<description/> <author/> <company>Parametric Technology Corporation</company> <keywords/> <revisedBy>UNIBS</revisedBy> </userData> <identityInfo> <revision>181</revision> <documentID>80ED9498-764B-40AF-B415-B80A121BB729</documentID> <versionID>F9E1C681-054E-4317-9EF4-6ABD442BFAFF</versionID> <parentVersionID>00000000-0000-0000-0000-000000000000</parentVersionID> <branchID>00000000-0000-0000-0000-000000000000</branchID> </identityInfo> </metadata> <settings> <presentation> <textRendering> <textStyles> <textStyle name="Normal"> <blockAttr margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit"/> <inlineAttr font-family="Arial" font-charset="0" font-size="14" font-weight="normal" font-style="normal" underline="false" line-through="false" vertical-align="baseline" color="#f00"/> </textStyle> <textStyle name="Heading 1"> <blockAttr margin-left="0" margin-right="0" text-indent="inherit" text-align="left" list-style-type="inherit" tabs="inherit"/> <inlineAttr font-family="Arial" font-charset="0" font-size="14" font-weight="bold" font-style="normal" underline="false" line-through="false" vertical-align="baseline"/> </textStyle> <textStyle name="Heading 2"> <blockAttr margin-left="0" margin-right="0" text-indent="inherit" text-align="left" list-style-type="inherit" tabs="inherit"/> <inlineAttr font-family="Arial" font-charset="0" font-size="12" font-weight="bold" font-style="italic" underline="false" line-through="false" vertical-align="baseline"/> </textStyle> <textStyle name="Heading 3"> <blockAttr margin-left="0" margin-right="0" text-indent="inherit" text-align="left" list-style-type="inherit" tabs="inherit"/> <inlineAttr font-family="Arial" font-charset="0" font-size="12" font-weight="normal" font-style="normal" underline="false" line-through="false" vertical-align="baseline"/> </textStyle> <textStyle name="Paragraph"> <blockAttr margin-left="0" margin-right="0" text-indent="21" text-align="left" list-style-type="inherit" tabs="inherit"/> <inlineAttr font-family="Arial" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false" line-through="false" vertical-align="baseline"/> </textStyle> <textStyle name="List"> <blockAttr margin-left="14.25" margin-right="0" text-indent="-14.25" text-align="left" list-style-type="inherit" tabs="inherit"/> <inlineAttr font-family="Arial" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false" line-through="false" vertical-align="baseline"/> </textStyle> <textStyle name="Indent"> <blockAttr margin-left="108" margin-right="0" text-indent="inherit" text-align="left" list-style-type="inherit" tabs="inherit"/> <inlineAttr font-family="Arial" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false" line-through="false" vertical-align="baseline"/> </textStyle> <textStyle name="Title"> <blockAttr margin-left="0" margin-right="0" text-indent="inherit" text-align="center" list-style-type="inherit" tabs="inherit"/> <inlineAttr font-family="Times New Roman" font-charset="0" font-size="24" font-weight="bold" font-style="normal" underline="false" line-through="false" vertical-align="baseline"/> </textStyle> <textStyle name="Subtitle" base-style="Title"> <blockAttr margin-left="0" margin-right="0" text-indent="inherit" text-align="center" list-style-type="inherit" tabs="inherit"/> <inlineAttr font-family="Times New Roman" font-charset="0" font-size="18" font-weight="normal" font-style="normal" underline="false" line-through="false" vertical-align="baseline"/> </textStyle> </textStyles> </textRendering> <mathRendering equation-color="#000"> <operators multiplication="narrow-dot" derivative="derivative" literal-subscript="large" definition="colon-equal" global-definition="triple-equal" local-definition="left-arrow" equality="bold-equal" symbolic-evaluation="right-arrow"/> <mathStyles> <mathStyle name="Variables" font-family="Times New Roman" font-charset="0" font-size="10" font-weight="normal" font-style="italic" underline="false"/> <mathStyle name="Constants" font-family="Times New Roman" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false"/> <mathStyle name="User 1" font-family="Bell MT" font-charset="0" font-size="16" font-weight="normal" font-style="normal" underline="false"/> <mathStyle name="User 2" font-family="Courier New" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false"/> <mathStyle name="User 3" font-family="Arial" font-charset="0" font-size="10" font-weight="bold" font-style="normal" underline="false"/> <mathStyle name="User 4" font-family="Times New Roman" font-charset="0" font-size="10" font-weight="normal" font-style="italic" underline="false"/> <mathStyle name="User 5" font-family="Times New Roman" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false"/> <mathStyle name="User 6" font-family="Arial" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false"/> <mathStyle name="User 7" font-family="Times New Roman" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false"/> <mathStyle name="Math Text Font" font-family="Times New Roman" font-charset="0" font-size="16" font-weight="bold" font-style="normal" underline="false"/> </mathStyles> <dimensionNames mass="mass" length="length" time="time" current="current" thermodynamic-temperature="temperature" luminous-intensity="luminosity" amount-of-substance="substance" display="false"/> <symbolics derivation-steps-style="vertical-insert" show-comments="false" evaluate-in-place="false"/> <results numeric-only="true"> <general precision="3" show-trailing-zeros="false" radix="dec" complex-threshold="10" zero-threshold="15" imaginary-value="i" exponential-threshold="3"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="true" simplify-units="true" fractional-unit-exponent="false"/> </results> </mathRendering> <pageModel show-page-frame="false" show-header-frame="false" show-footer-frame="false" header-footer-start-page="1" paper-code="8" orientation="landscape" print-single-page-width="false" page-width="1190.25" page-height="841.5"> <margins left="14.4" right="17.26299" top="14.4" bottom="19.68"/> <header use-full-page-width="false"/> <footer use-full-page-width="false"/> </pageModel> <colorModel background-color="#fff" default-highlight-color="#ffff80"/> <language math="it" UI="it"/> </presentation> <calculation> <builtInVariables array-origin="1" convergence-tolerance="0.001" constraint-tolerance="0.001" random-seed="1" prn-precision="4" prn-col-width="8"/> <calculationBehavior automatic-recalculation="true" matrix-strict-singularity-check="false" optimize-expressions="false" exact-boolean="true" strings-use-origin="false" zero-over-zero="error"> <compatibility multiple-assignment="MC12" local-assignment="MC11"/> </calculationBehavior> <units> <currentUnitSystem name="si" customized="false"/> </units> </calculation> <editor view-annotations="false" view-regions="false"> <ruler is-visible="false" ruler-unit="in"/> <plotTemplate> <polar item-idref="1"/> <xy item-idref="2"/> </plotTemplate> <grid granularity-x="6" granularity-y="6"/> </editor> <fileFormat image-type="image/png" image-quality="75" save-numeric-results="true" exclude-large-results="true" save-text-images="false" screen-dpi="96"/> <miscellaneous> <handbook handbook-region-tag-ub="770" can-delete-original-handbook-regions="true" can-delete-user-regions="true" can-print="true" can-copy="true" can-save="true" file-permission-mask="4294967295"/> </miscellaneous> </settings> <regions> <region region-id="211" left="6" top="24" width="875.25" height="22.5" align-x="18" align-y="42" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> <f size="20"> Uso delle coordinate principali: sistema a due gdl smorzato (con smorzamento proporzionale) </f> </text> </region> <region region-id="215" left="6" top="60" width="675" height="282" align-x="6" align-y="60" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <picture> <metafile mapping-mode="8" x-extent="941.9528" y-extent="394.6394" item-idref="3"/> </picture> <rendering item-idref="4"/> </region> <region region-id="216" left="6" top="371.25" width="123" height="15.75" align-x="20.25" align-y="384" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="true"> Parametri geometrici </text> </region> <region region-id="217" left="6" top="399" width="87.75" height="18.75" align-x="18.75" align-y="414" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve">a</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:mult/> <ml:real>80</ml:real> <ml:apply> <ml:pow/> <ml:real>10</ml:real> <ml:real>-3</ml:real> </ml:apply> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>0.08</ml:real> </result> </ml:eval> </ml:define> </math> <rendering item-idref="5"/> </region> <region region-id="218" left="6" top="429" width="91.5" height="18.75" align-x="18" align-y="444" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve">b</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:mult/> <ml:real>180</ml:real> <ml:apply> <ml:pow/> <ml:real>10</ml:real> <ml:real>-3</ml:real> </ml:apply> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>0.18</ml:real> </result> </ml:eval> </ml:define> </math> <rendering item-idref="6"/> </region> <region region-id="219" left="6" top="459" width="90.75" height="18.75" align-x="17.25" align-y="474" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define warning="WarnRedefinedBIUnit" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve">c</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:mult/> <ml:real>220</ml:real> <ml:apply> <ml:pow/> <ml:real>10</ml:real> <ml:real>-3</ml:real> </ml:apply> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>0.22</ml:real> </result> </ml:eval> </ml:define> </math> <rendering item-idref="7"/> </region> <region region-id="7" left="6" top="497.25" width="106.5" height="15.75" align-x="20.25" align-y="510" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="true"> Parametri inerziali </text> </region> <region region-id="695" left="6" top="525" width="36.75" height="16.5" align-x="23.25" align-y="534" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="1">J</ml:id> <ml:real>1.2</ml:real> </ml:define> </math> <rendering item-idref="8"/> </region> <region region-id="696" left="6" top="549" width="30" height="16.5" align-x="23.25" align-y="558" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="2">J</ml:id> <ml:real>3</ml:real> </ml:define> </math> <rendering item-idref="9"/> </region> <region region-id="697" left="6" top="573" width="30.75" height="12.75" align-x="19.5" align-y="582" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define warning="WarnRedefinedBIUnit" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve">m</ml:id> <ml:real>15</ml:real> </ml:define> </math> <rendering item-idref="10"/> </region> <region region-id="679" left="6" top="599.25" width="102.75" height="15.75" align-x="38.25" align-y="612" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="true"> Parametri elastici </text> </region> <region region-id="15" left="6" top="627" width="39" height="12.75" align-x="18.75" align-y="636" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve">k</ml:id> <ml:real>2500</ml:real> </ml:define> </math> <rendering item-idref="11"/> </region> <region region-id="683" left="6" top="680.25" width="237" height="27.75" align-x="46.5" align-y="696" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define warning="WarnRedefinedBIUnit" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:function> <ml:id xml:space="preserve">V</ml:id> <ml:boundVars> <ml:id xml:space="preserve">α</ml:id> <ml:id xml:space="preserve">β</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:mult/> <ml:apply> <ml:div/> <ml:real>1</ml:real> <ml:real>2</ml:real> </ml:apply> <ml:parens> <ml:apply> <ml:plus/> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:real>2</ml:real> <ml:id xml:space="preserve">k</ml:id> </ml:apply> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">b</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">α</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:real>2</ml:real> <ml:id xml:space="preserve">k</ml:id> </ml:apply> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:minus/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve">a</ml:id> <ml:id xml:space="preserve">α</ml:id> </ml:apply> <ml:apply> <ml:mult/> <ml:id xml:space="preserve">a</ml:id> <ml:id xml:space="preserve">β</ml:id> </ml:apply> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve">k</ml:id> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">c</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">β</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:apply> </ml:parens> </ml:apply> </ml:define> </math> <rendering item-idref="12"/> </region> <region region-id="687" left="342" top="689.25" width="111" height="15.75" align-x="358.5" align-y="702" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> Energia potenziale </text> </region> <region region-id="684" left="6" top="723.75" width="175.5" height="34.5" align-x="60.75" align-y="744" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:symEval style="default" hide-keywords="false" hide-lhs="false" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:apply> <ml:derivative style="derivative"/> <ml:lambda> <ml:boundVars> <ml:id xml:space="preserve">α</ml:id> </ml:boundVars> <ml:apply> <ml:id xml:space="preserve">V</ml:id> <ml:sequence> <ml:id xml:space="preserve">α</ml:id> <ml:id xml:space="preserve">β</ml:id> </ml:sequence> </ml:apply> </ml:lambda> </ml:apply> <ml:symResult> <ml:apply> <ml:plus/> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:real>162.0</ml:real> <ml:id xml:space="preserve">α</ml:id> </ml:apply> <ml:apply> <ml:mult/> <ml:real>-32.0</ml:real> <ml:id xml:space="preserve">β</ml:id> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:real>32.0</ml:real> <ml:id xml:space="preserve">α</ml:id> </ml:apply> </ml:apply> </ml:symResult> </ml:symEval> </math> <rendering item-idref="13"/> </region> <region region-id="685" left="6" top="771.75" width="177" height="34.5" align-x="61.5" align-y="792" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:symEval style="default" hide-keywords="false" hide-lhs="false" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:apply> <ml:derivative style="derivative"/> <ml:lambda> <ml:boundVars> <ml:id xml:space="preserve">β</ml:id> </ml:boundVars> <ml:apply> <ml:id xml:space="preserve">V</ml:id> <ml:sequence> <ml:id xml:space="preserve">α</ml:id> <ml:id xml:space="preserve">β</ml:id> </ml:sequence> </ml:apply> </ml:lambda> </ml:apply> <ml:symResult> <ml:apply> <ml:plus/> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:real>121.0</ml:real> <ml:id xml:space="preserve">β</ml:id> </ml:apply> <ml:apply> <ml:mult/> <ml:real>32.0</ml:real> <ml:id xml:space="preserve">β</ml:id> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:real>-32.0</ml:real> <ml:id xml:space="preserve">α</ml:id> </ml:apply> </ml:apply> </ml:symResult> </ml:symEval> </math> <rendering item-idref="14"/> </region> <region region-id="17" left="6" top="827.25" width="178.5" height="15.75" align-x="30" align-y="840" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> Matrici di massa e di rigidezza </text> </region> <region region-id="20" left="6" top="858" width="171" height="42.75" align-x="21" align-y="882" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17">J</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:matrix rows="2" cols="2"> <ml:id xml:space="preserve" subscript="1">J</ml:id> <ml:real>0</ml:real> <ml:real>0</ml:real> <ml:parens> <ml:apply> <ml:plus/> <ml:id xml:space="preserve" subscript="2">J</ml:id> <ml:apply> <ml:mult/> <ml:id xml:space="preserve">m</ml:id> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">c</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:apply> </ml:parens> </ml:matrix> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="2"> <ml:real>1.2</ml:real> <ml:real>0</ml:real> <ml:real>0</ml:real> <ml:real>3.726</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </math> <rendering item-idref="15"/> </region> <region region-id="21" left="408" top="858" width="158.25" height="42.75" align-x="501.75" align-y="882" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:apply> <ml:id xml:space="preserve">diag</ml:id> <ml:matrix rows="2" cols="1"> <ml:id xml:space="preserve" subscript="1">J</ml:id> <ml:parens> <ml:apply> <ml:plus/> <ml:id xml:space="preserve" subscript="2">J</ml:id> <ml:apply> <ml:mult/> <ml:id xml:space="preserve">m</ml:id> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">c</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:apply> </ml:parens> </ml:matrix> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="2"> <ml:real>1.2</ml:real> <ml:real>0</ml:real> <ml:real>0</ml:real> <ml:real>3.726</ml:real> </ml:matrix> </result> </ml:eval> </math> <rendering item-idref="16"/> </region> <region region-id="23" left="6" top="912.75" width="222.75" height="41.25" align-x="26.25" align-y="936" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17">K</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:matrix rows="2" cols="2"> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:real>2</ml:real> <ml:id xml:space="preserve">k</ml:id> </ml:apply> <ml:parens> <ml:apply> <ml:plus/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">a</ml:id> <ml:real>2</ml:real> </ml:apply> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">b</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:parens> </ml:apply> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:real>-2</ml:real> <ml:id xml:space="preserve">k</ml:id> </ml:apply> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">a</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:real>-2</ml:real> <ml:id xml:space="preserve">k</ml:id> </ml:apply> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">a</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:id xml:space="preserve">k</ml:id> <ml:parens> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:real>2</ml:real> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">a</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">c</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:parens> </ml:apply> </ml:matrix> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="2"> <ml:real>194</ml:real> <ml:real>-32</ml:real> <ml:real>-32</ml:real> <ml:real>153</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </math> <rendering item-idref="17"/> </region> <region region-id="654" left="6" top="975" width="42.75" height="16.5" align-x="24.75" align-y="984" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#ff0" tag=""> <math optimize="false" disable-calc="false"> <ml:define warning="WarnRedefinedUDScalar" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="R">α</ml:id> <ml:real>0.02</ml:real> </ml:define> </math> <rendering item-idref="18"/> </region> <region region-id="655" left="120" top="975" width="43.5" height="16.5" align-x="139.5" align-y="984" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#ff0" tag=""> <math optimize="false" disable-calc="false"> <ml:define warning="WarnRedefinedUDScalar" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="R">β</ml:id> <ml:real>0.03</ml:real> </ml:define> </math> <rendering item-idref="19"/> </region> <region region-id="656" left="348" top="971.25" width="137.25" height="15.75" align-x="384.75" align-y="984" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="true"> Coefficienti di Rayleigh </text> </region> <region region-id="657" left="6" top="1026.75" width="170.25" height="29.25" align-x="24" align-y="1044" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17">C</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="R">α</ml:id> <ml:id xml:space="preserve" font="17">J</ml:id> </ml:apply> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="R">β</ml:id> <ml:id xml:space="preserve" font="17">K</ml:id> </ml:apply> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="2"> <ml:real>5.8439999999999994</ml:real> <ml:real>-0.96</ml:real> <ml:real>-0.96</ml:real> <ml:real>4.6645199999999996</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </math> <rendering item-idref="20"/> </region> <region region-id="658" left="348" top="1031.25" width="645" height="15.75" align-x="372.75" align-y="1044" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="true"> Matrice di smorzamento, calcolata con l'ipotesi di "smorzamento proporzionale" (o smorzamento di Rayleigh) </text> </region> <region region-id="34" left="6" top="1079.25" width="186" height="15.75" align-x="30.75" align-y="1092" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="true"> Calcolo delle pulsazioni proprie </text> </region> <region region-id="643" left="6" top="1116.75" width="171" height="29.25" align-x="40.5" align-y="1134" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve">omega</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:sqrt/> <ml:apply> <ml:id xml:space="preserve">genvals</ml:id> <ml:sequence> <ml:id xml:space="preserve" font="17">K</ml:id> <ml:id xml:space="preserve" font="17">J</ml:id> </ml:sequence> </ml:apply> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="1"> <ml:real>12.788144031903128</ml:real> <ml:real>6.2604185817191329</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </math> <rendering item-idref="21"/> </region> <region region-id="644" left="318" top="1112.25" width="149.25" height="33.75" align-x="416.25" align-y="1134" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:apply> <ml:sqrt/> <ml:apply> <ml:id xml:space="preserve">eigenvals</ml:id> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve" font="17">J</ml:id> <ml:real>-1</ml:real> </ml:apply> <ml:id xml:space="preserve" font="17">K</ml:id> </ml:apply> </ml:apply> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="1"> <ml:real>12.78814403190313</ml:real> <ml:real>6.2604185817191338</ml:real> </ml:matrix> </result> </ml:eval> </math> <rendering item-idref="22"/> </region> <region region-id="698" left="6" top="1167" width="90.75" height="20.25" align-x="40.5" align-y="1182" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:function> <ml:id xml:space="preserve" font="17">Δ</ml:id> <ml:boundVars> <ml:id xml:space="preserve">ω</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:minus/> <ml:id xml:space="preserve" font="17">K</ml:id> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> <ml:id xml:space="preserve" font="17">J</ml:id> </ml:apply> </ml:apply> </ml:define> </math> <rendering item-idref="23"/> </region> <region region-id="699" left="6" top="1215.75" width="79.5" height="19.5" align-x="40.5" align-y="1230" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:function> <ml:id xml:space="preserve">det</ml:id> <ml:boundVars> <ml:id xml:space="preserve">ω</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:determinant/> <ml:apply> <ml:id xml:space="preserve" font="17">Δ</ml:id> <ml:id xml:space="preserve">ω</ml:id> </ml:apply> </ml:apply> </ml:define> </math> <rendering item-idref="24"/> </region> <region region-id="700" left="6" top="1263" width="58.5" height="12.75" align-x="19.5" align-y="1272" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve">ω</ml:id> <ml:range> <ml:sequence> <ml:real>0</ml:real> <ml:real>0.1</ml:real> </ml:sequence> <ml:real>15</ml:real> </ml:range> </ml:define> </math> <rendering item-idref="25"/> </region> <region region-id="688" left="6" top="1302" width="637.5" height="270.75" align-x="6" align-y="1302" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <plot disable-calc="false" item-idref="26"/> <rendering item-idref="27"/> </region> <region region-id="702" left="6" top="1632.75" width="150" height="29.25" align-x="19.5" align-y="1650" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve">ω</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:sqrt/> <ml:apply> <ml:id xml:space="preserve">genvals</ml:id> <ml:sequence> <ml:id xml:space="preserve" font="17">K</ml:id> <ml:id xml:space="preserve" font="17">J</ml:id> </ml:sequence> </ml:apply> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="1"> <ml:real>12.788144031903128</ml:real> <ml:real>6.2604185817191329</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </math> <rendering item-idref="28"/> </region> <region region-id="703" left="6" top="1680.75" width="172.5" height="29.25" align-x="24.75" align-y="1698" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17">Φ</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:id xml:space="preserve">genvecs</ml:id> <ml:sequence> <ml:id xml:space="preserve" font="17">K</ml:id> <ml:id xml:space="preserve" font="17">J</ml:id> </ml:sequence> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="2"> <ml:real>1</ml:real> <ml:real>-0.070123541776235282</ml:real> <ml:real>0.21773359721521066</ml:real> <ml:real>1</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </math> <rendering item-idref="29"/> </region> <region region-id="717" left="6" top="1726.5" width="109.5" height="31.5" align-x="32.25" align-y="1746" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17" subscript="1">X</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:matcol/> <ml:id xml:space="preserve" font="17">Φ</ml:id> <ml:real>1</ml:real> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="1"> <ml:real>1</ml:real> <ml:real>-0.070123541776235282</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </math> <rendering item-idref="30"/> </region> <region region-id="716" left="186" top="1737" width="54.75" height="18" align-x="202.5" align-y="1746" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:provenance expr-id="1"> <originRef doc-id="80ED9498-764B-40AF-B415-B80A121BB729" version-id="4F69ECB6-AFEE-41EC-BA77-938639597761" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315097276" is-modified="false" region-id="0" href="C:\Users\Giovanni\Documents\ARCHIVIO UNIBS\02) Didattica\Vibrazioni Meccaniche\3) Esercizi ed esempi non codificati\Esercitazioni in aula\Esercitazioni aula - 2021-2022 (S2)\File 30 - Coordinate principali (sistema non smorzato).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </originRef> <parentRef doc-id="80ED9498-764B-40AF-B415-B80A121BB729" version-id="4F69ECB6-AFEE-41EC-BA77-938639597761" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315097116" is-modified="false" region-id="0" href="C:\Users\Giovanni\Documents\ARCHIVIO UNIBS\02) Didattica\Vibrazioni Meccaniche\3) Esercizi ed esempi non codificati\Esercitazioni in aula\Esercitazioni aula - 2021-2022 (S2)\File 30 - Coordinate principali (sistema non smorzato).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </parentRef> <comment xmlns="http://schemas.mathsoft.com/provenance10"/> <originComment xmlns="http://schemas.mathsoft.com/provenance10"/> <contentHash xmlns="http://schemas.mathsoft.com/provenance10">55b4cc6f922b13aa8697bdd68483cf82</contentHash> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:provenance> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>12.788144031903128</ml:real> </result> </ml:eval> </math> <rendering item-idref="31"> <element-image-map> <box left="1.5" top="0.75" width="10.5" height="16.5" expr-idref="1" xmlns="http://schemas.mathsoft.com/worksheet30"/> </element-image-map> </rendering> </region> <region region-id="718" left="6" top="1768.5" width="108.75" height="31.5" align-x="32.25" align-y="1788" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17" subscript="2">X</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:matcol/> <ml:id xml:space="preserve" font="17">Φ</ml:id> <ml:real>2</ml:real> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="1"> <ml:real>0.21773359721521066</ml:real> <ml:real>1</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </math> <rendering item-idref="32"/> </region> <region region-id="719" left="186" top="1779" width="45.75" height="18" align-x="202.5" align-y="1788" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:provenance expr-id="1"> <originRef doc-id="80ED9498-764B-40AF-B415-B80A121BB729" version-id="4F69ECB6-AFEE-41EC-BA77-938639597761" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315096476" is-modified="false" region-id="0" href="C:\Users\Giovanni\Documents\ARCHIVIO UNIBS\02) Didattica\Vibrazioni Meccaniche\3) Esercizi ed esempi non codificati\Esercitazioni in aula\Esercitazioni aula - 2021-2022 (S2)\File 30 - Coordinate principali (sistema non smorzato).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </originRef> <parentRef doc-id="80ED9498-764B-40AF-B415-B80A121BB729" version-id="4F69ECB6-AFEE-41EC-BA77-938639597761" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315096796" is-modified="false" region-id="0" href="C:\Users\Giovanni\Documents\ARCHIVIO UNIBS\02) Didattica\Vibrazioni Meccaniche\3) Esercizi ed esempi non codificati\Esercitazioni in aula\Esercitazioni aula - 2021-2022 (S2)\File 30 - Coordinate principali (sistema non smorzato).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </parentRef> <comment xmlns="http://schemas.mathsoft.com/provenance10"/> <originComment xmlns="http://schemas.mathsoft.com/provenance10"/> <contentHash xmlns="http://schemas.mathsoft.com/provenance10">6e521c359e593e28066b07240b4c796c</contentHash> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:provenance> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>6.2604185817191329</ml:real> </result> </ml:eval> </math> <rendering item-idref="33"> <element-image-map> <box left="1.5" top="0.75" width="10.5" height="16.5" expr-idref="1" xmlns="http://schemas.mathsoft.com/worksheet30"/> </element-image-map> </rendering> </region> <region region-id="706" left="6" top="1840.5" width="170.25" height="31.5" align-x="46.5" align-y="1860" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="#ff0" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17" subscript="star">J</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:transpose/> <ml:id xml:space="preserve" font="17">Φ</ml:id> </ml:apply> <ml:id xml:space="preserve" font="17">J</ml:id> </ml:apply> <ml:id xml:space="preserve" font="17">Φ</ml:id> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="2"> <ml:real>1.2183219012004929</ml:real> <ml:real>1.6653345369377348E-16</ml:real> <ml:real>1.6653345369377348E-16</ml:real> <ml:real>3.7828895032275307</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </math> <rendering item-idref="34"/> </region> <region region-id="707" left="6" top="1882.5" width="193.5" height="31.5" align-x="51" align-y="1902" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="#ff0" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17" subscript="star">K</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:transpose/> <ml:id xml:space="preserve" font="17">Φ</ml:id> </ml:apply> <ml:id xml:space="preserve" font="17">K</ml:id> </ml:apply> <ml:id xml:space="preserve" font="17">Φ</ml:id> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="2"> <ml:real>199.24025527369929</ml:real> <ml:real>1.4210854715202004E-14</ml:real> <ml:real>1.4210854715202004E-14</ml:real> <ml:real>148.262186133344</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> <resultFormat numeric-only="true"> <general precision="3" show-trailing-zeros="false" radix="dec" complex-threshold="10" zero-threshold="13" imaginary-value="i" exponential-threshold="3"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="true" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="35"/> </region> <region region-id="708" left="6" top="1926.75" width="244.5" height="29.25" align-x="49.5" align-y="1944" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="#ff0" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17" subscript="star">C</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="R">α</ml:id> <ml:id xml:space="preserve" font="17" subscript="star">J</ml:id> </ml:apply> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="R">β</ml:id> <ml:id xml:space="preserve" font="17" subscript="star">K</ml:id> </ml:apply> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="2"> <ml:real>6.0015740962349877</ml:real> <ml:real>4.2965631052993554E-16</ml:real> <ml:real>4.2965631052993554E-16</ml:real> <ml:real>4.52352337406487</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> <resultFormat numeric-only="true"> <general precision="3" show-trailing-zeros="false" radix="dec" complex-threshold="10" zero-threshold="13" imaginary-value="i" exponential-threshold="3"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="true" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="36"/> </region> <region region-id="57" left="6" top="2001" width="54.75" height="18" align-x="22.5" align-y="2010" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>12.788144031903128</ml:real> </result> </ml:eval> </math> <rendering item-idref="37"/> </region> <region region-id="58" left="174" top="1975.5" width="105" height="63.75" align-x="240.75" align-y="2010" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:apply> <ml:sqrt/> <ml:apply> <ml:div/> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve" font="17" subscript="star">K</ml:id> <ml:sequence> <ml:real>1</ml:real> <ml:real>1</ml:real> </ml:sequence> </ml:apply> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve" font="17" subscript="star">J</ml:id> <ml:sequence> <ml:real>1</ml:real> <ml:real>1</ml:real> </ml:sequence> </ml:apply> </ml:apply> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>12.788144031903128</ml:real> </result> </ml:eval> </math> <rendering item-idref="38"/> </region> <region region-id="63" left="6" top="2103" width="45.75" height="18" align-x="22.5" align-y="2112" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>6.2604185817191329</ml:real> </result> </ml:eval> </math> <rendering item-idref="39"/> </region> <region region-id="64" left="174" top="2077.5" width="96" height="63.75" align-x="240.75" align-y="2112" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:apply> <ml:sqrt/> <ml:apply> <ml:div/> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve" font="17" subscript="star">K</ml:id> <ml:sequence> <ml:real>2</ml:real> <ml:real>2</ml:real> </ml:sequence> </ml:apply> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve" font="17" subscript="star">J</ml:id> <ml:sequence> <ml:real>2</ml:real> <ml:real>2</ml:real> </ml:sequence> </ml:apply> </ml:apply> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>6.2604185817191338</ml:real> </result> </ml:eval> </math> <rendering item-idref="40"/> </region> <region region-id="597" left="6" top="2156.25" width="134.25" height="47.25" align-x="25.5" align-y="2172" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="1">μ</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:div/> <ml:real>1</ml:real> <ml:apply> <ml:sqrt/> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:transpose/> <ml:id xml:space="preserve" font="17" subscript="1">X</ml:id> </ml:apply> <ml:id xml:space="preserve" font="17">J</ml:id> </ml:apply> <ml:id xml:space="preserve" font="17" subscript="1">X</ml:id> </ml:apply> </ml:apply> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>0.90598075894246666</ml:real> </result> </ml:eval> </ml:define> </math> <rendering item-idref="41"/> </region> <region region-id="691" left="324" top="2156.25" width="134.25" height="47.25" align-x="343.5" align-y="2172" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="2">μ</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:div/> <ml:real>1</ml:real> <ml:apply> <ml:sqrt/> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:transpose/> <ml:id xml:space="preserve" font="17" subscript="2">X</ml:id> </ml:apply> <ml:id xml:space="preserve" font="17">J</ml:id> </ml:apply> <ml:id xml:space="preserve" font="17" subscript="2">X</ml:id> </ml:apply> </ml:apply> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>0.51414802519436142</ml:real> </result> </ml:eval> </ml:define> </math> <rendering item-idref="42"/> </region> <region region-id="80" left="6" top="2220.75" width="72" height="29.25" align-x="30.75" align-y="2238" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17" subscript="1">X</ml:id> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="1"> <ml:real>1</ml:real> <ml:real>-0.070123541776235282</ml:real> </ml:matrix> </result> </ml:eval> </math> <rendering item-idref="43"/> </region> <region region-id="690" left="324" top="2220.75" width="71.25" height="29.25" align-x="348.75" align-y="2238" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17" subscript="2">X</ml:id> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="1"> <ml:real>0.21773359721521066</ml:real> <ml:real>1</ml:real> </ml:matrix> </result> </ml:eval> </math> <rendering item-idref="44"/> </region> <region region-id="77" left="6" top="2268.75" width="126" height="29.25" align-x="32.25" align-y="2286" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17" subscript="1">X</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="1">μ</ml:id> <ml:id xml:space="preserve" font="17" subscript="1">X</ml:id> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="1"> <ml:real>0.90598075894246666</ml:real> <ml:real>-0.063530579598167408</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </math> <rendering item-idref="45"/> </region> <region region-id="689" left="324" top="2268.75" width="120.75" height="29.25" align-x="350.25" align-y="2286" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17" subscript="2">X</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="2">μ</ml:id> <ml:id xml:space="preserve" font="17" subscript="2">X</ml:id> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="1"> <ml:real>0.11194729902666507</ml:real> <ml:real>0.51414802519436142</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </math> <rendering item-idref="46"/> </region> <region region-id="84" left="6" top="2316.75" width="91.5" height="29.25" align-x="23.25" align-y="2334" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17">Φ</ml:id> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="2"> <ml:real>1</ml:real> <ml:real>-0.070123541776235282</ml:real> <ml:real>0.21773359721521066</ml:real> <ml:real>1</ml:real> </ml:matrix> </result> </ml:eval> </math> <rendering item-idref="47"/> </region> <region region-id="83" left="6" top="2358.75" width="200.25" height="29.25" align-x="24.75" align-y="2376" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17">Φ</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:id xml:space="preserve">augment</ml:id> <ml:sequence> <ml:id xml:space="preserve" font="17" subscript="1">X</ml:id> <ml:id xml:space="preserve" font="17" subscript="2">X</ml:id> </ml:sequence> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="2"> <ml:real>0.90598075894246666</ml:real> <ml:real>-0.063530579598167408</ml:real> <ml:real>0.11194729902666507</ml:real> <ml:real>0.51414802519436142</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </math> <rendering item-idref="48"/> </region> <region region-id="87" left="6" top="2434.5" width="90.75" height="31.5" align-x="54.75" align-y="2454" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:transpose/> <ml:id xml:space="preserve" font="17">Φ</ml:id> </ml:apply> <ml:id xml:space="preserve" font="17">J</ml:id> </ml:apply> <ml:id xml:space="preserve" font="17">Φ</ml:id> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="2"> <ml:real>1</ml:real> <ml:real>4.163336342344337E-17</ml:real> <ml:real>5.5511151231257827E-17</ml:real> <ml:real>0.99999999999999978</ml:real> </ml:matrix> </result> </ml:eval> </math> <rendering item-idref="49"/> </region> <region region-id="724" left="6" top="2494.5" width="170.25" height="31.5" align-x="27.75" align-y="2514" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17">W</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:transpose/> <ml:id xml:space="preserve" font="17">Φ</ml:id> </ml:apply> <ml:id xml:space="preserve" font="17">K</ml:id> </ml:apply> <ml:id xml:space="preserve" font="17">Φ</ml:id> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="2"> <ml:real>163.53662778069963</ml:real> <ml:real>8.8817841970012523E-15</ml:real> <ml:real>7.1054273576010019E-15</ml:real> <ml:real>39.192840818334204</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> <resultFormat numeric-only="true"> <general precision="3" show-trailing-zeros="false" radix="dec" complex-threshold="10" zero-threshold="13" imaginary-value="i" exponential-threshold="3"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="true" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="50"/> </region> <region region-id="723" left="270" top="2499" width="71.25" height="24" align-x="298.5" align-y="2514" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:provenance expr-id="1"> <originRef doc-id="80ED9498-764B-40AF-B415-B80A121BB729" version-id="4F69ECB6-AFEE-41EC-BA77-938639597761" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315097276" is-modified="true" region-id="0" href="C:\Users\Giovanni\Documents\ARCHIVIO UNIBS\02) Didattica\Vibrazioni Meccaniche\3) Esercizi ed esempi non codificati\Esercitazioni in aula\Esercitazioni aula - 2021-2022 (S2)\File 30 - Coordinate principali (sistema non smorzato).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </originRef> <parentRef doc-id="80ED9498-764B-40AF-B415-B80A121BB729" version-id="4F69ECB6-AFEE-41EC-BA77-938639597761" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315096916" is-modified="true" region-id="0" href="C:\Users\Giovanni\Documents\ARCHIVIO UNIBS\02) Didattica\Vibrazioni Meccaniche\3) Esercizi ed esempi non codificati\Esercitazioni in aula\Esercitazioni aula - 2021-2022 (S2)\File 30 - Coordinate principali (sistema non smorzato).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </parentRef> <comment xmlns="http://schemas.mathsoft.com/provenance10"/> <originComment xmlns="http://schemas.mathsoft.com/provenance10"/> <contentHash xmlns="http://schemas.mathsoft.com/provenance10">3c408a1afb6dda03b82a169b706baaba</contentHash> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> </ml:provenance> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>163.5366277806996</ml:real> </result> </ml:eval> </math> <rendering item-idref="51"> <element-image-map> <box left="1.5" top="0.75" width="22.5" height="22.5" expr-idref="1" xmlns="http://schemas.mathsoft.com/worksheet30"/> </element-image-map> </rendering> </region> <region region-id="722" left="396" top="2499" width="66.75" height="24" align-x="424.5" align-y="2514" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:provenance expr-id="1"> <originRef doc-id="80ED9498-764B-40AF-B415-B80A121BB729" version-id="4F69ECB6-AFEE-41EC-BA77-938639597761" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315096756" is-modified="true" region-id="0" href="C:\Users\Giovanni\Documents\ARCHIVIO UNIBS\02) Didattica\Vibrazioni Meccaniche\3) Esercizi ed esempi non codificati\Esercitazioni in aula\Esercitazioni aula - 2021-2022 (S2)\File 30 - Coordinate principali (sistema non smorzato).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </originRef> <parentRef doc-id="80ED9498-764B-40AF-B415-B80A121BB729" version-id="4F69ECB6-AFEE-41EC-BA77-938639597761" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315096796" is-modified="true" region-id="0" href="C:\Users\Giovanni\Documents\ARCHIVIO UNIBS\02) Didattica\Vibrazioni Meccaniche\3) Esercizi ed esempi non codificati\Esercitazioni in aula\Esercitazioni aula - 2021-2022 (S2)\File 30 - Coordinate principali (sistema non smorzato).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </parentRef> <comment xmlns="http://schemas.mathsoft.com/provenance10"/> <originComment xmlns="http://schemas.mathsoft.com/provenance10"/> <contentHash xmlns="http://schemas.mathsoft.com/provenance10">79bf8969aaa0c8df129a1b4143e96fc7</contentHash> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> </ml:provenance> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>39.1928408183342</ml:real> </result> </ml:eval> </math> <rendering item-idref="52"> <element-image-map> <box left="1.5" top="0.75" width="22.5" height="22.5" expr-idref="1" xmlns="http://schemas.mathsoft.com/worksheet30"/> </element-image-map> </rendering> </region> <region region-id="614" left="6" top="2549.25" width="56.25" height="15.75" align-x="34.5" align-y="2562" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> Forzante </text> </region> <region region-id="101" left="6" top="2589" width="36" height="12.75" align-x="20.25" align-y="2598" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define warning="WarnRedefinedBIUnit" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve">F</ml:id> <ml:real>500</ml:real> </ml:define> </math> <rendering item-idref="53"/> </region> <region region-id="102" left="6" top="2628.75" width="53.25" height="29.25" align-x="23.25" align-y="2646" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17">F</ml:id> <ml:matrix rows="2" cols="1"> <ml:real>0</ml:real> <ml:apply> <ml:mult/> <ml:id xml:space="preserve">F</ml:id> <ml:id xml:space="preserve">c</ml:id> </ml:apply> </ml:matrix> </ml:define> </math> <rendering item-idref="54"/> </region> <region region-id="107" left="156" top="2628.75" width="55.5" height="29.25" align-x="171.75" align-y="2646" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17">F</ml:id> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="1"> <ml:real>0</ml:real> <ml:real>110</ml:real> </ml:matrix> </result> </ml:eval> </math> <rendering item-idref="55"/> </region> <region region-id="110" left="6" top="2680.5" width="101.25" height="31.5" align-x="28.5" align-y="2700" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:apply> <ml:transpose/> <ml:id xml:space="preserve" font="17">Φ</ml:id> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="2"> <ml:real>0.90598075894246666</ml:real> <ml:real>0.11194729902666507</ml:real> <ml:real>-0.063530579598167408</ml:real> <ml:real>0.51414802519436142</ml:real> </ml:matrix> </result> </ml:eval> </math> <rendering item-idref="56"/> </region> <region region-id="111" left="6" top="2728.5" width="114" height="31.5" align-x="24.75" align-y="2748" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17">Q</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:mult/> <ml:apply> <ml:transpose/> <ml:id xml:space="preserve" font="17">Φ</ml:id> </ml:apply> <ml:id xml:space="preserve" font="17">F</ml:id> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="1"> <ml:real>-6.9883637557984146</ml:real> <ml:real>56.556282771379756</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </math> <rendering item-idref="57"/> </region> <region region-id="115" left="6" top="2789.25" width="420.75" height="19.5" align-x="39" align-y="2802" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="true"> Equazioni di moto in formato matriciale (nelle coord. principali p<f family="Arial" charset="0" size="14"> <inlineAttr font-weight="normal" font-style="normal" underline="false" line-through="false"> <c val="#f00"> 1 </c> </inlineAttr> </f> e p<f family="Arial" charset="0" size="14"> <inlineAttr font-weight="normal" font-style="normal" underline="false" line-through="false"> <c val="#f00"> 2 </c> </inlineAttr> </f>) </text> </region> <region region-id="727" left="6" top="2821.5" width="387" height="51.75" align-x="360.75" align-y="2850" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#80ffff" tag=""> <math optimize="false" disable-calc="false"> <ml:apply xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:equal/> <ml:apply> <ml:plus/> <ml:apply> <ml:plus/> <ml:matrix rows="2" cols="1"> <ml:apply> <ml:id xml:space="preserve" subscript="1">p''</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> <ml:apply> <ml:id xml:space="preserve" subscript="2">p''</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:matrix> <ml:apply> <ml:mult/> <ml:parens> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="R">α</ml:id> <ml:matrix rows="2" cols="2"> <ml:real>1</ml:real> <ml:real>0</ml:real> <ml:real>0</ml:real> <ml:real>1</ml:real> </ml:matrix> </ml:apply> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="R">β</ml:id> <ml:matrix rows="2" cols="2"> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> <ml:real>0</ml:real> <ml:real>0</ml:real> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> </ml:matrix> </ml:apply> </ml:apply> </ml:parens> <ml:matrix rows="2" cols="1"> <ml:apply> <ml:id xml:space="preserve" subscript="1">p'</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> <ml:apply> <ml:id xml:space="preserve" subscript="2">p'</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:matrix> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:matrix rows="2" cols="2"> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> <ml:real>0</ml:real> <ml:real>0</ml:real> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> </ml:matrix> <ml:matrix rows="2" cols="1"> <ml:apply> <ml:id xml:space="preserve" subscript="1">p</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> <ml:apply> <ml:id xml:space="preserve" subscript="2">p</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:matrix> </ml:apply> </ml:apply> <ml:matrix rows="2" cols="1"> <ml:id xml:space="preserve" subscript="1">Q</ml:id> <ml:id xml:space="preserve" subscript="2">Q</ml:id> </ml:matrix> </ml:apply> </math> <rendering item-idref="58"/> </region> <region region-id="728" left="6" top="2901" width="102" height="24" align-x="74.25" align-y="2916" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:apply> <ml:plus/> <ml:id xml:space="preserve" subscript="R">α</ml:id> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> <ml:id xml:space="preserve" subscript="R">β</ml:id> </ml:apply> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>4.9260988334209879</ml:real> </result> </ml:eval> </math> <rendering item-idref="59"/> </region> <region region-id="729" left="252" top="2901" width="71.25" height="24" align-x="280.5" align-y="2916" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>163.5366277806996</ml:real> </result> </ml:eval> </math> <rendering item-idref="60"/> </region> <region region-id="730" left="450" top="2901.75" width="60.75" height="20.25" align-x="471.75" align-y="2916" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve" font="17">Q</ml:id> <ml:real>1</ml:real> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>-6.9883637557984146</ml:real> </result> </ml:eval> </math> <rendering item-idref="61"/> </region> <region region-id="731" left="6" top="2949" width="102" height="24" align-x="74.25" align-y="2964" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:apply> <ml:plus/> <ml:id xml:space="preserve" subscript="R">α</ml:id> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> <ml:id xml:space="preserve" subscript="R">β</ml:id> </ml:apply> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>1.1957852245500258</ml:real> </result> </ml:eval> </math> <rendering item-idref="62"/> </region> <region region-id="732" left="252" top="2949" width="66.75" height="24" align-x="280.5" align-y="2964" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>39.1928408183342</ml:real> </result> </ml:eval> </math> <rendering item-idref="63"/> </region> <region region-id="733" left="450" top="2949.75" width="60" height="20.25" align-x="471.75" align-y="2964" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve" font="17">Q</ml:id> <ml:real>2</ml:real> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>56.556282771379756</ml:real> </result> </ml:eval> </math> <rendering item-idref="64"/> </region> <region region-id="599" left="6" top="3003" width="68.25" height="36.75" align-x="30.75" align-y="3024" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#0ff" tag=""> <math optimize="false" disable-calc="false"> <ml:apply xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:equal/> <ml:apply> <ml:id xml:space="preserve" font="17">p</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> <ml:matrix rows="2" cols="1"> <ml:apply> <ml:id xml:space="preserve" subscript="1">p</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> <ml:apply> <ml:id xml:space="preserve" subscript="2">p</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:matrix> </ml:apply> </math> <rendering item-idref="65"/> </region> <region region-id="601" left="6" top="3083.25" width="543" height="15.75" align-x="30.75" align-y="3096" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> Calcolo dei fattori di smorzamento modali e delle corrispondenti pulsazioni proprie smorzate </text> </region> <region region-id="624" left="6" top="3123" width="129.75" height="44.25" align-x="23.25" align-y="3150" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="1">ξ</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:div/> <ml:apply> <ml:plus/> <ml:id xml:space="preserve" subscript="R">α</ml:id> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> <ml:id xml:space="preserve" subscript="R">β</ml:id> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:real>2</ml:real> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:apply> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>0.19260413478029489</ml:real> </result> </ml:eval> </ml:define> </math> <rendering item-idref="66"/> </region> <region region-id="735" left="366" top="3133.5" width="126" height="25.5" align-x="388.5" align-y="3150" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="s1">ω</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:mult/> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> <ml:apply> <ml:sqrt/> <ml:apply> <ml:minus/> <ml:real>1</ml:real> <ml:apply> <ml:pow/> <ml:id xml:space="preserve" subscript="1">ξ</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:apply> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>12.548705724159033</ml:real> </result> </ml:eval> </ml:define> </math> <rendering item-idref="67"/> </region> <region region-id="622" left="666" top="3141" width="54.75" height="18" align-x="682.5" align-y="3150" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>12.788144031903128</ml:real> </result> </ml:eval> </math> <rendering item-idref="68"/> </region> <region region-id="626" left="6" top="3183" width="129.75" height="44.25" align-x="23.25" align-y="3210" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="2">ξ</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:div/> <ml:apply> <ml:plus/> <ml:id xml:space="preserve" subscript="R">α</ml:id> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> <ml:id xml:space="preserve" subscript="R">β</ml:id> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:real>2</ml:real> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>0.095503616007546491</ml:real> </result> </ml:eval> </ml:define> </math> <rendering item-idref="69"/> </region> <region region-id="734" left="366" top="3193.5" width="121.5" height="25.5" align-x="388.5" align-y="3210" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="s2">ω</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:mult/> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> <ml:apply> <ml:sqrt/> <ml:apply> <ml:minus/> <ml:real>1</ml:real> <ml:apply> <ml:pow/> <ml:id xml:space="preserve" subscript="2">ξ</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:apply> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>6.2318027281454569</ml:real> </result> </ml:eval> </ml:define> </math> <rendering item-idref="70"/> </region> <region region-id="627" left="666" top="3201" width="45.75" height="18" align-x="682.5" align-y="3210" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>6.2604185817191329</ml:real> </result> </ml:eval> </math> <rendering item-idref="71"/> </region> <region region-id="323" left="6" top="3251.25" width="510.75" height="15.75" align-x="36.75" align-y="3264" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> Soluzione delle equazioni in coord. principali (dalla teoria dei sistemi a 1 gdl smorzati): </text> </region> <region region-id="663" left="6" top="3282.75" width="253.5" height="46.5" align-x="34.5" align-y="3306" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#0ff" tag=""> <math optimize="false" disable-calc="false"> <ml:apply xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:equal/> <ml:apply> <ml:id xml:space="preserve" subscript="1">p</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">e</ml:id> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:neg/> <ml:id xml:space="preserve" subscript="1">ξ</ml:id> </ml:apply> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:apply> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> <ml:parens> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="1">A</ml:id> <ml:apply> <ml:id xml:space="preserve">cos</ml:id> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="s1">ω</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="1">B</ml:id> <ml:apply> <ml:id xml:space="preserve">sin</ml:id> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="s1">ω</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </ml:parens> </ml:apply> <ml:apply> <ml:div/> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve" font="17">Q</ml:id> <ml:real>1</ml:real> </ml:apply> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </math> <rendering item-idref="72"/> </region> <region region-id="629" left="6" top="3336.75" width="253.5" height="46.5" align-x="34.5" align-y="3360" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#0ff" tag=""> <math optimize="false" disable-calc="false"> <ml:apply xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:equal/> <ml:apply> <ml:id xml:space="preserve" subscript="2">p</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">e</ml:id> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:neg/> <ml:id xml:space="preserve" subscript="2">ξ</ml:id> </ml:apply> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> <ml:parens> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="2">A</ml:id> <ml:apply> <ml:id xml:space="preserve">cos</ml:id> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="s2">ω</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="2">B</ml:id> <ml:apply> <ml:id xml:space="preserve">sin</ml:id> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="s2">ω</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </ml:parens> </ml:apply> <ml:apply> <ml:div/> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve" font="17">Q</ml:id> <ml:real>2</ml:real> </ml:apply> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </math> <rendering item-idref="73"/> </region> <region region-id="406" left="6" top="3401.25" width="207.75" height="15.75" align-x="33" align-y="3414" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> Derivate temporali prime (velocità): </text> </region> <region region-id="240" left="6" top="3437.25" width="423" height="27.75" align-x="36.75" align-y="3456" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#0ff" tag=""> <math optimize="false" disable-calc="false"> <ml:apply xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:equal/> <ml:apply> <ml:id xml:space="preserve" subscript="1">p'</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:parens> <ml:apply> <ml:neg/> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">e</ml:id> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:neg/> <ml:id xml:space="preserve" subscript="1">ξ</ml:id> </ml:apply> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:apply> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> <ml:parens> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="1">A</ml:id> <ml:id xml:space="preserve" subscript="s1">ω</ml:id> </ml:apply> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="1">B</ml:id> <ml:id xml:space="preserve" subscript="1">ξ</ml:id> </ml:apply> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:apply> </ml:apply> </ml:parens> </ml:apply> </ml:apply> </ml:parens> <ml:apply> <ml:id xml:space="preserve">sin</ml:id> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="s1">ω</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">e</ml:id> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:neg/> <ml:id xml:space="preserve" subscript="1">ξ</ml:id> </ml:apply> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:apply> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> <ml:parens> <ml:apply> <ml:minus/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="1">B</ml:id> <ml:id xml:space="preserve" subscript="s1">ω</ml:id> </ml:apply> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="1">A</ml:id> <ml:id xml:space="preserve" subscript="1">ξ</ml:id> </ml:apply> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:apply> </ml:apply> </ml:parens> </ml:apply> <ml:apply> <ml:id xml:space="preserve">cos</ml:id> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="s1">ω</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </math> <rendering item-idref="74"/> </region> <region region-id="743" left="6" top="3479.25" width="423" height="27.75" align-x="36.75" align-y="3498" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#0ff" tag=""> <math optimize="false" disable-calc="false"> <ml:apply xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:equal/> <ml:apply> <ml:id xml:space="preserve" subscript="2">p'</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:parens> <ml:apply> <ml:neg/> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">e</ml:id> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:neg/> <ml:id xml:space="preserve" subscript="2">ξ</ml:id> </ml:apply> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> <ml:parens> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="2">A</ml:id> <ml:id xml:space="preserve" subscript="s2">ω</ml:id> </ml:apply> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="2">B</ml:id> <ml:id xml:space="preserve" subscript="2">ξ</ml:id> </ml:apply> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:apply> </ml:parens> </ml:apply> </ml:apply> </ml:parens> <ml:apply> <ml:id xml:space="preserve">sin</ml:id> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="s2">ω</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">e</ml:id> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:neg/> <ml:id xml:space="preserve" subscript="2">ξ</ml:id> </ml:apply> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> <ml:parens> <ml:apply> <ml:minus/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="2">B</ml:id> <ml:id xml:space="preserve" subscript="s2">ω</ml:id> </ml:apply> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="2">A</ml:id> <ml:id xml:space="preserve" subscript="2">ξ</ml:id> </ml:apply> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:apply> </ml:parens> </ml:apply> <ml:apply> <ml:id xml:space="preserve">cos</ml:id> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="s2">ω</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </math> <rendering item-idref="75"/> </region> <region region-id="244" left="6" top="3575.25" width="460.5" height="19.5" align-x="31.5" align-y="3588" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> Occorre assegnare le condizioni iniziali per calcolare le costanti A<f family="Arial" charset="0" size="14"> <inlineAttr font-weight="normal" font-style="normal" underline="false" line-through="false"> <c val="#f00"> 1 </c> </inlineAttr> </f>, B<f family="Arial" charset="0" size="14"> <inlineAttr font-weight="normal" font-style="normal" underline="false" line-through="false"> <c val="#f00"> 1 </c> </inlineAttr> </f>, A<f family="Arial" charset="0" size="14"> <inlineAttr font-weight="normal" font-style="normal" underline="false" line-through="false"> <c val="#f00"> 2 </c> </inlineAttr> </f>, B<inlineAttr font-weight="normal" font-style="normal" underline="false" line-through="false"> <c val="#f00"> <f family="Arial" charset="0" size="14">2</f> </c> </inlineAttr> </text> </region> <region region-id="264" left="6" top="3617.25" width="746.25" height="47.25" align-x="13.5" align-y="3630" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> <f family="Arial Baltic" charset="186">Le condizioni iniziali </f> <f family="Arial Baltic" charset="186"> vengono sempre assegnate nelle coordinate fisiche </f> <f family="Arial Baltic" charset="186">; occorre quindi convertirle nelle coordinate principali.</f> <f family="Arial Baltic" charset="186">Si utilizzano le seguenti trasformazioni:</f> </text> </region> <region region-id="267" left="6" top="3693.75" width="78" height="25.5" align-x="30.75" align-y="3714" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#0ff" tag=""> <math optimize="false" disable-calc="false"> <ml:apply xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:equal/> <ml:apply> <ml:id xml:space="preserve" font="17">p</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve" font="17">Φ</ml:id> <ml:real>-1</ml:real> </ml:apply> <ml:apply> <ml:id xml:space="preserve" font="17">x</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> </ml:apply> </math> <rendering item-idref="76"/> </region> <region region-id="271" left="210" top="3701.25" width="89.25" height="15.75" align-x="221.25" align-y="3714" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> per le posizioni </text> </region> <region region-id="269" left="6" top="3747.75" width="87" height="25.5" align-x="35.25" align-y="3768" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#0ff" tag=""> <math optimize="false" disable-calc="false"> <ml:apply xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:equal/> <ml:apply> <ml:id xml:space="preserve" font="17">p'</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve" font="17">Φ</ml:id> <ml:real>-1</ml:real> </ml:apply> <ml:apply> <ml:id xml:space="preserve" font="17">x'</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> </ml:apply> </math> <rendering item-idref="77"/> </region> <region region-id="273" left="210" top="3755.25" width="83.25" height="15.75" align-x="221.25" align-y="3768" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> per le velocità </text> </region> <region region-id="275" left="6" top="3815.25" width="171" height="15.75" align-x="6" align-y="3828" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> All'istante iniziale t=0 si avrà: </text> </region> <region region-id="737" left="6" top="3855.75" width="79.5" height="25.5" align-x="31.5" align-y="3876" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#0ff" tag=""> <math optimize="false" disable-calc="false"> <ml:apply xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:equal/> <ml:apply> <ml:id xml:space="preserve" font="17">p</ml:id> <ml:real>0</ml:real> </ml:apply> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve" font="17">Φ</ml:id> <ml:real>-1</ml:real> </ml:apply> <ml:apply> <ml:id xml:space="preserve" font="17">x</ml:id> <ml:real>0</ml:real> </ml:apply> </ml:apply> </ml:apply> </math> <rendering item-idref="78"/> </region> <region region-id="738" left="6" top="3891.75" width="88.5" height="25.5" align-x="36" align-y="3912" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#0ff" tag=""> <math optimize="false" disable-calc="false"> <ml:apply xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:equal/> <ml:apply> <ml:id xml:space="preserve" font="17">p'</ml:id> <ml:real>0</ml:real> </ml:apply> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve" font="17">Φ</ml:id> <ml:real>-1</ml:real> </ml:apply> <ml:apply> <ml:id xml:space="preserve" font="17">x'</ml:id> <ml:real>0</ml:real> </ml:apply> </ml:apply> </ml:apply> </math> <rendering item-idref="79"/> </region> <region region-id="293" left="6" top="3959.25" width="330.75" height="15.75" align-x="6" align-y="3972" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> Assegniamo le condizioni iniziali nelle coordinate fisiche </text> </region> <region region-id="297" left="6" top="3993" width="57" height="36.75" align-x="30.75" align-y="4014" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#0ff" tag=""> <math optimize="false" disable-calc="false"> <ml:apply xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:equal/> <ml:apply> <ml:id xml:space="preserve" font="17">x</ml:id> <ml:real>0</ml:real> </ml:apply> <ml:matrix rows="2" cols="1"> <ml:id xml:space="preserve" subscript="0">α</ml:id> <ml:id xml:space="preserve" subscript="0">β</ml:id> </ml:matrix> </ml:apply> </math> <rendering item-idref="80"/> </region> <region region-id="741" left="222" top="3993" width="63.75" height="36.75" align-x="251.25" align-y="4014" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#0ff" tag=""> <math optimize="false" disable-calc="false"> <ml:apply xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:equal/> <ml:apply> <ml:id xml:space="preserve" font="17">x'</ml:id> <ml:real>0</ml:real> </ml:apply> <ml:matrix rows="2" cols="1"> <ml:id xml:space="preserve" subscript="0">α'</ml:id> <ml:id xml:space="preserve" subscript="0">β'</ml:id> </ml:matrix> </ml:apply> </math> <rendering item-idref="81"/> </region> <region region-id="407" left="6" top="4053" width="105" height="16.5" align-x="24" align-y="4062" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="0">α</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:mult/> <ml:real>20</ml:real> <ml:id xml:space="preserve">deg</ml:id> </ml:apply> <ml:unitOverride> <ml:id xml:space="preserve">rad</ml:id> </ml:unitOverride> <result xmlns="http://schemas.mathsoft.com/math30"> <unitedValue> <ml:real>0.3490658503988659</ml:real> <unitMonomial xmlns="http://schemas.mathsoft.com/units10"> <unitReference unit="radian"/> </unitMonomial> </unitedValue> </result> </ml:eval> </ml:define> </math> <rendering item-idref="82"/> </region> <region region-id="740" left="222" top="4053" width="105.75" height="16.5" align-x="240.75" align-y="4062" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="0">β</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:mult/> <ml:real>10</ml:real> <ml:id xml:space="preserve">deg</ml:id> </ml:apply> <ml:unitOverride> <ml:id xml:space="preserve">rad</ml:id> </ml:unitOverride> <result xmlns="http://schemas.mathsoft.com/math30"> <unitedValue> <ml:real>0.17453292519943295</ml:real> <unitMonomial xmlns="http://schemas.mathsoft.com/units10"> <unitReference unit="radian"/> </unitMonomial> </unitedValue> </result> </ml:eval> </ml:define> </math> <rendering item-idref="83"/> </region> <region region-id="409" left="6" top="4107" width="33" height="16.5" align-x="26.25" align-y="4116" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="0">α'</ml:id> <ml:real>2</ml:real> </ml:define> </math> <rendering item-idref="84"/> </region> <region region-id="739" left="222" top="4107" width="39" height="16.5" align-x="243" align-y="4116" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="0">β'</ml:id> <ml:real>-3</ml:real> </ml:define> </math> <rendering item-idref="85"/> </region> <region region-id="322" left="6" top="4187.25" width="348.75" height="15.75" align-x="6" align-y="4200" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> Convertiamo le condizioni iniziali nelle coordinate principali </text> </region> <region region-id="325" left="6" top="4229.25" width="94.5" height="15.75" align-x="17.25" align-y="4242" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> Per le posizioni: </text> </region> <region region-id="308" left="6" top="4269.75" width="79.5" height="25.5" align-x="31.5" align-y="4290" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#0ff" tag=""> <math optimize="false" disable-calc="false"> <ml:apply xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:equal/> <ml:apply> <ml:id xml:space="preserve" font="17">p</ml:id> <ml:real>0</ml:real> </ml:apply> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve" font="17">Φ</ml:id> <ml:real>-1</ml:real> </ml:apply> <ml:apply> <ml:id xml:space="preserve" font="17">x</ml:id> <ml:real>0</ml:real> </ml:apply> </ml:apply> </ml:apply> </math> <rendering item-idref="86"/> </region> <region region-id="310" left="6" top="4311" width="147.75" height="36.75" align-x="42.75" align-y="4332" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="1"> <ml:id xml:space="preserve" subscript="10">p</ml:id> <ml:id xml:space="preserve" subscript="20">p</ml:id> </ml:matrix> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve" font="17">Φ</ml:id> <ml:real>-1</ml:real> </ml:apply> <ml:matrix rows="2" cols="1"> <ml:id xml:space="preserve" subscript="0">α</ml:id> <ml:id xml:space="preserve" subscript="0">β</ml:id> </ml:matrix> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="1"> <ml:real>0.3381817820345262</ml:real> <ml:real>0.38124781233483812</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </math> <rendering item-idref="87"/> </region> <region region-id="311" left="342" top="4323" width="56.25" height="16.5" align-x="364.5" align-y="4332" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="10">p</ml:id> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>0.3381817820345262</ml:real> </result> </ml:eval> </math> <rendering item-idref="88"/> </region> <region region-id="312" left="498" top="4323" width="56.25" height="16.5" align-x="520.5" align-y="4332" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="20">p</ml:id> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>0.38124781233483812</ml:real> </result> </ml:eval> </math> <rendering item-idref="89"/> </region> <region region-id="326" left="6" top="4373.25" width="88.5" height="15.75" align-x="6" align-y="4386" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> Per le velocità: </text> </region> <region region-id="317" left="6" top="4407.75" width="88.5" height="25.5" align-x="36" align-y="4428" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#0ff" tag=""> <math optimize="false" disable-calc="false"> <ml:apply xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:equal/> <ml:apply> <ml:id xml:space="preserve" font="17">p'</ml:id> <ml:real>0</ml:real> </ml:apply> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve" font="17">Φ</ml:id> <ml:real>-1</ml:real> </ml:apply> <ml:apply> <ml:id xml:space="preserve" font="17">x'</ml:id> <ml:real>0</ml:real> </ml:apply> </ml:apply> </ml:apply> </math> <rendering item-idref="90"/> </region> <region region-id="318" left="6" top="4449" width="157.5" height="36.75" align-x="45" align-y="4470" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="1"> <ml:id xml:space="preserve" subscript="10">p'</ml:id> <ml:id xml:space="preserve" subscript="20">p'</ml:id> </ml:matrix> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve" font="17">Φ</ml:id> <ml:real>-1</ml:real> </ml:apply> <ml:matrix rows="2" cols="1"> <ml:id xml:space="preserve" subscript="0">α'</ml:id> <ml:id xml:space="preserve" subscript="0">β'</ml:id> </ml:matrix> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="1"> <ml:real>2.8844986402102353</ml:real> <ml:real>-5.4784731079585773</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </math> <rendering item-idref="91"/> </region> <region region-id="319" left="342" top="4461" width="58.5" height="16.5" align-x="366.75" align-y="4470" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="10">p'</ml:id> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>2.8844986402102353</ml:real> </result> </ml:eval> </math> <rendering item-idref="92"/> </region> <region region-id="320" left="498" top="4461" width="63.75" height="16.5" align-x="522.75" align-y="4470" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="20">p'</ml:id> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>-5.4784731079585773</ml:real> </result> </ml:eval> </math> <rendering item-idref="93"/> </region> <region region-id="343" left="6" top="4511.25" width="219" height="15.75" align-x="42" align-y="4524" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> Imponendo le condizioni iniziali si ha: </text> </region> <region region-id="742" left="6" top="4541.25" width="253.5" height="200.25" align-x="37.5" align-y="4644" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:matrix rows="4" cols="1"> <ml:id xml:space="preserve" subscript="1">A</ml:id> <ml:id xml:space="preserve" subscript="1">B</ml:id> <ml:id xml:space="preserve" subscript="2">A</ml:id> <ml:id xml:space="preserve" subscript="2">B</ml:id> </ml:matrix> <ml:eval placeholderMultiplicationStyle="default"> <ml:provenance expr-id="1"> <originRef doc-id="0188C78A-80C3-4B5A-9564-83EA9BCA226A" version-id="531BC4DB-5DD3-4B33-B2A9-739C49F261FE" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315097276" is-modified="true" region-id="40" href="C:\Users\giova\Documents\3) TEMP\aaa.xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </originRef> <parentRef doc-id="0188C78A-80C3-4B5A-9564-83EA9BCA226A" version-id="531BC4DB-5DD3-4B33-B2A9-739C49F261FE" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315096916" is-modified="true" region-id="40" href="C:\Users\giova\Documents\3) TEMP\aaa.xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </parentRef> <comment xmlns="http://schemas.mathsoft.com/provenance10"/> <originComment xmlns="http://schemas.mathsoft.com/provenance10"/> <contentHash xmlns="http://schemas.mathsoft.com/provenance10">ac4a1fe8354cea5436b2e915ca7011e8</contentHash> <ml:matrix rows="4" cols="1"> <ml:apply> <ml:div/> <ml:apply> <ml:minus/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="10">p</ml:id> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve" font="17">Q</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:apply> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:apply> <ml:div/> <ml:apply> <ml:minus/> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="10">p</ml:id> <ml:id xml:space="preserve" subscript="1">ξ</ml:id> </ml:apply> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="10">p'</ml:id> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="1">ξ</ml:id> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve" font="17">Q</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="s1">ω</ml:id> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:apply> </ml:apply> <ml:apply> <ml:div/> <ml:apply> <ml:minus/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="20">p</ml:id> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve" font="17">Q</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:apply> <ml:div/> <ml:apply> <ml:minus/> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="20">p</ml:id> <ml:id xml:space="preserve" subscript="2">ξ</ml:id> </ml:apply> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="20">p'</ml:id> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="2">ξ</ml:id> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve" font="17">Q</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="s2">ω</ml:id> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:apply> </ml:matrix> </ml:provenance> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="4" cols="1"> <ml:real>0.38091449488690149</ml:real> <ml:real>0.3046298119254186</ml:real> <ml:real>-1.0617780462276529</ml:real> <ml:real>-0.98098457611047762</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </math> <rendering item-idref="94"> <element-image-map> <box left="40.5" top="0.75" width="156.75" height="198.75" expr-idref="1" xmlns="http://schemas.mathsoft.com/worksheet30"/> </element-image-map> </rendering> </region> <region region-id="645" left="6" top="4775.25" width="669" height="19.5" align-x="15" align-y="4788" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> La soluzione nelle coordinate principali è ora calcolabile, perché sono state determinate le costanti A<f family="Arial" charset="0" size="14"> <inlineAttr font-weight="normal" font-style="normal" underline="false" line-through="false"> <c val="#f00"> 1 </c> </inlineAttr> </f>, B<f family="Arial" charset="0" size="14"> <inlineAttr font-weight="normal" font-style="normal" underline="false" line-through="false"> <c val="#f00"> 1 </c> </inlineAttr> </f>, A<f family="Arial" charset="0" size="14"> <inlineAttr font-weight="normal" font-style="normal" underline="false" line-through="false"> <c val="#f00"> 2 </c> </inlineAttr> </f>, B<inlineAttr font-weight="normal" font-style="normal" underline="false" line-through="false"> <c val="#f00"> <f family="Arial" charset="0" size="14">2</f> </c> </inlineAttr> </text> </region> <region region-id="646" left="6" top="4824.75" width="255.75" height="46.5" align-x="36" align-y="4848" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:function> <ml:id xml:space="preserve" subscript="1">p</ml:id> <ml:boundVars> <ml:id xml:space="preserve">t</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">e</ml:id> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:neg/> <ml:id xml:space="preserve" subscript="1">ξ</ml:id> </ml:apply> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:apply> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> <ml:parens> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="1">A</ml:id> <ml:apply> <ml:id xml:space="preserve">cos</ml:id> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="s1">ω</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="1">B</ml:id> <ml:apply> <ml:id xml:space="preserve">sin</ml:id> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="s1">ω</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </ml:parens> </ml:apply> <ml:apply> <ml:div/> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve" font="17">Q</ml:id> <ml:real>1</ml:real> </ml:apply> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:apply> </ml:define> </math> <rendering item-idref="95"/> </region> <region region-id="647" left="6" top="4908.75" width="255.75" height="46.5" align-x="36" align-y="4932" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:function> <ml:id xml:space="preserve" subscript="2">p</ml:id> <ml:boundVars> <ml:id xml:space="preserve">t</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">e</ml:id> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:neg/> <ml:id xml:space="preserve" subscript="2">ξ</ml:id> </ml:apply> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> <ml:parens> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="2">A</ml:id> <ml:apply> <ml:id xml:space="preserve">cos</ml:id> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="s2">ω</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="2">B</ml:id> <ml:apply> <ml:id xml:space="preserve">sin</ml:id> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="s2">ω</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </ml:parens> </ml:apply> <ml:apply> <ml:div/> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve" font="17">Q</ml:id> <ml:real>2</ml:real> </ml:apply> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:apply> </ml:define> </math> <rendering item-idref="96"/> </region> <region region-id="387" left="6" top="4985.25" width="572.25" height="15.75" align-x="31.5" align-y="4998" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> Occorre infine applicare la formula 5.195 per calcolare la soluzione nelle coordinate fisiche α e β </text> </region> <region region-id="392" left="6" top="5037" width="70.5" height="36.75" align-x="32.25" align-y="5058" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:function> <ml:id xml:space="preserve" font="17">p</ml:id> <ml:boundVars> <ml:id xml:space="preserve">t</ml:id> </ml:boundVars> </ml:function> <ml:matrix rows="2" cols="1"> <ml:apply> <ml:id xml:space="preserve" subscript="1">p</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> <ml:apply> <ml:id xml:space="preserve" subscript="2">p</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:matrix> </ml:define> </math> <rendering item-idref="97"/> </region> <region region-id="397" left="6" top="5091.75" width="68.25" height="19.5" align-x="31.5" align-y="5106" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:function> <ml:id xml:space="preserve" font="17">x</ml:id> <ml:boundVars> <ml:id xml:space="preserve">t</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" font="17">Φ</ml:id> <ml:apply> <ml:id xml:space="preserve" font="17">p</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> </ml:define> </math> <rendering item-idref="98"/> </region> <region region-id="398" left="6" top="5127.75" width="56.25" height="20.25" align-x="30.75" align-y="5142" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:function> <ml:id xml:space="preserve">α</ml:id> <ml:boundVars> <ml:id xml:space="preserve">t</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:indexer/> <ml:apply> <ml:id xml:space="preserve" font="17">x</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> <ml:real>1</ml:real> </ml:apply> </ml:define> </math> <rendering item-idref="99"/> </region> <region region-id="399" left="6" top="5157.75" width="57" height="20.25" align-x="31.5" align-y="5172" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:function> <ml:id xml:space="preserve">β</ml:id> <ml:boundVars> <ml:id xml:space="preserve">t</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:indexer/> <ml:apply> <ml:id xml:space="preserve" font="17">x</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> <ml:real>2</ml:real> </ml:apply> </ml:define> </math> <rendering item-idref="100"/> </region> <region region-id="483" left="6" top="5217" width="41.25" height="16.5" align-x="34.5" align-y="5226" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="max">T</ml:id> <ml:real>9</ml:real> </ml:define> </math> <rendering item-idref="101"/> </region> <region region-id="484" left="6" top="5259" width="48" height="12.75" align-x="25.5" align-y="5268" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve">Δt</ml:id> <ml:real>0.001</ml:real> </ml:define> </math> <rendering item-idref="102"/> </region> <region region-id="768" left="6" top="5283" width="41.25" height="16.5" align-x="34.5" align-y="5292" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define warning="WarnRedefinedUDScalar" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="max">T</ml:id> <ml:real>5</ml:real> </ml:define> </math> <rendering item-idref="103"/> </region> <region region-id="485" left="6" top="5313" width="69.75" height="16.5" align-x="16.5" align-y="5322" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve">t</ml:id> <ml:range> <ml:sequence> <ml:real>0</ml:real> <ml:id xml:space="preserve">Δt</ml:id> </ml:sequence> <ml:id xml:space="preserve" subscript="max">T</ml:id> </ml:range> </ml:define> </math> <rendering item-idref="104"/> </region> <region region-id="416" left="6" top="5358" width="761.25" height="266.25" align-x="6" align-y="5358" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <plot disable-calc="false" item-idref="105"/> <rendering item-idref="106"/> </region> <region region-id="764" left="834" top="5481" width="36" height="16.5" align-x="851.25" align-y="5490" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:globalDefine xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="R">α</ml:id> <ml:real>0.1</ml:real> </ml:globalDefine> </math> <rendering item-idref="107"/> </region> <region region-id="765" left="942" top="5481" width="36.75" height="16.5" align-x="960" align-y="5490" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:globalDefine xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="R">β</ml:id> <ml:real>0.1</ml:real> </ml:globalDefine> </math> <rendering item-idref="108"/> </region> <region region-id="766" left="834" top="5580.75" width="91.5" height="29.25" align-x="850.5" align-y="5598" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17">C</ml:id> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="2"> <ml:real>5.8439999999999994</ml:real> <ml:real>-0.96</ml:real> <ml:real>-0.96</ml:real> <ml:real>4.6645199999999996</ml:real> </ml:matrix> </result> </ml:eval> </math> <rendering item-idref="109"/> </region> <region region-id="754" left="6" top="5676" width="756" height="266.25" align-x="6" align-y="5676" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <plot disable-calc="false" item-idref="110"/> <rendering item-idref="111"/> </region> <region region-id="213" left="6" top="6029.25" width="300" height="15.75" align-x="21" align-y="6042" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> Verifica dei calcoli mediante integrazione numerica </text> </region> <region region-id="457" left="6" top="6072.75" width="110.25" height="29.25" align-x="18" align-y="6090" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17">I</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:id xml:space="preserve">identity</ml:id> <ml:real>2</ml:real> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="2"> <ml:real>1</ml:real> <ml:real>0</ml:real> <ml:real>0</ml:real> <ml:real>1</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </math> <rendering item-idref="112"/> </region> <region region-id="458" left="222" top="6072.75" width="86.25" height="29.25" align-x="240.75" align-y="6090" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:provenance expr-id="1" xmlns:ml="http://schemas.mathsoft.com/math30"> <originRef doc-id="1C97A390-190F-4DBF-BEDE-2FCBA53F3DB1" version-id="80C8BBE0-DCBC-478A-AA76-7303644D9E84" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315096916" is-modified="false" region-id="0" href="C:\Users\giova\Documents\2) HOMEWORK\Materiale per Video Lezioni\Mathcad\Nuovi files\File 22 - Vibrazioni torsionali (avviamento).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </originRef> <parentRef doc-id="1C97A390-190F-4DBF-BEDE-2FCBA53F3DB1" version-id="80C8BBE0-DCBC-478A-AA76-7303644D9E84" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315097276" is-modified="false" region-id="0" href="C:\Users\giova\Documents\2) HOMEWORK\Materiale per Video Lezioni\Mathcad\Nuovi files\File 22 - Vibrazioni torsionali (avviamento).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </parentRef> <comment xmlns="http://schemas.mathsoft.com/provenance10"/> <originComment xmlns="http://schemas.mathsoft.com/provenance10"/> <contentHash xmlns="http://schemas.mathsoft.com/provenance10">40b468a951fb4f0d6bf3a1b86a328624</contentHash> <ml:define> <ml:id xml:space="preserve" font="17">O</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:mult/> <ml:real>0</ml:real> <ml:id xml:space="preserve" font="17">I</ml:id> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="2"> <ml:real>0</ml:real> <ml:real>0</ml:real> <ml:real>0</ml:real> <ml:real>0</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </ml:provenance> </math> <rendering item-idref="113"> <element-image-map> <box left="1.5" top="0.75" width="84.75" height="27.75" expr-idref="1" xmlns="http://schemas.mathsoft.com/worksheet30"/> </element-image-map> </rendering> </region> <region region-id="463" left="6" top="6120.75" width="183" height="29.25" align-x="46.5" align-y="6138" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:provenance expr-id="1" xmlns:ml="http://schemas.mathsoft.com/math30"> <originRef doc-id="1C97A390-190F-4DBF-BEDE-2FCBA53F3DB1" version-id="80C8BBE0-DCBC-478A-AA76-7303644D9E84" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315097316" is-modified="false" region-id="0" href="C:\Users\giova\Documents\2) HOMEWORK\Materiale per Video Lezioni\Mathcad\Nuovi files\File 22 - Vibrazioni torsionali (avviamento).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </originRef> <parentRef doc-id="1C97A390-190F-4DBF-BEDE-2FCBA53F3DB1" version-id="80C8BBE0-DCBC-478A-AA76-7303644D9E84" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315096756" is-modified="false" region-id="0" href="C:\Users\giova\Documents\2) HOMEWORK\Materiale per Video Lezioni\Mathcad\Nuovi files\File 22 - Vibrazioni torsionali (avviamento).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </parentRef> <comment xmlns="http://schemas.mathsoft.com/provenance10"/> <originComment xmlns="http://schemas.mathsoft.com/provenance10"/> <contentHash xmlns="http://schemas.mathsoft.com/provenance10">66700cb9cbec92ceab73718f47b5bd7c</contentHash> <ml:define> <ml:id xml:space="preserve" font="17" subscript="sup">A</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:id xml:space="preserve">augment</ml:id> <ml:sequence> <ml:id xml:space="preserve" font="17">O</ml:id> <ml:id xml:space="preserve" font="17">I</ml:id> </ml:sequence> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="4"> <ml:real>0</ml:real> <ml:real>0</ml:real> <ml:real>0</ml:real> <ml:real>0</ml:real> <ml:real>1</ml:real> <ml:real>0</ml:real> <ml:real>0</ml:real> <ml:real>1</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </ml:provenance> </math> <rendering item-idref="114"> <element-image-map> <box left="1.5" top="0.75" width="181.5" height="27.75" expr-idref="1" xmlns="http://schemas.mathsoft.com/worksheet30"/> </element-image-map> </rendering> </region> <region region-id="692" left="6" top="6165.75" width="339.75" height="32.25" align-x="42.75" align-y="6186" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:provenance expr-id="1" xmlns:ml="http://schemas.mathsoft.com/math30"> <originRef doc-id="1C97A390-190F-4DBF-BEDE-2FCBA53F3DB1" version-id="80C8BBE0-DCBC-478A-AA76-7303644D9E84" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315097396" is-modified="false" region-id="0" href="C:\Users\giova\Documents\2) HOMEWORK\Materiale per Video Lezioni\Mathcad\Nuovi files\File 22 - Vibrazioni torsionali (avviamento).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </originRef> <parentRef doc-id="1C97A390-190F-4DBF-BEDE-2FCBA53F3DB1" version-id="80C8BBE0-DCBC-478A-AA76-7303644D9E84" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315096916" is-modified="false" region-id="0" href="C:\Users\giova\Documents\2) HOMEWORK\Materiale per Video Lezioni\Mathcad\Nuovi files\File 22 - Vibrazioni torsionali (avviamento).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </parentRef> <comment xmlns="http://schemas.mathsoft.com/provenance10"/> <originComment xmlns="http://schemas.mathsoft.com/provenance10"/> <contentHash xmlns="http://schemas.mathsoft.com/provenance10">936b4fb243f1803ebac9a0bdf12e07fb</contentHash> <ml:define> <ml:id xml:space="preserve" font="17" subscript="inf">A</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:id xml:space="preserve">augment</ml:id> <ml:sequence> <ml:apply> <ml:mult/> <ml:apply> <ml:neg/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve" font="17">J</ml:id> <ml:real>-1</ml:real> </ml:apply> </ml:apply> <ml:id xml:space="preserve" font="17">K</ml:id> </ml:apply> <ml:apply> <ml:mult/> <ml:apply> <ml:neg/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve" font="17">J</ml:id> <ml:real>-1</ml:real> </ml:apply> </ml:apply> <ml:id xml:space="preserve" font="17">C</ml:id> </ml:apply> </ml:sequence> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="4"> <ml:real>-161.66666666666669</ml:real> <ml:real>8.588298443370908</ml:real> <ml:real>26.666666666666668</ml:real> <ml:real>-41.062801932367151</ml:real> <ml:real>-4.87</ml:real> <ml:real>0.25764895330112725</ml:real> <ml:real>0.8</ml:real> <ml:real>-1.2518840579710144</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </ml:provenance> <resultFormat numeric-only="true"> <general precision="3" show-trailing-zeros="false" radix="dec" complex-threshold="10" zero-threshold="15" imaginary-value="i" exponential-threshold="6"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="true" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="115"> <element-image-map> <box left="1.5" top="0.75" width="338.25" height="30.75" expr-idref="1" xmlns="http://schemas.mathsoft.com/worksheet30"/> </element-image-map> </rendering> </region> <region region-id="693" left="6" top="6206.25" width="289.5" height="62.25" align-x="24" align-y="6240" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:provenance expr-id="1" xmlns:ml="http://schemas.mathsoft.com/math30"> <originRef doc-id="1C97A390-190F-4DBF-BEDE-2FCBA53F3DB1" version-id="80C8BBE0-DCBC-478A-AA76-7303644D9E84" branch-id="00000000-0000-0000-0000-000000000000" revision-num="246826596" is-modified="false" region-id="0" href="C:\Users\giova\Documents\2) HOMEWORK\Materiale per Video Lezioni\Mathcad\Nuovi files\File 22 - Vibrazioni torsionali (avviamento).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </originRef> <parentRef doc-id="1C97A390-190F-4DBF-BEDE-2FCBA53F3DB1" version-id="80C8BBE0-DCBC-478A-AA76-7303644D9E84" branch-id="00000000-0000-0000-0000-000000000000" revision-num="246826396" is-modified="false" region-id="0" href="C:\Users\giova\Documents\2) HOMEWORK\Materiale per Video Lezioni\Mathcad\Nuovi files\File 22 - Vibrazioni torsionali (avviamento).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </parentRef> <comment xmlns="http://schemas.mathsoft.com/provenance10"/> <originComment xmlns="http://schemas.mathsoft.com/provenance10"/> <contentHash xmlns="http://schemas.mathsoft.com/provenance10">fd52f0e20f32b4c58069e06bff674eba</contentHash> <ml:define> <ml:id xml:space="preserve" font="17">A</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:id xml:space="preserve">stack</ml:id> <ml:sequence> <ml:id xml:space="preserve" font="17" subscript="sup">A</ml:id> <ml:id xml:space="preserve" font="17" subscript="inf">A</ml:id> </ml:sequence> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="4" cols="4"> <ml:real>0</ml:real> <ml:real>0</ml:real> <ml:real>-161.66666666666669</ml:real> <ml:real>8.588298443370908</ml:real> <ml:real>0</ml:real> <ml:real>0</ml:real> <ml:real>26.666666666666668</ml:real> <ml:real>-41.062801932367151</ml:real> <ml:real>1</ml:real> <ml:real>0</ml:real> <ml:real>-4.87</ml:real> <ml:real>0.25764895330112725</ml:real> <ml:real>0</ml:real> <ml:real>1</ml:real> <ml:real>0.8</ml:real> <ml:real>-1.2518840579710144</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </ml:provenance> <resultFormat numeric-only="true"> <general precision="3" show-trailing-zeros="false" radix="dec" complex-threshold="10" zero-threshold="15" imaginary-value="i" exponential-threshold="6"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="true" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="116"> <element-image-map> <box left="1.5" top="0.75" width="288" height="60.75" expr-idref="1" xmlns="http://schemas.mathsoft.com/worksheet30"/> </element-image-map> </rendering> </region> <region region-id="694" left="6" top="6276.75" width="40.5" height="29.25" align-x="21" align-y="6294" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17">o</ml:id> <ml:matrix rows="2" cols="1"> <ml:real>0</ml:real> <ml:real>0</ml:real> </ml:matrix> </ml:define> </math> <rendering item-idref="117"/> </region> <region region-id="468" left="6" top="6318.75" width="55.5" height="29.25" align-x="21.75" align-y="6336" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17">F</ml:id> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="1"> <ml:real>0</ml:real> <ml:real>110</ml:real> </ml:matrix> </result> </ml:eval> </math> <rendering item-idref="118"/> </region> <region region-id="469" left="6" top="6368.25" width="158.25" height="62.25" align-x="21" align-y="6402" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17">b</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:id xml:space="preserve">stack</ml:id> <ml:sequence> <ml:id xml:space="preserve" font="17">o</ml:id> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve" font="17">J</ml:id> <ml:real>-1</ml:real> </ml:apply> <ml:id xml:space="preserve" font="17">F</ml:id> </ml:apply> </ml:sequence> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="4" cols="1"> <ml:real>0</ml:real> <ml:real>0</ml:real> <ml:real>0</ml:real> <ml:real>29.522275899087496</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </math> <rendering item-idref="119"/> </region> <region region-id="673" left="378" top="6462.75" width="63" height="29.25" align-x="390" align-y="6480" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:provenance expr-id="1"> <originRef doc-id="1C97A390-190F-4DBF-BEDE-2FCBA53F3DB1" version-id="80C8BBE0-DCBC-478A-AA76-7303644D9E84" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315097116" is-modified="false" region-id="0" href="C:\Users\giova\Documents\2) HOMEWORK\Materiale per Video Lezioni\Mathcad\Nuovi files\File 22 - Vibrazioni torsionali (avviamento).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </originRef> <parentRef doc-id="1C97A390-190F-4DBF-BEDE-2FCBA53F3DB1" version-id="80C8BBE0-DCBC-478A-AA76-7303644D9E84" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315096916" is-modified="false" region-id="0" href="C:\Users\giova\Documents\2) HOMEWORK\Materiale per Video Lezioni\Mathcad\Nuovi files\File 22 - Vibrazioni torsionali (avviamento).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </parentRef> <comment xmlns="http://schemas.mathsoft.com/provenance10"/> <originComment xmlns="http://schemas.mathsoft.com/provenance10"/> <contentHash xmlns="http://schemas.mathsoft.com/provenance10">f38c1c39d8f7d46e5f8a7de790f0d96e</contentHash> <ml:id xml:space="preserve">ω</ml:id> </ml:provenance> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="1"> <ml:real>12.788144031903128</ml:real> <ml:real>6.2604185817191329</ml:real> </ml:matrix> </result> </ml:eval> <resultFormat numeric-only="true"> <general precision="3" show-trailing-zeros="false" radix="dec" complex-threshold="10" zero-threshold="6" imaginary-value="i" exponential-threshold="3"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="true" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="120"> <element-image-map> <box left="1.5" top="9" width="6" height="11.25" expr-idref="1" xmlns="http://schemas.mathsoft.com/worksheet30"/> </element-image-map> </rendering> </region> <region region-id="471" left="6" top="6452.25" width="174.75" height="62.25" align-x="19.5" align-y="6486" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:provenance expr-id="1" xmlns:ml="http://schemas.mathsoft.com/math30"> <originRef doc-id="1C97A390-190F-4DBF-BEDE-2FCBA53F3DB1" version-id="80C8BBE0-DCBC-478A-AA76-7303644D9E84" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315097316" is-modified="false" region-id="0" href="C:\Users\giova\Documents\2) HOMEWORK\Materiale per Video Lezioni\Mathcad\Nuovi files\File 22 - Vibrazioni torsionali (avviamento).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </originRef> <parentRef doc-id="1C97A390-190F-4DBF-BEDE-2FCBA53F3DB1" version-id="80C8BBE0-DCBC-478A-AA76-7303644D9E84" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315097236" is-modified="false" region-id="0" href="C:\Users\giova\Documents\2) HOMEWORK\Materiale per Video Lezioni\Mathcad\Nuovi files\File 22 - Vibrazioni torsionali (avviamento).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </parentRef> <comment xmlns="http://schemas.mathsoft.com/provenance10"/> <originComment xmlns="http://schemas.mathsoft.com/provenance10"/> <contentHash xmlns="http://schemas.mathsoft.com/provenance10">35ffc4f48040b35e74bbe0ca271ce37f</contentHash> <ml:define> <ml:id xml:space="preserve">λ</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:id xml:space="preserve">eigenvals</ml:id> <ml:id xml:space="preserve" font="17">A</ml:id> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="4" cols="1"> <ml:complex> <ml:real>-2.4630494167104953</ml:real> <ml:imag>12.548705724159028</ml:imag> </ml:complex> <ml:complex> <ml:real>-2.4630494167104953</ml:real> <ml:imag>-12.548705724159028</ml:imag> </ml:complex> <ml:complex> <ml:real>-0.59789261227501378</ml:real> <ml:imag>6.2318027281454569</ml:imag> </ml:complex> <ml:complex> <ml:real>-0.59789261227501378</ml:real> <ml:imag>-6.2318027281454569</ml:imag> </ml:complex> </ml:matrix> </result> </ml:eval> </ml:define> </ml:provenance> <resultFormat numeric-only="true"> <general precision="3" show-trailing-zeros="false" radix="dec" complex-threshold="5" zero-threshold="5" imaginary-value="i" exponential-threshold="3"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="true" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="121"> <element-image-map> <box left="1.5" top="0.75" width="173.25" height="60.75" expr-idref="1" xmlns="http://schemas.mathsoft.com/worksheet30"/> </element-image-map> </rendering> </region> <region region-id="508" left="6" top="6534" width="334.5" height="249.75" align-x="6" align-y="6534" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <plot disable-calc="false" item-idref="122"/> <rendering item-idref="123"/> </region> <region region-id="478" left="240" top="6827.25" width="102" height="15.75" align-x="261.75" align-y="6840" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="true"> Condizioni iniziali </text> </region> <region region-id="479" left="6" top="6810.75" width="101.25" height="77.25" align-x="21" align-y="6852" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17">y</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:matrix rows="4" cols="1"> <ml:id xml:space="preserve" subscript="0">α</ml:id> <ml:id xml:space="preserve" subscript="0">β</ml:id> <ml:id xml:space="preserve" subscript="0">α'</ml:id> <ml:id xml:space="preserve" subscript="0">β'</ml:id> </ml:matrix> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="4" cols="1"> <ml:real>0.3490658503988659</ml:real> <ml:real>0.17453292519943295</ml:real> <ml:real>2</ml:real> <ml:real>-3</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </math> <rendering item-idref="124"/> </region> <region region-id="509" left="6" top="6939" width="68.25" height="18.75" align-x="24" align-y="6954" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve">Δt</ml:id> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>0.001</ml:real> </result> </ml:eval> </math> <rendering item-idref="125"/> </region> <region region-id="510" left="6" top="6988.5" width="111" height="31.5" align-x="21.75" align-y="7008" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:provenance expr-id="1" xmlns:ml="http://schemas.mathsoft.com/math30"> <originRef doc-id="1C97A390-190F-4DBF-BEDE-2FCBA53F3DB1" version-id="80C8BBE0-DCBC-478A-AA76-7303644D9E84" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315097396" is-modified="false" region-id="0" href="C:\Users\giova\Documents\2) HOMEWORK\Materiale per Video Lezioni\Mathcad\Nuovi files\File 22 - Vibrazioni torsionali (avviamento).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </originRef> <parentRef doc-id="1C97A390-190F-4DBF-BEDE-2FCBA53F3DB1" version-id="80C8BBE0-DCBC-478A-AA76-7303644D9E84" branch-id="00000000-0000-0000-0000-000000000000" revision-num="246826396" is-modified="false" region-id="0" href="C:\Users\giova\Documents\2) HOMEWORK\Materiale per Video Lezioni\Mathcad\Nuovi files\File 22 - Vibrazioni torsionali (avviamento).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </parentRef> <comment xmlns="http://schemas.mathsoft.com/provenance10"/> <originComment xmlns="http://schemas.mathsoft.com/provenance10"/> <contentHash xmlns="http://schemas.mathsoft.com/provenance10">639b1c04d0ad87f57a07112da323d20e</contentHash> <ml:define warning="WarnRedefinedBIUnit"> <ml:id xml:space="preserve">N</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:id xml:space="preserve">ceil</ml:id> <ml:apply> <ml:div/> <ml:id xml:space="preserve" subscript="max">T</ml:id> <ml:id xml:space="preserve">Δt</ml:id> </ml:apply> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>5000</ml:real> </result> </ml:eval> </ml:define> </ml:provenance> <resultFormat numeric-only="true"> <general precision="3" show-trailing-zeros="false" radix="dec" complex-threshold="10" zero-threshold="15" imaginary-value="i" exponential-threshold="6"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="true" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="126"> <element-image-map> <box left="1.5" top="0.75" width="109.5" height="30" expr-idref="1" xmlns="http://schemas.mathsoft.com/worksheet30"/> </element-image-map> </rendering> </region> <region region-id="511" left="6" top="7065.75" width="122.25" height="19.5" align-x="77.25" align-y="7080" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#ff0" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:function> <ml:id xml:space="preserve">EQMOTO</ml:id> <ml:boundVars> <ml:id xml:space="preserve">t</ml:id> <ml:id xml:space="preserve" font="17">y</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" font="17">A</ml:id> <ml:id xml:space="preserve" font="17">y</ml:id> </ml:apply> <ml:id xml:space="preserve" font="17">b</ml:id> </ml:apply> </ml:define> </math> <rendering item-idref="127"/> </region> <region region-id="514" left="354" top="7067.25" width="227.25" height="15.75" align-x="374.25" align-y="7080" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="true"> Equazioni di moto nello spazio di stato </text> </region> <region region-id="513" left="6" top="7101.75" width="174.75" height="21.75" align-x="30" align-y="7116" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#0f0" tag=""> <math optimize="false" disable-calc="false"> <ml:provenance expr-id="1" xmlns:ml="http://schemas.mathsoft.com/math30"> <originRef doc-id="1C97A390-190F-4DBF-BEDE-2FCBA53F3DB1" version-id="80C8BBE0-DCBC-478A-AA76-7303644D9E84" branch-id="00000000-0000-0000-0000-000000000000" revision-num="246826836" is-modified="false" region-id="0" href="C:\Users\giova\Documents\2) HOMEWORK\Materiale per Video Lezioni\Mathcad\Nuovi files\File 22 - Vibrazioni torsionali (avviamento).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </originRef> <parentRef doc-id="1C97A390-190F-4DBF-BEDE-2FCBA53F3DB1" version-id="80C8BBE0-DCBC-478A-AA76-7303644D9E84" branch-id="00000000-0000-0000-0000-000000000000" revision-num="246828636" is-modified="false" region-id="0" href="C:\Users\giova\Documents\2) HOMEWORK\Materiale per Video Lezioni\Mathcad\Nuovi files\File 22 - Vibrazioni torsionali (avviamento).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </parentRef> <comment xmlns="http://schemas.mathsoft.com/provenance10"/> <originComment xmlns="http://schemas.mathsoft.com/provenance10"/> <contentHash xmlns="http://schemas.mathsoft.com/provenance10">7a8e4d7e7d7c90a9c423f2a096bf0ee8</contentHash> <ml:define> <ml:id xml:space="preserve">TAB</ml:id> <ml:apply> <ml:id xml:space="preserve">rkfixed</ml:id> <ml:sequence> <ml:id xml:space="preserve" font="17">y</ml:id> <ml:real>0</ml:real> <ml:id xml:space="preserve" subscript="max">T</ml:id> <ml:id xml:space="preserve">N</ml:id> <ml:id xml:space="preserve">EQMOTO</ml:id> </ml:sequence> </ml:apply> </ml:define> </ml:provenance> </math> <rendering item-idref="128"> <element-image-map> <box left="1.5" top="0.75" width="173.25" height="20.25" expr-idref="1" xmlns="http://schemas.mathsoft.com/worksheet30"/> </element-image-map> </rendering> </region> <region region-id="515" left="354" top="7109.25" width="249.75" height="15.75" align-x="373.5" align-y="7122" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="true"> Soluzione per via numerica (Runge-Kutta) </text> </region> <region region-id="499" left="6" top="7179.75" width="68.25" height="18" align-x="37.5" align-y="7194" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:provenance expr-id="1" xmlns:ml="http://schemas.mathsoft.com/math30"> <originRef doc-id="1C97A390-190F-4DBF-BEDE-2FCBA53F3DB1" version-id="80C8BBE0-DCBC-478A-AA76-7303644D9E84" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315097116" is-modified="false" region-id="0" href="C:\Users\giova\Documents\2) HOMEWORK\Materiale per Video Lezioni\Mathcad\Nuovi files\File 22 - Vibrazioni torsionali (avviamento).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </originRef> <parentRef doc-id="1C97A390-190F-4DBF-BEDE-2FCBA53F3DB1" version-id="80C8BBE0-DCBC-478A-AA76-7303644D9E84" branch-id="00000000-0000-0000-0000-000000000000" revision-num="246829636" is-modified="false" region-id="0" href="C:\Users\giova\Documents\2) HOMEWORK\Materiale per Video Lezioni\Mathcad\Nuovi files\File 22 - Vibrazioni torsionali (avviamento).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </parentRef> <comment xmlns="http://schemas.mathsoft.com/provenance10"/> <originComment xmlns="http://schemas.mathsoft.com/provenance10"/> <contentHash xmlns="http://schemas.mathsoft.com/provenance10">4fda78194e5f9756f491d7c034e7cee2</contentHash> <ml:define> <ml:id xml:space="preserve">tempo</ml:id> <ml:apply> <ml:matcol/> <ml:id xml:space="preserve">TAB</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:define> </ml:provenance> </math> <rendering item-idref="129"> <element-image-map> <box left="1.5" top="0.75" width="66.75" height="16.5" expr-idref="1" xmlns="http://schemas.mathsoft.com/worksheet30"/> </element-image-map> </rendering> </region> <region region-id="523" left="456" top="7181.25" width="602.25" height="15.75" align-x="462.75" align-y="7194" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> Se il procedimento è corretto le due tracce (metodo analitico e numerico) devono sempre sovrapporsi </text> </region> <region region-id="502" left="6" top="7215.75" width="60" height="18" align-x="29.25" align-y="7230" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve">alfa</ml:id> <ml:apply> <ml:matcol/> <ml:id xml:space="preserve">TAB</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:define> </math> <rendering item-idref="130"/> </region> <region region-id="503" left="174" top="7215.75" width="62.25" height="18" align-x="199.5" align-y="7230" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve">beta</ml:id> <ml:apply> <ml:matcol/> <ml:id xml:space="preserve">TAB</ml:id> <ml:real>3</ml:real> </ml:apply> </ml:define> </math> <rendering item-idref="131"/> </region> <region region-id="677" left="408" top="7221" width="24.75" height="12.75" align-x="420" align-y="7230" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve">q</ml:id> <ml:real>1</ml:real> </ml:define> </math> <rendering item-idref="132"/> </region> <region region-id="678" left="486" top="7217.25" width="88.5" height="15.75" align-x="504.75" align-y="7230" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="true"> Variabile on/off </text> </region> <region region-id="505" left="6" top="7284" width="764.25" height="342" align-x="6" align-y="7284" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <plot disable-calc="false" item-idref="133"/> <rendering item-idref="134"/> </region> <region region-id="518" left="6" top="7644" width="760.5" height="342" align-x="6" align-y="7644" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <plot disable-calc="false" item-idref="135"/> <rendering item-idref="136"/> </region> </regions> <binaryContent> <item item-id="1" content-encoding="gzip">H4sIAAAAAAAA/2yQwW6CQBCGZ1wqSFG89NI1wWfwCXowTQ+NJvoAZItUMRIbSpMe++b0n9k9 GWczw8/s9w/LZkTEyD0yVW1Q88OqPHbu61R+XrvW9TFJPCEn2l673mmLnpFJW20/znXV+9bO P2i8RDFhvnhz1fKWut/m+1VHR8oukO8cWB8DFvHiXjdMifvOVfVqbXTHBq7gObHlQRSzHcFT 8IitgbNgwzYapBOBoQHiAczfC7bGwogrVgYiEUbgicwRewpG7Y9g1J6BUftUvwUxUwYiF0Zg f9x889PWXVO5i//zRI/9hjxQg2XCFaXh2m4ik/IPAAD//wMAHK222K8BAAA=</item> <item item-id="2" content-encoding="gzip">H4sIAAAAAAAA/4yQwW7CMAyG7aZbS9etu3AhSOUZeIIdENph2iT2ACiUAkVUTF2ROPLm3W8n u+w0R3b+Ot/vRsmJiJGfyEy1QS228/W+c1+H9e7cta5PSGKMHGl74XqnLZog07b62Bzrqvet ld8omqGYMF+8hWr5yty1+V7q6FjZKfKNA+tjwCKe/r/7OzzpO1fV84XRIxvAkp+JLQ+imG0E U8kRWwNryYZtPEgnBkMDxB2Y2wuO7oURV6IMRCqMwCOZI/YMjNofwKg9B6P2R/0XxJMyEIUw AvvrFu+Xtu6ayp38g6R67VfklhosE14uC6/5J3IpPwAAAP//AwARvY6sxgEAAA==</item> <item item-id="3" content-encoding="gzip">H4sIAAAAAAAA/+z9DZQV13ng/e44PUa2ZNSTmKu0HV3kQKQ3imxIFEUWUqzOAK3EEsEvhNdq OUYd37ZIgBj6GkF0V5x2Msh+EznGtkysJYWhNUFgY3rQAnmJxmaBpEYwYCTALbW4SxMU85G5 0lLUBA+SwurVt55iqiiqa1ftc07V2fXxf7J+Qe7ep7rqfNSp2s+z9/459T6lfv6lJe1KqVNf +bDz//8frUpd+94r1a9c8+k5HTN/Tkn8nPqLv5Z/73qP8/+uUeofZv2++ofu31fy26XqYqx5 r1I/fs/F//3CXyn1ud92HjJ7TodsRR7z8+72Lo0vT1Dq4y1K/YXzi6Oh33X9+D+oT/3Wz6v/ 0/nvX3Yer/tbXnzD+fkh5+efcv77/3D8uWPD/+vC465RP/Z/L7/7u//93z8nBxVoF4yxsTEA AAAAAAAAAAAAAEotulbgQvi1AlMv1ArY3lcAAAAAAAAAAAAAANA4o1qBFmoFAAAAAAAAAAAA AAAoC6NagZO/R60AAAAAAAAAAAAAAAAlYVQrMEitAAAAAAAAAAAAAAAAZWFUK7CBWgEAAAAA AAAAAAAAAMrCqFZgNbUCAAAAAAAAAAAAAACUhVGtQDe1AgAAAAAAAAAAAAAAlIVRrcAsagUA AAjauHFjYpuf/vSnqre312WyzZdeeslvn7ftl+EY2H6+X+N/+Zd/UW+//ba7/fXr1xvtCwAA AAAAAACgfka1AlOpFUjTnDlzEtvs3btXeWGyzS1btvjt87b9MhwD2y/Ga/zDH/5QvfXWW0Zt AQAAAAAAAACwbdu2bX4/eEdHR2L7YHR1dRVu+2U4Braf/9f4ySefVH/6p39qtK9VZ1Qr0EKt AAAAAAAAAADkSd5yA1XIPbD9cr/GfX19/u9M598DAKDIjGoFTt5BrQAAAAAAACXy4osvqnvv vdfV09Pj9o2LHTt2RLb32opVq1b57Z955plCbL8Mx8D28/sa//7v/74Kx9atW61/zgEAAAAg jlGtwCC1AgAAAIg2MDDg94m3t7cntl+/fr3fvrOzs3DbL8MxsP18vcbhmDZtGusqAQAAAAAA IHNGtQIbqBUAkI285QbKlntg++V8jf/+7/9eRcXcuXPJLQEAAAAAAAAACunIkSNun7pYunSp 2rhxo2vnzp2R7WX+MK/9ihUr/PbPPfdcIbafh2MwqhVYHV0rkLfns+jbz/pvHD58WF1xxRXj cksyb57tzz4AAAAAAAAAAGnYvXu33/89Y8aMxPbSp+7F/PnzC7f9MhwD22/ua/zkk0+q1tZW FQ4ZA2WyLwDKw6hWoJt5BQAAAAAAAFB+ecsNFC33wPbzt/00/0ZfX58Kx5VXXqm2bt1qtB8A AADIF6NagVnUCgAAAACAZ+/evZE/HxoaUvPmzXMtWrRI9ff3u3TbefbZZ/32y5cv99vnbftl OAa2b/c1/t3f/d3I3NKLL75o/fMMAAAAAEBVGdUKTKVWAACAtMyZMyexjfS9e2GyzS1btvjt 87b9MhwD27fzGg8MDKiomDZtmnrrrbeM/iYAAAAAAAAAIJpRrUALtQJ5kbfcQNbbL8MxsP3m v8avvfaa+tVf/VUVjrlz55JbAgAAAAAAAACUwpIlS9T27dtd+/fvj2wzPDys7rrrLpfXVujm +JL+e6993rZfhmNg+819jQ8fPqxaW1vH5Yvuvfde65/fvDCqFTjZQa0AAAAAAAAAAJTEtm3b /P7yjo6OxPbB6OrqKtz2y3AMbL95r3FfX19kbmnNmjVG+wEAQFEY1QoMUisAAAAAAAAuyFtu oEi5B7afz+2n9Te+8Y1vqHBceeWVav369Ub7AAAAAADNZFQrsIFaAQAAAKRj3759bn+6uO++ +9TAwIDr4MGDke29tqKnp8dvf/To0UJsvwzHwPazf43l36jckm6uPQAAAAAAAKBRRrUCq6kV AJA/0v/uRXt7e2J7GcfhRWdnZ+G2X4ZjYPvpvsYjIyPqYx/7mArHtGnT1PHjx43+HgAAAAAA AAAARZe3/v685RN0f8OoVqA7uVag6M9n0bdfy9947bXX1PTp01U4br/9dvXWW28Z/S0AAAAA AAAAAMrgwIEDbv+4uPfee9WePXtcL7zwQmT7p59+2m+/dOlSv/3Q0FAhtl+GY2D76bzGhw8f Vq2trePyRfI3bH8uATSHUa3ALOYVAAAAAAAAAOLs3r3b72OfMWNGYvuNGzf67efPn1+47Zfh GNh+fa9xX19fZG5pzZo1RtsEAABAPhjVCkylVgAAAAAAspS33AD5JbafxWv8jW98Q4Xjyiuv dOfJM9keAAAAAABIj1GtQAu1AgAA5MHw8HDkzw8dOqRuueUWl6xD8/zzz7t025G1brz2ixYt 8tvnbftlOAa2n85rfN1110Xmll588UXrn0sAAAAAAAAAKCKjWoGTs6kVKIE5c+Ykttm7d6/f /26yzS1btvjt87b9MhwD22/sNR4ZGVHt7e0qHNOmTSO3BAAAAAAAAACorLz192e9/TIcA9uv /zV+7bXX1PTp08PpIjdf9NZbbxntW1kZ1QoMUisAAAAAAAAAACC/VMRjYPv1vcaHDx9Wra2t Khz33nuv0T4BAJB3RrUCG6gVAAAAAAAAjdu2bZvfz97R0ZHYPhhdXV2F234ZjoHt1/4a9/X1 qajo7e012h4AAAAANINRrcBqagUAAABgX95yAzZyD0U7BrZf22scNR+exPr16422BQAAAAAA AJgyqhXoplYAQPHlLTfQ7NxDEY+B7df2GofjyiuvVC+++KLRdgAAAAAAAAAAqArpO5c1ZURP T487N5jYsWNHZHuvrVi1apXf/plnninE9nV/w6hWYNaFWoEiHS/bT34/dHZ2ql/4hV8Yl1ua Nm0auSUAAAAAAAAAABIMDAz4fevt7e2J7WXOMC+kj75o2y/DMbD98W0OHz4cOc+d5Iveeust 488DgOIxqhWYyrwCAAAAAAAAQJrykBto5vbLcAxs/9LfS26ptbVVhUPGM9XyWQAAAIAdRrUC LdQKAAAAAECeHDlyxO3jF0uXLlUbN2507dy5M7K9zFHmtV+xYoXf/rnnnivE9stwDGz/4vZl HryoWLZsmfXPFgAAAAAAVWFUK3ByFrUCAACU1O7du/3++RkzZiS2lz5+L+bPn1+47ZfhGKq8 /auvvlpFhYyPauRzAAAAAAAAAABVY1QrMEitAKKVKfdAfqn82w/HlVde6f6+kc8AAAAAAAAA AABVsnfv3sifDw0NqXnz5rkWLVqk+vv7XbrtPPvss3775cuX++3ztv0yHAPbv2D//v1ubigc kydPVi+++KL1z5YNRrUCG6gVAAAAAAAAAAA0Zs6cOYltpH/fC5NtbtmyxW+ft+2X4RjY/pg6 fPiwmj59eji1pKZNm6beeusto78BAEAeGdUKrKZWAAAAAAAA2FXG3EPc9stwDFXfvuSWWltb w6klde+995JbAgAAAGCdUa1AN7UCAAAAKLYlS5ao7du3u2S+sag2w8PD6q677nJ5bYVuDjLJ JXjt87b9MhxDlbf/pS99aVxeSWLZsmXWP0sAAAAAAAAoB6NagVnUCgCotm3btvl99B0dHYnt g9HV1VW47ZfhGKq8/ahYv359Q58BAAAAAAAAAABQrnyCUa3A1Au1AuSXyrn97373uyoq3vOe 96jHHnusoc8KAAAAAAAAAAC4YN++fW4/v7jvvvvUwMCA6+DBg5Htvbaip6fHb3/06NFCbL8M x1Cl7Y+MjKhf/dVfjcwZSVx55ZXaefMAFJNRrUAL8woAAAAAAAAAeSF9/F60t7cntpe5xrzo 7Ows3PbLcAxl3v7hw4fVNddcE5VWuiTIMQEAAOSLUa3AyZnUCgAAAABASRQp95DG9stwDGXd /p49e1Rra2tUOkkbrK0EAAAAAEA6jGoFBqkVAAAAFxw4cEDdfvvtrnvvvdft4xcvvPBCZPun n37ab7906VK//dDQUCG2X4ZjKOP2//zP/zwyfzRnzhx3rBI5JgAAAAAAAACIZ1QrsIFaAaRv 9+7dfp/9jBkzEttv3LjRbz9//vzCbb8Mx1Cm7evyRjIPHjkmAAAAAAAAAACaJ8/5BHJG5dv+ yMiI+sQnPjEu/yP5oa1bt17SVvJGkydP1qWM3JDxUFl8LprBqFZgNbUCAAAAAAAAAAB7hoeH I39+6NAhdcstt7hkrZvnn3/epduOrKXjtV+0aJHfPm/bL8MxlG37kluaPn16ZG5JcklR237r rbfUtGnTSptjAgAUm1GtQDe1AgAAAAAAoLhknZqkNnv37vX77E22uWXLFr993rZfhmMo0/YP Hz6srrnmmnG5IckdSQ4pbpumOaak7QAAAABAmFGtwCxqBQAAAFBdec49ZLH9MhxDWba/Z88e 1draOi4nNHfuXOOckLST9nFhkqsCAAAAAABAuRjVCkylVgAA6rVt2za/H76joyOxfTC6uroK t/0yHEOZth+Oeue0k8fFBTkmAAAAAAAAAADi5S2fYFQr0HKhViDrfWnG8bJ98+0HY82aNQ29 701yTLr1nAAAAAAAAAAAqLo85xPIGZVr+yMjI+pTn/qUCseVV16p1q9fn8r7WfJOcSF/i7wR kD2jWoGT/4l5BQAAAAAAAICCkr52Gcshenp6VF9fn2vHjh2R7b22YtWqVX77Z555phDbL8Mx FHH7kluaPn16U/I9kqsixwQAANAYo1qBQWoFAAAAAABjamBgwO+Db29vT2wf7Mfv7Ows3PbL cAxF2P7hw4dVa2vruDyPzEF3/PjxTN7LJjmmtMZMAQAAAACQR0a1AhuoFQAAAI0rY27D9t+o +vaffPLJyNzS7bffrt56661M38+7d+9280hxQY4JAAAAAAAAQF4Z1QqsplYA9h05csTNIYil S5eqjRs3unbu3BnZXuZL89qvWLHCb//cc88VYvtlOIY8b3/WrFmROR2ZR69Z72mZB48cEwAA AAAAAAAA9ZPxHF7MmDEjsb3kEbyYP39+4bZfhmPI0/a7uroi8zNr1qxp+nvZJG+Udh7LqFag m1oBAAAAAAAAAEC5FDm3Uc/2y3AMedj+yMiI+tSnPjUuf2N7DSOZX0/WcIqLZo6VAgDkn1Gt wCxqBQAAAAAAQDXt3bs38udDQ0Nq3rx5rkWLFqn+/n6XbjvPPvus33758uV++7xtvwzHkNft S25p+vTpkbklGUNk+71OjgkAAACoFqNaganUCgAAAAD1mDNnTmIbyR94YbLNLVu2+O3ztv0y HEMet3/48GHV2to6Ll8j+Zw85JY8kmO6/fbbx+1neJ+lne19BQAAAAAAqDqjWoEWagUAwIYy 5DZq2X4ZjiFv27/pppu0uaW85mlknFJc5HnfAQAAAAAAAADIwpIlS9T27dtd+/fvj2wzPDys 7rrrLpfXVujGmhjVCpz8XbdWQHIV3raz2JdGtt+Mv1Gl7UfNhydRhHnmTHJMx48ft76fAAAA AAAAAAA0w7Zt2/w+8o6OjsT2wejq6irc9stwDHnZvvwuKnp7e62/r03JvsZFXtaDAmwwqhUY /F3mFQAAAAAAAAAqqKi5jXq3X4ZjsL39kZER1d7erqJi/fr11t/TtZJ9jgtyTAAAoKiMagU2 UCsAAAAAAKjdvn373JyDuO+++9TAwIDr4MGDke29tqKnp8dvf/To0UJsvwzHYHP7r732WuQ8 d0XPwZjkmLZu3Wp9PwEAAAAA1WJUK7CaWgEAAGCX5BC8kPEpSe2DffKdnZ2F234ZjqGZ2//9 3/991draOi73ImsPFTm35Nm9e7ebR4qLIo7PAgAAAAAAAGCPUa1AN7UCKLY85zbIL9nf/nvf +95x+RbJLb311lvW37tpkTwZOSYAAAAAAAAAQNUdOHBA3X777a57771X7dmzx/XCCy9Etn/6 6af99kuXLvXbDw0NFWL7ZTiGZmx/6tSpkbkTebzt92wWvLyRUa3ArAu1AnGxbNky68cEAAAA AAAAAEAzyPxfXsyYMSOx/caNG/328+fPL9z2y3AMWWy/q6urkjmT48ePu+Ot4qKs+TUAKBOj WoGpzCsAAAAAAACQljzkNpq5/TIcQ5rbHxkZcefEi4qqzPkmc/mRYwIAAADsMqoVaKFWAAAA AGi24eHhyJ8fOnRI3XLLLS5Zq+f555936bYja/V47RctWuS3z9v2y3AMWW//tddeU9OnTx+X S5E54yQnZfs920wmOaayrSsFAAAAAACQJqNagZPt1AoAQMHMmTMnsc3evXv9vnSTbW7ZssVv n7ftl+EYst5+a2vruBzK5MmT3XWJbL9fbZFxSnFBjgkAAAAAAAAAUHS6fIJRrcDg+FqBMuVO ynAMzXiOyJ2MR44JAAAAAAAAAFBm27Zt8/u8Ozo6EtsHo6urq3DbL8MxZLH9b3zjG5F5EMmT kAe5aNmyZZHPkxcyL2CVx24hf4xqBTYwrwAAAAAAAACA2uQht9HM7ZfhGNLevmwjKiSXYvv9 mUfr16+PfL68IMcEAADSZFQrsJpaAQAAAABAc9nObTR7+2U4hrS2PzIyotrb2yNzJJJDsf3e zDOTHNPu3but7ycAAAAAwD6jWoFuagUAAECxyDgLmQNN9PT0qL6+PteOHTsi23ttxapVq/z2 zzzzTCG2X4ZjSGP7//k//2c1ZcoU8iIN2Lp1q/t8xQV5OgAAAAAAAKD4jGoFZlErgOoaGBjw +8VlXEtS++AYjs7OzsJtvwzH0Mj2w8G8brWT54scEwAAAAAAAACgbMqUDyFnFG3FihWReY1p 06apt956y/p7MG1GtQJTm1crYJJj6u3ttf68AQAAAAAAAABg6siRI26OQixdulRt3LjRtXPn zsj2Msea117yFl775557rhDbL8Mx1LL9L3/5y5H5jLlz55Yyt9RMx48fd3N0cSFzDtreTwAo IqNagRbmFQAAAAAAAMgDWVfHixkzZiS2l5yGF/Pnzy/c9stwDHHb7+rqIueRMcnRkWMCAAAA xjOqFTh5O7UCAAAAQAkUKXeSxvbLcAxR2x8ZGVHTp0+PzHWw1k/6THJMt99+O+PFAAAAAABA oRjVCgxSKwAAVbJ3797Inw8NDal58+a5Fi1apPr7+1267Tz77LN+++XLl/vt87b9MhyD6fb/ 7u/+Tn3kIx8Zl9+Q9X+2bt1q/b1XVpI7knFKcVHW9a4AAAAAAAAAAPlkVCuwwbxWYM6cOYlt JP/hhck2t2zZ4rfP2/bLcAwm2//a174WmdeQ3NKLL75o/X1cBeSYAAAAAAAAAABFUsR8CDmj +LZ9fX3kKHIiKW9EDg8mjGoFVjOvAAAAAAAAAIDmWbJkidq+fbtr//79kW2Gh4fVXXfd5fLa Cl2/uORCvPZ5234ZjiFu+1/+8pcj8xhz584lt2SJrF1FjgkAgGozqhXoplYAAAAAAFAc27Zt 8/u5Ozo6EtsHo6urq3DbL8Mx6LYv/x0VMobG9vus6sgxAQAAAECxGdUKzKJWAAAAVEdRcidp bb8MxxDe/sjIiJo+fXpk3mLNmjXW32O4QHJMkkeKC2ljez8BAAAAAACAMjKqFZhKrQBgat++ fW5OQ9x3331qYGDAdfDgwcj2XlvR09Pjtz969Gghtl+GYwhu/4/+6I/UlClTxuUpJI9BriJ/ ZIwSOSYAAAAAAAAAQN5JnsKL9vb2xPbBebw6OzsLt/2iH8OePXvU+9///sh8UZnmTzOqFWgp Z62ASY6JcWcAAAAAAAAAAJvylDtpxvaLfAx9fX2RuYZp06ap48ePW38vIZ7kjeS1igvWwgJQ Jka1Aic/UcpaAQAAAAAAgLI7cOCAuv32213Sty3jY8QLL7wQ2f7pp5/22y9dutRvPzQ0VIjt F/UYli9fHpmPkMe89dZb1t9HMCOvFTkmAAAAFIVRrcAgtQIAAABA1e3evdvv454xY0Zi+40b N/rt58+fX7jtF+EYRkZGVFdXF3mIEjHJMc2dO5e8IQAAAAAAMPKj0THVMnOd26dwo+ONgYvz 4xjVCmyIrhVYOzKmVNvKyO1G+ZnjkwOnnJbtqsP5/wPfalej/7bb+vMDAMhe3vI/Vc8v3Xzz zWr69OmR+QfW3yk2yR1JfjAuJAdFjgkAAAAAACR53THpqwf9PoVd97c4Pz8d2fZVx8Qv7jJq Kw45JnxmyyV9Fg+0KzXycq/14waAqjCqFVjdnHkFhoeHI39+6NAhdcstt7hk3aDnn3/epduO rBvktV+0aJHfPm/bL+IxfOxjH1OXX375uJzDlVde6a7vZPv9jHSQYwIAAAAAAI0675i584xS qu1C/sf5Z3RkbWTbdxy/+b3TF3NFMW1FOB/19Xal/mFoh/q/PrZajby02PqxAwAaN2fOnMQ2 e/fu9b8LTLa5ZcvF2oS8bb9oxyDrKbW2tkbmi1588UXr7x+kKylvNHnyZF73gjKqFehmDQIA AAAAQHPVMpfd4uFRp0WHUdszjimPHPP7NB6aotTY+c1q49kx9cD1f6XePanPTQEAiiFv+Z+s t1+kY+jr64vMMcjYFHIM5SVzGMYFuUUAAOwwqhWYRa0AAAAoluAaTBJx897V0lYE81ELHG8P Xxi31HtiVP3w878Z+1gAQP5t27bN/07o6OhIbB+Mrq6uwm2/KMewfPnyqNQC855VhMxlGBfk mAAAAACgdka1AlOpFUC1hMcZxc17F267xGn77ole7baDY6NkBr7hR9ucn5+3fswAgHTkLf+T 9fbzfgz/6T/9J/cxUSFzp9l+v6B5knJMEqypBQAAAAAAopjOZRdegykpDxReV2md8yfGRndb P15Uh1GtQAu1AkAz5C3/k/X2834MH/jAByLzCL29vdbfK2g+GaMkY5XighwTAAAAAAAIC44d koibyy7c9uvtSo2e3RzZNpyPCs57BwAoPumTlrEuoqenx13HR+zYsSOyvddWrFq1ym//zDPP FGL7eT0GWVfpiiuuKG1OwKhW4OTvUCvQIHJMAAAAAIAsyPz7u3fvRoHUsmZCeJxR3Lx34bY3 OF7b/DGlG8PEvHcAUA0DAwP+d0N7e/Q42KDgnFydnZ2F235ejmHPnj2qtbV1XB6AdXPgkffB 5MmTY/NGzIkIIGtGtQKD1AoAAACUjeQqkmoZifyFvG6mr3F4nFFczii8rpJE3BimcD6qf6Fy fj5k/X0NAEhX3vI/VcgvybimqJg2bRq5JVxC6o7kfREX5JgAAAAQx6hWYAO1AgAulVTvSOQz askvieAaTBJxOaNg2ymOX7j2Wu24pHA+Km5sFACguI4cOeLmQMTSpUvVxo0bXTt37oxsL/O9 ee1XrFjht3/uuecKsX3bxyDrLUWF5BBqGcOM6jDNMfH+AQAAAADorFmzxr+HlLpY2/O4obY5 74xqBVbXXivwo9Ex1TJznbsdGcV07bW/oIYf/WUVlTMKt/3rv25TfzJhuXr3RG/ktoP5qBsd bwyY1YUDAIpJvrO8mDFjRmJ7yZl4MX/+/MJtv9nH8KEPfUibG7D92iPf5Fpy7ty5uvSSG+Qo AQAAAABR5F7Rm+vu9ttvt74/yFZw7JDkgb79lTZ1X9tfR+aBwvPYfbtDqZs27lBbuiaosdEj 49oHc0wSu+5vcX5+2voxA0ARGNUKdBd3XoG85X/Kll+KimXLlll/3VEckouMC3JMAAAAAICw 4L3k8ePHre8PshVeV+nRDqVmPv18ZM4o3Fbmsfv//utaNenuLZFrMTHvHQCU0969eyN/PjQ0 pObNm+datGiR6u/vd+m28+yzz/rtly9f7rfP2/ZtHsN/+S//Rf2H//AfIvv3ZYy57fcCisck b8TaXOkxqhWYVdxaAQAAAADwBOtke3t7re8PmmPtyJhSbSvd132R49iJXm3OKNhW5rH7/w20 q879Z9WfttwWOeaJee8AoHzmzJmT2EbyK16YbHPLli1++7xt39YxPPnkk6q1tXVc/7+MM691 DUUgKDgXdlTIe4wcEwAAFxjVCkylVgAAAG8938mTJzPfRYUcckz4zJZLckC9J0ZV14QuNfLS 4kvaRs15d2LsDfXLDx5UX29XavTs5kvaB/NREgf/ZoLz83esHzMAoH55y/9kvX0bx9DX1xfZ 7y/XaPT7Iw0y/i0uyDEBAAAAKAujWoEWagUaEbzH3Lp1q/X9QfOE57GTnNE7Y6fVTd87qXpv a1HvnlyrbevNeXdgdExdvrBf9X+u5ZJ59cJrNq3rUM7vd1s/ZgBA/ZYsWaK2b9/u2r9/f2Sb 4eFhddddd7m8tkLXXy25Fq993rbf7GP4gz/4g8j+fta7QdpMckzMwwgAAAAA1SP9D3JPKHH7 7bdb3x80X3BuOi+v4+WB+j7bokb/7Uduu3ccv/m9035fwgLH28MXxi3JOKaWls+pgRWTnMcP uT8L55gemuJs+/xmK8eIYjGqFTh5G7UCQM5t27bN/w7o6OhIbB+Mrq6uwm2/2ccQFbJmDrkl ZEHmWvTuGXRBjgkAAAAAqmXZsmX+PSFzW1TT5vNjSk15yH0PBHNGG8+OqZbb/latbFfq1L4v qBNjo+78dl44P1Zn9y9w2553fOGls0q1LVR/99kWNfLSN/0clRfklwCg+PKW08l6+80+hnDI dZrt17xeRrUCg9QKFIncK5BjAgAAAAAIuUcsQ/8FGiNjj1Tb59z3QZtj+NE25+fn3d998+So arntzyP7D4K5KPEzxycHTqkLmafxwZx3AFB8+/btc3Mm4r777lMDAwOugwcPRrb32oqenh6/ /dGjRwux/az/xuHDh1VLSwv99LDKJG8kY+Ns7yeA5jOqFdhArQAAAEAVyXx4EnI/yVwq1bJg /3n12ZYPqev/8i//d1bpYsi6SmNjp/2260+Oqsvv/rtx/QzhNZSEjGPqffmsarnt/kvahvNW AIDik5yJF+3t7Yntg2u4dHZ2Fm77af+NJ598UrW2to77fpXrMpmLzPbri2qRewFZuysuyDEB AAAUj1GtwGpqBQDUJtg/Qn1sdW0+Map6e7/l6FX3f/vb6vK7/2/1QJtSoyNrL2k3NDqmFv3j Pj9v9O3Ptqh3T27Ubnf3v42pBX073PnyJLe0pWeSGv233daPFwCQnrzlf4qUX/rGN74R2X8/ efJk5iuGNeSYAAAAAKCa5H7Qm9dCxiXZ3h9cyqhWoJtaAQBAcRw4cMC95hDS57xnzx7XCy+8 ENn+6aef9tsvXbrUbz80NFSI7af1N37v934vst9e+vUZTw7b5D3ozXGgC96rAAAAAFAuMv7E C+peAQBIn1GtwKxq1grIfG5ezJgxI7H9xo0b/fbz588v3Pbr+RtPPfWUtr9+7ty59NcjVyQf So4JAAAAAMrv+PHj/r0ec1YAAIA05C2nk7ec0WuvvaamTp0a2ffO9RjyyiRvJPcWtvczS0a1 AlOrWSsAAAAAoHq8OSxkfjzqCAEAQC2Gh4cjf37o0CF1yy23uGStoeeff96l246sNeS1X7Ro kd8+b9tP428cPnxYtba2RvbPs64l8i4470FUyD0F8yEAAPLMqFaghVoBAACSbN261b8XXLNm jfX9AQAAxTJnzpzENnv37vWvN0y2uWXLFr993rbf6N/o6+uLzC1Jn7xcl9l+PQETkgclxwQA AADAFqNagZO3UiuQIRmrNHnyZPc5l7ksbO8PAAAonrzlf7LefiN/4xvf+AZ98SgNkxwTOVMA AAAAKI7gfBWyboDt/QFQbka1AoPUCgBllrf8T9bbr/dvdHV1RfbBS40P8xOjqOR+Q/JIccGc jwAAAACQf7KWrhesCw0AAOq1bds2/5qio6MjsX0wJI9StO3X+jdGRkbURz/60ci+9Llz5xYu X2RUK7CBWgFEk7F35JgAAAAAoNikP0NC7u+K1q8BAADyI2/5nzzll1577TU1ffr0yD506ntQ JiZ5o2XLllnfTwAXGNUKrKZWAAAAAOPJ3BReyBx5tvcHAAAUl/QrS65E9PT0qL6+PteOHTsi 23ttxapVq/z2zzzzTCG2b/o3Dh8+rFpbWyP72RmfgTKS+RFkDse4IK8KAACQDqNagW5qBQCk b/Lkye65Rv61vS8AAKA8BgYG/H7k9vb2xPaSZ/Gis7OzcNvX/Q3JNUXllmR8B7kllJnMi0CO CQAAAACKQ8YgeSFjk2zvT5UY1QrMolYAAFANecv/2MgvfeMb34jsU5fckoyVsv0aAVkzyTHJ 75mjGwAAAADsknkovLnOZU0l2/sDAEDRGNUKTKVWwMSRI0fcHItYunSp2rhxo2vnzp2R7WU+ Oa/9ihUr/PbPPfdcIbYf/hs33HCDti9drtlsvz5AM8k4JXJMAAAAAJBfklPyamLptwAAAFkK ruE4Y8aMxPaSm/Fi/vz5hdr+yMiImj59emS/+O23306/OCorr3kjo1qBFmoFAAAAAEAE+0hk jjzb+wMAAMotT/mfLLd/+PBhbW6JtWOAMbVs2bLYHBPzQQIA6mFUK3ByBrUCAACkwJvXfPLk ydTQAgCA1Ozduzfy50NDQ2revHmuRYsWqf7+fpduO88++6zffvny5X77vG0/+Dckt9Ta2hrZ Z75mzRrrrw2QF8G1x8gxAQAAABBGtQKD1ArYJv0bXmzdutX6/gAAgPKYM2dOYhvJ4Xhhss0t W7b47fO2fe9v9PX1afvJpS/d9usC5I1JjknGDdreTwAAAAAoMxl/JPdfEjKnv+39AVBsRrUC G6gVABAtb/mfrLcv8eUvf1nbP84YDEBPat28+xhdkJ8FAAAAgOwE18k9fvy49f0BAADlsmTJ ErV9+3bX/v37I9sMDw+ru+66y+W1Fbr8iuRovPZ52/7VV18d2c8t8wsX5VrLqFZgNbUCsE8+ w+SYAAAAAKD5gmtN9/b2Wt8fAABQPtu2bfOvNzo6OhLbB6Orq6sw2x8ZGVHTp0/X5pZYsxIw Z5I34v4FiGdUK9BNrQAAAADqI/PhSUyePJk+DwAAkIm85H+y3P7hw4e1uSUZK277NQCKSMb7 SW42Lvh8AQCAMjOqFZhFrQCA5guuicucEQAAICv79u1z8zLivvvuUwMDA66DBw9Gtvfaip6e Hr/90aNHc7l9yS21trZG9n0zvgJojNTAkWMCAAAAgGzJvZc3h4SMS7K9P2ViVCswlVoBAACS SF7Gi/b29sT2wXqYzs7O3G2/r69P2+dN/Q6QDpMck9z/MB8DAAAAANRn2bJl/v2Vbq1qAACq yqhWoIVagayVKb90zTXXRPZzS70P12JA+mScUlywzhkAAAAA1E76MLyQPJPt/QEAANVx4MAB d+yAkP7fPXv2uF544YXI9k8//bTffunSpX77oaEhq9v/pV/6JW2fNfkiIDu15I2MagVO3kKt AAAAAAAYkj4Rr16Wmj0AAGDL7t27/T7hGTNmJLbfuHGj337+/PlWtj8yMqKmT5/OeAjAoqQc E+MCAaA8jGoFBqkVAACgGYLzujDPPwAAsKlo+aXDhw9r57mT/m7bzydQJcH7GnJMAAAAQH4Z 1QpsoFYg76SedvLkye7rJmOYbO8PAACohuHh4cifHzp0SN1yyy0uWc/o+eefd+m2I+sZee0X LVrkt2/G9mXOvNbW1sh+7N7eXuvPMVBF5JgAAAAAoH7Sn+GF1Oja3h8A+WVUK7CaWgEA6Zsz Z05im7179/rXNCbb3LJli98+6+3r5sOTYDw4YJd8BiWPFBd8TgEAAADgUsePH/fvmZiTBQAA NFPRc0ZRIX3Uea/XMaoV6KZWANUiY5TIMQEAAACAOZnjzusLYS1pAADQTEXML/3X//pftX3P Mocw82gB+WKSN1qzZo31/QRqZVQrMItaAQAAAGRj69at3FMBAABrtm3b5l+LdHR0JLYPRldX V9O3PzIyop3nbtq0adTqADklOSb5jMYFczkAAIBmM6oVmEqtAID8kf4PqbH1+kNs7w8AAKie IuWXDh8+rK655hptvzS5JSDf5DNKjgkAAAAAovX29vr3Rnmf9z9PjGoFWqgVAAAgbUXJL+3Z s0e1trZG9kcvW7bM+vMIwIxJjmnu3LnkiwEAAABUyvHjx/15xeWeyPb+AADQTEa1Aic/Tq1A zsicVTJeQPT09Ki+vj7Xjh07Itt7bcWqVav89s8880xm2+/u7tb2Q69fv976cwigNpI7ks94 XDDfJQAAAIAqkZyShOSYJNdke38AAAAGBgb8/tr29vbE9pKv8aKzszPT7YdDrqEY/w3YZVQr MJhOrQA5JgAAAAAYc/tCvJA58mzvDwAAgMhTfmlkZETddtttkf3IsialjHWy/XwBaFxS3ojP OwBky6hWYAPzCgAAkBdyj+TdK9neFwAAAM+RI0fcPJFYunSp2rhxo2vnzp2R7WWeO6/9ihUr /PbPPfdcQ9uX3NL06dMZowBURNxYRQkZr0iOCQAAAIhmVCuwmlqBMpDxSl4wpwsAAMir4Hjr GTNmJLaXPJEX8+fPr3v7hw8fVtdcc01kH7PML0xuCSgnckwAAAAAqkb6OORex+vzsL0/AOwx qhXoplYAQDHYyC89+eSTqrW1NbJvWebQsv2cAMhWUo5JQtrY3k8AAAAASENwvvDjx49b3x8A AIC9e/dG/nxoaEjNmzfPtWjRItXf3+/SbefZZ5/12y9fvtxvb7r9P/uzP9P2Ea9du9b68xTF qFZgFrUCQFZkjJJXv0eOCQAAAEBZBetuZd472/sDAAAg5syZk9hGckRemGxzy5YtfnuT7X/y k5+kfxiAr2x5o/OOmTvPOHvd5u77Asfbw4u17X/muOl7J9328oi+nknq3RObrR9HWRnVCkyl VgAAAAD2yPrSEpMnT2YtAAAAkBs280sjIyPqU5/6VGwfsnf9ZPt5AtBckmOSz35cFGkuzLUj Y0q1rXT3W3JGw4+2OT8/H9n2R6NjqmXmukuOdaHzoNN7O7WPAQCgyoxqBVqoFQBQTGvWrPHv C7Zu3Wp9fwAAADxLlixR27dvd+3fvz+yzfDwsLrrrrtcXlsh/b9R7SUf5bXXbV9yS9OnT0/M LXENBVSX1OV5dXq6+NKXvmR9P0286pj4xV3+fvcvlHz9UGTb3lNjklEad6x3Ot7av8D6sQAA AACon9znePM13H777db3Jy+MagVO3kytAAAAObJt2za/77KjoyOxfTC6urrq2v7hw4dVa2tr ZF/xr/zKr0T+nGsuoJpMckwyjinv69uecUx55Ji/zw+0KTU6Mn5tOd1ceqfl55teUZ9ruU2N vKSfWw8AAABAvsn9ixd5v48BAKAWRrUCg9QKlEmz80tPPvmkNrfk9RHrgusuoJokxzR37tzY HJPMGWF7P5MsHh6VM6G7vzc63hhoH9cmPM5pndN8bHS3+ztZl+mTA6fUX0yapN49OT43BQAA ACDfZB4YL3p7e63vDwAAQNi+ffvcXI6477771MDAgOvgwYOR7b22oqenx29/9OjRxO23t7dr +3vXr1/vP0bXN1yk9VOAojCqFdhQvFoB3TgmmVuiCGvcBtdV0q3BlNRGckwLdpxWxzfNHPdY AAAAAPkmc7MU6R4GAABUm+SJvJBcUFJ7yQl50dnZGdtWxi/p+nqDuSWxe/dubR6KayqgmoLz joejKLV8rzsmffWgv9/B8UYek3FLABBmVCuwuni1AkCV3XLLLf41w549exLbz5s3z2+/cePG Qm/f9nMP5EGwvyXcZwIUhdSie3Hdddcltn/jjTf89hMnTiz99gGgbLLIL42MjKhPfepTKiqk r1jGe0c9Tjf2oCj9yADSJZ/9qJg8ebL1fTOlWy9J93vduksAAKCY8pzTIWdkn1GtQDe1AlUQ rK1jfWkUWdHzP+SXAKA2Bw4ccK9dhMxFJ/cM4oUXXohs//TTT/vtly5d6rcfGhpyfy+5penT p0f2CUv+KG6NpGDuKhhF6ksGkI64tdaKVsu3dmRMqbaV7r7f4Dj6neudn7/u/s5kfBMAAMgn 717oX/7lX7RtFi9e7N8/9fX1+Y/513/918j2X/rSl/z2Dz/8cOLfyPP2bb8+gG3Lli3zr/N1 dbZAEfyP//E//HP7/Pnz/e+C/fv3R7aXvkGvvayd4bV/9tlnC7193fNjVCswi1oBAOUXnKNu xowZie2l3s0LOT8fPnxYtba2RvYHyznZZJ473VxYRetPBtAYyXeXJd98yDHhM1v8Y5CRSgMr Jqmx0aFL1lOSOPg3E5zHvGN9nwEAQDIvJO9i0t5bV0Vi586die1lvoha/kbetg9UmeSTvJA8 k+39AdLyyiuv+O/tj3zkI4ntz5w547efMGFCobdv+7kHABsayRn91m/9ljZfJH2/pvugm+NK xjOldZxGtQJTqRUAiqDRsUuSy5ZzVF7WbjvjmPLIMf8Y2h2T5s1TfZ9tUd/86U/8sU2svQQA QLF4QX4JQJj3eZEa2rzclwBpyHP+h/wSANRveHg48ueHDh1y58wWcv/w/PPPu3TbkfWYpO01 11yj7d9ds2ZNTfsm11K6kPyX7ecOQPYaGY8k54n3v//9bvtPfvKT1o/Fs3h41NmjDne/ZF2l V19arK7/yj7lraUkschx/lSv9X0tCqNagRZqBQAA+eDdZ33zm9/077Pi7rVkrjnvMZLP8drr 5pr7y7/8y8i/UZTtA1W1detW/36AuVhQNEnfBf/8z//sfxfceeedfvsf//jHke1/9rOf+e1/ 53d+p9Dbt/3aAECW5syZk9hm7969Rjn3rq6uyH5gqbup99pI17dcyzgoAMVU73gkyU0H5yz3 4o477rB+TCI4t51klIYfbVOnx0bVzE1SD9Xu/pz1lAAAVWNUK3Dyt0tVKxC+1zF5zOzZs/32 27dvr+lvFHH7QFXIPYzU0EmkOWcL0Cy1fBf80z/9k9/+Qx/6UGL7d955p9DbB4AySyO/JOvX tbe3q6iQ3FIj61EG5x4Oh/Q9237+AGSnnrFLcs6Q+zFdfPjDH3brAm0e16uOiV/c5e+Tl0s6 7/z8Cy+dVaptIespAQBQUOSMyBkBnuCc/1WZg8WoVmCwXLUCuKCW74285X/ILwFA/bZt2+af Ezs6OhLbB0PGK7322mtq+vTpKiqkjzeNHFBwvu5gsNYlUF71jF3SrdkWDmln89gkjzRzp8zF fGE+vAecf0ZH1vq//+bJUTVx2kp19DvXO//7deuvBQAAqF8w4ubd8dx1111++/7+/tJvHyir 4P0M86+g6ILx0Y9+NLH9qVOn/PYf/OAHS799oGyMagU2UCuACxrJL/3BH/yBam1tVVEhuaW0 1qIMzkEcDNa7BMqrlrFLcu+my0OHQ9ZjysN5Y+2Icz5tW+nu042ONwbaL/n9eskx3f01NbBi khobHbK+vwAAoD7BKGL+J+vtA2Xl3Z/Qb4EyCEYR8z9Zbx8AyqaRnNF73/teFRVZ1Nt4cwuH g3UsAcNagdXlrBWoZeyS/G+5ZzMN22OXPIccEz6zxd+vXfe3OD8/fUmbH5wdU5ff/Zj69mdb 1LsnN1rfZwAAULtgFDH/k/X2gTKSufC8WLNmjfX9ARoVjCLmf7LePgCUjaw/Ivkg0dPTo/r6 +lw7duyIbC/tfvu3f1vpIqv+WLnOioq4tVUAFJPJeCSp65s7d672XBQVeaoHPOOY8sgxf9+8 dZXC7TaeHVMtt/2tWtim1Om9nc7PzlvfdwDZMKoV6C5nrQCa50ejzvfKzHXG351R42aRHe++ THzzm9/0783+5V/+JbK93CN57R966CG/vaz1UOTt234dgGbyamnp20BZBL8LlixZ4p/b/9t/ +2+R7f/1X//Vb9/d3e23f+KJJ0q5fQAos4GBAf9esr09+j5S1lHSRZZjiaRPWDfuQObEs/3c AUhH3Hgkb51b+beWcUhe5GU8kmfx8KizVx3uvi1wvD28OLadtNz39+3MgQcAiCRr9q0dOqva lj067jtQvkP+27fa1bsnNlvfzyq75ZZb/Ndkz549ie3nzZvnt9+4MXl8cp63b/u5bxajWoFZ 1ArgguCasN69DlAmR48e9d/j1113XWL7N954w28/ceLE0m8fAMomLr80MjLi/iwqpJ9XxkFl vX/Lli2L/PsyV7Ht5w5AOnRrI8nPJc+sOw8kRZ7GLnmCNeRTHD/dfP24NtJX2Ln/rJyV/WOR sUyb+haokaG11o8BAJA/B5zvl8sX9l/yPdi/UPr3qU+wLc/5H/JLQOOkVs6rg5O5FmzvD5CF oud0TLZvVCswdXytAPklAFV35MgR1dnZ6Vq6dKl7jyH+8R//UU2fPl1FxbRp05qSWxJx4xrk d7afPwCNCc49Ho7HHnvMPd8kxXve857In5uMXfLm3GvWvOevOiZ+cZe/j8F1lUznL2LeIgBA mNQmzNx5xvmGaPO/L3TzsCI93r2Tbu448Zd/+Zf+/dbDDz/sP0Y3f9zXv/51v73MH5f0N/K8 fduvD9AM3vzdkmOijwJl9c///M/+uf3zn/+8/10g67tHtf+3f/s3v/1nP/tZv/33vve9Qm/f 9usAADYE+25bWloi+yql/7bZNf5Ra6gY1Qq0XKgVkJB5Tm0/vwAaoxu75M1JnhQ333xz5M9N xi7JPJuXX365217+bcb9YLj/7wHnn9ERxiQBABoXrlOIm4cV6fDCdP2g4HXPzp07E9tLv1Yt fyNv2wfKLtjfkrd5uYGsvPLKK/77/iMf+Uhi+zNnzvjtJ0yYUOjt237uAcCGuLEBErZyNHH7 lbf5rACYSzrnxIXkhDZs2KBdZynunk03j15ra2tT5jVfOzKmVNtK92+2O87uX2D9tbDFqFbg 5E2sQQAAKTvjmPLIMf87kPGu2fOC/BJQTV5NnPxre1+AZslz/of8EgDUb+/evZE/X7x48bj+ Vi9s19fo5r2yvV8A6qcbj5QUMqZRckTB9W6DETceSfJHSeOdsl6zLVgzLiOThh9tc35+3vrr AQAotqh574Jzq2bFqFZgkFoBWWtIyDxz/f39Pl37L33pS/5jvv3tb/vtT5+Ofj1lrrmov1GU 7QNlJvNre9GMejagWZK+C06dOuV/FyxcuNBv/4Mf/CCy/blz5/z2CxYsKPT2bb82AJClOXPm jPtZV1eXtq91/fr11vdZ9iEqqP0BiqmesUuSN/LWPpL8Ua1jl3T5qHDceuutmR57uGacNS8A AGkJjpGVYG7V5pB5HoJh8pjZs2f77bdv317T3yji9oEyCd6LZF2b1ixGtQIbqBXApWr53pC1 +7z40Ic+lNj+nXfeKfT2AaDMgvmlkZERNX36dBUVcr2Up9oaXV9yHvJfAGpT69glaR9c80iX K5Kcc3js0osvvqgdAxkVzTjvLR4edf5Sh/v3FjnOn+q1/poAAIrvVcfEL+7yv9PaVbXnYW0W 8kvkl1Atwc9LM9ZlBZqplu+CvOV0yBmh6oxqBVZTK4DGLVmyxL0H+Na3vqWuvfZaFRXSRyt9 srb3NUjXnyz9xrb3DYC5WscuhccjyT2cLsL5ZhnvpMtNR4XMvdeM52Dzeee6fcpD7t9kzQsA QFrCY2SZh7U55Hrjrrvu8sm9ludf//VfIx8j88d57WX+OK+9bv644N8o4vaBsgjeyzBfP8oo 6rtgz549kW3/5//8n377T3/60377gYGByPb//u//XujtA0CZbdu2TV199dXaPlPJwejWI7FJ 9kkXeRo7BZgyqhXornatwI033miU65HzVlSeO1wj7EVwvkw5t9SzvlOz6g/D9eXNWB8DAFAN wTGyEqbzsErtw6PXPMicrTUg4sP26wNkwZsXIWreBKDoiOSw/RoBQFa8PtioMT7SF5vn6x5d X7H83Pa+AUhmOh5p2bJlkecik/FIW7durWkcko3zSHhd9YemONee5zdbf30A1M6oVmBWtWsF kF8/Gh1TLTPX+d+FpvOwHnL8vz+2mfWaakDEh+3XB0ibjPPzQu5PbO8PkDYiOWy/RgDQDMF8 jfTn2t6fJJIb0wVzGQP5J59TXZ5YImktt7jxSJKPkjnr6g3Tc0haOfjeU3JNvtD92wscbw8v tv76AACKLzzu1XRuVckZTZq3hXlYa0DEh+3XpxZGtQJTqRWAGblf8OrdZJ4E2/sDZIFIDtuv EQA0i4zZDq9Dkme6eayKkB8DcMGRI0fUe9/73ks+w5IbisvdxI1dWrVqlZtjSorw3/TCZOzS a6+95s7Xl9Y9YrBmnDUvAABpCY+RlTCZW9X7Xjr4NxOc//2O9eMoAiI+bL8+QJqkv8ELaltR VkUPo1qBlgu1As0I268nACCajC2PCqkPyvPcfgAu5c2V9773vc8ox60bu/SBD3zA6Npu9uzZ 2t8l3SPKeWfChAl++zTy2WtHnOv3tpX+NllXCQCQlvB3zANtKnEuO28tJtM1mFD8frisw/br A6QlOI8Kda0oMyI5bL9GAFB2RrUCJ3+r4XkFdOMUijQOC8CY+5k1qf+LG7uUFHK+kFyW7rwR N3ZJcta6vNacOXNqrl1csP+888gpkdsz6fsDAMBEeF2luHlYh5y2i/5xn2q57X63rekaTKAf Lilsvz5AWrw5VKhpRdkRyWH7NQKQf7pzxsDAgHriiSdcS5cuVffcc4/r6NGjkdt59tln/fYr Vqzw28vPbR9jXgXXvwyG9Anb3jcA6Ytbhyku5HFyXye5KV3o8j5xeSYvJk6cWNfxbD4xqnp7 v+XoVX/xta+ptmWPGq95kTdGtQKDjdcKAACyccYx5ZFjdX3PFvW7ywYiPmy/PkAagvcc1LOi 7IjksP0aAcg/k3NGcP2fnTt3Jm7z7rvv9tv39fVZP8a8Cq6BGQ6Zm8r2/gFITz3jkeT8EDwX 1DoeSfI+JvHpT3/a+vMDAEAUbw47Lx6a4tyznN88rp2swWRUK7DhQq3AI0NnVfu3Njk/bHd/ z5ytAHBpH0Vaa7ACAIByC4auDfml7ATXzAwG13JAudQ6dknOAcG5KGoZuyTzpU+bNs3o71x+ +eXMeQEAyK3wukpx894JyTN94aWzzmMWsq4SAIxd2ucg9wm29yeJUa3AauYVAAAgL4Kha/P0 00+7fZuip6dHdXV1uQ4fPhzZfs+ePX77VatW+e1tH2sexY1pqHWdEwD5VMvYJakXlLkzw9sw Hbskj9WNi4wKGeNk+/kBAEDnVcfEL+7yv7dkRb+fbr4+9jE/c3xy4JRaoNpjc1EAUHbB+5C4 NVgBAEiTUa1AN7UCZREMk/azZ8/22z/11FOJ7YM1+7aPNa/mzp1r1G8MoJhuvPFGo1yPjDmK qik0GbskY5CCY01NgvV6AQB5J+ORZu4843xrtfnfXyZz2R0YHVNtC77PukoAKs27P+C6HwAA 1CIYJu3JGaVv9+7d2j5druvQDEa1ArOoFcia5ItMIm4ckW7sksx1Ib+XNZpqGbNk8jcBAMgL 0zWYoh73oz/7ZVWUdZUIgiAIgiCqGravwwBcShf79u1LfOydd97pt+/v7zf6W7aPN69065/Q pwsUn+SQf/3Xfz3yMy75oLi5zWUOu6iQHNFrr72mHdOYFKZ1ibJvsv+2n0MAyYxqBaZSK4By +9HomGqZuc7/vktag6mICIIgCIIgCPqYgbzQBfml5tLNaSV9wLb3DUA6guM/JWRMUVyOR36n G2skY0N1Y5WC8fM///ORPzfJXUsbiYkTJ7LGGwDAmjOOKY8c87/DTNZVKhKCCIZRrUDLhVoB giAIgqhy2L6GA3CBLsgvNZ+uH1lyT7b3DUDjJF8kOaH3v//97jx1Se29/E44JkyYYHStpVtj KWnskuSSwo+V/Wa+TgCALQv2n1cXMksXwmRdpaIgCIIgCKJcYVQrcPJGagUaDNvXcAAuCMbe vXt9nZ2dasaMGa4XXngh8rEvv/yy337RokV++x/+8IeR7aWd7ePNM11fssydZ3vfAKRDcjcm eZq4sUtJIbmg7du315Wzlt/pHnfZZZe55ynyTACAZls74ty3tK30v5NM11WKsvn8mHr0mgfV 2Ohu68clCIIgCIIgiAth+7oMwAW6z+XHP/5x/+d79uxJ3M68efP89ps2bbJ+XLUwqhUYvDFX axBIn60uWP8EqBZdvjkpZN48OZfIv1EhuaeovyePMV3LibnyAADN9qpj4hd3+d9FjayrdMhx /fWbc7MuE0EQBEEQBHEhbF+XAbhA97msUn4pT3T9vNKXa3vfADRHPeORpL03j57kdHQRNR5J ctKmf2/+/PnWnx8do1qBDfmqFQAApC/NNZgkv3T53f1q+NE253+ft35sBEEQBEEQxIWwfV0G 4ALd5/LQoUNun6OQnEd7e7vr4MGDkdv5yU9+4rdfunSp337Hjh3Wj7FIXnzxRe15k3EAQDXU Oh5J1j8KzkNnOh5JHrNs2bKa/hbnIQBAloxqBVbH1wq84/jN752+5Pur3jWYvLn21nWoXMyR pwvb+wWgWIL3G8x/AiDvuP4B8s3kcxlcu33nzp2J27z77rv99n19fdaPsWiCz3cwpB/Y9r4B yJbkfCZOnKi9fgqGjDlas2bNJY83HbskuWxZr62WkLyV7ecHAFBN5x0zd55xvo3a/O+letZV OuH45QcPuo9vZN68NOnC9n4BKA65B/DmI8jD3CdGtQLdzCsAwAzXSkC+mXwuyS81l8xxFRVy vRgcowCgfFatWqW9dgqG5IYkRxR+vMnYpXrXa2LsEgDABskt9b58VrXcdv8l30u15IdOO/58 28vq8rv/b//xkqnKw7x3urD9vAMoDm9tVOkz4JodQBFw/ZN/RrUCs6gVgFl+6cCBA+48d+Lz n/+8uuOOO1z79++PbH/kyBG/fU9Pj9/e9rEWifQFR0V4rAKActF99oMh+aGoxyaNXZLf68ZH JgXjJwEAzSZrH034zJa6vrdMIw/z3unC9vMPoBhkfjsvdPcJAJA3XP8A+Vbr53L27Nl++6ee eiqxfbA+3vaxFonkhqIivCYK4DGqFZhKrUCZyBhFGcP0nve8J/JcETc/etzYJckveXNm1Bqm YydlPBV5KAAAaqML2/sFoBi8Oa/pVwBQJFz/APlW6+eS/FJzSP+srn9X5sSzvX8AsrV9+/ZL PveSi4nL28SNR/qN3/gN7e+8kHWcLrvsssjfmdQ2BufRY+wkUD+jWoEWagWAqtKF7f0CkH/B Glb6FAAUCdc/QL4FY86cOb5XXnklsv2+ffvUtm3bXEuWLPHbP//885HtpUbday/tbB9vkUh/ clTInFW29w1A9uQcIHkfk/s/3XikqLFN4ZA6Rt35Jmk8UtQ8ej//8z+vHnvsMevPHwAARaAL 2/uF+hnVCpz8TWoF0DTB+lX6EwAUDddKQL7pQvJISY+98847/fb9/f1Gf8v28RZJ3HgEydvZ 3j8A2TOZay7uXJEUMu4obrxk3NilpHn05N6VcxUAAPF0YXu/AORbsL5M7gds7w9QNka1AoPU CjQD10pAvumC/FI+zJ07N/L1kWtJ2/sGIB90Y5fiQuZc93I/wbntgqEbuyQ/052bwhG3BhQA AKDPBEDt5BrbC5O5rAEgb7j+AfItGFu2bPEtWrRIzZ8/3/XSSy9FPnZwcNBvv3z5cr/9c889 F9le2mV1HEa1AhvKXSsQvG4Mh8m4BgDlVs/YpeBaTrWOXZJzUtyYpWDcfPPN1p8fAADyThe2 9wtAfsn81hJSM0a/AIAi4voHyDfd5/LjH/+4//M9e/YkbmfevHl++02bNlk/rjLxrgdN+nMB VEt4PaO4kFxPeH0m0/FI8t+6dZd0Uc94JKNagdXlrhUAAEBHF7b3C0A+yRwEXsh81bb3BwDq wfUPkG+6zyX5pfyQ68CokP5f2/sGwJ64MYvhkDnqwvWKpuOR5L5U6h1rCdYCBoDqMqoV6KZW oB66sL1fAPJLruWlTsz2fgBAvbj+AfJN97kcGBhQTzzxhGvp0qXqnnvucR09ejRyO88++6zf fsWKFX57+bntYywDXR8wNUhAdcmcckkh5441a9ZEPt5k7JKuTVKwlhIAAGZ0YXu/AFSbUa3A LGoFAGSPayUg30w+l8H5l3bu3Jm4zbvvvttv39fXZ/0Yy0DXxytz59neNwB23HvvvbE5Hjk/ yJpLUY9NGrskj9PNzZkUjF0CgPKQ7wOZW1W+G6QG3vb+lJEubO8XAABAVrj+aQ6jWoGp1Aqg MSafYfJL9klfsC4YJwBUl/T7XX311ePOC0nrs8WNXfrKV76izT2ZhMk5Sfoo5buFvkoAyI9g LknO0eHvAq45s6EL2/sFAACQFa5/gHwz+Vw+/fTT7txqoqenR3V1dbkOHz4c2V7WYPLar1q1 ym9v+1iLTjf+QNZKsb1vSIdRrUALtQKon+SfP/zhD7vvqauuuiqx/y9u7JLJOkvy2Ouuuy7y dyZjl4K5LcZgAoAd8l0g3xdyTpbrTpO6AvJL2dCF7f0CAADICtc/QL7V+rmcPXu23/6pp55K bB/Midg+1qKT2n1d6Oa1AlAN0o8na/F6ayHF0Y1Hes973qM9x3ghOaFNmzZpfx/Xn6ibR0/6 KuXnJvsOFIFRrcDJ36BWALkUziWZ1BLU+n2A+unC9n4BAABkhagtbL9eqJ5a33/kl+wKzj8Y DOlXtr1vAPJPcjz1hjePnu48FDeWcs2aNYn17tKHyRx4ANCYtPJDUUHOKBu6sL1fVWdUKzBI rQAAAFkjagvTMQhR7Zrx2KjaYpN6Y2kTbhf1M9P9beSxuFQwTNqTX7JL5rmPCum3pfYfQBLd PJtxIWOLvLyP7hyku4aR85L0b5qGnMvIMQGAOcn5SA5fzu9p5pKiop78kjwG8XRh+70FAEDR GdUKbKBWIO+I2sMkJyL3DldccYW64YYbXPLfEiavifTdhB8rPzN9PWW9Bulr8tZTSFoXXMg9 j8Q111yjpk+f7v4rIevzJD1WxmVI/Pqv/7q6+eab1ZQpU9z/LX1cSY/1+tF0tdaEPvbt25f4 /N55551++/7+fqP3j+1zUhno+g7kc2Z73wDkVz1jl8JjI3XnH/m+Df89+Z42WaMjHPIYk+94 AKiaYC4par5Rolxh+/0GAACQFaL2SMqjxPX5JNWJxa3JklQDvH379sjHydrfSe+DW2+9NfKx c+bMSXysl18Kx5/8yZ8kPjbrurwyh42ckVGtwGpqBWrh5WvDIZ8N2/sGIL9qGbsk55Pw9Ydc y+giWBsjY5a82pBGwqQGBQDKilwSYfs9CAAAkBWi9oiq6w2K67NJGh+k62uWSBrPoFvjWyLp faCrSU7q45Z+J13IvVPcYxtZN4JgTFKeyedC95mijh9AlFq+E2WOuqg5NE3GI0kfp1GtQPeF WgGTOhD5vv/ud7+bOL6JcZkAqsLLzZNLIryw/Z4EAADIClF7JOVc4mqKZW63uMfGrWsQt9a2 iJs3Lm7cVNyYKYm4Of7i1m6QiFtDJi4PR0TH3r17fZ2dnWrGjBmuF154IfI5fvnll/32ixYt 8tv/8Ic/jGwv7Wyfk8pCV++fdA4AUE0m45Hi5pozGY8UV4cSDDl/ed/fco2QlDdKqrsBgDKR c6qci+WcKtd19cwdmkYY1QrMulArQNgP2+9bAACArHD9E09XcxaXc0mq9Y37e3H3J0nrKsVF 3LipuDFTEnFz2iTNmRM3VqOedcmrHsHn7+Mf/7j/8z179iS+l+fNm+e337Rpk/XPVtnFjUVg vXsAYVIHopurVkL6MOu59pDvWjnnmNTQy3VGVD2K/N2kx5NjAlBleck5Ec0N2+87AMVnVCsw lTUIAOQb10rxdPkTXc7FZH4b3ViipHFEErp+adlmXMSNmYgbMyUR12eU1N8UXks8iLWXao/g 80d+Kf90ny36YQHohGs+pI8yaV65uLFLX/rSl4z6OXVz7nnkd0nf+fL7uG0AQJWEc07NuPeR 7wu5L2wG289vM+nC9n4BAABkheufeME53YxqBVrGzyug6x82mfNNN5YoaRyRhK6PyWTOG93z kdTvpJsPMG7tJS90azDp8nBJazZVke41PHTokH9/J+/H9vZ218GDByO385Of/MRvv3TpUr/9 jh07rB9jWcXlfemDBaDj1bZMmTLFaLyjrs/yqquuSvyejptzL0zOW0k1KeSYAEBPzo9yfSj3 bnI+TTvnVLW8T7PowvZ+AQAAZIXrn3i6vIguj2Iyj5tufFBSP4yEbl0lk7lsou4hTMZBSUTN s5O0npIXUX1Hutxa3BimqgqGrk1wPa2dO3cmbvPuu+/22/f19Vk/xjLTfTbj5qFEtoxqBU5O Z14BFIJ8xyaNWfI0spZh0px7OknXRXI9xTygAGAmzZwT+aVs6ML2fgEAAGSF659ktayrZHqN H/V3TOakiVpXyWRskERUf7bJOCiJqDn+ktZT8iKq1lnX32RaF10lwdC1Ib+UX7r+3KQ10gDA RL19i6Y5KZ2kawA5x5E3QpaMagUGqRVA+YVzTiZ1hxLkl7KhC9v7BQAAkBWuf5KZrqtksp6S 7nredByRRLi/xnQcUdS4KZMxUxJRc/yZ3rtEjUnS9YcxT854wdC1OXDggDt3nfj85z+v7rjj Dtf+/fsj2x85csRv39PT47e3faxlpXu/69ZmAwAT9YxHku/utPI+SX+/lvn0AADJ5B5S6gPk 3izqXqzR2gFE04Xt/SoDo1qBDdQKAACQZ1wrJdPlb8I5l1r6ecJjiUzHEUmE7xtMxxFJhI/N ZMyURHg+QNMxUxLhdZJYT6k2ca9flNmzZ/vtn3rqqcT2wbFkto+1rHRrnvGeB1Av+R6udexS FvNymlz7kEsHgOwEc06cb7OhC9v7BQBAHhjVCqymVqDMuFZKZroGk8naS16ExxKZjiOSCK/B ZDqOSCI4bko3ZuqDH/xg5M+D8wHqcm5XXHFF5M+D45JYe6k2wTBpT34pf+LyscxNAqAeurx1 VMj1SpbnGrmeSKpXoaYeAFBUurC9XwAAAFnh+seMybpKutrgiRMnJj7HpmODJIJrsej6on/x F38x8ufBemTdOCiZ+ywqgv09unFQt956a+TPg3PesJ5SbYIxZ84c3yuvvBLZft++fWrbtm2u JUuW+O2ff/559/dGtQLd0bUC0i/obVu2afu5KSvdZyScLwaAJHKdcNlll0WeU8Ih555mzD1r kmOKmmsXAIC804Xt/QIAAMgK1z9mktZVipvHTTfeyKsP1o0NknFLIiq89RB0Y4Pmz5+v3aZ3 TLr90uWdgn3bunzbQw89FPnz4Hgj1lOqjS4kj5T02DvvvNNv39/fb/S3bB9vWcWtuRbMUwNA EvmeNqoVmHWhViApbKx9JOe9pDHV5JgAAEWjC9v7BQAAkBWuf8wkrasUN4+bLl/jjSXS/V5+ rpvTxhtLFJf30uVxvGPS1Q7r3hfeuKm4OQCT1ktiPaXa6YL8UvHo8sXM9wigVnIdMGHChMhz ipxrbNVsyN9NyhvJ76kpAQDDeQWmsgZBkejC9n4BAABkhesfM0nrKsXN+RY3PkkeqxtHJI+T MU5R4Y0lipu3T7dPss2kfYobN5WUa4sbn8R6SrULxpYtW3yLFi1yx6mJl156KfKxg4ODfvvl y5f77Z977rnI9tLO9vGWme6zI7lb+loB1Oq1115TV111lX8ukfl487B2kZzPktaKJMcEACgK XdjeLwD5YFQr0EKtAIDy4lrJXFwuJ2nOt7ixQlG/C66xFBVef3RUeDkvXS5HxkQljamKGzeV NFdgXK6NtZdqF4zgzz/+8Y/7P9+zZ0/idubNm+e337Rpk/XjqirduSIPfcIAiserF5kyZUru 5trUfed7IedDb75fAADyShe29wsAACArVbv+MaoVODmt4VoBXV5Ft+ZQcM43XR3vY489Fvnz 4FpHurFEX//61yN/7o0j0s1FJ9tLWhMqbtxUXJ5NHhs3Rom1l2oXjODPyS8Vk+7z4eWFAaBW ec7R6K6dvJB6mTzvPwAAurC9XwAAAFnh+secbr6qW2+9NfLnwXncdGOA7rjjjsifB8cn6MYH feITn4j8uTc2SOhyNHFjpOLeGzKfTlQE+7t1Y6V+7dd+LfLnrKcUT/f6DAwMqCeeeMK1dOlS dc8997iOHj0auZ1nn33Wb79ixQq/vfzc9jFWiXw+dJ8/xuaZM6oVGGy8VgBAc+ny6l7I+ZNz JQAgr3Rhe78AAACywvWPOV3e5IorrkjsK9atYfTBD34w8ufB+lzd+CBdH3Vwzpuk+WaC4a2n 5NGNhYoKbxyUJ2m97mCwnlK8YOjaBF+rnTt3Jm7z7rvv9tv39fVZP8aq0dXohz+DAFAWSXkj iWB9DKrBqFZgA7UCAIpDF7b3CwAAICtc/9SmlrxJeB43XT4oHMH1lJJep3CE580y6c/xwltP yaMbNxUV4T6hpDlugkEdcrxg6NqQXyoW3XyUEsz9BKCs5PyWdC3E+nIAgLzRhe39ygujWoHV 1AoAAFBWXCvVxjRvEjXnm24do3AE11PymI4lCo8jiuvHDoe3npJHN24qKsLrhOvmEowK1lOK Fwxdm6efftrN8Ymenh7V1dXlOnz4cGR7WYPJa79q1Sq/ve1jrRLd2MLwZxgAysQkx8R5EACQ J7qwvV8AAKTFqFagm1oBRONaqTameZOoOd90azCFI6p213QsUdTcMro1mMJRy/sjGOExU0I3 l2A4WHspWdJrFDZ79my//VNPPZXYPpjnsH2sVRKXvyXnCqDMpCYlaTw4OSYAQF7owvZ+AQAA ZIXrn9qY5kKi5nHTrasUjqg5r0zHB4XHBgmTdZV0a7mYjIXS9euYzA/IekrJguH9zKhWYFZy rQD5peLQfZ7Cc1UCQNnItVfSNYX8nnw7AMA2giAIgiAI4kLYvi7LM5O8ia6PI2mel6j1lEyv VaPGBgmTdZV0fdQmY6F0a2ybzA/IekrJdLFv377Ex955551++/7+fqO/Zft4q0T32Yw7DwAo D6NaganMKyDkuipp7mByTAAA2wiCIAiCIIgLYfu6LM+S8iZx87gl9Y1ErafkSRpLpBtHZLKu Ung9JY/JuKmoMVPCZH5A+oGS6YL8Ujno5qTU5W0BoGySxlnLeTJqbDcA5IFRrUALtQJVQRAE QRAEQVwI29dleZaUN4mb8y1pXaWo9ZQ8SWOJ4vqjk9ZVqvcaWTdmSiTNJch6SmZ0QX6pHHSf az4fAKokqXZHxnWSYwIA2EAQRPnDqFbg5MfcWgGCIAjCLGxfw+VZUt4kbs63pDWY4vpOksYS 6cYRibjaYN3aS564cVNJa2rHzSXI2ktmgrF3715fZ2enmjFjhuuFF16IfOzLL7/st1+0aJHf /oc//GFke2ln+3irJu58ohtXCABllDSfr+SYmFcXANBsBEEQBEEQxIWwfV0Wx6hWYPBjuZlX IC5vkjTnm24NJpM1V3QRN45IxPXZ6NZe8sSNm0qawyuuHpk+IjPBCP784x//uP/zPXv2JG5n 3rx5fvtNmzZZPy5cpMv/xs2XCQBlZLJmJPOHAgCajSAIgiAIgsh3fikP/viP/zjyebv66qsT H9ve3h752FtvvTXxsbpc1Zw5c2IfF7euUtK4h7ixUHHjoETc/ICsp2QmGMGfkzMqj7jPZ9Jn rMiMagU25KdWAED+yFhuXS2OF3HzCQMAkAWCIAiCIIiqhu3rsKLw8iaTJk1Sv/Zrv+bmlSRM 5nHz1lW65ppr1PTp091/JUz6P7zxQeHHmtTnyrilK664Qt1www0u+W/T11wi/NikcVDCm8/r 8ssvV9ddd52aMmWK+79ZL8ac7vN56NAhN/cnZEyL5C3FwYMHI7fzk5/8xG+/dOlSv/2OHTus HyP0c0syNySAKjLJGyXNu4vmMKoVWE2tAAAAKA+pA5W+Femjk2vSuLUiqhpyLS/Pi/RpSH+n PF/UmQPARXJODI8riPqZ7rHhc2rUz9J+bNS+mY6NaOSxUetEse62uWDo2gSvZXbu3Jm4zbvv vttv39fXZ/0YoR/rJ9dkXIMBqCK5zoibY1iCHBMAAI0zqhXoplYA1ST9V1LTLXkS6XtJqoEi 4kNq1WV+f8nNST8I/R0AAGQrGLo25JfKQa6zooK5nQBUldxvJuWY5PfclwIAyoqID9uvD/JP rhMljyH5DPJDzYtgHilpXQ6kw6hWYFaxagWI+LD9+gAoBpPzxoEDB9x57sTnP/95dccdd7j2 798f2f7IkSN++56eHr+97WOtOt1a9SZzUQJAWUmfgNybxgU5JgBAWRHxYfv1Qf7IGHi5t5Zx 7kl1SlmHN0ecR8ZASb7FI/vprWOQhuC2g3P3eWyH9xwwbqnaiPiw/foAsMuoVmBqbbUCtZ5j Zs+e7bd/6qmnEtvL9QbnsHyQ6ytdLZVcf9nePwCwKfh9FRWSi2f+XQBA2RDxYfv1gX1yH+3l k3RzgmQRkrsK5oyCuSLbz4nJc+btq8yXEsxFNfs5lOevCM8Z0kPEh+3XB0Ax1HreIGdUbHK9 FBVy7WZ73wCMZ1Qr0FKseQXKQnc+9UJy9OSYAABlQsSH7dcHdkg+Qq4Lsx6f5OWQgvkj07XI i06uqb0xUc1ae0r+huS7uJ4vNyI+bL8+AIohGHPmzPG98sorke337duntm3b5lqyZInf/vnn n49sL9/FXntpZ/t4q06uP3XBdRMA6OcH9ULuZRnHCaAWRrUCJz9KrQByj4gP268PmsMboyRz JmeR4/DmsfPySNynx78W3rgnL++URcg4Kqmd5h6gfIj4sP36ACgGXUgeKemxd955p9++v7/f 6G/ZPl7o53WSn9veNwDIg6Qck4S0sb2fAAA0iogP268PsiO1l5KXMB13ZFQrMHihVsA0grkk yZOwDlA6JCcn1+pZ5Zwktyjb5/UqPiI+bL8+AIpBF+SXykuuW3XB9REAXCD3pUk1qdInYXs/ AQDIwpYtW3yLFi1S8+fPdw0NDUW2Hxwc9NsvX77cb//cc88Vdvu2XwOkr9ackkl46/ZIvqEq 89XlhVyvy+sp+Z4012oif1Qewe+Cf/iHf/C/C77whS9Etj937pzf/oknnvDb/9Ef/VEpt19F RrUCG5hXAKhFMHTX4C+99FLkY+u5xrd9vLhAdz0tdVO29w0A8sIkx8TYTwBA2d1yyy3+996e PXsS28+bN89vv3HjxsJu3/bzjnSknVPy8kkyVxr5h3yR11ryQnJ9nla+iVxTeRw9etR/Xa+7 7rrE9m+88YbffuLEiaXfPgAEBSP4849//OP+z2u9bt+0aZP140I83XxO0jdqe9+CjGoFVlMr ACAf5D41qT+CHBMAoMzymv/Jevu2n3fUz1sjKY250YJr75BjKBYv35TWWlmswVRsRc//kF8C 0Ey6sL1fyJ6uRod1QgDgUtI/kJQ3kt/TjxDNqFagm1oBAMi7e+65x/XMM89o2wwMDLjz7Iil S5f6j5G+rqj2zz77rN9+xYoViX8jr9u3/dqgdtL3r1tvuJaQfISMeWK+u3KRtQJk7FmjY9mk 30W2w/ujWOS+zvsuePTRR/3vAnkto9q/8847fvvHH3/cb79w4cJCb9/26wAAyDeZ3y4q5PrJ 9r4BQN7INbr0H8QFOSYAQNF40dfXF9vOqFZg1qW1AsHxIDt37kzcl87OTuP9yeP2kX/Sxy/3 wY3MhyZjWxijVC3evIlJ9wJJIfcKzJ9XPK+88or/Gn7kIx9JbH/mzBm//YQJEwq9fdvPPYrp vGPmTnkftbnvowWOt4cXa9v/zHHT90667eURfT2T1LsnNls/DgDJ5JpGF1KrY3v/ACCPkupc pb9C1mKyvZ8AAJjwwiTfIvKW0yFnVF2S32mkvz+YJ7J9LLDLmyMxjfdT3scpGdUKTGVegaA8 53/ILyGv1o4415htK933keSMhh9tc35+PrLtj0bHVMvMdZecUxc6Dzq9t1P7GAD5oesnlesq 2/sGAHkl8wok3V+SYwIAFIH0qYpVq1aprq4un679008/7T+mp6fHb3/48OHI9rKGUdTfKMr2 kS+SB5BxJPWOPyKnhCRp5Jokj826A/aYfJeNjIz43wXf+c53/PbLly+PbP/v//7vfvt169YV evu2Xx8U16uOiV/c5Z/r+hdKDnIosm3vKalhWjju/Hin4639C6wfS9EZ1Qq0UCuA5pDaGl3k ve4GQDmEw/b+mJJr87iQ/gv6LgAARRGuOzN5zOzZs/3227dvr+lvFHH7sEfuTetdK4mcEurl 5ZqC4x5rCcmDynyMzIfXXMEwaf9P//RPfvsPfehDie1lraIibx+o1xnHlEeO+e+3B9qUGh1Z O66dbn680/LzTa+oz7XcpkZe0s+XByAfdNc/urX8ANTGqFbg5A3UChgIh+39qUVSjkmCGjEA QBGQXyK/lEcyb3u9ffveGBL69pGGRtbuKsrceWURDJP2ecv/kF9Cni0eHnXeaR3u++1GxxsD 7ePahMc5rXOaj43udn8nay19cuCU+otJk9S7J8fnpgDkh9Rm6a5ruL4GkLVw2N6fWsncdnK+ jAuZm8X2fgJojFGtwGC6tQJR84sHI3j/BdgUjOeffz6x/V133eW37+/vL/32kS3JC9XTj++N F6EfH1mSvGe94+lk3j3Wu26eYHz0ox9NbH/q1Cm//Qc/+MHSbx+oVfBeRreuUlIbyTEt2HFa Hd80c9xjAeSL7nqcPlEAWQuH7f2ph0mOSe4rbe8ngPwYcu6lrv/KPuXNBfHldkVdHnIlGEXM 6WS9fWSj3lyR9MObzGlnVCuwgXkFkL5G1gOT8XXkmbIXjCLmf7LePlCr1x2TvnrQf59F1caZ jHECUAy6eZvk2sf2vgFAEUjt67Rp02LvDckxAQgKzgcRt+YtYEMwipj/yXr7SI/0u8s4oqRa nXBIe5nTnfFHyIN6xymRO8pWMIqY08l6+0VlVCuwmloB23RrK+l+r1ujCUAxyDW97nqe9UAA wIycS5NyTPJ75h4FIIL3VNxPIW/27t3r6+zsVDNmzHC98MILke1ffvllv/2iRYv89jt37izs 9m2/BmVXb15JaiC5R0VeeWsv1fq+Js+UjeB3wZYtW/zvgrvvvjuy/bvvvuu3l+8Xr/2sWbNK uX2gHmtHxpzbl5XuuesGx9HvXO/8/HX3dyZjlgAUi9Rz6a5dGtmuUa1AN7UCAKpH+kpkjpa4 IMcE5JvkfR4ZOqvav7XJ+cS2+59dqaz7Ts8k1dfbq949sTmVv+XdnzEfBPLslltu8T8He/bs SWw/b948v/3GjRsLu33bz3uZ1dP/zlo0KJp65nskz5Sdo0eP+s/zddddl9j+jTfe8NtPnDix 9NsHTB1yTPjMFv/9JfdIAysmqbHRoXHrxx78mwnOY96xvs8A6ie1M7qQ9UJs7x8AFEnSnBdy /1ikc6tRrcAsagVQLbKG7CcHZG6R9kvuleR3m0+MqvZvbfZ/19czqeE8U3COPO6/kFd5zf9k vX3bz3sZ1dPfLtdfzIGHIpN8keSNagnyTOkrev6H/BLy4oxjyiPH/PdXu2OSc+3U99kW9c2f /sQf20T9HFAeuv5Q1gEBgNrpxoV6IbW4RcoxAVXQe2LUuc/5nH//c3b/gkt+L+OVel8+q1pu u99tIyvMDm+Se6HX6/p7wXnvmBMCWTOqFZiqrxVob2937dixQ/s3Dh065PZzeuuLeI85ePBg ZPuhoSG//dKlSxP/Rl63b/u1LZOtW7fWlFeS6ykZ40ReCWUi55WkeQ7CIXkmPgfp+NnPfuZ/ F3z/+9/3vwtkDtWo9v/+7//ut5fvF6/97NmzC719268DymHx8Oj/vmu6sK7Sqy8tVtd/ZZ/y 1lKSWOQ4f6rX+r4CaJx8l+iC6xQAqJ3U3ib1idAvBeRDuL5Oov9zLWps9Mi4tt88Oapabvtz t014LvFa9Z6SNZkXjlvHFmgGL/r6+ozaB+vqdesZBUlfVi1/I2/bR2NqHYvh5YqYGxhlJn0r SXMdhCM4Ns+oVqCFeQXS8sorr/ivw0c+8pHE9mfOnPHbT5gwodDbt/3coxyC8+BJRmn40TZ1 emxUzdwk78129+fU2QHloltvXq7zbe8bgPIJh+39yUJSjkmCtacB+8JrzEpI7ui1zR9zfn/+ krYy7qhz/1n/nkjXzoR3z+Xdb9WzDaBeXpBfQpqkH7yWsRfklVBFteaZ+JykJ885HXJGKILg vN0SXn5I7pG+8JJzj9S2kPm8ExjVCpz8dWoFUEi6flC5lrG9bwDKJxy29ycrMg9e0trU5JgA u6LGL0no7o1OOH75wYv5qCVtSr17orehv9u/UM6DQ9afC1SHzIMjenp61B133OHTtT9w4ID/ mPvuu89vv3///sj2R44cifwbRdk+aiP93tL/bRr0lwO155lk/kjuG/RMvstk/jjvu2Dz5s1+ +3vuuSey/fnz5/32P/jBDwq9fduvD8ohOMe3xAPOP6Mja/3fy1wPE6etbGiOBwD5o5vDmusS VJVRrcAgtQJZC4ft/cmSSY6J9e4Au4LrLymlEscUBecTl9h1f4vz89M1/11vO+F7M6BZwn2b Jo+RdR+82L59e01/o4jbRzy5r0y6zgkGeSXgUnKvUMt8ktKWtVzHq/W77J/+6Z/89h/60IcS 27/zzjuF3j6QlrUjzuetbaX73rvR8cbApWt2rZcc091fUwMrJqmxUerngDLQ1ZFJ3sn2vgEo l3DY3p+syX2dbm5RL8gbAdkwqhXYYF4rILV4vS+fVS233e/mlpLuh8JzQ9Q7hsmbIy/q3gxo BvJL5JfqVWufeHAdGQDj1bpu2bJly8jVBtT6XZa3/A/5JRTFIceEz2zx339RdXY/ODumLr/7 MfXtz7aod09utL7PABoj1xu6YG15AGkKh+39aQY5xyblmOQ+kXs/IP/C8z3Uuy5SMPdU77gm oBHS779t2zbfnDlzfMPDw5GP2bdvn99+yZIlfvvnn38+8W8Ucfu4lFynSF+1aaQ1fsKoVmA1 8wqgWmRMoG4emnDI2EHmprlA9733Z3/2Z5Ht/9f/+l9+++9+97t++8985jOR7UdHRwu9fSAt 4bnFvTWYwu02nh1TLbf9rVro3FSd3tupWGsWKDbdnL6y9qrtfQNQHuGwvT/NIn0ySbWGkoMi xwTkX3C+B4mvtys1enZzTdsI5ql091sAkBdbt2417suWdtQoAvWT+WVM55OU+wvG/KEIjGoF uqkVKJPgXOELHG8PL45tJy33/X078+UBBSbXJLrgegVAWsJhe3+aLWl9XskxMVc6kG/hee/q nbfOy1PF3W8BgE1yHyj1hiYh/eFr1qyxvs9AGdQ6XlByUrb3GUD5efN2S0xx/HTz9ePaSB1d 5/6zzm/b/XOUjE/a1LdAjQyxlixQRF69vFGtwKwLtQLhYG0QAFEkDyL1qULqWuW+Jkj6I+Qc FBSO8O898tjw9uRveH+vyDmYpByT9M8U+fiAsgvP99DoHHn1Ph4AsiS5ItMxFKzxAmSjljXM qFMDkLVwnV1wnu9g7ikuWFMWKB7pj40KuVfgHgCoFi8fFM7/mM530syQfQrnmfKeV5J5zeNC zruNzhdjVCswlXkFgKwF53uQqGeOu2CeijnyAOSFjFky7c9Oa40lAPHkPoN8LwDbwmvRPuD8 MzrCmCSgCnR9x8xfAJSP3Et4OSS5t5D7ftN7kSKFl3+SY5RzmRxzHu6jknJMEqyvC+RTuOau 3nnrek+MOrdcn+N+C1pGtQIt1Aqg+UzHLEkbrmeA5qplzjy5VyL3CyALwbVo2x1n9y+wvk8A sqfr75RrDtv7BqB+cs8gn+8y55FqDXkOvLyTPDc27qsk15X0WtAnk3/y3gnP0Yh8a/RzFZ7v QTeneBLvnou5HwDYVMs4JJm7Nw91OkCRGNUKnLw+9VoBuUaVufBMgnWZAKQtWJPHPN9Adci9 gq6vkz5OoBjkc+ytcWTaV0BcDHnOvDWcmtF/Ivd9STkm1rbLL5PXj8hfyOe8kdc9PN+DRHBO 8VofW8vjASBNcr1j8j0m9YaNzt0LID1yv2Ly2WWtJQBpCq8vW6W5u41qBQbTrxUAikg3jrrR vhgA2ZCaU8n/Sg4ij+sjFT3kOZXnVp5jea6zeA1NagvJMeVPMLfkjYdDMci1TqOvf3C+B4la 57jbfGJU9fZ+Sy1at0613HY/c+QBaCqpoZG1K02CNVqAfKpl7CFrHgBIS3B92UWO86d6re8T gOzJdYcuqGVBURjVCmwof62ANz7JRj5J+iHuv/9+1+OPP676+/tdch45duyY780332z4OGUb wW3K3/D+nvxtbz9M+0bSCi/flPb4JtlWUo5J7h/p38mHcG6J79LqOeSY8Jkt/uezXTGnOIBi kHFIJteQjFkCisF0PTTuJQCkYfP5MaWmPOSeV5jnG6gW6Q+NCmriATukP1ruBbKew+6mm25y 8zAPP/zwJbmgc+fOWX8Oksg+BnNLcgxyLHJMWYa8JvLapJEzkHu4pNdY8krc69kVzu9J/tD2 PpWVUa3A6vzXCoTnhmBOcQBFIHNqmQRjloBiMR3LJHko8sYAGhFei5Z5voHqkGsIXWQ15xOA S0mftdyvZzHuyMsjSR5mcHDQzcvYPt6syTHKscoxZ5V3ktdKXrNG8w26HL8XzI1uTzi3xHqD 1Rac70GiSnOKAygWkxoW71om7f5ko1qB7vzXCgBlYzqWSXLNtvcVQDGF15N9aIpzv3R+s/X9 AtAcurmXuLYAsuPNZWdynW8akkd58MEH3bxKUcYiNYs35kmeG3mO0sw5yWvozY1Xz74l5ZiY Z635yC0h7EejY6pl5jr/PbHA8fbwYuv7BQBBki8yubaUaw/GIQHFZ7JeqwRz4KEWRrUCs6gV wHi9p8acN8hC7peACpJ+M12fpu19A8pE7vnTzCl1d3e7aw7JWJ001jaqGnnO5LmT51CeyzTC yzXVWg+sOw8Ht8ucFc0TXL+LWguI8HwPzCkOIG9M5riT6wnmegXKR+ZVMPn8cz8BoFbBOjvm /gaKw6hWYGq6tQK6ebmo2QYaI/NMpjX3nZdPkjo1xialT55TeW7Tyjd5c+iZzjWalGOS4Jyc veB4MtYihCc834MEc4oDyAMZkxCsi9CFjF9g/nOgvEzHL1I7BaAWa0fGnFugldwDARWlq2GT Pk/b+wYUjdy7S9++yfwDcSHzsj388MOMObLEG7Mkr0Gjc+TJe0HeE7q5JoxqBVourRUwuS8k x5QdckuIE763eqBNqdGRtdb3C0B1SV2MSQ0T/clANZiuvyY5aebLA6CzYP9550wxJfL8wT0Q UC1yvaALxkUDZuS+vdE57bxxRydOnLB+PLiUvCaNjlfy5sSLWyNJfpf0HiKfkb5gnYXcR9ve H+RPeF2ldsfZ/Qus7xfyw6hW4OSvsQYBmk7qTZKuLZgPC6imNWvWJN7DSK0ca7wCiLP5xKhz T/0t9776L772NdW27FHmCQcqSLduPP1sQDy5Zzep/dKF5CsGBgYYo1Qg8lrJa9ZIrkneM7rx RSbr75JjSk9wvkF53qnRRJQzjimPHPPfK8wpDsAWk/VT5DqD7zOgdka1AoPlqhUwmReBNdkA AEASmWNbF8y/DVxK7telLqPesUrklMqj0VyTvIfkvRTuA5L/nZRjou+oceSWUIvFw6POO6XD f8+sc/5zbHS39f0CUA2m6ycxxx0AYToHnoxhsr2vAJAmo1qBDeWqFQDyQnft8Sd/8ifW9w3I A8m16sb6JYX0B8j8auSUykteW3mNTfp+okLeW8F8vsk9ITmR+kkNJs8jarH5/JhSUx7y3zcL HG8PL7a+XwDKT64PkupOGF8AIIrJmEfmRgAAADqrVq2KvH6QPkvb+4byMqoVWF2cWgGZM6De nMHDDz+sjh07Zv0Y0FzymstrX0/Iey24BkJSTpM50WsXXOdK/uX5g4lDjgmf2eJ/9pgjD0Az mKzNyLUAgDgm67BRbwUAAMKS6lTk97b3EWg26bevZ70kmf9scHBQnTt3zvoxwC55D8h7oZ45 8eS95+WOkvJG5D3MkS/KhlGtQHdxagUa9aPRMdUyc90ln9MlbUq9e6LX+r4BKCeTPmGpS6FP GEASuT6ePHly7PmEXDUAABCm8+zq1pYHyqjevBLjjxCn3nFKXp4puP5PVEifUnAsE8Yjt4R6 nHesHTqr2pY9GvnZW9mu1PAPZO67163vK4DySPrel0h7/SSjWoFZ1akVAKrMpK+I62kAAKrN pCaF6wVUST15pZtuukn19/czVgnG5L0i7xl579QS8t7UzWMaDOoBosk9cnA9CtadgIkF+887 75Ypxp/TB9qUGh1Za32/ARSbybhlvu8B1MvkHMO1cnEZ1QpMpVYAANAYuVYwmVM3uH48UFaS Q601ryTzi8i8Z7b3HcUm76Fa1+uSc/MVV1wR24Y+p0uFc0s8PwCAvGJOXADNYDJGkmtmAADs