Mathcad Professional 14.0 <description/> <author>chris</author> <company/> <keywords/> <revisedBy>chris</revisedBy> </userData> <identityInfo> <revision>32</revision> <documentID>80A7763D-6FCB-4D99-89A9-24B3C6427FAD</documentID> <versionID>24228E6B-6A15-4756-90F1-25C9E0B1D36D</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="12" font-weight="normal" font-style="normal" underline="false" line-through="false" vertical-align="baseline" color="#000080"/> </textStyle> </textStyles> </textRendering> <mathRendering equation-color="#000"> <operators multiplication="narrow-dot" derivative="derivative" literal-subscript="small" definition="colon-equal" global-definition="triple-equal" local-definition="left-arrow" equality="bold-equal" symbolic-evaluation="right-arrow"/> <mathStyles> <mathStyle name="Variables" font-family="MS Serif" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false"/> <mathStyle name="Constants" font-family="MS Serif" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false"/> <mathStyle name="User 1" font-family="MS Sans Serif" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false"/> <mathStyle name="User 2" font-family="Courier" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false"/> <mathStyle name="User 3" font-family="System" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false"/> <mathStyle name="User 4" font-family="Script" font-charset="255" font-size="10" font-weight="normal" font-style="normal" underline="false"/> <mathStyle name="User 5" font-family="Roman" font-charset="255" font-size="10" font-weight="normal" font-style="normal" underline="false"/> <mathStyle name="User 6" font-family="Modern" font-charset="255" font-size="10" font-weight="normal" font-style="normal" underline="false"/> <mathStyle name="User 7" font-family="MS Serif" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false"/> <mathStyle name="Math Text Font" font-family="MS Serif" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false"/> </mathStyles> <dimensionNames mass="mass" length="length" time="time" current="charge" 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"> <engineering use-e-notation="false" precision="3" show-trailing-zeros="true" radix="dec" complex-threshold="20" zero-threshold="20" imaginary-value="i"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="false" 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="9" orientation="landscape" print-single-page-width="false" page-width="842.25" page-height="595.5"> <margins left="28.31811" right="28.31811" top="28.31811" bottom="28.31811"/> <header use-full-page-width="false"/> <footer use-full-page-width="false"/> </pageModel> <colorModel background-color="#fff" default-highlight-color="#ffff80"/> <language math="en" UI="en"/> </presentation> <calculation> <builtInVariables array-origin="0" 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="true" optimize-expressions="false" exact-boolean="false" strings-use-origin="false" zero-over-zero="0"> <compatibility multiple-assignment="MC11" 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> <xy item-idref="1"/> </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="8173" 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="6490" left="216" top="15.75" width="347.25" height="18" align-x="227.25" align-y="30" 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"> <p style="Normal" margin-left="inherit" margin-right="inherit" text-indent="inherit" text-align="center" list-style-type="inherit" tabs="inherit"> <f family="Arial" charset="0" size="16"> <b> <inlineAttr line-through="false"> <c val="#000080">FM Range Calculation</c> </inlineAttr> </b> </f> </p> </text> </region> <region region-id="7690" left="18" top="42.75" width="759" height="149.25" align-x="26.25" align-y="54" 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"> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit">This sheet is to estimate of the range that can be expected from an FM or ASK modulated system. </p> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit"/> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit"> <f family="Arial" charset="0"> <b> <inlineAttr line-through="false"> <c val="#000080">Chris Haji-Michael<sp count="2"/>www.sunshadow.co.uk </c> </inlineAttr> </b> </f> </p> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit"/> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit"> <inlineAttr font-weight="normal" font-style="normal">It uses a modified-version of the Friss transmission equation.<sp count="2"/>It starts by calculating the minimum signal strength in dBm that is required in the receiver.<sp count="2"/>It uses three probability-error-function curves, the first one is for FSK with average white gaussian noise (AWGN), a second one is form ASK and the third is for FSK with Raleigh fading.<sp count="2"/> </inlineAttr> </p> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit"/> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit"> <f family="Arial" charset="0"> <inlineAttr font-weight="normal" font-style="normal" line-through="false"> <c val="#000080">Many of the RF equations are adapted from Pozar, Microwave & RF Design of Wireless Systems.<sp count="2"/>The propagation equations were found on http://www.sss-mag/indoor.html<sp count="2"/>and<sp count="2"/>http://people.deas.harvard.edu/~jones/es151/prop_models/propagation.html.<sp count="2"/> </c> </inlineAttr> </f> <inlineAttr font-weight="normal" font-style="normal">The error function calculations are from "Wireless Communications", by Andrea Goldsmith.</inlineAttr> </p> </text> </region> <region region-id="8146" left="678" top="206.25" width="69.75" height="13.5" align-x="690" align-y="216" 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">x</ml:id> <ml:range> <ml:sequence> <ml:real>-5</ml:real> <ml:real>-4.9</ml:real> </ml:sequence> <ml:real>25</ml:real> </ml:range> </ml:define> </math> <rendering item-idref="2"/> </region> <region region-id="7855" left="6" top="258.75" width="773.25" height="41.25" align-x="6" align-y="270" 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"> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit"> <b> <c val="#000080">Probability Error Function for coherent 2FSK</c> </b> </p> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit"> <c val="#000080">The probability error function curve for coherent FSK can be used to calculate BER for different Eb/No.<sp count="2"/>This equation is found in "An Intro to Analogue & Digital Communications" by Simon Haykin and Andrea Goldsmith equation 5.41 & table 6.1 where x is in dB.</c> </p> </text> </region> <region region-id="7856" left="12" top="338.25" width="24.75" height="13.5" align-x="24" align-y="348" 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">x</ml:id> <ml:real>1</ml:real> </ml:define> </math> <rendering item-idref="3"/> </region> <region region-id="7857" left="48" top="338.25" width="26.25" height="13.5" align-x="60" align-y="348" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:id xml:space="preserve" xmlns:ml="http://schemas.mathsoft.com/math30">Given</ml:id> </math> <rendering item-idref="4"/> </region> <region region-id="7858" left="90" top="312" width="114.75" height="48" align-x="125.25" align-y="348" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:apply xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:equal/> <ml:id xml:space="preserve" subscript="2fsk">PEF</ml:id> <ml:apply> <ml:mult style="auto-select"/> <ml:real>0.5</ml:real> <ml:apply> <ml:id xml:space="preserve">erfc</ml:id> <ml:apply> <ml:sqrt/> <ml:apply> <ml:div/> <ml:apply> <ml:pow/> <ml:real>10</ml:real> <ml:apply> <ml:div/> <ml:id xml:space="preserve">x</ml:id> <ml:real>10</ml:real> </ml:apply> </ml:apply> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </math> <rendering item-idref="5"/> </region> <region region-id="7859" left="228" top="338.25" width="151.5" height="15.75" align-x="339.75" align-y="348" 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">EbNo_2FSK_dB</ml:id> <ml:boundVars> <ml:id xml:space="preserve" subscript="2fsk">PEF</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:Find auto-method="true" method="levenberg" derivative-estimation="central" variable-estimation="tangent" linear-check="false" multistart="false" evolutionary="false"/> <ml:id xml:space="preserve">x</ml:id> </ml:apply> </ml:define> </math> <rendering item-idref="6"/> </region> <region region-id="7860" left="420" top="338.25" width="126" height="13.5" align-x="511.5" align-y="348" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#80ff80" 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">EbNo_2FSK_dB</ml:id> <ml:real>0.1</ml:real> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>2.1547217099124909</ml:real> </result> </ml:eval> <resultFormat numeric-only="true"> <general precision="3" show-trailing-zeros="true" radix="dec" complex-threshold="20" zero-threshold="15" imaginary-value="i" exponential-threshold="3"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="false" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="7"/> </region> <region region-id="7861" left="582" top="318" width="130.5" height="48" align-x="632.25" align-y="354" 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="2fsk">PEF</ml:id> <ml:boundVars> <ml:id xml:space="preserve">x</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:mult style="auto-select"/> <ml:real>0.5</ml:real> <ml:apply> <ml:id xml:space="preserve">erfc</ml:id> <ml:apply> <ml:sqrt/> <ml:apply> <ml:div/> <ml:apply> <ml:pow/> <ml:real>10</ml:real> <ml:apply> <ml:div/> <ml:id xml:space="preserve">x</ml:id> <ml:real>10</ml:real> </ml:apply> </ml:apply> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </ml:define> </math> <rendering item-idref="8"/> </region> <region region-id="7862" left="420" top="356.25" width="130.5" height="13.5" align-x="516" align-y="366" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#80ff80" 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">EbNo_2FSK_dB</ml:id> <ml:real>0.01</ml:real> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>7.33349316296293</ml:real> </result> </ml:eval> <resultFormat numeric-only="true"> <general precision="3" show-trailing-zeros="true" radix="dec" complex-threshold="20" zero-threshold="15" imaginary-value="i" exponential-threshold="3"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="false" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="9"/> </region> <region region-id="7847" left="6" top="402.75" width="773.25" height="41.25" align-x="6" align-y="414" 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"> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit"> <b> <c val="#000080">Probability Error Function for coherent ASK</c> </b> </p> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit"> <c val="#000080">The probability error function curve for 2 level pulsed amplitude modulation (also called amplitude shift keying or on/off keying).<sp count="2"/>This equation is from </c>Andrea Goldsmith 5.41 & table 6.1 for MPAM where x is in dB<c val="#000080"> <sp/> </c> </p> </text> </region> <region region-id="7816" left="12" top="488.25" width="24.75" height="13.5" align-x="24" align-y="498" 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">x</ml:id> <ml:real>1</ml:real> </ml:define> </math> <rendering item-idref="10"/> </region> <region region-id="7817" left="48" top="488.25" width="26.25" height="13.5" align-x="60" align-y="498" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:id xml:space="preserve" xmlns:ml="http://schemas.mathsoft.com/math30">Given</ml:id> </math> <rendering item-idref="11"/> </region> <region region-id="7818" left="84" top="462" width="152.25" height="48" align-x="120.75" align-y="498" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:apply xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:equal/> <ml:id xml:space="preserve" subscript="2ask">PEF</ml:id> <ml:apply> <ml:mult style="auto-select"/> <ml:apply> <ml:div/> <ml:real>2</ml:real> <ml:apply> <ml:mult/> <ml:real>2</ml:real> <ml:apply> <ml:id xml:space="preserve" font="1">log</ml:id> <ml:sequence> <ml:real font="0">2</ml:real> <ml:real>2</ml:real> </ml:sequence> </ml:apply> </ml:apply> </ml:apply> <ml:apply> <ml:id xml:space="preserve">erfc</ml:id> <ml:apply> <ml:sqrt/> <ml:apply> <ml:div/> <ml:apply> <ml:pow/> <ml:real>10</ml:real> <ml:apply> <ml:div/> <ml:id xml:space="preserve">x</ml:id> <ml:real>10</ml:real> </ml:apply> </ml:apply> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </math> <rendering item-idref="12"/> </region> <region region-id="7819" left="258" top="488.25" width="154.5" height="15.75" align-x="372.75" align-y="498" 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">EbNo_2ASK_dB</ml:id> <ml:boundVars> <ml:id xml:space="preserve" subscript="2ask">PEF</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:Find auto-method="true" method="levenberg" derivative-estimation="central" variable-estimation="tangent" linear-check="false" multistart="false" evolutionary="false"/> <ml:id xml:space="preserve">x</ml:id> </ml:apply> </ml:define> </math> <rendering item-idref="13"/> </region> <region region-id="7827" left="432" top="488.25" width="141" height="13.5" align-x="534" align-y="498" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#80ff80" 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">EbNo_2ASK_dB</ml:id> <ml:real>0.001</ml:real> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>10.345308465201784</ml:real> </result> </ml:eval> <resultFormat numeric-only="true"> <general precision="3" show-trailing-zeros="true" radix="dec" complex-threshold="20" zero-threshold="15" imaginary-value="i" exponential-threshold="3"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="false" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="14"/> </region> <region region-id="7828" left="600" top="468" width="168" height="48" align-x="651.75" align-y="504" 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="2ask">PEF</ml:id> <ml:boundVars> <ml:id xml:space="preserve">x</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:mult style="auto-select"/> <ml:apply> <ml:div/> <ml:real>2</ml:real> <ml:apply> <ml:mult/> <ml:real>2</ml:real> <ml:apply> <ml:id xml:space="preserve" font="1">log</ml:id> <ml:sequence> <ml:real font="0">2</ml:real> <ml:real>2</ml:real> </ml:sequence> </ml:apply> </ml:apply> </ml:apply> <ml:apply> <ml:id xml:space="preserve">erfc</ml:id> <ml:apply> <ml:sqrt/> <ml:apply> <ml:div/> <ml:apply> <ml:pow/> <ml:real>10</ml:real> <ml:apply> <ml:div/> <ml:id xml:space="preserve">x</ml:id> <ml:real>10</ml:real> </ml:apply> </ml:apply> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </ml:define> </math> <rendering item-idref="15"/> </region> <region region-id="7721" left="6" top="546.75" width="774" height="42" align-x="6" align-y="558" 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"> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit"> <b>Probability Error Function for coherent 2FSK with fading</b> </p> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit">A<f family="Arial" charset="0"> <inlineAttr font-weight="normal" line-through="false"> <c val="#000080"> real signal is likely to be subjected to fading of which the most severe version is </c> </inlineAttr> </f> <f family="Arial" charset="0"> <b> <inlineAttr line-through="false"> <c val="#000080">Raleigh fading</c> </inlineAttr> </b> </f> <f family="Arial" charset="0"> <inlineAttr font-weight="normal" line-through="false"> <c val="#000080">.<sp count="2"/>This is best represented as an alternate Error Function Curve and is calculated here according to "Wireless Communications", by Andrea Goldsmith, equation 6.59.</c> </inlineAttr> </f> </p> </text> </region> <region region-id="7744" left="30" top="632.25" width="24" height="13.5" align-x="41.25" align-y="642" 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">y</ml:id> <ml:real>1</ml:real> </ml:define> </math> <rendering item-idref="16"/> </region> <region region-id="7745" left="66" top="632.25" width="24.75" height="13.5" align-x="77.25" align-y="642" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:id xml:space="preserve" xmlns:ml="http://schemas.mathsoft.com/math30">Given</ml:id> </math> <rendering item-idref="17"/> </region> <region region-id="7746" left="102" top="606" width="159" height="64.5" align-x="159" align-y="642" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:apply xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:equal/> <ml:id xml:space="preserve" subscript="2fsk_fad">PEF</ml:id> <ml:apply> <ml:mult style="auto-select"/> <ml:real>0.5</ml:real> <ml:parens> <ml:apply> <ml:minus/> <ml:real>1</ml:real> <ml:apply> <ml:sqrt/> <ml:apply> <ml:div/> <ml:apply> <ml:pow/> <ml:real>10</ml:real> <ml:apply> <ml:div/> <ml:id xml:space="preserve">y</ml:id> <ml:real>10</ml:real> </ml:apply> </ml:apply> <ml:apply> <ml:plus/> <ml:real>2</ml:real> <ml:apply> <ml:pow/> <ml:real>10</ml:real> <ml:apply> <ml:div/> <ml:id xml:space="preserve">y</ml:id> <ml:real>10</ml:real> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </ml:parens> </ml:apply> </ml:apply> </math> <rendering item-idref="18"/> </region> <region region-id="7767" left="318" top="632.25" width="195" height="15.75" align-x="473.25" align-y="642" 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">EbNo_2FSK_fad_dB</ml:id> <ml:boundVars> <ml:id xml:space="preserve" subscript="2fsk_fad">PEF</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:Find auto-method="true" method="levenberg" derivative-estimation="central" variable-estimation="tangent" linear-check="false" multistart="false" evolutionary="false"/> <ml:id xml:space="preserve">y</ml:id> </ml:apply> </ml:define> </math> <rendering item-idref="19"/> </region> <region region-id="7771" left="558" top="606" width="174" height="64.5" align-x="629.25" align-y="642" 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="2fsk_fad">PEF</ml:id> <ml:boundVars> <ml:id xml:space="preserve">x</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:mult style="auto-select"/> <ml:real>0.5</ml:real> <ml:parens> <ml:apply> <ml:minus/> <ml:real>1</ml:real> <ml:apply> <ml:sqrt/> <ml:apply> <ml:div/> <ml:apply> <ml:pow/> <ml:real>10</ml:real> <ml:apply> <ml:div/> <ml:id xml:space="preserve">x</ml:id> <ml:real>10</ml:real> </ml:apply> </ml:apply> <ml:apply> <ml:plus/> <ml:real>2</ml:real> <ml:apply> <ml:pow/> <ml:real>10</ml:real> <ml:apply> <ml:div/> <ml:id xml:space="preserve">x</ml:id> <ml:real>10</ml:real> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </ml:parens> </ml:apply> </ml:define> </math> <rendering item-idref="20"/> </region> <region region-id="7768" left="348" top="656.25" width="143.25" height="13.5" align-x="456.75" align-y="666" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#80ff80" 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">EbNo_2FSK_fad_dB</ml:id> <ml:real>0.1</ml:real> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>5.50907468880581</ml:real> </result> </ml:eval> <resultFormat numeric-only="true"> <general precision="3" show-trailing-zeros="true" radix="dec" complex-threshold="20" zero-threshold="15" imaginary-value="i" exponential-threshold="3"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="false" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="21"/> </region> <region region-id="7838" left="90" top="704.25" width="79.5" height="13.5" align-x="101.25" align-y="714" 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">y</ml:id> <ml:range> <ml:sequence> <ml:real>-10</ml:real> <ml:real>-9.9</ml:real> </ml:sequence> <ml:real>40</ml:real> </ml:range> </ml:define> </math> <rendering item-idref="22"/> </region> <region region-id="7843" left="126" top="720" width="492.75" height="267.75" align-x="126" align-y="720" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <plot disable-calc="false" item-idref="23"/> <rendering item-idref="24"/> </region> <region region-id="7831" left="132" top="996.75" width="558.75" height="42.75" align-x="132" align-y="1008" 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"> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit"> <b> <c val="#f00">RED</c> </b> <c val="#000080"> curve is with average white gaussian noise for 2FSK (frequency shift keying)</c> </p> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit"> <b>BLUE</b> curves with average white gaussian noise for ASK (amplitude keying or on/off keying)</p> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit"> <b> <c val="#0f0">GREEN</c> </b> <c val="#000080"> curve is with Raleigh fading for 2FSK</c> </p> </text> </region> <region region-id="7865" left="12" top="1092.75" width="768" height="54.75" align-x="20.25" align-y="1104" 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"> <p style="Normal" margin-left="inherit" margin-right="inherit" text-indent="inherit" text-align="center" list-style-type="inherit" tabs="inherit"> <f family="Arial" charset="0"> <b> <inlineAttr line-through="false"> <c val="#000080">Required receiver bandwidth for 2FSK</c> </inlineAttr> </b> </f> </p> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit">FM systems require a receive bandwidth which <f family="Arial" charset="0"> <inlineAttr font-weight="normal" line-through="false"> <c val="#000080">depends on the deviation and bit period.<sp count="2"/>This is called the Carson's BW.<sp count="2"/>More advanced FM receivers that use a PLL to track the input signal effectivly reduces this bandwidth.<sp count="2"/>Remember, the bigger the bandwidth the more noise is seen by the receiver and the worse the receive sensitivity.</c> </inlineAttr> </f> </p> </text> </region> <region region-id="7887" left="162" top="1172.25" width="187.5" height="13.5" align-x="266.25" 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">BT</ml:id> <ml:boundVars> <ml:id xml:space="preserve">fm_dev</ml:id> <ml:id xml:space="preserve">bit_period</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:mult/> <ml:id xml:space="preserve">fm_dev</ml:id> <ml:id xml:space="preserve">bit_period</ml:id> </ml:apply> </ml:define> </math> <rendering item-idref="25"/> </region> <region region-id="7886" left="372" top="1164.75" width="274.5" height="29.25" align-x="522" 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">Carsons_BW</ml:id> <ml:boundVars> <ml:id xml:space="preserve">fm_dev</ml:id> <ml:id xml:space="preserve">bit_period</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:mult/> <ml:real>2</ml:real> <ml:parens> <ml:apply> <ml:plus/> <ml:apply> <ml:div/> <ml:real>1</ml:real> <ml:apply> <ml:mult style="narrow-dot"/> <ml:real font="0">2</ml:real> <ml:id xml:space="preserve">bit_period</ml:id> </ml:apply> </ml:apply> <ml:id xml:space="preserve">fm_dev</ml:id> </ml:apply> </ml:parens> </ml:apply> </ml:define> </math> <rendering item-idref="26"/> </region> <region region-id="7864" left="6" top="1212.75" width="771" height="95.25" align-x="14.25" align-y="1224" 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"> <p style="Normal" margin-left="inherit" margin-right="inherit" text-indent="inherit" text-align="center" list-style-type="inherit" tabs="inherit"> <f family="Arial" charset="0"> <b> <inlineAttr line-through="false"> <c val="#000080">Limit of sensitivity for 2FSK in AWGN</c> </inlineAttr> </b> </f> </p> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit">The limit of sensitivity for FM systems uses the PEF curve and adds to this the noise floor (-174dBm/Hz) and noise you would get with the bandwidth you specify, as well as the noise-figure of the receiver.<sp count="2"/>There is also an adjustment for the BT which is a measure of how large the eye.<sp count="2"/>A low data rate has a large eye size and is easier to receive. </p> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit"/> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit"> In advanced receivers that use a PLL as the demodulator, this effectivly reduces the noise bandwith.<sp count="2"/>The IF bandwidth needs to be greater than the Carson's bandwidth.<sp count="2"/>The results here can be directly compared to the receiver measurements.</p> </text> </region> <region region-id="7884" left="60" top="1326.75" width="729" height="29.25" align-x="372.75" align-y="1344" 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">min_detect_signal_fsk_dBm</ml:id> <ml:boundVars> <ml:id xml:space="preserve">BER</ml:id> <ml:id xml:space="preserve">fm_dev</ml:id> <ml:id xml:space="preserve">bit_period</ml:id> <ml:id xml:space="preserve">rx_NF_dB</ml:id> <ml:id xml:space="preserve">BW</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:minus/> <ml:apply> <ml:plus/> <ml:apply> <ml:plus/> <ml:apply> <ml:plus/> <ml:real>-174</ml:real> <ml:apply> <ml:mult/> <ml:real>10</ml:real> <ml:apply> <ml:id xml:space="preserve">log</ml:id> <ml:apply> <ml:div/> <ml:id xml:space="preserve">BW</ml:id> <ml:id xml:space="preserve">Hz</ml:id> </ml:apply> </ml:apply> </ml:apply> </ml:apply> <ml:apply> <ml:id xml:space="preserve">EbNo_2FSK_dB</ml:id> <ml:id xml:space="preserve">BER</ml:id> </ml:apply> </ml:apply> <ml:id xml:space="preserve">rx_NF_dB</ml:id> </ml:apply> <ml:apply> <ml:mult style="auto-select"/> <ml:real>10</ml:real> <ml:apply> <ml:id xml:space="preserve">log</ml:id> <ml:apply> <ml:mult style="auto-select"/> <ml:real font="0">2</ml:real> <ml:apply> <ml:id xml:space="preserve">BT</ml:id> <ml:sequence> <ml:id xml:space="preserve">fm_dev</ml:id> <ml:id xml:space="preserve">bit_period</ml:id> </ml:sequence> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </ml:define> </math> <rendering item-idref="27"/> </region> <region region-id="7885" left="144" top="1376.25" width="633.75" height="31.5" align-x="446.25" align-y="1404" 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">min_detect_signal_fsk_W</ml:id> <ml:boundVars> <ml:id xml:space="preserve">BER</ml:id> <ml:id xml:space="preserve">fm_dev</ml:id> <ml:id xml:space="preserve">bit_period</ml:id> <ml:id xml:space="preserve">rx_NF_dB</ml:id> <ml:id xml:space="preserve">BW</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:real>10</ml:real> <ml:apply> <ml:div/> <ml:apply> <ml:id xml:space="preserve">min_detect_signal_fsk_dBm</ml:id> <ml:sequence> <ml:id xml:space="preserve">BER</ml:id> <ml:id xml:space="preserve">fm_dev</ml:id> <ml:id xml:space="preserve">bit_period</ml:id> <ml:id xml:space="preserve">rx_NF_dB</ml:id> <ml:id xml:space="preserve">BW</ml:id> </ml:sequence> </ml:apply> <ml:real>10</ml:real> </ml:apply> </ml:apply> <ml:id xml:space="preserve">mW</ml:id> </ml:apply> </ml:define> </math> <rendering item-idref="28"/> </region> <region region-id="7926" left="36" top="1434.75" width="175.5" height="29.25" align-x="173.25" align-y="1452" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#80ff80" 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">Carsons_BW</ml:id> <ml:sequence> <ml:apply> <ml:mult style="auto-select"/> <ml:real>20</ml:real> <ml:id xml:space="preserve">kHz</ml:id> </ml:apply> <ml:apply> <ml:div/> <ml:id xml:space="preserve">s</ml:id> <ml:apply> <ml:mult/> <ml:real>2</ml:real> <ml:real>20000</ml:real> </ml:apply> </ml:apply> </ml:sequence> </ml:apply> <ml:unitOverride> <ml:id xml:space="preserve">kHz</ml:id> </ml:unitOverride> <result xmlns="http://schemas.mathsoft.com/math30"> <unitedValue> <ml:real>80</ml:real> <unitMonomial xmlns="http://schemas.mathsoft.com/units10"> <unitReference unit="kilohertz"/> </unitMonomial> </unitedValue> </result> </ml:eval> <resultFormat numeric-only="true"> <general precision="0" show-trailing-zeros="true" radix="dec" complex-threshold="20" zero-threshold="15" imaginary-value="i" exponential-threshold="3"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="false" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="29"/> </region> <region region-id="7935" left="234" top="1434.75" width="301.5" height="29.25" align-x="502.5" align-y="1452" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#80ff80" 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">min_detect_signal_fsk_dBm</ml:id> <ml:sequence> <ml:real>0.001</ml:real> <ml:apply> <ml:mult style="auto-select"/> <ml:real>20</ml:real> <ml:id xml:space="preserve">kHz</ml:id> </ml:apply> <ml:apply> <ml:div/> <ml:id xml:space="preserve">s</ml:id> <ml:apply> <ml:mult/> <ml:real>2</ml:real> <ml:real>20000</ml:real> </ml:apply> </ml:apply> <ml:real>5</ml:real> <ml:apply> <ml:mult style="auto-select"/> <ml:real>100</ml:real> <ml:id xml:space="preserve">kHz</ml:id> </ml:apply> </ml:sequence> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>-109.20017743095602</ml:real> </result> </ml:eval> <resultFormat numeric-only="true"> <decimal precision="0" show-trailing-zeros="true" radix="dec" complex-threshold="20" zero-threshold="15" imaginary-value="i"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="false" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="30"/> </region> <region region-id="8011" left="564" top="1452.75" width="211.5" height="121.5" align-x="564" align-y="1464" 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"> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit"> <c val="#000080">These figures show that the sensitivity and BW for different deviations and bit period.<sp count="2"/>The 2 for the bit period because the system is manchester encoded.</c> </p> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit"/> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit"> <c val="#000080">The sensitivity limit is for AWGN</c> </p> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit"/> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit"> <c val="#000080">Note the BW is the noise bandwidth and not the 3dB bandwidth </c> </p> </text> </region> <region region-id="7925" left="36" top="1476.75" width="175.5" height="29.25" align-x="173.25" align-y="1494" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#80ff80" 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">Carsons_BW</ml:id> <ml:sequence> <ml:apply> <ml:mult style="auto-select"/> <ml:real>30</ml:real> <ml:id xml:space="preserve">kHz</ml:id> </ml:apply> <ml:apply> <ml:div/> <ml:id xml:space="preserve">s</ml:id> <ml:apply> <ml:mult/> <ml:real>2</ml:real> <ml:real>10000</ml:real> </ml:apply> </ml:apply> </ml:sequence> </ml:apply> <ml:unitOverride> <ml:id xml:space="preserve">kHz</ml:id> </ml:unitOverride> <result xmlns="http://schemas.mathsoft.com/math30"> <unitedValue> <ml:real>80</ml:real> <unitMonomial xmlns="http://schemas.mathsoft.com/units10"> <unitReference unit="kilohertz"/> </unitMonomial> </unitedValue> </result> </ml:eval> <resultFormat numeric-only="true"> <general precision="0" show-trailing-zeros="true" radix="dec" complex-threshold="20" zero-threshold="15" imaginary-value="i" exponential-threshold="3"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="false" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="31"/> </region> <region region-id="7934" left="234" top="1476.75" width="301.5" height="29.25" align-x="502.5" align-y="1494" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#80ff80" 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">min_detect_signal_fsk_dBm</ml:id> <ml:sequence> <ml:real>0.001</ml:real> <ml:apply> <ml:mult style="auto-select"/> <ml:real>30</ml:real> <ml:id xml:space="preserve">kHz</ml:id> </ml:apply> <ml:apply> <ml:div/> <ml:id xml:space="preserve">s</ml:id> <ml:apply> <ml:mult/> <ml:real>2</ml:real> <ml:real>10000</ml:real> </ml:apply> </ml:apply> <ml:real>5</ml:real> <ml:apply> <ml:mult style="auto-select"/> <ml:real>100</ml:real> <ml:id xml:space="preserve">kHz</ml:id> </ml:apply> </ml:sequence> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>-113.97138997815264</ml:real> </result> </ml:eval> <resultFormat numeric-only="true"> <decimal precision="0" show-trailing-zeros="true" radix="dec" complex-threshold="20" zero-threshold="15" imaginary-value="i"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="false" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="32"/> </region> <region region-id="7924" left="36" top="1518.75" width="171" height="29.25" align-x="168.75" align-y="1536" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#80ff80" 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">Carsons_BW</ml:id> <ml:sequence> <ml:apply> <ml:mult style="auto-select"/> <ml:real>35</ml:real> <ml:id xml:space="preserve">kHz</ml:id> </ml:apply> <ml:apply> <ml:div/> <ml:id xml:space="preserve">s</ml:id> <ml:apply> <ml:mult/> <ml:real>2</ml:real> <ml:real>5000</ml:real> </ml:apply> </ml:apply> </ml:sequence> </ml:apply> <ml:unitOverride> <ml:id xml:space="preserve">kHz</ml:id> </ml:unitOverride> <result xmlns="http://schemas.mathsoft.com/math30"> <unitedValue> <ml:real>80</ml:real> <unitMonomial xmlns="http://schemas.mathsoft.com/units10"> <unitReference unit="kilohertz"/> </unitMonomial> </unitedValue> </result> </ml:eval> <resultFormat numeric-only="true"> <general precision="0" show-trailing-zeros="true" radix="dec" complex-threshold="20" zero-threshold="15" imaginary-value="i" exponential-threshold="3"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="false" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="33"/> </region> <region region-id="7933" left="234" top="1518.75" width="297" height="29.25" align-x="498" align-y="1536" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#80ff80" 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">min_detect_signal_fsk_dBm</ml:id> <ml:sequence> <ml:real>0.001</ml:real> <ml:apply> <ml:mult style="auto-select"/> <ml:real>35</ml:real> <ml:id xml:space="preserve">kHz</ml:id> </ml:apply> <ml:apply> <ml:div/> <ml:id xml:space="preserve">s</ml:id> <ml:apply> <ml:mult/> <ml:real>2</ml:real> <ml:real>5000</ml:real> </ml:apply> </ml:apply> <ml:real>5</ml:real> <ml:apply> <ml:mult style="auto-select"/> <ml:real>100</ml:real> <ml:id xml:space="preserve">kHz</ml:id> </ml:apply> </ml:sequence> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>-117.65115783109859</ml:real> </result> </ml:eval> <resultFormat numeric-only="true"> <decimal precision="0" show-trailing-zeros="true" radix="dec" complex-threshold="20" zero-threshold="15" imaginary-value="i"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="false" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="34"/> </region> <region region-id="7923" left="36" top="1560.75" width="177.75" height="29.25" align-x="175.5" align-y="1578" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#80ff80" 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">Carsons_BW</ml:id> <ml:sequence> <ml:apply> <ml:mult style="auto-select"/> <ml:real>37.5</ml:real> <ml:id xml:space="preserve">kHz</ml:id> </ml:apply> <ml:apply> <ml:div/> <ml:id xml:space="preserve">s</ml:id> <ml:apply> <ml:mult/> <ml:real>2</ml:real> <ml:real>2500</ml:real> </ml:apply> </ml:apply> </ml:sequence> </ml:apply> <ml:unitOverride> <ml:id xml:space="preserve">kHz</ml:id> </ml:unitOverride> <result xmlns="http://schemas.mathsoft.com/math30"> <unitedValue> <ml:real>80</ml:real> <unitMonomial xmlns="http://schemas.mathsoft.com/units10"> <unitReference unit="kilohertz"/> </unitMonomial> </unitedValue> </result> </ml:eval> <resultFormat numeric-only="true"> <general precision="0" show-trailing-zeros="true" radix="dec" complex-threshold="20" zero-threshold="15" imaginary-value="i" exponential-threshold="3"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="false" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="35"/> </region> <region region-id="7932" left="234" top="1560.75" width="303.75" height="29.25" align-x="504.75" align-y="1578" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#80ff80" 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">min_detect_signal_fsk_dBm</ml:id> <ml:sequence> <ml:real>0.001</ml:real> <ml:apply> <ml:mult style="auto-select"/> <ml:real>37.5</ml:real> <ml:id xml:space="preserve">kHz</ml:id> </ml:apply> <ml:apply> <ml:div/> <ml:id xml:space="preserve">s</ml:id> <ml:apply> <ml:mult/> <ml:real>2</ml:real> <ml:real>2500</ml:real> </ml:apply> </ml:apply> <ml:real>5</ml:real> <ml:apply> <ml:mult style="auto-select"/> <ml:real>100</ml:real> <ml:id xml:space="preserve">kHz</ml:id> </ml:apply> </ml:sequence> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>-120.96109002151283</ml:real> </result> </ml:eval> <resultFormat numeric-only="true"> <decimal precision="0" show-trailing-zeros="true" radix="dec" complex-threshold="20" zero-threshold="15" imaginary-value="i"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="false" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="36"/> </region> <region region-id="7918" left="6" top="1626.75" width="771" height="27.75" align-x="14.25" align-y="1638" 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"> <p style="Normal" margin-left="inherit" margin-right="inherit" text-indent="inherit" text-align="center" list-style-type="inherit" tabs="inherit"> <f family="Arial" charset="0"> <b> <inlineAttr line-through="false"> <c val="#000080">Limit of sensitivity for 2FSK in Raleigh Fading</c> </inlineAttr> </b> </f> </p> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit">A similar analysis can be carried out for the fading environment, but of course we cannot measure this directly.<sp count="2"/> </p> </text> </region> <region region-id="7919" left="36" top="1674.75" width="759.75" height="29.25" align-x="361.5" align-y="1692" 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">min_detect_signal_fskfad_dBm</ml:id> <ml:boundVars> <ml:id xml:space="preserve">BER</ml:id> <ml:id xml:space="preserve">fm_dev</ml:id> <ml:id xml:space="preserve">bit_period</ml:id> <ml:id xml:space="preserve">rx_NF_dB</ml:id> <ml:id xml:space="preserve">BW</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:minus/> <ml:apply> <ml:plus/> <ml:apply> <ml:plus/> <ml:apply> <ml:plus/> <ml:real>-174</ml:real> <ml:apply> <ml:mult/> <ml:real>10</ml:real> <ml:apply> <ml:id xml:space="preserve">log</ml:id> <ml:apply> <ml:div/> <ml:id xml:space="preserve">BW</ml:id> <ml:id xml:space="preserve">Hz</ml:id> </ml:apply> </ml:apply> </ml:apply> </ml:apply> <ml:apply> <ml:id xml:space="preserve">EbNo_2FSK_fad_dB</ml:id> <ml:id xml:space="preserve">BER</ml:id> </ml:apply> </ml:apply> <ml:id xml:space="preserve">rx_NF_dB</ml:id> </ml:apply> <ml:apply> <ml:mult style="auto-select"/> <ml:real>10</ml:real> <ml:apply> <ml:id xml:space="preserve">log</ml:id> <ml:apply> <ml:mult style="auto-select"/> <ml:real font="0">2</ml:real> <ml:apply> <ml:id xml:space="preserve">BT</ml:id> <ml:sequence> <ml:id xml:space="preserve">fm_dev</ml:id> <ml:id xml:space="preserve">bit_period</ml:id> </ml:sequence> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </ml:define> </math> <rendering item-idref="37"/> </region> <region region-id="7920" left="126" top="1718.25" width="659.25" height="31.5" align-x="441" 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:function> <ml:id xml:space="preserve">min_detect_signal_fskfad_W</ml:id> <ml:boundVars> <ml:id xml:space="preserve">BER</ml:id> <ml:id xml:space="preserve">fm_dev</ml:id> <ml:id xml:space="preserve">bit_period</ml:id> <ml:id xml:space="preserve">rx_NF_dB</ml:id> <ml:id xml:space="preserve">BW</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:real>10</ml:real> <ml:apply> <ml:div/> <ml:apply> <ml:id xml:space="preserve">min_detect_signal_fskfad_dBm</ml:id> <ml:sequence> <ml:id xml:space="preserve">BER</ml:id> <ml:id xml:space="preserve">fm_dev</ml:id> <ml:id xml:space="preserve">bit_period</ml:id> <ml:id xml:space="preserve">rx_NF_dB</ml:id> <ml:id xml:space="preserve">BW</ml:id> </ml:sequence> </ml:apply> <ml:real>10</ml:real> </ml:apply> </ml:apply> <ml:id xml:space="preserve">mW</ml:id> </ml:apply> </ml:define> </math> <rendering item-idref="38"/> </region> <region region-id="7922" left="234" top="1776.75" width="316.5" height="29.25" align-x="517.5" align-y="1794" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#80ff80" 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">min_detect_signal_fskfad_dBm</ml:id> <ml:sequence> <ml:real>0.001</ml:real> <ml:apply> <ml:mult style="auto-select"/> <ml:real>37.5</ml:real> <ml:id xml:space="preserve">kHz</ml:id> </ml:apply> <ml:apply> <ml:div/> <ml:id xml:space="preserve">s</ml:id> <ml:apply> <ml:mult/> <ml:real>2</ml:real> <ml:real>2500</ml:real> </ml:apply> </ml:apply> <ml:real>7</ml:real> <ml:apply> <ml:mult style="auto-select"/> <ml:real>100</ml:real> <ml:id xml:space="preserve">kHz</ml:id> </ml:apply> </ml:sequence> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>-101.78425660370898</ml:real> </result> </ml:eval> <resultFormat numeric-only="true"> <decimal precision="0" show-trailing-zeros="true" radix="dec" complex-threshold="20" zero-threshold="15" imaginary-value="i"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="false" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="39"/> </region> <region region-id="8174" left="6" top="1902.75" width="773.25" height="41.25" align-x="14.25" align-y="1914" 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"> <p style="Normal" margin-left="inherit" margin-right="inherit" text-indent="inherit" text-align="center" list-style-type="inherit" tabs="inherit"> <f family="Arial" charset="0"> <b> <inlineAttr line-through="false"> <c val="#000080">Required receiver bandwidth for ASK</c> </inlineAttr> </b> </f> </p> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit">An ASK system requires an IF bandwith that is equal to twice the maximum data rate.<sp count="2"/>This is less than the required BW for FSK, where the deviation is very much greater than the modulation, then ASK gives a big reduction in the required RX BW which will help with limiit of sensitivity.<sp count="4"/> </p> </text> </region> <region region-id="8175" left="234" top="1968.75" width="180.75" height="29.25" align-x="332.25" align-y="1986" 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">ASK_BW</ml:id> <ml:boundVars> <ml:id xml:space="preserve">bit_period</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:mult/> <ml:real>2</ml:real> <ml:parens> <ml:apply> <ml:div/> <ml:real>1</ml:real> <ml:apply> <ml:mult style="narrow-dot"/> <ml:real font="0">2</ml:real> <ml:id xml:space="preserve">bit_period</ml:id> </ml:apply> </ml:apply> </ml:parens> </ml:apply> </ml:define> </math> <rendering item-idref="40"/> </region> <region region-id="8176" left="450" top="1968.75" width="128.25" height="29.25" align-x="540" align-y="1986" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#80ff80" 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">ASK_BW</ml:id> <ml:apply> <ml:div/> <ml:id xml:space="preserve">s</ml:id> <ml:apply> <ml:mult/> <ml:real>2</ml:real> <ml:real>20000</ml:real> </ml:apply> </ml:apply> </ml:apply> <ml:unitOverride> <ml:id xml:space="preserve">kHz</ml:id> </ml:unitOverride> <result xmlns="http://schemas.mathsoft.com/math30"> <unitedValue> <ml:real>40</ml:real> <unitMonomial xmlns="http://schemas.mathsoft.com/units10"> <unitReference unit="kilohertz"/> </unitMonomial> </unitedValue> </result> </ml:eval> <resultFormat numeric-only="true"> <general precision="0" show-trailing-zeros="true" radix="dec" complex-threshold="20" zero-threshold="20" imaginary-value="i" exponential-threshold="3"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="false" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="41"/> </region> <region region-id="8134" left="12" top="2022.75" width="771" height="27.75" align-x="20.25" align-y="2034" 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"> <p style="Normal" margin-left="inherit" margin-right="inherit" text-indent="inherit" text-align="center" list-style-type="inherit" tabs="inherit"> <f family="Arial" charset="0"> <b> <inlineAttr line-through="false"> <c val="#000080">Limit of sensitivity for ASK in AWGN</c> </inlineAttr> </b> </f> </p> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit">A similar analysis can be carried out for the AWGN environment with ASK</p> </text> </region> <region region-id="8135" left="144" top="2064.75" width="547.5" height="29.25" align-x="423.75" align-y="2082" 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">min_detect_signal_ask_dBm</ml:id> <ml:boundVars> <ml:id xml:space="preserve">BER</ml:id> <ml:id xml:space="preserve">bit_period</ml:id> <ml:id xml:space="preserve">rx_NF_dB</ml:id> <ml:id xml:space="preserve">BW</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:plus/> <ml:apply> <ml:plus/> <ml:apply> <ml:plus/> <ml:real>-174</ml:real> <ml:apply> <ml:mult/> <ml:real>10</ml:real> <ml:apply> <ml:id xml:space="preserve">log</ml:id> <ml:apply> <ml:div/> <ml:id xml:space="preserve">BW</ml:id> <ml:id xml:space="preserve">Hz</ml:id> </ml:apply> </ml:apply> </ml:apply> </ml:apply> <ml:apply> <ml:id xml:space="preserve">EbNo_2ASK_dB</ml:id> <ml:id xml:space="preserve">BER</ml:id> </ml:apply> </ml:apply> <ml:id xml:space="preserve">rx_NF_dB</ml:id> </ml:apply> </ml:define> </math> <rendering item-idref="42"/> </region> <region region-id="8136" left="162" top="2114.25" width="569.25" height="31.5" align-x="431.25" align-y="2142" 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">min_detect_signal_ask_W</ml:id> <ml:boundVars> <ml:id xml:space="preserve">BER</ml:id> <ml:id xml:space="preserve">bit_period</ml:id> <ml:id xml:space="preserve">rx_NF_dB</ml:id> <ml:id xml:space="preserve">BW</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:real>10</ml:real> <ml:apply> <ml:div/> <ml:apply> <ml:id xml:space="preserve">min_detect_signal_ask_dBm</ml:id> <ml:sequence> <ml:id xml:space="preserve">BER</ml:id> <ml:id xml:space="preserve">bit_period</ml:id> <ml:id xml:space="preserve">rx_NF_dB</ml:id> <ml:id xml:space="preserve">BW</ml:id> </ml:sequence> </ml:apply> <ml:real>10</ml:real> </ml:apply> </ml:apply> <ml:id xml:space="preserve">mW</ml:id> </ml:apply> </ml:define> </math> <rendering item-idref="43"/> </region> <region region-id="8088" left="216" top="2174.25" width="264" height="13.5" align-x="330.75" align-y="2184" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:apply xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve">min_detect_signal_ask_W</ml:id> <ml:sequence> <ml:id xml:space="preserve">BER</ml:id> <ml:id xml:space="preserve">bit_period</ml:id> <ml:id xml:space="preserve">rx_NF_dB</ml:id> <ml:id xml:space="preserve">BW</ml:id> </ml:sequence> </ml:apply> </math> <rendering item-idref="44"/> </region> <region region-id="7992" left="48" top="2196.75" width="128.25" height="29.25" align-x="138" align-y="2214" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#80ff80" 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">ASK_BW</ml:id> <ml:apply> <ml:div/> <ml:id xml:space="preserve">s</ml:id> <ml:apply> <ml:mult/> <ml:real>2</ml:real> <ml:real>20000</ml:real> </ml:apply> </ml:apply> </ml:apply> <ml:unitOverride> <ml:id xml:space="preserve">kHz</ml:id> </ml:unitOverride> <result xmlns="http://schemas.mathsoft.com/math30"> <unitedValue> <ml:real>40</ml:real> <unitMonomial xmlns="http://schemas.mathsoft.com/units10"> <unitReference unit="kilohertz"/> </unitMonomial> </unitedValue> </result> </ml:eval> <resultFormat numeric-only="true"> <general precision="0" show-trailing-zeros="true" radix="dec" complex-threshold="20" zero-threshold="15" imaginary-value="i" exponential-threshold="3"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="false" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="45"/> </region> <region region-id="8018" left="210" top="2196.75" width="265.5" height="29.25" align-x="442.5" align-y="2214" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#80ff80" 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">min_detect_signal_ask_dBm</ml:id> <ml:sequence> <ml:real>0.001</ml:real> <ml:apply> <ml:div/> <ml:id xml:space="preserve">s</ml:id> <ml:apply> <ml:mult/> <ml:real>2</ml:real> <ml:real>20000</ml:real> </ml:apply> </ml:apply> <ml:real>5</ml:real> <ml:apply> <ml:mult style="auto-select"/> <ml:real>50</ml:real> <ml:id xml:space="preserve">kHz</ml:id> </ml:apply> </ml:sequence> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>-111.66499149143803</ml:real> </result> </ml:eval> <resultFormat numeric-only="true"> <decimal precision="0" show-trailing-zeros="true" radix="dec" complex-threshold="20" zero-threshold="15" imaginary-value="i"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="false" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="46"/> </region> <region region-id="8085" left="522" top="2208.75" width="240.75" height="121.5" align-x="522" align-y="2220" 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"> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit"> <c val="#000080">These figures show that the sensitivity and BW for different data rates.<sp count="2"/>The 2 in the data rate<sp count="2"/>for the bit period because the system is manchester encoded.</c> </p> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit"/> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit"> <c val="#000080">The sensitivity limit is for AWGN</c> </p> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit"/> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit"> <c val="#000080">Note the BW is the noise bandwidth and not the 3dB bandwidth </c> </p> </text> </region> <region region-id="7994" left="48" top="2238.75" width="128.25" height="29.25" align-x="138" align-y="2256" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#80ff80" 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">ASK_BW</ml:id> <ml:apply> <ml:div/> <ml:id xml:space="preserve">s</ml:id> <ml:apply> <ml:mult/> <ml:real>2</ml:real> <ml:real>10000</ml:real> </ml:apply> </ml:apply> </ml:apply> <ml:unitOverride> <ml:id xml:space="preserve">kHz</ml:id> </ml:unitOverride> <result xmlns="http://schemas.mathsoft.com/math30"> <unitedValue> <ml:real>20</ml:real> <unitMonomial xmlns="http://schemas.mathsoft.com/units10"> <unitReference unit="kilohertz"/> </unitMonomial> </unitedValue> </result> </ml:eval> <resultFormat numeric-only="true"> <general precision="0" show-trailing-zeros="true" radix="dec" complex-threshold="20" zero-threshold="15" imaginary-value="i" exponential-threshold="3"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="false" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="47"/> </region> <region region-id="8019" left="210" top="2238.75" width="265.5" height="29.25" align-x="442.5" align-y="2256" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#80ff80" 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">min_detect_signal_ask_dBm</ml:id> <ml:sequence> <ml:real>0.001</ml:real> <ml:apply> <ml:div/> <ml:id xml:space="preserve">s</ml:id> <ml:apply> <ml:mult/> <ml:real>2</ml:real> <ml:real>10000</ml:real> </ml:apply> </ml:apply> <ml:real>5</ml:real> <ml:apply> <ml:mult style="auto-select"/> <ml:real>50</ml:real> <ml:id xml:space="preserve">kHz</ml:id> </ml:apply> </ml:sequence> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>-111.66499149143803</ml:real> </result> </ml:eval> <resultFormat numeric-only="true"> <decimal precision="0" show-trailing-zeros="true" radix="dec" complex-threshold="20" zero-threshold="15" imaginary-value="i"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="false" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="48"/> </region> <region region-id="7996" left="48" top="2280.75" width="123.75" height="29.25" align-x="133.5" align-y="2298" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#80ff80" 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">ASK_BW</ml:id> <ml:apply> <ml:div/> <ml:id xml:space="preserve">s</ml:id> <ml:apply> <ml:mult/> <ml:real>2</ml:real> <ml:real>5000</ml:real> </ml:apply> </ml:apply> </ml:apply> <ml:unitOverride> <ml:id xml:space="preserve">kHz</ml:id> </ml:unitOverride> <result xmlns="http://schemas.mathsoft.com/math30"> <unitedValue> <ml:real>10</ml:real> <unitMonomial xmlns="http://schemas.mathsoft.com/units10"> <unitReference unit="kilohertz"/> </unitMonomial> </unitedValue> </result> </ml:eval> <resultFormat numeric-only="true"> <general precision="0" show-trailing-zeros="true" radix="dec" complex-threshold="20" zero-threshold="15" imaginary-value="i" exponential-threshold="3"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="false" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="49"/> </region> <region region-id="8020" left="210" top="2280.75" width="261" height="29.25" align-x="438" align-y="2298" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#80ff80" 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">min_detect_signal_ask_dBm</ml:id> <ml:sequence> <ml:real>0.001</ml:real> <ml:apply> <ml:div/> <ml:id xml:space="preserve">s</ml:id> <ml:apply> <ml:mult/> <ml:real>2</ml:real> <ml:real>5000</ml:real> </ml:apply> </ml:apply> <ml:real>5</ml:real> <ml:apply> <ml:mult style="auto-select"/> <ml:real>20</ml:real> <ml:id xml:space="preserve">kHz</ml:id> </ml:apply> </ml:sequence> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>-115.64439157815841</ml:real> </result> </ml:eval> <resultFormat numeric-only="true"> <decimal precision="0" show-trailing-zeros="true" radix="dec" complex-threshold="20" zero-threshold="15" imaginary-value="i"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="false" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="50"/> </region> <region region-id="7999" left="48" top="2322.75" width="119.25" height="29.25" align-x="133.5" align-y="2340" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#80ff80" 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">ASK_BW</ml:id> <ml:apply> <ml:div/> <ml:id xml:space="preserve">s</ml:id> <ml:apply> <ml:mult/> <ml:real>2</ml:real> <ml:real>2500</ml:real> </ml:apply> </ml:apply> </ml:apply> <ml:unitOverride> <ml:id xml:space="preserve">kHz</ml:id> </ml:unitOverride> <result xmlns="http://schemas.mathsoft.com/math30"> <unitedValue> <ml:real>5</ml:real> <unitMonomial xmlns="http://schemas.mathsoft.com/units10"> <unitReference unit="kilohertz"/> </unitMonomial> </unitedValue> </result> </ml:eval> <resultFormat numeric-only="true"> <general precision="0" show-trailing-zeros="true" radix="dec" complex-threshold="20" zero-threshold="15" imaginary-value="i" exponential-threshold="3"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="false" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="51"/> </region> <region region-id="8021" left="210" top="2322.75" width="261" height="29.25" align-x="438" align-y="2340" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#80ff80" 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">min_detect_signal_ask_dBm</ml:id> <ml:sequence> <ml:real>0.001</ml:real> <ml:apply> <ml:div/> <ml:id xml:space="preserve">s</ml:id> <ml:apply> <ml:mult/> <ml:real>2</ml:real> <ml:real>2500</ml:real> </ml:apply> </ml:apply> <ml:real>5</ml:real> <ml:apply> <ml:mult style="auto-select"/> <ml:real>10</ml:real> <ml:id xml:space="preserve">kHz</ml:id> </ml:apply> </ml:sequence> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>-118.65469153479822</ml:real> </result> </ml:eval> <resultFormat numeric-only="true"> <decimal precision="0" show-trailing-zeros="true" radix="dec" complex-threshold="20" zero-threshold="15" imaginary-value="i"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="false" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="52"/> </region> <region region-id="8036" left="6" top="2394.75" width="773.25" height="14.25" align-x="6" align-y="2406" 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"> <p style="Normal" margin-left="inherit" margin-right="inherit" text-indent="inherit" text-align="center" list-style-type="inherit" tabs="inherit"> <f family="Arial" charset="0"> <b> <inlineAttr line-through="false"> <c val="#000080">This plot shows the receive sensitivity curves for FSK (with and without fading) and for ASK without fading</c> </inlineAttr> </b> </f> </p> </text> </region> <region region-id="8167" left="6" top="2418.75" width="771.75" height="69" align-x="6" align-y="2430" 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"> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit"> <c val="#000080">This part is a bit confusing, it plots the PEF curves in a complex way because it sweeps signal stength on the x-axis to get the Bit Error Rate (BER) rather than the other way around.<sp count="2"/>It does this by having a</c> <c val="#000080"> starting BER to make the convergence work, I have put 0.01 here but it does not matter. </c> </p> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit"/> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit">A<c val="#000080"> useful plot to test receiver in 2FSK is to use the </c> <b> <c val="#f00">RED</c> </b> <c val="#000080"> curve.</c> </p> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit">A useful plot to test receiver in 2ASK is to use the <b>BLUE</b> curve.</p> </text> </region> <region region-id="8168" left="96" top="2510.25" width="657" height="13.5" align-x="327.75" align-y="2520" 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">offset_fsk</ml:id> <ml:boundVars> <ml:id xml:space="preserve">BER</ml:id> <ml:id xml:space="preserve">fm_dev</ml:id> <ml:id xml:space="preserve">bit_period</ml:id> <ml:id xml:space="preserve">rx_NF_dB</ml:id> <ml:id xml:space="preserve">BW</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:minus/> <ml:apply> <ml:id xml:space="preserve">min_detect_signal_fsk_dBm</ml:id> <ml:sequence> <ml:id xml:space="preserve">BER</ml:id> <ml:id xml:space="preserve">fm_dev</ml:id> <ml:id xml:space="preserve">bit_period</ml:id> <ml:id xml:space="preserve">rx_NF_dB</ml:id> <ml:id xml:space="preserve">BW</ml:id> </ml:sequence> </ml:apply> <ml:apply> <ml:id xml:space="preserve">EbNo_2FSK_dB</ml:id> <ml:id xml:space="preserve">BER</ml:id> </ml:apply> </ml:apply> </ml:define> </math> <rendering item-idref="53"/> </region> <region region-id="8169" left="96" top="2534.25" width="591.75" height="13.5" align-x="294.75" align-y="2544" 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">offset_ask</ml:id> <ml:boundVars> <ml:id xml:space="preserve">BER</ml:id> <ml:id xml:space="preserve">bit_period</ml:id> <ml:id xml:space="preserve">rx_NF_dB</ml:id> <ml:id xml:space="preserve">BW</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:minus/> <ml:apply> <ml:id xml:space="preserve">min_detect_signal_ask_dBm</ml:id> <ml:sequence> <ml:id xml:space="preserve">BER</ml:id> <ml:id xml:space="preserve">bit_period</ml:id> <ml:id xml:space="preserve">rx_NF_dB</ml:id> <ml:id xml:space="preserve">BW</ml:id> </ml:sequence> </ml:apply> <ml:apply> <ml:id xml:space="preserve">EbNo_2ASK_dB</ml:id> <ml:id xml:space="preserve">BER</ml:id> </ml:apply> </ml:apply> </ml:define> </math> <rendering item-idref="54"/> </region> <region region-id="8170" left="180" top="2568.75" width="227.25" height="29.25" align-x="367.5" align-y="2586" 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">offset_fsk</ml:id> <ml:sequence> <ml:real>0.1</ml:real> <ml:apply> <ml:mult style="auto-select"/> <ml:real>20</ml:real> <ml:id xml:space="preserve">kHz</ml:id> </ml:apply> <ml:apply> <ml:div/> <ml:id xml:space="preserve">s</ml:id> <ml:apply> <ml:mult/> <ml:real>2</ml:real> <ml:real>20000</ml:real> </ml:apply> </ml:apply> <ml:real>5</ml:real> <ml:apply> <ml:mult style="auto-select"/> <ml:real>100</ml:real> <ml:id xml:space="preserve">kHz</ml:id> </ml:apply> </ml:sequence> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>-119</ml:real> </result> </ml:eval> <resultFormat numeric-only="true"> <general precision="1" show-trailing-zeros="true" radix="dec" complex-threshold="20" zero-threshold="15" imaginary-value="i" exponential-threshold="3"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="false" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="55"/> </region> <region region-id="8171" left="432" top="2568.75" width="191.25" height="29.25" align-x="583.5" align-y="2586" 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">offset_ask</ml:id> <ml:sequence> <ml:real>0.1</ml:real> <ml:apply> <ml:div/> <ml:id xml:space="preserve">s</ml:id> <ml:apply> <ml:mult/> <ml:real>2</ml:real> <ml:real>20000</ml:real> </ml:apply> </ml:apply> <ml:real>5</ml:real> <ml:apply> <ml:mult style="auto-select"/> <ml:real>40</ml:real> <ml:id xml:space="preserve">kHz</ml:id> </ml:apply> </ml:sequence> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>-122.97940008672038</ml:real> </result> </ml:eval> <resultFormat numeric-only="true"> <general precision="1" show-trailing-zeros="true" radix="dec" complex-threshold="20" zero-threshold="15" imaginary-value="i" exponential-threshold="3"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="false" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="56"/> </region> <region region-id="8178" left="6" top="2622.75" width="773.25" height="69.75" align-x="6" align-y="2634" 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"> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit"> <b> <c val="#f00">RED</c> </b> <c val="#000080"> curve is r</c>eceiver sensitivity curves for FSK 20KHz dev, 25us bit period, 5dB NF <c val="#000080">with average white gaussian noise</c> </p> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit"> <b> <c val="#000080">BLUE</c> </b> <c val="#000080"> <sp/> </c>curve is receiver sensitivity curves for ASK, 25us bit period, 5dB NF with average white gaussian noise.