MKoVaLFXKyDzKCSF3NcwNzfKTvKnSffw4bj//vuZAw+pk/eUvLdqife+972J53Hbx5UH5JYA AEUQ/r6KCur/AKTJpO54zZo11vcTAABkR+5DTGrfuSZA2clnwSR3GgxyRYhjVCtw8v9ItVag njxTXJBjGrvkOzLtdSoAAEiDSW5JxtpT/wcgbcH1SbmnAACgWqR2Lek+hHlzUQUyHiHpmjgY 5JVgQ5q5oyr3MQXHHHKvCyCJUa3AYLq1AkAtTHJLfN8ByJJJ3xLnIQAAymX37t1G6ycxNzfK TD4HSdfBwSCvhDxIK88k7/2q5ZjILQEA8k7uv5KuT5ctW2Z9P4EiMaoV2ECtQCPIdQMAUB0y 111SyHxAVet3RHXIe7uWdZPIKyGP0sgzVamOIDj/pXzH2d4fAADCTOaZYh1AAM1kcu/M+tsA msGoVmA1tQJA1kz61Guth5PrCBkHwroUKALJr5rOhSd90OSVkHfyHjVZR08X8nkoe45J+uK8 qOK4LQBA/pFbApBnSX1JXGMDAFA8JuOP5R4l6T4kmB+SPsrJkydfsg2ZS9f2sSIfjGoFuvNZ KyD37LLmjEncdNNNanBw0Po+A7WQ96y8d+sJ+a6Q7wHbx5AFcksAgLxLyi2Z3NMBQNbIMQEA UB4m9W2SJwrXpCflkqJiw4YN1o8XCAvOdZUU/f396ty5c9b3GaiHvHflPVxvlK0/auvWrf6x cQ9bH6NagVn5rBUAgLIxyS2VfZwxgOKQ+W7igutzAADyL1iLHfedfuTIEbcfrpZcUlRcd911 1o8Z8JiseeyFrE3z5ptvWt9nIA3yXq53vaWy5JiCfXD0twEA8qhZ+SKjWoGp1AoASF9SnxQ5 JgAA8iupVkTiqquuMl5fxjRqXZMJyILpmCXJp9LvjLKS93Y96y3JfBa2973R4ya3BADIM8Yi ASgyk7yR7X0sG6NagRZqBQAAjZPaD9O1Y7IIuRei/gS21DJmibnwUAX1zplX1BwTuSUAQN6R WwJQBkk5pqLeTwAAkBWjWoGT11mtFZD7EBmTZCuuvfZa7oVgjemYJZk37MSJE9b3F2gmec/X Omee1CoUqV5A9jWYX5Z5X23vEwAAQfJdFTcXObklAEVCjgkAgPwz7TPPImRepSL1LaI8jh8/ bjQO76abblIDAwPW9xfFYVQrMGi3ViBL8nmRz41pFGVO9HBuqSzrSAEAyiP8XRUOcksAiogc EwAA+dfe3m7cFxgX0l8v6yPdeuutiW0lr2X7uFFNMubAZF0wGYvx5ptvWt9fIA/ks1DL+CTp 38pzHxb5IgBxjGoFNpS3VgDFQ24JQJmRYwIAIN/kfqSWkPsTySVJjkj66r17laT7Gu+x9OPB Fsl/JgVjlgC9WsYn5bkvS8bPekG9AwAgj8gXAY0xqhVYTa1AUSTlmNasWWN9HwEAqLIbb7xR e+8SzCXJvGJRj09aR1ZC5gLnPgg2yPs2Kfcp0d3dzZglIIF8RuSzYhIf+MAH1O7du63vc5DU N3pBrSMAII+C31VR92fcUwEoi6QaUOqTgWozqhXoplYAyAP5Tr/iiivcPFNSLilMvu+TcktF WWMD5WM6H15/f7/1fQWKRD4zppGX+0JySwCAvIvLLUnIta3tfQSANHHeAwCgXEzmEKNfrjqM agVm5bNWQHKlSSHzZB07dsz6vgJFJJ+d4FxzcWE7xyRzZgQ/97afOwAAwoLfVXn8LgWArDBu EwCA4pPxRTIXXt77CFFN8v406cd+8MEH1blz56zvL1Bk8hmSz5JJdHZ2WtnH4BzsjJFNZlQr MDWftQIAUAVJa4twDwag7OLu9yXHZDrHDgAAaD6pBUlan6aWmhHp55M1N2T8SHt7u7r66qsZ 04y6mbw/JQYGBqzvK1Am8pkyiTlz5jR1v8gtAQDyTq5f40LmjKh1m0a1Ai3UCgAoJrmmj7vv 57ofAIB8MlmfRr7HdbUiwVyS1JtMnjw5chvye9vHiuKR91bS+1PedydOnLC+r0AZyWfLZOxg s+735JzAPSYAIM8ktxR3/cpc4wCqJClvJPPo2N7HZjKqFTh5LbUCAIBCSJrj27v/8frvJMck +SjJFck1QFK/fzCuuuoq68eLYkmaJ0Ti/vvvZz48IGPyGZPPWlLIWNUs9yPYX8c87ACAPKIf FQDGI+8OACgTo1qBwXLXCsh9T9z6iV58+tOfriuXpAvmDYcpk/dnf3+/9f0EqkQ+c0mR1b0h uSUAQBHErWfLuFsAVZaUY6K/CAAAe5Lq5LIO+vkQxySfedNNN/E+Qk2MagU2lLtWwAb5nMrn NS6C417TIGNnyS0BAPJO1lTShXx/kVsCUHVJc5TIXNi29xEAgKqRfrYrrrgi9js6y7j22mvp 64OWSe6T9ZMA+0zWWUqr7jp8XpB5V20fPwA7jGoFVlMrgPyL6zOlNgIALopbp0HOl7q1vwEA QPrkPsVmSF8kdXjQkevCpNxSd3c36ycBOSGfRflMxoV8phu55wvnlpgHAwCQR0nzOFEbAVxk VCvQTa0ALoqb34S5RAEAaK64Ob7rCbmPuuqqqxLbybpMto8d+ZV0Ty7x8MMPW99PAOPJZzPp e6KeOmxySwCAIpDvq8mTJ3MfBAA1iqsvzWqdVgDpMKoVmEWtAFBGcv9z2WWXxfYFxvURSn5K 7pGkBu/IkSOJ403kMfQJIo5Jbqm/v9/6fgLQk89o0ndBrTmmYJ0ifXMAgLyKq9+jfxQA9JLy 8zI3nu19BACgKhYtWhTbtych39vB/FB4ziKTfn7ZBvOB54NRrcDUfNcKmLznBgcHre8ngGTy WY2LWnJMwdwSfXMAgLyS+ypdMGcTACRL6hOg/wkAgOaQfFEwJA8k6x3JPc/u3bsT723i1pjl Hgmm5H0Ud2140003cX0IFIx8ZuWzqwuTcarki/SMagVa8l0rAABVFL7/Cn83suY8AJiJ64+S 8yn9UAAAZE/uX0xzSWHLli3TfpfTHwhTSTlK6Z8+ceKE9f0EUDv57MblmCR0OSaZ18ILqXuw fSwAAESR+6m4Oim5z9I91qhW4OSvUisAABpx/VIyD4/t/QMAAONJHipuvvCkPkPAQ24JKL96 ckzBcwNjYAEAeRa3rizrAAJAfYp0bjWqFRikVgAAACHzHcWtoyhRz3rs0ne4fft2d74I28eI 5kjKLXV3d5NbAkpCPsvymY4LL8dEbgkAUBRxdfOMvQWA+sk9QL1jQwEAqJVRrcAGagUaJbmf uO93ry8wac5w+b1cC0jNidx3XX311f7jb731VuvHieyZ5JbOnTtnfT8BpEc+00k5plWrVl3y fUJuCQCQV3I/owupx+M7DAAaw3kWAIB8k/xOUsj6SeHvbMkPSa5JHi/z5CXlnCRsHyuyFXfd J0G+CCaMagVWUytQZCY5Jol6xsECANAsSXX1fIcBQDri+q0YJwoAgB1yPyR5o6SQtdXl3qjW XFJUrFixwvpxIxvyHol7X5BbAsovKW/0cz/3c+q73/2u9f0EkD2jWoFuagVQbNKnqQu5h7K9 fwBQJnHrf7PuNwAAzSVjj+LWSZSYMGGC+vVf//XYNvUEayuVD7klAJ6kHBNjkgAAeSU5IV1I v6bt/QOazahWYBa1AmiOuDGj8vOkNRoAAEA6ZA6zescfpRHUlZQLuSUAYfKZ7+rq0p4XyDEB APJG+iXj+i1Z3wMAsiE1yLqQumjb+wcUnVGtwFRqBYCiOO+YufOM8+Ftcz/HCxxvDy/Wtv+Z 46bvnXTbyyP6eiapd09srvvvS27HVrzvfe9Tjz32mPXXAOmR+2xZT1MX5JaAakrKL0mwFi8A IE/i5naQ+jzb+wcAZbZs2TLtOVjWabC9fwAAZG3tyJhSbSvd7z7JAw0/2ub8/Hxk2x+NjqmW mesu+b5c6Dzo9N5O7WPiJM1zl1XI36X+3IxRrUBLsWoFpF847r0n89GTWwKqJzw/3u/8zu/E fo+QYwIA2Ba3brz0edrePwAou6T+BfqeAABl96pj4hd3+d99/QuV8/OhyLa9p8acFgvHfV/e 6Xhr/4Ka/7Z8D8taSY2GzPsg84rfcccdiW0ld0CfYPnEXc/ddNNN6sSJE9b3EUDzPfzww/65 oL+/3z0XyDlBF2Wdx8KoVuDk1ELVCgBA1UmfZdz3Gfc8ANAcSedj2/sHAECWzjimPHLM/+57 oE2p0ZG149rp5sc7LT/f9Ir6XMttauQl/Xx5OjLvXC0h8xJJjkhq9WSuW28dxHvvvTfxsYxN Lqe4157cElBdwdyS/Lf386Qck5xTbO87AKCa4mqmjGoFBi/UClAvDwDNFTeelL4oAEDZLR4e db7xOtzvvRsdbwy0j2sTHue0zmk+Nrrb/Z2stfTJgVPqLyZNUu+eHJ+biiN1dboI5pJk7vCo GrykscgSMr5J1m+y/TwjfXHXcBLkloBq0uWWPHJuiAvuAQEAzUbfJADkT9pz3xnVCmxgXgEA QLEE11XSrcGU1EZyTAt2nFbHN80c99gkkke6+uqr3ZrxuFxSmHyXS+4oLmTb1O+Vk+QM42Jw cND6PgJovoGBAf88cP/992vbyTkiLqhLAAA0i8zJoLuvYW4lALCHue8AAGFGtQKrq1Ur8Lpj 0lcP+t+RwbFJHpMxTs0k/X5JuSXmHS+vuGs8CXJLQDUFc0bd3d3q3Llzxu3DId8x1CcAAJpB 1pLVBd9FAGBX3PjSNWvWWN8/AACyoFsvSfd73bpLzbJs2bLYfIFEPWtiSH6Je7L8k9cpLl8Y Nb8Vqs2oVqC7WrUCVVdrbskTnEsvHHJeok4BAJCluDH5zIsHAPmgmydP7he8dcEBACibtSNj SrWtdL/zbnAc/c71zs9fd39nMmapGaTfLq5ezwuTeYpkWzK3ntyHybpNbW1t/ve97dcC8a9b 3JzGDz74oPV9BNB8x44d888Dck43zRd55NyhC+ayAPLBqFZgFrUCKK+4Giu+qwAgP+LmUJF7 Fdv7BwBAFg45Jnxmi/+dJ9mWgRWT1Njo0CXrKUkc/JsJzmPeaer+yfdzXF5BQjc/kdSHbN26 1c0lSX4qaa486knyS8al6aKWMQgAyuPEiRPqpptucs8D8q/871q3IecOOYfoop4xsQAAJIm7 tmVeBZSRUa3AVGoFUHxx8+RJ/5Tt/QMAIG1nHFMeuVj/3e6YNG+e6vtsi/rmT3/ij22ysZ6S fPearJ8keaFac0lRsWrVKuuvB8aLmw9E+pTffPNN6/sIoLnSyC155BzibSsqTMbGAgBgSuZS 0IXMB257/wAA402ePDnyvC0/Z65sVIVRrUALtQJAFS0eHnVOAh3u+UDWYHr1pcXq+q/sU966 SxKLHOdP9TZtn2T9w6SQ3NKtt95aVy5JF9QL5kvc+HKJRvqUARRTMB/UaG7JI9vguwEA0Ay6 uRnoowSA/IqrDWAdPABAGQXntpMs0fCjber02KiauekVdWGUUvPWU5L7pLg5ILIOuVez/Xqk xahW4OSUUtQKyPtGVyMkMTg4aH0fATRXeD67NM8Dsq247xH6/AAAjYqroWPeJADIt7h+LdZb AACUzauOiV/c5X/Xebmk887Pv/DSWaXaFjZlPSXpj5syZYr2OzjrmDRpEnXnOSJzF+ri4Ycf tr5/AJory3yRR84turC1zq5RrcBgOWoFAAAXyH2Rbt4FuUa2vX8AgHicxwEAVSJ5pJk7zyhv jrsHnH9GR9b6v//myVE1cdpKdfQ71zv/+/XM9kP67myFzD1BvXl+xNVrSv+y7f0D0HwDAwP+ eSDLMYnBHFY45Nxk+3kAABSPUa3Ahou1AtS9A0DxxfVryBx6tvcPAIA0rR0ZU6ptpfs9d6Pj jYH2S36/XnJMd39NDayYpMZGhzLZB8nvvP/979d+/9YSUiciNSE333xzYlu5f7P9/OOiuHWU ZP0UWU/F9j4CsEPGFz3++OOZ/o3gmk1RwThXAECt4q5vWYsDAIrBqFZg9aXzCsSto2f7eAAA SNMhx4TPbPG/63bd3+L8/PQlbX5wdkxdfvdj6tufbVHvntyYyX784Ac/0N576cLLJcm9mcxF LjV9kquKm1/Ni/Xr11t/7nFR0jpKx44ds76PAMovrh+Q9ZUAALXS3ZfwnQIAxSNjj3TBfAcA kG9GtQLdrEFQjzOOKY8c878TvTWYwu02nh1TLbf9rVrYptTpvZ3Oz86nvi/ve9/7tN/VUbmk 8OOlX1BXIxLcDjXo+RM3F0h/f7/1/QNQHXLO0QXjXgEApuSeRRfUugFAMen6LqSviboBAECZ LB4edb7hOtzvuQWOt4cXx7aTlvv+vj31OfC8mr0rrrgiMT8UJu106yJ6Ifkk5iLPn7j7adZR qi6jWoFZ1ArAvri1mOT8Znv/AAD5pxubz/ruAFBc0v+k66di3lMAQJn8aHRMtcxc537HTXH8 dPP149qcd3TuP+v8tt3/PpTxSZv6FqiRobWp7Id899aT/4lbD9ELqRuhPiR/4q63WEcJgC1x 6ytRbwiYM6oVmEqtAKpNxifpgrXdUVbyvpf+dfrYUXbyHtcF9c8AgLJ41THxi7v877jgukrB 3FNc3Oh4Y6C9qfst/Xtxc6V5wTVrfsWtf8XchABsiltfiXpyAICO3KPo6qeYOxVFZFQr0FJb rUDwPtD28QFZ4jsBAFAFMjZp5s4zzrdbm/sd94Dzz+hIOmOSsiLf0SbrJ9UyN5HUjkj7ZcuW ude71JJkK27c2cMPP2x9/wBAzkW6YF1eAEAUatVRVeSMUCVxY0851yOPjGoFTv4K8woAqNva kTGl2la655V2x9n9C6zvk47UlCettSRzmMeNf/FySXL/J9fBUdtj3YzsxM2LN3fuXHXu3Dnr +wgAci6Sc1JUyDmMe0cAQFBcnTpzKqDsyC+halg7DwBQdsG57WRk0vCjbc7Pz1vfr7C4uo/g 93NwvQvJHXlzNuvyQ1Hx6U9/2tpxGtUKDFajViBuXrxjx45Z3z8A8Mg5Ke67yfb+AQDyQzd2 ibX7UAXkl1A1UpOsC9bPAwCUwRnHlEcu9out63Cu80Z3W9+vIJP1k+655x43l+TNW9dItLS0 sK5PBuJyhI8//rj1/QOAMDk36SJu/KpRrcCGatQKAAAuYuwSqo78EqpI10dFHRoAoCwWD486 32wd7vfbIsf5U73W90nI/VejuaJ6Q9Zton4w3deSue4AFE3S3Hd8TwBAeRnVCqyurVaAsUuo OvJLqCIZp6QLxjABAMpg8/kxpaY85H633eh4Y6Dd+j7J+CHdPLXNiBtuuIE1M1IUNwaNue4A 5Fnc3HdybrO9fwAAe+JqqNasWWN9/4BmIGeEPDOqFejOfl4BxjABAMrsVcfEL+7yv9923d/i /Py0tf2R+o3LL79c25+XdYTXcULjr6cumBcPQBHEzZNHzSEAVJduPJLUydneN6BZyC+hqhiX BADNYVQrMIs1CGw675i584zzYrS5r8tDU5zrwvObre2PjF36wAc+oP2erjfe+973JraR9Zts vx5loxuHxrx4AIoibp48+hABoJrixi7JuqO29w9oFvJLqDLGJQEAyqz31JjzrbbQ/W5b4Hh7 eLHV/ZG10BsJ6cOT/j2pE9y+fbu6+eabEx/DvV0G7ytNnaaE5AZt7x+yYVQrMJVaAZSXnN90 wZrsAFA9jF0CLiC/hCqLG8NE/wgAoOh+NDqmWmauc7/XZGTS8KNtzs/PW92nKVOmaL97w/dl Xi5Jvq+D89WZrM0ktYR8l6dP1qTS1Wk++OCD1vcPAGol5y7d9wjr8KFsjGoFWqgVAJIwdgm4 iPwSqk43hom1WgEARbd2ZEyptpX+d5vtdZVEVJ1fXC4pTMZA6e7lvJg2bRrrJ2VEro+i4qab blJvvvmm9f0DgFrJuUvOYdwTAkC16cYjyb2H7X0DgoxqBU5+pGm1AuSXUHVxY5ioVwMAFM2C /eedb7DoMUIPtCk1OrLW6v7J925nZ6f6yle+UvN6h3HzsgX7AsktZffa6aK/v9/6/gFAveQc pgvW5gWAatDNj8AcqKgickYA45KQLaNagcHm1QoAQNjmE6POvdC33Puhv/ja11TbskfVjc7p 6Y2Bduv7VivJF+nWVQ/GmjVrrO9rmemureS1sb1vANAo3fcMa/OiiCQv2izU9aAMZE67qJCx S7zHUUXkl4D4NcT5bgAAwB4ZHyxz2MWF3MvVUzMu3/Gm44+NagU2UCsgdPfcEseOHbO+fwDy 58SJE+75oSiefPJJ7Xlu1apV4/rUpaZBajjKSvqVyoxILxjjhzJg7BJwKfJLwAV8PwAAkC/S B2GyftKLL76YuC3JI0k9idcP5m132bJl1o+zCHTXSffff7/1fUO5Sd5B3mdlpFvHh8gujGoF Vl+oFSAIIv2444473GsKk+svbx3N9vZ29alPfcr9V/63SR+N5HSjHis1MEmPle1PnDhR/fZv /7b6xCc+oa699lp3302+s+Rv3HDDDequu+5yyX/L8Zo8ViL8WLnOTXqc1EtJyPWttz6p7e/u MoubA5r6dFQV+SXggrjxrbb3DQCAqokbA+OFzE8UdR+nyyVFBXMZJYtby0rWure9fygubyzI wMCAu6aNePjhhy/JwXR3dyeeCwiCIAj7YZIL+eM//uPIx954442Jj5UcUVTMmTMn8bEf/vCH Ix+7adOm2MdJDZMuksbAN5KH0M2xI+MjbY9XzAOjWoHuC7UCaf/t6667LvK1kfe/7eclS5Kz beZcmiiW4HwjpvODAGWlq801qXEBAADpkPUPk8Kre5X7fvmelv9d7xw28njqDaPJ86LLz0ke wPb+If/OnTvn55C83BHjdgiCIMoZSddTcXMeJ32f6K5HTMYH6UL6zeMeJ33qukjqJ5Jt60Ly R3GPlevaqPj0pz+t3SZBEERWkXSuBOphVCswq3hrEOi+/03HMwMAgPpJn0TSWkvve9/71OzZ s1NfD4Fakmi6ayPJD0jewPb+IX9kTNvg4KCbS5IxhgRBEER1Iu56ypvHTRdSA697bNw4Iom4 ennZri6SxrHHfY9JPVTcY+OuVZPmB9RdD8v4JYIgiGYH+SVUXVzdbdz1CwCUnVGtwNTi1Qqg mKTfQDeOuBlBLcl4cddQMo+Z7f1DPkieUT6/5JMIgrAZ0pdfZrbnxgrTjaOJy7no5nzzIq7/ Mm4ckURcXituHJFE3Hdc3BzLSdeOcRE3l6AuD+fNv0YQBNHs8L6H5FwbnBfP9n0I0Ey6eXaS 6k0AAEB95Nrzsssua/KV7/hgXNKlGI9UPka1Ai3p1grIe0XGKD344IPN+BgXJmStqODaUWXj rZFVVt56YOLo0aPqox/9aOTrLPeQttc5qIftcxXKRZcDicu56OZ88yJuLFFS/UJc307SmHfd 5yNpzJSEbtxU3JgpL3RzCerycPL8kV8iCCKPIed+OdfKuVjOU3IOZB0mlI28p3XB+x0AgHTF jXtJK2T7Xh2V3Ifr1otOmvekSuJeF1kbx/b+wY6q5YnkWOWYbT/vsEPySFEh50bW4YNNRrUC J6/J1bwCujncdH0sSXMgS+j+VtJ1ZVxeKyl046aSxkxJ6OqYTPJAujWYdHk4aU9+iSCIIkX4 np1rLRSdrmaFOSQBAEif3G+nFV49lHddGtVvEVdLQt32Bbo+CakJtr1vaK4TJ06489nJuLW0 wrt/9OoWg/NnBO8lwxHeN2nrPU62IduSbcq208xby7HLcyDPhe3XA82lGwfBfSGgz21E5VGS 1lPyIuo6zGRskETUNZ/J2CBdfZHJPK66sVAma3/q1lXS5dZq6Wu1PQayCB577LHI5/nyyy9X 27dvt75/VeflSMvMqFZg8EKtQBp/T845d9xxh7ruuuvUhAkTEs9ReQg5H8p+y3vC9nceUCtd PxfrLQAAkI2bb7655utN+V6We39vXH0t993Mh6sXN3aJsRzVIa+1zHPWaMh9oXyupLa71nvD cNRzHPI35W/LPpjUwyeFPCd8DqpDXuuoYAwToJ+bLepaKmk9JS+icrcmY4MkovJapmN4oo7P pE5B10dkElHrKsWtp2T79S4b7gVQVJJzl5xLGte1tkPOs3I/L+dvrqtQFLq1wHVjjVFuRrUC G/I1rwAAlJX0AceFXD/Xm0uq9e9VfT5cxi5Vm/Snm9Rsx31W5Z43jXktwpHG8ck+yb41el8u zxF5pmpgDBMQrZZ1lZLWU/IiaiyR6XdSVF7AZByRRLj+wXTMlET4ujHpmjYY4e/JuPWUbL/e ZRJXSyWvve39A8LkPCPXHbp+7bKEnO/po0fe0V8CAEDzBOehu+GGG+oewxDHqFZgdXStgK7P ocr38FUbu3Ts7Jh6on+XumHhQ5cc60ML29QP+r+v3j39Y+v72CyN5JW8usO0c7PhyOK4ZZ9l 3xs59jJ+NnARY5gAPdN1lWrJ54f/hul8p1F5LdMI54tNx0xJhMdN1bLuUbgfNW49JduvdZno 5ixinBjyRt6rpnnyWqK7u9sdky/zP8t6k0Kud44dO5bIay9kGyLNebQl5Lwv58Oq130in+LW W+DeAACA9DV7rmRd3WeV+wGrUl8juaKV63Y5R9auFrYptfv7D6nRs8fc3406dh87qxY+9H3V 5vzff13ZXurcUb25ombMTxGOrJ8LOZZ680fkjprHqFag2968AoxhAqKZrMGkG+ekyxsFrx11 44haW1sjfx7si6xlHFF43FQt3xnhcVO19AWHa5rSWHsJyXSvUdQci0CzeWOV0lhLVPJIXg5J ckNvvvmm8X4E58k2fYz8Dbl29nJPaeSd5PPK+kvIG911gtSn2N43AADQON28AVXtB9Q9HwMD A9b3LQ2SO+o/dla1ta9Ubc5x9a9s9/NKUQZPj6obFn5ddThth/tXOj87a/0Y0iL3dHIfWUvI +0Oug5tVHxiOZj4/coxyrLXOLSL3D/Lc2n59kS7p+9B9JmzvG2CTybpKujaf+MQnIn8evAbT jQ0y6fPX1cxcdtllkT8PHlctfbXh80BUfOADH4j8eXAMTNXWUzKqFZhldw0C6s6RV/Le1K0L ZhqS0/FySefOnWtof+rJL0U5ceKEm3OSPFcjc3DLeZkcMPKCcbAAAJSb7ru+in2GuudCaska vefIizccN67c5R/brpVtzs/PxD7m5OiY6nhgl1E+qgikFvHBBx+s6R5N7u9s1AKGw9ZzJsde 6z2uPMe11H0i3+QcqKurpf8CVWayrpJu3NJf/dVfRf48OD4oruY3KoJ5LV0Oqr29PfLn3vec bhyUbC9pjj/dOCg5Dl29gpenYD2l5tDlHaPW5AKaoZFckZxbJBcj562079cef/xxf067NLcr OSSpXay1zs0Lckf5YlQrMNVurUAesI4fAADlFbeGUNWuW3X9HlLXZnvf0iLjlx7YdcY5qjb3 2B5w/hk9syvxca+NjqkbPndh/EL/59rU2OhJ68dSD3kta5l7Qu51bc5lHg7bz1+t9//yXJfp 81N1ujFM1B6i6pJyLnG/112DeduO+31UBPNaUSHb0+WmvHFTcb9PmuNPl7uQbeq+P7z1kpJ+ j3Tornd5ntFs9eaV5PpScj+Sp7F9DI2SWqx6c03kmWCb7vNLXQiAojGqFWihVgCIo+sLqFKf Ydy6A2UZu+TZdWZMqbaV7rHd6Hhj10qjx/WfHHUe9zk3M7XzgXY1Nnra+rGYkvvPWu7bbOeV POGwvT+eWvsD5LkvQx9A1cm5UBfM9Y8qi8u5JI1v0o1Pks9U3DgieaxufJKco+PGESVtN26f kuYD1O2T/E3dOHGvHyppfBMap3vtqzhvAeyRz7Tu/jMuZGx8mcc6SK5JanlqnTOANZdgC98p AACUm64/Q6Iq15+6a3OZQ8H2vqUtOO+d5H+G13U4Px9NfNzbjgXrjrmPu8HxWv/njB6XNqNa gZOT66oV0I25iIq85JU84bC9P2G15pkYy1R8cv6MCjnf2t43wJa4nEtSPiZurFDSOCNd/6zk ceLGEclj48ZFxf0uKV8WFbI9+Z1uLR+pZ4r7ne3Xt0x0uVBqzdEscn6qZX03byx81eZcln77 4JpOJiHfK+Tj0WyMiQUAoNx0/b7Bef3LKm494jLenwTzRBLrOpQaGzVbJyk4nmlJm1Lvnsxv DkBeO9NxSPI+z2ONYzhs74+OfIZ0dejhkNekjJ+rqpDXThd5yrci/4xqBQbrqxXIs7icS9J8 cnFjieLGEcljdWOU5PovbhyRPFa37ccee0y7P97x6ub727RpU+TPg/lnXT/U2rVrI39O3iNd rH8BW2q5rvTOG4ODg9b32zYZ/6+r7YkKbw5U2/uN6mBNPwAAyi0ux1L2PkNdf07a67Pmybpj o84RdrjHucDx9rF1Ro8L5qZk7NN/f6jd+flZ68cTJn0fJmsnSd9VnuulwmF7f5LIc6nrDwyG vDb0TxWXrkbWGycBVJEuj/Jrv/ZrkT8PXlvp+vGT1lsSUaE7D3tjg4RuvNMdd9wR+fPg51t3 3aTLQwX7L3X1TL/7u78b+fM8f0cXjW58HOO+0AijWoEN5rUCtcyFR14pmtQC1ZJnklxe2e/3 kQ+6Pqfg9QkAACg2XZ1Y2etJqlifOTw6pto61rnHWcsceaLe3FSzyBq+JiH3r3mfFyIctvfH RC3z5MtrZXt/UTvdGAruDVFlupxLVITXGqhl/YzwuPJaa/y9x+k+xx/84Acjfx6cL1mXp7jq qqsifx68ntKtq/SBD3wg8ud5/54uEl1uj/ELyIr0Jety7+EgV2SmltwR45PQLLrPOTUixWFU K7C6fPMKJDnvmLnzjLrQaxYfbt/Y8GLr+wwAtunmTpEo6/29rp+j7OuDNDJHXiO5qSydO3fO aD48uf4tSu4wHLb3pxbyHJv0KchrJq+d7f1FbXT94d48XkDV6HIuURGu29GNJYqK8DjBWmr9 w/2MtaxrEnxc3Lqd4QjnnePGy4eDcTXp0r3ejFtAFkzXTvLWTLK9v1Fkvrljx465bO9LmOyT 6ZpLcs1W1nt55IPuOqbsdcoortOOmZtecd6l7bHnT+nv2v/VSc5jXre+zwCQxKhWoLs5tQK6 OVXKOu+Rru43r/c5aap3HNIbjhtX7vKfq1pyU1mR+z+T+u+ivY/DYXt/6mHS9yl9C/Ia2t5X mJNzZFRUYc0+IEotOZdwHlY3ligqguOIRFxtUDjCtRWm46aiPtemYxKi+pZM5lGVYD2l9Oje J+TwkDY5F+rGyoVDxuHkucYomL+xvS86MubLZE5wyfWFvz+AtDDvHYroZ46uwVPq/9M2V717 cu2438vYpa+/dFb9+cf+So2OjP89AFSNboyOfN+XrZYpri42z/cvafnx+TGlpjzkHm8tY4vC 45lsz3tnsn6S9E8V8T4pHLb3p17y3Cf1EVZtnSWjWoFZ5ZhXQOZn0QW18Kgq05xL1GfEdCxR 1N81iag+HtNxU1G1GabzAUaNaTTte2ZenfToXi/mzkKaTMeuS247j2OCwoqQXxJyj2s6Pzif eWSFOfJQRJJD+sJLZ9UX2+aqkZd7I9tsPjumhh/9gvPf563vLwDYpusHLttcRrqxElIfZ3vf mqHesUXyvbqo/7T/uBsdb+xaaeUYpA4vKYo8z0M4bO9PI+Q1MKl/Zz794njwwQcjX8Oijf0D 0mKSRwmvp+QxOT/qxvyZrKsUNZev6VioqPoM0zn+ovJjulqmcBThu9uoVmBqsWoFdPcB1AMg LXL+MMmTSx6kKDV/RckveSRnZ/K9Izn+IpyLUSy6mhXGIqNe0kf1yNBZ1f6tTSo4j53UUX+n Z5Lqc+5P3z2xOZW/0/vyWfXZtmVq5CXWTwKAOLq8i65PpKh0989VGUMx6nhg18X1B+90vPXj hxIfF84v2VpXaWBgIPGeqOj93OGwvT9pMJkDrwpzTpaBrm+6bN8VgCmTnItufQGT8UG67zST 86quDt2kjzfqcSZz/OnOBSbrKjEXW3p052qeY6TF5PxVlHFIQUXLGQnT8Uny+SdvdJFRrUBL sWoF8kx3HcA9BOohc9d9cuCUkryS9E0NrJikxkaH3N9tPjGq2r+12f9dX8+kVPJM3zw5qj7b 9kVyTAAQQ641df0NZRmzrJtbPqq+t8x2nRlTqm2lf/xfb1dq9OyPYx8Tzi9J/PihKaqZY4Bl jFlcyPu3DO/VcNjen7SY1K1WZRxh0elqX4s4HyXQKJOci24suMlYIt3nymRdJV3tTFL9etw6 aUlzW8Wt1Z00Zyo1zOnR5R95jpEGkzksZbxzUcYhBRUxv+QxnT+8KnWVaA7ddQHvM9Sq98So Um2fc98/7Y6z+xdc8ntvHFHLbRfO07JS+PCmdud3rzf0d8kbFZtRrcDJ/ye1AkAT6e4VyrIe u+74pN7L9r41U3iOPKnp2PmA8708erqmxzRz/FJSbqlM90rhsL0/aZLXKKl/kRxT/ulqZOP6 lYEyS8q5xM0xlpR3j/u7cRG3ZnbSuIO4ccBJ8wHGzauc1CddhhqRvKC/D1mQfLpJbqnI8x4X Ob8kZP3L7u7u2NdHvh84FyAtrN+HNJxxTHnk2CXvof7Ptaix0SPj2ko+qOW2P3fb3OA4+p3r FTkmAMhW3HwkZbiu1PXLyLW17X1rpvC8d+1i4UL17YVt6t3T/z2y/dd/fFYF56mtZ00lo1qB wcZqBZJyS8z1UCzyWiX1xZJjyjc5v0ZFXH82UGZxOZekeV/ixhIl1ffErcEUNzY7adxU3FjE pPkA43JpSWsw8V2eDuYgQhZMrt9k7MyJEyes72sjip5f8lRl3gfYx3ysSMPrjklfPXjJe0hy R69t/pgKz5kj45g691/sy9K1q4Vs8wsvnVVdEz5DjgkANHR1ZkWvNdf1cSxYsKBy+SWx7tio ujBK+EKuaGf/SnXDwnVqYZtSP/j+Q+rd0xfmwJPakK/vPKZU28JLnrcHnHajZ3Zlvp9J84OT Lyom8kbFp6t3pf+h/IxqBTYwr4BOXM4l6VorbixR0nqCcWswJdUNx42bintc3HyASfmLuJon +qHSo8vjFf26H/aYXOPJNUQR58MLK0t+SZisWxs35hQwpbum4J4epqLGL0kc/JsJzu/fGdf+ hOOXH7yYj1rSptS7J3ob2gcvb/WnLbc1vC0AKKO4Ofrj6kzzTlfze8stt/j/LfcI0qfd39/v 1tYUvaYuzvDomGrrWOcf+66VbeonZ8+4OaakcGs++j+nsp7vTubLiAvp++A6tNiS5k0p8pwp Zafrh6BPElUUl0dJ6o+LGx+UtE5Z3DVb0rjz2bNnRz7OJHczZcqUyMfOnz8/8bG/+Iu/GPlY 1vpJj+6al/w/goxqBVbXVitgWj9UhtySKFN+SZisyUSOCY3S3f/x3kItgusvSbhrMjzapnTj khYPX6yvlth1f4vzc/2aDyZOOz7298Oq97YW9e7JtdafEwDIG90cKkW994/r95GxS5JPigup sZN1Z6Wd9HcfO3bM+jE16m3HgnUXaz688UinR8fUwq//d+XNnRcOyS0Nfn1h7PpLaTDJLdl+ DpEOckzFxLx3wKW8PtWrrrpKffjDH/Y/Eya1ORMnTnTbfvCDH3R5YfJ3vQg+VraX9DhvLNRl l12mfumXfsn9VyJpHJTw5vgLP9akb8g75//H//gf1dVXX63e9773uf+b3Ed6qA1HWqo43rxs OSMhNZPkjZAlxsLaY1Qr0F2+eQVk/FDvy2dVy233uz1XAysmqbHRIW37Vx0Tv3hxjfA0xjCJ A6Nj6vKF/arvsy1q9N9+ZP15AYA80V0fyP1qEe9NdccjOSOvjVx3B+8nTELqQ+UxMoeb1PJL 3qlI9XjBOfIWON4+ts79uay3tPvYWbVyneTd2t3fy3f2oyvb1ekfP6myHrckr0VccJ1aPkk5 pjLkdMtId86knxhVJHXf4WukpPFHusfKf5uue1nvY6PaRR1DFMmZNfLYcM7N9LEwez9ERdL6 XECUpNyS3P/Y3se0lTG/JOT+Km4NP7nPL8N6y7BDV8/LWn7IkuSYZu68uK540himWsh4pzbV ofZ/dZLzv1+3fqwAys2oVmBWMWsF5FogKpLm6M8jXd911LgI+VlSfVdSyOODc+1J33ge13gK zpHnfhev61BZ546SJNXXxa1BjmKLyzGVYQ3oMtKNMyQHDAB26NbZMhmXBgRVdXx5WfNLQuog detnSpBjQiN0+WjeU8jS2pExpdpW+u+3r7crNXp2c8Pb/dHomGqZue5CHfbwYuvHCQB5olsX uoh1Jbq5P3Tjh+Tnku/JIoLz58n1k82xFmcd7Q/92N+3dR3OvdGovf1Juo+R69Aq1CyHw/b+ NEvSvCp5Wd/ZqFZgajFrBbLEHHkAkC+6Oa7p30MtqppbEmXOLwmTHFOR11yGPbr6hiLWJ6M4 wvPe3eh4Y6C94e2+7pj01YPuNtNYqwkAykT6enV5mSLNuSxzL0WF3A8kPVZyP3HX1GmGN39e cN2mrPvSZW67B3ZdHCMcnPfOBsm76aIquSURDtv700xJOabgPJXIB905knnvAOSZUa1AS7lr BaKCnD9qoZtX3Isy55ZE2fNLgvo/ZEHmBI4K5h9Bls44pjxycV3xtObIC+aXLtRs77Z+rACQ J7q6ErmOtL1vpnQ1dbXMAS45n0bnwasnmlE/uuvMxTHCNue9k+dYF1WrjQuH7f1ptrjctoS8 V2zvIy6Sc2lULFu2zPq+AUCV0F+HRuneQ16UcQ2lsCrkjERZ5hk3qhU4eXWpawWKKiqocUDW ZK0kb11xiTTyQcFxUQ9NcbZ3vvE59wCgTKRPXxem61DbplsbqtY1jqR93PiatKNZ1/RvOG5c eXGMsI058mSsVlxUbZ6WcNjeHxvkNY/LMdmcVxKX0s17V8S5VAGgyJhvCI2Q+764ay+ZO9z2 PjZDVfJLIinHxFpsqBXzrsIGb/0jL0zXQbp/eFQd+dZM579fv+Tn5x1feOmsUm0L3e0xJil7 RrUCg9QKAEWmG/9ThJomuY6JChnj38g25dizjscff7wpz5HtOfJkzoW457NIczEiXXFzrsi9 cB7WYsIFunlTuJcEgOahXw/1SpqfuCq5JVGl/JJIqvNjvmPUgjoH2BBeV2mK46ebr0983AnH dX99QG3+q+vVuycujDk67ejc/oqfW7rB8drmj6lG59ADgDLS5V0k8j4PmVybREWj+Rjpq46b oy2NkBqxZj1PwXnv3HULd63UtjWqFdiQTa1A3JgwyW3afr/BLpljTResxZQfcv6NCu4lAaB5 ooK5hmBCV08oITUkVarpqVp+Scg6Wbqo2jzlaEyz5mlNyokT+YysxkTKeKOZOy/WTEvsur/F +d3pxMceGR1TbV/8UeT+ytYGVkxSY6ND1j9bAJBXuhrHvPfp6/Y7rdyNbCd4X5FWNDJmqh7h ee92rZR1C89k/nfjcpdFWrcL2Yq7H6jC3P5FoKtnlXOw7X1DNKNagdXMKwCUke76qwjzDcAu GZ+iiyqOF69ifknoaoW4h0OtoiLNebPJLRU3spxzc+3IxfpqiQfalBodWWv02J85vvrcKdW2 7FH/8SvblRr+gcyx93pm+wwAZRC3fqt8Z9veP52okHuftP+O1HHFzUddazS7z1xqOBb1n770 +/XMrkz/ptx/6p4zat8QFDfHv7yHal0fDdnQfZ5t7xcAVIFuzD5jQ8vNqFagO51agaQ1l5o5 90JeyDFLjU0V1/qMq7FkLSaY0tUDp9EXEM4tyfeh9GuhGLLsDzrkmPCZLf57o91xdv8C658H AKgCqSMp0vWjrr4uq3myJF8i842nEVI/KjmmZvWbv+W486Ef+38/aY68NMTNdVfUubv37Nmj Ojs7XStWrFAbN250Pffcc5Htjxw54rdfunSp337nzp2ZbN/289OIuHpZ5r7LB91nWu4PbO8b AJSdbs4yzsGIo+vblWCMePXE1f9JsD4bTOjmLE/jHj94zirT2stGtQKzmFcgLWccUx65OP+G zG03/KjM4cO6SQCQNfn+joq8ztWuu66RsUZZ/l2pX5P8UFohfbZZ7HP/aRnftVD7d7OcIy9u XryyzM3yxBNP+Mc0f/78xPbBMYIzZszIZPu2n5NGxX2uuN+1T/qhokLOxbb3DQDKTlcHZnu/ kF+6MW8SRa/daeS6+g//8A8z2b7t58SUbs5jCea+gwldXWCj9wTBOoq8r9MAOxYPjzrvjg7/ fbLO+c+x0d3W9wtANRjVCkylVkDo5kTIY+2Ibp7dZo0J6u/vT3XOPNmWjI9Ke/6F06POvj75 Y3d/xaOOtvaVmc2RJ3VxujyBvL/yPN9iLcgvpU/eG7pzkLynqjbnf97IuTUq6IsAgGzJ92NU sAYedJLmHi76NRX5pcbIPaEu8jp3CfJDzi9pfyeRW4KJH42OqZaZ6/z3ygLH28OLre8XAFSB XCNGRZrrKqZBd+/c7PEu0ocaN990OOQeTcYfJT1GjkOu5Yu4lkzcPUhR57qL8uyzz7q5HLF8 +XK1ZcsW1969eyPbDw0N+e0XLVrkt09z+7afkzTEzX0n7y3T7RjVCrRQK5AlXe64LHljAMgj 3TqpjB+FTtyY8DKsJfSTn/wk02vwerZv+zmpVXd3t/Y9wrwBSJLmmNrgvDTklhDnVcfEL+7y 3y/uug8D7db3CwCqIG5MQJ7GPOj6oGVMj439ketqk3nwgvsnuSPpL096nOSiJCdVhNpBOSbd OK2yzHUXRe4hvZgzZ05ie7k/9SKL7Rdd3GeiiDnXMtGtK5en7wegjM47Zu48oy7MCp9cg/kz x03fO+m2l0f09UxS757YbP04amVUK3Dyl6kVCNDViuVxfgHYp8tHSjz++OPW9y9ttV6Df//7 36/pGrzW7RfFiRMntO8TxkYiie7ertbcZHCNBuZEQJLwtbPErvtbnN+dtr5vAFAFujWB83Tt qLt3zno9pTiS/5H7sLiQa/Oox0ptoPTbJs2hJ23yXCOm63uWnKWMjbe9f1khv5SuuHlabOWQ cYGcY6OC+VGA7K0dGXNukVe6n7mktYfDc4JILHQedHpvp/YxyK+0+uZQDbqxAmWdP5icUf3i 5p1IMydtVCswSK1AWaVR9xDOLTEfAkwEr50l3HUfRtZa3y8AqALdHLkSebmPlVxXVORhbIPk kKLmvzMdvyP9t7KOblzItiSXlYfjDR63LsrS96x7/8t951133eVasmSJ2r59u2v//v2R7V9+ +WW/vddWpLX9MtDdh0iUYa6WotKtq5Sn+gOgrMLzfPQvlL7O6LlRe08599Nq4bjP6p2Ot/Yv sH4sqE2acwuh3OKun/JyH1ePeq6rDx8+HNle7rXSuG7Xbb9odHPfyXmHPnzoNDoXK/ki1Ctc Q9XuOMu1bVMY1QpsoFYAwEW6Osm8zHEbFXmbf21gYOCS8Ujyv2t5vPTjymOS5s+TewJpZ7se UbemVN7W7mpEV1dXYptt27b5x97R0ZHYPhhZbL/IdP1p8l6zvW9VphtnaXu/gLI745jyyDH/ M6erwdTNpXdafr7pFfW5ltvUyEusb1wUujVHyesjLG6e86JfO0nccccdRu3qva7OYvtFIHVb uihLjSDS18h3k+S6vXOV/EtuCbUIXw8njekHAKQrbn5t23OX6fZNxvHYft7CJI/jzQPXyPgh Ge8jY5CS5sST50CuwYxqBVbbqRWIuy8p03z/5JeaK1jXFg7GMNmjG19Z5LpooCgWD4/K2d/9 zOnWKQ6Pc1rnNB8b3e3+TtZl+uTAKfUXkyapd08yP0gRNFojjuqIG7uUpzkP6iFBfik7cXOc 0/cPnah8tvws7jHh3BL3D6hH8Ho4fK0LAMiejD2OCtu1SWvWrIncr1rH/DRTmvdpcl2VNCee 5KEkH6VbwyltuvFIZVtLs6enx32fiaNHj0a22bdvn5vLEffdd5/f/uDBg5HtvbYii+0XnW5+ y6LX1haZbv59OTfb3reyMaoV6GZeAUQLzgmiq9dMaiM5pgU7Tqvjm2aOeyzyR3eNzPkZQXHz m8t3vO39a1RersFr3X5RyH2trubRdj8B8kt3T6erXSa3hLRsPj+m1JSH/PecN1bf9n4BQFXo xgPYrk2SefeiompjFGRslNyj6ObB9kLmzJN2WdUixq2lJHW0tp+nNAU/E/fcc09ie3nevWhv H1833uztF1HcWMqqfebzQjcGMS9zogJl9rpj0lcP+p+7qBpMk3FLKA4Ze1SFa6wyM6oVmNW8 WgHdvZTkDGzPoZ2WHTt2ZHoNnvX2805Xa2S7nwD5pRszGfVdJu+h4Nzn5JbQiEOOCZ/Z4r+f mCMPAJpPt6aJzTpI3bgq28+VTd78eUnrL8m4J1mnNs2/rZsjoYxz/ZNfskNX7ybvPdv7VlVR UbbxikAe6dZL0v1et+4SikP3HWh7v5AfurVNJMowHslDzihbkltkXBJqoatHDr9f5BwV7MMp 6pz4RrUCU5lXIG+CY/W9WOJcH797otf6vgFAVejm2ZC8k619igoZw2P7ucoLqe/R5Xy8kHsH adPo/HkyJkoXW7dutf5cpO3pp592+3nE0qVL1Z49e1xDQ0OR7Q8cOOC3l3nXvfYvvPBCJtu3 /fxkJW4MU9HXCCgq3bhJ2/sFVMHaEedaqG2l+5m7wXH0O9c7P3/d/Z3J+CYUS9SaFTavw5E/ unEBUndme9/S1Mh19Z/92Z9let0u27f9/KSBcUmohe4eLTinQVlyS8gHqaNaO3RWtS17NPK9 t7JdqeEfSN3V69b3FUA+GdUKtFArkDa5Hoi6r7V1baC7hmEcw3hSgyZjlUzmz5N7iXr66XX3 IFXo93jiiSf8450/f35i++B7d8aMGZls3/ZzkiXduEUZt2d736pIl8Mu+/sQyIOoeT4GVkxS Y6ND42o0D/7NBOcx71jfZ9RHNy6ljGPEUZ+4e7W05yzIk0auq//wD/8wk+3bfk7SEDeGiXXc ECXpO4rcEtKyYP955100JfI9FxWM4QeA7Onmcrdxv6obIyVzf9l+nvJM8kGS30maE+/+++93 7y9N51PX3VOU7XpQPgPyfpfnx3PXXXf5x3vttdde8rsod999t9/+wx/+cGL7erYv+1imOSmM agVOfnhcrYBu/gV5v9o+pirS5ZbLdp4A8uiMY8ojF9dBa3dMmjdP9X22RX3zpz/xxzax9lLx 6eqqynRdgMbEXR+VYS0lOQZZ9zHsH//xH/1j/Y3f+I3INkF/93d/57f/vd/7vcT29Wxf/i3D c17l+kHULmo9Bcl5y++Ca72xTisAAOVz/PjxyOtGiWbXn+tyXXKNbvt5Kgp5rmQ8gS435N1n Spu49TElDxUV3jVimcTNuZa3KPOYEN0ab2Wuuc0rOY9EhZyjbe8bUAWLh0edT1yH+7mTdZVe fWmxuv4r+5S3lpLEIsf5U73W99WoVmBwfK0AaqPLHTCOAB7ddVRZ1l2KG0+Tt5CavzLkl+Ke 8zLOf47G6NYI7Ozs9P+b3BIAAOUVrCcJXxs3cz901yRluD5vNm9OvAcffDDx/kfmFQvPiad7 XFnrZHXvvTxF2efA0a0ZIOO3bO9b1cj5o4rvQSAvgnPbSUZp+NE2dXpsVM3c9Iq6MEqJ9ZTK QPe9V+ZaEpiLq38q072RboxM3qJM9U5Rcxsb1QpsuFArIFG2Nb3QON13mhfklgAAKDcZx6IL Gd/UrP2ImjOca9fGSe5I5hhMmj9P1meSe6dTp07l4v3QTEUYw1T2/qa4sZT1rAmGxkTVq5Zx /CKQR686Jn5xl//Z83JJspbxF146q1TbQtZTKgHddVlZr7VQG139X9nWmi3CuKSy3Y/KdbUu OP8gSLd+gYSsp2R7/wBcyqhWYDXzCgDIjm78RrPqTHTrFjN2IV0nTpxInD9PF2W7twrL8xim qtR76fraWIOt+eTcGxW29wuoAskjzdx5Rnnz4YXXJf7myVE1cdpKdfQ71zv/+3Xr+4v66K47 bO8X7NPdF0nItbzt/Uubbl7uvESZxi55dNd5ZZ2rAvXR1WBeddVV7nnK9v4BAIDsybzIUSE1 6M24HtBdj5RlHvAwo1qB7vzUCsi4taT584Ihc//b3ucsxY2fsR1VqSXUrS8hY+ts71vVyLyZ UVH2cXRAXqwdGVOqbaX7ubvR8cZA+yW/Xy85pru/pgZWTFJjo0PW9xe1iwrqwSGqeD2UNM+C rShrraMupyfreNneN+SHbp4bWVPJ9r4BAIDm0a312ozaJN29EWMRsidzTcjzLPehcSH9GDLu vcz5i7j1xbKcvz5uvo+qjEfyRM19KcG8d80l54SoKHvuuFZGtQKz8lMrgHI65JjwmS3+53TX /S3Oz09f0uYHZ8fU5Xc/pr792Rb17smN1vcZtYkK1rmD0OVayjiOxhM3hklqE48dO5aZuDWg 5Pe2n5us6O5TJKdge9+QH3xXAQAAXY6nGbVJujUey3ydnkdPPPGE9p4pGHIvW8Z+5rhxSVmO pdPdqzZr/GCe6HJ85JqbS869UcFcKEBznHFMeeTi59BbVyncbuPZMdVy29+qhW1Knd4rNcLn re87kunG7XOOtcOoVmBq/moF4ubGy7IuKg90dXFZjyHS5fPKOnbJI3OmR8WyZcus7xvyQXKN P/dzPzfuPcI4NwAAqkc3diDrXILuWp0xC82lm19bF/J+kXxAmWrXdLlOqdvL4l49buxSFfuZ dHN1lnmelzzSredc9rXVgDxZPDzqfOo63M/eAsfbw4tj20nLfX/fzhx4BUB+CTq6ej+Zp9r2 vmVNV9sikVXNYdxYqLLXOcr6XFFBPgBC7u91fUMStvcvilGtQEv+agUAACgbXd961teZrFts n+Q5dCH3ujI/XlzIe0TeP0WfP09qQXXX0lmMYdKtXVPFsUse3fNf9lrcvIkK5rgAmudHo2Oq ZeY697M3xfHTzdePa3Pe0bn/rPPbdv9zKmOZNvUtUCNDa60fA6LJfMNRITUWtvcNdlVxvrsg Xa1bVnVGuudbxuvYfi6aQXf8ZaodRO3i7oe9qOp9KgAgW0a1Aic/RK1AzsXNs5DlWu3NvI9A NN2anMHxCpI7khySbq0uL6QPWnJSRb3ujBvDlOaYOt0YEYkq1zBzr5sPUa+D3Gva3i+gKl51 TPziLv/zF1xXKZh7iosbHW8MtFs/FlxKd52R5bU2ikEXVamxiRvDlHaOTbfWpERV5tDQ1blJ Dtz2vsEOuX8P1pXq7sv4vgIAoHp0a5pkWYseFWWfmzpvdPcMuvkO5TpR3itx9UrenHhFq7GN q8NKs0ZRNz95UeaaMKoVGMy2VkA3N0xVaknzQldDbHu/gKqQsUkzd55xPnVt7mfvAeef0RHG JJWB7rq86GPA0RjdHMFVmBsvSHctneYcvXHzU1fpelNXX8h49WoK55bknox6CAAA4JH7VV1k cS+ru1aVfIft56JKdGvfJr3mcm0p15O6eQu9kJyJrOtalP4QXd5CIo06xbjxSFmvYZZ3unMQ 6/k0ly7nzNgwoHnWjowp1bbS/ey1O87uX5D63zCqFdjAvAK2MC80osg1dVTIOBvb+9ZMcdfT aY1hkjmvoyLtuQqKQJdns71faL5wbkl+pst7M8YNAIBq0t3LSg1l2n9Lt25xFuvXIJpuPaVa x9FIXkCuH5PmxJPr0SLMiac7jjRqQ3XjPYoydsnWc1+1+3ibdP0p1CACzROc205GJg0/2ub8 /Lz1/UJjyBkhim790hMnTljft2bTjUuS/Eaj8//FjUcq8v2nUa3A6sZqBeQeiGtDBMfVBvuH dP06VZ7LHQCAKtNdG0ikPf5EN06kKmvT5oFuDFkj+UTZZtL8ed7fyOv8eXFjmGTO93q3Gzdf fF6fi2bTzQfE2JnmkXNwVFR9fB3QTGccUx65+J2xrkOpsdHd1vcLjYm6NmKOqWrTrWtb1bHb Uk+UVQ5INz47jdxVEenWliInUB26fJHQ9RPw/gCyYVQr0M28AgCKQ1dDl/a1hG6e3kb671Eb 3TiFtPqRZTu69T69kL4Wmesjb/kD3eegkfXBdGOX6Fu69D0TFUWuKy0aXR6U+0mguRYPjzqf vA7387fIcf5Ur/V9QmO4BkCYrravSuv7hMXNS1dvHihuPr2qXmPK+DjOSdUVl1vy8P4AAABB uj5b6dtPc66youaMjGoFZpWvVkCX70g71yPvMZk/T5ez8UJ+L+3yMH9e3Fi+et7PuhowCeaW uEi3BlMjeT3UhvwSkA+bz48pNeUh9/N3o+ONgXbr+4TGRAV9ddWmu3eq2npKQVnMY6ebd0/q 4Ko4dsnDukrVFOwbkvtvXTu+swAAQJhuXZM05zxiXnn7bNwnSO5ExiAlzYkn93C259jSvUe7 u7tr3pZubBbX3eNFvTfkvWp7v6pE95m0vV9pMqoVmFq+WgGU06uOiV/c5X9ed93f4vz8tPX9 Qn109RPk7qtNdy1ZxbWXgnRjmCRqXZMzbuxS1edlb1atIfIjnFuKq+uMCu5zAQCoNl19nOSd 0vob5Jfskvo729eBso6Qbg0dLyTXIG1s3LvEjUuq5R5Tt0aNBPdk4+nODbX2EaB+ts8NAMbU ecfMnWecT1+b+xl8aIpzjXR+s/X9Qn1Y0zwbRrUCLeWoFdDV/9neL9vknkaXe6t17kBdDqVs NTb1yHqudORLLbklEXX/Jvfxto8DAADYI9cPuvElkhNI429EXYMwRqF5dPNaydiiZu+LvN/k GjZpTjy5r5Y58aSut1n7prtfreU+U7cN3fzVVSfvwajI+9yZZRIV5JeA5us9JZ/Hhe5ncIHj 7eHF1vcJ9SG/hDC5/o0K5vm9IK4+y7TmSHe/I1H18UhCNyc356XyCb7WpuseNLMe2KhW4GRb KWoFAACoEl0fb1p9jFH5K+6nmkfmao8Kyd/Y3C/JHcl7T1ejGXwfSk4q6/WXdHPUSMjff/zx x9358qJC3s+6OkiJZubJikS3BlyV1xdotqj3bZrjVwGY+dHomGqZuc79DMoopuFH25yfn7e+ X6idLr9k+7oL+XtPyLWl7X3LC12NllynyLgl6TOXcTUyTsm77pbnT34muSXddTj3nBfo5gOk pqhc5HPi9b3Iv6bzZzDfDACgFka1AoPUClRNXL+63A81un2u9e3SzXGQxmubFtkXGeMTt1aT N39elvudNIdfPWFjnFhR6Ppb6l2nGbXT9cfY3i+gataOONdLbSv9zyDrKhWX5JGiIk/XXWgu 6mmSxY1haiQYE39RVMicErb3C+moN7ckyC8BAAAdXV95GnN1RQU5o+bR9QnncZyMNyee7rrV CxkzIbmYtI9BtheXt6o1Jk6cmPlYqiRGtQIb8l8rwHwx9pFfAuxZsP+882mbEvkZfKBNqdGR tdb3EbXTrW1Kfqm6dO8Jch+XipsLoJ7getLs+bW9X2hceM2DWtf91Z2j8ti3AAAAmks3NiCN a4WoePDBB60fc1UU9f5A3ndS15s0J57U0qU5J55uzo16otZ6sCqKCu7xm6eo5wegTDafGFW9 vd9y+2z+4mtfU23LHlU3Op/DNwba/TZGtQKr818rgEuRX0KY7jrUdG2hqohbQ6meeOaZZ6wf U57IvXpUkEMoNrlfDq5tLPfQtW6D7y0AABBHN2akkTm7dOuDMqdV89x0003jnv+irZki76Ok OfEkpM3WrVvr/ju6OUkaCXJM8YL3OMGwvV9VoZsT0/a4OwAoKvreEMZ8UmbSnvdO7oFOnDhh /biCjGoFuu3XChRhznTUJo3ckuA7DgAAxJF++aiQ/vF6+xpZM8W+qCjy+qxyLZw0xkjes5IX rSWvE5yHOu2QfB799dHoc7GL/gMASBd9bwiLGosv17K29ytPslpTKY85Jlt013yN1ObBrjTy RYLvLaA2RrUCs+zXCgCALbq5yOSao57tkV+yLyqKnF/ySL5G5s/TjX/xQn4v7eLyO/K7pHn4 Gg36EaLpcoXnzp2zvm9VQH4JANKl66ezvV+wJyqYC/giyf9EzbeQVnR3d1s/xjzQzSlY730+ 7Aqun93omtnklwAAQBLdnF/1zpFmO2dkVCswlVoBUZX7CBl7JOOVksYfSS4jqrZLd02ddnCN Ph7rWNtFfgkA0hXs8wuG7f2CPVFBfuki3VqQaQa1jdW5L6yCNHNLgvwSAABIImMzdP3u9Yyj tp1fqroq3hvIvA0mc+LJ9bXkmuLe82lHGcaCpY2ckV1FyRkZ1Qq0UCsAoFiY9xVBx48fj3w/ PP7449b3LQ+ymhcvHDI+qupj42WcWFSU+R6yjNLOLQld/07e7h0AAIBduv5emTus1m2RX7Kr ivklj+SNJCeaNCfeL/7iL8b+Pu2QvgPbz02ekF+yqyj5JQAoCnJGeka1Aid/qbK1Atw3xWvG 2CUvBgYGrB+vbVHBHN7FEZyXpp5+HB3ySwAAwISudq6e6wbd9YfUn9k+ziqocn4pSN7TMide 1usmmUTVnvskst5VVJBfag7ySwCQLvJLCCJnpPfmm2/WdzFdZ7C2UnnX2q2CcL4obr3gWunO UzIvSFR7o1qBwerWCgAAgAt088HXev2pu1ah77g5dPmleuY6LAu5Tpb3d7PmxAsH93CX4hxh l9TnRgX5JQCoD/klBJFf0mvWfHfBkJyW7eO2KSq4N8m/LHNLQneeoi4SAMrLqFZgA7UCaEzc GKZa5vei79guXX6JvuML8+c99NBD2vd5ViF5LdvHnie6c4SsjWV736qAcwQApIv8EoLIL+nJ ulLNjqrfg0YF+aV8k3siL+Q+Mu3ckiC/BAAAaqG7561lvUdyRnbRHxxP9/7MOmrdT6NagdXl rxXQvV7Me98cnE8AFInUOMn5Kekc5bWJq1WQPjqvXRq8Pj/dtXYt+50G02OP66uUbaS5T3l9 zZKOvZHXTDcP8MMPP+x+BwfFja2R30tOSsS1kzFBXrtGxc1nLvvgtYu7Bwzud9inPvUp8wvt lEJyWmk9P7Uce15es6gI55fkfSt5hUZl/X1QBXIe8ubckH+zqr8jvwQAQLE1+z5Jd48j4T22 3vsk6Q8O3yedOHFCuz/nzp3z28l/69rJNsLbbUTc61HrfqfB9NiD+xgVkiNM414g7j46eL8R N45NxvCnsS8ibi4/2Yfwfuuuj7OO8LGb7ncZX7NVq1ZFPkde34npPfmDDz7oruWchrjPvddG +hziPotp7YuIO8fIftSy32GyvkBUyHwa0ocQRdYni/vu8NrF3dfKNnTbr0fcsde632kwPXaT /Y5jGnH3/PI7L+LOD7X8vaSIO/bgedl0v9MIk2M33W9dGNUKdF+oFagleH2TI+6zVut+pxHe a5H0vJvsdxpheuzNeg9J5PU1Szr2Zr1mcWOaguslxl0/yXVAWhF3HRK8HzHd7zyE5PXSjKK+ ZsEIf+bSOlfHfW6QrFm5JUF+CQCAYrN1nxR3/ZBmmF7jNuuaWyLu9ah1v9MI02PP6u+Ho+h9 WrbzS2n1WdYSeXvNJI8RFV6u1Itm9kcU/XMft99pBP2QyZG3fsi8fe7zmH9oRn6pluA1S468 fu7JGekjr69ZXs7VRc0/VC1nZFQrMGt8rUBRX99gkF/KH6n7Da4VnPW84eSXAAAotjzll7x1 YdIM8kvJQX4pPmrt/wrOUd2sCK6pRJ+lUlOmTIn8Ofmli0F+KTnijr3M45dMxY2RlN/Vut+N Mn0tTPe7Wa9ZM99Dpsee1/HBuvNDmuNx4z4/te53GrzXQnd+q2W/m/maNes9lNZrJr+v4px3 8rtmz+VgOkeD6X6H6cZQZxmbNm1KdW6JZr1mce/BWkRF0vUImkvOS8H6u7hzYlp095tJ5z+g yIxqBaaWfw0CANWT1X1wlOD95OTJkyOvN0zuOWUetqiQPtF65+2udU5q+Rnz410a8rqkcV8e 9x4KrnvQrPn6TefhD+53syN4D+cdexbrB9g+X5mS80RUpHkvDT1dbq5I7yEAyBNdfsn2fsEO Xb9tXN1MVcgcv82Oql9fRgX5pfywkVsS5JcAAEA9dH26kktKemwa90lGtQIt1Ao0SpdfSqoh rZK0x1EkBc/9pcgv2UV+CQDSRX4JQeSX9KQGsNkRV6tXBVExd+5c6/uFC2zklsTWrVsj3xvc DwAAgDjheXqDIdcXcY/lPskuckbJdHORZBVZz3NdNOSM7GpmzsioVuDkVdQKAKiM/z97/wNd 1XUfaMNnXE1IYwfl7YTXo7ReSgY3/uq4UaYemhi7lWZsK9M4VB1YfLGaBtEsYjpAIusNmHqt umrfms60TqMmhMbLHkYw40BC0SgLnDEi9nfBRobXBMsQbJlvMpEbMJ0vXq5E8BC7LK377X1P 7vHhnLP32ffec+8+f55nrcd/pH2P9v13zj6//du/rVq7b7tfaEe5Zj4KuXbHdt9sK2tStBJZ j8/2c7aprHURBfeQ6dB/7ZD/3cq/rbp3Y34JERER41Td/8atkWd+yb5RkHv2trr506SReWa2 n2/aVJ1bip4z2ipZk4SImKyq2JvtfiWlUa7AEXIF6jUK3T6ORXLLli01jLobY2JiwvrztSk5 iunV5tySlPklRERErFdVPp1Etx6D+SX7RkHt7CtVjZOTJm69XxGlNpBdmV9CRExWYm8YNArm jFxV8xhJs2TJksLnLjFnlE5tzRcZ5Qo8Fs4V4BqHiIiIjSjXvEShGwfJuacomF9qnfJ+Kgjr aK60FWuYmNOL1l873I/tfhVF1fySzCmw3TdExCxK7A2Dyj1rg1BL4G3lXFuzKfp6JKlqzEf+ mz3lvklVbN6fc91CRMymRrkCD1JXANOlai2SRBeLjGLr1q3Wn09RVN2z2e5X2tR9vhtFzl0R r482CnJ6WyfnB0TEZCVOh0FZq61X7q0UlQ+XFEXfS6kqa9bTZXBuSeY72uoL1y1ERERsVNU9 z+DgoPIxURATbp2qmLDNcWla9Y/dk0LOF8m1eUa5AmvIFVApP6+cS+zK/BIiYrISp8OgqhoQ cl7Fdt/SoqrmRaPIuH3R6+JVVe1fRQ5c603T3JJUdd3is4GIiIimquLvMoauGutEQUy4dZJ/ VptJzjFV55ZsP6e0qloHtnPnTut9K4rMGSHWplGuwB3kCqCZIyMjkedgxmjFVRW7lfvh2O5b mjxy5EgNI3IzZOz+7Nmz1p9bGmR8mA79c6mydqbtuSUpaywRERExCaPqgkvkPXJU+yiYX2qd soZ4Le8Xlp39+/c7V111le4WNBY59iaPS69qLo8911pnVPxAzova7hciYlZV5U4w7iquqvEO +/yElfNoH/jAB+ofgEcg6+kF542McgWuL26uQBTsydta5dxSdc/fNOUsMr+EiIiISajKtZTz TlHto8Ygcpxv+3kURXmfFoWupmHRHRgYUN6jxiG/BzKOYPs5ZEFydO0bhTxn2+4XImJWVc0v yWue7b5huj4TrNcOK+sANoK8x5R5SnL/3uDPizy+VL2ujPlaZ1rni6TMGWFWNcoVaCturgAi YpqV67KrY6MgUXF1xit2lXXEuZ8wN1jT/ROf+ETlcy3r4as+93JOSc5JjY+PW+9/llSdG9hj oHVybkBETFbmlzAoe0iaG1xX/Wd/9meVuaE45Dhdziv5908KzjFJZF0928/Rhqo9qzgvtUZ/ /CRtc0vSqHsy6hkgIiJiParWEkSthZdjEKNcgXP/ZyVXIArbz7dIqu7LbPcrjQbH11E17mTs SJq2e4OsGTVfx9rG1hqFjNHY7hciYlZVxXFZN15sVfXFbfcrTQZrLsj5oervZH07WU9QjlGq yPkkOV+ky0tijslVtR8vNRear5xbkvGUNL/mUZBvhoiIiPUo4+gqgvsMq+ai2BO1dar2XWXO 40rlmiI/5Ok1T9U5hPzc1qmqh6n73BvlChz5P6krgIhoaBTE6oqtas02905v6587kvlG/rVI Vf33P6bHZY6J+0ZbZmFuSco1CxEREZNUte9MMJ+dfVPsSx6amf58UbleRo7zg22C2O5zVlXt X83+Aq2znvklRESMN4o0xd+McgUeI1fAlqp7J7kmx3bf0qB8HfzI+5yodvXML0UdX1KkOSZq WdgxC3NL0rRf3xARETFbqmp6SPx1wkZGRiLbFGmcblvVeyXnCG33LS0G7+VV43rutZJR1v6J gtzI1inPwVGk+Z4WETELRtV0jaohjcVRtdeWv65bUZVrj/xzGrrau/XOGUmjxj1FeP3lurco mBNorv5c3GbVPDXKFXgwuVwB9uZCRETEZqiq3+Cft1DdT6lyzTB55X1bFHK9TrP+5un5stPz N8+Jv9JT+VurOhznv+1Y6bx1dk+o7RvCT0y8WmnbK/458dUeZ/4npZa9PnI+1B8L0sWAgth+ b7OqP0fPj64uPiaral1jsMYpIiLWpmp8bLtfaE+5Jj6KVu5jeF6Mze/b8b0rxubf+duHnLfO fy/U9i3hnzx13hubP7X9Pmf+YnNqT8i16350uUaNzC9JizjHFLUeS8KcQPP059GlPZ9TFavh 84GI2BqNcgXWUFcA861qPOKvLaZqQx2s1uqvU+7Hv9YsKb9zsexcffejkX9P3qMe/Zsepzx/ 2mv/pLjfbbt9+xXt/qTHcd46t60lr02w1iPx9eaq2nuplTEWDMdzqrCGDBGxMZlfwiht5tZ8 X4zNb1q1PfLvy7H5/7PjPjE2P++1nxZj847eiLH5+acS7VdwbU3cPpyNzi9JizbHtGXLlsj3 nfud5lmtA572uSVpcP/hKrIGje2+ISIiYrb170Pjx5/HEkXcPQEma9QetJKk61tdEC5+OHqv Fj87PtPmzP/kycpjgnNGx/9igfOdi5edP+z5atPnjYJzmrp5C6NcgTvIFUhS1d5Leb63T6Oq /Ztt9wsRMeuq9ihtRk4PZkdVbeBm76v0U+HK7Qbj+FUdzvzF6cpjgvNL33tosfN9MY5/6L7t ic4xBccicr7JtH0jf1fm0gT3GcrrOJT9lOyYlfk71X5vWek/IiIipldV/FeuS6q2iWLNmjXW +14kVTWQk14HEpwr6hDuHVrk1bAr/aTs9HxV1l7o8eaYfiB+vvCLT3mPeWpTm2h73hk+O+98 69d+zSnPn2za6xLMGyae03xVsTT2WWut8hysO28j5lWjXIHryRXA1ki8DqNUrROQ60ua+XeD c0VyHD92X49X7+7MxbJz3/an3HH8z+aYXhM/v/k+3zj+vg7R9oIzdm7eGVu7VozjzzXcr+B9 jMn8TlLzS1I5lxWce5HHl3XFbX9WkvLMmeh5RfZewqpcrxAREbGZRu1NLKmujaH2h31V+yol HU8eflXOJ67yjr+9V7zP86VQu1Fxz3n13X9VmWO6/JMnndsPXnDcu1jHeWixeMzlPc550W5s 2+mm7a8UnBuldnRrVJ0v8nSPngWjIIaAiNi4slZQFHLOwHbf0K4qmjkGGjsfNTYP74905Py8 c9OqL1fmjS5fnHbufyo4Nv9epU7B00+dSWR/JX89bjl3Y/Ia+OeMjHIF2pLLFYiaY5K5OnkZ v6pqIVP7DKuqauhX9zpAREREbERVLousnSd/rxqLtKKGOL6tqsZVkvEO//ySuBV1frTnRmVb uffSwrsfdSY2LnLWvXjOcau6O85K4U+n1zX1tZDjYH9tR/8+YNg8Vfm4rGdsrfLcGwX7WiEi Ni57oKNK1T1RM9dw++eXKmPzsVXKtnLvpZtXbXcO3t/jbJ8OjM3PbE+uT2NjVzx/+f+23pM8 zxupPm/sn4lVyQNG2xrlCpxbRF0BRMQCKOPyKuQ9tmr+Sa7Dt933IilrtkeR5H6itcwvSeUc 06Lljzr/+Ws3Om23/2XlcTJTcvoRWVvjctNei+BnMul9qDBaVW28Zu8ngFeqqoFC7BMRsXFV 80tyTx3bfUO7quqKN7PuXS3zS1I5x9SzarvzXx5Z5XT0PvT22Hx7r/j9fMP9kfM2/vmcNOS2 yDmm4FxM1ueY5HOKopr/iSiNqivBZwQRERFr0ShX4IhZroAqbixzYlR1Qogpt1bVmoUka+Tt uSzuYRc/9PY9bMz8klTus/SrbZ91Ftxyi9en6h5MzXgd5B5L/rF0V1eX9femKKpq47GWsbWq 5pqZZ0VETEbVmNh2v9CucgyqollzGd8Ljs1j5pekcu+kmzs+6yzu6Xl7bP6zfZUa7Y/cO8lP WtbSyNc/uDel/H85T2O7b/WoqnfHPDf65VqFiIiIzVZ3D/Too49G/txmfYOiGrwXqpJUrPgH woVfdPftNV1b9IZwybfOOdW67ZL7xX/Oz21rymsQnN+sZZ/RILbfzyypqneXhlzUImmUK/DY 27kCqpgD+/MiIiZjFOSzoFRVj6pZNfJeE958n28cb7AO6S3h+rGIcfyFpxrqS3BNjazdbfv9 8Bs1xyTXWmVxjilY869KWubz0L7ysxBFkrVNEBEREaWqus39/f2RP0/bfUIRVK1FSCrGL/fy Xfzw2/W0xlbJOZjTsY97br7sXL3q7frqPcKLx1Ym/vyDdWhqfd5BbL+fWVJ1fmBtYutV7a/G vmKIiMnY7H0qjHIFHmQPgqyrqpHXrL0ofypcuT04jo+vEfCKGMff9NnAOP57DzXUl+BYJY3r 1/MwxyTnKqOg7hn6Za9AREREbJWqcYfk3e9+d+hnrFtovaq6dxK5Bi2Jv7Fuet6p7vFrug7p srD/2MWf3ZE2b1+lYLyn1uccxPb7mRV16xvTGC/Iu1F5qknWvkRELLqqnIqkxlqYXXV70TZr XmL7mcDY3GBt0bzwy98LjM0b2FcpuP5Bro+2/V6ozPq8kSqPiLmA9GmUK7DGfq6Aao9sWZ/C 9muIiIiI+VPW84hC5kBFkeW9ULNqs+85/DXybha+NtFj9Lgn58tO2+3bvf6IW1inPF9K7HkH c0Dreb5BbL+XWVF1T8IaxtYrz7lRUD8dETE5Vdc9aouiVLUXrdxvqBl/z1/3rjI2f+o+o8dN i7F5R29wbH6mrj7451zlXE3a7wFl/4L7P8l+nzlT3/Nvla3IJcR8KPfX4jqFiNi4RrkCd9jP FUDMkqo6DgsWLIj8edrH6Hm02XUQgmuRxj7bJu5FT8Y+LlhbL8k9mGSuqH+OU67VqKcWWBDb 72VWVM0vN2v/AFQrz7lRsIczImJyqsbDIyMj1vuG9lXVfGjWvEtwLdLYZzvE2Pxc7OOC9fLq 3VdJ7rnrp97ayP4cuVa9V8E5prSPX1X7ZpJHhEGbXccVERERMagqPhwF+6nYUbU3q4xxJHF8 /x5JdwlnDfdH8tfLq2U9U5zV3GCjXIHr3VyBRl8Txt+1q4qxZSF3NY+q9mBL6jyBiIjsVYHx qu6t5FxMM/6ef4+kyjjecH8kf229WtY+VZVjPf89SiP10avzS83af0plVuaYgq+1H+qYYdCo cxD1shEREbGZynxLU5pV2wH1qvLVZM3CpP7G8Flxj9nx2cpxTdclbZsri8fc5/Xn+F8sED9/ s6F+yPoOcvxbpZGcvCC238csqMp3S3Mt/TwbFfeQUN8Cs6RRrkAbdQUwG6r22GENAVZV3VvJ +YFm/c2xc/5xvNkapqcuXDmO/95Di51a9kcNjlEaqV0h53Tk/JuNuZ0szDEF14lVSapGBeZL rlGIiIjYauV9sj+er6PVOWXoqqu3nVSc+Q3hkm+dc+Quv6ZrmJqxr1Kwbn0jzy+I7fcx7apy siXyM2i7f0U0uAc1n2VExOYYNRYmdotVdfdLzZqLeEu4fsw3NjdYl9TIvkpnz5694nkF99I0 yhU4995M5gpEzd+kZY5Jt3aJupwYVHU/J++xbfcNERER861qD8gobPe1qKrWMSSZi3RS3pN+ 8cnKcb/c4zjzF/do258QLvj0Xq8vK4U/nV5X998PjocbHQfz2a1N1dqlLVu2WO9bUY0iyXWL iIjoyn4VGGe1fnOQRurCxXlOjM1773/KNzb/nrb9j4SLV709V1IZm5/ZbvS3/HsgSfKQWxS1 N20a1uSr1iPVu48s5ltVTXLqsmI9GuUKHMlmrgAiItpV1iQzpZHaB1i/8nVXkWStrO9cLDtX 3/2o0yGOO7FxkVOeP61sG5xfanQPJn9sR95fyc9lI88liO33MM3q1i7xnbej6jtP/iEiYvKq cq2oR4pVdfdLzdyj9vtibH7Tqu2VsfnB+8U4e/68sm1wfsl0X6Wpqakrnk8a5mCSMmqOyWbN d916JOYLMErV3DbXJ0RExGJqlCvwmJ1cgWBdMhv3T6g3mFdYJel6ytU5ppvEsU99/Ubxsx9H tpP7L933S593Fn7hP3p9eWpTm/id+r5XZTAvK4n7qyC23780q8rbDtZGwdapym2lFgoiYvKq 9szhnIt+VfdLcr5Azhs06+9W55jk2Pz721eJn12MbCf3VHrgrvucm/+vh94em9/XIX53QXt8 uQarVc/FhmmaY2LtEtaq6j6t0VxMRERERBODuWgqqH9lT926pPHx8UT/VrX+nbw3/e9/eqMz /5NS6Pe/NPSkc+KRDmfxwy95/bi/w3Hm57bV9LeCderlHgZJ3DMFsf3+pVXWIzVXo1yBBxvL FZDn5Sjked3280dEzJuq66Zc12S7b5gedWuY5LxBM/92tVaeHMcfeGiVM3/xTOj3d4nfP7+9 11m5ffrKcfyFp5THDc535DWXUM4xBdcNtXqOSdYcZO0S1mrU3m/yZ7b7hYiIiMVRle/ip5l1 wzFe1bqkZuwr/YZw9ZFXHadjldMr/sZjX+2p3M+s3b7dabttk7fv0rrpefHb3ko/6tlXKbiO X65PSqL/QWy/d2lVfnaiYD2SXVUxBdv9QkTMozKvJYrqum+jXIE17EGAV6rbt7bZexa9Jfza /3PeG8f/7fb7KvNDjwg7eu7z9mjafiYwjlfswRSs1Zb3e8KzZ8+GxmIyn6dV67VU++mydglV xl3HEBEREVuhXANjQh72cM2qujVMzcplOz1fdjb9p2edq+92a9vJmu47hhY5b53d47Up/UTO E407H/7TXc70I78kfnbZ6NjB3M4kx79BbL93aVRV40fC2iV7ynNsFNwfIiI2T1W+he1+YboM rrv306o6D+fF2Pwb3/6ec9Oqh7yx+X+5r8d56/z3vDZnLsr1Rk85n33ob53p7XeJn82HjhOc w0hy7CfXAMn5LPlv2++Z36h5ozVr1jR93sjGPRyGNcoVuCObuQKsrUVERMS0qLq39pO2+4Si qaqFlcV8Nn8Nd0mSe48Gsf1c06YuPsLaJbtG1f6XEH9ARGyewTFJFWqNYtDg2vssfl7kPEsz x37VmgtpHFPamGOSx48iqbrgmE9V55qk6n0gImZRo1yB67OZK4CYF+VYJQ5be5yiq2ptgyRL tSqCOVZyH+Qkjx/E9vNNm6p9pyWsUbSrqiZK0vusISLi2xLHw1pU5eTJsXiraqg1YrDmdtJj vzTPL0lbOccU3LeK8wuaSt4DIiIipkXdOgX/vZDtfhZd3b1Hkut6GtUoV6AtPlegq6vLe37y 8ylr5SXZzyC2X7c0qaqfINm5c6f1/hVd1b0k+a2IiM2T2kJYi7q64mkfS8nYdLP7m/b5Jamc SwquK5L/n+RcW3CdmB/qHmOc1G1FRETENKmr41CFNQt2De6b6ycLtROCa9+aUcuLsXW08rOh uv+Qn6ks5NHmWdWaQznHartvWFyNcgXO/TPqCmBhVO2NzrkaVaryQSRp3sfS3+9mjROzML8k jZpjkq+JnBdK4viqungS1qCgzuA+xVWYl0RERERbqu6Z/bCvkn2D+YR+kq4fl6TBNXLNmg8L Yvt5p0WZW833Or2q9lMiJx4RsfmS/421KGO6qroPaax9Z5QrcOTtXIFgvYSJiYmm9Csr80vS Zs0x6WpTsKcmxqlaI8lnBxEREW2q229Fwr5K6TBYD91PWvdfCa6Pa1Yt8SC2n3ca1NVnycI9 fRHcsmVLpr7PiIh5UjX+TVPtYUyXIyMjyrGVvKbb7p/fYP2DZtY8z9KcUfW1SXLeSK5JUyHn sW0/X0y/qpxA7gnQKFfgMeoKICKifVVrrP3jbdt9RLeWlqpOnsynTFudvODnijX7rVO3t5r8 DFHzMh2qvs+2+4WIWARVcwXkgKNOOZ5V0aw1P/Uo8wP9NLMuX9bml6Ryjin4GtUzx6SrYy6h 1h2aqDqvJL0PMSIiIrZeo1yBB/ORK6C7V5IkVWsaG1Pet6qwMX8j5zBkfCbqd8Ha8+QDt86s xD6KrCrXlXlYRMTWqKo93Mx1Hph9dXXyWnnPJGusyfmcqJwh2Qc/zZ73yeL8UtXgHJOklhrS uvoSzFWjqVGw9g0RERHToIzn65D3Jbb7iK6qOlmSVu7F4o+1yDGtf04o+HlK875PeTNYZ9BP Fu/lW61RrsCa1uYK7Ny5M/L9VM3XIiJi8kbNE8if2e4XpltdDWK5lqUVezH55zXkPZ3/bwbn PJq9bj3L80vSeueYVGM5SVdXl/XnhdlQFbPhXhsRERHTohzbqpB1p233D13jais0a9+ioPLv BJHrKWSupv+zJGMvrNdvjWmIYaCZwVr+VaiNgojYuEa5AnfUliugWu/LeRvj1O1fK8cDzR6f Be8b5P/LOZHgujw5B9Ls1yLr80vSWueY5O9UcJ+EtajKIyTnDBEREdNi1HyBH/ZhSY+6vWHl fUorYh2qvUWDUO+hNcr3XFeDhfhXepTn0iiobYGI2FqJ1WG9yv0rdbl5cg6iWX9bNY4I0ur1 UVmeM5JGrTGKmjcK1hQMIvO9bD+XvGiUK3B9PvYgMJE8B0RERMyCMr6pgr1Y0qWsb6FCzjPI +95m/n3dPXUr+4FujEP33W1FPiqaq9objdoWiIitVVVriH2V0MS43J5mzTGp9gML0qr65nmZ X5JGrTXyj6Pl3JKujkQra5VjPoyC2quImCeNcgXaipMrgFgEdXu3yH1/bPcPr1S3T6yc/2nm 3I4pwb2ZWtEX2+9LK43Ln83DvX7eVH1vyXdFRGy9URDbQ1N1tYklJnvz1KouxyxIcL+lZpin +SVp1ByTnCuMq1Eu15bY7jtmS3IcEBERMSvK+LMut449WdKlfD/kmDKpexejXIFzv1BTroBq LKyjujdTM16zILbfw1aq+6zI3/H9Tpfy/YiCWCYioh2pN4SNKusp6kh6jkmXXxZFdb+lZj3/ vM0vSaPmmJYuXap8jZud14f5VFVvnhqtiIiImEZ1e+U0834D6zOu9kIz6mjF3RvrkJ+vpO+p gth+T1qlbg9o+ZmQnw3bfcQrVe3bTL07TFKjXIEjteUKIKJ6/MP+kViLuvGbJMn7Ld09go41 a9ZU9nBN+rnncX5JqhrfBZE5Qs3KocN8q6pBwecJERER06gco6gYGhqy3j8MG1dbPenYddx9 sQ5ZAy/pcXAQ2+9HK4x7D8ilTqeydmgUo6Oj1vuGiFhEVWMo6ldhrSY9b2SUK/CYOlfg9ddf 1/YnjmbkFeZ1fkn6P/7H/3AWLVqkfD3l3BLjc6xHmZsZhbyvtt03RERERJX9/f3KsTE1tNJp XN5cknNMuj18dFAPIhlbmfeKyamqdyfhe4GIaE9VbWjOzViLcftcJjFGi8spszE+zOuckZyf k+u4dDR731jMrzK3LApqGuRPo1yBB6krgIiI+VO3Z85//+//3Xr/MNq4PXyTGq/Wg8wDJk7T uHFzSzt37rTeR4x2YmIi8j1jX15ERLuqrq3j4+PW+4bZ0mSOqZGxWtxYPwpZI6+Z62byOL8U V39cct1113Fvg3XLdQcRETE7GuUKrCFXIMobbrghcsxDvZB0u3XrVu29kBzLNnIvpJt71P1N 269LHoybW5Lvve0+olpVDiz3kYiIdiWPHJPUZI6p3jFbdS7HlFbsx5m3+SW575TpvlXUZsB6 Zd0sIiIiZlXV/bPk1Vdftd4/VBs3b9TI/Y1qv2oVg4OD1l+PrNvM2EMeNcoVuCO9uQKqPRDk vaXtviEiFl3V3haco7FeTcZ5Mu+k1rrjpvMe1eM3e25JKv+GnJNpxd9qtnH1xiVy3ZIf5piw VlX5muTxIiIiYla85pprIsczDzzwgPW+oV6TOaZ66l/ErYvxI+cobb8OWXdmZoa5pRwo69tE Qb47ImK0RrkC19vJFVBdl5tZVwzzrckck6yRazovo8pViaKeuauiG3efJZH3QVHvq5yL5lyB pspczShkzqftviEiIiKa+PnPf155f2O7bxhv3L2PvL+pda/ZuHtf/z2V7eefdeW9p6oeQhXm lrKhPGdGwV7PiIjpULU+m3XY2IhyfkE1Bqgi1xnJtTBxx5LjQhNk7Tnmi8yVr5WqLnGV4JwQ 80bmGuUKtKW3roBtOzs7Iz+TfNYQERExK8qx81VXXRU5ppG1DWz3D+M1ya+rJf8pDhv3VkFs v+ZJqKtPWYW5pWyoigfJ+0XbfUNERFfO1dhMTdb/y3Gdbl5obGyMsWHCyu99XB1B1b0Nc0xY j7I2BdcaRMyaRrkC5/4PcgUQUalqTTb3L9nRpD64vO+Nqweuqv9s+54qiO3XuxHle2ASg5iY mLDeVzRTNcc7PDxsvW+IiPi25IljM5XX/Th0NfDkuiQdshav7eeYFeU8nqp2sZ+4/ZKi1qcx x4Q6WSuLiIiIeVGVNyOhnkL6NMoVOKLPFTCZY4rbk0k1Hja5/2qmQWy/X/UqX3uT+oMmNVQw HcrzqQp5HrbdP0REfFv2ucBmK9enx9U+lsi1SsHH6tbZMDY0V87fxdXDk8h5I9N7m2BumHyP qRWOUbJ/HyIiIubJ7u7uyLEN6yKypbxHiqvrIFHFRlRrZWzOF0mD2H6d61E3H1dFvnemezVj OpTnyCjkOdV23zDdGuUKPEZdAURbqmrkyTGR7b5hfjTNJ5JzINV65K+//rpyHMnckpmma5Yk 9awxj7qnYo4J/apyfOV8pO2+ISIiItajan8Xmatlu29Ym/Ke0yQPT8a/g2smou5v5f2R7ecU xHZ/alG+xqr5Wz/yPZPvne3+Ym2q9ukmhoCImE5Vde9YR4r1aJQr8KA+VyCqrpoKOSdy7Nix 0M/TkqMk+ydr96W5Pp+c0zN5vWWcX9YDr/fvMMeEOlW5h2m490ZERESsV1V9BtZnZ0+Zk6fa 5yV43+TPyUvr+DaI7f6YKl9bk7oncXs4YzpV5baTd4iImF5Va7XZBw+boYwhm4wFr7/++iv+ Py3zRdLqXk/y37b7ElS+RnF7UVWR+V5J1GOIqp3JvBFKVWsRx8fHrfcN9RrlCqyhrgAiImKc qjrzW7Zssd43rE9ZI8OkXp4cCwdzqNJ0nxTEdn/ilLmOJrVOJNSgzK6qeAZ78iIipld5jY5C rmuy3TfMp6Y18Kr8u3/371KVd5TG+SW55t8kl65K0vumRdX+YI662FLvDhER0a5GuQJ3kCvQ bFVjIgl1u7Kr6b61ftI0tyQNYrs/KuV3SJUXHUS+J2nJS8XaVe19IKHGEiJiulXVyGPNPjZT Of9gipzLqe63hG9b67ySnNdr1vc6ao4pLbUfsPWqcnX5TCAiImIeVNWXlmNz233D+q1lX1qJ vKdNoiZEUgax3Z+g8rWqJQ4g34s05Zra0ChX4Pp85AqoYhvsWYeImH5VcUDWn2KzlXMdJvty VpH5SLIege1+27bWeSW5ZiTpNUtRMseEVclbQERExDyrqgMi66YVPR6eB2Vuo+mewNX9ltIw zxTEdn+qVueVTGrlS+RrT35p9pXnQhWN7O2MiJg1jXIF2rKdK8D+eWhb032Z/ONNWVO5aPdu cowta7XXgnytWrmWnDkmpO4qIiIiFkFVPs3Y2Jj1vmHjyntN+V6aUp1nslnHK4jt11C+FrXM K1W/P0W7z8+rqu8P94WIiNlRNd5lb3VslTJPSbU+Todcm5PnfCW5/kjOj5nmxFWR9e1s5fnI vxu8L5DzRmnI0zPRKFfg3HsynSuQFlX10VkTi4iIiHkyKq9KwhqmfCn386nuwWuKHA/bWLcf xNZrJp+76Z5JVeRrzN5J+VGeA+W5MIq07UWGiIhq5ZoQ1VjHdt+wWNayJ6cfOf8iay7nYZwp x1eytl+t648kcq44DWMweZ8QnGOSc15ZmWPC+pTvryrnkH1WEbGVGuUKHCFXABFbp2qMRE3v /CnvherJD5T3ca26XwrSytdHPkf5XOVzrgX5mvJ9yZ8ynzYK6iQhImZLGfdTQTwYbSjXwNSy 31Jw3CnnmrK0rqm6TqmeOSVJWuaV/DLHVDxVubnyfbfdN0RERMSklfW8VPcjtvuWB41yBR5L b66ArPmlWpehQt4/yXzLZteSCdKK10M+J/ncaqmBJ5GvIbUk86tqLlaeX233DRERa1MVy5dr m2z3DYurnGeqZz2THzlnI+du0jTfJOeTZO6VrO9Xa26bnzTOK/lljqlYch1BRETEIqlbu806 i/xa3W+p1rkjiX/+KOl7oiDNeO6yz/XOE0mqc0XUhcyv8tyn+uwTByimRrkCD6Y3VwARzVXl nbOfHqbBat28esawQWS9Zrm+qTrnVMvYtlpPW/7b9DGyZp+cZ5HjaDnX1ch8UhUZx0/zvJJf +dyDtQ+YY8qfrINFRETEIsoapuLayDyT/75OfoaS2Ds3SFLPU/ZN9rHe+iIS5pWKI+uREBHz pyoen8T4BbOhUa7AmmzkCtRbq9lkvCvnjKRy3Cutzj/5XbNmTaW9/Lf/59X5I6lckySPk8Q8 kp9qrpuN/V+TeN+C75mcu87ic8FoBwcHIz+37OOHiIiIeVaOc1Wwhqk4yvc6qTxCGYeXa4Rq vVcKUs/zkH9T/u1G55OqsGdSsWTtUvLK103Gb/Nq9XyTV2WcRJ5L/RIHa67y9Q2+5ti41157 beT5Xf7cdt9sK+Oets81eKVr1651PvrRjyayTinNyHF2VtYf6YyaN5LvHdfLfEh+Qro0yhW4 Ixu5AoiIiHlSVc+bNUzFU+YeynzDJJH3W/KzJO+XZd3palw2GK8PEtU/f6xaHkseUx5bxkeS 5IEHHojM10yj/lzRPCprt1RzaZvt9ddfH/l5aGb8MencY4BmQzylucrXFwAgr1TnlPKWt8Mc Uz6lZioiImLyGuUKXE+uQNbU1Qlm7UYxlXXg5PxKtf4FAACAhPml5sr8EgDkCRmHl2thm7F3 a9qUzy+Y88YcU7ZV5TDKPEfbfUNERERshbp1SewjU2xff/115o8KjFGuQJubKwAA4OfjH/+4 Fy+Mu9bIeKKMrfX09Di/8zu/U/m3/H+59jTusTJ2E/VYk1pK8viy7a233lrp780331zpu8n1 UT7upptucj75yU9WlP9tmqcskX/zU5/6lLNixYpKLrs07nHVWsXVWmUAAFlFnseq80kyh9H2 PY8Ng+dxeV2Rr4ftfmFtynlBFXmfK0VERESsKsf0qnrBsu6U7f5hOqzONW3ZsqWyry8AABQT k7mQ3//93498rJzDiXusnCOKYtmyZbGPlXM8UezevVv7OF18KC72qVtLFBdbkrHEZmOUK3Cu nVwBAEgcOY9UrX9dz76reTcqVyAPe00VSVW+h/y57b4hIiIitlI55o9CziOwhgmjlHv6yPlH 5psAAIpH3LyJbs+wuOuLKufFZH2Qiri1UHIdlIq4WJ9qDCWJy0WX+ftRfOhDH2pozzYAAFPe +c53Vv4tz7HyfFZVnvuq+5kyL1SbzZg3MsoVONLOHgQttLquOArqBCMiImLRlGMjVTxn586d 1vuH6Veub5L3nnLOadOmTdTUg8Ihz6GNxIPTbjX/OK/K+YVqHC2P1hsbVM1/6OZNdPEWiS7m oltHJNGtJdKtI5KfYd3zlJ9vFXE5yLr5nLj6gKp5OGrqJK9uH1Fi5+lTvl+2z5tFsVobNO48 ibXJ2qRsqsoZMVm7jYgYp1GuwGPkCiBidtXl38q5A9v9w2wqPztyrdORI0cqc0/VNU9yDsrv H/zBHzi//uu/rvQ3fuM3nN/93d+ttJWP37p1a2Xus3rMPCjrD8rXKq+ePXvW+ufR/7lUYbLn C2JRVNVv082bxNV8033HdOuIJLrYnG4cI9E9T1WOjSRu3ZQOXTxKNQ9HDKt5UscIMWx1jpz5 peSV16wg8jpnu1+oVjUeYG4QERERi6qMXaj2IpAxetv9a7VGuQIPkiuAmHflPGUU8nzJugHE t61nDkS15qmKLoapW0ck0c0DxNWFU62bilszJVGtm9KtmaqiOqfUM3eHjal7v+L22ULMq8wv NdeoOSbmtNNp1HslManPi4iIiJhnVeMkiVxTYLt/iIiNKs9lKsg3zJ9GuQJryBVIylpruOn2 Xqqi+lu6dUQSXYwnDtW6qbg1UxLVeSRuzZREVUuwntqD2LzPM/N6WFSZX2q+zDFlQ1VeLrUN EBEREdX30jJP2HbfEBEbVbXmgTpTiNHWMrcRt59Slaj1QSZrgyRRa0dM1gap4qFx66Akqthe 3DooiWougv2U7KjKpZLzlbz26dUoV+AOcgWaLfNLrZE5pnTLdQQRERFRry5GI/d9sd0/RMR6 lecwFap6WIhFt5babHH7KVWJyu81WRskiVofZLI2SBL1/OLWQUlUa6FMiJq7Zj8lu5J3jvi2 zBm1TjnWDF5zmDdKh6qcD96fZDXKFbieXAFERMQ8qMrlXbJkSWWve9v9Q0SsVXnukuewKFif iai2lrmQuP2UqkTFMU3WEUmiYj0m64gkwXlk0zVTkuC6KZM1U1WC+czsp2RXcs8R35b5pbfd c3beWbljt+N0rPLOCzcJd/7pjc6rR/5MtLnc8N+Q153gHJO8nnLusSf77SEiIpprlCvQRq4A livjKFU+75YtW6z3DxGxVuW5SxVP5N4RUa9pLTeT/ZSqBP+GyToiSdRaIlOC61NM10xJguum TNdMSYK1BNlPya7yc6v6vLGGCYsm80tl57zw9t0vi1ehR3su/6vPtDlzL36l4b/HHFO6VOWo sHYJERERMawuFiL3vLfdP0SM1yhX4NxCcgV8yvObCmKJiPGazIeo1jm9613vivy5fy2Rah3R e97znsif++eEa1lHFIyfmq6ZkgTjTKZrpiTBdUnsvWRf1ZiYNUxYNIs+v/SGcMm3zolXoMPo fN4rnN7dIx7744b+LnNM6ZC1S4iIiIi1KcerqprzMsZy6dIl631ERIxTnqtUcWHVPimIeKUm NdpUbVS1Jv3ztaq1Qap5Gf/6IFXs/53vfGfkz/3Py3QdlCR4voji3e9+d+TP/fUB69lPyShX 4Ai5As2SNUyIrkWfX9o2J879HfddcQ6QM02PDi1y5k5v89rJNU5/NvaSc+Of7nZuEi1Off1G J4k5pmC+AXNMrZW1S4iIiIi1q8vR2blzp/X+ISLGKc9VKoL7qyBitCZzIqp1S3/6p38a+XN/ fFI1B6yad/LHclTxnp6ensifV7/3qnVQ8niqNUPV/GTV+Eg+D1VuTjUGyH5K6ZF1SYjFnjO6 LLz94AXHvx5J7qGkmw+Sjxl+6aKzsOuLNc8bGeUKPKbOFZDnpeD1SV5z5PXM9muZd3VxEe4n EBEREfXq6sGcPXvWev8QEVXqat3Jc5vt/iFmybg6bbo5GdXaj+qxdb+Pwr+WKAp5PNXcVHW9 ie73qrmy6rop1byEPKac+4qiWh+Q/ZTSI+uSEMuV85r8vAf3jSuCPxYu+g/Hr/jub+8V1575 Uuxjn/xJ2Vm8fk8ia5Oi5o3kuYl5o+aqyk8p4lwrIpprlCvwIHUFEBH96u6916xZY71/iIgq 5TkqCnLTEWtXNy+iWt9UnQdS5arInF/dOiL5WFX8R85b6dYRxR1X1yfVGqPquilVn+TflDHa KKrrk9hPKV2yhgmxuJ4QLvj0Xu97L1cxTT/SIX532ejxJ+fLzof//Xedi8dWNtwX5phaK+uR EBERERtXlbcrGRsbs94/lUa5AmvIFUAsuvI8pkKe/2z3DzFr6uq6xc3H6NYKxa0zUsX/qzn3 uu+4bl2U7ndx82VRyOPJ38l5ryiq+2Sofmf7/S2qrGFCLK7B+aUeYRJzRfXKHFPrVOV6sHYJ ERERsTZV+bcSat8hYprU1brjXjB7GuUK3EGuQJrUzY3E1ZPTrSXSrSOSj1XlGMu5K906IvlY 1bEfffRRZX+qz1cVe9q9e3fkz/01N1V7MD322GORP2fvJbuyhgmxmAbr4/U4dueXqgbrrMpz ETVUk1O1zlhS3WcREREREc3U7Ykg4ySXLl2y3kdERHkuUsWJ5TmMe0HE+lXNo9x0002xsRfV GCJuvyVpFKp5meraIKlqvdPHP/7xyJ/716Co5sFU5xf/WkfVvkqf/OQnI39OLNCuunVJ1XV1 2LhGuQLXkyuA6fSCcPHDb+cvLRb+aM+N1vsljbrmFHGPrGaoGmtwbUBERESsT12dvJ07d1rv HyKiPBepoNYdYmOq5lyiqNaHq6qal4kiuM5Qt4Y6iH9tkGot1LXXXhv5c/8+Cqo6fqrH+msS qfKd3/ve90b+nHUv9lWtS5KQl4CYX9dNz4tveW/lu17rnkrNlnmj5NXFMzjXm2mUK9BGrgAi IiKq1cV4qPeMiDZVxZIl1MVDbFzVnEsUwdxeXUwnSHAvG13sP0hwHlm1LiUK/+NUNf6i8K+Z kqr2VYqC/ZTSoypP3T9niYj5ctfFstN221963/exVfJacNp6v6oyx5ScrFVFRER0NcoVOPdu cgWwaerq5C1ZsoQ6eYhoRXnukeegKKiLh5iMtcy5BONfuvnfIP51RFLVvkpRBHNdTNdNRc1B q+oBBomaf1DNVQRhP6X0qNtnI/iZRMR8GKx7d3+H48zPbbPeL79R+RnMMdWubv887hMQERER G1d3T71p0ybr/UPMk0a5AkfIFahFeZ5SwT04YnKazrlExWpM1xJF/V0TguuIpKbrpoJrpqSm 9QCjam+q9mAKwt5L6VL1+WadGWJ+HT477zgdn61819NWI69q1L06a27M1a0rjrr+IyIiImJ9 6nJ8x8bGrPcPEYujPOeooFYRYrKazKME91OqarI+SFWf0mRfpajvu+laqKg1J6Y1/qJq/gbj e0a5Ao+5uQIq2HspXerWypGrgJhP3xAu+dY5x51dcpwv9zjO/MU91vsVlDmm+lWNVWReCtdh RERExOSUYytdPZczZ85Y7yMi5l95rlEhz1HcByImq8mciyqGZbI+SJUbbLKvUtTaIKnJWqio x5nU+ItaByU12VeJdS7pVDWXSWwRMb8+N192rl7l5ivdJHxlz4edqHVJRrkCD15ZV+Cy8Asv XnScjlXO/T2OM/fScEueE3NMtUveACIiImJr1Y2/2F8JEZutbh8lCXtgICavyZyLKgZjspZI 9b012Vcpah2RNG7dlGrNlDSuxp9ujWTcvkrsp5ROdXODvGeI+fUr5+adttv+sPJdX9/hOG+d HVa2/YFw4Ref8s4NrVzHJPM8gnkTzBupVV2LyfFALK5GuQJr2IMAEbFV6vKJ16xZY71/iJhf 5TlGBfXREZtn3JyLbu/ruLVEur+rQ7WOSBq3bkp3voirB6haMyWN21eJ/ZTSq+59V81jImK2 9a8zkujmjC4IFz/89hp60dS5eGxly/oqz0PB66nMlWCN5ZXqYhXkoSEiIiI2V90+Bzt37mzo 2Ea5AneQK4CIauV5SIVuLQIiNq4u9q7ae6mqbi1R3HdXNzbRrSOKWzelizHF1QPUzTVE1Sby Qxwwvcr3RjUXyjUGMb/KOabhly46bbdtqnzfd3ymzZn/yZORbU/Ol52OLz5ZaXez8LWJnpb2 NWqOSeZ/cG1xlbkuqvM4+7MiIiIiNl/dfbXkyJEj1vuIiPlRnlNUsOdFOjXKFbieXIE8qptz iavRo1tLFLfuUJeHrFtHJNWNaXSP09UD1K2ZkurqrFGXJ/3q5gfZhwMx3z75k7LTMfi3le+7 bs+kbXNlx+m4z1kp2v10el3L+8kck1pVPot8vXTrrBERERExOeP2Vzp79qz1PiJi9pXnEvZR QrSrbh4lLpauWx8U9/3VjTXi6pDdeeedkY8zmbtZvHhx5GPlMeMeu2jRosjHsjdPNlTVdyxq LoNRrkAbuQJYPN8QDk++6lx99390OsRX4etDi5yXxofF735c+f3p+bLT8zfPOTc5Pc6pr9/o /bzVymtlcI+hos8x6cYW1NBGREREbK26PGMZD7506ZL1PiJidpXnEN3cUtyaBERMzptuuqny vXv3u9/t/MIv/IL3PTTJ85WPkVxzzTUVq5j83Sr+x8rjxT3OP0Z573vf6yxYsKDy3ybzPNUa f/Ix8rG1nHOq+yoFH8t+StlQNx8at/4OEfOhrIP38OmLztrt271aeJLqPNKrR/5MtLtstY9y fig4Hy7/v4h7wMnXIjiHViWuLm9RNMoVOHcNuQKIiIhoRd3+1GvWrGGOCRHrUp475DmEOB9i OpQxK/9ckoznmK4fDD5W/rdpDEy28+dZmz42qn/BfqiM+hvyWCb53o08FtOhbg8x1swiYlqM mmOSay2LNsfEORsREYugUa7AEXIFMJtGjWv9bN261XofETF7ynOHiqLX9UBExOap22dU5sJz /UHEtFj0OSbdmlO5x5Lt/iEiIiIWWZl/q9vveufOndb7iNhKjXIFHiNXIAnl+UUF++4iImKz lfUMVbA/ByKmySLPMbFnHiIiImK61eUDSY4cOWK9j4iYfuW5QkcR7n8RMR0a5Qo8SK4Alp3u 7m6uW4iYGYN16+UcS1x9OPkYk30J06ic61fBHq2IiIiI6XF0dJS4MCLWbdw8tTzH2O4jIiIW Q936fJkHb7t/iGimUa7AmuLlCkTtjawaa/vbZm2fIt39hcwNsN0/RERERLxS3Z6ZS5Yscc6e PWu9j4iYPuW5QZ4jVGQ1XxIREbOrzGtXQe07REyjJvNGUeuXslQjTlfrjtrYiPnUKFfgjuLl CiAi5tWoMS1zTIioMm5uSZ5TbPcRERGLqa72Xdby+BGxGOrmmFT36n19fdb7bSK17hARERGz qVGuwPVurkDUvqF+ZBz59ddft/6cENG+8lygm1uS55Is5VMiImK+1NXJ6+zs5BqFiKk0ah5G ziHpSPv8jJzTV0GtO0RERMT0GzdvtGbNGufSpUvW+4mI9pTnAHkuUMF8kV2NcgXaqCuAiOhX VyePNbaImFbj9kcOkub6cvL+Qc7pZ63fiIiIiHiluhxOCXNMiMU1bm6Jez9EREyTutp34+Pj 1vun0ihX4NzV5AogYs1zTDIXzHafo9TV4E/7uitEREREvNKpqSnmmBDxCk3mluS5w3Y/ERER q+rypsiJQMS0WuuckUTWy7Pdb79yDl9FUWrdGeUKHCFXABEREfMpc0yIWJW5JUREzKrEOBEx S+rOWXHI/Yts91/K3D4iIuZFo1yBx8gVQFSp219TwhwTYv6Nm1tK070sIiJilLo979OW84+I xTUuxzMOuX9RGvZB1e3pnObapIiIiIhoZtx6e+aNME0a5Qo8SK5AMzSZW5LnE9v9RERE1Knb N15CngQi2rbRuaUqch8jm89jcHAwtX1DRERExORkjgmxeDJfhIi2NMoVWEOuADZP3bp8GdNN Q84/IhZTXT25erC1RkhX2y8ta6sQERERMTmZY0IsjswtIWIjXhbefvCCOFN0VM4XK4U/nV6n bP+GcMm3zlXay0fsGFrkvHV2j/XngflT1rdTwf5KiMlqlCtwB7kC9SjnZnTrLGvBxh5HcXNk 7NWKiIiImE/j5phkvfqzZ89a7yci1q/8Duv2npAwt4SIcW6bKztOx32Vc4acM5p+pEP8/HJk 2yfny07b7duvOM+sEg86P9mvfAxivcp5JBWyVpPt/iFicZVrdnQ142qhlXPmst+6fZRGRkas v7aIWJ9GuQLXkyuAiIjmxs0xLVmyhDkmxIwqv7vyO6yDuSVENPEHwoVffMo7d4ytcsTPT0e2 HX61LFqsCp1v7hLOHltp/blgvpRxUF3ePPvJI6Jt5fod3XyNKXLNZiv6K/dKUiHz1my/noiI iIhF1ihXoK21uQLMMSHmT+aWEDFJLwgXP3zGO3/c3+E483PbQu1UtfTOy5/vftn5bNttztyL 6tp6iPUYt78StZkQMQ3KdT+N7rXU7POZLjbAPkqIiIiIxVKODePGr0eOHLHeT0SMV35Xdcjv OvNFrdMoV+Dcu6grgLly3fS8+HD3Vj7nNwtfm+gJtQmuc9oumpfnS5XfyX2ZPjHxqvNHixY5 b50Lz00hNqJuLya5boCYKCKmQblXUVztah3NPJ/JuSvdvQZz9YiIiIjFU44B4+aYJiYmrPcT EdXK76gO7vcQsV79eyWp9lWKayPnjVYeOO/M7L499FjERtXFYRut02SUK3CEXAFETF5Zu7Pe tUzN2FtOznvp+kMeGyIiImJxNZlj2rp1q/V+ImJY+d3UwdwSIjbij4WL/sNx75ziX29U1WTd EmKzjNtnvhlxVkTEepXnLHleqock94uLO3fKPZZsv1ZJaZQr8Bi5AoiIiIiNKOPPsl6yjk2b NjmXLl2y3ldELFe+i/I7qUN+p5lbQsRGVO2XpPq9at8lxGYalytFXj0ipk25L5xuficKeZ5L qvadnD9SIftl+/VBRET0a5Qr8CC5Aoi2jcthkqxZs8Y5e/as9b4iFln5HZTfRR3sJYGISblt ruw4HfdVzi03CU99/Ubx8x9XfmeyvgmxFcq8fh3kWyBiGpV7w9VSB6/Rup9S3fqpJOewEBER ETEfyvFhd3e3dpy6ZMkS58yZM9b7iunVKFdgDbkCzVbGx+T3VYf8vnNfiIhJeUK44NN7vXOM XKk0sXGRU54/fcXeS5Ljf7FAPOZN633GYirjtLqYKXNMiJhG5d5GcffrfkZGRur+W3I9pw7O k4iIiIioUrcGvsrExIT1fiIWSfmdiyNP9c8R0a5GuQJ3XJkrcEG4+OEz3jmpR7ho+XJnx2fa nK/86Pve2ib2XsI0GFfzifwLREyrch2myVqmeufLZU0+HdQSRURERMQ4Za5THFu2bGGfJcQm K79j8rsWRyP5iYiIOtdNz4uzTG/lXCP3VfrBi+ucG//8qFPdS0myVnj51WHrfcViG1fDmXkj LJpGuQLXU1cgi8pzma5+nf+8V8tx4/apk3/T9nNHRERExGxokhfFPkuIzdNk/yT5HZXfVdt9 RcT86q9tJ2eUph/pcM6X553bd7/suKuU2E8J06OMuerGr6z1RcS0K9cPxe13bDrPwzkRMfsa 5Qq0kSuAiIjpVOYvdXZ2ase2co+XI0eOWO8rYp6U36m4/ZPkd5Pa54jYbH8gXPjFp7xzT3Uu 6bL4+RdevOg4HavYTwlTZVz+PfFURMyCur3iJHIeSvd41nQiIiIipkOjXIFzP5/bXAE55jTZ R3Tr1q3Uy0NsUPkdkt+lOOR3kvtBRGyFch7p9oMXnGo9vPvFv+bntnm//8q5eWdh133Oqa/f KP7/x9b7iyiVa3t1UFcWEbPgzMyM8l5c5pqp7gfi5pZ0j0VERERENNWkZnNfXx818BDrVH53 5HcoDmqZN1+jXIEj+c0VQGzEbXNlx+m4r/J9uVn42kTPFb8flXNMd3/Jmdi4yCnPn7beX0Sp 3INeB3vUI2JWlOerqHWZ8j4jqr1cp6lCHocaCIiIiIiYlCb7LEkmJias9xUxS46NjcV+r9g/ CRFteUK44NN7vfPRU5vaxM/PX9HmOxfLztV3P+p87TNtzlvndlnvM6JRrsBjb+cKxOVSMceE iFlRrjeKmjcKnseYW0JERETEVivHmHF7iEo2bdrkvP7669b7i5hm5XdEflfikN857u8Q0ZYX hIsfPuOdk6r7KgXb7bpYdtpu+0tnVYfjnJ/sFz+7bL3viLr4aVS8FRExzcq9lPz7Hst5IFkb T/4uj+c7o1yBB6krgIiIiJglVflQQZYsWcJaJkSF8rshvyNxyO8aNc8R0bbrpufFGam3cl5a Kfzp9DptO9ny6N/0UAMPUyE5+oiYN4eHh73zmNx3KY9zS4iImA+NcgXWkCuAWERVtZ2DsJYJ 8W1N1yzJ7xb3gYiYFp+cLzttt2+vnJ8WC3+058ZQm8vC/mMXxW97vHOZXMu0e8dKZ+70NuvP AYtr3P71zDEhYhaV65bk3FIcIyMj1vuKiIiIiMVRjlNNauCxPik7GuUK3EGuQLM1XbMkv3/V OheIiGnwB8KFX3zKO0/592Dyzz3puFn42kSP9eeCxZQ5JkTMozIfTYdc12S7j4iIiIhYTP1r 7nWwPgmLqOk6JIn8LtnuLyJmX6Ncgeubkysg1ybdfvCC+CMdlb93v/jX/BxrkjBbMseEiHmS uSVERERETLvyHtu/f6iOsbEx6/1FbIXys26C/O4Qp0LENLttruw4HfdVzlk9wovHVlrvE2Kt Mm+EWdIoV6CNugKol7klRERERMyK8p59cHDQKJ7e19fnnDlzxnqfEZuh/GzLz7gJ8jsjvzu2 +4yIqNNf206uTJp+pEP8/LL1fiHWqrzm6vYFZY4JEdNs3P0280WI6dIoV+DcO8kVQERE9Fkq lYzXMm3ZsoWaeZgb5WdZfqZNkN8R+V2x3WdERBMvCBc/fMY7h23vdZzyfMl6vxDrUc4fxc0x cY1GxLQp5450MLeEiIiImLxGuQJHyBVI2lrWMi1ZsqRSR+zSpUvW+41Yj/KzKz/D8rNsAmuW EDGLrpueF2ew3sp5bK3w8qvD1vuEWK9xc0wSWVfKdj8REaXMLSEiIiJiXpT347ra9X5kjbAj R45Y7zNiLcrPrGl9O/ldoI5O8zXKFXiMXAHEZrrnctlxFj9U+d7dLHxtosd6nxAbkTkmRMyC zC0hIiIiYh4dHh6OvSevsmbNGvZbwtQrP6Pys2qC/OzL74DtPiMiNuIPhAu/+JR3bntqU5v4 +Xnr/cJ8apQr8GBrcwVM8qaYY0JEG8p6B8wtISIiImKenZmZMV7nIdm0aRPzTJg65WdSfjZN kZ95+dm33W9ExEa9LLz94AVxZuuonN8eWuw45ct7rPcLsVFlXDZu3og8EURspSbnpSzMFxnl CqyhrgAiIiIimiv3QO7s7DSOz2/dutV5/fXXrfcbi638DMrPoinyM85+34iYN4dfLYsz3KrK eW6l8KfT66z3CTEJ8xLLRcTsy/kIERHTpFGuwB3kCiBi662lZp6EeSa0Ya3zStTCQ8Q8++R8 2Wm7fXvlfCdXMU0/0iF+ftl6vxCT0CSmK9cly3a2+4qI+dRkrzfmlhARERExz8p77sHBQeN4 vIS5I71GuQLXkyuQpGfPnq1pXkkiP/fEnBAxz26bKztOx33eeY89mDBvmswxyd9zvUfEpDWZ WxoZGbHeT0RERETEVljrfksS9lzCZljrnkkS9k1CxKQ1yhVoS1+uwMpjl0XnFkeeK+/vcJz5 uW3W+4iYtHJ9gA5ZB1fGgm33ExHz4ejoaOz9iWxju5+IiIiIiK1W7kHT3d1tHNeXyLmAI0eO WO87Zlv5Gap1Xkl+Vtk3CRGL4p6z887w8FcrdT3/6EtfcjoGH3FuFufC1yZ6rPcNsRnGzRvJ tQOMA1CnUa7AuQWpyxXA9CqvwXEwt4SIiIiIRbeeeSa5hmRiYsK5dOmS9f5jNpSfFfmZqXXt HPNKiIiIxZBYLiI2S1k702Qem/WPiHY1yhU4Qq4AIiJiGq1nnmnJkiWVvXDknji2+4/ptLpf kvys1ALzSoiIiMXTpBaVjBHb7iciZkeT/duorYmIiIiYjEa5Ao+RK5Bn65lnkqxZs6ZS94w1 TSg/A/KzID8TtcK8EiIiYrGV4wC5jiBuvCBjxrb7iojpVs4ZxZ1P5NwT5xNERERExHjrnTti nVJxrXf9UTX2w1yRHY1yBR4kVwAREdOpjAnL9QQ6WG+AiDpN1kPK2t3MLSEiIiIi1qa8F4+r P60bg8t9dF5//XXrzwObo3xv69krqYr8bBHvQUREbEyjXIE1+c0VMKlpJdclsCcTIgY1udcd HBy03k9ERERExCw7MzNT2Us5rmaACln/TM5DUBMv+8r3UL6X9dS0k8jPkPwsyc+U7eeCiIiI 6dYk9ss+S4golfcXcXPNkrTONRvlCtyR31wBRERERMy/Mj9UjsdNxu0qtmzZwrqmjFldp7Rp 06a633f5mZGfHepKICIiYi2OjIwYjTPIXUEsruPj47G5kPL31E5ARMRmapQrcD25AoiIUrlf Tr2186pU1zWxX1P6lO9JI+uUqsjPCHsrISIiYiPKsYRJ7FjGmG33FRFbq6xfF4ecgybPDRER ERGxNcqxt8wVbWSdkkTuy7N169ZKnlirauMZ5Qq0kSsQVL4/8n2S71e9+ylVkZ8b+fnhHg4R ERGT0rT2FXulIBZD03MCNTQREREREe0p5xzkfXq9ey/5kWthxsbGnDNnzlh/XnlVvrbyNW50 3ZFEvufyvaeOBCK2WqNcgXPvIFcAsWDK3BWT9fMy5sz4BTG/mtTDk6R1ryVERERExCIqx/Hy nj6JuSaJ3M9HzoW0cn1TnqyuOdq5c2dDeyP5ke+tfI+pL4OIiIitVMaBTcYpco207b6imUa5 AkfIFUBzTeeYOzs7mWNGREREREyxSc81Sar19OS+P6xxCitfE/nabNmypeEadn6YU0JERMQ0 KOPBMi5sMmakDi9i9uU7j5h+jXIFHiNXABEREeu3GXNN/nsJOZ9SXed09uxZ68+32crnKJ+r fM5JzyVVYU4JERER06iMIZuMfeRYhnEMYnYdHh42um9hzSIiIiJiYxrlCjxIrgCmw+p+TSZ7 rTaC3F+oWl+vuqdTltY8Vftb7b98LknsmaRDvifsp4SIiIhZUMaUTXKXZL4M6xoQs6O8FzG5 V6TGHSIiIiJi/pyZmanUx5f38ia1DJKkOqdUXc8krdbSq/r6668n9lzlsfzHln+r+ndlH1ox JxREvubytZfvgXwvbH8e0FyjXIE15AogIiJWrSUOXSqVrPcXEfWarlli3hgRERERsRjK+36Z XyrrmLR6vqkIVOeT5GtM/h4iIqJdjXIF7iBXIElljFmuvzaBPVoQ06npXDF1LxERERERi211 fVMrauTlke7u7sprx/okREREzLtyzZFJDTxizojpUc7hmq5Dkvc2abynMcoVuJ5cAURERETE NChjB3L9jVyHI+8xwKU6lyRfG2q6ICIiYhGVsWq57sh07JTGWDViUZT3LKY1K+Q9ju3+IiJi fjXKFWgjVwARMa/K2IDMQ5W5b3mfd5LPTT5H+VzlcyYugoiIiHilcoxkupZJjqls9xexSNYy DyxrWFDXGxERERER4zTKFTj3T2vKFZD3LjIvrjr3JNf3yPkZqUnModXIPlX7J/tanUOSz4H6 /4iIiIjm1hLDlmsoWP+N2HzlOiTT+zDmfhERERER0YbVeaWq8t7Er4w1VOdx/AaJalNVHiN4 XP/fZD4IEYuoUa7AkdpyBRARG9F0LZNEju9YG46YvPL+yHQ/XWpXIiIiIiJiFg1iuz+IiIiI GK3M5ZHrvU2o1sEj/8dMo1yBx8gVwHjlPJHpOkL5PWWfJUREREREzKrMLyEiIiJmS7kuQta3 M0G2Gx0dtd5nxCwq52flPK0prAdEtKtRrsCD5AogIiIiJiXzS4iIiIjZVMa9Tevgyfpb7LeE aK6clzX9frGfGSIiImIyGuUKrCFXADEtMr+EiIiImF1rqcVVnWdifQWiWrl/men6QAl1KBER ERERMU8yZ4SNapQrcAe5AoiIiGmxlpp5koGBAeaZEH3K75CcfzWFWniIiIiIiJhHmV9CRERE o1yB68kVyJu11Mxjngmx9nklauEhIiIiImKeZX4JERERMV/K+ltyLqgWmDvColnrXJGcix0Z GbHeb51GuQJt5AogIiIiImIyMr+EiIiImE/lfFEt8XPmmbAI1jqvJGHfJEREbJVGuQLn2sgV QETEVMj8EiIiImK+rSeezjwT5k2+B4iIiIiI2AqNcgWOkCuAiIiIiNlxfHy8sh9MLfT19bF/ DGba0dHRuj73zCshIiIiIiIiIhZTo1yBx8gVQMTiWU+8Xa77kI+z3XdEE2UtO1nTrp7POfOp iIiIiIiIiIiIiFg065k7ku3Tur+MUa7Ag+QKoKtccyRr2rW3tzOvhIiIiIiIiIiIiIhYo/XM M0lkbH5qasp6/xFlPcda90tirggxnRrlCqwhVwARERERERERETEtynmmrq6ummP08jHysWlc 04T5Va4/qqeunUTOjTKvhIiIiNgcjXIF7iBXABERERERMW/KuHs9a0FknTHWNGGzlWuV+vr6 av58VueV5LyU7eeAiIiIiIiYVoPY7g/a0ShX4HpyBRARETFaOU8k4/H1INeUjIyMEMvHRJSf xcHBwZr3SpLIx6R1zzBERERERMS0yfwSIiJiPjTKFWgjVwCba7UOWT2xfYlca0L9PKxV+bmT c5T11L+TyMfxuUNERERERKxN5pcQERER0dR6913yzx/JmmW2nwem0+o8UaOfsSzvl2SUK3Du 58gVQERERERE6wax3R9ERERETL+N1MOTyPVNrFVCaRJzSnL9kVw3R41FRERMg0a5AkfIFUBE RERERETE4irnhhpd0ySpzjUxP1AM5fyknA9K4nPDejhERERERExao1yBx8gVQERERERMQjln MDg4WPfeS1XknIM8jjye7eeEySjnIeU8kFzzVu8eSVXk4+V6J+YiERERERERERFRpVGuwIPk CiAipk05lyDXljRKtY4e8wnZs7pGqbu7O5HPAXOOiIiIiIiIiIiIiIjZtVoTz3T+yChXYI2b KxCHXLsi18BQSy99yrkfOQ8oPxeNrmGTyGPI95r6d4iIiIiIiIiIiIiI+bLWuSYT5BxSdc1S qVSy/hyLonwv5eud1JqjKswTIeZDo1yBO6grgIiIiIiIiIiIiLXZjLmmKnK+Q9ZHk8enRloy yrkkOYcn537knldJUl1nxpwSIiIiYno0yhW4nlwBxKIbxHZ/EBEREbF4yrkFOccg5xqagZxz ksevrnOS81u2n3MalXUE5Xsh1yXJub+k55KqyOOyRxIiIiIiImL6DGK7P1i/RrkCbeQKICIi Yn6s7s2TZN21KGQttuq8k5xPkfMqRaixV61tJ9d3NXseyf9ay7/DXliIiIiIiIjpNojt/iAi IhZZo1yBc1eRK4CoUM6FyLUuzZ4D8VOde5LKORj//FOa19xU547880fytZPPo5Wvn6S6L1aa Xy9ERERERES80iC2+4OIiIiIqFLOiVRrsjV7zZIJ/rkl//omv3LupjqPU49y3iV4TDkf4/+7 tqmuOarWFLT9ObGtUa7AEXIFEBEREREx2wax3R9ERERExFqszsHI+Y1m7ckEVyLXN8m5NDl3 xpojRETMo0a5Ao+RK4CIiMU2iO3+ICIiIiI2YrXuW3WdD3NOjVFdl8XaJEREREREzJJGuQIP kivQiEFs9wcRERERsRn69xmScybMO12JXJMk5+Oq+0exLgkREREREbF4BrHdH0Qstka5AmvI FUBERDvKeZTqeqfBwcHK3JOca8kj1f2a5HOtrkeamZmx/h4gIiIiIiJiOgxiuz+IiIiImG2N cgXuyGeugJyDqe7xJOdlquufqtqmvb3d60t1/ZFUrtWS/WYdEiIiIiIiIpoaxHZ/EBERERGz bHWOKWh1LsfU6pqhoMwBIWIrNMoVuD6fuQKIiIiIaCbzS4iIiIiIiIiIiIj50ihXoI1cAUSs X+aXEBERERERERERERERkTmjdGmUK3Dun5ArgIiIiIiIiIiIiIiI1mR+CRERMVmNcgWOkCuA iIiIiIiIiIjZlfklRERERETEKzXKFXiMXAFERERERCyuzC8hIiIiIiIiImLeNMoVeJBcAURE xHplfgkREREREREREREREdOmUa7AGnIFbMn8EiIiIiIiIiIiIiIiIjJnhIhJa5QrcAe5AoiI iIiIiIiIiIhZlfklRERERAxqlCtwPbkCiIiIiIiIiIiIiIiIiIiIedEoV6CNXAFERERERERE RERERERERMS8aJQrcM4hVwARERERERERERERERERETEnGuUKHCFXABERERERERERERERERER MS8a5Qo8Rq4AIiIiIiIiIiIiIiIiIiJiXjTKFXiQXAFERERERERERERERERERMS8aJQrsIZc AURERERERERERERERERExLxolCtwB7kCiIiIiIiIiIiIiIiIiIiIedEoV+B6cgUQERERERER ERERERERERHzolGuQBu5AoiIiIiIiIiIiIiIiIiIiHlxzVVursD/py06V0DmCUjIFUDENLhQ nI/e+7Nz1Ht+9t/yNPVz/+Sdzj8V/75a5jhV/uuaq7531cg7Xv3zX3T+9dXvd77nnLjqr98h f1f99/9L+FfvkP8vz3r/vHLE//jujzpBygAAAAAAAAAAAGAb4nQAAAAAAAAAAAD2IU4HAAAA AAAAAABgH+J0AAAAAAAAAAAA9iFOBwAAAAAAAAAAYB/idAAAAAAAAAAAAPYhTgcAAAAAAAAA AGAf4nQAAAAAAAAAAAD2IU4HAAAAAAAAAABgH+J0AAAAAAAAAAAA9omJ0+3atUv1qx/96EfD FTQPf/HFF902rTxOCruU1+M0u0t///d//9Of/lQcZ3R0VPOHAAAAAAAAAAByQEycbtmyZapf TU5OOhU0D9+7d6/bppXHSWGX8nqc1nTpu9/97uzsrKYBAAAAAAAAAEAOiInT7du3zw2j9Pb2 Kg9RZfXq1Wk4Tgq7lNfjtKBL3/72t//9v//3mg4AAAAAAAAAAOQD4nRZemppO06zu7Rjxw73 h/oFtgAAAAAAAAAAOSAmTjc1NTVQYWhoaEeFAwcOBNoMVNm8ebPb5vDhwxaPk8Iu5fU4Te3S b/3Wbzk+xsfHw38dAAAAAAAAACA3mO73OjEx4YZLenp6VG1GR0fdNv39/Wk4Tgq7lNfjNKNL frq6uihRBwAAAAAAAAD5hjhdJp9a2o6T4KH+5m/+JhCk6+vrI0gHAAAAAAAAALnHNE538uTJ /gobNmzYVeHgwYOBNocPH3bbbNy40W3zzDPPWDxOCruU1+MkdagXXnjhmmuu8QfpBgYGwn8L AAAAAAAAACB/mMbpPEqlkhtAWbp0qarNrl273DYrVqxIw3FS2KW8HqeRQ337299+z3ve4w/S jY6Oav4QAAAAAAAAAECeIE5np0t5PU7dh/K2dnVpb29n4wgAAAAAAAAAKBQxcbrJycnAT06f Pr28wtq1a8cqhB/19NNPu23uvfdet00rj5PCLuX1OEkd6l//638dCNJNTU2FjwwAAAAAAAAA kGNi4nTLli1T/WpyctKNqmgevnfvXrdNK4+Twi7l9TiNH8rbWcKDrV0BAAAAAAAAoJgQp2tF l/J6nAYP9corr/zyL/+yP0jH1q4AAAAAAAAAUFhi4nTr16/fX+HYsWOBX01PT3+ywv4q4bWK k5OTbptWHieFXcrrcRo51AsvvBDYNYKtXQEAAAAAAACgyMTE6fbt2+fGUHp7e5WHqLJ69eo0 HCeFXcrrceo+1I4dOwJBupGREc1fAQAAAAAAAADIPcTpsvTU0nac+g7113/91/4IXXt7++jo qOZPAAAAAAAAAAAUgZg43dGjR3sr3HPPPRMVjh8/HmjTW2VoaMhtc+rUKYvHSWGX8nqcOg4l /hkI0rG1KwAAAAAAAABAOTZO5+Hty9nT06NqMzo66rbp7+9Pw3FS2KW8HsfkUHNzcx/+8If9 Qbqurq6ZmRnNMQEAAAAAAAAAigNxukw+tbQdJ/ZQr7zyykc+8hF/kK67u5utXQEAAAAAAAAA PEzjdM8991x3hYGBgUMVnn/++UCbJ554wm2zYcMGt83p06ctHieFXcrrcfSHYmtXAAAAAAAA AIBYTON0HqVSyQ21LF26VNVm165dbpsVK1ak4Tgp7FJejxM+FFu7AgAAAAAAAACYQJzOTpfy epzAodjaFQAAAAAAAADAkJg43fT0dOAnJ06cuKVCf3//sxXCj5qYmHDbrF271m3TyuOksEt5 PY7+UDfccEMgSMfWrgAAAAAAAAAAKmLidMuWLVP9anJy0o2/aB6+d+9et00rj5PCLuX1OKpD zc3N9fT0+IN0XV1dBOkAAAAAAAAAADQQp2tFl/J6nMhDhbd27erqYmtXAAAAAAAAAAA9xOla 0aW8Hid8KLZ2BQAAAAAAAACoj5g43b59+9xoS29vr/IQVVavXp2G46SwS3k9TuBQO3bscK5k eHhY81gAAAAAAAAAAPAgTpelp5a24/gPFVjrKmBrVwAAAAAAAAAAc4jTZemppe04/kP5YWtX AAAAAAAAAIBaiYnTTU1NDVQYGhraUeHAgQOBNgNVNm/e7LY5fPiwxeOksEt5PY6gv7//F37h F/xBOrZ2BQAAAAAAAACog5g4ncfExIQbhenp6VG1GR0dddv09/en4Tgp7FLOjvPCCy+wtSsA AAAAAAAAQCIQp8vkU0vDcdjaFQAAAAAAAAAgQUzjdCdPnuyvsGHDhl0VDh48GGhz+PBht83G jRvdNs8884zF46SwS7k5Tnhr18HBwTIAAAAAAAAAANSLaZzOo1QquXGZpUuXqtrs2rXLbbNi xYo0HCeFXcr0ca677rpAkI6tXQEAAAAAAAAAGoQ4nZ0uZfo4ftrb28XPVQ8BAAAAAAAAAABD YuJ0k5OTgZ+cPn16eYW1a9eOVQg/6umnn3bb3HvvvW6bVh4nhV3Kx3GOHTvW3t7uD9J1dnay tSsAAAAAAAAAQCLExOmWLVum+tXk5KQbrNE8fO/evW6bVh4nhV3KwXHY2hUAAAAAAAAAoKkQ p2tFl7J+nMitXQnSAQAAAAAAAAAkSEycbv369fsrHDt2LPCr6enpT1bYXyW8BHJyctJt08rj pLBLmT7OAw884FwJW7sCAAAAAAAAACROTJxu3759bmimt7dXeYgqq1evTsNxUtilTB8nAFu7 AgAAAAAAAAA0A+J0WXpqrTzON7/5zUCE7qqrrnr00UdV7QEAAAAAAAAAoBFi4nRHjx7trXDP PfdMVDh+/HigTW+VoaEht82pU6csHieFXcrccebm5n75l385nEzX3t7OBq8AAAAAAAAAAM0g Jk7nMTEx4UZqenp6VG1GR0fdNv39/Wk4Tgq7lInjvPDCC+9///vDQTpCdQAAAAAAAAAAzYM4 XSafWvOOc+jQocDWrpFQpQ4AAAAAAAAAIFlM43TPPfdcd4WBgYFDFZ5//vlAmyeeeMJts2HD BrfN6dOnLR4nhV1K+XH+8A//MBCPW7ZsWXt7O6E6AAAAAAAAAIBmYxqn8yiVSm6YZunSpao2 u3btctusWLEiDcdJYZdSe5xwJG5qaopQHQAAAAAAAABAsyFOZ6dLqTrO3Nzcb/7mb/oDcO3t 7ePj416Dqampzs7OyFDdwMCA6u8CAAAAAAAAAIA5MXG66enpwE9OnDhxS4X+/v5nK4QfNTEx 4bZZu3at26aVx0lhl9J8nLm5uY985COBIF14p4jZ2dmuri5CdQAAAAAAAAAATSImTrds2TLV ryYnJ90wjebhe/fuddu08jgp7FJqjxPe2rWrq2t2djby4fpQnepRAAAAAAAAAABgAnG6VnQp nccJb+3a19enD7eJ34o2kaE6TYAPAAAAAAAAAABiiYnT7du3z43C9Pb2Kg9RZfXq1Wk4Tgq7 lNrj+DFfvipaEqoDAAAAAAAAAEgW4nRZemrNi9ONjIyoGkeiCdWFa9sBAAAAAAAAAEAsxOmy 9NQaP87c3Nzv/M7v+CNr7e3to6OjqqNpGBkZiQzVRW5DAQAAAAAAAAAAemLidFNTUwMVhoaG dlQ4cOBAoM1Alc2bN7ttDh8+bPE4KexSSo5juLWrOaOjo4TqAAAAAAAAAAASISZO5zExMeGG YHp6elRtvKhNf39/Go6Twi5ZPM4LL7wQ2DWiq6trZmZG9XBDNKG6+tL0AAAAAAAAAACKCXG6 TD61Wo/z7W9/OxCk6+7uTmrPh1Kp1N7eHhmtI1QHAAAAAAAAAGCIaZzu5MmT/RU2bNiwq8LB gwcDbQ4fPuy22bhxo9vmmWeesXicFHbJynHuuOOOQPjMfGtXQ6ampgjVAQAAAAAAAAA0gmmc zqNUKrnxl6VLl6ra7Nq1y22zYsWKNBwnhV1q2XFWr14dCJzVurWrIZpQXeJhQQAAAAAAAACA /EGczk6XWnCcBLd2NWR2drarq4tQHQAAAAAAAABAHcTE6SYnJwM/OX369PIKa9euHasQftTT Tz/ttrn33nvdNq08Tgq71PrjJL61qyGE6gAAAAAAAAAA6iMmTrds2TLVryYnJ934i+bhe/fu ddu08jgp7FKLjxO5tWsLgnQus7Oz3d3dkaE60Y2kNq8AAAAAAAAAAMgZxOla0aVWHmfJkiXh IF3ro2MDAwOE6gAAAAAAAAAAzImJ061fv35/hWPHjgV+NT09/ckK+6uEM7YmJyfdNq08Tgq7 1LLjBNa6OlZXm2pCdTMzM7Z6BQAAAAAAAACQTmLidPv27XNjK729vcpDVFm9enUajpPCLrXm OOGtXYeHh1WHag2iA5GhutYUywMAAAAAAAAAyBDE6bL01FTHmZub6+npCcTCmrq1qzmiG4Tq AAAAAAAAAABiiYnTHT16tLfCPffcM1Hh+PHjgTa9VYaGhtw2p06dsnicFHapqcd55ZVXrGzt ao4mVDc+Pm67dwAAAAAAAAAAqSAmTucxMTHhxlZ6enpUbbxwTH9/fxqOk8IuJX6c3/qt37K4 tas5pVKpvb09MlqXkrw/AAAAAAAAAAC7EKfL5FPzjvOOd7wjEKRL7W6qU1NThOoAAAAAAAAA AFSYxumee+657goDAwOHKjz//POBNk888YTbZsOGDW6b06dPWzxOCruU4HGuv/76QLTL4tau hmhCdYODg7Z7BwAAAAAAAABgE9M4nUepVHIDK0uXLlW12bVrl9tmxYoVaThOCrvU4HHCW7tm Jc41MzPT1dUVGapLf5wRAAAAAAAAAKB5EKez06W6j5PmrV0NmZ2dJVQHAAAAAAAAABAgJk43 PT0d+MmJEyduqdDf3/9shfCjJiYm3DZr165127TyOCnsUlLHidzatVQqhY+fcjShujSX2AMA AAAAAAAAaB4xcbply5apfjU5OekGVjQP37t3r9umlcdJYZeSOk5ga9fOzs4Ubu1qzsDAAKE6 AAAAAAAAAAAX4nSt6FJSx8lfMItQHQAAAAAAAACAS0ycbt++fW7cpLe3V3mIKqtXr07DcVLY pQaP89d//deBMNbAwEBuwliDg4ORobr29vZMZwsCAAAAAAAAANQEcbq0P7Xsbu1qzujoKKE6 AAAAAAAAACg4xOnS+9RysLWrOZpQXRY3ygAAAAAAAAAAqJWYON3U1NRAhaGhoR0VDhw4EGgz UGXz5s1um8OHD1s8Tgq7VMdx/uzP/mzx4sWFiliNj4+L5xgZrctrdBIAAAAAAAAAwCMmTucx MTHhRkx6enpUbbyUqP7+/jQcJ4Vdquk4gSBdEVaAiudIqA4AAAAAAAAAiglxutQ9tY0bNwZC VIXa/FQTqhseHrbdOwAAAAAAAACAZmEapzt58mR/hQ0bNuyqcPDgwUCbw4cPu202btzotnnm mWcsHieFXYo9zp/8yZ8EglN9fX3FCdK5zMzMdHV1RYbqBgYGbPcOAAAAAAAAAKApmMbpPEql khsxWbp0qarNrl273DYrVqxIw3FS2KXI44S3di1sWGp2dpZQHQAAAAAAAAAUCuJ0droUOM7c 3NxHPvKRQECq4BXZNKG67u7uouUYAgAAAAAAAEDuiYnTTU5OBn5y+vTp5RXWrl07ViH8qKef ftptc++997ptWnmcFHZJf5y/+qu/+sAHPuCPQ7W3t4+Pj4cfVTRmZ2cHBgYiQ3WFqtkHAAAA AAAAAEUgJk63bNky1a8mJyfdiInm4Xv37nXbtPI4KeyS5jhf+tKXAhGogmztag6hOgAAAAAA AAAoAsTpWtElVYMdO3YQezJBFaojpgkAAAAAAAAAuSEmTrd+/fr9FY4dOxb41fT09Ccr7K8S jphMTk66bVp5nBR2KfI4bO1aE6Ojo4TqAAAAAAAAACDHxMTp9u3b50ZDent7lYeosnr16jQc J4VdCh+HrV3rgFAdAAAAAAAAAOQY4nStfmqRW7uOjIxong54jI6Otre3R0brCr49LgAAAAAA AABknZg43dGjR3sr3HPPPRMVjh8/HmjTW2VoaMhtc+rUKYvHSWGXvOP83u/93uLFiwO5YASY amJqaopQHQAAAAAAAADkj5g4ncfExIQbCunp6VG18ZYl9vf3p+E4aevSoUOH3vWudwWCdCzY rANNqI7MRAAAAAAAAADIKMTpWtSlyK1dZ2ZmNJ0HDVNTU+IFjAzVUekPAAAAAAAAALKIaZzu ueee664wMDBwqMLzzz8faPPEE0+4bTZs2OC2OX36tMXjpKdL9957byCWJJqxtWuDiBeQUB0A AAAAAAAA5AbTOJ1HqVRyQyFLly5Vtdm1a5fbZsWKFWk4jsUuzc3NsbVr89CE6vr6+oiEAgAA AACAhifny223bxe3Dzc7zmsTEeuits2VnY77NA0Eb5TLn5h41XF6eh1n4qs98z8pNbfTAJBr iNM1sUsf/ehH2dq12czOzg4MDESG6rq6ugjVAQAAAACAih+Xy4v+w3H39uGpTW3l8vlAgx+U ywu/+JSmgeBEubzg03u925D7e5y5l4Zb0XsAyCMxcbrp6enAT06cOHFLhf7+/mcrhB81MTHh tlm7dq3bppXHSUmXPvzhD1999dX+sBFbuzYPQnUAAAAAAFArl8vl2w9ecJwOGV/rcObntgUa vFku/9q3zv8sABfVoHxlLO/LPc5/On3g//3hB+deXNeSZwAAeSMmTrds2TLVryYnJ90zkebh e/f+bFahlcdJQ5cOHTr0nve8JxCkY2vXpqIK1XV2dvLKAwAAAABAJLErW9dNzztOr6bBhXJ5 8cNn3LuPhxY75ct7dl0s33/jn751LiKoBwCghzhd8l2K3NqVUFELGBkZiQzVESQFAAAAAIBI vBJ1jmJla2yDsi+Wt9JxfjotM+mGz85/93O/FtkYAEBDTJxu37597vmot7dXeYgqq1evTsNx 7HYpvLUrSy9byejoKKE6AAAAAAAwxJ8NF7my1d9gfYfz1tnh8EG8pLwOx5l+pKOyoBYAoB6I 0yXWpX/zb/4NW7umAVWoTkB9QAAAAAAACKBf2eqvYacKw/lL1G3vdcrzpRZ1HQByB3G6xLr0 7ne/OxAVGh4e1nQSmsfU1FR7ezuhOgAAAAAAiMXLhnMUK1v9Db7c48xf3BNo4I/leUtfAQDq ICZONzU1NVBhaGhoR4UDBw4E2gxU2bx5s9vm8OHDFo/T+i4tW7bsmmuuIR6UKgjVAQAAAEDW mZ2dLUHDxJYh8mfDRS599Te4yXFe2fPhcEodS18BIBFi4nQeExMT7lmppydigxsXb71hf39/ Go7Tmi6xtWtqEe9CZ2dnZKiO9cgAAAAAkHJKpZJq4hlqQryS+pfanw0XGYbzl6hzFCl1/lje 2Cpxo3062c8DABQE4nQNdYmtXVPO7OyseEcir9aE6gAAAAAgzaimnKFWYuN0ZV+JOkcRhvMa LHacX/jgB8MZc7H7UQAAmGAapzt58mR/hQ0bNuyqcPDgwUCbw4cPu202btzotnnmmWcsHqfZ XQrvGsHWrilEH6rj/QIAAACAFDIyMuIOWUdHR62uGc0DJmP+J+fLbbdvdyo5dR/84C9MP/JL gTCcv8H//X93/MGCe8Mbv+r3owAAMME0TuchTnPuBWPp0qWqNrt27XLbrFixIg3HaUaX3ve+ 94WDPpoOgEXEhbmvry8yVEdoFQAAAADShhiguiteu7u7bfelKHjZcB2O87U/77in4/8OhOH8 y1q/1uss2XVg7+oF5fmT/jZeLM9R7EcBABALcbo6uxRgcHBQ89chDQwMDBCqAwAAAID04w1c Z2ZmbPelKPhL1D3S69z+xLOBMJy/wf0dzv/3H7YtuntvoJIdS18BoHFi4nSTk5OBn5w+fXp5 hbVr145VCD/q6aefdtvce++9bptWHqepXfrP//k//9N/+k8DgR72D80KmlAdVQUBAAAAIA14 2QPDw8O2+1IsvA1b1zrOmbPD4TCc1+Bmx/n/TfT0H7v479tuC6TdsfQVABokJk63bNky1a8m Jyfd64fm4Xv37nXbtPI4zevSt7/97fDWriWDoqSQHrxKHwHYpRcAAAAA0oBbW7mzs5M1Hy3m RLm84NN7vRDb8Nn51QtWz724zmsQWNZ6tvzaL205Hth0wovlCY7/xYJy+U0bTwUAMgxxOtMu hbd2FddOIjtZxNu9l1AdAAAAAKQKb6Q6Pj5uuy+Fw79q9alNbW+Wzy/51rnh29reOrct3MBd 1vrcfPnqVWNjn23zVsj6y9ht73XK8yVrzwcAsklMnG79+vX7Kxw7dizwq+np6U9W2F8lHOOY nJx027TyOM3o0m//9m8HYjoUNcs0mlAdq5gBAAAAwApsH2Edb9WqG2Jzw3A7PtM2/5Mny5XU uF/71nn3xmGl4/x0WqbaDZ+db2v77MTGReX50+UrY3kPLXbKl/fE/EkAgCuJidPt27fPPcX0 9vYqD1Fl9erVaThOM7oUYGBggCBd1imVSu4wKAyhOgAAAABoPYODg+5wlEUetthzuewsfsgf htt1sdx221/e1+O8evQLZ8vzv7TluPse9TjOxWMry5XydV948aLTseqvPtM29+JX3NAecToA qBvidDXH6djaNTeIARChOgAAAABIA2Joyu2GdYbPzjsdn3Uqu7pOP9LhbiLxlXPzbbf9YeB+ wQvkCd4olz8x8WoldncFrHsFgDqIidMdPXq0t8I999wzUeH48eOBNr1VhoaG3DanTp2yeJyk DvXCCy+0tbURvsk3mlDdwMCA7d4BAAAAQFHo7u52KmVYWLjTelYeu/yZtvfd+Md/XAnQ/Yyn NrWVy+fdBqPn5q+++6/8Nwv+mnTlSjxv+KWLbbdt8hr4I30AAObExOk8JiYm3NNNT49yb2mv 5ld/f38ajtPIodjatTiIkZC7qRahOgAAAACwgnfbQlqARfacnR8e/urw8PCmr33t6rv/o7tT hPfb0/Pltf/1qBuJ+9pn2t46tyt8hNJPyit3HHA6VnU4zt6hRfM/KbWu9wCQF4jTRRzqr//6 rwPxGrZ2zTeE6gAAAADAFmwfAQAAHqZxuueee667wsDAwKEKzz//fKDNE0884bbZsGGD2+b0 6dMWj1Pfof7tv/23gUgNW7sWAfEWu2sNwvABAAAAAIDmMTw87A47yQwAAADTOJ1HqVRyryJL ly5Vtdm1a5fbZsWKFWk4juGhHn/88XCMpq+vjxhNcRgYGCBUBwAAAAAtY2ZmhmUcAADgQZzu Z7zyyivXX399IDrDxbKAaEJ1YhRlu3cAAAAAkCvYPgIAAPzExOmmp6cDPzlx4sQtFfr7+5+t EH7UxMSE22bt2rVum1Yep45DvfDCC4FdIxxquBYYb+lBADF+YjECAAAAACTF+Pi4O84cGRmx 3RcAAEgFMXG6ZcuWqX41OTnpXlQ0D9+7d6/bppXHqfVQO3bsCG/tKi6ZmoNA7vF2ICFUBwAA AADNYHZ2trOz0123YbsvAACQFooepwtv7UogBlw0oTrCuAAAAADQIN4ajlKpZLsvAACQFgod p1u9enUgBMN2AeBHjJna29sjo3UsiwYAAACAumH7CAAAiCQmTrdv3z73+tHb26s8RJXVq1en 4Tgmh5qbm/vVX/3VQOSFrV0hzNTUFKE6AAAAAEgWcevhsH0EAACEKGKc7pVXXvnIRz4SiLkw kQUqNKG6wcFB270DAAAAgIxRKpXcweTw8LDtvgAAQLqIidNNTU0NVBgaGtpR4cCBA4E2A1U2 b97stjl8+LDF4+gPxdauUAczMzNdXV2RoToivAAAAABQE+72EeKftjsCAACpIyZO5zExMeFG JXp6elRtvLr7/f39aThO+FCRW7sSpAMTZmdnCdUBAAAAQIOwfQQAAGgoUJyOrV2hQTShOnYg AQAAAIBYZmZm3IIqfX19tvsCAABpxDROd/Lkyf4KGzZs2FXh4MGDgTaHDx9222zcuNFt88wz z1g8jv9QN910UziwIi6Thk8fwGNgYIBQHQAAAADUgbd9BHciAAAQiWmczsMrerp06VJVm127 drltVqxYYf04c3Nz4V0juru7CalA3RCqAwAAAIBaYfsIAACIJedxuhdeeIGtXaEZDA4ORobq WEwNAAAAAJG4FVQ6OzuZ2QUAABUxcbrJycnAT06fPr28wtq1a8cqhB/19NNPu23uvfdet00r j+MdKnJr15GREf1TBjDEq35IqA4AAAAA9IjbEHesOD4+brsvAACQXmLidMuWLVP9anJy0r3S aB6+d+9et00rj+MeaseOHeHoCVu7QrJoQnVs4AUAAAAALrOzs+72Ed3d3bb7AgAAqSafcbo/ +ZM/IcUJWsP4+Lg76gpDXBgAAAAAyr7qxmwfAQAAemLidOvXr99f4dixY4FfTU9Pf7LC/irh QNjk5KTbppXHue666wLhErZ2haYiPrGE6gAAAAAgEraPAAAAc2LidPv27XMvKr29vcpDVFm9 erXd40Ru7coWnNACNKE6BmQAAAAARaa7u9th+wgAADAjP3E6tnYFu8zMzLh7eIXhcwgAAABQ TLxyxiyzAAAAE2LidEePHu2tcM8990xUOH78eKBNb5WhoSG3zalTp1p8nMitXcljghYzOztL qA4AAAAAXNg+AgAAaiUmTucxMTHhhht6enpUbbzJov7+/lYeJ7y1KxNWYAtNqE6Mz1jsAAAA AFAcBgcH3XEgO9oBAIAhmY/Tvf/97w9EQ9jaFazjbekVgGqJAAAAAAVB3JK4I8DBwUHbfQEA gMxgGqd77rnnuisMDAwcqvD8888H2jzxxBNumw0bNrhtTp8+3dTj/PN//s/DcRCCdJAGCNUB AAAAFBl3+4j29nbGfgAAYI5pnM7D21Z86dKlqja7du1y26xYsaJJx2FrV0g/qlAdKZ8AAAAA +YbtIwAAoD4yGad74YUXwstdqdMPKcQbohGqAwAAACgIs7OznZ2dDttHAABA7cTE6aanpwM/ OXHixC0V+vv7n60QftTExITbZu3atW6bBI9z6NAhtnaFDEGoDgAAAKBQiHsTd7xXKpVs9wUA ADJGTJxu2bJlql9NTk66lx/Nw/fu3eu2Seo44bWuJJND+hEf0fb29shoHZ9eAAAAgDwxMzPD ch8AAKibjMXpwhlJTFJBJpiamiJUBwAAAJB72D4CAAAaIRtxuv/yX/5LOLrR2dnJskHIEJpQ 3cjIiO3eAQAAAECjjI+PM7oDAIBGiInT7du3z73S9Pb2Kg9RZfXq1c04Dlu7Qm6YmpoSH93I UB0rIwAAAAAyjbd9hBjv2e4LAABklbTH6VRbuxKkg4wiPrqE6gAAAADyB9tHAABA46Q6The5 tevg4GDMcwJIN5pQXV9fHzFoAAAAgMwxMzPjVjgRwznbfQEAgAwTE6ebmpoaqDA0NLSjwoED BwJtBqps3rzZbXP48OHGj7NmzZpwFIOK+5APZmdnxUc9MlTHmm4AAACAzNHX1+dUto+YmZmx 3RcAAMgwMXE6j4mJCTeI0NPTo2ozOjrqtunv70/kOH7Y2hXyB6E6AAAAgBwg7lPcUdzw8LDt vgAAQLZJXZxubm7utttuC4Qt2NoV8ooqVMdnHgAAACAruNtHiH/a7ggAAGQe0zjdyZMn+yts 2LBhV4WDBw8G2hw+fNhts3HjRrfNM888U9Nx2NoVCkhkAqmbQ0qoDgAAACDlsH0EAAAkiGmc zsNL6l66dKmqza5du9w2K1asMD9O5NaulNWHIkCoDgAAACCLiFsVto8AAIAESUuc7tvf/nZ4 a9eBgYFauweQUVShOoftUwAAAADSilfDhO0jAAAgEWLidJOTk4GfnD59enmFtWvXjlUIP+rp p59229x7771uG/1xPv/5z4djE9u2bWvkiQFkjqmpKXc+llAdAAAAQPph+wgAAEicmDjdsmXL VL+anJx0L0uah+/du9dtoznOJz7xCQITAC5pDtVdLpdvP3jBcTpEZ1Y6zk+n14XbvFEuL/nW OdFGNNoxtOits3ta308AAACA1tDV1eVUto+gUA8AACSFzTjd3Nzc7/zO76jW+rFfEhSTqakp d8uwMNZXgm+bKzsd9zmVWN30Ix2V2N0VPDlfbrt9u9fhVR3O+cn+cDMAAACArDMyMuIOeMbH x233BQAA8kNMnG79+vX7Kxw7dizwq+np6U9W2F8lXPB+cnLSbRM+TuTWrgG45kExmZ2ddadn wzzwwAMWO/aDcnnhF59yezK2Spw9TgcaDL9adpxV/g7f5Tizx1Za6S0AAABAk/C2j+ju7rbd FwAAyBUxcbp9+/a5N9u9vb3KQ1RZvXq14XFeeOGF8K4R/+Jf/IvAT7jsQWHRhOoGBgZsFSq+ UC4vfviM2437O5z5uSuKSIYXxp4XP9n98mfbbpt7MWKRLAAAAEBGYfsIAABoEhbidKqtXcVF LhyS4MoHhWV2dravry8yVDcyMmKrV+um58X3WPThZsd5baLH/yt/tt32Xqc8XypXKtZ9YuLV P1q06K1z7AwDAAAAeWBqasod8LB9BAAAJE5MnO7o0aO9Fe65556JCsePHw+06a0yNDTktjl1 6pTqOD09PeGgg1cgPxyVsF6QC8Au4ay69vZ2i7WKvQp04RJ1ql+9US6vPHB+ZvftFKoDAACA HNDd3W19SAYAAHklJk7nMTEx4cYIenp6VG1GR0fdNv39/ZENVq9eHY44+Hex9LY298P1DwqL V/rEj92Z2x+Xy4v+w/FA0pyLJtUOAABywy233OJeBQ4dOqRqs3z5crfNrl27UnUcVQMAc7xb Hv9dDBScU6dOuZ+KG264QdXmtddec9ssXLgwK8cBACu0KE4XubVre3t7eOuJcPYQ+eRQWMSH P/B1sL4PcrgIXfjn4dJ1AACQG4jTQZFh+wiIJG3xNeJ0AJnGdLzy3HPPdVcYGBg4VOH5558P tHniiSfcNhs2bHDbnD4tt4OM3Nq1q6srsvacF+xLT2ACwAqRFRvTMHO7ba7sdNwnOnOT45z6 +o2VHDtdnh0AAGQOdyD393//9+FfrVu3zh3v7dixw232D//wD4E2DzzwgNtm69atqkNZOU6t rwNAgMHBQXe0E842gCLzP//n/3RPMitWrHBPRMeOHQu0ETfFbpve3l63zdNPP52q4zT8MgBA MtQ8r+gtTV26dKmqza5du9w24rwQubWrOB1oVrOGF/qlITYB0GK8fcTSFrM+US4v+PRet0sd jjOxcVF5/rRXnE5w/C8WlMtv2u4mAADUj3s+37Fjh6aNW6JLcPDgQVWb/v7+2EO18jgAjeBt HzE4OGi7L5BSXn75ZfdD8oEPfEDV5sKFC26bBQsWpOo4qgYA0GKaG6f7V//qX0Vu7ar/E+G1 fl1dXbX2EyDT1JpMNzs7K75ZrSnmeKFcXvzwGbdLPY6zaPnyHZ9p+8qPvu8m2VGcDgAgBxCn AwjD9hEQC3E6AGicmG/j9PR04CcnTpy4pYIYMD1bIfyoiYkJ0eD9739/ONAwMjIS2ydx5Qs/ sFQqmT4ngOxTUzKd+Ha8613vEm0+8YlPtKZ73pYRKx3nBy+uu/HPj7qV6QRrHefyq8Ot6QYA ADQJd7D3la985dkq4Tbr1q1zm+3YscNtE15w+sd//MeBQ9k9DkDdjI+Px86bQnFQnYj+7u/+ zj0R3XXXXW6b733ve4E2b7zxhtvmN37jN1J1nBqePwA0k5g43bJly1S/mpyc1MTdY7d21RMO UsRm4QHkBvNkutnZWa9OisvHP/7xFvTQW+Xa4TjTj3ScL8/fvvvlSnYdxekAAPKDfzymaXbn nXe6bfbv3x97qJQcB6AmxIirs7PTYZUPVIk9Ef3whz9027zvfe9TtXnzzTdTdRwASAnJx+nm 5uZ6enrCQbqaiq161R/8RO47AZA/DJPpxNckvD+y4Bd/8RfHx8eb2sMflMsLv/iU++fcwNzl cvkLL150OlZRnA4AIDcQpwNw8crysMQHXFIVXyNOB5AzYuJ0+/btc7/Pvb29ykNUWb169Suv vGK+taser8iIBxVboQgYJtOFyzj6Eb9taicvl8u3H7zgrnW9v8OZn9vm/vwr5+YXdt3nbQIL AAC5wbvERC6tcvnkJz/pthkbG8vKcQBi8cZmrO+BAN6J6Fd/9VdVbV599VW3zXvf+96sHAcA 7JJknO63f/u3w7tGdHV11Vdp1asB4UHRVigCscl0YrAYjmL7ede73tWCb8q2uXLkxhGj5+YX 3v0ldxPYZvcBAABaRlJxsbQdByAWto8AFUnFxdJ2HACwS5Jxune84x2BeEGDk05uGQg/1G2F fBObTCf+WwwTw238NDuZzuVEubzg03vdv/jUprZy+bz3q+9cLF9996Nf+0zbW+d2taAnAADQ AryrTEria0kdB0BPqVRyP0Umu+FB0fBORCmJryV1HACwS0ycbmpqaqDC0NDQjgoHDhwItBG/ /fVf//VmBAvE5TBwTM2WlwA5QJNMNzs729fXF/6iBWjZZO+Fcnnxw2fcPxreO2LXxXLbbX+5 qsM5P9lfWSYLAJAWvJ1wNAQyhaFcuUK5fOUrX3HHhH//938faCNGbm6bhx56yG3zwx/+MFXH afhlgMLh5g1wDwKReCei9evXuyeZ//bf/lugzT/8wz+4bdasWeO2+cY3vpHy4wCAXWLidB4T ExPuyLWnJzhsDW/t6iSU+DY7OxtOHWp2gXwAW0Qm07nlisU/Y9PoXFqTTOeybnrecXrFH13p OD+dXhf5W/Hro3/TwxpYAEgbl8vlbacvdgw+4j+FilPWf/tqz1tn99juXaq55ZZb3Jfr0KFD qjbLly932+zapUystnIcVQOASNg+Agw5deqU+1G54YYbVG1ee+01t83ChQuzchwAsEJDcbpE tnbVMzg4GDh+d3d3UgcHSBXhqnPiJ7Ozs+FvgYoWV07xclIWO86P9tzo/5W4Ae4/dlGcMNyO repwdu9YOXd6W8v6BgBgwnPz5atXjXln0bFVYlzEvEIMxOmgIMzMzLizpH19fbb7AmknbfE1 4nQAmcZ0vHLy5Mn+Chs2bNhV4b/+1/8aubVrgkG6siLDqI7dYwFSjlf9xM+jjz4qvlPRMTnH ueqqqwI/0STTuctmky2t8oNyeeEXn3L/tFeiTr+gjKVkAJAq/LtXO1Gr+AuLO9gLrzYV/PEf /7E7Jty6davbLLzg9Mtf/rLb5qGHHlIdyspxan4hoMC49Uba29u59YBY/u7v/s49yXzuc59z T0T79u0LtPnJT37itvnMZz7jtvnWt76VquM0/DIAQDLUPK/oRRPa2trCQbpm5PKEa3KxJzrk j3AyXXgfFT8f/ehHAz/RJNONj49fffXVoo34Z4JjTf/97f0dzvwc6XIAkD38swuRq/iLifuC 6Au6eVeugwcPqtqIO8DYQ7XyOACGeLc8rawoAjng5Zdfdj85H/jAB1RtLly44LZZsGBBqo6j agAALab+OF3LYmeRf5Ft0SFPqL5WkbS3tz/22GPhcnWR48jwstn3vOc9CdZY2TZXdjrucyoL XC8eW5nUYQEAWoZ/Vxxyfj3cF4Q4HRQWto+A+iBOBwCNE/NtnJycDPxk3bp14cBBsyeawkv/ mNqCPBFOplPR19c3OzvrVTX2iEymK5VKqqS8pOo8enkoHY4z/UgHW7sCQOYILH31VvEXnOUV HnroobEq4TYPPPCA2+xrX/ua2+b8+eBL9+UvfzlwKLvHATBhZGTEPSGwfQSoUJ2IXn31VfdE tGrVKrfNd77znUCbS5cuuW1WrlyZquPU8PwBoJnExOmWLVvm/9/mbe2qR/yJwB9ldgtyg2Ey XXt7u1tdLnIf5HDkOhzL83Prrbcm0nl/Hgp1nQAgo3ipwQ6r+K9kYGDAu3Bomt15551um/37 98ceKiXHAVDhDbTYvA40xJ6IfvjDH7pt3ve+96navPnmm6k6DgCkBNM43dzcXHjXCHENa9ks Uzgw0YL4IEALMEmmE228unLhAFxnZ6c/mW5qakqz+4RLgt/cddPzjtMrjrnWcS6/OpzUYQEA WoZ/V5weVvH7IE4HBcT7jLF9BGiIPRERpwOAuomJ061fv14Mcb761a9+8IMfDIcGkt3aVU84 NtHV1dWyvw7QJEyS6fy5cpE7IPtj1iMjI+GgdoC+vr4En8Key2Vn8UMOdZ0AILP4U4NZxe9H XFM+WWV/lX/4h38INHvggQfcNl/72tfcNuEFp96hUnIcgEjYPgIMCZyIDh06FGjwv/7X/3Lb fOpTn3LbTExMBNr84z/+Y6qOAwApISZOt2/fvuuuuy58n9+krV01iD8X7gY1IyDr3HzzzZqA mviiBaLh/tQGF28NuPiOGNa5S3Z+2J+HQl0nAMgoXmqwE7eKf8/l8iPv35L7Zf4mV5MsYvt1 hbTjrkgIrFQACGD7TNZEbL+0ACCJ+Sq6MYJALtvAwICVS1c4QtG8TWYBWoA+mW5wcDDwRdMk 042Pj8em0TXpW+Mvwf7QYqd8eU+yxwcAaAHerjhO3Cr+E+Xy//XhPbmvYWdyQckitl9XSDXe 9hFiWGW7L5Bq7J7HmortlxYAJKZfRS9GNjg42Mz+6JiamgqfSqgcAdlFfHrD0WdHXfkxMplu dna2r6/P/Oqr/8rUF4IfflWMV1aJg690nJ9Or6vjCAAAdvGnButX8Z8olxct35v7Zf7ml5Vs Yft1hfTC9hFgju0zWROx/dICgKSGr2JXV5f1rRvCy/osxg0BEuHkyZPveMc7vI90X19fZLAs Mplu8+bNnZ2dqgut/7AummS6V1555eabb65vbOrloVDXCQAyij812NGu4nfPeMf/YkG5/GaL O9lK6rvBSz+2X1dIL+Kewv2QkAQAsdg9jzUV2y8tAEgy9lUcHx8PnEra29upHwFZx10A+/M/ //OaUHg4me7d73635irr7XbnRzX0FN+sBQsWuG3qiH1vmys7Hfe5D6dEHQBkFP+p7P4OR7Wy 1a1kp69hlwNqv7nLBrZfV0gp3qodMgDABLvnsaZi+6UFAEn2vorh7CHrWX4AjSM+xpr528hk OhXiO1IqlcLflMhkutnZ2XAEcNmyZSaTySuPXXacxYHHam5uAQDSjL9EXeQq/tPz5bX/9Wjb bZucuBp2OcD8opMtbL+ukFLcJTtM/4Mhts9kTcT2SwuQXsJfk4mJiW9U2LBhw+9WOHXqVOBR Tz/9tNtm48aNbhvxk/i/lXDfm49X4dXD2+8SIK9ElrGLxN3mZXR0NPyrcOgtMpznsnDhQsO+ 7Tk7Pzz81eHh4T/60pc6Bh/R13UCAEgtF8rlxQ+fMTzZ5v5cZ/g6ZA7bryukEW/UxNw/GGL3 PNZUbL+0AOlF8zXx6rMdPHhQ9fC7777bbbNjx474v9VQT23gFXn1w65MkGMMk+nE98L7Ipgk 0wX2cQ7wqU99qsVPEwDAOu6aVvc0GN7A+nK5/PDpiz1f3e04PQ7L/AFyAdtHAACACcTpYvDq vHpwZYUcY5JMJ74C3kqN2GS6qamprq4uzdGuvvpq1n0AQAHxl6hTbWB9uVz+wosXnY5VuS9R B1AEvNsKMTqy3RcAAEgvmjjdE088MVphaGhodYUXXngh0ObQoUNum82bN7ttdH8ryY63isj0 IvZmglwSm0zX3t4+MjLif4g+mU40DmekBhgeHm7tswQASAU/KJcXfvEp90y42HF+tOfGyGZv lMufmHh1pdMTGcgDgKzgjbIia/gCAAB4aOJ0Ht5ejo8//riqjZeFo/tb9XfTKn19fZpIBEBu uPnmmzUBta6ursD0ryaZbnZ21svI1UARZQAoLJfL5dsPXnCcDvd8qFnZ+tx8uWPl3+a7RB1A 7mH7CAAAMIQ4XTylUikcX+ASCzljampKE1CLzHoLJ9MNDg6Kn4+Pj8em0WkOCwBQEPQl6gIt n/z8L9ktUWdyVgcAAEgnFi+gALUS/gAfPXpU1fiuu+5y24yNjWkOqPxVo521R7jAFvEFyB+l UulDH/pQ4KPe2dkZWUUlvBtye3v7K6+8Es4/VaGfUhZ/VPSnac8VAMA+T86X227f7p4SVSXq UoLhiR0AACDN2L6cAhgR/ugSp4sgvL6vvb3ddqcAmoKXQOtU8uMiQ2mRWyEPDAyEM+w8fu7n fi7wE02w290fduHChdSCBIAcc6FcXvzwGfeUqClRZx2TOx8AAID0Y/uKCmBE+KNLnC6acFRi dHTUdqcAkmd2drazs/Nd73rX+Pi4qo0bR/OzYMECzRUxXKtOlUw3MzPjbyx6whpzAMgxK49d rsToJJoSdXaJv+8BAADIAravqABGeJ/YySr9/f1LKzz//POBxi+99JLbZu3atW6b7373u4E2 4rfKv5V891tIODDR1dVlu1MATWFmZkYTHYtMplPR2dm5f/9+wzC3+GG45Tvf+U7x7SNaBwC5 ZNtc2em4zz3d6UvUBdhzufzI+7eU50vN7N3PqOEGCAAAIMW04KIJ0DjhT+zHPvYx9yeHDh1S PWr58uVum927d9fwtxrqqW1mZ2fD33PqZ0EBCcesVQwMDIgvjrfLjEdnZ2fgmKKZvrAdC2AB IJf8oFxe+MWn3BNdTSXqTpTLN964pzUl7QzP+QAAACmnBRdNgMYJf2KJ0ykJhxv6+vpsdwqg pRgm04k27rLZmZmZ8G8DyXSlUkl/zBUrVlh5sgAAzabuEnUnyuWr7x6bfqSjXL7c1B6WidMB AEBeaPYVEyARwp/YEydOlCoMDAz0VDh+/HjgUd///vfdNhs2bHDbHDhwIP5vJdz3ljM1NRX+ qpPmA4XCJJmuu7vbW6aqT6YTzQYHB2MPyLcMAPLKm+Xyr33rvHe6My9R5y6Y3d7rtGDpK/c5 AHXgDZlYfwNgC65fkFE0n1ivmPvBgwdVD7/77rvdNjt27Ij/Ww31NB2Ey+EPDg7a7hRAi5id nV24cKEmoNbe3j4yMuK11yfTTU1NdXV1aY7mMjAwYOO5AgC0gsvl8u0HLzhOh3vGMyxRd7Zc /qUtx50al8rWDfc5ALUihkDuWgEW3wBYhOsXZBTNJ5Y4XQTj4+PhwAQV7qEgbN68WRNQ6+rq mpqa8rfXJNOZF7kjmQ4A8splcTJ86WLbbZu8M15s3O18ufyH+166+u7/6LbvcJwWLH0Nn5mb +ucAcoBbdVfcJjCMAbAI1y/IKJpP7HPPPXegwuc+97mPVzh27FigzcmTJ902Q0NDbhvd30qy 4/bo7OwMfNv9CUQAOSb84fcYHh4ONFYl04mfh/NSVZCvCgC55ES5vODTew3PhHpasPQ1/Eeb +ucAsk6pVHK/KeHREQC0Eq5fkFFMPrF33nmn2+bxxx9XtfHyZnR/q/5upomRkZHAtz28eSVA Lpmdnd28efNVV10V+PxHFl6JTKYbHR012YbCRZ+sOjU1RRQPAKAFcJ8DUBNuWQ9uEACsw/UL MgpxupqJ3O/S3doSoAjs37/f++QPDg5GhtIik+n+5b/8l6qQ3MKFC9/5zncGfqiZhfaWzZLN CgDQbLjPATDHm9Hn7gDAOly/IKN4n9hlVV5++eVAm6NHj+6rsH79erfNs88+G2gzNTXlthG/ Vf6t5LtvifAOld3d3bY7BdA6xFdg4cKFmgFoOJkukIXnp6urK/ydUiXTBZbN/tzP/dyjjz7a vGcKAADc5wAY4k3nc2sAkAa4fkFGCX90jx49qmp81113uW3GxsY0B1T+qtHOpobIXKFABX2A fKNZkRr5BVExPDwcmaMamUynWjYrRsN8AQEAmgT3OQCGePOUbB8BkAa4fkFGCX90idMZ4e7i 5EdcmG13CiAVhJPpIuns7HSDa+G9X8PJdOJ/w186P5E18gAAoHG4zwEwge0jANIG1y/IKN4n dm+VtWvXrqjw4osvBhofOXLEbXPvvfe6bZ555plAG/Fb5d9Kvvv28K7EfjQZRgAFwTCZzits Z5JMJ75u+t0nPvrRj1p5sgAARYD7HAATvO0juCMASAlcvyCjhD+xH/vYx9yfHDp0SPWo5cuX u212795dw99qqKfpw70Ya4ILAAXEXzwukvb2dn9hO30ynfiPcOm6MCTTAQA0D+5zAGKZmppy vx2jo6O2+wIAP4PrF2SU8CeWOJ0p4jIcji/Y7hSATSLzTP309fX5J5n1yXRiyNvZ2ak/oEOp ZgCAJsN9DoAJYtwyODhouxcA8DZcvyCjhD+xExMT36iwYcOG361w6tSpwKOefvppt83GjRvd NuIn8X8r4b6ngHCIgTk0KDIf/ehHVdE08WUZGRkJtNck04V/pYJkOgCApsJ9DgAAZBGuX5BR NJ9Yb/nawYMHVQ+/++673TY7duyI/1sN9TSVhEMJXV1dtjsFYA3VDhLiexHe+EyVTCdahheV qyCZDgCahDgXjY+Pi5MS20lznwMAAFmE6xdkFM0nljhdPLOzs+EvP9k9UGTEne11110XCL1F toxMpvvzP/9z/ZYR5l83cWstzmLcYAOAIV5gTpw6/CciLuvc5wAAQBbh+gUZRfOJfeKJJ0Yr DA0Nra7wwgsvBNocOnTIbbN582a3je5vJdnx1BBOIOrr67PdKQCbzM7O/uIv/qL4Llx77bWq +9vIZDpNNTrR+IYbbgj8UJNM5wUBSXEFABXiRCTOUeJ0IS7cmhkC4nTc5wAAQBbh+gUZxeQT e+edd7ptHn/8cVUbL1ql+1v1dzPFeLs7+Qkv8QMoFOK2dnBw0L9lRIBwMt1VV12luknu7u7e vXu34c1zeNmsuAMXP9F0BgAKgj8wZ7JNjeZUUyjCr4ntHgEAAMRjeKHPNLZfY2gKJu8vcboY vBXCHmz2BKBhZmbG/NrjLpsNf8siE1dHRkZUSTHinpw1sABFo77AXADidOHXxHaPAAAA4qnv up8t9BlCgd8m2DiQA6FJiZitoPpffR9qapwnvPdX04Y4XQzj4+OBb4u3ZyUAhFFtNxGgq6vL jayFv2Lha5L4xon7cP0BxReTUB1A7imVSiMjI+I8U3dgLoBhnK6UX8KvSZPfQwAAgARIZBiQ fjThKjEWuuaaa26qIP7D0V7Bxb2Sv7H4X/1re+2114r7Nbc2kaoiebmSSCEavP/97//IRz4i /in+e3R0VNV4cHBQNPjQhz700Y9+dPHixeK/xZ2gqrF7UxnO58glR48eVb0Od911l9tmbGxM 834pf6X6RQ4I3wyIj6PtTgGkEcNkOn9Savj7JU7K/mOK07fh7hOimeZcDwBZpFQNzJlvFQ0N Yvs9BwAAiMf21bJFqMJekbddJcXsY2Q5L1WKw/79+wMtf/EXf1H1Ltx6662BxsuWLVM1dgN5 fv7gD/5A1TipGdlMQJyuHtwgsR/xobHdKYA0EptMJ747/uuHuPCE23izRrOzs+6sS01o5nAA IP0QmLOO7Y8AAABAPLavli0ikMHgEXkbpUp8Cwc0HHXuUbjUuKMeG0RuHhjZUtzZhQ+r2hWw pkpKOYA4XT1Ebl5J2g5AgNjzaV9fX2DNuCaZTtyra2ZRxDn9m9/8pirPjoxXgGzhBuUJzKUE 2x8HAACAeGxfLVuEKuwVmR7R3d0d2TiyglBkQfByVOlwR5GpF5mm5yjW6kYWOxJElhSLjELm j8kq/f39Sys8//zzgZfipZdectusXbvWbfPd73430Eb8NvKtLOc7TleuLqX2o/oOABQWTTJd 5IpUTTJd5DSOh7fbrLg2qEJ1qqknAEgt4usvThTi6y+usIar3aEZ2P4gAAAAxJPX61d42jIy 7KVKaIg8ZuSwSlWiLvKwkZl6kWl6jmJ5k2qZVGT+k2HF86zjPd+Pfexj7k8OHToU+aYIli9f 7rbZvXu3qk3Eu2neNItEJgpRtB7AT6lUClcocCpBbcOrizgji6+VJqdGXE4CkzniyKr2hOoA Mg1hO1vYfucBAADiyev1KxzPCoe9NMuYwolvqqw3JyqgEbnBlKPIUlJt9Bd5F6a6ZfMXLvco SHE67/kSp6uf8KeQKABAGP+8iri1Vi1BjUyme+CBBzR34+Flsy7ih6rzvvg5uzMD5AN/2C7B 0Zs4RyW7fart16lmwq+J7R4BAADEk9frV3h9aDjsoFkWGk58U2W9OVHFgjSrmsJdVd24hdfq RhancwmXqAtHIVVl7DJK+CU9ceKEO4wU73VPhePHjwce9f3vf99ts2HDBrfNgQMH4v9Wwn1P H5GhZUIAAGHcWaDFixdrck7Dt9nXXnut6vQdu5Gr+Caq5nMI1QHkEvG9FtdlMZoU3/1GwnZZ jKwlS/g1sd0jAACAePJ6/QqHtMJhL82y0HDim+ouyYkqUadZ2BQYMmnS9JzQWl1VcTqXwM1a OAoZmXOXXbznFf6VVxzw4MGDqofffffdbpsdO3bE/62GepoRwp9a1Y4qAEVGnGr1OznUVBlU tWw2jOqKJa5trFIHyDd1h+2I04VfE9s9AgAAiCfH16/YEnX6oU7gaJrlSoESdZqsNycU+tCk 6Tmhtbqq4nQugYSM8D1dzvbwVL1TZeJ09REOLqiKLwKABvO76Fp3blVdA8RXlVAdQHHwh+30 28gSpwu/JrZ7BAAAEE+Or1/6EnWa4nThsY0+6825skSdPustkKmnSdNzQmt19YOxQLpc+FYx Z6ujvOcV/tVzzz13oMLnPve5j1c4duxYoM3JkyfdNkNDQ24b3d9KsuMpJvyhidzNBABUGCbT ibN5fZE11fFjF88CQI4RY9aRkRExagyMFGudDMgf4bOl7R4BAADEk+Prl75EXezNlD/xTZ/1 FhgI6bPeAq+wfo8v/1pdfZqec2X5udwXpytr43Qed955p9vm8ccfV7XxEg91f6v+bmaKcGHF /H1uAJqHOE2bJNM1uKJcc/UisA4AZV/YjnNC+Dxpu0cAAADx5Pj6pS9RpylO5+JPfNNnvTlX lqjTZ705vky9cJree9/73sBPvLW64bDjNddcE/iJlzGX++J0ZeJ0zSAyGMyqGQBDNFsIuYiL UCJfKHHxUE3ykD4DAOARPkna7hEAAEA8+b5+aUrUhZMeFi5cqHop9Flvjq+QVzjQ8c/+2T8L /MTLpQin6X384x8P/MS75wqn6d16662Bn3jLnnJfnK7s++guq/Lyyy8H2hw9enRfhfXr17tt nn322UAbccPrthG/Vf6t5LufVsIfnfA+KQAQRpz93/nOd0ZcH6qIL1eC1Qc0obrw7uYAAMUk fIa03SMAAIB48n39UpWoi1wWGk6ac/Mewllv3RUCP3RrDYWz3lasWBF+uNu98F8MR+68IEk4 5vjQQw8FfuIlzeW+OF056qN79OhRVeO77rrLbTM2NqY5oPJXjXY2O0QWbjTcjxKgyGjqIzSp eJz4YqrytwnVAQCU836fAwAAeSXf1y9VibrIZaHhmyw38S38c/GT8PImN/EtMjIYjpq53Qsn Q5RD74ibqRe5hldVhK4IxenKxOmaRzgInb9V0wDNQFwGFixYEPj6iC9U8+ZJxJFVoTrx8/zN zwAA1ET43Gi7RwAAAPHk+/qlKlEXuSw0Mm+uHJX1JlqWSqXAD93Et8iVtuE/Jx6u+nORmXqq gGNk3lwRitOVfR/dvVXWrl27osKLL74YaHzkyBG3zb333uu2eeaZZwJtxG+Vfyv57qeY8Ket vb2dG34AE1555ZVrr73W/eIsXLiwBdXixHdTVUKVUB0AFJzwidF2jwAAAOLJ/fUrMnCmWhYa meAW+KFXii4ylBH4oRsWDAfOhoeHVel7kZl6qgW8kQHHIhSnK0ftI/Gxj33M/cmhQ4dUj1q+ fLnbZvfu3TX8rYZ6mkHC3xCK0wMY4k7CLF68uJULxlVbI4nvsluUAQCggITPirZ7BAAAEE/u r1/hCFe4rJu3LDSclPDoo48GfuIVjAsnvn35y18O/MTNegsvRBWPVZXDi8zUU22IEZk6V4Ti dGXidE0l/MHyb5YMAHqshMbCVzuX9vZ2QnUAUEzCp0TbPQIAAIgn99ev8Bq+8Dap3rLQmjZg DSe+/eZv/mbgJ27WWzkqPykyd88l8PPwRrRezCScwfcrv/IrgZ/ksjhdOSpONzEx8Y0KGzZs +N0Kp06dCjzq6aefdtts3LjRbSN+Ev+3Eu576hEfrPAHNJdpmQB5Ihxh9643fH8BoICEz4e2 ewQAABBP7q9f4UjWNddco4o/hGvGvfe97w38xMtLCCe+hSMb3rIn1ZokD28T2HJUpl4A/1Z+ qhriHrksTleOitN5eC/gwYMHVQ+/++673TY7duyI/1sN9TSbhHNz/J9RAEgnqlCd45s4AgAo COEzoe0eAQAAxFOE61dsJMu/LDQca/PjFadz0R/Wv1JQc+vk4hancwln6gXw322pljp55DWL wnuC4V8Rp0uA8GptxxelBoDUIr6nqisZhSYBoFCET4O2ewQAABBPEa5f+khWYFmoat88F684 nYs+8c2f9RYZ9PDjFqdzCWfqBfBXJw8v7A2Qy+J0ZW2c7oknnhitMDQ0tLrCCy+8EGhz6NAh t83mzZvdNrq/lWTHs0M4C9T/mQaA1KIJ1fEtBoDiED4H2u4RAABAPEW4fukjWYFloeESdX4C uQj6xLfAGqNwiTrNy65pGSjoH17Y6yevxenK2jidx5133um2efzxx1VtvGCU7m/V380sExkw zmvcFyBnzMzMqJLJCdUBQEGIHXADAACkkCJcv/SRrMCy0HCJOj+BZX/6xDd/1ltZW6IuXPhL k6kXvsPSLOzNa3G6MnG61hD+bPlXaANAmhEXP9XlQfycmDsA5B7V+BgAACBb2L6iNgVNJCt8 q6JaLRQoTueiOmwg662sLVEXDn1oMvXCpcA1C3vzWpyuHPXKHz16VNX4rrvuctuMjY1pDqj8 VaOdzSzhT23k1wAA0om4wqmqORCqA4DcoxofAwAAZAvbV9SmoIpkRS4LVd3UBIrTuagS38JZ b5oSdf7idC6aTL1Aml5Zu7A3x3dh4SdLnK4phBdss2skQLZQpXOLbzebwwBAjlGNjwEAALKF 7StqU1BFsiKXhapK1EVulKdKfIsMZahK1EX2ObJlOE2vrF7Ym+PidGXidC0j/BHP9wcLIJeo Zqva29sJ1QFAXok87wEAAGQO21fUpqCKZEUuC1WVqIu8l1ElvoWz3sqKnIZwcTqXyEw9Vfnv yIW9OS5OV/YNvSar9Pf3L63w/PPPBxq/9NJLbpu1a9e6bb773e8G2ojfKv9W8t3PDpFfnnAK KACkHFXxhfb29hyXSACAIhN50gMAAMgctq+ozSIykqVaFhouUaepyhU+bGTWW1lxl6Sqyx+Z qadacRiZKpHvO6/wJ/ZjH/uY+5NDhw6pHrV8+XK3ze7du2v4Ww31NPuEA8yRi8ABIOVo6qSy nh0AconqpAcAAJAhbF9Om8Xv//7vB57pddddp2rc09MTaHzrrbeqGocjgMuWLYtsGVmiTpWZ FJmpF5mmV1Ys7M1xcboycbpWEvnBVX0WASDNTE1NqTZLiizuAACQdSLPeAAAAJnA9lW0ubiR rEWLFv3Kr/zKdddd52iXhbol6t7//vd/5CMfEf90tPcvbuKbv7EmL6Gzs/Oaa665qYL4D/3L Ln7rb6xK0ytX1yZeffXVN9xww+LFi50C1BALf25PnDhRqjAwMNBT4fjx44FHff/733fbbNiw wW1z4MCB+L+VcN8zSHgZdr6XVQPkGE2oTlVbAQAAAFLOzMyMuMkRt6biaq7a6zCjiHGLeEbi 7kPck4vnmO9sFIBCIb7O/gSgwP+GG/u//oH/baRx4I/qc5Jqahwon5f7yuDeeTv8K+/CdPDg QdXD7777brfNjh074v9WQz3NBeGMTXG95BoJkFHE5SSyGIRDqA4AACAjiPu90dHRwcFBcfOj moHLK52dnX19fcPDw+ImhVsSAICU4J2lw78iTtcUwtsVs0oOILuIQa0qVCd+zpAXACCWJgQf UoHt1xWUiKvz+Pj48PBwAQNzerywHZvdpQfbH4pmYft1BUg1mm/Kc889d6DC5z73uY9XOHbs WKDNyZMn3TZDQ0NuG93fSrLjmSVcgV6zEhsA0o8Y7otBbeQQhFAdAEAsTbwRtIrt1xWuYGZm RgzCBwYGVLNrSeGuLXUZHBwcriL+eqlehn24q3FdmvpEBO5TINXOLs1+l21h+3UFSDUm35Q7 77zTbfP444+r2nh7mer+Vv3dzBHiOheeuMv3psIARSC8obNLZ2dn7gsoAAA0QhNuAFOB7dcV 5Kjbjc2Fl7M0SFdXlxeG8wJwFp+m24GRkREvkNeMpyyeL3l2rSfZ9zE92H5dAVKNyTeFOF3C iItc4Dwlrqa2OwUAjRL+aru0t7cTqgMAUNGEG8BUYPt1LS6lUklckZPKm3NDcl48Tl/pPG2I 4Yebjpds9T1xqJGREcY2rSGRtyyF2H5dAVKN901ZVuXll18OtDl69Oi+CuvXr3fbPPvss4E2 4kTtthG/Vf6t5LufTcQFPnyq4lIHkAPCC9tdxMiYtFkAgEiadRdoG9uva7FwU+f6+voaDEW5 q1bdqFwuB+du8t3IyIgbuWvwQ97Z2TkwMMAIp6k0+B6lFtuvK0CqCX9ljh49qmp81113uW3G xsY0B1T+qtHO5ojwEjl2hwTIB6pQnUD8ynbvAABSR3L3fenC9utaCGZmZkZGRhpJnfMCc6VS qZhV2Pzb3Tbyge/r6xPHKeZr2FQaeVPSjO3XFSDVhL8yxOlagRgKhF96LmwA+UAMeVXz+ezv DAAQy94qa9euXVHh9OnTgTZHjhxx29x7771um2eeeSY9x2n4NQAdDYbn3FJro6Oj2VrE2hrE GEa8tn19fXVXuCNg1yS8E9F/+k//yT0RfeELXwi0uXTpktvmG9/4htvm937v91J+HAAI451R wyOQF198MdDYcCSj/FvJdz/LhMcWw8PDtjsFAMmgCdWRPAsAYMgtt9zinjkPHTqkarN8+XK3 za5du9JzHNVvoREaCc+5sTm2Lq0Jb5Pc+mJ2BOyaxKlTp9xX+IYbblC1ee2119w2CxcuzMpx AMDDO5F6P/nYxz7m/sRkJLN79+4a/lZDPc0d4cVx4q7edqcAIDHE6FZ1L0GoDgDABOJ0UK7W nqtjVaZXPY1QUeO4Mbv6KgBSwy5Z0hZfI04HkDjhE2kT/1bzDp1RwnNTlK8CyBPixkAVqhM/ 57YBAEDwuxUOHz4c/tXExMQ3KmzYsMFtJm4IA22efvppt83GjRtVh2r9cWp+FSCK8fHxcE3n WPr6+kZGRljT2jzq21FX3PiIR/G+NI4YQLonokceecQ9EYkXNtDmzTffdNvs3LnTbbNq1apU Haex1wAAEoM4XZDh4eHwrbvtTgFAkoixi7hhIFQHAKDCPSXu2LFD08bLpTp48KCqTX9/f+yh WnkcqJuZmRkxSK5prWV7ezupc63HXYmsGueoEOMf1sMmwssvv+y+pB/4wAdUbS5cuOC2WbBg QaqOo2oAaeNyuXz7QfGudYh3baXj/HR6XbjNG+Xykm+dE21Eox1Di946u6f1/YS64dsYRFyf wpeuUqlku18AkDCqdABxEzI1NWW7dwAANnHPh8TpoFxJoKsp6OOF52x3vOi4a5PreO9Ir2sE 4nTQGrbNlZ2O+5xKrG76kY5K7O4Knpwvt92+3ft2r+pwzk/2h5tBOuHbGEH47l1c4Wx3CgCS Z3BwUDVOJVQHAEVmtMLmzZtXVwm3eeKJJ9xmQ0NDbpsXXngh0ObQoUOBQ9k9DpgzOzs7MjJi nkBHeC611BGw6+7upvKPBs2JcW5uzj0Rff3rX3fb3HvvvYE2//iP/+i22b59e6qOY/4KgHV+ UC4v/OJT7hd2bJVTLgd3PB9+tew4q/zf67scZ/bYSiu9hVohThfBzMxM+HLFzBJAgvi/XHZ7 Et49xrvf4GYDAAqOf+ZS0+zOO+902+zfvz/2UCk5DmgQg17zCnSE5zJErbt/dHZ2Dg8Psxg2 jMmJ8Yc//KHb5n3ve5+qzZtvvpmq40CGuFAuL374jPue3t/hzM9t8/82vDD2vPjJ7pc/23bb 3IsRi2QhbRCniyZ8AQuX3gSAuvF/uWz3RRmqc9hGBgCKDXG6olEqlcyDOG7KFUGcLFJTtUEW w4YxOTESp4Nms2563nF6xXt6s+O8NtHj/5U/2257r1OeL5UrFes+MfHqHy1a9Na5bdFHhNRg /w45nYyPj4cvUQxEAJLC/+Wy3RfJ1NSU+I5HDk9HRkZs9w4gFQQKnXh44z/IN947/uyzz6ra fPKTn3TbjI2NZeU44DI6OmoYtXFzrIja5INSqWSeO9nX10fN7gDei/Orv/qrqjavvvqq2+a9 731vVo4DmcAbmIVL1Kl+9Ua5vPLA+Zndt1OoLuWk4g45nYQHK9yuAySF/5tluy8/QxOqE0NY 270DSAun58s3/vlRdyXFn/Q4TMkWB++UmJL4WlLHAfMIXV9fH+tbc0lNtQi7u7uJ1nl4L0tK 4mtJHQcywY/L5UX/4bj7bgYmTTWpdpAJ0nKHnELCS+HE1ct2pwCgiczMzHR1dUWOSgnVAXh4 iyki6xZDXvHOhymJryV1nMIyOzs7PDysmqDyI9oMDg6SQFcEzNPriNa5eC9ISuJrSR0HMkG4 CF345+HSdZAJiNMpEcOX8NiFYlUA+UZ88VWhOvFzFr8DlH3jPwZ/hWKySn9//9IKzz//fKDN Sy+95LZZu3at2+bgwYPpOU7Dr0FOMI/QdXZ2MvQtIG71OpNPCNE670S0d+9e90R09913B9q8 9dZbbhtxHnPb3HHHHSk/DmSFbXNlp+M+8WW8yXFOff3GSo6dLs8OsgJxOh2Dg4Phq5HtTgFA cxE3MH19fYTqIE9cLpcfPn2x56u7HafH/TB3OM7XhxbtGB5+6+yeOg7ojgtZTFFMbrnlFvdT dOjQIVWb5cuXu2127dqVnuOoflsoDOMvVCKDsvGaaKJ1glOnTrmvxg033KBq89prr7ltFi5c mJXjQMo5US4v+PReb2g3sXFRef60v6Dw8b9YUC6/abubUDMMWXTMzMyEL0VTU1O2+wUATUe1 7kMMWDkJQLZwt/dynB5vACd+uOfsfM9X97g/3DG0qNZonbf0lfFfASFOl1EMYy7s7AkBDHcB Lni0Lm3xNeJ0BeFCubz44TPu+9jjOIuWL9/xmbav/Oj7bpId86nZpehDlljC9+qUqQIoCOGM Wpf29nZCdZAhhs/OOx2fdQdwF4+t9H5+WfzqpYttt21yKqWGp3f3uMslTPCWvrKeIq/0VDhw 4ED4VydOnChVECMit9nx48cDbU6fPu222bBhg+pQrT9OrS9CbhgfH4+N0IlLG7u4ggbxDVKt NghE64r5KXrjjTfcE9Hf/u3fuiei/v7+QJt//Md/dNuI85jb5s4770zVcRp+GcAC3pYRKx3n By+u8zb7Eqx1nMuvDtvuINQDcboYxJkrfAUq5uUHoICE95Px7meKfMsHGcI/0SoY+2xbef6k v8FXzs233faHzpWVTUwYfrXsOKv8dYshT7gfmB07dmjaeCk24eJxHuLOMPZQrTxO0TDJhHIj dFR1ABPETZDJRhNFzsp8+eWX3RfhAx/4gKrNhQsX3DYLFixI1XFUDSDNeKtcOxxn+pGO8+X5 23e/7NY5YTI1u/BtjCdcVF6MZmx3CiDb+L9QtvsSgypU57CxDGQBfy1hpxKMe2XPhyv5cD9D /Ff/sYvueC78Ww3uuNAdFBo+BDKE+4EhTpddZmZmYrOfiNBBfZhE6wr76SJOBy3GK0XiVANz Ykz2hRcvOh2rKE6SXfg2xhO+SxcXHtudAsg2/i+U7b7EMzU1paq6TagOUk4gn86Jqih3tlz+ pS0/i+Wt73DeOjtc05HHVolv8emm9B7scaDC0NDQx6uE2zz33HNus3vuucdtc+zYsUCbkydP Bg5l9zhFwN3OlRgKNBuTaF1etwzWnBjfeOMN90S0Z88et83v/u7vBtpcvnzZbfOd73wnVcep 4SWA1OCVIhHfuPs7nPm5be7Pv3JufmHXfTUtlYD0kIE75DQQLuqRy0sOQMvIVpyurA3VUbMS Uo5Xn86prokIp795xU0ET21qK5fPmxzZfZR/UAg5w38Trml25513um32798fe6iUHCeviAFq 7HauROggQcQAKXZttWiQs8K+JifGH/7wh26b973vfao2b775ZqqOAxll21w5cuOI0XPzC+/+ kreHGGSIbNwhWyc8LdnZ2Wm7UwAZxv9tst0XU8QQM7wK3oVQHaQZb78I/36vAfyLJsxT6tyl r+wmlmOI02UIk3BJkauGQVMxqYQ4ODiYmwCxyYmROB20jBPl8oJP73Xf38Bs63culq+++9Gv fabtrXPKbdMhhWTmDtku4qISvthQRR6gbvxfJdt9qQFxKlCF6sTwNDejTygg/kUT5iXnvOie eQoeZIupqal9VZZVmZ6eDjQ7evSo22b9+vVum2effVZ1qJQcJ0+Iq49qg3L/RSpnCU2QQkZH R/U7C7e3t+djTVL4xPj5z38+0OZ//+//7bb55je/6bb59Kc/HWgzPz+fquNARvEXOQnvHbHr Yrnttr9c1eGcn+ynoHBWyNIdsl3C9Rf6+vpsdwogq2Q0Tleu3A6pZoy7uroI1UF28RZNCL7c 48xf3BP7EC+6x4ZiALYYHx/XR0bEb5lahlYyPDysX3wtxlHkdQIki1fAZKXj/HR6XeRvxa+P /k0Pa2AzQcbukC0iLifhywzXGID6yG6czkVVOLmrq4uEBcgo/qWv5ktZ3ehe5KAQAJpK7I6u 7e3tIyMjtrsJRcQkx3N4eNh2NwHyg1uKRHyzFjvOj/bc6P/V5XK5/9hFx+lxv3qrOpzdO1bO naaycKrJ5B2yLcJJNBSlAggwNTVVqjA+Pj7sQ9xLdPvwf4+6Q4jG/seKQ7nHTFUITBWqE/dF qeongCH+RRO1Ln01bw8AiTAyMqJPWcpTLTDIKLE1E5ndBEgK/2yrV43EC95FQn3hNEOcrgbG x8fDN+SMgaCAuME4fwBOv+gmccSf84fzbIXwRkdHI7snzgwsMoIs4t/11XApqxfdY+krQGuY mZnRxz4oRQepInYPYmLKAI3jLzR8f4czP0e6XLYhTlcb4WAECwog34iRkxuSE6MoMfTXj7Ss 48bvRFfFF1N0uwXDPlWoTpCPSslQKPzzruZLWYfPzjsdn2VQCNAC9Gl0uSnSDzkjdhmsGL8R XAZoEK/QcI/jXDy20nZ3oCGI09VG+J5cXFdsdwogScQ4SXzOMxGVM0E8BTdyJ55Uk4aApVJJ 9UJxv1Q34s0ahoap9RPoXzQRrm+iwh0XsnoCoKnEptENDAyQlARpRlzZu7q6NJ/hYSrWATSA N9tKNZIcQJyuNsQAKHxDzq04ZBrxqXZryelvAHKDeJrDlZp3Cd7PiKGnKlRHFcs60LyeUBPi 017TK+9fNOH46puYNI5tDwB1I65ZmrMiO7pChhjW7gZLxTqAuvEXGqYaSdYhTlcz4bTtWm+E AKwzMzMzOjo6MDDQ4rpyaUM8ffEiiJei8b2bNbPEhOpqwgvSubmQ0AjiglXr6+8tmnDM6pvs OTs/PPzVtdu3t922iaWvAIkzOzur39SV2l6QOWKTQykrBFAfXqHhtY5z+dVh292B+iFOVzPi 0hK+nDDzA+nHzZtrUmxO3EVsqrBz586xCuJLcabK66+/XmtvxUO8h4tDuccUB3f/iv6mpT7c mF0jeXbigapQnRiPch9lgj9Ix3nVCifK5QWf3ut+bnuobwJglVKppLlkk0YHmUZfbJGBE0Ad 7LlcdhY/5LCXa/YhTlcP4mY+cC0hXwZSy9TUlBgJ6ectzVmyZMmmTZu2bt3qReIuXbpk66mJ P+1F8USXRMdE9xJ5muLlEi9aHXEiMaZUvdRdXV2MOPX4A53j4+O2u1NQ/IsmqG8CYJHh4WHN dYo0OsgB+sS69vZ2ItEANeEvNEw1kkxDnK4exDUjfC1pfN0cQIKMj4+LQXyDqXNuVG5sbOzI kSNnzpyx/ZxMEV0VHRbdbjxyJ15A8TLWGjMKh/JdqLqiwR+ko+inXbxFEw71TQBsoJnycUij g9yhT6xjcwkAc/y1gx9a7JQv77HdI6gT4nR1El7dxlUE0oC7srXuGvxLlizZsmXL2NiY3US5 ZHHT7sSTEk+t7rCdeEndVbGGf1QVqmM5ZyQE6VKFt1+YYKXj/HR6ne0eARQIzR7iDpu6Qk7R bwXLGlgAc4ZfLTvOKoZwWYc4XZ2IO8nw7bftTkFxEcP6usNza9as2blz55EjR+qoIpdFxNMU T1Y8ZfHE63i53ICdSS5D+CzhHYFUiABewUEmPNKAf9EE9U0AWolmrau4dlAQAPJNeLM+xk4A teLNtlK9JNMQp6uf8IpC0kCgxczMzNS3uNWNzU1NTeUmaa4+xNMXL0J9MTt3Sax+wbsqVMfp wo+Xe0ihz5TgXzThUN8EoCXo93Xt7u6mvgoUAX0+KZN5ALFsmys7HfcxhMs6xOnqJzznKe7b bXcKCoEYzY+OjmoWCESyZMmSrVu3FidvrlbcPDvxEtW6Nla8EeLtUK3I0Iw4CdWVCdKlFf8g 7/4OZ35um+0eAeSZqakpzZQbsQkoFPr6jH19fayBBQiz8thlx1kc+L4whMsuxOnqR1wkwhcP UrKhqYihfK3rW93UubNnz9rue5YQL1etSXbuetjI2nPih6q3rODBKW+2Qwy7bfcFrsBfoq7H cS4eW2m7RwC5ZXR0VHWNYK0fFJaRkRHViItduQBU7Dk7Pzz8VTHA/qMvfalj8BGql2QX4nQN ES4Vz90mNAkxjtfMLoZZs2bNxMQEqXMNIl5A8TLWFLATb1M4UU5TILmwoTpvUbB4ZZgbTxsX yuXFD59x3yDqmwA0D01BLmrnQ8HRrEigXCMA5BvidA0xMzMTvnJQQAQSRIzRh4eHzRPoCM81 iVoDduItE2+c/xbLv6UpN2ME6dLPuul5x+l136btvU55vmS7RwC5Ql+QjrWuAOW4NbAjIyO2 OwgA0BSI0zVK+OLxB3/wB7Y7BXlgZmYmnLCpQoz1d+7cSXiuBYgXWbzUmpurAOJN9GL3muFm ocJV4+PjBXzWmWPP5bKz+CH3nVrpOD+dXme7RwD5QVwXVDM3JAoBBNCknRZ2UQIA5BvidI2y efPmwAVD3Ifb7hRkm1KpZB4G2rp165kzZ2x3uYiIl128+IZvk3hDvRpDqvBrQeqteNX6xD+L 8Hyzy4lyecGn97ofTpa+AiSIpmhpQS4EALWiKePInB8A5A/idA2hmt4RP7fdNcgkpVLJsAjd mjVrjhw5cunSJdtdLjriLRBvhOF6WPHmutE6Vagu96ErgnQZwr+VhGB9h/PW2WHbnQLIPJpw AxtZAmjQbItMgBsAcgZxujrRl0sIV5EH0GMeoSOBLp2Yp9e50TqvQFuAHO/uR5AuK1wul7ed vtgx+Ejgw3lfjzP9nXXl8o9tdxAgq6jO/A4F6QAM0Nx/MbQAgDxBnK4eNPM5XCSgVgwjdEuW LBkbGyOBLuWIN0i8TeLNin1DxZseXjXvkb9Yv38bDUovpZmVxy47zmL9p/f+Dmd+bpvtngJk DE0mdf7O+QDNQ/NVYoABAPmAOF3NiAuApj4Cm72COVNTUyYRur6+viNHjtjuLNSGeMtMigyK k8Y111wT+as83bb5g3R5el4AAIYUttwBQDPQpKYyzACAHECcrjaGh4dVVwUxAqOqCBhiuJfr pk2bWOKaacTbJ97E2Df6He94h+qsYvsZJABBOgAoMv5zYADmdwHqRpM5MTIyYrt3AAANQZzO FDHM0mTHcD0AQ8QHSRPt9SBClycMo3WR5CBU5505qb4EAEVDE6Tr7u5mfhegETRbJ+dg+AQA RYY4nREzMzOqYRalEMAczS5vHkTo8krd0bpM38t5eaOMmAGgaGiCdJwSARJBc4/GtwwAsgtx unhKpZKmIB1VRcAE8SlSDSM8iNAVgfqideLDk8VQHUE6ACgsYnyouu4PDg7a7h1AfiAgDgD5 gzhdDCMjI6o7576+vizeOUOLER+S2FJ0ROiKRh3RuszNCnjru8Wp0nZfAABaimY5HmU6ARJH M9imgDgAZBHidDo04RWTuVBxVSiVSpRkKjIjIyP6ha59fX1E6AqLeOtN9oT1yNC2gN5GbBnN BAQAqBuCdABWUN24MRQBgMxBnC4aTQa1GHuphlleYE7ce3d2dnoPYTOvAiKG6d3d3ZqYy5Il S44cOWK7m2Af8TEQHwajQF3l/CNOMra7HANBOgAoLKognWb0CABJQagOAPIBcboINBOhnZ2d /nwWVWAuwGOPPWbv2YAFYnd0HRsbu3Tpku1uQloQHwbxkdB/Zvyk+WZvfHycMTEAFBNNkC4r 2dAAWWdwcDBy7MSwBAAyBHG6IF4mSOT5/eTJk+IuNDYwF+CGG26w/bSgRWjqRrts2rTp9ddf t91NSCPig2FetC6doTrvHpWbUgAoGgTpAFKC6m6OUB0AZAXidFegmoERXHvttfpCY3rY26sI 6NPo+vr6GKlDLOJDYli0Lm27mBGkA4DCQpAOIFVoQnW2uwYAEA9xup8xOzurrybWIGKgxgRO jolNo2OhK5hjvgw2PaE6gnQAUFgI0gGkEFWoLj1jJwAAFcTpJGIUde2115rcFdfHBz/4QQZq OUafRrdp06azZ8/a7iNkD/GxMVkG293dbX0OwL/xzvj4uN3OAAC0EnECjCyEQpAOwDqE6gAg oxCnk8RW/W+Evr4+63fR0CRmZmY0aZhLliyZmJiw3UfINuIjFLsbrN16K/4gXTqr5gEANAn/ CdAPQTqAlECoDgCyCHG6n9HT02MaePPR3d09ODh46623qhoMDw/bfmbQLMbHxzUlC9kvApLC ZH8JcaNo5Z6QIB0AFBaCdACZgFAdAGQO4nQ/Qwy29LfB7sCru7t7eHh4fHzcHYGphmhuY+5a c4xmyxHS6KAZxCbWWbkz9La8YE4CAIoGQTqArKAK1Y2MjNjuGgBABMTp3ubmm2/WBOZmZmYC 7VVlgwWdnZ2M0vKK+CRotoxYs2YNaXTQJMRHS3zANKG6d7/73aVSqWX9GRgYcP8uM9IAUDS8 EyBBOoBMoJpiJ68CAFIIcbq3Eafva6655uabb1YF5vyIc7oqSGe3VhQ0Ff1a17GxMdsdhPwT uxVsa0acBOkAoLBEBukcNtIBSDd8cwEgKxCnqwfNmkduWXOMZr+Rvr6+M2fO2O4gFAXxYfMW nFoJ1Y2MjHif/Kb+IQCAtOGdAFt84gWAxiETFgAyAXG62pidndXs78kQLa+I910TFtmyZcul S5ds9xGKhfjIiQ+eJlTX39/fpD/tFXkhdxgAioaqyhUjQICsEDmkb29v1y+lAgBoJcTpamBq akqza4R+HkbczZZKpeHh4Z6enuuuu4786gyhed8FbBkBFhEfP02obtmyZYn/RYJ0AFBYxHgg 8mQ7ODhou2sAYIpqG0AGNgCQHojTmaIpTCZO6+EZGC8w19fX19nZGXgIeyNmBfEmqt538c6e PXvWdgeh6IgPoSbZM9lBp/g6MJYFgGKi2j2MgicAmUMVquvu7rbdNQAACXE6I1S1SNzxmXu/ OjMzMz4+Pjw8LE7xmq0GXK699lrbzwniUS1vEWzatIm1rpASxEdRfCBVn9Xrrrsukb/i3aNS xgUAigZ39QA5g8g7AKQZ4nQxiJGZam8gwac+9SnDwFwYSpmkHM37zr6ukEI0+8A2PugkSAcA RSayNjGZxQCZRhWq4x4NAKxDnE6Havo0KbjdTSea4OySJUt41yC1iA+n+IhGfnS9zN86mJmZ IUgHAIVlcHAwfFIV50OCdABZR7V0plQq2e4aABQa4nRKxO3oNddcU28ILp4PfvCD3PGmEE1w loJ0kH405erqS/3wfyPYAAcAikbkbTyTFgC5IbK6Edu/AoBdiNNFo9rSKynEjTTTsClEXJJV Qbo1a9ZQkA4ygfigio9r5Mc4ctMbDf4gHctAAKBoqJbFMWkBkCcil9GwsB0ALEKcTklkLRJD xKju2muvVf2WzV7TiWo4Lti6davt3gHUhvjQqs5OhmkgBOkAoMiIc2BnZyejOIAiEDlPz54S AGAL4nRKxPjsne98Z3xMrnrr293dLYZu4+PjJ0+eVOVkiWbc7qYTTZCOXSMgo6h2ljAM1XnT y9yUAkABiZyv5b4dIJeo4vIjIyO2uwYARYQ4nY61a9eqAnPiVO4F5vzryDThHvEQqpmkE827 duTIEdu9A6gf8QGuL1TnBem4KQWAAiIGeOEzJ+vgAHKM6naA2zcAaD3E6XSMj4/7o2x9fX1i 3FYqlVSjNNWeQYzt0ox41yKvymztCvlAtQmsJr2XIB0AFBn/8M9/zqSuPEC+Ue0bw00cALQY 4nQ6xIBMH5jzMzg4qArSca+bWlSh1SVLlrC1K+QG8WGODNU5UYXnvI3P+vr6LPQVAMAqYuwX OXsnRoO2uwYATSfyhq67u9t2vwCgWBCnS4DZ2VnNphMUpEstBOmgOBiG6rwvBSnAAFBMIksM U6YToDhwEgAA6xCna5SpqanIsqOOcbF2cTO8f//+8fHxpvcVfKiCdGvWrCFIB7lEfLDFx1sT qiNIBwAFJzKVhuRigEIhhkAk1QKAXYjTNcT4+Lhq/wFxo6uqYyJ+Lk70w8PDYuR33XXXue1v vfXW1va90GiCdJcuXbLdO4BmIT7eqlDd5s2bCdIBQJERY7PwubGzs5NTIkDR4GwAAHYhTlc/ kXuBuQwMDPjP4zMzM+Pj46J9d3e3Kq4nsPdUikXkpZcgHRQETajOMc4CBgDIGaoMGk6JAMUk 8kaP7FoAaA3EhupBDOa8/RDDjIyMiFGdSWAuwMaNG20/s/yj2nOdIB0UB1Wo7p/8k3/yzW9+ 03bvAAAsIG6/I0d0tvsFANaIrD9O5XEAaAHE6WpmZmYmsryoYMGCBR/60IcMo3KRUKWuqRCk A3BRherIpwOAAuLtc+2HHR4BCk5kmq34iaq0EQBAUhCnq41SqWSeH1cHTNE0D4J0AH7Ex371 6tXhbwShOgAoFOKWO/JWnEJUADA+Ph4eKXV1ddnuFwDknFzF6S6Xy7cfvOA4HeIEutJxfjq9 LtzmjXJ5ybfOiTai0Y6hRW+d3WN+fNXmA4nw8z//848++mhyLwZcgRhtR27LS5AOCosqTudQ KRkAikTkIgk2dgQAl8htoIeHh233CwDyTK7idIJtc2Wn4z6nEqubfqSjEru7gifny223b/dO sqs6nPOT/eFmkaiWuzaOODIJLM1jdnY28r3r6+sjSAfFxL/u9Td+4zciT0qE6gAg90SWihe3 5bb7BQBpQXUfwb0bADSPvMXpflAuL/ziU+7Zc2yVeHanAw2GXy07zir/SfYux5k9ttLk4OI0 vWDBglpjcO3t7d3d3R//+Mf//+zdf2wl13nY/dO8i8qOIjFtvHAvXIMoNohRdRuiFVgjjhuy WHhVJDboxlnYsuGlIxBVKydh/cpcVy8SsH4DJk3W8YXNbCxI774U29qLrH1fBjXchnq1YH/Q rxVbLmtb9pqoAAa+q/3DBsFdr0FFWFyc94izGo/mOTN37szcOWdmvh8Ywpoc3nvu3Plx5jnP eU7SBnNzczwPj5X15jo9Pd3v9103DXBjdXX1znWy1zMngjkd5DnCtA4AzWYes62XPnplAKKS rhWu2wWgsZoWp7up9YnHd4Or52MdNbhxIfpbOTH2uvnJpe8+dOztN75tmSQrPfnkk0MDc5OT k3Nzc8vLyxsbG0Gd0ZTFYcmaHjfrzidIhzYLg3TmH8FPkkJ15vRx2lIAGCNyZABkZM295TkO wJg0LU5nPHJ1oNRpc+m8X6kfbM5GfxXNtrt4WunBlj6qWPfLmy/+9vHjL1+7YH/FiIODg5TA 3NbWVmwMNilTWh3l2bFqxLhZ76kGQTq0lgzSBcxJYT1Z6IMCaCSeugGMhMg+gMo0ME4XVqCT JeqSfvUjrc/8+fW9S6eyFKqbnJx885vfPD8/bw3MRSUtMBqE9risj1vSuh/b29uumwa4sbm5 GZwFS0tL8rfm1LCeMowoAGgY6xqvzGIDkILZrwAq08A43fe1Pv77XwsunWHSXCAl1a505sk2 KUhH6ZMKWG+lBOnQZmEYLmWZY2uozlzKGFcA0CQzMzPyWseFDkA6ax5ut9t13S4ATdPAOJ0s Qid/LkvXlcu6gHcgY72ng4MD+ou5mb1nDZLGJvoB7ZElSBcIJ8bGQnWMLgBoBmu6PTNeAWQh Z7+aPlJQkRwAytLAOJ1x4YZWnXPmunlSqW9+5r6jHLu0PLsSmUdZ6yBtIGX6mPnDra0t002c m5vrdDrBRX8cLWy8pJqAKysrrpsGuLG7e2d1HXN5SQ/SBczJIs8gZnYAaADrSB7XNwAZWafs mP6V63YBaJRmxum+rvVdH/hCcN3sKLX50eN68HxYnM742h/cpfVflf6+5sKdsmpELD9ub29v Y2NjeXl5ZmYmaYYsgzM5WBd4HZpDBDRVuJZr9mWOzcliThl5HrH8K4C6s3YSmMEAIDvr7Ffz WOe6XQCao5lxuptan3j8Tv7IrFLHf/VXn/rgsU9971tBkt2YitOZq3NKQbq9I0MDczEf+9jH Sm9ns1kns0xPT+/v77tuGuBAjiBdwJwywR/GsKYEgPra2tqSl7XFxUXX7QJQM5OTk7ErifkJ FUIAlKWZcTodWTLijFIvfPuR+37vK0FlOuNhpW6/uFzu23W73aRY29TU1C/+4i9mDMxJjPFm l7R2xEjhCaAxwljbqEG6gPkTLkoAmkROeuDRGkAO1qA/ZS4BlKWxcbpwlmtHqatPdK7rwalL 3z3Kriu5OJ3p3lnnUJTF9CDLamqzmS9CDm0pFnhFW0XnruY+C6zLv/JYC6COrEOqTFUDkI/1 AZCaRQBK0dg43Qta3/voleCKGQTmbmv9W9++pTpnSyxOZx5WT5w4kT8IN8zx48dJXcnIunwH C7yinUoJ0gWsy79SLxlAvViXjzA9B9ftAlBXXFUAjE9j43S3tT719M1grutjHTW4cSH4+aeu De6dOhcuAluQeVgtIRqXYGpqiqSVjKyD5AsLC67bBbixubkZnAWl5JNa15QwJ13xVwaAapD5 AqB01geQra0t1+0CUHuNjdMZF25o68IRa9cG977vE8EisAXf4uDg4Cd/8idzxOAmJiZmZmbe +ta3Jm3AuorZWcvSsXYEWm51dXV9fb2Ul0paU4JsXwC1YO0nUEkKQHHWqpeuGwWg9pocp/u6 1nd94AvBFfPK0jGtr4e/+tItfff7nvzjDx57+drnCr7Ll770peyBOdMp3NjY2NvbOzg4sM7T DLCiYnZJZel2d3ddNw1oDutTLoXqANSC7HFx+QJQCuuCEsw5AFBQk+N0N7U+8fhucLmUa0d8 7pY+9vY/PNtR17/84NE02fxe//rXDw3MRbc3T7xy7CX8K1JURmKdydLr9Vy3C2gac1rJc43M XwCeM90wee1iQBRAWeTDiHmgYyQAQBFNjtMZj1wdKHXaXC7PKPXS1UesvzW//sqfzBaZAxuM 0/7UT/1UUmAuyvxW1hwNTE1NUSplJNbON2XpgDGxFqpjtUQAPpNJ9xR6B1Ai8/gmH+6YWQ+g iIbH6Z4Z6GOnLppr5Qmlvnf5vuivbmv94LO3lJoNLqZnO+rSU2duPH8hx7vsHcmypbXaaGB+ fp6Bl5FYb4qUpQPGx1qojkFjAN5aW1uTPS6qvGMoc+QsH3HdENSDOVTkpYb0CwC5NTxO94LW 9z56JbhWhiXqwuCdVWzRibKY51jrDM0A/YAcrAX+mDUMjJW1UB3JKQA8ZLpecjyP2frIIuxk um4I6oGrDYByNfz2c1vrU0/fVKpjrpWPddTgRp50ueLMtTulIN3QWWN7e3tmm8XFRdNpYGQm YM1MXF1ddd0uoPnMiSbPPkomA/ANGS7IjTgdRmXN3uWCAyCf5t9+LtzQqnNOHU1wvfXsmeob sLOzk1SQbnJy0pr/FQTmTP/S9BJif0spKJ0w43Vubu7w8NB104DmMyeaOd3kkAOdUQD+sKa3 MH0BGRGnQw5UwwRQlubffsJZrh2lrj7RKbi066isQyvhhTss6rSzsxMUwpCBuZj3vve9Vbbf T9YZr7u7u67bBbSFOd2s1zTX7QKAO2QyHcU0kR1xOuRgXeCOgpgAcmj+7eem1icev/NIefG0 0oOtyt46pSDd+9///rW1tWAqa0pUTjp27FjLS7BZQ5/r6+uu2wW0iznp5JlIwi8AH5BMh4KI 0yEf+WTHKCaAHFpx+3nk6kCp0+ZC+bBSt19cruAdTQdx1ABcdlNTU60dELb2vJnxClQvafZr a69OAPxBMh0KIk6HfLa2tuSzGyl1AEbVitvP5dtanTivxraWa8zOzo4sT1CikydPtrYOlDVF kRmvgBPW2a+sbgbALeuQHmvdYCTE6ZAbKXUAimvF7ecFre999EpwobyydEzr6+N7r62trbvv vrtoKC5ZtKpd21hHqJjxCjhknf3KuDEAh2Qy3eTkpOtGoWaI0yE3UuoAFNeK289trU89ffNo JQl1/oTSty+P7712dnbuueeeIpG4v/7X/3rSrxYXF8fXcv/JLEVmvAJuWWe/8kgMwBVrMt3a 2prrdqFmiNOhCFLqABTUltvP8otaqbPmKnlGqZeuPjLW97Ku9ZPCPNOaB93l5eUvfvGLb33r W5M2a3kvUw6PGy1fUgPwgTkN5blJvXYATpBMh1IQp0MR1pQ6HlsAZNeW288zA33s1EV1lFN3 9YnOUY7dGJ04cSJLYM5cxMNJrClV7SYmJlp+Zd/b25PD4ysrK67bBeAV5mSUV63WltEE4ArJ dCgLcToUJFPqKOALILu23H4u3NCqcy64So67RJ0Ww7nWwFzUxsaG7FkG2ry6a0guHzE9Pb2/ v++6XQBeYU5Gc0rSHwXglnWZV9eNQi0Rp0NB1pQ6hjABZNTw28+ZZ28rFU9te6yjBjcujPV9 zaX5wQcf/L3f+70sRUOtMzrDB12CdNb7XK/Xc90uAD9mTkl5nlI1GUCV5LwE5uAjH+J0KI6U OgC5teL2c7k/WF7+tOmr/fYnPtFZfOJ+pX6wOeu6Ua84ODiQJdhD3W7XdQO9IG9yZqe5bhSA OHk1o2oyAltlYwQL0traWuwSNDExwaGCfIjToThryXIuSgCy4PbjzN7e3tTUlDVCZ3qWGfNQ zLW+2RnUsttt7O7uum4X4Jd+v7/r2p/92Z/Js/VjH/tYNLzS7XaX62CmJqx3kDYgVRMSyXQo 0QxxOpSB6xKAfLj9uGGeMVIK0qWsGrG3t7exsRE8RgavsLi4WF27Kydvb0tLS64bhXL0+/0l 78myawCce+CBB8zdIeX2F1R9nZ2dffe7323+a/6d8mjU7XZjG6esPGBe59577/1H/+gf/dIv /dLP/dzPqdTHePNSJ0+efOcR84/0hUfNS0U3Np2BpC0PDg7UUb7q8lHd25TXbA9riQzyVpAb cTqUwprn67pRAGqA248D1hyxwNzcXKxbKQNzMTPNnVm2bKvcx/IR9RJkWm1ubvaOrK6uhiGw hYWFrA/lACCkRLJ+/dd/Pbbx/fffn7Tx7OxsbON3vetdSRu/6U1vim186dIl65Y7OzuyzUkp 8COFmeRcqo997GMuEk898pa3vEUeHq4blUm32y19YjiKC6e8NHvaCiogcw5YhBrAUMTpqiaX Lg0FqQGmZ28u3+bf2ec0me2bN2hsPpGMS66urrpuFxIdHh4GIbkgGEcmGoBxS7r3WctKJF27 5L0mJfFNvuxyQqZet9uVGyc9ni3bxqU2NjasG5seQmzL9773vVl3GYBRJJ3gQEby8p6eWw0A mjhdlczjRFJBute//vXveMc7ihQbat7IjLyrTU9PHx4eum4XXmN/f397e3t1dTVlRRQAGBPr vS+YFipt2aaIWrPeVEISzZYt6y0pq916VUxa7M/aAUia2Cv7Eh/72MeG7CkAuRCnQ0HWzAPr /QgAQsTpKmKeBGTac4kaNjJjvaX1ej3X7cIrDg8PzfFMbA4Yq+pXpcinyhmCMmvMGvayLrGn Ep63rVlvKiECaM16UwmZetZSFUk3a+vLWif2yihkMMHT+goACgqucuaCEE6JtfeNgGRyNlXS mA0ABIjTVcHc3V/3uteNuyfRpJQ6kuk8ZPb/9vb2ysrKuI/kyiwsLLhboGIEvZoIF3795je/ +ff//t+P7W3TJXVSY0hyfSYhPxmisoa95LTQwIwt8S1pvMH6EDWTkPYuj6ukND1ly9TbsqXp BeTEXhmFNJ+XOB1QMXPxMRcEc6FYPlrOhTJ2SGEOD3kIccwASEGcbuysqWH5mNcJhvVMN10W ybY+gdSRdY9tbm66bld71TE8Zxpsmu16z7VXr9eLfSPmpG5eGU1UT075lI86SSUmlC3xLekG bY0AJr2szNRLStNTtkG1lCibLFEno5DBYlNJrwCgGtFeOvc7xMhhHmuKNwAEiNNVIWWB13TB YF1wy489ilhHZpqRKiKfN+bm5lw3qo36/f7q6mru5SCCDmsw1BxOGAl7rtEto29qNgi2NH9i /tD8+UzCSsdZmMabj2A+iIPd13oyTYkuKYqTUapY2CupOF0gdpdMyXpTIgKYkvUmx8lSygLI TL2kND1lK1Eno5BDIwLVZqxW6sknn4ztjbvvvvuLX/yi63b5LojtNpU5ax544IG3vOUtd911 V8oJPm7mVDUt2WpEzxzFyYfBhtUsAlAu4nQVeetb35rlpm4u2aZzv3yUQj+0593IYgfWZDoS oypmdvjS0lKOLqk5ArvdbpZeafQPM7bKvKx5cfMWKckySczH4SiqmNnhsW+BlDoUJ2d9xm58 ScXpAsuvDRanZL0pEQFcTs1Zi7UzZXRBPpulvGysRJ21OF3+vVl/jewIIZ+dnZ3FxcUcPYRx M1cD07c31xPugC0nK5UnLeoNAMTpKrKVMA5v+hPZA3NZXrPuxQ7kgxDJdFXa3t4eaXWIYLg4 xxSP6IvkaKd5O/Omo/bIzUcjWlclUupQuqEl6pKK0wViiW/pl7tYxCcl6029NlMvPU1PvfZO ndQ9CEWvrtbidGXt29qxDuyZne+6XaiUOZvMnWWsa7WVyFxzCM20Fs84ALIjTleRcJrqyZMn s+ccDSUfG2rdZa9jMt3uLf3Z3pWTZ8+HDT5/tvOl3udfvv6c66aNZqQIXTAyXCQoHH21gi03 zTCNGanxnh9UjUFKHcYhvUTd0Nh99KXS59THIoDpLxuNQaen6anXZuqlp+mp1yZcWIvTlblz a0XOI2t5dmHbmAMgPXo+VLCc1OrqarAakrln7SYLF00KlnjKXRVEHd0Kzblc95F1jMpas4hO EQAr4nTVGUeJCjm0XuvH4HoNNO3e0ucuXlFq9mxHbX3+/ODWrvnhwHzRu7fOnv98R3X+3bnZ WkTrskfoSpy4EX3Z4q8WMA3LHrAjWlcNUupQupQSdTLbTkbiwnuxzHr76Z/+6dhPwgfpoVlv 0Uy9oVehaKbe0EBDdPgtR3G6BpO7rkkL3yNJkECXo3DtwsJCEJLb3d3d399Pf5ew+kf6Zual TF8iCN7liNyZY5gCdq0i7w7dbtd1owD4iDhd7clU//o+BsvP4ucyrwOte7u3OrPnOkr1zs0G EbqY7euDk2c/eVqpq71zWt+qvpFZmP6l6bYO7Uea78V0I8od+I2+fokvGzBNNQ3OMgvGdJjM Tii9AQjJhV8pnIyCUkrUyV/90i/9Uuwn4S1SZr2lxH3kMNLrXve62E/CFg6NIETPgtiv7rnn nthPwhwxitNFkZnSQuZLlxUJ0y0tLQWBucPDw5HeK2OcLqbf729vb6+uro5UQsRcEAgxtwRZ wAAyIk5Xe41ZP0h+kOnp6VH7VdX4gdb3n7sSNPLKuY7WN62bXRvo049dSYnlObS/v7+ysjK0 72g6mmMa6Y2+yzheP2Aan6WvbHbF0KF15GNOYZliwAMJikgpUSdT7T7+8Y/HfhImvlnzGmI/ CSOAMoQ3Ozsb+0lwtZRpeuZvk+bqyjQ90yo5xhCEnyhOFyUjp6wg0WAjRejMSbS6umrOxCJ9 yPX19WB+a+5X6Pf7m5ubWUZDA0TrWoKqmgCyIE5Xe9aabnW808snk16v57pRdgOtH7tyU6mO aeRjHTW4eSVpy78c6JMPvZJP1HuoowfXqmxkCrNjh87OMB3isVZOib7X+N4lkKV/b3aIt8db 3cmUOkaPUVBS2Mv6c3mLDF7E+vPYT8IIYOzn5m9lUC/I1LP+PGmurgw2Batax34YFKFL+nk7 sXJiS2SP0Jn7+Pr6er/fd93kuP39/ewBO6J1jSeP5zaPuABIQpyuCWRHv3aPwdbSP34m0wWu 3NSqc8408n6lfnDlXMqWvWsD1Xmoo9TTj83qwfXKWmhl+q9De4rjjtAFou847vcKZOnrm53j YRe/7syJLHc1FXlQhDXslZRnJ/PmzOFnzXrTtrw5c+mwZr0lvYL17ZLm6sq3My8rs8uDp7ik PLsWkju/pjMJkMIc3kNXWQmsrKzUIiNpf3+/1+tlSfOnbl2DcfkCkAVxuiaQDyeqbo/Bstey urrqulFpwqmvHaWuXjx9lGNn95LWZy7umi1PKvWXvYdSthw3mdYUU02ELhB932reMZAlWkdi XenM6RzbyT4vEQP/WcNeSbEwa4JbUjacjAusra1Zs950Qkae9YdJMcTYD83falvltampKesP nex8H8hALQkpDWPOu6F1HoNE+DqWrdjZ2Qnr36UwV7DWxuKbjXRgAEMRp2sIGXqIrj3nOWs1 aM87XmH0zbh4WulBWvm5MPnuwx318jUHMSCzM9PT6MzRUvFYdPTdq3zfgDnk0tdYNLvL8yOw XszOlDu5sqAwmsca9kqaW2pNfLNmvWlbcre5vVqz3rRthOnJJ5+U7xW0Wc7JvXTpUuwnYfxa PsVduHAh9pM2R6Yo8NRgQ2/Qqikrtvf7fTmIFRNMsXfdUpSM8poAhiJO1xDWUFddHoPlw1WR wr2Vubg7UOq0ae0ZpV7avZiyZRjU6yj1F+dnK17+1Ty9pFSjM0+DTgbxom2o/t0D5oOnrAlr dhoPfiWSuQP1XZkaPpBhr7/7d/9u0k1QhnWSitZpW4k6+bfBljIp74EHHkg6zuWdzrqQRbCx HHv7J//kn8R+0tr8C5k42ebUwoYZOtG1GRG6qP39/aHRupmZmbp06ZGFfGoLbysAECBO1xxy +LEugzM1HRi/OtCd0xdVhqmvepSgXrnW19dTen6mQ+xqSkW0GU4aEBha/sbsQIfNaxKZ00Sv FEXIsFdMtOLP0IJQ0Qz0LLk8wZbyqH7DG94Q+0lYg0JGl974xjfGfhLe+2SJunvuuSf2k9ZO iJNBTBKOGmBvb08G32PnXcMidFFDo3Uk1jWMPNpbO/TSWre1PvX0nWUJrV55Zrz6iOtmwhni dM1hXYrB/368fBqpS+Gqkaa+jhTUK8Xh4WHKXFfTP3AbDI02xmEzAmZXpDwemN3o85ImNSJj Jaxqh9xk2CsmOlIlE99iotmdQzN6ok/LQ0tohVtaK8lGRSPX1hz5qDZnkMl9TqpR3aVXo3Oy IHu/3989UuWbmrdLr1tnbqP+d+yRhbwr1SW7AiW6rvWpS99VatZ6vptnxmd//7jW33fdTLjh /gkZJZLTc/yfWSYzF2pUxT97lly47oTKENQrzvQvU/JHfDgqou1x3ZY7Uh7OzUMC68AWJ1cy qVEZTfhmaNgrGgWWiW8x0ZWXrINeUdFBjvRMvdgRnp4uFHtIS5mVr1pcnE5+O20OWTaAOZHT F3daXV11MlQWxsuqf+vt7e2UciUTExP1WikOVkx9ReBHWn9o+8X/ozP38rUL0Z/f1vqT3771 r3/+44MbF5L+Fs3myxMySiFz08xF3+eRN2vKQI1yl567rdWJ82qUVV/V+Ke+phSkMw9+nnTv oq1y3ZYfMzsn6dmYcnXFsZoEypUe9oodWumJb7FXTtky9iiVnqkXGxRJn6sbSy9ND160doaU 3IdMBqyv9GT2ubm5itPZohzG6fTRlIj0uiUc9g3A1FcEbmv9W9++9Whn7sZ3lmO/unxLX33i t442Qet49ISMUsgog88zy2QG0+rqqutGjSB7lpy5vj7cux5seb9SP7hybkxN2t7eTuny+hO0 jTbMdVtew+yilASZBhfHqcbKykpsl/qQ3YmaSgl7RYvTBVLOa5nXmVKiLlaZIT1TLzYukj5X NxZYlANvUf5czCsmOznE+mvKnA4p0fP19XW3o7Zu43SB3d3dlAvX/Px8a68DzSCHeVqbKF1H 5snu8edvzX76UjhrtaPUZz5y/Knl5Zf7l3O82vJ3bn2ws3jj2xSkwx1+PSGjOBn5ko8r/pAd 7nqlLA20fuzKnQqgv2IenJ47n7RlNE43vhJ1m5ubSf0536Ih0ba5botFyhzYGs3L9pAMavh8 gYLnUsJestBPSuKbvDymXAFkGktKrCG2ZcpcXXkipJSoa+1MT3kBae2uqLuU89FtGl3Ihzid HpZYZ45/QnX1JS/y9Ijq4kda//Lmi0rNmme6zY8e14PnzQ8v9wezn74c/PCpjxzPEa371LXB BzuPEqpDwMcnZBRhbtjymcHPPGpZZaYuK0hEXbmpVedc0P5PzqrBreesm0XjdMZz50+UnsOc tFKYOR48PACiLXTdFruUof56ZX36RmYHeDIXG7WTEvaSieQpiW/yCEwpUScHk5ISXqzlF5Nm +VkriCdNw29tzoWMn7Z2V9RaypzulZUVT4qfeBKnC6TXM6nXCDei5B2Bb7MWlvsD1XlIHaXS 3Xr2TPjzIC3u2NtfuXqcVurqpdlRV4EgVIeQF7cflEt2gPws1i7bub6+7rpRI4tOfe0o9fRj s3pwfehmpefTJQXpvO3ARRvpui2JzK5Lek4mVJebTA1gjTPklhT2ss6FTIq8W1/ZuqW1zndS ZpA1izlprq61QkVSOMPDoZdq8Exbd+mrRnhVWcKrOJ0+KvC6sLCQdF3iRKgpCm7W0U2tTzy+ G35lvYeO6cE3oht86trg2Nv/tfnVSaW++Zn7CNUhH19uPyiRdbKMh7dw+chkeiGuGzWy6NTX WfO/s2f/+Gzn5et/Edvmk8/dCusXlF6fLilIx4SI4swOTAoEEKrLR64mwRpnyM0a9kqaOmRN fEsax7KWqLMmfSdl6lkTRZPm6loDi0kl6tp5YWeOWN2l3E89XFTdtzhdoF4zJzAUc/nr6Pta H//9r4Vf2Uml/vLyz0enSZl/Pfjsnec++duhgmUlPnTXBwjVtZxftx+URQ5X+paxIp9Vzpw5 U8c4nXFxd3CU3fxKAO7p3rmTZy+e7agvff78y9dfmQN7U+tPPr2rOmfDT/pYRw1uXinr3ZMK lxCkKwuhutLJpAAeMJCPNeyVdL+zJr4l1e60lqhLynSwZupZt7TO1U2KN1lH3Vr7FCejlr51 bJAi5U5q7giezHWN8jNOp1MrEfu8cBySyNsHvXfPxfLpjK/9wV1a/1V0m77Wf3vlTizvwx31 cn95pLcIIn3/8tjbR/1DNIl3tx+Uwlpbx6s10WRewy/8wi8E/zB9o9XV1V6vt7Oz49v4qtXV ge6cvhg0/sq5zrdu3Tx59mJSL+qVcZXeQ2VNek1a3ZVVwEqXNFXHq3k6dSEfM3jeRj7WsFfS w6o18S2pPKL1NpqUmf6Od7wjtmVKNO3EiROxjd/znvckbfwzP/MzsY1bW5FNdhuI79dF+nCX h0E67XGcTqeWqyNUVzuye8mX6L+wPp0Kyhk90ZEZc49cvZPGYVxZOqa1pSxSCrP1z//J1eW3 H3v52oXyGo468fH2g1LIOTv+dO6tT1b7+/u9Xs/a7VhYWFhZWTG/3d7e9mEVsJiXtD5z8c64 SpArd32gz37yL4LJsFEnldr+5FlrAbscUoJ0pbw+YgjVlYWpryhR8Pz/xje+8U1velNwOKUM St17771mgzccCTZOeeVgg3Bj87dJWwaZeq973ev+1t/6W+a/KnWJ7WCubnTjlKey4LLzN/7G 33jzm9/8+te/XrU4OEXWSU3VNCfd5zid0e/3CdU1A5nCdRSuFxFd7zXmBdPlePROafIcKXXG Vwf67rO9pz54bPDDZ0poNOrG09sPipPXfdPH9aRTK9u2srIS/Mr0PMK+UYq5uTmz2fr6+ubm 5u7urvPB2HDq6xmlXtq9qI/y5bZ2b5272AvKE5jr+BPnZq8/92dlZdKZHWXdM9zdxyopVOdh +Nhz8jRvbfQBBe3s7ERvbenLB0c3Nv9Ir9yafePYb2NNitnb2xtp42jYMX3jBpO5kH4ukAUp KUhn+m+um5bG8zidPuoHWmtusqxEvcjcBSpvNsNtrU89faeCeVLO3VCPXB101Olnf//4qItR oAH8vf2gOLlUpSerCMlgRywjyfzfpHHCJGb7cMLs7u5uxaXuwqmv41jLVUoaR7XWOEe5rKE6 D2tge05mgxJiBpBElgtMyViEP+qbh+5/nM44PDy0LgJLqK5eWMm6qS7c0KpzLvhOPzmrBrcu j/oKzwz0sVMXX8kCucqaEq3j9e0HBcma2Z4M0cjZKzIhzvwkaVmr7MIJs+aGN9aMp1taz55/ LnjTi6eVHozxvZK6ZfVaOCLactdtGU3SFB4/K2F7i6mvALKTpTx4jvVffYN0uiZxOp0aqvOq LDVSyHEIT/IqUFB06uv9Sv1gc3bUVwjXls1R4Q515/vtB0UcHBzIiJjzuhVyeT7TGUraeHd3 19r/yC2YMBuWuisrsDLQ+rErd3Kbw6mvY7KysiI/V72CdLrOcTqdHKoLp28jC1Z9BZBR7FpB WN9/ssJJoBZBOl2fOJ1uyvBtm8lli5gf0wzRlWHzTX0N43RHWSBb42kmPFWD2w+KkEM0KYvQ VUOOrw6tUdLr9UadBptRiQPyV27eyW0e69RX61IbdRw1jbbfdVvysAbBDfMFuW5abchVX/1Z 6waAP3iIrR3rcsnK+5p0UTWK02nKodSf7Ni7bhHKEV31NUesLczIO39C6dsjT5tFrdXj9oPc 9vb25G07vcz2uMmqeVlqyZltrHlkRZTbffmB1vefuxK5EJc/9XV3d9f6Qeo4/Sfaftdtycns dmuojjUlMpJTXz2ZmA/AK0wKqxfT87TeHH1e3VWqV5xOJ4fqqORYC0ztb6qgwFzwnaaXmVu6 OvjGp09F14u4rfVvffuW6pxV5NO1Um1uP8hN5q85HF6TS7YtLCyM9OfWxa3yWV9fL/GjjXvq 6+HhofWzO5/I3GbWeT2mo0yhuozkVB06pgBieIKtkaS6EPUK0ukaxul08mguNSX8x2hEU0VL 1J1Q6nuX70vasq/1W/7Pr17++H0v91/Jm7uu9YNf/G4QpDup1F9e/vkcy8Wi1up0+0E+MjRm uJomKZe2GDVYdnh4aJ37mUPpC3SGU19fqRV65Vy5L25NJ2SJTOcWFxfl90KhuozM6R/bdXRM AcTErhLMCPOZde2IOq6zVMc4nbatpa7qWR2lbcqd3Z8ULseoiqej3tb61NN30jjUsOUgvjHQ nUefibXB/OXmR4/rwfMFW4LaqdntB/nIsWhX8R3ZknzBMvNXYRcqn5Hy+DKKTn29cq5zVD+0 HNZgq/NSgwhYO0M1qsLjkBz8N5cI140C4BF5+6PklrfkQmGqtjnmNY3TadsAGD3GWoh9Zbkr gRCkK1Ep08Yv3LiTxmE81lGDGxdSNv6R1r//31/sLD4RbH9uVl390iPRybBoj/rdfpCDtaCv k0WgYm0wvbcir7a9vZ17fYlxhFFua/1w7/qPL8Q3r5TysqaDKz8mo6P+sNbiMV9ZlsKLkMe2 6xYB8IhMwyfr1k9JZelKn7tQDdPs3SOuG5KHdSSbQnWek6kMOfr50SCduVRuoZhSHrW+rvVd H/hC8KXMKnXr2TPFXxNtwBNRW8jVG6q/YcuB1uLTAw8PD1dXV2V3ZKi5ubnNzc1yIykHWv/K +eeC1y9x6qt1xqs/1Ub+y3/5Lw8e+ehHP/q5I//9v//32Dbf+MY3gm1+4zd+I9jm6aefLvg6 Y/xIo7MmETD7NQt5eG85XegGgFfkPEouEX6SUQZFarkj1vFdRWFHv8k6Kjm6+uFpSPVqf9zU +sTjd6aPdJS6+kSHSnPIgjhdW8iC99VXeJF3oO3t7VJeeXd3N/f6EisrKwWb0buulTorX7mU qa/WGa9+zvr57Gc/GzTvPe95T9I2W6+mdr7tbW8r+DoFW1s66xFIn3go8xQX22nmQuG6UQB8 IUcZXbcIFjLtUfk3WDVSD+TXfu3XCr5O0eYWY11TgtmvPpMjvqN2h8JRDapX++aRqwOlTgff Diu3IiO6Oy0i5yNUPNgiyyWUm87W6/VyT4M1f7i6ulpkgsP1ge792XO9I0/0ep3Zc8WnvlrX eDXfo5M5y0O1PE5nvhR5ipmvr451eapkLgKxncaDBICAua7Grg+UsPRQUvEH325/rYrT6aNe sezuMvvVW+Y8KnK5I0jns2cG+tipi8EXdEapl64+4rpFqAHidC0i1/zOXaM0B9nbHkdSmHnm H7q+hOk7bm9vJ21mWmV6Np5UFrP2sfyZ8Rrz3/7bf3vPkX/1r/7VF458+ctfjm3z/PPPB9s8 /PDDwTa5X2dcH6MA6+xX8yW6bpfvZDDaz0g0gIptieq65Nt6yJpO7mFlt29961ul9EAyvs5Y PsOIFhYW5FdDpr+3cqcPhzOWCNL56QWt7330zkqDr1RG2px13SLUAHG6FrHm+1SWlyRDGKur q2N6L9MFSZkGG77v/v5+r9dL2nJpaWl7e9vhaLBpnkwP9HPGa4zpswatfde73pW0jen4Du2C ZHkd31gPJ0/Cvt6SJSY9zJdEO93W+tTTN49KyiSOgf9I6+k/vWa2MRs99ZHjL/cvV9/OppLj ixRd8o2MpRrr6+uu25UmSw/k85//fCk9Gbf6/b78dkhK9ZbsQ2YJqoaljZiO4K1oX8K4snRM 6+uuGwXf+XtrwTjIesyV3a1lb7us4nRWh4eH1pXplW3psd3d3dXV1aQ5s+ZXTsYeZfCiLmu8 tjlOZ53+M76QdDOYS0FsjzExB/64cEOrzjmVXP45Op/FONtR17/8IFWiS5HvqRVVkhlA/hd8 aE+cTifMzCDe7accIxPRIB1zEXwW9iWMVyoj3bjgukXwnde3FpRO1j6orNcrFwKrIMmo3+/H 5rem56Ntb29bF1cN/nB9fb2yxCjrEKifwQt5/Jie6zuPfPjDH/7ikWeffTa2zXe+851gmy++ Kt/reEh2s5SXM4D8IUvUMdoPf0Snq/TOml5TfNL98ovxpYR+RamDZ884aW3DsIiE56z3O69i qRl7IP/zf/7P2DamQ5ijJyNfxwdy9qs5s4jpeGjUmf4E6WokOqQ3q9QtOgkYhh5P68jR6Wpq GcTetMr5m5ubm2GunPn30O339/fNZknzYU13x/x23GPFsnxelcUER/KhD30o6Vf/8T/+x6Dx p0+fTtom/IAFX8cr8tnSfKGuG+U1mc3qukXAHTe1PvH4nZUT5Ri4nBh73fzk0ncfOvb2G9+m UHQhLCLhOWs1Fd9udqZJDzzwQMpvs/dACr6OQ9a1X/0c+m25kS56Ozs7wQno7fpyiIr2JZLS 84EonoVax1pJZNyzKeWbrqysjPUdYw4PD4NppCMlxPX7/fX19aT5sOYjjGnQ2Nqj8naSAnE6 KRzhjCKlLoXMY/UqIwMt98jVgbn8KFv552i23cXTSg+29FHFul/efPG3jx9/+RoTW/JjEQnP WZPpfKvHqojTHbGWUiG446FY7Nv8X+tm0SAd/aW6CPsS0Q4DkIQ4XRtNTU3F7tbjHlXrdrux d8yS11a63N1HcwtMmg87PT29vr4ua94VIZPpfC4N+5GPfGTzyDe/+c3Yr77yla+cPvLP//k/ D7b52te+Ftvm9KsKvo5v5ERv37IMvCIL6JiLhutGAXeE01XkGHjSr36k9Zk/v7536RQD5rnJ ngOXBX9Y66h4uL55NT2QLK/jlnVpMlLqPCR7jzKXgiBdTV2+rdWJ88HXmrQyFRAiTtdGMtln 3KNqcv2KOuYWHR4emr6XdZF7dTST1/y2+EiytTKdz8tfhofT+9///qRtzJ4JtpmdTVyJvKzX 8YQ1cbWOh301ZA5pNfPxgSy+r/Xx3/9acGTGxsBTUu1Q0OLiYo1uhW0j+3XT09N+Lh/x53/+ 56X0QMp6HVfkeBgpdR6Saaqx6575ysLiKgTp6uXrWt/1gTtL5DH1FUMRp2spWT9rrMPUMoNv fO9VgWA+bFIBu5WVlSJL2cq5CZ5X5CFOl0QOirLwa4rYvvI5hxRtI4vQyZ+zfFvp5CXUdYtw h6yipbxMpgsQpwscHh6SUuc/mUsR/Y7MqRc+UnlbDwdJYqvDf7ijXu4vu24U/EWnp6XkdJKx LlMQe6+FhYXxvVeVdnZ2ZFgtYDpD5lejzoeVC18aGxsb42l+Of7zf/7PM0d+4zd+478cef75 +HqIX/3qV4NtPvShDwXb/I//8T8Kvs4YP1JJrCl1vtXu8YfMVHXdIuDHLtzQqnPOHJYnlfrm Z+47yrFLy7NDcbE6Td4uptRCMuWnysXBRjVSD+Q3f/M3S+nJmNcZ40fKi5Q6/8muYzi9gCBd fd02vYjnb3UWn4h9uedm1dUvPRL0KIAYHoRayrpK15gu+vKW07CsosPDw+3t7ZT5sKZjlDE6 I7tQNXoy+exnPxu0+T3veU/SNuHB8La3va3g6xRsbTVkJun6+rrrRnlKhrzr8i2jDWLTVTY/ elwPno+OjX/tD+7S+q9cN7M5WOzVW9YOZJFpBJUZqQfya7/2awVfp2hzx8CaUkfZR98kXfoI 0tXUmWdvK3VCpSIlHxJxuvaSlV/G1An2ZBGJCuzv7/d6vaT5sEtLS6Yjm169RfaffL4Zm0PI HDNLr3rnO98ZtPnnfu7nlhK8733vC7Z505velLRNxtcxb+3/fA05f8F8xa4b5SkZpPb54Efb 3NT6xON3qijOKnX8V3/1qQ8e+9T3vhUk2VGcrnRyhM//C35LWO9rHlamM03afa1//+//fdDg f/AP/sFugj/6oz8Ktvmn//SfJm2T8XXMfz3cLbUeD26JWG2iYMnXsCIk1XuBliBO117WtbrG kcAiA4KNr6ZvPuDq6qoMuqlX58NaK79ub2/HNk5ajt0T1nmdVapFvpWsBVmLvIPqyaUkzKXD daOAHwuXjDij1AvffuS+3/tKUJnOeFip2y8uu25go8hgEFk/npA3NT8r01lzx6o0NzfnYZzO uls8r6/SNrI054MPPhj8gyAd0B7E6VpNLtc1jgoj8n7jYcdlHIL5sCsrK0kduPX19eh8WLml /+kD8sutTF3mQMk6PktLS64b5SNzvtT0K0ZLhLNcg2XaruvBqUvfPcquozhd+YYueggnrONz 3nbqZO5Ylbwdk5NVJnwuL9hC8uoXIEgHtApxulbb2dmRt4G9vb1y3yVWx6SFvYH9/f3Nzc2k +bALCwumM/fiiy9W8F2UzmFKXV2e2ayJq6wmYRUb5Pc8nxRt84LW9z56JTg4g8Dcba1/69u3 VOcsxelKJ++Y/t8Q20CO7/pccdhhSp3PfV3rkmWcX/6Q9YKMqakp1+0CUCnidG0n86HKHa6R paDbnEzU7/eT5sPWq5MX5SSlrl6DivKBs6klGgsyF4fYjnLdIuDHbmt96umbwVzXaMnnT10b 3Dt1LlwEFqWQdxbXLYKlR2eMuq59xWRFkWp4m0wXkHdb/ydwtIccAn/jG9/IsrxA29DvabuN jY3YzaDcNdrlzcbPOiYV29nZSZoPG6pLEX1rvti41WvgVxZaWlhYcN0oH62vr8d2VF2yJtES F25o68IRa9cG977vE8EisA6b1ySxSwG5JD6o6b0saTbD+Pg/IC3Dl6wm4Q852+nBBx903SgA VSNOB0tJ4BJH1WSvjkyi0OHhodkbpptr7eeZx5Jut1uLgJS10GEpBWusk1bqlUwXiM3+Vkx9 tTGnQ2wv1SVajZb4utZ3feALwcF5ZemY1tfDX33plr77fU/+8QePvXztcw5b2BixSwHVKn0g A16eZ40FrCl1vV4vaanWkVhL4NVitTTZubIucQYnuAACIE4HSyitxFE1WQy1Ft2Xin32s5+V /byQ6Rl7Hq2wptSVkjgpe8Dl5ntWRoYyCVhLcslXZuLAKze1PvH4naNUrh3xuVv62Nv/8GxH Xf/yg0fTZJGTzMTnUuCcddKrtytIxMgB0bJS3mTs0v9kuoBcTYI11j2xs7Pz1/7aX4t+NWQ7 Ai1EnA6vkMk+ZQWGZA+GNCJJFgqRzHc0Pz/v7WinDMhOT08X7MFbk+lq+rQmJ5jXYrpQxWRx 67pUaUR7PHJ1oNRpc3CeUeqlq49Yf2t+/ZU/mWUObG7E6Twkx3RXVlZcNyorOQikyhg2tmbq 1WU0ut/vx1pOMMgHpp8vH8oUBTqB9uG0xytkkKWsuzWloIc6PDyU92PTIZ6ampI/D74a8335 Nh/24OBAdiwKptTJamU1TaYLyP1Tl0yEKsV2EXM94JtnBvrYqYvm4Dyh1Pcu3xf91W2tH3z2 llKzwdF7tqMuPXXmxvMXXDW1vuRyhxsbG64b1XY1nfQakgOixUfL5D7xefVbSbbf28HglrD2 pQP17f0CyIegCV5hnc5QSgX30ntFzSPrxYY5RHt7e8vLy7KAYGBmZmZtbc2fO7c1pS53+qRM rVI1T6mgQ5xFbC+ZDqvrFgGv8YLW9z56JTg+wxJ1YfDOKrboBIaSdxOWlHFOHtj1GmqyptQV CTXKgqqqblNG5Ghot9t13aj2Mv35cIRe9hi5BgJtQ5wOd8j6WaVkssResy5lO6ok+0ly0rG5 PZsvyDrIFsyH9SHXwDoMmHtsWVZOqfuMDDlpqF4D79WQKQ+uWwS8xm2tTz19U6mOOTgf66jB DdLlyic7JL6lkLeNLN1Qo0mvIdmvyF1awVqXo3b3dDlOTA67K9EgnekuMlYBgEcg3GFdCqBg z1j2ANbX10tqb3PI8sZJu93cxc3NW04lDkxOTi4uLrp9mJGhKJVreNmaTOf5YhpDyVOM4muS DFuTdQjfXLihVeecOprgeuvZM66b00BUzPCN6V3EvpE6LoVk7VrkS6mTi1wVmUDgkIw2um5R S0WDdNoWGSfVEWgbLsf4Mdkznp+fL/KCshR0KWuANoksTpcla2xvb8/csJPmw5qbvcP5sLJV OUbdZVJV3ZPpAnLn1LFbP1by4YcxZPgmnOXaUerqEx2Wdi0dcTrfyIK5/X7fdaPykCl1Oda8 sibT1bR/a3po3HOdCzOIw8cu1tIBQNcHPybvCqpYSp3MrqpX1eEKyJTDkWKj5s+T5sMGL1X9 fFhrSt1Iy59Zi8j4MLG3ODmZi2SxGLl8Xt3zKNE8N7U+8fidy9TF00oPtly3qGliNzXm4rkl SxjXNxl8f3+/eIhN5n0XX+DeFVllj3hQxWSQTtueDvhegLYhTofXkEOmRW4MsrxCXZarr4zM HsoXlTB/JYvOBswDz+LiYpXxIHkUjVSXUCbTNeYhTQYxazoCPz4ySkvfFB565OpAqdPm+HxY qdsvLrtuTtM09RZQU3IQt3aF2KKsU1azR9msk2freyvv9/ucbg5Zg3QBvheg5YjT4TVkHGFi YiL3DEridEPJmFSRgJr5prrdrgyTBczPzW8rmA9rTczM+NXLIUTVoFkYskQdK6vEEKdDLVy+ rdWJ84q1XMcjdhHgAdUt2ZerY3G6UMFZq9bFKGqaTBegRJ0r4TOX6Z/L33IZBFqOazHiZAmt 3PPOKDEz1Ji6Rzs7O4uLi0nzYU2fctxzCeVXv7CwkOUPZVZgw7omsS/FHACuW+Qdebi6bhEQ 94LW9z56JThErywd0/q66xY1hxzPIFjvlrwv17Q4XUim1Kls5WJLXInCH+UOGCOjaJDOOoLe 7M4wgKGImyBODpzmLuFPnC6dXESi9NvwxsaGrIkWmJiYML8aU2/MmlI3tC8ra5M1r78oTwqW koihbwr/3db61NM3j1aSUOdPKH37susWNQcF1H0jh29dt6go0/uSwccsk3llSKsBI0llFWBB dkODdFp0F02nvdImAnCt9vdalM7cMGQeVr4q/rF7DKlDMXKK3+Li4jjeyHynpk+QNB/WdMG7 3W6RBUOsZCd4aHdW/knBFYc9ZL7i2GdkMnhMbP8Qp4Ofll80x+pZc4ieUeqlq4+4bk5zEKfz ilxEohnlGqzjgunDZtZFruqeTKdZsqBy4Q5PryxErgPQcpzzsJChhHyPyrF4XzP6diWSy2x1 u92xvuPe3p75cuXYePgtr62tlVXATs5dUkeDtOvr6wsLC7GfLx2R25cePXROloCsdaGfcYgd CbnzeYGxemagj526qI5y6q4+0TnKsUMJZJxu3HdGpJBfh7mJu25UOeTQoLn7HB4e7uzs9Hq9 1dXVoGdiPq/5v7u7u7KX0oxurZzMy/DY+JijK3g4Mv9Nny9CnA5oOc55WFgjLDlq+TeyQ1Mi OdegsgUTzBvNz89bC9gF82FLaUnSlNuMxpRd6JZ85qnvOnFjIp+FXLcIsLhwQ6vOueAQpURd ibrdrqs7I6QGjy1ZU+pG0ph0+Njnsi5rgOKyB+k0cTqg9TjnYScjLDlmIMZegThdjIxHVJw+ FsyHlV2BwOTk5OLiYpEmmb9NWstiqHvvvbeCpWmr19Q5RCUiTgefnXn2tlInYofoYx01uHHB ddMaQhbJJU7nkPw6GhOc0rbbTXZNundz261AtKxQlsrL8tRr3hQTACm4EMPOug7AqHeI2J+v rKyMp7F15U/HyHyz3W43aT7s1NRU7vmwcl5JRlkGG2sq9kmb1NcvhT/nBZDkcn+wvPxp8xz1 25/4RGfxifuV+sHmrOtGNQRxOq/Im3iT1j6ylpzL6L/+1//quvmlMf3z2KcjJFQu04UOi0Rn XKaDKyHQcjz/IJFMsxppHqIsTMv8vpjp6eno/vGhDpf51pLmw6qjnMqRVhSR82VG0tRQnVzQ w3WL/CLngzcysxKAFU+nXmn25LsiU19NF67f77v+BOVwWIalDXIE6TRXQqD1GnW7Rbk2NjZi d4j0lYliqMM1VGz/eFW41/QkklLhzGGwuLg4NIIWluEoYnJysnkxmmY/9hTHAwPQZjydeiWW aD903fYaKV6frjGhOnnbHWlQFulyBOk0V0Kg9Xg+RBo5ETL7Yu3E6YaK7R+v4nSBg4ODbrcr 878C5ufmt9Y4mvlh0izaUTXpqSAgA6CHh4euG+UR4nRAm8mnU9ctarXYd9GYQg39fj82pyGf hYUF1x+lBHL+b/bePtKF9b5HLfNNnA5oOXo/SCPnLWafm0mcLl29ekU7OzuLi4tJ+XFzc3Ox QULZvSiiYV2TZpflLo44HdBmchkr1y1qtdh30Zg4XZEVJGIa0LmtV4+0RnIH6TRxOqD16P0g TXRxolDGtG3idOlq2iva2NhImQ9rOiI7OzvWw6YIDzMNiyBOl444HdBmVAbwx97eXuy7WF9f d92oEhSf8Ro1PT1d96T4fr8f+1C16JF6rkiQTtseo+gLAa1C7wdDyJjC1NRUlj8kTpeupnG6 wMHBwdraWtJ82J/5mZ/J3sHNqElLjxGnS0ecDmgz4nT+aGpHrsRkusDm5qbrz1RU7BM1r+RI xcIJSRkfmiTidEDL0fvBEHI0NeOtQt5gtre3x9/e2qh1nC5kDo/FxcWyStGlqOPOSdLtdmOf jjhdFHE6oM2I0/mjkXG6/f39cvsnqhFV6mKfqGHzGCoWDdLlXgxNnn0s7gG0Cr0fDCeLxWS5 f8sbDMGIKBmnG2kdKN+Y3oM5Tsqd7jrqIVcXnBrpNjc3Y/uHOB3QHsTp/NHIOF25k15D+/v7 rj9ZIbGP06ROV8VKCdJp29nXpBFrAEPR+8Fw1pS6ofMQCUakk3G6BgQjTI/k/Pnz+fu5ySYm Jlx/uNLIU2NnZ8d1ozzSyFMDQEbE6fzRyDjd+vp6uf2TQN27uLGPQ5wuH9OdC3ag6bUWCdJp 4nRA69H7QSay3zy0KipxunRNDUbI770srj9ZaeQuakBpmxI19dQAamRvb2/riPW3wa+sAwzm 0XQrr+CxVvY3hrYkh/TGWx+wzZ/kfrvqd2NS40fajbJKw+rq6m6ENYnM/Lx3xPrb7e3tXi7W 2inmLYLfWnuYYUui3v3ud5faN7ljfX093+ca2vhqdmPs40TjdOZIWB6dfJfGM2dZMK3E/Lf4 +OsWcTqg3Zrz6IvQOLp3sq9mBBtn795tbm5Gu3f9fl++1+HhYfBb6+JZ5k9287LuqywtySG9 8eFbx/bP/Px8jp6QtdsddqqsaY9ra2s53siwzsw1bxFtiexYlCXa+PSWeL4bP/axj8U+2u7R 0056f31lZWUpF/lq+qiKtmFduc8cqPneyLCeSuZdhrYkamFhIbZ/pqamZoTFxUX5auZiFfzW 2kU2fyJfJyNr47O0JIf0xqe0xCr9tFq29fuXX13qxHpeDH3NJNbGh1eM9JbkkNL49Jbk0ODd KF9Nv5p6U/puDHZU0p5JaUkO6Y0v9ytLb/yYdmNS48vdjT1bel3v1Rqj1tvZUt41HKw3kbAr ld6SCqyurub+W392Yyh6WOa7gMh3abZyg3SaOB3Qeq27jLbB+Lp31htG7vdK7yuU2y9RqX3T jL2W7NIbX/z1o3x7Phx3nK4Bj9lyndwggBv8u/SHDflq2umRb21JDjxml7IbfbuAJLUkB+J0 pexG+WqaON3oqt+NSY0vdzf6E2AiTpcRcbpxODg4CAs0l1XJhDgd0HLtuoy2RGVxuqBkWO73 Ik6X+/WjfHs+DGtzlCusT9eAx+wTJ07EfkKcLgcfHrODhDXP8+nSWVNTzQ+ztGRU6TsqvSU5 pDS+9K8svfHV5zXHbOWathaQr6aPro1DW5JDsKNmxHk6tCU5pDe+3K8svfFJLTE/T5qvkM75 vFfzwyxzC0aVPvUhvSVRMmW7FJcuXcr3uYY2ftTdaP2Kh4p9nBnq02Vmzrhw/LXEFeFknzbp /AXQSMTpGqh4Lzkm7JtOTk7G7hkp3Va5Sqx5XM9SIyN7PQ7zf7N3g6z7KviVP/Nec/TIrV9Z WFqo3CI76WV0wpYU6uomCLuMQeMLFvSRf1ulZTE0nbtj3UgyKOn8KwNQGRmnc92i9tpiHYnM 6n4fj30c4nQZjSlIp4nTAa1H7wcjkPGFycnJpI0b2b0rkYzTJQ251458yiquMTtHE6cbhjgd 0GbE6fzRyI7c9vZ2uf2TgHWAtkZiH2dubs51i+phTEE6Y2NjI/al0BcCWoXeD0YQrb8QMjcS 68aN7N6VqMFxOuuqIwWVVe/DB8Tp0hGnA9pMJuO7blF77e3txb4L6+pD9SLXNi1uYWHB9ccq pN/vxz5RY3qkYxVerMw/Sn9x2VekLwS0Cr0fjEZ2oJNy44nTDRXbP40ZvbTGc4uYmppy/ZnK JE+iuo/Dl4s4HdBm8unUdYtaLfZdJK3cXS8rKysldlGMzc1N15+pkAaPHI/PWIN0mjgd0Hr0 fjAaObiqEnKdiNMNFds/TaoGIrsXRSTlbNYUs7rSEacD2oynU6/EvotmxOlkWKqI6enpug+2 Eacb1biDdJorIdB6PB9iZHNzc7E7h/UuJdf9JE4XY/p20f3TpKyxElPqmhS+DITVTEKuW+QX GaezLpYCoJF4OvVKbAGxxiT+hwuRF1f3ZDptu+02bHy0XGtra8FeGmu/nSsh0HI8H2JkMlFO JTxIx7ZZXV2tvLFek91E1y0qk/U4GdXExETzYjSxz9iM9IQSNfu8AJCOp1OvNDUBfH9/PzZW mk/dK9MFSGPPLhqkOzg4GN8bcSUEWq4ht1tUTPbbFhcX5WaxbYhHxMh4xFhv+dULezP5TExM NGn5iID5ijkv0hGnA9qMp1OvyCkU+/v7rhtVDjntY1RTU1N1n/EakAX7mjdEWorKgnTadiXk SwFahecf5CHjLxMTE/KOFduGeERMGwYwc4fqGhmk07Y0wwYsn1cu4nRAm8kVw5t3Z6yRZi9Q vr29na+LEpiamur3+64/RAm47WYRBnYnJycrGFZvaiorgIw455FTrGSJYfrWsW1iGxCni9nc 3By6Dxvgi1/84k/8xE+M1Pc1vZOmDhvKwCV1G2NiDwwTExOuWwSgOnIwo5F3xrqQ96wGlGOL 2t3d/Tt/5++M1EWJmp6ebkCoLvahmlQuuSw7OztB2eXKRpGJ0wEtxzmPnOSI9+TkZGyb2D3G 9GZctNRfcoEt6/ThugtXxcrCHEXmqcB1k8eo2bkJpYjtn+YtJAIghYzTsfSkQ43PAd/f38/e RVFHXdler7e6uhr9Sa3v43IPcNuNqT5Ip4nTAa3HOY+crAt6xiIs3GPSHR4eNr5vFK3/8su/ /MvmCJmbm5NHzuTk5Pz8fBvWF5MnRWNq/ZSl8ScFgBTE6bzS+Jqq0Qzu3/3d301ZXML0Xnq9 XliQLhqqM7a3t91+kNxknT7OuKjweafieiyx7iJzC4C2IW6C/GRmUCxVnjjdULJH6LpFJYse A7GprFtHGlmELkUsRkmSqSQfjVy3CEB1ZNSgkZnmNSLrnLhuUWmi0xpWV1eDH/b7/c3NzXAB jV6vt729bR1Ra0aoTtZKbva0hpEcHByYRxsnuyX2pTBmCbRNc+61qN7e3p4SovWeZSCvAVU8 yiVr9zYpbrWxsRF+LoZnte2UaVhiQnFyMjhHDtA2PKB6RY65NqYvFwbjpqenYyu3ht2z9Fdo QKiu2R3RIhwG6TSXQaD1iNOhEFl6LJr8QimuoZo9jBkOwsvlgKMf2VHrHJAFuRtW6Kc44nQA eED1iuzLNWMpiehaXnJBp4xxOi3WBKtdqK7xEztycxik01wGgdbjWoxC5PwUFZneKNeaqF33 ZdzkDpyfn3fdqHJEe/ayi9POHuHi4mLs62bUOsZcImK7qEmRawBZxOoDsPqkW7JiYDhFtL4O Dw/D+JS1ukL2OJ0Wd64a7Z9+vx/7cokHBcJEBCfz7ikaCKBFT8gYEzkhIow0yb6dHLFsObmU hFw2N4fnB3r2T76q1Kx5wbMd9f88debl/uXoBj/S+pc3XzQbnFZq89Ozgx9uFX/TqL29vfBB y/qIFf3I5b61z8Kx2RCLSMTIDNPoVHoAbUBxW6/IpSRKLxt6faDPPfVc2Gn50ufPv3z9uegG L2v9b65cDzotVy6eG9wqOjljfX09/DjWAbOR4nS6tqG6WDKgIh50JBxVdTV2znI6AOj6oCh5 LwknOcpfMctPCsujhGLrLYzqS7f03e97Mvaapmv7lT+Z1YPng22eGehjpy6Gv/03s+rlaxdK +DCvik6IJs4SkMXpWCFBij47BUg5BNqGOJ1vxjrI9K1b+uTZi7HXN52Wv3jqnB5cD7a5OtCd 06/ttFy/kvsdo0lkSVViR43T6XqG6lZWVmJ7nj6bfrVKicMJLtH6zoFut+uqMQCcoOuDEsi1 wMJhn9jPqZovxYoQq2IT/W5qfeLxeIWv0FMfPDb44TP6tXG6r/3BXV+6dftfz366rFBdND5L KCoki9PVohNfMVnQ2nWLAFRNlr4tOHyFgmTRhrJK1L2k9ZmLyZ2Ws53Brav6tXG6586f+Nat 2+fPXcwdqoveaJLWxMgRp9NH0xWj5d78v8tTnC6J23ilLApJ/BRoGy7HKIEMQExMTAS/iv18 YWHBaUt9JItQFIltRQNwHaW+8JHjwZzWrR/q2U9vKjUbhOpe0PreR68Em11ZOqb19eX+4E// 4T/Ug28U/0TRVAgerkLyyZNyjZK5RFivJADag2dU38jsnpWVlVJeORqAM52W3rnZYE7r7i19 7uKVVzotR6G6H2h9/7lXOy3nOlrf7F0b9B5+WA+ujfqO0U5XShwtX5xOHyXrRYNf5nViK8n6 Qy7cRHE6T3ANBECcDuWIVX1WryaFMXtlKFmirkhsYvlFrdTZ4HUunlZ6sBX97dq1wd3v+6On Pnjs9g+fOfX0zaNesTp/Qunbl69r3bvwfPFCddGgLdU0ouQ54m3f3SGeGQDIRag2NjZcN6rt lFDKLax3PdZpeU3hue3rg5NnP/nU2c7tW1cfuxLttDx3U+v/dmU3R6G6sNjI9PR0ykfIHafT IlS3sLDg5+1eFppgcqUnZEmcoKAQgPYgaIJyyJGfYD0EeaehcL4k5/rlfiYJ43QnlPre5fvk Bl+6pe9935ObHz3+yLevHVWAUWeUeunqI4U+wKtMNyKcBB2WKYS2JSOQWyqZi0NsLzFvGmgh aqh7SHbnSkkJD+N0r3RaemflBt+6pe8/e/Hpx2YvXo10WnYv5ny7yFJFY13ZrBahOvmdUhDW E2Q5AOC0RznkcmDqKElbxu92d4uu0tU8cr2t3MVrh8bp9FGo7vivPvl///F9x079oToanr76 REfr2/k/QPjuka+7SJW95pGTXssq7tMkcg4OD+dAC8k43eLioutGtZ2scFLK1NehcTp9FKqb PXvx3z1xtnP6/J1Oy8XTWg9Gfa/Dw8MwdlbBIFC/348GwnwL1UUX0wgE4+vwQWwGBl8N0ELE 6VAaGYmYmZmRs1cIT0gyjSj31NfLt7U6cV6lxumM5f7g7x976K5f+IXg7YISdTlb/6q9vb2w YzE1NVXw1RpGTnolsVSSAWuivUA7ye6E6xa1nVyyXJUx9fW5aKclIU5n9K4N7u88dGJ29k6n 5ahE3ajvFV22q5rEMbN/okVXzb+Tlq2onpz0SjTcH1wAARCnQ2msfbgnn3wy9pOxTjSor1j5 /NwRinCBiPQsuR9pPf2n14JSL8ZjHTW4UXSx12igdmi92+gnLfi+/pOTXpnOaSUfGyicDLRT 7FLA2I8P5Fy84lNfwwUi0rPkXtb6w73XdlpujrbYazR9bGlpqWCzs4uF6qanpz0J1cmVXpn0 6gm5vlzuSTYA6qv5T8iokix18eCDD8Z+UmX3qEZkJlG+UM5NrU88fmfyYO+sOcGfT9ryqwN9 99k7hVpmlbr17Jn8rX/tNKUsLY9+0iLvWwvyvCCr1EoWaqTEIdBOlGfykJz6WrzQ6ktan7kY 7bQkpvb/5UCffCjSaXnu/EhvFL2/VJzP7mGobnt7O/ZVMrPSHxToBKCJ06Fc8tZi3HPPPdH/ SyaRlZz6auzt7eV4qUeuDoJay+lZcre1fvDZW0fd3RJK1EWfqbI0uz1PX9Y8Uya9WsWG94ss fAyg1uTwRr4bIkpkrURcPOR0cTfSaUnOkhto/cnnIp2WUUrURROU1tfXCzY4B99CdXJUjEiQ P2Rpbxa8Blqo4U/IqN7U1FTs7hIuABryqpKuP8rqNoVTX+9X6gebsylbPjPQx05dDN7r4mml B1v5Wh4dY8/Y5ujHzPemdSH7W6SUWpnLQmxHUZAFaC155WQWvA9kJeLV1dWCrxlOfX2l03Ll XMqWVwe6czraacm6LlkY9p2ennbVBTXvGy2QZ1rial21EgeGMQ6Li4tc/QA0/AkZ1ZPTIu66 667YT1jy1aqsaQjRRLneQ8f04BtJW0YnyeYuUXdwcBCGYicmJjJOVIx+zBxvWiMyTl28oE8j ycVeqWkNtJbsS3S7XdeNgmXaRPHIVzRRrvdQRw+uJW0ZnSSbvURdr9cLW5u96EQ4dJpx+4yi oTpX/QFZCpZRMa8w6x+AJk6HcZCBiRiKcyWRZX3zrSYR1p77FaUOUgvPhZNkhybfJYlmPWRv bUv6H/JR0+FgvudY7BVAiApN3pJ9vOLrg4W1517ptKQWngsnyQ5NvguYG27Ysxqp7koQpyte gE9yG6qL7pAQ0yq9EjvFqAECtFOTn5DhSrfbVamKz5JoKjnImXuRu+X+QHUeUsNS6i7c0Kpz Lnivr/3BXVr/1Ujvsre3ZzoQwZ+PNB4b/YwjvWO9yEFRJ5VxaiH26KKY6AG0mCyFRsqPJ2Qf b3p6uvjL9q6FnZa0lLorN3/caXnu/ImhdXWjd5aRJnNsb2/3er0xBdEchuqi2YUBVpDwDZc+ AJo4HcbBdK/D2I3VOMYnm8FaNCRftOJHWk//6TWlOukpdQVL1EVL1YzUzugHHOkda8S6rAor SCSJ1thu9oEBIItYR4JogiesfbzikaaXtf5w79VOS3JK3Ugl6vr9fthC3yrDxuJl1YTqrMl0 zCj3iuw6mp6260YBcIAHIYyFrIHKE3hGMqso90jaN0x39tFnzCt8clYNbl22bvN1re/6wBeC Nzqj1EtXH8n++tHOxKjdiDYcDDKZbmVlxXWj/BXbV7kzSQE0A0WavCVX+RhpSmmSawN9+rEr r3ZanrNu8z2tT5zt/bjTsnsx5QWjy3N5OEgWq0pcQbq9TKbLXlYY1ZD1UpjyD7QTnR6Mxd7e nkrFUhJJZDV9VWAC4Jdu6bvf92RHqc2PHteD5+UG0TjdqCXqwoco088bdaWwxj96WZPpOOyT yMOeAWSg5Vj00FvWPl4ppYe/dUufPHvRdFqefmxWD67LDaJxuvQSdTs7O2HbvK04EQvVjbUs jDWZjhiQb1jqGkCgmU/I8EF0RuSY+nNNFR0BDhQpThGE6k4q9c3P3Kf192O/vXBDn/vbv3nv b/3b4I2uLB3T2tIzlqIjfjn6edFPN+rf1oLMBPFt0o1X5CA/M3GAlpN10Lgs+EP28cpaJSkI 1ZlOy7cuntX6Vuy3V27q3/mVc/f/7+fvdFrOdY7WrreYm5srt2FjUlmojmS6WpC9x1EHwgE0 QzOfkOGD6EimxATAFNaUuiKrcQUTYE2v9z99/L7BD7eiP//bH3nm6090Tjz+neBdHuuowY0L Q18wWp5mcnIyRz8v+tFG/Vv/kUw3KnNBiO0ucwFx3SgALskL6eLioutG4Q5rSl3xhV8DwQRY 02n58/NnB7d2oz//lceu/I+Lp89cvPrjTsvNK/IVojEp/weGt7e3o5lu4wjV7e/vk0xXC7Hi jyz2CrRWA5+Q4Q85KBQqpZRJg8mUuoIltH+k9Ye2X1Sds6eV+g+fnjWds4cvXjz29qWgdN0j VwdKmd9kLVEXTctfW1vL0Z7oR8vx554zX1bs6yOZLp18fnDdIgCOseSr56yViMsqA/ey1n/8 F9eDTsvnL57r9XpP9Hqd2XNB6bqLu5FOiyhRF53gWZfeZr/fj94HV1ZWys0BlLWPSabzEBc9 ACGehTBGGxsbsg9XemeukawpdcVHPp8f6KX/6/+7+32vzHLtKPXUR46/3L+zvsTWD/Xy8sbP f/xzV5/421rfTnmR6Ch67g5EgyMycq6WIpkulVzmmI4pAG0b83DdIvyYdeHXcmdLXB/oz/7Z cyfPng86Lf/u3OzL1++sL7F7S/d6Vx46//mrF39F60H0r6Ixqdw33+3t7V6vV80yrIFYqG5h YaGsUN2YupQoHUnEAEL0eDBespMdqrL3U0dyJqAng59hzRdVoLptU5+7rM8tJNOli1Xn4fkB QCB6uwkwI94rsua98++o3++XcvMNpjVUfPseU6jOvE7sO8pXsQTjJk+ofHNWADRAo56Q4SG5 vnhorMtaNYBMMlIezOCIjvUVWZEz+qHKa5171uVTSB1NJ+fjFKnGCKAxeGr1nxyONR0Vh+s2 RMuGFLn5OonT6TGE6uTyEZxH3mJkAkCoUU/I8JA1vSjsyblune+svSu3C7RPTU0FzTBfa5El qKKfqLzWOWZdPmJ9fd11u3wnO6aM8wPQzAKrA2uFE1c3vugKZgXb4CpOp4/q60Uz4My/cwcc o9mFISpLeIuZ/gBCnP8YO+u0iAB5RumitZBDDmcrRLMjC85MbF4vxHwpsoNlvj6HaQW1IPNG p6amXDcKgBdkVXWuDx6SYy3KUVXWsCXFb74O43RahOrMx+n3+zleR854VaRo+UquoUxEFWiz hjwhw2eynx2iRN1Q0cHhUJEJp7lFUyOLxwqjH6ekBjpmXfmOI3woWZyOfBkAIRJM/Le3tydn TlQ/+zU6BWFzc7Pgq7mN0+kyQnXWORmUf/WWTE3lywLajO4OqmAt2qUoUZdNtNhKqPoCXtG8 yOKVTRr20GWd+MPyEVnI9VIoTgcgJPsPbos/wMq60Hm5a7+mi84/KKWsivM4nS4WqrOu8To5 OTnO9qIQOdxLdwhosyY8IcN/Mpc77HO4bloN7O/vy9mvFa/9Gv0GycOPsRZhNF8Z07qzkMe2 6xYB8IgMAJFj4ifTN5DdvOJ5bRlF1yMqZcqtD3E6fRSqi360jKE6a9UUxYxXv8kzqEgZaAB1 xxMRKmLtwBn5Km60jenpyl03pnjZxMSEeS6K/TBafYZchhi3Dye1Jgf8iQIDiJLFH1iEyk/W 2a+ld/N6vd7S0lJsGCy6WkJZkTVP4nSB2KroQ0tqWOdhEOD2XOz7IvkRaDnidKiIdSlMw3S5 XDetHuT0QDWGSl7hE5HpH4TxuOh356Q0ns+sy6R40rP33/r6emzXyRgxgJaLRX/M/3XdIthZ S0CUu55SGIEyvcfwZaNhqbIy2b2K0+lRQnXyxqpYgMV78imJ/jbQcsTpUB3TS5Bdh4WFBdft qoekWQzFS8VFRVd0VUfJTXt7e+EXZ56OSMKPquCZpNnkUnTMygEQI3OWuVB4y1qP2Fzqy7ot RjtC5t/b29vRjMv19fVS3kX7F6fT2UJ1cmkmOm+1IAd9GbYEWo44HaoTiwGFKOOVkbUqsOl+ lfjEYl20NMSkiSiz261zfHiAzMic+LFdxywPABJPsDVycHBgHZQtZd0wedeIGkfinldxOi1y 5WKhuuj83yiWI/AfoxEAYojToVLmOVx2IKjklV2v15M7sMQ1Jazd69LfpQHMrrAezCUO5jee rLrILA8AkpwRRok6nyUNYhUP1clihVHl1lHxM06nRcZc2Ovo9/vWWRelF0jBOMgut+sWAXCM OB0qZS3mtbKy4rpddWItDzw1NVVKEC2lB6xeW7SuxDcq5QWrlJQv4GGH3mfySGbMH4AVD7H1 Yi0KoTIsgJDOOlQZFS1aV5C3cTotQnWrq6tJpVFYmqkWGIoAINXvCRm1dnBwYB1lpZ5XdmZf RVdfLbE3lrTWh3yj4oVOoi9Y8KWqZ93/5occxtmZfRXbgTx4A0jCpLDa6Xa71i5EkVCddZwy JihaV7z9PsfptAjVve1tb5O7oqwRXIybrDnD1H4A9XtCRt1ZK6CV0qlqj6TZDQWnDSb1qq3M 91ik/xd9qSJtrp61SLb5OsyX4rppdSJrXTPpFUASeXuiXqr/rLfLIl0+a8/HamFhYXd3t0jj PY/T6YQlI0KsHVEjcooG3x2Amj0howHMvUf2Jz7ykY+4blfNJFVpKRLsSOpSW01OThbpRkRf KveLVC9pF5HZMaqVlZXYPix35WIATSJveUzoq4USQ3Xpi0iU8hZR/sfpjP/1v/7X8ePH5Wcv d4UxjNXBwUHs62NNLQCaOB2cePDBB2WvgjmDo0oaSs0dqktZRCKmnZMpSk8NaC056dVo4REF IDtZNIOLhv+SyrnmuHWmLyJR+n3Z/zjd/v7+wsKC9eOXVUoYFVhbW4t9fUwvAKCJ08EJax20 //Sf/pPrdtVPUk3lfPf4jN3fmZmZFj4dJQXpWOA1B7nSKyWTAaSTF2FWnqmFlFDdSDfQoYtI BKanp0tJJfM8TpdU/8R485vf3MJOWn1xZQNgRZwObrzlLW+J3ZaYw5LP6uqqtaNmbvwjddQy LiLRzlG+pCCd2fmum1ZLMgWAXimAdGSd1FdKqC77bTTjIhJl1Yr1OU63u7ubXqqvnZMeaopM YQBWxOnghuxwGy+++KLrdtVSUqhupI5alkUkFhcXx/gxvFTK0wWiZI0hVnoFMJSs4sSlo0ZS bqYLCwtZKp8MXUTCvE6JCzqZl9rd3fVwhaiU5SPe/OY3h/8mVFcLcoycrAUAAeJ0cOanfuqn Yjen3/md33HdqLpKCdVlnAAydBGJFpb539vbI0hXuvX19djOJCkGQBbygkyx/BpJCdXNzc2l R8SGLiKRMdhXd0mdvaCTFtvDrCbhv8XFxdj32O12XTcKgBeI08GZ3/zN35QdNdeNqrGk3pvp qGWpKJy+iEQLg3SmdysnIwQI0hVhTvPY/qTiNYAsZN53C7O8a+3g4EDeAgLT09Mpiz+kLyKx tLTU+CCd+YBJq0ZE43GE6uplcnIy9m3yfQEIEKeDM6Yz8RM/8ROx+9Pu7q7rdtVYykDr0AG6 pD8cUycv+halv3hx1nnZAYJ0RchnLdNJdd0oAPXABaQZUvL3zR3WGnFLWUSiDTdlc+QnTfuV nTRCdXWxt7fHBQ1AEh+fkNEeMt+7Df2tsUopXJKyskTSIhLj695F32Ucr5+b2UUpjxCbm5uu G1hvMpS8vLzsulEAaoP0k2YwV/6k+6x1DmzSIhKNX3L98PBQFosIJRWhi+UtEqrzEwnCAFL4 9YSMtpFDSUbjJy+MW0qoLqlcnXURibEWIY6+0ZjeIgezc1Lm/6ZMyUEW5tSWe9VcBFy3C0Bt UM6pMdbW1pKKSxi9Xi+6sTWbrPE35X6/nzTXVR0FNNM7adFBR7OrW1jAxHMU3ASQwqMnZLTT zMxM7C5FylJxpm+XsjKafKqRGWTjXiks+l7je5eRpKx4a3amh4u+1Y45tWM7lnXNAIxETn01 dyvXjUJO6WNjCwsLQS0UuYhEejG7BkhPo1OZU9FjvTtCdf6QmQosYA0gypcnZLSWLATGahKl MP3alGHYmZmZaB5TrKNcwfqb0bcb93sNZXaFjBeHzG40O9N1G5tAlg/nmQHAqOTUV9Jy6ytl ZYnA+vr6s88+G/1JNSNn5n2XlpaczKvd2dlJ2ScZFwcLEarzkxwbrqDvDaBG3D8hA3LiA4nf pTg8PExZWcLs9nA8tvqOQvQdK3i7FGYnpEy9SappjVHJLBiGjgHkIBPAKXNZd91uN+VG/LM/ +7PhvytLbw8q4pn/VvBeIfPRkirxBWZmZnLMdYjNFidU5wOZSbqxseG6UQA8QpwO7slyMysr K64b1Rzb29spc2BNRyE6pldZ7y3ahmreUdra2kqZcaOYgl0q+exBvWQAOciFj1gksQHS58AG /tk/+2eVjZxVHKfb399PGVgNFCnFGJu8QmjbLSa9AhiKOB3cs64mwUzDEqWXIg5VOcQafd/K 3jRkDrmURV3V0VxXCtKVSFYXUkxVA5AXq742Vco6sIGlpaWgaF1jZInQJS0CNpJYqI5Zlg7J BAW+DgAxxOngBVmJw/RaXDeqUYbWJFZH46tjXTsiKvq+1bxjwHzAoY8BZkcx17Vc8iGEMpQA cpNPueTnNsbOzk5KxdjAwsJCA9aRyBKhm5iYKHFFY0J1nmCkAcBQxOngBTmNZXp6mlhJ6XZ3 d9MLNgdF6yqI1kXfdNzvFQgidCkVcNRR8KhhA/U+MCey3NUjlcEGgCjqXTZeesW68Ja9ublZ x76i6WmsrKykf7rgA5aeeE6ozjlm7gPIgjgdfCEHl3q9nutGNZDp0Zodm941DKJ1Y52WGH27 8b1LwHyQoRG64HirY3fff/J4o0sKoCDZZ6AKe8McHBzIxEmr1dXVWoyx7e/vb25upg+XBqam psY3mmVeOdojmp+fr2wuBbRtJRzSgQFIxOngi9gQnyKlbpyGrikW9t7GlIoffZdxvH7AND69 Dl3A7Aqq0Y2JOYXlMiYsNgegoOgKSOENy3WjUL6h9WRDc3Nz6+vrHt7NzX1we3s7SwKdOhrH quAWaXpH0VDd1NQUobpqmP0sh40p1wtAIk4Hj8hbVwPqj/jMdNQyjuuaXmO5fbjo65f4sgHT VNPgoSvHqaNuPcfYWG1ubsb2OdPTABRnXYGKWENTbW1tDS1aF72zr6+vO8+wC7LnMobnVFUR uhChOidkUoLZ864bBcBHxOngEVngn2LzFej1ejLjSTL9ufn5+bImFkVfuZQXDJjmmUYOneKq jrI1mVhdARkINqe560YBaAIZuCmx4j48tLW1lTG3LrSysrK5uVlZzG5/f397e3t1dTXLIGio 4ghdiFBd9bhqAciIOB08Ys0GJ92pAkHRuizROhUJ2BXpz0VfsGDjTTOyh+fUqxE6plRXwJy8 8uDhMQBAKWRmCrUv2yCYCZvxjh+1tLS0vr4ehO2G9gGC2iDmv+mb9fv9nZ0d06lYWVkZKTYX mJmZcVsIwjQ+OvmAUN1YkQUMIDvidPALKXUOjRStC7uY5ivLUe04+iL5Wmve1Lx19okwighd 5UimAzBWMljDWtItkb3ARUqXYOlI70gQvwstLCyYbcx/w58E8ThjdXXV/FWOqFwoGO8cU/3f UZk9Gd2Nk5OTnjSseeS6KFTVBJCEOB38YroLskNDSl3FzA7PNyy8vLy8sbGRpYcX/cOMrTIv a1581NhcgDp01Wt5Mp35pFt1EJxT/jOPNzMRPEYGzH6Yabc3vvGNseuM+YnrRhVlHt1dn3B1 8vDDD7/1rW/NkV7nhOmNeLiSUixUZ3Ym19hxYFwBQHbE6eAdWXyElDondnd3V1dXc3dGTZ/P fHGmD93tdoOIQDRGE90y9r5hgMP8oflz8yIzowfmQr/zO78TGySvWDgC77n19fWlUv3sz/5s 7Lso5fm5SPYEGoNnm4DZD66/CgDDBeE5n0eqCNWNG/P0AYyEOB28Yy3fQDKUK4eHh5ubm8EE EADwAXG6AHE6wFuTk5OLi4sFi/lWybQzOixKqK5ccsiZFSQApCBOBx9ZU+ooK+bW/v4+ATsA PnjggQeCZ+Ck65V5NjYPmbOzs+9+97vNf82/l5NrI5qHpdjGKRPTzOuYDX7xF3/RtOH+++9X qTP3zZYnT5585xHzj/TsCfNS5mXf+973vuc975k6krRlUCAimCDp+JsAEGHOyiA2t7e3l3Ky +yx6VTFXMPNZXLeoCXZ2duTRUpcALgAniNPBR6Z/I4s49Ho91+3CK4KA3crKykgrTgBAuVIi Wb/+678e2/j+++9P2nh2dja28bve9a6kjScnJ2MbX7p0ybql9cEs6endmhmX9BRnnpzz7zUA JZmZmQnqe2SszFsXsQEADwvq1Y4cU2EFCQDpiNPBU8ti4dfp6WlS6nyzu7vb6/WI2QFwIimS Za1jmHQdk8NCKYlv8mWTMvW63a7cOOmJV97yjKRMFrlo4N/7e39vpDqPWfcv0Fave93r1NGl IFywwpy8QfHcJoXkkhCqK5F1iTyqNwBIR5wOnjJ3NfnstL6+7rpdSLS/vx+smbC0tMT0WORg TvniC01UIEig8F+4hIvnsj/0yviUNZJlfShSCc9F1qw3lZD4tmXLejOHhLW11lWzk3IoZmyx s6SJvTIK2fL5U9aytm0IppTI7EO31wHfBLPak87uNiBUV5ZlMQyTkgwOAAHidPCXvLEZ+/v7 rtuFrMyXtbu7u729HawourKyEl0P9F/8i3/xj2z+8T/+x+9///vNBmb71dXV9fV1h6ugpnO7 kmx2/X6/sm9cnrPLyXXBgJHI+Z7WSFbStFDroWjNekt6KLXelVRCpp4calLJmXrWl7U+y8ko JI98mmllKFsQOm9znE6LJUpZ9yAfeS8g6AlgKOJ08Jd5GpGVgFZWVly3C4Dd0tKSjEq0PNMH JcoYopJpdwHrI7c16y0pymPNelO2TL2kND1ly9TbSl62VZ4+GYOVbWPdh/Wt5Q/niNMFYqE6 wt+jiu1AlVpXAQBCxOngNXl7M3Z3d123C0CcOTHl2cqgMcqVZcqntThdQL6gNest6VEq6WVl pl5Smp71pEhK01O2ib0ZJ/+2kPzeiWAiN+J0IUJ1RciEAyYZAMiCOB18J3vec3NzrhsFIE7m JTEdD6UbGqVKKk4XiCW+pWS9KZGNlZL1Jh/mk9L0lO0pNylNT9kiTRSnSyIH9iYmJtg5yIc4 XRShuny4KAHIjTgdfGd9NOr1eq7bBeDHzCkpz1M5GRAoaOisz6TidIFYIkNK1psSiW8pWW9K ZOolpekpW6ZeysvGgt0Up0tH6grKQpwuxtzQo5c1QnVZyGEV9huAjIjToQZkYsL09DQLSgCe MCejOSVjJyl5rxiHoYGqpOJ0gdhTd0rWm3ygSsl6U6+NSqen6anXZuqlpOkFoskXFKdLR/YK ytKYON3l/uDMU5dU52xwRpxUav3j9724/bta3x71pcyVLRqqM9deTq4UFM0EUARxOtSAuavJ 3AQWlAA8YU5G+WxMZxRjkj7xM6U4XSD6UilZb0okvqW/bDRvKz1NT702Uy89TU+9dmIvxenS mSNBfqek1CGHBsTprmt96tJ3lZq1Xlj+6IPHbnz7U6O+JqG67OTQDsl0ALIjTod6sD7JsKAE 4Jx1+QgejDE+KbEqmW33kz/5k7GfhIlvMuvtp3/6p2M/CcPNQ7Peos/z6Wl66rVPa+lpeuq1 GXMUpxtK9hZIqUMOdY/T/Ujr6T+9plQn5dpyWqmrl2a1/v5Ir0yoLguS6QAURJwO9WA6AbLu jHkWOjw8dN00oL3MCShDEtaFMoGypMz9lL+SM7LDILLMepMhszDxTUZ/Xve618V+ErYwPU0v do7EfnXPPffEfhJO7KU4XRak1KEUdY/TXbihVedceAp0lHryI8dvPH8h+O11rX+39537Pn7p pOp88zP35QjVRccMCNVJJNMBKIg4HWrDOja1vr7uul1Ae5kTUJ6VLB+BsUoJV8lUu49//OOx n4QP3jLELCN34ZOVfOianZ21HvkyTc/8rcyDCxIr5H3NtEoOSgXPwBSny4iUOhRX6zjdba1P PX0zTKY7qZQ1GGc2W/7OrXunHs0RqjMnVPSyZq5a5tJX2geoOesDC10jACMhToc6sU4m6vf7 rtsFtJF1xivLR6ACSdM/reEwmV0VvIj157GfhIlvsZ+bv5VBvSBpy/pzGUAMMvVkRMn8+fz8 fOyHwcReitNlREodijNnqDlmYos+18X3tT7++18LD/6Lp5UebCVt/MwP9YkPXy4eqjMnHaG6 gBzXqWnAF4BDxOlQJ9bO98LCgut2AW1kTr3YyUjSCqphjVjJPLsgyiYHeLa2tqxZb9r2fLW3 t2fNekt6BevbyVS4IFNPvp15WbloaZA3R3G67EipQ5t9Xeu7PvCF4MjvKHX1iU766q7fGOif /5f/761nz4z6RoTqJJLpAJSCOB1qxrqOXq/Xc90uoF3MSSfPRHN6um4XWsE6AzQpFmZNcEvK hpPxnSCtxnqoWzPyrD9MiiHGfmj+Vh8tcR77eVD+Sf7Qyc6vBVLq0GbRON2sUjkCcNkRqouR Ayok0wHIgTgd6se6Oh6zX4HKWGe80hNFZaxBq6S5pdbEN2vWm7alQszPz1uz3rQtde7JJ59M Oi/kw9ulS5diPwmnjcsSdf/hP/yH2E8oTpeOlDq0VnTe6+yY43SB6Gx9c6K1dkq+zIZWLPMK IBfidKgfa70h1n4FqmFd49WckvREUSUZ9jp58mTS05G8ZSQVrdO2EnXyb4MtZVLeAw88EPtJ mMMlw4jWhSyCjWWJune+852xn7T2STgja0odSy6iDW5qfeLxO8NpJ5T63uX7KnjT2FWrpqX9 CpL3C645APIhTodass5+Ze1XoALWNV6Z8YqKybBXTLgEhE5Ygygqmg1qTdmOCrPeZKbeG9/4 xthPwrJEclqu3DicLyaTMt7whjfEfkJq2FAypU6R24J2eOTqQKnTKlt9urK0PFRnfTbhggMg H+J0qCvro1TLi2IA4yYDE4oZr3BBhr1iolkM1senqGjlMmtwJyoalZYZWzHhlnKubkyYpqdt JepiKE6XkUxvYU1qtMHnbuljb//D4JjvnTUXouered/WhupI4AVQLuJ0qCvr7Nfp6WlmvwJj Yk4uc4rJ4ALDxaje0LBX9PnQGl+Oii7GZ12tLyo6IJSeqRcLYcu5ulGx+JEMMEVRnC4ja7ko 1l5E40Wnvj7WUYMbFyp769i4SEtCddaCmPSOAORGnA41Zu1/Ly0tuW4X0Ezm5JJnXEu64PBQ etgr9oCUnvgWe+WULaNZb3pYpl5sgdH0ubqxyeOyRF0Uxemyk8cJ2Yhog+X+QHUeUtVOfQ3E +ueNTyuzZkCzwDSAIojTod6suQy9Xs91u4CmMaeVPNeYQQaHUsJe0eJ0gZTENzlxO6VEXeyY T8/Ui+Vtpc/VjdVtsA5EhShOl501QZIBBjTej7Se/tNrR2E69clZNbh1ucp3b1Wozrq4Fldp AEUQp0O9mbugdXLQ7u6u66YBzWFOKHmWmVOPbigcSgl7yWfClMQ3mfWQUqJOLpmSkqkX2zJl rm4sTU+nlqgjHWxUMvDKUzTa4KsDfffZV8bYTir1l5d/3ppSZ370W9++pTpnH5tVN76zXOK7 tyRUx0gAgHEgTofas94gKVQHlMValk5R4wmupYS95DNSSuKbPJJTStTJ1YqSMvWs66skzdW1 pqYmlaijON2orEFPdiPa4FPXBsfe/q/NAf/hjnq5vyw3eEHrex+9EpwUpafdbWxsREcyGhmq kxdqhlIAFEecDk1gzX1YWFhw3S6gCcypJM8vCq/AB0lhL2v17qTEN+srW7eUWW86OVPPeo4k zdWVaXo6uUQdxelysO551ohH44XpciohDBddcWJWqVvPnim3AeYsi157Z2ZmmpTKan0AYRQT QHHE6dAQ1nJC6+vrrtsF1Js5ieSZZU0UAqpnDb7I4nQBa+Jb0sFsvadYs96SMvWsj2pJc3Wt AaOkEnVNesqtjNlpMlDLpQxtcFvr5e/cOvb2V1aCeuqDxwY/fCa2wTcGuvPoM+a39yv1g83Z 0hsQC9VNTU014yK2t7cnryrU7QVQCuJ0aAhrF9zY3t523TSgrszpI88p6jrBH9awV9LUKmvi W1JmqDVLwpr1phMy9axbWufqWtP0dMJsTWZU5WaNe1JGCi3xzA91Z/Hz5pi31qG7cEOrzrkz Sr109ZFxvHsjQ3XW5SOs2dwAMCridGiOpEJ1/X7fddOA+jEnDmXp4Dlr2Csp8mJNfEs6nq03 lKRpku94xztiW6ZE006cOBHb2Px50sbHjx+PbUxVtSLkRGkGHtAeP9J6+csv3v2+f9tR6jMf Of6djWWtv29+/vxAz/7JV0+q2W9+5r7gJ+Ngrp/RUm51D9VZ7xGUBAFQFuJ0aBRrugRrSgCj Slo7IimfCHDl5MmT5si85557/ubf/JvBUZqSzmA2Mxv81JFg45RXDjYINzZ/m7RleOt5wxve cNddd6VH04K5umYzs/HQ0yooURfdmOJ0RVhjtY2sbQ8kua3148/fevjixWAmrBGE7V7c/l3r grAlOjg4iMbKzb9rWiPSfBC5fERSyQUAyIE4HZrGWnh7YWGBUB2QkTlZrGtH8DQLD5nHvDAw Z56d0vM9oxubf6Q/Iprfhuke6RvH3jf6LlLspcwfpiSVjLQxsrDWNCRNGKhGLFQ3MTFRx1Ad lxEA40acDk0T6wGEVldXXTcNqAdzssgzqO5TVABAJ1SznZyc5PoGVKPuoTprWi7LRwAoF3E6 NJB1ASbF8q9ABtYFXimNDKAxrMuPUFgKqEytQ3WUuQRQAeJ0aCbrYJdi+VcglXWBV5VcPh8A 6mhmZoYLHeBWtFLNxMRE0rxRs5k/6+eMtBQ4AORGnA6Ntba2RsQByC4pup20eiYA1JQ17z5l lV4A4xArKi37G+EGPlR/s3aTZmZmXLcLQAMRp0OTWeu8Tk9P9/t9100D/GJOCusCr/4MYgNA iawLxDP7FahYSqgulnDnfG6pdcYrVUEAjANxOjScdflXQnVAVFKQjgVeATSYdfarD2k7QKtY Q3WyA+92rQZmvAKoEnE6NFzS8q/T09P7+/uuWwe4Z04Ea5COBV4BNJt19itrvwLVi0XB5ubm ZLfEYVxsa2tLNoYZrwDGhzgdmi8pVLewsHB4eOi6dYBL5hQwJwJBOgDtZJ39SioxUL2kutJR TuaZmu7Q5OSkDy0B0B7E6dAK1jFzQnVouaQgHb1PAO1hnf26sbHhul1A62QJ1VW/3ou1hA4z XgGMFXE6tMXOzg6hOiCUEqRjTWQA7WEdyWO4Aqheljidqna9l42NDdkAZrwCGDfidGgRQnVA gCAdAIR4FAecs56GSapZ74UgPgBXiNOhXayFYAnVoVWSgnSVdXwBwDfWuvVVpu0AbZY0lJ6k mvVerOWtmRQPoALE6dA6SUn1hOrQBilBOnNquG4dALhhLRXP6AVQgVGDdIFxr/eyuLhY/ZsC QIA4HdqIUB3aiSAdACSxZtxPTEyw+DUwPklLvWUxvtQ26yTcapL4AEATp0NrEapD2xCkA0Zy W+tTT99UqmPOkTNKvXT1EbnNj7Se/tNrZhuz0VMfOf5y/3L17USJlpeX5RWSQnXAWG1sbFiz WYcaU6m4pNAhBXwBVIY4HdorKVQ3NzfX7/ddtw4okzmkrdWXCNIBKS7c0KpzTh3F6q4+0TmK 3b3GMwN97NTF8Gw621HXv/yg3Aw1MjMzI6+Ti4uLrtsFNNnBwYF1nulQpYfRTUusZem63W65 bwQAKYjTodWSQnXT09OE6tAY5mA2hzRBOmBUL2h976NXgpOld9Z0mZ6PbbD8olbqbPSc+hWl Dp4946S1KIV5Srem0lA8Hhi3nZ0da4wsXbnrvczPz8u3mJubK/EtAGAo4nRoO0J1aDaCdEBu N7U+8fhucL481lGDGxeiv5UTY6+bn1z67kPH3n7j25ZJsqiLpEJ1zHoDKtDtdketWFfWuWl9 KKAsHYDqEacDXrkrJ3UItre3XbcOyM8cwNYD2xzwBOmALB65OlDqtDlr7lfqB5uz0V9Fs+0u nlZ6sKWPKtb98uaLv338+MvXLthfEXVgLVQ3NTXF4zpQgb29vaRiHValnJs7OzvW/hIBegDV I04HvCJlSfjNzU3XrQPyMIduUpCOTieQUViBTpaoS/rVj7Q+8+fX9y6dolBdrVnDBEx/Ayqz sbGRPbGuYBHJpLUjGNQE4ARxOuCOlFDd6uqq69YBozEHLUE6oLjva338978WnD5h0lwgJdUO DZBUTp41JYDKjLS+RO4ikkkn+/z8fJkfBgAyI04H/NjOzk7SwvBLS0uHh4euGwgMZw5Uc7ha D2NzeBOkA0Yii9DJn8vSdWiGpAE8UmyAKm1tbWVZX8Kcrflmv1rXjjDvWPLHAIDMiNMBr5E0 pGYsLCywsgQ8Zw5Rc6BaD2AqKwH5XLihVeecOYlOKvXNz9x3lGOXlmeHJtnY2LBeURnzACq2 vLw8dBpsjpnp1ny93CE/ACgFcTogztyYZ2ZmrLf/6enp3d1d1w0E7MxzY9LSruaQpscJ5PN1 re/6wBeCU6mj1OZHj+vB82FxOuNrf3CX1n/lupkYF+uaEtQQAKq3t7eX1EUPdbvd7C9oXeBV EYgH4BpxOsDOmgMfYGUJeChp1QhFgRWgmJtan3h8NzibZpU6/qu/+tQHj33qe98KkuwoTtcG SdPiGP8Aqpe+vkT2GPrW1pb1FZjYDsA54nRAom63m9QJWFlZoVwdPGEORXNAJh2rIw0sA7AK l4w4o9QL337kvt/7SlCZznhYqdsvLrtuIMYrqSYGoTrAifT1JbKUlkuqPslCMQB8QJwOSJMy ZEe5OvggpSCdOXRzr30GICqc5dpR6uoTnet6cOrSd4+y6yhO1xYHBwfW/gAJy4ArKetLpIfb OJ0BeI44HTBEyiKw09PT29vbrhuI9jKHX1JBOpZ2BUr0gtb3PnolOLmCwNxtrX/r27dU5yzF 6dojKQGHZ3vAIWsFSWNra8u6PemxAPxHnA4YLmVlCWN1dZU5sKiYOeTMgZd0TLJqBFCu21qf evpmMNf1sY4a3LgQ/PxT1wb3Tp0LF4FF4yUt/0qFAcAh6/oSk5OTsi+UFKSzbgwArhCnA7JK KYQxNzfHHFhUxhxs5pBLOhoprQKMw4Ub2rpwxNq1wb3v+0SwCKzD5qEySQtEUnsecMucg7GM V9NZim1jXROG5ZsB+IY4HTCC9BWmWAcWFej1eklHIAXpgPH5utZ3feALwbl2ZemY1tfDX33p lr77fU/+8QePvXztcw5biMokjdsRqgPcOjg4iEXiomclQToAdUGcDhiNuZcn1aw1lpaW9vf3 XbcRzWQOLXOAJR175rCkowmMz02tTzy+G5xucu2Iz93Sx97+h2c76vqXHzyaJouGsz7wE6oD fLC1tRWWlp6YmNjb29OcswBqhTgdMDI5WBc1PT1NYh1KZw6qpCUj1FEVc+qqAOP2yNWBUqfN GXdGqZeuPmL9rfn1V/5kljmwbUBuDuCzcH2JmZkZgnQA6oU4HZCTrIIRRWIdypKeRmcOQnqZ QDWeGehjpy6a8+6EUt+7fF/0V7e1fvDZW0rNBifm2Y669NSZG89fcNVUVCCpID2hOsAT1vUl Qiz/AsBbxOmA/MztP2UOLIl1KC49jc4cfsFsDgAVeEHrex+9Epx9YYm6MHhnFVt0Ag1DqA7w XNLCL/Pz866bBgCJiNMBRYV59VYk1iGf9DQ6wxx4rtsItMttrU89fVOpjjkBH+uowQ3S5UCo DvAXQToANUWcDiiB6YuHBWuter2e6zaiTlIWdTXMwcbjH+DEhRtadc6powmut54947o58AKh OsBDBOkA1BdxOqAcppu+uLiYEluZm5vb3d113Uz4zhwk5lBJOZDMYcaSEYAr4SzXjlJXn+iw tCsC5rJsLVlLqA5wIqlPTpAOQC0QpwPKFF0J3mplZYVpsLAyB4Y5PFIOHnNomQPMdTOBVrup 9YnHd4NT8uJppQdbrlsEX+zs7CSF6rh0A1VKWt2VIB2AuiBOB5RsaGLd9PR0r9c7PDx03VL4 whwM5pBIWS9CkUYHeOORqwOlTpuz8mGlbr+47Lo58EhSqM5gYW6gGgTpADQAcTpgLExnPWUp WHU0DXZ7e9t1M+GeOQzSJ7qaA4lpU4A/Lt/W6sR5xVqusCFUBzhEkA5AMxCnA8ZoeXk5qb8e WFhYoGhda5mv3hwAKYeHOXhY1BXwzQta3/voleAkvbJ0TOvrrlsEv6QM1BGqA8bk4OCAIB2A xiBOB4zX3t5eeraUsbS0RLSuVczXbb709KPCHDbm4HHdUgBxt7U+9fTNo5Uk1PkTSt++7LpF 8E7SCrAGoy9A6VLOOIJ0AOqIOB1QhaHrSxirq6ssMdF45is2X3T6kcB6EYDnll/USp01Z+sZ pV66+ojr5sBHBA6AanCuAWge4nRAdYZOgyVa12BZInRMdAVq4ZmBPnbqojrKqbv6ROcoxw6I SwkfzM3NsTQQUFxKRUiCdADqizgdUKmhq8ESrWuefr8/NEKnWNEVqI8LN7TqnAvOXErUIUVK qM78nGs+UERKkK7b7bpuHQDkR5wOcCBL0TpF3br6y1KHTlGKDqiJM8/eVupE7Px9rKMGNy64 bhq8llTefnJykuW8gXzW1taSulUs2AKg7ojTAc5sbW3NzMxkidZtb2+7bixGY76yLBE6cwBQ ig6ol8v9wfLyp5eXl3/7E5/oLD5xv1I/2Jx13Sj4LilUNzExwV0AGJW5AhOkA9BgxOkAxzJG 6+bm5jY3Nw8PD123F2nMF2S+pizJkkToAKBViCwAxR0cHKREvUlQBdAMxOkAL2SM1k1PT6+u rvb7fdftRVxQhM58QUToAABWKTP1qHkPDJVS8JFZ5ACahDgd4JGM0TpjYWFhe3ub9DrnzFdg vgjzdWT51ojQAUDLmbtAUuV7c49gZQkgScqqEazKAqBhiNMB3skerSO9zqHsCXRE6AAAoZ2d ncnJSevNgpwgwColF3Vubo4gHYCGIU4HeMr01JMKcFj7KJubm/v7+65b3XxmJ2esQBcwXyIP XQCAqJTpexMTE5SrA6JS+sOLi4uuWwcA5SNOB3htb29veXk5Kc9fWlhYYLmJcQgWiMg4vzV4 0DJfnPn6XDccAOCplOgD5eoAfdQNTopoKxZgAdBcxOmAGjg4ODB9kZSeirSyskKGXXFB9tzS 0lL2PW++JvNlMQUDADBUt9tNuZsw2IM229jYSBqoZmlXAM1GnA6ok62treyTYQNBhh017EZi dtdI2XMB89VQhA4AMJKUlSXMzzc2Nlw3EHBgcXExqbvFqhEAGo84HVA/pnfS7XZHSq9TRzXs VldXd3Z2mBVrZXaL2TlmF2WvPRf2F83XQZcRAJBP+uQ+KnChVdJPB6aEA2gD4nRAje3s7Jju e/bqdaGFhYVer7e7u+v6E7hndoLZFaOmzqmjNAez85l2AQAo7uDgICVffmpqitsN2iBlrqui IB2A1iBOBzSB6daY/n2OgJ2xtLTU6/Xak2cX5M2tr6+PVHUuZHay2dVMRAIAlG5tbS3l7tPt dl03EBiX9FD15OQkoWoA7UGcDmiUIgE79erc2M3NzYal2pmPYz7UysrKqHNaow9IhOcAAOO2 s7MzOTmZcpumzAKah8MeAKKI0wHNVDBgF3aMVlZWgmy7Gq1EYZpqGmyaXSQwFyA8BwCo2MHB QcrNi8Ul0DDLy8spPTHSSAG0EHE6oOGCGnajLjqRZGFhIZgnG5S3c552F7QhaI9pWI4yc1Zm d1F7DgDgULfbTRlsm5+fJ8MIdWc6WikdVOa6Amgt4nRAW+zt7a2trZmefcrMgtyC+F2QfGcE M2cD+/v7+Rps/jB8EfOCwSubtygxHhdldovZOWYXmR1V6o4HACCPoVGMra0t120EckpPoyMS DaDNiNMBbWS6/t1ud25ubhwxuxoJYnNmVzBgCwDw0MHBweLiYsqNjNJdqJ30ADQzuwGAOB3Q dkGeXYlzYz03MzNjPix5cwCAutja2kqZA0tcA3VxcHCQnkZnOml0zwCAOB2A1zAPA91ud35+ 3nSVxhAlcyAIzJkPxfwgAEBNpS8uoQhwwHumG5Y+jYMlIwAgQJwOQBrT6d/Y2FheXq5L5M40 0jTVNNg0mycWAECTmFtbemKduf25biMQNzTKPDU1RQUSAAgRpwMwGtPZ2traCoJ3i4uLM0dS HhvGwbxd8L6mAUFIzjSJAj0AgMYbGvJgfQl4JX3lYoPgMgDEEKcDUJoghBdYjjBPFDOvFe2f zdiYP4m+QviyBOMAAEhPrFNH60uQVA63TLctvfYxk7UBwIo4HQAHor00120BAKB+hi4FG0yD ZXwL1dvb20vP+jQHJ9XoACAJT8gAHCBOBwBAcUNr85vfrq2tuW4m2mLoiq6KZE8AGIYnZAAO EKcDAKAsy8vL6dNgZ2ZmKFqHcVtbW0s/DimeCABZ8IQMwAHidAAAlGjoTENFOTCMzcbGRnpe pzpaL4JZ2ACQBU/IABwgTgcAQOmGToM15ufnidahLOaQi60PJjHRFQBGwhMyAAeI0wEAMCZD p8ESrUNxWSJ0THQFgBx4QgbgAHE6AADG5+DgYH5+Pj2GQrQO+WSJ0LGiKwDkxhMyAAeI0wEA MG57e3tD4ylE65BdlgidohQdABTDEzIAB4jTAQBQjYyxFaJ1SMFRBACV4QkZAAAAaLgsK3Kq o5L/FBRD1NraWsYjhwgdAJSCOB0AAADQChljLjMzM2ZL142FSwcHB8vLyxmPFmK7AFAi4nQA AABAi2SM1pltKDTWQnt7e/Pz80OXDCZCBwBjQpwOAAAAaJ2M0Tp1VHRsZ2fHdXsxdhsbG1mK 0BGhA4CxIk4HAAAAtNTa2trU1FSW0IzZzGxMel3z7O3tZZziGgRtidABwFgRpwMAAABaLeNq nsbExATpdY2xsbExNzeX5XtXrOUKAFUhTgfAgWi3z3VbAADAK3Z2dubn5zNGbSYnJ7vdLoGb OjJf9OLiYpYKdEFkljKFAFAlnpABOECcDgAAPwWzIDMGcYy5uTnmw9aC+Wa73W7G+a1BKJZv FgCqxxMyAAeI0wEA4LnspevCgN3GxobrViMuCM+N+lVShA4AXOEJGYADxOkAAKiFkSbDqqNp kmTY+SBHeG5ycnJ5eZm5zADgFk/IAAAAANIcHByMml6nXp0SS9ynSjs7O8vLyzm+KXIhAcAT xOkAAAAAZDLSEgShqakp81esEjsmBwcHGxsb8/Pz2WvPBVgMBAA8RJwOAAAAwGg2Njbm5uZG igqpV2fFEhsqRZA6NzMzk+NbIGwKAN4iTgcAAAAgj2A+bI6AnTpK5pqfn2di7Eh2dna63a7Z 4aOmNAbhObPDmd8KAJ4jTgcAAACgkCIBuyBmF+TZscxojNmxZp/ky5sjPAcAdUScDgAAAEA5 CgbsAjMzM4uLi+Z12jk3c2trq9vtzs/Pj7ocRFSQrkh4DgBqhzgdAAei/UjXbQEAAGORb3ED a9jOvE6QbXdwcOD6Y5Vsb2/P7Kjl5eW5ubkigbkAS3YAQN3xhAzAgWiH0nVbAADAeAVV1XLP 3IyZmJgIInfLy8sbGxs1miobTGJdW1srKyoX7hDzalT6A4Bm4AkZgAPRzqXrtgAAgOpsbW0t Li6WFaKKxqpmjiwfCeJ3TtLKgmBcGI8zH9a0qvTPawQV/UidA4CG4QkZgAPRXqbrtgAAAAcO Dg6C+Z5l5dmlCKN4YSJeaG1tbWsU3W43+udzc3PhK1fwKVhtAwAajydkAA5EO52u2wIAANwL QmBzc3PF69k1ydTU1Pz8fGuX1ACAFuIJGYAD0Q6o67YAAAC/BFNHg2y1toXtootmuP4eAAAO 8IQMwIFof9R1WwAAgO/Ccm8zMzNNitxNTU3Nzc0FBfXImAMAaOJ0AJyI9lBdtwUAANTPzs5O kHM3voUayhUucxHkyrE2KwDAiidkAA5Eu62u2wIAAJojttpDlYs8BG8U5MeFK1SQJQcAGAlP yAAcIE4HAABckau4LmcQ5MFFEYMDAJSOJ2QADhCnAwAAAAAghidkAA4QpwMAAAAAIIYnZAAO EKcDAAAAACCGJ2QADhCnAwAAAAAghidkAA4QpwMAAAAAIIYnZAAOEKcDAAAAACCGJ2QADhCn AwAAAAAghidkAA4QpwMAAAAAIIYnZAAOEKcDAAAAACCGJ2QADhCnAwAAAAAghidkAAAAAAAA wD3idAAAAAAAAIB7xOkAAAAAAAAA94jTAQAAAAAAAO4RpwMAAAAAAADcI04HAAAAAAAAuEec DgAAAAAAAHCPOB0AAAAAAADgHnE6AAAAAAAAwD3idAAAAAAAAIB7xOkAAAAAAAAA94jTAQAA AAAAAO6p/0294v8XAAAA//8DAAIszgOq14AA</item> <item item-id="4">iVBORw0KGgoAAAANSUhEUgAAA4QAAAF4CAYAAAAMgTdwAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAHK1SURBVHhe7d0L7F3VfeD7v8EuNoGM 3QYBVUfYYBouapWQQChoJP8xNiEzUAxMlNArFTOlM7pII6czMHNH987YBnuundSDzWOoZ4pi U+KEFAtjpgqmiTCPODxqMARqLF0VGoOrJGJqD7lNIuuvff+/fdY653d+Z61z9j7P/fh+0E/4 v/Ze5+y9z36tvddjKgEAAAAA1BIFQgAAAACoKQqEAAAAAFBTFAgBAAAAoKYoEAIAAABATVEg BAAAAICaokAIAAAAADVFgRDIaWpqqhkAAABAmXFHC+REgRAAAABVwR0tkBMFQgAAAFQFd7RA ThQIAQAAUBXc0QI5USAEAABAVXBHC+REgRAAAABVwR0tkBMFQgCop2effTb5R//oHyXLli1L Q18P+g3/eTfccENy3nnnJffee6/7NgAYD+5ogZz0hRwAUB1S4JNCmYQ+1xchpOD4qU99Knn9 9dfd0gLAcHBHC+SkL9AAgHKSN3FSyNLn9DLGrbfeyltFAAPhjhbISV+IAQDFJ2/+1qxZ03b+ HkZIVU95m7h27do05N/yXd1CCqEyr1QRHWbVUx0UEAHkwR0tkJO+6AIAimnQt39SuJMqmlKI m4S///u/T77+9a+nhcdB10UKnwAQwx0tAAAoPXkrJgWfUIGoW0ihr2xv1PybxtD69ArJBwAa BUIAAFBKTzzxRLDQ0y3kzd+7777rPqE6pLOZvIVEqUZLJzUAKBACAIDSkKqUeQo+da0uKYVl 6XAmtE1CwZtDoL4oEAI56QsoAGA8shYCZT46Vekk2yS0vUIxqXaTACaDO1ogJ33RBACMjlRn 1OfcWEghUN4cIpushUPp0IbtClQfd7QAAKBQpHfNUAFFhxQCaf82uKztMHlrCFQXBUIAAFAY vQqDDKEwOtLjamib+6BQCFQTBUIAADBx0vtnqBDiQ6ZTfXH0snREQ8EQqBYKhEBO+qIIABjM smXL2s6rNuj9cjLkTW2vQroUHgGUH3e0QE76YggA6E+vQeQpbBRHr0I7vboC5cYdLZCTvggC APKR6ob6PGqjioPGV0WvgiG/HVBO3NECOemLHwAgGxnCQJ8/bVCYKI9uY0JSxRcoH+5ogZz0 hQ8A0J10BKPPmzakrRrKac2aNcHfVIJqpEB5cEeL2pObFRmHSS5svarD9BPytFS68paQ7wGA uujWTpDhI6qj23AV8mYYQLFRIERtSLuVbtVcJhVyIeVJKoAq6fZWkAJCNfV6EwyguDhCUUmv v/56Wvjr1WV2UUN616OQCKBspFAQq0ZI27J66FaNlFoyQDFRIERl9OrCPGvcfvvtycaNG5Pd u3cn27dvT44cOZJ8+OGH7lu6e/nll9P59+3bl+afN29e8DvyhrxFlEIuABSVPMQKnb8kGMi8 fmKdCPFgACgeCoQoPXnimLcqqBT4pIB19OhR9ynjJd+7ZcuW3AVGLqQAiihWXZBzFkL7BTVg gGKhQIhSylsAlDj11FPTN35FI28U169fH1zmbiHbgOo3ACZNegkNnaMkpKCIepOHr6F9Q4Ka L0AxUCBEaXSrjhSKzZs3u5zlI4XEpUuXBtcrFFI45MYLwLjFHs7xZhBWbF+RNvMAJosCIQpN CoHdurPWsXjx4olVAR21nTt3Btc5FPK0nsIhgFELnX8oCKIbOhwCiokCIQopawcx0nnLP/zD P7hc9SDrK4Xf0PawQXUcAMMWe0jHmx5k8e6770b3IQCTwdGHQskyMPw999zj5oa8EV24cGFw O+mghz8AwxCr9sfDJ+QVa3tKDRdg/CgQohB6FQSl0FPV6qDDIgXl0LbTQcEQQL9C5xQJoF+x 3ml5wACMF2dyTIxUGwldCHS8+OKLbm7ksWLFiuD29CFVcoEiOvnSF83+usRNwSS1/yaNoN0X hiW0f0kAGA+ONkxErNqRj6wDwaO7Xm8NZeBgoIg+4ffThQ+6FExC7A0O1fowbLH7AgCjx5GG serVWQwFwdHoNc6hvK0FiuNkstDtmxf/8OcuDeMWKwwCo0JnM8BkcJRhLLoNTHv77ben4+5h 9GQ7h34DCap/oTBObmrul8dcEsYrds4GRi324BjA6HCEYeRiT5kvvPDC2g0ZURSPPvpo8DeR MaKoCoZJmzl8h9sn/8ClYJykMBiqvgeMk93/GNYEGB3O8BiZWEHQB9VDJ2vjxo3B30VCCobA pGxv7ou0Hxy32HmbHooxblIAtPshhUJgNCgQYiSksxJ7Ivexe/duNxeKIPQb+QDGr9V+8Kz9 J1waxiFWGGQIAExKaKxCCoXA8HHHh6GL9RS2ZAndxxfVvn37gr+ZhFyQgbGZ+dPmviftBx++ am7b/uhjet9PG/NjKCgMoqjOO++8jv3y3nvvdVMBDAMFQgxNrBMCaSsoBQ4UX6xgSIczGJcj Wxe7/e4sl5IkmxY29sPp//qD5K0Zl4ih0se7hBzzFAYxSVJN2e+PoUKhBIDh4GjCUEibs9DJ moJgOYV+Swlg1Ja6wt/UJY+lf//x9NxkatVUsuv9X6Z/Y/jscS4BTIrUSrE1jUSoTSEPK4Hh 4KyPgcTeCkrQg2i53XfffcHflQswRued5n724PFfJqvk33NOSX7ipmL47I23BDApdl+U0D1f h8YplDQAg+HMj75JHX57YpZYvXq1mwNld/To0eBvLAEM28zbF7n9a6Ha1+a4qRi20M01MAmx WkahHq9DDzHoGRsYDGd/9CV0IyFBFdFqCv3WEvrJLTCY55JN55za2r+m1zX//Qs3B4Yn1BM0 xzPG7d133+3YD310q42i2xf6oM0r0D8KhMgt9HTu9ttvT98mobrkza/93SW4CGMYTuyZ0xxu 4uKH3pxN+UlzH3vwOD3JDFPoHM5xjHGLPViWuOGGG9xccaG3igD6w9GDXEI3Evfff7+bijqw v78EN5MY1OY5rf3pL135b67fx/7khUZC8rNk+Y4vJifdX8hPbrT9dvZBdTuMU+g+wkZWoc8C kB9HDjKzJ12JF1980U1FnYT2BcYrRP9OqH2pNV7pk5/xaWcl73xzOlk6tTyZmvsVNxV5harZ yQ01MA6xfgds5K26bPPLPk31ZyAfCoTIRG727UmXKqL1tmTJko59gkIh+vP/tvaj39/t0mZ9 0N7O7YsvcZM3CN6mYFK69UiuQ9q29sN+TpYqpwBauBqgJ3uilVi+fLmbirqK9UBKoRB5SfvB xv6zKJl/32GXOmvmf7T2rTnTVBUdQKgTGaqKYhyyVBH10a/Qd9CUAciOAiG6sidYCelABhAy 1mRoH6FQCBSHVJ+zxyhV6jAO3XoRtfHEE0+4XP0JfSaAbDhaEBV64kZhEFasUMjTWaAY7LHZ b7U8oB9ZqovK/cYwjOpzgaqjQIggCoPIQwqFsn/YfUaeDgOYnPPOO6/tmOQGGZMQ6odAx7DI W0b72dKZDYDuKBCiQ2hsILnhB3oJFQqpmgZMRqi6HjBuofarOob94DBURRpAdxwlaBMa6JXe RJFHqPdRAOMV6uJfhp0AxunWW29t2wft36PqDdQWQnkzDnTHnRqaQvX8aQeGftj9SALA+Njj jxtijJst/Hk6fZT0d0tQdRSI4y4NqVCbwZ07d7qpQH52f5J9jOqjwOiFzufAuNgqm5N8GKGX QwJAGEcHglWL6EAGg4r1Porq0b9vqD2QpJ9xxhnNeSw9/cwzz3SpDf78NHfu3PT/toqZ/C3p n/jEJ9L/27cA/m2Enz5o1/ZFF6rpQVVRdDqZbHtgKrnrnzSOKxkD9J4XP3DT+lekwqCwzWCk kyUAnbg7q7lQ42sJYBhCg9dP+gYBw2V79bM3XKEHTtqyZcu6Tp83b17X6XaazK/Z6dJpVpXZ 9eV4g/Xn/2Kh2z8WJmuf+OvkJ7Npr2xt7C93HP55Y6Y+2IcRRakRopdJgt6vgU7c+ddcr5sx YFBS9Zh9rLp6PYH3b/B0aL3OQXbasKdXiX3AJ9sW0D766/8r3TfmrtrhUhq++0eL0vTpfT91 KfnpqspFKQwK29vuqDqyAcqsuldG9CTViPRJUoKqohgFu59JoBpCv62MOeaFpusqjKHpujOr 0HRt0OlVYteTTsGgzby/rblvfPujGZcqDrX2m/0nXFo+ujBYxLfwzfUr8DICk8RdWY3ZE+SR I0fcFGC4ZN+y+xtPaavB/q4S+i1haLp+cxWa7qs5htrDSfgCZWgQaglfEIoNhl2ktxfDYh/w UVUU7X7W3DcufuQ9l+adTG5aNDtt+ivu73z0fidtdoso9AC8iucBoF8UCGtKP82TuPPOO90U YHT0PidBW45ys1UUdYhYgU5ChG7SfAh7nvLhC5TylD803ReGpGAaml7FtwN2HQHtyDo/Puyi 2eLf8Oh9rqiFQU8vqwQPTYAWrho11KuTB2BUQgUElFesQCYhYgU6CRFqP+hDhNJ9DGN6VdhB uHn7Dqu5f/z6RpcyGDvGoK4mXlShB1g8lAQaqnVVRCb2hCjDAwDjMj093bb/0Q14eenf0Uas uqaPbm8HJbq9XZSw33/aaae1/W2n+2ErfFSJXq+qrRuG4b3W/rHpRy6tf2UsDHqhWgMAZq8j 7v+oCfvEfvfu3W4KMD56H5SgLUc56d/Qvi20N1723GPfDvaa3/5t2w/a5bE1Iezbgarsc7aX V/kbaHewuX8s2vRDl9buL/+PqeSLL33k/orTx7kck769bpnYc5VvkwzUGQXCGgk9kQcmIdTJ DMolVMDSf9sQtlCnQ4TSfdgCnr2ps/ntd9npkr8K9DpJoDj8Q47J+0nbPiLjDracTP7tKWck F596iknvZAuDZaa3hwRQdxwFNWGfIktQVRSTZKuOSjsolEevApkNEWq/7EP0alMYK1D6aseh aRK+8GfTy463g8Xm9+ciuF3tJ+1xSvr/XoVBfexV4WGKPdfQlhB1V/4rIjLRJz6JjRuH07Ac GITdL+kMozzsbydsIVGHF5rmC3QiNF1u3kSsQOnbMMUKlFK9VIQKsWVF52DFV6QCYZIcS6bN /iKx7e3jbnpc1QqDnt4OEkCdcQTUQOgmDSgCacPKvllO+jfzN4mxcQF9gU6EputOKULTtUGm V6kdoV4PCd4OFk+xCoT52Y6hij6sRF62xkFV2hUD/eDuq+JC7XoYgB5FYvfPsrdNqYNuBate D6BC1T41O22U08v6toP24OVQ5gJh1QuDnl5HXVMBqBuuIhVnx6eSQD4vv/xysmHDhrSa7V13 3ZXG4sWLk8997nPNuPTSS5NVq1Yl69evT3bu3Jm++RpXbN++PS3kjzuOHj3qttBgPvzwQ/bR kgn18OmF3hJqvao69jpn2QKlvYnT0yRsoa/btLKw3f5X9Wa97MpcINT7lxyzVWXPJ0BdsfdX nD7RSezYscNNgSWFHCn02W1GEET30EK1ErTQ+IKaffulq5uKXgVCfxPuwxb67BtMmb9b6HkJ op+QhxwS8tatDIUrvey6OncV2Y6ZGIICdUWBsMLsU+RHH33UTYH32GOPtW0jgiDyRegtm57+ 2c9+1qW26OlnnnmmS23R07/5zW+61AZboLS9A9oCpW0XFGvnSBCTCnnwIMdREXq61MtV9cKg p9dZAqgj9vwK4yQXtm3bto5tQxBE/ijz0/TQ+hBEUWLcbxLtm/0yDjjfL6lloNcdqCP2/Iqy 1SBefPFFN6WeZMzFCy+8sG2bdAt5Whu7IOr5upELep4qZ7zBLedDDLmRkoLRuEPedPmqaOMM Obf46pRlvmmUqqd+PcoU+vg4++yzg/NMIqRGSmh/KWvIfh7qAGncIcsw6uOszoVBT68/ncug jigQVpQ+uUnU2dKlSzu2hw256NqqZTE6Xx5yA5/lBkM6iqkreXChtwVd6QMtvMkYPXnYordx r5BOxA4dOuRyt5MOyGSeEOmUa8uWLcm8efM6PjMWoyioSOFPf0fW62DV6G0gUeWOdIAQrigV ZLuLruPbQbnY6m0QCtlO/Vz89Gf0S763V+Hwtttuc3PXi90OABo4NkbHtrkPxQ033JD2JD0q 0uOyfH7ou3UMq2Bor0F1LQwKKQDqbSEB1Al7fAXV/aQ2PT3dsQ18yAVw0Ib7+vOGodeNiPR+ WidSdVavvzyxB+rOvsmRGgcYXKggYGMS52ApHC5YsCC4PD6kgNov+1l1Lgx6dpsAdcIeXzFS 2NEntFE+zSyaXm8Fh3XB0585TLbdp47Vq1e7uerBrj9Qd/ZtDgant6eNFStWuLkmLzRWq468 bf50XsawbLHH2CAFbqBsuKpUTF1vGu6777629fZhxxwbhmF/nmWXWYd0jlMHK1eubFtvoO70 8UCnF4Pp9VZw8eLFbs7BdGtD2I9eBcMsDz31/NJ5DlrsA3UJoC7Y2ytGn8ikukkdLFmypG29 ffiqocNuJ6E/a5RsBxI+5E1oHeh15gYYdWarixZhvLqystcDH+vWretIG5R0OCPXp2Gz+4OO boU8PV9dxhjMS28jCaAu2NsrxD7dqsPbJNsrpYRc8DX7NDg0kHYe+rNGLXbhr0NHQXaduQlG Xdnq5OiP7XDNbs/LLrusLb3o51lpSqCX10fooaduq05hMM7W0MlbHRcoK64sFSEXgFNOOaXt RFZ1el112IthqBtxW2jMQ3/OOEg7Bv2dPu6//343RzXdc889bevLW0LUlT4OJJCf3YY+dI2L 0Fi1RT7PykNfu7w+dEFGF3IGufbVgX2AzPZCXXBlqYgzzjij7SR2/fXXuynVpNfVR7cTd2h+ ibI8KY29Kax6odCuL1BH+higo4t85AGh3n46rIMHDwbnu/32290cxRQrGErhhsJgfrKd9HYE 6oA9vSL0yUvigw8+cFOqx66rRK9qoKE8PsrSy1rsxqbKhUK7rnS1j7qxbyyQXeyc2a2AF5pf op+2gDKWrOQdF7vMOgZtKlEntlaRrXUEVBFXlwo499xz205eElVl11MiS4EulE9HmS6WoXEL q9qmcM+ePR3rCtQJbyv6EysMSkcv3YTy6Mhzrh12L6NZxDpZQz562/FmFXXAWaIC9IlLQgZm r6I5c+Z0rGtW9qYqFFkbj+s8kxIqFFa191G7nkBd2LeDy5Ytc1PQi95uPrII5bOR1SQKhKGO 1iQo1ORjtx9QdezlJRcazLyKvYveeeedHeuZR6hjGR15erDU+SYp1NlMFdl1pNoo6sLu+/R4 mI3dbhJZhTqW8ZG3Jsa4C4SxwqAPqo1mp9teSgBVx15ecvqEVdUT15EjRzrWsZ86/fYzfJRZ 6M1n1Tz22GNt60dvo6gLvd9LoDu5Lgx6Tox1LNPPg9ZxFgilKqxeXhGqPsoDtez0duNhDKqO K0yJhd56LV261E2thg0bNnSsY789g+rP0DcNZelUJkavl4S8Ta2axYsXt60jUAd6n2e/781u L4l+CnI+79y5c9s+K69xFQilY7HYcobeGiIbvc2ocouq48xQYvpk5aNK7cg+/PDDjvUbpMqL nNClTY6nq1yWnd5GEv30iFdk9i0xUHW2/SDDTXQnNQf09pLo93oo1Ubl+iPkM/znyXkoj1EX CEPDTYQM2uSirthmqBP28JIK9aC2bt06N7Ua7PpJDJv/3DxP/0a5PP2SakB6uSTytncpOr1u tIVB1dkCjtQIQZi0AdfbSkJqlwyL1Lzxn7tv3z6X2tsoC4S2MNjrIaCeV2Lt2rVuCmJoR4g6 YQ8vKTmZ6xOVhH+iWQWhJ5+jGAtIqp/6z89KL1OR2LYzl156qZtSDXrdKBCi6vT+LoG4cXSw 5T83z3l1lAVCGUvRL1PWN6F+fh/ozj5oAKqMPbyk9Emqiierca6f//ysbQnHsUz9CLUprdJD glNPPbVt3YAq0/s6+3t3dlvt2LHDTRke/flZz6ujKhDqNtXdBtm3fB4f/bbHrxO9vehYBlXG VaaEQtUDJarCtheTN1953g4e+NN/3sz77aO/TNOe+DfLkrnrfiU5mf7VLm9bQj9v1vnHyb4l LOIy9st2jgBUmd7X2d/j7Dmvr5v2mWPJpoWtaqF/dWz2uvHRkeSxzYuTLzx8OJ1FtyWUyGLF ihWZ581K9xyapzDo+bw+0J3eVnmalgBlw9mghPQJyse8efPc1PKz65bZzBvNPNO/ujQ56zdm /z19RjK11X/WjW7GTj5flhO+n1eiiGzbo7wdIRSZXi+e1qKq7EM/qkiHhdoO5vW9/zDdzLt7 9+5kafrv+c20h9/4/9yc7U0Z8rQlHBb/3RL9tpFcv3592+fIWMaI09uKAiGqjAJhyYQugBLb t293c5SbfTu4bNkyN6WHj/5idv45aZ6fuKTU8Y2tz7vkMZfYSbcl7DVIffPzZqOo9DIWeTnz 0uvExRlVZd966d6R0aK3kUTeISYevknyzU+WP/xDl9KwdLpVJfOES/Mm1Tu1/04JGWZiEPqz JCgUxsk9iN5WQFWxd5dIbNDdKp2k+luvnzXn//ZHMy7Nmfnj5rSL3/65Swzz80l0k3W+SbIX saq0JdTrJAFUkT3Po5PtaVuqZ+bx/u7VjbzTf+xSWras8J97lktp57/zD//wD13K6MhbS/99 EjIA/aAeffTRts+UQJh+WMx2QpWxd5dIrDAoURV6nbK+Ado9fXojz/Rul9Iyc6j1eb9waTH6 7Wu3xvZ+Hoki08spnRBUwbZt29rWC6givY9nriVRMwMVmmfea+Z73zxDFCvctPM3v+RS2t13 332ZvnfQNoR2wPlhDiWkP1dCqikjTG8noKrYu0tEn5RsVIG/eOZbp3ea879hL+wze5Ltzc87 1yV2p28yYvz0bvMUgW1LWAWHDh2q3DoBlt7HqS4aprfRwoULXWo2T/o3gIsDzQhmDjc/d/uJ QGnR8fN8+tOfdimdBullVBc6JYY9rqy8adSfL4EwvY0YDxRVxRmgJLq9HVy5cqWbq9zsevV2 Ijmy9fzG/Av/N5fWsvPaVscAiza1txHpxueRtiIhfrpEkdn2po89Fm9DWSZ6nUYxNiUwaXof 79WmuY6kkKy3UdZx+Bp+3sz3aKDAt3O69bnx4mCSdmrl5xt2lXw9xmCvAecH4b/DB8L0NqKD J1QVZ4CS0CckG1W40ZcLql6nTOMjndyUbL6gMf/8rz7vEhtmjt7Z9nkbjne7tLeT8Qh9vtDJ X39u0ellLcPyZqHXh3G0UEV6H0cnvX1yb6OTLzXzdVQqef+x1ucuvMKlxjXnnY1hGVdhUOgh LCRiD0HrTm8jqXkDVBFXmxKQHsD0CcnGyy+/7OYsr0suuaRtnTL5YG3y+27+i7/9I5c466O/ SBatan3W1NSi4PiD3bTylvst1EDtbApKrw83MKgaqZKm93F00tsn9/ALx3wnLee7hIaHb/q1 ZOrLrc+du70x/mA3ehgK6fxlUIOOMZiXHVtRAp2qeB0FLPbsEtAnIokq9nql10dOvpl88E+S 32jmm588+79mkn33Nf6eu2pd8/OmlmxOFk4tTrbleEtYpW2s12PY7VAmQa9P5n0FKAluPruz YzTmHWoieb81FNH0V2YLcR+5oY5uWpwc/uinzWn739uZTC9Z5zLF6WEopN2flqcN4bgLg54d lxCd9G8sQQc8qCKO/hLQJ6K1a9d2pFWBXp/s1QBPJEf+5IK2vBJzV+1IkpnvNv8+dfaCt3X6 /3R5stNP6svMr4PEoONXFYG/yfIBVImuss7+3cnWmMmv1YZQx2EZskh3KPNv7km+d+yXLk93 +nO0rAVC3R5xEudo/91ZlrXIRtXhi9x36W1ExzKoIq42BWfHk5Pqi1WrUqSr3Ujk6kQhHZC+ lXdqajp5S14EzvyPtvQH3892Ybd8ft2WUH9uGejllSj7mIS2dzygShgIO86OPdjv9nm4rUnB VPIvd7hmFzNvtNKne78d9PRnSeHOy1Ig1Hl7zTsq+vvLOsyJf7MuD1SGzb6VpmYKqoirTcHp k5CEqFqVoi1bthR2ffSTQd+WsKjLGmP3F6kiVGb79+9vWx+gSvS+TY+G7WxhWcYlLQJ5qKnb wXu9CoR+fptv3IqyHIPyyz/szsb0G1wJxgZFFXE3VWC26pCvplC1J8iXXXZZodcntGzyW+gn wUVmh5+Q7V1mR464Nj8ugCrR+zYFwnZ2bNV8w02Mnl8uaUsoyzZnzpz0b+n4xtbM8PNKTLpt 94UXXti2PGVl3yAPk/5cCoSoIu6mCkyfgCS8KhUI7UDjRbwBsk8HQ1H0rqjt8pZdldYF0PS+ 7duMo0FvG4mi+cM//MOOZbSxdOnStr+L0NHXwYMH25apzKRQ6GvFDLNqp94+Zd9GQAh7dUHZ Xi5lIF5Pp0uVlDKTMRT1+uj1LAr7W3SLor41tMtZdlVaF0DT+3YRz4eTpLeNRNHY5esVRXrD qZerCoZdKNTbpyrbCNDYqwuq28lHpy9atMillpPtMdK30ysK24FPlsjVKc6Y2GUsuyqtC6Dp fZveDNvpbSNRFFIddN68eR3L1yuKRC/XsNvgTYpfn2HUPNLbRwKoGvbqArJtvuzg23raMAbD nSS9LhJFY5cvaxSNXBCLvHx56XWpys0LIPS+TYGwnd42K1eudKmTp5crT3z60592nzB5ermq 1HbVr9OgvY/q7SMBVA17dQHZXiEtPY0C4ehIdS27fFmjaFVHbQdF0nazzPS6cNOMKtH7Nlrs g1IZyL0I5O2gXq68URR6marWdtWv1yCFQr19JICqYa8eMrloyck0S9iTk3RMIun6pOPTdOjp 0juYFAqlFzNNLlKSbns2k/n8/NJbY7fQ5O/nnnvO/dUghQqbJxbSLbcn7SYkTeh1kZCb+15h q2TKGyIZJ0iTwlworw3bRkc+x79xssuWJ3wvZPL5unAoVWJDy+FjVOx+47d/Wel1GeV2A8ZN 79tokeNcb5uiPAxdt25d23LljaI8nNPLNMzOWIrCr1u/NUr09pEAqoa9esjsRatXaKHpeUKT G35Jszf+Nk+30LqlZQm9HHpsJjtflpDCjebTNT1/r9B82iBvB334z9NdVA9j/9Bsj7NZw+8f +qZK/m3n6xaa/G07OLLzdwstNnaXzROL2PbW9Py9Qgul5dlX7AMIn67p+buF7dnWp2u2qnC3 0Pw66e+Qf9s83UKTv201NDt/t9D8cugbuzzLptdJPkPS9D4jbJ5uoYXS/HdkidC8lp0eC72e wqdrebab5tNC2zJL2Ad4Pl3T8/cKzZ/HdEHrtttu68gTC5nX871g28KnzTOuCJ2vbYHS5olF t/O1Px50WrcoAz0kRT81ePT6SgBVw149ZL3eAOkIvdWyJ51QPj19+/bt6c29fXsnb+QkXb+Z EzKfpGcJTf4exxtC/SY0FrINNLl4xd629gp70ySfI59nt3M/IeTz9Y1rnjfI9iY1tJ7y2aG8 Nv79v//3bcv28ssvpzcU/ncQ8u+NGzemNwpZQpO/d+7c6f5qkBsrmycU+gZMyOdIuqXz6HWR goZsGx9r1qxxORpDhkiafeJt83QLTf62BRv/HVnC3oj45dBsHgm9vj7kd9V8uhbLGwrN7//6 O+TfNk+30ORvWRbNzt8tNL8c+jyQZz31coTWU9g83UILpeU5l4Tmtez0WMS2t9bv/uHTQtsy S+jfTvh0Tc/fKzRfUNLnNnvO6Bb63COfIWlFLhDq9RQ2Tyz0ego9zf+uOq1blIWcf2V55XqQ t1BY1nUGsmKvLhB7wonVd9fz2ItB2eh1kSiKr33tax3LljeKxN6sbd682U0pJ70u9uYSGAd5 GGP3PfnbvgXO85BQv8WI7ds2TyzsDa88gLQPIWWeUN5QaD5Nf0fe9dRkm0m6ZvPYB6br169P r3/ycEuTNCks6eYSMtafpGUJPS6gb3phr7Ny/vTz62XqJ2Q9/Gf1Cr0cofUUvllIr7DjH+pl 8gVC+b88LOkVZaL7aLD7YTd6+0gAVcNeXRC2wXy3E46eZ//+/S61nPS6SEyS/Aa2Q59+Qz85 LwJ7M2VvcMpGr4u9kQTKTO/baJHjXG8bKdQUgRSs9HLlDVugmxS9TEW7fg2brt6ctVCot48E UDXs1QVhCyK2ipum5yvKRbFfel0kJiFWCDz11FOD6VnCdnwzafIUVy8fBUKgmPS+jRbbRtFW UZ8kvVx5oyj0MtmhrqpI709Z6O2TNQ9QJuzVBZHnZKPno0CYnzwR7PYmUC4UXmh6ligaucDr 5StKz3b90utCgRBVovdttNPbxraBm6Q8ndboKNL1Wy9X2aqB9kua5fh17kVvnyzzA2XDXl0A 0rGEPtF0ezso9LwUCLOzhSIdsSeivhF6nsjTLmFc7D5WdnpdbLsooMz0vs3DjnZ620gUSd6q o0UZR9HTy1anc6q+Nnajt0+veYEyYq8uAHui6VXdUM87b948l1pOtve3YVa1lIKZrWakQ6Zl