</p> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit"> <b> <c val="#0f0">GREEN</c> </b> curve is receiver sensitivity curves for FSK 20KHz dev, 25us bit period, 5dB NF with Raleigh Fading</p> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit"/> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit">Note, the ASK curve uses a reduced BW</p> </text> </region> <region region-id="8177" left="132" top="2706" width="517.5" height="201.75" align-x="132" align-y="2706" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <plot disable-calc="false" item-idref="57"/> <rendering item-idref="58"/> </region> <region region-id="7852" left="6" top="2933.25" width="773.25" height="44.25" align-x="16.5" align-y="2946" 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"> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit"> <f family="Arial" charset="0" size="14"> <b> <inlineAttr line-through="false"> <c val="#000080">Calculate the expected range</c> </inlineAttr> </b> </f> </p> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit"> <f family="Arial" charset="0"> <inlineAttr font-weight="normal" line-through="false"> <c val="#000080">Using a modifie</c> </inlineAttr> </f> <f family="Arial" charset="0"> <inlineAttr font-weight="normal" line-through="false"> <c val="#000080">d form of the Friss equation from </c> </inlineAttr> </f> <b>Rappaport & Theodore</b> <f family="Arial" charset="0"> <inlineAttr font-weight="normal" line-through="false"> <c val="#000080">.<sp count="2"/>N is the propagation coefficient, refer to the picture below.<sp count="2"/>These range calculations use both the </c> </inlineAttr> </f> <inlineAttr line-through="false"> <c val="#000080"> <f family="Arial" charset="0"> <b>Raleigh</b> </f> </c> </inlineAttr> <f family="Arial" charset="0"> <inlineAttr font-weight="normal" line-through="false"> <c val="#000080"> fading model and </c> </inlineAttr> </f> <f family="Arial" charset="0"> <b> <inlineAttr line-through="false"> <c val="#000080">non Raleigh</c> </inlineAttr> </b> </f> <f family="Arial" charset="0"> <inlineAttr font-weight="normal" line-through="false"> <c val="#000080"> fading. </c> </inlineAttr> </f> </p> </text> </region> <region region-id="8173" left="360" top="2994" width="413.25" height="243" align-x="360" align-y="2994" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <picture> <png item-idref="59" display-width="411.75" display-height="241.5"/> </picture> <rendering item-idref="60"/> </region> <region region-id="8117" left="102" top="2990.25" width="69" height="13.5" align-x="123.75" align-y="3000" show-border="false" show-highlight="true" 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">freq</ml:id> <ml:apply> <ml:mult/> <ml:real>860</ml:real> <ml:id xml:space="preserve">MHz</ml:id> </ml:apply> </ml:define> </math> <rendering item-idref="61"/> </region> <region region-id="8118" left="192" top="2990.25" width="32.25" height="13.5" align-x="203.25" align-y="3000" show-border="false" show-highlight="true" 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">n</ml:id> <ml:real>2.5</ml:real> </ml:define> </math> <rendering item-idref="62"/> </region> <region region-id="7568" left="24" top="3036.75" width="291.75" height="82.5" align-x="32.25" align-y="3048" 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"> <p style="Normal" margin-left="inherit" margin-right="inherit" text-indent="inherit" text-align="center" list-style-type="inherit" tabs="inherit">The Friis equation is modified according plots from (RT) <f family="Arial" charset="0"> <b> <inlineAttr line-through="false"> <c val="#000080">Rappaport, Theodore</c> </inlineAttr> </b> </f>., <inlineAttr font-style="normal">Wireless Communications - Principles & Practice</inlineAttr>, IEEE Press, 1996.</p> <p style="Normal" margin-left="inherit" margin-right="inherit" text-indent="inherit" text-align="center" list-style-type="inherit" tabs="inherit"/> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit"> In this sheet I have set <b>n=</b> <b>2.5</b> <f family="Arial" charset="0"> <inlineAttr font-weight="normal" line-through="false"> <c val="#000080"> as communication is same floor.</c> </inlineAttr> </f> </p> </text> </region> <region region-id="8042" left="180" top="3255.75" width="241.5" height="30" align-x="327" align-y="3282" 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">antenna_gain</ml:id> <ml:boundVars> <ml:id xml:space="preserve">antenna_gain_dB</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:pow/> <ml:real>10</ml:real> <ml:apply> <ml:div/> <ml:id xml:space="preserve">antenna_gain_dB</ml:id> <ml:real>10</ml:real> </ml:apply> </ml:apply> </ml:define> </math> <rendering item-idref="63"/> </region> <region region-id="8043" left="420" top="3255.75" width="237" height="30" align-x="552.75" align-y="3282" 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">tx_power_W</ml:id> <ml:boundVars> <ml:id xml:space="preserve">tx_power_dBm</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:real>10</ml:real> <ml:apply> <ml:div/> <ml:id xml:space="preserve">tx_power_dBm</ml:id> <ml:real>10</ml:real> </ml:apply> </ml:apply> <ml:id xml:space="preserve">mW</ml:id> </ml:apply> </ml:define> </math> <rendering item-idref="64"/> </region> <region region-id="8044" left="12" top="3304.5" width="781.5" height="49.5" align-x="411.75" align-y="3342" 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">RangeRT_fad</ml:id> <ml:boundVars> <ml:id xml:space="preserve">tx_power_dBm</ml:id> <ml:id xml:space="preserve">BER</ml:id> <ml:id xml:space="preserve">fm_dev</ml:id> <ml:id xml:space="preserve">bit_period</ml:id> <ml:id xml:space="preserve">rx_NF_dB</ml:id> <ml:id xml:space="preserve">antenna_gain_dB</ml:id> <ml:id xml:space="preserve">BW</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:mult style="auto-select"/> <ml:apply> <ml:div/> <ml:apply> <ml:mult style="auto-select"/> <ml:apply> <ml:mult/> <ml:real>3</ml:real> <ml:apply> <ml:pow/> <ml:real>10</ml:real> <ml:real>8</ml:real> </ml:apply> </ml:apply> <ml:apply> <ml:div/> <ml:id xml:space="preserve">m</ml:id> <ml:id xml:space="preserve">s</ml:id> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:real>4</ml:real> <ml:id xml:space="preserve">Ï€</ml:id> </ml:apply> <ml:id xml:space="preserve">freq</ml:id> </ml:apply> </ml:apply> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:div/> <ml:apply> <ml:mult style="dot"/> <ml:apply> <ml:pow/> <ml:apply> <ml:id xml:space="preserve">antenna_gain</ml:id> <ml:id xml:space="preserve">antenna_gain_dB</ml:id> </ml:apply> <ml:real>2</ml:real> </ml:apply> <ml:apply> <ml:id xml:space="preserve">tx_power_W</ml:id> <ml:id xml:space="preserve">tx_power_dBm</ml:id> </ml:apply> </ml:apply> <ml:apply> <ml:id xml:space="preserve">min_detect_signal_fskfad_W</ml:id> <ml:sequence> <ml:id xml:space="preserve">BER</ml:id> <ml:id xml:space="preserve">fm_dev</ml:id> <ml:id xml:space="preserve">bit_period</ml:id> <ml:id xml:space="preserve">rx_NF_dB</ml:id> <ml:id xml:space="preserve">BW</ml:id> </ml:sequence> </ml:apply> </ml:apply> </ml:parens> <ml:apply> <ml:div/> <ml:real>1</ml:real> <ml:id xml:space="preserve">n</ml:id> </ml:apply> </ml:apply> </ml:apply> </ml:define> </math> <rendering item-idref="65"/> </region> <region region-id="8041" left="30" top="3370.5" width="750.75" height="49.5" align-x="411.75" align-y="3408" 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">RangeRT</ml:id> <ml:boundVars> <ml:id xml:space="preserve">tx_power_dBm</ml:id> <ml:id xml:space="preserve">BER</ml:id> <ml:id xml:space="preserve">fm_dev</ml:id> <ml:id xml:space="preserve">bit_period</ml:id> <ml:id xml:space="preserve">rx_NF_dB</ml:id> <ml:id xml:space="preserve">antenna_gain_dB</ml:id> <ml:id xml:space="preserve">BW</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:mult style="auto-select"/> <ml:apply> <ml:div/> <ml:apply> <ml:mult style="auto-select"/> <ml:apply> <ml:mult/> <ml:real>3</ml:real> <ml:apply> <ml:pow/> <ml:real>10</ml:real> <ml:real>8</ml:real> </ml:apply> </ml:apply> <ml:apply> <ml:div/> <ml:id xml:space="preserve">m</ml:id> <ml:id xml:space="preserve">s</ml:id> </ml:apply> </ml:apply> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:real>4</ml:real> <ml:id xml:space="preserve">Ï€</ml:id> </ml:apply> <ml:id xml:space="preserve">freq</ml:id> </ml:apply> </ml:apply> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:div/> <ml:apply> <ml:mult style="dot"/> <ml:apply> <ml:pow/> <ml:apply> <ml:id xml:space="preserve">antenna_gain</ml:id> <ml:id xml:space="preserve">antenna_gain_dB</ml:id> </ml:apply> <ml:real>2</ml:real> </ml:apply> <ml:apply> <ml:id xml:space="preserve">tx_power_W</ml:id> <ml:id xml:space="preserve">tx_power_dBm</ml:id> </ml:apply> </ml:apply> <ml:apply> <ml:id xml:space="preserve">min_detect_signal_fsk_W</ml:id> <ml:sequence> <ml:id xml:space="preserve">BER</ml:id> <ml:id xml:space="preserve">fm_dev</ml:id> <ml:id xml:space="preserve">bit_period</ml:id> <ml:id xml:space="preserve">rx_NF_dB</ml:id> <ml:id xml:space="preserve">BW</ml:id> </ml:sequence> </ml:apply> </ml:apply> </ml:parens> <ml:apply> <ml:div/> <ml:real>1</ml:real> <ml:id xml:space="preserve">n</ml:id> </ml:apply> </ml:apply> </ml:apply> </ml:define> </math> <rendering item-idref="66"/> </region> <region region-id="8048" left="6" top="3456.75" width="772.5" height="27.75" align-x="6" align-y="3468" 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"> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit">The range is calculated with the TX power in dBm and required BER for <f family="Arial" charset="0"> <b> <inlineAttr line-through="false"> <c val="#000080">Rappaport & Theodore, </c> </inlineAttr> </b> </f> <f family="Arial" charset="0"> <inlineAttr font-weight="normal" line-through="false"> <c val="#000080">with Raleigh fading on the left, non Raleigh on the right.<sp count="2"/>I would expect communication to work reliably to the Raleigh limit, with decreasing performance to the higher limit</c> </inlineAttr> </f> </p> </text> </region> <region region-id="8139" left="342" top="3512.25" width="321.75" height="13.5" align-x="381.75" align-y="3522" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:apply xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve">RangeRT</ml:id> <ml:sequence> <ml:apply> <ml:id xml:space="preserve">txpower</ml:id> <ml:id xml:space="preserve">dBm</ml:id> </ml:apply> <ml:id xml:space="preserve">BER</ml:id> <ml:id xml:space="preserve">dev</ml:id> <ml:id xml:space="preserve">bitperiod</ml:id> <ml:id xml:space="preserve">NF</ml:id> <ml:apply> <ml:id xml:space="preserve">Antennagain</ml:id> <ml:id xml:space="preserve">dB</ml:id> </ml:apply> <ml:id xml:space="preserve">BW</ml:id> </ml:sequence> </ml:apply> </math> <rendering item-idref="67"/> </region> <region region-id="8079" left="12" top="3511.5" width="201" height="27.75" align-x="12" align-y="3534" 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"> <p style="Normal" margin-left="inherit" margin-right="inherit" text-indent="inherit" text-align="center" list-style-type="inherit" tabs="inherit"> <f family="Arial" charset="0" size="24"> <b> <inlineAttr line-through="false"> <c val="#000080">Summary</c> </inlineAttr> </b> </f> </p> </text> </region> <region region-id="8065" left="264" top="3534.75" width="487.5" height="54.75" align-x="264" align-y="3546" 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"> <p style="Normal" margin-left="inherit" margin-right="inherit" text-indent="inherit" text-align="center" list-style-type="inherit" tabs="inherit">This range is calculated with the TX power in dBm for</p> <p style="Normal" margin-left="inherit" margin-right="inherit" text-indent="inherit" text-align="center" list-style-type="inherit" tabs="inherit"> <sp/> <f family="Arial" charset="0"> <b> <inlineAttr line-through="false"> <c val="#000080">Rappaport & Theodore.<sp count="2"/> </c> </inlineAttr> </b> </f> <f family="Arial" charset="0"> <inlineAttr font-weight="normal" line-through="false"> <c val="#000080">Note, the bit period is divided by 2 as the data is Manchester encoded over the air.<sp count="2"/>AWGN on the left, Raleigh fading values on the right.<sp count="2"/>The range is between the two values.</c> </inlineAttr> </f> </p> </text> </region> <region region-id="8055" left="12" top="3582.75" width="195.75" height="68.25" align-x="20.25" align-y="3594" 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"> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit"> <b>Range 1</b> </p> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit">6dBm TX, 5dB NF, 20kHz dev, 20kHz air data rate, average antenna gain -15dB,</p> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit">200bits without error, no FEC.</p> </text> </region> <region region-id="8062" left="228" top="3606.75" width="260.25" height="29.25" align-x="456.75" align-y="3624" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#80ff80" 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">RangeRT</ml:id> <ml:sequence> <ml:real>6</ml:real> <ml:real>0.005</ml:real> <ml:apply> <ml:mult style="auto-select"/> <ml:real>20</ml:real> <ml:id xml:space="preserve">kHz</ml:id> </ml:apply> <ml:apply> <ml:div/> <ml:id xml:space="preserve">s</ml:id> <ml:apply> <ml:mult/> <ml:real>2</ml:real> <ml:real>20000</ml:real> </ml:apply> </ml:apply> <ml:real>5</ml:real> <ml:real>-15</ml:real> <ml:apply> <ml:mult style="auto-select"/> <ml:real>100</ml:real> <ml:id xml:space="preserve">kHz</ml:id> </ml:apply> </ml:sequence> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <unitedValue> <ml:real>82.16356521989222</ml:real> <unitMonomial xmlns="http://schemas.mathsoft.com/units10"> <unitReference unit="meter"/> </unitMonomial> </unitedValue> </result> </ml:eval> <resultFormat numeric-only="true"> <decimal precision="0" show-trailing-zeros="true" radix="dec" complex-threshold="20" zero-threshold="15" imaginary-value="i"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="false" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="68"/> </region> <region region-id="8063" left="504" top="3606.75" width="277.5" height="29.25" align-x="750" align-y="3624" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#80ff80" 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">RangeRT_fad</ml:id> <ml:sequence> <ml:real>6</ml:real> <ml:real>0.005</ml:real> <ml:apply> <ml:mult style="auto-select"/> <ml:real>20</ml:real> <ml:id xml:space="preserve">kHz</ml:id> </ml:apply> <ml:apply> <ml:div/> <ml:id xml:space="preserve">s</ml:id> <ml:apply> <ml:mult/> <ml:real>2</ml:real> <ml:real>20000</ml:real> </ml:apply> </ml:apply> <ml:real>5</ml:real> <ml:real>-15</ml:real> <ml:apply> <ml:mult style="auto-select"/> <ml:real>100</ml:real> <ml:id xml:space="preserve">kHz</ml:id> </ml:apply> </ml:sequence> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <unitedValue> <ml:real>27.927625755358296</ml:real> <unitMonomial xmlns="http://schemas.mathsoft.com/units10"> <unitReference unit="meter"/> </unitMonomial> </unitedValue> </result> </ml:eval> <resultFormat numeric-only="true"> <decimal precision="0" show-trailing-zeros="true" radix="dec" complex-threshold="20" zero-threshold="15" imaginary-value="i"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="false" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="69"/> </region> <region region-id="7609" left="12" top="3666.75" width="196.5" height="41.25" align-x="12" align-y="3678" 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"> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit"> <b>Range 2</b> </p> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit">TX 6dBm TX, 1:10 FEC, reduce the data rate by quarter, no Manchester.</p> </text> </region> <region region-id="8074" left="228" top="3666.75" width="238.5" height="29.25" align-x="430.5" align-y="3684" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#80ff80" 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">RangeRT</ml:id> <ml:sequence> <ml:real>6</ml:real> <ml:real>0.1</ml:real> <ml:apply> <ml:mult style="auto-select"/> <ml:real>20</ml:real> <ml:id xml:space="preserve">kHz</ml:id> </ml:apply> <ml:apply> <ml:div/> <ml:id xml:space="preserve">s</ml:id> <ml:real>5000</ml:real> </ml:apply> <ml:real>5</ml:real> <ml:real>-15</ml:real> <ml:apply> <ml:mult style="auto-select"/> <ml:real>50</ml:real> <ml:id xml:space="preserve">kHz</ml:id> </ml:apply> </ml:sequence> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <unitedValue> <ml:real>435.38359807705893</ml:real> <unitMonomial xmlns="http://schemas.mathsoft.com/units10"> <unitReference unit="meter"/> </unitMonomial> </unitedValue> </result> </ml:eval> <resultFormat numeric-only="true"> <decimal precision="0" show-trailing-zeros="true" radix="dec" complex-threshold="20" zero-threshold="15" imaginary-value="i"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="false" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="70"/> </region> <region region-id="8077" left="504" top="3666.75" width="255.75" height="29.25" align-x="723.75" align-y="3684" show-border="false" show-highlight="true" is-protected="true" z-order="0" background-color="#80ff80" 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">RangeRT_fad</ml:id> <ml:sequence> <ml:real>6</ml:real> <ml:real>0.1</ml:real> <ml:apply> <ml:mult style="auto-select"/> <ml:real>20</ml:real> <ml:id xml:space="preserve">kHz</ml:id> </ml:apply> <ml:apply> <ml:div/> <ml:id xml:space="preserve">s</ml:id> <ml:real>5000</ml:real> </ml:apply> <ml:real>5</ml:real> <ml:real>-15</ml:real> <ml:apply> <ml:mult style="auto-select"/> <ml:real>50</ml:real> <ml:id xml:space="preserve">kHz</ml:id> </ml:apply> </ml:sequence> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <unitedValue> <ml:real>319.66710279200549</ml:real> <unitMonomial xmlns="http://schemas.mathsoft.com/units10"> <unitReference unit="meter"/> </unitMonomial> </unitedValue> </result> </ml:eval> <resultFormat numeric-only="true"> <decimal precision="0" show-trailing-zeros="true" radix="dec" complex-threshold="20" zero-threshold="15" imaginary-value="i"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="false" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="71"/> </region> <region region-id="8138" left="6" top="3726.75" width="768" height="40.5" align-x="6" align-y="3738" 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"> <p style="Normal" margin-left="0" margin-right="0" text-indent="0" text-align="center" list-style-type="none" tabs="inherit">Improvements.<sp count="2"/>The ASK has not been included in the range calculation because this work needs to be double checked and needs to be compared to measurements.<sp count="2"/>The FSK curves and plots are correct and have been checked and are in the range calculation.<sp count="2"/>This work is ongoing but provides quite a useful sheet as it is.</p> </text> </region> </regions> <binaryContent> <item item-id="1" 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="2">iVBORw0KGgoAAAANSUhEUgAAAF0AAAASCAYAAAA5f9J6AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA ATNJREFUWEftltkNwyAMhtN5Mk/2YR7mYR8KORCnMWAoUUHqSwM2/nz8fKRa21pjCWjoa40l sI11t7ydk2VhGE9gOuhquGqNMT9qJNp2aj1+W3zGbPgxTQm9JWjoLATVTgaUmJz95ztkb0G/ KVFAtxOCgu63dM8Wx1YLZcU/EFJV/BPo93vdiTPZZoLJXc3Gg1NiuWz1SvZI6D63oKB9bLnL ufuFZLsrfMYBQUZqZ2sqplhh+SOBSkxBsY59pAo2tF2WJNw98jZLX0Q4v3GsubPnXf6h0lMC B1Vj7eAshh6bRbXOa89RCxo0aqi7PHV3/39T6UWvl45CaotpCIXLQ7dng15AYFAPByB2aIzZ WjHdOx3TIZwxKTAbJ93zMuiXaDYU+hRpeBn0KZg1X2JBb0ZYbuAL4VL0R3JRkf4AAAAASUVO RK5CYII=</item> <item item-id="3">iVBORw0KGgoAAAANSUhEUgAAACEAAAASCAYAAADVCrdsAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA AJBJREFUSEtj/A8EDAMNQI4YaMAw0A4Ax8SIdwQwKYLDYMBCAuSAAXcENFeihgTMZXDXQV0K 49Mi7WANCXQLcTrgSsN/baAjQ1dR5jSc0YEcEoStuPK/QRsStxiYCBfiTRO0jAJkjw3ekCA6 TRCOJ6JUYIQESbmDCgkTJQ0R5WQaKxqwEhMlgdLYk0QZPyhCAgArDypKpi0BoQAAAABJRU5E rkJggg==</item> <item item-id="4">iVBORw0KGgoAAAANSUhEUgAAACMAAAASCAYAAADR/2dRAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA ALFJREFUSEvtVEEOwCAI2/7/6E2XYbBWhSUkHEayk6yWlnpepY4sVclkqSMLkceh9GTKCtU9 at+7V413PYuoAZVdJMQiCGjMjsxq4ig1PpGRn7RKzEpUEXvEchmuw+uYGXdBq4SKec6w12ST Z5k9ZFAIE5ldmrTkwwUkmYjXrLPaZJl4ZRkmkfWaor1Thk06m14HYWkTJoYlAdMwA2dYzMKu L/oh8+DHvOseBqr3JzMT7gZR0T01ABoJ5AAAAABJRU5ErkJggg==</item> <item item-id="5">iVBORw0KGgoAAAANSUhEUgAAAJkAAABACAYAAAAJbdVdAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA BNBJREFUeF7tndFx2zAMhtWnLpENvESX0CDZwPP4rbt4H5WKQ5tiCQKEABmxf9/12otICPz5 EQThXPlrSZ8JHyjgqcAKGT5BFbjMKQDMyyWoew+3Lss8TctMODqF9/9NHbzM0zJRsxZUk9Xn 0/n6n3eALOCEXc8nMioEdHfj0up7DRogizZr1/NyUkSwlFKtufXT/pQyXubTUgY0QBYKsuty PulysBWwMJ91oZzOS944A3kWRqLnOZImZ27kNBKHQkGWHC6jGSCTzOBBba7nebPNjLw2GmSJ sntuBshGZtK1rX6r/K51uno3bjyVNb63TEA2rp5TjzQpioQ/O+MRyfJBIkM89o7HogFkTsgM mw2aj2WwxgC7jT7nZYBsmAanDkUOM/oGDQAj79DazzkmIBtR27Ntgky7W2ohkA5Haz8XlQGZ VGnvdkEh27NdAjJvaEbtB4RsX+K/LIBsFALv9gEh2ztkQLZXQev+nx/Lx6fOqDZn0r1N3uvv n99fY0JOJtfMtyUima++sP5VVAp7utTOD7ZLrXJe/QJApvlVoZ4cgMwLFq3dAJBpXaf6ATJr RffaA2R7FUR/VgElZFFPlut4EcnYWT+4QVDIaojLAi2nECDjFDr6eUDIaqBK4CQRFJAdDRH3 voCQrS5TYAEybkIjPgdkEWflxXwCZC82oRGHA8gizsqL+eQMWSuHkpwUkZO9EmeOkLVgkp4U ARkg25z+enK06l25PXVSLL/LLNtKTpYoxkaE0zGS1aWIXmnCUhrUySzVtLAFyCxUhI2uAgrI pNsWIhnYuykAyECCuwKAzF1ivACQgQF3BRwha5Uicp42kteNatA8XbZ+x7s0TP0OeF1DqduN OufZnqtye/jOvfOInMxTU8o2WcLoFe24U4rk+TMGWy6C1r/rhWTpozhSOEYyy/GM2DKDrH6p WNQRb43aSr5KsfZfbA+QPWaZmyjuuREvKjMS3yRQcF+3ZBtcLrR5FyDbQkaJVyaT1Jey3HYs SUip3JDLfaSQ9ez0/KfG3NuO7/beDTIOol5eQ01k6+e9qME964FGhTkJZFx+xkHGpQ/kuN4N st5exG0n3PPycMCBpNoTO52sIKsBly661sHo7q4xZFy0t3y++3TJrczR570ttdxuekBwAh0Z yfZEvo2fg5BJFrP1Ih21Jz5dchBJcixqIjiQqJXvCRkX7ay3S21O9mMh6+VirQhUJ7qS/qIk ODVqJdGjK6nVvmW3BQ63lVN2JLaaOr1TJLOYSGpyuahY52s/YbWa6QXI9kvZ2oZaEOWf1X/v 9yC4hQCQWe8eh/9mbOtEVif6VD70FhHtyZBxuahmiR4OmcbJt+qTIGvdeis5KVvrZLWocVmE 9czstTd47Y0VCNL8WTM8XHujUc21z9gFXl6Q2dnFBV6uuOiM968iHK1H6nzY/i8+Whu3friK cJ9+Tr2pS1UltT0Ll+yi2MoYLlW1mBN7G0xe5nECLAdhCRmuh7bHw8iibMu0hCE7bgvwNr/E jSRGeJiZSdvMRNxJ6FmgbtUxtWMqo9hqA5BplXTsl4uYVHnBI5KZDadR7wNkZuraGrrMU/Ma HOuvfky9JqIwIDNV2dbYCto0zakYsP3Ei2QpB5uSr9Q2bysLrB2hQDzI+qP+BzSER1c64oer AAAAAElFTkSuQmCC</item> <item item-id="6">iVBORw0KGgoAAAANSUhEUgAAAMoAAAAVCAYAAAAHDXFSAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA A3dJREFUeF7tWwGSpCAMnHuP7/E/vMf3+B8OttZZjEk6AXFGi6m6qt0VQpN0J4Hx/sX0eY3P 8MDwgO6BLJTx+YwHlhDi+pmlHauuMYTFMf6ZQ1/P3Nb372oNU5zvwr9ljlP4fkn3jPoQSk/v SrbXEOebEW8Nc7wZ5FMjuxNKatLyeeXw7/cc8/N36bPNK5+XtlpRI/vcWuWcEjsdW4uTYtL8 V+6fI53k920eso2et/o/dhY34p4XP8eXjcdWWzvO0ElUDJRg2iISma3ANBGWhKFi5H7nhGGZ 58Wq+YvHnHr+aY5c14VstT737m0/fonz1PdMhfbnxc8ldi3Za1o4lAhUNT4hlBqCI6d7EgDa s4bvkMVSZp4Ewnkwc5g8JPCSLqZrhzBNXduvs/Ejf1p8sNlQhSIthMoaImFtq8OVTmkt5PS3 A5R2ErWZ1gq6w5IOxi/hFI8Ca/GrVHktpEBjlvnV9QKC21/pY+lnmqgkfnH+5WyyOLhyoy2k BYsjnyf4KFBSKbXi5RyK1vS0ppuQSzwUc77tahGKlmS0dT2ilpJMFop4+5UrZUo6LTd5En7K OWtyRNxDibd8XlVRLOcFKWNbwGuZXO0jheogCYxzFBIOwq+tlW1noUhk04hOsaJ1EJkkMWg+ 0bD/+S23aPylkJQgOD4h/GiORQTUhsoTlDFpFrb+frZQpMBqAfdiRSKxOB8R2FNRPLGxil6K i+ar7ZlNKBYv8mMsMdYSVU0Sk5L+QUSeYHiBWMp9LdGtG7RUPyvJniIUrn0rYyVlc7X1ehPp nIoiCbeHULTk8X6GhCI5zUoar7i4XOPBUArDO88i2pr97OY4DvOe2FjE7m1n6F6vPMxfJRRr PM1fOFICcgtYKsgWUG6sVLTLDEiDLdkps4QFK5dlW/CI9oTrYbQ+9QHdt2W+pxU9Jg1wPdx4 mNfw02rHcYDjp0UEnG0uoYxXWOpb6sqZ8heOlQbhNK5San/jk0/CPff9whFupHGApWOQqvgQ SqPza6Zf/d4UmyGZG8KyCh9auc6vsNT4sWaORyw7v9Us1mMOai16rGltrTxtog3nkl5dv+5t XK51Re1M2crkn68Wt82P/lG3F4p/y/eesab/i3KXt+xjQnqlsL8xsqP1+lhU+r9keM7Wrj9T nYP7XCtDKOf6c1h7qAeGUB4a2LGtcz3wHzBLlhDmyzsdAAAAAElFTkSuQmCC</item> <item item-id="7">iVBORw0KGgoAAAANSUhEUgAAAKgAAAASCAYAAADPGpWnAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA AlhJREFUaEPtWYFRwzAMDPOwBEt0Cpbo5oUGDI6i17/sxJfeGY7rkcjy6/WWlPTt/rg/lvkz GbgqA0+Bzt/JwFUZWK4KbOKaDKzdfdIwGbgyAxuBfo8hz3l09/c7p67XUTBlXX2/9tVLAvPv 7VWvqbFb21acFlPEn43f49KL0ePNro3y0sN7D54oH6pft4JGwTMikIh6SCqHo/hgySn3PUHa wxP9r2JmeCx+JY6WQqDiVe0sf9nCpBwsBcuuxTNyIqdnCbRFWEw4agIYiWwfT6CoeiLxqtWX YW29rxQm5bAzP66oo+AR+ahEo+rl7ZEp86pAM6LzsKoJtK2cJScrYpZIJnA1DtUuiwfZMz+y QL35oVSCSASKQJVkRe0kErvnOyKriTAzh6M9EYdKy2e4RgqUYUEdAnXTbGEKWzwTU3QfVSjm k40QmYoftczWCsrwK+JhNkwUbP2Wo8/Hx7v/8LvcbvQdOMOCDhwbVxS/q29lvkGbjRRob9tQ sCot7/UEqkTl28giIm933NYdrNmMTGcJ1BsJUDtQKMwKNCuk1mSwfdSYaz8My6gKynCwKhlV V9k3E2hEHEsOmkPYQ0WmjUfCzWBnraqli6hr7N4seTmBKsd/b9NzYNDajM8/7qxYiqjqT2Sj XvcE54kXUWkx1YlHfgoZ6JDY6yhe1J48e4+7jJjUA63Ytcnyf1XEuZp3FDs7gGGL7w1srucM ZBLEvf1YnOFT3ftMu/ldPH2OPYf+IwV1pK9zom33ehmBqu2xPVR95QgsR4rqSF86S2MsLyPQ MeHOXV6NgS+LeWH2nUbepgAAAABJRU5ErkJggg==</item> <item item-id="8">iVBORw0KGgoAAAANSUhEUgAAAK4AAABACAYAAACDQkwcAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA BUxJREFUeF7tndF15CAMRb1f20Q6cBPbhAtJB67Hf9vL9OPFk5BggkACMSvsN+fkI2OQpce1 ENiJf+3uM+EDBUZT4AAXnxspsC0uUS37Zj7kbV+maV8IRyfz/sNBNQW2ZdonigS1s+gaOnye 18cPowBXV2ez1h7rTGYvs05/Onb4HsMLcK2PmoZ/j3WfKzKtK3uP9c9/+wlD35Z5DxMvwNUA w7SNx77OdTXtAa2Zz3HxzevuiwZDnpmR6FqOuAFfEjUiJ0hT4DqHw6wLcDkjOHCbx7qcplhJ KNbAdeR+1boAVzKSw7WtLxOOUM2B6zbxls9yAeAOB6PEYTfQFYsyf4Ye4PrFnr8wZOf4vhAB roSD0doarW89rDJoP8T3dS7AHQ1Gib9BTSjp9ooyoQbawy9fswNc6YiO1N6BW1sp1ILFlafW vr+RAnC5So/Yzii4LaUCwB0RRKnPBsFtW5wdpcLHrWtkXCkMI7U3CG6rfAC3VcER+r+/7W/v dY7W1qB1Z+P3+vvn9zMmZFy+ZuO1RMYdb8zg8XPT0+yuQu34oFSoVW6kfgbArXksMicxwB0J wFpfDYBb6zrVD+BqK2rRHsC1OCrwqahAJbhWdxSOeJFxi6N+gQZGwY0vjPCmREl1gFtS6ArH DYIbQxpCzMn0APcKYJZiMAju4TIFK8AtDehdjgPcu4z0xeIEuBcb0LuEA3DvMtIXi7MzuKma lLNDgBr3Ypyph9MR3BSg3B0CgKs+0hcz2BHceHcgt1sQqho+u+C/52Rp3xbbYRdjNBmOQXBb ZQe4rQqO0B/gjjBK8PGHAhXgcm4ChFN8XAZQx7RGBxlXS0nLdgCu5dGBb6QCABdwDKkAwB1y 2OB0R3BT21p+S0xSJ0sHKVnjpv4+iNqDy+3HxXakzpXaS4SRtC2dlzpe2ofsoUfpnE9fO4Jb q1VrP3JxlnrIl1o5+itMcrzV8dQ5czZ7g8u5W6TtA9sewP1GoyRa6XgruDX2a/pw/QS4XKV0 2qlkXOohC+9iD2BSM0I4bVIljI5sP61ogVu6FerPU6otT/og454zLiVeWJynajDquxiHXP1W ulg45cvpfMfbXDJvMCwBzwVXElNss7p8uxu4JTBzGZUayNT3uYxMHSt9zwE7DePxb9qJd3pl /sksB9zcGqB0oXHiIXW8G7gtix1OeRBOe7mVeuqYJuylbMo5rgVumCxKNksZ+MtvZXBjH3v+ TmnPrnFT03gL2LlyIpxOOZk7lck4Gcp6xm3J0KfYhOBykg7nYu7ZRg1c9tX/GQ0XSCorc8CU +tQidCk7xqWA1H9p+9P57gJurrZNZcowQ8bHOdN8DrDYNlVXxz6nYshmksbFWRh3LlNS8YRx pdpIvzvFehdwWzJPqS8nc4TZggtu6bypxQ+nzyXaANz2YUxNqTmYqVJBCuIIdVu7uoQFA+CW Zhpp7C9/HjdegcZTbKrsyJUH3IABLlcp/Vegcup/vncfLV8OrtRBtFdQwGXcWfDm9J4XuZZt vKBPgQvzJoSvRNWCi7Mwr9UOr0StVW6ofrKXUPcCV88uXkI9FH71zn4PNCcL6gF2PpueXXch zuv+cObxuqh6Kobo6WvC2Flqf1g7KD1onWdBzQ5wtUfKmr1Cndtj5R9KoAmur2+fO1LWdIY/ 2grwygVNwHwEuhfFuV4HuNqcWLTnptiJeDQzd6OnNZTU3n2tzTDbIuPWqjhgP79xTy3SemRc NZkS+9HIuGrq2je0LVPyFanat2VVlSBmC4CrqrJ9Ywe807TsW+SqvYzralr3J1VkiWNfanj4 CgXsgZuP+h+S14M22dN/5gAAAABJRU5ErkJggg==</item> <item item-id="9">iVBORw0KGgoAAAANSUhEUgAAAK4AAAASCAYAAADCBOXgAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA AllJREFUaEPtWQFywjAMY+/ZJ/YJXrFP8HM2eusIri3JTintFnYct+AksqQ4Bt4u18v1NB6D gaMxcDPu+BsMHI2B09EAD7yDgalLGDQMBo7IwINxv9ucW7+7eP70wdN4lOQ8r32/XauXHLa+ t1c7p8VuY6s4LSbEn83f49LL0ZsX6WDHkV5VPWyOGT8gHym5P3iLEWoFRwlH5qqSNM9DGCKx PKPaQ4X+VzErZolMGu2vGNPbV8Gi5oUM6emi5BLpmPHY797MuBlxn2XciuGYiBWyPDHZPnOV YTwqeFiMckB6jcvyUP2kHE5YJNFGqJohkhSCs1dDdMqVU4wMV7lO0XWpmIfFKKKyNVhV7DWw ypuSS/YwTMXAM24rjF1UubaVmAqxkVgqXpSLKiSrsNbUFYMpYlfWVXNU4phxo8KUHQ9blkrF zVQ/RWiFKO/KtWOK4BY7E4BdfSy//bYKn9ePd//D+Ol8pt/tq7xlNIk0dm9MJgwq40i0+T0m 7LMIYHmhWwH2VuabFZbffo2rlotlnKoZMmLW0As9mcBV486gmbAqCdlEs/tWcbB99mvcesVV uXqpcXv6Va+fUYTuaV/aViCDXb2mGH6l92QxyiFla0TtXL3O3mcy40a8Z8fhzWcr6my29jWK Ucc9I3qmjoBaTNacSMTo8NjxKF+3vzI/0nh40HrImMrBiPJVCsWzjOvhRjgjT6j4xk++9GOI SqUexyqWvhKP3HIvjma9iGHcFxhXbUt6Zf6rpp346yVnrflei/Iq4rfAskVuW+yxlv7ZdXZj 3CzwEf+/GfgCZvNzSa1tgmgAAAAASUVORK5CYII=</item> <item item-id="10">iVBORw0KGgoAAAANSUhEUgAAACEAAAASCAYAAADVCrdsAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA AKFJREFUSEvllQEKgCAMRe08nqf7eJ7O431MTcVltkUbCxqEDIT/+vvqEmIZ7UoQ2mW0AfIk fg8Ro5g9UHMiAahDlFMJnahkja6Q1l4iO5dOnAWnAN4FGyHX7R3adBy9E7iED84esx0+AuFt JiRH0P/Yd50gZwKfE2nH4MSj08EQTJAhErLwpnZjGgcvT6zn5MrKSbAXxXpOgPZ2VADqyg2x A+5WBPfOrd88AAAAAElFTkSuQmCC</item> <item item-id="11">iVBORw0KGgoAAAANSUhEUgAAACMAAAASCAYAAADR/2dRAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA ALFJREFUSEvtVEEOwCAI2/7/6E2XYbBWhSUkHEayk6yWlnpepY4sVclkqSMLkceh9GTKCtU9 at+7V413PYuoAZVdJMQiCGjMjsxq4ig1PpGRn7RKzEpUEXvEchmuw+uYGXdBq4SKec6w12ST Z5k9ZFAIE5ldmrTkwwUkmYjXrLPaZJl4ZRkmkfWaor1Thk06m14HYWkTJoYlAdMwA2dYzMKu L/oh8+DHvOseBqr3JzMT7gZR0T01ABoJ5AAAAABJRU5ErkJggg==</item> <item item-id="12">iVBORw0KGgoAAAANSUhEUgAAAMsAAABACAYAAAC0jIMoAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA BiRJREFUeF7tXduV3CAMdb7SRDpwE2mCQtKB6/FfenE/BGaWHYYAehjG2L4+Z38GIcSVLpLw zNkf1j0THiAABGgEPFnwAIGuCKzGHcjGrl0XaaF8tWaarCkYOrVYAjqAQAmB1Ux2KkXfoLB5 m+dl+886kGVQh13BrG2Zi6f06PvztqeEAVlG99pZ7dsWOysyimscfA992F8M92pmGycYkOWs wTi03ZtdZl2P4okyzOMJPy82FGQDWTYMRDBkLwIuyEym5ueoHYoszuA4u4AsHA9CRoTAtpi3 8kUyeTSyOLZ89y4gi8STkGUgoC/BvPLhyOIuvM1XKQayMNwPEQkCLrgUjX1YoQdZwoVBIKNs jRf5QRZJHECWRmDQfiUQREaU53ZD3wKy0O6HhASBqMaXTPtECaYhircr9GAgi9SjkK8j4Mii rcK0wcx1iVZ/eLkKsnCRhhwPgUHJsqcMA1l4roeUFIEBybKvwfdl2PNrO8gs0mCA/GnLMK3r QBYtcphXR+DPL/vrjw4kbU+hW40/6+/vn489IbPwMYMkB4EByzCO2TUZZJa9CGJ+HgGQBZEB BJgIDEAWzVf8kVmY/oVYQwQGIEvD3TxUoQxrjSj0PREAWRAJQICJgJIso96EIbMw/Q4xBQKD kiUlY/yiktolyjAKIYzrEBiQLCkxYuJwMhrIogsFzKIQGJAs3uQSQUAWyqEY74cAyNIPW2i+ GAIgy8Uciu30QwBk6YftXTRLbl9OjUlnsuR6DA626FlOElXS25eTbCtvZkey5EjBxRZkOWFU cW5fTritl8kdyZLeatVuuWIM4++Khc852SjI4ur4oIgEWfLAc3HJvVyMCdDDrSBLD1QJndyA OMC0dksOmFn2bg5k2YugYj7Isi+raMswhavepoAsexEUzr8FUTwmiswiwQZlmDDwziguCYgz 7u/bZpDl1O473Hju9ebhhrYwAGRpgeJ9daQ/c700Eh3JkrsCDn1Mz8yd7VlSp5Z+A1Byfm5+ z01cOugG3Bzr3URHshwFSbHBrzVQ1G0EZ/yoDWPdfQiwDz2Q5QU0BRo1vs9lmH0UAmy/gix5 suQAvFUze1TkNlqX+hpI8CXVK7zFAcjyTpYSeHGzlatvWTVvFAiUfKlHouY1irVTq6HeV6jL 8buRhSJDiBJJZtFmnFr6B1n0fKXIkmqmyPOp9yz6Hetnsht8CjTpeO4SoLYNdq0swIIi2dXG c9Dk9sg9BKs+bJxZPumLUggNQxaqXKtlJApIAX9uJ0plbOoQLM4XkqXHYdjamc3Iwk7PXzso gRM3k/FmS5/n+qOUPK1Bu5K+1mXYt767kKXWq9SCM07ftea61OOkc9JscobT54xEKvmbyvZV f9+FLD0dLrkMiGviGvl62gvdSgRAFiVw0bRc70H1Iylp9lvxvwYOGTkyEttqGVaiJ872udJV qquJ/ABkae2vj/+epdRPpKUAVZI1cWimf6JKPWpcYpe0XyjpLh02LW2V7OshezBZageweC9f Ez5OFq2hn5pHBRg1zrWT0kONc4gTZ2SuXc3kHFnmZWOr0+6Xs0Ar3dtirN8S/qdkJsvkHJHL BqV0n2ZRyWmndXAtW3ECq5nMtlhzMbKsZgZZQoBwApTTX+VOdEkQc+zgEPnQzGJXa/w/jWc+ 2j1T6tvp3ewyG7crZJYH5hxgQRYqPMP4K7g4xOZgz105lmun15F/XqwvLG9fhmVBdaXEPE02 PiB7k0Xr3NI8rT5NYKZzQo2ffp4rW3vY2VRn1IOBLI4UnIdLlpCpav0M58Tl2FTLik0DhmvM d3Kp9y2SHk66NLdS4OoN/cpDL3fSFeUkTuOShQrS3LjEjlKpIemN+vuSV4pRWGns1GKZX+u9 /7o9WUIGoByXZoravHiME8Q1effiwhqX/aZM0yxdRxN86jmufMnZHJ/8FOaatetYyjTGWeX2 mUUGHU+6ZeaIV1yXZ5PJfXoEInftVzU2v/V9aVYcwcbinjLvi26dWaTO58hTZJHX1L6keb9s oOwYKQhXk7e91NNRe/vIeCErgiwd0KdKgd7B3Fu/FDJPmGl6vqso9VxSnX3kyyUvyrA+iEMr E4HRSE2Z/Q8gsExonPVDYwAAAABJRU5ErkJggg==</item> <item item-id="13">iVBORw0KGgoAAAANSUhEUgAAAM4AAAAVCAYAAAAO5tEoAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA A6NJREFUeF7tWwFyrDAI3X8ez+N9PI/n8T5p3L9aROCBSezYZmc602pCHg8ewaz9l/Ln1T+d gc5AjIFVOP3zswzM05SWn4UQWH1J0zQHxv/Ooa/f6dZzvFqmIY1Py8N5TMP0HKm3yIYunBas em0uUxofmoDLNKaHQvdGxxx3EE5u8tbnndPP5znofV37bPPofWqrFK1kn6/lwUZ94Piu4uXY LB4pRin5NP63ecg2ul8ah31+Y9GjXIz6oeWPldN8jUPuWDc3wdCgWYA18USdNAEr4tUIkIRi CS5CZNTO0XZ+VhjGJHVpHEPtv0vj8X/+nMah7bMZ8jvqhxTbSLxDwvFWdW1XigDzEGE57yHG CkYJVk+QD2NyxR6UxEO2EE5038MzHrMKf2jartX2A/GKfX6fQL+HnXovXqEl4aBtz7JBBXaF mFLhaLvZVSwbF56gHMbkB+yXciqAbHn49XYJnmTRxszjq+nBhuQnzT3td56z3hjxcdI8UzjW Ql5haYFDSYFawUhrSTFowtCKAEoo5AflUFpjPU0rEY4WI16YJJyRImHxswpHPV1bd9RcnUtO DDmHUk5xfJ78tIqKJ66XdxyPKHZlsmcSBKyFcDxERZJpS06rjZXs0WurcLSk0xJGSxyrmGiJ FPFXG2v58I1pbenkQyetcCA/veJAuWYVUynG9JrZqkWru+TQVfBobb6bWBXYm+AlyYT85IGI 7DheLiLtWYmv2zo+4aC9W7+PEhsVMBQTiwO0djPh0JbBm7iluw2qVIhorcpIuK4Ehe843lat tXCkdoxe0yq82artoOvsOFoOWXG4EqPDriKc4O6dFAqKd1vUkvIqeA9RqCpI4kV4vOJBdlCr lgKHA5EYefAj3jTf+PU7Dwc8+cB9L45RNqhywQHRlseqRJJBbbwUeGmstuNwTFQQCMdeIT5f 7Gr+0t3Ki03CxbHRMQf/lONodfxnshQfXtws/Jqg+RzJ5jE+4Di68HDA4oFilXBrfHiEJNnm 9t4xttqjfq8lA/oXoK1WRbugtdOc52b8Y9svQFvxQItkdI29EEcn9vH1GLj7fS+xcpI+3hLO qQVs/MpNPZZtS1IxsTqfXXR3AUTroBYEzW9xvz2mOb+if99bxp7WkraaUmXeEu1u0beI76kY gEUOhacVoG7Xx8CS/xfnaf9VsL6ndqfgfUzeO6o/49zLt7Ba+5cl67p4/7NZXfx1rHXh1OGx W/ljDHTh/LGAd3frMPAF2ADL6JDAgSMAAAAASUVORK5CYII=</item> <item item-id="14">iVBORw0KGgoAAAANSUhEUgAAALwAAAASCAYAAADhX7Q1AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA AndJREFUaEPtWm1WwzAMG+fhElxip+ASu3lhY93LUluS7a3to4HHD9oklmVFNh8fl+kyncbH YOAoDFwFPz4HA0dh4HSUREeeg4HbNDNoGAwciYEnwf+Ocdd5fvF1n/Nvzz1y5n3t+/asKqnW +X0sBVubQ48vi7fHhnjsMVqcslxRPdDePhaq5yvqZeXKYrLcvfqxc2csC4dHpLBDPdG/kjwP g/JcwcdyRJeKXUBP3N4+NadHMRtDsuq4luAZz9G82vx6wUe1RQXPimg5emRPGLDRZWYSooKa ndISTBVXBouKxys6E4OCKZp3xgAUU1E6n3LOosugFus5gtd2LOEh4CnARcF7+WaxzFwo7hld E3XC/yx4VVvswpoOj4qISFVAKUVXXaN3ROt7NO+yd4g8lkfLITOINUYaxA0TSeY94geZC+Oq 15+33tVQxuG9EcC6DIowVEKVlszIfJXDZ/Jie/Y10nxPX5/2LzFO5zP9201W8Ja2WOfyLrOl q9IMryTFiqyOEqi9926adWYVi9JZspczWlxvvRI/IhTVlCJmqNZJqa9au7cJ3hsX2AWojDPM HbJCNZ2i+1lCySu6Bl1ylmtd8PtxeG/kU54vOjoaaViLZQW05iu2h4kr44BojyqMCE/qvMxi byv4qKc/r1e6f2SczlxwOtL0rcMTbPS5lZh1BnL3FlvbPSxi27Pn9yrmPg5ru9Z6hUc0TqhG 4V0Y9DxjOFHpexxW8kKjkmcMVPDRxMb6GgORQtUi+bv3gOFduQ3B098trEn9X6wtBbdl7PWZ vvO9VWA05kTGinfiV0eTCoYtRbdl7Apnlb3jvyV36PqVgo69mIEfAqffucTB8b8AAAAASUVO RK5CYII=</item> <item item-id="15">iVBORw0KGgoAAAANSUhEUgAAAOAAAABACAYAAAADMXsPAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA BpNJREFUeF7tXdGR2zoMVL7SRDpQE2lChVwHqkd/6cX9KNT56KN5IAFQpAhJ65k3eRODILDA EgBlx79W9xrwAgJAoA8CGwHxAgKnQ2CZXOGY1sW84cs6DcM6JQwdzNsPA4FAhMAyDeuQymij aG02j/Pjh3UgoNGAwSwagcc8JquJdcw222MSgoDWowb7vhF4zOtYUPnccLfdc3T7LwzhMo1r WAhBQCT4SRB4rPNYNvNt5DPz2g6RcV59M2rIMjMQwRCLCLjEnYgZSmKqKQI6g8MqCAJKIgiZ 7gg85umtddMYZI2AjoGvWRAE1EQSsp0QKG8/N4PNEdA9PJm+2lAQsFNKYVsNAi5hCy5f/A4t COgvdTzBdXt8HyggoCYPINsHAaPznyedjnxPCP0cCAL2SSnsqkEgmJk0y45oP0vIt9nlZ1oQ UBtRyB+PgCNgaQdaShCpk6X6/QcKQEAp0pDrh4BRAu5pQUHAfumEnbUIGCTgvkuYrQV9fqQO FVCbDJA/HgGDBNwLAgi4F0GsPw6Bjz/rn4+y7UpntLLd5Kv+/f396RMqoBwzSPZCABWwF/LY Fwg4BEBApAEQ6IiAAQKWfJ0phxhmwI75hK2VCBggoNJiVhwEZCGCgBkEQEAzoYAhd0SgkIBW b0C3EKIC3jGRz+qzUQLGBA8fznNQg4AcQnjfDgIGCRiTLSSjpPKCgHbSC5ZwCBgk4GZyinQg IBdQvH8uBEDAc8UL1l4MARDwYgGFO+dCAAQ8V7zuYq3m1u3UmDQmIDWzSbDFDHjqrNpnvPbW bd9unVc3JCBFNCm2IGDnvLCyveTWzYqtRXY0JGB8m5m73QxtDz8b6v9eUjW9LB5DFGWCzUUg IB0XKS7UA/WQVC2iDgK2QLWDTmmSdTCt3pYGK+Be50DAvQgaWQ8C7qt+pS3o3vCDgHsRNLD+ FuTbcC6ogBps0IIaSOYzmqBJsjP697IZBDx1+C5pvPSq/BLOg4CXCOOlnIj/iYRLORc705CA 1OMEPxe27DDIGTAOaur7TqngU+tbOqHRrZG9dDIbc0707KwhAXvBkbyEyQ2k3I2R5P2aDmtI pZGtaSN0pREQxwQE/AaRA417v1ZCluxTsqaWvdDzEwFxPEBAmoAUgEddDuTa47CHP8oeEOyJ ADVThW2mjwc3e73FFwR8J2AKvDgAKZJIkzU3H3DkV7fD7kcgx2Eo/hksqU9XlsuNL1QsxePO 3QjIEcwnEUeCVPURtx5fJyqVtCkd4Qkbr5Ptu/188PB2kr/wKP2RuiuzLvCNIyAXj2R87kbA XL5wScy9T1Wmkv1y+3DkbMGH8NC6w/+nDsXYd+lhnc2LygQ8Mj6pXBPfgnKnlvb9GOhUa0JV slRFDW1ABWxxvMh0ag9FrgV97aokoKQIyDxqJ1WNgGIQv3zhqlNKn2QfTuYMgWkX8vaaa7eg L313IWBYmqUtRlyVqIrGtSHxGkonV2lj2ylfsgTEJUwVhqZyiOt0whz5IXsXAlaJQEIJlfy5 ljJsJbm1UrtRAaVIGZMDAfcHhCKblICpAV1DKI3sXm9zXUB80u/dS9JdaPdIVSutnmryBggo ianG38O/Dxi2Jrm2lWtHqQsXieNHETB3qHBttMSPlIx2/pLo0fiyx3Z2bWcCtsDhcAKyIF9Q gCM9974UEk4P976EjKkuRGrjLjlHwHF+iFWU+ivZoJbuxzytm0v4jXgJ6oUyXLCoqpVqdeIO QnMqc3aYJ6C7IJsuRsBlGkHAQl6JlkmSXjIDU5Un127WanNrXXyJwGKFlnVSfApJgj27JSFQ T+/2aatpXdweqIAlkRCskQQLBBQA+SnynbDUCs2BJN1Rsk+5LnegjLPzCgQsxzCzkiQf8Zyx NQElh4Am0Ur11QDZz0xUhT+CgFV9D2ZaVMAa2RHpkAZLSkDfhubmQw2ROJdT9kv94vQXvc/M gZqZuGT/mr77+e8zriXGYE0aAU0iSAnIBZ+b17j1oTdSm47PAVkbqvFV6oMmprzO93kWBOQR U0n4KpWqVnGyx8HNVblQd4o0/u/zdrgkcN99HIiLjdy6FsmtAte1bpTNvkMI/1TpZYQ1MeX2 DasfKiCHlpH3a1a40KVlfl4ESF/dCegM9Q+wKZslh57U1yZyxPNMVMAmSNdVyhFQf/I/v3Ss uNl/+z30ut7ptS0TbbtpAiaqNwioj3+XFVwb1Lo6tdavBXUj4TA8n6Vx7bhWd135dLuPFrQu 0tBmAAFrBwUHyX/JZIhHb0qJ3QAAAABJRU5ErkJggg==</item> <item item-id="16">iVBORw0KGgoAAAANSUhEUgAAACEAAAASCAYAAADVCrdsAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA AJ9JREFUSEtj/A8EDAMNQI4YaMAw0A4Ax8SIdwQwKYLDYMBCAuSAAXcENFeihgTMZXDXQV0K 49Mi7WANCXQLcTrgSsN/baAjQ1dR5jTKHAG3+8r/Bm1I3GJgIlyIM00gRwdl/iSsm0qOoFFI IKdawn6hTAXeLEowR1AhYaKkIXS/EHQAZZ7HqhteYtIzQWJ4HCaAXIzSwLN4jRywugPZVQCt QypK4zg3zgAAAABJRU5ErkJggg==</item> <item item-id="17">iVBORw0KGgoAAAANSUhEUgAAACMAAAASCAYAAADR/2dRAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA ALFJREFUSEvtVEEOwCAI2/7/6E2XYbBWhSUkHEayk6yWlnpepY4sVclkqSMLkceh9GTKCtU9 at+7V413PYuoAZVdJMQiCGjMjsxq4ig1PpGRn7RKzEpUEXvEchmuw+uYGXdBq4SKec6w12ST Z5k9ZFAIE5ldmrTkwwUkmYjXrLPaZJl4ZRkmkfWaor1Thk06m14HYWkTJoYlAdMwA2dYzMKu L/oh8+DHvOseBqr3JzMT7gZR0T01ABoJ5AAAAABJRU5ErkJggg==</item> <item item-id="18">iVBORw0KGgoAAAANSUhEUgAAAL4AAABWCAYAAABxYyxyAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA BsRJREFUeF7tndF5qzwMhvmvzhLdgCXOEgzSDZiHu7NL9uHHaWkcKtuyLUsGPp6nN42R5E9v hGxI8t+6HQMOKHA3BRz4OAwVWKat8EzrYhjCNV0v6zQM6xQQdrjmpM8xq2Ua1iGUmXNMofso ncbj/PgVJ8A3St1jHoPVyCiky7p1Wh/hB/gW6X7M69hRpd/ae7fOu9yfn9plGle/8AN8dfAf 6zz21dM76C9/uGIzzuve9Nxgxp2ldEvARPScllHeAvxNYL/qA3xl4h7z9HbJVXZPursL+Bv5 P70+wFclr7825/s+jqoKds62Lc7vdgfgq2ZhE76jRe0+9R4qvh9Du3hehQfga4LfYX/fS8XX Af/V5wN8TfC9HlPTbcpXuwqb8vx6XQv8fY0F8Pm5qR+5gd9hp/Pcv7c+9MD/unFoP2NrxTX9 dwh+D9D7a43W8ex3zAE+wNdUIOqrNfTOOcC3SDcqPsC34M7c5+fH+vFpHsVbABpVljNjrTj+ /f3zzAFaHU5WpMag4kspWWwHrU6xdBUnAvwK8WROBfgyOuZZAfhPvUoegc4TOjwa4EspmWMH 4Oeo1WQswG8ia8IowLdQ/c0nwLdIAcC3UB3gm6sO8IMpOG5n7usA6Zyh4ksryrEH8EmVjpC3 fG4H4HNAlR4D8FkVH+BLg2dtD+ADfGsGTfwDfIBvAp61U4AP8K0ZNPHfGfgtHgyjbHJ2aEJ9 vXSMWNxakH9x8CnAuQtVgG8BpJbPi4PvZKT243d5Q9Xbf3bHHytd7Z1tVHwt2H0/AN9C9Tef AN8iBQDfQnWAb646wDdPASq+RQoAvoXqqPjmqgN88xSg4lukAOBbqI6Kb676xcGntiX3Lc4W W5Ml+SQrPvVZSN946LOSx73X47iSAHs/J3U3ktSACb4WJFp+esplsNWJ3YBI3aDgvN6TCKWx cO5GklAxwE+9oUpjps4D+J4queAfBb2DmC3Bp4qHJOzHK3gr273aFan4oQeS/NanVwFq4gL4 NerZnlsFfmgR4y9kQg8spa4oJYshzlUmFk9q7RK7quU8f+J+g4nzNeGc+Ujgo+VHIlYpG1Hw U2DHKnqoGlL/jwnPTQp3XGj9EYKeGxvH/88YgC/Fb7EddsXP7eFzQODCFZslx59068Vpdche uhp89xtOgR9i5lxKImu5Y7yxgnCG10LMmINPXVX8doQDV8qGD7x2q9MG/OJC9+vEnIIh59Xe khj4nJ49tJMQg3t/LZWgWI99bG+oWFu1OuTcqiu+HDgpXeU89WUpeQOLCjcESaqyhlqN2JuG qtChmEKX6RT4NSkJXUGoWH7+xwCfuorVxBk6F+C3UJWwSQkd+18J+FSV5bRMShK435pk7epo xAPwNVTefKSgPF4VSlqdlA/zZAN8JdrCbtSfzjy2SXsb4gMeqs4xYI9XBqpV0GofklkF+EmJ Wg9QB7/1hE5hH+CLpKmmfT0d+NTCWkRFTSMAX0TtW4Evopi1EYAvkgGALyKjohGALyI2wBeR UdEIwBcTm7vdfXR4uh5fTDFLQwBfTP3SrWmAL5aCDEMAP0Os+FCALyalgiGALyJyKfTOOSq+ SAoyjQD8TMHkhwN8eU3TFjsBv6ZipifZ9wiAb5EfgJ+tOnXjMvW/mBOAn50CgRMAvoCIdSYA fp1+ZWcD/DLdBM8C+IJisk0BfLZUrQYC/FbKxuwCfBHVj4vznLu4AF8kBZlGAH6mYL+HU5+/ 2EdxdqsAfnUKCgwA/ALRaPgp2AG+iLwNjHx+rB+fDexmmuQAkmlSdXjJJ/X2AP/9/fPMwaAa 8d2doeKLEFADPlodkRRkGgH4mYLRwwG+iIyKRk4Ifs6OSa6SVMvF8Qfwc5W2Hn8y8Gs+6ZSS mgKc6w/gp9Tt7fUN/HF+mEdVsrgtOSc1UWo/PrU16T+n44/lxveYp9WlAIvbVHYkX3/M63RZ 8Ldvcp7mNedtXQJ+bTqWaQT4tSLmn7+sU+bXeef7SJ/BrY6p6vvu6Qzgu69Zn9ZlCxwVP82J 4IiX8IJGk6a4vXPIEO+Ncgbwt8Izfl2VAH4SG9kBe48pazVsLXaLnxNDHPrID1V8Axbzod7q eGssgM/JvuQYoz5/h4xXvV8T5o/vv+Lv/b2bHcCXhJply7bd4YNMf8N1eIq9g/++vgL4LFiF B22X3EF5kVtS8f2tw/QbJg98alvyWYmHr9/4kj78ao+KL61uhr39mZGMU6qHtoKqOrDWBoj7 J/JvrdaTuJD9ZRpUfyHlluAHrq4A3/iN5OAfhq+9ZY2jRRuhEXe+j62nd21ToKUE+PmKnvqM +4AfT9P/Q6Iwi/hcUsEAAAAASUVORK5CYII=</item> <item item-id="19">iVBORw0KGgoAAAANSUhEUgAAAPMAAAAVCAYAAACT69igAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA A+tJREFUeF7tWwt2ozAMzJ6H83AfzsN5uI8XuoV1FI1GwthNg/Ne32uKP9JoRhIu/Enr59E/ HYGOwO9HYBNz/3weAvM0peXz3AIeLWma5tt4ixx93B6BDwRgmYY03o3b85iG6T7pS6NtF/On iXmZ0nhTUi/TmG7q+heLn8S83jRs988vP9/31V9/hyX+e15+PV+rVDP7Wmh9ba98Tm67HBu1 U7PF8i86XsPbik2+t0ZoFNN9HlubXS+N7WXzKycypo+oH4gXls7kHk+8ti7upMqDHiVtxDAr USAb5Pr7d028MhFY3z22RIJ3BgfkG7Z7vXccxqR12Gyt0usRLOqNndM41D0rYDhFfdN4EeFK SMwRwqPqGXWQJQyPTQx0KXavjRGgPXaW+vpkz1qZBkDmCB6aTWf99uJ6zbgtmQ1VW+2rcWBx 8eByFDCzbIu2Oq96VkZhQsnbFY+x3orK9tXWiQQH2W21S1Ff8/GeQD+NWQ+BHuDki63FsGPX o3GsNX4eH1UP/zQc8vij3yX3LC6hsXunLPlmitlLWkQQK/CMVJFKJZ3T1kZiRQJkJLPEH/Hb U/2QPyg+2yl2iZitxMOSjCeuUcw9iVauuYkZnmpvnctaoEpO+hEOMvaICwwnzWfvnJcTrQgh rU2ObAGq+05mT8CssZa9nnmR/ffkoXUzKFtG/PQGDXUqm5gRkS0xSr8YoRhRvQkvmrjR+Hw/ C4P/87d2XD/sRclQiyPDgc3R+MSwt+aYYrZacGbI1WJGBPGK0ZN4WFX2+szGeUjpXUMS2VuZ WWyj13N731/MnkjrYzw8jHCNJXCZECyxVxNz3gKjSoIqnacFZRWPgcSunxFcpKvx+Oix8d3E nHcAeYy05G4JQ+t0NDzkOLPNPkC/pjIjXr+tmCMERWCXijlig0agM1XuN4o5BQ7AWOX1JBIP mdE6rKNiFRBxouUBmMd/yT2G65mu5kiU0qA8s8qsl1dbT5ZElVfL3lbjI22SgjVbD+fDLF6b LEy0wHnX1XyShGWxSeBfU8wGbV20d2lHoeHnXTOfq4uZ/Guq8ADMwlHapuHHYsw6VZrgzt89 9JnvhwB+aKSWrVYi1SrXGTFrwoViHus+NFILx+hto4ptbeP6+m0RaP18MhOaJOnREhqPBlut qTb/sKHy45ytIunF9AWnVgayfVirx+Zfeb2mLTXX/ofBvL4O2O7tIe0WiLWcrArtJGWtvmx7 WyeyKzmnVVoNB7nnE0a1DOrr/hwCy/ou893egGydxH4uunjn/grkO0al2Kb6LxwUm/i9AD3U c23U/qzAZVbjQV3MjQHv23UEaiHQxVwL2b5uR6AxAn8BIbsgLznufCkAAAAASUVORK5C YII=</item> <item item-id="20">iVBORw0KGgoAAAANSUhEUgAAANMAAABWCAYAAABVeqOyAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA B1NJREFUeF7tndF15CoMhn2ftol04Ca2CReSDlyP37aX6ccXT9Yb4hEgQCCZ+eecPSebgBC/ +CyBPTP/7e414QUFoEC9AgdMeA2mwLa4C+Syb4NNS386275M074EhJ30HYQHkgpsy7RPoWhL DvTGtg6N5/XxogBgGmhRPNY5eNUcaJompnJofQUKMJkIjYATj3WfDWUktwE59uLD/fMjtS3z 7icowCSwjvVNPPZ1trVHOkAa/nVcwOZ1Pwu+N5jx8CHddxfUhajhNWf+FjA5gf3sBJg0V5zQ 2I91+VFuCJmtMvMuMDma/u2dAFPVkrHQ2V6Jd6jyNjC5GxDL31IPMFngocoHF0xDBw/nVLRh Og8/TrDb+fN9MQNMVQvZQGeD+yUrmekEqB1IX/E/902AyQAPVS54NXuVHeHOrRcw190efpx7 VsDEjYrVdg4mg1WemT1TH5i+bpYDJquQcP0yCFOPBcyRp1eZdz55Apg4UbHcBjCR0el3AHHc 5kNmsowI3zfAxNeqUUvA1EjY7mY/P/aPz+6jRge0Uub1UuXP71/PGKDM66V4q3GQmVopy7aL zMSWynhDwKQeIMCkHgIhBwDTU8iSt3sIRQAHEFJCqtsBTOohQGZSD4GQA4BJSMhyM4CpXDtb PQGTejwAk3oIhBwATEEhr0f0/o1cIfWfZgCTpJqatgATqf4VHB8s6ftggEkTAMmxARMrMwEm yUU3qi3ABJhGXdvd5wWYAFP3RTfqgIAJMI26trvPyxhM0pv7Q0/KJudkLrRPkvYRBxDdV32j AQeHiYKGe5gAmBqtuWHNDg4TlZk4MPnP6p2x52SzknWCzFSimsU+gEk9KoBJPQRCDgAmISHL zQCmcu1s9QRM6vEATOohEHIAMAkJWW4GMJVrZ6snYFKPB2BSD4GQA4BJSMhyM4CpXDtbPQeH iTriPo/LpW++lgaWhIl6H70/QOh99tdz/Gu7Uidb98sJRk7bUr9T90FIXZkw9fD/XOSl879r v2Bmot5QdQWq5v+WBMtZYDltS+bIvRH5YpsBUwrSEn9DfVrrJOmrlC0xmK4O3UXMEj9L+nAD 1hKmnhmjpUZcLXu3E4Ep9ACiX/b1nhh3PCoD+1fw0OMoXPu57QBTrmJ22lfBFNoU+htDqrQI /Y7KbjlXOE7b69ipCwF1NY+Oc3zztvuG8dKvd+HC9KIho8xDZmoLXhSmFCyxzBNaFNTvY4uT A0juIuEu2JBdnk/H1zJO9IciRkjj+EbuVQFTW1IY1tmZKXdPxFlwZxuLMMUA5cyNoT3ZRA+m MvipSaTi6V+k7/hzKLbqMIX2JBRooSClbPgZlFPm+SBxykBa3LLFqQdTKf6v/VpebOS8lLck BtNVwJSgnDIwtajJcueiUQpKDiy5c6sJEwcmso2hMi8V+xp9LPdN3rQNpXEqPccyQGzhxxbr NZOExKRs+H1z4L3242azp2+VBxDnBSQF8IsuDJioDN5icQKmFqoSNjmZ4JqROMFJwZL6e870 Of7k2BNpy4BJZByGEZP6MPyubdL92TxqUccAo8q0ULb0M+P151j5lBP8nLa1wcnqD5iy5GrR WAWma7lBlVUxMGKlZ6zUDJU5OYDktG0RsKBNwNRVbmqw7jCpz3hUBwBTdWRjF3WO8dvBFDv4 4Ex42DaASSS03G0FMpOI3EaNACaxwJSW8rfLTGKKjWYIMIlFFDCJSXlTQ4BJJHAo80RkvLkR wFQdwLc7gKhWbFQDgEk9stgzqYdAyAHAJCRkuRnAVK6drZ6AST0egEk9BEIOGIGp9CRMSAVV M4BJVX7BwQFTtpjUAwCp38UGAUzZITDaATCpBwYwqYdAyAHAJCRkuRnAVK6drZ6AST0egEk9 BEIOACYRIal3OHMPVQCTSAgMGAFM1UG4fhRA7A2l1GCAqToERgwAJpFA1Hy8AWASCYEBI58f +8envh/ckkjfU9qDGpj+/P71jMFkdXLwi6kAMhNTqHizGpiQmURCYMAIYBIJAmASkfHmRm4I 03XDLxmB0CdepcpQwCQZhbvauhlMuSdlOWGhIOWOB5hylB61rYNpXh/qs0td+SkHS/qkJkrd Lzr7hMbzn8vz23L9e6zLfoQABxCp6Fj/u/tY5mVYmNyXICzrnnOpKIGpNsTbMgOmWhFt9N/2 pfQb1gQnwL2Kp7LET5fuANPxrSfLvjnHkZkEF5SOqe9g9hyfuxcJ+cSD7w4wuYvZ/JU9AVPP FdhorLNmb2T+xWzs8RuOD3GQIt9v9XfRxsboXuZ5e1bAxIm+9TZK+6Zz4fKyzLeI/Pb2M9O5 XzpmB5isg8LyT7fU48PhFpz7Em3+vsk6TD/3q4CJtVhv0MiVG1Png4iSzOQfQ6chzIOJOuJ+ ZgwHcHqs/Bj7WQmZKV8/0z3OZ8R6OtlqofacQ9FYxP09ZKYiJe122pZp75mg3hKmQBUAmOxy UezZAdQ0fd376PFqUUL18Dt/DLdHOkrGwNUKMOUrih4XBd4Hpnjo/weUQJehalCwcwAAAABJ RU5ErkJggg==</item> <item item-id="21">iVBORw0KGgoAAAANSUhEUgAAAL8AAAASCAYAAAAKaA82AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA ApBJREFUaEPtWltywjAMpOfpJXoJTtFL9Oa0MDVjhPYhh0CGuB0+SPxY7a4kJ+3Hz+nndJg/ k4E9MnA2//ydDOyRgcMeg54xTwYuJ55Jw2RgrwzcmP/v2Hc+/999/p8LLtcRUW1ef79faynB av1sr35Ojz2OreLMsLD4quMzvpk2ce9MJxdDnMs0H9U0xpKtw8agWNwY2353lZ8Fr4hABh0l 6QqySzolTrufmT0mJvvOknwkHsUdMoDCiIyO5o0UsJF4VTFQayKcUVfHI1BLp2pkG7BsVYKp wCvEOSS1SjpiiAo/FdwOByrRVVwjxnCSycG+lAtHVycRKA4mLiKftZ0oCGvBj66GiAxWVSsY +lYcE0nF6e6j2r1KRmVehUPNX2r8LGGdrqcSuaL9da2MTFdklhyoWzjVDLap5JlDmYVVEGWE iigq6Z29FDcxVteoFWO4ay5JAuSvrKAwTl5S+R1jN2COoC6RShhmdFQxHVOi41LPQxPU4cZN bsXd648936evz/wlyeF4tP5+5PDvmtxJqhstVRtlwjtZqQR0gmetcnR+pRq6HLA1HZwjXKk5 qiM5+jvY3QLm7MfGOLq5eOXbHld4VIWUODZQ8JrVrfZr4ahUJWWQEYxqzrrm31blR10XdlqV iZVqlgmhxHHMX8EQjyKVY0gVCzOWitt5nnDWqI5RMWb31RyV1G4lR4XWLTDshJDyHTc8bxQ/ aIx7PQu+7eEQh/D01xHB2T7ZPLZWFie6FoVy141JG3FnulSMingY1dDRjY2p4EH+UboojPPf G6zHMkXjdu4/ukpXq+l2mNBIpvnfzPyPNusayaRt+ZwRmzG/29afQcuaWNZcGz3fLOFsmv8N q+MSQ8y578HAL5bccGYWc/YJAAAAAElFTkSuQmCC</item> <item item-id="22">iVBORw0KGgoAAAANSUhEUgAAAGMAAAASCAYAAABPRWDxAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA AWJJREFUWEftlwsOgyAMht15dh7vw3k8j/dhgpZ0PORHsCGKybLhuj7+r9D50ds1jasPBQyM cfWhwNRHGiMLe0INGfpRoFsY2yEeqGTuxe7XyIn4RGzQHPz8ue8uYcSK50W0AoL4RGxKQKT8 2ZpRR9J2sQ6iHB4Pg7qRCvXXA4Z9BLgsA9c11lTBzjjrxr8sVqW/W2Lzcjm37A+ftjPug+Gk XLX67kM1eFWSkoBxPPC63FMdUntKnM0JvkuCfRcjmG1j2ACHVw8Dj0VQcmVcPab8RoWOKZ4U Fris4Fyx/Pt6GCXRsHmAaXIet/jfVIugZVKE1pIwkHoRG6Tm9jBuHuCpM5ruh0UvejZzq3BO pfz5giVBcB1ATWJN5kaDX1irDkC6pLXNopReWzsV9OcG+KT2j47SsXbDxlsL5giE2mdX4cYA /MqaWAIGhH0dDzW05iAIlmx674rmYBAU5P1dEslV+wMRJSyzr7FVfgAAAABJRU5ErkJg gg==</item> <item item-id="23" content-encoding="gzip">H4sIAAAAAAAA/2xUy1ITURDtO3nMJATy4CFkMBNBXiKQTLGBFfLSKhAt+ABqkgwQlQSHgcJd yg9w59qFLvwAy7WWe38pntsdhkc5U53uPnNO33u7Z5IhIgU7gKU5juHX8t/v+8fNditB+lqG pRrtumBxxlY07bT+qvbGr4eM0D7rDfwmz0/O1ttXAu/CTAD7EfEl7DvsF9ZagU0lbnJ9fS3J pgwyYhwUosWfhWHQrF2EvmLmKixLt6/46sSdPMEF4mHg+zYDLqz/jMOkO/abqJMw3S/wa0lL vJkSb6XFp/pcA4uE6I+hVJUow/kP5CllVWiRKtFd1dVZ1kkNiKyfZRVsVGRRTpRz03/AtPLs 13IFt/AXPj/IzEYBC1ivaYu2sYZLR3ROb4mG3E/YfqdAw8z6NqTrfdDlRljeyT8Q+chduSfy UZGP0JjIRyM5FUWfs0VfxFEy95c/hPeoQTQudYr0UOqM39ShknTAdKSBpbJ45xFTL8vXHZjg /GeU06QInceuHuv6JKApDj9OcuFpSUqczAg5OSvVZ+bEzz6Rvs8RxZSxjKHQvPt5B8w5eioj mOdHVeIhLEiZ2UWRLVxvZkm4CzfjqshJZ65PWuWXPdtwD48D7+zk8KgdnHqhyZsb1t8Mw5te 6Elbir130sCB+TvTr+YGbIJjnaW9q+b5NteR7wxtpW3V4/ILvUU1WqI9ahN1cZMaf377eYTu /gel6S0KcLdhZcz1glpUp5CaQFpANoAEdEk+Zl3GpIW3AX8CLIC1wNbKA9rpbdkMA6/uu5vy 8dq9RR2VI2UrrDlooH8IsQUdGjrsMhpjAqNxRjlMIOysMSGp7BjXMpUd7+rAkkKOSinbQBlH pcHpaqQPHJRwVEYKOKpfc7RqgDkIspqjybL17N7FqR806947abnFR3gBa6AlTZ6R/ntJ9+Z1 78rcTv4BAAD//wMA8Vd+PUoFAAA=</item> <item item-id="24">iVBORw0KGgoAAAANSUhEUgAAApEAAAFlCAYAAACzwLNZAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA OwRJREFUeF7tnYt13LgSRP3icTzOx/FsPM5HTzPyyBRFEh8WGgXg2sdnvRYING41McUGyfnf 2/uvH/yCAAQgAAEIQAACEIBACYGHieQXBCAAAQhAAAIQgAAESgj8KGlMWwhAAAIQgAAEIAAB CDx3ssEAAQhAAAIQgAAEIACBUgKYyFJitIcABCAAAQhAAAIQoBJJDkAAAhCAAAQgAAEIlBOg ElnOjCMgAAEIQAACEIDA8gQwkcunAAAgAAEIQAACEIBAOQFMZDkzjoAABCAAAQhAAALLE8BE Lp8CAIBAPoH3d9A+vpzg25/8Hu637BXDY9wWv/bzeYzRaqw78b/ivNPH9tiaeatjUM3l1U8q N7fxbzXeHvfS332uanb0NyaBNqvimCyIGgIQyCCwNzg9DE9UDK3ndtS/o3lQc7gzb3UsGSn/ rclVDGe5eWQa96b66v9r4uQYCLQmgIlsTZj+ITAZAfcPcSXulnMtMSLKOdX0peRwd97KWGpY pCrFZ/GlLnz2JrM2No6DQCQBTGQkbcaCwAQEzj7sjrbqzrbvXh/E+6rbVfuzKs0rnlRfZ+1e /V5tKR5VB/ftt3Pa//1I9pQZKpnPEc+r+EoqgUf93NEvNe8zPbb/fsU3R5d9LuWyPpt3KjdT xnP781w+EywlTGECApjICURkChCIJLD9kD6qruT+29UHb+qD9CyGq2rPkVHYG5Mjo3LUpnac szmf6VcyzpFRuapulVS+ruLYj3tkuFvOu2SOd+aRy6v0/Nib5shzmbEgcJcAJvIuQY6HwGIE cj+0zwxaqdk5wnsWQ8p05VSMctrUjqM2U3s2tXHlmPar2HMN1pH2KX2PTGqORqnjSvMwd445 7c4qwbl8FltymK4xAUyksTiEBgFHAleG4+rDUWVCrj5o1SZKbVZbm8gtm7OK8LZSlhtPaQXv Km9ThvWuucutUr/GyW2fYw5LTGBJrjquA8QEgWe+gwECEIBACQFM5PdX8JRWZ1Nm49VfjdHI OfZM7zNto0xkTuw1TM54l+TyXRNZwjCVHyXnK20h0JIAJrIlXfqGwIQESj/Ec6qTuR/QL5w5 ZudVYTo7Jsf4taxE5hib/fi5nEqZl3AtrRQenQKp+EpyLEfHHNb7NiUxbOd4lZu5sab0mHBZ YUqDEsBEDiocYUOgB4H91t+RUTv68D0zDfvjXx/kqQrR0XHbD95tRevo70fjbOd2ZArO5pqa 71Ws2zjO2m3//ezv+1xIGZlUzEf9nR1T2tdep9S8U/3X6nIUx173UhN/lkNbg3qV80fjXZ0L PdYAxoTAl/MEHBCAAAQgAAEIQAACECglQCWylBjtIQABCEAAAhCAAAR4sIYcgAAEIAABCEAA AhAoJ0AlspwZR0AAAhCAAAQgAIHlCWAil08BAEAAAhCAAAQgAIFyApjIcmZDHMETfUPIRJAQ gAAEIACBYQlgIoeV7jzw1CtFJpwyU4IABCAAAQhAIJgAJjIYeNRwVCKjSDMOBCAAAQhAYE0C mMhJdcdETios04IABCAAAQiYEMBEmgihDuPsmw+236jA33+8wQAG5AA5QA6QA6vlgMpzYCJV JM362ZtIs/AI5y+B96UbFqYE0MZUGM4db2Heo+Pc8ZZIuVPJJ5i31tXRYSKr0YUeyGIbirto MLQpwhXeGH3CkWcPiDbZqLo0xER2wT7WoN+2s5/XhvyGAAQgAAEIQGBpAj909UNdT2N5rKmj 3d7b8Zio8qpjanAdJscVewfomUOiTSaoTs3QpxP4jGHRJgNSxyZKT4CJ7Chk1NAfpvJhJjP/ ULGEAAQgAAEIQGBOAlQio+zXHONkm8dck7lrNwelPrPgir0P95xR0SaHUr826NOPfWpktEkR 6vtzKpF9+Q83eknCtDKcw0ELCpjFNgh0xTBoUwEt8BD0CYRdOBTaFAILbl7iCVKhsZ2dIjTB z5/b2a1+V1Yvv5nVVvHRLwQgAAEIQAAC/wiwnT2BswucgvKqoyZsVXWzZmz3Y7hi91UIbXy1 eUSGPr76oI2vNs9zBxPpLZBbdMqEaTG3uyazRUxRfbLYRpEuHwdtyplFHoE+kbTLxkKbMl7R rZWegO3saPU6jNd0Oztqi6Bm2zwqNsaBAAQgAAEIjEKASmQHJzbwkMqrDjcMNVVMpzlwxe6k xtdY0MZXG7az0cabgHd0Sk9AJdJba0l0yoSRBBTQSam5DAjpcAiMSi/y6XHRJs2oZwv06Un/ emy08dXmeQFGJdJbILfoptjOVm0TlGyLq8akHwhAAAIQgIALAUykm03zjkd51eE907rocquW db1zxd6CW0SfVFMiKNePgT717FofiTatCd/rX+kJ2M6+p8UQRysTZogJC4LMMZaCYXhNiQJi oz74IGwEVtQt+ohANugGbRpAFXap9ASYSKEwrl0pE8Z1jq3jamUqWWxbK1ffP9rUs4s4En0i KNeNgTZ13KKOUnoCTGSUah3HUXxbTcfwLYdWmUoWW0t5n0Ghja826IM23gS8o8NEeutjF53C RNJHgkDqgR2XG6qJAwIQgAAE1ibAgzV2Ps06oC9XHTkltF2b1gbSGl5lcFeYt11S7aoEHHAY 2gRAvjEE+tyA1/hQtGkM+Gb3VCJvAlzt8NsJU2g8W5nOUXW7xPd+PcwvTwJ8EHrq8ooKfXz1 QRtfbZ63glCJ9BbILTplwlzOrdBsvmeyfEvBjf0+ntwKpfs8VoiPD0JvldHHVx+08dUGE+mt jWV0YSYyZ/YVRlNpNnNCjGqTQhEVB+McE+CD0Dsz0MdXH7Tx1QYT6a2NZXQjfWPNwzDW/tHX NQN7vJq2vF4bOC9ihwAEIAABLwJsZ1t6NdugrCqRdynVmMy/Y96xTnfDPjv+7Ir9bJqt4qDf 7wSopnhnBfr46oM2vtpQifTWxjK6qUzkqRsrrGDu+qk1mHcFTy22mMm7hOuPT2lT3zNHKgig j4Jimz7Qpg1XVa9KT8CjoSpVjPtRJozxNI9DK6lcnkyuxmDmcipZbI+mkjsO7coJlGhT3jtH 3CWAPncJtjsebdqxVfSs9ASYSIUi5n0oE8Z8qnnhlRjLi3tHSs3lUXA1iy3VyTyZ77aq0ebu mByfTwB98llFt0SbaOJl4yk9ASayjP2QrZUJMySAnKBLjGWiP4W5vBNyzrG0SRPggzDNqGcL 9OlJ/3pstPHV5hGZ0hNgIr21lkSnTBhJQCN1kmMuM+bT2liy1Z0hQmETPggLgQU3R59g4AXD oU0BrA5NlZ4AE9lBwOghlQkTHbvdeClTWRBwibHM7RYzmUsq3Y4PwjSjni3Qpyd9KpG+9NOR KT0BJjLNe/gWI70nssRYObRNvdOyW4z7h9W93lJGNBCAAAQg0IsA74kc3teFTkB51REa+GiD VVQpc6opuUb0CNc+pNGQ9ow3R5ue8a0+Nvr4ZgDa+GrziEzpCahEemstiU6ZMJKAVukk44uy axfbHGP5wswWd13C1WpTNxpHlRJAn1Jice3RJo51zUhKT4CJrFFgsGOUCTPY1H3CzTCUd4LN MZWYyTLCfBCW8YpujT7RxPPHQ5t8Vj1aKj0BJrKHgsFjKhMmOPQ5h6vY9i4FkTSVm3smS/te pT0fhN5Ko4+vPmjjq80jMqUnwER6ay2JTpkwkoDo5B+BAEP5XDRSv/+aSqTZSPPOjF++BDAq aONLwDsypSdglfTWWhKdMmEkAdHJJ4FvH4QBX0eTNJR/DefqMmFSvDMAfXz1QRtfbahEemtj GR0m0lKWZ1CXi22AocyqUi5akeOD0Pe8SZ473qFPHx3njrfESk9AJdJba0l0yoSRBEQn55XI IzZBZvI19HO4i9+ryMcHobfS6OOrD9r4akMl0lsby+gwkZaypCuRuYaywfS++NbE/ZQNhrfo kg9CCxlOg0AfX33QxlcbTKS3NmHRPb+F5uKt89ufYSLDZCkeqHqxDXhfz9GLylP3UxYDMD6g WhvjOc0UGvr4qok2vtpgIr21CYkuZRD3BhMTGSJL1SC3F9tOZvI12Zm3vW9rU5URHJRLAH1y ScW3Q5t45iUjKj0B90SWkDdpmzKR+ysNZcKYIJgmDNli29hM5nx94myGUqbNNNnqNRH08dJj Gw3a+GpDJdJbm5DoMJEhmEMGkS+2gWbyCtAMhlKuTUhGrTMI+vhqjTa+2mAivbUJia7GRL62 uPnvx72ks/95n+Djawm+/NHNedttBsurB3MW0ELHPYM1PKc/t8knzgNFDqjMCtvZKpKB/dSY yMDwGKqAQPMr9kaVyZzt7SMMIz2Y01ybgjyh6XcC6OObFWjjqw2VSG9tQqKrMZGpD29+PjeB w8pk6qsQUz/fFztT7fk5BCAAAQj0J3DxZpdSE0MlspSYQfsaE2kQNiEcEAi/Yq8tIV6ot+3y jshu91GGa3MH3oLHoo+v6Gjjqw2VSG9twqK7eo3P9n6JV8LMXWdjdqUEvt0veffaeFuVvNsX x0MAAhCAQDsCVCLDvNoUA/GKH18Zu16xi79OUVWR3Kt1ZpBbq9pVm9aTm6B/9PEVEW18taES 6a2NZXSYSEtZnkFZLLbCh29aGcmXgpGG0kIb39TtHhn6dJfgNAC08dUGE+mtjWV0z+1tfkMg g4Bkm5ut7QzSnJEQgAAEOhFgO9vSq9kGRSXSVhqPSuS3/ePdY9cV+FpXJLchtapOUk2pED7w EPQJhF04FNoUAgturvQEPJ0dLF6P4ZQJ0yP+mce0XmxvOsEGD4InU0FpKK21SZKYvwH6+GqM Nr7asJ3trY1ldGxnd9oymGBT8/b2Nu+SnCALOH8gAIGpCLCdbenVbIOiEmkrjed2dsPt7R5K 1FYnqab0UCt/TPTJZxXdEm2iiZeNp/QEbGeXsR+ytTJhhgRgHPRQi61oe7unHFfVhG/e+d3i 88uXwFDnji/GJpGhTROssk6VnoBVUiaLb0dsZ0+1EdF1e/TW9jZPbXfVjrMAAhCAwJMA29m+ hs0xMmXCOM5v5JiGvGK/8cTMzWJmM6lrt7ybBUTHSQJDnjvJWc3RAG28dVR6AiqR3lpLolMm jCQgOvkkMPRiW+kIKw8LyZqS7e6QgBjklMDQ587kuqKNt8BKT4CJ9NZaEp0yYSQB0ckcJvIx i0pHeKOYGZY9VCfDUFcNhFGpwhZyENqEYK4eROkJMJHVMoxzoDJhxpn1GJFOsdhOaiS32mAo /c6nKc4dP6ySiNBGgrFZJ0pPgIlsJpNPxzxYw83UrQnwwE1rwvQPAQhAQESAB2t8DNoIkSiv OkaY70gxTnXFfmOPurKY2VTqlDZHy3nTgOj8C4GUPuDqRwBt+rHPGVnpCahE5hAfvI0yYQZH YRf+lIttpSOsPKyZprnasNXdTILLjnP16RPd2qOijbf+Sk+AifTWWhKdMmEkAdHJJ4FpF9tK R/g6zCFFSrXBTMaqVqpPbHRrj4Y23vorPQEm0ltrSXTKhJEERCfzm8jHDAc3knc+CDGU7U/y O/q0j27tEdDGW3+lJ8BEemstiU6ZMJKA6GQNE1lpJCu9pzyrFB+EV7fBywNerEOFPoshC5su 2oShrhpI6QkwkVUSjHUQT2eLnmjjS+uqCGwrktlK8BWJVayz+dI7BCCwLgGezh7LxPWOVnnV 0Xsus42/zBV7RXmx4hBperTUhu3u+1K11Od+dGv3gDbe+is9AZVIb60l0SkTRhIQnayznb3V usIV9nzQJuqDkFcF1S0IUfrURbf2UWjjrb/SE2AivbWWRKdMGElAdLKmiXzMeiAjGf1BSHWy bGGI1qcsurVbo423/kpPgIn01loSnTJhJAHRybomssJIVvhOSYb1+iDETObJ10ufvOjWboU2 3vorPQEm0ltrSXTKhJEERCdrm8i9kcy4ybvHtrbDByFb3eeLhYM+LGXHBNDGOzOUngAT6a21 JDplwkgCohNM5I2KZFT6OH0QUp38rrqTPlE5Oco4aOOtlNITYCK9tZZEp0wYSUB0gonc5kBm mTF6W9vxgxAz+S9xHPVhafsggDbemaD0BJhIb60l0fGeSN6e50yg6D2SvD9y3XfbMXMIQEBD IOMWolzzgYnMJTVwO+VVx8AYLEPniv2vLAVlxszC5W29R9Fm1erkKPrcTsQBO0Abb9GUngAT 6a21JDplwkgCohO2s49ywMxIjvZBuJqZHE2flZY9tPFWW+kJMJHeWkuiUyaMJCA6wUSe5UCm kcxsdivTRv4gXOGp7pH1uZWYAxyMNt4iKT0BJtJba0l0yoSRBEQnmMirHMjcr85sVp1tM3wQ Xt0LWw3G5MAZ9DFBKQ8DbeRIpR0qPQEmUiqNZ2fKhPGc4bhRsdieaJfpEDObVSXITNrMaCZn 0qcqQY0PQhtjcd5DU3oCTKS31pLolAkjCYhOqESmciBzvzqzWWq0w5/P/EE4w3b3zPpUJazR QWhjJMZBKEpPgIn01loSnTJhJAHRCSYyJwcyHWKrauQKH4QjP4yzgj45p4ljG7RxVOVfTEpP gIn01loSnTJhJAHRCSYyNwc6GsmVPghHNJMr6ZN7uri0QxsXJY7jUHoCTKS31s/oni8LP3k5 6NnPtu2VCTMArqFCZLHNkCuj1JjRJGOgr01W1WaUre5V9SlO5A4HoE0H6AVDKj0BJrIAfI+m V2bw7Gd7Y6lMmB4MZh6TxTZT3QyXmNEkc7CPZqtr424mV9enKJmDG6NNMPDC4ZSeABNZCD+6 eY2JfFUvX7EqEyZ6/rOPx2KbqXDGtnZGk8zBMJFbUK5b3Zw7Rekc2hhtQnEXD6b0BJjIYvyx B2AiY3lHj8ZiW0A8o9SY0SR7QLT5isrNTKJPdiqHN0SbcORFA2Iii3CN3VhlIl9b3Pz34/5S /ozJ4F24x03CCf2eTdA4ySnF8eLnz83+3e+W49E3+UwOSHNA5YyoRKpINupHZSIbhUe3Nwlw xV4IMGPPWlWNRJu0Nj2rk+iT1qdXC7TpRT5vXCqReZymaKUykWeLPf8OgdEIfFYjj6phr3/7 KFheteBnEIAABNYkcPK2lxrTRCWyhlrgMSoTGRgyQxUQ4Iq9ANa2aaLcqKhGok2dNlFPdaNP nT4RR6FNBOX6MahE1rMb8sirV/YcvSdye7/fY8LKhBkSoHHQLLaV4gRsa6NNpTZ/D2u91Y0+ 9/RpeTTatKR7v2+lJ6ASeV8P+x6eppLfEJiMQHJb+7Wlzbb2ZMqzmkEAArcIsJ1t79usAlRe dVhNbIJguGK/KWLDbW20uanNweHK6iT66PVR9Yg2KpJt+lF6AiqRbTSy6pVK5K1rNqo4xgRe 1cjna3/OfvOQjbGCnJsQgEA4ASqRVh7NPhjlVYf9ZAcLkCt2gWCNqpFoI9Amo4vaB3HQJwNu pyZo0wl85rBKT0AlMhP6yM2UCTMyB8fYWWxFqjQwkmgj0iazm9KtbvTJBNuhGdp0gF4wpNIT YCILwI/alO3s8M0Ctg+DCSS3tdnSDlaEcw4CELAlwHb2qHauT9zKq44+M5h3VK7YhdqKq5Fo I9SmsqurrW70qYQacBjaBEC+MYTSE1xWIvffL/yK+eh7h3N+dmPOn4emJp/6uSKG0fqAia9i LLZibS6MZOkLyNFGrM2N7kq3um8MxaECApw7AogNu1B6guR29tk3ptR+k8pdLqnJp35+d/wR j4eJr2ostmJthNVItBFrI+gOMymAGNAF504A5BtDKD2BxETu53IW4Nm3qxwdf9RH7sRz293Q YKhDuSfS9s4U7lNrQODyJeTcG9mAOOcXBCAwFIHIeyJTlcgrs1dz7HZbPGVOU/0P5fQaBoup bgj3Ztdcsd8EeHR44isRc7e10aaBNsIuX/pc3TcpHI6uCghw7hTA6tBU6QmyKpFnFcSjf3/w 2N4zebzGfwx7NZGcSiQmMi/7lAmTNyKtcgmw2OaSKmyXcW9k6mIcbQqZBzff64OZDBbgYjjO HR8trjyYIsosE1lqBF+mJbWtvTeh2/8/Moj7/jCReSmAiczj1KMVi21D6jeNJNo01EbQ9Zk+ 3DcpgHuzC86dmwAbH670BLdNZKnLzTV+R0YUE1mXWdwTOdTdKtyzJiJweW/kYwzujxSR5vyC AASGIpDahimwGhITeWXutrGk2u0NZqr9a0s8p10Bk+maKq86poPTeUJcsTcW4MbT2mjTWJub 3ZfoQ3XyJuzCw0u0Keya5gICSk+Q/Z7IvRnc3vd4ZP5yjN3VfY9n92Hu+eXcOylgPnQXyoQZ GoRh8Cy2jUXBRDYG3K/72nOHeyfba1arTfvIGOFVfFORSFYiZQNtyqdX90zuf5Yyo6mfq+If uR9MpK96LLYB2lQaSbQJ0ObGEHf1wUzegJ849K427SKj56FN5LZ6+ZrI1jTm3C951uaVGhim 7ycJTHwXDhbbAG0wkQGQ44dQnTtsdeu1U2mjj4wehzWRSNePACayH/vUyCy2KUKin1cYSbQR sW/UjVofzKROKLU2usjoCRNJDhQT4OnsoZ6b44nZBgR4UptzAAIQgMBfApFPZxc7Fg6wI0Al 0k6Sz4C4Yg/UJuO9kdto0CZQm4qhIvThvskKYd4PidCmLjKOohJJDhQTwEQWIws7gMU2DPXj K7I+/pz82v8YbQK1qRgqUh/MZJlAkdqURUZrTCQ5UEwAE1mMLOwAFtsw1B8DFVQj0SZYm8Lh eujDfZN5IvXQJi8yWmEiyYFiApjIYmRhB7DYhqFOmsi9x0SbYG0Kh+upD2byWqye2hSm0ZLN lZ7geG/n8/vAXt8LFvTfJeVsP2llwrSPdq0RWGw76J1ZjUSbDtoUDOmiD1vd30Vz0aYgnZZq qvQE301kLwMpfFpoqWzImKwyYTKGo0kBARbbAliqpts17qDP7Xdqq4akHz0Bt3OH6uQ/jd20 0Wff2D0qPUHYN9aMjXzs6JUJMzYJv+hZbDtpcmEkMZGdNCkc1vXcOTOTrvEWYs9qvtJcs4CY NVJ6AkykmbgtwuE9kVfLOj9blcDluyNfd/A0eGflqryZNwQgYEJAuPOLiWzh2sz6VF51mE1t +HC4Yu8s4cn9kYm3AXUOmuEfBEY7d1a6d3I0bVY7o5SeABM5Yfa8vqP8NTVlwkyIq+uUWGy7 4j995Q8msrMuGcOPeu6sYCZH1SYj7aZoovQEmMgpUuLfJLbJ8fq7MmEmw9V9Oiy23SXASBpI UBPC6OfOzGZydG1q8nGkY5SeABM5kvKFsWIiC4F1aM5i2wH6fki2tA1EKA9hlnNnxqe6Z9Gm PCvHOAITOYZO3aPERHaXIBkAi20SUUyDAyP5/HC//qbEmNgY5ZDAbOfOTGZyNm1mOwUxkbMp Kp4P90SKgTbsjsW2IdySrjGRJbQs2s587oy+1T2zNhbJfzOIMBP5MiNHpqTmZzfn/eXwFITU z5WxxPT15+33z0dlZPfn5++3PycBUImMUebOKCy2d+iJj90ZSSqRYr7i7lY4d0Y1kytoI07n 0O6U/ih5T+TRgxqP2Z79e+pnKlIpCKmfq+Jw7gcT6azOR2wstkYaHZjIj/Xs4w+/vAisdO6M ttW9kjZeZ0VeNEp/lFwac0zkPuyzAPcVzdR0z9rnAshtl4rjzs9LY9i3r2H2ihcTeUe5mGNZ bGM4Z4+ycYwvbTCR2fRCG6547oxiJlfUJjT5bw5W6kuuhrttIo+COTIvV5XLywAPSgBHRmtv nD4qCMnp3ZTi+vAaA1hj2o9MfC3vpkDo/BsBFluzpDgwkR9rCdVIM6WWr+I7b3WzrrmdLV/j UXqjpMva3oO3DePs31/m7cpAlUzgyqSmjGPJOK0kL41BYSKPTGWr+dHvPQIstvf4yY/GRMqR tuqQc+eDrGN1Em1aZb2m31JfcjVqlok86uBqqzS1jbo3SldbuEemapRK5MtQP/77368fb7/+ 25D879fbj4OHYjCRmpNklF5YbA2V+mskt9pQifTTiXNnV106sZM9lEObHtTzx7QykVcGs+Rn Z8YztTW+NWpXf8/Hq225FethJH/+fn+W+mEgvzjKf2O2MpHbyjF/P3jKff/UO////U0AizB5 7V1/P09e29rkD2uIeQ6c1ScXOYfJz3R+qpzO7Urk3rgd/f8r2LOt6f3Wd06lcg/gaPtc6bZr ge9j+PP756mBbGWIHTjU8pv9OK7YTRU+em8k90VaicW5k5aj11Y32qS16dlC6QkuTeTWzW8n fOTyt0Yx19CdVd22ZqqmryvTGi3cdo4PA9mrEhk9b8bLI8Bim8cpvBVfhRiOvHRAzp0yYpEP 4qBNmTbRrcNMpHJiqXsbz+5zTG1znxlGJaQ7HF5xfG5lvzoLvify7IqUf4cABI4JfG5rb7cG /1YjYQYBCEBgWALCN9ckt7PvGKir6uVZ5TK1tX1kGs+2yVWx1/ZzVsk96++o/VEltjQeF0Nd GvcK7bliN1aZLW1jcXhR/11xWlYmWdfuqtP2eKUnCDORbZHQ+xWBpxHlNwQgUETgsBL56IFq ZBFH1h4IQMCMwIiVSGxePwLKq45+s5hzZK7YfXV9akM10lYgzh2tNMqHcNBGq426N6UnoBKp VsewP2XCGE5v6JBYbH3lw0T6avOIjHOnnT53t7rRpp02ip6VngATqVDEvA+2s822EtgMHIoA D9hw/kAAAlMRYDvb3LWZhae86jCb2vDhcMXuK+GnNmxpW4rEuRMnS2llEm3itKkZSekJqETW KDDYMcqEGWzq9uGy2PpKhIn01Ybt7D7a5N43ybrWR5/cUZWe4NBEfj59uLmvPOLfcgHQrowA 29lTbUQMtRU8C3m2tGdRknlAAAJNTWSEWTwbo8wa0TqXgDJhcsekXR4BrtjzOPVo9UUbtrR7 SHA5JueOhyS51UmPaIniWcXnnkgSoYSAMmFKxqVtmgAfhGlGvVp802ZnJE++GbFXuMuNy7nj JTlm0kuPq2iUnoB7IsfRvTpStrPZvoDAfQJsad9nSA8QgIABASqR1X5qyQOVVx1LAmw4aaop DeHe7DpVifzYFvr4w694Apw78cxzR3xpQ3Uyl1hsO6UnYPmL1a7LaMqE6TKBiQflg9BX3ENt 2NK2EYxzx0aKb4EcaXNkKH1nMHdkSk+AiZw7V56zUybMArhCp8gHYSjuosFyTCTVyCKk0sac O1Kc0s6utMFMSlFXdab0BJjIKgnGOkiZMGPN3D9aPgh9NcJE+mrzNO+P7zbnlyWBHG0wk/2k U3oCzsJ+OoaNzIM1Bjcy83bHaQh8e8Dm9T7daWbI+QIBCExNQHgjNyYyzMr1G0h51dFvFnOO nHPFPufM/Wd1qg3vjLQQj3PHQobDIGq04SGcOD2VnuDSRD4rWJs/rynu/30b0NXP4hClR0pB TP08PYJPi5nm4kNVE0nNYqsZmV5SBDCRKUJ9f86505f/1eh3tWGru622Sk+QrETuDeLWSB79 /fFvZ8e0xVLWewpi6udlo/VtPdNc+pLUj353sdVHRI+f69rVPXc8pd09UTh3uktwGoBKG8xk G42VnkBiIvfTPAvwVaXctj9qe9Tu8qonY39/22cuwNx2bWTW9TrLPHREfHpSLbY+M5onkktt 2NLuLjTnTncJmpvI7QXd3lD6zt4/MqUnuG0iz0zgVUXydczVRHInmdtuG8/+mJxqq39anEf4 NND8hgAEpAR4wIZVBQIQGJJARuEt1/NkmcizCuJZxXB7X+RRIJjIXHk07UqMtmZEesklQDUl l1R8u6Q2bGnHi7IZMalP1+jWHry1NlfGbW3yebNXeoIsE1lqBFMmcWsy91XAo2NT2+NnfXyW wncPB61YicxLLVpFE2i92EbPZ6bxktqwpd1V7qQ+XaNbe/BIbXiquzzXrEzklcFM/exsG/lq 63nf55XB3PZz1ecK29nlacYREQQiF9uI+cw0RpY2VCO7SZ6lT7fo1h64lzY8iJOXd3Ym8qqy t51Sql2qopgypS+juN1mPzKIZ/dxpuLLk8evlTJh/GY3dkS9FtuxqcVEn6UNJjJGjINRsvTp Ft3aA/fWBjN5nX9KT5D9nsi9GUxtSecYsjMztzWDqVMxVUUsMZG5Vc5UTG4/VyaM29xGj6f3 Yjs6v5bxZ2nDlnZLCS77ztKnW3RrD+yiDVvdx3mo9ATJeyJVp0KpmUvdV/mKq8ZEvkxqSZVU xaFHP8qE6RH/zGO6LLYzM66dW7Y2VCNrEd86LlufW6NwcA0BN20wk19VVHqCUBO5rV7uq43b Lei9ybua8NFxuf92ZESv/q3mZHI4RpkwDvOZKQa3xXYmtnfnkq0NJvIu6qrjs/Wp6p2D7hBw 1oat7q9fCHNH56dXu9sBx/sTeJpqfkMAAk0IfHtf5GOU95X1+YffEIAABNwIRL4n0sUi7e/B pLqWrwys8llFt3S+Yo9m4TZekTZUI8PlK9InPLq1BxxJmxUrk0pPQCVygXNdmTAL4Aqd4kiL bSgYg8GKtMFEhitWpE94dGsPOKI2K903qfQEmMiJz/Xch5MmRmA/tREXW3uoogCLtMFEiqjn d1OkT363tBQQGFmbFcwkJlKQ5LN3sX24SJkws3OLnt/Ii200q+jxirXBSIZKVKxPaHRrDzaL NrNudSs9AZXISc91TOQYws6y2I5BuyzKYm0wkWWAb7Yu1ufmeByeT2A2bWYzk81NZK8nCvNT lJZXBPbb2MqEgbyWwGyLrZZO396KtcFEhgpWrE9odGsPNqs2V95oJMWVnuBbJbKXgZw16Xok FiayB/W6Mcn7Om4RRxVrw7fXRMjyOUaxPqHRrT3Y7NqMbiabmsi1U9959n/efv98T93HOx+3 f37+fvuzCTv3m4GcZ7pSbLMvtiNrWaUN1cgwyav0CYtu7YFW02a07W5M5Nrn5+Xs9ybz0ViZ MKDXElhtsdXSa9tblTaYyLaibC+Y+a6MMNalA1WdO6WDGLYfxUwqPQEP1jROxFKx9u33X+FY Ei6v+Cmh1aftqottH9plo1Zpg4ksg3yjdZU+N8bj0HwCq2vjbiZLfcmV8pjI/POiuGWpAdy3 P9qaLgkCE1lCq0/b1RfbPtTzRq3WBiOZB/hmq2p9bo7L4WkCaPPB6OzeyTTBti0wkW35Snsv FevMOJb2s53EnWOlMOjsGwEWW9+kqNYGExkiarU+IdGtPQjafNXfzUwqPQGVyMbn+kus/379 ePv132aw/369/dg9FPO8ctl8MfrdSuRrNGXCNMa1XPcstr6SV2uDiQwRtVqfkOjWHgRtzvV3 2OpWegJMZONzfSvWw0j+/P3+LPXDQH5xlP+CaGUivz3VvX/Km////uQ7TGBSmQPv4B5XhBt+ z/+FZyVP1q+DN3PAcuzz6WizO1BTlfXBRKpInvSzd/x/fv88NZBUIhuLYdg9V+yGovwN6ZY2 VCObC3tLn+bRrT0A2uTr36MySSUyX5/uLbdiPQxkt0rk6S2+Z3dr8O8QgEAtgc9K5Ou8+yhM chZCAAIQ6E9gc9vcXZNEJfIuwcTx23sinwby9Yt7IhuTH6N7rth9dbqtzaYaefBlNr4THySy 2/oMMs8Rw0SbetUiHsKhElmvT+iR2/t4cgY+al/6mqCjcZ598BsCEAglQDWSVQcCELAkQCUy x5LR5kVAedUBVS0Brti1PJW93daG+yKVcnzr67Y+TaNbu3O00eqvvm9S6QnYztZqbdmbMmEs JzhwUCy2vuLd1gYT2VTc2/o0jW7tztGmjf6qrW6lJ8BEttHaqle2sy03FEK3ViHQh8CXLW0e riHnIQABBwJsZ1t5NPtglFcd9pMdLECu2H0Fk2hDNbKZwBJ9mkW3dsdoE6d/TXVS6QmoRMZp 3W0kZcJ0m8SkA7PY+gor0QYT2UxgiT7Nolu7Y7Tpo3/uvZNKT4CJ7KN16KhsZ/fZymRUCDw/ TF/fXsM7Ix028ogBAhBgOzvUgw0/mPKqY3gYZhPgit1MkE04Mm2oRjYRWaZPk+jW7hRtPPQ/ q0wqPQGVSA+tm0ahTJimgS7YOYutr+gybQ7eNM7Lx+/rLtPnfij0sCOANl4pkbvNXRM1JrKG 2mDHsJ3NtioE+hH4tp392EzjSW22FCEAgY4EVDYGE6kiadwPlUhfcbhiX0QbtrTlQnPuyJHK OkQbGcomHSk9ASayiURenSoTxmtm40fDYuuroVQbTKRcaKk+8ujW7hBtvPVXegJMpLfWkuiU CSMJiE4+CbDY+iaDVBvui5QLLdVHHt3aHaKNt/5KT4CJ9NZaEp0yYSQB0QkmcoAckH4QYiLl ikv1kUe3dodo462/0hNgIr21lkTHgzX9HqpgZAi8CPC+SHIBAhCwIMB7IiXeaplOlFcdy0AL mihX7EGgK4aRa8N9kRUqnB8i10ca3dqdoY23/kpPQCXSW2tJdMqEkQREJ2xnD5AD8g9CtrSl qsv1kUa3dmdo462/0hNgIr21lkSnTBhJQHSCiRwgB5p8EFKNlCnfRB9ZdGt3hDbe+is9ASbS W2tJdMqEkQREJ5jIAXKgyQchJlKmfBN9ZNGt3RHaeOuv9ASYSG+tJdEpE0YSEJ1gIgfIgWYf