Kbx1e5toI2/PZeNil7PsqrQu6HSB+n3T2PNjN6X69HpTIGynt41E0djl6xZFo5etaE0eRi1L oVBvn27zAWXFXj1h9q1VlhsAPX+Rqs3047HHHmtbH9tDXz+6vQm0w0z0IhfG0Od0iyIqwzLm UaV1QcTxB5q/8Y9cUh3ofXsY58Mq0dtGokhWrFjRsXzd4sYbb3Q5J086ttHLVkf+wW+shpbe PnXdRqg29uoJ6+ckIz2A5c1TVH7gXx/ypK4fUsVFf44OOcH3+8TTflaIvREo4ltCvXwSZVel dUHYiR1Xu994jkupB71v16UtV1Z620gURagwGJJlnknYsmVLIZdr3Pz6y32IrT2kt48EUDXs 1RNkqzNmfRpcpQKhuOyyy/paH9letm2cj17tMLOQt7X6M6enp92UTno+iSKxbzlle5eZ9JCq 1wfVtPkC9xtf8phLqQe9b1e9+/+8bO2PIvQ0+g//8A9tyyQRG0rCvomTgmQR9HsNrqLYdtDp HJeoIu6mJkifYCSykmqP/eQrqjxPJ+XtW6xNn6QP6+1cqO1hN1L9R89bpEb5dn+R7V1mMvam Xh9U0Ynm77vsg5MurR70vs2NZztbE0SGBpq0Cy+8sG2Zli5d6qaE6XklpEA5aXp52Oc6ex+1 ncuxjVBF3E1NkD7ByAkoK/vmquzsE9YYO7i6hLwhHEU7G1uI2rhxo5sSp+fP21ZxlOy6FGUg 5H5Jz7p6fVBBM991v++ipF7FwerVABk2Wytk0vSySPQq4NnrXRF6CtfLQ0dGDb5QKP+391xU 5UYVcbWZEP0ESiJPGzf7tKoK9ProcQClMBN7IxgbKmJQ0nbAfleWQpTNU5RCoV6mIjxRH5R0 167XCdVzYs8c9/vGq2lXlX2Ag3ZFKhCuX7++bVmyLk8/eUalivcTw+K3ib0HobMnVBFH/4To k4tEXoPkLSK9Pt1CbpZGPc6fPflLlaAsbPsQiVEvaxZ6eQ4cOOBSy0uvj/xWqJ6Hr5rb+I0X 71BvC21c4OauFnv+QSe9fWT8v0nRy5FnWfbt29eWTwqWkzJnjn/40gi0hB4OSzD2LaqIo38C bPs0/UYsK53/5ZdfdqnlZdth6JC3qcNqG9hLaJiJPG08pP2IzjvpAksVby71+ozqLTEmqdV+ 8OK3f95I+miXS7smmX7gB420iqpak4BR0NtnUttIOhnTyyCdXeWh80pMYhiKe+65p20ZeMDW yb5BlQCqiD17AoZxctH5ZSy/MgqdaH3IhanfoSIGEVqWQWMS6+HZZakCvT79PExBwR3f2Px9 j83+OfP+15OpVVPJ3BVbG9NrQO/j6FSEB136+4cV42a/f1wPXstGb6Nzzz3XpQLVwtVmzGwh KE9nMpr+jJUrV7rU4pNqlQsWLGhb/liMm30yP8yYBPu2s6wPDiy9TqieJz/jf99zk4/eviOZ O/vvix56002tB72PT/KBUlHZqnzypmuc5s51VZqHHOMchiLUxAFhdjtJO1agajgDjFGoOmK/ itbTWi/btm1rW14f0n7Bd9iyYcOGtmnjpr972DGJntuq2DnFoUOHKrdOaCcFwPT3Xdf6nes8 9AQdWITpbSQxLraX0GFHniYKg1i4cGHb91JdNE5vJx/9PswHioo7qjGy1VwG6YXSDsFQNHJR k6edehl1xBrf63nG3UunFLLvuuuu5tPfU045Jf1bh182m66fGNtp55xzzkS6qfbLIyE9c1aB 7dUPVfNc6/ddNZUs9P/e/KqbXg/NbTAbjHkWZq+B4+pcRq5t0l5Qwp+PpHDl03z45bLp3fLI tHEUCEOFWsTZ7eT/TZOF/C5Q29LGg8dn3FyYBM4CY2R3/kEN87OGZefOnW3LpSNLtR6bZ9z8 98rg52+99ZZLTZJNmzal6dKRgEzTsuQZNzuWWZZhM8pAr5MEquSkGm5iKvnJbMpzd/vful7t dorQRq4M9DYa93by3ynn/ddee82lJslDDz2Upve6VoTyXHvttS5ltIrW+VmR2U4Aha6yTKEw r5PJfSva305PTa1Kz/eYLK40Y2Iv8MM4AevP2759u0sdnqyFCBm0XS+Ljrzt1o4ePdqWf9x1 9f33atdcc00zXRfuJF1P837wgx9E84yL/24fVaHXiZuYipn5VrL5VP+m/cFG0tsXub/nNAeo P/b0P0+u/ma129XZ6wXVRsPseL6PPvqomzJaofO+0Ok33XSTS41fK3z6okWLOvKMim07KPta EYZHKir7cNXThUI648npZONhucT8+w43z+2YrOrcKRac3/l9DKOjAP158+bNc6mD02MkxUhB T3+/DikgDsJ+3jitXr3a/aslthw+3ebZu3dvNM842LaDUhWpKvR6cRGuliNbF7d+3z0/dqnv NNMufvtY8s43pav/6eTB93/ppleTjHPW3BazQScWcXo7SYyDfM/nP/9591dLbBl8us3j00PX nVHx3+mDwmB3elvZh5C6k0C2Y3b6XP+X1BItjMncsdaMfYopMQyxJ1f9CrUrkDd2nhQs/JNM G1JVdFjVEu1bQjkJj6unPV3I8J3cSMc369atc6lJ2lZF0p966qk0dB6/zJKu84yLfbMgURV+ u1dtvdBw6cLWb3vIpYnfUL/51NTy5MaX6nHj1VrnRiBM2mfr7SRjk466HZ6c31966SX3V/t5 f+vW1vAocg2QdLmGdMsjnWWNw+7du5vfK2ELOOjUa3vpKqUUCrP5RHObjr/2FOK4yoyBvUkf Vo+T9inyIKRQIxdS/XkS999/f1rQsxcSCek0ZlQXXjtQ/SQGIH/88cfT777++utdSsP3v//9 ND3EL++k+O/3UZW2g0L2Rb1uqJKTyaLmb/tFl9bw/mOXNtJv2+FS6kHv6xKIs9tKHlCOk/9e 68///M/TdHsNEbE8o+S/cxLfXVZ6e8VqpfhCIW/ys2id6+fe9T2XhiLgjDAG+oQyzJ4zdR12 iX5v/m3hK0tIAbGnmWPJH+/Ync67dOqcZGrRf3ATepO2IPY7x/X0zT/VXbVqVVoolAKg55dF 0jWfR0LeLGoPPPCA+9doVfntoKjyugEW+3t2oYeZ4+it0z+kWrx4ccc1wS+HvYZ0yzPKWiWh h7rozg4V1o2fh+Eoepj50+a2mt7WqoGGyeOMMGKjHgtOf7YtiPRy3333teXvFbk+/1jnxWfh pfk6mLH5JUZNOoDx33X11Ve71CR54403kltuuSW4HL3y/NZv/ZZLGR37tlgi9jSzrPS68SQW VWevHZMYy7RM9LbyMWrym8j3XHnllS6lJbYM3fJ84xvfcP8aLoaZ6I9cZ/Jsszzz1hXtB4uL vXbE9AlCYtjs5+s2fzG2jV6vkCeZ/Xp06QXNz3nyRL6jP3QRo81DmN1OVRl30JP2q3r9gKqz 4+xx7uvOvs2RGOTaNQy33XZbuhyTZrfLJMbFLSO73XqRWky2EFn1yGtps614uP3gBy80+m6Q uOMvDrtUjAN3ViNkq7GMomMUe9PQ6wKou77OE/2ZaQ0sPTXHpeXjO3bRwbg/7ez2yfumuAxk v9brCNSB3ufZ73sLVR2VmjCTctddd030d7MP0iR4sJBdv9utToXCfFq9Rk8t7mwT/sq61ji0 PuZ+6z2GpRgTrjAjZHfsUcnyPf20E9TRV/XDE2poirmNccX6EbqojavX0RC9HJNW9XaDwg5x wg0N6kLv9xLoLfSmUIZSmoRJFghjNYGQnd5uVWuCMQmtcWVlGKGfu9SWq+67sTlA/UdvX9uY 95TNyberPcpQYXB2GBH75m6Ub7X090joxvR52wnakILk/v37+2qgv3+zb1t3VrL9SOfBn0fo TeGkunjWyzBJoaeQVWTXUdpLAnVg931uSrPRQwH4kGFrxm1SBcJQcwsJhkXIbtjDeiFJtjer i87J8NbvJ415Vzyc7KKt4Viwl4+IPpFIjJLtfEAKT3naCcrYgps3bx76EAVz3efP3X44mZn9 b+f2z7nvPCfZ81fH3FzZSbs4vdwSdRUa2zJL+9EysuuJ4vEFlVjHJ3663JCGCjXdpus0+b+9 qZU3Qr2m+xoFoelFZqtAyk0qsgmdI8f9pnASBcLYtd8fI8jGbj8M6qRqQtS7WYt/m3jH4cFe JiA79vIR8L2I+Rj1GHpyg6O/LxRz585Nu52Wt31jMXO4+d1PnnijbVl8rN79nps5u9DnjHr7 Fk3oRmcST7/HgeqixWfPP7Y2hNyI6ukSmj1f2unz5s1rm2Z7mL3gglbHVRK/9mu/5qY0yN96 us1fdLZaOLKLteXqp8ZLP8ZdIAwdaxK0u89Pb79hDhdWdaub221R21vAmcOuCuhs/MujveqA HkvmfuL0ZO50vcaenTSuLkMWar8wDvY7Jz0g+Ynt5zaX5dzfnzv7/4XJXx37ZXLkmdZ4fVNT /fWE2crfirLd5PUjVvCXca2qyq4r1UWLx95025unXu1cbdUsO91OG/b0orPVH5FPrFA4jhoV 4ywQ+vENbSA/W7AuU62CyXqvbbv9wqXK28HpZvpqlxZzMrl5ajq5qOd8GDbOFkNmb37GVVCx 1UbnzOmvV89hubRZV3x+cszU/9ZDUfTbVtjn1zGut0f6O8clNM6gRJULg8KuL4rH/kYS+o1E aLquWhqarqu3haZrg04vA73sdasRMQyh3kclRt0j8zgKhHKshDqNozZF/+xDBGR1orXdpne6 tJPJH81tbUvfaUzMH00tSv7FolXuL4wTe/qQNQ8GF+NkC4WTM5Mscsvwtb/qfLJ2bPcfuGVc NDtn//S6+hjH+Er6+8ZFf6cPqQJcZfYpLdV2ikn/Rj6kWrMXmq6P09B06ZTLC033Ym/NvVCN DT29LPSy16E2xCjYjt58SC/WozLqAmGs8xj2kf7ZcwbXnXw+2NfoTPATd+5LZo5+O/n2fa5z wdt6V/+ceX8bhcEJKt+VscBCPZuNk21bNq52Eh1OvtRchiOBEt/+LVe56YO9xZR2c/57dIz6 yaj+rlELta/yUXV2fXUhA8VhfycfXmiahBea5o/h2E2871wlVh3Qv6G0D8l8lK3qsa15gv7E alpMT4cHyR7UKAuEO3bs6FgPH1Rx7J891ihc53Uy+bOtre131h89lFz8B70Lgx+9fUdy9cJV yR1vH3cJ307u+G8MTD9OXFmGSJ9EJNasWeOmjI/+/jPPPNOljteR7We5ZTjLpbR7eIUbfHSA sQm15cuXN9dZh652Nkz6O0bJXph8VLU3UUuvM09piyl2gy0hYgU6CRFqP+hDxI4BCRFKl/AF ytA0ibLd5NkHQ77XVOQXe6ssMexaF7fddlv6ucMUGpfXh9/v0T+7TTF673zTD1GmY36y7jjj TYwTe/uQTKozGcveQE2CHm6i08+by7ZtwLEJtZ07dzY/V4dsj2G/DdCfPwq9boLrYOXKlbVc 77KJvaGTkHNir305lO4j7/TTTjut7W87XdpV67/LRi87N/6D67Zvrlixws1VHNJRXGhZJWRd dDVr9Kco93H1crLZxKg9vuimY1zY24fE7syTGi/KntDWr1/vpoxLawykUHXRI9v828Ph9yDV 7YIpMayn6vozhyk0nISP22+/3c1VD3b9UUz6N7I32La6pp1u3w7a+bu9XZSwb3q++c1vtv1t z4UPPvhg299lY7cfBhcqAOiQzlom3WO3fThmg4Lg8ITOWUBdcFUZAntjMumTiF4WiXE68eQV 7nsDQ0rM+C6JO3seHabVq1e77wnHoAVD/VnD0K0gKFGXKqLeo48+2rb+ukdKFIv+nXoV4HpN t22wbQHI/m3fTtrlCRWg9N9la0doO1kq2/IXmTTv0Ns2FFILZVzs+KuhmNRD5yqz2xioE/b4 IbBPlSbN3ljt2bPHTRm9J1ctaXzvv2lvizFzTDqAkfEI5yRHPhp9vfBebwsl5Aa1n8Kh/ox+ yffaG1YbGzdudHPXi90OKCbbflDov22Ibvu86Nam0BaIbIhQug87vYydRejll8Bw9XpjqGPz 5s09O25bsqRxPexFHvqtW6fH6I0H7alHY9KdAgKTxh4/y18EzjjjjL6qX+gTiNzwFIFeJolx +t62VW3fvejLX0vmTy1Mpv9l756mRkHag+jlCYX8blkLhzpfHqELTiik99S6sj3HTqJjJmTT 6w2dDdGtUCdiver6tyGxAqW/SY4VKH3hL7TMZVK0h49VleWBXSikar8005DxDaXzlxtvvDFN l39LSKc1Ug31sssu68jbK3gjOFp2e1MVF3XDFWXW5Zdf3nEy0CGD2saq59gLdNZCxajZKjB1 LmR4WQqGPuRmIFZVUc/XjVxQ8txU8Bt1XpRRXPp38gUyW+DyoW9mQ9P1W4/QdH8sxgqU/rwb K1D6bvhDbzXLxDZPYJD60etVpX+UIdcPjAfbHXVX+zuubl1Qdws5YYRufoqkyMs2Sffcc0/H tplULFq0qHZtBGPk6bneNrwdLDb9W/lx/2LnU/1wJTRdP0gLTdeGOT1WgCWISYV/m43x4c07 MHttdP+vtfnz57edDPoNf1NUFPYtoXTWgXaxwe0JYlQhb8smEWvXrh1afP7zn29bJ63Xg7LQ W3NNlrXbdDttkOkUCIlJhRRCZF8vSq2iOrO/DVBH7PlO6MbAjlvVK4pIL5+0W+jVCL7OpCOa rA37CYJohRZ6S6iFqn1qttqn3DRrUk1ST5cqfZotUNo3zRQCiXGHPERBMdnzVaypCFB1FAgV fVLQIW3Bet1EFLUBcq+n8egkhebt27cn999/f1+N/wmibmHlmX7mmWe61BY9/ZOf/KRLbZBz rZ5ua2bYAqFt/02BkBh32IcWKA6qiwIN7P1GqAAlYZ8aSTUPubHw8xeZXRfp7QyDue2225K7 7rprrEHhlChanHbaacG2nnJenJ6eTucJvR3R00NV7T/+8Y8nn/vc59LpIdIj9HXXXZf+P0Ty yfTf+q3fcikt/o2AffNYJqHOc1A8/uEEiksfQ3JeAuqqFGeqC9QBm8aeH7sp/fMX1FD1AFsl yUdZe3SzT8wlgCKwHcmwb46fnAP7DSlcYTLscUMnTMVDgbDYbG0Bzmeos/KcqY4/0Dxof+SS BnHBBRe0nQh86CEmQm8Ly/oEya5HXQc9R7HY/ZKqVUA2ttMwCRQLBcJi4/gBWkpzBJzYcbU7 aOe4lP5JmxV7IsgbZXsaK9W27DrQwQwm6ZprrmnbH+l4AcjHFgqL1tN13VEgLC5bc0o6uwLq rDRnqs0XuAP3ksdcSv8+9rGPtZ0I+o1Y+5Wikipedh2ASZB2rOyLwOA4joqLAmFxcdwA7Upy FJxoHrTLPjjp0gYT6ho9b5Sx+qitBrtjxw43BRgfvQ9K0HYD6I99S0hbwuKgQFhM9u2gPCwH 6q4cZyrVfnA4xcEWfVLw4Qt60r25PXHoKCu7HlQdxTj53iV9SLffAPqnjycJFAMFwuIJ9Q0B oCQFwic/4w7chVckycx3Ow7mRlzg5s4vNi5VqE2TFBLlCWyZb2LtOF4SwDj0GpQcQH72LSGd MxUDBcJiCdUMkyHEAJSkQDjXHbgX3+37Fz2ZTKsD+qx12ao9yrzS7k8KgPYkECokSZS1V9Fe 7FOy22+/3U0BRkfvcxJUFQWGwx5bmDzp5IfOsoqDt4NAXAmOhueaB+6umUbKf1/V+PuW7x/L XIX0L/7iL5qfEwp5khcrFEpUkV1HBqzHqITeDJZ1XE+giGS4JH18VfVhJtCPUKd6PJAEWopf 0jn+oDt4G1VCZ97/ejJ31fzkLVc4zOr0009vOxHEIvQESaKKVXDsTfpll12WfPjhh24qELZ+ /Xr3r+wuvPDCtn1NAsBwcYwBYbZpEG3XgXaFv2I02w9OnZt89PYdafXRix56003NTp8I+o15 8+a5T6uO0HoCMXv27Mm9n9x///1t+5eEvI0HMFzy1l0fZ9z0AuG2g/QsCrQr/N2/bz84ta51 IPcz9ETohNBPVE1suwAx0t40635y9OjRtv1KgqpswOhwvAHt7DFBRzJAp4Lf+bfaD06tmkoW +n9vftVNzy9WJVTeWHQbYkLiS1/6kvuUapGG73Zd6WQG3SxZsiTdT+T/MTKcid2vJACMTqit lDQPAOpIOvXRxwIPSICwAt+dnUxO7JnTPIh/Mpvy3N3+oD63MUufYsNM2HaC8vZMCokLFixI p1dZaJtIVT8gxu8nsYcHel+SkH0MwOiFHnwCdcRxAGRT3KNj5lvJ5lPnuoP4wUbS2xe5v+c0 exc99vQ/T67+Zvz1/z/+x/84LehJD2xW6KJZ56dHoZ4gX3zxRTcV6OT3kzvvvNOlNOh9SIKn ssB4cQyi7uwxQNt1IK6wBcIjWxe3DuQ9P3ap7zTTLn77WPLON6dn/z2dPPj+L930dl/72tda n2FCGt/LG4tQoVCirmz1CglpBwbE+P3Ev1H21Ul1ABiv0LmcqqOoi9D+DyCusEfIpQtbB/Eh lyZ+Qx3cU1PLkxtfio8j05qvv6jrE9VQ9VFpDwbE2P1FB4DJCNX6AKou1FkegO4KepScTBY1 D+QvurSG9x+7tJF+2w6XEqdPBoNEHYXenNLRDGLkgcHq1as79hm5MAOYHHsup+ooqkyaB+n9 XULeFgLorvKlnViV0DxRVxQKkUWsN1GqpwHFYI9N2lKhquy+zgMQIJtalHZCdcn1iaLbkBN1 F9omFArhxQqDElyIgWIIVaHj7T2qxu7jEgCyqc3REmpLIRF7iyEXyz/7sz9zf9VbaLvR0QxE qJqoHteSm06gGNasWdN2nErHakBVhB56yJicALKpXIEwNsSEyDr+INqFBq6XoFBYb6HeRCWE 3mcAFIM+Tjk+USXs28BgKnfEhE4K5513XloYfPfdd6OFG6q39RZqU8g4hfVk9wMJObY0edDi pwGYvFCHG1z7UHb23kT+pnYKkE+l7tRibwBDESrcSKC70Hbz48+hHuzvLxGrei3V0vw8ACYv dJ2kF0aUVWh/BpBfpY4ce1LoN+gdsbtQofCaa66hCmnFLV++vON3l5A3793oC3asOjeA8Qmd w7nuoWykQ0C7H/NmEOhP5R6l5HlLGAsujL3pNz86du/e7eZAlYR+a4msF199A8rxBUye7WRG gptplIVcR+z+SycyQP8q+W499NRIQm5K/RAToSekEmeffbb7FPQSa48pb5JQDfLWN/QbS+Sl L+DceAKTF7oOAkUX2m/pHBAYTKXP/rFCnyU3p34cQqq05RN6SudDxqhDeW3YsCH4u0r0Sz9E oFAITJ4+rn0ARRWqnST3egAGU4kz/xlnnJF89rOfTd/+WaFqMRLcjA5XbPB/qpCWU+i3lLA9 ifZDv8EHMHn6GOfYRFHpnqt9UBgEhqP0Z30p2NkThA85UUibwthbLE4kwyWdi4S284UXXpjs 27fPzYUik98p9BtKDLN9BkNSAMWij3UJuT7S3hdFEWuiAmA4Sn80hU4QeYO3hcMlNxKh7SwD maOYuhUEJUZxY6g7gAIwWbGHqxQKMWmhwiBtBoHhKv2d2GmnndZxougnKBQOV6wKqcSjjz7q 5sKkSTvP0G+kY5THhn94wNt6YPIoFKJoKAwC41GJR/OhdoLSW6hN6xYYjdC2luAGoxhiw4f4 GAdfKGSfACZPjkN7HuCBDSbF7otSswTA8FWmJGRPGj7kQuZvOGOxdOlS9ykYNrm56DY2JD2R Toa8pQ39HjqG0YFMFvoGlBtPYPJC10yOTYyb3QclqM0FjEalXo3FCh7yBtGTk4kMMaHfjEhn KBit0FNnH/JbHDlyxM2JUZLtHPoNbIx7gF9dLYgnwMDkxc7ZwKiFehOVoDAIjE7lzu7dBqXH 5PWqokjBcDRuvPHG4Pa2IYUxuRGcFL8ctBEBJo9CIcYt9mCfwiAwWpU8s8sbv9hJBcXQqxov BcPhyFoQlCjKmzm/PNIxEYDJinU0ww06hi12XwBg9Cp9pMV6uqSKaDHExi3U8eKLL7q5kceK FSuC2zMWRXojp29Ax9WOEUBcrFBIzRsMS2j/kgAwHpU/2mJVXrjRLBZp1xn6nXTw1rC7bdu2 BbdbryjisaBvQCdZhbUsTr70xbbftC0W/gc3FzCY4P41G0C/Yg8bnnjiCTcHgHGozZncnmx0 RzMojiwFQyn4oCFrJzHnnXdeML3IhS39MIdCYTZ/fNVc9fte5VKB4YlV6+MYRV66MzH2JWCy alsgRLHFehmzsW/fvuTDDz90uepB1nfx4sXB7WGjW3vaMrQBkqfEfnmL0saxyFq/71nJxW// 3KUCwxUrFNLuF1nItSf28BfAZNTi6LNVEmj3UB7yBDHLW0MJKSQdPXrU5ayWnTt3Btc5FLoK aBUa6eunyNJLLSJm9jS306JNP0xOumRgVPz+poPrK7qhJ3gM09Y/uDg544z/e3YfWpRM7/up S0U/alEgtNXlUE6xC0ks1q9fX8qB72WZpcpM1reAEnIxtW/8QvMVsb1gVn4dGJIi7MSeOc1t tOH4jEsFRiv20Emuu4AW21d40Ie8pN38BWY/4ro3mFqUjvQOw1Ooaoi1iQuFFKzK0FupVAVd t25dcB1CIftybAD5UNuMcQ82Pwp+XdDpe//2DLd9LnApwHjEevSmYxB40m9DaB+pwnUJEzTz XbcvLaJWzIAqf2clJxt98uHtQnXIzUbWtoY2LrzwwrSQOKmeS+V7N2zYkFx22WXB5esWUhDs 9mAjtE2q8iBEt4dEu7nN3/sslwKMj5xj/LGpoyrnHvQvtm/QLhyDOrHjarc/zXEp6Ffl76rs CQjVlbdKaSxWrlyZLFmyJNm9e3eyZcuWtPD28ssvu2+Jkzd8fl7JK+3+7rrrruB35A25oPbq eS3WfXfVLrr+5oIbTe255u8t7QeBSYi9KZTgTVD90F4Qo7b5ArdfXfKYS0G/Kl1CsjfItGmo D+ldU37vPFVLixTyli9Pez8pLMY+p4piT5yrGr30aj944hn/FHU2Fl7hUoHhk+uutAlr7m8q KAjUQ7cHA1JIBIbjRHO/WvYBFUYHVekCoS0MoN6k0CQ3JEUrTMgbvEEukqH2ghJl7kAmC7np lBuPqkeWB1kPq/EHY2beO6sxz54fuxRgdOShnN8nbVT93FRXsVoqPoChOv5Ac9+iODi4Sh+h +kTEk0l0IwUyufkeRWFRbuil8CLDZwx7wN1Ye0GqZ9VJ6ynp1NSDLq3T9oWNef6SjtgwRt1q aUhHI6iGbtdOfmcM3zvJk585pbGPSa2XZucyNuhkLavKFgjtjbI8rQSGQe9Xk6Q7WPHBg48a Uk9Jp56Mvf07mSxM56HDGYxft7eFEjzAKi95kBr6TSVkGjAKM29f1OxI7eK7f+RSTybTzf3v V5IH3zru0pFFJQuEoe6NgWEpwn4VehpLj21VdjL5o09ItdBzk7fMG74nP+P3gTnxajMzf9qY h4b3Q6Xbyl1//fVph1Se/FvSL7jggnTagQMH3JTWOUTSDx8+7FJbeebMmZOsWLHCpTb0k6do YkMP+ODBbXlIjZfQbyhRtAeTfrnk2PnX//pfu9Qkeeutt9L0+fPnJ1/4whdcakPePL/4xS+a 89s8+rPs96A/m85ptZvf5a6J/31V4+9bvn+skYBcKllS8juJD9orYJj0vjVuvh2kXgaJqnYe g4Yj65a0fu9NL7hUcbSZPve2fS6t05Gti9N5HmDg3qF5+umnk4ULFza3v7Zp06Zmuowt6r3x xhtpvmHkeeWVV9rSdZ6ii7V7lpDzm7RFQzGFminoGHaziGHwy6bt2bOnecz9+q//ukttHNeh 4+1nP/tZmv7Vr341mEc+z36Hz2M/S/jvQT98jReJJWnKnUtn/71wquOBKbJr30MrwI47aA9C YFCT2rfq2nkMkuTldeo3/9Z7LjVJvqL2g26N6n8jnYeBe4fJb/eQ2LRbbrklTd+xY4dLacmb R7+hKatYb6Q+aHtWHL0KgmX7rfxyWz79tNNOcykN77zzTpouQ1JZsc/qJw8y8DVe0tjQrCb6 NIXBgVRub2ztJI2QCw5QdrGLcRGfxmL4Zo7e2fzN5Qnot+6eUm0lppOfuPnCGjclU1Ob3d8Y hunp6XS7agsWLEjTZJqE1vgNGtP0m4FYnlWrVqXpS5cu7cijPyuUp2xCtR50UANiMuQBe7eq oRJZekEuEr3s+tj5yU9+0pauq3/rdHlA49k8Wj95kI2v8SLXPr8t0+2586duDvSj8gVCoOxi XXlTrapePnrrT9TvvyhZu3ZH8u2jv3RTuzj+YJpneh8Xy1G74oor0m29f/9+l9Lifzsrluem m25K03ft2uVSWmKf5fOUVbeCYdHapVVdt2q9EmXtCMgv/yc/+UmX0vDTn/40Tf/4xz/uUlp8 HmvYeZDN4//7PLd9z00+cee+1rBLm191c6Af5b1yBMjQAf4g9AGUWax3PjqQQVa+0xnaD46W PzavvPLK5LXXXnOpSdrRi6QvXrw4neb5+eUtgc2jP+uZZ55xqa10idNPP92lNug8ZSUPuXq9 LZTp1PwZnW7DhPgoY2HQL/tFF12UHiNf/vKX3ZT2Y0d3zKTznHPOOS61IW+ev/3bv43mQR6t 9oMXP/RmWjvmubsbf0sBEf2rVInJH2w+BhnsG4jR+9goSXVQ/V3j+E5USGBcpmcpE46M38bW ddddl6bv3r3bpTT4+XXvo17ss/rJU1a93lBJSOGFnkkHF+p7IRRlfSso/Dr89m//tktp8dOs Yeb54IMPonmQw8y3mtvRj6krQ1A00nxP2z9Llu/4Im3mc6rMnmnfpPAEEaOi97NRid0MASgO 3bbX8sNBPPXUUy6lZVx5qiBrYYUqpfmEalSFQvbxshe6/bpYf/M3f5Om6x5Dvbx5/LATIbHP Qn6t9oO6ox7fTn4qufjtY7P/n06m5n7FTUNWldlD/c7gAygruy9LcLMDFIs/NlevXp2Gpqcd OnTIpbY6opHqo4Pmufzyy6N5qihLdVIfMt8TTzzhcsLLWgiUqFLv1XJ8dDvevvKVVuFBbwOd R44vSZPxPmN57Hf4PKFpPh35XLrQ/T6/vtGlNDR60vax3KUij0oWCLl5RhnFOo+hvSBQPP74 1Pbt25d84xvfCE6TcQJ9uh4zMJbn+eefT9N9z6E6z86dO9PeR2N5qk7eWGVp6+ajrs1H5O2q DAcR2iahkHunqndWJsdH6HiTN3w6/Vd+5VfclEYef/zqISRsHq2fPOjlZLLIbbc7Dv/cpTW8 /9iljW16W+eQPsimEnujfWoIlE2svSDdrQPlQS+j45fnrZcPechWxYJP3oKyhFxj6vQ21a83 vYyizPz+pSPPdSekElcOvUGyrjhQFLH2gnSKBBRX6JgtStSVnDOlsBfaJt1CxtorW6FIHiJm rUJrow5vArXQNihKAP2w+9HNN9+cvP32225qq6mBxNVXX+1SGyQtpPR7o3Qe41dagoG6USah 6jxUeQaKzx63RQrka3MYCykoygO7SXaqIoXctWvX9lXQ1VHn60poexQlgH5024/uvvvu5JRT TknTN2zY4FIbD5H27t3bMb9X+r1Rb5DYSgJFZB9mSFAYBMrBHrtFCnSSgpXuFXYYIedrKTTK gz0ptEl1TWm3lyVkfp9HPiP0+YMEbc9bQtunKAH0o9v+E5vmz38xpd4bpYqHX3EJqtihDORp c+hpLxdwoDzs8VukQG/yBlHOuYO+RSxCyMNFKVQiLLTNihJAP0L7j96vpMqo5zsmk56qP//5 z7vUTqXdG+WmWq+8BFB0dB4DFEPoOCQIgihzFJl/ibNgwYLgsspLHUmXgktoum9iE5sub9vP OOOMNELTJc1Pl3ktmX7qqaem/y96bS1ZRr+OBw8eTGsd+LSvfvWrabq4//77k/nz56fpO3Z0 74G1c4uVhK36wUD0KAO9z/qo0nhPQFmEjkWCIIiyR1HddtttXZdTd4QSmm5rVll6Wpbptm2w nV7kjpf8Morf+Z3fSf9d215G/Ur6kNIxUGR2n5WgzSAwfqFjkSAIogpRVHY5bc++drrtJNJO 71Wgs+x0+5bQTi9yMx6/jN///veb/9YFwr/+679um6ZJekgpC4ShbvqBopKnTHZ/laAwCExG 6HgkCIKoQhSVXU5b4LLT7T2Sna4LdKFmZPpFka5SqcOzfZLY6UXTazn/2T/7Z2n67t27XUpL aH5R3LXtQm8ECalXDBRR6CQlUacxoICiCR2TBEEQVYgiij0Y92L9K3ixAp1/SyiFQztNd7QU 68nXs9VR7fSi6bacOl0XCHVTu5Dirm1E6AYbKKJeJzgAk8ExCaAqynA+61XgivX268UKdP4t YWiahH9LGJom4QuUoWkSRX1475dPe+ihh9pqUHpSlVTSv/SlL3Xk0eJTCsruNEWu44v6ClVr lgAweRyXAKqiDOczu4w+fDvC0DQJ344wNM1Ht+n+LWFomkSvAmVRyxh++TSf9swzz7iUhi9/ +ctpeuV6GfUrLEEbLAzKv8XzT4mGQe+jPthXgeKwxycAlFUZzmd2GX34AldomoS/dwpN8xFr muMjVt3Uhx/uIhZFpJfN9zIqvbT+3u/9XvLmm2+m6cLPJ+nPP/+8Sw0r1ZXQvnKmHRYGYU8C gz4JitWR5y02UCz2GAWAsir6+czeG9n2fvZezNYE7FVgC7Uf1BGrbuqjV/4i8sv2+OOPN8dl 1L2Mvvjii+m00DpIekiproR+xUIrCOTR7QTTz4OGWHtBBpwHiscepwBQVkU/n9mXObZJTa8C oJ1uP8+GnV9Ht2kSoc8u4ssnu4xWpXsZlZ5E9crr7mSBfth9SsfatWvdXL3F2gvKSQ2oKtm/ ffuLsrHHKgCUVdHPZ6Hls2k6uk2XAl2sNpaEvA3s9sBfpnUrUEqbRvvGsIi1vPyyXX/99cm1 117rUhv0tAMHDrjUJLnhhhua6SGluRL6FfQBDIPdr2z0KtSFCpVywgKqQi6Q/kISijKqwjoA gCj6+Uwvm3+IqNN09OoExt+ThaZJeKFpEqJbgVKUYTSD0HJt2rQpefrppzumvfHGG2n6jTfe 2HVdireWEX4FJfK8vQGEnABChbvY2z0d3TqcsfNSGEQVyPEiF+Ze1WskyqgK6wAAoujnM71s /n4q1m7Pi117vNA0CS80TcKL1RDz9Jh9Or0oQsvl0yrdy6jdMYpYnxfF5vedUIFN71uxkDck WqzKAfsmykYelPRqdN8tpPp+PzFJdh0AoKyKfD6TGiahZRu0QBi7B/NC0yQ8u1x2un1ZUDR6 uQ4ePJheU33aV7/61TRd3H///cn8+fPT9EoUCP1K+gDysPuPhB/7RtgTU7e3IlLg63WiAopI nszKRbTb/l3XAICyKvL5zBbcvNhQEV6soz5PF4B8+OqmInSd89VVPTvdthW004sYwg87oXsZ 9fR8vWSba4Lk6bVfIQl9Iw9kofcfHXLC8G8q7DRJz3PjDBSZPMiIPVElOH4BlFfonFbE6FUg 0wU6YafLPZlma7bomiehAqVt/mOn2xpednqR48orr0xee+01t+RJsnz58jRdhqSQaZqkhxT+ SqhXOLYSQDexJ1E+5CRjC3/+xBGrVuDDnqCAssj70KPKAQBlFTqnFTGkGqZmp+sCnbDTpZCn PfXUU23TrY997GNdp/vx+2LTY9VaixhW5YadsCV8244LsOSEIo2FLb0fZQ0tdONsT05A2ckD EDnP5rkQllEV1gEAhD2fFTUs/cbOvr0TvaYL/1Yv1H+DpHWbrj+z1/SiiW1TEZvmO8qJKeyV UH4cv1KxlQM0OXhj+0tof+oV9mmV9G4r6bwVRJ3IsdOtx9EyqsI6AIDgfFY/od/6oYceausM x5O2hZL+pS99qev+Udg9x958cBOOXvT+4kMPNWH3qVijZR22UAigQY6tstbasMc5AJQV57P6 Cf3WPq1yw074FfMB9GL3GR1SFc6+JZQCoqTF3nz4AFAtHOMYhH+YaNtEAZPA+ax+9G/texmd np5Ofu/3fi95880303Th55P0559/3qWGFXLPCd2gA73YKqM2ZL+y+5b8LeRNoE7XwVtCoFrs MQ7kxb6DotDnMvbJevC/9eOPP97sHEcPO/Hiiy+m00L7hKSHFHLP8Svgg847YEnHMR//+Mfd Xy1238kS8pbQCz2MYP8DqsUe40BeUl1a9h3blT4wbpzP6qfXb16JXkZDnX8Alt83pGCoC3T2 LeEll1zS9rfE/Pnz2/6WzmI033mMbn8IoDr08S8B9IP9B0Wgz2Xsj/Xgf+vrr78+ufbaa11q w913352cf/756bQDBw641EZV971796bpIYXbc2JV+gBP7x8+dKHOTgu99bNhMcQJMGoHg8di z1jxsMvfP/uZQD8+9alPpfuPfagIjBPns/rp9lvHppVu2Am/IhJyI6/f/gBC7yM6/MMDv9Pr 8G/9YiF5AIzbyeSPFraOw4t/+HOXrh1L1jXnWZTMv++wS++f/z4fQL/YhzBp+lzGvlgPod9a 7wPSwYy3atWqNE3aGn7+8593qZ0Ktef4Ovk+ijwoJCZHqonq/cSG9Chq06Q3uFB1ZB0Axm// pkaDeIljLi1kqRQKT3kw+csZlzAAfdwTBEFUKVB9+rc+ePBgW8eIX/3qV9N0cf/99zebSZVq 2Am/Mj4AeSgQekts9xUbp556akeaZx88+KDzGGD8Lm2+/ZvjUsLWLZ6fXPx26A1ifvbYJwiC qEqg+vRv7Yed0L2Menn2icLsOXp0fQnG94HQ+4QuGPZ6SxgKyeNJQXPZsmXNaVRNBibhROsY Xdz96eUwNb+TIAiiYoHqs7/5lVdembz22mtuapIsX748TZdqojJNk/SQwuw5esViC4v68AP/ 2ujWeYzUmbZpNiz5HsYZBCZj5u2LmsdmqP3gb6y8qGs10kHo8wJBEERVAtXX6zcv7bATdqgA OviA0PuEDt95jH2rLNGrnaB+Swhgsp78TOvYtAW/D/bJA57u1UiBIpExCWVfppdqAKPkr5sh sWml6GXUDgsAiFBvoTqk8xhbKPQPE7oNNQGgGFY0j0td8DuZ/NlWlz6EISaAceI6A2DUQucZ nybVRHUvo772nKSvXr3apXYqxFnLr4SEPGFDPYXajep9IxShgp8nVUG7TbcuMPNN7fmxmwJg 6Gb2tB9vgZje+VM3c/GElrcogcnxDyl9TRZMnj0+ihRAP/T+s2/fvuQb3/hGM+2+++5L08W6 deua6fLvbnrujTLw6ig7ePEDu/qgc496iu0Dvd4ShsJWOdbDUPR0/IHmvD9ySQCG78SeOc1j bcPx9rEkfNvCp4cwxMSo+GUvYmCy/O/A0FnFoI+NogVQFNG9UTrbsIN5j6Kwpj9fAvUSagco odv62UJht+qgPkKy7L8ndlztPoO2S8AoPXzVXHesXeBStIPR47go/LmmiIHJ0m3ZMXn62Cha AEUR3Rv37t3bsePK27xh0idNCXp7rKfPfvazbfuBDz0uoE6XasV237HR75PZ7/3bM9L8c+/6 nksBMArN9oOhdoInv51sXPzf3B/FpM83RQtMnq/9NIoH6cjHHh9FCqAoonvjF77whbQeqt15 h1kv3r7pQT3JgwC9H+jwbUptulxk77333q5vC/vh8xa57RJQfoeax9qiTT90aeWjzz9PPfVU 8sgjj7gpSbJy5co0fevWrem0Y8da/ajqPP/zf/5Pl9qeR3+W6CcPJkdf1zBZ+tjRg3e/9NJL afqv/uqvJk8++aRLbcib5xe/+EVzfptHf5b9HqAoomcquch40iuN36F9DMreyNOZTL3pfSEU 0o23/ls/mLBVm33kpjq5+MsCt10Cyq5b+8GyuOmmm5JTTjmluR7aqlWrmum6kPZf/st/SfMN I89//I//sS2dwmDx+Pscaj9Nlj12hPS86I+fhQsXutTGcR063j744IM0/fd///eDefzxq/k8 9rOE/x6gKNr30C78Dq1jkKoQw/wslF+3t4Sx0PuM/FtP6+cC3LpJbXXXC2D4tjeP1fK21fXn mpDYtFtuuSVN37Fjh0tpyZtn2bJl0TwoDn6jYor9Lj79tNNOcykN77zzTpq+ZMkSl9IS+6x+ 8gCT0nNv9DuthDyB1H9L6HZeWdm3PRKA3Sd6hX5L6Mm+JVVJ+9Hs5GLx7I3XzHeTh6/2nV7o CHWAASCzmVdax9OKLS6xfHbt2pX85//8n91fDb/zO7+Trpd07y3TNb/Okv43f/M3LjWex799 vPvuuzvy6M8K5UFx+PsdBqsvBn/sLF26tO2t+o9+9KM0fe7cuekx9dhjj7kp7ceb9K/h6Tz2 gU2WPPrYBSYteuWQgp7t3dHTaXZaFjavDAsASEcwer+QasS2arGN4b1ZPtH8zIvf/rlLS5JN Cxtp0w/8wKUA6EerV9HOmN5X/ja7cr1ctGhRuj66/ZA8oNLXUk/SYnnkvOcLd7rQ6fPYzxI6 D4rF/140jZkce+z85m/+ppuSJL/7u7/bHLxbCmpet+Mtbx5p9yvpoTxAEUSvHL6X0Wuuucal tPgd3UaWm3M9JpwPwIvtG7GC4emnnz6cQuHxjc3PlK4fZt7/evLHq2ZvYFdNJbve/2VjHgCI 8OcP67rrrkvTd+/e7VIa/PwHDhxwKS2xz+onDyZPN4kY3kNM5OG3/2//9m+7lBY/zRpmHmlP GMsDFEF0z/Q7ri4Qfv/732+mSyxevLjtb4leVUjt/FSjgBZ6S+jFOo8ZxnAoT37Gf965yUdv 35HMnf33RQ+9mfzETQcAS78J8OH53j996AKhTteFO5tH0+lZ86A49LUN46WPD11Qk2rYepqm 07Pk8b2M+tB5dLoEUETRPVP3MioWLFiQzJnT6HRD27dvX8fOHmvDZTv+oPoEQvQ+IqHZfWhY pACYfua61mcv++CkmwoA7a644opmdU97Lrroooua6bqd0n/6T/8pzTeMPHfccUdbOr2MFp+v 6fL1r3/dpWAc7LEjpKOX+fPnp+nyf+/Tn/508Hj727/92zT9n/7TfxrM49sCaz6P/Szhvwco ivY9tIvQDi2kUaykSxe8fh6J0FsbW+0PCOn2ltCTN8vDq3rzXOv7Vs3uy/7fm19NfuHmAADN nzNCYtPoZRT8ZsUQ+x18Or2Mom567o1+p5WGsLrR+0MPPZSmX3vttWn6W2+9lT7x8PNLSAFQ 09N4O4hu9L4y6v1Fj4kmVUSfu9t/77nJgyUdIw3AaMl1T18ThX/LJz2G2mn+HCPpf/d3f+dS 43n8uGbSy6jNoz8rlAfFtWbNmvQ3svdHGA9/7Pjjx3vvvffa0p9//nk3pf14k4HpPZtH6ycP MEm5CoSaVHmQdHl6qdlB7H3VCDvOHA2r0Y2tGioxKq/+P+47Vjyc/j1z+A73nZ+tRO+HAMbD Vw8L3ejFzmOxPH5A61DX9LHP8nlQbLHfD6Pnt/0nP/lJl9Lw05/+NE3/+Mc/7lJaYr/XsPMA kxQ9I8lTSb9DSzvBV1991U1p7eiS/uabb7rU9jy9AujGj92ko9/xBXtpth/8kxcaCTN/qr73 wdmEnyVzF04ncx490pgOAECffPMZeVsIAEUQLZmdf/756QlLvxk8fPhwswttCW3FihXNdMlj 2xTqGEavkKgu2T9C+420LRy+Q83Pf6BZPfRkqx1hM1a7aQAADMZfWwCgCKJno+uvv979q8UP OxESOrn59gw2gBjb8ZCEdO0+Kq32g4tmi4EtG+aoQbTnTLdNAwBgEFLjRa4vtCUEUATR0pkU /jR/c2wH19XVRDdu3OhSk+SBBx5I0/ybRh2jvMFHedn9RILuuQEAVeSvc6Op/QIA2fV8XXfj jTc2u72W8J577rk0fenSpWn61Vdf7aY08vjxXa688so0zef3QbVRaHb/kHj99dfdVAAAqkV3 niYd7wHApPQsEPqTVdZeRoXPY9l2hVJVgpNgvcnbYr1P+KAwCACoOhlSyV/3AGBSomcgXRVU BsR9+umn3ZRWgU/S5U2hZ/NoPn3x4sXNf/ugp616inUew5AkAIC68L1qy/8BYBKiBcK9e/em J6hrrrnGpbT4G3cra55Dh1o9O/qgCmm9hDqPkQAAoG64BgKYpOjZZxi9jIpYHnmD6PP4oLet eqAwCABAyxNPPJFeB7kPAjAJ0bvwlStXun81LFiwIJkzp9FFvyaD019xxRUdN/UHDx5M06+6 6qqOPJIeqjrqA9UV+r3pSRQAUHf+mkjfCgDGLXPpy5+orF27dqXpN998s0tpkBOapPteRjX7 WbYKKcNSVJPviEgHnccAANDqdXTZsmUuBQDGo2eB8PTTT09PTv4G3vvOd76TpvthJ2QQek/y +J6z5N+ez2M/S/g0H1SbqJZQb6L8xgAAtPjrI52rARinngVCf3IaxrATWfLYoDphuclFLdSb KJ0IAQDQTmrN+OskAIxL9Iyjh5CQdoKvvvqqm9IqvEn6m2++6VLb82zevNmlNmTNE+qBlCqk 5fTuu+92/Jb8ngAAxPmO13ggDmBcogXC888/Pz0h6TeDhw8fTq677rrmjb22YsWKZnqWPNL7 qE7XeeTfPt0H1QvLRT/l1LF27Vo3BwAACPHXTAAYh+jZZtTDTjz++OPRPD593bp1zX9LUCgs h1DnMRI87QQAoDc/WL30xwAAoxYtEEpBTvM39bt373YpDbrK58aNG11qkjzwwANpmrxptHn0 Z4XyyJAUPk+oCimKa82aNR2/lxTk6U0UAIDs/DWUDmYAjFrP0tWNN96YdgLjT0zec889l6b7 XkavvvpqN6WRZ/78+Wm6HnbC57GfJWJ5RKgKqbRPQ7GEehKl8xgAAPLzw3fJQ1XueQCMUs8C ob+x1238RJYeQ61+8gg/zQZVEIuBnkQBABg+38GMBACMSvQMo6uC7tixI3n66afdlFYBTdLl rZ9n82iD5JEqpNKeUKfpwOTEOo+hJ1EAAAbnx3WmUzYAoxItTfkb+2uuucaltDqI8aHp9Cx5 dKcyEjqPTpdYvXq1mxIuEEpQx378fHUWG7y5BQBgePz1FQBGIXp2KUIvoyF+2tatW5v/9vHE E0+4uTBqvvovvwEAAKPlO2yjKQaAUQiXumbJ+IHaggULknPPPTc5cOCAS2mQgeavuOKK9A2f nnbw4ME0XdoL2jySvmTJkjQ9lEfaK2b9noULF7YVSDAeeptL0JMoAACj46+3ADBsmc8ssRPR rl270vSbb77ZpTT46oS2x1AR+6xueWLf89577zU/zwfVR0fLbm8JekADAGB0fFtCGaMQAIap Z4HQ3/DLW7v9+/e71CR56KGH0vRrr702TX/rrbfclFYeSX/ttddcaiuP/SwRyyP0NP09mzZt an6en8cHb6uGTwradjtLUAAHAGD0/HUXAIYpelb5wQ9+kFbP9CcfPeyETl+1apVL7cyj9Zsn NM3nkd5HJd0vm60+qvNgMLHeRAEAwHj49vvSTAMNvz71xWShui/ZcHwmTd8yre5XVj+WpiXJ yeSjv17bSp+Nk24KUGfRO/q9e/emB4oUvCx/EFmjyqN7GRVZ8ujgDdZg9DhIOgAAwHj5azA1 oVpmjl7stsuitIC3aXH7/crUlLw4OJlML7LpU8m3f5l+BFBr0bv6L3zhC8lTTz2VvPTSSy6l 1WOopN9zzz0utcEfWFnzbNiwIZpHp0vok14sj/6eU045pTmfD06c/bHbUYInkwAATIZuvoGG I1sbNcamVvxJ8v5jU8ncuVPJd//XTLJ/k0ufujHZPX16+u+30heIh1w6BUJARM8mK1eudP9q kGqevq2e9vzzzyc33XRT88DypK2fpMvn2DzLly9vzh/KY9NF7HtEbNlsFVLGx8tHbzsfFAYB AJgsGX5CrskyHAWSZOlCf58yJ1l8yurkjrePp+mfaN6/zJmN+cm3P2pUJ5055NOnkl+kKUC9 tZeguvAHjjXpXkZF7PN8Hh233nqrm4qYWOcxjH8EAEAx+GszTjS3xSmz8UajzDfrnWZ6Gt96 z6UnyXaftvAKlwLUW88ziT6YHnnkEZeaJOvXr0/T5O2cFLxeeOEFN6WVR9KfeeYZl9rKI3H3 3Xe71IZYHqGn6e/Ztm1bc1po2Xwe2wspb7ni6EkUAIDiu/fee4PX66rGE0884da83czbFzXn mX/f2y61PX3hKa2OEXUBcmrPj10aUG/RAqG0uZO3ac2DZjY8ST/77LPTtKuuusqldubR+s0T mubzXHLJJR3T9PfodBmywqf5oAppO9mudhtJAACA4ol1+lbFiBUIn/yMn2dxW4+h25vVSKea VUVTxx9oph9ySUDdRe/2y97LqOXz2JACJFrbzQYAAEBRrfD3LJf4oSXESTUUxQaXJt6ZLUD6 jgfPcmkAalcglDx+Hh91rw7pxzXSQbVaAABQbK3qnw+68QcbWulrj6puRI8/mPy7cxvp5/63 wy4RQLRAeP3117t/tfihHUL8gWfF8jz++OPRPLF0EZuWZ9l27NjRTPMh1SXrSFfJZXsAAIDS UNU/j7kkodsP6vTm8BSzcfEPdyVTi69omw7UVbgENevw4fYnJ/4AOnDggEtp8IUrGUpCT9uy ZUuafs4553Tk0Z8VyuOnabHvEXnz6O/RIQ206+SGG24Ibgc6kAEAAEX33N3u3sX0FvroknD6 4zfPVfc7C5vDUwB1Fy0QeqeffnqybNmy5gHkfec730nTly5dmqZLb6Oe5DnvvPPSdPm35/PY zxKxPOLyyy8Pfs8rr7wy0LJJSHVU/2+Jugyt4Mcw0iEFRAqDAACgDB6+qlHAm973U5fS8O9c hzI2/YN908m0u+d59n/pKqZAvfUsEPrCggzdoPl2Z7fccotLafF5rH7yiNi0ffv2pemxZQvl CaXbKqRVbz8X6pWMznUAAACA+gmXwGbJOIG+sCAFr1dffdVNaRWqJP3NN990qe15Nm/e7FIb BsmzePHiZOvWrS61lUc6iOm2bKE8ErHvsVHFKqShwiDDbwAAAAD1FC0Qnn/++WlhQb99k3aF 1113XbMgoa1YsaKZniWPdAKj03Ue+axu00Lpw1o2/28fUo2yCkI9iUrQeQwAAABQX9ECYZV7 GfViefz8PspehZSeRAEAAACEhEtQs6SwpPlCxO7du11Kg66KuXHjRpeaJA880OgKWN402jz6 s0J5pIpo1u8R+vO0XssWyuPTQz2RllGo8xgJOo8BAAAA0LOUc+ONN6adwPiChPfcc8+l6b4n z6uvvtpNaeSZP39+mn7llVe61FYe+1kilkdcddVVzTz6e954442uyxbKo7+nWx7P/+2jTAWp UHtBCQAAAAAQPUsHvhCh296JKvUyKrIsm48yVLUMFQbXrl3rpgIAAADAbFnH/b+Drm4pwzI8 /fTTbkqrgCTp8mbNs3m0QfJIFdJ169a51FYeKQh2W7ZQHj9N0+mxZQtFUYWWVQq8AAAAAKBF SzV79+5NCxIytIPlCxnWqPLI4PHapJbNDmK/Zs2aNL1I9NtRH3QeAwAAACAkWiD0hQldgPK9 cvrQdHqWPLqXUQmdR6dL6AKhTi/CskmnLUUR6k207D2kAgAAABidaIFw5cqV7l8NCxYsSObM mZMWMjRpx3fFFVc0CyDewYMH03TfIYwm6VINNJbHpovY94h+ly2UJ+uy6Zh0oUs6ugktV5EK qwAAAACKp7001IUvZFi7du1K02+++WaX0vDss8+m6bbHUBH7rG55Yt8jYp/Xa9lCeWLpdtmO Hz/enDeWZxzefffdjuWQkLeFAAAAANBNz1KML2BIBy779+93qUny0EMPpenXXnttmv7WW2+5 Ka08kv7aa6+51FYe+1kilkfoafp7Nm3aFPw8/z2hPDo9lCfPsunv8THOYSmkbaD9fokitm0E AAAAUDzRAuEPfvCDtO2cL2RIQcnT6atWrXKpnXm0fvOEpvk8vmpnbNlCeWy60On9rI+NcXTi Euo8RoLeRAEAAABk1V7KUSbVk6fm8xSll1HN5pFCqU+z04ZNxhO030XnMQAAAADyipZavvCF LyRPPfVU8tJLL7mUVq+ckn7PPfe41AZfMMmaZ8OGDdE8Ol1Cv3GL5ZnEssXy6Bh2FVJ6EgUA AAAwLNECoe1lVKpS+rdg2vPPP5/cdNNNzcKJJ+32JF0+x+ZZvnx5c/5QHpsuYt8j+l22UJ5+ lk3nkc9cuHBh82+JYVQhpSdRAAAAAMPWXrLpwhdArLr2MqqF8kgh1adL3HvvvW5KfrHOY+hJ FAAAAMAgOks+hi6APPLIIy41SdavX5+mScFHCl4vvPCCm9LKI+nPPPOMS23lkbj77rtdakMs j9DT9Pds27atOS20bKE8Oj2WJ+uyZcmjo5+3ebrwqoPOYwAAAAAMKnOBUKpCar6Xy1tuucWl tPg8Vj95RGyaDDwv6bFlC+WJpY9qfQ4dOtT8t4SMG5iHzish7QXlewEAAABgUOES2Cx54+UL ITt27EiefvppN6VVSJH05557zqV25tEGySPDS6xbt86ltvJIQbDbsoXy+GmaTh90fXy6zvPp T3+6LT1rJzA6j49xDGkBAAAAoB6iBcIiDe1QhmEnPJ9u8/h0Hd2E5qcwCAAAAGCYoqWS66+/ 3v2rxQ/TEOILLVYsz+OPPx7NE0sXsWmTXjafJ7TdfJ79+/d39EBqh6WI9SY67OErAAAAACBc gpp1+PBh968GXzA5cOCAS2mQapGSLkMv6GlbtmxJ088555yOPPqzQnn8NC32PSJvnm7fo9P7 yaO3WyyPrUL6xBNPpOlSlVSn+wAAAACAUehZ2jj99NOTZcuWdRROvvOd76TpS5cuTdOlt1FP 8px33nlpuvzb83nsZ4lYHnH55ZcHv+eVV14ZaNlCeWy6GOb62Dw+zjzzzI40CQAAAAAYlZ4l Dl8woZfR4eeRKqR+vlAAAAAAwChFSx26h00peL366qtuSquwI+lvvvmmS23Ps3nzZpfaMEge 6WV069atLrWVRzpu6bZsoTwSw1w2m8en2zw+TTrI0XmOHz/elkfiYx/7WDoNAAAAAEYpWiA8 //zz08KJfvsm7eOuu+66ZsFFW7FiRTM9Sx7p0EWn6zzyWd2mhdIntWxerzyhdJtHx5o1a9xc AAAAADAa7SUnhV5G8y1bll5GLZvH9kD6qU99Kk0HAAAAgFHoLKU4UljSfCFl9+7dLqVBV6vc uHGjS02SBx54IE2TN402j/6sUB6pIpr1e4T+PK3XsoXy6PSsy6bz6O3WT55NmzY1p0lkHcQe AAAAAPKKFggtX0Cxdu3alabffPPNLqXh2WefTdOvvPJKl9IS+6xueWLfI2Kf12vZQnli6cNe n155dEhHNAAAAAAwbJ0lEkMXTB555BGXmiTr169P02RIByl4vfDCC25KK4+kP/PMMy61lUdC 3t5psTxCT9Pfs23btua00LKF8uj0WJ6syzbMPH5dpI2h5PFjKPq49dZb3ZwAAAAAMBzRAuHr r7+eFkJ0ocST9LPPPjtNu+qqq1xqZx6t3zyhaT7PJZdc0jFNf08oj00XOs+g63PDDTcE80ha tzyLFi3qyHPo0KFmmgTVRwEAAAAMU3spR9m7d29aCJGhHSxfQLFGlUeGatCKtGyWT7d5fLpd F+GnhfhpOqhCCgAAAGAYwqWQWb7woQs2vldOH5pOz5JH9+QpofPodAldiNLpRVg2L2sevS42 j6bTQyFvGwEAAABgEO2lEIVhJ/Itm88zyLATWiiP9Fbq0yWoQgoAAABgEJ2lFEcGbdd8IeTA gQMupcF3frJ8+fK2aVu2bEnTzznnnI48+rNCefw0LfY9Im+ebt+j0/vJo7fbqPLooFAIAAAA oF/RAqF3+umnJ8uWLWsWQLzvfOc7afrSpUvTdOlt1JM85513Xpou//Z8HvtZIpZHXH755cHv eeWVVwZatlAemy6GuT6xPH5dsuSR9fH/9gEAAAAAefUsSfgChwyHoEnHJpJ+yy23uJSWWCGl nzwiNm3fvn1pemzZQnli6eNan1ie2LqIWB5bhfTdd991UwAAAACgt84SiyNj5PmChhRWXn31 VTelVdiR9DfffNOltufZvHmzS20YJI8UfLZu3epSW3mk45ZuyxbKIzHMZbN5fLrN49OkU5lY HrsuQk8LLZtsAz+PBFVIAQAAAGQVLRCef/75aQFDv7GStm7XXXdds/ChrVixopmeJY906KLT dR75rG7TQumTWjavV55Qus2jxb5H2M+zg9hLAAAAAEAv0ZIDvYzmW7Zx9DLqhaa99957yZIl S5rTJP7+7//eTQUAAACATuESxywpLGnSkYn01rl7926X0vD8888nN910U1qtc+PGjS41Sd56 6600/V/9q3/VkUc+Rz5P0kN5LrzwwszfI/pdtlCefpZN59HbbRR5uq2PbOs5c+Y0C4RUHwUA AADQTbRAaPlChrVr1640/eabb3YpDc8++2yafuWVV7qUlthndcsT+x4R+7xeyxbKE0sf9voM kqfXtvbz+QAAAACAkJ6lBV2weOSRR1xqkqxfvz5Nk7dcUvB64YUX3JRWHkl/5plnXGorj4R0 iqLF8gg9TX/Ptm3bmtNCyxbKo9NjebIu2zDz+HWRNoHd8nRbH53Hp/ug+igAAAAAK1ogfP31 15Nbb721rVDhSfrZZ5+dpl111VUutTOP1m+e0DSf55JLLumYpr8nlMemC51n0PW54YYbgnkk rVueRYsWdeQRsTxZl00HAAAAAGjRUsLevXvTQoQMa2DFChijyiNDNWhFWjbLp9s8Pt2ui/DT QmLTsi6b74F07dq16d8AAAAA4IVLIbPmzp3r/tWyf//+YOFE6EKIFsuzYcOGaJ5YuohNm/Sy +Tyh7TbMPCI2LbQ+f/d3f5dWR7333ntdCgAAAAA0hEscAAAAAIDKo0AIAAAAADVFgRAAAAAA aooCIQAAAADUFAVCAAAAAKilJPn/AQy35+okI3TiAAAAAElFTkSuQmCC</item> <item item-id="5">iVBORw0KGgoAAAANSUhEUgAAAHUAAAAZCAYAAAAPMmGdAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAHrSURBVGhD7ZmNzcQgCIY7lwM5j9O4 jMN4oNb6hx82l95Xw5M0uRioyFtEc4cXtkNE3RARdUNE1A0RURucUf44DniUNy4NvgwRtcQZ r5OSQVxtw++3IaJSWO3VS0tVRB3ivDHvrFJERG2BLViFnnpIpf5foOpUFKl7Jj0z9FRlwPt9 SKWSWK9F1M2Qg9I6132w6V25p/19TxxeOxb8W8qYvn6d4cRF2JC5IviRqLC1nYGHhZyLaMc1 jIzJC62Sz/d/Fk5clA2VK5rfiEoFDVveJVI84MwKpqvURf/H4MRF2VC5mtCL6sAxnRaVMZOX 4AR3k5ZOpOhc9K5WJKvn201rv+r/FJy4aJtxrmbUoqKTAhFtdGwnGpMmTXt+9Ux940dR2uB8 ZdB3RF3xv8f6ejlxzW36XM0oREVHKPNiLpyI+Z5lnNGwA6QEpasDvbDxroD2z4u6Dieumc0o VzOyqO1LYYBo6C03KhXfnYOL/sGU03sKWlFX/e9xY72cuCgbKlcTsqj4ZeR/KKzxRpeTfJmu +Z+B4u/Uw6vFjOlEXfR/Dk5chA2ZK5pr+8V+il+bwlKPCVPaVNtxDW8CiiAIzgdPv0PgeFog BX7Zyb8Sluv/NKO40lgOn4idzBVBfVAStkBE3RARdUNE1O3w/gMlrkKAURTy6AAAAABJRU5E rkJggg==</item> <item item-id="6">iVBORw0KGgoAAAANSUhEUgAAAHoAAAAZCAYAAAD+OToQAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAHySURBVGhD7ZeNzYQgDIadi4GYh2lc hmH4WkTl39bz/MLRJzG5cLaWvqXA4oQpEKEnQYSeBBF6EkRoAtYotywLPMoZGwYHQ4S+whqn g7pecL3636MhQnNYtVODLmkRmox1xoy5mhERmgK0b+X36EVW9FjA6lSbcMXT2YP9Hq0MWI+H rGgWq9Mi9AT80mFsuzN+/75Yu6qc99VsLzz2yOu4qlcghn1OHNPjVytiXKxcNais6O+3pyPI JHj47j5hn4B98vm4hpE6NL9t+3ehxcXLVZtS6JfaU1GlyWSjiUA853vbISqZc0bhl2n/Goy4 yLnqkAmNH1ROGxAbq0i1qh+df5awIvgwWT8WFVv+3qr7rSp/n2v/Fpy4qLnqkQq9V4q3s/7j if+C8EEsivzpG1aCR7YCisfxvXgid4Tm2N+DnwdOXNRc9UiERoex3bXQ96kFb6GTGBuSFs4J 7YTUu0rut23/v3DiouaqRyQ0GsWtGhLZbN07D65o7CZHwJtf//cQe/SNPHy6R9dy1eEUOu71 dgXjdHU/TTX45ICxB4+/oxPmRfWW1c+zfw96XPRctUlXtAaHWIWwks22UTegOW+C1YzfwSdy 4icUxpM25ieG4xeny4Zfsv3b1OIKY0f43Fw1SA9jws8iQk+CCD0JIvQUOPcH0P0f25G0qcsA AAAASUVORK5CYII=</item> <item item-id="7">iVBORw0KGgoAAAANSUhEUgAAAHkAAAAZCAYAAAAVDoETAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAHnSURBVGhD7ZmLrcMgDEWZKwMxxxuB aVgmw/AwIYQ/NopSkfhIUVurLo6vbYIqDPN6WOQPwCJ/ABb5A7DIA3a1GSGEvTajdm9cDBa5 x66M9Mo6saV271eDRcaipdkWbWUWGcVulFqziwEWeYQd2ZvbkwV38jrYrtwO0Yqrs+e6PXlT 1ns9uJPRaCNZ5JfDD15Y4lEZnztbdkvYE8fn1Ooxh+Cfc52R7XX38WkY10SuGjwq8q5kCEpL G6Qffy27G5HnjbikSGupEwRJxMD7P8s4Lnqu2vxuXLeSHtvtiLxEOyq411BFJxP9H4MaFyZX HQqRoVLOMVJfGKrwhmRBgLUqjOy5aFC5vX0x/z7V/ynIcSFy1SMR2bW/Xdw52WqL4qgQ7w3Z 1Xc8aD3IRHZIRvydGZEp/nPQ80COC5GrHpfIyKq4h9ZxJLW3k1GfJr8RmQ4tLlyuegSR84XH zHeylvV9pLDbSr1+a7x35SJT/eeYyAMhLnSuOqQi+5V2ff37cjewznV/6b88pR06NnoKHVRu ITLR/zlwcdFy1SYZ19JXZL+jIcC5joCxlFb7caMtuwOSkNtqQHec/nFwWP+nqcXlbRD+VK4a JA9ezDsJIou/VO/RZ2YdnHIgYCzi6DOzFkFkyiuzEsb8A633ua2538VaAAAAAElFTkSuQmCC</item> <item item-id="8">iVBORw0KGgoAAAANSUhEUgAAADEAAAAWCAYAAABpNXSSAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAELSURBVFhH7ZXtEYMgDECZi4EyD9Ow DMOkCcH6UcGABT3Pd8ePSq7mkQQNPoBX4i48WSKgswaNkQU+PR5EcBZN9qXL3Cy6UKwEB0vQ SKIAJ5iRCA6+OXmgOOtKEh6BAgY7RMqVWBAcWgN5Cf4j21QGkj/ZglUS+Ur8o5XWc7VaBwmq JTzEg96XSIa/DnLK00D1Qicxt/uuRLGVyJ6H6dihbyU8AGkIIrFILHCJ7BywhW8ETaXPcCTB +99t6hqRiFOeTokFssfM7ZQXFE4ONh/otmIpP/4Zr9VpPy4Sligl/ILm7PpRJTGilVqokNC0 0jWoJXTX3jUoJKZvg6y2r3hf6gb7prwSd+EBEogfSUP4kxjbNW4AAAAASUVORK5CYII=</item> <item item-id="9">iVBORw0KGgoAAAANSUhEUgAAACgAAAAWCAYAAACyjt6wAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAD2SURBVEhL7ZQBDsMgCEU9FwfiPJzG y3gYBsVuc5tY6+q6pD8xaaypD/6ngU+uC3BU/wqYmCBwCLYw5u0JSgT5XmBKbgcV0g5NUyLG fOECKp1xACMjkGD+SBEZBLYKqBXogX5JYcOxEPfIPuBkcNTeMsfFcumtQD1X76BkAV7t1Xzk CwGPt96GBT8DvtsrVUnVtmOdadu/t4OrtJMroAQy5I4lDSegvK5rfz47pEz3KVZL1+oUrnF3 RHTyuX9IzNayy5UhcfT0r5qhPsDJcKrtgAJHLe8P0CbAFOMjlwtofp6gJmAR3GX5E/5t9Q/J ZF2Aozo5IPMN0zUMEMW2cTMAAAAASUVORK5CYII=</item> <item item-id="10">iVBORw0KGgoAAAANSUhEUgAAACkAAAARCAYAAABASYU2AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAD2SURBVEhL7ZONEYMgDIWZKwNljo7A NCyTYdIEUg2IP63W2jvfnWeCAh8vIfAf6IY8SjfkFlEEDpgseykxhsAhP/hbyAyoIA0kReRI logu6OToIhjpCEmRQT9CZJIYwX7y8URlwUm13lC/3CLj0X1rJxMyYOSUyCaL7fITyXh3oUHE UQ5Seqh5Vk4wC5mlJjQ9mXB0LCFIXCbr+B63lrQMqXtXkEoN1rDlBHlqtt3iWX3LSV3XQ/qS uvi1CPVa8gAtQmr7+Z70JdVSW9XzOChkSRvtvDhqRut4Z6y+OBfVABkeNe9afqbyzgrgIbbk Z2qA/OR9jpifV3P384a7S9oAAAAASUVORK5CYII=</item> <item item-id="11">iVBORw0KGgoAAAANSUhEUgAAADQAAAARCAYAAACSGY9uAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAEnSURBVEhL7VSLDcUgCHQuB2IepnEZ h+GBn4pK32tsmvQ1vaRNRAN3HOroYXgF3R1PFxQJvSPnPGEsoUtQ61i1AkGKywe8KohI3jo/ xA2HuBgg/69DRNhIBWAyvtXTew0isghJAqrQOT4L4g2YM16HiWB2x2sOgYlC9Sq7m5ZGfBIU 0ROAL5YbyTfk4lu+VYgg5VBCEtnqCqdGXHTkPStu3CGkwAkxHHVJ34fhO6KWu7zfsOycENdn tKAx3guS5J4PMZnTnT8EJj26oxDgpKDAowbsTOxm8xfWHaqEbUjesr92h1S36kWNHMP9kmcg 3W1cjIeoG0UZP/Waba7O8SaoS1DUfhmHnGxtNNNodG4WUtLxGhsTpyarsxVDfHgU/h+voLvj YYKIPrJBoZ8Q69LSAAAAAElFTkSuQmCC</item> <item item-id="12">iVBORw0KGgoAAAANSUhEUgAAATwAAAAlCAYAAADfq1XhAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAYiSURBVHhe7ZvBcbMwEIVdFwUxKSKH v4Ac6CL3HLilh1wzPqWDdMC/T0ZBCElISBhhv2+GScCyvNpdPS3CvgyEEPIkUPAIIU8DBS+T 9nIZLtbR9uOLhJBD6NvlvLzIxKTgZdJS3Qg5Ab2aqxQ8D9euUSvCGhQ8Qs4ABc+LEruxBF6j HsG7Dl2jy/dWwptDyb7IMdQew9L2rfVHwQtytgrv2rVDd739r/Yvmk5SYBsl+yLHUHsMS9u3 3h8FL8ipb2mv3dCUWtVL9kWOofYYlrbP2R8FL8i59/AkuMUSqGRf5Bhqj2Fp+1z9yTUKnp+z V3itru9zKdkXOYbaY1jaPmd/FLwgZxa8viu3X1OyL3IMtcewtH3u/ih4Qc4qeLC7lEkl+yLH UHsMS9vn769iwfv5+Rm+vr7GswPo2/Hxthwr0ahJ8BDs5q+U74cu4zahZF+kHClzo/YYbrXP 54Nwfy7BMye63vST++FmvDaf29LBDhuhb29vw8fHx/D5+Tm8vr6OV+WzKv1qhFvwzO8ENX+P y93otmvtJhBYu716FP8Xu7T+bEr2NWdfv+yVk7WQMjf2iyHYEps5W+1z+yCmP5fgAQic5cC+ RZk4uyKJVdKBNzCA7+9v9f/v768y/A8lvPUls0vw0r9jJAnUpgh6vQtAiH39sk9O1kJ9cyM1 Z/MJ+mAVn+ChyjMnsZzbTzyQrM7CJgOoNg4NFHwxINu2CnBXeAYxyShtkp5SiR+m0r0GNiR/ Yb/skZO1cJ+5kRjD1JzNJMoHQTyCZ98HLyY0EtWxMuO++uXlRakwyk4Yo9VY/w2hjUdb+/0T EpQmtIpjlb9v4kcJ3kolA5+3UkVjzPrwC9rNB61US2qroUlf2XNjtWSj4JXyS6ivq+TEeBvd dDJJA1Vgeb94SLAJoB2ADdvnxhppMUzLWbQf81XGumV+xvkghEfwzJWyb5eTSe0fWRYjUWAA VFcDNcY1vPb+/j5edYPyFIkG0B7vxbmp6Jq4lRzBnwIxO7Z4O8Cq4K1WY7C1G3qZtN1s28AD JjcqI9X0GumPidxYudkgeAX94spJBT4DIjC+39tO2McvDhJsAuXnho+UGKblLOzCGFVLGX+q jSk+8OMUPAzktkqY+y0mCJCdqEgKbZAGT1FwHYkCg0MgycxEA1Bxu0/g+vxSwF7fMTGvIsOC h5XcTKL5exXjBMDqt+xq2R7jN8/Xk3zeB8aSE6sJ5IrbV+t7c2X94s4JtNELw42573L9Ehj/ 0uCRNJvAvnNjYwxTcjZUfTvJ84Ef6Vc6tQRPB8R/f+5yKhxkG6STJUaFcQthJxQG41pV44K6 JRm3ERI8V4Vsox8IXSWJ1m3DuMw+JV6Jt7TwQU6s3IhdCRVeab+4cmJxTVfG46nNPn6Zk2oT KD83fMTHMDc2qaT4wI9L8JQaS8kZGDgGYA8SSYHkMNH32THY78fgcG25qsLZrlXlOHyCBz/9 veRdQCBYo6918mN/p/MMECur7kfaqb28RF/kxspN/GTZwy+unFR5MvZ97eXWS0Q2NDn38cuc VJuAbdd+cyM2humxaUajMGZfIRUixQd+xEaxYx5NKPbKiqMGaZWo2DyEAUgQfehrUOdp5ZQP lWvaAUDvj+DQ78V73BuSqHBCG7O3/rcHPR2X4CHxMPbp8NhsCpgam7QNlv9ISpnc6FMWpqg9 P4v4WKUQN1l284sjJ1Uf+Az4SV64TTzsO42vW+zjF4tEm8rOjTXiYrglNtMDmnTj0nwQwiV4 keStJPJ+47duuF2INl6cvbYi3htfhUfuS25O1sjZ50YJknwQxBK8y7+59oXP5Y2i2FtWE5S1 5ooGtY5Cl8/jaS1Q8GoB1X1OhVMfZ58bJYj2wSqG4EHMTEGLPZ9voG8jbkBI5nsFdCzTcesR MYFwC31rOx3UwKNA7B5n4tc3N+7PVsFbbp/IYQpezt9HIv3nT4SQs3AxRQv/p54/NA98q0DI MzITPJB6/tC4nv4RQk7LE6nXBmaP4AkhZ4eC56WfvmBJCHkIKHgeYn7+RAg5FxQ8B3E/fyKE nA0KnkX0z58IIaeDgkcIeRooeISQp4GCRwh5Gih4hJAnYRj+A8CWkCXhqFVYAAAAAElFTkSu QmCC</item> <item item-id="13">iVBORw0KGgoAAAANSUhEUgAAAOoAAAAuCAYAAAAm7YXsAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAUXSURBVHhe7ZwxcqQwEEW502YcYQ9C zSFc5Qs44AibOXdA5mBv4NRF5Bv4Blp9IYEkBEgghpH3vypqjRaEWs3vbjHMVIIQ8vBQqIQU AIVKSAFQqIQUAIVKSAFQqIQUAIVKSAFQqIQUAIVKSAFQqIQUAIVKSAFQqIQUwKpQ+7YRdVWJ qm5Ep9sIIfdnUahd14muH/7u21o0VCohlxFX+nataLVoCSH3JyzUvhNNXYtKlr21TKVd27L0 JeRC5kLt5LoUa1Kr7K2ZTgm5FFeofTuIVO+CntmUkMtxhNo1lfPQqC94bfr19SU+Pj70Hvlf +Kl+t4Qq16VVrYXZS9E2+570ytIZa9th09kZmVq3uX3imvk/+nl5eRFvb2/i/f1dPD096Vas u1tp2f3B8qEKTmYv2tqel2m/Gn0RYJzPlWMeAGX3bJzn2Rj2ex7ubYuPl1H1YGT5u7dDhSqh XVGg78YsfBV2YMgHHPT5+an+/v7+VvaMqAm772fCo4NnQoX9bjs+tzbzgeqmCgYWa94usCca ObZGG2MHqrNsXPX7Ue5sS4iM1lggq9o3ptw3hhr8MjsHiKbYDIisM4f5Y9uB7aAYbOcODJF4 9SHdklOd8Q/95J5H1W+TsfqQYw7amsnGKL8H2WHnybYscYpQcWNOxsiI4o8MRgWiD9YXt9tN RUeUMZhsEyXNv2sY5+BY//wJTNbRTC5tSuhjJlTt1AZRWI7RFbFmYY78vhDwlgS/fz5zClX2 1S7cmRlsBHF+D5Fq57m2rPnrFKHa2RJrXd+02Y0rwSAxMERDA6Ik2vB/r6+vujUMyh0YCXA8 zsW+HWkNWbK5Ehv62Xazb++wL+cFTWM/w/+NLERunGu3Lzn+2HzmEirKviEYBW/OgzaCFL/P SbHzXFu2/HWCUKXxOtsslYj+4AEGZCbcgKd3aMdNBYesAQNtIwGikt8nCF3fAeWKvG7sttqX BNfzhbruPGTs8A20fO5wI5nLYFxp8wm/he0Lr71WjveijrJ/VhYetxGk+H0g1U6Xs2zB9df8 NQrVGXDCNgcDkIbITOGvSw3+4AH68ifcDDQmOqJc8G8+GB7KHKHrJ6Myof+ALIxyridUe98X aqgKGYlc8xybT9lvttIX6HtC74EcNoIUv8/ZY+c5tmz5K39GVbU61l/LE+DfqAADwsBsTJ0e g38+nIe2UCaGMJYcHwci6NE1qjkfjp/6wrGTX0PBzjpezXV4no/NZ2ahOjdrPhtBit/n7LDz JFu2/JVfqDBkVhp4BAaMRTMGhcGZzbQhak7RBobLDGRNlllzYTPn4pzwAwVEtUkYe0h66qvm Q1cg1pjtdtOMADIeqzbLwdZxZn/tc7n4+QxxXKgqOKkxys2yO6eNaX4PEWfnPWzZ8ld+oUZy NKvhiwLGZpQG0c7xIiIplyS/F85lQpWKcUq+FPBqo700RMSJQkW2jWxPiiHa7z+AC4UKUIYe F06cwxAYKNKfBIVKCHkoKFRCCmBVqHi6qZ5WZShPCSH7WRQqf9yMkMchrvTlj5sRcilhoXo/ bvbn+Vn8vu3/Vgsh5BhzoXZyXYo1qc6gf59/KWHab7Kodw9lm3kzhBByLq5Q8UKA9+AIgkQ2 tcE7iWiHSOPeqSSEHMERqv9aH94A8rMpMELd/hYGISQHllDtV/qmHzeDIPd/C4MQkgMvo+pv Ccjy1zzlNW/wL73Vv/4tDEJIDpgWCSkACpWQAqBQCSkACpWQh0eIf/K1tOOMRdiBAAAAAElF TkSuQmCC</item> <item item-id="14">iVBORw0KGgoAAAANSUhEUgAAAOwAAAAuCAYAAAAr8/WrAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAUsSURBVHhe7Zw9cqQwEIU51QacZ4vy IZw7cUC6J3DugMxX2HLqIvINfANtP43ESEIIMfyMmH1fFWXDCEYt6XW3BEylCCGngYIl5ERQ sIScCAqWkBNBwRJyIihYQk4EBUvIiaBgCTkRFCwhJ4KCJeREULCEnAgKlpATkRRs3zaqripV 1Y3qzDFCyP2YFGzXdarrL//3ba0aKpaQu5OXEnetao14CSH3Iy7YvlNNXatK0uFaQmvXtkyJ CSmAsWA7mbdizuqkwzXDKyFF4Au2by9iNbugZ3QlpBg8wXZN5S0u9Sefu35/f6vPz0+zR8Aj tcn/2L+OYGXeWtVGoL2It7l9ZVjSasx/L5uJ2Ije5ph/XXzv9reNXl9f1fv7u/r4+FDPz8/m KObmrVh3PJhaVJ7hvWpr20a23a+MywcM7Tk+d4p4m2yDru+oPmkbBwqzJcXRdoYEEdZURtLi Wy84oNNrXxy4fmMnxxrXSWwHOvDr60v///Pzo20a0I127H3loZMdAeIet7UbmU3ltFWsvI/T bpn2JNtkLVKHxhjjOpqUjVcKsyXFwXbG2M9SRFl3wMm+NdYSpuBbAK+LzQIPPOrQsG434HZS Dm4Hj4h0YLK8V/+Ld0+Zk9UmUeTazcKMROoWXaScGqQl25JiZzun2E2wGHBXg8S7hLWDYRFP hHnJ09OT9qJIe9AZ1pvavyls56FseP4VNNjayC42LbjGrGCDtkiVDz+D40ut5Oe1SYylg1zK t/k2gnJtSbGvnSkN7CZYN3piPhyaFxuQqCgqB69pgTfFMXz29vZmjsZBegRDAcrjXOy7Htmy SXTX3hTXmR8GKQFKZUadlyqPz9zyqc5f0iZjlgxypHySDsoWrctERCrTlhT72jmngZ0EK41j os9U6hgaAFAp2yEWrALiOMSKDksBI11DATxUeE0Q+34PpDDyvblb8loCvi8uQETqeY/sMt35 l8HknrakTS6g7+I2xudmPrreo5QwbiMoy5ZE+aAv9rIT35XSwCBYr3ILtzGohBgjESict1pC AwCuFXaIrWyOF0UKEYoaxscic+z7F6MjbLiQFkd3cESAsewDTJXXLJgPLWmTMXLtxVHJ9L3Z A1M2aoq2JcU+ds5pYJ8Iq/P4Ruat0w0UG5CoVHhfzebwOYTno3NxLBaZ4eGmBkYe8Kbr5rA4 du1D37klBasHi7PimIh8S9pkzA2D3BuYaRsvFGxLip3sDG0Ergb2ESyMGaULAZFKY2KNiqGC drPH4F2vngfGS0rhNBjye3habPZcnBNfkICHyxdbjEWrxLo9TEZi6gyHMRzTm1OfSHndXrLv Doq5e3rL2iRG3iDXziWsrzBpY8G2pDjCzjkN7CPYTNZGObyUYO1GupDdeYF3fFQWtUnhPJIt a7irYEU51zRhIXhs0p06wvtkob3cTPR/ELLb5AQ8ki1ruLNgAdLT9QLK61A4iP9DrICCfTwK ECwhJJebBIsFgHAlixCyP0nBYiVUr2o5KStWrbAAgBVbpimEHMukYGM/wgaB2pU6ew+MEHIc eYrrWvX7z61vSRBCtiKuuMiPsFlxIsIiLcY+74sRcixjwXYyb8Wc1UmHf738HR5IxoITIi32 3YhLCNkfX7B4qCC4J4ofYXuR9Dd8IBlR1oqYEHIMnmDDRwXtj7BhsSl8wDr/LQlCyFY4gnUf E/R/hA3z1dvfkiCEbEUQYc3bCJIW2+d7178lQQjZivGiUwDfkiCkHGYFy6eZCCkHCpaQEzEr WEJIKSj1D/B5MpkhW3HtAAAAAElFTkSuQmCC</item> <item item-id="15">iVBORw0KGgoAAAANSUhEUgAAAOQAAAA5CAYAAAAm94d8AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAcHSURBVHhe7Z3bkas6EEWdwPm+KUwM zuIkQR6TAdHwdTJxMFxtgYzAenTjBknQq4oqlz0G9FjdkgDPY1QUpRoKCzmM3eMxPjZbN8wf K6cxdJ/t8NCGOJ3yQmqjV4q2TQlUSCWCtk0JGhHyNfbPZSjVfD959ePTluU59q/5vepQIUvQ UIaElDV3YCqmzE5EK2Zn3qkRFbIEDQlp/vbZGy0bZ+i8xZIp89fZ71XIEjQj5Kt/js/206Mt h796idXNOsulQpagoTnkFYarn4FFhVR82hASc63gcNV8v/rFkTUqpJKiCSGTw1XMyVqaW+oc 0vIa+rEfmmk1Ft+UrU4hPcle5vXzGV+JRIaps0PHQFb3VlmrDSbHCTl03eXvxkK/7XaMfOoU 8n2dzmyQMVoudO5aLxskuPF1yFd/fRnfIJkwG7iRRZ0Iq+GfIssBQiIQJfa5XYFe498ccmAg Ew6WQ8fbT9NCtjdcbQlpIY1QZqga1Q0yQoTIMZFZXcdGux+zbmDK7E8nJEZf2A/jXBsWEpXX 4HC1GYSFRMfMpIp0hvSQkmXLasQlt+DGyZKFhfwdf35+59c8yI2n7MS0zX+dWBZCe+U6JUvI AzLk9vhil6QYc8kGMyQyo5tL1HoN7wqYehbr9Mg2+YxGFnLHYgkFHN/fr9w1Ynpdtr2ooxyI pJC0dqYJKXlea44TkhaQQFBIO2n2stB6kxy7q5D1ItjxzRCTck2OIiSuYR7WYw6aQwLqPDKZ IbETX0SRYLFChawXQSGJQ8yckPh88YUmOQ9T5oNu2vBXiVMkhbQVdEhmdKiQ9SIrZLaZkZ1c X3N/bFdTpyz1OWqjr1yymI8pvf9VMEmgQioRThby4qiQypeokJKokMqXqJCSqJDKlwgK+fsz 7rz/4zL8+/uHVAcqpBJBM6QkJ2dI03jmb/iVrkLWiwopCV9It9zrNQI/Q04XU5fveFvwbOoR 8spPsAN++WJCctvYoELyhfSv87gvroSUipYr6hDyDk+wA95T7JohJWELuci3ZMKVkKQabS9D 3uoJdmDkoN2fqRlSEraQtgFQqe9G8Cv+oLsiSguJYfole4rfdp9TDdp9lYIZUldZ966ymoZc 3b/6MB22T/ymjWOSmd+3SwqJsh6xclye7NP1pPs0BYXUDLknQ5agoJDolMek/bqwi3V7sqQK KYkKmQEVdAcfbR2HVsizc0kVUhIVMgnmWNccrn5gMmR4ZTUnXGEhbWbHtEkmcEKI9HoIAtcy VcM2nXNuLWX5PFVGFTJJqeOez9DHpMoFJVkhedMDyDF3/siQm4UXlKyYobYfhtUx3IPQ6fn4 LDGhL4k8D3k8hcSIZo1rkYvK6XmkoJDc+jYCL518ykBi3YQUHMwxu0DZV8FhOi9qoBH5xYDj KSQkO2K3B2RcyjiMfaC86agtKCSznbdZDJlJpr2MRD3hPGIBBEK6Opnl7NwNNcny5UYjC20J aSsBFUCLNlHIc5opCq7nFPVDfbo+nUElhaR3SLAOJlJCmvLM9ZHbVzRQeYF8ChqmTCjU3C8l 6rKwkJzfZUWFCs0ryEICdKYvA0ClpIU8/3dZHccIOWFFSvYd096h4epGKtY5MkZk7WRIUyix eQVLSHp0CxNr4PKkhWS0DQUEUapUkm39AQJ7Sshwube/dmfF9v4uJSR1/giaEZJTAVkYQm4j IR+ikG44DvnN684tGPivI2CI5Yby1HKBU4W0YlFHNZDGGw2JDZ0NK9kDBPrGqp7QHnO7PN9T Ae98P+DVY1NC+p3yHCHRiejRLQwjQ5rzsrcqDi9bXsxRsBiDpzRinQj1gM/s/snlmjhXSIMt H3GftsPH578cbF3afU119WY+hv/WNhPa+nXftZt3PmiX+f1YsTjZEaiQKULRGRFyXuiBPOEz mIZZSyN6WyLa+2VCQz7nFUG8P50vIrHX+KHzY3C6kAZk8wN2WyfoZ8w+ets5JEXmbRBw5zu9 M51Dfj/UDAnZXDTF6zlS2ygeHup9nh+P7GWPg8y5w/On1/oPykG8DvtlZsD3g5UF6ef95v6V OqAJQRTSDzjeaxzDDkkDO7DHd383RMqUIHtjALlt+Fz5Fxpe/f6yNSSkwWYLDP144/JPIsd9 7x9DSyNj5hiI9PnzoAm5DEvxeimfHcaaD4LfR2Bxw2d2hSDDpwLOsUIqYdoSUoxcZyQQy7LN YOo+OcpQIUtwUyHh0xdZtnkZDRiSJ8ugQpbgtkLa4ekeqcz3rjD3yS/Hq5AluK+QO4atLzyi 4zqxFXN+3RyUelchS3BjIQ0YthGPb1c73YKP3b6cgxaEdrFahSzBvYU03OpCNTBBiDb/VSFL cHshwR0uVAP2DyWrkKdTXsjVMHDaSvSDy/8rgczFalzv3LbD6r5P5RQKC6koysI4/g+fHv4H YF73RwAAAABJRU5ErkJggg==</item> <item item-id="16">iVBORw0KGgoAAAANSUhEUgAAANMAAAA5CAYAAACs2B49AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAebSURBVHhe7Z3btas4DIbTwDxPC6kh XUwT9HE6oBqeTicUw+iXcbATX2RiE0P0r8Va2YaAL/ok2UD2bVGpVFV0MEzTMtxuy+1luwfK hmn9isrTNLz31e0eKNMOPFzHw/Q2yKEylVzap71IYTq9tE97kcIU1LyMjy1l6tsuFaZepDBF BaAeyzivf3YrhakXKUxRUb0eIyHVuxSmXqQwRTSPj+XRf1giKUy9SGEK6iwpHqQw9SKFKaR5 XB7BFI/qyosSPYGmMPUihSmgZIo3Dcutq7nU8X06T+MyTqcI28X6pG0KE+QAMtPnx2OgWoWF JxD6Yv/YPp2G4fJPp8AGhh2ph8IEIa2zj+EApGg/Ul1vcdC+o+P6dB6vD9JTcKqFQClMJUIE 666uB/UpHE7inEiN433j3gRvON98OsU615iGsvMoTAXqL8WDjuhTgoHSu9gZGSQYceSaiGjW KPlB3SZzTmqzhYihqpBBRBeiwlKYxMJg9ZbiQQf0KYwq46LTkclRLUN/lZc1mEhYowtKotPB MP1Z7vc/62erUFl/EhvL4aL++3d48Z6hsv1C23MGVQRTg8j0en1EwCo33QvmThqZskJEsvl+ pQGqKqrfm3GGyvYKXj4fScQw7ZjYS4Tru+etBlNBXypMp1drmGTjI4OpZr18tYNJ5kwghen0 ChloRaOltExyz0UCE+5RNRvpRnMmSDpvUphOr8YwCdOyHEzYv9m6DNAyUZvd1byKEdBdjUxJ YTq92sOUHR5EBTuvtAfzqp2JDrwcbvfzJl8hK9J6zdrn9xxBQgrT6dUBTBeXwvQzUphaqxJM NChVw6bCVF8KU2vVi0zOZA4n/exREIWpvhrD9Oe+nOCeelP9/e8fUR9kYQJAddbrIYWpvhrD pJHpg8g0YyDMqssw0Wf7HJVdsXHP6hz7GMdtaZKGcRwoinGKaDfso+MVpsrCGChMLbUPJgaG jH59oQcvSUVvhNG+B34nwR6LFJB3muO2aIaBtZB9DtOV3/KEyttXApMZG9/JrVtsDBSmHTDx Gj1FIWcEvJPY/fwHDVb0WLPPssRAut97q1WoLKxfeMsTKnvTMwROqGynFKZymPAFb26EyPOE wN//dqwHmtn/9Hjem6v7Yfqptzwh9L8IqBKYNDLtUTFMuEttvOG8TDT/Gcl4/RTPpHQ4YjuW 9iAtoYjhDUY0VdkJE2CVtOZ0co17c0ZWsmfCSmDaIV3N27Gax5GIBpUiCQaQo88wmgHmyAM6 1+GOHbuOnnl8hI5HmQfVHpjI4Fo+IPlFZd9ARb9noWgMk0Ymtm9JH/gLEBWEqGb4oSjGUcu9 N7UDJhhU3j2fXy+pslU+OilMrfUdmKjjAY4XjMhIEKFMUTlMaMgvsMT9EIAJfZp2JgpTa30p MtF8izypeXLXbEj/tkEthQlzimumeG+C0wlCkwOjQ5g4yppUv4YjhDEbe4qdj9rr2Bw2U2d3 Thr67rY/1cavpXlphcAJlVml9l1LSI/DdpdzKCFwQmU7lY2Mr6JrW8ONpK5FcpwMQxWyB5rL u6X2JcT0nBT1pDKBfV3jfaaot76Wcp4vPW8KgRMq26nSMUCq/2yM8fwCe5VJBDZd08uGVnlg m3pJncQ13rQt9ornE0Da2jgtY6C9ac/YGKbU+AT0Gj0QEeqMIQEwCuoRgx8w2T5ZwRp41TkX nXKZwabuYZKNo/E0fr7cv8wtBHcLe8B05GoNk9yYIN85mDZ+DhO1Z+2j3LmijsdxzAZ4ahMa xWClbEbelwfDVPi7eWKYIAy6LByfTWmY+vjdPKs2MBkxBMk5GNlAKMV7AaKojgXZ0UUiEyT3 IGHFBuL7SsNU2Kd7BO8tBYLGrNmcCe1KwhRu9+uvIjGUznEpmKTzJegyML16m3IJYVrTAl4Z os94BYWv636OCCmIXTKWtgv6OkwMhTTVo2u7q3nV0k2SB2pAAXvx+g5jtI4VnugxQ+XU901l /XihOZPcg4RVEJmoXvz41DTzYCH/xsKB/8qKL54f0T4zfiUR98Ug3lTYp3vFbRaek401Pgcs Efcvn8v031PrNdyi1wjEfW6/y5tTH4zVWh5rVklUgq4BU8gDwgutixL+jWNXJg3ZOtvZEh7V TQv4JvW6yoRyU19qE53jWfdQ/QrUBUwkRNYGp+1TsL1CT9A9TJLUDcbmH2fOaUoMMPnzSCMT ndtLEVZvyJ4ynAq9169M2aXxkj79UL/wTtk1/3MgokuoUQjRq6dHw1P/NhOSGbMQJlzb1tf5 zOkIfZ4DJ+Dr2+OmSJsSyt60feu/djBBV37becbrRzvb1jdMsX0cBWw6RiBl2g5vmrdfGUxb KofPm5Fz6kc7gt93U85CkLheSWdxPEyqsDqHKWdIAsWi22lE/ZOcbylMvahzmMBC2YqKp9OD REIam2yDwtSLuoeJU7o9QND3rpDX55dnFaZe1D9MO1K9GY/kWwNkqNbPp5MECoWpF50AJhJS HaFx8KqaXZzg7cM51xclu2moMPWic8BE+qkbhhA5ENl8T2HqRaeBCfqFG4ZQ8Y9QKkxd6HiY vBTMbPdAWcwWrnzDEMrdNHx/3oy2e6BMYTpcB8OkUl1Vy/I/x4Rtth0E3JMAAAAASUVORK5C YII=</item> <item item-id="17">iVBORw0KGgoAAAANSUhEUgAAASkAAAA3CAYAAABQMrzdAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAmWSURBVHhe7Z3bkas4EIYngX3eFE4M zmKTII/JwNHwdDJxMN7+xcWS0KV1ReD+qqgajwFJ3a1fLYHh5y04mef5/Vr/vgYvqvO1aiwI HAYWqfk9/fy8f6xtmtevWzI/388r9vcX1buHfb6UeTrG40+XgPxuxhapUwLg9X5euKfPz+fF MsArc1aMfhciUjaUjVw67ubpmlngJRGR6oGIlMXrmdrJKfN6bOn/RLXuQaBMiOzXq1Qvn4hI 9UBEygDBnRbUuqipNYtH++lWuEyyW4c6jEw/n4hI9UBEyqCwg1MW8+iWTa0cykwX2lvT1Cci Uj0QkdJBQBeVSXXuLVKOMufpIetSOy19IiLVAxEpnXkqu6R8xnqQo0xMcaTvrDT1iYhUD0Sk NF7PR5FInXH531UmROohqZSirU/axugL9+vd5AbdkraISGlApHI7N47tPaj6yixpx51o75N2 MTpP0+2y4RfNVHKyWhEpjdzObR43v58dBCJUpohUL5+0iVFcnbybQO2QUKXG5kVFSr8PJrZI vO0bX0xGYKdO99QlblUPbn3SUHWyzhkrcxyRauOnGK198qGBSEUu3rhidImRpa2uQ1usUX7K 1G3L83fqhZ1LilT6fTBkvImxNlG6cN4EskPibRGwyQgi1cxPw1BbpNB+/5XIXRj0MiFqm13x t30lU/2vskhpFyN00WT7W68zg+tP91yOsdGMGqSLSCV2xIz0uMXIWUxNPw1DZZGCjSLttzMp fNaPsQcoXDh4towHX3xG/J2STd1DpCKqDEdOZJQtJcbmNaxX/ZHRbMdSZ8qeQqSIFO37oLrT CIXR8Id5k+aQ90nV9NNGNZ/kUlek0P5Y/V0ipX82RIoEBF+1G7QoPn0/xo/5O2HwHVikft9/ /vyufweINhYd/fmeyWjxS6AIeodhUQaJxXa8HRhpJIgUHI3RSO38YgYb2ssTs65U9RNR1Se5 UIz+O/F8GYXnt0M7yQ767xM/IkXnWwWkjUhRXwkNJFF/e/qag4tnUnZDF8MZh63B7J6X2/u7 AgX7bEKxgEBJczrO+8kOjC3gKLscXrDZNmlJoF1GRWv7qYZPalDT1nQuRgNcYoy4+Nh+zca0 Z6KlixTXr2t9DtM6jl1cfc3NLlJLYY5KYTMqtgSMvU9YNXOIOw33ksQaianPRNGMezRsA7vQ F/8A7GK0bctu1o/pkHNYmZTtRDieUS7Vb7R1ndp+qu+TXDidkQnTby6R2tFsZwpXqz4KoAem 7Tn+BtxliUMmZYqV2WF1FiNQYFFK2aLpqvGBYEU996+9DtaCaAtkrGOEHmpnnUuNQutnddcs OYAjdn6YIoXMYqsH1VmtTfE8z3J8L1r4qb5PctHqXYru7wBekYJAeYQ6PZNKQBNGwPP3gp0Q +DhO91RjPyLlahsqknIJMQ8KAI9lj6OER5ENx68pbLTelojgHCiDshicSrWdvtenGmkkZFLb IjLKZhY40pM5m/mpuk9yqStSUSGhfXZbbjtv/wvUo7ZIKaG060Gw/b1iCFqARJFCAFmpdjP8 ItUc9Tuj9e8rQSPXJet9WTqL1M2oL1JI3XA1pYc+KU4UKeJ1ybfFiEL1RUSqhKoihcXMB9af uhrxXJEShDgiUiVUEik6iXFznXuNqg0iUsLoVBSp3z9vzm2Bd+Lvf/+w2pwoUrTVckoUESlh dCSTKqHqdO+wap9kTXIkHZPuABEpYXREpEqovHC+Xhbe/59zY9jxHPvmrKmIlDA6PpFKjXVC RMoLU6TAkhF9vmt9pU9EShgdyaRKaCBSBO4CNoSKvmd7SDIp4W74REoyKQ7ZIoUDP0Z13LZu iBg2KqjJT2NEpK4A7s2q7/uzwT1nnFZVzKTk6p6XXaRMcbK3NaM6CJS++aZ/S/aVrjciUsOj /dL+drDu3q8oUpJJeTlO94ZBRGpsaEpz89/gxH8HKSJVgoiU0Bb8TOru7iHhCGeKIlIliEhd Dn2x1bpgMSDcx2yk09sOgfIijxoZSaQ+yzX6sovetryr8eq8h4pRu9U5TZu56+BHROpipL9Z 5UwQ/G0EpLcdwuXFRKiuSGU/XUQTU11USm25i47VD50DlKcOIZzncSAiNSLk8HOeNMmlYucM 0dsOh/JiYlzRDlonL8Indpm2PAoOtXnNoryiyhTc7CdzjsMXi5QKhIFFCgHfxTe97XAsL9yR aP9qYl0j3klUfRcz4LOMunqzIiV6LqEK1MGAn40PLFLMt8XckVqjaitopOSk88X0toOjPEyT /E3t/7YYPxDYQIaTOZ0MT91sUY/UwYAv8JJJDchIjwB2wV1zKKW3HVzlQaT8Ha5ujMKu7qIg YEvnP2xW+co3h+yTLwg2MV+7XrrgroNFgmiKSA0GHDx6s1HHnFE5hd528JUXbmvlGMUUqtiu VKfDlDUiGAHQfr9I+bK/Yx1suOtRQERqIMwOMdPcvq0Q5BLuuOX0tkOovHBba8do6ZSPsKbi qP/+MWP6jOO9IuXLhqLLAWl2E5E6oKfWMbXf9uWPCj7UJWIjlS8/Zxr8trgDN8VufnrbIVZe X5Ei0PETz6n8sdVfO7bYlhAb+7yu/xG+OrhIyaKAiJRF+r0l1DlZr6i6Asy2OEbK0ntyRgVt 6SpSBGx5Quj3gWInNZsTkQqBNYLYAmDvK1At4bbFIVIGHLtVo+0gAZHyN7VdjGId6W5ChRe6 5PQVEakQ6GyRjABprv0ceP/IS+CZXOu06PEkUWg8nUmB3ZaYXRh2q2eHNJFClqJeLErlccIr ep9UwxhVb2fu/8bTJrzIx7ltEZEKQcofFBx0EOqMM3VKlgNwPry7cN1XzeOHGS5T2gKBCQhD zG5V7cAXKTUNpXLUvhjVo0XCJqGMcIAY/QK+UKQQeJ9MwdiM8uyOSJ9pH3OXpbNhZD5W1d4f nyngtd6kMpcWTcwhqS2hzhuzWw07BHzoE09OdnfAbosNfT+MA++LZFIeOPeWYCowUW/DXDuW CaAjGtkFOk23dZs4KW0Bvh+HxuxW3w68TOpQLgeqW3gNRUSqByJSDoyR3Ruo2ii7dTSss3h+ t6QWYNfzqLUG6sz5U5zapLVF4bALx2717ZAgUms5KNftUwsS7PBu58XoNyEiZaHWLYwphGfh FNOj/Yt1+hGaGmB/NR1ZAn/pNFgDWr8/k9S2KExxSLJbVTvwREqJ5r5QzyuM9WROEanmiEgJ +airT+vfd4NELd42idEeiEgJRbxu+7YYTuxJjPZgbJEypg/LJjEhnMVxSkubBGRzBhYpQRCE 9/t/Nit7t9nq/dUAAAAASUVORK5CYII=</item> <item item-id="18">iVBORw0KGgoAAAANSUhEUgAAADkAAAAWCAYAAAB64jRmAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAG2SURBVFhH3VSxkcMgEKQuFaRxEQ6+ AAfqwrkDZe7BqUeRO3AH/C5w4gTSPPwoYJjRnrjlgFsOMNbi6xywOdkbYHOyN8DmZG+Azckm sBg7GCQITGjXxEyD54QHGztbwiiJByFzYcwyRcHzCIEUnA5sAjMSQ4LiszIjuOqYIB6tSH4+ xl4uxl6vxt5u6ETA++375F8D7nS2cAFYDS2AFRnA1cY4kbqSFEhRz2cMejw8x777PfK10Hdk A5WkBgXohI9E/hXDapNDyxNclFXUQa+X5ynw+/WcO+cq0QH+osacgbNEjthc5gbPE0xYV5EQ kayo5lkZudxsp5OnqK3kGXeSouWxgg0NLEpR4hNyLzVHyA6xvXtMTsDm5VTrlcSwyqtg9MF6 hw8LBVGYQDg+RGuVMVhELdxNWSjMo/Hfh8eBiWP8Zv7AaQFpTHqdHNZJC6En4e7NkkDDgA2N n6TjwGd1eNbp6wqm8S0B1ieokzz0eTxZyeDLsV37Vby0WwBsTKrkz+ppn8d1L47/VmB0Qmzv +hDiLnjw16c5+PIQiC/jW8FGpCNO9lsAbE72Btic7A2wOdkXjP0FjDyP4ZufA2gAAAAASUVO RK5CYII=</item> <item item-id="19">iVBORw0KGgoAAAANSUhEUgAAADoAAAAWCAYAAACR1Y9lAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAHPSURBVFhH5VbBcYMwEFRdFMS4CD9S gB904b8f/NyDvx5e7sAdKLtICwcSGWBIQshM9ny3OqQ7rRBx3uPvHwA2JY8I2JQ8ImBTUni9 nH888mN/DbApSVwuzt9uzt/vzp/P+ZxvR+N84VAkUMFfktNUgRMPth8U2NjzGfz3OySPc34C pYqPzdRzc+CXaJTjbcNlplGqSCimor/SaB0KVFwVKB7cmpyCzQ5IQE1RUR5fxlJ3DbjjyeIz ICUU1/BZ8NKcKsawPcljejoFnxcRlWVsFV4L7jY3LYEp1IJN2KKnGv0qh5vMNciB6Qd4TAnL UVU1T3AyW2iBuDH5W2GLRpXT1mpJXkJU1XJs8nodclRHNxz98eQ5LFV0s3cUaJW1BBe2303d uOPmS0woFXO7uBUGN6pZc1FO3Ax4geA7SeUIHlfCfmbsg2qs4SRayOYYrL2MWrB4PD9YI3Ld nJmc7rgSUXHY4PDCmXO72neUO1irgJ0DNjofo4GJmArxo8zYKjnO3xtgQ5G20MlY5z3GOsLd uMmXvxfA9oXN+aWKNubRzeXxd09wtij62RjNtC98jPU/p2JdDIr1/J4waLQlNo73AtiUPCJg U/KIgE3J48H5T8VE0x6S2qmVAAAAAElFTkSuQmCC</item> <item item-id="20">iVBORw0KGgoAAAANSUhEUgAAAOMAAAAnCAYAAAD99//uAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAbrSURBVHhe7Z3NceM8DIbdwNfH1qAu tglPishl7zmoi9xz8OlLD7lmfEoH6UCLlz8SSBEiSNleO4NnRrMRRZGghJeAKNt7mAzDuAtM jEY359M4jadz2DPAnmtiYjS6OB2P0/EUdoyE84muzdguSBOj0cx5NCFWIUEOjYI0MRptnMdp EJV4mo6Hw3Rw25H21pzHIRw/FJ31dDwUhS6V7wJjcbYMk6Qbbu+6//M0DtIx2Cy3W8LEaDRA zkfpqaQJRMxt54NYg4M6IWTOGsSxcmypfBe5LYVxoXwYadTh76ROmHi2jOLnKzAxGnrgXKLa lqgo1kkcmokhcBpHEvtadFL5LiiNXITkI1zePqIiHwuis9/39eVrsdASHU2Mhho4Z9WxnOAk Rw1pHbw+f6bCoocrzkQhle/EpZ+swUVoC2KdMKkcad+lsFuGNTw7mhgNJRCSnKKmIOpJdUvp HbU9+v1UdFL5fuSox0D0ZOOIdbxIqRwHwuQj20bjVaaqJkZDCTlVgxrw6qNU2z9XhggZnRTv 5oK3JqKTylWEPiD8fKOGVGIkUL6c6zMD7bke/SS2IUaaAehiHIdlNQnGDNRwfKkpXfB7Jb2w +XafY3GzcNFe2hLvDBEn22QnaYQigP7dmeCAiCJzlPBiwRBK9wV2S+UXAVFvvn6LLSKsvo+M S+VtMeK47rmxLEZc+DirMPE5zgi78eI8lhgjuDjLDa6tAN4HqShlm70DDyQcmv1D2UVoePYR 64ZnLe8zfvLIBQD7S6KQyvtB/0EkySRRIEtX/TiiwFg7AvVVZs9ajK6j5aaXxx9nrccUY+7Y DzEG5xDbNrv0qWEpvQnqf1MM3D5eMfhTLOLXviTY24mRmH2diYnbG8dUuqZsvDW7MGaN7ZkY 01RnuwHUNTHejE0xIs1Kn2MuTk2MhkiXGFud9NGeGSO3FOPX19f08fER9nYgiZFmcjzXX1OH DhNjNx1i9A+x8w3/wVf+VmJ8eXmZ3t7epvf39+n5+TmUdlIQIz6QjGeXm9wqE2M3HWJMU1QT 4z4gvs/PT/f39/e362sXiRjp5maLUFe/W39+Tb/+hL+NJv7//Z/q2pkYVY5MWcNRvzCCaIgt gsiIvnaxKUbarrVwE7HI2M3+yLjr5vq2em8enrOenp5cdEGqB3tilIn/7uHaYkS7ALbm9neT iNHb7Fe02XZNtZgYu+kQY35zL5H6ZM+hfBOsgxBxHNEkgiiDMhx7fX0NpUri8jWbXK4pRqSk mEgA7IXt2OeRsouCGEvXV7ei2n5fTIz9dIlxdtyw/YuLj36jM0ewGolyCBHODvKoMMxiSaMy rxfLEjGKGcCGw25kDZhE+EQCEB35mGTbgZBVFMUIsoym8gK6GxNjN31iJHSOSvXo5shfMo20 z8A4ljtzFGMaXdD28ikI9FOKCst4FgdOxqjyMOpLGRmRWscJIwIhphFdZ3uCKEYCn4qaj4Xj m8213xcTYz/dYgSpILMbe8anb673SQ/0mb+Xi89dKeSAzAZEm7JDB0ed63JH1EYRvRhz++NK aipQre0LyT0hsa2qJ2L1Y7voR+JsNbWbjtXUDIhupOiXzaD8g+IyXgA9MykWOtAPBBi3WIao M0dNRObgkYjS28KCmLgz0zhIXNVhzOjEGJ9psUXb+SuOmQbbUxHmW4iQKyHyTTvhVKA+LDL2 sSsyPgL8uQsThF5Y1wNptGbV9B5tr2Ji7OaHixGRF1EBKeeFZv4LgChY5z5tr2Ji7OZnixFp WRwd/V1d/LgRKjHeqe1V9oqx9A2JDJ6Sr/vC5CUdq4NsZPs8qf1SeViHcFt4VNjgB4sxjygh 0jyETz+w7bsmDowzjNuJsuDAKI+LWqs6wfl7VAjCRCCfLrVfLtd+PzHS/33Ge2aeXdMZKT6D 9d6rm/DItgOyv+dXsh08GwiRJh8vogcXO66L3/f192QQ278uJ7UvlQeB0qa1ad83/Q1jBTlh 54zh0k927iK0BbFOiJKqX2IrEdJrtFc8VWq/1m+YXOuChKgLmUABE6OhRO9UOXLUYyB6ZhkD 6niRUjkOBAHo9Ug2V35dTmpf1y+iZO2aUB3lO3kTo6EGDloOBD6lcxEk38iDVWIkUL6c6/vS nltE8etyUvt6mytibHjWNjEaehAhlI6VoHhmTGD1IQqeIraIEXWTiYG2/FypfV2/9WzBflHc uBK9qSrSObaaupW2Zemqqz+/DmHtNAIxFScAqX1Nv9WoR+cVOy1jYjTagAM2ONiMc+4l/eRl rjknQjpeEmo8Fut2kIiR9wuk9kvlrIxHzhItURGYGI1m8N6s4ocGibb1VZCJ0ejC/udiGfuf i42bY/+n/5ozPmDQeU1MjIZxF0zTX8tr/Vx23hkFAAAAAElFTkSuQmCC</item> <item item-id="21">iVBORw0KGgoAAAANSUhEUgAAAOQAAAAnCAYAAAAfK+SXAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAccSURBVHhe7Z3rkaM6EIUngc1jY3AW NwnnsRkQDb9uJgTD9mk9aD1aSBh7YKe/KqqwhKSW1Ecv7Jmv1TCMy2CCNG7NMk/rNC/+0zV4 xSYTpHFb5udzfc7+w8VYZrJtGhelCdK4Jct0XTFGSJSPQVGaIP8B5nleL7ZoI5veaNEyrY+G GpfpsX4l8cs6Pb7Wry9cj7WukXl9cvx2hSw4Px8mBaaFS+anVl4dEyQRGtWu913nQeKipaom xygSIUjMpkEU85PiHlM5gNGgJvPEcth9hlC9qDAQREFr4RmIq5WnYIIkznWYT0LOOWmu+f3M U78jdgMHr3r+RjlDClg8uqAdEL23PXk+F2ElvMLILGmCJG4rSHKKxsrt+6E9VK8j9gKx7eW5 K8i9GQvtGgvxy13kl+wJtfAKA3tJEyRxV0HKpdglSRz7DCCCvdkNxTYE2SGOsl39/rLIUwvP oec6l60/XpC3Xq52OOf30u+IfVB+u87fEmSPPWK56nEC9TOiSK+Fl/T3lQnytoIsnYsdESN2 7UoclNJWnuldVgE+HKnk4a7gfCcPGp0zribI7aCmRSb6ZInrxMfRWrhC7z7SBEkOdEvgEIoH pMLUl7VOVA9y8uOvTeBorbJGj/2bdO7FaoJEWAxqCZvKSJKinZPDGyHIWrhC7/bCBEkNeUvI cdS9C+KESGpPwUFHjuM1cvHnZUH0LUcdIhdLDVl3/3A5m/tBgkWV2lebRWUd5YCghddIBoQG JkhqzDvCzqD1cFOQWF6RGHuG6w56BHlWWahXj1NfERNkJ3cWpOromiCxVCMxnqUPsCfIpp2j mCD/fa4qyODoWieOChJfdsa3Sc52aBNkHybITuBI1wSnqKWDB1gIWg8ngiRHyA5ezvTpjwry z+/19x9/fzP+/+9Xl+0/WpBwosuCGa01pEJ0hwRJ1wmHOYE9Qdoe0nF4hnRLG9fAj+T7fL4j eR/iG1nee5L0MXxZp9wpwkkXR2M2CGkoTxmXsH+8PALKK2nbWtbP2RSeifFkZDjdi+1AcYkY GvXG64JQTxyZF22KvLSGQJx/PoikOGk8qRF7BHlWf6Fep+X1YQ4Jkt8pkcO4X84saWPC0Uig +FkNd8LzuU7kHHDA0LkIx6jukof3ZO6l6SZOOKEQHPLFZ/9zHZf3nuUuz8TBwrWbdgPPp7Rt rdfP36PdZhIV6uE/47m0PpR/+DJ4s94oNwgXNon2CogyCtAnsU2CSMo22+rZCcpEWlEu2x3z rAmyYjtzoA+pXlrU1UE79di+eSQ3dtqgUpC4Dx2IRn54x4rPID3dkERJpJg5Q0dgBtleirKA Yzkuzvsk02v4GaDzUxq2qvVzNj8wQHm7+XOIRB7BgfGnHdxNu96UJhELlV1+ZUyKNoPtDg4u +xTlCsdXVyJ10NchbTAHdsf8CnsgOln+i1C9PuUbZ9Pr19EjEycCLFApqlRgnLcQcewYctT8 mx9pp22OWC+zpwMPjK4V8HxOy1YXltfP2bLVQ7QPQJ3gqEJU7Xojv4pQ4IxJYMPZVUESWCbH OB+fl6Wwtc2WZ9JeRdtTWdqg4dtts0NcWh/+OEGGFH5vE50GHRzixD13Bt0v9BinD89T+sSp lD/6w7Orf4j/MBDNMmpnvEjobEn+mVFsVesXBOdiYvugTRxOAHE/jpBWvX1+WP5C9vHHuHk5 hPZ1LO4XX1+Ip3gmESyuchCt48Uc7ZCiqgwiGIRqBh7lZ52yisbFLCCcMi5L+X5reITHPRWP hiF9Ouq6pQ7l6fegEXIMtydxTuMGBXpGPJLiHOKoZmGbJP8MVFuV+iVCJVybyDrA5m1GYRr1 5mVyzN8LgK/KCJs5fCrE/PI2FGKUlxSVKzsO0hEMEmk5ap9RWcK816H8jvb9d4O+6bG9MkWc C3417jrL7b24M9VlzPuBAwXkPbiarftAHO+175Vf/Z/+FwNMkC/Co302emJUf7MTtQgizMV4 RVu74CW2vz8ZDErVma8HarvT7TJBvgpendByjJwfAsAl91LfRbAl5Zq29rBc8q/OvUE5Lwty 25aV+ezsh5tp97nMkvWKBMEZN4MEmZxOD+H344oq5AEZnyMkW5V22h7s95A7mCBvyOFTWze7 dYuZyklfQw2kVdC/IJFigjRuBM1Ux9aLLLAnn6B3zHR4PsyQo2mrQNTZSbuCeaVxI/odW8Kv g/A+FwlZYDv7QLE0Hk5bhQaSztN6E6RxKyCQ0dUj0hTvitVMUvGMpVUY2PuaII17gVlqUBBu ltumtZao8r+pM5JWo3f/CEyQxs04sGzlpWYQBU5M6wKB+KL2KA0fIHWm1Rnb95ogjfuBJeDo Ro7S8KGM3AOKPSG/6vDx7hLCq6XtZGR2BCZI45bgvd6oOD4OCXn0NY0J0rgt9h+UDeNi8I8A Dn/h9j0s03GbTJCGcRnW9S9PWQT5o6iTGAAAAABJRU5ErkJggg==</item> <item item-id="22">iVBORw0KGgoAAAANSUhEUgAAAMcAAAAtCAYAAAAa2n8iAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAZzSURBVHhe7ZzptaM4EIWdwOTRMTiL ScJ5dAZEw6/JhGCYutooCRVI7EvdPpy2AS1I99Nq3qdXqVRZKRwqlSCFQ6USVATH5/PR40GH qkzFcLxB7e/X/1r35QHqWnqepnPfVLVSOJy65llgBBEgXwVkkRQOqGv674FktL/PoYZtf99e +aiXwtF3fUPDqaPQ6JqvKc9DW3PA/23oSfdV1zZ9016LwjV5UjhgnIObVQBydJp79x5Xnq8t nXvNuv7pQyoY9WCfngLHnnOPW8zXFjz/y+GgIdX3uCGV1ylw0FP+9hhazczXzDAyuo4y98vK UsNEeTXXh8NHYeJz53gZSue5anvPl8NBlbC6yeOVnRxC3OfAsUdDMD1fC4Zl5YBexj86FiY+ OWDbNooTQzb7HdA4gwPKAJd0PlHl3OvdcFBhnbEPcA4cMFldyzkrmG0mQgOI1AAZI88BCwCd oaP7UyAy5zOqKYN3w3HSHsBZcPBWewvhOebim4VjriWne4YGzPXSiC+qO+l8RhV1/no4pHrb S8YsGGrQcTQgSHu75y0bpk3CUWDUMdDoGeKhmpV0PhXdVzi0Ujg2M8v1tS0cZLKCyGQ4SkzK hlROFhbXU7Dw0vmxyqCGFI6CUjITR7RK2aOsoGvEe5fREWXYtZbJIbXGm8IRDXdkSXAMk+wp JQBGwzALgrksnRdUOu9QOOZrKAiFOphw2/F7TjEkcnoWXjJ+0zqD5LUpHAVDIigHR5SPKcjS +gEE0cSbwZE7L6h07qVwTBRiqtSsFUGXifI3lx7yVLo8uTUcs3Hx/Lubx70wX3GKTZ3rXXgd cDil8zmVlsOk85HQo3VrODB8qFv1OhyOi0rhKNFd4cBQhMCo4MJI4bBSOJy8obOFcUM48CM6 7AAvMabCYXUKHMY8hePf44QlQ8HIt4KDKjRZEKhNf1M4/v7p//x1n2+m//79pyjvj+85YDDx h3G3hoOOyoZoUzgqy+5KKi2HRXDYrt1WUJgQ+ooMqdKEMa1MvzKBq6M4/Jq9vSdcp/j8CkeU FjdGh97B30PjcZYOll99lrCEF6WJNPyNBToXDpveaLWnwqGlS5hForxVJH0p7QYHIkZLbMoY y28hFb75Yj8PxoN5GRhSHPhMpm9bMjje3nLfcZ8xJk+rcZ9hcMTt3vaK70O6HiK7uhOZAxNb yS1IG+ZjEJq4gzHPgcM+hz9nj1LA5c2vcZzhCGWZSOFILjsjd/gHA3OzGTP5CkQvMLRS6AVC 5U7EYaGhcC7j5ru/iDi8UfHqo/1g0uFvQUYPDnBCBkmAISoVCi+UEm+h/S0RHAya9cJzZBYN snBA9v7hmmR6LgDA41gphSO+HMxBhk53YyMjk2IjDQaW47CtV9TbcEMAKhiSGTxN09wTwmR6 CgiVGk7KhhnyP1yPnukIZ4hwkDCUDNfc9fRZI9H9ItDac+QU3O8Lgyv9HpmRKmcwnzMi1U4o fOHFdjEOb357xRqDnqAbThgzfNkP0dC6e6ObF+mp1wmV6eLDEA0IhpdyknSQH5+dWM584V5u ICnMtopgJPOP0ozgsfkSf0JC90YNyVq9bbUKBcyVfjeG8QZhvYExHCqmHXC0wxI6R2ZuI0jy cUTQkBAeIAxBYdak9USFu3gQ1MThwpihXIjfGd0cSauBvIumAVTcoGme9lEMRXq4MhiBwY8x vDUv+RSJ0n9Nz+GFwuX/L1HbeAPZeYUxmNilny30CAl0jxM1Dls7WeFYINNiJ60rtc7oQa4J B4nyPKy6PU+b9xrQG+GAbNe8tOfoTGX4/QQcfJ5wVWEP4JF8wMSbk0FaDccwhxvHMze/mwo7 rxPhuK/0D0lXCL3t4rjdHFAobL5Zaeau0ZB8OmyJVr/P8UY4oDV/PvJK6mjet+tzYKi8CA7b 6heDZRZ7/JywMqyg0mGmwqFaqIWTfGf2n99knYsD9/ueozZsVgCsbAFGCVAtVLnJuMwyNfac ENCYfWbewIZv1WGzIqgLV04VDtViway1IxyE4cMis58lRhIbuS6soIq5ksKhWi603pXmtK3/ 0NxPGTx9h7wmrKSaZW2FQ7VCC4ZWZjjkDYqVp7xZAULggMKYyX9hWFl18ySFQ7VOGKbUDvwp jJlQ8zkDm0OY5Vt33R4MglzYQtVuhiocqtW6xQYqQVW79KxwqDbRlTdQl26GKhyqzXTFDdQ1 m6EKh0qVVd//D9c1NSnU/qJKAAAAAElFTkSuQmCC</item> <item item-id="23">iVBORw0KGgoAAAANSUhEUgAAAHkAAAAbCAYAAABYxiAYAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAMJSURBVGhD7Zk7kqswEEVZF3vwNigv wtlLHRB7A84dkDn2hE5dRA6deQd6uvqAJIRpAfLAoFOlGrD4iL59W4LJWOLPk0TeAEnkDZBE 3gBJ5EVSszLPWJahFaxSv44libxA6rJgZS23q4ILnZdc9vGQRd7tdiKzDoeD+iXxFeqS5RPd TBL5er2q0iHb4/FQPeuhLnPrGaxWmCGsWOE5JtfW+joYz7DIwvGecYumjunl/X53Tlqzm22x 27LoIoOWs6KsJpXKyXAnFwEJVhXd5xsU+XK5iBNOp5NxcsZut5s6YmVUhfEcfocgEfKJ8+Bc VGXYONwkxvN9FNl0MbZNoTFHr5KPImNVywX+tdJsA8GsmYRAsMjaxfgLXq+XcYGVurlPZJRF LvBC9JXVpBlMxUriwIJEdl2sobuZtmCQ8GO/VR49Itf8txzzL22w0ekuoujJFySyFhMraxN3 IeZ3MwQOdMUMrwokLJG5sM5CZSE6j4Yssi7LfU7VZVw30+kAmRjiiuZ8CBDbTh9F5u2XF1xu LEMhi6xd3Dfnum7Wc7YAjvQECq9dz+dT7bWcz2dxDYlc+PRXAPkOOykPPOW6Ux4jJxotFuMg iTzkYk2fm8VNnCDhWCsRDHTCaGhVwPy267Shkz0i+64Xa4UdEotBxBTHx2uYiiTykIs1fW7G TdwAod8EGWs+6PF4VFv+82fFKzKQVaLto6wpwpMNfSafYtHFrmRmBdK/WSIr8a074nMlOqnv wO7nTghPERn7ulzhnuZD0kSe28mK2hWa98+cb7iuCfb7YjFEK2j7HJbIKhbWHTFXoBMiY5vS mgvyhqwUN3ECjT4M/n6/i0zVx+ok0aUehC7aQrGCwIPTyScrCdDm/bSJa1JjMYxKyqZcm8nf VqJGZGST7JzWXj//OgsvPJTu3+/31r79zw4MMs7CyxbXbcoJHYHNFvhK2AM9FlR4zJw3hLwo rQpk146ZGO1GBDmmjTdKFJGl4wIz/1sfQzZIJJEBSi9VNCRFEjgWEUVOLIUk8gZIIv95GPsP Bn3JukUimKMAAAAASUVORK5CYII=</item> <item item-id="24">iVBORw0KGgoAAAANSUhEUgAAAGoAAAAaCAYAAABfA8lWAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAALSSURBVGhD7Zg/lvIgFMWzruzBbXBc hN20FtRuwN4inbVfaeuhsrRzB3xcwiMPTCbEgZzxDL85zOQPyHtcLjA2uvIR/EmhmuYz0uZx VqF+MVWoKtRnUIX6EJYItdlsbP3dbueerEcVKlGo8/ls61K53W7uzTpUoRKEej6fgUgoa7uK x1mFmuB0Otl6h8PBC4VyuVxcjfLwOKtQI3A34ZqLhT1rLXicVagRyE34Cx6PhxcKZS1X8Thn hOq0sMG1Wir3KBm0FeZ3CqZuK/XiLt6ED0BM7CYi3VX58uZxzghlUFK3CYOoJA8OwS4UF/0k J/gz+ADEkCA48XHiw8W4q/LmzeOcFUrJVrczPavOdCaYTKLR7HYWP3M7oZslDd+EDwCHlrgp x9CSSIU7DuTOm8f5GrGCHftARBfaWJkPbF2QJB6Co8BtXxMOxNH2fr+7u4Hj8cgCUlq2381I xLNsMMYY+gshN03tQbGraA+zFMh7eB8LBWVh3a5vAWFIaessc23fICg/WpGYpl48O5BQkBSD kifSZiUSGwYsKPONbb2YOTcRU64qkTd/P1zRWslURee2sRNG4ccsc4KrH9l2bKnkHQLMJp7A fr93V+PtcxPHA+bcREy5qkTevL2/eqlol7neKXhnAzMCCelcZelnNp8JKQHjnpYDfC3Dgx9r /0peRyEGPEv9Hyn+agnilcibt/dX1nq2otKdlFqaUxwlHXyI2cP8eupdCK/1oG48WOgQQV2v VzuLcI/ZRQnT8gGWbsjvwAcAYB/BMwiF65SC+lSQS4m8UYcYruig0AorghVHSLf3DAcMvPfL I5Y91Dcu84xsqgjWtjVlu90G9+EXnesfJjDDKZaflMe/r+x5ox4RTq1MvO2KaL8rBR+AnOTO u7hQ/cz/zhkj0DLqbktSSqjcea8gFICdUwc+POKXppxQIF/eKwn1eykrVD6qUFWoShm0/g/3 9KtpaLKHegAAAABJRU5ErkJggg==</item> <item item-id="25">iVBORw0KGgoAAAANSUhEUgAAAE4AAAARCAYAAABzcpo/AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAFvSURBVFhH7ZbBrYQgEIapi4KIRViC B7rw7oGbPXg1nOzADuYxIyq6yCJhIy/xS0iAHWD4+YVl8JLEK1wir3CJvMIl8qxwWgJnDBjj ILXtCxEZryUHJpRt3cc/XoGgtbGIJ4XDRKwAJIgwPSHi4mnTuLlE4a7GaykOhxUt3DzPtpYJ ZU5tS06D5AyCe70Rn99xu9u4Ve8gXF3XME2Tbe20bUuDPlkmTMnxnJwSe1I+7sTnF85irwpc d1Oj6zoqPtBtfuFWFgeQxc/lYgOYnLvxGOFi438mHIFmce443KQLuswVsmkaW8vD/xUO1w4I h+31sx3H8dKNC/cdZ1Z3fiv9jnPBtU/CoTjDMJC7sI2u6/ue6tkfh/MryaVJKUR8/E+FMweI Tt+EQ9FWl1RVdWij4/ykPw6EvWw//5ct8/LzxL5427eFojMpxpTDeDfXQN6+8Z6+4/dZGEp+ c+FzFCucVhJUqaoZinZcybzCJQHwByuTSy2McRV7AAAAAElFTkSuQmCC</item> <item item-id="26" content-encoding="gzip">H4sIAAAAAAAA/4xTzW8SQRR/s8t+sEChxaJ0KWjVUgVLGa0fPdWmaUysmuDNmNQtrBQjgnRr eiT9A7x59uIf4MF496bG+B8ZfB+U1p6czdt58+b3+82bNzNJAFBoT9E89k38u+HbRtju9N5Y QO0WWrzVa0osxrF7BOs2n+y+CpsRR6DBfAP/9v5ef6N3KOFtNAcDjQnwEXHRvtsAz3C9unsy pvbZkKQMMEx2ZiaL34+iQWf3IAoVI9fQ0nC6xdYW/hlbLBCLBmHoc0Cjpfrs2jr/DWBoOfoj 9uu2K70Tl971pI8ntP0TYMNLagMXixLIVNYqLEMdVuA2ymnrF+okpmQ+BWAqg+YgzZEvGDGU ojKqDGsdedP6w0PUzsCMQKZZtI6idzDFuwjNimrmnKhmT1RnhZI9UYUcpzqMn9dUj40cBi+w e5Tjreb1zG+cd+eY2spjScwWhIAn4uv3WI5hHgo898kn2R90DWBeVJ2iFGK+JH3xIiPflTgB TOgSj79OxrAgxOJlSYeO5IqkI6dzVQbzPFgUsF0W9cUl6cvXZJWl8cZXAa7LOkuTdSpCLVd1 7A9SKjdke1UsptWDLm6wDQHAshSsyjy6ADXGDysrgq+dwdcFX2N8Xa6NFGfxuDg3+aKnW3qn PQj6ezsve4NuEDmMnaX3wuHNIArkHs6N7yOx+I2R84J2z+dHl9QLDjv7W6wjb6yA9lyNX6i0 EX6gCtv/HT0Wd6JB0Az1Jp8r+GNgSWVA+QpxvqmU8jGpUUkZyjeRW1Km8mMjisQQBCN0LMQM 13HKJgwJOIxBxyUMgeOkQ3QPMUxPIIbpScQwPcVroTPFGHTShCGw5Jt+fNANB51m8Foq4nLe D9Ba0MHPHL98b1zOMy15evAXAAD//wMA7JN7Z+UEAAA=</item> <item item-id="27">iVBORw0KGgoAAAANSUhEUgAAA1IAAAFpCAYAAACWBBlAAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAB8aSURBVHhe7d3NceNIEgZQ2SWDGGvE GMHzOrAe8DbnsWGCp/FgPOCyKJZUKIE/IJEkUPVeRM5I3S21hAKy8hNI9scBAACASQQpAACA iQQpAACAiQQpAACAiQQpAACAiQQpAACAiQQpAACAiQQpAACAiQQpAACAiQQpAACAiQSpuey3 h8/N7vwOAADQMkFqFrvD5uPj8CFIAQBAFwSpGew2m8POHSkAAOiGIPWs3eZwyk+CFAAAdEOQ esr+sP38OHykh/Wd63O7P/8eAADQqk6C1E/gufem0W4zEorSXadTYPo8/MpL7kgBAEA3OghS 018IYr/9HLm7lMJYDlDHz/m5Pf5KQZACAIBuNB+kRu8s3SGFqcHH7TaDMJY+r9wEAAB9ajtI nR6KtzlsjqHn9Bymc/LZb9MLRPyEpH16wYgqbNVB6tb7SX6eVF0AAEBbmp7yTw/RSy9NfspP Xw/xy3eRchBKISo9JG8YiR4LUiUBCgAA2tV8kCrDTv0wv/T+R/1cpzNBCuAC/Q1gSF/sUtur Xj2vqQw/+e1770hNfY6UIAU0S38DGNIXu9T4qqeH8+VX2vt51b36OVIpJN16jlT58cffPHxe uJOVCVJAs/Q3gCF9sUvtr3q6k3Q8uVNdu4NUSiEqf8wgTF37d6QqghQAALTLtB9EkAIAgHaZ 9oMIUsCy7Q+7zfnu++fm8HXD/vwPmH8c39+nf3g835Uv3072h+35Y7//IfL99vRnTp/vXD+/ tTndzf/cHv9M8esAy6IvMo1pP0i6WACWKT3n82cAGDwnND2EebM97HZfL8bzcXx7u92d3047 /fFjT5t+HiTS80XToFE8H7V4MZ7T5z6+c/qt00Ot83ACsCT6ItOZ9oMIUsBipY27eMGccmAo N/jTPxlRvp3+zPFj009RjxPE6QV4vmaI9AI850Eg/QQ2Dw+nXy9emOd76Di9c/4p78/gAvA2 i+iLX2FOsFoP034QQQpYqu/N/+Rr4/7ax9Pb+Seo5U9Tf95OH5v6W3rYyzb9dDb9dvoT+eEw xz+XXxU1/dnv+WDw96RZYnseFMq/B+A9ltAXv6RfE6TWwrQfRJAClurnJ637w+749vdPYcuf lJY/JT29/fXrpyHgPGykz/MTjI4DxNcvf/seGNJPadM/O3Hhp6xp2Kg/FuCVltMXBak1Me0H EaSA5dp9PwH6+3H6Rz+DRLHZn98+PT8g/VYaKqqfsH5t/OefxqbnDeRfTcNI/jtOw8hxOPg1 HRy/lvwXAbzNUvqiILUmpv0gghTQi902/+R2d3x78zUYnH7ntp+H+AG04/G+KEitiWk/iCAF NKvsb+XDXk6OQ8DmviHg5ye96SEuP58BYHVm6ounPytIrYZpP4ggBTRr0N+Kf3cl1XEAuCcT pYfFfH/MsTy6D1i1Yx/78VhfTL574yCIsVSm/SDpIgBokv4GMKQvdsmqBxGkAACgXab9IIIU AAC0y7QfRJACAIB2mfaDCFJAs/Q3gCF9sUtWPYggBTRLfwMY0he7ZNWDCFJAs/Q3gCF9sUtW PYggBQAA7TLtBxGkAACgXab9IIIUAAC0y7QfRJACmqW/AQzpi12y6kEEKaBZ+hvAkL7YJase RJACmqW/AQzpi12y6kEEKQAAaJdpP4ggBQAA7TLtBxGkAACgXab9IIIU0Cz9DWBIX+ySVQ8i SAHN0t8AhvTF9Xpi7ax6EEEKaJb+BjCkL65TWjdBankEKQAAWKgcogSp5RGkAABgoQSp5RKk AABgoQSp5RKkgGbpbwBD+uK65ACV60FWPYggBTRLfwMY0hfXJQeoJ9fNqgcRpIBm6W8AQ/ri euQAlesJVj2IIAUAAAszU4hKTPtBBCkAAFiQMkQJUsslSAEAwILMGKIS034QQQpolv4GMKQv rkMOUTOtl1UPIkgBzdLfAIb0xeXLASrXDKx6EEEKaJb+BjCkLy5fDlAzrpVVDyJIAQDAApQh SpBaPkEKAAAWICBEJab9IIIUAAAsgCC1LoIU0Cz9DWBIX1yuHKByzciqBxGkgGbpbwBD+uJy 5QAVsEZWPYggBTRLfwMY0heXKQeoXDOz6kEEKQAAeKPAEJWY9oMIUgAA8CZliBKk1kWQAgCA NwkOUYlpP4ggBTRLfwMY0heXJ4eowLWx6kEEKaBZ+hvAkL64LDlA5Qpi1YMIUkCz9DeAIX1x WXKACl4Xqx5EkAIAgBcrQ5QgtU6CFAAAvNiLQlRi2g8iSAEAwIsJUusnSAHN0t8AhvTFZcgB Klcwqx5EkAKapb8BDOmLy5AD1IvWw6oHEaSAZulvAEP64vvlAJXrBax6EEEKAABe5MUhKjHt BxGkAADgBcoQJUitnyAFAAAv8IYQlZj257LfHj43u/M7ghTQMP0NYEhffK8col68DlZ9FrvD 5rhwH4IU0AP9DWBIX3yfdOzLeiGrPoPdZnPYuSMF9EJ/AxjSF98nB6g3rIFVf9ZuczjlJ0EK AABepwxRgtTa7A/bz49TaMr1ud2ffkeQAgCAQGnezvUG3Uz7++3nd8i5x27zE4q+pbtOx4X6 +Pg8/PpU7kgBAMBrlCHqTXN3J9P+14tB3BukUugq7y59SXefcoA6fr7P7fFXCoIU0Av9DWBI X3y9dMxzvUkXq55eDGI78Y7UrztYu83gVfnSHavi3V8EKaBZ+hvAkL74WjlA5XqT9ld9vz1s j4GnDEb7bXqBiJ+QtE8vGFGFrDpI3Xo/SeFprACaoq8BDOmLr5WOd643anzV94ft5usheJeC UApR6SF5w0h0OziNBamSAAUAAAEEqRfYbb9fFGIs+KSH533Uz3U6E6QAAGBhyhAlSEX5/dLk qXL4yUHo3jtSniMFAABvlmbsXG/WzbRfBqP6OVIpJN16jtTxV35etS+9Qt+FO1mZIAU0S38D GNIXXyMHqFxv1s2q33ooXin92foO1sm1f0eqIkgBzdLfAIb0xddIxznXAlj1IIIU0Cz9DWBI X4yXA1SuBbDqQQQpAACYycJCVGLaDyJIAQDADMoQJUi1T5ACAIAZLDBEJab9IIIU0Cz9DWBI X4yTA1SuBbHqQQQpoFn6G8CQvhgnB6gFHmOrHkSQApqlvwEM6Ytxcoha4DG26kEEKQAAeEIZ ogSpfghSAADwhAWHqMS0H0SQAgCAB5UhSpDqiyAFNEt/AxjSF+eXA9SCj61VDyJIAc3S3wCG 9MV55QCVa6GsehBBCmiW/gYwpC/OKweohR9Xqx5EkAIAgInKECVI9UmQAgCAiVYSohLTfhBB CgAAJihDlCDVL0EKaJb+BjCkL84jB6iVHE+rHkSQApqlvwEM6YvzyCFqJcfTqgcRpIBm6W8A Q/ri83KAyrUCVj2IIAUAAHdaWYhKTPtBBCkAALhDGaIEKQQpAAC4wwpDVGLaDyJIAc3S3wCG 9MXH5QCVa0WsehBBCmiW/gYwpC8+LgeoFR5Dqx5EkAKapb8BDOmLj8kBKtfKWPUgghQAAFyx 4hCVmPaDCFIAAHBBGaIEKUqCFAAAXLDyEJWY9oMIUkCz9DeAIX1xmhygcq2UVQ8iSAHN0t8A hvTFaXKAWvlxs+pBBCmgWfobwJC+eL8coHKtmFUPIkgBAEClkRCVmPaDCFIAAFAoQ5QgxSWC FAAAFBoKUYlpP4ggBTRLfwMY0hdvywEqVwOsehBBCmiW/gYwpC/elgNUQ8fKqkcpT5ZnCmBp 9CaAIX3xunK2behYWfUo9QnzjgIAgHdrdD41bUcpT5ilFwAARGh47jRFB5nlOVL1iffKAgCA ZzU8X5qYg7z9xSbKkzaygP649gGG9MVx5czY4DGy6kFW86p99Qk+RwFtc50DDOmL4xqfD616 kFW//Hl50s9VQDtc0wBD+uJv5RzY6PGx6kG6+Hek6gtkagEA0KYOZj7TbJAugtSY8qJ5pAAA WLdO5juTa5Bug9SY+mKaWgAArENHc5wpNYggdUN9kU0t4H1cgwBD+uKPjuY1qx5EkHpAeeE9 UsBruN4AhvTFL+Vc1sExsepBBKmZ1BfklAJiuL4AhvTFL53NYVY9iCAVqLxI7y0AAOJ0OHuZ MIMIUi9WX7zXCgCAeXU4a5kqgwhSb1ZezPcUAACP6XSuMkEGEaQWpL647yngMtcIwFDvfbHT GcpuGESQWqjyQr+3gCHXBcBQz32xnJk6Ow4dr3osQWpF6gZwrQDXAkCt577Y8ZxkNwwiSK1Y 2RBuFQBArzqfi0yCQQSpRtQN4loBAPSk8znI9BdEkGpQ2SyuFQBA68w/glQUQaoDdQMZK2iR cxtgqMe+aN4RpKIIUp0pm8mlglY4nwGGeuuL5XzT8Z7Q73ceTJDqVN1YrhWslfMXYKi3vmie ObEbBhGkGDSZWwUAsAZmmG8muLnst4fPze78TjqvHFoKddO5VAAAS2VuGXAEZrE7bI4n04cg xb3KJnSpAACWxJwy4CjMYLfZHHbuSPGosimNFSyN8xJgqIe+WM4m9oETR+FZu83hlJ8EKZ5V N6ixgiVwLgIM9dAXzSO/TDoSu83H8dh9fAWHe1Th4pbd5vOw3Z/fWYX9Yfv5dUxyfZ6/AUGK p6Tz51rBOzkHAYZa74vlDGIP+DbxSKTnAm2O/71gt/0JQilEfW6PUWOK9PnnDFNl0LnydY9I oTGHom/pezp9rpGv0R0pIuSGda0AAKKYOy6adjR2m8ELKgwdQ8smB6cUYKYFlx/HMDU5gI3b bzffged0N+3Oz7vffh7PkzpIpe8pB6iRr1GQIlrZxMYKAGBuZo2Lbh6R/TE8ne7CHENCCiNf WSGFpnPYSL+wP79qXb5TMxK4/v3338N//vOf83s//ve//50+T+nn75nR6W7S/eEuhalBkKq+ p1tfoyBFmHRu3SoAgGeZL666ekROYSLfednlIPJ1Z2azOyWm7zsz6c/mYPErhBz997//Pfzz zz/n936kgFWHjrGPf14Ke19BKt2p+vr6v6SwuKn+vvpruPV+kr6PsYIw6fy6VhDBuQUw1Gpf NFNcdfmo1M9xyndk0h2q9P/97hyo0m8OH8p3KUhl6e36/VJIkDp+P2VYyn/H6Y7b8Zuo/7Zb wenW1yhA8VJlo7tUMBfnE8BQi32xnCH0/VEXj8rwoWvpztPX+6fnGh0P5scxOG13xyCSfrsK XWMhowxL6eP//vvv09vpLlV6eF/pVkh5xG5bPafp6PS91M91OhOkWK3c8C4VPMt5BDDUWl8s 5wY9/6KLR+Y7SKU7T+mhcOeHxZ1+/RwgUpg4/Zl0V2f7nbpO79fPkUrBIj2M76+//jq9nf6f QlT+9dIwxD3v++ss5CB07x2p+nu69TUKUrxdOgdvFQBAzaxwl4tHJwWMFAZOIeN0x+kYpFKu OL1oQ7orlZ8n9RUqPjfbr98/+f2qfX/88cfp86X6888/v9/Od6Z+jLwi3hOGgSiFwmN4qp4j lULSredIfX1P5xfTqB/2OCJ9b7AY6Xy8VgAAiRnhbnFH5xS4hmHqtiKszCAFvBzYvuq+z51C VP6YQZgqQuStz5M+FhYnnZe3CgDol5ngbrFHKAWPa49/q+w284WodxOkWLyyUY4VXOL8ABhq pS+Wc4Bef5MjFESQYjXqpjlWUHJOAAy10hft/ZM4SkEEKVapbKBjBYlzAWCohb5Y7vf6/F0c pSCCFKtWN9OxAgDaYI9/iCMVRJCiGXVzrQsAWDf7+kN+H63yQEZW4wQpmlNfw2MFAKyLvfxh w6NVH8joapggRdPqa7ku2maNAYbW3Bft3w/7fcTKgxlZjROk6EJ9XZdFu6wvwNBa+2K5b+vt kzliQQQpulM341y0x7oCDK2xL5Z7tb7+EEctiCBFt+rGnAsAWA579NMcuSCCFF0rm/NYAQDv Y1+ehSMXRJCCo7pR1wUAvJa9eDaOXhBBCgp10x4r1sN6AQytqS/ae2fjCAYRpOCCsoGPFctn nQCG1tIXy/1WL3+aIxhEkIIb6mZeFstmjQCG1tIX7bWzchSDCFIwQdnYywIA5mGPnZ2jGESQ ggfUTT4XAPA4+2oIRzKIIAUPqpt9XQDANPbREI5mEEEKnlQ2/bHifRx/gKEl98Vy79S/Z+Vo BhGkYCb1BjBWvJZjDjC01L5Y7pV69+wc0SCCFASoN4S6eA3HGmBoqX3RHhnKUQ0iSEGgcmOo CwCwP76AoxpEkIIXqTeKXADQM3tiOEc2iCAFL1ZuGGUBQG/shS/hyAYRpOAN6o2jLubhWAIM Lakvlvuefh3K0Q0iSMEb1ZtIXTzHMQQYWlJftN+9jCMcRJCCBSg3k7p4nOMHMLSUvljuc3p1 OEc4iCAFC1NvLrkAoAX2t5dzlIMIUrBQ9UaTCwDWyp72Fo50EEEKFqzecOoCgDWxh72Fox1E kIIVKDeesWKcYwMw9M6+WO5b+vNLOdpBBClYkXoTKovfHBeAoXf1xXK/0ptfzhEPIkjBStWb Ui5+OB4AQ+/qi/apt3LUgwhSsHLl5lQWACyB/entHPUgghQ0oN6kcgHAO9mXFsGRDyJIQUPq DSsXALyavWgxHP0gghQ0qN68cvWmx+8Z4JpX9sWe95+FsQJBBCloVLmBjVUPevk+Ae71qr5Y 7jd68dtZgSCCFDSu3szqalnr3x/AVK/oi+Ueow8vglUIIkhBJ+qNrSwAmIv9ZXGsRBBBCjpU bnJlAcAz7CuLZCWCCFLQsXrDywUAU9lLFstqBBGkoHP1xlfXmq396weYW1RfLPcNvXdxrEgQ QQo4qTfButZorV83QJSIvljuFfruIlmVIIIUMFBviGWtzRq/ZoBIc/fFco/QcxfLygQRpICL 6g0yFwAk9oZVsDpBBCngpnKjLAuAftkTVsPqBBGkgLvUG2YuAPpjL1gVKxREkAImqTfPXEu0 1K8L4F3m6Itl79dnV8EqBRGkgIfUG2muJVna1wPwbnP0xaX2fC6yUkEEKeBh5WY6Vu+2hK8B YEme7Ytlj9djV8NKBRGkgKfVG2tdAKyf3r5aViuIIAXMpt5kywJg3fT01bJiQQQpIES54ZYF wPro5atmxYIIUkCoevPN9Qqv+nsA1uKRvlj2bn11laxaEEEKCFdvwrmiveLvAFiTqX2x7Nl6 6mpZuSCCFPAy9YacK0rk5wZYoyl9sezT+umqWb0gghTwcvXmnAuAZdCfm2IFgwhSwFvUm3Rd ALyPftwUqxhEkALeqtysxwqA19KHm2MVgwhSwCLUG3dZj3rmYwFadKsvlr1XD22GlQwiSAGL U2/kuaZ65GMAWnatL5b9Vv9sitUMIkgBi1Vv6rnuNeXPAvTgUl8se6ze2RwrGkSQAhat3txz ATAP/bV5VjWIIAWsQr3R5wLgcXpqF6xsEEEKWJV6088FwDT6aDesbhBBClidevOvKyvfBuCn L5Y9U69snhUOIkhB25q+xssh4FJBB+zl3K3ukc6d1XjmOrfKQTRfaFsX13geBsYKOmAv5256 5GoJUguk+ULburvG83BQFzTMXs7d9MXVEqQWIi2EUkq1XMf/jNbYn1VKqR7q+B/9cOX1qMc/ kqueWRRg+bq+xtP3PlbQGHs5N+mDqydILZDmC23rPkjl/48VNMJezlV6XxMEqQXSfIFm1f0t DxF1AbRKz+PIqgcRpIBmjfW3cpgYK4BWjPU3fa5LVj2IIAV0qRwuxgpgzfQ0Cs6AIL+C1H57 +Dz+2sfH52G7P/8asHK7w+Z0XafaHN/jW+qBlwpWYLf5OHzWG7a9vG96WXNGr/MJe7uzIMgw SO0P28/cdI+L87k9/gqwerutYeoeeeioCxZqv/08nqL1gGUv75r+1Zzx6/xowt7uTAgyCFK7 zeFj85NnU/ot3gVWKQ1VXz+x6u56fnSISB83VrBAacgaDFj28n7d07P0slX6dZ1P3NutepAy SNWL9HvRgPXZH/bpMj4/1KergeqZgSF97LWChbi1d9vLO3Fvj9K/Vun3dTxtb7fqQQQp6Ehq uD09zGeOgSF9jmsFb3Zr77aXd2BKX9K3VunqdXzH3m7VgwhS0JP0UAAvNvGQckipC97o1t5t L2+cftSF69fx7b3dmRGkDFIeVw2tOzbbjSeeP60eXHLBG/wasOzl/dCDunEzSN3Y250dQQZB 6pRoz6/009tDgKAHxwHLT6ZnVA8xueCFfg9Y9vLm6TvduRqk7tjbnSFBhkHq6PykNf/2BDQi /XT6dE0fq7cfS79iuEh/x1jBC6ThKl/fg0HKXt6u3GPKmmLqn+ftRq/ziXu7VQ+SFgCgSa/s b+nvGiuAuczRYx75GFbPqgcRpIBmvaO/5eGmLoBnzNVT9KMuWfUgghTAzMphZ6wAptBDeJKz JoggBRCkHn7qArhF32AGzpwgghRAsHoQKgvgEv2CmTh7gghSQLOW2N/KoagsgFJUj9BvumTV gwhSQLOW3N/ycFQXQGRf0Ge6ZNWDCFJAs5be3/KQdKmAvryiD0R8ThbPqgcRpADerByaxgpo n2ufQM6mIIIUwELUQ9RYAe1xrRPMGRVEkAJYoHqoqgtog2ubF3BmBRGkgGa10N/KAasuYL3e dU2/6u9hUax6EEEKaFZr/S0PW3UB6/LO6/iVfxeLYdWDCFJAs1rtb3nwqgtYvndfu6/++1gE qx5EkAJYoXIIGytgeVynvImzLYggBbBi9WBWF/B+rk3ezBkXRJACaEA9pNUFvIfrkQVw1gUR pIBm9drfyoGtLuB1lngNLuFr4OWsehBBCmhW7/0tD251AfGWet3pAV2y6kEEKaBZ+tuXPMjV Bcxv6dfa0r4eXsKqBxGkADpRDnZlAc8bu7ZSwQI4E4MIUgAdqYe8uoDpxq6lVLAQzsYgghRA h+qBry7gtrFrJxUsjLMyiCAFNEt/u60c/i4VMDR2naRag7V8nczKqgcRpIBm6W/T5GHwUgHj 10aqtVjT18psrHoQQQpolv72mDwYXivozdh1kGpt1vg18zSrHkSQAuCicmAcK2jd2HmfClbE GRtEkALgpnqIHCtozdh5ngpWxlkbRJACYJJ6qKwL1m7svE4FK+XsDSJIAc3S32KVA+algjUZ O4dTtaS174e7WPUgghTQLP3tdfLAealg6cbO21StafF74iarHkSQApqlv71eHj6vFSzJ2Dma qlUtf29cZNWDCFIAhCiH0rGCdxk7H3NBg5zZQQQpAELVg+pYwSuMnXtlQaOc3UEEKQBeph5c xwrmNnaelQWNc5YHEaSAZulvy1UOsbcKnjF2TqXqVc/fe8esehBBCmiW/rYOebC9p+BeY+dP qt45Bl2y6kEEKaBZ+tv65GH3noLa2HmSiy+ORZesehBBCoDFKgfhW0W/xs6HsqBzroIgghQA q1APx9eK9o2te13AiashiCAFwOrUA/O1oi1ja1wW8IsrI4ggBTRLf+tHOUjfKtZnbB3r4j6O VZesehBBCmiW/tanPFjfUyzT2FqNFdM5bl2y6kEEKaBZ+ht54L6neK+xNblUPM7x65JVDyJI AdCNchi/p4g1dswvFfAwV1AQQQqALtWD+r3F48aO56UCZuOKCiJIAdC9eoifWvw2dpxuFRDC 1RVEkAKapb/xjHLAf6R6Mvb931u8lmPeJaseRJACmqW/Mbc8/M9ZSzb29T5bvJc16JJVDyJI Ac3S33iFHBDeUY8a+1wRxfJYly5Z9SCCFADMrAwTvRSwWK7QIIIUALxJHUaWWMDquZKDCFIA sEJ14JlaQDdc8XPZbw+fm935ndRLHVqgUfobwJC+2CWrPovdYXO8gD4EKaAH+hvAkL7YJas+ g91mc9i5IwX0Qn8DGNIXu2TVn7XbHE75SZACAIBumPafsj9sPz9OoSnX53Z/+h1BCgAA2tX4 tF8Gnc3h537RbbvNTyj6lu46nT7X56H+LXekAACgH01P+/vt5jvwpGD08bk9Rqvb9tvPUxAa BqkUynKA2h029ecSpIBe6G8AQ/pil/pZ9dPdpPvvSqUwNQhSu83gVflSMCve/UWQApqlvwEM 6Ytd6mjV00uUfwWpdKdqs/sJSfv0ghHVY/XqIHXr/eTrIYS/C6Ap+hrAkL7YpX5Wfb8dhKUc hFKISg/JG0aix4IUAADQhxUGqd+vlPddVx5rt9v+fn7UtedNCVIAAMAlXdyRSqGnzlg5CN17 R2rqc6QAAIB2NR+khoFod9im8FQ9RyqFpFvPkTr+ys+r9qUXrrjzFQABAID2NB2kTg/dGzz8 b+TffxqRQlT+mEGYOr3y3/2fBwAAaFM/LzYBAAAwE0EKAABgIkEKAABgIkEKAABgIkEKAABg IkEKAABgIkEKAABgIkEKAABgIkEKAABgIkEKAABgIkEKAABgIkEKAABgIkEKAABgIkEKAABg IkEKAABgIkEKAABgIkEKAABgIkEKAABgIkEKAABgIkEKAABgIkEKAABgIkEKAABgIkEKAABg IkEKAK74999/D//5z3/O7/343//+d/j4sI0C9MoOAABX/Pe//z38888/5/d+pIAlSAH0yw4A AFekIJWlt+v3AeiTIAUAV5RhKd2B+vvvv09vp7tU6eF9APRJkAKAK1J4Sg/j++uvv05vp/+n EJV/HYA+CVIAcMUff/xxCk2p/vzzz++3850pAPokSAEAAEwkSAEAAEwkSAEAAEwkSAEAAExy OPwfHv4At60FDWIAAAAASUVORK5CYII=</item> <item item-id="28">iVBORw0KGgoAAAANSUhEUgAAAMgAAAAnCAYAAABKSgfJAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAaXSURBVHhe7Z25ceQ8EIWVwOaxMUwW m8SUgpCzvgxmIV8GrV85yFXRUgbKgNsPB9k4mgRIcMT51d8WqoYHjgHew8Wh9mFUFEVEDaI0 Zei7sesHd3QO9pRJDaI0o79ex2vvDk7G0FPZunqTqEGUJgzdec0xQSa5VJpEDXIAfd+PJ5tk UJkOLNHQjZcFdwzdZXwIrg9jd3kYHx4QLmNes/14Ndfn4JMw6blzXPDSeU5/lfLLc0qD+C+p 4bjQDhI7Ta0ke0yiZQbBaONF2l/p2qVLOxTqZHiamL7ZYxjHiRzGnAwmnY/AtVx+Aqc1yH1C YukkqXw/fVcujGIguKwSZ9IRhGHELBvMAhO6sgf3x6bInM9QM4qoQVpCjbQw0/h+aA5eKoxS IP61NFcNstajo16nTNz0DOkFawrpfIaKtYgapCF86nBKAqG1AKJc6/2R7YJBCsSa1qtbnyRp Sudj6L7CadbplHjX06sCsXwv5cIog9JbFeOSQUrKw6ZXDmsYN2Kw+NL5lPK2UoM0I21sIwz0 aLkQCIbiZu4pnQYAs9jNpGGDF0NjExeOSJJB5oX3EpEJgymZNYO5LJ0XKF2HqEFagQYSWiQ0 ijwNsyK/kOi2bxOj4Zfyqt3mXKRwLp8zCM5Np5aMRnkEUVHPwWKcGSR3XqB0OqwGaQU1pDj3 xTUm2txdEEzN9qNEbMY4L5hwSThVxOLNwb+7uzkd7ZxpjcjD8uVGGf4duUGl8zkCgy6gBmmE aRypxhcNgukAmaOkOyugxCCt8sL3KhHZGVGD3BhUuCg8ySCYWpA5WukVrBlksZy1qEFuz1kN 4oUnVWqtQfDjOTztbS0wNUgZapDmYJcqFZzHCFOq8cAg1DDRQrqlxm5qkL+/x99/3ec7478/ v4rKXqzGr68v9+k40KinBT3+UpcDE2wyCIUGi3PPmkF0DWLZNII8PT2Nn5+f7mjm5eXFVHjK +nZaDfk8aBEbC8rveuAqhOvO24a3ZfL3TNepkH73ZBIIhMvFOWCU8PfQ+oDlg+1R/z2xRRjm SSAtqSJwzd3vRZvs5DSqxBKDtGovfK9mad2YaoO8vr6akAOjBypcxj6YmRuGhYoaxP0hNt25 x4OAmTkwXaD0zSG2CH1e+EzC73sSOX7m7Y5xnxHQVCZK3/+4EEZC2u5n4eF9yJc/hJrLMMHy SMgYxKbD6olCdc+OPBGX5btukEzZDRva8CcZBBXBwajBDfP8/Ow+HUdcBiNMauTJEEZortGd IQb8gxES41A8VwHm2F9EGl5QeBXTfjD58FcmggqEebiqKO/0JxbcRBFZgwDky4TIRqwS+Cjk ixMYJCkPTJCaZjM/2SA49tOtj48PcXSxbOh9MuD+mLDBZxFP58kY4ZNnW5Zg1OGi9D09EznS ig0wP5VFehnhQhzByQXxiQYhMK2brrnrcV4Cc93MaQb1ldT9goldvc3lYEFqw59mEJjg/f3d jBY4xijy9vZmPrdepPvK58THBuGF+0DUJLJJxN4A9ooVJ9XEMJ8wgrywH8CZeblLy7zgT6PP JAqXHqZrsOH0clCcDyH9fCEQLYk5uScwEEJseglnrqkcXOQZU6NTyBVwKz9pFwvm8A30+PgY HGMEyWMbaGsvgrQ58TGw0wgSDAk6fG0UvaETQzSy8NEA8WGGOSrKHPXiJFA7l7fiNWm4OGZa N6XvBGlCpgeKBBgaIw6uDIk5eOAit3mnO2kwbZhP+H0ZlFdLfyC9rW3/3aBtSsqe6bJvCxrU wz8DvAFnG9quM4wQxCnCGYBYjy3fnrcCm79RqAY5Hm+K2BymZ8XUhrcoeuiDBbgbMyV0nxuD TiI7MpRAdde8XGqQ2wBzJAYhG2BL0j9vQODrhjMznPKvmhyg5N0GmddMaTor66nFuOv8Dwyi nB4ySLD7V4Vbzwkq5RseZh0aTK2X45Zwd++DqEHukM27Yrb3LzYX5RNuu1fEFSh9cUwNouyA evJt8xsj+Kt/0LmWBu73I0ht3CwwWbSTKaCqVHZQLjSO2f7G8yRENIJfWUewqVx13Cxk7MLd UDWIsgsItna2gzjJsyoxkVDMdXEFKtZOahBlH+jFKwVqR4G5218SefxOek1ciZo/XKEGUXay YZplpkZepNiRygsWZpi8QHHMhkBhXJm6dZMaRNkPpiy1CwGKYxbZfA3B1hRma9ddt4EZIRe3 kJrRA6hBlCbguUKtWG8OGat2W1oNojRD/4cpRVlhz/8HeBRDt71MahBFERnHfzKp13mpNx18 AAAAAElFTkSuQmCC</item> <item item-id="29">iVBORw0KGgoAAAANSUhEUgAAAOYAAAAnCAYAAAAb3jSqAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAbVSURBVHhe7Z07cuM4EIZ1gb3HnEF3 cLCXUPkQTiZ3wNgXcO6A0Th26tTFyKEz34DbP17EkwTBhlce9z+FKguEAAjoD90AKc1pFolE VycBU8SmaRzmYZzMK9GR8RAwRSwaL5f5MpoXIqdppHEZ9sMpYIoOaxoEylURnOedcAqYzBrH cb6uYG6iPnXs0TTMZyYqp+E8n3oTjv6eTvPpdJ7zrEzzcMb1fJlcH1WeKn8qAjheSu3lJWBy CnuKjgw0i4xx6GLvZMQUwnJU7Yy7K5jjfLGwKUDTvsP72zkcL9Sf8+AW2nwf4zoLAOKaV9eW BEw2kZH2sX4WjUO9UVQLxsa4EnX3mBRSLvVrz7jaXAbepI9BGQ/SjPZ4TQGTSzRBPW3qsMgo GRlSgpFy1tkbzLh+eMTVhSXj5dI+mtAXeVt7yR17TQGTSX4IdJXCwsHaQRgkTxhr9RVg+mBs gpkBKd9HeMqaMJzKVYazRTDv7u5Uurm5cRtbpNvbW5X/+PhoSop6GCm/6o2iTlQfM0TXBWZ+ vHJ91Iuy8ZyrY1xvJ5se8+3tLQBTlFM6iWoCvXELUjCx9N5MmdWVPJI6pMjUoZM1BObFg90D mzHrCCY84FK/BqnUHO7L5i4lfQzC3fU6odp9poDJIUxOYTZCQMvhrobrTMbefrsFk77W1t4j +1Xt2C/VqjuYahE0Y5DZP1qhH64b0QKUBTM4/FkHs3bLUyTt9fV1fnp6UmHrMtmn+f7+XuW/ v7+bkiIYadGgcM2NX3kV3nOUXlK8CMRtAX42u6fPxVYX5I8Ta8WRFEhox1ukTB6aTaMPr1yh j/64by1WAfQrSsAEkHZfCSgBoesMJVzHPhN/o5wAaiamNNqrYCL0Cfc9R1QDJpuX4wbzh6gJ TD9sBZRxHhL0+fkZHAp9fHyo/J8qDHbR4EtgIkQiKLk4gbbAXO3nXgmYTWoC8+HhwU0sPCVU 2mPiVDYu+1O1F0w82IwnRLgNW8C8fjWB6e8nX15eVF4JTD/EFTBrQ1malOiAhtO2vxTM37/m X7/N36Jq/fn3n6pxE4/JIcDXBCYlhkMfqy0wZY/5/6vJY+7ZY/p5+T3m9tHxuqZ5iI3YOyHT 4aDO18Zm7wfqMu46dcCetDmjBCweEGldWjjatid49nMseVF9pQ+Ka6a8hSU5+WsfpEA1YDI1 pT4XW10/SE1gQjh1tZMLOJ+fn73JbjmV1Tdd/TpcKvZQv2eBhKDzDkpUSEbvVS9x1G3rwd8E 3DjiuVW6al6jnDJa1x7Vbx44L9Wl4LH5zgj1KarHrpbXTqIMmLoem6eTvyBUCW3ivf4Cswlm 6bCpYY4EzCY1g2ll72NaCG2y9zFxvZ/g/ZYbsfBoztAMPBP+AcAEWHqf+eDqtb2IOqwR269n lepaA42upY+iYeHYAyZkPbxNJWjy8r2u7U4AZtIfwJfC2iwBs0mHwbQq7THr1bAak0IjI4My VubyCaLwKRndTuBlfRAsbB5YpbqQv+rBYJTB9RWjL4JJmmI4l8+5pWV8ljqDMUvGdmXxMGO3 9MNLpTkSMJuEOaoZty8As1HwahkrDaAhw3ahZezlAASNwLRkKAjOl6VMqS6Vb0ZPeVLdwPKl 4IxHLT1qFcBCECVlAnCR4gWnJAO164cPV8b7YkHKdbBVcirbpKZTWV/YXyK1f7tEG07rqqpD NTJSAin8aQys/KY/kSf1vRzeDwiXt6I/kccq1KWM2OSHHtiU9Q6DnCLDD4GMk+lHAqWffLh0 23axWITFImwn/MyeqC1OLlFf69z+ZMEuasbtC11gvfBte21ceu+njK8Yhl2LAEnfPh75FQL2 XzAQMJv0fcGEFyEIg1Uf3qiz0bNIhd/mb2Zhgcp6whrR+LH3S8Bs0jf2mBPNOYWlUXh29VAa TVf5K3kdCDoKJi0W9h5xMcTOlvG3FDr1WiDUdoS58m8dyoq+gQjM1ZPrVQEuA5uCL977Q4Uy tMj4ZUtfaD4qBSXAZwezbq8vYIraRLA0n/Jiu+IMXp8mJ/ZfUwb5HaOpHh6T7RcMRKK8yKM1 Gm1s8OoEPbLWmjKHFocK8YOJBabOwwuYokbVG1ksGHxyaysD5nYZ5ltAkfjBpMWs8u6CgClq Fgw3D4YOPf0DGpfI0HnA7BvGQuxg7tiXC5iiduFQptLQArHsMdtD6Vpxg7nnx9AETNEBtYaz 0YlrNrzbKEPgduaSGcx9C4mAKTomhGctxgvYVHjreRGT56rLlTHqdZvECR7bC7+Pau9PhwqY osPCIQybY/kbBe++h0qSgClikfyP0nnhu8Qtt3QETBGb1BcOmh/o/fs0De3jIWCKRFenef4P WR9q5JzRkbUAAAAASUVORK5CYII=</item> <item item-id="30">iVBORw0KGgoAAAANSUhEUgAAAJIAAAAqCAYAAABY4gKWAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAQ0SURBVHhe7ZwxcqswEIa5wLtHzsAd UrxLMD6Em/QuqHOB9CmoXuq0bjNULt35Bnr6hSBClkDIKwub/Wc0E4u1grUfuysZUwgWi0AM EtRUoqpb/cJSW/uPsQYxSKIRVVWLKVTauhLM0rQ2D1JThUAiYSunYdu6tg2STGmlg6KmKq77 p9IfkdqmFnWTD9db/v+GQXJHmbYuRVE4QJJqqjJZikNkrBr9IqPayAtmsyABGJ/jcMwFEgrv MoG3UYOtAaJBnkg9JY5I+pUpH0hJIhIhnCqaEo219LNyjeSYLSdISWqkVtQypVG4XkEkUzIV SArwBQuMbYMk5Vq1XYOUaNUGZxHCSRmRoCVRafMgKUisfSQbpFT7SPg/lONSg+SL2C4xSJCV tkYgJdvZlmmtpElrvchBWhCJnwqk/X6v2uvra1cv6Lbb7VT/x8eHtvRLOUO/jzLtXAuRkNLp KUAKh30AyZxAu/Xn5rJJO9lx+vn5GZ3jKpUg0tGDhGAdln5Hs4zNqNJwQFE4aMQE4JgMeRk3 YSf1ECAtqD9ClQKk0PrwepZtmOwTw3Hik6XS8XgUn5+fKo2ZIB0OB9V/Op205QqEuoxyGuV4 Xp/dIMAZMpzzcrVT2DDQwr2FewkA9XURIAI05vnjOOok/A27VQBFDVIi3QSSfLsssn4doVJc iwqedpVBITONASK7Dw26XC6jIvx8Pqv+bNoGSFKIPoYziiLB1wMEen9/H84RkQjy1UhYtdm2 2bQZkKS8KW5FMuuh7+9v1ecDyUx52UF6exEvb/rvFevf3z9B5zkJkjPF6SNrEUektCKJSPiw ZVlOr+KcakUjl41lEHiy9pLjxk7qkhrJ7OMaKUw0NZJeoYWmuFYV5KbtkghmRz+jzXwSrMp6 W8D09fU1en/6VVvEuW8DJHuFFpDiFHhycjKmwn4fqYemb/0+Eo6vRs8PEqBxrNDsVZx39C5V dXb3iUi2fDVSOnFEGs0ybv5WsHg2He0Uhx3ua7tYkOh0f5Ai9JyrNvcVZX4XhF9W2Me7ZsOy FKTOnuLqRH2Edsu3/3fTZortaOWPSA8hBmlODFKQGKQ5MUhBuhWkYfEz8dWV08b0T9emziMj SGa9xSB5JUGKvx8JMGg4FCyuefbYNM3IFj9+mPJR/P1IN4tBCpJ0bvQdkhLC322Fbr6vokaI DfpnHqARdYckjRikMMmIEZIzHFLbMMZ7Xc8qCLGZhxm+DPMhPUhDXu5atdb7cbMr3Em2AIm9 NeMCad5mLm1J2O/+KxKEUgMgu0VefE8tONvtSDOqW01OJA1I82kNPrXH9SlBamMFC9E70FEj kdRI86mVf2n7MIpNb9aKzJl+ZmwkaNMcLavhGKTcQvpY4LBBQy1qRA3dNwznstGaW/bz00ge UGt8PtLSrQkGaSXiJ7axyJT9GZI1P0OSlVVC/AeyDdiDWFUziAAAAABJRU5ErkJggg==</item> <item item-id="31">iVBORw0KGgoAAAANSUhEUgAAAEkAAAAYCAYAAAC2odCOAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAGwSURBVFhH7ZWxjYUwDECZi4EihmAE Cragp0jHDrSI6m/ABrk4OMGBGMzpI6G7PCkSMQTwww6FyVySJQnIkgRkSQJeJWluS1MojTNg Nm1ZmKKAUZp2xnCENsqd34a/hbsfxkqymItzvEZSeHEiaW5VEKOVPVe2VtsOra2mDa0UzkEe ip1bUwbJXJxHLGlZFjx6jmMlEVxCXgCHrTyFIqPr92IS8RMiSXVdm8/ng7ONruvcV36aS0mp SqLYa1TIGFsV7qcVaSsuzhMy7/vejRRQRXJJdB/ZDU4AcipJkBBtzxXcrw735OJpQuaQBAWq h0prmgaPnoOXZJO6qiL4OL7VkFUafjSynotzsJJg7ltvmia2yo58v5K2zfgMK5KujdpzfSd3 moufEEkCEeM4uqqBOVTTMAzumG7c8KeR9PJdUpIgFkLRnrPDtmO0FGREGzSRlIqfECSBIP/F q6qK5lBJHpeIjX1dkk3SP8+Lcr99H3OD/qHi5FLV5t8VBn1fLs4R95gQeMgTlfRWsiQBWZKA LElAliQgSxJwWxIIuvP7/Av8qpL+G1mSgCxJQJZ0iTE/H2TauERfX/gAAAAASUVORK5C YII=</item> <item item-id="32">iVBORw0KGgoAAAANSUhEUgAAAJEAAAAqCAYAAACz1bmVAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAASOSURBVHhe7Zw9cqswFIW9gbePrIE9 uHibYLyH5yZ9CupsIL0LqqROmzZD5TJddqB3jxBYEvoDhIVBZ0YzsYRBlj7uPZKJDywra6Yy RFBdsrJqxAtNTWVvy+LKELGalWXFXJg0VckyR3btHqK6DAGEQCvcoO1Z+4aI0lihENSwqjiw wwGlJHQkuVJeJDV1xao6Dapzrr1jiIbRRU5bdUkgae11WSyW1hARS4Xa+6uZeKPsFqKmKtyT Roa60KMR6haYacCbGqBeg+jsV45E4tVQ1K5BtEgkcoHJQUZqtV1XTr/DY3CjHLRz8zp+/MEK y9jPmT2RbbT0pf0inoggoDRmRggQi8k0RUWSK/32sCgQ6ee0wIK2EQuJfUNEsq3O6koeRF/U mihMlnEWSQTtDYA24jhTngG0QSRSjpGAMmhMNNo9RHwwtX0i3S8ttU+E69jOqwOASOP0Kobo MYBIwMjrfN5nhDfKEEFSqsLA3wavZtX530JLe0yoLZXp/QiAyDDpQ4ggRCA9zZkUHn03BdH5 fObleDz25hHldDrx+re3N3GkWdxXSO+zG9oYQgS0T+Q4iMwTboKojaoiIjkhcUMuq4eIX1AZ wFvp+mE6xnl3JNL397fSx1WK0o8zwo3wRPB1pqYBRErKc58TCvVFyghjs6ldUnbF0Dl8eLRR ZxJtrnr1EBB5PQfSjphEg9/pBFBurKlgGiFSjLUbolAvOBxhHST9Kmh3XTmhvr6+2OVy4alL hujl5YXXX69XceQKROPoHUY+6fgMUkQQdXivM/3S+ft66UJyNvFlEQVQh4y3qXwhlP5Ejjsi pQBP54MAEICR+492+CL8jeNWAVMIRIk1CyJ6e2u8+omgENjAvIUZrXtKTl0ASK9DgX5/fxXD /fPzw+uTafsQkfpQ2pUlVyrT9fr62vcREQiyeSKszvRjk2kXEJGsaW1Fkv3P5+cnr7NBJKe5 5BA9P7GnZ/H3SvX+909QH50QGdOaaFmLciRaTlEiET5oURTu1Zos7puk6EUeyv+gk3+p6dIY TyTXZU/kVxxPJFZiQWlt4KE8xw+kRz2peE6A1Vd3LED6+PhQ3r/86mxC37cPkb4S86U1tJPx 7qNOmjTY7RN1wHSl2ydC+2q0bYgEEHoW0iONfHZKY/XgYm2aao8PWdlNuJstsnmi5TSh71uF CA9rc1AcW+zyAGHn2s6GPLD3NeT3h2iCtrc6M99J+rfIentbbIDcIpH/O6I42wfwQyhTv8W/ q3ZhrOeqT3/3jUIPowyRX23kWucu9yqUIfKIR6GwDuxWkSHifjXygCeECNsDGSCvCKJYD/Rx gGAdokM09XmimTL+9wR2sj0/mrA7UbSO+ez2EpFo0pONc9XfEYYS667bjnBjxZv0+BBhxR62 KIoGER6tNcHTlZzedIVPUojiQwRbEpY94kDkASgv883CxMcK0NEhGuHZFjDWWcHCKjYSRbEh yv8B+zCKl9LiQjTOr2WIUgtpY+7ky3YiAkj5V0EeUNiPiZiJ5omAHLv1kCFaifIvpWVFUdLf bKzybzZmJRNj/wFOLOhPbq3DFgAAAABJRU5ErkJggg==</item> <item item-id="33">iVBORw0KGgoAAAANSUhEUgAAAD0AAAAYCAYAAABJA/VsAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAGBSURBVFhH7ZbLzYQgEICpi4KIRViC B7rw7oGbPXg1nuzADuaf4SXsKr+47sPol5iIOJFvZwaWwQW5pa/CLf19BpCcAWMMhLKPPPMc YxzkYB97UrExPyStQJDQyooHKbyoEvgel6jpSMc+sll6miZ79w5Mlvhz+pYZJHAmUFUP8mKR SLosSxjH0Y5m6rrWZfM2rISgDG7JGL3vMp0bi3iTpmn0tQRlebt02HsP12rpcpzDzNG0lvin L5Xwmc2ORbwJLSqEshv+CFVV2bvjoYWH5Uk9u16u2L9BP+fFGlalaexKve/71Sp4Zm+m57nU whVl1d4TObGOSJrEuq7TWaUxZbttW31vNrJQKP74S+iydMcQ7cRLR5IR9H4YI+iljbEhXpqE jQyDoiiiMWWaSB8bL4J96r4Xirke1d+z8+YK5JZiE8Q1nYNe0IHZ/iD7pXUpXU3a9dQJ2S2t 5IH9/GF2SUe76AnJlo7/DCiQJyzxLOnksXEi9m9kJ+aWvgoXlAb4A/K8mKxCIY5TAAAAAElF TkSuQmCC</item> <item item-id="34">iVBORw0KGgoAAAANSUhEUgAAAOMAAAAqCAYAAABBaewwAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAZYSURBVHhe7Z09cuwqEIVnA28fdw3a g4O3iSkvwsnNHSj2Bpw7UPQcO3XqUuTQmXegxwFaahBIIMH89ldF1YwGEII+dIM0M4dBEISL QMQoCBeCiFEQLgQRoyBcCCLGgnTHw3A4BNKxszluk75rh7br7bv7Zk9fiBiL0Q1HJToMQ982 Q9PSgHRqcOzLG6Q7Hocbn2uy6TvVJ+P4pyNiLE4/tE0zbBiLq6NvRYhRlCCnCTkNEWNp+nZo mlZ7yJsG17mgREQH8fAcExaF8fOJK1RWH7Nhf66RJ4NrirRpC90xrx4RY2HcEPVWUWJS4WlU aiSciBjhUamL9DqbTV7hsmoJQALRgqkRefjniF9fMpkTs4ixKHcSosLIVi4y5N2CBAx/VtbJ w0RTEhVWTuc0njul+WvkeEcRY0kyZ8JrBWJZM7AsMXp9Ni9rw1oc27AWS8E/Jzx2kfNktFfE WBAM6F2EqM16CJcsxoCxhsvCI8ZD3734Y1dMjGh34gQtYozw9PSk08PDw7hxgPT4+KiPv76+ 2pzAGopNlewlSF47S6CuNeEC08QYNtRQWbPOtB6yQvRRT4xpkxcIijF681qnAgvbK+Lr68u5 /tKU6uva7RxRYWXKPbQUMeIeZSjHrKwTypZbzzlUWjOC1HXj4qihEm4YRSaKK+NURr63r08m xsQ10JoY8flk+67Ag2J0NnBqRB+o14omsI7dA989XmJx1HSnjAN8Xx7x8/NzeHt706EeN/Ln 52d9/Pv72+Ysw9a+PnU7IcZVIcDLUFsosxaUEdE8GmCeI1RWwfunTPgYwLax1H1Gwpl4FhAx esC4af0FA4dBT31w0J9jPYbXyFfK2HP7+lztTBKj4CBi3AAP9WDg/jEk8Pv762yY/Pz86ON7 yOnrc7ZTxJiPiHEDLy8v4/XC04DYWgy7lH7ePeT09TnbKWLMR8S4Ab7u+vj40MdiRs7DwlOL 8ZztHP7+Gf78ta+FJP7795+kPhMxMsQzJiCeMZsL8ozd0Nba/Zqxb9ubG/TaWowfu+Q1Iz8m a8bzkC9G2tZl91dKiLHckww52Cc1xraztNIr2IWkvDD09/d3p3yRXcoCfV2mnRv6ScSYTbYY +b0fKugYyIaboFT+9GLcD4wZYR0ZNCW6f4fP03E9dl5fL3v7su1MQMSYTbYYJ2OYZmXHQDJH AD89QGWvyTP6xNZieyjd12BbO8UznoJsMdIMPM3KfKBCTySo/PQ5yvTqvT2jY1g88Rap/O2x sU88mNQcPe87PhGBZNoAkePY0rfMS1JDjPl9vU6ddgaQ3dRsNu6mKqNwnpE0Apn/2BUZj53Z ISy85wJZ8owkMpVffwIh27x+ODwJWxmpakg/CjS0rjL1lNAp1mFI/KY5EsJBHN//bYjUvl6m fjs9xDNms8Ez5kDiYbM4RJIoxmnNROW5Z3BFxsVI9ejyYhHnQcSYzYnEaEQS/ErNkmfsaDeR hBcP03jIK0ZwAYgYs6ksRlckOnnh5aIYGfjR12PD144ixoumpBjHJYc75qWZbCh2Hu5cXFvj 9sfteL3OCeStKkZAmynUWEeQa2LEOpHWnfQNbp0/Jsa6AyYkgjEvMhAQgB1TLcrQHkABVN0U uWlbCqmi65xzT1969ts4vV6tk1Hk+4xJjKIyaWzXkhiZiE1+EePVwAxxF7CP0VjM+K/Y9H6S JhLVFtrVdyYJJkxOQp1FvukfBwLkYSkaysWFQzExcuHxC6VjIsbLRo1VAdX4HqX+k1rK7lL+ Z8GZbKytop1B0aXUiTrSvP6uDRzsnurm0a0JLlA9q1iB4WLUe/OHIFyMRry4R7nuGWsPlpBG unEtgXHl41lXjNNkv3aOeUhpy7KJw5Bap8rn76dE2C5GCJGHqBgg74y4MBKkf9uDFu5HNbPo YrTDavPqY1zQNs36RDg5ENJe3ZxWjAYzsS9NJGqi8R48WftVutU6k0Jjw/41o3B/YJLcKxws Y8aZ1Rh7/YkW3mxJjMbJjOA6RwHG2rhcp/yiuFCZEqEqjNgaqmP0FXEmgADqc+djHZnRdaK9 ATEu1umJewURo7ANhF8ZhhZkXIbU25wzYaRd5vD22nPzQ6HfceXlKRqI1umR4xWBiFHYDNZT e/V4s8DLZs4wIkZhF/LPxXPkn4uFsyH/6T/Rt/Kf/oJw5QzD/56c7lg54xUWAAAAAElFTkSu QmCC</item> <item item-id="35">iVBORw0KGgoAAAANSUhEUgAAAQIAAAAqCAYAAABYx1W/AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAdTSURBVHhe7Z07Uuw6EIZnA3cfZw3e A8HdhItFkJycwDEbICdwdIlJSamJCMnYgW//etgtWfJTkgfcX5WqGPmlR/evlmRmLp0gCKdH hEAQBBECQRBECARBIEQICtDWl+5yCaS6NWecj2vbdE17NZ+EFOxpUxGC7LRdTQ6P7rk2VVc1 tqNa6jTz58lo67o7sQZm5dpS2/Y2thwRgmJcu6aqug199Ku4NiIC2SExGAacZYgQlOLadFXV qMjgtKANJlQAEZM/XVJ5ZirFD8XyByC8dhoWFmBM2YqLEtpgokypaOt19xchKIQ7LTgj5Jg0 JYj5Xe/YrrcP4qkcyFwfy2cg8rDNrdZofBE2DllWCGiaaAUgUu5k8DZagAhBEWRaoAxzpgH8 iMAXTzg0PsfyowScrm0aEqbCQkAh+1A/HbHkfP6aqECEoAQr1fk3AuedM8qQEPDPXAhC+VH8 9seCGl2O63I6os/qcu9lxVqBCEEBYABZO/zmweg3Hwb7jgJDvrCRvHecWH4MxyGoLGa75ggh 4OXMLgTUQvXCAUiEYCUPDw8q3d3d6TmtSff39yr/+fnZnAkwJxzOKWl0U6yrQwr0FuocIyEg 4CxDGYeoIpY/xnMG7LWze/xuIVgmwKAXAtUJfcN6yWkt17htyluh2+Pj48OpfwpK90GOOgSh 0HzJ3nZICHoQBYSOxfINeGfBaTlHQHQqZrtOWfOvEYCl6wSj3neNcVh59dENWlEH65dlzkZO JyrVB8WEwAnN40SFAA7kLfYpYvkG3G/wu7EYlY4I6InurkGBdSO+ezLFuPdV4w5GGGonNPBZ F7/e39+7l5cXFUJzJ3p8fFT5n5+f5swdZO6DInXgUH1mHY7X2Z5s8/x6xvLhXJSPy8cj/3hk LC8EhCnj9HQmHY4YTrBSCBDOnHPhC85j59RwIDjM0E4XdRxzbPyN83Y5U6Y+KFoHDtWnuMMJ ivRCgNDqpHvhPISGA/l5SOD7+9tZgPv6+lL5q8nQB8XrwBEhOIykQoB/ZKgwFz1pZz49PfVt glEUxObXWHH3z11Nhj4oXgeOCMFhJBICukntLlydsT/5XPrt7U3lxZyIh9tphCBNHxSvA+fv n+7PX/O3UJT//v1nUduvFAJKJ1wkPDYiSNMHEhGck6RTg9EKbJJebbum2IKD3nffWmzuMHPz a56Xco1gbx8UrwNHhOAwEi8W6pcfuIHs3TmAYZfffRjXo08zrYUVdXsuHOn19dW5Pv+uwf4+ SFOHDW0oQnAY6XcNzKg6HNu+g4DC4R4/cRsSzoJw2TqMTXYPHseXE4lUdvfBdASUtg4LECE4 jAxCQFx9Q6TjK30Zq9/2+p8UEfjE5tdJyNwHlm11kIjgJ7FZCOxorVPg9UTHSJHoQc4rrmSo 1lCwqAXDNSVx780SLymd39SVeftKp6oOv0Gmj+tRUW+v4dwyFpdTCPb3wTKyihlHdg0OY/Wu QdRJVTKj0sgAeYJD2tHCnA+nxmfunOweo4jAOjidr47w0c9bKR/KS8+lIfHai4M3gir0fVJo BObWSPylGySE2cjf8597afpA3WqSnHUIQmUupM+CB2xqSdsnHgas4zKDhIMuFIJhZdxez8NQ 18G5ENj7qOvF4m4PEYLDOFgItIMG//V0KiJozaje/w81FwJ3tOOjpxjZjSNCcBgHCYHroCr5 L79MCQEDP9aA9+r9tQCLCMEPIpcQ9NPBZVOiVCjbi1QIUSk/xO00bO/xwU4zHNf3nTvf5TAh AHbhzjaAIwZzQoB1AVVRigr6NQekmBCUNQJhA7CH5J2E6NP0vRKE0NpQenq7C3mXEabhkF/G sa1Of9uyibDZs2a/ndlj+/cRpKJ3aJ36ukwJARMQfb4Iwa+AnGDLr+9MAjvqjUrbCfOXrCjb Czxs9M3IjkAxUYjhnK/rNCmgzvlhNn9D0T7g/AFF6x0bWTEh4E7PG8/miRD8XKgfE3up74wY HdNHHWGCQkB2jSx3amBsWh+YLx8c2/qPcfIaoz7s3H8e4OcHwfOXRUpZFguxS6ArYxyZF1ZV kFWOPusfbuRCoBsT7yAMeTEhKGcAwlaWG+RS0P+8348VAqpf9JuRjQ+4mWGYWOhnUJvhMuMz o1vMigs9e2bqYEkvBFRa9RKRdWAYgFcSzFusGPhbizofOw5GTOxOgjlX5XExMWlJOwvHAcNO 6ac3JQQT34ys5+hmkJt0Stdp5+u3wMmXRCGGfGsEgsCBeKd0VDLywRm1o5UaDHwhgJPyQQlJ 1RV17p11uoz+ty2HnuEKw3yEJb90JNwgqacHNCLa6aLjcPkZRQQMJyJQkautM8obFgLcj1+j FlbVtdaRWV2J4Pkj6JpIGUOIEAjlQKi6wjhnUc6CETjttGMSRCJ25A/UZTw1INEw5/cjuik3 zhtHE6wu7Fn2npPnM+TXkIWbBnPmlFogBCABWbtdK0IgFAfzWxGDPOBlvi3vbIgQCIeAV8j1 trGQiiteaNrYpiIEgnB6uu5/gJCD/Eyom90AAAAASUVORK5CYII=</item> <item item-id="36">iVBORw0KGgoAAAANSUhEUgAAAUYAAAAnCAYAAABzCOG7AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAgLSURBVHhe7Z3Pcaw4EMYngc3jxTBZ bBJTDsKXd/eBLHz3gdM6B19dnJyBM2D7k9QgCST0B5iB6V+Vat8AI1pN61NLwrOXXhAEQXAQ YRSEE9G1Td+0nfn03NT4QoRREE5Ce7v1t9Z8EBRdSz5p8sVRhFEQTkDXiCgGIXG8ZoqjCKMg HJ2u6a9RVez65nrpL5fLvHji+3Tucrn2jn7MHh/rmly/JiGbCmlvefWIMArCoSGhoil0WBbb /gaBCQonzhvRUGLEdc0fR2bKAtPeqN5rQxasTcimClBPhq0ijIJwZNDhg6mQzu6i00iaZo6i qa9XH0PHbdYSLZ+UexeQkzWKMArCgemaSGc3wnVDZhfIGvF9+ziyQAhp6LhDZhaWStK9S8hY axRhFITDgmwqnLFpgaHzuECJ5DTzwjW2WNjCOHfcoWBTI4WkexdBU/REIRdhFITDQh19Jgtk UgSmXBjTRSaX7YQxPpDYRISRnNM21HgaddTuEAoZTBXzS5N4byrlJoILHvToU7+IT2Og08z7 jYojEljAn16zTgd7ECgLjL2jp3y1NCUtXGPctO9vtMYIUtcZ54URDidjVDBZQqjoMFJwoEkn rgEPaey0426fsIwrkGHf6UHoSgLSUhc7GUtTWTV9ZiHAQDEnCtZxZ80wdFz7ftStuDiXEb53 LfaueoypMCpnjgE3L3ydCTgRxhr8zi2+zABZxYLv4N8tNgceBvLBYiZl+ckWM2e9cejznnDO HJ/OdrzvrEXIpkocUY/gCSOUemx0vAJcK525BhHGCqLCiOmXu051SlKEUXAoEsbcjiprjHU8 ozD+/Pz0X19f5lMFIWGkTAPr4mfXRIUIYzYFwqgXOYdgE49vzrMJ49vbW//x8dF/fn72r6+v 5mghM8KIHwzAmtrThK4IYzYFwuhOo0UYt+eZhBFC+P39rf79+/ur2lyFI4wU7N5G1lNE798/ /Z+/5t9CEv/9+0+Sz0QY78hxhZFmF7f0TQ1kiSgMMka0uYqoMFI586YLIxljNvUZ42aB1fbN bgtAuk2lwYP1sJeXF5XtYBoIv3DWw/+tYR1h3NOfTJ4won0APvP9WIwjjNp3kx3TVVTjHv5N RIQxmwJh9ANrmwwG99h/t9BbP7VLwEsQRZxHdsMg68ExnHt/fzdHE+HXD7z3wUZbyvx9N38m CiOmzRhcAPwGH+KznUEWMSOMc8+51jf7+Tc/RuEDEcY8ioRx6LymrO10FoIjvEYBO7lDM9hN xXGIIjo88LOU6yAYbrZqX8fHHGEsyND39Wek40Zsx8BiDy4AWaPt27APQSDrnxVG4M18Kt6D e/h4FWHMpkwYidTOqnYAM54Krud69w+0/NEY5/wOzcLoZjuomzsfTbvoPnPtG/06dmLH15kR fnd/JmaMWIbgQYSBKLoZd5oPHYLCSOCvs4Zz5nymi/b3b36MijDmUyyMwBVHL6g6/NXL3F8U UDDyg8U5BKexwKnPLraFdH2Deq3zbuZAOBmt7kj6FQ1cu26E4B7++3a8PuaCdo92hqdeprMO 19odYS6reWR/pgsj6rb9yDvSrlim+nDE9cHMn3k5wqnb5/5Z4CP7NxHZlc6mYFfaAwLY0EP0 RjH7RyRGuJObkRtBg8/2w7cCdRL0HEB0vTqDIDXX+lnrGLQUaGRHNwSflzUodD0lMYjNAdwH YsiFjyELGrJJBLppj84y5kSOgaC4nQ6daZrNPKo/mTRh5LVYFPah/drOQIYPg6KlirHZ8s20 oO5H928iZGNJbD8zeB4pPgsLYxYcGFZAIwASA21cY+Lvc+CiuAFkBxrXo75/pwix18cwaExF roRz+BNLDim7z9v4MMZJ4lWEMZs7CaMOgNlf24gEGqWmehRFp1AH7ECzgpcYA239zaF80G7Y DHtdO+s4hz+RHS6zlQ9jnCReRRiz2VkY3QBQxZtSRAPNgn8DUk83UB5YGNEmNoL+HWtXLmfw Z5IwbujDGKeIV7JxtTqHKf4+gxOy5rDt9sC1PPgw8To1eB4pPltNGAEvLHMgOMG2FGhYp1EN J0cMa0AooUDb5wGG8TMck/msaNP5/bm9D2Mc3r+wf5VK4XdjnxJIapM+sQ1GhEMCNfebiYv/ d8KFOpny32OsZQgYXQZDY4FmBai+/sGFcRhdUcYg4rWnlBEpmbP6c08fxjiyf8mHq/xILNo6 NFy3ZUv/t03TN8HsDiId8DujYscV73idI3W/4J0NgstW8LFxg6HBQLODihs7fv9hOvKuiD+3 5Sz+pfsuKUECykarHgxOQVGqhfyKWy1Oe83AOWsHznnZfVKd6tm5ghpi1c0X7OopYzEKIxhs 4+0MAdbTZ/3ajx1oumF4p2w8Fgq0DR/e3RF/bstZ/Jve0WPARtu27YSR7G20tcsiBvBcZtpH Qjjal1Mn1ecMiGHWE0YEGQKMAwQPzLMA83sONv/VCH0cO4QmWHnnz1yrjtnBasqyc4+I+HNb zuNfiFqthu0mjOQjrjZNGHGdL4x4Zna2mFGnI6hx1l9jFARhPyC+tSJGgrHHGiOEyx4kUOK2 TzNiXyhz6kxdXwQijIJwaNaYTlMWxksAENrE6WYNSRmjl+Ehsx31e7rxFK9TzxJSEWEUhKMD AalN8YZpf/3UPAVHxMy91Wdkr5z9WW2aZoZTO2PCmJMtAhFGQTgBWA+t1cbTQmKb+1qTCKMg nASsv4k4uuAl/pJ3PUUYBeFE4E8U9WtFQoeXvgt9IcIoCILg0Pf/AwUotftTdqKCAAAAAElF TkSuQmCC</item> <item item-id="37">iVBORw0KGgoAAAANSUhEUgAAAEkAAAAYCAYAAAC2odCOAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAGwSURBVFhH7ZWxjYUwDECZi4EihmAE Cragp0jHDrSI6m/ABrk4OMGBGMzpI6G7PCkSMQTwww6FyVySJQnIkgRkSQJeJWluS1MojTNg Nm1ZmKKAUZp2xnCENsqd34a/hbsfxkqymItzvEZSeHEiaW5VEKOVPVe2VtsOra2mDa0UzkEe ip1bUwbJXJxHLGlZFjx6jmMlEVxCXgCHrTyFIqPr92IS8RMiSXVdm8/ng7ONruvcV36aS0mp SqLYa1TIGFsV7qcVaSsuzhMy7/vejRRQRXJJdB/ZDU4AcipJkBBtzxXcrw735OJpQuaQBAWq h0prmgaPnoOXZJO6qiL4OL7VkFUafjSynotzsJJg7ltvmia2yo58v5K2zfgMK5KujdpzfSd3 moufEEkCEeM4uqqBOVTTMAzumG7c8KeR9PJdUpIgFkLRnrPDtmO0FGREGzSRlIqfECSBIP/F q6qK5lBJHpeIjX1dkk3SP8+Lcr99H3OD/qHi5FLV5t8VBn1fLs4R95gQeMgTlfRWsiQBWZKA LElAliQgSxJwWxIIuvP7/Av8qpL+G1mSgCxJQJZ0iTE/H2TauERfX/gAAAAASUVORK5C YII=</item> <item item-id="38">iVBORw0KGgoAAAANSUhEUgAAAIwAAABVCAYAAACb8QzZAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAU7SURBVHhe7Z2NkaQgEEYnLgMyHqMx GYNx+YDWhgGFVce/71VRdafo7Wy/ahp0uM9ISAUUhlRBYUgVRcJ8Ph+2gvYGioUhBFAYUgWF IVVQGFLFv4QZusYeS7a2971AP7aJPk03+PPkbmzKMKE47ZjzoG9xvhnbrh+pyr3ZJIwxIRBG 5xYBUjVNR1EewoHCDGPXGFk4/DyKY4QZurE1stCV57G7MIM51qBeSY1P5PbsKIyRpA2LYDrz PA4UxjQWu49jR2FcRnFTaNV2GZv6sWNBdAlWhUHQsySEcbMjJYxpW2dKkJCzrWtwgDAgXuH9 /4xJFgcpzDU4SBjDEEtjzlfGHDMuuZ7CXINNwqw+GgiEQosfDxipZPhCgQzJfM2TfV6layLT vzOFdqPON21UaA+dOu8ynZv6o2+gOCngX8Jkg2mbzzRfsuiGwEmt4/sj+Pi7DuJShhERTH97 Rme0aHY2/7zm3zVpbpgkirIiWWVThtmGBFjVNwhkoTDzbEyu18V2KIIWRu5jr2eGqeYCwrhA tnEGAUsZpvdZohE5tDBKQoPOiHRkGycKEwbStnihb0kYxWDkwbOruFYRKMx+nCoMkAJUAhpI syYM6hapg6aaCC0nTHic1HO6MJYp8K5NWWBxSJpFc/0pzC84URhIooeguaZZFyZV4IY1EYU5 hnOFMffGrMjGUKbFWiK9hgKLzN8xLQ6FcYJhDaek6F2qhcg65woDWfRwhBlPFM+hm4eeeMrt jqvFQJk5+b72mJbON30bofZ51Vufb50ozHWQDFQqQG3/J0FhPJCgRoDa/k+BwngoTBkUxkNh yqAwHgpTBoXxUJgyKIyHwpRBYQwIPj4nWiiBX1yMxMj3fz4UpoC+T6z0HYyVMlhhzD8rm/Gr 5arJLXKS18pPYVYYjCy17yJvZQqiEgYr3hJP+/JX/CoIwM/q/wj6Vj9n85LZlW8RLnc8D4W5 KN8ZRmGDKzLkMBlJ3m8O+seSJI4vQGEuyqowqQyjMX3mtxj9cIb74Rnc6vE8FOZQwqfqQcvJ 4FkUpiC4eghz+Prm656542mqhfn64Gy27U1eGBPgtewCUaOv2ziBvMDq+tzxHIuf9IhfxLvY P8PMhewSRip9bTCEuZ/Jns4dX4DCXJSUMDg2HQpqlAgzZAWXQozozcRJmNTxBSjMFTEBjzPR /D0saXqmEwY6lYWsgP7aw9ZhcBNCNBSGVEFhSBUUhlSxqzDfhZluJdNBcnUOyTB9tJtmQfFN bsIhwuipGjPLs6AwpAoKQ6qgMKQKCkOqoDCkigsLw+3ir8h2YfzT0vClnO3CvHU7jauzWRi9 uiuP2ANhCt7iipHrKcz12CzMLMecSQJh1t7IieB28ddmszCoNexLxNGrfi7oqa8t4J1Ufx7X YAcqL1U4lKmmpcPWZNwu/jR2EAYYSaL/XAtB/P4CmMjks5HsS6eDuJRhRATT356BbL5vPPTN 8hlhuF38buwkTCkSYJV5EMhCYeZ6Sa7X2SwUQQsj97HXM8Ns4iRhXCC5Xfz9+LEwYSBti2dR hUUvt4s/h58LA7hd/H05RRjLFHjXpiywOCTNorn+FObX/FgYSKKHoLmmWRcmVeCGNRGFOZ6s Efhl748LMLeLvy+/Fway6OGI28Xfih8Lcx0kA5UKUNv/qbxWGAAJagSo7f9EKAyFqYLCUJgq JisgiJaEwnxDYaIMQ2GWoTCRMEBEoTDfUJgXC4Pg4zOihRL4xcVIjHz/d5G0Qn4xb+aM7eLv AIVJcMZ28Xcha8XbMwxJQ2FIBeP4BxVmT7fykcDSAAAAAElFTkSuQmCC</item> <item item-id="39">iVBORw0KGgoAAAANSUhEUgAAAD0AAAAYCAYAAABJA/VsAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAGBSURBVFhH7ZbLzYQgEICpi4KIRViC B7rw7oGbPXg1nuzADuaf4SXsKr+47sPol5iIOJFvZwaWwQW5pa/CLf19BpCcAWMMhLKPPPMc YxzkYB97UrExPyStQJDQyooHKbyoEvgel6jpSMc+sll6miZ79w5Mlvhz+pYZJHAmUFUP8mKR SLosSxjH0Y5m6rrWZfM2rISgDG7JGL3vMp0bi3iTpmn0tQRlebt02HsP12rpcpzDzNG0lvin L5Xwmc2ORbwJLSqEshv+CFVV2bvjoYWH5Uk9u16u2L9BP+fFGlalaexKve/71Sp4Zm+m57nU whVl1d4TObGOSJrEuq7TWaUxZbttW31vNrJQKP74S+iydMcQ7cRLR5IR9H4YI+iljbEhXpqE jQyDoiiiMWWaSB8bL4J96r4Xirke1d+z8+YK5JZiE8Q1nYNe0IHZ/iD7pXUpXU3a9dQJ2S2t 5IH9/GF2SUe76AnJlo7/DCiQJyzxLOnksXEi9m9kJ+aWvgoXlAb4A/K8mKxCIY5TAAAAAElF TkSuQmCC</item> <item item-id="40">iVBORw0KGgoAAAANSUhEUgAAAIAAAABVCAYAAACBzexXAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAU+SURBVHhe7Z0BkqQgDEX7XB7I83ga L+NhnARIE2hFabXFyX9VVO20OLuzeYSALvuagWkggHEggHF2CfB6vdB2tCeyWwDwP4EAxoEA xoEAxvlKgGno3GeLrR9DL2ac+4U+3TCF6+BuDmWAVIR+Xovr2PP1bu6HcUbo2+KQABTZRAA9 9gWWpOsGBL5RLhRgmoeOgo903zTXCDANc0/BR+zb53QBJvqs4/l+aT4AzXGiABT0Pi0K4UD7 XCgANRR/zXOiAH7E+yWfaqfMBeM8oKC4hE0BOIirLAjgq38lALWjKwGWCquJa7hAACbfAfx+ RSCbTRDgGi4SgJhyCeh6ZQx5RSH3Q4BrOCTA5lZwIgi3fDuYJJHpggtGlibUDKvPG3RNQf0H Kjw7db3rs8JzGtR1n4n8UpX7Jsqa5CsBVoPjWsgEH8HXjQMhtULoz8Hkr3VQShlAAkv93RWd cbLVR/zz0u9LaWh6S5FlLYMcygDHkICp+oADs1OAuNqQ+3XxmQZWCyDfx92PDNCCAD4wfT7C mVIGGMMo7iTYWgAlFaEzFmKecqMAaWBcyzeOSgIoJpKBnz3kc70AAda5VQBGCjIJUCLBlgA8 70sd8a4puK0JkH4OGhDA8Q6kb+9RWpwCoji+PwT4hhsF4KDrlB9rgm0Blgq+tKaAAPu4VwD6 3lz1u5jIMk5LodfwbAV9zcu4VAAvDO8h7CkCS7WERe4VgIOv0z9X9Fl8piGm+nyJ6D9Xm0uy Mgh93WdaotD0t0llSpePy9T2b5sbBWgDFkySgtsbyFciGbX9W8e8AAkuW1SM6tr+DQIBErgO qQlobf/2gAAaGtGLO5Jr1PZvEAigGIe6+by2f4tAgAAvFdPVQZna/q0CAQgOZtwf2H7/sLZ/ y5gXID5WlqY3kfzmlN48Kvd/HsgAOxjHO3J93HD6nGr0ZtSSgKV7UyDABhMFv/ZdxuP4zLP2 wkp5M6p8bw4EaA4/enc/s0g2oyrvJSBAa4SA9lJrbI1k7i8ZoPZeAgJcip6rs7aa3ju6RiOa L7uAbszj/FAsjPjqe4lqAT5+EDTXziJdYvo5fj2l03yv5v+6ez3FP/mZP5hNvs0A8VopiCOP 9vBrpuZeAQK0hkvdsrTjin55n4GD/Y413eOeSey8VwMBWkS9CqeDLHO6W/qF676pQC/dWwAC GAcCGAcCGAcCGOdUAT6LE92e/erUf+WSDDBmp4VtLUXAfVwigNuQUAJg5LcLBDAOBDAOBDAO BDAOBDBOwwI8+3Xrp3BcgPCUSr+YeIYAe55lg+McFkDv/snjx0SAL/75tNwPAa7nsAAx2HGk JwLseSitwPGwv+WwADxXp0e76Neglt5I4ffYwnW+h08ICZKkU4dqWiI+CgbHw57GCQIwFPTs P4vgoHz+gwqRI2QLOddHB6WUASSw1N9dYXlC33yqiTKRADgedpWTBNiLBExlBg7MTgFivSH3 62yTBlYLIN/H3Y8MkHCTAD4wOB72fn4sQBoY1/JVws4iEMfDnsPPBWBwPGw73CKA4x1I396j tDgFRHF8fwhwlB8LwEHXKT/WBNsCLBV8aU0BAepZjTD/5Z2PDxiOh22H3wvAwdfpH8fD3sqP BWgD68fDakwKkOCyRcWoru3fOBCAp6WqgNb2bxsIQCPa2vGwmneUfYETg25FAIvHw2qSKFsT gJeK6eqgTG3/J/ARZQn8fxeAgxn3B7bfP6zt/xRMCqBfY/NNbyL5zSm9eVTu/2wWoyw/qGXu OR7290CABe45HvYeVqNsPQNYAQKYZp7/AAWHirsYEdR8AAAAAElFTkSuQmCC</item> <item item-id="41">iVBORw0KGgoAAAANSUhEUgAAALMAAAA/CAYAAACrZvlQAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAARkSURBVHhe7Z1NcrQgEIbnXB7IymGy 8BbZZ+Eud8g25So3yA0MzY8CI4oOE5rO+1RRX5iRGfl4bBo0ldsMgBAgMxADZAZigMxADJC5 ENPQzbd+tDVQA8hcAC3y7QaZKwOZC4HIXB/IXAjIXB/IXAjIXB/IXAjIXB/IXAjIXB/IXAjI XB/IXIKxN1tz2J6rCmQGZZiGudMXdDcPk30tQs9e9qIPrvndttM8dBttNoDMoADj3DsRtZi9 eiWCXu8Gpab9eTlmry29lz/bQWbwOJRmLcKZSBr7R1G588Lu2N9MPdnW/Oy3OeJOZgrnHx8f tmbqb29vtnYWc2VlXligUeLF7yKqR+qYZFsbpXtV16lJhkSbMn9/f9uaqX9+ftraVda8567A dGacH6tk1PXRi+Q1hfBl3mprJFfHUwMt9smcmaSlk3Z8fX0F9RCbz+wk/I8S/EeiFC+lyJJZ Qa+v32+82ZM55zN9gh69vr4GnaT0guo/Pz+63EFXm0vqd0FkbocLY5WRMwf4xyfamsi8fshp memESWiHq7+/vweph4O+YPekwT8h2pHYC3BRupFsq1MLN+t7x+xwJ7MT2BWqby8A6Qv8k9rC pCIQ/h9g89og7fRzXS2xen9L9K22hGvjPuOAReY4Xz4kmB4AqM9iL0Xfl5cXWzuGQ4rhrlqU 80UiS69IZMqN88hJMZ6P1EEB17hkQ7zSrAVkBj4nbTALOjdVHW2VPJsaMlN65fofFKwfqtN0 aCOJ/hZ1MStp6RION/XHeYDL1YHMl6DN/eN9T/C3QOYr+Jv7gA3NylwvKscpBuACZD4NUgyu QOazIMVgC2Q+CVIMvkDmbMI9dmwr80OEzPqOpCeaX5x0W8f8VYRN3mjR5f6xAO794YqYyDyN vX2M0JWNZ0dUvqujq8p5xwrjPva+gP3uIrKF/nBDjMyaWIA4F6D3K+YHYTTNeFCLeX+4IUtm RTz9LmPNYBfitMwKzv3hhjiZ1ShHv8OmpJnU4q3Lk+eZXJGZc3+4IVBmBUWtZfCp8LjJcU1m BdP+cEOmzIrk9FyRyzIrOPaHG2JlVsN/Pz3bd2rxiMwc+8ONJmWmwTyEVvpdt78bsMk0jwPt IpSX5SGZGfaHGzJl9lb6udPzpBdVj0TOCJfnejsOl2Xm0J8GEChzvNLPmJ61LL1qd3DcLup7 VTsnl3/Xz70WiBhsq4VtQ2r1pz2EyUwDvbHSd1HSlVQ4s1KVGPxV3PVzApmT5+DDpz8tIEbm abQDnLiREE/PdOfs/riSg28/azkfP6Ieb63x6w9/BMgcT7um+A/dpB/0iQe49OCrcwuexyDp jp6j4Nwf3oiJzGWQNviQmT2QORfIzB7InAtkZs/zZPbzVQmDL60/+0DmAMjcMpA5ADK3DHuZ zUCEp/k0maObEX3rv4skrT8HNBGZfXn9n4sxrn9uYKtk3azjhLT+ZNKEzAQNgv8vADGQGYih KTNIZMgMUkBmIIbmzIDMIAVkBkKY518BmWUT+30JNAAAAABJRU5ErkJggg==</item> <item item-id="42">iVBORw0KGgoAAAANSUhEUgAAALMAAAA/CAYAAACrZvlQAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAASPSURBVHhe7Z1NUoQwEEbnXByI8jAu uIX7WbDzDm4tVt7AG2A3CZCE/0xm0mm/V5XSDDjQ5tEJIaO3HgAlQGagBsgM1ACZgRogcyK6 pupvdWtrIAeQOQGDyLcbZM4MZE4EMnN+IHMiIHN+IHMiIHN+IHMiIHN+IHMiIHN+IHMiIHN+ IHMK2tpMzWF6LiuQGaSha/pquKCrvunsawvavh4v+ltNtZm9nq2tb/2ZHAGZQQJYUivxILUv 6kjX1KuiDyJv9Wr2IoHM4DXwMGuyreubak2+OStXK0ZvZea2afomNjPzwT4/P23N1D8+Pmzt KiaAMycCyiUUkYcFa8IO2Ewbbl+VmS4Sfil6mMHy/vz82Jqpf3192Vos5mrl91oUmC6M623F Irpy7so8wEnuaMxM59GYepTMLC2f9Mj397dXn6Crq7YBV3VDh30O0y8R5SklFddl5n0OZG5p eGHfIkrm9/d3L0geXnD99/d3KAa6quidzXHMVXx04uN+7i9yKmfOEryQiLY6NWZ24X32ZWaB w+MfeebJzD/AQo+M9fv97g09XMKrEvxHgtmM6qC3JvlDZ5bDjJmozDxeASzwWLi+dwPI3cW2 y7gB/DfYGztvntmdVtt7sLS3jbgsczhePgWPnZGVgRAmezn7vr292doJBIg8Xc0ol4tGpqhY ZB4bn4JEbtr8GVlro4A4LtvQtW0/eTxIbb/PAGQGLpdsGO44na4qXCzyanLIvDZlNBTc5Wan 6NTGEr2WeY7dn5Jss/ZQwACZo+BJ/72ljiAHkDkGnj89ejAAXk6xMufLyuEQA0gBMl8GQwyp QOarYIghFsh8EQwx5AKZT2MWTfFxuWBaWR4qZF4+zFlKt7bPqzLs5oOWoSwfPEmPRypqMnPH a2S9xl15OsmLo3gbjXlzLC1pa1fAvaWzZcQjDTUyD4QChGMB3p5xfOBn0xNLAYTHIw1dMhNh 9zu1tYBZiMsyE5LjkYY6mamVg8+wkTQd3bwFnznLQYzMkuORhkKZCc5aU+NzkfGQI05mQmg8 0tApM7HZPWckWmZCYjzSUCszNf+ye7ZbcvGIzBLjkUaRMnNjHsJ3+lW1PxvgMoxDHdloTJr6 o2EPySwwHmnolNm50z/VPS/GpAf7n2F8T2fGIVpmCfEUgEKZwzv9o+6Zt9MN1fzBxsjunI5L +4+yuE/9xtc8Eb1pNf9nfXLFUx7KZLYNOVtiCDOVaw11x+2idY1cZv+4mYNZ3FkeT+ZTKVJO PCWgRuauXXbrLmH3zE/OttvUzWaxmcwKNJ2P+57HQsmLRz4KZHYbai7uopvthT5bDTtnsscW 79C5eesxWLqjdRSS45GNmsyclKkbV5LFtMWzAWRewWQ+PWNLbfFsAZlDhixW6ZnC0hbPDpDZ g8aWlaaG1xbPPpDZYfVvTfOTtCf+q4tnoi2eIyCzJZzqckuJMwDa4jmDeJnHBnAJ64/CH1Ea j7NWSuumtcVzliIyMzfAiPt9Eg4avrjpLG3xXKAImRluCPcrACGQGaihKDNMNwmZwTqQGaih ODMgM9gCMgMl9P0fdoZqBpz7ZH0AAAAASUVORK5CYII=</item> <item item-id="43">iVBORw0KGgoAAAANSUhEUgAAAGAAAAAnCAYAAADwxU3sAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAALJSURBVGhD7ZvtkYMgEIZt4PpIDXRx TdhHOqAafl0nFMPtIjFIQD5cgojvDDNJJAj7sMsqOqlbTXUJAFJwxYU0376vI+fvHoCYZzUL 86WhpIB+8HwIXQOQ/BzGXwUQWCaEfgFIrhiR9SVnaiJqS8xM5TDoFIBUHEIPhcm08aeJDICe GIxDD9PUJwAcZEG8DYnSA1A5XtAlADQYof3JAeSsBR0CgPDDaMLPS+QAoHdzYhjqEAAMjtRY NQCkT5IVgO4ELkae8uqbrw5lLE4SxP+SfHtP9ADS14GNB+DFBNsY2EMRDYDHwMWaXHwW5Nox 1QCA1yjZALRcCG7H8DhxZ7ME5yc9PbQXHOsBIdSU5rxrgBtq1oYyc9wqogZQSYcAwN9hEXkD 0KFI4spOm30UaQwAIJztlhdME23uXaxhAICCoailng/1eJrPJ9bf709SP3cBeEOROdJMI3kA DpYxtp8VeSWVgDSM1QA21BpgMp7UUCT1Qm3XjQFwPcwqoZOMAcDNeBJCkQYGxqkdsq4PAI3t yXjcrCjYOsBLBnB7wAYAbi5rIwcuttxQhFfEn/VyABTomlmQfyba91zE/Hl8Ka6RKwMYZhEu 1g0AdQNorBtASGsysXNrxVvHHtdS9vrREIC9ntQBUL4fgEY0RtVG9vUvUEeITV18IGxvbOX7 AYdVGQAYpXhHDOC909ulnx+zNKUO/j7v35Yv2hGjUWUA0GLpnrBOo63/YmbnelNKnfgkQBuk jZ0ewBo/lzKT71umD84VGtdNrX0A4nVi4QUmydefikDXtQzvlsJJ6xUayW8A2/ucAh2gARAP P2gLt92QKoSgLwi9LHGAG5GsAfEQePkn4xbDlIQhJ8PxholIHQC0b/+8NapTACB084yBrlrX KGuWmt/W5nx1jGLp5yBPRy864/sBuSly1wBQ9xsyJ1Dzd8T4wO+I9S2l/gHSVkQoQnAnmAAA AABJRU5ErkJggg==</item> <item item-id="44">iVBORw0KGgoAAAANSUhEUgAAAF8AAAAnCAYAAABpPZRZAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAMlSURBVGhD7ZvtlawgDIZtYPvYGuzi NmEf24HV+Ot2YjFsAshECF9jHBb1PYdzdgcG5CEkQZ1BPWqmS8Bfl1nNy2r/+6yOjN09/GWa 1LTYfxppXeAa5voF6Br+OrcH7wQLMFYuQL/w11mNMfJYNwxqGEbF81jVPGI932adRzV4fevP dPshCnmZYuPx6hQ+wAN3w6Nf1LQB1YsQtsMds0FaJgA6ztCjkYO8g+/3GYGMdaSvnPqEj5OM mRhs/xc4Y+FJ18QsUGD5uzZkIRjVWH+X8BFObII+OLTspC9mrDWAbxdRf5bz7RW+v0P4CCLm cgw4OvksfAZWCB+FFu+7I07QrtD1dAgfJpcAUAefB8XBN3HC7oAk3LRxUDn4ekAbzf2yXQfX JmlVZwjcRDKnrvD5eEbgqgL4O9eU7hNV6vd3lo+HBZOibYW5OJw81sHFNDlUZn0qCYiMP9+E gF9rtF9QFv4u4Kbh02wqpdDt+Avgj4L1qZHPFoyfHV7DwusnFmg/w+/q9JLOkbaD/rm5012f 2+27hU2I9fl0ICyuo4QlfUwl8BvrEHz4ugksbgFgy60YnMoCyam6PnyQ27pbKQsip+sW8EFR 99NSP9/q+8f+/Uf1/99X0TUm4bPux9Y0010sHyc6jnBoIdYfZD9UOi6Q3QIxQvwhx218vs1s itxPECMy7bX8nUVK7EvXh+9nNjn3g/UQkJ2Vn+iurg3fgvS9hW/ZtHdwN0swmDkJmvaxTOmx fCd8GKwBJ47kFBCedFmmWhSsoOVfL9vhLdC/O+jXmxID+7J80Ztvtwi4R+XclKDVox74eZmd csKp+IGfkbb6sguoljB8HceEL7QhfHPQOgU8CuBLxRANHl2jOPx37+cfFD4dCgbGk+8kdCsa dtU7b4fFdIblv/Uk66icJTFFLuPBhZSDJQ8fM8eyJEMMPj6C5KBvRW5+5ZMrkTx8dLtlu1wG fga8dLqJwKQ2kjj8iph0QsD9gDCbEqIvDf/yb6wBMjHXIwu/Lh51Ch+E2/soNOouBRbgJm8p G/219/NrU+Cu4aOeX6Y0VtPfZM03/k1Wv1LqF+GWaa3P3yENAAAAAElFTkSuQmCC</item> <item item-id="45">iVBORw0KGgoAAAANSUhEUgAAAKgAAAAnCAYAAACbrQO5AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAASsSURBVHhe7ZzNcaQwEIVJYPNwDGSx SUw5Dd99IAvffeC0zsFXFydn4AzYfvphJCGJFgxYAr0qyohpgab1qVuCMc1YVZWxKqAn0tB3 Y9cPqpSPtrSrAnoS9bfbeOtVIUMNPbWvS4e0AnoCDV3ecE4iSNtESCugpWvoxpZDJ+yaZmya dgwxMnQtfQ6bxgY+WncYu9ZTJ6D+Fr6+TxXQokVwUGpf5qIfbxouAZunDo63HZ1R7U82sbr4 jOBMCd/mdRiqgJYsdDYnHFFqvUMkI57LFKKnea7+1shysK7cT03ZUEoUrYAWLEDF6WiRug0i J/gMhWyCdVU0vVFZTAsMm0UlzEUroMUKEYyT3iV8JhA+QAFNY6RvE1BfXQku2aOCgJU3B5Wi qQEzzVdAixV1MpMIFqAkHNeLJL0gigHKOadf/ME1ASpGxNQ4e9N+8NnwG1WGivEDRS32fUXG HNSSaR+oK3xgnCQNUNjzpidWBMXNVHk7QW8eyuEYfEYhOsOHFg9REX5AG9lAOCvxWHoFkLOV uqcu9vVx04Yp3Lvl2M9TvNs57lDD5+6xMyp3P9D1ky4vgMJ3MUBSx8R56Hzie/rg9dWFdB19 jgQhAnPqeOegbgqbTmSOoAsoaz+kApqZNgFK1cVc4945FPIHrLx4E9vzKGM/XBtQ0hTW9ZY2 xziNcvXD5QElBVPcxZSlH16exqcXtV+g/v39w2p/FFDqmnmKU59cSxn6oUZQEjmhbVs7xbG8 Mox9h1UwpyNxi2JbVEK7Pj4+VEmW397eVOkBOsQPiapz0PtKlZviBrGAMG1TOsaNUsa28E1g 8/39rUqy/Pn5qUobdYgfVnz3awPqrlQZKU50JDnt4FQIEHEtra+vL6tsS0Zre6ETi+AZ++G6 gKITPCtVdzUbPLuGILVj1kXQ19dXYaOF1I7yz8+P2GaijvXejJ7pSD+s+O5XBBT/3CScH+hA N8XhScrcbi2g64TrAFItXX5/f7fSvhaeGS85pgg/XGsV7x/B5rNe+5cu5uY6P7VjpP3aaKDb ASj1hrJ/kYRrxdr0m35I1OUXSau1c8cYcuefi0J6P6xXK6AxXQJQRMnn52dVWhYnvT9OFdCY LgEo4MRckye0a9/22Moc0GmxF3l0G7W5T4fcdrDm+b8HqDmPOxKIuMTCZlOPpmpnPxCg638k jcGjoBMQ+toXs1GDz+dPBfUyoGt/D7pZuQFqRrK0X31v085+IBDWvKlDyJqLy3bOgArayP2Q H/uuGztGBF31i/rHKDdAf0t7+4EG3hIFAbnZBCnZBS5oo6Kp97851bRjOcXDNzyfPB5QFeJl x6ChK0d56drdD/xOdgX4TCBDgPpsJLh0XVzYSufUnk62ZhlQGlyshyWPBBQpwegQd4s3+EQ6 0A+AxeFKyYzezkYN2Aqoty7eYKcOLwJKPnKvF9IOKb7qMCGCMTvaUnB+aShgIyPo3VgDir/u YAi1rb5Z5DJam+adFbo33QZsRFrXgBk2huIRNG3uXAEtXUiXCR0+Sc0frXuc6th0Op8NZExj fJeOAVrfbndBlfR+0NRbYxXQk6i+Ybkqe2X7jnrcvF/ZrgpoVcYax//HGdoK7u2owQAAAABJ RU5ErkJggg==</item> <item item-id="46">iVBORw0KGgoAAAANSUhEUgAAAKEAAAAnCAYAAABnuChzAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAASgSURBVHhe7ZxLkqQgEIbrAnOPPoO3 mEsYfY3e18Jb9L4XrqbvUNsOV32DuoGTP4KFCAk+8IH8EUYMagMmH5kJOnVrs7J2VoYwITV1 1VZ1I0vbaknbGcJEVJdlW9aysJOamvpQTQcxQ5iAmmp/AHsRiMVEEDOEZ1dTtYWLQFy73drb rWg5LpqqaG+WOsbnm7YqUB9fZ13y7ZnKEJ5aBAWFYTuCdVsqUASM9vsEaIDKgNB2Hh5XwVWX dK2oqAcWoT3XNYsyhGcWBtvtjjSAOg9mcXZC4Z5QEwM2NMUbZghPLEDiGmgTIHguF7CzIeS8 3YTcMEN4WsG7uT0RANIhWB1CL2SUDgSG5AzhaUWD7AKEFBfCEMD4SaKrh1A0KFY940P1w3YP PxvOp9PYgcIhuycXMSfEnqSjqoFC88KBJ8RmY7ekV4elMTw8rtFM2GlzPrpOYYeQcKivjhnP NQVCnOtPeSaCvprmNA7H5gCYncN1S4eT09HtQO17mwd8ov+aR5Ln+r+leqzPaDkvtmXUOXHw nm4ALCNrTihmgNZYX5FnRqWmQ9shBMKdtQhC+nNtZxwHhaMGyWhYLpCODmyH9CEk9a5cHWFJ ZnI6qh0uASHJGY4upkPa4eOtffuQ/z6o/v39E9RHFkIy/zgcySvX0gHtcBVPiActioJfJeoS +ZLmNSh38n/oiK2EZd4FbX1/f8tSV/78/JSlFbSJHSbqMjmhXAEGhaNR7uS5fyTT22iHpwLc 8/v7K0td+fF4yNJCbWKHGc+ePoTmCtAXjnCdEvZ+tm8XvgAb2lD6+fkZlHsRHKXsU1Hq2yuc Jz6wHdKGUBryNUqdzBmu107hpx411g1ud3/IitIcMO1gnuR+v4t7lBCGUX4+n+LoRH2hOrou dO34X7NtaYcZz54qhPjPKsLAjo1YMxzhjYF7KHXDxvOEqB8gKqny19fXIETrwnNwEJ7CDumt ju0zUR+o8SsbdbgM+/IA/necoXnjWKofAE8dKHMLE7yAn+KRtrHDRF1iYbJUfdiK5wXNfDBI yA3XhMGnWHbIEPrVeYyQXHC+4O3e399lKUBbA0iKZocMoUdi9od1YIkAIHK/IFGfVt+r8ymm HUIg7L0wNwn0hdPQW4vc19EIJpev/R0hpIeiVWUUw89UQ0vW164JYJT/jqrIdiAI/bm2hE/A aE8HXN/8CQABpu0BJNx+COd+T7hQ1qQfbxAGe3PbqTemY7bHUnQ7EAjLv6wGqJ1dbEC7PGFd 0UQO8ISzvqxeqvGAv45VV4YH1zZ2ANBuCkyAED6dbUvPZl63QijTAH84BvhhE341CPFJvM3o 6uA7nI62swM/yABIh4qFUAhe0ZcTUpsyl/FDSPUFfvi7DoQew28VAnfXxnYAJG7nNhVC3OOB EL+8JavwQki28LWnFGFhkrWZEEZdA40J0VPiygl1jT2rCSHAMyeWq/38CwyXEReSEV611bEv NFo81zgcv8R7Qj5fNZUhPLsAj2vA5YJjsE8oz4k/0dMHsw7uGomDMP8q1wV1tN8nnPpWKkOY iLCo2BvE/EutWQTBDq8mpRpsYM9sO0OYtbPa9j+L4dqKSfclZwAAAABJRU5ErkJggg==</item> <item item-id="47">iVBORw0KGgoAAAANSUhEUgAAAHoAAAAnCAYAAADASVzNAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAOlSURBVHhe7Zw/sqowFMbZwNvHXQN7 sHibYFyEze0tqN2AvQXVs7a1dags7dxBXr6QYMAEQjjBqPlmmLmGXDie3/kTBM1Y0lcoatB1 VbKyquWrpDn+iBZ0VRSsqOSLpFZ1xf1STocdJei6TJAHxWHnE2HHB7ouWU5EuS5zloWOGNib ZSzLcmb2fc3KHPvNc0w2ijExP7MCrQrb+cyKDDR3Ci/ZFGhaZwUFXbFCwRPAn21HdVJAqoLb k5f8XTYy29g/pgUo9mnHGlNcoGH8lDAdUfCM5iX0cfwmcwdPZwiGJxs7czToBk3J6qhA400T cg4Oun98ZOxgoBqy8NlGWeoxNtaLJ/TqiEDjDdKUbaUlQOuOHgVtAGO2EZns0nb4PMfybQW9 2WzEtlqt2oUBtvV6Lcb3+72cSSVuNDGUuECboZhsbPq6zOxBkO7JMZrRl8ulAzqYeFnzuT4c UmjQyNDH8Yd7ND4XMO16srFT3oePCbn26XhAT+g3rgoOGlmqr5At2Qc7HvHQDWgj6M5ibBi0 vqofkpXc+Xxmh8NBlGkd9Ha7FePX61XOJBIHTcoE2absDglbgMF5tMySYzituKRSdvTnWWwU 8OX4WPB3gmhAT6ABWPVlQAbU1hi+YT/6NP7GPDLg1KC/RF6g9TINyP0xbND9fu8s0m63mxif pQTaS16gd7tdCw+ZDNl6NFbd/bmzlEB7yQu03o9Pp5MYs4HWSzoJ6N8f9vMr/05y1r+/f5z8 ljL6zRW8R+tjqUe/TrNW3QogYB+Pxw5Ut1W3/FRH+792s1mVQHvJG7SSuo5WUNWmrqOxn1QJ tJdmg1ay9ehhpYxeSi8G7aG06vaS16pbF/oztsXuXqWM9hJZRi+mBNpLCfSX6PtAm+4i9WWc 09wK1NtTqIADFOo7aW8J2v9+dO++sOFpTOucqurMtT0gMFcCMgKJHPTM+9GLizvf+wkTHiQP B1qeynCZg/HC7RksH4XIaLInTJYTzzhPJ/QdaHp2y2XOrGBzED1oBKxbBYoItLvRfcGBOjQb 6PE5bmXQV/SgeXLMfQr0FYIjzI5uSq2+YGo37jga0GHLNkQOesK6JirQ3BPOhndE0qP9W4er qEG/7Tc1GgA+5bu3ojaWs5E5PBACcyYGPS0wIwPNhXLk4wzAE+Vci3I51h7ONEcq1GVVK1QU rd3M1Zt/m7IRFkVkgf+JQvWZQpkrStBQ+sUDsz7qFw+U0m+YdFWXH/gbJkmUYuw/Rf97Hduf wh0AAAAASUVORK5CYII=</item> <item item-id="48">iVBORw0KGgoAAAANSUhEUgAAAQsAAAAnCAYAAAAYTG2rAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAjQSURBVHhe7Z07kts4EIbnAnsPn0F3 cLCXUM0hnDh3oNgXcO5A0Tp26tSlyKEz34Dbf+PBBgSQDRDkjEf9VbGWAvFokd0/GqDG+zQZ hmEoMLEw3gS362W6XG/+k1Fjy30ysTD+eq7n83S++g/GKrcr3a9Lu2CYWBh/NbeLCUUXJBin RsEwsTD+Xm6X6aRRCtR7epqenk5TLT5ulxNdR52nVHwW296my6nQphWFfYBtLAx0Xz7btdTn 9bw8Xo6JxUE80pr6mO9KAUHLj/UYvU7nEDAclIU2KD9dqEd/HusstcU1CsZNKgEU9hFRzLLx SuXItoIIXM90LXy3HPm9FZhYHMAjrql718Vq4Oia/smOOZDcjJs/CwSc7AsBxp+rbd15axpf RGFfQJ9ZCBYECLRkFyYWO/PQa+qOdbEWBIim6zyQohAIanWqbX0AnjFrb8wuNPYF8rqBWjmz lj00PCMTiz3Bg9rgSG+B1nWxDszA9dlSgkCSwVAMRszuYvaVYlFq64KT6qMBC0f/noXKPk9N FGrlzKoY0DJIuRSpisWHDx/4eP/+vVNPfzw/P3P5ly9ffE2jDDm0ak39xlmb2bogB1dGpzYY UT77uRO4WtuWAF+jpa+aKNTKqTeFEOiFdzWz+Pnzp7iJloioQZAUHjo/WHE/5RGed6lOrzPm vMT4w7MLurfq/ZCGPQFG1q+0zYNzi1i02JePG6iVY6+s0lWC9vmYWOwEHmDtAWDzz70qC0fh oSIgcI1mhtEvFg4fH+ONVIum/rK3DUszLQI3uReVtjgP5bJOF3r7WsQCZbEIz3LBQPn2ZIlq 9P/48WP6+vUrLzlmp3qaPn36xOW/fv3yNY17FKldHrC5E+B6wTGGcej4mnS4AbKtyTQObnxP EdS+jPuh/vgelGwstQWhTehjC2v2ATFe8qwK5emSCseymCXCssCdWEAkwj4FhALCIAfGdexb 4Bz1TDRKUHAo7j7PCOLexiZwlJHBVeG48fXrYhWtYmEs0iUWcskBocjLcIA/f/4kG5+/f//m csNDwaZbU7s16nx/KaBumIUHBtYix40/dN/CxGIoXWLx+fPn6DjIKEBtzwJvQ/K6hoecWb2m jiloOAYGlYaDxteui1WYWAylSyzk/sT379+5rCYWcnliYuFAQPA9b3Tm6nLgII4YX+uQKj6+ m9599OfGZv779x/V/bTMYhD4e4i4Idg88xWWA/7KMew//lCxsMxiKF2ZRcuehSwr71ngldDG WYrXz26M0wWv8lyKjBk82SnmVDo4ODk+rY+lfZxaI5hxjo077ClQv7xUkOce+WrRlbvvElL0 eN0bIHefuajVmdHf6RTH5EPVwW268r3YGNyt4/NzEff4REuMlferJhavly6xAHjbEZwAgvHt 27fZKehofxuSz1riWLLQO3BwQnwhrk+Oer25PkPzeM2PNQe+c+r5I/V5vkxX6tO1ISeniwj+ YAvKkSFwE4jQPAi/IbiS6Jxhk//suoaYiIBtcWbRD9sk7k+tj1serFvEonV81Bd1Vut70Hf5 eod/mFgMpf5sUu7EIhB+ZxGEIRzhdxa4vh8++FwkMskXYocVmUQUDtcuiAOLgAgk+Us77M6f Lu4Kyrk9+qUTkg9qi4xjFhqMf4K4+M74c7goxIZROzOCXgZ6HjgFEeDgpv7X6qloHR/XZwFX 2evROqQKE4uhbBaLQG3PQk/uUOKoWJgEIkjEIb3OS4I4w7trsX8EQuwGQhKC34sRTkXfsS0F xJlUYbbAfYckY4n2uGvJV1E5sw+8eRAH2+Ptx1HtCDaEevO90dMxPrK6u4GkHYX+PHWHbPcP E4ux1J9NygFi0Q7P9N7r+B9SoRl9dhwR9OS5vGZHYAcnRX2ZkgTIwWIf4pwFgs7RHudRECgw YjDxbD4LUmjPYwaxoQ/yejS3AG+G4n7KPgVRtPwRl0UJ/WIxZvyADPa6HVqHVGFvQ4bS9TZE gv0KHP1/deqcuctBKNicM7slBQcx9hrgsRycfuanz7y0CNe4Keyk635vIhCXGnw+z4CufQgG sjk4vshKEhEhkjEhHLAzrE9AVSzKs2jed37dHXkgasTC1Yn7LkPHD8x2yH5yhorFihgbbWif zTGpwkFcL0E03J4DvxWpzJ67cogza8TCgfuy2z2Iy5ZlG0wsXi+PJxY8w88ZBkOOjAzjkcUC opnck8G4TKS+VxEwsXi9PGBmcSMfouVCDCC3VDhcKAA581JKPgZ9ZrEbYUmoGPxV/dw7ZkML IrdYZ17O5XZAPJtt09iz8LwR7POeXorGngcUi1cEMprdxUK3sbgf5Lx4a6QcePQfkvWLMYLO 28JBWrp3S3V80Ja+uA967T1xaOzBpbLYslBstEcr5CYWu0AO0OYxHbysWOBfYbpzMPxYrJjN wdaBNlIQdIsxCc0cWO4e3j2qah13XhMq7A1dWjMLjT0sKO5Zl8auZRZae7RCbmKxC4ODo4hz rJcQizibFY5yIJGzD91o7hfjPLCQpuc2V+v4mb/4r3r7pVHrMkRjT8RnCmv2Mmp79L5qYrET eIC1Zz4E7zghSPkn6AfgfhU7j5sfRcekNtUA6KJfjPFcpC01sSjVcUFJ42LgJMUne/yr8x6x WLMnBVlG+t2dXUlJgz16ITex2As40+JD76QnWEexMnYtwxm6X+FBgJT7lBlXdtDN0QRnrU61 LV7T++L14EzR2JOT/0O86CPNchrsaRByE4vd6J/93hb9S4ZFesUYghftccJyZ16lTh6UIbDx 31yY1LZp7Em496uSXVp7WoTcxGJPoNrLT/7Ns0dW4egVY6Tx3iYITjEFr9ThpUf4PqKOYHUm v0Njj6CQCdxlFoJle9qE3MRiZ/Ba6mH1ghx711fIvWLMQY8ZVwS7L4vdleoAZAJ+ti4N3S4W xJo9Ysw7UVi6RizZ0yrkJhYHYP9j5P14aDHeQsfzMbE4CP5blYPeWLw0N7zfP/C7PqIYb6FX yE0sjDfBI4nxFrYIuYmFYRgKpul/CaXj2NW4c7YAAAAASUVORK5CYII=</item> <item item-id="49">iVBORw0KGgoAAAANSUhEUgAAAHkAAAAqCAYAAACX4PQQAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAOXSURBVHhe7Zw9krJAEIa5wHePPQN3 MNhLUB7CZHMDYi9gbkC0xqamFpGhmTeYnXdkYOBjoOcHUGaeKqp2WVjbfuie4UcTFlk9UXIA RMkBECUHgFfJRZawJOlZsqLaYp2URc7yoqx+mwaX1/AouWAZl4kwyjxlaS4DKnhw1Y8rpMgy NtcxXBb8teq80pmgXZcsT1NmEcvHUebzCa7hopsCouFfcpmzNM1FRa8avM8Bw+hmVsMU/q8Y 5vSFUmRmReRdcrtVrxXerXib1ikUgq3mInzIk3KFbM1rGBaSZ8mBtGokeeRNWlUyb8XNPshl oh0OTKrZr2TDI+xTgcCxBNtI7u6DsxXtwWQwNnuVjCCDaNWpvlVLbCWr+RuUjNZOLCit5N1u J5bNZtM6591ut2L98XistgQYS5ptDN+bE2Zx+uB1qjjG9JJpBxsYreTb7dZKnm+0F1DEQnsT YOo4a/iQRDlXtZGMFtzsMzwmA+q4vLhkCQJuXicznrzNJpk4FlpJFh1RmV2PtGOcpztJvl6v 7HQ6iZanJm+/34v19/u92tIPIin169AreO44IXnUHSpSxmIqGnLFvrTJHeXf/ycZSZPjGxKH RNUB8wV/x3iHn7GdrySaSl4qTpLkmbCSrLY8JK67Dgt4Pp+tic7j8RDrXTCRvGScHy/5cDjU CUFlAN1Yh1lrd1sXTCQvGefHS1bHtcvlItbpkqe2x7klLxkn+/liXz/Vzwvz+/2PFEtLcqxk AiGNyeq6dx6T1XVxTK7ArFQmBQk8n8+tRHmZtcrTBOU80EQy8BPn64KDul+96LK3BskSJAnt TSZKLvL8E3+n87rsKQNSr3LJdS3JrYsA7X27+I2TwJokS3RjnQuN0KZiW5ItsmgXZ+CVLJlC sqzOpmLVZNPvk6pME2cPnz67VsE4h0W9mIAFbRHr3e/ucLGt69UJS7OcmT6QOH2cHdZYyZEO UXIARMkBQJFscCepDzEJJdiLkqeCSx6+n9y5J0w451cRgnGAkCQ73k+OaODiBp8M4QdBI2j8 6Y4+qJXs7cmQSBdeqQMCuoKGn9PqhyYZBxCtS0TJxgwnF4LoD+P1Q5PMDzbXpzUjeiBB5202 yaNzg4Yo2QZMqHQJnmlMXu4TFMEw1LI7s2tiS1UZlzw8L+gSJduCdqlLtMt5MjpBdWlWJ9qk ikGU7MBSn082/SB6lOxIoN80EB6zfGdI/hbfGRJ5Txj7A48iea4pWgYrAAAAAElFTkSuQmCC</item> <item item-id="50">iVBORw0KGgoAAAANSUhEUgAAAOMAAAAqCAYAAABBaewwAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAZvSURBVHhe7Z0xcuwoEIbnAnuPd4a5 g4O9hMqHcPJyBxP7As4dKFrHTp26JnLozDfQ9g9IahAgQEIaW/1VUWVLIATqv7tBY8+pEwTh JhAxCsKNIGIUhBtBxCgIN8JhxNg2p+508pSmNTWOx7W9dJf2an4T1mDJnB5EjG3XkOgwRdfL uTtf+slqaeLMjwejbZruwH6oKteW5nawsXQOlqZeu8v53BXM06/iehEhVocEOTr9NI4lxuul O58vKkIeFsxBRInIHPypOxyZTu3706quSvdjDo6ykmFZ0NBvGn9bXleX6k4D8zG5j3Vom7xr HkqMdop6REhQlJ6G7HsQyEQBRiT8OBlxn4qFBYxzzdQgQ23b1ro3pNL+q64FxmUEo0S5cn+4 ZobzP5AYJUVVxjEzAVNh6YgYbRdMycZIF2wfbAvHUTmLob7HsepxBnxKMTnR8ThizPRSvxEI bc4wJmI0EaPpd6Mn1kpGPLcLpq7hE2SkLbUp2QTJwR0rdtyjTqeEjLXjYcSIiV99on8U8Pzz aZhroPp3aodDRlTj6YTIN4C6vP94W296uzKuTVQRI8aZGAR+pBgfHh5Uubu7097alPv7e3X8 +fnZ1ATjQ0eZOPadyBvDGtA8JAzeJ8Y5g1VtEtZbvjWgv+0GKSqxjRjTnCAwYrQNdihK0biY 5xwKe2jWS/WN0sGPjw/rftZAGwcbCy+WMfvnLPdh1hiDl8S0T43fEaP7nKdjxFzMGVzIKH1t 0xzHYjZYM4LUdeP49K+OcTmCwovM0Wj8F0dqYRtsXWoasi3KcMqkndCZDF1/qCCXzcSYuHZx xUcHKDXtnzdsxPPsLaMOEOrf15aObWNGbDwYZ6Ugkppy208fEzMYxnTSrejnmS08yC0m8f39 vXt5eVHp3Hi/p+7x8VEd//z8NDUXYM2F3+tjvKUPcJMxcFIMnI+ZV2bH+8OWs+J1+boycL1g W0P9VxoMc7813jP2pOrCccVOSupeIWqgvnRjXWDA/RoLRgyjHe/npM5jzYWfUW+RQUfHinkq 2xDadAwcGk+KQQjrUyhG3XA0DscIBy+ii9UBjLfi0+bpHIzYPYYCvr+/rU2Rr68vdTybkBhp DprC95Wbj4EjYtyNYjHSU7PWjvwiuOiZDLE/x8WHFLbmw356ehr6RTQBofUWdiLdutl4xIh1 M9ZPpePcfAwcEeNuLBCjFtZgIMOaSKdmFys69hGjforK11Zvb2/qWMiQeeq3jhhpMptIxpDI 5mPg/P3T/flrfhY25b9//0mae68YXUNUKRlEqORtryvVIdSv7Hb3jYyuGKkUbNxIZDwmiyKj KzhsVPAL4uf+HESY9plPnf6WGgQ32rn1Fj+25prRyhhQMgez+Rg4IsbdWChGR3DYOeQvbJ2N nLwo4ezY8jJzx9hp7OvCmF9fX6329XdTp/eeu6O6zhgK5lDEuBuLxegKzja6aeTcEhgsUrfe aPvSv6PD+XQCEdsrRmBvcIXfT8UzgXXHkICIcTeWi5EY07KpwY2RMyVF5ZRHRpfQemsVgmIk 3E8r4XyhPyobg0TGn8QqYhwM0peG9pGz8BMoa1BTjFaaTmKbOBxLrCg04QUfiavqUDiym7ob y3ZTB3QE8Keh2junp6jxtC0HrLVQ+ItxFKR8OL7kLx5sEbrFRMiJEHlJyxRqjsGLRMbdWCcy Cr8HEeNuiBgFGxHjbogYBZstxDjswOdu6q3DuMSw++dLj9gcqHpOhVDbUF8+UFfEKIyQGOu+ gsKegDFMJUpnB7o21Kf3P87hXvpNxsh9DeKyFedvG+orQNnfMwq/F2ZAVSCxj0apN/dSokEV mOOBWLgTwuu6kFNyhZXUNsHJ5f+lv/DLochVUR2uIceMvi7kCNh/nMu5L58Y423tvvzAMaVl CSLGw5BuFCUkRZHqjB/GGPpGxGapaY4Y4209fXmheonv4kWMBwLGVksftyFGjRKVIyK92YIS noOJGIm5tm5fExLS2B4R45HAJkQtgSCKDIa885pRRS2PQKx7nOIT40CwbaAvg/xHcSFAzVQV RmkMj+9C7oFPODgWi2BEUIyxtlGB05wEz00RMR4NMp7Yt1AtQm3/x1PBWuh00aSTfHxKSHTM dQ7mXoeqfT3ePtA22JeDfAuVMAvee9XSo2AgIee+ShIxHhT55uJ6yDcXC9nId/qvz/Ui3+kv CD+crvsfpHc8jwDvIrIAAAAASUVORK5CYII=</item> <item item-id="51">iVBORw0KGgoAAAANSUhEUgAAAF8AAAAgCAYAAAB0OKThAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAK6SURBVGhD7ZlNzoMgEEA9lwciPUSP 0AW36L4Ldr1Dt42r3qA34JtBkFEG/OPTNPASE0Wh+BgHrI2unEaVfyJV/olU+SdysPxOy7bR TYOb0MqWlsqh8jsptOz6fSVgAFoJw1Eu56WdTuq28Og/MecrLar8EKXUznTQmTaSQOQLl4MK JZSv5JCXdwFyZcK/kmXne2QiH1YjKWMrUYIX3MlWi5LzjWUsX4m8Upj2UHw7PFoKBrvc+B/J V8IvBbOAeZ3YN8tLs8Z3W5v193BgGzZ6/PuFO22une0DLgpcX/3igK9Lr+23uUAm8rGDuVcf 0KGD1vKDkOCOrRRaTib7+IDhOSYYY3VhgUFbwUDmW/V4+dBou1LU9/u1ezFwQPNGd4pQZB/x Ps0xQGrkz/tIjtaP1oXfjcx3FC8fGuIi4Hq96s/nY4889/vddGwOTDWRwMpOIB8DCtKFcOku 6AhImltgmDa4AUjUJU9HiqT8x+NhNg6M+v+R30erkTXdZhqayu+P4fHHIivRn14Q2QN4LU0j 6bpsumIY7IWPLJyExikY7XQwbreb3YuD8udvLg+cfPrbXF9MnQVv2lwO5+suSznISP60Y1P5 eOxS0Pv9jj4VFK7dNLkj3x/zgTCNag7sE3cNVxfKZvrpmI18FPx6vUyU4zFG//P5NPt0wo1F +Hr52wnuwaQaN+GjKGbyj8x1I2ITK1cXyha6T8tH8S7qLpfL6Bgj32HqQtmp8lGE7dvoPki5 K3b9Da6l80KkvWhdy5IlpsPnFW4UVxCTvH7CLQcvH0e9yj8UMqNCTtzxNsrLx4nquJesX4PI j83oy4jLP+bvhV+EyMcUsT1KefnLl10lMpKPk+5WV6z8He2VwFg+pokNH1NQvFt+0QGoX6vS TOQD2T4jquRnxAonH+iO+IBe4eVXjqHKPw2t/wBiOE1e2LitFgAAAABJRU5ErkJggg==</item> <item item-id="52">iVBORw0KGgoAAAANSUhEUgAAAFkAAAAgCAYAAAB5JtSmAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAKYSURBVGhD7ZlNkoMgEEY9lweicogc IQtvkX0W7HKHbFOucoPcgOFDkeav1WiYGfVVWaUpAX00DZhKHXydQ3IBDskFOCQXoLDkVjV1 paoKh1Cy/3XrFJXcNkI1bXcuhRZdN1r79vm9dNE2qt5JNP9iTpZK7FmylHLhMG5NHSw6koXN HRsnliybIW8uQktsGM+y2Uc+BoFkPftzZmYiRVpk29RK7CFP9PiSpVj35RP1QXA9DBWpO3X7 8exJlsItsVYBeZdYNss2s0a2R71ae+i8VJ3u94oNIHNfcEOubK6tHEQyNgprz/Z6BVFiLUwm UU8Wlom2fZxnVjODNN9kumyuLQYnmVY6kff73Z/lQMetF62T0CnKpiNIcKmpG0n0mhIKm1SW tMXhJOsCqV45n8/q9Xr1V47r9Wp6fww83ITOXgl/4g7FzZXMl52+SGAl3243c6RAFH9HMqK/ y4PRwVaEzU133yAD70RSxBzJfNlEWwyDpagRDSqhIHqp9Mvl0p/l4V7sG5j3COQMncRMVKn3 HysbtpXDkxzKCCXj2qaO5/OZjXJKql6eTyPZktmuZ9KhJSV5IFt22qeB0UiGyMfjYaIW14jm +/1uzruJj0qJG5wveSEpIcHQT5GVzJUd6TgLKxmCO3mVOp1O3jUiGYx9viwh2Tx7/1zeOxhB 8TPpAnpJRuYKex8tnymbbYvB5YOJvcJC15M9EL+02v+OkwxBi23EOeqQTCVD0MzNSATZDXUg XxfejPxBiGQI4SeHMeLPl6hzYcdtACIZQ/vzqMOEEKcFPTr2nis0nmRMfp848VcQ5PPlh/Vt DV8yhvfMj/b+rgiHGw17+veDI5CsWe3vJx3RRxQbYsmatsQfqTsiKflgXQ7JX0epH78BUz9G 7/qCAAAAAElFTkSuQmCC</item> <item item-id="53">iVBORw0KGgoAAAANSUhEUgAAADAAAAARCAYAAACb8i8UAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAEOSURBVEhL7VSJDcMgDGQuBmKOjuBp WIZhXBsINU9DKKgoVU+KIhwn3B3nKLw5/gJ24/cEWKNQqcZlbOxYDYsm7WNoFeEAta9pBBdr jKLeOAGHoBVKvg406uwr6+DA5AQ9WFQk7gkfwup6QwA1aSAZBGoy3G2hsckKvNzPDLJELDko DG3UawHUdHyMne8nJ5Do950gxkLuKyPLseZnrXpnBor8nSI6kt4V1yV1bESIShlZKaCsFwJE fDwhMVRfgDWTAqoGOMRcwewJCMM+mwEujERmMcTs0SL/26RU1PUgIA5RcGw0NhNDzI6+O6XE qTC1qFdDfDckAeqRaxld74JnwWQkodH1TiQBM/d9QHwCShOAuYRCtasAAAAASUVORK5C YII=</item> <item item-id="54">iVBORw0KGgoAAAANSUhEUgAAAEcAAAAnCAYAAABdRFVFAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAJWSURBVGhD7ZrtcYQgEIZpIH1cDXaR JuzjOqAafqUTiiG7ICgIuKLE9eI740wuBrM87AfuIcyjotjD0UoaqfT06XzVns8ajhpHM6rp Q0dpBf9HrgGxhaPl34AJAkBDAognHC3N0EIGxwlhhBhMxhE2pcZ4HEM42kgIp/1olBk9FAup 4Rk4bpBggRM/OGhg27IbEbwNAA+iKSyX3sMOjpZtIYHjZjg4SdEM2Y9jBgdXvCWkHJwljGY4 GJ5TaDGDA4Y1lqjz4MwLFOBYt7SZnn41zqMsyDe5/QZJJ+UclM87a8/BmAsAMvGvFVQTB/J0 OJm9Bl1JtVpUnb3CPVYeji2DFTiT0G17wDn0zGB7W1L3wihCO/bBgXvSG390Ijn1eGaDiHBi 7/CDuumucPDlLIV1uu4GJ726Gv9+mdd7+vlC/Xx/WTuInqPh166KPZ6zguNEyzlYUhsh3hkO 3gvValNuI+afE10lAreG01v/B84neg4YGME5stXcK67VCmlFq5tc9BX95IR8pR44FT1wKiLC wY5ALvSLiX6n2MKh9XPWzSycUHsvKFa5n3OlYBtBq45znzeMwa91z2EDa+RaNbzg4KRpcRW8 xIcARf79EPtU5TGZHjIPzYbVFOccWtfPjgEi9k+ruW32SmZwnCfUJ7sIqSLMZK+Fu35qT3nh lezg2IlU6KSJV8ntSe9J1qy/8Sx7Awrv0cJoKQtnciM8j1NO+nHOYwgHhK6dJoXohXg7L0XC ija9BNc86AanLJyuOJ+TehRbOKjnZNeGup8JhIR+yzOB18qYX2cQ1vRZkHKrAAAAAElFTkSu QmCC</item> <item item-id="55">iVBORw0KGgoAAAANSUhEUgAAAEoAAAAnCAYAAACout71AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAIxSURBVGhD7ZrtkYQgDIZp4PrYGuzi mrCP7YBq+HWdUAyXiOAXuyYR3ej6zjiz6ILkMSGImHCLJJWgvLPBOt+X6mlLu+pAubYNresL O8g7aN/yYakC5e2+kLIAVsOEpQeUt6HhUsI6xgRjmsB1Etfy6igB5YOFkONhcqFNgDpgzPpY p7FwZ5p0gMJO810imOyBALox7LDleJUKUN7yQwfrDKDQaCOCTa2jABR6AzfsIqixkSJQGL7E 8FMACjorSHV1QNEfUgbVuXKXQeiHwL6lYHySzGswbLaOUSjqOLX0KIzbDKPQiHeQoSLUKqAE c5qoWdZjZLCxcO4mA9Wl2jegeqGr1wIlbif3lZ8MkjCSKPfngYJrNjW6xcCxarUjVCVQU6+h NsrS1UDhy+QcXBVdBdT8qG7U8xEez/73B/T3+0O6P9GjPJyO2fD2qKR7jCqKBQqv5axXVJz4 pbqT41VvLglqD30nqG/yKOj4BJR0ykvV2bIekp14wOzY7amf1qOO1g2KqBsUUURQ3dBQ+GPx fE5I66sKpwK1th7VwUDDZxaVz/O+zsjXo44WGEPJrGSPAvBDeX3lU77Cebhoa+ZUUPPy+7V0 wZr550TrLAcU/aPDqb7CROPW3H8XUITxMUkFKLButcNUUGj8UH4/Rp3uS3E0aC07EUFhOI2z 3svQoo2NSUpAgTAMXj/6/Cq18J7SecI86qS7WaKO3B/FfdlXBQp177hjaLc9nPZCezh1KoR/ udWSUlfBqecAAAAASUVORK5CYII=</item> <item item-id="56">iVBORw0KGgoAAAANSUhEUgAAAIcAAAAqCAYAAACZZUg6AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAASrSURBVHhe7Zw/cusgEMZ9gXePnMF3 SPEuockh0qRPoToXSJ9C1UudNm1GVcp0uYHefghshFi0IOQ/yf5mmNgI0LJ8LMgm3g2KwqDi UFhUHAqLikNhqSqOrtkNu10kNZ0tUY++a4e26+07hWONnyqKoxsaEgHM6Nv9sG+dQR0ZZ19W omuaYQO9/Vj6jvx1GA85Gywr/dDu90OBLSL6VoVRBAnkOGFl1BdH3w77fWsiSHXQNqcMXDPL GC9MRDS31E2aSdaF2CN11iCwtdQmLO2cnV2TN2mri2O6pNSEHELLSbzftKQ5JxqnRsoh34l2 UiZVF9doEKqpAghsLbXJlE2IGNczJm5lcWy4pKBjXMMUMo/OGmdV6KBQtJhh5j1bd3xdXegC W0tt6lrafCYiB8iJHnXFkanMHDC4XKfMcuF55DDwHlwZtq6dsQ29r/nEtcbWpE0kKLxF2aSp VC68H0dVcaBT0hvngRkTC78j4X1jDodTdl54dmW4uuMAUXlUMIOy4HQhElvzbSL/2EdClE3b ScuScAKz4ri/vzfp9vZ2VKlNd3d3Jv/5+dmWBHYdtKmGE6dQ+4lGJQ4HyD/2ZYxEqYGQtDln DP3H+3iJ+iBpN9smfJZhs5GX9n96ovksRo6Pj49JB88CzZLkc7pkHffxyzN1MRB+2JaLYwGJ rZk24a8/RkgpW6X7jusQBzkrPTDB7j4VNuH42e4/UteEbedEr8xqJLaW27QcOdBMI+oLO9rv 7+/Dy8uLWUJ8cTw+Ppr8z89PW/IE0IAudXh0HGz0HGbzTF0jCroeG4xYXeDquDZqsWSr9z7X Jpk49qL+zMQBUbh9BoQBIThjkHAd+w68RrmTiEQiDkVMkTj8JQTCCPOQwPf392Sj+vX1ZfI3 Q8VRlSJxPD09HQYcEQNwew48rYRlN0PFUZUicfj7i7e3N5PHicNfbjYXx8PNcPNgXyur+ff3 j8ifGjl+IZvvOfw83XNcF6ueVtygQyCvr68TIax/Whk/1PHbPCTOYhVHVYrF4XCfczghuOQ+ 58D1k6HiqMpqcTi4PUc5GjnOzQWLowB9WqlK0dOKD/YbSLJvZTdGI0dVqkWOi0DFURUVh8Ly +8TBfYsZAMfENr7zfH/jnG5zEZFt+Are3W96GIezGUi+hQ35ceLIOs8RONdhnAznB56J5ftn HsxhmtQZkSRS2+JnLDibDVZ0+eJYeZ7joiAn1DoJxs1CLt+QGNRFRLYdo0ZsEnC2SU6bx6h2 EuwyIOclPBA6DzOdizTF4iiMHDm2mftEBBK1zS61+csKBCoT+pWII90hOM93aHVxLC5rPDm2 jSCKTPs6t438IT5tHkLtC4V+JeIYHcT5NGcAOBFw+TnOjJFjmwP/KD6RQmhb1mnzgAyhX404 yEN8pzbcc4QDlU2GbSPzKBnaBkGMTzXHJB3w8/3H26aklhaEYu+JIDHTc8SBvOO4LmyKWeS2 GSIzm7MZ5EUOskVe+JrEQcBxXOfsZi55ohuz2M02v51I/nx2ymfcjCXbOLtA6hqRI46z/5f9 1ujvcxRCIsuNfFcnDqC/7JPHBf2yz2nQ3wST0eODsvP/JpjysxiG/1U48dSu3S5/AAAAAElF TkSuQmCC</item> <item item-id="57">iVBORw0KGgoAAAANSUhEUgAAAJgAAAAqCAYAAABPwJJfAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAUGSURBVHhe7Zw9cqswEMd9gXePnIE7 pHiXYHyINOlTUOcC6VNQvdRp02aoUqbLDXi7+mKlaIWQhMFYvxnNGH0B0l+7i4x9GiuVFakC q6xKFVhlVarAKqtyWIH17Wk8nTyp7VWN/TH03dj1gzraDznXdVCB9WMLQsIhGbpmbDo9OD0M lPq4M/q2HXesfRAZXJ8Zx3gO7iKHsWuaMWFcLsrQ7VtcBhDZtFjjOLbAhm5smk5Yst2C15is LlxA0vX7ukDrrUMDWk7zqWC4fErfLluwhxaY7R73CAgEXGOavCAMQDFw4qSLCz+f9HmwnRKJ yNeC4fIdaL8RHFhgV+AecbKSLlBarlBbd3HhQ484DorNk+9hiRU7rsAWrrQtQBHETpSFEkOr n5Q9Vky4O5JvBKbEKcqsmIrL97AgFjuswK7CPTZp7lGKB9piYyE2TwwGIjgZi0QFJo4Y98rl u0C9yMV7FQJ7eHgQ6f7+3gShmM7ns8h/eXlRNRE1SCrNjtVmwHWyF6esCbkPk6AN6/4c7L3A yVriU2s3qHMQoXD5v4lfHLzA4EToaxvrRuG47cYeyrbg8/OTXEuZtSGsAekzJhURLVielH0l hHd/DGjNdH0rdJBiEkVcPkNsHOadJVQyml0czKaTG5Yiv5fmWOTD2SP6L8oaAjNgXGH69gze 0MMTnxRjaOCjWRDH/EK4RX2NaLEDk+24StlWH0trbwTmy2eQ1k4dBPg1S3RFewdAXLCaiCIj Pc/Hx8f4+voq3KE5N6SnpyeR//X1pWpmIAZY981PGFqLUgLL6ofMg+lH3YM41uUeV8fN8ezc E7BuzPXbAosaZOWjVb0ig82AwtJxF4oLxaTPiwnLMQ7Dz1gvS2ihe4cy8xVTrjA0pfrZiCSB WUFhIMiLrZcDdYcoLjcPE/Lz82MF/9/f3yJ/MZbA7IUTO5iLuD2B2ZYp5P6oKQ25kxyen5/N OdByIVwMhk+Rbt3FMALDL3npcTFuT2D2430wvlKDrtMaA0Xjrff3d5HHCYy6zlICc1Pxe3y8 G+8e1ecr5N/fP1HXv1uB7cOCDZAtny6L32O1YHzrOBc5/6gbYkkMRvNqDHYZEgSG9zwNcLkg 34ntaJq5QnxK1HVRZG9vb1b79Z4inYUBZeEXFRPu8RYFhjc9DU7cNkXyZuEC9D6YFpNOeh8M y+NhLGtIYGtwkwIDqHWa3WiN2qJIt2AuXAxWhCyBVQvG4Zkl+R2kHiDuq6K19r9CrCowmHBL YGtb5tt7irRBMbXeVQnKJaLjyQvyKRh/YYp7m2IZuBJpn25azcrcrgVzoC6RfmlayaMKTHP5 oP4mqAKjgLujIoOeq8wyiRIY3Zt0vce08P39cG1z+pwoLDAEgn/ynphO1aIlAgKbfyWGe+dK iSQww1zbnD4pye+DVS7EMPdG62RpbCFKKxMWJ9c2p0+brDdaK5cAJjvGWqj9OTP54hjcq96v DPXhttXk9ClAQcY98FWBbUb8JEnLI+uKbZW5XxRZTG1tMvuM3AetAtsQnNhYr4R/jqLFQC0S fvMy59p0W5fkPiPiR00V2JagtYiaqMnaSWszyWVeYJylTO+z/rL7aoh0k9RiCBemJxjd3Mxk c9YmuU8on43RJqrAtgYn2jdhkC8Cbl/QTcpMEY2duLZL+/SwxHohVWA7APeUQpO6G0CES18C qALbCfUfDiurs9v/aO3qf7RWdsk4/gdm+gwAi+bb/QAAAABJRU5ErkJggg==</item> <item item-id="58">iVBORw0KGgoAAAANSUhEUgAAAgQAAABFCAYAAAA1mf52AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAABL3SURBVHhe7Z2xjuM4Eob7BS5bYKPJ J5+NJrhw0ekBlxxg3GbGBIsGLrtgg5t8gn6LzTdwNu8w6cDRvsG+gY8/JVoUVSSLUlEm1RV8 gC3bsqrq568SJVtP/7vdboqiKIqivG20IUhyuX14ero9BXy4UO9V6qP1WMPpvMzZ03ti2flC fl5xqP7WQOpPtdYk2hAkMQagwm0IrYcMVB41t3k0RzJoHltFG4IkKty20HrIQOVRc5tHcySD 5rFVtCFIosJti0fX43p7/uimPc+3E/meHqDyqFrPo/qTQbXWKtoQJFHhtsVj6/Hyer49X4fH 9rzox9fbS/CePqDyqFrPo/qTQbXWKtoQJFHhtkVD9bi+3t51e5RG5VG1nkf1J4NqrVW0IUii wm2LluphtkUbgjeG6k8G1VqraEOQRIXbFrx6nC6XjVOpV7sO+rURc4T24fVKv9Y8VB5V63lU fzKo1lpFG4IkKty2YNTj8no/z7oJY7jPid+Xn157PX8LqDyq1vOo/mRQrbWKNgRJVLhtkavH 9fb8Klev05k23ZfXj53/GQ2VR9V6HtWfDKq1Vkk2BC/odi9tT0vV3UYVbltk6nE5yxolsT6Y 8bv7VO3F7AB6nLal8lim9Va94aF+oPpj0q/WHrEt0t+ZWl+0ITidhcVdkRcMnCoDo0y4Sm3S 9YBmRaZrHThP633f8i9YP8p+X4j5/ndVvofKI1/rrXvDo/xA9celb63V09eSWvHHYiAbAvze tZdm4I4JcOqcpeALV9mDVD3wpy3SV12b73vYb73NdzsjtsYsGRuVR57Wu/GG3f1A9cfnAFqr oq851eMnYlg2BCg+o1gU6GDdF/iPeUCAMdHxBsbp/LhOVtmDRD2g20Lz/O9ff5HLJ2DylY/C YpjBOt0ABtsheRMdKo8MrQt5Aw+zPT35geqvgLpa49Givjw48V8vt2ezDW7G6F3kmpMUYQxB Q2AKf17bCfqiKRUQikMnF12S3R4kKNelrhiUaRjCVXYkUY+ZgU38/bffbv/588/F8n/+/rsd ROHyEOzIqnbpEXCu2I+nfIeagspjTutS3sChQz9Q/RVQU2scWtWXgxG/3T5cXOpqMowxSoNJ ghjmDQFeXF10k+T7iv3HeWKix8UPsy4pMuh8ZLu2z7cf3n8mltdnj4tXHnGBzDYS9SC08a8/ /rD4yxw4OmvdkP2xKGvIJo8/noPxSS3zEPOGPF36geqvgJpay9Ouvkay8aOhIWLA9uaaGAI/ hllDABGkgkMiIeIPFwzw4WKTaaMutxP5OEPQoTjcdw3f55ajC8oUwCRFTkwmzowgarDnhTR7 XiCznXg9wiMaEBoujsp8g/75y5fZ6xSyRsinriFTO2hq2YScN2To1A9UfyXU1FqGpvU1wIqf 0uHKhsCPwWsIEHxuZaYASP7r+E9c2IBEYTlQA2kA37XcHiQjXfy02Mow64oYQC0eciFNBVHX IV6P0MBAaMh47qZvf/3+PXr05kOtdxcwtu6xDtOBcrqgxkhq3OznDb36geqvhMdprW19gVz8 2E56m6KNQpYpBq8hMAszK1skcyaaHEMg4U9YooKPrDs/QDiC4pLPiSjoXhPfFxdzBqyXyL1P lakvceL1oHIDA4bpfvr2zR6N4TmO0n75+tU+9i/qwmCidJXXWy0wXsaaRI5q1mPWvVgftcx7 LaO7Rf6z3oD4juMHqr8S9tBaj/oCmfitl1NePcS7bBToPMyZYpgaAvNF6aljfMhfaficAZIe CIFOONZNd0GcASK3c8uLUw4Tc+JCEjsIUNji7YEgxnxYMUW+Q3zQ1yBeD5uf4DWYsc2Z4adP n2bPcYQ2+6xZRumKo7dq2HrlBvMaTB4XtaaWjdTyhgP5geqvhJ201p2+DLn4bU2W32dnB2I5 JfIQ4mKYGgLzoWTg2JD7Sq92BaVCpaZfqIE0BG12XNfrIghqHSGYdpcpUNwAxEHMmXySucoB Mdw/Exc+EBV2FRL1mMVZTmzgc/TWH5QhJ0y6kjccyg9UfwXso7X+9GXIxT96OGLwc5C6dqAk hllDkPqQTeTY3aJjnH7uwMWIgNroWYFHMLg+mg1c/C94ohv0wLbKDKKEAQiDbebEVWo64Wcg jqjgsmJ8NIl6QEcbaoU8UbG/HUNOm7S8NxzMD1R/BeyhtR71ZcjEbzExDBdTDjmA7shcWiJ5 CHAxMBsCXmKSIOmRAcMWfWIdPrsYgCjIL69oaxoC32iSDUFqoDZBqh7btj3M04CA7puEylUi fzW84XB+oPrjs4PWutSXIRn/xMvVNAEb8+DjYuA1BMyVpkgXwQiEOC8yA50do9MB+xiAJLzv QVyldQiNJt0Q8BqTx5HK07ZtD/M0gHWuN/l2oQyZWjZSwRuO5weqPz71tdanvgyp+EeGUwRP w6yGmyHBvxYuZjhyeZhwMWQbArzRTdHEdyQ5UIBcclMDivP5iX0MQBBMATFya2tRuj2zQYQc pwXS9nUE6Xps2Xbkdqnvneq/OyYugYZgvTeY7zqgH6j+uNTWWq/6MjAaAsvib4up0wb8OFwM U0Pw+f3th8/zN0lhi7mjsP/9j78JxbLTgDQi4BjqujxCFKNRoevNHHGIXiAjTqYe3MFEgNwu arBhfW1j8ljSEAh7w2H9QPXHpK7W+tWXQXCsleTBxcC+qHAdpvBjB8Pr7GRAImRiyRiAFJzc m/e4PBaL3U5/4bP5Ixi53NUgVw/T9RPTZjnswCE0enpNN0/9QhlywqTFvOHofqD641FLa73r yyAy1srz4GKo3BA8hv0MQIiGci8qbnEY9cD9GSRmODAld5DxsIQyZGrZSOfesKsfqP4YHEtr 7TUE5WhDwIJhABK8oYYAf92Kf26jXsvDq8fLJfUzHA7X28msg37tCFCGfCyT9tnbD1R/OY6l NW0IGkcbgvXUbAh+/vLF/lsb/roVt4Wl3pNmp3ocHsqQj2XSPt35weE5lta0IWic7gyg4gWd pYheIOOBBsD9XSv31q9L1JBloAz5WCbtow1BaxxLa9oQNA6vQNfb8zkiyjs7GUBDuZfL3QRm BYB77m7u4r+HhxqyDJQhH8ukfbrzg8NzLK1pQ9A4exgAzoXjhiU48sVUOHZw7gjYv3EJi4M3 BG7nj7yEuSpDDVkGypCPZdI+2hC0xrG0FtMXfC5F+H6LNgTy1DYANAMoKI503TJ3JzO8hluc +u/PcuCGAKcH0DjhMXKDPLk7v4XvzaOGLANlyMcyaR9tCFrjWFrThqBx4gZgBv39phABpEBp A8D73U7OgSvnsRzNgLvPOf420v+Od3fDMes1z+/b2EVDUJq7ATRNfuMEMEvg5y+eJ+DnSg1Z BsqQ/WXt6nMNtf1AKeVYWovrawXaEMjDK9D6IwIYRriTcw3B/MgXhuP+DOhizYf8k4guGgIf Tu4GcErFNUgONAPzWRRmniL1UErxDTm1bOSwDYHPej9QSjmW1mL6mjWYBOH7LdoQyLNHQxD+ lt6dG/eXhcLHkTC5o+vuVwb8hiDMlfuFwbxJYOZJDVkIypCpZSPaEIyo/mQ4lta0IWic2gaA C+JQUDQBDrcMR8T32QOTU7djezGPo38d3FDu5XI3XDOAmQDg8uT//PAON09qyEJQhnwsk/bR hqA1jqU1nr6YPCh+bQhYbDMA/9z4O9xZK/aXpg3lXlLcOH3C+TUBO09qyEJQhnwsk/ZpxQ8U x7G0pg1B47RhAOaz9taT5sjjfn48wkEbAswGUMvnFORJDVkIypCpZSPaEIyo/mQ4lta0IYjg n/uNnweOYUQSvXdzQkAETRiAyef9roTmcTIXnNwX3LGQAjnh3CVRUtyshqAkT2rIGdBUudmW 2FgC1HhKjDGOPjO8eT94E2zR34iA1ni0qC9DSfy4AdbZu1Mm87ouChfD1BBsvrBtEMMQjP+Y A4pD7+hwf35bNLtDTIlsYrf7n0cJj3ZH8cV25JwdoctPQR4cthmAaFgNwXlVw7GOwjytrsfb wK+dPQ0TNTWTx8Vr1LIRUW/g4Ok9eK1PP3gbbNPfyC4XWLeqLwM3frt92Im7/cYwxopvjT/i YhCcIfCLnCg4AcRDfffLxQTtB2i2cd+j3BUGYAs1dGx+l2wHiFlGbpf5zIfMDMIUd6m5Dtim gBHLyXSc1EARZ02e1tTjrZI0NGp8JsasqDfkOZQfvFWK9TeyWWt52tWXgRW/yR/lkdheZhMT 4mIoaggms0ZB8dhPxOV2Ih9ngHAIcbjvmu8cwiNKmv4MIP094c4cuSmbfl2ugwb5XSeofdir HkcAprFfQ8D3hgzqBwehVH8jm7WWoWl9GbjxUzrcuyEYimw++Dre7xsbECssk/iOihYUkpHb zv4MIL0jRjx+A4Ac1GkIEgO1CfaqR11wr/ttOWbcL98YX3zWiapzovY7eoP6QX3a1N9IZa21 rS9DNn5sJ71N0UaBgYuhqCFYJBOF4GwAxGE7ueWFD1gnuXOLrDv6fg+5Au1nANjmWCcaxlyt ITA5L13vvuxXj2pcXmVOyZgx9ZzQ+Ok1ZZAmj8INwUJfKW9QP3gczepvREJr3erLkIvfxIZr B5Y1NDkNG4VEHkJcDLOGIB14OH3Cm05xg2jYGHxmvjOjEz68j0oMr0BSF8btaAAodCyumVjj uUmBvFGC99nt+oHV9G7IpnbmqIZ+rZxTZJDnDYoyZGrZiKg3qB88jpb1N7JZaz3ry5CLP9IQ 2NmBWU7TeQhxMUwNAbqJ3Ibcv/Bqdx65RFGECcbzxY7KBn2+na7XhWg4UzhyO7Y9DQBFi502 MNvhRDCrAx8yzzP2jHUtPWxjAjPYc9otgljffHxdzA6AGqMmjwsNUctGKnqD+sGONK2/EWGt 9aUvQy7+ceeOGPwc5K4dCPMQ4mKYGoKM2G0in4bph+EijtRGxzmdg25qVuARI7Qns3NcdrNI Ri75qR1rKTsbgIl7dpWrjxXtkPti8SGfrnaR9YuKuhrC9diS0xUstL8VmIeXD3uU4OqcjIsy 5IRJV/SGN+0Hqj/6NUGt9aUvwNAY8o6mYMwB9hl0LifStZ9i8BqCVGCcxDBAIESXwunCLChc dkCmxFaKsAEwwNSNaBfPweQ13ZW2gmQ9zLqcYVljlhzUFNLGAdZqnfpcal2VvOFN+4FZl+qP eE1Qa93pC/Dq9HI1TcDGPExMMXgNwdB9kclmJSZDcqO8wUG+buAOGrOtqamRMsx2iRkAH3Rz ezUFuJlQH80AEKzHTNMYhEyTWAv0W2gc4W2jlxQa5B3KxKhlE+Le8Nb9QPVXV2td6msgGv/I cIrgaZjVcDMk+NdC6vqQZB5GvBhmDYFNQvBhf4pmdeBmvfcNj5LqjFBARnEMslPfn28/vP9M LK8P/iQjn7NtvLzW/w5Z5Ophde2ZC44apAf2jIiZ4e+dcVfIcDnuEIkxFy4PYR/tzDB5/PEc GDK1zEPSG9QPVH81tdatvkaI+Bcs/raYOG3AysM8hnlDkEzSOl4ul+mvaO0Gzl+XxRSSEP16 pNenbEOuHjAYf9A9wpBxN0jgL3Pg6KyeIVN5zOVWxhvUDwZUf3W01re+HNvHGj8P8xiChsCA 6QNB0bsOZkC22QiR79ZqFVxZR7+GbMdCsO2h4eKozDfon798mb1OsW67qTwycrvRG9QPJlR/ 8lrrX18eG8ZaSR7CGJYNgeEhF7ZtxSRQ/ly4nAEoEgjWw+hlMkh05HXP4YY7AIDBGj5307e/ fv8ePXrzodabh8ojL7fdeEPrfqD6I5bPaVprVfQ1p3r8RAxkQwD2vLBtK/UujBM0AEUAyXqY dflXeRdecFWK7dqDbYcBw3Q/fftmj8bwHEdpv3z9ah8PF3UNOws8pzr9vRsC0Lo39OEHqj/6 tTktam3PC7FrxR+LIdoQgD0ubNtK3QvjJA1A2Y5wPWDE1ugqTv2NUIYMMx6M9un206dPs+c4 Qhs+N/1+GNOz4X+2P6IhAK16Q1d+oPpj0ZLWHnEhtnT8qRiSDYEibADKRjquh+nIQ0Muxu5A 5kdpMOnyIwgqj6r1PKq/evpTWkAbgiQq3LbouB4w083bbuLXhuCBqP7q6U9pAW0Ikqhw26Ln epht33qe2Jj6/Lwfzu+umW6m8qhaz6P6q6c/pQW0IUiiwm2LnusB81xelFXC8payWOcak6fy qFrPo/qrpz+lBbQhSKLCbYu+67Hld8u4eGs5Nbs2H9TnVOt5VH/z5ZL6U1pAG4IkKty26Lwe +KlP8fnWwYynK7m9W8quXB+dR9V6HtVfPf0pLaANQRIVblv0Xo8rfQOSBPanXuNPwQamo7zl FC4XKo+q9Tyqv3r6U1pAG4IkKty2OEA98JvildO2M3Bzk1VHZ4DKo2o9j+rvjrj+lBbQhiCJ CrctjlEP3Hhk3ZGV43o7mXXQr3Gg8qhaz6P6G6ihP6UFtCFIYoQ7my4bWHfeTNmO1mMNy2lf w3timZp0BtXfGkj9qdaaRBsCRVEURXnz3G7/B8uUad++yGnzAAAAAElFTkSuQmCC</item> <item item-id="59">iVBORw0KGgoAAAANSUhEUgAAAIgAAAAgCAYAAADJ9TDRAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAPeSURBVHhe7ZpLjsIwDIY5Vw9UcQiO wKK3YM8iO+7AFnXFDbhBxn9aFyeTV0OpIpFPqoaWPhz7t510OOhGI0ITSCNKE0gjShNII8qP CWTUQ3fQhwO2Xqv5aCPMTwlkHHo9jNNn1ZNIuoEk04jxuy1mHHTXqkiSH56DKN03gSSpUiBK qQ9L/2juEYUqSM/9phGkPoGoYZknfAQJYIhoRA1t/pFDZQKhVUYsqitRvV8E49DpvvWWLOoS iOq3DZznfhBHt5QoRYLMqyPP51Pf7/d573eoSiCqfy9DNwHzDKEQs7Q170B467Kedz6f9fV6 1bfbTZ9Op/nofsDuUOJA8Dye9znyfY9vjO/vUwlZkUBg9NarClqpfPiuA4J4PB7m8+v1Mk7d FbMcDwQS3/H4zHmT/+Lve7B6o2MpZczsMFoKfGAuYCEHmwkCFgeiy6sSPlA1sDGoIHsLBJPp IVBB7HY5iUHuG4RwJn94zolgjRZ99ng8mqxBWYUzOHv473oyBULzBZ+qYQvscrlcLlnBipXn FHx/jN31xy7Mc6jQGEx7EV8EBeJUmZ7Ow1hyqsjiYQQBFyFLGGQPjuE7BKSMcoG4GSzJLfel AsH9kSwA44cd2A/ZE2fKXBMUdwsaR9fMK7rgGOAz8bLPKxA6h49NgqLzcYERS9o3i4dhLDuE wawdxyEOLucwQg6w8wY/4pBAG3GzAeB8CeyQAUJWp/A67R9TX5aPR6LIZAF4nvRRni8KEe+D ggIhbBvcdmrPwbJakoMlENchLBA7axB8NoSWicmelldBXOMBni3BPrcblPqcbPbdNwe0NneO A3HYlTTXF5GECUTeFR+26Dg8FRirQnnETcLVAnHX+dx3bWxVph+SLxBfBYEIYBfbggDxZFEG MGRHqUBcf3BLs0Wz1hdl4L6hCmJwWg3AuJdrqJ2YfyuYtvIWdJ+xzF+ij4yEAxAI3vgYsmmp LrKnGcNSDykXCMSB52Pj/s/7crJorqVjWwmE51zY2Bdyubuw2hdlWAKRcwfzTPKH07ZxPvtp 2oRdfA1tUdHNuOUhiXx4h/cWWzkEhudYHCAkhGT2eYAQ/4nBw9d8URErBYKyhFIme+9GIDMq EQiqRZov+qIi1glEZjl9Xlu649j9fC1+gZQFL0sgX/VFPawQiOvsOYM28wvuj4wsIyyQctGF +bYv6iFPIPPEaOq37yByD/6gM1iovrxU+wVCgdvKOGYnX9TC6knqV6FSXepgr0A+uF9joi6B oHQX/GAI4pgy2l7qtl+NfU5lAiE2+8mhiv7ksJFHfQIhxj1+tNzIokqBNOqhCaQRQes/tXa+ 8lQliSIAAAAASUVORK5CYII=</item> <item item-id="60">iVBORw0KGgoAAAANSUhEUgAAAF8AAAAgCAYAAAB0OKThAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAK6SURBVGhD7ZlNzoMgEEA9lwciPUSP 0AW36L4Ldr1Dt42r3qA34JtBkFEG/OPTNPASE0Wh+BgHrI2unEaVfyJV/olU+SdysPxOy7bR TYOb0MqWlsqh8jsptOz6fSVgAFoJw1Eu56WdTuq28Og/MecrLar8EKXUznTQmTaSQOQLl4MK JZSv5JCXdwFyZcK/kmXne2QiH1YjKWMrUYIX3MlWi5LzjWUsX4m8Upj2UHw7PFoKBrvc+B/J V8IvBbOAeZ3YN8tLs8Z3W5v193BgGzZ6/PuFO22une0DLgpcX/3igK9Lr+23uUAm8rGDuVcf 0KGD1vKDkOCOrRRaTib7+IDhOSYYY3VhgUFbwUDmW/V4+dBou1LU9/u1ezFwQPNGd4pQZB/x Ps0xQGrkz/tIjtaP1oXfjcx3FC8fGuIi4Hq96s/nY4889/vddGwOTDWRwMpOIB8DCtKFcOku 6AhImltgmDa4AUjUJU9HiqT8x+NhNg6M+v+R30erkTXdZhqayu+P4fHHIivRn14Q2QN4LU0j 6bpsumIY7IWPLJyExikY7XQwbreb3YuD8udvLg+cfPrbXF9MnQVv2lwO5+suSznISP60Y1P5 eOxS0Pv9jj4VFK7dNLkj3x/zgTCNag7sE3cNVxfKZvrpmI18FPx6vUyU4zFG//P5NPt0wo1F +Hr52wnuwaQaN+GjKGbyj8x1I2ITK1cXyha6T8tH8S7qLpfL6Bgj32HqQtmp8lGE7dvoPki5 K3b9Da6l80KkvWhdy5IlpsPnFW4UVxCTvH7CLQcvH0e9yj8UMqNCTtzxNsrLx4nquJesX4PI j83oy4jLP+bvhV+EyMcUsT1KefnLl10lMpKPk+5WV6z8He2VwFg+pokNH1NQvFt+0QGoX6vS TOQD2T4jquRnxAonH+iO+IBe4eVXjqHKPw2t/wBiOE1e2LitFgAAAABJRU5ErkJggg==</item> <item item-id="61">iVBORw0KGgoAAAANSUhEUgAAAFEAAAAbCAYAAAAETGM8AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAHySURBVGhD7ZjtccMgDIY9FwMxD9Ow jIehEp8y0YuThjptzXPnHxHmEI8lfM4WFm+zJE5gSZzAkjiBJXECS+IEsMR9D96aYMwWtq1c 9Nu64Gls0VAl7s4Gk8UZ50NRtnvX4rbF786DxN2ZWnnGKZq8reMbiVz0EvdWady6mkO6KTjR 4stjJ9HbJmczDrbrs/fdBSHxWGGjVpUtjyv2SlruWtoyXzmOjq7TI61DSPTB5onxGvWpPBfp +mxL57xREnxElW6Jx5WlGQzPywUQ46UYUBzzxyWmChxVC1eVHOejKP4eClXiA74lcdTONckr yBu25YxWco65injLLx8BPEZF0XJGcczUF0uRixfuzl15DR4aIgmiquGpUajSFbFrSmWl3Ft+ uXAeJ4G4zkFiWrBsDJXxUUQvrG+f9xhL79dCXXB46GJf/FHh6OsrrnEoBj2OOEok5IKqDCla WWAs8Scqsc1DEiuce7mfK7fmn/KKQyg+4EEiT+Rv5rI59NmHntDcSjwhtnCprJOXQNfWaa58 gQiJWnyAIjHBwqxaNSZYIbbnUomM6Iy62Sgi/y7joGvKXJkziiOgxIpsX/kkAZdL/AWcS6Tn J8+xM0FLIoTOBimS+kTT9Gob/BeelMjQC0f8z3hHWYgXJC4QS+IElsS3CeELmxSBdjhyvuQA AAAASUVORK5CYII=</item> <item item-id="62">iVBORw0KGgoAAAANSUhEUgAAAIgAAAAgCAYAAADJ9TDRAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAPFSURBVHhe7ZrNkcIwDIWpKwVlKIIS OKQL7hx8oweuTE7bAR14/ewokR3jv4SMZ/A3k1kSsrYiPUk2cJKNRoAmkEaQJpBGkCaQRpAf E8goh+4kTyccvRTT1cZnfkog49DLYTSvRa9E0g1KMo0Qv9tixkF2rYpE+eE1iJB9E0iUKgUi hNhY+kc9RhBVQXrqN42P1CcQMczrhE0oAQwBjYihrT9SqEwgapcRimomoveLYBw62bfekkRd AhH9voHzjAdxdHOJEkqQaXXk7+9PPp/P6ex3qEogol+2obuAdQZTiN7a6s9A6OiS5rter/J+ v8vH4yEvl8t09Rgg6FMka3z36GvTc67/ffk8KDJ0TQKB0XvvKtROZeNnHRDE6/XSr9/vt3bq UcxBDkTRew+28PTcq+08dm/hMTkHPK0K/Ie1gAV/qEQQsDAQXVqV8IGqgYNABTlSIKCkguB8 aaOmcppzUzn4ezGsp0WfPZ/POmtQVuEMyh76m0+iQNR6wecI2AK7XG63W1Kw4JzEZFlB4+PZ XX8cRalA+PkskKma9NRqExwzexhBwD8hSwhkD67hPQSkjHKBuBnMSS33pQLB+EgWgOeHHTj/ ZE8Yk7k6KO4RMa5EINqXrK2QQMx96jre0GLJWIPAWHIIgVU7rkMcVM4xGX/Azhv8gEM+tBGf I3A/B3bwACGrY8zZE8T0ZT49EoUnC8B83EdpvthGkUAUtm2mzeI+7osU31gCcR1CArGzBsGn vq62idGellZBXOMB5ubgnNoNSn1KNvvGTQGtzV3jQBx2JU31xcEVhMMqs3tftkDcfT71XRt7 ZxCfJF0g7kNibogAdpEtCBAtFk0AufPXu6BSgWA87g9qabZocn2RzyaBOK1G3ajayiLoPmGb P0cfGQkHIBB00DVk01xd1KTkhFEbEJukXCAQhwm8aX/8nBaLsa/wSwRCay4c5Au+3Z3J9kUm esxJ/OQbd+3gu4eu+do5u9+nKRe3PETRQZgm6PC5xV4OgeEpFoeYVul8FNibOyyEuBKDh6/5 oiIyBYKyhADw3rsTCO5Wgcz2LZQIBNUizhd9URF5AuFZrl7v22/tfl6EEpn9FX5Z8JIE8lVf 1EOGQFxnTxm0m18wvp39uay/wseYG0Xn5du+qIc0gUwLI9NvlyBSD97cGSZEX16qsRhd26EC t5dxxEG+qIXsRepXUaW6xMH2ToV9hV84XmOhLoGgdGf+YIjvJMyxVKH2q7HtVCYQxW4/OVSV pFWPzdQnEMV4xI+WG0lUKZBGPTSBNAJI+Q88psNUwx5aYwAAAABJRU5ErkJggg==</item> <item item-id="63">iVBORw0KGgoAAAANSUhEUgAAAFkAAAAgCAYAAAB5JtSmAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAKYSURBVGhD7ZlNkoMgEEY9lweicogc IQtvkX0W7HKHbFOucoPcgOFDkeav1WiYGfVVWaUpAX00DZhKHXydQ3IBDskFOCQXoLDkVjV1 paoKh1Cy/3XrFJXcNkI1bXcuhRZdN1r79vm9dNE2qt5JNP9iTpZK7FmylHLhMG5NHSw6koXN HRsnliybIW8uQktsGM+y2Uc+BoFkPftzZmYiRVpk29RK7CFP9PiSpVj35RP1QXA9DBWpO3X7 8exJlsItsVYBeZdYNss2s0a2R71ae+i8VJ3u94oNIHNfcEOubK6tHEQyNgprz/Z6BVFiLUwm UU8Wlom2fZxnVjODNN9kumyuLQYnmVY6kff73Z/lQMetF62T0CnKpiNIcKmpG0n0mhIKm1SW tMXhJOsCqV45n8/q9Xr1V47r9Wp6fww83ITOXgl/4g7FzZXMl52+SGAl3243c6RAFH9HMqK/ y4PRwVaEzU133yAD70RSxBzJfNlEWwyDpagRDSqhIHqp9Mvl0p/l4V7sG5j3COQMncRMVKn3 HysbtpXDkxzKCCXj2qaO5/OZjXJKql6eTyPZktmuZ9KhJSV5IFt22qeB0UiGyMfjYaIW14jm +/1uzruJj0qJG5wveSEpIcHQT5GVzJUd6TgLKxmCO3mVOp1O3jUiGYx9viwh2Tx7/1zeOxhB 8TPpAnpJRuYKex8tnymbbYvB5YOJvcJC15M9EL+02v+OkwxBi23EOeqQTCVD0MzNSATZDXUg XxfejPxBiGQI4SeHMeLPl6hzYcdtACIZQ/vzqMOEEKcFPTr2nis0nmRMfp848VcQ5PPlh/Vt DV8yhvfMj/b+rgiHGw17+veDI5CsWe3vJx3RRxQbYsmatsQfqTsiKflgXQ7JX0epH78BUz9G 7/qCAAAAAElFTkSuQmCC</item> <item item-id="64">iVBORw0KGgoAAAANSUhEUgAAAFAAAAAbCAYAAADrjggCAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAHmSURBVGhD7ZjtkcQgCIZTlwVRj9XY TIrhIImKHn5syP24jc+Ms5s1oL6AJrvhwsQS0MgS0MgS0MgS0MgS0EhbwH3HAA6d23DbYqNr 8Biob3GiCrh7QHeJ5nzAKNcefP4d8u9v5peAu3cp45xXJAqQ+jcS8e2UAu45w7hcNf3oJvSi rN+uYSFggCzM5nyzRGfvewNCwDKzeuUpy7ydqU8SENJ4QFeSPG99yi1bi8+MEFA6pNazlPsg tb8uYz7U9CBdc+4GW7e1+JT8AwHzvMpD7cwS9aBLtGwtPktuCaiXsNwC6pJ4gOuAS4s7rgEh 7se9KNa2EYvPi8cOEVkSR79qL0Wu2lQac5DP4BxBBPp+XhxC9F1k2xKLz0rAsjTnHmPUdL8i ORj7FoEXSJ+8WDk2B02diyDa1lh8lgISMrtUYylyM0tb0bZmINvnxUqb8WKzbYnFpyIgO+R3 4Liw1qtc9/mPMhAGA9+CgpcWdGR5rBIO2OBxStpKLD4JRcATFgvUbHEkTv89OPiOuJ8iM77O UtGXuuTe1bL91GeHpoAJOdjEvsZlMDPwtzAWkHJpeGhclJtwQD/K/y9gQkCG9gMpIqVYLY08 fM423j++gUkBGTpcxP+EsfUy8g18IOBCYwloZAloAvEH9J8erLWrvlcAAAAASUVORK5C YII=</item> <item item-id="65">iVBORw0KGgoAAAANSUhEUgAAAFsAAAAxCAYAAAC1WdWlAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAPUSURBVHhe7ZzhtdsgDIWzQPfoDNmi S3iPbuBpskyGSXUdlCOEBNiPuEDy4zuNEcZwERfbL+nl8ng8vpyEWTgat5W427GzyfXFLByJ ZaHBGeX/kxv1aTUETwpGYu1QaMYSPDoYiTst1+Vmx5pAbV9IMC92peubMcFypX6K4yg4DJQx sA8z1gIIrYRisJrwLybbnYwA6shJiYKjgEFYntiK5WLbEzY/uZpgFaXVJbM7CY7A6mQdA7Fo ZJcbljs+U/1ab9fZyHCbW7tcThN+LfRFencS7B4MsGQhwQbWkHUYcI3HAkykma1o07iutwpe YMLDtdNg71DnS0tXC2YudzreMlVlJs61LMqzDK/+C5EcabBzin6NwUkB9bHAynhTPLThZHBR bIJ92wz2jPRAi8hzqR4G6tW3LECvCoA2YSF3o52ijRC4gxlW7NzgINZmD8Ei3Md42Ai1pcut DXJbAQTvAS+o7dIGCdAn9NkM9kxW7MrBA7TjeX9NtoJcG5Ipxa4dPMgKiqwvTBpbixXTTCe2 tI/ShuVZSARWiVen5nzBsGL//U0Y5XvA4GtXQAv+/Hr22Qz2TM5GiiAjQ/ZXrYBGTOnZvZKI jUFEs05ZsL1XAKh8UhaUmEJsgB2axeZ7TTkJySBzGwUmS92vtmBKsV8DEj4XiYdyasS6Pdrz zncvc4uNWyAuF8J596lH3vnu4XPEDplsPdJmz3dWANf10PXBx4mNk81shbWI7Ge8VcDX8tD1 wcfZCE627lE9y/DqH2FusZGtoYytwxQvTIolhCc2t+uh64MpxYawsAwMjst4kDhZZ/CRd77c roeuD+bMbBJTerV8qHnHO99a5rYRBy9bNRDne+v37HNUuEfs3EMN832oefJzsQEswhMTk9FY aHCa2Ln+U8x6zvBIxMYgWGim5fJvRYv32UUyq/bIq4jPfJ9dibcfHX0VYdrICNSIzXbYw9fP wNRisw108fUzYmqxtWB6uW/ZG7J0UZOAc1u/ihha7Ozg9NLWxyTYSzTESHDZnileqGdNcpXY 1Gdc3wz2DLIyN7jIc6le7utnQIuFY53BfOdx9Otnw37XL8pMA4gFe9igz6W/neIXDHKDszbI zfOxovR1qe3iBok6dC4+p8HeEZ03Y6XBC7xVUpOtwPPxCIrz5KXBAUD2WoJWDT6QtSNqo9Wr CLnHJMERsISS9uGKGMD5xa9mULzFq4jhf1OTFaIAHnR48JvoItYcupZcaXFwIPZYBhNtnuDg hNUisxpEwdHA/etbM/MHSK9mooMR+f52/WR6+l8Z1kxfzMIvb+Dx+Af3EnXt0xYClAAAAABJ RU5ErkJggg==</item> <item item-id="66">iVBORw0KGgoAAAANSUhEUgAAAK0AAAA7CAYAAAAJkAodAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAVgSURBVHhe7Z1BcqMwEEV9Lg5E5RA5 QhbcIvsstMsdsk15lRvkBow+0FgSEkiAJpL5r4qamNiYiKdWq5E9t56QyqC0pDooLakOSkuq g9L+Gfe+a2797Yat7dW0l2xDaf+Ie9f23X38WbVa3KbTGpMYKG0J3Lu+YbSNhtIWgepbShsN pd1AKXVw2L4Px1hFR9pWcgWyCaVdQ3Vz3nkILWW34q3qmM+mQGmD6Nn9mmmJqNYv5r1r+pZ5 QRKUNoRqz5XJczwI28yhXOlOEhdvf35++q+vr+nR9aC0AVT7KEmdAvJWw9qhzDXUaGVrot7v 7e2t//j46D8/P/vX19dp77WgtF5Q+D97Nq/69mAtFpJ+f38PP//+/g6yX5GL/dVaxkBuaYG6 aaJgkGgddIS4aOoD0RWbgEhLaQsDedvLy8sQXTAk4gJJlJF/04mUVuefN09Ci3PBebm8v79H CYSUYG+eLMfH3+62x9UoUlqIgYuCaCIgymAffgdJ9rFfWjfSmcQO1XulxfHRgQH+fpwHHofO 59kpUloIIBdJwGwZ+yGsDMXuZKbxColh2X7evAVSAMzqXWnxfBOchykNot8WON9HtSAE7o7Z cqPzmh0Y4P3MNopri+egWGndiyTS2tHFzBPVIOe6FHGR1i5FjeC9TfBYUgUM0zFRz3fcGJCW uDkzhLVHnNS2qJdipXXrkJLH2dgz8u1IFi+tL9JCTJyXnAukkQmRKVXoPPZK67aHpCO2yKlt US9FSovIhYsCOWSTfYg6cxTWuadcmDvy0M1a535pISzeH5vkk/LYnBANr9X7zpJWcnhs0hZm 6WsmuS3qpUhpYzHzuAZ11bMukmcilkJITpxv6mHRORaCesjWFgVSsbSynM/M5U4CddpCpEVU 3SZjWxRIvdKa0dAYGs/Bzg9T8Uu7T6goabO2RXlUKq0rwBRpTrtWOD4i1z7C0u7vCGFyt0V5 1Ccthu4pdzM/ECg53YFR3UK1+4dZv7RaprNOTvhPbVEaVU/EsqKH2b0X3SvtgeMRG0obRA+7 OxaBQ9gx8tllL3464TyC0qIeeHlO+7iNWv24TVXMKcl6+jR0XmdoMTu0+avH/riUzCut3DKV 7crc/8cHG6sBk7xJrEFe/2R1ltA287Hc03yt/lk+1OkT3cfCSLnTg1uEKGq797zJhTFLa0if mvBkzxUQj610SU8WfXn/cgK7ZCEthHUXq6SB3vi8M9cr44roFW/CJ+36a+PnEF5pZWFIzO3D MGNPHIYJd6PRBZB+faKi5YQrKaKoW5Z7vHYMdHjv0PFMFtJiMYgse/PfjZE32F/HXGPRgNwO b2dxSFoNnv84r6U/w2sCebKJ9RdJhN0EvWbz7g4jbdnsuD4HcloL6zgmCIiJ0por4ddAjwmd D3lmnOrBSuAKSuukCRZBmW0sadHLUJ+VFfl+tnrDmD5Q6idlKFchIhvD+7RvvuaDmE7Uln2O 6GNK4Dx3A0taWYUvm7eKENkbCMmFN0tHpEXEhbguTA3IX7OwEmUuVA6Q3y5LXnGJcp2YE5M8 lRFyDt5IG2J1Rlg5/Dr5eoiUdpxcSa4bUwCuGvPeeCK87Z2fpEh7GTbKObjp4quwIK3yzQPI ubCFfajwwg3cgMHmQ76PgOSFLbxAp0JWlLXrzq6UiK6mxFyHnB9K64AvU17LZV1p8Tj165HI MSitAaojc3FE57W+/3EGksoaDallI9qmfD0SOQalnbBXIGHz12ohrDxn79cjkWNQ2oxAXEp7 PpQ2I5Q2D5Q2I5Q2D5Q2I5Q2D5Q2I5Q2D5Q2I5Q2D5Q2ExBWSmEU91woLakOSkuqg9KS6qC0 pDL6/h9xfd+E3jzX3QAAAABJRU5ErkJggg==</item> <item item-id="67">iVBORw0KGgoAAAANSUhEUgAAAKgAAAAiCAYAAADLYJIKAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAOBSURBVHhe7ZnBsYMgEIZTlwU5KSIl 5GAXuefgLT3kmskpHaQDHr+CrggCeRAx7jfjjBgV2P3ZXcxBMEzBsECZomGBMkXDAjU4HA6b OfYAC9RgL47fCuwNAxZoWbA3DMoV6FM0lU7vtWjV1V+HBWpQqkCfTS2aZ3/e1lKkVSMl+/uw QAmbSe/PRlQ7iaIsUMJ26s9W1CzQ/bGlCFrrfP/jsEAJWxFo2+yj/gQsUMIWBPpsKlHvZQsv YYESShcoxFkNqb0VzQ7SPAuUYBMoRHHIGLLO57M6W6b7tCTHNx7V8NkpBfN50u+uS31hw6bv m2/cMG762q4fdX+IWVmgBBiNMhgzk0Dv9/vgLLPvb2KbZ+h3V3rfjO5zGBEi2vo9gZ/Kgq3y fr/VWZmkGJ9NJLki6PV67frDuB+Px+r2XZynU0xj9BxLjxFs5hoSQdEHvQ/Ctz1HmXjkdDqJ 1+ulWiOXyyXRCu8n9Km/c47P9XwugaK/2+2mWuvjFejSP1cqUk7E1tadn2mKN/uIEihWNA4b WN1pBKqh9Y1xOIyUe3yu53MKFPNBmkcETUO8XTWL85Ri8wmpDz46yspxNP3ZpAaV76F1apRA MQkKohIVRFgxr0N+2gIe/G98/shtvl+zLNDPBXE8HrsD80BmmJPPljbc85TjCPzfv62V+FqZ 2tUDE4FK0B7t5J+bU6Bo63SKFe6KXjOwSrwTincsfqOg/dH4HJjv1+SIoDpyegmyJSV9BB1E 5wV99/dORdgfs0iJuQXYdSJQbThEI7SxulEn4Ty0iDdXTCpixheSOkzwDhs5BIrIGUIuW9qw zRPXhkuyzlz8e9VRBljnYKT6JQavwPlwEg4YkLZpjYTPCiiIcX0+IFqH2PCnWhfh45OGlteS CLQzZN9HSpHifVhktg3fiM+WCbHMcx4FVTpWG6LutgD7TASq74/ICvaw4QRhfKFuwAASOvJT INJUETQHOurrw7qbL8SWaxPvFYT6kNWyIj6BamFQzPY3QATV5YpJKbZcm8+8Ilf3vB75Ykry EBJB1xYoyhLU0ChXaInSU44t1ybCKzK96x2d5cNtV/sVsuRDUzwV5RoR1EVJtlybCK/0Gxw4 EgXzaD96PX5zkoNYgZYjzvJsuTblhI2EhAoUaDEwZfJznunSY0QEYoGWDXtGwgItF/aMhAVa LuwZpmhYoEzBCPEHLAczKBkjW6MAAAAASUVORK5CYII=</item> <item item-id="68">iVBORw0KGgoAAAANSUhEUgAAAEkAAAAYCAYAAAC2odCOAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAGwSURBVFhH7ZWxjYUwDECZi4EihmAE Cragp0jHDrSI6m/ABrk4OMGBGMzpI6G7PCkSMQTwww6FyVySJQnIkgRkSQJeJWluS1MojTNg Nm1ZmKKAUZp2xnCENsqd34a/hbsfxkqymItzvEZSeHEiaW5VEKOVPVe2VtsOra2mDa0UzkEe ip1bUwbJXJxHLGlZFjx6jmMlEVxCXgCHrTyFIqPr92IS8RMiSXVdm8/ng7ONruvcV36aS0mp SqLYa1TIGFsV7qcVaSsuzhMy7/vejRRQRXJJdB/ZDU4AcipJkBBtzxXcrw735OJpQuaQBAWq h0prmgaPnoOXZJO6qiL4OL7VkFUafjSynotzsJJg7ltvmia2yo58v5K2zfgMK5KujdpzfSd3 moufEEkCEeM4uqqBOVTTMAzumG7c8KeR9PJdUpIgFkLRnrPDtmO0FGREGzSRlIqfECSBIP/F q6qK5lBJHpeIjX1dkk3SP8+Lcr99H3OD/qHi5FLV5t8VBn1fLs4R95gQeMgTlfRWsiQBWZKA LElAliQgSxJwWxIIuvP7/Av8qpL+G1mSgCxJQJZ0iTE/H2TauERfX/gAAAAASUVORK5C YII=</item> <item item-id="69">iVBORw0KGgoAAAANSUhEUgAAAK0AAAA7CAYAAAAJkAodAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAWISURBVHhe7Z3BcaswFEVdlwtiUkRK yIIuss9Cu/SQbcardJAOiK7gGUlIIAmIkblnhvk2xgSLw+PpSfa/dIRUBqUl1UFpSXVQWlId lPZh3Lr2eukuFyxNp4a1ZBlK+yBubdO1t/6xarS411ZrTFKgtEfg1nZXRttkKO0hUF1DaZOh tAsopVbetm9mH7PoSNtIrkAWobRzqPaed65CS9nOeKta5rM5UNoounc/Z1omqgmLeWuvXcO8 IAtKG0M128oU2B+Evd5DudIXSVq8/fn56b6+voZn54PSRlDNWJLaBOStlrWmzGVqtLJck/7e 29tb9/Hx0X1+fnavr6/D2nNBaYOg8L91b151zcpaLCT9/v42j39/f43sZ+Rkn1rLGMktHVA3 zRQMEs2DCyEtmoZAdMUiINJS2oOBvO3l5cVEF9wScYIkysi/+SRKq/PPSyChxbHguHze39+T BEJKUJony/7x2f32OBuHlBZi4KQgmgiIMliH1yBJGeXS+pHOJvVWXSot9o8LGODz4zjwPHY8 z84hpYUAcpIE9JaxHsLKrdjvzFyDQuK27G53XyIpAHr1vrTY3gbHYUuD6LcEjnesFsTA6Jgr Ny5e+wIG+Ht2G6W1xXNwWGn9kyTSutHFzhOVkXNeirRI65aievC3bfBcUgXcplOiXmi/KSAt 8XNmCOvecXLbol4OK61fh5Q8zsXtkS9HsnRpQ5EWYuK45FggjXSIeqnsqD6tPpRKi/3Z7SHp iCtyblvUyyGlReTCSYEcssg6RJ17FNa5p5yYG/LQxVpnubQQtpexT13s59IhWppuWCKt5PBY pC3s0ted7Laol0NKm4qdx11RV93qJAU6YtkEphvieHN3i4tjImiA3drigFQsrUzns3O5jYBw a6W9H99IibSIqsvs2BYHpF5p7Who3Rq3wc0Pi8CwrXNMZUIlSbtrWxyPSqX1BRgizWbnCvt3 o2Qu0+mG2OfKCyHI3m1xPOqT1uSKkr+NYklOt/quPqCa8tssOlzT49AybXVwwj+1xdGouiO2 K/o2W3LS3QqBNd2wcH9kCqWNom+7mZPA7R58v4zRmt9O2I6otKgHnp7Nvm6jI+6zRNl7SjKT PkW2MfXv4YKe3nWQm8decwlKK0OmspyZ2398sbEa0MkbRDRihjqrkW3wWDqik/fiPdq1xPxp YqSM9GCIEEVtf8ybnBi7tDZExolnkW380UCkUv3zfpucMt1EWgjrT1bJo79qEi8aUhHm9m6d 2FG8kdg20fcOUbeR/kCCOEFpZWJIyvBhnDFHmSw0+gDkn594tByJboMI7JXlsL6XWa/HC0bg gpwWk0Fk2ttkNEbvtBk+6F7zNScNyGX1shWrpB0ej8fV570p+/RxPpFE2DB9cbzfXUoewkh7 bArOz4qc1sHapo+04wbZ0toz4ZfwrxByBrzKQHBYemEbL00w28j29ntncKTFVYb6rMzInyP+ uwDsiD01Q97p1GCHdWOADWxjZNXrQqLLa/Y+ZnCklVn4skSrCPqg+INp5FEEs3REWkRciDuB wpIHM7ESZS5UDpDfTkpeWtj2aee82R2T5byKPI5gpA2B4cy7r0be4fGTwJ+Tr4ckaU1Zwsp1 Q980fSpMR6LsM3LYe3+SI+2pgLQzkRaDLqEKC9KqYD+AbApbOISKf88KAzBYQsjvEZB9YQtP UN6XGt26sy8loqstMech7w+l9cCgyVwu60uL57k/j0TWQWkt0OG0R3VC9WhIKnM0pJaNaJvz 80hkHZR2wJ2BhCVcq4Wwsk3pzyORdVDavVlRPiNhKO3uoCNHabeE0u5NJDcm5VDaneHvHWwP pd0RpxpBNoPS7oT7zY70/42RLENpdyC1fEbKoLSkOigtqQ5KS6qD0pLK6Lo/XeylkLwCqS0A AAAASUVORK5CYII=</item> <item item-id="70">iVBORw0KGgoAAAANSUhEUgAAAKIAAAAiCAYAAADcQgLDAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAOMSURBVHhe7ZpNsqsgEIVdlwuysogs IQN3kXkGzrKHTFMZZQfZAY+DtLYI/l1R9PVXRUWjUTgcmoZKpgQhAcSIQhKIEYUkECM6ZFl2 mHImxIgOZ+vgoyCqO4gR90FUd0jXiB9V5jQtF6qy354FMaJDqkb8lIUqP/VxVWgz5qW25nkQ IzIOMy1/SpWfLCqKERnHyQ8rVYgRz8uRImJB8/RJECMyjmLEqjxXfgjEiIwjGPFT5qo425JZ I0ZkpG5EmDBvpuRKlSeansWIDJ8R0flZxBB0u93s0TBmy0bXry15s52zDu0+Zb+5fA+z+16j T+/78P0hxIgMCMdpRI5kxNfrZTurLvuBVXi4ncE9TLZo4gN2yZ7n5Nb/fj97lCZr1M9nhlgR 8fF4mPeh3u/3e0d96+jVTvkjhPYwq8L/jIl7nh3lr9er+n6/9qzlfr+vNGLrkbe0X2PWL/T7 WEbE+57Ppz3bEWuUgqb+sbbi/l6E02YuA7/z3t+nUR8jFMUHRus6RiR4DuGUgBCx6xf6fUwj oj2YnhER12G+rnX7dMTCZWPKkUDRi3x2WtfFGxFDkdKhUR8P4iDK8I4fTap1IworQl6sv8/1 t/qNR2L3+cSwEed3PHG5XExBOxDpO0TWkoP2caMgpwsbR+sYiG5Gp94UHL7fJWhEnNM0iBEb ikY1+oVa+PqFU3KO+R2IaxycT6/fOO7ziRgRkSKhn7lacpZGxPbakBErRE573AeDvXt9+P4u HSOSQIguOMdoRR6D4znJtDvK1mBa/XhHTBcB4Dc+YhgRkXAqMbTsYKZj2mKBmfzbLahHIwMi tnuTnoK5TqP3OzTqo5PrDsyMUPyc5zBYmiOPwPdDI8d/CQ1dtliZUr+//FUKz+kBce071jQj nofB5Ft4uYS1XBHWTm4eyheNlvZ6XWqzmkFK3zF9QvcP4Q8DQRBxRh46wf2bYIScMTVowbaC ojiV4Oo5FS03YL76ECcUHZISrp+zEGQAjnu+BYiIlGb0+I9MCJapr0N5TyQtXFklJNxIR+5t RKQTyHGRZvDUx5CalhswQ309LdMqSAvFNyk/VaUa3YyI9nhHpvxViptvj4joI0Utt2CG+vVC Ax2G5JNm507Casq81WoMOiu2Ach89Lk3KWq5FWn0wIqgM9vV/PhfpajThX05VQ8s2jYQIyaB 9IBGjLg/0gMaMeL+SA8ISSBGFBJAqX+5LBhIQX7DDAAAAABJRU5ErkJggg==</item> <item item-id="71">iVBORw0KGgoAAAANSUhEUgAAAD0AAAAYCAYAAABJA/VsAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAGBSURBVFhH7ZbLzYQgEICpi4KIRViC B7rw7oGbPXg1nuzADuaf4SXsKr+47sPol5iIOJFvZwaWwQW5pa/CLf19BpCcAWMMhLKPPPMc YxzkYB97UrExPyStQJDQyooHKbyoEvgel6jpSMc+sll6miZ79w5Mlvhz+pYZJHAmUFUP8mKR SLosSxjH0Y5m6rrWZfM2rISgDG7JGL3vMp0bi3iTpmn0tQRlebt02HsP12rpcpzDzNG0lvin L5Xwmc2ORbwJLSqEshv+CFVV2bvjoYWH5Uk9u16u2L9BP+fFGlalaexKve/71Sp4Zm+m57nU whVl1d4TObGOSJrEuq7TWaUxZbttW31vNrJQKP74S+iydMcQ7cRLR5IR9H4YI+iljbEhXpqE jQyDoiiiMWWaSB8bL4J96r4Xirke1d+z8+YK5JZiE8Q1nYNe0IHZ/iD7pXUpXU3a9dQJ2S2t 5IH9/GF2SUe76AnJlo7/DCiQJyzxLOnksXEi9m9kJ+aWvgoXlAb4A/K8mKxCIY5TAAAAAElF TkSuQmCC</item> <item item-id="72">iVBORw0KGgoAAAANSUhEUgAAAVIAAAA+CAYAAAB5lJN6AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAkGSURBVHhe7Z0/etQwEMVzLg6Uj0Nw BIrUXICeIh01LS1fKm7ADZZ5iscZz45kWfauLe8rfmCPHFmakZ4k/9unp8vlQgghZAWhkRBC SD2hkRBCSD2hkRBCSD2hkRBCSD2hkRBCSD2hkRBCzsTb2+Xp+dPl6dOTiJ4i+88v72nR3ywh NBJCyFl4ef4Qz5fXD/uriKjan429hdBICCFn4EVmnaOIBjPPVyOya8Q0NBJCSO+8mRknlvFv wTEXEVe73H+NjqkgNBJCSO88G4H8JKIaHQNqjysRGh+Zv38vT79+XZ7+/YvT7wWu34QjKCFk HjfTLC3b7fI/O3OdITQ+KhBROPPz53eiY+4C7jA2joyEEEGEcxRHoSSk9jopaFneh8ZHBU78 8ydOmwMz2Eh8v39/z9fbc7xpA2gcGQkhAoV0P+DEyF7Dt2/vM1pvh8AuzRdLjdaL3oQQYYGQ lpb2uH4a3e33hMZT4K6RWHJOtcdE10jTRenMTBFCarf9ftrWMskIqGlX4JhSOiGkirU3m1Rg H1tIF5KbUU4QAR5FDiOeEVUrnHC+XiJAnljea9qVULp88MhG651DQsgHkyV7ZgLkJ1xeNCGm FNIFwImR3YLAlGazmMXijj+28b/evJrMbr2QOnAO+/YFIaQdO9uceyA/msBQSBeizsTsMVrW g9K1yy9fPvL4+fNj++rm1YyQIvDp/d8gjRCyEOlveMde+2PuFdHcKpBC2gAEEHfe4dir9BkB rGarfITXjmauu5ZVfN6Tr7bgcP7eOQYQzfCeCYSyUC4KaQOYPWJJbq93Kptdu9xISF+2KMud OERZpbPUdIgzcFh/HyAGk+umFf2QQroxtQ6dQ6/ZrBLlnkThQGXFV4Ai+6k4uL93j4H4pnRz yUMh3RI4Xxx6lOuWaIy9XEM9UlmxquhmAGrk6P4+RAxksLFimruBDBGtFdzQuAopVHbKjAoc YdmxAARenXmIskPUa0Z1OW7310xry3ovbtH+UMehfYztZK869+Dvo2iA+AqDjo0baBX50NgM RDQzc9MpfRKmIwW7NyobIkbT0tscd+EAnUbbXeJWQjOIaXqiQ7bTkxcb+x7xnPVlD/6+VQx2 JjS2ggYUPR6EO2a2YZWexyRlanyX/C3H7b2Eaolz7tGzFny7A7k2OgFiuESQ5BxWwPYaxHrx 901isDOTHVQQ01s8ppCWK9IgZis8gJlmNBpqnilftWNUysxc9dgc/niLvSO3+3WYGzHbWSQN da+9SF5iXPq4dmD9bBu7XSrh3Lmy4pnb6C2ypR94KRG2u8G+dSdGPdXXEJPsWzQVeB/Cpv5O vhRS+nAOm9aLv28Rg72ZGhAkdMAhGAhM7VIhOwojT8nH23PO1GDk8Mcr9vwQ9bAsJ2ButqOP vuC42cZaAPHR86AdaF7pB8TkHEko0NjluJQm235wjMr648c71qZgdlSK8SLkvFG7Q5lmB5iF nRg+0Pa5amkd+HC0q58FnbRAtF8lTfd78fctYrA3kx0fiHCEg8PQaFzAc87JjZJVzqxkFE7J D43rSHfYVzP4WztRyW9IS7EZCIU0ip87R251AbvvLHZAHOMw7EdlxXnsPmZFtqN//TpND/F1 cOUHi9ud2PxNI6UojnKOMV3ygD/C432Zjc2W2/tQbdbvqAMu3ejfaZ168ffWMYiOvyepDGOB UAl1VLRvgNN8xULnDI6xDldyzvSF9PjjAfJCGsp79vfUUdeowaKzjX4uxA5E8bPkYuPtqYO7 8yBvPSYqq48h9nXZiRcicrMnT7EOqL/ku6TdTZD02tmQra/uRzMzTSv5XfF5Tsos/6Nu4z5E bThfL/7eOgZHYNxApxgdhUoUKmtnIUoURB1Jo9+NjvIACHQJfzywgcGHkU81I3WgQXs/YxY+ iZVsl3yQ871iY2nztvbkZ8knpQmYISW7aUdRWRFDdN7fv99nQ9jHLEm/T1B786NUh5Z2NwH1 GepQRI6zfk7nlfx9nZXiuTM+1HNgCX+VJqiPUYZe/L1pDA7CuIFOAscmTOCukEBFI64PMEij mHA1Sxwax2ZiJ/mjU6PsON9ZRRT4zjLGbYiJdmaN45UvMvGzpFgOeVwJ9GC3nVuXenpO7SRR x0an1mPxXQO7bz/wEt10GZmpQ7bdCVt14omfFal7TkRn/Y50k4+WMZ0H+0O+dtIAUB+UNSek R/T3eYVUCl0rbFGwlCoHCaU8yAziNz9gLeGuvm8t60x7bK4D8pW/DdNuzF383oO/d4zBLUn/ LHFUUSwlj3AWZEgj7AkdeTdWNsTawW4TVpQV7STXJpvr0Co0G3AXv/fg7x1jcEsmS/qrKb1H nDArgqVg1vw9mQXLsJrVwxU7+L+5rAIG+KXLzBIQi9k2fgvu6Pej+3u3GGyBlFsvbXmfTHbm sDcbyI5IDFoa4y7xW1pWOTa86TKwpg57fV7urn4/uL8P8Ym/RuwghVm69dW4UUQcqbNW0O2I ciIWNcid49dcVunE45JybR3k7+/ebnfy+2H9vUcMbgQGHTsrnSSSvtj1K+gL2bWs0nnP/nyx 53D+PlsMpC4UUkLIaVk9iIjoz+Xhr/VOEgkhpGfwAknrzTYLhLL0pIK/fDLZIYSQbtn48kHu hQDcgPMiO9khhJBeweNbTc+7Zojyg4iOS3oRbd2eHEQIIb2Cx7i2WNYrWN7bR7/wyJMo5gci qnq+8SBCCOkWmRlu/uqpiGjtW1ihkRBCeiJ6mWCO2S9fQZzNrLNEaCSEkJ7IfS9k7c+s1H5n IDQSQkhPREKKTwYCa1Nqf2aFQkoIeRiibwJ4oWz5mRUIac1rraGREEJ6YvJY0oAXUuwv/ZmV KN+I0EgIIT2Rm5FCLGt+ZiU386SQEkIehkhIIaIQTFD8mRX5W9gopISQhyZ3176WnGDyZhMh 5GHwbyEthUJKCCEioksfyLeEQir7fCCfEPI4QPRWvCKaFVK+IkoIeSSeK2ePEaGQyiy39nJB aCSEkN5Y8xm9SEiX5BcaCSGkO0QIWz7sDBEVJbx6BGrJjwiGRkII6ZHNfmpEBHnJ7DY0EkJI r9zjx+88oZEQQkg9oZEQQkgll8t/lvzh7O05KpcAAAAASUVORK5CYII=</item> <item item-id="73">iVBORw0KGgoAAAANSUhEUgAAAVIAAAA+CAYAAAB5lJN6AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAldSURBVHhe7Z0xlts6DEW9rlnQnCwi S0gxdTaQPoW71GnT/jNVdpAd+ONxBBnEgBIpyZZov+ImEsShSIAEQVKST6fL5UIIIWQFoZAQ Qkg9oZAQQkg9oZAQQkg9oZAQQkg9oZAQQkg9oZAQQh6J9/fL6fXlcno5idNT5Pz17eNa9Dct hEJCCHkU3l6vzvPtfJWfxYmq/NXIlxAKCSHkEXiTqHN0okHkeTZOdo0zDYWEENI77ybixDT+ PUhzEedqp/vnKE0FoZAQQnrn1TjIF3GqURpQm26KUPjM/P17Of3+fTn9+xdfvxdYvwlHUELI PC7SnJq22+l/MXKdIRQ+K3CiUOaXLx9Eae4CdhgXjoyEEEEc5+gchSlHatdJwZLpfSh8VqDE //6Lr82BCDZyvj9+fOTr5SXetQEsHBkJIQId6X5AiZG8hu/fPyJaL4eDbc0XU42li96EEKHB kYZTe7s0II42+jtLKHwI3BqJpaRUm8avkeqzaC8FpcKR2mN/no6NcaNHMRIod4XhCCHTrNls Qn/XGSGuz21ChcJnpBRRAjxGoY4Po1c6hlM002/rOGEQXSJAnpjep79VY9i/dfngXkt3Dgkh V7Ipu+ljGS7gigKc9BjVTHATCp8RKDGSe2CcKKLF3yOKxY4/jvG/bl5FTwDgdbXIsMjfvn1B CFmOjTbnHsgvBjAIduhI61BlInqMHJ8CxUfrl1+/XvP49et6HG5eiWFKywswfHr/N7hGCGlE nCeCFu2PpVdEp2aBdkZaIhQ+K3CA2HmHYqPrUw6whXGKv5LzBmW5F7uWVTpBT7ragsPpe2cb wGmGeybiZOdmgDX9NRQ+K4geMSW3652WLRygXWOdG+Wm2MoZ34NDlHWlvnvisPo+gA2yddOZ 6TqofYImFJLPYESL5C3Y9Rqw+BGnnpzCgcqKndhI/lAcXN+720B0M7e5pIxBD85n9BoKSc44 JRFFHmEjyD6acXSOVNaata7eObq+D2ED6cPWmUbLdT7oKe76D4TCVUihiiEzKnCEaUcD2cO6 wu4PyksjrHrOVNLt/pppbVnvxS3aH+po2gfYrc496PsoPkB0hUHH2g0sdfKhcDFwogXPrSF9 zTNZZILKhogBYIuNsVUcoNNou0vcytEMzjQNsnKcnrzYWPew56wue9D3rWywM6FwKWhAUcSG 9UXbsErPYpJ5anSX9C3p9p5CLbHz1KNnrfh2B0ptNAPOsMUhyT2sA9trEOtF3zexwc5kJ6gg wlusCabpijSI2QoPINKMRkPNM+WrcoxKhchV05bw6S12R+5R18JmO4tcQ92zhfKFjFMf1w6s nm1jt1Ml3LtUVjxzG71F1vqBlynCdjfIt+7EqKfqGs5kbj1tCq9DyFTfSZdCuj7cw17rRd+3 sMHe5AIYCR1wMAYMUztVKI7CyFPy8fKSMtUYJXx6xd4fTj0sywMwF+3ooy9IN9tYJ4B99D5o B5pX+gExuUdyFGjski5dk2M/OEZl/fnzAytTEB1N2bgJuW/U7lCm2QGmsRNDB9o+V02tAx2O ctWzoEELnPZZrul5L/q+hQ32JjvxhvAjXDLY0GB8JUvKKY2SVcqsZHSckh8aVyna7RI0UNOJ pvSGa0ireEdatJ+7h3bM8bqR+85iB8TRDsN5VFbcx54jKrId/du3/LonrIMrP2hudyLTfD2T zlHuMV6XPKAPn75W75rWl9vrHXXA0o3+ndapF31vbYMo/T1JZRgLhEpIBUcH5M9FSaOihgpb ZYTKGdJZhSslZfpCenx6gLxwDeV99PfUUdeowaLBj3qG3iVdNpjM2M9Sso2Xpw7u7oMOpWmi snob4lynnXghohQ9JWrrMFxraXcZct0HCiVsffU8G2wa9K74PLMy+zwkb71fL/re2gZHYDxI I48WHJWYqaxXRmREHUmj3422kYwFhp7Cpwe2LPgw8kNFpI5o5EcUntlKjud0MNWYrS1t3lae 9Cz2SNcEREhJbtpRVFbYEJ33z5+PaAjniJL0+wQtmx+lOixpdxmoz1CHSSSd1XO6r+Tv62wp 6h33DHSo98AU/tM1QXWMMvSi701tcBDGAygJik0Yw5WA0W1H9QYGMCwedfgUJUremzo7yR+d GmXH/TbL94D4zjLaTeQ4186sdizpwtvPkmw55JF1HNhtkNvOnaKiQZ7azpA+6tjo1JoW3zWw 5/YDL9Gmi6dUh2K7E7bqxJmeFan7lBMFRb0XdJjug/MhX+/MUB+UFXn2ou/HdaRS6BbHBuNG yq5SkBAZnFQievMDVisl+23O0rJWtMdFdUC+0vbCa3fg5nrvQd872+BWpH9aHNukQiWPqSgI 4O81eiILWNkQb96ZLSvKinKW2uTiOix1NBtwF733oO8dbXBLsin9nLKg0Lkp/6QxRYl0ouvB NKx29mCpst/GLC0rwADv2+SaOix2CCu5p96Pru+9bLAJUm5d2vJ+LDuZAms0aqCkXHON3Bmx RWtj3M1+rWWVtFiLwzHKaaOXtXXY4/Nyd9f7wfV9iE/8LcQOUljGtLoaD6bINqICb0zuT0uD 3Nt+TZ1HOu9YTim3dt7VdWh1MBuwl94Pq+8dbHArMLBYnWQXSV8gYojkR2TXskrnffTniz2H 0/ej2UDqQkdKCHlYVg8i4vTn8kBEaqPr7CIhhPQMXiDRdcw1zK0J++WT7IQQQrpl4+WD0gsB WD/2TjY7IYSQXsHjW61PFkwR5QcnOk7pxWnrcZaIEEJ6pfj67UIwvbcvKeCRJ/GYV8Sp6v3G RIQQ0i0SGW7+6qk40dq3sEIhIYT0BKLH1ldPZ798Bedsos4pQiEhhPRE6Xsha39mpfZDTKGQ EEJ6InKk+GQgsDKl9mdW6EgJIU8DdtO9I/WOsvVnVgAcac1rraGQEEJ6InssacA7UpxX/8zK QJRvRCgkhJCeKEWkcJaTP7OCDSU5xnn0YRY6UkLI0xA5UjjR5CCF0s+sTH0aL12nIyWEPAul XfsW/KfxADebCCFPg38LaRHy93SkhJDnRZxg6wP5HjjjbBqP9VOZ2vOBfELIcwCnF2wWtfDp lwWQZ6VzDoWEENIbr5XRYwQ2lT5N4SXKrV0uCIWEENIbSz+jl+3Mi+PU45b8QiEhhHSHOMDW DztjM0m84BUT1bb8iGAoJISQHtnsp0bEIbdEt6GQEEJ65R4/fucJhYQQQuoJhYQQQiq5XP4H AtDG+jqaMj8AAAAASUVORK5CYII=</item> <item item-id="74">iVBORw0KGgoAAAANSUhEUgAAAjQAAAAlCAYAAABVnnMUAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAptSURBVHhe7Z0/mts2EMV1i1whZ/CB 9vMh3KRPsXUukN7Fdq7Tps23lW/gGyjzuBwuCA5AgALxR3zFz5YAaMABZp4ASOLebvf7nRBC CCFkaMzCYXkTh25b3qy2hDwJL0bMv0guWG0JIeRZMQuHRUScQk4uD/OAEHJBzMJhoZATwjwg hFwSs3BYKOSEMA8IIZfELByWxkL+8+f99s8/99uvX3Z9Ld5e77d3o5xcBC5ouoB6QEhdzMJh aSjkEC8ZzdvXrx9YbarwLmMgAmbWkWvABU1zqAeE1McsHJaGQg7x+u8/u24P7OAs0fv77w+7 fnmId/Ed7W9fuCO7NFzQNId6QEh9zMJhaSjkOULj89dfHzs6vxzClmv3VcSLP1O/OFzQNId6 QEh9zMJhKSXk7/fbF+xsDEL23TbWZ+bTvUICOyUImPvYfz491mt6+azbgDaxenINuKApC/WA kCEwC4elkZBDZKwd1Qq5rkVc5LErZq5gQQD1qBo2ccysdRuB8uy8v0o9Py8njfKAfEA9IKQN ZuGwNBJyiI5V7vImwhPbzWEXh19E4DH+h3hp+dJ2Z8eFPl4b+E86o1EekA+oB4S0wSwclkZC DqEB2D1Zx8sg9ln2t2+fNn78+Hy8+VLhjoDhGBu/aLCOscmFaJQH5APNX+oBIXUxC4eloZBD ePDLBAjPpn5HeJIpZUd44xveuEgcROevUh40jaG9MWgM9YBUo2Eu9KYBqyfDI861WtBg94Sj Yffzb6XYZ9mFBIw32hofxFTw1ysV8uC1RDw/ivj4Kjlh1jWGekBqEtWDk+hRA9aVo1NByI+A 4+USwqt/VfkhMZTr4Ofqz0Hwhmln54HY7mUh8VrohKIm1ANyBlVvoNipBqwqhueP++13waxr hUz6F+eXB63BFwV5X4rnIDiXkgO/nfhGDwHpJZ6xM+1FWJOgHpCTqDmXvWrApjIGdgRZ4iGr uOB9EqSu+E8KxWZPJzQYaOygHt5FFeQlJRBljp/udul4I5nnYpmTUGxm8uefdvnZIL7MeD8j txSMY6FxK8KZvhZmGD04MVe6paDmdacHpelYA7aVIeYgT14BSiehG0fpEdGU4CUHRvrs8SOn bkgMRByJP+U4ujEMAZPHj/r577+fog+sNqch126+MYbKd8C8777uoO2SrD5m6k1cRyI2difk Ss+U0rwu9aA0HWvAZ+EemU4gAazFD76A5gYOjsmKJYrYeeakexQsIPfmcJofmZOzjvFDP2M9 BIQ2Iyb9GH5UxL5//xAt+IQvgRb1Tdi1h0S2Ng2enyU5kq8lx8XXDxDSmhW5sXIBonpQOFdC lM6ZI5TSvG71oDA9a8CmEBOCn0JNR44yOItQSNmuaMyEEkXtT31oeWQStG0Iv/0j36HBJKnd oT6Tz2A3EKUOvkO8HhkD3EfDulNq7h/X20WuMedNCv6rX0iK0AliKvAFP8+16lJ5dKzMN/Od 79Bgd+PHusb/FB/CVD+Pj1sXiqEac27qx1y+q00HYkX7uqIeoK5UrlTTgyOI//DzUc0D8KVL PfBY8l98dtu6Me/miq8XPWvAqvEkZJjY+WJx4Ud2esHVPOyLTb88NAl64SH89rAfStAY7vVW +xyyATEBA/ozPIzHXlKEwC4FWHVYpZvzdhRJrpw3KTcZSpxgwA58xTHz5qZnCZQYKzN3Ynkg Y2ZuIFDu2NJNCd7M3qROn1u5XW3OpV9LP5LejDJihXog5TJnJXKlWmwcpITmKfAFvnanBw6o 1znH/GvbF/Ef8zzpAnJF7UAXpM7Vi541YFPoXujeG+BkXC7UX72r8VVbIWQv1D4bsb2xjwnB NRqg7SJY0g7iXePIrhriF/zUoLUCUUGdOzZmUsz2VvPt9YHH7muwOncDPekLc1Y/SmQ+o8Ir Npd6sYGENdsn+KjgxmkAPmJ34tZN+LY8O3jstj8yVvBjkzvSzzLPxrVbb9Ioc4ViihVH8DRH rVwt4cfeWIFs/ZCy3FihHghSVipX8Nh9TU09MH1zgP9uezfWFhJ8VLrTA88+YtuaRz/3wbKg mevdsexZAz4LERjupPnPA6ADf5BMgYE97+IUs70wORfBb48B2AtiHw1q+Prs92MIjTMCdpmX nXm35tvFnxc812NI7FrcII+x18+EXGvqrhv2XN/x3Frpa91e3/ADOzGrziVmq8RYmXOakAf+ eKzsyP/I1eW52NKxQjvfdpU5n68pRz9WSH1KrMAWrv/KeuDHBp4fzZUqsXGAkpoHutaDnTq/ fFrgeOOB69Y2aN+rBiwFcGIxJBU4ggoNjIu7klMsh6dBkot9N2xaNgAGJYbf/uiCRv18l9em LOJGxZoX7EJX8yyPY2MQmisF86LJjVU5nmO1rn+TBseP1ut89vqZkGtNWtB4Pk2xKPZDsZLS N3ZiVrlPzFbOWMGOlY9u/C6E8gDjJTmIx36+Y3zw0dKmToDIwB7GTx9rHcjxI0ZsrI7oxwr4 7vgU4up6UDpXasRGLqU1D3StB06dzqM7Bm75FPNqW7D0omcNWArgFDqekMcqblHEKWvl7gsi mFZewmbXI/0UEw2xHUq8INIeEwi/cX3PKl7AF7Blzuc5VPHSGNiMhbzWmm8XBLXaQJK7z7FS 13bWF1MXEvqZQMJ5ceaz8knxxmFFYt+wg8TVXYjJjq3ksZrnKVnApF/TP1zPbH/K8bl8GiM8 n1/j24RYYJwRD5aY1ZjzoH4IMRFcSIiVCbF/VT04I1dqxEYOZ2gewOu71YMZ+KqxvWojj5eY l9cv7/243rnc1YueNeCjAA5Zk7mD5ZiSJDJCzEY2YqeYrSfk0bEuNlc78VY0JjJJ7Vt3Hgqe +21K+hESKjPPpM/Txk/s+puVJM6ac9iV15p1JMqj8VksvqkH2WTpQWnEh141YPrnqKHo4Ik9 c8XrMK2O5wspgvTZKvBHAOP9yPiUTJbYtVRJygC5fWNHpkesfl1JP7pZ0Djikcspcy72Dokr oR4kkNt3l3pQmo41YBoYXVVaAxREjOwuRmKOp7w+F7F5mpA/A87EZ3PCfGEhvYm5M+Iilcy+ cZSKz4lxxOoeq04U9sMUMOSXtWmQvs/MAxwbxzYqMUrPOQQyS7fIJzLu1IMImX13qwcn0KsG bCpTwYB2t3iQ6+GCJgIC/mDQFJtvuQbzi6kzLeOqZN+l/YA9U8CsNyTp99QxFNuba4lx4pzr fUTIARA/1IMgJfsu7QfsJevBGYgvPWrAqiIJ6VRPdLJPdc5Grq1V8I8Cfr2WtbIuPd+uPbmW 5YixZVyV7PskP0wBk77MeA+VFyRrIeGOSck5l9dXjZMnhHpgULLvk/zI0oOT6FED1pWjU3lC RwTHfYc+pyRNsQQsOJeV8kB/FdUEGYtnv09MDagHY5KlByfSmwasngyPOMcFzQ58IxgOiJe1 gwnukJgHJBXqwXBk68GFMAuHhUKeBG6qVOOLY+Q8cAOs4G6MeUAyoB6MT1QPLoRZOCwU8mSa HhWSx5BdWXT+mAckE+rBwOzpwYUwC4dFJlWP4ly4ciXPDO7f4Mc8FzSEkKthFhJCCCGEDMP9 /j+tcOi9brbOwwAAAABJRU5ErkJggg==</item> <item item-id="75">iVBORw0KGgoAAAANSUhEUgAAAjQAAAAlCAYAAABVnnMUAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAqLSURBVHhe7Z2xmds4EIXVxbVwNWxB +7kIJ5c72NgNOHewmWOnTv0pcgfuQDePErhDcAACJEAC4gt+mwKoAYGZeQJAiXu53G43Qggh hJCuMQu75V06dJnzbp1LyJPwasT8q+SCdS4hhDwrZmG3iIhTyMnpYR4QQk6IWdgtFHJCmAeE kFNiFnYLhZwQ5gEh5JSYhd1ysJD/+XO7/Px5u/z9a9fvxfvb7XI1yslJ4ISmCagHhOyLWdgt Bwo5xEtG8/Lp0x3rnF24yhiIgJl15BxwQnM41ANC9scs7JYDhRzi9fu3XbcEVnCW6H37drfr l4e4St9x/uWFK7JTwwnN4VAPCNkfs7BbDhTyHKHx+fr1vqLzyyFsuXbfRLz4M/WTwwnN4VAP CNkfs7BbSgn59XZ5wcrGIGRfn+PfM397vZe/yP+63AEB08f+6+FY2nX23+T6XP0EXHegDXIi SuUBuUM9IKQLzMJuOUjIITLWigpc3z4EB6ul4RhipLaBtWBBoNxWNWxim3l4r7sPrt/r2UFb L7xfTg7KA3KHekDIMZiF3XKQkEN0rHKfd1ktWdeH92MVh19E4Bj/Q7xcuX/+qxItDey/HdB/ 0hgH5QG5Qz0g5BjMwm45SMghNACrJ0twHBAY6372588fNn78+Dg2v1QY6SMegY9fNFjiRk7E QXlA7rj8pR4Qsi9mYbccKOQQHvwyAcJj1Ze6tnGreSPvB40TKcB1wX+FYm2JQ2NoaQwOhnpA duPAXGhNAyYvukc6d9SEBqsnbA3r+9+aEsKj77m7+/Br4IO2+gffjwj+emWHPCj1QbqJjXlQ E+oB2ZOoHlSiRQ2YVvbODkK+BgiGVZ6D/xeVVwevOJ/31Z+D4APTaueB2G5lIoFfDFnlLUM9 IDXY9QGKjWrApKJ7/rtd/hXMuoMYt8QaEY7QfXvSH0FfSg78U/GDHgLSyope/2qoB6gHpBZ7 +rJVDZhVxsCqIEs8JGEvIWGVuuI/KRSbLe3QYEu4yCqqIK8pgSg+frrHpUuf/GeJlHpGx5cv dnltkMhmvNfILQfGsdC4FaFmXwvTjR5UzJVmKah5zelBaRrWgHlliEeQJyehNKKfiaBxW0Rw QHDCswZps8VbTs2QGIgQ3qccRx3DEDA53trPX78+RB9Y51RDrt38MA+VLwC/L75vpe2STG4z tSauPREbuwq50jKlNK9JPShNwxrwUbhEZieQANbkB/ePdeBgm6xYooidZ066rWACueTDwT/i k1rb+LGfsWYDoc2IST+Gt4rY9+930UKf8CXQon0TFu0hka1Fg9fPkqzJ15Lj4usHCGnNhNxY OQFRPSicKyFK58waSmles3pQmJY1YFYIh+A+77DlKIMzCoWULYrGg1CiOPtDG6484gR3bgj/ /C3foYGTnN2e7snnsBiIUoe+Q7y2jAGeo2E9KTX3j+stIteY8yGF/rt+ISlCO4ipoC/4ea5V l8rWsTI/zBe+Q4PVjR/rLv6H+BCG+sf46LpQDO3hc1M/HuWL2rQiVlxbZ9QD1JXKld30YA3S f/Rzq+YB9KVJPfAY81/6rM/VMa9zxdeLljVgcvIgZHDs42Jx4WtWesHZPOyLTb885AR34SH8 82E/lKAx9PXudh/yAGICBtzP8DAeS0kRAqsUYNVhlm76bS2SXDkfUjoZSuxgwA76im1m86Fn C5QYKzN3YnkgY2YuIFCubLlFCT7M3qXOvbZyezefS7uWfiR9GGXECvVAysVnJXJlt9hYSQnN c6Av6GtzeqBAvfM5/O/OxdOm4edBF5Arzg50Qeq0XrSsAbNCfaGxgB8ETi4SF+oLhTOuy0DI Xuj8bMT2zD4c8rhOH5w7CpacB/HeY8tuN6Rf6KcLWisQHajTY+MnRdDfXhs4HusEzM51oC99 YS4WVwMRf0aFV65zrBcbSFj//NQ+OvDgNIA+YnWi60xbhccKoB+z3JF2Rj8b1z7GvFemhWKI FSV4LketXN3aj5SxAtn6IWW5sTKOjbz3tHogZaVyBcdjnbCnHph9U6D/+nwdayC1j47m9MCz P1yD7ocq9ycJ44TmUa/HsmUN+ChEYOjk9V9rxOBoFOdJo7qDpsA8zvODAJjnC+hMDP/8yXUl 4oIafX325zGExhnBNPoFfpLzJn5f8LfG9wteu21IrFp0kM/IaGdA6kyRM0AiaFt4PUnizLbR D6zErLpUW5vG6oHpU91+AH88Jnb8axZbbqxwnm97F58/6nL0Y4LUp8QKbOH6z6wHJXNll9hY QUnNA03rwUKdXz5McLzx0DGB88f+PNjFz4+6mAaMBcMMySW8VGALyjRq4A+I1eFhkGRQroZN PRvUYFBi+OdPBiYRfe1XeW9wEvcEWH7BKnTiZzleGgPf3xr4xSU3ZuV4jdm6+5s02H603mcR a2dA6pImNF6fhliUa4nFylLbWIlZ5RYhW0ljhWuXY7z2V1HAtB3KAzkPuy449vMd44NbS7M6 AWIGexg/d+zqQFI/1PkxQmO1Rj8moO+qTyF0+2fUg9K5skds5FJa80DTeqDqnB/1GOjyIebF 9lAnWHrRsgaMBTAyDBKQYyduKaDTOhh8QQQYBPy0arbqkXaKiYbYjiWeycOB6Deu71nFC+jA da8Hf0u/8dqJl4uB0Fj4/tYgqJ0NJLl+jZm6O8/6YqpPrJ0Bed/Sh9SkTw5vHCyW2oYdJK5b hcQI2UoZK4yTey+S1s8rM/lDeSBlzv6Q44/yYYzw+vEe3ybaxTjjOiwx28PnQf0QSk5ozqwH NXJlj9jIoYbmAby/WT14gL662J6cI8djzMv7x89+8btrX+tFyxpwL0CHIs6MgUGyLixJZARr cFYjdorZekJKjHXI31kkxFuRdlaS0rZbeTjw2jqvZD9gy1+VmXlWMw/Eri+iSdTyOex6Y0LS oB6kkdJ203pQmoY1YPhnbWBHGxd7sRkvsByyCWmTE5owGO8t47Mq2ALErqVkO7nkto0Vmdti 9euK9wM5lSJgNfNA+rN2AlHF52JvlbiSqD9SKBnfVWKjALltN6kHpZE+tKoBH1twQo4xNL54 WyrWccMZmxGb1YT8GVCOzyXJ35lgIu3HXI12UsltG1upuE+MLVa9rQpq9AM2J+Mlx+aKp3Ie 6G3vXEr7fDYmJB3qQZTctpvVgwq0qgGzyhRwr911ZrgQVXcolYW8exDwEkxmXYSi/pZrwD1S HMOWFtQj46pk27X6Mftz/fCnXwak/ap5ILazJhEVfT4bE5IO9SDIU+lBDaRPLWrApCIFvaMz sCIhqiEDwwlNHPx6zQVPCsX9Lf4ZbYltF7xHxlXJtmv1A3ZniR6K9x3yIGsiUcvnYjdLVMkM 6sGckm3X6gfsJutBJVrUgGll7+zs0B7Bdt8sEUjTIOnHpFUJHPTlTnmAlZVVvgsyBs/+nJg9 oB70R7YeVKQ1DZi86B7pHCc0C/CDoCvwJb/JCkbEzK2ogysk5gFJhXrQFav04ESYhd1CIU8C D1XK2WYm7YEHYAVXY8wDkgH1oH+ienAizMJuoZAnc+hWIdmGrKqj/mMekEyoBx2zpAcnwizs FnHquBWn4MyVPDOzbWiBExpCyNkwCwkhhBBCuuF2+x8Jtr8UOTYoCAAAAABJRU5ErkJg gg==</item> <item item-id="76">iVBORw0KGgoAAAANSUhEUgAAAGgAAAAiCAYAAACz35yrAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAK9SURBVGhD7Zg9UsMwEIV9rtyBa2Q4 BEegcM0F6CncUdPSMqko6biB2Wd7lZX8BFg4sTxs8c04zytps8/6sZum73unYqjo1AMVnXqg olMPVHTOHJu+aU/83jWgojPSHqRAblDdwCQ3qGLcoMpxg66JFPogewr2lZRjR+IFN6hy3KDK cYMqBuboEriVSVTcE3d3Izc352KC29tRf3zk7fYCFffI21tsEIvZI1TcI25Qpby+9s3T07ic WYPu70f9/Z232wtU3AMwRvcdmAMzrEG4j30I14jbq1HhojvGf7CVF7fwUienmW7Do2aKXc5g TqoBaJ+f8eHh4yPuZw3sSW9A6phqHWn3W6If+LSunR7aUbPGzQYSE5FQpJl72sfaPDycc8LM gZbbg3CKS2PXxhpi64brUxK7tGbRD2tQMAMdJoMHXRKbJSC0UwInic8m8wfsfvPyMmo5g+zS dymDgK3dsPqw/11Qs3ABqEGytIWlzjRELJu6nQxgv2vhScp95yqlthkEhsKa8VltSmoWRPCj QZP7SIYtX9n2madGY3Ok8cqSPchql9iDLPT/T5TW7HfBqk+NsObSWSEaW9LQb5ow0LFypPEW nNI0DiY9P8dttzjF2VmUmlFas5moA1CDpo4wGPs2lVvOcvFroO9Baoai70G4z9oVg4JK/+yB s4eFNKa0ZpFIDZoSAvpU0MHkN4zMJc6S035zpPHfkduDrkU4tWVmUWnNItEaNAwmGgZWTTtC 49T1ITGJPREjLrHEpWxpEDZ5ezo7yrXmoXUqrdlM1I476czuPfZFlW14wxMk4IhpdbvhRfpK YP8BW33NtjVDsWGEzQPAmNKaRTcig4zOyM2KlNwa+x8pqVl0Y4lB3710KTqF2b1/SUHNopuL DAKYijkDkIybM2dhzcKFPQwovjRtDxWdeqCiUwl9/wW597cKk0uqOAAAAABJRU5ErkJg gg==</item> <item item-id="77">iVBORw0KGgoAAAANSUhEUgAAAHQAAAAiCAYAAACOTf3NAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAL9SURBVGhD7Zk9UsMwEIV9rtyBa2Q4 BEegcM0F6CncUdPSMqko6biB2Wezzkp+GztCwT+j4ps4K1la75N25aSq2rYt7AhqLGwXaixs F2osbBdqLExzrNqqPvG2JaHGwmXqgwSuCLovIGoRdEcUQXdGEXTNiDAHqYmoizHHhvQXiqA7 owi6M4qgOwJiakpem6jUuAceHnru7s7BB/f3vf35md+3dahxDqe6D9DhyNvXwsdHKCjrszQ5 Y0mNc7BppyHta2ELguaMJTXOYe2Cvr+31ctLn17VT/D42Ns/P/l9S7AKQTVNVCtLuRBS6ybE hHgaLIB21FFco98ahM0Zy+GikcH0oUEtL9TDy7asoCY+zUk72g7iTGBfEJteIWZsA7B9f4eH pa+vcJwc2F3XIfGNbcNuzBjL4Av+EtLJdHArdJwO3L+Q4KC32rBQbrQInp7OvmJnwubVUJxy 4765sQLaeOL6FPXNFcvgixU0Xj3WKaWWiUY5H/3lQWKHAfrjs0sxnpN/wNbLt7fe5glqU/Gt BAU2pl3Wc547VyyHC0AFlVUzpN4ZImCMkWNCIxPb30WxUr3fSVNZ2w4FQ338hcXGIyWWgxFM CuqsFgXOs3TqjuuMp3094v7KNTXU2m5RQy30+SdIjeW8zmqfEBQ1g+46sbHdjfnYA6oPHnF/ C06x2g+ivr6G9y5xyrW7lInESI3lyKgTU0EnUi6cYIXdS69e/xzoe6iKp+h7KNrZfckg0DI+ W6D2cOT1iUmNZWCkgv46CqZWF3VCvmNBeA/KnNb5POL+l/Bq6H8xnGqv3KWpsQyMVtDOCbHB IbVNrSwMGq+e7kFkjBMR7hYpN2ZJQXF4sWXqKNfqB02nhtRYjow6YSOD2do5+mGBwAp5t0IF HNmt3RbywJ4J1E+w1L8tNpYQAQJZP8AlUVNjGTQEghr7NXi7LsarBYUzKbEMGnIIeullWNHU wdoKhoRYBo1ZBAVIAZ5gcLKIOZ8rYzlc2MOPUlLi9qDGwnahxsJGadsfrTlGi+er9IkAAAAA SUVORK5CYII=</item> <item item-id="78">iVBORw0KGgoAAAANSUhEUgAAAGoAAAAiCAYAAAC3KkyWAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAALYSURBVGhD7Zg9UsMwEIV9rtyBa2Q4 BEegSM0F6CncUdPSMqko03EDo2drlaf1KiG2wdawxTdjP0kreZ/1YzdN13VOBZiisz1M0dke puhsD1N0cvZN1xyOdtlfYYrOmcMuJMmNqgOY5UZVgBtVCW7UGoSE78Keg31Hs2+N+gE3qhLc qEpwoyoAJsnSuKZZplgbDw8Dd3fnpIL7+0F/frbb1YQp1srHR26UVadWTLFW3KiN8/7eNS8v wzLHRj0+Dvrnp92uJkyxFmCQ7EswCaawUSjHPoVr1KvZsHTR7vOHPISPv/RhGE4+7crHUw0v czBJawDa11d+yDid8jhLwCfDnpBLrbVGu1vIbvA7XwLvDoPGBo46C2ZiUHJ/DG36umGQx1gu cZbm6ek8LswkaKU9Cqc+XXdp2BjOHa77XDAT8pbdsFHJFASNWtYYugRW933HcSB8vSS8H729 DVrJKF4Sf8sowPnrVyTruSfmLV0A06iw5KUlkBqiLs8wvD3pX1lsI+VZ2UJsbUaBNDMioxUo MDVvqQG4ahQ5r6cmpj6bgVjpSx4x+C0ipL8Sur5wyx7F2m/sUYyZw8icvKUKUmnUiWGUDg6g 8S+WrMN4rwcOpL8Suj6DU53Ug1mvr3nbNU59PKuumSLaT/KWBBGlE9OouPTp4JamO7TaLIV8 R4kpgnxHodxqN5mQ7CxHBJ6Tx8B15uQtCVJp1EEcFJA3xOrw0loLrDZAYpfQ9S9R2qP+CuQA OSrNqjl5SwJgo/oOg4ZAokkANNZTWJ9e9LRHbB6AILFL6PqXWNOoFubE58f9PlzLOCRXc/I2 EiV4GwLw3sQfvFZA0aW+DLgHb4rWFgT7E1jr7znnDdsDDOFxABg0J29Zg8wo0i1KM8Qim97/ nKl5ywpuMYqnrFke6d+W0KFV9i+ZmLes8CajAKbmJRMwKDdpzIS8pQs+NAi+XG0HU3S2hyk6 G6PrvgFlxF8jFZ+akgAAAABJRU5ErkJggg==</item> <item item-id="79">iVBORw0KGgoAAAANSUhEUgAAAHYAAAAiCAYAAACKuC3wAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAMPSURBVGhD7Zk7coNADIY5l++Qa3hy iBwhBXUukD4FXeq0aT2uUqbLDYh+QBut0AKGdXiMim8GtC+hf1cCuyjqunYOiGl09o9pdPaP aXT2j2l0pnEu6qK82m1rYxqdccoTBc+FPSYQ14U9IC7sQXFh9wAJdKKaibqpOVdGf8KFPSgu 7EFxYQ8IROVUvUVxTeNReHpqeXj4EwE8Prb211d73BEwjVO4lm2QTme7fUtcLrGwVp+1yR1P 0zgFmYoqo31L7EHY3PE0jVPYg7Cfn3Xx9tamXfYVPD+39q8ve9wabEZYTh3FBlMxBOW6ClEh IgcNoB11FtfotwWBc8czXFQ0IT84KOmjPHyw026q9JsftaPtRA5F9pWRaReiahuA7ecnfqn6 /o7nyYE8hQ0UY20LpzNzPKMb/A3FC/ICUnCdIpJ/W8FJsfPCbqSHunbt99oQLy9//uKkwpaq sXgr1n1zI4WUMcV1EwtBznhGN1JYvZOAHlzSYr16gP68oLpvHOoclNc5kfX046O1pYSVKfpe wgIZ1yYTJp47ZzzDBTCFpR0UUvIEITCHdA67M/zW2s3F7VFbJrZ2YkE4YR098QaYG88wAIwK K3eOAR6gd6ppjBQPa4R0g7kTc7IfKXR/5pYaK233qLESM7YjLIln6MCdeovfIKxelG2ybkSO dPfWg7IfKXR/Cd56uR/EfX+Px67xVixPrRYrxZJ4BgMbeXFT2JFUrBe1bNoRa0wu+DuWRWT4 Oxbt1rjZkAhR7AR4TumD1UezJJ7BwJ16C3fOgrGdZjkyVBOANQbwmil0/yFSNfa/QAwQu1tP 7ZJ4BgOQwjaOkA0TsW1sl2FSnTr0W5x+IKxpzctrptD9h1hT2Apids+P+zNdsx+9WCmWxLNn 5EUrmkDW1t4PFAbWQmzneaIajR2nbRlBfQVr/bsj44kyBqGkH2BI3CXxjAZEwgr7LaROoEWU VhyTufGMGnIIK1OF2d7R7DpyxGpzBDPjGTVmERYgJQyJBmdd1OnMiGe4kC9JjKfJ/WIanf1j Gp2dU9e/sxjulWaQGZoAAAAASUVORK5CYII=</item> <item item-id="80">iVBORw0KGgoAAAANSUhEUgAAAEwAAAAxCAYAAABwK080AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAPLSURBVGhD7ZuxdeMwDIa1wO1xM2SL W8IvQ6S5PoW3SJ8iXXZIm+cqG2QDHT5Z0EEkSEqy8kI6Lr6XCJBk6jcAQhLddX3f31iBa2yN l6Nw8n1byJ3PNbbE4SAX59gv5UXOe3REiwwtcfwisRRPtNlGS5wkbQ4vvm9PDnfyWWZ75mwG +dZJRde3M3wxd4Juz5ytwEV49eWrsFEWOVvgGKRJyMdH393f993DQ989PspFdn33/n726d81 2FoWOatHBn6XSUfEQqDX1/+25+ezDd/T03z/RUit1LSMnbUjg88Ve4Qhuqzt7e1sR6zPz9FO HZRIxc4XkItY+yXFzsop1S8EsNEFKhiRpraDbB/GqCHlSjOu1jHXWTOphlJBGASyNq1juo3o nUnrcCb0oOdrVrBcs0pRRxxEUtTGJED0DdFlIopzFgWTCONzXWfNlAQrQj0Swew5EKOUkj9X MBHGpqMnoMePFSysV5xvJmCCZgX7+1tw7IuRCNM24kS0LYgu+PPr/Lmus2YuTklhiCoRqiNq MjOu5efWsI1EgmEYVFdkYKHtOwYaUo1galRxtDAyQP6Pbh2C2WZoBjlWzjHsS60wxXUvqhIM aOpUtONYIMN9BrFUmGDbdtFhR70H1Qk2RcqINzhEtXYuYmr8gr5m5tuB6gQDG2Xh4MI+BjiZ FYXjp/s9BLTRaNDPSBHuD1UKZqOsJI7a7A3xTLBx27tI/YwU4f5QpWA47cDtAENxPFsomHfM VqoTjAENs2IiyryL55hUDYOUYHr+FOH+UJVgvPG1s58+kQQVhAOjIi7bdpYM0/gqU5KLmgYr g2KHaXsEoTxBYIrIUbjJR8SFtguoLiWXkIoYj1m67kCTgkWNa4Ih6uQCPd9WtgjG26LwsfVa LhMMSLWcGIi6s1iwVjAeUfPyg0fTPKL29lnC5YJ9E2uehyGQvrjl9Ro1NtxnKVf/PIyosq/V iLBLBGs2wpYKpuIQYfqabcsyAeWqBSP99O03BZ9IY9tG3FquWjDSL3z7TZTNlhDIpLV4qYDQ tGCl+1KK/bSGYgSx7EIUesk1SwWaffNNb1cSjKixfZfOkCpi2B+m7l4sza6toL/LRYMuaQJd KmDbCxiiy5yDCMsKZnrO2Fk7hYaZwp6dDTleBLN1kPqUTUnxqaCxswG4wFSRLnbzcvGzOxBH wBBbNyNnC+TqWEmwsF4hRukWTusX/0fOJiikZRbSS45FgEVLBWQfm65zZ0MsaQVSDFElQvHE pbRUwEYXzJytcfslyAZuvzXawN6/ZjtmzucabyTo+3+Nim2Y5NoU1wAAAABJRU5ErkJg gg==</item> <item item-id="81">iVBORw0KGgoAAAANSUhEUgAAAFUAAAAxCAYAAACrkOUWAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAQhSURBVHhe7Zu9eaQwEIZp4Pq4GtzF NbGPi3Di3MF24dyBM/fg1M9G7sAdcPOyDDcMkpD48QHe4H120bACfXwaCdBWVV3XNxYmWHgU Xs/CJRybS6ruYOEROJ2k0YHyJXmVY5wDwg4KjsD5GwRVQsL2No7ARbrl6TUcy+FUhd2X4nQn xzXbveDuETHo9sFYDvL7OxG11OVcyDtBt3vBvUPjSl3WQxxuxSnBunUQ3DNn1w09n591dX9f Vw8PdfX0JI0XV358XGPNp4g6NRfb3DoI7ha6bqLrIygivr39K3t5uZYRe37u71+McfkwuFek UakBCvFwqS17f7+WI+jXVz/WQI4W97MPFyzVC+xFHQZ3ylg+RRjrUlBRcawtV5gJnFr30b3H ZhWaV4PBPRKbiCuIh4i2TPOqLVO4SJVJJ36ED8H8+HCipgYZBiIEREhFyxi4vIsblxpnUv+o qOJUziEY3CNjohZBfhRRbX0INtb9b6KmEPFs18+9KZglapNv5CCpKcx3s6SoPn9Sd0/kCLNE 5ceICou5YyaPv4VA+STEqTqFuuDazHb++XU9h2BwjC2Kumj3Fxp30kbcl5hVWBbp/jld4rtY WtQpDES17lPBfFl30m2XmPrwYQ02KaoWqoAqGCfLd3+LFn3u6EbOztVSd1MH+WqFi7FZUQGx VNhzm7D9PhB8uo6gKp7bbsRt67Lfl2LTonbOaik5US6I3Z+GdhNmN9frxRZg06KCdWvuiXIx fLfmIFa4XspAZOtqgx47ht8fNi+qdWtu/vMCapnNuz4Pe2creuwYfn/YvKgEbSNyTtYLGCrz ooZ+M5VNi8rJNaN9oVtDAlFXLKdCTFQ9bgy/P2xWVFZd2NFbn3rD2KBChYN9ZNuO/v7iHL77 08DuxOUE2aHbbkkJGxJNy5vft+J2MZzry2aw6e4/h5jzQvRSwwIcVtTB5D9C414RIRSbyhRR eYvqX7HMYR1RgW6dEgzhFxYUSkXldQov/HiNwusULfczlBLWE/U/UfI8FRF1EQWvpsn5TSww Qylh1vPULZLrVNxpX0nj1E5U6UW5NzshDufUXFFVQJyqr6jVtYg61aXwI0Wlq+sqFQYpHMt2 bDFFKT9SVLq6f7+PW/1yoA7JsdnLfoRDijo2ajNA+TVTCBpbnMZMoGTZz+FWqDD3HRMVx9l5 qY78ocVpfi4du2O0HG4tFYNMykm6XBJ02Y+dWnkal5r6cGpSVKZj7UUYBveKaVQIBqOYgAOo S0S1OZp8mez+ElPRh8EdQ8Njg4m9axpFBOrd9QVE9ticPgjumVReLRHV508EG7u11nzK90Fw 14ykgGzoylIPImUt+5F9bGroBw9AztQnh8adIiZP3caW/ViXQi94FG7/+FuJ239TV2LNf1Gf E3UHC2/MoK7/AtsybOGL6asRAAAAAElFTkSuQmCC</item> <item item-id="82">iVBORw0KGgoAAAANSUhEUgAAAIwAAAAWCAYAAAASPXQbAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAANrSURBVGhD7VhLsuMgDPS5fCAqh8gR suAW2WfhXe6Qbcqr3CA3YNTiJ7CxTZ7zKnlDT1EvBiykpiXwdKahoQJNMA1VaIJpqEITTEMV PlAwo9F9Z7oOrTd6dN3AqE0/17+KwaiX3tsRG3wfdT8ft8OgOqMG90CI89P+/RH5+zjBjFoF skBQ12uSED+R045IJl9RTwXwTrD129jgO/UrFzgLIVeAE1zolvGUbO4Jt95nH0mSiEEJEm0V qskqbEI/l7a/gVrfaX7u66C10aLC5PEgud4Zn18vEczj8TCHw8Ecj0dzOp241N3vdx7zf7fB lrCaDZ2FyKI861YJGskHd7SpAf7EDByxIVxipQ3aSBVLvG37HGF1vpMfOiOO/MXr8kjabJOT DrFQ/PxbxCQ46kmQoQpmY5K/IBiIBYPX69X1GHO5XLgPY+fz2fXWQN5HsiaCLUJkWlVGIaMR /ODexbNbj+3Qbx4Bgdxv/Yz2QFZJLPUxbfcdG2NtxfEoICkYG2NMglU+lDYD8aH7WLF7xOg5 kgJc4C8IBk6iukjcbjfuh1iez6frHcmey0QsXvDx58CmxTtHmXRLciDSH2PCL7zL404gI/4N lFFBFDaDvHkmSGzGT7FdMBa8eX598tNPTQRDwDPvAzcZy3Reup6Nt8hRaYyQCEZWF8ALBpXG g50ktVrfSKXSswlerzCDyjZMqNzbnTORbw776Mi3G0Frk1AUZa2kMIzxeEpYihdi2uh7hNtQ /EpEYdtEbIn9HNGWx4QjLxL+WeYPSAQDgUj4e0yAMMzA8xu+POB05Nd/PSBwl0UL63IW2kn2 okhfXZ7MhAw6o7U8dpDJbyuX23wPKAiAY8u7MTcTRIIZW5Ej0IDLdJyzxB8Q1IBLLcQBkfjm +3AJRvVhtcvF4cxs8CBoLYvmwWvQu7GJTWXBZn05OCNoDlUJzGGR4Py2g+Eil1cRuy4J1Z31 u2POd9cHnuBniLlAHG+mH2KhII5l8SXveJQ4gqFF/oRg1jEtpRxkIbhvAjLJasTebfhr6Q2V 8y+gQjD5WbjlLP4CuCqZFBXKfFSaJpgptgsmP3u5JC6cnV8D+9VnjwvbUIKbWOZRV2H8uY// 1CFi/8Bp1FCJCsEQ/IUIl8N3XAwbPh51gmn479EE01CFJpiGKjTBNFTAmH8ra8K4gJs2JAAA AABJRU5ErkJggg==</item> <item item-id="83">iVBORw0KGgoAAAANSUhEUgAAAI0AAAAWCAYAAAD9/x8lAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAANtSURBVGhD7VhB0rIwDOVcHKjjITyC C27h3gU77+DWYeUNvEH/vDSFtKWVfoM6/tPnMEMphOT1JSl2tqGhEk00DdVoommoRlE0j8fD 3m43GTU0OGRFczqd7OVysdfr1R6PR7n6WUxDbzszykgwDbbvOtt1vR0mubYJozV/em5HbPQ9 jdv7vhzLtJ4zNHoXFv5WRQOR3O93Pn8+n+zQp8HEwcmEPCGcF6CSJDzTD/Y7mtnm+2rc4xjc O5rl2Wkwn0sC4S9RA6oLDg9Umm+IBkgybjRqPNmh1xn3GrDXf6vMVPieVhoNetZ44S9V5hNx ef4SNXiBoNKgRWHsq04dXEA1ixojJi8ej+YFWRP5QIuDGAxlq1HZPdEiulahbWBBJNPnY592 VuN7UTSU7SZ+jitXgQuZ5/bF5yomxVE/kO3MnOYvEA1a0eFw4HNsglFxMNaVpx4uq8KFkOOF otZEo4kpigaZDQJGNw+ReFtsh855BiTydefnYg+E5QRTH1ON7yXR5NtRmBQJOP6BOh18l/uQ OIjRc6TfW+AvEA1aEQ4NVBsvJIeJ7JFxEISXr8e9C2Ly8sSDMFXVOJtC3/Asz4tIJvxGyqxZ GI50b55JKi1CJfK+p4jjXoBKmN+TLXudiA9C+j4RmboUcJSbIwSiwQYY1UYDgjmfzzJyL4di 2R6Uuhqcxn6VBu9bxs7umol4gdhPEQDbxLtJLGaQaiOY53g+JC3EH2La6DuQxD2DFjr3ENv0 ookhApERkHDkhcKnef6AQDQIWv8v47+cZiEpwwyM3/g1kpKH4KUyFN4NYbu+T1WR+vRAJd3b CQihnj3oFkSVx5fj/bHNdyArGlq8rGawsHMgEQLBOiwcgQaKm6qUv6fEHzCLBnsYVBQcaEk4 9Kc34KqMejmcyQYPkvLZ9BKwvZa9LFxcV4sdAwTiHso83MNCQT93k/PmLq4mHB/ZNdL7d8ea 73JtDjEXN0F/ajMK92qwCOLpHEfwq8ifEg02u+WvpLSkZjPiB4GMcjpxex3+inpjFf1lzKJ5 /a9v3BfLffmngIwlgQTFhSoAKk4TTYrtoon7MJfGqFz+LNwXoWsd7kA5boJZR7ARLgN7AdkD 4E8fIvY/6UwNlagQDcFvkLBZfMdGseEnUCeahgZCE01DNZpoGqrRRNNQCWv/AZk3R+tb2GfI AAAAAElFTkSuQmCC</item> <item item-id="84">iVBORw0KGgoAAAANSUhEUgAAACwAAAAWCAYAAAC7ZX7KAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAFXSURBVFhH7ZVBzoMgEIU5FwciHsIj uOAW7l2w8w5uDStv4A3m5+GgoKZRWv6mTV9CWofAfDxnUNCH6QdcWt8PbJQgqS0/lZYlLQUJ gSEJaW8CLxsow4+FZbXykBCMElLfBTak3KL/8jeR1SSFSoGnaaKqqqiua2qaxr+KcRz93PJr yNx21x3S7fP0WwFw7DBgAdj3PUeIuq7zMcy1bcvRXMX1uBtXTmOU750VGAvhbqxhGHwcsPM8 czSWdfvIJalUZIrVylaKCXDsLhSA4fSZfCMormk48NCpfIeNcmbw/wQYgLFCHZ+Km2BNxTX2 apOtllv9uxwrDZoKcIAMI8TQhHv3F3cjZ5zDuHaOwPlN53O4tdtw5cdzN3W8j+HEpeZ5UpnA cC0qh5MDlFIe8L5eUQ7JAcop3+FwjdkXfRguKhPYCdcYN4IqdwEflA/8Jv2AS+vDgIn+AHbu Or8QFZtFAAAAAElFTkSuQmCC</item> <item item-id="85">iVBORw0KGgoAAAANSUhEUgAAADQAAAAWCAYAAACPHL/WAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAGBSURBVFhH7ZZNjoMwDIVzrhwo4hAc gQW3YM8iO+7AFmXVG3ADj19+ICHDtMoMClP1SZHqSo38+dlOBb2ZPkB3149Aj8eD5nn20f/Q KVDXdTSOI03TRG3b+m+JtBIke+Oj+jK9JCEEH0lI61sgACzLYj+v62p/4GSol4KU9mFtmZ6U L64F48QyILiCEwSHdiBNSvaMdUNpZTsnAwrJwyG0HeLgFoB0kTtcCL7nOme5c3p3eQKE9mqa xn7GQoBTiGPHfifXsq7nD6eY1hULd2QOob1wYsGlAJnLsNN+KKUi/ee9+HoB3HJQKRCWAVyK BZhhGHyUChtPKD9T6OGnVb7CoSA4dQDCxfG7EzbcEdKKN4zkC7Y0ENdcGFzQZMthZuAEDtoM J17fRzl3oqriwlOga5bC/gbtuWxAGPyz5HPl71F4B2prA4r/DTyX69c9/RywlsqAjvOCdksA 6ylZCq+LHQpr2lwzH6UqBGJhTduBlAxTbbdlKge6qT5Ad9ebARF9AY9C5REHjtZ/AAAAAElF TkSuQmCC</item> <item item-id="86">iVBORw0KGgoAAAANSUhEUgAAAGoAAAAiCAYAAAC3KkyWAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAALYSURBVGhD7Zg9UsMwEIV9rtyBa2Q4 BEegSM0F6CncUdPSMqko03EDo2drlaf1KiG2wdawxTdjP0kreZ/1YzdN13VOBZiisz1M0dke puhsD1N0cvZN1xyOdtlfYYrOmcMuJMmNqgOY5UZVgBtVCW7UGoSE78Keg31Hs2+N+gE3qhLc qEpwoyoAJsnSuKZZplgbDw8Dd3fnpIL7+0F/frbb1YQp1srHR26UVadWTLFW3KiN8/7eNS8v wzLHRj0+Dvrnp92uJkyxFmCQ7EswCaawUSjHPoVr1KvZsHTR7vOHPISPv/RhGE4+7crHUw0v czBJawDa11d+yDid8jhLwCfDnpBLrbVGu1vIbvA7XwLvDoPGBo46C2ZiUHJ/DG36umGQx1gu cZbm6ek8LswkaKU9Cqc+XXdp2BjOHa77XDAT8pbdsFHJFASNWtYYugRW933HcSB8vSS8H729 DVrJKF4Sf8sowPnrVyTruSfmLV0A06iw5KUlkBqiLs8wvD3pX1lsI+VZ2UJsbUaBNDMioxUo MDVvqQG4ahQ5r6cmpj6bgVjpSx4x+C0ipL8Sur5wyx7F2m/sUYyZw8icvKUKUmnUiWGUDg6g 8S+WrMN4rwcOpL8Suj6DU53Ug1mvr3nbNU59PKuumSLaT/KWBBGlE9OouPTp4JamO7TaLIV8 R4kpgnxHodxqN5mQ7CxHBJ6Tx8B15uQtCVJp1EEcFJA3xOrw0loLrDZAYpfQ9S9R2qP+CuQA OSrNqjl5SwJgo/oOg4ZAokkANNZTWJ9e9LRHbB6AILFL6PqXWNOoFubE58f9PlzLOCRXc/I2 EiV4GwLw3sQfvFZA0aW+DLgHb4rWFgT7E1jr7znnDdsDDOFxABg0J29Zg8wo0i1KM8Qim97/ nKl5ywpuMYqnrFke6d+W0KFV9i+ZmLes8CajAKbmJRMwKDdpzIS8pQs+NAi+XG0HU3S2hyk6 G6PrvgFlxF8jFZ+akgAAAABJRU5ErkJggg==</item> <item item-id="87">iVBORw0KGgoAAAANSUhEUgAAAMUAAAAxCAYAAABqO23/AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAfvSURBVHhe7Z07cuwqFEV7Am8edww9 BwdvEl0ehJObO+jYE3DuQNFz7NSpqyOHzjwDPTYf6YD4HGiE+sOqUt3bqIVgw+YAsuXd2Ol0 LKKmOA3H8Tic9Kd1aXmva6C2Hl1fPkFTDIfDeBj0h0acBnHPY2+4tbTv+vLwmuJ0bG+ICdFw +ztuuNW1v3N9OSxNcTqO+80coRgO+/Eu262R9nerLxPHFKfxKEL3tpYQoHPsj6I0t8Fw2DFG 54ba35i+tbFNAbEuZAi5ldHsdNyPux3DFI21b6Hvlov7c+5tmQINGBMKIx4a+DAM42GP/+/X m//e0NwXuqbqktL++/t7fHx8HJ+ensbn52fZDl9fX/Kc+TeLlfXdYqPGpXRjgZhChO99KnwL M8AIx0GFXnHT3WphGMa7jRCfNkVcexgCJnh/f9cp4/j29ibTcO719VWn5rCevptu1LgUmJ+Y QoiUqImcCtDvwBSLa2AcFUXsspzE19VUYocOkCwnx6TXQdoUce2hGaIE5fPzU6bDEL+/vzo1 R+OV9I1tFuCct2/MyD7m+U46PTxFzZ0qzqYQBY6HGohIM3c/EzwRRE69DjoN7k0YEFzmugL1 Vo2wOAJ1SpoioT3yplECGFMgYhhyNa6vr9AmuFmAwVLfT5rD8z2ig+zspvyh9EWegfrgXEZU nE2RCjNWxmpECjsT6w79AcgCExGYhUQYjhXpWkiaIqE9Oj9MQDHriokCjavri3uGMsRAOXUK NbBEPRvShKZbdSYG8ZAzAFimiBWShim1wA7dAYWzRwE1gpEUCMQyxYoL+YZwTBGrJxbS0B1G MIdJw8IbUaRE49r6Ir9QNWX/ITdDecOaCNOIdesSN11HbeRLzeIjdZ7ANAVuznQaGsPKaDkq uAKFqN1oWyDrqgeTYKNEtedQpnFdfVEGz5RIg3vR+odNgUHVp1ciPVkR8T3GQAx4plh09DCL qZMsNBVr2YAhWpkCoy2Oh4eHqQPjMFugZbs7GZxtijKN6+oryhDJjG8KBb6/c2YcwE1XU0BV 33hkjJuWkjSFKoTPoT7cxhG4c1sYzFNZH3UbLY2ZkpijGeeaolDjqvqKMkQ3alCm6WYc03r6 koSkW/VO58ldV8wt//fP+Oev/n8h0kCLUolKmO3BEyrEixLgv3//ObtMOWxmirO1L9O4qr6i 08cHTZRLd0rXxD4sExFoOvKZjJOuN3djgTd9SqIKZDrTQhwIJs9hZGKUStMqUmBnB1ubmCpR U2BBi3Q8IFuVs7TXFGhcVV9OHWQnVmWcuohOw7VyUNXaU0OE0gE9l5rJcOtbyRTrULXRPMAM Zh0BQ5inxObAeawr8H98bzVzbKR9VX0vsP+4dFMkoFMlGMJNwwHwtJguwH9+fmR6VbopmtBN keDl5WXq6OapcGhNgd0n97tV6aZoQjdFArp++Pj4kGkhU9BpVTdFgAobNWvD3VjokYJ09B4p zqBHijbEK8HfevSRs6agaX1NEaCbog28SuinmaTjTkfiYvOTpjhgDPwMEb3+fnefCjTtpmhD 1ZEsgnlOYQxgDvOcAufr4kS5izNFAd0UbeBVojxSuITWFKtzcabokUJxtaaoRzfFGfTdpzbE G+28hTYF6wkc1/tTsmVUNUWPFG1oHSk2o5uiCd0U10SB9tgJO3cDoJvCTzfFJZCpvdkVwxay ecZSQnNT+H5K1gFlUtNX+ztz+vKnYeU5RkW49Z1NcYELpda/T7EZGdrDBNgQAObBYimb/j6F 7xeIRHruWzsms7BM0fT3KdaB6+yrh6k9ogP9MRPzsLGUqvqSDu1F1HHuuOnfkrNMZpmIGERj GyhM/m/eMRpGvjFCNEKT12YKuilsjAEQKTCFwmcTNUqoq6/oE5HM3I6b9zYP/dwE11OzaHim QB6e6OQhM1Iol7Z5baaqbLpMNwBDe0yVsEUMsMg2T+Bp5Milrr7xTod70c5c/DYPT4F5psBA zuurWaZY3BymoJ8RQvWT0L15U91E7msz1f2Sdb0FGNpjqoSDgmhhjKLI07i2vsgvNPjjHM8U CtnXyLoj9tYO+d1URYTGsftRLFPEL0KBaKXdzyp8qo+q8K4Iua/NrP4Gu0uF0WBYYM/vjFXA EPTBYq7G1fXF3D+UoSjP3HFV/4gXD5FBmwL5Rt7awTFF2RsCUwslp2C4SawhrZEB19LdBiuv MDkVuWpS2gsw+tPnEmbnaTJKgcb19UWHJWWwQCfX9+O0PzWRVTc1jcozhRqwucymSFyowpkY ieSBsBtXE3+fwAiuRjCSNyqcNEVM4Fsjrj3WEIgIODBlwkG3ZkG+xivpG4tQsnOr/jOZUafh EquPOXnQc9ZgjHoGrjHkmp+YIiYSzmVkLCo6j3y4Nj/ciaqwF0bXT7yDYjEd32Uq0Xg9fTEt SzZvK4RpUlHYhZhCCem9Hm7k1tIyBBDi07DuaUAvGHEyK3PNBLUXpJ9aF2i8sr438peMBAhl TibBsOVDXL/4O2PIk45IMJjVgH7uZj1h8GhvSJqiQOMW+m76N++Olf7mncgqGsZjnIZh3gKU 5tD/xyiGPHGO/UpH8b2th5nmlGufr/E96svHMYUAYTVTMBpN1OE0LvKU6ekFOri7KGEo0H4i Q+O71ZfJ0hSCTRdKonFL5oG3wura37m+HLymAFsslEoXRrfGWtp3fXkETQFaLpTOWRjdIrW1 7/ryiZqi07k/xvF/F6y6kT4O+dUAAAAASUVORK5CYII=</item> <item item-id="88">iVBORw0KGgoAAAANSUhEUgAAAEsAAAAWCAYAAACIXmHDAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAG9SURBVFhH7ZaNjcMgDIWZi4GYh2my DMNw/oHIGJzCXXUSLZ9UqXWCY56fSV0+THPEWuCItcARa4F9xEoxe+eycz7HVGKKFD1c7+95 HXfZW0kFm4h15VA3SqIFiCggHsqGSYRQ7rDiXU67CZU9xLqC2GTK0bt8/xwB9w+dIuON6EK4 BxqxrsCWDBcshoLQto9F/ROtI7hOe2xAzDgqWsdZdMpriatQzmKFAySlpdhRH/n72yhFlrOi +RidQbHkZmyxsH7O1V5/EZ90RCOW7iCJ1SWqD9a2TXB7OTA92Hu0l18yLxZD+xicazqeYoA9 lOZNmEKIhYukAPq3YOA4GuFQYmhrs1vrzqLn3dd4vZmewIYOXgIyjmfWvYeZnFIstRhdYnUP hWkSN4cl0OR6B7jJ0riZ3MOJAGS8qZmnZVostmjtMh7sVjl919hVIjJw3p+hzXFtdw9LDB/d 1C9qseKIvPY01pUi1sPIabqu9RamIlRhnwCLZdl2QDeCndPm5n9H3JoV+xGkUZAjh8Lrez4E 8TZ8zXi8QMD6VyHNHZS7MikWi2A6ENxUD1/7xbA/S876do5YCxyxFjhiTZPzD2yHHP9G02C1 AAAAAElFTkSuQmCC</item> <item item-id="89">iVBORw0KGgoAAAANSUhEUgAAAEsAAAAWCAYAAACIXmHDAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAHESURBVFhH7ZbtEYMgDIaZi4GYh2lY hmFoQoIm8Qts+0OP9653ChKTh5dUV6a6NWENaMIa0IQ1oOfAyrF454pzvsTMY0Y5epjfPrOO u+LN4joXEt+d6yGwUgkNQIUWYMQIxgOD0ADs2hXkAvFVsFIQBeUSvSun9cHzi4MUXAGOddtZ KZBVQ4KgkBDauTPOX2ULwjztcVoFMKNMmuDW9RIi64tjSOQDvKyGxB31ka5/Jk6ee4j6HSSN Bckij2Fh/hRLz/P4TvzbsDYLlf1B2Be4UB8sxAyPw3pMyoPtf0i4Hxap1iH6Wo4Bjh5vktn8 m7AwmDzP9h52B4LSLb3YFuAaQLT7YQLjztKbRuvP60MnMSzsWQug7dp7sExQdMnV7i3zqomC VKxfiNpDfV1PbAlX5UbH8WtYZN22y9jYz0tNAa3dro0r/tHratGU27KHPIavVvmb4uWcMgDm ebBmTwzLHrkLQZLtm4bW3t+tJ4lgqZ5wIQUKJfpDVU9PeabcoUX3BKCiPZ54FOSRq9aW8N4j 8W94rgwfqgunCo2v0VntUyFvG+ib1AVLN3/8GeeAm1rzvfpjeLK6nTU1YQ1pwhrQhNWtUj4v aR+3V5xsRwAAAABJRU5ErkJggg==</item> <item item-id="90">iVBORw0KGgoAAAANSUhEUgAAAHYAAAAiCAYAAACKuC3wAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAMPSURBVGhD7Zk7coNADIY5l++Qa3hy iBwhBXUukD4FXeq0aT2uUqbLDYh+QBut0AKGdXiMim8GtC+hf1cCuyjqunYOiGl09o9pdPaP aXT2j2l0pnEu6qK82m1rYxqdccoTBc+FPSYQ14U9IC7sQXFh9wAJdKKaibqpOVdGf8KFPSgu 7EFxYQ8IROVUvUVxTeNReHpqeXj4EwE8Prb211d73BEwjVO4lm2QTme7fUtcLrGwVp+1yR1P 0zgFmYoqo31L7EHY3PE0jVPYg7Cfn3Xx9tamXfYVPD+39q8ve9wabEZYTh3FBlMxBOW6ClEh IgcNoB11FtfotwWBc8czXFQ0IT84KOmjPHyw026q9JsftaPtRA5F9pWRaReiahuA7ecnfqn6 /o7nyYE8hQ0UY20LpzNzPKMb/A3FC/ICUnCdIpJ/W8FJsfPCbqSHunbt99oQLy9//uKkwpaq sXgr1n1zI4WUMcV1EwtBznhGN1JYvZOAHlzSYr16gP68oLpvHOoclNc5kfX046O1pYSVKfpe wgIZ1yYTJp47ZzzDBTCFpR0UUvIEITCHdA67M/zW2s3F7VFbJrZ2YkE4YR098QaYG88wAIwK K3eOAR6gd6ppjBQPa4R0g7kTc7IfKXR/5pYaK233qLESM7YjLIln6MCdeovfIKxelG2ybkSO dPfWg7IfKXR/Cd56uR/EfX+Px67xVixPrRYrxZJ4BgMbeXFT2JFUrBe1bNoRa0wu+DuWRWT4 Oxbt1rjZkAhR7AR4TumD1UezJJ7BwJ16C3fOgrGdZjkyVBOANQbwmil0/yFSNfa/QAwQu1tP 7ZJ4BgOQwjaOkA0TsW1sl2FSnTr0W5x+IKxpzctrptD9h1hT2Apids+P+zNdsx+9WCmWxLNn 5EUrmkDW1t4PFAbWQmzneaIajR2nbRlBfQVr/bsj44kyBqGkH2BI3CXxjAZEwgr7LaROoEWU VhyTufGMGnIIK1OF2d7R7DpyxGpzBDPjGTVmERYgJQyJBmdd1OnMiGe4kC9JjKfJ/WIanf1j Gp2dU9e/sxjulWaQGZoAAAAASUVORK5CYII=</item> <item item-id="91">iVBORw0KGgoAAAANSUhEUgAAANIAAAAxCAYAAACvSfduAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAhRSURBVHhe7Z07cuQqFIZ7A3cfs4be g4O7iS4vwsnkDhR7A84dKLqOnTp1deTQmXegy89DfUC8QRLdzVelGjeSEJzDzwH0mMPU6XSK 8QrpPA7TMJ7lr+3Y67qtsqY9uq3r4BTSeDpNp1H+2IHzyK4/dAdv4Ydu63KsQjoP+4pohjn4 eMcO3tQPd27rUpZCOg/TMdN74+lQ3Rnj6TjdpX8L/AByfHG3tq6AIaTzNLChRJ772LnHQ/0e FA3qOLDcb4O4Bl7iB5Dpixuz9ZboQoIhs7ukcTqt5IRb6SnPw3E6HCKEVOQHkO+LLWzd6gJH Sbk0IcHRPiOiN0VDOI1wFP4+kl5vnFjyOtzQ+B02DtUl5Ifv7+/p8fFxenp6mp6fn7lPvr6+ +D7xb4EvVrb13otYIXIXXoiQMBwIDSeYgCCeYRS9HbvoYZOhwHrRbmvCQvL7ASKCcN7f32XK NL29vfE07Ht9fZWpuaxn62YWsUJkdCZESMyAgVryoQk9BkLyngPhicill+vMThXDnAMaTbDM MSK/DsJC8vsBNkM0onx+fvJ0iOj391emUlLsvZKtoxZPxNyOl3PRZi7wdsiP0YfJrnQFRlTB IkhSh7gXIbGK+kMaKkkzN387sEQtPkQ8yTSoP6J2bc6TqOONzVGnoJACfkDeNBoBJSREJhup 9q5va2aniMUTRCx1XV5ma2RE5yzLB3HOgnOlS3hawgIMjk+IzBchhcKZlrHo4fw9q2DRC/AK EaNGFpga+ZoJCingBwgGwqGoeZKVDHtXtzWumZqhWW6Flm6Kx5IuGYeBiTltJTOlQ9GE5LsI DZtikSHmCqiQbgzRO5IUdt2YeRaun2KEVokRkq+eWEyADyAetak0LD6Y0SrH3rVtjfxSdcRO cghejgJQQK3TcaUzpE0XnXoIMx8PkUKKHMaZsDz1IY6oLE3iAo2o3S0IiddVdkZOB3n9kEqe vevaGmWwRJYQ3kaMDtroIDi2dHb9QfxOFhLyi+jkQZyQFoKIY1lwM0ItHe1iKyGhV8f28PAw N3psarm5fFUsQFUh5dm7rq1ZGZIz8zdgMfQUdaHR1ZqOe0PygHQhIa+4TiAopKhe1IrpRIYZ riFQ8xgHWwlJoYZLatuMmkLKtHdVW7MypN6Xwb0m5+W1OpGOwZHOh7bEj9hS2nHsPOnSQv7+ mf78lX9XgAtw4Q30NMxIKNgZQovvIf7795+q5Quxm5Cq+iHP3lVtzcSb0nA1EdtECMHMnQGp kyudkB6RkG3cwkvc0C4JUQmn+mFYvg8GyzTwimBFDMvIGMZRIWFSj3Tc9FyVmhEJZNi7qq0T 6rOMHjIacJFcRMA7aXkMbV+udEWekOJssYKQ1mFtIUFAal4EEamnBdSG/Zgn4W8ct5qgGvDD XkJqkS6kBOgwDiIy07ABPDVAFyF+fn54elW6kJqiCymBl5eXWRzq6QDXHAmrduaxVelCaoou pATofOjj44OnuYREh3xdSBFUXsTamtiFly4kRo9IOj0iXbiziBS/tGsjZY5E0/ocKYIupLaI q5C8q00a+7wFTlZPUGODmPDMGj2/r9qBDPt2IbVF1V7Sg7qPpESjNnUfCfvrYkTTpoWUQRdS W8RVKD8imbjmSKvTtJB6RHLRI5KDLiT5o5S+atcWfueWLTZQMD/CdhtPf+dRVUg9IrXF1hFp N7qQmqIL6VrJ8ANWEGsugnQhXdhFSHi6NuWR+RS6kOyo1UQs16t7YKDEF20Jib5N4HlPiaE/ 3a2/hYBN7UP9VFrIRulCKp4UXl6mWoOt30fajQQ/QDhYFAHqZrGgzBd7vo9kEv0hFuNVC3wh k1b/8rIgBEZfz/C/uLfD+0isgPRtzMr0iKSDKEQfUVI3kAVlvqhqa9ZYc75cKrhElZAY/V8J Yh2L+hzZ4gVAv5DS35CNcKB68WrzTxYzupB0lGgQkTC8w28VnUp9UdfWrK2UZiajjVNM0mbO F/c0Mct7YeKEgEBxrH84qUiMSELB23+yuLZzGybCDxjGYTkeYKFBPYlBI1QJdW0d3xj9oO3Z 8mH5B74StByeIS8pJi8IGHHtO0lIMLB2cQjJdRJ6AXkX/KjC6kzqJ4vFtYP1vgUi/IBhnPn9 OkQlJa4lafaubWvkZ+/4ZXRAuczNUgDrR1GCXwli1zDan/VrQzaYL2Lnd5qQwmGOGsT8TRHh XOwSBaZ58yFi4ieLoyed106E87DIYH7jGyJy3SxOtXd1W2NoVpwh2tFSSLxuhgj1a4m2OIOy zOIRbdNljrwvrWrjSAtGAXCRWOOgR5qPRT40RGv5ukmp1FUT8gMDjYXeN1Irdqa4OBn2rm9r uwiSiOhgrBGJnaelafYQQzy7kAwBBrgIKXAiH9ZJxad+AQghWdlA9I7kOqyi4XlWBUdcDX4/ YE6EyIMNwzlsdBncJN3eK9k6IhIuQFlVm6PncjEsBWATkm04SNuyS5ypnQkRks+A2JfZS2k9 LPLRK8srFTQwa1wRUes28DdkLCi4RLMkx97r2RpDxqCrWwBRLLGxEyEJI1vPR8+QYwFNRIA5 SVt5WTraCnqzLBVfJ04/MOjTC2Ey7L2yrREhWhZThf+xj4GQaWQSEwatsLwW/x8n8qe9HQSq OdrO3cyPFBY/KJKElGHvLWzd7P8hi5u6meXShcR7rHDDDnHG4xmqPFxQ8m/0kMgf+6I/ocuO a7kLW4U6fki39z3aug6GkBgI7QXGpBFMbEaDQP48PW7B4u6ikaLQDzMJ9r5bW1dgKSRGM5NC 1ghyxqu3wqZ+uHNbl2IVEth7Upg76bs1tvBDt3U5TiGBvSaFJZO+W2RNP3Rb18ErpE6nE8M0 /Q9pQHmkegQvawAAAABJRU5ErkJggg==</item> <item item-id="92">iVBORw0KGgoAAAANSUhEUgAAAE4AAAAWCAYAAABud6qHAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAHASURBVFhH7ZbtkYQgDIapi4Koh2po xmK4hKCGEFbNuLd7Hs+MP8y6MXnzgS5PTEzhjEzhjEzhjFwSLgWXfVzq3adYcvQuO4eXz6Nw lujrM23MI/sK5hhSvXnBBeEo4DNO38kSwyYWJul8hMgkKYdV1CVmvwk8sleK7Xbh4KVqkB+k JBogMkFjl2Ip9kqKMUdLx5UKFsVRJBqF3UnKYP4uUAi1mHWcMfgU2EiO7ADck9nUcVSFEBMF A870UbgLvq/EdSZ6mXwD5qL50ewQB+SMmIQri5P/C4V76aUGIXcFBJJCXcIeRuMtyuNUjItK u7AWhj2n2hOMaH3AIBw64wLI+wFKV5aRD2tQ0BXDSGoCRXxxHUSfgrLbVpoRpncUdwP7uqL4 Ne5kYhdOOMWOOfoz0lWoWcDAcA/ZwcnY3gn+g4yziYGmYhNOszMudxz/vqFD4Uyq+PK28tRt zHLznuy7g5+Ye9K/9B13ciwl3Q5kY1Hp9uZDIOEODwGdvjqyA3shn4I7at0x/ZiWceFjiQWR zzwEdqpeQx9BEHP9/Fj05fsUDMKRIMMuxc+P8tvZA+ZvYu64/84UzsgUzsgUzkTOP86VEI7Q TAyoAAAAAElFTkSuQmCC</item> <item item-id="93">iVBORw0KGgoAAAANSUhEUgAAAFUAAAAWCAYAAACxOdCYAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAH3SURBVFhH7ZeNzesgDEWZi4GYh2lY hmF4NjbUJiSFqDxF+ThSpKStwb7+ITVp83O2qAvYoi5gi7qAKVGDM8n6yE9PJSRnTDL5cvB0 Dsbj6g+kHV3lu+ht/Wwk/glRY/JWOvFMondpKO/RJyuESyGoBARXEoJiW1oz2/D9BROiwuLW g7RP5lNt3yoqeJ+8qlQJFJDjWLOQHYEvUKJiO1DZo4B4b8WmkMmuAw+Eq/BU2OByXLr9BWDv qi11qCGDO+1PmXA+UJZgEbO0Otlhri519UtoAoylN1NhT4gPORP1OEK4AwZ9UqLmgSwNUdSz hTCbLIgtrVKJYMrD3UJgP8vKXBI+c1EQoO3Zn76oovUZEpn3HigyISoayXnRPksgc+ANfUWb ybbIY6Q4hi1zlhi2HRVpDlz7KGoZcfLSLU2xVXCUVCHJ32+ufURtjLHSRuYHghVef5vnmQhG rfsfGZh/3UoFO/WZiofGwLCo8l2MDqhxGbDNiv9UpWLX5XNZgHuVGKQPWZijGD1ReyPj5nvq Vat/ARxuT0odTzOn/wAkKmb4TuBKUKQ9ccdm0Nsws6VdAUF9OyKwzWSr53Y8ttPbEaf/OBH/ 0tWOR3H5Hiu1vELFsaH+RqZF1QcaXk0lQnXioTB72L2JW5W6uWaLuoAt6gK2qD8npX9BEodr yQHzsAAAAABJRU5ErkJggg==</item> <item item-id="94">iVBORw0KGgoAAAANSUhEUgAAAVIAAAELCAYAAACLVA7QAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAACKpSURBVHhe7Z27ccO81kXdwI3/Fr4a 3MVtQuMinHy5A3fh3AGj6x6cehS5A3eg/2yAEEEQIHEAkCKlvWY4Fi3xhcfiAfjA04UQQkgV dyrS7nJ6ero8BdOp678mhFzpTtO68sTKouJ+RcqCQEghrD9aKFJCSADrjxaKlOyE8+X92TUt T5KD5Haw/mihSMkuOL+fLu9n+9n02T2/i1rJbWD90UKRkv1xfr88Myq9Iaw/WihSskMk/9YS 6fksEe/z5fnajYBJ5k/vl06+I4D1RwtFSlR0XVfZ5BaRyTpmkYj05Nr5DUH3wXMvz+f34TjO HSLg/v9SbqhT1h8tFCnJR4TTxG8iSvFYku69ff/o+f35GoE+xw6iO12/5z2UrD9aKFKSyfny Pmc/JZ00pWOyhPCaZ53pc+0lKc34+MnAv2vg0R/eYP3RQpGSPCRia5qkkfVBokO02Im428Sl oyd3Zu4GyP3d/cP6o4UiJVl0p+H2pCagH9TLo+ljiqnIUcs40pxrtvvN/3bbPyKsP1ooUpIB ZNT6Krrk0SZRn2znKsd5kSJKHkT6yM171h8tFClZBn2MSun9/f31n1JAzltEfRSpHtYfLRTp gXHN4VMnx2uarytcqAEQTGTFr6+vl9/f335u4OPjw+zXEtj/9bMpX6RzTXvsa/Rq/11CkWq5 U5H+e/nnn3/7z/cMJCHydPdEQnhrNJcjIv38/DRTDESj+xGp3c5VkAUXm5xgH0ekUn/+79S+ HN0xjEgPjKng/nFGI0cXkYXNaPuEj5XG6dKNvrPLuFVNtiNgOR9Eob5Y397e+k9p9FFecOHI n+bye9RkT3UnjNcd7hfS4KEi0k36r+8HivSwhH2MM32OkUjVRF/uXk75Hk/0pIhJBLLxwbxr 5v/8/CSjVZ8t5eRHm9Ft+rKNSIQiJXNQpEdldAGof348UdEhkVFymBvUvavwCxeTUhEpZPn9 /W2iT8wjKv36+jKf/YtNqchzWzl5EbhMqUdEU01/ipTMQZEeFCM3V/nNRaZUsZe0CF4AYqNR 7z+RiNUnJlJI1G3/5eVlNI+I1OH28/YitUCa9sJcOHl9zREoUjIHRXpIFLcOQZKjtLB9gf6/ YqIcMVmHjpSEJpHylvhN+Yw3TVGkZA6K9IgoxDaVVRihTsU6AU3/ivTcpUj743YyXZIkRUrm oEgPhokeMyv/VJpC2B9qIrOliKyuYsUlpIiqVwPH5clUykxsd3Rpfg9QpFoo0jvGCGCSDqgk Ik7UkrN8FjksJxWkt9z8TZEW6R4q6/nSee8pfSxhpqBItVCkd4kVZFIKEoVaccxdpBqDK96l bomL9NHzaM9QpFooUpKHyLc0SaMirVgfWRuKVAtFSjKRpnjBi51T/YtrvAWftIIi1UKRknya DTXSzQ41Qm4NRaqFIiUqzlsMfkduDEWqhSIlhARQpFooUkJIAEWqhSIlhARQpFoo0gPiroJz ut1031CkWihSQkgARaqFIiWEBFCkWihSQkgARaqFIiWEBFCkWihSQkgARaqFIiU7xn/5cvlr /IgWilQLRUp2y/n9dH2234wzxcq9ERSpFoqUHINw5FOyIhSpFoqUHATJU4p0IyhSLRQpqQZv c6qrdBlvhJKI9NTkHX5kGYpUC0VK6mj2jlJZz0yW8UXQW0KRaqFISQVlb81P0Z3ilRdv2Wd2 bglFqoUiJeW0Hncpsr7xeE+diJvVe30oUi0UKSmmOw23JzUB/aBevplbnkZvXbr1OPiPAkWq hSIlhdSNdR+HFXgfMB+0UKSkDNzXqaxsf39//acUkDOjzttDkWqhSEkZ3enyFEnj19fXy+/v bz838PHxYZrnS6A5z6y7NRSpFoqUlBER6efnp5liIBqlSI8CRaqFIiVF4Gp6KNJQlIhCfbG+ vb31n9JApMNVenIbKFItFCkpYnxbkiUUKeZdM//n5ycZrfrE1ku2hiLVQpGSIlIRKWT5/f1t ok/MIyr9+voyn/2LTanIkyLdAxSpFoqUFBETKSQKYWJ6eXkZzSMidZhl5X8U6V6hSLVQpKSM xFX7XFLC5MWmPUCRaqFISRm4j5QivVMoUi0UKSmkrrLFRcob8vcBRaqFIiWF1D0imhYpK/Dt oUi1UKSkmO5UHj3GRcp82wcUqRaKlJRT8Rq9qEgr1kdaQpFqoUhJBdIUL3ixMyTqbovyZcq3 4O8FilQLRUrqaDbUSDc71AjZEopUC0VKqjlvMfgd2RCKVAtFSggJoEi1UKSEkACKVAtFSggJ oEi1UKSEkACKVAtFSggJoEi1UKSEkACKVAtFSggJoEi1UKSEkACKVAtFSggJoEi1UKSEkACK VAtFSggJoEi1UKQkAC9Xtm9menoqf3EzOTIUqRaKlIw4v5+ub3PC+ElPrFAPCEWqhSIlaTDA HaPSB4Qi1UKRkhkkHSnS/XE+m2Fenq9dMJhk/vR+6eS7eihSLRTpQcH7O+sKesY7QCUiPTV5 azNpBbpenq+jCwxl4Nyh9dD/X8p+Xa5RpFoo0iPS7K30sp6ZZOLQH/siNUTLle50/f6pqvxT pFoo0sNRNk5Sik6ag7EKg0p750H9sTD91b0kpRkfP5H6d1w8VeQfRaplVqRoLrx37ZKz9frS 3LFIW4+0GVnfeITPTsTNKnVrzB0UTqQzksv93TwUqZakSLvTOkPjnlFxV6+Y+xbp29tb/0kP 8qVp8qEf1EurUUU0U/nY9Vqw7WiT9U4oz/dxpDnXbPeb/+V5R5FqiYoUHdqrekhkum6F2a9I v7+/vYKu7VlBhWp9FX0vlcbKYsfnvyrq8l3yyFt2tv/T7yeVqSw9KVIt0xxFX0x26kvhT/Sx LYHbN9Zz6T5F+vn5aQr339/f5efnx/xVgbxRFvDlbUBgNXlRXgbG3KbyqvOggOp8p0h3TyBS VIr8iMc0I0qFVSCFfPJE6pqxpw4FB5/XvcCCbX19ffVzBaCSRHbw9fX18vv7288NfHx8mG0u gXQoP+58kc6nd3dZa0Tm2vSppTrfFSKda9rnd51QpFrGpQhyyw1NcOFIpFvTRF8vKs0Tqfkd KrO7Hw+iWrEAoXAjOkEzD5GJmohIsT5MMRD5YJtLbCXSrdMbtEifWqrzXXAnITPNpFnqd06w FOk6jEoREjtPbPZKLn5f1dcpFalq+SR5Ip1E1NGID5UfBTNMG/t0iS2wEsWPvrPLhKt6eXkx EyIhRElTwm2N1zPZXwHb98G6fXHkXODIj1Qc6A7oK2s4zVTAvPT2qU97/ManJH0sM8c8ewz1 +W5AWl23maqn430M8zS/vsr2KVIVXilDJuQ1692N2siYhTK0wFoZJutd3DEcr18gw3mPSORk zvwuEsMJYWF7LiJZJLItR6wioML4YN41YxH9pKIxn/wKFkPSLSsiVaS3T2Xat0ifGlrku8OP NqP5hXX038fWRZGuh1fKcuRjM+OaWTLFF5F1me+XKgoqU+ur0CDjWEZ9tP2zy4mdRQEerQ7L +s+gZ/T3IiLJYbItD5P2wZfIA1dZEV1hHpEP+uTw2b+wgXXHjnETkSrS26c27VukTw0t8n3A i8RlSj0iqjkRx6FItQwilQK5eH+n/GZ4qGYhosg4wwIUjMZlV1gW6fiEgMg6eSByUhjLHoV+ 0kRdOFZsBxU5dtFjYLotn5hIIQl3HKi0/rzfH+eO91YizU9vn/q0b5E+NWCdtfkeAmnai3Xh 5PU/R8jPZ9kfilTFIFIpkHOJbDJv9P28SPPOsMjcxjeYG6QgzG584STgg4o6WheWHR+bqYQL B+siIDdFr+JOthWw9P0CqYqUm1flKNLbp1Ha55Ivmnya5HsKLHdd97KI9yLS7Z5w1FGzXyOR pvLSFFY/s0zzymVgrILkn2Gx7kb1wGNBpIqCO5VMeGzTyj0HIhPXxAxZFBrSPXdDEVIVaXWR FoqiddovsYZIHVX5nsQev62Hy9H0HkS61hOTrSh98jJLpGoUFWdrkWJ7uQXPrCc8IYR9cjjW zJMGmpLon0MT029WWiLbmlBXwOMVqTBazESX3j5t0z6HfNHoqM/3OVAmPJlKuY8dgbrcryDS 1Z+YbIWUK205WEWkmjMsMrh94kpBaLBSU/gm60Ehk4KPdD6jErSJiOLbCoH0yitdXBRY5zrR Rw1bpr0jnj7rkpfvS5wvnfee0nxhpkA6Ny4Tua2pa2s3fXI3aRb5zfD/6bFr01l77WYFkaKA 51d2HGDLymCRfahaqa2kyQKJM5b5DvuuSO0oC9sKqLk4h7Serr82rVqzZdqPiafPWujyfVtk 35qKVE7WWU9MIk368m2EGllG/u+a3mM5hssO9cT8DumsKedYhyINBpH++8/ln3/7zxWMD26Z //33P022O0YSdVdyaEjFCS8qioYtkaOzrUj3TGORQko56SplcXCH7f+dLZs4qQ629MTrSbVH 6yWgCVoaRqTYef0ZFgfYviLfsUhRwIZ70LIxBSmSN3wLviWVPo9JW5EibXOSNJQdugjTeRHW Aytes7wv2J5w3VlE1pNinYtNCnCA7bd7zyIVcJtGi1J+7rz7gglxtBQpBJfX1QcXjE7ySZEO Qdv4+/7/kbpfJFJFOlCkB+W8xeB35EFpKdL8upgvUouRo9ePau9J7yPTYP/LRJp/EqBICSEB DUXqXRxaRBw0yM4Kcb4ay346kY4uDk2XLRMpdimvW4IiJYQENBSpeGUuqhwDMfbiyrlq7ot3 crGpjUhzn7ykSAkhAW1FqqqKRojo//Qiwf5/WI8Rovl+2hfqfzeSN4SbWGaJXD89pEivicqJ 0wNPaW4o0p1BkR459wi5KRSpgyKlSAkphCJ1UKQUKSGFNBRpoycmb0Xuk5cUKSEkgBGpgxEp RUpIIRSpgyKlSAkphCJ1UKQUKSGFUKSOlUTaP8fq349W8aJhQJHeB/5onGuxxTYIoEgdK0ak /nOs8vkkMq1IKYr0OLy+vkZHw8QwGvM3eOezxTbIEhSpY0WRjhMZG6JI7x8MY4wpBiLFFpLb YhskB4rUsZ5I5XfuOVYMX+q/0r8EivQYhBJDhOhLDyNkLiP5IusZnqO28y6r2myD1EOROlYT Kd4RiAJvJkns2jSiSI9BKDnMuyY4RsZMRZITpJyF74p0NNsGqYQidawkUj+Bz1aq0QS3kUY4 yl8MivQYQGoQ2ff393V8dkSMX19f5nPuhSCUmVTWaLax9NJfUsMNRRp7+1MAnIHyMPlNclnn I0y6i+PriFR+M309VWLH8F1GZlCkxwCCswXxyYzP7s/7Y7Xj/Y22MMdEhwKdLsj527AViSJd i1uJFOWjl+Do/aIe8v+8UUSHZXPfKRpjBZHiav1g+jMOCIU8sdBc5OFDkd4T4zIyASfXRvmC ckORrsWNRDoqH/7dQQnk99cykFwWgk2d2JdpK9JryOxPUpBlgfiuYefzQmiK9M7ACTaR9rkn 1xwo0jW5jUiRp/6Jdr77RmTpjdy4uGzvMG2ZaStSLaOzwzwU6R2CsjQpsPkn1xwo0jW5nUj9 PE2LFGVpHGXmLasvgzcVqSbyoEjvBYkQTn0hxdk/qIjI59yTaw5hxSEt2btILaZM9WLMXbZz ZTQTrDdn/1cQqc76uTuqgyLdHuT70O0zJL////mKoSGsOKQltxEpfhvv50zhuSZrWfz/ICLF hjWRB0VKSqBI1+RGIjVi9K68L+3DSJ4Zy8rvtWXmBiLFgegjD4qUaDEn68YRLvG5lUiF/qLQ 6F7Q/n9Yj5/3k4Attixkm/p9BjcQaRkUKSF744Yi3RkUKUVKSCEUqYMipUgJKYQidVCkFCkh hVCkDoqUIiWkEIrUcScilQx9KnmskCIlpByK1HFnEam9wfZ6G4M/RRemSAkpJyVSbT0UKNJt yN1RHRQpIeUwInUwIj1y7hFyUxiROhiRHjn3CLkpjEgddyJSyVA52+n3iyIlpByK1MGIlCIl pBCK1EGRUqSEFEKROjYS6fnyfqpLcIqUWPwLGbp3RpLW7EukcMTsm5tib326MpQruwq/nMV+ P2YTkS4eYAYUKQH+SI94u3nOCLRkLfYjUuMYSC+5EtlX/z2ko5Mwvhsvqy1n64u0ezdDS9S+ D5IiJRMmFYJsy35ECmYDNln/8J2NNu2s/Tzrp4xytrJIu8u77CA2QpGS9iCSWEmk57MUdSm3 1+YdJpk/vV86+Y6A44g0/O46VlMvyROizlREi9/cMiLt3u3GW0iQIj0WXZcagjsXEZmsYxYp 4NNRSOtBs872paGyDcdxltbV9f9Sbtpv+WgcS6R+MOdEapeRkzEWM1KN3EYp+7YUCOb6SS1S s4N9ocM0WQaVoD/b4yy/lBm5O6qDIl0FdOe0qF1SRrwhySe4E3VL/HIbrTxS/t33qUr7OGwl Uv/CTzB5C1kpxleC71Iijf1/IO8Yc/2kE+moAiARwqteVmD2Xxl9FELujuqgSNsj+TlnPyVd 4iS7SnnoIxJbSVNXaseV+rGLz1YizQNlInlywwnw+p3NQ8yGy4QizR2WublI0fwZN7diIh2D naBI74QGFWJEZH3j8mL74VuASnSNdGYEkfu7++dAIsW++lft3X6bk6fzk/cbYeQc+d1cN1Ku n7JEag7EFLDe4llneKxyuNUgRe6O6qBIW5OTlypQgL08GklsoVzpGEea6Qrpl/OW2z8iOxKp LD/Ju94/1/VefRTkmbes+622nDUVaRELpndQpEcAMmp9Fb1hZZ0F0YhXcebKhV9pZXrcIrQj kd6Y24o0U6KAIj0AOOMrK9bf31//KcVy11AbKFI9FKnjdiKVSvfe5WcBRVqOa6acOhR8fF4j LQUIJrLi19fXy+/vbz838PHxYfZrCez/+tmUL1KUxevvrk0+v2tgpXtbdwdF6riJSM9Soa8O NULtP89AkdYASUj6uXsiIbw1mssRkX5+fpopBqJRiGeJbURqt3MV5Ez6xH73mI+uUqSOzUU6 Pptjant7gY7HEKlJc/84Q+Ghi6WPpqb39NonfKw0JK9GX0LQg+Qm2xGwnA+iUF+sb29v/ac0 ENPSXR1jggtH/jSX30iX629T3QnjdUf3C10cDxGVUqSOzUVaCkVaStjHGM7bNLCzVhLj++gg n76ySN7jiZ4UyKNQLJCND+ZdM//n5ycZrfrE1rsWfrQZ3aYv26REcIKhSFVQpNtAkRYyugDU Pz8+I6WRtMLIarSuKVg2FpFClt/f3yb6xDyi0q+vL/PZXmzyo7ypgLYUKfblGoHLlHpEdLbp jgh/s/29JRSpgyI9cu5lYOTmKr+5yDRf7P37QG006qUPojGlSCFRt/2Xl5fRPCJSsNS/uK1I LebBkmj3gKShJ9cYazy6uk8oUgdFeuTcWyRsxi8wiqRslOgnT0yUIyDa2vQMo2ABcr1ZNuGY rhJdbq6vU073CkXqoEjvudRrxDZpjkrajMQxFesESLA6PcPt4jBuKNL+uJ1Ms7tF5AhaPbq6 XxqK9N9/Lv/8238+IP/773+y9p8iPRhIr5zKbxABTu7phRT9SmIis6WIrEHFmghdGVWvAo7L k6mUmXB3/ItUdrr1Pm9BQ5EyIt0GinQd0vf0opKIOPHdWT6LHJaTCtJbbv7OMe1fxDobVdYq zrJvw3tK3bR13+2+oEgdFOmRc68SP2q1UyBByWv3koeli1QOXPEudUs8j3my2y8UqYMiZSVt S2H5QP5G+xcPXsHumx2JFF1R/Uk/dSL3Awd/W/7/wxaG+S5jx3L9RJGSTKQpnvPMr8dc/+Lj 3Ep0RNqKtLybRPbDlRkj1Ej3kt/nP/pNuOxQ9q6CzRJp3usjKVKST7OhRiQyZfbsmIYiFYkV P8QgThpkZ++yCKs1/OGLGidvM5+Sao+RaYYjcru0KFKiAhexCqtFT8bgd+TGNBRpRV0MZXeV pEf6N1a85rtIVJwn0vyLrBQpISSgpUjzZRQCN0SjTR9ErV6Tf/wbOY5EEz5PpPnpoBRpb3m/ 36vythiKlJC90VKkto6H/rNEfOImqb9ZIhXw/2FZvy8U/Zv9NoLjyRJpJJJNMYg0+wkEu2N2 H+QzDqJCWrlPDuigSAkpp61IxVrZQhqBaPNaj33vJPB/j21ej2G6bI5INbf8FTTtx4mcZfYZ sHzF4gnuW6SjMzenm0z3TWORGpGVtFxlP/wr73P7BIn6V/UnF5u0ItU5RC9S+Z07u9jXj+Vb OwZFSsjeaC1SAd4oqZNGiDh5eZ7p/2dWZwQq30f218jSLBt0CbhlMCX2SfsAilqko/4I2fla XVGkhOyNFUQqoM/yENUSLlRGh0qR+gmMF+VamY42KWeL9PAWUyhSQvbGOiIFeC/unqvmuUCi YCTSxQ7h8DcyP36O2wrM/sJ28C6tM/fJAR0UKSHlrCdSgC5BzUjDW3F+L9+vQaSIJGeNBjEO /QZn/H6mjwEg2lwSac3LMNJQpISUs65I75FBpHPyuXb4+pNI8hp9xvGHt4hTejVvCYr0PrCt Glve1ignJA5FqsUTaWOpLUa4YK0Mo0jvgaUxn8haUKRaPJGi4DZqZmdJVMjply2CIr07TKuI Uek2UKRaRiI1hbVWbLKO3A7bdfpHAUV6f0ieUqQbQZFqGYtUkq6meZ8e3iLGmrKjSLcEb3Oq q3QZb4TKbeWQBlCkWgKRCmhuF0jIf4og5+LAetEooEg3o9k7SudPvHwR9JZQpFqmIhVWfwJB ZL1udEGRboO0YBq+oblLPMCxzkMbJA1FqiUqUrDWEwilTw7ooEg3AXnZMpkj6xvfi/wIY8rv AYpUS1KkoPUTCDVPDuigSLdg+T5hJegH9fJt/J5JTGt2B5EBilTLrEiPC0W6Pms8TMEKvA+Y D1ooUlKGRI+z74eM8Pf3139KATkz6rw9FKkWipSU0XlvI/d4fX29/P7+9nMDHx8fpnm+BJrz zLpbQ5FqoUhJGRGRfn5+mikGolGK9ChQpFooUlKEuW84SONQlIhCfbG+vb31n9JApOs8Nkzy oUi1UKSkiPFtSZZQpJh3zfyfn59ktOoTWy/ZGopUC0VKikhFpJDl9/e3iT4xj6j06+vLfLYX m3BByd3ONL3qT5HuAYpUC0VKioiJFBK1gny6vLy8jOYRkYKlV+NRpHuAItVCkZIyElftVeAW qiAq5cWmPUCRaqFISRmQYHUaSz5RpDuEItVCkZJCGlQ2PBI6asbzhvx9QJFqoUhJIfWPiE5f jYd1sgLfHopUC0VKiql5pywuKk2ziPm2DyhSLRQpKafwNXrjK/Peq/EK10daQ5FqoUhJBdIU V77Y2dzy1N8SZachquVb8PcCRaqFIiV1NBtqRCJTZtlOoEi1UKSkGgx6WFfpMga/IxtCkWqh SAkhARSpFoqUEBJAkWqhSAkhARSpFoqUEBJAkWqhSAkhARSpFoqUEBJAkWqhSAkhARSpFoqU EBJAkWqhSAkhARSpFoqUEBJAkWqhSAkhARSpFoqUBMyP8kkeAYpUC0VKRiyN8kkeAYpUC0VK 0kRG+SSPAEWqhSIlM0g6UqT743w2w7w8X7tgMMn86f3SyXf1UKRaKNKDgvd31hX0jHeATkb5 JLcGXS/PvTyf34cycO7Qeuj/L2W/LtcoUi0U6RFp9lZ6Wc9MMnHoj32Bsa5cBDqMeeXRna7f P1WVf4pUC0V6OPTjJM3RSXMwVmHio3ySm2H6q3tJeuNcjfHvuHiqyD+KVAtFejRaj7QZWV9y lE9yM0aDBs5ILvd381CkWijSG/D29tZ/0tOdhtuTmoB+UC+t5kb5XBtsO9pkvRPK830cac41 2/3mf3neUaRaKNKN+f7+9gq6NvlRoVpfRd9LpbGy2PH5r4q6fJc88pad7f/0+0llKktPilQL Rbohn5+fpnD//f1dfn5+zF8V6CdTFvDlbUBgNVGnLJ/oZ9Vxm8qrzoMCqvOdIt09Dy1S14w9 dSg4+LzuBRZs6+vrq58rAJUksoOvr6+X39/ffm7g4+PDbHMJpEP5ceeLdD69u8taIzLXpk8t 1fmuEGm8aW+jffu/nBYNRarlwSNSFFCpzO5+PIhqxQKEgozoBM08RCZqIiLF+jDFQOSDbS6x lUi3Tm/QIn1qqc53wZ2EzDSTZrHf6R/7pUi1PLRIzdnb/10oKlyI6c/keGpkXLDs0yW2wMpZ fvQlhDGV08vLi5kQCSFKGhHd1ng9k/0V8HsfrNsXR84FDlQu3UUeP8IJppkKuJjePo3SHr/x KUkfy8wxp46hpzbfDUir6zbzbn+K5im6hxajUtk+RarigUWKQucXyHDersPO2gLqF0xzZncF Xwo5niaZw0Ukcea35YCIwv+jwvhg3jVjEf2kojGf2Hrzkf2diC7GUnr7tEv7FulTQ4t8d/jR ZvR3vmyTIpRtUqTNeVyR4sx8LSz9s8szhXgkm/CsPlpXHEQkuaTENonoBFQaV1kRXWEekQ/6 5PDZXtiwldRWsmkl2kSkyvT2qUn7FulTQ4t8H/AicZlSj4jONt0lvZYf+6VItTysSI2UXMEz Fz3mi41//6aNiLz1IxJYKHjYDipy7KJHSOpe0ZhIIQl3HKi0/rzrj1vqI9tCpNr09qlJ+xbp UwO2U5vvIZCm6w4YT17/c4K8x34pUi0PKlJEIKlmZYTRWdxGL/7qY4ILcRGQm5JXceciBkhj YTuLhBGdAHnUrnYeZXr7NEh7FZH0qaFJvqfwm/IZ+4y0yksqilTLY4pUI6RJAZd1jwrttHLP gcjENTEnLFUmVPJqaYT7j+RYWaSlJ4DGaZ/HNH1aUJXvSezxO5nOtSrGrY6lx34lDShSFQ8n Ur+JudiclQL+HjZBITO/kJmoIK/ioSmJ/jk0MV2z8kpsWxMaFPBJpa2IFjNQpbdP47TPZpI+ 9dTn+xwoE55MpdyHazPdFf33dlrKb4pUy4M27Zc5d91wW40p7P1nU8ik8uK7s3yWglkbEaW3 FQLp1Ylj2keGde6r0myZ9iFbvjowP9+XOMt+D+8pdVN5vzfSmSLVQJFG8KMoOwXykkjIFlrd RZMYi9sKwFXb0vqBbU2TpS6tWrNl2ofE02cdtPm+LRSpFor0aIhISg4NFTfaR1a4vnsjmT4P CUWqhSI9HNIUV7YB5/rI+Bb8+fR5TChSLRTpEWk21IhEXoxGyQSKVAtFelBwoaKuoJ+XB78j DwpFqoUiJYQEUKRaKFJCSABFqoUiJYQEUKRaHlKk4yu0nDg95pSGItXCiJQQEkCRaqFICSEB FKkWipQQEkCRaqFICSEBFKkWipQQEkCRaqFICSEBFKkWipQQEkCRaqFISRPsaJzrssU2CKBI tdypSP+9/PPPv/1n0orX19foaJgYRmP+Bu98ttgGWULqz/+dKFIFjEhJFhjGGFMMRIotJLfF NkgOrD9aKFKSRSgxRIi+9DBC5iznd2ku2kcTn6/j4Es+ybzLquptkEaw/mihSEkWoeQw75rg GBkzFUlabH5YedohhGMDs9Vtg7SD9UcLRUqygNQgsu/v7+v47IgYv76+zGfNhaDx+EgDeduw Isb8vgaMuydYf7RQpCQLCM7K68mMz+7P+2O1n71hgVPDAXenU3SolJxtYP1uWTPWEq8urwDr jxaKlDQE0eLCwHHoK539gQJZ1zOj0hVg/dFCkZK2QJSptG8pUYPkM0W6Aqw/WihS0h6MlR8K UyT63jVuhDcXM7Gw/mihSEkjpFl/6qNDNLm9vkuMeHp1qBFq/7kSjsm/Fqw/WihS0ghJ8/7C 0NPT8/XeUFyhdxeM7NSmKY71MovXgvVHC0VKDsf49qnu8s7mfWNYf7RQpORQmFueRhHuwl0C pADWHy0UKSEkgPVHC0VKCAlg/dFyvyIdNf/sxLJByJRpd4lMrCwq7lSkhBCyFZfL/wP1Q0Bw u/1uEAAAAABJRU5ErkJggg==</item> <item item-id="95">iVBORw0KGgoAAAANSUhEUgAAAVUAAAA+CAYAAACbSIgDAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAkfSURBVHhe7Z1NtpwqFIXvuGpArgwi Q0ij2plA+mnYemmnm+5dtjKDzMDHBlHAAyJi+VP7W8uktLyI58DmyI/10RNCCKkGRZUQQipC USWEkIpQVAkhpCIUVUIIqQhFlRBCKkJRJYS8F13Xt82jfzw++o8Pu6n95tm36rutUFQJIW9D 92z6xyCkj2fbWwnt2ud0vJmOl0BRJYS8Bd3zMUamj6cgm20zfv+hhLUUiioh5P50UySKR31J U9VJ/dPpEijVVYoqIeT2tM0klh+PZ/TxPve8FBTVBH///u1///7d//v3bzhyEO0z0rISQpbx I9DUo73bRRCPaNNQVCNAUGHYL1++6O04VIFoylpMQgho+2YUyrSoev2qaivpAqCoRoBBPz8/ h711ILKVhPjHjx863Ww6WxjKWkxCCKCongIYtJTv37/rSDcEYrs2XTyOlDiWEGLJF9XU4z/6 W8VZAwFvIqpBn4q7RQzsniP1qZoObTmChKha8DncN9g8NcrlMXBO6ntCSA5bB6qs2FJUC4lF mj6q9RsFDy3hJLCuiMIRthsBaaILYCIUTT8dPQ2kcASSEOLgPdbHutP84CsUUAgrRbUQGHQR OCkR5SK6xcwBfMb/duDLj3oXIlF1Daz6IIRsx41CFyf/C8EMRXUD1rCIKqVHf5Dq6/z69euY xq9fv8bP84GvtKjqPhy9Hnk4QAjZgFnzb+tjbJlqrHuAoroRiCFG8GHkObX6OmulowS43bZe +ZUcm1dVsdT134nz2ftYH0BAG3GMRQVKjtCGUFQ3gqgSj+1u/+hItb7OOqLaPVU0O3w+O+fI a9s/MyrHHTivvU/gA6+fdbkeUlR3JNe4S4x9PJsEulWt68GFM5vz5BVvK7pKQ1TOue19vA8Q 1FhRXR7Zp6juBhwRGz18PSiYZ8nLEqfKq3rauE5jVMbp7X0KH6iGxxXWRn78h6Dmiu8Ooopp QbEWCDdw4SlCeOwfDLstuqxFbveBOu/wpa51ujrqsUdZ9CMfU06Ouucr2PssetD1rWqAprdY ma30abSyqEJQ5ShuDPW1MJ3J2Vcmr1DqVjaxiuQ1HF+B/MfNvUTHCKsxt/qMLp7Kttf+XLTl Fex9NuGvQ1VRRR+hVH70dAX3i8QcT7KCHDviDVcN5rseWb0UBT6PTWcrYVYGFbHy6gNhXCNO vpgd1qBdxN77+OBYPFG1AydNi4KBzyvWnSMCFVpGm6ZJdzgIIyX7JdX1vfPX06lCNf48wtGC sheLFceMsOZ2sKdA1GHs6ZcJz85OwZ/OH+wfySvm9Eqr11a/fCaBXAbN8eUytrJCwx6Drc3c x/L+95kNAeyIYzrjpp7YFUKjL/DdRey9iw8OJrAinOTM1YJjMh8h4i0y0pyH+NnGDPuo7Jb4 Yy0i6nudb4j98oUuyVIU1D6N73DeFhPoSmLtqcqETUtPpFblwyxOwMRq69N5oynl9efPn3qT QNQEP9dBLoO6nCwq3roKrW1lyyhsMxxfz9yGBlMnRlMOwUyrBLyBI4b9/y5i7z18cDSeFWcF X2zt/NbREjVOpMXMM2YBg4iq+ExHCs2JRuq3Y2xvzZmyofalrdzO3/hIvvSvoe0pNaw4HlQc r6FU3zfORaW8hpUY0ZJb6b99+zZ8ShHeQ5B/sLoMGuFy7Tduki1G1LXH700jI5+fYXcQ2FAT 2F3fQ9P0djWzvaer2Lu2D8TzX7jpPOh/NWHLGO47wGjBjcnGMYYR7JswpkvCsFKiCqSrv1d5 T62OuAP6XmXjjpXM2DDiRyD40iXmp9lxXdmD66i07ZQZKa/wkwv27aMpFl/EoqoZyXuoUAZz oySVDy895EuI2DQLdh9xbAj8PJt7m/YhcOZ6V7F3dR+cgMnKqBSjodDKxm/Wi0gGJCfqNOHk DnGjj5RGDTwndW1aUK4OCndgRB2dezeMQh63wZIftD2HE9y03eOwM/rgjd1RAQYhccuUkFdU alTkP3/+6CgJ+4ie7PsScgdOkvewuQzmVmjfzp26ro7oIhdIXztiQ3sN9Ziv973vFION9U/X X8TedX1wDkZR1aKIQqA39MHFbmFqDT1CBwPdoqlHkylsGvAL4BzhkSIbU8H1fWC6xlU8UUJQ cSYfOhXS8enc3hFfuqg0rD39RhY+HNJWvpzKi/GdveaYPaGSo4Kb88zP1rj77stnxAGbkYV7 iJZBfFWpQnt2tptpdOS/W7J7xIb6Otg3B7wAQoH7GV/AcxF731hUl0TOQXCWJc9AikQaZA2q gIcN2Rpe6ofSvC6UzeJ7QLoLDcpevMTuV7D3gT7YESOqKwyVFk60YgvirFvb+xnyGLYVyuxG sAob8opoOZLR8nvY2CBt4DV2v4K9j/PBnny4j/3zUD8EornkqJQzc/6erAGPallPGDNe74vy vCpUwz9fJ77hHiAcxZnZwuvsfnp7H+aDGkDnjG6Gg5H+cOACWoD3b2LJKspeoXaML9fmVRVc ccDGsOUejnol3mvtfm57X+mVlSFug4Xo3Z0NkSmqaKGsKudEtOSVrCucx/qyPK/OgM3meyhr iLZxjN3Pa+8jfLATaICcaHVVpErOy7Fvd1/H4W+iF0am78z57H03H6DRoagSQt6A7Q2KagCG KWxRgr5hiioh5J60z/KBOhclmqnA2r5jw0JRJYTckK6XFiCU0kYWH2DwLhy7o6gSQu4HpoTV 01QxPQjqNGg3DbxRVAkht6PFm7tqPPpb0G/qqKqeRuXMiMBsCXs9iioh5GakFiCV0mav/qKo EkLuhbBwYYnlN3RBqPPej0JRJYTci8i7TLb+lEzuew8oqoSQeyGIKl5ziE0i96dkKKqEkLdE ekdBKJolPyUDUc1ZoktRJYTcCn+qkyEUVeyv/SkZKV0Jiioh5FbEIlUIZ85PycQiUooqIeQt kUQVggrxxJb+KRn1t+oYRZUQQiyR0f9cYuLJgSpCyHuCeaoUVUIIqUX+6icJWVQ5+Z8Q8rZs W6YaF1UuUyWEvCltkxdVSsiiqqLfzC4Fiioh5H5sePWfKKor0qOoEkJuiHpcL3hJNQTVTrVy hTV8u38Kiioh5J5U+zmVNvlzKiEUVULIbele8cN/ARRVQgipCEWVEEKq0ff/A6hIohCm8shA AAAAAElFTkSuQmCC</item> <item item-id="96">iVBORw0KGgoAAAANSUhEUgAAAVUAAAA+CAYAAACbSIgDAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAltSURBVHhe7Z1NtpwqFIXvuGpArgwi Q0jDdiaQfhq2XtrppnuXrcwgM/CdDaKAgIhU+VP7W8ukyvIingObw4/6MRBCCKkGRZUQQipC USWEkIpQVAkhpCIUVUIIqQhFlRBCKkJRJYS8F30/dM1jeDw+ho8Ps8n3ph06+W0vFFVCyNvQ t83wGIX00XaDkdC+a+f9zby/BIoqIeQt6NvHFJk+2oBsds30+4cIaykUVULI/ennSBRd/ZCm ykFDaw0JlOoqRZUQcnu6ZhbLj0cb7d7nHpeCoprg79+/w+/fv4d///6New6iayMtKyFkHTcC TXXt7SGCeESbhqIaAYIKw3758kVtxyEFoilrMQkhoBuaSSjTouqMq8pWMgRAUY0Ag35+fo7f toHINiTEP378UOlm05vCUNZiEkIARfUUwKClfP/+XUW6PhDbremiO1LiWEKIIV9Uw91/e/ig kdTSvImoemMq9hYxsH2MP6Y6rXV7hA0MUTXgs/9dMzs6uLxDgXyvO5EQkmbPRBXqu6mi6veV CSxGqgFikaaib4dmtDBaNS2IEMi5i26LKJxjhhGQJoYAQN+2o1jaf+umg3M9CmcgCSEWTrc+ NpzmBl/BYEctzUoHOhTVADBoFnBUINLF3yO6xcoBfMb/ZuIrtJIAt8wFnSzp464PQsh+7Ch0 dfF/NJhB4ENR3YwxLKLKkAhOiBNCowdfv36d0vj169f0OTzxJU6KDEGgEOj7kccdhJAd6Hv+ TX2M3aaa7N5bPdUYFNUIEEPM4MPIYbqhTQx45zIPA+yj6/bdr/xKjs2rVCw5/ztxPnsf6wMI aBOcY3mIYKZt1Ul9XbMlRTUCokp02+3xUZsc465hj8m2K61filrC/ArOkdd99r4S57X3CXzg jLOuTwjnrsShqBaAlm5vQXVmGWUrD3q71e7IeThPXjGje7zYPJtz2/t4H2RMTI3MARBINwgU 1Y30U1dKHHKCSSR7ucfZOVVeM8bGrs7p7X0KH0jDYwurRDd+jvwAaO1mnCeIqmQyGkrjAq67 RMhdGLwnuqxF7jpWOe7wW11z8/oqnlEW3chHbYdd8xXsfRY96IdOGqD5KVZ6S0WuKSqLKgQ1 rOJTqJ+xzovkklcoVWNweAtwfAVyu5vPEh0trNrc8hlRTmXbK3+u2vIK9j6b8NehqqgiTA6V H7Vcwf4hsr6TbCTHjnjCVYP1rkdWL6HA58nlbBtZlEEhVl5dIIxbxMkVs8MatIvY+zk+OBZH VM3YQdOhYODzhvvOEYEGWkaTpk533AkjPVLjEnJ+5/jt9FKoptcjHC0oz2K14ugBdXeQvQxE Hdqebplw7GwV/Pn40f6RvGJNb+jutc0Pn0kQLoN6/3oZ21ihYY/R1kpYVsbfUixsCGBH7FMZ 1/XEjPFNvsBvF7H3U3xwMJ4V4SRrrRYck9mFiLfISHMZ4mcb0x+jMlvij5WIyO8q3xD79RNd krUoyCz7wnF7TKAqibGnlAmTllpILeVD35yAhdXGp8tGM5TXnz9/qi0Eoib4uQ7hMqjKyari bavQylamjMI24/7tLG2o0XViMuUYzHQi4A0cMX7/7yL2foYPjsax4qLg+62dOMzMlNlRCYga J9Ji5hmzgFFEJT5TkUKTjIivho5MjDlTNlS+NJXb+puJqC/dcyh7hhpW7PcqjtNQIn3rpKG8 4tw2iJbsSv/t27fxU4TgNXj5B5vLoBYuYztnC9liQs49/a4bmcXxuXYHng0Vnt3VNTTNYBai mGu6ir1r+yB4/As3lQf1r8JvGf3vYjwxlP6qL9g2Rtg4+riAfRPGtEkYNpSogHTV75L3tbsj ro661rBxp0qmbeg3LGlf2sT8tNivKrt3HkS1445QXuEnG3w3XVPcfBGLqjS511ChDHoBRBS5 Xic9iIvT8OTbfcKyIXDz7KcBgdPnu4q9q/vgBMxWRqVwWtn0xfrGCDlRpQkn94gbXZyopiJO vvpOHHynSNUjEBGo6Ny5YBTytA1SBVv9Np7DTtveDzsjgtFpoAKMQmKXqUBeUalRkf/8+aOi JHxH9GSel7Bl4iR6DbvLYG6Fdu3cy3kRwS3qhEXc7hEbmnNIN199d34TRhurV9dfxN51fXAO JlGFkWBYvWEMLn0JHboc9iG+gwEc+5DjFovk3QK4BC1uqejqCq6uA8s1ruKJEryKM/vQqpCW T2P2XvjSRtKYunyuw8WHY9riy7m8aN+Zc07ZC1RyVHB9nH5tjf3dfvhMcMLGI3oN0TKInypV aMfOZtONTurv4naP2FCdB9/1Dl/YcD3olitXXMTeNxbVNZHzQEULHJxnICHgcFICGpCdhS3i y/qU5jWjbBZdA9IdG58jeLrdr2Dvg33wJLSobhG5pEHRwq47xJ/gIKXsLJRPr9g2O/KKfMbK Z/E1VGiQSnmJ3a9g7wN98EQ+7G5/LNSfEINiPCdNypkQXQpqTdBVKyrjWb6sS3FegTT8i8q8 5xqKxWEnL7T76e19lA+qAJ3Tuuk/4cqdDkyAB4lM9lTGHT+Tg9n+CLXjfLk1r1Jwm7HASj7t Mfu913DEI/Feb/dz2/tKj6z0sRssDHvay7yyRNWdxFoqMzmWLYXzaF9uq0jo2Zh8zhM2+69h e0O0l6Psfl57v94HTwMNkGWT7EiVnJtjn+6+jcOfRP9m3azz2ftuPnCHNSmqhJDbsr9BkQZA 0kgikao9NkxRJYTck64tn6izWRlD9l+tRFElhNyQfqj5Zo4ucvMBxpv91WcUVULI/cCSsHqa GkwPgjovQ50n3iiqhJDbkbz1ugSMm1qqqpZROSsi5pueKKqEkJuRugGplPy7vyiqhJB7gXWj G29/XX9CF4Q67/koFFVCyL2IPMtk76tkch8YRVElhNyLgKjiMYfYQuS+SoaiSgh5S9RttZ76 +aK5+VUyAkR19aFTAkWVEHIr3KVOGl9U8T3/VTKaULohKKqEkFsRi1QhnOlXyWAyyiyRWq4e oKgSQt6SkKhCULVYxl8lk3qcH6CoEkLek8js/ya8x/kBTlQRQt4TCOJeUcVif4oqIYSA/Luf ouC2VKerz8X/hJC3Zf9tqv7j/HSavE2VEPKmdE1eVBkCE1LLbr5Ev5lDChRVQsj9KHz0nzvD b71Ha0N6FFVCyA2R7vrGh1SrZVTjMiu9zdHucjggDkWVEHJPqr1ORSLWDfpMUSWE3Jb+FS/+ 86CoEkJIRSiqhBBSjWH4H6aWeYH2HorXAAAAAElFTkSuQmCC</item> <item item-id="97">iVBORw0KGgoAAAANSUhEUgAAAF4AAAAxCAYAAABTcB7hAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAPESURBVHhe7ZzrlaMwDIXTwPYxNdDF NkEf0wHV8Gs7oRhW1w/wQ7IdQhIQ3HOYExgg+LN0ZRySx3zrK1IHfhqHeRgnt/Zdla5FFfix 7+d+dCsH0TTSNQ05fDXgp+F40BcR/C6BrwP8NMzdW6mPc//o6S8n+l83zDVzG/tuDtkrAD/N A1nM+7ADegzNC1lm3hcdL3aME/YJOuj84NEgjspOGvsHa2EonFGWkZ08KlkXRv3pwU8DH41e APd4AB4sAa+79lqQRKmXP6c9r9uIzOvK14LO8UFycvBobM1mrFX0w2gBIjIbPBlCp/JRzHu+ lB2r1npwcvDUkEr4ZvBYSwBImw1hxOJY1sYEWxH3X7QGyrnBkxVwY+RVafoX7IDJBB4kziH4 fhU83sa+/7nBB57JKvLoyTRa2p+ziSxbIJwTNjNNUSdBdavB4b0O8KWGGnCuCNqiKnUS79lx xzmZzCB4VDNiFbIpEK4J16wYfBsII8AUTtQSxUaFc4TSD74RBFSGi2yodKC3H7dakmrwocXU ip0FW4OG7JH2aTl+lQ7wvz/zz697vVGmkxozYw/9+/vHXLPq4lqWH7u3ZsY+0u/xB1UOHsUo iADcYtu5DSzY+TMR8ZRUgCehui/w3fgVn6AsnZE1slRY0HHJGHhvaQS/NijwwggktvPDrKfm qV+RbvB2bsJuXyFKY98t89SbdR3wLsK5W2mSfHzpBsRm1CaAVwOPg/ko5j1fyo5Y4fski3Tw 1awGB7PjXsFWxP1flW7weXHlQdoO4kC0gb8j3i4E2QzdEcVum28kDs5gvDhPvUk6I54iPfD2 6AZq13nqu7gm4N1GQc1RjA55F50rgqe9xRuoRfcNVKYdwEOwEQksP7TcVR8DX2oLLLl9aiQH D0tw0Jfl6OG0w3x8XXJmb5kauefjGyXVsq1TI6zVnE4N4L19HuIRPtJlwNNOxiqO8QjfhcBn 8FJLoKj29ytdH3cIjt17akQN+HJD0/RP1ylyiYJdxf/iz155kHY/hnsjeA1PkiFaSw2NPLr8 CB+UgsN6Ftl+BLNxakTHs5MuYiUZcK4ItnxujC+vRf3CFVdTI7Y+wod9bG04Ofi1IblaQAQS sqclio0E34+FkZXtyJODBy8BbhMIp6Jl2VFRsQO9/bhVUUFNOj140+iESmgxtWKH4+tfSC5l Fj+05KTqO1BlKGVNdFO1MDcd4F6/RXE9UgCehBRutRWnuPBi2dZ5rQqjHdIBnoTx8ZPsPycK jLSGqAEP3b9l8EUd6tc7hov8esd5NM//AdiN/tsE4R93AAAAAElFTkSuQmCC</item> <item item-id="98">iVBORw0KGgoAAAANSUhEUgAAAFsAAAAaCAYAAADYMiBQAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAKGSURBVGhD7Zg90sIgEIZzrtzBa2Q8 hEewoPYC9hbprG1tHSpLO2/Ax0sgYWH5JBjjz+SdYUY3CywPmwWt1KLZtMCeUQvsGbXAnlEL 7Bn1M7A3m41pq9VKVVXVt/V6bez7/d56vk8/l9mXy4XA/iQtsGfUz8A+n8/qcDiYkuHD3m63 xn69Xq3n+/T1sAHZ1WmABlgfNp6jbuMz/N4JvYctRU2CrKpGicDWtNb5Q+SXDIAObWjQ/X4n B+ftdjN2orYh/RrRqqZ232u9dmkdy0UymwCvhTLDIwj9OZ5LB6M3hOePQG3/F2q32/XxIqOh VM3GbST0DdU2Qz+3fultQpxs4xhEZcSfsNtdbjBMUivB0JTC+kuh6mQg08ivz6fTydhSsP3y kgN7AIu1WjuBN55BBLtz4CYdhKA4u2x1X/8BsoJznEivzOwhbKlEX04GcCUMYtha/KRW2Aym RKQD5Xe/U5c1XNA5GlOzfRtbs7Uew7ZrKWTAwibZHQxq6jpLB+DispHKACp/QUF70Bm3DecL 4MfjkfQfcxvJhV3KgIUd3kz8cfGs5lI1UTKS/hPL3bMdWNfcPRvPqeK36jHsDmQpgxi27mBe kUR28xN1ATHzZMIuz+xQqZqdIx52tynGbjmUMiDRmOLunbBt42W4HsVNFAEwG6N3XUrj4yuv jEynqWADrLnuIlutza2jlEEfDZlIdwp/0JiGHtzhgID0FVHoqyIVdvt1B6Qv1Gu0Z/71o5mt Y/NqNflRU8hg3NZbZWdrooZ9qihsa0yohEERbD2Czsj/MlbLvVb26zdoDOwSBoWwIbweKZgI 5LtAQ+NgQ+MYPAH7x+QdhH3LI56tBfaMWmDPJqX+APu9YTsCcCw6AAAAAElFTkSuQmCC</item> <item item-id="99">iVBORw0KGgoAAAANSUhEUgAAAEsAAAAbCAYAAAA0wHIdAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAHqSURBVGhD7ZbNjcIwEIVTVwqKKIIS OLgL7hx8oweuyCc6oINZv8EOdjKWMrFZtFp/kgVxfubN83iSgTqb6WYp6GYp6GYp6GYp6GYp 6GYp6GYp6GYp6GYp+JNmOTPSMAzJmMgs5iYbLm7ITrMsTV6grMefGw25cPQpMsNiPDvxf7sK 3kbvDrMQeCQjPN2ZIMgZGovi2mGnpJIMkpZittOrNgsCpRJ31gdMT2CVP7EXUjjJxDAhXEu9 mVmPx4MOhwMdj0c6nU4s4H6/8zn+hTihZLMVnuM5MqO8oi+w4nIiGuTYgQZ6ce0YJmezYBRu vl6vYYbocrnwHM6dz2cf2/cJMTskvi7j0qrmQORbfDa2OJlW18KYWr2xL67MwiSqKuV2u/E8 jHo+n3xzvDGjUMLF6xsSE4pjmWyt3nQuMyutKhDNQoUBOfirMqQFLIrNqKgsnzRvs0J1tdBb NAvmpMS+FcGNqwRYqC9p57ItALZtw31wg07ecnZKKswHxXQLvaJZaOAIBIPiiHNo+Fx1UsNE SftXtvGv7hy5Yb7Z3+CR1GyMT3z5QcoDD26gVzRrK5urpdAXfptavVVm+acWP/JmYqmHw+9S p7fSLICSLZkhv5a/yz693PPCtoZhO836n3SzFHSzFHSzNkP0A8VLsMJn8P0CAAAAAElFTkSu QmCC</item> <item item-id="100">iVBORw0KGgoAAAANSUhEUgAAAEwAAAAbCAYAAADWHGlkAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAIeSURBVGhD7ZZBzsIgEIV7rh6IeAiP 4IJbuHfBzju4Nay8gTcYeRSaoR1Mx+Lvv+BLSKStzPB4M+1AHRVdMCVdMCVdMCVdMCVdMCVd MCVdMCVdMCVdMCVvBXs8HnS73dLsf+HtSMMwsGHILq4Zlx5uSFWw0+lEl8uFrtcrHY/HdJXj yIQk5ZzCvdGST7NvUYiW4zkTf7tV8Db5ioJBoPv9Hn8/n8+YUAmCj2SFCN6mpLylsZpgO5xh jrLYuBSzXb4rweAqjAwcthQMSUp29y4E5Tdw2t+oC07cKBNNCNcy35VgWRw4DGWJeXZbBAkK 9i1Oeo7pyY7yyU7g5OXNaJBjJ3bli3l+bnJfIRjK73A4xN9o+HAa5txxsW+IO8Tm15aunW4J T2wxtqjJXbYQZ0++KNcsXhQ4rF0IhvLD4MBlWUSABEbJMhU7V59vSNH8w+BpNMs39bhCMDR7 uIwDsc7nc5rVFpwcIh3kNsF2OCxsPJZcxWXt8p0cWQiGYPy7K78huYhYcLWJpL7zvigHsK0k PyM2bfb2c4Y5LQTF5Wb5hv+YEGgWDD0LTsJAGWLwz4sZBFs2Udg7vM5teK2X4CS/0/RjT8ni hM0vP1rjwMKN8nV2WmMWDI19JU6Fza6p9Im/Zm++cGm+PAsmf83XgDPeOSeQbZ+mv+XzfMue 5j4VDMC+NUHkV/Zv0edblj1GKPt0r7ORLpiSLpiSLpgKohfoWRQMKUXpewAAAABJRU5ErkJg gg==</item> <item item-id="101">iVBORw0KGgoAAAANSUhEUgAAADcAAAAWCAYAAABkKwTVAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAFxSURBVFhH7VaBjcMgDGQuBvI8noZl GMZ/NiZAPpFoq9Km7UmRHAOSz3c4CfLB+JG7Kr6JXBaOQUI4eCj5nvdF5rjVq+WO5DILcfYw SvRY8/zu3FBjjAx5PA50ZktVMErldgUMYgCJoKDHI/ouLEUSckvdCrNkd/CU3L4L63HH3U+w IaxYV0/IXc+SFUZoawSU9HzDyyzZ48GprSpi345cNvavteSD6OzZyNn4bB0aCNY1VRQxoau2 3scVGUPBux4Z67AHUfn+lH1QRd8P3XH/QCmkvEZPHQ6UQ+BwJJaUsg2cQCSMYrNbYNuj9xV7 FMMEswbpmXWWnybX2zWh89G/6pov9WvXYYeuciXXVFg/qCbJaeG1MCehoauh8b/PR7dWUIbE sOfJmCO3s16Nq+0y6jUFvfCc8LsG225ngGSWRs7uLey8gOMUuWY9jZu1zKpKrrz40MG9QsKU BCFmEBpUh1UX/ajOD5QL4kfuqvhgciJ/uV+xn7HiGT8AAAAASUVORK5CYII=</item> <item item-id="102">iVBORw0KGgoAAAANSUhEUgAAAEAAAAARCAYAAABtu6qMAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAFgSURBVFhH7ZVLkoQgDIY5FwfiFB7B Bbdw74Kdd3BrsfIG3iDDz8P2QcSZoqYX+lVR2hhI8hPSgh7OK0B8PpZXgPhkWZaFhBA0TVOc +UesJul8CyFJ2zh3hLO5WGu1JKGMfy8K0DSNFwDPHFYrClvVxpBKwftkcn44G36tTx7C3BGg bdu1AjCOWOM2jxtVx6g1SOeJtBR0csXZFNbeqoC+72kcx/UdAszz7H8Do4IoGNvN9+Akrr7z bIME8CcPtczZlNYWBcB9R9IJJI5Eu66LMwDJ3S3/cApJsN1g1EGQ26A5AXI2pbWXAqDkc/c9 BbyyK7P6fE0AJA8RjqRmGGDuJMvvK2AvMOOvdg9A0+P+7tAPEPAwDNghdFbr1I7f63Po5FJn fHE212uzAiCx0+lkhr8eUFgq0noja5a/N0GPFxp+YzKbuc8BZ2wAN4/Y/bwbbpPTFXgarwDx +VgeLgDRDzF1hTaE2MP8AAAAAElFTkSuQmCC</item> <item item-id="103">iVBORw0KGgoAAAANSUhEUgAAADcAAAAWCAYAAABkKwTVAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAF5SURBVFhH7VbbkcMgDKQuClIdVwLV 0AzF6LRYsoWj3I/j2OSyMx6zgEGr1zjxB+Mrblb8J3GNS06cUvBQ1T13RmVabaaduFaYStNh 5qxjzJcJtLVCbCYDT9ISEczDxvtji5oFJRYnkcq5iMR3YzHwUAXAdhUYihtS8hIcrX04aV9z HTOm5CMqReIuS0mPo5HD9w/imijeCnJaVNrVnBaieWgQaGuIqIxJvNrX/djQJN/V67nIespM lN2Z4lXwMDsONBQRZLZbdMOGEgLeoMK1tt5wkuR0EWMbDjVrsAf1KnuAZZ+udQfhm/el/Cou /Yw6I24RqvC8riONYT84OpTq6hziTBu4b1T2/ZnoN+Aif9kzvhhWt3WNhnETv3KIdRxNAnuM G/z4lein2uF/vZFenSMNg3lE0M8XtGLHq6Q03qg1vJv4wa+fgeQP7pcH3KceHp9aeJaAbhHE eq835UmaCuaMk/6oGj8Lgzjgav5KnHfyDfAVNyeYfwFelztSD55LzwAAAABJRU5ErkJg gg==</item> <item item-id="104">iVBORw0KGgoAAAANSUhEUgAAAF0AAAAWCAYAAACi7pBsAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAJFSURBVGhD7Za9scMgEIRVFwVRhUtw QBfOHZC5B6ceInfgDni3BwiQZUnooZ+Ab0ZjhJA4L3cLnW3sThP9AJroB9BEP4B9RTfKiq6z XSesMr6vkM/nQ+939vV6+Z5aGKsEYhu5pPZj6jAqulHS1p0GaCuD2Cz+ujkulwsLgd8xVsdO MUmfCUYJK0JWUL+qLMaX6EaTIJVXltEyyRiXVaXTXK/XPtNxDakTO2JbX4lLyCLXMpbUeOzI 1nKxALInLVPM1WfTAu73u30+n30bMb7fb74H87EvBFUoFEm/HYN0gahLyrPc/7KSJUpEh39D 6ADExly32833gKWxTzOMcwty0TMLqMta0WEnY/4dFrmnSuzbWwtIoi7x2fJMz0VZPhcEh/BD wobqKIl9gh2sBUTRw4nCmI0mHZxeFvw5bJy/jobwd4j+eDwqxW6KLO8/RNGRiUJaNXk+Wr+R MiwOKmJYwu676ckDYmYV9ONi65mMPY35R/x9XO7KhA/PkCTUllRR/DxtBwx93zuAUPSc/qeU dIDov0kViXs3+hxoCjRbi7NAiyqkslpTJfEpDAtM7dQyMQb7AY0B2WmNFw7vuP93GtFxxvbx no7UdjRlqvAVhf5YQbA37mYgeqwo7Dmxuk+V6ecEggbBvLho+uxF++uYmTxzuI0+jGmizzGw kNAO9kF7t8t4LygqVpH99O8Qmq0J+w72BdNEnyNaCNrRIthyILq78Zst+TZ1cOaT0EqR0FmV kOWQNTXRD6CJfgBN9ANoou+OtX8Nch0Qa8XFBgAAAABJRU5ErkJggg==</item> <item item-id="105" content-encoding="gzip">H4sIAAAAAAAA/2xTyW7TYBCe30u81ImblKQ0TuOQsNxQMYugp1JVCKksUvoAleuY1og0wXVQ j1EfgBtnOPACSIg7L8Bz8BZhliS0iN8az/bN8o/HHgAopAMkl2Ud33b6vp8eZ6NTE+g8QHIG o0RsBtueEGyYvD56myYFW6DP8Rq+S2cn493RuZhfIFlo6C+BLykW6XcAMMN6+5t/dTpflDSl gaazUFsWf1oUeXY0KVLFyG0kHy4fY7t7RTc5gVHkaSrZI6TymMVStPETYGpa0WfkOyVbuOUI t13hzkqkYZEPOB9TGVtwF+7DYwAv+rQPcOFCmd3fPUyoTHLfg0f44KlwgqnjR9TlbgVNqyxe VLiBalT7hX67xhkGVbyy+qbDWvQRG5xW4Rrbv66RXWZXl4xWQ1qrrwtvXJcW1wmptgA2WP+x 1KEpgY1AWmmiqSWtNDnxpih1VtoCLoWSvd0RHt6QKh3O+hCgK1U6yyo9CQxvSpUemm5J4h4n vi0Xai8udIfXxR9Eh8d5PD45fDPKh3FhyVVp69i8FxexfM3m/KtSFG8qCROkLu8efWo3Ps/O nnEe2dQW7aCa7znwMkIKCYzgFAaQ4QriA6p1BaPnEKP3v765dVHQKvI4SaM9nV3BHBiqVVCB QlyAs0EJo0KlqUDH2FDpKjBmZDHINUPBVIE23UFXiTCUwGIMCvYi3EEMh7uI4fAVxHC4hxgO LxOGoiqMQcEnDIGlX//VZJjmWRK/kynZ3PdzJJpGxmOlf8qdj/if411W/gAAAP//AwCsGeBP PwQAAA==</item> <item item-id="106">iVBORw0KGgoAAAANSUhEUgAAA/cAAAFjCAYAAAB47vuzAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAB5ESURBVHhe7d1LcvJKtgZQj4sBETWI GoTadyD0ql1jqKBVM6gZ6JKYtNNYYB7aUmZqrYh9zm9eypdBnxHiYwQAAACaJtwDAABA44R7 AAAAaJxwDwAAAI0T7gEAAKBxwj0AAAA0TrgHAACAxgn3AAAA0DjhHgAAABon3AMAAEDjhHsA AABonHB/z3EYdx8f48fHbhyOl8tuOA67cffXjQAAACCAcH/TcRx2OdQfxv1uOF1yy+n6jw/h HgAAgFUI97cc9uPH/nD5If34MRY//nDY78fBO/cAAACsRLi/4fow+5uH3R+HcTiF/nT9x/kQ /t8FAAAAkSTPGx4L98dx2H8ern99vVAPAADAUiTQGx4K94fh60R7wj0AAABrkUBv+fMz9+mE e78Pwc8BX7gHAABgKRLoTcXZ8tNX4t09W7537gEAAFiPBHrP1Pfcny/7/b33wj0AAABrkUCD CPcAAAAsRQINItwDAACwFAk0yM1wny4X/AEAAJiRlBnkV7jPob4sAAAAmIGEGeRHuL8O9bkA AABgBhJmkK9wPxXmpy4DAACAF0mXQX6F+1K+7PpyAAAAeIF0GeRuuE/y5VPXAQAAwBMkyyDn cH8vwN+7DgAAAJ4gWQb5Ee5vEe4BAACYgWQZRLgHAABgKZJllGfC/b3bAAAAwB+kyiiPhnbh HgAAgDdJlVGEewAAABYiVUYR7gEAAFiIVBnl2XAv4AMAAPAiiTLKM2FduAcAAOANEmUU4R4A AICFSJRBzt9z/yjhHgAAgDdIlEGEewAAAJYiUQZ5KdwL+HHKMTbWAABAZyScIE+F+0TgjJHH 9VYBAAB0QLoJItxXII/p9biWl19fBwAA0CDJJohwv7I8nrfGtLz+1m0AAAAaIdUEeTncC5rv e3QsH70dAABA5aSaIE+H+0TQnMcz45hva9wBAICGSTRBhPuV5DF8dByfvT0AAECFJJogwv1K XhnDfB9jDwAANEqaCSLcryCPn7EHAAA2RpoJItyv4J3xy/c1/gAAQIMkmSDC/QreHT/jDwAA NEqSCfJWuBcwnzfH2M3xGAAAACuQYoK8FO4T4fI1c42b8QcAABokxQQR7heUx2yOcZvzsQAA ABYiwQQR7hc095iZAwAAoDESTBDhfkFzj5k5AAAAGiPBBBHuF5LHa84xi3hMAACAQNJLkJfD fSJYPi5qrMwBAADQEOkliHC/kKixyo9rHgAAgAZILkGE+4VEjpV5AAAAGiG5BBHuF5DHKWqs zAMAANAIySWIcL+A6HHKj28uAACAykktQYT7BSwxTuYCAABogNQSZJZwL1TettQYmQcAAKAB UkuQt8J9IlTet9T45O2YCwAAoGISSxDhPtiS42MuAACAykksQYT7YEuOj7kAAAAqJ7EEEe4D 5bFZanyW3h4AAMCTpJUgwn2gNcbGfAAAABWTVoII94HWGBvzAQAAVExaCSLcB1pjbPI2zQkA AFAhSSXI2+E+ESZ/y2OyxriYDwAAoFKSShDhPsiaY2I+AACASkkqQYT7IGuOSd62OQEAACoj pQQR7oOsOSZ52+YEAACojJQSRLgPsvaYmBMAAKBCUkoQ4T5AHo81x6SGNgAAAFyRUIII9wFq GQ/zAgAAVEZCCTJLuE8EyW+1jIU5AQAAKiOhBBHuA9QyFuYEAACojIQSRLgPUMtY5HaYFwAA oBLSSRDhfmZ5HGoZC/MCAABURDoJItzPrLZxMC8AAEBFpJMgwv3MahuH3B5zAwAAVEAyCSLc z6zGcTA3AABAJSSTIML9zGocB3MDAABUQjIJMnu433KIrHUMam0XAACwOVJJkNnCfbL1AFlz /7c+NwAAQBWkkiDC/Yxq7v/W5wYAAKiCVBJEuJ9Rzf3Pbdvy/AAAAKuTSIII9zOquf+5bVue HwAAYHUSSRDhfia57zX3f8vzAwAAVEEiCSLcz6SFvuc2bnWOAACA1UkjQYT7mbTQ99zGrc4R AACwOmkkyKzhPtlqeGyl31udHwAAoArSSBDhfiat9Hur8wMAAFRBGgki3M+klX7ndm5xjgAA gNVJIkGE+xnkPrfS7y3OEQAAUAVJJIhwP4PW+rzFOQIAAKogiQQR7mfQWp9ze7c2TwAAwOqk kCDC/Qxa7PMW5wkAAFidFBJEuJ9Bi33e4jwBAACrk0LuOQ7j7hTUPj5243C8XPbDcRx26fpU +/FwuTQR7mfQYp9zm7c2VwAAwKokkJtScM+h/jDud8Ppkp+Ow/4r9B/2p4Bf3CYs3G8lNLba 31bbDQAANE0CueWwHz/23+/Fp/Be/Pjb+V3+73fvZw/3yZZCY8t93dI8AQAAVZBAbjgOu3FX HIt//fNvh1Oey4fo/6zZpMea8/Fq1nJfc9u3MlcAAMDqpI8bng73x2HcF9fPGuqzLQXGlvua 276VuQIAAFYnfdzwbLg/DD8/ky/cv6n1vm5prgAAgNVJH7c88Zn7FPyvrxPu39R6X7c0V9Ql r71HCwCALtizu6k4W346Wd7E2fKTn+/oH8bh8m/h/k2t9zW3fyvzxbrK9fZOAQDQLHtz90x9 z/35ss+fz19/d74+1/fthPs39NLPrcwX68lrrKxHTd03FwAAzbEXFyQ03Pe+891LH7cyXyyv XFtzrK/rx8sFAEAz7L0FCQn3yRZ2unvpY+5H7/PFcso1FbWurrcRtR0AAGZlry2IcP+Gnvq4 hfliGXktLbWeyu0tuV0AAF5iby2IcP+Gnvq4hfkiVl5Da6yl623nAgCgOvbSggj3L8r966WP vfWHZdWyfsp21NAeAAB+sYcWRLh/UY/9633OiJHXTU1rp2xTbW0DANg4e2ZBhPsX9di/3Kee 54151bxmyrbV2kYAgA2yVxZEuH9Rj/3Lfep53phPK+ulbGcL7QUA6Jy9sSDC/Yt67V/v88Y8 8jppZa2U7W2p3QAAHbInFkS4f1Gv/cv96nnueE/La6Rse4vtBwDogL2wIGHhPul1Bzr3q8e+ JT33jff0sPbLPrTeFwCABtn7CiLcv6D3QNB7/3hNXhc9rI2yL730CQCgEfa8ggj3L+g9DOT+ 9dxHntPrmij71VvfqM/1ertXANAxr3RBhPsXbGHnawt95HE9r4fct7LgHVNraq4CgA54RQsi 3L9gCztZuY+995O/bWUtlP3sva/M63rt3KpHTN3vrwKAxnj1CiLcv2ALO1S5j733k/u2tg7K /m6lz7zmeq2UFWVqW9cFAA3wihVEuH/BVnaittJPpuX539oaKPu9xf5z29TaSLWWqbaUBQCV 8ioVRLh/wVZ2nHI/t9BXftv63Jfrf8vjwO+1UON6mGpjWQBQEa9MQULDfdLbjsXWdpS21l8+ 5Xnf+tyX47D1sdiiVuf/ut3XBQAr82oURLh/0tZ2jnJ/t9TnrTPnP5XjYVy2oaf5vu5LWTC3 qXV2r4DN8gwQRLh/0tZekHJ/t9TnLTPft5VjY3z61PscX/evLPjL1LpZqoDu+M0OItw/aYsv NLnPW+v3Fpnn+8rfBePUjy3O63Wfc8HUuni0njF1/2cLaJbf4CDC/ZO2+IKS+7y1fm+NeX5M OU7Gq23m8fcY5GI7puZ/qtYw1Y6/CmiC39Ygwv2Ttvrikfu9xb5vgfl9Xjlmxq0t5u636zEp i75MzXFZLZhq972CZ02to7lr44xAEOH+CbkvvfTnGVvu+xaY29eUvxfGr37m6zHX45SLNk3N ZVk9merfVLFNU2uhlepQn72qgHD/hI5/wR6S+7/lMeiReX1POX7GsV7m53nlmJVF/abmLdeW TPV/qujH1Py+UpGmtjdHNWbGFh/H4+FUl5+2Trh/Qk99eUXu/5bHoDfmdD7lWBrPepiX912P YS7qMjVHqfg2NT5TRd2m5uyRatVUX16pyjzQouM47Pfj/s/anfr3Me4Pl7ttnHD/hJ768qo8 Blsfh16Yy3mVvx/Gdl3mYX7XY1oW65iai1Q8bmr8pop1TM3FvdqiqXF4tlbwwFYP4/7UuBRW H6nd4L37JI1FuLSNJbYTrZd+vCOPwdbHoQfmMkY5rsZ3HcY+XjnGZRFrasxzMY+psZ0q5jU1 xreK50yN4bMV4IFHfS7cf+wGh+afpLEIl7axxHai9dKPd+VxMBbtMofxyjE21ssw3su7HvOy mM/U+KYi3tS4TxWPmxq/W0WsqTF/tZ70wD1SuN+f/vvtOOzG/eF3hD8Oe4flXwj3T+ilH3Mw Fm0zf8vI41wWMYzxusrxvy5eYyzrNDUvt2rrpsbkVlGnqbm6V0947tZnv8P+t9N13rk/E+4f lPvQej/mYjzaZe6WV465sZ+Xca3P9ZyUxW1T45WLek3N173q0VQ/7xV9eXFuX1gJn4fp7/bD eEhnxz9+1uEwnIJ9OjT/VvDfFuH+QT30YW55TIxLO8zZesqxNwfvM5b1u56jsvg2NT6paNfU fD5SNZtq718Fd7y0Qg77q8/Zl+W4/LM0FuHSNpbYTqQe+jC3PCbGph3man3l74z5eI2xa085 Z9e1NVNjkIq+Tc35qzWXqcd+peAFL66c4yngf371XVnp3XyH5H8S7h/UQx8i5HExNvUzV/Uo 58KcPM6Yte96Dq+rV1N9zQVT66K2gpm9uarSIfmH8ZAOzb9csorjMO5uHDGQ/gixxrfzCfcP 8uR2Wx4b41Mvc1Sncl7Mz33GqD/lnE5Vy6b6kwteNbWe3ilY0cwrMIX9yz+XkoL91En8DsMl 1KdzBCwf8BcN9y0/kbTe/mg9zHHPzE3dyt8fc/WTcdmG63meqppNtbcsAH548ZnxFOLTCfT2 +6vaTQftMMdx2E2dwO90+Y+PCCx/Fv9Fwn3S+gucF+j78vgYp/qYlzaU82S+PhmLbSrn/a9a w1Q7rguAu156prx7Qr0Zz5b/n//85/yY//znP8//Tz/ny88O+98n8Dt+ns3/4+rd+tTmJc/1 l9q7iLSdpbY1t5bbvqQ8TsarHuajPeWcbXXutt5/frpeD4/WO6Ye714B8JQXnjlzeL5RM71D /t///vf8eP/73//OP//73/8+//yvf/3rK9wfh924mzjePl3+K/PfuG2U1NZFpO0sta25tdz2 peWxKov1mIN2lb9DW5nHrfWX112vlSULgLe98Gyawn1+d/7q8PfjMA4zvTuewvE//vGPy0+f zn88KF4ApgP79KH6wn2FWm77WvKYlcWyjH37yjksqze9949lXa+nVwqAUC8806bwvDuF+PSZ +8N4TIfGf5yC8+Uz93O9c5/CcXq3vnR92WRgv3GCPeG+Qi23fU153K7rXRGP2Rtj05dyPstq XW/9AQAe8tKrfgrKKbx+fr4+hf3isPyZPnN/HeTz5+9/mPrM/emy3cThAz5zX6GW216DPH5r 1BZtvf89K+f2ulrSctsBgLe9+Op/PIXl9E54TsvpbPSf4X4qWL8if8Y+nUzv//7v/86ftU+H 6aef0+fxP/0+BD+F+N1+GA8/3qR3tvwqtdz2muRxfLeuTd3murZki33eonJ9l1WzltoKAIRp fy8gHYZ/92iBy8cIlkz2J4uH+xZ36Fpt95aV6+26eraVfvKtnPPrqkGt7QIAVvPS3sD5XftT YN4teZz7PSng32hLauvSwT5ZLNwnLe7Y5Ta31m6+lXNYVm967ht/K+d/qpY0tf1UAAAnL+wV fH8VXjr8fYXc3ATh/g8ttplpeS7L6kmPfeI15Rq/VXOaevxcAABXXtpDOOxvHQZ/HI+HU11+ 2jLh/g92UPuT57Ss1vXUF+ZVro1n6trUbW4VAMAdr+0tHA/jcAr4w+EwHo+nMJ8rnal+prPl t064/4Od1T7leS2rVT30geWU62WuAgB4wgt7D9+H5U+XcJ+ksVhM2taS25tDi23mcXl+y2pJ q+2mXuWaKgsAYCYv7FksEO5PjxNewdJYLGahPs2qxTbzvDzPrc13a+0FAGDzXth7TeF+N+4P h/Hwq2Y4LL8MApEVTLj/Q4tt5jV5rsuqWSvtBACAgr3XIML9Hbm9LbWZ95RzXvPc194+AAC4 wR5sEOH+DuFpu/Lc17gGam0XAAA8wF5skEXDfdJSKBGgti3Pf1lrq6ktAADwAnuyQYT7O4Qo 8hooay01tAEAAN5kbzaIcH+HIEWW18Jaa2LNbQMAwIzs0QYR7u8Qpijl9VDWEpbeHgAABLJX G0S4v0OgYkpeF2VFWWIbAACwIHu2QYT7O4Qqbslro6w5RT42AACsyN5tEOH+BqGKR+R1Uta7 5nwsAACojL3cIML9DcIVj8prZaqe8c59AQCgEfZ0gywe7pMWwouAxbPymnm3AACgY/Z4gwj3 NwhavCOvn2cKAAA2wJ5vEOH+BoELAABgdlJWEOH+BuEeAABgdlJWEOF+Qm5fzW0EAABokJQV RLifINgDAACEkLSCCPcThHsAAIAQklYQ4X6CcA8AABBC0goi3E8Q7gEAAEJIWkFWDfe1Buia 2wYAANAwSSvIKuE+Ee4BAAA2R9IKItxPEO4BAABCSFpBhPsrgj0AAEAYaSuIcH9FuAcAAAgj bQUR7q8I9wAAAGGkrSDC/RXhHgAAIIy0FUS4vyLcAwAAhJG2gqwW7pMag7RwDwAAEEbaCiLc F3J7amoTAABAR6StIMJ9QbAHAAAIJXEFEe4Lwn1XVl3bEMjapmfWN72ytunZs+vbb0OQVZ9o 0rZreqKrrT28xYsovbK26Zn1Ta+sbXr27Pr22xBk1SeatO2anuhqaw9v8SJKr6xtemZ90ytr m549u779NswoDX4NdfrPuaauW6NO/6mqPUoppZRSSinVQj3juVvzsGcnYlZp22tuv5TbUkt7 eNuqaxsCWdv0zPqmV9Y2PXt2ffttCLLqE03adi1PdDW1hVl4EaVX1jY9s77plbVNz55d334b gqz+RJO2X8OTXS3tAAAA6JjUFUS4vxDuAQAAwkldQYT7C+EeAAAgnNQV5Fe4Pw7j7nTZx8du HI6XyyLVEqpraQezO+w/xt0iixmWchyHXT477X48XC6FXhyH3WV9L7QvAgtLa9y+Cf05jPvz c/ff+ydSV5Cf4T7tMOYX0tPk7IbTJcHS9n+0YSW1tINZ5R1EL6D05DjsvwJP+uPVxxLP1bCU 4zDuLwv8/By+9+crevMZgOyb0J3D8PAfZKWuID/C/WH/40U07TSGv6bWEqpraQez89dxunY+ 2sq793TqtF/i+ZveHPb7cbBvQne+jyp8JD9KXUHKcH8dghYJRTWE6hraQBjhnr6ld4CEe3p0 2lEcrGw6cxzGtKztm9Cf43hMS/ryEe+/Ar7kFWT1cJ+sHa7X3j6hvIDSteIQZujGZefQx6ro y3Ec9p8fo7JvQtfSc/gfHxmUvIII9ydrb59QXkDp2WHweXv6lZ6/nVOCbhSfR7ZvQt/SIfpO qLeKMtyv8pn7RLgnkBdQepXW9iLP0bCahU7uC+G+P49clv0T+vR9lMotkleQH+H+/MRzOVv+ A4dTzCa14Uc7Frb29gkl3NOjn+v6MA7WOD1yQj06Zd+Erj3w3C15BfkZ7k++Pue24HfLrh2u 194+YdKLp7+O05vz199d1vVn+S5w+lE+b/saPHol3NOddAT4E8/dkleQNAGrS21Yqx1rbhsA AGBjpK8gwv2K2wYAANgY6SuIcL/itgEAADZG+gpSVbhfoy1rbRcAAGCDpK8gVYT7RLgHAADo nvQVRLhfabsAAAAbJH0FEe5X2i4AAMAGSV9BNh3uBXsAAIBFSWBBhHtLCwAAYCkSWJDqwv2S 7Vl6ewAAABsngQWpJtwnwj0AAEDXJLAgwr2lBQAAsBQJLMhmw33eVk39BwAA6JwEFmTz4R4A AIDFSGFBhHsAAACWIoUFEe4BAABYihQWRLgHAABgKVJYkCrD/RJtWmo7AAAAfJHCglQV7pMl QnfeRm19BwAA6JwUFmTT4R4AAIBFSWJBhHsAAACWIokFEe4BAABYiiQWpNpwH9mu6McHAABg kiQWpLpwnwj3AAAAXZLEgmwu3OfHrrHfAAAAnZPEgmw23AMAALA4aSyIcA8AAMBSpLEgwj0A AABLkcaCVB3uI9oW9bgAAAD8SRoLUmW4TyJCeH7MWvsMAADQOWksyCbDPQAAAKuQyIII9wAA ACxFIgsi3AMAALAUiSyIcA8AAMBSJLIg1Yf7udo39+MBAADwNIksSLXhPpkzjAv2AAAAq5PK ggj3AAAALEUqCyLcAwAAsBSpLEgT4X6ONs71OAAAALxMKgtSdbhP5gjl+TFq7ysAAEDnpLIg mwr3AAAArEoyCyLcAwAAsBTJLIhwDwAAwFIksyDNhPtX2/nu/QEAAJiNZBak+nCfvBPOBXsA AIBqSGdBhHsAAACWIp0FaSrcP9vWV+8HAABACOksyCbCPQAAAFWQ0II0Ee4T4R4AAKB5EloQ 4R4AAIClSGhBmgv3j7b32dsDAAAQTkIL0ky4T54J64I9AABAdaS0IF2G+3y7lvoGAACwAVJa kCbD/V9tfuQ2AAAALE5SC9JUuE+EewAAgGZJakGaDfe32v3X9QAAAKxGUgvSbbgHAACgOtJa kObCfZID/HXbb10OAABAFaS1IE2G+6QM8tcFAABAlSS2IM2G++Q61LfcFwAAgA2Q2oI0He4B AABoigQaRLgHAABgKRJoEOEeAACApUigQYR7AAAAliKBBhHuAQAAWIoEGkS4BwAAYCkSaBDh HgAAgKVIoEGEewAAAJYigQYR7gEAAFiKBBpEuAcAAGApEmgQ4R4AAIClSKBBhHsAAACWIoHe cxzG3Smkf3zsxuF4ueyH4zjs0vWp9uPhcmki3AMAALAUCfSmFNxzqD+M+91wuuSn47D/Cv2H /SngF7cR7gEAAFiKBHrLYT9+7L/fi0/hvfjxt/O7/N/v3gv3AAAALEUCveE47MZdcSz+9c+/ HS6H5/8uAAAAiCR53vB0uD8O4/5u+AcAAIAYGwv35QnwrurqmPtnw/1h+P2ZfAAAAFiCd+5v eeIz9yn43/08PgAAAAQS7m8qzpafTpY3cbb85Oc7+odxcGg+AAAACxPu75n6nvvzZZ8/n7/+ 7sfh/be+Dx8AAADiCPcAAADQOOEeAAAAGifcAwAAQOOEewAAAGiccA8AAACNE+4BAACgccI9 AAAANE64BwAAgMYJ9wAAANA44R4AAAAaJ9wDAABA44R7AAAAaJxwDwAAAI0T7gEAAKBxwj0A AAA0TrgHAACAxgn3AAAA0DjhHgAAABon3AMAAEDjhHsAAABonHAPAAAAjRPuAYB1HYZxOF7+ DQC8RLgHAFZ0HIf9cPovAPAO4R4AWMfxMO4/PsaPj5137gHgTcI9ALCa47Ab94fLDwDAy4R7 AGAlx3HY7UfZHgDeJ9wDAOs4DuNu5/P2ADAH4R4AWMdhP+4G79sDwByEewBgFYf9x7jbD+PB W/cA8DbhHgAAABon3AMAAEDjhHsAAABonHAPAAAAjRPuAQAAoHHCPQAAADROuAcAAIDGCfcA AADQOOEeAAAAGifcAwAAQOOEewAAAGiccA8AAACNE+4BAACgccI9AAAANE64BwAAgMYJ9wAA ANA44R4AaNdxGHcfH+PHpXbD8XLFSb5uf7hcAAD9Eu4BgMYdxv1EuD8Ou0vo359uAQB9E+4B gMZNh3vv3AOwJcI9ANC4G+EeADZEuAcAxuOw//HZ9Y/dMJYx+Xgort/tx8NVhv5x/cdu3F+H 7OMw7nf5+s/HGMoHOd3/67qPdN0w7vLtr9pyerDTzfMh92lbp8e+3Pcr3F89nvfuAeidcA8A W3c5fD0H4+M5WH8H6vzZ9c/rj+NwDt3fgfnrs+3nw9+/30X/Oho+Hx5/eszPi/JjFLc5+f6M /GVb+X7554vD/vOyfLh9+sPCj/tdfN1OuAdgA4R7ANi6rxC9+/lu+lkO66frLlfl0PyZrX+H +c/r8+2/g3wZvL+C/MQfEb7D+NTh9hN/PJi83dTjAUC/hHsA4BTIi3fN98Vh8D8Ob/9Z5yBd XF++C/9tOnh/3+/7jwYPhfvJ7Qn3ACDcAwBnPz83fwnEf4X3F8P9d/AW7gFgDsI9AFBIJ6sr wvONz71/Ka6f/sq5mQ/LL7Yn3APAN+EeALYuBebiDPjpzPlTn7E/X3a5UTrp3u/rU+DO16cz 3p//mW7wef1XkP/rhHp529Oh/Wt7pzunS7/v91n5Mb/bJdwD0D/hHgC27hTu01fXfR+Wv/sK 6Z/Kr547Vfoau/LqdP0pYH+9g7+7vv9J2sYl0H89Rnmb4nD7fP2P25/q+w8Bx3EovgpvOKY/ Anx+Jd7XQ/56vOI8AgDQIeEeAAAAGifcAwAAQOOEewAAAGiccA8AAACNE+4BAACgccI9AAAA NG0c/x9owOPaGEy55wAAAABJRU5ErkJggg==</item> <item item-id="107">iVBORw0KGgoAAAANSUhEUgAAADAAAAAWCAYAAACG9x+sAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAFXSURBVFhH7ZbBjYMwEEVdlwuyKIIS OLgL7hy40QNXxIkO6GDWf9aGWa+txEl2AYknWUkGCf//hzFRdHFuA0dzG/gosyWtFCmlyc6+ lmC2mpTp+fuJDPRkgnA2YlzlNyweJk9noDebKCeTrFa0/YzIdmBZFqqqiuq6pqZp2Ok0TXwt fD7HtwBOKl4ZVVIU6I0inXmOkgYgHhsMw+ArRF3XcQ3X2rb11b8BoqTgYgMQivQl4zhyHeLX deUabsxJ+qWNdXnHvNaBtw3I9EEwgE7sQFw4JXoWmtuoiHdnAEIhWBLm4CfutNB76umkyjvA 95WnkNgjJmkAQ4oNIDqsUMNQb91xSQXBM1J7cGYXkXoP+Nrmm/fcw4jjfYicAa3dWf0p8S9S aABtxgtGzsGxlBmQgyYepSMpMBCn7rtxiUdoGy6s/T9KmIfswfIPFA/x2bgNHM3FDRB9ASH1 U3F0LWUHAAAAAElFTkSuQmCC</item> <item item-id="108">iVBORw0KGgoAAAANSUhEUgAAADEAAAAWCAYAAABpNXSSAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAF4SURBVFhH7ZbNrYQgFIWpi4KIRViC C7pw74KdPbg1ruzADu7jMIB3HPENvnkOmcyXkAgLPef+oaAP4GuiFA5NzPNMwzD4XbkkTTRN Q13XUd/3VNe1P72ASZMUgoSQpCd/tsOkJQll3POuCYgex9E9L8tiX3hV1RlSQbwzo+zJI84A jKZMIPpYAWTiMhNGRWFWKmkpKG43HGYiCEYmUFLYh6zkcRPhIrZdCWVcGDBKkEzUVNIESqeq KveMpkZGsOeZ+U8gjIs+ZQKlg8VBNoIxgBfzqEqlbcz3OJeJP5tAQyMbHBho29bvAMSFyWGc 0NSHsnlFTyBK/F4Ik+nemJ0gco1+Olr5mXDv5tOJfWfLrgn0ACKOhRLC4qM2YqMVRE+I3C/z PJu9e8KfRe/uu2tAogk07zNTiPeElHaOv9LASaKJ525lpBsXEO+L95NngjceK6t3c9fYx2yj 77NSUjkdEpsNa/2fCf2RHDYXkZGJcvmaKIUPMEH0AzhNqlnH2uM2AAAAAElFTkSuQmCC</item> <item item-id="109">iVBORw0KGgoAAAANSUhEUgAAAHoAAAAnCAYAAADASVzNAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAARlSURBVHhe7VztcaUwDHwNXB+pgS6u CfpIB1TDr+uEYjitP55lY2HZEM65eGeYCcYf8q4kbCDvtQ/8CHQr9LYu+7Ju7mwAuMJJl0Kv 87zPqzsZiLCtxM1SL3Z3Qm/LELkIEnuqFLsvobdln0SV131+vfaXOWY6O2JbJnf9lSVinV9Z J5LKLwFzMbZMu6QJt/c4/rYvk3QNNsv95tCR0DQxStkS34j084nBEdzkDckJEY74A2lS+SWk tmTmhfJpoVm7v6M6zqnPjOLtFehHaBguKhmiWawTkcWIdlgXWshkIlcqvwRKrUEkG5lp/4hm PhdkFXtu68tcBNREdTdCY+JFo42YEgku1YHR9B6GBYwpTgiXyi/CpGTWYRAxQKzjHHamc5PW zwyruFd3IjREktN2DESrVDeX8qjvxZ7Hgkrl1yFHKwOins3D17EOQOW44Bxbto3mq0zfnQhN Blcwje1Xrra9j7vI9gRg7+mYiASVylVwY8Cp0oM6UglNQHloazOatq2FPkBOhCbvIjLmKawM YcxEHftNu0R4Nchz9XtDYXLw/rd3WyEgXkymPUCcVH4LEK1vzwm2iGD1xZQuQHufzgsN4r3H MmENNqQLT85NQlfca8S67t5m7bEpPCUXpOUIl8rbgfGdAJEDZpCkcDsPLx7rR0B5N2JxFNoM VBLSR8R9Qp8SbchwNvGKzlZfZKLB1cs5w3NCE948MqG4vX5OOSdg8y3ZhTlrbE+EtpGgGwR1 HxJ6QEST0DwiNNF62z16CN2MBqGTleSTzA+hm9EgdJy2HxX682P/+HR/D1Thz+9fKu76EHpE dDOuR7TyicstGEI3o2kxZrdM/mhdaCX3en5IFg2hm9EkNLVie+gv2FtKGEI3o01oAhq+I/Ak feOTlvxHAiOin0Sz0EAsNqVwrvaGp2J45n3jPXysupvRsOpOAEEXitokOvlLjdswIroZlyL6 cQyhmzGE/iH4WULn3hQl4OuO41hhAdliR/ntl9R/rpw/zyhvcb+d0O0v/UEMe/ebIwflfvF4 qOOIbVEYcE4mN5f6z5dr3y97tL+P/hcgslr++8CAnCSQZSMk5RRezx0JEWjPbf12J6O+Tr8i lfqXykM0a2269oXJ46AJ5pkqwqRk1jaIGCDWcdGt+uIyB3fLEVO31H9pXJclymLDYXRPMDsR Wm9wCjlaGRD1LF37OtYBqBwXHLl6rcnmwlekUv+6cRHdJU6ojvJ5RidC28nnHdimObs4SQ5i RyU0AeWhrR1L2zYLxVekUv96mwtCV6xtuhGaZq82OoLiHh2B1QfhPG3WCI26kdPRkbaV+teN W85y3/I/NTQTywMpjq26z1JZksJN/feWjPVTCQiVdS6pf824xWildtlB8+hIaAImV2H8G4a4 kJJ5menOCEzXc07gr/m6DYiE5uMCUv+5clbGIz6HmmgG+hKagH1hYY4D5BC129HuhAbGLx7I +G9+8cBj/IbJERsezjRy0q3QA3di3/8CuaCAUwMZ6qEAAAAASUVORK5CYII=</item> <item item-id="110" content-encoding="gzip">H4sIAAAAAAAA/2xTy27TUBCd67fduAkhKaROYuhriYJFF3RVqgqx4CGFD6hcx22NSB1cB3UZ 8QGIP2DDF7Bgzw/wMXTTXZiHE1rEjcZ37pkzjzt3UgMAhfIWxWNdx6+Tfhimp1l+bgKtJyju KE8EMxh7SrRx8ub4XZqUjMCQ/TX8Whdnk4P8UuCXKDYCwyXxFfmifOkBXGO+q/7fM63fmhSl gaaz0lwmf1aWRXY8LVPFzD2UOtxcxt7GrbPJAYyySNOAgQjFn7BqRZ2fADPTjr7ivm85stuu 7I4nu7sSaZjkI/bHUuZjeIQxdqkpNcZ/CD5AfIAWaozPjjN3NaLqDnyqktVPPiduRM1faHfu cIBRA6+qvuvQjD5jYbMG3GX8W5Nw6VlLItptKam1Jnv7npS2Rkw1ALgvJS3P0BHH9rqU0kEo kFI6HLgrhxYfekK2+hK9F8refyBZQo66C/BQsoTLLBvi2N+ULPQEWxJYXmNbLtRbXGiHx6Q+ io5Oi3hydnSSF+O4tJnbpmlj+DAuY3nF9eo1yYsnlJQpJeaZoyf24svs4jnHkQnt0uypar6B hxBSSCCHcxhBBjDHH6juLY5eQIzW/9oqdJHQLos4SaNDnU1BRQxVA1SgkBdgb1BDr1BpKtDR N1S6Cow5IQaZ5qiYKtBm+2iyiEMBbOag4izcXeSwu4ccdl9BDrvXkMPuPnHIa5U5qNSJQ2Sp t/56Ok6LLInfS5ccrvsFCnUj47bSf8mrWvzPqt08/AEAAP//AwApv0ZiNwQAAA==</item> <item item-id="111">iVBORw0KGgoAAAANSUhEUgAAA/AAAAFjCAYAAACaMuDKAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAB7CSURBVHhe7d3LcfPMsQZgxcWAWCcI B4H1SeBkwJ3XjsHFlTNwBjiEyJFGECSRbACcy/NUjf3rRgLdTWrejxe9jQAAAEDxBHgAAACo gAAPAAAAFRDgAQAAoAICPAAAAFRAgAcAAIAKCPAAAABQAQEeAAAAKiDAAwAAQAUEeAAAAKiA AA8AAAAVEOAvTse38TCcbx8tOY3Ht7fx7X0dLx8BAADAvroP8Ofh8B7Mfw3wp2H8Nd8DAADA xjwCfzGF+J8D/HkcDtdH348eegcAAOBFBPiLvwL8efrSeRgPH0+j/74AAABgS5Lnxe8BPjOF +MNwifRXgjsAAAB7kUAv7g7w70+n/3wTOwEeAACAvUigFw8F+KNH4AEAANifBHpxd4A/Hb98 nwAPAADAXrpPoFN4n4L4tD7C+fsb1h2ufzruEtrT199mb0MvwAMAALAXCTRAgAcAAGAvEmiA AA8AAMBeJNAAAR4AAIC9SKABAjwAAAB7kUADBHgAAAD2IoEGCPAAAADsRQINEOABAADYiwQa IMADAACwFwk0QIAHAABgLxJogAAPAADAXiTQAAEeAACAvUigAQI8AAAAe5FAAwR4AAAA9iKB BgjwAAAA7EUCDRDgAQAA2IsEGiDAAwAAsBcJNECABwAAYC8SaIAADwAAwF4k0AABHgAAgL1I oAECPAAAAHuRQAMEeAAAAPYigQYI8AAAAOxFAg0Q4AEAANiLBBogwAMAALAXCTRAgAcAAGAv EmiAAA8AAMBeJNAAAR4AAIC9SKABAjwAAAB7kUADBHgAAAD2IoEGCPAAAADsRQINEOABAADY iwQaIMADAACwFwk0QIAHAABgLxJogAAPAADAXiTQAAEeAACAvUigAQI8AAAAe5FAAwR4AAAA 9iKBBgjwAAAA7EUCDRDgKzT17LcFAABQKIklQICvyDyo/7UAAAAKI6kECPAVuDeYz78vLQAA gEJIKAECfOGeCeL5zzz6swAAABuSTgIE+IJFA3j+889eBgAAwIokkwABvlBrBe/8cqKXBQAA ECSVBAjwBVo7cOeXt9ZlAgAAPEEiCRDgC7NV0M4vd+3LBgAAuJM0EiDAF2TrgL315QMAAPxB GgkQ4AuxV7je63oAAAAWSCIBAnwh9grV6Xr2uj4AAICMFBIgwBdg70C99/UBAADcSCEBAvyL vSpMv+p6AQCArkkgAQL8i70yRKfrNgMAAMBOpI8AAf6FXh2gX339AABAd6SPAAH+hUoIz+kY zAEAALADySNAgH+RUoJzKccBAAB0QfIIEOBfoLTQXNrxAAAAzZI6AgT4FygxLKdjMg8AAMCG JI4AAX5npQblUo8LAABoisQRIMDvrOSQnI7NTAAAABuRNgIE+B2VHpBLPz4AAKB60kaAAL+j GsJxOkZzAQAAbEDSCBDgd1JLMK7lOAEAgCpJGgEC/E5qCsXpWM0GAACwMikjQIDfQW2BuLbj BQAAqiFlBAjwO6gxDKdjNh8AAMCKJIwAAX5jtQbhWo8bAAAomoQRIMBvrOYQnI7djAAAACuR LgIE+I3VHIDTsZsRAABgJdJFgAC/oRYCcAvnAAAAFEOyCBDgN9RC8E3nYE4AAIAVSBYBAvxG Wgq+rZwHAADwcpJFgAC/kZZCbzoXswIAAARJFQEC/EZaCrzpXMwKAAAQJFUECPAbaDHwtnhO AADA7iSKAAF+Ay0G3XRO5gUAAAiQKAIE+JW1HHRbPS8AAGA3EkWAAL+ylkNuOjczAwAAPEma CBDgV9Z6wBXgAQCAAGkiQIBfUQq3Lde0h3MEAAA2I0kECPAr6iHYpnM0N2wpn7OlBQBAtezm AgT4FfUSLtJ5mh3WlM/VIwsAgKrYwQUI8CvpKVD0dK5sL5+nv+Zq6XvTAgCgCnZudzod38bD cL59dCXAr6S3ECE0EZVm6NlZmv98WgAAFM2O7Q7n4XDZ2wrwm+ktPKTzNT88Kp+dNeZnfnlr XCYAAJuxW7vTFOIF+A30GBx6PGdi8pnZYm62vnwAAFZhp3anFOCn0L60eNJUux7rl87b7PCX vWYlv549rg8AgIfZod3JI/Ab6Dko9Hzu3O8Vc5Jf557XCwDAn+zO7iTAb6D3gCAg8Zs0H6+Y kfy6X3H9AAAssjO7kwC/gd7DQTp/c8RcCbORH8MrjwMAgA92ZXcS4DfQezBI52+OyJU0F/mx lHA8AACdsyO7wxTep7A+rTzEC/ABKRD0XkM1IFfi7SI/ppKOCwCgQ3ZjAQJ8gDBwleqgFpQ8 C/mxlXh8AACdsBMLEOCfJAh8pRbUcJvIj7Hk4wQAaJhdWIAA/yQB4Cv16Fvqfw0zkB9rDccL ANAYO7AAAf5JNv9fpXqoSZ9q630+rzUdNwBAA+y+AgT4J9j4L1OTPtV6e8iPu7ZjBwComJ1X gAD/BBv+ZakuatOP2nte+/EDAFTIzitAgH+CDf+yVBe16UMr/W7lPAAAKmHXFSDAP8Fm/2dq 049Wep3Oo5XzAQAonB1XgAD/IBv936lPH1rrc34+rZwTAECh7LYCBPgH2eD/LtVHjdrVao9b PS8AgMLYbQUI8A+wwb+PGrWt5f6mczO/AACbsdMKEOAfYGN/n1QntWpPD73t4Rx5nXy+/loA 0Ci/5QIE+AfYVN1PrdqTetp6X/PzbP1c2d58niILABrht1qAAP8Am6j7qVV7euppOlczzDPy +Zmveyz93HwBQMX8JgsQ4O9k4/QY9WpLj/3s8ZyJyWdmzdlZuty0AKBCfoMFCPB3sll6nJq1 IfWxt17m593bufOYPWdlfl1pAUBF/OYKEODvZJP0uFQzdatbzz00w/wmn4+9Z2R+3WkBQAX8 xgoQ4O9gc/QcdaufHqoB3+UzUcJczI+nhGMCgF/4TRUgwN/Bhuh5alc3/btKdVALSp2F/Ljy BQAF8hsqQIC/g43Q81Lt1K8+evcpr4V69KmWGZgfZ8nHCkC3/HYKEODvYBMUo371ST3Tt09q 0q8ae58fc23HDkDz/FYKEOD/YPMTp3710bNlqS5q04e837X2vIVzAKA5fiMFCPB/sOmJSzVU xzro18/y2qhP21rqc34urZwTAFXzmyhAgP+Dzc461LEeevW7VB81alerPc7Pq7VzA6AqfgsF CPC/sNFZj1rWQZ/uo07tar23+fm1eo7saz5TkQV0wy0+QID/hV8o60m1VM+y6dF98nlWrzb0 1NP5ubZ+vqxnaXa2XkCTVrx1n8fz6bJuH/VAgP+FXx7rUs+ypf7o0X3Uqx299jI/797OnfvN 52S+1rB0uT8toAl33JrP43A8jsc/1+Fy3/A2Hk+3H+uAAP8DvyzWp6Zl05vHmen69d7D/Px7 rQHfzecirT0tXf98AdW64xZ8Go+XG/oUVu9Zh6Gfx+Cn82XBVBe1WVeqqbqWR2+ek9dN7eqj d1d5HdSjX0tzMK1SLB1bvoCq3HGrfSzAvx2Gbp5GP50vC6a6qM361LU8qSf68hz1q5O+fZXX Q136UmPvl445LaAKd9xapwB/vPzvp/NwGI+n7zH9PBw9hR6/CLaS6qq25dCPOHNdF/36WV4b 9WlbK72en0e+gGI9cQv9Hug/Xb7mEfi+ufPfltqWw6yvI6+jWpZNn/6W10id2jLvbWv9XTq/ acEjlmZoq9WxJ87++pT6w3EYT9O7zp+v63QaLuF9ehr9T+G+PQL8AjeqbalvOfRiPamW6lku PbpfXis1q19v/Vw637QgWZqPklbjnjrD03H2uvd8dfQc+ul8mZlqoi7bSfVV49fSh/Wpabn0 5nF5zdSuXr33Lz//fNGXpRn4ae1h6XrvWQ158mzOlxB//bNx+Zoelfd34Ds21SMttqHGZdCD 9ZntMulLTF4/NayHvn01r0datGmp1/NVqqVj/W1VKnjk09PnT+Npehr97TNb+e9//zv+61// un204DyMhx8e/Z/+sWGLv24nwM9UfmOohjq/Vqq/HqxPbcuiH+vI66iWZdOr383rky/qtdTP fNVu6Zx+WpVY+UinQH/7zxX985//HP/3f//3/f//53/+5/bZzBTel9487zTcgvv0uv31Q7wA P1PZ8Fcr1VmtX0Ptt2W+y6AP68rrqabl0Z/HzWuWFnVY6l1aPVg676VVqCeP7BLUpzetOx5n 67AcpAOm0D6t5HtoPo/DYemN8y6f//KU/vXfIV+Anyl82Jui1q+R6q7221Hj19ODbeR1Vd8y 6EfcvIZpUZalHqXFcl2WViGeOpJf38Ru5Xehzx9xn55CP13HF6fj9zfOO1/fKf9t9qj7dNxr vsfet2Pp2VSLtNieWr+Guu8j1Vmt96f228rrq86vowfrm9c0LV5nqR9p8bulmi2tF3nimlM4 /mGt+Cj3v//97/dH36f//7//+7/3y5/+O3ceDuNh4bnx0+e/5fofvvdZ0/FwM9VCPfaT6q3m +1Hzfan3a6j5PvL5Vu99qfu28vrOF9tbqvu0iFmq6dLayRPXNAX49Cj77Gnq52EcVnyE+x// +Mf7///nP//5ePR9ei18bjmULz+tXoDf0M6Dy4Wa70u995Xqreb7UfN95fVW8+2p977m9c4X 61qq8bTYzlK9f1obeOJSp3B8uAT16TXwp/E8PYX97RKMb6+BX/MR+Pkb1k2Bfh6aF0P5D29q J8BvZOMh5Qfqvh+1fg11349av0Zed/Xfhvq+3rwHafG8pXpOi9dZ6sfSWsFTlzIF4Sm8Xl/v PgX6/Gn067wGfvqzcXlATh/Pn0K/+Br4y+cOC08F8Br4jUx1UIv9pbqr/fbU+XXM+fbU+LXy +uvDutS0LHk/5ou/LdVtWpRpqVc/rQc9/hPvzpcwPD2andLw9A7v1wC/FJyfMb3mfXr9+/So e3rkffnvwH9/uvwU1A/HYTx9ebDdu9Bv5snhYwVqv71UY3V+jbz+erA+tS1H3gv9iFHLss37 ky++WqrRtKjPUh/z9YDHvntH6fXvd5meMv/rI/+3p/2vmd4vBPibJwaPlaTaq/921Pf1zPk2 1LU8eU/05XHqV595z/LVq6VaTIv2PNnfp6bh/dH3SyA+rPl89Jn5m9X9aQrxPxzPdLxrh/eJ AH/x5OCxEvXflvqWQy/WpZ7lynujR/dTr7rl/VtarVs652nBgicm4/PPyE1PU98gF1dDgL9w B/N6erAdtS1H6oWexKljHfI+6dXP1Kk9857OVwuWzist+MNTU3I6/vR09fN4Pl3W7aPWCfAX 7mxeL/VAH9alruXRkzg1rEveL337Tl3al/d4adVi6djzBQ94bmLOp3G4hPjhdBrP50tgT2t6 9/eV3oW+Bt0HeHc85dCH9alpmVJf9OZxalenvG/6d6UefZr3fWmVYunY8gUBT0zQ51Pol5cA 343p/HuvQSlSL/RjHepZNv15jprVLZ/7XnvZ+/nzaT4Lf62tLF3X0oIVPTFROwT4y+VsvlYw nW/XVqwlQakX+rEOtSxbPu/6dB/1akPex5762eM587j5nLxywYaemLApwB/G4+k0nr6tFZ5C v3Qj2GKtQIBfr5asQD/WkeqolmXTp/upVXvynrbe2x7Oke3k87PFghcweQFdB3h3XuXRk3Wo YT3M/N/UqF15b1vsccvnBhDgHjFAgDc+RUk90ZfnqWF99OxnatOHvM8t9LulcwHYgHvGAAHe +BQn9UVvnqN29clnXu8+qUl/8p7X1vuajx1gZ+4hA7oN8H7Blktvnqd29dK7r9SjX3nv81Wq mo4VoBDuKQO6D/CUSX+eo251S/3rvYfqwCSfg/kqQYnHBFAJ95oBAjxFSv3Ro/upWf3yHvba x97Pn2X5XORrbyUcA0AD3HsGCPAUKfVHj+6nXm3IZ7+3fvZ63twvn5Gltbal60gLgKe5Fw3o MsD7BVwHPXqMerUj9bKnnvZ2vsTlM3PP+snS9/60AFiFe9SArgM8ZUt90qu/qVV7euppT+fK NvIZWnMBsAn3sAECPEXTq7+lGqlTe3robQ/nyGvlM7a0ANide98AAZ6ipV7p18/Up20t3wZa PjcA4Ed+8wd0F+BtGOuiX39Tn/a1eDto8ZwAgLv47R/QbYCnHnr2s1Qb9Wlb3ucWet3SuQAA D7MDCBDgKV7qmb59pS59yftdc89bOAcAIMQuIECApwr69p2a9Cf1vNbe13zsAMBq7AQCugrw No/10rvv1KNP+W2hpv7XeMwAwCbsBgK6DPDUJ/VO/67Ug3wGSp6DWo4TANiNHUGAAE81Uv96 76E6kOSzUOI8lHxsAMDL2BkEdBPgbSTrp4dXakAuv12UNBclHhMAUAS7g4DuAjx1672P6fzN Mrl8Ll49GyUdCwBQJDuEAAGeqqQ+9tpLc8xP8tvGq+bkldcNAFTDTiFAgKc6vfYynbc55jf5 nOw1L3tfHwBQNbuFgC4CvI1lW3rtpxnmXvltJF9r2/ryAYAm2TUEdBXgaUPqZ0897fGcicvn Jl9Ra18eANAVu4cAAZ4qpZ720lczTER+e/lp/WTpe9MCAHiCXURA8wHeZrNNPfW1p3NlW/ks RRYAQIDdREA3AZ72pN6aYXhefjtaWgAAK7PDCBDgqVbqbcv97eEcAQDoip1tgABP1VJ/W+2x +QUAoDF2twFNB/gUfgSgdrXc45bPDQCAbtndnofxcNnkv70dxuF8+9w3p/H4/j3TOl4+uuoi wNO21OeWet3iOQEAwEXnO9zzOBxScL+E9MNw+cyC07AY7gV4qpf63FKvzS4AAI3qe5d7Oo5v x/R4+vTh25h9eDOF/Ouj7/OvCfA0IfW6hX63dC4AADDT9S73PBzGQ/bQ+vzjq/N4nj718VT7 5dWU6XzSon0t9dvcAgDQsK53uvcF+MwU4rOn2TcX3BMhqD+p5zX3vYVzAACAX3S90304wL8/ nb6DN7ETgvqTel5z380tAACN63u3e9dr4HOXAH9s/BH4FIIEof7U3Puajx0AAO7U+W43exf6 2dPjF10Cf/4IfdMBnv6k3tfW/1qPGwAAHmTHu/R34N8/d/t4epT+/euXNXt4XoCnOan/tcxA bccLAAABdr0BAjzNSf2vZQ5qOU4AAFiBnW9AcwE+hSGBqG+1zEEtxwkAACux8w1oNsBDmoVS 56H04wMAgA3Y/QYI8DQrzUKp82BWAQDokB1wQFMBPgUioYik1Jko9bgAAGBjdsABTQZ4yKW5 KGU2SjseAADYkV1wgABP89JclDIfpRwHAAC8gJ1wgABPF9JsvHo+SjkOAAB4ETvhgGYCvGDE X149I6++fgAAKIDdcEBzAR5+k+Zk71l51fUCAEBh7IgDBHi6kuZkz3nZ+/oAAKBgdsUBTQR4 AYlH5POy9czsdT0AAFAJO+OApgI83CvNzJazs/XlAwBAheyOAwR4upXmZov52epyAQCgcnbI AQI8XUuzk6+oNS8LAAAaY5ccUH2AF5aIymcoMktrXAYAADTOTjmgmQAPUWmW5usvj34/AAB0 zI45QICHTJqnZxcAAPAru+aAqgO84MSW8vn6bQEAAHezgw5oIsADAABQBQkuQIAHAABgLxJc gAAPAADAXiS4gGoDfArvAjwAAEA1JLiA6gM8AAAA1ZDiAgR4AAAA9iLFBVQZ4FN4F+ABAACq IsUFVB3gAQAAqIokFyDAAwAAsBdJLqC6AJ/CuwAPAABQHUkuoNoADwAAQHWkuQABHgAAgL1I cwFVBfgU3gV4AACAKklzAVUGeAAAAKok0QUI8AAAAOxFoguoJsCn8C7AAwAAVEuiC6guwAMA AFAtqS5AgAcAAGAvUl1AFQE+hXcBHgAAoGpSXUBVAR4AAICqSXYBAjwAAAB7kewCig/wKbwL 8AAAANWT7AKqCfAAAABUT7oLEOABAADYi3QXUHSAT+FdgAcAAGiCdBdQRYAHAACgCRJegAAP AADAXiS8gGIDfArvAjwAAEAzJLyA4gM8AAAAzZDyAgR4AAAA9iLlBRQZ4FN4F+ABAACaIuUF FB3gAQAAaIqkF1BcgE/hXYAHAABojqQXUWqABwAAoDnSXkRJgTkdiwAPAADQJGkvosQADwAA QJMkvggBHgAAgJ1IfBEpNL86OJdyHAAAAGxG4osoJTgL7wAAAM2T+gLe/4xcvl7h1dcPAADA LqS+gKICPAAAAE2T/ALeA/z1P14XogV4AACALkh+Ad8C/N5B+lXXCwAAwO4kv4BiAjwAAADN k/4CPgL8JIXpvQL13tcHAADAS0l/AUUEeAAAALogAQZ8CfCTFKq3DtZ7XQ8AAADFkAADXh7g AQAA6IYUGPAtwE9SuN4qYG99+QAAABRJCgzYPcBvedkAAAAUTRIMWAzwk62C9haXCQAAQBWk wYBdA/wWlwkAAEA1pMGAHwP8ZO3AveZlAQAAUB2JMODXAD9JoTsavNe6HAAAAKolEQbsEuDX uAwAAACqJxUG/BngJ5EAHvlZAAAAmiIZBtwV4CfPBPFnfgYAAIBmSYcBdwf4SR7I//q5e78P AACAbkiIAQ8F+EkezNPK/fY1AAAAuiYlBjwc4CfzkP7TAgAAgIykGPBUgE/mgT1yWQAAADRP agwIBXgAAAB4gAQaIMDTKrNNq8w2LTPftMps07JH59utIcCdCa0y27TKbNMy802rzDYte3S+ 3RoC3JnQKrNNq8w2LTPftMps07JH59ut4UFTgS3LsizLsizLsixrjfUIAT7g0WJDLcw2rTLb tMx80yqzTcsenW+3hgB3JrTKbNMqs03LzDetMtu07NH5dmsAAACACgjwAAAAUAEBHgAAACog wK/pPIyHt+mdBA/jcL59Dip2Or6NB8NMU87jcEjv+nocT7fPQivOw+E23/YitGmacXsT2nMa j+/33X/vTwT41UybwvTL8tKAw3D5DNQrbQL9kqQl5+H4EWqmf6B6c19NS87DeLwN+Pt9+NE/ UdGaa8ixN6E5p+Huf3QV4NdyOn75RTltDP3epHb+lZumvT9ryqPwNOqyL3H/TWtOx+M42JvQ nM9nB96THwX4lcyDjuBDC8wxbZseyRHgadFlMziYbBpzHsZprO1NaM95PE8jfXs59l8hXoBf iQBPi8wxTcuebgzNuG0AvQSKtpzH4Xh9yZO9CU2b7sP/eHmfAL8SAZ4WmWNadhq8/p12Tfff 3uOBZmSvD7Y3oW3T0+m9id0+vAaeBvklSaum2XYfTdu8oS6t+Hx9cL7sT2jT57NNfiLAr2a6 c7m9C/0dT32AGgjwtOjrXJ/GwYzTIm9iR6PsTWjaHffdAvyaPl535m+vUr/pF6R/5aY17386 7jbX1+X+mnbk99v+hBytEuBpzvRM7gfuuwV4AAAAqIAADwAAABUQ4AEAAKACAjwAAABUQIAH AACACgjwAAAAUAEBHgAAACogwAMAAEAFBHgAAACogAAPAAAAFRDgAQAAoAICPAAAAFRAgAcA tncaxuF8+28A4CkCPACwsfM4HIfL/wIAEQI8ALCd82k8vr2Nb28Hj8ADQJAADwBs6jwcxuPp 9gEA8DQBHgDY0HkcDsdRfgeAOAEeANjOeRgPB69/B4A1CPAAwHZOx/EwePwdANYgwAMAmzkd 38bDcRhPHoIHgDABHgAAACogwAMAAEAFBHgAAACogAAPAAAAFRDgAQAAoAICPAAAAFRAgAcA AIAKCPAAAABQAQEeAAAAKiDAAwAAQAUEeAAAAKiAAA8AAAAVEOABAACgAgI8AAAAVECABwAA gAoI8AAAAFABAR4AKNt5GA9vb+PbbR2G8+0LF+lrx9PtEwDQLgEeAKjAaTwuBPjzcLgF++Pl OwCgbQI8AFCB5QDvEXgAeiLAAwAV+CHAA0BHBHgA6MR5OH55LfnbYRjzKHw+ZV8/HMfTLCd/ +frbYTzOg/R5GI+H9PXrZQz5hVx+/uNrb9PXhvGQvn92LJcLu3x7enr8dF2Xy7797EeAn12e x+ABaJ0ADwA9uD3VPIXf83t4/gzN6bXk16+fx+E9WH+G4o/Xmr8/Vf3z0fCPZ66np7JfLvP6 qXQZ2fdcfL5m/XZd6efSxzen4/Vz6anx0z8efPm5m4/vE+AB6IAADwA9+AjKh6+Pir9Lgfzy tduXUjC+5ufvgf369fT9n2E9D9cfYX3hHwo+A/fSU+MX/oFg8fuWLg8A2iXAA0AnPp+SfgnB x+wp61+eiv51vYfl7Ov5o+mflsP15899/sPAXQF+8foEeAAQ4AGgI19fx34LvX8F9CcD/Ge4 FuABYA0CPAB0Z3qDuCwg//A69A/Z15f/XNvKT6HPrk+AB4BPAjwA9GAKxdk7y0/vSL/0mvf3 z92+aXqju+9fn0J1+vr0TvLv/zl9w/XrH2H9rzexS9e9HMw/ru/yw9NnP3/uutJlfh6XAA9A +wR4AOjBJcBPf/bt8yn0h48gfpX/2bbLmv4EXP7l6euXEP3xSPxh/vMX03XcQvvHZeTfkz01 Pn39y/df1mfYP49D9mfkhvMU9K9/Tu7jIr9dXva6fgBokAAPAAAAFRDgAQAAoAICPAAAAFRA gAcAAIAKCPAAAABQAQEeAAAAijeO/w8Q8nTV1NOYqwAAAABJRU5ErkJggg==</item> <item item-id="112">iVBORw0KGgoAAAANSUhEUgAAAJMAAAAnCAYAAAALvnp2AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAQ+SURBVHhe7ZztlawgDIZt4PaxNdjF bcI+tgOr8dd2YjHcvAE0oiCgMxd38u6Zc2Zk+JA8JgHd6YxKdZMUpgdpnkYzTrP79Bpd6UNh eoimYTDD5D68WPNEfY3lQClMD9A8vg+kRQRUXwiUwtS65tH0CZLmsTddDWlot+tM1/Umxsw0 xMuOpDA1rdmMFN5iqDBIAKIYpskMHiKGKtIHyvqRRpEnhallwZgnrqHKM1EIW+sQsH0XDaMl 3klhalgA5cyQNTCFdaahi0NbkDspTM0KHiMe4rxqYZKAJGFCSMwMdQpTsyIjZkDyepjyoIYW mNAgJ3PBK9fFqW4W5Us5ez01MCF0rXXSOROUmzdtPRPi4wJS2bJQdbMyc5UqmOD15GruJIxh n6scJl4mKkxNiGA6ZQQexturFKjF1nlJfk7zClOryoHpTVKYni6F6S4hprcCNMbiVzM2Wd3O T/7SuUgKU744cUwZAWO5aCQkjn4OjvqT5ccSiSpJJqK8+vXt8bzlLZ+z9f1lvr7d+/+sn79/ ssbSbJiD8a9sS+C5nOQN0pNyCMBEvxIChGT4TlfyOZ7JhqHqk8XEow/ZwIxwYfseJhlaULRu WVjAbP9+jEu5a0/umfGhoL+jPbUFXHwXHufMM+7KEQJvvABpHNXze7PeFOZkDhG8kr0HG2Vs bOrPPeEHOHx99lD0nkswPl/JGXMiDzOg3sa4WxipMNiYC2BFCFwLzTSO5ic4thONMfSccU9W MU+fB1OluB9nTP9e9LUMHmX0ZsYfoBFXvoWMchh3kvzZFwoYWbI/KCyXn8lDos1NezvBi+69 VrpOoT4PpjrPJCd9ZwCaRG94lHFbBNFAFl6/Zftd60lPE3qhsI99OZ+3g2OiEGsPxcHAI7Sy ule8zid6JjakP9G8BsqFibUhjY2H0MAGmDm8jLRi8hO8MQ48hvdMwvgs51lmX8aezraPdmV/ +3KSaw8h058y+j4y9GZiqZ68f5ZM2Ev15NXcUVKK1/GVZhPgqoljY8IgrrIHuLfLbgZo8EZF OHFjwZ1rN5QNZCSMnevgEMBCW2v82/W3KWfhfAJP7ACTh/ZzJOs4aGWFK6Jx3gbmRW0uoIS2 nulT5fKkUEWexnnH26QwPUk2iZ7IA41Rd3LgsY7E3u84j6qWwvQkWVDOnxlC+EqBgnZuBgnK gYkhDsNtvgBJjjdVmJ4ugim2mrQSXrPCMzJIADELpvU2UkoKU6siQJJec5Oj2a2HHO8hleuZ 6p60VDUk8jwJQ4cg8Io2x+JCeTCdhflVClOzShsRIOy2R14CE0EdbJHEpDA1LBg7xsfbYDrN 3VYpTC0LiXXMkG/KmfQ/en+NUqEuWM1lhiKpc5jSeVsohal1IczEDHplnwmezd8WirSvv4Ly C4V9ngIHcY8Itpx/ApVSmB4i/eU41a16y29a4jGgyj4UJtVNMuYftFhmMicU+4MAAAAASUVO RK5CYII=</item> <item item-id="113">iVBORw0KGgoAAAANSUhEUgAAAHMAAAAnCAYAAAA8XHcHAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAALbSURBVHhe7ZzRcYMwDEBZoHtkBrbo EuzRDTyNv7qJh3ElAkY4WEgGnJbq3XHXHDU4eki2CUkXjdtgMhUE76LzYXp1DUfOYTKF+GGI g59eXEzwcC6nF2oyBQTXTmQChPZKoSZzj+BiXzKJ+7oudl0f1YkkaOsH3XFNJkuIDsrrtkof h1nEKKb0f1sI2+K+3kEvZJhMDgxmOW1ilzIWpPedvBQr2mqy02QyBFcOJO5bhGDQO/EYp2qr GDtNZhHMmHLpRCE0yFqZ8rZQkoWl1mQWgSAydbOdTP6iopRlBrwioByMM65l6+HAVy+cpWAQ 8v6NfRQGlQXGS3at12jMRKTj5qZMbLwExqcUx7sTz+k0bIpZ1qXgmDL3qWaJUGJ3rMpmpKp4 6NriOrdK5jg4T8HZfDN4Vc3B+w1CMRhzf06WuZtp6dwV51W0RSeSrF/LFAXmWRZmoaeUtCO8 U2YjqmSuxiAm66T/1wSTmSAysY4TSUxrWopPDSBFWoZMZuIEmYpZnJhsgsDdKrtK5tcjPr6m v9/M9+eHqC+/UyZOstJBd6bulpkJInM9sTleZp8XR01AxuOThuyi2mQmGkyAsouEboUeYuep vOMy9X348zJrAnPFGy7L3Mh2y8zEWiaADWdRm9mA49kcPNG7rcuKZd+zffFUlpmJF5kIFTqQ +7D0dh5++n5WErySzWa5Ug5BpzJPE/C3Z7MZwUc3QLlbXdH4GpYJQaKxfgI0kjKuXDpXYzfZ iuOrhrtkpgGYzBthMm+ERKZgKCiiaGsyj4ITKzbK2STtiqfzJqo/zzQmIMi3eNLAQCB7mAiP yzeyn71LlaFri7KPPgP07+GDiEKoAK1MeVu4qISfGZtMBgx6KcbNZO6O3QsmkwMnJ0yQW4yZ 9kT7aXClNpuRqh6fkbblx+0ck7kHlrlSQMdlxXXrTE1WIiZTwLu+n6n9wq3JFGLfnL4ZTX7T wNlvGhgxxh9MiiCOdjnidgAAAABJRU5ErkJggg==</item> <item item-id="114">iVBORw0KGgoAAAANSUhEUgAAAPQAAAAnCAYAAAA4hWV/AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAcMSURBVHhe7Zzpsaw4DIU7gcnjxdBZ TBLk8TIgGn5NJgTD6MgLcrcxMvv11amiqoH2Kn3yAt2vyWQyNSMD2vQjNQ791A+jP2tLe9pm QJt+nIaum7rBnzSqcaA29vVQG9CmH6Wxbx/mKIL6XQm1Ad2ghmGY7pmMjlT2iSWP/fReohn3 Xq/p9XpP1QPbnrSksX9Pry1RRlHu0NXVyYBuTVh/ncjUqshJ+1NG0HHqaaqdz3qYugAFQ7L0 vZz2pEUSghlQVgOtLBf33r06QBvQTYmc/hyaqjT0egdUC469FKloajoDRX3wfumn5XvSem0a oSvKrRmlDeiWRE5f61eniJxV64BaAZqlPD+BGrqXeu25J23QFqCryq1YSxvQDQkbRpW+eI4Q WA6tCEaw5akw4JAOXwv01rRBW4HWl0vTc+W024BuRmWnv1Z6B9SJ8isA0z7QetuuA80L9vp1 helqKSAa8R1yPt5ZnY83OUvNiwxwvs88OJ/okAcHl7URv/E1NKRdR68CzZWFwWorbLpWCLwF G8EhZvDmx1p4K8k9OqGjZlTFui6kyzx2qX3cUtTqGpICldwxrpod7EnrtAnoynK1y6kVoFHo stFMD1IS8VPFoExHFgykDXbWOjScMKTJAn3grI7qt5pXrM8GP92TVvZdbYMryoUNNdmXgZaV paMcJU13anGUWAHPyU35quysAPowf9EA3bgOABpGpkzEVO1V+dDddJ1g8BxAyXq3MPpqvxe1 AvRSfTbJgD4AaBiMDSun3bY59lTlAUptV5oSIn38nmbqaUBfqt1AyzVQdfQ2XSZslsBMDOSX xbcCrQjcVwL998/056///Ev137//qPpgAWg4gpheJ2tpRfQ2XSLeoZ6j7qOAtjX0sdo1Qn9H 1w2bJrfIOfAu4/Oz2tDOnvJzzoqREA4c82aHDkGP+ifZa8BB6cIjIcxq6PvIl/tOfvbC718D IO56gNGXH+77CshZE1/KAp3aTQ+0ImgrgC4UVycDmu2j6YMM0B9OkD2evjlWaEOpVwDNGyA6 72Qnx/cJ8mF0eYbk8Z4va4YTAUE4OPLsev5ZoUvTTT3dBKChLriOkZaTAJS5EN7HGCgwdKiT P3dZA3hhh+TeLO1yqXpZtQr0UlDYYBsDmrp7K9DC0RIlBjww+j5GHhDhhEkncvvFiBzhdumC 8zKoAjQ59YST46UO99mnR770gRCntBi5ZxAc6HiLS5yHm192QiDJgLgCnlMKWZLtkor5Ir8D g74BzbbX9MEH0NJRv1UdxW9T/SiQwAIlAKf3efor2o97MX84cswGsAdnFyOqyDumxSNC8QYX 3eE2JCN/rE/OTssQyfolbQziIOTrryWnCPRCcGHV28aAdjbU9IEAeqR+g+GXojhJGp4O+Qqh M6K/B2NiLRpq8JWOUuWuZb4LJ4/5Ut146nmCeMT0deA/aaORcXYwwOT7ZaDpN9bTaHuoCr6f qxfaIvvAf2bA6DPS43NsO/UZnv3zKYCRUPj0XGYICHQS75NKrwdKqGUfylc/47RfI6qPBDo0 k0X1O/TXVrbLXbvLnYmaSYQtRFX+XrgfRiA4Jp0LK+dGidw1KDcTcFNZdy1xnkQAr3S/oOCg +KECFcigYe3LhcPp/QhK5zyNDvc4KepF9/1aOYiDhK+LXFO69AEeGQgBKV905X/0SSwTfYF6 Un0SrYEEu2Dan9gS5y445OX6NK7rSa6934cMyqVqVIvy22TThgR/0PRBZlNsi5zRk9EdEIga bAV6ziKUQUcSbO4V/p3DQejWwLzbfVv9KLAS9GeUXfMvJIf/Y4kBfRfQODCSfZtzP9BylpBf K14uHinnkZqFUfIkqFTi6b//fJAQqJI2lkTtP7p8A9qxciHQrsAAIR8fo9SxQBfW+ZfK7TvM a0lqx50we423/uvnCeRpgOZl0UbfuCstiRlQkHo50FB8+SEcAuo2gTZdIvhV0eCYIXqfYMBq ZnB3pRX+rwJaty9xKNAs7G6LTZdQ12OBfsiU23SNCJZH/mPJnrRezIAi0fKLOqmOW0MnU2xE rhTI/UDPeT5pU8x0hcj2Baf/hAL+Ux7RZ92VNugzj7wQLHSD2KGbYvFRDEbpT/DiWsM3OnxH XvOSQMdNJ0RDf221/abGVHZoQPHpPzVQ3pE2SAc0saIcxI4DGjDL6bZ4phoUfuCA+4B/QGP8 uQRVAt0NIk9ar5z1Yonp2YLjL7HSPNCrewizjl9DH6AUaH/R9LuFGd6SUze+htaunyED2vRD VJp2Y/kmdpur9ljuSuu0DjSVUQGBAW36OcLUc8kh4h6NfjSLuistRnjv50tQ14zO0POAlo1c aazp9wn7ML/GHYiF2h+5PHKENplKGrr2ocZLWlt+sWZAm36k+IcwjT7xGPvtbTOgTaZmNE3/ A3eVgAC0+HjlAAAAAElFTkSuQmCC</item> <item item-id="115">iVBORw0KGgoAAAANSUhEUgAAAcUAAAArCAYAAAAJ+JPCAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAvfSURBVHhe7Z3rsesoEISdwOZxY3AW m4TzuBkoGv3aTByMdxqQGBAgkAC95qty1bElYADRDUL2ef0EQRAEQVCIKd6E7zj8hvFr3glX R/pTEI5BTPEGjJ/P7zOaN8Jt+I7Ur4MYoyD0REzx4nwHMcRbQ8b4FmMUhG6IKV6Z7/B7n8QR x8/rkeLdo97j5/0TXxSEPogpXpbvb/h8fmewxO/w/r1ezzPFbvXG5Oc9UI+3RfYxhbuw51oW U7wqEMoTmRAM4okrxV71br1alH1p4W5s3ZMXU7woEOMzeZCYYmMa7i3KvrRwWzaMGzHFS/L9 De9z3DqdEFNszfj7tLiFumNfGnV/BdPi+nypW8v8cPx8Tjjt8nNqD/ob76fXatZBbL6vV2ii GYuH0zvmfLDnHStD9YeJw17DHWLENafyjk/sw7Fto/Qui5jiJaELt+lo4kLhvSLl9jOHlpy5 3oit9kSI8ty4Lz2L1qJdjKh6n8fP54TTBj8fRydu3P5N5RwDq+Sp+2AgL2fiEYuH0z/mbIz5 hMNHfMYs1Hnm7+Yx+uWG8o/EthXkUTChFFO8ItTJZ/v+2j1MsZye9a6+rwix2JGhMjpHcfWk Ipbn8nxOLG06Tw3MvcIq2hHpzHKPjjnBOAxURsQUnboyE3JoEOP4YdeAbqdFfFmxlVEydsQU r8gJv7smptgevqqpAWLfk9/C5IyYfbDiCqyekqYYS7uSp4LOqTJJRFnTiiKz3MNjjoGHTKjY +O1TbUgqtpieNIjRvwbCX2nKiK2UgnzEFK+IueDPgrrQMfjp1csgzkDveqO8ev0O4dl3a8wX OP2e8sRHyhhcQfbP58TSruUJqk0WmHDmlXuCmINQ3w66wNSeIh2lVVjEtIkWMaJt+FgJmyJI x1YO5Zd5C1VM8YqczBSFPkBQ6vU7iUQ0MzNThyj5L5ZGi7/7PiV4/vmcWNq1PFWsVW7xuaK5 Xu6RMa/0D76jZzJH2bFu1qZn8loYRq12dclpV5CObQvIiyYq5l2KdVOMzHSEA6lgirgYg4NK vfIunhIwGMJl0cupjJkheq/QwNlC73ovIdEk0fq8eXuQUNCAnb5sHHu4AW1YbRzSuN57a0z1 KQvIf+8Lnn+cE0u7lid9kjD3fPw2Xy/3+JhjoDz3ul7GTkGyh0+0+bghNYqRtMu2TahcYjW2 beTuK66aoupgNGyLBhK2UXGliAvFDp6Wt3Q08/W0Up4e2GQCw2gGR11611sBI8LMF2UyE1R8 sVKx8YS6F21XbRiyW4Vb8cUf9bNPCmJy44rQ4nxOLO1KnjXGgtOu6CMUsFYuODDmXDCOgmWp GKfrDDF65zWL0Wun0CpwLbaN5N4OXjFFHZAeqF7HCsdR8YL1TarJOOBQ7GvlIaaSR6i30L3e aqCvlfdVItbLFHflxfuRZ8Q+d/IPnW/aZD4vI63zObH3KwPLVRXTuVC5J4i5BMcUvdj5GPAn SE1jnMcCa+uC2LaSO37Spsg6tmZwwk6oX3I6N4fu5uBcU355uFXi7jm0om+9+eRyKZIuODcc T+6gzqLiNSQIV6CCKWqB+ni3mWQcnYA7miLNFLHP1mve1bPepWV12VMUUxQexn5TxHJW3cIq meUKXbiZKeKHe7EH0/Pa6ldv/aDAXNaOSoopCsJ2dpsivxft3HdvvNcjxMFGseqS25giXaQH 3YnoV293UnkaU/z75/fnr/lbEB7Af//+k3XNR0zR29fwhEy2FvuDx/jnH26+rSnSq9Ok6/Gm WPEaEoQrkDt+gqaIxO7DDu4toOCDEDTI1BNFe0VNPZrOBAtfuAxmqMVm18Bmj8G/h4Hy04aP FZnzhJl6MmoSTmoLX8iRDqaFv1F/tT9m2on/bdC3C6dy8fkknKb86bgJgK/U1Uc4zvLbw7Gm qMtbPAG4q1PzOMwUd4yP3EfKs6B+6NDMgnAadpiitwcSfC1FxArbjpXkbBbIg8WRrEki3lQ6 lIUHO4zjKpHE+WSUoyl7Sj4fM2VZQ9IGbt9Snp/hN1KeOg2JGB2EyU2x4HOs+FQSmK0tRO3h jvhiN2Iy73XWEFbW5jDaLY2MPNEuTJhVnHObHWOKFMWiD2uZvuLgerumv72s+JePN4wB6ofY IUG4IxjzOdf80hSZgDtMwmJei1NgCDi2YybsG+v0BeuqAqkwJsOydRpM1dUVbH1Mp5vCUWbH RA7xT7Ei9rf/+4PIl/4gm6S0WEFakdNmiS90s/fTwUWfUBzO+4m0OHJxnpI75rCj75agrcLX yVyeYxD6fHssZgAhTl7vtbGTBeq43VAXUD9si0MQromj8Qk8U+QGsISLS10hAVzY6gliCMdw gGOC7nF1KzO2wnBu7UIMp7jxt8mP5T2nJTN0f6lF18HGxNKbY25V8JmNNxcbu03r1KeHSkZN kcDt7PmYOW4baTNnqLdTXmLsqOstGA+1TTRd+RgQUxSeBsZgzjXPTBG/poGBmzAkR9Ag4kbY nc9Nem92jHPtXhxMgRXinWvTxALZh1q5mbyxYsM/WrXiwcxtpFUG9hdRzykUnB9SarTBlAf7 W4kh/Y30+HuuExkAvgeq3qL+XPBMelXmZKr0Zj5OIK/y5jGmM5fFxXRLfuU45kD1WpTpXWP6 WuETiC0cX2/g1N3fK6f+VXcXYsZH7VJ1PMjTp8LDKHz6NDDTdAZnYiaqzguLjCsCOj+72vTF qFSotNDNXlYCBEbFpEVZmRX2AlGmMiEjxPQe8c7HVFId38fsHU4oo5090cav00+ijtm+qSMT RVU+q7BTJkwCcVI8DohzvZECUDvPkxP94vVrhWuG/sus4BaGyF/8mtB9H15RxTim3gtgfpho eeOJ/yB4CH5NVYHaetPYEYSLAg3KueYDD9psYd0UJwHnn7kBlppif/CfrLVu6T1BJbKxmX1z 0F72duATQX+c8DJpAE0CajuYmKLwMC5hiu5K5+SmqFZs3sqCVmtYMR4WKsVUtlq6D5iUdF/l HUT1VSIQUxQehphidcyej4ldxX+kIRrw3TURtxsD82oxGCqZYnw8c8yernrZuxuxtHl5todv iUyo2FINp7ZfEHtIw6zGTVnYum7QvGRZIKWpZX1Si3T7lcZb5hkoO+eaF1O8AfgB6ZzOFq4F nkRtYogAdxl25w2hMmPV7MWHsgz/6EAsbV6ezTGGw8fVrF3RwebHzrc3jKi7GdoH/pJmESJV loa3u3oWgm31lPVJHdbarzTe1PkhwnVeIqZ4E9Qe51PuJz6A79C4P5kgb8YRYyaoDsYM/PEe S5uVZ3uwXz2UrhRpomGPaT3Tb/XfyUlI6SQlWlaERbsW9ElFss0/J16Oc36Y3G0IMUVBeCQk NDnilMSMWeSzJupKtPiYj6UtyLMVVK4uvswU/WNIr+I3gv3Bagb6tkhPdfafLl8hWlYMxOCv pLL7pB6p9nPIipcROt8BdUub5sRpTTGn3QRB2Eq+SKQxs/isAYtzeZmxtCV51obaxRjUFlPk mjYZlU5D9UYyI+w2C1NXegXFPkKsrChRk8vtkzqk2s8hO17DqolTupXbqxN1TJECmoxOvVC4 6Xz++Qdfkvc/Q+0C56pXo44RBEELVFJHMtD7NGZCmyE6/B8ox9KW5lmGyTemN9iGMAXWNMXQ 5xyV78rtP05Onpa0IeT0yTor7WpItZ8lP15NhuEVrHwrrRQFQbgcmIxmCkUQ55aVFsW03uEc I2ixtMV51gXm4os6b6OkqGNxMB+zsftpwgYWWQHFiJQVYmkinIw+qUiOKWbHa0ifryn5WpOY oiA8lqXAFAERnYUcor4iony2HktbmmdDYF5+2WlRR7xGfLnBqDpNoszO4Tgml0OkLA/EO2dL 5y0ersrpk4qsmWJRvMTq+QqqS0FFxBQF4clAZHYonxI5Ek9nRaXE1QgqxN4c98UwmJaIfd6b hSmG6sLryt4vHhZkaW1SW88yQzSEymLxLFe95rwNfVKFlfYrjTd6vkfpj1+IKQrCw8E+kqeN gnAPyFBLv3okpigIAmmHGKNwL7b++IWYoiAICvkBCOEu7PnxCzFFQRAEQVD8fv8D7AE8qM1a mlcAAAAASUVORK5CYII=</item> <item item-id="116">iVBORw0KGgoAAAANSUhEUgAAAYIAAABTCAYAAACS7wC3AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAA1ESURBVHhe7Z3rsesoEIRPApvHjeFk sUk4j5uBo/GvzcTBeKcRkgYE0iChl+mvylW2zGNAzDQgWf75EEIIaRoKAWma9+v5eb7e/hMh 92XLWKYQkGZ5PR6fx8t/IOQLeL9kTD/LxYBCQJrk/aQIkC9FxOC3UAwoBKQ93s/P71EqgLp+ fj4/P7+fFRO163Gz9ryfv5+fqyr+jn35epSVSSEgjfH+PB+PzzGh4fV59E7unP6oevfiXu1x IoBAe0kh2LkvUebvU0a7DQoBaQs4SO3pVw5Zoo9BSATo9+fe21E3bM9lVwQH9GXJqoBCQJoC geEoHYiD0Ovxc5wI7cAd23NVITikLwuuFVAISENg5nXcdgacXTviNwjB3dpzZSHYvy9fn4dx e4hCQBpCHOPAoEAhOJ+2hcA+8aEQkHZ4P1fdY70aXiM4nasKwVF9ab1OQCEg7VCwZ1qH6M6Q grs4rsn92nNZITioL/F7GQoBIRoRgsNjApycvyM4B8y6na0XvYX0gL6EEFqaTiEg7XCGEBBy IhQCQmIoBKQxKASExFAISGNQCAiJ+fvn8+evf09IA/z37z+mMU8hIO3AFQFpDK4ICImhEJDG oBAQEkMhII1BISAkhkJAGoNCQEgMhYA0BoWAkBgKAWkMCgEhMRQC0hgUAkJiKASkMSgEhMRQ CEhjUAgIiTlDCPj00fO4ga0I1Hs+GZVCUIH36/l5vr7Be+/B7v19uBBEz5z/Oe5vMvfhTu25 vq1OBHZ+RDaFYCOvB7cRzuCNYL3X9O1oIZD6jvgXqsO4U3tuYitXBBcG/+pz2QHeAuLEu/yT 2MFCEDv5N/xn8V3acxdbKQRXBcvIHU8MsWH9r9UiThACHXy+QQju0p672EohuCSyhHzcfR/3 S4Ag1/4fVwrBJigE9bmPELgLLTff27SCtpYMlm/qmwu2pfqq4GAhQH2jk/MawaHcxNbbCIEz dOcr21cBbS3Tge/pm0u2RZy56izuaCGI71ypvcI5nDu15x62Or/bcVCifEvxC0KAzsR9uHhV np1dDswaSraFvqlvrtoWsaumAx8uBIJfaX2N/9ypPVe3FauW3u92Gph1hEAbKq+r7gfWQYJO ycn4pr65bFtKxXmBM4SAkBOpIARwQink4bcM3OuLL6TK7MF+//o39c2121L1OgGFgDTGdiEY 9tX0tsHdL4wi6KEdieAiQcI8E/6mvrl4W/CbDgoBIevYLAS43aovAO/7APFz8kUXBIZtvixi 8Ei0oSBIXLVv1nD1tlgHsgkKAWmMjUKA2aEKuOJAQ4A48cILnkWz+cdeuS0gc5C4Zt+s4/pt oRAQsp5NQoDM4TZJv6XSvZa3ULpthrVOh1l/d7V/rEvPVody31KPt+v3KQFeBa+xjLAjXMcE ++G+DmOQ2N4312FVW6SfXL9uXTG4c6fOAy4KJwq0DmQTFALSGFb/SQhBGAzSL+v2zExZWeuQ JzUbnc5ef5HORw80uC/TiYa8d98Ezo+ynxJwEk+5NAWJmn1zNuvaMgryhhVDLyauDGVH4gTk B/KM/bkTSSEgjbFeCMRZko6Ei4rK2XZ1KGzfxBUEdnlRUIFoaPBw8TMBApCIB9oxsd8SJK7Q N7VY2xb0Ib7bsCKIxQR3BuFzagViHcgmLOeYkC9ipRB0s6xcxvKLiStmbT1w2iEwhHahcUHQ cMGrm71OvlMg4DxEPfCo40n9i0Gidt+cyZlt0WNieVWRH8grxhaFgDTGCiF4u0A565wIoMrh fp9++6Ua4tz9Q9/0zL4P9G8J4vIRgaoXCfdnJpKnd34nBP17+W4UE+xJx+XJMWmDYzZIbO0b 1O2/gw2ot69skk9ypY4l0sL2oVyxDSK3zIa2BMd9/mgFgbRPP8N3NulKorRjnrzd1oFsQuyv VhYhN6BQCBKzq2AmODP7Ss4YJUDJd+VO1+XrylYNQAD6fXyeKmh32xPdPeZd8Mfev3wnwWa8 gKwsQ57hs2+Ptj0bJLb2Tf99L3ASKPFZVQb7+zy9jaljIDVbdyscfyxsQ9ef451WtdqC1ygk 2ta+vNHOWHDSZeSwDmQT2XNMyHdi9Z/ExeJG2S1I9OKmgh5mxqqytUIwFqEENAjsHa/n9Ng6 loUgZX/YrxQCQo7C6j8Ugp7dhaALfKnfMCwF0mUh0MHVrzw82B4z7RiZWCcE2v5cGTmsA9kE hYA0htV/KAQ9EiTCgFUPHRTdK5q1lwTSZSFYDq7rOUMIrvWICdeuZCFju/TX+fSadN7pcT2p SKW3snQOcvZojrbZDnwkV0d6XB5g43B9LD/m8z6zHpRpaQuFoAfXFqpFnCnYxw8ulCoxSA2A 3KBoTQhwYbtaezYKwdCmSSE+kETH8+k16bzJ46+XHB15rfw3PS2ubjwFE5OcPZrjbTbjA27a fNjnx5NL59/vbmNcb6r8jG0bwRicPZUeCsGAnAhLj21B/RIar766kkBaujVUl6OFAGkrtmeX FUHXnrCNI9P0mlze+TI7JE3qmVmlBIHJWO/ZNs+A62HP3IogaKsKvAE72CjjbhwDXT9N7DPZ Vg7GX3b4KSgEAzhBewRRBP/EjEte/QkqCaRpIRjLTF0srkc3iLv6jxCCuO82socQeAd+9Ocl qmCSXpPLu1CmQ9JUWcGirr6PjfWebnMOf37zW0N+7HWJ0qK1g43xGIB907oNtq0Adaf7IoRC oECn1R+nXZDGXUKuaKwK4Cg6wDkn6hzIDYA+jT7m0UKAMtyFYMw4/DHLSV9PfSGYtbeiQzh8 oNhC7NTdZ5lA4JA/j7qOOL0ml3epTFDt2onqY1u9F7A5iYwrf3t5XgiA961Mgj1sRN/EPpwe 1/O2rQF1W4qjEGgwgKuPVDm5cibcj8j6AJh4wBoGIJzHBU5J/4Jj+c949SdTC8FDvK78B2Ub UILjXhAz7/T6+EMCgv7sjsH+RFr3yozUqtcHwKwQaIGKXipTF/DCz3NOHqfX5PIulelsrbJ9 gfEzlrNc75k2L5wf/LDUF466c+e5C/S+LD0Zc9Tq1xBLv4J529aBunN9oaEQBOAkSJD2n64I BlHvAJYTfF86Aa3KbiuC8XPs5PH3mlzepTJr9U18UXS53vNtzoH6AnGQV2y7GKmeQ9YF3NCk nWzEBGooN1WvsGjbOnBeLOVQCGKwVN5jMFRCD/gLm7mZ6qsBsIMQOAcetskkkETXPSbpNbm8 C2XWasdQhtTn9sWX6gUn2mwFPpKsy9nYix9sjNLtZmPUT6nZ/pJtK6EQbABLtH0GxHaaEAI4 ZBBFKrHV0SV/3/dBcFfHg/JT6Z3Dq3SGvMFxYevtjdPZswraqXovYHMJgRBEtjth9jbGK4Zd bfR2BH1dYNtaKAQbwaCwdOChKEcbXpczchv4vcVud5ag7O/qLkJmoRBUwD3ZdO8LsGTgjXvA 9+xvCgFpDAoBITEUAtIYFAJCYigEpDEoBITEUAhIY1AICImhEJDGoBAQEkMhII1BISAkhkJA GoNCQEgMhYA0BoWAkJi/fz5//vr3hDTAf//+YxrzFALSDlwRkMbgioCQGAoBaQwKASExFALS GBQCQmIqCQGcq3/oX/4pkd2jhLt041Mtc3ltZe5P6hHOzra5jks9WXPA/9GKfN8XMbY1lX6B 2brAWN80Tdk5qcV8/5XaO5d+Cuq2jHkKAWkHEYLtjh49Wz7jjOm/PMzltZW5Oz7I6sAxBMls NIlt149y9oEsLHB4uux8gEwxV1eH7nf3uG317P+yc1KHpf4rtXcufYp0m6dQCEg7qCC0miAA qSAS4AOgvALhyeU1lbk/Lzz9tXRFIOI6fqf/Wat7Pyu8pcKcrSvDpF8LzklFzIJnsVcTpE9j /YMnCgFpCHEui0PO4pfmKGcpkDlH1Y6cy1tQ5l5IvV31ZUIQf4f8zn4fpB6YtSKgTfJLm/2f zVvJ1pUDNsQzZvM5qUeRECzaq0ilD0Db5oWih0JAGsLuGPP42ZrFuV1aXWcub0mZtRmD8hoh 0EGqD85dHmk3svlgNhbh2yqvksCbqytLNrBbz0kdzEJgttezKFySb2HrqIdCQJoCTjnrOwa6 fVc/kzQ4mv4LxFze0jLL8OX64Bu8EKDwB0y+wppCkDquceUubG1oLGWOzAdByzlZZqFfPXP9 N2K3t8MQ5AtWOBQC0haYnRqdI0mwHO8CwbyPI4134lze4jLrgoAaBzLdR7OBTILN+N1oe5wn HbQzM90cmbpSzP//sOGcVMQiBGZ7PfPpO6zXBwCFgDTG1KmKQOAYghcC2ULg0LOyXN7SMncE ATuuez6QwV4fcHRQdW3qA5FKowkCu4VMXRGwdyhW0k1uELCck4osCUGRvcJieoe0paAhFALS HnCsDd7uHFsCRjBzdgHFBxEEOP99HACSeYXc8aOZCEGqLbqt6vPkvnaVd8w6trNMBDypupQ9 09WNT7finFRhof9K7c2mjyhZDQAKAWkS7AtH8YCQ70BEpPQ2aQoBaRbss1IMyDfxXiECgEJA muaNO2Ze5Y5DyNV44weBK8cyhYAQQprm8/kfSA14MBf91F4AAAAASUVORK5CYII=</item> <item item-id="117">iVBORw0KGgoAAAANSUhEUgAAADYAAAAnCAYAAABEz7vjAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAHiSURBVGhD7ZntjcMgDIZZ4PboDNni lsge3YBp+HWbMIwP81XCYRfahDrRvVKltqDIj21sAgouKpFg1mjQxsZftLh54sDMusJq4o8O WePm679wosCsHoPKcnBLBScHzGpYKCocUwqUWqARHC+zbseEgFnQLgXbWAbWBOQBiXk4tmj3 pCAZYGgUHQpQOZLOAYsi07WMmggwq+kUw7EHGBqvWCekMQFgGAUqDQNYCcKCYdrGdBQA5oxh SuEY2MNJnwdz66vVh7IG1hgqrbPPgzV60FZVVSwqX0vYC8WAcRHw8mWe72NJmLr4vHOADYgB s2BcOIOHgpdwR/DEUa9rFhhWHQ8UYXCTGQDpkvyWpoBliDKXQyVKsLvrfoPbPX7fQT/fX/55 G7AcLQrsiKgdHzEKoPyfqkpYkvn+Qko2WFI5t/pQ1s9YYxdNRadm8Qhpxno9S2jEUClqqXcd Xu5nVMWgukEjJL8/S1F9yfOzIjZd/2B9OhfYWXf3Q+9jT4qYnPexy75BY0QYSzG1yh54njOP wpiWEKz/MMc5Sc4pVTCesnUIrFivIsBwnXHG9q4xcSfBwWAqHauqSJ5SbdeqEDAnTCMqFB19 TOhtS9A792N1yxAFhrrkjWZS9x20PtEd9D4C+AWi9vyF3QMcmQAAAABJRU5ErkJggg==</item> <item item-id="118">iVBORw0KGgoAAAANSUhEUgAAAEoAAAAnCAYAAACout71AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAIxSURBVGhD7ZrtkYQgDIZp4PrYGuzi mrCP7YBq+HWdUAyXiOAXuyYR3ej6zjiz6ILkMSGImHCLJJWgvLPBOt+X6mlLu+pAubYNresL O8g7aN/yYakC5e2+kLIAVsOEpQeUt6HhUsI6xgRjmsB1Etfy6igB5YOFkONhcqFNgDpgzPpY p7FwZ5p0gMJO810imOyBALox7LDleJUKUN7yQwfrDKDQaCOCTa2jABR6AzfsIqixkSJQGL7E 8FMACjorSHV1QNEfUgbVuXKXQeiHwL6lYHySzGswbLaOUSjqOLX0KIzbDKPQiHeQoSLUKqAE c5qoWdZjZLCxcO4mA9Wl2jegeqGr1wIlbif3lZ8MkjCSKPfngYJrNjW6xcCxarUjVCVQU6+h NsrS1UDhy+QcXBVdBdT8qG7U8xEez/73B/T3+0O6P9GjPJyO2fD2qKR7jCqKBQqv5axXVJz4 pbqT41VvLglqD30nqG/yKOj4BJR0ykvV2bIekp14wOzY7amf1qOO1g2KqBsUUURQ3dBQ+GPx fE5I66sKpwK1th7VwUDDZxaVz/O+zsjXo44WGEPJrGSPAvBDeX3lU77Cebhoa+ZUUPPy+7V0 wZr550TrLAcU/aPDqb7CROPW3H8XUITxMUkFKLButcNUUGj8UH4/Rp3uS3E0aC07EUFhOI2z 3svQoo2NSUpAgTAMXj/6/Cq18J7SecI86qS7WaKO3B/FfdlXBQp177hjaLc9nPZCezh1KoR/ udWSUlfBqecAAAAASUVORK5CYII=</item> <item item-id="119">iVBORw0KGgoAAAANSUhEUgAAANMAAABTCAYAAAAFtzIBAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAdTSURBVHhe7Z3rkaQgFEY7gc1jYugs NonJYzIwGn9tJgbj3g9B8YGgfUEcv1NlVU/3iAj3yMPXqyeEqECZyGV0bdM3bWf/qoNP8kSZ yCW039/9d2v/qIyulbw1x4WiTKQ4XVOvSCMi1PugUJSJlKVr+remSUjv9epfr3d/ojHZpf0+ liZlIgXp+ka6d3oqtf23k8hIpZm2gDTfjeQ6DcpEyoHg1Gw+pCv2Gls5EfX9Uu8+HmmdKBMp RtfodsWQ3iQTAv+lKys4MHaiTKQQaDl0u2GQyQ/0LDKhK5nY1aNMpBASlMp9sDIypR8EKBMp g4yXzpy72aXAmAmkjpsoEynDifM2cRazeQdm3o6A82KUidSDyJSj1TASZTrP5EB3MiXvlImU IZdMBaBMpC4oEyFKUCZClPj56r9+7Oeb8e/vn6S8UyZSBrZMhChBmZ5FjXd+liL7vlOm51Dz nZ+lOHuHaRKU6Rnc4s7PUkjQ61+pIFCmB4Az6Der5TwXdE4cvcM0Ccr029G+8zM/qNjXK69M 5gCjfZ0bZfrlIGhyBmUmULm5863eOlGm3w0K6YYuFZEJwa+6Dcr0m9G/87MURWTC7Q2aXb1c MvGq8RrQv/PzHMNNbRgHrZZA/srIpHywySLT4n4m7acTWShTDCn8bOdUMlNGJsS/4tE+h0yS Zok7bSlTDKmIEgGZg1Iypd5hmkQGmVAO2Z9OJFCmGFm6HfkxAWS7gbmFSg2iJDLJ5JcBZbqK m8pUEso0cFgmrOAPfn99oBWUCZXsl+180R80L+syZdkqi9plQprVjpn8Ss+Rqaq4oGXCgH4K YMXxSAjZx2HaGMvGZELX9o3N01ZZVC/TcjYv29OJKNM+F8g0bzEKnOMaz8FgCc/Mod63yqJ+ mYRazzNRprxUJZP81rgMBMriFjIVQEEm6YOO3RIklkn7q0iq3K5vm3lXCVeYny2Ja2WaHyBT AoQyDXwskwsa3DC2VRn3wF1dsNEFSKjcsTxWZXFOhFpkcvsR23/KNPCxTNPKGOTZ7zMN8PbA icPP6gAt7Ea+Y5U7iuOL6OSU5URkXC3TcontAmUaUJTJC6ASAeCB5xJ8fOOeBNTmZUORyp3K IiDTibK4WqZhfzv5eui6xoqWMg1kkik0YzK0XmcLy1UutuFOum3mpZPt2Ly8G5HEy8+UxnzH TUHMpqTtNqRywyf4QtKklEWYOmQaSAmQ2i8nKkVhmRz+/y6WYG6wzla6ENQLOAQ//s9OhJjA tGmafMtn88us0pB207cSVKsn74RaLIO/HydlcoHsdY1rkgm/jbN5AcIXup6oZ8oEvILLNWZC YC9zK4U/VYwVy9v4uIMImFC+rIAIqHVhSJrrL0emsvADKiTZmq2ynMmkWpaBnsGeTFGwr4rC UyYwVBS+i1/3dOKI5UBhj+kP6bhVsDOzbZsgGSp69ZsHjqyY0jezV6vtR4IF65i8+zJNZRHb n0mcaRszmWLlocFHMsm+BoU/Uc9PlgmFgoIcp4OztEpSKe6BJn4L42TpRAT5E/lyopmHJco6 rtKMTO6z/DYJ6QXDmJ585/VtsG7AQ4Mrj/Vpgh0JR6x4Y7n5ARjpImoh+Z3JdGSjWFczk8+T SVaSYOu8gT6W99bU8owhcI4Xlg04s3gZRtBKq9G4wHdBge8kI4NAGAvJbyLKNCnh5XIWDDaQ lwcESLYbMMuTtqGyGPZjPfOIg4U/ThrWXw7ftEH5+NtcLin1pHpjIHiaTM8DkqW0Mmm0Teyg cxfk4KAd+ZTpAaAFU6hldDFztzilUG+VAGV6BjifcteKVgdBr26SkEsmMx5G9zXfOJQyHYQP 7kfrmkkkkEUmjFWtRG6SafhBFcp0guyvVamYTsZ7t3uljKQ5TcUPk0w5DoiUidRFBpkQ5P55 LZzKUJ3Ot1AmUheZZPLloUzkGVAmQpTgmIkQJXLItJzNy3LJG2UitZFFJoHnmcjjyCVTASgT qQvKRIgSlIkQJSgTIUpQJkKUoEyEKEGZCFGCMhGiBGUiRImfr/7rx36+Gf/+/knKO2UiZWDL RIgSlIkQJSgTIUpEZbIPCt24AhzBHH+Apv9AU/dglVCa4W1tQZlIXYhMe3fB+q+vMY+l9h9t vXzMNT4v2Hr9TSjN4LYCbKW9BWUiZRARkh8j5kmDViF+a/rUKgWFDYm4I6gj9aGclIkU4sAj lxHgYyty4AlERozA716aM0Lfj6BLuC+bgzKRQqQHpRgzCSGf/beO7MpkQCu1sZ1QNzPS/TTp RbqBDspEioFWZjduDevgNeMa241LmTDA03nnMoWESBAlKtsEZSLlQJcqEphrETzQSkW7iusW MJTm7rYsR15iQJlIQfa7erMpaBFvNmGx6O4FWbQkoTR3tzUiLVdU3gnKRMqCYN8I0HlXzuvO GYnk72V3DK2cfG+Scv+DxUs7lGZwWwuOvlqHMpHi4LzNgQP+NYigR98IQpnIJdT8Cp+zr9ah TOQyanyFzyev1qFMhKjQ9/8B5Qz6m7NBrKgAAAAASUVORK5CYII=</item> <item item-id="120">iVBORw0KGgoAAAANSUhEUgAAAFQAAAAnCAYAAACR3W+uAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAMWSURBVGhD7ZrNsaMwDMdpYPt4NdDF NsGkiFzePQe6yD0HnzY95JrhlA7SgdfyB8ggGXvhGWfhP8NMMMhYP8uyY6jkoVW1OdBOtLIV nT0rQ0vatClQ0TSyEfakMHVCta1Nh7oZ0K4tF2YvBbVOhLoN0K6VdYBm19ay8q53sq0rWVVw 1JL2UchGXx8OV4Wuz5ZhQFw5lmi459HaAKiCo4Y6h7N3EgGFaHZOiUZdq1tVy0hCeHVCOjHn ANpCgY7sO4QrHwmuUc9jlB8oNJBs+aBphCJp5/kOMYJOsxC8+8cQiXJCKVGaHSjAmmvcLNC5 iFH3DBOKTRdQn5cTuXJCCbk0M1BwYi66gEcAaIRzOEUY2fw6qZMrH0vdFznsMwNVDZttfAho jGNouFsZwDYikT1XPlVcIIDyAvWGIi8O6DDRhDTqNC9FGHj6MlfOKDaP5gUamYsooFDWF4U6 Rj3DMwVw3uSDgFLljKZphFZ2oKFGa6l73NrQQdVLJVemDzxT+yCoKNYdZG1xh3LllLwODag8 oIXqALqyDqArq0yg31/y69v+/jD9+f0rqu3RQN/vt/21QHuL0PP5LF+vlz0bdL1e9Uy4WHsC ervd9EEJonMK1P7DsMsO7+CevCegAAILohIDvlwu9tcC7RkonLvh/3w+ieg9IpSSBxSgPR4P HY1wDlF6v9/171UmpT3N8gDTRdjpdPLOIUJX0d5m+R/XAXRlHUBX1mKgw0Q4rQdPktTeZch2 XsUCjX03M5XZs+RWEOE3o2HbGJW5HxraGA7KRFd0Z3ibx4m2jMrcsYdI+bfxpgE1bqN5rg64 30Voqi0p6JSY1y/ZgcY3DAvyVwU78WCoAc3kQZRakm1JqUAIvsQblBmocTB19IENHrKQI/kh 7DufZssoIfdnB6qjJNEhE2VDWIWgjN8ppdhyKvrLEeVi+rDXQ9U5BTM27SDA69kpGz0BRtry Ssv7GwBVgiGUmsiUjVljohyIcqJeKtnr5kDgKNtIfcDXd0awrkt1LrtUR6Qu8zYDCjq+YP4B FfmNffuh39j/f5LyL2RfcV0bb4pNAAAAAElFTkSuQmCC</item> <item item-id="121">iVBORw0KGgoAAAANSUhEUgAAAOkAAABTCAYAAAB6ZiDwAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAt/SURBVHhe7Z3Lcdw6EEWVgPNwDMrC SagchDfeezGxeDGrF4T3s3IGzmBeX3wbxIfAEBqhxHtcU2WSIAhw+rABSqRe7oSQpaGkJ+d2 vdwv15tbIu/FkfNMSU/M9e3t/nZ1C+TduV3lfF/GRaWkJ+V2oaAfgoj6OigqJT0jt8v9ddjQ 2/3y+nJ/ecHn9b4XZ9e3l8JFINbht90ur911jmLqThrR24fr/c2UwedNllK2fYt90OttHfk5 wP5jfaWkp0MCVYa5hdhpgszrAwtB+vJ6kZoq4CKQBagL/DS6w/AvF+oYQRxVZ28fdLmMbd+w 7Osx2zrOrd6nA0p6NhAgI5fxEjvBeL1c5EKgJbUZrHncrmEgLjD9wd0Uv9qHmEVL7dn2DcfQ 5SB/z/kdyaaU9GQgqHqDo0orE+DmiARwMiR0Qrwhe22zqUHku+S65EyWtGM0kAhX6Nv2GEFS s3/jXA/MTSnpqUBGGx/qZlQDLMqWB7IcF8su+GNctzNXykRJuyRB2/z5KvcN9eh5q5VUhvGm Tzvz3s4hLyU9FRIYtaBFEIabKptPsk8juPCzQLdhK6kWImQbhRGqOPxstGsnyOuS9guCH1OZ Gip9A1iO7XJi7mVS069Sf3Mo6ZmQwHnk53SaELQF0mC1H8i4laUkqaxVWavGnEza6kNKFKnW twRk1Xhl2pEUxdvbPZT0TAzMg0og6EPM7wifZJskYCFjITh1gFc5LulIH2rnK+mbZzPsTftc pnkXWUFJz4QE0q4HFfJM4gLQBGMetFkgmyC2+/r1RiJfX1fDBiRVx/N1d/WhsN+WpG++fDJ8 RgZ2dTSG1ckFowElPRMHJCXzoaQkh5IuBSUlOZR0KSgpyfn59f71p/s/+XD++/al6/ugpGeC mXQpmElJDiVdCkpKcijpUlBSkkNJl4KSkhxKuhSUlORQ0qWgpCSHki4FJSU5lHQpKCnJoaRL QUk/kOv12vekRpWb1HGshiKHJFVPduw8ggWKj3OpOvw2BGpvnaOYupNG9PYBj9P5cvmzp9u+ xT7o9baO/BxEKOlHoZ7gP8RN6un4Aoc4ICnfFujY9g3Lvh6zrfeBchSnpB+AXKknmoU309Vi 5SFmDXd3gvH93hY4RlP8ah9iFi0+8D3pbYGAkn4E8sVPkcAjgTs1bmdKWsuk7hjJkNAJcfxt gWPsStoxGkiEK/Rte4wgqdm/PYSnpB9A7+swupEv+ug7iRJmSSr1lLNFlC0PZMlaWHbBH9vR zlxHaEpa7YMGbfPZttw31PPY2wJt+3q+D0o6DQzp6kPAx5AgaV3tR2lKaoekJtNtP8lOjTY9 +22BOxFel7T/vL7f2wJt+3a6YKCk0xgQKssmNSaLPyGTfuzbAseoSbrK2wIp6bPBl9JpgAke fNEd5Uf/uE+Tg5ImQSX9ff7bAscoSTrSB7Qpv5hs+uZB+/VFhpIuSHeQIUj91XhfwGJAPMoB SfNM4tpugjFvY9ZuE8R2X78+XKzwmdZJhzqer7urD4X9tiR98+WTUZQaojdGV5T0yZSu2kV0 EMindKXWICD2ynQjx+5pInkOlPTJ4ITvy4QrrHwxMoSNorbnR331dkJJl+JhSX/9+mWC59+/ f24N8ZhsKeemdGK7ZMKwygx/9JC3XJ+Hkn5eHpYUIHB+//7tlj4GI0Tv3dKngTu45cxn2rtz xvVcJpkf7cxbKOnn5JCkEBTBQzZIkFfv4Mq2tqSbHzGgvJe0cQOJc9LPyyFJMdRF8Pz588et qWGHbUe++BsC3wVrCEYfwKFimcsl87g0sPM6/HDSlgnbpT6fwZJj6Ux2Q7b0ZfCbI/E4+HGI bxJ+uyg5JuppnIg8I6o7gLo9G3T2PYy0cVpd5DCHJAU/fvy4f//+3S3tkQZc8tkLXNluwhPz tVDW1mcX7f9jEEMiJWitDjf/u15FNDz2FeaDdp/kAuB/bxQyo273mFhaDsf1MqNNsQ0GVX9O 4/yET3kYXf85aaPO0OYNlHQpDknqh7v4/P37162djBPqhn8QSQc9toWgRVaMvxOLrBgCulGH lVf2cyfBLPuNqMMLFX7dyx5HP8aZnEQIHBooyLHTP8irJd6A45W+DdPPKFdeBCKW5X0ISroU D0uqMygCB3d79xm/qqOBZruI9SYm6eBOhBJCWVM+ilSvw7Ynyb46U0EOCKVE2x7TlAn7oL5C RkPQh5U1oWxbal9G+wZSQ3xXb9hXf+oHo6QLgZjr+T4SSSGnHuK+5w2kRAqZB0YJnBBiYghO ZDud4hzVOryEdosJTgTuLa4wc9ZX9bcuzdzP1YWsfJEsHILd1YehMy4F2Ga2bI6TPwVzk3px ISkI7kHblGCv+mKDi0h1xwegpEsxLCmELM1BETj1H8fYYH/si0eWcMGpsqMJfAnqN/Nck8Vm G1knUqWvFSnXsc2K2B9Cxl3R7k3WkwA2Q0+pB7uaOtw+Zogd6rd9tlJtTnIiVSHTJVmxkQl9 OTnuTEcp6VoMS7oqeBLeymHnneYubxLsK4EsO69ty76ZgUzhc0hqMpjOgAKy1UQRpmOG5u7/ R5B+TqlHQ0mX4pNkUjun03dA9TxyVW5T3hb4DjbNlNRMS/CdNObbydQgTi8QnP771O3R65Ob eFOI04v8HOipR9qf2Ca9vlbe9rf3HH8SSclUpkmKYHTBaWTdzO8dxdfJoLyfriT7butMZTmG u1hUOq/bae5/qPaFm4mQ1e1fLT8IJSU5syTFNCRUZLNKXm/MojorIjCzm3pYrglbpXf+b9vX nZmTdiikz8U6auU7oKQkZ5KkOquAIFoJE8Rxe31fN4TEtpoQCZ2SOonqbyrcgPJZZpRj1W4Q 6PLmWP0jAEpKciZKqiVqSmpAZnTZBllYZZ503/awNKVPUntRkOOhSnfBaFafXSDKI4JAKO/L UVJyhG5JXVZzwZl8pIJxSVEmFTPWGYPazvXcsYvzvEa7KvPCsbaKaI168t+v3pRnJiWHmZRJ UU/Mdlacdr0osw1wQdeTDDM76+zOpLGilqTttwgiU6bbs/KUlBxmlqQmYF0wJnJVkONmYkBQ HfQmwP2yHTrOkDQVR7V7QyKM7JP9OmZyYaqUT461DyUlOdMkFUxAYqipgtKtM8cwErqhqD6o X18Q22Q9t8/e8FlKd97dFVRbtFi+renwGx/bJ92ebTbOy9vsb5b3LloOSkpyZkpKDkNJSQ4l XQpKSnIo6VJQUpJDSZeCkpIcSroUlJTkUNKloKQkh5IuBSUlOZR0KSgpyaGkS0FJSc7Pr/ev P93/yYfz37cvXd8HJT0TzKRLwUxKcijpUlBSkkNJl4KSkpyZkpaegsmwj5zZJ0XiY2kITv8E iW6PXr//FMwo8SmV/ByoJ1g2/Ylt0utr5XsesYtQUpIjks4J/s3zpNkbCyx8W2Cb4vkpQEnP hAq6Q4jsMeBtVsnjP2ZRfWFAsOvl8KaEmrCHse3rvjgl7VDULnC18h3U/6xlCiU9FRL8PeOr HXRWAUG0EiaI4/b6vm4IiW3TMr7gJFrvbYHob5/clPRU9AdGi2o2rILM6I6LLKwyT7pve1j6 CPaiIMdDle6C0aw+u0CURwSBUN6X65VUyncOkynpyUDQ7geRy2ouOJOPRPi4pCiTihnrjO05 9LbAinljba2LY2TPhrWb8iOZdGC0QEnPBgKpMziqIBsGKaw47eRXyeC6Hj1s7KqzD5tJY0Ut SZ/5tsDe+SigpKejIswQCFgXZIlcFUpZA4LqoDcB7pdR/xxJU3FUuzdA5nA82ed93xYo7Rjo HCU9I5DmqAEmIDHUVEHp1pmqjYSFoahfXxDbDintPoezvUa1RYvl25oOv/GxfdLt2WbjvLwa iu9ctEayKKCkJwXzP+0OeRJywRj9MRglPTGYU1HU53F7QFBASU/Ozfxl8vHAIWPcLo+fZ0pK yNLc7/8DGo3zrvCoTxQAAAAASUVORK5CYII=</item> <item item-id="122" content-encoding="gzip">H4sIAAAAAAAA/2xTy27TUBCdazt+NYmblKQ0DnFIAqEgIWoKEl2VqkIgSpGCWFeuY9ogQoLr oi6jfgA71mz4BMSGFRuWfBFSmEdIC/Ra4zt3fObM+HicBwCF9gLNZV/Hu5286yUHg9HbHNBa R3P6o1hiBsceEGwYP99/ncQZR6DH+RrezaPD8dboRMI7aBYGenPgM8olawH8wOov22dnWr+U NKWBprNTnhd/mGXpYP84SxQjN9A8OL+MjdZf5xwTGFmaJD4HQrTCmF0zXPkOMMlZ4SfcN01b dsuR3XZldxZCDYu8R30sZa4hw224h6KgBPnw41OAUxcKDPmS/x8CUGSaieOF1OtWEUOL7J4W uY1SWP6Jz+0yc/RLALr2BIYAS+EHbHRSgkv85PMSSqK+0QeCinBaVWmxsix79bK0ukxIdQdg hc9f52eoSWLVl2ZqGKpLMzVu5oocKnxoCNgMhL3RlD24yqxZk1nXAFoiQxPaIgN+AlPlSIR1 uA93iaojVME1qdvB0HUp1eFSXRGhcUNE6JIIPUgAVkWELtwUEVbPRLjFw+b1w72DNBof7r0a pcMos5iPXsDh8HaURTILtdlMUBbPOTnYPrR5cmlQ3OhkcPSIeWTO62i7avaXyJriBap+cXTn gugfcitLozgJt7l58GfAQC2C8glXdxQuH7uaBkpTvo7BQOnKN6YUMZSvYIpODjGTTXxkEoYY LMagYxOGwA7xULqLGE5fQAyn5xHD6QWuhU6RMeh4hCGwNOztHg+TdBBHb0QSmxt/jNaHAV76 7PdzZ3r+s/LnD78BAAD//wMAtcFV3moEAAA=</item> <item item-id="123">iVBORw0KGgoAAAANSUhEUgAAAb4AAAFNCAYAAAB/iwpeAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAABOKSURBVHhe7dxNktqItoVRxsWACA9H bU+gZkCv2jWGClo1A89AF5EoLTD5YydnXw5ai1A8g11VLzaZ+iwg72YEgBURPgBWRfgAWBXh A2BVhA+AVRE+AFZF+ABYFeEDYFWED4BVET4AVkX4AFgV4QM4OYzDdjNuNtOxG/fnR08Ow7g9 Pb4dh8P5MdoSPoCjw7B7jdp+d4zcdjimcDIFcQ7efty9Pk5Xwgdw7XSFd77q2+/Gze7n9d8U xcVdGlpF+I5/d3Nzc3P7vdv00uZ0G87HfDu95HnjcIvdvmo14SPL5nk2v6PjFd/u/LrnYdiO 28Ube8v7U/DoR/goYfM8m9/Pfvj5Pp7wPR/ho4TN82x+H1PYLt7De+c9PuHrSfgoYfM8m3/d 5dXdfhxOv158qnP60MviU53C15PwUcLmeTb/mtOPMEwfUnk9Fj+z98bP8QlfT8JHCZvn2TxP +HoSPkrYPM/mecLXk/BRwuZ5Ns8Tvp6EjxI2z7N5nvD1JHyUsHmezfOEryfho4TN82yeJ3w9 CR8lbJ5n8zzh60n4KGHzPJvnCV9PwkcJm+fZPE/4ehI+Stg8z+Z5wteT8FHC5nk2zxO+noSP EjbPs3me8PUkfJSweZ7N84SvJ+GjhM3zbJ4nfD0JHyVsnmfzPOHrSfgoYfM8m+cJX0/CRwmb 59k8T/h6Ej5K2DzP5nnC15PwUcLmeTbPE76ehI8SNs+zeZ7w9SR8lLB5ns3zhK8n4aOEzfNs nid8PQkfJWyeZ/M84etJ+Chh8zyb5wlfT8JHCZvn2TxP+HoSPkrYPM/mecLXk/BRwuZ5Ns8T vp6EjxI2z7N5nvD1JHyUsHmezfOEryfho4TN82yeJ3w9CR8lbJ5n8zzh60n4KGHzPJvnCV9P wkcJm+fZPE/4ehI+Stg8z+Z5wteT8FHC5nk2zxO+noSPEjbPs3me8PUkfJSweZ7N84SvJ+Gj hM3zbJ4nfD0JHyVsnmfzPOHrSfgoYfM8m+cJX0/CRwmb59k8T/h6Ej5K2DzP5nnC15PwUcLm eTbPE76ehK+L6RtsPhpwEs6zeZ7w9bSKZ639CWEZvfl4cE7CeTbPE76eVvGstT4hXAdveTww J+E8m+cJX0+reNaEL89JOM/mecLX0yqeNeHLcxLOs3me8PW0imet/QnhOngNvtmchPNsnid8 Pa3iWXuKE0Kj6E2chPNsnid8Pa3iWXNCyLN5ns3zhK8n4aOEzfNsnid8PQkfJWyeZ/M84etJ +Chh8zyb5wlfT8JHCZvn2TxP+HoSPkrYPM/mecLXk/BRwuZ5Ns8Tvp6EjxI2z7N5nvD1JHyU sHmezfOEryfho4TN82yeJ3w9CR8lbJ5n8zzh60n4KGHzPJvfx363GbfD4Xzv7DCM22PkNpvt uPwt4etJ+Chh8zybf91h2J5idhm+wzhs5+Dtx912OD7yQvh6Ej5K2DzP5vcxxe8ifPvduNnt z3dergjnu8LX02rC5+bm5vap23A+3rp/esnzxuEWu33VasJHls3zbH4f11d8792fgkc/wkcJ m+fZ/D6E7/kJHyVsnmfz+7gOnff4no/wUcLmeTa/j1/Ct/xU5/RjDT7V2Z7wUcLmeTb/uil6 pw+qHI+L+Pk5vqcifJSweZ7N84SvJ+GjhM3zbJ4nfD0JHyVsnmfzPOHrSfgoYfM8m+cJX0/C Rwmb59k8T/h6Ej5K2DzP5nnC15PwUcLmeTbPE76ehI8SNs+zeZ7w9SR8lLB5ns3zhK8n4aOE zfNsnid8PQkfJWyeZ/M84etJ+Chh8zyb5wlfT8JHCZvn2TxP+HoSPkrYPM/mecLXk/BRwuZ5 Ns8Tvp6EjxI2z7N5nvD1JHyUsHmezfOEryfho4TN8x5y8ykM8/GEhK+nVTxrTsJ5Ns97uM2X 0ZuPJyN8Pa3iWXMSzrN53kNtfh285fFEhK+nVTxrTsJ5Ns8Tvjzh62kVz5qTcJ7N84QvT/h6 WsWz5iScZ/O8h9v8OnhPGAnh62kVz5qTcJ7N8x5y8yeO3kT4elrFs+YknGfzPJvnCV9PwkcJ m+fZPE/4ehI+Stg8z+Z5wteT8FHC5nk2zxO+noSPEjbPs3me8PUkfJSweZ7N84SvJ+GjhM3z bJ4nfD0JHyVsnmfzPOHrSfgoYfM8m+cJX0/CRwmb59k8T/h6Ej5K2DzP5nnC15PwUcLmeTbP E76ehI8SNs+zeZ7w9SR8lLB5ns3zhK8n4aOEzfNsnid8PQkfJWyeZ/M84etJ+Chh8zyb5wlf T8JHCZvn2TxP+HoSPkrYPM/mecLXk/BRwuZ5Ns8Tvp6EjxI2z7N5nvD1JHyUsHmezfOEryfh o4TN82yeJ3w9CR8lbJ5n8zzh60n4KGHzPJvnCV9PwkcJm+fZPE/4ehI+Stg8z+Z5wteT8FHC 5nk2zxO+noSPEjbPs3me8PUkfJSweZ7N84SvJ+GjhM3zbJ4nfD0JHyVsnmfzPOHrSfgoYfM8 m+cJX0/CRwmb59k8T/h6Ej5K2DzP5nnC15PwUcLmeTbPE76ePnjWDuOwPX47HZ/czWY7Dofz w59xGMbtbn++87797jf/3b/JCSHP5nk2zxO+nj7xrO3H3Z9Ebzscs3npJaC3/pN/8N/4Db44 82yeZ/M8m/f08bP2RsTeNl0l7o4p+9WPHz9OXyj//fff+ZGlY/x+67/zeb4482yeZ/M8m/f0 4bN2GLbj9nQpNl2VTVdsx6gdpkhtfj7++uvp7m7cvPMS57dv3978YtnvNuMnXx39Lb4482ye Z/M8m/f0wbM2Xb0tXoI8vW83jPv9YTycAjeMw7A///qlWD9Dedt0tTd9sUxXf9c++mf/1PTf czgcDsdzHF/1/r/h6mXOU5iOgZvuT1dnF78+B+ujeP3777+n/8e/f/9+fuSnyvCRZfM8m+fZ vKd3n7XLEC2v/pYfRln++v14/fXXX6cvlH/++efmF4zwPQ+b59k8z+Y9vfOs3XiZc776W76P d37J8zVXb7zHN32BLL9Ipl///fff53svyt7j++gVXe7O5nk2zxO+nm4/a1PkzqE6fZjl9NDP q7FloF5e8hzG/Wv5fv1U5/Sy5vUXyBS9y8emD8kUfarTCSHO5nk2zxO+nmqetVM4b/9Iw21X V5d35oSQZ/M8m+cJX091z9rpE6CfS9//63+5ZbqKfbmqrf3vr9Htzae/4Ex7T8fv/MWIz3jr 63yy/AAa9/NL+F5fLXNOeWR14XsgN08Ixy/Q3fkr8xTAijcXV+zW5odh93oymE7Em6KXttfq rfDNf8ETvvu7DN/VBwB9fT+s9YZvab9zUrizDzf/7ZfD+ch7m1d9YnrtLsJ39cG+qg/r8XVv f6dMT+h8vOVef6bY+yfh49/SBl+d9/Zh+Ka/EQvfXQlf3jJ81xvb/HHd/k5Zxmo+rt3rzwS8 eUJ4fT3ey0D39mH4Fi81cx/Clyd8Pf36nTIH6tYxu/V78zG79XvzUWb54Ymr453XHKYvUO83 /ak/23w/2PvP/f7mTsI1ps1nwtfHrxWansi3jtmt35uP2a3fm4+w4+ng/Ku3eCP63t7bfDoh eO/j/j7a3En4/pbh8x5fH79+p1xHannMbv3efMxu/d58hH0YPh9uubu3Nr88Ae/Hwe53I3x5 F+E7XYmfP9U5vY3iL9MP6/Z3ynWoLp7cs3v9mYBbJ4TpRPDRy0P8uVubn36EYd78dPhZp3u6 tflk+bUufvc1bXrh9XMDvrYf2dslmp7Q+XjLvf5MsbdOCNSxeZ7N834JHy2s4llzQsizeZ7N 84SvJ+GjhM3zbJ4nfD0JHyVsnmfzPOHrSfgoYfM8m+cJX0/CRwmb59k8T/h6Ej5K2DzP5nnC 15PwUcLmeTbPE76ehI8SNs+zeZ7w9SR8lLB5ns3zhK8n4aOEzfNsnid8PQkfJWyeZ/M84etJ +Chh8zyb5wlfT8JHCZvn2TxP+HoSPkrYPM/mecLXk/BRwuZ5Ns8Tvp6EjxI2z7N5nvD1JHyU sHmezfOEryfho4TN82yeJ3w9CR8lbJ5n8zzh60n4KGHzPJvnCV9PwkcJm+fZPE/4ehI+Stg8 z+Z5wteT8FHC5nk2zxO+noSPEjbPs3me8PUkfJSweZ7N84SvJ+GjhM3zHnLzKQzz8YSEr6dV PGtOwnk2z3u4zZfRm48nI3w9reJZcxLOs3neQ21+Hbzl8USEr6dVPGtOwnk2zxO+POHraRXP mpNwns3zhC9P+HpaxbPmJJxn87yH2/w6eE8YCeHraRXPmpNwns3zHnLzJ47eRPh6WsWz5iSc Z/M8m+cJX0/CRwmb59k8T/h6Ej5K2DzP5nnC15PwUcLmeTbPE76ehI8SNs+zeZ7w9SR8lLB5 ns3zhK8n4aOEzfNsnid8PQkfJWyeZ/M84etJ+Chh8zyb5wlfT8JHCZvn2TxP+HoSPkrYPM/m ecLXk/BRwuZ5Ns8Tvp6EjxI2z7N5nvD1JHyUsHmezfOEryfho4TN82yeJ3w9CR8lbJ5n8zzh 60n4KGHzPJvnCV9PwkcJm+fZPE/4ehI+Stg8z+Z5wteT8FHC5nk2v4/9bjNuh8P53tlhGLfH yG0223H5W8LXk/BRwuZ5Nv+6w7A9xewyfIdx2M7B24+77XB85IXw9SR8lLB5ns3vY4rfRfj2 u3Gz25/vvFwRzneFr6fVhM/Nzc3tU7fhfLx1//SS543DLXb7qtWEjyyb59n8Pq6v+N67PwWP foSPEjbPs/kt0/tzx2Wur86mY/Hy5ZLwPT/ho4TN82x+H9eh8x7f8xE+Stg8z+b38Uv4lp/q nH6swac62xM+Stg8z+ZfN0Vvfin0In5+ju+pCB8lbJ5n8zzh60n4KGHzPJvnCV9PwkcJm+fZ PE/4ehI+Stg8z+Z5wteT8FHC5nk2zxO+noSPEjbPs3me8PUkfJSweZ7N84SvJ+GjhM3zbJ4n fD0JHyVsnmfzPOHrSfi6mL7B5qMBJ+E8m+cJX0+reNbanxCW0ZuPB+cknGfzPOHraRXPWusT wnXwlscDcxLOs3me8PW0imdN+PKchPNsnid8Pa3iWRO+PCfhPJvnCV9Pq3jW2p8QroPX4JvN STjP5nnC19MqnrWnOCE0it7ESTjP5nnC19MqnjUnhDyb59k8T/h6Ej5K2DzP5nnC15PwUcLm eTbPE76ehI8SNs+zeZ7w9SR8lLB5ns3zhK8n4aOEzfNsnid8PQkfJWyeZ/M84etJ+Chh8zyb 5wlfT8JHCZvn2TxP+HoSPkrYPM/mecLXk/BRwuZ5Ns8Tvp6EjxI2z7N5nvD1JHyUsHmezfOE ryfho4TN82yeJ3w9CR8lbJ5n8zzh60n4KGHzPJvnCV9PwkcJm+fZPE/4ehI+Stg8z+Z5wteT 8FHC5nk2zxO+noSPEjbPs3me8PUkfJSweZ7N84SvJ+GjhM3zbJ4nfD0JHyVsnmfzPOHrSfgo YfM8m+cJX0/CRwmb59k8T/h6Ej5K2DzP5nnC15PwUcLmeTbPE76ehI8SNs+zeZ7w9SR8lLB5 ns3zhK8n4aOEzfNsnid8PQkfJWyeZ/M84etJ+Chh8zyb5wlfT8JHCZvn2TxP+HoSPkrYPM/m ecLXk/BRwuZ5Ns8Tvp6EjxI2z7N5nvD1JHyUsHmezfOEryfho4TN82yeJ3w9CR8lbJ5n8zzh 60n4KGHzPJvnCV9PwkcJm+fZPE/4ehI+Stg8z+Z5wteT8FHC5nk2zxO+noSPEjbPs3me8PUk fJSweZ7N84SvJ+GjhM3zbP5Vh3HYHlc8xmyz2Y3786Mnh2Hcnh7fjsPh/NiR8PUkfJSweZ7N v+Yw7F6jtt8d19wOxxROpiDOwduPu9fHha8r4aOEzfNsfkenK7zzVd9+N252P6//pijOd4Wv p9WEz83Nze23btNLm9NtOB/z7fSS543DLXb7Kn9dAbh2vOLbnV/3PAzbcbt4Y+/6Pv0IH/DE lh9YuToWL19e2w8/38cTvucjfAALU9gumvjOe3z0JHwAZ5dXc/txOP168anO6UMvi0910pPw ARydfoTh4uXQxc/svfFzfPQkfACsivABsCrCB8CqCB8AqyJ8AKyK8AGwKsIHwKoIHwCrInwA rIrwAbAqwgfAqggfAKsifACsivABsCrCB8CqCB8AqyJ8AKyK8AGwKsIHwKoIHwCrInzwsPbj brMZN/Ox3R0f+YTDMG53n/qT4363HYfD+Q6shPDBI5sitnkJ3hSp7UeVmv78dhiv/9Qcz19N cRU/1kX44JHtd69Xb4fho/AdxuGNq8IfP36cwvfff/+dH1k6xu9GLOFZCR88sNeXIg/XV2bH yB1/b4rZ68uax0hu3nmJ89u3b29c9U3/6Gb85Kuj0J7wwcOaruDm9/eO0dvP1Zse3x5DNd3/ ebX20RXhdLU3/bumq79rH19NwvMQPnhU8/t11x9WmV/+PF4FvgTw5eGP4vXvv/+ewvf9+/fz Iz8JH2sifPCgfsbo8r276WXJl6vA3ekqcM7Ve/H666+/Tv/MP//8c/q/14SPNRE+eEgvL2fO LTq9B3e+s/z1FKzXi8E33uM7RXIRu+nXf//99/neC+/xsSbCB4/m9CMM81Xd+dOWU9Sm+6eX OOffn9/nm11eGU6mlzWX0ZtM0bt8zKc6WRfhg2dyiuInf9D95PLKEtZA+ODZXH8Y5h3+l1tY I+EDYFWED4AVGcf/AUhSVCR1OIx/AAAAAElFTkSuQmCC</item> <item item-id="124">iVBORw0KGgoAAAANSUhEUgAAAIcAAABnCAYAAAAqo1GIAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAjSSURBVHhe7Z3BtZs6EIbdwOsjNbiL 14RPisgm+yzoIvssWL30kG2OV7eD2wFvfglhIUtoJIQQMN85Poltrgzo82iQNfZtEIQATcrx 7Luh65/jvbJs2fbZaE6O/vEYHv14ZyOePb1GJ4LEaEqOZ7e9GBMkyF0EWaQdOZ7dcK9mhqZ/ 3AfxI0wjcjyHjoaTumoQEPLe0asLPtqQA52001tYokeYJuR4drwO+vj4GL5+/Tp8+/Zt+PHj x3C73Ya/f/+q58y/yUjuEaQBOWhIuceHFIgBGX7//j0+Mgy/fv1Sj+G5nz9/jo+m0g8PGVq8 NCAHdQ4jEYUEiBo2f/78UY9DjM/Pz/HRpxoq8PgN0kV7nSfnFdlfDso3OHMObtQARg5EEEP/ ICkeYyTAkMEQT/IOP/vLwRzzIQFksDF5xwQS25sVBZhXI5hf2VKOPWdl17x2E3JwpjeQcEIE CGFu5jEkqIgqOmpYjVHbN5Yc980m32rM+MbInRE+jBxxkDvcZm2h02eyBNhKjqozvjGYEdrm RHJQYmsPKR5ZQmwiB4a0UKNq+KModwvnOkrshW0QJe3mX9uHjzk1tzqPHG5+Qe3eZrKEKS/H 0owvJB47yc2RDPS4GQZUp7s7N8o1PWwfe6hNYG/HYH85vn8Zvnwf/78KzFfQSVEnHR3Aixrg v3//KbQPI+iE0FsU0r56NR7dPMNB31GSaUUOCGRvg6gSev2U6HGiYYXAiRxD8SMhQy8dOdBe qAPcSLDUkUqeztmx8XzZw0pSmx7ZQpxLjkzKyoFoEB7O8Fq8d7mOfsghXs+/ZLHlwDm0h9Bl 4RBheUOLyEGUlYNO/kJjfDk0KiqYjsecxbjpTA4C901CupToKsGYM8IiB1FUDso3FucU8C6f XoyRc0C2UY65APr2JtasfT/cvEPkIIrKQcezFAlog/nVSizEBzrbjRwKbBu6UrHgzgiLHERp OaJtqctNJ/yPj+Fv9VAyRodAYzM5lBS0LTOX4B6vyEFUl2NnRI4ERA4/IgchcvgROYiichSb 8d0O7oywyEFI5PAjchAihx+RgxA5/BxWDqw4d5cN5iJy+DmkHFgiiEXFWBqIJYJrETn8HE4O yGAKmFCOgJnBtYgcfg4lB6KFXYaAyCFypHNKOYwIiBymLCG7DNJC5PBzGDkwhJiKNySjiCC4 b0eSXEQOP4eRA0OIW/GG6DEvkUwthdRUl8P3qawH7Nf8U9nX6jBzez1tP7f8sf3p5EAi+qqH 1UAMu4BaLYZJLIUEdeVw1nMEOlKJgY62G+v72bYomDL3U6r2TicHTpQ9r2GuVCZh3BON+4XX N7CIHQ89/+rw5ZVgSpBgY/S35o2ghNNRY3mhkeZUcpivWMDNlELal7RARw2rIXRCg3K4HY79 DnXoohwk/9tyxHG4iglyKjmQdC5flby/AxdPrENtOezOy5UjPIwgilwo54jPgronhLNw98Xx 5LCHlHfsXMTHteRw8wtqk1sKCWrKofZt2iA356A3Q/BF0OaF5IhDJyuzFBJUlQP7ylx9HpRj 6TXouUvlHCxwUlTGjgNfPjkudeUgxsQxtPpcQe3oOQsn0Sbeho2FbX1cT44VVJdjZ9LkmEy2 bibc2VYmjONsRI7qJEcOfHfUSxB3Wne8GkiL1jxEjuokywHwR1OUsP+aIkvOd0qxEDmqkyWH jhAmeryGEEy4bHa8Ikd1MuXQf2iih74kImGiLaVfPk6IHNXJlsN0tBKEktI+6WD1pI6Ra3YL NdK0HMc8nhgr5MDx2SdiwyEFSOSozio56K+nK5fYbNsciRznl4PQ0WN5pVIRJHJUZ7UcOEju egjaWBJSw1XkqHKQIkd10uWgg0KecTdfZdjRlYr638aIHNVJlmO6QsFQsuWMqEuhk7m0aCZG m3LYyXDZ3C89ckCIcWewaruSGoVOpj6Rue20KIe9DFC9cdn5X5x0OfaiyMmkhHjFyWszclio qYVy802nkgOrz1GjguWCbhmk/rdHSUc2h5BDIsc7EAMy2NVuWI2Ox0zJwlqal4PazM2nfJxG DkgwL3kM/SqkTVpZZNtyrBsyfZxKDrdG1verkDYqgUsoi2xZjliZQQ6nkiP6q5A2bvLGGK9b lWO2X7iaLDS0nEYOJJwQwZRB4mYeQ4LqRhUdNawGqf3YZWCLckzzTtOt3FzHaeRI432+Ayci +AnqSItybMlF5cAHgPYYzZscEzn8nEsON7+gtjmLlUQOP+eLHBllkSKHn5PJQVB7ehUbTgAv gxM5/JxPjgxEDj8iByFy+BE5CJHDj8hBiBx+RA6iqBzyS00FkchRHYkcCYgcfkQOQuTwI3IQ R5ED+1niE1qRI4HScpRc0jdhredQkqzYYfkt+wSKylFwUU6QlQJiCaXIwaSoHPjwb9MDeg7d WJWYB5Yx8JYeHlIOrDov9cuQoKwc/JOfjv6kGXlHfuTAJ9e8BcuHkwPLBLGwGMsD7a+9bqYc kkB76buiFybNlwaON2fn0H72l+okDEn7y5EwowgZTDGT+b0VDW/FVwjujCEbLDrKfmdzQATJ k4Obb4DDRA5EC7sUAZHjJQc/VPooHTm0rFsNLQSds7yrlbR86DByGBEQOUxpgokiOOhmyiEN CN8FG9VDiX+Y4ZISNcAh5MAQYqrekIwiguB+qKgplU3kIDCfsEW7WeA8Jw51h5ADQ4hbn4Lo 4ZZJvtixHNIBFWt7C/LMEAM0IUcseUMi6tbEQoxQEXV6OST/lxVzwPfKd5t8cXycZ5f/2vvL wZhRRARY/GVIG1wpJJZDpo7FV2F/OSIZtPmahaVfhrRJL4fc+MriwDQgx3LnIOkMifDO+3yH yvIXh5V1l8FnpgE5dAeGwro9CxrHnRxiTI4xcp6r0oQcKi8IdFCSHG5+gSElMpMo+UaYNuSI DC18MERQO+hsVjnkcr5zdRqRg2BccrJAO5jfYJRDStRYph05iKoziiTR5otyDk5TcoAaM4q5 M4ZXozk5wJYzimtmDK9Gk3IILTAM/wNzavVgOEoTywAAAABJRU5ErkJggg==</item> <item item-id="125">iVBORw0KGgoAAAANSUhEUgAAAFsAAAAZCAYAAABeplL+AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAHFSURBVGhD7ZjNsYMgFEaty4KowhJc 0IV7F+7swa3Dyg7sgHc//IlRBFQkb3HPDBN1JgROLh8kmWaSwbITwrITwrITwrIfoGSusyyj lmup5ocOWPZdlNRiNmyki8Zcu2DZMWiEzgNKm2U/Rmkp/VUNWPYTKEpyk9kZV/Z1qErzSd6h OTLZZHYu6d1uWHYUGi1YdiJibZDjOJpl1Pf9/OR9Qo9SV7D2uWZu2Dl5y+eM7Y6YLV7ZRVGY DvFqQ0lBiyge6yQiyrb3SUt/kWykx52HDafssizXykbboxoaZOQKBEkqm5b+537aGF+Yyhen suu61l3XrdeQPQyDuQeNmJcQtdiDdMsmMWJXhfg15xnEvs/9PeYTkrtPsMpGPkPwAiRDalVV 8xOAZehaeveOUcBf2dj9589GBAScBGyyt3J/IhuxYcvnRdTK1zKMi182oSCchAm/aPAvZUM0 hO9ZNsqJkIx7s7KJRXZAVYNDn7/ObGyIZ0c85DdEtW2LkU+7t1JBE72KX/bzGDF9bE8jgV/a E1bZkHioQEszEYOqoMmG/gFzCfS9fJ5V+I0N8qxPUzR4fv2cfYdDjDDvwbITwrITwrKTofUf FM79puoFLHkAAAAASUVORK5CYII=</item> <item item-id="126">iVBORw0KGgoAAAANSUhEUgAAAJQAAAAqCAYAAABV/HLRAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAUBSURBVHhe7ZzNcaQwEIVJYPNwDGSx SRCFD+v7HMjCdx90Wufgq4uTM5gM2H4tCf0MCIQY0Ape1ZSRGIRAn7pbDZ6qv3RpQ50KqE60 fSs6VTpOufTjGToNUKJp+kaoQgbqBPWnLQ+qUwDVtXnBNIigqguDqnyguravgzR1fVtXfVWN fHagUDR1XxJThQNFsJCrC2JBwGnX07W1sRhU3+5h1QB83VJPy1DZQGGwFk9/WKpjrEVJVqpo oGBxlvN0oKUoKJYqGChYnBl3Z8lxd7tL9E0hbq9goGiQFgfVx7k7qTj4c1a5QFnB9qwyCIxL iaPKBWpxXNLRV6vDYxjkyi6gchYy0XM+BJbJyjs5UOl9sFywdrXab29rdYiBdBu0v6rJ3dZW m0hfUDlgBRHDZZl8jdS5gZoTrFzT9kJ0POBVQ1aEAMFjkyHpie8g/lLP5uT31D6GEsfMu9ML qNy1AVC2K0SMU6tMJ+pl22SZCBj7Oa8LxvJg/wIqdyUDBVg0DAocbCqrg21A8Ogm7dUagPLc 44QuoHLX20v/8qa218hza3obA4/tjhhhS6Vg4VdSyCUOx5AEu0uq4ziMXGWAq7+/f6X1NxNd FmpCxq1h27gtdoMAShZU4C5XaGyxCKKWVmyVY93I+sw8GCzTQrHJxmrFS7JhlnH9MvN9lNh6 UB95YBKB2lvbAwWQ5Zg54zmMsRfbxdZPaMRCTfh9HqC8YGKAnKU43USdcT45UON5LSsudOK9 2PppPQJFByIW8DPHgpa+2Y8PXJAelVMDZayTYxjonpgYTxoOLsbWB/QAVMfg4GDbxAmKC2Za Uhcxd8KQMKu0ebXbQd5H1ls3CBeLOuuLiHWGIu1P6cve2t7lkZS70veMLbpzv+S+2PqQPKAI JAUOGjODF/OymSRZ+m7vE7hj6Cz28xktGLgfuh43aGjDnzEEtG1ViwFq3f00wkSXrsoZU5IN Tkx9SA9BuQHHdATuLtxMogDK2GMJBRBdGi/LG9tq8uyzfDqsmH2xM0CNDtAOnylNA5Uu/IMG mt4dKOnujNBAI+Duwo24ip9Rfse1UM/HEUhYdtvfcI/BOW0XTboslBKOVxOP7ok5RrbLxdj6 gCyg6ADfr6HBhcvFFDEcqqdsidQJHWg6AnuABhdH23jGxkVp4QQdO1xBamJzZz0tsUljaCYe vI66h45XiK2flgSKwZHUu5aCGlzQiBROPk/wqKiz5mm9f341GzHL9C5cHAfuGkLqv70forpV fTlI0xZqhazxfLBifO+wT09Opdj6CU079f9dZwbqQA1AVX9ctmLL2ekC6hAxFYDDBmRNOTs9 Aaj7/c5u5Pv7W9Vsp+KA2uJvViKg3HgsXa+vrwwU/o4Jidm1TBTzCrANA7ZTylkJgf6GI3S7 3QYLhY8vrE5N0jVexfyTgg9Eajkf0QpxIx/y8fHRf319DdsA6ufnh8sQ8nUatHWnRBpkvXXL SbnSsIG2GSTES4BICyABnPf3d1UDIWWScq6Y9EzeKhgoeL00NwIXNxYvaWs0CHmfFGv4hHjv KBUNFOKolIECTIDKlw7OpZY9kgiplPgJKhuoBLeHIHwqPYB4CkB9fn5KaOHu8M642h+n7WK9 HFQ4UCS4k8gBAyjarYU+7A7h7gja+ffFxlWSdYLKB4qEHE+WRoBg3DK1kYNOARR0/WjrPjoN UFAuP+eM986un5W+dGlWff8PFKtaX6V8K0IAAAAASUVORK5CYII=</item> <item item-id="127">iVBORw0KGgoAAAANSUhEUgAAAKMAAAAaCAYAAADbqew9AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAARYSURBVHhe7VsBluogDPRcHsjzeBov 42H8MzzCHyAU0JXVbt/bYbeBBJIModXu6fHAzx/B7XZ63B35T4H2PfmBMaCthbsEiHK9O/Kf BOxfD0I+DbS1cI+4Xn35T+OGed5ZffcMtLVwd0DFuqyqWCsq8E6BthbuDXdUq2UEIfEXVeG9 AW0t3BuuZxyfjvxduGC+46ieB9pauDesJsdq8rMan08Lb0UmwZPphPUZWutEWwt3BSbq4sjf iBvmW3nfmJK92M8ZMCYHGeH46iQx8Cur1CUmmfjUh6eDjECoGr9AxvOqhxhuNiHjsnkncZAR IBmfTlC8F9Nkn+z+syCB3iO+NOckeH96kUSXa3HxpF+voCTj1SEn2raSImMznFFjvCfLHhDE oZYz4WixAHTsMeDWlwHjwpgN/VeJcYcvmrjyGKQft0K2jIyY9xxjqEd1q/IonvHrFSivLD9c g8m4ZoysFcNuE4d4nRbLAERl7a+OwjiRR8ZsfM8e+u1zuyzJkIev3jr61KnWJmB/b/cHG5gj QG3J2hSryMgEm99ZEYH/uplbmPXrFZSV0eRpEzFnqpAGoCMkCEokoSaMymosANc0qEnl12K0 U+44yrmbTT5qj8g2RURXn39roAWhOjT6SqSgybpaRGbgV5CRa0rzR58NZZxamPHLA0+kEeK3 yBgKh61BFQKYoBhIDtRFBYNe8mIgdCy/C67Ig3GsZna0zNgLFTDqmWxIn387Y1rBaUGriMWH 92rlOIK2t2y6G2gSXE9JeE3s6GaY8cvDW8moSmW5bwWxIgXGkIRZUnDN0q9BHLYHeMEf0ndI bKD+aAUgON7iwtfFvLkJzj9SmbJEKArfPTR1BaO+jfrl4a1kTAliEjUovEafF+SSFPbmipLR 3prh5MHGhD0i6ZlsQt/9Bgb9I0lXaECJVrK51hmST6O19hgTW18ZQy+uxKhfhuZG8OIc0SUj f6tC6WQgEGT6oFAmP0xSLMKIZ9WMYxLBbeyEvUxPZaPrwXUZYMq8xGwizsngbR2Do18/NpMq OfCwtXZN+hY5Mgz65eHVysgNYvNC8r+jdNIe/y2RIXgyuU2QJRo6RpBwPwIdvVZnaU8/hnHt RbkXpFF9zpuRNgafH10MJUtgc5SbIAHyd761050f8We/gXGbIUvTbgPPkNE4lD7aQR55jZH4 wQJsZ1SAYjLKQJOQsU+JQASyxr5ACExm5LYdEPQsWR175boqQvb0dZzqMgjQ81645Tppp5Qn RF13HgL9vYS2jsseNL4BxTqq/sY4Fz2/GpghY3hm2MgZ2lypBxojvL5PxmjQiM23tZG0LSJ9 7ZveHb9WAG0tbCGVWjA7/a8HiOlVmI8D1jvy/yk8OrJvHnDN6mxVmWQrbwMSGIuBOT4CM34t AtpauAkG3EhZlNlPB4k2u1bdgOGI2dh43/TfgTN+rQLaWnhAwETFe5xv2nhdfKBfaGvhgQO/ AbS18MCB9Tg9/gHgrf6xVJxQCgAAAABJRU5ErkJggg==</item> <item item-id="128">iVBORw0KGgoAAAANSUhEUgAAAOkAAAAdCAYAAABPNEvsAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAVMSURBVHhe7ZmLcRw3EAUZlwJyPI5G yTgYWX3eJp+mgF0svUseWVNdKPzmD+COJ728/Gqa5qmJYXMXP7fWNDP27kgMmzv463frB9qs wD35+3X2Rgybq6Hg/UCbM4weagybK/nnd+NbtGnOwr3h/kgMmyvpB9q8Fx7oj9dZP9JboMij 3xZNs0p+m25dcyU80PxzpWnOkr9Nt665kvxTpWnei/do65or6d+jzRX0I72J2e9R1ql2Ng+B P21yvWnA36Xb9A0uTl4Y2+jboa5VXS/hR6H/2e9B4mX/aswXv7P/kIb6GGuco7URj4Pb2oo8 nDnXO8nYk6zN6j+6eZ61YUvwl3I13/Q7gz3v8pG9ozof6Sf+28Y2/Q8WLBC9Y9YzcWBv5IAg lWV/L4g7mPkzH2Oy6FdAfbRH7rVWCTGMDihrfwS6yNP+PMExads6uL4X651Qr5rvUe1G5H1z Tl5gfep+vSPsz+qY8kf2juq8Go+g/4gtF5NMtoIihmuRAT1hfxbAHWSRKhnX1eBTv9QmD2EE VbfJLO6KtRfyOvKX7J3rR4F/cqhnslqDRBvUoJ5/fRDAvN56dEZ1qeur9mBk74w+4P+xn4uC 8VrABGVbgkHXdF4DlVHAK2APXW2nD3xz+O7TyMMxe8bFGHnGxlwvjrIpA9aHdWWE+VFe+k27 +F4B+ZQ1jxWM+7MxXmLx7GA1D6HOaSuhLqOa1vMCbNRYPEftnrE3qvMZfSGux34uCpuzgrmu gYQgsEirQc5ATp1so4QEv+zTpx/GFKjGxTzzQU4Z5PFX82VuDMg4Rg959ekzVubu7WGexrKi A8SVseK7xj6j6n4WxpDxnKmBkHvWMckzStCp5+X5Ks/c2Ixv1R6knpzRF2ygs03/xMteQQkN W3WahcJxLdxVYLsWgVjwN4oLWfOhr1nXfBlbOO2ypm7KWkhhXP2PwL51rPHsgb/MfVSLGTXP zwD/WR9zX80hURebnpdz9ka5sp7+9Yu+667tnbtUezC6T2f0hTjY26ZvYAgnFdbTWA2EvSz0 w/jrbA52kKsti15hv2KRaVU38yHGjBPYzzXG+Bit53yUY63DDA+OtiIvNT9iZO2I2bl+NDVX z+1MDaDWAX3WaNY27ycgX2ugX3qaNrNeZ+ylnpzRT4jnoZuLgKKBy6iIGE+nOTcoE76Sejhi svqmN8mURw4bwh5z41fPfJm7l7VRpsai/grojg7vCHWMbYWM/TOpMVD799SgnqN2hP08G2ud oKNf4vKcnWesK/ZgVudV/QQZ4tmm2ySajuhdG8lZnNoyoBHIZJFXwW7VY14LSgPWlTd25jTG HgrjaoM1emXUcQ35UZ5HuQv2jHMG/qq9PANjkyqfsrTM0T3zoWc/xynLmjbo8ZM2mWc+yoC1 S3lJnRFpp+aTreZtvHUPcg+8E+A6zViP7NW4ao5H+iOsy9Z9X1aKcTUW94i8GHvUAz/ijDz+ qRE9eozpXVeGnIxVOfByznyeiWWP99ghNtpX5ds/UhLkgK66JGfA5+hycMnzQq/Ehs6Zi3ZW 3kc5GvsoiTltIuMeWOvK2VhmvMcO8RN3frhgY6XmzwAxG+vWfT84HD+JPprZZciL8ywXJh+g t4G5Y2LMOHNPyOcZcqkQqzWnMf4qEOvrubjYXMvoA4Kis/4sF4ZPa+PIMQ/OS0LvA1RGOWDM uh88Xqzm/5E1jmFzJXnpnxUfmGMfGGNjZ59bkt/+7NGzrg7jZ/w2/YrkWUAMm6vh0voImmYF 7kv9sIthcwd8KvZDbVYYPVCIYXMXFL8farPH3l9dMWya5ul4+fUvgoaNfGN/4EgAAAAASUVO RK5CYII=</item> <item item-id="129">iVBORw0KGgoAAAANSUhEUgAAAFsAAAAYCAYAAACV+oFbAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAKPSURBVGhD7ViLbcQgDM1cGYh5mIZl GIb6GRwMmN7l00rX8qRIgQSfeX42zm1p4dewyL6D4JLzsQw6RD88W2RfRkjO+TShmhG9S5rv RfZFBNcSaYMCsteALLKvgMrHbjAd3DbOq1KzyD6NVq2C6Pe0bQbZhOB2zoJF9kmAVBfKoAOe WWTjsNxp0SL7NGxlAzOyl7LvYFKzTbJXzb4PqxsZyV7dyEMY++ye7H/WZ8fk9427hOEaTjl6 tyHPWLs7olih+4JsyH73CxIRmRy4nwW14Z4I320Qz80AEOF5GsGwgpTB60tQhrpdMJAdQ25T /hZAWu4ITNCePdXgkaS25toBeR8N2fgCkuiIzYiTV0cMPSPGcALKocjL/HFPEFsuwGHct/1p Y3f6HwOtZRtleBXwedKu4Tc8+dwoX6C6DhYh7aF/5Qw6ZWNztYSwA7RTtg+HZddwgggKITu5 4WSGwzRfIw9bRDDlK6/Hs7Jh9J24p+WEyIH5nlCjfsr1RiRMIguCzz7hnd6UFh/7W+avoiVb k1XIJQo5qk6lof4PAMTtpQBq0nIQlHtiG3ZVQIHXZN/BvISwjypwrQ+6hGRBiFiuQpGtDwPl CDl6qJORFZudV5nQkNhvsI4HlfE6m4yKG8qGfYskmq+HpBEQZK+egFg6kZxFJVvIitAyhoqU SHVNnNHqV/eiZFqebTWqqLbY7hFRqKdmyfPIiuztc6Y2c3Nx8Ij2g7PjZWBfoJIN4qiP9Ee4 MxGsHvSX5Yd1yss3f77HQZcz4MgKvlALj10QlEppQ+2zHsiciyWGxSM+VMKrbzoj5T3aDx+E dZ3MH2fXDXQH5BMwUnKB8TzZuswsNHiUbF0+fq4Ofy5+oIws2EjpC7Kx2h3QtMDtAAAAAElF TkSuQmCC</item> <item item-id="130">iVBORw0KGgoAAAANSUhEUgAAAFAAAAAYCAYAAABtGnqsAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAI9SURBVFhH7ZfrbcUgDIUzFwOxRDsE 07AMw1AfHsF2gOYW5UptOVJ+hARMPr/IEbeWtAFqeRutC+VGKbjLsw1QyEdrXRzgSwrORs5w A2TyVsLpiyCbBnkDrKLUNYJeiM4c8ThwWcLGxNJ8A0ySUQXxVPWWIKrn3pr0fAMkBWeiFSGm RM3D6CjEGE3aAJOuEShFzxXAHYFalxrIpI8vuwb2NerC3vHo3F14IoKjzoG6Pi6dA7EY2jpf ELXg0uYfFT9eqOvSCehdAaQz14yPKPjeltY+us+P1T8RhG8zWA3UgvoWsXokPpDGneKXHN6D ShDzMAD3wIOjAn30v/E1gCi0pzFs5J2RpwX7E8d5Ako17doYZA3rQ76vK8AAA5m6oeJpGXlE WrJFIE/PVOOTeVL0Hr2zsOcsnMOGRw9KNzIuU7CIddtAkM1wn/ckAWJxeNUXA8I70nN8I/N5 I3XqUb1u0O3CKapdE+/opURq0ves+pEBRGRQSrI9iQ1wYCQ8y7ffzHtE4/SFbe4MuQ8eBKjd GWJnmds6AcKw8ChS5OyuesOt/s3nzbQQgaP0FY2kA1kFAe5XTxAnQHjj7G6lALf6ljfsaTyN 4L48m857RDlydPrCtjxi9Jze7gN9A2rx6l5bCsM7WJAiC0ZSZNEZCqkZ4ClEXDXOU3Qyr6+F JgLHwVa5KkTYzGMlmsR7BC01izavjiMIhtu8KdlEbgq/PE/G2G/SCwBL3UPot7z493oRIIX+ Ytf6a/pRCm9VxfgFmxsBKzvVS50AAAAASUVORK5CYII=</item> <item item-id="131">iVBORw0KGgoAAAANSUhEUgAAAFMAAAAYCAYAAACGLcGvAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAJcSURBVGhD7ZfRke4gCIVTVwqyHqux GYvxAooCapLZ5M7uA2cmMxuj+c3HAdyjuD6Tw7xSCiXE3G6McpyeOcytUgkhlg1KUo6hSJ4O c6MUNKi1APg5gDvMlSC9T0Myx7McxwHXqSGLUuAwJ2m3kUR9JKgh0d+sFCpgh2mEsAwrrYVr EfYJixzmpIUzu3KJcSbtzrzSzn1UMw/9zGvmvXbdnGpmd65384fanTMHwNtzZj0CmPb/ZwU1 7KypN11TF4G5Cs5i7RkAlZBI4S4uAc/+A7oqwHthlK6a4H+RObL0Wgbjtk+sjjQwSkDrMMJe BaGt3QZpaIa5Kr43yqkeDX5PCOUim2B/EWrg/F3aOGvgz2Vg1k0FcBl1LmP7jKBbhHhjCaPZ xvo+Mm6S54F7tmUD5sl1PxV22m02JTjOZAI1wRTGIUO8LG8aJrV/AEgvzASKP5Q2Azf1kXQi AhHQcYPokvoSWncf7UX94usB6SWopgTBxCc4x75KGgE79P0vXUvBtD/YYTZ4EF+KYJApBfDG Bzew4rtWH/Gt9ilOgRSB0fuQKV6NM448P5OAiZuSaY0/Vu/7pqgENHeSqqN4kzhPOYSd3m73 euHMXYrD+GhCC+C2N6ApHu11rwFTvhxqHtXOFaT2jG4ZVkbP4isAbJuHDsai/6ag36s6yqY4 ZY8aszD1fYbvwNr9dq/amfA/JrkBHMk1rwpd2pyCbuVHGE2cyxbAgPB6mENBgLOdepUSlgWb fg9FgWx7gouBjtRuLlPzACA1mrGOx3s/eCHdgFyv5DA/lMP8UA7zM5XyDzkdHnctOWwdAAAA AElFTkSuQmCC</item> <item item-id="132">iVBORw0KGgoAAAANSUhEUgAAACEAAAARCAYAAABTnsXCAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAC3SURBVEhL7ZKJDcMgDEU9lwfyPEzD Mh7mx0YgSnATqbkqtU+KxBHB49uEL+Av0fhtCU0MklzGj0gUAaJnJZzNJDQL2C1ZkG1MnKB1 r5Mh9k894yPeSpQNv7zcqshC4DQrdBSJTdil19+OYSyhyRIwgTqd5icTSvhif3V94W7eJyfh 0Zc1tXqLQJixWYmDxOUYGtJKETZk42BjesO/JDY0ZmMszfXMEl6OCxsyYpRYxXQXYTnuBVgA rkAqt0W0ml4AAAAASUVORK5CYII=</item> <item item-id="133" content-encoding="gzip">H4sIAAAAAAAA/2xTS08TURQ+d96dlhYqoO0glQKC+AiOjygrJMSY4COpP4AM7QBj6INhalg2 /AB3rt34B0yMcWvcufC3uHWF3z2nVDDeyZl7Ht95zpkCESnQa5DPvIm3Fx824r2k27FJn/ug XKvbFJ3Fusca1m6+2nkTNzPWUIP9Dbydo/3eRvdY1M9BLhSNEfCF9gX9WiQyEe7r0l9Zn99K ijLIMJkpj5I/ybI02elnsWLkGqhE54+1Vr8g2xzAytI4DlgRgsZ6zDph5RvRwHbDD7jXHU9u Nye358udy4cGkrzFfGxlrdIdukePiArh+y2iE5/G2Py5gIDK1ua79BAPTpEDDHKlUFe5UYRq nNmTIhcwEfrfYffKYfknEk1c4kitMlpXn0yaDN+h0EGZplj/cVLreYbTofMDlonL4jCNyqyI DmiXIqIrgp7W6EPOU5E63Ko0VAnkrs5IY4FGqlWiqyx/Gck0K47VmjQwC9U1aWCWA8+JUGGh LmBnXqLXF+SeX5QsCxz1AdF1ybIwyrIkjvPLkgXbQDck8BIHXpEx1W9KtyvDMdySPiE6dkYx talHXY2/zUtYaoXbe2nU29/e7abtKHM51pTeZVZvRlkkO1I9W5Y5kMmLR9QHLfNG6wXyo+Pk 6CnHkf2f0Zuthn8P8YqjhCYK6FCLEqJTPKRmLmDMFN+n9X/bUHuW0M3SqBmHmyabgiGwpsZJ BQq4vJGLkEp/9IQykE6tFIwIlDe8Dhpoo6L0zGSowESCmjJVYCFVTVkafArGVoExWIfJ0Rid xWUMGE8C1lQOGHb3gWH3PDDsXgCG3cc0RnsVGQOmpDEaLE2VXvbbcZo0owMZpcfNPQPpkSU8 e/07+8Pv8M8pnBf+AAAA//8DALl10H26BAAA</item> <item item-id="134">iVBORw0KGgoAAAANSUhEUgAAA/sAAAHICAYAAAARCfoQAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAACc4SURBVHhe7d1dkrJK1gZQx+WAiB5E D4LrbyDc9XWPocOrnkHPgI9UUlOlqvBvC8laEXlOqQiZO32reARx1wMAAABVEfYBAACgMsI+ AAAAVEbYBwAAgMoI+wAAAFAZYR8AAAAqI+wDAABAZYR9AAAAqIywDwAAAJUR9gEAAKAywj4A AABURth/xKHt97tdv9vt+/Yw3veDQ7vv938tBAAAAB8g7M926Nt9Dvld3+zb4Z6fDI/vdsI+ AAAAXyHsz9U1/a7pxhvp5q4vbl7pmqZvHdkHAADgS4T9mW5Py//xNP1D27fd6fHd8ZT/+wYA AACfJHnONC/sH/q2OZ3ef/u4kA8AAEAUCXSmWWG/a88X7hP2AQAA+BYJdK4/P7OfLuB3f8p+ DvzCPgAAAFEk0NmKq/Gnr+D79Wr8juwDAADwPRLoI6a+Z/943/337gv7AAAAfIsEGkTYBwAA IIoEGkTYBwAAIIoEGkTYBwAAIIoEGkTYBwAAIIoEGuQ27KebuQEAAMA7iZpByrBfBv3cAAAA 4F3EzCA57N+G/NwAAADgXcTMILdhPxP2AQAAeDcxM0gK+znYl+H+9jYAAAC8SswMUob9Ur7v 9n4AAAB4logZ5Kewnwj7AAAAvJOIGUTYBwAAIIqIGUTYBwAAIIqIGSQH+t/C/tRjAAAA8Cjx MshfYV7YBwAA4F3EyyDCPgAAAFHEyyDCPgAAAFHEyyDCPgAAAFHEyyBzw77ADwAAwKtEyyBz QrywDwAAwDuIlkGEfQAAAKKIlkGEfQAAAKKIlkF2M1K8sA8AAMA7iJZBhH0AAACiiJZBHgn7 Av886gQAADBNXAoyJ+wnwv7fco3KBgAAwIWYFETYf49cn9sGAADAhZgURNh/Xa5NWZ+p+wAA ALZORAoi7L8m12WqNr89BgAAsEXiURBh/zW/1SU/pm4AAAAn4lEQYf95uSa/1WXOMgAAAFsh GgUR9p83pyZ5GbUDAAAYstH4fz7s0bAvtJ48Ug91AwAAOBGNgswN+4nQevFILfKyagcAAGyd WBRE2H/OI7XIy6odAACwdWJREGH/cbkOj9RC7QAAAIZcNP6fDxP2H/dMHfJz1A8AANgykSiI sP+4Z+ugfgAAwNaJREGE/cfkGjxTB/UDAAC2TiQKIuw/5pUa5OduvYYAAMB2iUNBngn7Ww6r r45/6/UDAAC2TRwK8kjYT7YeVl8d/9brBwAAbJs4FETYf8yr48/P33INAQCA7RKFggj78+Wx vzr+LdcQAADYNlEoiLA/37vGvuUaAgAA2yYKBRH253vX2LdcQwAAYNtEoSDC/nzvGntez1br CAAAbJcYFETYnyeP+11j32odAQCAbRODggj787x73FutIwAAsG1iUBBhf553jzuvb4u1BAAA tksECvJs2N9aSH33mLdaRwAAYNtEoCCPhv1kiyH1E2PeYh0BAIBtE4GCCPt/y+N995i3VkcA AAARKIiw/7dPjXdrdQQAABCBggj7f/vUePN6t1RLAABg28SfIML+3z453q3VEgAA2DbxJ4iw /7dPjndrtQQAALZN/Aki7P/tk+PdWi0BAIBtE3+CCPu/y2P91Hg/vX4AAIAlEX2CvBL2txBQ I8a5lVoCAACIPkGeCfvJVgJqxDi3UksAAADRJ4iw/7uIcW6llgAAAKJPEGH/dxHjzNvYQj0B AIBtE3uCCPu/ixhn3sYW6gkAAGyb2BNE2P9ZHmPEOLdQTwAAALEniLD/s8gxbqGeAAAAYk8Q Yf9nkWPcQj0BAADEniDC/s8ix5i3VXtNAQCAbRN5ggj7P4scY95W7TUFAAC2TeQJIuz/LHqM W6gpAACwbSJPEGH/Z9Fj3EJNAQCAbRN5gjwb9pOaw2keW+T4vrFNAACASOJOEGF/2jfGlrdZ a00BAADEnSDC/rRvja3mmgIAAIg7QYT9ad8aW801BQAAEHeCCPvTvjW2mmsKAAAg7gQR9qd9 a2x5u7XWFQAA2DZRJ4iwP+1bY8vbrbWuAADAtok6QYT9e3lc3xpbrXUFAAAQdYII+/e+Pa5a 6woAACDqBBH27317XLXWFQAAQNQJ8krYT2oMpt8eU95+bXUFAAAQc4II+/eWMKYa6woAACDm BBH27y1hTDXWFQAAQMwJIuzfW8KYaqwrAACAmBNE2L+3hDHVWFcAAAAxJ4iwfy2P59tjWko/ AAAA3knECSLsX1vSeGqrLQAAgIgTRNi/tqTx1FZbAAAAESeIsH9tSeOprbYAAAAiThBh/9qS xpP7UlN9AQCAbRNvggj715Y2ntrqCwAAbJt4E0TYv7a08dRWXwAAYNvEmyDC/kUey5LGU1N9 AQAAxJsgr4b9pJZAusRx1FJbAACARLwJIuxfLHEcuU811BcAAEC0CSLsXyx1HLXUFwAAQLQJ IuxfLHUctdQXAABAtHnEoe33Qxrc7fZ9exjvu3Lo2316PLWm78Z7E2H/YqnjqKW+AAAAos1s KcjnkN/1zb4d7rl2aJvzmwBdMwT+Yhlh/2Kp48j9qqHGAADAtok1c3VNv2sux+pTmC9u3jue BXA5ui/sXyx5HLXUGAAA2DaxZqZDu+/3xbn7t7fvdUNozKf0X7dnpae+8PTFWPI4aqkxAACw bWLNTA+H/UPbN8Xjr4T8rIYgmsew1HHUUGMAAACxZqZHw37XXn+mX9g/WfoYaqgxAACAWDPX A5/ZT28E3D4m7J8sfQy5f2uvM59Rvj68RgAAWDK7q7MVV+NPF9+buBp/cn3Ev+vb8Wdh/2QN Y6ihzrxPfj3MaQAAsBR2Tx8x9T37x/tOt49ft3d8PLfLcsL+yRrGUEOdeV1+HZQtm3qsbAAA 8G12S4O8I+wnaw8Ta+j/2mvMa/L8P/I6uH3O3OcBAMCn2CUNIuyfrKH/uY9rrjOPK+f92bm/ Xcez6wEAgFfZFQ0i7J+spf9rrzOPyfP9rjkv1/eudQIAwCPshgYR9i99X0P/11xnHvOp12W5 3k+sHwAAfmP3M4iwv66+r7nOzJfn+ZNzXW7jk9sBAICSXc8gwv66+p77utZa87fIOS63FbE9 AACw2xlE2F9f39dca36X5zZyfsttRm4XAIBtsssZRNhfX9/XXGt+luf1G3Nbbvsb2wcAYDvs bgYR9tfX9zXXmp99e17z9r/dDwAA6mZXM4iwv76+5/6utd7cW8qclv34dl8AAKiT3cwgwv76 +p77u9Z6c21p81n2Zyl9AgCgHnYxg7wr7CdrDQdr7Pdaa829Jc5l7tMS+wYAwLrZvQwi7K+z 32utNdfyPC5xLsu+LbF/AACsk13LIFsP+7nP+k20Ncxh2ccl9xMAgPWwWxlE2F9fn7M19531 zF/u51r6CwDAstmlDCLsr6/P2Zr7vnV57tYyf2V/19JnAACWye5kEGF/fX3O1tz3rVvj3OU+ r7HvvOZ27h9pAAC37CIEEfbX1+cs932t/d+qNc9b2fc19p/5buf6HQ0AILFbEETYX1+fS2vv /9bk+arhNbf2cXDvdm5ze8bUesoGAGyXXYEgwv76+lxae/+3ppb5yuOoZTxbdzufn5jTqW3k BgBsiz//QYT99fW5lPu/5jFsRW1zVY6nljFtzTfm8HabZQMAtsGf/SDC/vr6XMr9X/MYtqLG eSpff7WNrWZLmbfbfuQGANTNn/sgWw77texY1jKOmuU5qnGeyrHVOL6aLHWubvuVGwBQJ3/m gwj7440Vq2UcNat9jvL4vA6Xaw1zVPaxbABAXfx5DyLsjzdWLI+jhrHUaCvzU46z9rGuyRrn 5bbPuVG/qXmf2wBYD7+2g7wz7Cdr+qNb0w5CTWOpzZbmJo/Va3EZ1j4fZf/XPA7uTc3tpxoA y+PXcxBhf7yxcnkstYynFlucly2OeYlqmodyLLWMaWum5nCqPWNqPb81AL7Pr+Mgwv54Y+Xy WGoZTy22Oidej99T1r6m+t+OKzeWa2q+coswtd2pBkC8On79Htp+33TjjWtds+/bw3jji4T9 8UYFahvP2uX52OqcbH3837CFmpdjrH2sazQ1P6ktwVS/bhvb5fUAsdb/TywF/X3b3+X5rh1D ftc3u+8HfmF/vFGB2sazdlufjzz+rdchytZqXY53S+Neoqm5SG3ppvpcNtZraj6jGjDPyv+5 HPp23wxx/tZwf1O+ATAE/qk3BAIJ++ONCuTx1DSmtTIXF2rxWWV9t1bj27FvbfzfVlP9p8ZS NpZpaq6ebaWpx9/RgItF/5P4z3/+M/yj3fX//Oc/j/9Pt/P9R13T725P3z+kI/m7Yfnro/ld s+t/ONM/hLA/3qhEjWNaI/NwkWuhJu+nridlHdTjs7ZS66lx5sZ3TM3FT+0bpvoxt8EWLfal /9///nf4h7nr//e//x1v//vf/z7e/te//nUO+4d23+8nzs9P99+9B/DDslG2GvZzP9fQ10fU Oq41MQf3ypqoy3uo572yJmrzXluu7dTYc+Nzpup929Ziqu9/NajdYl/mKRz/4x//GG+dpPvK 0Dwd4KdP7Rf2v2Mt/XxUHleNY1sL9Z9WvjbV5zXq+LOyNmr0OrW8uK3FbeM1UzUtW42mxvlb g5q88SV96A/d0MZbr0rhOB3NL93eNxngf7hgn7D/HWvp5zNqHtvS5dqr/7SyPmr0HPWbp6yT ej1O7X53W5/bxt+m6la2rZqqxV8N1mjGSzdd7K7pmz/bfviH8L7Pxd8G+/z5/StTn9kf7tu3 953wmf3vWEs/n5HHVuv4lkzd/1a+PtXqMer2mLJeZeNnavW425rdNk6malM2fjdVs78aLNmM l2i+4N289q6j5/kz+unifP/3f/93/Kx+Oq0/3U6f5z+5P2U/hfp90/bdVTfquxp/klb5gdW+ 1Rr6+Irax7dEuebq/reyVuo1j3o9r6ydGk5Tn/e4reNt25Kp8ZeN103V9bcGSzLjJflY2N9F h+p02v5u6uv3svSGQH3fs5+kVX5gtW+1hj6+Io+v5jEujXo/pnyNqtvv1Ok9yjqqp3p82lR9 b1tNpsZXNuJM1T81WJIZL8kU9q/DdPr8e3N96Pzo0DbfOVU+Bf4fNtw13w/6ibBfpzy+mse4 NOr9uPJ1qnb31Of9bmua25ZsffzfMFXzqbYWU32/bQC/eeLXxH34v/j+6fJLJezXK4+x9nEu gVq/pqyfGp6ox2eV9S1bzbY01qW7nYuf2hJM9WuqATziiV8bp9P6j5+LT1ffP5xa17VD0E+n 8v92Sv12Cfv1ymOsfZzfps7vUdZx67VUhzhlrctWk5rHVovbOZrT3mlq/b81gFc99askXQTv +Pn8qfbNS94vWKrNu6VVfmC1b7WGPr5DHucWxvot6vs+5et1qzXd+vi/pax72dasprFsze3c fbMBfMKTv14Ox8/C3wb9dLTfKfzTthj2c/+W3Md32dJYv0V936t8zW6trlsd95KUc1C2tVhz 35lnao5fbQDRXvzVk07h7/ounco/3vMWU78h392CbTnsb0Ue75bGHEVtP6Os61Zqu7XxLl05 H7dtaab6mBoALNWb/0yl8D/++Kypv6SfaMGE/frl8W5t3BHU9HPK12ztNd7KONeonJvb9k1T /UkNANbgyT9ZQ6hPF+Rrmpu27/euxj9J2N+GPOatjfuT1PTzyhrXWuuax1abcq6m2qdNbTM1 AFibp/58/XqBPlfjn5Rq825plR9Y7dssvX+fkse9xbF/glrGKV+7tdS8xjFtye38/dSeMbWe 2wYAa/bEn7LTV+/dh/yxObI/KdXm3dIqP7Dat1l6/z4pj32r438XdYxX1nztda9lHJyU8/nJ BgC1eOLPWgr7+ej9oW/LK/Af2r51WH+SsL8teexbrsE7qN93lK/dtc7BmvvOfOU8P9oAoHZP /LkbAv5+P4T69Jn9rj90zfBHc9/vx8/sO7I/Tdjfnjz+sjGfun1XWf81zcMa+wwA8AlP7Qod 2vwd++kIfwr/5an8PrM/5RNhP0mr/dCqX7bkvkXJNbhtc0w9b6rVqvbxrUX5Wlv6nKyhjwAA UZ7cJTr0Xbry/vmc/a5vxsB/uY+SsL9tuRafbrWocUxrVs7HUudlyX0DAPgGu0VBthb27XRP y3WZ0/4y9Zzc1q6WcdSmfI0tZY6W1h8AgKV4atfoeFR/nz6n/62j+OM3AhTXB0h9WvJHCIR9 PinXu2xrVcMYalbOT9mifXv7AABL98Qu0uWr9/bllfiDpesG7NvT1o/XEGi6Y+Af71ocYZ8I ue5lW5u19ntrytdY2SJEbw8AYI2e2lXqmp+OoB/6Qze08dbnjN8IcNxQul7AckN+JuwTJde+ bGuxxj5vXTlnt+2dPr1+AIDaPLe7dOj6dgj8bdf1h8MQ7nPrmn7/5lPpD21aZ7rwX3s8o+D0 yYEU8Nvxa//GbwEYP1IwvfyU00UG03Obrh2e89k3DIR9ouU5WNNcrKmvXCtfa1PtGVPrSQ0A gL89sdt0OY1/ur0v7B9P1R/S+ukAfgr247rTzznFpzcYzkH/h+UndM3Q13HZ48cAis//f0Kq zSek1X5o1S9Zar+2Js/DGuZjLf3kb+VcvqsBAPCYJ3ahgsL+oe33ZQAvAn4K6jnrp6B+/PmX 5e+kx64u7pfOBPhk1E87q5/ZW02r/dCqX7LUfm1Rnoulz8nS+8dzytfeow0AgOc9sTuVwn4K 2F3f3bX3ncZfBvohyfftvjiF/7yNdP/p55+XT059znn+atn0JsGHT+FPhH2+Kc/HUudlyX0D AIA1Wuyu9TmQp+sDtM0l4B+P2KfrBaTHhqA+pvYfl59wWbbt2/S5/Q+fwp8I+3xbnpMlzs0S +wQAAGu22N3r4+foh73/42fwj6foD+F9COgpqJef0R9//HH5KeWyx/UFXMr/U2E/Sav+4Oqf ssQ+cZmXJc3P0voDAAA1WPXu9c9fAThX+RV+n7WlsJ/7s6Q+cVHOz7fnaCn9AACA2qxwF3sI 6CnkF6fwP+14BsDnT+FPthj2Wa48R9+eq29vHwAAarXC3ezTxfde/Zx9PpX/eDq/0/jfamn9 YVqep2/N1ze3DQAAtbObHUTYZ4nyXEXP2Te2CQAAW2JXO4iwz1Ll+Yqct8htAQDAFtndDiLs s2R5ziLmLmo7AACwZYvc3S7DwKdaNGGfpcvz9sn5+/T6AQCAk8Xtcpdh4JMtmrDPGuS5+8Qc fmq9AADAPbvdQYR91iLP3zvn8d3rAwAAfmfXO8hWwn7uy1L6w3PKeXzHXL5rPQAAwDx2v4Ns Leyzfnkuy/aoV58PAAA8x+53EGGftcpzWrY5Hl0eAAB4H7vhQT4Z9pOlhCrhrk55Xm/blDnL AAAAn2VXPIiwTw3y/M5tAADAd9gdDyLsU5s817cNAAD4PrvmQYR9AAAAoohlQYR9AAAAoohl QYR9AAAAoohlQYR9AAAAoohlQYR9AAAAoohlQbYQ9gV9AACAZRDNggj7AAAARBHNggj7AAAA RBHNgnw67CffDtvCPgAAwDKIZkGEfQAAAKKIZkGEfQAAAKKIZkGEfQAAAKKIZkGEfQAAAKKI ZkGEfQAAAKKIZkFqD/uCPgAAwHKIZ0GEfQAAAKKIZ0GEfQAAAKKIZ0GEfQAAAKKIZ0GEfQAA AKKIZ0GEfQAAAKKIZ0GEfQAAAKKIZ0Eiwn7yrdAt7EPdon6HAXyK32PA2j36e8xvvSDCPrBm dpKBtfN7DFg7YX+hhH1gzewkA2vn9xiwdsL+gqTJiG9pu9Hbztv81pg1TdM0TdM0TdPqb494 bGme9ujEPCttJmhTZ9/YJhAr6ncYwKf4PQasnbC/UMI+sGZ2koG183sMWDthf6GEfQAAAKKI aEGEfQAAAKKIaEGEfQAAAKKIaEHuwv6h7ffDfbvdvm8P431v8I3g/Y1tAtEOfbvPV4Jt+m68 F2CNDu2+379zBwwg1GW/rPllp0xEC3Id9tPk5JDf9c2+He55n+jwHb09IN6hbc5vTHbN8Mfl zb+3AOIM+17DjouwD6zT6XfY7reUPxLRglyF/a65mpy04zxjrmaLDt/R2wO+7HhmkqP7wDp1 TdO3juwDK5Wy49zfXyJakDLs35469u5TySLDt6APW5TeURb2gRU6tH07/PJyGj+wSuMBlyad ZTmEsL+O7otpQYR9oBrDH5rGTjKwOoe+bU4fQRL2gTVKv7t2TdN3x4x/Op3fZ/YXQNgHatG1 Pq8PrFDXnq89IuwDa3T7u+uvU/rFtCC1fmZf2IdtSX9k3vn7CiBG+Y0ilybwA6tykyP/euNS TAtyFfaPf3DGq/Gnz12s+Gr8kdsCvuv6D0rXt3aSgZVyZB9Yp3Tqfv5Wt/Ib3qaJaUGuw/7g eHGF9K7ye79nP4kM4JHbAr7n+HV7x99Zub3/dxdAFGEfWK10dH/cH/vrbEsxLUiajChpU1Gb i9wWAAAA84hpQYR9AAAAoohpQYR9AAAAoohpQb4R9iM2GbUdAAAA5hPTgkSG/SQihAv6AAAA yySqBRH2AQAAiCKqBRH2AQAAiCKqBRH2AQAAiCKqBRH2AQAAiCKqBRH2AQAAiCKqBRH2AQAA iCKqBRH2AQAAiCKqBakt7Av6AAAAyyWuBRH2AQAAiCKuBflW2P/UZj+5bgAAAF4jrgWJDvuJ sA8AALBN4loQYR8AAIAo4loQYR8AAIAo4loQYR8AAIAo4loQYR8AAIAo4loQYR8AAIAo4lqQ msJ+Xu8XhgQAAMAM4lqQGsM+AAAAyySyBflG2E8+EcyFfQAAgGUT2YII+wAAAEQR2YII+wAA AEQR2YII+wAAAEQR2YII+wAAAEQR2YII+wAAAEQR2YLUEvbz+r40HAAAAGYQ2YLUFvYBAABY LrEtiLAPAABAFLEtyLfD/rs2/851AQAA8BliW5Bvhf1E2AcAANgWsS2IsA8AAEAUsS2IsA8A AEAUsS1IDWE/r+eLQwEAAGAGsS1ITWEfAACAZRPdggj7AAAARBHdgiwh7L/ahXesAwAAgM8T 3YJ8M+wnwj4AAMB2iG5BhH0AAACiiG5BhH0AAACiiG5BhH0AAACiiG5B1h72BX0AAID1EN+C CPsAAABEEd+CCPsAAABEEd+CLCXsP9uNV54LAABALPEtyLfDfiLsAwAAbIP4FmTNYT8/bwFD AAAAYAbxLUgNYR8AAIB1EOGCCPsAAABEEeGCLCnsP9qVZ54DAADA94hwQYR9AAAAoohwQZYQ 9hNhHwAAoH4iXJC1hv28/EK6DwAAwAwiXJC1h30AAADWQ4wLIuwDAAAQRYwLsrSwP7c7jywL AADAMohxQYR9AAAAoohxQZYS9pO5AT4vt6CuAwAAMIMYF2TNYR8AAIB1EeWCLDHs/9WlOcsA AACwPKJcEGEfAACAKKJckCWF/eSvIJ8fX1i3AQAAmEGUC7LWsA8AAMD6iHNBlhr2f+rWb48B AACwbOJckDWF/d8eAwAAYPnEuSBLC/vJT4Fe0AcAAFg3kS7IksP+bdem7gMAAGA9RLogSwz7 yW2wz7cX2l0AAABmEOmCLD3s5+6VPwMAALBOYl2QpYb9JAf8sgEAALBeYl2QJYf9RNAHAACo h2gXZOlhHwAAgHpIoEGEfQAAAKJIoEGEfQAAAKJIoEGEfQAAAKJIoEGEfQAAAKJIoEGEfQAA AKJIoEGEfQAAAKJIoEGEfQAAAKJIoEGEfQAAAKJIoEGEfQAAAKJIoEGEfQAAAKJIoEGEfQAA AKJIoEGEfQAAAKJIoEGEfQAAAKJIoI84tP1+CO273b5vD+N9Vw59u0+Pp9b03XhvIuwDAAAQ RQKdLQX5HPK7vtm3wz3XDm1zfhOga4bAXywj7AMAABBFAp2ra/pdczlWn8J8cfPe8SyAy9F9 YR8AAIAoEuhMh3bf74tz929v3+vG0/nvGwAAAHyS5DnTw2H/0PbNr28GAAAAwGdsPOyXF9S7 aTfn6D8a9rv2/jP9AAAAEMGR/bke+Mx+eiPg18/zAwAAwAcJ+7MVV+NPF9+buBp/cn3Ev+tb p/IDAAAQTNh/xNT37B/vO90+ft3e1ccBfvo+fgAAAPgcYR8AAAAqI+wDAABAZYR9AAAAqIyw DwAAAJUR9gEAAKAywj4AAABURtgHAACAygj7AAAAUBlhHwAAACoj7AMAAEBlhH0AAACojLAP AAAAlRH2AQAAoDLCPgAAAFRG2AcAAIDKCPsAAABQGWEfAAAAKiPsAwAAQGWEfQAAAKiMsA8A AACVEfYBAACgMsI+APAeXdu3h/Hn38xdDgB4mrAPALzBoW+bdvjvX+YuBwC8QtgHAF5z6Ppm t+t3u/3vR+zvlkvBfz/c3vX7phtu58ebvkvL7of7jwuWPw/PGX5O62japt8Pyx+fenTou3F9 u/2wjvFeANgiYR8AeNmhHcL3jHR9WS6F9vRzDvPj0f5DOwT/tu+G+w9d0++Gn9u2G38eN5CW yW8YnO8/vQlwekPgtJ38MwBskbAPALwoBe05R9KL5YaQfjyaf+jG0H9c4BTShxunHH864n/+ eSrI57Cf/p/fMBgI+wBsnbAPALwmHWkvgvaPiuVSeM+n27fpKP5pgWPwP2X0dEr/1M/lMmk9 pzcKyjcDTsuUp/cDwPYI+wDAa9JR+nZGsi6WS+G8GcP5+dT+8k2DfMT+/HNx/7hMPgsguRzJ P/Td8HN5lB8AtkjYBwBecjrdvu2PH78/S0fjy6PtN8sdP3c/XmhvfGJ56v3xzYBz1r88L/18 PCMgrTu/AXB0uohfvtjf5X4A2CZhHwD4mK4bE/tbpDcQXGUfAOYQ9gGAjzgMQf/6aP+LylP7 AYBfCfsAwPKloJ9P3y8+GgAATBP2AQAAoDLCPgAAAFRG2AcAAIDKCPsAAABQGWEfAAAAKiPs AwAAQGWEfQAAAKiMsA8AAACVEfYBAACgMsI+AAAAVEbYBwAAgMoI+wAAAFAZYR8AAAAqI+wD APU4tP1+t+t3Y9u3h/GBQX6s6cY7AKBewj4AUJmubybC/qHdj28CNMMSAFA3YR8AqMx02Hdk H4AtEfYBgMr8EPYBYEOEfQDgzqFtrj77vtu3fRmbD13x+L7pu5tMffX4bt83t6H70PbNPj9+ WkdbrmR4/vmxXXqs7fd5+Zu+DCsbFs+n6KdtDesen3sO+zfrc2wfgNoJ+wDAtfF09xyUD8eg fQnY+bPvp8cPfXsM4ZcAff5s/PF0+ctR9vPZ8/l0+mGdp7vyOoplBpfP2I/bys/Lt0ddc7ov n56f3mi4et7ovJywD8AGCPsAwLVzqN5fH20/yuF9eGx8KIfoU9a+D/enx/Pyl2BfBvFzsJ94 U+ESzqdOz594M2Fyuan1AUC9hH0A4M7ltPghMDfFafNXp8Nft2OwLh4vj9JfTAfxy/MubyLM CvuT2xP2AUDYBwAmXX/ufgzIf4X5J8P+JYgL+wDwDsI+APCLdPG7Ikz/8Ln5s+Lx6a+4e/Np /MX2hH0AuBD2AYBrKUAXV9hPV+af+oz+8b5xoXQRv/vHUwDPj6cr6h9/TAucHj8H+78u0Je3 PR3iz9sbnpzuvTzv1PI6L/0S9gGon7APAFwbwn76qrzLafz7c2g/Kb/qbmjpa/PKh9PjQ+A+ H+Hf3z5/kLYxBvzzOsplitPz8+NXyw/t8sbAoW+Lr95rD+lNgdNX8J1Xebe+4joEAFAhYR8A AAAqI+wDAABAZYR9AAAAqIywDwAAAJUR9gEAAKAywj4AAABURtgHAACAygj7AAAAUBlhHwAA ACoj7AMAAEBlhH0AAACojLAPAAAAlRH2AQAAoDLCPgAAAFRG2AcAAIDKCPsAAABQGWEfAAAA KiPsAwAAQGWEfT6va/rdbje2pu/Gu/tD2+/Tfc35np89siwAAMDGCfuE6Jr7sH9o9/dvAIy6 5vq+35YFAADgmrBPiMmw/tPR+uOZADeh3pF9AACA2YR9Qsw+Mp9DvSP4AAAATxP2N+vQd20z ButT2zfdcO/o5nP2bTeE8P14e99eljv6Y12Du7A/8Tn+w806ji1t66fP/A/SY5fn7Pumve5Z evOgyf1ObZ/GcrMMAABAZYT9bxhC58fbH06foR/C8Sll983wnBSG90VYvgT08f7zUffr5R5b 1yWsP/I5/l+XPW64O2/3eDPJ/d2343MOfTsG//MyAAAAFfo7FfJeQ9AMaX/IQXnfXgfl3wP6 78s9tq759yW/9SUH9/ymw2mzl2A/2Y+7sxMAAADq8XcqpGqHru2bZj95xP63gF0ulz22rvn3 JXf3F6f2Tx+l/6Gv5+flNwUAAADqI+xvVj7yncLz3KPxP4X9Z9Y1/77kXWH/sh5hHwAAqJew v0mXU9xvP+/+eNh/dl3z70vu7i+uHzD9dXxO4wcAALZL2N+kSyBPQfnww4X3LgE7HwWfCvLz 1jV1gb0/L9B3GNY9ruP356ej+6fl0tX5j5cOSPLR/3Owv7wBMPn+AAAAQCWE/Y3qzke4T19F 1+bgPNw+5ubiNPl8/9VX2A0tB+bH13X7dXrjfae1nbezb8b7flw2feXf5RoBu336RoDTI2fp 2wHKfo99BAAAqJmwDwAAAJUR9gEAAKAywj4AAABURtgHAACAygj7AAAAUBlhHwAAACoj7AMA AEBlhH0AAACojLAPAAAAlRH2WbdD2+93u37XdOMdAAAACPus2qHd97sU9ndNL+4DAACcCPus myP7AAAAd4R9AAAAqIywv0VdM576fjr9ve3afr8fb+/b/jAudrtcNzzS5uWGtm/HJa+W2w/r 6/rmvL5h/cNih6tt3J9yfxjWcTxCnx/PnchH7lNLR+/zcsPP6TmnbaZ2vc6r9Q19anJfs2G9 5z6mlvp53igAAMC6CftfMGTLj7e/XD7rPob2IlSfQ/yga3IgzmF6CPITy92t7+qNgSFsH4P0 78+9ft4lvJ/7cHwT4Prx+/4VfTme2n/Z5vlM/zzWfTs+59JXnwYAAABqMCMW8k5Dngxpf7m/ sN1fIX7ucvvjkfzkEtLz2QKX514+Y5/vu39eXuR2PcdtDQ+efz4+/75/18/P678E+8n+l2c2 AAAArNSMWEiNHg/xHwr7V6fiX7e8/vN6zm8QXNz1r1jfxOKD6f5fnnfpPwAAwFoJ+xu1xLA/ Hc6L9Xww7E/1HwAAYK2E/Y1aTNj/4VoBpYfC/u0F/e44jR8AAKifsL9R9+F8Xti/ugJ+Eaaf DvuD83LpueMV8dPV++/Wc37D4WLqsct96eh+Xl+62v7xx7TA6fFzv1ygDwAAqIuwv0VlYD+G 3ub6a+iGdgm9QxBuLkG+GUL4ObDn5W7XNwTv9hz+x9YM4f1mG5c3FQ7DKorl09fgnR8qjtSn Vqbxu3Fcwns3bP/8vH3+NoDCsF5fvQcAANRK2AcAAIDKCPsAAABQGWEfAAAAKiPsAwAAQGWE fQAAAKiMsA8AAACVEfYBAACgMsI+AAAAVKXv/x/hb/l2/bAQuQAAAABJRU5ErkJggg==</item> <item item-id="135" content-encoding="gzip">H4sIAAAAAAAA/2xTy27TUBCdaye282rSlkASt4SkkBJAKFh0QVelqhALHlL6AZWbmDaoaVLX RV1GfABrdmz4AhbsETsWiC+inJlJQou41vjeO3Nmzsx4nCciA9mFZOVs4+1FJ93oYDA6ThOv x5BMf9RTXUp0Txg27L3efxv1EtFQV/wtvJ3Tw/H26FzVLyAuFN058CX7Qn6tE/0G38f23zsv x9KkLLJsOSzNyZ8mSTzYP0siI8hNSJEur9Rm88o9LQFSSRxFvigCSGGsPEH1G9Ek7QafsG85 nu5uRncvq3smF1ggeYf+OCb9iB4ixgY3JS/6r6rvQN+BhRtTEMdJZiHg7LYLnKUc3xeEuBRk v8PuLQZLP0FQWpJA/UWUbL7YtBx8QIKTRbom+s/LrJfelQPnByyl6+pQRn2pfYoooZDohqLL jD4Rnorm4Va1kEpN96qvBdUYaTpEK1rI/E6r6li9qQWsQlXXAlYl8C29VOTSULDT1OiNNd2b t5VlTaJuEN1RlrU5S0sdm+vK0oLqrgZuSeC2tqlxT6ttT9twX+vE1UknKH5IYxox/oEMX7Ef 7B3E4fhw780oHoaJK7HKPMOi3gmTUGejNhuSBsSWgSM64yxkknlwsuH54PSZxNG5X+GJNtO/ hmS0kUIPCRxTnwZEF3jIrFzB2DG+T///tql2RugmcdiLgh1bTP4UWDclMr4BLmdlQlCFdASy BMLUxsCIQDnLO0YBQ2QUz0yW8W0Q1I1t/BSo6ibF4Asc0sa3JlswOYxhFlcwOHgasG4ywIh7 FhhxzwEj7nlgxL3AGPZaEAwORcYwWIsqvjobRvGgFx5pKz0p7jmEWzaQ3vNvnJ1+h39W/vLl DwAAAP//AwBGbYTNsgQAAA==</item> <item item-id="136">iVBORw0KGgoAAAANSUhEUgAAA/YAAAHICAYAAADk93GgAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAACjlSURBVHhe7d3dsfI6mgZQ4iIg1wTR Qfh6EpgMuOvrjqGLq86gM/BYgECAYfNjGUtaq0rnbMD455U/0ION2QwAAABAsQR7AAAAKJhg DwAAAAUT7AEAAKBggj0AAAAUTLAHAACAggn2AAAAUDDBHgAAAAom2AMAAEDBBHsAAAAomGAP AAAABRPsn9h1m2Hb70+3puyGbrMZNofWjbcAAABgWYL9A/t+ewjsT4P9rh+e5n4AAADITLB/ IoT7x8F+P/Tb49H6zqF6AAAAfkSwf+KvYL8PD+37YXs+Hf++AQAAQE6S5xPPg30ihPttP0b9 I4EeAACApUigT7wc7A+n5V8unifYAwAAsBQJ9Im3gn3niD0AAADLk0CfeDnY77qr6QR7AAAA liKBPhBCfQjooZ1D++FCedvjT9yNYT4+vrm5LL5gDwAAwFIk0AwEewAAAJYigWYg2AMAALAU CTQDwR4AAIClSKAZCPYAAAAsRQLNQLAHAABgKRJoBoI9AAAAS5FAMxDsAQAAWIoEmoFgDwAA wFIk0AwEewAAAJYigWYg2AMAALAUCTQDwR4AAIClSKAZCPYAAAAsRQLNQLAHAABgKRJoBoI9 AAAAS5FAMxDsAQAAWIoEmoFgDwAAwFIk0AwEewAAAJYigWYg2AMAALAUCTQDwR4AAIClSKAZ CPYAAAAsRQLNQLAHAABgKRJoBoI9AAAAS5FAMxDsAQAAWIoEmoFgDwAAwFIk0AwEewAAAJYi gWYg2AMAALAUCTQDwR4AAIClSKAZCPYAAAAsRQLNQLAHAABgKRJoBoI9AAAAS5FAMxDsAQAA WIoEmoFgDwAAwFIk0AwEewAAAJYigWYg2AMAALAUCTQDwR4AAIClSKAZCPYAAAAsRQLNQLAH AABgKRJoBoI9AAAAS5FAMxDsAQAAWIoEmoFgDwAAwFIk0AwEewAAAJYigWYg2AMAALAUCTQD wR4AAIClSKAZCPYAAAAsRQLNQLAHAABgKRJoBoI9AAAAS5FAMxDslxNK/agBAAC0QPzJQLDP 7zbEP2oAAAC1E30yEOzz+iu4p48/mgYAAKAWYk8Ggn0+rwb2dLq/pgUAACiZyJOBYJ/Hu0E9 nf7V5wAAAJRG3MlAsJ/fpwE9fd67zwUAACiBqJOBYD+vb4P5t88HAABYM1EnA8F+XnOE8jgP XQMAANRGzMlAsJ/PnIF8znkBAACshYiTgWA/nzmDeJyX7gEAAGoi4mQg2M8jRxDPMU8AAIBf Em8yEOznkSOAx3nqIgAAoBbiTQaC/fdyBvCc8wYAAFiaaJOBYP+9nME7zls3AQAANRBtMhDs v7NE8F5iGQAAAEsQazIQ7L+zROCOy9BVAABA6cSaDAT7zy0ZuJdcFgAAQC4iTQaC/eeWDNpx WboLAAAomUiTgWD/mV8E7aWXBwAAMDeRJgPB/jO/CNlxmboMAAAolTiTgWD/mV8FbMEeAAAo mTiTgWD/vhiuf1G6Xy4bAADgW6JMBoL9+34ZrOOydRsAAFAiUSYDwf49awjWv14+AADAp0SZ DAT796whVMd10HUAAEBpxJgMBPv3rCFQx3XQdQAAQGnEmAwE+9etKVCvZT0AAADeIcZkINi/ bk1hOq6L7gMAAEoiwmQg2L9ubUFasAcAAEojwmQg2L8mhug1lWtt6wMAAPAXESYDwf41awzR cZ10IQAAUArxJQPB/jVrDdCCPQAAUBLxJQPB/m8xPK+xVGteNwAAgFuiSwaC/d/WHJzjuulG AACgBKJLBoL939YenAV7AACgFKJLBoL93wR7AACAeYguGQj2z8XQvOYylbCOAAAAgdiSgWD/ XCmBWbAHAABKILZkINg/J9gDAADMR2zJQLB/LIblEkpU0roCAADtElkyEOwfKy0oC/YAAMDa iSwZCPaPCfYAAADzElkyEOwfKzXY61IAAGCtxJUMBPtpJYbkEtcZAABoi7iSgWA/rdSALNgD AABrJq5kINhPKz3Y61YAAGCNRJUMBPt7JYfjktcdAACon6iSgWB/r/RgLNgDAABrJapkINjf E+wpXdwH0gYAAGtgaJqBYH+v9CAU11/XtiXt92cNAAB+yZA0A8H+XukBKK6/rm1D2t9T/T71 eGgAAPALhqIZCPbXagk+NWwDf3tnf02nffU5AAAwN8PQL+26zbDt96dbR4L9tVoCT9wO3Vun tH/f7eNvngsAAN8yBP3Cvt+Og3jB/i+1hJ24Hbq3PnP1bTqfb+cFAACvMvT8Ugj3gv1jtYWc mraFo7n30XR+c80TAACeMez8Ugz2IcxPtdaFEtRUhtq2p3WxP+fu03S+c88bAABuGXJ+yRH7 52oLNnF7dHH5cvdl7vkDAEBkyPklwf652oJN3B5dXLal+nGp5QAA0DbDzS8J9s/VGGoEtfIt 1YdxOUstDwCANhlqfkmwf6zWQFPjNrVk6f0yXd5SywQAoC2GmV8IoT6E+NDScC/YH9UaZOJ2 6eby/KrvfrVcAADaYJiZgWB/VHOQEdLK9Mt+i8u23wAAMDdDzAwE+/pDTM3bVqtf75Pp8n+1 DgAA1MnwMgPBvv7wIpyVJfbXr/tsLesBAEBdDC8zEOzrDy9x+3R1GdbUV/YdAADmZmiZgWDf RnARzsoQ+2ktfZWuz1rWCQCAshlWZiDYtxFaBLMyrLGf4jrZfwAAmINhZQatB/tWQksL21i6 Ne+La143AADKYkiZgWDfRliJ29l4d6/amvvH/gMAwFwMKTMQ7NsJK4LZesW+WXP/lLCOAACs n+FkBoJ9O0FFKFun2C8l9E1J6woAwDoZSmbQcrBvLaS0tr2lKKlP7EMAAHzLUDIDwf50owFx exvu8lUqrU/sRwAAfMMwMgPB/nSjEQLZusT+KKlPSlxnAADWwzAyA8H+dKMRAtm6lNofcb3t SwAAvMsQMgPB/nSjEXGbG+721Si5L0ped+aT7gf2BwDgVYYMGbQa7FsdiLa63WtUej/Yl9qT 9vmrDQDgliFCBq0H+xYZcP9e7IOS+6GGbeBvaT/fttTU42kDAIgMDTIQ7NtjoP17tfRB3A77 U33Svv2kj6eeHxoAgCFBBoJ9e+K2t7r9v1ZT/WvaFo7SPp2jX2/nFxsA0C5DgQwE+zYZXP9O bbWP22N/Kl/OvkznnWsZAEAZDAMyaDHYG1ja/l+pcd+rcZtatFQ/pstZYnkAwPp4+8+g5WDf MjX4jVrrHrfLPlWetO+W6r/bZS61XABgHbz1ZyDYtynWoPU6LK3WmtufyvTrfkuX/6t1AACW 520/A8G+TbEGrddhSbXXvPbtq81a+itdj1+vCwCwDG/5GbQW7A0gL9RhWbXXO26ffWr91tZX 6fqsab0AgDy81WfQarBHLZYUa117vVvZzpKttY/S9Vrj+gEA8/E2n4Fg365YC/XIr5U626fW rYT+SddxzesJAHzOW3wGgn3b1GMZLdU5bqv9al1K6pd0XUtYXwDgPcW8vf/3v/8d/vWvf51u Tdj3w7bbnW5c23Xbod+fbixAsG+beuQXa9xKnVvb3hKU2CclrjMA8Joi3t7/+c9/Dv/7v/97 +P///M//nO5NhFC/7Ye77L7rT4F+N3Sb5cJ9S8HeQPGeeuTXYo3jNtu3fq/kvkjXvcT15zu3 /T/VACjT6l/CQ5gPLboPzfuh33ZjdL813t+lYX8M91PhP4MWgz0XsSbqkker9W11u9emhn6o YRv4W9rP3zYA1m/1L9fpEfpwKv5daN51w+b2FPx9OEK/Gae9Pkq/6zbDg7P1ZyXYoy75tFzb uO32rd+ppf72pTql/TrVnpmafqoBsE6rfon+97//fThaH/7/f//3f+Mbyubwd2rfb4ftxDn2 4f67vP9g2rkJ9qhLPi3XNm67fes3aqt/bdvTsrQv5+zTqfmmDYD1WPXL8j/+8Y/D///zn/+c j9aH79qnpsP69On5gv28vLk/pi552OfU4FdqrXut29WCtO+W6MOp5cUGwO/N+HK8H/a7sZ1u zeH2Qnkh6N+G5smw/uBieoL9vLyhPxZroz7zUlP71i/UXPN022rcvhrd9tkv+m1qHUID4Hde eBkOF6Hrhu7Pth1f1Of7Dnv4ebs0IMfbt6fiT37Hfrxv29+viO/YzytsZiOb+hH1mVesp5qq w9Jqr3fcvtq3swZr66d0fdIGwPJeePmNF6J7rc11RDx8pz58vz4cpY9H6qd/x/7+tPsQ4Ldd P+yuVsVV8ecWNrORTf2I+sxLPS9iLdQjv1ZqnW5n7dtaorX3z+36xQbAcl542X0v2G9mCs/x +/UvCafeb6Z+8i4K4d/v2M8tbGYjm/oR9ZmXel5Tj/xijVupc7q9rWzz2pXWJ7frGxttmdoH bhswvxf+aYVgfx2aw3fVu+vD4Qf7vpvtVPfbi+T9KYT7BwvfdcuF+kCwJ4j1UaPvqeU9Ncmv xfqm+1Vr2742JfdDuu6lbgPPTfXxnA143wf/dO6D/sVyp7uvWQvB3ovva9RoHuo4TV3yibVt sb4tb/sapPUvvQ9ut6X07WnVVD8+a89MTf9KA/72wT+V46n5h++wh6vg749tt+vHUB9Ox392 SnwbWgr2PKdO81DHabEuajMvdVWDX6mx7uk2pY11m+qztOUytaypBtz76J9GuDjd4fv0U22J y86vXKhD7cImNrCZX4t1UqvPqeFjajM/Nb1Qi+Wkta613rfbWOt2lmyqj0L7pan1uW3A0Yf/ HPaH763fhvpwFL/10/ADwZ4o1kmtPqd+z9nH5qWWF+m+pSb5tFbjdHtb2u61muqP0NZqal1v G7Tqy90/nIa/G3bhdPzTPbOY+lc6d8uo9mC/UBmroVbfUb/nYn3U6HtqeS+tibrMr9Xaptvd 4vb/2lT9QyvR1HakDVoy8y4fgv7pz09N/avM0TJqJdjzGvX6XKyd+j2nTt9Tw8fS2qjPfNT0 ugat12IJtdd7avvSBrX7cDcfA3y4WF7X3bTtsHVV/PHFo+5XDy+Q71Gvz6nda2Kd1Opz6vdc uo+p03fU8p6a5NVifW+3OW1Qq49276cXz3NV/EMdahY2r/JNnFWsl5q9T91eZz/7nNq9Rp2+ p4aPpbVRo3mo59FtHdIGNflglz7+3N19oD81R+wPdahZ2LzKN3F2ava+WDN1e416fUbd3qNe n1O716R1Uq/3qd9zU/WJDUr3wW4cgn08Kr8f+vRK+Pt+6P3a3fjiUO+rgxfAz6jZ+9TsfbFm 6vY69Xqf/ex9avaetF7q9ho1e89UvdIGJfpg1x3D/HY7BvjwHfvdsN914z+A7bA9fcfeEfvw glDvK4IXvM+o23tivdTsPer2HvX6TFo3tfubWn0urZ0aTlOf793W8LZBKT7aXfd9/A37cOQ+ BP30dHzfsRfsuRXrpnavUavPqd3r1OpzsXZq+JgazeO2jrG1Tj3yuK3rVIO1+nD33A+7cAX8 83n3u6E7hfvLfe0S7Jmidq9Tq8/F2qnfc+r0vbSG6nhNXeaX1rTl2qrBcm5rPdVgTeySGQj2 TFG718Q6qdXn1PA59ZlPWkv1PFKPvNL6tlTnFrd5bW77QD+wNh/tkoej9dvwvfr8R+ePP623 HfqCvrhfa7D3QvYd9XuNGn3Pvvac2swr3d9ar6s6LCetdc01b2EbgXl88BJx+bm7bXpF/Fz2 /bB99YJ8u34VHwDUHuz5TKyfGj6nRvNQx2mxLmozr7Surda29e3/hbTmaStdjdsE5PfRS8Wu e3SBvP2w343tdGsO4UJ9r50YcPPTez8k2POIGj4X66NG31PLe2qSV1rf1mrc6navRVr/tJWm hm0Afuezl4z9bgzR3dDvdsN+Pwb52HbdsJ31qvjHn9Y7/Ize1en4IcQfr8x/+DrAuD7Hswgu 0+z7sC7hYn794bHHHw4cLwQY5tXt+vE535/2L9jziBo+pz7zivVU0yO1yC/d51qodWvbu3a3 /RHbmpW2vsB6ffDycTkVf7rNGOzDb+SfTsMPR+63h8R9Cvu78He4Gv/l8RjeD9OONw75/PA7 +4/X6fAd/tO0h5/xm+F3+EMdahQ2q9JNW0ysoTpOU5t52d8u1GI5aa1rrncL21iqtG9u2xpM rVdoAN/44GVkuWAfjqRfhfUQ7MNZAeHOcNbAIeAfHh3/Pi339jv5IdjHmdxKPjg43gxH+L+N 9eHFuc5X57BZlW7aYmIN1fGe2uShpkfqsKxY71rrXvO21Sbtq9u2pKnlhwYwlw9eUkKwD4F6 N+zu2pyn4l+Oxh+D+/EU+eNV8sc2Bvk+fp8/CfPh8UuOD89Lbx/XPWb3q2nDPGY4DT+oMdh7 E5qPOk5TlzxiXVuurRr8Rlr3mmpf4za1IO23R21OU/OPDSCH9b68hA8JDil7P+xOp9Yf7x7D +Cl9n0+/P0ybPB7+DEf0+24M8s9Pwz9O2x+/sz/DafhBzcGe76nlNHXJJ9a2xfq2vO1rkNa/ hj6oaVtalvbjUg0gt/W+1IQL4m2PR+fP35cPDkfWw/0h1B/vDQE9/PReuHn4nnx8zuFI/hjs HyT7dNrDPOY4XD8S7Hkm1lI9L9Qkr5bra7/6vXT/K7UvatgGHrvt3zkawNK89BxcTvWfg2DP X9TzmnrkF2vcUp1b3Oa1SvuitP4odb0BaIu3qSD5jv4cagv2BjXzU89r6pFfrHErdW5te0uQ 9kkp/VLa+gLQrubfquLp+IdT8p2KP8mgZn5qehFroR75tVRr+9Q6pfvgmvuohHUEgJS3qwwE e/4Sa6qu6rCkVva7VrazVGn/rLGf1rpeAPCMt60MBHteoa6XGti/llN7zWvfvpqkfbWGPlvT ugDAu7x1ZSDY8wp1VYNfiDWvte72qbKk++Mv++7XyweAb63yLSx9g83VchLseUWsa8u1tW/9 Rq37Xq3b1YK075bsw18sEwByWN3b2O2bbK6WU03Bfqmataj12ra+/b9UY+1r3KbWpH2Ytrkt sQwAWJq3swxqDPbk0XJ97Vu/FetfSx/Yn+qR7pu37VNT8woNAGrhbS0DwZ5XtVxf+9bvxT4o vR9q2Q7upX07VwOAGnmLy0Cw51Wxvq3VuNXtXpsa+qGGbeBvaT9/0gCgdt7uMhDseUeLNbZf rUfsixL7o+R1BwCYk+FQBrUEe4PmZbRYY/vVesS+KLFPSlxnAIAcDIkyqC3Yk1escyu1bm17 S1Bin5S4zgAAuRgSZSDY845Y51Zqbb9ap5L2w5LWFQBgCYZFGQj2vKulWtuv1in2Swn9U8I6 AgAsydAoA8Ged7VS67id9qt1KqF/SlhHAIClGRplUEOwN3heViv1tk+t35r3xTWvGwDALxke ZVBTsGc5LdTcflWG2E9r6qs1rhMAwFoYImUg2POJ2mset89+tX5pX62hv9a0LgAAa2SYlIFg zydizWutu32qLGvZH9eyHgAAa2aolIFgz6dqrrt9qjyxz37Vb79ePgBAKQyXMig92BtM/06t dbdPletXffer5QIAlMiQKYNagj3Li7Wvrf72qXKl++RSfbj08gAASmfYlIFgz6di7Wuqf43b 1Jq0D3P341LLAQCoiaFTBoI936it/vanOsR+TNvccs4bAKBmhk8ZCPZ8o7b625/qEvtz7n7N MU8AgFYYQmVQcrA3uP69mvqgpm3hIu3Xb/t3rvkAALTMMCqDGoI9v1VLP9if6hX79ra949Pn AQBwzXAqA8Geb9XQD3Eb7E91S/t5qqWmHg8NAIDvGFJlINjzrdgPJfeFfakt6T77agMAYB6G VhkI9syh9L6wL7Ut9n/aAADIw1ArA8GeOZTcF3Hd7UsAAJCfYXcGpQZ7YWxdSu4P+xEAACzH 0DuD0oM961Fin8R1ti8BAMAyDL0zEOyZS+yTkvrFfgQAAMsy/H5k3w/bMZ1sNtuh35/uu7Mb usM0oXXjrSPBnrnEPimpX+xHAACwLMPvSfuh38ZAP4b3bT/eM2HXT4b+EoN9DGMC2fqU1C/2 IwAAWJ7h95RdN2y6ePw93NwMyc2TEP6PR+tvHys52LM+sW9K6B/7EQAALM8QfMK+3w7b5FD8 7e2j/bAPd51P2Z9upQirWtDqNiX2TQn9Yz8CAIDlGYJPeC3YJ0K4T07XLynQRwLZupXQP3Ed 7UcAALAsQ/AJbwf7w2n5ZV88TyBbt9g/a+4j+xAAAPyGYfiUl75jnxqDfeeIPXmtuY/iutmH AABgeYbhk5Kr4t+cZj9p110d0S8t2AtlZVhzH9l/AADgdwzFH5n6HfvDfafb4aj+4fGx3RzO LzXYs26xn9bWV2tdLwAAaIWheAaCPbmssa/sPwAA8FuG4xkI9uQS+2ot/bW29QEAgBYZjmdQ UrAXzMqzpv6y7wAAwO8ZkmdQYrCnHLHPft1va1kPAABonSF5BoI9OcU++3W/2XcAAGAdDMsz EOzJLfbbr/ru18sHAAAuDMszEOzJLfbbr/rOfgMAAOthaJ6BYM8SYt8t3X+/Wi4AADDN0DyD UoK9gFa2X/WffQYAANbF8DyD0oI95Yp9uFQ/Lr08AADgb4bnGQj2LCX24VL9aJ8BAID1MUTP QLBnSbEfc/flUssBAADeY4ieQQnBXkirxxJ9ucQyAACAzximZ1BSsKcOsT9z9an9BQAA1stQ PQPBnqXF/szRr7nmCwAAzMNQPQPBnl+IfTpnv+aYJwAAMC/D9QwEe34l9utcfWs/AQCA9TNk z2DtwT6GNYGtTnP171zzAQAA8jJkz6CUYE+dYv9+08/fPh8AAFiOYXsGgj2/Fvv4k77+9HkA AMBvGLpnINizBrGf0/bMO9MCAADrYfiegWDPWsS+vm2pZ48BAADrZxifwZqDvQDXprTfnzUA AKA8hvIZlBDsaVfcB+wLAABQB8P6DAR7AAAAliLiZSDYAwAAsBQRLwPBHgAAgKWIeBmsNdjH UC/YAwAA1EPEy2DtwR4AAIB6iHkZCPYAAAAsRczLQLAHAABgKWJeBoI9AAAASxHzMlhjsI+h XrAHAACoi5iXwZqDPQAAAHUR9TIQ7AEAAFiKqJeBYA8AAMBSRL0M1hbsY6gX7AEAAOoj6mWw 1mAPAABAfcS9DAR7AAAAliLuZSDYAwAAsBRxL4M1BfsY6gV7AACAOol7Gawx2AMAAFAnkS8D wR4AAICliHwZCPYAAAAsReTLYC3BPoZ6wR4AAKBeIl8Gawv2AAAA1Evsy0CwBwAAYCliXwaC PQAAAEsR+zIQ7AEAAFiK2JfBGoJ9DPWCPQAAQN3EvgzWFOwBAACom+iXgWAPAADAUkS/DAR7 AAAAliL6ZfDrYB9DvWAPAABQP9Evg7UEewAAAOon/mUg2AMAALAU8S8DwR4AAICliH8Z/DLY x1Av2AMAALRB/MtgDcEeAACANoiAGQj2AAAALEUEzOBXwT6GesEeAACgHSJgBr8O9gAAALRD DMxAsAcAAGApYmAGvwrXgj0AAEB7xMAMfhGw4zIFewAAgLaIgRn8MtgDAADQFlEwA8EeAACA pYiCGSwdsuPyBHsAAID2iIIZLB20hXoAAIB2iYMZhJ+7E+wBAABYgjiYgWAPAADAUsTBDJYM 9nE5SywLAACA9REHM0iDfe7AvcQyAAAAWC+RMIMQ7I//zx+6BXsAAIC2iYQZLBXs4/xzLgMA AIB1EwkzuA32uYJ3znkDAABQBrEwgxjsA8EeAACAnMTCDJYI9nG+OeYNAABAOcTCDKaC/dwB PMc8AQAAKI9omEHuYJ9jngAAAJRJNMwgDfbB3CFcqAcAACASDzN4FOznCuNzzgsAAICyiYcZ 3Ab7YK4wHuczx7wAAAAon3iYwbNg/20gn2MeAAAA1ENEzCBXsJ9jHgAAANRFRMxgKtgH34Zy oR4AAIBbYmIGfwX7T8L5N88FAACgXmJiBo+CffBpQP/kOQAAANRPVMxg7mD/yXMAAABog6iY wbNgH7wT1N+ZFgAAgPaIixm8E+z/CuyvTAMAAEC7RMYM/gr2QQzsz4L7X48DAACAyJjBK8E+ SIP77VMe3Q8AAAApsTGDV4N9kAb4qQYAAADPiI4ZvBPsI4EeAACAT4iQGXwS7AEAAOATEmgG gj0AAABLkUAzEOyBknkNA0rndQwo3buvY171MvBmApTMaxhQOq9jQOkE+xXwZgKUzGsYUDqv Y0DpBPsfCYXXNE3TNE3TNE3TtDnaOwT7DN7tBIA18RoGlM7rGFA6wX4FvJkAJfMaBpTO6xhQ OsEeAAAAGiLYAwAAQMEEewAAACiYYL+EfT9sN+HKhtuh35/uA1it/dBv4xVZu2F3uhegRPt+ O2wNwIBiXcZl3ZNBmWCfXeiIGOh3Q7ftx3sA1mvfd+cPIXfd+EbidQso1jj2GgfDgj1QpuNr 2OZZoj8R7HPbdVcdEQbJL/QLwDoczjhy1B4o067rht4Re6BQITu++vol2Gd2e/qX08GAsoRP igV7oED7fujHFy9jL6BIp4MrXTh78oWj9oJ9ZoI9ULTxTaXzmgUUZz/03fFrRMZeQInCa9em 64bdIc8fT8n3HfsfEuyBku16368HCrTrz9cKMfYCSnT72vXXafmCfW6+Yw8UKryheL0CypP+ sselCfdAUW5y5F8fUgr22SVXxQ/fk3B1aaAA128eu6E3IAYK5Yg9UKZw+n38dbX0l9amCfZL OFz4IHxa7HfsgfU7/MTd1ZEur11AuQR7oFjhqP1pPPbXWZSCPQAAABRMsAcAAICCCfYAAABQ MMEeAAAACibYAwAAQMEEewAAACiYYA8AAAAFE+wBAACgYII9AAAAFEywBwAAgIIJ9gAAAFAw wR4AAAAKJtgDAO/b9UO/P/39zKvTAQAfE+wBgDfth77rx//+5dXpAIBvCPYAwOv2u6HbbIbN Zvv8SPzddCHkb8fbm2Hb7cbb8fFu2IVpt+P9hwnTv8fnjH+HeXR9N2zH6Q9PPdgPu9P8Nttx Hqd7AaBFgj0A8JZ9PwbtF5L0ZboQ0MPfMbifjuLv+zHk98NuvH+/64bN+Hff705/nxYQpokf DpzvPwb+Y/g/Lif+DQAtEuwBgDeEUP3KEfJkujGQH47S73engH+Y4BjIxxvHzH48kn/+eyq0 x2Af/h8/HBgJ9gC0TrAHAF4XjqAnofqhZLoQ1OMp8304On+c4BDyj3k8nJY/9Xc6TZjP8UOB NPgfp0lP0QeA9gj2AMDrwtH3/oUUnUwXgnh3CuLn0/PTDwjikfjz38n9p2ni0f3gcoR+P+zG v9Oj9wDQIsEeAHjZ8ZT5fjh8Xf4sHGVPj6LfTHf4nvzpIninJ6anzx+C/znXX54X/j4c6Q/z jmH/4HiBvXghvsv9ANAmwR4AmMVud0rnswgfFrjaPQC8QrAHAL62H0P99VH8L6Wn5wMATwn2 AMC6hFAfT8FPTu8HAKYJ9gAAAFAwwR4AAAAKJtgDAABAwQR7AAAAKJhgDwAAAAUT7AEAAKBg gj0AAAAUTLAHAACAggn2AAAAUDDBHgAAAAom2AMAAEDBBHsAAAAomGAPAAAABRPsAYAy7fth u9kMm1Pb9vvTA6P4WLc73QEA9RLsAYCC7YZuItjv++0p8HfjFABQN8EeACjYdLB3xB6Algj2 AEDBHgR7AGiIYA8Ajdv33dV31Tfbfkgj8n6XPL7tht1Nfr56fLMdutuAve+HbhsfP86jT2cy Pv/82CY81g/bOP3NuowzGyePp9mHZY3zPj33HOxv5ueYPQC1E+wBoGWnU9ZjKN4fQvUlTMfv qh8f3w/9IXBfwvL5u+yHU94vR8/PZ8DHU+LHeR7vivNIphldvhN/WlZ8Xrx9suuO98VT7MOH ClfPOzlPJ9gD0ADBHgBadg7Q2+uj6AcxqI+PnR6KgfmYq++D/PHxOP0lxKeh+xziJz5AuATx qVPsJz44mJxuan4AUC/BHgAadzm1fQzHXXLq+9Up7dftEKKTx9Oj7xfTofvyvMsHBi8F+8nl CfYAINgDADffkz+F4b+C+4fB/hK6BXsAmINgDwCchAvTJcH5wffcz5LHp39WbuZT8ZPlCfYA cCHYA0DLQlhOrnQfrpA/9Z36w32nicIF9u4fD2E7Ph6ubH/4M0xwfPwc4v+6eF5c9nRgPy9v fHK49/K8Y4vzvKyXYA9A/QR7AGjZGOzDz9NdTsXfngP6UfrzcmMLP1WXPhweH8P1+cj99vb5 o7CMU5g/zyOdJjnFPj5+Nf3YLh8C7Ic++bm7fh8+ADj+7N15lnfzS64bAAAVEuwBAACgYII9 AAAAFEywBwAAgIIJ9gAAAFAwwR4AAAAKJtgDAABAwQR7AAAAKJhgDwAAAAUT7AEAAKBggj0A AAAUTLAHAACAggn2AAAAUDDBHgAAAAom2AMAAEDBBHsAAAAomGAPAAAABRPsAQAAoGCCPQAA ABRMsGdeu27YbDan1g27093Dvh+24b7ufM9j70wLAADQOMGe2e26+2C/77f3Yf9k113f92xa AAAArgn2zG4ymD86Cn84wn8T4B2xBwAAeJlgz+xePuIeA7wj8wAAAB8T7JuwH3Z9dwrRx7bt duO9Jzffi+93Y+Denm5v+8t0B3/Ma3QX7Ce+d7+/mcehhWU9+o7+KDx2ec526PrrNQsfFHRx vUPbhm25mQYAAKAygn1uY8DM3v5w/M77GISPiXroxueE4LtNgvEljJ/uPx9Nv57uvXldgvk7 37t/Ou1hwbvzcg83g7i+2/70nP3Qn0L+eRoAAIAK/Z0K+dwYKhdpf4iheNtfh+LnYfz5dO/N 6/X7gmfrEkN6/IDhuNhLiJ9cj7uzDgAAAOrxdyqkGvtdP3TddvJI/LMwnU4XvTev1+8L7u5P Ts+fPvr+YF3Pz4sfAAAAANRHsG9CPKIdgvKrR9kfBftP5vX6fcFcwf4yH8EeAACol2Bfvctp 6rffT38/2H86r9fvC+7uT77vP/0TeE7FBwAA2iXYV+8SvkMo3j+4KN4lTMej21Oh/bV5TV38 7s+L5+3HeZ/m8fz54aj9cbpwlfzDV/2DeFT/HOIvYX/yswAAAIBKCPYN2J2PXB9//q2PIXm8 fcjIyanu8f6rn40bWwzH78/r9ifsTvcd53ZezrY73fdw2vAze5fv9G+24cr8x0fOwlX60/U+ rSMAAEDNBHsAAAAomGAPAAAABRPsAQAAoGCCPQAAABRMsAcAAICCCfYAAABQMMEeAAAACibY AwAAQMEEewAAACiYYE859v2w3WyGTbc73QEAAIBgTzH2/XbYhGC/6QbRHgAA4EiwpxyO2AMA ANwR7AEAAKBggn3tdt3p9PXjKez9rh+229PtbT/sT5PdTrcbH+njdGPb9qcpr6bbjvPbDd15 fuP8x8n2V8u4P21+P87jcOQ9Ph5XIh6RDy0clY/TjX+H5xyXGdr1PK/mN65TF9c1Gud7XsfQ wnqeFwoAAFA2wT6zMUdmb3+5fDf9FNCTAH0O7KNdF8NvDM5jaJ+Y7m5+Vx8CjMH6EJqfP/f6 eZegfl6HQ+C/fvx+/ZJ1OZyef1nm+Wz9uK3b/vScy7o6ox8AAKjBC7GQT43ZcZH2l/uLzv0V 2F+dbns4Qh9cAnk8C+Dy3Mt34uN998+Lk9zO57Cs8cHz34fn36/f9fPj/C8hfnL90zMWAAAA CvVCLKR07wf2TMH+6nT66xbnf57P+cOAi7v1S+Y3Mfloev0vz7usPwAAQKkE+wasMdhPB/Fk PhmD/dT6AwAAlEqwb8Bqgv2D7/an3gr2txfbu+NUfAAAoH6CfQPug/hrwf7qSvRJcP442I/O 04Xnnq5MH66ifzef84cLF1OPXe4LR+3j/MJV7w9/hgmOj5/Xy8XzAACAugj2tUvD+SHgdtc/ /Ta2S8AdQ293Ce3dGLjP4TxOdzu/MWT356B/at0Y1G+WcfkAYT/OIpk+/PTc+aHkCHxoafK+ 245LUN+Nyz8/bxuvyp8Y5+vn7gAAgFoJ9gAAAFAwwR4AAAAKJtgDAABAwQR7AAAAKJhgDwAA AAUT7AEAAKBggj0AAAAUTLAHAACAYg3D/wPDo9R+h0+NMwAAAABJRU5ErkJggg==</item> </binaryContent> </worksheet>