hBsjydcf1idCM33qQ+LIvwTQxjsVlJ4AE+mttSQ6ns62eB6u4xdcMX8nAjyl7aQGsUBgQQI8 nS3xVst0orzqWAZa0ES5Yg8CXTFMU22oRlYo8vWQpvrcjm7tDtDGW3+lJ6AS6a21JDplwkgC ohO2swfIgaYfhJjI2xnQVJ/b0a3dAdp466/0BJhIb60l0SkTRhIQnWAiB8iBph+EmMjbGdBU n9vRrd0B2njrr/QEmEhvrSXRKRNGEhCdYCIHyIGmH4Q8pX07A5rqczu6tTtAG2/9lZ4AE+mt tSQ6ZcJIAqITTOQAOdD8g5Bq5K0saK7PrejWPhhtvPVXegJMpLfWkuiUCSMJiE4wkQPkQPMP QkzkrSxors+t6NY+GG289Vd6Akykt9aS6JQJIwmITjCRA+RA8w9CTOStLGiuz63o1j4Ybbz1 V3oCTKS31pLoeE/kgu8B462U9gR4XyTnJQQg0IUA74mUeKtlOlFedSwDLWiiXLEHga4YJkQb qpEVynwcEqJPdXRrH4g23vorPQGVSG+ti6N7Vh03f56LrfCqozggDrgkwGLrmyAh2mAiqxMg RJ/q6NY+EG289Vd6Akykt9bF0R0lhzJhigPiAEzkoDkQ8kGIiazOjhB9qqNb+0C08dZf6Qkw kd5aF0eHiSxG1vUAFtuu+PsbfN4XWZ0AnDvV6JofiDbNEd8aABN5C9/cB++3sh+zVSbM3PTi Z8diG888d8QwbahG5krypV2YPlXRrX0Q2njrr/QEVCK9tS6Obpscr78rE6Y4IA7oX+1CgyoC YR+EmEhvfaqiW/ugsHNnbczVs1d6AkxktQzRB/55+/3z60Mzz6rjz99vf05CwURGa1Q+Hott ObOoI8K0wURWSRqmT1V0ax+ENt76YyK99bGJDhNpI8VpICy2vhqFaYOJrEqCMH2qolv7ILTx 1h8T6a3Pl+hKxdq3f93jmDtltrNzSXm0Y7H10OEoilBtMJLFiRCqT3F0ax+ANt76l/qSq9mw nd1Q6xoDeGQCHyGWiL4ft+TYhjjo+oAAi61vWoRqg4ksToRQfYqjW/sAtPHWX+kJMJGNtS4V S2Ei91MqjaExErrfEGCx9U2HUG0wkcWJEKpPcXRrH4A23vorPQEmsrHWL7H++/Xj7dd/m8H+ +3X4UAwmsrEgZt2z2JoJ0svgYyKLE4FzpxhZ2AFoE4a6aiBMZBW2PgdtxXoYyZ+/35+lfhjI L47yX2ytTOT2/ZH8/eAp993XRcIIRpE58H6/yuOelb9fWfr865evL42MhbHIfXJg/hxQOSIq kSqSJ/3sHf+f3z9PDeSji1YmsvE06b6SAFfsleACDgvXhmpkkarh+hRFt3ZjtPHWn0qktz5f otuK9TCQ3SqRb4/Tmt8QgIArgc9q5OM8/ShMcsZCAAIQ0BN4LC6iX7qeRAHN1s32nsingXz9 4p7I2aSumg9X7FXYQg4K14ZKZJGu4foURbd2Y7Tx1p9KpLc+n9Ft7yvJCfmofelrgo7GUSZM zjxok0+AxTafVXTLcG0wkUUSh+tTFN3ajdHGW3+lJ6AS6a21JLqnEeU3BCBgT4AtbVYqCECg OQG2syXeaplOlFcdy0ALmihX7EGgK4bpog3VyGyluuiTHd3aDdHGW3+lJ6AS6a21JDoqkc2v 6+wrXBAYgwCVyDF0IkoIDE2ASqTEWy3TifKqYxloQRPlij0IdMUwXbShEpmtVBd9sqNbuyHa eOuv9ARUIr21lkSnTBhJQHTySYDF1jcZumiDicxOiC76ZEe3dkO08dZf6Qkwkd5aS6JjO3vo jQe2yhcjwJY25ysEINCUANvZEm+1TCfKq45loAVNlCv2INAVw3TThmpkllrd9MmKbu1GaOOt v9ITUIn01loSnTJhJAHRCdvZA+RAtw9CTGRWdnTTJyu6tRuhjbf+Sk+AifTWWhKdMmEkAdEJ JnKAHOj2QYiJzMqObvpkRbd2I7Tx1l/pCTCR3lpLouOeyKZ3lyx2xx4sIwh83hfJd2hzfkEA AmoC3BMp8VbLdKK86lgGWtBEuWIPAl0xTFdtqEYmFeuqTzK6tRugjbf+Sk9AJdJba0l0yoSR BEQnbGcPkANdPwgxkckM6apPMrq1G6CNt/5KT4CJ9NZaEp0yYSQB0QkmcoAc6PpBiIlMZkhX fZLRrd0Abbz1V3oCTKS31pLouCcy4i42xoCAjgDvitSxpCcIQGBHgHsiJd5qmU6UVx3LQAua KFfsQaArhumuzd9q5KYoWTGLeQ/prs+8aG/PDG1uI2zagdITUIlsKpVH58qE8ZjRPFGw2Ppq 2V0btrQvk6O7Pr6p2z0ytOkuwfW5QyXSWyC36DCRbor8i4fFFm1OCWAiMZG+pwfaDKrNI2yl J6ASOXAi5IauTJjcMWmXRwATmcepR6vu2mAiMSo9El8wZvdzRzCHmbtQegJM5MyZ8nduyoRZ AFfoFFlsQ3EXDdZdG0wkJrIoY30adz93fFBYRqL0BJhIS4m1QfF0Ns/mQWBMAnxzzZi6ETUE rAlwT6TWZM3em/KqY3ZW0fPjij2aeP54FtpQjTwVzEKf/HRaqiXaeMut9ARUIr21lkSnTBhJ QHTySYDF1jcZLLTBRGIifU8RtBlQm0fISk+AiRw0CUrCViZMybi0TROwMCrpMJdsYaENJhKj MuDZZ3HuDMgtKmSlJ8BERqnWcRxlwnScxpRDs9j6ymqhDSYSE+l7iqDNgNpQiRxUtJ5hYyJ7 0r8e28Ko+OLpGpmNNnxzzWEe2OjTNUs9B0cbT11eUSk9AZVIb60l0SkTRhIQnXwSYLH1TQYb bahGYiJ9TxO0GUwbKpEDCtY7ZExkbwXOx7cxKr6IukVmow0mEqPS7SyoG9jm3KkLf/qjlJ6A SuT06fLxJBa/IQCBMQl8vivycRa/r9jPP/yGAAQgUEuA90Qu4PyEU1RedQjDoqt3Alyx+6aB jTZUIqlE+p4maDOYNmxnDyhYdMjPyuPmSgMTGa1A/ng2RiU/5GVaWmnDwzXf8s5Kn2XOiryJ ok0ep16tlJ6A7exeKjYa98g8KhOmUdjLdsti6yu9lTaYSEyk76mCNgNpQyVyMLGiwz0yjJjI aBXyx7MyKvlhL9HSShu2tDEqA511VufOQNyiQlV6AiqRUaoFjYOJDAItGobFVgSyQTdW2mAi MZENcrxVl1bnTqtJDtwvJnJg8VqH/rofknsiW5PW9M9iq+HYohcrbTCRmMgWSd6oT6tzp9Ec R+4WEzmyetWx/3n7/fPjgZkvf37+fvuz6ZN7IqsBdzmQxbYL9qxB7bThvsgvutnpk5VVazRC G2+dMZHe+nSNDhPZFX/x4Cy2xcjCDrDTBhOJiQzL/nsD2Z0796Yz3dGYyIEkLRVr337/up7U 1DGRKUJeP2ex9dJjG42dNmxpYyJ9Txe0GUSbR5ilvuRqajxY01D4GgN4di9jiei8J7KhqOKu 7YyKeH4jd2enDSYSozLICWV37gzCLSrMEj+RigkTmSJ08+elYilM5D7k0hhuTpnDCwiw2BbA Cm5qpw0mEhMZfA7UDmd37tROZNLjlJ4AE9k4SV5i/ffrx9uv/zaD/ffr7cfuoZh9mVn1hLUy YRrjWq57FltfyS214b7Iz4Sx1Mc3nUMjQ5tQ3MWDKT0BJrIYf9kBW7EeRvLn7/dnqR8G8ouj /Ndnq0rkt6e690958//fn3yHCUzMcuBdkMcNTa//oI+ZPqyzB28QQSPL87TMyZy3xkSqSJ70 s3f8f37/PDWQrSqRjadI9xCAAAQgAAEILEgAE9lY9K2JfBjIHpXIxlOkewhAAAIQgAAEFiSA iWws+vaeyKeBfP0KvCey8RTpHgIQgAAEIACBBQlgIhuKvr0/JmeYo/alrwnKGYc2EIAABCAA AQhA4C4BTORdghwPAQhAAAIQgAAEFiSAiVxQdKYMAQhAAAIQgAAE7hLARN4lOPDxd79iceCp 24XObQt2kjwD4hzx1OWlDfp46nO0nrHG+Wm1f6VgzfsjMZF+uoZEdPXViDWJFBL0pIOoXio/ KZ5u0+Ic6YY+OfDROcN5lMQW0gBtQjDfHmS7vt05dzCRt6UYt4MWLzYfl0a/yO+cwP2iXmNk zhF/nV8acR75aYU2fpq8IsJE+mozTGR8QHpIxYefhw5HUXCO+Gqz/TB8bW/v/80/+rkjxER6 6rvX5c5nEJVIT41DouIDMgRzcpA7J3CycxrcIsA5cgtf84PRpzniqgG4FaQKW9hBmMgw1KMM 9Oft98+T7yw9+Y7uqyt37omM1R0TGcu7ZDRMSgmt2Lb7dYrzKJZ/zmhUInMoxbZR37NKJTJW P6vR+ID0kIMPPw8djqLgHPHU5uhCl/PITytMpKcmr0qxQh9MpJ/GYRHxARmG+nIgPvw8dMBE +uqwj0xdTRln5v6Roo2/Rq8IMZHjaGUX6f5K5LW9zVZ2H6n29xD1iYJRtwQ4R3zzYavN3rSw hvXX7Wg9Y43rr0vqYqzm3KES6acrEUEAAhCAAAQgAAF7AphIe4kIEAIQgAAEIAABCPgRwET6 aUJEEIAABCAAAQhAwJ4AJtJeIgKEAAQgAAEIQAACfgQwkX6aEBEEIAABCEAAAhCwJ4CJtJeI ACEAAQhAAAIQgIAfAUyknyZEBAEIQAACEIAABOwJYCLtJSJACEAAAhCAAAQg4EcAE+mnCRFB AAIQgAAEIAABewKYSHuJCBACEIAABCAAAQj4EcBE+mlCRBCAAAQgAAEIQMCeACbSXiIChAAE IAABCEAAAn4EMJF+mhARBCAAgSYEfvz4t+Rv/95kMDqFAASmJ4CJnF5iJggBCEDggwAmkkyA AASUBDCRSpr0BQEIQMCYACbSWBxCg8CABDCRA4pGyBCAAARqCGAia6hxDAQgcEYAE0luQAAC EFiIwMNIcj/kQoIzVQg0JICJbAiXriEAAQi4EcBAuilCPBAYlwAmclztiBwCEIBAMQFMZDEy DoAABE4IYCJJDQhAAAKLEMBALiI004RAEAFMZBBohoEABCAAAQhAAAIzEcBEzqQmc4EABCAA AQhAAAJBBDCRQaAZBgIQgAAEIAABCMxEABM5k5rMBQIQgAAEIAABCAQRwEQGgWYYCEAAAhCA AAQgMBMBTORMajIXCEAAAhCAAAQgEEQAExkEmmEgAAEIQAACEIDATAQwkTOpyVwgAAEIQAAC EIBAEAFMZBBohoEABCCgIvD6/uvtfx99X30v9utF4/s2+z5UMdIPBCAwPwFM5PwaM0MIQGBC Attvnzn7+3baV234JpsJE4QpQSCAACYyADJDQAACEFATODN+Of9+VI1Ux0d/EIDA/AQwkfNr zAwhAIEJCWy3p/cVx6Nt7X0lMlWZpDo5YdIwJQiICWAixUDpDgIQgEAEgbN7GXO2ufcGNOeY iDkxBgQgMBYBTORYehEtBCAAgSeBq0rkC1GqzdHPc+6vRAIIQAACz3UIDBCAAAQgMB6B3Hsf t4Zzby5fP6MSOZ7+RAwBBwKYSAcViAECEIBAIYEc45eqRGIiC6HTHAIQ+EIAE0lCQAACEBiM wNl7IreVxiuTmXo6++p9k4OhIlwIQKAhAUxkQ7h0DQEIQAACEIAABGYlgImcVVnmBQEIQAAC EIAABBoSwEQ2hEvXEIAABCAAAQhAYFYCmMhZlWVeEIAABCAAAQhAoCGB/wN8l64Ky1e+agAA AABJRU5ErkJggg==</item> <item item-id="25">iVBORw0KGgoAAAANSUhEUgAAAPwAAAASCAYAAAB/5bOVAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA ArhJREFUeF7tWguWgjAMdM/jebwP5/E83ocVFbcbkmbSNlAgvLfvrVjzmcw0KfozPq9LXIFA IHAOBCbBxxUIBALnQOByjjQjy0AgEHhN8wFDIBAInAeBf4J/HmKm8/zib4ZDej+9n0I33afX vLYlxB42rXFzubbMMWerxDfFrMSGNT+POnnY/DzXeulAutbAq6Xvr0Y5Ykv3aJK1r62EaU16 q3+0yOg6q//W69M4vWP2tO9lG7WLrmtdP6u9l+gtgkfWptOAZb01+DVt53Li4t4TAbSJrLYu VuxK/HnhjdpF15Xk1vIzqwqejv3pKFbaaVKb3LTBja3Ur2UknH1IdtNRUBsHJVupQDQ/0uhJ saailt73Iu5ea3/EerMdPicktCNw5JFESYmLEI+zJXWT3IaC+KIi5PxYNy1OzGj8XrlnsXgM 4/U5Et7uZT1nj7VHa4RwCLU1a0HiHPe+pEkWc2Q0lhLSuphmWxIJCqAlUURMGpVRoa0Zfy0B 0A38b91jHK78w91LZjfoETutTj3GXFvvTc/wnoLPTSnpqKaJPCeILeLXJieUpJQ43GsLNtpa NC5NhFrctO6S34UfZoLpIebW9T6s4HME3IPgpfhbE+DoHV6aMltsLNLUyNUO3Ty0qTg6/Ach qdtKnQspuAa+Z4dHjyu1BLALXuvl/Pso4ZG6oLZqa4/68Yy59QZv+uENJcc8PiE72gw+FUna bbn/tU6djnC18b0/fx9v0/eVwnlU84fu+rkjR4oVRzrtnhQjtZsj9AL3Bg/t+q/9km1Hq7fb T2uRXa+sV6zzqfswjA9HVz3i02NMjiVY1XQv2LoJXhqnVkW5yNn7KXTp10+oy14IgE4kaF6x DjvSbIVT14KnY2/uCLEVgJYjB5dPL3H3uAH1jJd0jJU42wt3XQXfC5kjjkAgEHgj8Au4iqga 5+3YfAAAAABJRU5ErkJggg==</item> <item item-id="26">iVBORw0KGgoAAAANSUhEUgAAAWUAAAAnCAYAAADXYlloAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA BoFJREFUeF7tXe2R2zgM9f26JtKBm7gmVEg6cD3+l17cj07aDbM0FiAAkhKo8O1MZrJrEh8P 4CME0dI/6/Zzww8QAAJAAAiMgcBOyvgBAkAgAIHnshVEy/oMUA2VkQg81+V2Wxch8LdI06Ab CMyKwHO5rTdpVc4KymR+7zlwf7y+eQ1SniwR4G48Aq/HXayS4q2DBWcisOcCJWaQ8pkRgC4g 8Hqs94Er5K2pihidjMBzua95wYwInBwAqJsZgdf6uI/bQ94JGaQckJ/7Rn1/rKmRAVIOiAFU TorAtvgWpoc4Ehog5Zho5NUySDkmBtA6IQKvx/J2mToiBCDloKhsJ3FSbxmkHBQDqJ0NgbFb FykaIOWovNyOyf1uYYCUo2IAvZMhsC26gW/wgZSj0/Fr0wYpR8cC+udA4AL95D0QqJTj0jH1 lUHKcTGA5pkQyHqGI7sNUo6LTrrnAFKOiwE0z4TARsoX6F6gUg7MyfSlIpByYBCgeiIELkDK 6ZwyquWYvCySch6cFKArBYraTw/F0+TLP+f+T33nsDji4P0RMmm6aToi416jm4v10UtMw/BD /wVI+WicouSb4uM07giZIimfRThODNzDJT+ku8zS5qPJOfrmSA0xecGy6rCO8+rvPT6382ib zbpAyr3DbJJ3ZPx7y2ZJuaSktwEmRBsGaWQqVb9aVazJbTCZnXoG7lYd1nG9MfDK02LolWcd X8Tn54/1x0+rJIzrhcCROdtb9q///v3IkbeeslUJd4lYqjS5RaLJaK3YLeSpLV6rjNwX+n+p Mi8lXZJBMUpVuYRdHgNurqSTzstt9siUcMj15r5JenI/tblUhoRZr0WuxU38HJVyxxBsZ3qX r2dFlPL6SmvTXSlzjuekZV0MJSLkyKaG1Ch5SQTVg5STLo48KLlYNj0NH6pHwseiiyNF6g+N sbayPDH05E8tLlx83nzYHwZTeOC45q85P0HKVigN42ykfLW12UTKUrVD0bRWmhZi8ZCMtBBL 9uQVYWkht/hk8aGWfLQNxlPhSUR8pv2lzcHzmUrKf4DZv1H1+ZS0b/8MZ9lUbEDKBrK1Dmkj ZY5v1Phtk45em9Wk7F2wGlmUKjFtrodoNKK+CilTwuAqZ0uCSRV3j0q5VM1L9mubXe2CsJOy lRC+j2PxphW4QsrsZsBtEBP8jY9EYdPMHnupFYZe/vKsk9a16SZlSlpatVJamDWfmS8Tfw/U FjmVdxVStmxEI5OyZL8Wr+NJub5SNuGNSrl+1/s2c/xKWdocSrniPhKnlfwlZdZq2FIZmxYA c6lRqpgsekubgpUwLLZbZWmVv2cFWHUeaX88KXsQ+xpbyu03iTh9UQcwO2t8UtbymXOLPX2R E09einOsz1WW3OKWjEvyadVMkzzXYyGFRFbc5aC1SrNuMBQv7vfcT85nziZqO4eRZKNs++cb dKWXdUrxpjZrMSjZTmNTin0+lstLKSelvBIZofFGn+bvH72olMNI2cIpsWvzExp8zboyRTRS qhR72rTnQz9K1GLMiPgMYRNIuSWtppgLUm4I8xCL3G3/Z8/UcJDALZlWvE0COk8eJlYOUrZU bdqYSL9rdGv+dE6LIcVdnpS59kRNMtREp1VPpO01/mqtAk+rqId+j4zWWHl0FccaSZleapdk Wn2zjuvma6WgEeyMXJuXJ+XKuGMaEIhBoOJ5yhpJaZ8nR63jYoD50noVO4/CCc9TPgpZyAUC HAIVbx7RSCp9Ti/989/zyk8KDDe+dHWUk31uw/53rg1RsoFWpjMnD948MnP04XsAAr539GmE zBGg1ProKSvp5YiZq8yp7lJ7xmJnQOBOUol39J0ENNQAgYSA723WFoKyEl5PWRwpc1W11DoB KUsrAm+zBlcAgdMRSD1DTTFLosx56jNJWWozcLZaibdkv4bRX/d5ds8Br4P666ILh4ZFwNhX tlS2tGItVbAWeVaCL1XFaF/UZ17qJ3/EsV4MZgIBIOBDQG9hlKpMjRBbe8q5/NpqV2tvWOX6 cL366Pf7DSDlq8cT9l8Lge0yVfqqeyI0y4mJvCotnXjgqlcOMO0UhGSTVTc3rsbfawXbZm1e JaNStmGGUUCgKwLpSwJdhTYKs7Q4GlVgOocAc34dlTJSBQgEIPBcjv/Ku8ctkLIHrU5jhasm kHInfCEGCHgR2In5dlvWp3di5XjaouB+rxSNaS4ElCc2umRhMBAAAkAACByKwP/O1oLW/yey 6wAAAABJRU5ErkJggg==</item> <item item-id="27">iVBORw0KGgoAAAANSUhEUgAAA6wAAAAnCAYAAAALpMXCAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA Dr5JREFUeF7tnduR4zgSRbVf68R4ICfWCRkyHsie+htfyh8t1dXsYUFA3psgSFGl0xETMV0E E5knX8imqPrPbfpz4g8EIAABCEAAAhCAAAQgAAEIQOBoBO4DK38gAAEIQAACEHgBAh+X6R+Z L7ePzVX9uF1Op9tl+402t4QNIAABCEDgtQmcXlt9tIcABCAAAQi8B4GPy+l22nmCvO95vn6+ B2CshAAEIACBQxJgYD2kW1AKAhCAAAQg8C+Bz+v5aU8773sztBKNEIAABCDwLAIMrM8iz74Q gAAEIAABh8Dn9XYOnqxOrxrdv4vi4b9Z9PLa/Wfz31v/f79e/vm4nG88aHWcxRoIQAACEBhN gIF1NFHkQQACEIAABIYR+Lxdz/qd1dqQufxZeX3+e+vnD+rfh+bz9caHg4c5FkEQgAAEIGAS YGA1QbEMAhCAAAQgsDuBaVC8GI82Nx9YJ8N5yrq799kQAhCAAAQmAgyshAEEIAABCEDgoAQ+ rxfro7hqYL2bp56m1mR8wzJ9QzHvsh40UFALAhCAwA8mwMD6g52LaRCAAAQg8MoEvI8Dz8No +R5rafnqgXX6ZToXPhb8ygGF7hCAAARekgAD60u6DaUhAAEIQODnE5gGRPPX2DhPWJdPWct3 WOXT1V+w/QH65/sGCyEAAQhAYC8CDKx7kWYfCEAAAhCAQIaA+f7qchBdio+G2L6BlfdYM+5j LQQgAAEIjCHAwDqGI1IgAAEIQAACYwkk3hnd5wnr9IzVfKd2LAikQQACEIDAOxNgYH1n72M7 BCAAAQgcl8A0sJqfCH74QqXWU9faz72PA39h+ryebZ2OCxbNIAABCEDglQgwsL6St9AVAhCA AATeh4A5sJZftjT/vQVKfflSBJiB9X3CD0shAAEIHIVAemDN/EusMlI1VXX/HtdH2rvUN2N7 tDYjJzq8lL9gvnYA6uVdOxwp+eoApu53dR3Br9xrC5nZPbaKW4drz957MKvpXtt3GVvzPSoe FZdyn7Xy7vspGaUdSx1q/1++06hs2vK6qhktX47WqRbLTqw6ayxdzYHVkjVo0eiBNYpjxdHN 317TlfyoVsw5WqshZf5m9FtTS1RerbG3xmJpe8bGPdcqm4/cA1xOmTqmcs7dM/J9tEemr6m1 ZQ6WvszaEq0fzc09ryzX9Zy9RjHo2btWy0p9lnLTA+so47KFrAfGURzZYubYNGqN8lutkUWB o+Qp9q0CGt2ndHRYKb0ydqniNUrWmvhpFeytdeuRn/Vfzx5uE+iJT6WPit8eXyk9W3tGujzL D9Fh4lvTOj22rS0OC62DrNu7hnB8g4G1FvfK32Udd3JL5Wd4UCpizskrFT89+d6KPVUHoph1 7lV8s9ezvth6veMrFaeOjorTmpiIzgnZej+kdv1WKLt3i8EsRzHMXnf85q4Zye0dz32qFjGw upG4wTonuEetUeqrJO8tpC39VWA6+zk6R3Y7bBW3tUWlV77Dxz1Yr9Fh5L1b+kPFgRNLa/Xb Yg+VR87BesRBbGQcqNhu2bzWPzUbeg5bQ/Pu779uf/09mu46ef/877/DdYp8p/zqDhtrrHZy 18mjpRxll6pZy+uqDkQ5tebeVqw7Mtf4Y/S9bgyt8ZkTH6r29dqdrWNr7XRr4JrzobPH3nE4 ilsm9595Bu2Nx6h2RT77M7AuE7ZVVMs187pasivg8z21RCrl1da27p8TvqarE0wtG5fJsdy7 1KPFwi2IpSNb9zk+UsFU2uE2QCdWVDFxCknLxkyTjJI5imelf+twuyauo5iODtOtfKnlQqT3 cn25LsrBZZON4rXl8x67l7VnRM6XBwUVn04tKfWq1boaZ5W3bp66eaJszegzam2Lb6Sr9sn0 O0Qv1+k3ifp/Ip+p/Wr3qv7wkAsv+4Q1xzqq9ctcd/2vao0fAV8r3ThwY0Kt6+kvqpb05FSt hjj1wllT94EXN2Xvfuce4MSyG79Rz0jXrkreqBht7V/LwWita0ctjqK9evKydT4r65pbE1r1 sLSl5a8lG6e2tvRfcqrt9dDLFvCW16LYLP347QlrzeDSeWVjiRpNy7mtA5ebVO66EqhKbFVk 1b4OC2dNi08rkbIyVQMui38UjMu1jtwyWXqDOrNXy+81f9YS2CkkKjZ6Yt6J15bvszFRK0pR 0a8Vvdr6Xi5Z29fEw9LnqmFG8VrqrApxWbSdOKvtERV/h4uqew++mH436Hn6iKT77bXKl06f UH5p1cbvsr3DsFt/lb+iHHSv3d5oYG3lVlSbWj3eyT03Llv54epbxpOKGycfWr1L9dOWbKeG ZGSna8ofxfwcbZ1LovxVZwHVNxWnZ/QAJ44zfbispZl7Vd65vsn0xiifnDhcxlH5/4ptL5sM p1bPV+ctp85EtVXZpq63fK1sj3z2MLBmIKiNs4XXBeCuc5PDOQypg59iUSv2bkApO2Y5jryW rUr/qIhFRb6lk1tIlG9qXFWRcRioxpVpTJl4HeXDZ+k/stlFflRx1csxyqWRTGv1pPWzLIew 4P9+F0/VDI/fdLg8n+pf/rRymlX+VX2qzsw/DGdrhOp13TXgjQZWh7nbh1U/8+L7SyPVq9xY XcrK7K9sdnSMaouyT9X0+brqxZ7Nfo4q7t5+jz5+tR7gnHcy9Uf5O3Pei3yQvab8rWqwU19G 9vgM895Ydc/lrRjp1VHVjF65qpYdbmBdFr1W8JRrnCBzA6IXtHufo+syuFRglM3CKV4jGmDk G6W/CspWYSlty7KM7t868VtxvdUhJstGxa+TlzW/KblOrLiNyI0bJ46cvFO5Ftnu5kB2DyW3 PIz1HAKUTu3r/oDr8o/y6kuPYM+z/niwHb+Vp85RDrrX1BPWWi8c9bOWH9vfEtzPOurPmTwq 49s5pEbxvDY/7PgRSZVhkLXZyTV1fnL6TVtGX9xkYkbVrJr+DpeMXOfc1arfYR0z/nEwG4du feqJy7aN/9LMsM/GgctCxbzyZ60O1847zj7ROanlK0euYtHqr8o/Sq5rT1nLDjewtgqAUxCj RHCcVyv0NfCzE13oIwNK6eja2VNoHDuU3FYzVclfiwt3Lyemyv3deHMOB8on83W1rrTDLQqO XFeW0iHimLmWPQg8FLbKN8lm4kAVZKWfE89b7NGqgWWMqZhzYmbtISpi2MNG6+w/vRmZ124s 1PqKGlidOBy9xvu1NjnWke9UnW/1aLc/Z+Mwc87I6p6pUZF9Tszt3Yt1ft418uOmJU/VNvcs 0VODVH9U8RDF1po8zvb36PzTyrcMLxULo2Q5clq2Kh1Vjqn7e+I0s6faPyNLxXXmbKdyIPLZ oQdWN5Cc5HKcVyvgkWy3Ibp2OIVU6egWvDVBEwWnkrumSTqye/2cscnxkxOT6sChGpTbhBwm rixVmNT1LBf3AKeKb+ZQetfRaXRr/NPKU8dXKm5aMpz8ifJT2Tvyeg9/zc4/DLc4uP1gTR58 4/iy3xKcY91bf1p5r2Jdx8r3jwIrect4yda4rC5urcvorA6kSpaTr46dawdWpafbT16pBzh1 N5Nfw2rXJEjFhduronNtFFdqf859339RjFu7FNdMvDk5O6+xv3Tpzw2Lw1yZ1K0krxXDeW1N WadYtO6fA7AmQxXM1j2lE2t7KxbL6zWWUSGt7V8eWtWa1pAV2bK81vLTknf5/9GhvJQdxYHS MWJxu33cLveYrXxsJvJZ6S8VO2XcZeI6KsaR/irWo+KTycnMPq0YiHK6nct535W5pfwW1aFa XCl5Ltda/NdlawZRnpY2RH93r902/tIl1yfqkP1Y8/JDlNMTsrW75uemD9/gHVYnF6KYaPko ykX3kJ/pPcs6HsVNrVc7daUVIw6/I/Rix8bswBqxbPeV7953640rb3wPcKK1vUblTnQ2nK9F tkf9J1Xrfm/mxnN0Llnq3fJHaVt5dmmdWVz/9ur3Jb/d9zPnMZVz6uyQiZ2S1+iY+WX3ulR4 r7tbyfdeFHLWqoTJSetf/XHV76v1S9/+zq31P4qfaiS3tn17763fYW8GR46H9TSPJSHsKy87 sB6LMdrUn3btyYWasiftffZSZ+Jn+/zZ+6/1wtZ9/4h8ophiYE1ElErOhKi3WvrcpPj6Qgfj ewkO6pN99H+uj1ro97H9oI7/rdb+DI4ZC8f20hrtRg6stX/V7n0y1MzK6/mF6+kaT73+vc/K 7Wft+/oeO7YFzpn42b5/9v59Htyn7x+NjYqnTQbWzCP9Pmf237VWN/UIvUeztTq19txKbtbG oyVFVv95/VF4jtZ/9IG2V7+t7tvSb1vK3oqHkvtT8lXZeaTrzb7S8YRVNf21dntfurR2l33u P1L+7qHLs3L7Wfvu0bv38Ns+2dC3izoTH8H3fZZ93fXq/m3pf8Rzn+pdmwysa4KDeyEAAQhA AAIQmAhMA+v5+plCoZp+Slhl8ef1ckuqtHZL7ocABCAAgTcnwMD65gGA+RCAAAQgcFAC0xdc XZLToTOw1v7V3SXwcTkzsLqwWAcBCEAAAkMIMLAOwYgQCEAAAhCAwGgC07dFJl/Adz7CVg61 /sf27u9WXabvsOQPBCAAAQhAYD8CDKz7sWYnCEAAAhCAQIJAfkB0nrAuFfCH1ftd0wB9fu1v XE/AZykEIAABCByEAAPrQRyBGhCAAAQgAIGSQPad0czAmhtW7/Nq/p1aPAoBCEAAAhBYS4CB dS1B7ocABCAAAQhsRSD5Hqs7sKaH1V/zKu+vbuVm5EIAAhCAQJsAAyvRAQEIQAACEDgsgdzH gp2Bte8d1vz7tIdFimIQgAAEIPBSBBhYX8pdKAsBCEAAAm9HYPoo7sn48qXa79ZTP5uvK6Y8 XVWEuA4BCEAAAlsRYGDdiixyIQABCEAAAoMIfF7PN2NmHbRbIYZ3V7fhilQIQAACELAIMLBa mFgEAQhAAAIQeC6Bj8tp/6HVfLr7XDLsDgEIQAACP5kAA+tP9i62QQACEIDAjyJwH1pPpz1+ F+r0zupp2utpj3V/lNswBgIQgAAEVhBgYF0Bj1shAAEIQAACEIAABCAAAQhAYDsC/weSHWR9 5T9WvQAAAABJRU5ErkJggg==</item> <item item-id="28">iVBORw0KGgoAAAANSUhEUgAAAukAAAAoCAYAAABD72QzAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA C1FJREFUeF7tnQtyGzkMRLPnyXlyH5/H58l9tHGyStFcAN3gfDSynqtSZWtIEHgAB019on9u v36+8QMBCEAAAhCAAAQgAAEIXIfAh0jnBwIQgAAEIAABCEAAAhC4DoFv13EFTyAAAQhAAAIQ gAAEIACB3+90AQMEIAABCEDgagR+vd685NLHvNW5SwsGk7au78QQreHMm90d59x/X7HzW1AM OatszdfceVV+tjK/+3+knT1sb2Wwmltnb8y2xzyPfOfHVUx3biv16dbh6EPktxP/EWPW7oJH eIJNCEAAAhCAwA4EKjG0IpRW5mwNYzWGFV9nkXz3vWtrHp/ZjQT9LJI6PnT9VKJwa+7m+KKD 0R5rrNrYk5eKrVMTWTxb69Otwy01uJoLNQ+RrghxHQIQgAAEnorAqsB1RMJZIFZjWBFgW0VQ JkpXxJES8BH/lZiPzvVq/s6orz15RSJ9Ne9OTirb3fnVYao6XJyRo7+H1DMXYy0IQAACEIDA /PL1vVlGL2vP1yp60cvo98dGO3Ojjxpy5kslBKr1Iz9GW+N1JR6isaO/fxu8eMtQ5u88X+Vr i0iPBGNHiI0xOH46+Rvjj7hW+ZnnRnU052+ug6zm5xqaa9qta+cAobgqjlkNZjwje259qjuq e2hwx6n19rzOM+l70sQWBCAAAQhYBLKGODdxV7BF49zHRtGl5mRiIhNameBVwtYRUpENVwRV /CMR6eYrOnhkQjwTq5E4q4pK+aZEtcq54qFqoqrp7tyoVp094+4jx76Ti+pwUx1SInEfHT6q /TH7V9WTUzuqfqwb3uIgRPoiOKZBAAIQgMA6AdUcoybfFa5jc87szQ08E5QqUkfozc2+Eh9u rJFNR8Ao/srX6vChhFZ2KHJsuqK28j8ScdHaWW2s8s2E61Z/Km7dfZQJUifmSuB3c9utT+fg EMWWHXDmeN341X2iex2R3iXGeAhAAAIQ2ExANeGuuFAi2RGORwqUKh7X9yiGTFyqBCn+Z4n0 jujOYurG4oribJwj2KqcqvluPWRCUs3vrF/VnJO7VbG7Naejb5mt6lCx6rfad93riPQuMcZD AAIQgMBmAqoJ7y3SlT1XRFeBZ8/KRWKg81glKiORnh02HOGi8pKJ90j4OeIo8rWa5wjDSFhW wrQjapXArdaOasyNR/nYqScVgyP+3QPTqtjt1qGzL7N94fiomG2+ISYGEOlHkcUuBCAAAQik BFQTVqI6Ezcf82bbVROex98budO4R0FWxTOusTIuEukjH8WyElSzb/e/FbPscDAzqfhGa82P fZ7/fvvxkd8f759CcvkqIdetnT/2Yp+iOsrylOU3ykFV207dr/DMas3xO/LJFbxZLUQ1leVh tJGt6+x11+e9b/mI9L2JYg8CEIAABCAwEXhUkz8zEWfF+P7+WaSfGWO21p4+ncXxHsuevj86 F0fEcnY+RoaI9EdXFOtDAAIQgIBNYH5m7MwGurJ2/KyfF+7KetWzm1t8cTw+Phc/b2/fv92m J9Id136P2YPn/xdb9ynyJ3p1wQ6wPXDN9z057mdrLRYH2fF1nXuBSHcyxBgIQAACEIAABCAA AQicSACRfiJsloIABCAAAQhAAAIQgIBDAJHuUGIMBCAAAQhsJqBe3uf6nw+98g8G1MB1a2Dz jbBhAJHegMVQCEAAAhCAAAQgAAEInEEAkX4GZdaAAAQgAAEIQAACEIBAgwAivQGLoRCAAAQg AAEIQAACEDiDACL9DMqsAQEIQAACEIDAYQSiL6R55H+dd1igGH4pAoj0l0o3wUIAAhCAAAS+ FoH5/38fxTlC/Wvl+tWiQaS/WsaJFwIQgAAEIPDFCGTCHJH+xRL9YuEg0l8s4YQLAQhAAAIQ +GoEEOlfLaPE80EAkU4dQAACEIAABCDw1AQQ6U+dPpxPCCDSKQ0IQAACEIAABJ6aACL9qdOH 83uJ9D3f3zV/2OOKWdoz3jG+TuzV2I6d+/rjN5n9fjnlv2+4y36PPjUfxTJ/Q1pnTPTtaiv1 sMJDrXOEzXlNtcZRdahiv9eEM261vru2q/ERx7nexzqvarazTvbtgJ3YlI3VfXtW7UTrqLru 8GEsBNSeHHtc9DsEIfBoAu37+BUcdnzY2mi2znd87I5xfNprTCQKZ1EVCbKs8Sp7ji1H/HeY Oqw69iI+q/Odea7/7jhnzSPHPMrPTKirmu36q+o32gOKt9pv2ZqVL924lI/RdcX8DB9W/GbO 1yEw1lj2+9eJlkiemUDnPv7wt7u4N293XJa4rfOPKAjHp73GuAJFCY/MHyUuHMGi1lY5cFgp G4+sH9d/d9xqrHvNe5SfSjDe49vqn1Ov3TXUPurc3B99yEQo7bWTsKMI3Pf8XHPd/afW4ToE 9iDQuY//FeljY6tOpPdr8/jOZhg3VOTs3GSrDZg1tcg/x0cV1+z77FskEO4C1Wnqc2PNBIeT I1VMyh8lGJQIqOw7tp18jYIry92qWNhap1HdVjmJ9tZcO45NVcOjzZnN6ENVt6rOs+tR/LO/ kX9O3LP4VnlX1ytfs32pDsPdPRkdbvfct8qfzvXufu/YZiwEIACBqxOIell0D+/c1z89k94V OXOjckRV50ZeCdFMIGb2V3ybQSrfnabvjKnErxK3TpyzmHH/zoptFlWrAk6JD7VBo/mR8HUY qVx360/5Hglit/6V8N1Sx925Ve1WDBzR6+Rt9FfttVH4O7ZVfc72HJuRqFd+OX60Dgs/327f f30u5ce7U6X5mM6e2bYSsyEAAQhck4CjozeJ9EiwuWLBaUqdG/nKupkPK77tKVDGxpuJ4kxs KeEzCtFu2c5zla0O306uI6G/GkunhrM1Or5XYtCpu6we3Prv1k0ntj33gBLpVRyrHKt6ViJe CV0llqMbsarpKM7qsWz/Kt9jP37e3r5/+/tB8k+HBUPBd+tKseA6BCAAgWcj0Onbjv763zPp HYHjNCnVLOaAPjWGX8/uZP4445S4PVLcOIlyhIfTtKMDgFvYTpE4HF0/XaHmsOnWViaGV+og q79RmHVjcEWOY1fZ6uwf9xCycj9wcuLEm9WoK9Kd/aLic/aAWse1sde+Vf50rqua69hiLAQg AIHLEwhehXS039z3qidbLifSs6RUQmFVRK6slQlit0G5cSjx0ikERxhXRbLKV4majsh2NutK DpwaGIX3/Hsl8LeIy2pNx67LQuVAxe4KeJW/LKZKZHdy54pfZ69ke/OINbJ6O0ak80y6qlOu QwACEHD7hNJ7zn380iLdFaKOUHCETdR8FWSnYbtxVIKvElNdMayEWcVKialK0Cs/t4ocV5g6 teDaUiKqcztz1zzSf5UD10fnMKMOf6peXlmkR3xX9m2nPtXYTm0oW1yHAAQg8IwEunpP9Tn7 g6Oj4o9+vzcNV0B8jLv/m5t19PjclLL5ox8q+EisRnNm6NHas8/V3xm/TKRH60fMxgODkwfn gKEE/czbyWvFT/G/3d5vPz5qJ3iPrGLe4R7F5dZpLVBz/1XtZpu/qptqL1X7z6mvTNDne7Of uzlnqq6r+uvkrzoAqPqt7k1O/Y7zq3g797eS2w4fHFX7/hmbLT5DAAIQ6BIY+2r2+9yLyr+7 Drzy+EyUvBITJZLOYvH+9nb7edZiB6xztP9XyVOE7ujYD0jX7ibPZnDletgdLgYhAAEIfBEC D/8yo2fiiEj/k63HNvw/75s1/rOJi5bWOf4/NkcZ+nNiv2ji/3PrfAbXrIVrZwnvIAABCFyB wCEifX65NnvJ/REAtvrmvhTdiW2rT9laR9rtxHfVsUfxOSvezP/67RZneXfcOkfm7UjbxxGp LSPSH0WedSEAAQhsI3CISN/mErMhAAEIQAACEIAABCDw2gT+BY4I3BH6yk8RAAAAAElFTkSu QmCC</item> <item item-id="29">iVBORw0KGgoAAAANSUhEUgAAAOoAAAAnCAYAAAAB4tQkAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA BKtJREFUeF7tXdtx4zAM1M3cd/5chZpwE/q4GvyVDlRP6kk/iuRHjqYJYEGKlEkimUxmJEog F1hi+TD9Z17mZbAfQ8AQeG8ENqLabwICX+Pa0Y3LZChmRGBaxmFYxq8EP2WsXYlaDSWMtGpj moZlmIyipfy74X2aL5VTLg4tI2qk2y/zqesePi7c0p/acO+RrEbUGKJ+n5eTZdIY5HZ5ZppO y/k7nfQ1vcGIqg6dy3IebUx6aJBvHeV4XnoSwUZULVHXIBk7HScdSk7PT71lVSOqkqiXeexO dmkIuq5xbMt9SlQ1Fu5l19n2nsaqRlRVSJns5ShVhKC//lqXbDqSv0ZUFVHX4LBJJBKxskTt q9M0omqIauNTEa2SZO1pnGpEFUPPEXudjYsiRo5XNEuRtaf5AiOqkqg9b2NDiVuOqP1sOjGi GlE1CLBli8343mvR0+6wIFEfgLvAl+ol0V5bmn302+DW3733kGqP+36bn4Jvlb5SRqWC1b8u BbVfR7eeNfliD39S78hBVNR/oTrl9OkLUUNBIFUgpzNi30214/E+/75LVNfmUzmBqH5nELJF laEc7183kv5HZG+iav2H+oyMp7syQHz6RFTuAeRlsaTK8VwWon5+LB+fWG1zOb02P2BoxZX6 d/4L+wOxkMtnRYnqGwtJydCMXygbc7LOl6KcXUn+StmIyqpU1p0B6avJohSGpP3AjKqLZY3K ByFQKem7N1Gp2Efi8sWvEttRMqRUyg1MBCzEuUgA5yIq915N+0JtUNd5t6kiBPWyZfaWvlSS iB2uUKpOis2Qj2HpG8psUgMk+elnYDTAkXBAbLv2H+X9/0+2wIyKtoO1RaxHctKXu4dgVlsZ mqjbrqXbnuOXP2ZnGdKJShjn8qmaqEhjuCyNBjElo9FgOoqokl0fv5AS0UhfrqyEFZWx3+36 20rf7eN23hExLnahekvxQXEHJmqop5AyqjRe02RUbUCigFCZNNhzAhlVsqvp6KR3aTGRiFvb /XfOqKFEk3INXp6RSIdKspSMSjV0z56LtSHM+iIk5Do3DhuKlMgztREQrW8Ns76If6Qy1yxN BTmXwl255makkEEqK4Qkn3/Nt8N1BtQYWpIiKokNrKNS9mLb6xObwohu5+0EvxYPYSs1mfSI EST+kBiO8altIdTMigLSF80GpctNc3tHl+Qgamm/oPaMqM0T9TYDGrv1kVNc/r2QauCykba8 b8+Iqgneg8q6Ug+Rt2jPxZarOKNy7eLGzSEyauYrUsbtktQ0oh5Evl3IlLPuHXweVSKHPxdQ irSh2LDPo+YM9prf3cEJDzUR1U54qJlMWeve9plJsST1Z873krt8fezMpKyh/vbylm1928FR F1HtFEIjKoNAq+OiIEkDW+SQ3TVFMmoH8wVP8wF1Z7gDat/oODUlmx4hfXsan17xPSDUK8/Y 7cnf+pZn2p4rCK5dG1EjENi+vLihg7j9NWlpzVWzTTLHhofesqll1ITc3tNie0RXloCsYK2z senvOvVbOSGfe7O8efsGbGlrnuG7IwKNKRkNMjZGTaTwRtZhsO9L1QSdvmy7nwBCsTCiJhIV BdrKGQIpCPwAf1X3HUuRH+cAAAAASUVORK5CYII=</item> <item item-id="30">iVBORw0KGgoAAAANSUhEUgAAAZIAAAAnCAYAAAAl8s6IAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA BstJREFUeF7tXduR3DgMnKu6b/9NFEpiktDHxbBfzmDicTzORyftlmwthwQaFEmRmLZry94R RQKNBpqkHvPPc3kuN/4hAkSACBABIpCLwCYk/EsEmiHwa1onLtMyk3VE4C0QmJfpdlumX80y 7BJUb77do3c9ITDPt+U2U0J6igltaYPAxv378+OSIt/CQwqJ29C2oA8+xsfz7n5WhqPBlu+I wJYDXsWEQkIhqY/A78dy50qkPs4coXsE5vm+PH77k1EKSffUG510H8tj4jWR0aNI+wshsE2q psfibZOLQkIhqYvAmjiT473hQuWlbgzYe1cIeFyVUEi6opi/svTxnFwu5f1F6vMxgM8fj751 5dN656K3ayUUEqZNRQS4rdVVARMiTQFpGan1lmBn21sUkopltCU1+xxrTRheZB+CYRSSlhnk b4JFIRkizVuSvOBYvD4yFLsoJgW5r0Te23USCslQqd6O6EVGcrgXXASXjjlHMWkTYW/XDikk HSd1G0pXHGUVEu+vhqiI3iXMpJC0iai3B3QpJJekaxuyXj4KhWQYdvGOrbbZ8vZCUnLGMgJ5 S/p7pKrFd6mtpZ9UqoR97L+H/5pTDRASxP5UGw0Xq73oOJrNR9x2G2KfWe3z3v4s32K52mtM rxCSM/ho3Lt8RYIWarSdVCw1MFofR3wq1UbzLRwnRTqtn2/HFSE5jpHyM9VGOlcr9DEfrONo cSmC3zDrGRMrkl5pmEqjxGLec0xbC0kJfET8y1AgvxeUPGg7CkleLKoIyc8fy4+faXtqCck2 4hm+WAuQJkzHVUledN7jrDMxi8XcGkdt/JKTg/8e/4q5USPiUo4jWEFCclQsqdP9WNheC0Js WyelkuHn+++hXamZZ9g+dj6yzaPZd+w3hkvM5/AzZJYVIwASI42MMfulopeKffKci1YkrYVE m+2l8Anxt+SQFtsRj5/1/2yhjNUXLV9TuanVnNYrkhJCCwnJPpClWMQKvkZgNNih46nCKbU7 HkNIGmtjGVdqGysmOTZZbURXaNaEOMY+/P+fMQcUEpSfWqxjIiH1fVb8tLwb4bhWfDUfzsau ZUzdC4lW8NDCmlPAcoigCYk0ww5ttBZpCxaxJCktJEh/0uzYMvtK+f7NhjcQkhTmGpcQ7mmF 0/NxC5cRTiN83du0iGlaSLan3r/eefbyc/ItEWh9PS4q0Dh8u9huAVtbKuUKSQheStiQdlph jNloBbsUiVG8UrgfVwVIgcmZHVv58RxMSLTiH/ovYa71lcPN8JxosYkVoAs/Q7iI5CHSD5q7 Eo9bxbTcigQXHhSfHG52JyRIQZVU0jKbyBnrmLwS4Oaim7iXBfEnNZaUfBSSV3S04o/EVFoB a/0jxfJd2qAz4ZI5qOURGj/E9t5XJBKPo8KfCgSSNFoxQmYaUvAQG8IZesomJLip1Q8CKqr2 GllDzKxCEluxWOOQ6gOJxzd7B7pry+qbxn2k6Gh9vItoaHmH4nA2B7V4lIxpz3dtIfXupU7F lst7IdmB22fh0ufhMW02LM3sj+NJQie1ix3TxCR1TlhkYraH50q/x3CVVkex8VNxKxGHo39a nEROZD6QGEvoVBJLn78eW99GvG3zRPaaQ58lfGMYx3IkxZtjokrjPpe0vWhx7a+d7FMqpzUs EJ72EdMvK8ptbWERtuCTjkF6rMsfSMRg6KMVMiPpw9JOrACE5ApL5+dYX3U6mr1ITHN9yj0P sallm9ZCUts3Conh6WEKiZGO3QnJ14XJcV4kidlrmUFqq/iXLYvEtyZa+/nbL+bTK9NyzzNy 1lAPzvRMIQGARrZGzgThzLlnbZO3IvIsO2uTti1Ww2bI0+6EBLIaYHg//YTbZ5Jlqba1P+8H rX4soZAMlWb9EOctLeH3kTTNltiKWZpk7McoJO2zk99H0jQ12geYIxZEgN+Q2DRbKCQFuVs5 cvyGxMoAj0MFWqojwO9s1zEq0yJXRLbRuSIpEwO8F35nO2WECBgQ8JcweLFo25JC0hbvc6Ot E6xprDsHNX9515ahLGpg8vgrAt72gnuMcVRE1m3F+3rHVXiHmnbnYY3VSY+YXWqTw2uHFBIK SV0EeJ2kLr7G716hkFwqIZ9c8HZ95HN79HpYaYFvBLi9VTO+vP23Jro1+vZ53ZBCUn2+WIOM g/W5LuVjryUZzIsumWJ5Rkhqa33A0Nqesf5CwONqhCuSLkuDz5Tz9gCWzyjRq6oIOLw2suPF FQnFpBkC8zzS60mqlpRmmNOLThBwviqnkDClmyKwicntNq3vte0kwWkHEaiKgMc3OL/mLoWk KolYLIkAESAC/hH4H/M6nviou/swAAAAAElFTkSuQmCC</item> <item item-id="31">iVBORw0KGgoAAAANSUhEUgAAAOoAAAAnCAYAAAAB4tQkAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA BKxJREFUeF7tncuR2zAMhpWZnPfmKtSEm9AhNfi0HaierWf74Ur2yqFpgAD4Eh9IJpMZmxZB AB9/kKLlP6tZzaR/1APqgbo9sIOqfyM88DVvE91sFvViRg8sZp4mM39FxCmjdSWsmkp00msf yzKZaVFES8V39/dlvTWOXJi3FNTAsN/Wy9AzfFi6xX9q9/uIsCqoIaB+X81FlTTEc0k+sywX c/2Oh76lKyio4tS5meusa9JTk3yfKOerGakIVlCloG5JMg+6TjoVTidOo6mqgioE9bbOw5Vd EkC3exz77T6hVyU9/LbddttHWqsqqKKU0rLXh1QRQJ/x2m7ZDFT+KqgiULfk0E0k1GNlQR1r 0lRQJaDq+pT0VklYR1qnKqhk6lnF3mDrooCV492bpWAdab9AQRWCOvIxNi645UAd59CJgqqg SjzgbVtsx/fXipFOh4GgHg63HV9qluTO2tTuozsG2377vaNUO953x/ySfFvpSykqlqzu61RS uzbadrYUixTxxK6RA1Ru/CCbcsb0DVQoCSgDcgYj9NrYOI7rue/boNp9vrQjQHUnA6gvrA0W ePd1hfS/R1KDKo0fN2ZoPv1WBpyYvoDq+wDnYqFQ5fhcFlA/P8zHJ8/aXEFvLQ48b4W1+nf9 y44Hp4dcMSsKqtsZVEpCO36QGvvKOrcU9fVLlb+UGmGqiqnuyih9JSqK+RDtH9hRtX3ZYuXD AahU6ZsaVCz3OXn5FleKdi4MMUbZiclxFie4nATOASrlB8n4oDGIbU62VcTxetk2qUtfTCRC lytYVUflJhRjdukLKRs1AKr8dBXYl4TSko/Tt93/0d79/yX1MikqNjbuGHwKXBadsr3hoO6n lh5njt/+eU6WcSZRKg+9+YPcY/Zd83k9qaJyBuO7pgRGSj24as8pJc8CFVJgjr1U+cRBBlPs 2l6vtvTdv27nPCLG9h1kt2TyfWGNCyo0U1CKSq3XJIoqVQ2uQzBAwVkuk6K6fpCCSs3yHGhb bFOzoqaOKfv2DAUdR74h432wc2FLOXNhDr73Qez6cqoNyXg544+pOlqE07a5hV1fTnyoNneV xpLcJ+F2uWYrEtQhlmxQyee+5vbDVQ6sdMMSk3LU83MMRYXGdcBP+Qcarws25iM8Xo8n+PX4 ELZSm0lY/Hzs2MLmMhISUz1CKNkVZYBaq0ota3+PLskBaq3xU1C7B/WxAxp69JFbhfhUR1Jl SNRLQZUk70ltpeVtkpmyYUX1jd+3buaUd75NwZh1O7XUUVBPgi8JTDltH+D7qBQc2I50KiAl k4Z+HzVnsrd87QGe8NASqPqEh5Zhymp7389M4kIquc2WSmnfqy19ZlLWVK++vPWOvu/kaAtU fQqhgurxQK/rIhBS4IhcNWvUAfYLbFHT2zPSaanTdapETWsofUdan9793XYpeob1/ZW/kp3W OhS1770C8JbYGanefJ/7jxd39CBu9540FR/suGKqgw3YdQ67RlNTVVRp2Wu1H+lmOwVu0fcH W5s+K5iiTo4Ao0Y791/Apo7m1Wh3szZ1VslI4qBr1MjJY4d1mvT3UiVJJ2/b7zeAuL5QUCNB 5Tpa26kHYjzwA9nq/xkwMkI5AAAAAElFTkSuQmCC</item> <item item-id="32">iVBORw0KGgoAAAANSUhEUgAAAZIAAAAnCAYAAAAl8s6IAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA BulJREFUeF7tXduR3CAQXFf5238bhZLYJPThGO7LGWw8jsf5yNJdySWzzEyDAMFs23Vln4Rg pufRA+jx7bk8lxv/EAEiQASIABHIRWAjEv4lAs0Q+D2thcu0zPQ6IvAWCMzLdLst0+9mEXYJ qjff6lG7nhCY59tym0khPdmEsrRBYPP9+/PjkiTfQkMSiVvTtnAffIyP5919VYajwZbviMAW A17JhERCIqmPwJ/HcudMpD7OHKF7BOb5vjz++KNREkn3rje6030sj4l7IqNbkfIXQmArqqbH 4m2Ri0RCIqmLwBo4k+O14ULppa4N2HtXCHiclZBIunIxf2np4zm5nMr7s9TnYwCfPx5160qn 9c5Fb3slJBKGTUUEuKzVVQJTLE0CaWmp9ZZgZ8tbJJKKabSla/Y51how3GQfwsNIJC0jyF+B RSIZIsxbOnnBsbg/MpR3kUwK+r5heW/7JCSSoUK9naMXGcnhWnARXDr2OZJJGwt72zskkXQc 1G1cuuIoK5F4fzVERfQu8UwSSRuLentAl0RySbi2cdbLRyGRDONdvGOrbbS8PZGUrFhGcN6S +h5dNUV3rW1KP1KohH3sv4f/JocaQCSI/FIbC5dUedFxLJmPuO0yxI6lyue9/Vl/i8Vqrza9 gkikXKblODT/XT4jgQU9eX87Ok7LYEVkKtXG0iscRwpKq5//zhtEchwDcXKpfUx2BLeQ2I9J P+X/MUyK4DfMfCbJK0StUm1mFWaIvyA+GPMF7RiCRmsiQQnVwlQsRhGla7ZBnQdtp1XdNfXI 6RvRqVQbS74qRPLrx/LjlzwyEsSpyeBsgG/X544pEVMJmSz7eTiP+Lqmp+bDvdn05+O7Ghs1 7IkUa6eJ5MhYGuj7ubB9ihPs18ZY8njuGIBh21g7qb3WNjSYpVcoe9h3CvNbmElyx7AIdUcc 0bJDDJs90R79QEyUF81IQjJAsJBIAE1AOUQc86VUWT21t+LB0rUkkUi5SYqJWO6RcsHWtvWM RIuJGO6x+FZJXGKgVGJBgxc1NloZau2O5xAn1QCN6RcmGSnpIElKMlJqgkL0tHQ5kgVCZpKv PAcjEit5IKSiJRTLlqjtrIQ66nnJ11B90NyC2FHKBRb5WzbedXFPJLHqEgG+VyIRq+XISu1Z IpEII0zMZ2QqUT2gRKJVX5BPDEYkOb4vJX/Ll6zKFk2eXtvlkGopImlhU5lItqfev9559vJz 8i0RqF5IQfziv0i1DCWNYG05p8LW2D5W/ccqGBSsXPkQvNDZFBIsSELKqeSsyskaF/IJ50Sy 4x7zJQs/rehAyCGaaGLJ5+JjiC4oflZfJYiklU3LzUhw4kFzY+hbFu6f+Q5JjFDSKEQkSILX EjAKlgaOxshS0j7rxIjeqbZCdURmKFnk6JxItJklSiRIMYEEsrc2ObicjUGrEi9p055nJDlF TtdEkkNgkjOlOCY6rgY40gciE0KM1uwCqfiQPpJ1GuiurVTdLLyQpGP14Y0cLH2sRJ5yfU7h Y9mjpE1HuGsrVmCKBW+YDHew9sp778w6HrazqmGtsj+OrVXhWrvYOStxS9eEDh6TPbxW+z2G pTYjiY0v2a2EHY76WXbS/MPabJdkjQW0FMTa8ddz69uIt6UeYa3Z8icUc80/NF9KlddKrH2e z7PBc8GuO+M7VsxK59Ns+mWVcktbmJWtOJZyo5Uz/83KMTHYSmJnFOi3RBB4sv0KXObnWJ86 HU1exKa5OuVeh8jUsk1rIqmt2+VPttdWsGT/UvVbcgxXfXVHJF8bk+O8SBKTV5pJabPc8FzK bEyb9dqyYDq9yp57XZ8RRSIBXtuALI1cZd6zsoXXl9DjrEzWslgNmSG9uyMSSGrAw/vpJ1xW sSSLJXqpj1LHLZne8TyJZKgwe0cX7Uhnfo+kabSgy6yxfYR/a92Hd9qRSOrFEr9H0jQ06hmS PTdAgF9IbBotJJIGPl3IovxCYiEgxzE5Jc1HgN9sz8cu7UqURPb9jWPvpWYeqctsaRp6as1v tpNGiEACAv4Cptd0RiLp1TIxudYCaxrrzkELXd61lZAWLTB5/hUBb2vBPdo4SiLrsuJ93e+I 3aHGPZKLrehw75BEQiKpiwD3SeriC76aSFrKCpe6aixzXZy2q+Ofqp+3/ZFPH0oFge2JQBoC XN5Kwyutdc6+BGckaRiXbe1z35BE0l29UtZtu+htncpLryXpQr6BfSD1GSHrVRmhPUo9qEg7 fyHgcTbCGcnACWS0wPT2ANZo+FPeDhBwuDeyo8oZCcmkGQLzPNLrSTpIPM0sQ12rI+B8Vk4i YbA2RWAjk9ttWt/lWj10OQIR6AAB/a3FXqKARNKBq3lxJupBBIjAeyLwF3JktO2IXIJ0AAAA AElFTkSuQmCC</item> <item item-id="33">iVBORw0KGgoAAAANSUhEUgAAAOQAAAAnCAYAAAAfK+SXAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA BJ1JREFUeF7tnV1yozAMgOlMn/uWU3CJXIKHPUOeegPO0/P0PiyQJmMUyZJsHLCl3dmZbTHY lvTpxzjOxziNU+d/XAIugXNIYAHS/2ZI4KefHVo/DS7FHSQwTH3XTf1Phj52GMWRvXdHdl57 38PQTd3gKO6tx0Wul/FWOVppUnEgE9V+Gy+mPXmaucnvWuRrEUoHMgXI3+t08ciYIjnVPcNw ma6/cohbaOlAqkxkUfltuvZeM77F+BfH119nib+lt1P04kBq1TAbSW+0vjkCC2tR0oFUAnkb e3NpFAfi/L5geXWmlCT31L/r8yq2pVrSgVSZkaerEKNiID71Mr8KMZS2OpAqIGfj8MWcjcTK A2nLCTqQGiC9fkSlVRpKS3WkA6kB0lg9I6zyVgmWhNJS3e5AKoG0vK0rBmhZIO1swnAgHUiN BF7aFl1h/evN0q4oFMiHkENhl/SAmtRI0haOHxpNeP2Rbj3mB+e8uXdOWbkIiRkoJc+YTOEY w3HWpAuJvrg2JYCkHInEwXBtcnT3AiSmbG4AnECPuE7N4zEWeD0EMhzvph0DZNiW+j/5bCRO cXM4Qq5H9Lk3kBI9cU4v5Tp3z+p0pQYiedgRyqL65Iw5Ccjvr+nrWzZLB1ImJ0mrf9dPsdwl z2sCSAgunFQswmDGD6Nu+HNOOkFFQGwlkIKSfIYgZcXupZwZJUPtHMIUqTbHKQHo7BESs1cu IMAS5KlDaYTEBLeXQYUGKPFeEiVCI5UKKCoPAZCUcjilUePj5sE5FYmszt5mbyBDGCh9c46N y4AwvXG6Wu9JATLsTOPNuSgVGzAnIGhUEgDD8cAIj/YnADImDzh/2GfqHLg+zw4cNz4ayGUX z30f7cu/yI4qidPn7K2U7tRASiYTg1wDHedRYoqsBUgscmqdHOboOCMnUybMuN/0O2rMe0dI iQ1v7Gf5GBg4WgQLSlxwoyDfjId7CDQOyWRinj4HSI3h1QKkFj7pvCRA1tLmzBGSskmpnl54 0KZJrQHJpdEb+TCrrBLZUG2k4+CyBi7VqgXCcJw1rLJyepHod426mIJgPo5Bi9Vc2KAoT4Gl avB34c+x1I4aHzcPKjqRRi2oIalxps4XAkzJiE6h7ie51XwY194pa5iux2ybK4kw3cBSILQx ie5865xm45gAyLNGoGGs9yiMEkCeVU8OZPNA3lciU7b8abMnTXYQi1KwXwdSY6QHtYXpqCal TfaOFUdILv2KLS5JFv5y6meu7nUgD4IsGZR3jdfA5yE5OKjrZYG0c46Rp6wamA2cGHBGIP3E AI2Rmmrb9pk6HIzUCiJc0s+Jlq9Zkp+pYwoxXZrctnFIgKTqyRwI4/36qXMOZEQCrZ7vgkKB bBl7O5AG6vaNTHURwluPjdaR2ui4Z5oa69tS/bjK1BHTSqC9tJVKN6n3kNgrkjIpa9s1Oypf rTl6+1kCy5e0NnRgsnSLIdwGBm1h740B1qKjR8iMatnSy+pDnLCx2vHp7A4RdgYIZxrv8k2/ 3Ja0M423mrE0loFo5O41ZKZzWL/WvPPvi9QYHd22/k+m5MrBgcwEMlcBfr9LIJTAfxzihqoW x4WPAAAAAElFTkSuQmCC</item> <item item-id="34">iVBORw0KGgoAAAANSUhEUgAAAYwAAAAnCAYAAAAclX/TAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA BuBJREFUeF7tXUuW2zgM9Lw36+x8Cl3Cl9AiZ+hVbuDz5Dy5jyJ3t2dkNkEUJJIC0ZW8vO7I FAkWCij+JP9zX+7LhX+IABEgAkSACGgIPASDf4lANwR+T+sAZVpmso4IhEJgXqbLZZl+d4uk U9C7xO4ee+cJgXm+LJeZUuHJJ7SlLgIPjl/vb6ck87o9yddGwQjr2h70wdt4u1/Dj75wNFgy MgIPrkcVDQoGBaM9An9uy5Uzi/Y4swU3CMzzdbn9iSeLFAw3FItHro8evS23iXsWUb3LfgkI PAZJ021lfyyEKBjBHOqOnmvgTIHXdN3hTT67QSDiLIOC4YZeMVPP230KOTWP4q31FOXjWD2j oAUC64nAaHsZFIwWRGGdnwhwOcqzsFAoWntnPWobbFmKgsHk3hCBNWC42d0Q32MJj4JxDD/9 7ngDJgqG23DW6ei+BPcv3LOLotE2iqLtY1Aw3Id0W0I3rT3gGm5TvE7iIkWjnVej7eFRME4K 0nYUdVTzKhjRX5XgCO3dTKZgtPNitAdWKRi7w6wdycLUTMFwzS6ekGofad9eMGqORkYgbM3+ bulp6XuprKUeKTzSOp7/T3+awwsQDMR+qYyGi9XeXH05DDSbt/c8bchds9oXpfxRXuViEuVI b9+dIRhSzrLgJuaKs0mIJmS0XCkpnt3XtH2kT7XKaH1P25HIpdXz8rkiGNs2EJJL5XO2I7il Ar5N7rnfpfI5TKrg53p+YmLCS0+svtEGWggvEK6VfL7X5t6CgQrno68WTP7DZr/b69yJOgIt R8HY55cmgvHrx/Ljl2wPQlhrMtASPYKO1S4KBoLq/2Vqx7KVI1r7NcX+5+3fYgzYkMNKI4Ov w4KxVaaSA56fpeU1J+RGCTk1fF5LbUjL5sptk0XOPsRGrV/bdtPfn06QCIckZQmn0igrhxVC rZz9yCgL4cp7PSfNMNJgQLCQZg9I8Ek8TtvN+b/kA6vdo5RH4rDUl1Ic7REPJC73+q73DKPE fRS3IvZSIkKSQi6Ja6S1GI04X1PKrcBZbUPrzgkFmmisNkliVEr0yAjYGhCSsL7UM5hgSIlf E4TSAMEiykcTqcYlL59vMdljE5pDkPzR2neeBaM0wJX88nJKygowoszIKEtKdog9WlK3JFJr Mpbsy6l8LkiQBIFgbBFFBGtplIL4Y2TB0LiS6z8yMNDqPToj2pN0vdyDxMDeHKLxtYfvZMF4 PAX+8R6vL/8Ovh0B7Vcpfw0jGNKILO0cUi6dPSFBgo5eNKHSyIomCU3EtqMES/BpQqS1i/Qv