Mathcad Professional 14.0 <description/> <author/> <company>Parametric Technology Corporation</company> <keywords/> <revisedBy>UNIBS</revisedBy> </userData> <identityInfo> <revision>181</revision> <documentID>80ED9498-764B-40AF-B415-B80A121BB729</documentID> <versionID>F9E1C681-054E-4317-9EF4-6ABD442BFAFF</versionID> <parentVersionID>00000000-0000-0000-0000-000000000000</parentVersionID> <branchID>00000000-0000-0000-0000-000000000000</branchID> </identityInfo> </metadata> <settings> <presentation> <textRendering> <textStyles> <textStyle name="Normal"> <blockAttr margin-left="0" margin-right="0" text-indent="0" text-align="left" list-style-type="none" tabs="inherit"/> <inlineAttr font-family="Arial" font-charset="0" font-size="14" font-weight="normal" font-style="normal" underline="false" line-through="false" vertical-align="baseline" color="#f00"/> </textStyle> <textStyle name="Heading 1"> <blockAttr margin-left="0" margin-right="0" text-indent="inherit" text-align="left" list-style-type="inherit" tabs="inherit"/> <inlineAttr font-family="Arial" font-charset="0" font-size="14" font-weight="bold" font-style="normal" underline="false" line-through="false" vertical-align="baseline"/> </textStyle> <textStyle name="Heading 2"> <blockAttr margin-left="0" margin-right="0" text-indent="inherit" text-align="left" list-style-type="inherit" tabs="inherit"/> <inlineAttr font-family="Arial" font-charset="0" font-size="12" font-weight="bold" font-style="italic" underline="false" line-through="false" vertical-align="baseline"/> </textStyle> <textStyle name="Heading 3"> <blockAttr margin-left="0" margin-right="0" text-indent="inherit" text-align="left" list-style-type="inherit" tabs="inherit"/> <inlineAttr font-family="Arial" font-charset="0" font-size="12" font-weight="normal" font-style="normal" underline="false" line-through="false" vertical-align="baseline"/> </textStyle> <textStyle name="Paragraph"> <blockAttr margin-left="0" margin-right="0" text-indent="21" text-align="left" list-style-type="inherit" tabs="inherit"/> <inlineAttr font-family="Arial" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false" line-through="false" vertical-align="baseline"/> </textStyle> <textStyle name="List"> <blockAttr margin-left="14.25" margin-right="0" text-indent="-14.25" text-align="left" list-style-type="inherit" tabs="inherit"/> <inlineAttr font-family="Arial" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false" line-through="false" vertical-align="baseline"/> </textStyle> <textStyle name="Indent"> <blockAttr margin-left="108" margin-right="0" text-indent="inherit" text-align="left" list-style-type="inherit" tabs="inherit"/> <inlineAttr font-family="Arial" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false" line-through="false" vertical-align="baseline"/> </textStyle> <textStyle name="Title"> <blockAttr margin-left="0" margin-right="0" text-indent="inherit" text-align="center" list-style-type="inherit" tabs="inherit"/> <inlineAttr font-family="Times New Roman" font-charset="0" font-size="24" font-weight="bold" font-style="normal" underline="false" line-through="false" vertical-align="baseline"/> </textStyle> <textStyle name="Subtitle" base-style="Title"> <blockAttr margin-left="0" margin-right="0" text-indent="inherit" text-align="center" list-style-type="inherit" tabs="inherit"/> <inlineAttr font-family="Times New Roman" font-charset="0" font-size="18" font-weight="normal" font-style="normal" underline="false" line-through="false" vertical-align="baseline"/> </textStyle> </textStyles> </textRendering> <mathRendering equation-color="#000"> <operators multiplication="narrow-dot" derivative="derivative" literal-subscript="large" definition="colon-equal" global-definition="triple-equal" local-definition="left-arrow" equality="bold-equal" symbolic-evaluation="right-arrow"/> <mathStyles> <mathStyle name="Variables" font-family="Times New Roman" font-charset="0" font-size="10" font-weight="normal" font-style="italic" underline="false"/> <mathStyle name="Constants" font-family="Times New Roman" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false"/> <mathStyle name="User 1" font-family="Bell MT" font-charset="0" font-size="16" font-weight="normal" font-style="normal" underline="false"/> <mathStyle name="User 2" font-family="Courier New" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false"/> <mathStyle name="User 3" font-family="Arial" font-charset="0" font-size="10" font-weight="bold" font-style="normal" underline="false"/> <mathStyle name="User 4" font-family="Times New Roman" font-charset="0" font-size="10" font-weight="normal" font-style="italic" underline="false"/> <mathStyle name="User 5" font-family="Times New Roman" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false"/> <mathStyle name="User 6" font-family="Arial" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false"/> <mathStyle name="User 7" font-family="Times New Roman" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false"/> <mathStyle name="Math Text Font" font-family="Times New Roman" font-charset="0" font-size="16" font-weight="bold" font-style="normal" underline="false"/> </mathStyles> <dimensionNames mass="mass" length="length" time="time" current="current" thermodynamic-temperature="temperature" luminous-intensity="luminosity" amount-of-substance="substance" display="false"/> <symbolics derivation-steps-style="vertical-insert" show-comments="false" evaluate-in-place="false"/> <results numeric-only="true"> <general precision="3" show-trailing-zeros="false" radix="dec" complex-threshold="10" zero-threshold="15" imaginary-value="i" exponential-threshold="3"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="true" simplify-units="true" fractional-unit-exponent="false"/> </results> </mathRendering> <pageModel show-page-frame="false" show-header-frame="false" show-footer-frame="false" header-footer-start-page="1" paper-code="8" orientation="landscape" print-single-page-width="false" page-width="1190.25" page-height="841.5"> <margins left="14.4" right="17.26299" top="14.4" bottom="19.68"/> <header use-full-page-width="false"/> <footer use-full-page-width="false"/> </pageModel> <colorModel background-color="#fff" default-highlight-color="#ffff80"/> <language math="it" UI="it"/> </presentation> <calculation> <builtInVariables array-origin="1" convergence-tolerance="0.001" constraint-tolerance="0.001" random-seed="1" prn-precision="4" prn-col-width="8"/> <calculationBehavior automatic-recalculation="true" matrix-strict-singularity-check="false" optimize-expressions="false" exact-boolean="true" strings-use-origin="false" zero-over-zero="error"> <compatibility multiple-assignment="MC12" local-assignment="MC11"/> </calculationBehavior> <units> <currentUnitSystem name="si" customized="false"/> </units> </calculation> <editor view-annotations="false" view-regions="false"> <ruler is-visible="false" ruler-unit="in"/> <plotTemplate> <polar item-idref="1"/> <xy item-idref="2"/> </plotTemplate> <grid granularity-x="6" granularity-y="6"/> </editor> <fileFormat image-type="image/png" image-quality="75" save-numeric-results="true" exclude-large-results="true" save-text-images="false" screen-dpi="96"/> <miscellaneous> <handbook handbook-region-tag-ub="770" can-delete-original-handbook-regions="true" can-delete-user-regions="true" can-print="true" can-copy="true" can-save="true" file-permission-mask="4294967295"/> </miscellaneous> </settings> <regions> <region region-id="211" left="6" top="24" width="875.25" height="22.5" align-x="18" align-y="42" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> <f size="20"> Uso delle coordinate principali: sistema a due gdl smorzato (con smorzamento proporzionale) </f> </text> </region> <region region-id="215" left="6" top="60" width="675" height="282" align-x="6" align-y="60" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <picture> <metafile mapping-mode="8" x-extent="941.9528" y-extent="394.6394" item-idref="3"/> </picture> <rendering item-idref="4"/> </region> <region region-id="216" left="6" top="371.25" width="123" height="15.75" align-x="20.25" align-y="384" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="true"> Parametri geometrici </text> </region> <region region-id="217" left="6" top="399" width="87.75" height="18.75" align-x="18.75" align-y="414" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve">a</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:mult/> <ml:real>80</ml:real> <ml:apply> <ml:pow/> <ml:real>10</ml:real> <ml:real>-3</ml:real> </ml:apply> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>0.08</ml:real> </result> </ml:eval> </ml:define> </math> <rendering item-idref="5"/> </region> <region region-id="218" left="6" top="429" width="91.5" height="18.75" align-x="18" align-y="444" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve">b</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:mult/> <ml:real>180</ml:real> <ml:apply> <ml:pow/> <ml:real>10</ml:real> <ml:real>-3</ml:real> </ml:apply> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>0.18</ml:real> </result> </ml:eval> </ml:define> </math> <rendering item-idref="6"/> </region> <region region-id="219" left="6" top="459" width="90.75" height="18.75" align-x="17.25" align-y="474" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define warning="WarnRedefinedBIUnit" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve">c</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:mult/> <ml:real>220</ml:real> <ml:apply> <ml:pow/> <ml:real>10</ml:real> <ml:real>-3</ml:real> </ml:apply> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>0.22</ml:real> </result> </ml:eval> </ml:define> </math> <rendering item-idref="7"/> </region> <region region-id="7" left="6" top="497.25" width="106.5" height="15.75" align-x="20.25" align-y="510" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="true"> Parametri inerziali </text> </region> <region region-id="695" left="6" top="525" width="36.75" height="16.5" align-x="23.25" align-y="534" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="1">J</ml:id> <ml:real>1.2</ml:real> </ml:define> </math> <rendering item-idref="8"/> </region> <region region-id="696" left="6" top="549" width="30" height="16.5" align-x="23.25" align-y="558" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="2">J</ml:id> <ml:real>3</ml:real> </ml:define> </math> <rendering item-idref="9"/> </region> <region region-id="697" left="6" top="573" width="30.75" height="12.75" align-x="19.5" align-y="582" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define warning="WarnRedefinedBIUnit" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve">m</ml:id> <ml:real>15</ml:real> </ml:define> </math> <rendering item-idref="10"/> </region> <region region-id="679" left="6" top="599.25" width="102.75" height="15.75" align-x="38.25" align-y="612" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="true"> Parametri elastici </text> </region> <region region-id="15" left="6" top="627" width="39" height="12.75" align-x="18.75" align-y="636" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve">k</ml:id> <ml:real>2500</ml:real> </ml:define> </math> <rendering item-idref="11"/> </region> <region region-id="683" left="6" top="680.25" width="237" height="27.75" align-x="46.5" align-y="696" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define warning="WarnRedefinedBIUnit" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:function> <ml:id xml:space="preserve">V</ml:id> <ml:boundVars> <ml:id xml:space="preserve">α</ml:id> <ml:id xml:space="preserve">β</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:mult/> <ml:apply> <ml:div/> <ml:real>1</ml:real> <ml:real>2</ml:real> </ml:apply> <ml:parens> <ml:apply> <ml:plus/> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:real>2</ml:real> <ml:id xml:space="preserve">k</ml:id> </ml:apply> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">b</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">α</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:real>2</ml:real> <ml:id xml:space="preserve">k</ml:id> </ml:apply> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:minus/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve">a</ml:id> <ml:id xml:space="preserve">α</ml:id> </ml:apply> <ml:apply> <ml:mult/> <ml:id xml:space="preserve">a</ml:id> <ml:id xml:space="preserve">β</ml:id> </ml:apply> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve">k</ml:id> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">c</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">β</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:apply> </ml:parens> </ml:apply> </ml:define> </math> <rendering item-idref="12"/> </region> <region region-id="687" left="342" top="689.25" width="111" height="15.75" align-x="358.5" align-y="702" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> Energia potenziale </text> </region> <region region-id="684" left="6" top="723.75" width="175.5" height="34.5" align-x="60.75" align-y="744" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:symEval style="default" hide-keywords="false" hide-lhs="false" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:apply> <ml:derivative style="derivative"/> <ml:lambda> <ml:boundVars> <ml:id xml:space="preserve">α</ml:id> </ml:boundVars> <ml:apply> <ml:id xml:space="preserve">V</ml:id> <ml:sequence> <ml:id xml:space="preserve">α</ml:id> <ml:id xml:space="preserve">β</ml:id> </ml:sequence> </ml:apply> </ml:lambda> </ml:apply> <ml:symResult> <ml:apply> <ml:plus/> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:real>162.0</ml:real> <ml:id xml:space="preserve">α</ml:id> </ml:apply> <ml:apply> <ml:mult/> <ml:real>-32.0</ml:real> <ml:id xml:space="preserve">β</ml:id> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:real>32.0</ml:real> <ml:id xml:space="preserve">α</ml:id> </ml:apply> </ml:apply> </ml:symResult> </ml:symEval> </math> <rendering item-idref="13"/> </region> <region region-id="685" left="6" top="771.75" width="177" height="34.5" align-x="61.5" align-y="792" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:symEval style="default" hide-keywords="false" hide-lhs="false" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:apply> <ml:derivative style="derivative"/> <ml:lambda> <ml:boundVars> <ml:id xml:space="preserve">β</ml:id> </ml:boundVars> <ml:apply> <ml:id xml:space="preserve">V</ml:id> <ml:sequence> <ml:id xml:space="preserve">α</ml:id> <ml:id xml:space="preserve">β</ml:id> </ml:sequence> </ml:apply> </ml:lambda> </ml:apply> <ml:symResult> <ml:apply> <ml:plus/> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:real>121.0</ml:real> <ml:id xml:space="preserve">β</ml:id> </ml:apply> <ml:apply> <ml:mult/> <ml:real>32.0</ml:real> <ml:id xml:space="preserve">β</ml:id> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:real>-32.0</ml:real> <ml:id xml:space="preserve">α</ml:id> </ml:apply> </ml:apply> </ml:symResult> </ml:symEval> </math> <rendering item-idref="14"/> </region> <region region-id="17" left="6" top="827.25" width="178.5" height="15.75" align-x="30" align-y="840" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> Matrici di massa e di rigidezza </text> </region> <region region-id="20" left="6" top="858" width="171" height="42.75" align-x="21" align-y="882" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17">J</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:matrix rows="2" cols="2"> <ml:id xml:space="preserve" subscript="1">J</ml:id> <ml:real>0</ml:real> <ml:real>0</ml:real> <ml:parens> <ml:apply> <ml:plus/> <ml:id xml:space="preserve" subscript="2">J</ml:id> <ml:apply> <ml:mult/> <ml:id xml:space="preserve">m</ml:id> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">c</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:apply> </ml:parens> </ml:matrix> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="2"> <ml:real>1.2</ml:real> <ml:real>0</ml:real> <ml:real>0</ml:real> <ml:real>3.726</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </math> <rendering item-idref="15"/> </region> <region region-id="21" left="408" top="858" width="158.25" height="42.75" align-x="501.75" align-y="882" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:apply> <ml:id xml:space="preserve">diag</ml:id> <ml:matrix rows="2" cols="1"> <ml:id xml:space="preserve" subscript="1">J</ml:id> <ml:parens> <ml:apply> <ml:plus/> <ml:id xml:space="preserve" subscript="2">J</ml:id> <ml:apply> <ml:mult/> <ml:id xml:space="preserve">m</ml:id> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">c</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:apply> </ml:parens> </ml:matrix> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="2"> <ml:real>1.2</ml:real> <ml:real>0</ml:real> <ml:real>0</ml:real> <ml:real>3.726</ml:real> </ml:matrix> </result> </ml:eval> </math> <rendering item-idref="16"/> </region> <region region-id="23" left="6" top="912.75" width="222.75" height="41.25" align-x="26.25" align-y="936" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17">K</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:matrix rows="2" cols="2"> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:real>2</ml:real> <ml:id xml:space="preserve">k</ml:id> </ml:apply> <ml:parens> <ml:apply> <ml:plus/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">a</ml:id> <ml:real>2</ml:real> </ml:apply> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">b</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:parens> </ml:apply> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:real>-2</ml:real> <ml:id xml:space="preserve">k</ml:id> </ml:apply> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">a</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:real>-2</ml:real> <ml:id xml:space="preserve">k</ml:id> </ml:apply> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">a</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:id xml:space="preserve">k</ml:id> <ml:parens> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:real>2</ml:real> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">a</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">c</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:parens> </ml:apply> </ml:matrix> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="2"> <ml:real>194</ml:real> <ml:real>-32</ml:real> <ml:real>-32</ml:real> <ml:real>153</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </math> <rendering item-idref="17"/> </region> <region region-id="654" left="6" top="975" width="42.75" height="16.5" align-x="24.75" align-y="984" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#ff0" tag=""> <math optimize="false" disable-calc="false"> <ml:define warning="WarnRedefinedUDScalar" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="R">α</ml:id> <ml:real>0.02</ml:real> </ml:define> </math> <rendering item-idref="18"/> </region> <region region-id="655" left="120" top="975" width="43.5" height="16.5" align-x="139.5" align-y="984" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#ff0" tag=""> <math optimize="false" disable-calc="false"> <ml:define warning="WarnRedefinedUDScalar" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="R">β</ml:id> <ml:real>0.03</ml:real> </ml:define> </math> <rendering item-idref="19"/> </region> <region region-id="656" left="348" top="971.25" width="137.25" height="15.75" align-x="384.75" align-y="984" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="true"> Coefficienti di Rayleigh </text> </region> <region region-id="657" left="6" top="1026.75" width="170.25" height="29.25" align-x="24" align-y="1044" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17">C</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="R">α</ml:id> <ml:id xml:space="preserve" font="17">J</ml:id> </ml:apply> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="R">β</ml:id> <ml:id xml:space="preserve" font="17">K</ml:id> </ml:apply> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="2"> <ml:real>5.8439999999999994</ml:real> <ml:real>-0.96</ml:real> <ml:real>-0.96</ml:real> <ml:real>4.6645199999999996</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </math> <rendering item-idref="20"/> </region> <region region-id="658" left="348" top="1031.25" width="645" height="15.75" align-x="372.75" align-y="1044" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="true"> Matrice di smorzamento, calcolata con l'ipotesi di "smorzamento proporzionale" (o smorzamento di Rayleigh) </text> </region> <region region-id="34" left="6" top="1079.25" width="186" height="15.75" align-x="30.75" align-y="1092" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="true"> Calcolo delle pulsazioni proprie </text> </region> <region region-id="643" left="6" top="1116.75" width="171" height="29.25" align-x="40.5" align-y="1134" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve">omega</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:sqrt/> <ml:apply> <ml:id xml:space="preserve">genvals</ml:id> <ml:sequence> <ml:id xml:space="preserve" font="17">K</ml:id> <ml:id xml:space="preserve" font="17">J</ml:id> </ml:sequence> </ml:apply> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="1"> <ml:real>12.788144031903128</ml:real> <ml:real>6.2604185817191329</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </math> <rendering item-idref="21"/> </region> <region region-id="644" left="318" top="1112.25" width="149.25" height="33.75" align-x="416.25" align-y="1134" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:apply> <ml:sqrt/> <ml:apply> <ml:id xml:space="preserve">eigenvals</ml:id> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve" font="17">J</ml:id> <ml:real>-1</ml:real> </ml:apply> <ml:id xml:space="preserve" font="17">K</ml:id> </ml:apply> </ml:apply> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="1"> <ml:real>12.78814403190313</ml:real> <ml:real>6.2604185817191338</ml:real> </ml:matrix> </result> </ml:eval> </math> <rendering item-idref="22"/> </region> <region region-id="698" left="6" top="1167" width="90.75" height="20.25" align-x="40.5" align-y="1182" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:function> <ml:id xml:space="preserve" font="17">Δ</ml:id> <ml:boundVars> <ml:id xml:space="preserve">ω</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:minus/> <ml:id xml:space="preserve" font="17">K</ml:id> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> <ml:id xml:space="preserve" font="17">J</ml:id> </ml:apply> </ml:apply> </ml:define> </math> <rendering item-idref="23"/> </region> <region region-id="699" left="6" top="1215.75" width="79.5" height="19.5" align-x="40.5" align-y="1230" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:function> <ml:id xml:space="preserve">det</ml:id> <ml:boundVars> <ml:id xml:space="preserve">ω</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:determinant/> <ml:apply> <ml:id xml:space="preserve" font="17">Δ</ml:id> <ml:id xml:space="preserve">ω</ml:id> </ml:apply> </ml:apply> </ml:define> </math> <rendering item-idref="24"/> </region> <region region-id="700" left="6" top="1263" width="58.5" height="12.75" align-x="19.5" align-y="1272" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve">ω</ml:id> <ml:range> <ml:sequence> <ml:real>0</ml:real> <ml:real>0.1</ml:real> </ml:sequence> <ml:real>15</ml:real> </ml:range> </ml:define> </math> <rendering item-idref="25"/> </region> <region region-id="688" left="6" top="1302" width="637.5" height="270.75" align-x="6" align-y="1302" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <plot disable-calc="false" item-idref="26"/> <rendering item-idref="27"/> </region> <region region-id="702" left="6" top="1632.75" width="150" height="29.25" align-x="19.5" align-y="1650" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve">ω</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:sqrt/> <ml:apply> <ml:id xml:space="preserve">genvals</ml:id> <ml:sequence> <ml:id xml:space="preserve" font="17">K</ml:id> <ml:id xml:space="preserve" font="17">J</ml:id> </ml:sequence> </ml:apply> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="1"> <ml:real>12.788144031903128</ml:real> <ml:real>6.2604185817191329</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </math> <rendering item-idref="28"/> </region> <region region-id="703" left="6" top="1680.75" width="172.5" height="29.25" align-x="24.75" align-y="1698" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17">Φ</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:id xml:space="preserve">genvecs</ml:id> <ml:sequence> <ml:id xml:space="preserve" font="17">K</ml:id> <ml:id xml:space="preserve" font="17">J</ml:id> </ml:sequence> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="2"> <ml:real>1</ml:real> <ml:real>-0.070123541776235282</ml:real> <ml:real>0.21773359721521066</ml:real> <ml:real>1</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </math> <rendering item-idref="29"/> </region> <region region-id="717" left="6" top="1726.5" width="109.5" height="31.5" align-x="32.25" align-y="1746" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17" subscript="1">X</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:matcol/> <ml:id xml:space="preserve" font="17">Φ</ml:id> <ml:real>1</ml:real> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="1"> <ml:real>1</ml:real> <ml:real>-0.070123541776235282</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </math> <rendering item-idref="30"/> </region> <region region-id="716" left="186" top="1737" width="54.75" height="18" align-x="202.5" align-y="1746" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:provenance expr-id="1"> <originRef doc-id="80ED9498-764B-40AF-B415-B80A121BB729" version-id="4F69ECB6-AFEE-41EC-BA77-938639597761" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315097276" is-modified="false" region-id="0" href="C:\Users\Giovanni\Documents\ARCHIVIO UNIBS\02) Didattica\Vibrazioni Meccaniche\3) Esercizi ed esempi non codificati\Esercitazioni in aula\Esercitazioni aula - 2021-2022 (S2)\File 30 - Coordinate principali (sistema non smorzato).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </originRef> <parentRef doc-id="80ED9498-764B-40AF-B415-B80A121BB729" version-id="4F69ECB6-AFEE-41EC-BA77-938639597761" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315097116" is-modified="false" region-id="0" href="C:\Users\Giovanni\Documents\ARCHIVIO UNIBS\02) Didattica\Vibrazioni Meccaniche\3) Esercizi ed esempi non codificati\Esercitazioni in aula\Esercitazioni aula - 2021-2022 (S2)\File 30 - Coordinate principali (sistema non smorzato).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </parentRef> <comment xmlns="http://schemas.mathsoft.com/provenance10"/> <originComment xmlns="http://schemas.mathsoft.com/provenance10"/> <contentHash xmlns="http://schemas.mathsoft.com/provenance10">55b4cc6f922b13aa8697bdd68483cf82</contentHash> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:provenance> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>12.788144031903128</ml:real> </result> </ml:eval> </math> <rendering item-idref="31"> <element-image-map> <box left="1.5" top="0.75" width="10.5" height="16.5" expr-idref="1" xmlns="http://schemas.mathsoft.com/worksheet30"/> </element-image-map> </rendering> </region> <region region-id="718" left="6" top="1768.5" width="108.75" height="31.5" align-x="32.25" align-y="1788" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17" subscript="2">X</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:matcol/> <ml:id xml:space="preserve" font="17">Φ</ml:id> <ml:real>2</ml:real> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="1"> <ml:real>0.21773359721521066</ml:real> <ml:real>1</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </math> <rendering item-idref="32"/> </region> <region region-id="719" left="186" top="1779" width="45.75" height="18" align-x="202.5" align-y="1788" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:provenance expr-id="1"> <originRef doc-id="80ED9498-764B-40AF-B415-B80A121BB729" version-id="4F69ECB6-AFEE-41EC-BA77-938639597761" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315096476" is-modified="false" region-id="0" href="C:\Users\Giovanni\Documents\ARCHIVIO UNIBS\02) Didattica\Vibrazioni Meccaniche\3) Esercizi ed esempi non codificati\Esercitazioni in aula\Esercitazioni aula - 2021-2022 (S2)\File 30 - Coordinate principali (sistema non smorzato).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </originRef> <parentRef doc-id="80ED9498-764B-40AF-B415-B80A121BB729" version-id="4F69ECB6-AFEE-41EC-BA77-938639597761" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315096796" is-modified="false" region-id="0" href="C:\Users\Giovanni\Documents\ARCHIVIO UNIBS\02) Didattica\Vibrazioni Meccaniche\3) Esercizi ed esempi non codificati\Esercitazioni in aula\Esercitazioni aula - 2021-2022 (S2)\File 30 - Coordinate principali (sistema non smorzato).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </parentRef> <comment xmlns="http://schemas.mathsoft.com/provenance10"/> <originComment xmlns="http://schemas.mathsoft.com/provenance10"/> <contentHash xmlns="http://schemas.mathsoft.com/provenance10">6e521c359e593e28066b07240b4c796c</contentHash> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:provenance> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>6.2604185817191329</ml:real> </result> </ml:eval> </math> <rendering item-idref="33"> <element-image-map> <box left="1.5" top="0.75" width="10.5" height="16.5" expr-idref="1" xmlns="http://schemas.mathsoft.com/worksheet30"/> </element-image-map> </rendering> </region> <region region-id="706" left="6" top="1840.5" width="170.25" height="31.5" align-x="46.5" align-y="1860" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="#ff0" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17" subscript="star">J</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:transpose/> <ml:id xml:space="preserve" font="17">Φ</ml:id> </ml:apply> <ml:id xml:space="preserve" font="17">J</ml:id> </ml:apply> <ml:id xml:space="preserve" font="17">Φ</ml:id> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="2"> <ml:real>1.2183219012004929</ml:real> <ml:real>1.6653345369377348E-16</ml:real> <ml:real>1.6653345369377348E-16</ml:real> <ml:real>3.7828895032275307</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </math> <rendering item-idref="34"/> </region> <region region-id="707" left="6" top="1882.5" width="193.5" height="31.5" align-x="51" align-y="1902" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="#ff0" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17" subscript="star">K</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:transpose/> <ml:id xml:space="preserve" font="17">Φ</ml:id> </ml:apply> <ml:id xml:space="preserve" font="17">K</ml:id> </ml:apply> <ml:id xml:space="preserve" font="17">Φ</ml:id> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="2"> <ml:real>199.24025527369929</ml:real> <ml:real>1.4210854715202004E-14</ml:real> <ml:real>1.4210854715202004E-14</ml:real> <ml:real>148.262186133344</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> <resultFormat numeric-only="true"> <general precision="3" show-trailing-zeros="false" radix="dec" complex-threshold="10" zero-threshold="13" imaginary-value="i" exponential-threshold="3"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="true" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="35"/> </region> <region region-id="708" left="6" top="1926.75" width="244.5" height="29.25" align-x="49.5" align-y="1944" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="#ff0" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17" subscript="star">C</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="R">α</ml:id> <ml:id xml:space="preserve" font="17" subscript="star">J</ml:id> </ml:apply> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="R">β</ml:id> <ml:id xml:space="preserve" font="17" subscript="star">K</ml:id> </ml:apply> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="2"> <ml:real>6.0015740962349877</ml:real> <ml:real>4.2965631052993554E-16</ml:real> <ml:real>4.2965631052993554E-16</ml:real> <ml:real>4.52352337406487</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> <resultFormat numeric-only="true"> <general precision="3" show-trailing-zeros="false" radix="dec" complex-threshold="10" zero-threshold="13" imaginary-value="i" exponential-threshold="3"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="true" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="36"/> </region> <region region-id="57" left="6" top="2001" width="54.75" height="18" align-x="22.5" align-y="2010" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>12.788144031903128</ml:real> </result> </ml:eval> </math> <rendering item-idref="37"/> </region> <region region-id="58" left="174" top="1975.5" width="105" height="63.75" align-x="240.75" align-y="2010" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:apply> <ml:sqrt/> <ml:apply> <ml:div/> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve" font="17" subscript="star">K</ml:id> <ml:sequence> <ml:real>1</ml:real> <ml:real>1</ml:real> </ml:sequence> </ml:apply> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve" font="17" subscript="star">J</ml:id> <ml:sequence> <ml:real>1</ml:real> <ml:real>1</ml:real> </ml:sequence> </ml:apply> </ml:apply> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>12.788144031903128</ml:real> </result> </ml:eval> </math> <rendering item-idref="38"/> </region> <region region-id="63" left="6" top="2103" width="45.75" height="18" align-x="22.5" align-y="2112" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>6.2604185817191329</ml:real> </result> </ml:eval> </math> <rendering item-idref="39"/> </region> <region region-id="64" left="174" top="2077.5" width="96" height="63.75" align-x="240.75" align-y="2112" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:apply> <ml:sqrt/> <ml:apply> <ml:div/> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve" font="17" subscript="star">K</ml:id> <ml:sequence> <ml:real>2</ml:real> <ml:real>2</ml:real> </ml:sequence> </ml:apply> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve" font="17" subscript="star">J</ml:id> <ml:sequence> <ml:real>2</ml:real> <ml:real>2</ml:real> </ml:sequence> </ml:apply> </ml:apply> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>6.2604185817191338</ml:real> </result> </ml:eval> </math> <rendering item-idref="40"/> </region> <region region-id="597" left="6" top="2156.25" width="134.25" height="47.25" align-x="25.5" align-y="2172" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="1">μ</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:div/> <ml:real>1</ml:real> <ml:apply> <ml:sqrt/> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:transpose/> <ml:id xml:space="preserve" font="17" subscript="1">X</ml:id> </ml:apply> <ml:id xml:space="preserve" font="17">J</ml:id> </ml:apply> <ml:id xml:space="preserve" font="17" subscript="1">X</ml:id> </ml:apply> </ml:apply> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>0.90598075894246666</ml:real> </result> </ml:eval> </ml:define> </math> <rendering item-idref="41"/> </region> <region region-id="691" left="324" top="2156.25" width="134.25" height="47.25" align-x="343.5" align-y="2172" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="2">μ</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:div/> <ml:real>1</ml:real> <ml:apply> <ml:sqrt/> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:transpose/> <ml:id xml:space="preserve" font="17" subscript="2">X</ml:id> </ml:apply> <ml:id xml:space="preserve" font="17">J</ml:id> </ml:apply> <ml:id xml:space="preserve" font="17" subscript="2">X</ml:id> </ml:apply> </ml:apply> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>0.51414802519436142</ml:real> </result> </ml:eval> </ml:define> </math> <rendering item-idref="42"/> </region> <region region-id="80" left="6" top="2220.75" width="72" height="29.25" align-x="30.75" align-y="2238" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17" subscript="1">X</ml:id> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="1"> <ml:real>1</ml:real> <ml:real>-0.070123541776235282</ml:real> </ml:matrix> </result> </ml:eval> </math> <rendering item-idref="43"/> </region> <region region-id="690" left="324" top="2220.75" width="71.25" height="29.25" align-x="348.75" align-y="2238" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17" subscript="2">X</ml:id> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="1"> <ml:real>0.21773359721521066</ml:real> <ml:real>1</ml:real> </ml:matrix> </result> </ml:eval> </math> <rendering item-idref="44"/> </region> <region region-id="77" left="6" top="2268.75" width="126" height="29.25" align-x="32.25" align-y="2286" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17" subscript="1">X</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="1">μ</ml:id> <ml:id xml:space="preserve" font="17" subscript="1">X</ml:id> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="1"> <ml:real>0.90598075894246666</ml:real> <ml:real>-0.063530579598167408</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </math> <rendering item-idref="45"/> </region> <region region-id="689" left="324" top="2268.75" width="120.75" height="29.25" align-x="350.25" align-y="2286" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17" subscript="2">X</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="2">μ</ml:id> <ml:id xml:space="preserve" font="17" subscript="2">X</ml:id> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="1"> <ml:real>0.11194729902666507</ml:real> <ml:real>0.51414802519436142</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </math> <rendering item-idref="46"/> </region> <region region-id="84" left="6" top="2316.75" width="91.5" height="29.25" align-x="23.25" align-y="2334" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17">Φ</ml:id> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="2"> <ml:real>1</ml:real> <ml:real>-0.070123541776235282</ml:real> <ml:real>0.21773359721521066</ml:real> <ml:real>1</ml:real> </ml:matrix> </result> </ml:eval> </math> <rendering item-idref="47"/> </region> <region region-id="83" left="6" top="2358.75" width="200.25" height="29.25" align-x="24.75" align-y="2376" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17">Φ</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:id xml:space="preserve">augment</ml:id> <ml:sequence> <ml:id xml:space="preserve" font="17" subscript="1">X</ml:id> <ml:id xml:space="preserve" font="17" subscript="2">X</ml:id> </ml:sequence> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="2"> <ml:real>0.90598075894246666</ml:real> <ml:real>-0.063530579598167408</ml:real> <ml:real>0.11194729902666507</ml:real> <ml:real>0.51414802519436142</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </math> <rendering item-idref="48"/> </region> <region region-id="87" left="6" top="2434.5" width="90.75" height="31.5" align-x="54.75" align-y="2454" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:transpose/> <ml:id xml:space="preserve" font="17">Φ</ml:id> </ml:apply> <ml:id xml:space="preserve" font="17">J</ml:id> </ml:apply> <ml:id xml:space="preserve" font="17">Φ</ml:id> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="2"> <ml:real>1</ml:real> <ml:real>4.163336342344337E-17</ml:real> <ml:real>5.5511151231257827E-17</ml:real> <ml:real>0.99999999999999978</ml:real> </ml:matrix> </result> </ml:eval> </math> <rendering item-idref="49"/> </region> <region region-id="724" left="6" top="2494.5" width="170.25" height="31.5" align-x="27.75" align-y="2514" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17">W</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:transpose/> <ml:id xml:space="preserve" font="17">Φ</ml:id> </ml:apply> <ml:id xml:space="preserve" font="17">K</ml:id> </ml:apply> <ml:id xml:space="preserve" font="17">Φ</ml:id> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="2"> <ml:real>163.53662778069963</ml:real> <ml:real>8.8817841970012523E-15</ml:real> <ml:real>7.1054273576010019E-15</ml:real> <ml:real>39.192840818334204</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> <resultFormat numeric-only="true"> <general precision="3" show-trailing-zeros="false" radix="dec" complex-threshold="10" zero-threshold="13" imaginary-value="i" exponential-threshold="3"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="true" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="50"/> </region> <region region-id="723" left="270" top="2499" width="71.25" height="24" align-x="298.5" align-y="2514" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:provenance expr-id="1"> <originRef doc-id="80ED9498-764B-40AF-B415-B80A121BB729" version-id="4F69ECB6-AFEE-41EC-BA77-938639597761" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315097276" is-modified="true" region-id="0" href="C:\Users\Giovanni\Documents\ARCHIVIO UNIBS\02) Didattica\Vibrazioni Meccaniche\3) Esercizi ed esempi non codificati\Esercitazioni in aula\Esercitazioni aula - 2021-2022 (S2)\File 30 - Coordinate principali (sistema non smorzato).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </originRef> <parentRef doc-id="80ED9498-764B-40AF-B415-B80A121BB729" version-id="4F69ECB6-AFEE-41EC-BA77-938639597761" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315096916" is-modified="true" region-id="0" href="C:\Users\Giovanni\Documents\ARCHIVIO UNIBS\02) Didattica\Vibrazioni Meccaniche\3) Esercizi ed esempi non codificati\Esercitazioni in aula\Esercitazioni aula - 2021-2022 (S2)\File 30 - Coordinate principali (sistema non smorzato).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </parentRef> <comment xmlns="http://schemas.mathsoft.com/provenance10"/> <originComment xmlns="http://schemas.mathsoft.com/provenance10"/> <contentHash xmlns="http://schemas.mathsoft.com/provenance10">3c408a1afb6dda03b82a169b706baaba</contentHash> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> </ml:provenance> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>163.5366277806996</ml:real> </result> </ml:eval> </math> <rendering item-idref="51"> <element-image-map> <box left="1.5" top="0.75" width="22.5" height="22.5" expr-idref="1" xmlns="http://schemas.mathsoft.com/worksheet30"/> </element-image-map> </rendering> </region> <region region-id="722" left="396" top="2499" width="66.75" height="24" align-x="424.5" align-y="2514" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:provenance expr-id="1"> <originRef doc-id="80ED9498-764B-40AF-B415-B80A121BB729" version-id="4F69ECB6-AFEE-41EC-BA77-938639597761" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315096756" is-modified="true" region-id="0" href="C:\Users\Giovanni\Documents\ARCHIVIO UNIBS\02) Didattica\Vibrazioni Meccaniche\3) Esercizi ed esempi non codificati\Esercitazioni in aula\Esercitazioni aula - 2021-2022 (S2)\File 30 - Coordinate principali (sistema non smorzato).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </originRef> <parentRef doc-id="80ED9498-764B-40AF-B415-B80A121BB729" version-id="4F69ECB6-AFEE-41EC-BA77-938639597761" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315096796" is-modified="true" region-id="0" href="C:\Users\Giovanni\Documents\ARCHIVIO UNIBS\02) Didattica\Vibrazioni Meccaniche\3) Esercizi ed esempi non codificati\Esercitazioni in aula\Esercitazioni aula - 2021-2022 (S2)\File 30 - Coordinate principali (sistema non smorzato).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </parentRef> <comment xmlns="http://schemas.mathsoft.com/provenance10"/> <originComment xmlns="http://schemas.mathsoft.com/provenance10"/> <contentHash xmlns="http://schemas.mathsoft.com/provenance10">79bf8969aaa0c8df129a1b4143e96fc7</contentHash> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> </ml:provenance> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>39.1928408183342</ml:real> </result> </ml:eval> </math> <rendering item-idref="52"> <element-image-map> <box left="1.5" top="0.75" width="22.5" height="22.5" expr-idref="1" xmlns="http://schemas.mathsoft.com/worksheet30"/> </element-image-map> </rendering> </region> <region region-id="614" left="6" top="2549.25" width="56.25" height="15.75" align-x="34.5" align-y="2562" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> Forzante </text> </region> <region region-id="101" left="6" top="2589" width="36" height="12.75" align-x="20.25" align-y="2598" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define warning="WarnRedefinedBIUnit" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve">F</ml:id> <ml:real>500</ml:real> </ml:define> </math> <rendering item-idref="53"/> </region> <region region-id="102" left="6" top="2628.75" width="53.25" height="29.25" align-x="23.25" align-y="2646" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17">F</ml:id> <ml:matrix rows="2" cols="1"> <ml:real>0</ml:real> <ml:apply> <ml:mult/> <ml:id xml:space="preserve">F</ml:id> <ml:id xml:space="preserve">c</ml:id> </ml:apply> </ml:matrix> </ml:define> </math> <rendering item-idref="54"/> </region> <region region-id="107" left="156" top="2628.75" width="55.5" height="29.25" align-x="171.75" align-y="2646" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17">F</ml:id> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="1"> <ml:real>0</ml:real> <ml:real>110</ml:real> </ml:matrix> </result> </ml:eval> </math> <rendering item-idref="55"/> </region> <region region-id="110" left="6" top="2680.5" width="101.25" height="31.5" align-x="28.5" align-y="2700" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:apply> <ml:transpose/> <ml:id xml:space="preserve" font="17">Φ</ml:id> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="2"> <ml:real>0.90598075894246666</ml:real> <ml:real>0.11194729902666507</ml:real> <ml:real>-0.063530579598167408</ml:real> <ml:real>0.51414802519436142</ml:real> </ml:matrix> </result> </ml:eval> </math> <rendering item-idref="56"/> </region> <region region-id="111" left="6" top="2728.5" width="114" height="31.5" align-x="24.75" align-y="2748" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17">Q</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:mult/> <ml:apply> <ml:transpose/> <ml:id xml:space="preserve" font="17">Φ</ml:id> </ml:apply> <ml:id xml:space="preserve" font="17">F</ml:id> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="1"> <ml:real>-6.9883637557984146</ml:real> <ml:real>56.556282771379756</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </math> <rendering item-idref="57"/> </region> <region region-id="115" left="6" top="2789.25" width="420.75" height="19.5" align-x="39" align-y="2802" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="true"> Equazioni di moto in formato matriciale (nelle coord. principali p<f family="Arial" charset="0" size="14"> <inlineAttr font-weight="normal" font-style="normal" underline="false" line-through="false"> <c val="#f00"> 1 </c> </inlineAttr> </f> e p<f family="Arial" charset="0" size="14"> <inlineAttr font-weight="normal" font-style="normal" underline="false" line-through="false"> <c val="#f00"> 2 </c> </inlineAttr> </f>) </text> </region> <region region-id="727" left="6" top="2821.5" width="387" height="51.75" align-x="360.75" align-y="2850" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#80ffff" tag=""> <math optimize="false" disable-calc="false"> <ml:apply xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:equal/> <ml:apply> <ml:plus/> <ml:apply> <ml:plus/> <ml:matrix rows="2" cols="1"> <ml:apply> <ml:id xml:space="preserve" subscript="1">p''</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> <ml:apply> <ml:id xml:space="preserve" subscript="2">p''</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:matrix> <ml:apply> <ml:mult/> <ml:parens> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="R">α</ml:id> <ml:matrix rows="2" cols="2"> <ml:real>1</ml:real> <ml:real>0</ml:real> <ml:real>0</ml:real> <ml:real>1</ml:real> </ml:matrix> </ml:apply> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="R">β</ml:id> <ml:matrix rows="2" cols="2"> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> <ml:real>0</ml:real> <ml:real>0</ml:real> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> </ml:matrix> </ml:apply> </ml:apply> </ml:parens> <ml:matrix rows="2" cols="1"> <ml:apply> <ml:id xml:space="preserve" subscript="1">p'</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> <ml:apply> <ml:id xml:space="preserve" subscript="2">p'</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:matrix> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:matrix rows="2" cols="2"> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> <ml:real>0</ml:real> <ml:real>0</ml:real> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> </ml:matrix> <ml:matrix rows="2" cols="1"> <ml:apply> <ml:id xml:space="preserve" subscript="1">p</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> <ml:apply> <ml:id xml:space="preserve" subscript="2">p</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:matrix> </ml:apply> </ml:apply> <ml:matrix rows="2" cols="1"> <ml:id xml:space="preserve" subscript="1">Q</ml:id> <ml:id xml:space="preserve" subscript="2">Q</ml:id> </ml:matrix> </ml:apply> </math> <rendering item-idref="58"/> </region> <region region-id="728" left="6" top="2901" width="102" height="24" align-x="74.25" align-y="2916" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:apply> <ml:plus/> <ml:id xml:space="preserve" subscript="R">α</ml:id> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> <ml:id xml:space="preserve" subscript="R">β</ml:id> </ml:apply> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>4.9260988334209879</ml:real> </result> </ml:eval> </math> <rendering item-idref="59"/> </region> <region region-id="729" left="252" top="2901" width="71.25" height="24" align-x="280.5" align-y="2916" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>163.5366277806996</ml:real> </result> </ml:eval> </math> <rendering item-idref="60"/> </region> <region region-id="730" left="450" top="2901.75" width="60.75" height="20.25" align-x="471.75" align-y="2916" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve" font="17">Q</ml:id> <ml:real>1</ml:real> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>-6.9883637557984146</ml:real> </result> </ml:eval> </math> <rendering item-idref="61"/> </region> <region region-id="731" left="6" top="2949" width="102" height="24" align-x="74.25" align-y="2964" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:apply> <ml:plus/> <ml:id xml:space="preserve" subscript="R">α</ml:id> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> <ml:id xml:space="preserve" subscript="R">β</ml:id> </ml:apply> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>1.1957852245500258</ml:real> </result> </ml:eval> </math> <rendering item-idref="62"/> </region> <region region-id="732" left="252" top="2949" width="66.75" height="24" align-x="280.5" align-y="2964" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>39.1928408183342</ml:real> </result> </ml:eval> </math> <rendering item-idref="63"/> </region> <region region-id="733" left="450" top="2949.75" width="60" height="20.25" align-x="471.75" align-y="2964" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve" font="17">Q</ml:id> <ml:real>2</ml:real> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>56.556282771379756</ml:real> </result> </ml:eval> </math> <rendering item-idref="64"/> </region> <region region-id="599" left="6" top="3003" width="68.25" height="36.75" align-x="30.75" align-y="3024" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#0ff" tag=""> <math optimize="false" disable-calc="false"> <ml:apply xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:equal/> <ml:apply> <ml:id xml:space="preserve" font="17">p</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> <ml:matrix rows="2" cols="1"> <ml:apply> <ml:id xml:space="preserve" subscript="1">p</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> <ml:apply> <ml:id xml:space="preserve" subscript="2">p</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:matrix> </ml:apply> </math> <rendering item-idref="65"/> </region> <region region-id="601" left="6" top="3083.25" width="543" height="15.75" align-x="30.75" align-y="3096" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> Calcolo dei fattori di smorzamento modali e delle corrispondenti pulsazioni proprie smorzate </text> </region> <region region-id="624" left="6" top="3123" width="129.75" height="44.25" align-x="23.25" align-y="3150" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="1">ξ</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:div/> <ml:apply> <ml:plus/> <ml:id xml:space="preserve" subscript="R">α</ml:id> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> <ml:id xml:space="preserve" subscript="R">β</ml:id> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:real>2</ml:real> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:apply> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>0.19260413478029489</ml:real> </result> </ml:eval> </ml:define> </math> <rendering item-idref="66"/> </region> <region region-id="735" left="366" top="3133.5" width="126" height="25.5" align-x="388.5" align-y="3150" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="s1">ω</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:mult/> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> <ml:apply> <ml:sqrt/> <ml:apply> <ml:minus/> <ml:real>1</ml:real> <ml:apply> <ml:pow/> <ml:id xml:space="preserve" subscript="1">ξ</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:apply> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>12.548705724159033</ml:real> </result> </ml:eval> </ml:define> </math> <rendering item-idref="67"/> </region> <region region-id="622" left="666" top="3141" width="54.75" height="18" align-x="682.5" align-y="3150" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>12.788144031903128</ml:real> </result> </ml:eval> </math> <rendering item-idref="68"/> </region> <region region-id="626" left="6" top="3183" width="129.75" height="44.25" align-x="23.25" align-y="3210" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="2">ξ</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:div/> <ml:apply> <ml:plus/> <ml:id xml:space="preserve" subscript="R">α</ml:id> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> <ml:id xml:space="preserve" subscript="R">β</ml:id> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:real>2</ml:real> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>0.095503616007546491</ml:real> </result> </ml:eval> </ml:define> </math> <rendering item-idref="69"/> </region> <region region-id="734" left="366" top="3193.5" width="121.5" height="25.5" align-x="388.5" align-y="3210" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="s2">ω</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:mult/> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> <ml:apply> <ml:sqrt/> <ml:apply> <ml:minus/> <ml:real>1</ml:real> <ml:apply> <ml:pow/> <ml:id xml:space="preserve" subscript="2">ξ</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:apply> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>6.2318027281454569</ml:real> </result> </ml:eval> </ml:define> </math> <rendering item-idref="70"/> </region> <region region-id="627" left="666" top="3201" width="45.75" height="18" align-x="682.5" align-y="3210" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>6.2604185817191329</ml:real> </result> </ml:eval> </math> <rendering item-idref="71"/> </region> <region region-id="323" left="6" top="3251.25" width="510.75" height="15.75" align-x="36.75" align-y="3264" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> Soluzione delle equazioni in coord. principali (dalla teoria dei sistemi a 1 gdl smorzati): </text> </region> <region region-id="663" left="6" top="3282.75" width="253.5" height="46.5" align-x="34.5" align-y="3306" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#0ff" tag=""> <math optimize="false" disable-calc="false"> <ml:apply xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:equal/> <ml:apply> <ml:id xml:space="preserve" subscript="1">p</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">e</ml:id> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:neg/> <ml:id xml:space="preserve" subscript="1">ξ</ml:id> </ml:apply> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:apply> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> <ml:parens> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="1">A</ml:id> <ml:apply> <ml:id xml:space="preserve">cos</ml:id> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="s1">ω</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="1">B</ml:id> <ml:apply> <ml:id xml:space="preserve">sin</ml:id> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="s1">ω</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </ml:parens> </ml:apply> <ml:apply> <ml:div/> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve" font="17">Q</ml:id> <ml:real>1</ml:real> </ml:apply> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </math> <rendering item-idref="72"/> </region> <region region-id="629" left="6" top="3336.75" width="253.5" height="46.5" align-x="34.5" align-y="3360" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#0ff" tag=""> <math optimize="false" disable-calc="false"> <ml:apply xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:equal/> <ml:apply> <ml:id xml:space="preserve" subscript="2">p</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">e</ml:id> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:neg/> <ml:id xml:space="preserve" subscript="2">ξ</ml:id> </ml:apply> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> <ml:parens> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="2">A</ml:id> <ml:apply> <ml:id xml:space="preserve">cos</ml:id> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="s2">ω</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="2">B</ml:id> <ml:apply> <ml:id xml:space="preserve">sin</ml:id> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="s2">ω</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </ml:parens> </ml:apply> <ml:apply> <ml:div/> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve" font="17">Q</ml:id> <ml:real>2</ml:real> </ml:apply> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </math> <rendering item-idref="73"/> </region> <region region-id="406" left="6" top="3401.25" width="207.75" height="15.75" align-x="33" align-y="3414" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> Derivate temporali prime (velocità): </text> </region> <region region-id="240" left="6" top="3437.25" width="423" height="27.75" align-x="36.75" align-y="3456" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#0ff" tag=""> <math optimize="false" disable-calc="false"> <ml:apply xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:equal/> <ml:apply> <ml:id xml:space="preserve" subscript="1">p'</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:parens> <ml:apply> <ml:neg/> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">e</ml:id> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:neg/> <ml:id xml:space="preserve" subscript="1">ξ</ml:id> </ml:apply> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:apply> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> <ml:parens> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="1">A</ml:id> <ml:id xml:space="preserve" subscript="s1">ω</ml:id> </ml:apply> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="1">B</ml:id> <ml:id xml:space="preserve" subscript="1">ξ</ml:id> </ml:apply> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:apply> </ml:apply> </ml:parens> </ml:apply> </ml:apply> </ml:parens> <ml:apply> <ml:id xml:space="preserve">sin</ml:id> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="s1">ω</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">e</ml:id> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:neg/> <ml:id xml:space="preserve" subscript="1">ξ</ml:id> </ml:apply> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:apply> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> <ml:parens> <ml:apply> <ml:minus/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="1">B</ml:id> <ml:id xml:space="preserve" subscript="s1">ω</ml:id> </ml:apply> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="1">A</ml:id> <ml:id xml:space="preserve" subscript="1">ξ</ml:id> </ml:apply> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:apply> </ml:apply> </ml:parens> </ml:apply> <ml:apply> <ml:id xml:space="preserve">cos</ml:id> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="s1">ω</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </math> <rendering item-idref="74"/> </region> <region region-id="743" left="6" top="3479.25" width="423" height="27.75" align-x="36.75" align-y="3498" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#0ff" tag=""> <math optimize="false" disable-calc="false"> <ml:apply xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:equal/> <ml:apply> <ml:id xml:space="preserve" subscript="2">p'</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:parens> <ml:apply> <ml:neg/> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">e</ml:id> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:neg/> <ml:id xml:space="preserve" subscript="2">ξ</ml:id> </ml:apply> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> <ml:parens> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="2">A</ml:id> <ml:id xml:space="preserve" subscript="s2">ω</ml:id> </ml:apply> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="2">B</ml:id> <ml:id xml:space="preserve" subscript="2">ξ</ml:id> </ml:apply> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:apply> </ml:parens> </ml:apply> </ml:apply> </ml:parens> <ml:apply> <ml:id xml:space="preserve">sin</ml:id> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="s2">ω</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">e</ml:id> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:neg/> <ml:id xml:space="preserve" subscript="2">ξ</ml:id> </ml:apply> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> <ml:parens> <ml:apply> <ml:minus/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="2">B</ml:id> <ml:id xml:space="preserve" subscript="s2">ω</ml:id> </ml:apply> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="2">A</ml:id> <ml:id xml:space="preserve" subscript="2">ξ</ml:id> </ml:apply> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:apply> </ml:parens> </ml:apply> <ml:apply> <ml:id xml:space="preserve">cos</ml:id> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="s2">ω</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </math> <rendering item-idref="75"/> </region> <region region-id="244" left="6" top="3575.25" width="460.5" height="19.5" align-x="31.5" align-y="3588" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> Occorre assegnare le condizioni iniziali per calcolare le costanti A<f family="Arial" charset="0" size="14"> <inlineAttr font-weight="normal" font-style="normal" underline="false" line-through="false"> <c val="#f00"> 1 </c> </inlineAttr> </f>, B<f family="Arial" charset="0" size="14"> <inlineAttr font-weight="normal" font-style="normal" underline="false" line-through="false"> <c val="#f00"> 1 </c> </inlineAttr> </f>, A<f family="Arial" charset="0" size="14"> <inlineAttr font-weight="normal" font-style="normal" underline="false" line-through="false"> <c val="#f00"> 2 </c> </inlineAttr> </f>, B<inlineAttr font-weight="normal" font-style="normal" underline="false" line-through="false"> <c val="#f00"> <f family="Arial" charset="0" size="14">2</f> </c> </inlineAttr> </text> </region> <region region-id="264" left="6" top="3617.25" width="746.25" height="47.25" align-x="13.5" align-y="3630" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> <f family="Arial Baltic" charset="186">Le condizioni iniziali </f> <f family="Arial Baltic" charset="186"> vengono sempre assegnate nelle coordinate fisiche </f> <f family="Arial Baltic" charset="186">; occorre quindi convertirle nelle coordinate principali.</f> <f family="Arial Baltic" charset="186">Si utilizzano le seguenti trasformazioni:</f> </text> </region> <region region-id="267" left="6" top="3693.75" width="78" height="25.5" align-x="30.75" align-y="3714" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#0ff" tag=""> <math optimize="false" disable-calc="false"> <ml:apply xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:equal/> <ml:apply> <ml:id xml:space="preserve" font="17">p</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve" font="17">Φ</ml:id> <ml:real>-1</ml:real> </ml:apply> <ml:apply> <ml:id xml:space="preserve" font="17">x</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> </ml:apply> </math> <rendering item-idref="76"/> </region> <region region-id="271" left="210" top="3701.25" width="89.25" height="15.75" align-x="221.25" align-y="3714" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> per le posizioni </text> </region> <region region-id="269" left="6" top="3747.75" width="87" height="25.5" align-x="35.25" align-y="3768" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#0ff" tag=""> <math optimize="false" disable-calc="false"> <ml:apply xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:equal/> <ml:apply> <ml:id xml:space="preserve" font="17">p'</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve" font="17">Φ</ml:id> <ml:real>-1</ml:real> </ml:apply> <ml:apply> <ml:id xml:space="preserve" font="17">x'</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> </ml:apply> </math> <rendering item-idref="77"/> </region> <region region-id="273" left="210" top="3755.25" width="83.25" height="15.75" align-x="221.25" align-y="3768" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> per le velocità </text> </region> <region region-id="275" left="6" top="3815.25" width="171" height="15.75" align-x="6" align-y="3828" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> All'istante iniziale t=0 si avrà: </text> </region> <region region-id="737" left="6" top="3855.75" width="79.5" height="25.5" align-x="31.5" align-y="3876" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#0ff" tag=""> <math optimize="false" disable-calc="false"> <ml:apply xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:equal/> <ml:apply> <ml:id xml:space="preserve" font="17">p</ml:id> <ml:real>0</ml:real> </ml:apply> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve" font="17">Φ</ml:id> <ml:real>-1</ml:real> </ml:apply> <ml:apply> <ml:id xml:space="preserve" font="17">x</ml:id> <ml:real>0</ml:real> </ml:apply> </ml:apply> </ml:apply> </math> <rendering item-idref="78"/> </region> <region region-id="738" left="6" top="3891.75" width="88.5" height="25.5" align-x="36" align-y="3912" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#0ff" tag=""> <math optimize="false" disable-calc="false"> <ml:apply xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:equal/> <ml:apply> <ml:id xml:space="preserve" font="17">p'</ml:id> <ml:real>0</ml:real> </ml:apply> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve" font="17">Φ</ml:id> <ml:real>-1</ml:real> </ml:apply> <ml:apply> <ml:id xml:space="preserve" font="17">x'</ml:id> <ml:real>0</ml:real> </ml:apply> </ml:apply> </ml:apply> </math> <rendering item-idref="79"/> </region> <region region-id="293" left="6" top="3959.25" width="330.75" height="15.75" align-x="6" align-y="3972" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> Assegniamo le condizioni iniziali nelle coordinate fisiche </text> </region> <region region-id="297" left="6" top="3993" width="57" height="36.75" align-x="30.75" align-y="4014" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#0ff" tag=""> <math optimize="false" disable-calc="false"> <ml:apply xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:equal/> <ml:apply> <ml:id xml:space="preserve" font="17">x</ml:id> <ml:real>0</ml:real> </ml:apply> <ml:matrix rows="2" cols="1"> <ml:id xml:space="preserve" subscript="0">α</ml:id> <ml:id xml:space="preserve" subscript="0">β</ml:id> </ml:matrix> </ml:apply> </math> <rendering item-idref="80"/> </region> <region region-id="741" left="222" top="3993" width="63.75" height="36.75" align-x="251.25" align-y="4014" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#0ff" tag=""> <math optimize="false" disable-calc="false"> <ml:apply xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:equal/> <ml:apply> <ml:id xml:space="preserve" font="17">x'</ml:id> <ml:real>0</ml:real> </ml:apply> <ml:matrix rows="2" cols="1"> <ml:id xml:space="preserve" subscript="0">α'</ml:id> <ml:id xml:space="preserve" subscript="0">β'</ml:id> </ml:matrix> </ml:apply> </math> <rendering item-idref="81"/> </region> <region region-id="407" left="6" top="4053" width="105" height="16.5" align-x="24" align-y="4062" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="0">α</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:mult/> <ml:real>20</ml:real> <ml:id xml:space="preserve">deg</ml:id> </ml:apply> <ml:unitOverride> <ml:id xml:space="preserve">rad</ml:id> </ml:unitOverride> <result xmlns="http://schemas.mathsoft.com/math30"> <unitedValue> <ml:real>0.3490658503988659</ml:real> <unitMonomial xmlns="http://schemas.mathsoft.com/units10"> <unitReference unit="radian"/> </unitMonomial> </unitedValue> </result> </ml:eval> </ml:define> </math> <rendering item-idref="82"/> </region> <region region-id="740" left="222" top="4053" width="105.75" height="16.5" align-x="240.75" align-y="4062" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="0">β</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:mult/> <ml:real>10</ml:real> <ml:id xml:space="preserve">deg</ml:id> </ml:apply> <ml:unitOverride> <ml:id xml:space="preserve">rad</ml:id> </ml:unitOverride> <result xmlns="http://schemas.mathsoft.com/math30"> <unitedValue> <ml:real>0.17453292519943295</ml:real> <unitMonomial xmlns="http://schemas.mathsoft.com/units10"> <unitReference unit="radian"/> </unitMonomial> </unitedValue> </result> </ml:eval> </ml:define> </math> <rendering item-idref="83"/> </region> <region region-id="409" left="6" top="4107" width="33" height="16.5" align-x="26.25" align-y="4116" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="0">α'</ml:id> <ml:real>2</ml:real> </ml:define> </math> <rendering item-idref="84"/> </region> <region region-id="739" left="222" top="4107" width="39" height="16.5" align-x="243" align-y="4116" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="0">β'</ml:id> <ml:real>-3</ml:real> </ml:define> </math> <rendering item-idref="85"/> </region> <region region-id="322" left="6" top="4187.25" width="348.75" height="15.75" align-x="6" align-y="4200" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> Convertiamo le condizioni iniziali nelle coordinate principali </text> </region> <region region-id="325" left="6" top="4229.25" width="94.5" height="15.75" align-x="17.25" align-y="4242" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> Per le posizioni: </text> </region> <region region-id="308" left="6" top="4269.75" width="79.5" height="25.5" align-x="31.5" align-y="4290" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#0ff" tag=""> <math optimize="false" disable-calc="false"> <ml:apply xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:equal/> <ml:apply> <ml:id xml:space="preserve" font="17">p</ml:id> <ml:real>0</ml:real> </ml:apply> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve" font="17">Φ</ml:id> <ml:real>-1</ml:real> </ml:apply> <ml:apply> <ml:id xml:space="preserve" font="17">x</ml:id> <ml:real>0</ml:real> </ml:apply> </ml:apply> </ml:apply> </math> <rendering item-idref="86"/> </region> <region region-id="310" left="6" top="4311" width="147.75" height="36.75" align-x="42.75" align-y="4332" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="1"> <ml:id xml:space="preserve" subscript="10">p</ml:id> <ml:id xml:space="preserve" subscript="20">p</ml:id> </ml:matrix> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve" font="17">Φ</ml:id> <ml:real>-1</ml:real> </ml:apply> <ml:matrix rows="2" cols="1"> <ml:id xml:space="preserve" subscript="0">α</ml:id> <ml:id xml:space="preserve" subscript="0">β</ml:id> </ml:matrix> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="1"> <ml:real>0.3381817820345262</ml:real> <ml:real>0.38124781233483812</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </math> <rendering item-idref="87"/> </region> <region region-id="311" left="342" top="4323" width="56.25" height="16.5" align-x="364.5" align-y="4332" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="10">p</ml:id> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>0.3381817820345262</ml:real> </result> </ml:eval> </math> <rendering item-idref="88"/> </region> <region region-id="312" left="498" top="4323" width="56.25" height="16.5" align-x="520.5" align-y="4332" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="20">p</ml:id> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>0.38124781233483812</ml:real> </result> </ml:eval> </math> <rendering item-idref="89"/> </region> <region region-id="326" left="6" top="4373.25" width="88.5" height="15.75" align-x="6" align-y="4386" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> Per le velocità: </text> </region> <region region-id="317" left="6" top="4407.75" width="88.5" height="25.5" align-x="36" align-y="4428" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#0ff" tag=""> <math optimize="false" disable-calc="false"> <ml:apply xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:equal/> <ml:apply> <ml:id xml:space="preserve" font="17">p'</ml:id> <ml:real>0</ml:real> </ml:apply> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve" font="17">Φ</ml:id> <ml:real>-1</ml:real> </ml:apply> <ml:apply> <ml:id xml:space="preserve" font="17">x'</ml:id> <ml:real>0</ml:real> </ml:apply> </ml:apply> </ml:apply> </math> <rendering item-idref="90"/> </region> <region region-id="318" left="6" top="4449" width="157.5" height="36.75" align-x="45" align-y="4470" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="1"> <ml:id xml:space="preserve" subscript="10">p'</ml:id> <ml:id xml:space="preserve" subscript="20">p'</ml:id> </ml:matrix> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve" font="17">Φ</ml:id> <ml:real>-1</ml:real> </ml:apply> <ml:matrix rows="2" cols="1"> <ml:id xml:space="preserve" subscript="0">α'</ml:id> <ml:id xml:space="preserve" subscript="0">β'</ml:id> </ml:matrix> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="1"> <ml:real>2.8844986402102353</ml:real> <ml:real>-5.4784731079585773</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </math> <rendering item-idref="91"/> </region> <region region-id="319" left="342" top="4461" width="58.5" height="16.5" align-x="366.75" align-y="4470" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="10">p'</ml:id> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>2.8844986402102353</ml:real> </result> </ml:eval> </math> <rendering item-idref="92"/> </region> <region region-id="320" left="498" top="4461" width="63.75" height="16.5" align-x="522.75" align-y="4470" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="20">p'</ml:id> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>-5.4784731079585773</ml:real> </result> </ml:eval> </math> <rendering item-idref="93"/> </region> <region region-id="343" left="6" top="4511.25" width="219" height="15.75" align-x="42" align-y="4524" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> Imponendo le condizioni iniziali si ha: </text> </region> <region region-id="742" left="6" top="4541.25" width="253.5" height="200.25" align-x="37.5" align-y="4644" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:matrix rows="4" cols="1"> <ml:id xml:space="preserve" subscript="1">A</ml:id> <ml:id xml:space="preserve" subscript="1">B</ml:id> <ml:id xml:space="preserve" subscript="2">A</ml:id> <ml:id xml:space="preserve" subscript="2">B</ml:id> </ml:matrix> <ml:eval placeholderMultiplicationStyle="default"> <ml:provenance expr-id="1"> <originRef doc-id="0188C78A-80C3-4B5A-9564-83EA9BCA226A" version-id="531BC4DB-5DD3-4B33-B2A9-739C49F261FE" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315097276" is-modified="true" region-id="40" href="C:\Users\giova\Documents\3) TEMP\aaa.xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </originRef> <parentRef doc-id="0188C78A-80C3-4B5A-9564-83EA9BCA226A" version-id="531BC4DB-5DD3-4B33-B2A9-739C49F261FE" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315096916" is-modified="true" region-id="40" href="C:\Users\giova\Documents\3) TEMP\aaa.xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </parentRef> <comment xmlns="http://schemas.mathsoft.com/provenance10"/> <originComment xmlns="http://schemas.mathsoft.com/provenance10"/> <contentHash xmlns="http://schemas.mathsoft.com/provenance10">ac4a1fe8354cea5436b2e915ca7011e8</contentHash> <ml:matrix rows="4" cols="1"> <ml:apply> <ml:div/> <ml:apply> <ml:minus/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="10">p</ml:id> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve" font="17">Q</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:apply> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:apply> <ml:div/> <ml:apply> <ml:minus/> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="10">p</ml:id> <ml:id xml:space="preserve" subscript="1">ξ</ml:id> </ml:apply> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="10">p'</ml:id> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="1">ξ</ml:id> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve" font="17">Q</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="s1">ω</ml:id> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:apply> </ml:apply> <ml:apply> <ml:div/> <ml:apply> <ml:minus/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="20">p</ml:id> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve" font="17">Q</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:apply> <ml:div/> <ml:apply> <ml:minus/> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="20">p</ml:id> <ml:id xml:space="preserve" subscript="2">ξ</ml:id> </ml:apply> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="20">p'</ml:id> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="2">ξ</ml:id> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve" font="17">Q</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="s2">ω</ml:id> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:apply> </ml:matrix> </ml:provenance> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="4" cols="1"> <ml:real>0.38091449488690149</ml:real> <ml:real>0.3046298119254186</ml:real> <ml:real>-1.0617780462276529</ml:real> <ml:real>-0.98098457611047762</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </math> <rendering item-idref="94"> <element-image-map> <box left="40.5" top="0.75" width="156.75" height="198.75" expr-idref="1" xmlns="http://schemas.mathsoft.com/worksheet30"/> </element-image-map> </rendering> </region> <region region-id="645" left="6" top="4775.25" width="669" height="19.5" align-x="15" align-y="4788" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> La soluzione nelle coordinate principali è ora calcolabile, perché sono state determinate le costanti A<f family="Arial" charset="0" size="14"> <inlineAttr font-weight="normal" font-style="normal" underline="false" line-through="false"> <c val="#f00"> 1 </c> </inlineAttr> </f>, B<f family="Arial" charset="0" size="14"> <inlineAttr font-weight="normal" font-style="normal" underline="false" line-through="false"> <c val="#f00"> 1 </c> </inlineAttr> </f>, A<f family="Arial" charset="0" size="14"> <inlineAttr font-weight="normal" font-style="normal" underline="false" line-through="false"> <c val="#f00"> 2 </c> </inlineAttr> </f>, B<inlineAttr font-weight="normal" font-style="normal" underline="false" line-through="false"> <c val="#f00"> <f family="Arial" charset="0" size="14">2</f> </c> </inlineAttr> </text> </region> <region region-id="646" left="6" top="4824.75" width="255.75" height="46.5" align-x="36" align-y="4848" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:function> <ml:id xml:space="preserve" subscript="1">p</ml:id> <ml:boundVars> <ml:id xml:space="preserve">t</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">e</ml:id> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:neg/> <ml:id xml:space="preserve" subscript="1">ξ</ml:id> </ml:apply> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:apply> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> <ml:parens> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="1">A</ml:id> <ml:apply> <ml:id xml:space="preserve">cos</ml:id> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="s1">ω</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="1">B</ml:id> <ml:apply> <ml:id xml:space="preserve">sin</ml:id> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="s1">ω</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </ml:parens> </ml:apply> <ml:apply> <ml:div/> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve" font="17">Q</ml:id> <ml:real>1</ml:real> </ml:apply> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:apply> </ml:define> </math> <rendering item-idref="95"/> </region> <region region-id="647" left="6" top="4908.75" width="255.75" height="46.5" align-x="36" align-y="4932" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:function> <ml:id xml:space="preserve" subscript="2">p</ml:id> <ml:boundVars> <ml:id xml:space="preserve">t</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">e</ml:id> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:neg/> <ml:id xml:space="preserve" subscript="2">ξ</ml:id> </ml:apply> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> <ml:parens> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="2">A</ml:id> <ml:apply> <ml:id xml:space="preserve">cos</ml:id> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="s2">ω</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="2">B</ml:id> <ml:apply> <ml:id xml:space="preserve">sin</ml:id> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="s2">ω</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </ml:parens> </ml:apply> <ml:apply> <ml:div/> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve" font="17">Q</ml:id> <ml:real>2</ml:real> </ml:apply> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:indexer/> <ml:id xml:space="preserve">ω</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:parens> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:apply> </ml:define> </math> <rendering item-idref="96"/> </region> <region region-id="387" left="6" top="4985.25" width="572.25" height="15.75" align-x="31.5" align-y="4998" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> Occorre infine applicare la formula 5.195 per calcolare la soluzione nelle coordinate fisiche α e β </text> </region> <region region-id="392" left="6" top="5037" width="70.5" height="36.75" align-x="32.25" align-y="5058" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:function> <ml:id xml:space="preserve" font="17">p</ml:id> <ml:boundVars> <ml:id xml:space="preserve">t</ml:id> </ml:boundVars> </ml:function> <ml:matrix rows="2" cols="1"> <ml:apply> <ml:id xml:space="preserve" subscript="1">p</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> <ml:apply> <ml:id xml:space="preserve" subscript="2">p</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:matrix> </ml:define> </math> <rendering item-idref="97"/> </region> <region region-id="397" left="6" top="5091.75" width="68.25" height="19.5" align-x="31.5" align-y="5106" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:function> <ml:id xml:space="preserve" font="17">x</ml:id> <ml:boundVars> <ml:id xml:space="preserve">t</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" font="17">Φ</ml:id> <ml:apply> <ml:id xml:space="preserve" font="17">p</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> </ml:apply> </ml:define> </math> <rendering item-idref="98"/> </region> <region region-id="398" left="6" top="5127.75" width="56.25" height="20.25" align-x="30.75" align-y="5142" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:function> <ml:id xml:space="preserve">α</ml:id> <ml:boundVars> <ml:id xml:space="preserve">t</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:indexer/> <ml:apply> <ml:id xml:space="preserve" font="17">x</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> <ml:real>1</ml:real> </ml:apply> </ml:define> </math> <rendering item-idref="99"/> </region> <region region-id="399" left="6" top="5157.75" width="57" height="20.25" align-x="31.5" align-y="5172" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:function> <ml:id xml:space="preserve">β</ml:id> <ml:boundVars> <ml:id xml:space="preserve">t</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:indexer/> <ml:apply> <ml:id xml:space="preserve" font="17">x</ml:id> <ml:id xml:space="preserve">t</ml:id> </ml:apply> <ml:real>2</ml:real> </ml:apply> </ml:define> </math> <rendering item-idref="100"/> </region> <region region-id="483" left="6" top="5217" width="41.25" height="16.5" align-x="34.5" align-y="5226" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="max">T</ml:id> <ml:real>9</ml:real> </ml:define> </math> <rendering item-idref="101"/> </region> <region region-id="484" left="6" top="5259" width="48" height="12.75" align-x="25.5" align-y="5268" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve">Δt</ml:id> <ml:real>0.001</ml:real> </ml:define> </math> <rendering item-idref="102"/> </region> <region region-id="768" left="6" top="5283" width="41.25" height="16.5" align-x="34.5" align-y="5292" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define warning="WarnRedefinedUDScalar" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="max">T</ml:id> <ml:real>5</ml:real> </ml:define> </math> <rendering item-idref="103"/> </region> <region region-id="485" left="6" top="5313" width="69.75" height="16.5" align-x="16.5" align-y="5322" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve">t</ml:id> <ml:range> <ml:sequence> <ml:real>0</ml:real> <ml:id xml:space="preserve">Δt</ml:id> </ml:sequence> <ml:id xml:space="preserve" subscript="max">T</ml:id> </ml:range> </ml:define> </math> <rendering item-idref="104"/> </region> <region region-id="416" left="6" top="5358" width="761.25" height="266.25" align-x="6" align-y="5358" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <plot disable-calc="false" item-idref="105"/> <rendering item-idref="106"/> </region> <region region-id="764" left="834" top="5481" width="36" height="16.5" align-x="851.25" align-y="5490" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:globalDefine xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="R">α</ml:id> <ml:real>0.1</ml:real> </ml:globalDefine> </math> <rendering item-idref="107"/> </region> <region region-id="765" left="942" top="5481" width="36.75" height="16.5" align-x="960" align-y="5490" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:globalDefine xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="R">β</ml:id> <ml:real>0.1</ml:real> </ml:globalDefine> </math> <rendering item-idref="108"/> </region> <region region-id="766" left="834" top="5580.75" width="91.5" height="29.25" align-x="850.5" align-y="5598" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17">C</ml:id> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="2"> <ml:real>5.8439999999999994</ml:real> <ml:real>-0.96</ml:real> <ml:real>-0.96</ml:real> <ml:real>4.6645199999999996</ml:real> </ml:matrix> </result> </ml:eval> </math> <rendering item-idref="109"/> </region> <region region-id="754" left="6" top="5676" width="756" height="266.25" align-x="6" align-y="5676" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <plot disable-calc="false" item-idref="110"/> <rendering item-idref="111"/> </region> <region region-id="213" left="6" top="6029.25" width="300" height="15.75" align-x="21" align-y="6042" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> Verifica dei calcoli mediante integrazione numerica </text> </region> <region region-id="457" left="6" top="6072.75" width="110.25" height="29.25" align-x="18" align-y="6090" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17">I</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:id xml:space="preserve">identity</ml:id> <ml:real>2</ml:real> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="2"> <ml:real>1</ml:real> <ml:real>0</ml:real> <ml:real>0</ml:real> <ml:real>1</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </math> <rendering item-idref="112"/> </region> <region region-id="458" left="222" top="6072.75" width="86.25" height="29.25" align-x="240.75" align-y="6090" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:provenance expr-id="1" xmlns:ml="http://schemas.mathsoft.com/math30"> <originRef doc-id="1C97A390-190F-4DBF-BEDE-2FCBA53F3DB1" version-id="80C8BBE0-DCBC-478A-AA76-7303644D9E84" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315096916" is-modified="false" region-id="0" href="C:\Users\giova\Documents\2) HOMEWORK\Materiale per Video Lezioni\Mathcad\Nuovi files\File 22 - Vibrazioni torsionali (avviamento).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </originRef> <parentRef doc-id="1C97A390-190F-4DBF-BEDE-2FCBA53F3DB1" version-id="80C8BBE0-DCBC-478A-AA76-7303644D9E84" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315097276" is-modified="false" region-id="0" href="C:\Users\giova\Documents\2) HOMEWORK\Materiale per Video Lezioni\Mathcad\Nuovi files\File 22 - Vibrazioni torsionali (avviamento).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </parentRef> <comment xmlns="http://schemas.mathsoft.com/provenance10"/> <originComment xmlns="http://schemas.mathsoft.com/provenance10"/> <contentHash xmlns="http://schemas.mathsoft.com/provenance10">40b468a951fb4f0d6bf3a1b86a328624</contentHash> <ml:define> <ml:id xml:space="preserve" font="17">O</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:mult/> <ml:real>0</ml:real> <ml:id xml:space="preserve" font="17">I</ml:id> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="2"> <ml:real>0</ml:real> <ml:real>0</ml:real> <ml:real>0</ml:real> <ml:real>0</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </ml:provenance> </math> <rendering item-idref="113"> <element-image-map> <box left="1.5" top="0.75" width="84.75" height="27.75" expr-idref="1" xmlns="http://schemas.mathsoft.com/worksheet30"/> </element-image-map> </rendering> </region> <region region-id="463" left="6" top="6120.75" width="183" height="29.25" align-x="46.5" align-y="6138" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:provenance expr-id="1" xmlns:ml="http://schemas.mathsoft.com/math30"> <originRef doc-id="1C97A390-190F-4DBF-BEDE-2FCBA53F3DB1" version-id="80C8BBE0-DCBC-478A-AA76-7303644D9E84" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315097316" is-modified="false" region-id="0" href="C:\Users\giova\Documents\2) HOMEWORK\Materiale per Video Lezioni\Mathcad\Nuovi files\File 22 - Vibrazioni torsionali (avviamento).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </originRef> <parentRef doc-id="1C97A390-190F-4DBF-BEDE-2FCBA53F3DB1" version-id="80C8BBE0-DCBC-478A-AA76-7303644D9E84" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315096756" is-modified="false" region-id="0" href="C:\Users\giova\Documents\2) HOMEWORK\Materiale per Video Lezioni\Mathcad\Nuovi files\File 22 - Vibrazioni torsionali (avviamento).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </parentRef> <comment xmlns="http://schemas.mathsoft.com/provenance10"/> <originComment xmlns="http://schemas.mathsoft.com/provenance10"/> <contentHash xmlns="http://schemas.mathsoft.com/provenance10">66700cb9cbec92ceab73718f47b5bd7c</contentHash> <ml:define> <ml:id xml:space="preserve" font="17" subscript="sup">A</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:id xml:space="preserve">augment</ml:id> <ml:sequence> <ml:id xml:space="preserve" font="17">O</ml:id> <ml:id xml:space="preserve" font="17">I</ml:id> </ml:sequence> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="4"> <ml:real>0</ml:real> <ml:real>0</ml:real> <ml:real>0</ml:real> <ml:real>0</ml:real> <ml:real>1</ml:real> <ml:real>0</ml:real> <ml:real>0</ml:real> <ml:real>1</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </ml:provenance> </math> <rendering item-idref="114"> <element-image-map> <box left="1.5" top="0.75" width="181.5" height="27.75" expr-idref="1" xmlns="http://schemas.mathsoft.com/worksheet30"/> </element-image-map> </rendering> </region> <region region-id="692" left="6" top="6165.75" width="339.75" height="32.25" align-x="42.75" align-y="6186" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:provenance expr-id="1" xmlns:ml="http://schemas.mathsoft.com/math30"> <originRef doc-id="1C97A390-190F-4DBF-BEDE-2FCBA53F3DB1" version-id="80C8BBE0-DCBC-478A-AA76-7303644D9E84" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315097396" is-modified="false" region-id="0" href="C:\Users\giova\Documents\2) HOMEWORK\Materiale per Video Lezioni\Mathcad\Nuovi files\File 22 - Vibrazioni torsionali (avviamento).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </originRef> <parentRef doc-id="1C97A390-190F-4DBF-BEDE-2FCBA53F3DB1" version-id="80C8BBE0-DCBC-478A-AA76-7303644D9E84" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315096916" is-modified="false" region-id="0" href="C:\Users\giova\Documents\2) HOMEWORK\Materiale per Video Lezioni\Mathcad\Nuovi files\File 22 - Vibrazioni torsionali (avviamento).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </parentRef> <comment xmlns="http://schemas.mathsoft.com/provenance10"/> <originComment xmlns="http://schemas.mathsoft.com/provenance10"/> <contentHash xmlns="http://schemas.mathsoft.com/provenance10">936b4fb243f1803ebac9a0bdf12e07fb</contentHash> <ml:define> <ml:id xml:space="preserve" font="17" subscript="inf">A</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:id xml:space="preserve">augment</ml:id> <ml:sequence> <ml:apply> <ml:mult/> <ml:apply> <ml:neg/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve" font="17">J</ml:id> <ml:real>-1</ml:real> </ml:apply> </ml:apply> <ml:id xml:space="preserve" font="17">K</ml:id> </ml:apply> <ml:apply> <ml:mult/> <ml:apply> <ml:neg/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve" font="17">J</ml:id> <ml:real>-1</ml:real> </ml:apply> </ml:apply> <ml:id xml:space="preserve" font="17">C</ml:id> </ml:apply> </ml:sequence> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="4"> <ml:real>-161.66666666666669</ml:real> <ml:real>8.588298443370908</ml:real> <ml:real>26.666666666666668</ml:real> <ml:real>-41.062801932367151</ml:real> <ml:real>-4.87</ml:real> <ml:real>0.25764895330112725</ml:real> <ml:real>0.8</ml:real> <ml:real>-1.2518840579710144</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </ml:provenance> <resultFormat numeric-only="true"> <general precision="3" show-trailing-zeros="false" radix="dec" complex-threshold="10" zero-threshold="15" imaginary-value="i" exponential-threshold="6"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="true" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="115"> <element-image-map> <box left="1.5" top="0.75" width="338.25" height="30.75" expr-idref="1" xmlns="http://schemas.mathsoft.com/worksheet30"/> </element-image-map> </rendering> </region> <region region-id="693" left="6" top="6206.25" width="289.5" height="62.25" align-x="24" align-y="6240" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:provenance expr-id="1" xmlns:ml="http://schemas.mathsoft.com/math30"> <originRef doc-id="1C97A390-190F-4DBF-BEDE-2FCBA53F3DB1" version-id="80C8BBE0-DCBC-478A-AA76-7303644D9E84" branch-id="00000000-0000-0000-0000-000000000000" revision-num="246826596" is-modified="false" region-id="0" href="C:\Users\giova\Documents\2) HOMEWORK\Materiale per Video Lezioni\Mathcad\Nuovi files\File 22 - Vibrazioni torsionali (avviamento).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </originRef> <parentRef doc-id="1C97A390-190F-4DBF-BEDE-2FCBA53F3DB1" version-id="80C8BBE0-DCBC-478A-AA76-7303644D9E84" branch-id="00000000-0000-0000-0000-000000000000" revision-num="246826396" is-modified="false" region-id="0" href="C:\Users\giova\Documents\2) HOMEWORK\Materiale per Video Lezioni\Mathcad\Nuovi files\File 22 - Vibrazioni torsionali (avviamento).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </parentRef> <comment xmlns="http://schemas.mathsoft.com/provenance10"/> <originComment xmlns="http://schemas.mathsoft.com/provenance10"/> <contentHash xmlns="http://schemas.mathsoft.com/provenance10">fd52f0e20f32b4c58069e06bff674eba</contentHash> <ml:define> <ml:id xml:space="preserve" font="17">A</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:id xml:space="preserve">stack</ml:id> <ml:sequence> <ml:id xml:space="preserve" font="17" subscript="sup">A</ml:id> <ml:id xml:space="preserve" font="17" subscript="inf">A</ml:id> </ml:sequence> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="4" cols="4"> <ml:real>0</ml:real> <ml:real>0</ml:real> <ml:real>-161.66666666666669</ml:real> <ml:real>8.588298443370908</ml:real> <ml:real>0</ml:real> <ml:real>0</ml:real> <ml:real>26.666666666666668</ml:real> <ml:real>-41.062801932367151</ml:real> <ml:real>1</ml:real> <ml:real>0</ml:real> <ml:real>-4.87</ml:real> <ml:real>0.25764895330112725</ml:real> <ml:real>0</ml:real> <ml:real>1</ml:real> <ml:real>0.8</ml:real> <ml:real>-1.2518840579710144</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </ml:provenance> <resultFormat numeric-only="true"> <general precision="3" show-trailing-zeros="false" radix="dec" complex-threshold="10" zero-threshold="15" imaginary-value="i" exponential-threshold="6"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="true" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="116"> <element-image-map> <box left="1.5" top="0.75" width="288" height="60.75" expr-idref="1" xmlns="http://schemas.mathsoft.com/worksheet30"/> </element-image-map> </rendering> </region> <region region-id="694" left="6" top="6276.75" width="40.5" height="29.25" align-x="21" align-y="6294" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17">o</ml:id> <ml:matrix rows="2" cols="1"> <ml:real>0</ml:real> <ml:real>0</ml:real> </ml:matrix> </ml:define> </math> <rendering item-idref="117"/> </region> <region region-id="468" left="6" top="6318.75" width="55.5" height="29.25" align-x="21.75" align-y="6336" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17">F</ml:id> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="1"> <ml:real>0</ml:real> <ml:real>110</ml:real> </ml:matrix> </result> </ml:eval> </math> <rendering item-idref="118"/> </region> <region region-id="469" left="6" top="6368.25" width="158.25" height="62.25" align-x="21" align-y="6402" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17">b</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:id xml:space="preserve">stack</ml:id> <ml:sequence> <ml:id xml:space="preserve" font="17">o</ml:id> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve" font="17">J</ml:id> <ml:real>-1</ml:real> </ml:apply> <ml:id xml:space="preserve" font="17">F</ml:id> </ml:apply> </ml:sequence> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="4" cols="1"> <ml:real>0</ml:real> <ml:real>0</ml:real> <ml:real>0</ml:real> <ml:real>29.522275899087496</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </math> <rendering item-idref="119"/> </region> <region region-id="673" left="378" top="6462.75" width="63" height="29.25" align-x="390" align-y="6480" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:provenance expr-id="1"> <originRef doc-id="1C97A390-190F-4DBF-BEDE-2FCBA53F3DB1" version-id="80C8BBE0-DCBC-478A-AA76-7303644D9E84" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315097116" is-modified="false" region-id="0" href="C:\Users\giova\Documents\2) HOMEWORK\Materiale per Video Lezioni\Mathcad\Nuovi files\File 22 - Vibrazioni torsionali (avviamento).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </originRef> <parentRef doc-id="1C97A390-190F-4DBF-BEDE-2FCBA53F3DB1" version-id="80C8BBE0-DCBC-478A-AA76-7303644D9E84" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315096916" is-modified="false" region-id="0" href="C:\Users\giova\Documents\2) HOMEWORK\Materiale per Video Lezioni\Mathcad\Nuovi files\File 22 - Vibrazioni torsionali (avviamento).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </parentRef> <comment xmlns="http://schemas.mathsoft.com/provenance10"/> <originComment xmlns="http://schemas.mathsoft.com/provenance10"/> <contentHash xmlns="http://schemas.mathsoft.com/provenance10">f38c1c39d8f7d46e5f8a7de790f0d96e</contentHash> <ml:id xml:space="preserve">ω</ml:id> </ml:provenance> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="2" cols="1"> <ml:real>12.788144031903128</ml:real> <ml:real>6.2604185817191329</ml:real> </ml:matrix> </result> </ml:eval> <resultFormat numeric-only="true"> <general precision="3" show-trailing-zeros="false" radix="dec" complex-threshold="10" zero-threshold="6" imaginary-value="i" exponential-threshold="3"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="true" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="120"> <element-image-map> <box left="1.5" top="9" width="6" height="11.25" expr-idref="1" xmlns="http://schemas.mathsoft.com/worksheet30"/> </element-image-map> </rendering> </region> <region region-id="471" left="6" top="6452.25" width="174.75" height="62.25" align-x="19.5" align-y="6486" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:provenance expr-id="1" xmlns:ml="http://schemas.mathsoft.com/math30"> <originRef doc-id="1C97A390-190F-4DBF-BEDE-2FCBA53F3DB1" version-id="80C8BBE0-DCBC-478A-AA76-7303644D9E84" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315097316" is-modified="false" region-id="0" href="C:\Users\giova\Documents\2) HOMEWORK\Materiale per Video Lezioni\Mathcad\Nuovi files\File 22 - Vibrazioni torsionali (avviamento).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </originRef> <parentRef doc-id="1C97A390-190F-4DBF-BEDE-2FCBA53F3DB1" version-id="80C8BBE0-DCBC-478A-AA76-7303644D9E84" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315097236" is-modified="false" region-id="0" href="C:\Users\giova\Documents\2) HOMEWORK\Materiale per Video Lezioni\Mathcad\Nuovi files\File 22 - Vibrazioni torsionali (avviamento).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </parentRef> <comment xmlns="http://schemas.mathsoft.com/provenance10"/> <originComment xmlns="http://schemas.mathsoft.com/provenance10"/> <contentHash xmlns="http://schemas.mathsoft.com/provenance10">35ffc4f48040b35e74bbe0ca271ce37f</contentHash> <ml:define> <ml:id xml:space="preserve">λ</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:id xml:space="preserve">eigenvals</ml:id> <ml:id xml:space="preserve" font="17">A</ml:id> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="4" cols="1"> <ml:complex> <ml:real>-2.4630494167104953</ml:real> <ml:imag>12.548705724159028</ml:imag> </ml:complex> <ml:complex> <ml:real>-2.4630494167104953</ml:real> <ml:imag>-12.548705724159028</ml:imag> </ml:complex> <ml:complex> <ml:real>-0.59789261227501378</ml:real> <ml:imag>6.2318027281454569</ml:imag> </ml:complex> <ml:complex> <ml:real>-0.59789261227501378</ml:real> <ml:imag>-6.2318027281454569</ml:imag> </ml:complex> </ml:matrix> </result> </ml:eval> </ml:define> </ml:provenance> <resultFormat numeric-only="true"> <general precision="3" show-trailing-zeros="false" radix="dec" complex-threshold="5" zero-threshold="5" imaginary-value="i" exponential-threshold="3"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="true" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="121"> <element-image-map> <box left="1.5" top="0.75" width="173.25" height="60.75" expr-idref="1" xmlns="http://schemas.mathsoft.com/worksheet30"/> </element-image-map> </rendering> </region> <region region-id="508" left="6" top="6534" width="334.5" height="249.75" align-x="6" align-y="6534" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <plot disable-calc="false" item-idref="122"/> <rendering item-idref="123"/> </region> <region region-id="478" left="240" top="6827.25" width="102" height="15.75" align-x="261.75" align-y="6840" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="true"> Condizioni iniziali </text> </region> <region region-id="479" left="6" top="6810.75" width="101.25" height="77.25" align-x="21" align-y="6852" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" font="17">y</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:matrix rows="4" cols="1"> <ml:id xml:space="preserve" subscript="0">α</ml:id> <ml:id xml:space="preserve" subscript="0">β</ml:id> <ml:id xml:space="preserve" subscript="0">α'</ml:id> <ml:id xml:space="preserve" subscript="0">β'</ml:id> </ml:matrix> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:matrix rows="4" cols="1"> <ml:real>0.3490658503988659</ml:real> <ml:real>0.17453292519943295</ml:real> <ml:real>2</ml:real> <ml:real>-3</ml:real> </ml:matrix> </result> </ml:eval> </ml:define> </math> <rendering item-idref="124"/> </region> <region region-id="509" left="6" top="6939" width="68.25" height="18.75" align-x="24" align-y="6954" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve">Δt</ml:id> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>0.001</ml:real> </result> </ml:eval> </math> <rendering item-idref="125"/> </region> <region region-id="510" left="6" top="6988.5" width="111" height="31.5" align-x="21.75" align-y="7008" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:provenance expr-id="1" xmlns:ml="http://schemas.mathsoft.com/math30"> <originRef doc-id="1C97A390-190F-4DBF-BEDE-2FCBA53F3DB1" version-id="80C8BBE0-DCBC-478A-AA76-7303644D9E84" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315097396" is-modified="false" region-id="0" href="C:\Users\giova\Documents\2) HOMEWORK\Materiale per Video Lezioni\Mathcad\Nuovi files\File 22 - Vibrazioni torsionali (avviamento).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </originRef> <parentRef doc-id="1C97A390-190F-4DBF-BEDE-2FCBA53F3DB1" version-id="80C8BBE0-DCBC-478A-AA76-7303644D9E84" branch-id="00000000-0000-0000-0000-000000000000" revision-num="246826396" is-modified="false" region-id="0" href="C:\Users\giova\Documents\2) HOMEWORK\Materiale per Video Lezioni\Mathcad\Nuovi files\File 22 - Vibrazioni torsionali (avviamento).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </parentRef> <comment xmlns="http://schemas.mathsoft.com/provenance10"/> <originComment xmlns="http://schemas.mathsoft.com/provenance10"/> <contentHash xmlns="http://schemas.mathsoft.com/provenance10">639b1c04d0ad87f57a07112da323d20e</contentHash> <ml:define warning="WarnRedefinedBIUnit"> <ml:id xml:space="preserve">N</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:id xml:space="preserve">ceil</ml:id> <ml:apply> <ml:div/> <ml:id xml:space="preserve" subscript="max">T</ml:id> <ml:id xml:space="preserve">Δt</ml:id> </ml:apply> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>5000</ml:real> </result> </ml:eval> </ml:define> </ml:provenance> <resultFormat numeric-only="true"> <general precision="3" show-trailing-zeros="false" radix="dec" complex-threshold="10" zero-threshold="15" imaginary-value="i" exponential-threshold="6"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="true" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="126"> <element-image-map> <box left="1.5" top="0.75" width="109.5" height="30" expr-idref="1" xmlns="http://schemas.mathsoft.com/worksheet30"/> </element-image-map> </rendering> </region> <region region-id="511" left="6" top="7065.75" width="122.25" height="19.5" align-x="77.25" align-y="7080" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#ff0" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:function> <ml:id xml:space="preserve">EQMOTO</ml:id> <ml:boundVars> <ml:id xml:space="preserve">t</ml:id> <ml:id xml:space="preserve" font="17">y</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:plus/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" font="17">A</ml:id> <ml:id xml:space="preserve" font="17">y</ml:id> </ml:apply> <ml:id xml:space="preserve" font="17">b</ml:id> </ml:apply> </ml:define> </math> <rendering item-idref="127"/> </region> <region region-id="514" left="354" top="7067.25" width="227.25" height="15.75" align-x="374.25" align-y="7080" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="true"> Equazioni di moto nello spazio di stato </text> </region> <region region-id="513" left="6" top="7101.75" width="174.75" height="21.75" align-x="30" align-y="7116" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#0f0" tag=""> <math optimize="false" disable-calc="false"> <ml:provenance expr-id="1" xmlns:ml="http://schemas.mathsoft.com/math30"> <originRef doc-id="1C97A390-190F-4DBF-BEDE-2FCBA53F3DB1" version-id="80C8BBE0-DCBC-478A-AA76-7303644D9E84" branch-id="00000000-0000-0000-0000-000000000000" revision-num="246826836" is-modified="false" region-id="0" href="C:\Users\giova\Documents\2) HOMEWORK\Materiale per Video Lezioni\Mathcad\Nuovi files\File 22 - Vibrazioni torsionali (avviamento).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </originRef> <parentRef doc-id="1C97A390-190F-4DBF-BEDE-2FCBA53F3DB1" version-id="80C8BBE0-DCBC-478A-AA76-7303644D9E84" branch-id="00000000-0000-0000-0000-000000000000" revision-num="246828636" is-modified="false" region-id="0" href="C:\Users\giova\Documents\2) HOMEWORK\Materiale per Video Lezioni\Mathcad\Nuovi files\File 22 - Vibrazioni torsionali (avviamento).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </parentRef> <comment xmlns="http://schemas.mathsoft.com/provenance10"/> <originComment xmlns="http://schemas.mathsoft.com/provenance10"/> <contentHash xmlns="http://schemas.mathsoft.com/provenance10">7a8e4d7e7d7c90a9c423f2a096bf0ee8</contentHash> <ml:define> <ml:id xml:space="preserve">TAB</ml:id> <ml:apply> <ml:id xml:space="preserve">rkfixed</ml:id> <ml:sequence> <ml:id xml:space="preserve" font="17">y</ml:id> <ml:real>0</ml:real> <ml:id xml:space="preserve" subscript="max">T</ml:id> <ml:id xml:space="preserve">N</ml:id> <ml:id xml:space="preserve">EQMOTO</ml:id> </ml:sequence> </ml:apply> </ml:define> </ml:provenance> </math> <rendering item-idref="128"> <element-image-map> <box left="1.5" top="0.75" width="173.25" height="20.25" expr-idref="1" xmlns="http://schemas.mathsoft.com/worksheet30"/> </element-image-map> </rendering> </region> <region region-id="515" left="354" top="7109.25" width="249.75" height="15.75" align-x="373.5" align-y="7122" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="true"> Soluzione per via numerica (Runge-Kutta) </text> </region> <region region-id="499" left="6" top="7179.75" width="68.25" height="18" align-x="37.5" align-y="7194" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:provenance expr-id="1" xmlns:ml="http://schemas.mathsoft.com/math30"> <originRef doc-id="1C97A390-190F-4DBF-BEDE-2FCBA53F3DB1" version-id="80C8BBE0-DCBC-478A-AA76-7303644D9E84" branch-id="00000000-0000-0000-0000-000000000000" revision-num="315097116" is-modified="false" region-id="0" href="C:\Users\giova\Documents\2) HOMEWORK\Materiale per Video Lezioni\Mathcad\Nuovi files\File 22 - Vibrazioni torsionali (avviamento).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </originRef> <parentRef doc-id="1C97A390-190F-4DBF-BEDE-2FCBA53F3DB1" version-id="80C8BBE0-DCBC-478A-AA76-7303644D9E84" branch-id="00000000-0000-0000-0000-000000000000" revision-num="246829636" is-modified="false" region-id="0" href="C:\Users\giova\Documents\2) HOMEWORK\Materiale per Video Lezioni\Mathcad\Nuovi files\File 22 - Vibrazioni torsionali (avviamento).xmcd" xmlns="http://schemas.mathsoft.com/provenance10"> <hash/> </parentRef> <comment xmlns="http://schemas.mathsoft.com/provenance10"/> <originComment xmlns="http://schemas.mathsoft.com/provenance10"/> <contentHash xmlns="http://schemas.mathsoft.com/provenance10">4fda78194e5f9756f491d7c034e7cee2</contentHash> <ml:define> <ml:id xml:space="preserve">tempo</ml:id> <ml:apply> <ml:matcol/> <ml:id xml:space="preserve">TAB</ml:id> <ml:real>1</ml:real> </ml:apply> </ml:define> </ml:provenance> </math> <rendering item-idref="129"> <element-image-map> <box left="1.5" top="0.75" width="66.75" height="16.5" expr-idref="1" xmlns="http://schemas.mathsoft.com/worksheet30"/> </element-image-map> </rendering> </region> <region region-id="523" left="456" top="7181.25" width="602.25" height="15.75" align-x="462.75" align-y="7194" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> Se il procedimento è corretto le due tracce (metodo analitico e numerico) devono sempre sovrapporsi </text> </region> <region region-id="502" left="6" top="7215.75" width="60" height="18" align-x="29.25" align-y="7230" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve">alfa</ml:id> <ml:apply> <ml:matcol/> <ml:id xml:space="preserve">TAB</ml:id> <ml:real>2</ml:real> </ml:apply> </ml:define> </math> <rendering item-idref="130"/> </region> <region region-id="503" left="174" top="7215.75" width="62.25" height="18" align-x="199.5" align-y="7230" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve">beta</ml:id> <ml:apply> <ml:matcol/> <ml:id xml:space="preserve">TAB</ml:id> <ml:real>3</ml:real> </ml:apply> </ml:define> </math> <rendering item-idref="131"/> </region> <region region-id="677" left="408" top="7221" width="24.75" height="12.75" align-x="420" align-y="7230" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve">q</ml:id> <ml:real>1</ml:real> </ml:define> </math> <rendering item-idref="132"/> </region> <region region-id="678" left="486" top="7217.25" width="88.5" height="15.75" align-x="504.75" align-y="7230" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="true"> Variabile on/off </text> </region> <region region-id="505" left="6" top="7284" width="764.25" height="342" align-x="6" align-y="7284" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <plot disable-calc="false" item-idref="133"/> <rendering item-idref="134"/> </region> <region region-id="518" left="6" top="7644" width="760.5" height="342" align-x="6" align-y="7644" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <plot disable-calc="false" item-idref="135"/> <rendering item-idref="136"/> </region> </regions> <binaryContent> <item item-id="1" content-encoding="gzip">H4sIAAAAAAAA/2yQwW6CQBCGZ1wqSFG89NI1wWfwCXowTQ+NJvoAZItUMRIbSpMe++b0n9k9 GWczw8/s9w/LZkTEyD0yVW1Q88OqPHbu61R+XrvW9TFJPCEn2l673mmLnpFJW20/znXV+9bO P2i8RDFhvnhz1fKWut/m+1VHR8oukO8cWB8DFvHiXjdMifvOVfVqbXTHBq7gObHlQRSzHcFT 8IitgbNgwzYapBOBoQHiAczfC7bGwogrVgYiEUbgicwRewpG7Y9g1J6BUftUvwUxUwYiF0Zg f9x889PWXVO5i//zRI/9hjxQg2XCFaXh2m4ik/IPAAD//wMAHK222K8BAAA=</item> <item item-id="2" content-encoding="gzip">H4sIAAAAAAAA/4yQwW7CMAyG7aZbS9etu3AhSOUZeIIdENph2iT2ACiUAkVUTF2ROPLm3W8n u+w0R3b+Ot/vRsmJiJGfyEy1QS228/W+c1+H9e7cta5PSGKMHGl74XqnLZog07b62Bzrqvet ld8omqGYMF+8hWr5yty1+V7q6FjZKfKNA+tjwCKe/r/7OzzpO1fV84XRIxvAkp+JLQ+imG0E U8kRWwNryYZtPEgnBkMDxB2Y2wuO7oURV6IMRCqMwCOZI/YMjNofwKg9B6P2R/0XxJMyEIUw AvvrFu+Xtu6ayp38g6R67VfklhosE14uC6/5J3IpPwAAAP//AwARvY6sxgEAAA==</item> <item item-id="3" content-encoding="gzip">H4sIAAAAAAAA/+z9DZQV13ng/e44PUa2ZNSTmKu0HV3kQKQ3imxIFEUWUqzOAK3EEsEvhNdq OUYd37ZIgBj6GkF0V5x2Msh+EznGtkysJYWhNUFgY3rQAnmJxmaBpEYwYCTALbW4SxMU85G5 0lLUBA+SwurVt55iqiiqa1ftc07V2fXxf7J+Qe7ep7rqfNSp2s+z9/459T6lfv6lJe1KqVNf +bDz//8frUpd+94r1a9c8+k5HTN/Tkn8nPqLv5Z/73qP8/+uUeofZv2++ofu31fy26XqYqx5 r1I/fs/F//3CXyn1ud92HjJ7TodsRR7z8+72Lo0vT1Dq4y1K/YXzi6Oh33X9+D+oT/3Wz6v/ 0/nvX3Yer/tbXnzD+fkh5+efcv77/3D8uWPD/+vC465RP/Z/L7/7u//93z8nBxVoF4yxsTEA AAAAAAAAAAAAAEotulbgQvi1AlMv1ArY3lcAAAAAAAAAAAAAANA4o1qBFmoFAAAAAAAAAAAA AAAoC6NagZO/R60AAAAAAAAAAAAAAAAlYVQrMEitAAAAAAAAAAAAAAAAZWFUK7CBWgEAAAAA AAAAAAAAAMrCqFZgNbUCAAAAAAAAAAAAAACUhVGtQDe1AgAAAAAAAAAAAAAAlIVRrcAsagUA AAjauHFjYpuf/vSnqre312WyzZdeeslvn7ftl+EY2H6+X+N/+Zd/UW+//ba7/fXr1xvtCwAA AAAAAACgfka1AlOpFUjTnDlzEtvs3btXeWGyzS1btvjt87b9MhwD2y/Ga/zDH/5QvfXWW0Zt AQAAAAAAAACwbdu2bX4/eEdHR2L7YHR1dRVu+2U4Braf/9f4ySefVH/6p39qtK9VZ1Qr0EKt AAAAAAAAAADkSd5yA1XIPbD9cr/GfX19/u9M598DAKDIjGoFTt5BrQAAAAAAACXy4osvqnvv vdfV09Pj9o2LHTt2RLb32opVq1b57Z955plCbL8Mx8D28/sa//7v/74Kx9atW61/zgEAAAAg jlGtwCC1AgAAAIg2MDDg94m3t7cntl+/fr3fvrOzs3DbL8MxsP18vcbhmDZtGusqAQAAAAAA IHNGtQIbqBUAkI285QbKlntg++V8jf/+7/9eRcXcuXPJLQEAAAAAAAAACunIkSNun7pYunSp 2rhxo2vnzp2R7WX+MK/9ihUr/PbPPfdcIbafh2MwqhVYHV0rkLfns+jbz/pvHD58WF1xxRXj cksyb57tzz4AAAAAAAAAAGnYvXu33/89Y8aMxPbSp+7F/PnzC7f9MhwD22/ua/zkk0+q1tZW FQ4ZA2WyLwDKw6hWoJt5BQAAAAAAAFB+ecsNFC33wPbzt/00/0ZfX58Kx5VXXqm2bt1qtB8A AADIF6NagVnUCgAAAACAZ+/evZE/HxoaUvPmzXMtWrRI9ff3u3TbefbZZ/32y5cv99vnbftl OAa2b/c1/t3f/d3I3NKLL75o/fMMAAAAAEBVGdUKTKVWAACAtMyZMyexjfS9e2GyzS1btvjt 87b9MhwD27fzGg8MDKiomDZtmnrrrbeM/iYAAAAAAAAAIJpRrUALtQJ5kbfcQNbbL8MxsP3m v8avvfaa+tVf/VUVjrlz55JbAgAAAAAAAACUwpIlS9T27dtd+/fvj2wzPDys7rrrLpfXVujm +JL+e6993rZfhmNg+819jQ8fPqxaW1vH5Yvuvfde65/fvDCqFTjZQa0AAAAAAAAAAJTEtm3b /P7yjo6OxPbB6OrqKtz2y3AMbL95r3FfX19kbmnNmjVG+wEAQFEY1QoMUisAAAAAAAAuyFtu oEi5B7afz+2n9Te+8Y1vqHBceeWVav369Ub7AAAAAADNZFQrsIFaAQAAAKRj3759bn+6uO++ +9TAwIDr4MGDke29tqKnp8dvf/To0UJsvwzHwPazf43l36jckm6uPQAAAAAAAKBRRrUCq6kV AJA/0v/uRXt7e2J7GcfhRWdnZ+G2X4ZjYPvpvsYjIyPqYx/7mArHtGnT1PHjx43+HgAAAAAA AAAARZe3/v685RN0f8OoVqA7uVag6M9n0bdfy9947bXX1PTp01U4br/9dvXWW28Z/S0AAAAA AAAAAMrgwIEDbv+4uPfee9WePXtcL7zwQmT7p59+2m+/dOlSv/3Q0FAhtl+GY2D76bzGhw8f Vq2trePyRfI3bH8uATSHUa3ALOYVAAAAAAAAAOLs3r3b72OfMWNGYvuNGzf67efPn1+47Zfh GNh+fa9xX19fZG5pzZo1RtsEAABAPhjVCkylVgAAAAAAspS33AD5JbafxWv8jW98Q4Xjyiuv dOfJM9keAAAAAABIj1GtQAu1AgAA5MHw8HDkzw8dOqRuueUWl6xD8/zzz7t025G1brz2ixYt 8tvnbftlOAa2n85rfN1110Xmll588UXrn0sAAAAAAAAAKCKjWoGTs6kVKIE5c+Ykttm7d6/f /26yzS1btvjt87b9MhwD22/sNR4ZGVHt7e0qHNOmTSO3BAAAAAAAAACorLz192e9/TIcA9uv /zV+7bXX1PTp08PpIjdf9NZbbxntW1kZ1QoMUisAAAAAAAAAACC/VMRjYPv1vcaHDx9Wra2t Khz33nuv0T4BAJB3RrUCG6gVAAAAAAAAjdu2bZvfz97R0ZHYPhhdXV2F234ZjoHt1/4a9/X1 qajo7e012h4AAAAANINRrcBqagUAAABgX95yAzZyD0U7BrZf22scNR+exPr16422BQAAAAAA AJgyqhXoplYAQPHlLTfQ7NxDEY+B7df2GofjyiuvVC+++KLRdgAAAAAAAAAAqArpO5c1ZURP T487N5jYsWNHZHuvrVi1apXf/plnninE9nV/w6hWYNaFWoEiHS/bT34/dHZ2ql/4hV8Yl1ua Nm0auSUAAAAAAAAAABIMDAz4fevt7e2J7WXOMC+kj75o2y/DMbD98W0OHz4cOc+d5Iveeust 488DgOIxqhWYyrwCAAAAAAAAQJrykBto5vbLcAxs/9LfS26ptbVVhUPGM9XyWQAAAIAdRrUC LdQKAAAAAECeHDlyxO3jF0uXLlUbN2507dy5M7K9zFHmtV+xYoXf/rnnnivE9stwDGz/4vZl HryoWLZsmfXPFgAAAAAAVWFUK3ByFrUCAACU1O7du/3++RkzZiS2lz5+L+bPn1+47ZfhGKq8 /auvvlpFhYyPauRzAAAAAAAAAABVY1QrMEitAKKVKfdAfqn82w/HlVde6f6+kc8AAAAAAAAA AABVsnfv3sifDw0NqXnz5rkWLVqk+vv7XbrtPPvss3775cuX++3ztv0yHAPbv2D//v1ubigc kydPVi+++KL1z5YNRrUCG6gVAAAAAAAAAAA0Zs6cOYltpH/fC5NtbtmyxW+ft+2X4RjY/pg6 fPiwmj59eji1pKZNm6beeusto78BAEAeGdUKrKZWAAAAAAAA2FXG3EPc9stwDFXfvuSWWltb w6klde+995JbAgAAAGCdUa1AN7UCAAAAKLYlS5ao7du3u2S+sag2w8PD6q677nJ5bYVuDjLJ JXjt87b9MhxDlbf/pS99aVxeSWLZsmXWP0sAAAAAAAAoB6NagVnUCgCotm3btvl99B0dHYnt g9HV1VW47ZfhGKq8/ahYv359Q58BAAAAAAAAAABQrnyCUa3A1Au1AuSXyrn97373uyoq3vOe 96jHHnusoc8KAAAAAAAAAAC4YN++fW4/v7jvvvvUwMCA6+DBg5Htvbaip6fHb3/06NFCbL8M x1Cl7Y+MjKhf/dVfjcwZSVx55ZXaefMAFJNRrUAL8woAAAAAAAAAeSF9/F60t7cntpe5xrzo 7Ows3PbLcAxl3v7hw4fVNddcE5VWuiTIMQEAAOSLUa3AyZnUCgAAAABASRQp95DG9stwDGXd /p49e1Rra2tUOkkbrK0EAAAAAEA6jGoFBqkVAAAAFxw4cEDdfvvtrnvvvdft4xcvvPBCZPun n37ab7906VK//dDQUCG2X4ZjKOP2//zP/zwyfzRnzhx3rBI5JgAAAAAAAACIZ1QrsIFaAaRv 9+7dfp/9jBkzEttv3LjRbz9//vzCbb8Mx1Cm7evyRjIPHjkmAAAAAAAAAACaJ8/5BHJG5dv+ yMiI+sQnPjEu/yP5oa1bt17SVvJGkydP1qWM3JDxUFl8LprBqFZgNbUCAAAAAAAAAAB7hoeH I39+6NAhdcstt7hkrZvnn3/epduOrKXjtV+0aJHfPm/bL8MxlG37kluaPn16ZG5JcklR237r rbfUtGnTSptjAgAUm1GtQDe1AgAAAAAAoLhknZqkNnv37vX77E22uWXLFr993rZfhmMo0/YP Hz6srrnmmnG5IckdSQ4pbpumOaak7QAAAABAmFGtwCxqBQAAAFBdec49ZLH9MhxDWba/Z88e 1draOi4nNHfuXOOckLST9nFhkqsCAAAAAABAuRjVCkylVgAA6rVt2za/H76joyOxfTC6uroK t/0yHEOZth+Oeue0k8fFBTkmAAAAAAAAAADi5S2fYFQr0HKhViDrfWnG8bJ98+0HY82aNQ29 701yTLr1nAAAAAAAAAAAqLo85xPIGZVr+yMjI+pTn/qUCseVV16p1q9fn8r7WfJOcSF/i7wR kD2jWoGT/4l5BQAAAAAAAICCkr52Gcshenp6VF9fn2vHjh2R7b22YtWqVX77Z555phDbL8Mx FHH7kluaPn16U/I9kqsixwQAANAYo1qBQWoFAAAAAABjamBgwO+Db29vT2wf7Mfv7Ows3PbL cAxF2P7hw4dVa2vruDyPzEF3/PjxTN7LJjmmtMZMAQAAAACQR0a1AhuoFQAAAI0rY27D9t+o +vaffPLJyNzS7bffrt56661M38+7d+9280hxQY4JAAAAAAAAQF4Z1QqsplYA9h05csTNIYil S5eqjRs3unbu3BnZXuZL89qvWLHCb//cc88VYvtlOIY8b3/WrFmROR2ZR69Z72mZB48cEwAA AAAAAAAA9ZPxHF7MmDEjsb3kEbyYP39+4bZfhmPI0/a7uroi8zNr1qxp+nvZJG+Udh7LqFag m1oBAAAAAAAAAEC5FDm3Uc/2y3AMedj+yMiI+tSnPjUuf2N7DSOZX0/WcIqLZo6VAgDkn1Gt wCxqBQAAAAAAQDXt3bs38udDQ0Nq3rx5rkWLFqn+/n6XbjvPPvus33758uV++7xtvwzHkNft S25p+vTpkbklGUNk+71OjgkAAACoFqNaganUCgAAAAD1mDNnTmIbyR94YbLNLVu2+O3ztv0y HEMet3/48GHV2to6Ll8j+Zw85JY8kmO6/fbbx+1neJ+lne19BQAAAAAAqDqjWoEWagUAwIYy 5DZq2X4ZjiFv27/pppu0uaW85mlknFJc5HnfAQAAAAAAAADIwpIlS9T27dtd+/fvj2wzPDys 7rrrLpfXVujGmhjVCpz8XbdWQHIV3raz2JdGtt+Mv1Gl7UfNhydRhHnmTHJMx48ft76fAAAA AAAAAAA0w7Zt2/w+8o6OjsT2wejq6irc9stwDHnZvvwuKnp7e62/r03JvsZFXtaDAmwwqhUY /F3mFQAAAAAAAAAqqKi5jXq3X4ZjsL39kZER1d7erqJi/fr11t/TtZJ9jgtyTAAAoKiMagU2 UCsAAAAAAKjdvn373JyDuO+++9TAwIDr4MGDke29tqKnp8dvf/To0UJsvwzHYHP7r732WuQ8 d0XPwZjkmLZu3Wp9PwEAAAAA1WJUK7CaWgEAAGCX5BC8kPEpSe2DffKdnZ2F234ZjqGZ2//9 3/991draOi73ImsPFTm35Nm9e7ebR4qLIo7PAgAAAAAAAGCPUa1AN7UCKLY85zbIL9nf/nvf +95x+RbJLb311lvW37tpkTwZOSYAAAAAAAAAQNUdOHBA3X777a57771X7dmzx/XCCy9Etn/6 6af99kuXLvXbDw0NFWL7ZTiGZmx/6tSpkbkTebzt92wWvLyRUa3ArAu1AnGxbNky68cEAAAA AAAAAEAzyPxfXsyYMSOx/caNG/328+fPL9z2y3AMWWy/q6urkjmT48ePu+Ot4qKs+TUAKBOj WoGpzCsAAAAAAACQljzkNpq5/TIcQ5rbHxkZcefEi4qqzPkmc/mRYwIAAADsMqoVaKFWAAAA AGi24eHhyJ8fOnRI3XLLLS5Zq+f555936bYja/V47RctWuS3z9v2y3AMWW//tddeU9OnTx+X S5E54yQnZfs920wmOaayrSsFAAAAAACQJqNagZPt1AoAQMHMmTMnsc3evXv9vnSTbW7ZssVv n7ftl+EYst5+a2vruBzK5MmT3XWJbL9fbZFxSnFBjgkAAAAAAAAAUHS6fIJRrcDg+FqBMuVO ynAMzXiOyJ2MR44JAAAAAAAAAFBm27Zt8/u8Ozo6EtsHo6urq3DbL8MxZLH9b3zjG5F5EMmT kAe5aNmyZZHPkxcyL2CVx24hf4xqBTYwrwAAAAAAAACA2uQht9HM7ZfhGNLevmwjKiSXYvv9 mUfr16+PfL68IMcEAADSZFQrsJpaAQAAAABAc9nObTR7+2U4hrS2PzIyotrb2yNzJJJDsf3e zDOTHNPu3but7ycAAAAAwD6jWoFuagUAAECxyDgLmQNN9PT0qL6+PteOHTsi23ttxapVq/z2 zzzzTCG2X4ZjSGP7//k//2c1ZcoU8iIN2Lp1q/t8xQV5OgAAAAAAAKD4jGoFZlErgOoaGBjw +8VlXEtS++AYjs7OzsJtvwzH0Mj2w8G8brWT54scEwAAAAAAAACgbMqUDyFnFG3FihWReY1p 06apt956y/p7MG1GtQJTm1crYJJj6u3ttf68AQAAAAAAAABg6siRI26OQixdulRt3LjRtXPn zsj2Msea117yFl775557rhDbL8Mx1LL9L3/5y5H5jLlz55Yyt9RMx48fd3N0cSFzDtreTwAo IqNagRbmFQAAAAAAAMgDWVfHixkzZiS2l5yGF/Pnzy/c9stwDHHb7+rqIueRMcnRkWMCAAAA xjOqFTh5O7UCAAAAQAkUKXeSxvbLcAxR2x8ZGVHTp0+PzHWw1k/6THJMt99+O+PFAAAAAABA oRjVCgxSKwAAVbJ3797Inw8NDal58+a5Fi1apPr7+1267Tz77LN+++XLl/vt87b9MhyD6fb/ 7u/+Tn3kIx8Zl9+Q9X+2bt1q/b1XVpI7knFKcVHW9a4AAAAAAAAAAPlkVCuwwbxWYM6cOYlt JP/hhck2t2zZ4rfP2/bLcAwm2//a174WmdeQ3NKLL75o/X1cBeSYAAAAAAAAAABFUsR8CDmj +LZ9fX3kKHIiKW9EDg8mjGoFVjOvAAAAAAAAAIDmWbJkidq+fbtr//79kW2Gh4fVXXfd5fLa Cl2/uORCvPZ5234ZjiFu+1/+8pcj8xhz584lt2SJrF1FjgkAgGozqhXoplYAAAAAAFAc27Zt 8/u5Ozo6EtsHo6urq3DbL8Mx6LYv/x0VMobG9vus6sgxAQAAAECxGdUKzKJWAAAAVEdRcidp bb8MxxDe/sjIiJo+fXpk3mLNmjXW32O4QHJMkkeKC2ljez8BAAAAAACAMjKqFZhKrQBgat++ fW5OQ9x3331qYGDAdfDgwcj2XlvR09Pjtz969Gghtl+GYwhu/4/+6I/UlClTxuUpJI9BriJ/ ZIwSOSYAAAAAAAAAQN5JnsKL9vb2xPbBebw6OzsLt/2iH8OePXvU+9///sh8UZnmTzOqFWgp Z62ASY6JcWcAAAAAAAAAAJvylDtpxvaLfAx9fX2RuYZp06ap48ePW38vIZ7kjeS1igvWwgJQ Jka1Aic/UcpaAQAAAAAAgLI7cOCAuv32213Sty3jY8QLL7wQ2f7pp5/22y9dutRvPzQ0VIjt F/UYli9fHpmPkMe89dZb1t9HMCOvFTkmAAAAFIVRrcAgtQIAAABA1e3evdvv454xY0Zi+40b N/rt58+fX7jtF+EYRkZGVFdXF3mIEjHJMc2dO5e8IQAAAAAAMPKj0THVMnOd26dwo+ONgYvz 4xjVCmyIrhVYOzKmVNvKyO1G+ZnjkwOnnJbtqsP5/wPfalej/7bb+vMDAMhe3vI/Vc8v3Xzz zWr69OmR+QfW3yk2yR1JfjAuJAdFjgkAAAAAACR53THpqwf9PoVd97c4Pz8d2fZVx8Qv7jJq Kw45JnxmyyV9Fg+0KzXycq/14waAqjCqFVjdnHkFhoeHI39+6NAhdcstt7hk3aDnn3/epduO rBvktV+0aJHfPm/bL+IxfOxjH1OXX375uJzDlVde6a7vZPv9jHSQYwIAAAAAAI0675i584xS qu1C/sf5Z3RkbWTbdxy/+b3TF3NFMW1FOB/19Xal/mFoh/q/PrZajby02PqxAwAaN2fOnMQ2 e/fu9b8LTLa5ZcvF2oS8bb9oxyDrKbW2tkbmi1588UXr7x+kKylvNHnyZF73gjKqFehmDQIA AAAAQHPVMpfd4uFRp0WHUdszjimPHPP7NB6aotTY+c1q49kx9cD1f6XePanPTQEAiiFv+Z+s t1+kY+jr64vMMcjYFHIM5SVzGMYFuUUAAOwwqhWYRa0AAAAoluAaTBJx897V0lYE81ELHG8P Xxi31HtiVP3w878Z+1gAQP5t27bN/07o6OhIbB+Mrq6uwm2/KMewfPnyqNQC855VhMxlGBfk mAAAAACgdka1AlOpFUC1hMcZxc17F267xGn77ole7baDY6NkBr7hR9ucn5+3fswAgHTkLf+T 9fbzfgz/6T/9J/cxUSFzp9l+v6B5knJMEqypBQAAAAAAopjOZRdegykpDxReV2md8yfGRndb P15Uh1GtQAu1AkAz5C3/k/X2834MH/jAByLzCL29vdbfK2g+GaMkY5XighwTAAAAAAAIC44d koibyy7c9uvtSo2e3RzZNpyPCs57BwAoPumTlrEuoqenx13HR+zYsSOyvddWrFq1ym//zDPP FGL7eT0GWVfpiiuuKG1OwKhW4OTvUCvQIHJMAAAAAIAsyPz7u3fvRoHUsmZCeJxR3Lx34bY3 OF7b/DGlG8PEvHcAUA0DAwP+d0N7e/Q42KDgnFydnZ2F235ejmHPnj2qtbV1XB6AdXPgkffB 5MmTY/NGzIkIIGtGtQKD1AoAAACUjeQqkmoZifyFvG6mr3F4nFFczii8rpJE3BimcD6qf6Fy fj5k/X0NAEhX3vI/VcgvybimqJg2bRq5JVxC6o7kfREX5JgAAAAQx6hWYAO1AgAulVTvSOQz askvieAaTBJxOaNg2ymOX7j2Wu24pHA+Km5sFACguI4cOeLmQMTSpUvVxo0bXTt37oxsL/O9 ee1XrFjht3/uuecKsX3bxyDrLUWF5BBqGcOM6jDNMfH+AQAAAADorFmzxr+HlLpY2/O4obY5 74xqBVbXXivwo9Ex1TJznbsdGcV07bW/oIYf/WUVlTMKt/3rv25TfzJhuXr3RG/ktoP5qBsd bwyY1YUDAIpJvrO8mDFjRmJ7yZl4MX/+/MJtv9nH8KEPfUibG7D92iPf5Fpy7ty5uvSSG+Qo AQAAAABR5F7Rm+vu9ttvt74/yFZw7JDkgb79lTZ1X9tfR+aBwvPYfbtDqZs27lBbuiaosdEj 49oHc0wSu+5vcX5+2voxA0ARGNUKdBd3XoG85X/Kll+KimXLlll/3VEckouMC3JMAAAAAICw 4L3k8ePHre8PshVeV+nRDqVmPv18ZM4o3Fbmsfv//utaNenuLZFrMTHvHQCU0969eyN/PjQ0 pObNm+datGiR6u/vd+m28+yzz/rtly9f7rfP2/ZtHsN/+S//Rf2H//AfIvv3ZYy57fcCisck b8TaXOkxqhWYVdxaAQAAAADwBOtke3t7re8PmmPtyJhSbSvd132R49iJXm3OKNhW5rH7/w20 q879Z9WfttwWOeaJee8AoHzmzJmT2EbyK16YbHPLli1++7xt39YxPPnkk6q1tXVc/7+MM691 DUUgKDgXdlTIe4wcEwAAFxjVCkylVgAAAG8938mTJzPfRYUcckz4zJZLckC9J0ZV14QuNfLS 4kvaRs15d2LsDfXLDx5UX29XavTs5kvaB/NREgf/ZoLz83esHzMAoH55y/9kvX0bx9DX1xfZ 7y/XaPT7Iw0y/i0uyDEBAAAAKAujWoEWagUaEbzH3Lp1q/X9QfOE57GTnNE7Y6fVTd87qXpv a1HvnlyrbevNeXdgdExdvrBf9X+u5ZJ59cJrNq3rUM7vd1s/ZgBA/ZYsWaK2b9/u2r9/f2Sb 4eFhddddd7m8tkLXXy25Fq993rbf7GP4gz/4g8j+fta7QdpMckzMwwgAAAAA1SP9D3JPKHH7 7bdb3x80X3BuOi+v4+WB+j7bokb/7Uduu3ccv/m9035fwgLH28MXxi3JOKaWls+pgRWTnMcP uT8L55gemuJs+/xmK8eIYjGqFTh5G7UCQM5t27bN/w7o6OhIbB+Mrq6uwm2/2ccQFbJmDrkl ZEHmWvTuGXRBjgkAAAAAqmXZsmX+PSFzW1TT5vNjSk15yH0PBHNGG8+OqZbb/latbFfq1L4v qBNjo+78dl44P1Zn9y9w2553fOGls0q1LVR/99kWNfLSN/0clRfklwCg+PKW08l6+80+hnDI dZrt17xeRrUCg9QKFIncK5BjAgAAAAAIuUcsQ/8FGiNjj1Tb59z3QZtj+NE25+fn3d998+So arntzyP7D4K5KPEzxycHTqkLmafxwZx3AFB8+/btc3Mm4r777lMDAwOugwcPRrb32oqenh6/ /dGjRwux/az/xuHDh1VLSwv99LDKJG8kY+Ns7yeA5jOqFdhArQAAAEAVyXx4EnI/yVwq1bJg /3n12ZYPqev/8i//d1bpYsi6SmNjp/2260+Oqsvv/rtx/QzhNZSEjGPqffmsarnt/kvahvNW AIDik5yJF+3t7Yntg2u4dHZ2Fm77af+NJ598UrW2to77fpXrMpmLzPbri2qRewFZuysuyDEB AAAUj1GtwGpqBQDUJtg/Qn1sdW0+Map6e7/l6FX3f/vb6vK7/2/1QJtSoyNrL2k3NDqmFv3j Pj9v9O3Ptqh3T27Ubnf3v42pBX073PnyJLe0pWeSGv233daPFwCQnrzlf4qUX/rGN74R2X8/ efJk5iuGNeSYAAAAAKCa5H7Qm9dCxiXZ3h9cyqhWoJtaAQBAcRw4cMC95hDS57xnzx7XCy+8 ENn+6aef9tsvXbrUbz80NFSI7af1N37v934vst9e+vUZTw7b5D3ozXGgC96rAAAAAFAuMv7E C+peAQBIn1GtwKxq1grIfG5ezJgxI7H9xo0b/fbz588v3Pbr+RtPPfWUtr9+7ty59NcjVyQf So4JAAAAAMrv+PHj/r0ec1YAAIA05C2nk7ec0WuvvaamTp0a2ffO9RjyyiRvJPcWtvczS0a1 AlOrWSsAAAAAoHq8OSxkfjzqCAEAQC2Gh4cjf37o0CF1yy23uGStoeeff96l246sNeS1X7Ro kd8+b9tP428cPnxYtba2RvbPs64l8i4470FUyD0F8yEAAPLMqFaghVoBAACSbN261b8XXLNm jfX9AQAAxTJnzpzENnv37vWvN0y2uWXLFr993rbf6N/o6+uLzC1Jn7xcl9l+PQETkgclxwQA AADAFqNagZO3UiuQIRmrNHnyZPc5l7ksbO8PAAAonrzlf7LefiN/4xvf+AZ98SgNkxwTOVMA AAAAKI7gfBWyboDt/QFQbka1AoPUCgBllrf8T9bbr/dvdHV1RfbBS40P8xOjqOR+Q/JIccGc jwAAAACQf7KWrhesCw0AAOq1bds2/5qio6MjsX0wJI9StO3X+jdGRkbURz/60ci+9Llz5xYu X2RUK7CBWgFEk7F35JgAAAAAoNikP0NC7u+K1q8BAADyI2/5nzzll1577TU1ffr0yD506ntQ JiZ5o2XLllnfTwAXGNUKrKZWAAAAAOPJ3BReyBx5tvcHAAAUl/QrS65E9PT0qL6+PteOHTsi 23ttxapVq/z2zzzzTCG2b/o3Dh8+rFpbWyP72RmfgTKS+RFkDse4IK8KAACQDqNagW5qBQCk b/Lkye65Rv61vS8AAKA8BgYG/H7k9vb2xPaSZ/Gis7OzcNvX/Q3JNUXllmR8B7kllJnMi0CO CQAAAACKQ8YgeSFjk2zvT5UY1QrMolYAAFANecv/2MgvfeMb34jsU5fckoyVsv0aAVkzyTHJ 75mjGwAAAADsknkovLnOZU0l2/sDAEDRGNUKTKVWwMSRI0fcHItYunSp2rhxo2vnzp2R7WU+ Oa/9ihUr/PbPPfdcIbYf/hs33HCDti9drtlsvz5AM8k4JXJMAAAAAJBfklPyamLptwAAAFkK ruE4Y8aMxPaSm/Fi/vz5hdr+yMiImj59emS/+O23306/OCorr3kjo1qBFmoFAAAAAEAE+0hk jjzb+wMAAMotT/mfLLd/+PBhbW6JtWOAMbVs2bLYHBPzQQIA6mFUK3ByBrUCAACkwJvXfPLk ydTQAgCA1Ozduzfy50NDQ2revHmuRYsWqf7+fpduO88++6zffvny5X77vG0/+Dckt9Ta2hrZ Z75mzRrrrw2QF8G1x8gxAQAAABBGtQKD1ArYJv0bXmzdutX6/gAAgPKYM2dOYhvJ4Xhhss0t W7b47fO2fe9v9PX1afvJpS/d9usC5I1JjknGDdreTwAAAAAoMxl/JPdfEjKnv+39AVBsRrUC G6gVABAtb/mfrLcv8eUvf1nbP84YDEBPat28+xhdkJ8FAAAAgOwE18k9fvy49f0BAADlsmTJ ErV9+3bX/v37I9sMDw+ru+66y+W1Fbr8iuRovPZ52/7VV18d2c8t8wsX5VrLqFZgNbUCsE8+ w+SYAAAAAKD5gmtN9/b2Wt8fAABQPtu2bfOvNzo6OhLbB6Orq6sw2x8ZGVHTp0/X5pZYsxIw Z5I34v4FiGdUK9BNrQAAAADqI/PhSUyePJk+DwAAkIm85H+y3P7hw4e1uSUZK277NQCKSMb7 SW42Lvh8AQCAMjOqFZhFrQCA5guuicucEQAAICv79u1z8zLivvvuUwMDA66DBw9Gtvfaip6e Hr/90aNHc7l9yS21trZG9n0zvgJojNTAkWMCAAAAgGzJvZc3h4SMS7K9P2ViVCswlVoBAACS SF7Gi/b29sT2wXqYzs7O3G2/r69P2+dN/Q6QDpMck9z/MB8DAAAAANRn2bJl/v2Vbq1qAACq yqhWoIVagayVKb90zTXXRPZzS70P12JA+mScUlywzhkAAAAA1E76MLyQPJPt/QEAANVx4MAB d+yAkP7fPXv2uF544YXI9k8//bTffunSpX77oaEhq9v/pV/6JW2fNfkiIDu15I2MagVO3kKt AAAAAAAYkj4Rr16Wmj0AAGDL7t27/T7hGTNmJLbfuHGj337+/PlWtj8yMqKmT5/OeAjAoqQc E+MCAaA8jGoFBqkVAACgGYLzujDPPwAAsKlo+aXDhw9r57mT/m7bzydQJcH7GnJMAAAAQH4Z 1QpsoFYg76SedvLkye7rJmOYbO8PAACohuHh4cifHzp0SN1yyy0uWc/o+eefd+m2I+sZee0X LVrkt2/G9mXOvNbW1sh+7N7eXuvPMVBF5JgAAAAAoH7Sn+GF1Oja3h8A+WVUK7CaWgEA6Zsz Z05im7179/rXNCbb3LJli98+6+3r5sOTYDw4YJd8BiWPFBd8TgEAAADgUsePH/fvmZiTBQAA NFPRc0ZRIX3Uea/XMaoV6KZWANUiY5TIMQEAAACAOZnjzusLYS1pAADQTEXML/3X//pftX3P Mocw82gB+WKSN1qzZo31/QRqZVQrMItaAQAAAGRj69at3FMBAABrtm3b5l+LdHR0JLYPRldX V9O3PzIyop3nbtq0adTqADklOSb5jMYFczkAAIBmM6oVmEqtAID8kf4PqbH1+kNs7w8AAKie IuWXDh8+rK655hptvzS5JSDf5DNKjgkAAAAAovX29vr3Rnmf9z9PjGoFWqgVAAAgbUXJL+3Z s0e1trZG9kcvW7bM+vMIwIxJjmnu3LnkiwEAAABUyvHjx/15xeWeyPb+AADQTEa1Aic/Tq1A zsicVTJeQPT09Ki+vj7Xjh07Itt7bcWqVav89s8880xm2+/u7tb2Q69fv976cwigNpI7ks94 XDDfJQAAAIAqkZyShOSYJNdke38AAAAGBgb8/tr29vbE9pKv8aKzszPT7YdDrqEY/w3YZVQr MJhOrQA5JgAAAAAYc/tCvJA58mzvDwAAgMhTfmlkZETddtttkf3IsialjHWy/XwBaFxS3ojP OwBky6hWYAPzCgAAkBdyj+TdK9neFwAAAM+RI0fcPJFYunSp2rhxo2vnzp2R7WWeO6/9ihUr /PbPPfdcQ9uX3NL06dMZowBURNxYRQkZr0iOCQAAAIhmVCuwmlqBMpDxSl4wpwsAAMir4Hjr GTNmJLaXPJEX8+fPr3v7hw8fVtdcc01kH7PML0xuCSgnckwAAAAAqkb6OORex+vzsL0/AOwx qhXoplYAQDHYyC89+eSTqrW1NbJvWebQsv2cAMhWUo5JQtrY3k8AAAAASENwvvDjx49b3x8A AIC9e/dG/nxoaEjNmzfPtWjRItXf3+/SbefZZ5/12y9fvtxvb7r9P/uzP9P2Ea9du9b68xTF qFZgFrUCQFZkjJJXv0eOCQAAAEBZBetuZd472/sDAAAg5syZk9hGckRemGxzy5YtfnuT7X/y k5+kfxiAr2x5o/OOmTvPOHvd5u77Asfbw4u17X/muOl7J9328oi+nknq3RObrR9HWRnVCkyl VgAAAAD2yPrSEpMnT2YtAAAAkBs280sjIyPqU5/6VGwfsnf9ZPt5AtBckmOSz35cFGkuzLUj Y0q1rXT3W3JGw4+2OT8/H9n2R6NjqmXmukuOdaHzoNN7O7WPAQCgyoxqBVqoFQBQTGvWrPHv C7Zu3Wp9fwAAADxLlixR27dvd+3fvz+yzfDwsLrrrrtcXlsh/b9R7SUf5bXXbV9yS9OnT0/M LXENBVSX1OV5dXq6+NKXvmR9P0286pj4xV3+fvcvlHz9UGTb3lNjklEad6x3Ot7av8D6sQAA AACon9znePM13H777db3Jy+MagVO3kytAAAAObJt2za/77KjoyOxfTC6urrq2v7hw4dVa2tr ZF/xr/zKr0T+nGsuoJpMckwyjinv69uecUx55Ji/zw+0KTU6Mn5tOd1ceqfl55teUZ9ruU2N vKSfWw8AAABAvsn9ixd5v48BAKAWRrUCg9QKlEmz80tPPvmkNrfk9RHrgusuoJokxzR37tzY HJPMGWF7P5MsHh6VM6G7vzc63hhoH9cmPM5pndN8bHS3+ztZl+mTA6fUX0yapN49OT43BQAA ACDfZB4YL3p7e63vDwAAQNi+ffvcXI6477771MDAgOvgwYOR7b22oqenx29/9OjRxO23t7dr +3vXr1/vP0bXN1yk9VOAojCqFdhQvFoB3TgmmVuiCGvcBtdV0q3BlNRGckwLdpxWxzfNHPdY AAAAAPkmc7MU6R4GAABUm+SJvJBcUFJ7yQl50dnZGdtWxi/p+nqDuSWxe/dubR6KayqgmoLz joejKLV8rzsmffWgv9/B8UYek3FLABBmVCuwuni1AkCV3XLLLf41w549exLbz5s3z2+/cePG Qm/f9nMP5EGwvyXcZwIUhdSie3Hdddcltn/jjTf89hMnTiz99gGgbLLIL42MjKhPfepTKiqk r1jGe0c9Tjf2oCj9yADSJZ/9qJg8ebL1fTOlWy9J93vduksAAKCY8pzTIWdkn1GtQDe1AlUQ rK1jfWkUWdHzP+SXAKA2Bw4ccK9dhMxFJ/cM4oUXXohs//TTT/vtly5d6rcfGhpyfy+5penT p0f2CUv+KG6NpGDuKhhF6ksGkI64tdaKVsu3dmRMqbaV7r7f4Dj6neudn7/u/s5kfBMAAMgn 717oX/7lX7RtFi9e7N8/9fX1+Y/513/918j2X/rSl/z2Dz/8cOLfyPP2bb8+gG3Lli3zr/N1 dbZAEfyP//E//HP7/Pnz/e+C/fv3R7aXvkGvvayd4bV/9tlnC7193fNjVCswi1oBAOUXnKNu xowZie2l3s0LOT8fPnxYtba2RvYHyznZZJ473VxYRetPBtAYyXeXJd98yDHhM1v8Y5CRSgMr Jqmx0aFL1lOSOPg3E5zHvGN9nwEAQDIvJO9i0t5bV0Vi586die1lvoha/kbetg9UmeSTvJA8 k+39AdLyyiuv+O/tj3zkI4ntz5w547efMGFCobdv+7kHABsayRn91m/9ljZfJH2/pvugm+NK xjOldZxGtQJTqRUAiqDRsUuSy5ZzVF7WbjvjmPLIMf8Y2h2T5s1TfZ9tUd/86U/8sU2svQQA QLF4QX4JQJj3eZEa2rzclwBpyHP+h/wSANRveHg48ueHDh1y58wWcv/w/PPPu3TbkfWYpO01 11yj7d9ds2ZNTfsm11K6kPyX7ecOQPYaGY8k54n3v//9bvtPfvKT1o/Fs3h41NmjDne/ZF2l V19arK7/yj7lraUkschx/lSv9X0tCqNagRZqBQAA+eDdZ33zm9/077Pi7rVkrjnvMZLP8drr 5pr7y7/8y8i/UZTtA1W1detW/36AuVhQNEnfBf/8z//sfxfceeedfvsf//jHke1/9rOf+e1/ 53d+p9Dbt/3aAECW5syZk9hm7969Rjn3rq6uyH5gqbup99pI17dcyzgoAMVU73gkyU0H5yz3 4o477rB+TCI4t51klIYfbVOnx0bVzE1SD9Xu/pz1lAAAVWNUK3Dyt0tVKxC+1zF5zOzZs/32 27dvr+lvFHH7QFXIPYzU0EmkOWcL0Cy1fBf80z/9k9/+Qx/6UGL7d955p9DbB4AySyO/JOvX tbe3q6iQ3FIj61EG5x4Oh/Q9237+AGSnnrFLcs6Q+zFdfPjDH3brAm0e16uOiV/c5e+Tl0s6 7/z8Cy+dVaptIespAQBQUOSMyBkBnuCc/1WZg8WoVmCwXLUCuKCW74285X/ILwFA/bZt2+af Ezs6OhLbB0PGK7322mtq+vTpKiqkjzeNHFBwvu5gsNYlUF71jF3SrdkWDmln89gkjzRzp8zF fGE+vAecf0ZH1vq//+bJUTVx2kp19DvXO//7deuvBQAAqF8w4ubd8dx1111++/7+/tJvHyir 4P0M86+g6ILx0Y9+NLH9qVOn/PYf/OAHS799oGyMagU2UCuACxrJL/3BH/yBam1tVVEhuaW0 1qIMzkEcDNa7BMqrlrFLcu+my0OHQ9ZjysN5Y+2Icz5tW+nu042ONwbaL/n9eskx3f01NbBi khobHbK+vwAAoD7BKGL+J+vtA2Xl3Z/Qb4EyCEYR8z9Zbx8AyqaRnNF73/teFRVZ1Nt4cwuH g3UsAcNagdXlrBWoZeyS/G+5ZzMN22OXPIccEz6zxd+vXfe3OD8/fUmbH5wdU5ff/Zj69mdb 1LsnN1rfZwAAULtgFDH/k/X2gTKSufC8WLNmjfX9ARoVjCLmf7LePgCUjaw/Ivkg0dPTo/r6 +lw7duyIbC/tfvu3f1vpIqv+WLnOioq4tVUAFJPJeCSp65s7d672XBQVeaoHPOOY8sgxf9+8 dZXC7TaeHVMtt/2tWtim1Om9nc7PzlvfdwDZMKoV6C5nrQCa50ejzvfKzHXG351R42aRHe++ THzzm9/0783+5V/+JbK93CN57R966CG/vaz1UOTt234dgGbyamnp20BZBL8LlixZ4p/b/9t/ +2+R7f/1X//Vb9/d3e23f+KJJ0q5fQAos4GBAf9esr09+j5S1lHSRZZjiaRPWDfuQObEs/3c AUhH3Hgkb51b+beWcUhe5GU8kmfx8KizVx3uvi1wvD28OLadtNz39+3MgQcAiCRr9q0dOqva lj067jtQvkP+27fa1bsnNlvfzyq75ZZb/Ndkz549ie3nzZvnt9+4MXl8cp63b/u5bxajWoFZ 1ArgguCasN69DlAmR48e9d/j1113XWL7N954w28/ceLE0m8fAMomLr80MjLi/iwqpJ9XxkFl vX/Lli2L/PsyV7Ht5w5AOnRrI8nPJc+sOw8kRZ7GLnmCNeRTHD/dfP24NtJX2Ln/rJyV/WOR sUyb+haokaG11o8BAJA/B5zvl8sX9l/yPdi/UPr3qU+wLc/5H/JLQOOkVs6rg5O5FmzvD5CF oud0TLZvVCswdXytAPklAFV35MgR1dnZ6Vq6dKl7jyH+8R//UU2fPl1FxbRp05qSWxJx4xrk d7afPwCNCc49Ho7HHnvMPd8kxXve857In5uMXfLm3GvWvOevOiZ+cZe/j8F1lUznL2LeIgBA mNQmzNx5xvmGaPO/L3TzsCI93r2Tbu448Zd/+Zf+/dbDDz/sP0Y3f9zXv/51v73MH5f0N/K8 fduvD9AM3vzdkmOijwJl9c///M/+uf3zn/+8/10g67tHtf+3f/s3v/1nP/tZv/33vve9Qm/f 9usAADYE+25bWloi+yql/7bZNf5Ra6gY1Qq0XKgVkJB5Tm0/vwAaoxu75M1JnhQ333xz5M9N xi7JPJuXX365217+bcb9YLj/7wHnn9ERxiQBABoXrlOIm4cV6fDCdP2g4HXPzp07E9tLv1Yt fyNv2wfKLtjfkrd5uYGsvPLKK/77/iMf+Uhi+zNnzvjtJ0yYUOjt237uAcCGuLEBErZyNHH7 lbf5rACYSzrnxIXkhDZs2KBdZynunk03j15ra2tT5jVfOzKmVNtK92+2O87uX2D9tbDFqFbg 5E2sQQAAKTvjmPLIMf87kPGu2fOC/BJQTV5NnPxre1+AZslz/of8EgDUb+/evZE/X7x48bj+ Vi9s19fo5r2yvV8A6qcbj5QUMqZRckTB9W6DETceSfJHSeOdsl6zLVgzLiOThh9tc35+3vrr AQAotqh574Jzq2bFqFZgkFoBWWtIyDxz/f39Pl37L33pS/5jvv3tb/vtT5+Ofj1lrrmov1GU 7QNlJvNre9GMejagWZK+C06dOuV/FyxcuNBv/4Mf/CCy/blz5/z2CxYsKPT2bb82AJClOXPm jPtZV1eXtq91/fr11vdZ9iEqqP0BiqmesUuSN/LWPpL8Ua1jl3T5qHDceuutmR57uGacNS8A AGkJjpGVYG7V5pB5HoJh8pjZs2f77bdv317T3yji9oEyCd6LZF2b1ixGtQIbqBXApWr53pC1 +7z40Ic+lNj+nXfeKfT2AaDMgvmlkZERNX36dBUVcr2Up9oaXV9yHvJfAGpT69glaR9c80iX K5Kcc3js0osvvqgdAxkVzTjvLR4edf5Sh/v3FjnOn+q1/poAAIrvVcfEL+7yv9PaVbXnYW0W 8kvkl1Atwc9LM9ZlBZqplu+CvOV0yBmh6oxqBVZTK4DGLVmyxL0H+Na3vqWuvfZaFRXSRyt9 srb3NUjXnyz9xrb3DYC5WscuhccjyT2cLsL5ZhnvpMtNR4XMvdeM52Dzeee6fcpD7t9kzQsA QFrCY2SZh7U55Hrjrrvu8sm9ludf//VfIx8j88d57WX+OK+9bv644N8o4vaBsgjeyzBfP8oo 6rtgz549kW3/5//8n377T3/60377gYGByPb//u//XujtA0CZbdu2TV199dXaPlPJwejWI7FJ 9kkXeRo7BZgyqhXornatwI033miU65HzVlSeO1wj7EVwvkw5t9SzvlOz6g/D9eXNWB8DAFAN wTGyEqbzsErtw6PXPMicrTUg4sP26wNkwZsXIWreBKDoiOSw/RoBQFa8PtioMT7SF5vn6x5d X7H83Pa+AUhmOh5p2bJlkecik/FIW7durWkcko3zSHhd9YemONee5zdbf30A1M6oVmBWtWsF kF8/Gh1TLTPX+d+FpvOwHnL8vz+2mfWaakDEh+3XB0ibjPPzQu5PbO8PkDYiOWy/RgDQDMF8 jfTn2t6fJJIb0wVzGQP5J59TXZ5YImktt7jxSJKPkjnr6g3Tc0haOfjeU3JNvtD92wscbw8v tv76AACKLzzu1XRuVckZTZq3hXlYa0DEh+3XpxZGtQJTqRWAGblf8OrdZJ4E2/sDZIFIDtuv EQA0i4zZDq9Dkme6eayKkB8DcMGRI0fUe9/73ks+w5IbisvdxI1dWrVqlZtjSorw3/TCZOzS a6+95s7Xl9Y9YrBmnDUvAABpCY+RlTCZW9X7Xjr4NxOc//2O9eMoAiI+bL8+QJqkv8ELaltR VkUPo1qBlgu1As0I268nACCajC2PCqkPyvPcfgAu5c2V9773vc8ox60bu/SBD3zA6Npu9uzZ 2t8l3SPKeWfChAl++zTy2WtHnOv3tpX+NllXCQCQlvB3zANtKnEuO28tJtM1mFD8frisw/br A6QlOI8Kda0oMyI5bL9GAFB2RrUCJ3+r4XkFdOMUijQOC8CY+5k1qf+LG7uUFHK+kFyW7rwR N3ZJcta6vNacOXNqrl1csP+888gpkdsz6fsDAMBEeF2luHlYh5y2i/5xn2q57X63rekaTKAf Lilsvz5AWrw5VKhpRdkRyWH7NQKQf7pzxsDAgHriiSdcS5cuVffcc4/r6NGjkdt59tln/fYr Vqzw28vPbR9jXgXXvwyG9Anb3jcA6Ytbhyku5HFyXye5KV3o8j5xeSYvJk6cWNfxbD4xqnp7 v+XoVX/xta+ptmWPGq95kTdGtQKDjdcKAACyccYx5ZFjdX3PFvW7ywYiPmy/PkAagvcc1LOi 7IjksP0aAcg/k3NGcP2fnTt3Jm7z7rvv9tv39fVZP8a8Cq6BGQ6Zm8r2/gFITz3jkeT8EDwX 1DoeSfI+JvHpT3/a+vMDAEAUbw47Lx6a4tyznN88rp2swWRUK7DhQq3AI0NnVfu3Njk/bHd/ z5ytAHBpH0Vaa7ACAIByC4auDfml7ATXzAwG13JAudQ6dknOAcG5KGoZuyTzpU+bNs3o71x+ +eXMeQEAyK3wukpx894JyTN94aWzzmMWsq4SAIxd2ucg9wm29yeJUa3AauYVAAAgL4Kha/P0 00+7fZuip6dHdXV1uQ4fPhzZfs+ePX77VatW+e1tH2sexY1pqHWdEwD5VMvYJakXlLkzw9sw Hbskj9WNi4wKGeNk+/kBAEDnVcfEL+7yv7dkRb+fbr4+9jE/c3xy4JRaoNpjc1EAUHbB+5C4 NVgBAEiTUa1AN7UCZREMk/azZ8/22z/11FOJ7YM1+7aPNa/mzp1r1G8MoJhuvPFGo1yPjDmK qik0GbskY5CCY01NgvV6AQB5J+ORZu4843xrtfnfXyZz2R0YHVNtC77PukoAKs27P+C6HwAA 1CIYJu3JGaVv9+7d2j5druvQDEa1ArOoFcia5ItMIm4ckW7sksx1Ib+XNZpqGbNk8jcBAMgL 0zWYoh73oz/7ZVWUdZUIgiAIgiCqGravwwBcShf79u1LfOydd97pt+/v7zf6W7aPN69065/Q pwsUn+SQf/3Xfz3yMy75oLi5zWUOu6iQHNFrr72mHdOYFKZ1ibJvsv+2n0MAyYxqBaZSK4By +9HomGqZuc7/vktag6mICIIgCIIgCPqYgbzQBfml5tLNaSV9wLb3DUA6guM/JWRMUVyOR36n G2skY0N1Y5WC8fM///ORPzfJXUsbiYkTJ7LGGwDAmjOOKY8c87/DTNZVKhKCCIZRrUDLhVoB giAIgqhy2L6GA3CBLsgvNZ+uH1lyT7b3DUDjJF8kOaH3v//97jx1Se29/E44JkyYYHStpVtj KWnskuSSwo+V/Wa+TgCALQv2n1cXMksXwmRdpaIgCIIgCKJcYVQrcPJGagUaDNvXcAAuCMbe vXt9nZ2dasaMGa4XXngh8rEvv/yy337RokV++x/+8IeR7aWd7ePNM11fssydZ3vfAKRDcjcm eZq4sUtJIbmg7du315Wzlt/pHnfZZZe55ynyTACAZls74ty3tK30v5NM11WKsvn8mHr0mgfV 2Ohu68clCIIgCIIgiAth+7oMwAW6z+XHP/5x/+d79uxJ3M68efP89ps2bbJ+XLUwqhUYvDFX axBIn60uWP8EqBZdvjkpZN48OZfIv1EhuaeovyePMV3LibnyAADN9qpj4hd3+d9FjayrdMhx /fWbc7MuE0EQBEEQBHEhbF+XAbhA97msUn4pT3T9vNKXa3vfADRHPeORpL03j57kdHQRNR5J ctKmf2/+/PnWnx8do1qBDfmqFQAApC/NNZgkv3T53f1q+NE253+ft35sBEEQBEEQxIWwfV0G 4ALd5/LQoUNun6OQnEd7e7vr4MGDkdv5yU9+4rdfunSp337Hjh3Wj7FIXnzxRe15k3EAQDXU Oh5J1j8KzkNnOh5JHrNs2bKa/hbnIQBAloxqBVbH1wq84/jN752+5Pur3jWYvLn21nWoXMyR pwvb+wWgWIL3G8x/AiDvuP4B8s3kcxlcu33nzp2J27z77rv99n19fdaPsWiCz3cwpB/Y9r4B yJbkfCZOnKi9fgqGjDlas2bNJY83HbskuWxZr62WkLyV7ecHAFBN5x0zd55xvo3a/O+letZV OuH45QcPuo9vZN68NOnC9n4BKA65B/DmI8jD3CdGtQLdzCsAwAzXSkC+mXwuyS81l8xxFRVy vRgcowCgfFatWqW9dgqG5IYkRxR+vMnYpXrXa2LsEgDABskt9b58VrXcdv8l30u15IdOO/58 28vq8rv/b//xkqnKw7x3urD9vAMoDm9tVOkz4JodQBFw/ZN/RrUCs6gVgFl+6cCBA+48d+Lz n/+8uuOOO1z79++PbH/kyBG/fU9Pj9/e9rEWifQFR0V4rAKActF99oMh+aGoxyaNXZLf68ZH JgXjJwEAzSZrH034zJa6vrdMIw/z3unC9vMPoBhkfjsvdPcJAJA3XP8A+Vbr53L27Nl++6ee eiqxfbA+3vaxFonkhqIivCYK4DGqFZhKrUCZyBhFGcP0nve8J/JcETc/etzYJckveXNm1Bqm YydlPBV5KAAAaqML2/sFoBi8Oa/pVwBQJFz/APlW6+eS/FJzSP+srn9X5sSzvX8AsrV9+/ZL PveSi4nL28SNR/qN3/gN7e+8kHWcLrvsssjfmdQ2BufRY+wkUD+jWoEWagWAqtKF7f0CkH/B Glb6FAAUCdc/QL4FY86cOb5XXnklsv2+ffvUtm3bXEuWLPHbP//885HtpUbday/tbB9vkUh/ clTInFW29w1A9uQcIHkfk/s/3XikqLFN4ZA6Rt35Jmk8UtQ8ej//8z+vHnvsMevPHwAARaAL 2/uF+hnVCpz8TWoF0DTB+lX6EwAUDddKQL7pQvJISY+98847/fb9/f1Gf8v28RZJ3HgEydvZ 3j8A2TOZay7uXJEUMu4obrxk3NilpHn05N6VcxUAAPF0YXu/AORbsL5M7gds7w9QNka1AoPU CjQD10pAvumC/FI+zJ07N/L1kWtJ2/sGIB90Y5fiQuZc93I/wbntgqEbuyQ/052bwhG3BhQA AKDPBEDt5BrbC5O5rAEgb7j+AfItGFu2bPEtWrRIzZ8/3/XSSy9FPnZwcNBvv3z5cr/9c889 F9le2mV1HEa1AhvKXSsQvG4Mh8m4BgDlVs/YpeBaTrWOXZJzUtyYpWDcfPPN1p8fAADyThe2 9wtAfsn81hJSM0a/AIAi4voHyDfd5/LjH/+4//M9e/YkbmfevHl++02bNlk/rjLxrgdN+nMB VEt4PaO4kFxPeH0m0/FI8t+6dZd0Uc94JKNagdXlrhUAAEBHF7b3C0A+yRwEXsh81bb3BwDq wfUPkG+6zyX5pfyQ68CokP5f2/sGwJ64MYvhkDnqwvWKpuOR5L5U6h1rCdYCBoDqMqoV6KZW oB66sL1fAPJLruWlTsz2fgBAvbj+AfJN97kcGBhQTzzxhGvp0qXqnnvucR09ejRyO88++6zf fsWKFX57+bntYywDXR8wNUhAdcmcckkh5441a9ZEPt5k7JKuTVKwlhIAAGZ0YXu/AFSbUa3A LGoFAGSPayUg30w+l8H5l3bu3Jm4zbvvvttv39fXZ/0Yy0DXxytz59neNwB23HvvvbE5Hjk/ yJpLUY9NGrskj9PNzZkUjF0CgPKQ7wOZW1W+G6QG3vb+lJEubO8XAABAVrj+aQ6jWoGp1Aqg MSafYfJL9klfsC4YJwBUl/T7XX311ePOC0nrs8WNXfrKV76izT2ZhMk5Sfoo5buFvkoAyI9g LknO0eHvAq45s6EL2/sFAACQFa5/gHwz+Vw+/fTT7txqoqenR3V1dbkOHz4c2V7WYPLar1q1 ym9v+1iLTjf+QNZKsb1vSIdRrUALtQKon+SfP/zhD7vvqauuuiqx/y9u7JLJOkvy2Ouuuy7y dyZjl4K5LcZgAoAd8l0g3xdyTpbrTpO6AvJL2dCF7f0CAADICtc/QL7V+rmcPXu23/6pp55K bB/Midg+1qKT2n1d6Oa1AlAN0o8na/F6ayHF0Y1Hes973qM9x3ghOaFNmzZpfx/Xn6ibR0/6 KuXnJvsOFIFRrcDJ36BWALkUziWZ1BLU+n2A+unC9n4BAABkhagtbL9eqJ5a33/kl+wKzj8Y DOlXtr1vAPJPcjz1hjePnu48FDeWcs2aNYn17tKHyRx4ANCYtPJDUUHOKBu6sL1fVWdUKzBI rQAAAFkjagvTMQhR7Zrx2KjaYpN6Y2kTbhf1M9P9beSxuFQwTNqTX7JL5rmPCum3pfYfQBLd PJtxIWOLvLyP7hyku4aR85L0b5qGnMvIMQGAOcn5SA5fzu9p5pKiop78kjwG8XRh+70FAEDR GdUKbKBWIO+I2sMkJyL3DldccYW64YYbXPLfEiavifTdhB8rPzN9PWW9Bulr8tZTSFoXXMg9 j8Q111yjpk+f7v4rIevzJD1WxmVI/Pqv/7q6+eab1ZQpU9z/LX1cSY/1+tF0tdaEPvbt25f4 /N55551++/7+fqP3j+1zUhno+g7kc2Z73wDkVz1jl8JjI3XnH/m+Df89+Z42WaMjHPIYk+94 AKiaYC4par5Rolxh+/0GAACQFaL2SMqjxPX5JNWJxa3JklQDvH379sjHydrfSe+DW2+9NfKx c+bMSXysl18Kx5/8yZ8kPjbrurwyh42ckVGtwGpqBWrh5WvDIZ8N2/sGIL9qGbsk55Pw9Ydc y+giWBsjY5a82pBGwqQGBQDKilwSYfs9CAAAkBWi9oiq6w2K67NJGh+k62uWSBrPoFvjWyLp faCrSU7q45Z+J13IvVPcYxtZN4JgTFKeyedC95mijh9AlFq+E2WOuqg5NE3GI0kfp1GtQPeF WgGTOhD5vv/ud7+bOL6JcZkAqsLLzZNLIryw/Z4EAADIClF7JOVc4mqKZW63uMfGrWsQt9a2 iJs3Lm7cVNyYKYm4Of7i1m6QiFtDJi4PR0TH3r17fZ2dnWrGjBmuF154IfI5fvnll/32ixYt 8tv/8Ic/jGwv7Wyfk8pCV++fdA4AUE0m45Hi5pozGY8UV4cSDDl/ed/fco2QlDdKqrsBgDKR c6qci+WcKtd19cwdmkYY1QrMulArQNgP2+9bAACArHD9E09XcxaXc0mq9Y37e3H3J0nrKsVF 3LipuDFTEnFz2iTNmRM3VqOedcmrHsHn7+Mf/7j/8z179iS+l+fNm+e337Rpk/XPVtnFjUVg vXsAYVIHopurVkL6MOu59pDvWjnnmNTQy3VGVD2K/N2kx5NjAlBleck5Ec0N2+87AMVnVCsw lTUIAOQb10rxdPkTXc7FZH4b3ViipHFEErp+adlmXMSNmYgbMyUR12eU1N8UXks8iLWXao/g 80d+Kf90ny36YQHohGs+pI8yaV65uLFLX/rSl4z6OXVz7nnkd0nf+fL7uG0AQJWEc07NuPeR 7wu5L2wG289vM+nC9n4BAABkheufeME53YxqBVrGzyug6x82mfNNN5YoaRyRhK6PyWTOG93z kdTvpJsPMG7tJS90azDp8nBJazZVke41PHTokH9/J+/H9vZ218GDByO385Of/MRvv3TpUr/9 jh07rB9jWcXlfemDBaDj1bZMmTLFaLyjrs/yqquuSvyejptzL0zOW0k1KeSYAEBPzo9yfSj3 bnI+TTvnVLW8T7PowvZ+AQAAZIXrn3i6vIguj2Iyj5tufFBSP4yEbl0lk7lsou4hTMZBSUTN s5O0npIXUX1Hutxa3BimqgqGrk1wPa2dO3cmbvPuu+/22/f19Vk/xjLTfTbj5qFEtoxqBU5O Z14BFIJ8xyaNWfI0spZh0px7OknXRXI9xTygAGAmzZwT+aVs6ML2fgEAAGSF659ktayrZHqN H/V3TOakiVpXyWRskERUf7bJOCiJqDn+ktZT8iKq1lnX32RaF10lwdC1Ib+UX7r+3KQ10gDA RL19i6Y5KZ2kawA5x5E3QpaMagUGqRVA+YVzTiZ1hxLkl7KhC9v7BQAAkBWuf5KZrqtksp6S 7nredByRRLi/xnQcUdS4KZMxUxJRc/yZ3rtEjUnS9YcxT854wdC1OXDggDt3nfj85z+v7rjj Dtf+/fsj2x85csRv39PT47e3faxlpXu/69ZmAwAT9YxHku/utPI+SX+/lvn0AADJ5B5S6gPk 3izqXqzR2gFE04Xt/SoDo1qBDdQKAACQZ1wrJdPlb8I5l1r6ecJjiUzHEUmE7xtMxxFJhI/N ZMyURHg+QNMxUxLhdZJYT6k2ca9flNmzZ/vtn3rqqcT2wbFkto+1rHRrnvGeB1Av+R6udexS FvNymlz7kEsHgOwEc06cb7OhC9v7BQBAHhjVCqymVqDMuFZKZroGk8naS16ExxKZjiOSCK/B ZDqOSCI4bko3ZuqDH/xg5M+D8wHqcm5XXHFF5M+D45JYe6k2wTBpT34pf+LyscxNAqAeurx1 VMj1SpbnGrmeSKpXoaYeAFBUurC9XwAAAFnh+seMybpKutrgiRMnJj7HpmODJIJrsej6on/x F38x8ufBemTdOCiZ+ywqgv09unFQt956a+TPg3PesJ5SbYIxZ84c3yuvvBLZft++fWrbtm2u JUuW+O2ff/559/dGtQLd0bUC0i/obVu2afu5KSvdZyScLwaAJHKdcNlll0WeU8Ih555mzD1r kmOKmmsXAIC804Xt/QIAAMgK1z9mktZVipvHTTfeyKsP1o0NknFLIiq89RB0Y4Pmz5+v3aZ3 TLr90uWdgn3bunzbQw89FPnz4Hgj1lOqjS4kj5T02DvvvNNv39/fb/S3bB9vWcWtuRbMUwNA EvmeNqoVmHWhViApbKx9JOe9pDHV5JgAAEWjC9v7BQAAkBWuf8wkrasUN4+bLl/jjSXS/V5+ rpvTxhtLFJf30uVxvGPS1Q7r3hfeuKm4OQCT1ktiPaXa6YL8UvHo8sXM9wigVnIdMGHChMhz ipxrbNVsyN9NyhvJ76kpAQDDeQWmsgZBkejC9n4BAABkhesfM0nrKsXN+RY3PkkeqxtHJI+T MU5R4Y0lipu3T7dPss2kfYobN5WUa4sbn8R6SrULxpYtW3yLFi1yx6mJl156KfKxg4ODfvvl y5f77Z977rnI9tLO9vGWme6zI7lb+loB1Oq1115TV111lX8ukfl487B2kZzPktaKJMcEACgK XdjeLwD5YFQr0EKtAIDy4lrJXFwuJ2nOt7ixQlG/C66xFBVef3RUeDkvXS5HxkQljamKGzeV NFdgXK6NtZdqF4zgzz/+8Y/7P9+zZ0/idubNm+e337Rpk/XjqirduSIPfcIAiserF5kyZUru 5trUfed7IedDb75fAADyShe29wsAACArVbv+MaoVODmt4VoBXV5Ft+ZQcM43XR3vY489Fvnz 4FpHurFEX//61yN/7o0j0s1FJ9tLWhMqbtxUXJ5NHhs3Rom1l2oXjODPyS8Vk+7z4eWFAaBW ec7R6K6dvJB6mTzvPwAAurC9XwAAAFnh+secbr6qW2+9NfLnwXncdGOA7rjjjsifB8cn6MYH feITn4j8uTc2SOhyNHFjpOLeGzKfTlQE+7t1Y6V+7dd+LfLnrKcUT/f6DAwMqCeeeMK1dOlS dc8997iOHj0auZ1nn33Wb79ixQq/vfzc9jFWiXw+dJ8/xuaZM6oVGGy8VgBAc+ny6l7I+ZNz JQAgr3Rhe78AAACywvWPOV3e5IorrkjsK9atYfTBD34w8ufB+lzd+CBdH3Vwzpuk+WaC4a2n 5NGNhYoKbxyUJ2m97mCwnlK8YOjaBF+rnTt3Jm7z7rvv9tv39fVZP8aq0dXohz+DAFAWSXkj iWB9DKrBqFZgA7UCAIpDF7b3CwAAICtc/9SmlrxJeB43XT4oHMH1lJJep3CE580y6c/xwltP yaMbNxUV4T6hpDlugkEdcrxg6NqQXyoW3XyUEsz9BKCs5PyWdC3E+nIAgLzRhe39ygujWoHV 1AoAAFBWXCvVxjRvEjXnm24do3AE11PymI4lCo8jiuvHDoe3npJHN24qKsLrhOvmEowK1lOK Fwxdm6efftrN8Ymenh7V1dXlOnz4cGR7WYPJa79q1Sq/ve1jrRLd2MLwZxgAysQkx8R5EACQ J7qwvV8AAKTFqFagm1oBRONaqTameZOoOd90azCFI6p213QsUdTcMro1mMJRy/sjGOExU0I3 l2A4WHspWdJrFDZ79my//VNPPZXYPpjnsH2sVRKXvyXnCqDMpCYlaTw4OSYAQF7owvZ+AQAA ZIXrn9qY5kKi5nHTrasUjqg5r0zHB4XHBgmTdZV0a7mYjIXS9euYzA/IekrJguH9zKhWYFZy rQD5peLQfZ7Cc1UCQNnItVfSNYX8nnw7AMA2giAIgiAI4kLYvi7LM5O8ia6PI2mel6j1lEyv VaPGBgmTdZV0fdQmY6F0a2ybzA/IekrJdLFv377Ex955551++/7+fqO/Zft4q0T32Yw7DwAo D6NaganMKyDkuipp7mByTAAA2wiCIAiCIIgLYfu6LM+S8iZx87gl9Y1ErafkSRpLpBtHZLKu Ung9JY/JuKmoMVPCZH5A+oGS6YL8Ujno5qTU5W0BoGySxlnLeTJqbDcA5IFRrUALtQJVQRAE QRAEQVwI29dleZaUN4mb8y1pXaWo9ZQ8SWOJ4vqjk9ZVqvcaWTdmSiTNJch6SmZ0QX6pHHSf az4fAKokqXZHxnWSYwIA2EAQRPnDqFbg5MfcWgGCIAjCLGxfw+VZUt4kbs63pDWY4vpOksYS 6cYRibjaYN3aS564cVNJa2rHzSXI2ktmgrF3715fZ2enmjFjhuuFF16IfOzLL7/st1+0aJHf /oc//GFke2ln+3irJu58ohtXCABllDSfr+SYmFcXANBsBEEQBEEQxIWwfV0Wx6hWYPBjuZlX IC5vkjTnm24NJpM1V3QRN45IxPXZ6NZe8sSNm0qawyuuHpk+IjPBCP784x//uP/zPXv2JG5n 3rx5fvtNmzZZPy5cpMv/xs2XCQBlZLJmJPOHAgCajSAIgiAIgsh3fikP/viP/zjyebv66qsT H9ve3h752FtvvTXxsbpc1Zw5c2IfF7euUtK4h7ixUHHjoETc/ICsp2QmGMGfkzMqj7jPZ9Jn rMiMagU25KdWAED+yFhuXS2OF3HzCQMAkAWCIAiCIIiqhu3rsKLw8iaTJk1Sv/Zrv+bmlSRM 5nHz1lW65ppr1PTp091/JUz6P7zxQeHHmtTnyrilK664Qt1www0u+W/T11wi/NikcVDCm8/r 8ssvV9ddd52aMmWK+79ZL8ac7vN56NAhN/cnZEyL5C3FwYMHI7fzk5/8xG+/dOlSv/2OHTus HyP0c0syNySAKjLJGyXNu4vmMKoVWE2tAAAAKA+pA5W+Femjk2vSuLUiqhpyLS/Pi/RpSH+n PF/UmQPARXJODI8riPqZ7rHhc2rUz9J+bNS+mY6NaOSxUetEse62uWDo2gSvZXbu3Jm4zbvv vttv39fXZ/0YoR/rJ9dkXIMBqCK5zoibY1iCHBMAAI0zqhXoplYA1ST9V1LTLXkS6XtJqoEi 4kNq1WV+f8nNST8I/R0AAGQrGLo25JfKQa6zooK5nQBUldxvJuWY5PfclwIAyoqID9uvD/JP rhMljyH5DPJDzYtgHilpXQ6kw6hWYFaxagWI+LD9+gAoBpPzxoEDB9x57sTnP/95dccdd7j2 798f2f7IkSN++56eHr+97WOtOt1a9SZzUQJAWUmfgNybxgU5JgBAWRHxYfv1Qf7IGHi5t5Zx 7kl1SlmHN0ecR8ZASb7FI/vprWOQhuC2g3P3eWyH9xwwbqnaiPiw/foAsMuoVmBqbbUCtZ5j Zs+e7bd/6qmnEtvL9QbnsHyQ6ytdLZVcf9nePwCwKfh9FRWSi2f+XQBA2RDxYfv1gX1yH+3l k3RzgmQRkrsK5oyCuSLbz4nJc+btq8yXEsxFNfs5lOevCM8Z0kPEh+3XB0Ax1HreIGdUbHK9 FBVy7WZ73wCMZ1Qr0FKseQXKQnc+9UJy9OSYAABlQsSH7dcHdkg+Qq4Lsx6f5OWQgvkj07XI i06uqb0xUc1ae0r+huS7uJ4vNyI+bL8+AIohGHPmzPG98sorke337duntm3b5lqyZInf/vnn n49sL9/FXntpZ/t4q06uP3XBdRMA6OcH9ULuZRnHCaAWRrUCJz9KrQByj4gP268PmsMboyRz JmeR4/DmsfPySNynx78W3rgnL++URcg4Kqmd5h6gfIj4sP36ACgGXUgeKemxd955p9++v7/f 6G/ZPl7o53WSn9veNwDIg6Qck4S0sb2fAAA0iogP268PsiO1l5KXMB13ZFQrMHihVsA0grkk yZOwDlA6JCcn1+pZ5Zwktyjb5/UqPiI+bL8+AIpBF+SXykuuW3XB9REAXCD3pUk1qdInYXs/ AQDIwpYtW3yLFi1S8+fPdw0NDUW2Hxwc9NsvX77cb//cc88Vdvu2XwOkr9ackkl46/ZIvqEq 89XlhVyvy+sp+Z4012oif1Qewe+Cf/iHf/C/C77whS9Etj937pzf/oknnvDb/9Ef/VEpt19F RrUCG5hXAKhFMHTX4C+99FLkY+u5xrd9vLhAdz0tdVO29w0A8sIkx8TYTwBA2d1yyy3+996e PXsS28+bN89vv3HjxsJu3/bzjnSknVPy8kkyVxr5h3yR11ryQnJ9nla+iVxTeRw9etR/Xa+7 7rrE9m+88YbffuLEiaXfPgAEBSP4849//OP+z2u9bt+0aZP140I83XxO0jdqe9+CjGoFVlMr ACAf5D41qT+CHBMAoMzymv/Jevu2n3fUz1sjKY250YJr75BjKBYv35TWWlmswVRsRc//kF8C 0Ey6sL1fyJ6uRod1QgDgUtI/kJQ3kt/TjxDNqFagm1oBAMi7e+65x/XMM89o2wwMDLjz7Iil S5f6j5G+rqj2zz77rN9+xYoViX8jr9u3/dqgdtL3r1tvuJaQfISMeWK+u3KRtQJk7FmjY9mk 30W2w/ujWOS+zvsuePTRR/3vAnkto9q/8847fvvHH3/cb79w4cJCb9/26wAAyDeZ3y4q5PrJ 9r4BQN7INbr0H8QFOSYAQNF40dfXF9vOqFZg1qW1AsHxIDt37kzcl87OTuP9yeP2kX/Sxy/3 wY3MhyZjWxijVC3evIlJ9wJJIfcKzJ9XPK+88or/Gn7kIx9JbH/mzBm//YQJEwq9fdvPPYrp vGPmTnkftbnvowWOt4cXa9v/zHHT90667eURfT2T1LsnNls/DgDJ5JpGF1KrY3v/ACCPkupc pb9C1mKyvZ8AAJjwwiTfIvKW0yFnVF2S32mkvz+YJ7J9LLDLmyMxjfdT3scpGdUKTGVegaA8 53/ILyGv1o4415htK933keSMhh9tc35+PrLtj0bHVMvMdZecUxc6Dzq9t1P7GAD5oesnlesq 2/sGAHkl8wok3V+SYwIAFIH0qYpVq1aprq4un679008/7T+mp6fHb3/48OHI9rKGUdTfKMr2 kS+SB5BxJPWOPyKnhCRp5Jokj826A/aYfJeNjIz43wXf+c53/PbLly+PbP/v//7vfvt169YV evu2Xx8U16uOiV/c5Z/r+hdKDnIosm3vKalhWjju/Hin4639C6wfS9EZ1Qq0UCuA5pDaGl3k ve4GQDmEw/b+mJJr87iQ/gv6LgAARRGuOzN5zOzZs/3227dvr+lvFHH7sEfuTetdK4mcEurl 5ZqC4x5rCcmDynyMzIfXXMEwaf9P//RPfvsPfehDie1lraIibx+o1xnHlEeO+e+3B9qUGh1Z O66dbn680/LzTa+oz7XcpkZe0s+XByAfdNc/urX8ANTGqFbg5A3UChgIh+39qUVSjkmCGjEA QBGQXyK/lEcyb3u9ffveGBL69pGGRtbuKsrceWURDJP2ecv/kF9Cni0eHnXeaR3u++1GxxsD 7ePahMc5rXOaj43udn8nay19cuCU+otJk9S7J8fnpgDkh9Rm6a5ruL4GkLVw2N6fWsncdnK+ jAuZm8X2fgJojFGtwGC6tQJR84sHI3j/BdgUjOeffz6x/V133eW37+/vL/32kS3JC9XTj++N F6EfH1mSvGe94+lk3j3Wu26eYHz0ox9NbH/q1Cm//Qc/+MHSbx+oVfBeRreuUlIbyTEt2HFa Hd80c9xjAeSL7nqcPlEAWQuH7f2ph0mOSe4rbe8ngPwYcu6lrv/KPuXNBfHldkVdHnIlGEXM 6WS9fWSj3lyR9MObzGlnVCuwgXkFkL5G1gOT8XXkmbIXjCLmf7LePlCr1x2TvnrQf59F1caZ jHECUAy6eZvk2sf2vgFAEUjt67Rp02LvDckxAQgKzgcRt+YtYEMwipj/yXr7SI/0u8s4oqRa nXBIe5nTnfFHyIN6xymRO8pWMIqY08l6+0VlVCuwmloB23RrK+l+r1ujCUAxyDW97nqe9UAA wIycS5NyTPJ75h4FIIL3VNxPIW/27t3r6+zsVDNmzHC98MILke1ffvllv/2iRYv89jt37izs 9m2/BmVXb15JaiC5R0VeeWsv1fq+Js+UjeB3wZYtW/zvgrvvvjuy/bvvvuu3l+8Xr/2sWbNK uX2gHmtHxpzbl5XuuesGx9HvXO/8/HX3dyZjlgAUi9Rz6a5dGtmuUa1AN7UCAKpH+kpkjpa4 IMcE5JvkfR4ZOqvav7XJ+cS2+59dqaz7Ts8k1dfbq949sTmVv+XdnzEfBPLslltu8T8He/bs SWw/b948v/3GjRsLu33bz3uZ1dP/zlo0KJp65nskz5Sdo0eP+s/zddddl9j+jTfe8NtPnDix 9NsHTB1yTPjMFv/9JfdIAysmqbHRoXHrxx78mwnOY96xvs8A6ie1M7qQ9UJs7x8AFEnSnBdy /1ikc6tRrcAsagVQLbKG7CcHZG6R9kvuleR3m0+MqvZvbfZ/19czqeE8U3COPO6/kFd5zf9k vX3bz3sZ1dPfLtdfzIGHIpN8keSNagnyTOkrev6H/BLy4oxjyiPH/PdXu2OSc+3U99kW9c2f /sQf20T9HFAeuv5Q1gEBgNrpxoV6IbW4RcoxAVXQe2LUuc/5nH//c3b/gkt+L+OVel8+q1pu u99tIyvMDm+Se6HX6/p7wXnvmBMCWTOqFZiqrxVob2937dixQ/s3Dh065PZzeuuLeI85ePBg ZPuhoSG//dKlSxP/Rl63b/u1LZOtW7fWlFeS6ykZ40ReCWUi55WkeQ7CIXkmPgfp+NnPfuZ/ F3z/+9/3vwtkDtWo9v/+7//ut5fvF6/97NmzC719268DymHx8Oj/vmu6sK7Sqy8tVtd/ZZ/y 1lKSWOQ4f6rX+r4CaJx8l+iC6xQAqJ3U3ib1idAvBeRDuL5Oov9zLWps9Mi4tt88Oapabvtz t014LvFa9Z6SNZkXjlvHFmgGL/r6+ozaB+vqdesZBUlfVi1/I2/bR2NqHYvh5YqYGxhlJn0r SXMdhCM4Ns+oVqCFeQXS8sorr/ivw0c+8pHE9mfOnPHbT5gwodDbt/3coxyC8+BJRmn40TZ1 emxUzdwk78129+fU2QHloltvXq7zbe8bgPIJh+39yUJSjkmCtacB+8JrzEpI7ui1zR9zfn/+ krYy7qhz/1n/nkjXzoR3z+Xdb9WzDaBeXpBfQpqkH7yWsRfklVBFteaZ+JykJ885HXJGKILg vN0SXn5I7pG+8JJzj9S2kPm8ExjVCpz8dWoFUEi6flC5lrG9bwDKJxy29ycrMg9e0trU5JgA u6LGL0no7o1OOH75wYv5qCVtSr17orehv9u/UM6DQ9afC1SHzIMjenp61B133OHTtT9w4ID/ mPvuu89vv3///sj2R44cifwbRdk+aiP93tL/bRr0lwO155lk/kjuG/RMvstk/jjvu2Dz5s1+ +3vuuSey/fnz5/32P/jBDwq9fduvD8ohOMe3xAPOP6Mja/3fy1wPE6etbGiOBwD5o5vDmusS VJVRrcAgtQJZC4ft/cmSSY6J9e4Au4LrLymlEscUBecTl9h1f4vz89M1/11vO+F7M6BZwn2b Jo+RdR+82L59e01/o4jbRzy5r0y6zgkGeSXgUnKvUMt8ktKWtVzHq/W77J/+6Z/89h/60IcS 27/zzjuF3j6QlrUjzuetbaX73rvR8cbApWt2rZcc091fUwMrJqmxUerngDLQ1ZFJ3sn2vgEo l3DY3p+syX2dbm5RL8gbAdkwqhXYYF4rILV4vS+fVS233e/mlpLuh8JzQ9Q7hsmbIy/q3gxo BvJL5JfqVWufeHAdGQDj1bpu2bJly8jVBtT6XZa3/A/5JRTFIceEz2zx339RdXY/ODumLr/7 MfXtz7aod09utL7PABoj1xu6YG15AGkKh+39aQY5xyblmOQ+kXs/IP/C8z3Uuy5SMPdU77gm oBHS779t2zbfnDlzfMPDw5GP2bdvn99+yZIlfvvnn38+8W8Ucfu4lFynSF+1aaQ1fsKoVmA1 8wqgWmRMoG4emnDI2EHmprlA9733Z3/2Z5Ht/9f/+l9+++9+97t++8985jOR7UdHRwu9fSAt 4bnFvTWYwu02nh1TLbf9rVro3FSd3tupWGsWKDbdnL6y9qrtfQNQHuGwvT/NIn0ySbWGkoMi xwTkX3C+B4mvtys1enZzTdsI5ql091sAkBdbt2417suWdtQoAvWT+WVM55OU+wvG/KEIjGoF uqkVKJPgXOELHG8PL45tJy33/X078+UBBSbXJLrgegVAWsJhe3+aLWl9XskxMVc6kG/hee/q nbfOy1PF3W8BgE1yHyj1hiYh/eFr1qyxvs9AGdQ6XlByUrb3GUD5efN2S0xx/HTz9ePaSB1d 5/6zzm/b/XOUjE/a1LdAjQyxlixQRF69vFGtwKwLtQLhYG0QAFEkDyL1qULqWuW+Jkj6I+Qc FBSO8O898tjw9uRveH+vyDmYpByT9M8U+fiAsgvP99DoHHn1Ph4AsiS5ItMxFKzxAmSjljXM qFMDkLVwnV1wnu9g7ikuWFMWKB7pj40KuVfgHgCoFi8fFM7/mM530syQfQrnmfKeV5J5zeNC zruNzhdjVCswlXkFgKwF53uQqGeOu2CeijnyAOSFjFky7c9Oa40lAPHkPoN8LwDbwmvRPuD8 MzrCmCSgCnR9x8xfAJSP3Et4OSS5t5D7ftN7kSKFl3+SY5RzmRxzHu6jknJMEqyvC+RTuOau 3nnrek+MOrdcn+N+C1pGtQIt1Aqg+UzHLEkbrmeA5qplzjy5VyL3CyALwbVo2x1n9y+wvk8A sqfr75RrDtv7BqB+cs8gn+8y55FqDXkOvLyTPDc27qsk15X0WtAnk3/y3gnP0Yh8a/RzFZ7v QTeneBLvnou5HwDYVMs4JJm7Nw91OkCRGNUKnLw+9VoBuUaVufBMgnWZAKQtWJPHPN9Adci9 gq6vkz5OoBjkc+ytcWTaV0BcDHnOvDWcmtF/Ivd9STkm1rbLL5PXj8hfyOe8kdc9PN+DRHBO 8VofW8vjASBNcr1j8j0m9YaNzt0LID1yv2Ly2WWtJQBpCq8vW6W5u41qBQbTrxUAikg3jrrR vhgA2ZCaU8n/Sg4ij+sjFT3kOZXnVp5jea6zeA1NagvJMeVPMLfkjYdDMci1TqOvf3C+B4la 57jbfGJU9fZ+Sy1at0613HY/c+QBaCqpoZG1K02CNVqAfKpl7CFrHgBIS3B92UWO86d6re8T gOzJdYcuqGVBURjVCmwof62ANz7JRj5J+iHuv/9+1+OPP676+/tdch45duyY780332z4OGUb wW3K3/D+nvxtbz9M+0bSCi/flPb4JtlWUo5J7h/p38mHcG6J79LqOeSY8Jkt/uezXTGnOIBi kHFIJteQjFkCisF0PTTuJQCkYfP5MaWmPOSeV5jnG6gW6Q+NCmriATukP1ruBbKew+6mm25y 8zAPP/zwJbmgc+fOWX8Oksg+BnNLcgxyLHJMWYa8JvLapJEzkHu4pNdY8krc69kVzu9J/tD2 PpWVUa3A6vzXCoTnhmBOcQBFIHNqmQRjloBiMR3LJHko8sYAGhFei5Z5voHqkGsIXWQ15xOA S0mftdyvZzHuyMsjSR5mcHDQzcvYPt6syTHKscoxZ5V3ktdKXrNG8w26HL8XzI1uTzi3xHqD 1Rac70GiSnOKAygWkxoW71om7f5ko1qB7vzXCgBlYzqWSXLNtvcVQDGF15N9aIpzv3R+s/X9 AtAcurmXuLYAsuPNZWdynW8akkd58MEH3bxKUcYiNYs35kmeG3mO0sw5yWvozY1Xz74l5ZiY Z635yC0h7EejY6pl5jr/PbHA8fbwYuv7BQBBki8yubaUaw/GIQHFZ7JeqwRz4KEWRrUCs6gV wHi9p8acN8hC7peACpJ+M12fpu19A8pE7vnTzCl1d3e7aw7JWJ001jaqGnnO5LmT51CeyzTC yzXVWg+sOw8Ht8ucFc0TXL+LWguI8HwPzCkOIG9M5riT6wnmegXKR+ZVMPn8cz8BoFbBOjvm /gaKw6hWYGq6tQK6ebmo2QYaI/NMpjX3nZdPkjo1xialT55TeW7Tyjd5c+iZzjWalGOS4Jyc veB4MtYihCc834MEc4oDyAMZkxCsi9CFjF9g/nOgvEzHL1I7BaAWa0fGnFugldwDARWlq2GT Pk/b+wYUjdy7S9++yfwDcSHzsj388MOMObLEG7Mkr0Gjc+TJe0HeE7q5JoxqBVourRUwuS8k x5QdckuIE763eqBNqdGRtdb3C0B1SV2MSQ0T/clANZiuvyY5aebLA6CzYP9550wxJfL8wT0Q UC1yvaALxkUDZuS+vdE57bxxRydOnLB+PLiUvCaNjlfy5sSLWyNJfpf0HiKfkb5gnYXcR9ve H+RPeF2ldsfZ/Qus7xfyw6hW4OSvsQYBmk7qTZKuLZgPC6imNWvWJN7DSK0ca7wCiLP5xKhz T/0t9776L772NdW27FHmCQcqSLduPP1sQDy5Zzep/dKF5CsGBgYYo1Qg8lrJa9ZIrkneM7rx RSbr75JjSk9wvkF53qnRRJQzjimPHPPfK8wpDsAWk/VT5DqD7zOgdka1AoPlqhUwmReBNdkA AEASmWNbF8y/DVxK7telLqPesUrklMqj0VyTvIfkvRTuA5L/nZRjou+oceSWUIvFw6POO6XD f8+sc/5zbHS39f0CUA2m6ycxxx0AYToHnoxhsr2vAJAmo1qBDeWqFQDyQnft8Sd/8ifW9w3I A8m16sb6JYX0B8j8auSUykteW3mNTfp+okLeW8F8vsk9ITmR+kkNJs8jarH5/JhSUx7y3zcL HG8PL7a+XwDKT64PkupOGF8AIIrJmEfmRgAAADqrVq2KvH6QPkvb+4byMqoVWF2cWgGZM6De nMHDDz+sjh07Zv0Y0FzymstrX0/Iey24BkJSTpM50WsXXOdK/uX5g4lDjgmf2eJ/9pgjD0Az mKzNyLUAgDgm67BRbwUAAMKS6lTk97b3EWg26bevZ70kmf9scHBQnTt3zvoxwC55D8h7oZ45 8eS95+WOkvJG5D3MkS/KhlGtQHdxagUa9aPRMdUyc90ln9MlbUq9e6LX+r4BKCeTPmGpS6FP GEASuT6ePHly7PmEXDUAABCm8+zq1pYHyqjevBLjjxCn3nFKXp4puP5PVEifUnAsE8Yjt4R6 nHesHTqr2pY9GvnZW9mu1PAPZO67163vK4DySPrel0h7/SSjWoFZ1akVAKrMpK+I62kAAKrN pCaF6wVUST15pZtuukn19/czVgnG5L0i7xl579QS8t7UzWMaDOoBosk9cnA9CtadgIkF+887 75Ypxp/TB9qUGh1Za32/ARSbybhlvu8B1MvkHMO1cnEZ1QpMpVYAANAYuVYwmVM3uH48UFaS Q601ryTzi8i8Z7b3HcUm76Fa1+uSc/MVV1wR24Y+p0uFc0s8PwCAvGJOXADNYDJGkmtmAADs MKoVaLFXKyDzKCSF3NcwNzfKTvKnSffw4bj//vuZAw+pk/eUvLdqife+972J53Hbx5UH5JYA AEUQ/r6KCur/AKTJpO54zZo11vcTAABkR+5DTGrfuSZA2clnwSR3GgxyRYhjVCtw8v9ItVag njxTXJBjGrvkOzLtdSoAAEiDSW5JxtpT/wcgbcH1SbmnAACgWqR2Lek+hHlzUQUyHiHpmjgY 5JVgQ5q5oyr3MQXHHHKvCyCJUa3AYLq1AkAtTHJLfN8ByJJJ3xLnIQAAymX37t1G6ycxNzfK TD4HSdfBwSCvhDxIK88k7/2q5ZjILQEA8k7uv5KuT5ctW2Z9P4EiMaoV2ECtQCPIdQMAUB0y 111SyHxAVet3RHXIe7uWdZPIKyGP0sgzVamOIDj/pXzH2d4fAADCTOaZYh1AAM1kcu/M+tsA msGoVmA1tQJA1kz61Guth5PrCBkHwroUKALJr5rOhSd90OSVkHfyHjVZR08X8nkoe45J+uK8 qOK4LQBA/pFbApBnSX1JXGMDAFA8JuOP5R4l6T4kmB+SPsrJkydfsg2ZS9f2sSIfjGoFuvNZ KyD37LLmjEncdNNNanBw0Po+A7WQ96y8d+sJ+a6Q7wHbx5AFcksAgLxLyi2Z3NMBQNbIMQEA UB4m9W2SJwrXpCflkqJiw4YN1o8XCAvOdZUU/f396ty5c9b3GaiHvHflPVxvlK0/auvWrf6x cQ9bH6NagVn5rBUAgLIxyS2VfZwxgOKQ+W7igutzAADyL1iLHfedfuTIEbcfrpZcUlRcd911 1o8Z8JiseeyFrE3z5ptvWt9nIA3yXq53vaWy5JiCfXD0twEA8qhZ+SKjWoGp1AoASF9SnxQ5 JgAA8iupVkTiqquuMl5fxjRqXZMJyILpmCXJp9LvjLKS93Y96y3JfBa2973R4ya3BADIM8Yi ASgyk7yR7X0sG6NagRZqBQAAjZPaD9O1Y7IIuRei/gS21DJmibnwUAX1zplX1BwTuSUAQN6R WwJQBkk5pqLeTwAAkBWjWoGT11mtFZD7EBmTZCuuvfZa7oVgjemYJZk37MSJE9b3F2gmec/X Omee1CoUqV5A9jWYX5Z5X23vEwAAQfJdFTcXObklAEVCjgkAgPwz7TPPImRepSL1LaI8jh8/ bjQO76abblIDAwPW9xfFYVQrMGi3ViBL8nmRz41pFGVO9HBuqSzrSAEAyiP8XRUOcksAiogc EwAA+dfe3m7cFxgX0l8v6yPdeuutiW0lr2X7uFFNMubAZF0wGYvx5ptvWt9fIA/ks1DL+CTp 38pzHxb5IgBxjGoFNpS3VgDFQ24JQJmRYwIAIN/kfqSWkPsTySVJjkj66r17laT7Gu+x9OPB Fsl/JgVjlgC9WsYn5bkvS8bPekG9AwAgj8gXAY0xqhVYTa1AUSTlmNasWWN9HwEAqLIbb7xR e+8SzCXJvGJRj09aR1ZC5gLnPgg2yPs2Kfcp0d3dzZglIIF8RuSzYhIf+MAH1O7du63vc5DU N3pBrSMAII+C31VR92fcUwEoi6QaUOqTgWozqhXoplYAyAP5Tr/iiivcPFNSLilMvu+TcktF WWMD5WM6H15/f7/1fQWKRD4zppGX+0JySwCAvIvLLUnIta3tfQSANHHeAwCgXEzmEKNfrjqM agVm5bNWQHKlSSHzZB07dsz6vgJFJJ+d4FxzcWE7xyRzZgQ/97afOwAAwoLfVXn8LgWArDBu EwCA4pPxRTIXXt77CFFN8v406cd+8MEH1blz56zvL1Bk8hmSz5JJdHZ2WtnH4BzsjJFNZlQr MDWftQIAUAVJa4twDwag7OLu9yXHZDrHDgAAaD6pBUlan6aWmhHp55M1N2T8SHt7u7r66qsZ 04y6mbw/JQYGBqzvK1Am8pkyiTlz5jR1v8gtAQDyTq5f40LmjKh1m0a1Ai3UCgAoJrmmj7vv 57ofAIB8MlmfRr7HdbUiwVyS1JtMnjw5chvye9vHiuKR91bS+1PedydOnLC+r0AZyWfLZOxg s+735JzAPSYAIM8ktxR3/cpc4wCqJClvJPPo2N7HZjKqFTh5LbUCAIBCSJrj27v/8frvJMck +SjJFck1QFK/fzCuuuoq68eLYkmaJ0Ti/vvvZz48IGPyGZPPWlLIWNUs9yPYX8c87ACAPKIf FQDGI+8OACgTo1qBwXLXCsh9T9z6iV58+tOfriuXpAvmDYcpk/dnf3+/9f0EqkQ+c0mR1b0h uSUAQBHErWfLuFsAVZaUY6K/CAAAe5Lq5LIO+vkQxySfedNNN/E+Qk2MagU2lLtWwAb5nMrn NS6C417TIGNnyS0BAPJO1lTShXx/kVsCUHVJc5TIXNi29xEAgKqRfrYrrrgi9js6y7j22mvp 64OWSe6T9ZMA+0zWWUqr7jp8XpB5V20fPwA7jGoFVlMrgPyL6zOlNgIALopbp0HOl7q1vwEA QPrkPsVmSF8kdXjQkevCpNxSd3c36ycBOSGfRflMxoV8phu55wvnlpgHAwCQR0nzOFEbAVxk VCvQTa0ALoqb34S5RAEAaK64Ob7rCbmPuuqqqxLbybpMto8d+ZV0Ty7x8MMPW99PAOPJZzPp e6KeOmxySwCAIpDvq8mTJ3MfBAA1iqsvzWqdVgDpMKoVmEWtAFBGcv9z2WWXxfYFxvURSn5K 7pGkBu/IkSOJ403kMfQJIo5Jbqm/v9/6fgLQk89o0ndBrTmmYJ0ifXMAgLyKq9+jfxQA9JLy 8zI3nu19BACgKhYtWhTbtych39vB/FB4ziKTfn7ZBvOB54NRrcDUfNcKmLznBgcHre8ngGTy WY2LWnJMwdwSfXMAgLyS+ypdMGcTACRL6hOg/wkAgOaQfFEwJA8k6x3JPc/u3bsT723i1pjl Hgmm5H0Ud2140003cX0IFIx8ZuWzqwuTcarki/SMagVa8l0rAABVFL7/Cn83suY8AJiJ64+S 8yn9UAAAZE/uX0xzSWHLli3TfpfTHwhTSTlK6Z8+ceKE9f0EUDv57MblmCR0OSaZ18ILqXuw fSwAAESR+6m4Oim5z9I91qhW4OSvUisAABpx/VIyD4/t/QMAAONJHipuvvCkPkPAQ24JKL96 ckzBcwNjYAEAeRa3rizrAAJAfYp0bjWqFRikVgAAACHzHcWtoyhRz3rs0ne4fft2d74I28eI 5kjKLXV3d5NbAkpCPsvymY4LL8dEbgkAUBRxdfOMvQWA+sk9QL1jQwEAqJVRrcAGagUaJbmf uO93ry8wac5w+b1cC0jNidx3XX311f7jb731VuvHieyZ5JbOnTtnfT8BpEc+00k5plWrVl3y fUJuCQCQV3I/owupx+M7DAAaw3kWAIB8k/xOUsj6SeHvbMkPSa5JHi/z5CXlnCRsHyuyFXfd J0G+CCaMagVWUytQZCY5Jol6xsECANAsSXX1fIcBQDri+q0YJwoAgB1yPyR5o6SQtdXl3qjW XFJUrFixwvpxIxvyHol7X5BbAsovKW/0cz/3c+q73/2u9f0EkD2jWoFuagVQbNKnqQu5h7K9 fwBQJnHrf7PuNwAAzSVjj+LWSZSYMGGC+vVf//XYNvUEayuVD7klAJ6kHBNjkgAAeSU5IV1I v6bt/QOazahWYBa1AmiOuDGj8vOkNRoAAEA6ZA6zescfpRHUlZQLuSUAYfKZ7+rq0p4XyDEB APJG+iXj+i1Z3wMAsiE1yLqQumjb+wcUnVGtwFRqBYCiOO+YufOM8+Ftcz/HCxxvDy/Wtv+Z 46bvnXTbyyP6eiapd09srvvvS27HVrzvfe9Tjz32mPXXAOmR+2xZT1MX5JaAakrKL0mwFi8A IE/i5naQ+jzb+wcAZbZs2TLtOVjWabC9fwAAZG3tyJhSbSvd7z7JAw0/2ub8/Hxk2x+NjqmW mesu+b5c6Dzo9N5O7WPiJM1zl1XI36X+3IxRrUBLsWoFpF847r0n89GTWwKqJzw/3u/8zu/E fo+QYwIA2Ba3brz0edrePwAou6T+BfqeAABl96pj4hd3+d99/QuV8/OhyLa9p8acFgvHfV/e 6Xhr/4Ka/7Z8D8taSY2GzPsg84rfcccdiW0ld0CfYPnEXc/ddNNN6sSJE9b3EUDzPfzww/65 oL+/3z0XyDlBF2Wdx8KoVuDk1ELVCgBA1UmfZdz3Gfc8ANAcSedj2/sHAECWzjimPHLM/+57 oE2p0ZG149rp5sc7LT/f9Ir6XMttauQl/Xx5OjLvXC0h8xJJjkhq9WSuW28dxHvvvTfxsYxN Lqe4157cElBdwdyS/Lf386Qck5xTbO87AKCa4mqmjGoFBi/UClAvDwDNFTeelL4oAEDZLR4e db7xOtzvvRsdbwy0j2sTHue0zmk+Nrrb/Z2stfTJgVPqLyZNUu+eHJ+biiN1dboI5pJk7vCo GrykscgSMr5J1m+y/TwjfXHXcBLkloBq0uWWPHJuiAvuAQEAzUbfJADkT9pz3xnVCmxgXgEA QLEE11XSrcGU1EZyTAt2nFbHN80c99gkkke6+uqr3ZrxuFxSmHyXS+4oLmTb1O+Vk+QM42Jw cND6PgJovoGBAf88cP/992vbyTkiLqhLAAA0i8zJoLuvYW4lALCHue8AAGFGtQKrq1Ur8Lpj 0lcP+t+RwbFJHpMxTs0k/X5JuSXmHS+vuGs8CXJLQDUFc0bd3d3q3Llzxu3DId8x1CcAAJpB 1pLVBd9FAGBX3PjSNWvWWN8/AACyoFsvSfd73bpLzbJs2bLYfIFEPWtiSH6Je7L8k9cpLl8Y Nb8Vqs2oVqC7WrUCVVdrbskTnEsvHHJeok4BAJCluDH5zIsHAPmgmydP7he8dcEBACibtSNj SrWtdL/zbnAc/c71zs9fd39nMmapGaTfLq5ezwuTeYpkWzK3ntyHybpNbW1t/ve97dcC8a9b 3JzGDz74oPV9BNB8x44d888Dck43zRd55NyhC+ayAPLBqFZgFrUCKK+4Giu+qwAgP+LmUJF7 Fdv7BwBAFg45Jnxmi/+dJ9mWgRWT1Njo0CXrKUkc/JsJzmPeaer+yfdzXF5BQjc/kdSHbN26 1c0lSX4qaa486knyS8al6aKWMQgAyuPEiRPqpptucs8D8q/871q3IecOOYfoop4xsQAAJIm7 tmVeBZSRUa3AVGoFUHxx8+RJ/5Tt/QMAIG1nHFMeuVj/3e6YNG+e6vtsi/rmT3/ij22ysZ6S fPearJ8keaFac0lRsWrVKuuvB8aLmw9E+pTffPNN6/sIoLnSyC155BzibSsqTMbGAgBgSuZS 0IXMB257/wAA402ePDnyvC0/Z65sVIVRrUALtQJAFS0eHnVOAh3u+UDWYHr1pcXq+q/sU966 SxKLHOdP9TZtn2T9w6SQ3NKtt95aVy5JF9QL5kvc+HKJRvqUARRTMB/UaG7JI9vguwEA0Ay6 uRnoowSA/IqrDWAdPABAGQXntpMs0fCjber02KiauekVdWGUUvPWU5L7pLg5ILIOuVez/Xqk xahW4OSUUtQKyPtGVyMkMTg4aH0fATRXeD67NM8Dsq247xH6/AAAjYqroWPeJADIt7h+LdZb AACUzauOiV/c5X/Xebmk887Pv/DSWaXaFjZlPSXpj5syZYr2OzjrmDRpEnXnOSJzF+ri4Ycf tr5/AJory3yRR84turC1zq5RrcBgOWoFAAAXyH2Rbt4FuUa2vX8AgHicxwEAVSJ5pJk7zyhv jrsHnH9GR9b6v//myVE1cdpKdfQ71zv/+/XM9kP67myFzD1BvXl+xNVrSv+y7f0D0HwDAwP+ eSDLMYnBHFY45Nxk+3kAABSPUa3Ahou1AtS9A0DxxfVryBx6tvcPAIA0rR0ZU6ptpfs9d6Pj jYH2S36/XnJMd39NDayYpMZGhzLZB8nvvP/979d+/9YSUiciNSE333xzYlu5f7P9/OOiuHWU ZP0UWU/F9j4CsEPGFz3++OOZ/o3gmk1RwThXAECt4q5vWYsDAIrBqFZg9aXzCsSto2f7eAAA SNMhx4TPbPG/63bd3+L8/PQlbX5wdkxdfvdj6tufbVHvntyYyX784Ac/0N576cLLJcm9mcxF LjV9kquKm1/Ni/Xr11t/7nFR0jpKx44ds76PAMovrh+Q9ZUAALXS3ZfwnQIAxSNjj3TBfAcA kG9GtQLdrEFQjzOOKY8c878TvTWYwu02nh1TLbf9rVrYptTpvZ3Oz86nvi/ve9/7tN/VUbmk 8OOlX1BXIxLcDjXo+RM3F0h/f7/1/QNQHXLO0QXjXgEApuSeRRfUugFAMen6LqSviboBAECZ LB4edb7hOtzvuQWOt4cXx7aTlvv+vj31OfC8mr0rrrgiMT8UJu106yJ6Ifkk5iLPn7j7adZR qi6jWoFZ1ArAvri1mOT8Znv/AAD5pxubz/ruAFBc0v+k66di3lMAQJn8aHRMtcxc537HTXH8 dPP149qcd3TuP+v8tt3/PpTxSZv6FqiRobWp7Id899aT/4lbD9ELqRuhPiR/4q63WEcJgC1x 6ytRbwiYM6oVmEqtAKpNxifpgrXdUVbyvpf+dfrYUXbyHtcF9c8AgLJ41THxi7v877jgukrB 3FNc3Oh4Y6C9qfst/Xtxc6V5wTVrfsWtf8XchABsiltfiXpyAICO3KPo6qeYOxVFZFQr0FJb rUDwPtD28QFZ4jsBAFAFMjZp5s4zzrdbm/sd94Dzz+hIOmOSsiLf0SbrJ9UyN5HUjkj7ZcuW ude71JJkK27c2cMPP2x9/wBAzkW6YF1eAEAUatVRVeSMUCVxY0851yOPjGoFTv4K8woAqNva kTGl2la655V2x9n9C6zvk47UlCettSRzmMeNf/FySXL/J9fBUdtj3YzsxM2LN3fuXHXu3Dnr +wgAci6Sc1JUyDmMe0cAQFBcnTpzKqDsyC+halg7DwBQdsG57WRk0vCjbc7Pz1vfr7C4uo/g 93NwvQvJHXlzNuvyQ1Hx6U9/2tpxGtUKDFajViBuXrxjx45Z3z8A8Mg5Ke67yfb+AQDyQzd2 ibX7UAXkl1A1UpOsC9bPAwCUwRnHlEcu9out63Cu80Z3W9+vIJP1k+655x43l+TNW9dItLS0 sK5PBuJyhI8//rj1/QOAMDk36SJu/KpRrcCGatQKAAAuYuwSqo78EqpI10dFHRoAoCwWD486 32wd7vfbIsf5U73W90nI/VejuaJ6Q9Zton4w3deSue4AFE3S3Hd8TwBAeRnVCqyurVaAsUuo OvJLqCIZp6QLxjABAMpg8/kxpaY85H633eh4Y6Dd+j7J+CHdPLXNiBtuuIE1M1IUNwaNue4A 5Fnc3HdybrO9fwAAe+JqqNasWWN9/4BmIGeEPDOqFejOfl4BxjABAMrsVcfEL+7yv9923d/i /Py0tf2R+o3LL79c25+XdYTXcULjr6cumBcPQBHEzZNHzSEAVJduPJLUydneN6BZyC+hqhiX BADNYVQrMIs1CGw675i584zzYrS5r8tDU5zrwvObre2PjF36wAc+oP2erjfe+973JraR9Zts vx5loxuHxrx4AIoibp48+hABoJrixi7JuqO29w9oFvJLqDLGJQEAyqz31JjzrbbQ/W5b4Hh7 eLHV/ZG10BsJ6cOT/j2pE9y+fbu6+eabEx/DvV0G7ytNnaaE5AZt7x+yYVQrMJVaAZSXnN90 wZrsAFA9jF0CLiC/hCqLG8NE/wgAoOh+NDqmWmauc7/XZGTS8KNtzs/PW92nKVOmaL97w/dl Xi5Jvq+D89WZrM0ktYR8l6dP1qTS1Wk++OCD1vcPAGol5y7d9wjr8KFsjGoFWqgVAJIwdgm4 iPwSqk43hom1WgEARbd2ZEyptpX+d5vtdZVEVJ1fXC4pTMZA6e7lvJg2bRrrJ2VEro+i4qab blJvvvmm9f0DgFrJuUvOYdwTAkC16cYjyb2H7X0DgoxqBU5+pGm1AuSXUHVxY5ioVwMAFM2C /eedb7DoMUIPtCk1OrLW6v7J925nZ6f6yle+UvN6h3HzsgX7AsktZffa6aK/v9/6/gFAveQc pgvW5gWAatDNj8AcqKgickYA45KQLaNagcHm1QoAQNjmE6POvdC33Puhv/ja11TbskfVjc7p 6Y2Bduv7VivJF+nWVQ/GmjVrrO9rmemureS1sb1vANAo3fcMa/OiiCQv2izU9aAMZE67qJCx S7zHUUXkl4D4NcT5bgAAwB4ZHyxz2MWF3MvVUzMu3/Gm44+NagU2UCsgdPfcEseOHbO+fwDy 58SJE+75oSiefPJJ7Xlu1apV4/rUpaZBajjKSvqVyoxILxjjhzJg7BJwKfJLwAV8PwAAkC/S B2GyftKLL76YuC3JI0k9idcP5m132bJl1o+zCHTXSffff7/1fUO5Sd5B3mdlpFvHh8gujGoF Vl+oFSAIIv2444473GsKk+svbx3N9vZ29alPfcr9V/63SR+N5HSjHis1MEmPle1PnDhR/fZv /7b6xCc+oa699lp3302+s+Rv3HDDDequu+5yyX/L8Zo8ViL8WLnOTXqc1EtJyPWttz6p7e/u MoubA5r6dFQV+SXggrjxrbb3DQCAqokbA+OFzE8UdR+nyyVFBXMZJYtby0rWure9fygubyzI wMCAu6aNePjhhy/JwXR3dyeeCwiCIAj7YZIL+eM//uPIx954442Jj5UcUVTMmTMn8bEf/vCH Ix+7adOm2MdJDZMuksbAN5KH0M2xI+MjbY9XzAOjWoHuC7UCaf/t6667LvK1kfe/7eclS5Kz beZcmiiW4HwjpvODAGWlq801qXEBAADpkPUPk8Kre5X7fvmelv9d7xw28njqDaPJ86LLz0ke wPb+If/OnTvn55C83BHjdgiCIMoZSddTcXMeJ32f6K5HTMYH6UL6zeMeJ33qukjqJ5Jt60Ly R3GPlevaqPj0pz+t3SZBEERWkXSuBOphVCswq3hrEOi+/03HMwMAgPpJn0TSWkvve9/71OzZ s1NfD4Fakmi6ayPJD0jewPb+IX9kTNvg4KCbS5IxhgRBEER1Iu56ypvHTRdSA697bNw4Iom4 ennZri6SxrHHfY9JPVTcY+OuVZPmB9RdD8v4JYIgiGYH+SVUXVzdbdz1CwCUnVGtwNTi1Qqg mKTfQDeOuBlBLcl4cddQMo+Z7f1DPkieUT6/5JMIgrAZ0pdfZrbnxgrTjaOJy7no5nzzIq7/ Mm4ckURcXituHJFE3Hdc3BzLSdeOcRE3l6AuD+fNv0YQBNHs8L6H5FwbnBfP9n0I0Ey6eXaS 6k0AAEB95Nrzsssua/KV7/hgXNKlGI9UPka1Ai3p1grIe0XGKD344IPN+BgXJmStqODaUWXj rZFVVt56YOLo0aPqox/9aOTrLPeQttc5qIftcxXKRZcDicu56OZ88yJuLFFS/UJc307SmHfd 5yNpzJSEbtxU3JgpL3RzCerycPL8kV8iCCKPIed+OdfKuVjOU3IOZB0mlI28p3XB+x0AgHTF jXtJK2T7Xh2V3Ifr1otOmvekSuJeF1kbx/b+wY6q5YnkWOWYbT/vsEPySFEh50bW4YNNRrUC J6/J1bwCujncdH0sSXMgS+j+VtJ1ZVxeKyl046aSxkxJ6OqYTPJAujWYdHk4aU9+iSCIIkX4 np1rLRSdrmaFOSQBAEif3G+nFV49lHddGtVvEVdLQt32Bbo+CakJtr1vaK4TJ06489nJuLW0 wrt/9OoWg/NnBO8lwxHeN2nrPU62IduSbcq208xby7HLcyDPhe3XA82lGwfBfSGgz21E5VGS 1lPyIuo6zGRskETUNZ/J2CBdfZHJPK66sVAma3/q1lXS5dZq6Wu1PQayCB577LHI5/nyyy9X 27dvt75/VeflSMvMqFZg8EKtQBp/T845d9xxh7ruuuvUhAkTEs9ReQg5H8p+y3vC9nceUCtd PxfrLQAAkI2bb7655utN+V6We39vXH0t993Mh6sXN3aJsRzVIa+1zHPWaMh9oXyupLa71nvD cNRzHPI35W/LPpjUwyeFPCd8DqpDXuuoYAwToJ+bLepaKmk9JS+icrcmY4MkovJapmN4oo7P pE5B10dkElHrKsWtp2T79S4b7gVQVJJzl5xLGte1tkPOs3I/L+dvrqtQFLq1wHVjjVFuRrUC G/I1rwAAlJX0AceFXD/Xm0uq9e9VfT5cxi5Vm/Snm9Rsx31W5Z43jXktwpHG8ck+yb41el8u zxF5pmpgDBMQrZZ1lZLWU/IiaiyR6XdSVF7AZByRRLj+wXTMlET4ujHpmjYY4e/JuPWUbL/e ZRJXSyWvve39A8LkPCPXHbp+7bKEnO/po0fe0V8CAEDzBOehu+GGG+oewxDHqFZgdXStgK7P ocr38FUbu3Ts7Jh6on+XumHhQ5cc60ML29QP+r+v3j39Y+v72CyN5JW8usO0c7PhyOK4ZZ9l 3xs59jJ+NnARY5gAPdN1lWrJ54f/hul8p1F5LdMI54tNx0xJhMdN1bLuUbgfNW49JduvdZno 5ixinBjyRt6rpnnyWqK7u9sdky/zP8t6k0Kud44dO5bIay9kGyLNebQl5Lwv58Oq130in+LW W+DeAACA9DV7rmRd3WeV+wGrUl8juaKV63Y5R9auFrYptfv7D6nRs8fc3406dh87qxY+9H3V 5vzff13ZXurcUb25ombMTxGOrJ8LOZZ680fkjprHqFag2968AoxhAqKZrMGkG+ekyxsFrx11 44haW1sjfx7si6xlHFF43FQt3xnhcVO19AWHa5rSWHsJyXSvUdQci0CzeWOV0lhLVPJIXg5J ckNvvvmm8X4E58k2fYz8Dbl29nJPaeSd5PPK+kvIG911gtSn2N43AADQON28AVXtB9Q9HwMD A9b3LQ2SO+o/dla1ta9Ubc5x9a9s9/NKUQZPj6obFn5ddThth/tXOj87a/0Y0iL3dHIfWUvI +0Oug5tVHxiOZj4/coxyrLXOLSL3D/Lc2n59kS7p+9B9JmzvG2CTybpKujaf+MQnIn8evAbT jQ0y6fPX1cxcdtllkT8PHlctfbXh80BUfOADH4j8eXAMTNXWUzKqFZhldw0C6s6RV/Le1K0L ZhqS0/FySefOnWtof+rJL0U5ceKEm3OSPFcjc3DLeZkcMPKCcbAAAJSb7ru+in2GuudCaska vefIizccN67c5R/brpVtzs/PxD7m5OiY6nhgl1E+qgikFvHBBx+s6R5N7u9s1AKGw9ZzJsde 6z2uPMe11H0i3+QcqKurpf8CVWayrpJu3NJf/dVfRf48OD4oruY3KoJ5LV0Oqr29PfLn3vec bhyUbC9pjj/dOCg5Dl29gpenYD2l5tDlHaPW5AKaoZFckZxbJBcj562079cef/xxf067NLcr OSSpXay1zs0Lckf5YlQrMNVurUAesI4fAADlFbeGUNWuW3X9HlLXZnvf0iLjlx7YdcY5qjb3 2B5w/hk9syvxca+NjqkbPndh/EL/59rU2OhJ68dSD3kta5l7Qu51bc5lHg7bz1+t9//yXJfp 81N1ujFM1B6i6pJyLnG/112DeduO+31UBPNaUSHb0+WmvHFTcb9PmuNPl7uQbeq+P7z1kpJ+ j3Tornd5ntFs9eaV5PpScj+Sp7F9DI2SWqx6c03kmWCb7vNLXQiAojGqFWihVgCIo+sLqFKf Ydy6A2UZu+TZdWZMqbaV7rHd6Hhj10qjx/WfHHUe9zk3M7XzgXY1Nnra+rGYkvvPWu7bbOeV POGwvT+eWvsD5LkvQx9A1cm5UBfM9Y8qi8u5JI1v0o1Pks9U3DgieaxufJKco+PGESVtN26f kuYD1O2T/E3dOHGvHyppfBMap3vtqzhvAeyRz7Tu/jMuZGx8mcc6SK5JanlqnTOANZdgC98p AACUm64/Q6Iq15+6a3OZQ8H2vqUtOO+d5H+G13U4Px9NfNzbjgXrjrmPu8HxWv/njB6XNqNa gZOT66oV0I25iIq85JU84bC9P2G15pkYy1R8cv6MCjnf2t43wJa4nEtSPiZurFDSOCNd/6zk ceLGEclj48ZFxf0uKV8WFbI9+Z1uLR+pZ4r7ne3Xt0x0uVBqzdEscn6qZX03byx81eZcln77 4JpOJiHfK+Tj0WyMiQUAoNx0/b7Bef3LKm494jLenwTzRBLrOpQaGzVbJyk4nmlJm1Lvnsxv DkBeO9NxSPI+z2ONYzhs74+OfIZ0dejhkNekjJ+rqpDXThd5yrci/4xqBQbrqxXIs7icS9J8 cnFjieLGEcljdWOU5PovbhyRPFa37ccee0y7P97x6ub727RpU+TPg/lnXT/U2rVrI39O3iNd rH8BW2q5rvTOG4ODg9b32zYZ/6+r7YkKbw5U2/uN6mBNPwAAyi0ux1L2PkNdf07a67Pmybpj o84RdrjHucDx9rF1Ro8L5qZk7NN/f6jd+flZ68cTJn0fJmsnSd9VnuulwmF7f5LIc6nrDwyG vDb0TxWXrkbWGycBVJEuj/Jrv/ZrkT8PXlvp+vGT1lsSUaE7D3tjg4RuvNMdd9wR+fPg51t3 3aTLQwX7L3X1TL/7u78b+fM8f0cXjW58HOO+0AijWoEN5rUCtcyFR14pmtQC1ZJnklxe2e/3 kQ+6Pqfg9QkAACg2XZ1Y2etJqlifOTw6pto61rnHWcsceaLe3FSzyBq+JiH3r3mfFyIctvfH RC3z5MtrZXt/UTvdGAruDVFlupxLVITXGqhl/YzwuPJaa/y9x+k+xx/84Acjfx6cL1mXp7jq qqsifx68ntKtq/SBD3wg8ud5/54uEl1uj/ELyIr0Jety7+EgV2SmltwR45PQLLrPOTUixWFU K7C6fPMKJDnvmLnzjLrQaxYfbt/Y8GLr+wwAtunmTpEo6/29rp+j7OuDNDJHXiO5qSydO3fO aD48uf4tSu4wHLb3pxbyHJv0KchrJq+d7f1FbXT94d48XkDV6HIuURGu29GNJYqK8DjBWmr9 w/2MtaxrEnxc3Lqd4QjnnePGy4eDcTXp0r3ejFtAFkzXTvLWTLK9v1Fkvrljx465bO9LmOyT 6ZpLcs1W1nt55IPuOqbsdcoortOOmZtecd6l7bHnT+nv2v/VSc5jXre+zwCQxKhWoLs5tQK6 OVXKOu+Rru43r/c5aap3HNIbjhtX7vKfq1pyU1mR+z+T+u+ivY/DYXt/6mHS9yl9C/Ia2t5X mJNzZFRUYc0+IEotOZdwHlY3ligqguOIRFxtUDjCtRWm46aiPtemYxKi+pZM5lGVYD2l9Oje J+TwkDY5F+rGyoVDxuHkucYomL+xvS86MubLZE5wyfWFvz+AtDDvHYroZ46uwVPq/9M2V717 cu2438vYpa+/dFb9+cf+So2OjP89AFSNboyOfN+XrZYpri42z/cvafnx+TGlpjzkHm8tY4vC 45lsz3tnsn6S9E8V8T4pHLb3p17y3Cf1EVZtnSWjWoFZ5ZhXQOZn0QW18Kgq05xL1GfEdCxR 1N81iag+HtNxU1G1GabzAUaNaTTte2ZenfToXi/mzkKaTMeuS247j2OCwoqQXxJyj2s6Pzif eWSFOfJQRJJD+sJLZ9UX2+aqkZd7I9tsPjumhh/9gvPf563vLwDYpusHLttcRrqxElIfZ3vf mqHesUXyvbqo/7T/uBsdb+xaaeUYpA4vKYo8z0M4bO9PI+Q1MKl/Zz794njwwQcjX8Oijf0D 0mKSRwmvp+QxOT/qxvyZrKsUNZev6VioqPoM0zn+ovJjulqmcBThu9uoVmBqsWoFdPcB1AMg LXL+MMmTSx6kKDV/RckveSRnZ/K9Izn+IpyLUSy6mhXGIqNe0kf1yNBZ1f6tTSo4j53UUX+n Z5Lqc+5P3z2xOZW/0/vyWfXZtmVq5CXWTwKAOLq8i65PpKh0989VGUMx6nhg18X1B+90vPXj hxIfF84v2VpXaWBgIPGeqOj93OGwvT9pMJkDrwpzTpaBrm+6bN8VgCmTnItufQGT8UG67zST 86quDt2kjzfqcSZz/OnOBSbrKjEXW3p052qeY6TF5PxVlHFIQUXLGQnT8Uny+SdvdJFRrUBL sWoF8kx3HcA9BOohc9d9cuCUkryS9E0NrJikxkaH3N9tPjGq2r+12f9dX8+kVPJM3zw5qj7b 9kVyTAAQQ641df0NZRmzrJtbPqq+t8x2nRlTqm2lf/xfb1dq9OyPYx8Tzi9J/PihKaqZY4Bl jFlcyPu3DO/VcNjen7SY1K1WZRxh0elqX4s4HyXQKJOci24suMlYIt3nymRdJV3tTFL9etw6 aUlzW8Wt1Z00Zyo1zOnR5R95jpEGkzksZbxzUcYhBRUxv+QxnT+8KnWVaA7ddQHvM9Sq98So Um2fc98/7Y6z+xdc8ntvHFHLbRfO07JS+PCmdud3rzf0d8kbFZtRrcDJ/ye1AkAT6e4VyrIe u+74pN7L9r41U3iOPKnp2PmA8708erqmxzRz/FJSbqlM90rhsL0/aZLXKKl/kRxT/ulqZOP6 lYEyS8q5xM0xlpR3j/u7cRG3ZnbSuIO4ccBJ8wHGzauc1CddhhqRvKC/D1mQfLpJbqnI8x4X Ob8kZP3L7u7u2NdHvh84FyAtrN+HNJxxTHnk2CXvof7Ptaix0SPj2ko+qOW2P3fb3OA4+p3r FTkmAMhW3HwkZbiu1PXLyLW17X1rpvC8d+1i4UL17YVt6t3T/z2y/dd/fFYF56mtZ00lo1qB wcZqBZJyS8z1UCzyWiX1xZJjyjc5v0ZFXH82UGZxOZekeV/ixhIl1ffErcEUNzY7adxU3FjE pPkA43JpSWsw8V2eDuYgQhZMrt9k7MyJEyes72sjip5f8lRl3gfYx3ysSMPrjklfPXjJe0hy R69t/pgKz5kj45g691/sy9K1q4Vs8wsvnVVdEz5DjgkANHR1ZkWvNdf1cSxYsKBy+SWx7tio ujBK+EKuaGf/SnXDwnVqYZtSP/j+Q+rd0xfmwJPakK/vPKZU28JLnrcHnHajZ3Zlvp9J84OT Lyom8kbFp6t3pf+h/IxqBTYwr4BOXM4l6VorbixR0nqCcWswJdUNx42bintc3HyASfmLuJon +qHSo8vjFf26H/aYXOPJNUQR58MLK0t+SZisWxs35hQwpbum4J4epqLGL0kc/JsJzu/fGdf+ hOOXH7yYj1rSptS7J3ob2gcvb/WnLbc1vC0AKKO4Ofrj6kzzTlfze8stt/j/LfcI0qfd39/v 1tYUvaYuzvDomGrrWOcf+66VbeonZ8+4OaakcGs++j+nsp7vTubLiAvp++A6tNiS5k0p8pwp Zafrh6BPElUUl0dJ6o+LGx+UtE5Z3DVb0rjz2bNnRz7OJHczZcqUyMfOnz8/8bG/+Iu/GPlY 1vpJj+6al/w/goxqBVbXVitgWj9UhtySKFN+SZisyUSOCY3S3f/x3kItgusvSbhrMjzapnTj khYPX6yvlth1f4vzc/2aDyZOOz7298Oq97YW9e7JtdafEwDIG90cKkW994/r95GxS5JPigup sZN1Z6Wd9HcfO3bM+jE16m3HgnUXaz688UinR8fUwq//d+XNnRcOyS0Nfn1h7PpLaTDJLdl+ DpEOckzFxLx3wKW8PtWrrrpKffjDH/Y/Eya1ORMnTnTbfvCDH3R5YfJ3vQg+VraX9DhvLNRl l12mfumXfsn9VyJpHJTw5vgLP9akb8g75//H//gf1dVXX63e9773uf+b3Ed6qA1HWqo43rxs OSMhNZPkjZAlxsLaY1Qr0F2+eQVk/FDvy2dVy233uz1XAysmqbHRIW37Vx0Tv3hxjfA0xjCJ A6Nj6vKF/arvsy1q9N9+ZP15AYA80V0fyP1qEe9NdccjOSOvjVx3B+8nTELqQ+UxMoeb1PJL 3qlI9XjBOfIWON4+ts79uay3tPvYWbVyneTd2t3fy3f2oyvb1ekfP6myHrckr0VccJ1aPkk5 pjLkdMtId86knxhVJHXf4WukpPFHusfKf5uue1nvY6PaRR1DFMmZNfLYcM7N9LEwez9ERdL6 XECUpNyS3P/Y3se0lTG/JOT+Km4NP7nPL8N6y7BDV8/LWn7IkuSYZu68uK540himWsh4pzbV ofZ/dZLzv1+3fqwAys2oVmBWMWsF5FogKpLm6M8jXd911LgI+VlSfVdSyOODc+1J33ge13gK zpHnfhev61BZ546SJNXXxa1BjmKLyzGVYQ3oMtKNMyQHDAB26NbZMhmXBgRVdXx5WfNLQuog detnSpBjQiN0+WjeU8jS2pExpdpW+u+3r7crNXp2c8Pb/dHomGqZue5CHfbwYuvHCQB5olsX uoh1Jbq5P3Tjh+Tnku/JIoLz58n1k82xFmcd7Q/92N+3dR3OvdGovf1Juo+R69Aq1CyHw/b+ NEvSvCp5Wd/ZqFZgajFrBbLEHHkAkC+6Oa7p30MtqppbEmXOLwmTHFOR11yGPbr6hiLWJ6M4 wvPe3eh4Y6C94e2+7pj01YPuNtNYqwkAykT6enV5mSLNuSxzL0WF3A8kPVZyP3HX1GmGN39e cN2mrPvSZW67B3ZdHCMcnPfOBsm76aIquSURDtv700xJOabgPJXIB905knnvAOSZUa1AS7lr BaKCnD9qoZtX3Isy55ZE2fNLgvo/ZEHmBI4K5h9Bls44pjxycV3xtObIC+aXLtRs77Z+rACQ J7q6ErmOtL1vpnQ1dbXMAS45n0bnwasnmlE/uuvMxTHCNue9k+dYF1WrjQuH7f1ptrjctoS8 V2zvIy6Sc2lULFu2zPq+AUCV0F+HRuneQ16UcQ2lsCrkjERZ5hk3qhU4eXWpawWKKiqocUDW ZK0kb11xiTTyQcFxUQ9NcbZ3vvE59wCgTKRPXxem61DbplsbqtY1jqR93PiatKNZ1/RvOG5c eXGMsI058mSsVlxUbZ6WcNjeHxvkNY/LMdmcVxKX0s17V8S5VAGgyJhvCI2Q+764ay+ZO9z2 PjZDVfJLIinHxFpsqBXzrsIGb/0jL0zXQbp/eFQd+dZM579fv+Tn5x1feOmsUm0L3e0xJil7 RrUCg9QKAEWmG/9ThJomuY6JChnj38g25dizjscff7wpz5HtOfJkzoW457NIczEiXXFzrsi9 cB7WYsIFunlTuJcEgOahXw/1SpqfuCq5JVGl/JJIqvNjvmPUgjoH2BBeV2mK46ebr0983AnH dX99QG3+q+vVuycujDk67ejc/oqfW7rB8drmj6lG59ADgDLS5V0k8j4PmVybREWj+Rjpq46b oy2NkBqxZj1PwXnv3HULd63UtjWqFdiQTa1A3JgwyW3afr/BLpljTResxZQfcv6NCu4lAaB5 ooK5hmBCV08oITUkVarpqVp+Scg6Wbqo2jzlaEyz5mlNyokT+YysxkTKeKOZOy/WTEvsur/F +d3pxMceGR1TbV/8UeT+ytYGVkxSY6ND1j9bAJBXuhrHvPfp6/Y7rdyNbCd4X5FWNDJmqh7h ee92rZR1C89k/nfjcpdFWrcL2Yq7H6jC3P5FoKtnlXOw7X1DNKNagdXMKwCUke76qwjzDcAu GZ+iiyqOF69ifknoaoW4h0OtoiLNebPJLRU3spxzc+3IxfpqiQfalBodWWv02J85vvrcKdW2 7FH/8SvblRr+gcyx93pm+wwAZRC3fqt8Z9veP52okHuftP+O1HHFzUddazS7z1xqOBb1n770 +/XMrkz/ptx/6p4zat8QFDfHv7yHal0fDdnQfZ5t7xcAVIFuzD5jQ8vNqFagO51agaQ1l5o5 90JeyDFLjU0V1/qMq7FkLSaY0tUDp9EXEM4tyfeh9GuhGLLsDzrkmPCZLf57o91xdv8C658H AKgCqSMp0vWjrr4uq3myJF8i842nEVI/KjmmZvWbv+W486Ef+38/aY68NMTNdVfUubv37Nmj Ojs7XStWrFAbN250Pffcc5Htjxw54rdfunSp337nzp2ZbN/289OIuHpZ5r7LB91nWu4PbO8b AJSdbs4yzsGIo+vblWCMePXE1f9JsD4bTOjmLE/jHj94zirT2stGtQKzmFcgLWccUx65OP+G zG03/KjM4cO6SQCQNfn+joq8ztWuu66RsUZZ/l2pX5P8UFohfbZZ7HP/aRnftVD7d7OcIy9u XryyzM3yxBNP+Mc0f/78xPbBMYIzZszIZPu2n5NGxX2uuN+1T/qhokLOxbb3DQDKTlcHZnu/ kF+6MW8SRa/daeS6+g//8A8z2b7t58SUbs5jCea+gwldXWCj9wTBOoq8r9MAOxYPjzrvjg7/ fbLO+c+x0d3W9wtANRjVCkylVkDo5kTIY+2Ibp7dZo0J6u/vT3XOPNmWjI9Ke/6F06POvj75 Y3d/xaOOtvaVmc2RJ3VxujyBvL/yPN9iLcgvpU/eG7pzkLynqjbnf97IuTUq6IsAgGzJ92NU sAYedJLmHi76NRX5pcbIPaEu8jp3CfJDzi9pfyeRW4KJH42OqZaZ6/z3ygLH28OLre8XAFSB XCNGRZrrKqZBd+/c7PEu0ocaN990OOQeTcYfJT1GjkOu5Yu4lkzcPUhR57qL8uyzz7q5HLF8 +XK1ZcsW1969eyPbDw0N+e0XLVrkt09z+7afkzTEzX0n7y3T7RjVCrRQK5AlXe64LHljAMgj 3TqpjB+FTtyY8DKsJfSTn/wk02vwerZv+zmpVXd3t/Y9wrwBSJLmmNrgvDTklhDnVcfEL+7y 3y/uug8D7db3CwCqIG5MQJ7GPOj6oGVMj439ketqk3nwgvsnuSPpL096nOSiJCdVhNpBOSbd OK2yzHUXRe4hvZgzZ05ie7k/9SKL7Rdd3GeiiDnXMtGtK5en7wegjM47Zu48oy7MCp9cg/kz x03fO+m2l0f09UxS757YbP04amVUK3Dyl6kVCNDViuVxfgHYp8tHSjz++OPW9y9ttV6Df//7 36/pGrzW7RfFiRMntO8TxkYiie7ertbcZHCNBuZEQJLwtbPErvtbnN+dtr5vAFAFujWB83Tt qLt3zno9pTiS/5H7sLiQa/Oox0ptoPTbJs2hJ23yXCOm63uWnKWMjbe9f1khv5SuuHlabOWQ cYGcY6OC+VGA7K0dGXNukVe6n7mktYfDc4JILHQedHpvp/YxyK+0+uZQDbqxAmWdP5icUf3i 5p1IMydtVCswSK1AWaVR9xDOLTEfAkwEr50l3HUfRtZa3y8AqALdHLkSebmPlVxXVORhbIPk kKLmvzMdvyP9t7KOblzItiSXlYfjDR63LsrS96x7/8t951133eVasmSJ2r59u2v//v2R7V9+ +WW/vddWpLX9MtDdh0iUYa6WotKtq5Sn+gOgrMLzfPQvlL7O6LlRe08599Nq4bjP6p2Ot/Yv sH4sqE2acwuh3OKun/JyH1ePeq6rDx8+HNle7rXSuG7Xbb9odHPfyXmHPnzoNDoXK/ki1Ctc Q9XuOMu1bVMY1QpsoFYAwEW6Osm8zHEbFXmbf21gYOCS8Ujyv2t5vPTjymOS5s+TewJpZ7se UbemVN7W7mpEV1dXYptt27b5x97R0ZHYPhhZbL/IdP1p8l6zvW9VphtnaXu/gLI745jyyDH/ M6erwdTNpXdafr7pFfW5ltvUyEusb1wUujVHyesjLG6e86JfO0nccccdRu3qva7OYvtFIHVb uihLjSDS18h3k+S6vXOV/EtuCbUIXw8njekHAKQrbn5t23OX6fZNxvHYft7CJI/jzQPXyPgh Ge8jY5CS5sST50CuwYxqBVbbqRWIuy8p03z/5JeaK1jXFg7GMNmjG19Z5LpooCgWD4/K2d/9 zOnWKQ6Pc1rnNB8b3e3+TtZl+uTAKfUXkyapd08yP0gRNFojjuqIG7uUpzkP6iFBfik7cXOc 0/cPnah8tvws7jHh3BL3D6hH8Ho4fK0LAMiejD2OCtu1SWvWrIncr1rH/DRTmvdpcl2VNCee 5KEkH6VbwyltuvFIZVtLs6enx32fiaNHj0a22bdvn5vLEffdd5/f/uDBg5HtvbYii+0XnW5+ y6LX1haZbv59OTfb3reyMaoV6GZeAUQLzgmiq9dMaiM5pgU7Tqvjm2aOeyzyR3eNzPkZQXHz m8t3vO39a1RersFr3X5RyH2trubRdj8B8kt3T6erXSa3hLRsPj+m1JSH/PecN1bf9n4BQFXo xgPYrk2SefeiompjFGRslNyj6ObB9kLmzJN2WdUixq2lJHW0tp+nNAU/E/fcc09ie3nevWhv H1833uztF1HcWMqqfebzQjcGMS9zogJl9rpj0lcP+p+7qBpMk3FLKA4Ze1SFa6wyM6oVmNW8 WgHdvZTkDGzPoZ2WHTt2ZHoNnvX2805Xa2S7nwD5pRszGfVdJu+h4Nzn5JbQiEOOCZ/Z4r+f mCMPAJpPt6aJzTpI3bgq28+VTd78eUnrL8m4J1mnNs2/rZsjoYxz/ZNfskNX7ybvPdv7VlVR UbbxikAe6dZL0v1et+4SikP3HWh7v5AfurVNJMowHslDzihbkltkXBJqoatHDr9f5BwV7MMp 6pz4RrUCU5lXIG+CY/W9WOJcH797otf6vgFAVejm2ZC8k619igoZw2P7ucoLqe/R5Xy8kHsH adPo/HkyJkoXW7dutf5cpO3pp592+3nE0qVL1Z49e1xDQ0OR7Q8cOOC3l3nXvfYvvPBCJtu3 /fxkJW4MU9HXCCgq3bhJ2/sFVMHaEedaqG2l+5m7wXH0O9c7P3/d/Z3J+CYUS9SaFTavw5E/ unEBUndme9/S1Mh19Z/92Z9let0u27f9/KSBcUmohe4eLTinQVlyS8gHqaNaO3RWtS17NPK9 t7JdqeEfSN3V69b3FUA+GdUKtFArkDa5Hoi6r7V1baC7hmEcw3hSgyZjlUzmz5N7iXr66XX3 IFXo93jiiSf8450/f35i++B7d8aMGZls3/ZzkiXduEUZt2d736pIl8Mu+/sQyIOoeT4GVkxS Y6ND42o0D/7NBOcx71jfZ9RHNy6ljGPEUZ+4e7W05yzIk0auq//wD/8wk+3bfk7SEDeGiXXc ECXpO4rcEtKyYP955100JfI9FxWM4QeA7Onmcrdxv6obIyVzf9l+nvJM8kGS30maE+/+++93 7y9N51PX3VOU7XpQPgPyfpfnx3PXXXf5x3vttdde8rsod999t9/+wx/+cGL7erYv+1imOSmM agVOfnhcrYBu/gV5v9o+pirS5ZbLdp4A8uiMY8ojF9dBa3dMmjdP9X22RX3zpz/xxzax9lLx 6eqqynRdgMbEXR+VYS0lOQZZ9zHsH//xH/1j/Y3f+I3INkF/93d/57f/vd/7vcT29Wxf/i3D c17l+kHULmo9Bcl5y++Ca72xTisAAOVz/PjxyOtGiWbXn+tyXXKNbvt5Kgp5rmQ8gS435N1n Spu49TElDxUV3jVimcTNuZa3KPOYEN0ab2Wuuc0rOY9EhZyjbe8bUAWLh0edT1yH+7mTdZVe fWmxuv4r+5S3lpLEIsf5U73W99WoVmBwfK0AaqPLHTCOAB7ddVRZ1l2KG0+Tt5CavzLkl+Ke 8zLOf47G6NYI7Ozs9P+b3BIAAOUVrCcJXxs3cz901yRluD5vNm9OvAcffDDx/kfmFQvPiad7 XFnrZHXvvTxF2efA0a0ZIOO3bO9b1cj5o4rvQSAvgnPbSUZp+NE2dXpsVM3c9Iq6MEqJ9ZTK QPe9V+ZaEpiLq38q072RboxM3qJM9U5Rcxsb1QpsuFArIFG2Nb3QON13mhfklgAAKDcZx6IL Gd/UrP2ImjOca9fGSe5I5hhMmj9P1meSe6dTp07l4v3QTEUYw1T2/qa4sZT1rAmGxkTVq5Zx /CKQR686Jn5xl//Z83JJspbxF146q1TbQtZTKgHddVlZr7VQG139X9nWmi3CuKSy3Y/KdbUu OP8gSLd+gYSsp2R7/wBcyqhWYDXzCgDIjm78RrPqTHTrFjN2IV0nTpxInD9PF2W7twrL8xim qtR76fraWIOt+eTcGxW29wuoAskjzdx5Rnnz4YXXJf7myVE1cdpKdfQ71zv/+3Xr+4v66K47 bO8X7NPdF0nItbzt/Uubbl7uvESZxi55dNd5ZZ2rAvXR1WBeddVV7nnK9v4BAIDsybzIUSE1 6M24HtBdj5RlHvAwo1qB7vzUCsi4taT584Ihc//b3ucsxY2fsR1VqSXUrS8hY+ts71vVyLyZ UVH2cXRAXqwdGVOqbaX7ubvR8cZA+yW/Xy85pru/pgZWTFJjo0PW9xe1iwrqwSGqeD2UNM+C rShrraMupyfreNneN+SHbp4bWVPJ9r4BAIDm0a312ozaJN29EWMRsidzTcjzLPehcSH9GDLu vcz5i7j1xbKcvz5uvo+qjEfyRM19KcG8d80l54SoKHvuuFZGtQKz8lMrgHI65JjwmS3+53TX /S3Oz09f0uYHZ8fU5Xc/pr792Rb17smN1vcZtYkK1rmD0OVayjiOxhM3hklqE48dO5aZuDWg 5Pe2n5us6O5TJKdge9+QH3xXAQAAXY6nGbVJujUey3ydnkdPPPGE9p4pGHIvW8Z+5rhxSVmO pdPdqzZr/GCe6HJ85JqbS869UcFcKEBznHFMeeTi59BbVyncbuPZMdVy29+qhW1Knd4rNcLn re87kunG7XOOtcOoVmBq/moF4ubGy7IuKg90dXFZjyHS5fPKOnbJI3OmR8WyZcus7xvyQXKN P/dzPzfuPcI4NwAAqkc3diDrXILuWp0xC82lm19bF/J+kXxAmWrXdLlOqdvL4l49buxSFfuZ dHN1lnmelzzSredc9rXVgDxZPDzqfOo63M/eAsfbw4tj20nLfX/fzhx4BUB+CTq6ej+Zp9r2 vmVNV9sikVXNYdxYqLLXOcr6XFFBPgBC7u91fUMStvcvilGtQEv+agUAACgbXd961teZrFts n+Q5dCH3ujI/XlzIe0TeP0WfP09qQXXX0lmMYdKtXVPFsUse3fNf9lrcvIkK5rgAmudHo2Oq ZeY697M3xfHTzdePa3Pe0bn/rPPbdv9zKmOZNvUtUCNDa60fA6LJfMNRITUWtvcNdlVxvrsg Xa1bVnVGuudbxuvYfi6aQXf8ZaodRO3i7oe9qOp9KgAgW0a1Aic/RK1AzsXNs5DlWu3NvI9A NN2anMHxCpI7khySbq0uL6QPWnJSRb3ujBvDlOaYOt0YEYkq1zBzr5sPUa+D3Gva3i+gKl51 TPziLv/zF1xXKZh7iosbHW8MtFs/FlxKd52R5bU2ikEXVamxiRvDlHaOTbfWpERV5tDQ1blJ Dtz2vsEOuX8P1pXq7sv4vgIAoHp0a5pkWYseFWWfmzpvdPcMuvkO5TpR3itx9UrenHhFq7GN q8NKs0ZRNz95UeaaMKoVGMy2VkA3N0xVaknzQldDbHu/gKqQsUkzd55xPnVt7mfvAeef0RHG JJWB7rq86GPA0RjdHMFVmBsvSHctneYcvXHzU1fpelNXX8h49WoK55bknox6CAAA4JH7VV1k cS+ru1aVfIft56JKdGvfJr3mcm0p15O6eQu9kJyJrOtalP4QXd5CIo06xbjxSFmvYZZ3unMQ 6/k0ly7nzNgwoHnWjowp1bbS/ey1O87uX5D63zCqFdjAvAK2MC80osg1dVTIOBvb+9ZMcdfT aY1hkjmvoyLtuQqKQJdns71faL5wbkl+pst7M8YNAIBq0t3LSg1l2n9Lt25xFuvXIJpuPaVa x9FIXkCuH5PmxJPr0SLMiac7jjRqQ3XjPYoydsnWc1+1+3ibdP0p1CACzROc205GJg0/2ub8 /Lz1/UJjyBkhim790hMnTljft2bTjUuS/Eaj8//FjUcq8v2nUa3A6sZqBeQeiGtDBMfVBvuH dP06VZ7LHQCAKtNdG0ikPf5EN06kKmvT5oFuDFkj+UTZZtL8ed7fyOv8eXFjmGTO93q3Gzdf fF6fi2bTzQfE2JnmkXNwVFR9fB3QTGccUx65+J2xrkOpsdHd1vcLjYm6NmKOqWrTrWtb1bHb Uk+UVQ5INz47jdxVEenWliInUB26fJHQ9RPw/gCyYVQr0M28AgCKQ1dDl/a1hG6e3kb671Eb 3TiFtPqRZTu69T69kL4Wmesjb/kD3eegkfXBdGOX6Fu69D0TFUWuKy0aXR6U+0mguRYPjzqf vA7387fIcf5Ur/V9QmO4BkCYrravSuv7hMXNS1dvHihuPr2qXmPK+DjOSdUVl1vy8P4AAABB uj5b6dtPc66youaMjGoFZpWvVkCX70g71yPvMZk/T5ez8UJ+L+3yMH9e3Fi+et7PuhowCeaW uEi3BlMjeT3UhvwSkA+bz48pNeUh9/N3o+ONgXbr+4TGRAV9ddWmu3eq2npKQVnMY6ebd0/q 4Ko4dsnDukrVFOwbkvtvXTu+swAAQJhuXZM05zxiXnn7bNwnSO5ExiAlzYkn93C259jSvUe7 u7tr3pZubBbX3eNFvTfkvWp7v6pE95m0vV9pMqoVmFq+WgGU06uOiV/c5X9ed93f4vz8tPX9 Qn109RPk7qtNdy1ZxbWXgnRjmCRqXZMzbuxS1edlb1atIfIjnFuKq+uMCu5zAQCoNl19nOSd 0vob5Jfskvo729eBso6Qbg0dLyTXIG1s3LvEjUuq5R5Tt0aNBPdk4+nODbX2EaB+ts8NAMbU ecfMnWecT1+b+xl8aIpzjXR+s/X9Qn1Y0zwbRrUCLeWoFdDV/9neL9vknkaXe6t17kBdDqVs NTb1yHqudORLLbklEXX/Jvfxto8DAADYI9cPuvElkhNI429EXYMwRqF5dPNaydiiZu+LvN/k GjZpTjy5r5Y58aSut1n7prtfreU+U7cN3fzVVSfvwajI+9yZZRIV5JeA5us9JZ/Hhe5ncIHj 7eHF1vcJ9SG/hDC5/o0K5vm9IK4+y7TmSHe/I1H18UhCNyc356XyCb7WpuseNLMe2KhW4GRb KWoFAACoEl0fb1p9jFH5K+6nmkfmao8Kyd/Y3C/JHcl7T1ejGXwfSk4q6/WXdHPUSMjff/zx x9358qJC3s+6OkiJZubJikS3BlyV1xdotqj3bZrjVwGY+dHomGqZuc79DMoopuFH25yfn7e+ X6idLr9k+7oL+XtPyLWl7X3LC12NllynyLgl6TOXcTUyTsm77pbnT34muSXddTj3nBfo5gOk pqhc5HPi9b3Iv6bzZzDfDACgFka1AoPUClRNXL+63A81un2u9e3SzXGQxmubFtkXGeMTt1aT N39elvudNIdfPWFjnFhR6Ppb6l2nGbXT9cfY3i+gataOONdLbSv9zyDrKhWX5JGiIk/XXWgu 6mmSxY1haiQYE39RVMicErb3C+moN7ckyC8BAAAdXV95GnN1RQU5o+bR9QnncZyMNyee7rrV CxkzIbmYtI9BtheXt6o1Jk6cmPlYqiRGtQIb8l8rwHwx9pFfAuxZsP+882mbEvkZfKBNqdGR tdb3EbXTrW1Kfqm6dO8Jch+XipsLoJ7getLs+bW9X2hceM2DWtf91Z2j8ti3AAAAmks3NiCN a4WoePDBB60fc1UU9f5A3ndS15s0J57U0qU5J55uzo16otZ6sCqKCu7xm6eo5wegTDafGFW9 vd9y+2z+4mtfU23LHlU3Op/DNwba/TZGtQKr818rgEuRX0KY7jrUdG2hqohbQ6meeOaZZ6wf U57IvXpUkEMoNrlfDq5tLPfQtW6D7y0AABBHN2akkTm7dOuDMqdV89x0003jnv+irZki76Ok OfEkpM3WrVvr/ju6OUkaCXJM8YL3OMGwvV9VoZsT0/a4OwAoKvreEMZ8UmbSnvdO7oFOnDhh /biCjGoFuu3XChRhznTUJo3ckuA7DgAAxJF++aiQ/vF6+xpZM8W+qCjy+qxyLZw0xkjes5IX rSWvE5yHOu2QfB799dHoc7GL/gMASBd9bwiLGosv17K29ytPslpTKY85Jlt013yN1ObBrjTy RYLvLaA2RrUCs+zXCgCALbq5yOSao57tkV+yLyqKnF/ySL5G5s/TjX/xQn4v7eLyO/K7pHn4 Gg36EaLpcoXnzp2zvm9VQH4JANKl66ezvV+wJyqYC/giyf9EzbeQVnR3d1s/xjzQzSlY730+ 7Aqun93omtnklwAAQBLdnF/1zpFmO2dkVCswlVoBUZX7CBl7JOOVksYfSS4jqrZLd02ddnCN Ph7rWNtFfgkA0hXs8wuG7f2CPVFBfuki3VqQaQa1jdW5L6yCNHNLgvwSAABIImMzdP3u9Yyj tp1fqroq3hvIvA0mc+LJ9bXkmuLe82lHGcaCpY2ckV1FyRkZ1Qq0UCsAoFiY9xVBx48fj3w/ PP7449b3LQ+ymhcvHDI+qupj42WcWFSU+R6yjNLOLQld/07e7h0AAIBduv5emTus1m2RX7Kr ivklj+SNJCeaNCfeL/7iL8b+Pu2QvgPbz02ekF+yqyj5JQAoCnJGeka1Aid/qbK1Atw3xWvG 2CUvBgYGrB+vbVHBHN7FEZyXpp5+HB3ySwAAwISudq6e6wbd9YfUn9k+ziqocn4pSN7TMide 1usmmUTVnvskst5VVJBfag7ySwCQLvJLCCJnpPfmm2/WdzFdZ7C2UnnX2q2CcL4obr3gWunO UzIvSFR7o1qBwerWCgAAgAt088HXev2pu1ah77g5dPmleuY6LAu5Tpb3d7PmxAsH93CX4hxh l9TnRgX5JQCoD/klBJFf0mvWfHfBkJyW7eO2KSq4N8m/LHNLQneeoi4SAMrLqFZgA7UCaEzc GKZa5vei79guXX6JvuML8+c99NBD2vd5ViF5LdvHnie6c4SsjWV736qAcwQApIv8EoLIL+nJ ulLNjqrfg0YF+aV8k3siL+Q+Mu3ckiC/BAAAaqG7561lvUdyRnbRHxxP9/7MOmrdT6NagdXl rxXQvV7Me98cnE8AFInUOMn5Kekc5bWJq1WQPjqvXRq8Pj/dtXYt+50G02OP66uUbaS5T3l9 zZKOvZHXTDcP8MMPP+x+BwfFja2R30tOSsS1kzFBXrtGxc1nLvvgtYu7Bwzud9inPvUp8wvt lEJyWmk9P7Uce15es6gI55fkfSt5hUZl/X1QBXIe8ubckH+zqr8jvwQAQLE1+z5Jd48j4T22 3vsk6Q8O3yedOHFCuz/nzp3z28l/69rJNsLbbUTc61HrfqfB9NiD+xgVkiNM414g7j46eL8R N45NxvCnsS8ibi4/2Yfwfuuuj7OO8LGb7ncZX7NVq1ZFPkde34npPfmDDz7oruWchrjPvddG +hziPotp7YuIO8fIftSy32GyvkBUyHwa0ocQRdYni/vu8NrF3dfKNnTbr0fcsde632kwPXaT /Y5jGnH3/PI7L+LOD7X8vaSIO/bgedl0v9MIk2M33W9dGNUKdF+oFagleH2TI+6zVut+pxHe a5H0vJvsdxpheuzNeg9J5PU1Szr2Zr1mcWOaguslxl0/yXVAWhF3HRK8HzHd7zyE5PXSjKK+ ZsEIf+bSOlfHfW6QrFm5JUF+CQCAYrN1nxR3/ZBmmF7jNuuaWyLu9ah1v9MI02PP6u+Ho+h9 WrbzS2n1WdYSeXvNJI8RFV6u1Itm9kcU/XMft99pBP2QyZG3fsi8fe7zmH9oRn6pluA1S468 fu7JGekjr69ZXs7VRc0/VC1nZFQrMGt8rUBRX99gkF/KH6n7Da4VnPW84eSXAAAotjzll7x1 YdIM8kvJQX4pPmrt/wrOUd2sCK6pRJ+lUlOmTIn8Ofmli0F+KTnijr3M45dMxY2RlN/Vut+N Mn0tTPe7Wa9ZM99Dpsee1/HBuvNDmuNx4z4/te53GrzXQnd+q2W/m/maNes9lNZrJr+v4px3 8rtmz+VgOkeD6X6H6cZQZxmbNm1KdW6JZr1mce/BWkRF0vUImkvOS8H6u7hzYlp095tJ5z+g yIxqBaaWfw0CANWT1X1wlOD95OTJkyOvN0zuOWUetqiQPtF65+2udU5q+Rnz410a8rqkcV8e 9x4KrnvQrPn6TefhD+53syN4D+cdexbrB9g+X5mS80RUpHkvDT1dbq5I7yEAyBNdfsn2fsEO Xb9tXN1MVcgcv82Oql9fRgX5pfywkVsS5JcAAEA9dH26kktKemwa90lGtQIt1Ao0SpdfSqoh rZK0x1EkBc/9pcgv2UV+CQDSRX4JQeSX9KQGsNkRV6tXBVExd+5c6/uFC2zklsTWrVsj3xvc DwAAgDjheXqDIdcXcY/lPskuckbJdHORZBVZz3NdNOSM7GpmzsioVuDkVdQKAKiM/z97/wNd 1XUfaMNnXE1IYwfl7YTXo7ReSgY3/uq4UaYemhi7lWZsK9M4VB1YfLGaBtEsYjpAIusNmHqt umrfms60TqMmhMbLHkYw40BC0SgLnDEi9nfBRobXBMsQbJlvMpEbMJ0vXq5E8BC7LK377X1P 7vHhnLP32ffec+8+f55nrcd/pH2P9v13zj6//du/rVq7b7tfaEe5Zj4KuXbHdt9sK2tStBJZ j8/2c7aprHURBfeQ6dB/7ZD/3cq/rbp3Y34JERER41Td/8atkWd+yb5RkHv2trr506SReWa2 n2/aVJ1bip4z2ipZk4SImKyq2JvtfiWlUa7AEXIF6jUK3T6ORXLLli01jLobY2JiwvrztSk5 iunV5tySlPklRERErFdVPp1Etx6D+SX7RkHt7CtVjZOTJm69XxGlNpBdmV9CRExWYm8YNArm jFxV8xhJs2TJksLnLjFnlE5tzRcZ5Qo8Fs4V4BqHiIiIjSjXvEShGwfJuacomF9qnfJ+Kgjr aK60FWuYmNOL1l873I/tfhVF1fySzCmw3TdExCxK7A2Dyj1rg1BL4G3lXFuzKfp6JKlqzEf+ mz3lvklVbN6fc91CRMymRrkCD1JXANOlai2SRBeLjGLr1q3Wn09RVN2z2e5X2tR9vhtFzl0R r482CnJ6WyfnB0TEZCVOh0FZq61X7q0UlQ+XFEXfS6kqa9bTZXBuSeY72uoL1y1ERERsVNU9 z+DgoPIxURATbp2qmLDNcWla9Y/dk0LOF8m1eUa5AmvIFVApP6+cS+zK/BIiYrISp8OgqhoQ cl7Fdt/SoqrmRaPIuH3R6+JVVe1fRQ5c603T3JJUdd3is4GIiIimquLvMoauGutEQUy4dZJ/ VptJzjFV55ZsP6e0qloHtnPnTut9K4rMGSHWplGuwB3kCqCZIyMjkedgxmjFVRW7lfvh2O5b mjxy5EgNI3IzZOz+7Nmz1p9bGmR8mA79c6mydqbtuSUpaywRERExCaPqgkvkPXJU+yiYX2qd soZ4Le8Xlp39+/c7V111le4WNBY59iaPS69qLo8911pnVPxAzova7hciYlZV5U4w7iquqvEO +/yElfNoH/jAB+ofgEcg6+kF542McgWuL26uQBTsydta5dxSdc/fNOUsMr+EiIiISajKtZTz TlHto8Ygcpxv+3kURXmfFoWupmHRHRgYUN6jxiG/BzKOYPs5ZEFydO0bhTxn2+4XImJWVc0v yWue7b5huj4TrNcOK+sANoK8x5R5SnL/3uDPizy+VL2ujPlaZ1rni6TMGWFWNcoVaCturgAi YpqV67KrY6MgUXF1xit2lXXEuZ8wN1jT/ROf+ETlcy3r4as+93JOSc5JjY+PW+9/llSdG9hj oHVybkBETFbmlzAoe0iaG1xX/Wd/9meVuaE45Dhdziv5908KzjFJZF0928/Rhqo9qzgvtUZ/ /CRtc0vSqHsy6hkgIiJiParWEkSthZdjEKNcgXP/ZyVXIArbz7dIqu7LbPcrjQbH11E17mTs SJq2e4OsGTVfx9rG1hqFjNHY7hciYlZVxXFZN15sVfXFbfcrTQZrLsj5oervZH07WU9QjlGq yPkkOV+ky0tijslVtR8vNRear5xbkvGUNL/mUZBvhoiIiPUo4+gqgvsMq+ai2BO1dar2XWXO 40rlmiI/5Ok1T9U5hPzc1qmqh6n73BvlChz5P6krgIhoaBTE6oqtas02905v6587kvlG/rVI Vf33P6bHZY6J+0ZbZmFuSco1CxEREZNUte9MMJ+dfVPsSx6amf58UbleRo7zg22C2O5zVlXt X83+Aq2znvklRESMN4o0xd+McgUeI1fAlqp7J7kmx3bf0qB8HfzI+5yodvXML0UdX1KkOSZq WdgxC3NL0rRf3xARETFbqmp6SPx1wkZGRiLbFGmcblvVeyXnCG33LS0G7+VV43rutZJR1v6J gtzI1inPwVGk+Z4WETELRtV0jaohjcVRtdeWv65bUZVrj/xzGrrau/XOGUmjxj1FeP3lurco mBNorv5c3GbVPDXKFXgwuVwB9uZCRETEZqiq3+Cft1DdT6lyzTB55X1bFHK9TrP+5un5stPz N8+Jv9JT+VurOhznv+1Y6bx1dk+o7RvCT0y8WmnbK/458dUeZ/4npZa9PnI+1B8L0sWAgth+ b7OqP0fPj64uPiaral1jsMYpIiLWpmp8bLtfaE+5Jj6KVu5jeF6Mze/b8b0rxubf+duHnLfO fy/U9i3hnzx13hubP7X9Pmf+YnNqT8i16350uUaNzC9JizjHFLUeS8KcQPP059GlPZ9TFavh 84GI2BqNcgXWUFcA861qPOKvLaZqQx2s1uqvU+7Hv9YsKb9zsexcffejkX9P3qMe/Zsepzx/ 2mv/pLjfbbt9+xXt/qTHcd46t60lr02w1iPx9eaq2nuplTEWDMdzqrCGDBGxMZlfwiht5tZ8 X4zNb1q1PfLvy7H5/7PjPjE2P++1nxZj847eiLH5+acS7VdwbU3cPpyNzi9JizbHtGXLlsj3 nfud5lmtA572uSVpcP/hKrIGje2+ISIiYrb170Pjx5/HEkXcPQEma9QetJKk61tdEC5+OHqv Fj87PtPmzP/kycpjgnNGx/9igfOdi5edP+z5atPnjYJzmrp5C6NcgTvIFUhS1d5Leb63T6Oq /Ztt9wsRMeuq9ihtRk4PZkdVbeBm76v0U+HK7Qbj+FUdzvzF6cpjgvNL33tosfN9MY5/6L7t ic4xBccicr7JtH0jf1fm0gT3GcrrOJT9lOyYlfk71X5vWek/IiIipldV/FeuS6q2iWLNmjXW +14kVTWQk14HEpwr6hDuHVrk1bAr/aTs9HxV1l7o8eaYfiB+vvCLT3mPeWpTm2h73hk+O+98 69d+zSnPn2za6xLMGyae03xVsTT2WWut8hysO28j5lWjXIHryRXA1ki8DqNUrROQ60ua+XeD c0VyHD92X49X7+7MxbJz3/an3HH8z+aYXhM/v/k+3zj+vg7R9oIzdm7eGVu7VozjzzXcr+B9 jMn8TlLzS1I5lxWce5HHl3XFbX9WkvLMmeh5RfZewqpcrxAREbGZRu1NLKmujaH2h31V+yol HU8eflXOJ67yjr+9V7zP86VQu1Fxz3n13X9VmWO6/JMnndsPXnDcu1jHeWixeMzlPc550W5s 2+mm7a8UnBuldnRrVJ0v8nSPngWjIIaAiNi4slZQFHLOwHbf0K4qmjkGGjsfNTYP74905Py8 c9OqL1fmjS5fnHbufyo4Nv9epU7B00+dSWR/JX89bjl3Y/Ia+OeMjHIF2pLLFYiaY5K5OnkZ v6pqIVP7DKuqauhX9zpAREREbERVLousnSd/rxqLtKKGOL6tqsZVkvEO//ySuBV1frTnRmVb uffSwrsfdSY2LnLWvXjOcau6O85K4U+n1zX1tZDjYH9tR/8+YNg8Vfm4rGdsrfLcGwX7WiEi Ni57oKNK1T1RM9dw++eXKmPzsVXKtnLvpZtXbXcO3t/jbJ8OjM3PbE+uT2NjVzx/+f+23pM8 zxupPm/sn4lVyQNG2xrlCpxbRF0BRMQCKOPyKuQ9tmr+Sa7Dt933IilrtkeR5H6itcwvSeUc 06Lljzr/+Ws3Om23/2XlcTJTcvoRWVvjctNei+BnMul9qDBaVW28Zu8ngFeqqoFC7BMRsXFV 80tyTx3bfUO7quqKN7PuXS3zS1I5x9SzarvzXx5Z5XT0PvT22Hx7r/j9fMP9kfM2/vmcNOS2 yDmm4FxM1ueY5HOKopr/iSiNqivBZwQRERFr0ShX4IhZroAqbixzYlR1Qogpt1bVmoUka+Tt uSzuYRc/9PY9bMz8klTus/SrbZ91Ftxyi9en6h5MzXgd5B5L/rF0V1eX9femKKpq47GWsbWq 5pqZZ0VETEbVmNh2v9CucgyqollzGd8Ljs1j5pekcu+kmzs+6yzu6Xl7bP6zfZUa7Y/cO8lP WtbSyNc/uDel/H85T2O7b/WoqnfHPDf65VqFiIiIzVZ3D/Too49G/txmfYOiGrwXqpJUrPgH woVfdPftNV1b9IZwybfOOdW67ZL7xX/Oz21rymsQnN+sZZ/RILbfzyypqneXhlzUImmUK/DY 27kCqpgD+/MiIiZjFOSzoFRVj6pZNfJeE958n28cb7AO6S3h+rGIcfyFpxrqS3BNjazdbfv9 8Bs1xyTXWmVxjilY869KWubz0L7ysxBFkrVNEBEREaWqus39/f2RP0/bfUIRVK1FSCrGL/fy Xfzw2/W0xlbJOZjTsY97br7sXL3q7frqPcKLx1Ym/vyDdWhqfd5BbL+fWVJ1fmBtYutV7a/G vmKIiMnY7H0qjHIFHmQPgqyrqpHXrL0ofypcuT04jo+vEfCKGMff9NnAOP57DzXUl+BYJY3r 1/MwxyTnKqOg7hn6Za9AREREbJWqcYfk3e9+d+hnrFtovaq6dxK5Bi2Jv7Fuet6p7vFrug7p srD/2MWf3ZE2b1+lYLyn1uccxPb7mRV16xvTGC/Iu1F5qknWvkRELLqqnIqkxlqYXXV70TZr XmL7mcDY3GBt0bzwy98LjM0b2FcpuP5Bro+2/V6ozPq8kSqPiLmA9GmUK7DGfq6Aao9sWZ/C 9muIiIiI+VPW84hC5kBFkeW9ULNqs+85/DXybha+NtFj9Lgn58tO2+3bvf6IW1inPF9K7HkH c0Dreb5BbL+XWVF1T8IaxtYrz7lRUD8dETE5Vdc9aouiVLUXrdxvqBl/z1/3rjI2f+o+o8dN i7F5R29wbH6mrj7451zlXE3a7wFl/4L7P8l+nzlT3/Nvla3IJcR8KPfX4jqFiNi4RrkCd9jP FUDMkqo6DgsWLIj8edrH6Hm02XUQgmuRxj7bJu5FT8Y+LlhbL8k9mGSuqH+OU67VqKcWWBDb 72VWVM0vN2v/AFQrz7lRsIczImJyqsbDIyMj1vuG9lXVfGjWvEtwLdLYZzvE2Pxc7OOC9fLq 3VdJ7rnrp97ayP4cuVa9V8E5prSPX1X7ZpJHhEGbXccVERERMagqPhwF+6nYUbU3q4xxJHF8 /x5JdwlnDfdH8tfLq2U9U5zV3GCjXIHr3VyBRl8Txt+1q4qxZSF3NY+q9mBL6jyBiIjsVYHx qu6t5FxMM/6ef4+kyjjecH8kf229WtY+VZVjPf89SiP10avzS83af0plVuaYgq+1H+qYYdCo cxD1shEREbGZynxLU5pV2wH1qvLVZM3CpP7G8Flxj9nx2cpxTdclbZsri8fc5/Xn+F8sED9/ s6F+yPoOcvxbpZGcvCC238csqMp3S3Mt/TwbFfeQUN8Cs6RRrkAbdQUwG6r22GENAVZV3VvJ +YFm/c2xc/5xvNkapqcuXDmO/95Di51a9kcNjlEaqV0h53Tk/JuNuZ0szDEF14lVSapGBeZL rlGIiIjYauV9sj+er6PVOWXoqqu3nVSc+Q3hkm+dc+Quv6ZrmJqxr1Kwbn0jzy+I7fcx7apy siXyM2i7f0U0uAc1n2VExOYYNRYmdotVdfdLzZqLeEu4fsw3NjdYl9TIvkpnz5694nkF99I0 yhU4995M5gpEzd+kZY5Jt3aJupwYVHU/J++xbfcNERER861qD8gobPe1qKrWMSSZi3RS3pN+ 8cnKcb/c4zjzF/do258QLvj0Xq8vK4U/nV5X998PjocbHQfz2a1N1dqlLVu2WO9bUY0iyXWL iIjoyn4VGGe1fnOQRurCxXlOjM1773/KNzb/nrb9j4SLV709V1IZm5/ZbvS3/HsgSfKQWxS1 N20a1uSr1iPVu48s5ltVTXLqsmI9GuUKHMlmrgAiItpV1iQzpZHaB1i/8nVXkWStrO9cLDtX 3/2o0yGOO7FxkVOeP61sG5xfanQPJn9sR95fyc9lI88liO33MM3q1i7xnbej6jtP/iEiYvKq cq2oR4pVdfdLzdyj9vtibH7Tqu2VsfnB+8U4e/68sm1wfsl0X6Wpqakrnk8a5mCSMmqOyWbN d916JOYLMErV3DbXJ0RExGJqlCvwmJ1cgWBdMhv3T6g3mFdYJel6ytU5ppvEsU99/Ubxsx9H tpP7L933S593Fn7hP3p9eWpTm/id+r5XZTAvK4n7qyC23780q8rbDtZGwdapym2lFgoiYvKq 9szhnIt+VfdLcr5Azhs06+9W55jk2Pz721eJn12MbCf3VHrgrvucm/+vh94em9/XIX53QXt8 uQarVc/FhmmaY2LtEtaq6j6t0VxMRERERBODuWgqqH9lT926pPHx8UT/VrX+nbw3/e9/eqMz /5NS6Pe/NPSkc+KRDmfxwy95/bi/w3Hm57bV9LeCderlHgZJ3DMFsf3+pVXWIzVXo1yBBxvL FZDn5Sjked3280dEzJuq66Zc12S7b5gedWuY5LxBM/92tVaeHMcfeGiVM3/xTOj3d4nfP7+9 11m5ffrKcfyFp5THDc535DWXUM4xBdcNtXqOSdYcZO0S1mrU3m/yZ7b7hYiIiMVRle/ip5l1 wzFe1bqkZuwr/YZw9ZFXHadjldMr/sZjX+2p3M+s3b7dabttk7fv0rrpefHb3ko/6tlXKbiO X65PSqL/QWy/d2lVfnaiYD2SXVUxBdv9QkTMozKvJYrqum+jXIE17EGAV6rbt7bZexa9Jfza /3PeG8f/7fb7KvNDjwg7eu7z9mjafiYwjlfswRSs1Zb3e8KzZ8+GxmIyn6dV67VU++mydglV xl3HEBEREVuhXANjQh72cM2qujVMzcplOz1fdjb9p2edq+92a9vJmu47hhY5b53d47Up/UTO E407H/7TXc70I78kfnbZ6NjB3M4kx79BbL93aVRV40fC2iV7ynNsFNwfIiI2T1W+he1+YboM rrv306o6D+fF2Pwb3/6ec9Oqh7yx+X+5r8d56/z3vDZnLsr1Rk85n33ob53p7XeJn82HjhOc w0hy7CfXAMn5LPlv2++Z36h5ozVr1jR93sjGPRyGNcoVuCObuQKsrUVERMS0qLq39pO2+4Si qaqFlcV8Nn8Nd0mSe48Gsf1c06YuPsLaJbtG1f6XEH9ARGyewTFJFWqNYtDg2vssfl7kPEsz x37VmgtpHFPamGOSx48iqbrgmE9V55qk6n0gImZRo1yB67OZK4CYF+VYJQ5be5yiq2ptgyRL tSqCOVZyH+Qkjx/E9vNNm6p9pyWsUbSrqiZK0vusISLi2xLHw1pU5eTJsXiraqg1YrDmdtJj vzTPL0lbOccU3LeK8wuaSt4DIiIipkXdOgX/vZDtfhZd3b1Hkut6GtUoV6AtPlegq6vLe37y 8ylr5SXZzyC2X7c0qaqfINm5c6f1/hVd1b0k+a2IiM2T2kJYi7q64mkfS8nYdLP7m/b5Jamc SwquK5L/n+RcW3CdmB/qHmOc1G1FRETENKmr41CFNQt2De6b6ycLtROCa9+aUcuLsXW08rOh uv+Qn6ks5NHmWdWaQznHartvWFyNcgXO/TPqCmBhVO2NzrkaVaryQSRp3sfS3+9mjROzML8k jZpjkq+JnBdK4viqungS1qCgzuA+xVWYl0RERERbqu6Z/bCvkn2D+YR+kq4fl6TBNXLNmg8L Yvt5p0WZW833Or2q9lMiJx4RsfmS/421KGO6qroPaax9Z5QrcOTtXIFgvYSJiYmm9Csr80vS Zs0x6WpTsKcmxqlaI8lnBxEREW2q229Fwr5K6TBYD91PWvdfCa6Pa1Yt8SC2n3ca1NVnycI9 fRHcsmVLpr7PiIh5UjX+TVPtYUyXIyMjyrGVvKbb7p/fYP2DZtY8z9KcUfW1SXLeSK5JUyHn sW0/X0y/qpxA7gnQKFfgMeoKICKifVVrrP3jbdt9RLeWlqpOnsynTFudvODnijX7rVO3t5r8 DFHzMh2qvs+2+4WIWARVcwXkgKNOOZ5V0aw1P/Uo8wP9NLMuX9bml6Ryjin4GtUzx6SrYy6h 1h2aqDqvJL0PMSIiIrZeo1yBB/ORK6C7V5IkVWsaG1Pet6qwMX8j5zBkfCbqd8Ha8+QDt86s xD6KrCrXlXlYRMTWqKo93Mx1Hph9dXXyWnnPJGusyfmcqJwh2Qc/zZ73yeL8UtXgHJOklhrS uvoSzFWjqVGw9g0RERHToIzn65D3Jbb7iK6qOlmSVu7F4o+1yDGtf04o+HlK875PeTNYZ9BP Fu/lW61RrsCa1uYK7Ny5M/L9VM3XIiJi8kbNE8if2e4XpltdDWK5lqUVezH55zXkPZ3/bwbn PJq9bj3L80vSeueYVGM5SVdXl/XnhdlQFbPhXhsRERHTohzbqpB1p233D13jais0a9+ioPLv BJHrKWSupv+zJGMvrNdvjWmIYaCZwVr+VaiNgojYuEa5AnfUliugWu/LeRvj1O1fK8cDzR6f Be8b5P/LOZHgujw5B9Ls1yLr80vSWueY5O9UcJ+EtajKIyTnDBEREdNi1HyBH/ZhSY+6vWHl fUorYh2qvUWDUO+hNcr3XFeDhfhXepTn0iiobYGI2FqJ1WG9yv0rdbl5cg6iWX9bNY4I0ur1 UVmeM5JGrTGKmjcK1hQMIvO9bD+XvGiUK3B9PvYgMJE8B0RERMyCMr6pgr1Y0qWsb6FCzjPI +95m/n3dPXUr+4FujEP33W1FPiqaq9objdoWiIitVVVriH2V0MS43J5mzTGp9gML0qr65nmZ X5JGrTXyj6Pl3JKujkQra5VjPoyC2quImCeNcgXaipMrgFgEdXu3yH1/bPcPr1S3T6yc/2nm 3I4pwb2ZWtEX2+9LK43Ln83DvX7eVH1vyXdFRGy9URDbQ1N1tYklJnvz1KouxyxIcL+lZpin +SVp1ByTnCuMq1Eu15bY7jtmS3IcEBERMSvK+LMut449WdKlfD/kmDKpexejXIFzv1BTroBq LKyjujdTM16zILbfw1aq+6zI3/H9Tpfy/YiCWCYioh2pN4SNKusp6kh6jkmXXxZFdb+lZj3/ vM0vSaPmmJYuXap8jZud14f5VFVvnhqtiIiImEZ1e+U0834D6zOu9kIz6mjF3RvrkJ+vpO+p gth+T1qlbg9o+ZmQnw3bfcQrVe3bTL07TFKjXIEjteUKIKJ6/MP+kViLuvGbJMn7Ld09go41 a9ZU9nBN+rnncX5JqhrfBZE5Qs3KocN8q6pBwecJERER06gco6gYGhqy3j8MG1dbPenYddx9 sQ5ZAy/pcXAQ2+9HK4x7D8ilTqeydmgUo6Oj1vuGiFhEVWMo6ldhrSY9b2SUK/CYOlfg9ddf 1/YnjmbkFeZ1fkn6P/7H/3AWLVqkfD3l3BLjc6xHmZsZhbyvtt03RERERJX9/f3KsTE1tNJp XN5cknNMuj18dFAPIhlbmfeKyamqdyfhe4GIaE9VbWjOzViLcftcJjFGi8spszE+zOuckZyf k+u4dDR731jMrzK3LApqGuRPo1yBB6krgIiI+VO3Z85//+//3Xr/MNq4PXyTGq/Wg8wDJk7T uHFzSzt37rTeR4x2YmIi8j1jX15ERLuqrq3j4+PW+4bZ0mSOqZGxWtxYPwpZI6+Z62byOL8U V39cct1113Fvg3XLdQcRETE7GuUKrCFXIMobbrghcsxDvZB0u3XrVu29kBzLNnIvpJt71P1N 269LHoybW5Lvve0+olpVDiz3kYiIdiWPHJPUZI6p3jFbdS7HlFbsx5m3+SW575TpvlXUZsB6 Zd0sIiIiZlXV/bPk1Vdftd4/VBs3b9TI/Y1qv2oVg4OD1l+PrNvM2EMeNcoVuCO9uQKqPRDk vaXtviEiFl3V3haco7FeTcZ5Mu+k1rrjpvMe1eM3e25JKv+GnJNpxd9qtnH1xiVy3ZIf5piw VlX5muTxIiIiYla85pprIsczDzzwgPW+oV6TOaZ66l/ErYvxI+cobb8OWXdmZoa5pRwo69tE Qb47ImK0RrkC19vJFVBdl5tZVwzzrckck6yRazovo8pViaKeuauiG3efJZH3QVHvq5yL5lyB pspczShkzqftviEiIiKa+PnPf155f2O7bxhv3L2PvL+pda/ZuHtf/z2V7eefdeW9p6oeQhXm lrKhPGdGwV7PiIjpULU+m3XY2IhyfkE1Bqgi1xnJtTBxx5LjQhNk7Tnmi8yVr5WqLnGV4JwQ 80bmGuUKtKW3roBtOzs7Iz+TfNYQERExK8qx81VXXRU5ppG1DWz3D+M1ya+rJf8pDhv3VkFs v+ZJqKtPWYW5pWyoigfJ+0XbfUNERFfO1dhMTdb/y3Gdbl5obGyMsWHCyu99XB1B1b0Nc0xY j7I2BdcaRMyaRrkC5/4PcgUQUalqTTb3L9nRpD64vO+Nqweuqv9s+54qiO3XuxHle2ASg5iY mLDeVzRTNcc7PDxsvW+IiPi25IljM5XX/Th0NfDkuiQdshav7eeYFeU8nqp2sZ+4/ZKi1qcx x4Q6WSuLiIiIeVGVNyOhnkL6NMoVOKLPFTCZY4rbk0k1Hja5/2qmQWy/X/UqX3uT+oMmNVQw HcrzqQp5HrbdP0REfFv2ucBmK9enx9U+lsi1SsHH6tbZMDY0V87fxdXDk8h5I9N7m2BumHyP qRWOUbJ/HyIiIubJ7u7uyLEN6yKypbxHiqvrIFHFRlRrZWzOF0mD2H6d61E3H1dFvnemezVj OpTnyCjkOdV23zDdGuUKPEZdAURbqmrkyTGR7b5hfjTNJ5JzINV65K+//rpyHMnckpmma5Yk 9awxj7qnYo4J/apyfOV8pO2+ISIiItajan8Xmatlu29Ym/Ke0yQPT8a/g2smou5v5f2R7ecU xHZ/alG+xqr5Wz/yPZPvne3+Ym2q9ukmhoCImE5Vde9YR4r1aJQr8KA+VyCqrpoKOSdy7Nix 0M/TkqMk+ydr96W5Pp+c0zN5vWWcX9YDr/fvMMeEOlW5h2m490ZERESsV1V9BtZnZ0+Zk6fa 5yV43+TPyUvr+DaI7f6YKl9bk7oncXs4YzpV5baTd4iImF5Va7XZBw+boYwhm4wFr7/++iv+ Py3zRdLqXk/y37b7ElS+RnF7UVWR+V5J1GOIqp3JvBFKVWsRx8fHrfcN9RrlCqyhrgAiImKc qjrzW7Zssd43rE9ZI8OkXp4cCwdzqNJ0nxTEdn/ilLmOJrVOJNSgzK6qeAZ78iIipld5jY5C rmuy3TfMp6Y18Kr8u3/371KVd5TG+SW55t8kl65K0vumRdX+YI662FLvDhER0a5GuQJ3kCvQ bFVjIgl1u7Kr6b61ftI0tyQNYrs/KuV3SJUXHUS+J2nJS8XaVe19IKHGEiJiulXVyGPNPjZT Of9gipzLqe63hG9b67ySnNdr1vc6ao4pLbUfsPWqcnX5TCAiImIeVNWXlmNz233D+q1lX1qJ vKdNoiZEUgax3Z+g8rWqJQ4g34s05Zra0ChX4Pp85AqoYhvsWYeImH5VcUDWn2KzlXMdJvty VpH5SLIege1+27bWeSW5ZiTpNUtRMseEVclbQERExDyrqgMi66YVPR6eB2Vuo+mewNX9ltIw zxTEdn+qVueVTGrlS+RrT35p9pXnQhWN7O2MiJg1jXIF2rKdK8D+eWhb032Z/ONNWVO5aPdu cowta7XXgnytWrmWnDkmpO4qIiIiFkFVPs3Y2Jj1vmHjyntN+V6aUp1nslnHK4jt11C+FrXM K1W/P0W7z8+rqu8P94WIiNlRNd5lb3VslTJPSbU+Todcm5PnfCW5/kjOj5nmxFWR9e1s5fnI vxu8L5DzRmnI0zPRKFfg3HsynSuQFlX10VkTi4iIiHkyKq9KwhqmfCn386nuwWuKHA/bWLcf xNZrJp+76Z5JVeRrzN5J+VGeA+W5MIq07UWGiIhq5ZoQ1VjHdt+wWNayJ6cfOf8iay7nYZwp x1eytl+t648kcq44DWMweZ8QnGOSc15ZmWPC+pTvryrnkH1WEbGVGuUKHCFXABFbp2qMRE3v /CnvherJD5T3ca26XwrSytdHPkf5XOVzrgX5mvJ9yZ8ynzYK6iQhImZLGfdTQTwYbSjXwNSy 31Jw3CnnmrK0rqm6TqmeOSVJWuaV/DLHVDxVubnyfbfdN0RERMSklfW8VPcjtvuWB41yBR5L b66ArPmlWpehQt4/yXzLZteSCdKK10M+J/ncaqmBJ5GvIbUk86tqLlaeX233DRERa1MVy5dr m2z3DYurnGeqZz2THzlnI+du0jTfJOeTZO6VrO9Xa26bnzTOK/lljqlYch1BRETEIqlbu806 i/xa3W+p1rkjiX/+KOl7oiDNeO6yz/XOE0mqc0XUhcyv8tyn+uwTByimRrkCD6Y3VwARzVXl nbOfHqbBat28esawQWS9Zrm+qTrnVMvYtlpPW/7b9DGyZp+cZ5HjaDnX1ch8UhUZx0/zvJJf +dyDtQ+YY8qfrINFRETEIsoapuLayDyT/75OfoaS2Ds3SFLPU/ZN9rHe+iIS5pWKI+uREBHz pyoen8T4BbOhUa7AmmzkCtRbq9lkvCvnjKRy3Cutzj/5XbNmTaW9/Lf/59X5I6lckySPk8Q8 kp9qrpuN/V+TeN+C75mcu87ic8FoBwcHIz+37OOHiIiIeVaOc1Wwhqk4yvc6qTxCGYeXa4Rq vVcKUs/zkH9T/u1G55OqsGdSsWTtUvLK103Gb/Nq9XyTV2WcRJ5L/RIHa67y9Q2+5ti41157 beT5Xf7cdt9sK+Oets81eKVr1651PvrRjyayTinNyHF2VtYf6YyaN5LvHdfLfEh+Qro0yhW4 Ixu5AoiIiHlSVc+bNUzFU+YeynzDJJH3W/KzJO+XZd3palw2GK8PEtU/f6xaHkseUx5bxkeS 5IEHHojM10yj/lzRPCprt1RzaZvt9ddfH/l5aGb8MencY4BmQzylucrXFwAgr1TnlPKWt8Mc Uz6lZioiImLyGuUKXE+uQNbU1Qlm7UYxlXXg5PxKtf4FAACAhPml5sr8EgDkCRmHl2thm7F3 a9qUzy+Y88YcU7ZV5TDKPEfbfUNERERshbp1SewjU2xff/115o8KjFGuQJubKwAA4OfjH/+4 Fy+Mu9bIeKKMrfX09Di/8zu/U/m3/H+59jTusTJ2E/VYk1pK8viy7a233lrp780331zpu8n1 UT7upptucj75yU9WlP9tmqcskX/zU5/6lLNixYpKLrs07nHVWsXVWmUAAFlFnseq80kyh9H2 PY8Ng+dxeV2Rr4ftfmFtynlBFXmfK0VERESsKsf0qnrBsu6U7f5hOqzONW3ZsqWyry8AABQT k7mQ3//93498rJzDiXusnCOKYtmyZbGPlXM8UezevVv7OF18KC72qVtLFBdbkrHEZmOUK3Cu nVwBAEgcOY9UrX9dz76reTcqVyAPe00VSVW+h/y57b4hIiIitlI55o9CziOwhgmjlHv6yPlH 5psAAIpH3LyJbs+wuOuLKufFZH2Qiri1UHIdlIq4WJ9qDCWJy0WX+ftRfOhDH2pozzYAAFPe +c53Vv4tz7HyfFZVnvuq+5kyL1SbzZg3MsoVONLOHgQttLquOArqBCMiImLRlGMjVTxn586d 1vuH6Veub5L3nnLOadOmTdTUg8Ihz6GNxIPTbjX/OK/K+YVqHC2P1hsbVM1/6OZNdPEWiS7m oltHJNGtJdKtI5KfYd3zlJ9vFXE5yLr5nLj6gKp5OGrqJK9uH1Fi5+lTvl+2z5tFsVobNO48 ibXJ2qRsqsoZMVm7jYgYp1GuwGPkCiBidtXl38q5A9v9w2wqPztyrdORI0cqc0/VNU9yDsrv H/zBHzi//uu/rvQ3fuM3nN/93d+ttJWP37p1a2Xus3rMPCjrD8rXKq+ePXvW+ufR/7lUYbLn C2JRVNVv082bxNV8033HdOuIJLrYnG4cI9E9T1WOjSRu3ZQOXTxKNQ9HDKt5UscIMWx1jpz5 peSV16wg8jpnu1+oVjUeYG4QERERi6qMXaj2IpAxetv9a7VGuQIPkiuAmHflPGUU8nzJugHE t61nDkS15qmKLoapW0ck0c0DxNWFU62bilszJVGtm9KtmaqiOqfUM3eHjal7v+L22ULMq8wv NdeoOSbmtNNp1HslManPi4iIiJhnVeMkiVxTYLt/iIiNKs9lKsg3zJ9GuQJryBVIylpruOn2 Xqqi+lu6dUQSXYwnDtW6qbg1UxLVeSRuzZREVUuwntqD2LzPM/N6WFSZX2q+zDFlQ1VeLrUN EBEREdX30jJP2HbfEBEbVbXmgTpTiNHWMrcRt59Slaj1QSZrgyRRa0dM1gap4qFx66Akqthe 3DooiWougv2U7KjKpZLzlbz26dUoV+AOcgWaLfNLrZE5pnTLdQQRERFRry5GI/d9sd0/RMR6 lecwFap6WIhFt5babHH7KVWJyu81WRskiVofZLI2SBL1/OLWQUlUa6FMiJq7Zj8lu5J3jvi2 zBm1TjnWDF5zmDdKh6qcD96fZDXKFbieXAFERMQ8qMrlXbJkSWWve9v9Q0SsVXnukuewKFif iai2lrmQuP2UqkTFMU3WEUmiYj0m64gkwXlk0zVTkuC6KZM1U1WC+czsp2RXcs8R35b5pbfd c3beWbljt+N0rPLOCzcJd/7pjc6rR/5MtLnc8N+Q153gHJO8nnLusSf77SEiIpprlCvQRq4A livjKFU+75YtW6z3DxGxVuW5SxVP5N4RUa9pLTeT/ZSqBP+GyToiSdRaIlOC61NM10xJguum TNdMSYK1BNlPya7yc6v6vLGGCYsm80tl57zw9t0vi1ehR3su/6vPtDlzL36l4b/HHFO6VOWo sHYJERERMawuFiL3vLfdP0SM1yhX4NxCcgV8yvObCmKJiPGazIeo1jm9613vivy5fy2Rah3R e97znsif++eEa1lHFIyfmq6ZkgTjTKZrpiTBdUnsvWRf1ZiYNUxYNIs+v/SGcMm3zolXoMPo fN4rnN7dIx7744b+LnNM6ZC1S4iIiIi1KcerqprzMsZy6dIl631ERIxTnqtUcWHVPimIeKUm NdpUbVS1Jv3ztaq1Qap5Gf/6IFXs/53vfGfkz/3Py3QdlCR4voji3e9+d+TP/fUB69lPyShX 4Ai5As2SNUyIrkWfX9o2J879HfddcQ6QM02PDi1y5k5v89rJNU5/NvaSc+Of7nZuEi1Off1G J4k5pmC+AXNMrZW1S4iIiIi1q8vR2blzp/X+ISLGKc9VKoL7qyBitCZzIqp1S3/6p38a+XN/ fFI1B6yad/LHclTxnp6ensifV7/3qnVQ8niqNUPV/GTV+Eg+D1VuTjUGyH5K6ZF1SYjFnjO6 LLz94AXHvx5J7qGkmw+Sjxl+6aKzsOuLNc8bGeUKPKbOFZDnpeD1SV5z5PXM9muZd3VxEe4n EBEREfXq6sGcPXvWev8QEVXqat3Jc5vt/iFmybg6bbo5GdXaj+qxdb+Pwr+WKAp5PNXcVHW9 ie73qrmy6rop1byEPKac+4qiWh+Q/ZTSI+uSEMuV85r8vAf3jSuCPxYu+g/Hr/jub+8V1575 Uuxjn/xJ2Vm8fk8ia5Oi5o3kuYl5o+aqyk8p4lwrIpprlCvwIHUFEBH96u6916xZY71/iIgq 5TkqCnLTEWtXNy+iWt9UnQdS5arInF/dOiL5WFX8R85b6dYRxR1X1yfVGqPquilVn+TflDHa KKrrk9hPKV2yhgmxuJ4QLvj0Xu97L1cxTT/SIX532ejxJ+fLzof//Xedi8dWNtwX5phaK+uR EBERERtXlbcrGRsbs94/lUa5AmvIFUAsuvI8pkKe/2z3DzFr6uq6xc3H6NYKxa0zUsX/qzn3 uu+4bl2U7ndx82VRyOPJ38l5ryiq+2Sofmf7/S2qrGFCLK7B+aUeYRJzRfXKHFPrVOV6sHYJ ERERsTZV+bcSat8hYprU1brjXjB7GuUK3EGuQJrUzY3E1ZPTrSXSrSOSj1XlGMu5K906IvlY 1bEfffRRZX+qz1cVe9q9e3fkz/01N1V7MD322GORP2fvJbuyhgmxmAbr4/U4dueXqgbrrMpz ETVUk1O1zlhS3WcREREREc3U7Ykg4ySXLl2y3kdERHkuUsWJ5TmMe0HE+lXNo9x0002xsRfV GCJuvyVpFKp5meraIKlqvdPHP/7xyJ/716Co5sFU5xf/WkfVvkqf/OQnI39OLNCuunVJ1XV1 2LhGuQLXkyuA6fSCcPHDb+cvLRb+aM+N1vsljbrmFHGPrGaoGmtwbUBERESsT12dvJ07d1rv HyKiPBepoNYdYmOq5lyiqNaHq6qal4kiuM5Qt4Y6iH9tkGot1LXXXhv5c/8+Cqo6fqrH+msS qfKd3/ve90b+nHUv9lWtS5KQl4CYX9dNz4tveW/lu17rnkrNlnmj5NXFMzjXm2mUK9BGrgAi IiKq1cV4qPeMiDZVxZIl1MVDbFzVnEsUwdxeXUwnSHAvG13sP0hwHlm1LiUK/+NUNf6i8K+Z kqr2VYqC/ZTSoypP3T9niYj5ctfFstN221963/exVfJacNp6v6oyx5ScrFVFRER0NcoVOPdu cgWwaerq5C1ZsoQ6eYhoRXnukeegKKiLh5iMtcy5BONfuvnfIP51RFLVvkpRBHNdTNdNRc1B q+oBBomaf1DNVQRhP6X0qNtnI/iZRMR8GKx7d3+H48zPbbPeL79R+RnMMdWubv887hMQERER G1d3T71p0ybr/UPMk0a5AkfIFahFeZ5SwT04YnKazrlExWpM1xJF/V0TguuIpKbrpoJrpqSm 9QCjam+q9mAKwt5L6VL1+WadGWJ+HT477zgdn61819NWI69q1L06a27M1a0rjrr+IyIiImJ9 6nJ8x8bGrPcPEYujPOeooFYRYrKazKME91OqarI+SFWf0mRfpajvu+laqKg1J6Y1/qJq/gbj e0a5Ao+5uQIq2HspXerWypGrgJhP3xAu+dY5x51dcpwv9zjO/MU91vsVlDmm+lWNVWReCtdh RERExOSUYytdPZczZ85Y7yMi5l95rlEhz1HcByImq8mciyqGZbI+SJUbbLKvUtTaIKnJWqio x5nU+ItaByU12VeJdS7pVDWXSWwRMb8+N192rl7l5ivdJHxlz4edqHVJRrkCD15ZV+Cy8Asv XnScjlXO/T2OM/fScEueE3NMtUveACIiImJr1Y2/2F8JEZutbh8lCXtgICavyZyLKgZjspZI 9b012Vcpah2RNG7dlGrNlDSuxp9ujWTcvkrsp5ROdXODvGeI+fUr5+adttv+sPJdX9/hOG+d HVa2/YFw4Ref8s4NrVzHJPM8gnkTzBupVV2LyfFALK5GuQJr2IMAEbFV6vKJ16xZY71/iJhf 5TlGBfXREZtn3JyLbu/ruLVEur+rQ7WOSBq3bkp3voirB6haMyWN21eJ/ZTSq+59V81jImK2 9a8zkujmjC4IFz/89hp60dS5eGxly/oqz0PB66nMlWCN5ZXqYhXkoSEiIiI2V90+Bzt37mzo 2Ea5AneQK4CIauV5SIVuLQIiNq4u9q7ae6mqbi1R3HdXNzbRrSOKWzelizHF1QPUzTVE1Sby Qxwwvcr3RjUXyjUGMb/KOabhly46bbdtqnzfd3ymzZn/yZORbU/Ol52OLz5ZaXez8LWJnpb2 NWqOSeZ/cG1xlbkuqvM4+7MiIiIiNl/dfbXkyJEj1vuIiPlRnlNUsOdFOjXKFbieXIE8qptz iavRo1tLFLfuUJeHrFtHJNWNaXSP09UD1K2ZkurqrFGXJ/3q5gfZhwMx3z75k7LTMfi3le+7 bs+kbXNlx+m4z1kp2v10el3L+8kck1pVPot8vXTrrBERERExOeP2Vzp79qz1PiJi9pXnEvZR QrSrbh4lLpauWx8U9/3VjTXi6pDdeeedkY8zmbtZvHhx5GPlMeMeu2jRosjHsjdPNlTVdyxq LoNRrkAbuQJYPN8QDk++6lx99390OsRX4etDi5yXxofF735c+f3p+bLT8zfPOTc5Pc6pr9/o /bzVymtlcI+hos8x6cYW1NBGREREbK26PGMZD7506ZL1PiJidpXnEN3cUtyaBERMzptuuqny vXv3u9/t/MIv/IL3PTTJ85WPkVxzzTUVq5j83Sr+x8rjxT3OP0Z573vf6yxYsKDy3ybzPNUa f/Ix8rG1nHOq+yoFH8t+StlQNx8at/4OEfOhrIP38OmLztrt271aeJLqPNKrR/5MtLtstY9y fig4Hy7/v4h7wMnXIjiHViWuLm9RNMoVOHcNuQKIiIhoRd3+1GvWrGGOCRHrUp475DmEOB9i OpQxK/9ckoznmK4fDD5W/rdpDEy28+dZmz42qn/BfqiM+hvyWCb53o08FtOhbg8x1swiYlqM mmOSay2LNsfEORsREYugUa7AEXIFMJtGjWv9bN261XofETF7ynOHiqLX9UBExOap22dU5sJz /UHEtFj0OSbdmlO5x5Lt/iEiIiIWWZl/q9vveufOndb7iNhKjXIFHiNXIAnl+UUF++4iImKz lfUMVbA/ByKmySLPMbFnHiIiImK61eUDSY4cOWK9j4iYfuW5QkcR7n8RMR0a5Qo8SK4Alp3u 7m6uW4iYGYN16+UcS1x9OPkYk30J06ic61fBHq2IiIiI6XF0dJS4MCLWbdw8tTzH2O4jIiIW Q936fJkHb7t/iGimUa7AmuLlCkTtjawaa/vbZm2fIt39hcwNsN0/RERERLxS3Z6ZS5Yscc6e PWu9j4iYPuW5QZ4jVGQ1XxIREbOrzGtXQe07REyjJvNGUeuXslQjTlfrjtrYiPnUKFfgjuLl CiAi5tWoMS1zTIioMm5uSZ5TbPcRERGLqa72Xdby+BGxGOrmmFT36n19fdb7bSK17hARERGz qVGuwPVurkDUvqF+ZBz59ddft/6cENG+8lygm1uS55Is5VMiImK+1NXJ6+zs5BqFiKk0ah5G ziHpSPv8jJzTV0GtO0RERMT0GzdvtGbNGufSpUvW+4mI9pTnAHkuUMF8kV2NcgXaqCuAiOhX VyePNbaImFbj9kcOkub6cvL+Qc7pZ63fiIiIiHiluhxOCXNMiMU1bm6Jez9EREyTutp34+Pj 1vun0ihX4NzV5AogYs1zTDIXzHafo9TV4E/7uitEREREvNKpqSnmmBDxCk3mluS5w3Y/ERER q+rypsiJQMS0WuuckUTWy7Pdb79yDl9FUWrdGeUKHCFXABEREfMpc0yIWJW5JUREzKrEOBEx S+rOWXHI/Yts91/K3D4iIuZFo1yBx8gVQFSp219TwhwTYv6Nm1tK070sIiJilLo979OW84+I xTUuxzMOuX9RGvZB1e3pnObapIiIiIhoZtx6e+aNME0a5Qo8SK5AMzSZW5LnE9v9RERE1Knb N15CngQi2rbRuaUqch8jm89jcHAwtX1DRERExORkjgmxeDJfhIi2NMoVWEOuADZP3bp8GdNN Q84/IhZTXT25erC1RkhX2y8ta6sQERERMTmZY0IsjswtIWIjXhbefvCCOFN0VM4XK4U/nV6n bP+GcMm3zlXay0fsGFrkvHV2j/XngflT1rdTwf5KiMlqlCtwB7kC9SjnZnTrLGvBxh5HcXNk 7NWKiIiImE/j5phkvfqzZ89a7yci1q/8Duv2npAwt4SIcW6bKztOx32Vc4acM5p+pEP8/HJk 2yfny07b7duvOM+sEg86P9mvfAxivcp5JBWyVpPt/iFicZVrdnQ142qhlXPmst+6fZRGRkas v7aIWJ9GuQLXkyuAiIjmxs0xLVmyhDkmxIwqv7vyO6yDuSVENPEHwoVffMo7d4ytcsTPT0e2 HX61LFqsCp1v7hLOHltp/blgvpRxUF3ePPvJI6Jt5fod3XyNKXLNZiv6K/dKUiHz1my/noiI iIhF1ihXoK21uQLMMSHmT+aWEDFJLwgXP3zGO3/c3+E483PbQu1UtfTOy5/vftn5bNttztyL 6tp6iPUYt78StZkQMQ3KdT+N7rXU7POZLjbAPkqIiIiIxVKODePGr0eOHLHeT0SMV35Xdcjv OvNFrdMoV+Dcu6grgLly3fS8+HD3Vj7nNwtfm+gJtQmuc9oumpfnS5XfyX2ZPjHxqvNHixY5 b50Lz00hNqJuLya5boCYKCKmQblXUVztah3NPJ/JuSvdvQZz9YiIiIjFU44B4+aYJiYmrPcT EdXK76gO7vcQsV79eyWp9lWKayPnjVYeOO/M7L499FjERtXFYRut02SUK3CEXAFETF5Zu7Pe tUzN2FtOznvp+kMeGyIiImJxNZlj2rp1q/V+ImJY+d3UwdwSIjbij4WL/sNx75ziX29U1WTd EmKzjNtnvhlxVkTEepXnLHleqock94uLO3fKPZZsv1ZJaZQr8Bi5AoiIiIiNKOPPsl6yjk2b NjmXLl2y3ldELFe+i/I7qUN+p5lbQsRGVO2XpPq9at8lxGYalytFXj0ipk25L5xuficKeZ5L qvadnD9SIftl+/VBRET0a5Qr8CC5Aoi2jcthkqxZs8Y5e/as9b4iFln5HZTfRR3sJYGISblt ruw4HfdVzi03CU99/Ubx8x9XfmeyvgmxFcq8fh3kWyBiGpV7w9VSB6/Rup9S3fqpJOewEBER ETEfyvFhd3e3dpy6ZMkS58yZM9b7iunVKFdgDbkCzVbGx+T3VYf8vnNfiIhJeUK44NN7vXOM XKk0sXGRU54/fcXeS5Ljf7FAPOZN633GYirjtLqYKXNMiJhG5d5GcffrfkZGRur+W3I9pw7O k4iIiIioUrcGvsrExIT1fiIWSfmdiyNP9c8R0a5GuQJ3XJkrcEG4+OEz3jmpR7ho+XJnx2fa nK/86Pve2ib2XsI0GFfzifwLREyrch2myVqmeufLZU0+HdQSRURERMQ4Za5THFu2bGGfJcQm K79j8rsWRyP5iYiIOtdNz4uzTG/lXCP3VfrBi+ucG//8qFPdS0myVnj51WHrfcViG1fDmXkj LJpGuQLXU1cgi8pzma5+nf+8V8tx4/apk3/T9nNHRERExGxokhfFPkuIzdNk/yT5HZXfVdt9 RcT86q9tJ2eUph/pcM6X553bd7/suKuU2E8J06OMuerGr6z1RcS0K9cPxe13bDrPwzkRMfsa 5Qq0kSuAiIjpVOYvdXZ2ase2co+XI0eOWO8rYp6U36m4/ZPkd5Pa54jYbH8gXPjFp7xzT3Uu 6bL4+RdevOg4HavYTwlTZVz+PfFURMyCur3iJHIeSvd41nQiIiIipkOjXIFzP5/bXAE55jTZ R3Tr1q3Uy0NsUPkdkt+lOOR3kvtBRGyFch7p9oMXnGo9vPvFv+bntnm//8q5eWdh133Oqa/f KP7/x9b7iyiVa3t1UFcWEbPgzMyM8l5c5pqp7gfi5pZ0j0VERERENNWkZnNfXx818BDrVH53 5HcoDmqZN1+jXIEj+c0VQGzEbXNlx+m4r/J9uVn42kTPFb8flXNMd3/Jmdi4yCnPn7beX0Sp 3INeB3vUI2JWlOerqHWZ8j4jqr1cp6lCHocaCIiIiIiYlCb7LEkmJias9xUxS46NjcV+r9g/ CRFteUK44NN7vfPRU5vaxM/PX9HmOxfLztV3P+p87TNtzlvndlnvM6JRrsBjb+cKxOVSMceE iFlRrjeKmjcKnseYW0JERETEVivHmHF7iEo2bdrkvP7669b7i5hm5XdEflfikN857u8Q0ZYX hIsfPuOdk6r7KgXb7bpYdtpu+0tnVYfjnJ/sFz+7bL3viLr4aVS8FRExzcq9lPz7Hst5IFkb T/4uj+c7o1yBB6krgIiIiJglVflQQZYsWcJaJkSF8rshvyNxyO8aNc8R0bbrpufFGam3cl5a Kfzp9DptO9ny6N/0UAMPUyE5+oiYN4eHh73zmNx3KY9zS4iImA+NcgXWkCuAWERVtZ2DsJYJ 8W1N1yzJ7xb3gYiYFp+cLzttt2+vnJ8WC3+058ZQm8vC/mMXxW97vHOZXMu0e8dKZ+70NuvP AYtr3P71zDEhYhaV65bk3FIcIyMj1vuKiIiIiMVRjlNNauCxPik7GuUK3EGuQLM1XbMkv3/V OheIiGnwB8KFX3zKO0/592Dyzz3puFn42kSP9eeCxZQ5JkTMozIfTYdc12S7j4iIiIhYTP1r 7nWwPgmLqOk6JIn8LtnuLyJmX6Ncgeubkysg1ybdfvCC+CMdlb93v/jX/BxrkjBbMseEiHmS uSVERERETLvyHtu/f6iOsbEx6/1FbIXys26C/O4Qp0LENLttruw4HfdVzlk9wovHVlrvE2Kt Mm+EWdIoV6CNugKol7klRERERMyK8p59cHDQKJ7e19fnnDlzxnqfEZuh/GzLz7gJ8jsjvzu2 +4yIqNNf206uTJp+pEP8/LL1fiHWqrzm6vYFZY4JEdNs3P0280WI6dIoV+DcO8kVQERE9Fkq lYzXMm3ZsoWaeZgb5WdZfqZNkN8R+V2x3WdERBMvCBc/fMY7h23vdZzyfMl6vxDrUc4fxc0x cY1GxLQp5450MLeEiIiImLxGuQJHyBVI2lrWMi1ZsqRSR+zSpUvW+41Yj/KzKz/D8rNsAmuW EDGLrpueF2ew3sp5bK3w8qvD1vuEWK9xc0wSWVfKdj8REaXMLSEiIiJiXpT347ra9X5kjbAj R45Y7zNiLcrPrGl9O/ldoI5O8zXKFXiMXAHEZrrnctlxFj9U+d7dLHxtosd6nxAbkTkmRMyC zC0hIiIiYh4dHh6OvSevsmbNGvZbwtQrP6Pys2qC/OzL74DtPiMiNuIPhAu/+JR3bntqU5v4 +Xnr/cJ8apQr8GBrcwVM8qaYY0JEG8p6B8wtISIiImKenZmZMV7nIdm0aRPzTJg65WdSfjZN kZ95+dm33W9ExEa9LLz94AVxZuuonN8eWuw45ct7rPcLsVFlXDZu3og8EURspSbnpSzMFxnl CqyhrgAiIiIimiv3QO7s7DSOz2/dutV5/fXXrfcbi638DMrPoinyM85+34iYN4dfLYsz3KrK eW6l8KfT66z3CTEJ8xLLRcTsy/kIERHTpFGuwB3kCiBi662lZp6EeSa0Ya3zStTCQ8Q8++R8 2Wm7fXvlfCdXMU0/0iF+ftl6vxCT0CSmK9cly3a2+4qI+dRkrzfmlhARERExz8p77sHBQeN4 vIS5I71GuQLXkyuQpGfPnq1pXkkiP/fEnBAxz26bKztOx33eeY89mDBvmswxyd9zvUfEpDWZ WxoZGbHeT0RERETEVljrfksS9lzCZljrnkkS9k1CxKQ1yhVoS1+uwMpjl0XnFkeeK+/vcJz5 uW3W+4iYtHJ9gA5ZB1fGgm33ExHz4ejoaOz9iWxju5+IiIiIiK1W7kHT3d1tHNeXyLmAI0eO WO87Zlv5Gap1Xkl+Vtk3CRGL4p6z887w8FcrdT3/6EtfcjoGH3FuFufC1yZ6rPcNsRnGzRvJ tQOMA1CnUa7AuQWpyxXA9CqvwXEwt4SIiIiIRbeeeSa5hmRiYsK5dOmS9f5jNpSfFfmZqXXt HPNKiIiIxZBYLiI2S1k702Qem/WPiHY1yhU4Qq4AIiJiGq1nnmnJkiWVvXDknji2+4/ptLpf kvys1ALzSoiIiMXTpBaVjBHb7iciZkeT/duorYmIiIiYjEa5Ao+RK5Bn65lnkqxZs6ZS94w1 TSg/A/KzID8TtcK8EiIiYrGV4wC5jiBuvCBjxrb7iojpVs4ZxZ1P5NwT5xNERERExHjrnTti nVJxrXf9UTX2w1yRHY1yBR4kVwAREdOpjAnL9QQ6WG+AiDpN1kPK2t3MLSEiIiIi1qa8F4+r P60bg8t9dF5//XXrzwObo3xv69krqYr8bBHvQUREbEyjXIE1+c0VMKlpJdclsCcTIgY1udcd HBy03k9ERERExCw7MzNT2Us5rmaACln/TM5DUBMv+8r3UL6X9dS0k8jPkPwsyc+U7eeCiIiI 6dYk9ss+S4golfcXcXPNkrTONRvlCtyR31wBRERERMy/Mj9UjsdNxu0qtmzZwrqmjFldp7Rp 06a633f5mZGfHepKICIiYi2OjIwYjTPIXUEsruPj47G5kPL31E5ARMRmapQrcD25AoiIUrlf Tr2186pU1zWxX1P6lO9JI+uUqsjPCHsrISIiYiPKsYRJ7FjGmG33FRFbq6xfF4ecgybPDRER ERGxNcqxt8wVbWSdkkTuy7N169ZKnlirauMZ5Qq0kSsQVL4/8n2S71e9+ylVkZ8b+fnhHg4R ERGT0rT2FXulIBZD03MCNTQREREREe0p5xzkfXq9ey/5kWthxsbGnDNnzlh/XnlVvrbyNW50 3ZFEvufyvaeOBCK2WqNcgXPvIFcAsWDK3BWT9fMy5sz4BTG/mtTDk6R1ryVERERExCIqx/Hy nj6JuSaJ3M9HzoW0cn1TnqyuOdq5c2dDeyP5ke+tfI+pL4OIiIitVMaBTcYpco207b6imUa5 AkfIFUBzTeeYOzs7mWNGREREREyxSc81Sar19OS+P6xxCitfE/nabNmypeEadn6YU0JERMQ0 KOPBMi5sMmakDi9i9uU7j5h+jXIFHiNXABEREeu3GXNN/nsJOZ9SXed09uxZ68+32crnKJ+r fM5JzyVVYU4JERER06iMIZuMfeRYhnEMYnYdHh42um9hzSIiIiJiYxrlCjxIrgCmw+p+TSZ7 rTaC3F+oWl+vuqdTltY8Vftb7b98LknsmaRDvifsp4SIiIhZUMaUTXKXZL4M6xoQs6O8FzG5 V6TGHSIiIiJi/pyZmanUx5f38ia1DJKkOqdUXc8krdbSq/r6668n9lzlsfzHln+r+ndlH1ox JxREvubytZfvgXwvbH8e0FyjXIE15AogIiJWrSUOXSqVrPcXEfWarlli3hgRERERsRjK+36Z XyrrmLR6vqkIVOeT5GtM/h4iIqJdjXIF7iBXIElljFmuvzaBPVoQ06npXDF1LxERERERi211 fVMrauTlke7u7sprx/okREREzLtyzZFJDTxizojpUc7hmq5Dkvc2abynMcoVuJ5cAURERETE NChjB3L9jVyHI+8xwKU6lyRfG2q6ICIiYhGVsWq57sh07JTGWDViUZT3LKY1K+Q9ju3+IiJi fjXKFWgjVwARMa/K2IDMQ5W5b3mfd5LPTT5H+VzlcyYugoiIiHilcoxkupZJjqls9xexSNYy DyxrWFDXGxERERER4zTKFTj3T2vKFZD3LjIvrjr3JNf3yPkZqUnModXIPlX7J/tanUOSz4H6 /4iIiIjm1hLDlmsoWP+N2HzlOiTT+zDmfhERERER0YbVeaWq8t7Er4w1VOdx/AaJalNVHiN4 XP/fZD4IEYuoUa7AkdpyBRARG9F0LZNEju9YG46YvPL+yHQ/XWpXIiIiIiJiFg1iuz+IiIiI GK3M5ZHrvU2o1sEj/8dMo1yBx8gVwHjlPJHpOkL5PWWfJUREREREzKrMLyEiIiJmS7kuQta3 M0G2Gx0dtd5nxCwq52flPK0prAdEtKtRrsCD5AogIiIiJiXzS4iIiIjZVMa9Tevgyfpb7LeE aK6clzX9frGfGSIiImIyGuUKrCFXADEtMr+EiIiImF1rqcVVnWdifQWiWrl/men6QAl1KBER ERERMU8yZ4SNapQrcAe5AoiIiGmxlpp5koGBAeaZEH3K75CcfzWFWniIiIiIiJhHmV9CRERE o1yB68kVyJu11Mxjngmx9nklauEhIiIiImKeZX4JERERMV/K+ltyLqgWmDvColnrXJGcix0Z GbHeb51GuQJt5AogIiIiImIyMr+EiIiImE/lfFEt8XPmmbAI1jqvJGHfJEREbJVGuQLn2sgV QETEVMj8EiIiImK+rSeezjwT5k2+B4iIiIiI2AqNcgWOkCuAiIiIiNlxfHy8sh9MLfT19bF/ DGba0dHRuj73zCshIiIiIiIiIhZTo1yBx8gVQMTiWU+8Xa77kI+z3XdEE2UtO1nTrp7POfOp iIiIiIiIiIiIiFg065k7ku3Tur+MUa7Ag+QKoKtccyRr2rW3tzOvhIiIiIiIiIiIiIhYo/XM M0lkbH5qasp6/xFlPcda90tirggxnRrlCqwhVwARERERERERETEtynmmrq6ummP08jHysWlc 04T5Va4/qqeunUTOjTKvhIiIiNgcjXIF7iBXABERERERMW/KuHs9a0FknTHWNGGzlWuV+vr6 av58VueV5LyU7eeAiIiIiIiYVoPY7g/a0ShX4HpyBRARETFaOU8k4/H1INeUjIyMEMvHRJSf xcHBwZr3SpLIx6R1zzBERERERMS0yfwSIiJiPjTKFWgjVwCba7UOWT2xfYlca0L9PKxV+bmT c5T11L+TyMfxuUNERERERKxN5pcQERER0dR6913yzx/JmmW2nwem0+o8UaOfsSzvl2SUK3Du 58gVQERERERE6wax3R9ERERETL+N1MOTyPVNrFVCaRJzSnL9kVw3R41FRERMg0a5AkfIFUBE RERERETE4irnhhpd0ySpzjUxP1AM5fyknA9K4nPDejhERERERExao1yBx8gVQERERERMQjln MDg4WPfeS1XknIM8jjye7eeEySjnIeU8kFzzVu8eSVXk4+V6J+YiERERERERERFRpVGuwIPk CiAipk05lyDXljRKtY4e8wnZs7pGqbu7O5HPAXOOiIiIiIiIiIiIiIjZtVoTz3T+yChXYI2b KxCHXLsi18BQSy99yrkfOQ8oPxeNrmGTyGPI95r6d4iIiIiIiIiIiIiI+bLWuSYT5BxSdc1S qVSy/hyLonwv5eud1JqjKswTIeZDo1yBO6grgIiIiIiIiIiIiLXZjLmmKnK+Q9ZHk8enRloy yrkkOYcn537knldJUl1nxpwSIiIiYno0yhW4nlwBxKIbxHZ/EBEREbF4yrkFOccg5xqagZxz ksevrnOS81u2n3MalXUE5Xsh1yXJub+k55KqyOOyRxIiIiIiImL6DGK7P1i/RrkCbeQKICIi Yn6s7s2TZN21KGQttuq8k5xPkfMqRaixV61tJ9d3NXseyf9ay7/DXliIiIiIiIjpNojt/iAi IhZZo1yBc1eRK4CoUM6FyLUuzZ4D8VOde5LKORj//FOa19xU547880fytZPPo5Wvn6S6L1aa Xy9ERERERES80iC2+4OIiIiIqFLOiVRrsjV7zZIJ/rkl//omv3LupjqPU49y3iV4TDkf4/+7 tqmuOarWFLT9ObGtUa7AEXIFEBEREREx2wax3R9ERERExFqszsHI+Y1m7ckEVyLXN8m5NDl3 xpojRETMo0a5Ao+RK4CIiMU2iO3+ICIiIiI2YrXuW3WdD3NOjVFdl8XaJEREREREzJJGuQIP kivQiEFs9wcRERERsRn69xmScybMO12JXJMk5+Oq+0exLgkREREREbF4BrHdH0Qstka5AmvI FUBERDvKeZTqeqfBwcHK3JOca8kj1f2a5HOtrkeamZmx/h4gIiIiIiJiOgxiuz+IiIiImG2N cgXuyGeugJyDqe7xJOdlquufqtqmvb3d60t1/ZFUrtWS/WYdEiIiIiIiIpoaxHZ/EBERERGz bHWOKWh1LsfU6pqhoMwBIWIrNMoVuD6fuQKIiIiIaCbzS4iIiIiIiIiIiIj50ihXoI1cAUSs X+aXEBERERERERERERERkTmjdGmUK3Dun5ArgIiIiIiIiIiIiIiI1mR+CRERMVmNcgWOkCuA iIiIiIiIiIjZlfklRERERETEKzXKFXiMXAFERERERCyuzC8hIiIiIiIiImLeNMoVeJBcAURE xHplfgkREREREREREREREdOmUa7AGnIFbMn8EiIiIiIiIiIiIiIiIjJnhIhJa5QrcAe5AoiI iIiIiIiIiIhZlfklRERERAxqlCtwPbkCiIiIiIiIiIiIiIiIiIiIedEoV6CNXAFERERERERE RERERERERMS8aJQrcM4hVwARERERERERERERERERETEnGuUKHCFXABERERERERERERERERER MS8a5Qo8Rq4AIiIiIiIiIiIiIiIiIiJiXjTKFXiQXAFERERERERERERERERERMS8aJQrsIZc AURERERERERERERERERExLxolCtwB7kCiIiIiIiIiIiIiIiIiIiIedEoV+B6cgUQERERERER ERERERERERHzolGuQBu5AoiIiIiIiIiIiIiIiIiIiHlxzVVursD/py06V0DmCUjIFUDENLhQ nI/e+7Nz1Ht+9t/yNPVz/+Sdzj8V/75a5jhV/uuaq7531cg7Xv3zX3T+9dXvd77nnLjqr98h f1f99/9L+FfvkP8vz3r/vHLE//jujzpBygAAAAAAAAAAAGAb4nQAAAAAAAAAAAD2IU4HAAAA AAAAAABgH+J0AAAAAAAAAAAA9iFOBwAAAAAAAAAAYB/idAAAAAAAAAAAAPYhTgcAAAAAAAAA AGAf4nQAAAAAAAAAAAD2IU4HAAAAAAAAAABgH+J0AAAAAAAAAAAA9omJ0+3atUv1qx/96EfD FTQPf/HFF902rTxOCruU1+M0u0t///d//9Of/lQcZ3R0VPOHAAAAAAAAAAByQEycbtmyZapf TU5OOhU0D9+7d6/bppXHSWGX8nqc1nTpu9/97uzsrKYBAAAAAAAAAEAOiInT7du3zw2j9Pb2 Kg9RZfXq1Wk4Tgq7lNfjtKBL3/72t//9v//3mg4AAAAAAAAAAOQD4nRZemppO06zu7Rjxw73 h/oFtgAAAAAAAAAAOSAmTjc1NTVQYWhoaEeFAwcOBNoMVNm8ebPb5vDhwxaPk8Iu5fU4Te3S b/3Wbzk+xsfHw38dAAAAAAAAACA3mO73OjEx4YZLenp6VG1GR0fdNv39/Wk4Tgq7lNfjNKNL frq6uihRBwAAAAAAAAD5hjhdJp9a2o6T4KH+5m/+JhCk6+vrI0gHAAAAAAAAALnHNE538uTJ /gobNmzYVeHgwYOBNocPH3bbbNy40W3zzDPPWDxOCruU1+MkdagXXnjhmmuu8QfpBgYGwn8L AAAAAAAAACB/mMbpPEqlkhtAWbp0qarNrl273DYrVqxIw3FS2KW8HqeRQ337299+z3ve4w/S jY6Oav4QAAAAAAAAAECeIE5np0t5PU7dh/K2dnVpb29n4wgAAAAAAAAAKBQxcbrJycnAT06f Pr28wtq1a8cqhB/19NNPu23uvfdet00rj5PCLuX1OEkd6l//638dCNJNTU2FjwwAAAAAAAAA kGNi4nTLli1T/WpyctKNqmgevnfvXrdNK4+Twi7l9TiNH8rbWcKDrV0BAAAAAAAAoJgQp2tF l/J6nAYP9corr/zyL/+yP0jH1q4AAAAAAAAAUFhi4nTr16/fX+HYsWOBX01PT3+ywv4q4bWK k5OTbptWHieFXcrrcRo51AsvvBDYNYKtXQEAAAAAAACgyMTE6fbt2+fGUHp7e5WHqLJ69eo0 HCeFXcrrceo+1I4dOwJBupGREc1fAQAAAAAAAADIPcTpsvTU0nac+g7113/91/4IXXt7++jo qOZPAAAAAAAAAAAUgZg43dGjR3sr3HPPPRMVjh8/HmjTW2VoaMhtc+rUKYvHSWGX8nqcOg4l /hkI0rG1KwAAAAAAAABAOTZO5+Hty9nT06NqMzo66rbp7+9Pw3FS2KW8HsfkUHNzcx/+8If9 Qbqurq6ZmRnNMQEAAAAAAAAAigNxukw+tbQdJ/ZQr7zyykc+8hF/kK67u5utXQEAAAAAAAAA PEzjdM8991x3hYGBgUMVnn/++UCbJ554wm2zYcMGt83p06ctHieFXcrrcfSHYmtXAAAAAAAA AIBYTON0HqVSyQ21LF26VNVm165dbpsVK1ak4Tgp7FJejxM+FFu7AgAAAAAAAACYQJzOTpfy epzAodjaFQAAAAAAAADAkJg43fT0dOAnJ06cuKVCf3//sxXCj5qYmHDbrF271m3TyuOksEt5 PY7+UDfccEMgSMfWrgAAAAAAAAAAKmLidMuWLVP9anJy0o2/aB6+d+9et00rj5PCLuX1OKpD zc3N9fT0+IN0XV1dBOkAAAAAAAAAADQQp2tFl/J6nMhDhbd27erqYmtXAAAAAAAAAAA9xOla 0aW8Hid8KLZ2BQAAAAAAAACoj5g43b59+9xoS29vr/IQVVavXp2G46SwS3k9TuBQO3bscK5k eHhY81gAAAAAAAAAAPAgTpelp5a24/gPFVjrKmBrVwAAAAAAAAAAc4jTZemppe04/kP5YWtX AAAAAAAAAIBaiYnTTU1NDVQYGhraUeHAgQOBNgNVNm/e7LY5fPiwxeOksEt5PY6gv7//F37h F/xBOrZ2BQAAAAAAAACog5g4ncfExIQbhenp6VG1GR0dddv09/en4Tgp7FLOjvPCCy+wtSsA AAAAAAAAQCIQp8vkU0vDcdjaFQAAAAAAAAAgQUzjdCdPnuyvsGHDhl0VDh48GGhz+PBht83G jRvdNs8884zF46SwS7k5Tnhr18HBwTIAAAAAAAAAANSLaZzOo1QquXGZpUuXqtrs2rXLbbNi xYo0HCeFXcr0ca677rpAkI6tXQEAAAAAAAAAGoQ4nZ0uZfo4ftrb28XPVQ8BAAAAAAAAAABD YuJ0k5OTgZ+cPn16eYW1a9eOVQg/6umnn3bb3HvvvW6bVh4nhV3Kx3GOHTvW3t7uD9J1dnay tSsAAAAAAAAAQCLExOmWLVum+tXk5KQbrNE8fO/evW6bVh4nhV3KwXHY2hUAAAAAAAAAoKkQ p2tFl7J+nMitXQnSAQAAAAAAAAAkSEycbv369fsrHDt2LPCr6enpT1bYXyW8BHJyctJt08rj pLBLmT7OAw884FwJW7sCAAAAAAAAACROTJxu3759bmimt7dXeYgqq1evTsNxUtilTB8nAFu7 AgAAAAAAAAA0A+J0WXpqrTzON7/5zUCE7qqrrnr00UdV7QEAAAAAAAAAoBFi4nRHjx7trXDP PfdMVDh+/HigTW+VoaEht82pU6csHieFXcrccebm5n75l385nEzX3t7OBq8AAAAAAAAAAM0g Jk7nMTEx4UZqenp6VG1GR0fdNv39/Wk4Tgq7lInjvPDCC+9///vDQTpCdQAAAAAAAAAAzYM4 XSafWvOOc+jQocDWrpFQpQ4AAAAAAAAAIFlM43TPPfdcd4WBgYFDFZ5//vlAmyeeeMJts2HD BrfN6dOnLR4nhV1K+XH+8A//MBCPW7ZsWXt7O6E6AAAAAAAAAIBmYxqn8yiVSm6YZunSpao2 u3btctusWLEiDcdJYZdSe5xwJG5qaopQHQAAAAAAAABAsyFOZ6dLqTrO3Nzcb/7mb/oDcO3t 7ePj416Dqampzs7OyFDdwMCA6u8CAAAAAAAAAIA5MXG66enpwE9OnDhxS4X+/v5nK4QfNTEx 4bZZu3at26aVx0lhl9J8nLm5uY985COBIF14p4jZ2dmuri5CdQAAAAAAAAAATSImTrds2TLV ryYnJ90wjebhe/fuddu08jgp7FJqjxPe2rWrq2t2djby4fpQnepRAAAAAAAAAABgAnG6VnQp nccJb+3a19enD7eJ34o2kaE6TYAPAAAAAAAAAABiiYnT7du3z43C9Pb2Kg9RZfXq1Wk4Tgq7 lNrj+DFfvipaEqoDAAAAAAAAAEgW4nRZemrNi9ONjIyoGkeiCdWFa9sBAAAAAAAAAEAsxOmy 9NQaP87c3Nzv/M7v+CNr7e3to6OjqqNpGBkZiQzVRW5DAQAAAAAAAAAAemLidFNTUwMVhoaG dlQ4cOBAoM1Alc2bN7ttDh8+bPE4KexSSo5juLWrOaOjo4TqAAAAAAAAAAASISZO5zExMeGG YHp6elRtvKhNf39/Go6Twi5ZPM4LL7wQ2DWiq6trZmZG9XBDNKG6+tL0AAAAAAAAAACKCXG6 TD61Wo/z7W9/OxCk6+7uTmrPh1Kp1N7eHhmtI1QHAAAAAAAAAGCIaZzu5MmT/RU2bNiwq8LB gwcDbQ4fPuy22bhxo9vmmWeesXicFHbJynHuuOOOQPjMfGtXQ6ampgjVAQAAAAAAAAA0gmmc zqNUKrnxl6VLl6ra7Nq1y22zYsWKNBwnhV1q2XFWr14dCJzVurWrIZpQXeJhQQAAAAAAAACA /EGczk6XWnCcBLd2NWR2drarq4tQHQAAAAAAAABAHcTE6SYnJwM/OX369PIKa9euHasQftTT Tz/ttrn33nvdNq08Tgq71PrjJL61qyGE6gAAAAAAAAAA6iMmTrds2TLVryYnJ934i+bhe/fu ddu08jgp7FKLjxO5tWsLgnQus7Oz3d3dkaE60Y2kNq8AAAAAAAAAAMgZxOla0aVWHmfJkiXh IF3ro2MDAwOE6gAAAAAAAAAAzImJ061fv35/hWPHjgV+NT09/ckK+6uEM7YmJyfdNq08Tgq7 1LLjBNa6OlZXm2pCdTMzM7Z6BQAAAAAAAACQTmLidPv27XNjK729vcpDVFm9enUajpPCLrXm OOGtXYeHh1WHag2iA5GhutYUywMAAAAAAAAAyBDE6bL01FTHmZub6+npCcTCmrq1qzmiG4Tq AAAAAAAAAABiiYnTHT16tLfCPffcM1Hh+PHjgTa9VYaGhtw2p06dsnicFHapqcd55ZVXrGzt ao4mVDc+Pm67dwAAAAAAAAAAqSAmTucxMTHhxlZ6enpUbbxwTH9/fxqOk8IuJX6c3/qt37K4 tas5pVKpvb09MlqXkrw/AAAAAAAAAAC7EKfL5FPzjvOOd7wjEKRL7W6qU1NThOoAAAAAAAAA AFSYxumee+657goDAwOHKjz//POBNk888YTbZsOGDW6b06dPWzxOCruU4HGuv/76QLTL4tau hmhCdYODg7Z7BwAAAAAAAABgE9M4nUepVHIDK0uXLlW12bVrl9tmxYoVaThOCrvU4HHCW7tm Jc41MzPT1dUVGapLf5wRAAAAAAAAAKB5EKez06W6j5PmrV0NmZ2dJVQHAAAAAAAAABAgJk43 PT0d+MmJEyduqdDf3/9shfCjJiYm3DZr165127TyOCnsUlLHidzatVQqhY+fcjShujSX2AMA AAAAAAAAaB4xcbply5apfjU5OekGVjQP37t3r9umlcdJYZeSOk5ga9fOzs4Ubu1qzsDAAKE6 AAAAAAAAAAAX4nSt6FJSx8lfMItQHQAAAAAAAACAS0ycbt++fW7cpLe3V3mIKqtXr07DcVLY pQaP89d//deBMNbAwEBuwliDg4ORobr29vZMZwsCAAAAAAAAANQEcbq0P7Xsbu1qzujoKKE6 AAAAAAAAACg4xOnS+9RysLWrOZpQXRY3ygAAAAAAAAAAqJWYON3U1NRAhaGhoR0VDhw4EGgz UGXz5s1um8OHD1s8Tgq7VMdx/uzP/mzx4sWFiliNj4+L5xgZrctrdBIAAAAAAAAAwCMmTucx MTHhRkx6enpUbbyUqP7+/jQcJ4Vdquk4gSBdEVaAiudIqA4AAAAAAAAAiglxutQ9tY0bNwZC VIXa/FQTqhseHrbdOwAAAAAAAACAZmEapzt58mR/hQ0bNuyqcPDgwUCbw4cPu202btzotnnm mWcsHieFXYo9zp/8yZ8EglN9fX3FCdK5zMzMdHV1RYbqBgYGbPcOAAAAAAAAAKApmMbpPEql khsxWbp0qarNrl273DYrVqxIw3FS2KXI44S3di1sWGp2dpZQHQAAAAAAAAAUCuJ0droUOM7c 3NxHPvKRQECq4BXZNKG67u7uouUYAgAAAAAAAEDuiYnTTU5OBn5y+vTp5RXWrl07ViH8qKef ftptc++997ptWnmcFHZJf5y/+qu/+sAHPuCPQ7W3t4+Pj4cfVTRmZ2cHBgYiQ3WFqtkHAAAA AAAAAEUgJk63bNky1a8mJyfdiInm4Xv37nXbtPI4KeyS5jhf+tKXAhGogmztag6hOgAAAAAA AAAoAsTpWtElVYMdO3YQezJBFaojpgkAAAAAAAAAuSEmTrd+/fr9FY4dOxb41fT09Ccr7K8S jphMTk66bVp5nBR2KfI4bO1aE6Ojo4TqAAAAAAAAACDHxMTp9u3b50ZDent7lYeosnr16jQc J4VdCh+HrV3rgFAdAAAAAAAAAOQY4nStfmqRW7uOjIxong54jI6Otre3R0brCr49LgAAAAAA AABknZg43dGjR3sr3HPPPRMVjh8/HmjTW2VoaMhtc+rUKYvHSWGXvOP83u/93uLFiwO5YASY amJqaopQHQAAAAAAAADkj5g4ncfExIQbCunp6VG18ZYl9vf3p+E4aevSoUOH3vWudwWCdCzY rANNqI7MRAAAAAAAAADIKMTpWtSlyK1dZ2ZmNJ0HDVNTU+IFjAzVUekPAAAAAAAAALKIaZzu ueee664wMDBwqMLzzz8faPPEE0+4bTZs2OC2OX36tMXjpKdL9957byCWJJqxtWuDiBeQUB0A AAAAAAAA5AbTOJ1HqVRyQyFLly5Vtdm1a5fbZsWKFWk4jsUuzc3NsbVr89CE6vr6+oiEAgAA AACAhifny223bxe3Dzc7zmsTEeuits2VnY77NA0Eb5TLn5h41XF6eh1n4qs98z8pNbfTAJBr iNM1sUsf/ehH2dq12czOzg4MDESG6rq6ugjVAQAAAACAih+Xy4v+w3H39uGpTW3l8vlAgx+U ywu/+JSmgeBEubzg03u925D7e5y5l4Zb0XsAyCMxcbrp6enAT06cOHFLhf7+/mcrhB81MTHh tlm7dq3bppXHSUmXPvzhD1999dX+sBFbuzYPQnUAAAAAAFArl8vl2w9ecJwOGV/rcObntgUa vFku/9q3zv8sABfVoHxlLO/LPc5/On3g//3hB+deXNeSZwAAeSMmTrds2TLVryYnJ90zkebh e/f+bFahlcdJQ5cOHTr0nve8JxCkY2vXpqIK1XV2dvLKAwAAAABAJLErW9dNzztOr6bBhXJ5 8cNn3LuPhxY75ct7dl0s33/jn751LiKoBwCghzhd8l2K3NqVUFELGBkZiQzVESQFAAAAAIBI vBJ1jmJla2yDsi+Wt9JxfjotM+mGz85/93O/FtkYAEBDTJxu37597vmot7dXeYgqq1evTsNx 7HYpvLUrSy9byejoKKE6AAAAAAAwxJ8NF7my1d9gfYfz1tnh8EG8pLwOx5l+pKOyoBYAoB6I 0yXWpX/zb/4NW7umAVWoTkB9QAAAAAAACKBf2eqvYacKw/lL1G3vdcrzpRZ1HQByB3G6xLr0 7ne/OxAVGh4e1nQSmsfU1FR7ezuhOgAAAAAAiMXLhnMUK1v9Db7c48xf3BNo4I/leUtfAQDq ICZONzU1NVBhaGhoR4UDBw4E2gxU2bx5s9vm8OHDFo/T+i4tW7bsmmuuIR6UKgjVAQAAAEDW mZ2dLUHDxJYh8mfDRS599Te4yXFe2fPhcEodS18BIBFi4nQeExMT7lmppydigxsXb71hf39/ Go7Tmi6xtWtqEe9CZ2dnZKiO9cgAAAAAkHJKpZJq4hlqQryS+pfanw0XGYbzl6hzFCl1/lje 2Cpxo3062c8DABQE4nQNdYmtXVPO7OyseEcir9aE6gAAAAAgzaimnKFWYuN0ZV+JOkcRhvMa LHacX/jgB8MZc7H7UQAAmGAapzt58mR/hQ0bNuyqcPDgwUCbw4cPu202btzotnnmmWcsHqfZ XQrvGsHWrilEH6rj/QIAAACAFDIyMuIOWUdHR62uGc0DJmP+J+fLbbdvdyo5dR/84C9MP/JL gTCcv8H//X93/MGCe8Mbv+r3owAAMME0TuchTnPuBWPp0qWqNrt27XLbrFixIg3HaUaX3ve+ 94WDPpoOgEXEhbmvry8yVEdoFQAAAADShhiguiteu7u7bfelKHjZcB2O87U/77in4/8OhOH8 y1q/1uss2XVg7+oF5fmT/jZeLM9R7EcBABALcbo6uxRgcHBQ89chDQwMDBCqAwAAAID04w1c Z2ZmbPelKPhL1D3S69z+xLOBMJy/wf0dzv/3H7YtuntvoJIdS18BoHFi4nSTk5OBn5w+fXp5 hbVr145VCD/q6aefdtvce++9bptWHqepXfrP//k//9N/+k8DgR72D80KmlAdVQUBAAAAIA14 2QPDw8O2+1IsvA1b1zrOmbPD4TCc1+Bmx/n/TfT0H7v479tuC6TdsfQVABokJk63bNky1a8m Jyfd64fm4Xv37nXbtPI4zevSt7/97fDWriWDoqSQHrxKHwHYpRcAAAAA0oBbW7mzs5M1Hy3m RLm84NN7vRDb8Nn51QtWz724zmsQWNZ6tvzaL205Hth0wovlCY7/xYJy+U0bTwUAMgxxOtMu hbd2FddOIjtZxNu9l1AdAAAAAKQKb6Q6Pj5uuy+Fw79q9alNbW+Wzy/51rnh29reOrct3MBd 1vrcfPnqVWNjn23zVsj6y9ht73XK8yVrzwcAsklMnG79+vX7Kxw7dizwq+np6U9W2F8lHOOY nJx027TyOM3o0m//9m8HYjoUNcs0mlAdq5gBAAAAwApsH2Edb9WqG2Jzw3A7PtM2/5Mny5XU uF/71nn3xmGl4/x0WqbaDZ+db2v77MTGReX50+UrY3kPLXbKl/fE/EkAgCuJidPt27fPPcX0 9vYqD1Fl9erVaThOM7oUYGBggCBd1imVSu4wKAyhOgAAAABoPYODg+5wlEUetthzuewsfsgf htt1sdx221/e1+O8evQLZ8vzv7TluPse9TjOxWMry5XydV948aLTseqvPtM29+JX3NAecToA qBvidDXH6djaNTeIARChOgAAAABIA2Joyu2GdYbPzjsdn3Uqu7pOP9LhbiLxlXPzbbf9YeB+ wQvkCd4olz8x8WoldncFrHsFgDqIidMdPXq0t8I999wzUeH48eOBNr1VhoaG3DanTp2yeJyk DvXCCy+0tbURvsk3mlDdwMCA7d4BAAAAQFHo7u52KmVYWLjTelYeu/yZtvfd+Md/XAnQ/Yyn NrWVy+fdBqPn5q+++6/8Nwv+mnTlSjxv+KWLbbdt8hr4I30AAObExOk8JiYm3NNNT49yb2mv 5ld/f38ajtPIodjatTiIkZC7qRahOgAAAACwgnfbQlqARfacnR8e/urw8PCmr33t6rv/o7tT hPfb0/Pltf/1qBuJ+9pn2t46tyt8hNJPyit3HHA6VnU4zt6hRfM/KbWu9wCQF4jTRRzqr//6 rwPxGrZ2zTeE6gAAAADAFmwfAQAAHqZxuueee667wsDAwKEKzz//fKDNE0884bbZsGGD2+b0 6dMWj1Pfof7tv/23gUgNW7sWAfEWu2sNwvABAAAAAIDmMTw87A47yQwAAADTOJ1HqVRyryJL ly5Vtdm1a5fbZsWKFWk4juGhHn/88XCMpq+vjxhNcRgYGCBUBwAAAAAtY2ZmhmUcAADgQZzu Z7zyyivXX399IDrDxbKAaEJ1YhRlu3cAAAAAkCvYPgIAAPzExOmmp6cDPzlx4sQtFfr7+5+t EH7UxMSE22bt2rVum1Yep45DvfDCC4FdIxxquBYYb+lBADF+YjECAAAAACTF+Pi4O84cGRmx 3RcAAEgFMXG6ZcuWqX41OTnpXlQ0D9+7d6/bppXHqfVQO3bsCG/tKi6ZmoNA7vF2ICFUBwAA AADNYHZ2trOz0123YbsvAACQFooepwtv7UogBlw0oTrCuAAAAADQIN4ajlKpZLsvAACQFgod p1u9enUgBMN2AeBHjJna29sjo3UsiwYAAACAumH7CAAAiCQmTrdv3z73+tHb26s8RJXVq1en 4Tgmh5qbm/vVX/3VQOSFrV0hzNTUFKE6AAAAAEgWcevhsH0EAACEKGKc7pVXXvnIRz4SiLkw kQUqNKG6wcFB270DAAAAgIxRKpXcweTw8LDtvgAAQLqIidNNTU0NVBgaGtpR4cCBA4E2A1U2 b97stjl8+LDF4+gPxdauUAczMzNdXV2RoToivAAAAABQE+72EeKftjsCAACpIyZO5zExMeFG JXp6elRtvLr7/f39aThO+FCRW7sSpAMTZmdnCdUBAAAAQIOwfQQAAGgoUJyOrV2hQTShOnYg AQAAAIBYZmZm3IIqfX19tvsCAABpxDROd/Lkyf4KGzZs2FXh4MGDgTaHDx9222zcuNFt88wz z1g8jv9QN910UziwIi6Thk8fwGNgYIBQHQAAAADUgbd9BHciAAAQiWmczsMrerp06VJVm127 drltVqxYYf04c3Nz4V0juru7CalA3RCqAwAAAIBaYfsIAACIJedxuhdeeIGtXaEZDA4ORobq WEwNAAAAAJG4FVQ6OzuZ2QUAABUxcbrJycnAT06fPr28wtq1a8cqhB/19NNPu23uvfdet00r j+MdKnJr15GREf1TBjDEq35IqA4AAAAA9IjbEHesOD4+brsvAACQXmLidMuWLVP9anJy0r3S aB6+d+9et00rj+MeaseOHeHoCVu7QrJoQnVs4AUAAAAALrOzs+72Ed3d3bb7AgAAqSafcbo/ +ZM/IcUJWsP4+Lg76gpDXBgAAAAAyr7qxmwfAQAAemLidOvXr99f4dixY4FfTU9Pf7LC/irh QNjk5KTbppXHue666wLhErZ2haYiPrGE6gAAAAAgEraPAAAAc2LidPv27XMvKr29vcpDVFm9 erXd40Ru7coWnNACNKE6BmQAAAAARaa7u9th+wgAADAjP3E6tnYFu8zMzLh7eIXhcwgAAABQ TLxyxiyzAAAAE2LidEePHu2tcM8990xUOH78eKBNb5WhoSG3zalTp1p8nMitXcljghYzOztL qA4AAAAAXNg+AgAAaiUmTucxMTHhhht6enpUbbzJov7+/lYeJ7y1KxNWYAtNqE6Mz1jsAAAA AFAcBgcH3XEgO9oBAIAhmY/Tvf/97w9EQ9jaFazjbekVgGqJAAAAAAVB3JK4I8DBwUHbfQEA gMxgGqd77rnnuisMDAwcqvD8888H2jzxxBNumw0bNrhtTp8+3dTj/PN//s/DcRCCdJAGCNUB AAAAFBl3+4j29nbGfgAAYI5pnM7D21Z86dKlqja7du1y26xYsaJJx2FrV0g/qlAdKZ8AAAAA +YbtIwAAoD4yGad74YUXwstdqdMPKcQbohGqAwAAACgIs7OznZ2dDttHAABA7cTE6aanpwM/ OXHixC0V+vv7n60QftTExITbZu3atW6bBI9z6NAhtnaFDEGoDgAAAKBQiHsTd7xXKpVs9wUA ADJGTJxu2bJlql9NTk66lx/Nw/fu3eu2Seo44bWuJJND+hEf0fb29shoHZ9eAAAAgDwxMzPD ch8AAKibjMXpwhlJTFJBJpiamiJUBwAAAJB72D4CAAAaIRtxuv/yX/5LOLrR2dnJskHIEJpQ 3cjIiO3eAQAAAECjjI+PM7oDAIBGiInT7du3z73S9Pb2Kg9RZfXq1c04Dlu7Qm6YmpoSH93I UB0rIwAAAAAyjbd9hBjv2e4LAABklbTH6VRbuxKkg4wiPrqE6gAAAADyB9tHAABA46Q6The5 tevg4GDMcwJIN5pQXV9fHzFoAAAAgMwxMzPjVjgRwznbfQEAgAwTE6ebmpoaqDA0NLSjwoED BwJtBqps3rzZbXP48OHGj7NmzZpwFIOK+5APZmdnxUc9MlTHmm4AAACAzNHX1+dUto+YmZmx 3RcAAMgwMXE6j4mJCTeI0NPTo2ozOjrqtunv70/kOH7Y2hXyB6E6AAAAgBwg7lPcUdzw8LDt vgAAQLZJXZxubm7utttuC4Qt2NoV8ooqVMdnHgAAACAruNtHiH/a7ggAAGQe0zjdyZMn+yts 2LBhV4WDBw8G2hw+fNhts3HjRrfNM888U9Nx2NoVCkhkAqmbQ0qoDgAAACDlsH0EAAAkiGmc zsNL6l66dKmqza5du9w2K1asMD9O5NaulNWHIkCoDgAAACCLiFsVto8AAIAESUuc7tvf/nZ4 a9eBgYFauweQUVShOoftUwAAAADSilfDhO0jAAAgEWLidJOTk4GfnD59enmFtWvXjlUIP+rp p59229x7771uG/1xPv/5z4djE9u2bWvkiQFkjqmpKXc+llAdAAAAQPph+wgAAEicmDjdsmXL VL+anJx0L0uah+/du9dtoznOJz7xCQITAC5pDtVdLpdvP3jBcTpEZ1Y6zk+n14XbvFEuL/nW OdFGNNoxtOits3ta308AAACA1tDV1eVUto+gUA8AACSFzTjd3Nzc7/zO76jW+rFfEhSTqakp d8uwMNZXgm+bKzsd9zmVWN30Ix2V2N0VPDlfbrt9u9fhVR3O+cn+cDMAAACArDMyMuIOeMbH x233BQAA8kNMnG79+vX7Kxw7dizwq+np6U9W2F8lXPB+cnLSbRM+TuTWrgG45kExmZ2ddadn wzzwwAMWO/aDcnnhF59yezK2Spw9TgcaDL9adpxV/g7f5Tizx1Za6S0AAABAk/C2j+ju7rbd FwAAyBUxcbp9+/a5N9u9vb3KQ1RZvXq14XFeeOGF8K4R/+Jf/IvAT7jsQWHRhOoGBgZsFSq+ UC4vfviM2437O5z5uSuKSIYXxp4XP9n98mfbbpt7MWKRLAAAAEBGYfsIAABoEhbidKqtXcVF LhyS4MoHhWV2dravry8yVDcyMmKrV+um58X3WPThZsd5baLH/yt/tt32Xqc8XypXKtZ9YuLV P1q06K1z7AwDAAAAeWBqasod8LB9BAAAJE5MnO7o0aO9Fe65556JCsePHw+06a0yNDTktjl1 6pTqOD09PeGgg1cgPxyVsF6QC8Au4ay69vZ2i7WKvQp04RJ1ql+9US6vPHB+ZvftFKoDAACA HNDd3W19SAYAAHklJk7nMTEx4cYIenp6VG1GR0fdNv39/ZENVq9eHY44+Hex9LY298P1DwqL V/rEj92Z2x+Xy4v+w/FA0pyLJtUOAABywy233OJeBQ4dOqRqs3z5crfNrl27UnUcVQMAc7xb Hv9dDBScU6dOuZ+KG264QdXmtddec9ssXLgwK8cBACu0KE4XubVre3t7eOuJcPYQ+eRQWMSH P/B1sL4PcrgIXfjn4dJ1AACQG4jTQZFh+wiIJG3xNeJ0AJnGdLzy3HPPdVcYGBg4VOH5558P tHniiSfcNhs2bHDbnD4tt4OM3Nq1q6srsvacF+xLT2ACwAqRFRvTMHO7ba7sdNwnOnOT45z6 +o2VHDtdnh0AAGQOdyD393//9+FfrVu3zh3v7dixw232D//wD4E2DzzwgNtm69atqkNZOU6t rwNAgMHBQXe0E842gCLzP//n/3RPMitWrHBPRMeOHQu0ETfFbpve3l63zdNPP52q4zT8MgBA MtQ8r+gtTV26dKmqza5du9w24rwQubWrOB1oVrOGF/qlITYB0GK8fcTSFrM+US4v+PRet0sd jjOxcVF5/rRXnE5w/C8WlMtv2u4mAADUj3s+37Fjh6aNW6JLcPDgQVWb/v7+2EO18jgAjeBt HzE4OGi7L5BSXn75ZfdD8oEPfEDV5sKFC26bBQsWpOo4qgYA0GKaG6f7V//qX0Vu7ar/E+G1 fl1dXbX2EyDT1JpMNzs7K75ZrSnmeKFcXvzwGbdLPY6zaPnyHZ9p+8qPvu8m2VGcDgAgBxCn AwjD9hEQC3E6AGicmG/j9PR04CcnTpy4pYIYMD1bIfyoiYkJ0eD9739/ONAwMjIS2ydx5Qs/ sFQqmT4ngOxTUzKd+Ha8613vEm0+8YlPtKZ73pYRKx3nBy+uu/HPj7qV6QRrHefyq8Ot6QYA ADQJd7D3la985dkq4Tbr1q1zm+3YscNtE15w+sd//MeBQ9k9DkDdjI+Px86bQnFQnYj+7u/+ zj0R3XXXXW6b733ve4E2b7zxhtvmN37jN1J1nBqePwA0k5g43bJly1S/mpyc1MTdY7d21RMO UsRm4QHkBvNkutnZWa9OisvHP/7xFvTQW+Xa4TjTj3ScL8/fvvvlSnYdxekAAPKDfzymaXbn nXe6bfbv3x97qJQcB6AmxIirs7PTYZUPVIk9Ef3whz9027zvfe9TtXnzzTdTdRwASAnJx+nm 5uZ6enrCQbqaiq161R/8RO47AZA/DJPpxNckvD+y4Bd/8RfHx8eb2sMflMsLv/iU++fcwNzl cvkLL150OlZRnA4AIDcQpwNw8crysMQHXFIVXyNOB5AzYuJ0+/btc7/Pvb29ykNUWb169Suv vGK+taser8iIBxVboQgYJtOFyzj6Eb9taicvl8u3H7zgrnW9v8OZn9vm/vwr5+YXdt3nbQIL AAC5wbvERC6tcvnkJz/pthkbG8vKcQBi8cZmrO+BAN6J6Fd/9VdVbV599VW3zXvf+96sHAcA 7JJknO63f/u3w7tGdHV11Vdp1asB4UHRVigCscl0YrAYjmL7ede73tWCb8q2uXLkxhGj5+YX 3v0ldxPYZvcBAABaRlJxsbQdByAWto8AFUnFxdJ2HACwS5Jxune84x2BeEGDk05uGQg/1G2F fBObTCf+WwwTw238NDuZzuVEubzg03vdv/jUprZy+bz3q+9cLF9996Nf+0zbW+d2taAnAADQ AryrTEria0kdB0BPqVRyP0Umu+FB0fBORCmJryV1HACwS0ycbmpqaqDC0NDQjgoHDhwItBG/ /fVf//VmBAvE5TBwTM2WlwA5QJNMNzs729fXF/6iBWjZZO+Fcnnxw2fcPxreO2LXxXLbbX+5 qsM5P9lfWSYLAJAWvJ1wNAQyhaFcuUK5fOUrX3HHhH//938faCNGbm6bhx56yG3zwx/+MFXH afhlgMLh5g1wDwKReCei9evXuyeZ//bf/lugzT/8wz+4bdasWeO2+cY3vpHy4wCAXWLidB4T ExPuyLWnJzhsDW/t6iSU+DY7OxtOHWp2gXwAW0Qm07nlisU/Y9PoXFqTTOeybnrecXrFH13p OD+dXhf5W/Hro3/TwxpYAEgbl8vlbacvdgw+4j+FilPWf/tqz1tn99juXaq55ZZb3Jfr0KFD qjbLly932+zapUystnIcVQOASNg+Agw5deqU+1G54YYbVG1ee+01t83ChQuzchwAsEJDcbpE tnbVMzg4GDh+d3d3UgcHSBXhqnPiJ7Ozs+FvgYoWV07xclIWO86P9tzo/5W4Ae4/dlGcMNyO repwdu9YOXd6W8v6BgBgwnPz5atXjXln0bFVYlzEvEIMxOmgIMzMzLizpH19fbb7AmknbfE1 4nQAmcZ0vHLy5Mn+Chs2bNhV4b/+1/8aubVrgkG6siLDqI7dYwFSjlf9xM+jjz4qvlPRMTnH ueqqqwI/0STTuctmky2t8oNyeeEXn3L/tFeiTr+gjKVkAJAq/LtXO1Gr+AuLO9gLrzYV/PEf /7E7Jty6davbLLzg9Mtf/rLb5qGHHlIdyspxan4hoMC49Uba29u59YBY/u7v/s49yXzuc59z T0T79u0LtPnJT37itvnMZz7jtvnWt76VquM0/DIAQDLUPK/oRRPa2trCQbpm5PKEa3KxJzrk j3AyXXgfFT8f/ehHAz/RJNONj49fffXVoo34Z4JjTf/97f0dzvwc6XIAkD38swuRq/iLifuC 6Au6eVeugwcPqtqIO8DYQ7XyOACGeLc8rawoAjng5Zdfdj85H/jAB1RtLly44LZZsGBBqo6j agAALab+OF3LYmeRf5Ft0SFPqL5WkbS3tz/22GPhcnWR48jwstn3vOc9CdZY2TZXdjrucyoL XC8eW5nUYQEAWoZ/Vxxyfj3cF4Q4HRQWto+A+iBOBwCNE/NtnJycDPxk3bp14cBBsyeawkv/ mNqCPBFOplPR19c3OzvrVTX2iEymK5VKqqS8pOo8enkoHY4z/UgHW7sCQOYILH31VvEXnOUV HnroobEq4TYPPPCA2+xrX/ua2+b8+eBL9+UvfzlwKLvHATBhZGTEPSGwfQSoUJ2IXn31VfdE tGrVKrfNd77znUCbS5cuuW1WrlyZquPU8PwBoJnExOmWLVvm/9/mbe2qR/yJwB9ldgtyg2Ey XXt7u1tdLnIf5HDkOhzL83Prrbcm0nl/Hgp1nQAgo3ipwQ6r+K9kYGDAu3Bomt15551um/37 98ceKiXHAVDhDbTYvA40xJ6IfvjDH7pt3ve+96navPnmm6k6DgCkBNM43dzcXHjXCHENa9ks Uzgw0YL4IEALMEmmE228unLhAFxnZ6c/mW5qakqz+4RLgt/cddPzjtMrjrnWcS6/OpzUYQEA WoZ/V5weVvH7IE4HBcT7jLF9BGiIPRERpwOAuomJ061fv14Mcb761a9+8IMfDIcGkt3aVU84 NtHV1dWyvw7QJEyS6fy5cpE7IPtj1iMjI+GgdoC+vr4En8Key2Vn8UMOdZ0AILP4U4NZxe9H XFM+WWV/lX/4h38INHvggQfcNl/72tfcNuEFp96hUnIcgEjYPgIMCZyIDh06FGjwv/7X/3Lb fOpTn3LbTExMBNr84z/+Y6qOAwApISZOt2/fvuuuuy58n9+krV01iD8X7gY1IyDr3HzzzZqA mviiBaLh/tQGF28NuPiOGNa5S3Z+2J+HQl0nAMgoXmqwE7eKf8/l8iPv35L7Zf4mV5MsYvt1 hbTjrkgIrFQACGD7TNZEbL+0ACCJ+Sq6MYJALtvAwICVS1c4QtG8TWYBWoA+mW5wcDDwRdMk 042Pj8em0TXpW+Mvwf7QYqd8eU+yxwcAaAHerjhO3Cr+E+Xy//XhPbmvYWdyQckitl9XSDXe 9hFiWGW7L5Bq7J7HmortlxYAJKZfRS9GNjg42Mz+6JiamgqfSqgcAdlFfHrD0WdHXfkxMplu dna2r6/P/Oqr/8rUF4IfflWMV1aJg690nJ9Or6vjCAAAdvGnButX8Z8olxct35v7Zf7ml5Vs Yft1hfTC9hFgju0zWROx/dICgKSGr2JXV5f1rRvCy/osxg0BEuHkyZPveMc7vI90X19fZLAs Mplu8+bNnZ2dqgut/7AummS6V1555eabb65vbOrloVDXCQAyij812NGu4nfPeMf/YkG5/GaL O9lK6rvBSz+2X1dIL+Kewv2QkAQAsdg9jzUV2y8tAEgy9lUcHx8PnEra29upHwFZx10A+/M/ //OaUHg4me7d73635irr7XbnRzX0FN+sBQsWuG3qiH1vmys7Hfe5D6dEHQBkFP+p7P4OR7Wy 1a1kp69hlwNqv7nLBrZfV0gp3qodMgDABLvnsaZi+6UFAEn2vorh7CHrWX4AjSM+xpr528hk OhXiO1IqlcLflMhkutnZ2XAEcNmyZSaTySuPXXacxYHHam5uAQDSjL9EXeQq/tPz5bX/9Wjb bZucuBp2OcD8opMtbL+ukFLcJTtM/4Mhts9kTcT2SwuQXsJfk4mJiW9U2LBhw+9WOHXqVOBR Tz/9tNtm48aNbhvxk/i/lXDfm49X4dXD2+8SIK9ElrGLxN3mZXR0NPyrcOgtMpznsnDhQsO+ 7Tk7Pzz81eHh4T/60pc6Bh/R13UCAEgtF8rlxQ+fMTzZ5v5cZ/g6ZA7bryukEW/UxNw/GGL3 PNZUbL+0AOlF8zXx6rMdPHhQ9fC7777bbbNjx474v9VQT23gFXn1w65MkGMMk+nE98L7Ipgk 0wX2cQ7wqU99qsVPEwDAOu6aVvc0GN7A+nK5/PDpiz1f3e04PQ7L/AFyAdtHAACACcTpYvDq vHpwZYUcY5JMJ74C3kqN2GS6qamprq4uzdGuvvpq1n0AQAHxl6hTbWB9uVz+wosXnY5VuS9R B1AEvNsKMTqy3RcAAEgvmjjdE088MVphaGhodYUXXngh0ObQoUNum82bN7ttdH8ryY63isj0 IvZmglwSm0zX3t4+MjLif4g+mU40DmekBhgeHm7tswQASAU/KJcXfvEp90y42HF+tOfGyGZv lMufmHh1pdMTGcgDgKzgjbIia/gCAAB4aOJ0Ht5ejo8//riqjZeFo/tb9XfTKn19fZpIBEBu uPnmmzUBta6ursD0ryaZbnZ21svI1UARZQAoLJfL5dsPXnCcDvd8qFnZ+tx8uWPl3+a7RB1A 7mH7CAAAMIQ4XTylUikcX+ASCzljampKE1CLzHoLJ9MNDg6Kn4+Pj8em0WkOCwBQEPQl6gIt n/z8L9ktUWdyVgcAAEgnFi+gALUS/gAfPXpU1fiuu+5y24yNjWkOqPxVo521R7jAFvEFyB+l UulDH/pQ4KPe2dkZWUUlvBtye3v7K6+8Es4/VaGfUhZ/VPSnac8VAMA+T86X227f7p4SVSXq UoLhiR0AACDN2L6cAhgR/ugSp4sgvL6vvb3ddqcAmoKXQOtU8uMiQ2mRWyEPDAyEM+w8fu7n fi7wE02w290fduHChdSCBIAcc6FcXvzwGfeUqClRZx2TOx8AAID0Y/uKCmBE+KNLnC6acFRi dHTUdqcAkmd2drazs/Nd73rX+Pi4qo0bR/OzYMECzRUxXKtOlUw3MzPjbyx6whpzAMgxK49d rsToJJoSdXaJv+8BAADIAravqABGeJ/YySr9/f1LKzz//POBxi+99JLbZu3atW6b7373u4E2 4rfKv5V891tIODDR1dVlu1MATWFmZkYTHYtMplPR2dm5f/9+wzC3+GG45Tvf+U7x7SNaBwC5 ZNtc2em4zz3d6UvUBdhzufzI+7eU50vN7N3PqOEGCAAAIMW04KIJ0DjhT+zHPvYx9yeHDh1S PWr58uVum927d9fwtxrqqW1mZ2fD33PqZ0EBCcesVQwMDIgvjrfLjEdnZ2fgmKKZvrAdC2AB IJf8oFxe+MWn3BNdTSXqTpTLN964pzUl7QzP+QAAACmnBRdNgMYJf2KJ0ykJhxv6+vpsdwqg pRgm04k27rLZmZmZ8G8DyXSlUkl/zBUrVlh5sgAAzabuEnUnyuWr7x6bfqSjXL7c1B6WidMB AEBeaPYVEyARwp/YEydOlCoMDAz0VDh+/HjgUd///vfdNhs2bHDbHDhwIP5vJdz3ljM1NRX+ qpPmA4XCJJmuu7vbW6aqT6YTzQYHB2MPyLcMAPLKm+Xyr33rvHe6My9R5y6Y3d7rtGDpK/c5 AHXgDZlYfwNgC65fkFE0n1ivmPvBgwdVD7/77rvdNjt27Ij/Ww31NB2Ey+EPDg7a7hRAi5id nV24cKEmoNbe3j4yMuK11yfTTU1NdXV1aY7mMjAwYOO5AgC0gsvl8u0HLzhOh3vGMyxRd7Zc /qUtx50al8rWDfc5ALUihkDuWgEW3wBYhOsXZBTNJ5Y4XQTj4+PhwAQV7qEgbN68WRNQ6+rq mpqa8rfXJNOZF7kjmQ4A8splcTJ86WLbbZu8M15s3O18ufyH+166+u7/6LbvcJwWLH0Nn5mb +ucAcoBbdVfcJjCMAbAI1y/IKJpP7HPPPXegwuc+97mPVzh27FigzcmTJ902Q0NDbhvd30qy 4/bo7OwMfNv9CUQAOSb84fcYHh4ONFYl04mfh/NSVZCvCgC55ES5vODTew3PhHpasPQ1/Eeb +ucAsk6pVHK/KeHREQC0Eq5fkFFMPrF33nmn2+bxxx9XtfHyZnR/q/5upomRkZHAtz28eSVA Lpmdnd28efNVV10V+PxHFl6JTKYbHR012YbCRZ+sOjU1RRQPAKAFcJ8DUBNuWQ9uEACsw/UL MgpxupqJ3O/S3doSoAjs37/f++QPDg5GhtIik+n+5b/8l6qQ3MKFC9/5zncGfqiZhfaWzZLN CgDQbLjPATDHm9Hn7gDAOly/IKN4n9hlVV5++eVAm6NHj+6rsH79erfNs88+G2gzNTXlthG/ Vf6t5LtvifAOld3d3bY7BdA6xFdg4cKFmgFoOJkukIXnp6urK/ydUiXTBZbN/tzP/dyjjz7a vGcKAADc5wAY4k3nc2sAkAa4fkFGCX90jx49qmp81113uW3GxsY0B1T+qtHOpobIXKFABX2A fKNZkRr5BVExPDwcmaMamUynWjYrRsN8AQEAmgT3OQCGePOUbB8BkAa4fkFGCX90idMZ4e7i 5EdcmG13CiAVhJPpIuns7HSDa+G9X8PJdOJ/w186P5E18gAAoHG4zwEwge0jANIG1y/IKN4n dm+VtWvXrqjw4osvBhofOXLEbXPvvfe6bZ555plAG/Fb5d9Kvvv28K7EfjQZRgAFwTCZzits Z5JMJ75u+t0nPvrRj1p5sgAARYD7HAATvO0juCMASAlcvyCjhD+xH/vYx9yfHDp0SPWo5cuX u212795dw99qqKfpw70Ya4ILAAXEXzwukvb2dn9hO30ynfiPcOm6MCTTAQA0D+5zAGKZmppy vx2jo6O2+wIAP4PrF2SU8CeWOJ0p4jIcji/Y7hSATSLzTP309fX5J5n1yXRiyNvZ2ak/oEOp ZgCAJsN9DoAJYtwyODhouxcA8DZcvyCjhD+xExMT36iwYcOG361w6tSpwKOefvppt83GjRvd NuIn8X8r4b6ngHCIgTk0KDIf/ehHVdE08WUZGRkJtNck04V/pYJkOgCApsJ9DgAAZBGuX5BR NJ9Yb/nawYMHVQ+/++673TY7duyI/1sN9TSVhEMJXV1dtjsFYA3VDhLiexHe+EyVTCdahheV qyCZDgCahDgXjY+Pi5MS20lznwMAAFmE6xdkFM0nljhdPLOzs+EvP9k9UGTEne11110XCL1F toxMpvvzP/9z/ZYR5l83cWstzmLcYAOAIV5gTpw6/CciLuvc5wAAQBbh+gUZRfOJfeKJJ0Yr DA0Nra7wwgsvBNocOnTIbbN582a3je5vJdnx1BBOIOrr67PdKQCbzM7O/uIv/qL4Llx77bWq +9vIZDpNNTrR+IYbbgj8UJNM5wUBSXEFABXiRCTOUeJ0IS7cmhkC4nTc5wAAQBbh+gUZxeQT e+edd7ptHn/8cVUbL1ql+1v1dzPFeLs7+Qkv8QMoFOK2dnBw0L9lRIBwMt1VV12luknu7u7e vXu34c1zeNmsuAMXP9F0BgAKgj8wZ7JNjeZUUyjCr4ntHgEAAMRjeKHPNLZfY2gKJu8vcboY vBXCHmz2BKBhZmbG/NrjLpsNf8siE1dHRkZUSTHinpw1sABFo77AXADidOHXxHaPAAAA4qnv up8t9BlCgd8m2DiQA6FJiZitoPpffR9qapwnvPdX04Y4XQzj4+OBb4u3ZyUAhFFtNxGgq6vL jayFv2Lha5L4xon7cP0BxReTUB1A7imVSiMjI+I8U3dgLoBhnK6UX8KvSZPfQwAAgARIZBiQ fjThKjEWuuaaa26qIP7D0V7Bxb2Sv7H4X/1re+2114r7Nbc2kaoiebmSSCEavP/97//IRz4i /in+e3R0VNV4cHBQNPjQhz700Y9+dPHixeK/xZ2gqrF7UxnO58glR48eVb0Od911l9tmbGxM 834pf6X6RQ4I3wyIj6PtTgGkEcNkOn9Savj7JU7K/mOK07fh7hOimeZcDwBZpFQNzJlvFQ0N Yvs9BwAAiMf21bJFqMJekbddJcXsY2Q5L1WKw/79+wMtf/EXf1H1Ltx6662BxsuWLVM1dgN5 fv7gD/5A1TipGdlMQJyuHtwgsR/xobHdKYA0EptMJ747/uuHuPCE23izRrOzs+6sS01o5nAA IP0QmLOO7Y8AAABAPLavli0ikMHgEXkbpUp8Cwc0HHXuUbjUuKMeG0RuHhjZUtzZhQ+r2hWw pkpKOYA4XT1Ebl5J2g5AgNjzaV9fX2DNuCaZTtyra2ZRxDn9m9/8pirPjoxXgGzhBuUJzKUE 2x8HAACAeGxfLVuEKuwVmR7R3d0d2TiyglBkQfByVOlwR5GpF5mm5yjW6kYWOxJElhSLjELm j8kq/f39Sys8//zzgZfipZdectusXbvWbfPd73430Eb8NvKtLOc7TleuLqX2o/oOABQWTTJd 5IpUTTJd5DSOh7fbrLg2qEJ1qqknAEgt4usvThTi6y+usIar3aEZ2P4gAAAAxJPX61d42jIy 7KVKaIg8ZuSwSlWiLvKwkZl6kWl6jmJ5k2qZVGT+k2HF86zjPd+Pfexj7k8OHToU+aYIli9f 7rbZvXu3qk3Eu2neNItEJgpRtB7AT6lUClcocCpBbcOrizgji6+VJqdGXE4CkzniyKr2hOoA Mg1hO1vYfucBAADiyev1KxzPCoe9NMuYwolvqqw3JyqgEbnBlKPIUlJt9Bd5F6a6ZfMXLvco SHE67/kSp6uf8KeQKABAGP+8iri1Vi1BjUyme+CBBzR34+Flsy7ih6rzvvg5uzMD5AN/2C7B 0Zs4RyW7fart16lmwq+J7R4BAADEk9frV3h9aDjsoFkWGk58U2W9OVHFgjSrmsJdVd24hdfq RhancwmXqAtHIVVl7DJK+CU9ceKEO4wU73VPhePHjwce9f3vf99ts2HDBrfNgQMH4v9Wwn1P H5GhZUIAAGHcWaDFixdrck7Dt9nXXnut6vQdu5Gr+Caq5nMI1QHkEvG9FtdlMZoU3/1GwnZZ jKwlS/g1sd0jAACAePJ6/QqHtMJhL82y0HDim+ouyYkqUadZ2BQYMmnS9JzQWl1VcTqXwM1a OAoZmXOXXbznFf6VVxzw4MGDqofffffdbpsdO3bE/62GepoRwp9a1Y4qAEVGnGr1OznUVBlU tWw2jOqKJa5trFIHyDd1h+2I04VfE9s9AgAAiCfH16/YEnX6oU7gaJrlSoESdZqsNycU+tCk 6Tmhtbqq4nQugYSM8D1dzvbwVL1TZeJ09REOLqiKLwKABvO76Fp3blVdA8RXlVAdQHHwh+30 28gSpwu/JrZ7BAAAEE+Or1/6EnWa4nThsY0+6825skSdPustkKmnSdNzQmt19YOxQLpc+FYx Z6ujvOcV/tVzzz13oMLnPve5j1c4duxYoM3JkyfdNkNDQ24b3d9KsuMpJvyhidzNBABUGCbT ibN5fZE11fFjF88CQI4RY9aRkRExagyMFGudDMgf4bOl7R4BAADEk+Prl75EXezNlD/xTZ/1 FhgI6bPeAq+wfo8v/1pdfZqec2X5udwXpytr43Qed955p9vm8ccfV7XxEg91f6v+bmaKcGHF /H1uAJqHOE2bJNM1uKJcc/UisA4AZV/YjnNC+Dxpu0cAAADx5Pj6pS9RpylO5+JPfNNnvTlX lqjTZ705vky9cJree9/73sBPvLW64bDjNddcE/iJlzGX++J0ZeJ0zSAyGMyqGQBDNFsIuYiL UCJfKHHxUE3ykD4DAOARPkna7hEAAEA8+b5+aUrUhZMeFi5cqHop9Flvjq+QVzjQ8c/+2T8L /MTLpQin6X384x8P/MS75wqn6d16662Bn3jLnnJfnK7s++guq/Lyyy8H2hw9enRfhfXr17tt nn322UAbccPrthG/Vf6t5LufVsIfnfA+KQAQRpz93/nOd0ZcH6qIL1eC1Qc0obrw7uYAAMUk fIa03SMAAIB48n39UpWoi1wWGk6ac/Mewllv3RUCP3RrDYWz3lasWBF+uNu98F8MR+68IEk4 5vjQQw8FfuIlzeW+OF056qN79OhRVeO77rrLbTM2NqY5oPJXjXY2O0QWbjTcjxKgyGjqIzSp eJz4YqrytwnVAQCU836fAwAAeSXf1y9VibrIZaHhmyw38S38c/GT8PImN/EtMjIYjpq53Qsn Q5RD74ibqRe5hldVhK4IxenKxOmaRzgInb9V0wDNQFwGFixYEPj6iC9U8+ZJxJFVoTrx8/zN zwAA1ET43Gi7RwAAAPHk+/qlKlEXuSw0Mm+uHJX1JlqWSqXAD93Et8iVtuE/Jx6u+nORmXqq gGNk3lwRitOVfR/dvVXWrl27osKLL74YaHzkyBG3zb333uu2eeaZZwJtxG+Vfyv57qeY8Ket vb2dG34AE1555ZVrr73W/eIsXLiwBdXixHdTVUKVUB0AFJzwidF2jwAAAOLJ/fUrMnCmWhYa meAW+KFXii4ylBH4oRsWDAfOhoeHVel7kZl6qgW8kQHHIhSnK0ftI/Gxj33M/cmhQ4dUj1q+ fLnbZvfu3TX8rYZ6mkHC3xCK0wMY4k7CLF68uJULxlVbI4nvsluUAQCggITPirZ7BAAAEE/u r1/hCFe4rJu3LDSclPDoo48GfuIVjAsnvn35y18O/MTNegsvRBWPVZXDi8zUU22IEZk6V4Ti dGXidE0l/MHyb5YMAHqshMbCVzuX9vZ2QnUAUEzCp0TbPQIAAIgn99ev8Bq+8Dap3rLQmjZg DSe+/eZv/mbgJ27WWzkqPykyd88l8PPwRrRezCScwfcrv/IrgZ/ksjhdOSpONzEx8Y0KGzZs +N0Kp06dCjzq6aefdtts3LjRbSN+Ev+3Eu576hEfrPAHNJdpmQB5Ihxh9643fH8BoICEz4e2 ewQAABBP7q9f4UjWNddco4o/hGvGvfe97w38xMtLCCe+hSMb3rIn1ZokD28T2HJUpl4A/1Z+ qhriHrksTleOitN5eC/gwYMHVQ+/++673TY7duyI/1sN9TSbhHNz/J9RAEgnqlCd45s4AgAo COEzoe0eAQAAxFOE61dsJMu/LDQca/PjFadz0R/Wv1JQc+vk4hancwln6gXw322pljp55DWL wnuC4V8Rp0uA8GptxxelBoDUIr6nqisZhSYBoFCET4O2ewQAABBPEa5f+khWYFmoat88F684 nYs+8c2f9RYZ9PDjFqdzCWfqBfBXJw8v7A2Qy+J0ZW2c7oknnhitMDQ0tLrCCy+8EGhz6NAh t83mzZvdNrq/lWTHs0M4C9T/mQaA1KIJ1fEtBoDiED4H2u4RAABAPEW4fukjWYFloeESdX4C uQj6xLfAGqNwiTrNy65pGSjoH17Y6yevxenK2jidx5133um2efzxx1VtvGCU7m/V380sExkw zmvcFyBnzMzMqJLJCdUBQEGIHXADAACkkCJcv/SRrMCy0HCJOj+BZX/6xDd/1ltZW6IuXPhL k6kXvsPSLOzNa3G6MnG61hD+bPlXaANAmhEXP9XlQfycmDsA5B7V+BgAACBb2L6iNgVNJCt8 q6JaLRQoTueiOmwg662sLVEXDn1oMvXCpcA1C3vzWpyuHPXKHz16VNX4rrvuctuMjY1pDqj8 VaOdzSzhT23k1wAA0om4wqmqORCqA4DcoxofAwAAZAvbV9SmoIpkRS4LVd3UBIrTuagS38JZ b5oSdf7idC6aTL1Aml5Zu7A3x3dh4SdLnK4phBdss2skQLZQpXOLbzebwwBAjlGNjwEAALKF 7StqU1BFsiKXhapK1EVulKdKfIsMZahK1EX2ObJlOE2vrF7Ym+PidGXidC0j/BHP9wcLIJeo Zqva29sJ1QFAXok87wEAAGQO21fUpqCKZEUuC1WVqIu8l1ElvoWz3sqKnIZwcTqXyEw9Vfnv yIW9OS5OV/YNvSar9Pf3L63w/PPPBxq/9NJLbpu1a9e6bb773e8G2ojfKv9W8t3PDpFfnnAK KACkHFXxhfb29hyXSACAIhN50gMAAMgctq+ozSIykqVaFhouUaepyhU+bGTWW1lxl6Sqyx+Z qadacRiZKpHvO6/wJ/ZjH/uY+5NDhw6pHrV8+XK3ze7du2v4Ww31NPuEA8yRi8ABIOVo6qSy nh0AconqpAcAAJAhbF9Om8Xv//7vB57pddddp2rc09MTaHzrrbeqGocjgMuWLYtsGVmiTpWZ FJmpF5mmV1Ys7M1xcboycbpWEvnBVX0WASDNTE1NqTZLiizuAACQdSLPeAAAAJnA9lW0ubiR rEWLFv3Kr/zKdddd52iXhbol6t7//vd/5CMfEf90tPcvbuKbv7EmL6Gzs/Oaa665qYL4D/3L Ln7rb6xK0ytX1yZeffXVN9xww+LFi50C1BALf25PnDhRqjAwMNBT4fjx44FHff/733fbbNiw wW1z4MCB+L+VcN8zSHgZdr6XVQPkGE2oTlVbAQAAAFLOzMyMuMkRt6biaq7a6zCjiHGLeEbi 7kPck4vnmO9sFIBCIb7O/gSgwP+GG/u//oH/baRx4I/qc5Jqahwon5f7yuDeeTv8K+/CdPDg QdXD7777brfNjh074v9WQz3NBeGMTXG95BoJkFHE5SSyGIRDqA4AACAjiPu90dHRwcFBcfOj moHLK52dnX19fcPDw+ImhVsSAICU4J2lw78iTtcUwtsVs0oOILuIQa0qVCd+zpAXACCWJgQf UoHt1xWUiKvz+Pj48PBwAQNzerywHZvdpQfbH4pmYft1BUg1mm/Kc889d6DC5z73uY9XOHbs WKDNyZMn3TZDQ0NuG93fSrLjmSVcgV6zEhsA0o8Y7otBbeQQhFAdAEAsTbwRtIrt1xWuYGZm RgzCBwYGVLNrSeGuLXUZHBwcriL+eqlehn24q3FdmvpEBO5TINXOLs1+l21h+3UFSDUm35Q7 77zTbfP444+r2nh7mer+Vv3dzBHiOheeuMv3psIARSC8obNLZ2dn7gsoAAA0QhNuAFOB7dcV 5Kjbjc2Fl7M0SFdXlxeG8wJwFp+m24GRkREvkNeMpyyeL3l2rSfZ9zE92H5dAVKNyTeFOF3C iItc4Dwlrqa2OwUAjRL+aru0t7cTqgMAUNGEG8BUYPt1LS6lUklckZPKm3NDcl48Tl/pPG2I 4Yebjpds9T1xqJGREcY2rSGRtyyF2H5dAVKN901ZVuXll18OtDl69Oi+CuvXr3fbPPvss4E2 4kTtthG/Vf6t5LufTcQFPnyq4lIHkAPCC9tdxMiYtFkAgEiadRdoG9uva7FwU+f6+voaDEW5 q1bdqFwuB+du8t3IyIgbuWvwQ97Z2TkwMMAIp6k0+B6lFtuvK0CqCX9ljh49qmp81113uW3G xsY0B1T+qtHO5ojwEjl2hwTIB6pQnUD8ynbvAABSR3L3fenC9utaCGZmZkZGRhpJnfMCc6VS qZhV2Pzb3Tbyge/r6xPHKeZr2FQaeVPSjO3XFSDVhL8yxOlagRgKhF96LmwA+UAMeVXz+ezv DAAQy94qa9euXVHh9OnTgTZHjhxx29x7771um2eeeSY9x2n4NQAdDYbn3FJro6Oj2VrE2hrE GEa8tn19fXVXuCNg1yS8E9F/+k//yT0RfeELXwi0uXTpktvmG9/4htvm937v91J+HAAI451R wyOQF198MdDYcCSj/FvJdz/LhMcWw8PDtjsFAMmgCdWRPAsAYMgtt9zinjkPHTqkarN8+XK3 za5du9JzHNVvoREaCc+5sTm2Lq0Jb5Pc+mJ2BOyaxKlTp9xX+IYbblC1ee2119w2CxcuzMpx AMDDO5F6P/nYxz7m/sRkJLN79+4a/lZDPc0d4cVx4q7edqcAIDHE6FZ1L0GoDgDABOJ0UK7W nqtjVaZXPY1QUeO4Mbv6KgBSwy5Z0hZfI04HkDjhE2kT/1bzDp1RwnNTlK8CyBPixkAVqhM/ 57YBAEDwuxUOHz4c/tXExMQ3KmzYsMFtJm4IA22efvppt83GjRtVh2r9cWp+FSCK8fHxcE3n WPr6+kZGRljT2jzq21FX3PiIR/G+NI4YQLonokceecQ9EYkXNtDmzTffdNvs3LnTbbNq1apU Haex1wAAEoM4XZDh4eHwrbvtTgFAkoixi7hhIFQHAKDCPSXu2LFD08bLpTp48KCqTX9/f+yh WnkcqJuZmRkxSK5prWV7ezupc63HXYmsGueoEOMf1sMmwssvv+y+pB/4wAdUbS5cuOC2WbBg QaqOo2oAaeNyuXz7QfGudYh3baXj/HR6XbjNG+Xykm+dE21Eox1Di946u6f1/YS64dsYRFyf wpeuUqlku18AkDCqdABxEzI1NWW7dwAANnHPh8TpoFxJoKsp6OOF52x3vOi4a5PreO9Ir2sE 4nTQGrbNlZ2O+5xKrG76kY5K7O4Knpwvt92+3ft2r+pwzk/2h5tBOuHbGEH47l1c4Wx3CgCS Z3BwUDVOJVQHAEVmtMLmzZtXVwm3eeKJJ9xmQ0NDbpsXXngh0ObQoUOBQ9k9DpgzOzs7MjJi nkBHeC611BGw6+7upvKPBs2JcW5uzj0Rff3rX3fb3HvvvYE2//iP/+i22b59e6qOY/4KgHV+ UC4v/OJT7hd2bJVTLgd3PB9+tew4q/zf67scZ/bYSiu9hVohThfBzMxM+HLFzBJAgvi/XHZ7 Et49xrvf4GYDAAqOf+ZS0+zOO+902+zfvz/2UCk5DmgQg17zCnSE5zJErbt/dHZ2Dg8Psxg2 jMmJ8Yc//KHb5n3ve5+qzZtvvpmq40CGuFAuL374jPue3t/hzM9t8/82vDD2vPjJ7pc/23bb 3IsRi2QhbRCniyZ8AQuX3gSAuvF/uWz3RRmqc9hGBgCKDXG6olEqlcyDOG7KFUGcLFJTtUEW w4YxOTESp4Nms2563nF6xXt6s+O8NtHj/5U/2257r1OeL5UrFes+MfHqHy1a9Na5bdFHhNRg /w45nYyPj4cvUQxEAJLC/+Wy3RfJ1NSU+I5HDk9HRkZs9w4gFQQKnXh44z/IN947/uyzz6ra fPKTn3TbjI2NZeU44DI6OmoYtXFzrIja5INSqWSeO9nX10fN7gDei/Orv/qrqjavvvqq2+a9 731vVo4DmcAbmIVL1Kl+9Ua5vPLA+Zndt1OoLuWk4g45nYQHK9yuAySF/5tluy8/QxOqE0NY 270DSAun58s3/vlRdyXFn/Q4TMkWB++UmJL4WlLHAfMIXV9fH+tbc0lNtQi7u7uJ1nl4L0tK 4mtJHQcywY/L5UX/4bj7bgYmTTWpdpAJ0nKHnELCS+HE1ct2pwCgiczMzHR1dUWOSgnVAXh4 iyki6xZDXvHOhymJryV1nMIyOzs7PDysmqDyI9oMDg6SQFcEzNPriNa5eC9ISuJrSR0HMkG4 CF345+HSdZAJiNMpEcOX8NiFYlUA+UZ88VWhOvFzFr8DlH3jPwZ/hWKySn9//9IKzz//fKDN Sy+95LZZu3at2+bgwYPpOU7Dr0FOMI/QdXZ2MvQtIG71OpNPCNE670S0d+9e90R09913B9q8 9dZbbhtxHnPb3HHHHSk/DmSFbXNlp+M+8WW8yXFOff3GSo6dLs8OsgJxOh2Dg4Phq5HtTgFA cxE3MH19fYTqIE9cLpcfPn2x56u7HafH/TB3OM7XhxbtGB5+6+yeOg7ojgtZTFFMbrnlFvdT dOjQIVWb5cuXu2127dqVnuOoflsoDOMvVCKDsvGaaKJ1glOnTrmvxg033KBq89prr7ltFi5c mJXjQMo5US4v+PReb2g3sXFRef60v6Dw8b9YUC6/abubUDMMWXTMzMyEL0VTU1O2+wUATUe1 7kMMWDkJQLZwt/dynB5vACd+uOfsfM9X97g/3DG0qNZonbf0lfFfASFOl1EMYy7s7AkBDHcB Lni0Lm3xNeJ0BeFCubz44TPu+9jjOIuWL9/xmbav/Oj7bpId86nZpehDlljC9+qUqQIoCOGM Wpf29nZCdZAhhs/OOx2fdQdwF4+t9H5+WfzqpYttt21yKqWGp3f3uMslTPCWvrKeIq/0VDhw 4ED4VydOnChVECMit9nx48cDbU6fPu222bBhg+pQrT9OrS9CbhgfH4+N0IlLG7u4ggbxDVKt NghE64r5KXrjjTfcE9Hf/u3fuiei/v7+QJt//Md/dNuI85jb5s4770zVcRp+GcAC3pYRKx3n By+u8zb7Eqx1nMuvDtvuINQDcboYxJkrfAUq5uUHoICE95Px7meKfMsHGcI/0SoY+2xbef6k v8FXzs233faHzpWVTUwYfrXsOKv8dYshT7gfmB07dmjaeCk24eJxHuLOMPZQrTxO0TDJhHIj dFR1ABPETZDJRhNFzsp8+eWX3RfhAx/4gKrNhQsX3DYLFixI1XFUDSDNeKtcOxxn+pGO8+X5 23e/7NY5YTI1u/BtjCdcVF6MZmx3CiDb+L9QtvsSgypU57CxDGQBfy1hpxKMe2XPhyv5cD9D /Ff/sYvueC78Ww3uuNAdFBo+BDKE+4EhTpddZmZmYrOfiNBBfZhE6wr76SJOBy3GK0XiVANz Ykz2hRcvOh2rKE6SXfg2xhO+SxcXHtudAsg2/i+U7b7EMzU1paq6TagOUk4gn86Jqih3tlz+ pS0/i+Wt73DeOjtc05HHVolv8emm9B7scaDC0NDQx6uE2zz33HNus3vuucdtc+zYsUCbkydP Bg5l9zhFwN3OlRgKNBuTaF1etwzWnBjfeOMN90S0Z88et83v/u7vBtpcvnzZbfOd73wnVcep 4SWA1OCVIhHfuPs7nPm5be7Pv3JufmHXfTUtlYD0kIE75DQQLuqRy0sOQMvIVpyurA3VUbMS Uo5Xn86prokIp795xU0ET21qK5fPmxzZfZR/UAg5w38Trml25513um32798fe6iUHCeviAFq 7HauROggQcQAKXZttWiQs8K+JifGH/7wh26b973vfao2b775ZqqOAxll21w5cuOI0XPzC+/+ kreHGGSIbNwhWyc8LdnZ2Wm7UwAZxv9tst0XU8QQM7wK3oVQHaQZb78I/36vAfyLJsxT6tyl r+wmlmOI02UIk3BJkauGQVMxqYQ4ODiYmwCxyYmROB20jBPl8oJP73Xf38Bs63culq+++9Gv fabtrXPKbdMhhWTmDtku4qISvthQRR6gbvxfJdt9qQFxKlCF6sTwNDejTygg/kUT5iXnvOie eQoeZIupqal9VZZVmZ6eDjQ7evSo22b9+vVum2effVZ1qJQcJ0+Iq49qg3L/RSpnCU2QQkZH R/U7C7e3t+djTVL4xPj5z38+0OZ//+//7bb55je/6bb59Kc/HWgzPz+fquNARvEXOQnvHbHr Yrnttr9c1eGcn+ynoHBWyNIdsl3C9Rf6+vpsdwogq2Q0Tleu3A6pZoy7uroI1UF28RZNCL7c 48xf3BP7EC+6x4ZiALYYHx/XR0bEb5lahlYyPDysX3wtxlHkdQIki1fAZKXj/HR6XeRvxa+P /k0Pa2AzQcbukC0iLifhywzXGID6yG6czkVVOLmrq4uEBcgo/qWv5ktZ3ehe5KAQAJpK7I6u 7e3tIyMjtrsJRcQkx3N4eNh2NwHyg1uKRHyzFjvOj/bc6P/V5XK5/9hFx+lxv3qrOpzdO1bO naaycKrJ5B2yLcJJNBSlAggwNTVVqjA+Pj7sQ9xLdPvwf4+6Q4jG/seKQ7nHTFUITBWqE/dF qeongCH+RRO1Ln01bw8AiTAyMqJPWcpTLTDIKLE1E5ndBEgK/2yrV43EC95FQn3hNEOcrgbG x8fDN+SMgaCAuME4fwBOv+gmccSf84fzbIXwRkdHI7snzgwsMoIs4t/11XApqxfdY+krQGuY mZnRxz4oRQepInYPYmLKAI3jLzR8f4czP0e6XLYhTlcb4WAECwog34iRkxuSE6MoMfTXj7Ss 48bvRFfFF1N0uwXDPlWoTpCPSslQKPzzruZLWYfPzjsdn2VQCNAC9Gl0uSnSDzkjdhmsGL8R XAZoEK/QcI/jXDy20nZ3oCGI09VG+J5cXFdsdwogScQ4SXzOMxGVM0E8BTdyJ55Uk4aApVJJ 9UJxv1Q34s0ahoap9RPoXzQRrm+iwh0XsnoCoKnEptENDAyQlARpRlzZu7q6NJ/hYSrWATSA N9tKNZIcQJyuNsQAKHxDzq04ZBrxqXZryelvAHKDeJrDlZp3Cd7PiKGnKlRHFcs60LyeUBPi 017TK+9fNOH46puYNI5tDwB1I65ZmrMiO7pChhjW7gZLxTqAuvEXGqYaSdYhTlcz4bTtWm+E AKwzMzMzOjo6MDDQ4rpyaUM8ffEiiJei8b2bNbPEhOpqwgvSubmQ0AjiglXr6+8tmnDM6pvs OTs/PPzVtdu3t922iaWvAIkzOzur39SV2l6QOWKTQykrBFAfXqHhtY5z+dVh292B+iFOVzPi 0hK+nDDzA+nHzZtrUmxO3EVsqrBz586xCuJLcabK66+/XmtvxUO8h4tDuccUB3f/iv6mpT7c mF0jeXbigapQnRiPch9lgj9Ix3nVCifK5QWf3ut+bnuobwJglVKppLlkk0YHmUZfbJGBE0Ad 7LlcdhY/5LCXa/YhTlcP4mY+cC0hXwZSy9TUlBgJ6ectzVmyZMmmTZu2bt3qReIuXbpk66mJ P+1F8USXRMdE9xJ5muLlEi9aHXEiMaZUvdRdXV2MOPX4A53j4+O2u1NQ/IsmqG8CYJHh4WHN dYo0OsgB+sS69vZ2ItEANeEvNEw1kkxDnK4exDUjfC1pfN0cQIKMj4+LQXyDqXNuVG5sbOzI kSNnzpyx/ZxMEV0VHRbdbjxyJ15A8TLWGjMKh/JdqLqiwR+ko+inXbxFEw71TQBsoJnycUij g9yhT6xjcwkAc/y1gx9a7JQv77HdI6gT4nR1El7dxlUE0oC7srXuGvxLlizZsmXL2NiY3US5 ZHHT7sSTEk+t7rCdeEndVbGGf1QVqmM5ZyQE6VKFt1+YYKXj/HR6ne0eARQIzR7iDpu6Qk7R bwXLGlgAc4ZfLTvOKoZwWYc4XZ2IO8nw7bftTkFxEcP6usNza9as2blz55EjR+qoIpdFxNMU T1Y8ZfHE63i53ICdSS5D+CzhHYFUiABewUEmPNKAf9EE9U0AWolmrau4dlAQAPJNeLM+xk4A teLNtlK9JNMQp6uf8IpC0kCgxczMzNS3uNWNzU1NTeUmaa4+xNMXL0J9MTt3Sax+wbsqVMfp wo+Xe0ihz5TgXzThUN8EoCXo93Xt7u6mvgoUAX0+KZN5ALFsmys7HfcxhMs6xOnqJzznKe7b bXcKCoEYzY+OjmoWCESyZMmSrVu3FidvrlbcPDvxEtW6Nla8EeLtUK3I0Iw4CdWVCdKlFf8g 7/4OZ35um+0eAeSZqakpzZQbsQkoFPr6jH19fayBBQiz8thlx1kc+L4whMsuxOnqR1wkwhcP UrKhqYihfK3rW93UubNnz9rue5YQL1etSXbuetjI2nPih6q3rODBKW+2Qwy7bfcFrsBfoq7H cS4eW2m7RwC5ZXR0VHWNYK0fFJaRkRHViItduQBU7Dk7Pzz8VTHA/qMvfalj8BGql2QX4nQN ES4Vz90mNAkxjtfMLoZZs2bNxMQEqXMNIl5A8TLWFLATb1M4UU5TILmwoTpvUbB4ZZgbTxsX yuXFD59x3yDqmwA0D01BLmrnQ8HRrEigXCMA5BvidA0xMzMTvnJQQAQSRIzRh4eHzRPoCM81 iVoDduItE2+c/xbLv6UpN2ME6dLPuul5x+l136btvU55vmS7RwC5Ql+QjrWuAOW4NbAjIyO2 OwgA0BSI0zVK+OLxB3/wB7Y7BXlgZmYmnLCpQoz1d+7cSXiuBYgXWbzUmpurAOJN9GL3muFm ocJV4+PjBXzWmWPP5bKz+CH3nVrpOD+dXme7RwD5QVwXVDM3JAoBBNCknRZ2UQIA5BvidI2y efPmwAVD3Ifb7hRkm1KpZB4G2rp165kzZ2x3uYiIl128+IZvk3hDvRpDqvBrQeqteNX6xD+L 8Hyzy4lyecGn97ofTpa+AiSIpmhpQS4EALWiKePInB8A5A/idA2hmt4RP7fdNcgkpVLJsAjd mjVrjhw5cunSJdtdLjriLRBvhOF6WPHmutE6Vagu96ErgnQZwr+VhGB9h/PW2WHbnQLIPJpw AxtZAmjQbItMgBsAcgZxujrRl0sIV5EH0GMeoSOBLp2Yp9e50TqvQFuAHO/uR5AuK1wul7ed vtgx+Ejgw3lfjzP9nXXl8o9tdxAgq6jO/A4F6QAM0Nx/MbQAgDxBnK4eNPM5XCSgVgwjdEuW LBkbGyOBLuWIN0i8TeLNin1DxZseXjXvkb9Yv38bDUovpZmVxy47zmL9p/f+Dmd+bpvtngJk DE0mdf7O+QDNQ/NVYoABAPmAOF3NiAuApj4Cm72COVNTUyYRur6+viNHjtjuLNSGeMtMigyK k8Y111wT+as83bb5g3R5el4AAIYUttwBQDPQpKYyzACAHECcrjaGh4dVVwUxAqOqCBhiuJfr pk2bWOKaacTbJ97E2Df6He94h+qsYvsZJABBOgAoMv5zYADmdwHqRpM5MTIyYrt3AAANQZzO FDHM0mTHcD0AQ8QHSRPt9SBClycMo3WR5CBU5505qb4EAEVDE6Tr7u5mfhegETRbJ+dg+AQA RYY4nREzMzOqYRalEMAczS5vHkTo8krd0bpM38t5eaOMmAGgaGiCdJwSARJBc4/GtwwAsgtx unhKpZKmIB1VRcAE8SlSDSM8iNAVgfqideLDk8VQHUE6ACgsYnyouu4PDg7a7h1AfiAgDgD5 gzhdDCMjI6o7576+vizeOUOLER+S2FJ0ROiKRh3RuszNCnjru8Wp0nZfAABaimY5HmU6ARJH M9imgDgAZBHidDo04RWTuVBxVSiVSpRkKjIjIyP6ha59fX1E6AqLeOtN9oT1yNC2gN5GbBnN BAQAqBuCdABWUN24MRQBgMxBnC4aTQa1GHuphlleYE7ce3d2dnoPYTOvAiKG6d3d3ZqYy5Il S44cOWK7m2Af8TEQHwajQF3l/CNOMra7HANBOgAoLKognWb0CABJQagOAPIBcboINBOhnZ2d /nwWVWAuwGOPPWbv2YAFYnd0HRsbu3Tpku1uQloQHwbxkdB/Zvyk+WZvfHycMTEAFBNNkC4r 2dAAWWdwcDBy7MSwBAAyBHG6IF4mSOT5/eTJk+IuNDYwF+CGG26w/bSgRWjqRrts2rTp9ddf t91NSCPig2FetC6doTrvHpWbUgAoGgTpAFKC6m6OUB0AZAXidFegmoERXHvttfpCY3rY26sI 6NPo+vr6GKlDLOJDYli0Lm27mBGkA4DCQpAOIFVoQnW2uwYAEA9xup8xOzurrybWIGKgxgRO jolNo2OhK5hjvgw2PaE6gnQAUFgI0gGkEFWoLj1jJwAAFcTpJGIUde2115rcFdfHBz/4QQZq OUafRrdp06azZ8/a7iNkD/GxMVkG293dbX0OwL/xzvj4uN3OAAC0EnECjCyEQpAOwDqE6gAg oxCnk8RW/W+Evr4+63fR0CRmZmY0aZhLliyZmJiw3UfINuIjFLsbrN16K/4gXTqr5gEANAn/ CdAPQTqAlECoDgCyCHG6n9HT02MaePPR3d09ODh46623qhoMDw/bfmbQLMbHxzUlC9kvApLC ZH8JcaNo5Z6QIB0AFBaCdACZgFAdAGQO4nQ/Qwy29LfB7sCru7t7eHh4fHzcHYGphmhuY+5a c4xmyxHS6KAZxCbWWbkz9La8YE4CAIoGQTqArKAK1Y2MjNjuGgBABMTp3ubmm2/WBOZmZmYC 7VVlgwWdnZ2M0vKK+CRotoxYs2YNaXTQJMRHS3zANKG6d7/73aVSqWX9GRgYcP8uM9IAUDS8 EyBBOoBMoJpiJ68CAFIIcbq3Eafva6655uabb1YF5vyIc7oqSGe3VhQ0Ff1a17GxMdsdhPwT uxVsa0acBOkAoLBEBukcNtIBSDd8cwEgKxCnqwfNmkduWXOMZr+Rvr6+M2fO2O4gFAXxYfMW nFoJ1Y2MjHif/Kb+IQCAtOGdAFt84gWAxiETFgAyAXG62pidndXs78kQLa+I910TFtmyZcul S5ds9xGKhfjIiQ+eJlTX39/fpD/tFXkhdxgAioaqyhUjQICsEDmkb29v1y+lAgBoJcTpamBq akqza4R+HkbczZZKpeHh4Z6enuuuu4786gyhed8FbBkBFhEfP02obtmyZYn/RYJ0AFBYxHgg 8mQ7ODhou2sAYIpqG0AGNgCQHojTmaIpTCZO6+EZGC8w19fX19nZGXgIeyNmBfEmqt538c6e PXvWdgeh6IgPoSbZM9lBp/g6MJYFgGKi2j2MgicAmUMVquvu7rbdNQAACXE6I1S1SNzxmXu/ OjMzMz4+Pjw8LE7xmq0GXK699lrbzwniUS1vEWzatIm1rpASxEdRfCBVn9Xrrrsukb/i3aNS xgUAigZ39QA5g8g7AKQZ4nQxiJGZam8gwac+9SnDwFwYSpmkHM37zr6ukEI0+8A2PugkSAcA RSayNjGZxQCZRhWq4x4NAKxDnE6Havo0KbjdTSea4OySJUt41yC1iA+n+IhGfnS9zN86mJmZ IUgHAIVlcHAwfFIV50OCdABZR7V0plQq2e4aABQa4nRKxO3oNddcU28ILp4PfvCD3PGmEE1w loJ0kH405erqS/3wfyPYAAcAikbkbTyTFgC5IbK6Edu/AoBdiNNFo9rSKynEjTTTsClEXJJV Qbo1a9ZQkA4ygfigio9r5Mc4ctMbDf4gHctAAKBoqJbFMWkBkCcil9GwsB0ALEKcTklkLRJD xKju2muvVf2WzV7TiWo4Lti6davt3gHUhvjQqs5OhmkgBOkAoMiIc2BnZyejOIAiEDlPz54S AGAL4nRKxPjsne98Z3xMrnrr293dLYZu4+PjJ0+eVOVkiWbc7qYTTZCOXSMgo6h2ljAM1XnT y9yUAkABiZyv5b4dIJeo4vIjIyO2uwYARYQ4nY61a9eqAnPiVO4F5vzryDThHvEQqpmkE827 duTIEdu9A6gf8QGuL1TnBem4KQWAAiIGeOEzJ+vgAHKM6naA2zcAaD3E6XSMj4/7o2x9fX1i 3FYqlVSjNNWeQYzt0ox41yKvymztCvlAtQmsJr2XIB0AFBn/8M9/zqSuPEC+Ue0bw00cALQY 4nQ6xIBMH5jzMzg4qArSca+bWlSh1SVLlrC1K+QG8WGODNU5UYXnvI3P+vr6LPQVAMAqYuwX OXsnRoO2uwYATSfyhq67u9t2vwCgWBCnS4DZ2VnNphMUpEstBOmgOBiG6rwvBSnAAFBMIksM U6YToDhwEgAA6xCna5SpqanIsqOOcbF2cTO8f//+8fHxpvcVfKiCdGvWrCFIB7lEfLDFx1sT qiNIBwAFJzKVhuRigEIhhkAk1QKAXYjTNcT4+Lhq/wFxo6uqYyJ+Lk70w8PDYuR33XXXue1v vfXW1va90GiCdJcuXbLdO4BmIT7eqlDd5s2bCdIBQJERY7PwubGzs5NTIkDR4GwAAHYhTlc/ kXuBuQwMDPjP4zMzM+Pj46J9d3e3Kq4nsPdUikXkpZcgHRQETajOMc4CBgDIGaoMGk6JAMUk 8kaP7FoAaA3EhupBDOa8/RDDjIyMiFGdSWAuwMaNG20/s/yj2nOdIB0UB1Wo7p/8k3/yzW9+ 03bvAAAsIG6/I0d0tvsFANaIrD9O5XEAaAHE6WpmZmYmsryoYMGCBR/60IcMo3KRUKWuqRCk A3BRherIpwOAAuLtc+2HHR4BCk5kmq34iaq0EQBAUhCnq41SqWSeH1cHTNE0D4J0AH7Ex371 6tXhbwShOgAoFOKWO/JWnEJUADA+Ph4eKXV1ddnuFwDknFzF6S6Xy7cfvOA4HeIEutJxfjq9 LtzmjXJ5ybfOiTai0Y6hRW+d3WN+fNXmA4nw8z//848++mhyLwZcgRhtR27LS5AOCosqTudQ KRkAikTkIgk2dgQAl8htoIeHh233CwDyTK7idIJtc2Wn4z6nEqubfqSjEru7gifny223b/dO sqs6nPOT/eFmkaiWuzaOODIJLM1jdnY28r3r6+sjSAfFxL/u9Td+4zciT0qE6gAg90SWihe3 5bb7BQBpQXUfwb0bADSPvMXpflAuL/ziU+7Zc2yVeHanAw2GXy07zir/SfYux5k9ttLk4OI0 vWDBglpjcO3t7d3d3R//+Mf//+zdf2wl13nY/dO8i8qOIjFtvHAvXIMoNohRdRuiFVgjjhuy WHhVJDboxlnYsuGlIxBVKydh/cpcVy8SsH4DJk3W8YXNbCxI774U29qLrH1fBjXchnq1YH/Q rxVbLmtb9pqoAAa+q/3DBsFdr0FFWFyc94izGo/mOTN37szcOWdmvh8Ywpoc3nvu3Plx5jnP eU7SBnNzczwPj5X15jo9Pd3v9103DXBjdXX1znWy1zMngjkd5DnCtA4AzWYes62XPnplAKKS rhWu2wWgsZoWp7up9YnHd4Or52MdNbhxIfpbOTH2uvnJpe8+dOztN75tmSQrPfnkk0MDc5OT k3Nzc8vLyxsbG0Gd0ZTFYcmaHjfrzidIhzYLg3TmH8FPkkJ15vRx2lIAGCNyZABkZM295TkO wJg0LU5nPHJ1oNRpc+m8X6kfbM5GfxXNtrt4WunBlj6qWPfLmy/+9vHjL1+7YH/FiIODg5TA 3NbWVmwMNilTWh3l2bFqxLhZ76kGQTq0lgzSBcxJYT1Z6IMCaCSeugGMhMg+gMo0ME4XVqCT JeqSfvUjrc/8+fW9S6eyFKqbnJx885vfPD8/bw3MRSUtMBqE9risj1vSuh/b29uumwa4sbm5 GZwFS0tL8rfm1LCeMowoAGgY6xqvzGIDkILZrwAq08A43fe1Pv77XwsunWHSXCAl1a505sk2 KUhH6ZMKWG+lBOnQZmEYLmWZY2uozlzKGFcA0CQzMzPyWseFDkA6ax5ut9t13S4ATdPAOJ0s Qid/LkvXlcu6gHcgY72ng4MD+ou5mb1nDZLGJvoB7ZElSBcIJ8bGQnWMLgBoBmu6PTNeAWQh Z7+aPlJQkRwAytLAOJ1x4YZWnXPmunlSqW9+5r6jHLu0PLsSmUdZ6yBtIGX6mPnDra0t002c m5vrdDrBRX8cLWy8pJqAKysrrpsGuLG7e2d1HXN5SQ/SBczJIs8gZnYAaADrSB7XNwAZWafs mP6V63YBaJRmxum+rvVdH/hCcN3sKLX50eN68HxYnM742h/cpfVflf6+5sKdsmpELD9ub29v Y2NjeXl5ZmYmaYYsgzM5WBd4HZpDBDRVuJZr9mWOzcliThl5HrH8K4C6s3YSmMEAIDvr7Ffz WOe6XQCao5lxuptan3j8Tv7IrFLHf/VXn/rgsU9971tBkt2YitOZq3NKQbq9I0MDczEf+9jH Sm9ns1kns0xPT+/v77tuGuBAjiBdwJwywR/GsKYEgPra2tqSl7XFxUXX7QJQM5OTk7ErifkJ FUIAlKWZcTodWTLijFIvfPuR+37vK0FlOuNhpW6/uFzu23W73aRY29TU1C/+4i9mDMxJjPFm l7R2xEjhCaAxwljbqEG6gPkTLkoAmkROeuDRGkAO1qA/ZS4BlKWxcbpwlmtHqatPdK7rwalL 3z3Kriu5OJ3p3lnnUJTF9CDLamqzmS9CDm0pFnhFW0XnruY+C6zLv/JYC6COrEOqTFUDkI/1 AZCaRQBK0dg43Qta3/voleCKGQTmbmv9W9++pTpnSyxOZx5WT5w4kT8IN8zx48dJXcnIunwH C7yinUoJ0gWsy79SLxlAvViXjzA9B9ftAlBXXFUAjE9j43S3tT719M1grutjHTW4cSH4+aeu De6dOhcuAluQeVgtIRqXYGpqiqSVjKyD5AsLC67bBbixubkZnAWl5JNa15QwJ13xVwaAapD5 AqB01geQra0t1+0CUHuNjdMZF25o68IRa9cG977vE8EisAXf4uDg4Cd/8idzxOAmJiZmZmbe +ta3Jm3AuorZWcvSsXYEWm51dXV9fb2Ul0paU4JsXwC1YO0nUEkKQHHWqpeuGwWg9pocp/u6 1nd94AvBFfPK0jGtr4e/+tItfff7nvzjDx57+drnCr7Ll770peyBOdMp3NjY2NvbOzg4sM7T DLCiYnZJZel2d3ddNw1oDutTLoXqANSC7HFx+QJQCuuCEsw5AFBQk+N0N7U+8fhucLmUa0d8 7pY+9vY/PNtR17/84NE02fxe//rXDw3MRbc3T7xy7CX8K1JURmKdydLr9Vy3C2gac1rJc43M XwCeM90wee1iQBRAWeTDiHmgYyQAQBFNjtMZj1wdKHXaXC7PKPXS1UesvzW//sqfzBaZAxuM 0/7UT/1UUmAuyvxW1hwNTE1NUSplJNbON2XpgDGxFqpjtUQAPpNJ9xR6B1Ai8/gmH+6YWQ+g iIbH6Z4Z6GOnLppr5Qmlvnf5vuivbmv94LO3lJoNLqZnO+rSU2duPH8hx7vsHcmypbXaaGB+ fp6Bl5FYb4qUpQPGx1qojkFjAN5aW1uTPS6qvGMoc+QsH3HdENSDOVTkpYb0CwC5NTxO94LW 9z56JbhWhiXqwuCdVWzRibKY51jrDM0A/YAcrAX+mDUMjJW1UB3JKQA8ZLpecjyP2frIIuxk um4I6oGrDYByNfz2c1vrU0/fVKpjrpWPddTgRp50ueLMtTulIN3QWWN7e3tmm8XFRdNpYGQm YM1MXF1ddd0uoPnMiSbPPkomA/ANGS7IjTgdRmXN3uWCAyCf5t9+LtzQqnNOHU1wvfXsmeob sLOzk1SQbnJy0pr/FQTmTP/S9BJif0spKJ0w43Vubu7w8NB104DmMyeaOd3kkAOdUQD+sKa3 MH0BGRGnQw5UwwRQlubffsJZrh2lrj7RKbi066isQyvhhTss6rSzsxMUwpCBuZj3vve9Vbbf T9YZr7u7u67bBbSFOd2s1zTX7QKAO2QyHcU0kR1xOuRgXeCOgpgAcmj+7eem1icev/NIefG0 0oOtyt46pSDd+9///rW1tWAqa0pUTjp27FjLS7BZQ5/r6+uu2wW0iznp5JlIwi8AH5BMh4KI 0yEf+WTHKCaAHFpx+3nk6kCp0+ZC+bBSt19cruAdTQdx1ABcdlNTU60dELb2vJnxClQvafZr a69OAPxBMh0KIk6HfLa2tuSzGyl1AEbVitvP5dtanTivxraWa8zOzo4sT1CikydPtrYOlDVF kRmvgBPW2a+sbgbALeuQHmvdYCTE6ZAbKXUAimvF7ecFre999EpwobyydEzr6+N7r62trbvv vrtoKC5ZtKpd21hHqJjxCjhknf3KuDEAh2Qy3eTkpOtGoWaI0yE3UuoAFNeK289trU89ffNo JQl1/oTSty+P7712dnbuueeeIpG4v/7X/3rSrxYXF8fXcv/JLEVmvAJuWWe/8kgMwBVrMt3a 2prrdqFmiNOhCFLqABTUltvP8otaqbPmKnlGqZeuPjLW97Ku9ZPCPNOaB93l5eUvfvGLb33r W5M2a3kvUw6PGy1fUgPwgTkN5blJvXYATpBMh1IQp0MR1pQ6HlsAZNeW288zA33s1EV1lFN3 9YnOUY7dGJ04cSJLYM5cxMNJrClV7SYmJlp+Zd/b25PD4ysrK67bBeAV5mSUV63WltEE4ArJ dCgLcToUJFPqKOALILu23H4u3NCqcy64So67RJ0Ww7nWwFzUxsaG7FkG2ry6a0guHzE9Pb2/ v++6XQBeYU5Gc0rSHwXglnWZV9eNQi0Rp0NB1pQ6hjABZNTw28+ZZ28rFU9te6yjBjcujPV9 zaX5wQcf/L3f+70sRUOtMzrDB12CdNb7XK/Xc90uAD9mTkl5nlI1GUCV5LwE5uAjH+J0KI6U OgC5teL2c7k/WF7+tOmr/fYnPtFZfOJ+pX6wOeu6Ua84ODiQJdhD3W7XdQO9IG9yZqe5bhSA OHk1o2oyAltlYwQL0traWuwSNDExwaGCfIjToThryXIuSgCy4PbjzN7e3tTUlDVCZ3qWGfNQ zLW+2RnUsttt7O7uum4X4Jd+v7/r2p/92Z/Js/VjH/tYNLzS7XaX62CmJqx3kDYgVRMSyXQo 0QxxOpSB6xKAfLj9uGGeMVIK0qWsGrG3t7exsRE8RgavsLi4WF27Kydvb0tLS64bhXL0+/0l 78myawCce+CBB8zdIeX2F1R9nZ2dffe7323+a/6d8mjU7XZjG6esPGBe59577/1H/+gf/dIv /dLP/dzPqdTHePNSJ0+efOcR84/0hUfNS0U3Np2BpC0PDg7UUb7q8lHd25TXbA9riQzyVpAb cTqUwprn67pRAGqA248D1hyxwNzcXKxbKQNzMTPNnVm2bKvcx/IR9RJkWm1ubvaOrK6uhiGw hYWFrA/lACCkRLJ+/dd/Pbbx/fffn7Tx7OxsbON3vetdSRu/6U1vim186dIl65Y7OzuyzUkp 8COFmeRcqo997GMuEk898pa3vEUeHq4blUm32y19YjiKC6e8NHvaCiogcw5YhBrAUMTpqiaX Lg0FqQGmZ28u3+bf2ec0me2bN2hsPpGMS66urrpuFxIdHh4GIbkgGEcmGoBxS7r3WctKJF27 5L0mJfFNvuxyQqZet9uVGyc9ni3bxqU2NjasG5seQmzL9773vVl3GYBRJJ3gQEby8p6eWw0A mjhdlczjRFJBute//vXveMc7ihQbat7IjLyrTU9PHx4eum4XXmN/f397e3t1dTVlRRQAGBPr vS+YFipt2aaIWrPeVEISzZYt6y0pq916VUxa7M/aAUia2Cv7Eh/72MeG7CkAuRCnQ0HWzAPr /QgAQsTpKmKeBGTac4kaNjJjvaX1ej3X7cIrDg8PzfFMbA4Yq+pXpcinyhmCMmvMGvayLrGn Ep63rVlvKiECaM16UwmZetZSFUk3a+vLWif2yihkMMHT+goACgqucuaCEE6JtfeNgGRyNlXS mA0ABIjTVcHc3V/3uteNuyfRpJQ6kuk8ZPb/9vb2ysrKuI/kyiwsLLhboGIEvZoIF3795je/ +ff//t+P7W3TJXVSY0hyfSYhPxmisoa95LTQwIwt8S1pvMH6EDWTkPYuj6ukND1ly9TbsqXp BeTEXhmFNJ+XOB1QMXPxMRcEc6FYPlrOhTJ2SGEOD3kIccwASEGcbuysqWH5mNcJhvVMN10W ybY+gdSRdY9tbm66bld71TE8Zxpsmu16z7VXr9eLfSPmpG5eGU1UT075lI86SSUmlC3xLekG bY0AJr2szNRLStNTtkG1lCibLFEno5DBYlNJrwCgGtFeOvc7xMhhHmuKNwAEiNNVIWWB13TB YF1wy489ilhHZpqRKiKfN+bm5lw3qo36/f7q6mru5SCCDmsw1BxOGAl7rtEto29qNgi2NH9i /tD8+UzCSsdZmMabj2A+iIPd13oyTYkuKYqTUapY2CupOF0gdpdMyXpTIgKYkvUmx8lSygLI TL2kND1lK1Eno5BDIwLVZqxW6sknn4ztjbvvvvuLX/yi63b5LojtNpU5ax544IG3vOUtd911 V8oJPm7mVDUt2WpEzxzFyYfBhtUsAlAu4nQVeetb35rlpm4u2aZzv3yUQj+0593IYgfWZDoS oypmdvjS0lKOLqk5ArvdbpZeafQPM7bKvKx5cfMWKckySczH4SiqmNnhsW+BlDoUJ2d9xm58 ScXpAsuvDRanZL0pEQFcTs1Zi7UzZXRBPpulvGysRJ21OF3+vVl/jewIIZ+dnZ3FxcUcPYRx M1cD07c31xPugC0nK5UnLeoNAMTpKrKVMA5v+hPZA3NZXrPuxQ7kgxDJdFXa3t4eaXWIYLg4 xxSP6IvkaKd5O/Omo/bIzUcjWlclUupQuqEl6pKK0wViiW/pl7tYxCcl6029NlMvPU1PvfZO ndQ9CEWvrtbidGXt29qxDuyZne+6XaiUOZvMnWWsa7WVyFxzCM20Fs84ALIjTleRcJrqyZMn s+ccDSUfG2rdZa9jMt3uLf3Z3pWTZ8+HDT5/tvOl3udfvv6c66aNZqQIXTAyXCQoHH21gi03 zTCNGanxnh9UjUFKHcYhvUTd0Nh99KXS59THIoDpLxuNQaen6anXZuqlp+mp1yZcWIvTlblz a0XOI2t5dmHbmAMgPXo+VLCc1OrqarAakrln7SYLF00KlnjKXRVEHd0Kzblc95F1jMpas4hO EQAr4nTVGUeJCjm0XuvH4HoNNO3e0ucuXlFq9mxHbX3+/ODWrvnhwHzRu7fOnv98R3X+3bnZ WkTrskfoSpy4EX3Z4q8WMA3LHrAjWlcNUupQupQSdTLbTkbiwnuxzHr76Z/+6dhPwgfpoVlv 0Uy9oVehaKbe0EBDdPgtR3G6BpO7rkkL3yNJkECXo3DtwsJCEJLb3d3d399Pf5ew+kf6Zual TF8iCN7liNyZY5gCdq0i7w7dbtd1owD4iDhd7clU//o+BsvP4ucyrwOte7u3OrPnOkr1zs0G EbqY7euDk2c/eVqpq71zWt+qvpFZmP6l6bYO7Uea78V0I8od+I2+fokvGzBNNQ3OMgvGdJjM Tii9AQjJhV8pnIyCUkrUyV/90i/9Uuwn4S1SZr2lxH3kMNLrXve62E/CFg6NIETPgtiv7rnn nthPwhwxitNFkZnSQuZLlxUJ0y0tLQWBucPDw5HeK2OcLqbf729vb6+uro5UQsRcEAgxtwRZ wAAyIk5Xe41ZP0h+kOnp6VH7VdX4gdb3n7sSNPLKuY7WN62bXRvo049dSYnlObS/v7+ysjK0 72g6mmMa6Y2+yzheP2Aan6WvbHbF0KF15GNOYZliwAMJikgpUSdT7T7+8Y/HfhImvlnzGmI/ CSOAMoQ3Ozsb+0lwtZRpeuZvk+bqyjQ90yo5xhCEnyhOFyUjp6wg0WAjRejMSbS6umrOxCJ9 yPX19WB+a+5X6Pf7m5ubWUZDA0TrWoKqmgCyIE5Xe9aabnW808snk16v57pRdgOtH7tyU6mO aeRjHTW4eSVpy78c6JMPvZJP1HuoowfXqmxkCrNjh87OMB3isVZOib7X+N4lkKV/b3aIt8db 3cmUOkaPUVBS2Mv6c3mLDF7E+vPYT8IIYOzn5m9lUC/I1LP+PGmurgw2Batax34YFKFL+nk7 sXJiS2SP0Jn7+Pr6er/fd93kuP39/ewBO6J1jSeP5zaPuABIQpyuCWRHv3aPwdbSP34m0wWu 3NSqc8408n6lfnDlXMqWvWsD1Xmoo9TTj83qwfXKWmhl+q9De4rjjtAFou847vcKZOnrm53j YRe/7syJLHc1FXlQhDXslZRnJ/PmzOFnzXrTtrw5c+mwZr0lvYL17ZLm6sq3My8rs8uDp7ik PLsWkju/pjMJkMIc3kNXWQmsrKzUIiNpf3+/1+tlSfOnbl2DcfkCkAVxuiaQDyeqbo/Bstey urrqulFpwqmvHaWuXjx9lGNn95LWZy7umi1PKvWXvYdSthw3mdYUU02ELhB932reMZAlWkdi XenM6RzbyT4vEQP/WcNeSbEwa4JbUjacjAusra1Zs950Qkae9YdJMcTYD83falvltampKesP nex8H8hALQkpDWPOu6F1HoNE+DqWrdjZ2Qnr36UwV7DWxuKbjXRgAEMRp2sIGXqIrj3nOWs1 aM87XmH0zbh4WulBWvm5MPnuwx318jUHMSCzM9PT6MzRUvFYdPTdq3zfgDnk0tdYNLvL8yOw XszOlDu5sqAwmsca9kqaW2pNfLNmvWlbcre5vVqz3rRthOnJJ5+U7xW0Wc7JvXTpUuwnYfxa PsVduHAh9pM2R6Yo8NRgQ2/Qqikrtvf7fTmIFRNMsXfdUpSM8poAhiJO1xDWUFddHoPlw1WR wr2Vubg7UOq0ae0ZpV7avZiyZRjU6yj1F+dnK17+1Ty9pFSjM0+DTgbxom2o/t0D5oOnrAlr dhoPfiWSuQP1XZkaPpBhr7/7d/9u0k1QhnWSitZpW4k6+bfBljIp74EHHkg6zuWdzrqQRbCx HHv7J//kn8R+0tr8C5k42ebUwoYZOtG1GRG6qP39/aHRupmZmbp06ZGFfGoLbysAECBO1xxy +LEugzM1HRi/OtCd0xdVhqmvepSgXrnW19dTen6mQ+xqSkW0GU4aEBha/sbsQIfNaxKZ00Sv FEXIsFdMtOLP0IJQ0Qz0LLk8wZbyqH7DG94Q+0lYg0JGl974xjfGfhLe+2SJunvuuSf2k9ZO iJNBTBKOGmBvb08G32PnXcMidFFDo3Uk1jWMPNpbO/TSWre1PvX0nWUJrV55Zrz6iOtmwhni dM1hXYrB/368fBqpS+Gqkaa+jhTUK8Xh4WHKXFfTP3AbDI02xmEzAmZXpDwemN3o85ImNSJj Jaxqh9xk2CsmOlIlE99iotmdQzN6ok/LQ0tohVtaK8lGRSPX1hz5qDZnkMl9TqpR3aVXo3Oy IHu/3989UuWbmrdLr1tnbqP+d+yRhbwr1SW7AiW6rvWpS99VatZ6vptnxmd//7jW33fdTLjh /gkZJZLTc/yfWSYzF2pUxT97lly47oTKENQrzvQvU/JHfDgqou1x3ZY7Uh7OzUMC68AWJ1cy qVEZTfhmaNgrGgWWiW8x0ZWXrINeUdFBjvRMvdgRnp4uFHtIS5mVr1pcnE5+O20OWTaAOZHT F3daXV11MlQWxsuqf+vt7e2UciUTExP1WikOVkx9ReBHWn9o+8X/ozP38rUL0Z/f1vqT3771 r3/+44MbF5L+Fs3myxMySiFz08xF3+eRN2vKQI1yl567rdWJ82qUVV/V+Ke+phSkMw9+nnTv oq1y3ZYfMzsn6dmYcnXFsZoEypUe9oodWumJb7FXTtky9iiVnqkXGxRJn6sbSy9ND160doaU 3IdMBqyv9GT2ubm5itPZohzG6fTRlIj0uiUc9g3A1FcEbmv9W9++9Whn7sZ3lmO/unxLX33i t442Qet49ISMUsgog88zy2QG0+rqqutGjSB7lpy5vj7cux5seb9SP7hybkxN2t7eTuny+hO0 jTbMdVtew+yilASZBhfHqcbKykpsl/qQ3YmaSgl7RYvTBVLOa5nXmVKiLlaZIT1TLzYukj5X NxZYlANvUf5czCsmOznE+mvKnA4p0fP19XW3o7Zu43SB3d3dlAvX/Px8a68DzSCHeVqbKF1H 5snu8edvzX76UjhrtaPUZz5y/Knl5Zf7l3O82vJ3bn2ws3jj2xSkwx1+PSGjOBn5ko8r/pAd 7nqlLA20fuzKnQqgv2IenJ47n7RlNE43vhJ1m5ubSf0536Ih0ba5botFyhzYGs3L9pAMavh8 gYLnUsJestBPSuKbvDymXAFkGktKrCG2ZcpcXXkipJSoa+1MT3kBae2uqLuU89FtGl3Ihzid HpZYZ45/QnX1JS/y9Ijq4kda//Lmi0rNmme6zY8e14PnzQ8v9wezn74c/PCpjxzPEa371LXB BzuPEqpDwMcnZBRhbtjymcHPPGpZZaYuK0hEXbmpVedc0P5PzqrBreesm0XjdMZz50+UnsOc tFKYOR48PACiLXTdFruUof56ZX36RmYHeDIXG7WTEvaSieQpiW/yCEwpUScHk5ISXqzlF5Nm +VkriCdNw29tzoWMn7Z2V9RaypzulZUVT4qfeBKnC6TXM6nXCDei5B2Bb7MWlvsD1XlIHaXS 3Xr2TPjzIC3u2NtfuXqcVurqpdlRV4EgVIeQF7cflEt2gPws1i7bub6+7rpRI4tOfe0o9fRj s3pwfehmpefTJQXpvO3ARRvpui2JzK5Lek4mVJebTA1gjTPklhT2ss6FTIq8W1/ZuqW1zndS ZpA1izlprq61QkVSOMPDoZdq8Exbd+mrRnhVWcKrOJ0+KvC6sLCQdF3iRKgpCm7W0U2tTzy+ G35lvYeO6cE3oht86trg2Nv/tfnVSaW++Zn7CNUhH19uPyiRdbKMh7dw+chkeiGuGzWy6NTX WfO/s2f/+Gzn5et/Edvmk8/dCusXlF6fLilIx4SI4swOTAoEEKrLR64mwRpnyM0a9kqaOmRN fEsax7KWqLMmfSdl6lkTRZPm6loDi0kl6tp5YWeOWN2l3E89XFTdtzhdoF4zJzAUc/nr6Pta H//9r4Vf2Uml/vLyz0enSZl/Pfjsnec++duhgmUlPnTXBwjVtZxftx+URQ5X+paxIp9Vzpw5 U8c4nXFxd3CU3fxKAO7p3rmTZy+e7agvff78y9dfmQN7U+tPPr2rOmfDT/pYRw1uXinr3ZMK lxCkKwuhutLJpAAeMJCPNeyVdL+zJr4l1e60lqhLynSwZupZt7TO1U2KN1lH3Vr7FCejlr51 bJAi5U5q7giezHWN8jNOp1MrEfu8cBySyNsHvXfPxfLpjK/9wV1a/1V0m77Wf3vlTizvwx31 cn95pLcIIn3/8tjbR/1DNIl3tx+Uwlpbx6s10WRewy/8wi8E/zB9o9XV1V6vt7Oz49v4qtXV ge6cvhg0/sq5zrdu3Tx59mJSL+qVcZXeQ2VNek1a3ZVVwEqXNFXHq3k6dSEfM3jeRj7WsFfS w6o18S2pPKL1NpqUmf6Od7wjtmVKNO3EiROxjd/znvckbfwzP/MzsY1bW5FNdhuI79dF+nCX h0E67XGcTqeWqyNUVzuye8mX6L+wPp0Kyhk90ZEZc49cvZPGYVxZOqa1pSxSCrP1z//J1eW3 H3v52oXyGo468fH2g1LIOTv+dO6tT1b7+/u9Xs/a7VhYWFhZWTG/3d7e9mEVsJiXtD5z8c64 SpArd32gz37yL4LJsFEnldr+5FlrAbscUoJ0pbw+YgjVlYWpryhR8Pz/xje+8U1velNwOKUM St17771mgzccCTZOeeVgg3Bj87dJWwaZeq973ev+1t/6W+a/KnWJ7WCubnTjlKey4LLzN/7G 33jzm9/8+te/XrU4OEXWSU3VNCfd5zid0e/3CdU1A5nCdRSuFxFd7zXmBdPlePROafIcKXXG Vwf67rO9pz54bPDDZ0poNOrG09sPipPXfdPH9aRTK9u2srIS/Mr0PMK+UYq5uTmz2fr6+ubm 5u7urvPB2HDq6xmlXtq9qI/y5bZ2b5272AvKE5jr+BPnZq8/92dlZdKZHWXdM9zdxyopVOdh +Nhz8jRvbfQBBe3s7ERvbenLB0c3Nv9Ir9yafePYb2NNitnb2xtp42jYMX3jBpO5kH4ukAUp KUhn+m+um5bG8zidPuoHWmtusqxEvcjcBSpvNsNtrU89faeCeVLO3VCPXB101Olnf//4qItR oAH8vf2gOLlUpSerCMlgRywjyfzfpHHCJGb7cMLs7u5uxaXuwqmv41jLVUoaR7XWOEe5rKE6 D2tge05mgxJiBpBElgtMyViEP+qbh+5/nM44PDy0LgJLqK5eWMm6qS7c0KpzLvhOPzmrBrcu j/oKzwz0sVMXX8kCucqaEq3j9e0HBcma2Z4M0cjZKzIhzvwkaVmr7MIJs+aGN9aMp1taz55/ LnjTi6eVHozxvZK6ZfVaOCLactdtGU3SFB4/K2F7i6mvALKTpTx4jvVffYN0uiZxOp0aqvOq LDVSyHEIT/IqUFB06uv9Sv1gc3bUVwjXls1R4Q515/vtB0UcHBzIiJjzuhVyeT7TGUraeHd3 19r/yC2YMBuWuisrsDLQ+rErd3Kbw6mvY7KysiI/V72CdLrOcTqdHKoLp28jC1Z9BZBR7FpB WN9/ssJJoBZBOl2fOJ1uyvBtm8lli5gf0wzRlWHzTX0N43RHWSBb42kmPFWD2w+KkEM0KYvQ VUOOrw6tUdLr9UadBptRiQPyV27eyW0e69RX61IbdRw1jbbfdVvysAbBDfMFuW5abchVX/1Z 6waAP3iIrR3rcsnK+5p0UTWK02nKodSf7Ni7bhHKEV31NUesLczIO39C6dsjT5tFrdXj9oPc 9vb25G07vcz2uMmqeVlqyZltrHlkRZTbffmB1vefuxK5EJc/9XV3d9f6Qeo4/Sfaftdtycns dmuojjUlMpJTXz2ZmA/AK0wKqxfT87TeHH1e3VWqV5xOJ4fqqORYC0ztb6qgwFzwnaaXmVu6 OvjGp09F14u4rfVvffuW6pxV5NO1Um1uP8hN5q85HF6TS7YtLCyM9OfWxa3yWV9fL/GjjXvq 6+HhofWzO5/I3GbWeT2mo0yhuozkVB06pgBieIKtkaS6EPUK0ukaxul08mguNSX8x2hEU0VL 1J1Q6nuX70vasq/1W/7Pr17++H0v91/Jm7uu9YNf/G4QpDup1F9e/vkcy8Wi1up0+0E+MjRm uJomKZe2GDVYdnh4aJ37mUPpC3SGU19fqRV65Vy5L25NJ2SJTOcWFxfl90KhuozM6R/bdXRM AcTErhLMCPOZde2IOq6zVMc4nbatpa7qWR2lbcqd3Z8ULseoiqej3tb61NN30jjUsOUgvjHQ nUefibXB/OXmR4/rwfMFW4LaqdntB/nIsWhX8R3ZknzBMvNXYRcqn5Hy+DKKTn29cq5zVD+0 HNZgq/NSgwhYO0M1qsLjkBz8N5cI140C4BF5+6PklrfkQmGqtjnmNY3TadsAGD3GWoh9Zbkr gRCkK1Ep08Yv3LiTxmE81lGDGxdSNv6R1r//31/sLD4RbH9uVl390iPRybBoj/rdfpCDtaCv k0WgYm0wvbcir7a9vZ17fYlxhFFua/1w7/qPL8Q3r5TysqaDKz8mo6P+sNbiMV9ZlsKLkMe2 6xYB8IhMwyfr1k9JZelKn7tQDdPs3SOuG5KHdSSbQnWek6kMOfr50SCduVRuoZhSHrW+rvVd H/hC8KXMKnXr2TPFXxNtwBNRW8jVG6q/YcuB1uLTAw8PD1dXV2V3ZKi5ubnNzc1yIykHWv/K +eeC1y9x6qt1xqs/1Ub+y3/5Lw8e+ehHP/q5I//9v//32Dbf+MY3gm1+4zd+I9jm6aefLvg6 Y/xIo7MmETD7NQt5eG85XegGgFfkPEouEX6SUQZFarkj1vFdRWFHv8k6Kjm6+uFpSPVqf9zU +sTjd6aPdJS6+kSHSnPIgjhdW8iC99VXeJF3oO3t7VJeeXd3N/f6EisrKwWb0buulTorX7mU qa/WGa9+zvr57Gc/GzTvPe95T9I2W6+mdr7tbW8r+DoFW1s66xFIn3go8xQX22nmQuG6UQB8 IUcZXbcIFjLtUfk3WDVSD+TXfu3XCr5O0eYWY11TgtmvPpMjvqN2h8JRDapX++aRqwOlTgff Diu3IiO6Oy0i5yNUPNgiyyWUm87W6/VyT4M1f7i6ulpkgsP1ge792XO9I0/0ep3Zc8WnvlrX eDXfo5M5y0O1PE5nvhR5ipmvr451eapkLgKxncaDBICAua7Grg+UsPRQUvEH325/rYrT6aNe sezuMvvVW+Y8KnK5I0jns2cG+tipi8EXdEapl64+4rpFqAHidC0i1/zOXaM0B9nbHkdSmHnm H7q+hOk7bm9vJ21mWmV6Np5UFrP2sfyZ8Rrz3/7bf3vPkX/1r/7VF458+ctfjm3z/PPPB9s8 /PDDwTa5X2dcH6MA6+xX8yW6bpfvZDDaz0g0gIptieq65Nt6yJpO7mFlt29961ul9EAyvs5Y PsOIFhYW5FdDpr+3cqcPhzOWCNL56QWt7330zkqDr1RG2px13SLUAHG6FrHm+1SWlyRDGKur q2N6L9MFSZkGG77v/v5+r9dL2nJpaWl7e9vhaLBpnkwP9HPGa4zpswatfde73pW0jen4Du2C ZHkd31gPJ0/Cvt6SJSY9zJdEO93W+tTTN49KyiSOgf9I6+k/vWa2MRs99ZHjL/cvV9/OppLj ixRd8o2MpRrr6+uu25UmSw/k85//fCk9Gbf6/b78dkhK9ZbsQ2YJqoaljZiO4K1oX8K4snRM 6+uuGwXf+XtrwTjIesyV3a1lb7us4nRWh4eH1pXplW3psd3d3dXV1aQ5s+ZXTsYeZfCiLmu8 tjlOZ53+M76QdDOYS0FsjzExB/64cEOrzjmVXP45Op/FONtR17/8IFWiS5HvqRVVkhlA/hd8 aE+cTifMzCDe7accIxPRIB1zEXwW9iWMVyoj3bjgukXwnde3FpRO1j6orNcrFwKrIMmo3+/H 5rem56Ntb29bF1cN/nB9fb2yxCjrEKifwQt5/Jie6zuPfPjDH/7ikWeffTa2zXe+851gmy++ Kt/reEh2s5SXM4D8IUvUMdoPf0Snq/TOml5TfNL98ovxpYR+RamDZ884aW3DsIiE56z3O69i qRl7IP/zf/7P2DamQ5ijJyNfxwdy9qs5s4jpeGjUmf4E6WokOqQ3q9QtOgkYhh5P68jR6Wpq GcTetMr5m5ubm2GunPn30O339/fNZknzYU13x/x23GPFsnxelcUER/KhD30o6Vf/8T/+x6Dx p0+fTtom/IAFX8cr8tnSfKGuG+U1mc3qukXAHTe1PvH4nZUT5Ri4nBh73fzk0ncfOvb2G9+m UHQhLCLhOWs1Fd9udqZJDzzwQMpvs/dACr6OQ9a1X/0c+m25kS56Ozs7wQno7fpyiIr2JZLS 84EonoVax1pJZNyzKeWbrqysjPUdYw4PD4NppCMlxPX7/fX19aT5sOYjjGnQ2Nqj8naSAnE6 KRzhjCKlLoXMY/UqIwMt98jVgbn8KFv552i23cXTSg+29FHFul/efPG3jx9/+RoTW/JjEQnP WZPpfKvHqojTHbGWUiG446FY7Nv8X+tm0SAd/aW6CPsS0Q4DkIQ4XRtNTU3F7tbjHlXrdrux d8yS11a63N1HcwtMmg87PT29vr4ua94VIZPpfC4N+5GPfGTzyDe/+c3Yr77yla+cPvLP//k/ D7b52te+Ftvm9KsKvo5v5ERv37IMvCIL6JiLhutGAXeE01XkGHjSr36k9Zk/v7536RQD5rnJ ngOXBX9Y66h4uL55NT2QLK/jlnVpMlLqPCR7jzKXgiBdTV2+rdWJ88HXmrQyFRAiTtdGMtln 3KNqcv2KOuYWHR4emr6XdZF7dTST1/y2+EiytTKdz8tfhofT+9///qRtzJ4JtpmdTVyJvKzX 8YQ1cbWOh301ZA5pNfPxgSy+r/Xx3/9acGTGxsBTUu1Q0OLiYo1uhW0j+3XT09N+Lh/x53/+ 56X0QMp6HVfkeBgpdR6Saaqx6575ysLiKgTp6uXrWt/1gTtL5DH1FUMRp2spWT9rrMPUMoNv fO9VgWA+bFIBu5WVlSJL2cq5CZ5X5CFOl0QOirLwa4rYvvI5hxRtI4vQyZ+zfFvp5CXUdYtw h6yipbxMpgsQpwscHh6SUuc/mUsR/Y7MqRc+UnlbDwdJYqvDf7ijXu4vu24U/EWnp6XkdJKx LlMQe6+FhYXxvVeVdnZ2ZFgtYDpD5lejzoeVC18aGxsb42l+Of7zf/7PM0d+4zd+478cef75 +HqIX/3qV4NtPvShDwXb/I//8T8Kvs4YP1JJrCl1vtXu8YfMVHXdIuDHLtzQqnPOHJYnlfrm Z+47yrFLy7NDcbE6Td4uptRCMuWnysXBRjVSD+Q3f/M3S+nJmNcZ40fKi5Q6/8muYzi9gCBd fd02vYjnb3UWn4h9uedm1dUvPRL0KIAYHoRayrpK15gu+vKW07CsosPDw+3t7ZT5sKZjlDE6 I7tQNXoy+exnPxu0+T3veU/SNuHB8La3va3g6xRsbTVkJun6+rrrRnlKhrzr8i2jDWLTVTY/ elwPno+OjX/tD+7S+q9cN7M5WOzVW9YOZJFpBJUZqQfya7/2awVfp2hzx8CaUkfZR98kXfoI 0tXUmWdvK3VCpSIlHxJxuvaSlV/G1An2ZBGJCuzv7/d6vaT5sEtLS6Yjm169RfaffL4Zm0PI HDNLr3rnO98ZtPnnfu7nlhK8733vC7Z505velLRNxtcxb+3/fA05f8F8xa4b5SkZpPb54Efb 3NT6xON3qijOKnX8V3/1qQ8e+9T3vhUk2VGcrnRyhM//C35LWO9rHlamM03afa1//+//fdDg f/AP/sFugj/6oz8Ktvmn//SfJm2T8XXMfz3cLbUeD26JWG2iYMnXsCIk1XuBliBO117WtbrG kcAiA4KNr6ZvPuDq6qoMuqlX58NaK79ub2/HNk5ajt0T1nmdVapFvpWsBVmLvIPqyaUkzKXD daOAHwuXjDij1AvffuS+3/tKUJnOeFip2y8uu25go8hgEFk/npA3NT8r01lzx6o0NzfnYZzO uls8r6/SNrI054MPPhj8gyAd0B7E6VpNLtc1jgoj8n7jYcdlHIL5sCsrK0kduPX19eh8WLml /+kD8sutTF3mQMk6PktLS64b5SNzvtT0K0ZLhLNcg2XaruvBqUvfPcquozhd+YYueggnrONz 3nbqZO5Ylbwdk5NVJnwuL9hC8uoXIEgHtApxulbb2dmRt4G9vb1y3yVWx6SFvYH9/f3Nzc2k +bALCwumM/fiiy9W8F2UzmFKXV2e2ayJq6wmYRUb5Pc8nxRt84LW9z56JTg4g8Dcba1/69u3 VOcsxelKJ++Y/t8Q20CO7/pccdhhSp3PfV3rkmWcX/6Q9YKMqakp1+0CUCnidG0n86HKHa6R paDbnEzU7/eT5sPWq5MX5SSlrl6DivKBs6klGgsyF4fYjnLdIuDHbmt96umbwVzXaMnnT10b 3Dt1LlwEFqWQdxbXLYKlR2eMuq59xWRFkWp4m0wXkHdb/ydwtIccAn/jG9/IsrxA29DvabuN jY3YzaDcNdrlzcbPOiYV29nZSZoPG6pLEX1rvti41WvgVxZaWlhYcN0oH62vr8d2VF2yJtES F25o68IRa9cG977vE8EisA6b1ySxSwG5JD6o6b0saTbD+Pg/IC3Dl6wm4Q852+nBBx903SgA VSNOB0tJ4BJH1WSvjkyi0OHhodkbpptr7eeZx5Jut1uLgJS10GEpBWusk1bqlUwXiM3+Vkx9 tTGnQ2wv1SVajZb4utZ3feALwcF5ZemY1tfDX33plr77fU/+8QePvXztcw5b2BixSwHVKn0g A16eZ40FrCl1vV4vaanWkVhL4NVitTTZubIucQYnuAACIE4HSyitxFE1WQy1Ft2Xin32s5+V /byQ6Rl7Hq2wptSVkjgpe8Dl5ntWRoYyCVhLcslXZuLAKze1PvH4naNUrh3xuVv62Nv/8GxH Xf/yg0fTZJGTzMTnUuCcddKrtytIxMgB0bJS3mTs0v9kuoBcTYI11j2xs7Pz1/7aX4t+NWQ7 Ai1EnA6vkMk+ZQWGZA+GNCJJFgqRzHc0Pz/v7WinDMhOT08X7MFbk+lq+rQmJ5jXYrpQxWRx 67pUaUR7PHJ1oNRpc3CeUeqlq49Yf2t+/ZU/mWUObG7E6Twkx3RXVlZcNyorOQikyhg2tmbq 1WU0ut/vx1pOMMgHpp8vH8oUBTqB9uG0xytkkKWsuzWloIc6PDyU92PTIZ6ampI/D74a8335 Nh/24OBAdiwKptTJamU1TaYLyP1Tl0yEKsV2EXM94JtnBvrYqYvm4Dyh1Pcu3xf91W2tH3z2 llKzwdF7tqMuPXXmxvMXXDW1vuRyhxsbG64b1XY1nfQakgOixUfL5D7xefVbSbbf28HglrD2 pQP17f0CyIegCV5hnc5QSgX30ntFzSPrxYY5RHt7e8vLy7KAYGBmZmZtbc2fO7c1pS53+qRM rVI1T6mgQ5xFbC+ZDqvrFgGv8YLW9z56JTg+wxJ1YfDOKrboBIaSdxOWlHFOHtj1GmqyptQV CTXKgqqqblNG5Ghot9t13aj2Mv35cIRe9hi5BgJtQ5wOd8j6WaVkssResy5lO6ok+0ly0rG5 PZsvyDrIFsyH9SHXwDoMmHtsWVZOqfuMDDlpqF4D79WQKQ+uWwS8xm2tTz19U6mOOTgf66jB DdLlyic7JL6lkLeNLN1Qo0mvIdmvyF1awVqXo3b3dDlOTA67K9EgnekuMlYBgEcg3GFdCqBg z1j2ANbX10tqb3PI8sZJu93cxc3NW04lDkxOTi4uLrp9mJGhKJVreNmaTOf5YhpDyVOM4muS DFuTdQjfXLihVeecOprgeuvZM66b00BUzPCN6V3EvpE6LoVk7VrkS6mTi1wVmUDgkIw2um5R S0WDdNoWGSfVEWgbLsf4Mdkznp+fL/KCshR0KWuANoksTpcla2xvb8/csJPmw5qbvcP5sLJV OUbdZVJV3ZPpAnLn1LFbP1by4YcxZPgmnOXaUerqEx2Wdi0dcTrfyIK5/X7fdaPykCl1Oda8 sibT1bR/a3po3HOdCzOIw8cu1tIBQNcHPybvCqpYSp3MrqpX1eEKyJTDkWKj5s+T5sMGL1X9 fFhrSt1Iy59Zi8j4MLG3ODmZi2SxGLl8Xt3zKNE8N7U+8fidy9TF00oPtly3qGliNzXm4rkl SxjXNxl8f3+/eIhN5n0XX+DeFVllj3hQxWSQTtueDvhegLYhTofXkEOmRW4MsrxCXZarr4zM HsoXlTB/JYvOBswDz+LiYpXxIHkUjVSXUCbTNeYhTQYxazoCPz4ySkvfFB565OpAqdPm+HxY qdsvLrtuTtM09RZQU3IQt3aF2KKsU1azR9msk2freyvv9/ucbg5Zg3QBvheg5YjT4TVkHGFi YiL3DEridEPJmFSRgJr5prrdrgyTBczPzW8rmA9rTczM+NXLIUTVoFkYskQdK6vEEKdDLVy+ rdWJ84q1XMcjdhHgAdUt2ZerY3G6UMFZq9bFKGqaTBegRJ0r4TOX6Z/L33IZBFqOazHiZAmt 3PPOKDEz1Ji6Rzs7O4uLi0nzYU2fctxzCeVXv7CwkOUPZVZgw7omsS/FHACuW+Qdebi6bhEQ 94LW9z56JThErywd0/q66xY1hxzPIFjvlrwv17Q4XUim1Kls5WJLXInCH+UOGCOjaJDOOoLe 7M4wgKGImyBODpzmLuFPnC6dXESi9NvwxsaGrIkWmJiYML8aU2/MmlI3tC8ra5M1r78oTwqW koihbwr/3db61NM3j1aSUOdPKH37susWNQcF1H0jh29dt6go0/uSwccsk3llSKsBI0llFWBB dkODdFp0F02nvdImAnCt9vdalM7cMGQeVr4q/rF7DKlDMXKK3+Li4jjeyHynpk+QNB/WdMG7 3W6RBUOsZCd4aHdW/knBFYc9ZL7i2GdkMnhMbP8Qp4Ofll80x+pZc4ieUeqlq4+4bk5zEKfz ilxEohnlGqzjgunDZtZFruqeTKdZsqBy4Q5PryxErgPQcpzzsJChhHyPyrF4XzP6diWSy2x1 u92xvuPe3p75cuXYePgtr62tlVXATs5dUkeDtOvr6wsLC7GfLx2R25cePXROloCsdaGfcYgd CbnzeYGxemagj526qI5y6q4+0TnKsUMJZJxu3HdGpJBfh7mJu25UOeTQoLn7HB4e7uzs9Hq9 1dXVoGdiPq/5v7u7u7KX0oxurZzMy/DY+JijK3g4Mv9Nny9CnA5oOc55WFgjLDlq+TeyQ1Mi OdegsgUTzBvNz89bC9gF82FLaUnSlNuMxpRd6JZ85qnvOnFjIp+FXLcIsLhwQ6vOueAQpURd ibrdrqs7I6QGjy1ZU+pG0ph0+Njnsi5rgOKyB+k0cTqg9TjnYScjLDlmIMZegThdjIxHVJw+ FsyHlV2BwOTk5OLiYpEmmb9NWstiqHvvvbeCpWmr19Q5RCUiTgefnXn2tlInYofoYx01uHHB ddMaQhbJJU7nkPw6GhOc0rbbTXZNundz261AtKxQlsrL8tRr3hQTACm4EMPOug7AqHeI2J+v rKyMp7F15U/HyHyz3W43aT7s1NRU7vmwcl5JRlkGG2sq9kmb1NcvhT/nBZDkcn+wvPxp8xz1 25/4RGfxifuV+sHmrOtGNQRxOq/Im3iT1j6ylpzL6L/+1//quvmlMf3z2KcjJFQu04UOi0Rn XKaDKyHQcjz/IJFMsxppHqIsTMv8vpjp6eno/vGhDpf51pLmw6qjnMqRVhSR82VG0tRQnVzQ w3WL/CLngzcysxKAFU+nXmn25LsiU19NF67f77v+BOVwWIalDXIE6TRXQqD1GnW7Rbk2NjZi d4j0lYliqMM1VGz/eFW41/QkklLhzGGwuLg4NIIWluEoYnJysnkxmmY/9hTHAwPQZjydeiWW aD903fYaKV6frjGhOnnbHWlQFulyBOk0V0Kg9Xg+RBo5ETL7Yu3E6YaK7R+v4nSBg4ODbrcr 878C5ufmt9Y4mvlh0izaUTXpqSAgA6CHh4euG+UR4nRAm8mnU9ctarXYd9GYQg39fj82pyGf hYUF1x+lBHL+b/bePtKF9b5HLfNNnA5oOXo/SCPnLWafm0mcLl29ekU7OzuLi4tJ+XFzc3Ox QULZvSiiYV2TZpflLo44HdBmchkr1y1qtdh30Zg4XZEVJGIa0LmtV4+0RnIH6TRxOqD16P0g TXRxolDGtG3idOlq2iva2NhImQ9rOiI7OzvWw6YIDzMNiyBOl444HdBmVAbwx97eXuy7WF9f d92oEhSf8Ro1PT1d96T4fr8f+1C16JF6rkiQTtseo+gLAa1C7wdDyJjC1NRUlj8kTpeupnG6 wMHBwdraWtJ82J/5mZ/J3sHNqElLjxGnS0ecDmgz4nT+aGpHrsRkusDm5qbrz1RU7BM1r+RI xcIJSRkfmiTidEDL0fvBEHI0NeOtQt5gtre3x9/e2qh1nC5kDo/FxcWyStGlqOPOSdLtdmOf jjhdFHE6oM2I0/mjkXG6/f39cvsnqhFV6mKfqGHzGCoWDdLlXgxNnn0s7gG0Cr0fDCeLxWS5 f8sbDMGIKBmnG2kdKN+Y3oM5Tsqd7jrqIVcXnBrpNjc3Y/uHOB3QHsTp/NHIOF25k15D+/v7 rj9ZIbGP06ROV8VKCdJp29nXpBFrAEPR+8Fw1pS6ofMQCUakk3G6BgQjTI/k/Pnz+fu5ySYm Jlx/uNLIU2NnZ8d1ozzSyFMDQEbE6fzRyDjd+vp6uf2TQN27uLGPQ5wuH9OdC3ag6bUWCdJp 4nRA69H7QSay3zy0KipxunRNDUbI770srj9ZaeQuakBpmxI19dQAamRvb2/riPW3wa+sAwzm 0XQrr+CxVvY3hrYkh/TGWx+wzZ/kfrvqd2NS40fajbJKw+rq6m6ENYnM/Lx3xPrb7e3tXi7W 2inmLYLfWnuYYUui3v3ud5faN7ljfX093+ca2vhqdmPs40TjdOZIWB6dfJfGM2dZMK3E/Lf4 +OsWcTqg3Zrz6IvQOLp3sq9mBBtn795tbm5Gu3f9fl++1+HhYfBb6+JZ5k9287LuqywtySG9 8eFbx/bP/Px8jp6QtdsddqqsaY9ra2s53siwzsw1bxFtiexYlCXa+PSWeL4bP/axj8U+2u7R 0056f31lZWUpF/lq+qiKtmFduc8cqPneyLCeSuZdhrYkamFhIbZ/pqamZoTFxUX5auZiFfzW 2kU2fyJfJyNr47O0JIf0xqe0xCr9tFq29fuXX13qxHpeDH3NJNbGh1eM9JbkkNL49Jbk0ODd KF9Nv5p6U/puDHZU0p5JaUkO6Y0v9ytLb/yYdmNS48vdjT1bel3v1Rqj1tvZUt41HKw3kbAr ld6SCqyurub+W392Yyh6WOa7gMh3abZyg3SaOB3Qeq27jLbB+Lp31htG7vdK7yuU2y9RqX3T jL2W7NIbX/z1o3x7Phx3nK4Bj9lyndwggBv8u/SHDflq2umRb21JDjxml7IbfbuAJLUkB+J0 pexG+WqaON3oqt+NSY0vdzf6E2AiTpcRcbpxODg4CAs0l1XJhDgd0HLtuoy2RGVxuqBkWO73 Ik6X+/WjfHs+DGtzlCusT9eAx+wTJ07EfkKcLgcfHrODhDXP8+nSWVNTzQ+ztGRU6TsqvSU5 pDS+9K8svfHV5zXHbOWathaQr6aPro1DW5JDsKNmxHk6tCU5pDe+3K8svfFJLTE/T5qvkM75 vFfzwyxzC0aVPvUhvSVRMmW7FJcuXcr3uYY2ftTdaP2Kh4p9nBnq02Vmzrhw/LXEFeFknzbp /AXQSMTpGqh4Lzkm7JtOTk7G7hkp3Va5Sqx5XM9SIyN7PQ7zf7N3g6z7KviVP/Nec/TIrV9Z WFqo3CI76WV0wpYU6uomCLuMQeMLFvSRf1ulZTE0nbtj3UgyKOn8KwNQGRmnc92i9tpiHYnM 6n4fj30c4nQZjSlIp4nTAa1H7wcjkPGFycnJpI0b2b0rkYzTJQ251458yiquMTtHE6cbhjgd 0GbE6fzRyI7c9vZ2uf2TgHWAtkZiH2dubs51i+phTEE6Y2NjI/al0BcCWoXeD0YQrb8QMjcS 68aN7N6VqMFxOuuqIwWVVe/DB8Tp0hGnA9pMJuO7blF77e3txb4L6+pD9SLXNi1uYWHB9ccq pN/vxz5RY3qkYxVerMw/Sn9x2VekLwS0Cr0fjEZ2oJNy44nTDRXbP40ZvbTGc4uYmppy/ZnK JE+iuo/Dl4s4HdBm8unUdYtaLfZdJK3cXS8rKysldlGMzc1N15+pkAaPHI/PWIN0mjgd0Hr0 fjAaObiqEnKdiNMNFds/TaoGIrsXRSTlbNYUs7rSEacD2oynU6/EvotmxOlkWKqI6enpug+2 Eacb1biDdJorIdB6PB9iZHNzc7E7h/UuJdf9JE4XY/p20f3TpKyxElPqmhS+DITVTEKuW+QX GaezLpYCoJF4OvVKbAGxxiT+hwuRF1f3ZDptu+02bHy0XGtra8FeGmu/nSsh0HI8H2JkMlFO JTxIx7ZZXV2tvLFek91E1y0qk/U4GdXExETzYjSxz9iM9IQSNfu8AJCOp1OvNDUBfH9/PzZW mk/dK9MFSGPPLhqkOzg4GN8bcSUEWq4ht1tUTPbbFhcX5WaxbYhHxMh4xFhv+dULezP5TExM NGn5iID5ijkv0hGnA9qMp1OvyCkU+/v7rhtVDjntY1RTU1N1n/EakAX7mjdEWorKgnTadiXk SwFahecf5CHjLxMTE/KOFduGeERMGwYwc4fqGhmk07Y0wwYsn1cu4nRAm8kVw5t3Z6yRZi9Q vr29na+LEpiamur3+64/RAm47WYRBnYnJycrGFZvaiorgIw455FTrGSJYfrWsW1iGxCni9nc 3By6Dxvgi1/84k/8xE+M1Pc1vZOmDhvKwCV1G2NiDwwTExOuWwSgOnIwo5F3xrqQ96wGlGOL 2t3d/Tt/5++M1EWJmp6ebkCoLvahmlQuuSw7OztB2eXKRpGJ0wEtxzmPnOSI9+TkZGyb2D3G 9GZctNRfcoEt6/ThugtXxcrCHEXmqcB1k8eo2bkJpYjtn+YtJAIghYzTsfSkQ43PAd/f38/e RVFHXdler7e6uhr9Sa3v43IPcNuNqT5Ip4nTAa3HOY+crAt6xiIs3GPSHR4eNr5vFK3/8su/ /MvmCJmbm5NHzuTk5Pz8fBvWF5MnRWNq/ZSl8ScFgBTE6bzS+Jqq0Qzu3/3d301ZXML0Xnq9 XliQLhqqM7a3t91+kNxknT7OuKjweafieiyx7iJzC4C2IW6C/GRmUCxVnjjdULJH6LpFJYse A7GprFtHGlmELkUsRkmSqSQfjVy3CEB1ZNSgkZnmNSLrnLhuUWmi0xpWV1eDH/b7/c3NzXAB jV6vt729bR1Ra0aoTtZKbva0hpEcHByYRxsnuyX2pTBmCbRNc+61qN7e3p4SovWeZSCvAVU8 yiVr9zYpbrWxsRF+LoZnte2UaVhiQnFyMjhHDtA2PKB6RY65NqYvFwbjpqenYyu3ht2z9Fdo QKiu2R3RIhwG6TSXQaD1iNOhEFl6LJr8QimuoZo9jBkOwsvlgKMf2VHrHJAFuRtW6Kc44nQA eED1iuzLNWMpiehaXnJBp4xxOi3WBKtdqK7xEztycxik01wGgdbjWoxC5PwUFZneKNeaqF33 ZdzkDpyfn3fdqHJEe/ayi9POHuHi4mLs62bUOsZcImK7qEmRawBZxOoDsPqkW7JiYDhFtL4O Dw/D+JS1ukL2OJ0Wd64a7Z9+vx/7cokHBcJEBCfz7ikaCKBFT8gYEzkhIow0yb6dHLFsObmU hFw2N4fnB3r2T76q1Kx5wbMd9f88debl/uXoBj/S+pc3XzQbnFZq89Ozgx9uFX/TqL29vfBB y/qIFf3I5b61z8Kx2RCLSMTIDNPoVHoAbUBxW6/IpSRKLxt6faDPPfVc2Gn50ufPv3z9uegG L2v9b65cDzotVy6eG9wqOjljfX09/DjWAbOR4nS6tqG6WDKgIh50JBxVdTV2znI6AOj6oCh5 LwknOcpfMctPCsujhGLrLYzqS7f03e97Mvaapmv7lT+Z1YPng22eGehjpy6Gv/03s+rlaxdK +DCvik6IJs4SkMXpWCFBij47BUg5BNqGOJ1vxjrI9K1b+uTZi7HXN52Wv3jqnB5cD7a5OtCd 06/ttFy/kvsdo0lkSVViR43T6XqG6lZWVmJ7nj6bfrVKicMJLtH6zoFut+uqMQCcoOuDEsi1 wMJhn9jPqZovxYoQq2IT/W5qfeLxeIWv0FMfPDb44TP6tXG6r/3BXV+6dftfz366rFBdND5L KCoki9PVohNfMVnQ2nWLAFRNlr4tOHyFgmTRhrJK1L2k9ZmLyZ2Ws53Brav6tXG6586f+Nat 2+fPXcwdqoveaJLWxMgRp9NH0xWj5d78v8tTnC6J23ilLApJ/BRoGy7HKIEMQExMTAS/iv18 YWHBaUt9JItQFIltRQNwHaW+8JHjwZzWrR/q2U9vKjUbhOpe0PreR68Em11ZOqb19eX+4E// 4T/Ug28U/0TRVAgerkLyyZNyjZK5RFivJADag2dU38jsnpWVlVJeORqAM52W3rnZYE7r7i19 7uKVVzotR6G6H2h9/7lXOy3nOlrf7F0b9B5+WA+ujfqO0U5XShwtX5xOHyXrRYNf5nViK8n6 Qy7cRHE6T3ANBECcDuWIVX1WryaFMXtlKFmirkhsYvlFrdTZ4HUunlZ6sBX97dq1wd3v+6On Pnjs9g+fOfX0zaNesTp/Qunbl69r3bvwfPFCddGgLdU0ouQ54m3f3SGeGQDIRag2NjZcN6rt lFDKLax3PdZpeU3hue3rg5NnP/nU2c7tW1cfuxLttDx3U+v/dmU3R6G6sNjI9PR0ykfIHafT IlS3sLDg5+1eFppgcqUnZEmcoKAQgPYgaIJyyJGfYD0EeaehcL4k5/rlfiYJ43QnlPre5fvk Bl+6pe9935ObHz3+yLevHVWAUWeUeunqI4U+wKtMNyKcBB2WKYS2JSOQWyqZi0NsLzFvGmgh aqh7SHbnSkkJD+N0r3RaemflBt+6pe8/e/Hpx2YvXo10WnYv5ny7yFJFY13ZrBahOvmdUhDW E2Q5AOC0RznkcmDqKElbxu92d4uu0tU8cr2t3MVrh8bp9FGo7vivPvl///F9x079oToanr76 REfr2/k/QPjuka+7SJW95pGTXssq7tMkcg4OD+dAC8k43eLioutGtZ2scFLK1NehcTp9FKqb PXvx3z1xtnP6/J1Oy8XTWg9Gfa/Dw8MwdlbBIFC/348GwnwL1UUX0wgE4+vwQWwGBl8N0ELE 6VAaGYmYmZmRs1cIT0gyjSj31NfLt7U6cV6lxumM5f7g7x976K5f+IXg7YISdTlb/6q9vb2w YzE1NVXw1RpGTnolsVSSAWuivUA7ye6E6xa1nVyyXJUx9fW5aKclIU5n9K4N7u88dGJ29k6n 5ahE3ajvFV22q5rEMbN/okVXzb+Tlq2onpz0SjTcH1wAARCnQ2msfbgnn3wy9pOxTjSor1j5 /NwRinCBiPQsuR9pPf2n14JSL8ZjHTW4UXSx12igdmi92+gnLfi+/pOTXpnOaSUfGyicDLRT 7FLA2I8P5Fy84lNfwwUi0rPkXtb6w73XdlpujrbYazR9bGlpqWCzs4uF6qanpz0J1cmVXpn0 6gm5vlzuSTYA6qv5T8iokix18eCDD8Z+UmX3qEZkJlG+UM5NrU88fmfyYO+sOcGfT9ryqwN9 99k7hVpmlbr17Jn8rX/tNKUsLY9+0iLvWwvyvCCr1EoWaqTEIdBOlGfykJz6WrzQ6ktan7kY 7bQkpvb/5UCffCjSaXnu/EhvFL2/VJzP7mGobnt7O/ZVMrPSHxToBKCJ06Fc8tZi3HPPPdH/ SyaRlZz6auzt7eV4qUeuDoJay+lZcre1fvDZW0fd3RJK1EWfqbI0uz1PX9Y8Uya9WsWG94ss fAyg1uTwRr4bIkpkrURcPOR0cTfSaUnOkhto/cnnIp2WUUrURROU1tfXCzY4B99CdXJUjEiQ P2Rpbxa8Blqo4U/IqN7U1FTs7hIuABryqpKuP8rqNoVTX+9X6gebsylbPjPQx05dDN7r4mml B1v5Wh4dY8/Y5ujHzPemdSH7W6SUWpnLQmxHUZAFaC155WQWvA9kJeLV1dWCrxlOfX2l03Ll XMqWVwe6czraacm6LlkY9p2ennbVBTXvGy2QZ1rial21EgeGMQ6Li4tc/QA0/AkZ1ZPTIu66 667YT1jy1aqsaQjRRLneQ8f04BtJW0YnyeYuUXdwcBCGYicmJjJOVIx+zBxvWiMyTl28oE8j ycVeqWkNtJbsS3S7XdeNgmXaRPHIVzRRrvdQRw+uJW0ZnSSbvURdr9cLW5u96EQ4dJpx+4yi oTpX/QFZCpZRMa8w6x+AJk6HcZCBiRiKcyWRZX3zrSYR1p77FaUOUgvPhZNkhybfJYlmPWRv bUv6H/JR0+FgvudY7BVAiApN3pJ9vOLrg4W1517ptKQWngsnyQ5NvguYG27Ysxqp7koQpyte gE9yG6qL7pAQ0yq9EjvFqAECtFOTn5DhSrfbVamKz5JoKjnImXuRu+X+QHUeUsNS6i7c0Kpz Lnivr/3BXVr/1Ujvsre3ZzoQwZ+PNB4b/YwjvWO9yEFRJ5VxaiH26KKY6AG0mCyFRsqPJ2Qf b3p6uvjL9q6FnZa0lLorN3/caXnu/ImhdXWjd5aRJnNsb2/3er0xBdEchuqi2YUBVpDwDZc+ AJo4HcbBdK/D2I3VOMYnm8FaNCRftOJHWk//6TWlOukpdQVL1EVL1YzUzugHHOkda8S6rAor SCSJ1thu9oEBIItYR4JogiesfbzikaaXtf5w79VOS3JK3Ugl6vr9fthC3yrDxuJl1YTqrMl0 zCj3iuw6mp6260YBcIAHIYyFrIHKE3hGMqso90jaN0x39tFnzCt8clYNbl22bvN1re/6wBeC Nzqj1EtXH8n++tHOxKjdiDYcDDKZbmVlxXWj/BXbV7kzSQE0A0WavCVX+RhpSmmSawN9+rEr r3ZanrNu8z2tT5zt/bjTsnsx5QWjy3N5OEgWq0pcQbq9TKbLXlYY1ZD1UpjyD7QTnR6Mxd7e nkrFUhJJZDV9VWAC4Jdu6bvf92RHqc2PHteD5+UG0TjdqCXqwoco088bdaWwxj96WZPpOOyT yMOeAWSg5Vj00FvWPl4ppYe/dUufPHvRdFqefmxWD67LDaJxuvQSdTs7O2HbvK04EQvVjbUs jDWZjhiQb1jqGkCgmU/I8EF0RuSY+nNNFR0BDhQpThGE6k4q9c3P3Kf192O/vXBDn/vbv3nv b/3b4I2uLB3T2tIzlqIjfjn6edFPN+rf1oLMBPFt0o1X5CA/M3GAlpN10Lgs+EP28cpaJSkI 1ZlOy7cuntX6Vuy3V27q3/mVc/f/7+fvdFrOdY7WrreYm5srt2FjUlmojmS6WpC9x1EHwgE0 QzOfkOGD6EimxATAFNaUuiKrcQUTYE2v9z99/L7BD7eiP//bH3nm6090Tjz+neBdHuuowY0L Q18wWp5mcnIyRz8v+tFG/Vv/kUw3KnNBiO0ucwFx3SgALskL6eLioutG4Q5rSl3xhV8DwQRY 02n58/NnB7d2oz//lceu/I+Lp89cvPrjTsvNK/IVojEp/weGt7e3o5lu4wjV7e/vk0xXC7Hi jyz2CrRWA5+Q4Q85KBQqpZRJg8mUuoIltH+k9Ye2X1Sds6eV+g+fnjWds4cvXjz29qWgdN0j VwdKmd9kLVEXTctfW1vL0Z7oR8vx554zX1bs6yOZLp18fnDdIgCOseSr56yViMsqA/ey1n/8 F9eDTsvnL57r9XpP9Hqd2XNB6bqLu5FOiyhRF53gWZfeZr/fj94HV1ZWys0BlLWPSabzEBc9 ACGehTBGGxsbsg9XemeukawpdcVHPp8f6KX/6/+7+32vzHLtKPXUR46/3L+zvsTWD/Xy8sbP f/xzV5/421rfTnmR6Ch67g5EgyMycq6WIpkulVzmmI4pAG0b83DdIvyYdeHXcmdLXB/oz/7Z cyfPng86Lf/u3OzL1++sL7F7S/d6Vx46//mrF39F60H0r6Ixqdw33+3t7V6vV80yrIFYqG5h YaGsUN2YupQoHUnEAEL0eDBespMdqrL3U0dyJqAng59hzRdVoLptU5+7rM8tJNOli1Xn4fkB QCB6uwkwI94rsua98++o3++XcvMNpjVUfPseU6jOvE7sO8pXsQTjJk+ofHNWADRAo56Q4SG5 vnhorMtaNYBMMlIezOCIjvUVWZEz+qHKa5171uVTSB1NJ+fjFKnGCKAxeGr1nxyONR0Vh+s2 RMuGFLn5OonT6TGE6uTyEZxH3mJkAkCoUU/I8JA1vSjsyblune+svSu3C7RPTU0FzTBfa5El qKKfqLzWOWZdPmJ9fd11u3wnO6aM8wPQzAKrA2uFE1c3vugKZgXb4CpOp4/q60Uz4My/cwcc o9mFISpLeIuZ/gBCnP8YO+u0iAB5RumitZBDDmcrRLMjC85MbF4vxHwpsoNlvj6HaQW1IPNG p6amXDcKgBdkVXWuDx6SYy3KUVXWsCXFb74O43RahOrMx+n3+zleR854VaRo+UquoUxEFWiz hjwhw2eynx2iRN1Q0cHhUJEJp7lFUyOLxwqjH6ekBjpmXfmOI3woWZyOfBkAIRJM/Le3tydn TlQ/+zU6BWFzc7Pgq7mN0+kyQnXWORmUf/WWTE3lywLajO4OqmAt2qUoUZdNtNhKqPoCXtG8 yOKVTRr20GWd+MPyEVnI9VIoTgcgJPsPbos/wMq60Hm5a7+mi84/KKWsivM4nS4WqrOu8To5 OTnO9qIQOdxLdwhosyY8IcN/Mpc77HO4bloN7O/vy9mvFa/9Gv0GycOPsRZhNF8Z07qzkMe2 6xYB8IgMAJFj4ifTN5DdvOJ5bRlF1yMqZcqtD3E6fRSqi360jKE6a9UUxYxXv8kzqEgZaAB1 xxMRKmLtwBn5Km60jenpyl03pnjZxMSEeS6K/TBafYZchhi3Dye1Jgf8iQIDiJLFH1iEyk/W 2a+ld/N6vd7S0lJsGCy6WkJZkTVP4nSB2KroQ0tqWOdhEOD2XOz7IvkRaDnidKiIdSlMw3S5 XDetHuT0QDWGSl7hE5HpH4TxuOh356Q0ns+sy6R40rP33/r6emzXyRgxgJaLRX/M/3XdIthZ S0CUu55SGIEyvcfwZaNhqbIy2b2K0+lRQnXyxqpYgMV78imJ/jbQcsTpUB3TS5Bdh4WFBdft qoekWQzFS8VFRVd0VUfJTXt7e+EXZ56OSMKPquCZpNnkUnTMygEQI3OWuVB4y1qP2Fzqy7ot RjtC5t/b29vRjMv19fVS3kX7F6fT2UJ1cmkmOm+1IAd9GbYEWo44HaoTiwGFKOOVkbUqsOl+ lfjEYl20NMSkiSiz261zfHiAzMic+LFdxywPABJPsDVycHBgHZQtZd0wedeIGkfinldxOi1y 5WKhuuj83yiWI/AfoxEAYojToVLmOVx2IKjklV2v15M7sMQ1Jazd69LfpQHMrrAezCUO5jee rLrILA8AkpwRRok6nyUNYhUP1clihVHl1lHxM06nRcZc2Ovo9/vWWRelF0jBOMgut+sWAXCM OB0qZS3mtbKy4rpddWItDzw1NVVKEC2lB6xeW7SuxDcq5QWrlJQv4GGH3mfySGbMH4AVD7H1 Yi0KoTIsgJDOOlQZFS1aV5C3cTotQnWrq6tJpVFYmqkWGIoAINXvCRm1dnBwYB1lpZ5XdmZf RVdfLbE3lrTWh3yj4oVOoi9Y8KWqZ93/5occxtmZfRXbgTx4A0jCpLDa6Xa71i5EkVCddZwy JihaV7z9PsfptAjVve1tb5O7oqwRXIybrDnD1H4A9XtCRt1ZK6CV0qlqj6TZDQWnDSb1qq3M 91ik/xd9qSJtrp61SLb5OsyX4rppdSJrXTPpFUASeXuiXqr/rLfLIl0+a8/HamFhYXd3t0jj PY/T6YQlI0KsHVEjcooG3x2Amj0howHMvUf2Jz7ykY+4blfNJFVpKRLsSOpSW01OThbpRkRf KveLVC9pF5HZMaqVlZXYPix35WIATSJveUzoq4USQ3Xpi0iU8hZR/sfpjP/1v/7X8ePH5Wcv d4UxjNXBwUHs62NNLQCaOB2cePDBB2WvgjmDo0oaSs0dqktZRCKmnZMpSk8NaC056dVo4REF IDtZNIOLhv+SyrnmuHWmLyJR+n3Z/zjd/v7+wsKC9eOXVUoYFVhbW4t9fUwvAKCJ08EJax20 //Sf/pPrdtVPUk3lfPf4jN3fmZmZFj4dJQXpWOA1B7nSKyWTAaSTF2FWnqmFlFDdSDfQoYtI BKanp0tJJfM8TpdU/8R485vf3MJOWn1xZQNgRZwObrzlLW+J3ZaYw5LP6uqqtaNmbvwjddQy LiLRzlG+pCCd2fmum1ZLMgWAXimAdGSd1FdKqC77bTTjIhJl1Yr1OU63u7ubXqqvnZMeaopM YQBWxOnghuxwGy+++KLrdtVSUqhupI5alkUkFhcXx/gxvFTK0wWiZI0hVnoFMJSs4sSlo0ZS bqYLCwtZKp8MXUTCvE6JCzqZl9rd3fVwhaiU5SPe/OY3h/8mVFcLcoycrAUAAeJ0cOanfuqn Yjen3/md33HdqLpKCdVlnAAydBGJFpb539vbI0hXuvX19djOJCkGQBbygkyx/BpJCdXNzc2l R8SGLiKRMdhXd0mdvaCTFtvDrCbhv8XFxdj32O12XTcKgBeI08GZ3/zN35QdNdeNqrGk3pvp qGWpKJy+iEQLg3SmdysnIwQI0hVhTvPY/qTiNYAsZN53C7O8a+3g4EDeAgLT09Mpiz+kLyKx tLTU+CCd+YBJq0ZE43GE6uplcnIy9m3yfQEIEKeDM6Yz8RM/8ROx+9Pu7q7rdtVYykDr0AG6 pD8cUycv+halv3hx1nnZAYJ0RchnLdNJdd0oAPXABaQZUvL3zR3WGnFLWUSiDTdlc+QnTfuV nTRCdXWxt7fHBQ1AEh+fkNEeMt+7Df2tsUopXJKyskTSIhLj695F32Ucr5+b2UUpjxCbm5uu G1hvMpS8vLzsulEAaoP0k2YwV/6k+6x1DmzSIhKNX3L98PBQFosIJRWhi+UtEqrzEwnCAFL4 9YSMtpFDSUbjJy+MW0qoLqlcnXURibEWIY6+0ZjeIgezc1Lm/6ZMyUEW5tSWe9VcBFy3C0Bt UM6pMdbW1pKKSxi9Xi+6sTWbrPE35X6/nzTXVR0FNNM7adFBR7OrW1jAxHMU3ASQwqMnZLTT zMxM7C5FylJxpm+XsjKafKqRGWTjXiks+l7je5eRpKx4a3amh4u+1Y45tWM7lnXNAIxETn01 dyvXjUJO6WNjCwsLQS0UuYhEejG7BkhPo1OZU9FjvTtCdf6QmQosYA0gypcnZLSWLATGahKl MP3alGHYmZmZaB5TrKNcwfqb0bcb93sNZXaFjBeHzG40O9N1G5tAlg/nmQHAqOTUV9Jy6ytl ZYnA+vr6s88+G/1JNSNn5n2XlpaczKvd2dlJ2ScZFwcLEarzkxwbrqDvDaBG3D8hA3LiA4nf pTg8PExZWcLs9nA8tvqOQvQdK3i7FGYnpEy9SappjVHJLBiGjgHkIBPAKXNZd91uN+VG/LM/ +7PhvytLbw8q4pn/VvBeIfPRkirxBWZmZnLMdYjNFidU5wOZSbqxseG6UQA8QpwO7slyMysr K64b1Rzb29spc2BNRyE6pldZ7y3ahmreUdra2kqZcaOYgl0q+exBvWQAOciFj1gksQHS58AG /tk/+2eVjZxVHKfb399PGVgNFCnFGJu8QmjbLSa9AhiKOB3cs64mwUzDEqWXIg5VOcQafd/K 3jRkDrmURV3V0VxXCtKVSFYXUkxVA5AXq742Vco6sIGlpaWgaF1jZInQJS0CNpJYqI5Zlg7J BAW+DgAxxOngBVmJw/RaXDeqUYbWJFZH46tjXTsiKvq+1bxjwHzAoY8BZkcx17Vc8iGEMpQA cpNPueTnNsbOzk5KxdjAwsJCA9aRyBKhm5iYKHFFY0J1nmCkAcBQxOngBTmNZXp6mlhJ6XZ3 d9MLNgdF6yqI1kXfdNzvFQgidCkVcNRR8KhhA/U+MCey3NUjlcEGgCjqXTZeesW68Ja9ublZ x76i6WmsrKykf7rgA5aeeE6ozjlm7gPIgjgdfCEHl3q9nutGNZDp0Zodm941DKJ1Y52WGH27 8b1LwHyQoRG64HirY3fff/J4o0sKoCDZZ6AKe8McHBzIxEmr1dXVWoyx7e/vb25upg+XBqam psY3mmVeOdojmp+fr2wuBbRtJRzSgQFIxOngi9gQnyKlbpyGrikW9t7GlIoffZdxvH7AND69 Dl3A7Aqq0Y2JOYXlMiYsNgegoOgKSOENy3WjUL6h9WRDc3Nz6+vrHt7NzX1we3s7SwKdOhrH quAWaXpH0VDd1NQUobpqmP0sh40p1wtAIk4Hj8hbVwPqj/jMdNQyjuuaXmO5fbjo65f4sgHT VNPgoSvHqaNuPcfYWG1ubsb2OdPTABRnXYGKWENTbW1tDS1aF72zr6+vO8+wC7LnMobnVFUR uhChOidkUoLZ864bBcBHxOngEVngn2LzFej1ejLjSTL9ufn5+bImFkVfuZQXDJjmmUYOneKq jrI1mVhdARkINqe560YBaAIZuCmx4j48tLW1lTG3LrSysrK5uVlZzG5/f397e3t1dTXLIGio 4ghdiFBd9bhqAciIOB08Ys0GJ92pAkHRuizROhUJ2BXpz0VfsGDjTTOyh+fUqxE6plRXwJy8 8uDhMQBAKWRmCrUv2yCYCZvxjh+1tLS0vr4ehO2G9gGC2iDmv+mb9fv9nZ0d06lYWVkZKTYX mJmZcVsIwjQ+OvmAUN1YkQUMIDvidPALKXUOjRStC7uY5ivLUe04+iL5Wmve1Lx19okwighd 5UimAzBWMljDWtItkb3ARUqXYOlI70gQvwstLCyYbcx/w58E8ThjdXXV/FWOqFwoGO8cU/3f UZk9Gd2Nk5OTnjSseeS6KFTVBJCEOB38YroLskNDSl3FzA7PNyy8vLy8sbGRpYcX/cOMrTIv a1581NhcgDp01Wt5Mp35pFt1EJxT/jOPNzMRPEYGzH6Yabc3vvGNseuM+YnrRhVlHt1dn3B1 8vDDD7/1rW/NkV7nhOmNeLiSUixUZ3Ym19hxYFwBQHbE6eAdWXyElDondnd3V1dXc3dGTZ/P fHGmD93tdoOIQDRGE90y9r5hgMP8oflz8yIzowfmQr/zO78TGySvWDgC77n19fWlUv3sz/5s 7Lso5fm5SPYEGoNnm4DZD66/CgDDBeE5n0eqCNWNG/P0AYyEOB28Yy3fQDKUK4eHh5ubm8EE EADwAXG6AHE6wFuTk5OLi4sFi/lWybQzOixKqK5ccsiZFSQApCBOBx9ZU+ooK+bW/v4+ATsA PnjggQeCZ+Ck65V5NjYPmbOzs+9+97vNf82/l5NrI5qHpdjGKRPTzOuYDX7xF3/RtOH+++9X qTP3zZYnT5585xHzj/TsCfNS5mXf+973vuc975k6krRlUCAimCDp+JsAEGHOyiA2t7e3l3Ky +yx6VTFXMPNZXLeoCXZ2duTRUpcALgAniNPBR6Z/I4s49Ho91+3CK4KA3crKykgrTgBAuVIi Wb/+678e2/j+++9P2nh2dja28bve9a6kjScnJ2MbX7p0ybql9cEs6endmhmX9BRnnpzz7zUA JZmZmQnqe2SszFsXsQEADwvq1Y4cU2EFCQDpiNPBU8ti4dfp6WlS6nyzu7vb6/WI2QFwIimS Za1jmHQdk8NCKYlv8mWTMvW63a7cOOmJV97yjKRMFrlo4N/7e39vpDqPWfcv0Fave93r1NGl IFywwpy8QfHcJoXkkhCqK5F1iTyqNwBIR5wOnjJ3NfnstL6+7rpdSLS/vx+smbC0tMT0WORg TvniC01UIEig8F+4hIvnsj/0yviUNZJlfShSCc9F1qw3lZD4tmXLejOHhLW11lWzk3IoZmyx s6SJvTIK2fL5U9aytm0IppTI7EO31wHfBLPak87uNiBUV5ZlMQyTkgwOAAHidPCXvLEZ+/v7 rtuFrMyXtbu7u729HawourKyEl0P9F/8i3/xj2z+8T/+x+9///vNBmb71dXV9fV1h6ugpnO7 kmx2/X6/sm9cnrPLyXXBgJHI+Z7WSFbStFDroWjNekt6KLXelVRCpp4calLJmXrWl7U+y8ko JI98mmllKFsQOm9znE6LJUpZ9yAfeS8g6AlgKOJ08Jd5GpGVgFZWVly3C4Dd0tKSjEq0PNMH JcoYopJpdwHrI7c16y0pymPNelO2TL2kND1ly9TbSl62VZ4+GYOVbWPdh/Wt5Q/niNMFYqE6 wt+jiu1AlVpXAQBCxOngNXl7M3Z3d123C0CcOTHl2cqgMcqVZcqntThdQL6gNest6VEq6WVl pl5Smp71pEhK01O2ib0ZJ/+2kPzeiWAiN+J0IUJ1RciEAyYZAMiCOB18J3vec3NzrhsFIE7m JTEdD6UbGqVKKk4XiCW+pWS9KZGNlZL1Jh/mk9L0lO0pNylNT9kiTRSnSyIH9iYmJtg5yIc4 XRShuny4KAHIjTgdfGd9NOr1eq7bBeDHzCkpz1M5GRAoaOisz6TidIFYIkNK1psSiW8pWW9K ZOolpekpW6ZeysvGgt0Up0tH6grKQpwuxtzQo5c1QnVZyGEV9huAjIjToQZkYsL09DQLSgCe MCejOSVjJyl5rxiHoYGqpOJ0gdhTd0rWm3ygSsl6U6+NSqen6anXZuqlpOkFoskXFKdLR/YK ytKYON3l/uDMU5dU52xwRpxUav3j9724/bta3x71pcyVLRqqM9deTq4UFM0EUARxOtSAuavJ 3AQWlAA8YU5G+WxMZxRjkj7xM6U4XSD6UilZb0okvqW/bDRvKz1NT702Uy89TU+9dmIvxenS mSNBfqek1CGHBsTprmt96tJ3lZq1Xlj+6IPHbnz7U6O+JqG67OTQDsl0ALIjTod6sD7JsKAE 4Jx1+QgejDE+KbEqmW33kz/5k7GfhIlvMuvtp3/6p2M/CcPNQ7Peos/z6Wl66rVPa+lpeuq1 GXMUpxtK9hZIqUMOdY/T/Ujr6T+9plQn5dpyWqmrl2a1/v5Ir0yoLguS6QAURJwO9WA6AbLu jHkWOjw8dN00oL3MCShDEtaFMoGypMz9lL+SM7LDILLMepMhszDxTUZ/Xve618V+ErYwPU0v do7EfnXPPffEfhJO7KU4XRak1KEUdY/TXbihVedceAp0lHryI8dvPH8h+O11rX+39537Pn7p pOp88zP35QjVRccMCNVJJNMBKIg4HWrDOja1vr7uul1Ae5kTUJ6VLB+BsUoJV8lUu49//OOx n4QP3jLELCN34ZOVfOianZ21HvkyTc/8rcyDCxIr5H3NtEoOSgXPwBSny4iUOhRX6zjdba1P PX0zTKY7qZQ1GGc2W/7OrXunHs0RqjMnVPSyZq5a5tJX2geoOesDC10jACMhToc6sU4m6vf7 rtsFtJF1xivLR6ACSdM/reEwmV0VvIj157GfhIlvsZ+bv5VBvSBpy/pzGUAMMvVkRMn8+fz8 fOyHwcReitNlREodijNnqDlmYos+18X3tT7++18LD/6Lp5UebCVt/MwP9YkPXy4eqjMnHaG6 gBzXqWnAF4BDxOlQJ9bO98LCgut2AW1kTr3YyUjSCqphjVjJPLsgyiYHeLa2tqxZb9r2fLW3 t2fNekt6BevbyVS4IFNPvp15WbloaZA3R3G67EipQ5t9Xeu7PvCF4MjvKHX1iU766q7fGOif /5f/761nz4z6RoTqJJLpAJSCOB1qxrqOXq/Xc90uoF3MSSfPRHN6um4XWsE6AzQpFmZNcEvK hpPxnSCtxnqoWzPyrD9MiiHGfmj+Vh8tcR77eVD+Sf7Qyc6vBVLq0GbRON2sUjkCcNkRqouR Ayok0wHIgTgd6se6Oh6zX4HKWGe80hNFZaxBq6S5pdbEN2vWm7alQszPz1uz3rQtde7JJ59M Oi/kw9ulS5diPwmnjcsSdf/hP/yH2E8oTpeOlDq0VnTe6+yY43SB6Gx9c6K1dkq+zIZWLPMK IBfidKgfa70h1n4FqmFd49WckvREUSUZ9jp58mTS05G8ZSQVrdO2EnXyb4MtZVLeAw88EPtJ mMMlw4jWhSyCjWWJune+852xn7T2STgja0odSy6iDW5qfeLxO8NpJ5T63uX7KnjT2FWrpqX9 CpL3C645APIhTodass5+Ze1XoALWNV6Z8YqKybBXTLgEhE5Ygygqmg1qTdmOCrPeZKbeG9/4 xthPwrJEclqu3DicLyaTMt7whjfEfkJq2FAypU6R24J2eOTqQKnTKlt9urK0PFRnfTbhggMg H+J0qCvro1TLi2IA4yYDE4oZr3BBhr1iolkM1senqGjlMmtwJyoalZYZWzHhlnKubkyYpqdt JepiKE6XkUxvYU1qtMHnbuljb//D4JjvnTUXouered/WhupI4AVQLuJ0qCvr7Nfp6WlmvwJj Yk4uc4rJ4ALDxaje0LBX9PnQGl+Oii7GZ12tLyo6IJSeqRcLYcu5ulGx+JEMMEVRnC4ja7ko 1l5E40Wnvj7WUYMbFyp769i4SEtCddaCmPSOAORGnA41Zu1/Ly0tuW4X0Ezm5JJnXEu64PBQ etgr9oCUnvgWe+WULaNZb3pYpl5sgdH0ubqxyeOyRF0Uxemyk8cJ2Yhog+X+QHUeUtVOfQ3E +ueNTyuzZkCzwDSAIojTod6suQy9Xs91u4CmMaeVPNeYQQaHUsJe0eJ0gZTENzlxO6VEXeyY T8/Ui+Vtpc/VjdVtsA5EhShOl501QZIBBjTej7Se/tNrR2E69clZNbh1ucp3b1Wozrq4Fldp AEUQp0O9mbugdXLQ7u6u66YBzWFOKHmWmVOPbigcSgl7yWfClMQ3mfWQUqJOLpmSkqkX2zJl rm4sTU+nlqgjHWxUMvDKUzTa4KsDfffZV8bYTir1l5d/3ppSZ370W9++pTpnH5tVN76zXOK7 tyRUx0gAgHEgTofas94gKVQHlMValk5R4wmupYS95DNSSuKbPJJTStTJ1YqSMvWs66skzdW1 pqYmlaijON2orEFPdiPa4FPXBsfe/q/NAf/hjnq5vyw3eEHrex+9EpwUpafdbWxsREcyGhmq kxdqhlIAFEecDk1gzX1YWFhw3S6gCcypJM8vCq/AB0lhL2v17qTEN+srW7eUWW86OVPPeo4k zdWVaXo6uUQdxelysO551ohH44XpciohDBddcWJWqVvPnim3AeYsi157Z2ZmmpTKan0AYRQT QHHE6dAQ1nJC6+vrrtsF1Js5ieSZZU0UAqpnDb7I4nQBa+Jb0sFsvadYs96SMvWsj2pJc3Wt AaOkEnVNesqtjNlpMlDLpQxtcFvr5e/cOvb2V1aCeuqDxwY/fCa2wTcGuvPoM+a39yv1g83Z 0hsQC9VNTU014yK2t7cnryrU7QVQCuJ0aAhrF9zY3t523TSgrszpI88p6jrBH9awV9LUKmvi W1JmqDVLwpr1phMy9axbWufqWtP0dMJsTWZU5WaNe1JGCi3xzA91Z/Hz5pi31qG7cEOrzrkz Sr109ZFxvHsjQ3XW5SOs2dwAMCridGiOpEJ1/X7fddOA+jEnDmXp4Dlr2Csp8mJNfEs6nq03 lKRpku94xztiW6ZE006cOBHb2Px50sbHjx+PbUxVtSLkRGkGHtAeP9J6+csv3v2+f9tR6jMf Of6djWWtv29+/vxAz/7JV0+q2W9+5r7gJ+Ngrp/RUm51D9VZ7xGUBAFQFuJ0aBRrugRrSgCj Slo7IimfCHDl5MmT5si85557/ubf/JvBUZqSzmA2Mxv81JFg45RXDjYINzZ/m7RleOt5wxve cNddd6VH04K5umYzs/HQ0yooURfdmOJ0RVhjtY2sbQ8kua3148/fevjixWAmrBGE7V7c/l3r grAlOjg4iMbKzb9rWiPSfBC5fERSyQUAyIE4HZrGWnh7YWGBUB2QkTlZrGtH8DQLD5nHvDAw Z56d0vM9oxubf6Q/Iprfhuke6RvH3jf6LlLspcwfpiSVjLQxsrDWNCRNGKhGLFQ3MTFRx1Ad lxEA40acDk0T6wGEVldXXTcNqAdzssgzqO5TVABAJ1SznZyc5PoGVKPuoTprWi7LRwAoF3E6 NJB1ASbF8q9ABtYFXimNDKAxrMuPUFgKqEytQ3WUuQRQAeJ0aCbrYJdi+VcglXWBV5VcPh8A 6mhmZoYLHeBWtFLNxMRE0rxRs5k/6+eMtBQ4AORGnA6Ntba2RsQByC4pup20eiYA1JQ17z5l lV4A4xArKi37G+EGPlR/s3aTZmZmXLcLQAMRp0OTWeu8Tk9P9/t9100D/GJOCusCr/4MYgNA iawLxDP7FahYSqgulnDnfG6pdcYrVUEAjANxOjScdflXQnVAVFKQjgVeATSYdfarD2k7QKtY Q3WyA+92rQZmvAKoEnE6NFzS8q/T09P7+/uuWwe4Z04Ea5COBV4BNJt19itrvwLVi0XB5ubm ZLfEYVxsa2tLNoYZrwDGhzgdmi8pVLewsHB4eOi6dYBL5hQwJwJBOgDtZJ39SioxUL2kutJR TuaZmu7Q5OSkDy0B0B7E6dAK1jFzQnVouaQgHb1PAO1hnf26sbHhul1A62QJ1VW/3ou1hA4z XgGMFXE6tMXOzg6hOiCUEqRjTWQA7WEdyWO4Aqheljidqna9l42NDdkAZrwCGDfidGgRQnVA gCAdAIR4FAecs56GSapZ74UgPgBXiNOhXayFYAnVoVWSgnSVdXwBwDfWuvVVpu0AbZY0lJ6k mvVerOWtmRQPoALE6dA6SUn1hOrQBilBOnNquG4dALhhLRXP6AVQgVGDdIFxr/eyuLhY/ZsC QIA4HdqIUB3aiSAdACSxZtxPTEyw+DUwPklLvWUxvtQ26yTcapL4AEATp0NrEapD2xCkA0Zy W+tTT99UqmPOkTNKvXT1EbnNj7Se/tNrZhuz0VMfOf5y/3L17USJlpeX5RWSQnXAWG1sbFiz WYcaU6m4pNAhBXwBVIY4HdorKVQ3NzfX7/ddtw4okzmkrdWXCNIBKS7c0KpzTh3F6q4+0TmK 3b3GMwN97NTF8Gw621HXv/yg3Aw1MjMzI6+Ti4uLrtsFNNnBwYF1nulQpYfRTUusZem63W65 bwQAKYjTodWSQnXT09OE6tAY5mA2hzRBOmBUL2h976NXgpOld9Z0mZ6PbbD8olbqbPSc+hWl Dp4946S1KIV5Srem0lA8Hhi3nZ0da4wsXbnrvczPz8u3mJubK/EtAGAo4nRoO0J1aDaCdEBu N7U+8fhucL481lGDGxeiv5UTY6+bn1z67kPH3n7j25ZJsqiLpEJ1zHoDKtDtdketWFfWuWl9 KKAsHYDqEacDXrkrJ3UItre3XbcOyM8cwNYD2xzwBOmALB65OlDqtDlr7lfqB5uz0V9Fs+0u nlZ6sKWPKtb98uaLv338+MvXLthfEXVgLVQ3NTXF4zpQgb29vaRiHValnJs7OzvW/hIBegDV I04HvCJlSfjNzU3XrQPyMIduUpCOTieQUViBTpaoS/rVj7Q+8+fX9y6dolBdrVnDBEx/Ayqz sbGRPbGuYBHJpLUjGNQE4ARxOuCOlFDd6uqq69YBozEHLUE6oLjva338978WnD5h0lwgJdUO DZBUTp41JYDKjLS+RO4ikkkn+/z8fJkfBgAyI04H/NjOzk7SwvBLS0uHh4euGwgMZw5Uc7ha D2NzeBOkA0Yii9DJn8vSdWiGpAE8UmyAKm1tbWVZX8Kcrflmv1rXjjDvWPLHAIDMiNMBr5E0 pGYsLCywsgQ8Zw5Rc6BaD2AqKwH5XLihVeecOYlOKvXNz9x3lGOXlmeHJtnY2LBeURnzACq2 vLw8dBpsjpnp1ny93CE/ACgFcTogztyYZ2ZmrLf/6enp3d1d1w0E7MxzY9LSruaQpscJ5PN1 re/6wBeCU6mj1OZHj+vB82FxOuNrf3CX1n/lupkYF+uaEtQQAKq3t7eX1EUPdbvd7C9oXeBV EYgH4BpxOsDOmgMfYGUJeChp1QhFgRWgmJtan3h8NzibZpU6/qu/+tQHj33qe98KkuwoTtcG SdPiGP8Aqpe+vkT2GPrW1pb1FZjYDsA54nRAom63m9QJWFlZoVwdPGEORXNAJh2rIw0sA7AK l4w4o9QL337kvt/7SlCZznhYqdsvLrtuIMYrqSYGoTrAifT1JbKUlkuqPslCMQB8QJwOSJMy ZEe5OvggpSCdOXRzr30GICqc5dpR6uoTnet6cOrSd4+y6yhO1xYHBwfW/gAJy4ArKetLpIfb OJ0BeI44HTBEyiKw09PT29vbrhuI9jKHX1JBOpZ2BUr0gtb3PnolOLmCwNxtrX/r27dU5yzF 6dojKQGHZ3vAIWsFSWNra8u6PemxAPxHnA4YLmVlCWN1dZU5sKiYOeTMgZd0TLJqBFCu21qf evpmMNf1sY4a3LgQ/PxT1wb3Tp0LF4FF4yUt/0qFAcAh6/oSk5OTsi+UFKSzbgwArhCnA7JK KYQxNzfHHFhUxhxs5pBLOhoprQKMw4Ub2rpwxNq1wb3v+0SwCKzD5qEySQtEUnsecMucg7GM V9NZim1jXROG5ZsB+IY4HTCC9BWmWAcWFej1eklHIAXpgPH5utZ3feALwbl2ZemY1tfDX33p lr77fU/+8QePvXztcw5biMokjdsRqgPcOjg4iEXiomclQToAdUGcDhiNuZcn1aw1lpaW9vf3 XbcRzWQOLXOAJR175rCkowmMz02tTzy+G5xucu2Iz93Sx97+h2c76vqXHzyaJouGsz7wE6oD fLC1tRWWlp6YmNjb29OcswBqhTgdMDI5WBc1PT1NYh1KZw6qpCUj1FEVc+qqAOP2yNWBUqfN GXdGqZeuPmL9rfn1V/5kljmwbUBuDuCzcH2JmZkZgnQA6oU4HZCTrIIRRWIdypKeRmcOQnqZ QDWeGehjpy6a8+6EUt+7fF/0V7e1fvDZW0rNBifm2Y669NSZG89fcNVUVCCpID2hOsAT1vUl Qiz/AsBbxOmA/MztP2UOLIl1KC49jc4cfsFsDgAVeEHrex+9Epx9YYm6MHhnFVt0Ag1DqA7w XNLCL/Pz866bBgCJiNMBRYV59VYk1iGf9DQ6wxx4rtsItMttrU89fVOpjjkBH+uowQ3S5UCo DvAXQToANUWcDiiB6YuHBWuter2e6zaiTlIWdTXMwcbjH+DEhRtadc6powmut54947o58AKh OsBDBOkA1BdxOqAcppu+uLiYEluZm5vb3d113Uz4zhwk5lBJOZDMYcaSEYAr4SzXjlJXn+iw tCsC5rJsLVlLqA5wIqlPTpAOQC0QpwPKFF0J3mplZYVpsLAyB4Y5PFIOHnNomQPMdTOBVrup 9YnHd4NT8uJppQdbrlsEX+zs7CSF6rh0A1VKWt2VIB2AuiBOB5RsaGLd9PR0r9c7PDx03VL4 whwM5pBIWS9CkUYHeOORqwOlTpuz8mGlbr+47Lo58EhSqM5gYW6gGgTpADQAcTpgLExnPWUp WHU0DXZ7e9t1M+GeOQzSJ7qaA4lpU4A/Lt/W6sR5xVqusCFUBzhEkA5AMxCnA8ZoeXk5qb8e WFhYoGhda5mv3hwAKYeHOXhY1BXwzQta3/voleAkvbJ0TOvrrlsEv6QM1BGqA8bk4OCAIB2A xiBOB4zX3t5eeraUsbS0RLSuVczXbb709KPCHDbm4HHdUgBxt7U+9fTNo5Uk1PkTSt++7LpF 8E7SCrAGoy9A6VLOOIJ0AOqIOB1QhaHrSxirq6ssMdF45is2X3T6kcB6EYDnll/USp01Z+sZ pV66+ojr5sBHBA6AanCuAWge4nRAdYZOgyVa12BZInRMdAVq4ZmBPnbqojrKqbv6ROcoxw6I SwkfzM3NsTQQUFxKRUiCdADqizgdUKmhq8ESrWuefr8/NEKnWNEVqI8LN7TqnAvOXErUIUVK qM78nGs+UERKkK7b7bpuHQDkR5wOcCBL0TpF3br6y1KHTlGKDqiJM8/eVupE7Px9rKMGNy64 bhq8llTefnJykuW8gXzW1taSulUs2AKg7ojTAc5sbW3NzMxkidZtb2+7bixGY76yLBE6cwBQ ig6ol8v9wfLyp5eXl3/7E5/oLD5xv1I/2Jx13Sj4LilUNzExwV0AGJW5AhOkA9BgxOkAxzJG 6+bm5jY3Nw8PD123F2nMF2S+pizJkkToAKBViCwAxR0cHKREvUlQBdAMxOkAL2SM1k1PT6+u rvb7fdftRVxQhM58QUToAABWKTP1qHkPDJVS8JFZ5ACahDgd4JGM0TpjYWFhe3ub9DrnzFdg vgjzdWT51ojQAUDLmbtAUuV7c49gZQkgScqqEazKAqBhiNMB3skerSO9zqHsCXRE6AAAoZ2d ncnJSevNgpwgwColF3Vubo4gHYCGIU4HeMr01JMKcFj7KJubm/v7+65b3XxmJ2esQBcwXyIP XQCAqJTpexMTE5SrA6JS+sOLi4uuWwcA5SNOB3htb29veXk5Kc9fWlhYYLmJcQgWiMg4vzV4 0DJfnPn6XDccAOCplOgD5eoAfdQNTopoKxZgAdBcxOmAGjg4ODB9kZSeirSyskKGXXFB9tzS 0lL2PW++JvNlMQUDADBUt9tNuZsw2IM229jYSBqoZmlXAM1GnA6ok62treyTYQNBhh017EZi dtdI2XMB89VQhA4AMJKUlSXMzzc2Nlw3EHBgcXExqbvFqhEAGo84HVA/pnfS7XZHSq9TRzXs VldXd3Z2mBVrZXaL2TlmF2WvPRf2F83XQZcRAJBP+uQ+KnChVdJPB6aEA2gD4nRAje3s7Jju e/bqdaGFhYVer7e7u+v6E7hndoLZFaOmzqmjNAez85l2AQAo7uDgICVffmpqitsN2iBlrqui IB2A1iBOBzSB6daY/n2OgJ2xtLTU6/Xak2cX5M2tr6+PVHUuZHay2dVMRAIAlG5tbS3l7tPt dl03EBiX9FD15OQkoWoA7UGcDmiUIgE79erc2M3NzYal2pmPYz7UysrKqHNaow9IhOcAAOO2 s7MzOTmZcpumzAKah8MeAKKI0wHNVDBgF3aMVlZWgmy7Gq1EYZpqGmyaXSQwFyA8BwCo2MHB QcrNi8Ul0DDLy8spPTHSSAG0EHE6oOGCGnajLjqRZGFhIZgnG5S3c552F7QhaI9pWI4yc1Zm d1F7DgDgULfbTRlsm5+fJ8MIdWc6WikdVOa6Amgt4nRAW+zt7a2trZmefcrMgtyC+F2QfGcE M2cD+/v7+Rps/jB8EfOCwSubtygxHhdldovZOWYXmR1V6o4HACCPoVGMra0t120EckpPoyMS DaDNiNMBbWS6/t1ud25ubhwxuxoJYnNmVzBgCwDw0MHBweLiYsqNjNJdqJ30ADQzuwGAOB3Q dkGeXYlzYz03MzNjPix5cwCAutja2kqZA0tcA3VxcHCQnkZnOml0zwCAOB2A1zAPA91ud35+ 3nSVxhAlcyAIzJkPxfwgAEBNpS8uoQhwwHumG5Y+jYMlIwAgQJwOQBrT6d/Y2FheXq5L5M40 0jTVNNg0mycWAECTmFtbemKduf25biMQNzTKPDU1RQUSAAgRpwMwGtPZ2traCoJ3i4uLM0dS HhvGwbxd8L6mAUFIzjSJAj0AgMYbGvJgfQl4JX3lYoPgMgDEEKcDUJoghBdYjjBPFDOvFe2f zdiYP4m+QviyBOMAAEhPrFNH60uQVA63TLctvfYxk7UBwIo4HQAHor00120BAKB+hi4FG0yD ZXwL1dvb20vP+jQHJ9XoACAJT8gAHCBOBwBAcUNr85vfrq2tuW4m2mLoiq6KZE8AGIYnZAAO EKcDAKAsy8vL6dNgZ2ZmKFqHcVtbW0s/DimeCABZ8IQMwAHidAAAlGjoTENFOTCMzcbGRnpe pzpaL4JZ2ACQBU/IABwgTgcAQOmGToM15ufnidahLOaQi60PJjHRFQBGwhMyAAeI0wEAMCZD p8ESrUNxWSJ0THQFgBx4QgbgAHE6AADG5+DgYH5+Pj2GQrQO+WSJ0LGiKwDkxhMyAAeI0wEA MG57e3tD4ylE65BdlgidohQdABTDEzIAB4jTAQBQjYyxFaJ1SMFRBACV4QkZAAAAaLgsK3Kq o5L/FBRD1NraWsYjhwgdAJSCOB0AAADQChljLjMzM2ZL142FSwcHB8vLyxmPFmK7AFAi4nQA AABAi2SM1pltKDTWQnt7e/Pz80OXDCZCBwBjQpwOAAAAaJ2M0Tp1VHRsZ2fHdXsxdhsbG1mK 0BGhA4CxIk4HAAAAtNTa2trU1FSW0IzZzGxMel3z7O3tZZziGgRtidABwFgRpwMAAABaLeNq nsbExATpdY2xsbExNzeX5XtXrOUKAFUhTgfAgWi3z3VbAADAK3Z2dubn5zNGbSYnJ7vdLoGb OjJf9OLiYpYKdEFkljKFAFAlnpABOECcDgAAPwWzIDMGcYy5uTnmw9aC+Wa73W7G+a1BKJZv FgCqxxMyAAeI0wEA4LnspevCgN3GxobrViMuCM+N+lVShA4AXOEJGYADxOkAAKiFkSbDqqNp kmTY+SBHeG5ycnJ5eZm5zADgFk/IAAAAANIcHByMml6nXp0SS9ynSjs7O8vLyzm+KXIhAcAT xOkAAAAAZDLSEgShqakp81esEjsmBwcHGxsb8/Pz2WvPBVgMBAA8RJwOAAAAwGg2Njbm5uZG igqpV2fFEhsqRZA6NzMzk+NbIGwKAN4iTgcAAAAgj2A+bI6AnTpK5pqfn2di7Eh2dna63a7Z 4aOmNAbhObPDmd8KAJ4jTgcAAACgkCIBuyBmF+TZscxojNmxZp/ky5sjPAcAdUScDgAAAEA5 CgbsAjMzM4uLi+Z12jk3c2trq9vtzs/Pj7ocRFSQrkh4DgBqhzgdAAei/UjXbQEAAGORb3ED a9jOvE6QbXdwcOD6Y5Vsb2/P7Kjl5eW5ubkigbkAS3YAQN3xhAzAgWiH0nVbAADAeAVV1XLP 3IyZmJgIInfLy8sbGxs1miobTGJdW1srKyoX7hDzalT6A4Bm4AkZgAPRzqXrtgAAgOpsbW0t Li6WFaKKxqpmjiwfCeJ3TtLKgmBcGI8zH9a0qvTPawQV/UidA4CG4QkZgAPRXqbrtgAAAAcO Dg6C+Z5l5dmlCKN4YSJeaG1tbWsU3W43+udzc3PhK1fwKVhtAwAajydkAA5EO52u2wIAANwL QmBzc3PF69k1ydTU1Pz8fGuX1ACAFuIJGYAD0Q6o67YAAAC/BFNHg2y1toXtootmuP4eAAAO 8IQMwIFof9R1WwAAgO/Ccm8zMzNNitxNTU3Nzc0FBfXImAMAaOJ0AJyI9lBdtwUAANTPzs5O kHM3voUayhUucxHkyrE2KwDAiidkAA5Eu62u2wIAAJojttpDlYs8BG8U5MeFK1SQJQcAGAlP yAAcIE4HAABckau4LmcQ5MFFEYMDAJSOJ2QADhCnAwAAAAAghidkAA4QpwMAAAAAIIYnZAAO EKcDAAAAACCGJ2QADhCnAwAAAAAghidkAA4QpwMAAAAAIIYnZAAOEKcDAAAAACCGJ2QADhCn AwAAAAAghidkAA4QpwMAAAAAIIYnZAAOEKcDAAAAACCGJ2QADhCnAwAAAAAghidkAAAAAAAA wD3idAAAAAAAAIB7xOkAAAAAAAAA94jTAQAAAAAAAO4RpwMAAAAAAADcI04HAAAAAAAAuEec DgAAAAAAAHCPOB0AAAAAAADgHnE6AAAAAAAAwD3idAAAAAAAAIB7xOkAAAAAAAAA94jTAQAA AAAAAO6p/0294v8XAAAA//8DAAIszgOq14AA</item> <item item-id="4">iVBORw0KGgoAAAANSUhEUgAAA4QAAAF4CAYAAAAMgTdwAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAHK1SURBVHhe7d0L7F3VfeD7v8EuNoGM 3QYBVUfYYBouapWQQChoJP8xNiEzUAxMlNArFTOlM7pII6czMHNH987YBnuundSDzWOoZ4pi U+KEFAtjpgqmiTCPODxqMARqLF0VGoOrJGJqD7lNIuuvff+/fdY653d+Z61z9j7P/fh+0E/4 v/Ze5+y9z36tvddjKgEAAAAA1BIFQgAAAACoKQqEAAAAAFBTFAgBAAAAoKYoEAIAAABATVEg BAAAAICaokAIAAAAADVFgRDIaWpqqhkAAABAmXFHC+REgRAAAABVwR0tkBMFQgAAAFQFd7RA ThQIAQAAUBXc0QI5USAEAABAVXBHC+REgRAAAABVwR0tkBMFQgCop2effTb5R//oHyXLli1L Q18P+g3/eTfccENy3nnnJffee6/7NgAYD+5ogZz0hRwAUB1S4JNCmYQ+1xchpOD4qU99Knn9 9dfd0gLAcHBHC+SkL9AAgHKSN3FSyNLn9DLGrbfeyltFAAPhjhbISV+IAQDFJ2/+1qxZ03b+ HkZIVU95m7h27do05N/yXd1CCqEyr1QRHWbVUx0UEAHkwR0tkJO+6AIAimnQt39SuJMqmlKI m4S///u/T77+9a+nhcdB10UKnwAQwx0tAAAoPXkrJgWfUIGoW0ihr2xv1PybxtD69ArJBwAa BUIAAFBKTzzxRLDQ0y3kzd+7777rPqE6pLOZvIVEqUZLJzUAKBACAIDSkKqUeQo+da0uKYVl 6XAmtE1CwZtDoL4oEAI56QsoAGA8shYCZT46Vekk2yS0vUIxqXaTACaDO1ogJ33RBACMjlRn 1OfcWEghUN4cIpushUPp0IbtClQfd7QAAKBQpHfNUAFFhxQCaf82uKztMHlrCFQXBUIAAFAY vQqDDKEwOtLjamib+6BQCFQTBUIAADBx0vtnqBDiQ6ZTfXH0snREQ8EQqBYKhEBO+qIIABjM smXL2s6rNuj9cjLkTW2vQroUHgGUH3e0QE76YggA6E+vQeQpbBRHr0I7vboC5cYdLZCTvggC APKR6ob6PGqjioPGV0WvgiG/HVBO3NECOemLHwAgGxnCQJ8/bVCYKI9uY0JSxRcoH+5ogZz0 hQ8A0J10BKPPmzakrRrKac2aNcHfVIJqpEB5cEeL2pObFRmHSS5svarD9BPytFS68paQ7wGA uujWTpDhI6qj23AV8mYYQLFRIERtSLuVbtVcJhVyIeVJKoAq6fZWkAJCNfV6EwyguDhCUUmv v/56Wvjr1WV2UUN616OQCKBspFAQq0ZI27J66FaNlFoyQDFRIERl9OrCPGvcfvvtycaNG5Pd u3cn27dvT44cOZJ8+OGH7lu6e/nll9P59+3bl+afN29e8DvyhrxFlEIuABSVPMQKnb8kGMi8 fmKdCPFgACgeCoQoPXnimLcqqBT4pIB19OhR9ynjJd+7ZcuW3AVGLqQAiihWXZBzFkL7BTVg gGKhQIhSylsAlDj11FPTN35FI28U169fH1zmbiHbgOo3ACZNegkNnaMkpKCIepOHr6F9Q4Ka L0AxUCBEaXSrjhSKzZs3u5zlI4XEpUuXBtcrFFI45MYLwLjFHs7xZhBWbF+RNvMAJosCIQpN CoHdurPWsXjx4olVAR21nTt3Btc5FPK0nsIhgFELnX8oCKIbOhwCiokCIQopawcx0nnLP/zD P7hc9SDrK4Xf0PawQXUcAMMWe0jHmx5k8e6770b3IQCTwdGHQskyMPw999zj5oa8EV24cGFw O+mghz8AwxCr9sfDJ+QVa3tKDRdg/CgQohB6FQSl0FPV6qDDIgXl0LbTQcEQQL9C5xQJoF+x 3ml5wACMF2dyTIxUGwldCHS8+OKLbm7ksWLFiuD29CFVcoEiOvnSF83+usRNwSS1/yaNoN0X hiW0f0kAGA+ONkxErNqRj6wDwaO7Xm8NZeBgoIg+4ffThQ+6FExC7A0O1fowbLH7AgCjx5GG serVWQwFwdHoNc6hvK0FiuNkstDtmxf/8OcuDeMWKwwCo0JnM8BkcJRhLLoNTHv77ben4+5h 9GQ7h34DCap/oTBObmrul8dcEsYrds4GRi324BjA6HCEYeRiT5kvvPDC2g0ZURSPPvpo8DeR MaKoCoZJmzl8h9sn/8ClYJykMBiqvgeMk93/GNYEGB3O8BiZWEHQB9VDJ2vjxo3B30VCCobA pGxv7ou0Hxy32HmbHooxblIAtPshhUJgNCgQYiSksxJ7Ivexe/duNxeKIPQb+QDGr9V+8Kz9 J1waxiFWGGQIAExKaKxCCoXA8HHHh6GL9RS2ZAndxxfVvn37gr+ZhFyQgbGZ+dPmviftBx++ am7b/uhjet9PG/NjKCgMoqjOO++8jv3y3nvvdVMBDAMFQgxNrBMCaSsoBQ4UX6xgSIczGJcj Wxe7/e4sl5IkmxY29sPp//qD5K0Zl4ih0se7hBzzFAYxSVJN2e+PoUKhBIDh4GjCUEibs9DJ moJgOYV+Swlg1Ja6wt/UJY+lf//x9NxkatVUsuv9X6Z/Y/jscS4BTIrUSrE1jUSoTSEPK4Hh 4KyPgcTeCkrQg2i53XfffcHflQswRued5n724PFfJqvk33NOSX7ipmL47I23BDApdl+U0D1f h8YplDQAg+HMj75JHX57YpZYvXq1mwNld/To0eBvLAEM28zbF7n9a6Ha1+a4qRi20M01MAmx WkahHq9DDzHoGRsYDGd/9CV0IyFBFdFqCv3WEvrJLTCY55JN55za2r+m1zX//Qs3B4Yn1BM0 xzPG7d133+3YD310q42i2xf6oM0r0D8KhMgt9HTu9ttvT98mobrkza/93SW4CGMYTuyZ0xxu 4uKH3pxN+UlzH3vwOD3JDFPoHM5xjHGLPViWuOGGG9xccaG3igD6w9GDXEI3Evfff7+bijqw v78EN5MY1OY5rf3pL135b67fx/7khUZC8rNk+Y4vJifdX8hPbrT9dvZBdTuMU+g+wkZWoc8C kB9HDjKzJ12JF1980U1FnYT2BcYrRP9OqH2pNV7pk5/xaWcl73xzOlk6tTyZmvsVNxV5harZ yQ01MA6xfgds5K26bPPLPk31ZyAfCoTIRG727UmXKqL1tmTJko59gkIh+vP/tvaj39/t0mZ9 0N7O7YsvcZM3CN6mYFK69UiuQ9q29sN+TpYqpwBauBqgJ3uilVi+fLmbirqK9UBKoRB5SfvB xv6zKJl/32GXOmvmf7T2rTnTVBUdQKgTGaqKYhyyVBH10a/Qd9CUAciOAiG6sidYCelABhAy 1mRoH6FQCBSHVJ+zxyhV6jAO3XoRtfHEE0+4XP0JfSaAbDhaEBV64kZhEFasUMjTWaAY7LHZ b7U8oB9ZqovK/cYwjOpzgaqjQIggCoPIQwqFsn/YfUaeDgOYnPPOO6/tmOQGGZMQ6odAx7DI W0b72dKZDYDuKBCiQ2hsILnhB3oJFQqpmgZMRqi6HjBuofarOob94DBURRpAdxwlaBMa6JXe RJFHqPdRAOMV6uJfhp0AxunWW29t2wft36PqDdQWQnkzDnTHnRqaQvX8aQeGftj9SALA+Njj jxtijJst/Hk6fZT0d0tQdRSI4y4NqVCbwZ07d7qpQH52f5J9jOqjwOiFzufAuNgqm5N8GKGX QwJAGEcHglWL6EAGg4r1Porq0b9vqD2QpJ9xxhnNeSw9/cwzz3SpDf78NHfu3PT/toqZ/C3p n/jEJ9L/27cA/m2Enz5o1/ZFF6rpQVVRdDqZbHtgKrnrnzSOKxkD9J4XP3DT+lekwqCwzWCk kyUAnbg7q7lQ42sJYBhCg9dP+gYBw2V79bM3XKEHTtqyZcu6Tp83b17X6XaazK/Z6dJpVpXZ 9eV4g/Xn/2Kh2z8WJmuf+OvkJ7Npr2xt7C93HP55Y6Y+2IcRRakRopdJgt6vgU7c+ddcr5sx YFBS9Zh9rLp6PYH3b/B0aL3OQXbasKdXiX3AJ9sW0D766/8r3TfmrtrhUhq++0eL0vTpfT91 KfnpqspFKQwK29vuqDqyAcqsuldG9CTViPRJUoKqohgFu59JoBpCv62MOeaFpusqjKHpujOr 0HRt0OlVYteTTsGgzby/rblvfPujGZcqDrX2m/0nXFo+ujBYxLfwzfUr8DICk8RdWY3ZE+SR I0fcFGC4ZN+y+xtPaavB/q4S+i1haLp+cxWa7qs5htrDSfgCZWgQaglfEIoNhl2ktxfDYh/w UVUU7X7W3DcufuQ9l+adTG5aNDtt+ivu73z0fidtdoso9AC8iucBoF8UCGtKP82TuPPOO90U YHT0PidBW45ys1UUdYhYgU5ChG7SfAh7nvLhC5TylD803ReGpGAaml7FtwN2HQHtyDo/Puyi 2eLf8Oh9rqiFQU8vqwQPTYAWrho11KuTB2BUQgUElFesQCYhYgU6CRFqP+hDhNJ9DGN6VdhB uHn7Dqu5f/z6RpcyGDvGoK4mXlShB1g8lAQaqnVVRCb2hCjDAwDjMj093bb/0Q14eenf0Uas uqaPbm8HJbq9XZSw33/aaae1/W2n+2ErfFSJXq+qrRuG4b3W/rHpRy6tf2UsDHqhWgMAZq8j 7v+oCfvEfvfu3W4KMD56H5SgLUc56d/Qvi20N1723GPfDvaa3/5t2w/a5bE1Iezbgarsc7aX V/kbaHewuX8s2vRDl9buL/+PqeSLL33k/orTx7kck769bpnYc5VvkwzUGQXCGgk9kQcmIdTJ DMolVMDSf9sQtlCnQ4TSfdgCnr2ps/ntd9npkr8K9DpJoDj8Q47J+0nbPiLjDracTP7tKWck F596iknvZAuDZaa3hwRQdxwFNWGfIktQVRSTZKuOSjsolEevApkNEWq/7EP0alMYK1D6aseh aRK+8GfTy463g8Xm9+ciuF3tJ+1xSvr/XoVBfexV4WGKPdfQlhB1V/4rIjLRJz6JjRuH07Ac GITdL+kMozzsbydsIVGHF5rmC3QiNF1u3kSsQOnbMMUKlFK9VIQKsWVF52DFV6QCYZIcS6bN /iKx7e3jbnpc1QqDnt4OEkCdcQTUQOgmDSgCacPKvllO+jfzN4mxcQF9gU6EputOKULTtUGm V6kdoV4PCd4OFk+xCoT52Y6hij6sRF62xkFV2hUD/eDuq+JC7XoYgB5FYvfPsrdNqYNuBate D6BC1T41O22U08v6toP24OVQ5gJh1QuDnl5HXVMBqBuuIhVnx6eSQD4vv/xysmHDhrSa7V13 3ZXG4sWLk8997nPNuPTSS5NVq1Yl69evT3bu3Jm++RpXbN++PS3kjzuOHj3qttBgPvzwQ/bR kgn18OmF3hJqvao69jpn2QKlvYnT0yRsoa/btLKw3f5X9Wa97MpcINT7lxyzVWXPJ0BdsfdX nD7RSezYscNNgSWFHCn02W1GEET30EK1ErTQ+IKaffulq5uKXgVCfxPuwxb67BtMmb9b6HkJ op+QhxwS8tatDIUrvey6OncV2Y6ZGIICdUWBsMLsU+RHH33UTYH32GOPtW0jgiDyRegtm57+ 2c9+1qW26OlnnnmmS23R07/5zW+61AZboLS9A9oCpW0XFGvnSBCTCnnwIMdREXq61MtV9cKg p9dZAqgj9vwK4yQXtm3bto5tQxBE/ijz0/TQ+hBEUWLcbxLtm/0yDjjfL6lloNcdqCP2/Iqy 1SBefPFFN6WeZMzFCy+8sG2bdAt5Whu7IOr5upELep4qZ7zBLedDDLmRkoLRuEPedPmqaOMM Obf46pRlvmmUqqd+PcoU+vg4++yzg/NMIqRGSmh/KWvIfh7qAGncIcsw6uOszoVBT68/ncug jigQVpQ+uUnU2dKlSzu2hw256NqqZTE6Xx5yA5/lBkM6iqkreXChtwVd6QMtvMkYPXnYordx r5BOxA4dOuRyt5MOyGSeEOmUa8uWLcm8efM6PjMWoyioSOFPf0fW62DV6G0gUeWOdIAQrigV ZLuLruPbQbnY6m0QCtlO/Vz89Gf0S763V+Hwtttuc3PXi90OABo4NkbHtrkPxQ033JD2JD0q 0uOyfH7ou3UMq2Bor0F1LQwKKQDqbSEB1Al7fAXV/aQ2PT3dsQ18yAVw0Ib7+vOGodeNiPR+ WidSdVavvzyxB+rOvsmRGgcYXKggYGMS52ApHC5YsCC4PD6kgNov+1l1Lgx6dpsAdcIeXzFS 2NEntFE+zSyaXm8Fh3XB0585TLbdp47Vq1e7uerBrj9Qd/ZtDgant6eNFStWuLkmLzRWq468 bf50XsawbLHH2CAFbqBsuKpUTF1vGu6777629fZhxxwbhmF/nmWXWYd0jlMHK1eubFtvoO70 8UCnF4Pp9VZw8eLFbs7BdGtD2I9eBcMsDz31/NJ5DlrsA3UJoC7Y2ytGn8ikukkdLFmypG29 ffiqocNuJ6E/a5RsBxI+5E1oHeh15gYYdWarixZhvLqystcDH+vWretIG5R0OCPXp2Gz+4OO boU8PV9dxhjMS28jCaAu2NsrxD7dqsPbJNsrpYRc8DX7NDg0kHYe+rNGLXbhr0NHQXaduQlG Xdnq5OiP7XDNbs/LLrusLb3o51lpSqCX10fooaduq05hMM7W0MlbHRcoK64sFSEXgFNOOaXt RFZ1el112IthqBtxW2jMQ3/OOEg7Bv2dPu6//343RzXdc889bevLW0LUlT4OJJCf3YY+dI2L 0Fi1RT7PykNfu7w+dEFGF3IGufbVgX2AzPZCXXBlqYgzzjij7SR2/fXXuynVpNfVR7cTd2h+ ibI8KY29Kax6odCuL1BH+higo4t85AGh3n46rIMHDwbnu/32290cxRQrGErhhsJgfrKd9HYE 6oA9vSL0yUvigw8+cFOqx66rRK9qoKE8PsrSy1rsxqbKhUK7rnS1j7qxbyyQXeyc2a2AF5pf op+2gDKWrOQdF7vMOgZtKlEntlaRrXUEVBFXlwo499xz205eElVl11MiS4EulE9HmS6WoXEL q9qmcM+ePR3rCtQJbyv6EysMSkcv3YTy6Mhzrh12L6NZxDpZQz562/FmFXXAWaIC9IlLQgZm r6I5c+Z0rGtW9qYqFFkbj+s8kxIqFFa191G7nkBd2LeDy5Ytc1PQi95uPrII5bOR1SQKhKGO 1iQo1ORjtx9QdezlJRcazLyKvYveeeedHeuZR6hjGR15erDU+SYp1NlMFdl1pNoo6sLu+/R4 mI3dbhJZhTqW8ZG3Jsa4C4SxwqAPqo1mp9teSgBVx15ecvqEVdUT15EjRzrWsZ86/fYzfJRZ 6M1n1Tz22GNt60dvo6gLvd9LoDu5Lgx6Tox1LNPPg9ZxFgilKqxeXhGqPsoDtez0duNhDKqO K0yJhd56LV261E2thg0bNnSsY789g+rP0DcNZelUJkavl4S8Ta2axYsXt60jUAd6n2e/781u L4l+CnI+79y5c9s+K69xFQilY7HYcobeGiIbvc2ocouq48xQYvpk5aNK7cg+/PDDjvUbpMqL nNClTY6nq1yWnd5GEv30iFdk9i0xUHW2/SDDTXQnNQf09pLo93oo1Ubl+iPkM/znyXkoj1EX CEPDTYQM2uSirthmqBP28JIK9aC2bt06N7Ua7PpJDJv/3DxP/0a5PP2SakB6uSTytncpOr1u tIVB1dkCjtQIQZi0AdfbSkJqlwyL1Lzxn7tv3z6X2tsoC4S2MNjrIaCeV2Lt2rVuCmJoR4g6 YQ8vKTmZ6xOVhH+iWQWhJ5+jGAtIqp/6z89KL1OR2LYzl156qZtSDXrdKBCi6vT+LoG4cXSw 5T83z3l1lAVCGUvRL1PWN6F+fh/ozj5oAKqMPbyk9Emqiierca6f//ysbQnHsUz9CLUprdJD glNPPbVt3YAq0/s6+3t3dlvt2LHDTRke/flZz6ujKhDqNtXdBtm3fB4f/bbHrxO9vehYBlXG VaaEQtUDJarCtheTN1953g4e+NN/3sz77aO/TNOe+DfLkrnrfiU5mf7VLm9bQj9v1vnHyb4l LOIy9st2jgBUmd7X2d/j7Dmvr5v2mWPJpoWtaqF/dWz2uvHRkeSxzYuTLzx8OJ1FtyWUyGLF ihWZ581K9xyapzDo+bw+0J3eVnmalgBlw9mghPQJyse8efPc1PKz65bZzBvNPNO/ujQ56zdm /z19RjK11X/WjW7GTj5flhO+n1eiiGzbo7wdIRSZXi+e1qKq7EM/qkiHhdoO5vW9/zDdzLt7 9+5kafrv+c20h9/4/9yc7U0Z8rQlHBb/3RL9tpFcv3592+fIWMaI09uKAiGqjAJhyYQugBLb t293c5SbfTu4bNkyN6WHj/5idv45aZ6fuKTU8Y2tz7vkMZfYSbcl7DVIffPzZqOo9DIWeTnz 0uvExRlVZd966d6R0aK3kUTeISYevknyzU+WP/xDl9KwdLpVJfOES/Mm1Tu1/04JGWZiEPqz JCgUxsk9iN5WQFWxd5dIbNDdKp2k+luvnzXn//ZHMy7Nmfnj5rSL3/65Swzz80l0k3W+SbIX saq0JdTrJAFUkT3Po5PtaVuqZ+bx/u7VjbzTf+xSWras8J97lktp57/zD//wD13K6MhbS/99 EjIA/aAeffTRts+UQJh+WMx2QpWxd5dIrDAoURV6nbK+Ado9fXojz/Rul9Iyc6j1eb9waTH6 7Wu3xvZ+Hoki08spnRBUwbZt29rWC6givY9nriVRMwMVmmfea+Z73zxDFCvctPM3v+RS2t13 332ZvnfQNoR2wPlhDiWkP1dCqikjTG8noKrYu0tEn5RsVIG/eOZbp3ea879hL+wze5Ltzc87 1yV2p28yYvz0bvMUgW1LWAWHDh2q3DoBlt7HqS4aprfRwoULXWo2T/o3gIsDzQhmDjc/d/uJ QGnR8fN8+tOfdimdBullVBc6JYY9rqy8adSfL4EwvY0YDxRVxRmgJLq9HVy5cqWbq9zsevV2 Ijmy9fzG/Av/N5fWsvPaVscAiza1txHpxueRtiIhfrpEkdn2po89Fm9DWSZ6nUYxNiUwaXof 79WmuY6kkKy3UdZx+Bp+3sz3aKDAt3O69bnx4mCSdmrl5xt2lXw9xmCvAecH4b/DB8L0NqKD J1QVZ4CS0CckG1W40ZcLql6nTOMjndyUbL6gMf/8rz7vEhtmjt7Z9nkbjne7tLeT8Qh9vtDJ X39u0ellLcPyZqHXh3G0UEV6H0cnvX1yb6OTLzXzdVQqef+x1ucuvMKlxjXnnY1hGVdhUOgh LCRiD0HrTm8jqXkDVBFXmxKQHsD0CcnGyy+/7OYsr0suuaRtnTL5YG3y+27+i7/9I5c466O/ SBatan3W1NSi4PiD3bTylvst1EDtbApKrw83MKgaqZKm93F00tsn9/ALx3wnLee7hIaHb/q1 ZOrLrc+du70x/mA3ehgK6fxlUIOOMZiXHVtRAp2qeB0FLPbsEtAnIokq9nql10dOvpl88E+S 32jmm588+79mkn33Nf6eu2pd8/OmlmxOFk4tTrbleEtYpW2s12PY7VAmQa9P5n0FKAluPruz YzTmHWoieb81FNH0V2YLcR+5oY5uWpwc/uinzWn739uZTC9Z5zLF6WEopN2flqcN4bgLg54d lxCd9G8sQQc8qCKO/hLQJ6K1a9d2pFWBXp/s1QBPJEf+5IK2vBJzV+1IkpnvNv8+dfaCt3X6 /3R5stNP6svMr4PEoONXFYG/yfIBVImuss7+3cnWmMmv1YZQx2EZskh3KPNv7km+d+yXLk93 +nO0rAVC3R5xEudo/91ZlrXIRtXhi9x36W1ExzKoIq42BWfHk5Pqi1WrUqSr3Ujk6kQhHZC+ lXdqajp5S14EzvyPtvQH3892Ybd8ft2WUH9uGejllSj7mIS2dzygShgIO86OPdjv9nm4rUnB VPIvd7hmFzNvtNKne78d9PRnSeHOy1Ig1Hl7zTsq+vvLOsyJf7MuD1SGzb6VpmYKqoirTcHp k5CEqFqVoi1bthR2ffSTQd+WsKjLGmP3F6kiVGb79+9vWx+gSvS+TY+G7WxhWcYlLQJ5qKnb wXu9CoR+fptv3IqyHIPyyz/szsb0G1wJxgZFFXE3VWC26pCvplC1J8iXXXZZodcntGzyW+gn wUVmh5+Q7V1mR464Nj8ugCrR+zYFwnZ2bNV8w02Mnl8uaUsoyzZnzpz0b+n4xtbM8PNKTLpt 94UXXti2PGVl3yAPk/5cCoSoIu6mCkyfgCS8KhUI7UDjRbwBsk8HQ1H0rqjt8pZdldYF0PS+ 7duMo0FvG4mi+cM//MOOZbSxdOnStr+L0NHXwYMH25apzKRQ6GvFDLNqp94+Zd9GQAh7dUHZ Xi5lIF5Pp0uVlDKTMRT1+uj1LAr7W3SLor41tMtZdlVaF0DT+3YRz4eTpLeNRNHY5esVRXrD qZerCoZdKNTbpyrbCNDYqwuq28lHpy9atMillpPtMdK30ysK24FPlsjVKc6Y2GUsuyqtC6Dp fZveDNvpbSNRFFIddN68eR3L1yuKRC/XsNvgTYpfn2HUPNLbRwKoGvbqArJtvuzg23raMAbD nSS9LhJFY5cvaxSNXBCLvHx56XWpys0LIPS+TYGwnd42K1eudKmTp5crT3z60592nzB5ermq 1HbVr9OgvY/q7SMBVA17dQHZXiEtPY0C4ehIdS27fFmjaFVHbQdF0nazzPS6cNOMKtH7Nlrs g1IZyL0I5O2gXq68URR6marWdtWv1yCFQr19JICqYa8eMrloyck0S9iTk3RMIun6pOPTdOjp 0juYFAqlFzNNLlKSbns2k/n8/NJbY7fQ5O/nnnvO/dUghQqbJxbSLbcn7SYkTeh1kZCb+15h q2TKGyIZJ0iTwlworw3bRkc+x79xssuWJ3wvZPL5unAoVWJDy+FjVOx+47d/Wel1GeV2A8ZN 79tokeNcb5uiPAxdt25d23LljaI8nNPLNMzOWIrCr1u/NUr09pEAqoa9esjsRatXaKHpeUKT G35Jszf+Nk+30LqlZQm9HHpsJjtflpDCjebTNT1/r9B82iBvB334z9NdVA9j/9Bsj7NZw+8f +qZK/m3n6xaa/G07OLLzdwstNnaXzROL2PbW9Py9Qgul5dlX7AMIn67p+buF7dnWp2u2qnC3 0Pw66e+Qf9s83UKTv201NDt/t9D8cugbuzzLptdJPkPS9D4jbJ5uoYXS/HdkidC8lp0eC72e wqdrebab5tNC2zJL2Ad4Pl3T8/cKzZ/HdEHrtttu68gTC5nX871g28KnzTOuCJ2vbYHS5olF t/O1Px50WrcoAz0kRT81ePT6SgBVw149ZL3eAOkIvdWyJ51QPj19+/bt6c29fXsnb+QkXb+Z EzKfpGcJTf4exxtC/SY0FrINNLl4xd629gp70ySfI59nt3M/IeTz9Y1rnjfI9iY1tJ7y2aG8 Nv79v//3bcv28ssvpzcU/ncQ8u+NGzemNwpZQpO/d+7c6f5qkBsrmycU+gZMyOdIuqXz6HWR goZsGx9r1qxxORpDhkiafeJt83QLTf62BRv/HVnC3oj45dBsHgm9vj7kd9V8uhbLGwrN7//6 O+TfNk+30ORvWRbNzt8tNL8c+jyQZz31coTWU9g83UILpeU5l4Tmtez0WMS2t9bv/uHTQtsy S+jfTvh0Tc/fKzRfUNLnNnvO6Bb63COfIWlFLhDq9RQ2Tyz0ego9zf+uOq1blIWcf2V55XqQ t1BY1nUGsmKvLhB7wonVd9fz2ItB2eh1kSiKr33tax3LljeKxN6sbd682U0pJ70u9uYSGAd5 GGP3PfnbvgXO85BQv8WI7ds2TyzsDa88gLQPIWWeUN5QaD5Nf0fe9dRkm0m6ZvPYB6br169P r3/ycEuTNCks6eYSMtafpGUJPS6gb3phr7Ny/vTz62XqJ2Q9/Gf1Cr0cofUUvllIr7DjH+pl 8gVC+b88LOkVZaL7aLD7YTd6+0gAVcNeXRC2wXy3E46eZ//+/S61nPS6SEyS/Aa2Q59+Qz85 LwJ7M2VvcMpGr4u9kQTKTO/baJHjXG8bKdQUgRSs9HLlDVugmxS9TEW7fg2brt6ctVCot48E UDXs1QVhCyK2ipum5yvKRbFfel0kJiFWCDz11FOD6VnCdnwzafIUVy8fBUKgmPS+jRbbRtFW UZ8kvVx5oyj0MtmhrqpI709Z6O2TNQ9QJuzVBZHnZKPno0CYnzwR7PYmUC4UXmh6ligaucDr 5StKz3b90utCgRBVovdttNPbxraBm6Q8ndboKNL1Wy9X2aqB9kua5fh17kVvnyzzA2XDXl0A 