wpKUxKHcAEDjGxrIKE+zySaXgDpcQ2xGZrpIPWiMljj6xA61aa/v6s0wcIFBbQ0hGKKybd6o WUqMKFglYpaAlBLFURIj/S4lL2QUW0pomoCUBFLEa7AlKQ1DSCQ/zwNpgqu1hSTOCGUsgxxk doz4SEuUNX3HGUYhcSPORxMrmqDQkT5iW46QGrlqkbiWYOyduTQRjIFOSR1NNFb8cn6ycHRk sUBjCo0Jz74Le0pqu7Sx5/f0Hm0EXxqpS9PFlBilcrnPtICU7pHazV3fCoj0+fO6ZH86Eyi1 k8MdqXd7X1p+6xvNTyWuaEtSEmdyyVca9ZWuf/1sfXvuY3lGWCPW+IT6JYeZdi3fdtle36Kx D+v7gt13hCMS39OYSGO5lAek3FJvSQrzthavKG6iWGNmsJQ0ctdE6FsjBzzpfQY+832sr84c zd6tT/favve+M/hUarO3YLTuP98lZXiaVhrNtnbSsPW7E4yPjcNxXoiI2Xt0RllaHbDMul55 itn+ldt77/MZJRQMIMEiSxpnufeoben9Nfpx1CZx+iicjqlhM1SHO8GArAYY7qeedJmkZJk0 G5bqsF73g4ofSygYQ4WTH+J8S0v4fRhdo0VbHqVg9I9Cfh9G1xDo72C2WBEBfuNe12ihYFTk biXP8Rv3KgHpz7W0qD4C/E7v+pjma9TEYrtPkTsp86zVugyFtNsLA3/t8Du9KRdEwIBAvIDx l5Q+LLIm7iPCYNk38YpXH7vWAdM01ok8DReekjKkPw1Mfv4VgWhruB59nBWLdTnwuh56kE6E UTA6eDLgHh4Fg4LRFgHuY7TFd8fsIp2R1BKPDim4OZY1+xBt/+KdNzUBYl1EIDPHWJ99mNbn d4lNCwQsy0O1hMHSZos+j1FnzP07CgYTWXsE1qm59DqOMYLfr5XW54KsD+JZy/tFqq9lEWcX nGG0T5Vs4ROBaA8w9U0/bG0oBALuXTzx5wyDKb0bAvM80ms5hkpR3XxIVBQEgs+mKRgMta4I PETjcuGeBhNvNARGfrMw7gsKRtd0iTuGJYkAESAC3hD4C3OKOIAq7ckoAAAAAElFTkSuQmCC</item> <item item-id="35">iVBORw0KGgoAAAANSUhEUgAAAO0AAAAnCAYAAADjPs9dAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA BNtJREFUeF7tXcuR2zAMVWZy3purUBNuQofU4NN2oHq2nu1HEe2Vh4KJH0nJ5ALJZCZj8fuA hwdSNP1nXuZl8D+OgCPQDwKBtP63AIGvcQ164zI5iichMC3jMCzjV4HNThrpUSMcjmrYQrvT NCzD5HR9h60D9pf51jn98pBz0maa/TZfTEf7PHerWyvYwCJxnbQ5pP2+LhdX2BzkqteZpsty /a4bDFpvzUmrdqPbch19DduMY4cAOl4XS4myk1ZL2tVJRqNrqWaICmxmTW2dtErS3ubRXDpW g6zr+5TwalGJtrDndQff0trWSatyI0+NhTTaoXoYWZ+9rK+BDKXITloVaVfn8A0oFWI/h3fU dXTBwVYwddJq3MnXsxq0TlVbS+taJ63GDY2tnXRqx5c+Mk22tNfgpFWS1vLxOZ6WdIljSWvn sIuT1kmrQSCr7KE7xz8jsnRCLUnaDeQY7COjZGkEh/Xh+KHTxM+3jZJtfnDOu7preswpbcpB U3hyY4bjSmEE5xHX6cleNex/BGmxYCMJQlyZEtu9kDZlbG4ANUCv3QY2j60f+DwmbTyWXTmG tHFZyf/RfiI948jHzbM2rq22V5u0Evvl2IazOdfmPTBzjWBO3qrxqPFihohfS2Bkvrf7+bF8 fMpmjoFf63PpPGWj7b/Uv+tfsW0ks/0VpIXkhpOilCpFBKjeqbQUSyG1oHPqKn0+C9LjXDJJ yIyl3imcUvZKpWQSLHso07rS1rSdWGlThsOIq3Fcqg1KGTlHih0US+9JVQ1pCDx2JyAt1les 5hiWnGGxVJibKzdPDssentcm7WYvCjsuleX8N2U3zlb3OtL0GIvcGoKmHFeSsnIOzwUUyRhh ppA0iIC0kr44EqQwkSgx1bfkGTeulp/jpA2npR7nnl/+EafbML/kiJjiU23bqUkrmQwVCDSR i4s6lBNhqoSlkkeTlovKGGaxo5UEp5zAF6sNpgpnf47ZvLbSSvx8Z9PwFUFwDc5RthOTFjo1 dALKKXPVVEq8EmfGyFpbaUtIq1XvEtxaVlNqbC0rLRYwpXZ6ES9MeajIn3KiXknLpew7fJjd Y2lw4oKMph2J4Usyll5I3MPuscQOXJm7emMOREn79ix2+PizmNSYU0nKw36kaoWlbJgDckA9 6wnWtKl5SSNtLiaQ5LCdFB57LB43HPZ8SV3t9DheGqSETeKLmB3gsgPyBbPn0469RNImxikg bRPjzDpsOC/T3O+1LUeQtlVb+tljjYP/WtI+dlhzjmhS2Usqg8EyOCpDkaiak1bjyG8qq02B q0TNX0taHh1qnU3ti0jqYWUkdbe+nbRvIiLvOm8u4d+nvXuGRPk0m5V1SGvn7i5PjzUBym+u aJa0fnOFxpFNlfU7onJVFtsxhcotUd3XfMvviDJFQ13Cbcs5uPfJHHYUwSXklK9p/TZGJy2B gKW7iKgNpuezxPE9ybq3KmmN7TX4mlYbogyva0tS45wdZqnSWlrP3gMil+L4c4iAzRRZSqCc NapWdfcWsbfP4KTVKm0oH35I2til5fC9OBXMqbLaQxRY+a1/ayrrSptDWIO3/zWbbRlbyz7f fTdrkAJCnTWn8Gvk3NG/s8Zirh+D2Y6TtlJQCMQdBv+92vOCRv/fSCrFyte0lchbagiv7whI EfgPVB9wRXqEcewAAAAASUVORK5CYII=</item> <item item-id="36">iVBORw0KGgoAAAANSUhEUgAAAZUAAAAnCAYAAADHLtXxAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA Bw9JREFUeF7tXcmR3DgQ7I3Yt35tBZ0YJ/hYG+YlD9oe2SN/uKRG7GBjUKgsHCRQnVIoRtON oyrryALA45/H8lhu/EMEiAARIAJEoAYCG6nwLxE4DYFf01rETMtMryMCb4fAvEy32zL9Oi3a LkH45ls9atcTAvN8W24z6aQnm1CW8xHY4uD++Lwk4Z+hLUnFrWnPcB98js/H3X2FhqPBlu+O wBYPXomFpEJSaY/A74/lzhVKe5w5w1AIzPN9+fjtj15JKkO54YgO+Ll8TDxDGdFylLkxAlux NX0s3jbCSCoklbYIrIEzOd4/bpx22tqGo1+OgMfVCknlcrfynZY+H5PLJb5vq71qt15lut12 wEhpgcB6NaS3sxWSSgtH4Zh/EeDW1+jkQzJpbcH1MmNnW2AkFRJAQwTWgOEBfUN8Wye8PzdG Dy1/e4RKZ/BXeJFUGDLtEOB5SjtsTxyZxFJKHOn+3s5VSConBmdb1+xwdIf7xR2ifIoHk1ja Wd7buSNJ5ZSQbOeQXY+8kor3R1J0jX9F3yaptLO0txuDSSoVA6+d2w06MklleO/ilV/tY+/t SaVmxTKCw9bU9+ieFt1TbS3jSOERjrH/Hv40hxdAKoj8UhsNF6u8sfEQDGJtNL2OfXY5Y59Z dfDSHsE9pWssblE/Ott2V5CKBZ8NZ0sevHylggqLtkslzt4CDtGpVhtN93Aeyem0cV6+V0jl OIekp9Qm1VdLCjEdcuY5koE2Jvo9Ym+TDQZdK5XgIBUHMXshdm9tu7NJxYLPTigWe5BULgw6 xFC12miJqAmp/Pyx/Pgpz9yKVKyVVSghQnDhqhNNPFo/xN6aLT18X4pDyp8RItHmr1J0/c09 /338m4yTFvZM6Veq25NUjuyVAn3/LmyvGSG29SMxZvj5/nsol1SRhu1j/ZGtIE2+47gxXGI6 a0lFwik1VgwXxBFj8qeqb8n2Yp+LViotSMUahKEMki9phBbayBJniA/02qZUz5qk0tp2Z69U tPioRirhMsdKMpqgUuKRCCwWlCVjIE6qgak5akqXmOw5MlllTJFniuC0ZCYVFS/yDUYqUpGi +bZUtGj+EMM45WOaHL0SRI5clkIQWSVKtrB+foxjJEaQIrInUpFyEpKrnthISueAjUysJWar PCnyQQyqtckhvVjwx4KkFK+QqJDxEGKWkpfZJwYjFXHFZTiojGFkCdSUDS32zUnkvfbJ0RvN M5pPn2E7mVS2u+2/nrv27V/hkyoselmLmZczFQ1gLYEjxteMHYInVfhIO40wSiocCxYIruiK AiUsLUHkVMSIHi/jOiEVxK81P0V8LRW8Fhm0ajqapGKJq/Jnmk9aYkAbS8szkr1i2LW2Xb2V Ck5CMX+qVdB0RyqIY+UobwlKKXnuQb8HZYq0zAlYuGAAqShS8iJ4SkQV9jXrRFJ5WhUNYrSd llQ9fW+JXWQlbvVj1CZou9A2vaxUcvJqlHClxGgFHl0ioRVEaiWQM4bFMRHdUwkXlR2RyUoq NewgjYHg8iLvQFd/aYVEKkkjuOQQN9rHE4FoKwhUVzRH9GC7Hq7+0gpTJFc9bReSyt75WI0j /z9W8Zrh9/Gkij/2eSxRp9pJY2vJIeaMIeBH+cPlMvJ7DOPUiiI2v2S3GnYI7ZOyUzjfS9vM mx81G0gYh59/D4T1qcnblo6wH434XaoS1uSOxZHkWzFd4vKlddJi8drv8+zxWLB+mj1SfhRi 3cZ2XxLU2/7CrJnKX9acHJvx8vtUMBj6aBVjawuD96HFiVIApHKiNM+tqPnh7xWuI+uUK3tu vyt8LjXn2aTSWn+SiuHmR5KK0R27I5Wvg0xfD7nEdJJWYdFKM/KmR2R3IRxLnxOT/buMuf2M /mvIDSUjk1QAoJHtkxIjlPQtlS0VXLlylcqkbZ21kBnStTtSgaQGPHysccIttpT0UltpRY60 52o+7S8kFXchN1aCGEpavk+lu2ixJHiEjEgq5RHJ96l0FyblRuUIjRDgmx+7ixaSSiNfL7A0 3/xYAF5/5qREbRHgO+rb4msbPZdQtlliVwztn+9SIKsWm8Tv0JrvqCelEAEDAv4CZuQ0V0Iq R70R8kC2zkbGsp7sa+E1+boakVd/GVJkPUd6n5G87ReParkooazbk/f1Sq/wajiNfEgqFb3A 4bkjSYWk0hYBnqu0xRccXSMKaSUSS58klXqk4u085c+WaD14OBIRiCHALbCr/cKyFVVCGEjf q7Hoa36fZ44kFbDS68sZB5NmXeJLj0YZTJMhvcVyn1LNmxz1mx/f2/oeVylcqQyZIsYMRG83 eI1pBUrdDQIOz1KeVwF2AzITvHsE5tnbI1IYPUQgAwHnK3duf7lP5RlO3xCTjVhut2l9zmxf clEaItAegZGfKI2jQ1JhciMCRIAIEIFqCPwPwgMqF49EF6sAAAAASUVORK5CYII=</item> <item item-id="37">iVBORw0KGgoAAAANSUhEUgAAA9QAAAAnCAYAAADuOgCuAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA Dw5JREFUeF7tndtx40gSRblf68R4QCfWCRoyHtAe/Y0v8ocLjpoKqFSV92YVQIDgUURHdwv1 yDz5LAGg/nObvk58QQACEIAABCAAAQhAAAIQgAAEIJAjcD9Q8wUBCEAAAhCAwJsQ+LhMP0i/ 3D5WV/fjdjmdbpf1N1pdEzaAAAQgAAEItAicQAMBCEAAAhCAwHsQ+Licbqcnn3Dve56vn+8B GC0hAAEIQODtCHCgfjuTozAEIAABCLwjgc/rebO7xfe9OVS/o9ehMwQgAIHjE+BAfXwboyEE IAABCLw7gc/r7RzcmZ5eFrt/nsqvPw9s82v37z3+3/r3/Xr59XE537hR/e6OiP4QgAAEjkeA A/XxbIpGEIAABCAAgRmBz9v1rN+Zrh2C598rrz/+3/r+LxPcD/Xn642Hv3FOCEAAAhA4EgEO 1EeyJrpAAAIQgAAESgLTQfZi3Bpe/UA9ycVdatwTAhCAAASORoAD9dEsij4QgAAEIACB+f3p 68V61FodqO9LqrvRtTV+GGP6hHHepcY9IQABCEDgSAQ4UB/JmugCAQhAAAIQ+EHAe9z7cVgu 36MuYQ4fqKdf1nXhsW98FAIQgAAEDkSAA/WBjIkqEIAABCAAgZ8EpgOs+WuynDvUj4N39Hds Af+AjyUhAAEIQAACr0CAA/UrWAkZIQABCEAAAj0EzPen5wfk+TbRIbv8UDL5uPefhXmPuseQ zIEABCAAgb0S4EC9V8sgFwQgAAEIQGCUQOKd5efcob7dPs13ukdVZz4EIAABCEDgGQQ4UD+D MntAAAIQgAAEtiAwHajNJ75/feDYXdzWXWf1LnWk6uf1bMu0BTL2hAAEIAABCGQIcKDO0GIs BCAAAQhA4JUImAfq8sPIHv9vqcqB+pWcAFkhAAEIQGBNAukDtfuOlCO0KtjOGmuPWVLfuawZ 3aOxzjrOmF7ZHvPKPbLNmbJjrXmr7VHTo/WptWq+kqmluzsvGpe1Wc+eao+1fN+RtWdvpY+z b8+Y2r5z3yr9RH2KcnSAmXNZIsbUGqUec11r/37I12O/HvYqhqJ8UJu7hty1NR1fdcZYzMwD tbXWQoOWvkMd+bHi6MZvr+pq/ShX3PdsxfzjmtJvpLZn1l6iF9iqzq8R973+Us5T/rPn+uIy yOTIHp/M1Il5XLVqRC3fKDvU4ryMb3pK12Py43pivPS1lp9+2y0v1rIzXCXdcVEzuqzk46s5 Oo2OceYv1VjWimGtOPSQU448T0RlAzHyfyVrL1+1bivROvMyY1z53XGZvdcYu5WcraZH+X9W 3jViTMVWa89IlqxeS/uCskemgRuRrUeOKF91yfIGB+pavpzbWPmjslMX92lSJIMTV45cSrdW bXR1ctfP1NlWfVO5aK0636Ojy2+JcY4fqBhw5Fijvqh9lW6uT6h9Wtd76lgkk2KYvd6rV23e M/zc3cMdt6T+PWupOvLjes8GS85xobrj3KBZUofetRydRsc485cKPJUoWkVU8Wvp4CRapb8j cySfWl/pttXa2SZrTT1HGJVzt5JTNQVZ3m4ec2JA8VVrtGJExc5WtnBYt3ReQ+aehs3RQdn1 +/rff93++tse/ZSB//zvv4vLFNlO2dWN3xE4Kl5aNh/xn7m8ioGbcxwGjq7OmFbP4M51mfb2 Jg6LJca4/tlr4605ZX18VE83v470ns4eqvYu4TtL5ICMHK5t3HGZvdcY6+Sax5jvR77nATtf oPz398TT6funr7VgVweFx5yasOV6tbGt+Y/EWOrjylibVzrkfO9SjtY+bkJs7VX7fi1gHb2V jC6rknXK8Qr/iZLPSFJr+XKmgESNRstf1L7Omj2xEcVFbc9aPM/tWoun1jqtteasW7E9b2ha sbKG39b2LfVo+V+tUCm7q+sRWzeHOPKqIqtiWV3fQ4Pakzc0u+l3KF+ut89E5Y5Yqf2cHFCL rx++8rJ3qHOso9hyclPpszXbZPNrFGcteV2fUON6eomWLznuPmfTkx964rXVA5Xy9shT19nz yXn8tepJxpfma7i9kusfo3ZzfEONyebIRfLiJJRiNOKTkZ2i/rP0Hdf29JRfBKLYc3Ji2fNm csePd6izhvxRtA3nrDVZThFUhc5ZQwVOqwF01i6NGDWT0XqtZjrT/DrGd4pQD68oyZW+UnP8 WuJQLKNioAJD+aMqAq35ZQGNdIhsm/E95SMq2SqfcP2hJbMqmOp6S7+Mv5cMWj7Zw9Itespf azIqH8mu2eIwX6dHDjXnx/XpdyOfpx+quZ8+7cSiu3/Nl1u55/e+XkOtbFaLN5X/e2Pk9kYH 6pYPq17Fid/RXKPirvSZVs6t5S0nPqIc7taaqI4on1d5Sumr8pO6rvK6U+OmX/hm/0CN+uJ4 5deYTG4rx2fmZmJYxYvyZyfHR7VoPn+en/xaleM6mgNq8ka2cuItytvK7up6b08Z9g/Ooi3Q GedsOZdrxJ5xKoE6DVjvvjU2kXP0Fqq5wzrB+RivZHGdXRXRiPGoXzn6RvYrWTg6K59xdVLN 85JFIyprbtJx2LhrKd3UdTcmVTlv6ZQpWLW8Fs1X/pjxr1oj0vpexgfUuqV+imNsh6lBPX89 7fTrz+Bp25UrZxO/oe6peW6+zMTIOx2oHeY1/675gOpvnJzYkkfZz5mX2T+K6T30AlHeelad 93j68a/yj7fflyc4/un6VKZnc9irOutcz/QOSs9MfxDZIHtN2dvp+dzc5Nb5DNcMt0w/0VrX 8f+l5F/bZ757olZwORBUwXEcqNynbKpqhaXWfEUFyHW+jDP3GijXvNUfSalxL4ui01iMBpCy v0rGjo8tWWiVPzoBXq7hBryztlqrFRtzRs4+bkEd9dWe2G7Fu5OnMnGuxo5w/E6u0yFR+Ytq NkZjTK3f4hDFbqlfTyPhyFUf4x/AVf6Z+9o8tn7vG+x51o9/q7j+3q9y197N0aGfiDvUtXq6 1Pdadm5/ync/6yhmM3HUil+1RkvX0fiw/UcElbtOj55qjhOLGU5qvyjvZvb5WqfPJzP+qPJh LQ84TDPrqv631VNKRsYPRl3fXLo/yPvClwQZ9lk/cFlE67ZsVfpRmedrfZazT22espWzrmLR kl/ZR63r6lPq+OuR7wyEbFJTAes6njsughIlmlryqjVfLvTWej0OlbFPhvfStnSSjsslk/Sy emTHR4W61Dnyo5r/ZYK8JYfjU6N+24qdXvnXTH5unI8WkpruSi/V7Kj4dWKsZw+17kOv8m8V G3VZ/AOyo4vK+So+1PXMI59uU+P6qMpV8+bix5pv9Mh3T25qxZnD2/HJTP3qiXnts19SuvlZ 6Z2tXSqftOIk+n5WRme8x3H7O9QPnq9SX3piRPVOqkcv68AjN7o1SvlChn20lrNOS1clYybm o5h29onq7hbyK65uLlQMyxy16wO1awgnuDJO4e476kSOTE4x7nUOh5vb7GUdrxzfI4sKGkcm Z41eBqooqOSeZeL4U++eztpL+GGtiepdVxVyx/aO3m5D3mose/3LbVSX4NBqbrPNgpJlyeuO fVU8/JbHb6gdZsq/3BwQrvOyn/KdYz2SJ2pzo/XcWHbtVzsAOP7TGhM1yCrHKr2za7t5yul1 XCaODs6Ykfh38o+K/0hfZ32VTxWDJfZQMqg+TcmQiXsn3zt9m5KpjOcWA2edljyO72TYOLIs acs15Ve6jHCJ1rY/lOyxyP3v2r8fQeFCeqzTKmQOkLkstYKkkkUtuFryzBNbTfZSluj/LX6t QlUGlCtjyyalDZVuURDNWdTkbF0vZVN+1bKVs74jY9TQ3G4ft8vd7yuPLik7Z2w9Z1Lz7VbM 6KaqLX+5Z6axUzaJrse8f35SYySjYtZju9JmKqdFuczJS6pIOf4b+4b23yiOouYgYhNyW/lD yVybOA3WzzH5Q14Uy67taznajr83uENd+m8mf2Z8ReVg1ybROlHNqM1zZVJ+GNVv5adRHnZs 85iv+Cm7ZvjUarfW04//Vm9wvPqiqbk9pOoLyjw4lBenybUapWqh68+qb1E+X9N17lO1WKhx jvRRuaNdw9+3p2z5zL/2HAuF95odgXwvEs/VNmzMnyjKx1W/L/lEcdJbrS3/XuxUA7O27mlj bDDh2Qz27A8b4F91y7A2veyBelVkLL4zAlvni63335k5DiGO6tm3tvnW+48aee2eYo98OFCP es2f+So4F9qGZSoEtg2sr/c8jc/W2KntniP/tjZqoX+O7js1/B+xns9gn76wbyuNSLfkgTpz l69X5vaHkvWuyLwjENgqb2y17xFstmcdnJ59a9tvvX+f/Z7TU+yNjfKnVe5QZx6J6DNm/6xR 2aLHJ3qlGpWpd19n3l5k21tgOexqY/bCc2n5a0147x57nLem3dZceyuWR4nXrfj17NusTR13 qFXj0CPffM6RDtR7it81ZVlz7YdvbJU3ttp3rneN72ic3ec/w25LyLnWGqpn34PtR3R/dfu2 5N9jT6nq4ioH6hHnYC4EIAABCEAAAgsRmA7U5+tnajHVOKQWqwz+vF5uSZFGt2Q+BCAAAQhA YDUCHKhXQ8vCEIAABCAAgY0JTB8Ad0meXp0D9cgdtY/LmQP1xm7B9hCAAAQgsBwBDtTLsWQl CEAAAhCAwM4ITJ/ImvwACOcxwvLQ7T86eX//7jL97gS+IAABCEAAAscgwIH6GHZECwhAAAIQ gECFQP4A69yhnm/kH6bvs6YD/vm1f2MCbgYBCEAAAhD4UQfBAQEIQAACEIDAcQlk31nOHKhz h+n7eTr/TvdxLYNmEIAABCBwBALcoT6CFdEBAhCAAAQg0CKQfI/aPVCnD9P/nqd5fxpHhQAE IACBYxHgQH0se6INBCAAAQhAoCCQe+zbOVD3vUOdf58bU0IAAhCAAAT2ToAD9d4thHwQgAAE IACBUQLTo9Yn48PJar//U33vcV2JyN1pRYjrEIAABCDwigQ4UL+i1ZAZAhCAAAQgkCTweT3f jDN1clVzOO9Om6AYBgEIQAACr0aAA/WrWQx5IQABCEAAAp0EPi6n5x+qzbvjnSoxDQIQgAAE ILApAQ7Um+JncwhAAAIQgMBzCdwP1afTM34X9PTO9Gnaa7Pb4s/lym4QgAAEIPCeBDhQv6fd 0RoCEIAABCAAAQhAAAIQgAAEBgn8H8Wny0uKbXO6AAAAAElFTkSuQmCC</item> <item item-id="38">iVBORw0KGgoAAAANSUhEUgAAAwcAAAAoCAYAAACrSoYxAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA C51JREFUeF7tnQtWWzsMRXnj6XiYD+NhPJ1PXoF1W2P0ObJ9P0l21+oqNLYsbcn2URLIf7c/ f174AwEIQAACEIAABCAAAQhA4KM54A8EIAABCEAAAhCAAAQgAIEXEEAAAhCAAAQgAAEIQAAC EPh8RxEYIAABCEAAAo9I4M97A4bC+pg3OndoQWPS7PpKDNYa0TzFJ2XdT/HRMN6+Vuf2uFq/ Ilv9Y+q8KKcKk6wmRuO2OHg5zXyYeVxhsCpGy8/edpvnttb6/8/yusU1Up9qHbY+WH7P5GVm 7tjJObMicyEAAQhAAAIXJxAJHkUMRQL2qNBHY5gVmCqfXpxvXNT53njP7iYUR+Z5onRVLqsx e+uO5nxVHJng3mudPu7oe5X1bH2qddg3CKP7YCVbmoOVNLEFAQhAAAIPQWC1yFIFyUp4ozHc U3OQ+ZqJxhlhtjKnq2yN5nxl3Y00LrPrW68eeLlV1zq7OeibWdXvFeNoDlZQxAYEIAABCBxK oH/Jf7tIrbcC9I9lz25aQsOz69lubfRvF4jEkzW2/T8r7vaZxujZyn5cP7a1bY31xkd2rVi9 GFs7rTCyWPZjVZFvxWjZysR6lpO+hpScW3msNDdtLVr1Gvmk1pVn16tByyevJvqxkU+9DbU5 yPLq+avsK8snr07VxkPxd4+Dl+ZgD6rYhAAEIACB3QlkF6wndiOh5gnUXqxGgtLyK7vkPRGY 2bLsRgJYEcIVbpWxLUPra0u8qjnumwNPCFuNSS9ss1xlDFUhqeR2pDnwajiLS/HH2gdRDags VLvZ3vVqP8p7Xztefr2ainyK6jCro36P7H6gNgvQHBxJm7UgAAEIQGAZAVU4qgLFE0e9sOjH RY9bAiATOFkjEsXdipFIDEaiMxPpm3/eWuq6Wf6yZsITT1G+PfHoMY+Ktep/b6tab5m493hF MWfCuK3tSHhnaytC14pPiTlqLLxGqbIHozocqY/I32hfLjs4BUM0BwIkhkAAAhCAwPUIZOIs ekbTisYTa4qo80RWpTnIRE4Uj+p75E9rP2seItF1D82Bmv8R8TfbjHr1pgjlqA6y+WoNebWR za+sP7pvtj00KrKzM0VpcizfM2ZZ43X06UtzcDRx1oMABCAAgSUEsot8dXOQ2VPFuyI4VVvq uKjB8YR+xtdqZjLxVLHZ28rEZ0WUWX6qoj5bR7WTxaPa8RrTvjYq4rxSV+rYyvpebXl7p2d1 9eYgq79qc7TkQG2M0BysJoo9CEAAAhA4hEAmNDMxbzn5MWf7217Qkdjox28Xf0WgWGv262fx 9L5HYqxdL1vbiifipIhAa32PYyTGLcaWb20Miu/ffXm/vX7Uxev7t5LpY8iahlX19mXH9snL lSI2M27eflCE+QjPtt6VWo7OA6u2okZDrU8vDy1Lz3flfMj20l4HLc3BXmSxCwEIQAACEFhI 4CyhsDCEXUwdxeX9/XtzsEswRaMrfTqK4xbiSt+L2JYP3yOWo/PRQqE5WF4iGIQABCAAgSsT 6J/VO/ISHlm78qxnz31kvRW5O3Ld/fP3+/b26+XWvXAgY9qHxbhPlj/eqwZykKWBY76v5LjO 1lgsCq7969r3guZAyRBjIAABCEAAAhCAAAQg8AQEaA6eIMmECAEIQAACEIAABCAAAYUAzYFC iTEQgAAEIHAJAtlbInj83w9UwwIW1MDj1MCRBzDNwZG0WQsCEIAABCAAAQhAAAIXJkBzcOHk 4BoEIAABCEAAAhCAAASOJEBzcCRt1oIABCAAAQhAAAIQgMCFCdAcXDg5uAYBCEAAAhCAwPkE rA+sOvNXTZ5PBA8emQDNwSNnl9ggAAEIQAACEJgi0H/OhPdJvFOLMBkCFyJAc3ChZOAKBCAA AQhAAALXI+A1BLx6cL1c4dE8AZqDeYZYgAAEIAABCEDggQnQHDxwcgntBwGaA4oCAhCAAAQg AAEIBARoDiiPZyJAc/BM2SZWCEAAAhCAAATKBGgOysiYcMcEys3ByvfX9T/kc0WOK+Nt46vE Ho1V7Chjqr61n7r4Mbddw/ra+k0P1pr9pzlWxlifBDlSU1Veyhp72OzXzdbYq5bV+JVx1Tqs 2lTGWxz7em9rPqrZaD3rhxxna9j7NNTNj9F9e1TtWOtkda3klDEQmCVAczBLkPlnEiif/Wc6 u12wig+zl9PsfMXH6hjFp9kxynzLb2WeJ/izRsDLuycMesE4832WIyXuzIb3+J62W/Gn+HeE L4of2Ziz/PQaBKshi+qxGp+yB6o2+/02sm+PyEPG/AgfMrY8/rwEaA6eN/ePEnnl7C+/crAa knrgq+POFGZVNkpMs2OU+Uc3B55PijDK4lEakyhPmf1qjmeE48haqv/quBEfVs45y89MqFab MfVcUvZAxjezUbkgzq5fT5BlDHgcAisJbOdBX49nnU8rY8PW8xConP1/m4P2Mow65O2xfnxl k7QbzXK2v5ijjeldhJZ/io9ZXL3vvW+WqNieuasKVytuy5Z3YKnzW6HjzRkVN5lQycRHxEyx reTcir/nPCpSZmt9NB/eHmrjithk+8CzY50j1p6I4oqYRXW4xdP+O3MObDFGjcBIXWQ10cdY qeHeZ8/37CxS9tYeV2p1v+/hAzYhAAEI3BMB696dvQu+vXLgCQLvArQESAa0cvhH63qi0rOv XLDZhZj5rggFZUwkmGd8jERHJhaOag4yYag8njUcUY1aHHqx6W26TNRFuVcfU/aXsm/U/aDY skR0tleq+zeLu2/mVFGc1XW2X7N6rNRE33D156vCQLWR7ffsnPnhy++326+Xl9vru+qlPa5S N3MrMRsCEIDA4xBQ9LulXVzNrFzSIwIhu3Sty1u9GNRxVZGYXYiVdT2RmYmNqphok5353zLv hUcmFhRB3YroSEB7wlTxP+NXFWsV3tnao7YscV2tXUUMn+V/Jb4VdejlQWmIonMpq0/FfhZf toZylak2vP1a2Z8//fl9e/v18vcXFHzbj0LnUDljFRaMgQAEIPAMBFSdbmnG/i74HHO15sAT d33gyrgRgVW5nCKxpSRqREx4nV8v9mfFouJbv8bRzYG14asCeFTQK3yyWooaGWuzKgdctqZX FxlLS+BX9uDIXokaTMXfkf0/0xzM5seLV6m1LNaoYZjdt0rc6hi1flV7jIMABCDwcASMV2oV zVnRbJdrDrwkqqJv7lmvr9UzIWMJcfVSU+PIRJxaCCN+jYqyWZERCZiMh9IMKQfEKC9FrFZy fw/NgbJXI7FdfSzLX7b3KyLbqgOlPqs+RvWm1ny/puqnt18zjnGMvHKQ1QCPQwACEFhNQNWE rb6LNNulmwM12EzMj4rdrFFokzsiKhWxolzU6tpRPIpIiYq5Ml+JSRX7yroKZ2W9iri3ak6p 06imlMNErQWFiWpL2Sdq7Eo+R5uSyjnwDM1BVqNK06HUZGVMpeYqdhkLAQhA4JEJqHpZ0Tqf d0MvRtpOovL1dtGoouNj3PbX8iF79sub3/pRFRqRPy1Qa+1+bvS9xTUSPH3SM+HSrp3FZNm2 5mSbqsLay23LNauNKAcZn9vt/fb6UX/Ge6CzvFVy19ei55e3Z/y95Puf1b93gFj57fNhicWs vvp9Y+2jyO5PBvXc9TnzuX554p0t1fxle9qrtYy7Ur+5jX+x9udv5Ld7Diz4geSIe5az7Hzi cQhAAAKPTMDTfZ6eyzTb6Z9zcE/J8kTtPcVwBV+vctG/v73dfl8ByKAPe/t/lTxZePaOfTAl h047msGV6+FQ8CwGAQhA4MEJ0BwUEkxzUICVDD1XaHy9L1r45SnrAl5q6Rj/z82RB+yY2Jem a7mx4xlcsxaWg8UgBCAAAQj8IbBLc9C/pO29rHFGBmZ9U1+ur8Q261NlrerYvXx7FLGxF59q nkbHe/57b/EYXedq8/bM2562z+L4KPv1LH6sCwEIQOCeCOzSHNwTAHyFAAQgAAEIQAACEIAA BL4I/A8DAtMJlL98hAAAAABJRU5ErkJggg==</item> <item item-id="39">iVBORw0KGgoAAAANSUhEUgAAAaYAAAAnCAYAAABE6uzKAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA B2pJREFUeF7tXcmRGzkQ5EbsW7+xgk6ME3ysDfOSB7RH9sif3m6NOGqBdSSubqCYu6HQaIij KrOqEkAf/Oe+3JcL/yMCRIAIEAEiMAoCmzDxfyIwFQI/ruti6rrcGLlE4OUQuC3Xy2W5/pgq Y7NZusR2j95FQ+B2uyyXGyUpGq/0Jw+BLQ/e7h/ZBT9vlvNaU5jCUnteUPWa+eP+Fn6l2As7 jhsPgS0foooThYnCNAcCP9+XN+6U5uCKVh6GwO32trz/jCe6FKbDQihe8Bzn0cfyfuU1pePw 5kzTILAt2K7vS7RDPQoThWl8BNbkuwY+T5+mCI4fKS9pYcRdE4XpJUN5rlL4cb+GPK6Yi4U6 a9e7kLfHUphtPRBY71KNdq2JwtQjUDhmQwR4jFcnCef3piD15mC9hTzYcR6FqWEJ7R1+rzn+ mnS86WHqKKUw9c7ceIs3CtPUKd874AcYn9eXQkQoxalvLkW7zkRhCpH2fYP+1NEDnp+fiueJ 8U5x6sd8tOuwFKYTE7VfmAYaeRWm6K9fCcSWmU0Upn5MR3v4nMJEYRobAQrT2PwA1vGOvH6C 9Bj55YWp5apnhoBt6e8+PHN8t9oi4yBtSm179EvnePw7/Ts7RQFhQvzT2njY5torjYdgILXx /Nr3SXnoFbe5eJzZHsHdsk/CEI2jo7k7Q5hq8PHi4vQdE5pAaDvN4dr+HpAlnyM21bZB+ku2 l/RL+2iBm4WVI0z7OTSbtTZWX6+weJghc6aLAW9M9PMS7rI4AXZJI4yHxIZVL6x4Rvj1eGiS H7+5OFqYtAXYfoHk/WwuCs4OII88ybkSm9F5SsYu7YPYVNsG6Y8WPM/PLsL0/dvy7bs+M1J8 SotIKXabtYhIUpi8iGr3eQmXMwnTf+//mnnSDsk/I6H4IHGe2ve1Y9oroJXIj8/S9jnEP/pq qpv+ft9+L1TaqjZtL/VHVkqefZIfnk05JGl2S/ikuDyKo+XDUzD8fjpf80ETMMtOaY69bWkR f4qjk3ZMlrggSS7lg5UjiJBpXGoYS7mSwy3i5wxtcmqTlZ9WXZQW0Gl7q5BL/dMctrg7esck 5YeGT07N+8Ih7aQJjwY8YkwJ2VbBsgjWPkOC0ysm3rwIFkgbFC8vGbziatlSgpeEjyTelog+ 2TyZMFnFwxMmT3i0+Ewx9gogwu0MgoPaWOqvl+9e/nm5LvFWyt0MwpTDw1/XmGpWBF4RtFYE uZ+hAZOr1LXC5AlKWngQorxA3ePu2S+toBHstQLg2ebZg8TbfTJh0vBE80NbkGmx4mGccofE HFrwZ2lX6jNaZ7w4PoI7XZi2t0J8vqfw6U/lG1VQfKycUGuLVkw9sKVEQwLAcyYFz9qp7dt6 ziO2eT55ttdgiRZ/zcZU9BDBsVZ0CF4UJr00e9hYRT3lReNWE7ucQuiJi1jQpCJ30O88e1sK MZrvVq08irt2OyZcyFB8chZlX3WrppiWJF+OM4htaCAihVYTwdQOKdhQvywxkJIOKTI5AoMs ONBA8viXbEfm/6tfkB1Ti/jz8GyxW8st/KO3z8HdqyW5sevleg2fqa2j75hyeQh/lPcAJAcY JABbB3HOjikVDlQUrX6eyCCiiYyBYPvXOBPdlZezQLDiJ2ccpLghvIwuMKX25eR965z2cG/J 3ch35XkCLdYWaTfwKGD7ou79nPbxjiq28R5/kB2JVFSl/ns7vMCQAlHqkwIr2Z7aYv1bwtIS Jml+jTeLu/2KWhLsvV9oQmt90t9bXJs2Fz5g6/EoYYHF4fq28y12lfN5KyaRwufZvR/fikMr Zp+5tX0qFYVj+vl8yHb4/byYtXJQqod9uPu0ot1RHsaaV7+1XILrCmYGW6XCuC9sRKcjAoAw dZxdfZT0do/3ddYz+1Rqe2m/M2LOmvNoYert/+lvfujtYMvxka13y/k41orAcML0eXE41otl MZ+03aAUp95piLcz9Ha0f+bEbH+2sbTfmFlJYQJeR4Ic45xFb61tJcddnq+1Nnnj13x+um3D CVMNmvP2TY8GLU+0ttoxDtIePQKaF+E6yylMgDDVQczeRGCHAL+PabiMyxEJRNAoTPUZz+9j Gi5N6knlCAMjwG+wHS7jKEzj5Qu/wXa4NBkvSGhRSwTWu6cqn1Bvac2rj1UqShtu1p1c+2tK 3s+vzoF8vey63ALVZt78EIjMmAm7XaSOlXQz81QjTHu/c4/vcuadGd8y29fF2zXWXaIUJgrT 8AhEOz8vKz7n9xLFYT1qfVuf60rvUvSEhMLUkM+A12EpTMOX5YYBPKuvvM40BHOe2Gg7IimC KUzt8jra9aVfx77t4OFIRKAXAjzO64UsOi5yd513bahGjHJEEfUpRruY12ApTEOsRWOkSFcv 1uMK7TVAXedlfPxCIOf5Paut9pBu7u/J+ScCEXdL3DGx6EyFQLSHCFlciUAVAgGvLX3tuquA maqs0dMICNxu0V4HFIEV+nA4AsFPEHiUR3GdDoFNnC4X3kJ+eDGcLlIiIjTzm+BxPihMTDYi QASIABEYCoH/AU8LSasrOp/VAAAAAElFTkSuQmCC</item> <item item-id="40">iVBORw0KGgoAAAANSUhEUgAAAOoAAAAnCAYAAAAB4tQkAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA BQ1JREFUeF7tXdlx4zAMVb62iXTgJtKECkkHrsd/24v70YrJ0qFhnKRDQiY8szMbiSSARzwC oK63bf8t8QsEAgHfCCSixi8Q6IbAZd0Dw7pdugk8iqDLti7LthLALEcxI/Q8PgKXddkWyhOP b95TLEgYnc7Xh7GCqE+BNwaRELieT2S0kPrOdj5hBckaRJ3NC0bYez1vJ8eRdC9OR6DCyrys p60MrP40dAdZKNSGwHU7n/zWpImkHom6pcXtdN5yEhxEbfPC6C0hsDvcitRcUree510SdQeg jKpB1J4eMaGs63m9S+E8QuCVqDtTb7VqENWj57yMTr7T3gyzW6LuF7HW/+lvEPVlSOHRkN3R HG8i+Sfqz0IXRPXo36+i0wHq0wS134j6U6cGUV+FFB7tKGosj+r5j6jbvvn7XeMHUT170NF1 24l6gMzXdUTNN4oEUY9OBs/6H4Co+Tqq1/RXJCqneHmRuGwHjbaAULbF+lFj5xoj60HJxPSk fHzkpNXIhhfta8aw8l11o8ABiGq1u3f7aqJKDg+dxOI0WFsorwSqJCd2nDum1UvbrvcEQnkc Ts/WTS0riNoMPUtUigDYDhlHTKuT/xZRpXE5NK02NM9M5QAtC2SlyK9uLD6f79v7Z8vo0ffv x58vDNEa1UJUamWvcXANoSSH1IxROhiWNkppNIzUsD2GiSQHpvDljiSVZubj2Pka/GtowcqJ iFoD6V0fMqJKRKAcCjpWjYbQ8TSk0+pLkafUW/o/ZRNGQmwsGIEkglmyFQ0ON/3TDd/MQ8ra uRMXgyCqFkqyXRNRKSeEkcqqpYaYmAwuA6DSMy0JRGdE0r+yj1aOZpHQjkvZ/Dgf6c6X76dH Hv4prquI2ARRrRR4aM8SlUupsMgEU0GOyJzmr0pUCk/JXgvJTRG12X2I2hRGaoGo6AKBLRoT HKOmBCUqtUK2rOTiqltoKDkuXABgJNXor01JLYuNhVDUona0iKqa14iozUtiE1EtxNWnYfgq rSUf5ziaBYCySeOQFqJy2GE1rDZb6RlRJRtuOseubzNRH3Z9yzQEOkc+B+tDzDnKtrk9PIZp T6VBlKUWx+TIjumG2SCl7BR+HAZa2RR+EDMpWt/ON24mcXLvcIqI2kxU8YaHZgmOBtBExRZ1 f3v8Gt1c6BRErZm6uz5TEdWSftcg64IUQq1fY1dzHwNRLVmXNstq1t8wQI0PaGweQlRremvA SWxaAyRVAoy0QzR0b9Bqq0aGqo2SqOqa12CbGwwEoCQ9hxBVNbnR6HUQqHgeVXJc6XwGT9tu NNiSnvE86ugZmkF+xRseJMfN56lNy1zmSGlleV6zWVcuAKUOpTwqA4NTrd6M2zvmNxHG86gz EGaYjbZ3JkkkxUhBpc3PHCvLxciKRXAom0vteT3jnUnDXHcuwba3EGrJBSOXhiwY7rWEwvTU kpGT+ahjvIVwLr4MtFb7Xl+UpMj1Xi25nkF6KkXtRtR4r+9Az51NtLJO1RALpqBYSmrZTNKS HqsxuWOWcTm74035s5FlqL1y+suljRJJWmtUTRrNLQiatFubFt/bel/fx2bSUCeeRHj6eDHz 2JxlF7TcUMIil5X0pWxsUcB2hDXHtONSETW+5jYJN7yZ6fH7qNp0uzuWyPXniKjdZ2Fegelr 2orn0bsB5JKoRPYRRO3mFiEoIZDIuiz9vpcK02rsbx8zs9ek6QF5YiULovqYpdAiEGAR+Afk BKHdykIU4wAAAABJRU5ErkJggg==</item> <item item-id="41">iVBORw0KGgoAAAANSUhEUgAAAKsAAAAnCAYAAABwmri6AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA A7tJREFUeF7tnU1yhCAQhU1V1tnNKbyEl3CRM8wqN/A8OU/uQyAzWkzbTTeoEGiSymIclZ/+ eDxAzNtiFjP0n14DNdSAg1Xd7/doG+ho5qpKPptxGMz4rS5aW5QGbUWf58EMc12Y+jFy+b8t 96qa2VmMqYL1vtyaUCZXDo3A6oH1ZzK3ihUVqtM838z0c5Zm1XEfJbDezTTW5lEZgFzjGyej yRDogNUGdmzQ52lTVxWw3pcxa5dpZ4HcdOD1gyA7q6HJuyqANa8FyALp1gzsdJYiK6AAVhvQ jAOrvLDmbYilh2Htw1rAr+YEVpNvbR/WQr4uF7C5/XhJdVUBa4klynywtrHQIWkEHdaTx+zZ ZgKe+W5lVe4QrCFl8APin7ceX4/Bz6EM+edi11H3fj41tk0V7a61NqCEskoq/4xzroQVMiBp iNw5kthS9UIqKwUrBqd/c6yA0qBgacL0sLSCaXZYk/oOCF0oDjC+nAXi4hwFq6+MXEZCoHCZ 5u69quZ6HpVWENavD/PxJW0u9Z33Ob1fUr4mYaWAiwUVgskBKv1+6coarayYWKUqK2YLJMqK 2kJO3agbh2xCCqi+98R8jRTOXdod1uKwYoxhMHJ2cudZYzxnqIWkACtpcb4CQwVA01QLq1vd ejyjsPsLrOhRCpqirCFB40QSHZtIqOdG8VSLiAW2BKxoMLEAFz5GxeDs2QBYH1iP9hIn96gi 2G4T6hmldg9NNyS9bEYfmw1fuhnucwz4XMG6si6GhjVNWSXCw4kQp8KxorRxmAJrKDPSjGDQ xlwrbhR9NiDas3IiFQMrJjiSOGPx3TwrJd3wuK9m6A3Bs5xcl+BXDNYlU0oshlWtZw31YbLv qPhyV69xhLGFg2gs9kEeuISr/77Dmqys/y32/dkAL5RQETh/TXVnVx6HeTp7gPXfAH2xqTkz R428L82DUFm5QUHswIO635HjWD11WJvpOGx4E55n5QYQpcDFYc27v+xSYWG4a98GJOwUqAnW vlOgJWW1b7SK2YOVCiqcojnS3cstSd+D1RSqdto86gUXdcHad7c2Bqtb5ZH5OhRUZDmRW1m7 SmV3fjHBj5f0nEfTbt+zuqYn9K1HVPUqQEN50uRX/+r3KO11XM9bAblPfH0e4gpvKstLnBev I07hXCqB9TGFFXovK5wDliwIYIsI1MLCWcfXfGlTVUXK+ghxMxPoyrzq2kD1KOtz2OjeHF31 blemh2ihu6fKoA5WVxF/r2qv9H8K1PyK+aMNSSWsRyutX1+mBn4B3GUQaduidPIAAAAASUVO RK5CYII=</item> <item item-id="42">iVBORw0KGgoAAAANSUhEUgAAAroAAAAnCAYAAADgmuutAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA C31JREFUeF7tnetx4zgQhHW/LonNQElcEgxkM1A8+ne5OB8daR+9MISZ7gFIgo921VbZIghg vnn10rD012v8uulLBERABERABERABERABM5GYBK6+hIBERABERCBSxF4DuNDnuH1XN3o52u4 3V7D+gutbokWEIEjErgdcdPaswiIgAiIgAjUEngOt9dtY+U5rXl/fNRuWfeJgAhUEpDQrQSn 20RABERABI5H4ONx7/Z0dVpbYvd4MaMdH5uAhO6x/afdi4AIiIAIsAQ+Hq+78yR3PJo4/c3K 2795+vTa9Nr8s/X9dD3/eg73lx7ssg7TOBFoJyCh285QM4iACIiACOyewMfrccdnckviNH0t vz7/bL3+hmUS2/fHS4cYdh8w2uBJCEjonsSRMkMEREAERMAhMArMgXiUurrQHbeop7qKVBHY joCE7nastZIIiIAIiEAnAh+PgToygITutH309LY0xw+zx3d80FndToGgZS9HQEL3ci6XwSIg AiJwNQLcsYVZxObndHNazUJ3fFOzQccXrhaEsrcTAQndTuC1rAiIgAiIwFYERmFJvp0Y80Q3 faqbn9GFT3M/TeaF91aEtI4InJWAhO5ZPSu7REAEREAEvgiQ53NTAZui88RvndDVOV2Fpghs RUBCdyvSWkcEREAERKAPgcCZ2G2e6E7amzsz3AeYVhWB8xCQ0D2PL2WJCIiACIhAicAodMmT C29/aGY95S29zh1bmB8y9/vgCgWJCFyJgITulbwtW0VABETgigRIoYs+MCJHh/4ozUPd8xPa rhgCsvm6BMJCN/I/VoQ1/VQZNLbX9SXtTW1Y2vYl5svniBZ95KNSU/A+hWh+YuKNQfcze/J8 vJb/0b68p0jevUvEAbO3UsP3/DuPb42pNWIU7Sm9nsak9X1+ZrOG51L3oJwrrbNGzFvHAdBa i8UzKXSX4s7Ms7TQ9eIYcSxdz+Oesckaw6zP3JvGi5WXKKZQ7UL1IO/jpb6ezxEZw/S0qI0t vovey8ZShLNVp6x4eIuNqBFLj2cdxo7zkmXpvbfO12pTKWGX3pPVoGrWYecqNWdUXLzraK+s H9hxaL21r/fap1XgUJxG94viYxagEc4oNq01vb1E7YrslxmL/GHZvPS+a/bRks9FNhcQuqW4 z4WAFzfIT0zMWYJkft2KLeZ1Zn+1sVtbU1DdsOxG60Wv1/pmrfsYX6F4ZfaGOM1rhJ/oMotH xrCByY6T0I3Qfx/LBk50FaaQsUVhbeF0pBhCjSXqp5bxtcUtuuYaMYoaVo3QrRHcURZovFc3 txK6qKGh2o6uIwaf13//ev36TY3cbNC///y9+J6i/k6NZfO3BZCXZygHW+PI23dNTend0xbJ ixZnOveysdRqA+u3b6Gbbiy9Of9+/jkfH9nwfK8FI389HZ+KodL93wr+dvv+o4LS/Z6YKdlY WrdkB9pTXliYOGPntHyF1kD+sIQlEzOWePVe94K3phgy9qdxk/u6FFOlOZmc8GI5LeRsblhx iWzO7c1j3vqZaRZW/Sj5nK0btTHKcCg1UNRUmevWvOyelhhn8fXyCPtkfA/Y4TG+Eyz/Fc1p r0569TyPk+91D/tEN8bayz2vr+W1B9VnqyehiIjGHYrF1Ca0dqmPWbHU0mei90bHo576004u fkq9PGfj5Z2nEVAsxez5+lTCqN9+PNG1GrWVPHmwo6BEzYEthuw4K3mthEABh9b1ikxLk2fv Zda3AtIKNrNx/H8DEzNew0fzp4LMKq6eX5ji58VxlCk7V86kZJu3NnsN2Y/WjYi1UtOxcqqm aNbGKGJQijFUC1Ato4r3+N6u97Fos+8GwNiB8ptpOriOc80z3QuqnZ5tbKy7deBCQtcTAQxL ZgwV35lTa3Iqzc1SjFg9AeUK6hlsX/L25NW4fH6Utwy7ss18rp65F7wJXSSqIklgBYFVANli yI7zCi2zt5IYqdl73kijRaKUMF7g40b1xwom4b0ktOIBzcs0XBSL6fWIcIoUFS/emRhi8wUV skjMt/g/Z14713xf1K4o0zxHo7kVicN0rdw+ixPHb/qUrJ9PKb7juVEFs/uKxTnfPC2+kfVq c+iH7RcSugzzUt6UOKM6zsX3145QLbDGMDka2QeyvXWvUTut8aincTbzuYpqBbfeu6/30At2 J3RT51oFLh/DFE3WSbWCgr2P2euSYrokHKz5mQKQj/F85K3DFC9WtESZ9hS6THwzAp9t/lH/ Mz6z5mQaJWpm1xS6yEv5dV4YM0139onfWJ017/gYA1sfp08wy59ys7HuijIgdEs9ZanXLO/a 77pQz9rrcxHRaokTNAdTG/IaY9XEaC1gezzT55i8QcIw0me9nhRZ52ueuviJxA6qWHvrBbsT ukyi1DikNglKiT0nJhvIrBhEiY2Sr0bwocJVuyaady2hu7SQijKlG3rm7Ahn7z8bUftREa3N m1Q8of9YRIom06SiDKzxnk9yIYA4+jbywhWxYlgjn6Lrn4200xldVFdS0fSDxQWf6KJYYFmy fc6LTVTfrDWYPXr7Y/REbU2J5DyyI+orvsbxuYrswXXhjxWWbkJ2ovqGOFpxtmuhywpET4zk DQmB9ESE52hW4LQIJ1QsUBAgIV2T8DU+QoUJ2YGu80Xgayc1vqspoOw689xMXKP9o3hHMRVh ubfihmyPxmHJL4hPpDlE9suOZfwbbz5882SYIUa1efDDrsO+60KMdaTGoDo6XWfGoFhke4RV 91j/W7G2Rt/z8p7JuRqu6J6yH/j4qdl3pA8y80diie19n3GcF/tUGEa+nxdFRSsdZyVS6fXc qHmMBY9J0lKBR806XTdPQPbnElfPwakt3r25zRZHy27rfsvm3Of5Pr0CVYoDFA8ln1tx8D7X 8zVMAW+ceWTnQfHtscpttnIvLdilmEIx/37dtt3zubdfL4Y8AenlLZMDNTH6NS9mgHzHiLYS F9Oulf8YzYtpxNpvOHzzjMa8JUqa8+ACT3Tz+EX1tFR/Sv2PzXVP9OSxiOpt2ucsbRHpB15c 1dYUpg4jH0RsKO3Tz9PpKp+rZ+4F3d9HFztqPyOswN7PDve7EyQQt9r584HPE7bsZS92lmxY 2/YWblvduzWDPcfDVsy3Wsetz4cVulvR0zpRAr1zu/f6UV49x0voBuhL6AZgFYb2Tcyvc5CN f8QOAfS10dreNrZDOF0HbM9gn7HQ1QmrLr6k0I08aas1aumPAK7dh+6rJ9Arx3utW0+q752r CF3mVyi9zG7dW37/Ena07gntYe350frz9bMkp8XT+nUcy2fv49aMozXn7sX1LPHei1/NumZ9 rniiu/aDjTMJ3T3m7xZ76pXjvdZNe3mJb03O5ves4bdVhO4SxmoOERABERABEViEwCh074/I Z7lx7/vasrePx/AKbqllOd0rApclIKF7WdfLcBEQARG4CIHxD/+GoKpknui2PNV6DncJ3YuE n8zsS0BCty9/rS4CIiACIrA6gfFdN4IH9JlfoeZimP+V8nRmfBjfC0RfIiACaxOQ0F2bsOYX AREQARHoTCAuLJknuqlRvMid7hqFN/GJcp2haXkROAUBCd1TuFFGiIAIiIAIeASiZ2IjQjcm ciedGz8zLO+KgAjUEZDQreOmu0RABERABI5EIHhOlxW6YZH7qXN1PvdIoaO9HpuAhO6x/afd i4AIiIAIUARixxcYoVt3Rjd+XpgyT4NEQASKBCR0FRgiIAIiIALXIDAeGbA+BjwFUHpPbPTa fB2B1NNcREjXRWBZAhK6y/LUbCIgAiIgAjsm0PWDGnQ2d8eRoa2dlYCE7lk9K7tEQAREQASK BJ7D+h8H/rYw+TRZLhMBEViWgITusjw1mwiIgAiIwAEITGL3dtvivWzHM7m3ca3g+/geAKG2 KAKHICChewg3aZMiIAIiIAIiIAIiIAJRAv8Bs9rv6fPh/I4AAAAASUVORK5CYII=</item> <item item-id="43">iVBORw0KGgoAAAANSUhEUgAAApgAAAAoCAYAAABaZIqVAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA CiBJREFUeF7tnQtWIzkMRXvW0+thP6yH9bCfDDRTjHHr82S7Pklun9PnAGXL0pXKeuUk8M/t 498v/kEAAhCAAAQgAAEIQGAVgU+ByT8IQAACEIAABCAAAQisIvBrlSHsQAACEIAABCAAAQhA 4M+r42CAAAQgAIHHJfDxatdQcJ/zRucOLWhMml1/NoaR+e2c7esRO38adJO7yFZ/TZ1n5cnz dTYXWU0o9kc5ZmtvrD1u7fWWdWZ3thbUnLd+9P4pXLM4Rq+P7TyjqzEPAhCAAATuhkDUnEYa 18icWViza47M74XKFkPVVj/es2uJ0V50VHxYnffZHHqxrLTbM8y+V3M5WwtqzmfyvZrjd63t ZRi7EIAABCBw3wRWCw21Ka+kNrvmyPxZUWGJm4qI7PlFIsVivTrvV8pn5Et/QhoJfDWm2VqY FZheLan+z4zjBHOGHnMhAAEInEhgaz7WS3FbY/GuKY22b279et717xOM5mX2/qU7b/12nGU/ iycTCZb4soREG2sbzwi3fn6WtxmBaYnDEYFpcc5yaMXl1UJfn2otKeLXqqG+LjxhrQg65aHD YlXNQ+WhQvH76K0KgXk0cdaDAAQgsJBAdkJSFUqWPfVnbUPM5ngNvv155HvWrK25mTi0hJ1q J8qDJW7UvPU8PKEUib9MGFqiOxLFCke1Fnq+Wd1k4s6zl82z/O3nqByrtZDxjHJuPfxYfi/c cmRTCEwZFQMhAAEIXI+AKlQyQRY1qraxeqKvb75WQ680eVWgWKc8bUPO1rREQ88qsxGdNGX5 8eZm/DwREc1T4sj8yWx4AjGrD6+uIrbKQ4r10ODdxZHY9eox80HJfyYwrXszEsQIzOvt03gE AQhA4O4IZA0satxqc6yKHa+pZ+Kkb6RV0eGJm0xQWP62ayt+Z3moitAq88hHz7cKFysXq+pn 9GEiW98TZmrco0KtWgsIzLvbdnEYAhCAwOMTyJrZaoGZ2VNFodJUVVvquH7NSHhlp1mqrSw/ nvCcEZiRMBoVy6sEpmdHzWHmvxd7Ns/Kw9UFZvRg1NanEvseOyUvke9BFZsQgAAEDiKQCZhM EHqnQJ/zetuZcLGuV5q0tWZ7CtVej+L2ximxZj5kwrhfe/s+Y9eftnkse/82kdGuY+WtrYP/ r7/dXj7z/PL2V1ijHKJ4o3pSau2nT7bvFv+WUXR6mcVssVftefn5mh/HEq2r3F/9mIO2Jn7R +lGgWQcCEIAABOYJnNUs5z2vWzgq1re3vwVm3duvGUf5vPm30vfRmFfN2yOWo/PRsuAEc1Vl YAcCEIDAHRHoT1SObEQja1dOj/o0jKynnEzN+KSUyv45eb+9/v51Mw4wU/cspt7pZWpsaMCY 79eshbFYFGz715DvBQJTyRBjIAABCEAAAhCAAARkAghMGRUDIQABCEAAAhCAAAQUAghMhRJj IAABCFyIQPbyJNe/PqDEfxhQAz9r4MhtDIF5JG3WggAEIAABCEAAAk9AAIH5BEkmRAhAAAIQ gAAEIHAkAQTmkbRZCwIQgAAEIAABCDwBAQTmEySZECEAAQhA4PEJWL90+8xfU/P4xIkwIoDA pD4gAAEIQAACd04g+ys0dx4e7t8hAQTmHSYNlyEAAQhAAAI9gf7PLW7XOcWkVs4ggMA8gzpr QgACEIAABBYTQGAuBoq5KQIIzCl8TIYABCAAAQhcgwAC8xp5wIsvAghMKgECEIAABCDwAAQQ mA+QxAcKoSwwV76Xo39T8hW5roy3jW917CP22r9w8Odp47+/fOF9bX1C0Yqp/8sJlTHWX12o 1EXGYa98Kj6OrJ3Fo6w7MsZat6+Xtk6inEfrWx9MWFUDnk+jdT+Sv1H2/byz6mDEf+acRwCB eR77Z1i5vHeeDUXdtNVxXjyz8/fgtNqnEXueaMzE5CYurEaY/SyznV3PcqFyUMdl6+19/Sw/ PZFZzW/GR8l3lYE13mu+bS1HvlR9yOK2rmfMj/BhxG/mXIMAAvMaeXhkLyqaoXyCuRqcumGq 4xCYtQxViqW17OUja+yeMI1sV3OvjlfH1YiuH32Wn5nY2SKd9e9eBKZSuyuyf7bIXREDNo4n sN2vvcicvT+Pj4QVr0ygohm+BWbbTKKnoO1aP75SxO2NYDnbN7boxvEEjeWf4mMWV+9775vV lLfGpDRS9VTDsjm6sWR+KaIxEhvVhpn5k918Vo32vKya6u1mtdDa9Nh7vnhzW46Kj70wb9ez 1qjY7G15AsvbL6I8ZXuAlYss79kDUFZX2XUEZiUDjIUABO6JQN/vRnt6u0/+OMH0GqrXQCwh mAGtiI1oXa+ZePZVcRk1tsx3pdEqYzyG2dzsumLXaqJVgRmJh170KLaV3HlCq4+nyiiq8Uo9 RH5Urin3VyaUVJ4VgTkiXL0NrK8f1V+rBiK/RjiFvry/3n5/vI/55S3LUnw9q6s568yGAAQg YBNQNKCiEb57hyLURoTeiJhRN1Z1nBeb6lul8SvNKhIrUcFbTTISZqMN+ceTx0ejtArJEwXR z6v5ygRqtjmo6ymcVFtZrWTXI+Gr+KnmZdSWJTatNRX7yr3S51ixm93vyj0TxRndDz/9fb+9 /v71/aG5H/UsqM9KzWX3AtchAAEIqARUrRdphR82riYwPXHRB66MyxqOBb2yuauioJK0qLEq jbkXsdXC6hus19grP8/8rsSsxKPmUBEtma1KHY7Uiy5qvshEMSnxZvtBJNKU3Fg+KvUx47vy 8DNb92rsyris5hQbjIEABCAQEjBecaloFW/PvLTA9IBEzVkRkpUGlQkBS8SpTUGNw2qKWXOv 2ra4zTbazMdMBCkCJNs2RnKh1F3vW1XMZXXlCaHR2o3ymzH08qDkN7OdCco91vDqTtkklf3l Z8ycYGY1wHUIQOB6BJ5KYKrBKo17tEmrom1E1Cg+ZesrsatlnDV+pdEq4iBbR7ERxTSSixUC c1WuMj6V2GcEu+XHbG4Uf/ZYI3qwqfBW7ln1fltRc7NrMR8CEICAdbiR9TNpL+9FQ/tEX/l6 W0zZgD/HtP8tH7Im481v/ag0Ds//bZ02Adba3rh+XpuUfk7UcNpcVH1Vb58KLy8/fW49keLV QFYX/8fydnv5rCPnPW2qnaxeo1rNai0TaGoN/+2jH3tUr5G/fY1E/NT7NRPC0X2Ucb/dcga5 jS8PZ+v+O84FH/JR61a9pxkHAQhAQCXQ9g/v637/D79XF2ac/f62TKA8KrerxP32+np73xHy VeK0Qtw79h2xLjN9NIMr18MyqBiCAAQgsIDA6b9ofUEMh5nwTu0Oc+BiC53bbL/e5yZ8KHeK 2rkxeq4fE/sUuN0nH8/gmrWwO2gWgAAEIDBEYBeBab1MepXNedY39WW3SjZmfcrW2sv+VXKa xZ9d9/hEL2FnNu/h+l518Rn7nrbPYvso9X4WP9aFAASei8AuAvO5EBItBCAAAQhAAAIQgEBL 4F+thkGkWJqYXwAAAABJRU5ErkJggg==</item> <item item-id="44">iVBORw0KGgoAAAANSUhEUgAAAU4AAAASCAYAAAA0aDdRAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA BAZJREFUeF7tW1F24jAMpOfZ8/Q+nIfz9D7Z0rdhHaHRjBybZFnxBbUtS6PRSIHXj+X7dalX IVAIFAKFgI7AXTjrVQgUAoVAIaAjcNG31s5CoBAoBAqBn6f0gqEQKAQKgUIgh0BaOL+/BMjd EOy+2xppb5hjjaFZ/o2OvcfeemaNsbXhvbdYeJ9bm55P3nprh51nOWY4zMon8+vPbwnKts0e Fk/aoHiA5W41w/LJrrP37LW34hzxqJf3G56ywGavq0RW9yF/956fgcNon3rsITFkIomEwPOB 2cqus1yoOKj72H2z14/yE4mnjZflj+GjnM9iwHjYw/sSTpbJF61nycDc6rHXQ6BoemKEVSYv pZAiLFQc1H0M99nrR/nZK5xZPJR8ZzFgPNzL+8dzdwvSRlmbR3O7Z93nAcyIvZ7xArD2vL3o vB3Vvc+Kb+25dn97r32PzkS2FJIhfD2c7COUat/GGH0eKZyM4IrQogkI8UjlBOI74oOHfVQj KodV0fBq2N4R3Rnh2MYWTZxZX2eJcw+vMgK++cIyEkYPuKhwUcFGzqlr6r626JSEMrDZve06 uk/Zo2CX9ZU1i5HC6TWUqPlEhdgrnAjnLP6sOSHcmCAwLrHGFXGECYBSC6h2EO96BBmJseWP 6i8bcLI8i2rsSTiRQLK/qwTvJUxEeKUYFPCzYqT6hCYcxafM2dWeatcrTmtDaQCsyDM5t/Hu iYVxVrGd8Z3xUOULE75ME7QYKDFHuGVrxPOVxcfuiOJXJ2OEC6uhx3rUMbPEU5LCiGi7DfJB 2ccKOptU5vseLKMJIurMqMsq5DqDcLIcKJzKTBJM3LK2Mjw8UjjVwYZxeIaoMSHd67siptmB 4dQTJ0siK7oe4bRJ8pK6FktkHxXJnsJlpM3a/heEc0TRKLlQGhfjBuPbkcLJJimlljzej84P spdtoKxW3m7iVMSyZ/LLAN9TaKpPWXFj+9ViVCZQpfNH3VslPbtHJf1esVM4oeZVEVU1Vwyf KJcKdkrcXi7fUThRnIxb8o9Dbcfy3q8OKElZE4AmNyVBrQ3v8QrZZqTzSGsJ791t74s+I/yi ZLVnENbMByaemYJFBWpzi/BEHEB5fc7nbfm8/wPF580NS7XD+Bpxtc0D442XM5XDzz7i2BEH LOcycbMnK1SvSq1F/kb4LAvHgOUODQERNg8cWTHV+l8EkFj8jxixwnsVJrfrdfmaeNlZ4vRC nB37RFiHmX41BiWcHakr4dyCdqyofC3XX5cFDJwd2fWPHBsjCuM1sQ8DcYqh12PQcmHcP543 4HiPa2ch4F7f1PE/w5W9PrG7Ztk/S05Z/Gwd4RM/KjKr51+fxQv7Fca74DhdOM9PmfKwECgE CoF+BH4D9rWHxc5RXQUAAAAASUVORK5CYII=</item> <item item-id="45">iVBORw0KGgoAAAANSUhEUgAAAKsAAAAnCAYAAABwmri6AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA A7tJREFUeF7tnU1yhCAQhU1V1tnNKbyEl3CRM8wqN/A8OU/uQyAzWkzbTTeoEGiSymIclZ/+ eDxAzNtiFjP0n14DNdSAg1Xd7/doG+ho5qpKPptxGMz4rS5aW5QGbUWf58EMc12Y+jFy+b8t 96qa2VmMqYL1vtyaUCZXDo3A6oH1ZzK3ihUVqtM838z0c5Zm1XEfJbDezTTW5lEZgFzjGyej yRDogNUGdmzQ52lTVxWw3pcxa5dpZ4HcdOD1gyA7q6HJuyqANa8FyALp1gzsdJYiK6AAVhvQ jAOrvLDmbYilh2Htw1rAr+YEVpNvbR/WQr4uF7C5/XhJdVUBa4klynywtrHQIWkEHdaTx+zZ ZgKe+W5lVe4QrCFl8APin7ceX4/Bz6EM+edi11H3fj41tk0V7a61NqCEskoq/4xzroQVMiBp iNw5kthS9UIqKwUrBqd/c6yA0qBgacL0sLSCaXZYk/oOCF0oDjC+nAXi4hwFq6+MXEZCoHCZ 5u69quZ6HpVWENavD/PxJW0u9Z33Ob1fUr4mYaWAiwUVgskBKv1+6coarayYWKUqK2YLJMqK 2kJO3agbh2xCCqi+98R8jRTOXdod1uKwYoxhMHJ2cudZYzxnqIWkACtpcb4CQwVA01QLq1vd ejyjsPsLrOhRCpqirCFB40QSHZtIqOdG8VSLiAW2BKxoMLEAFz5GxeDs2QBYH1iP9hIn96gi 2G4T6hmldg9NNyS9bEYfmw1fuhnucwz4XMG6si6GhjVNWSXCw4kQp8KxorRxmAJrKDPSjGDQ xlwrbhR9NiDas3IiFQMrJjiSOGPx3TwrJd3wuK9m6A3Bs5xcl+BXDNYlU0oshlWtZw31YbLv qPhyV69xhLGFg2gs9kEeuISr/77Dmqys/y32/dkAL5RQETh/TXVnVx6HeTp7gPXfAH2xqTkz R428L82DUFm5QUHswIO635HjWD11WJvpOGx4E55n5QYQpcDFYc27v+xSYWG4a98GJOwUqAnW vlOgJWW1b7SK2YOVCiqcojnS3cstSd+D1RSqdto86gUXdcHad7c2Bqtb5ZH5OhRUZDmRW1m7 SmV3fjHBj5f0nEfTbt+zuqYn9K1HVPUqQEN50uRX/+r3KO11XM9bAblPfH0e4gpvKstLnBev I07hXCqB9TGFFXovK5wDliwIYIsI1MLCWcfXfGlTVUXK+ghxMxPoyrzq2kD1KOtz2OjeHF31 blemh2ihu6fKoA5WVxF/r2qv9H8K1PyK+aMNSSWsRyutX1+mBn4B3GUQaduidPIAAAAASUVO RK5CYII=</item> <item item-id="46">iVBORw0KGgoAAAANSUhEUgAAAWIAAAAnCAYAAAA1vkIRAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA Bk9JREFUeF7tXUGS2zgM9FbtOTe/Qp/wJ3TYN8wpP/B78p78R5EyqxmaBokGLVIk3Lu1laxF kUCj0QQpyv7nvtyXC/8hAkSACBCB8xDYhJj/EoHmCPya1gJgWmayjwhkEZiX6XJZpl/NGdo0 Lhff7tG7HhGY58tymSnBPcamV5s2zlzvH03FsSUWFGK3oW1JI3ysj/vVfXWDo8GWFgQ27ngV YwoxhbgdAr9vy5WVcDu8HY40z9fl9tsi32O0pRA7JGuf1PtYbhP3hPuMzUBWbZP5dFu8bVJQ iCnEbRBYE2hyvMc3kJS1iXfFUTxWxRTiioRhcn4j8HGfXC4pkRivZ6K2I6Jk2lEIrCduvO0V U4iPIgf7ySDwvtsSFGBkqrK2WY+0OdueoBBTQBsgsCbOmz6koxBbRRZp729ipxA3kCGEWq7b vPn+MMX4eHZ72yemEFOI6yPgcE/PKi0UYyti+fbenjlQiOvLEEdYhdj7K6qazFCINYRs1729 GEQhpkzWR+CNhZgnJmwCi7Z+eyE+cmYfgaRH+huS7Gjfj+gv7mP///hPNFm+2gFCjNifapO7 t1b8zBjUn+6SI5TGL3dfiispXMK+9jbSZyiuZwhxikvS5wifH/QAdbxWOzRR0HY5ItTyobTf V32Kxz2iv7iPFMlMPitCHI6BkD3VXrL9CExMvp4ouEdz/5VYSLYcwqX/8W0txJYiAOHzU+6e TTI0UdB2R5OxJj6v+jSMEP/8sfz4mUYSIS4ivkcmes24t+67lGc9C/F/t3+znKqBMYJHaU5+ 7RGHip8j/X4tbm8J9n6vNMuE13JLGKldqn2urQSc5KPUt+RH6cyZIw7aZxw3NCZaPFLkQjjz 996TKuJtbBSDGonbS5+lGCDCg2hFmDuaUMVcTNnQuiLOcSmHL4r9w8O6lLiiYCODxm1SfceO l7QL+yixLWeDdC1no0RGxKaQuFr/2nVkuSjFRxNqaeJ66IdCfOqGhaUQifkmFQE5PdBEVxPa nD6Eto0gxJb8fhJiTTBQQUxVA62FOEcMbXa2CnFKNPd+YlKjgZISSbo3FES0GtOInxsHniwp xKcKcY6XJTzRhDjFa41LSD7ubdJCvL119/ndHk//vfh2p8Uv62qsOyFOVV9x8JF2JQQ8YqJA BQoRYsvEV1L5UIgf019MYCmpT/4MFVCLuOX61MQ3VWxoOWgVN12Ircjgwm2xFcntB2yQpS8S BHQGsAgdYhtKNAswmvjFla3kO4IZYlPOFlTw0QTT/JBWF4if3CO2ikO99gjnpNGROFu4KnEp 91lsU+8VsRXn7ipiRHwtYr63tQCDkE6bAJA+EJss5NaqWy3BqgkxT02cujWhcSgl+1YOa/yT +F5yz2Zvz6cmSvCGH9aFgib9PVyaaPP5XlGmKkvpc6n6y7VDqlZJTCVixMCG9oezuNROup7C L5cQ8YSSwy5lk9Z/3GccJ23M8PpD28IXOrRYaH5Kcfq85/OXgX39gGnep1SuaFhoORYXTqkc yOVMLr8k8d7GbP2wLsUlza+U/U/ao4kmr38jIIGKAv3WOAJCfAY+893fT+6U+lR63xlxO0OI a/vJ75owLBopxIV07E6IPx/QaF9ElK4in3GwVI651aO1n29LMJ+eLS+9r5ALhnzLjdC6Iq7t bRUhRpa0tR1Dl+GWZAsTyHpfzt/aeNXuX41ld0KsWvzwIoi26omX1uGWSc2/6174bUEhPmhG 80sRevaEwODfR6wJcbxPWlN8LbZ4ZiK/j5hCTASsCAz+Cx0W8WtZHXsWWs03/kKHNQnZnghs pxRefKtJS8xa10tFeN/Gqlkd1/K5/375m3UUFSJQgMC4iUMh7lGW+SvOBUnYYyBpU2sERtzT E0V43Wa5rmeQ4xMX2omaGlsWrWPYzXiDP3OQcKxyaqKbgHHa6AeBAfeJX6mGuTVRTwW87Q// 5Uo9uNgzEQgRGGt7IlXBitXMWiHX3Au22OKfc+M+b8jFhkLcT83o35J1STnKa8Xx2etsEkXf xBYfZ0ttWxzxuX/hffTQYzXMiti/9HXnobeD+O8mhKf663Bv+GsldSqw3ckE0WiBwDzrrxe3 sINjDITAQKupElS5NcHJ4BQENjG+XKb1hHEJbXnP+yDg8ZvynqNHIaYQEAEiQARORuAPR6RY L3aYeQIAAAAASUVORK5CYII=</item> <item item-id="47">iVBORw0KGgoAAAANSUhEUgAAAKsAAAAnCAYAAABwmri6AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA A8dJREFUeF7tnUtyrCAUhk1Vxpn1KtyEm3Bw19Cj7MD1ZD3ZDxe7W4vgeUEDCoekUpXYKK+P n5+Dmo/FLGboX70FamiBFVZ13z+jHaCjmauq+WzGYTDjj7re2ntp0Fb1eR7MMNeFqdtHa/lv y72qYZaKMVWw3pdbE8q01kMjsHpg/Z3MrWJF9dVpnm9m+k2lWXVcRwmsdzONtXlUBqB18I2T 0WQIdMBqO3Zs0OdpU1cVsN6XseiUaaNAazgw/yLIRjU0eVcFsJa1AEUg3YeBDWcpsgIKYLUd WnBhVRbWsgPx7GVY+7Ce4FdLAqvJt7YP60m+rhSwpf34meqqAtYztijLwdrGRodkEHRYE6/Z i0UCXuVuZVfuLVgpZXA7xE23Hd+O+X9TBXLTQudh137dNbaHig7nWhtwhrJKGj9FmhywYgNO MhC5NJK+xdoFVVYMVghO9+L+eSHTIZTWzw/Ki8yzwxo0d2DtTfWDDxfX51w/B8HqKiNXEAoU rtDctTfV3NJheZGwfn+Zr+8UGnbNa/ybPrPVTwIu1Cpcv58GKwYcV2BpJTml5j5furIGKSs2 S8YqK2QLJLC6dmH/nVM37MKUTYgB1fWekK/h1BX7vMMaNyO8M2NyYIMgetvTUP4Hz8oqlTNO qRESA6xkxLnWwLcrYJ5qlXXd3Xreo3D4EezopYCVEjROJMG1CXSSXzluFS+ZOiTj+wxYsVF+ teNY++WKBlAw/emn9VZF73Ebamb01yHojPgSxT8qTa3koQtxyst9HgI+V7GurIvBYY1XVk40 uFlTYgMkynpgKQZWqjBcRVPACkGMNmCPBgQtsLC+5QCkOJIIGJfmodauem7y7WfsHnfVDMrA vwY3JUD5S87hKrfXQa1nlRivYxrKAkJ8QLlgDPiLaIo90IbFVamiszqsQcp65Z7t9wZ40Q3O j3G2CVOfVMd9mHIssK4KbFFYQWnP/fiHUFlDPJk7nUkiIaE+MKQsHdZmJg6LUsT9rFJ1pXxz KkC5svT7WVuCNeJJAQ4QLDaYA1CuLP1JgZZgtW+0CnkGi4NDGqLJAe7RS/ZnsJpC1YbNg15w URes/enWxmBdd3lk7w0AQQW2Ey9jAyL8+FVX+pJyFY0GSAqUJY3Qt4aoKreLVsIGaPKrj/bO AsfltJm3AiHhomsoa5gXb6GflcD6DGFR72WlthmpLUVsy9k/J/WmgDZVVaSsT3SaCaAr86r7 TNbC9BBSh/XN0VU/7crMECFtUVtaPTbA8dGPV7VX+j8Fan7F/LuDQyWs7zZaP/+cFvgPmywa ZGMYY6IAAAAASUVORK5CYII=</item> <item item-id="48">iVBORw0KGgoAAAANSUhEUgAAAWIAAAAnCAYAAAA1vkIRAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA BjVJREFUeF7tXcFx2zAQVGby9k9VsAk1wUdq8MsdqB7Xk34Y0gptEDrg9iAAJMBNJmNHPAK4 vb3FEQTFX/fpPl34hwgQASJABPZDYBFi/iUC1RH4HOYCYJhGso8IRBEYp+FymYbP6gytGpdL 3+7RuyMiMI6X6TJSgo8Ym6OOaeHM9f5eVRxrYkEh7ja0NWmE9/V+v3Zf3eBo0NKCwMKdXsWY QkwhrofA39t0ZSVcD+8OexrH63T7a5HvNmwpxB2S9ZjUe59uA9eEjxmbhka1TObDbeptkYJC TCGug8CcQEPHa3wNSVmdeBfspceqmEJckDBMzh8E3u9Dl5eUSIznPVHLFlEyLRcC846b3taK KcS5yMF2Igicd1mCAoxMVVabeUtbZ8sTFGIKaAUE5sQ56U06CrFVZBH7/iZ2CnEFGUKo1bXN ydeHKcb52d3bOjGFmEJcHoEO1/Ss0kIxtiIWt+/tngOFuLwMsYdZiHt/RFWTGQqxhpDteG8P BlGIKZPlETixEHPHhE1gUevTC3HOmb0Fkub01yVZbt9ztOe3sf7f/4kmy7cdIMTI+EM2sXNL xc+MQfnpLthDavxi54W4EsLFbWu1kT5Dcd1DiENckj5H+LzRA9TxUnZooqB2MSKU8iG13Vd9 8vvN0Z7fRohkJp8VIXb7QMgespfGngMTk687Cm5u7r8SC2ksWbj0H9/aQmwpAhA+P+Xu3iRD EwW1y03Gkvi86lMzQvzxNr19hJFEiIuIb85ELxn32m2n8uzIQvzn9jvKqRIYI3ik5uT3GrGr +DHSr8d8e0uw13OlWcY9FruEkexC9jFbCTjJR6ltyY/UmTNGHLRNP25oTLR4hMiFcObr3J0q 4qVvFIMSiXuUNlMxQIQH0Qo3dzSh8rkYGkPtijjGpRi+KPabm3UhcUXBRjr1bUJt+46n2Llt pIwtNgbpWGyMEhmRMbnE1drXjiOXi1J8NKGWJq5NOxTiXRcsLIWIzzepCIjpgSa6mtDG9MEd WwtCbMnvJyHWBAMVxFA1UFuIY8TQZmerEIdEc23HJzUaKCmRpHNdQUSrMY34sX7gyZJCvKsQ x3iZwhNNiEO81riE5ONqExbi5am7x3d7PP178elOi1/Wq7HDCXGo+vKDj9ilEDDHRIEKFCLE lokvpfKhEG/TX0xgKal3/gwVUIu4xdrUxDdUbGg5aBU3XYityODCbRkrktsbbJBLXyQI6Axg ETpkbCjRLMBo4udXtpLvCGbImGJjQQUfTTDND+nqAvGTa8RWcShnj3BO6h2Js4WrEpdin/lj OnpFbMX5cBUxIr4WMV9tLcAgpNMmAKQNZEwWcmvVrZZgxYSYuyZ2XZrQOBSSfSuHNf5JfE85 ZxnvkXdNpOAN36xzBU363b000ebztaIMVZbS51L1F7NDqlZJTCVi+MC643dncclOOh7CL5YQ /oQSwy40Jq19v00/Tlqf7vGNbeIDHVosND+lOD3OebwZuK8XmMZ9CuWKhoWWY37hFMqBWM7E 8ksS76XP2jfrQlzS/AqN/0l7NNHk8R8EJFBRoE+NIyDEe+Az3vt75U6qT6nn7RG3PYS4tJ/8 rgnDRSOFOJGOhxPixw0a7YuIwlWkjEOIHyU//xkJ5tPzyFPPS+SCId9iPdSuiEt7W0SIkUva 0o6hl+EpyRa+5E3zqjRepdtXvT6cEKsj3jwIglz1hJZt3OWT3L/rXvRrQSHONKP1SxF69oRA 499HjAjx4rO0pp1bfNGx9M5Cfh8xhZgIWBFo/A0dqPhRiOvJP9/QYU1C2hOBZZfCi0811Uvx bU+oCLMirhkhvrOOokIEEhBoN3EoxDUFFu2Lb3FOSEIUXNr1jECLa3qiCM/LLNd5D7K044JL E5UY3Pg9BwmlIrsmKoWDk0JLCDS4Tmyphrk0US/re1sf/uJOPfjY07kRaGt5wn/aC4kdK2IE pVdt2r3fEPOcQtxSVdn6WOdLylYeK/b3XmvyoT0C658f2r9u/VwbV2/He6yGWRG3LmwNjr+3 jfi9Cd2h/elwbfh7n/mhgW9QaIinjsA46o8X663Q4lQINHQ1lRIXLk1Q7HdBYBHjy2WYdxin 0JbnnAeBHr8p7zl6FGIKAREgAkRgZwT+AeQdXizhBc8WAAAAAElFTkSuQmCC</item> <item item-id="49">iVBORw0KGgoAAAANSUhEUgAAAKUAAAAnCAYAAABuU4gJAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA A8JJREFUeF7tnMuRpDAMhtmqPc+toyCJToLDxtCnyYB4Jp7Jx4u7G8ptJEsCZGzk2ZqqHcAv 9PmXZAN/Rje6rv20O1DSHfBQmvv3008TsXdDkSMfXN91rv8xZ5XFGp21oQ9D57qhTBxDW/h+ 3sZHkdNGmxlTUD7GW1UK5PtrEUw7UP7e3a0ChYxVaBhu7v6rrU1l1W8Eyoe796XGkAQQfjL1 d2fJkduAcjJsX3F8Zk0tTUD5GHt1FzitqPilNZ3EZFotsBRbGoBS33WrwbggPi0TGXLhBqCc DKqc4OhDqT+xSkp1rg9lpnhSG0xLceX1ocwYj2mCmSMuLkUtTUCZa8tOF8q6Fv73AN6gPCBf Vs283/2rbTdKBcrUrA+NEF43H5+PxX+nOhpeC5XD6n4/5bQsx6zKTu47l1LuMQRVVgtKyM6c SUZdw7EhNmZUKTEoIQjDyuNyEpeG3aC5fqzuZJsNStQXQGDF9qWERHqewwMIZah0caMUdJJB UXXPKrgLyu8v9/VN6VD55//d/6qMI2VPCqAt56kyT5tD5pBAiYHFaVwKZQwp5++xKWUyaj4K Skp1U8KyCt0oMKQxBxVrUHIfd1Dav1V/G5TZoIRstQIu2oqFJsVKKSn3nIofQ+XSUkqojZSy X18p/W7Pa9999cvYyTpCKTFbUzkC6mU5dFPqhoEqBZM7iBjCPVCCxoQMnOkYdq9zZd9oTuAf oYte0wjvXSoMpIRslTuICzDkl1PnnkEcCWX5Kc6rhziU5yslFNvvOfbhvjkyHMeMHHfPVUyu UrISnDmSatl3lpiSaxOKlydfYVaEZVDh8VClwCA1eq6QkniofU4ZanCLArZEh1ynxOxIeRFI oGI+4nAAK/MRRlENV3++QXnARmpeCtred2AyyFNgsS+m5lgd0uNxu1qJTl7ceK1lhRLLdHld 3XgVUynRrBPQGW7sHYYmkv9DI21QVif4CWA3PE9JJWbnQKn/ntHGaX84LVmV8pRBb3jyvEQo 25Pnh7N/Co7vUcje0aGAnJc+sJUKiZvmhwztHZ1LITktO4s+RMCBEtscwCCTHl9P4fY248Wg 9LshvHgMBBLYXssO5Ya4+EzftLft68eUfoox40qpSsa7GFJF5LpvS/Hk857upbqO8rQL5wKi BSI+IWQxcR32SPfSCJTTTfAfSk08yhWvoVLGlS6GS6+f27emkoaU8mXi6hagjcWSy8oFpQhX O++/kFvF242Esl/NLh/J45UHh43t+Ynpwr95XsMnsLXYsRNTXm6hSwuJ8+v9D5PToeZOxL1d AAAAAElFTkSuQmCC</item> <item item-id="50">iVBORw0KGgoAAAANSUhEUgAAAVwAAAAnCAYAAABDhPCaAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA BkxJREFUeF7tXUuS2zoMdKqyzs6n0CV8CS3eGWaVG/g8OU/uo0gzpXkaDj5Nih+T6kylkrEp EmgADRCmrB/P5bnc+IcIEAEiQATKI7ARLn+IQHUE/kxrop+Wmd5HBN4RmJfpdlumP9U9sSr+ t7HVo3aviMA835bbTKp9Rdu0lmnzjfvzrSoJ1tSZhDusaWu6Eb7W2/M+fBWDo8GREgKbj4xK uiRcEm49BP4+ljsr23p4d7zSPN+Xx9/xEhIJt2On7Msd35bHxJ5tXzZrKO2WnKfHMlpzgYRL wq2DwBpA08C9uYbUVMd+DVYZscol4TZwpCsG59tzGnKLqNlyPV+0Hbekd51BYD3JMlovl4R7 xiF4LYjAtdoJJNpcJcV6VGywtgIJF6SMXC50zXnWwLnQh2Uk3FxePl6iJuGScMsjcMH+LUk3 D+mO1scl4ZanG64wYC8OoROSLoKSPWa03j8Jl3RYHoGVcEe/ZVOiDRJuDsId60YZEm55uuEK FyNcnlA4T7T7DKPdmRhNuDmzdg+OmVPfoxvm1j3HfOEc++/hv9HhBBAuIr82xrq2lP2iMaiQ ds/YCcXWs9NRhh0j6TUUvxaEq/mM9TrqZ9GEiwKFjoMFPXmmEV0HlTvHuNwy5ZgvnEOaM3od h3CP8yHOro2XZI+WtQIx5vCdnC0MBE/ERkeCDeVLtUNtwkUTj6WrZV8SbsMAS3VC67D92WAu Qri/fy2/fuuSIcEcSwqpAXEWv5bX5/CnVJzD3Vsuwv3v8dP0nRJ4I0l/WzcF70/CPTK7Bfr+ Xjg+ZvH9WimbHN+ztiTSOG28NVZyDElHaW5Jj5gMiWKGzhnaLXZ+zR5a8CA+835towo3NShK BHGNOVF7mxXYYSeJkm9Kkg5jR5O9doVr+Qyip2fnLxWuRqIIAaPObQmNvoeOO8qEOKM0RtM9 1DckK209az7XWEowSNUcoq9kMwlbKbGECWif65isPvUh4VbZR8UUFpKvoXHl+bBEph5Z9Uq4 WjGkxfI3wvWCFyUgdcGgF4vOlzLO2+Yg257UdS3ikjD2qg6E0EWyc0I9JRCsBCzqRsKtQrix /u75v2dnpKhAfN0qDvQKd7sL7eP7Kr79PXlXI6qXl3jEpKYZyQMbqY5SsqhWTYXyIONSHBDN 8lKFewZLJEEh5BidcY0EqO1aon2jI8LVqrNWr3u7HsRvkDkkkrHsbPmZN1dMXOZrKeAEfSnC RRzIyogoWF5FqWXmY+BZjhNNSkoN5GVRZB1UVzSJImt+sUNHhIuQUw9jrBhBCiGroIjxSavC RWRkhWv0EREAc1SQKXMgsknO4TmXRcxn5vOuRd5HdEaqZmvriZDvk6cUqrQUUF89a8+zPoMm 9m1cL6cUkFh71zus0vYLj1sG5P87iMjC4fZMksHblmiV5lEOzzEkx5OuCR1ZWvuIkYRFiGEK XkdHDdfz1vSqXEsnxF7h+l/kS7zxwbPFMelo/iLhtD8hts8HWX483VaTXdZ3Q0q/DrWvhrfE D1rMhHNYcbyNzddSwPYlmjze69jsAeGiF111nJcEroqLqzdAuO4cBWrE+dnvI1xSZU+9roV9 WhBuaT2b3/hQWsGc85NwE9F8OcL9+ADF+0IdvVr8ikNshWjtbvw1Mdm/Wyr1ukSbZ0qQtSvc 0toWIdzQAX0nKq3m//Oflc0KrlQtzsrkrVt6fm9978YH9/pMwRuzTrgl9loy0vvaHLGvx8g9 2lgSbgPnH82JLqdP59+HK+1sjjbU3o8l1hiSv4oP8ftwSbhEIBaBzp/4QMJtR+984kNssHE8 Edg+IT9590+rkPfI9tiPlU5V7HKfqXZb6d5+XT7TjORBBBIQ6DdwEMLV2gtnSDZ23fbkWEIC PrU3IdhKGIJz9oZAj704kfTW9sh9PQernXDIRbIk3NXDO+/9ix+k9ha4lLdTBDrs46aQHgk3 n3+O1r99bz/lg4czEQELgb7aCjEnBkqQbArZj+V//fb9zSOEYxmJ2rw0AusWsZfbaWPPW2tn zXO9/tJ2LdBoHLG6ZYVbwFGuFhix+o52kD1Wf44HEBiwd/t5WgVQn7REBLIiMM/+bbX0y4si 0NEuKMVC7OFmpZIUE1zzmo10b7dpPaF7Tf2pdYiA/S1oo+BFwmXAEwEiQAQqIfAPn6ndsuDp T5IAAAAASUVORK5CYII=</item> <item item-id="51">iVBORw0KGgoAAAANSUhEUgAAAJ8AAAAnCAYAAAARgpr4AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA A61JREFUeF7tnEtyhCAQhk1V1tnNKbyEl3CRM8wqN/A8OU/uQ2ASLQb7BYIjDUllEZWH9Mff 3aC+LWYxQ//pI/CKEXDwNff7PdoJN5r5knc+m3EYzPit3yqD/lt8vsN5HswwXxM7v6eun7fl fsnpkYuZpuC7L7eqFMX1VzOA7cD3M5lbBYoXqso838z0k0trrlVPI/DdzTReNcZjgHCTZpyM RgfcBnzWgGPF8ZNW9WsCvvsyFndddqXCLVmVSRBsdq4x9msAvvIutxh0G8p2+UWh620APmu4 wolGefjKT6BXpCL64Tsp3isNoMa4Tz98J8ZLJQE8I249W/2agO+sraqy8NW1QC4BucOXIT8t mun+96+23ZlD8FGz2B9s/7r1+Hos/J/qkH8tVA6r+/+pnG2ZY1fWut2zlE8y4KnX5IYvHG+s X9zEktgKrZtqFDoHweZfF0Ib44qga8P2oLbINjt8oLYftQtl81UQuIkGul1fucIKOLgoWNjO AIu0h+H7+jAfX1zL1z//Ob1nvQ8V8GFwxtzcWgenfNCM4ibD0pUvq/JBblhqt11oFatsYYwF lU8Bz6+XihU52HZtd/hQ+Lh4DhIFDDQoZufCsZ3b5Ywr9fUpAEpmkK9+YXgAtqkePrf78bev vPsT7uxwtqLCMCy+o+rc6sOUi1IfjmjKjXIZL6fEueEDjQYZ8qRj2PjkznZRG7pHuILH+DkW pKIRgvqkfBitkqAfA46bVVIlpbJccmZ25WNXMjkbUfaPUb6dV+WMv6M1eHRI4qa5m4tJOKIh 79kum3Bw9uFsLFE+qI5N+TBpDY/7KgNWCMC51sG5XMgFYmW4AdnKqVc+alTpcxK7QAmmXw7i hju2nU/veiUlO3ys232VJfvermcaqRJAauAfg+rB6pa2WTLhaAI+LLMsevNC5eOCaiw2lpTD rpGUXdvt8F1WwAl8E57n4wJwCYiSa+LgK/8eSlERANjR73YTnmS+Inz9SeYalc9+kSXmHY5U 8MKYL6/y9Xc4qkRvsa9bx7wwfgS+I8DR7fa31yqFz+InfG8XBADYbsJW9YvBlxC3nh2/pbSn P+ZzU0YY9+VSPWhXCNqZkSYcGuO9xxilEFtfGd71SkE4CpZk2eV5fONi1pps0wh81iTug5DE I0bhGiRlROra2MVkbpFZq+o1pHx/KFW3UKs01ttCkJpkOkdf3Rc/q3ibjVHqHGPx6jracbte rv74NO7Fv8lcw6d7j8LbJHxHB62XzzMCv+7WH3yFG4xSAAAAAElFTkSuQmCC</item> <item item-id="52">iVBORw0KGgoAAAANSUhEUgAAAVwAAAAnCAYAAABDhPCaAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA BlxJREFUeF7tXUGS2yAQdKpy3ptfoU/4EzrkDT7tD/yefc/+R5HsyGExw/QgQAZ1Uqns2giG np6eASPr1226TSf+IQJEgAgQgfIILILLv0SgOgJfw5zoh2kk+4jAHYFxGk6nafiqzsSq+J/6 nh5n944IjONpOo2U2nf0zd42Ldw4365VRbDmnCm43bq2Jo3wsa63c/dVDI4GW4YQWDjSq+hS cCm49RD4vkxnVrb18G54pHE8T5fv/hISBbdhUrZFx+t0Gbhn25bPdrR2Sc7DZeptc4GCS8Gt g8AcQEPHe3M7SlMd/+0wSo9VLgV3ByIdMTivt6HLJaLky/l80XLckuzagsB8kqW3vVwK7hZC 8FoQgWNtJ1Boc5UU81GxzrYVKLigZOSi0DH7mQPnQB+WUXBzsby/RE3BpeCWR+CA+7cU3Tyi 29s+LgW3vNxwhA734hA5oegiKMXb9Lb3T8GlHJZHYBbc3m/ZDMkGBTeH4PZ1owwFt7zccISD CS5PKGwX2rWH3u5MNAtuzqzdAjFzztelYe655+jP72P93f/fHE6A4CL2S21i15bynxmDCml3 q59CWEmc0I7DuX25dllx20NwJc4g+GjzMwuu1qH1fTQg0HYxIlhtK91+65x8+3L05/chkcyE jSK4fnBqy3Opfcj2HJiY5lpBWEtwPJS0UJwRHqb6obbgWpI6wtsXbPYmE+oItF0JMpbCaOuc EKJbbS8iuJ8f08enbAlCXCT4sySHHQXT6qvc/o/5HvHRak9OP/y5/I5yZytmWnL3V6XS72gs PytcV9lj5F7f89ujAy4Gr9dKWdV/3W3vOlXLRr6tiI3avHzbfds0m2IO1JZpoQBDfKWRUvOH FNgIZ+7X7lThrlzT5t/L+wi/Y3NNEVwphrVkEIqjkG21K9wYZ1B8ohj7AiAJaih7hYRRI6/F 6JiYoJnGnY/VNh98zXbJXtRWi32h4ELGRwIhxIlQ0nOTZ+jnZz8U3Cp1s+QjjVeh2I5xX+OZ VJTEig0pWXQvuJKwaq+j1YQmWog4WfqQyBQioVXEYsQL2ZiSoFwhQ4iO+gEJMqkvJBH+mD8F t4rgxgQNEV00rrQiRosjLem778uCu9yF9vi+ipd/G+9qlMRfmpcf16YKVxNWi8ggoub354Mn 2YO0SyEgSjpr9avhKjnJgndKhROb7xEFV6rO9nodEUokzpB+UO5rPLMIriRuq735KlxcoC2C ayno7vFkrSgtAoAQQavarEKVAlYs4yKVakiYzFWgUANp+CDjRDOu941WmgDHEo1oKyvcLitc JCHHBEkT2+Xad69wuxJcVEyQJIA4VxP3WB+W6sDipFTBlYJBW84VEVyeUqgiuBpXtCo3hcNa sSFxHeHZcu07n1KwxPGzrV/hrkCsy4YVUO11v51WVUnLkthyxSeUa2NoHqhTXeCkitZv449t +T2EJYKXS+7Q3DUbpDFczCWMNX/FeKKdUpC4o/ki5je3z9dE+XhCbJsPsozbLsXE+lRcac6I fyW8Q1qhxarPOamYybeloKWbx/saDqF5YT3/69/S+OhtQ6SQiHJ0rH7MH7jTbA+8xlu7j3BJ tT31uj38s4xZW3BLz3P3O81KTzBn/xTcRDTfTnAfH6BoX6gjV4uvOMRWHMhqRKvW/4+I2f5q Yep1iT7PtIlCwQWADC0X3qUS3GqbtPzeQsutNmljl+5fGx/ZUlD7AHiXsw9/6RjrW2orcR5p /y7xkhPTlL4ouJWJn+IkXvNmCDT+fbgW8UOEmoKL85Pfh0vBJQJWBBp/4gMFFxfI3C35xAdr sLE9EZjafaZZqtguwhP7xNvds9V+zi1i7fTHZ5pRPIhAAgLtBs4WwXWFzbqNYBm3HQG1Wsqn 9iYEmxVktu8RgRb34oKiN2+PnOczvP4JB00gKbgJrG587z80Yx4LYwqpg0CD+7iaiEoVbDDQ nFuoreKbIFV1fFp4lN72b+/bTEd1JuddG4G2thWQ0wba3qtVWC1j1vZe/fHa3fePHiGsDyRH PCwC8xKxldtpLeetY21DNz24H6j5XJDaH40zPVa3rHALL4mOFiTIfHs7yI7MmW2MCHS4d/tc DRmhoEQRgc0IjKN+Wy15eVAEGloFpXiIe7ib5SMFdl6ziO7pNMwndIkFEVgQaPkb3HAPUnAZ 8ESACBCBSgj8BXri66sP9UchAAAAAElFTkSuQmCC</item> <item item-id="53">iVBORw0KGgoAAAANSUhEUgAAA1AAAAASCAYAAABVYz/LAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA CSRJREFUeF7tXIt13EgMy9WTetKP63E97sexby1nlksSADUjaRPmvbxLrBl+QBCkzuv89/7x 60f/agQagUagEWgEGoFGoBFoBBqBRqARwAh8vkD1r0agEWgEGoFGoBFoBBqBRqARaAQaAYzA D3ykTzQCjUAj0Ag0Ao1AI9AINAKNQCPQCHwi0C9QzYNGoBFoBBqBRqARaAQagUagEWgESASk F6iPDwR+/rzUt2n7d9KndCzzwfofY/7/rfErD/vfMTB0xnsuJfZ1mM1Bsb3CpvWPfFjMlfj3 nq34RvnsjSm67/kdubXdQ3xE8UW9m/UAazOyYfMYY/D+vNWtUj8Uq/p8ZgxncUvJeWa+no4y sczQ+szPih6wuSozhJlFPWfudw5vDjHcWnGm0jNnaUHPmfu9b+u92bzI5jSqPVujaszIfrZ3 WLyi2a72xB5Ntr6YHQmdYfR29Eu/QHnBVgvJkpcpRvWMdw/lqD5H+DCxIxvZYl69y95j42fP sX5XnTsrzkjY0LKgxov4y/ZltjR6Quudz2JR81rFh5l22ZzYc2f2vYoLk9OsMyi2FT2w+WRm itdjKCb0XM0ZnVeeM3VT7HlnWR/sub3x7L1/Vpw9Z24vUVm/7q0t6m9Ue7ZGapxZ3pG+2DtI h1Bu7Myq6mi0v6hxK+f/+heoqKhMkRAhENCI5Mg+up89X2lbFaAjYtmDlZrPDF/2pYLh0l4c V/hAfcSIMxo6s/E+wx5bO/YcO4zOyNX6ZHKadQblu6IHosFdHegIPwYrqy8Il+pzNZaKH9YH e64Sw8w7Z8XJLud741vRY880ZzL8ELZsjfbwkakPM5NHOygvZVdFtb6C3m4xPrxAbQW04IyF 9c5sSXnFsaSI7ntLZWTPW3oV8qlFUs8jglsMLL6VpX60WakDUxdvMEf1HTmBROWbkOZjoiMO yI/CQc8uKwJeraJ6MjbH3LO6VwQLcQItawyPkQ0k2ExvoTjY50yfeVoXcQsNBqSVmc6i/tnL uaznLG8yvfJ0W1ngEUas1ke1qPYAwxWk0wy3UV+j/mE5aDmM/FbxjHTa8o3RxnFZQvpvn3u6 1HMGfxemwotqjym6/SxzJsOP4Z99QYg0BPE9699In5Xaj7mwdWT2LlUz0azJ9LPq6zv3zDlD BHbhY0ihNghjMxJpb0GIBDci6p7BZmP3hptCUIUkSl2Z4RwtOGi5yBYvNkavNspd1HxoiWD7 B+HIcnnkLRItxE+mBxgf2fCMeI5wT5ert5f3nx8v2r9eUXT+85Ez9s8Rl5XBgnJme5U9N/pL cftKDg0N5JfBgjmj9o5qE9Vh1NyorxiueH5GHjH9kfU16mPUBd79cbb0nLkhqPAe6TU7g5ja ofozPR9pRKQFPWdQZeLnWb+PepJxTuEPW/9Ip5R4rWYrvr3ZGmHAaKZXAXuvwm/Ub3e9xA4x ZBQNZSQ4ERjsohUVUvk6ElAbIyoyakHWH0NS1haqA+OLaYRMANRaK7kp+SFOZ/VDvKrimC0z MzHNxBTxlu1J75zND+GYx/L2/vLz9kPmD7+Tt6sIR+brEW6eNkQ4sXxmz6F6oNhm9sxYh0gn 1P63dqq9lS0sTK2y/lN4rNa158z9B2XYPlV5VqmLOgtZ/UC9k82JSDM93Dze9pxRJuDjWUUn GM6hPWWPHiLtz3hY4SCjwYiTWQ+pd6Pz3hxz95ls+CpEiICJxJ8pOgvG5oMRTIYQSLy850w+ aJFZORiyIbyiEWaKsI0d2WZ5i4Qpk9Gs3ioXKoONkXiUH9NfyA9rw3IMcU7FEMW5Z0FGODK6 Y2vM6KLKe2U5y3Ka8Qz1KNJCNE/YeqPaIf6ymhzxFdlnaqZiibBlc5rJ6zFPtbdZPjJ2kS2m L5n5z84gxOOeMzeEmD7yOFafM7X/OYf6WdGjKHZkI+KUolGV3kdcjvZqJh/mDMsTpBOM3n7X JlosbDCVBPY2P1NwlCxjgy2sMpgYMiExj+KaTW4kMmxDrhzMbAwKb1lOM3jbWqEmRX3HDoyM Zyi/FT6QiFmuKf35mGttyDE83bsAsb2daUpFP6u88+L9/JrCIQZXpY+zXmbzVOJHi3GFE0yP oVxQDmjWsFxEcXi9jeavp3OMH6SPqFYsz9j4UR1ZjNGCjXaMnjN/EMpqMnfOoA67f67oNupt VYOVfQDtl0xsWZ9WehDtD2rMSLOY3S7lWUWoMhFghwwjosxypRY5K4BqC4kqajtWdCtYsXVA hFQacuYCxcaPasBiPGOwIf4ogoLyQtxihGOFD1YbEN4M5xkMkEAynK3Utcq7Ku/twsBgw+SO FjrGBlPLGVqPas30hD3D5KdwnomB4RuDaaTtbE4qnrPjvlL8SCur/Y76FPlFOor2O8a+GuMK HihxMv737D178ECxec/VO6hvkb0RG3YeKXgi/0ytmXnB6Aejx9kZ6l/h2wxYMLO/2yIwoHjD Gvn4fI7iiuxud62NMd/ozPh1K1L3ub6+//qM0fmZjCz28ZkXX9QkKC6FeDcfcfwWpwyHyK/H k4grWZ2Q7wjDjANZ7lHtttijOmRcZIYdwhFxPcI75hjmb47hn4iQcGb53+W18B+RsPVjtC8a qKy+eDVj9TPqiSgmL58sR0ZfI/31sERY2byZvs5y9bRc6YFIhz1cUO/1nPE/itVzBmtspAc9 Zx67f9qcQcLiPLeaj3re0zcmfrbuaL/K7GT67d1TYopmI4Pfpuln6u03NgWOPNUVdeFcldzr y8v72yrjB9hdHf9V6uRBuTr3A8q328XRGFyZD7vBvLgBD/uuR160q+BzdJ/OpvLq+K9Sp54z PnNW199bumdzuO2tR+DsPv5nXqCit9X1Jd483H5Oo/pPLh8XZ+TpmPjPbgg/+2NyP7/GWQTH Y3BNLly7SjOj6xeoGprn8vb4Pq2h1HPmEYFnr90MJhyPwbn9OgOzf9vGWfUb/T58hO/okrDf stsT11lA74nZu3sEVrNjHu1F8Svf+l0Z3yrbK+u20vYqPJDdZ+nXK2O/N7boIxaodtnzvTFF tlfZVXN9Ft6ivK6CJ4pT5UPPmSqit49eer/rFs+/+bf064bklWp0RCxn1e9SL1Dnt1FH0Ag0 Ao1AI9AINAKNQCPQCDQCjQCHwG93Dcys28eeYAAAAABJRU5ErkJggg==</item> <item item-id="54">iVBORw0KGgoAAAANSUhEUgAAAvYAAAASCAYAAAAwq5qdAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA CFBJREFUeF7tnY1xWzkMhHP1pJ7043pcj/vRWfE9HUUD2A8knyjb8MxNEj3+ALuLBWQruX8u 71+/6qsQKAQKgUKgECgECoFCoBAoBL42AtfBvr4KgUKgECgECoFCoBAoBAqBQuBrI/Dra4df 0RcChUAhUAgUAoVAIVAIFAKFwN9P4RQMhUAhUAgUAoVAIVAIFAKFQCHw9RFIDfbvHzq6fh7/ lnX/511w0Dja2P++q/kvn/7XNg+1xnqewUHF3secOXt27cjdKp/ZmLz91r0tN8c+xaeKz6uB SEP0TO+MPo82Buv3B28j/KlYs89XxrBLW5mcV+Zr+VAmlmjtCizPqIU+54wXE08vv75XxQod jGiy/Pp+/ji0O4IlqXOrtyjuKUejMZP7Sb9vPdfrlVlfnvG2/i4yc6g1xLfucKCkWMHSvda6 LNDqLnKetYa8pnJXz1fEflbhq9hGnhMuRs5Vezwj6vet5otoiMQexenFHOWyiweV68xzmhNd FzWOmTjP2Dubk6qDkZhJLY3GTetKxZB9rnCg+dB16r6zn++Ks/z6Y7g/vs7iYaZHUI6yGiV5 e3j0e1fXt8fHqB95s1s27sx6/B17degMsdm9I28UiEgyhLYxzGJDC5quW4HnzBm74qQmNBsf 4Tt7hzIN704VSzaOGd4fsZfmQ9fVYD/HmtKf19TUreXXCqF1z2drZTSS8uv7wX60VhT+Eb+K e8qRiiF6HvU+1RctzMibBhLviLft9q3j/k+D/UFkD05LsLXmANgCoxeHt98b2D1yPQIzYswK J7s+IyAPpxZbVaQ3YruPTbVvWtQ9GS6tc5VZ9Hva4uy1kdVKX+iKr2ysVFv0XO/NpDIs8vys RtHrg9Sh5R+kNnrtj/pLpKNZzUU1157d/96rs6j+CGaWRq0zLY/PnJ+thV4DGe1n1np69M4Y rVXlox4Pnv9Ffc7jimqv59XSouLe4m+2dsqv71HPalFxpvAl84I6g84nXqxRj1b92zqzzYng 066J6oLE4vGX3Ztd33vb3WBvNU1qhtlAsgLOmDWJpTfX3qAo2XTAigRmNQgLd4IZPcsq1lH+ o32qsKzmN3qeVdCqWVJMez1E+iBnqkZLNExwCmN5e7n8fn8D+OdVsWQ/7xu9wj/SJjX9yAe8 ZyO6JhwqjtS9ytNIQ1W+Qgdiki/xutZHvdpudWJpxrqn/PrzxzaUvjzPIr4R1aPan9WSqoNe U8qtSHzZM5/Cr1XizvMoV+XJytOJJ1CPorPmcaenM8t3CHQrdKNiarlQmrKeR3NIy8XTD/YW EFFDjYC1yFXNmTTXFSZBTJgY5iqzV3lHZkzijAaOO4E2n0FUxWnFpPglsZKCV0WYiZ0MY73h ZnV/H8/b5eX3x1+M//RfMPV7GiCvR8NbZF5UdyPrlGkSjkfvtTSmmq4avqJYVtQYbepED6rG SE1Ya7z6jxpstlZJfhFXxPuVH6vnVJfZ3HvMiZ9GPJVf409HK6puzzPck/lB1eqoBrwaj86L 9JKJg3iZ0mbU07J7R33rNhesaKIRIf2goAa6EZM7AO0bIWnUWcCtalINVFUgKaZVg1CGD2oI qtCj/LNFS8+6Cdx4Y7Cj4LMa8Pju84rypJpRsZGhhAw3IzpRtUH03K8h9Uo1ouIb8TOCZZYT r3FFnqm8jjTDaOCk/qJ8PKsrwj/1GZof4cs6i+jb6qmjulReUH79gRCZG9p1nm/3ePv4jn3z RfUAVTtWrWT2ZOqo1bGn+6wnUR8nXkY49+6b2Uvqu1+z7Dv2mcuV2OhZmQb4KMBpblGzyuSl jIE2MSW8RzeK3ghVw4kGF4WROnvEyDKGsqdRtFmPNQ2i05HhbaQ2Io5HhpEMfypHa3im9UTr 9wzPzDZRomOimchDlU8R/1X1POMHM3z1sWd1q3RoaYTgFdXjp4Fi8CesRxyE3xl+iEbV+Zkz +j6m+lrGd0icil9VC9bz7B4vzgzXVNveHECwGslL7fFqbiROctdNX6NN1BMgJYAIWJkkvWt1 o8gAPCsohYEaWilGqsjoIKKMRMU7IvioYam8ZvnJmHx0VyZOor/IUEjOZA0Z0qj+qPnT80bW tVwSf7IwpvVK64mep2Ihz0nOSntKx0Qz5dcfbFGNZLlVHM76FNGRl5/ST9aXFI6jPpmJM4M3 xY7gEPVF8oxylMlPadXLv/cN6u8ZflUehHMS/wz2an467kf/Ks5BsALXS/z6OgHFEmu79xa0 cV5/h/VnC5RjXftru856buFgieL+tdfLn2vczmeW1f1UoH28Uc5WntFrXoytPmyu/dw93nqu leFF+Hl8Zs3RukPp4/87NAaKO2oIfb5unif+5VmrVlWNenFGuChNtphFPmQ9o5qjNZPxT+p7 Z3pmlg9SC31enqdb2GfO9zRDffZyKb+2elX5NfsfW0YeHPmK8pxMz7L6sudz2dfpXJHxEFWb rVdYM2mfL/F+NRNaZ9Cek9mrvE1h088G6/+mRlZ5D1q/umBGw359ebm8jW4G+54lTyvUs3MH 8Gxf8mgMnlkP28k4OQBvOD352m9x/LPo9ux6fZY8y6/tsjmbf2uw/BYF/EOT2F3Ptzc/Pwn/ vaB/fI559J8WpDztzdGL8jG5U4z2rHs8Bs+phT3o77i1Bvs51Pfq9zH1ujfH8mtfoY/hv73/ ObUwV8M/cfcuHtt7t3/HXv0IYqUwdgG+MofrWR5m0Y+IVsew47wztXLm2TuwOnSy6+7Mvc+M /Wxs/f4MLt7a2ZhUDGefr+4/npdfU6Sec92ZOjrz7F1ofhe9t/Vr8bQL32h2WhnTLh6farBf CWidVQgUAoVAIVAIFAKFQCFQCPxUBP4Fy8hq5RpzNFwAAAAASUVORK5CYII=</item> <item item-id="55">iVBORw0KGgoAAAANSUhEUgAAAS8AAAAnCAYAAABe+s4BAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA BVZJREFUeF7tXdF1nTAMpV9dIhuwRJdgkGzAPPx1l7cPhby8lGcs68o2AizlnH6UY1vSlXQt C4f8mpefzn8cAUfAEbgbAit5mfqZhoWsh3kyZbQb2w4C0zx03Tx4AM9dO07lLZmGbu7c6zxQ PuLyCKyx3I+Py+t5pIJmyOsx9r5bHRlJvrY6AmtMWyYwG+T1GOfeKy715HKBxyMwDf1stQAz QF6Peey9x3V8GrmEUxBYN+Z+nC0eINsnr8W5g9Wt6ZRscqHaCFitvponr8c4mC2rtZMolLe8 eV+v4ZytRvvylzfoFntfjUeWHxnPylwnLU3kl+sTBo+OjZPX4lRv1Gtm0Y8sJy9N2G1u0m2T l/e7NDNoJ8sJTA9+i32vtsnLaC9AL2V4SU5gPEY1Rljs7TZPXn5qrJEa+Ws4eeVjJ5lp8RK2 k5ckQnwsjIC/aYShqjLQyYuBMQxIjQBNyWDlL8dGrvJi1/jGJKeCoNaW4vgav9Uh9qxKFjS0 yBajXP+h1z/O9ukZ5EVhGnuO5BkyZusPuPIKFcoJhjfBwP0fREZyDENeIRlQeSsFdV2HWht5 HtODCoiGuKa6KUj8SHyO+A6NqTBGXnrk6qxNXujGnMoFig9QDNomr8+P+eOTzonSQEOzTRr0 Tl4osulxaBKkCAxJMMS/R/v075/fyVivg+j7KmjlheQZMmZXBVNlceqIQh1ZYmwcG0vNDwMl tV5sp9qNr1R5UbskGhBIcMfw5irVWDW8xbY0eVH7rjqu1P4YvlTcIc9T61GVV+hPyibtyiuV EyhuyMaQiq23yksiNNcRkvJYKmPn2AuQF4opt/PEgpjDpzR5r0pKqF7IJplMjqC1kbMJpeK9 pk+dvEBnxVg3liho4uaU7WFQRBP1huRFEQ6CL1epoUnf4rgcIkfjlyM1DZ/S5LXevn/+junu H/c2iwkEqV0v+dwRGvVVtcqLIrQQsJLKi5OxA+Vk8uIIJwx61LkchqjzqWM6VRFc4XkuseZi ghxtUuSl5dN6lRdOdhLyksQs6quq5BWSS0oJREEEHCpwvsC6GXlxR4wwcTlyzE30Fuch8cbh y1VYqfhHfYWOC3W9euV1e/JKEk0kY6TktavMTnzbWBLoNY7lsTVaJKWUTdJ4O5K8jvbpld82 csQlKXreKuGYw2KlbviM+/9WYap05nZDREZyTOYl1VCv7XHpHa/nX3KJ/VGP8IgVO6Klnm3t isnnnsUxp/W9L7GlbaKPbdg8KhaovNH36VNivWMjFglUTqTiEtkcOE5Ikhem+k1GAeRVw5Jp vNdneO+mL+KjXJty5yE6aY7RJi9N2yhZ8CXVKygr1uFw8no2Nwtf2ojNyp+A6UtXK3vJqaqa 6t/UeP5fE8ymvea58/LRP3Kmk9eR6BJrh8crSeKw6h5OXqwGtxsg6ROV9PVK5t4OVAWFnbwU QFYV4d/zKoJb1H/Y3BEsISZkbpFRjU7273m15lj/kmqRR528iuBTnexfUlWFW0OYf8M+F+Vc 4lrlIdVTyZhcm9qd59+wb9C3Np1aw5FOXjVQ1FrD/3qQFtKqciz2AkoBjhLX+peZl75W+GaV enP40qGkwpK8PCi1+dbzjfZ2274qsUak973EeVlSdfmxUQx38QSL/a6vOCtG7vIL+NFR4iJJ tXNEVYWsKbGn/bF2+7oGyGsJ36Wsjv0KT/uBLbcwvHeXWiE19ujLq3LL2pxhteoyUnk9g9bi Jb4209Wt+kHAaK/rp59qKRSm4U6/ymPJM26rGAE/TVjoeb2HxUpgXTfMkzhafIIjcAUEWvwy SB6uNnpeedj4LEfAEbgwAv8AboueFnZaRT0AAAAASUVORK5CYII=</item> <item item-id="56">iVBORw0KGgoAAAANSUhEUgAAAP8AAAAnCAYAAADAZZ6IAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA BLtJREFUeF7tXcGR2zAMVF5p4jpwE2lChVwHqke/9OJ+FMmOfDRNAEuaomACnslMzkeRwGIX AGlZ92tZX4O/HAFHwB4Cm/hNvuZxTXrjMpt03rLT8zIOwzJ64JfBIg3mcVgGj77F0D983jhw ma6mMTAn/ut08axvmvI/zm9csJwAbIn/Oi0Xr/gu/QCBebwsVhsAQ+K/LtPF9/iu/AiBrSBc psXiBsCO+Ncgj1ZTvCueRcBq9Tcj/us0dtPerZ9JbR/PuqRrIbB+8mNx72+EQf20/C76WooP 51k//jPY+hsR/xrcTg76XPxHiL+f4pCDjg3xd7bf9wSQQ3FsrMV9vw3xd7in8wSAiRod1dOZ EOqzGfF30vU/4uriRymOjbN485eLH+OGmlF+0n9MKFz8Aq4x8bQQUbRjbfulyi/O8R8br7i3 b4E+/SuRY4xjLrdCG/b1U++htp0h/hSXKB4i/ETGhHjAlT8VLBTY1LjaImLnE8QfXsvNkwvu O/hovvbd2KWEHgo49X+UQ6W2tRZ/iksUDxF+ImNiDG2I//tr+fqm5ZQDXCm5NIs517Z3MdAo /r9/frMcycUIGS8VGjThbeNyOPxIri/ZIGjpqHaKaq+obEa1dAiJ0DYodv5p7kqVPwYZCXCP Y5C4UX7v1+ZWOalSUt1CyFWuc2td+SUuURgj76Pxear88UVcNuHGcoFAW7pUUuKuZTOfi79q DqKSP7LIO+LnChVXkMLrKGFoEn/KRi5xVan8GsWfCioFDplNXfyILovGoFWGI6jUBSDVDikq nK20+Le7/54POB+clE6RBURz/JL8Y4sfYUe1yp8SHtdyIaTJ7TzI7Hii+FMYaHyvSPnRXlOa I/Y7Reg45lzF44oA1zmm7KxX+fFkkWO/evHntB/vih+tJDfQThS/JIhP/z0Sx5SPUrVHuISK B7FRS+WXbEW6BWmORyLh9kM5lVfK4kgwuWwtBZrbsix+2l81x5S0mGhsczgncYLqRlNgaDjt z02IlHaLxL+DlWq14vekn8NkQLVuiJHhtfv4bPsKb/JJJZS0L/cnwvb1UFDeJ7odx7CgsI0J ndoGUDzdbUpdk/pduFa9th/Ls4itHBZUYkM0laz8mNkfOAoQfw2v5qm/x0GV+lR6XY04lMzR WvwlNta+Br7Jp/bCTec7XPz3Qx7p8Jeulq9ocN0S1e7WeP/HEsynV8tLr2vKiJfFXPwn4B+2 P1JrVmze4eKXLcvZJ+fu/WqNl73od4SLv9fYKvs+f9a+LHhWXy2R5ySiXikR++Xf5+810sqe 5OPi10c0f5KPvphUskjPM/xKhR+f7h7RBVQC+wOn8Wf4fWDQUJP1BNfFj8as5Th/em9LtJuv pWFPlxT+9hdjEn81ljq534Hzyl+RQsrOhCp6xk5l46O+DQIF+/53qr63/cdJwuJ+/8an4yDV NvO5rX/OCfsRVR2ZU1vE2tij5zyojb8/qxgS/+r02t6ddQtufD8DF2hu7NE3/7Qm4NnrWa36 xir/nWYWb+Y4W2Bq1ze613+cG6kNzIGGzaN8K+6By/vUGhA4sQvU4L7Jyr8DvyWAYRiXWUsk 3I5GCGDfOmxkzKnL2Nrznwq1L+4I6ELgH32TrIvQttMCAAAAAElFTkSuQmCC</item> <item item-id="57" content-encoding="gzip">H4sIAAAAAAAA/3RVT1cbVRS/MxPyj0AgLVJIMBEtFkGaDKAFsVzSEKhNWwytVoXGSTLQWCCY DD3VVY4rV+5cd+MHcOFx5cbTRc9x4XfxE+B9974mk1Yn5+Xde9/v/vu9N29iAGDQ2KURZdmi /7D7bdk9bDRPBkA9SzQi9WZNbAG2rSjYce1u9Ru35rEFyuxv0n+w/eg033wq5hKNEBnKXeBt Gr/SuDQMUKV8l+M9XT3/pAFGqCoTTIurS3STb3heq1E981yDkas04uB/AqvTffoABwh4LddN ssGmMXTKYtCe+BOgMxCyn9GMwbDMoYjM4ajMkUHbpCQe8WMaRg4gxvpvpEeMcBYWINv95VR0 dutEhsVtiN2yVKi4dXVq0o4+J2R4lGccSdiJv2kevcDIeoIShHdgE4qUw4YDaMNjgIv2T1R+ JwFj9o8vSLj4hqDHqE2rDQ04BBjnQJ2xS7I0DjAYbVIAFcIFDyovg01IsHGYlFImklLKZErm 5JTMqTelmSli1AhIz9Rr2g7+RV5TGVmlfbMM06Y1eIstv6RVUY9hG75XZ2Na4Km3BT6t4csA 7whcWcw/qAsCXxa6kkwXQWBGvCffFe8ZCm1YOaYdroj/TF86fmaFitH3hIrZfk4doWFOaJiF eeF07n1Bz/s4XZBA81dlaeF1TnWwrARbgJxwmrWFw9yizPaSNLDYz+WytLf4gawu98j5UJpb 9pFzTcixu+SsiHduVbxX2HtJUfOReK+8Tg2sSUsjH0tLa3R2Y6+eN3VUHKgDXJe21mBdOLqO KG7rPpJwQ0KuY14WceN/zx7ekIgEwYJQhTdwU0jCAha1tIlbWiritrSHW/3c4U1pH7fwE424 2TuLeEsoUDYfB1jSTkW8rZ1KPdLxjnYq+WjHu8I7bnaJxx0dpYCf6ig7/pOJZR1n5z82AHBX LosQ3pPLBnfxvpbu4Wfs+gTvv7wy8HO2/N6zAD6QCAT/wlaXYR4fKPOXrPzACqlfaXVX1D1x CuK+TraHD7W0jxX751tKegj4te63wsRcA87o8HpHrVf1uqNbXmQEYE0XtY91TUqt24OrfWq9 ixAPNEl7vrMEeMhfk3jdrhy2nNNHlYNm69jxQtzCmPoosbngeI6wOalZNTP0Z4HwfJXGOMtK izpPG+0ix5EPWYpG0YDeniTLdP4zVMIJnMIZHdgMSEEn9B4ckXZEx/gJDSVfoTcjD8dA18o5 /cBIbYEvlpWnt6ncXSv582grzJUpVo1Gg6O2OJ9L2VRWj61q/o7sNaqnxag2aeqYnrFUZUSG 6lX+DWhSVdJuyGs5NdcuWNKaTp82RsBIGpT9gklbQCIVo0RTiedstRjA1gBbWRwgsYMMCBpJ i2OFjGTgXAlhCZQ2IkbSpDBpI0qYc2UZJAyFSBsxCZA2hhRGeQ0zhoS4wiiwlB6/c3bstho1 50i2K8wtbNOoU4sN3l/17Y/qvX7lifmVfwEAAP//AwAJzQ3q5wgAAA==</item> <item item-id="58">iVBORw0KGgoAAAANSUhEUgAAArIAAAENCAYAAADzDYa9AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA MzFJREFUeF7tndt13LwORnPqcT3uIUX8D67HjaSBvKSLdDDHmolsDk0SIEXwuu2VFdviBdiE yE8Qpfnf7ePrB18QgAAEIAABCEAAAhCYjcAhZPmCAAQgAAEIQAACEIDAbAR+zGYw9kIAAhCA AAQgAAEIQOC+qwAMEIAABCAAAQhAAAIQmJEAQnbGUcNmCEAAAhCAAAQgAAEyssQABCAAAQhA AAIQgMCcBMjIzjluWA0BCEAAAhCAAAS2J4CQ3T4EAAABCEAAAhCAAATmJICQnXPcsBoCEIAA BCAAAQhsTwAhu30IAAACEFiRwMc7zY8Pu3n6Z+3n0d/sXzEftL5py83M6Ywr14fasRbqw++v JcOScZV8aGn/yn3NP+usPDr4BgEIQOACAX/xLVmML3Q/fdWrvK7WHxGg61Ps51p2a/lpy9Wy K6edkW3L8WPksgjZkUcH2yAAAQhcIICQvQDvo+pVEXK1/jXr7WsjZGXGq8eATMC+BELWnjE9 QAACEOhCQCNk3VvCvpGxY6G/u7dR/eOp8r4YOn9PCQDp1vbpR8immI/H32M+aH2T6ru+pW47 x/hJfsXa1/IPtZ8KXEnI+j6mxi3Uj+9Pyv9QrMfq++1IMaiNt1C7CFn7qQ8ha8+YHiAAAQh0 ISAJ2dTx2DFtHW25U0BqRVRIGEh9xQSX1Ja2nqacNBau/26wuGIs9HdfgEvt+LxD/DXBGhOO kp+S+PV9LGEbi6VU3zExG+IpxVtsnDRcKZNPACGbz4waEIAABKYg4GbiJNGmFTTaRVxbTttv TKDlCjNJSJUIp6t1NMInh2dtJqFgT2UacwRj6kTS+hyzRVtfE4OlPqU4TTGJTGAkQnaCQcJE CEAAAiUENKItJnZT4kBbJ5VN1LahEVF+W1rRkSP4YqKoRCxpRWBK4Epj6/oWsjF1kSOJr5zj KT4h/iU+lwrZnBjUxpTkb8l5TJ00AYQsEQIBCEBgUQLSolpDUGmEh2SHJiPpD5FGyElC9Wwz 1pZGvMwqZKWQL4kNiWdMuJYK0VB/mnjMFdB++ZIxl3hzvJwAQracHTUhAAEIDE0gJBA04iwl AHMWca1w0YrSmEjJsSmHiWRXTluS6IodP/vwbZFsS/WnubDQiMvcC4AzA5q6KCkRohpbr4jR K3Wl7PXQE8gkxiFkJxkozIQABCCQQ8C9beqLg5AoiomykPjQtp3KfOW0kbLfFWwpW/1yp20h URcSaLG2Q6JMe7EQG08NG9eemEC05K9hLTFP2V0yrrGxSAlorR9+udgYuXGl8S/nnKZsmABC lsiAAAQgAAEINCBAdq4BZKcLeLfl3as3hGwv8vQLAQhAAAJbEAhtD9jC8c5OImQ7D0Cj7hGy jUDTDQQgAAEI7EmAW8ztxz1167+9NfRoSQAha0mXtiEAAQhAAAIQgAAEzAggZM3Q0jAEIAAB CEAAAhCAgCUBhKwlXdqGAASaEmBPXFPcdAYBCECgOwGEbPchwAAIQKAGAfYh1qBIGxCAAATm IoCQnWu8sBYCEEgQICNLeEAAAhDYiwBCdq/xxlsILE0AIbv08OIcBCAAgW8EELIEBQQgsAyB 2CdWua/i4ecfNxjAgBggBnrEgMVig5C1oEqbEIBAFwKpjwbtYtCgnX4s4YNa1t8s2KTHAD5x PrARYueHzbxj02r/uQgLIACBDQl8y8h+CDa+IQABCEBgAAII2Q1XZVyGAASyCCBkB1isuHSA AAQgECKAkM1azygMAQhsRsD/SEoe/OIWaMkpwO1hthaUxM1Rh9hha0Fp7FAPAhCAwDcCD2H7 sbjE/pEzgQAEIACBdgTIyLJSQwACENATSIrYlMD9d0zf03wlyRyRrS6NWmKH2CmOHYRsKTrq QQACOxK4Z2Rj3wohSyaX/bYQgAAEKhJAyO64FOMzBCBQSqB0j2xpJrfUzh71yKqRVSuNO2KH 2CmOHYRsKTrqQQACOxIoFbIpVrkid1TuiBHESGlsEjvETnHsIGRL0VEPAhDYkUBya0HNxxty tinU7Je2IAABCMxEACG741KMzxCAQCkBSciWtqupp83catqyKENWjaxaaVwRO8ROcewgZEvR UQ8CENiRQMVHFL7lPEp4asRtSbsldRAjiJGSuDnqEDvETnHsIGRL0VEPAhDYkkBEOVoK3Ky2 pS0JM90yxFYIQAACEgGE7JZLMU5DAAKFBIoe9hLSpllCVZrUgx/hGP8Ah0IMwWpk1ciqlcYT sUPsFMcOQrYUHfUgAIEdCRQJ2RxQir0C1YVvpQ9rQIwgRnJC3S1L7BA7xbGDkC1FRz0IQGBH AuZCVgNVIXaPz9Ct9a0x6SiDGEGMaGPFL0fsEDvFsYOQLUVHPQhAYEcC0lsLaonHK+0cIlbz 70of1IUABCAwBAGE7I5LMT5DAAKlBIbIyJYaf0+bxkXut0OFOd0r5q1al4xjemThQ0a29Ny3 mpM/Zkq+IAABCKxHwGrS7E7KU7ExvVu6ZaG7f50NQKghZEtDkNgRYoeMbGloUQ8CENiRwLJC NjSYxz7bSAL36cC/urm3GXeKH8QIQrY03okdhGxp7FAPAhCAwDcCWwlZz/uQqA0qXb9exhaF VUMOMYKQLY1tYgchWxo71IMABCAQFLK5mcflygeytLHU7XK+Z4hyfIcABBoQYGsBKzUEIAAB PYGdM7I+JXWG9iyYwJyz3OlHa5ySZNXIyJZGI7FDRrY0dqgHAQhAgK0FihjIFrTKDIpW3CpM 7F4EMYKQLQ1CYgchWxo71IMABCCAkFXGQPShsLN+6t222j4Ut/WVTTUvhhhByJYGHbGDkC2N HepBAAIQQMhmxMA9gxraP+u3IapeXaeajK2uJftSiBGEbGmUETsI2dLYoR4EILAJgfundSVu d7vH2CMbDwp3wb0kaJVbD77pYyFj2zOcESMI2dL4I3YQsqWxQz0IQGADApJI9UUuQlYnZI9S KjF7NlcpS/vZ3GDbEBAjCNnS6ZTYQciWxg71IACBDQhIQvYhyL4+rBAhqxeyKY0abaXCXtpQ 29I2BOtQR4wgZEtjjNhByJbGDvUgAIENCGhEqqbMBqhEF6UF19eocoOaDbdiK98K9BC1Ept8 L9aqAZ/8C8S1IqDcG6vkwlf6otw2akIAAhAwJ6ARqX6Zc7sB/z/2Fuf987ccyPVDexTy+kz0 odiCUK2vbFYyG2yDETFgIzltWjVf0ugAAhDYjUCJkN2NkdZfbVYta++s23lxRa0HHyI78a1v 5XtJLZsrfcxcFz5kZEvjl4xsKTnqQQACSxAoEbLSrWmOKwn4Owc02dGPMsEMrbKu0jJagwAE