0rGEPtF0ezso9LwUCLOzhSIdsSeivhF6nsjTLmFc7D5WdnpdbLsooMz0vs3DjnZ620gUSd6q o0UZR9HTy1anc6q+Nnajt0+veYEyYq8uAHui6VXdUM87b948l1pOtve3YVa1lIKZrWakQ6Zl Kbx1e5toI2/PZeNil7PsqrQu6HSB+n3T2PNjN6X69HpTIGynt41E0djl6xZFo5etaE0eRi1L oVBvn27zAWXFXj1h9q1VlhsAPX+Rqs3047HHHmtbH9tDXz+6vQm0w0z0IhfG0Od0iyIqwzLm UaV1QcTxB5q/8Y9cUh3ofXsY58Mq0dtGokhWrFjRsXzd4sYbb3Q5J086ttHLVkf+wW+shpbe PnXdRqg29uoJ6+ckIz2A5c1TVH7gXx/ypK4fUsVFf44OOcH3+8TTflaIvREo4ltCvXwSZVel dUHYiR1Xu994jkupB71v16UtV1Z620gURagwGJJlnknYsmVLIZdr3Pz6y32IrT2kt48EUDXs 1RNkqzNmfRpcpQKhuOyyy/paH9letm2cj17tMLOQt7X6M6enp92UTno+iSKxbzlle5eZ9JCq 1wfVtPkC9xtf8phLqQe9b1e9+/+8bO2PIvQ0+g//8A9tyyQRG0rCvomTgmQR9HsNrqLYdtDp HJeoIu6mJkifYCSykmqP/eQrqjxPJ+XtW6xNn6QP6+1cqO1hN1L9R89bpEb5dn+R7V1mMvam Xh9U0Ynm77vsg5MurR70vs2NZztbE0SGBpq0Cy+8sG2Zli5d6qaE6XklpEA5aXp52Oc6ex+1 ncuxjVBF3E1NkD7ByAkoK/vmquzsE9YYO7i6hLwhHEU7G1uI2rhxo5sSp+fP21ZxlOy6FGUg 5H5Jz7p6fVBBM991v++ipF7FwerVABk2Wytk0vSySPQq4NnrXRF6CtfLQ0dGDb5QKP+391xU 5UYVcbWZEP0ESiJPGzf7tKoK9ProcQClMBN7IxgbKmJQ0nbAfleWQpTNU5RCoV6mIjxRH5R0 167XCdVzYs8c9/vGq2lXlX2Ag3ZFKhCuX7++bVmyLk8/eUalivcTw+K3ib0HobMnVBFH/4To k4tEXoPkLSK9Pt1CbpZGPc6fPflLlaAsbPsQiVEvaxZ6eQ4cOOBSy0uvj/xWqJ6Hr5rb+I0X 71BvC21c4OauFnv+QSe9fWT8v0nRy5FnWfbt29eWTwqWkzJnjn/40gi0hB4OSzD2LaqIo38C bPs0/UYsK53/5ZdfdqnlZdth6JC3qcNqG9hLaJiJPG08pP2IzjvpAksVby71+ozqLTEmqdV+ 8OK3f95I+miXS7smmX7gB420iqpak4BR0NtnUttIOhnTyyCdXeWh80pMYhiKe+65p20ZeMDW yb5BlQCqiD17AoZxctH5ZSy/MgqdaH3IhanfoSIGEVqWQWMS6+HZZakCvT79PExBwR3f2Px9 j83+OfP+15OpVVPJ3BVbG9NrQO/j6FSEB136+4cV42a/f1wPXstGb6Nzzz3XpQLVwtVmzGwh KE9nMpr+jJUrV7rU4pNqlQsWLGhb/liMm30yP8yYBPu2s6wPDiy9TqieJz/jf99zk4/eviOZ O/vvix56002tB72PT/KBUlHZqnzypmuc5s51VZqHHOMchiLUxAFhdjtJO1agajgDjFGoOmK/ itbTWi/btm1rW14f0n7Bd9iyYcOGtmnjpr972DGJntuq2DnFoUOHKrdOaCcFwPT3Xdf6nes8 9AQdWITpbSQxLraX0GFHniYKg1i4cGHb91JdNE5vJx/9PswHioo7qjGy1VwG6YXSDsFQNHJR k6edehl1xBrf63nG3UunFLLvuuuu5tPfU045Jf1bh182m66fGNtp55xzzkS6qfbLIyE9c1aB 7dUPVfNc6/ddNZUs9P/e/KqbXg/NbTAbjHkWZq+B4+pcRq5t0l5Qwp+PpHDl03z45bLp3fLI tHEUCEOFWsTZ7eT/TZOF/C5Q29LGg8dn3FyYBM4CY2R3/kEN87OGZefOnW3LpSNLtR6bZ9z8 98rg52+99ZZLTZJNmzal6dKRgEzTsuQZNzuWWZZhM8pAr5MEquSkGm5iKvnJbMpzd/vful7t dorQRq4M9DYa93by3ynn/ddee82lJslDDz2Upve6VoTyXHvttS5ltIrW+VmR2U4Aha6yTKEw r5PJfSva305PTa1Kz/eYLK40Y2Iv8MM4AevP2759u0sdnqyFCBm0XS+Ljrzt1o4ePdqWf9x1 9f33atdcc00zXRfuJF1P837wgx9E84yL/24fVaHXiZuYipn5VrL5VP+m/cFG0tsXub/nNAeo P/b0P0+u/ma129XZ6wXVRsPseL6PPvqomzJaofO+0Ok33XSTS41fK3z6okWLOvKMim07KPta EYZHKir7cNXThUI648npZONhucT8+w43z+2YrOrcKRac3/l9DKOjAP158+bNc6mD02MkxUhB T3+/DikgDsJ+3jitXr3a/aslthw+3ebZu3dvNM842LaDUhWpKvR6cRGuliNbF7d+3z0/dqnv NNMufvtY8s43pav/6eTB93/ppleTjHPW3BazQScWcXo7SYyDfM/nP/9591dLbBl8us3j00PX nVHx3+mDwmB3elvZh5C6k0C2Y3b6XP+X1BItjMncsdaMfYopMQyxJ1f9CrUrkDd2nhQs/JNM G1JVdFjVEu1bQjkJj6unPV3I8J3cSMc369atc6lJ2lZF0p966qk0dB6/zJKu84yLfbMgURV+ u1dtvdBw6cLWb3vIpYnfUL/51NTy5MaX6nHj1VrnRiBM2mfr7SRjk466HZ6c31966SX3V/t5 f+vW1vAocg2QdLmGdMsjnWWNw+7du5vfK2ELOOjUa3vpKqUUCrP5RHObjr/2FOK4yoyBvUkf Vo+T9inyIKRQIxdS/XkS999/f1rQsxcSCek0ZlQXXjtQ/SQGIH/88cfT777++utdSsP3v//9 ND3EL++k+O/3UZW2g0L2Rb1uqJKTyaLmb/tFl9bw/mOXNtJv2+FS6kHv6xKIs9tKHlCOk/9e 68///M/TdHsNEbE8o+S/cxLfXVZ6e8VqpfhCIW/ys2id6+fe9T2XhiLgjDAG+oQyzJ4zdR12 iX5v/m3hK0tIAbGnmWPJH+/Ync67dOqcZGrRf3ATepO2IPY7x/X0zT/VXbVqVVoolAKg55dF 0jWfR0LeLGoPPPCA+9doVfntoKjyugEW+3t2oYeZ4+it0z+kWrx4ccc1wS+HvYZ0yzPKWiWh h7rozg4V1o2fh+Eoepj50+a2mt7WqoGGyeOMMGKjHgtOf7YtiPRy3333teXvFbk+/1jnxWfh pfk6mLH5JUZNOoDx33X11Ve71CR54403kltuuSW4HL3y/NZv/ZZLGR37tlgi9jSzrPS68SQW VWevHZMYy7RM9LbyMWrym8j3XHnllS6lJbYM3fJ84xvfcP8aLoaZ6I9cZ/Jsszzz1hXtB4uL vXbE9AlCYtjs5+s2fzG2jV6vkCeZ/Xp06QXNz3nyRL6jP3QRo81DmN1OVRl30JP2q3r9gKqz 4+xx7uvOvs2RGOTaNQy33XZbuhyTZrfLJMbFLSO73XqRWky2EFn1yGtps614uP3gBy80+m6Q uOMvDrtUjAN3ViNkq7GMomMUe9PQ6wKou77OE/2ZaQ0sPTXHpeXjO3bRwbg/7ez2yfumuAxk v9brCNSB3ufZ73sLVR2VmjCTctddd030d7MP0iR4sJBdv9utToXCfFq9Rk8t7mwT/sq61ji0 PuZ+6z2GpRgTrjAjZHfsUcnyPf20E9TRV/XDE2poirmNccX6EbqojavX0RC9HJNW9XaDwg5x wg0N6kLv9xLoLfSmUIZSmoRJFghjNYGQnd5uVWuCMQmtcWVlGKGfu9SWq+67sTlA/UdvX9uY 95TNyberPcpQYXB2GBH75m6Ub7X090joxvR52wnakILk/v37+2qgv3+zb1t3VrL9SOfBn0fo TeGkunjWyzBJoaeQVWTXUdpLAnVg931uSrPRQwH4kGFrxm1SBcJQcwsJhkXIbtjDeiFJtjer i87J8NbvJ415Vzyc7KKt4Viwl4+IPpFIjJLtfEAKT3naCcrYgps3bx76EAVz3efP3X44mZn9 b+f2z7nvPCfZ81fH3FzZSbs4vdwSdRUa2zJL+9EysuuJ4vEFlVjHJ3663JCGCjXdpus0+b+9 qZU3Qr2m+xoFoelFZqtAyk0qsgmdI8f9pnASBcLYtd8fI8jGbj8M6qRqQtS7WYt/m3jH4cFe JiA79vIR8L2I+Rj1GHpyg6O/LxRz585Nu52Wt31jMXO4+d1PnnijbVl8rN79nps5u9DnjHr7 Fk3oRmcST7/HgeqixWfPP7Y2hNyI6ukSmj1f2unz5s1rm2Z7mL3gglbHVRK/9mu/5qY0yN96 us1fdLZaOLKLteXqp8ZLP8ZdIAwdaxK0u89Pb79hDhdWdaub221R21vAmcOuCuhs/MujveqA HkvmfuL0ZO50vcaenTSuLkMWar8wDvY7Jz0g+Ynt5zaX5dzfnzv7/4XJXx37ZXLkmdZ4fVNT /fWE2crfirLd5PUjVvCXca2qyq4r1UWLx95025unXu1cbdUsO91OG/b0orPVH5FPrFA4jhoV 4ywQ+vENbSA/W7AuU62CyXqvbbv9wqXK28HpZvpqlxZzMrl5ajq5qOd8GDbOFkNmb37GVVCx 1UbnzOmvV89hubRZV3x+cszU/9ZDUfTbVtjn1zGut0f6O8clNM6gRJULg8KuL4rH/kYS+o1E aLquWhqarqu3haZrg04vA73sdasRMQyh3kclRt0j8zgKhHKshDqNozZF/+xDBGR1orXdpne6 tJPJH81tbUvfaUzMH00tSv7FolXuL4wTe/qQNQ8GF+NkC4WTM5Mscsvwtb/qfLJ2bPcfuGVc NDtn//S6+hjH+Er6+8ZFf6cPqQJcZfYpLdV2ikn/Rj6kWrMXmq6P09B06ZTLC033Ym/NvVCN DT29LPSy16E2xCjYjt58SC/WozLqAmGs8xj2kf7ZcwbXnXw+2NfoTPATd+5LZo5+O/n2fa5z wdt6V/+ceX8bhcEJKt+VscBCPZuNk21bNq52Eh1OvtRchiOBEt/+LVe56YO9xZR2c/57dIz6 yaj+rlELta/yUXV2fXUhA8VhfycfXmiahBea5o/h2E2871wlVh3Qv6G0D8l8lK3qsa15gv7E alpMT4cHyR7UKAuEO3bs6FgPH1Rx7J891ihc53Uy+bOtre131h89lFz8B70Lgx+9fUdy9cJV yR1vH3cJ307u+G8MTD9OXFmGSJ9EJNasWeOmjI/+/jPPPNOljteR7We5ZTjLpbR7eIUbfHSA sQm15cuXN9dZh652Nkz6O0bJXph8VLU3UUuvM09piyl2gy0hYgU6CRFqP+hDxI4BCRFKl/AF ytA0ibLd5NkHQ77XVOQXe6ssMexaF7fddlv6ucMUGpfXh9/v0T+7TTF673zTD1GmY36y7jjj TYwTe/uQTKozGcveQE2CHm6i08+by7ZtwLEJtZ07dzY/V4dsj2G/DdCfPwq9boLrYOXKlbVc 77KJvaGTkHNir305lO4j7/TTTjut7W87XdpV67/LRi87N/6D67Zvrlixws1VHNJRXGhZJWRd dDVr9Kco93H1crLZxKg9vuimY1zY24fE7syTGi/KntDWr1/vpoxLawykUHXRI9v828Ph9yDV 7YIpMayn6vozhyk0nISP22+/3c1VD3b9UUz6N7I32La6pp1u3w7a+bu9XZSwb3q++c1vtv1t z4UPPvhg299lY7cfBhcqAOiQzlom3WO3fThmg4Lg8ITOWUBdcFUZAntjMumTiF4WiXE68eQV 7nsDQ0rM+C6JO3seHabVq1e77wnHoAVD/VnD0K0gKFGXKqLeo48+2rb+ukdKFIv+nXoV4HpN t22wbQHI/m3fTtrlCRWg9N9la0doO1kq2/IXmTTv0Ns2FFILZVzs+KuhmNRD5yqz2xioE/b4 IbBPlSbN3ljt2bPHTRm9J1ctaXzvv2lvizFzTDqAkfEI5yRHPhp9vfBebwsl5Aa1n8Kh/ox+ yffaG1YbGzdudHPXi90OKCbbflDov22Ibvu86Nam0BaIbIhQug87vYydRejll8Bw9XpjqGPz 5s09O25bsqRxPexFHvqtW6fH6I0H7alHY9KdAgKTxh4/y18EzjjjjL6qX+gTiNzwFIFeJolx +t62VW3fvejLX0vmTy1Mpv9l756mRkHag+jlCYX8blkLhzpfHqELTiik99S6sj3HTqJjJmTT 6w2dDdGtUCdiver6tyGxAqW/SY4VKH3hL7TMZVK0h49VleWBXSikar8005DxDaXzlxtvvDFN l39LSKc1Ug31sssu68jbK3gjOFp2e1MVF3XDFWXW5Zdf3nEy0CGD2saq59gLdNZCxajZKjB1 LmR4WQqGPuRmIFZVUc/XjVxQ8txU8Bt1XpRRXPp38gUyW+DyoW9mQ9P1W4/QdH8sxgqU/rwb K1D6bvhDbzXLxDZPYJD60etVpX+UIdcPjAfbHXVX+zuubl1Qdws5YYRufoqkyMs2Sffcc0/H tplULFq0qHZtBGPk6bneNrwdLDb9W/lx/2LnU/1wJTRdP0gLTdeGOT1WgCWISYV/m43x4c07 MHttdP+vtfnz57edDPoNf1NUFPYtoXTWgXaxwe0JYlQhb8smEWvXrh1afP7zn29bJ63Xg7LQ W3NNlrXbdDttkOkUCIlJhRRCZF8vSq2iOrO/DVBH7PlO6MbAjlvVK4pIL5+0W+jVCL7OpCOa rA37CYJohRZ6S6iFqn1qttqn3DRrUk1ST5cqfZotUNo3zRQCiXGHPERBMdnzVaypCFB1FAgV fVLQIW3Bet1EFLUBcq+n8egkhebt27cn999/f1+N/wmibmHlmX7mmWe61BY9/ZOf/KRLbZBz rZ5ua2bYAqFt/02BkBh32IcWKA6qiwIN7P1GqAAlYZ8aSTUPubHw8xeZXRfp7QyDue2225K7 7rprrEHhlChanHbaacG2nnJenJ6eTucJvR3R00NV7T/+8Y8nn/vc59LpIdIj9HXXXZf+P0Ty yfTf+q3fcikt/o2AffNYJqHOc1A8/uEEiksfQ3JeAuqqFGeqC9QBm8aeH7sp/fMX1FD1AFsl yUdZe3SzT8wlgCKwHcmwb46fnAP7DSlcYTLscUMnTMVDgbDYbG0Bzmeos/KcqY4/0Dxof+SS BnHBBRe0nQh86CEmQm8Ly/oEya5HXQc9R7HY/ZKqVUA2ttMwCRQLBcJi4/gBWkpzBJzYcbU7 aOe4lP5JmxV7IsgbZXsaK9W27DrQwQwm6ZprrmnbH+l4AcjHFgqL1tN13VEgLC5bc0o6uwLq rDRnqs0XuAP3ksdcSv8+9rGPtZ0I+o1Y+5Wikipedh2ASZB2rOyLwOA4joqLAmFxcdwA7Upy FJxoHrTLPjjp0gYT6ho9b5Sx+qitBrtjxw43BRgfvQ9K0HYD6I99S0hbwuKgQFhM9u2gPCwH 6q4cZyrVfnA4xcEWfVLw4Qt60r25PXHoKCu7HlQdxTj53iV9SLffAPqnjycJFAMFwuIJ9Q0B oCQFwic/4w7chVckycx3Ow7mRlzg5s4vNi5VqE2TFBLlCWyZb2LtOF4SwDj0GpQcQH72LSGd MxUDBcJiCdUMkyHEAJSkQDjXHbgX3+37Fz2ZTKsD+qx12ao9yrzS7k8KgPYkECokSZS1V9Fe 7FOy22+/3U0BRkfvcxJUFQWGwx5bmDzp5IfOsoqDt4NAXAmOhueaB+6umUbKf1/V+PuW7x/L XIX0L/7iL5qfEwp5khcrFEpUkV1HBqzHqITeDJZ1XE+giGS4JH18VfVhJtCPUKd6PJAEWopf 0jn+oDt4G1VCZ97/ejJ31fzkLVc4zOr0009vOxHEIvQESaKKVXDsTfpll12WfPjhh24qELZ+ /Xr3r+wuvPDCtn1NAsBwcYwBYbZpEG3XgXaFv2I02w9OnZt89PYdafXRix56003NTp8I+o15 8+a5T6uO0HoCMXv27Mm9n9x///1t+5eEvI0HMFzy1l0fZ9z0AuG2g/QsCrQr/N2/bz84ta51 IPcz9ETohNBPVE1suwAx0t40635y9OjRtv1KgqpswOhwvAHt7DFBRzJAp4Lf+bfaD06tmkoW +n9vftVNzy9WJVTeWHQbYkLiS1/6kvuUapGG73Zd6WQG3SxZsiTdT+T/MTKcid2vJACMTqit lDQPAOpIOvXRxwIPSICwAt+dnUxO7JnTPIh/Mpvy3N3+oD63MUufYsNM2HaC8vZMCokLFixI p1dZaJtIVT8gxu8nsYcHel+SkH0MwOiFHnwCdcRxAGRT3KNj5lvJ5lPnuoP4wUbS2xe5v+c0 exc99vQ/T67+Zvz1/z/+x/84LehJD2xW6KJZ56dHoZ4gX3zxRTcV6OT3kzvvvNOlNOh9SIKn ssB4cQyi7uwxQNt1IK6wBcIjWxe3DuQ9P3ap7zTTLn77WPLON6dn/z2dPPj+L930dl/72tda n2FCGt/LG4tQoVCirmz1CglpBwbE+P3Ev1H21Ul1ABiv0LmcqqOoi9D+DyCusEfIpQtbB/Eh lyZ+Qx3cU1PLkxtfio8j05qvv6jrE9VQ9VFpDwbE2P1FB4DJCNX6AKou1FkegO4KepScTBY1 D+QvurSG9x+7tJF+2w6XEqdPBoNEHYXenNLRDGLkgcHq1as79hm5MAOYHHsup+ooqkyaB+n9 XULeFgLorvKlnViV0DxRVxQKkUWsN1GqpwHFYI9N2lKhquy+zgMQIJtalHZCdcn1iaLbkBN1 F9omFArhxQqDElyIgWIIVaHj7T2qxu7jEgCyqc3REmpLIRF7iyEXyz/7sz9zf9VbaLvR0QxE qJqoHteSm06gGNasWdN2nErHakBVhB56yJicALKpXIEwNsSEyDr+INqFBq6XoFBYb6HeRCWE 3mcAFIM+Tjk+USXs28BgKnfEhE4K5513XloYfPfdd6OFG6q39RZqU8g4hfVk9wMJObY0edDi pwGYvFCHG1z7UHb23kT+pnYKkE+l7tRibwBDESrcSKC70Hbz48+hHuzvLxGrei3V0vw8ACYv dJ2kF0aUVWh/BpBfpY4ce1LoN+gdsbtQofCaa66hCmnFLV++vON3l5A3793oC3asOjeA8Qmd w7nuoWykQ0C7H/NmEOhP5R6l5HlLGAsujL3pNz86du/e7eZAlYR+a4msF199A8rxBUye7WRG gptplIVcR+z+SycyQP8q+W499NRIQm5K/RAToSekEmeffbb7FPQSa48pb5JQDfLWN/QbS+Sl L+DceAKTF7oOAkUX2m/pHBAYTKXP/rFCnyU3p34cQqq05RN6SudDxqhDeW3YsCH4u0r0Sz9E oFAITJ4+rn0ARRWqnST3egAGU4kz/xlnnJF89rOfTd/+WaFqMRLcjA5XbPB/qpCWU+i3lLA9 ifZDv8EHMHn6GOfYRFHpnqt9UBgEhqP0Z30p2NkThA85UUibwthbLE4kwyWdi4S284UXXpjs 27fPzYUik98p9BtKDLN9BkNSAMWij3UJuT7S3hdFEWuiAmA4Sn80hU4QeYO3hcMlNxKh7SwD maOYuhUEJUZxY6g7gAIwWbGHqxQKMWmhwiBtBoHhKv2d2GmnndZxougnKBQOV6wKqcSjjz7q 5sKkSTvP0G+kY5THhn94wNt6YPIoFKJoKAwC41GJR/OhdoLSW6hN6xYYjdC2luAGoxhiw4f4 GAdfKGSfACZPjkN7HuCBDSbF7otSswTA8FWmJGRPGj7kQuZvOGOxdOlS9ykYNrm56DY2JD2R Toa8pQ39HjqG0YFMFvoGlBtPYPJC10yOTYyb3QclqM0FjEalXo3FCh7yBtGTk4kMMaHfjEhn KBit0FNnH/JbHDlyxM2JUZLtHPoNbIx7gF9dLYgnwMDkxc7ZwKiFehOVoDAIjE7lzu7dBqXH 5PWqokjBcDRuvPHG4Pa2IYUxuRGcFL8ctBEBJo9CIcYt9mCfwiAwWpU8s8sbv9hJBcXQqxov BcPhyFoQlCjKmzm/PNIxEYDJinU0ww06hi12XwBg9Cp9pMV6uqSKaDHExi3U8eKLL7q5kceK FSuC2zMWRXojp29Ax9WOEUBcrFBIzRsMS2j/kgAwHpU/2mJVXrjRLBZp1xn6nXTw1rC7bdu2 BbdbryjisaBvQCdZhbUsTr70xbbftC0W/gc3FzCY4P41G0C/Yg8bnnjiCTcHgHGozZncnmx0 RzMojiwFQyn4oCFrJzHnnXdeML3IhS39MIdCYTZ/fNVc9fte5VKB4YlV6+MYRV66MzH2JWCy alsgRLHFehmzsW/fvuTDDz90uepB1nfx4sXB7WGjW3vaMrQBkqfEfnmL0saxyFq/71nJxW// 3KUCwxUrFNLuF1nItSf28BfAZNTi6LNVEmj3UB7yBDHLW0MJKSQdPXrU5ayWnTt3Btc5FLoK aBUa6eunyNJLLSJm9jS306JNP0xOumRgVPz+poPrK7qhJ3gM09Y/uDg544z/e3YfWpRM7/up S0U/alEgtNXlUE6xC0ks1q9fX8qB72WZpcpM1reAEnIxtW/8QvMVsb1gVn4dGJIi7MSeOc1t tOH4jEsFRiv20Emuu4AW21d40Ie8pN38BWY/4ro3mFqUjvQOw1Ooaoi1iQuFFKzK0FupVAVd t25dcB1CIftybAD5UNuMcQ82Pwp+XdDpe//2DLd9LnApwHjEevSmYxB40m9DaB+pwnUJEzTz XbcvLaJWzIAqf2clJxt98uHtQnXIzUbWtoY2LrzwwrSQOKmeS+V7N2zYkFx22WXB5esWUhDs 9mAjtE2q8iBEt4dEu7nN3/sslwKMj5xj/LGpoyrnHvQvtm/QLhyDOrHjarc/zXEp6Ffl76rs CQjVlbdKaSxWrlyZLFmyJNm9e3eyZcuWtPD28ssvu2+Jkzd8fl7JK+3+7rrrruB35A25oPbq eS3WfXfVLrr+5oIbTe255u8t7QeBSYi9KZTgTVD90F4Qo7b5ArdfXfKYS0G/Kl1CsjfItGmo D+ldU37vPFVLixTyli9Pez8pLMY+p4piT5yrGr30aj944hn/FHU2Fl7hUoHhk+uutAlr7m8q KAjUQ7cHA1JIBIbjRHO/WvYBFUYHVekCoS0MoN6k0CQ3JEUrTMgbvEEukqH2ghJl7kAmC7np lBuPqkeWB1kPq/EHY2beO6sxz54fuxRgdOShnN8nbVT93FRXsVoqPoChOv5Ac9+iODi4Sh+h +kTEk0l0IwUyufkeRWFRbuil8CLDZwx7wN1Ye0GqZ9VJ6ynp1NSDLq3T9oWNef6SjtgwRt1q aUhHI6iGbtdOfmcM3zvJk585pbGPSa2XZucyNuhkLavKFgjtjbI8rQSGQe9Xk6Q7WPHBg48a Uk9Jp56Mvf07mSxM56HDGYxft7eFEjzAKi95kBr6TSVkGjAKM29f1OxI7eK7f+RSTybTzf3v V5IH3zru0pFFJQuEoe6NgWEpwn4VehpLj21VdjL5o09ItdBzk7fMG74nP+P3gTnxajMzf9qY h4b3Q6Xbyl1//fVph1Se/FvSL7jggnTagQMH3JTWOUTSDx8+7FJbeebMmZOsWLHCpTb0k6do YkMP+ODBbXlIjZfQbyhRtAeTfrnk2PnX//pfu9Qkeeutt9L0+fPnJ1/4whdcakPePL/4xS+a 89s8+rPs96A/m85ptZvf5a6J/31V4+9bvn+skYBcKllS8juJD9orYJj0vjVuvh2kXgaJqnYe g4Yj65a0fu9NL7hUcbSZPve2fS6t05Gti9N5HmDg3qF5+umnk4ULFza3v7Zp06Zmuowt6r3x xhtpvmHkeeWVV9rSdZ6ii7V7lpDzm7RFQzGFminoGHaziGHwy6bt2bOnecz9+q//ukttHNeh 4+1nP/tZmv7Vr341mEc+z36Hz2M/S/jvQT98jReJJWnKnUtn/71wquOBKbJr30MrwI47aA9C YFCT2rfq2nkMkuTldeo3/9Z7LjVJvqL2g26N6n8jnYeBe4fJb/eQ2LRbbrklTd+xY4dLacmb R7+hKatYb6Q+aHtWHL0KgmX7rfxyWz79tNNOcykN77zzTpouQ1JZsc/qJw8y8DVe0tjQrCb6 NIXBgVRub2ztJI2QCw5QdrGLcRGfxmL4Zo7e2fzN5Qnot+6eUm0lppOfuPnCGjclU1Ob3d8Y hunp6XS7agsWLEjTZJqE1vgNGtP0m4FYnlWrVqXpS5cu7cijPyuUp2xCtR50UANiMuQBe7eq oRJZekEuEr3s+tj5yU9+0pauq3/rdHlA49k8Wj95kI2v8SLXPr8t0+2586duDvSj8gVCoOxi XXlTrapePnrrT9TvvyhZu3ZH8u2jv3RTuzj+YJpneh8Xy1G74oor0m29f/9+l9Lifzsrluem m25K03ft2uVSWmKf5fOUVbeCYdHapVVdt2q9EmXtCMgv/yc/+UmX0vDTn/40Tf/4xz/uUlp8 HmvYeZDN4//7PLd9z00+cee+1rBLm191c6Af5b1yBMjQAf4g9AGUWax3PjqQQVa+0xnaD46W PzavvPLK5LXXXnOpSdrRi6QvXrw4neb5+eUtgc2jP+uZZ55xqa10idNPP92lNug8ZSUPuXq9 LZTp1PwZnW7DhPgoY2HQL/tFF12UHiNf/vKX3ZT2Y0d3zKTznHPOOS61IW+ev/3bv43mQR6t 9oMXP/RmWjvmubsbf0sBEf2rVInJH2w+BhnsG4jR+9goSXVQ/V3j+E5USGBcpmcpE46M38bW ddddl6bv3r3bpTT4+XXvo17ss/rJU1a93lBJSOGFnkkHF+p7IRRlfSso/Dr89m//tktp8dOs Yeb54IMPonmQw8y3mtvRj6krQ1A00nxP2z9Llu/4Im3mc6rMnmnfpPAEEaOi97NRid0MASgO 3bbX8sNBPPXUUy6lZVx5qiBrYYUqpfmEalSFQvbxshe6/bpYf/M3f5Om6x5Dvbx5/LATIbHP Qn6t9oO6ox7fTn4qufjtY7P/n06m5n7FTUNWldlD/c7gAygruy9LcLMDFIs/NlevXp2Gpqcd OnTIpbY6opHqo4Pmufzyy6N5qihLdVIfMt8TTzzhcsLLWgiUqFLv1XJ8dDvevvKVVuFBbwOd R44vSZPxPmN57Hf4PKFpPh35XLrQ/T6/vtGlNDR60vax3KUij0oWCLl5RhnFOo+hvSBQPP74 1Pbt25d84xvfCE6TcQJ9uh4zMJbn+eefT9N9z6E6z86dO9PeR2N5qk7eWGVp6+ajrs1H5O2q DAcR2iahkHunqndWJsdH6HiTN3w6/Vd+5VfclEYef/zqISRsHq2fPOjlZLLIbbc7Dv/cpTW8 /9iljW16W+eQPsimEnujfWoIlE2svSDdrQPlQS+j45fnrZcPechWxYJP3oKyhFxj6vQ21a83 vYyizPz+pSPPdSekElcOvUGyrjhQFLH2gnSKBBRX6JgtStSVnDOlsBfaJt1CxtorW6FIHiJm rUJrow5vArXQNihKAP2w+9HNN9+cvP32225qq6mBxNVXX+1SGyQtpPR7o3Qe41dagoG6USah 6jxUeQaKzx63RQrka3MYCykoygO7SXaqIoXctWvX9lXQ1VHn60poexQlgH5024/uvvvu5JRT TknTN2zY4FIbD5H27t3bMb9X+r1Rb5DYSgJFZB9mSFAYBMrBHrtFCnSSgpXuFXYYIedrKTTK gz0ptEl1TWm3lyVkfp9HPiP0+YMEbc9bQtunKAH0o9v+E5vmz38xpd4bpYqHX3EJqtihDORp c+hpLxdwoDzs8VukQG/yBlHOuYO+RSxCyMNFKVQiLLTNihJAP0L7j96vpMqo5zsmk56qP//5 z7vUTqXdG+WmWq+8BFB0dB4DFEPoOCQIgihzFJl/ibNgwYLgsspLHUmXgktoum9iE5sub9vP OOOMNELTJc1Pl3ktmX7qqaem/y96bS1ZRr+OBw8eTGsd+LSvfvWrabq4//77k/nz56fpO3Z0 74G1c4uVhK36wUD0KAO9z/qo0nhPQFmEjkWCIIiyR1HddtttXZdTd4QSmm5rVll6Wpbptm2w nV7kjpf8Morf+Z3fSf9d215G/Ur6kNIxUGR2n5WgzSAwfqFjkSAIogpRVHY5bc++drrtJNJO 71Wgs+x0+5bQTi9yMx6/jN///veb/9YFwr/+679um6ZJekgpC4ShbvqBopKnTHZ/laAwCExG 6HgkCIKoQhSVXU5b4LLT7T2Sna4LdKFmZPpFka5SqcOzfZLY6UXTazn/2T/7Z2n67t27XUpL aH5R3LXtQm8ECalXDBRR6CQlUacxoICiCR2TBEEQVYgiij0Y92L9K3ixAp1/SyiFQztNd7QU 68nXs9VR7fSi6bacOl0XCHVTu5Dirm1E6AYbKKJeJzgAk8ExCaAqynA+61XgivX268UKdP4t YWiahH9LGJom4QuUoWkSRX1475dPe+ihh9pqUHpSlVTSv/SlL3Xk0eJTCsruNEWu44v6ClVr lgAweRyXAKqiDOczu4w+fDvC0DQJ344wNM1Ht+n+LWFomkSvAmVRyxh++TSf9swzz7iUhi9/ +ctpeuV6GfUrLEEbLAzKv8XzT4mGQe+jPthXgeKwxycAlFUZzmd2GX34AldomoS/dwpN8xFr muMjVt3Uhx/uIhZFpJfN9zIqvbT+3u/9XvLmm2+m6cLPJ+nPP/+8Sw0r1ZXQvnKmHRYGYU8C gz4JitWR5y02UCz2GAWAsir6+czeG9n2fvZezNYE7FVgC7Uf1BGrbuqjV/4i8sv2+OOPN8dl 1L2Mvvjii+m00DpIekiproR+xUIrCOTR7QTTz4OGWHtBBpwHiscepwBQVkU/n9mXObZJTa8C oJ1uP8+GnV9Ht2kSoc8u4ssnu4xWpXsZlZ5E9crr7mSBfth9SsfatWvdXL3F2gvKSQ2oKtm/ ffuLsrHHKgCUVdHPZ6Hls2k6uk2XAl2sNpaEvA3s9sBfpnUrUEqbRvvGsIi1vPyyXX/99cm1 117rUhv0tAMHDrjUJLnhhhua6SGluRL6FfQBDIPdr2z0KtSFCpVywgKqQi6Q/kISijKqwjoA gCj6+Uwvm3+IqNN09OoExt+ThaZJeKFpEqJbgVKUYTSD0HJt2rQpefrppzumvfHGG2n6jTfe 2HVdireWEX4FJfK8vQGEnABChbvY2z0d3TqcsfNSGEQVyPEiF+Ze1WskyqgK6wAAoujnM71s /n4q1m7Pi117vNA0CS80TcKL1RDz9Jh9Or0oQsvl0yrdy6jdMYpYnxfF5vedUIFN71uxkDck WqzKAfsmykYelPRqdN8tpPp+PzFJdh0AoKyKfD6TGiahZRu0QBi7B/NC0yQ8u1x2un1ZUDR6 uQ4ePJheU33aV7/61TRd3H///cn8+fPT9EoUCP1K+gDysPuPhB/7RtgTU7e3IlLg63WiAopI nszKRbTb/l3XAICyKvL5zBbcvNhQEV6soz5PF4B8+OqmInSd89VVPTvdthW004sYwg87oXsZ 9fR8vWSba4Lk6bVfIQl9Iw9kofcfHXLC8G8q7DRJz3PjDBSZPMiIPVElOH4BlFfonFbE6FUg 0wU6YafLPZlma7bomiehAqVt/mOn2xpednqR48orr0xee+01t+RJsnz58jRdhqSQaZqkhxT+ SqhXOLYSQDexJ1E+5CRjC3/+xBGrVuDDnqCAssj70KPKAQBlFTqnFTGkGqZmp+sCnbDTpZCn PfXUU23TrY997GNdp/vx+2LTY9VaixhW5YadsCV8244LsOSEIo2FLb0fZQ0tdONsT05A2ckD EDnP5rkQllEV1gEAhD2fFTUs/cbOvr0TvaYL/1Yv1H+DpHWbrj+z1/SiiW1TEZvmO8qJKeyV UH4cv1KxlQM0OXhj+0tof+oV9mmV9G4r6bwVRJ3IsdOtx9EyqsI6AIDgfFY/od/6oYceausM x5O2hZL+pS99qev+Udg9x958cBOOXvT+4kMPNWH3qVijZR22UAigQY6tstbasMc5AJQV57P6 Cf3WPq1yw074FfMB9GL3GR1SFc6+JZQCoqTF3nz4AFAtHOMYhH+YaNtEAZPA+ax+9G/texmd np5Ofu/3fi95880303Th55P0559/3qWGFXLPCd2gA73YKqM2ZL+y+5b8LeRNoE7XwVtCoFrs MQ7kxb6DotDnMvbJevC/9eOPP97sHEcPO/Hiiy+m00L7hKSHFHLP8Svgg847YEnHMR//+Mfd Xy1238kS8pbQCz2MYP8DqsUe40BeUl1a9h3blT4wbpzP6qfXb16JXkZDnX8Alt83pGCoC3T2 LeEll1zS9rfE/Pnz2/6WzmI033mMbn8IoDr08S8B9IP9B0Wgz2Xsj/Xgf+vrr78+ufbaa11q w913352cf/756bQDBw641EZV971796bpIYXbc2JV+gBP7x8+dKHOTgu99bNhMcQJMGoHg8di z1jxsMvfP/uZQD8+9alPpfuPfagIjBPns/rp9lvHppVu2Am/IhJyI6/f/gBC7yM6/MMDv9Pr 8G/9YiF5AIzbyeSPFraOw4t/+HOXrh1L1jXnWZTMv++wS++f/z4fQL/YhzBp+lzGvlgPod9a 7wPSwYy3atWqNE3aGn7+8593qZ0Ktef4Ovk+ijwoJCZHqonq/cSG9Chq06Q3uFB1ZB0Axm// pkaDeIljLi1kqRQKT3kw+csZlzAAfdwTBEFUKVB9+rc+ePBgW8eIX/3qV9N0cf/99zebSZVq 2Am/Mj4AeSgQekts9xUbp556akeaZx88+KDzGGD8Lm2+/ZvjUsLWLZ6fXPx26A1ifvbYJwiC qEqg+vRv7Yed0L2Menn2icLsOXp0fQnG94HQ+4QuGPZ6SxgKyeNJQXPZsmXNaVRNBibhROsY Xdz96eUwNb+TIAiiYoHqs7/5lVdembz22mtuapIsX748TZdqojJNk/SQwuw5esViC4v68AP/ 2ujWeYzUmbZpNiz5HsYZBCZj5u2LmsdmqP3gb6y8qGs10kHo8wJBEERVAtXX6zcv7bATdqgA OviA0PuEDt95jH2rLNGrnaB+Swhgsp78TOvYtAW/D/bJA57u1UiBIpExCWVfppdqAKPkr5sh sWml6GXUDgsAiFBvoTqk8xhbKPQPE7oNNQGgGFY0j0td8DuZ/NlWlz6EISaAceI6A2DUQucZ nybVRHUvo772nKSvXr3apXYqxFnLr4SEPGFDPYXajep9IxShgp8nVUG7TbcuMPNN7fmxmwJg 6Gb2tB9vgZje+VM3c/GElrcogcnxDyl9TRZMnj0+ihRAP/T+s2/fvuQb3/hGM+2+++5L08W6 deua6fLvbnrujTLw6ig7ePEDu/qgc496iu0Dvd4ShsJWOdbDUPR0/IHmvD9ySQCG78SeOc1j bcPx9rEkfNvCp4cwxMSo+GUvYmCy/O/A0FnFoI+NogVQFNG9UTrbsIN5j6Kwpj9fAvUSagco odv62UJht+qgPkKy7L8ndlztPoO2S8AoPXzVXHesXeBStIPR47go/LmmiIHJ0m3ZMXn62Cha AEUR3Rv37t3bsePK27xh0idNCXp7rKfPfvazbfuBDz0uoE6XasV237HR75PZ7/3bM9L8c+/6 nksBMArN9oOhdoInv51sXPzf3B/FpM83RQtMnq/9NIoH6cjHHh9FCqAoonvjF77whbQeqt15 h1kv3r7pQT3JgwC9H+jwbUptulxk77333q5vC/vh8xa57RJQfoeax9qiTT90aeWjzz9PPfVU 8sgjj7gpSbJy5co0fevWrem0Y8da/ajqPP/zf/5Pl9qeR3+W6CcPJkdf1zBZ+tjRg3e/9NJL afqv/uqvJk8++aRLbcib5xe/+EVzfptHf5b9HqAoomcquch40iuN36F9DMreyNOZTL3pfSEU 0o23/ls/mLBVm33kpjq5+MsCt10Cyq5b+8GyuOmmm5JTTjmluR7aqlWrmum6kPZf/st/SfMN I89//I//sS2dwmDx+Pscaj9Nlj12hPS86I+fhQsXutTGcR063j744IM0/fd///eDefzxq/k8 9rOE/x6gKNr30C78Dq1jkKoQw/wslF+3t4Sx0PuM/FtP6+cC3LpJbXXXC2D4tjeP1fK21fXn mpDYtFtuuSVN37Fjh0tpyZtn2bJl0TwoDn6jYor9Lj79tNNOcykN77zzTpq+ZMkSl9IS+6x+ 8gCT0nNv9DuthDyB1H9L6HZeWdm3PRKA3Sd6hX5L6Mm+JVVJ+9Hs5GLx7I3XzHeTh6/2nV7o CHWAASCzmVdax9OKLS6xfHbt2pX85//8n91fDb/zO7+Trpd07y3TNb/Okv43f/M3LjWex799 vPvuuzvy6M8K5UFx+PsdBqsvBn/sLF26tO2t+o9+9KM0fe7cuekx9dhjj7kp7ceb9K/h6Tz2 gU2WPPrYBSYteuWQgp7t3dHTaXZaFjavDAsASEcwer+QasS2arGN4b1ZPtH8zIvf/rlLS5JN Cxtp0w/8wKUA6EerV9HOmN5X/ja7cr1ctGhRuj66/ZA8oNLXUk/SYnnkvOcLd7rQ6fPYzxI6 D4rF/140jZkce+z85m/+ppuSJL/7u7/bHLxbCmpet+Mtbx5p9yvpoTxAEUSvHL6X0Wuuucal tPgd3UaWm3M9JpwPwIvtG7GC4emnnz6cQuHxjc3PlK4fZt7/evLHq2ZvYFdNJbve/2VjHgCI 8OcP67rrrkvTd+/e7VIa/PwHDhxwKS2xz+onDyZPN4kY3kNM5OG3/2//9m+7lBY/zRpmHmlP GMsDFEF0z/Q7ri4Qfv/732+mSyxevLjtb4leVUjt/FSjgBZ6S+jFOo8ZxnAoT37Gf965yUdv 35HMnf33RQ+9mfzETQcAS78J8OH53j996AKhTteFO5tH0+lZ86A49LUN46WPD11Qk2rYepqm 07Pk8b2M+tB5dLoEUETRPVP3MioWLFiQzJnT6HRD27dvX8fOHmvDZTv+oPoEQvQ+IqHZfWhY pACYfua61mcv++CkmwoA7a644opmdU97Lrroooua6bqd0n/6T/8pzTeMPHfccUdbOr2MFp+v 6fL1r3/dpWAc7LEjpKOX+fPnp+nyf+/Tn/508Hj727/92zT9n/7TfxrM49sCaz6P/Szhvwco ivY9tIvQDi2kUaykSxe8fh6J0FsbW+0PCOn2ltCTN8vDq3rzXOv7Vs3uy/7fm19NfuHmAADN nzNCYtPoZRT8ZsUQ+x18Or2Mom567o1+p5WGsLrR+0MPPZSmX3vttWn6W2+9lT7x8PNLSAFQ 09N4O4hu9L4y6v1Fj4kmVUSfu9t/77nJgyUdIw3AaMl1T18ThX/LJz2G2mn+HCPpf/d3f+dS 43n8uGbSy6jNoz8rlAfFtWbNmvQ3svdHGA9/7Pjjx3vvvffa0p9//nk3pf14k4HpPZtH6ycP MEm5CoSaVHmQdHl6qdlB7H3VCDvOHA2r0Y2tGioxKq/+P+47Vjyc/j1z+A73nZ+tRO+HAMbD Vw8L3ejFzmOxPH5A61DX9LHP8nlQbLHfD6Pnt/0nP/lJl9Lw05/+NE3/+Mc/7lJaYr/XsPMA kxQ9I8lTSb9DSzvBV1991U1p7eiS/uabb7rU9jy9AujGj92ko9/xBXtpth/8kxcaCTN/qr73 wdmEnyVzF04ncx490pgOAECffPMZeVsIAEUQLZmdf/756QlLvxk8fPhwswttCW3FihXNdMlj 2xTqGEavkKgu2T9C+420LRy+Q83Pf6BZPfRkqx1hM1a7aQAADMZfWwCgCKJno+uvv979q8UP OxESOrn59gw2gBjb8ZCEdO0+Kq32g4tmi4EtG+aoQbTnTLdNAwBgEFLjRa4vtCUEUATR0pkU /jR/c2wH19XVRDdu3OhSk+SBBx5I0/ybRh2jvMFHedn9RILuuQEAVeSvc6Op/QIA2fV8XXfj jTc2u72W8J577rk0fenSpWn61Vdf7aY08vjxXa688so0zef3QbVRaHb/kHj99dfdVAAAqkV3 niYd7wHApPQsEPqTVdZeRoXPY9l2hVJVgpNgvcnbYr1P+KAwCACoOhlSyV/3AGBSomcgXRVU BsR9+umn3ZRWgU/S5U2hZ/NoPn3x4sXNf/ugp616inUew5AkAIC68L1qy/8BYBKiBcK9e/em J6hrrrnGpbT4G3cra55Dh1o9O/qgCmm9hDqPkQAAoG64BgKYpOjZZxi9jIpYHnmD6PP4oLet eqAwCABAyxNPPJFeB7kPAjAJ0bvwlStXun81LFiwIJkzp9FFvyaD019xxRUdN/UHDx5M06+6 6qqOPJIeqjrqA9UV+r3pSRQAUHf+mkjfCgDGLXPpy5+orF27dqXpN998s0tpkBOapPteRjX7 WbYKKcNSVJPviEgHnccAANDqdXTZsmUuBQDGo2eB8PTTT09PTv4G3vvOd76TpvthJ2QQek/y +J6z5N+ez2M/S/g0H1SbqJZQb6L8xgAAtPjrI52rARinngVCf3IaxrATWfLYoDphuclFLdSb KJ0IAQDQTmrN+OskAIxL9Iyjh5CQdoKvvvqqm9IqvEn6m2++6VLb82zevNmlNmTNE+qBlCqk 5fTuu+92/Jb8ngAAxPmO13ggDmBcogXC888/Pz0h6TeDhw8fTq677rrmjb22YsWKZnqWPNL7 qE7XeeTfPt0H1QvLRT/l1LF27Vo3BwAACPHXTAAYh+jZZtTDTjz++OPRPD593bp1zX9LUCgs h1DnMRI87QQAoDc/WL30xwAAoxYtEEpBTvM39bt373YpDbrK58aNG11qkjzwwANpmrxptHn0 Z4XyyJAUPk+oCimKa82aNR2/lxTk6U0UAIDs/DWUDmYAjFrP0tWNN96YdgLjT0zec889l6b7 XkavvvpqN6WRZ/78+Wm6HnbC57GfJWJ5RKgKqbRPQ7GEehKl8xgAAPLzw3fJQ1XueQCMUs8C ob+x1238RJYeQ61+8gg/zQZVEIuBnkQBABg+38GMBACMSvQMo6uC7tixI3n66afdlFYBTdLl rZ9n82iD5JEqpNKeUKfpwOTEOo+hJ1EAAAbnx3WmUzYAoxItTfkb+2uuucaltDqI8aHp9Cx5 dKcyEjqPTpdYvXq1mxIuEEpQx378fHUWG7y5BQBgePz1FQBGIXp2KUIvoyF+2tatW5v/9vHE E0+4uTBqvvovvwEAAKPlO2yjKQaAUQiXumbJ+IHaggULknPPPTc5cOCAS2mQgeavuOKK9A2f nnbw4ME0XdoL2jySvmTJkjQ9lEfaK2b9noULF7YVSDAeeptL0JMoAACj46+3ADBsmc8ssRPR rl270vSbb77ZpTT46oS2x1AR+6xueWLf89577zU/zwfVR0fLbm8JekADAGB0fFtCGaMQAIap Z4HQ3/DLW7v9+/e71CR56KGH0vRrr702TX/rrbfclFYeSX/ttddcaiuP/SwRyyP0NP09mzZt an6en8cHb6uGTwradjtLUAAHAGD0/HUXAIYpelb5wQ9+kFbP9CcfPeyETl+1apVL7cyj9Zsn NM3nkd5HJd0vm60+qvNgMLHeRAEAwHj49vvSTAMNvz71xWShui/ZcHwmTd8yre5XVj+WpiXJ yeSjv17bSp+Nk24KUGfRO/q9e/emB4oUvCx/EFmjyqN7GRVZ8ujgDdZg9DhIOgAAwHj5azA1 oVpmjl7stsuitIC3aXH7/crUlLw4OJlML7LpU8m3f5l+BFBr0bv6L3zhC8lTTz2VvPTSSy6l 1WOopN9zzz0utcEfWFnzbNiwIZpHp0vok14sj/6eU045pTmfD06c/bHbUYInkwAATIZuvoGG I1sbNcamVvxJ8v5jU8ncuVPJd//XTLJ/k0ufujHZPX16+u+30heIh1w6BUJARM8mK1eudP9q kGqevq2e9vzzzyc33XRT88DypK2fpMvn2DzLly9vzh/KY9NF7HtEbNlsFVLGx8tHbzsfFAYB AJgsGX5CrskyHAWSZOlCf58yJ1l8yurkjrePp+mfaN6/zJmN+cm3P2pUJ5055NOnkl+kKUC9 tZeguvAHjjXpXkZF7PN8Hh233nqrm4qYWOcxjH8EAEAx+GszTjS3xSmz8UajzDfrnWZ6Gt96 z6UnyXaftvAKlwLUW88ziT6YHnnkEZeaJOvXr0/T5O2cFLxeeOEFN6WVR9KfeeYZl9rKI3H3 3Xe71IZYHqGn6e/Ztm1bc1po2Xwe2wspb7ni6EkUAIDiu/fee4PX66rGE0884da83czbFzXn mX/f2y61PX3hKa2OEXUBcmrPj10aUG/RAqG0uZO3ac2DZjY8ST/77LPTtKuuusqldubR+s0T mubzXHLJJR3T9PfodBmywqf5oAppO9mudhtJAACA4ol1+lbFiBUIn/yMn2dxW4+h25vVSKea VUVTxx9oph9ySUDdRe/2y97LqOXz2JACJFrbzQYAAEBRrfD3LJf4oSXESTUUxQaXJt6ZLUD6 jgfPcmkAalcglDx+Hh91rw7pxzXSQbVaAABQbK3qnw+68QcbWulrj6puRI8/mPy7cxvp5/63 wy4RQLRAeP3117t/tfihHUL8gWfF8jz++OPRPLF0EZuWZ9l27NjRTPMh1SXrSFfJZXsAAIDS UNU/j7kkodsP6vTm8BSzcfEPdyVTi69omw7UVbgENevw4fYnJ/4AOnDggEtp8IUrGUpCT9uy ZUuafs4553Tk0Z8VyuOnabHvEXnz6O/RIQ206+SGG24Ibgc6kAEAAEX33N3u3sX0FvroknD6 4zfPVfc7C5vDUwB1Fy0QeqeffnqybNmy5gHkfec730nTly5dmqZLb6Oe5DnvvPPSdPm35/PY zxKxPOLyyy8Pfs8rr7wy0LJJSHVU/2+Jugyt4Mcw0iEFRAqDAACgDB6+qlHAm973U5fS8O9c hzI2/YN908m0u+d59n/pKqZAvfUsEPrCggzdoPl2Z7fccotLafF5rH7yiNi0ffv2pemxZQvl CaXbKqRVbz8X6pWMznUAAACA+gmXwGbJOIG+sCAFr1dffdVNaRWqJP3NN990qe15Nm/e7FIb BsmzePHiZOvWrS61lUc6iOm2bKE8ErHvsVHFKqShwiDDbwAAAAD1FC0Qnn/++WlhQb99k3aF 1113XbMgoa1YsaKZniWPdAKj03Ue+axu00Lpw1o2/28fUo2yCkI9iUrQeQwAAABQX9ECYZV7 GfViefz8PspehZSeRAEAAACEhEtQs6SwpPlCxO7du11Kg66KuXHjRpeaJA880OgKWN402jz6 s0J5pIpo1u8R+vO0XssWyuPTQz2RllGo8xgJOo8BAAAA0LOUc+ONN6adwPiChPfcc8+l6b4n z6uvvtpNaeSZP39+mn7llVe61FYe+1kilkdcddVVzTz6e954442uyxbKo7+nWx7P/+2jTAWp UHtBCQAAAAAQPUsHvhCh296JKvUyKrIsm48yVLUMFQbXrl3rpgIAAADAbFnH/b+Drm4pwzI8 /fTTbkqrgCTp8mbNs3m0QfJIFdJ169a51FYeKQh2W7ZQHj9N0+mxZQtFUYWWVQq8AAAAAKBF SzV79+5NCxIytIPlCxnWqPLI4PHapJbNDmK/Zs2aNL1I9NtRH3QeAwAAACAkWiD0hQldgPK9 cvrQdHqWPLqXUQmdR6dL6AKhTi/CskmnLUUR6k207D2kAgAAABidaIFw5cqV7l8NCxYsSObM mZMWMjRpx3fFFVc0CyDewYMH03TfIYwm6VINNJbHpovY94h+ly2UJ+uy6Zh0oUs6ugktV5EK qwAAAACKp7001IUvZFi7du1K02+++WaX0vDss8+m6bbHUBH7rG55Yt8jYp/Xa9lCeWLpdtmO Hz/enDeWZxzefffdjuWQkLeFAAAAANBNz1KML2BIBy779+93qUny0EMPpenXXnttmv7WW2+5 Ka08kv7aa6+51FYe+1kilkfoafp7Nm3aFPw8/z2hPDo9lCfPsunv8THOYSmkbaD9fokitm0E AAAAUDzRAuEPfvCDtO2cL2RIQcnT6atWrXKpnXm0fvOEpvk8vmpnbNlCeWy60On9rI+NcXTi Euo8RoLeRAEAAABk1V7KUSbVk6fm8xSll1HN5pFCqU+z04ZNxhO030XnMQAAAADyipZavvCF LyRPPfVU8tJLL7mUVq+ckn7PPfe41AZfMMmaZ8OGDdE8Ol1Cv3GL5ZnEssXy6Bh2FVJ6EgUA AAAwLNECoe1lVKpS+rdg2vPPP5/cdNNNzcKJJ+32JF0+x+ZZvnx5c/5QHpsuYt8j+l22UJ5+ lk3nkc9cuHBh82+JYVQhpSdRAAAAAMPWXrLpwhdArLr2MqqF8kgh1adL3HvvvW5KfrHOY+hJ FAAAAMAgOks+hi6APPLIIy41SdavX5+mScFHCl4vvPCCm9LKI+nPPPOMS23lkbj77rtdakMs j9DT9Pds27atOS20bKE8Oj2WJ+uyZcmjo5+3ebrwqoPOYwAAAAAMKnOBUKpCar6Xy1tuucWl tPg8Vj95RGyaDDwv6bFlC+WJpY9qfQ4dOtT8t4SMG5iHzish7QXlewEAAABgUOES2Cx54+UL ITt27EiefvppN6VVSJH05557zqV25tEGySPDS6xbt86ltvJIQbDbsoXy+GmaTh90fXy6zvPp T3+6LT1rJzA6j49xDGkBAAAAoB6iBcIiDe1QhmEnPJ9u8/h0Hd2E5qcwCAAAAGCYoqWS66+/ 3v2rxQ/TEOILLVYsz+OPPx7NE0sXsWmTXjafJ7TdfJ79+/d39EBqh6WI9SY67OErAAAAACBc gpp1+PBh968GXzA5cOCAS2mQapGSLkMv6GlbtmxJ088555yOPPqzQnn8NC32PSJvnm7fo9P7 yaO3WyyPrUL6xBNPpOlSlVSn+wAAAACAUehZ2jj99NOTZcuWdRROvvOd76TpS5cuTdOlt1FP 8px33nlpuvzb83nsZ4lYHnH55ZcHv+eVV14ZaNlCeWy6GOb62Dw+zjzzzI40CQAAAAAYlZ4l Dl8woZfR4eeRKqR+vlAAAAAAwChFSx26h00peL366qtuSquwI+lvvvmmS23Ps3nzZpfaMEge 6WV069atLrWVRzpu6bZsoTwSw1w2m8en2zw+TTrI0XmOHz/elkfiYx/7WDoNAAAAAEYpWiA8 //zz08KJfvsm7eOuu+66ZsFFW7FiRTM9Sx7p0EWn6zzyWd2mhdIntWxerzyhdJtHx5o1a9xc AAAAADAa7SUnhV5G8y1bll5GLZvH9kD6qU99Kk0HAAAAgFHoLKU4UljSfCFl9+7dLqVBV6vc uHGjS02SBx54IE2TN402j/6sUB6pIpr1e4T+PK3XsoXy6PSsy6bz6O3WT55NmzY1p0lkHcQe AAAAAPKKFggtX0Cxdu3alabffPPNLqXh2WefTdOvvPJKl9IS+6xueWLfI2Kf12vZQnli6cNe n155dEhHNAAAAAAwbJ0lEkMXTB555BGXmiTr169P02RIByl4vfDCC25KK4+kP/PMMy61lUdC 3t5psTxCT9Pfs23btua00LKF8uj0WJ6syzbMPH5dpI2h5PFjKPq49dZb3ZwAAAAAMBzRAuHr r7+eFkJ0ocST9LPPPjtNu+qqq1xqZx6t3zyhaT7PJZdc0jFNf08oj00XOs+g63PDDTcE80ha tzyLFi3qyHPo0KFmmgTVRwEAAAAMU3spR9m7d29aCJGhHSxfQLFGlUeGatCKtGyWT7d5fLpd F+GnhfhpOqhCCgAAAGAYwqWQWb7woQs2vldOH5pOz5JH9+QpofPodAldiNLpRVg2L2sevS42 j6bTQyFvGwEAAABgEO2lEIVhJ/Itm88zyLATWiiP9Fbq0yWoQgoAAABgEJ2lFEcGbdd8IeTA gQMupcF3frJ8+fK2aVu2bEnTzznnnI48+rNCefw0LfY9Im+ebt+j0/vJo7fbqPLooFAIAAAA oF/RAqF3+umnJ8uWLWsWQLzvfOc7afrSpUvTdOlt1JM85513Xpou//Z8HvtZIpZHXH755cHv eeWVVwZatlAemy6GuT6xPH5dsuSR9fH/9gEAAAAAefUsSfgChwyHoEnHJpJ+yy23uJSWWCGl nzwiNm3fvn1pemzZQnli6eNan1ie2LqIWB5bhfTdd991UwAAAACgt84SiyNj5PmChhRWXn31 VTelVdiR9DfffNOltufZvHmzS20YJI8UfLZu3epSW3mk45ZuyxbKIzHMZbN5fLrN49OkU5lY HrsuQk8LLZtsAz+PBFVIAQAAAGQVLRCef/75aQFDv7GStm7XXXdds/ChrVixopmeJY906KLT dR75rG7TQumTWjavV55Qus2jxb5H2M+zg9hLAAAAAEAv0ZIDvYzmW7Zx9DLqhaa99957yZIl S5rTJP7+7//eTQUAAACATuESxywpLGnSkYn01rl7926X0vD8888nN910U1qtc+PGjS41Sd56 6600/V/9q3/VkUc+Rz5P0kN5LrzwwszfI/pdtlCefpZN59HbbRR5uq2PbOs5c+Y0C4RUHwUA AADQTbRAaPlChrVr1640/eabb3YpDc8++2yafuWVV7qUlthndcsT+x4R+7xeyxbKE0sf9voM kqfXtvbz+QAAAACAkJ6lBV2weOSRR1xqkqxfvz5Nk7dcUvB64YUX3JRWHkl/5plnXGorj4R0 iqLF8gg9TX/Ptm3bmtNCyxbKo9NjebIu2zDz+HWRNoHd8nRbH53Hp/ug+igAAAAAK1ogfP31 15Nbb721rVDhSfrZZ5+dpl111VUutTOP1m+e0DSf55JLLumYpr8nlMemC51n0PW54YYbgnkk rVueRYsWdeQRsTxZl00HAAAAAGjRUsLevXvTQoQMa2DFChijyiNDNWhFWjbLp9s8Pt2ui/DT QmLTsi6b74F07dq16d8AAAAA4IVLIbPmzp3r/tWyf//+YOFE6EKIFsuzYcOGaJ5YuohNm/Sy +Tyh7TbMPCI2LbQ+f/d3f5dWR7333ntdCgAAAAA0hEscAAAAAIDKo0AIAAAAADVFgRAAAAAA aooCIQAAAADUFAVCAAAAAKilJPn/AQy35+okI3TiAAAAAElFTkSuQmCC</item> <item item-id="5">iVBORw0KGgoAAAANSUhEUgAAAHUAAAAZCAYAAAAPMmGdAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAHrSURBVGhD7ZmNzcQgCIY7lwM5j9O4 jMN4oNb6hx82l95Xw5M0uRioyFtEc4cXtkNE3RARdUNE1A0RURucUf44DniUNy4NvgwRtcQZ r5OSQVxtw++3IaJSWO3VS0tVRB3ivDHvrFJERG2BLViFnnpIpf5foOpUFKl7Jj0z9FRlwPt9 SKWSWK9F1M2Qg9I6132w6V25p/19TxxeOxb8W8qYvn6d4cRF2JC5IviRqLC1nYGHhZyLaMc1 jIzJC62Sz/d/Fk5clA2VK5rfiEoFDVveJVI84MwKpqvURf/H4MRF2VC5mtCL6sAxnRaVMZOX 4AR3k5ZOpOhc9K5WJKvn201rv+r/FJy4aJtxrmbUoqKTAhFtdGwnGpMmTXt+9Ux940dR2uB8 ZdB3RF3xv8f6ejlxzW36XM0oREVHKPNiLpyI+Z5lnNGwA6QEpasDvbDxroD2z4u6Dieumc0o VzOyqO1LYYBo6C03KhXfnYOL/sGU03sKWlFX/e9xY72cuCgbKlcTsqj4ZeR/KKzxRpeTfJmu +Z+B4u/Uw6vFjOlEXfR/Dk5chA2ZK5pr+8V+il+bwlKPCVPaVNtxDW8CiiAIzgdPv0PgeFog BX7Zyb8Sluv/NKO40lgOn4idzBVBfVAStkBE3RARdUNE1O3w/gMlrkKAURTy6AAAAABJRU5E rkJggg==</item> <item item-id="6">iVBORw0KGgoAAAANSUhEUgAAAHoAAAAZCAYAAAD+OToQAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAHySURBVGhD7ZeNzYQgDIadi4GYh2lc hmH4WkTl39bz/MLRJzG5cLaWvqXA4oQpEKEnQYSeBBF6EkRoAtYotywLPMoZGwYHQ4S+whqn g7pecL3636MhQnNYtVODLmkRmox1xoy5mhERmgK0b+X36EVW9FjA6lSbcMXT2YP9Hq0MWI+H rGgWq9Mi9AT80mFsuzN+/75Yu6qc99VsLzz2yOu4qlcghn1OHNPjVytiXKxcNais6O+3pyPI JHj47j5hn4B98vm4hpE6NL9t+3ehxcXLVZtS6JfaU1GlyWSjiUA853vbISqZc0bhl2n/Goy4 yLnqkAmNH1ROGxAbq0i1qh+df5awIvgwWT8WFVv+3qr7rSp/n2v/Fpy4qLnqkQq9V4q3s/7j if+C8EEsivzpG1aCR7YCisfxvXgid4Tm2N+DnwdOXNRc9UiERoex3bXQ96kFb6GTGBuSFs4J 7YTUu0rut23/v3DiouaqRyQ0GsWtGhLZbN07D65o7CZHwJtf//cQe/SNPHy6R9dy1eEUOu71 dgXjdHU/TTX45ICxB4+/oxPmRfWW1c+zfw96XPRctUlXtAaHWIWwks22UTegOW+C1YzfwSdy 4icUxpM25ieG4xeny4Zfsv3b1OIKY0f43Fw1SA9jws8iQk+CCD0JIvQUOPcH0P0f25G0qcsA AAAASUVORK5CYII=</item> <item item-id="7">iVBORw0KGgoAAAANSUhEUgAAAHkAAAAZCAYAAAAVDoETAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAHnSURBVGhD7ZmLrcMgDEWZKwMxxxuB aVgmw/AwIYQ/NopSkfhIUVurLo6vbYIqDPN6WOQPwCJ/ABb5A7DIA3a1GSGEvTajdm9cDBa5 x66M9Mo6saV271eDRcaipdkWbWUWGcVulFqziwEWeYQd2ZvbkwV38jrYrtwO0Yqrs+e6PXlT 1ns9uJPRaCNZ5JfDD15Y4lEZnztbdkvYE8fn1Ooxh+Cfc52R7XX38WkY10SuGjwq8q5kCEpL G6Qffy27G5HnjbikSGupEwRJxMD7P8s4Lnqu2vxuXLeSHtvtiLxEOyq411BFJxP9H4MaFyZX HQqRoVLOMVJfGKrwhmRBgLUqjOy5aFC5vX0x/z7V/ynIcSFy1SMR2bW/Xdw52WqL4qgQ7w3Z 1Xc8aD3IRHZIRvydGZEp/nPQ80COC5GrHpfIyKq4h9ZxJLW3k1GfJr8RmQ4tLlyuegSR84XH zHeylvV9pLDbSr1+a7x35SJT/eeYyAMhLnSuOqQi+5V2ff37cjewznV/6b88pR06NnoKHVRu ITLR/zlwcdFy1SYZ19JXZL+jIcC5joCxlFb7caMtuwOSkNtqQHec/nFwWP+nqcXlbRD+VK4a JA9ezDsJIou/VO/RZ2YdnHIgYCzi6DOzFkFkyiuzEsb8A633ua2538VaAAAAAElFTkSuQmCC</item> <item item-id="8">iVBORw0KGgoAAAANSUhEUgAAADEAAAAWCAYAAABpNXSSAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAELSURBVFhH7ZXtEYMgDECZi4EyD9Ow DMOkCcH6UcGABT3Pd8ePSq7mkQQNPoBX4i48WSKgswaNkQU+PR5EcBZN9qXL3Cy6UKwEB0vQ SKIAJ5iRCA6+OXmgOOtKEh6BAgY7RMqVWBAcWgN5Cf4j21QGkj/ZglUS+Ur8o5XWc7VaBwmq JTzEg96XSIa/DnLK00D1Qicxt/uuRLGVyJ6H6dihbyU8AGkIIrFILHCJ7BywhW8ETaXPcCTB +99t6hqRiFOeTokFssfM7ZQXFE4ONh/otmIpP/4Zr9VpPy4Sligl/ILm7PpRJTGilVqokNC0 0jWoJXTX3jUoJKZvg6y2r3hf6gb7prwSd+EBEogfSUP4kxjbNW4AAAAASUVORK5CYII=</item> <item item-id="9">iVBORw0KGgoAAAANSUhEUgAAACgAAAAWCAYAAACyjt6wAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAD2SURBVEhL7ZQBDsMgCEU9FwfiPJzG y3gYBsVuc5tY6+q6pD8xaaypD/6ngU+uC3BU/wqYmCBwCLYw5u0JSgT5XmBKbgcV0g5NUyLG fOECKp1xACMjkGD+SBEZBLYKqBXogX5JYcOxEPfIPuBkcNTeMsfFcumtQD1X76BkAV7t1Xzk CwGPt96GBT8DvtsrVUnVtmOdadu/t4OrtJMroAQy5I4lDSegvK5rfz47pEz3KVZL1+oUrnF3 RHTyuX9IzNayy5UhcfT0r5qhPsDJcKrtgAJHLe8P0CbAFOMjlwtofp6gJmAR3GX5E/5t9Q/J ZF2Aozo5IPMN0zUMEMW2cTMAAAAASUVORK5CYII=</item> <item item-id="10">iVBORw0KGgoAAAANSUhEUgAAACkAAAARCAYAAABASYU2AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAD2SURBVEhL7ZONEYMgDIWZKwNljo7A NCyTYdIEUg2IP63W2jvfnWeCAh8vIfAf6IY8SjfkFlEEDpgseykxhsAhP/hbyAyoIA0kReRI logu6OToIhjpCEmRQT9CZJIYwX7y8URlwUm13lC/3CLj0X1rJxMyYOSUyCaL7fITyXh3oUHE UQ5Seqh5Vk4wC5mlJjQ9mXB0LCFIXCbr+B63lrQMqXtXkEoN1rDlBHlqtt3iWX3LSV3XQ/qS uvi1CPVa8gAtQmr7+Z70JdVSW9XzOChkSRvtvDhqRut4Z6y+OBfVABkeNe9afqbyzgrgIbbk Z2qA/OR9jpifV3P384a7S9oAAAAASUVORK5CYII=</item> <item item-id="11">iVBORw0KGgoAAAANSUhEUgAAADQAAAARCAYAAACSGY9uAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAEnSURBVEhL7VSLDcUgCHQuB2IepnEZ h+GBn4pK32tsmvQ1vaRNRAN3HOroYXgF3R1PFxQJvSPnPGEsoUtQ61i1AkGKywe8KohI3jo/ xA2HuBgg/69DRNhIBWAyvtXTew0isghJAqrQOT4L4g2YM16HiWB2x2sOgYlC9Sq7m5ZGfBIU 0ROAL5YbyTfk4lu+VYgg5VBCEtnqCqdGXHTkPStu3CGkwAkxHHVJ34fhO6KWu7zfsOycENdn tKAx3guS5J4PMZnTnT8EJj26oxDgpKDAowbsTOxm8xfWHaqEbUjesr92h1S36kWNHMP9kmcg 3W1cjIeoG0UZP/Waba7O8SaoS1DUfhmHnGxtNNNodG4WUtLxGhsTpyarsxVDfHgU/h+voLvj YYKIPrJBoZ8Q69LSAAAAAElFTkSuQmCC</item> <item item-id="12">iVBORw0KGgoAAAANSUhEUgAAATwAAAAlCAYAAADfq1XhAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAYiSURBVHhe7ZvBcbMwEIVdFwUxKSKH v4Ac6CL3HLilh1wzPqWDdMC/T0ZBCElISBhhv2+GScCyvNpdPS3CvgyEEPIkUPAIIU8DBS+T 9nIZLtbR9uOLhJBD6NvlvLzIxKTgZdJS3Qg5Ab2aqxQ8D9euUSvCGhQ8Qs4ABc+LEruxBF6j HsG7Dl2jy/dWwptDyb7IMdQew9L2rfVHwQtytgrv2rVDd739r/Yvmk5SYBsl+yLHUHsMS9u3 3h8FL8ipb2mv3dCUWtVL9kWOofYYlrbP2R8FL8i59/AkuMUSqGRf5Bhqj2Fp+1z9yTUKnp+z V3itru9zKdkXOYbaY1jaPmd/FLwgZxa8viu3X1OyL3IMtcewtH3u/ih4Qc4qeLC7lEkl+yLH UHsMS9vn769iwfv5+Rm+vr7GswPo2/Hxthwr0ahJ8BDs5q+U74cu4zahZF+kHClzo/YYbrXP 54Nwfy7BMye63vST++FmvDaf29LBDhuhb29vw8fHx/D5+Tm8vr6OV+WzKv1qhFvwzO8ENX+P y93otmvtJhBYu716FP8Xu7T+bEr2NWdfv+yVk7WQMjf2iyHYEps5W+1z+yCmP5fgAQic5cC+ RZk4uyKJVdKBNzCA7+9v9f/v768y/A8lvPUls0vw0r9jJAnUpgh6vQtAiH39sk9O1kJ9cyM1 Z/MJ+mAVn+ChyjMnsZzbTzyQrM7CJgOoNg4NFHwxINu2CnBXeAYxyShtkp5SiR+m0r0GNiR/ Yb/skZO1cJ+5kRjD1JzNJMoHQTyCZ98HLyY0EtWxMuO++uXlRakwyk4Yo9VY/w2hjUdb+/0T EpQmtIpjlb9v4kcJ3kolA5+3UkVjzPrwC9rNB61US2qroUlf2XNjtWSj4JXyS6ivq+TEeBvd dDJJA1Vgeb94SLAJoB2ADdvnxhppMUzLWbQf81XGumV+xvkghEfwzJWyb5eTSe0fWRYjUWAA VFcDNcY1vPb+/j5edYPyFIkG0B7vxbmp6Jq4lRzBnwIxO7Z4O8Cq4K1WY7C1G3qZtN1s28AD JjcqI9X0GumPidxYudkgeAX94spJBT4DIjC+39tO2McvDhJsAuXnho+UGKblLOzCGFVLGX+q jSk+8OMUPAzktkqY+y0mCJCdqEgKbZAGT1FwHYkCg0MgycxEA1Bxu0/g+vxSwF7fMTGvIsOC h5XcTKL5exXjBMDqt+xq2R7jN8/Xk3zeB8aSE6sJ5IrbV+t7c2X94s4JtNELw42573L9Ehj/ 0uCRNJvAvnNjYwxTcjZUfTvJ84Ef6Vc6tQRPB8R/f+5yKhxkG6STJUaFcQthJxQG41pV44K6 JRm3ERI8V4Vsox8IXSWJ1m3DuMw+JV6Jt7TwQU6s3IhdCRVeab+4cmJxTVfG46nNPn6Zk2oT KD83fMTHMDc2qaT4wI9L8JQaS8kZGDgGYA8SSYHkMNH32THY78fgcG25qsLZrlXlOHyCBz/9 veRdQCBYo6918mN/p/MMECur7kfaqb28RF/kxspN/GTZwy+unFR5MvZ97eXWS0Q2NDn38cuc VJuAbdd+cyM2humxaUajMGZfIRUixQd+xEaxYx5NKPbKiqMGaZWo2DyEAUgQfehrUOdp5ZQP lWvaAUDvj+DQ78V73BuSqHBCG7O3/rcHPR2X4CHxMPbp8NhsCpgam7QNlv9ISpnc6FMWpqg9 P4v4WKUQN1l284sjJ1Uf+Az4SV64TTzsO42vW+zjF4tEm8rOjTXiYrglNtMDmnTj0nwQwiV4 keStJPJ+47duuF2INl6cvbYi3htfhUfuS25O1sjZ50YJknwQxBK8y7+59oXP5Y2i2FtWE5S1 5ooGtY5Cl8/jaS1Q8GoB1X1OhVMfZ58bJYj2wSqG4EHMTEGLPZ9voG8jbkBI5nsFdCzTcesR MYFwC31rOx3UwKNA7B5n4tc3N+7PVsFbbp/IYQpezt9HIv3nT4SQs3AxRQv/p54/NA98q0DI MzITPJB6/tC4nv4RQk7LE6nXBmaP4AkhZ4eC56WfvmBJCHkIKHgeYn7+RAg5FxQ8B3E/fyKE nA0KnkX0z58IIaeDgkcIeRooeISQp4GCRwh5Gih4hJAnYRj+A8CWkCXhqFVYAAAAAElFTkSu QmCC</item> <item item-id="13">iVBORw0KGgoAAAANSUhEUgAAAOoAAAAuCAYAAAAm7YXsAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAUXSURBVHhe7ZwxcqQwEEW502YcYQ9C zSFc5Qs44AibOXdA5mBv4NRF5Bv4Blp9IYEkBEgghpH3vypqjRaEWs3vbjHMVIIQ8vBQqIQU AIVKSAFQqIQUAIVKSAFQqIQUAIVKSAFQqIQUAIVKSAFQqIQUAIVKSAFQqIQUwKpQ+7YRdVWJ qm5Ep9sIIfdnUahd14muH/7u21o0VCohlxFX+nataLVoCSH3JyzUvhNNXYtKlr21TKVd27L0 JeRC5kLt5LoUa1Kr7K2ZTgm5FFeofTuIVO+CntmUkMtxhNo1lfPQqC94bfr19SU+Pj70Hvlf +Kl+t4Qq16VVrYXZS9E2+570ytIZa9th09kZmVq3uX3imvk/+nl5eRFvb2/i/f1dPD096Vas u1tp2f3B8qEKTmYv2tqel2m/Gn0RYJzPlWMeAGX3bJzn2Rj2ex7ubYuPl1H1YGT5u7dDhSqh XVGg78YsfBV2YMgHHPT5+an+/v7+VvaMqAm772fCo4NnQoX9bjs+tzbzgeqmCgYWa94usCca ObZGG2MHqrNsXPX7Ue5sS4iM1lggq9o3ptw3hhr8MjsHiKbYDIisM4f5Y9uB7aAYbOcODJF4 9SHdklOd8Q/95J5H1W+TsfqQYw7amsnGKL8H2WHnybYscYpQcWNOxsiI4o8MRgWiD9YXt9tN RUeUMZhsEyXNv2sY5+BY//wJTNbRTC5tSuhjJlTt1AZRWI7RFbFmYY78vhDwlgS/fz5zClX2 1S7cmRlsBHF+D5Fq57m2rPnrFKHa2RJrXd+02Y0rwSAxMERDA6Ik2vB/r6+vujUMyh0YCXA8 zsW+HWkNWbK5Ehv62Xazb++wL+cFTWM/w/+NLERunGu3Lzn+2HzmEirKviEYBW/OgzaCFL/P SbHzXFu2/HWCUKXxOtsslYj+4AEGZCbcgKd3aMdNBYesAQNtIwGikt8nCF3fAeWKvG7sttqX BNfzhbruPGTs8A20fO5wI5nLYFxp8wm/he0Lr71WjveijrJ/VhYetxGk+H0g1U6Xs2zB9df8 NQrVGXDCNgcDkIbITOGvSw3+4AH68ifcDDQmOqJc8G8+GB7KHKHrJ6Myof+ALIxyridUe98X aqgKGYlc8xybT9lvttIX6HtC74EcNoIUv8/ZY+c5tmz5K39GVbU61l/LE+DfqAADwsBsTJ0e g38+nIe2UCaGMJYcHwci6NE1qjkfjp/6wrGTX0PBzjpezXV4no/NZ2ahOjdrPhtBit/n7LDz JFu2/JVfqDBkVhp4BAaMRTMGhcGZzbQhak7RBobLDGRNlllzYTPn4pzwAwVEtUkYe0h66qvm Q1cg1pjtdtOMADIeqzbLwdZxZn/tc7n4+QxxXKgqOKkxys2yO6eNaX4PEWfnPWzZ8ld+oUZy NKvhiwLGZpQG0c7xIiIplyS/F85lQpWKcUq+FPBqo700RMSJQkW2jWxPiiHa7z+AC4UKUIYe F06cwxAYKNKfBIVKCHkoKFRCCmBVqHi6qZ5WZShPCSH7WRQqf9yMkMchrvTlj5sRcilhoXo/ bvbn+Vn8vu3/Vgsh5BhzoXZyXYo1qc6gf59/KWHab7Kodw9lm3kzhBByLq5Q8UKA9+AIgkQ2 tcE7iWiHSOPeqSSEHMERqv9aH94A8rMpMELd/hYGISQHllDtV/qmHzeDIPd/C4MQkgMvo+pv Ccjy1zzlNW/wL73Vv/4tDEJIDpgWCSkACpWQAqBQCSkACpWQh0eIf/K1tOOMRdiBAAAAAElF TkSuQmCC</item> <item item-id="14">iVBORw0KGgoAAAANSUhEUgAAAOwAAAAuCAYAAAAr8/WrAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAUsSURBVHhe7Zw9cqQwEIU51QacZ4vy IZw7cUC6J3DugMxX2HLqIvINfANtP43ESEIIMfyMmH1fFWXDCEYt6XW3BEylCCGngYIl5ERQ sIScCAqWkBNBwRJyIihYQk4EBUvIiaBgCTkRFCwhJ4KCJeREULCEnAgKlpATkRRs3zaqripV 1Y3qzDFCyP2YFGzXdarrL//3ba0aKpaQu5OXEnetao14CSH3Iy7YvlNNXatK0uFaQmvXtkyJ CSmAsWA7mbdizuqkwzXDKyFF4Au2by9iNbugZ3QlpBg8wXZN5S0u9Sefu35/f6vPz0+zR8Aj tcn/2L+OYGXeWtVGoL2It7l9ZVjSasx/L5uJ2Ije5ph/XXzv9reNXl9f1fv7u/r4+FDPz8/m KObmrVh3PJhaVJ7hvWpr20a23a+MywcM7Tk+d4p4m2yDru+oPmkbBwqzJcXRdoYEEdZURtLi Wy84oNNrXxy4fmMnxxrXSWwHOvDr60v///Pzo20a0I127H3loZMdAeIet7UbmU3ltFWsvI/T bpn2JNtkLVKHxhjjOpqUjVcKsyXFwXbG2M9SRFl3wMm+NdYSpuBbAK+LzQIPPOrQsG434HZS Dm4Hj4h0YLK8V/+Ld0+Zk9UmUeTazcKMROoWXaScGqQl25JiZzun2E2wGHBXg8S7hLWDYRFP hHnJ09OT9qJIe9AZ1pvavyls56FseP4VNNjayC42LbjGrGCDtkiVDz+D40ut5Oe1SYylg1zK t/k2gnJtSbGvnSkN7CZYN3piPhyaFxuQqCgqB69pgTfFMXz29vZmjsZBegRDAcrjXOy7Htmy SXTX3hTXmR8GKQFKZUadlyqPz9zyqc5f0iZjlgxypHySDsoWrctERCrTlhT72jmngZ0EK41j os9U6hgaAFAp2yEWrALiOMSKDksBI11DATxUeE0Q+34PpDDyvblb8loCvi8uQETqeY/sMt35 l8HknrakTS6g7+I2xudmPrreo5QwbiMoy5ZE+aAv9rIT35XSwCBYr3ILtzGohBgjESict1pC AwCuFXaIrWyOF0UKEYoaxscic+z7F6MjbLiQFkd3cESAsewDTJXXLJgPLWmTMXLtxVHJ9L3Z A1M2aoq2JcU+ds5pYJ8Iq/P4Ruat0w0UG5CoVHhfzebwOYTno3NxLBaZ4eGmBkYe8Kbr5rA4 du1D37klBasHi7PimIh8S9pkzA2D3BuYaRsvFGxLip3sDG0Ergb2ESyMGaULAZFKY2KNiqGC drPH4F2vngfGS0rhNBjye3habPZcnBNfkICHyxdbjEWrxLo9TEZi6gyHMRzTm1OfSHndXrLv Doq5e3rL2iRG3iDXziWsrzBpY8G2pDjCzjkN7CPYTNZGObyUYO1GupDdeYF3fFQWtUnhPJIt a7irYEU51zRhIXhs0p06wvtkob3cTPR/ELLb5AQ8ki1ruLNgAdLT9QLK61A4iP9DrICCfTwK ECwhJJebBIsFgHAlixCyP0nBYiVUr2o5KStWrbAAgBVbpimEHMukYGM/wgaB2pU6ew+MEHIc eYrrWvX7z61vSRBCtiKuuMiPsFlxIsIiLcY+74sRcixjwXYyb8Wc1UmHf738HR5IxoITIi32 3YhLCNkfX7B4qCC4J4ofYXuR9Dd8IBlR1oqYEHIMnmDDRwXtj7BhsSl8wDr/LQlCyFY4gnUf E/R/hA3z1dvfkiCEbEUQYc3bCJIW2+d7178lQQjZivGiUwDfkiCkHGYFy6eZCCkHCpaQEzEr WEJIKSj1D/B5MpkhW3HtAAAAAElFTkSuQmCC</item> <item item-id="15">iVBORw0KGgoAAAANSUhEUgAAAOQAAAA5CAYAAAAm94d8AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAcHSURBVHhe7Z3bkas6EEWdwPm+KUwM zuIkQR6TAdHwdTJxMFxtgYzAenTjBknQq4oqlz0G9FjdkgDPY1QUpRoKCzmM3eMxPjZbN8wf K6cxdJ/t8NCGOJ3yQmqjV4q2TQlUSCWCtk0JGhHyNfbPZSjVfD959ePTluU59q/5vepQIUvQ UIaElDV3YCqmzE5EK2Zn3qkRFbIEDQlp/vbZGy0bZ+i8xZIp89fZ71XIEjQj5Kt/js/206Mt h796idXNOsulQpagoTnkFYarn4FFhVR82hASc63gcNV8v/rFkTUqpJKiCSGTw1XMyVqaW+oc 0vIa+rEfmmk1Ft+UrU4hPcle5vXzGV+JRIaps0PHQFb3VlmrDSbHCTl03eXvxkK/7XaMfOoU 8n2dzmyQMVoudO5aLxskuPF1yFd/fRnfIJkwG7iRRZ0Iq+GfIssBQiIQJfa5XYFe498ccmAg Ew6WQ8fbT9NCtjdcbQlpIY1QZqga1Q0yQoTIMZFZXcdGux+zbmDK7E8nJEZf2A/jXBsWEpXX 4HC1GYSFRMfMpIp0hvSQkmXLasQlt+DGyZKFhfwdf35+59c8yI2n7MS0zX+dWBZCe+U6JUvI AzLk9vhil6QYc8kGMyQyo5tL1HoN7wqYehbr9Mg2+YxGFnLHYgkFHN/fr9w1Ynpdtr2ooxyI pJC0dqYJKXlea44TkhaQQFBIO2n2stB6kxy7q5D1ItjxzRCTck2OIiSuYR7WYw6aQwLqPDKZ IbETX0SRYLFChawXQSGJQ8yckPh88YUmOQ9T5oNu2vBXiVMkhbQVdEhmdKiQ9SIrZLaZkZ1c X3N/bFdTpyz1OWqjr1yymI8pvf9VMEmgQioRThby4qiQypeokJKokMqXqJCSqJDKlwgK+fsz 7rz/4zL8+/uHVAcqpBJBM6QkJ2dI03jmb/iVrkLWiwopCV9It9zrNQI/Q04XU5fveFvwbOoR 8spPsAN++WJCctvYoELyhfSv87gvroSUipYr6hDyDk+wA95T7JohJWELuci3ZMKVkKQabS9D 3uoJdmDkoN2fqRlSEraQtgFQqe9G8Cv+oLsiSguJYfole4rfdp9TDdp9lYIZUldZ966ymoZc 3b/6MB22T/ymjWOSmd+3SwqJsh6xclye7NP1pPs0BYXUDLknQ5agoJDolMek/bqwi3V7sqQK KYkKmQEVdAcfbR2HVsizc0kVUhIVMgnmWNccrn5gMmR4ZTUnXGEhbWbHtEkmcEKI9HoIAtcy VcM2nXNuLWX5PFVGFTJJqeOez9DHpMoFJVkhedMDyDF3/siQm4UXlKyYobYfhtUx3IPQ6fn4 LDGhL4k8D3k8hcSIZo1rkYvK6XmkoJDc+jYCL518ykBi3YQUHMwxu0DZV8FhOi9qoBH5xYDj KSQkO2K3B2RcyjiMfaC86agtKCSznbdZDJlJpr2MRD3hPGIBBEK6Opnl7NwNNcny5UYjC20J aSsBFUCLNlHIc5opCq7nFPVDfbo+nUElhaR3SLAOJlJCmvLM9ZHbVzRQeYF8ChqmTCjU3C8l 6rKwkJzfZUWFCs0ryEICdKYvA0ClpIU8/3dZHccIOWFFSvYd096h4epGKtY5MkZk7WRIUyix eQVLSHp0CxNr4PKkhWS0DQUEUapUkm39AQJ7Sshwube/dmfF9v4uJSR1/giaEZJTAVkYQm4j IR+ikG44DvnN684tGPivI2CI5Yby1HKBU4W0YlFHNZDGGw2JDZ0NK9kDBPrGqp7QHnO7PN9T Ae98P+DVY1NC+p3yHCHRiejRLQwjQ5rzsrcqDi9bXsxRsBiDpzRinQj1gM/s/snlmjhXSIMt H3GftsPH578cbF3afU119WY+hv/WNhPa+nXftZt3PmiX+f1YsTjZEaiQKULRGRFyXuiBPOEz mIZZSyN6WyLa+2VCQz7nFUG8P50vIrHX+KHzY3C6kAZk8wN2WyfoZ8w+ets5JEXmbRBw5zu9 M51Dfj/UDAnZXDTF6zlS2ygeHup9nh+P7GWPg8y5w/On1/oPykG8DvtlZsD3g5UF6ef95v6V OqAJQRTSDzjeaxzDDkkDO7DHd383RMqUIHtjALlt+Fz5Fxpe/f6yNSSkwWYLDP144/JPIsd9 7x9DSyNj5hiI9PnzoAm5DEvxeimfHcaaD4LfR2Bxw2d2hSDDpwLOsUIqYdoSUoxcZyQQy7LN YOo+OcpQIUtwUyHh0xdZtnkZDRiSJ8ugQpbgtkLa4ekeqcz3rjD3yS/Hq5AluK+QO4atLzyi 4zqxFXN+3RyUelchS3BjIQ0YthGPb1c73YKP3b6cgxaEdrFahSzBvYU03OpCNTBBiDb/VSFL cHshwR0uVAP2DyWrkKdTXsjVMHDaSvSDy/8rgczFalzv3LbD6r5P5RQKC6koysI4/g+fHv4H YF73RwAAAABJRU5ErkJggg==</item> <item item-id="16">iVBORw0KGgoAAAANSUhEUgAAANMAAAA5CAYAAACs2B49AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAebSURBVHhe7Z3btas4DIbTwDxPC6kh XUwT9HE6oBqeTicUw+iXcbATX2RiE0P0r8Va2YaAL/ok2UD2bVGpVFV0MEzTMtxuy+1luwfK hmn9isrTNLz31e0eKNMOPFzHw/Q2yKEylVzap71IYTq9tE97kcIU1LyMjy1l6tsuFaZepDBF BaAeyzivf3YrhakXKUxRUb0eIyHVuxSmXqQwRTSPj+XRf1giKUy9SGEK6iwpHqQw9SKFKaR5 XB7BFI/qyosSPYGmMPUihSmgZIo3Dcutq7nU8X06T+MyTqcI28X6pG0KE+QAMtPnx2OgWoWF JxD6Yv/YPp2G4fJPp8AGhh2ph8IEIa2zj+EApGg/Ul1vcdC+o+P6dB6vD9JTcKqFQClMJUIE 666uB/UpHE7inEiN433j3gRvON98OsU615iGsvMoTAXqL8WDjuhTgoHSu9gZGSQYceSaiGjW KPlB3SZzTmqzhYihqpBBRBeiwlKYxMJg9ZbiQQf0KYwq46LTkclRLUN/lZc1mEhYowtKotPB MP1Z7vc/62erUFl/EhvL4aL++3d48Z6hsv1C23MGVQRTg8j0en1EwCo33QvmThqZskJEsvl+ pQGqKqrfm3GGyvYKXj4fScQw7ZjYS4Tru+etBlNBXypMp1drmGTjI4OpZr18tYNJ5kwghen0 ChloRaOltExyz0UCE+5RNRvpRnMmSDpvUphOr8YwCdOyHEzYv9m6DNAyUZvd1byKEdBdjUxJ YTq92sOUHR5EBTuvtAfzqp2JDrwcbvfzJl8hK9J6zdrn9xxBQgrT6dUBTBeXwvQzUphaqxJM NChVw6bCVF8KU2vVi0zOZA4n/exREIWpvhrD9Oe+nOCeelP9/e8fUR9kYQJAddbrIYWpvhrD pJHpg8g0YyDMqssw0Wf7HJVdsXHP6hz7GMdtaZKGcRwoinGKaDfso+MVpsrCGChMLbUPJgaG jH59oQcvSUVvhNG+B34nwR6LFJB3muO2aIaBtZB9DtOV3/KEyttXApMZG9/JrVtsDBSmHTDx Gj1FIWcEvJPY/fwHDVb0WLPPssRAut97q1WoLKxfeMsTKnvTMwROqGynFKZymPAFb26EyPOE wN//dqwHmtn/9Hjem6v7Yfqptzwh9L8IqBKYNDLtUTFMuEttvOG8TDT/Gcl4/RTPpHQ4YjuW 9iAtoYjhDUY0VdkJE2CVtOZ0co17c0ZWsmfCSmDaIV3N27Gax5GIBpUiCQaQo88wmgHmyAM6 1+GOHbuOnnl8hI5HmQfVHpjI4Fo+IPlFZd9ARb9noWgMk0Ymtm9JH/gLEBWEqGb4oSjGUcu9 N7UDJhhU3j2fXy+pslU+OilMrfUdmKjjAY4XjMhIEKFMUTlMaMgvsMT9EIAJfZp2JgpTa30p MtF8izypeXLXbEj/tkEthQlzimumeG+C0wlCkwOjQ5g4yppUv4YjhDEbe4qdj9rr2Bw2U2d3 Thr67rY/1cavpXlphcAJlVml9l1LSI/DdpdzKCFwQmU7lY2Mr6JrW8ONpK5FcpwMQxWyB5rL u6X2JcT0nBT1pDKBfV3jfaaot76Wcp4vPW8KgRMq26nSMUCq/2yM8fwCe5VJBDZd08uGVnlg m3pJncQ13rQt9ornE0Da2jgtY6C9ac/YGKbU+AT0Gj0QEeqMIQEwCuoRgx8w2T5ZwRp41TkX nXKZwabuYZKNo/E0fr7cv8wtBHcLe8B05GoNk9yYIN85mDZ+DhO1Z+2j3LmijsdxzAZ4ahMa xWClbEbelwfDVPi7eWKYIAy6LByfTWmY+vjdPKs2MBkxBMk5GNlAKMV7AaKojgXZ0UUiEyT3 IGHFBuL7SsNU2Kd7BO8tBYLGrNmcCe1KwhRu9+uvIjGUznEpmKTzJegyML16m3IJYVrTAl4Z os94BYWv636OCCmIXTKWtgv6OkwMhTTVo2u7q3nV0k2SB2pAAXvx+g5jtI4VnugxQ+XU901l /XihOZPcg4RVEJmoXvz41DTzYCH/xsKB/8qKL54f0T4zfiUR98Ug3lTYp3vFbRaek401Pgcs Efcvn8v031PrNdyi1wjEfW6/y5tTH4zVWh5rVklUgq4BU8gDwgutixL+jWNXJg3ZOtvZEh7V TQv4JvW6yoRyU19qE53jWfdQ/QrUBUwkRNYGp+1TsL1CT9A9TJLUDcbmH2fOaUoMMPnzSCMT ndtLEVZvyJ4ynAq9169M2aXxkj79UL/wTtk1/3MgokuoUQjRq6dHw1P/NhOSGbMQJlzb1tf5 zOkIfZ4DJ+Dr2+OmSJsSyt60feu/djBBV37becbrRzvb1jdMsX0cBWw6RiBl2g5vmrdfGUxb KofPm5Fz6kc7gt93U85CkLheSWdxPEyqsDqHKWdIAsWi22lE/ZOcbylMvahzmMBC2YqKp9OD REIam2yDwtSLuoeJU7o9QND3rpDX55dnFaZe1D9MO1K9GY/kWwNkqNbPp5MECoWpF50AJhJS HaFx8KqaXZzg7cM51xclu2moMPWic8BE+qkbhhA5ENl8T2HqRaeBCfqFG4ZQ8Y9QKkxd6HiY vBTMbPdAWcwWrnzDEMrdNHx/3oy2e6BMYTpcB8OkUl1Vy/I/x4Rtth0E3JMAAAAASUVORK5C YII=</item> <item item-id="17">iVBORw0KGgoAAAANSUhEUgAAASkAAAA3CAYAAABQMrzdAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAmWSURBVHhe7Z3bkas4EIYngX3eFE4M zmKTII/JwNHwdDJxMN7+xcWS0KV1ReD+qqgajwFJ3a1fLYHh5y04mef5/Vr/vgYvqvO1aiwI HAYWqfk9/fy8f6xtmtevWzI/388r9vcX1buHfb6UeTrG40+XgPxuxhapUwLg9X5euKfPz+fF MsArc1aMfhciUjaUjVw67ubpmlngJRGR6oGIlMXrmdrJKfN6bOn/RLXuQaBMiOzXq1Qvn4hI 9UBEygDBnRbUuqipNYtH++lWuEyyW4c6jEw/n4hI9UBEyqCwg1MW8+iWTa0cykwX2lvT1Cci Uj0QkdJBQBeVSXXuLVKOMufpIetSOy19IiLVAxEpnXkqu6R8xnqQo0xMcaTvrDT1iYhUD0Sk NF7PR5FInXH531UmROohqZSirU/axugL9+vd5AbdkraISGlApHI7N47tPaj6yixpx51o75N2 MTpP0+2y4RfNVHKyWhEpjdzObR43v58dBCJUpohUL5+0iVFcnbybQO2QUKXG5kVFSr8PJrZI vO0bX0xGYKdO99QlblUPbn3SUHWyzhkrcxyRauOnGK198qGBSEUu3rhidImRpa2uQ1usUX7K 1G3L83fqhZ1LilT6fTBkvImxNlG6cN4EskPibRGwyQgi1cxPw1BbpNB+/5XIXRj0MiFqm13x t30lU/2vskhpFyN00WT7W68zg+tP91yOsdGMGqSLSCV2xIz0uMXIWUxNPw1DZZGCjSLttzMp fNaPsQcoXDh4towHX3xG/J2STd1DpCKqDEdOZJQtJcbmNaxX/ZHRbMdSZ8qeQqSIFO37oLrT CIXR8Id5k+aQ90nV9NNGNZ/kUlek0P5Y/V0ipX82RIoEBF+1G7QoPn0/xo/5O2HwHVikft9/ /vyufweINhYd/fmeyWjxS6AIeodhUQaJxXa8HRhpJIgUHI3RSO38YgYb2ssTs65U9RNR1Se5 UIz+O/F8GYXnt0M7yQ767xM/IkXnWwWkjUhRXwkNJFF/e/qag4tnUnZDF8MZh63B7J6X2/u7 AgX7bEKxgEBJczrO+8kOjC3gKLscXrDZNmlJoF1GRWv7qYZPalDT1nQuRgNcYoy4+Nh+zca0 Z6KlixTXr2t9DtM6jl1cfc3NLlJLYY5KYTMqtgSMvU9YNXOIOw33ksQaianPRNGMezRsA7vQ F/8A7GK0bctu1o/pkHNYmZTtRDieUS7Vb7R1ndp+qu+TXDidkQnTby6R2tFsZwpXqz4KoAem 7Tn+BtxliUMmZYqV2WF1FiNQYFFK2aLpqvGBYEU996+9DtaCaAtkrGOEHmpnnUuNQutnddcs OYAjdn6YIoXMYqsH1VmtTfE8z3J8L1r4qb5PctHqXYru7wBekYJAeYQ6PZNKQBNGwPP3gp0Q +DhO91RjPyLlahsqknIJMQ8KAI9lj6OER5ENx68pbLTelojgHCiDshicSrWdvtenGmkkZFLb IjLKZhY40pM5m/mpuk9yqStSUSGhfXZbbjtv/wvUo7ZIKaG060Gw/b1iCFqARJFCAFmpdjP8 ItUc9Tuj9e8rQSPXJet9WTqL1M2oL1JI3XA1pYc+KU4UKeJ1ybfFiEL1RUSqhKoihcXMB9af uhrxXJEShDgiUiVUEik6iXFznXuNqg0iUsLoVBSp3z9vzm2Bd+Lvf/+w2pwoUrTVckoUESlh dCSTKqHqdO+wap9kTXIkHZPuABEpYXREpEqovHC+Xhbe/59zY9jxHPvmrKmIlDA6PpFKjXVC RMoLU6TAkhF9vmt9pU9EShgdyaRKaCBSBO4CNoSKvmd7SDIp4W74REoyKQ7ZIoUDP0Z13LZu iBg2KqjJT2NEpK4A7s2q7/uzwT1nnFZVzKTk6p6XXaRMcbK3NaM6CJS++aZ/S/aVrjciUsOj /dL+drDu3q8oUpJJeTlO94ZBRGpsaEpz89/gxH8HKSJVgoiU0Bb8TOru7iHhCGeKIlIliEhd Dn2x1bpgMSDcx2yk09sOgfIijxoZSaQ+yzX6sovetryr8eq8h4pRu9U5TZu56+BHROpipL9Z 5UwQ/G0EpLcdwuXFRKiuSGU/XUQTU11USm25i47VD50DlKcOIZzncSAiNSLk8HOeNMmlYucM 0dsOh/JiYlzRDlonL8Indpm2PAoOtXnNoryiyhTc7CdzjsMXi5QKhIFFCgHfxTe97XAsL9yR aP9qYl0j3klUfRcz4LOMunqzIiV6LqEK1MGAn40PLFLMt8XckVqjaitopOSk88X0toOjPEyT /E3t/7YYPxDYQIaTOZ0MT91sUY/UwYAv8JJJDchIjwB2wV1zKKW3HVzlQaT8Ha5ujMKu7qIg YEvnP2xW+co3h+yTLwg2MV+7XrrgroNFgmiKSA0GHDx6s1HHnFE5hd528JUXbmvlGMUUqtiu VKfDlDUiGAHQfr9I+bK/Yx1suOtRQERqIMwOMdPcvq0Q5BLuuOX0tkOovHBba8do6ZSPsKbi qP/+MWP6jOO9IuXLhqLLAWl2E5E6oKfWMbXf9uWPCj7UJWIjlS8/Zxr8trgDN8VufnrbIVZe X5Ei0PETz6n8sdVfO7bYlhAb+7yu/xG+OrhIyaKAiJRF+r0l1DlZr6i6Asy2OEbK0ntyRgVt 6SpSBGx5Quj3gWInNZsTkQqBNYLYAmDvK1At4bbFIVIGHLtVo+0gAZHyN7VdjGId6W5ChRe6 5PQVEakQ6GyRjABprv0ceP/IS+CZXOu06PEkUWg8nUmB3ZaYXRh2q2eHNJFClqJeLErlccIr ep9UwxhVb2fu/8bTJrzIx7ltEZEKQcofFBx0EOqMM3VKlgNwPry7cN1XzeOHGS5T2gKBCQhD zG5V7cAXKTUNpXLUvhjVo0XCJqGMcIAY/QK+UKQQeJ9MwdiM8uyOSJ9pH3OXpbNhZD5W1d4f nyngtd6kMpcWTcwhqS2hzhuzWw07BHzoE09OdnfAbosNfT+MA++LZFIeOPeWYCowUW/DXDuW CaAjGtkFOk23dZs4KW0Bvh+HxuxW3w68TOpQLgeqW3gNRUSqByJSDoyR3Ruo2ii7dTSss3h+ t6QWYNfzqLUG6sz5U5zapLVF4bALx2717ZAgUms5KNftUwsS7PBu58XoNyEiZaHWLYwphGfh FNOj/Yt1+hGaGmB/NR1ZAn/pNFgDWr8/k9S2KExxSLJbVTvwREqJ5r5QzyuM9WROEanmiEgJ +airT+vfd4NELd42idEeiEgJRbxu+7YYTuxJjPZgbJEypg/LJjEhnMVxSkubBGRzBhYpQRCE 9/t/Nit7t9nq/dUAAAAASUVORK5CYII=</item> <item item-id="18">iVBORw0KGgoAAAANSUhEUgAAADkAAAAWCAYAAAB64jRmAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAG2SURBVFhH3VSxkcMgEKQuFaRxEQ6+ AAfqwrkDZe7BqUeRO3AH/C5w4gTSPPwoYJjRnrjlgFsOMNbi6xywOdkbYHOyN8DmZG+Azckm sBg7GCQITGjXxEyD54QHGztbwiiJByFzYcwyRcHzCIEUnA5sAjMSQ4LiszIjuOqYIB6tSH4+ xl4uxl6vxt5u6ETA++375F8D7nS2cAFYDS2AFRnA1cY4kbqSFEhRz2cMejw8x777PfK10Hdk A5WkBgXohI9E/hXDapNDyxNclFXUQa+X5ynw+/WcO+cq0QH+osacgbNEjthc5gbPE0xYV5EQ kayo5lkZudxsp5OnqK3kGXeSouWxgg0NLEpR4hNyLzVHyA6xvXtMTsDm5VTrlcSwyqtg9MF6 hw8LBVGYQDg+RGuVMVhELdxNWSjMo/Hfh8eBiWP8Zv7AaQFpTHqdHNZJC6En4e7NkkDDgA2N n6TjwGd1eNbp6wqm8S0B1ieokzz0eTxZyeDLsV37Vby0WwBsTKrkz+ppn8d1L47/VmB0Qmzv +hDiLnjw16c5+PIQiC/jW8FGpCNO9lsAbE72Btic7A2wOdkXjP0FjDyP4ZufA2gAAAAASUVO RK5CYII=</item> <item item-id="19">iVBORw0KGgoAAAANSUhEUgAAADoAAAAWCAYAAACR1Y9lAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAHPSURBVFhH5VbBcYMwEFRdFMS4CD9S gB904b8f/NyDvx5e7sAdKLtICwcSGWBIQshM9ny3OqQ7rRBx3uPvHwA2JY8I2JQ8ImBTUni9 nH888mN/DbApSVwuzt9uzt/vzp/P+ZxvR+N84VAkUMFfktNUgRMPth8U2NjzGfz3OySPc34C pYqPzdRzc+CXaJTjbcNlplGqSCimor/SaB0KVFwVKB7cmpyCzQ5IQE1RUR5fxlJ3DbjjyeIz ICUU1/BZ8NKcKsawPcljejoFnxcRlWVsFV4L7jY3LYEp1IJN2KKnGv0qh5vMNciB6Qd4TAnL UVU1T3AyW2iBuDH5W2GLRpXT1mpJXkJU1XJs8nodclRHNxz98eQ5LFV0s3cUaJW1BBe2303d uOPmS0woFXO7uBUGN6pZc1FO3Ax4geA7SeUIHlfCfmbsg2qs4SRayOYYrL2MWrB4PD9YI3Ld nJmc7rgSUXHY4PDCmXO72neUO1irgJ0DNjofo4GJmArxo8zYKjnO3xtgQ5G20MlY5z3GOsLd uMmXvxfA9oXN+aWKNubRzeXxd09wtij62RjNtC98jPU/p2JdDIr1/J4waLQlNo73AtiUPCJg U/KIgE3J48H5T8VE0x6S2qmVAAAAAElFTkSuQmCC</item> <item item-id="20">iVBORw0KGgoAAAANSUhEUgAAAOMAAAAnCAYAAAD99//uAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAbrSURBVHhe7Z3NceM8DIbdwNfH1qAu tglPishl7zmoi9xz8OlLD7lmfEoH6UCLlz8SSBEiSNleO4NnRrMRRZGghJeAKNt7mAzDuAtM jEY359M4jadz2DPAnmtiYjS6OB2P0/EUdoyE84muzdguSBOj0cx5NCFWIUEOjYI0MRptnMdp EJV4mo6Hw3Rw25H21pzHIRw/FJ31dDwUhS6V7wJjcbYMk6Qbbu+6//M0DtIx2Cy3W8LEaDRA zkfpqaQJRMxt54NYg4M6IWTOGsSxcmypfBe5LYVxoXwYadTh76ROmHi2jOLnKzAxGnrgXKLa lqgo1kkcmokhcBpHEvtadFL5LiiNXITkI1zePqIiHwuis9/39eVrsdASHU2Mhho4Z9WxnOAk Rw1pHbw+f6bCoocrzkQhle/EpZ+swUVoC2KdMKkcad+lsFuGNTw7mhgNJRCSnKKmIOpJdUvp HbU9+v1UdFL5fuSox0D0ZOOIdbxIqRwHwuQj20bjVaaqJkZDCTlVgxrw6qNU2z9XhggZnRTv 5oK3JqKTylWEPiD8fKOGVGIkUL6c6zMD7bke/SS2IUaaAehiHIdlNQnGDNRwfKkpXfB7Jb2w +XafY3GzcNFe2hLvDBEn22QnaYQigP7dmeCAiCJzlPBiwRBK9wV2S+UXAVFvvn6LLSKsvo+M S+VtMeK47rmxLEZc+DirMPE5zgi78eI8lhgjuDjLDa6tAN4HqShlm70DDyQcmv1D2UVoePYR 64ZnLe8zfvLIBQD7S6KQyvtB/0EkySRRIEtX/TiiwFg7AvVVZs9ajK6j5aaXxx9nrccUY+7Y DzEG5xDbNrv0qWEpvQnqf1MM3D5eMfhTLOLXviTY24mRmH2diYnbG8dUuqZsvDW7MGaN7ZkY 01RnuwHUNTHejE0xIs1Kn2MuTk2MhkiXGFud9NGeGSO3FOPX19f08fER9nYgiZFmcjzXX1OH DhNjNx1i9A+x8w3/wVf+VmJ8eXmZ3t7epvf39+n5+TmUdlIQIz6QjGeXm9wqE2M3HWJMU1QT 4z4gvs/PT/f39/e362sXiRjp5maLUFe/W39+Tb/+hL+NJv7//Z/q2pkYVY5MWcNRvzCCaIgt gsiIvnaxKUbarrVwE7HI2M3+yLjr5vq2em8enrOenp5cdEGqB3tilIn/7uHaYkS7ALbm9neT iNHb7Fe02XZNtZgYu+kQY35zL5H6ZM+hfBOsgxBxHNEkgiiDMhx7fX0NpUri8jWbXK4pRqSk mEgA7IXt2OeRsouCGEvXV7ei2n5fTIz9dIlxdtyw/YuLj36jM0ewGolyCBHODvKoMMxiSaMy rxfLEjGKGcCGw25kDZhE+EQCEB35mGTbgZBVFMUIsoym8gK6GxNjN31iJHSOSvXo5shfMo20 z8A4ljtzFGMaXdD28ikI9FOKCst4FgdOxqjyMOpLGRmRWscJIwIhphFdZ3uCKEYCn4qaj4Xj m8213xcTYz/dYgSpILMbe8anb673SQ/0mb+Xi89dKeSAzAZEm7JDB0ed63JH1EYRvRhz++NK aipQre0LyT0hsa2qJ2L1Y7voR+JsNbWbjtXUDIhupOiXzaD8g+IyXgA9MykWOtAPBBi3WIao M0dNRObgkYjS28KCmLgz0zhIXNVhzOjEGJ9psUXb+SuOmQbbUxHmW4iQKyHyTTvhVKA+LDL2 sSsyPgL8uQsThF5Y1wNptGbV9B5tr2Ji7OaHixGRF1EBKeeFZv4LgChY5z5tr2Ji7OZnixFp WRwd/V1d/LgRKjHeqe1V9oqx9A2JDJ6Sr/vC5CUdq4NsZPs8qf1SeViHcFt4VNjgB4sxjygh 0jyETz+w7bsmDowzjNuJsuDAKI+LWqs6wfl7VAjCRCCfLrVfLtd+PzHS/33Ge2aeXdMZKT6D 9d6rm/DItgOyv+dXsh08GwiRJh8vogcXO66L3/f192QQ278uJ7UvlQeB0qa1ad83/Q1jBTlh 54zh0k927iK0BbFOiJKqX2IrEdJrtFc8VWq/1m+YXOuChKgLmUABE6OhRO9UOXLUYyB6ZhkD 6niRUjkOBAHo9Ug2V35dTmpf1y+iZO2aUB3lO3kTo6EGDloOBD6lcxEk38iDVWIkUL6c6/vS nltE8etyUvt6mytibHjWNjEaehAhlI6VoHhmTGD1IQqeIraIEXWTiYG2/FypfV2/9WzBflHc uBK9qSrSObaaupW2Zemqqz+/DmHtNAIxFScAqX1Nv9WoR+cVOy1jYjTagAM2ONiMc+4l/eRl rjknQjpeEmo8Fut2kIiR9wuk9kvlrIxHzhItURGYGI1m8N6s4ocGibb1VZCJ0ejC/udiGfuf i42bY/+n/5ozPmDQeU1MjIZxF0zTX8tr/Vx23hkFAAAAAElFTkSuQmCC</item> <item item-id="21">iVBORw0KGgoAAAANSUhEUgAAAOQAAAAnCAYAAAAfK+SXAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAccSURBVHhe7Z3rkaM6EIUngc1jY3AW NwnnsRkQDb9uJgTD9mk9aD1aSBh7YKe/KqqwhKSW1Ecv7Jmv1TCMy2CCNG7NMk/rNC/+0zV4 xSYTpHFb5udzfc7+w8VYZrJtGhelCdK4Jct0XTFGSJSPQVGaIP8B5nleL7ZoI5veaNEyrY+G GpfpsX4l8cs6Pb7Wry9cj7WukXl9cvx2hSw4Px8mBaaFS+anVl4dEyQRGtWu913nQeKipaom xygSIUjMpkEU85PiHlM5gNGgJvPEcth9hlC9qDAQREFr4RmIq5WnYIIkznWYT0LOOWmu+f3M U78jdgMHr3r+RjlDClg8uqAdEL23PXk+F2ElvMLILGmCJG4rSHKKxsrt+6E9VK8j9gKx7eW5 K8i9GQvtGgvxy13kl+wJtfAKA3tJEyRxV0HKpdglSRz7DCCCvdkNxTYE2SGOsl39/rLIUwvP oec6l60/XpC3Xq52OOf30u+IfVB+u87fEmSPPWK56nEC9TOiSK+Fl/T3lQnytoIsnYsdESN2 7UoclNJWnuldVgE+HKnk4a7gfCcPGp0zribI7aCmRSb6ZInrxMfRWrhC7z7SBEkOdEvgEIoH pMLUl7VOVA9y8uOvTeBorbJGj/2bdO7FaoJEWAxqCZvKSJKinZPDGyHIWrhC7/bCBEkNeUvI cdS9C+KESGpPwUFHjuM1cvHnZUH0LUcdIhdLDVl3/3A5m/tBgkWV2lebRWUd5YCghddIBoQG JkhqzDvCzqD1cFOQWF6RGHuG6w56BHlWWahXj1NfERNkJ3cWpOromiCxVCMxnqUPsCfIpp2j mCD/fa4qyODoWieOChJfdsa3Sc52aBNkHybITuBI1wSnqKWDB1gIWg8ngiRHyA5ezvTpjwry z+/19x9/fzP+/+9Xl+0/WpBwosuCGa01pEJ0hwRJ1wmHOYE9Qdoe0nF4hnRLG9fAj+T7fL4j eR/iG1nee5L0MXxZp9wpwkkXR2M2CGkoTxmXsH+8PALKK2nbWtbP2RSeifFkZDjdi+1AcYkY GvXG64JQTxyZF22KvLSGQJx/PoikOGk8qRF7BHlWf6Fep+X1YQ4Jkt8pkcO4X84saWPC0Uig +FkNd8LzuU7kHHDA0LkIx6jukof3ZO6l6SZOOKEQHPLFZ/9zHZf3nuUuz8TBwrWbdgPPp7Rt rdfP36PdZhIV6uE/47m0PpR/+DJ4s94oNwgXNon2CogyCtAnsU2CSMo22+rZCcpEWlEu2x3z rAmyYjtzoA+pXlrU1UE79di+eSQ3dtqgUpC4Dx2IRn54x4rPID3dkERJpJg5Q0dgBtleirKA Yzkuzvsk02v4GaDzUxq2qvVzNj8wQHm7+XOIRB7BgfGnHdxNu96UJhELlV1+ZUyKNoPtDg4u +xTlCsdXVyJ10NchbTAHdsf8CnsgOln+i1C9PuUbZ9Pr19EjEycCLFApqlRgnLcQcewYctT8 mx9pp22OWC+zpwMPjK4V8HxOy1YXltfP2bLVQ7QPQJ3gqEJU7Xojv4pQ4IxJYMPZVUESWCbH OB+fl6Wwtc2WZ9JeRdtTWdqg4dtts0NcWh/+OEGGFH5vE50GHRzixD13Bt0v9BinD89T+sSp lD/6w7Orf4j/MBDNMmpnvEjobEn+mVFsVesXBOdiYvugTRxOAHE/jpBWvX1+WP5C9vHHuHk5 hPZ1LO4XX1+Ip3gmESyuchCt48Uc7ZCiqgwiGIRqBh7lZ52yisbFLCCcMi5L+X5reITHPRWP hiF9Ouq6pQ7l6fegEXIMtydxTuMGBXpGPJLiHOKoZmGbJP8MVFuV+iVCJVybyDrA5m1GYRr1 5mVyzN8LgK/KCJs5fCrE/PI2FGKUlxSVKzsO0hEMEmk5ap9RWcK816H8jvb9d4O+6bG9MkWc C3417jrL7b24M9VlzPuBAwXkPbiarftAHO+175Vf/Z/+FwNMkC/Co302emJUf7MTtQgizMV4 RVu74CW2vz8ZDErVma8HarvT7TJBvgpendByjJwfAsAl91LfRbAl5Zq29rBc8q/OvUE5Lwty 25aV+ezsh5tp97nMkvWKBMEZN4MEmZxOD+H344oq5AEZnyMkW5V22h7s95A7mCBvyOFTWze7 dYuZyklfQw2kVdC/IJFigjRuBM1Ux9aLLLAnn6B3zHR4PsyQo2mrQNTZSbuCeaVxI/odW8Kv g/A+FwlZYDv7QLE0Hk5bhQaSztN6E6RxKyCQ0dUj0hTvitVMUvGMpVUY2PuaII17gVlqUBBu ltumtZao8r+pM5JWo3f/CEyQxs04sGzlpWYQBU5M6wKB+KL2KA0fIHWm1Rnb95ogjfuBJeDo Ro7S8KGM3AOKPSG/6vDx7hLCq6XtZGR2BCZI45bgvd6oOD4OCXn0NY0J0rgt9h+UDeNi8I8A Dn/h9j0s03GbTJCGcRnW9S9PWQT5o6iTGAAAAABJRU5ErkJggg==</item> <item item-id="22">iVBORw0KGgoAAAANSUhEUgAAAMcAAAAtCAYAAAAa2n8iAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAZzSURBVHhe7ZzptaM4EIWdwOTRMTiL ScJ5dAZEw6/JhGCYutooCRVI7EvdPpy2AS1I99Nq3qdXqVRZKRwqlSCFQ6USVATH5/PR40GH qkzFcLxB7e/X/1r35QHqWnqepnPfVLVSOJy65llgBBEgXwVkkRQOqGv674FktL/PoYZtf99e +aiXwtF3fUPDqaPQ6JqvKc9DW3PA/23oSfdV1zZ9016LwjV5UjhgnIObVQBydJp79x5Xnq8t nXvNuv7pQyoY9WCfngLHnnOPW8zXFjz/y+GgIdX3uCGV1ylw0FP+9hhazczXzDAyuo4y98vK UsNEeTXXh8NHYeJz53gZSue5anvPl8NBlbC6yeOVnRxC3OfAsUdDMD1fC4Zl5YBexj86FiY+ OWDbNooTQzb7HdA4gwPKAJd0PlHl3OvdcFBhnbEPcA4cMFldyzkrmG0mQgOI1AAZI88BCwCd oaP7UyAy5zOqKYN3w3HSHsBZcPBWewvhOebim4VjriWne4YGzPXSiC+qO+l8RhV1/no4pHrb S8YsGGrQcTQgSHu75y0bpk3CUWDUMdDoGeKhmpV0PhXdVzi0Ujg2M8v1tS0cZLKCyGQ4SkzK hlROFhbXU7Dw0vmxyqCGFI6CUjITR7RK2aOsoGvEe5fREWXYtZbJIbXGm8IRDXdkSXAMk+wp JQBGwzALgrksnRdUOu9QOOZrKAiFOphw2/F7TjEkcnoWXjJ+0zqD5LUpHAVDIigHR5SPKcjS +gEE0cSbwZE7L6h07qVwTBRiqtSsFUGXifI3lx7yVLo8uTUcs3Hx/Lubx70wX3GKTZ3rXXgd cDil8zmVlsOk85HQo3VrODB8qFv1OhyOi0rhKNFd4cBQhMCo4MJI4bBSOJy8obOFcUM48CM6 7AAvMabCYXUKHMY8hePf44QlQ8HIt4KDKjRZEKhNf1M4/v7p//x1n2+m//79pyjvj+85YDDx h3G3hoOOyoZoUzgqy+5KKi2HRXDYrt1WUJgQ+ooMqdKEMa1MvzKBq6M4/Jq9vSdcp/j8CkeU FjdGh97B30PjcZYOll99lrCEF6WJNPyNBToXDpveaLWnwqGlS5hForxVJH0p7QYHIkZLbMoY y28hFb75Yj8PxoN5GRhSHPhMpm9bMjje3nLfcZ8xJk+rcZ9hcMTt3vaK70O6HiK7uhOZAxNb yS1IG+ZjEJq4gzHPgcM+hz9nj1LA5c2vcZzhCGWZSOFILjsjd/gHA3OzGTP5CkQvMLRS6AVC 5U7EYaGhcC7j5ru/iDi8UfHqo/1g0uFvQUYPDnBCBkmAISoVCi+UEm+h/S0RHAya9cJzZBYN snBA9v7hmmR6LgDA41gphSO+HMxBhk53YyMjk2IjDQaW47CtV9TbcEMAKhiSGTxN09wTwmR6 CgiVGk7KhhnyP1yPnukIZ4hwkDCUDNfc9fRZI9H9ItDac+QU3O8Lgyv9HpmRKmcwnzMi1U4o fOHFdjEOb357xRqDnqAbThgzfNkP0dC6e6ObF+mp1wmV6eLDEA0IhpdyknSQH5+dWM584V5u ICnMtopgJPOP0ozgsfkSf0JC90YNyVq9bbUKBcyVfjeG8QZhvYExHCqmHXC0wxI6R2ZuI0jy cUTQkBAeIAxBYdak9USFu3gQ1MThwpihXIjfGd0cSauBvIumAVTcoGme9lEMRXq4MhiBwY8x vDUv+RSJ0n9Nz+GFwuX/L1HbeAPZeYUxmNilny30CAl0jxM1Dls7WeFYINNiJ60rtc7oQa4J B4nyPKy6PU+b9xrQG+GAbNe8tOfoTGX4/QQcfJ5wVWEP4JF8wMSbk0FaDccwhxvHMze/mwo7 rxPhuK/0D0lXCL3t4rjdHFAobL5Zaeau0ZB8OmyJVr/P8UY4oDV/PvJK6mjet+tzYKi8CA7b 6heDZRZ7/JywMqyg0mGmwqFaqIWTfGf2n99knYsD9/ueozZsVgCsbAFGCVAtVLnJuMwyNfac ENCYfWbewIZv1WGzIqgLV04VDtViway1IxyE4cMis58lRhIbuS6soIq5ksKhWi603pXmtK3/ 0NxPGTx9h7wmrKSaZW2FQ7VCC4ZWZjjkDYqVp7xZAULggMKYyX9hWFl18ySFQ7VOGKbUDvwp jJlQ8zkDm0OY5Vt33R4MglzYQtVuhiocqtW6xQYqQVW79KxwqDbRlTdQl26GKhyqzXTFDdQ1 m6EKh0qVVd//D9c1NSnU/qJKAAAAAElFTkSuQmCC</item> <item item-id="23">iVBORw0KGgoAAAANSUhEUgAAAHkAAAAbCAYAAABYxiAYAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAMJSURBVGhD7Zk7kqswEEVZF3vwNigv wtlLHRB7A84dkDn2hE5dRA6deQd6uvqAJIRpAfLAoFOlGrD4iL59W4LJWOLPk0TeAEnkDZBE 3gBJ5EVSszLPWJahFaxSv44libxA6rJgZS23q4ILnZdc9vGQRd7tdiKzDoeD+iXxFeqS5RPd TBL5er2q0iHb4/FQPeuhLnPrGaxWmCGsWOE5JtfW+joYz7DIwvGecYumjunl/X53Tlqzm22x 27LoIoOWs6KsJpXKyXAnFwEJVhXd5xsU+XK5iBNOp5NxcsZut5s6YmVUhfEcfocgEfKJ8+Bc VGXYONwkxvN9FNl0MbZNoTFHr5KPImNVywX+tdJsA8GsmYRAsMjaxfgLXq+XcYGVurlPZJRF LvBC9JXVpBlMxUriwIJEdl2sobuZtmCQ8GO/VR49Itf8txzzL22w0ekuoujJFySyFhMraxN3 IeZ3MwQOdMUMrwokLJG5sM5CZSE6j4Yssi7LfU7VZVw30+kAmRjiiuZ8CBDbTh9F5u2XF1xu LEMhi6xd3Dfnum7Wc7YAjvQECq9dz+dT7bWcz2dxDYlc+PRXAPkOOykPPOW6Ux4jJxotFuMg iTzkYk2fm8VNnCDhWCsRDHTCaGhVwPy267Shkz0i+64Xa4UdEotBxBTHx2uYiiTykIs1fW7G TdwAod8EGWs+6PF4VFv+82fFKzKQVaLto6wpwpMNfSafYtHFrmRmBdK/WSIr8a074nMlOqnv wO7nTghPERn7ulzhnuZD0kSe28mK2hWa98+cb7iuCfb7YjFEK2j7HJbIKhbWHTFXoBMiY5vS mgvyhqwUN3ECjT4M/n6/i0zVx+ok0aUehC7aQrGCwIPTyScrCdDm/bSJa1JjMYxKyqZcm8nf VqJGZGST7JzWXj//OgsvPJTu3+/31r79zw4MMs7CyxbXbcoJHYHNFvhK2AM9FlR4zJw3hLwo rQpk146ZGO1GBDmmjTdKFJGl4wIz/1sfQzZIJJEBSi9VNCRFEjgWEUVOLIUk8gZIIv95GPsP Bn3JukUimKMAAAAASUVORK5CYII=</item> <item item-id="24">iVBORw0KGgoAAAANSUhEUgAAAGoAAAAaCAYAAABfA8lWAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAALSSURBVGhD7Zg/lvIgFMWzruzBbXBc hN20FtRuwN4inbVfaeuhsrRzB3xcwiMPTCbEgZzxDL85zOQPyHtcLjA2uvIR/EmhmuYz0uZx VqF+MVWoKtRnUIX6EJYItdlsbP3dbueerEcVKlGo8/ls61K53W7uzTpUoRKEej6fgUgoa7uK x1mFmuB0Otl6h8PBC4VyuVxcjfLwOKtQI3A34ZqLhT1rLXicVagRyE34Cx6PhxcKZS1X8Thn hOq0sMG1Wir3KBm0FeZ3CqZuK/XiLt6ED0BM7CYi3VX58uZxzghlUFK3CYOoJA8OwS4UF/0k J/gz+ADEkCA48XHiw8W4q/LmzeOcFUrJVrczPavOdCaYTKLR7HYWP3M7oZslDd+EDwCHlrgp x9CSSIU7DuTOm8f5GrGCHftARBfaWJkPbF2QJB6Co8BtXxMOxNH2fr+7u4Hj8cgCUlq2381I xLNsMMYY+gshN03tQbGraA+zFMh7eB8LBWVh3a5vAWFIaessc23fICg/WpGYpl48O5BQkBSD kifSZiUSGwYsKPONbb2YOTcRU64qkTd/P1zRWslURee2sRNG4ccsc4KrH9l2bKnkHQLMJp7A fr93V+PtcxPHA+bcREy5qkTevL2/eqlol7neKXhnAzMCCelcZelnNp8JKQHjnpYDfC3Dgx9r /0peRyEGPEv9Hyn+agnilcibt/dX1nq2otKdlFqaUxwlHXyI2cP8eupdCK/1oG48WOgQQV2v VzuLcI/ZRQnT8gGWbsjvwAcAYB/BMwiF65SC+lSQS4m8UYcYruig0AorghVHSLf3DAcMvPfL I5Y91Dcu84xsqgjWtjVlu90G9+EXnesfJjDDKZaflMe/r+x5ox4RTq1MvO2KaL8rBR+AnOTO u7hQ/cz/zhkj0DLqbktSSqjcea8gFICdUwc+POKXppxQIF/eKwn1eykrVD6qUFWoShm0/g/3 9KtpaLKHegAAAABJRU5ErkJggg==</item> <item item-id="25">iVBORw0KGgoAAAANSUhEUgAAAE4AAAARCAYAAABzcpo/AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAFvSURBVFhH7ZbBrYQgEIapi4KIRViC B7rw7oGbPXg1nOzADuYxIyq6yCJhIy/xS0iAHWD4+YVl8JLEK1wir3CJvMIl8qxwWgJnDBjj ILXtCxEZryUHJpRt3cc/XoGgtbGIJ4XDRKwAJIgwPSHi4mnTuLlE4a7GaykOhxUt3DzPtpYJ ZU5tS06D5AyCe70Rn99xu9u4Ve8gXF3XME2Tbe20bUuDPlkmTMnxnJwSe1I+7sTnF85irwpc d1Oj6zoqPtBtfuFWFgeQxc/lYgOYnLvxGOFi438mHIFmce443KQLuswVsmkaW8vD/xUO1w4I h+31sx3H8dKNC/cdZ1Z3fiv9jnPBtU/CoTjDMJC7sI2u6/ue6tkfh/MryaVJKUR8/E+FMweI Tt+EQ9FWl1RVdWij4/ykPw6EvWw//5ct8/LzxL5427eFojMpxpTDeDfXQN6+8Z6+4/dZGEp+ c+FzFCucVhJUqaoZinZcybzCJQHwByuTSy2McRV7AAAAAElFTkSuQmCC</item> <item item-id="26" content-encoding="gzip">H4sIAAAAAAAA/4xTzW8SQRR/s8t+sEChxaJ0KWjVUgVLGa0fPdWmaUysmuDNmNQtrBQjgnRr eiT9A7x59uIf4MF496bG+B8ZfB+U1p6czdt58+b3+82bNzNJAFBoT9E89k38u+HbRtju9N5Y QO0WWrzVa0osxrF7BOs2n+y+CpsRR6DBfAP/9v5ef6N3KOFtNAcDjQnwEXHRvtsAz3C9unsy pvbZkKQMMEx2ZiaL34+iQWf3IAoVI9fQ0nC6xdYW/hlbLBCLBmHoc0Cjpfrs2jr/DWBoOfoj 9uu2K70Tl971pI8ntP0TYMNLagMXixLIVNYqLEMdVuA2ymnrF+okpmQ+BWAqg+YgzZEvGDGU ojKqDGsdedP6w0PUzsCMQKZZtI6idzDFuwjNimrmnKhmT1RnhZI9UYUcpzqMn9dUj40cBi+w e5Tjreb1zG+cd+eY2spjScwWhIAn4uv3WI5hHgo898kn2R90DWBeVJ2iFGK+JH3xIiPflTgB TOgSj79OxrAgxOJlSYeO5IqkI6dzVQbzPFgUsF0W9cUl6cvXZJWl8cZXAa7LOkuTdSpCLVd1 7A9SKjdke1UsptWDLm6wDQHAshSsyjy6ADXGDysrgq+dwdcFX2N8Xa6NFGfxuDg3+aKnW3qn PQj6ezsve4NuEDmMnaX3wuHNIArkHs6N7yOx+I2R84J2z+dHl9QLDjv7W6wjb6yA9lyNX6i0 EX6gCtv/HT0Wd6JB0Az1Jp8r+GNgSWVA+QpxvqmU8jGpUUkZyjeRW1Km8mMjisQQBCN0LMQM 13HKJgwJOIxBxyUMgeOkQ3QPMUxPIIbpScQwPcVroTPFGHTShCGw5Jt+fNANB51m8Foq4nLe D9Ba0MHPHL98b1zOMy15evAXAAD//wMA7JN7Z+UEAAA=</item> <item item-id="27">iVBORw0KGgoAAAANSUhEUgAAA1IAAAFpCAYAAACWBBlAAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAB8aSURBVHhe7d3NceNIEgZQ2SWDGGvE GMHzOrAe8DbnsWGCp/FgPOCyKJZUKIE/IJEkUPVeRM5I3S21hAKy8hNI9scBAACASQQpAACA iQQpAACAiQQpAACAiQQpAACAiQQpAACAiQQpAACAiQQpAACAiQQpAACAiQQpAACAiQSpuey3 h8/N7vwOAADQMkFqFrvD5uPj8CFIAQBAFwSpGew2m8POHSkAAOiGIPWs3eZwyk+CFAAAdEOQ esr+sP38OHykh/Wd63O7P/8eAADQqk6C1E/gufem0W4zEorSXadTYPo8/MpL7kgBAEA3OghS 018IYr/9HLm7lMJYDlDHz/m5Pf5KQZACAIBuNB+kRu8s3SGFqcHH7TaDMJY+r9wEAAB9ajtI nR6KtzlsjqHn9Bymc/LZb9MLRPyEpH16wYgqbNVB6tb7SX6eVF0AAEBbmp7yTw/RSy9NfspP Xw/xy3eRchBKISo9JG8YiR4LUiUBCgAA2tV8kCrDTv0wv/T+R/1cpzNBCuAC/Q1gSF/sUtur Xj2vqQw/+e1770hNfY6UIAU0S38DGNIXu9T4qqeH8+VX2vt51b36OVIpJN16jlT58cffPHxe uJOVCVJAs/Q3gCF9sUvtr3q6k3Q8uVNdu4NUSiEqf8wgTF37d6QqghQAALTLtB9EkAIAgHaZ 9oMIUsCy7Q+7zfnu++fm8HXD/vwPmH8c39+nf3g835Uv3072h+35Y7//IfL99vRnTp/vXD+/ tTndzf/cHv9M8esAy6IvMo1pP0i6WACWKT3n82cAGDwnND2EebM97HZfL8bzcXx7u92d3047 /fFjT5t+HiTS80XToFE8H7V4MZ7T5z6+c/qt00Ot83ACsCT6ItOZ9oMIUsBipY27eMGccmAo N/jTPxlRvp3+zPFj009RjxPE6QV4vmaI9AI850Eg/QQ2Dw+nXy9emOd76Di9c/4p78/gAvA2 i+iLX2FOsFoP034QQQpYqu/N/+Rr4/7ax9Pb+Seo5U9Tf95OH5v6W3rYyzb9dDb9dvoT+eEw xz+XXxU1/dnv+WDw96RZYnseFMq/B+A9ltAXv6RfE6TWwrQfRJAClurnJ637w+749vdPYcuf lJY/JT29/fXrpyHgPGykz/MTjI4DxNcvf/seGNJPadM/O3Hhp6xp2Kg/FuCVltMXBak1Me0H EaSA5dp9PwH6+3H6Rz+DRLHZn98+PT8g/VYaKqqfsH5t/OefxqbnDeRfTcNI/jtOw8hxOPg1 HRy/lvwXAbzNUvqiILUmpv0gghTQi902/+R2d3x78zUYnH7ntp+H+AG04/G+KEitiWk/iCAF NKvsb+XDXk6OQ8DmviHg5ye96SEuP58BYHVm6ounPytIrYZpP4ggBTRr0N+Kf3cl1XEAuCcT pYfFfH/MsTy6D1i1Yx/78VhfTL574yCIsVSm/SDpIgBokv4GMKQvdsmqBxGkAACgXab9IIIU AAC0y7QfRJACAIB2mfaDCFJAs/Q3gCF9sUtWPYggBTRLfwMY0he7ZNWDCFJAs/Q3gCF9sUtW PYggBQAA7TLtBxGkAACgXab9IIIUAAC0y7QfRJACmqW/AQzpi12y6kEEKaBZ+hvAkL7YJase RJACmqW/AQzpi12y6kEEKQAAaJdpP4ggBQAA7TLtBxGkAACgXab9IIIU0Cz9DWBIX+ySVQ8i SAHN0t8AhvTF9Xpi7ax6EEEKaJb+BjCkL65TWjdBankEKQAAWKgcogSp5RGkAABgoQSp5RKk AABgoQSp5RKkgGbpbwBD+uK65ACV60FWPYggBTRLfwMY0hfXJQeoJ9fNqgcRpIBm6W8AQ/ri euQAlesJVj2IIAUAAAszU4hKTPtBBCkAAFiQMkQJUsslSAEAwILMGKIS034QQQpolv4GMKQv rkMOUTOtl1UPIkgBzdLfAIb0xeXLASrXDKx6EEEKaJb+BjCkLy5fDlAzrpVVDyJIAQDAApQh SpBaPkEKAAAWICBEJab9IIIUAAAsgCC1LoIU0Cz9DWBIX1yuHKByzciqBxGkgGbpbwBD+uJy 5QAVsEZWPYggBTRLfwMY0heXKQeoXDOz6kEEKQAAeKPAEJWY9oMIUgAA8CZliBKk1kWQAgCA NwkOUYlpP4ggBTRLfwMY0heXJ4eowLWx6kEEKaBZ+hvAkL64LDlA5Qpi1YMIUkCz9DeAIX1x WXKACl4Xqx5EkAIAgBcrQ5QgtU6CFAAAvNiLQlRi2g8iSAEAwIsJUusnSAHN0t8AhvTFZcgB Klcwqx5EkAKapb8BDOmLy5AD1IvWw6oHEaSAZulvAEP64vvlAJXrBax6EEEKAABe5MUhKjHt BxGkAADgBcoQJUitnyAFAAAv8IYQlZj257LfHj43u/M7ghTQMP0NYEhffK8col68DlZ9FrvD 5rhwH4IU0AP9DWBIX3yfdOzLeiGrPoPdZnPYuSMF9EJ/AxjSF98nB6g3rIFVf9ZuczjlJ0EK AABepwxRgtTa7A/bz49TaMr1ud2ffkeQAgCAQGnezvUG3Uz7++3nd8i5x27zE4q+pbtOx4X6 +Pg8/PpU7kgBAMBrlCHqTXN3J9P+14tB3BukUugq7y59SXefcoA6fr7P7fFXCoIU0Av9DWBI X3y9dMxzvUkXq55eDGI78Y7UrztYu83gVfnSHavi3V8EKaBZ+hvAkL74WjlA5XqT9ld9vz1s j4GnDEb7bXqBiJ+QtE8vGFGFrDpI3Xo/SeFprACaoq8BDOmLr5WOd643anzV94ft5usheJeC UApR6SF5w0h0OziNBamSAAUAAAEEqRfYbb9fFGIs+KSH533Uz3U6E6QAAGBhyhAlSEX5/dLk qXL4yUHo3jtSniMFAABvlmbsXG/WzbRfBqP6OVIpJN16jtTxV35etS+9Qt+FO1mZIAU0S38D GNIXXyMHqFxv1s2q33ooXin92foO1sm1f0eqIkgBzdLfAIb0xddIxznXAlj1IIIU0Cz9DWBI X4yXA1SuBbDqQQQpAACYycJCVGLaDyJIAQDADMoQJUi1T5ACAIAZLDBEJab9IIIU0Cz9DWBI X4yTA1SuBbHqQQQpoFn6G8CQvhgnB6gFHmOrHkSQApqlvwEM6Ytxcoha4DG26kEEKQAAeEIZ ogSpfghSAADwhAWHqMS0H0SQAgCAB5UhSpDqiyAFNEt/AxjSF+eXA9SCj61VDyJIAc3S3wCG 9MV55QCVa6GsehBBCmiW/gYwpC/OKweohR9Xqx5EkAIAgInKECVI9UmQAgCAiVYSohLTfhBB CgAAJihDlCDVL0EKaJb+BjCkL84jB6iVHE+rHkSQApqlvwEM6YvzyCFqJcfTqgcRpIBm6W8A Q/ri83KAyrUCVj2IIAUAAHdaWYhKTPtBBCkAALhDGaIEKQQpAAC4wwpDVGLaDyJIAc3S3wCG 9MXH5QCVa0WsehBBCmiW/gYwpC8+LgeoFR5Dqx5EkAKapb8BDOmLj8kBKtfKWPUgghQAAFyx 4hCVmPaDCFIAAHBBGaIEKUqCFAAAXLDyEJWY9oMIUkCz9DeAIX1xmhygcq2UVQ8iSAHN0t8A hvTFaXKAWvlxs+pBBCmgWfobwJC+eL8coHKtmFUPIkgBAEClkRCVmPaDCFIAAFAoQ5QgxSWC FAAAFBoKUYlpP4ggBTRLfwMY0hdvywEqVwOsehBBCmiW/gYwpC/elgNUQ8fKqkcpT5ZnCmBp 9CaAIX3xunK2behYWfUo9QnzjgIAgHdrdD41bUcpT5ilFwAARGh47jRFB5nlOVL1iffKAgCA ZzU8X5qYg7z9xSbKkzaygP649gGG9MVx5czY4DGy6kFW86p99Qk+RwFtc50DDOmL4xqfD616 kFW//Hl50s9VQDtc0wBD+uJv5RzY6PGx6kG6+Hek6gtkagEA0KYOZj7TbJAugtSY8qJ5pAAA WLdO5juTa5Bug9SY+mKaWgAArENHc5wpNYggdUN9kU0t4H1cgwBD+uKPjuY1qx5EkHpAeeE9 UsBruN4AhvTFL+Vc1sExsepBBKmZ1BfklAJiuL4AhvTFL53NYVY9iCAVqLxI7y0AAOJ0OHuZ MIMIUi9WX7zXCgCAeXU4a5kqgwhSb1ZezPcUAACP6XSuMkEGEaQWpL647yngMtcIwFDvfbHT GcpuGESQWqjyQr+3gCHXBcBQz32xnJk6Ow4dr3osQWpF6gZwrQDXAkCt577Y8ZxkNwwiSK1Y 2RBuFQBArzqfi0yCQQSpRtQN4loBAPSk8znI9BdEkGpQ2SyuFQBA68w/glQUQaoDdQMZK2iR cxtgqMe+aN4RpKIIUp0pm8mlglY4nwGGeuuL5XzT8Z7Q73ceTJDqVN1YrhWslfMXYKi3vmie ObEbBhGkGDSZWwUAsAZmmG8muLnst4fPze78TjqvHFoKddO5VAAAS2VuGXAEZrE7bI4n04cg xb3KJnSpAACWxJwy4CjMYLfZHHbuSPGosimNFSyN8xJgqIe+WM4m9oETR+FZu83hlJ8EKZ5V N6ixgiVwLgIM9dAXzSO/TDoSu83H8dh9fAWHe1Th4pbd5vOw3Z/fWYX9Yfv5dUxyfZ6/AUGK p6Tz51rBOzkHAYZa74vlDGIP+DbxSKTnAm2O/71gt/0JQilEfW6PUWOK9PnnDFNl0LnydY9I oTGHom/pezp9rpGv0R0pIuSGda0AAKKYOy6adjR2m8ELKgwdQ8smB6cUYKYFlx/HMDU5gI3b bzffged0N+3Oz7vffh7PkzpIpe8pB6iRr1GQIlrZxMYKAGBuZo2Lbh6R/TE8ne7CHENCCiNf WSGFpnPYSL+wP79qXb5TMxK4/v3338N//vOf83s//ve//50+T+nn75nR6W7S/eEuhalBkKq+ p1tfoyBFmHRu3SoAgGeZL666ekROYSLfednlIPJ1Z2azOyWm7zsz6c/mYPErhBz997//Pfzz zz/n936kgFWHjrGPf14Ke19BKt2p+vr6v6SwuKn+vvpruPV+kr6PsYIw6fy6VhDBuQUw1Gpf NFNcdfmo1M9xyndk0h2q9P/97hyo0m8OH8p3KUhl6e36/VJIkDp+P2VYyn/H6Y7b8Zuo/7Zb wenW1yhA8VJlo7tUMBfnE8BQi32xnCH0/VEXj8rwoWvpztPX+6fnGh0P5scxOG13xyCSfrsK XWMhowxL6eP//vvv09vpLlV6eF/pVkh5xG5bPafp6PS91M91OhOkWK3c8C4VPMt5BDDUWl8s 5wY9/6KLR+Y7SKU7T+mhcOeHxZ1+/RwgUpg4/Zl0V2f7nbpO79fPkUrBIj2M76+//jq9nf6f QlT+9dIwxD3v++ss5CB07x2p+nu69TUKUrxdOgdvFQBAzaxwl4tHJwWMFAZOIeN0x+kYpFKu OL1oQ7orlZ8n9RUqPjfbr98/+f2qfX/88cfp86X6888/v9/Od6Z+jLwi3hOGgSiFwmN4qp4j lULSredIfX1P5xfTqB/2OCJ9b7AY6Xy8VgAAiRnhbnFH5xS4hmHqtiKszCAFvBzYvuq+z51C VP6YQZgqQuStz5M+FhYnnZe3CgDol5ngbrFHKAWPa49/q+w284WodxOkWLyyUY4VXOL8ABhq pS+Wc4Bef5MjFESQYjXqpjlWUHJOAAy10hft/ZM4SkEEKVapbKBjBYlzAWCohb5Y7vf6/F0c pSCCFKtWN9OxAgDaYI9/iCMVRJCiGXVzrQsAWDf7+kN+H63yQEZW4wQpmlNfw2MFAKyLvfxh w6NVH8joapggRdPqa7ku2maNAYbW3Bft3w/7fcTKgxlZjROk6EJ9XZdFu6wvwNBa+2K5b+vt kzliQQQpulM341y0x7oCDK2xL5Z7tb7+EEctiCBFt+rGnAsAWA579NMcuSCCFF0rm/NYAQDv Y1+ehSMXRJCCo7pR1wUAvJa9eDaOXhBBCgp10x4r1sN6AQytqS/ae2fjCAYRpOCCsoGPFctn nQCG1tIXy/1WL3+aIxhEkIIb6mZeFstmjQCG1tIX7bWzchSDCFIwQdnYywIA5mGPnZ2jGESQ ggfUTT4XAPA4+2oIRzKIIAUPqpt9XQDANPbREI5mEEEKnlQ2/bHifRx/gKEl98Vy79S/Z+Vo BhGkYCb1BjBWvJZjDjC01L5Y7pV69+wc0SCCFASoN4S6eA3HGmBoqX3RHhnKUQ0iSEGgcmOo CwCwP76AoxpEkIIXqTeKXADQM3tiOEc2iCAFL1ZuGGUBQG/shS/hyAYRpOAN6o2jLubhWAIM Lakvlvuefh3K0Q0iSMEb1ZtIXTzHMQQYWlJftN+9jCMcRJCCBSg3k7p4nOMHMLSUvljuc3p1 OEc4iCAFC1NvLrkAoAX2t5dzlIMIUrBQ9UaTCwDWyp72Fo50EEEKFqzecOoCgDWxh72Fox1E kIIVKDeesWKcYwMw9M6+WO5b+vNLOdpBBClYkXoTKovfHBeAoXf1xXK/0ptfzhEPIkjBStWb Ui5+OB4AQ+/qi/apt3LUgwhSsHLl5lQWACyB/entHPUgghQ0oN6kcgHAO9mXFsGRDyJIQUPq DSsXALyavWgxHP0gghQ0qN68cvWmx+8Z4JpX9sWe95+FsQJBBCloVLmBjVUPevk+Ae71qr5Y 7jd68dtZgSCCFDSu3szqalnr3x/AVK/oi+Ueow8vglUIIkhBJ+qNrSwAmIv9ZXGsRBBBCjpU bnJlAcAz7CuLZCWCCFLQsXrDywUAU9lLFstqBBGkoHP1xlfXmq396weYW1RfLPcNvXdxrEgQ QQo4qTfButZorV83QJSIvljuFfruIlmVIIIUMFBviGWtzRq/ZoBIc/fFco/QcxfLygQRpICL 6g0yFwAk9oZVsDpBBCngpnKjLAuAftkTVsPqBBGkgLvUG2YuAPpjL1gVKxREkAImqTfPXEu0 1K8L4F3m6Itl79dnV8EqBRGkgIfUG2muJVna1wPwbnP0xaX2fC6yUkEEKeBh5WY6Vu+2hK8B YEme7Ytlj9djV8NKBRGkgKfVG2tdAKyf3r5aViuIIAXMpt5kywJg3fT01bJiQQQpIES54ZYF wPro5atmxYIIUkCoevPN9Qqv+nsA1uKRvlj2bn11laxaEEEKCFdvwrmiveLvAFiTqX2x7Nl6 6mpZuSCCFPAy9YacK0rk5wZYoyl9sezT+umqWb0gghTwcvXmnAuAZdCfm2IFgwhSwFvUm3Rd ALyPftwUqxhEkALeqtysxwqA19KHm2MVgwhSwCLUG3dZj3rmYwFadKsvlr1XD22GlQwiSAGL U2/kuaZ65GMAWnatL5b9Vv9sitUMIkgBi1Vv6rnuNeXPAvTgUl8se6ze2RwrGkSQAhat3txz ATAP/bV5VjWIIAWsQr3R5wLgcXpqF6xsEEEKWJV6088FwDT6aDesbhBBClidevOvKyvfBuCn L5Y9U69snhUOIkhB25q+xssh4FJBB+zl3K3ukc6d1XjmOrfKQTRfaFsX13geBsYKOmAv5256 5GoJUguk+ULburvG83BQFzTMXs7d9MXVEqQWIi2EUkq1XMf/jNbYn1VKqR7q+B/9cOX1qMc/ kqueWRRg+bq+xtP3PlbQGHs5N+mDqydILZDmC23rPkjl/48VNMJezlV6XxMEqQXSfIFm1f0t DxF1AbRKz+PIqgcRpIBmjfW3cpgYK4BWjPU3fa5LVj2IIAV0qRwuxgpgzfQ0Cs6AIL+C1H57 +Dz+2sfH52G7P/8asHK7w+Z0XafaHN/jW+qBlwpWYLf5OHzWG7a9vG96WXNGr/MJe7uzIMgw SO0P28/cdI+L87k9/gqwerutYeoeeeioCxZqv/08nqL1gGUv75r+1Zzx6/xowt7uTAgyCFK7 zeFj85NnU/ot3gVWKQ1VXz+x6u56fnSISB83VrBAacgaDFj28n7d07P0slX6dZ1P3NutepAy SNWL9HvRgPXZH/bpMj4/1KergeqZgSF97LWChbi1d9vLO3Fvj9K/Vun3dTxtb7fqQQQp6Ehq uD09zGeOgSF9jmsFb3Zr77aXd2BKX9K3VunqdXzH3m7VgwhS0JP0UAAvNvGQckipC97o1t5t L2+cftSF69fx7b3dmRGkDFIeVw2tOzbbjSeeP60eXHLBG/wasOzl/dCDunEzSN3Y250dQQZB 6pRoz6/009tDgKAHxwHLT6ZnVA8xueCFfg9Y9vLm6TvduRqk7tjbnSFBhkHq6PykNf/2BDQi /XT6dE0fq7cfS79iuEh/x1jBC6ThKl/fg0HKXt6u3GPKmmLqn+ftRq/ziXu7VQ+SFgCgSa/s b+nvGiuAuczRYx75GFbPqgcRpIBmvaO/5eGmLoBnzNVT9KMuWfUgghTAzMphZ6wAptBDeJKz JoggBRCkHn7qArhF32AGzpwgghRAsHoQKgvgEv2CmTh7gghSQLOW2N/KoagsgFJUj9BvumTV gwhSQLOW3N/ycFQXQGRf0Ge6ZNWDCFJAs5be3/KQdKmAvryiD0R8ThbPqgcRpADerByaxgpo n2ufQM6mIIIUwELUQ9RYAe1xrRPMGRVEkAJYoHqoqgtog2ubF3BmBRGkgGa10N/KAasuYL3e dU2/6u9hUax6EEEKaFZr/S0PW3UB6/LO6/iVfxeLYdWDCFJAs1rtb3nwqgtYvndfu6/++1gE qx5EkAJYoXIIGytgeVynvImzLYggBbBi9WBWF/B+rk3ezBkXRJACaEA9pNUFvIfrkQVw1gUR pIBm9drfyoGtLuB1lngNLuFr4OWsehBBCmhW7/0tD251AfGWet3pAV2y6kEEKaBZ+tuXPMjV Bcxv6dfa0r4eXsKqBxGkADpRDnZlAc8bu7ZSwQI4E4MIUgAdqYe8uoDpxq6lVLAQzsYgghRA h+qBry7gtrFrJxUsjLMyiCAFNEt/u60c/i4VMDR2naRag7V8nczKqgcRpIBm6W/T5GHwUgHj 10aqtVjT18psrHoQQQpolv72mDwYXivozdh1kGpt1vg18zSrHkSQAuCicmAcK2jd2HmfClbE GRtEkALgpnqIHCtozdh5ngpWxlkbRJACYJJ6qKwL1m7svE4FK+XsDSJIAc3S32KVA+algjUZ O4dTtaS174e7WPUgghTQLP3tdfLAealg6cbO21StafF74iarHkSQApqlv71eHj6vFSzJ2Dma qlUtf29cZNWDCFIAhCiH0rGCdxk7H3NBg5zZQQQpAELVg+pYwSuMnXtlQaOc3UEEKQBeph5c xwrmNnaelQWNc5YHEaSAZulvy1UOsbcKnjF2TqXqVc/fe8esehBBCmiW/rYOebC9p+BeY+dP qt45Bl2y6kEEKaBZ+tv65GH3noLa2HmSiy+ORZesehBBCoDFKgfhW0W/xs6HsqBzroIgghQA q1APx9eK9o2te13AiashiCAFwOrUA/O1oi1ja1wW8IsrI4ggBTRLf+tHOUjfKtZnbB3r4j6O VZesehBBCmiW/tanPFjfUyzT2FqNFdM5bl2y6kEEKaBZ+ht54L6neK+xNblUPM7x65JVDyJI AdCNchi/p4g1dswvFfAwV1AQQQqALtWD+r3F48aO56UCZuOKCiJIAdC9eoifWvw2dpxuFRDC 1RVEkAKapb/xjHLAf6R6Mvb931u8lmPeJaseRJACmqW/Mbc8/M9ZSzb29T5bvJc16JJVDyJI Ac3S33iFHBDeUY8a+1wRxfJYly5Z9SCCFADMrAwTvRSwWK7QIIIUALxJHUaWWMDquZKDCFIA sEJ14JlaQDdc8XPZbw+fm935ndRLHVqgUfobwJC+2CWrPovdYXO8gD4EKaAH+hvAkL7YJas+ g91mc9i5IwX0Qn8DGNIXu2TVn7XbHE75SZACAIBumPafsj9sPz9OoSnX53Z/+h1BCgAA2tX4 tF8Gnc3h537RbbvNTyj6lu46nT7X56H+LXekAACgH01P+/vt5jvwpGD08bk9Rqvb9tvPUxAa BqkUynKA2h029ecSpIBe6G8AQ/pil/pZ9dPdpPvvSqUwNQhSu83gVflSMCve/UWQApqlvwEM 6Ytd6mjV00uUfwWpdKdqs/sJSfv0ghHVY/XqIHXr/eTrIYS/C6Ap+hrAkL7YpX5Wfb8dhKUc hFKISg/JG0aix4IUAADQhxUGqd+vlPddVx5rt9v+fn7UtedNCVIAAMAlXdyRSqGnzlg5CN17 R2rqc6QAAIB2NR+khoFod9im8FQ9RyqFpFvPkTr+ys+r9qUXrrjzFQABAID2NB2kTg/dGzz8 b+TffxqRQlT+mEGYOr3y3/2fBwAAaFM/LzYBAAAwE0EKAABgIkEKAABgIkEKAABgIkEKAABg IkEKAABgIkEKAABgIkEKAABgIkEKAABgIkEKAABgIkEKAABgIkEKAABgIkEKAABgIkEKAABg IkEKAABgIkEKAABgIkEKAABgIkEKAABgIkEKAABgIkEKAABgIkEKAABgIkEKAABgIkEKAABg IkEKAK74999/D//5z3/O7/343//+d/j4sI0C9MoOAABX/Pe//z38888/5/d+pIAlSAH0yw4A AFekIJWlt+v3AeiTIAUAV5RhKd2B+vvvv09vp7tU6eF9APRJkAKAK1J4Sg/j++uvv05vp/+n EJV/HYA+CVIAcMUff/xxCk2p/vzzz++3850pAPokSAEAAEwkSAEAAEwkSAEAAEwkSAEAAExy OPwfHv4At60FDWIAAAAASUVORK5CYII=</item> <item item-id="28">iVBORw0KGgoAAAANSUhEUgAAAMgAAAAnCAYAAABKSgfJAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAaXSURBVHhe7Z25ceQ8EIWVwOaxMUwW m8SUgpCzvgxmIV8GrV85yFXRUgbKgNsPB9k4mgRIcMT51d8WqoYHjgHew8Wh9mFUFEVEDaI0 Zei7sesHd3QO9pRJDaI0o79ex2vvDk7G0FPZunqTqEGUJgzdec0xQSa5VJpEDXIAfd+PJ5tk UJkOLNHQjZcFdwzdZXwIrg9jd3kYHx4QLmNes/14Ndfn4JMw6blzXPDSeU5/lfLLc0qD+C+p 4bjQDhI7Ta0ke0yiZQbBaONF2l/p2qVLOxTqZHiamL7ZYxjHiRzGnAwmnY/AtVx+Aqc1yH1C YukkqXw/fVcujGIguKwSZ9IRhGHELBvMAhO6sgf3x6bInM9QM4qoQVpCjbQw0/h+aA5eKoxS IP61NFcNstajo16nTNz0DOkFawrpfIaKtYgapCF86nBKAqG1AKJc6/2R7YJBCsSa1qtbnyRp Sudj6L7CadbplHjX06sCsXwv5cIog9JbFeOSQUrKw6ZXDmsYN2Kw+NL5lPK2UoM0I21sIwz0 aLkQCIbiZu4pnQYAs9jNpGGDF0NjExeOSJJB5oX3EpEJgymZNYO5LJ0XKF2HqEFagQYSWiQ0 ijwNsyK/kOi2bxOj4Zfyqt3mXKRwLp8zCM5Np5aMRnkEUVHPwWKcGSR3XqB0OqwGaQU1pDj3 xTUm2txdEEzN9qNEbMY4L5hwSThVxOLNwb+7uzkd7ZxpjcjD8uVGGf4duUGl8zkCgy6gBmmE aRypxhcNgukAmaOkOyugxCCt8sL3KhHZGVGD3BhUuCg8ySCYWpA5WukVrBlksZy1qEFuz1kN 4oUnVWqtQfDjOTztbS0wNUgZapDmYJcqFZzHCFOq8cAg1DDRQrqlxm5qkL+/x99/3ec7478/ v4rKXqzGr68v9+k40KinBT3+UpcDE2wyCIUGi3PPmkF0DWLZNII8PT2Nn5+f7mjm5eXFVHjK +nZaDfk8aBEbC8rveuAqhOvO24a3ZfL3TNepkH73ZBIIhMvFOWCU8PfQ+oDlg+1R/z2xRRjm SSAtqSJwzd3vRZvs5DSqxBKDtGovfK9mad2YaoO8vr6akAOjBypcxj6YmRuGhYoaxP0hNt25 x4OAmTkwXaD0zSG2CH1e+EzC73sSOX7m7Y5xnxHQVCZK3/+4EEZC2u5n4eF9yJc/hJrLMMHy SMgYxKbD6olCdc+OPBGX5btukEzZDRva8CcZBBXBwajBDfP8/Ow+HUdcBiNMauTJEEZortGd IQb8gxES41A8VwHm2F9EGl5QeBXTfjD58FcmggqEebiqKO/0JxbcRBFZgwDky4TIRqwS+Cjk ixMYJCkPTJCaZjM/2SA49tOtj48PcXSxbOh9MuD+mLDBZxFP58kY4ZNnW5Zg1OGi9D09EznS ig0wP5VFehnhQhzByQXxiQYhMK2brrnrcV4Cc93MaQb1ldT9goldvc3lYEFqw59mEJjg/f3d jBY4xijy9vZmPrdepPvK58THBuGF+0DUJLJJxN4A9ooVJ9XEMJ8wgrywH8CZeblLy7zgT6PP JAqXHqZrsOH0clCcDyH9fCEQLYk5uScwEEJseglnrqkcXOQZU6NTyBVwKz9pFwvm8A30+PgY HGMEyWMbaGsvgrQ58TGw0wgSDAk6fG0UvaETQzSy8NEA8WGGOSrKHPXiJFA7l7fiNWm4OGZa N6XvBGlCpgeKBBgaIw6uDIk5eOAit3mnO2kwbZhP+H0ZlFdLfyC9rW3/3aBtSsqe6bJvCxrU wz8DvAFnG9quM4wQxCnCGYBYjy3fnrcCm79RqAY5Hm+K2BymZ8XUhrcoeuiDBbgbMyV0nxuD TiI7MpRAdde8XGqQ2wBzJAYhG2BL0j9vQODrhjMznPKvmhyg5N0GmddMaTor66nFuOv8Dwyi nB4ySLD7V4Vbzwkq5RseZh0aTK2X45Zwd++DqEHukM27Yrb3LzYX5RNuu1fEFSh9cUwNouyA evJt8xsj+Kt/0LmWBu73I0ht3CwwWbSTKaCqVHZQLjSO2f7G8yRENIJfWUewqVx13Cxk7MLd UDWIsgsItna2gzjJsyoxkVDMdXEFKtZOahBlH+jFKwVqR4G5218SefxOek1ciZo/XKEGUXay YZplpkZepNiRygsWZpi8QHHMhkBhXJm6dZMaRNkPpiy1CwGKYxbZfA3B1hRma9ddt4EZIRe3 kJrRA6hBlCbguUKtWG8OGat2W1oNojRD/4cpRVlhz/8HeBRDt71MahBFERnHfzKp13mpNx18 AAAAAElFTkSuQmCC</item> <item item-id="29">iVBORw0KGgoAAAANSUhEUgAAAOYAAAAnCAYAAAAb3jSqAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAbVSURBVHhe7Z07cuM4EIZ1gb3HnEF3 cLCXUPkQTiZ3wNgXcO6A0Th26tTFyKEz34DbP17EkwTBhlce9z+FKguEAAjoD90AKc1pFolE VycBU8SmaRzmYZzMK9GR8RAwRSwaL5f5MpoXIqdppHEZ9sMpYIoOaxoEylURnOedcAqYzBrH cb6uYG6iPnXs0TTMZyYqp+E8n3oTjv6eTvPpdJ7zrEzzcMb1fJlcH1WeKn8qAjheSu3lJWBy CnuKjgw0i4xx6GLvZMQUwnJU7Yy7K5jjfLGwKUDTvsP72zkcL9Sf8+AW2nwf4zoLAOKaV9eW BEw2kZH2sX4WjUO9UVQLxsa4EnX3mBRSLvVrz7jaXAbepI9BGQ/SjPZ4TQGTSzRBPW3qsMgo GRlSgpFy1tkbzLh+eMTVhSXj5dI+mtAXeVt7yR17TQGTSX4IdJXCwsHaQRgkTxhr9RVg+mBs gpkBKd9HeMqaMJzKVYazRTDv7u5Uurm5cRtbpNvbW5X/+PhoSop6GCm/6o2iTlQfM0TXBWZ+ vHJ91Iuy8ZyrY1xvJ5se8+3tLQBTlFM6iWoCvXELUjCx9N5MmdWVPJI6pMjUoZM1BObFg90D mzHrCCY84FK/BqnUHO7L5i4lfQzC3fU6odp9poDJIUxOYTZCQMvhrobrTMbefrsFk77W1t4j +1Xt2C/VqjuYahE0Y5DZP1qhH64b0QKUBTM4/FkHs3bLUyTt9fV1fnp6UmHrMtmn+f7+XuW/ v7+bkiIYadGgcM2NX3kV3nOUXlK8CMRtAX42u6fPxVYX5I8Ta8WRFEhox1ukTB6aTaMPr1yh j/64by1WAfQrSsAEkHZfCSgBoesMJVzHPhN/o5wAaiamNNqrYCL0Cfc9R1QDJpuX4wbzh6gJ TD9sBZRxHhL0+fkZHAp9fHyo/J8qDHbR4EtgIkQiKLk4gbbAXO3nXgmYTWoC8+HhwU0sPCVU 2mPiVDYu+1O1F0w82IwnRLgNW8C8fjWB6e8nX15eVF4JTD/EFTBrQ1malOiAhtO2vxTM37/m X7/N36Jq/fn3n6pxE4/JIcDXBCYlhkMfqy0wZY/5/6vJY+7ZY/p5+T3m9tHxuqZ5iI3YOyHT 4aDO18Zm7wfqMu46dcCetDmjBCweEGldWjjatid49nMseVF9pQ+Ka6a8hSU5+WsfpEA1YDI1 pT4XW10/SE1gQjh1tZMLOJ+fn73JbjmV1Tdd/TpcKvZQv2eBhKDzDkpUSEbvVS9x1G3rwd8E 3DjiuVW6al6jnDJa1x7Vbx44L9Wl4LH5zgj1KarHrpbXTqIMmLoem6eTvyBUCW3ivf4Cswlm 6bCpYY4EzCY1g2ll72NaCG2y9zFxvZ/g/ZYbsfBoztAMPBP+AcAEWHqf+eDqtb2IOqwR269n lepaA42upY+iYeHYAyZkPbxNJWjy8r2u7U4AZtIfwJfC2iwBs0mHwbQq7THr1bAak0IjI4My VubyCaLwKRndTuBlfRAsbB5YpbqQv+rBYJTB9RWjL4JJmmI4l8+5pWV8ljqDMUvGdmXxMGO3 9MNLpTkSMJuEOaoZty8As1HwahkrDaAhw3ahZezlAASNwLRkKAjOl6VMqS6Vb0ZPeVLdwPKl 4IxHLT1qFcBCECVlAnCR4gWnJAO164cPV8b7YkHKdbBVcirbpKZTWV/YXyK1f7tEG07rqqpD NTJSAin8aQys/KY/kSf1vRzeDwiXt6I/kccq1KWM2OSHHtiU9Q6DnCLDD4GMk+lHAqWffLh0 23axWITFImwn/MyeqC1OLlFf69z+ZMEuasbtC11gvfBte21ceu+njK8Yhl2LAEnfPh75FQL2 XzAQMJv0fcGEFyEIg1Uf3qiz0bNIhd/mb2Zhgcp6whrR+LH3S8Bs0jf2mBPNOYWlUXh29VAa TVf5K3kdCDoKJi0W9h5xMcTOlvG3FDr1WiDUdoS58m8dyoq+gQjM1ZPrVQEuA5uCL977Q4Uy tMj4ZUtfaD4qBSXAZwezbq8vYIraRLA0n/Jiu+IMXp8mJ/ZfUwb5HaOpHh6T7RcMRKK8yKM1 Gm1s8OoEPbLWmjKHFocK8YOJBabOwwuYokbVG1ksGHxyaysD5nYZ5ltAkfjBpMWs8u6CgClq Fgw3D4YOPf0DGpfI0HnA7BvGQuxg7tiXC5iiduFQptLQArHsMdtD6Vpxg7nnx9AETNEBtYaz 0YlrNrzbKEPgduaSGcx9C4mAKTomhGctxgvYVHjreRGT56rLlTHqdZvECR7bC7+Pau9PhwqY osPCIQybY/kbBe++h0qSgClikfyP0nnhu8Qtt3QETBGb1BcOmh/o/fs0De3jIWCKRFenef4P WR9q5JzRkbUAAAAASUVORK5CYII=</item> <item item-id="30">iVBORw0KGgoAAAANSUhEUgAAAJIAAAAqCAYAAABY4gKWAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAQ0SURBVHhe7ZwxcqswEIa5wLtHzsAd UrxLMD6Em/QuqHOB9CmoXuq0bjNULt35Bnr6hSBClkDIKwub/Wc0E4u1grUfuysZUwgWi0AM EtRUoqpb/cJSW/uPsQYxSKIRVVWLKVTauhLM0rQ2D1JThUAiYSunYdu6tg2STGmlg6KmKq77 p9IfkdqmFnWTD9db/v+GQXJHmbYuRVE4QJJqqjJZikNkrBr9IqPayAtmsyABGJ/jcMwFEgrv MoG3UYOtAaJBnkg9JY5I+pUpH0hJIhIhnCqaEo219LNyjeSYLSdISWqkVtQypVG4XkEkUzIV SArwBQuMbYMk5Vq1XYOUaNUGZxHCSRmRoCVRafMgKUisfSQbpFT7SPg/lONSg+SL2C4xSJCV tkYgJdvZlmmtpElrvchBWhCJnwqk/X6v2uvra1cv6Lbb7VT/x8eHtvRLOUO/jzLtXAuRkNLp KUAKh30AyZxAu/Xn5rJJO9lx+vn5GZ3jKpUg0tGDhGAdln5Hs4zNqNJwQFE4aMQE4JgMeRk3 YSf1ECAtqD9ClQKk0PrwepZtmOwTw3Hik6XS8XgUn5+fKo2ZIB0OB9V/Op205QqEuoxyGuV4 Xp/dIMAZMpzzcrVT2DDQwr2FewkA9XURIAI05vnjOOok/A27VQBFDVIi3QSSfLsssn4doVJc iwqedpVBITONASK7Dw26XC6jIvx8Pqv+bNoGSFKIPoYziiLB1wMEen9/H84RkQjy1UhYtdm2 2bQZkKS8KW5FMuuh7+9v1ecDyUx52UF6exEvb/rvFevf3z9B5zkJkjPF6SNrEUektCKJSPiw ZVlOr+KcakUjl41lEHiy9pLjxk7qkhrJ7OMaKUw0NZJeoYWmuFYV5KbtkghmRz+jzXwSrMp6 W8D09fU1en/6VVvEuW8DJHuFFpDiFHhycjKmwn4fqYemb/0+Eo6vRs8PEqBxrNDsVZx39C5V dXb3iUi2fDVSOnFEGs0ybv5WsHg2He0Uhx3ua7tYkOh0f5Ai9JyrNvcVZX4XhF9W2Me7ZsOy FKTOnuLqRH2Edsu3/3fTZortaOWPSA8hBmlODFKQGKQ5MUhBuhWkYfEz8dWV08b0T9emziMj SGa9xSB5JUGKvx8JMGg4FCyuefbYNM3IFj9+mPJR/P1IN4tBCpJ0bvQdkhLC322Fbr6vokaI DfpnHqARdYckjRikMMmIEZIzHFLbMMZ7Xc8qCLGZhxm+DPMhPUhDXu5atdb7cbMr3Em2AIm9 NeMCad5mLm1J2O/+KxKEUgMgu0VefE8tONvtSDOqW01OJA1I82kNPrXH9SlBamMFC9E70FEj kdRI86mVf2n7MIpNb9aKzJl+ZmwkaNMcLavhGKTcQvpY4LBBQy1qRA3dNwznstGaW/bz00ge UGt8PtLSrQkGaSXiJ7axyJT9GZI1P0OSlVVC/AeyDdiDWFUziAAAAABJRU5ErkJggg==</item> <item item-id="31">iVBORw0KGgoAAAANSUhEUgAAAEkAAAAYCAYAAAC2odCOAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAGwSURBVFhH7ZWxjYUwDECZi4EihmAE Cragp0jHDrSI6m/ABrk4OMGBGMzpI6G7PCkSMQTwww6FyVySJQnIkgRkSQJeJWluS1MojTNg Nm1ZmKKAUZp2xnCENsqd34a/hbsfxkqymItzvEZSeHEiaW5VEKOVPVe2VtsOra2mDa0UzkEe ip1bUwbJXJxHLGlZFjx6jmMlEVxCXgCHrTyFIqPr92IS8RMiSXVdm8/ng7ONruvcV36aS0mp SqLYa1TIGFsV7qcVaSsuzhMy7/vejRRQRXJJdB/ZDU4AcipJkBBtzxXcrw735OJpQuaQBAWq h0prmgaPnoOXZJO6qiL4OL7VkFUafjSynotzsJJg7ltvmia2yo58v5K2zfgMK5KujdpzfSd3 moufEEkCEeM4uqqBOVTTMAzumG7c8KeR9PJdUpIgFkLRnrPDtmO0FGREGzSRlIqfECSBIP/F q6qK5lBJHpeIjX1dkk3SP8+Lcr99H3OD/qHi5FLV5t8VBn1fLs4R95gQeMgTlfRWsiQBWZKA LElAliQgSxJwWxIIuvP7/Av8qpL+G1mSgCxJQJZ0iTE/H2TauERfX/gAAAAASUVORK5C YII=</item> <item item-id="32">iVBORw0KGgoAAAANSUhEUgAAAJEAAAAqCAYAAACz1bmVAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAASOSURBVHhe7Zw9cqswFIW9gbePrIE9 uHibYLyH5yZ9CupsIL0LqqROmzZD5TJddqB3jxBYEvoDhIVBZ0YzsYRBlj7uPZKJDywra6Yy RFBdsrJqxAtNTWVvy+LKELGalWXFXJg0VckyR3btHqK6DAGEQCvcoO1Z+4aI0lihENSwqjiw wwGlJHQkuVJeJDV1xao6Dapzrr1jiIbRRU5bdUkgae11WSyW1hARS4Xa+6uZeKPsFqKmKtyT Roa60KMR6haYacCbGqBeg+jsV45E4tVQ1K5BtEgkcoHJQUZqtV1XTr/DY3CjHLRz8zp+/MEK y9jPmT2RbbT0pf0inoggoDRmRggQi8k0RUWSK/32sCgQ6ee0wIK2EQuJfUNEsq3O6koeRF/U mihMlnEWSQTtDYA24jhTngG0QSRSjpGAMmhMNNo9RHwwtX0i3S8ttU+E69jOqwOASOP0Kobo MYBIwMjrfN5nhDfKEEFSqsLA3wavZtX530JLe0yoLZXp/QiAyDDpQ4ggRCA9zZkUHn03BdH5 fObleDz25hHldDrx+re3N3GkWdxXSO+zG9oYQgS0T+Q4iMwTboKojaoiIjkhcUMuq4eIX1AZ wFvp+mE6xnl3JNL397fSx1WK0o8zwo3wRPB1pqYBRErKc58TCvVFyghjs6ldUnbF0Dl8eLRR ZxJtrnr1EBB5PQfSjphEg9/pBFBurKlgGiFSjLUbolAvOBxhHST9Kmh3XTmhvr6+2OVy4alL hujl5YXXX69XceQKROPoHUY+6fgMUkQQdXivM/3S+ft66UJyNvFlEQVQh4y3qXwhlP5Ejjsi pQBP54MAEICR+492+CL8jeNWAVMIRIk1CyJ6e2u8+omgENjAvIUZrXtKTl0ASK9DgX5/fxXD /fPzw+uTafsQkfpQ2pUlVyrT9fr62vcREQiyeSKszvRjk2kXEJGsaW1Fkv3P5+cnr7NBJKe5 5BA9P7GnZ/H3SvX+909QH50QGdOaaFmLciRaTlEiET5oURTu1Zos7puk6EUeyv+gk3+p6dIY TyTXZU/kVxxPJFZiQWlt4KE8xw+kRz2peE6A1Vd3LED6+PhQ3r/86mxC37cPkb4S86U1tJPx 7qNOmjTY7RN1wHSl2ydC+2q0bYgEEHoW0iONfHZKY/XgYm2aao8PWdlNuJstsnmi5TSh71uF CA9rc1AcW+zyAGHn2s6GPLD3NeT3h2iCtrc6M99J+rfIentbbIDcIpH/O6I42wfwQyhTv8W/ q3ZhrOeqT3/3jUIPowyRX23kWucu9yqUIfKIR6GwDuxWkSHifjXygCeECNsDGSCvCKJYD/Rx gGAdokM09XmimTL+9wR2sj0/mrA7UbSO+ez2EpFo0pONc9XfEYYS667bjnBjxZv0+BBhxR62 KIoGER6tNcHTlZzedIVPUojiQwRbEpY94kDkASgv883CxMcK0NEhGuHZFjDWWcHCKjYSRbEh yv8B+zCKl9LiQjTOr2WIUgtpY+7ky3YiAkj5V0EeUNiPiZiJ5omAHLv1kCFaifIvpWVFUdLf bKzybzZmJRNj/wFOLOhPbq3DFgAAAABJRU5ErkJggg==</item> <item item-id="33">iVBORw0KGgoAAAANSUhEUgAAAD0AAAAYCAYAAABJA/VsAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAGBSURBVFhH7ZbLzYQgEICpi4KIRViC B7rw7oGbPXg1nuzADuaf4SXsKr+47sPol5iIOJFvZwaWwQW5pa/CLf19BpCcAWMMhLKPPPMc YxzkYB97UrExPyStQJDQyooHKbyoEvgel6jpSMc+sll6miZ79w5Mlvhz+pYZJHAmUFUP8mKR SLosSxjH0Y5m6rrWZfM2rISgDG7JGL3vMp0bi3iTpmn0tQRlebt02HsP12rpcpzDzNG0lvin L5Xwmc2ORbwJLSqEshv+CFVV2bvjoYWH5Uk9u16u2L9BP+fFGlalaexKve/71Sp4Zm+m57nU whVl1d4TObGOSJrEuq7TWaUxZbttW31vNrJQKP74S+iydMcQ7cRLR5IR9H4YI+iljbEhXpqE jQyDoiiiMWWaSB8bL4J96r4Xirke1d+z8+YK5JZiE8Q1nYNe0IHZ/iD7pXUpXU3a9dQJ2S2t 5IH9/GF2SUe76AnJlo7/DCiQJyzxLOnksXEi9m9kJ+aWvgoXlAb4A/K8mKxCIY5TAAAAAElF TkSuQmCC</item> <item item-id="34">iVBORw0KGgoAAAANSUhEUgAAAOMAAAAqCAYAAABBaewwAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAZYSURBVHhe7Z09cuwqEIVnA28fdw3a g4O3iSkvwsnNHSj2Bpw7UPQcO3XqUuTQmXegxwFaahBIIMH89ldF1YwGEII+dIM0M4dBEISL QMQoCBeCiFEQLgQRoyBcCCLGgnTHw3A4BNKxszluk75rh7br7bv7Zk9fiBiL0Q1HJToMQ982 Q9PSgHRqcOzLG6Q7Hocbn2uy6TvVJ+P4pyNiLE4/tE0zbBiLq6NvRYhRlCCnCTkNEWNp+nZo mlZ7yJsG17mgREQH8fAcExaF8fOJK1RWH7Nhf66RJ4NrirRpC90xrx4RY2HcEPVWUWJS4WlU aiSciBjhUamL9DqbTV7hsmoJQALRgqkRefjniF9fMpkTs4ixKHcSosLIVi4y5N2CBAx/VtbJ w0RTEhVWTuc0njul+WvkeEcRY0kyZ8JrBWJZM7AsMXp9Ni9rw1oc27AWS8E/Jzx2kfNktFfE WBAM6F2EqM16CJcsxoCxhsvCI8ZD3734Y1dMjGh34gQtYozw9PSk08PDw7hxgPT4+KiPv76+ 2pzAGopNlewlSF47S6CuNeEC08QYNtRQWbPOtB6yQvRRT4xpkxcIijF681qnAgvbK+Lr68u5 /tKU6uva7RxRYWXKPbQUMeIeZSjHrKwTypZbzzlUWjOC1HXj4qihEm4YRSaKK+NURr63r08m xsQ10JoY8flk+67Ag2J0NnBqRB+o14omsI7dA989XmJx1HSnjAN8Xx7x8/NzeHt706EeN/Ln 52d9/Pv72+Ysw9a+PnU7IcZVIcDLUFsosxaUEdE8GmCeI1RWwfunTPgYwLax1H1Gwpl4FhAx esC4af0FA4dBT31w0J9jPYbXyFfK2HP7+lztTBKj4CBi3AAP9WDg/jEk8Pv762yY/Pz86ON7 yOnrc7ZTxJiPiHEDLy8v4/XC04DYWgy7lH7ePeT09TnbKWLMR8S4Ab7u+vj40MdiRs7DwlOL 8ZztHP7+Gf78ta+FJP7795+kPhMxMsQzJiCeMZsL8ozd0Nba/Zqxb9ubG/TaWowfu+Q1Iz8m a8bzkC9G2tZl91dKiLHckww52Cc1xraztNIr2IWkvDD09/d3p3yRXcoCfV2mnRv6ScSYTbYY +b0fKugYyIaboFT+9GLcD4wZYR0ZNCW6f4fP03E9dl5fL3v7su1MQMSYTbYYJ2OYZmXHQDJH AD89QGWvyTP6xNZieyjd12BbO8UznoJsMdIMPM3KfKBCTySo/PQ5yvTqvT2jY1g88Rap/O2x sU88mNQcPe87PhGBZNoAkePY0rfMS1JDjPl9vU6ddgaQ3dRsNu6mKqNwnpE0Apn/2BUZj53Z ISy85wJZ8owkMpVffwIh27x+ODwJWxmpakg/CjS0rjL1lNAp1mFI/KY5EsJBHN//bYjUvl6m fjs9xDNms8Ez5kDiYbM4RJIoxmnNROW5Z3BFxsVI9ejyYhHnQcSYzYnEaEQS/ErNkmfsaDeR hBcP03jIK0ZwAYgYs6ksRlckOnnh5aIYGfjR12PD144ixoumpBjHJYc75qWZbCh2Hu5cXFvj 9sfteL3OCeStKkZAmynUWEeQa2LEOpHWnfQNbp0/Jsa6AyYkgjEvMhAQgB1TLcrQHkABVN0U uWlbCqmi65xzT1969ts4vV6tk1Hk+4xJjKIyaWzXkhiZiE1+EePVwAxxF7CP0VjM+K/Y9H6S JhLVFtrVdyYJJkxOQp1FvukfBwLkYSkaysWFQzExcuHxC6VjIsbLRo1VAdX4HqX+k1rK7lL+ Z8GZbKytop1B0aXUiTrSvP6uDRzsnurm0a0JLlA9q1iB4WLUe/OHIFyMRry4R7nuGWsPlpBG unEtgXHl41lXjNNkv3aOeUhpy7KJw5Bap8rn76dE2C5GCJGHqBgg74y4MBKkf9uDFu5HNbPo YrTDavPqY1zQNs36RDg5ENJe3ZxWjAYzsS9NJGqi8R48WftVutU6k0Jjw/41o3B/YJLcKxws Y8aZ1Rh7/YkW3mxJjMbJjOA6RwHG2rhcp/yiuFCZEqEqjNgaqmP0FXEmgADqc+djHZnRdaK9 ATEu1umJewURo7ANhF8ZhhZkXIbU25wzYaRd5vD22nPzQ6HfceXlKRqI1umR4xWBiFHYDNZT e/V4s8DLZs4wIkZhF/LPxXPkn4uFsyH/6T/Rt/Kf/oJw5QzD/56c7lg54xUWAAAAAElFTkSu QmCC</item> <item item-id="35">iVBORw0KGgoAAAANSUhEUgAAAQIAAAAqCAYAAABYx1W/AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAdTSURBVHhe7Z07Uuw6EIZnA3cfZw3e A8HdhItFkJycwDEbICdwdIlJSamJCMnYgW//etgtWfJTkgfcX5WqGPmlR/evlmRmLp0gCKdH hEAQBBECQRBECARBIEQICtDWl+5yCaS6NWecj2vbdE17NZ+EFOxpUxGC7LRdTQ6P7rk2VVc1 tqNa6jTz58lo67o7sQZm5dpS2/Y2thwRgmJcu6aqug199Ku4NiIC2SExGAacZYgQlOLadFXV qMjgtKANJlQAEZM/XVJ5ZirFD8XyByC8dhoWFmBM2YqLEtpgokypaOt19xchKIQ7LTgj5Jg0 JYj5Xe/YrrcP4qkcyFwfy2cg8rDNrdZofBE2DllWCGiaaAUgUu5k8DZagAhBEWRaoAxzpgH8 iMAXTzg0PsfyowScrm0aEqbCQkAh+1A/HbHkfP6aqECEoAQr1fk3AuedM8qQEPDPXAhC+VH8 9seCGl2O63I6os/qcu9lxVqBCEEBYABZO/zmweg3Hwb7jgJDvrCRvHecWH4MxyGoLGa75ggh 4OXMLgTUQvXCAUiEYCUPDw8q3d3d6TmtSff39yr/+fnZnAkwJxzOKWl0U6yrQwr0FuocIyEg 4CxDGYeoIpY/xnMG7LWze/xuIVgmwKAXAtUJfcN6yWkt17htyluh2+Pj48OpfwpK90GOOgSh 0HzJ3nZICHoQBYSOxfINeGfBaTlHQHQqZrtOWfOvEYCl6wSj3neNcVh59dENWlEH65dlzkZO JyrVB8WEwAnN40SFAA7kLfYpYvkG3G/wu7EYlY4I6InurkGBdSO+ezLFuPdV4w5GGGonNPBZ F7/e39+7l5cXFUJzJ3p8fFT5n5+f5swdZO6DInXgUH1mHY7X2Z5s8/x6xvLhXJSPy8cj/3hk LC8EhCnj9HQmHY4YTrBSCBDOnHPhC85j59RwIDjM0E4XdRxzbPyN83Y5U6Y+KFoHDtWnuMMJ ivRCgNDqpHvhPISGA/l5SOD7+9tZgPv6+lL5q8nQB8XrwBEhOIykQoB/ZKgwFz1pZz49PfVt glEUxObXWHH3z11Nhj4oXgeOCMFhJBICukntLlydsT/5XPrt7U3lxZyIh9tphCBNHxSvA+fv n+7PX/O3UJT//v1nUduvFAJKJ1wkPDYiSNMHEhGck6RTg9EKbJJebbum2IKD3nffWmzuMHPz a56Xco1gbx8UrwNHhOAwEi8W6pcfuIHs3TmAYZfffRjXo08zrYUVdXsuHOn19dW5Pv+uwf4+ SFOHDW0oQnAY6XcNzKg6HNu+g4DC4R4/cRsSzoJw2TqMTXYPHseXE4lUdvfBdASUtg4LECE4 jAxCQFx9Q6TjK30Zq9/2+p8UEfjE5tdJyNwHlm11kIjgJ7FZCOxorVPg9UTHSJHoQc4rrmSo 1lCwqAXDNSVx780SLymd39SVeftKp6oOv0Gmj+tRUW+v4dwyFpdTCPb3wTKyihlHdg0OY/Wu QdRJVTKj0sgAeYJD2tHCnA+nxmfunOweo4jAOjidr47w0c9bKR/KS8+lIfHai4M3gir0fVJo BObWSPylGySE2cjf8597afpA3WqSnHUIQmUupM+CB2xqSdsnHgas4zKDhIMuFIJhZdxez8NQ 18G5ENj7qOvF4m4PEYLDOFgItIMG//V0KiJozaje/w81FwJ3tOOjpxjZjSNCcBgHCYHroCr5 L79MCQEDP9aA9+r9tQCLCMEPIpcQ9NPBZVOiVCjbi1QIUSk/xO00bO/xwU4zHNf3nTvf5TAh AHbhzjaAIwZzQoB1AVVRigr6NQekmBCUNQJhA7CH5J2E6NP0vRKE0NpQenq7C3mXEabhkF/G sa1Of9uyibDZs2a/ndlj+/cRpKJ3aJ36ukwJARMQfb4Iwa+AnGDLr+9MAjvqjUrbCfOXrCjb Czxs9M3IjkAxUYjhnK/rNCmgzvlhNn9D0T7g/AFF6x0bWTEh4E7PG8/miRD8XKgfE3up74wY HdNHHWGCQkB2jSx3amBsWh+YLx8c2/qPcfIaoz7s3H8e4OcHwfOXRUpZFguxS6ArYxyZF1ZV kFWOPusfbuRCoBsT7yAMeTEhKGcAwlaWG+RS0P+8348VAqpf9JuRjQ+4mWGYWOhnUJvhMuMz o1vMigs9e2bqYEkvBFRa9RKRdWAYgFcSzFusGPhbizofOw5GTOxOgjlX5XExMWlJOwvHAcNO 6ac3JQQT34ys5+hmkJt0Stdp5+u3wMmXRCGGfGsEgsCBeKd0VDLywRm1o5UaDHwhgJPyQQlJ 1RV17p11uoz+ty2HnuEKw3yEJb90JNwgqacHNCLa6aLjcPkZRQQMJyJQkautM8obFgLcj1+j FlbVtdaRWV2J4Pkj6JpIGUOIEAjlQKi6wjhnUc6CETjttGMSRCJ25A/UZTw1INEw5/cjuik3 zhtHE6wu7Fn2npPnM+TXkIWbBnPmlFogBCABWbtdK0IgFAfzWxGDPOBlvi3vbIgQCIeAV8j1 trGQiiteaNrYpiIEgnB6uu5/gJCD/Eyom90AAAAASUVORK5CYII=</item> <item item-id="36">iVBORw0KGgoAAAANSUhEUgAAAUYAAAAnCAYAAABzCOG7AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAgLSURBVHhe7Z3Pcaw4EMYngc3jxTBZ bBJTDsKXd/eBLHz3gdM6B19dnJyBM2D7k9QgCST0B5iB6V+Vat8AI1pN61NLwrOXXhAEQXAQ YRSEE9G1Td+0nfn03NT4QoRREE5Ce7v1t9Z8EBRdSz5p8sVRhFEQTkDXiCgGIXG8ZoqjCKMg HJ2u6a9RVez65nrpL5fLvHji+3Tucrn2jn7MHh/rmly/JiGbCmlvefWIMArCoSGhoil0WBbb /gaBCQonzhvRUGLEdc0fR2bKAtPeqN5rQxasTcimClBPhq0ijIJwZNDhg6mQzu6i00iaZo6i qa9XH0PHbdYSLZ+UexeQkzWKMArCgemaSGc3wnVDZhfIGvF9+ziyQAhp6LhDZhaWStK9S8hY axRhFITDgmwqnLFpgaHzuECJ5DTzwjW2WNjCOHfcoWBTI4WkexdBU/REIRdhFITDQh19Jgtk UgSmXBjTRSaX7YQxPpDYRISRnNM21HgaddTuEAoZTBXzS5N4byrlJoILHvToU7+IT2Og08z7 jYojEljAn16zTgd7ECgLjL2jp3y1NCUtXGPctO9vtMYIUtcZ54URDidjVDBZQqjoMFJwoEkn rgEPaey0426fsIwrkGHf6UHoSgLSUhc7GUtTWTV9ZiHAQDEnCtZxZ80wdFz7ftStuDiXEb53 LfaueoypMCpnjgE3L3ydCTgRxhr8zi2+zABZxYLv4N8tNgceBvLBYiZl+ckWM2e9cejznnDO HJ/OdrzvrEXIpkocUY/gCSOUemx0vAJcK525BhHGCqLCiOmXu051SlKEUXAoEsbcjiprjHU8 ozD+/Pz0X19f5lMFIWGkTAPr4mfXRIUIYzYFwqgXOYdgE49vzrMJ49vbW//x8dF/fn72r6+v 5mghM8KIHwzAmtrThK4IYzYFwuhOo0UYt+eZhBFC+P39rf79+/ur2lyFI4wU7N5G1lNE798/ /Z+/5t9CEv/9+0+Sz0QY78hxhZFmF7f0TQ1kiSgMMka0uYqoMFI586YLIxljNvUZ42aB1fbN bgtAuk2lwYP1sJeXF5XtYBoIv3DWw/+tYR1h3NOfTJ4won0APvP9WIwjjNp3kx3TVVTjHv5N RIQxmwJh9ANrmwwG99h/t9BbP7VLwEsQRZxHdsMg68ExnHt/fzdHE+HXD7z3wUZbyvx9N38m CiOmzRhcAPwGH+KznUEWMSOMc8+51jf7+Tc/RuEDEcY8ioRx6LymrO10FoIjvEYBO7lDM9hN xXGIIjo88LOU6yAYbrZqX8fHHGEsyND39Wek40Zsx8BiDy4AWaPt27APQSDrnxVG4M18Kt6D e/h4FWHMpkwYidTOqnYAM54Krud69w+0/NEY5/wOzcLoZjuomzsfTbvoPnPtG/06dmLH15kR fnd/JmaMWIbgQYSBKLoZd5oPHYLCSOCvs4Zz5nymi/b3b36MijDmUyyMwBVHL6g6/NXL3F8U UDDyg8U5BKexwKnPLraFdH2Deq3zbuZAOBmt7kj6FQ1cu26E4B7++3a8PuaCdo92hqdeprMO 19odYS6reWR/pgsj6rb9yDvSrlim+nDE9cHMn3k5wqnb5/5Z4CP7NxHZlc6mYFfaAwLY0EP0 RjH7RyRGuJObkRtBg8/2w7cCdRL0HEB0vTqDIDXX+lnrGLQUaGRHNwSflzUodD0lMYjNAdwH YsiFjyELGrJJBLppj84y5kSOgaC4nQ6daZrNPKo/mTRh5LVYFPah/drOQIYPg6KlirHZ8s20 oO5H928iZGNJbD8zeB4pPgsLYxYcGFZAIwASA21cY+Lvc+CiuAFkBxrXo75/pwix18cwaExF roRz+BNLDim7z9v4MMZJ4lWEMZs7CaMOgNlf24gEGqWmehRFp1AH7ECzgpcYA239zaF80G7Y DHtdO+s4hz+RHS6zlQ9jnCReRRiz2VkY3QBQxZtSRAPNgn8DUk83UB5YGNEmNoL+HWtXLmfw Z5IwbujDGKeIV7JxtTqHKf4+gxOy5rDt9sC1PPgw8To1eB4pPltNGAEvLHMgOMG2FGhYp1EN J0cMa0AooUDb5wGG8TMck/msaNP5/bm9D2Mc3r+wf5VK4XdjnxJIapM+sQ1GhEMCNfebiYv/ d8KFOpny32OsZQgYXQZDY4FmBai+/sGFcRhdUcYg4rWnlBEpmbP6c08fxjiyf8mHq/xILNo6 NFy3ZUv/t03TN8HsDiId8DujYscV73idI3W/4J0NgstW8LFxg6HBQLODihs7fv9hOvKuiD+3 5Sz+pfsuKUECykarHgxOQVGqhfyKWy1Oe83AOWsHznnZfVKd6tm5ghpi1c0X7OopYzEKIxhs 4+0MAdbTZ/3ajx1oumF4p2w8Fgq0DR/e3RF/bstZ/Jve0WPARtu27YSR7G20tcsiBvBcZtpH Qjjal1Mn1ecMiGHWE0YEGQKMAwQPzLMA83sONv/VCH0cO4QmWHnnz1yrjtnBasqyc4+I+HNb zuNfiFqthu0mjOQjrjZNGHGdL4x4Zna2mFGnI6hx1l9jFARhPyC+tSJGgrHHGiOEyx4kUOK2 TzNiXyhz6kxdXwQijIJwaNaYTlMWxksAENrE6WYNSRmjl+Ehsx31e7rxFK9TzxJSEWEUhKMD AalN8YZpf/3UPAVHxMy91Wdkr5z9WW2aZoZTO2PCmJMtAhFGQTgBWA+t1cbTQmKb+1qTCKMg nASsv4k4uuAl/pJ3PUUYBeFE4E8U9WtFQoeXvgt9IcIoCILg0Pf/AwUotftTdqKCAAAAAElF TkSuQmCC</item> <item item-id="37">iVBORw0KGgoAAAANSUhEUgAAAEkAAAAYCAYAAAC2odCOAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAGwSURBVFhH7ZWxjYUwDECZi4EihmAE Cragp0jHDrSI6m/ABrk4OMGBGMzpI6G7PCkSMQTwww6FyVySJQnIkgRkSQJeJWluS1MojTNg Nm1ZmKKAUZp2xnCENsqd34a/hbsfxkqymItzvEZSeHEiaW5VEKOVPVe2VtsOra2mDa0UzkEe ip1bUwbJXJxHLGlZFjx6jmMlEVxCXgCHrTyFIqPr92IS8RMiSXVdm8/ng7ONruvcV36aS0mp SqLYa1TIGFsV7qcVaSsuzhMy7/vejRRQRXJJdB/ZDU4AcipJkBBtzxXcrw735OJpQuaQBAWq h0prmgaPnoOXZJO6qiL4OL7VkFUafjSynotzsJJg7ltvmia2yo58v5K2zfgMK5KujdpzfSd3 moufEEkCEeM4uqqBOVTTMAzumG7c8KeR9PJdUpIgFkLRnrPDtmO0FGREGzSRlIqfECSBIP/F q6qK5lBJHpeIjX1dkk3SP8+Lcr99H3OD/qHi5FLV5t8VBn1fLs4R95gQeMgTlfRWsiQBWZKA LElAliQgSxJwWxIIuvP7/Av8qpL+G1mSgCxJQJZ0iTE/H2TauERfX/gAAAAASUVORK5C YII=</item> <item item-id="38">iVBORw0KGgoAAAANSUhEUgAAAIwAAABVCAYAAACb8QzZAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAU7SURBVHhe7Z2NkaQgEEYnLgMyHqMx GYNx+YDWhgGFVce/71VRdafo7Wy/ahp0uM9ISAUUhlRBYUgVRcJ8Ph+2gvYGioUhBFAYUgWF IVVQGFLFv4QZusYeS7a2971AP7aJPk03+PPkbmzKMKE47ZjzoG9xvhnbrh+pyr3ZJIwxIRBG 5xYBUjVNR1EewoHCDGPXGFk4/DyKY4QZurE1stCV57G7MIM51qBeSY1P5PbsKIyRpA2LYDrz PA4UxjQWu49jR2FcRnFTaNV2GZv6sWNBdAlWhUHQsySEcbMjJYxpW2dKkJCzrWtwgDAgXuH9 /4xJFgcpzDU4SBjDEEtjzlfGHDMuuZ7CXINNwqw+GgiEQosfDxipZPhCgQzJfM2TfV6layLT vzOFdqPON21UaA+dOu8ynZv6o2+gOCngX8Jkg2mbzzRfsuiGwEmt4/sj+Pi7DuJShhERTH97 Rme0aHY2/7zm3zVpbpgkirIiWWVThtmGBFjVNwhkoTDzbEyu18V2KIIWRu5jr2eGqeYCwrhA tnEGAUsZpvdZohE5tDBKQoPOiHRkGycKEwbStnihb0kYxWDkwbOruFYRKMx+nCoMkAJUAhpI syYM6hapg6aaCC0nTHic1HO6MJYp8K5NWWBxSJpFc/0pzC84URhIooeguaZZFyZV4IY1EYU5 hnOFMffGrMjGUKbFWiK9hgKLzN8xLQ6FcYJhDaek6F2qhcg65woDWfRwhBlPFM+hm4eeeMrt jqvFQJk5+b72mJbON30bofZ51Vufb50ozHWQDFQqQG3/J0FhPJCgRoDa/k+BwngoTBkUxkNh yqAwHgpTBoXxUJgyKIyHwpRBYQwIPj4nWiiBX1yMxMj3fz4UpoC+T6z0HYyVMlhhzD8rm/Gr 5arJLXKS18pPYVYYjCy17yJvZQqiEgYr3hJP+/JX/CoIwM/q/wj6Vj9n85LZlW8RLnc8D4W5 KN8ZRmGDKzLkMBlJ3m8O+seSJI4vQGEuyqowqQyjMX3mtxj9cIb74Rnc6vE8FOZQwqfqQcvJ 4FkUpiC4eghz+Prm656542mqhfn64Gy27U1eGBPgtewCUaOv2ziBvMDq+tzxHIuf9IhfxLvY P8PMhewSRip9bTCEuZ/Jns4dX4DCXJSUMDg2HQpqlAgzZAWXQozozcRJmNTxBSjMFTEBjzPR /D0saXqmEwY6lYWsgP7aw9ZhcBNCNBSGVEFhSBUUhlSxqzDfhZluJdNBcnUOyTB9tJtmQfFN bsIhwuipGjPLs6AwpAoKQ6qgMKQKCkOqoDCkigsLw+3ir8h2YfzT0vClnO3CvHU7jauzWRi9 uiuP2ANhCt7iipHrKcz12CzMLMecSQJh1t7IieB28ddmszCoNexLxNGrfi7oqa8t4J1Ufx7X YAcqL1U4lKmmpcPWZNwu/jR2EAYYSaL/XAtB/P4CmMjks5HsS6eDuJRhRATT356BbL5vPPTN 8hlhuF38buwkTCkSYJV5EMhCYeZ6Sa7X2SwUQQsj97HXM8Ns4iRhXCC5Xfz9+LEwYSBti2dR hUUvt4s/h58LA7hd/H05RRjLFHjXpiywOCTNorn+FObX/FgYSKKHoLmmWRcmVeCGNRGFOZ6s Efhl748LMLeLvy+/Fway6OGI28Xfih8Lcx0kA5UKUNv/qbxWGAAJagSo7f9EKAyFqYLCUJgq JisgiJaEwnxDYaIMQ2GWoTCRMEBEoTDfUJgXC4Pg4zOihRL4xcVIjHz/d5G0Qn4xb+aM7eLv AIVJcMZ28Xcha8XbMwxJQ2FIBeP4BxVmT7fykcDSAAAAAElFTkSuQmCC</item> <item item-id="39">iVBORw0KGgoAAAANSUhEUgAAAD0AAAAYCAYAAABJA/VsAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAGBSURBVFhH7ZbLzYQgEICpi4KIRViC B7rw7oGbPXg1nuzADuaf4SXsKr+47sPol5iIOJFvZwaWwQW5pa/CLf19BpCcAWMMhLKPPPMc YxzkYB97UrExPyStQJDQyooHKbyoEvgel6jpSMc+sll6miZ79w5Mlvhz+pYZJHAmUFUP8mKR SLosSxjH0Y5m6rrWZfM2rISgDG7JGL3vMp0bi3iTpmn0tQRlebt02HsP12rpcpzDzNG0lvin L5Xwmc2ORbwJLSqEshv+CFVV2bvjoYWH5Uk9u16u2L9BP+fFGlalaexKve/71Sp4Zm+m57nU whVl1d4TObGOSJrEuq7TWaUxZbttW31vNrJQKP74S+iydMcQ7cRLR5IR9H4YI+iljbEhXpqE jQyDoiiiMWWaSB8bL4J96r4Xirke1d+z8+YK5JZiE8Q1nYNe0IHZ/iD7pXUpXU3a9dQJ2S2t 5IH9/GF2SUe76AnJlo7/DCiQJyzxLOnksXEi9m9kJ+aWvgoXlAb4A/K8mKxCIY5TAAAAAElF TkSuQmCC</item> <item item-id="40">iVBORw0KGgoAAAANSUhEUgAAAIAAAABVCAYAAACBzexXAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAU+SURBVHhe7Z0BkqQgDEX7XB7I83ga L+NhnARIE2hFabXFyX9VVO20OLuzeYSALvuagWkggHEggHF2CfB6vdB2tCeyWwDwP4EAxoEA xoEAxvlKgGno3GeLrR9DL2ac+4U+3TCF6+BuDmWAVIR+Xovr2PP1bu6HcUbo2+KQABTZRAA9 9gWWpOsGBL5RLhRgmoeOgo903zTXCDANc0/BR+zb53QBJvqs4/l+aT4AzXGiABT0Pi0K4UD7 XCgANRR/zXOiAH7E+yWfaqfMBeM8oKC4hE0BOIirLAjgq38lALWjKwGWCquJa7hAACbfAfx+ RSCbTRDgGi4SgJhyCeh6ZQx5RSH3Q4BrOCTA5lZwIgi3fDuYJJHpggtGlibUDKvPG3RNQf0H Kjw7db3rs8JzGtR1n4n8UpX7Jsqa5CsBVoPjWsgEH8HXjQMhtULoz8Hkr3VQShlAAkv93RWd cbLVR/zz0u9LaWh6S5FlLYMcygDHkICp+oADs1OAuNqQ+3XxmQZWCyDfx92PDNCCAD4wfT7C mVIGGMMo7iTYWgAlFaEzFmKecqMAaWBcyzeOSgIoJpKBnz3kc70AAda5VQBGCjIJUCLBlgA8 70sd8a4puK0JkH4OGhDA8Q6kb+9RWpwCoji+PwT4hhsF4KDrlB9rgm0Blgq+tKaAAPu4VwD6 3lz1u5jIMk5LodfwbAV9zcu4VAAvDO8h7CkCS7WERe4VgIOv0z9X9Fl8piGm+nyJ6D9Xm0uy Mgh93WdaotD0t0llSpePy9T2b5sbBWgDFkySgtsbyFciGbX9W8e8AAkuW1SM6tr+DQIBErgO qQlobf/2gAAaGtGLO5Jr1PZvEAigGIe6+by2f4tAgAAvFdPVQZna/q0CAQgOZtwf2H7/sLZ/ y5gXID5WlqY3kfzmlN48Kvd/HsgAOxjHO3J93HD6nGr0ZtSSgKV7UyDABhMFv/ZdxuP4zLP2 wkp5M6p8bw4EaA4/enc/s0g2oyrvJSBAa4SA9lJrbI1k7i8ZoPZeAgJcip6rs7aa3ju6RiOa L7uAbszj/FAsjPjqe4lqAT5+EDTXziJdYvo5fj2l03yv5v+6ez3FP/mZP5hNvs0A8VopiCOP 9vBrpuZeAQK0hkvdsrTjin55n4GD/Y413eOeSey8VwMBWkS9CqeDLHO6W/qF676pQC/dWwAC GAcCGAcCGAcCGOdUAT6LE92e/erUf+WSDDBmp4VtLUXAfVwigNuQUAJg5LcLBDAOBDAOBDAO BDAOBDBOwwI8+3Xrp3BcgPCUSr+YeIYAe55lg+McFkDv/snjx0SAL/75tNwPAa7nsAAx2HGk JwLseSitwPGwv+WwADxXp0e76Neglt5I4ffYwnW+h08ICZKkU4dqWiI+CgbHw57GCQIwFPTs P4vgoHz+gwqRI2QLOddHB6WUASSw1N9dYXlC33yqiTKRADgedpWTBNiLBExlBg7MTgFivSH3 62yTBlYLIN/H3Y8MkHCTAD4wOB72fn4sQBoY1/JVws4iEMfDnsPPBWBwPGw73CKA4x1I396j tDgFRHF8fwhwlB8LwEHXKT/WBNsCLBV8aU0BAepZjTD/5Z2PDxiOh22H3wvAwdfpH8fD3sqP BWgD68fDakwKkOCyRcWoru3fOBCAp6WqgNb2bxsIQCPa2vGwmneUfYETg25FAIvHw2qSKFsT gJeK6eqgTG3/J/ARZQn8fxeAgxn3B7bfP6zt/xRMCqBfY/NNbyL5zSm9eVTu/2wWoyw/qGXu OR7290CABe45HvYeVqNsPQNYAQKYZp7/AAWHirsYEdR8AAAAAElFTkSuQmCC</item> <item item-id="41">iVBORw0KGgoAAAANSUhEUgAAALMAAAA/CAYAAACrZvlQAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAARkSURBVHhe7Z1NcrQgEIbnXB7IymGy 8BbZZ+Eud8g25So3yA0MzY8CI4oOE5rO+1RRX5iRGfl4bBo0ldsMgBAgMxADZAZigMxADJC5 ENPQzbd+tDVQA8hcAC3y7QaZKwOZC4HIXB/IXAjIXB/IXAjIXB/IXAjIXB/IXAjIXB/IXAjI XB/IXIKxN1tz2J6rCmQGZZiGudMXdDcPk30tQs9e9qIPrvndttM8dBttNoDMoADj3DsRtZi9 eiWCXu8Gpab9eTlmry29lz/bQWbwOJRmLcKZSBr7R1G588Lu2N9MPdnW/Oy3OeJOZgrnHx8f tmbqb29vtnYWc2VlXligUeLF7yKqR+qYZFsbpXtV16lJhkSbMn9/f9uaqX9+ftraVda8567A dGacH6tk1PXRi+Q1hfBl3mprJFfHUwMt9smcmaSlk3Z8fX0F9RCbz+wk/I8S/EeiFC+lyJJZ Qa+v32+82ZM55zN9gh69vr4GnaT0guo/Pz+63EFXm0vqd0FkbocLY5WRMwf4xyfamsi8fshp memESWiHq7+/vweph4O+YPekwT8h2pHYC3BRupFsq1MLN+t7x+xwJ7MT2BWqby8A6Qv8k9rC pCIQ/h9g89og7fRzXS2xen9L9K22hGvjPuOAReY4Xz4kmB4AqM9iL0Xfl5cXWzuGQ4rhrlqU 80UiS69IZMqN88hJMZ6P1EEB17hkQ7zSrAVkBj4nbTALOjdVHW2VPJsaMlN65fofFKwfqtN0 aCOJ/hZ1MStp6RION/XHeYDL1YHMl6DN/eN9T/C3QOYr+Jv7gA3NylwvKscpBuACZD4NUgyu QOazIMVgC2Q+CVIMvkDmbMI9dmwr80OEzPqOpCeaX5x0W8f8VYRN3mjR5f6xAO794YqYyDyN vX2M0JWNZ0dUvqujq8p5xwrjPva+gP3uIrKF/nBDjMyaWIA4F6D3K+YHYTTNeFCLeX+4IUtm RTz9LmPNYBfitMwKzv3hhjiZ1ShHv8OmpJnU4q3Lk+eZXJGZc3+4IVBmBUWtZfCp8LjJcU1m BdP+cEOmzIrk9FyRyzIrOPaHG2JlVsN/Pz3bd2rxiMwc+8ONJmWmwTyEVvpdt78bsMk0jwPt IpSX5SGZGfaHGzJl9lb6udPzpBdVj0TOCJfnejsOl2Xm0J8GEChzvNLPmJ61LL1qd3DcLup7 VTsnl3/Xz70WiBhsq4VtQ2r1pz2EyUwDvbHSd1HSlVQ4s1KVGPxV3PVzApmT5+DDpz8tIEbm abQDnLiREE/PdOfs/riSg28/azkfP6Ieb63x6w9/BMgcT7um+A/dpB/0iQe49OCrcwuexyDp jp6j4Nwf3oiJzGWQNviQmT2QORfIzB7InAtkZs/zZPbzVQmDL60/+0DmAMjcMpA5ADK3DHuZ zUCEp/k0maObEX3rv4skrT8HNBGZfXn9n4sxrn9uYKtk3azjhLT+ZNKEzAQNgv8vADGQGYih KTNIZMgMUkBmIIbmzIDMIAVkBkKY518BmWUT+30JNAAAAABJRU5ErkJggg==</item> <item item-id="42">iVBORw0KGgoAAAANSUhEUgAAALMAAAA/CAYAAACrZvlQAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAASPSURBVHhe7Z1NUoQwEEbnXByI8jAu uIX7WbDzDm4tVt7AG2A3CZCE/0xm0mm/V5XSDDjQ5tEJIaO3HgAlQGagBsgM1ACZgRogcyK6 pupvdWtrIAeQOQGDyLcbZM4MZE4EMnN+IHMiIHN+IHMiIHN+IHMiIHN+IHMiIHN+IHMiIHN+ IHMK2tpMzWF6LiuQGaSha/pquKCrvunsawvavh4v+ltNtZm9nq2tb/2ZHAGZQQJYUivxILUv 6kjX1KuiDyJv9Wr2IoHM4DXwMGuyreubak2+OStXK0ZvZea2afomNjPzwT4/P23N1D8+Pmzt KiaAMycCyiUUkYcFa8IO2Ewbbl+VmS4Sfil6mMHy/vz82Jqpf3192Vos5mrl91oUmC6M623F Irpy7so8wEnuaMxM59GYepTMLC2f9Mj397dXn6Crq7YBV3VDh30O0y8R5SklFddl5n0OZG5p eGHfIkrm9/d3L0geXnD99/d3KAa6quidzXHMVXx04uN+7i9yKmfOEryQiLY6NWZ24X32ZWaB w+MfeebJzD/AQo+M9fv97g09XMKrEvxHgtmM6qC3JvlDZ5bDjJmozDxeASzwWLi+dwPI3cW2 y7gB/DfYGztvntmdVtt7sLS3jbgsczhePgWPnZGVgRAmezn7vr292doJBIg8Xc0ol4tGpqhY ZB4bn4JEbtr8GVlro4A4LtvQtW0/eTxIbb/PAGQGLpdsGO44na4qXCzyanLIvDZlNBTc5Wan 6NTGEr2WeY7dn5Jss/ZQwACZo+BJ/72ljiAHkDkGnj89ejAAXk6xMufLyuEQA0gBMl8GQwyp QOarYIghFsh8EQwx5AKZT2MWTfFxuWBaWR4qZF4+zFlKt7bPqzLs5oOWoSwfPEmPRypqMnPH a2S9xl15OsmLo3gbjXlzLC1pa1fAvaWzZcQjDTUyD4QChGMB3p5xfOBn0xNLAYTHIw1dMhNh 9zu1tYBZiMsyE5LjkYY6mamVg8+wkTQd3bwFnznLQYzMkuORhkKZCc5aU+NzkfGQI05mQmg8 0tApM7HZPWckWmZCYjzSUCszNf+ye7ZbcvGIzBLjkUaRMnNjHsJ3+lW1PxvgMoxDHdloTJr6 o2EPySwwHmnolNm50z/VPS/GpAf7n2F8T2fGIVpmCfEUgEKZwzv9o+6Zt9MN1fzBxsjunI5L +4+yuE/9xtc8Eb1pNf9nfXLFUx7KZLYNOVtiCDOVaw11x+2idY1cZv+4mYNZ3FkeT+ZTKVJO PCWgRuauXXbrLmH3zE/OttvUzWaxmcwKNJ2P+57HQsmLRz4KZHYbai7uopvthT5bDTtnsscW 79C5eesxWLqjdRSS45GNmsyclKkbV5LFtMWzAWRewWQ+PWNLbfFsAZlDhixW6ZnC0hbPDpDZ g8aWlaaG1xbPPpDZYfVvTfOTtCf+q4tnoi2eIyCzJZzqckuJMwDa4jmDeJnHBnAJ64/CH1Ea j7NWSuumtcVzliIyMzfAiPt9Eg4avrjpLG3xXKAImRluCPcrACGQGaihKDNMNwmZwTqQGaih ODMgM9gCMgMl9P0fdoZqBpz7ZH0AAAAASUVORK5CYII=</item> <item item-id="43">iVBORw0KGgoAAAANSUhEUgAAAGAAAAAnCAYAAADwxU3sAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAALJSURBVGhD7ZvtkYMgEIZt4PpIDXRx TdhHOqAafl0nFMPtIjFIQD5cgojvDDNJJAj7sMsqOqlbTXUJAFJwxYU0376vI+fvHoCYZzUL 86WhpIB+8HwIXQOQ/BzGXwUQWCaEfgFIrhiR9SVnaiJqS8xM5TDoFIBUHEIPhcm08aeJDICe GIxDD9PUJwAcZEG8DYnSA1A5XtAlADQYof3JAeSsBR0CgPDDaMLPS+QAoHdzYhjqEAAMjtRY NQCkT5IVgO4ELkae8uqbrw5lLE4SxP+SfHtP9ADS14GNB+DFBNsY2EMRDYDHwMWaXHwW5Nox 1QCA1yjZALRcCG7H8DhxZ7ME5yc9PbQXHOsBIdSU5rxrgBtq1oYyc9wqogZQSYcAwN9hEXkD 0KFI4spOm30UaQwAIJztlhdME23uXaxhAICCoailng/1eJrPJ9bf709SP3cBeEOROdJMI3kA DpYxtp8VeSWVgDSM1QA21BpgMp7UUCT1Qm3XjQFwPcwqoZOMAcDNeBJCkQYGxqkdsq4PAI3t yXjcrCjYOsBLBnB7wAYAbi5rIwcuttxQhFfEn/VyABTomlmQfyba91zE/Hl8Ka6RKwMYZhEu 1g0AdQNorBtASGsysXNrxVvHHtdS9vrREIC9ntQBUL4fgEY0RtVG9vUvUEeITV18IGxvbOX7 AYdVGQAYpXhHDOC909ulnx+zNKUO/j7v35Yv2hGjUWUA0GLpnrBOo63/YmbnelNKnfgkQBuk jZ0ewBo/lzKT71umD84VGtdNrX0A4nVi4QUmydefikDXtQzvlsJJ6xUayW8A2/ucAh2gARAP P2gLt92QKoSgLwi9LHGAG5GsAfEQePkn4xbDlIQhJ8PxholIHQC0b/+8NapTACB084yBrlrX KGuWmt/W5nx1jGLp5yBPRy864/sBuSly1wBQ9xsyJ1Dzd8T4wO+I9S2l/gHSVkQoQnAnmAAA AABJRU5ErkJggg==</item> <item item-id="44">iVBORw0KGgoAAAANSUhEUgAAAF8AAAAnCAYAAABpPZRZAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAMlSURBVGhD7ZvtlawgDIZtYPvYGuzi NmEf24HV+Ot2YjFsAshECF9jHBb1PYdzdgcG5CEkQZ1BPWqmS8Bfl1nNy2r/+6yOjN09/GWa 1LTYfxppXeAa5voF6Br+OrcH7wQLMFYuQL/w11mNMfJYNwxqGEbF81jVPGI932adRzV4fevP dPshCnmZYuPx6hQ+wAN3w6Nf1LQB1YsQtsMds0FaJgA6ztCjkYO8g+/3GYGMdaSvnPqEj5OM mRhs/xc4Y+FJ18QsUGD5uzZkIRjVWH+X8BFObII+OLTspC9mrDWAbxdRf5bz7RW+v0P4CCLm cgw4OvksfAZWCB+FFu+7I07QrtD1dAgfJpcAUAefB8XBN3HC7oAk3LRxUDn4ekAbzf2yXQfX JmlVZwjcRDKnrvD5eEbgqgL4O9eU7hNV6vd3lo+HBZOibYW5OJw81sHFNDlUZn0qCYiMP9+E gF9rtF9QFv4u4Kbh02wqpdDt+Avgj4L1qZHPFoyfHV7DwusnFmg/w+/q9JLOkbaD/rm5012f 2+27hU2I9fl0ICyuo4QlfUwl8BvrEHz4ugksbgFgy60YnMoCyam6PnyQ27pbKQsip+sW8EFR 99NSP9/q+8f+/Uf1/99X0TUm4bPux9Y0010sHyc6jnBoIdYfZD9UOi6Q3QIxQvwhx218vs1s itxPECMy7bX8nUVK7EvXh+9nNjn3g/UQkJ2Vn+iurg3fgvS9hW/ZtHdwN0swmDkJmvaxTOmx fCd8GKwBJ47kFBCedFmmWhSsoOVfL9vhLdC/O+jXmxID+7J80Ztvtwi4R+XclKDVox74eZmd csKp+IGfkbb6sguoljB8HceEL7QhfHPQOgU8CuBLxRANHl2jOPx37+cfFD4dCgbGk+8kdCsa dtU7b4fFdIblv/Uk66icJTFFLuPBhZSDJQ8fM8eyJEMMPj6C5KBvRW5+5ZMrkTx8dLtlu1wG fga8dLqJwKQ2kjj8iph0QsD9gDCbEqIvDf/yb6wBMjHXIwu/Lh51Ch+E2/soNOouBRbgJm8p G/219/NrU+Cu4aOeX6Y0VtPfZM03/k1Wv1LqF+GWaa3P3yENAAAAAElFTkSuQmCC</item> <item item-id="45">iVBORw0KGgoAAAANSUhEUgAAAKgAAAAnCAYAAACbrQO5AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAASsSURBVHhe7ZzNcaQwEIVJYPNwDGSx SUw5Dd99IAvffeC0zsFXFydn4AzYfvphJCGJFgxYAr0qyohpgab1qVuCMc1YVZWxKqAn0tB3 Y9cPqpSPtrSrAnoS9bfbeOtVIUMNPbWvS4e0AnoCDV3ecE4iSNtESCugpWvoxpZDJ+yaZmya dgwxMnQtfQ6bxgY+WncYu9ZTJ6D+Fr6+TxXQokVwUGpf5qIfbxouAZunDo63HZ1R7U82sbr4 jOBMCd/mdRiqgJYsdDYnHFFqvUMkI57LFKKnea7+1shysK7cT03ZUEoUrYAWLEDF6WiRug0i J/gMhWyCdVU0vVFZTAsMm0UlzEUroMUKEYyT3iV8JhA+QAFNY6RvE1BfXQku2aOCgJU3B5Wi qQEzzVdAixV1MpMIFqAkHNeLJL0gigHKOadf/ME1ASpGxNQ4e9N+8NnwG1WGivEDRS32fUXG HNSSaR+oK3xgnCQNUNjzpidWBMXNVHk7QW8eyuEYfEYhOsOHFg9REX5AG9lAOCvxWHoFkLOV uqcu9vVx04Yp3Lvl2M9TvNs57lDD5+6xMyp3P9D1ky4vgMJ3MUBSx8R56Hzie/rg9dWFdB19 jgQhAnPqeOegbgqbTmSOoAsoaz+kApqZNgFK1cVc4945FPIHrLx4E9vzKGM/XBtQ0hTW9ZY2 xziNcvXD5QElBVPcxZSlH16exqcXtV+g/v39w2p/FFDqmnmKU59cSxn6oUZQEjmhbVs7xbG8 Mox9h1UwpyNxi2JbVEK7Pj4+VEmW397eVOkBOsQPiapz0PtKlZviBrGAMG1TOsaNUsa28E1g 8/39rUqy/Pn5qUobdYgfVnz3awPqrlQZKU50JDnt4FQIEHEtra+vL6tsS0Zre6ETi+AZ++G6 gKITPCtVdzUbPLuGILVj1kXQ19dXYaOF1I7yz8+P2GaijvXejJ7pSD+s+O5XBBT/3CScH+hA N8XhScrcbi2g64TrAFItXX5/f7fSvhaeGS85pgg/XGsV7x/B5rNe+5cu5uY6P7VjpP3aaKDb ASj1hrJ/kYRrxdr0m35I1OUXSau1c8cYcuefi0J6P6xXK6AxXQJQRMnn52dVWhYnvT9OFdCY LgEo4MRckye0a9/22Moc0GmxF3l0G7W5T4fcdrDm+b8HqDmPOxKIuMTCZlOPpmpnPxCg638k jcGjoBMQ+toXs1GDz+dPBfUyoGt/D7pZuQFqRrK0X31v085+IBDWvKlDyJqLy3bOgArayP2Q H/uuGztGBF31i/rHKDdAf0t7+4EG3hIFAbnZBCnZBS5oo6Kp97851bRjOcXDNzyfPB5QFeJl x6ChK0d56drdD/xOdgX4TCBDgPpsJLh0XVzYSufUnk62ZhlQGlyshyWPBBQpwegQd4s3+EQ6 0A+AxeFKyYzezkYN2Aqoty7eYKcOLwJKPnKvF9IOKb7qMCGCMTvaUnB+aShgIyPo3VgDir/u YAi1rb5Z5DJam+adFbo33QZsRFrXgBk2huIRNG3uXAEtXUiXCR0+Sc0frXuc6th0Op8NZExj fJeOAVrfbndBlfR+0NRbYxXQk6i+Ybkqe2X7jnrcvF/ZrgpoVcYax//HGdoK7u2owQAAAABJ RU5ErkJggg==</item> <item item-id="46">iVBORw0KGgoAAAANSUhEUgAAAKEAAAAnCAYAAABnuChzAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAASgSURBVHhe7ZxLkqQgEIbrAnOPPoO3 mEsYfY3e18Jb9L4XrqbvUNsOV32DuoGTP4KFCAk+8IH8EUYMagMmH5kJOnVrs7J2VoYwITV1 1VZ1I0vbaknbGcJEVJdlW9aysJOamvpQTQcxQ5iAmmp/AHsRiMVEEDOEZ1dTtYWLQFy73drb rWg5LpqqaG+WOsbnm7YqUB9fZ13y7ZnKEJ5aBAWFYTuCdVsqUASM9vsEaIDKgNB2Hh5XwVWX dK2oqAcWoT3XNYsyhGcWBtvtjjSAOg9mcXZC4Z5QEwM2NMUbZghPLEDiGmgTIHguF7CzIeS8 3YTcMEN4WsG7uT0RANIhWB1CL2SUDgSG5AzhaUWD7AKEFBfCEMD4SaKrh1A0KFY940P1w3YP PxvOp9PYgcIhuycXMSfEnqSjqoFC88KBJ8RmY7ekV4elMTw8rtFM2GlzPrpOYYeQcKivjhnP NQVCnOtPeSaCvprmNA7H5gCYncN1S4eT09HtQO17mwd8ov+aR5Ln+r+leqzPaDkvtmXUOXHw nm4ALCNrTihmgNZYX5FnRqWmQ9shBMKdtQhC+nNtZxwHhaMGyWhYLpCODmyH9CEk9a5cHWFJ ZnI6qh0uASHJGY4upkPa4eOtffuQ/z6o/v39E9RHFkIy/zgcySvX0gHtcBVPiActioJfJeoS +ZLmNSh38n/oiK2EZd4FbX1/f8tSV/78/JSlFbSJHSbqMjmhXAEGhaNR7uS5fyTT22iHpwLc 8/v7K0td+fF4yNJCbWKHGc+ePoTmCtAXjnCdEvZ+tm8XvgAb2lD6+fkZlHsRHKXsU1Hq2yuc Jz6wHdKGUBryNUqdzBmu107hpx411g1ud3/IitIcMO1gnuR+v4t7lBCGUX4+n+LoRH2hOrou dO34X7NtaYcZz54qhPjPKsLAjo1YMxzhjYF7KHXDxvOEqB8gKqny19fXIETrwnNwEJ7CDumt ju0zUR+o8SsbdbgM+/IA/necoXnjWKofAE8dKHMLE7yAn+KRtrHDRF1iYbJUfdiK5wXNfDBI yA3XhMGnWHbIEPrVeYyQXHC+4O3e399lKUBbA0iKZocMoUdi9od1YIkAIHK/IFGfVt+r8ymm HUIg7L0wNwn0hdPQW4vc19EIJpev/R0hpIeiVWUUw89UQ0vW164JYJT/jqrIdiAI/bm2hE/A aE8HXN/8CQABpu0BJNx+COd+T7hQ1qQfbxAGe3PbqTemY7bHUnQ7EAjLv6wGqJ1dbEC7PGFd 0UQO8ISzvqxeqvGAv45VV4YH1zZ2ANBuCkyAED6dbUvPZl63QijTAH84BvhhE341CPFJvM3o 6uA7nI62swM/yABIh4qFUAhe0ZcTUpsyl/FDSPUFfvi7DoQew28VAnfXxnYAJG7nNhVC3OOB EL+8JavwQki28LWnFGFhkrWZEEZdA40J0VPiygl1jT2rCSHAMyeWq/38CwyXEReSEV611bEv NFo81zgcv8R7Qj5fNZUhPLsAj2vA5YJjsE8oz4k/0dMHsw7uGomDMP8q1wV1tN8nnPpWKkOY iLCo2BvE/EutWQTBDq8mpRpsYM9sO0OYtbPa9j+L4dqKSfclZwAAAABJRU5ErkJggg==</item> <item item-id="47">iVBORw0KGgoAAAANSUhEUgAAAHoAAAAnCAYAAADASVzNAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAOlSURBVHhe7Zw/sqowFMbZwNvHXQN7 sHibYFyEze0tqN2AvQXVs7a1dags7dxBXr6QYMAEQjjBqPlmmLmGXDie3/kTBM1Y0lcoatB1 VbKyquWrpDn+iBZ0VRSsqOSLpFZ1xf1STocdJei6TJAHxWHnE2HHB7ouWU5EuS5zloWOGNib ZSzLcmb2fc3KHPvNc0w2ijExP7MCrQrb+cyKDDR3Ci/ZFGhaZwUFXbFCwRPAn21HdVJAqoLb k5f8XTYy29g/pgUo9mnHGlNcoGH8lDAdUfCM5iX0cfwmcwdPZwiGJxs7czToBk3J6qhA400T cg4Oun98ZOxgoBqy8NlGWeoxNtaLJ/TqiEDjDdKUbaUlQOuOHgVtAGO2EZns0nb4PMfybQW9 2WzEtlqt2oUBtvV6Lcb3+72cSSVuNDGUuECboZhsbPq6zOxBkO7JMZrRl8ulAzqYeFnzuT4c UmjQyNDH8Yd7ND4XMO16srFT3oePCbn26XhAT+g3rgoOGlmqr5At2Qc7HvHQDWgj6M5ibBi0 vqofkpXc+Xxmh8NBlGkd9Ha7FePX61XOJBIHTcoE2absDglbgMF5tMySYzituKRSdvTnWWwU 8OX4WPB3gmhAT6ABWPVlQAbU1hi+YT/6NP7GPDLg1KC/RF6g9TINyP0xbND9fu8s0m63mxif pQTaS16gd7tdCw+ZDNl6NFbd/bmzlEB7yQu03o9Pp5MYs4HWSzoJ6N8f9vMr/05y1r+/f5z8 ljL6zRW8R+tjqUe/TrNW3QogYB+Pxw5Ut1W3/FRH+792s1mVQHvJG7SSuo5WUNWmrqOxn1QJ tJdmg1ay9ehhpYxeSi8G7aG06vaS16pbF/oztsXuXqWM9hJZRi+mBNpLCfSX6PtAm+4i9WWc 09wK1NtTqIADFOo7aW8J2v9+dO++sOFpTOucqurMtT0gMFcCMgKJHPTM+9GLizvf+wkTHiQP B1qeynCZg/HC7RksH4XIaLInTJYTzzhPJ/QdaHp2y2XOrGBzED1oBKxbBYoItLvRfcGBOjQb 6PE5bmXQV/SgeXLMfQr0FYIjzI5uSq2+YGo37jga0GHLNkQOesK6JirQ3BPOhndE0qP9W4er qEG/7Tc1GgA+5bu3ojaWs5E5PBACcyYGPS0wIwPNhXLk4wzAE+Vci3I51h7ONEcq1GVVK1QU rd3M1Zt/m7IRFkVkgf+JQvWZQpkrStBQ+sUDsz7qFw+U0m+YdFWXH/gbJkmUYuw/Rf97Hduf wh0AAAAASUVORK5CYII=</item> <item item-id="48">iVBORw0KGgoAAAANSUhEUgAAAQsAAAAnCAYAAAAYTG2rAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAjQSURBVHhe7Z07kts4EIbnAnsPn0F3 cLCXUM0hnDh3oNgXcO5A0Tp26tSlyKEz34Dbf+PBBgSQDRDkjEf9VbGWAvFokd0/GqDG+zQZ hmEoMLEw3gS362W6XG/+k1Fjy30ysTD+eq7n83S++g/GKrcr3a9Lu2CYWBh/NbeLCUUXJBin RsEwsTD+Xm6X6aRRCtR7epqenk5TLT5ulxNdR52nVHwW296my6nQphWFfYBtLAx0Xz7btdTn 9bw8Xo6JxUE80pr6mO9KAUHLj/UYvU7nEDAclIU2KD9dqEd/HusstcU1CsZNKgEU9hFRzLLx SuXItoIIXM90LXy3HPm9FZhYHMAjrql718Vq4Oia/smOOZDcjJs/CwSc7AsBxp+rbd15axpf RGFfQJ9ZCBYECLRkFyYWO/PQa+qOdbEWBIim6zyQohAIanWqbX0AnjFrb8wuNPYF8rqBWjmz lj00PCMTiz3Bg9rgSG+B1nWxDszA9dlSgkCSwVAMRszuYvaVYlFq64KT6qMBC0f/noXKPk9N FGrlzKoY0DJIuRSpisWHDx/4eP/+vVNPfzw/P3P5ly9ffE2jDDm0ak39xlmb2bogB1dGpzYY UT77uRO4WtuWAF+jpa+aKNTKqTeFEOiFdzWz+Pnzp7iJloioQZAUHjo/WHE/5RGed6lOrzPm vMT4w7MLurfq/ZCGPQFG1q+0zYNzi1i02JePG6iVY6+s0lWC9vmYWOwEHmDtAWDzz70qC0fh oSIgcI1mhtEvFg4fH+ONVIum/rK3DUszLQI3uReVtjgP5bJOF3r7WsQCZbEIz3LBQPn2ZIlq 9P/48WP6+vUrLzlmp3qaPn36xOW/fv3yNY17FKldHrC5E+B6wTGGcej4mnS4AbKtyTQObnxP EdS+jPuh/vgelGwstQWhTehjC2v2ATFe8qwK5emSCseymCXCssCdWEAkwj4FhALCIAfGdexb 4Bz1TDRKUHAo7j7PCOLexiZwlJHBVeG48fXrYhWtYmEs0iUWcskBocjLcIA/f/4kG5+/f//m csNDwaZbU7s16nx/KaBumIUHBtYix40/dN/CxGIoXWLx+fPn6DjIKEBtzwJvQ/K6hoecWb2m jiloOAYGlYaDxteui1WYWAylSyzk/sT379+5rCYWcnliYuFAQPA9b3Tm6nLgII4YX+uQKj6+ m9599OfGZv779x/V/bTMYhD4e4i4Idg88xWWA/7KMew//lCxsMxiKF2ZRcuehSwr71ngldDG WYrXz26M0wWv8lyKjBk82SnmVDo4ODk+rY+lfZxaI5hxjo077ClQv7xUkOce+WrRlbvvElL0 eN0bIHefuajVmdHf6RTH5EPVwW268r3YGNyt4/NzEff4REuMlferJhavly6xAHjbEZwAgvHt 27fZKehofxuSz1riWLLQO3BwQnwhrk+Oer25PkPzeM2PNQe+c+r5I/V5vkxX6tO1ISeniwj+ YAvKkSFwE4jQPAi/IbiS6Jxhk//suoaYiIBtcWbRD9sk7k+tj1serFvEonV81Bd1Vut70Hf5 eod/mFgMpf5sUu7EIhB+ZxGEIRzhdxa4vh8++FwkMskXYocVmUQUDtcuiAOLgAgk+Us77M6f Lu4Kyrk9+qUTkg9qi4xjFhqMf4K4+M74c7goxIZROzOCXgZ6HjgFEeDgpv7X6qloHR/XZwFX 2evROqQKE4uhbBaLQG3PQk/uUOKoWJgEIkjEIb3OS4I4w7trsX8EQuwGQhKC34sRTkXfsS0F xJlUYbbAfYckY4n2uGvJV1E5sw+8eRAH2+Ptx1HtCDaEevO90dMxPrK6u4GkHYX+PHWHbPcP E4ux1J9NygFi0Q7P9N7r+B9SoRl9dhwR9OS5vGZHYAcnRX2ZkgTIwWIf4pwFgs7RHudRECgw YjDxbD4LUmjPYwaxoQ/yejS3AG+G4n7KPgVRtPwRl0UJ/WIxZvyADPa6HVqHVGFvQ4bS9TZE gv0KHP1/deqcuctBKNicM7slBQcx9hrgsRycfuanz7y0CNe4Keyk635vIhCXGnw+z4CufQgG sjk4vshKEhEhkjEhHLAzrE9AVSzKs2jed37dHXkgasTC1Yn7LkPHD8x2yH5yhorFihgbbWif zTGpwkFcL0E03J4DvxWpzJ67cogza8TCgfuy2z2Iy5ZlG0wsXi+PJxY8w88ZBkOOjAzjkcUC opnck8G4TKS+VxEwsXi9PGBmcSMfouVCDCC3VDhcKAA581JKPgZ9ZrEbYUmoGPxV/dw7ZkML IrdYZ17O5XZAPJtt09iz8LwR7POeXorGngcUi1cEMprdxUK3sbgf5Lx4a6QcePQfkvWLMYLO 28JBWrp3S3V80Ja+uA967T1xaOzBpbLYslBstEcr5CYWu0AO0OYxHbysWOBfYbpzMPxYrJjN wdaBNlIQdIsxCc0cWO4e3j2qah13XhMq7A1dWjMLjT0sKO5Zl8auZRZae7RCbmKxC4ODo4hz rJcQizibFY5yIJGzD91o7hfjPLCQpuc2V+v4mb/4r3r7pVHrMkRjT8RnCmv2Mmp79L5qYrET eIC1Zz4E7zghSPkn6AfgfhU7j5sfRcekNtUA6KJfjPFcpC01sSjVcUFJ42LgJMUne/yr8x6x WLMnBVlG+t2dXUlJgz16ITex2As40+JD76QnWEexMnYtwxm6X+FBgJT7lBlXdtDN0QRnrU61 LV7T++L14EzR2JOT/0O86CPNchrsaRByE4vd6J/93hb9S4ZFesUYghftccJyZ16lTh6UIbDx 31yY1LZp7Em496uSXVp7WoTcxGJPoNrLT/7Ns0dW4egVY6Tx3iYITjEFr9ThpUf4PqKOYHUm v0Njj6CQCdxlFoJle9qE3MRiZ/Ba6mH1ghx711fIvWLMQY8ZVwS7L4vdleoAZAJ+ti4N3S4W xJo9Ysw7UVi6RizZ0yrkJhYHYP9j5P14aDHeQsfzMbE4CP5blYPeWLw0N7zfP/C7PqIYb6FX yE0sjDfBI4nxFrYIuYmFYRgKpul/CaXj2NW4c7YAAAAASUVORK5CYII=</item> <item item-id="49">iVBORw0KGgoAAAANSUhEUgAAAHkAAAAqCAYAAACX4PQQAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAOXSURBVHhe7Zw9krJAEIa5wHePPQN3 MNhLUB7CZHMDYi9gbkC0xqamFpGhmTeYnXdkYOBjoOcHUGaeKqp2WVjbfuie4UcTFlk9UXIA RMkBECUHgFfJRZawJOlZsqLaYp2URc7yoqx+mwaX1/AouWAZl4kwyjxlaS4DKnhw1Y8rpMgy NtcxXBb8teq80pmgXZcsT1NmEcvHUebzCa7hopsCouFfcpmzNM1FRa8avM8Bw+hmVsMU/q8Y 5vSFUmRmReRdcrtVrxXerXib1ikUgq3mInzIk3KFbM1rGBaSZ8mBtGokeeRNWlUyb8XNPshl oh0OTKrZr2TDI+xTgcCxBNtI7u6DsxXtwWQwNnuVjCCDaNWpvlVLbCWr+RuUjNZOLCit5N1u J5bNZtM6591ut2L98XistgQYS5ptDN+bE2Zx+uB1qjjG9JJpBxsYreTb7dZKnm+0F1DEQnsT YOo4a/iQRDlXtZGMFtzsMzwmA+q4vLhkCQJuXicznrzNJpk4FlpJFh1RmV2PtGOcpztJvl6v 7HQ6iZanJm+/34v19/u92tIPIin169AreO44IXnUHSpSxmIqGnLFvrTJHeXf/ycZSZPjGxKH RNUB8wV/x3iHn7GdrySaSl4qTpLkmbCSrLY8JK67Dgt4Pp+tic7j8RDrXTCRvGScHy/5cDjU CUFlAN1Yh1lrd1sXTCQvGefHS1bHtcvlItbpkqe2x7klLxkn+/liXz/Vzwvz+/2PFEtLcqxk AiGNyeq6dx6T1XVxTK7ArFQmBQk8n8+tRHmZtcrTBOU80EQy8BPn64KDul+96LK3BskSJAnt TSZKLvL8E3+n87rsKQNSr3LJdS3JrYsA7X27+I2TwJokS3RjnQuN0KZiW5ItsmgXZ+CVLJlC sqzOpmLVZNPvk6pME2cPnz67VsE4h0W9mIAFbRHr3e/ucLGt69UJS7OcmT6QOH2cHdZYyZEO UXIARMkBQJFscCepDzEJJdiLkqeCSx6+n9y5J0w451cRgnGAkCQ73k+OaODiBp8M4QdBI2j8 6Y4+qJXs7cmQSBdeqQMCuoKGn9PqhyYZBxCtS0TJxgwnF4LoD+P1Q5PMDzbXpzUjeiBB5202 yaNzg4Yo2QZMqHQJnmlMXu4TFMEw1LI7s2tiS1UZlzw8L+gSJduCdqlLtMt5MjpBdWlWJ9qk ikGU7MBSn082/SB6lOxIoN80EB6zfGdI/hbfGRJ5Txj7A48iea4pWgYrAAAAAElFTkSuQmCC</item> <item item-id="50">iVBORw0KGgoAAAANSUhEUgAAAOMAAAAqCAYAAABBaewwAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAZvSURBVHhe7Z0xcuwoEIbnAnuPd4a5 g4O9hMqHcPJyBxP7As4dKFrHTp26JnLozDfQ9g9IahAgQEIaW/1VUWVLIATqv7tBY8+pEwTh JhAxCsKNIGIUhBtBxCgIN8JhxNg2p+508pSmNTWOx7W9dJf2an4T1mDJnB5EjG3XkOgwRdfL uTtf+slqaeLMjwejbZruwH6oKteW5nawsXQOlqZeu8v53BXM06/iehEhVocEOTr9NI4lxuul O58vKkIeFsxBRInIHPypOxyZTu3706quSvdjDo6ykmFZ0NBvGn9bXleX6k4D8zG5j3Vom7xr HkqMdop6REhQlJ6G7HsQyEQBRiT8OBlxn4qFBYxzzdQgQ23b1ro3pNL+q64FxmUEo0S5cn+4 ZobzP5AYJUVVxjEzAVNh6YgYbRdMycZIF2wfbAvHUTmLob7HsepxBnxKMTnR8ThizPRSvxEI bc4wJmI0EaPpd6Mn1kpGPLcLpq7hE2SkLbUp2QTJwR0rdtyjTqeEjLXjYcSIiV99on8U8Pzz aZhroPp3aodDRlTj6YTIN4C6vP94W296uzKuTVQRI8aZGAR+pBgfHh5Uubu7097alPv7e3X8 +fnZ1ATjQ0eZOPadyBvDGtA8JAzeJ8Y5g1VtEtZbvjWgv+0GKSqxjRjTnCAwYrQNdihK0biY 5xwKe2jWS/WN0sGPjw/rftZAGwcbCy+WMfvnLPdh1hiDl8S0T43fEaP7nKdjxFzMGVzIKH1t 0xzHYjZYM4LUdeP49K+OcTmCwovM0Wj8F0dqYRtsXWoasi3KcMqkndCZDF1/qCCXzcSYuHZx xUcHKDXtnzdsxPPsLaMOEOrf15aObWNGbDwYZ6Ugkppy208fEzMYxnTSrejnmS08yC0m8f39 vXt5eVHp3Hi/p+7x8VEd//z8NDUXYM2F3+tjvKUPcJMxcFIMnI+ZV2bH+8OWs+J1+boycL1g W0P9VxoMc7813jP2pOrCccVOSupeIWqgvnRjXWDA/RoLRgyjHe/npM5jzYWfUW+RQUfHinkq 2xDadAwcGk+KQQjrUyhG3XA0DscIBy+ii9UBjLfi0+bpHIzYPYYCvr+/rU2Rr68vdTybkBhp DprC95Wbj4EjYtyNYjHSU7PWjvwiuOiZDLE/x8WHFLbmw356ehr6RTQBofUWdiLdutl4xIh1 M9ZPpePcfAwcEeNuLBCjFtZgIMOaSKdmFys69hGjforK11Zvb2/qWMiQeeq3jhhpMptIxpDI 5mPg/P3T/flrfhY25b9//0mae68YXUNUKRlEqORtryvVIdSv7Hb3jYyuGKkUbNxIZDwmiyKj KzhsVPAL4uf+HESY9plPnf6WGgQ32rn1Fj+25prRyhhQMgez+Rg4IsbdWChGR3DYOeQvbJ2N nLwo4ezY8jJzx9hp7OvCmF9fX6329XdTp/eeu6O6zhgK5lDEuBuLxegKzja6aeTcEhgsUrfe aPvSv6PD+XQCEdsrRmBvcIXfT8UzgXXHkICIcTeWi5EY07KpwY2RMyVF5ZRHRpfQemsVgmIk 3E8r4XyhPyobg0TGn8QqYhwM0peG9pGz8BMoa1BTjFaaTmKbOBxLrCg04QUfiavqUDiym7ob y3ZTB3QE8Keh2junp6jxtC0HrLVQ+ItxFKR8OL7kLx5sEbrFRMiJEHlJyxRqjsGLRMbdWCcy Cr8HEeNuiBgFGxHjbogYBZstxDjswOdu6q3DuMSw++dLj9gcqHpOhVDbUF8+UFfEKIyQGOu+ gsKegDFMJUpnB7o21Kf3P87hXvpNxsh9DeKyFedvG+orQNnfMwq/F2ZAVSCxj0apN/dSokEV mOOBWLgTwuu6kFNyhZXUNsHJ5f+lv/DLochVUR2uIceMvi7kCNh/nMu5L58Y423tvvzAMaVl CSLGw5BuFCUkRZHqjB/GGPpGxGapaY4Y4209fXmheonv4kWMBwLGVksftyFGjRKVIyK92YIS noOJGIm5tm5fExLS2B4R45HAJkQtgSCKDIa885pRRS2PQKx7nOIT40CwbaAvg/xHcSFAzVQV RmkMj+9C7oFPODgWi2BEUIyxtlGB05wEz00RMR4NMp7Yt1AtQm3/x1PBWuh00aSTfHxKSHTM dQ7mXoeqfT3ePtA22JeDfAuVMAvee9XSo2AgIee+ShIxHhT55uJ6yDcXC9nId/qvz/Ui3+kv CD+crvsfpHc8jwDvIrIAAAAASUVORK5CYII=</item> <item item-id="51">iVBORw0KGgoAAAANSUhEUgAAAF8AAAAgCAYAAAB0OKThAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAK6SURBVGhD7ZlNzoMgEEA9lwciPUSP 0AW36L4Ldr1Dt42r3qA34JtBkFEG/OPTNPASE0Wh+BgHrI2unEaVfyJV/olU+SdysPxOy7bR TYOb0MqWlsqh8jsptOz6fSVgAFoJw1Eu56WdTuq28Og/MecrLar8EKXUznTQmTaSQOQLl4MK JZSv5JCXdwFyZcK/kmXne2QiH1YjKWMrUYIX3MlWi5LzjWUsX4m8Upj2UHw7PFoKBrvc+B/J V8IvBbOAeZ3YN8tLs8Z3W5v193BgGzZ6/PuFO22une0DLgpcX/3igK9Lr+23uUAm8rGDuVcf 0KGD1vKDkOCOrRRaTib7+IDhOSYYY3VhgUFbwUDmW/V4+dBou1LU9/u1ezFwQPNGd4pQZB/x Ps0xQGrkz/tIjtaP1oXfjcx3FC8fGuIi4Hq96s/nY4889/vddGwOTDWRwMpOIB8DCtKFcOku 6AhImltgmDa4AUjUJU9HiqT8x+NhNg6M+v+R30erkTXdZhqayu+P4fHHIivRn14Q2QN4LU0j 6bpsumIY7IWPLJyExikY7XQwbreb3YuD8udvLg+cfPrbXF9MnQVv2lwO5+suSznISP60Y1P5 eOxS0Pv9jj4VFK7dNLkj3x/zgTCNag7sE3cNVxfKZvrpmI18FPx6vUyU4zFG//P5NPt0wo1F +Hr52wnuwaQaN+GjKGbyj8x1I2ITK1cXyha6T8tH8S7qLpfL6Bgj32HqQtmp8lGE7dvoPki5 K3b9Da6l80KkvWhdy5IlpsPnFW4UVxCTvH7CLQcvH0e9yj8UMqNCTtzxNsrLx4nquJesX4PI j83oy4jLP+bvhV+EyMcUsT1KefnLl10lMpKPk+5WV6z8He2VwFg+pokNH1NQvFt+0QGoX6vS TOQD2T4jquRnxAonH+iO+IBe4eVXjqHKPw2t/wBiOE1e2LitFgAAAABJRU5ErkJggg==</item> <item item-id="52">iVBORw0KGgoAAAANSUhEUgAAAFkAAAAgCAYAAAB5JtSmAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAKYSURBVGhD7ZlNkoMgEEY9lweicogc IQtvkX0W7HKHbFOucoPcgOFDkeav1WiYGfVVWaUpAX00DZhKHXydQ3IBDskFOCQXoLDkVjV1 paoKh1Cy/3XrFJXcNkI1bXcuhRZdN1r79vm9dNE2qt5JNP9iTpZK7FmylHLhMG5NHSw6koXN HRsnliybIW8uQktsGM+y2Uc+BoFkPftzZmYiRVpk29RK7CFP9PiSpVj35RP1QXA9DBWpO3X7 8exJlsItsVYBeZdYNss2s0a2R71ae+i8VJ3u94oNIHNfcEOubK6tHEQyNgprz/Z6BVFiLUwm UU8Wlom2fZxnVjODNN9kumyuLQYnmVY6kff73Z/lQMetF62T0CnKpiNIcKmpG0n0mhIKm1SW tMXhJOsCqV45n8/q9Xr1V47r9Wp6fww83ITOXgl/4g7FzZXMl52+SGAl3243c6RAFH9HMqK/ y4PRwVaEzU133yAD70RSxBzJfNlEWwyDpagRDSqhIHqp9Mvl0p/l4V7sG5j3COQMncRMVKn3 HysbtpXDkxzKCCXj2qaO5/OZjXJKql6eTyPZktmuZ9KhJSV5IFt22qeB0UiGyMfjYaIW14jm +/1uzruJj0qJG5wveSEpIcHQT5GVzJUd6TgLKxmCO3mVOp1O3jUiGYx9viwh2Tx7/1zeOxhB 8TPpAnpJRuYKex8tnymbbYvB5YOJvcJC15M9EL+02v+OkwxBi23EOeqQTCVD0MzNSATZDXUg XxfejPxBiGQI4SeHMeLPl6hzYcdtACIZQ/vzqMOEEKcFPTr2nis0nmRMfp848VcQ5PPlh/Vt DV8yhvfMj/b+rgiHGw17+veDI5CsWe3vJx3RRxQbYsmatsQfqTsiKflgXQ7JX0epH78BUz9G 7/qCAAAAAElFTkSuQmCC</item> <item item-id="53">iVBORw0KGgoAAAANSUhEUgAAADAAAAARCAYAAACb8i8UAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAEOSURBVEhL7VSJDcMgDGQuBmKOjuBp WIZhXBsINU9DKKgoVU+KIhwn3B3nKLw5/gJ24/cEWKNQqcZlbOxYDYsm7WNoFeEAta9pBBdr jKLeOAGHoBVKvg406uwr6+DA5AQ9WFQk7gkfwup6QwA1aSAZBGoy3G2hsckKvNzPDLJELDko DG3UawHUdHyMne8nJ5Do950gxkLuKyPLseZnrXpnBor8nSI6kt4V1yV1bESIShlZKaCsFwJE fDwhMVRfgDWTAqoGOMRcwewJCMM+mwEujERmMcTs0SL/26RU1PUgIA5RcGw0NhNDzI6+O6XE qTC1qFdDfDckAeqRaxld74JnwWQkodH1TiQBM/d9QHwCShOAuYRCtasAAAAASUVORK5C YII=</item> <item item-id="54">iVBORw0KGgoAAAANSUhEUgAAAEcAAAAnCAYAAABdRFVFAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAJWSURBVGhD7ZrtcYQgEIZpIH1cDXaR JuzjOqAafqUTiiG7ICgIuKLE9eI740wuBrM87AfuIcyjotjD0UoaqfT06XzVns8ajhpHM6rp Q0dpBf9HrgGxhaPl34AJAkBDAognHC3N0EIGxwlhhBhMxhE2pcZ4HEM42kgIp/1olBk9FAup 4Rk4bpBggRM/OGhg27IbEbwNAA+iKSyX3sMOjpZtIYHjZjg4SdEM2Y9jBgdXvCWkHJwljGY4 GJ5TaDGDA4Y1lqjz4MwLFOBYt7SZnn41zqMsyDe5/QZJJ+UclM87a8/BmAsAMvGvFVQTB/J0 OJm9Bl1JtVpUnb3CPVYeji2DFTiT0G17wDn0zGB7W1L3wihCO/bBgXvSG390Ijn1eGaDiHBi 7/CDuumucPDlLIV1uu4GJ726Gv9+mdd7+vlC/Xx/WTuInqPh166KPZ6zguNEyzlYUhsh3hkO 3gvValNuI+afE10lAreG01v/B84neg4YGME5stXcK67VCmlFq5tc9BX95IR8pR44FT1wKiLC wY5ALvSLiX6n2MKh9XPWzSycUHsvKFa5n3OlYBtBq45znzeMwa91z2EDa+RaNbzg4KRpcRW8 xIcARf79EPtU5TGZHjIPzYbVFOccWtfPjgEi9k+ruW32SmZwnCfUJ7sIqSLMZK+Fu35qT3nh lezg2IlU6KSJV8ntSe9J1qy/8Sx7Awrv0cJoKQtnciM8j1NO+nHOYwgHhK6dJoXohXg7L0XC ija9BNc86AanLJyuOJ+TehRbOKjnZNeGup8JhIR+yzOB18qYX2cQ1vRZkHKrAAAAAElFTkSu QmCC</item> <item item-id="55">iVBORw0KGgoAAAANSUhEUgAAAEoAAAAnCAYAAACout71AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAIxSURBVGhD7ZrtkYQgDIZp4PrYGuzi mrCP7YBq+HWdUAyXiOAXuyYR3ej6zjiz6ILkMSGImHCLJJWgvLPBOt+X6mlLu+pAubYNresL O8g7aN/yYakC5e2+kLIAVsOEpQeUt6HhUsI6xgRjmsB1Etfy6igB5YOFkONhcqFNgDpgzPpY p7FwZ5p0gMJO810imOyBALox7LDleJUKUN7yQwfrDKDQaCOCTa2jABR6AzfsIqixkSJQGL7E 8FMACjorSHV1QNEfUgbVuXKXQeiHwL6lYHySzGswbLaOUSjqOLX0KIzbDKPQiHeQoSLUKqAE c5qoWdZjZLCxcO4mA9Wl2jegeqGr1wIlbif3lZ8MkjCSKPfngYJrNjW6xcCxarUjVCVQU6+h NsrS1UDhy+QcXBVdBdT8qG7U8xEez/73B/T3+0O6P9GjPJyO2fD2qKR7jCqKBQqv5axXVJz4 pbqT41VvLglqD30nqG/yKOj4BJR0ykvV2bIekp14wOzY7amf1qOO1g2KqBsUUURQ3dBQ+GPx fE5I66sKpwK1th7VwUDDZxaVz/O+zsjXo44WGEPJrGSPAvBDeX3lU77Cebhoa+ZUUPPy+7V0 wZr550TrLAcU/aPDqb7CROPW3H8XUITxMUkFKLButcNUUGj8UH4/Rp3uS3E0aC07EUFhOI2z 3svQoo2NSUpAgTAMXj/6/Cq18J7SecI86qS7WaKO3B/FfdlXBQp177hjaLc9nPZCezh1KoR/ udWSUlfBqecAAAAASUVORK5CYII=</item> <item item-id="56">iVBORw0KGgoAAAANSUhEUgAAAIcAAAAqCAYAAACZZUg6AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAASrSURBVHhe7Zw/cusgEMZ9gXePnMF3 SPEuockh0qRPoToXSJ9C1UudNm1GVcp0uYHefghshFi0IOQ/yf5mmNgI0LJ8LMgm3g2KwqDi UFhUHAqLikNhqSqOrtkNu10kNZ0tUY++a4e26+07hWONnyqKoxsaEgHM6Nv9sG+dQR0ZZ19W omuaYQO9/Vj6jvx1GA85Gywr/dDu90OBLSL6VoVRBAnkOGFl1BdH3w77fWsiSHXQNqcMXDPL GC9MRDS31E2aSdaF2CN11iCwtdQmLO2cnV2TN2mri2O6pNSEHELLSbzftKQ5JxqnRsoh34l2 UiZVF9doEKqpAghsLbXJlE2IGNczJm5lcWy4pKBjXMMUMo/OGmdV6KBQtJhh5j1bd3xdXegC W0tt6lrafCYiB8iJHnXFkanMHDC4XKfMcuF55DDwHlwZtq6dsQ29r/nEtcbWpE0kKLxF2aSp VC68H0dVcaBT0hvngRkTC78j4X1jDodTdl54dmW4uuMAUXlUMIOy4HQhElvzbSL/2EdClE3b ScuScAKz4ri/vzfp9vZ2VKlNd3d3Jv/5+dmWBHYdtKmGE6dQ+4lGJQ4HyD/2ZYxEqYGQtDln DP3H+3iJ+iBpN9smfJZhs5GX9n96ovksRo6Pj49JB88CzZLkc7pkHffxyzN1MRB+2JaLYwGJ rZk24a8/RkgpW6X7jusQBzkrPTDB7j4VNuH42e4/UteEbedEr8xqJLaW27QcOdBMI+oLO9rv 7+/Dy8uLWUJ8cTw+Ppr8z89PW/IE0IAudXh0HGz0HGbzTF0jCroeG4xYXeDquDZqsWSr9z7X Jpk49qL+zMQBUbh9BoQBIThjkHAd+w68RrmTiEQiDkVMkTj8JQTCCPOQwPf392Sj+vX1ZfI3 Q8VRlSJxPD09HQYcEQNwew48rYRlN0PFUZUicfj7i7e3N5PHicNfbjYXx8PNcPNgXyur+ff3 j8ifGjl+IZvvOfw83XNcF6ueVtygQyCvr68TIax/Whk/1PHbPCTOYhVHVYrF4XCfczghuOQ+ 58D1k6HiqMpqcTi4PUc5GjnOzQWLowB9WqlK0dOKD/YbSLJvZTdGI0dVqkWOi0DFURUVh8Ly +8TBfYsZAMfENr7zfH/jnG5zEZFt+Are3W96GIezGUi+hQ35ceLIOs8RONdhnAznB56J5ftn HsxhmtQZkSRS2+JnLDibDVZ0+eJYeZ7joiAn1DoJxs1CLt+QGNRFRLYdo0ZsEnC2SU6bx6h2 EuwyIOclPBA6DzOdizTF4iiMHDm2mftEBBK1zS61+csKBCoT+pWII90hOM93aHVxLC5rPDm2 jSCKTPs6t438IT5tHkLtC4V+JeIYHcT5NGcAOBFw+TnOjJFjmwP/KD6RQmhb1mnzgAyhX404 yEN8pzbcc4QDlU2GbSPzKBnaBkGMTzXHJB3w8/3H26aklhaEYu+JIDHTc8SBvOO4LmyKWeS2 GSIzm7MZ5EUOskVe+JrEQcBxXOfsZi55ohuz2M02v51I/nx2ymfcjCXbOLtA6hqRI46z/5f9 1ujvcxRCIsuNfFcnDqC/7JPHBf2yz2nQ3wST0eODsvP/JpjysxiG/1U48dSu3S5/AAAAAElF TkSuQmCC</item> <item item-id="57">iVBORw0KGgoAAAANSUhEUgAAAJgAAAAqCAYAAABPwJJfAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAUGSURBVHhe7Zw9cqswEMd9gXePnIE7 pHiXYHyINOlTUOcC6VNQvdRp02aoUqbLDXi7+mKlaIWQhMFYvxnNGH0B0l+7i4x9GiuVFakC q6xKFVhlVarAKqtyWIH17Wk8nTyp7VWN/TH03dj1gzraDznXdVCB9WMLQsIhGbpmbDo9OD0M lPq4M/q2HXesfRAZXJ8Zx3gO7iKHsWuaMWFcLsrQ7VtcBhDZtFjjOLbAhm5smk5Yst2C15is LlxA0vX7ukDrrUMDWk7zqWC4fErfLluwhxaY7R73CAgEXGOavCAMQDFw4qSLCz+f9HmwnRKJ yNeC4fIdaL8RHFhgV+AecbKSLlBarlBbd3HhQ484DorNk+9hiRU7rsAWrrQtQBHETpSFEkOr n5Q9Vky4O5JvBKbEKcqsmIrL97AgFjuswK7CPTZp7lGKB9piYyE2TwwGIjgZi0QFJo4Y98rl u0C9yMV7FQJ7eHgQ6f7+3gShmM7ns8h/eXlRNRE1SCrNjtVmwHWyF6esCbkPk6AN6/4c7L3A yVriU2s3qHMQoXD5v4lfHLzA4EToaxvrRuG47cYeyrbg8/OTXEuZtSGsAekzJhURLVielH0l hHd/DGjNdH0rdJBiEkVcPkNsHOadJVQyml0czKaTG5Yiv5fmWOTD2SP6L8oaAjNgXGH69gze 0MMTnxRjaOCjWRDH/EK4RX2NaLEDk+24StlWH0trbwTmy2eQ1k4dBPg1S3RFewdAXLCaiCIj Pc/Hx8f4+voq3KE5N6SnpyeR//X1pWpmIAZY981PGFqLUgLL6ofMg+lH3YM41uUeV8fN8ezc E7BuzPXbAosaZOWjVb0ig82AwtJxF4oLxaTPiwnLMQ7Dz1gvS2ihe4cy8xVTrjA0pfrZiCSB WUFhIMiLrZcDdYcoLjcPE/Lz82MF/9/f3yJ/MZbA7IUTO5iLuD2B2ZYp5P6oKQ25kxyen5/N OdByIVwMhk+Rbt3FMALDL3npcTFuT2D2430wvlKDrtMaA0Xjrff3d5HHCYy6zlICc1Pxe3y8 G+8e1ecr5N/fP1HXv1uB7cOCDZAtny6L32O1YHzrOBc5/6gbYkkMRvNqDHYZEgSG9zwNcLkg 34ntaJq5QnxK1HVRZG9vb1b79Z4inYUBZeEXFRPu8RYFhjc9DU7cNkXyZuEC9D6YFpNOeh8M y+NhLGtIYGtwkwIDqHWa3WiN2qJIt2AuXAxWhCyBVQvG4Zkl+R2kHiDuq6K19r9CrCowmHBL YGtb5tt7irRBMbXeVQnKJaLjyQvyKRh/YYp7m2IZuBJpn25azcrcrgVzoC6RfmlayaMKTHP5 oP4mqAKjgLujIoOeq8wyiRIY3Zt0vce08P39cG1z+pwoLDAEgn/ynphO1aIlAgKbfyWGe+dK iSQww1zbnD4pye+DVS7EMPdG62RpbCFKKxMWJ9c2p0+brDdaK5cAJjvGWqj9OTP54hjcq96v DPXhttXk9ClAQcY98FWBbUb8JEnLI+uKbZW5XxRZTG1tMvuM3AetAtsQnNhYr4R/jqLFQC0S fvMy59p0W5fkPiPiR00V2JagtYiaqMnaSWszyWVeYJylTO+z/rL7aoh0k9RiCBemJxjd3Mxk c9YmuU8on43RJqrAtgYn2jdhkC8Cbl/QTcpMEY2duLZL+/SwxHohVWA7APeUQpO6G0CES18C qALbCfUfDiurs9v/aO3qf7RWdsk4/gdm+gwAi+bb/QAAAABJRU5ErkJggg==</item> <item item-id="58">iVBORw0KGgoAAAANSUhEUgAAAgQAAABFCAYAAAA1mf52AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAABL3SURBVHhe7Z2xjuM4Eob7BS5bYKPJ J5+NJrhw0ekBlxxg3GbGBIsGLrtgg5t8gn6LzTdwNu8w6cDRvsG+gY8/JVoUVSSLUlEm1RV8 gC3bsqrq568SJVtP/7vdboqiKIqivG20IUhyuX14ero9BXy4UO9V6qP1WMPpvMzZ03ti2flC fl5xqP7WQOpPtdYk2hAkMQagwm0IrYcMVB41t3k0RzJoHltFG4IkKty20HrIQOVRc5tHcySD 5rFVtCFIosJti0fX43p7/uimPc+3E/meHqDyqFrPo/qTQbXWKtoQJFHhtsVj6/Hyer49X4fH 9rzox9fbS/CePqDyqFrPo/qTQbXWKtoQJFHhtkVD9bi+3t51e5RG5VG1nkf1J4NqrVW0IUii wm2LluphtkUbgjeG6k8G1VqraEOQRIXbFrx6nC6XjVOpV7sO+rURc4T24fVKv9Y8VB5V63lU fzKo1lpFG4IkKty2YNTj8no/z7oJY7jPid+Xn157PX8LqDyq1vOo/mRQrbWKNgRJVLhtkavH 9fb8Klev05k23ZfXj53/GQ2VR9V6HtWfDKq1Vkk2BC/odi9tT0vV3UYVbltk6nE5yxolsT6Y 8bv7VO3F7AB6nLal8lim9Va94aF+oPpj0q/WHrEt0t+ZWl+0ITidhcVdkRcMnCoDo0y4Sm3S 9YBmRaZrHThP633f8i9YP8p+X4j5/ndVvofKI1/rrXvDo/xA9celb63V09eSWvHHYiAbAvze tZdm4I4JcOqcpeALV9mDVD3wpy3SV12b73vYb73NdzsjtsYsGRuVR57Wu/GG3f1A9cfnAFqr oq851eMnYlg2BCg+o1gU6GDdF/iPeUCAMdHxBsbp/LhOVtmDRD2g20Lz/O9ff5HLJ2DylY/C YpjBOt0ABtsheRMdKo8MrQt5Aw+zPT35geqvgLpa49Givjw48V8vt2ezDW7G6F3kmpMUYQxB Q2AKf17bCfqiKRUQikMnF12S3R4kKNelrhiUaRjCVXYkUY+ZgU38/bffbv/588/F8n/+/rsd ROHyEOzIqnbpEXCu2I+nfIeagspjTutS3sChQz9Q/RVQU2scWtWXgxG/3T5cXOpqMowxSoNJ ghjmDQFeXF10k+T7iv3HeWKix8UPsy4pMuh8ZLu2z7cf3n8mltdnj4tXHnGBzDYS9SC08a8/ /rD4yxw4OmvdkP2xKGvIJo8/noPxSS3zEPOGPF36geqvgJpay9Ouvkay8aOhIWLA9uaaGAI/ hllDABGkgkMiIeIPFwzw4WKTaaMutxP5OEPQoTjcdw3f55ajC8oUwCRFTkwmzowgarDnhTR7 XiCznXg9wiMaEBoujsp8g/75y5fZ6xSyRsinriFTO2hq2YScN2To1A9UfyXU1FqGpvU1wIqf 0uHKhsCPwWsIEHxuZaYASP7r+E9c2IBEYTlQA2kA37XcHiQjXfy02Mow64oYQC0eciFNBVHX IV6P0MBAaMh47qZvf/3+PXr05kOtdxcwtu6xDtOBcrqgxkhq3OznDb36geqvhMdprW19gVz8 2E56m6KNQpYpBq8hMAszK1skcyaaHEMg4U9YooKPrDs/QDiC4pLPiSjoXhPfFxdzBqyXyL1P lakvceL1oHIDA4bpfvr2zR6N4TmO0n75+tU+9i/qwmCidJXXWy0wXsaaRI5q1mPWvVgftcx7 LaO7Rf6z3oD4juMHqr8S9tBaj/oCmfitl1NePcS7bBToPMyZYpgaAvNF6aljfMhfaficAZIe CIFOONZNd0GcASK3c8uLUw4Tc+JCEjsIUNji7YEgxnxYMUW+Q3zQ1yBeD5uf4DWYsc2Z4adP n2bPcYQ2+6xZRumKo7dq2HrlBvMaTB4XtaaWjdTyhgP5geqvhJ201p2+DLn4bU2W32dnB2I5 JfIQ4mKYGgLzoWTg2JD7Sq92BaVCpaZfqIE0BG12XNfrIghqHSGYdpcpUNwAxEHMmXySucoB Mdw/Exc+EBV2FRL1mMVZTmzgc/TWH5QhJ0y6kjccyg9UfwXso7X+9GXIxT96OGLwc5C6dqAk hllDkPqQTeTY3aJjnH7uwMWIgNroWYFHMLg+mg1c/C94ohv0wLbKDKKEAQiDbebEVWo64Wcg jqjgsmJ8NIl6QEcbaoU8UbG/HUNOm7S8NxzMD1R/BeyhtR71ZcjEbzExDBdTDjmA7shcWiJ5 CHAxMBsCXmKSIOmRAcMWfWIdPrsYgCjIL69oaxoC32iSDUFqoDZBqh7btj3M04CA7puEylUi fzW84XB+oPrjs4PWutSXIRn/xMvVNAEb8+DjYuA1BMyVpkgXwQiEOC8yA50do9MB+xiAJLzv QVyldQiNJt0Q8BqTx5HK07ZtD/M0gHWuN/l2oQyZWjZSwRuO5weqPz71tdanvgyp+EeGUwRP w6yGmyHBvxYuZjhyeZhwMWQbArzRTdHEdyQ5UIBcclMDivP5iX0MQBBMATFya2tRuj2zQYQc pwXS9nUE6Xps2Xbkdqnvneq/OyYugYZgvTeY7zqgH6j+uNTWWq/6MjAaAsvib4up0wb8OFwM U0Pw+f3th8/zN0lhi7mjsP/9j78JxbLTgDQi4BjqujxCFKNRoevNHHGIXiAjTqYe3MFEgNwu arBhfW1j8ljSEAh7w2H9QPXHpK7W+tWXQXCsleTBxcC+qHAdpvBjB8Pr7GRAImRiyRiAFJzc m/e4PBaL3U5/4bP5Ixi53NUgVw/T9RPTZjnswCE0enpNN0/9QhlywqTFvOHofqD641FLa73r yyAy1srz4GKo3BA8hv0MQIiGci8qbnEY9cD9GSRmODAld5DxsIQyZGrZSOfesKsfqP4YHEtr 7TUE5WhDwIJhABK8oYYAf92Kf26jXsvDq8fLJfUzHA7X28msg37tCFCGfCyT9tnbD1R/OY6l NW0IGkcbgvXUbAh+/vLF/lsb/roVt4Wl3pNmp3ocHsqQj2XSPt35weE5lta0IWic7gyg4gWd pYheIOOBBsD9XSv31q9L1JBloAz5WCbtow1BaxxLa9oQNA6vQNfb8zkiyjs7GUBDuZfL3QRm BYB77m7u4r+HhxqyDJQhH8ukfbrzg8NzLK1pQ9A4exgAzoXjhiU48sVUOHZw7gjYv3EJi4M3 BG7nj7yEuSpDDVkGypCPZdI+2hC0xrG0FtMXfC5F+H6LNgTy1DYANAMoKI503TJ3JzO8hluc +u/PcuCGAKcH0DjhMXKDPLk7v4XvzaOGLANlyMcyaR9tCFrjWFrThqBx4gZgBv39phABpEBp A8D73U7OgSvnsRzNgLvPOf420v+Od3fDMes1z+/b2EVDUJq7ATRNfuMEMEvg5y+eJ+DnSg1Z BsqQ/WXt6nMNtf1AKeVYWovrawXaEMjDK9D6IwIYRriTcw3B/MgXhuP+DOhizYf8k4guGgIf Tu4GcErFNUgONAPzWRRmniL1UErxDTm1bOSwDYHPej9QSjmW1mL6mjWYBOH7LdoQyLNHQxD+ lt6dG/eXhcLHkTC5o+vuVwb8hiDMlfuFwbxJYOZJDVkIypCpZSPaEIyo/mQ4lta0IWic2gaA C+JQUDQBDrcMR8T32QOTU7djezGPo38d3FDu5XI3XDOAmQDg8uT//PAON09qyEJQhnwsk/bR hqA1jqU1nr6YPCh+bQhYbDMA/9z4O9xZK/aXpg3lXlLcOH3C+TUBO09qyEJQhnwsk/ZpxQ8U x7G0pg1B47RhAOaz9taT5sjjfn48wkEbAswGUMvnFORJDVkIypCpZSPaEIyo/mQ4lta0IYjg n/uNnweOYUQSvXdzQkAETRiAyef9roTmcTIXnNwX3LGQAjnh3CVRUtyshqAkT2rIGdBUudmW 2FgC1HhKjDGOPjO8eT94E2zR34iA1ni0qC9DSfy4AdbZu1Mm87ouChfD1BBsvrBtEMMQjP+Y A4pD7+hwf35bNLtDTIlsYrf7n0cJj3ZH8cV25JwdoctPQR4cthmAaFgNwXlVw7GOwjytrsfb wK+dPQ0TNTWTx8Vr1LIRUW/g4Ok9eK1PP3gbbNPfyC4XWLeqLwM3frt92Im7/cYwxopvjT/i YhCcIfCLnCg4AcRDfffLxQTtB2i2cd+j3BUGYAs1dGx+l2wHiFlGbpf5zIfMDMIUd6m5Dtim gBHLyXSc1EARZ02e1tTjrZI0NGp8JsasqDfkOZQfvFWK9TeyWWt52tWXgRW/yR/lkdheZhMT 4mIoaggms0ZB8dhPxOV2Ih9ngHAIcbjvmu8cwiNKmv4MIP094c4cuSmbfl2ugwb5XSeofdir HkcAprFfQ8D3hgzqBwehVH8jm7WWoWl9GbjxUzrcuyEYimw++Dre7xsbECssk/iOihYUkpHb zv4MIL0jRjx+A4Ac1GkIEgO1CfaqR11wr/ttOWbcL98YX3zWiapzovY7eoP6QX3a1N9IZa21 rS9DNn5sJ71N0UaBgYuhqCFYJBOF4GwAxGE7ueWFD1gnuXOLrDv6fg+5Au1nANjmWCcaxlyt ITA5L13vvuxXj2pcXmVOyZgx9ZzQ+Ok1ZZAmj8INwUJfKW9QP3gczepvREJr3erLkIvfxIZr B5Y1NDkNG4VEHkJcDLOGIB14OH3Cm05xg2jYGHxmvjOjEz68j0oMr0BSF8btaAAodCyumVjj uUmBvFGC99nt+oHV9G7IpnbmqIZ+rZxTZJDnDYoyZGrZiKg3qB88jpb1N7JZaz3ry5CLP9IQ 2NmBWU7TeQhxMUwNAbqJ3Ibcv/Bqdx65RFGECcbzxY7KBn2+na7XhWg4UzhyO7Y9DQBFi502 MNvhRDCrAx8yzzP2jHUtPWxjAjPYc9otgljffHxdzA6AGqMmjwsNUctGKnqD+sGONK2/EWGt 9aUvQy7+ceeOGPwc5K4dCPMQ4mKYGoKM2G0in4bph+EijtRGxzmdg25qVuARI7Qns3NcdrNI Ri75qR1rKTsbgIl7dpWrjxXtkPti8SGfrnaR9YuKuhrC9diS0xUstL8VmIeXD3uU4OqcjIsy 5IRJV/SGN+0Hqj/6NUGt9aUvwNAY8o6mYMwB9hl0LifStZ9i8BqCVGCcxDBAIESXwunCLChc dkCmxFaKsAEwwNSNaBfPweQ13ZW2gmQ9zLqcYVljlhzUFNLGAdZqnfpcal2VvOFN+4FZl+qP eE1Qa93pC/Dq9HI1TcDGPExMMXgNwdB9kclmJSZDcqO8wUG+buAOGrOtqamRMsx2iRkAH3Rz ezUFuJlQH80AEKzHTNMYhEyTWAv0W2gc4W2jlxQa5B3KxKhlE+Le8Nb9QPVXV2td6msgGv/I cIrgaZjVcDMk+NdC6vqQZB5GvBhmDYFNQvBhf4pmdeBmvfcNj5LqjFBARnEMslPfn28/vP9M LK8P/iQjn7NtvLzW/w5Z5Ophde2ZC44apAf2jIiZ4e+dcVfIcDnuEIkxFy4PYR/tzDB5/PEc GDK1zEPSG9QPVH81tdatvkaI+Bcs/raYOG3AysM8hnlDkEzSOl4ul+mvaO0Gzl+XxRSSEP16 pNenbEOuHjAYf9A9wpBxN0jgL3Pg6KyeIVN5zOVWxhvUDwZUf3W01re+HNvHGj8P8xiChsCA 6QNB0bsOZkC22QiR79ZqFVxZR7+GbMdCsO2h4eKozDfon798mb1OsW67qTwycrvRG9QPJlR/ 8lrrX18eG8ZaSR7CGJYNgeEhF7ZtxSRQ/ly4nAEoEgjWw+hlMkh05HXP4YY7AIDBGj5307e/ fv8ePXrzodabh8ojL7fdeEPrfqD6I5bPaVprVfQ1p3r8RAxkQwD2vLBtK/UujBM0AEUAyXqY dflXeRdecFWK7dqDbYcBw3Q/fftmj8bwHEdpv3z9ah8PF3UNOws8pzr9vRsC0Lo39OEHqj/6 tTktam3PC7FrxR+LIdoQgD0ubNtK3QvjJA1A2Y5wPWDE1ugqTv2NUIYMMx6M9un206dPs+c4 Qhs+N/1+GNOz4X+2P6IhAK16Q1d+oPpj0ZLWHnEhtnT8qRiSDYEibADKRjquh+nIQ0Muxu5A 5kdpMOnyIwgqj6r1PKq/evpTWkAbgiQq3LbouB4w083bbuLXhuCBqP7q6U9pAW0Ikqhw26Ln epht33qe2Jj6/Lwfzu+umW6m8qhaz6P6q6c/pQW0IUiiwm2LnusB81xelFXC8payWOcak6fy qFrPo/qrpz+lBbQhSKLCbYu+67Hld8u4eGs5Nbs2H9TnVOt5VH/z5ZL6U1pAG4IkKty26Lwe +KlP8fnWwYynK7m9W8quXB+dR9V6HtVfPf0pLaANQRIVblv0Xo8rfQOSBPanXuNPwQamo7zl FC4XKo+q9Tyqv3r6U1pAG4IkKty2OEA98JvildO2M3Bzk1VHZ4DKo2o9j+rvjrj+lBbQhiCJ CrctjlEP3Hhk3ZGV43o7mXXQr3Gg8qhaz6P6G6ihP6UFtCFIYoQ7my4bWHfeTNmO1mMNy2lf w3timZp0BtXfGkj9qdaaRBsCRVEURXnz3G7/B8uUad++yGnzAAAAAElFTkSuQmCC</item> <item item-id="59">iVBORw0KGgoAAAANSUhEUgAAAIgAAAAgCAYAAADJ9TDRAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAPeSURBVHhe7ZpLjsIwDIY5Vw9UcQiO wKK3YM8iO+7AFnXFDbhBxn9aFyeTV0OpIpFPqoaWPhz7t510OOhGI0ITSCNKE0gjShNII8qP CWTUQ3fQhwO2Xqv5aCPMTwlkHHo9jNNn1ZNIuoEk04jxuy1mHHTXqkiSH56DKN03gSSpUiBK qQ9L/2juEYUqSM/9phGkPoGoYZknfAQJYIhoRA1t/pFDZQKhVUYsqitRvV8E49DpvvWWLOoS iOq3DZznfhBHt5QoRYLMqyPP51Pf7/d573eoSiCqfy9DNwHzDKEQs7Q170B467Kedz6f9fV6 1bfbTZ9Op/nofsDuUOJA8Dye9znyfY9vjO/vUwlZkUBg9NarClqpfPiuA4J4PB7m8+v1Mk7d FbMcDwQS3/H4zHmT/+Lve7B6o2MpZczsMFoKfGAuYCEHmwkCFgeiy6sSPlA1sDGoIHsLBJPp IVBB7HY5iUHuG4RwJn94zolgjRZ99ng8mqxBWYUzOHv473oyBULzBZ+qYQvscrlcLlnBipXn FHx/jN31xy7Mc6jQGEx7EV8EBeJUmZ7Ow1hyqsjiYQQBFyFLGGQPjuE7BKSMcoG4GSzJLfel AsH9kSwA44cd2A/ZE2fKXBMUdwsaR9fMK7rgGOAz8bLPKxA6h49NgqLzcYERS9o3i4dhLDuE wawdxyEOLucwQg6w8wY/4pBAG3GzAeB8CeyQAUJWp/A67R9TX5aPR6LIZAF4nvRRni8KEe+D ggIhbBvcdmrPwbJakoMlENchLBA7axB8NoSWicmelldBXOMBni3BPrcblPqcbPbdNwe0NneO A3HYlTTXF5GECUTeFR+26Dg8FRirQnnETcLVAnHX+dx3bWxVph+SLxBfBYEIYBfbggDxZFEG MGRHqUBcf3BLs0Wz1hdl4L6hCmJwWg3AuJdrqJ2YfyuYtvIWdJ+xzF+ij4yEAxAI3vgYsmmp LrKnGcNSDykXCMSB52Pj/s/7crJorqVjWwmE51zY2Bdyubuw2hdlWAKRcwfzTPKH07ZxPvtp 2oRdfA1tUdHNuOUhiXx4h/cWWzkEhudYHCAkhGT2eYAQ/4nBw9d8URErBYKyhFIme+9GIDMq EQiqRZov+qIi1glEZjl9Xlu649j9fC1+gZQFL0sgX/VFPawQiOvsOYM28wvuj4wsIyyQctGF +bYv6iFPIPPEaOq37yByD/6gM1iovrxU+wVCgdvKOGYnX9TC6knqV6FSXepgr0A+uF9joi6B oHQX/GAI4pgy2l7qtl+NfU5lAiE2+8mhiv7ksJFHfQIhxj1+tNzIokqBNOqhCaQRQes/tXa+ 8lQliSIAAAAASUVORK5CYII=</item> <item item-id="60">iVBORw0KGgoAAAANSUhEUgAAAF8AAAAgCAYAAAB0OKThAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAK6SURBVGhD7ZlNzoMgEEA9lwciPUSP 0AW36L4Ldr1Dt42r3qA34JtBkFEG/OPTNPASE0Wh+BgHrI2unEaVfyJV/olU+SdysPxOy7bR TYOb0MqWlsqh8jsptOz6fSVgAFoJw1Eu56WdTuq28Og/MecrLar8EKXUznTQmTaSQOQLl4MK JZSv5JCXdwFyZcK/kmXne2QiH1YjKWMrUYIX3MlWi5LzjWUsX4m8Upj2UHw7PFoKBrvc+B/J V8IvBbOAeZ3YN8tLs8Z3W5v193BgGzZ6/PuFO22une0DLgpcX/3igK9Lr+23uUAm8rGDuVcf 0KGD1vKDkOCOrRRaTib7+IDhOSYYY3VhgUFbwUDmW/V4+dBou1LU9/u1ezFwQPNGd4pQZB/x Ps0xQGrkz/tIjtaP1oXfjcx3FC8fGuIi4Hq96s/nY4889/vddGwOTDWRwMpOIB8DCtKFcOku 6AhImltgmDa4AUjUJU9HiqT8x+NhNg6M+v+R30erkTXdZhqayu+P4fHHIivRn14Q2QN4LU0j 6bpsumIY7IWPLJyExikY7XQwbreb3YuD8udvLg+cfPrbXF9MnQVv2lwO5+suSznISP60Y1P5 eOxS0Pv9jj4VFK7dNLkj3x/zgTCNag7sE3cNVxfKZvrpmI18FPx6vUyU4zFG//P5NPt0wo1F +Hr52wnuwaQaN+GjKGbyj8x1I2ITK1cXyha6T8tH8S7qLpfL6Bgj32HqQtmp8lGE7dvoPki5 K3b9Da6l80KkvWhdy5IlpsPnFW4UVxCTvH7CLQcvH0e9yj8UMqNCTtzxNsrLx4nquJesX4PI j83oy4jLP+bvhV+EyMcUsT1KefnLl10lMpKPk+5WV6z8He2VwFg+pokNH1NQvFt+0QGoX6vS TOQD2T4jquRnxAonH+iO+IBe4eVXjqHKPw2t/wBiOE1e2LitFgAAAABJRU5ErkJggg==</item> <item item-id="61">iVBORw0KGgoAAAANSUhEUgAAAFEAAAAbCAYAAAAETGM8AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAHySURBVGhD7ZjtccMgDIY9FwMxD9Ow jIehEp8y0YuThjptzXPnHxHmEI8lfM4WFm+zJE5gSZzAkjiBJXECS+IEsMR9D96aYMwWtq1c 9Nu64Gls0VAl7s4Gk8UZ50NRtnvX4rbF786DxN2ZWnnGKZq8reMbiVz0EvdWady6mkO6KTjR 4stjJ9HbJmczDrbrs/fdBSHxWGGjVpUtjyv2SlruWtoyXzmOjq7TI61DSPTB5onxGvWpPBfp +mxL57xREnxElW6Jx5WlGQzPywUQ46UYUBzzxyWmChxVC1eVHOejKP4eClXiA74lcdTONckr yBu25YxWco65injLLx8BPEZF0XJGcczUF0uRixfuzl15DR4aIgmiquGpUajSFbFrSmWl3Ft+ uXAeJ4G4zkFiWrBsDJXxUUQvrG+f9xhL79dCXXB46GJf/FHh6OsrrnEoBj2OOEok5IKqDCla WWAs8Scqsc1DEiuce7mfK7fmn/KKQyg+4EEiT+Rv5rI59NmHntDcSjwhtnCprJOXQNfWaa58 gQiJWnyAIjHBwqxaNSZYIbbnUomM6Iy62Sgi/y7joGvKXJkziiOgxIpsX/kkAZdL/AWcS6Tn J8+xM0FLIoTOBimS+kTT9Gob/BeelMjQC0f8z3hHWYgXJC4QS+IElsS3CeELmxSBdjhyvuQA AAAASUVORK5CYII=</item> <item item-id="62">iVBORw0KGgoAAAANSUhEUgAAAIgAAAAgCAYAAADJ9TDRAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAPFSURBVHhe7ZrNkcIwDIWpKwVlKIIS OKQL7hx8oweuTE7bAR14/ewokR3jv4SMZ/A3k1kSsrYiPUk2cJKNRoAmkEaQJpBGkCaQRpAf E8goh+4kTyccvRTT1cZnfkog49DLYTSvRa9E0g1KMo0Qv9tixkF2rYpE+eE1iJB9E0iUKgUi hNhY+kc9RhBVQXrqN42P1CcQMczrhE0oAQwBjYihrT9SqEwgapcRimomoveLYBw62bfekkRd AhH9voHzjAdxdHOJEkqQaXXk7+9PPp/P6ex3qEogol+2obuAdQZTiN7a6s9A6OiS5rter/J+ v8vH4yEvl8t09Rgg6FMka3z36GvTc67/ffk8KDJ0TQKB0XvvKtROZeNnHRDE6/XSr9/vt3bq UcxBDkTRew+28PTcq+08dm/hMTkHPK0K/Ie1gAV/qEQQsDAQXVqV8IGqgYNABTlSIKCkguB8 aaOmcppzUzn4ezGsp0WfPZ/POmtQVuEMyh76m0+iQNR6wecI2AK7XG63W1Kw4JzEZFlB4+PZ XX8cRalA+PkskKma9NRqExwzexhBwD8hSwhkD67hPQSkjHKBuBnMSS33pQLB+EgWgOeHHTj/ ZE8Yk7k6KO4RMa5EINqXrK2QQMx96jre0GLJWIPAWHIIgVU7rkMcVM4xGX/Azhv8gEM+tBGf I3A/B3bwACGrY8zZE8T0ZT49EoUnC8B83EdpvthGkUAUtm2mzeI+7osU31gCcR1CArGzBsGn vq62idGellZBXOMB5ubgnNoNSn1KNvvGTQGtzV3jQBx2JU31xcEVhMMqs3tftkDcfT71XRt7 ZxCfJF0g7kNibogAdpEtCBAtFk0AufPXu6BSgWA87g9qabZocn2RzyaBOK1G3ajayiLoPmGb P0cfGQkHIBB00DVk01xd1KTkhFEbEJukXCAQhwm8aX/8nBaLsa/wSwRCay4c5Au+3Z3J9kUm esxJ/OQbd+3gu4eu+do5u9+nKRe3PETRQZgm6PC5xV4OgeEpFoeYVul8FNibOyyEuBKDh6/5 oiIyBYKyhADw3rsTCO5Wgcz2LZQIBNUizhd9URF5AuFZrl7v22/tfl6EEpn9FX5Z8JIE8lVf 1EOGQFxnTxm0m18wvp39uay/wseYG0Xn5du+qIc0gUwLI9NvlyBSD97cGSZEX16qsRhd26EC t5dxxEG+qIXsRepXUaW6xMH2ToV9hV84XmOhLoGgdGf+YIjvJMyxVKH2q7HtVCYQxW4/OVSV pFWPzdQnEMV4xI+WG0lUKZBGPTSBNAJI+Q88psNUwx5aYwAAAABJRU5ErkJggg==</item> <item item-id="63">iVBORw0KGgoAAAANSUhEUgAAAFkAAAAgCAYAAAB5JtSmAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAKYSURBVGhD7ZlNkoMgEEY9lweicogc IQtvkX0W7HKHbFOucoPcgOFDkeav1WiYGfVVWaUpAX00DZhKHXydQ3IBDskFOCQXoLDkVjV1 paoKh1Cy/3XrFJXcNkI1bXcuhRZdN1r79vm9dNE2qt5JNP9iTpZK7FmylHLhMG5NHSw6koXN HRsnliybIW8uQktsGM+y2Uc+BoFkPftzZmYiRVpk29RK7CFP9PiSpVj35RP1QXA9DBWpO3X7 8exJlsItsVYBeZdYNss2s0a2R71ae+i8VJ3u94oNIHNfcEOubK6tHEQyNgprz/Z6BVFiLUwm UU8Wlom2fZxnVjODNN9kumyuLQYnmVY6kff73Z/lQMetF62T0CnKpiNIcKmpG0n0mhIKm1SW tMXhJOsCqV45n8/q9Xr1V47r9Wp6fww83ITOXgl/4g7FzZXMl52+SGAl3243c6RAFH9HMqK/ y4PRwVaEzU133yAD70RSxBzJfNlEWwyDpagRDSqhIHqp9Mvl0p/l4V7sG5j3COQMncRMVKn3 HysbtpXDkxzKCCXj2qaO5/OZjXJKql6eTyPZktmuZ9KhJSV5IFt22qeB0UiGyMfjYaIW14jm +/1uzruJj0qJG5wveSEpIcHQT5GVzJUd6TgLKxmCO3mVOp1O3jUiGYx9viwh2Tx7/1zeOxhB 8TPpAnpJRuYKex8tnymbbYvB5YOJvcJC15M9EL+02v+OkwxBi23EOeqQTCVD0MzNSATZDXUg XxfejPxBiGQI4SeHMeLPl6hzYcdtACIZQ/vzqMOEEKcFPTr2nis0nmRMfp848VcQ5PPlh/Vt DV8yhvfMj/b+rgiHGw17+veDI5CsWe3vJx3RRxQbYsmatsQfqTsiKflgXQ7JX0epH78BUz9G 7/qCAAAAAElFTkSuQmCC</item> <item item-id="64">iVBORw0KGgoAAAANSUhEUgAAAFAAAAAbCAYAAADrjggCAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAHmSURBVGhD7ZjtkcQgCIZTlwVRj9XY TIrhIImKHn5syP24jc+Ms5s1oL6AJrvhwsQS0MgS0MgS0MgS0MgS0EhbwH3HAA6d23DbYqNr 8Biob3GiCrh7QHeJ5nzAKNcefP4d8u9v5peAu3cp45xXJAqQ+jcS8e2UAu45w7hcNf3oJvSi rN+uYSFggCzM5nyzRGfvewNCwDKzeuUpy7ydqU8SENJ4QFeSPG99yi1bi8+MEFA6pNazlPsg tb8uYz7U9CBdc+4GW7e1+JT8AwHzvMpD7cwS9aBLtGwtPktuCaiXsNwC6pJ4gOuAS4s7rgEh 7se9KNa2EYvPi8cOEVkSR79qL0Wu2lQac5DP4BxBBPp+XhxC9F1k2xKLz0rAsjTnHmPUdL8i ORj7FoEXSJ+8WDk2B02diyDa1lh8lgISMrtUYylyM0tb0bZmINvnxUqb8WKzbYnFpyIgO+R3 4Liw1qtc9/mPMhAGA9+CgpcWdGR5rBIO2OBxStpKLD4JRcATFgvUbHEkTv89OPiOuJ8iM77O UtGXuuTe1bL91GeHpoAJOdjEvsZlMDPwtzAWkHJpeGhclJtwQD/K/y9gQkCG9gMpIqVYLY08 fM423j++gUkBGTpcxP+EsfUy8g18IOBCYwloZAloAvEH9J8erLWrvlcAAAAASUVORK5C YII=</item> <item item-id="65">iVBORw0KGgoAAAANSUhEUgAAAFsAAAAxCAYAAAC1WdWlAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAPUSURBVHhe7ZzhtdsgDIWzQPfoDNmi S3iPbuBpskyGSXUdlCOEBNiPuEDy4zuNEcZwERfbL+nl8ng8vpyEWTgat5W427GzyfXFLByJ ZaHBGeX/kxv1aTUETwpGYu1QaMYSPDoYiTst1+Vmx5pAbV9IMC92peubMcFypX6K4yg4DJQx sA8z1gIIrYRisJrwLybbnYwA6shJiYKjgEFYntiK5WLbEzY/uZpgFaXVJbM7CY7A6mQdA7Fo ZJcbljs+U/1ab9fZyHCbW7tcThN+LfRFencS7B4MsGQhwQbWkHUYcI3HAkykma1o07iutwpe YMLDtdNg71DnS0tXC2YudzreMlVlJs61LMqzDK/+C5EcabBzin6NwUkB9bHAynhTPLThZHBR bIJ92wz2jPRAi8hzqR4G6tW3LECvCoA2YSF3o52ijRC4gxlW7NzgINZmD8Ei3Md42Ai1pcut DXJbAQTvAS+o7dIGCdAn9NkM9kxW7MrBA7TjeX9NtoJcG5Ipxa4dPMgKiqwvTBpbixXTTCe2 tI/ShuVZSARWiVen5nzBsGL//U0Y5XvA4GtXQAv+/Hr22Qz2TM5GiiAjQ/ZXrYBGTOnZvZKI jUFEs05ZsL1XAKh8UhaUmEJsgB2axeZ7TTkJySBzGwUmS92vtmBKsV8DEj4XiYdyasS6Pdrz zncvc4uNWyAuF8J596lH3vnu4XPEDplsPdJmz3dWANf10PXBx4mNk81shbWI7Ge8VcDX8tD1 wcfZCE627lE9y/DqH2FusZGtoYytwxQvTIolhCc2t+uh64MpxYawsAwMjst4kDhZZ/CRd77c roeuD+bMbBJTerV8qHnHO99a5rYRBy9bNRDne+v37HNUuEfs3EMN832oefJzsQEswhMTk9FY aHCa2Ln+U8x6zvBIxMYgWGim5fJvRYv32UUyq/bIq4jPfJ9dibcfHX0VYdrICNSIzXbYw9fP wNRisw108fUzYmqxtWB6uW/ZG7J0UZOAc1u/ihha7Ozg9NLWxyTYSzTESHDZnileqGdNcpXY 1Gdc3wz2DLIyN7jIc6le7utnQIuFY53BfOdx9Otnw37XL8pMA4gFe9igz6W/neIXDHKDszbI zfOxovR1qe3iBok6dC4+p8HeEZ03Y6XBC7xVUpOtwPPxCIrz5KXBAUD2WoJWDT6QtSNqo9Wr CLnHJMERsISS9uGKGMD5xa9mULzFq4jhf1OTFaIAHnR48JvoItYcupZcaXFwIPZYBhNtnuDg hNUisxpEwdHA/etbM/MHSK9mooMR+f52/WR6+l8Z1kxfzMIvb+Dx+Af3EnXt0xYClAAAAABJ RU5ErkJggg==</item> <item item-id="66">iVBORw0KGgoAAAANSUhEUgAAAK0AAAA7CAYAAAAJkAodAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAVgSURBVHhe7Z1BcqMwEEV9Lg5E5RA5 QhbcIvsstMsdsk15lRvkBow+0FgSEkiAJpL5r4qamNiYiKdWq5E9t56QyqC0pDooLakOSkuq g9L+Gfe+a2797Yat7dW0l2xDaf+Ie9f23X38WbVa3KbTGpMYKG0J3Lu+YbSNhtIWgepbShsN pd1AKXVw2L4Px1hFR9pWcgWyCaVdQ3Vz3nkILWW34q3qmM+mQGmD6Nn9mmmJqNYv5r1r+pZ5 QRKUNoRqz5XJczwI28yhXOlOEhdvf35++q+vr+nR9aC0AVT7KEmdAvJWw9qhzDXUaGVrot7v 7e2t//j46D8/P/vX19dp77WgtF5Q+D97Nq/69mAtFpJ+f38PP//+/g6yX5GL/dVaxkBuaYG6 aaJgkGgddIS4aOoD0RWbgEhLaQsDedvLy8sQXTAk4gJJlJF/04mUVuefN09Ci3PBebm8v79H CYSUYG+eLMfH3+62x9UoUlqIgYuCaCIgymAffgdJ9rFfWjfSmcQO1XulxfHRgQH+fpwHHofO 59kpUloIIBdJwGwZ+yGsDMXuZKbxColh2X7evAVSAMzqXWnxfBOchykNot8WON9HtSAE7o7Z cqPzmh0Y4P3MNopri+egWGndiyTS2tHFzBPVIOe6FHGR1i5FjeC9TfBYUgUM0zFRz3fcGJCW uDkzhLVHnNS2qJdipXXrkJLH2dgz8u1IFi+tL9JCTJyXnAukkQmRKVXoPPZK67aHpCO2yKlt US9FSovIhYsCOWSTfYg6cxTWuadcmDvy0M1a535pISzeH5vkk/LYnBANr9X7zpJWcnhs0hZm 6WsmuS3qpUhpYzHzuAZ11bMukmcilkJITpxv6mHRORaCesjWFgVSsbSynM/M5U4CddpCpEVU 3SZjWxRIvdKa0dAYGs/Bzg9T8Uu7T6goabO2RXlUKq0rwBRpTrtWOD4i1z7C0u7vCGFyt0V5 1Ccthu4pdzM/ECg53YFR3UK1+4dZv7RaprNOTvhPbVEaVU/EsqKH2b0X3SvtgeMRG0obRA+7 OxaBQ9gx8tllL3464TyC0qIeeHlO+7iNWv24TVXMKcl6+jR0XmdoMTu0+avH/riUzCut3DKV 7crc/8cHG6sBk7xJrEFe/2R1ltA287Hc03yt/lk+1OkT3cfCSLnTg1uEKGq797zJhTFLa0if mvBkzxUQj610SU8WfXn/cgK7ZCEthHUXq6SB3vi8M9cr44roFW/CJ+36a+PnEF5pZWFIzO3D MGNPHIYJd6PRBZB+faKi5YQrKaKoW5Z7vHYMdHjv0PFMFtJiMYgse/PfjZE32F/HXGPRgNwO b2dxSFoNnv84r6U/w2sCebKJ9RdJhN0EvWbz7g4jbdnsuD4HcloL6zgmCIiJ0por4ddAjwmd D3lmnOrBSuAKSuukCRZBmW0sadHLUJ+VFfl+tnrDmD5Q6idlKFchIhvD+7RvvuaDmE7Uln2O 6GNK4Dx3A0taWYUvm7eKENkbCMmFN0tHpEXEhbguTA3IX7OwEmUuVA6Q3y5LXnGJcp2YE5M8 lRFyDt5IG2J1Rlg5/Dr5eoiUdpxcSa4bUwCuGvPeeCK87Z2fpEh7GTbKObjp4quwIK3yzQPI ubCFfajwwg3cgMHmQ76PgOSFLbxAp0JWlLXrzq6UiK6mxFyHnB9K64AvU17LZV1p8Tj165HI MSitAaojc3FE57W+/3EGksoaDallI9qmfD0SOQalnbBXIGHz12ohrDxn79cjkWNQ2oxAXEp7 PpQ2I5Q2D5Q2I5Q2D5Q2I5Q2D5Q2I5Q2D5Q2I5Q2D5Q2ExBWSmEU91woLakOSkuqg9KS6qC0 pDL6/h9xfd+E3jzX3QAAAABJRU5ErkJggg==</item> <item item-id="67">iVBORw0KGgoAAAANSUhEUgAAAKgAAAAiCAYAAADLYJIKAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAOBSURBVHhe7ZnBsYMgEIZTlwU5KSIl 5GAXuefgLT3kmskpHaQDHr+CrggCeRAx7jfjjBgV2P3ZXcxBMEzBsECZomGBMkXDAjU4HA6b OfYAC9RgL47fCuwNAxZoWbA3DMoV6FM0lU7vtWjV1V+HBWpQqkCfTS2aZ3/e1lKkVSMl+/uw QAmbSe/PRlQ7iaIsUMJ26s9W1CzQ/bGlCFrrfP/jsEAJWxFo2+yj/gQsUMIWBPpsKlHvZQsv YYESShcoxFkNqb0VzQ7SPAuUYBMoRHHIGLLO57M6W6b7tCTHNx7V8NkpBfN50u+uS31hw6bv m2/cMG762q4fdX+IWVmgBBiNMhgzk0Dv9/vgLLPvb2KbZ+h3V3rfjO5zGBEi2vo9gZ/Kgq3y fr/VWZmkGJ9NJLki6PV67frDuB+Px+r2XZynU0xj9BxLjxFs5hoSQdEHvQ/Ctz1HmXjkdDqJ 1+ulWiOXyyXRCu8n9Km/c47P9XwugaK/2+2mWuvjFejSP1cqUk7E1tadn2mKN/uIEihWNA4b WN1pBKqh9Y1xOIyUe3yu53MKFPNBmkcETUO8XTWL85Ri8wmpDz46yspxNP3ZpAaV76F1apRA MQkKohIVRFgxr0N+2gIe/G98/shtvl+zLNDPBXE8HrsD80BmmJPPljbc85TjCPzfv62V+FqZ 2tUDE4FK0B7t5J+bU6Bo63SKFe6KXjOwSrwTincsfqOg/dH4HJjv1+SIoDpyegmyJSV9BB1E 5wV99/dORdgfs0iJuQXYdSJQbThEI7SxulEn4Ty0iDdXTCpixheSOkzwDhs5BIrIGUIuW9qw zRPXhkuyzlz8e9VRBljnYKT6JQavwPlwEg4YkLZpjYTPCiiIcX0+IFqH2PCnWhfh45OGlteS CLQzZN9HSpHifVhktg3fiM+WCbHMcx4FVTpWG6LutgD7TASq74/ICvaw4QRhfKFuwAASOvJT INJUETQHOurrw7qbL8SWaxPvFYT6kNWyIj6BamFQzPY3QATV5YpJKbZcm8+8Ilf3vB75Ykry EBJB1xYoyhLU0ChXaInSU44t1ybCKzK96x2d5cNtV/sVsuRDUzwV5RoR1EVJtlybCK/0Gxw4 EgXzaD96PX5zkoNYgZYjzvJsuTblhI2EhAoUaDEwZfJznunSY0QEYoGWDXtGwgItF/aMhAVa LuwZpmhYoEzBCPEHLAczKBkjW6MAAAAASUVORK5CYII=</item> <item item-id="68">iVBORw0KGgoAAAANSUhEUgAAAEkAAAAYCAYAAAC2odCOAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAGwSURBVFhH7ZWxjYUwDECZi4EihmAE Cragp0jHDrSI6m/ABrk4OMGBGMzpI6G7PCkSMQTwww6FyVySJQnIkgRkSQJeJWluS1MojTNg Nm1ZmKKAUZp2xnCENsqd34a/hbsfxkqymItzvEZSeHEiaW5VEKOVPVe2VtsOra2mDa0UzkEe ip1bUwbJXJxHLGlZFjx6jmMlEVxCXgCHrTyFIqPr92IS8RMiSXVdm8/ng7ONruvcV36aS0mp SqLYa1TIGFsV7qcVaSsuzhMy7/vejRRQRXJJdB/ZDU4AcipJkBBtzxXcrw735OJpQuaQBAWq h0prmgaPnoOXZJO6qiL4OL7VkFUafjSynotzsJJg7ltvmia2yo58v5K2zfgMK5KujdpzfSd3 moufEEkCEeM4uqqBOVTTMAzumG7c8KeR9PJdUpIgFkLRnrPDtmO0FGREGzSRlIqfECSBIP/F q6qK5lBJHpeIjX1dkk3SP8+Lcr99H3OD/qHi5FLV5t8VBn1fLs4R95gQeMgTlfRWsiQBWZKA LElAliQgSxJwWxIIuvP7/Av8qpL+G1mSgCxJQJZ0iTE/H2TauERfX/gAAAAASUVORK5C YII=</item> <item item-id="69">iVBORw0KGgoAAAANSUhEUgAAAK0AAAA7CAYAAAAJkAodAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAWISURBVHhe7Z3BcaswFEVdlwtiUkRK yIIuss9Cu/SQbcardJAOiK7gGUlIIAmIkblnhvk2xgSLw+PpSfa/dIRUBqUl1UFpSXVQWlId lPZh3Lr2eukuFyxNp4a1ZBlK+yBubdO1t/6xarS411ZrTFKgtEfg1nZXRttkKO0hUF1DaZOh tAsopVbetm9mH7PoSNtIrkAWobRzqPaed65CS9nOeKta5rM5UNoounc/Z1omqgmLeWuvXcO8 IAtKG0M128oU2B+Evd5DudIXSVq8/fn56b6+voZn54PSRlDNWJLaBOStlrWmzGVqtLJck/7e 29tb9/Hx0X1+fnavr6/D2nNBaYOg8L91b151zcpaLCT9/v42j39/f43sZ+Rkn1rLGMktHVA3 zRQMEs2DCyEtmoZAdMUiINJS2oOBvO3l5cVEF9wScYIkysi/+SRKq/PPSyChxbHguHze39+T BEJKUJony/7x2f32OBuHlBZi4KQgmgiIMliH1yBJGeXS+pHOJvVWXSot9o8LGODz4zjwPHY8 z84hpYUAcpIE9JaxHsLKrdjvzFyDQuK27G53XyIpAHr1vrTY3gbHYUuD6LcEjnesFsTA6Jgr Ny5e+wIG+Ht2G6W1xXNwWGn9kyTSutHFzhOVkXNeirRI65aievC3bfBcUgXcplOiXmi/KSAt 8XNmCOvecXLbol4OK61fh5Q8zsXtkS9HsnRpQ5EWYuK45FggjXSIeqnsqD6tPpRKi/3Z7SHp iCtyblvUyyGlReTCSYEcssg6RJ17FNa5p5yYG/LQxVpnubQQtpexT13s59IhWppuWCKt5PBY pC3s0ted7Laol0NKm4qdx11RV93qJAU6YtkEphvieHN3i4tjImiA3drigFQsrUzns3O5jYBw a6W9H99IibSIqsvs2BYHpF5p7Who3Rq3wc0Pi8CwrXNMZUIlSbtrWxyPSqX1BRgizWbnCvt3 o2Qu0+mG2OfKCyHI3m1xPOqT1uSKkr+NYklOt/quPqCa8tssOlzT49AybXVwwj+1xdGouiO2 K/o2W3LS3QqBNd2wcH9kCqWNom+7mZPA7R58v4zRmt9O2I6otKgHnp7Nvm6jI+6zRNl7SjKT PkW2MfXv4YKe3nWQm8decwlKK0OmspyZ2398sbEa0MkbRDRihjqrkW3wWDqik/fiPdq1xPxp YqSM9GCIEEVtf8ybnBi7tDZExolnkW380UCkUv3zfpucMt1EWgjrT1bJo79qEi8aUhHm9m6d 2FG8kdg20fcOUbeR/kCCOEFpZWJIyvBhnDFHmSw0+gDkn594tByJboMI7JXlsL6XWa/HC0bg gpwWk0Fk2ttkNEbvtBk+6F7zNScNyGX1shWrpB0ej8fV570p+/RxPpFE2DB9cbzfXUoewkh7 bArOz4qc1sHapo+04wbZ0toz4ZfwrxByBrzKQHBYemEbL00w28j29ntncKTFVYb6rMzInyP+ uwDsiD01Q97p1GCHdWOADWxjZNXrQqLLa/Y+ZnCklVn4skSrCPqg+INp5FEEs3REWkRciDuB wpIHM7ESZS5UDpDfTkpeWtj2aee82R2T5byKPI5gpA2B4cy7r0be4fGTwJ+Tr4ckaU1Zwsp1 Q980fSpMR6LsM3LYe3+SI+2pgLQzkRaDLqEKC9KqYD+AbApbOISKf88KAzBYQsjvEZB9YQtP UN6XGt26sy8loqstMech7w+l9cCgyVwu60uL57k/j0TWQWkt0OG0R3VC9WhIKnM0pJaNaJvz 80hkHZR2wJ2BhCVcq4Wwsk3pzyORdVDavVlRPiNhKO3uoCNHabeE0u5NJDcm5VDaneHvHWwP pd0RpxpBNoPS7oT7zY70/42RLENpdyC1fEbKoLSkOigtqQ5KS6qD0pLK6Lo/XeylkLwCqS0A AAAASUVORK5CYII=</item> <item item-id="70">iVBORw0KGgoAAAANSUhEUgAAAKIAAAAiCAYAAADcQgLDAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAOMSURBVHhe7ZpNsqsgEIVdlwuysogs IQN3kXkGzrKHTFMZZQfZAY+DtLYI/l1R9PVXRUWjUTgcmoZKpgQhAcSIQhKIEYUkECM6ZFl2 mHImxIgOZ+vgoyCqO4gR90FUd0jXiB9V5jQtF6qy354FMaJDqkb8lIUqP/VxVWgz5qW25nkQ IzIOMy1/SpWfLCqKERnHyQ8rVYgRz8uRImJB8/RJECMyjmLEqjxXfgjEiIwjGPFT5qo425JZ I0ZkpG5EmDBvpuRKlSeansWIDJ8R0flZxBB0u93s0TBmy0bXry15s52zDu0+Zb+5fA+z+16j T+/78P0hxIgMCMdpRI5kxNfrZTurLvuBVXi4ncE9TLZo4gN2yZ7n5Nb/fj97lCZr1M9nhlgR 8fF4mPeh3u/3e0d96+jVTvkjhPYwq8L/jIl7nh3lr9er+n6/9qzlfr+vNGLrkbe0X2PWL/T7 WEbE+57Ppz3bEWuUgqb+sbbi/l6E02YuA7/z3t+nUR8jFMUHRus6RiR4DuGUgBCx6xf6fUwj oj2YnhER12G+rnX7dMTCZWPKkUDRi3x2WtfFGxFDkdKhUR8P4iDK8I4fTap1IworQl6sv8/1 t/qNR2L3+cSwEed3PHG5XExBOxDpO0TWkoP2caMgpwsbR+sYiG5Gp94UHL7fJWhEnNM0iBEb ikY1+oVa+PqFU3KO+R2IaxycT6/fOO7ziRgRkSKhn7lacpZGxPbakBErRE573AeDvXt9+P4u HSOSQIguOMdoRR6D4znJtDvK1mBa/XhHTBcB4Dc+YhgRkXAqMbTsYKZj2mKBmfzbLahHIwMi tnuTnoK5TqP3OzTqo5PrDsyMUPyc5zBYmiOPwPdDI8d/CQ1dtliZUr+//FUKz+kBce071jQj nofB5Ft4uYS1XBHWTm4eyheNlvZ6XWqzmkFK3zF9QvcP4Q8DQRBxRh46wf2bYIScMTVowbaC ojiV4Oo5FS03YL76ECcUHZISrp+zEGQAjnu+BYiIlGb0+I9MCJapr0N5TyQtXFklJNxIR+5t RKQTyHGRZvDUx5CalhswQ309LdMqSAvFNyk/VaUa3YyI9nhHpvxViptvj4joI0Utt2CG+vVC Ax2G5JNm507Casq81WoMOiu2Ach89Lk3KWq5FWn0wIqgM9vV/PhfpajThX05VQ8s2jYQIyaB 9IBGjLg/0gMaMeL+SA8ISSBGFBJAqX+5LBhIQX7DDAAAAABJRU5ErkJggg==</item> <item item-id="71">iVBORw0KGgoAAAANSUhEUgAAAD0AAAAYCAYAAABJA/VsAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAGBSURBVFhH7ZbLzYQgEICpi4KIRViC B7rw7oGbPXg1nuzADuaf4SXsKr+47sPol5iIOJFvZwaWwQW5pa/CLf19BpCcAWMMhLKPPPMc YxzkYB97UrExPyStQJDQyooHKbyoEvgel6jpSMc+sll6miZ79w5Mlvhz+pYZJHAmUFUP8mKR SLosSxjH0Y5m6rrWZfM2rISgDG7JGL3vMp0bi3iTpmn0tQRlebt02HsP12rpcpzDzNG0lvin L5Xwmc2ORbwJLSqEshv+CFVV2bvjoYWH5Uk9u16u2L9BP+fFGlalaexKve/71Sp4Zm+m57nU whVl1d4TObGOSJrEuq7TWaUxZbttW31vNrJQKP74S+iydMcQ7cRLR5IR9H4YI+iljbEhXpqE jQyDoiiiMWWaSB8bL4J96r4Xirke1d+z8+YK5JZiE8Q1nYNe0IHZ/iD7pXUpXU3a9dQJ2S2t 5IH9/GF2SUe76AnJlo7/DCiQJyzxLOnksXEi9m9kJ+aWvgoXlAb4A/K8mKxCIY5TAAAAAElF TkSuQmCC</item> <item item-id="72">iVBORw0KGgoAAAANSUhEUgAAAVIAAAA+CAYAAAB5lJN6AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAkGSURBVHhe7Z0/etQwEMVzLg6Uj0Nw BIrUXICeIh01LS1fKm7ADZZ5iscZz45kWfauLe8rfmCPHFmakZ4k/9unp8vlQgghZAWhkRBC SD2hkRBCSD2hkRBCSD2hkRBCSD2hkRBCSD2hkRBCzsTb2+Xp+dPl6dOTiJ4i+88v72nR3ywh NBJCyFl4ef4Qz5fXD/uriKjan429hdBICCFn4EVmnaOIBjPPVyOya8Q0NBJCSO+8mRknlvFv wTEXEVe73H+NjqkgNBJCSO88G4H8JKIaHQNqjysRGh+Zv38vT79+XZ7+/YvT7wWu34QjKCFk HjfTLC3b7fI/O3OdITQ+KhBROPPz53eiY+4C7jA2joyEEEGEcxRHoSSk9jopaFneh8ZHBU78 8ydOmwMz2Eh8v39/z9fbc7xpA2gcGQkhAoV0P+DEyF7Dt2/vM1pvh8AuzRdLjdaL3oQQYYGQ lpb2uH4a3e33hMZT4K6RWHJOtcdE10jTRenMTBFCarf9ftrWMskIqGlX4JhSOiGkirU3m1Rg H1tIF5KbUU4QAR5FDiOeEVUrnHC+XiJAnljea9qVULp88MhG651DQsgHkyV7ZgLkJ1xeNCGm FNIFwImR3YLAlGazmMXijj+28b/evJrMbr2QOnAO+/YFIaQdO9uceyA/msBQSBeizsTsMVrW g9K1yy9fPvL4+fNj++rm1YyQIvDp/d8gjRCyEOlveMde+2PuFdHcKpBC2gAEEHfe4dir9BkB rGarfITXjmauu5ZVfN6Tr7bgcP7eOQYQzfCeCYSyUC4KaQOYPWJJbq93Kptdu9xISF+2KMud OERZpbPUdIgzcFh/HyAGk+umFf2QQroxtQ6dQ6/ZrBLlnkThQGXFV4Ai+6k4uL93j4H4pnRz yUMh3RI4Xxx6lOuWaIy9XEM9UlmxquhmAGrk6P4+RAxksLFimruBDBGtFdzQuAopVHbKjAoc YdmxAARenXmIskPUa0Z1OW7310xry3ovbtH+UMehfYztZK869+Dvo2iA+AqDjo0baBX50NgM RDQzc9MpfRKmIwW7NyobIkbT0tscd+EAnUbbXeJWQjOIaXqiQ7bTkxcb+x7xnPVlD/6+VQx2 JjS2ggYUPR6EO2a2YZWexyRlanyX/C3H7b2Eaolz7tGzFny7A7k2OgFiuESQ5BxWwPYaxHrx 901isDOTHVQQ01s8ppCWK9IgZis8gJlmNBpqnilftWNUysxc9dgc/niLvSO3+3WYGzHbWSQN da+9SF5iXPq4dmD9bBu7XSrh3Lmy4pnb6C2ypR94KRG2u8G+dSdGPdXXEJPsWzQVeB/Cpv5O vhRS+nAOm9aLv28Rg72ZGhAkdMAhGAhM7VIhOwojT8nH23PO1GDk8Mcr9vwQ9bAsJ2ButqOP vuC42cZaAPHR86AdaF7pB8TkHEko0NjluJQm235wjMr648c71qZgdlSK8SLkvFG7Q5lmB5iF nRg+0Pa5amkd+HC0q58FnbRAtF8lTfd78fctYrA3kx0fiHCEg8PQaFzAc87JjZJVzqxkFE7J D43rSHfYVzP4WztRyW9IS7EZCIU0ip87R251AbvvLHZAHOMw7EdlxXnsPmZFtqN//TpND/F1 cOUHi9ud2PxNI6UojnKOMV3ygD/C432Zjc2W2/tQbdbvqAMu3ejfaZ168ffWMYiOvyepDGOB UAl1VLRvgNN8xULnDI6xDldyzvSF9PjjAfJCGsp79vfUUdeowaKzjX4uxA5E8bPkYuPtqYO7 8yBvPSYqq48h9nXZiRcicrMnT7EOqL/ku6TdTZD02tmQra/uRzMzTSv5XfF5Tsos/6Nu4z5E bThfL/7eOgZHYNxApxgdhUoUKmtnIUoURB1Jo9+NjvIACHQJfzywgcGHkU81I3WgQXs/YxY+ iZVsl3yQ871iY2nztvbkZ8knpQmYISW7aUdRWRFDdN7fv99nQ9jHLEm/T1B786NUh5Z2NwH1 GepQRI6zfk7nlfx9nZXiuTM+1HNgCX+VJqiPUYZe/L1pDA7CuIFOAscmTOCukEBFI64PMEij mHA1Sxwax2ZiJ/mjU6PsON9ZRRT4zjLGbYiJdmaN45UvMvGzpFgOeVwJ9GC3nVuXenpO7SRR x0an1mPxXQO7bz/wEt10GZmpQ7bdCVt14omfFal7TkRn/Y50k4+WMZ0H+0O+dtIAUB+UNSek R/T3eYVUCl0rbFGwlCoHCaU8yAziNz9gLeGuvm8t60x7bK4D8pW/DdNuzF383oO/d4zBLUn/ LHFUUSwlj3AWZEgj7AkdeTdWNsTawW4TVpQV7STXJpvr0Co0G3AXv/fg7x1jcEsmS/qrKb1H nDArgqVg1vw9mQXLsJrVwxU7+L+5rAIG+KXLzBIQi9k2fgvu6Pej+3u3GGyBlFsvbXmfTHbm sDcbyI5IDFoa4y7xW1pWOTa86TKwpg57fV7urn4/uL8P8Ym/RuwghVm69dW4UUQcqbNW0O2I ciIWNcid49dcVunE45JybR3k7+/ebnfy+2H9vUcMbgQGHTsrnSSSvtj1K+gL2bWs0nnP/nyx 53D+PlsMpC4UUkLIaVk9iIjoz+Xhr/VOEgkhpGfwAknrzTYLhLL0pIK/fDLZIYSQbtn48kHu hQDcgPMiO9khhJBeweNbTc+7Zojyg4iOS3oRbd2eHEQIIb2Cx7i2WNYrWN7bR7/wyJMo5gci qnq+8SBCCOkWmRlu/uqpiGjtW1ihkRBCeiJ6mWCO2S9fQZzNrLNEaCSEkJ7IfS9k7c+s1H5n IDQSQkhPREKKTwYCa1Nqf2aFQkoIeRiibwJ4oWz5mRUIac1rraGREEJ6YvJY0oAXUuwv/ZmV KN+I0EgIIT2Rm5FCLGt+ZiU386SQEkIehkhIIaIQTFD8mRX5W9gopISQhyZ3176WnGDyZhMh 5GHwbyEthUJKCCEioksfyLeEQir7fCCfEPI4QPRWvCKaFVK+IkoIeSSeK2ePEaGQyiy39nJB aCSEkN5Y8xm9SEiX5BcaCSGkO0QIWz7sDBEVJbx6BGrJjwiGRkII6ZHNfmpEBHnJ7DY0EkJI r9zjx+88oZEQQkg9oZEQQkgll8t/lvzh7O05KpcAAAAASUVORK5CYII=</item> <item item-id="73">iVBORw0KGgoAAAANSUhEUgAAAVIAAAA+CAYAAAB5lJN6AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAldSURBVHhe7Z0xlts6DEW9rlnQnCwi S0gxdTaQPoW71GnT/jNVdpAd+ONxBBnEgBIpyZZov+ImEsShSIAEQVKST6fL5UIIIWQFoZAQ Qkg9oZAQQkg9oZAQQkg9oZAQQkg9oZAQQkg9oZAQQh6J9/fL6fXlcno5idNT5Pz17eNa9Dct hEJCCHkU3l6vzvPtfJWfxYmq/NXIlxAKCSHkEXiTqHN0okHkeTZOdo0zDYWEENI77ybixDT+ PUhzEedqp/vnKE0FoZAQQnrn1TjIF3GqURpQm26KUPjM/P17Of3+fTn9+xdfvxdYvwlHUELI PC7SnJq22+l/MXKdIRQ+K3CiUOaXLx9Eae4CdhgXjoyEEEEc5+gchSlHatdJwZLpfSh8VqDE //6Lr82BCDZyvj9+fOTr5SXetQEsHBkJIQId6X5AiZG8hu/fPyJaL4eDbc0XU42li96EEKHB kYZTe7s0II42+jtLKHwI3BqJpaRUm8avkeqzaC8FpcKR2mN/no6NcaNHMRIod4XhCCHTrNls Qn/XGSGuz21ChcJnpBRRAjxGoY4Po1c6hlM002/rOGEQXSJAnpjep79VY9i/dfngXkt3Dgkh V7Ipu+ljGS7gigKc9BjVTHATCp8RKDGSe2CcKKLF3yOKxY4/jvG/bl5FTwDgdbXIsMjfvn1B CFmOjTbnHsgvBjAIduhI61BlInqMHJ8CxUfrl1+/XvP49et6HG5eiWFKywswfHr/N7hGCGlE nCeCFu2PpVdEp2aBdkZaIhQ+K3CA2HmHYqPrUw6whXGKv5LzBmW5F7uWVTpBT7ragsPpe2cb wGmGeybiZOdmgDX9NRQ+K4geMSW3652WLRygXWOdG+Wm2MoZ34NDlHWlvnvisPo+gA2yddOZ 6TqofYImFJLPYESL5C3Y9Rqw+BGnnpzCgcqKndhI/lAcXN+720B0M7e5pIxBD85n9BoKSc44 JRFFHmEjyD6acXSOVNaata7eObq+D2ED6cPWmUbLdT7oKe76D4TCVUihiiEzKnCEaUcD2cO6 wu4PyksjrHrOVNLt/pppbVnvxS3aH+po2gfYrc496PsoPkB0hUHH2g0sdfKhcDFwogXPrSF9 zTNZZILKhogBYIuNsVUcoNNou0vcytEMzjQNsnKcnrzYWPew56wue9D3rWywM6FwKWhAUcSG 9UXbsErPYpJ5anSX9C3p9p5CLbHz1KNnrfh2B0ptNAPOsMUhyT2sA9trEOtF3zexwc5kJ6gg wlusCabpijSI2QoPINKMRkPNM+WrcoxKhchV05bw6S12R+5R18JmO4tcQ92zhfKFjFMf1w6s nm1jt1Ml3LtUVjxzG71F1vqBlynCdjfIt+7EqKfqGs5kbj1tCq9DyFTfSZdCuj7cw17rRd+3 sMHe5AIYCR1wMAYMUztVKI7CyFPy8fKSMtUYJXx6xd4fTj0sywMwF+3ooy9IN9tYJ4B99D5o B5pX+gExuUdyFGjski5dk2M/OEZl/fnzAytTEB1N2bgJuW/U7lCm2QGmsRNDB9o+V02tAx2O ctWzoEELnPZZrul5L/q+hQ32JjvxhvAjXDLY0GB8JUvKKY2SVcqsZHSckh8aVyna7RI0UNOJ pvSGa0ireEdatJ+7h3bM8bqR+85iB8TRDsN5VFbcx54jKrId/du3/LonrIMrP2hudyLTfD2T zlHuMV6XPKAPn75W75rWl9vrHXXA0o3+ndapF31vbYMo/T1JZRgLhEpIBUcH5M9FSaOihgpb ZYTKGdJZhSslZfpCenx6gLxwDeV99PfUUdeowaLBj3qG3iVdNpjM2M9Sso2Xpw7u7oMOpWmi snob4lynnXghohQ9JWrrMFxraXcZct0HCiVsffU8G2wa9K74PLMy+zwkb71fL/re2gZHYDxI I48WHJWYqaxXRmREHUmj3422kYwFhp7Cpwe2LPgw8kNFpI5o5EcUntlKjud0MNWYrS1t3lae 9Cz2SNcEREhJbtpRVFbYEJ33z5+PaAjniJL0+wQtmx+lOixpdxmoz1CHSSSd1XO6r+Tv62wp 6h33DHSo98AU/tM1QXWMMvSi701tcBDGAygJik0Yw5WA0W1H9QYGMCwedfgUJUremzo7yR+d GmXH/TbL94D4zjLaTeQ4186sdizpwtvPkmw55JF1HNhtkNvOnaKiQZ7azpA+6tjo1JoW3zWw 5/YDL9Gmi6dUh2K7E7bqxJmeFan7lBMFRb0XdJjug/MhX+/MUB+UFXn2ou/HdaRS6BbHBuNG yq5SkBAZnFQievMDVisl+23O0rJWtMdFdUC+0vbCa3fg5nrvQd872+BWpH9aHNukQiWPqSgI 4O81eiILWNkQb96ZLSvKinKW2uTiOix1NBtwF733oO8dbXBLsin9nLKg0Lkp/6QxRYl0ouvB NKx29mCpst/GLC0rwADv2+SaOix2CCu5p96Pru+9bLAJUm5d2vJ+LDuZAms0aqCkXHON3Bmx RWtj3M1+rWWVtFiLwzHKaaOXtXXY4/Nyd9f7wfV9iE/8LcQOUljGtLoaD6bINqICb0zuT0uD 3Nt+TZ1HOu9YTim3dt7VdWh1MBuwl94Pq+8dbHArMLBYnWQXSV8gYojkR2TXskrnffTniz2H 0/ej2UDqQkdKCHlYVg8i4vTn8kBEaqPr7CIhhPQMXiDRdcw1zK0J++WT7IQQQrpl4+WD0gsB WD/2TjY7IYSQXsHjW61PFkwR5QcnOk7pxWnrcZaIEEJ6pfj67UIwvbcvKeCRJ/GYV8Sp6v3G RIQQ0i0SGW7+6qk40dq3sEIhIYT0BKLH1ldPZ798Bedsos4pQiEhhPRE6Xsha39mpfZDTKGQ EEJ6InKk+GQgsDKl9mdW6EgJIU8DdtO9I/WOsvVnVgAcac1rraGQEEJ6InssacA7UpxX/8zK QJRvRCgkhJCeKEWkcJaTP7OCDSU5xnn0YRY6UkLI0xA5UjjR5CCF0s+sTH0aL12nIyWEPAul XfsW/KfxADebCCFPg38LaRHy93SkhJDnRZxg6wP5HjjjbBqP9VOZ2vOBfELIcwCnF2wWtfDp lwWQZ6VzDoWEENIbr5XRYwQ2lT5N4SXKrV0uCIWEENIbSz+jl+3Mi+PU45b8QiEhhHSHOMDW DztjM0m84BUT1bb8iGAoJISQHtnsp0bEIbdEt6GQEEJ65R4/fucJhYQQQuoJhYQQQiq5XP4H AtDG+jqaMj8AAAAASUVORK5CYII=</item> <item item-id="74">iVBORw0KGgoAAAANSUhEUgAAAjQAAAAlCAYAAABVnnMUAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAptSURBVHhe7Z0/mts2EMV1i1whZ/CB 9vMh3KRPsXUukN7Fdq7Tps23lW/gGyjzuBwuCA5AgALxR3zFz5YAaMABZp4ASOLebvf7nRBC CCFkaMzCYXkTh25b3qy2hDwJL0bMv0guWG0JIeRZMQuHRUScQk4uD/OAEHJBzMJhoZATwjwg hFwSs3BYKOSEMA8IIZfELByWxkL+8+f99s8/99uvX3Z9Ld5e77d3o5xcBC5ouoB6QEhdzMJh aSjkEC8ZzdvXrx9YbarwLmMgAmbWkWvABU1zqAeE1McsHJaGQg7x+u8/u24P7OAs0fv77w+7 fnmId/Ed7W9fuCO7NFzQNId6QEh9zMJhaSjkOULj89dfHzs6vxzClmv3VcSLP1O/OFzQNId6 QEh9zMJhKSXk7/fbF+xsDEL23TbWZ+bTvUICOyUImPvYfz491mt6+azbgDaxenINuKApC/WA kCEwC4elkZBDZKwd1Qq5rkVc5LErZq5gQQD1qBo2ccysdRuB8uy8v0o9Py8njfKAfEA9IKQN ZuGwNBJyiI5V7vImwhPbzWEXh19E4DH+h3hp+dJ2Z8eFPl4b+E86o1EekA+oB4S0wSwclkZC DqEB2D1Zx8sg9ln2t2+fNn78+Hy8+VLhjoDhGBu/aLCOscmFaJQH5APNX+oBIXUxC4eloZBD ePDLBAjPpn5HeJIpZUd44xveuEgcROevUh40jaG9MWgM9YBUo2Eu9KYBqyfDI861WtBg94Sj Yffzb6XYZ9mFBIw32hofxFTw1ysV8uC1RDw/ivj4Kjlh1jWGekBqEtWDk+hRA9aVo1NByI+A 4+USwqt/VfkhMZTr4Ofqz0Hwhmln54HY7mUh8VrohKIm1ANyBlVvoNipBqwqhueP++13waxr hUz6F+eXB63BFwV5X4rnIDiXkgO/nfhGDwHpJZ6xM+1FWJOgHpCTqDmXvWrApjIGdgRZ4iGr uOB9EqSu+E8KxWZPJzQYaOygHt5FFeQlJRBljp/udul4I5nnYpmTUGxm8uefdvnZIL7MeD8j txSMY6FxK8KZvhZmGD04MVe6paDmdacHpelYA7aVIeYgT14BSiehG0fpEdGU4CUHRvrs8SOn bkgMRByJP+U4ujEMAZPHj/r577+fog+sNqch126+MYbKd8C8777uoO2SrD5m6k1cRyI2difk Ss+U0rwu9aA0HWvAZ+EemU4gAazFD76A5gYOjsmKJYrYeeakexQsIPfmcJofmZOzjvFDP2M9 BIQ2Iyb9GH5UxL5//xAt+IQvgRb1Tdi1h0S2Ng2enyU5kq8lx8XXDxDSmhW5sXIBonpQOFdC lM6ZI5TSvG71oDA9a8CmEBOCn0JNR44yOItQSNmuaMyEEkXtT31oeWQStG0Iv/0j36HBJKnd oT6Tz2A3EKUOvkO8HhkD3EfDulNq7h/X20WuMedNCv6rX0iK0AliKvAFP8+16lJ5dKzMN/Od 79Bgd+PHusb/FB/CVD+Pj1sXiqEac27qx1y+q00HYkX7uqIeoK5UrlTTgyOI//DzUc0D8KVL PfBY8l98dtu6Me/miq8XPWvAqvEkZJjY+WJx4Ud2esHVPOyLTb88NAl64SH89rAfStAY7vVW +xyyATEBA/ozPIzHXlKEwC4FWHVYpZvzdhRJrpw3KTcZSpxgwA58xTHz5qZnCZQYKzN3Ynkg Y2ZuIFDu2NJNCd7M3qROn1u5XW3OpV9LP5LejDJihXog5TJnJXKlWmwcpITmKfAFvnanBw6o 1znH/GvbF/Ef8zzpAnJF7UAXpM7Vi541YFPoXujeG+BkXC7UX72r8VVbIWQv1D4bsb2xjwnB NRqg7SJY0g7iXePIrhriF/zUoLUCUUGdOzZmUsz2VvPt9YHH7muwOncDPekLc1Y/SmQ+o8Ir Npd6sYGENdsn+KjgxmkAPmJ34tZN+LY8O3jstj8yVvBjkzvSzzLPxrVbb9Ioc4ViihVH8DRH rVwt4cfeWIFs/ZCy3FihHghSVipX8Nh9TU09MH1zgP9uezfWFhJ8VLrTA88+YtuaRz/3wbKg mevdsexZAz4LERjupPnPA6ADf5BMgYE97+IUs70wORfBb48B2AtiHw1q+Prs92MIjTMCdpmX nXm35tvFnxc812NI7FrcII+x18+EXGvqrhv2XN/x3Frpa91e3/ADOzGrziVmq8RYmXOakAf+ eKzsyP/I1eW52NKxQjvfdpU5n68pRz9WSH1KrMAWrv/KeuDHBp4fzZUqsXGAkpoHutaDnTq/ fFrgeOOB69Y2aN+rBiwFcGIxJBU4ggoNjIu7klMsh6dBkot9N2xaNgAGJYbf/uiCRv18l9em LOJGxZoX7EJX8yyPY2MQmisF86LJjVU5nmO1rn+TBseP1ut89vqZkGtNWtB4Pk2xKPZDsZLS N3ZiVrlPzFbOWMGOlY9u/C6E8gDjJTmIx36+Y3zw0dKmToDIwB7GTx9rHcjxI0ZsrI7oxwr4 7vgU4up6UDpXasRGLqU1D3StB06dzqM7Bm75FPNqW7D0omcNWArgFDqekMcqblHEKWvl7gsi mFZewmbXI/0UEw2xHUq8INIeEwi/cX3PKl7AF7Blzuc5VPHSGNiMhbzWmm8XBLXaQJK7z7FS 13bWF1MXEvqZQMJ5ceaz8knxxmFFYt+wg8TVXYjJjq3ksZrnKVnApF/TP1zPbH/K8bl8GiM8 n1/j24RYYJwRD5aY1ZjzoH4IMRFcSIiVCbF/VT04I1dqxEYOZ2gewOu71YMZ+KqxvWojj5eY l9cv7/243rnc1YueNeCjAA5Zk7mD5ZiSJDJCzEY2YqeYrSfk0bEuNlc78VY0JjJJ7Vt3Hgqe +21K+hESKjPPpM/Txk/s+puVJM6ac9iV15p1JMqj8VksvqkH2WTpQWnEh141YPrnqKHo4Ik9 c8XrMK2O5wspgvTZKvBHAOP9yPiUTJbYtVRJygC5fWNHpkesfl1JP7pZ0Djikcspcy72Dokr oR4kkNt3l3pQmo41YBoYXVVaAxREjOwuRmKOp7w+F7F5mpA/A87EZ3PCfGEhvYm5M+Iilcy+ cZSKz4lxxOoeq04U9sMUMOSXtWmQvs/MAxwbxzYqMUrPOQQyS7fIJzLu1IMImX13qwcn0KsG bCpTwYB2t3iQ6+GCJgIC/mDQFJtvuQbzi6kzLeOqZN+l/YA9U8CsNyTp99QxFNuba4lx4pzr fUTIARA/1IMgJfsu7QfsJevBGYgvPWrAqiIJ6VRPdLJPdc5Grq1V8I8Cfr2WtbIuPd+uPbmW 5YixZVyV7PskP0wBk77MeA+VFyRrIeGOSck5l9dXjZMnhHpgULLvk/zI0oOT6FED1pWjU3lC RwTHfYc+pyRNsQQsOJeV8kB/FdUEGYtnv09MDagHY5KlByfSmwasngyPOMcFzQ58IxgOiJe1 gwnukJgHJBXqwXBk68GFMAuHhUKeBG6qVOOLY+Q8cAOs4G6MeUAyoB6MT1QPLoRZOCwU8mSa HhWSx5BdWXT+mAckE+rBwOzpwYUwC4dFJlWP4ly4ciXPDO7f4Mc8FzSEkKthFhJCCCGEDMP9 /j+tcOi9brbOwwAAAABJRU5ErkJggg==</item> <item item-id="75">iVBORw0KGgoAAAANSUhEUgAAAjQAAAAlCAYAAABVnnMUAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAqLSURBVHhe7Z2xmds4EIXVxbVwNWxB +7kIJ5c72NgNOHewmWOnTv0pcgfuQDePErhDcAACJEAC4gt+mwKoAYGZeQJAiXu53G43Qggh hJCuMQu75V06dJnzbp1LyJPwasT8q+SCdS4hhDwrZmG3iIhTyMnpYR4QQk6IWdgtFHJCmAeE kFNiFnYLhZwQ5gEh5JSYhd1ysJD/+XO7/Px5u/z9a9fvxfvb7XI1yslJ4ISmCagHhOyLWdgt Bwo5xEtG8/Lp0x3rnF24yhiIgJl15BxwQnM41ANC9scs7JYDhRzi9fu3XbcEVnCW6H37drfr l4e4St9x/uWFK7JTwwnN4VAPCNkfs7BbDhTyHKHx+fr1vqLzyyFsuXbfRLz4M/WTwwnN4VAP CNkfs7BbSgn59XZ5wcrGIGRfn+PfM397vZe/yP+63AEB08f+6+FY2nX23+T6XP0EXHegDXIi SuUBuUM9IKQLzMJuOUjIITLWigpc3z4EB6ul4RhipLaBtWBBoNxWNWxim3l4r7sPrt/r2UFb L7xfTg7KA3KHekDIMZiF3XKQkEN0rHKfd1ktWdeH92MVh19E4Bj/Q7xcuX/+qxItDey/HdB/ 0hgH5QG5Qz0g5BjMwm45SMghNACrJ0twHBAY6372588fNn78+Dg2v1QY6SMegY9fNFjiRk7E QXlA7rj8pR4Qsi9mYbccKOQQHvwyAcJj1Ze6tnGreSPvB40TKcB1wX+FYm2JQ2NoaQwOhnpA duPAXGhNAyYvukc6d9SEBqsnbA3r+9+aEsKj77m7+/Br4IO2+gffjwj+emWHPCj1QbqJjXlQ E+oB2ZOoHlSiRQ2YVvbODkK+BgiGVZ6D/xeVVwevOJ/31Z+D4APTaueB2G5lIoFfDFnlLUM9 IDXY9QGKjWrApKJ7/rtd/hXMuoMYt8QaEY7QfXvSH0FfSg78U/GDHgLSyope/2qoB6gHpBZ7 +rJVDZhVxsCqIEs8JGEvIWGVuuI/KRSbLe3QYEu4yCqqIK8pgSg+frrHpUuf/GeJlHpGx5cv dnltkMhmvNfILQfGsdC4FaFmXwvTjR5UzJVmKah5zelBaRrWgHlliEeQJyehNKKfiaBxW0Rw QHDCswZps8VbTs2QGIgQ3qccRx3DEDA53trPX78+RB9Y51RDrt38MA+VLwC/L75vpe2STG4z tSauPREbuwq50jKlNK9JPShNwxrwUbhEZieQANbkB/ePdeBgm6xYooidZ066rWACueTDwT/i k1rb+LGfsWYDoc2IST+Gt4rY9+930UKf8CXQon0TFu0hka1Fg9fPkqzJ15Lj4usHCGnNhNxY OQFRPSicKyFK58waSmles3pQmJY1YFYIh+A+77DlKIMzCoWULYrGg1CiOPtDG6484gR3bgj/ /C3foYGTnN2e7snnsBiIUoe+Q7y2jAGeo2E9KTX3j+stIteY8yGF/rt+ISlCO4ipoC/4ea5V l8rWsTI/zBe+Q4PVjR/rLv6H+BCG+sf46LpQDO3hc1M/HuWL2rQiVlxbZ9QD1JXKld30YA3S f/Rzq+YB9KVJPfAY81/6rM/VMa9zxdeLljVgcvIgZHDs42Jx4WtWesHZPOyLTb885AR34SH8 82E/lKAx9PXudh/yAGICBtzP8DAeS0kRAqsUYNVhlm76bS2SXDkfUjoZSuxgwA76im1m86Fn C5QYKzN3YnkgY2YuIFCubLlFCT7M3qXOvbZyezefS7uWfiR9GGXECvVAysVnJXJlt9hYSQnN c6Av6GtzeqBAvfM5/O/OxdOm4edBF5Arzg50Qeq0XrSsAbNCfaGxgB8ETi4SF+oLhTOuy0DI Xuj8bMT2zD4c8rhOH5w7CpacB/HeY8tuN6Rf6KcLWisQHajTY+MnRdDfXhs4HusEzM51oC99 YS4WVwMRf0aFV65zrBcbSFj//NQ+OvDgNIA+YnWi60xbhccKoB+z3JF2Rj8b1z7GvFemhWKI FSV4LketXN3aj5SxAtn6IWW5sTKOjbz3tHogZaVyBcdjnbCnHph9U6D/+nwdayC1j47m9MCz P1yD7ocq9ycJ44TmUa/HsmUN+ChEYOjk9V9rxOBoFOdJo7qDpsA8zvODAJjnC+hMDP/8yXUl 4oIafX325zGExhnBNPoFfpLzJn5f8LfG9wteu21IrFp0kM/IaGdA6kyRM0AiaFt4PUnizLbR D6zErLpUW5vG6oHpU91+AH88Jnb8axZbbqxwnm97F58/6nL0Y4LUp8QKbOH6z6wHJXNll9hY QUnNA03rwUKdXz5McLzx0DGB88f+PNjFz4+6mAaMBcMMySW8VGALyjRq4A+I1eFhkGRQroZN PRvUYFBi+OdPBiYRfe1XeW9wEvcEWH7BKnTiZzleGgPf3xr4xSU3ZuV4jdm6+5s02H603mcR a2dA6pImNF6fhliUa4nFylLbWIlZ5RYhW0ljhWuXY7z2V1HAtB3KAzkPuy449vMd44NbS7M6 AWIGexg/d+zqQFI/1PkxQmO1Rj8moO+qTyF0+2fUg9K5skds5FJa80DTeqDqnB/1GOjyIebF 9lAnWHrRsgaMBTAyDBKQYyduKaDTOhh8QQQYBPy0arbqkXaKiYbYjiWeycOB6Deu71nFC+jA da8Hf0u/8dqJl4uB0Fj4/tYgqJ0NJLl+jZm6O8/6YqpPrJ0Bed/Sh9SkTw5vHCyW2oYdJK5b hcQI2UoZK4yTey+S1s8rM/lDeSBlzv6Q44/yYYzw+vEe3ybaxTjjOiwx28PnQf0QSk5ozqwH NXJlj9jIoYbmAby/WT14gL662J6cI8djzMv7x89+8btrX+tFyxpwL0CHIs6MgUGyLixJZARr cFYjdorZekJKjHXI31kkxFuRdlaS0rZbeTjw2jqvZD9gy1+VmXlWMw/Eri+iSdTyOex6Y0LS oB6kkdJ203pQmoY1YPhnbWBHGxd7sRkvsByyCWmTE5owGO8t47Mq2ALErqVkO7nkto0Vmdti 9euK9wM5lSJgNfNA+rN2AlHF52JvlbiSqD9SKBnfVWKjALltN6kHpZE+tKoBH1twQo4xNL54 WyrWccMZmxGb1YT8GVCOzyXJ35lgIu3HXI12UsltG1upuE+MLVa9rQpq9AM2J+Mlx+aKp3Ie 6G3vXEr7fDYmJB3qQZTctpvVgwq0qgGzyhRwr911ZrgQVXcolYW8exDwEkxmXYSi/pZrwD1S HMOWFtQj46pk27X6Mftz/fCnXwak/ap5ILazJhEVfT4bE5IO9SDIU+lBDaRPLWrApCIFvaMz sCIhqiEDwwlNHPx6zQVPCsX9Lf4ZbYltF7xHxlXJtmv1A3ZniR6K9x3yIGsiUcvnYjdLVMkM 6sGckm3X6gfsJutBJVrUgGll7+zs0B7Bdt8sEUjTIOnHpFUJHPTlTnmAlZVVvgsyBs/+nJg9 oB70R7YeVKQ1DZi86B7pHCc0C/CDoCvwJb/JCkbEzK2ogysk5gFJhXrQFav04ESYhd1CIU8C D1XK2WYm7YEHYAVXY8wDkgH1oH+ienAizMJuoZAnc+hWIdmGrKqj/mMekEyoBx2zpAcnwizs FnHquBWn4MyVPDOzbWiBExpCyNkwCwkhhBBCuuF2+x8Jtr8UOTYoCAAAAABJRU5ErkJg gg==</item> <item item-id="76">iVBORw0KGgoAAAANSUhEUgAAAGgAAAAiCAYAAACz35yrAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAK9SURBVGhD7Zg9UsMwEIV9rtyBa2Q4 BEegcM0F6CncUdPSMqko6biB2Wd7lZX8BFg4sTxs8c04zytps8/6sZum73unYqjo1AMVnXqg olMPVHTOHJu+aU/83jWgojPSHqRAblDdwCQ3qGLcoMpxg66JFPogewr2lZRjR+IFN6hy3KDK cYMqBuboEriVSVTcE3d3Izc352KC29tRf3zk7fYCFffI21tsEIvZI1TcI25Qpby+9s3T07ic WYPu70f9/Z232wtU3AMwRvcdmAMzrEG4j30I14jbq1HhojvGf7CVF7fwUienmW7Do2aKXc5g TqoBaJ+f8eHh4yPuZw3sSW9A6phqHWn3W6If+LSunR7aUbPGzQYSE5FQpJl72sfaPDycc8LM gZbbg3CKS2PXxhpi64brUxK7tGbRD2tQMAMdJoMHXRKbJSC0UwInic8m8wfsfvPyMmo5g+zS dymDgK3dsPqw/11Qs3ABqEGytIWlzjRELJu6nQxgv2vhScp95yqlthkEhsKa8VltSmoWRPCj QZP7SIYtX9n2madGY3Ok8cqSPchql9iDLPT/T5TW7HfBqk+NsObSWSEaW9LQb5ow0LFypPEW nNI0DiY9P8dttzjF2VmUmlFas5moA1CDpo4wGPs2lVvOcvFroO9Baoai70G4z9oVg4JK/+yB s4eFNKa0ZpFIDZoSAvpU0MHkN4zMJc6S035zpPHfkduDrkU4tWVmUWnNItEaNAwmGgZWTTtC 49T1ITGJPREjLrHEpWxpEDZ5ezo7yrXmoXUqrdlM1I476czuPfZFlW14wxMk4IhpdbvhRfpK YP8BW33NtjVDsWGEzQPAmNKaRTcig4zOyM2KlNwa+x8pqVl0Y4lB3710KTqF2b1/SUHNopuL DAKYijkDkIybM2dhzcKFPQwovjRtDxWdeqCiUwl9/wW597cKk0uqOAAAAABJRU5ErkJg gg==</item> <item item-id="77">iVBORw0KGgoAAAANSUhEUgAAAHQAAAAiCAYAAACOTf3NAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAL9SURBVGhD7Zk9UsMwEIV9rtyBa2Q4 BEegcM0F6CncUdPSMqko6biB2Wezzkp+GztCwT+j4ps4K1la75N25aSq2rYt7AhqLGwXaixs F2osbBdqLExzrNqqPvG2JaHGwmXqgwSuCLovIGoRdEcUQXdGEXTNiDAHqYmoizHHhvQXiqA7 owi6M4qgOwJiakpem6jUuAceHnru7s7BB/f3vf35md+3dahxDqe6D9DhyNvXwsdHKCjrszQ5 Y0mNc7BppyHta2ELguaMJTXOYe2Cvr+31ctLn17VT/D42Ns/P/l9S7AKQTVNVCtLuRBS6ybE hHgaLIB21FFco98ahM0Zy+GikcH0oUEtL9TDy7asoCY+zUk72g7iTGBfEJteIWZsA7B9f4eH pa+vcJwc2F3XIfGNbcNuzBjL4Av+EtLJdHArdJwO3L+Q4KC32rBQbrQInp7OvmJnwubVUJxy 4765sQLaeOL6FPXNFcvgixU0Xj3WKaWWiUY5H/3lQWKHAfrjs0sxnpN/wNbLt7fe5glqU/Gt BAU2pl3Wc547VyyHC0AFlVUzpN4ZImCMkWNCIxPb30WxUr3fSVNZ2w4FQ338hcXGIyWWgxFM CuqsFgXOs3TqjuuMp3094v7KNTXU2m5RQy30+SdIjeW8zmqfEBQ1g+46sbHdjfnYA6oPHnF/ C06x2g+ivr6G9y5xyrW7lInESI3lyKgTU0EnUi6cYIXdS69e/xzoe6iKp+h7KNrZfckg0DI+ W6D2cOT1iUmNZWCkgv46CqZWF3VCvmNBeA/KnNb5POL+l/Bq6H8xnGqv3KWpsQyMVtDOCbHB IbVNrSwMGq+e7kFkjBMR7hYpN2ZJQXF4sWXqKNfqB02nhtRYjow6YSOD2do5+mGBwAp5t0IF HNmt3RbywJ4J1E+w1L8tNpYQAQJZP8AlUVNjGTQEghr7NXi7LsarBYUzKbEMGnIIeullWNHU wdoKhoRYBo1ZBAVIAZ5gcLKIOZ8rYzlc2MOPUlLi9qDGwnahxsJGadsfrTlGi+er9IkAAAAA SUVORK5CYII=</item> <item item-id="78">iVBORw0KGgoAAAANSUhEUgAAAGoAAAAiCAYAAAC3KkyWAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAALYSURBVGhD7Zg9UsMwEIV9rtyBa2Q4 BEegSM0F6CncUdPSMqko03EDo2drlaf1KiG2wdawxTdjP0kreZ/1YzdN13VOBZiisz1M0dke puhsD1N0cvZN1xyOdtlfYYrOmcMuJMmNqgOY5UZVgBtVCW7UGoSE78Keg31Hs2+N+gE3qhLc qEpwoyoAJsnSuKZZplgbDw8Dd3fnpIL7+0F/frbb1YQp1srHR26UVadWTLFW3KiN8/7eNS8v wzLHRj0+Dvrnp92uJkyxFmCQ7EswCaawUSjHPoVr1KvZsHTR7vOHPISPv/RhGE4+7crHUw0v czBJawDa11d+yDid8jhLwCfDnpBLrbVGu1vIbvA7XwLvDoPGBo46C2ZiUHJ/DG36umGQx1gu cZbm6ek8LswkaKU9Cqc+XXdp2BjOHa77XDAT8pbdsFHJFASNWtYYugRW933HcSB8vSS8H729 DVrJKF4Sf8sowPnrVyTruSfmLV0A06iw5KUlkBqiLs8wvD3pX1lsI+VZ2UJsbUaBNDMioxUo MDVvqQG4ahQ5r6cmpj6bgVjpSx4x+C0ipL8Sur5wyx7F2m/sUYyZw8icvKUKUmnUiWGUDg6g 8S+WrMN4rwcOpL8Suj6DU53Ug1mvr3nbNU59PKuumSLaT/KWBBGlE9OouPTp4JamO7TaLIV8 R4kpgnxHodxqN5mQ7CxHBJ6Tx8B15uQtCVJp1EEcFJA3xOrw0loLrDZAYpfQ9S9R2qP+CuQA OSrNqjl5SwJgo/oOg4ZAokkANNZTWJ9e9LRHbB6AILFL6PqXWNOoFubE58f9PlzLOCRXc/I2 EiV4GwLw3sQfvFZA0aW+DLgHb4rWFgT7E1jr7znnDdsDDOFxABg0J29Zg8wo0i1KM8Qim97/ nKl5ywpuMYqnrFke6d+W0KFV9i+ZmLes8CajAKbmJRMwKDdpzIS8pQs+NAi+XG0HU3S2hyk6 G6PrvgFlxF8jFZ+akgAAAABJRU5ErkJggg==</item> <item item-id="79">iVBORw0KGgoAAAANSUhEUgAAAHYAAAAiCAYAAACKuC3wAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAMPSURBVGhD7Zk7coNADIY5l++Qa3hy iBwhBXUukD4FXeq0aT2uUqbLDYh+QBut0AKGdXiMim8GtC+hf1cCuyjqunYOiGl09o9pdPaP aXT2j2l0pnEu6qK82m1rYxqdccoTBc+FPSYQ14U9IC7sQXFh9wAJdKKaibqpOVdGf8KFPSgu 7EFxYQ8IROVUvUVxTeNReHpqeXj4EwE8Prb211d73BEwjVO4lm2QTme7fUtcLrGwVp+1yR1P 0zgFmYoqo31L7EHY3PE0jVPYg7Cfn3Xx9tamXfYVPD+39q8ve9wabEZYTh3FBlMxBOW6ClEh IgcNoB11FtfotwWBc8czXFQ0IT84KOmjPHyw026q9JsftaPtRA5F9pWRaReiahuA7ecnfqn6 /o7nyYE8hQ0UY20LpzNzPKMb/A3FC/ICUnCdIpJ/W8FJsfPCbqSHunbt99oQLy9//uKkwpaq sXgr1n1zI4WUMcV1EwtBznhGN1JYvZOAHlzSYr16gP68oLpvHOoclNc5kfX046O1pYSVKfpe wgIZ1yYTJp47ZzzDBTCFpR0UUvIEITCHdA67M/zW2s3F7VFbJrZ2YkE4YR098QaYG88wAIwK K3eOAR6gd6ppjBQPa4R0g7kTc7IfKXR/5pYaK233qLESM7YjLIln6MCdeovfIKxelG2ybkSO dPfWg7IfKXR/Cd56uR/EfX+Px67xVixPrRYrxZJ4BgMbeXFT2JFUrBe1bNoRa0wu+DuWRWT4 Oxbt1rjZkAhR7AR4TumD1UezJJ7BwJ16C3fOgrGdZjkyVBOANQbwmil0/yFSNfa/QAwQu1tP 7ZJ4BgOQwjaOkA0TsW1sl2FSnTr0W5x+IKxpzctrptD9h1hT2Apids+P+zNdsx+9WCmWxLNn 5EUrmkDW1t4PFAbWQmzneaIajR2nbRlBfQVr/bsj44kyBqGkH2BI3CXxjAZEwgr7LaROoEWU VhyTufGMGnIIK1OF2d7R7DpyxGpzBDPjGTVmERYgJQyJBmdd1OnMiGe4kC9JjKfJ/WIanf1j Gp2dU9e/sxjulWaQGZoAAAAASUVORK5CYII=</item> <item item-id="80">iVBORw0KGgoAAAANSUhEUgAAAEwAAAAxCAYAAABwK080AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAPLSURBVGhD7ZuxdeMwDIa1wO1xM2SL W8IvQ6S5PoW3SJ8iXXZIm+cqG2QDHT5Z0EEkSEqy8kI6Lr6XCJBk6jcAQhLddX3f31iBa2yN l6Nw8n1byJ3PNbbE4SAX59gv5UXOe3REiwwtcfwisRRPtNlGS5wkbQ4vvm9PDnfyWWZ75mwG +dZJRde3M3wxd4Juz5ytwEV49eWrsFEWOVvgGKRJyMdH393f993DQ989PspFdn33/n726d81 2FoWOatHBn6XSUfEQqDX1/+25+ezDd/T03z/RUit1LSMnbUjg88Ve4Qhuqzt7e1sR6zPz9FO HZRIxc4XkItY+yXFzsop1S8EsNEFKhiRpraDbB/GqCHlSjOu1jHXWTOphlJBGASyNq1juo3o nUnrcCb0oOdrVrBcs0pRRxxEUtTGJED0DdFlIopzFgWTCONzXWfNlAQrQj0Swew5EKOUkj9X MBHGpqMnoMePFSysV5xvJmCCZgX7+1tw7IuRCNM24kS0LYgu+PPr/Lmus2YuTklhiCoRqiNq MjOu5efWsI1EgmEYVFdkYKHtOwYaUo1galRxtDAyQP6Pbh2C2WZoBjlWzjHsS60wxXUvqhIM aOpUtONYIMN9BrFUmGDbdtFhR70H1Qk2RcqINzhEtXYuYmr8gr5m5tuB6gQDG2Xh4MI+BjiZ FYXjp/s9BLTRaNDPSBHuD1UKZqOsJI7a7A3xTLBx27tI/YwU4f5QpWA47cDtAENxPFsomHfM VqoTjAENs2IiyryL55hUDYOUYHr+FOH+UJVgvPG1s58+kQQVhAOjIi7bdpYM0/gqU5KLmgYr g2KHaXsEoTxBYIrIUbjJR8SFtguoLiWXkIoYj1m67kCTgkWNa4Ih6uQCPd9WtgjG26LwsfVa LhMMSLWcGIi6s1iwVjAeUfPyg0fTPKL29lnC5YJ9E2uehyGQvrjl9Ro1NtxnKVf/PIyosq/V iLBLBGs2wpYKpuIQYfqabcsyAeWqBSP99O03BZ9IY9tG3FquWjDSL3z7TZTNlhDIpLV4qYDQ tGCl+1KK/bSGYgSx7EIUesk1SwWaffNNb1cSjKixfZfOkCpi2B+m7l4sza6toL/LRYMuaQJd KmDbCxiiy5yDCMsKZnrO2Fk7hYaZwp6dDTleBLN1kPqUTUnxqaCxswG4wFSRLnbzcvGzOxBH wBBbNyNnC+TqWEmwsF4hRukWTusX/0fOJiikZRbSS45FgEVLBWQfm65zZ0MsaQVSDFElQvHE pbRUwEYXzJytcfslyAZuvzXawN6/ZjtmzucabyTo+3+Nim2Y5NoU1wAAAABJRU5ErkJg gg==</item> <item item-id="81">iVBORw0KGgoAAAANSUhEUgAAAFUAAAAxCAYAAACrkOUWAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAQhSURBVHhe7Zu9eaQwEIZp4Pq4GtzF NbGPi3Di3MF24dyBM/fg1M9G7sAdcPOyDDcMkpD48QHe4H120bACfXwaCdBWVV3XNxYmWHgU Xs/CJRybS6ruYOEROJ2k0YHyJXmVY5wDwg4KjsD5GwRVQsL2No7ARbrl6TUcy+FUhd2X4nQn xzXbveDuETHo9sFYDvL7OxG11OVcyDtBt3vBvUPjSl3WQxxuxSnBunUQ3DNn1w09n591dX9f Vw8PdfX0JI0XV358XGPNp4g6NRfb3DoI7ha6bqLrIygivr39K3t5uZYRe37u71+McfkwuFek UakBCvFwqS17f7+WI+jXVz/WQI4W97MPFyzVC+xFHQZ3ylg+RRjrUlBRcawtV5gJnFr30b3H ZhWaV4PBPRKbiCuIh4i2TPOqLVO4SJVJJ36ED8H8+HCipgYZBiIEREhFyxi4vIsblxpnUv+o qOJUziEY3CNjohZBfhRRbX0INtb9b6KmEPFs18+9KZglapNv5CCpKcx3s6SoPn9Sd0/kCLNE 5ceICou5YyaPv4VA+STEqTqFuuDazHb++XU9h2BwjC2Kumj3Fxp30kbcl5hVWBbp/jld4rtY WtQpDES17lPBfFl30m2XmPrwYQ02KaoWqoAqGCfLd3+LFn3u6EbOztVSd1MH+WqFi7FZUQGx VNhzm7D9PhB8uo6gKp7bbsRt67Lfl2LTonbOaik5US6I3Z+GdhNmN9frxRZg06KCdWvuiXIx fLfmIFa4XspAZOtqgx47ht8fNi+qdWtu/vMCapnNuz4Pe2creuwYfn/YvKgEbSNyTtYLGCrz ooZ+M5VNi8rJNaN9oVtDAlFXLKdCTFQ9bgy/P2xWVFZd2NFbn3rD2KBChYN9ZNuO/v7iHL77 08DuxOUE2aHbbkkJGxJNy5vft+J2MZzry2aw6e4/h5jzQvRSwwIcVtTB5D9C414RIRSbyhRR eYvqX7HMYR1RgW6dEgzhFxYUSkXldQov/HiNwusULfczlBLWE/U/UfI8FRF1EQWvpsn5TSww Qylh1vPULZLrVNxpX0nj1E5U6UW5NzshDufUXFFVQJyqr6jVtYg61aXwI0Wlq+sqFQYpHMt2 bDFFKT9SVLq6f7+PW/1yoA7JsdnLfoRDijo2ajNA+TVTCBpbnMZMoGTZz+FWqDD3HRMVx9l5 qY78ocVpfi4du2O0HG4tFYNMykm6XBJ02Y+dWnkal5r6cGpSVKZj7UUYBveKaVQIBqOYgAOo S0S1OZp8mez+ElPRh8EdQ8Njg4m9axpFBOrd9QVE9ticPgjumVReLRHV508EG7u11nzK90Fw 14ykgGzoylIPImUt+5F9bGroBw9AztQnh8adIiZP3caW/ViXQi94FG7/+FuJ239TV2LNf1Gf E3UHC2/MoK7/AtsybOGL6asRAAAAAElFTkSuQmCC</item> <item item-id="82">iVBORw0KGgoAAAANSUhEUgAAAIwAAAAWCAYAAAASPXQbAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAANrSURBVGhD7VhLsuMgDPS5fCAqh8gR suAW2WfhXe6Qbcqr3CA3YNTiJ7CxTZ7zKnlDT1EvBiykpiXwdKahoQJNMA1VaIJpqEITTEMV PlAwo9F9Z7oOrTd6dN3AqE0/17+KwaiX3tsRG3wfdT8ft8OgOqMG90CI89P+/RH5+zjBjFoF skBQ12uSED+R045IJl9RTwXwTrD129jgO/UrFzgLIVeAE1zolvGUbO4Jt95nH0mSiEEJEm0V qskqbEI/l7a/gVrfaX7u66C10aLC5PEgud4Zn18vEczj8TCHw8Ecj0dzOp241N3vdx7zf7fB lrCaDZ2FyKI861YJGskHd7SpAf7EDByxIVxipQ3aSBVLvG37HGF1vpMfOiOO/MXr8kjabJOT DrFQ/PxbxCQ46kmQoQpmY5K/IBiIBYPX69X1GHO5XLgPY+fz2fXWQN5HsiaCLUJkWlVGIaMR /ODexbNbj+3Qbx4Bgdxv/Yz2QFZJLPUxbfcdG2NtxfEoICkYG2NMglU+lDYD8aH7WLF7xOg5 kgJc4C8IBk6iukjcbjfuh1iez6frHcmey0QsXvDx58CmxTtHmXRLciDSH2PCL7zL404gI/4N lFFBFDaDvHkmSGzGT7FdMBa8eX598tNPTQRDwDPvAzcZy3Reup6Nt8hRaYyQCEZWF8ALBpXG g50ktVrfSKXSswlerzCDyjZMqNzbnTORbw776Mi3G0Frk1AUZa2kMIzxeEpYihdi2uh7hNtQ /EpEYdtEbIn9HNGWx4QjLxL+WeYPSAQDgUj4e0yAMMzA8xu+POB05Nd/PSBwl0UL63IW2kn2 okhfXZ7MhAw6o7U8dpDJbyuX23wPKAiAY8u7MTcTRIIZW5Ej0IDLdJyzxB8Q1IBLLcQBkfjm +3AJRvVhtcvF4cxs8CBoLYvmwWvQu7GJTWXBZn05OCNoDlUJzGGR4Py2g+Eil1cRuy4J1Z31 u2POd9cHnuBniLlAHG+mH2KhII5l8SXveJQ4gqFF/oRg1jEtpRxkIbhvAjLJasTebfhr6Q2V 8y+gQjD5WbjlLP4CuCqZFBXKfFSaJpgptgsmP3u5JC6cnV8D+9VnjwvbUIKbWOZRV2H8uY// 1CFi/8Bp1FCJCsEQ/IUIl8N3XAwbPh51gmn479EE01CFJpiGKjTBNFTAmH8ra8K4gJs2JAAA AABJRU5ErkJggg==</item> <item item-id="83">iVBORw0KGgoAAAANSUhEUgAAAI0AAAAWCAYAAAD9/x8lAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAANtSURBVGhD7VhB0rIwDOVcHKjjITyC C27h3gU77+DWYeUNvEH/vDSFtKWVfoM6/tPnMEMphOT1JSl2tqGhEk00DdVoommoRlE0j8fD 3m43GTU0OGRFczqd7OVysdfr1R6PR7n6WUxDbzszykgwDbbvOtt1vR0mubYJozV/em5HbPQ9 jdv7vhzLtJ4zNHoXFv5WRQOR3O93Pn8+n+zQp8HEwcmEPCGcF6CSJDzTD/Y7mtnm+2rc4xjc O5rl2Wkwn0sC4S9RA6oLDg9Umm+IBkgybjRqPNmh1xn3GrDXf6vMVPieVhoNetZ44S9V5hNx ef4SNXiBoNKgRWHsq04dXEA1ixojJi8ej+YFWRP5QIuDGAxlq1HZPdEiulahbWBBJNPnY592 VuN7UTSU7SZ+jitXgQuZ5/bF5yomxVE/kO3MnOYvEA1a0eFw4HNsglFxMNaVpx4uq8KFkOOF otZEo4kpigaZDQJGNw+ReFtsh855BiTydefnYg+E5QRTH1ON7yXR5NtRmBQJOP6BOh18l/uQ OIjRc6TfW+AvEA1aEQ4NVBsvJIeJ7JFxEISXr8e9C2Ly8sSDMFXVOJtC3/Asz4tIJvxGyqxZ GI50b55JKi1CJfK+p4jjXoBKmN+TLXudiA9C+j4RmboUcJSbIwSiwQYY1UYDgjmfzzJyL4di 2R6Uuhqcxn6VBu9bxs7umol4gdhPEQDbxLtJLGaQaiOY53g+JC3EH2La6DuQxD2DFjr3ENv0 ookhApERkHDkhcKnef6AQDQIWv8v47+cZiEpwwyM3/g1kpKH4KUyFN4NYbu+T1WR+vRAJd3b CQihnj3oFkSVx5fj/bHNdyArGlq8rGawsHMgEQLBOiwcgQaKm6qUv6fEHzCLBnsYVBQcaEk4 9Kc34KqMejmcyQYPkvLZ9BKwvZa9LFxcV4sdAwTiHso83MNCQT93k/PmLq4mHB/ZNdL7d8ea 73JtDjEXN0F/ajMK92qwCOLpHEfwq8ifEg02u+WvpLSkZjPiB4GMcjpxex3+inpjFf1lzKJ5 /a9v3BfLffmngIwlgQTFhSoAKk4TTYrtoon7MJfGqFz+LNwXoWsd7kA5boJZR7ARLgN7AdkD 4E8fIvY/6UwNlagQDcFvkLBZfMdGseEnUCeahgZCE01DNZpoGqrRRNNQCWv/AZk3R+tb2GfI AAAAAElFTkSuQmCC</item> <item item-id="84">iVBORw0KGgoAAAANSUhEUgAAACwAAAAWCAYAAAC7ZX7KAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAFXSURBVFhH7ZVBzoMgEIU5FwciHsIj uOAW7l2w8w5uDStv4A3m5+GgoKZRWv6mTV9CWofAfDxnUNCH6QdcWt8PbJQgqS0/lZYlLQUJ gSEJaW8CLxsow4+FZbXykBCMElLfBTak3KL/8jeR1SSFSoGnaaKqqqiua2qaxr+KcRz93PJr yNx21x3S7fP0WwFw7DBgAdj3PUeIuq7zMcy1bcvRXMX1uBtXTmOU750VGAvhbqxhGHwcsPM8 czSWdfvIJalUZIrVylaKCXDsLhSA4fSZfCMormk48NCpfIeNcmbw/wQYgLFCHZ+Km2BNxTX2 apOtllv9uxwrDZoKcIAMI8TQhHv3F3cjZ5zDuHaOwPlN53O4tdtw5cdzN3W8j+HEpeZ5UpnA cC0qh5MDlFIe8L5eUQ7JAcop3+FwjdkXfRguKhPYCdcYN4IqdwEflA/8Jv2AS+vDgIn+AHbu Or8QFZtFAAAAAElFTkSuQmCC</item> <item item-id="85">iVBORw0KGgoAAAANSUhEUgAAADQAAAAWCAYAAACPHL/WAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAGBSURBVFhH7ZZNjoMwDIVzrhwo4hAc gQW3YM8iO+7AFmXVG3ADj19+ICHDtMoMClP1SZHqSo38+dlOBb2ZPkB3149Aj8eD5nn20f/Q KVDXdTSOI03TRG3b+m+JtBIke+Oj+jK9JCEEH0lI61sgACzLYj+v62p/4GSol4KU9mFtmZ6U L64F48QyILiCEwSHdiBNSvaMdUNpZTsnAwrJwyG0HeLgFoB0kTtcCL7nOme5c3p3eQKE9mqa xn7GQoBTiGPHfifXsq7nD6eY1hULd2QOob1wYsGlAJnLsNN+KKUi/ee9+HoB3HJQKRCWAVyK BZhhGHyUChtPKD9T6OGnVb7CoSA4dQDCxfG7EzbcEdKKN4zkC7Y0ENdcGFzQZMthZuAEDtoM J17fRzl3oqriwlOga5bC/gbtuWxAGPyz5HPl71F4B2prA4r/DTyX69c9/RywlsqAjvOCdksA 6ylZCq+LHQpr2lwzH6UqBGJhTduBlAxTbbdlKge6qT5Ad9ebARF9AY9C5REHjtZ/AAAAAElF TkSuQmCC</item> <item item-id="86">iVBORw0KGgoAAAANSUhEUgAAAGoAAAAiCAYAAAC3KkyWAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAALYSURBVGhD7Zg9UsMwEIV9rtyBa2Q4 BEegSM0F6CncUdPSMqko03EDo2drlaf1KiG2wdawxTdjP0kreZ/1YzdN13VOBZiisz1M0dke puhsD1N0cvZN1xyOdtlfYYrOmcMuJMmNqgOY5UZVgBtVCW7UGoSE78Keg31Hs2+N+gE3qhLc qEpwoyoAJsnSuKZZplgbDw8Dd3fnpIL7+0F/frbb1YQp1srHR26UVadWTLFW3KiN8/7eNS8v wzLHRj0+Dvrnp92uJkyxFmCQ7EswCaawUSjHPoVr1KvZsHTR7vOHPISPv/RhGE4+7crHUw0v czBJawDa11d+yDid8jhLwCfDnpBLrbVGu1vIbvA7XwLvDoPGBo46C2ZiUHJ/DG36umGQx1gu cZbm6ek8LswkaKU9Cqc+XXdp2BjOHa77XDAT8pbdsFHJFASNWtYYugRW933HcSB8vSS8H729 DVrJKF4Sf8sowPnrVyTruSfmLV0A06iw5KUlkBqiLs8wvD3pX1lsI+VZ2UJsbUaBNDMioxUo MDVvqQG4ahQ5r6cmpj6bgVjpSx4x+C0ipL8Sur5wyx7F2m/sUYyZw8icvKUKUmnUiWGUDg6g 8S+WrMN4rwcOpL8Suj6DU53Ug1mvr3nbNU59PKuumSLaT/KWBBGlE9OouPTp4JamO7TaLIV8 R4kpgnxHodxqN5mQ7CxHBJ6Tx8B15uQtCVJp1EEcFJA3xOrw0loLrDZAYpfQ9S9R2qP+CuQA OSrNqjl5SwJgo/oOg4ZAokkANNZTWJ9e9LRHbB6AILFL6PqXWNOoFubE58f9PlzLOCRXc/I2 EiV4GwLw3sQfvFZA0aW+DLgHb4rWFgT7E1jr7znnDdsDDOFxABg0J29Zg8wo0i1KM8Qim97/ nKl5ywpuMYqnrFke6d+W0KFV9i+ZmLes8CajAKbmJRMwKDdpzIS8pQs+NAi+XG0HU3S2hyk6 G6PrvgFlxF8jFZ+akgAAAABJRU5ErkJggg==</item> <item item-id="87">iVBORw0KGgoAAAANSUhEUgAAAMUAAAAxCAYAAABqO23/AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAfvSURBVHhe7Z07cuwqFEV7Am8edww9 BwdvEl0ehJObO+jYE3DuQNFz7NSpqyOHzjwDPTYf6YD4HGiE+sOqUt3bqIVgw+YAsuXd2Ol0 LKKmOA3H8Tic9Kd1aXmva6C2Hl1fPkFTDIfDeBj0h0acBnHPY2+4tbTv+vLwmuJ0bG+ICdFw +ztuuNW1v3N9OSxNcTqO+80coRgO+/Eu262R9nerLxPHFKfxKEL3tpYQoHPsj6I0t8Fw2DFG 54ba35i+tbFNAbEuZAi5ldHsdNyPux3DFI21b6Hvlov7c+5tmQINGBMKIx4a+DAM42GP/+/X m//e0NwXuqbqktL++/t7fHx8HJ+ensbn52fZDl9fX/Kc+TeLlfXdYqPGpXRjgZhChO99KnwL M8AIx0GFXnHT3WphGMa7jRCfNkVcexgCJnh/f9cp4/j29ibTcO719VWn5rCevptu1LgUmJ+Y QoiUqImcCtDvwBSLa2AcFUXsspzE19VUYocOkCwnx6TXQdoUce2hGaIE5fPzU6bDEL+/vzo1 R+OV9I1tFuCct2/MyD7m+U46PTxFzZ0qzqYQBY6HGohIM3c/EzwRRE69DjoN7k0YEFzmugL1 Vo2wOAJ1SpoioT3yplECGFMgYhhyNa6vr9AmuFmAwVLfT5rD8z2ig+zspvyh9EWegfrgXEZU nE2RCjNWxmpECjsT6w79AcgCExGYhUQYjhXpWkiaIqE9Oj9MQDHriokCjavri3uGMsRAOXUK NbBEPRvShKZbdSYG8ZAzAFimiBWShim1wA7dAYWzRwE1gpEUCMQyxYoL+YZwTBGrJxbS0B1G MIdJw8IbUaRE49r6Ir9QNWX/ITdDecOaCNOIdesSN11HbeRLzeIjdZ7ANAVuznQaGsPKaDkq uAKFqN1oWyDrqgeTYKNEtedQpnFdfVEGz5RIg3vR+odNgUHVp1ciPVkR8T3GQAx4plh09DCL qZMsNBVr2YAhWpkCoy2Oh4eHqQPjMFugZbs7GZxtijKN6+oryhDJjG8KBb6/c2YcwE1XU0BV 33hkjJuWkjSFKoTPoT7cxhG4c1sYzFNZH3UbLY2ZkpijGeeaolDjqvqKMkQ3alCm6WYc03r6 koSkW/VO58ldV8wt//fP+Oev/n8h0kCLUolKmO3BEyrEixLgv3//ObtMOWxmirO1L9O4qr6i 08cHTZRLd0rXxD4sExFoOvKZjJOuN3djgTd9SqIKZDrTQhwIJs9hZGKUStMqUmBnB1ubmCpR U2BBi3Q8IFuVs7TXFGhcVV9OHWQnVmWcuohOw7VyUNXaU0OE0gE9l5rJcOtbyRTrULXRPMAM Zh0BQ5inxObAeawr8H98bzVzbKR9VX0vsP+4dFMkoFMlGMJNwwHwtJguwH9+fmR6VbopmtBN keDl5WXq6OapcGhNgd0n97tV6aZoQjdFArp++Pj4kGkhU9BpVTdFgAobNWvD3VjokYJ09B4p zqBHijbEK8HfevSRs6agaX1NEaCbog28SuinmaTjTkfiYvOTpjhgDPwMEb3+fnefCjTtpmhD 1ZEsgnlOYQxgDvOcAufr4kS5izNFAd0UbeBVojxSuITWFKtzcabokUJxtaaoRzfFGfTdpzbE G+28hTYF6wkc1/tTsmVUNUWPFG1oHSk2o5uiCd0U10SB9tgJO3cDoJvCTzfFJZCpvdkVwxay ecZSQnNT+H5K1gFlUtNX+ztz+vKnYeU5RkW49Z1NcYELpda/T7EZGdrDBNgQAObBYimb/j6F 7xeIRHruWzsms7BM0fT3KdaB6+yrh6k9ogP9MRPzsLGUqvqSDu1F1HHuuOnfkrNMZpmIGERj GyhM/m/eMRpGvjFCNEKT12YKuilsjAEQKTCFwmcTNUqoq6/oE5HM3I6b9zYP/dwE11OzaHim QB6e6OQhM1Iol7Z5baaqbLpMNwBDe0yVsEUMsMg2T+Bp5Milrr7xTod70c5c/DYPT4F5psBA zuurWaZY3BymoJ8RQvWT0L15U91E7msz1f2Sdb0FGNpjqoSDgmhhjKLI07i2vsgvNPjjHM8U CtnXyLoj9tYO+d1URYTGsftRLFPEL0KBaKXdzyp8qo+q8K4Iua/NrP4Gu0uF0WBYYM/vjFXA EPTBYq7G1fXF3D+UoSjP3HFV/4gXD5FBmwL5Rt7awTFF2RsCUwslp2C4SawhrZEB19LdBiuv MDkVuWpS2gsw+tPnEmbnaTJKgcb19UWHJWWwQCfX9+O0PzWRVTc1jcozhRqwucymSFyowpkY ieSBsBtXE3+fwAiuRjCSNyqcNEVM4Fsjrj3WEIgIODBlwkG3ZkG+xivpG4tQsnOr/jOZUafh EquPOXnQc9ZgjHoGrjHkmp+YIiYSzmVkLCo6j3y4Nj/ciaqwF0bXT7yDYjEd32Uq0Xg9fTEt SzZvK4RpUlHYhZhCCem9Hm7k1tIyBBDi07DuaUAvGHEyK3PNBLUXpJ9aF2i8sr438peMBAhl TibBsOVDXL/4O2PIk45IMJjVgH7uZj1h8GhvSJqiQOMW+m76N++Olf7mncgqGsZjnIZh3gKU 5tD/xyiGPHGO/UpH8b2th5nmlGufr/E96svHMYUAYTVTMBpN1OE0LvKU6ekFOri7KGEo0H4i Q+O71ZfJ0hSCTRdKonFL5oG3wura37m+HLymAFsslEoXRrfGWtp3fXkETQFaLpTOWRjdIrW1 7/ryiZqi07k/xvF/F6y6kT4O+dUAAAAASUVORK5CYII=</item> <item item-id="88">iVBORw0KGgoAAAANSUhEUgAAAEsAAAAWCAYAAACIXmHDAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAG9SURBVFhH7ZaNjcMgDIWZi4GYh2my DMNw/oHIGJzCXXUSLZ9UqXWCY56fSV0+THPEWuCItcARa4F9xEoxe+eycz7HVGKKFD1c7+95 HXfZW0kFm4h15VA3SqIFiCggHsqGSYRQ7rDiXU67CZU9xLqC2GTK0bt8/xwB9w+dIuON6EK4 BxqxrsCWDBcshoLQto9F/ROtI7hOe2xAzDgqWsdZdMpriatQzmKFAySlpdhRH/n72yhFlrOi +RidQbHkZmyxsH7O1V5/EZ90RCOW7iCJ1SWqD9a2TXB7OTA92Hu0l18yLxZD+xicazqeYoA9 lOZNmEKIhYukAPq3YOA4GuFQYmhrs1vrzqLn3dd4vZmewIYOXgIyjmfWvYeZnFIstRhdYnUP hWkSN4cl0OR6B7jJ0riZ3MOJAGS8qZmnZVostmjtMh7sVjl919hVIjJw3p+hzXFtdw9LDB/d 1C9qseKIvPY01pUi1sPIabqu9RamIlRhnwCLZdl2QDeCndPm5n9H3JoV+xGkUZAjh8Lrez4E 8TZ8zXi8QMD6VyHNHZS7MikWi2A6ENxUD1/7xbA/S876do5YCxyxFjhiTZPzD2yHHP9G02C1 AAAAAElFTkSuQmCC</item> <item item-id="89">iVBORw0KGgoAAAANSUhEUgAAAEsAAAAWCAYAAACIXmHDAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAHESURBVFhH7ZbtEYMgDIaZi4GYh2lY hmFoQoIm8Qts+0OP9653ChKTh5dUV6a6NWENaMIa0IQ1oOfAyrF454pzvsTMY0Y5epjfPrOO u+LN4joXEt+d6yGwUgkNQIUWYMQIxgOD0ADs2hXkAvFVsFIQBeUSvSun9cHzi4MUXAGOddtZ KZBVQ4KgkBDauTPOX2ULwjztcVoFMKNMmuDW9RIi64tjSOQDvKyGxB31ka5/Jk6ee4j6HSSN Bckij2Fh/hRLz/P4TvzbsDYLlf1B2Be4UB8sxAyPw3pMyoPtf0i4Hxap1iH6Wo4Bjh5vktn8 m7AwmDzP9h52B4LSLb3YFuAaQLT7YQLjztKbRuvP60MnMSzsWQug7dp7sExQdMnV7i3zqomC VKxfiNpDfV1PbAlX5UbH8WtYZN22y9jYz0tNAa3dro0r/tHratGU27KHPIavVvmb4uWcMgDm ebBmTwzLHrkLQZLtm4bW3t+tJ4lgqZ5wIQUKJfpDVU9PeabcoUX3BKCiPZ54FOSRq9aW8N4j 8W94rgwfqgunCo2v0VntUyFvG+ib1AVLN3/8GeeAm1rzvfpjeLK6nTU1YQ1pwhrQhNWtUj4v aR+3V5xsRwAAAABJRU5ErkJggg==</item> <item item-id="90">iVBORw0KGgoAAAANSUhEUgAAAHYAAAAiCAYAAACKuC3wAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAMPSURBVGhD7Zk7coNADIY5l++Qa3hy iBwhBXUukD4FXeq0aT2uUqbLDYh+QBut0AKGdXiMim8GtC+hf1cCuyjqunYOiGl09o9pdPaP aXT2j2l0pnEu6qK82m1rYxqdccoTBc+FPSYQ14U9IC7sQXFh9wAJdKKaibqpOVdGf8KFPSgu 7EFxYQ8IROVUvUVxTeNReHpqeXj4EwE8Prb211d73BEwjVO4lm2QTme7fUtcLrGwVp+1yR1P 0zgFmYoqo31L7EHY3PE0jVPYg7Cfn3Xx9tamXfYVPD+39q8ve9wabEZYTh3FBlMxBOW6ClEh IgcNoB11FtfotwWBc8czXFQ0IT84KOmjPHyw026q9JsftaPtRA5F9pWRaReiahuA7ecnfqn6 /o7nyYE8hQ0UY20LpzNzPKMb/A3FC/ICUnCdIpJ/W8FJsfPCbqSHunbt99oQLy9//uKkwpaq sXgr1n1zI4WUMcV1EwtBznhGN1JYvZOAHlzSYr16gP68oLpvHOoclNc5kfX046O1pYSVKfpe wgIZ1yYTJp47ZzzDBTCFpR0UUvIEITCHdA67M/zW2s3F7VFbJrZ2YkE4YR098QaYG88wAIwK K3eOAR6gd6ppjBQPa4R0g7kTc7IfKXR/5pYaK233qLESM7YjLIln6MCdeovfIKxelG2ybkSO dPfWg7IfKXR/Cd56uR/EfX+Px67xVixPrRYrxZJ4BgMbeXFT2JFUrBe1bNoRa0wu+DuWRWT4 Oxbt1rjZkAhR7AR4TumD1UezJJ7BwJ16C3fOgrGdZjkyVBOANQbwmil0/yFSNfa/QAwQu1tP 7ZJ4BgOQwjaOkA0TsW1sl2FSnTr0W5x+IKxpzctrptD9h1hT2Apids+P+zNdsx+9WCmWxLNn 5EUrmkDW1t4PFAbWQmzneaIajR2nbRlBfQVr/bsj44kyBqGkH2BI3CXxjAZEwgr7LaROoEWU VhyTufGMGnIIK1OF2d7R7DpyxGpzBDPjGTVmERYgJQyJBmdd1OnMiGe4kC9JjKfJ/WIanf1j Gp2dU9e/sxjulWaQGZoAAAAASUVORK5CYII=</item> <item item-id="91">iVBORw0KGgoAAAANSUhEUgAAANIAAAAxCAYAAACvSfduAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAhRSURBVHhe7Z07cuQqFIZ7A3cfs4be g4O7iS4vwsnkDhR7A84dKLqOnTp1deTQmXegy89DfUC8QRLdzVelGjeSEJzDzwH0mMPU6XSK 8QrpPA7TMJ7lr+3Y67qtsqY9uq3r4BTSeDpNp1H+2IHzyK4/dAdv4Ydu63KsQjoP+4pohjn4 eMcO3tQPd27rUpZCOg/TMdN74+lQ3Rnj6TjdpX8L/AByfHG3tq6AIaTzNLChRJ772LnHQ/0e FA3qOLDcb4O4Bl7iB5Dpixuz9ZboQoIhs7ukcTqt5IRb6SnPw3E6HCKEVOQHkO+LLWzd6gJH Sbk0IcHRPiOiN0VDOI1wFP4+kl5vnFjyOtzQ+B02DtUl5Ifv7+/p8fFxenp6mp6fn7lPvr6+ +D7xb4EvVrb13otYIXIXXoiQMBwIDSeYgCCeYRS9HbvoYZOhwHrRbmvCQvL7ASKCcN7f32XK NL29vfE07Ht9fZWpuaxn62YWsUJkdCZESMyAgVryoQk9BkLyngPhicill+vMThXDnAMaTbDM MSK/DsJC8vsBNkM0onx+fvJ0iOj391emUlLsvZKtoxZPxNyOl3PRZi7wdsiP0YfJrnQFRlTB IkhSh7gXIbGK+kMaKkkzN387sEQtPkQ8yTSoP6J2bc6TqOONzVGnoJACfkDeNBoBJSREJhup 9q5va2aniMUTRCx1XV5ma2RE5yzLB3HOgnOlS3hawgIMjk+IzBchhcKZlrHo4fw9q2DRC/AK EaNGFpga+ZoJCingBwgGwqGoeZKVDHtXtzWumZqhWW6Flm6Kx5IuGYeBiTltJTOlQ9GE5LsI DZtikSHmCqiQbgzRO5IUdt2YeRaun2KEVokRkq+eWEyADyAetak0LD6Y0SrH3rVtjfxSdcRO cghejgJQQK3TcaUzpE0XnXoIMx8PkUKKHMaZsDz1IY6oLE3iAo2o3S0IiddVdkZOB3n9kEqe vevaGmWwRJYQ3kaMDtroIDi2dHb9QfxOFhLyi+jkQZyQFoKIY1lwM0ItHe1iKyGhV8f28PAw N3psarm5fFUsQFUh5dm7rq1ZGZIz8zdgMfQUdaHR1ZqOe0PygHQhIa+4TiAopKhe1IrpRIYZ riFQ8xgHWwlJoYZLatuMmkLKtHdVW7MypN6Xwb0m5+W1OpGOwZHOh7bEj9hS2nHsPOnSQv7+ mf78lX9XgAtw4Q30NMxIKNgZQovvIf7795+q5Quxm5Cq+iHP3lVtzcSb0nA1EdtECMHMnQGp kyudkB6RkG3cwkvc0C4JUQmn+mFYvg8GyzTwimBFDMvIGMZRIWFSj3Tc9FyVmhEJZNi7qq0T 6rOMHjIacJFcRMA7aXkMbV+udEWekOJssYKQ1mFtIUFAal4EEamnBdSG/Zgn4W8ct5qgGvDD XkJqkS6kBOgwDiIy07ABPDVAFyF+fn54elW6kJqiCymBl5eXWRzq6QDXHAmrduaxVelCaoou pATofOjj44OnuYREh3xdSBFUXsTamtiFly4kRo9IOj0iXbiziBS/tGsjZY5E0/ocKYIupLaI q5C8q00a+7wFTlZPUGODmPDMGj2/r9qBDPt2IbVF1V7Sg7qPpESjNnUfCfvrYkTTpoWUQRdS W8RVKD8imbjmSKvTtJB6RHLRI5KDLiT5o5S+atcWfueWLTZQMD/CdhtPf+dRVUg9IrXF1hFp N7qQmqIL6VrJ8ANWEGsugnQhXdhFSHi6NuWR+RS6kOyo1UQs16t7YKDEF20Jib5N4HlPiaE/ 3a2/hYBN7UP9VFrIRulCKp4UXl6mWoOt30fajQQ/QDhYFAHqZrGgzBd7vo9kEv0hFuNVC3wh k1b/8rIgBEZfz/C/uLfD+0isgPRtzMr0iKSDKEQfUVI3kAVlvqhqa9ZYc75cKrhElZAY/V8J Yh2L+hzZ4gVAv5DS35CNcKB68WrzTxYzupB0lGgQkTC8w28VnUp9UdfWrK2UZiajjVNM0mbO F/c0Mct7YeKEgEBxrH84qUiMSELB23+yuLZzGybCDxjGYTkeYKFBPYlBI1QJdW0d3xj9oO3Z 8mH5B74StByeIS8pJi8IGHHtO0lIMLB2cQjJdRJ6AXkX/KjC6kzqJ4vFtYP1vgUi/IBhnPn9 OkQlJa4lafaubWvkZ+/4ZXRAuczNUgDrR1GCXwli1zDan/VrQzaYL2Lnd5qQwmGOGsT8TRHh XOwSBaZ58yFi4ieLoyed106E87DIYH7jGyJy3SxOtXd1W2NoVpwh2tFSSLxuhgj1a4m2OIOy zOIRbdNljrwvrWrjSAtGAXCRWOOgR5qPRT40RGv5ukmp1FUT8gMDjYXeN1Irdqa4OBn2rm9r uwiSiOhgrBGJnaelafYQQzy7kAwBBrgIKXAiH9ZJxad+AQghWdlA9I7kOqyi4XlWBUdcDX4/ YE6EyIMNwzlsdBncJN3eK9k6IhIuQFlVm6PncjEsBWATkm04SNuyS5ypnQkRks+A2JfZS2k9 LPLRK8srFTQwa1wRUes28DdkLCi4RLMkx97r2RpDxqCrWwBRLLGxEyEJI1vPR8+QYwFNRIA5 SVt5WTraCnqzLBVfJ04/MOjTC2Ey7L2yrREhWhZThf+xj4GQaWQSEwatsLwW/x8n8qe9HQSq OdrO3cyPFBY/KJKElGHvLWzd7P8hi5u6meXShcR7rHDDDnHG4xmqPFxQ8m/0kMgf+6I/ocuO a7kLW4U6fki39z3aug6GkBgI7QXGpBFMbEaDQP48PW7B4u6ikaLQDzMJ9r5bW1dgKSRGM5NC 1ghyxqu3wqZ+uHNbl2IVEth7Upg76bs1tvBDt3U5TiGBvSaFJZO+W2RNP3Rb18ErpE6nE8M0 /Q9pQHmkegQvawAAAABJRU5ErkJggg==</item> <item item-id="92">iVBORw0KGgoAAAANSUhEUgAAAE4AAAAWCAYAAABud6qHAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAHASURBVFhH7ZbtkYQgDIapi4Koh2po xmK4hKCGEFbNuLd7Hs+MP8y6MXnzgS5PTEzhjEzhjEzhjFwSLgWXfVzq3adYcvQuO4eXz6Nw lujrM23MI/sK5hhSvXnBBeEo4DNO38kSwyYWJul8hMgkKYdV1CVmvwk8sleK7Xbh4KVqkB+k JBogMkFjl2Ip9kqKMUdLx5UKFsVRJBqF3UnKYP4uUAi1mHWcMfgU2EiO7ADck9nUcVSFEBMF A870UbgLvq/EdSZ6mXwD5qL50ewQB+SMmIQri5P/C4V76aUGIXcFBJJCXcIeRuMtyuNUjItK u7AWhj2n2hOMaH3AIBw64wLI+wFKV5aRD2tQ0BXDSGoCRXxxHUSfgrLbVpoRpncUdwP7uqL4 Ne5kYhdOOMWOOfoz0lWoWcDAcA/ZwcnY3gn+g4yziYGmYhNOszMudxz/vqFD4Uyq+PK28tRt zHLznuy7g5+Ye9K/9B13ciwl3Q5kY1Hp9uZDIOEODwGdvjqyA3shn4I7at0x/ZiWceFjiQWR zzwEdqpeQx9BEHP9/Fj05fsUDMKRIMMuxc+P8tvZA+ZvYu64/84UzsgUzsgUzkTOP86VEI7Q TAyoAAAAAElFTkSuQmCC</item> <item item-id="93">iVBORw0KGgoAAAANSUhEUgAAAFUAAAAWCAYAAACxOdCYAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAH3SURBVFhH7ZeNzesgDEWZi4GYh2lY hmF4NjbUJiSFqDxF+ThSpKStwb7+ITVp83O2qAvYoi5gi7qAKVGDM8n6yE9PJSRnTDL5cvB0 Dsbj6g+kHV3lu+ht/Wwk/glRY/JWOvFMondpKO/RJyuESyGoBARXEoJiW1oz2/D9BROiwuLW g7RP5lNt3yoqeJ+8qlQJFJDjWLOQHYEvUKJiO1DZo4B4b8WmkMmuAw+Eq/BU2OByXLr9BWDv qi11qCGDO+1PmXA+UJZgEbO0Otlhri519UtoAoylN1NhT4gPORP1OEK4AwZ9UqLmgSwNUdSz hTCbLIgtrVKJYMrD3UJgP8vKXBI+c1EQoO3Zn76oovUZEpn3HigyISoayXnRPksgc+ANfUWb ybbIY6Q4hi1zlhi2HRVpDlz7KGoZcfLSLU2xVXCUVCHJ32+ufURtjLHSRuYHghVef5vnmQhG rfsfGZh/3UoFO/WZiofGwLCo8l2MDqhxGbDNiv9UpWLX5XNZgHuVGKQPWZijGD1ReyPj5nvq Vat/ARxuT0odTzOn/wAkKmb4TuBKUKQ9ccdm0Nsws6VdAUF9OyKwzWSr53Y8ttPbEaf/OBH/ 0tWOR3H5Hiu1vELFsaH+RqZF1QcaXk0lQnXioTB72L2JW5W6uWaLuoAt6gK2qD8npX9BEodr yQHzsAAAAABJRU5ErkJggg==</item> <item item-id="94">iVBORw0KGgoAAAANSUhEUgAAAVIAAAELCAYAAACLVA7QAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAACKpSURBVHhe7Z27ccO81kXdwI3/Fr4a 3MVtQuMinHy5A3fh3AGj6x6cehS5A3eg/2yAEEEQIHEAkCKlvWY4Fi3xhcfiAfjA04UQQkgV dyrS7nJ6ero8BdOp678mhFzpTtO68sTKouJ+RcqCQEghrD9aKFJCSADrjxaKlOyE8+X92TUt T5KD5Haw/mihSMkuOL+fLu9n+9n02T2/i1rJbWD90UKRkv1xfr88Myq9Iaw/WihSskMk/9YS 6fksEe/z5fnajYBJ5k/vl06+I4D1RwtFSlR0XVfZ5BaRyTpmkYj05Nr5DUH3wXMvz+f34TjO HSLg/v9SbqhT1h8tFCnJR4TTxG8iSvFYku69ff/o+f35GoE+xw6iO12/5z2UrD9aKFKSyfny Pmc/JZ00pWOyhPCaZ53pc+0lKc34+MnAv2vg0R/eYP3RQpGSPCRia5qkkfVBokO02Im428Sl oyd3Zu4GyP3d/cP6o4UiJVl0p+H2pCagH9TLo+ljiqnIUcs40pxrtvvN/3bbPyKsP1ooUpIB ZNT6Krrk0SZRn2znKsd5kSJKHkT6yM171h8tFClZBn2MSun9/f31n1JAzltEfRSpHtYfLRTp gXHN4VMnx2uarytcqAEQTGTFr6+vl9/f335u4OPjw+zXEtj/9bMpX6RzTXvsa/Rq/11CkWq5 U5H+e/nnn3/7z/cMJCHydPdEQnhrNJcjIv38/DRTDESj+xGp3c5VkAUXm5xgH0ekUn/+79S+ HN0xjEgPjKng/nFGI0cXkYXNaPuEj5XG6dKNvrPLuFVNtiNgOR9Eob5Y397e+k9p9FFecOHI n+bye9RkT3UnjNcd7hfS4KEi0k36r+8HivSwhH2MM32OkUjVRF/uXk75Hk/0pIhJBLLxwbxr 5v/8/CSjVZ8t5eRHm9Ft+rKNSIQiJXNQpEdldAGof348UdEhkVFymBvUvavwCxeTUhEpZPn9 /W2iT8wjKv36+jKf/YtNqchzWzl5EbhMqUdEU01/ipTMQZEeFCM3V/nNRaZUsZe0CF4AYqNR 7z+RiNUnJlJI1G3/5eVlNI+I1OH28/YitUCa9sJcOHl9zREoUjIHRXpIFLcOQZKjtLB9gf6/ YqIcMVmHjpSEJpHylvhN+Yw3TVGkZA6K9IgoxDaVVRihTsU6AU3/ivTcpUj743YyXZIkRUrm oEgPhokeMyv/VJpC2B9qIrOliKyuYsUlpIiqVwPH5clUykxsd3Rpfg9QpFoo0jvGCGCSDqgk Ik7UkrN8FjksJxWkt9z8TZEW6R4q6/nSee8pfSxhpqBItVCkd4kVZFIKEoVaccxdpBqDK96l bomL9NHzaM9QpFooUpKHyLc0SaMirVgfWRuKVAtFSjKRpnjBi51T/YtrvAWftIIi1UKRknya DTXSzQ41Qm4NRaqFIiUqzlsMfkduDEWqhSIlhARQpFooUkJIAEWqhSIlhARQpFoo0gPiroJz ut1031CkWihSQkgARaqFIiWEBFCkWihSQkgARaqFIiWEBFCkWihSQkgARaqFIiU7xn/5cvlr /IgWilQLRUp2y/n9dH2234wzxcq9ERSpFoqUHINw5FOyIhSpFoqUHATJU4p0IyhSLRQpqQZv c6qrdBlvhJKI9NTkHX5kGYpUC0VK6mj2jlJZz0yW8UXQW0KRaqFISQVlb81P0Z3ilRdv2Wd2 bglFqoUiJeW0Hncpsr7xeE+diJvVe30oUi0UKSmmOw23JzUB/aBevplbnkZvXbr1OPiPAkWq hSIlhdSNdR+HFXgfMB+0UKSkDNzXqaxsf39//acUkDOjzttDkWqhSEkZ3enyFEnj19fXy+/v bz838PHxYZrnS6A5z6y7NRSpFoqUlBER6efnp5liIBqlSI8CRaqFIiVF4Gp6KNJQlIhCfbG+ vb31n9JApMNVenIbKFItFCkpYnxbkiUUKeZdM//n5ycZrfrE1ku2hiLVQpGSIlIRKWT5/f1t ok/MIyr9+voyn/2LTanIkyLdAxSpFoqUFBETKSQKYWJ6eXkZzSMidZhl5X8U6V6hSLVQpKSM xFX7XFLC5MWmPUCRaqFISRm4j5QivVMoUi0UKSmkrrLFRcob8vcBRaqFIiWF1D0imhYpK/Dt oUi1UKSkmO5UHj3GRcp82wcUqRaKlJRT8Rq9qEgr1kdaQpFqoUhJBdIUL3ixMyTqbovyZcq3 4O8FilQLRUrqaDbUSDc71AjZEopUC0VKqjlvMfgd2RCKVAtFSggJoEi1UKSEkACKVAtFSggJ oEi1UKSEkACKVAtFSggJoEi1UKSEkACKVAtFSggJoEi1UKSEkACKVAtFSggJoEi1UKSEkACK VAtFSggJoEi1UKQkAC9Xtm9menoqf3EzOTIUqRaKlIw4v5+ub3PC+ElPrFAPCEWqhSIlaTDA HaPSB4Qi1UKRkhkkHSnS/XE+m2Fenq9dMJhk/vR+6eS7eihSLRTpQcH7O+sKesY7QCUiPTV5 azNpBbpenq+jCwxl4Nyh9dD/X8p+Xa5RpFoo0iPS7K30sp6ZZOLQH/siNUTLle50/f6pqvxT pFoo0sNRNk5Sik6ag7EKg0p750H9sTD91b0kpRkfP5H6d1w8VeQfRaplVqRoLrx37ZKz9frS 3LFIW4+0GVnfeITPTsTNKnVrzB0UTqQzksv93TwUqZakSLvTOkPjnlFxV6+Y+xbp29tb/0kP 8qVp8qEf1EurUUU0U/nY9Vqw7WiT9U4oz/dxpDnXbPeb/+V5R5FqiYoUHdqrekhkum6F2a9I v7+/vYKu7VlBhWp9FX0vlcbKYsfnvyrq8l3yyFt2tv/T7yeVqSw9KVIt0xxFX0x26kvhT/Sx LYHbN9Zz6T5F+vn5aQr339/f5efnx/xVgbxRFvDlbUBgNXlRXgbG3KbyqvOggOp8p0h3TyBS VIr8iMc0I0qFVSCFfPJE6pqxpw4FB5/XvcCCbX19ffVzBaCSRHbw9fX18vv7288NfHx8mG0u gXQoP+58kc6nd3dZa0Tm2vSppTrfFSKda9rnd51QpFrGpQhyyw1NcOFIpFvTRF8vKs0Tqfkd KrO7Hw+iWrEAoXAjOkEzD5GJmohIsT5MMRD5YJtLbCXSrdMbtEifWqrzXXAnITPNpFnqd06w FOk6jEoREjtPbPZKLn5f1dcpFalq+SR5Ip1E1NGID5UfBTNMG/t0iS2wEsWPvrPLhKt6eXkx EyIhRElTwm2N1zPZXwHb98G6fXHkXODIj1Qc6A7oK2s4zVTAvPT2qU97/ManJH0sM8c8ewz1 +W5AWl23maqn430M8zS/vsr2KVIVXilDJuQ1692N2siYhTK0wFoZJutd3DEcr18gw3mPSORk zvwuEsMJYWF7LiJZJLItR6wioML4YN41YxH9pKIxn/wKFkPSLSsiVaS3T2Xat0ifGlrku8OP NqP5hXX038fWRZGuh1fKcuRjM+OaWTLFF5F1me+XKgoqU+ur0CDjWEZ9tP2zy4mdRQEerQ7L +s+gZ/T3IiLJYbItD5P2wZfIA1dZEV1hHpEP+uTw2b+wgXXHjnETkSrS26c27VukTw0t8n3A i8RlSj0iqjkRx6FItQwilQK5eH+n/GZ4qGYhosg4wwIUjMZlV1gW6fiEgMg6eSByUhjLHoV+ 0kRdOFZsBxU5dtFjYLotn5hIIQl3HKi0/rzfH+eO91YizU9vn/q0b5E+NWCdtfkeAmnai3Xh 5PU/R8jPZ9kfilTFIFIpkHOJbDJv9P28SPPOsMjcxjeYG6QgzG584STgg4o6WheWHR+bqYQL B+siIDdFr+JOthWw9P0CqYqUm1flKNLbp1Ha55Ivmnya5HsKLHdd97KI9yLS7Z5w1FGzXyOR pvLSFFY/s0zzymVgrILkn2Gx7kb1wGNBpIqCO5VMeGzTyj0HIhPXxAxZFBrSPXdDEVIVaXWR FoqiddovsYZIHVX5nsQev62Hy9H0HkS61hOTrSh98jJLpGoUFWdrkWJ7uQXPrCc8IYR9cjjW zJMGmpLon0MT029WWiLbmlBXwOMVqTBazESX3j5t0z6HfNHoqM/3OVAmPJlKuY8dgbrcryDS 1Z+YbIWUK205WEWkmjMsMrh94kpBaLBSU/gm60Ehk4KPdD6jErSJiOLbCoH0yitdXBRY5zrR Rw1bpr0jnj7rkpfvS5wvnfee0nxhpkA6Ny4Tua2pa2s3fXI3aRb5zfD/6bFr01l77WYFkaKA 51d2HGDLymCRfahaqa2kyQKJM5b5DvuuSO0oC9sKqLk4h7Serr82rVqzZdqPiafPWujyfVtk 35qKVE7WWU9MIk368m2EGllG/u+a3mM5hssO9cT8DumsKedYhyINBpH++8/ln3/7zxWMD26Z //33P022O0YSdVdyaEjFCS8qioYtkaOzrUj3TGORQko56SplcXCH7f+dLZs4qQ629MTrSbVH 6yWgCVoaRqTYef0ZFgfYviLfsUhRwIZ70LIxBSmSN3wLviWVPo9JW5EibXOSNJQdugjTeRHW Aytes7wv2J5w3VlE1pNinYtNCnCA7bd7zyIVcJtGi1J+7rz7gglxtBQpBJfX1QcXjE7ySZEO Qdv4+/7/kbpfJFJFOlCkB+W8xeB35EFpKdL8upgvUouRo9ePau9J7yPTYP/LRJp/EqBICSEB DUXqXRxaRBw0yM4Kcb4ay346kY4uDk2XLRMpdimvW4IiJYQENBSpeGUuqhwDMfbiyrlq7ot3 crGpjUhzn7ykSAkhAW1FqqqKRojo//Qiwf5/WI8Rovl+2hfqfzeSN4SbWGaJXD89pEivicqJ 0wNPaW4o0p1BkR459wi5KRSpgyKlSAkphCJ1UKQUKSGFNBRpoycmb0Xuk5cUKSEkgBGpgxEp RUpIIRSpgyKlSAkphCJ1UKQUKSGFUKSOlUTaP8fq349W8aJhQJHeB/5onGuxxTYIoEgdK0ak /nOs8vkkMq1IKYr0OLy+vkZHw8QwGvM3eOezxTbIEhSpY0WRjhMZG6JI7x8MY4wpBiLFFpLb YhskB4rUsZ5I5XfuOVYMX+q/0r8EivQYhBJDhOhLDyNkLiP5IusZnqO28y6r2myD1EOROlYT Kd4RiAJvJkns2jSiSI9BKDnMuyY4RsZMRZITpJyF74p0NNsGqYQidawkUj+Bz1aq0QS3kUY4 yl8MivQYQGoQ2ff393V8dkSMX19f5nPuhSCUmVTWaLax9NJfUsMNRRp7+1MAnIHyMPlNclnn I0y6i+PriFR+M309VWLH8F1GZlCkxwCCswXxyYzP7s/7Y7Xj/Y22MMdEhwKdLsj527AViSJd i1uJFOWjl+Do/aIe8v+8UUSHZXPfKRpjBZHiav1g+jMOCIU8sdBc5OFDkd4T4zIyASfXRvmC ckORrsWNRDoqH/7dQQnk99cykFwWgk2d2JdpK9JryOxPUpBlgfiuYefzQmiK9M7ACTaR9rkn 1xwo0jW5jUiRp/6Jdr77RmTpjdy4uGzvMG2ZaStSLaOzwzwU6R2CsjQpsPkn1xwo0jW5nUj9 PE2LFGVpHGXmLasvgzcVqSbyoEjvBYkQTn0hxdk/qIjI59yTaw5hxSEt2btILaZM9WLMXbZz ZTQTrDdn/1cQqc76uTuqgyLdHuT70O0zJL////mKoSGsOKQltxEpfhvv50zhuSZrWfz/ICLF hjWRB0VKSqBI1+RGIjVi9K68L+3DSJ4Zy8rvtWXmBiLFgegjD4qUaDEn68YRLvG5lUiF/qLQ 6F7Q/n9Yj5/3k4Attixkm/p9BjcQaRkUKSF744Yi3RkUKUVKSCEUqYMipUgJKYQidVCkFCkh hVCkDoqUIiWkEIrUcScilQx9KnmskCIlpByK1HFnEam9wfZ6G4M/RRemSAkpJyVSbT0UKNJt yN1RHRQpIeUwInUwIj1y7hFyUxiROhiRHjn3CLkpjEgddyJSyVA52+n3iyIlpByK1MGIlCIl pBCK1EGRUqSEFEKROjYS6fnyfqpLcIqUWPwLGbp3RpLW7EukcMTsm5tib326MpQruwq/nMV+ P2YTkS4eYAYUKQH+SI94u3nOCLRkLfYjUuMYSC+5EtlX/z2ko5Mwvhsvqy1n64u0ezdDS9S+ D5IiJRMmFYJsy35ECmYDNln/8J2NNu2s/Tzrp4xytrJIu8u77CA2QpGS9iCSWEmk57MUdSm3 1+YdJpk/vV86+Y6A44g0/O46VlMvyROizlREi9/cMiLt3u3GW0iQIj0WXZcagjsXEZmsYxYp 4NNRSOtBs872paGyDcdxltbV9f9Sbtpv+WgcS6R+MOdEapeRkzEWM1KN3EYp+7YUCOb6SS1S s4N9ocM0WQaVoD/b4yy/lBm5O6qDIl0FdOe0qF1SRrwhySe4E3VL/HIbrTxS/t33qUr7OGwl Uv/CTzB5C1kpxleC71Iijf1/IO8Yc/2kE+moAiARwqteVmD2Xxl9FELujuqgSNsj+TlnPyVd 4iS7SnnoIxJbSVNXaseV+rGLz1YizQNlInlywwnw+p3NQ8yGy4QizR2WublI0fwZN7diIh2D naBI74QGFWJEZH3j8mL74VuASnSNdGYEkfu7++dAIsW++lft3X6bk6fzk/cbYeQc+d1cN1Ku n7JEag7EFLDe4llneKxyuNUgRe6O6qBIW5OTlypQgL08GklsoVzpGEea6Qrpl/OW2z8iOxKp LD/Ju94/1/VefRTkmbes+622nDUVaRELpndQpEcAMmp9Fb1hZZ0F0YhXcebKhV9pZXrcIrQj kd6Y24o0U6KAIj0AOOMrK9bf31//KcVy11AbKFI9FKnjdiKVSvfe5WcBRVqOa6acOhR8fF4j LQUIJrLi19fXy+/vbz838PHxYfZrCez/+tmUL1KUxevvrk0+v2tgpXtbdwdF6riJSM9Soa8O NULtP89AkdYASUj6uXsiIbw1mssRkX5+fpopBqJRiGeJbURqt3MV5Ez6xH73mI+uUqSOzUU6 Pptjant7gY7HEKlJc/84Q+Ghi6WPpqb39NonfKw0JK9GX0LQg+Qm2xGwnA+iUF+sb29v/ac0 ENPSXR1jggtH/jSX30iX629T3QnjdUf3C10cDxGVUqSOzUVaCkVaStjHGM7bNLCzVhLj++gg n76ySN7jiZ4UyKNQLJCND+ZdM//n5ycZrfrE1rsWfrQZ3aYv26REcIKhSFVQpNtAkRYyugDU Pz8+I6WRtMLIarSuKVg2FpFClt/f3yb6xDyi0q+vL/PZXmzyo7ypgLYUKfblGoHLlHpEdLbp jgh/s/29JRSpgyI9cu5lYOTmKr+5yDRf7P37QG006qUPojGlSCFRt/2Xl5fRPCJSsNS/uK1I LebBkmj3gKShJ9cYazy6uk8oUgdFeuTcWyRsxi8wiqRslOgnT0yUIyDa2vQMo2ABcr1ZNuGY rhJdbq6vU073CkXqoEjvudRrxDZpjkrajMQxFesESLA6PcPt4jBuKNL+uJ1Ms7tF5AhaPbq6 XxqK9N9/Lv/8238+IP/773+y9p8iPRhIr5zKbxABTu7phRT9SmIis6WIrEHFmghdGVWvAo7L k6mUmXB3/ItUdrr1Pm9BQ5EyIt0GinQd0vf0opKIOPHdWT6LHJaTCtJbbv7OMe1fxDobVdYq zrJvw3tK3bR13+2+oEgdFOmRc68SP2q1UyBByWv3koeli1QOXPEudUs8j3my2y8UqYMiZSVt S2H5QP5G+xcPXsHumx2JFF1R/Uk/dSL3Awd/W/7/wxaG+S5jx3L9RJGSTKQpnvPMr8dc/+Lj 3Ep0RNqKtLybRPbDlRkj1Ej3kt/nP/pNuOxQ9q6CzRJp3usjKVKST7OhRiQyZfbsmIYiFYkV P8QgThpkZ++yCKs1/OGLGidvM5+Sao+RaYYjcru0KFKiAhexCqtFT8bgd+TGNBRpRV0MZXeV pEf6N1a85rtIVJwn0vyLrBQpISSgpUjzZRQCN0SjTR9ErV6Tf/wbOY5EEz5PpPnpoBRpb3m/ 36vythiKlJC90VKkto6H/rNEfOImqb9ZIhXw/2FZvy8U/Zv9NoLjyRJpJJJNMYg0+wkEu2N2 H+QzDqJCWrlPDuigSAkpp61IxVrZQhqBaPNaj33vJPB/j21ej2G6bI5INbf8FTTtx4mcZfYZ sHzF4gnuW6SjMzenm0z3TWORGpGVtFxlP/wr73P7BIn6V/UnF5u0ItU5RC9S+Z07u9jXj+Vb OwZFSsjeaC1SAd4oqZNGiDh5eZ7p/2dWZwQq30f218jSLBt0CbhlMCX2SfsAilqko/4I2fla XVGkhOyNFUQqoM/yENUSLlRGh0qR+gmMF+VamY42KWeL9PAWUyhSQvbGOiIFeC/unqvmuUCi YCTSxQ7h8DcyP36O2wrM/sJ28C6tM/fJAR0UKSHlrCdSgC5BzUjDW3F+L9+vQaSIJGeNBjEO /QZn/H6mjwEg2lwSac3LMNJQpISUs65I75FBpHPyuXb4+pNI8hp9xvGHt4hTejVvCYr0PrCt Glve1ignJA5FqsUTaWOpLUa4YK0Mo0jvgaUxn8haUKRaPJGi4DZqZmdJVMjply2CIr07TKuI Uek2UKRaRiI1hbVWbLKO3A7bdfpHAUV6f0ieUqQbQZFqGYtUkq6meZ8e3iLGmrKjSLcEb3Oq q3QZb4TKbeWQBlCkWgKRCmhuF0jIf4og5+LAetEooEg3o9k7SudPvHwR9JZQpFqmIhVWfwJB ZL1udEGRboO0YBq+oblLPMCxzkMbJA1FqiUqUrDWEwilTw7ooEg3AXnZMpkj6xvfi/wIY8rv AYpUS1KkoPUTCDVPDuigSLdg+T5hJegH9fJt/J5JTGt2B5EBilTLrEiPC0W6Pms8TMEKvA+Y D1ooUlKGRI+z74eM8Pf3139KATkz6rw9FKkWipSU0XlvI/d4fX29/P7+9nMDHx8fpnm+BJrz zLpbQ5FqoUhJGRGRfn5+mikGolGK9ChQpFooUlKEuW84SONQlIhCfbG+vb31n9JApOs8Nkzy oUi1UKSkiPFtSZZQpJh3zfyfn59ktOoTWy/ZGopUC0VKikhFpJDl9/e3iT4xj6j06+vLfLYX m3BByd3ONL3qT5HuAYpUC0VKioiJFBK1gny6vLy8jOYRkYKlV+NRpHuAItVCkZIyElftVeAW qiAq5cWmPUCRaqFISRmQYHUaSz5RpDuEItVCkZJCGlQ2PBI6asbzhvx9QJFqoUhJIfWPiE5f jYd1sgLfHopUC0VKiql5pywuKk2ziPm2DyhSLRQpKafwNXrjK/Peq/EK10daQ5FqoUhJBdIU V77Y2dzy1N8SZachquVb8PcCRaqFIiV1NBtqRCJTZtlOoEi1UKSkGgx6WFfpMga/IxtCkWqh SAkhARSpFoqUEBJAkWqhSAkhARSpFoqUEBJAkWqhSAkhARSpFoqUEBJAkWqhSAkhARSpFoqU EBJAkWqhSAkhARSpFoqUEBJAkWqhSAkhARSpFoqUBMyP8kkeAYpUC0VKRiyN8kkeAYpUC0VK 0kRG+SSPAEWqhSIlM0g6UqT743w2w7w8X7tgMMn86f3SyXf1UKRaKNKDgvd31hX0jHeATkb5 JLcGXS/PvTyf34cycO7Qeuj/L2W/LtcoUi0U6RFp9lZ6Wc9MMnHoj32Bsa5cBDqMeeXRna7f P1WVf4pUC0V6OPTjJM3RSXMwVmHio3ySm2H6q3tJeuNcjfHvuHiqyD+KVAtFejRaj7QZWV9y lE9yM0aDBs5ILvd381CkWijSG/D29tZ/0tOdhtuTmoB+UC+t5kb5XBtsO9pkvRPK830cac41 2/3mf3neUaRaKNKN+f7+9gq6NvlRoVpfRd9LpbGy2PH5r4q6fJc88pad7f/0+0llKktPilQL Rbohn5+fpnD//f1dfn5+zF8V6CdTFvDlbUBgNVGnLJ/oZ9Vxm8qrzoMCqvOdIt09Dy1S14w9 dSg4+LzuBRZs6+vrq58rAJUksoOvr6+X39/ffm7g4+PDbHMJpEP5ceeLdD69u8taIzLXpk8t 1fmuEGm8aW+jffu/nBYNRarlwSNSFFCpzO5+PIhqxQKEgozoBM08RCZqIiLF+jDFQOSDbS6x lUi3Tm/QIn1qqc53wZ2EzDSTZrHf6R/7pUi1PLRIzdnb/10oKlyI6c/keGpkXLDs0yW2wMpZ fvQlhDGV08vLi5kQCSFKGhHd1ng9k/0V8HsfrNsXR84FDlQu3UUeP8IJppkKuJjePo3SHr/x KUkfy8wxp46hpzbfDUir6zbzbn+K5im6hxajUtk+RarigUWKQucXyHDersPO2gLqF0xzZncF Xwo5niaZw0Ukcea35YCIwv+jwvhg3jVjEf2kojGf2Hrzkf2diC7GUnr7tEv7FulTQ4t8d/jR ZvR3vmyTIpRtUqTNeVyR4sx8LSz9s8szhXgkm/CsPlpXHEQkuaTENonoBFQaV1kRXWEekQ/6 5PDZXtiwldRWsmkl2kSkyvT2qUn7FulTQ4t8H/AicZlSj4jONt0lvZYf+6VItTysSI2UXMEz Fz3mi41//6aNiLz1IxJYKHjYDipy7KJHSOpe0ZhIIQl3HKi0/rzrj1vqI9tCpNr09qlJ+xbp UwO2U5vvIZCm6w4YT17/c4K8x34pUi0PKlJEIKlmZYTRWdxGL/7qY4ILcRGQm5JXceciBkhj YTuLhBGdAHnUrnYeZXr7NEh7FZH0qaFJvqfwm/IZ+4y0yksqilTLY4pUI6RJAZd1jwrttHLP gcjENTEnLFUmVPJqaYT7j+RYWaSlJ4DGaZ/HNH1aUJXvSezxO5nOtSrGrY6lx34lDShSFQ8n Ur+JudiclQL+HjZBITO/kJmoIK/ioSmJ/jk0MV2z8kpsWxMaFPBJpa2IFjNQpbdP47TPZpI+ 9dTn+xwoE55MpdyHazPdFf33dlrKb4pUy4M27Zc5d91wW40p7P1nU8ik8uK7s3yWglkbEaW3 FQLp1Ylj2keGde6r0myZ9iFbvjowP9+XOMt+D+8pdVN5vzfSmSLVQJFG8KMoOwXykkjIFlrd RZMYi9sKwFXb0vqBbU2TpS6tWrNl2ofE02cdtPm+LRSpFor0aIhISg4NFTfaR1a4vnsjmT4P CUWqhSI9HNIUV7YB5/rI+Bb8+fR5TChSLRTpEWk21IhEXoxGyQSKVAtFelBwoaKuoJ+XB78j DwpFqoUiJYQEUKRaKFJCSABFqoUiJYQEUKRaHlKk4yu0nDg95pSGItXCiJQQEkCRaqFICSEB FKkWipQQEkCRaqFICSEBFKkWipQQEkCRaqFICSEBFKkWipQQEkCRaqFISRPsaJzrssU2CKBI tdypSP+9/PPPv/1n0orX19foaJgYRmP+Bu98ttgGWULqz/+dKFIFjEhJFhjGGFMMRIotJLfF NkgOrD9aKFKSRSgxRIi+9DBC5iznd2ku2kcTn6/j4Es+ybzLquptkEaw/mihSEkWoeQw75rg GBkzFUlabH5YedohhGMDs9Vtg7SD9UcLRUqygNQgsu/v7+v47IgYv76+zGfNhaDx+EgDeduw Isb8vgaMuydYf7RQpCQLCM7K68mMz+7P+2O1n71hgVPDAXenU3SolJxtYP1uWTPWEq8urwDr jxaKlDQE0eLCwHHoK539gQJZ1zOj0hVg/dFCkZK2QJSptG8pUYPkM0W6Aqw/WihS0h6MlR8K UyT63jVuhDcXM7Gw/mihSEkjpFl/6qNDNLm9vkuMeHp1qBFq/7kSjsm/Fqw/WihS0ghJ8/7C 0NPT8/XeUFyhdxeM7NSmKY71MovXgvVHC0VKDsf49qnu8s7mfWNYf7RQpORQmFueRhHuwl0C pADWHy0UKSEkgPVHC0VKCAlg/dFyvyIdNf/sxLJByJRpd4lMrCwq7lSkhBCyFZfL/wP1Q0Bw u/1uEAAAAABJRU5ErkJggg==</item> <item item-id="95">iVBORw0KGgoAAAANSUhEUgAAAVUAAAA+CAYAAACbSIgDAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAkfSURBVHhe7Z1NtpwqFIXvuGpArgwi Q0ij2plA+mnYemmnm+5dtjKDzMDHBlHAAyJi+VP7W8uktLyI58DmyI/10RNCCKkGRZUQQipC USWEkIpQVAkhpCIUVUIIqQhFlRBCKkJRJYS8F13Xt82jfzw++o8Pu6n95tm36rutUFQJIW9D 92z6xyCkj2fbWwnt2ud0vJmOl0BRJYS8Bd3zMUamj6cgm20zfv+hhLUUiioh5P50UySKR31J U9VJ/dPpEijVVYoqIeT2tM0klh+PZ/TxPve8FBTVBH///u1///7d//v3bzhyEO0z0rISQpbx I9DUo73bRRCPaNNQVCNAUGHYL1++6O04VIFoylpMQgho+2YUyrSoev2qaivpAqCoRoBBPz8/ h711ILKVhPjHjx863Ww6WxjKWkxCCKCongIYtJTv37/rSDcEYrs2XTyOlDiWEGLJF9XU4z/6 W8VZAwFvIqpBn4q7RQzsniP1qZoObTmChKha8DncN9g8NcrlMXBO6ntCSA5bB6qs2FJUC4lF mj6q9RsFDy3hJLCuiMIRthsBaaILYCIUTT8dPQ2kcASSEOLgPdbHutP84CsUUAgrRbUQGHQR OCkR5SK6xcwBfMb/duDLj3oXIlF1Daz6IIRsx41CFyf/C8EMRXUD1rCIKqVHf5Dq6/z69euY xq9fv8bP84GvtKjqPhy9Hnk4QAjZgFnzb+tjbJlqrHuAoroRiCFG8GHkObX6OmulowS43bZe +ZUcm1dVsdT134nz2ftYH0BAG3GMRQVKjtCGUFQ3gqgSj+1u/+hItb7OOqLaPVU0O3w+O+fI a9s/MyrHHTivvU/gA6+fdbkeUlR3JNe4S4x9PJsEulWt68GFM5vz5BVvK7pKQ1TOue19vA8Q 1FhRXR7Zp6juBhwRGz18PSiYZ8nLEqfKq3rauE5jVMbp7X0KH6iGxxXWRn78h6Dmiu8Ooopp QbEWCDdw4SlCeOwfDLstuqxFbveBOu/wpa51ujrqsUdZ9CMfU06Ouucr2PssetD1rWqAprdY ma30abSyqEJQ5ShuDPW1MJ3J2Vcmr1DqVjaxiuQ1HF+B/MfNvUTHCKsxt/qMLp7Kttf+XLTl Fex9NuGvQ1VRRR+hVH70dAX3i8QcT7KCHDviDVcN5rseWb0UBT6PTWcrYVYGFbHy6gNhXCNO vpgd1qBdxN77+OBYPFG1AydNi4KBzyvWnSMCFVpGm6ZJdzgIIyX7JdX1vfPX06lCNf48wtGC sheLFceMsOZ2sKdA1GHs6ZcJz85OwZ/OH+wfySvm9Eqr11a/fCaBXAbN8eUytrJCwx6Drc3c x/L+95kNAeyIYzrjpp7YFUKjL/DdRey9iw8OJrAinOTM1YJjMh8h4i0y0pyH+NnGDPuo7Jb4 Yy0i6nudb4j98oUuyVIU1D6N73DeFhPoSmLtqcqETUtPpFblwyxOwMRq69N5oynl9efPn3qT QNQEP9dBLoO6nCwq3roKrW1lyyhsMxxfz9yGBlMnRlMOwUyrBLyBI4b9/y5i7z18cDSeFWcF X2zt/NbREjVOpMXMM2YBg4iq+ExHCs2JRuq3Y2xvzZmyofalrdzO3/hIvvSvoe0pNaw4HlQc r6FU3zfORaW8hpUY0ZJb6b99+zZ8ShHeQ5B/sLoMGuFy7Tduki1G1LXH700jI5+fYXcQ2FAT 2F3fQ9P0djWzvaer2Lu2D8TzX7jpPOh/NWHLGO47wGjBjcnGMYYR7JswpkvCsFKiCqSrv1d5 T62OuAP6XmXjjpXM2DDiRyD40iXmp9lxXdmD66i07ZQZKa/wkwv27aMpFl/EoqoZyXuoUAZz oySVDy895EuI2DQLdh9xbAj8PJt7m/YhcOZ6V7F3dR+cgMnKqBSjodDKxm/Wi0gGJCfqNOHk DnGjj5RGDTwndW1aUK4OCndgRB2dezeMQh63wZIftD2HE9y03eOwM/rgjd1RAQYhccuUkFdU alTkP3/+6CgJ+4ie7PsScgdOkvewuQzmVmjfzp26ro7oIhdIXztiQ3sN9Ziv973vFION9U/X X8TedX1wDkZR1aKIQqA39MHFbmFqDT1CBwPdoqlHkylsGvAL4BzhkSIbU8H1fWC6xlU8UUJQ cSYfOhXS8enc3hFfuqg0rD39RhY+HNJWvpzKi/GdveaYPaGSo4Kb88zP1rj77stnxAGbkYV7 iJZBfFWpQnt2tptpdOS/W7J7xIb6Otg3B7wAQoH7GV/AcxF731hUl0TOQXCWJc9AikQaZA2q gIcN2Rpe6ofSvC6UzeJ7QLoLDcpevMTuV7D3gT7YESOqKwyVFk60YgvirFvb+xnyGLYVyuxG sAob8opoOZLR8nvY2CBt4DV2v4K9j/PBnny4j/3zUD8EornkqJQzc/6erAGPallPGDNe74vy vCpUwz9fJ77hHiAcxZnZwuvsfnp7H+aDGkDnjG6Gg5H+cOACWoD3b2LJKspeoXaML9fmVRVc ccDGsOUejnol3mvtfm57X+mVlSFug4Xo3Z0NkSmqaKGsKudEtOSVrCucx/qyPK/OgM3meyhr iLZxjN3Pa+8jfLATaICcaHVVpErOy7Fvd1/H4W+iF0am78z57H03H6DRoagSQt6A7Q2KagCG KWxRgr5hiioh5J60z/KBOhclmqnA2r5jw0JRJYTckK6XFiCU0kYWH2DwLhy7o6gSQu4HpoTV 01QxPQjqNGg3DbxRVAkht6PFm7tqPPpb0G/qqKqeRuXMiMBsCXs9iioh5GakFiCV0mav/qKo EkLuhbBwYYnlN3RBqPPej0JRJYTci8i7TLb+lEzuew8oqoSQeyGIKl5ziE0i96dkKKqEkLdE ekdBKJolPyUDUc1ZoktRJYTcCn+qkyEUVeyv/SkZKV0Jiioh5FbEIlUIZ85PycQiUooqIeQt kUQVggrxxJb+KRn1t+oYRZUQQiyR0f9cYuLJgSpCyHuCeaoUVUIIqUX+6icJWVQ5+Z8Q8rZs W6YaF1UuUyWEvCltkxdVSsiiqqLfzC4Fiioh5H5sePWfKKor0qOoEkJuiHpcL3hJNQTVTrVy hTV8u38Kiioh5J5U+zmVNvlzKiEUVULIbele8cN/ARRVQgipCEWVEEKq0ff/A6hIohCm8shA AAAAAElFTkSuQmCC</item> <item item-id="96">iVBORw0KGgoAAAANSUhEUgAAAVUAAAA+CAYAAACbSIgDAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAltSURBVHhe7Z1NtpwqFIXvuGpArgwi Q0jDdiaQfhq2XtrppnuXrcwgM/CdDaKAgIhU+VP7W8ukyvIingObw4/6MRBCCKkGRZUQQipC USWEkIpQVAkhpCIUVUIIqQhFlRBCKkJRJYS8F30/dM1jeDw+ho8Ps8n3ph06+W0vFFVCyNvQ t83wGIX00XaDkdC+a+f9zby/BIoqIeQt6NvHFJk+2oBsds30+4cIaykUVULI/ennSBRd/ZCm ykFDaw0JlOoqRZUQcnu6ZhbLj0cb7d7nHpeCoprg79+/w+/fv4d///6New6iayMtKyFkHTcC TXXt7SGCeESbhqIaAYIKw3758kVtxyEFoilrMQkhoBuaSSjTouqMq8pWMgRAUY0Ag35+fo7f toHINiTEP378UOlm05vCUNZiEkIARfUUwKClfP/+XUW6PhDbremiO1LiWEKIIV9Uw91/e/ig kdTSvImoemMq9hYxsH2MP6Y6rXV7hA0MUTXgs/9dMzs6uLxDgXyvO5EQkmbPRBXqu6mi6veV CSxGqgFikaaib4dmtDBaNS2IEMi5i26LKJxjhhGQJoYAQN+2o1jaf+umg3M9CmcgCSEWTrc+ NpzmBl/BYEctzUoHOhTVADBoFnBUINLF3yO6xcoBfMb/ZuIrtJIAt8wFnSzp464PQsh+7Ch0 dfF/NJhB4ENR3YwxLKLKkAhOiBNCowdfv36d0vj169f0OTzxJU6KDEGgEOj7kccdhJAd6Hv+ TX2M3aaa7N5bPdUYFNUIEEPM4MPIYbqhTQx45zIPA+yj6/bdr/xKjs2rVCw5/ztxPnsf6wMI aBOcY3mIYKZt1Ul9XbMlRTUCokp02+3xUZsc465hj8m2K61filrC/ArOkdd99r4S57X3CXzg jLOuTwjnrsShqBaAlm5vQXVmGWUrD3q71e7IeThPXjGje7zYPJtz2/t4H2RMTI3MARBINwgU 1Y30U1dKHHKCSSR7ucfZOVVeM8bGrs7p7X0KH0jDYwurRDd+jvwAaO1mnCeIqmQyGkrjAq67 RMhdGLwnuqxF7jpWOe7wW11z8/oqnlEW3chHbYdd8xXsfRY96IdOGqD5KVZ6S0WuKSqLKgQ1 rOJTqJ+xzovkklcoVWNweAtwfAVyu5vPEh0trNrc8hlRTmXbK3+u2vIK9j6b8NehqqgiTA6V H7Vcwf4hsr6TbCTHjnjCVYP1rkdWL6HA58nlbBtZlEEhVl5dIIxbxMkVs8MatIvY+zk+OBZH VM3YQdOhYODzhvvOEYEGWkaTpk533AkjPVLjEnJ+5/jt9FKoptcjHC0oz2K14ugBdXeQvQxE Hdqebplw7GwV/Pn40f6RvGJNb+jutc0Pn0kQLoN6/3oZ21ihYY/R1kpYVsbfUixsCGBH7FMZ 1/XEjPFNvsBvF7H3U3xwMJ4V4SRrrRYck9mFiLfISHMZ4mcb0x+jMlvij5WIyO8q3xD79RNd krUoyCz7wnF7TKAqibGnlAmTllpILeVD35yAhdXGp8tGM5TXnz9/qi0Eoib4uQ7hMqjKyari bavQylamjMI24/7tLG2o0XViMuUYzHQi4A0cMX7/7yL2foYPjsax4qLg+62dOMzMlNlRCYga J9Ji5hmzgFFEJT5TkUKTjIivho5MjDlTNlS+NJXb+puJqC/dcyh7hhpW7PcqjtNQIn3rpKG8 4tw2iJbsSv/t27fxU4TgNXj5B5vLoBYuYztnC9liQs49/a4bmcXxuXYHng0Vnt3VNTTNYBai mGu6ir1r+yB4/As3lQf1r8JvGf3vYjwxlP6qL9g2Rtg4+riAfRPGtEkYNpSogHTV75L3tbsj ro661rBxp0qmbeg3LGlf2sT8tNivKrt3HkS1445QXuEnG3w3XVPcfBGLqjS511ChDHoBRBS5 Xic9iIvT8OTbfcKyIXDz7KcBgdPnu4q9q/vgBMxWRqVwWtn0xfrGCDlRpQkn94gbXZyopiJO vvpOHHynSNUjEBGo6Ny5YBTytA1SBVv9Np7DTtveDzsjgtFpoAKMQmKXqUBeUalRkf/8+aOi JHxH9GSel7Bl4iR6DbvLYG6Fdu3cy3kRwS3qhEXc7hEbmnNIN199d34TRhurV9dfxN51fXAO JlGFkWBYvWEMLn0JHboc9iG+gwEc+5DjFovk3QK4BC1uqejqCq6uA8s1ruKJEryKM/vQqpCW T2P2XvjSRtKYunyuw8WHY9riy7m8aN+Zc07ZC1RyVHB9nH5tjf3dfvhMcMLGI3oN0TKInypV aMfOZtONTurv4naP2FCdB9/1Dl/YcD3olitXXMTeNxbVNZHzQEULHJxnICHgcFICGpCdhS3i y/qU5jWjbBZdA9IdG58jeLrdr2Dvg33wJLSobhG5pEHRwq47xJ/gIKXsLJRPr9g2O/KKfMbK Z/E1VGiQSnmJ3a9g7wN98EQ+7G5/LNSfEINiPCdNypkQXQpqTdBVKyrjWb6sS3FegTT8i8q8 5xqKxWEnL7T76e19lA+qAJ3Tuuk/4cqdDkyAB4lM9lTGHT+Tg9n+CLXjfLk1r1Jwm7HASj7t Mfu913DEI/Feb/dz2/tKj6z0sRssDHvay7yyRNWdxFoqMzmWLYXzaF9uq0jo2Zh8zhM2+69h e0O0l6Psfl57v94HTwMNkGWT7EiVnJtjn+6+jcOfRP9m3azz2ftuPnCHNSmqhJDbsr9BkQZA 0kgikao9NkxRJYTck64tn6izWRlD9l+tRFElhNyQfqj5Zo4ucvMBxpv91WcUVULI/cCSsHqa GkwPgjovQ50n3iiqhJDbkbz1ugSMm1qqqpZROSsi5pueKKqEkJuRugGplPy7vyiqhJB7gXWj G29/XX9CF4Q67/koFFVCyL2IPMtk76tkch8YRVElhNyLgKjiMYfYQuS+SoaiSgh5S9RttZ76 +aK5+VUyAkR19aFTAkWVEHIr3KVOGl9U8T3/VTKaULohKKqEkFsRi1QhnOlXyWAyyiyRWq4e oKgSQt6SkKhCULVYxl8lk3qcH6CoEkLek8js/ya8x/kBTlQRQt4TCOJeUcVif4oqIYSA/Luf ouC2VKerz8X/hJC3Zf9tqv7j/HSavE2VEPKmdE1eVBkCE1LLbr5Ev5lDChRVQsj9KHz0nzvD b71Ha0N6FFVCyA2R7vrGh1SrZVTjMiu9zdHucjggDkWVEHJPqr1ORSLWDfpMUSWE3Jb+FS/+ 86CoEkJIRSiqhBBSjWH4H6aWeYH2HorXAAAAAElFTkSuQmCC</item> <item item-id="97">iVBORw0KGgoAAAANSUhEUgAAAF4AAAAxCAYAAABTcB7hAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAPESURBVHhe7ZzrlaMwDIXTwPYxNdDF NkEf0wHV8Gs7oRhW1w/wQ7IdQhIQ3HOYExgg+LN0ZRySx3zrK1IHfhqHeRgnt/Zdla5FFfix 7+d+dCsH0TTSNQ05fDXgp+F40BcR/C6BrwP8NMzdW6mPc//o6S8n+l83zDVzG/tuDtkrAD/N A1nM+7ADegzNC1lm3hcdL3aME/YJOuj84NEgjspOGvsHa2EonFGWkZ08KlkXRv3pwU8DH41e APd4AB4sAa+79lqQRKmXP6c9r9uIzOvK14LO8UFycvBobM1mrFX0w2gBIjIbPBlCp/JRzHu+ lB2r1npwcvDUkEr4ZvBYSwBImw1hxOJY1sYEWxH3X7QGyrnBkxVwY+RVafoX7IDJBB4kziH4 fhU83sa+/7nBB57JKvLoyTRa2p+ziSxbIJwTNjNNUSdBdavB4b0O8KWGGnCuCNqiKnUS79lx xzmZzCB4VDNiFbIpEK4J16wYfBsII8AUTtQSxUaFc4TSD74RBFSGi2yodKC3H7dakmrwocXU ip0FW4OG7JH2aTl+lQ7wvz/zz697vVGmkxozYw/9+/vHXLPq4lqWH7u3ZsY+0u/xB1UOHsUo iADcYtu5DSzY+TMR8ZRUgCehui/w3fgVn6AsnZE1slRY0HHJGHhvaQS/NijwwggktvPDrKfm qV+RbvB2bsJuXyFKY98t89SbdR3wLsK5W2mSfHzpBsRm1CaAVwOPg/ko5j1fyo5Y4fski3Tw 1awGB7PjXsFWxP1flW7weXHlQdoO4kC0gb8j3i4E2QzdEcVum28kDs5gvDhPvUk6I54iPfD2 6AZq13nqu7gm4N1GQc1RjA55F50rgqe9xRuoRfcNVKYdwEOwEQksP7TcVR8DX2oLLLl9aiQH D0tw0Jfl6OG0w3x8XXJmb5kauefjGyXVsq1TI6zVnE4N4L19HuIRPtJlwNNOxiqO8QjfhcBn 8FJLoKj29ytdH3cIjt17akQN+HJD0/RP1ylyiYJdxf/iz155kHY/hnsjeA1PkiFaSw2NPLr8 CB+UgsN6Ftl+BLNxakTHs5MuYiUZcK4ItnxujC+vRf3CFVdTI7Y+wod9bG04Ofi1IblaQAQS sqclio0E34+FkZXtyJODBy8BbhMIp6Jl2VFRsQO9/bhVUUFNOj140+iESmgxtWKH4+tfSC5l Fj+05KTqO1BlKGVNdFO1MDcd4F6/RXE9UgCehBRutRWnuPBi2dZ5rQqjHdIBnoTx8ZPsPycK jLSGqAEP3b9l8EUd6tc7hov8esd5NM//AdiN/tsE4R93AAAAAElFTkSuQmCC</item> <item item-id="98">iVBORw0KGgoAAAANSUhEUgAAAFsAAAAaCAYAAADYMiBQAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAKGSURBVGhD7Zg90sIgEIZzrtzBa2Q8 hEewoPYC9hbprG1tHSpLO2/Ax0sgYWH5JBjjz+SdYUY3CywPmwWt1KLZtMCeUQvsGbXAnlEL 7Bn1M7A3m41pq9VKVVXVt/V6bez7/d56vk8/l9mXy4XA/iQtsGfUz8A+n8/qcDiYkuHD3m63 xn69Xq3n+/T1sAHZ1WmABlgfNp6jbuMz/N4JvYctRU2CrKpGicDWtNb5Q+SXDIAObWjQ/X4n B+ftdjN2orYh/RrRqqZ232u9dmkdy0UymwCvhTLDIwj9OZ5LB6M3hOePQG3/F2q32/XxIqOh VM3GbST0DdU2Qz+3fultQpxs4xhEZcSfsNtdbjBMUivB0JTC+kuh6mQg08ivz6fTydhSsP3y kgN7AIu1WjuBN55BBLtz4CYdhKA4u2x1X/8BsoJznEivzOwhbKlEX04GcCUMYtha/KRW2Aym RKQD5Xe/U5c1XNA5GlOzfRtbs7Uew7ZrKWTAwibZHQxq6jpLB+DispHKACp/QUF70Bm3DecL 4MfjkfQfcxvJhV3KgIUd3kz8cfGs5lI1UTKS/hPL3bMdWNfcPRvPqeK36jHsDmQpgxi27mBe kUR28xN1ATHzZMIuz+xQqZqdIx52tynGbjmUMiDRmOLunbBt42W4HsVNFAEwG6N3XUrj4yuv jEynqWADrLnuIlutza2jlEEfDZlIdwp/0JiGHtzhgID0FVHoqyIVdvt1B6Qv1Gu0Z/71o5mt Y/NqNflRU8hg3NZbZWdrooZ9qihsa0yohEERbD2Czsj/MlbLvVb26zdoDOwSBoWwIbweKZgI 5LtAQ+NgQ+MYPAH7x+QdhH3LI56tBfaMWmDPJqX+APu9YTsCcCw6AAAAAElFTkSuQmCC</item> <item item-id="99">iVBORw0KGgoAAAANSUhEUgAAAEsAAAAbCAYAAAA0wHIdAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAHqSURBVGhD7ZbNjcIwEIVTVwqKKIIS OLgL7hx8oweuyCc6oINZv8EOdjKWMrFZtFp/kgVxfubN83iSgTqb6WYp6GYp6GYp6GYp6GYp 6GYp6GYp6GYp6GYp+JNmOTPSMAzJmMgs5iYbLm7ITrMsTV6grMefGw25cPQpMsNiPDvxf7sK 3kbvDrMQeCQjPN2ZIMgZGovi2mGnpJIMkpZittOrNgsCpRJ31gdMT2CVP7EXUjjJxDAhXEu9 mVmPx4MOhwMdj0c6nU4s4H6/8zn+hTihZLMVnuM5MqO8oi+w4nIiGuTYgQZ6ce0YJmezYBRu vl6vYYbocrnwHM6dz2cf2/cJMTskvi7j0qrmQORbfDa2OJlW18KYWr2xL67MwiSqKuV2u/E8 jHo+n3xzvDGjUMLF6xsSE4pjmWyt3nQuMyutKhDNQoUBOfirMqQFLIrNqKgsnzRvs0J1tdBb NAvmpMS+FcGNqwRYqC9p57ItALZtw31wg07ecnZKKswHxXQLvaJZaOAIBIPiiHNo+Fx1UsNE SftXtvGv7hy5Yb7Z3+CR1GyMT3z5QcoDD26gVzRrK5urpdAXfptavVVm+acWP/JmYqmHw+9S p7fSLICSLZkhv5a/yz693PPCtoZhO836n3SzFHSzFHSzNkP0A8VLsMJn8P0CAAAAAElFTkSu QmCC</item> <item item-id="100">iVBORw0KGgoAAAANSUhEUgAAAEwAAAAbCAYAAADWHGlkAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAIeSURBVGhD7ZZBzsIgEIV7rh6IeAiP 4IJbuHfBzju4Nay8gTcYeRSaoR1Mx+Lvv+BLSKStzPB4M+1AHRVdMCVdMCVdMCVdMCVdMCVd MCVdMCVdMCVdMCVvBXs8HnS73dLsf+HtSMMwsGHILq4Zlx5uSFWw0+lEl8uFrtcrHY/HdJXj yIQk5ZzCvdGST7NvUYiW4zkTf7tV8Db5ioJBoPv9Hn8/n8+YUAmCj2SFCN6mpLylsZpgO5xh jrLYuBSzXb4rweAqjAwcthQMSUp29y4E5Tdw2t+oC07cKBNNCNcy35VgWRw4DGWJeXZbBAkK 9i1Oeo7pyY7yyU7g5OXNaJBjJ3bli3l+bnJfIRjK73A4xN9o+HAa5txxsW+IO8Tm15aunW4J T2wxtqjJXbYQZ0++KNcsXhQ4rF0IhvLD4MBlWUSABEbJMhU7V59vSNH8w+BpNMs39bhCMDR7 uIwDsc7nc5rVFpwcIh3kNsF2OCxsPJZcxWXt8p0cWQiGYPy7K78huYhYcLWJpL7zvigHsK0k PyM2bfb2c4Y5LQTF5Wb5hv+YEGgWDD0LTsJAGWLwz4sZBFs2Udg7vM5teK2X4CS/0/RjT8ni hM0vP1rjwMKN8nV2WmMWDI19JU6Fza6p9Im/Zm++cGm+PAsmf83XgDPeOSeQbZ+mv+XzfMue 5j4VDMC+NUHkV/Zv0edblj1GKPt0r7ORLpiSLpiSLpgKohfoWRQMKUXpewAAAABJRU5ErkJg gg==</item> <item item-id="101">iVBORw0KGgoAAAANSUhEUgAAADcAAAAWCAYAAABkKwTVAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAFxSURBVFhH7VaBjcMgDGQuBvI8noZl GMZ/NiZAPpFoq9Km7UmRHAOSz3c4CfLB+JG7Kr6JXBaOQUI4eCj5nvdF5rjVq+WO5DILcfYw SvRY8/zu3FBjjAx5PA50ZktVMErldgUMYgCJoKDHI/ouLEUSckvdCrNkd/CU3L4L63HH3U+w IaxYV0/IXc+SFUZoawSU9HzDyyzZ48GprSpi345cNvavteSD6OzZyNn4bB0aCNY1VRQxoau2 3scVGUPBux4Z67AHUfn+lH1QRd8P3XH/QCmkvEZPHQ6UQ+BwJJaUsg2cQCSMYrNbYNuj9xV7 FMMEswbpmXWWnybX2zWh89G/6pov9WvXYYeuciXXVFg/qCbJaeG1MCehoauh8b/PR7dWUIbE sOfJmCO3s16Nq+0y6jUFvfCc8LsG225ngGSWRs7uLey8gOMUuWY9jZu1zKpKrrz40MG9QsKU BCFmEBpUh1UX/ajOD5QL4kfuqvhgciJ/uV+xn7HiGT8AAAAASUVORK5CYII=</item> <item item-id="102">iVBORw0KGgoAAAANSUhEUgAAAEAAAAARCAYAAABtu6qMAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAFgSURBVFhH7ZVLkoQgDIY5FwfiFB7B Bbdw74Kdd3BrsfIG3iDDz8P2QcSZoqYX+lVR2hhI8hPSgh7OK0B8PpZXgPhkWZaFhBA0TVOc +UesJul8CyFJ2zh3hLO5WGu1JKGMfy8K0DSNFwDPHFYrClvVxpBKwftkcn44G36tTx7C3BGg bdu1AjCOWOM2jxtVx6g1SOeJtBR0csXZFNbeqoC+72kcx/UdAszz7H8Do4IoGNvN9+Akrr7z bIME8CcPtczZlNYWBcB9R9IJJI5Eu66LMwDJ3S3/cApJsN1g1EGQ26A5AXI2pbWXAqDkc/c9 BbyyK7P6fE0AJA8RjqRmGGDuJMvvK2AvMOOvdg9A0+P+7tAPEPAwDNghdFbr1I7f63Po5FJn fHE212uzAiCx0+lkhr8eUFgq0noja5a/N0GPFxp+YzKbuc8BZ2wAN4/Y/bwbbpPTFXgarwDx +VgeLgDRDzF1hTaE2MP8AAAAAElFTkSuQmCC</item> <item item-id="103">iVBORw0KGgoAAAANSUhEUgAAADcAAAAWCAYAAABkKwTVAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAF5SURBVFhH7VbbkcMgDKQuClIdVwLV 0AzF6LRYsoWj3I/j2OSyMx6zgEGr1zjxB+Mrblb8J3GNS06cUvBQ1T13RmVabaaduFaYStNh 5qxjzJcJtLVCbCYDT9ISEczDxvtji5oFJRYnkcq5iMR3YzHwUAXAdhUYihtS8hIcrX04aV9z HTOm5CMqReIuS0mPo5HD9w/imijeCnJaVNrVnBaieWgQaGuIqIxJvNrX/djQJN/V67nIespM lN2Z4lXwMDsONBQRZLZbdMOGEgLeoMK1tt5wkuR0EWMbDjVrsAf1KnuAZZ+udQfhm/el/Cou /Yw6I24RqvC8riONYT84OpTq6hziTBu4b1T2/ZnoN+Aif9kzvhhWt3WNhnETv3KIdRxNAnuM G/z4lein2uF/vZFenSMNg3lE0M8XtGLHq6Q03qg1vJv4wa+fgeQP7pcH3KceHp9aeJaAbhHE eq835UmaCuaMk/6oGj8Lgzjgav5KnHfyDfAVNyeYfwFelztSD55LzwAAAABJRU5ErkJg gg==</item> <item item-id="104">iVBORw0KGgoAAAANSUhEUgAAAF0AAAAWCAYAAACi7pBsAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAJFSURBVGhD7Za9scMgEIRVFwVRhUtw QBfOHZC5B6ceInfgDni3BwiQZUnooZ+Ab0ZjhJA4L3cLnW3sThP9AJroB9BEP4B9RTfKiq6z XSesMr6vkM/nQ+939vV6+Z5aGKsEYhu5pPZj6jAqulHS1p0GaCuD2Cz+ujkulwsLgd8xVsdO MUmfCUYJK0JWUL+qLMaX6EaTIJVXltEyyRiXVaXTXK/XPtNxDakTO2JbX4lLyCLXMpbUeOzI 1nKxALInLVPM1WfTAu73u30+n30bMb7fb74H87EvBFUoFEm/HYN0gahLyrPc/7KSJUpEh39D 6ADExly32833gKWxTzOMcwty0TMLqMta0WEnY/4dFrmnSuzbWwtIoi7x2fJMz0VZPhcEh/BD wobqKIl9gh2sBUTRw4nCmI0mHZxeFvw5bJy/jobwd4j+eDwqxW6KLO8/RNGRiUJaNXk+Wr+R MiwOKmJYwu676ckDYmYV9ONi65mMPY35R/x9XO7KhA/PkCTUllRR/DxtBwx93zuAUPSc/qeU dIDov0kViXs3+hxoCjRbi7NAiyqkslpTJfEpDAtM7dQyMQb7AY0B2WmNFw7vuP93GtFxxvbx no7UdjRlqvAVhf5YQbA37mYgeqwo7Dmxuk+V6ecEggbBvLho+uxF++uYmTxzuI0+jGmizzGw kNAO9kF7t8t4LygqVpH99O8Qmq0J+w72BdNEnyNaCNrRIthyILq78Zst+TZ1cOaT0EqR0FmV kOWQNTXRD6CJfgBN9ANoou+OtX8Nch0Qa8XFBgAAAABJRU5ErkJggg==</item> <item item-id="105" content-encoding="gzip">H4sIAAAAAAAA/2xTyW7TYBCe30u81ImblKQ0TuOQsNxQMYugp1JVCKksUvoAleuY1og0wXVQ j1EfgBtnOPACSIg7L8Bz8BZhliS0iN8az/bN8o/HHgAopAMkl2Ud33b6vp8eZ6NTE+g8QHIG o0RsBtueEGyYvD56myYFW6DP8Rq+S2cn493RuZhfIFlo6C+BLykW6XcAMMN6+5t/dTpflDSl gaazUFsWf1oUeXY0KVLFyG0kHy4fY7t7RTc5gVHkaSrZI6TymMVStPETYGpa0WfkOyVbuOUI t13hzkqkYZEPOB9TGVtwF+7DYwAv+rQPcOFCmd3fPUyoTHLfg0f44KlwgqnjR9TlbgVNqyxe VLiBalT7hX67xhkGVbyy+qbDWvQRG5xW4Rrbv66RXWZXl4xWQ1qrrwtvXJcW1wmptgA2WP+x 1KEpgY1AWmmiqSWtNDnxpih1VtoCLoWSvd0RHt6QKh3O+hCgK1U6yyo9CQxvSpUemm5J4h4n vi0Xai8udIfXxR9Eh8d5PD45fDPKh3FhyVVp69i8FxexfM3m/KtSFG8qCROkLu8efWo3Ps/O nnEe2dQW7aCa7znwMkIKCYzgFAaQ4QriA6p1BaPnEKP3v765dVHQKvI4SaM9nV3BHBiqVVCB QlyAs0EJo0KlqUDH2FDpKjBmZDHINUPBVIE23UFXiTCUwGIMCvYi3EEMh7uI4fAVxHC4hxgO LxOGoiqMQcEnDIGlX//VZJjmWRK/kynZ3PdzJJpGxmOlf8qdj/if411W/gAAAP//AwCsGeBP PwQAAA==</item> <item item-id="106">iVBORw0KGgoAAAANSUhEUgAAA/cAAAFjCAYAAAB47vuzAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAB5ESURBVHhe7d1LcvJKtgZQj4sBETWI GoTadyD0ql1jqKBVM6gZ6JKYtNNYYB7aUmZqrYh9zm9eypdBnxHiYwQAAACaJtwDAABA44R7 AAAAaJxwDwAAAI0T7gEAAKBxwj0AAAA0TrgHAACAxgn3AAAA0DjhHgAAABon3AMAAEDjhHsA AABonHB/z3EYdx8f48fHbhyOl8tuOA67cffXjQAAACCAcH/TcRx2OdQfxv1uOF1yy+n6jw/h HgAAgFUI97cc9uPH/nD5If34MRY//nDY78fBO/cAAACsRLi/4fow+5uH3R+HcTiF/nT9x/kQ /t8FAAAAkSTPGx4L98dx2H8ern99vVAPAADAUiTQGx4K94fh60R7wj0AAABrkUBv+fMz9+mE e78Pwc8BX7gHAABgKRLoTcXZ8tNX4t09W7537gEAAFiPBHrP1Pfcny/7/b33wj0AAABrkUCD CPcAAAAsRQINItwDAACwFAk0yM1wny4X/AEAAJiRlBnkV7jPob4sAAAAmIGEGeRHuL8O9bkA AABgBhJmkK9wPxXmpy4DAACAF0mXQX6F+1K+7PpyAAAAeIF0GeRuuE/y5VPXAQAAwBMkyyDn cH8vwN+7DgAAAJ4gWQb5Ee5vEe4BAACYgWQZRLgHAABgKZJllGfC/b3bAAAAwB+kyiiPhnbh HgAAgDdJlVGEewAAABYiVUYR7gEAAFiIVBnl2XAv4AMAAPAiiTLKM2FduAcAAOANEmUU4R4A AICFSJRBzt9z/yjhHgAAgDdIlEGEewAAAJYiUQZ5KdwL+HHKMTbWAABAZyScIE+F+0TgjJHH 9VYBAAB0QLoJItxXII/p9biWl19fBwAA0CDJJohwv7I8nrfGtLz+1m0AAAAaIdUEeTncC5rv e3QsH70dAABA5aSaIE+H+0TQnMcz45hva9wBAICGSTRBhPuV5DF8dByfvT0AAECFJJogwv1K XhnDfB9jDwAANEqaCSLcryCPn7EHAAA2RpoJItyv4J3xy/c1/gAAQIMkmSDC/QreHT/jDwAA NEqSCfJWuBcwnzfH2M3xGAAAACuQYoK8FO4T4fI1c42b8QcAABokxQQR7heUx2yOcZvzsQAA ABYiwQQR7hc095iZAwAAoDESTBDhfkFzj5k5AAAAGiPBBBHuF5LHa84xi3hMAACAQNJLkJfD fSJYPi5qrMwBAADQEOkliHC/kKixyo9rHgAAgAZILkGE+4VEjpV5AAAAGiG5BBHuF5DHKWqs zAMAANAIySWIcL+A6HHKj28uAACAykktQYT7BSwxTuYCAABogNQSZJZwL1TettQYmQcAAKAB UkuQt8J9IlTet9T45O2YCwAAoGISSxDhPtiS42MuAACAykksQYT7YEuOj7kAAAAqJ7EEEe4D 5bFZanyW3h4AAMCTpJUgwn2gNcbGfAAAABWTVoII94HWGBvzAQAAVExaCSLcB1pjbPI2zQkA AFAhSSXI2+E+ESZ/y2OyxriYDwAAoFKSShDhPsiaY2I+AACASkkqQYT7IGuOSd62OQEAACoj pQQR7oOsOSZ52+YEAACojJQSRLgPsvaYmBMAAKBCUkoQ4T5AHo81x6SGNgAAAFyRUIII9wFq GQ/zAgAAVEZCCTJLuE8EyW+1jIU5AQAAKiOhBBHuA9QyFuYEAACojIQSRLgPUMtY5HaYFwAA oBLSSRDhfmZ5HGoZC/MCAABURDoJItzPrLZxMC8AAEBFpJMgwv3MahuH3B5zAwAAVEAyCSLc z6zGcTA3AABAJSSTIML9zGocB3MDAABUQjIJMnu433KIrHUMam0XAACwOVJJkNnCfbL1AFlz /7c+NwAAQBWkkiDC/Yxq7v/W5wYAAKiCVBJEuJ9Rzf3Pbdvy/AAAAKuTSIII9zOquf+5bVue HwAAYHUSSRDhfia57zX3f8vzAwAAVEEiCSLcz6SFvuc2bnWOAACA1UkjQYT7mbTQ99zGrc4R AACwOmkkyKzhPtlqeGyl31udHwAAoArSSBDhfiat9Hur8wMAAFRBGgki3M+klX7ndm5xjgAA gNVJIkGE+xnkPrfS7y3OEQAAUAVJJIhwP4PW+rzFOQIAAKogiQQR7mfQWp9ze7c2TwAAwOqk kCDC/Qxa7PMW5wkAAFidFBJEuJ9Bi33e4jwBAACrk0LuOQ7j7hTUPj5243C8XPbDcRx26fpU +/FwuTQR7mfQYp9zm7c2VwAAwKokkJtScM+h/jDud8Ppkp+Ow/4r9B/2p4Bf3CYs3G8lNLba 31bbDQAANE0CueWwHz/23+/Fp/Be/Pjb+V3+73fvZw/3yZZCY8t93dI8AQAAVZBAbjgOu3FX HIt//fNvh1Oey4fo/6zZpMea8/Fq1nJfc9u3MlcAAMDqpI8bng73x2HcF9fPGuqzLQXGlvua 276VuQIAAFYnfdzwbLg/DD8/ky/cv6n1vm5prgAAgNVJH7c88Zn7FPyvrxPu39R6X7c0V9Ql r71HCwCALtizu6k4W346Wd7E2fKTn+/oH8bh8m/h/k2t9zW3fyvzxbrK9fZOAQDQLHtz90x9 z/35ss+fz19/d74+1/fthPs39NLPrcwX68lrrKxHTd03FwAAzbEXFyQ03Pe+891LH7cyXyyv XFtzrK/rx8sFAEAz7L0FCQn3yRZ2unvpY+5H7/PFcso1FbWurrcRtR0AAGZlry2IcP+Gnvq4 hfliGXktLbWeyu0tuV0AAF5iby2IcP+Gnvq4hfkiVl5Da6yl623nAgCgOvbSggj3L8r966WP vfWHZdWyfsp21NAeAAB+sYcWRLh/UY/9633OiJHXTU1rp2xTbW0DANg4e2ZBhPsX9di/3Kee 54151bxmyrbV2kYAgA2yVxZEuH9Rj/3Lfep53phPK+ulbGcL7QUA6Jy9sSDC/Yt67V/v88Y8 8jppZa2U7W2p3QAAHbInFkS4f1Gv/cv96nnueE/La6Rse4vtBwDogL2wIGHhPul1Bzr3q8e+ JT33jff0sPbLPrTeFwCABtn7CiLcv6D3QNB7/3hNXhc9rI2yL730CQCgEfa8ggj3L+g9DOT+ 9dxHntPrmij71VvfqM/1ertXANAxr3RBhPsXbGHnawt95HE9r4fct7LgHVNraq4CgA54RQsi 3L9gCztZuY+995O/bWUtlP3sva/M63rt3KpHTN3vrwKAxnj1CiLcv2ALO1S5j733k/u2tg7K /m6lz7zmeq2UFWVqW9cFAA3wihVEuH/BVnaittJPpuX539oaKPu9xf5z29TaSLWWqbaUBQCV 8ioVRLh/wVZ2nHI/t9BXftv63Jfrf8vjwO+1UON6mGpjWQBQEa9MQULDfdLbjsXWdpS21l8+ 5Xnf+tyX47D1sdiiVuf/ut3XBQAr82oURLh/0tZ2jnJ/t9TnrTPnP5XjYVy2oaf5vu5LWTC3 qXV2r4DN8gwQRLh/0tZekHJ/t9TnLTPft5VjY3z61PscX/evLPjL1LpZqoDu+M0OItw/aYsv NLnPW+v3Fpnn+8rfBePUjy3O63Wfc8HUuni0njF1/2cLaJbf4CDC/ZO2+IKS+7y1fm+NeX5M OU7Gq23m8fcY5GI7puZ/qtYw1Y6/CmiC39Ygwv2Ttvrikfu9xb5vgfl9Xjlmxq0t5u636zEp i75MzXFZLZhq972CZ02to7lr44xAEOH+CbkvvfTnGVvu+xaY29eUvxfGr37m6zHX45SLNk3N ZVk9merfVLFNU2uhlepQn72qgHD/hI5/wR6S+7/lMeiReX1POX7GsV7m53nlmJVF/abmLdeW TPV/qujH1Py+UpGmtjdHNWbGFh/H4+FUl5+2Trh/Qk99eUXu/5bHoDfmdD7lWBrPepiX912P YS7qMjVHqfg2NT5TRd2m5uyRatVUX16pyjzQouM47Pfj/s/anfr3Me4Pl7ttnHD/hJ768qo8 Blsfh16Yy3mVvx/Gdl3mYX7XY1oW65iai1Q8bmr8pop1TM3FvdqiqXF4tlbwwFYP4/7UuBRW H6nd4L37JI1FuLSNJbYTrZd+vCOPwdbHoQfmMkY5rsZ3HcY+XjnGZRFrasxzMY+psZ0q5jU1 xreK50yN4bMV4IFHfS7cf+wGh+afpLEIl7axxHai9dKPd+VxMBbtMofxyjE21ssw3su7HvOy mM/U+KYi3tS4TxWPmxq/W0WsqTF/tZ70wD1SuN+f/vvtOOzG/eF3hD8Oe4flXwj3T+ilH3Mw Fm0zf8vI41wWMYzxusrxvy5eYyzrNDUvt2rrpsbkVlGnqbm6V0947tZnv8P+t9N13rk/E+4f lPvQej/mYjzaZe6WV465sZ+Xca3P9ZyUxW1T45WLek3N173q0VQ/7xV9eXFuX1gJn4fp7/bD eEhnxz9+1uEwnIJ9OjT/VvDfFuH+QT30YW55TIxLO8zZesqxNwfvM5b1u56jsvg2NT6paNfU fD5SNZtq718Fd7y0Qg77q8/Zl+W4/LM0FuHSNpbYTqQe+jC3PCbGph3man3l74z5eI2xa085 Z9e1NVNjkIq+Tc35qzWXqcd+peAFL66c4yngf371XVnp3XyH5H8S7h/UQx8i5HExNvUzV/Uo 58KcPM6Yte96Dq+rV1N9zQVT66K2gpm9uarSIfmH8ZAOzb9csorjMO5uHDGQ/gixxrfzCfcP 8uR2Wx4b41Mvc1Sncl7Mz33GqD/lnE5Vy6b6kwteNbWe3ilY0cwrMIX9yz+XkoL91En8DsMl 1KdzBCwf8BcN9y0/kbTe/mg9zHHPzE3dyt8fc/WTcdmG63meqppNtbcsAH548ZnxFOLTCfT2 +6vaTQftMMdx2E2dwO90+Y+PCCx/Fv9Fwn3S+gucF+j78vgYp/qYlzaU82S+PhmLbSrn/a9a w1Q7rguAu156prx7Qr0Zz5b/n//85/yY//znP8//Tz/ny88O+98n8Dt+ns3/4+rd+tTmJc/1 l9q7iLSdpbY1t5bbvqQ8TsarHuajPeWcbXXutt5/frpeD4/WO6Ye714B8JQXnjlzeL5RM71D /t///vf8eP/73//OP//73/8+//yvf/3rK9wfh924mzjePl3+K/PfuG2U1NZFpO0sta25tdz2 peWxKov1mIN2lb9DW5nHrfWX112vlSULgLe98Gyawn1+d/7q8PfjMA4zvTuewvE//vGPy0+f zn88KF4ApgP79KH6wn2FWm77WvKYlcWyjH37yjksqze9949lXa+nVwqAUC8806bwvDuF+PSZ +8N4TIfGf5yC8+Uz93O9c5/CcXq3vnR92WRgv3GCPeG+Qi23fU153K7rXRGP2Rtj05dyPstq XW/9AQAe8tKrfgrKKbx+fr4+hf3isPyZPnN/HeTz5+9/mPrM/emy3cThAz5zX6GW216DPH5r 1BZtvf89K+f2ulrSctsBgLe9+Op/PIXl9E54TsvpbPSf4X4qWL8if8Y+nUzv//7v/86ftU+H 6aef0+fxP/0+BD+F+N1+GA8/3qR3tvwqtdz2muRxfLeuTd3murZki33eonJ9l1WzltoKAIRp fy8gHYZ/92iBy8cIlkz2J4uH+xZ36Fpt95aV6+26eraVfvKtnPPrqkGt7QIAVvPS3sD5XftT YN4teZz7PSng32hLauvSwT5ZLNwnLe7Y5Ta31m6+lXNYVm967ht/K+d/qpY0tf1UAAAnL+wV fH8VXjr8fYXc3ATh/g8ttplpeS7L6kmPfeI15Rq/VXOaevxcAABXXtpDOOxvHQZ/HI+HU11+ 2jLh/g92UPuT57Ss1vXUF+ZVro1n6trUbW4VAMAdr+0tHA/jcAr4w+EwHo+nMJ8rnal+prPl t064/4Od1T7leS2rVT30geWU62WuAgB4wgt7D9+H5U+XcJ+ksVhM2taS25tDi23mcXl+y2pJ q+2mXuWaKgsAYCYv7FksEO5PjxNewdJYLGahPs2qxTbzvDzPrc13a+0FAGDzXth7TeF+N+4P h/Hwq2Y4LL8MApEVTLj/Q4tt5jV5rsuqWSvtBACAgr3XIML9Hbm9LbWZ95RzXvPc194+AAC4 wR5sEOH+DuFpu/Lc17gGam0XAAA8wF5skEXDfdJSKBGgti3Pf1lrq6ktAADwAnuyQYT7O4Qo 8hooay01tAEAAN5kbzaIcH+HIEWW18Jaa2LNbQMAwIzs0QYR7u8Qpijl9VDWEpbeHgAABLJX G0S4v0OgYkpeF2VFWWIbAACwIHu2QYT7O4Qqbslro6w5RT42AACsyN5tEOH+BqGKR+R1Uta7 5nwsAACojL3cIML9DcIVj8prZaqe8c59AQCgEfZ0gywe7pMWwouAxbPymnm3AACgY/Z4gwj3 NwhavCOvn2cKAAA2wJ5vEOH+BoELAABgdlJWEOH+BuEeAABgdlJWEOF+Qm5fzW0EAABokJQV RLifINgDAACEkLSCCPcThHsAAIAQklYQ4X6CcA8AABBC0goi3E8Q7gEAAEJIWkFWDfe1Buia 2wYAANAwSSvIKuE+Ee4BAAA2R9IKItxPEO4BAABCSFpBhPsrgj0AAEAYaSuIcH9FuAcAAAgj bQUR7q8I9wAAAGGkrSDC/RXhHgAAIIy0FUS4vyLcAwAAhJG2gqwW7pMag7RwDwAAEEbaCiLc F3J7amoTAABAR6StIMJ9QbAHAAAIJXEFEe4Lwn1XVl3bEMjapmfWN72ytunZs+vbb0OQVZ9o 0rZreqKrrT28xYsovbK26Zn1Ta+sbXr27Pr22xBk1SeatO2anuhqaw9v8SJKr6xtemZ90ytr m549u779NswoDX4NdfrPuaauW6NO/6mqPUoppZRSSinVQj3juVvzsGcnYlZp22tuv5TbUkt7 eNuqaxsCWdv0zPqmV9Y2PXt2ffttCLLqE03adi1PdDW1hVl4EaVX1jY9s77plbVNz55d334b gqz+RJO2X8OTXS3tAAAA6JjUFUS4vxDuAQAAwkldQYT7C+EeAAAgnNQV5Fe4Pw7j7nTZx8du HI6XyyLVEqpraQezO+w/xt0iixmWchyHXT477X48XC6FXhyH3WV9L7QvAgtLa9y+Cf05jPvz c/ff+ydSV5Cf4T7tMOYX0tPk7IbTJcHS9n+0YSW1tINZ5R1EL6D05DjsvwJP+uPVxxLP1bCU 4zDuLwv8/By+9+crevMZgOyb0J3D8PAfZKWuID/C/WH/40U07TSGv6bWEqpraQez89dxunY+ 2sq793TqtF/i+ZveHPb7cbBvQne+jyp8JD9KXUHKcH8dghYJRTWE6hraQBjhnr6ld4CEe3p0 2lEcrGw6cxzGtKztm9Cf43hMS/ryEe+/Ar7kFWT1cJ+sHa7X3j6hvIDSteIQZujGZefQx6ro y3Ec9p8fo7JvQtfSc/gfHxmUvIII9ydrb59QXkDp2WHweXv6lZ6/nVOCbhSfR7ZvQt/SIfpO qLeKMtyv8pn7RLgnkBdQepXW9iLP0bCahU7uC+G+P49clv0T+vR9lMotkleQH+H+/MRzOVv+ A4dTzCa14Uc7Frb29gkl3NOjn+v6MA7WOD1yQj06Zd+Erj3w3C15BfkZ7k++Pue24HfLrh2u 194+YdKLp7+O05vz199d1vVn+S5w+lE+b/saPHol3NOddAT4E8/dkleQNAGrS21Yqx1rbhsA AGBjpK8gwv2K2wYAANgY6SuIcL/itgEAADZG+gpSVbhfoy1rbRcAAGCDpK8gVYT7RLgHAADo nvQVRLhfabsAAAAbJH0FEe5X2i4AAMAGSV9BNh3uBXsAAIBFSWBBhHtLCwAAYCkSWJDqwv2S 7Vl6ewAAABsngQWpJtwnwj0AAEDXJLAgwr2lBQAAsBQJLMhmw33eVk39BwAA6JwEFmTz4R4A AIDFSGFBhHsAAACWIoUFEe4BAABYihQWRLgHAABgKVJYkCrD/RJtWmo7AAAAfJHCglQV7pMl QnfeRm19BwAA6JwUFmTT4R4AAIBFSWJBhHsAAACWIokFEe4BAABYiiQWpNpwH9mu6McHAABg kiQWpLpwnwj3AAAAXZLEgmwu3OfHrrHfAAAAnZPEgmw23AMAALA4aSyIcA8AAMBSpLEgwj0A AABLkcaCVB3uI9oW9bgAAAD8SRoLUmW4TyJCeH7MWvsMAADQOWksyCbDPQAAAKuQyIII9wAA ACxFIgsi3AMAALAUiSyIcA8AAMBSJLIg1Yf7udo39+MBAADwNIksSLXhPpkzjAv2AAAAq5PK ggj3AAAALEUqCyLcAwAAsBSpLEgT4X6ONs71OAAAALxMKgtSdbhP5gjl+TFq7ysAAEDnpLIg mwr3AAAArEoyCyLcAwAAsBTJLIhwDwAAwFIksyDNhPtX2/nu/QEAAJiNZBak+nCfvBPOBXsA AIBqSGdBhHsAAACWIp0FaSrcP9vWV+8HAABACOksyCbCPQAAAFWQ0II0Ee4T4R4AAKB5EloQ 4R4AAIClSGhBmgv3j7b32dsDAAAQTkIL0ky4T54J64I9AABAdaS0IF2G+3y7lvoGAACwAVJa kCbD/V9tfuQ2AAAALE5SC9JUuE+EewAAgGZJakGaDfe32v3X9QAAAKxGUgvSbbgHAACgOtJa kObCfZID/HXbb10OAABAFaS1IE2G+6QM8tcFAABAlSS2IM2G++Q61LfcFwAAgA2Q2oI0He4B AABoigQaRLgHAABgKRJoEOEeAACApUigQYR7AAAAliKBBhHuAQAAWIoEGkS4BwAAYCkSaBDh HgAAgKVIoEGEewAAAJYigQYR7gEAAFiKBBpEuAcAAGApEmgQ4R4AAIClSKBBhHsAAACWIoHe cxzG3Smkf3zsxuF4ueyH4zjs0vWp9uPhcmki3AMAALAUCfSmFNxzqD+M+91wuuSn47D/Cv2H /SngF7cR7gEAAFiKBHrLYT9+7L/fi0/hvfjxt/O7/N/v3gv3AAAALEUCveE47MZdcSz+9c+/ HS6H5/8uAAAAiCR53vB0uD8O4/5u+AcAAIAYGwv35QnwrurqmPtnw/1h+P2ZfAAAAFiCd+5v eeIz9yn43/08PgAAAAQS7m8qzpafTpY3cbb85Oc7+odxcGg+AAAACxPu75n6nvvzZZ8/n7/+ 7sfh/be+Dx8AAADiCPcAAADQOOEeAAAAGifcAwAAQOOEewAAAGiccA8AAACNE+4BAACgccI9 AAAANE64BwAAgMYJ9wAAANA44R4AAAAaJ9wDAABA44R7AAAAaJxwDwAAAI0T7gEAAKBxwj0A AAA0TrgHAACAxgn3AAAA0DjhHgAAABon3AMAAEDjhHsAAABonHAPAAAAjRPuAYB1HYZxOF7+ DQC8RLgHAFZ0HIf9cPovAPAO4R4AWMfxMO4/PsaPj5137gHgTcI9ALCa47Ab94fLDwDAy4R7 AGAlx3HY7UfZHgDeJ9wDAOs4DuNu5/P2ADAH4R4AWMdhP+4G79sDwByEewBgFYf9x7jbD+PB W/cA8DbhHgAAABon3AMAAEDjhHsAAABonHAPAAAAjRPuAQAAoHHCPQAAADROuAcAAIDGCfcA AADQOOEeAAAAGifcAwAAQOOEewAAAGiccA8AAACNE+4BAACgccI9AAAANE64BwAAgMYJ9wAA ANA44R4AaNdxGHcfH+PHpXbD8XLFSb5uf7hcAAD9Eu4BgMYdxv1EuD8Ou0vo359uAQB9E+4B gMZNh3vv3AOwJcI9ANC4G+EeADZEuAcAxuOw//HZ9Y/dMJYx+Xgort/tx8NVhv5x/cdu3F+H 7OMw7nf5+s/HGMoHOd3/67qPdN0w7vLtr9pyerDTzfMh92lbp8e+3Pcr3F89nvfuAeidcA8A W3c5fD0H4+M5WH8H6vzZ9c/rj+NwDt3fgfnrs+3nw9+/30X/Oho+Hx5/eszPi/JjFLc5+f6M /GVb+X7554vD/vOyfLh9+sPCj/tdfN1OuAdgA4R7ANi6rxC9+/lu+lkO66frLlfl0PyZrX+H +c/r8+2/g3wZvL+C/MQfEb7D+NTh9hN/PJi83dTjAUC/hHsA4BTIi3fN98Vh8D8Ob/9Z5yBd XF++C/9tOnh/3+/7jwYPhfvJ7Qn3ACDcAwBnPz83fwnEf4X3F8P9d/AW7gFgDsI9AFBIJ6sr wvONz71/Ka6f/sq5mQ/LL7Yn3APAN+EeALYuBebiDPjpzPlTn7E/X3a5UTrp3u/rU+DO16cz 3p//mW7wef1XkP/rhHp529Oh/Wt7pzunS7/v91n5Mb/bJdwD0D/hHgC27hTu01fXfR+Wv/sK 6Z/Kr547Vfoau/LqdP0pYH+9g7+7vv9J2sYl0H89Rnmb4nD7fP2P25/q+w8Bx3EovgpvOKY/ Anx+Jd7XQ/56vOI8AgDQIeEeAAAAGifcAwAAQOOEewAAAGiccA8AAACNE+4BAACgccI9AAAA NG0c/x9owOPaGEy55wAAAABJRU5ErkJggg==</item> <item item-id="107">iVBORw0KGgoAAAANSUhEUgAAADAAAAAWCAYAAACG9x+sAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAFXSURBVFhH7ZbBjYMwEEVdlwuyKIIS OLgL7hy40QNXxIkO6GDWf9aGWa+txEl2AYknWUkGCf//hzFRdHFuA0dzG/gosyWtFCmlyc6+ lmC2mpTp+fuJDPRkgnA2YlzlNyweJk9noDebKCeTrFa0/YzIdmBZFqqqiuq6pqZp2Ok0TXwt fD7HtwBOKl4ZVVIU6I0inXmOkgYgHhsMw+ArRF3XcQ3X2rb11b8BoqTgYgMQivQl4zhyHeLX deUabsxJ+qWNdXnHvNaBtw3I9EEwgE7sQFw4JXoWmtuoiHdnAEIhWBLm4CfutNB76umkyjvA 95WnkNgjJmkAQ4oNIDqsUMNQb91xSQXBM1J7cGYXkXoP+Nrmm/fcw4jjfYicAa3dWf0p8S9S aABtxgtGzsGxlBmQgyYepSMpMBCn7rtxiUdoGy6s/T9KmIfswfIPFA/x2bgNHM3FDRB9ASH1 U3F0LWUHAAAAAElFTkSuQmCC</item> <item item-id="108">iVBORw0KGgoAAAANSUhEUgAAADEAAAAWCAYAAABpNXSSAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAF4SURBVFhH7ZbNrYQgFIWpi4KIRViC C7pw74KdPbg1ruzADu7jMIB3HPENvnkOmcyXkAgLPef+oaAP4GuiFA5NzPNMwzD4XbkkTTRN Q13XUd/3VNe1P72ASZMUgoSQpCd/tsOkJQll3POuCYgex9E9L8tiX3hV1RlSQbwzo+zJI84A jKZMIPpYAWTiMhNGRWFWKmkpKG43HGYiCEYmUFLYh6zkcRPhIrZdCWVcGDBKkEzUVNIESqeq KveMpkZGsOeZ+U8gjIs+ZQKlg8VBNoIxgBfzqEqlbcz3OJeJP5tAQyMbHBho29bvAMSFyWGc 0NSHsnlFTyBK/F4Ik+nemJ0gco1+Olr5mXDv5tOJfWfLrgn0ACKOhRLC4qM2YqMVRE+I3C/z PJu9e8KfRe/uu2tAogk07zNTiPeElHaOv9LASaKJ525lpBsXEO+L95NngjceK6t3c9fYx2yj 77NSUjkdEpsNa/2fCf2RHDYXkZGJcvmaKIUPMEH0AzhNqlnH2uM2AAAAAElFTkSuQmCC</item> <item item-id="109">iVBORw0KGgoAAAANSUhEUgAAAHoAAAAnCAYAAADASVzNAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAARlSURBVHhe7VztcaUwDHwNXB+pgS6u CfpIB1TDr+uEYjitP55lY2HZEM65eGeYCcYf8q4kbCDvtQ/8CHQr9LYu+7Ju7mwAuMJJl0Kv 87zPqzsZiLCtxM1SL3Z3Qm/LELkIEnuqFLsvobdln0SV131+vfaXOWY6O2JbJnf9lSVinV9Z J5LKLwFzMbZMu6QJt/c4/rYvk3QNNsv95tCR0DQxStkS34j084nBEdzkDckJEY74A2lS+SWk tmTmhfJpoVm7v6M6zqnPjOLtFehHaBguKhmiWawTkcWIdlgXWshkIlcqvwRKrUEkG5lp/4hm PhdkFXtu68tcBNREdTdCY+JFo42YEgku1YHR9B6GBYwpTgiXyi/CpGTWYRAxQKzjHHamc5PW zwyruFd3IjREktN2DESrVDeX8qjvxZ7Hgkrl1yFHKwOins3D17EOQOW44Bxbto3mq0zfnQhN Blcwje1Xrra9j7vI9gRg7+mYiASVylVwY8Cp0oM6UglNQHloazOatq2FPkBOhCbvIjLmKawM YcxEHftNu0R4Nchz9XtDYXLw/rd3WyEgXkymPUCcVH4LEK1vzwm2iGD1xZQuQHufzgsN4r3H MmENNqQLT85NQlfca8S67t5m7bEpPCUXpOUIl8rbgfGdAJEDZpCkcDsPLx7rR0B5N2JxFNoM VBLSR8R9Qp8SbchwNvGKzlZfZKLB1cs5w3NCE948MqG4vX5OOSdg8y3ZhTlrbE+EtpGgGwR1 HxJ6QEST0DwiNNF62z16CN2MBqGTleSTzA+hm9EgdJy2HxX682P/+HR/D1Thz+9fKu76EHpE dDOuR7TyicstGEI3o2kxZrdM/mhdaCX3en5IFg2hm9EkNLVie+gv2FtKGEI3o01oAhq+I/Ak feOTlvxHAiOin0Sz0EAsNqVwrvaGp2J45n3jPXysupvRsOpOAEEXitokOvlLjdswIroZlyL6 cQyhmzGE/iH4WULn3hQl4OuO41hhAdliR/ntl9R/rpw/zyhvcb+d0O0v/UEMe/ebIwflfvF4 qOOIbVEYcE4mN5f6z5dr3y97tL+P/hcgslr++8CAnCSQZSMk5RRezx0JEWjPbf12J6O+Tr8i lfqXykM0a2269oXJ46AJ5pkqwqRk1jaIGCDWcdGt+uIyB3fLEVO31H9pXJclymLDYXRPMDsR Wm9wCjlaGRD1LF37OtYBqBwXHLl6rcnmwlekUv+6cRHdJU6ojvJ5RidC28nnHdimObs4SQ5i RyU0AeWhrR1L2zYLxVekUv96mwtCV6xtuhGaZq82OoLiHh2B1QfhPG3WCI26kdPRkbaV+teN W85y3/I/NTQTywMpjq26z1JZksJN/feWjPVTCQiVdS6pf824xWildtlB8+hIaAImV2H8G4a4 kJJ5menOCEzXc07gr/m6DYiE5uMCUv+5clbGIz6HmmgG+hKagH1hYY4D5BC129HuhAbGLx7I +G9+8cBj/IbJERsezjRy0q3QA3di3/8CuaCAUwMZ6qEAAAAASUVORK5CYII=</item> <item item-id="110" content-encoding="gzip">H4sIAAAAAAAA/2xTy27TUBCd67fduAkhKaROYuhriYJFF3RVqgqx4CGFD6hcx22NSB1cB3UZ 8QGIP2DDF7Bgzw/wMXTTXZiHE1rEjcZ37pkzjzt3UgMAhfIWxWNdx6+Tfhimp1l+bgKtJyju KE8EMxh7SrRx8ub4XZqUjMCQ/TX8Whdnk4P8UuCXKDYCwyXxFfmifOkBXGO+q/7fM63fmhSl gaaz0lwmf1aWRXY8LVPFzD2UOtxcxt7GrbPJAYyySNOAgQjFn7BqRZ2fADPTjr7ivm85stuu 7I4nu7sSaZjkI/bHUuZjeIQxdqkpNcZ/CD5AfIAWaozPjjN3NaLqDnyqktVPPiduRM1faHfu cIBRA6+qvuvQjD5jYbMG3GX8W5Nw6VlLItptKam1Jnv7npS2Rkw1ALgvJS3P0BHH9rqU0kEo kFI6HLgrhxYfekK2+hK9F8refyBZQo66C/BQsoTLLBvi2N+ULPQEWxJYXmNbLtRbXGiHx6Q+ io5Oi3hydnSSF+O4tJnbpmlj+DAuY3nF9eo1yYsnlJQpJeaZoyf24svs4jnHkQnt0uypar6B hxBSSCCHcxhBBjDHH6juLY5eQIzW/9oqdJHQLos4SaNDnU1BRQxVA1SgkBdgb1BDr1BpKtDR N1S6Cow5IQaZ5qiYKtBm+2iyiEMBbOag4izcXeSwu4ccdl9BDrvXkMPuPnHIa5U5qNSJQ2Sp t/56Ok6LLInfS5ccrvsFCnUj47bSf8mrWvzPqt08/AEAAP//AwApv0ZiNwQAAA==</item> <item item-id="111">iVBORw0KGgoAAAANSUhEUgAAA/AAAAFjCAYAAACaMuDKAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAB7CSURBVHhe7d3LcfPMsQZgxcWAWCcI B4H1SeBkwJ3XjsHFlTNwBjiEyJFGECSRbACcy/NUjf3rRgLdTWrejxe9jQAAAEDxBHgAAACo gAAPAAAAFRDgAQAAoAICPAAAAFRAgAcAAIAKCPAAAABQAQEeAAAAKiDAAwAAQAUEeAAAAKiA AA8AAAAVEOAvTse38TCcbx8tOY3Ht7fx7X0dLx8BAADAvroP8Ofh8B7Mfw3wp2H8Nd8DAADA xjwCfzGF+J8D/HkcDtdH348eegcAAOBFBPiLvwL8efrSeRgPH0+j/74AAABgS5Lnxe8BPjOF +MNwifRXgjsAAAB7kUAv7g7w70+n/3wTOwEeAACAvUigFw8F+KNH4AEAANifBHpxd4A/Hb98 nwAPAADAXrpPoFN4n4L4tD7C+fsb1h2ufzruEtrT199mb0MvwAMAALAXCTRAgAcAAGAvEmiA AA8AAMBeJNAAAR4AAIC9SKABAjwAAAB7kUADBHgAAAD2IoEGCPAAAADsRQINEOABAADYiwQa IMADAACwFwk0QIAHAABgLxJogAAPAADAXiTQAAEeAACAvUigAQI8AAAAe5FAAwR4AAAA9iKB BgjwAAAA7EUCDRDgAQAA2IsEGiDAAwAAsBcJNECABwAAYC8SaIAADwAAwF4k0AABHgAAgL1I oAECPAAAAHuRQAMEeAAAAPYigQYI8AAAAOxFAg0Q4AEAANiLBBogwAMAALAXCTRAgAcAAGAv EmiAAA8AAMBeJNAAAR4AAIC9SKABAjwAAAB7kUADBHgAAAD2IoEGCPAAAADsRQINEOABAADY iwQaIMADAACwFwk0QIAHAABgLxJogAAPAADAXiTQAAEeAACAvUigAQI8AAAAe5FAAwR4AAAA 9iKBBgjwAAAA7EUCDRDgKzT17LcFAABQKIklQICvyDyo/7UAAAAKI6kECPAVuDeYz78vLQAA gEJIKAECfOGeCeL5zzz6swAAABuSTgIE+IJFA3j+889eBgAAwIokkwABvlBrBe/8cqKXBQAA ECSVBAjwBVo7cOeXt9ZlAgAAPEEiCRDgC7NV0M4vd+3LBgAAuJM0EiDAF2TrgL315QMAAPxB GgkQ4AuxV7je63oAAAAWSCIBAnwh9grV6Xr2uj4AAICMFBIgwBdg70C99/UBAADcSCEBAvyL vSpMv+p6AQCArkkgAQL8i70yRKfrNgMAAMBOpI8AAf6FXh2gX339AABAd6SPAAH+hUoIz+kY zAEAALADySNAgH+RUoJzKccBAAB0QfIIEOBfoLTQXNrxAAAAzZI6AgT4FygxLKdjMg8AAMCG JI4AAX5npQblUo8LAABoisQRIMDvrOSQnI7NTAAAABuRNgIE+B2VHpBLPz4AAKB60kaAAL+j GsJxOkZzAQAAbEDSCBDgd1JLMK7lOAEAgCpJGgEC/E5qCsXpWM0GAACwMikjQIDfQW2BuLbj BQAAqiFlBAjwO6gxDKdjNh8AAMCKJIwAAX5jtQbhWo8bAAAomoQRIMBvrOYQnI7djAAAACuR LgIE+I3VHIDTsZsRAABgJdJFgAC/oRYCcAvnAAAAFEOyCBDgN9RC8E3nYE4AAIAVSBYBAvxG Wgq+rZwHAADwcpJFgAC/kZZCbzoXswIAAARJFQEC/EZaCrzpXMwKAAAQJFUECPAbaDHwtnhO AADA7iSKAAF+Ay0G3XRO5gUAAAiQKAIE+JW1HHRbPS8AAGA3EkWAAL+ylkNuOjczAwAAPEma CBDgV9Z6wBXgAQCAAGkiQIBfUQq3Lde0h3MEAAA2I0kECPAr6iHYpnM0N2wpn7OlBQBAtezm AgT4FfUSLtJ5mh3WlM/VIwsAgKrYwQUI8CvpKVD0dK5sL5+nv+Zq6XvTAgCgCnZudzod38bD cL59dCXAr6S3ECE0EZVm6NlZmv98WgAAFM2O7Q7n4XDZ2wrwm+ktPKTzNT88Kp+dNeZnfnlr XCYAAJuxW7vTFOIF+A30GBx6PGdi8pnZYm62vnwAAFZhp3anFOCn0L60eNJUux7rl87b7PCX vWYlv549rg8AgIfZod3JI/Ab6Dko9Hzu3O8Vc5Jf557XCwDAn+zO7iTAb6D3gCAg8Zs0H6+Y kfy6X3H9AAAssjO7kwC/gd7DQTp/c8RcCbORH8MrjwMAgA92ZXcS4DfQezBI52+OyJU0F/mx lHA8AACdsyO7wxTep7A+rTzEC/ABKRD0XkM1IFfi7SI/ppKOCwCgQ3ZjAQJ8gDBwleqgFpQ8 C/mxlXh8AACdsBMLEOCfJAh8pRbUcJvIj7Hk4wQAaJhdWIAA/yQB4Cv16Fvqfw0zkB9rDccL ANAYO7AAAf5JNv9fpXqoSZ9q630+rzUdNwBAA+y+AgT4J9j4L1OTPtV6e8iPu7ZjBwComJ1X gAD/BBv+ZakuatOP2nte+/EDAFTIzitAgH+CDf+yVBe16UMr/W7lPAAAKmHXFSDAP8Fm/2dq 049Wep3Oo5XzAQAonB1XgAD/IBv936lPH1rrc34+rZwTAECh7LYCBPgH2eD/LtVHjdrVao9b PS8AgMLYbQUI8A+wwb+PGrWt5f6mczO/AACbsdMKEOAfYGN/n1QntWpPD73t4Rx5nXy+/loA 0Ci/5QIE+AfYVN1PrdqTetp6X/PzbP1c2d58niILABrht1qAAP8Am6j7qVV7euppOlczzDPy +Zmveyz93HwBQMX8JgsQ4O9k4/QY9WpLj/3s8ZyJyWdmzdlZuty0AKBCfoMFCPB3sll6nJq1 IfWxt17m593bufOYPWdlfl1pAUBF/OYKEODvZJP0uFQzdatbzz00w/wmn4+9Z2R+3WkBQAX8 xgoQ4O9gc/QcdaufHqoB3+UzUcJczI+nhGMCgF/4TRUgwN/Bhuh5alc3/btKdVALSp2F/Ljy BQAF8hsqQIC/g43Q81Lt1K8+evcpr4V69KmWGZgfZ8nHCkC3/HYKEODvYBMUo371ST3Tt09q 0q8ae58fc23HDkDz/FYKEOD/YPMTp3710bNlqS5q04e837X2vIVzAKA5fiMFCPB/sOmJSzVU xzro18/y2qhP21rqc34urZwTAFXzmyhAgP+Dzc461LEeevW7VB81alerPc7Pq7VzA6AqfgsF CPC/sNFZj1rWQZ/uo07tar23+fm1eo7saz5TkQV0wy0+QID/hV8o60m1VM+y6dF98nlWrzb0 1NP5ubZ+vqxnaXa2XkCTVrx1n8fz6bJuH/VAgP+FXx7rUs+ypf7o0X3Uqx299jI/797OnfvN 52S+1rB0uT8toAl33JrP43A8jsc/1+Fy3/A2Hk+3H+uAAP8DvyzWp6Zl05vHmen69d7D/Px7 rQHfzecirT0tXf98AdW64xZ8Go+XG/oUVu9Zh6Gfx+Cn82XBVBe1WVeqqbqWR2+ek9dN7eqj d1d5HdSjX0tzMK1SLB1bvoCq3HGrfSzAvx2Gbp5GP50vC6a6qM361LU8qSf68hz1q5O+fZXX Q136UmPvl445LaAKd9xapwB/vPzvp/NwGI+n7zH9PBw9hR6/CLaS6qq25dCPOHNdF/36WV4b 9WlbK72en0e+gGI9cQv9Hug/Xb7mEfi+ufPfltqWw6yvI6+jWpZNn/6W10id2jLvbWv9XTq/ acEjlmZoq9WxJ87++pT6w3EYT9O7zp+v63QaLuF9ehr9T+G+PQL8AjeqbalvOfRiPamW6lku PbpfXis1q19v/Vw637QgWZqPklbjnjrD03H2uvd8dfQc+ul8mZlqoi7bSfVV49fSh/Wpabn0 5nF5zdSuXr33Lz//fNGXpRn4ae1h6XrvWQ158mzOlxB//bNx+Zoelfd34Ds21SMttqHGZdCD 9ZntMulLTF4/NayHvn01r0datGmp1/NVqqVj/W1VKnjk09PnT+Npehr97TNb+e9//zv+61// un204DyMhx8e/Z/+sWGLv24nwM9UfmOohjq/Vqq/HqxPbcuiH+vI66iWZdOr383rky/qtdTP fNVu6Zx+WpVY+UinQH/7zxX985//HP/3f//3/f//53/+5/bZzBTel9487zTcgvv0uv31Q7wA P1PZ8Fcr1VmtX0Ptt2W+y6AP68rrqabl0Z/HzWuWFnVY6l1aPVg676VVqCeP7BLUpzetOx5n 67AcpAOm0D6t5HtoPo/DYemN8y6f//KU/vXfIV+Anyl82Jui1q+R6q7221Hj19ODbeR1Vd8y 6EfcvIZpUZalHqXFcl2WViGeOpJf38Ru5Xehzx9xn55CP13HF6fj9zfOO1/fKf9t9qj7dNxr vsfet2Pp2VSLtNieWr+Guu8j1Vmt96f228rrq86vowfrm9c0LV5nqR9p8bulmi2tF3nimlM4 /mGt+Cj3v//97/dH36f//7//+7/3y5/+O3ceDuNh4bnx0+e/5fofvvdZ0/FwM9VCPfaT6q3m +1Hzfan3a6j5PvL5Vu99qfu28vrOF9tbqvu0iFmq6dLayRPXNAX49Cj77Gnq52EcVnyE+x// +Mf7///nP//5ePR9ei18bjmULz+tXoDf0M6Dy4Wa70u995Xqreb7UfN95fVW8+2p977m9c4X 61qq8bTYzlK9f1obeOJSp3B8uAT16TXwp/E8PYX97RKMb6+BX/MR+Pkb1k2Bfh6aF0P5D29q J8BvZOMh5Qfqvh+1fg11349av0Zed/Xfhvq+3rwHafG8pXpOi9dZ6sfSWsFTlzIF4Sm8Xl/v PgX6/Gn067wGfvqzcXlATh/Pn0K/+Br4y+cOC08F8Br4jUx1UIv9pbqr/fbU+XXM+fbU+LXy +uvDutS0LHk/5ou/LdVtWpRpqVc/rQc9/hPvzpcwPD2andLw9A7v1wC/FJyfMb3mfXr9+/So e3rkffnvwH9/uvwU1A/HYTx9ebDdu9Bv5snhYwVqv71UY3V+jbz+erA+tS1H3gv9iFHLss37 ky++WqrRtKjPUh/z9YDHvntH6fXvd5meMv/rI/+3p/2vmd4vBPibJwaPlaTaq/921Pf1zPk2 1LU8eU/05XHqV595z/LVq6VaTIv2PNnfp6bh/dH3SyA+rPl89Jn5m9X9aQrxPxzPdLxrh/eJ AH/x5OCxEvXflvqWQy/WpZ7lynujR/dTr7rl/VtarVs652nBgicm4/PPyE1PU98gF1dDgL9w B/N6erAdtS1H6oWexKljHfI+6dXP1Kk9857OVwuWzist+MNTU3I6/vR09fN4Pl3W7aPWCfAX 7mxeL/VAH9alruXRkzg1rEveL337Tl3al/d4adVi6djzBQ94bmLOp3G4hPjhdBrP50tgT2t6 9/eV3oW+Bt0HeHc85dCH9alpmVJf9OZxalenvG/6d6UefZr3fWmVYunY8gUBT0zQ51Pol5cA 343p/HuvQSlSL/RjHepZNv15jprVLZ/7XnvZ+/nzaT4Lf62tLF3X0oIVPTFROwT4y+VsvlYw nW/XVqwlQakX+rEOtSxbPu/6dB/1akPex5762eM587j5nLxywYaemLApwB/G4+k0nr6tFZ5C v3Qj2GKtQIBfr5asQD/WkeqolmXTp/upVXvynrbe2x7Oke3k87PFghcweQFdB3h3XuXRk3Wo YT3M/N/UqF15b1vsccvnBhDgHjFAgDc+RUk90ZfnqWF99OxnatOHvM8t9LulcwHYgHvGAAHe +BQn9UVvnqN29clnXu8+qUl/8p7X1vuajx1gZ+4hA7oN8H7Blktvnqd29dK7r9SjX3nv81Wq mo4VoBDuKQO6D/CUSX+eo251S/3rvYfqwCSfg/kqQYnHBFAJ95oBAjxFSv3Ro/upWf3yHvba x97Pn2X5XORrbyUcA0AD3HsGCPAUKfVHj+6nXm3IZ7+3fvZ63twvn5Gltbal60gLgKe5Fw3o MsD7BVwHPXqMerUj9bKnnvZ2vsTlM3PP+snS9/60AFiFe9SArgM8ZUt90qu/qVV7euppT+fK NvIZWnMBsAn3sAECPEXTq7+lGqlTe3robQ/nyGvlM7a0ANide98AAZ6ipV7p18/Up20t3wZa PjcA4Ed+8wd0F+BtGOuiX39Tn/a1eDto8ZwAgLv47R/QbYCnHnr2s1Qb9Wlb3ucWet3SuQAA D7MDCBDgKV7qmb59pS59yftdc89bOAcAIMQuIECApwr69p2a9Cf1vNbe13zsAMBq7AQCugrw No/10rvv1KNP+W2hpv7XeMwAwCbsBgK6DPDUJ/VO/67Ug3wGSp6DWo4TANiNHUGAAE81Uv96 76E6kOSzUOI8lHxsAMDL2BkEdBPgbSTrp4dXakAuv12UNBclHhMAUAS7g4DuAjx1672P6fzN Mrl8Ll49GyUdCwBQJDuEAAGeqqQ+9tpLc8xP8tvGq+bkldcNAFTDTiFAgKc6vfYynbc55jf5 nOw1L3tfHwBQNbuFgC4CvI1lW3rtpxnmXvltJF9r2/ryAYAm2TUEdBXgaUPqZ0897fGcicvn Jl9Ra18eANAVu4cAAZ4qpZ720lczTER+e/lp/WTpe9MCAHiCXURA8wHeZrNNPfW1p3NlW/ks RRYAQIDdREA3AZ72pN6aYXhefjtaWgAAK7PDCBDgqVbqbcv97eEcAQDoip1tgABP1VJ/W+2x +QUAoDF2twFNB/gUfgSgdrXc45bPDQCAbtndnofxcNnkv70dxuF8+9w3p/H4/j3TOl4+uuoi wNO21OeWet3iOQEAwEXnO9zzOBxScL+E9MNw+cyC07AY7gV4qpf63FKvzS4AAI3qe5d7Oo5v x/R4+vTh25h9eDOF/Ouj7/OvCfA0IfW6hX63dC4AADDT9S73PBzGQ/bQ+vzjq/N4nj718VT7 5dWU6XzSon0t9dvcAgDQsK53uvcF+MwU4rOn2TcX3BMhqD+p5zX3vYVzAACAX3S90304wL8/ nb6DN7ETgvqTel5z380tAACN63u3e9dr4HOXAH9s/BH4FIIEof7U3Puajx0AAO7U+W43exf6 2dPjF10Cf/4IfdMBnv6k3tfW/1qPGwAAHmTHu/R34N8/d/t4epT+/euXNXt4XoCnOan/tcxA bccLAAABdr0BAjzNSf2vZQ5qOU4AAFiBnW9AcwE+hSGBqG+1zEEtxwkAACux8w1oNsBDmoVS 56H04wMAgA3Y/QYI8DQrzUKp82BWAQDokB1wQFMBPgUioYik1Jko9bgAAGBjdsABTQZ4yKW5 KGU2SjseAADYkV1wgABP89JclDIfpRwHAAC8gJ1wgABPF9JsvHo+SjkOAAB4ETvhgGYCvGDE X149I6++fgAAKIDdcEBzAR5+k+Zk71l51fUCAEBh7IgDBHi6kuZkz3nZ+/oAAKBgdsUBTQR4 AYlH5POy9czsdT0AAFAJO+OApgI83CvNzJazs/XlAwBAheyOAwR4upXmZov52epyAQCgcnbI AQI8XUuzk6+oNS8LAAAaY5ccUH2AF5aIymcoMktrXAYAADTOTjmgmQAPUWmW5usvj34/AAB0 zI45QICHTJqnZxcAAPAru+aAqgO84MSW8vn6bQEAAHezgw5oIsADAABQBQkuQIAHAABgLxJc gAAPAADAXiS4gGoDfArvAjwAAEA1JLiA6gM8AAAA1ZDiAgR4AAAA9iLFBVQZ4FN4F+ABAACq IsUFVB3gAQAAqIokFyDAAwAAsBdJLqC6AJ/CuwAPAABQHUkuoNoADwAAQHWkuQABHgAAgL1I cwFVBfgU3gV4AACAKklzAVUGeAAAAKok0QUI8AAAAOxFoguoJsCn8C7AAwAAVEuiC6guwAMA AFAtqS5AgAcAAGAvUl1AFQE+hXcBHgAAoGpSXUBVAR4AAICqSXYBAjwAAAB7kewCig/wKbwL 8AAAANWT7AKqCfAAAABUT7oLEOABAADYi3QXUHSAT+FdgAcAAGiCdBdQRYAHAACgCRJegAAP AADAXiS8gGIDfArvAjwAAEAzJLyA4gM8AAAAzZDyAgR4AAAA9iLlBRQZ4FN4F+ABAACaIuUF FB3gAQAAaIqkF1BcgE/hXYAHAABojqQXUWqABwAAoDnSXkRJgTkdiwAPAADQJGkvosQADwAA QJMkvggBHgAAgJ1IfBEpNL86OJdyHAAAAGxG4osoJTgL7wAAAM2T+gLe/4xcvl7h1dcPAADA LqS+gKICPAAAAE2T/ALeA/z1P14XogV4AACALkh+Ad8C/N5B+lXXCwAAwO4kv4BiAjwAAADN k/4CPgL8JIXpvQL13tcHAADAS0l/AUUEeAAAALogAQZ8CfCTFKq3DtZ7XQ8AAADFkAADXh7g AQAA6IYUGPAtwE9SuN4qYG99+QAAABRJCgzYPcBvedkAAAAUTRIMWAzwk62C9haXCQAAQBWk wYBdA/wWlwkAAEA1pMGAHwP8ZO3AveZlAQAAUB2JMODXAD9JoTsavNe6HAAAAKolEQbsEuDX uAwAAACqJxUG/BngJ5EAHvlZAAAAmiIZBtwV4CfPBPFnfgYAAIBmSYcBdwf4SR7I//q5e78P AACAbkiIAQ8F+EkezNPK/fY1AAAAuiYlBjwc4CfzkP7TAgAAgIykGPBUgE/mgT1yWQAAADRP agwIBXgAAAB4gAQaIMDTKrNNq8w2LTPftMps07JH59utIcCdCa0y27TKbNMy802rzDYte3S+ 3RoC3JnQKrNNq8w2LTPftMps07JH59ut4UFTgS3LsizLsizLsixrjfUIAT7g0WJDLcw2rTLb tMx80yqzTcsenW+3hgB3JrTKbNMqs03LzDetMtu07NH5dmsAAACACgjwAAAAUAEBHgAAACog wK/pPIyHt+mdBA/jcL59Dip2Or6NB8NMU87jcEjv+nocT7fPQivOw+E23/YitGmacXsT2nMa j+/33X/vTwT41UybwvTL8tKAw3D5DNQrbQL9kqQl5+H4EWqmf6B6c19NS87DeLwN+Pt9+NE/ UdGaa8ixN6E5p+Huf3QV4NdyOn75RTltDP3epHb+lZumvT9ryqPwNOqyL3H/TWtOx+M42JvQ nM9nB96THwX4lcyDjuBDC8wxbZseyRHgadFlMziYbBpzHsZprO1NaM95PE8jfXs59l8hXoBf iQBPi8wxTcuebgzNuG0AvQSKtpzH4Xh9yZO9CU2b7sP/eHmfAL8SAZ4WmWNadhq8/p12Tfff 3uOBZmSvD7Y3oW3T0+m9id0+vAaeBvklSaum2XYfTdu8oS6t+Hx9cL7sT2jT57NNfiLAr2a6 c7m9C/0dT32AGgjwtOjrXJ/GwYzTIm9iR6PsTWjaHffdAvyaPl535m+vUr/pF6R/5aY17386 7jbX1+X+mnbk99v+hBytEuBpzvRM7gfuuwV4AAAAqIAADwAAABUQ4AEAAKACAjwAAABUQIAH AACACgjwAAAAUAEBHgAAACogwAMAAEAFBHgAAACogAAPAAAAFRDgAQAAoAICPAAAAFRAgAcA tncaxuF8+28A4CkCPACwsfM4HIfL/wIAEQI8ALCd82k8vr2Nb28Hj8ADQJAADwBs6jwcxuPp 9gEA8DQBHgDY0HkcDsdRfgeAOAEeANjOeRgPB69/B4A1CPAAwHZOx/EwePwdANYgwAMAmzkd 38bDcRhPHoIHgDABHgAAACogwAMAAEAFBHgAAACogAAPAAAAFRDgAQAAoAICPAAAAFRAgAcA AIAKCPAAAABQAQEeAAAAKiDAAwAAQAUEeAAAAKiAAA8AAAAVEOABAACgAgI8AAAAVECABwAA gAoI8AAAAFABAR4AKNt5GA9vb+PbbR2G8+0LF+lrx9PtEwDQLgEeAKjAaTwuBPjzcLgF++Pl OwCgbQI8AFCB5QDvEXgAeiLAAwAV+CHAA0BHBHgA6MR5OH55LfnbYRjzKHw+ZV8/HMfTLCd/ +frbYTzOg/R5GI+H9PXrZQz5hVx+/uNrb9PXhvGQvn92LJcLu3x7enr8dF2Xy7797EeAn12e x+ABaJ0ADwA9uD3VPIXf83t4/gzN6bXk16+fx+E9WH+G4o/Xmr8/Vf3z0fCPZ66np7JfLvP6 qXQZ2fdcfL5m/XZd6efSxzen4/Vz6anx0z8efPm5m4/vE+AB6IAADwA9+AjKh6+Pir9Lgfzy tduXUjC+5ufvgf369fT9n2E9D9cfYX3hHwo+A/fSU+MX/oFg8fuWLg8A2iXAA0AnPp+SfgnB x+wp61+eiv51vYfl7Ov5o+mflsP15899/sPAXQF+8foEeAAQ4AGgI19fx34LvX8F9CcD/Ge4 FuABYA0CPAB0Z3qDuCwg//A69A/Z15f/XNvKT6HPrk+AB4BPAjwA9GAKxdk7y0/vSL/0mvf3 z92+aXqju+9fn0J1+vr0TvLv/zl9w/XrH2H9rzexS9e9HMw/ru/yw9NnP3/uutJlfh6XAA9A +wR4AOjBJcBPf/bt8yn0h48gfpX/2bbLmv4EXP7l6euXEP3xSPxh/vMX03XcQvvHZeTfkz01 Pn39y/df1mfYP49D9mfkhvMU9K9/Tu7jIr9dXva6fgBokAAPAAAAFRDgAQAAoAICPAAAAFRA gAcAAIAKCPAAAABQAQEeAAAAijeO/w8Q8nTV1NOYqwAAAABJRU5ErkJggg==</item> <item item-id="112">iVBORw0KGgoAAAANSUhEUgAAAJMAAAAnCAYAAAALvnp2AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAQ+SURBVHhe7ZztlawgDIZt4PaxNdjF bcI+tgOr8dd2YjHcvAE0oiCgMxd38u6Zc2Zk+JA8JgHd6YxKdZMUpgdpnkYzTrP79Bpd6UNh eoimYTDD5D68WPNEfY3lQClMD9A8vg+kRQRUXwiUwtS65tH0CZLmsTddDWlot+tM1/Umxsw0 xMuOpDA1rdmMFN5iqDBIAKIYpskMHiKGKtIHyvqRRpEnhallwZgnrqHKM1EIW+sQsH0XDaMl 3klhalgA5cyQNTCFdaahi0NbkDspTM0KHiMe4rxqYZKAJGFCSMwMdQpTsyIjZkDyepjyoIYW mNAgJ3PBK9fFqW4W5Us5ez01MCF0rXXSOROUmzdtPRPi4wJS2bJQdbMyc5UqmOD15GruJIxh n6scJl4mKkxNiGA6ZQQexturFKjF1nlJfk7zClOryoHpTVKYni6F6S4hprcCNMbiVzM2Wd3O T/7SuUgKU744cUwZAWO5aCQkjn4OjvqT5ccSiSpJJqK8+vXt8bzlLZ+z9f1lvr7d+/+sn79/ ssbSbJiD8a9sS+C5nOQN0pNyCMBEvxIChGT4TlfyOZ7JhqHqk8XEow/ZwIxwYfseJhlaULRu WVjAbP9+jEu5a0/umfGhoL+jPbUFXHwXHufMM+7KEQJvvABpHNXze7PeFOZkDhG8kr0HG2Vs bOrPPeEHOHx99lD0nkswPl/JGXMiDzOg3sa4WxipMNiYC2BFCFwLzTSO5ic4thONMfSccU9W MU+fB1OluB9nTP9e9LUMHmX0ZsYfoBFXvoWMchh3kvzZFwoYWbI/KCyXn8lDos1NezvBi+69 VrpOoT4PpjrPJCd9ZwCaRG94lHFbBNFAFl6/Zftd60lPE3qhsI99OZ+3g2OiEGsPxcHAI7Sy ule8zid6JjakP9G8BsqFibUhjY2H0MAGmDm8jLRi8hO8MQ48hvdMwvgs51lmX8aezraPdmV/ +3KSaw8h058y+j4y9GZiqZ68f5ZM2Ev15NXcUVKK1/GVZhPgqoljY8IgrrIHuLfLbgZo8EZF OHFjwZ1rN5QNZCSMnevgEMBCW2v82/W3KWfhfAJP7ACTh/ZzJOs4aGWFK6Jx3gbmRW0uoIS2 nulT5fKkUEWexnnH26QwPUk2iZ7IA41Rd3LgsY7E3u84j6qWwvQkWVDOnxlC+EqBgnZuBgnK gYkhDsNtvgBJjjdVmJ4ugim2mrQSXrPCMzJIADELpvU2UkoKU6siQJJec5Oj2a2HHO8hleuZ 6p60VDUk8jwJQ4cg8Io2x+JCeTCdhflVClOzShsRIOy2R14CE0EdbJHEpDA1LBg7xsfbYDrN 3VYpTC0LiXXMkG/KmfQ/en+NUqEuWM1lhiKpc5jSeVsohal1IczEDHplnwmezd8WirSvv4Ly C4V9ngIHcY8Itpx/ApVSmB4i/eU41a16y29a4jGgyj4UJtVNMuYftFhmMicU+4MAAAAASUVO RK5CYII=</item> <item item-id="113">iVBORw0KGgoAAAANSUhEUgAAAHMAAAAnCAYAAAA8XHcHAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAALbSURBVHhe7ZzRcYMwDEBZoHtkBrbo EuzRDTyNv7qJh3ElAkY4WEgGnJbq3XHXHDU4eki2CUkXjdtgMhUE76LzYXp1DUfOYTKF+GGI g59eXEzwcC6nF2oyBQTXTmQChPZKoSZzj+BiXzKJ+7oudl0f1YkkaOsH3XFNJkuIDsrrtkof h1nEKKb0f1sI2+K+3kEvZJhMDgxmOW1ilzIWpPedvBQr2mqy02QyBFcOJO5bhGDQO/EYp2qr GDtNZhHMmHLpRCE0yFqZ8rZQkoWl1mQWgSAydbOdTP6iopRlBrwioByMM65l6+HAVy+cpWAQ 8v6NfRQGlQXGS3at12jMRKTj5qZMbLwExqcUx7sTz+k0bIpZ1qXgmDL3qWaJUGJ3rMpmpKp4 6NriOrdK5jg4T8HZfDN4Vc3B+w1CMRhzf06WuZtp6dwV51W0RSeSrF/LFAXmWRZmoaeUtCO8 U2YjqmSuxiAm66T/1wSTmSAysY4TSUxrWopPDSBFWoZMZuIEmYpZnJhsgsDdKrtK5tcjPr6m v9/M9+eHqC+/UyZOstJBd6bulpkJInM9sTleZp8XR01AxuOThuyi2mQmGkyAsouEboUeYuep vOMy9X348zJrAnPFGy7L3Mh2y8zEWiaADWdRm9mA49kcPNG7rcuKZd+zffFUlpmJF5kIFTqQ +7D0dh5++n5WErySzWa5Ug5BpzJPE/C3Z7MZwUc3QLlbXdH4GpYJQaKxfgI0kjKuXDpXYzfZ iuOrhrtkpgGYzBthMm+ERKZgKCiiaGsyj4ITKzbK2STtiqfzJqo/zzQmIMi3eNLAQCB7mAiP yzeyn71LlaFri7KPPgP07+GDiEKoAK1MeVu4qISfGZtMBgx6KcbNZO6O3QsmkwMnJ0yQW4yZ 9kT7aXClNpuRqh6fkbblx+0ck7kHlrlSQMdlxXXrTE1WIiZTwLu+n6n9wq3JFGLfnL4ZTX7T wNlvGhgxxh9MiiCOdjnidgAAAABJRU5ErkJggg==</item> <item item-id="114">iVBORw0KGgoAAAANSUhEUgAAAPQAAAAnCAYAAAA4hWV/AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAcMSURBVHhe7Zzpsaw4DIU7gcnjxdBZ TBLk8TIgGn5NJgTD6MgLcrcxMvv11amiqoH2Kn3yAt2vyWQyNSMD2vQjNQ791A+jP2tLe9pm QJt+nIaum7rBnzSqcaA29vVQG9CmH6Wxbx/mKIL6XQm1Ad2ghmGY7pmMjlT2iSWP/fReohn3 Xq/p9XpP1QPbnrSksX9Pry1RRlHu0NXVyYBuTVh/ncjUqshJ+1NG0HHqaaqdz3qYugAFQ7L0 vZz2pEUSghlQVgOtLBf33r06QBvQTYmc/hyaqjT0egdUC469FKloajoDRX3wfumn5XvSem0a oSvKrRmlDeiWRE5f61eniJxV64BaAZqlPD+BGrqXeu25J23QFqCryq1YSxvQDQkbRpW+eI4Q WA6tCEaw5akw4JAOXwv01rRBW4HWl0vTc+W024BuRmWnv1Z6B9SJ8isA0z7QetuuA80L9vp1 helqKSAa8R1yPt5ZnY83OUvNiwxwvs88OJ/okAcHl7URv/E1NKRdR68CzZWFwWorbLpWCLwF G8EhZvDmx1p4K8k9OqGjZlTFui6kyzx2qX3cUtTqGpICldwxrpod7EnrtAnoynK1y6kVoFHo stFMD1IS8VPFoExHFgykDXbWOjScMKTJAn3grI7qt5pXrM8GP92TVvZdbYMryoUNNdmXgZaV paMcJU13anGUWAHPyU35quysAPowf9EA3bgOABpGpkzEVO1V+dDddJ1g8BxAyXq3MPpqvxe1 AvRSfTbJgD4AaBiMDSun3bY59lTlAUptV5oSIn38nmbqaUBfqt1AyzVQdfQ2XSZslsBMDOSX xbcCrQjcVwL998/056///Ev137//qPpgAWg4gpheJ2tpRfQ2XSLeoZ6j7qOAtjX0sdo1Qn9H 1w2bJrfIOfAu4/Oz2tDOnvJzzoqREA4c82aHDkGP+ifZa8BB6cIjIcxq6PvIl/tOfvbC718D IO56gNGXH+77CshZE1/KAp3aTQ+0ImgrgC4UVycDmu2j6YMM0B9OkD2evjlWaEOpVwDNGyA6 72Qnx/cJ8mF0eYbk8Z4va4YTAUE4OPLsev5ZoUvTTT3dBKChLriOkZaTAJS5EN7HGCgwdKiT P3dZA3hhh+TeLO1yqXpZtQr0UlDYYBsDmrp7K9DC0RIlBjww+j5GHhDhhEkncvvFiBzhdumC 8zKoAjQ59YST46UO99mnR770gRCntBi5ZxAc6HiLS5yHm192QiDJgLgCnlMKWZLtkor5Ir8D g74BzbbX9MEH0NJRv1UdxW9T/SiQwAIlAKf3efor2o97MX84cswGsAdnFyOqyDumxSNC8QYX 3eE2JCN/rE/OTssQyfolbQziIOTrryWnCPRCcGHV28aAdjbU9IEAeqR+g+GXojhJGp4O+Qqh M6K/B2NiLRpq8JWOUuWuZb4LJ4/5Ut146nmCeMT0deA/aaORcXYwwOT7ZaDpN9bTaHuoCr6f qxfaIvvAf2bA6DPS43NsO/UZnv3zKYCRUPj0XGYICHQS75NKrwdKqGUfylc/47RfI6qPBDo0 k0X1O/TXVrbLXbvLnYmaSYQtRFX+XrgfRiA4Jp0LK+dGidw1KDcTcFNZdy1xnkQAr3S/oOCg +KECFcigYe3LhcPp/QhK5zyNDvc4KepF9/1aOYiDhK+LXFO69AEeGQgBKV905X/0SSwTfYF6 Un0SrYEEu2Dan9gS5y445OX6NK7rSa6934cMyqVqVIvy22TThgR/0PRBZlNsi5zRk9EdEIga bAV6ziKUQUcSbO4V/p3DQejWwLzbfVv9KLAS9GeUXfMvJIf/Y4kBfRfQODCSfZtzP9BylpBf K14uHinnkZqFUfIkqFTi6b//fJAQqJI2lkTtP7p8A9qxciHQrsAAIR8fo9SxQBfW+ZfK7TvM a0lqx50we423/uvnCeRpgOZl0UbfuCstiRlQkHo50FB8+SEcAuo2gTZdIvhV0eCYIXqfYMBq ZnB3pRX+rwJaty9xKNAs7G6LTZdQ12OBfsiU23SNCJZH/mPJnrRezIAi0fKLOqmOW0MnU2xE rhTI/UDPeT5pU8x0hcj2Baf/hAL+Ux7RZ92VNugzj7wQLHSD2KGbYvFRDEbpT/DiWsM3OnxH XvOSQMdNJ0RDf221/abGVHZoQPHpPzVQ3pE2SAc0saIcxI4DGjDL6bZ4phoUfuCA+4B/QGP8 uQRVAt0NIk9ar5z1Yonp2YLjL7HSPNCrewizjl9DH6AUaH/R9LuFGd6SUze+htaunyED2vRD VJp2Y/kmdpur9ljuSuu0DjSVUQGBAW36OcLUc8kh4h6NfjSLuistRnjv50tQ14zO0POAlo1c aazp9wn7ML/GHYiF2h+5PHKENplKGrr2ocZLWlt+sWZAm36k+IcwjT7xGPvtbTOgTaZmNE3/ A3eVgAC0+HjlAAAAAElFTkSuQmCC</item> <item item-id="115">iVBORw0KGgoAAAANSUhEUgAAAcUAAAArCAYAAAAJ+JPCAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAvfSURBVHhe7Z3rsesoEISdwOZxY3AW m4TzuBkoGv3aTByMdxqQGBAgkAC95qty1bElYADRDUL2ef0EQRAEQVCIKd6E7zj8hvFr3glX R/pTEI5BTPEGjJ/P7zOaN8Jt+I7Ur4MYoyD0REzx4nwHMcRbQ8b4FmMUhG6IKV6Z7/B7n8QR x8/rkeLdo97j5/0TXxSEPogpXpbvb/h8fmewxO/w/r1ezzPFbvXG5Oc9UI+3RfYxhbuw51oW U7wqEMoTmRAM4okrxV71br1alH1p4W5s3ZMXU7woEOMzeZCYYmMa7i3KvrRwWzaMGzHFS/L9 De9z3DqdEFNszfj7tLiFumNfGnV/BdPi+nypW8v8cPx8Tjjt8nNqD/ob76fXatZBbL6vV2ii GYuH0zvmfLDnHStD9YeJw17DHWLENafyjk/sw7Fto/Qui5jiJaELt+lo4kLhvSLl9jOHlpy5 3oit9kSI8ty4Lz2L1qJdjKh6n8fP54TTBj8fRydu3P5N5RwDq+Sp+2AgL2fiEYuH0z/mbIz5 hMNHfMYs1Hnm7+Yx+uWG8o/EthXkUTChFFO8ItTJZ/v+2j1MsZye9a6+rwix2JGhMjpHcfWk Ipbn8nxOLG06Tw3MvcIq2hHpzHKPjjnBOAxURsQUnboyE3JoEOP4YdeAbqdFfFmxlVEydsQU r8gJv7smptgevqqpAWLfk9/C5IyYfbDiCqyekqYYS7uSp4LOqTJJRFnTiiKz3MNjjoGHTKjY +O1TbUgqtpieNIjRvwbCX2nKiK2UgnzEFK+IueDPgrrQMfjp1csgzkDveqO8ev0O4dl3a8wX OP2e8sRHyhhcQfbP58TSruUJqk0WmHDmlXuCmINQ3w66wNSeIh2lVVjEtIkWMaJt+FgJmyJI x1YO5Zd5C1VM8YqczBSFPkBQ6vU7iUQ0MzNThyj5L5ZGi7/7PiV4/vmcWNq1PFWsVW7xuaK5 Xu6RMa/0D76jZzJH2bFu1qZn8loYRq12dclpV5CObQvIiyYq5l2KdVOMzHSEA6lgirgYg4NK vfIunhIwGMJl0cupjJkheq/QwNlC73ovIdEk0fq8eXuQUNCAnb5sHHu4AW1YbRzSuN57a0z1 KQvIf+8Lnn+cE0u7lid9kjD3fPw2Xy/3+JhjoDz3ul7GTkGyh0+0+bghNYqRtMu2TahcYjW2 beTuK66aoupgNGyLBhK2UXGliAvFDp6Wt3Q08/W0Up4e2GQCw2gGR11611sBI8LMF2UyE1R8 sVKx8YS6F21XbRiyW4Vb8cUf9bNPCmJy44rQ4nxOLO1KnjXGgtOu6CMUsFYuODDmXDCOgmWp GKfrDDF65zWL0Wun0CpwLbaN5N4OXjFFHZAeqF7HCsdR8YL1TarJOOBQ7GvlIaaSR6i30L3e aqCvlfdVItbLFHflxfuRZ8Q+d/IPnW/aZD4vI63zObH3KwPLVRXTuVC5J4i5BMcUvdj5GPAn SE1jnMcCa+uC2LaSO37Spsg6tmZwwk6oX3I6N4fu5uBcU355uFXi7jm0om+9+eRyKZIuODcc T+6gzqLiNSQIV6CCKWqB+ni3mWQcnYA7miLNFLHP1mve1bPepWV12VMUUxQexn5TxHJW3cIq meUKXbiZKeKHe7EH0/Pa6ldv/aDAXNaOSoopCsJ2dpsivxft3HdvvNcjxMFGseqS25giXaQH 3YnoV293UnkaU/z75/fnr/lbEB7Af//+k3XNR0zR29fwhEy2FvuDx/jnH26+rSnSq9Ok6/Gm WPEaEoQrkDt+gqaIxO7DDu4toOCDEDTI1BNFe0VNPZrOBAtfuAxmqMVm18Bmj8G/h4Hy04aP FZnzhJl6MmoSTmoLX8iRDqaFv1F/tT9m2on/bdC3C6dy8fkknKb86bgJgK/U1Uc4zvLbw7Gm qMtbPAG4q1PzOMwUd4yP3EfKs6B+6NDMgnAadpiitwcSfC1FxArbjpXkbBbIg8WRrEki3lQ6 lIUHO4zjKpHE+WSUoyl7Sj4fM2VZQ9IGbt9Snp/hN1KeOg2JGB2EyU2x4HOs+FQSmK0tRO3h jvhiN2Iy73XWEFbW5jDaLY2MPNEuTJhVnHObHWOKFMWiD2uZvuLgerumv72s+JePN4wB6ofY IUG4IxjzOdf80hSZgDtMwmJei1NgCDi2YybsG+v0BeuqAqkwJsOydRpM1dUVbH1Mp5vCUWbH RA7xT7Ei9rf/+4PIl/4gm6S0WEFakdNmiS90s/fTwUWfUBzO+4m0OHJxnpI75rCj75agrcLX yVyeYxD6fHssZgAhTl7vtbGTBeq43VAXUD9si0MQromj8Qk8U+QGsISLS10hAVzY6gliCMdw gGOC7nF1KzO2wnBu7UIMp7jxt8mP5T2nJTN0f6lF18HGxNKbY25V8JmNNxcbu03r1KeHSkZN kcDt7PmYOW4baTNnqLdTXmLsqOstGA+1TTRd+RgQUxSeBsZgzjXPTBG/poGBmzAkR9Ag4kbY nc9Nem92jHPtXhxMgRXinWvTxALZh1q5mbyxYsM/WrXiwcxtpFUG9hdRzykUnB9SarTBlAf7 W4kh/Y30+HuuExkAvgeq3qL+XPBMelXmZKr0Zj5OIK/y5jGmM5fFxXRLfuU45kD1WpTpXWP6 WuETiC0cX2/g1N3fK6f+VXcXYsZH7VJ1PMjTp8LDKHz6NDDTdAZnYiaqzguLjCsCOj+72vTF qFSotNDNXlYCBEbFpEVZmRX2AlGmMiEjxPQe8c7HVFId38fsHU4oo5090cav00+ijtm+qSMT RVU+q7BTJkwCcVI8DohzvZECUDvPkxP94vVrhWuG/sus4BaGyF/8mtB9H15RxTim3gtgfpho eeOJ/yB4CH5NVYHaetPYEYSLAg3KueYDD9psYd0UJwHnn7kBlppif/CfrLVu6T1BJbKxmX1z 0F72duATQX+c8DJpAE0CajuYmKLwMC5hiu5K5+SmqFZs3sqCVmtYMR4WKsVUtlq6D5iUdF/l HUT1VSIQUxQehphidcyej4ldxX+kIRrw3TURtxsD82oxGCqZYnw8c8yernrZuxuxtHl5todv iUyo2FINp7ZfEHtIw6zGTVnYum7QvGRZIKWpZX1Si3T7lcZb5hkoO+eaF1O8AfgB6ZzOFq4F nkRtYogAdxl25w2hMmPV7MWHsgz/6EAsbV6ezTGGw8fVrF3RwebHzrc3jKi7GdoH/pJmESJV loa3u3oWgm31lPVJHdbarzTe1PkhwnVeIqZ4E9Qe51PuJz6A79C4P5kgb8YRYyaoDsYM/PEe S5uVZ3uwXz2UrhRpomGPaT3Tb/XfyUlI6SQlWlaERbsW9ElFss0/J16Oc36Y3G0IMUVBeCQk NDnilMSMWeSzJupKtPiYj6UtyLMVVK4uvswU/WNIr+I3gv3Bagb6tkhPdfafLl8hWlYMxOCv pLL7pB6p9nPIipcROt8BdUub5sRpTTGn3QRB2Eq+SKQxs/isAYtzeZmxtCV51obaxRjUFlPk mjYZlU5D9UYyI+w2C1NXegXFPkKsrChRk8vtkzqk2s8hO17DqolTupXbqxN1TJECmoxOvVC4 6Xz++Qdfkvc/Q+0C56pXo44RBEELVFJHMtD7NGZCmyE6/B8ox9KW5lmGyTemN9iGMAXWNMXQ 5xyV78rtP05Onpa0IeT0yTor7WpItZ8lP15NhuEVrHwrrRQFQbgcmIxmCkUQ55aVFsW03uEc I2ixtMV51gXm4os6b6OkqGNxMB+zsftpwgYWWQHFiJQVYmkinIw+qUiOKWbHa0ifryn5WpOY oiA8lqXAFAERnYUcor4iony2HktbmmdDYF5+2WlRR7xGfLnBqDpNoszO4Tgml0OkLA/EO2dL 5y0ersrpk4qsmWJRvMTq+QqqS0FFxBQF4clAZHYonxI5Ek9nRaXE1QgqxN4c98UwmJaIfd6b hSmG6sLryt4vHhZkaW1SW88yQzSEymLxLFe95rwNfVKFlfYrjTd6vkfpj1+IKQrCw8E+kqeN gnAPyFBLv3okpigIAmmHGKNwL7b++IWYoiAICvkBCOEu7PnxCzFFQRAEQVD8fv8D7AE8qM1a mlcAAAAASUVORK5CYII=</item> <item item-id="116">iVBORw0KGgoAAAANSUhEUgAAAYIAAABTCAYAAACS7wC3AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAA1ESURBVHhe7Z3rsesoEIRPApvHjeFk sUk4j5uBo/GvzcTBeKcRkgYE0iChl+mvylW2zGNAzDQgWf75EEIIaRoKAWma9+v5eb7e/hMh 92XLWKYQkGZ5PR6fx8t/IOQLeL9kTD/LxYBCQJrk/aQIkC9FxOC3UAwoBKQ93s/P71EqgLp+ fj4/P7+fFRO163Gz9ryfv5+fqyr+jn35epSVSSEgjfH+PB+PzzGh4fV59E7unP6oevfiXu1x IoBAe0kh2LkvUebvU0a7DQoBaQs4SO3pVw5Zoo9BSATo9+fe21E3bM9lVwQH9GXJqoBCQJoC geEoHYiD0Ovxc5wI7cAd23NVITikLwuuFVAISENg5nXcdgacXTviNwjB3dpzZSHYvy9fn4dx e4hCQBpCHOPAoEAhOJ+2hcA+8aEQkHZ4P1fdY70aXiM4nasKwVF9ab1OQCEg7VCwZ1qH6M6Q grs4rsn92nNZITioL/F7GQoBIRoRgsNjApycvyM4B8y6na0XvYX0gL6EEFqaTiEg7XCGEBBy IhQCQmIoBKQxKASExFAISGNQCAiJ+fvn8+evf09IA/z37z+mMU8hIO3AFQFpDK4ICImhEJDG oBAQEkMhII1BISAkhkJAGoNCQEgMhYA0BoWAkBgKAWkMCgEhMRQC0hgUAkJiKASkMSgEhMRQ CEhjUAgIiTlDCPj00fO4ga0I1Hs+GZVCUIH36/l5vr7Be+/B7v19uBBEz5z/Oe5vMvfhTu25 vq1OBHZ+RDaFYCOvB7cRzuCNYL3X9O1oIZD6jvgXqsO4U3tuYitXBBcG/+pz2QHeAuLEu/yT 2MFCEDv5N/xn8V3acxdbKQRXBcvIHU8MsWH9r9UiThACHXy+QQju0p672EohuCSyhHzcfR/3 S4Ag1/4fVwrBJigE9bmPELgLLTff27SCtpYMlm/qmwu2pfqq4GAhQH2jk/MawaHcxNbbCIEz dOcr21cBbS3Tge/pm0u2RZy56izuaCGI71ypvcI5nDu15x62Or/bcVCifEvxC0KAzsR9uHhV np1dDswaSraFvqlvrtoWsaumAx8uBIJfaX2N/9ypPVe3FauW3u92Gph1hEAbKq+r7gfWQYJO ycn4pr65bFtKxXmBM4SAkBOpIARwQink4bcM3OuLL6TK7MF+//o39c2121L1OgGFgDTGdiEY 9tX0tsHdL4wi6KEdieAiQcI8E/6mvrl4W/CbDgoBIevYLAS43aovAO/7APFz8kUXBIZtvixi 8Ei0oSBIXLVv1nD1tlgHsgkKAWmMjUKA2aEKuOJAQ4A48cILnkWz+cdeuS0gc5C4Zt+s4/pt oRAQsp5NQoDM4TZJv6XSvZa3ULpthrVOh1l/d7V/rEvPVody31KPt+v3KQFeBa+xjLAjXMcE ++G+DmOQ2N4312FVW6SfXL9uXTG4c6fOAy4KJwq0DmQTFALSGFb/SQhBGAzSL+v2zExZWeuQ JzUbnc5ef5HORw80uC/TiYa8d98Ezo+ynxJwEk+5NAWJmn1zNuvaMgryhhVDLyauDGVH4gTk B/KM/bkTSSEgjbFeCMRZko6Ei4rK2XZ1KGzfxBUEdnlRUIFoaPBw8TMBApCIB9oxsd8SJK7Q N7VY2xb0Ib7bsCKIxQR3BuFzagViHcgmLOeYkC9ipRB0s6xcxvKLiStmbT1w2iEwhHahcUHQ cMGrm71OvlMg4DxEPfCo40n9i0Gidt+cyZlt0WNieVWRH8grxhaFgDTGCiF4u0A565wIoMrh fp9++6Ua4tz9Q9/0zL4P9G8J4vIRgaoXCfdnJpKnd34nBP17+W4UE+xJx+XJMWmDYzZIbO0b 1O2/gw2ot69skk9ypY4l0sL2oVyxDSK3zIa2BMd9/mgFgbRPP8N3NulKorRjnrzd1oFsQuyv VhYhN6BQCBKzq2AmODP7Ss4YJUDJd+VO1+XrylYNQAD6fXyeKmh32xPdPeZd8Mfev3wnwWa8 gKwsQ57hs2+Ptj0bJLb2Tf99L3ASKPFZVQb7+zy9jaljIDVbdyscfyxsQ9ef451WtdqC1ygk 2ta+vNHOWHDSZeSwDmQT2XNMyHdi9Z/ExeJG2S1I9OKmgh5mxqqytUIwFqEENAjsHa/n9Ng6 loUgZX/YrxQCQo7C6j8Ugp7dhaALfKnfMCwF0mUh0MHVrzw82B4z7RiZWCcE2v5cGTmsA9kE hYA0htV/KAQ9EiTCgFUPHRTdK5q1lwTSZSFYDq7rOUMIrvWICdeuZCFju/TX+fSadN7pcT2p SKW3snQOcvZojrbZDnwkV0d6XB5g43B9LD/m8z6zHpRpaQuFoAfXFqpFnCnYxw8ulCoxSA2A 3KBoTQhwYbtaezYKwdCmSSE+kETH8+k16bzJ46+XHB15rfw3PS2ubjwFE5OcPZrjbTbjA27a fNjnx5NL59/vbmNcb6r8jG0bwRicPZUeCsGAnAhLj21B/RIar766kkBaujVUl6OFAGkrtmeX FUHXnrCNI9P0mlze+TI7JE3qmVmlBIHJWO/ZNs+A62HP3IogaKsKvAE72CjjbhwDXT9N7DPZ Vg7GX3b4KSgEAzhBewRRBP/EjEte/QkqCaRpIRjLTF0srkc3iLv6jxCCuO82socQeAd+9Ocl qmCSXpPLu1CmQ9JUWcGirr6PjfWebnMOf37zW0N+7HWJ0qK1g43xGIB907oNtq0Adaf7IoRC oECn1R+nXZDGXUKuaKwK4Cg6wDkn6hzIDYA+jT7m0UKAMtyFYMw4/DHLSV9PfSGYtbeiQzh8 oNhC7NTdZ5lA4JA/j7qOOL0ml3epTFDt2onqY1u9F7A5iYwrf3t5XgiA961Mgj1sRN/EPpwe 1/O2rQF1W4qjEGgwgKuPVDm5cibcj8j6AJh4wBoGIJzHBU5J/4Jj+c949SdTC8FDvK78B2Ub UILjXhAz7/T6+EMCgv7sjsH+RFr3yozUqtcHwKwQaIGKXipTF/DCz3NOHqfX5PIulelsrbJ9 gfEzlrNc75k2L5wf/LDUF466c+e5C/S+LD0Zc9Tq1xBLv4J529aBunN9oaEQBOAkSJD2n64I BlHvAJYTfF86Aa3KbiuC8XPs5PH3mlzepTJr9U18UXS53vNtzoH6AnGQV2y7GKmeQ9YF3NCk nWzEBGooN1WvsGjbOnBeLOVQCGKwVN5jMFRCD/gLm7mZ6qsBsIMQOAcetskkkETXPSbpNbm8 C2XWasdQhtTn9sWX6gUn2mwFPpKsy9nYix9sjNLtZmPUT6nZ/pJtK6EQbABLtH0GxHaaEAI4 ZBBFKrHV0SV/3/dBcFfHg/JT6Z3Dq3SGvMFxYevtjdPZswraqXovYHMJgRBEtjth9jbGK4Zd bfR2BH1dYNtaKAQbwaCwdOChKEcbXpczchv4vcVud5ag7O/qLkJmoRBUwD3ZdO8LsGTgjXvA 9+xvCgFpDAoBITEUAtIYFAJCYigEpDEoBITEUAhIY1AICImhEJDGoBAQEkMhII1BISAkhkJA GoNCQEgMhYA0BoWAkJi/fz5//vr3hDTAf//+YxrzFALSDlwRkMbgioCQGAoBaQwKASExFALS GBQCQmIqCQGcq3/oX/4pkd2jhLt041Mtc3ltZe5P6hHOzra5jks9WXPA/9GKfN8XMbY1lX6B 2brAWN80Tdk5qcV8/5XaO5d+Cuq2jHkKAWkHEYLtjh49Wz7jjOm/PMzltZW5Oz7I6sAxBMls NIlt149y9oEsLHB4uux8gEwxV1eH7nf3uG317P+yc1KHpf4rtXcufYp0m6dQCEg7qCC0miAA qSAS4AOgvALhyeU1lbk/Lzz9tXRFIOI6fqf/Wat7Pyu8pcKcrSvDpF8LzklFzIJnsVcTpE9j /YMnCgFpCHEui0PO4pfmKGcpkDlH1Y6cy1tQ5l5IvV31ZUIQf4f8zn4fpB6YtSKgTfJLm/2f zVvJ1pUDNsQzZvM5qUeRECzaq0ilD0Db5oWih0JAGsLuGPP42ZrFuV1aXWcub0mZtRmD8hoh 0EGqD85dHmk3svlgNhbh2yqvksCbqytLNrBbz0kdzEJgttezKFySb2HrqIdCQJoCTjnrOwa6 fVc/kzQ4mv4LxFze0jLL8OX64Bu8EKDwB0y+wppCkDquceUubG1oLGWOzAdByzlZZqFfPXP9 N2K3t8MQ5AtWOBQC0haYnRqdI0mwHO8CwbyPI4134lze4jLrgoAaBzLdR7OBTILN+N1oe5wn HbQzM90cmbpSzP//sOGcVMQiBGZ7PfPpO6zXBwCFgDTG1KmKQOAYghcC2ULg0LOyXN7SMncE ATuuez6QwV4fcHRQdW3qA5FKowkCu4VMXRGwdyhW0k1uELCck4osCUGRvcJieoe0paAhFALS HnCsDd7uHFsCRjBzdgHFBxEEOP99HACSeYXc8aOZCEGqLbqt6vPkvnaVd8w6trNMBDypupQ9 09WNT7finFRhof9K7c2mjyhZDQAKAWkS7AtH8YCQ70BEpPQ2aQoBaRbss1IMyDfxXiECgEJA muaNO2Ze5Y5DyNV44weBK8cyhYAQQprm8/kfSA14MBf91F4AAAAASUVORK5CYII=</item> <item item-id="117">iVBORw0KGgoAAAANSUhEUgAAADYAAAAnCAYAAABEz7vjAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAHiSURBVGhD7ZntjcMgDIZZ4PboDNni lsge3YBp+HWbMIwP81XCYRfahDrRvVKltqDIj21sAgouKpFg1mjQxsZftLh54sDMusJq4o8O WePm679wosCsHoPKcnBLBScHzGpYKCocUwqUWqARHC+zbseEgFnQLgXbWAbWBOQBiXk4tmj3 pCAZYGgUHQpQOZLOAYsi07WMmggwq+kUw7EHGBqvWCekMQFgGAUqDQNYCcKCYdrGdBQA5oxh SuEY2MNJnwdz66vVh7IG1hgqrbPPgzV60FZVVSwqX0vYC8WAcRHw8mWe72NJmLr4vHOADYgB s2BcOIOHgpdwR/DEUa9rFhhWHQ8UYXCTGQDpkvyWpoBliDKXQyVKsLvrfoPbPX7fQT/fX/55 G7AcLQrsiKgdHzEKoPyfqkpYkvn+Qko2WFI5t/pQ1s9YYxdNRadm8Qhpxno9S2jEUClqqXcd Xu5nVMWgukEjJL8/S1F9yfOzIjZd/2B9OhfYWXf3Q+9jT4qYnPexy75BY0QYSzG1yh54njOP wpiWEKz/MMc5Sc4pVTCesnUIrFivIsBwnXHG9q4xcSfBwWAqHauqSJ5SbdeqEDAnTCMqFB19 TOhtS9A792N1yxAFhrrkjWZS9x20PtEd9D4C+AWi9vyF3QMcmQAAAABJRU5ErkJggg==</item> <item item-id="118">iVBORw0KGgoAAAANSUhEUgAAAEoAAAAnCAYAAACout71AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAIxSURBVGhD7ZrtkYQgDIZp4PrYGuzi mrCP7YBq+HWdUAyXiOAXuyYR3ej6zjiz6ILkMSGImHCLJJWgvLPBOt+X6mlLu+pAubYNresL O8g7aN/yYakC5e2+kLIAVsOEpQeUt6HhUsI6xgRjmsB1Etfy6igB5YOFkONhcqFNgDpgzPpY p7FwZ5p0gMJO810imOyBALox7LDleJUKUN7yQwfrDKDQaCOCTa2jABR6AzfsIqixkSJQGL7E 8FMACjorSHV1QNEfUgbVuXKXQeiHwL6lYHySzGswbLaOUSjqOLX0KIzbDKPQiHeQoSLUKqAE c5qoWdZjZLCxcO4mA9Wl2jegeqGr1wIlbif3lZ8MkjCSKPfngYJrNjW6xcCxarUjVCVQU6+h NsrS1UDhy+QcXBVdBdT8qG7U8xEez/73B/T3+0O6P9GjPJyO2fD2qKR7jCqKBQqv5axXVJz4 pbqT41VvLglqD30nqG/yKOj4BJR0ykvV2bIekp14wOzY7amf1qOO1g2KqBsUUURQ3dBQ+GPx fE5I66sKpwK1th7VwUDDZxaVz/O+zsjXo44WGEPJrGSPAvBDeX3lU77Cebhoa+ZUUPPy+7V0 wZr550TrLAcU/aPDqb7CROPW3H8XUITxMUkFKLButcNUUGj8UH4/Rp3uS3E0aC07EUFhOI2z 3svQoo2NSUpAgTAMXj/6/Cq18J7SecI86qS7WaKO3B/FfdlXBQp177hjaLc9nPZCezh1KoR/ udWSUlfBqecAAAAASUVORK5CYII=</item> <item item-id="119">iVBORw0KGgoAAAANSUhEUgAAANMAAABTCAYAAAAFtzIBAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAdTSURBVHhe7Z3rkaQgFEY7gc1jYugs NonJYzIwGn9tJgbj3g9B8YGgfUEcv1NlVU/3iAj3yMPXqyeEqECZyGV0bdM3bWf/qoNP8kSZ yCW039/9d2v/qIyulbw1x4WiTKQ4XVOvSCMi1PugUJSJlKVr+remSUjv9epfr3d/ojHZpf0+ liZlIgXp+ka6d3oqtf23k8hIpZm2gDTfjeQ6DcpEyoHg1Gw+pCv2Gls5EfX9Uu8+HmmdKBMp RtfodsWQ3iQTAv+lKys4MHaiTKQQaDl0u2GQyQ/0LDKhK5nY1aNMpBASlMp9sDIypR8EKBMp g4yXzpy72aXAmAmkjpsoEynDifM2cRazeQdm3o6A82KUidSDyJSj1TASZTrP5EB3MiXvlImU IZdMBaBMpC4oEyFKUCZClPj56r9+7Oeb8e/vn6S8UyZSBrZMhChBmZ5FjXd+liL7vlOm51Dz nZ+lOHuHaRKU6Rnc4s7PUkjQ61+pIFCmB4Az6Der5TwXdE4cvcM0Ccr029G+8zM/qNjXK69M 5gCjfZ0bZfrlIGhyBmUmULm5863eOlGm3w0K6YYuFZEJwa+6Dcr0m9G/87MURWTC7Q2aXb1c MvGq8RrQv/PzHMNNbRgHrZZA/srIpHywySLT4n4m7acTWShTDCn8bOdUMlNGJsS/4tE+h0yS Zok7bSlTDKmIEgGZg1Iypd5hmkQGmVAO2Z9OJFCmGFm6HfkxAWS7gbmFSg2iJDLJ5JcBZbqK m8pUEso0cFgmrOAPfn99oBWUCZXsl+180R80L+syZdkqi9plQprVjpn8Ss+Rqaq4oGXCgH4K YMXxSAjZx2HaGMvGZELX9o3N01ZZVC/TcjYv29OJKNM+F8g0bzEKnOMaz8FgCc/Mod63yqJ+ mYRazzNRprxUJZP81rgMBMriFjIVQEEm6YOO3RIklkn7q0iq3K5vm3lXCVeYny2Ja2WaHyBT AoQyDXwskwsa3DC2VRn3wF1dsNEFSKjcsTxWZXFOhFpkcvsR23/KNPCxTNPKGOTZ7zMN8PbA icPP6gAt7Ea+Y5U7iuOL6OSU5URkXC3TcontAmUaUJTJC6ASAeCB5xJ8fOOeBNTmZUORyp3K IiDTibK4WqZhfzv5eui6xoqWMg1kkik0YzK0XmcLy1UutuFOum3mpZPt2Ly8G5HEy8+UxnzH TUHMpqTtNqRywyf4QtKklEWYOmQaSAmQ2i8nKkVhmRz+/y6WYG6wzla6ENQLOAQ//s9OhJjA tGmafMtn88us0pB207cSVKsn74RaLIO/HydlcoHsdY1rkgm/jbN5AcIXup6oZ8oEvILLNWZC YC9zK4U/VYwVy9v4uIMImFC+rIAIqHVhSJrrL0emsvADKiTZmq2ynMmkWpaBnsGeTFGwr4rC UyYwVBS+i1/3dOKI5UBhj+kP6bhVsDOzbZsgGSp69ZsHjqyY0jezV6vtR4IF65i8+zJNZRHb n0mcaRszmWLlocFHMsm+BoU/Uc9PlgmFgoIcp4OztEpSKe6BJn4L42TpRAT5E/lyopmHJco6 rtKMTO6z/DYJ6QXDmJ585/VtsG7AQ4Mrj/Vpgh0JR6x4Y7n5ARjpImoh+Z3JdGSjWFczk8+T SVaSYOu8gT6W99bU8owhcI4Xlg04s3gZRtBKq9G4wHdBge8kI4NAGAvJbyLKNCnh5XIWDDaQ lwcESLYbMMuTtqGyGPZjPfOIg4U/ThrWXw7ftEH5+NtcLin1pHpjIHiaTM8DkqW0Mmm0Teyg cxfk4KAd+ZTpAaAFU6hldDFztzilUG+VAGV6BjifcteKVgdBr26SkEsmMx5G9zXfOJQyHYQP 7kfrmkkkkEUmjFWtRG6SafhBFcp0guyvVamYTsZ7t3uljKQ5TcUPk0w5DoiUidRFBpkQ5P55 LZzKUJ3Ot1AmUheZZPLloUzkGVAmQpTgmIkQJXLItJzNy3LJG2UitZFFJoHnmcjjyCVTASgT qQvKRIgSlIkQJSgTIUpQJkKUoEyEKEGZCFGCMhGiBGUiRImfr/7rx36+Gf/+/knKO2UiZWDL RIgSlIkQJSgTIUpEZbIPCt24AhzBHH+Apv9AU/dglVCa4W1tQZlIXYhMe3fB+q+vMY+l9h9t vXzMNT4v2Hr9TSjN4LYCbKW9BWUiZRARkh8j5kmDViF+a/rUKgWFDYm4I6gj9aGclIkU4sAj lxHgYyty4AlERozA716aM0Lfj6BLuC+bgzKRQqQHpRgzCSGf/beO7MpkQCu1sZ1QNzPS/TTp RbqBDspEioFWZjduDevgNeMa241LmTDA03nnMoWESBAlKtsEZSLlQJcqEphrETzQSkW7iusW MJTm7rYsR15iQJlIQfa7erMpaBFvNmGx6O4FWbQkoTR3tzUiLVdU3gnKRMqCYN8I0HlXzuvO GYnk72V3DK2cfG+Scv+DxUs7lGZwWwuOvlqHMpHi4LzNgQP+NYigR98IQpnIJdT8Cp+zr9ah TOQyanyFzyev1qFMhKjQ9/8B5Qz6m7NBrKgAAAAASUVORK5CYII=</item> <item item-id="120">iVBORw0KGgoAAAANSUhEUgAAAFQAAAAnCAYAAACR3W+uAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAMWSURBVGhD7ZrNsaMwDMdpYPt4NdDF NsGkiFzePQe6yD0HnzY95JrhlA7SgdfyB8ggGXvhGWfhP8NMMMhYP8uyY6jkoVW1OdBOtLIV nT0rQ0vatClQ0TSyEfakMHVCta1Nh7oZ0K4tF2YvBbVOhLoN0K6VdYBm19ay8q53sq0rWVVw 1JL2UchGXx8OV4Wuz5ZhQFw5lmi459HaAKiCo4Y6h7N3EgGFaHZOiUZdq1tVy0hCeHVCOjHn ANpCgY7sO4QrHwmuUc9jlB8oNJBs+aBphCJp5/kOMYJOsxC8+8cQiXJCKVGaHSjAmmvcLNC5 iFH3DBOKTRdQn5cTuXJCCbk0M1BwYi66gEcAaIRzOEUY2fw6qZMrH0vdFznsMwNVDZttfAho jGNouFsZwDYikT1XPlVcIIDyAvWGIi8O6DDRhDTqNC9FGHj6MlfOKDaP5gUamYsooFDWF4U6 Rj3DMwVw3uSDgFLljKZphFZ2oKFGa6l73NrQQdVLJVemDzxT+yCoKNYdZG1xh3LllLwODag8 oIXqALqyDqArq0yg31/y69v+/jD9+f0rqu3RQN/vt/21QHuL0PP5LF+vlz0bdL1e9Uy4WHsC ervd9EEJonMK1P7DsMsO7+CevCegAAILohIDvlwu9tcC7RkonLvh/3w+ieg9IpSSBxSgPR4P HY1wDlF6v9/171UmpT3N8gDTRdjpdPLOIUJX0d5m+R/XAXRlHUBX1mKgw0Q4rQdPktTeZch2 XsUCjX03M5XZs+RWEOE3o2HbGJW5HxraGA7KRFd0Z3ibx4m2jMrcsYdI+bfxpgE1bqN5rg64 30Voqi0p6JSY1y/ZgcY3DAvyVwU78WCoAc3kQZRakm1JqUAIvsQblBmocTB19IENHrKQI/kh 7DufZssoIfdnB6qjJNEhE2VDWIWgjN8ppdhyKvrLEeVi+rDXQ9U5BTM27SDA69kpGz0BRtry Ssv7GwBVgiGUmsiUjVljohyIcqJeKtnr5kDgKNtIfcDXd0awrkt1LrtUR6Qu8zYDCjq+YP4B FfmNffuh39j/f5LyL2RfcV0bb4pNAAAAAElFTkSuQmCC</item> <item item-id="121">iVBORw0KGgoAAAANSUhEUgAAAOkAAABTCAYAAAB6ZiDwAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAt/SURBVHhe7Z3Lcdw6EEWVgPNwDMrC SagchDfeezGxeDGrF4T3s3IGzmBeX3wbxIfAEBqhxHtcU2WSIAhw+rABSqRe7oSQpaGkJ+d2 vdwv15tbIu/FkfNMSU/M9e3t/nZ1C+TduV3lfF/GRaWkJ+V2oaAfgoj6OigqJT0jt8v9ddjQ 2/3y+nJ/ecHn9b4XZ9e3l8JFINbht90ur911jmLqThrR24fr/c2UwedNllK2fYt90OttHfk5 wP5jfaWkp0MCVYa5hdhpgszrAwtB+vJ6kZoq4CKQBagL/DS6w/AvF+oYQRxVZ28fdLmMbd+w 7Osx2zrOrd6nA0p6NhAgI5fxEjvBeL1c5EKgJbUZrHncrmEgLjD9wd0Uv9qHmEVL7dn2DcfQ 5SB/z/kdyaaU9GQgqHqDo0orE+DmiARwMiR0Qrwhe22zqUHku+S65EyWtGM0kAhX6Nv2GEFS s3/jXA/MTSnpqUBGGx/qZlQDLMqWB7IcF8su+GNctzNXykRJuyRB2/z5KvcN9eh5q5VUhvGm Tzvz3s4hLyU9FRIYtaBFEIabKptPsk8juPCzQLdhK6kWImQbhRGqOPxstGsnyOuS9guCH1OZ Gip9A1iO7XJi7mVS069Sf3Mo6ZmQwHnk53SaELQF0mC1H8i4laUkqaxVWavGnEza6kNKFKnW twRk1Xhl2pEUxdvbPZT0TAzMg0og6EPM7wifZJskYCFjITh1gFc5LulIH2rnK+mbZzPsTftc pnkXWUFJz4QE0q4HFfJM4gLQBGMetFkgmyC2+/r1RiJfX1fDBiRVx/N1d/WhsN+WpG++fDJ8 RgZ2dTSG1ckFowElPRMHJCXzoaQkh5IuBSUlOZR0KSgpyfn59f71p/s/+XD++/al6/ugpGeC mXQpmElJDiVdCkpKcijpUlBSkkNJl4KSkhxKuhSUlORQ0qWgpCSHki4FJSU5lHQpKCnJoaRL QUk/kOv12vekRpWb1HGshiKHJFVPduw8ggWKj3OpOvw2BGpvnaOYupNG9PYBj9P5cvmzp9u+ xT7o9baO/BxEKOlHoZ7gP8RN6un4Aoc4ICnfFujY9g3Lvh6zrfeBchSnpB+AXKknmoU309Vi 5SFmDXd3gvH93hY4RlP8ah9iFi0+8D3pbYGAkn4E8sVPkcAjgTs1bmdKWsuk7hjJkNAJcfxt gWPsStoxGkiEK/Rte4wgqdm/PYSnpB9A7+swupEv+ug7iRJmSSr1lLNFlC0PZMlaWHbBH9vR zlxHaEpa7YMGbfPZttw31PPY2wJt+3q+D0o6DQzp6kPAx5AgaV3tR2lKaoekJtNtP8lOjTY9 +22BOxFel7T/vL7f2wJt+3a6YKCk0xgQKssmNSaLPyGTfuzbAseoSbrK2wIp6bPBl9JpgAke fNEd5Uf/uE+Tg5ImQSX9ff7bAscoSTrSB7Qpv5hs+uZB+/VFhpIuSHeQIUj91XhfwGJAPMoB SfNM4tpugjFvY9ZuE8R2X78+XKzwmdZJhzqer7urD4X9tiR98+WTUZQaojdGV5T0yZSu2kV0 EMindKXWICD2ynQjx+5pInkOlPTJ4ITvy4QrrHwxMoSNorbnR331dkJJl+JhSX/9+mWC59+/ f24N8ZhsKeemdGK7ZMKwygx/9JC3XJ+Hkn5eHpYUIHB+//7tlj4GI0Tv3dKngTu45cxn2rtz xvVcJpkf7cxbKOnn5JCkEBTBQzZIkFfv4Mq2tqSbHzGgvJe0cQOJc9LPyyFJMdRF8Pz588et qWGHbUe++BsC3wVrCEYfwKFimcsl87g0sPM6/HDSlgnbpT6fwZJj6Ux2Q7b0ZfCbI/E4+HGI bxJ+uyg5JuppnIg8I6o7gLo9G3T2PYy0cVpd5DCHJAU/fvy4f//+3S3tkQZc8tkLXNluwhPz tVDW1mcX7f9jEEMiJWitDjf/u15FNDz2FeaDdp/kAuB/bxQyo273mFhaDsf1MqNNsQ0GVX9O 4/yET3kYXf85aaPO0OYNlHQpDknqh7v4/P37162djBPqhn8QSQc9toWgRVaMvxOLrBgCulGH lVf2cyfBLPuNqMMLFX7dyx5HP8aZnEQIHBooyLHTP8irJd6A45W+DdPPKFdeBCKW5X0ISroU D0uqMygCB3d79xm/qqOBZruI9SYm6eBOhBJCWVM+ilSvw7Ynyb46U0EOCKVE2x7TlAn7oL5C RkPQh5U1oWxbal9G+wZSQ3xXb9hXf+oHo6QLgZjr+T4SSSGnHuK+5w2kRAqZB0YJnBBiYghO ZDud4hzVOryEdosJTgTuLa4wc9ZX9bcuzdzP1YWsfJEsHILd1YehMy4F2Ga2bI6TPwVzk3px ISkI7kHblGCv+mKDi0h1xwegpEsxLCmELM1BETj1H8fYYH/si0eWcMGpsqMJfAnqN/Nck8Vm G1knUqWvFSnXsc2K2B9Cxl3R7k3WkwA2Q0+pB7uaOtw+Zogd6rd9tlJtTnIiVSHTJVmxkQl9 OTnuTEcp6VoMS7oqeBLeymHnneYubxLsK4EsO69ty76ZgUzhc0hqMpjOgAKy1UQRpmOG5u7/ R5B+TqlHQ0mX4pNkUjun03dA9TxyVW5T3hb4DjbNlNRMS/CdNObbydQgTi8QnP771O3R65Ob eFOI04v8HOipR9qf2Ca9vlbe9rf3HH8SSclUpkmKYHTBaWTdzO8dxdfJoLyfriT7butMZTmG u1hUOq/bae5/qPaFm4mQ1e1fLT8IJSU5syTFNCRUZLNKXm/MojorIjCzm3pYrglbpXf+b9vX nZmTdiikz8U6auU7oKQkZ5KkOquAIFoJE8Rxe31fN4TEtpoQCZ2SOonqbyrcgPJZZpRj1W4Q 6PLmWP0jAEpKciZKqiVqSmpAZnTZBllYZZ503/awNKVPUntRkOOhSnfBaFafXSDKI4JAKO/L UVJyhG5JXVZzwZl8pIJxSVEmFTPWGYPazvXcsYvzvEa7KvPCsbaKaI168t+v3pRnJiWHmZRJ UU/Mdlacdr0osw1wQdeTDDM76+zOpLGilqTttwgiU6bbs/KUlBxmlqQmYF0wJnJVkONmYkBQ HfQmwP2yHTrOkDQVR7V7QyKM7JP9OmZyYaqUT461DyUlOdMkFUxAYqipgtKtM8cwErqhqD6o X18Q22Q9t8/e8FlKd97dFVRbtFi+renwGx/bJ92ebTbOy9vsb5b3LloOSkpyZkpKDkNJSQ4l XQpKSnIo6VJQUpJDSZeCkpIcSroUlJTkUNKloKQkh5IuBSUlOZR0KSgpyaGkS0FJSc7Pr/ev P93/yYfz37cvXd8HJT0TzKRLwUxKcijpUlBSkkNJl4KSkpyZkpaegsmwj5zZJ0XiY2kITv8E iW6PXr//FMwo8SmV/ByoJ1g2/Ylt0utr5XsesYtQUpIjks4J/s3zpNkbCyx8W2Cb4vkpQEnP hAq6Q4jsMeBtVsnjP2ZRfWFAsOvl8KaEmrCHse3rvjgl7VDULnC18h3U/6xlCiU9FRL8PeOr HXRWAUG0EiaI4/b6vm4IiW3TMr7gJFrvbYHob5/clPRU9AdGi2o2rILM6I6LLKwyT7pve1j6 CPaiIMdDle6C0aw+u0CURwSBUN6X65VUyncOkynpyUDQ7geRy2ouOJOPRPi4pCiTihnrjO05 9LbAinljba2LY2TPhrWb8iOZdGC0QEnPBgKpMziqIBsGKaw47eRXyeC6Hj1s7KqzD5tJY0Ut SZ/5tsDe+SigpKejIswQCFgXZIlcFUpZA4LqoDcB7pdR/xxJU3FUuzdA5nA82ed93xYo7Rjo HCU9I5DmqAEmIDHUVEHp1pmqjYSFoahfXxDbDintPoezvUa1RYvl25oOv/GxfdLt2WbjvLwa iu9ctEayKKCkJwXzP+0OeRJywRj9MRglPTGYU1HU53F7QFBASU/Ozfxl8vHAIWPcLo+fZ0pK yNLc7/8DGo3zrvCoTxQAAAAASUVORK5CYII=</item> <item item-id="122" content-encoding="gzip">H4sIAAAAAAAA/2xTy27TUBCdazt+NYmblKQ0DnFIAqEgIWoKEl2VqkIgSpGCWFeuY9ogQoLr oi6jfgA71mz4BMSGFRuWfBFSmEdIC/Ra4zt3fObM+HicBwCF9gLNZV/Hu5286yUHg9HbHNBa R3P6o1hiBsceEGwYP99/ncQZR6DH+RrezaPD8dboRMI7aBYGenPgM8olawH8wOov22dnWr+U NKWBprNTnhd/mGXpYP84SxQjN9A8OL+MjdZf5xwTGFmaJD4HQrTCmF0zXPkOMMlZ4SfcN01b dsuR3XZldxZCDYu8R30sZa4hw224h6KgBPnw41OAUxcKDPmS/x8CUGSaieOF1OtWEUOL7J4W uY1SWP6Jz+0yc/RLALr2BIYAS+EHbHRSgkv85PMSSqK+0QeCinBaVWmxsix79bK0ukxIdQdg hc9f52eoSWLVl2ZqGKpLMzVu5oocKnxoCNgMhL3RlD24yqxZk1nXAFoiQxPaIgN+AlPlSIR1 uA93iaojVME1qdvB0HUp1eFSXRGhcUNE6JIIPUgAVkWELtwUEVbPRLjFw+b1w72DNBof7r0a pcMos5iPXsDh8HaURTILtdlMUBbPOTnYPrR5cmlQ3OhkcPSIeWTO62i7avaXyJriBap+cXTn gugfcitLozgJt7l58GfAQC2C8glXdxQuH7uaBkpTvo7BQOnKN6YUMZSvYIpODjGTTXxkEoYY LMagYxOGwA7xULqLGE5fQAyn5xHD6QWuhU6RMeh4hCGwNOztHg+TdBBHb0QSmxt/jNaHAV76 7PdzZ3r+s/LnD78BAAD//wMAtcFV3moEAAA=</item> <item item-id="123">iVBORw0KGgoAAAANSUhEUgAAAb4AAAFNCAYAAAB/iwpeAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAABOKSURBVHhe7dxNktqItoVRxsWACA9H bU+gZkCv2jWGClo1A89AF5EoLTD5YydnXw5ai1A8g11VLzaZ+iwg72YEgBURPgBWRfgAWBXh A2BVhA+AVRE+AFZF+ABYFeEDYFWED4BVET4AVkX4AFgV4QM4OYzDdjNuNtOxG/fnR08Ow7g9 Pb4dh8P5MdoSPoCjw7B7jdp+d4zcdjimcDIFcQ7efty9Pk5Xwgdw7XSFd77q2+/Gze7n9d8U xcVdGlpF+I5/d3Nzc3P7vdv00uZ0G87HfDu95HnjcIvdvmo14SPL5nk2v6PjFd/u/LrnYdiO 28Ube8v7U/DoR/goYfM8m9/Pfvj5Pp7wPR/ho4TN82x+H1PYLt7De+c9PuHrSfgoYfM8m3/d 5dXdfhxOv158qnP60MviU53C15PwUcLmeTb/mtOPMEwfUnk9Fj+z98bP8QlfT8JHCZvn2TxP +HoSPkrYPM/mecLXk/BRwuZ5Ns8Tvp6EjxI2z7N5nvD1JHyUsHmezfOEryfho4TN82yeJ3w9 CR8lbJ5n8zzh60n4KGHzPJvnCV9PwkcJm+fZPE/4ehI+Stg8z+Z5wteT8FHC5nk2zxO+noSP EjbPs3me8PUkfJSweZ7N84SvJ+GjhM3zbJ4nfD0JHyVsnmfzPOHrSfgoYfM8m+cJX0/CRwmb 59k8T/h6Ej5K2DzP5nnC15PwUcLmeTbPE76ehI8SNs+zeZ7w9SR8lLB5ns3zhK8n4aOEzfNs nid8PQkfJWyeZ/M84etJ+Chh8zyb5wlfT8JHCZvn2TxP+HoSPkrYPM/mecLXk/BRwuZ5Ns8T vp6EjxI2z7N5nvD1JHyUsHmezfOEryfho4TN82yeJ3w9CR8lbJ5n8zzh60n4KGHzPJvnCV9P wkcJm+fZPE/4ehI+Stg8z+Z5wteT8FHC5nk2zxO+noSPEjbPs3me8PUkfJSweZ7N84SvJ+Gj hM3zbJ4nfD0JHyVsnmfzPOHrSfgoYfM8m+cJX0/CRwmb59k8T/h6Ej5K2DzP5nnC15PwUcLm eTbPE76ehK+L6RtsPhpwEs6zeZ7w9bSKZ639CWEZvfl4cE7CeTbPE76eVvGstT4hXAdveTww J+E8m+cJX0+reNaEL89JOM/mecLX0yqeNeHLcxLOs3me8PW0imet/QnhOngNvtmchPNsnid8 Pa3iWXuKE0Kj6E2chPNsnid8Pa3iWXNCyLN5ns3zhK8n4aOEzfNsnid8PQkfJWyeZ/M84etJ +Chh8zyb5wlfT8JHCZvn2TxP+HoSPkrYPM/mecLXk/BRwuZ5Ns8Tvp6EjxI2z7N5nvD1JHyU sHmezfOEryfho4TN82yeJ3w9CR8lbJ5n8zzh60n4KGHzPJvfx363GbfD4Xzv7DCM22PkNpvt uPwt4etJ+Chh8zybf91h2J5idhm+wzhs5+Dtx912OD7yQvh6Ej5K2DzP5vcxxe8ifPvduNnt z3dergjnu8LX02rC5+bm5vap23A+3rp/esnzxuEWu33VasJHls3zbH4f11d8792fgkc/wkcJ m+fZ/D6E7/kJHyVsnmfz+7gOnff4no/wUcLmeTa/j1/Ct/xU5/RjDT7V2Z7wUcLmeTb/uil6 pw+qHI+L+Pk5vqcifJSweZ7N84SvJ+GjhM3zbJ4nfD0JHyVsnmfzPOHrSfgoYfM8m+cJX0/C Rwmb59k8T/h6Ej5K2DzP5nnC15PwUcLmeTbPE76ehI8SNs+zeZ7w9SR8lLB5ns3zhK8n4aOE zfNsnid8PQkfJWyeZ/M84etJ+Chh8zyb5wlfT8JHCZvn2TxP+HoSPkrYPM/mecLXk/BRwuZ5 Ns8Tvp6EjxI2z7N5nvD1JHyUsHmezfOEryfho4TN8x5y8ykM8/GEhK+nVTxrTsJ5Ns97uM2X 0ZuPJyN8Pa3iWXMSzrN53kNtfh285fFEhK+nVTxrTsJ5Ns8Tvjzh62kVz5qTcJ7N84QvT/h6 WsWz5iScZ/O8h9v8OnhPGAnh62kVz5qTcJ7N8x5y8yeO3kT4elrFs+YknGfzPJvnCV9PwkcJ m+fZPE/4ehI+Stg8z+Z5wteT8FHC5nk2zxO+noSPEjbPs3me8PUkfJSweZ7N84SvJ+GjhM3z bJ4nfD0JHyVsnmfzPOHrSfgoYfM8m+cJX0/CRwmb59k8T/h6Ej5K2DzP5nnC15PwUcLmeTbP E76ehI8SNs+zeZ7w9SR8lLB5ns3zhK8n4aOEzfNsnid8PQkfJWyeZ/M84etJ+Chh8zyb5wlf T8JHCZvn2TxP+HoSPkrYPM/mecLXk/BRwuZ5Ns8Tvp6EjxI2z7N5nvD1JHyUsHmezfOEryfh o4TN82yeJ3w9CR8lbJ5n8zzh60n4KGHzPJvnCV9PwkcJm+fZPE/4ehI+Stg8z+Z5wteT8FHC 5nk2zxO+noSPEjbPs3me8PUkfJSweZ7N84SvJ+GjhM3zbJ4nfD0JHyVsnmfzPOHrSfgoYfM8 m+cJX0/CRwmb59k8T/h6Ej5K2DzP5nnC15PwUcLmeTbPE76ePnjWDuOwPX47HZ/czWY7Dofz w59xGMbtbn++87797jf/3b/JCSHP5nk2zxO+nj7xrO3H3Z9Ebzscs3npJaC3/pN/8N/4Db44 82yeZ/M8m/f08bP2RsTeNl0l7o4p+9WPHz9OXyj//fff+ZGlY/x+67/zeb4482yeZ/M8m/f0 4bN2GLbj9nQpNl2VTVdsx6gdpkhtfj7++uvp7m7cvPMS57dv3978YtnvNuMnXx39Lb4482ye Z/M8m/f0wbM2Xb0tXoI8vW83jPv9YTycAjeMw7A///qlWD9Dedt0tTd9sUxXf9c++mf/1PTf czgcDsdzHF/1/r/h6mXOU5iOgZvuT1dnF78+B+ujeP3777+n/8e/f/9+fuSnyvCRZfM8m+fZ vKd3n7XLEC2v/pYfRln++v14/fXXX6cvlH/++efmF4zwPQ+b59k8z+Y9vfOs3XiZc776W76P d37J8zVXb7zHN32BLL9Ipl///fff53svyt7j++gVXe7O5nk2zxO+nm4/a1PkzqE6fZjl9NDP q7FloF5e8hzG/Wv5fv1U5/Sy5vUXyBS9y8emD8kUfarTCSHO5nk2zxO+nmqetVM4b/9Iw21X V5d35oSQZ/M8m+cJX091z9rpE6CfS9//63+5ZbqKfbmqrf3vr9Htzae/4Ex7T8fv/MWIz3jr 63yy/AAa9/NL+F5fLXNOeWR14XsgN08Ixy/Q3fkr8xTAijcXV+zW5odh93oymE7Em6KXttfq rfDNf8ETvvu7DN/VBwB9fT+s9YZvab9zUrizDzf/7ZfD+ch7m1d9YnrtLsJ39cG+qg/r8XVv f6dMT+h8vOVef6bY+yfh49/SBl+d9/Zh+Ka/EQvfXQlf3jJ81xvb/HHd/k5Zxmo+rt3rzwS8 eUJ4fT3ey0D39mH4Fi81cx/Clyd8Pf36nTIH6tYxu/V78zG79XvzUWb54Ymr453XHKYvUO83 /ak/23w/2PvP/f7mTsI1ps1nwtfHrxWansi3jtmt35uP2a3fm4+w4+ng/Ku3eCP63t7bfDoh eO/j/j7a3En4/pbh8x5fH79+p1xHannMbv3efMxu/d58hH0YPh9uubu3Nr88Ae/Hwe53I3x5 F+E7XYmfP9U5vY3iL9MP6/Z3ynWoLp7cs3v9mYBbJ4TpRPDRy0P8uVubn36EYd78dPhZp3u6 tflk+bUufvc1bXrh9XMDvrYf2dslmp7Q+XjLvf5MsbdOCNSxeZ7N834JHy2s4llzQsizeZ7N 84SvJ+GjhM3zbJ4nfD0JHyVsnmfzPOHrSfgoYfM8m+cJX0/CRwmb59k8T/h6Ej5K2DzP5nnC 15PwUcLmeTbPE76ehI8SNs+zeZ7w9SR8lLB5ns3zhK8n4aOEzfNsnid8PQkfJWyeZ/M84etJ +Chh8zyb5wlfT8JHCZvn2TxP+HoSPkrYPM/mecLXk/BRwuZ5Ns8Tvp6EjxI2z7N5nvD1JHyU sHmezfOEryfho4TN82yeJ3w9CR8lbJ5n8zzh60n4KGHzPJvnCV9PwkcJm+fZPE/4ehI+Stg8 z+Z5wteT8FHC5nk2zxO+noSPEjbPs3me8PUkfJSweZ7N84SvJ+GjhM3zHnLzKQzz8YSEr6dV PGtOwnk2z3u4zZfRm48nI3w9reJZcxLOs3neQ21+Hbzl8USEr6dVPGtOwnk2zxO+POHraRXP mpNwns3zhC9P+HpaxbPmJJxn87yH2/w6eE8YCeHraRXPmpNwns3zHnLzJ47eRPh6WsWz5iSc Z/M8m+cJX0/CRwmb59k8T/h6Ej5K2DzP5nnC15PwUcLmeTbPE76ehI8SNs+zeZ7w9SR8lLB5 ns3zhK8n4aOEzfNsnid8PQkfJWyeZ/M84etJ+Chh8zyb5wlfT8JHCZvn2TxP+HoSPkrYPM/m ecLXk/BRwuZ5Ns8Tvp6EjxI2z7N5nvD1JHyUsHmezfOEryfho4TN82yeJ3w9CR8lbJ5n8zzh 60n4KGHzPJvnCV9PwkcJm+fZPE/4ehI+Stg8z+Z5wteT8FHC5nk2v4/9bjNuh8P53tlhGLfH yG0223H5W8LXk/BRwuZ5Nv+6w7A9xewyfIdx2M7B24+77XB85IXw9SR8lLB5ns3vY4rfRfj2 u3Gz25/vvFwRzneFr6fVhM/Nzc3tU7fhfLx1//SS543DLXb7qtWEjyyb59n8Pq6v+N67PwWP foSPEjbPs/kt0/tzx2Wur86mY/Hy5ZLwPT/ho4TN82x+H9eh8x7f8xE+Stg8z+b38Uv4lp/q nH6swac62xM+Stg8z+ZfN0Vvfin0In5+ju+pCB8lbJ5n8zzh60n4KGHzPJvnCV9PwkcJm+fZ PE/4ehI+Stg8z+Z5wteT8FHC5nk2zxO+noSPEjbPs3me8PUkfJSweZ7N84SvJ+GjhM3zbJ4n fD0JHyVsnmfzPOHrSfi6mL7B5qMBJ+E8m+cJX0+reNbanxCW0ZuPB+cknGfzPOHraRXPWusT wnXwlscDcxLOs3me8PW0imdN+PKchPNsnid8Pa3iWRO+PCfhPJvnCV9Pq3jW2p8QroPX4JvN STjP5nnC19MqnrWnOCE0it7ESTjP5nnC19MqnjUnhDyb59k8T/h6Ej5K2DzP5nnC15PwUcLm eTbPE76ehI8SNs+zeZ7w9SR8lLB5ns3zhK8n4aOEzfNsnid8PQkfJWyeZ/M84etJ+Chh8zyb 5wlfT8JHCZvn2TxP+HoSPkrYPM/mecLXk/BRwuZ5Ns8Tvp6EjxI2z7N5nvD1JHyUsHmezfOE ryfho4TN82yeJ3w9CR8lbJ5n8zzh60n4KGHzPJvnCV9PwkcJm+fZPE/4ehI+Stg8z+Z5wteT 8FHC5nk2zxO+noSPEjbPs3me8PUkfJSweZ7N84SvJ+GjhM3zbJ4nfD0JHyVsnmfzPOHrSfgo YfM8m+cJX0/CRwmb59k8T/h6Ej5K2DzP5nnC15PwUcLmeTbPE76ehI8SNs+zeZ7w9SR8lLB5 ns3zhK8n4aOEzfNsnid8PQkfJWyeZ/M84etJ+Chh8zyb5wlfT8JHCZvn2TxP+HoSPkrYPM/m ecLXk/BRwuZ5Ns8Tvp6EjxI2z7N5nvD1JHyUsHmezfOEryfho4TN82yeJ3w9CR8lbJ5n8zzh 60n4KGHzPJvnCV9PwkcJm+fZPE/4ehI+Stg8z+Z5wteT8FHC5nk2zxO+noSPEjbPs3me8PUk fJSweZ7N84SvJ+GjhM3zbP5Vh3HYHlc8xmyz2Y3786Mnh2Hcnh7fjsPh/NiR8PUkfJSweZ7N v+Yw7F6jtt8d19wOxxROpiDOwduPu9fHha8r4aOEzfNsfkenK7zzVd9+N252P6//pijOd4Wv p9WEz83Nze23btNLm9NtOB/z7fSS543DLXb7Kn9dAbh2vOLbnV/3PAzbcbt4Y+/6Pv0IH/DE lh9YuToWL19e2w8/38cTvucjfAALU9gumvjOe3z0JHwAZ5dXc/txOP168anO6UMvi0910pPw ARydfoTh4uXQxc/svfFzfPQkfACsivABsCrCB8CqCB8AqyJ8AKyK8AGwKsIHwKoIHwCrInwA rIrwAbAqwgfAqggfAKsifACsivABsCrCB8CqCB8AqyJ8AKyK8AGwKsIHwKoIHwCrInzwsPbj brMZN/Ox3R0f+YTDMG53n/qT4363HYfD+Q6shPDBI5sitnkJ3hSp7UeVmv78dhiv/9Qcz19N cRU/1kX44JHtd69Xb4fho/AdxuGNq8IfP36cwvfff/+dH1k6xu9GLOFZCR88sNeXIg/XV2bH yB1/b4rZ68uax0hu3nmJ89u3b29c9U3/6Gb85Kuj0J7wwcOaruDm9/eO0dvP1Zse3x5DNd3/ ebX20RXhdLU3/bumq79rH19NwvMQPnhU8/t11x9WmV/+PF4FvgTw5eGP4vXvv/+ewvf9+/fz Iz8JH2sifPCgfsbo8r276WXJl6vA3ekqcM7Ve/H666+/Tv/MP//8c/q/14SPNRE+eEgvL2fO LTq9B3e+s/z1FKzXi8E33uM7RXIRu+nXf//99/neC+/xsSbCB4/m9CMM81Xd+dOWU9Sm+6eX OOffn9/nm11eGU6mlzWX0ZtM0bt8zKc6WRfhg2dyiuInf9D95PLKEtZA+ODZXH8Y5h3+l1tY I+EDYFWED4AVGcf/AUhSVCR1OIx/AAAAAElFTkSuQmCC</item> <item item-id="124">iVBORw0KGgoAAAANSUhEUgAAAIcAAABnCAYAAAAqo1GIAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAjSSURBVHhe7Z3BtZs6EIbdwOsjNbiL 14RPisgm+yzoIvssWL30kG2OV7eD2wFvfglhIUtoJIQQMN85Poltrgzo82iQNfZtEIQATcrx 7Luh65/jvbJs2fbZaE6O/vEYHv14ZyOePb1GJ4LEaEqOZ7e9GBMkyF0EWaQdOZ7dcK9mhqZ/ 3AfxI0wjcjyHjoaTumoQEPLe0asLPtqQA52001tYokeYJuR4drwO+vj4GL5+/Tp8+/Zt+PHj x3C73Ya/f/+q58y/yUjuEaQBOWhIuceHFIgBGX7//j0+Mgy/fv1Sj+G5nz9/jo+m0g8PGVq8 NCAHdQ4jEYUEiBo2f/78UY9DjM/Pz/HRpxoq8PgN0kV7nSfnFdlfDso3OHMObtQARg5EEEP/ ICkeYyTAkMEQT/IOP/vLwRzzIQFksDF5xwQS25sVBZhXI5hf2VKOPWdl17x2E3JwpjeQcEIE CGFu5jEkqIgqOmpYjVHbN5Yc980m32rM+MbInRE+jBxxkDvcZm2h02eyBNhKjqozvjGYEdrm RHJQYmsPKR5ZQmwiB4a0UKNq+KModwvnOkrshW0QJe3mX9uHjzk1tzqPHG5+Qe3eZrKEKS/H 0owvJB47yc2RDPS4GQZUp7s7N8o1PWwfe6hNYG/HYH85vn8Zvnwf/78KzFfQSVEnHR3Aixrg v3//KbQPI+iE0FsU0r56NR7dPMNB31GSaUUOCGRvg6gSev2U6HGiYYXAiRxD8SMhQy8dOdBe qAPcSLDUkUqeztmx8XzZw0pSmx7ZQpxLjkzKyoFoEB7O8Fq8d7mOfsghXs+/ZLHlwDm0h9Bl 4RBheUOLyEGUlYNO/kJjfDk0KiqYjsecxbjpTA4C901CupToKsGYM8IiB1FUDso3FucU8C6f XoyRc0C2UY65APr2JtasfT/cvEPkIIrKQcezFAlog/nVSizEBzrbjRwKbBu6UrHgzgiLHERp OaJtqctNJ/yPj+Fv9VAyRodAYzM5lBS0LTOX4B6vyEFUl2NnRI4ERA4/IgchcvgROYiichSb 8d0O7oywyEFI5PAjchAihx+RgxA5/BxWDqw4d5cN5iJy+DmkHFgiiEXFWBqIJYJrETn8HE4O yGAKmFCOgJnBtYgcfg4lB6KFXYaAyCFypHNKOYwIiBymLCG7DNJC5PBzGDkwhJiKNySjiCC4 b0eSXEQOP4eRA0OIW/GG6DEvkUwthdRUl8P3qawH7Nf8U9nX6jBzez1tP7f8sf3p5EAi+qqH 1UAMu4BaLYZJLIUEdeVw1nMEOlKJgY62G+v72bYomDL3U6r2TicHTpQ9r2GuVCZh3BON+4XX N7CIHQ89/+rw5ZVgSpBgY/S35o2ghNNRY3mhkeZUcpivWMDNlELal7RARw2rIXRCg3K4HY79 DnXoohwk/9tyxHG4iglyKjmQdC5flby/AxdPrENtOezOy5UjPIwgilwo54jPgronhLNw98Xx 5LCHlHfsXMTHteRw8wtqk1sKCWrKofZt2iA356A3Q/BF0OaF5IhDJyuzFBJUlQP7ylx9HpRj 6TXouUvlHCxwUlTGjgNfPjkudeUgxsQxtPpcQe3oOQsn0Sbeho2FbX1cT44VVJdjZ9LkmEy2 bibc2VYmjONsRI7qJEcOfHfUSxB3Wne8GkiL1jxEjuokywHwR1OUsP+aIkvOd0qxEDmqkyWH jhAmeryGEEy4bHa8Ikd1MuXQf2iih74kImGiLaVfPk6IHNXJlsN0tBKEktI+6WD1pI6Ra3YL NdK0HMc8nhgr5MDx2SdiwyEFSOSozio56K+nK5fYbNsciRznl4PQ0WN5pVIRJHJUZ7UcOEju egjaWBJSw1XkqHKQIkd10uWgg0KecTdfZdjRlYr638aIHNVJlmO6QsFQsuWMqEuhk7m0aCZG m3LYyXDZ3C89ckCIcWewaruSGoVOpj6Rue20KIe9DFC9cdn5X5x0OfaiyMmkhHjFyWszclio qYVy802nkgOrz1GjguWCbhmk/rdHSUc2h5BDIsc7EAMy2NVuWI2Ox0zJwlqal4PazM2nfJxG DkgwL3kM/SqkTVpZZNtyrBsyfZxKDrdG1verkDYqgUsoi2xZjliZQQ6nkiP6q5A2bvLGGK9b lWO2X7iaLDS0nEYOJJwQwZRB4mYeQ4LqRhUdNawGqf3YZWCLckzzTtOt3FzHaeRI432+Ayci +AnqSItybMlF5cAHgPYYzZscEzn8nEsON7+gtjmLlUQOP+eLHBllkSKHn5PJQVB7ehUbTgAv gxM5/JxPjgxEDj8iByFy+BE5CJHDj8hBiBx+RA6iqBzyS00FkchRHYkcCYgcfkQOQuTwI3IQ R5ED+1niE1qRI4HScpRc0jdhredQkqzYYfkt+wSKylFwUU6QlQJiCaXIwaSoHPjwb9MDeg7d WJWYB5Yx8JYeHlIOrDov9cuQoKwc/JOfjv6kGXlHfuTAJ9e8BcuHkwPLBLGwGMsD7a+9bqYc kkB76buiFybNlwaON2fn0H72l+okDEn7y5EwowgZTDGT+b0VDW/FVwjujCEbLDrKfmdzQATJ k4Obb4DDRA5EC7sUAZHjJQc/VPooHTm0rFsNLQSds7yrlbR86DByGBEQOUxpgokiOOhmyiEN CN8FG9VDiX+Y4ZISNcAh5MAQYqrekIwiguB+qKgplU3kIDCfsEW7WeA8Jw51h5ADQ4hbn4Lo 4ZZJvtixHNIBFWt7C/LMEAM0IUcseUMi6tbEQoxQEXV6OST/lxVzwPfKd5t8cXycZ5f/2vvL wZhRRARY/GVIG1wpJJZDpo7FV2F/OSIZtPmahaVfhrRJL4fc+MriwDQgx3LnIOkMifDO+3yH yvIXh5V1l8FnpgE5dAeGwro9CxrHnRxiTI4xcp6r0oQcKi8IdFCSHG5+gSElMpMo+UaYNuSI DC18MERQO+hsVjnkcr5zdRqRg2BccrJAO5jfYJRDStRYph05iKoziiTR5otyDk5TcoAaM4q5 M4ZXozk5wJYzimtmDK9Gk3IILTAM/wNzavVgOEoTywAAAABJRU5ErkJggg==</item> <item item-id="125">iVBORw0KGgoAAAANSUhEUgAAAFsAAAAZCAYAAABeplL+AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAHFSURBVGhD7ZjNsYMgFEaty4KowhJc 0IV7F+7swa3Dyg7sgHc//IlRBFQkb3HPDBN1JgROLh8kmWaSwbITwrITwrITwrIfoGSusyyj lmup5ocOWPZdlNRiNmyki8Zcu2DZMWiEzgNKm2U/Rmkp/VUNWPYTKEpyk9kZV/Z1qErzSd6h OTLZZHYu6d1uWHYUGi1YdiJibZDjOJpl1Pf9/OR9Qo9SV7D2uWZu2Dl5y+eM7Y6YLV7ZRVGY DvFqQ0lBiyge6yQiyrb3SUt/kWykx52HDafssizXykbboxoaZOQKBEkqm5b+537aGF+Yyhen suu61l3XrdeQPQyDuQeNmJcQtdiDdMsmMWJXhfg15xnEvs/9PeYTkrtPsMpGPkPwAiRDalVV 8xOAZehaeveOUcBf2dj9589GBAScBGyyt3J/IhuxYcvnRdTK1zKMi182oSCchAm/aPAvZUM0 hO9ZNsqJkIx7s7KJRXZAVYNDn7/ObGyIZ0c85DdEtW2LkU+7t1JBE72KX/bzGDF9bE8jgV/a E1bZkHioQEszEYOqoMmG/gFzCfS9fJ5V+I0N8qxPUzR4fv2cfYdDjDDvwbITwrITwrKTofUf FM79puoFLHkAAAAASUVORK5CYII=</item> <item item-id="126">iVBORw0KGgoAAAANSUhEUgAAAJQAAAAqCAYAAABV/HLRAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAUBSURBVHhe7ZzNcaQwEIVJYPNwDGSx SRCFD+v7HMjCdx90Wufgq4uTM5gM2H4tCf0MCIQY0Ape1ZSRGIRAn7pbDZ6qv3RpQ50KqE60 fSs6VTpOufTjGToNUKJp+kaoQgbqBPWnLQ+qUwDVtXnBNIigqguDqnyguravgzR1fVtXfVWN fHagUDR1XxJThQNFsJCrC2JBwGnX07W1sRhU3+5h1QB83VJPy1DZQGGwFk9/WKpjrEVJVqpo oGBxlvN0oKUoKJYqGChYnBl3Z8lxd7tL9E0hbq9goGiQFgfVx7k7qTj4c1a5QFnB9qwyCIxL iaPKBWpxXNLRV6vDYxjkyi6gchYy0XM+BJbJyjs5UOl9sFywdrXab29rdYiBdBu0v6rJ3dZW m0hfUDlgBRHDZZl8jdS5gZoTrFzT9kJ0POBVQ1aEAMFjkyHpie8g/lLP5uT31D6GEsfMu9ML qNy1AVC2K0SMU6tMJ+pl22SZCBj7Oa8LxvJg/wIqdyUDBVg0DAocbCqrg21A8Ogm7dUagPLc 44QuoHLX20v/8qa218hza3obA4/tjhhhS6Vg4VdSyCUOx5AEu0uq4ziMXGWAq7+/f6X1NxNd FmpCxq1h27gtdoMAShZU4C5XaGyxCKKWVmyVY93I+sw8GCzTQrHJxmrFS7JhlnH9MvN9lNh6 UB95YBKB2lvbAwWQ5Zg54zmMsRfbxdZPaMRCTfh9HqC8YGKAnKU43USdcT45UON5LSsudOK9 2PppPQJFByIW8DPHgpa+2Y8PXJAelVMDZayTYxjonpgYTxoOLsbWB/QAVMfg4GDbxAmKC2Za Uhcxd8KQMKu0ebXbQd5H1ls3CBeLOuuLiHWGIu1P6cve2t7lkZS70veMLbpzv+S+2PqQPKAI JAUOGjODF/OymSRZ+m7vE7hj6Cz28xktGLgfuh43aGjDnzEEtG1ViwFq3f00wkSXrsoZU5IN Tkx9SA9BuQHHdATuLtxMogDK2GMJBRBdGi/LG9tq8uyzfDqsmH2xM0CNDtAOnylNA5Uu/IMG mt4dKOnujNBAI+Duwo24ip9Rfse1UM/HEUhYdtvfcI/BOW0XTboslBKOVxOP7ok5RrbLxdj6 gCyg6ADfr6HBhcvFFDEcqqdsidQJHWg6AnuABhdH23jGxkVp4QQdO1xBamJzZz0tsUljaCYe vI66h45XiK2flgSKwZHUu5aCGlzQiBROPk/wqKiz5mm9f341GzHL9C5cHAfuGkLqv70forpV fTlI0xZqhazxfLBifO+wT09Opdj6CU079f9dZwbqQA1AVX9ctmLL2ekC6hAxFYDDBmRNOTs9 Aaj7/c5u5Pv7W9Vsp+KA2uJvViKg3HgsXa+vrwwU/o4Jidm1TBTzCrANA7ZTylkJgf6GI3S7 3QYLhY8vrE5N0jVexfyTgg9Eajkf0QpxIx/y8fHRf319DdsA6ufnh8sQ8nUatHWnRBpkvXXL SbnSsIG2GSTES4BICyABnPf3d1UDIWWScq6Y9EzeKhgoeL00NwIXNxYvaWs0CHmfFGv4hHjv KBUNFOKolIECTIDKlw7OpZY9kgiplPgJKhuoBLeHIHwqPYB4CkB9fn5KaOHu8M642h+n7WK9 HFQ4UCS4k8gBAyjarYU+7A7h7gja+ffFxlWSdYLKB4qEHE+WRoBg3DK1kYNOARR0/WjrPjoN UFAuP+eM986un5W+dGlWff8PFKtaX6V8K0IAAAAASUVORK5CYII=</item> <item item-id="127">iVBORw0KGgoAAAANSUhEUgAAAKMAAAAaCAYAAADbqew9AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAARYSURBVHhe7VsBluogDPRcHsjzeBov 42H8MzzCHyAU0JXVbt/bYbeBBJIModXu6fHAzx/B7XZ63B35T4H2PfmBMaCthbsEiHK9O/Kf BOxfD0I+DbS1cI+4Xn35T+OGed5ZffcMtLVwd0DFuqyqWCsq8E6BthbuDXdUq2UEIfEXVeG9 AW0t3BuuZxyfjvxduGC+46ieB9pauDesJsdq8rMan08Lb0UmwZPphPUZWutEWwt3BSbq4sjf iBvmW3nfmJK92M8ZMCYHGeH46iQx8Cur1CUmmfjUh6eDjECoGr9AxvOqhxhuNiHjsnkncZAR IBmfTlC8F9Nkn+z+syCB3iO+NOckeH96kUSXa3HxpF+voCTj1SEn2raSImMznFFjvCfLHhDE oZYz4WixAHTsMeDWlwHjwpgN/VeJcYcvmrjyGKQft0K2jIyY9xxjqEd1q/IonvHrFSivLD9c g8m4ZoysFcNuE4d4nRbLAERl7a+OwjiRR8ZsfM8e+u1zuyzJkIev3jr61KnWJmB/b/cHG5gj QG3J2hSryMgEm99ZEYH/uplbmPXrFZSV0eRpEzFnqpAGoCMkCEokoSaMymosANc0qEnl12K0 U+44yrmbTT5qj8g2RURXn39roAWhOjT6SqSgybpaRGbgV5CRa0rzR58NZZxamPHLA0+kEeK3 yBgKh61BFQKYoBhIDtRFBYNe8mIgdCy/C67Ig3GsZna0zNgLFTDqmWxIn387Y1rBaUGriMWH 92rlOIK2t2y6G2gSXE9JeE3s6GaY8cvDW8moSmW5bwWxIgXGkIRZUnDN0q9BHLYHeMEf0ndI bKD+aAUgON7iwtfFvLkJzj9SmbJEKArfPTR1BaO+jfrl4a1kTAliEjUovEafF+SSFPbmipLR 3prh5MHGhD0i6ZlsQt/9Bgb9I0lXaECJVrK51hmST6O19hgTW18ZQy+uxKhfhuZG8OIc0SUj f6tC6WQgEGT6oFAmP0xSLMKIZ9WMYxLBbeyEvUxPZaPrwXUZYMq8xGwizsngbR2Do18/NpMq OfCwtXZN+hY5Mgz65eHVysgNYvNC8r+jdNIe/y2RIXgyuU2QJRo6RpBwPwIdvVZnaU8/hnHt RbkXpFF9zpuRNgafH10MJUtgc5SbIAHyd761050f8We/gXGbIUvTbgPPkNE4lD7aQR55jZH4 wQJsZ1SAYjLKQJOQsU+JQASyxr5ACExm5LYdEPQsWR175boqQvb0dZzqMgjQ81645Tppp5Qn RF13HgL9vYS2jsseNL4BxTqq/sY4Fz2/GpghY3hm2MgZ2lypBxojvL5PxmjQiM23tZG0LSJ9 7ZveHb9WAG0tbCGVWjA7/a8HiOlVmI8D1jvy/yk8OrJvHnDN6mxVmWQrbwMSGIuBOT4CM34t AtpauAkG3EhZlNlPB4k2u1bdgOGI2dh43/TfgTN+rQLaWnhAwETFe5xv2nhdfKBfaGvhgQO/ AbS18MCB9Tg9/gHgrf6xVJxQCgAAAABJRU5ErkJggg==</item> <item item-id="128">iVBORw0KGgoAAAANSUhEUgAAAOkAAAAdCAYAAABPNEvsAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAVMSURBVHhe7ZmLcRw3EAUZlwJyPI5G yTgYWX3eJp+mgF0svUseWVNdKPzmD+COJ728/Gqa5qmJYXMXP7fWNDP27kgMmzv463frB9qs wD35+3X2Rgybq6Hg/UCbM4weagybK/nnd+NbtGnOwr3h/kgMmyvpB9q8Fx7oj9dZP9JboMij 3xZNs0p+m25dcyU80PxzpWnOkr9Nt665kvxTpWnei/do65or6d+jzRX0I72J2e9R1ql2Ng+B P21yvWnA36Xb9A0uTl4Y2+jboa5VXS/hR6H/2e9B4mX/aswXv7P/kIb6GGuco7URj4Pb2oo8 nDnXO8nYk6zN6j+6eZ61YUvwl3I13/Q7gz3v8pG9ozof6Sf+28Y2/Q8WLBC9Y9YzcWBv5IAg lWV/L4g7mPkzH2Oy6FdAfbRH7rVWCTGMDihrfwS6yNP+PMExads6uL4X651Qr5rvUe1G5H1z Tl5gfep+vSPsz+qY8kf2juq8Go+g/4gtF5NMtoIihmuRAT1hfxbAHWSRKhnX1eBTv9QmD2EE VbfJLO6KtRfyOvKX7J3rR4F/cqhnslqDRBvUoJ5/fRDAvN56dEZ1qeur9mBk74w+4P+xn4uC 8VrABGVbgkHXdF4DlVHAK2APXW2nD3xz+O7TyMMxe8bFGHnGxlwvjrIpA9aHdWWE+VFe+k27 +F4B+ZQ1jxWM+7MxXmLx7GA1D6HOaSuhLqOa1vMCbNRYPEftnrE3qvMZfSGux34uCpuzgrmu gYQgsEirQc5ATp1so4QEv+zTpx/GFKjGxTzzQU4Z5PFX82VuDMg4Rg959ekzVubu7WGexrKi A8SVseK7xj6j6n4WxpDxnKmBkHvWMckzStCp5+X5Ks/c2Ixv1R6knpzRF2ygs03/xMteQQkN W3WahcJxLdxVYLsWgVjwN4oLWfOhr1nXfBlbOO2ypm7KWkhhXP2PwL51rPHsgb/MfVSLGTXP zwD/WR9zX80hURebnpdz9ka5sp7+9Yu+667tnbtUezC6T2f0hTjY26ZvYAgnFdbTWA2EvSz0 w/jrbA52kKsti15hv2KRaVU38yHGjBPYzzXG+Bit53yUY63DDA+OtiIvNT9iZO2I2bl+NDVX z+1MDaDWAX3WaNY27ycgX2ugX3qaNrNeZ+ylnpzRT4jnoZuLgKKBy6iIGE+nOTcoE76Sejhi svqmN8mURw4bwh5z41fPfJm7l7VRpsai/grojg7vCHWMbYWM/TOpMVD799SgnqN2hP08G2ud oKNf4vKcnWesK/ZgVudV/QQZ4tmm2ySajuhdG8lZnNoyoBHIZJFXwW7VY14LSgPWlTd25jTG HgrjaoM1emXUcQ35UZ5HuQv2jHMG/qq9PANjkyqfsrTM0T3zoWc/xynLmjbo8ZM2mWc+yoC1 S3lJnRFpp+aTreZtvHUPcg+8E+A6zViP7NW4ao5H+iOsy9Z9X1aKcTUW94i8GHvUAz/ijDz+ qRE9eozpXVeGnIxVOfByznyeiWWP99ghNtpX5ds/UhLkgK66JGfA5+hycMnzQq/Ehs6Zi3ZW 3kc5GvsoiTltIuMeWOvK2VhmvMcO8RN3frhgY6XmzwAxG+vWfT84HD+JPprZZciL8ywXJh+g t4G5Y2LMOHNPyOcZcqkQqzWnMf4qEOvrubjYXMvoA4Kis/4sF4ZPa+PIMQ/OS0LvA1RGOWDM uh88Xqzm/5E1jmFzJXnpnxUfmGMfGGNjZ59bkt/+7NGzrg7jZ/w2/YrkWUAMm6vh0voImmYF 7kv9sIthcwd8KvZDbVYYPVCIYXMXFL8farPH3l9dMWya5ul4+fUvgoaNfGN/4EgAAAAASUVO RK5CYII=</item> <item item-id="129">iVBORw0KGgoAAAANSUhEUgAAAFsAAAAYCAYAAACV+oFbAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAKPSURBVGhD7ViLbcQgDM1cGYh5mIZl GIb6GRwMmN7l00rX8qRIgQSfeX42zm1p4dewyL6D4JLzsQw6RD88W2RfRkjO+TShmhG9S5rv RfZFBNcSaYMCsteALLKvgMrHbjAd3DbOq1KzyD6NVq2C6Pe0bQbZhOB2zoJF9kmAVBfKoAOe WWTjsNxp0SL7NGxlAzOyl7LvYFKzTbJXzb4PqxsZyV7dyEMY++ye7H/WZ8fk9427hOEaTjl6 tyHPWLs7olih+4JsyH73CxIRmRy4nwW14Z4I320Qz80AEOF5GsGwgpTB60tQhrpdMJAdQ25T /hZAWu4ITNCePdXgkaS25toBeR8N2fgCkuiIzYiTV0cMPSPGcALKocjL/HFPEFsuwGHct/1p Y3f6HwOtZRtleBXwedKu4Tc8+dwoX6C6DhYh7aF/5Qw6ZWNztYSwA7RTtg+HZddwgggKITu5 4WSGwzRfIw9bRDDlK6/Hs7Jh9J24p+WEyIH5nlCjfsr1RiRMIguCzz7hnd6UFh/7W+avoiVb k1XIJQo5qk6lof4PAMTtpQBq0nIQlHtiG3ZVQIHXZN/BvISwjypwrQ+6hGRBiFiuQpGtDwPl CDl6qJORFZudV5nQkNhvsI4HlfE6m4yKG8qGfYskmq+HpBEQZK+egFg6kZxFJVvIitAyhoqU SHVNnNHqV/eiZFqebTWqqLbY7hFRqKdmyfPIiuztc6Y2c3Nx8Ij2g7PjZWBfoJIN4qiP9Ee4 MxGsHvSX5Yd1yss3f77HQZcz4MgKvlALj10QlEppQ+2zHsiciyWGxSM+VMKrbzoj5T3aDx+E dZ3MH2fXDXQH5BMwUnKB8TzZuswsNHiUbF0+fq4Ofy5+oIws2EjpC7Kx2h3QtMDtAAAAAElF TkSuQmCC</item> <item item-id="130">iVBORw0KGgoAAAANSUhEUgAAAFAAAAAYCAYAAABtGnqsAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAI9SURBVFhH7ZfrbcUgDIUzFwOxRDsE 07AMw1AfHsF2gOYW5UptOVJ+hARMPr/IEbeWtAFqeRutC+VGKbjLsw1QyEdrXRzgSwrORs5w A2TyVsLpiyCbBnkDrKLUNYJeiM4c8ThwWcLGxNJ8A0ySUQXxVPWWIKrn3pr0fAMkBWeiFSGm RM3D6CjEGE3aAJOuEShFzxXAHYFalxrIpI8vuwb2NerC3vHo3F14IoKjzoG6Pi6dA7EY2jpf ELXg0uYfFT9eqOvSCehdAaQz14yPKPjeltY+us+P1T8RhG8zWA3UgvoWsXokPpDGneKXHN6D ShDzMAD3wIOjAn30v/E1gCi0pzFs5J2RpwX7E8d5Ako17doYZA3rQ76vK8AAA5m6oeJpGXlE WrJFIE/PVOOTeVL0Hr2zsOcsnMOGRw9KNzIuU7CIddtAkM1wn/ckAWJxeNUXA8I70nN8I/N5 I3XqUb1u0O3CKapdE+/opURq0ves+pEBRGRQSrI9iQ1wYCQ8y7ffzHtE4/SFbe4MuQ8eBKjd GWJnmds6AcKw8ChS5OyuesOt/s3nzbQQgaP0FY2kA1kFAe5XTxAnQHjj7G6lALf6ljfsaTyN 4L48m857RDlydPrCtjxi9Jze7gN9A2rx6l5bCsM7WJAiC0ZSZNEZCqkZ4ClEXDXOU3Qyr6+F JgLHwVa5KkTYzGMlmsR7BC01izavjiMIhtu8KdlEbgq/PE/G2G/SCwBL3UPot7z493oRIIX+ Ytf6a/pRCm9VxfgFmxsBKzvVS50AAAAASUVORK5CYII=</item> <item item-id="131">iVBORw0KGgoAAAANSUhEUgAAAFMAAAAYCAYAAACGLcGvAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAJcSURBVGhD7ZfRke4gCIVTVwqyHqux GYvxAooCapLZ5M7uA2cmMxuj+c3HAdyjuD6Tw7xSCiXE3G6McpyeOcytUgkhlg1KUo6hSJ4O c6MUNKi1APg5gDvMlSC9T0Myx7McxwHXqSGLUuAwJ2m3kUR9JKgh0d+sFCpgh2mEsAwrrYVr EfYJixzmpIUzu3KJcSbtzrzSzn1UMw/9zGvmvXbdnGpmd65384fanTMHwNtzZj0CmPb/ZwU1 7KypN11TF4G5Cs5i7RkAlZBI4S4uAc/+A7oqwHthlK6a4H+RObL0Wgbjtk+sjjQwSkDrMMJe BaGt3QZpaIa5Kr43yqkeDX5PCOUim2B/EWrg/F3aOGvgz2Vg1k0FcBl1LmP7jKBbhHhjCaPZ xvo+Mm6S54F7tmUD5sl1PxV22m02JTjOZAI1wRTGIUO8LG8aJrV/AEgvzASKP5Q2Azf1kXQi AhHQcYPokvoSWncf7UX94usB6SWopgTBxCc4x75KGgE79P0vXUvBtD/YYTZ4EF+KYJApBfDG Bzew4rtWH/Gt9ilOgRSB0fuQKV6NM448P5OAiZuSaY0/Vu/7pqgENHeSqqN4kzhPOYSd3m73 euHMXYrD+GhCC+C2N6ApHu11rwFTvhxqHtXOFaT2jG4ZVkbP4isAbJuHDsai/6ag36s6yqY4 ZY8aszD1fYbvwNr9dq/amfA/JrkBHMk1rwpd2pyCbuVHGE2cyxbAgPB6mENBgLOdepUSlgWb fg9FgWx7gouBjtRuLlPzACA1mrGOx3s/eCHdgFyv5DA/lMP8UA7zM5XyDzkdHnctOWwdAAAA AElFTkSuQmCC</item> <item item-id="132">iVBORw0KGgoAAAANSUhEUgAAACEAAAARCAYAAABTnsXCAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAAC3SURBVEhL7ZKJDcMgDEU9lwfyPEzD Mh7mx0YgSnATqbkqtU+KxBHB49uEL+Av0fhtCU0MklzGj0gUAaJnJZzNJDQL2C1ZkG1MnKB1 r5Mh9k894yPeSpQNv7zcqshC4DQrdBSJTdil19+OYSyhyRIwgTqd5icTSvhif3V94W7eJyfh 0Zc1tXqLQJixWYmDxOUYGtJKETZk42BjesO/JDY0ZmMszfXMEl6OCxsyYpRYxXQXYTnuBVgA rkAqt0W0ml4AAAAASUVORK5CYII=</item> <item item-id="133" content-encoding="gzip">H4sIAAAAAAAA/2xTS08TURQ+d96dlhYqoO0glQKC+AiOjygrJMSY4COpP4AM7QBj6INhalg2 /AB3rt34B0yMcWvcufC3uHWF3z2nVDDeyZl7Ht95zpkCESnQa5DPvIm3Fx824r2k27FJn/ug XKvbFJ3Fusca1m6+2nkTNzPWUIP9Dbydo/3eRvdY1M9BLhSNEfCF9gX9WiQyEe7r0l9Zn99K ijLIMJkpj5I/ybI02elnsWLkGqhE54+1Vr8g2xzAytI4DlgRgsZ6zDph5RvRwHbDD7jXHU9u Nye358udy4cGkrzFfGxlrdIdukePiArh+y2iE5/G2Py5gIDK1ua79BAPTpEDDHKlUFe5UYRq nNmTIhcwEfrfYffKYfknEk1c4kitMlpXn0yaDN+h0EGZplj/cVLreYbTofMDlonL4jCNyqyI DmiXIqIrgp7W6EPOU5E63Ko0VAnkrs5IY4FGqlWiqyx/Gck0K47VmjQwC9U1aWCWA8+JUGGh LmBnXqLXF+SeX5QsCxz1AdF1ybIwyrIkjvPLkgXbQDck8BIHXpEx1W9KtyvDMdySPiE6dkYx talHXY2/zUtYaoXbe2nU29/e7abtKHM51pTeZVZvRlkkO1I9W5Y5kMmLR9QHLfNG6wXyo+Pk 6CnHkf2f0Zuthn8P8YqjhCYK6FCLEqJTPKRmLmDMFN+n9X/bUHuW0M3SqBmHmyabgiGwpsZJ BQq4vJGLkEp/9IQykE6tFIwIlDe8Dhpoo6L0zGSowESCmjJVYCFVTVkafArGVoExWIfJ0Rid xWUMGE8C1lQOGHb3gWH3PDDsXgCG3cc0RnsVGQOmpDEaLE2VXvbbcZo0owMZpcfNPQPpkSU8 e/07+8Pv8M8pnBf+AAAA//8DALl10H26BAAA</item> <item item-id="134">iVBORw0KGgoAAAANSUhEUgAAA/sAAAHICAYAAAARCfoQAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAACc4SURBVHhe7d1dkrJK1gZQx+WAiB5E D4LrbyDc9XWPocOrnkHPgI9UUlOlqvBvC8laEXlOqQiZO32reARx1wMAAABVEfYBAACgMsI+ AAAAVEbYBwAAgMoI+wAAAFAZYR8AAAAqI+wDAABAZYR9AAAAqIywDwAAAJUR9gEAAKAywj4A AABURth/xKHt97tdv9vt+/Yw3veDQ7vv938tBAAAAB8g7M926Nt9Dvld3+zb4Z6fDI/vdsI+ AAAAXyHsz9U1/a7pxhvp5q4vbl7pmqZvHdkHAADgS4T9mW5Py//xNP1D27fd6fHd8ZT/+wYA AACfJHnONC/sH/q2OZ3ef/u4kA8AAEAUCXSmWWG/a88X7hP2AQAA+BYJdK4/P7OfLuB3f8p+ DvzCPgAAAFEk0NmKq/Gnr+D79Wr8juwDAADwPRLoI6a+Z/943/337gv7AAAAfIsEGkTYBwAA IIoEGkTYBwAAIIoEGkTYBwAAIIoEGkTYBwAAIIoEGuQ27KebuQEAAMA7iZpByrBfBv3cAAAA 4F3EzCA57N+G/NwAAADgXcTMILdhPxP2AQAAeDcxM0gK+znYl+H+9jYAAAC8SswMUob9Ur7v 9n4AAAB4logZ5Kewnwj7AAAAvJOIGUTYBwAAIIqIGUTYBwAAIIqIGSQH+t/C/tRjAAAA8Cjx MshfYV7YBwAA4F3EyyDCPgAAAFHEyyDCPgAAAFHEyyDCPgAAAFHEyyBzw77ADwAAwKtEyyBz QrywDwAAwDuIlkGEfQAAAKKIlkGEfQAAAKKIlkF2M1K8sA8AAMA7iJZBhH0AAACiiJZBHgn7 Av886gQAADBNXAoyJ+wnwv7fco3KBgAAwIWYFETYf49cn9sGAADAhZgURNh/Xa5NWZ+p+wAA ALZORAoi7L8m12WqNr89BgAAsEXiURBh/zW/1SU/pm4AAAAn4lEQYf95uSa/1WXOMgAAAFsh GgUR9p83pyZ5GbUDAAAYstH4fz7s0bAvtJ48Ug91AwAAOBGNgswN+4nQevFILfKyagcAAGyd WBRE2H/OI7XIy6odAACwdWJREGH/cbkOj9RC7QAAAIZcNP6fDxP2H/dMHfJz1A8AANgykSiI sP+4Z+ugfgAAwNaJREGE/cfkGjxTB/UDAAC2TiQKIuw/5pUa5OduvYYAAMB2iUNBngn7Ww6r r45/6/UDAAC2TRwK8kjYT7YeVl8d/9brBwAAbJs4FETYf8yr48/P33INAQCA7RKFggj78+Wx vzr+LdcQAADYNlEoiLA/37vGvuUaAgAA2yYKBRH253vX2LdcQwAAYNtEoSDC/nzvGntez1br CAAAbJcYFETYnyeP+11j32odAQCAbRODggj787x73FutIwAAsG1iUBBhf553jzuvb4u1BAAA tksECvJs2N9aSH33mLdaRwAAYNtEoCCPhv1kiyH1E2PeYh0BAIBtE4GCCPt/y+N995i3VkcA AAARKIiw/7dPjXdrdQQAABCBggj7f/vUePN6t1RLAABg28SfIML+3z453q3VEgAA2DbxJ4iw /7dPjndrtQQAALZN/Aki7P/tk+PdWi0BAIBtE3+CCPu/y2P91Hg/vX4AAIAlEX2CvBL2txBQ I8a5lVoCAACIPkGeCfvJVgJqxDi3UksAAADRJ4iw/7uIcW6llgAAAKJPEGH/dxHjzNvYQj0B AIBtE3uCCPu/ixhn3sYW6gkAAGyb2BNE2P9ZHmPEOLdQTwAAALEniLD/s8gxbqGeAAAAYk8Q Yf9nkWPcQj0BAADEniDC/s8ix5i3VXtNAQCAbRN5ggj7P4scY95W7TUFAAC2TeQJIuz/LHqM W6gpAACwbSJPEGH/Z9Fj3EJNAQCAbRN5gjwb9pOaw2keW+T4vrFNAACASOJOEGF/2jfGlrdZ a00BAADEnSDC/rRvja3mmgIAAIg7QYT9ad8aW801BQAAEHeCCPvTvjW2mmsKAAAg7gQR9qd9 a2x5u7XWFQAA2DZRJ4iwP+1bY8vbrbWuAADAtok6QYT9e3lc3xpbrXUFAAAQdYII+/e+Pa5a 6woAACDqBBH27317XLXWFQAAQNQJ8krYT2oMpt8eU95+bXUFAAAQc4II+/eWMKYa6woAACDm BBH27y1hTDXWFQAAQMwJIuzfW8KYaqwrAACAmBNE2L+3hDHVWFcAAAAxJ4iwfy2P59tjWko/ AAAA3knECSLsX1vSeGqrLQAAgIgTRNi/tqTx1FZbAAAAESeIsH9tSeOprbYAAAAiThBh/9qS xpP7UlN9AQCAbRNvggj715Y2ntrqCwAAbJt4E0TYv7a08dRWXwAAYNvEmyDC/kUey5LGU1N9 AQAAxJsgr4b9pJZAusRx1FJbAACARLwJIuxfLHEcuU811BcAAEC0CSLsXyx1HLXUFwAAQLQJ IuxfLHUctdQXAABAtHnEoe33Qxrc7fZ9exjvu3Lo2316PLWm78Z7E2H/YqnjqKW+AAAAos1s KcjnkN/1zb4d7rl2aJvzmwBdMwT+Yhlh/2Kp48j9qqHGAADAtok1c3VNv2sux+pTmC9u3jue BXA5ui/sXyx5HLXUGAAA2DaxZqZDu+/3xbn7t7fvdUNozKf0X7dnpae+8PTFWPI4aqkxAACw bWLNTA+H/UPbN8Xjr4T8rIYgmsew1HHUUGMAAACxZqZHw37XXn+mX9g/WfoYaqgxAACAWDPX A5/ZT28E3D4m7J8sfQy5f2uvM59Rvj68RgAAWDK7q7MVV+NPF9+buBp/cn3Ev+vb8Wdh/2QN Y6ihzrxPfj3MaQAAsBR2Tx8x9T37x/tOt49ft3d8PLfLcsL+yRrGUEOdeV1+HZQtm3qsbAAA 8G12S4O8I+wnaw8Ta+j/2mvMa/L8P/I6uH3O3OcBAMCn2CUNIuyfrKH/uY9rrjOPK+f92bm/ Xcez6wEAgFfZFQ0i7J+spf9rrzOPyfP9rjkv1/eudQIAwCPshgYR9i99X0P/11xnHvOp12W5 3k+sHwAAfmP3M4iwv66+r7nOzJfn+ZNzXW7jk9sBAICSXc8gwv66+p77utZa87fIOS63FbE9 AACw2xlE2F9f39dca36X5zZyfsttRm4XAIBtsssZRNhfX9/XXGt+luf1G3Nbbvsb2wcAYDvs bgYR9tfX9zXXmp99e17z9r/dDwAA6mZXM4iwv76+5/6utd7cW8qclv34dl8AAKiT3cwgwv76 +p77u9Z6c21p81n2Zyl9AgCgHnYxg7wr7CdrDQdr7Pdaa829Jc5l7tMS+wYAwLrZvQwi7K+z 32utNdfyPC5xLsu+LbF/AACsk13LIFsP+7nP+k20Ncxh2ccl9xMAgPWwWxlE2F9fn7M19531 zF/u51r6CwDAstmlDCLsr6/P2Zr7vnV57tYyf2V/19JnAACWye5kEGF/fX3O1tz3rVvj3OU+ r7HvvOZ27h9pAAC37CIEEfbX1+cs932t/d+qNc9b2fc19p/5buf6HQ0AILFbEETYX1+fS2vv /9bk+arhNbf2cXDvdm5ze8bUesoGAGyXXYEgwv76+lxae/+3ppb5yuOoZTxbdzufn5jTqW3k BgBsiz//QYT99fW5lPu/5jFsRW1zVY6nljFtzTfm8HabZQMAtsGf/SDC/vr6XMr9X/MYtqLG eSpff7WNrWZLmbfbfuQGANTNn/sgWw77texY1jKOmuU5qnGeyrHVOL6aLHWubvuVGwBQJ3/m gwj7440Vq2UcNat9jvL4vA6Xaw1zVPaxbABAXfx5DyLsjzdWLI+jhrHUaCvzU46z9rGuyRrn 5bbPuVG/qXmf2wBYD7+2g7wz7Cdr+qNb0w5CTWOpzZbmJo/Va3EZ1j4fZf/XPA7uTc3tpxoA y+PXcxBhf7yxcnkstYynFlucly2OeYlqmodyLLWMaWum5nCqPWNqPb81AL7Pr+Mgwv54Y+Xy WGoZTy22Oidej99T1r6m+t+OKzeWa2q+coswtd2pBkC8On79Htp+33TjjWtds+/bw3jji4T9 8UYFahvP2uX52OqcbH3837CFmpdjrH2sazQ1P6ktwVS/bhvb5fUAsdb/TywF/X3b3+X5rh1D ftc3u+8HfmF/vFGB2sazdlufjzz+rdchytZqXY53S+Neoqm5SG3ppvpcNtZraj6jGjDPyv+5 HPp23wxx/tZwf1O+ATAE/qk3BAIJ++ONCuTx1DSmtTIXF2rxWWV9t1bj27FvbfzfVlP9p8ZS NpZpaq6ebaWpx9/RgItF/5P4z3/+M/yj3fX//Oc/j/9Pt/P9R13T725P3z+kI/m7Yfnro/ld s+t/ONM/hLA/3qhEjWNaI/NwkWuhJu+nridlHdTjs7ZS66lx5sZ3TM3FT+0bpvoxt8EWLfal /9///nf4h7nr//e//x1v//vf/z7e/te//nUO+4d23+8nzs9P99+9B/DDslG2GvZzP9fQ10fU Oq41MQf3ypqoy3uo572yJmrzXluu7dTYc+Nzpup929Ziqu9/NajdYl/mKRz/4x//GG+dpPvK 0Dwd4KdP7Rf2v2Mt/XxUHleNY1sL9Z9WvjbV5zXq+LOyNmr0OrW8uK3FbeM1UzUtW42mxvlb g5q88SV96A/d0MZbr0rhOB3NL93eNxngf7hgn7D/HWvp5zNqHtvS5dqr/7SyPmr0HPWbp6yT ej1O7X53W5/bxt+m6la2rZqqxV8N1mjGSzdd7K7pmz/bfviH8L7Pxd8G+/z5/StTn9kf7tu3 953wmf3vWEs/n5HHVuv4lkzd/1a+PtXqMer2mLJeZeNnavW425rdNk6malM2fjdVs78aLNmM l2i+4N289q6j5/kz+unifP/3f/93/Kx+Oq0/3U6f5z+5P2U/hfp90/bdVTfquxp/klb5gdW+ 1Rr6+Irax7dEuebq/reyVuo1j3o9r6ydGk5Tn/e4reNt25Kp8ZeN103V9bcGSzLjJflY2N9F h+p02v5u6uv3svSGQH3fs5+kVX5gtW+1hj6+Io+v5jEujXo/pnyNqtvv1Ok9yjqqp3p82lR9 b1tNpsZXNuJM1T81WJIZL8kU9q/DdPr8e3N96Pzo0DbfOVU+Bf4fNtw13w/6ibBfpzy+mse4 NOr9uPJ1qnb31Of9bmua25ZsffzfMFXzqbYWU32/bQC/eeLXxH34v/j+6fJLJezXK4+x9nEu gVq/pqyfGp6ox2eV9S1bzbY01qW7nYuf2hJM9WuqATziiV8bp9P6j5+LT1ffP5xa17VD0E+n 8v92Sv12Cfv1ymOsfZzfps7vUdZx67VUhzhlrctWk5rHVovbOZrT3mlq/b81gFc99askXQTv +Pn8qfbNS94vWKrNu6VVfmC1b7WGPr5DHucWxvot6vs+5et1qzXd+vi/pax72dasprFsze3c fbMBfMKTv14Ox8/C3wb9dLTfKfzTthj2c/+W3Md32dJYv0V936t8zW6trlsd95KUc1C2tVhz 35lnao5fbQDRXvzVk07h7/ounco/3vMWU78h392CbTnsb0Ue75bGHEVtP6Os61Zqu7XxLl05 H7dtaab6mBoALNWb/0yl8D/++Kypv6SfaMGE/frl8W5t3BHU9HPK12ztNd7KONeonJvb9k1T /UkNANbgyT9ZQ6hPF+Rrmpu27/euxj9J2N+GPOatjfuT1PTzyhrXWuuax1abcq6m2qdNbTM1 AFibp/58/XqBPlfjn5Rq825plR9Y7dssvX+fkse9xbF/glrGKV+7tdS8xjFtye38/dSeMbWe 2wYAa/bEn7LTV+/dh/yxObI/KdXm3dIqP7Dat1l6/z4pj32r438XdYxX1nztda9lHJyU8/nJ BgC1eOLPWgr7+ej9oW/LK/Af2r51WH+SsL8teexbrsE7qN93lK/dtc7BmvvOfOU8P9oAoHZP /LkbAv5+P4T69Jn9rj90zfBHc9/vx8/sO7I/Tdjfnjz+sjGfun1XWf81zcMa+wwA8AlP7Qod 2vwd++kIfwr/5an8PrM/5RNhP0mr/dCqX7bkvkXJNbhtc0w9b6rVqvbxrUX5Wlv6nKyhjwAA UZ7cJTr0Xbry/vmc/a5vxsB/uY+SsL9tuRafbrWocUxrVs7HUudlyX0DAPgGu0VBthb27XRP y3WZ0/4y9Zzc1q6WcdSmfI0tZY6W1h8AgKV4atfoeFR/nz6n/62j+OM3AhTXB0h9WvJHCIR9 PinXu2xrVcMYalbOT9mifXv7AABL98Qu0uWr9/bllfiDpesG7NvT1o/XEGi6Y+Af71ocYZ8I ue5lW5u19ntrytdY2SJEbw8AYI2e2lXqmp+OoB/6Qze08dbnjN8IcNxQul7AckN+JuwTJde+ bGuxxj5vXTlnt+2dPr1+AIDaPLe7dOj6dgj8bdf1h8MQ7nPrmn7/5lPpD21aZ7rwX3s8o+D0 yYEU8Nvxa//GbwEYP1IwvfyU00UG03Obrh2e89k3DIR9ouU5WNNcrKmvXCtfa1PtGVPrSQ0A gL89sdt0OY1/ur0v7B9P1R/S+ukAfgr247rTzznFpzcYzkH/h+UndM3Q13HZ48cAis//f0Kq zSek1X5o1S9Zar+2Js/DGuZjLf3kb+VcvqsBAPCYJ3ahgsL+oe33ZQAvAn4K6jnrp6B+/PmX 5e+kx64u7pfOBPhk1E87q5/ZW02r/dCqX7LUfm1Rnoulz8nS+8dzytfeow0AgOc9sTuVwn4K 2F3f3bX3ncZfBvohyfftvjiF/7yNdP/p55+XT059znn+atn0JsGHT+FPhH2+Kc/HUudlyX0D AIA1Wuyu9TmQp+sDtM0l4B+P2KfrBaTHhqA+pvYfl59wWbbt2/S5/Q+fwp8I+3xbnpMlzs0S +wQAAGu22N3r4+foh73/42fwj6foD+F9COgpqJef0R9//HH5KeWyx/UFXMr/U2E/Sav+4Oqf ssQ+cZmXJc3P0voDAAA1WPXu9c9fAThX+RV+n7WlsJ/7s6Q+cVHOz7fnaCn9AACA2qxwF3sI 6CnkF6fwP+14BsDnT+FPthj2Wa48R9+eq29vHwAAarXC3ezTxfde/Zx9PpX/eDq/0/jfamn9 YVqep2/N1ze3DQAAtbObHUTYZ4nyXEXP2Te2CQAAW2JXO4iwz1Ll+Yqct8htAQDAFtndDiLs s2R5ziLmLmo7AACwZYvc3S7DwKdaNGGfpcvz9sn5+/T6AQCAk8Xtcpdh4JMtmrDPGuS5+8Qc fmq9AADAPbvdQYR91iLP3zvn8d3rAwAAfmfXO8hWwn7uy1L6w3PKeXzHXL5rPQAAwDx2v4Ns Leyzfnkuy/aoV58PAAA8x+53EGGftcpzWrY5Hl0eAAB4H7vhQT4Z9pOlhCrhrk55Xm/blDnL AAAAn2VXPIiwTw3y/M5tAADAd9gdDyLsU5s817cNAAD4PrvmQYR9AAAAoohlQYR9AAAAoohl QYR9AAAAoohlQYR9AAAAoohlQYR9AAAAoohlQbYQ9gV9AACAZRDNggj7AAAARBHNggj7AAAA RBHNgnw67CffDtvCPgAAwDKIZkGEfQAAAKKIZkGEfQAAAKKIZkGEfQAAAKKIZkGEfQAAAKKI ZkGEfQAAAKKIZkFqD/uCPgAAwHKIZ0GEfQAAAKKIZ0GEfQAAAKKIZ0GEfQAAAKKIZ0GEfQAA AKKIZ0GEfQAAAKKIZ0GEfQAAAKKIZ0Eiwn7yrdAt7EPdon6HAXyK32PA2j36e8xvvSDCPrBm dpKBtfN7DFg7YX+hhH1gzewkA2vn9xiwdsL+gqTJiG9pu9Hbztv81pg1TdM0TdM0TdPqb494 bGme9ujEPCttJmhTZ9/YJhAr6ncYwKf4PQasnbC/UMI+sGZ2koG183sMWDthf6GEfQAAAKKI aEGEfQAAAKKIaEGEfQAAAKKIaEHuwv6h7ffDfbvdvm8P431v8I3g/Y1tAtEOfbvPV4Jt+m68 F2CNDu2+379zBwwg1GW/rPllp0xEC3Id9tPk5JDf9c2+He55n+jwHb09IN6hbc5vTHbN8Mfl zb+3AOIM+17DjouwD6zT6XfY7reUPxLRglyF/a65mpy04zxjrmaLDt/R2wO+7HhmkqP7wDp1 TdO3juwDK5Wy49zfXyJakDLs35469u5TySLDt6APW5TeURb2gRU6tH07/PJyGj+wSuMBlyad ZTmEsL+O7otpQYR9oBrDH5rGTjKwOoe+bU4fQRL2gTVKv7t2TdN3x4x/Op3fZ/YXQNgHatG1 Pq8PrFDXnq89IuwDa3T7u+uvU/rFtCC1fmZf2IdtSX9k3vn7CiBG+Y0ilybwA6tykyP/euNS TAtyFfaPf3DGq/Gnz12s+Gr8kdsCvuv6D0rXt3aSgZVyZB9Yp3Tqfv5Wt/Ib3qaJaUGuw/7g eHGF9K7ye79nP4kM4JHbAr7n+HV7x99Zub3/dxdAFGEfWK10dH/cH/vrbEsxLUiajChpU1Gb i9wWAAAA84hpQYR9AAAAoohpQYR9AAAAoohpQb4R9iM2GbUdAAAA5hPTgkSG/SQihAv6AAAA yySqBRH2AQAAiCKqBRH2AQAAiCKqBRH2AQAAiCKqBRH2AQAAiCKqBRH2AQAAiCKqBRH2AQAA iCKqBRH2AQAAiCKqBakt7Av6AAAAyyWuBRH2AQAAiCKuBflW2P/UZj+5bgAAAF4jrgWJDvuJ sA8AALBN4loQYR8AAIAo4loQYR8AAIAo4loQYR8AAIAo4loQYR8AAIAo4loQYR8AAIAo4lqQ msJ+Xu8XhgQAAMAM4lqQGsM+AAAAyySyBflG2E8+EcyFfQAAgGUT2YII+wAAAEQR2YII+wAA AEQR2YII+wAAAEQR2YII+wAAAEQR2YII+wAAAEQR2YLUEvbz+r40HAAAAGYQ2YLUFvYBAABY LrEtiLAPAABAFLEtyLfD/rs2/851AQAA8BliW5Bvhf1E2AcAANgWsS2IsA8AAEAUsS2IsA8A AEAUsS1IDWE/r+eLQwEAAGAGsS1ITWEfAACAZRPdggj7AAAARBHdgiwh7L/ahXesAwAAgM8T 3YJ8M+wnwj4AAMB2iG5BhH0AAACiiG5BhH0AAACiiG5BhH0AAACiiG5B1h72BX0AAID1EN+C CPsAAABEEd+CCPsAAABEEd+CLCXsP9uNV54LAABALPEtyLfDfiLsAwAAbIP4FmTNYT8/bwFD AAAAYAbxLUgNYR8AAIB1EOGCCPsAAABEEeGCLCnsP9qVZ54DAADA94hwQYR9AAAAoohwQZYQ 9hNhHwAAoH4iXJC1hv28/EK6DwAAwAwiXJC1h30AAADWQ4wLIuwDAAAQRYwLsrSwP7c7jywL AADAMohxQYR9AAAAoohxQZYS9pO5AT4vt6CuAwAAMIMYF2TNYR8AAIB1EeWCLDHs/9WlOcsA AACwPKJcEGEfAACAKKJckCWF/eSvIJ8fX1i3AQAAmEGUC7LWsA8AAMD6iHNBlhr2f+rWb48B AACwbOJckDWF/d8eAwAAYPnEuSBLC/vJT4Fe0AcAAFg3kS7IksP+bdem7gMAAGA9RLogSwz7 yW2wz7cX2l0AAABmEOmCLD3s5+6VPwMAALBOYl2QpYb9JAf8sgEAALBeYl2QJYf9RNAHAACo h2gXZOlhHwAAgHpIoEGEfQAAAKJIoEGEfQAAAKJIoEGEfQAAAKJIoEGEfQAAAKJIoEGEfQAA AKJIoEGEfQAAAKJIoEGEfQAAAKJIoEGEfQAAAKJIoEGEfQAAAKJIoEGEfQAAAKJIoEGEfQAA AKJIoEGEfQAAAKJIoEGEfQAAAKJIoI84tP1+CO273b5vD+N9Vw59u0+Pp9b03XhvIuwDAAAQ RQKdLQX5HPK7vtm3wz3XDm1zfhOga4bAXywj7AMAABBFAp2ra/pdczlWn8J8cfPe8SyAy9F9 YR8AAIAoEuhMh3bf74tz929v3+vG0/nvGwAAAHyS5DnTw2H/0PbNr28GAAAAwGdsPOyXF9S7 aTfn6D8a9rv2/jP9AAAAEMGR/bke+Mx+eiPg18/zAwAAwAcJ+7MVV+NPF9+buBp/cn3Ev+tb p/IDAAAQTNh/xNT37B/vO90+ft3e1ccBfvo+fgAAAPgcYR8AAAAqI+wDAABAZYR9AAAAqIyw DwAAAJUR9gEAAKAywj4AAABURtgHAACAygj7AAAAUBlhHwAAACoj7AMAAEBlhH0AAACojLAP AAAAlRH2AQAAoDLCPgAAAFRG2AcAAIDKCPsAAABQGWEfAAAAKiPsAwAAQGWEfQAAAKiMsA8A AACVEfYBAACgMsI+APAeXdu3h/Hn38xdDgB4mrAPALzBoW+bdvjvX+YuBwC8QtgHAF5z6Ppm t+t3u/3vR+zvlkvBfz/c3vX7phtu58ebvkvL7of7jwuWPw/PGX5O62japt8Pyx+fenTou3F9 u/2wjvFeANgiYR8AeNmhHcL3jHR9WS6F9vRzDvPj0f5DOwT/tu+G+w9d0++Gn9u2G38eN5CW yW8YnO8/vQlwekPgtJ38MwBskbAPALwoBe05R9KL5YaQfjyaf+jG0H9c4BTShxunHH864n/+ eSrI57Cf/p/fMBgI+wBsnbAPALwmHWkvgvaPiuVSeM+n27fpKP5pgWPwP2X0dEr/1M/lMmk9 pzcKyjcDTsuUp/cDwPYI+wDAa9JR+nZGsi6WS+G8GcP5+dT+8k2DfMT+/HNx/7hMPgsguRzJ P/Td8HN5lB8AtkjYBwBecjrdvu2PH78/S0fjy6PtN8sdP3c/XmhvfGJ56v3xzYBz1r88L/18 PCMgrTu/AXB0uohfvtjf5X4A2CZhHwD4mK4bE/tbpDcQXGUfAOYQ9gGAjzgMQf/6aP+LylP7 AYBfCfsAwPKloJ9P3y8+GgAATBP2AQAAoDLCPgAAAFRG2AcAAIDKCPsAAABQGWEfAAAAKiPs AwAAQGWEfQAAAKiMsA8AAACVEfYBAACgMsI+AAAAVEbYBwAAgMoI+wAAAFAZYR8AAAAqI+wD APU4tP1+t+t3Y9u3h/GBQX6s6cY7AKBewj4AUJmubybC/qHdj28CNMMSAFA3YR8AqMx02Hdk H4AtEfYBgMr8EPYBYEOEfQDgzqFtrj77vtu3fRmbD13x+L7pu5tMffX4bt83t6H70PbNPj9+ WkdbrmR4/vmxXXqs7fd5+Zu+DCsbFs+n6KdtDesen3sO+zfrc2wfgNoJ+wDAtfF09xyUD8eg fQnY+bPvp8cPfXsM4ZcAff5s/PF0+ctR9vPZ8/l0+mGdp7vyOoplBpfP2I/bys/Lt0ddc7ov n56f3mi4et7ovJywD8AGCPsAwLVzqN5fH20/yuF9eGx8KIfoU9a+D/enx/Pyl2BfBvFzsJ94 U+ESzqdOz594M2Fyuan1AUC9hH0A4M7ltPghMDfFafNXp8Nft2OwLh4vj9JfTAfxy/MubyLM CvuT2xP2AUDYBwAmXX/ufgzIf4X5J8P+JYgL+wDwDsI+APCLdPG7Ikz/8Ln5s+Lx6a+4e/Np /MX2hH0AuBD2AYBrKUAXV9hPV+af+oz+8b5xoXQRv/vHUwDPj6cr6h9/TAucHj8H+78u0Je3 PR3iz9sbnpzuvTzv1PI6L/0S9gGon7APAFwbwn76qrzLafz7c2g/Kb/qbmjpa/PKh9PjQ+A+ H+Hf3z5/kLYxBvzzOsplitPz8+NXyw/t8sbAoW+Lr95rD+lNgdNX8J1Xebe+4joEAFAhYR8A AAAqI+wDAABAZYR9AAAAqIywDwAAAJUR9gEAAKAywj4AAABURtgHAACAygj7AAAAUBlhHwAA ACoj7AMAAEBlhH0AAACojLAPAAAAlRH2AQAAoDLCPgAAAFRG2AcAAIDKCPsAAABQGWEfAAAA KiPsAwAAQGWEfT6va/rdbje2pu/Gu/tD2+/Tfc35np89siwAAMDGCfuE6Jr7sH9o9/dvAIy6 5vq+35YFAADgmrBPiMmw/tPR+uOZADeh3pF9AACA2YR9Qsw+Mp9DvSP4AAAATxP2N+vQd20z ButT2zfdcO/o5nP2bTeE8P14e99eljv6Y12Du7A/8Tn+w806ji1t66fP/A/SY5fn7Pumve5Z evOgyf1ObZ/GcrMMAABAZYT9bxhC58fbH06foR/C8Sll983wnBSG90VYvgT08f7zUffr5R5b 1yWsP/I5/l+XPW64O2/3eDPJ/d2343MOfTsG//MyAAAAFfo7FfJeQ9AMaX/IQXnfXgfl3wP6 78s9tq759yW/9SUH9/ymw2mzl2A/2Y+7sxMAAADq8XcqpGqHru2bZj95xP63gF0ulz22rvn3 JXf3F6f2Tx+l/6Gv5+flNwUAAADqI+xvVj7yncLz3KPxP4X9Z9Y1/77kXWH/sh5hHwAAqJew v0mXU9xvP+/+eNh/dl3z70vu7i+uHzD9dXxO4wcAALZL2N+kSyBPQfnww4X3LgE7HwWfCvLz 1jV1gb0/L9B3GNY9ruP356ej+6fl0tX5j5cOSPLR/3Owv7wBMPn+AAAAQCWE/Y3qzke4T19F 1+bgPNw+5ubiNPl8/9VX2A0tB+bH13X7dXrjfae1nbezb8b7flw2feXf5RoBu336RoDTI2fp 2wHKfo99BAAAqJmwDwAAAJUR9gEAAKAywj4AAABURtgHAACAygj7AAAAUBlhHwAAACoj7AMA AEBlhH0AAACojLAPAAAAlRH2WbdD2+93u37XdOMdAAAACPus2qHd97sU9ndNL+4DAACcCPus myP7AAAAd4R9AAAAqIywv0VdM576fjr9ve3afr8fb+/b/jAudrtcNzzS5uWGtm/HJa+W2w/r 6/rmvL5h/cNih6tt3J9yfxjWcTxCnx/PnchH7lNLR+/zcsPP6TmnbaZ2vc6r9Q19anJfs2G9 5z6mlvp53igAAMC6CftfMGTLj7e/XD7rPob2IlSfQ/yga3IgzmF6CPITy92t7+qNgSFsH4P0 78+9ft4lvJ/7cHwT4Prx+/4VfTme2n/Z5vlM/zzWfTs+59JXnwYAAABqMCMW8k5Dngxpf7m/ sN1fIX7ucvvjkfzkEtLz2QKX514+Y5/vu39eXuR2PcdtDQ+efz4+/75/18/P678E+8n+l2c2 AAAArNSMWEiNHg/xHwr7V6fiX7e8/vN6zm8QXNz1r1jfxOKD6f5fnnfpPwAAwFoJ+xu1xLA/ Hc6L9Xww7E/1HwAAYK2E/Y1aTNj/4VoBpYfC/u0F/e44jR8AAKifsL9R9+F8Xti/ugJ+Eaaf DvuD83LpueMV8dPV++/Wc37D4WLqsct96eh+Xl+62v7xx7TA6fFzv1ygDwAAqIuwv0VlYD+G 3ub6a+iGdgm9QxBuLkG+GUL4ObDn5W7XNwTv9hz+x9YM4f1mG5c3FQ7DKorl09fgnR8qjtSn Vqbxu3Fcwns3bP/8vH3+NoDCsF5fvQcAANRK2AcAAIDKCPsAAABQGWEfAAAAKiPsAwAAQGWE fQAAAKiMsA8AAACVEfYBAACgMsI+AAAAVKXv/x/hb/l2/bAQuQAAAABJRU5ErkJggg==</item> <item item-id="135" content-encoding="gzip">H4sIAAAAAAAA/2xTy27TUBCdaye282rSlkASt4SkkBJAKFh0QVelqhALHlL6AZWbmDaoaVLX RV1GfABrdmz4AhbsETsWiC+inJlJQou41vjeO3Nmzsx4nCciA9mFZOVs4+1FJ93oYDA6ThOv x5BMf9RTXUp0Txg27L3efxv1EtFQV/wtvJ3Tw/H26FzVLyAuFN058CX7Qn6tE/0G38f23zsv x9KkLLJsOSzNyZ8mSTzYP0siI8hNSJEur9Rm88o9LQFSSRxFvigCSGGsPEH1G9Ek7QafsG85 nu5uRncvq3smF1ggeYf+OCb9iB4ixgY3JS/6r6rvQN+BhRtTEMdJZiHg7LYLnKUc3xeEuBRk v8PuLQZLP0FQWpJA/UWUbL7YtBx8QIKTRbom+s/LrJfelQPnByyl6+pQRn2pfYoooZDohqLL jD4Rnorm4Va1kEpN96qvBdUYaTpEK1rI/E6r6li9qQWsQlXXAlYl8C29VOTSULDT1OiNNd2b t5VlTaJuEN1RlrU5S0sdm+vK0oLqrgZuSeC2tqlxT6ttT9twX+vE1UknKH5IYxox/oEMX7Ef 7B3E4fhw780oHoaJK7HKPMOi3gmTUGejNhuSBsSWgSM64yxkknlwsuH54PSZxNG5X+GJNtO/ hmS0kUIPCRxTnwZEF3jIrFzB2DG+T///tql2RugmcdiLgh1bTP4UWDclMr4BLmdlQlCFdASy BMLUxsCIQDnLO0YBQ2QUz0yW8W0Q1I1t/BSo6ibF4Asc0sa3JlswOYxhFlcwOHgasG4ywIh7 FhhxzwEj7nlgxL3AGPZaEAwORcYwWIsqvjobRvGgFx5pKz0p7jmEWzaQ3vNvnJ1+h39W/vLl DwAAAP//AwBGbYTNsgQAAA==</item> <item item-id="136">iVBORw0KGgoAAAANSUhEUgAAA/YAAAHICAYAAADk93GgAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAACjlSURBVHhe7d3dsfI6mgZQ4iIg1wTR Qfh6EpgMuOvrjqGLq86gM/BYgECAYfNjGUtaq0rnbMD455U/0ION2QwAAABAsQR7AAAAKJhg DwAAAAUT7AEAAKBggj0AAAAUTLAHAACAggn2AAAAUDDBHgAAAAom2AMAAEDBBHsAAAAomGAP AAAABRPsn9h1m2Hb70+3puyGbrMZNofWjbcAAABgWYL9A/t+ewjsT4P9rh+e5n4AAADITLB/ IoT7x8F+P/Tb49H6zqF6AAAAfkSwf+KvYL8PD+37YXs+Hf++AQAAQE6S5xPPg30ihPttP0b9 I4EeAACApUigT7wc7A+n5V8unifYAwAAsBQJ9Im3gn3niD0AAADLk0CfeDnY77qr6QR7AAAA liKBPhBCfQjooZ1D++FCedvjT9yNYT4+vrm5LL5gDwAAwFIk0AwEewAAAJYigWYg2AMAALAU CTQDwR4AAIClSKAZCPYAAAAsRQLNQLAHAABgKRJoBoI9AAAAS5FAMxDsAQAAWIoEmoFgDwAA wFIk0AwEewAAAJYigWYg2AMAALAUCTQDwR4AAIClSKAZCPYAAAAsRQLNQLAHAABgKRJoBoI9 AAAAS5FAMxDsAQAAWIoEmoFgDwAAwFIk0AwEewAAAJYigWYg2AMAALAUCTQDwR4AAIClSKAZ CPYAAAAsRQLNQLAHAABgKRJoBoI9AAAAS5FAMxDsAQAAWIoEmoFgDwAAwFIk0AwEewAAAJYi gWYg2AMAALAUCTQDwR4AAIClSKAZCPYAAAAsRQLNQLAHAABgKRJoBoI9AAAAS5FAMxDsAQAA WIoEmoFgDwAAwFIk0AwEewAAAJYigWYg2AMAALAUCTQDwR4AAIClSKAZCPYAAAAsRQLNQLAH AABgKRJoBoI9AAAAS5FAMxDsAQAAWIoEmoFgDwAAwFIk0AwEewAAAJYigWYg2AMAALAUCTQD wR4AAIClSKAZCPYAAAAsRQLNQLAHAABgKRJoBoI9AAAAS5FAMxDslxNK/agBAAC0QPzJQLDP 7zbEP2oAAAC1E30yEOzz+iu4p48/mgYAAKAWYk8Ggn0+rwb2dLq/pgUAACiZyJOBYJ/Hu0E9 nf7V5wAAAJRG3MlAsJ/fpwE9fd67zwUAACiBqJOBYD+vb4P5t88HAABYM1EnA8F+XnOE8jgP XQMAANRGzMlAsJ/PnIF8znkBAACshYiTgWA/nzmDeJyX7gEAAGoi4mQg2M8jRxDPMU8AAIBf Em8yEOznkSOAx3nqIgAAoBbiTQaC/fdyBvCc8wYAAFiaaJOBYP+9nME7zls3AQAANRBtMhDs v7NE8F5iGQAAAEsQazIQ7L+zROCOy9BVAABA6cSaDAT7zy0ZuJdcFgAAQC4iTQaC/eeWDNpx WboLAAAomUiTgWD/mV8E7aWXBwAAMDeRJgPB/jO/CNlxmboMAAAolTiTgWD/mV8FbMEeAAAo mTiTgWD/vhiuf1G6Xy4bAADgW6JMBoL9+34ZrOOydRsAAFAiUSYDwf49awjWv14+AADAp0SZ DAT796whVMd10HUAAEBpxJgMBPv3rCFQx3XQdQAAQGnEmAwE+9etKVCvZT0AAADeIcZkINi/ bk1hOq6L7gMAAEoiwmQg2L9ubUFasAcAAEojwmQg2L8mhug1lWtt6wMAAPAXESYDwf41awzR cZ10IQAAUArxJQPB/jVrDdCCPQAAUBLxJQPB/m8xPK+xVGteNwAAgFuiSwaC/d/WHJzjuulG AACgBKJLBoL939YenAV7AACgFKJLBoL93wR7AACAeYguGQj2z8XQvOYylbCOAAAAgdiSgWD/ XCmBWbAHAABKILZkINg/J9gDAADMR2zJQLB/LIblEkpU0roCAADtElkyEOwfKy0oC/YAAMDa iSwZCPaPCfYAAADzElkyEOwfKzXY61IAAGCtxJUMBPtpJYbkEtcZAABoi7iSgWA/rdSALNgD AABrJq5kINhPKz3Y61YAAGCNRJUMBPt7JYfjktcdAACon6iSgWB/r/RgLNgDAABrJapkINjf E+wpXdwH0gYAAGtgaJqBYH+v9CAU11/XtiXt92cNAAB+yZA0A8H+XukBKK6/rm1D2t9T/T71 eGgAAPALhqIZCPbXagk+NWwDf3tnf02nffU5AAAwN8PQL+26zbDt96dbR4L9tVoCT9wO3Vun tH/f7eNvngsAAN8yBP3Cvt+Og3jB/i+1hJ24Hbq3PnP1bTqfb+cFAACvMvT8Ugj3gv1jtYWc mraFo7n30XR+c80TAACeMez8Ugz2IcxPtdaFEtRUhtq2p3WxP+fu03S+c88bAABuGXJ+yRH7 52oLNnF7dHH5cvdl7vkDAEBkyPklwf652oJN3B5dXLal+nGp5QAA0DbDzS8J9s/VGGoEtfIt 1YdxOUstDwCANhlqfkmwf6zWQFPjNrVk6f0yXd5SywQAoC2GmV8IoT6E+NDScC/YH9UaZOJ2 6eby/KrvfrVcAADaYJiZgWB/VHOQEdLK9Mt+i8u23wAAMDdDzAwE+/pDTM3bVqtf75Pp8n+1 DgAA1MnwMgPBvv7wIpyVJfbXr/tsLesBAEBdDC8zEOzrDy9x+3R1GdbUV/YdAADmZmiZgWDf RnARzsoQ+2ktfZWuz1rWCQCAshlWZiDYtxFaBLMyrLGf4jrZfwAAmINhZQatB/tWQksL21i6 Ne+La143AADKYkiZgWDfRliJ29l4d6/amvvH/gMAwFwMKTMQ7NsJK4LZesW+WXP/lLCOAACs n+FkBoJ9O0FFKFun2C8l9E1J6woAwDoZSmbQcrBvLaS0tr2lKKlP7EMAAHzLUDIDwf50owFx exvu8lUqrU/sRwAAfMMwMgPB/nSjEQLZusT+KKlPSlxnAADWwzAyA8H+dKMRAtm6lNofcb3t SwAAvMsQMgPB/nSjEXGbG+721Si5L0ped+aT7gf2BwDgVYYMGbQa7FsdiLa63WtUej/Yl9qT 9vmrDQDgliFCBq0H+xYZcP9e7IOS+6GGbeBvaT/fttTU42kDAIgMDTIQ7NtjoP17tfRB3A77 U33Svv2kj6eeHxoAgCFBBoJ9e+K2t7r9v1ZT/WvaFo7SPp2jX2/nFxsA0C5DgQwE+zYZXP9O bbWP22N/Kl/OvkznnWsZAEAZDAMyaDHYG1ja/l+pcd+rcZtatFQ/pstZYnkAwPp4+8+g5WDf MjX4jVrrHrfLPlWetO+W6r/bZS61XABgHbz1ZyDYtynWoPU6LK3WmtufyvTrfkuX/6t1AACW 520/A8G+TbEGrddhSbXXvPbtq81a+itdj1+vCwCwDG/5GbQW7A0gL9RhWbXXO26ffWr91tZX 6fqsab0AgDy81WfQarBHLZYUa117vVvZzpKttY/S9Vrj+gEA8/E2n4Fg365YC/XIr5U626fW rYT+SddxzesJAHzOW3wGgn3b1GMZLdU5bqv9al1K6pd0XUtYXwDgPcW8vf/3v/8d/vWvf51u Tdj3w7bbnW5c23Xbod+fbixAsG+beuQXa9xKnVvb3hKU2CclrjMA8Joi3t7/+c9/Dv/7v/97 +P///M//nO5NhFC/7Ye77L7rT4F+N3Sb5cJ9S8HeQPGeeuTXYo3jNtu3fq/kvkjXvcT15zu3 /T/VACjT6l/CQ5gPLboPzfuh33ZjdL813t+lYX8M91PhP4MWgz0XsSbqkker9W11u9emhn6o YRv4W9rP3zYA1m/1L9fpEfpwKv5daN51w+b2FPx9OEK/Gae9Pkq/6zbDg7P1ZyXYoy75tFzb uO32rd+ppf72pTql/TrVnpmafqoBsE6rfon+97//fThaH/7/f//3f+Mbyubwd2rfb4ftxDn2 4f67vP9g2rkJ9qhLPi3XNm67fes3aqt/bdvTsrQv5+zTqfmmDYD1WPXL8j/+8Y/D///zn/+c j9aH79qnpsP69On5gv28vLk/pi552OfU4FdqrXut29WCtO+W6MOp5cUGwO/N+HK8H/a7sZ1u zeH2Qnkh6N+G5smw/uBieoL9vLyhPxZroz7zUlP71i/UXPN022rcvhrd9tkv+m1qHUID4Hde eBkOF6Hrhu7Pth1f1Of7Dnv4ebs0IMfbt6fiT37Hfrxv29+viO/YzytsZiOb+hH1mVesp5qq w9Jqr3fcvtq3swZr66d0fdIGwPJeePmNF6J7rc11RDx8pz58vz4cpY9H6qd/x/7+tPsQ4Ldd P+yuVsVV8ecWNrORTf2I+sxLPS9iLdQjv1ZqnW5n7dtaorX3z+36xQbAcl542X0v2G9mCs/x +/UvCafeb6Z+8i4K4d/v2M8tbGYjm/oR9ZmXel5Tj/xijVupc7q9rWzz2pXWJ7frGxttmdoH bhswvxf+aYVgfx2aw3fVu+vD4Qf7vpvtVPfbi+T9KYT7BwvfdcuF+kCwJ4j1UaPvqeU9Ncmv xfqm+1Vr2742JfdDuu6lbgPPTfXxnA143wf/dO6D/sVyp7uvWQvB3ovva9RoHuo4TV3yibVt sb4tb/sapPUvvQ9ut6X07WnVVD8+a89MTf9KA/72wT+V46n5h++wh6vg749tt+vHUB9Ox392 SnwbWgr2PKdO81DHabEuajMvdVWDX6mx7uk2pY11m+qztOUytaypBtz76J9GuDjd4fv0U22J y86vXKhD7cImNrCZX4t1UqvPqeFjajM/Nb1Qi+Wkta613rfbWOt2lmyqj0L7pan1uW3A0Yf/ HPaH763fhvpwFL/10/ADwZ4o1kmtPqd+z9nH5qWWF+m+pSb5tFbjdHtb2u61muqP0NZqal1v G7Tqy90/nIa/G3bhdPzTPbOY+lc6d8uo9mC/UBmroVbfUb/nYn3U6HtqeS+tibrMr9Xaptvd 4vb/2lT9QyvR1HakDVoy8y4fgv7pz09N/avM0TJqJdjzGvX6XKyd+j2nTt9Tw8fS2qjPfNT0 ugat12IJtdd7avvSBrX7cDcfA3y4WF7X3bTtsHVV/PHFo+5XDy+Q71Gvz6nda2Kd1Opz6vdc uo+p03fU8p6a5NVifW+3OW1Qq49276cXz3NV/EMdahY2r/JNnFWsl5q9T91eZz/7nNq9Rp2+ p4aPpbVRo3mo59FtHdIGNflglz7+3N19oD81R+wPdahZ2LzKN3F2ava+WDN1e416fUbd3qNe n1O716R1Uq/3qd9zU/WJDUr3wW4cgn08Kr8f+vRK+Pt+6P3a3fjiUO+rgxfAz6jZ+9TsfbFm 6vY69Xqf/ex9avaetF7q9ho1e89UvdIGJfpg1x3D/HY7BvjwHfvdsN914z+A7bA9fcfeEfvw glDvK4IXvM+o23tivdTsPer2HvX6TFo3tfubWn0urZ0aTlOf793W8LZBKT7aXfd9/A37cOQ+ BP30dHzfsRfsuRXrpnavUavPqd3r1OpzsXZq+JgazeO2jrG1Tj3yuK3rVIO1+nD33A+7cAX8 83n3u6E7hfvLfe0S7Jmidq9Tq8/F2qnfc+r0vbSG6nhNXeaX1rTl2qrBcm5rPdVgTeySGQj2 TFG718Q6qdXn1PA59ZlPWkv1PFKPvNL6tlTnFrd5bW77QD+wNh/tkoej9dvwvfr8R+ePP623 HfqCvrhfa7D3QvYd9XuNGn3Pvvac2swr3d9ar6s6LCetdc01b2EbgXl88BJx+bm7bXpF/Fz2 /bB99YJ8u34VHwDUHuz5TKyfGj6nRvNQx2mxLmozr7Surda29e3/hbTmaStdjdsE5PfRS8Wu e3SBvP2w343tdGsO4UJ9r50YcPPTez8k2POIGj4X66NG31PLe2qSV1rf1mrc6navRVr/tJWm hm0Afuezl4z9bgzR3dDvdsN+Pwb52HbdsJ31qvjHn9Y7/Ize1en4IcQfr8x/+DrAuD7Hswgu 0+z7sC7hYn794bHHHw4cLwQY5tXt+vE535/2L9jziBo+pz7zivVU0yO1yC/d51qodWvbu3a3 /RHbmpW2vsB6ffDycTkVf7rNGOzDb+SfTsMPR+63h8R9Cvu78He4Gv/l8RjeD9OONw75/PA7 +4/X6fAd/tO0h5/xm+F3+EMdahQ2q9JNW0ysoTpOU5t52d8u1GI5aa1rrncL21iqtG9u2xpM rVdoAN/44GVkuWAfjqRfhfUQ7MNZAeHOcNbAIeAfHh3/Pi339jv5IdjHmdxKPjg43gxH+L+N 9eHFuc5X57BZlW7aYmIN1fGe2uShpkfqsKxY71rrXvO21Sbtq9u2pKnlhwYwlw9eUkKwD4F6 N+zu2pyn4l+Oxh+D+/EU+eNV8sc2Bvk+fp8/CfPh8UuOD89Lbx/XPWb3q2nDPGY4DT+oMdh7 E5qPOk5TlzxiXVuurRr8Rlr3mmpf4za1IO23R21OU/OPDSCH9b68hA8JDil7P+xOp9Yf7x7D +Cl9n0+/P0ybPB7+DEf0+24M8s9Pwz9O2x+/sz/DafhBzcGe76nlNHXJJ9a2xfq2vO1rkNa/ hj6oaVtalvbjUg0gt/W+1IQL4m2PR+fP35cPDkfWw/0h1B/vDQE9/PReuHn4nnx8zuFI/hjs HyT7dNrDPOY4XD8S7Hkm1lI9L9Qkr5bra7/6vXT/K7UvatgGHrvt3zkawNK89BxcTvWfg2DP X9TzmnrkF2vcUp1b3Oa1SvuitP4odb0BaIu3qSD5jv4cagv2BjXzU89r6pFfrHErdW5te0uQ 9kkp/VLa+gLQrubfquLp+IdT8p2KP8mgZn5qehFroR75tVRr+9Q6pfvgmvuohHUEgJS3qwwE e/4Sa6qu6rCkVva7VrazVGn/rLGf1rpeAPCMt60MBHteoa6XGti/llN7zWvfvpqkfbWGPlvT ugDAu7x1ZSDY8wp1VYNfiDWvte72qbKk++Mv++7XyweAb63yLSx9g83VchLseUWsa8u1tW/9 Rq37Xq3b1YK075bsw18sEwByWN3b2O2bbK6WU03Bfqmataj12ra+/b9UY+1r3KbWpH2Ytrkt sQwAWJq3swxqDPbk0XJ97Vu/FetfSx/Yn+qR7pu37VNT8woNAGrhbS0DwZ5XtVxf+9bvxT4o vR9q2Q7upX07VwOAGnmLy0Cw51Wxvq3VuNXtXpsa+qGGbeBvaT9/0gCgdt7uMhDseUeLNbZf rUfsixL7o+R1BwCYk+FQBrUEe4PmZbRYY/vVesS+KLFPSlxnAIAcDIkyqC3Yk1escyu1bm17 S1Bin5S4zgAAuRgSZSDY845Y51Zqbb9ap5L2w5LWFQBgCYZFGQj2vKulWtuv1in2Swn9U8I6 AgAsydAoA8Ged7VS67id9qt1KqF/SlhHAIClGRplUEOwN3heViv1tk+t35r3xTWvGwDALxke ZVBTsGc5LdTcflWG2E9r6qs1rhMAwFoYImUg2POJ2mset89+tX5pX62hv9a0LgAAa2SYlIFg zydizWutu32qLGvZH9eyHgAAa2aolIFgz6dqrrt9qjyxz37Vb79ePgBAKQyXMig92BtM/06t dbdPletXffer5QIAlMiQKYNagj3Li7Wvrf72qXKl++RSfbj08gAASmfYlIFgz6di7Wuqf43b 1Jq0D3P341LLAQCoiaFTBoI936it/vanOsR+TNvccs4bAKBmhk8ZCPZ8o7b625/qEvtz7n7N MU8AgFYYQmVQcrA3uP69mvqgpm3hIu3Xb/t3rvkAALTMMCqDGoI9v1VLP9if6hX79ra949Pn AQBwzXAqA8Geb9XQD3Eb7E91S/t5qqWmHg8NAIDvGFJlINjzrdgPJfeFfakt6T77agMAYB6G VhkI9syh9L6wL7Ut9n/aAADIw1ArA8GeOZTcF3Hd7UsAAJCfYXcGpQZ7YWxdSu4P+xEAACzH 0DuD0oM961Fin8R1ti8BAMAyDL0zEOyZS+yTkvrFfgQAAMsy/H5k3w/bMZ1sNtuh35/uu7Mb usM0oXXjrSPBnrnEPimpX+xHAACwLMPvSfuh38ZAP4b3bT/eM2HXT4b+EoN9DGMC2fqU1C/2 IwAAWJ7h95RdN2y6ePw93NwMyc2TEP6PR+tvHys52LM+sW9K6B/7EQAALM8QfMK+3w7b5FD8 7e2j/bAPd51P2Z9upQirWtDqNiX2TQn9Yz8CAIDlGYJPeC3YJ0K4T07XLynQRwLZupXQP3Ed 7UcAALAsQ/AJbwf7w2n5ZV88TyBbt9g/a+4j+xAAAPyGYfiUl75jnxqDfeeIPXmtuY/iutmH AABgeYbhk5Kr4t+cZj9p110d0S8t2AtlZVhzH9l/AADgdwzFH5n6HfvDfafb4aj+4fGx3RzO LzXYs26xn9bWV2tdLwAAaIWheAaCPbmssa/sPwAA8FuG4xkI9uQS+2ot/bW29QEAgBYZjmdQ UrAXzMqzpv6y7wAAwO8ZkmdQYrCnHLHPft1va1kPAABonSF5BoI9OcU++3W/2XcAAGAdDMsz EOzJLfbbr/ru18sHAAAuDMszEOzJLfbbr/rOfgMAAOthaJ6BYM8SYt8t3X+/Wi4AADDN0DyD UoK9gFa2X/WffQYAANbF8DyD0oI95Yp9uFQ/Lr08AADgb4bnGQj2LCX24VL9aJ8BAID1MUTP QLBnSbEfc/flUssBAADeY4ieQQnBXkirxxJ9ucQyAACAzximZ1BSsKcOsT9z9an9BQAA1stQ PQPBnqXF/szRr7nmCwAAzMNQPQPBnl+IfTpnv+aYJwAAMC/D9QwEe34l9utcfWs/AQCA9TNk z2DtwT6GNYGtTnP171zzAQAA8jJkz6CUYE+dYv9+08/fPh8AAFiOYXsGgj2/Fvv4k77+9HkA AMBvGLpnINizBrGf0/bMO9MCAADrYfiegWDPWsS+vm2pZ48BAADrZxifwZqDvQDXprTfnzUA AKA8hvIZlBDsaVfcB+wLAABQB8P6DAR7AAAAliLiZSDYAwAAsBQRLwPBHgAAgKWIeBmsNdjH UC/YAwAA1EPEy2DtwR4AAIB6iHkZCPYAAAAsRczLQLAHAABgKWJeBoI9AAAASxHzMlhjsI+h XrAHAACoi5iXwZqDPQAAAHUR9TIQ7AEAAFiKqJeBYA8AAMBSRL0M1hbsY6gX7AEAAOoj6mWw 1mAPAABAfcS9DAR7AAAAliLuZSDYAwAAsBRxL4M1BfsY6gV7AACAOol7Gawx2AMAAFAnkS8D wR4AAICliHwZCPYAAAAsReTLYC3BPoZ6wR4AAKBeIl8Gawv2AAAA1Evsy0CwBwAAYCliXwaC PQAAAEsR+zIQ7AEAAFiK2JfBGoJ9DPWCPQAAQN3EvgzWFOwBAACom+iXgWAPAADAUkS/DAR7 AAAAliL6ZfDrYB9DvWAPAABQP9Evg7UEewAAAOon/mUg2AMAALAU8S8DwR4AAICliH8Z/DLY x1Av2AMAALRB/MtgDcEeAACANoiAGQj2AAAALEUEzOBXwT6GesEeAACgHSJgBr8O9gAAALRD DMxAsAcAAGApYmAGvwrXgj0AAEB7xMAMfhGw4zIFewAAgLaIgRn8MtgDAADQFlEwA8EeAACA pYiCGSwdsuPyBHsAAID2iIIZLB20hXoAAIB2iYMZhJ+7E+wBAABYgjiYgWAPAADAUsTBDJYM 9nE5SywLAACA9REHM0iDfe7AvcQyAAAAWC+RMIMQ7I//zx+6BXsAAIC2iYQZLBXs4/xzLgMA AIB1EwkzuA32uYJ3znkDAABQBrEwgxjsA8EeAACAnMTCDJYI9nG+OeYNAABAOcTCDKaC/dwB PMc8AQAAKI9omEHuYJ9jngAAAJRJNMwgDfbB3CFcqAcAACASDzN4FOznCuNzzgsAAICyiYcZ 3Ab7YK4wHuczx7wAAAAon3iYwbNg/20gn2MeAAAA1ENEzCBXsJ9jHgAAANRFRMxgKtgH34Zy oR4AAIBbYmIGfwX7T8L5N88FAACgXmJiBo+CffBpQP/kOQAAANRPVMxg7mD/yXMAAABog6iY wbNgH7wT1N+ZFgAAgPaIixm8E+z/CuyvTAMAAEC7RMYM/gr2QQzsz4L7X48DAACAyJjBK8E+ SIP77VMe3Q8AAAApsTGDV4N9kAb4qQYAAADPiI4ZvBPsI4EeAACAT4iQGXwS7AEAAOATEmgG gj0AAABLkUAzEOyBknkNA0rndQwo3buvY171MvBmApTMaxhQOq9jQOkE+xXwZgKUzGsYUDqv Y0DpBPsfCYXXNE3TNE3TNE3TtDnaOwT7DN7tBIA18RoGlM7rGFA6wX4FvJkAJfMaBpTO6xhQ OsEeAAAAGiLYAwAAQMEEewAAACiYYL+EfT9sN+HKhtuh35/uA1it/dBv4xVZu2F3uhegRPt+ O2wNwIBiXcZl3ZNBmWCfXeiIGOh3Q7ftx3sA1mvfd+cPIXfd+EbidQso1jj2GgfDgj1QpuNr 2OZZoj8R7HPbdVcdEQbJL/QLwDoczjhy1B4o067rht4Re6BQITu++vol2Gd2e/qX08GAsoRP igV7oED7fujHFy9jL6BIp4MrXTh78oWj9oJ9ZoI9ULTxTaXzmgUUZz/03fFrRMZeQInCa9em 64bdIc8fT8n3HfsfEuyBku16368HCrTrz9cKMfYCSnT72vXXafmCfW6+Yw8UKryheL0CypP+ sselCfdAUW5y5F8fUgr22SVXxQ/fk3B1aaAA128eu6E3IAYK5Yg9UKZw+n38dbX0l9amCfZL OFz4IHxa7HfsgfU7/MTd1ZEur11AuQR7oFjhqP1pPPbXWZSCPQAAABRMsAcAAICCCfYAAABQ MMEeAAAACibYAwAAQMEEewAAACiYYA8AAAAFE+wBAACgYII9AAAAFEywBwAAgIIJ9gAAAFAw wR4AAAAKJtgDAO/b9UO/P/39zKvTAQAfE+wBgDfth77rx//+5dXpAIBvCPYAwOv2u6HbbIbN Zvv8SPzddCHkb8fbm2Hb7cbb8fFu2IVpt+P9hwnTv8fnjH+HeXR9N2zH6Q9PPdgPu9P8Nttx Hqd7AaBFgj0A8JZ9PwbtF5L0ZboQ0MPfMbifjuLv+zHk98NuvH+/64bN+Hff705/nxYQpokf DpzvPwb+Y/g/Lif+DQAtEuwBgDeEUP3KEfJkujGQH47S73engH+Y4BjIxxvHzH48kn/+eyq0 x2Af/h8/HBgJ9gC0TrAHAF4XjqAnofqhZLoQ1OMp8304On+c4BDyj3k8nJY/9Xc6TZjP8UOB NPgfp0lP0QeA9gj2AMDrwtH3/oUUnUwXgnh3CuLn0/PTDwjikfjz38n9p2ni0f3gcoR+P+zG v9Oj9wDQIsEeAHjZ8ZT5fjh8Xf4sHGVPj6LfTHf4nvzpIninJ6anzx+C/znXX54X/j4c6Q/z jmH/4HiBvXghvsv9ANAmwR4AmMVud0rnswgfFrjaPQC8QrAHAL62H0P99VH8L6Wn5wMATwn2 AMC6hFAfT8FPTu8HAKYJ9gAAAFAwwR4AAAAKJtgDAABAwQR7AAAAKJhgDwAAAAUT7AEAAKBg gj0AAAAUTLAHAACAggn2AAAAUDDBHgAAAAom2AMAAEDBBHsAAAAomGAPAAAABRPsAYAy7fth u9kMm1Pb9vvTA6P4WLc73QEA9RLsAYCC7YZuItjv++0p8HfjFABQN8EeACjYdLB3xB6Algj2 AEDBHgR7AGiIYA8Ajdv33dV31Tfbfkgj8n6XPL7tht1Nfr56fLMdutuAve+HbhsfP86jT2cy Pv/82CY81g/bOP3NuowzGyePp9mHZY3zPj33HOxv5ueYPQC1E+wBoGWnU9ZjKN4fQvUlTMfv qh8f3w/9IXBfwvL5u+yHU94vR8/PZ8DHU+LHeR7vivNIphldvhN/WlZ8Xrx9suuO98VT7MOH ClfPOzlPJ9gD0ADBHgBadg7Q2+uj6AcxqI+PnR6KgfmYq++D/PHxOP0lxKeh+xziJz5AuATx qVPsJz44mJxuan4AUC/BHgAadzm1fQzHXXLq+9Up7dftEKKTx9Oj7xfTofvyvMsHBi8F+8nl CfYAINgDADffkz+F4b+C+4fB/hK6BXsAmINgDwCchAvTJcH5wffcz5LHp39WbuZT8ZPlCfYA cCHYA0DLQlhOrnQfrpA/9Z36w32nicIF9u4fD2E7Ph6ubH/4M0xwfPwc4v+6eF5c9nRgPy9v fHK49/K8Y4vzvKyXYA9A/QR7AGjZGOzDz9NdTsXfngP6UfrzcmMLP1WXPhweH8P1+cj99vb5 o7CMU5g/zyOdJjnFPj5+Nf3YLh8C7Ic++bm7fh8+ADj+7N15lnfzS64bAAAVEuwBAACgYII9 AAAAFEywBwAAgIIJ9gAAAFAwwR4AAAAKJtgDAABAwQR7AAAAKJhgDwAAAAUT7AEAAKBggj0A AAAUTLAHAACAggn2AAAAUDDBHgAAAAom2AMAAEDBBHsAAAAomGAPAAAABRPsAQAAoGCCPQAA ABRMsGdeu27YbDan1g27093Dvh+24b7ufM9j70wLAADQOMGe2e26+2C/77f3Yf9k113f92xa AAAArgn2zG4ymD86Cn84wn8T4B2xBwAAeJlgz+xePuIeA7wj8wAAAB8T7JuwH3Z9dwrRx7bt duO9Jzffi+93Y+Denm5v+8t0B3/Ma3QX7Ce+d7+/mcehhWU9+o7+KDx2ec526PrrNQsfFHRx vUPbhm25mQYAAKAygn1uY8DM3v5w/M77GISPiXroxueE4LtNgvEljJ/uPx9Nv57uvXldgvk7 37t/Ou1hwbvzcg83g7i+2/70nP3Qn0L+eRoAAIAK/Z0K+dwYKhdpf4iheNtfh+LnYfz5dO/N 6/X7gmfrEkN6/IDhuNhLiJ9cj7uzDgAAAOrxdyqkGvtdP3TddvJI/LMwnU4XvTev1+8L7u5P Ts+fPvr+YF3Pz4sfAAAAANRHsG9CPKIdgvKrR9kfBftP5vX6fcFcwf4yH8EeAACol2Bfvctp 6rffT38/2H86r9fvC+7uT77vP/0TeE7FBwAA2iXYV+8SvkMo3j+4KN4lTMej21Oh/bV5TV38 7s+L5+3HeZ/m8fz54aj9cbpwlfzDV/2DeFT/HOIvYX/yswAAAIBKCPYN2J2PXB9//q2PIXm8 fcjIyanu8f6rn40bWwzH78/r9ifsTvcd53ZezrY73fdw2vAze5fv9G+24cr8x0fOwlX60/U+ rSMAAEDNBHsAAAAomGAPAAAABRPsAQAAoGCCPQAAABRMsAcAAICCCfYAAABQMMEeAAAACibY AwAAQMEEewAAACiYYE859v2w3WyGTbc73QEAAIBgTzH2/XbYhGC/6QbRHgAA4EiwpxyO2AMA ANwR7AEAAKBggn3tdt3p9PXjKez9rh+229PtbT/sT5PdTrcbH+njdGPb9qcpr6bbjvPbDd15 fuP8x8n2V8u4P21+P87jcOQ9Ph5XIh6RDy0clY/TjX+H5xyXGdr1PK/mN65TF9c1Gud7XsfQ wnqeFwoAAFA2wT6zMUdmb3+5fDf9FNCTAH0O7KNdF8NvDM5jaJ+Y7m5+Vx8CjMH6EJqfP/f6 eZegfl6HQ+C/fvx+/ZJ1OZyef1nm+Wz9uK3b/vScy7o6ox8AAKjBC7GQT43ZcZH2l/uLzv0V 2F+dbns4Qh9cAnk8C+Dy3Mt34uN998+Lk9zO57Cs8cHz34fn36/f9fPj/C8hfnL90zMWAAAA CvVCLKR07wf2TMH+6nT66xbnf57P+cOAi7v1S+Y3Mfloev0vz7usPwAAQKkE+wasMdhPB/Fk PhmD/dT6AwAAlEqwb8Bqgv2D7/an3gr2txfbu+NUfAAAoH6CfQPug/hrwf7qSvRJcP442I/O 04Xnnq5MH66ifzef84cLF1OPXe4LR+3j/MJV7w9/hgmOj5/Xy8XzAACAugj2tUvD+SHgdtc/ /Ta2S8AdQ293Ce3dGLjP4TxOdzu/MWT356B/at0Y1G+WcfkAYT/OIpk+/PTc+aHkCHxoafK+ 245LUN+Nyz8/bxuvyp8Y5+vn7gAAgFoJ9gAAAFAwwR4AAAAKJtgDAABAwQR7AAAAKJhgDwAA AAUT7AEAAKBggj0AAAAUTLAHAACAYg3D/wPDo9R+h0+NMwAAAABJRU5ErkJggg==</item> </binaryContent> </worksheet>