ZiFQ+JYTaQEjIysR4jgEIDAEAYRsZ2lXKGbvGTwvQ9vZk1mWfeyEwFoEELJDrKUYAQEIdCSQ esWWu//sMNHqNlZH96t1XXp7OPshsMbbDXxAJdsPStlUG5zBG4IPWwtKQ9RqTiYjWzoi1IMA BIYmcBe2fNcncCEzG8rOHtlaviEAgQ0IkJEdes3EOAhAYDACVlf/g7lZZE6NrJqbnS0zwlPE RY2UVZIkQ1mre9SqETurkoJNemSt5mQysqueUfgFgc0JkJGV5FqF464WLcir8jBYhTEo4E6v EOhCgIzs5qsy7kMAAlkErK7+s4wYtHDNzFH1zKzRYicNRcl+WqnNFY/XjJ3V+MCGjOxqMY0/ EIBARwJkZBvmXC5mZnmzQcOxIoMLgV4EjC5S2VrQcaGlawhAwI4AGdk4W4vM0eXM7GHupdci 1Iklnw2Z2meuFrFTZ+T6twIbMrL9oxALIACBZQggZNsKWV+HXgqkjoI2JUYQtY9PVOMrTAA2 CFnODQhAAALVCLC1oNPt6grbDNhq0Gnset1ypt89CLC1oNr6RkMQgMAGBMjIts/Inj1W2WbQ aatBblZttyxtLp8NpppPF2FDRnaneMdXCEDAmABCdgwhezkJ03ibQakY2UXQlvIxPt2HaB42 CNkhAhEjIACBNQggZPsJ2erJVF/MXlbHtmxWFrWINdvYWWP2DXthNSeza3vlqME3CGxMgD2y A+yzvPhxtr4H/lsNBvBwj72NeAmBGgSMLkARshsv9LgOgZUJWF39r8CsZVat2n7ZE3z1Bp9H 1IrNKllaKz6cVysQYGvB+qOIhxCAQDMCCNlxboFW157VG/xi1UKoxURts5PjQkct+Fwwr2tV 2CBkuwYgnUMAAmsRQMiOI2QPS6prz+oNPni1FCMzZmlb8pltRoINQna2mMVeCEBgYAII2cWF rK+OK+2/6yVGZsnS9uIz8FTzaRpsELIzxCk2QgACkxBAyI4lZE2ysgaN9hYjo2dpe/MZefqB DUJ25PjENghAYDICvLVg0Gf6K33yl+udu29hUK9rPPNNGxCYm0Cluyb+UsRbCyZbnDEXAhDQ ESAjO15G9rTIZHtrpUZHzKqNlKUdkY9uRrAvBRsysvZRRg8QgMA2BBCy4wpZd0dA1YCsIGZH FiMjCNqR+VSNpYLGYIOQLQgbqkAAAhAIE0DIziFkq95trPBxtrOIkV6idhY+PeZF2CBke8Qd fUIAAosSQMiOLWTdrOxIYnY2MdJa0M7Gp+X0BhuEbMt4oy8IQGBwAveHtSIKJ3bMLY+Q3VTI XlTIM4uRFq/wmpmP9ZQHmwGF7LlY+IuG/3d/8YjVqxFEqcXJYuGyXBhD9kqLt8tQKhtrXzMO obbdv6WERKhc6dhIsafxRVOmJGZ9n1oyC/Wt8XOWMqnzLnYsFCuz+NvazpEW3ArbWmN7S74+ hSED8EhsMsx+KmqZpV2BTylXqR5sBhSyjwvbrxcbaH5O1ZGCQHO8VBBp2vbLXBGxkp0lgvwq /5Tw9QXy+fvZZ6jvmA+1RFZOnyXjG/NZ01ZKNLVgVouxxtceZUqErPXc04ODVZ+jLbgjidnR 2FyNgdpZ2tX4XOX7tI58vByLrzgBSReVshOpa8WTRhTEMlYhARly2ApCDN5IQjYlXEqPaYKm hSgrtUNTT1umJLakcyNHiJcI05I6Wh69yyFkbUdgRDFyitmqnhc8/DUimxpMagnaVfnUYlyj nVXbKFlnNSwuC9mU4MxZ6ENiVvu3UyD7/WkzkGc/bnm3zVT7Ut3QIEjtaQRQjYVeFSD/MvKS TTn2hPyXbHEFtT+ukqDTxMEVmw7btXykc8KNJz+2cv2WmI58PCeeYhfRsXOYvz/2Ho/372sn QE3bnj4sYUi/O4xF7LX68BnwvOgQH4ZxYLHuqIRsSAikFn5JFLiiRJMNDS3uvnhwf08tgpr+ QvblLqy+uEqJ8pgQy+0zJoJiC70UUJLoio1jaBHS2JYzNrHxjonKUl9T9bR8QraGxtznJjGT BLzk88jHa8V+aq8gxwYkYPCpX/cMopOZHdDruT+tCeshoCVQ9fUkXyuYSsjGsoohMZkSGJoM U0pEhsShJhP29hK5onl9/3RN04428xPjkhKUfv+SSKmx0GtFWkwchi5wUhcYJYI6dUGQEtIl fcUumGoIbNdWKY6l8ypmp3TxNLJw9W2rEd8r8ag9dqPeHjbbK/s4qVQPf43KpnYMPM2RCSHy 7dxkH2h0KHaMnZy4tJqTLwvZlMiVjmkWK0121Rc0mjqahVNqJyakSoSsfwHQU8hKQuuqKNMG s1ROK2Q141EifGPxIV2MSXxLLwYkXjkTTu+ymrlB4nS/0OJ7PgJGWVk3M0tcQAACHQiMmpGV FhNtFjKU9ZPEnJ+ZSgkIaWEOLZwlQjYlbiWxlCPIayz0OZnGFB9pnCTBV8OOX79+3Zv5+/fv 537V//7777NpSeRJY11yUWbNTOIuxfzox+9CNPHWlJD/bh1pzEf339K+kTNHysRpGR5F4yOz KXO6rFZK5pS1uH4tYic9xlZzcjIjey4KqQUjtNj4f4tlxFKZKU0bmoUuB1zMl9N+V2jGmEhi 1RXfsfYkUR1qQ3sh8Ozj++31EAvOFgvX1xSPUH9+Xen3Lz+v2/Hz58/bIVyPNo+fj////Pnz uXH/a1zCfflj/Cgvl/XHSoqL2Nj6Aiz2e0pw58T6+kvK4wE8vicl0CAre2w14BsCEGhIoFdG ttaClyPOXJEUEgY5Nq20uFv58v7+tVc4h23tsqV2lHDJ6SunbG0msfaunhet7OzZT0lc9LS3 Zd8zZI4UydMyZELDM7Apc/x6rZjkud7yGi0QO+lxtJqTxT2ytcLrzDCFMk2hbFgs86QF4WcT a/mR247vt9b+UD9X6obt/n07HoTzErK5LlYof82OPC45feWUrYAhowmErAyLjGzDTItFbpOs rAVV2oRAPwKzZ2TlZYcSECgjkCdky/qg1nwEiIv4mM2SOTLLyj6yJ49/3tcsbHqdkS4f9tE+ jwKxs3hGttdJR78QgMCeBMjITp6RPfJGZGX7Zc/oGQK1CZCR3XMxxmsIQKCMABnZ+TOybuLU ZA0MpHzJqglZNeE9sjtnaYkdMrJlqxW1IAABCAQIIGQRsqoTwxOziJFrQtatvdvDYcQOQlY1 51AIAhCAgIYAWwsW2Fpw3to8txjUvtXJx9caEF0o7qBTl4DJbZWPre6aBYEyEIAABGYjQEZ2 jYys+fYCrwOyavUysn5Lq287IHZGysg+7bB3d9sb/zzbSom9EIDAsAQQsusIWVdrmgScs+Yh RuyE7OrbDoidUYRsLxFrlHI2mfRoFAIQGJ4AQnZNIWu2VJi+62v400VtYG2xtlKWtjYb9aBM UtBqTmZrwSQBgJkQgEAeAfbILrhXkddx1d2zSGsQaEnA6CoUIZu3NlIaAhCYhIDV1f8k7ifN nDVzZJ40Ne9g/uhpETuzvu2gBZuZI8hqTkbIzhwV2A4BCEQJWE2aKyCfecGNfCBXnWFByIoc W8bObIK2JRtxoAYsYDUnI2QHHGxMggAE8gnctxI4t66sJs18y8arMfOCa641zTsYLx5yLOoV OzOI2l5scsavZ1mrORkh23NU6RsCEKhCICRgrSbNKgZ3bmT2BdcyK3tnY9lB57G/2n3v2BlZ 0PZmc3VsretbzckIWeuRo30IQKApgXOy5GGvBR/28j8g4dCcBt+fQvbI8vMNAQjUIcDDXk3X QjqDAAQmJeAK2UldMDd7hcyRVdL0k41VB+aja9vBaLEz0uu7RmNjGwn5rZORzWdGDQhAYCMC 7JHVD/YKC66VzvwmZI2ySPrRGqvkyLHTe9vByGxGiKIuQvZcGEILRMmxmiBTQCxg9XqIxGfv MvT9lMqGuGhZSTFQa2xjcSW174/P0+3lwELk9nO07f8e6i/EW7KL41cJ/L69vTwe4nr69/J2 +x1pmoyszHyFBdfqmawnNlZqWR6iYUvMEDu9srQzsOkZWFq9kWujuEc2JuBSwq6F6LMCIgmY 3H5zy0v9x4RsyXikhK/UTw2/cnxNBbbrR4hDzNZcYZpbPvdkpHwdAghZmeMqC66FzgwKWbKy n0E1U+y0FrQzsZFnifolzHSDZKpGyPptpIRDjiMxoZXThuSf5vgVYV7D1lAbKVFVeiyHRQ2/ ZhOyZ+Y2JvI1/HYpkxsfoZjNaSPnAmaXMUhe/B1P5i/wZZGV/SZGLNTyxOxnFWstth3MyqZV OObM6Tk2ibOZJGRTIkuqqzFUI+JOgXEKX79fbebxtMfP8Lnt+m2lfg/ZExNvqYVcs0inxLb2 WM54xFinRJ5mHLTM3LFyBabESsvCH3O/v9jvGoarl9GMsx8nteaK1Piuzj3Hv5UW3No6Mypk ycreQ2z22LEUtLOzyZlDSsp2FbKhhSklOCQxkuOMRsiGRKCfPdP2WVMI+TZIA3/2XeJPjkBL ic1kFifysnnJ7hwOuULE7zs1frGLBUmUa9lK47vTce35FrogyI2BGNdcG7Yan0Uyso+5pe5r XxGy6TNhJbFWW9SuxMZiPrSak7MysiEBlBKaMaP9hSomMGJiVBK3IWF1/C340MjxEMnr+6dr OUI2ZJ8vijQDpxFSMXErCQFJ0Gvsk4SoRshKbYROGsk27VidfP0+JKYhmzV1LCaA2do8Ob2/ /rg5p9ft9v56+xF4UCsmXqUY0F54zcbP2t7VFtyaWdkgm9pq2XqADdtfLXbu83zkOxfjimxy GfSYky8L2VwBIonbmFDQZGl8QaWpkxI3MUEcqyMJbIlVyndJQKV81R6LBaAkJrRCNlfMavpN XTi4dmnGRmKsEbY1T/qZ23JZHmL25e3jHQOHiH1StV8eWglZNz74OfD2B/9tENP+fmZl7Xw8 U7/EkR3j7mxTr/2f9twYa7ws1rUqQlYjAGIixhcisSyn67xWDPcUsjFxpxHOZ5kcYa4Vq9qx ivFOifvj2K9fv+5V//79+/m59//9999ncxpx6vuvCXzJLo0I1YipEn4a+1cr43P6/fYSFbGh c74kBlLn1mp8r/qzWuaoZsI0yqZmJ1cHsGP91WInmGBKCNoU+h3YXAk9af0vbTspZEMi81x0 /Csnd+E5j0niUyO+NG35Zfx2Q20kg/HflZfvk+u7/3OorHYx9jlL/oREWcieEAd/TJ77er+9 elstQuMd8/Vo6+fPn7dDuJ4/H///+fPn8x2gX/2H+wpzjZcNMdbyc8vFxiDVVmwcSk/Gleq5 sXeI2G4Z2TofrEgrMxA498oa2crH1qZeZsUxCCgIGD0wKWZkay2uIdGqFbIhwZCr7HPL1/Lb sh0rn97fv/YM59hfYk9OXzllc+zWliUbqyX1+ICJ4+tzW8FZtfEeWcXUaiR76Lk5AeehL6u+ P7cXEDUQgEA+gRWEbCjDW5KNzMmE5WZj9Ut1Xkk/g10i+vwea7Tx3ObjU5Qi2xhFh/Psyekr p6xoZnEBhKwOXeg8T9VMzQu6HsOl8uLxSk/z1V31FmiNh76SbNheMP3rt2qcrakLpRrtr9qG 1ZzcLCO76sDg1xcBqyCFMQRKCNwFMt97ETDeXnAXuf/ELLEFAQhkEpg9I1uyEFEHAhCAQCkB Lqzi5FbPyF5ZL0U2NdK+pUE9QD2RzwA2tjah1uu7Wtvduj+rOZmMbOuRpD8IQKAJATKymdmS VXK3xllZHvraNK5WOT96+nHlCjOxaiBkmyypdAIBCLQmYHX139oPi/5WzqpdTZiq2FztxGJQ G7Wp4tPIltG6cdmQpf0+OlZzMkJ2tDMBeyAAgSoErCbNKsZ1bmRlMXL1eSwVm6uddB7/K92r +FzpYOK6ITY8GPY1oFZzMkJ24pMG0yEAgTgBthZsfAvYeHsBD31tHFs9b83P3nfLrQXuxWbL n1mUIQABCNQiYHX1X8u+nu2snlW7kjBVs7nSSc/Bv9i3ms/FfmasrmWz67YDqzn5W0a2pXD1 +5oxcLEZAhAYk4DVpDmmt3lWaRfcvFbHKl26jTWLTWknY6HKsiaLT1bL8xfOZbOboLWak9la MP+5gwcQgECAAFsLNr/9y/aC2W9EY/9qBFpuLWBVhAAEIDA7Aaur/9m5HPbnZo5m9Ln0zn8W m9JOZgT6z+YsPhP7WWJ6DTYrZ2mt5mQysiXRSh0IQGB4AlaT5vCOKwysseAquulepOTOfzab kk66kyk3IJtPeVfT1azJZsW3HVjNyQjZ6U4VDIYABDQErCZNTd+jl6m54I7sa4nGzGZT0snI 0ATbsvlM7Guu6RZsVhK0VnMyQjY3UikPAQhMQYA9spvvkT02UDTcJ3t0xjcEIJAgwB7ZKdZO jIQABAYhYHX1P4h7l8ywyBxdMsiwcm7CtIhNbieG/lo3XcTH2qhB2m/FZtZ9tFZzMhnZQU4A zIAABOoSsJo061rZp7VWC24f7557zdWYRWxyOxkBTKENRXwK+5qtWg82M4laqzkZITvbmYK9 EIBAksA5WVpNmivg77Hg9uKWqzGL2Gz09oIiPr0Gv3G/PdnMIGit5uSkkL3vMXP+nTHh/901 LnWscUypFrtQIQvYPqOaLEL2nuOg8U8qG2tf40Oobfdvsb79uAvFoaZ/P2ZjMZzTVqps7JyR 6rjHWzLzx9Yi9mux1bTjx5amzo5lei64rXnnasxiNrmKuTWISv0V86nU/8jNjMBmZEFrtb6I GdmYAEsJM0vRViuIrYBKwjG3X6l86njsmGZMDz9S5SS7YvVDbWrs9NvLiYOcPnPalcZa05Yv WiVbz+O1mK0uZHnwAgJ3Ag0f+oI4BCAQIdDrYS+t6PEzSrFFXrNwxrJ0MWGgFVWSCNAIj5Iy V4S95FuukE3xLz2mYRJiL42HJlY0fYdiU+Ka2+5ZvqRd6RyTxG3q3NPYY8G5lN+Ven48aXy/ 0t/MdUfIHLXkl5MsLWaTm/ptCaBiX8V8KtowalOjshklS2s1J1/OyIYMS4kWN7OWK8KuZsBS wukUz6EMWYmwdvvy2/aFSex3t17qwiBV3xdYpWL1qtjRCtmYqAtleCU+qXgJ1ZV81MTBFZtK s9hXmUl+j7po+HYhZPUjNeqCq/cgr2SOxrzEJkcx57kwTOlLfIbxwsaQ0dmkMsU2RJ5b7Spk Qwt4asGWFvOUoMzNbOWAifUbW8hTAiE26KE6sXY0olLyTyNiNGV8ERUTVbnjk2pHEh5uHPkx WGtsUhdWNftITRJSfKRiKhRDPrfQmGljosXklu7j9+3t5Xmv/t2/l7fbb6eidN7192MsC0Zf cC1oaTXmJTY5itnCyQZtXuLTwL6eXczCppeglfRM6dhlZWTdTlJiVBKqqYU2VDfmfEgw+4Ln FCru30PtSQvhcTy4oB6L6uv7JxpNOyGOvp0Sw5ig9PtPieUcsSq1oxH2sf5CYyYJ61xBnTqB YqxLfb5imzZ+ajK76mfp5FOzXmhOsZo0a9rdq61ZFtyafJoI2cfE9fi36NeOsaMdyhnZtNx2 YDUni2dbSkRqxUtKuEmCTHJcsk8SBilxHqorBbTUX0pESwI71LfUniRSUj5qj+XEQYyPxnfJ lxw7NBdlGptS8SDFbij2tfFTKmQ1PmntruF76vwvtSN2YSKduzsdn3HBvTo+2mTpZTYI2atD NW39y7HT0fMWgrZ0TpewVBGyWoEhlfMXcY3ToTbdRV4SBqMKWe1iLAnZR4Lga5hTY1B6TCMg Q/74f8uNj5gI8u2RYsC149evX/fqf//+/eT233//fTYpxWSKdQ4nd9xS9tdgJrUhTSIh3hIn /7yLcctpx4+Hkrq5vs5afuYF9wpzjca8zEarmK840rHuZT4dbbfuehU2VqLWak5Wv0c2tPCc gjEkQDWLoyTCNE5LC6AkYk7BkGrHFcaaE8Ev7wtrVziFOGnYuYt2rD2N71r/Y+P/+Pv77dXb YuG2m+LhCjZfvIW4+b4+t33djp8/f94O4Xq0e/x8/P/nz5/P9yl/cQj3FeIZ4+MzCo1p7G+x sc1n9nyh44+HJt5DZTTnrh9TvhC9akuuDaW+zlhvlQU3l30TIfsI3GW3F+waO5pYW41NbUFr NSeLGVnN4GnK5IgqX9Ck2i8RsiULpNUAaNjVLmPly/v7117h2jbntFdqRwmXnL5yyub4e6Vs zkVTTj9nu++vP27OFvIPTf/67UGt2MVMyXkaE8c5tu9QdrUFVztmmmRpFTYIWe2QLFWuSuwM SKTWw2Ela6wGR1Mhe2aLQhnEUNYulKHxnYpl+3yBm2pfEspW8DUD5DJw+V2x6UrdsM2PJ8uf BEuOc9XKXrMjj0tOXzllq8FQNWQtZA8jDjH78vbxjoFDxEaCRLogVTnjFfLPF34PvP3B+/TG PRidCVM7HmdGdg+edhzhNxjb48NFYt/KuaRkLpfqNBOykiEch0BvAnlCtre1Y/fvs/z99hIV sWRk24/lqpkjDUkpWVqFjSb1qzF2wDJV+AzoVw2TdmJTsu3Aao2dRsiGrsxqBB5tQAAC9Qm4 E9YhYrtlZFMZBI7tSaDBx9XeBc0/MZu6LcsxCGxFwOi1dNMI2fpLLS1CAAJWBNw9sncRe341 3iO71SKxpyzN9/pLY+bXzaiBkOXsg4BHACFrteTSLgQgUJOAvxdeajtU3t/TLrUROm51G6vE ltHq7HQLNBwb8ZcKVGOz6PaCanxGOykq2AObB8TUPtoKmL81QUbWgiptQgAC3QncxTDfEAgR YHsBcQGBLgQsFgaErAVV2oQABLoTICMbH4LdM0epB76qspGeLOt+luQbUJVPfvdD14BNeHjc hILFAAaFbK8shoWDtAkBCOxJgIxsr5l8gn7ZJ9slGzdBZMDFkkCrPbI9A23P5RavIQABCwJk ZMnIpuIqliytmlVbcJ9sVT4WJ37HNmGThm81J7O1oGPQ0zUEIGBHgIxsz7TEBH2zT9Yy90bb EPhOoFVG1m5ZoWUIQAAC7QhYXf2388CuJzJHn695/Qa5OpvFsrLV+diFefOWYUNGtnnQ0SEE ILAuAYQsWws0Wwv8JJGJGFnooS8TPotMQ7BByC4SyrgBAQiMQICtBRPc3u99+5XtBb1HgP53 IsDWghGWRmyAAARmIUBGloysFKuhRKlJVo2MrDQUSxw3iZ0lyDycsJqTedhroSDBFQhA4IuA 1aS5AmMWXDdOnj/ly4TNQvtkTfiscFIdQu3jm6/EBTQZWcIDAhCAgJ4AQpaMrCZa/GSpmRhZ JCtrxkczWIOXgU16gKzmZC4fBj8xMA8CECgjwB5Z9siqCLBPdqddmvjakwAZ2bLFjFoQgMCe BKyu/legSeao8daCozsysiucOkkfOK/IyC4f5DgIAQi0I4CQZWuBJtr87atmYmSRfbJmfDSD NXgZ2CBkBw9RzIMABGYigJBFyGrj1U2WmoqRBbKypny0AzZoOdggZAcNTcyCAARGJ3DfD+v8 e9zJ5RGA2Lix4D6TQcjqz3BihwtEfbT455nNnGzTaqmX1IMABCBQQCAkWhGyLLjaUELIaknx iqkUKUQ+GVn9mURJCEAAAg6BmJBVPbXe8yle+h6DQOM3FxwPfvENge0IGN0lIyOLHIAABKYn 4G8rYGuBkBnhxe3fAH0+i2XNZvJ9smQdudNRumBY3SVDyJaOCPUgAIFhCLgT5Pmz1aQ5jNMX DEGMfIeHkNUFFLGDkNVFSugcs5GcNq2Wekk9CEAAAk8Eft/eXp4f5LpnX1/ebr8jpBCycggh RhCycpSESxA7CNni2GFrQSk66kEAAjsRQMjKo40YGUDIGi3q8uhfK0HsIGRLI8jqLhkZ2dIR oR4EIBAlkDth+eXPPa9axGwt0JJ6lEOMJIRsi1Vx4n2yxA5CNm+2+Sqduy5o+2lxymptoRwE ILAAgRIRGhKid8GVkbXy+82puwD2LBcQI2FczfRls46ywkJVmNhByKoCJVDIak5GyJaOCPUg AAGTjGxM1Obitpo0c+0YsTxiZBAhm3GhNkocETsI2dJYtJqTEbKlI0I9CEBAFLLvrz9ur+9O sffX4INaNTKyvjFWk+YKw44Y6SxkH7cbHv8m+yJ2ELKlIWs1J893FpUSpB4EINCMgDthHWL2 5e3jHQOHiH1StV/mWAlZ9/2y/Bx4+4P30b4wOvWlPatTyMLcnjWMx2FssQghZC2o0iYENifg X3n/fnuJithHcuprKqq5tWC7T87h86KuE2j8KV/EKAS2IWB0BwIhu7ngwH0IWBBwxeghYntl ZC18W6FNbg8nbg+3uuPP1oIVTqUnHziv0kPK1oLlQh6HILAugXPC+txWcLraeI/sNpmO63lI WjgJNMrI3kXPPzFLnEJgCwJkZNdd9PEMAisRcPejafwKlc99hVeoH6urf41Po5chc5TIyB6C lqxsFBCxk46d0c/9nvZZzclsLeg5qvQNAQiYEbiLYb4hUEKgUVaWjCxn6FYEyMiarXc0DAEI LEjA6up/BVRk1cjIlsYxsUNGtjh2ELKl6KgHAQjsSICM7Fa5npK8a7xO44zskZnlGwLLE0DI 7rgU4zMEIFBKgIwsmaOS2LmLiVZ7ZA8Dm3ZWQuS5DhlZzqvSKLKak9kjWzoi1IMABIYmQEZ2 +fyObRazcVaW0YLA8gTIyA69ZmIcBCAwGAGrq//B3Cwyh6yanFVrliht1lFRqHyrROzIsVOH 9HqtWM3JZGTXixU8ggAE7ndsmd5igYAYkcVIM33ZrKM60wKxI8dOHdLrtWI1JzPTrxcreAQB CPwTssvfqrO9ub536423FvDAF2fr8gSMkgsIWZZ8CEBgSQJWV/8rwCKrJmfVmiZKm3Z2LYKJ HTl2rhFet7bVnIyQXTdm8AwCWxOwmjRXgIoY0YmRZvqyWUfXo5fY0cXOddLrtWA1JyNk14sV PIIABP5tLQBEmABiRCdGmunLZh1dPyOIHV3sXCe9XgsI2fXGFI8gAAFDArx+a/kdd/Z7eBvv k2XEILA0AfbIGq54NA0BCCxHwOrqfwVQZNV0WbVmidKzI6OFvmbMEju62KnJfJW2rOZkthas EiH4AQEIPBGwmjRXwIwY0YmRZkL2MKdpZ+VRTOzoYqec8Lo1reZkhOy6MYNnENiKwH0rgZPR spo0V4CKGNGJkaaJUoTs9KcW51V6CK3mZITs9KcODkAAAiEBazVprkCbBVcnZJsmShGy059a nFcI2emDGAcgAIE+BEKilYe9ln5sxP5Br7OHRg983UXQPzHLyEFgSQJGe8DJyPZZd+kVAhCo SCAmZCt2sVRTZI4GzMg2Tf+WhzOxo4+dcspr1rS6S4aQXTNe8AoCWxE498eyR1Y37IgRvRhp ese/aWe6WPFLETv62CkjvG4thOy6Y4tnEIBAlMDv29vL4yGup38vb7ffTh32yOaFEGJEL0aa asumneXFzFma2NHHThnhdWshZNcdWzyDAAQuEkDI5gFEjOSJkWb6sllHefHiliZ28mKnnPR6 NRGy640pHkFgWQK5E5Zf3n+VlgQKISsRej6OGMkTI031ZdPO8uLmKE3s5MVOPuF1a+SuC1oS 7JHVkqIcBCCgIlAiQmN7W3MmPt4jqxqeeyHESJ4Yaaotm3amj5mzJLGTFzv5hNetkTOf51BA yObQoiwEIKAikDth1RCyvmG5NqgcW6QQYiRPjDTVlk07yw9oYicvdvIJr1vDak5GyK4bM3gG gW4Ezgnr/fXH7fXdMeP99fbDe1DrniGMfCLXlYnvSt1u4Bp1jBjJFyPN9GWzjsqCjdjJj50y 0uvVspqTEbLrxQoeQaA7AXfCOsTsy9vHOwYOEfukar/MtBKy39524L/9gN+/vxECJhEm5+cV BN6iUZHZ54ciVGyT88B2zOCr52uxOCFkLajSJgQ2J+Bfef9+e4mKWKuM7OZDgPsQgAAEtiCA kN1imHESAm0JuEL2ELE9MrJtPaY3CEAAAhDoQQAh24M6fUJgcQLuHtm7iD2/Gu6RXRwx7kEA AhCAwAcBhCxhAAEIVCXg7hfTNBwqn/sKL00/lIEABCAAgfUIIGTXG1M8ggAEIAABCEAAAlsQ QMhuMcw4CQEIQAACEIAABNYjgJBdb0zxCAIQ8AhoPgJ31+0MsImfLqH3Xmp47XICaljsel6d MRDyf3cm7vlRgw9CdpcZBz8hsCmB1EfXngtx7D22qyODTVrESkJt17g5qBE78uwQio+dY8Yn VosPQlaORUpAAAKTE4h9oszuQvYUJKHhhc3zJ865GTbp58lPF7X5MVFG7DwQ1hJq6gGZrGAt PgjZyQYecyEAgXwCCNl05hEhG+YjbS3YPbuGkE3PRbWEWv6MN0eNWnwQsnOMN1ZCAAIXCHCL OE/ISgLlwlBMVRUhmy/U3Ezk7kL/ZOFuw4DJc0xptqhIkwZCViLEcQhAYCACv29vL5HP9X59 j9q5R0a2DpuQ6D/BxjgOFCARU+qwcUVa6ufxefgWXudTK7s2NrsyTvuI+zw+tWIGITv2WYN1 EIBABQJ7CNkyUFKGSDpe1usctcjIkpGtFansG/5OEiFbK7poBwIQWJ6AdKscsfYIgVoLyyoB hZBFyNaKZYQsQrZWLNEOBCCwGYFzD5Yv0qR9sztg8tm4v0u8VueTipvd2ZwXPaH9jZxXz2eG z8hlt/o5pPGvBh+2FmhIUwYCEIAABCAAAQhAYDgCCNnhhgSDIAABCEAAAhCAAAQ0BBCyGkqU gQAEIAABCEAAAhAYjgBCdrghwSAIQAACEIAABCAAAQ0BhKyGEmUgAAEIQAACEIAABIYjgJAd bkgwCAIQgAAEIAABCEBAQwAhq6FEGQhAAAIQgAAEIACB4QggZIcbEgyCAAQgAAEIQAACENAQ QMhqKFEGAhCAAAQgAAEIQGA4AgjZ4YYEgyAAAQhAAALPBEIflwsjCEDg46O1gQABCEAAAhCA AAQgAIEZCSBkZxw1bIYABCAAAQhAAAIQICNLDEAAAhCAAARGJHBsJ3D/nTaG/jai/dgEgRYE yMi2oEwfEIAABCAAgQwC/p7Y83f37+ybzQBK0WUJIGSXHVocgwAEIACBmQmERCtCduYRxXYL AghZC6q0CQEIQAACEKhAwM/EImQrQKWJpQggZJcaTpyBAAQgAIGVCCBkVxpNfLEggJC1oEqb EIAABCAAgQsEzge6zib8TKx//EJXVIXA1AQQslMPH8ZDAAIQgMCuBHjYa9eRx2+XAEKWeIAA BCAAAQhMRIBs7ESDhanmBBCy5ojpAAIQgAAEIAABCEDAggBC1oIqbUIAAhCAAAQgAAEImBNA yJojpgMIQAACEIAABCAAAQsCCFkLqrQJAQhAAAIQgAAEIGBOACFrjpgOIAABCEAAAhCAAAQs CCBkLajSJgQgAAEIQAACEICAOQGErDliOoAABCAAAQhAAAIQsCCAkLWgSpsQgAAEIAABCEAA AuYEELLmiOkAAhCAAASsCZwfEhD6sAD/mP+JWK0+IetqP6GPra3d5jlOIUbn31KspXG+aq/U Psf3I4CQ3W/M8RgCEIDAkgRckZQSqxZiyqLN0CBZiPCQ7dLfUqxTwdWK05IBjlNBAghZAgMC EIAABJYgoBWyFs62EmgthGzMlxjfXN9zy1uMF22uQwAhu85Y4gkEIACBrQlohGzslrl/2/wA Gftb7BZ7LIvp3oo/Byh0e97fOnDaoPHLLytlpLVtuu1KQlbi5fq+daDifFUCCNmqOGkMAhCA AAR6EQgJxpBw9O1zBZgvtkLiTfpbTLDl9uPaGarri23fV6m+1KbbfujnmPAO+V+6FaFXLNHv PAQQsvOMFZZCAAIQgECCQEyYxQRfDdGa6jMmBCUh7B93BWMs0xrL8KYE6FUhmyNYEbKculYE ELJWZGkXAhCAAASaEsi5XV6SrdRmVH2n/XpaISuVuyLQQ/7XEKbS9gNfWDcNEDpbkgBCdslh xSkIQAAC+xFAyD7GXMuBjOx+58iKHiNkVxxVfIIABCCwGQF/f6x/Oz60n9PPQMYyoLG9oP7f Q7f9UxnK2JaBkq0Fp4CNbT3wM7Baseu26wpfn3dpNnezMMVdAwIIWQOoNAkBCEAAAmsQ8IVh ba+s2y+118ouq3ZL/aTe/AQQsvOPIR5AAAIQgIABgVjWsUZXqQxxjfavtmElOK3aveov9ecl gJCdd+ywHAIQgAAEIAABCGxNACG79fDjPAQgAAEIQAACEJiXwP8B1pLC3+2V54wAAAAASUVO RK5CYII=</item> <item item-id="59">iVBORw0KGgoAAAANSUhEUgAAA88AAALMCAMAAADttnDrAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA AwBQTFRFAAAAAQEBAgICAwMDBAQEBQUFBgYGBwcHCAgICQkJCgoKCwsLDAwMDQ0NDg4ODw8P EBAQEREREhISExMTFBQUFRUVFhYWFxcXGBgYGRkZGhoaGxsbHBwcHR0dHh4eHx8fICAgISEh IiIiIyMjJCQkJSUlJiYmJycnKCgoKSkpKioqKysrLCwsLS0tLi4uLy8vMDAwMTExMjIyMzMz NDQ0NTU1NjY2Nzc3ODg4OTk5Ojo6Ozs7PDw8PT09Pj4+Pz8/QEBAQUFBQkJCQ0NDRERERUVF RkZGR0dHSEhISUlJSkpKS0tLTExMTU1NTk5OT09PUFBQUVFRUlJSU1NTVFRUVVVVVlZWV1dX WFhYWVlZWlpaW1tbXFxcXV1dXl5eX19fYGBgYWFhYmJiY2NjZGRkZWVlZmZmZ2dnaGhoaWlp ampqa2trbGxsbW1tbm5ub29vcHBwcXFxcnJyc3NzdHR0dXV1dnZ2d3d3eHh4eXl5enp6e3t7 fHx8fX19fn5+f39/gICAgYGBgoKCg4ODhISEhYWFhoaGh4eHiIiIiYmJioqKi4uLjIyMjY2N jo6Oj4+PkJCQkZGRkpKSk5OTlJSUlZWVlpaWl5eXmJiYmZmZmpqam5ubnJycnZ2dnp6en5+f oKCgoaGhoqKio6OjpKSkpaWlpqamp6enqKioqampqqqqq6urrKysra2trq6ur6+vsLCwsbGx srKys7OztLS0tbW1tra2t7e3uLi4ubm5urq6u7u7vLy8vb29vr6+v7+/wMDAwcHBwsLCw8PD xMTExcXFxsbGx8fHyMjIycnJysrKy8vLzMzMzc3Nzs7Oz8/P0NDQ0dHR0tLS09PT1NTU1dXV 1tbW19fX2NjY2dnZ2tra29vb3Nzc3d3d3t7e39/f4ODg4eHh4uLi4+Pj5OTk5eXl5ubm5+fn 6Ojo6enp6urq6+vr7Ozs7e3t7u7u7+/v8PDw8fHx8vLy8/Pz9PT09fX19vb29/f3+Pj4+fn5 +vr6+/v7/Pz8/f39/v7+////4rBdfQAA/IJJREFUeF7svYdbU0vXN/z9bd/7vc9z3+eoQNru vaQTCL1XadJtiCD2XrB3UewVe1fE3rF3zTe7hQSSkISEoGaucx1JMmXNmr32zKzyW/+PJ1mS HEhy4E/hwP/zp0wkOY8kB5Ic8CTlOfkQJDnw53AgKc9/zlomZ5LkQFKek89AkgN/DgeS8vzn rGVyJkkOJOU5+QwkOfDncCApz7/JWl5cOa++Zu6NcKl9cvft7cvD4dZO1vtTOJCU599iJYdm UykQj+lNhc9leo8vU8l+0vNa+evb0mc+MylFMQ6eDjtJ66z9wSf446v3tw+/BRuSRI7HgaQ8 j8ehqfD7DxueP6f75NUjVanWxxJBy/7PUYWuiv93pvLHyv9ZM0LpNQJBMoozc6ttRNq0+p/B pjALPav+NJS5OXClT12rpwIDkjSEyYGkPIfJqIRW22FoV8b/Xod0S/9egtcpX6Qbs5Q/yo0X Rkg8jxFN8r79/PGprJR5wWgvTelQf1r0n/rAlbalYJ8TOvXk4BFxICnPEbErMZW/OXlNWG9b 8FuAiM+sIn83WNgm//HBxowcnj03CeKmRupJnHsfhOxcw1z1l1aTJtmjqm6CyEeJmXRy1Gg4 kJTnaLg2yW126nu8I/YYt0t/i6L8zRYapR9If9yFSn2IukQ4Rz41GBcGpveHG9qg/lJnDCLP 2yBCen8ky2/CgaQ8/wYLVWG8PULlrkvS37Upd6R/5qAssVP6o2/afJ+JnECtI5fmPtz4MOAk X2UR2uV4ATo7MB8OmJiXvwGHkiSqHEjK89R/FI5h7jFEHvwf+R5dQndmNUp/LPy3z6fONdYx 8ukkh77RPr275qMFX8FyBeo5fiGqqtVGD7QaSp/6DEpS6OVAUp6n/sMw39A8hsjHM2rAd5/s 1U9LZYHLmeGrtrqO+8jzGswrkv05lNC0bZ9yyt4rMBzJLJD/Xo6VeDzv98/e/mrUSAvU+7nf 1x+P+OjepF++vJ36XPw7KEzK89Rf53T08BgifxklIT1DnvQs1EnCxVh9q1zGOa927FkWtVL5 7d5MBuYEDEZ1i2UZttOcxVYl/zQLYzo35fAQbN7kP1S3ya9j+cdD2RBWtsFno19uZ9xze72H +m/nOvNyOq5Ofcb+gRQm5XnKL+pDE6r6jPiSigvgUycQtpM6cOB+ia73/XEAxTRxetmWWv5L /u1Ato5vPnZ9hUhgxdLnt6sYovr6N/m3CppCaa68eyaWpgi4VjYgwnd/Dt2eT6UYINM0Vr63 g3KszJSaMn36DP0s5XOfC0qbMX1GWsbpKc/aP4/ApDxP+TX9iOABaCwkhz3DQgHYdVHG49kn b9LecpRiirbt2Lt2SWN+OmK7J39/2IzPvi/9sScTK5K/eWPFl6tNOhkqf7d0Yj+TaTro29NG fNT+3G+HbFW7rx4qgbGlcsVrAmSurGnurECnLZE+D+YaTelzOwroNNPGKc/bP47ApDxP+SX9 hbIBaFxFHfccnS7pp3OBPC8khn3r7MEF1gJTaUaoYH2/6so506Rt4S2abcsNa/aqFTStOoi1 Q36Wq1WoxW/wV1nG0rvSN2/bUUE+YS80OeSzwM+NmGwym4cza96Bfy/Nw3OmPG//OAKT8jz1 l9SFyT6e/uUosBh36aUjbZfu+KsqoM7yKYdw1DqnvSFzlrKFysUtHdDl0mHIVP7ISVukfjUf zlcNXCsN5b499cJmv56PkOmq7extnq5L+qlVrzVYs0X6PJ/JV1sc2jWK5uTHuHMgKc9xZ/GE B2iesXZsHw9Ifg4lW5n7DIIb87s+e87T1LFRTY4iXpV3pVHdOMtgrVkz0qrWXwz7Gcc2QKKf v+cyk1fXvgoulNrcsogbfR3Qrjm57aOu3BPmQLKDcDmQlOdwOZW4end424+xo1cgtH6O9PWQ QBCKk5i33BTTR4vUQpOsBJNKpUk1NpfD8o4KymyTqszybEJ4X5vVVtTiN/asFG1L9zxOx+Wr 9jo8hSru2uPVd28zp7KLT/gTlDjm/WUjJ+X5N1jwRXo1+kKidZW6WS+kGIPs+vnZTcE+5mbp q+MG++hpzTfJfidSaZrWIP/73WncoX7VaqpQ/1qGcUpIplLWGsVPvl2Vj5wVfmbqK+WfduUx epPe6o3Q2mozzoCZwt2/AWf/OBKT8vwbLOlzKttL5U6DfMoFVmCOwq7If5VT0CjvrrOYa/S0 zjNe7dSW1Cb51x9Wg3Yqn4k5VZeQHsQ87NN2F+qv1KpK69F+/egyaZFbn64cK0d01dqZ4Hl/ d1tWKtzkEyHyG3D5jyAxKc+/wzJWQMdVMn+WzlDtvld4OFf5colV3+k/i7sk9mT0vCqhPepX XSktyl9l8Hn1qxqCHVL+7IGtvkf1PljVnakV55FeR+97NofsQ66WA1kpvlS8XyDOUI4ByTKJ HEjK8yQyO+qh1httCtLQh9lYntrLMypN9fu6bEs56d/1EEmP8cA8gGoX6HYtXKvXcE5tV49o Yrsa9nPY3mP0P8qvI4HJWymLUP+Y6V1Gzo+KLRgS9YSTDaPkQFKeo2Tc5DYrQ/j2dTt2LXCy nV7b1cz/aJt2nnGUA9lVr3j6kDlTjXG+kpe6Qvn6Ea35k7QhGWrNjZhPqKXHcxDm/YKzXlYg 6p37EmmSQ0Iuavfkx7xL+nxY05kP2rDJZVJyNE8yP8bv8RBczDCk/PuP3kRrDiCA7AHHC5X4 2aODo44EwiQ5TUEVbXNWN2fQBk11lU7PXTsodVILafaqlZDXTi39cJBg/I1lvUaHHEB5qxqp kdxGPI3IZYWMA4R83i6BVSikPiYZmjXpT1dyf550lkc14Kczy+tyCrsDOJZI3Y2x9y4cFQAl j9kNFOF6vQ4lzOpd2TPHZNJTkn25LEUDGNye5ucQ1s/QQmnVwh07T17qVdxDynW5K3tXtFtN DcrrZHVqxu5z55/eXWk3XZQ+r4Etp6R/b+YRfp6jUU072ShCDiTlOUKG/dbVt5S6snuOnhtB ENqQbdJJwn1nkWaV+tThJ4WfCknMSOEm3f/O+L//3yZp8k+LyLTp06bDPSonfjYZ/532Pzoo dYainvvVRKalOx3ZLJtEEpz8hyUpz5PP80SOqIRa+ZQb2l4dhKpbB1d37j64eFFreXGP6hJ6 uLWgeYtPOOTeRS0FNcUzD2qvhFv1xSyqd8u7dLJMLgeS8jy5/P5LRntzf18wEMK/hAMJmmZS nhPE+OSwSQ7EgQNJeY4DU5NdJjmQIA4k5TlBjE8Om+RAHDiQlOc4MDXZZZIDCeJAUp4TxPjk sEkOxIEDSXmOA1OTXSY5kCAOJOU5QYxPDpvkQBw4kJTnODA11l3uzHNn5c/xZpgDGJpbm1Q3 kK3lkmdnG6slfh136ABIJ+O2SVb4bTiQlOepv1RbOZMZBX7XbVpOqkPs9P+7Taa7yfR/ARrf ldR/gqSTGz25993O3PqOfUfUEIrRPx8oDpr9/eX2oGmkpz4H/x4Kk/I85dd6H4vXnuw/tpQx yXBhoFSkIUr2uVUIJADMoediigx9PX5ZjWE4TAgoIsyRg6P8yzn2HwmZ++UCPyT8q3J0ZcW/ KnrC+IMkaySOA0l5Thzvwxv5IsMoEcensnAl4HknAqsBzC5qvoxb8GhfmIiauQRZv3rnlian abqCqe9XjlMzJFjAvf+4fL7+af8fabSsacZxPL192jypSn8T3uyStWLLgaQ8x5afse+tJVVD 6VqqIl23Mm5lc32BM4HiIkPQ4MBsw9LPj1cK0Jmx9dZZJKCTPUZfNMHnvEGKuHpWOwrTKNRM m6gZh2LPiWSP43MgKc/j8yihNd5ZTCo+gOcSjfYCWnZRBHVEpmkZQfkAf4ZDZgaqQHJ6PM1p ga7ccojUFtTqs7ve4k1j0+GFHOtbPS9QGlhZOFQl68SMA0l5jhkr49PRcYKQYQKkki9he711 o4xJRupaRdNklgxY8v6jVuXVgW2++PdPFQRQb3EaNBySdV7E7TF0r8U5nxzuN+zkrcimtm+G yGEaEnBkTZO1J8iBpDxPkIHxbr4SFoe1MZopHcAEacAZXIb2m4MxNFkGttSf+TY505znZYeL NLi92+n9WSJc7Le5lhk1eZ5nlLPVBCoLRyC4bz32bBBJ79tCqz0mitqvmxeLN7iQrfFmTLL/ QBxIyvMUfy62+gBsLiP04OJ80YwVyOfhj40EVfYU/LEfgjZJX5x0oLb6DAKrVea0LxdLd5io ep9MzeUkMG9JZbdolG64lzPvyR9vZ2ZLWTE2VksfOhCHgi94qATj8nhaNyh96JKPCT2ztqwv z2IWeOFDfx6cV9Z6+NqAPxvLTMqNIFkmmQNJeZ5khkc63Cb9SKq54wQmCXI+pKaZ20qKsjju hznpjv2QgxcNeV5s42fIMPkbcGz2jUsVRIo30Y3HU4qxC3ccP7Cxx0HC10Cd+dMUgME9MCZp 1rL/IwnyVkSG7P/ehduLnVaSN0k/HZwm5bV6xiMoipn1elG9uO/LhwwoZCQ0CGB1eiU67yUh 0gkn60+EA0l5ngj3JqHtat2IOA4ysOTUka/fqwy8HwGpYkEZMuESUOBsSAH1Oz5LkvpzVjp3 GPia5JCUmjEWfFnMoCjKsxTHc4J0ii5NVU7dh8lsKZlF/nQJhn+7khNjuTHjuufdOhujk/ba 1UYXOGVfEWiyZuvpbtEAy/gjp2mkduv5dRaeyPDjRVVqUh82CQ/H2CGS8pwQtoc/6C6jy1t5 EKfA379wVHX93AWR8m+PMQSI3GuLzTfzVD0pONafP96TzpMj+H9OnMmvqjZTDIDtlFoWQUp6 uT5S1nuXpUm5nbfJiTdOs2YZbX+WaJLcS1bB0jlhwIK6pYP5KatxAPxzxW6S3wcreTzLb0o9 hmQu9/DXOIY1k/IcQ2bGo6ur8Mj2epGSEk99RXEVPn8LpGSkeIyh4Lx9ifbFu/7sMos0RVMI Sfk4djnIYrCvnlk+J5dDL4GWlYiSPWcDZJd0XKVp0v68EZL253mw4nO23wZJ2H+rIOlufcuO rJG/LUEkgZ0Pu+SL9F0LVuM3+fmjM/DEgzXJPsdyICnPU/ypuIbSiu4alIVGCQj/F8GoB+i9 JlRWPT+neLAFD4m+mZuHLByb1TirMrO0W7ooq8VuXKz8ddCMSNkt5kKKH/gGRJTO6IUm6RS9 FgcZ2V9m0Iqta9ACSR2sQqTEVecY6Lr87SwYbMy/clHVUa0Q8deWV+p8gP+nOIf/KPKS8jzF l/Or2ahB3T+zkZIwfUdR6VQMygkEl1M4DtEYMBG/FHzl+YMTq/sGfvOPorAbtXPwTFRy+FwI nZB7Wo+ZpWN0KSX1t4kEm/UTG6f4d760Q1LauQ6dpIQ7QqYpb4dGafd+KqAqKG+dwZumTv65 yqBllp7i7P3TyEvK81Rf0cWUVc2N3qVsgh8QgyKEnnMYKfttH4BpcAL/Xkj6gGJ7ZmJydmi/ 8tVhkDzMpDKHkBLLtaPKJrxa2pLBdVov2baWoI5vnh/VlOJL+lKkpDP1WmlD9hymZA271BDY tV8LiOQKCspMk2bXVj7XocqxPFkmmQNJeZ5khkc83JcStPA02GlfzMYVt+13iKygAmUrDMvX 120mQtpdD9EtyveywXkfO1f55HPc9hRo2eQ81YgkgSuNSqBWMypnmC7+VwqkXEBIH9bjisP2 EE9Kg2xGpbv7ThJR5LlFJ70Yso2KOs0zSz69j5RFBnVwv2+TH+LOgaQ8x53FEx3gDAuxmXmV ToNTyb7+WqeXVFmgnNKhX6R/9+uJ2+CfL1ZD/a135xe5LdKt+l2+2PPE8+5oqckH66AUb1aa 9vMW6Tjdi8npX3c7aNlEVayTkklWw9KHAdEqvSQ8B624JM/rIanlPhJVsmCUGKQLcg+dJx8Q PuRiWlZJpfv1xnblj2SZXA4k5Xly+R3NaBvdLGqAkDz11uyx5WmX4nzl1nolLUPWi+0VjBxD pBgUmT3vMtLpIq7z1Xo32G2VXe0FNdUctkmqc0s0rN3TCXJUIfL+XARJ/5+llw/fpWkZy44N 7MoiEEme18ga6yEeVWYwD5YO1DccRKsk3+sEk38Adr9e7j5ZJpsDSXmebI5HMd7Xwwt7L/t4 bY7p4riqAj/RWko7XFrM1V63zWbmG33DlmdmEHodYkjTEbJnJ1BcpRZk4XqSI2SXsxqb9P/5 Rlk5vYs0puqMJsjkkj5tMfaB/z/mcaVdT4qsKN9khXLXd+WzFOof6HVgetLfM4qFnniTpDxP nIdTqofHklJbLZ+fXXvlD3Tw9GB77fwdh9etOKs6YA93tZSXLRt6pbhx/5K8wT03Fip+KYeK LZS1LKdORlH40i9/V0grfZ/IVVocSjciEEoxxn1+XDhNjfiwTCn2/OnEJOX5T1/hCczvx4Or ozFJPku2K99ypjw70+VgTeqVXvtJSzU/gdGTTaPgQFKeo2BasokPB758+HKlnoUHk0yZChxI yvNUWIXfnYZ5dK6saE+WRHMgKc+JXoHfffzH91/tNxs1jLPffTa/O/1Jef7dVzDB9LexiIWE S2VFWrIknANJeU74EvzeBDSgOsQ5Vw34+r2n8idQn5TnP2EVEzmHvs71qn95IqlIjq1wICnP ySchyYE/hwNJef5z1jI5kyQHkvKcfAaSHPhzOJCU5z9nLZMzSXIgKc/JZyDJgT+HA0l5/nPW MjmTJAeS8px8BpIc+HM4kJTnP2ctkzNJciApz8lnIMmBP4cDSXn+c9YyOZMkB5LynHwGkhz4 cziQlOc/Zy2TM0lyICnPyWcgyYE/hwNJef5z1jI5kyQHkvKcfAaSHPhzOJCU5z9nLZMzSXIg Kc/JZyDJgT+HA0l5/nPWMjmTJAeS8px8BpIc+HM4MKXl+dXDWz7ZWz58+f59+LPK+l9hLkG4 9cLsLlktyYEpzYEpLM+PyyhUJ4hibkl2nsPmyCEwm8CRYk5eQ0mOy16Wa8vNzHHYHaKj3ppR XVHZvbQiJ71yZnZGZUOVLd/JFxUUZOdV8kxlWUl+aZ69uKjA7XK7C3LKF8zJra2rmFnd0pST XVRZXt84y+Fobq3r6WrIslQsnV1YPrulbXbbgrktrR2NzY1L181e0710/9ldfTv3bV+9//ju vXt3nTp88N796/0Xbw/dGrx8+tzrT0M3b9x//fHt0L3nL6TUj6++eT4OT+llTxL3h3JgCsvz BZRdd/DyqQtDl0/sWbPn8L6d+weOntuzec+Go31rOhbu2de7Z93mFfMbmhdu37px58b12zbO bV+0dOm6Nfs2r2jpXLpi38aNVZ07F7fNaVy+YumS2pYlc+sbK6vqO6uXbJu7bENXe2VLV3td VXl27bLNTXNa82wtDSXlxaUlGRZHRkaWM8Nud6RnFVh5zmzLz3PkZ7C57gIXw1spmiZYwSAS nIFkCIYiUYjKZFCIszssJMJSZqfNSpitZlpMz3XZzQ6b1eVOzyuyZ5a4MooqGsHbZda8ztaa 5lVdXTt2rdm1Z/fWDTtW7924ft2a7efvXjt//8LBgQtn9x+5dPv0nce3Hj778mVoaPir5+ur ITkZc7JMNgduHB+dwmuyKYhovCksz3eZWRFNJa6Vf72Tuv/07MXQk8Ghe8Onzp64ePzCxXO3 Dpw8smtR76rVO06euXZ1YP+Bnbu3b9+7tu/QwV2Ll/R211a29HQ35Va0NDc0dTdXzy0rynSX FBQU5ApWW1auzWW3mwsKbWYTh6GCjaZYirbgKEqY9AwBwSZUR6AWM0EZCR5FENaW4c4ty66v z69+E9epJjsf4cDJKgIi8/JOTxWe3FuzcEXvxoOn1eygY8mawvJ8JE1KGf5nlk+eH+Dt4PH8 eAzm9/Xsrdu33/96ffvm/aH7124N3T998v7A/sP9x/b37xw4tGHLqm3r1u3Z3DWzpiwzNzuv GPtPz5/JlKk3qyNWPYrjSKp5iuQLuO2GYD2O6v4Vg+mFprA8H56+deqt8BSg6BDcPQWo+BtI uGM3ZW87fG6De/qyqTHdWTRjycw2W+iaYPRMYXk+bjg1Nbg4xaj4wSyYYhT9qeQ0YhnXpbld otdOjSnaOX79y693h54EJWcKy/NAysmpwcWpRgVeONUo+jPpecigc5WZbbs5NWbopMvHISQO 8nz9zqtPv156PO+9Q3+PihuH0w5G1e6Pb0SU/fFTnJQJPrgd2mawFaKOTwohYQ9i48Y7+MdC nu9s9E1ItteEMOYM0Z2BXlDo7C0wW5zlvWETrVVcM2NvxG3+igbW5P480XU+PW/z6hIagkKm re4mLOcnOlD47YHjwvvL9558CdHis5MvufrieSjzRgzk+YhT1+NDRB9rc1nNOIqlrpS/3cLA iJmCsZJIb8MNM06Ez42/qSZZ/zfNNi5zzTPgwJHAbEe7lO5XlpQVNyxc3t1YV7Vkw8LFd+Uv 57FCYWVZc/2kmFl2lRXXZDFma566Cwaa9mNaYGxO1lGy+kMwrkxYnr932Di61rf7O09f3TxR JdA5V6RvT1uR3LmHNy3MNKUDE00kpRm6Fkn1v6buR+MUssv/plzPIXHH3A0nNmDoZWkGj9BU GDIhsBFFjCYSNWTL0yqnOQZDKT0++378p1lC0iTpcPCEbVge7G5uRU1hZm5e9eL2qvL25bIG 7DxmMVtsBIXBzgNBKJqwPM/XoSyTNab3ZaxZuXvUGByyMuF6OrwrMq6sMp6LrMFfUvupvuIv mWncpvkrj6a2S727dQ3yIE2CxZlXZbOVA59hgedXyF9WiYS5bMmRJZl6cV3cSNE6zuY56+Kz Z5qoGU3yV2v/nW7QG6A0PYwY01L+T5303Q+nmDF766oGjjC54iXP2+qbzWzG6N6HS0nlJPOa Z/qUHzeZ5kTGlOV6eYNPllEcGJzenOTJxDjwKx1rk3vogWxqTyOHx5/P7ynfVQjMRunfLy0o E2+Hkh8ukjkkDTYPc8iDP2+rnbtw1ZL2+rbZMyuai+7IXz47Pij9c3SezbQlMAcmvD+DbmeS uaM7byI4ZXM9QdrUkKhBqjSyRVgLR6cXj2yU36/20xntvx/RU4viYTuh6Gdv08jHoKRV8IKi AH/qSOuL8wQ+uMh0eYgbGBfGvXQJysdPnpsg86jO73A0vV7+bj0yU/3tMWsN6nUakLaNRJx5 +Jt2/9HY85tSPmXIvm+mBmRiXlAmRf/0y/Pp06cPkmx//fBVpbMcs6h/5SPxdsn7mUFUy4Nd Q7RBQ3HrKGfQqPSvFov9eS42Wp7PimZKWCWN1DFjtTrgRwt5JKIFXc5EVP2vqfxJt/ivmWuc JvrExSgm1iEG3gD+uZfnzOAtFruQ68512vgM5UGdTWh6oVJIEbb4lQ9OQlHCPaAYRVLfnD9/ Aaix33/8+ho4+o8q9y3/XApITCzkeSE+5pVyflueMU26LzfrvUJcgrT4kfD92J7jt66c6gvG pNYgR4r4MfX36Hl4umpk+T3InYpUXqCoZzJd53FYskbdFyCChmlTmsFgTEvFEMUguBRxqcQ3 GZTrdvzKYztRKfe+HUNliVlhRQ0pTG6m0yYSdGbBVf+hHwlwYCt0TOQZE8ZO9E0bxIOb83yj 7AErlZlG/5dcA4pZyiqsFlnnEKDU0PFj3+/c89NUP/Pg7zyVRNF+nSKkwDZwM2aIbdK/jy/f uHH83vn+k8fOHj328K2yQ25B6AGFwlo42EMaqxk8cGCKFXIrgcoH2wZBsFs4AQTQ4zTlduz0 H+icGEDkpCqxkOdGU1GAaT22oYBn3Uav71ijwe+c+NUMNd4GKrugpvF6NFbM+rP6ea2v+rMm NPmzeWxWtbQfUOO+oMM/zyRKJAexh618fmSqn8hndD8dVg4Ft3lUUdUN37384MHzVy9fnH88 qDqNHV74VO25Hg7yTo+FPNcZArrNleIPPZ79Rq/VuR7a4zvPZzwPxDlEKU3Kc0DuXJ2R9CeJ XGD8WtyALKoSGcOOBe9rAwfTc7a2OdKKnk9wwHGbP3dArXKl+7RJtlsFKqX/2OWQhq9LaMtA 4CqxkOdWfWOAzr+nG2+BUDOkTPutRFDuLGp5QLtDz3JuUp4DMujoP5PigDjuI/g7V+jUQk6r loSaxjobDRn0ZFb83biHrSnKkf6OCTkTjKQNDIpXz1nQmoNTwVSisZDnhVB+AAqecAw4HVxF CfVA/V5QFHha+cKPo+9aBP3OT0z8aD//38jsBPGj5M/v+fq27oqWY5MBEjtHPdJ/yQzhdtXB IpDeaDTR1YNBeB8Lee7G7N7er1Zr/uSbDZKXyRuzQY2qOIzN9qfBSoWKJQGeMvCf/7xEM8OL /8iuisnyZ3IgqD5Jmu7+uc11tXU1vUGdYGIhz42I5jTn8eyYVgBCn0F5kq2Xb9Vz02bKGNp3 shxK1Iq3iFhw1xypUjvyZy7YRGc1OC1pf54oD//Y9rGQ57m6TC9/bmBwzoLt1/cvKUQQ+Vj4 Ih0uWbRzd7fdNOrW955DQ+/P3YbQv/+xazLOxF6kJOX5b137cecdC3lezC8dGWcuiZvgAgwm 61SP8UOiyQhjiDB/1C3kIU+N9Xvxpbd5xuC45P+NFU7+J+lP8jeue1hzjoU8e9R4FGXAw0uq 3U01S0dsUwfbisvrFozBArtKjpzSA5Jab/LrN6zp/A2VBv4TyJzwN8w8JnN891vh40c65ZjI 8+hBlRv0SAm4D1/AR7RoAamuRDXreaST+rPrX0jt+LMnmJxd9ByIizyHRc510hq63gIY+KMk yxgO3E5TIt6TJcmBMRxInDxfhnJCr0eTKfT9+m9dzVOpSfywv3Xtx5134uT5GuoKTV3S3zMw fw79q6JCj7u4yQp/HQcSJ8/3cQVYJWixJ/EMAvLmwrSev+4xTU44TA4kTp4HyYLQNJqpMOfw l1W7+O8Uyb7yl/F9YtMNHK48sT4DtE6cPB+HykLPhk0IPsnrc7v2xhv8bWKrODS9b2IdJFv/ uRxInDxfIMeRZzMJUgZMbvnRIdImg4HK2D3hcQ/NynLXtThdJbNWqYCIE+5S7WDgf0YFt8eq 4z+6n7hHPE4N7iVOnu/g/vFWY/jhQiYb3/NTA4yLPE9CEKbk9oi+3IcI3GhCIBjWE8B7riZk YpXIhrny79HIGiRr/z0cSJw8X4XGCcuvxsIALo3pSnXRXN2WC/fOrp3HBIFzCXu4QxDNurOa NvXt7j3z03MetcQu5u7MvyFC8MMmMFnxj+RA4uT5tmF+aI7Oi0amvq0vdNjzNke3Vk68Uj0S 3NRQSaPryOM5hrEdw97GWyEudr5uA//0R0tVst2fzoHEyfND48LQzK2PQh92o9JZuWh5DVEz kqw2ghV0wzHLPHELEQA6i1a2I0zsfN36/rM1gjklq/5VHEicPN9Fxjlv52ERr8RRW76cX2yP IyrIvI3MmCv95/6d+66MnJXfD7+78Ugm6+XH174qlm/vzp/zzVp2H2F8/P63k7zSKhZlxX8C Qy/Hou9kH785BxInz5fgcdKqOamIeVvMqFfLLablETcGsdoCPgrtsceFGdLowkZZ3z1ck5sl 8AhXvurW8Vaby8xWaLWPzUx34IgoQbOr5TJE+iT+3E3Aigz+2jSnomjBWW+1gc7K6pW+74Hz K8ZNfbZNdyOKuSWb/BUcSJw8H0MzfU6kAZhtoSNdgQFIizx6YwkEITxuf3sEJnvdiNf4twod U1PmsCIQLW22q1MQNq+tqspsNKMmMc+GmSDF83Iji7IcR2LkCJLbeYz2EcydCCxDyg0V6ox0 mh4pkdN/eq6XUxCMIlS3bJi7ne2qBK+PGX3jkLniP8EhZsedYbLCn82BxMnzbojIsOSFeDQj 9w87BCs5akEp4QPn9xlnNS+1cDqqQ0sBsN6EAKiu74PFKCFhJfXDllXSvfwIQ9I9w56PRwpx OSHKYZLd+ODxuS6eaPF2f4wSzbVNS1XL0m6GvQh+uuwyNey6umUBmZYnVXzCGzJbqrLcLFws fVwJwZi5uH1c569V03r/7GcyObvoOZA4ed4C8bVzl8jX3cDFRkU6rROYF/myQIy0sVr/2qJs jGtSzso5mJKD5KRoleT4LOmWtdRf0xlBPiN3KyGflVS+FPF92cKN3No3EzzIu63HB+QO9tC0 1GOzwf1O+tiAEdLRZLUxA6Qx+XShCsOkE3gHTrUNhkH1mn8iTKQdRp/JKn8IBxInz5uR8tBK aPs4eAdjV+Cl6EVTrqiJeoF+rcskKWnHfE/i+5Veyh3SibgfVvPi5uJKbs9NLAVM5DcJxr71 1dWziwQfeT5NcjkLl62Qk6l4PNtoCry4HqQjiiFt0IVILl7luALJdJEjpfSFs2k2dIIBdUaL px2Mem7JhpPBgcn2m/CZU+LkeTc6jnHIGfkWW6VlyHrtmogBebASSQMn5aeYlOFDKsVywr31 BjViu04V7B00Adzs11MCi1hN0k14JAnJNcLsk+ZgJ4WA/Xkfj6rJf7qNwF/svtk6rHRvJ6Vz QCM6TsCZumxLUv4S38XJEL0/bYzEyfNOfV1oZqaHkwjXv4vNtKr6XZSt5BuLslzOQICzyyuE VCWn1il1tNqgmrO6MOVgvYmgwPn5oFnIKCyatbChJmtk47yP23zQiPsF6hpIHUhrEaBHJN/1 i4yyy3s8FaQDSHYHFJ6RbUWqkmU8WZIcGMOBxMnzhrTW0Oshjs4qPf7yvXO6V55/8fD0kswQ 9/Lxu5FuuKXAPmXE7yiVywXpgrxEp0Z4dhqBOIKykZDSIJ2neFWO5cuxUu7gvmiHh2keaLSP sZo8b8FBT/2koGKnt1E2IKLzTcvCIc2z5J/Y+aaENWCy0u/DgcTJ83rTODCVzDj5rQIx+VyJ QXCI6Rtk61DkRUb+l8pstAT8P9O0Sf40aDZLfqDLjKoRrN4g/QquxYgV3Kuf05w3h6Z3yIvq FVv5op81gxfCSxuqpils0IN3WT9D3lN+bpRHa4HU2/Y4dPdMm9DZI3KuJFv8PhxInDzv148D a8cqQhNZ+bqi99jFaGPHO7U0u89KZevxVjRTNhS3o/JBe6VRPW/PMilXhY2Q/MopN6n44/2K WVkqJ5GRHAMezy7aKtGUYVA8NW9ZcUkRX6pXnV7suKQna0LHYBoHnPuS1Kkdnx3ZgiVrx5QD iZPno3DoGMIvhK9ExHTSwTqbnYLaWw5fu72nACuWzNdvXFDR3qGbK3FMFuR2varfrtIrG/Vq TL7ib0BZycdrsEk/Yjs+YdIux1KN5YhN6q/RJEgK80dlqeXStydQdkBur5O7y5umatPHmWvn jHgnI54UZicHiQcHEifPO9I6Q07oJSwroSaz3GZoEkFJitBzB+Rxd5VRFMsSNC7boVcbFXO0 p3WGcrVfg8ohIx/LENxSXmqHC0dcy26YMnwo32CSbW8PCnFTUd/OAmO64rFSgGUsPLprnojI AVPVaeHFTTXM8F4LJpM5ybHG4cDHF1OARYmT5926TSHnPwTJzleTWjZ0tzog1mnWzt2ewRYz RWZs2SnfWH90AiW1VM42KNawZysVe/JwhyNdwHJa1Ouw9NWvYt9o0FclSr7hT+2wEUKIAlUL f64MNyIwla7ki9wBST5k45emZN7N8Zn0t9ZInDz3pY0TpUxF5YI90YUc3H/p07CPovrG7Qdh wB7dvHruWChYIc3D4FlrrnXmdm8s9ONVBSzXrXmR7AiP9lkpsYvVCm/EZK3fhgOJk+el/45K ODmaZ9iId8Zvw84wCB2VJCDyHJrF0/7oDExhsDBZJSgHEifPi9NCp0n8bEqmdQm4bHOS/iRT WKDPz8nJq1hxM1EUJk6eF+lDp3n4DsUQQy9R7I3HuN0pUYGvxIOUZJ+jOPCmTaRyijiDeCVB rEmcPPcY2kLPGRrn9wRxLOHDtvwTVShowun+GwhYbZ8HXAq/7hLck41Nq3I3FvJ8YsB3qV6v XdA1v3PlMc2o8naof8uybbvH+EB0GMbBD0OT+3NAEZiVGmNA779B0CZnjhsZxVTh6UcblD+u XLx65vTAyYGBZzcnZdUmLs/DbYTZJ+DnbDGEiajRSJQoauHb2RZGxEmsUAMJ0Djbrgvtv/3Z qLJkcpbi9xllXsro/Nq/D+1/OKU5kGa6cKfJEfJbDEbabGMYhnPA3PAkzH7C8nylUcSMPobT fJPQcmhLq0CYFBvtvDSheseW9gyjdZRPdYOuPeT83qPJ83ZABq2YkYzHmATJiGYIgtQ24YZ/ 5SQmC3TssuPXn94ffHHO9c/iaLqMsM1E5fldPsmxyIio3meJeZJFZqtDcWB+54S3S5+PO/Wj 4P9axjlvP8cSkRb1zb3j+85djuhodGfnovZNFwbPD01Suur105PxkhE+5ZNVnfG6NK407pUG XQ1LSBVyWZ8yTjxhTIicqDxvQWwWljjtpeUEnKF4SXURiPTPBtNM5bctCOv/GC5IC21/vo+E 3r9jMv3RnSw3WwiYJorUAMhwxhhIx1AYJRmU5IAT2EcfjM9wWkdRZ+0/SftzFGybjCaFXqf9 FexsMOCnSoPiOAzKfdNk6IMmKs8/zl+fjeMABkstLxoVDB3PMQofBv+4ES1oiENO+LF0kSk0 LPzR8eKv4rBAZ2mTPYN1Z4po+CnfZqI4Zbeb3QxBAIf03v/EPRnNIkPy/hyHtY+sSx9IofcH 1+9UsZkXpEnPzdOVa1o4nKhZtaqQ0HsRKp8a10c2RFS1JyrPYNAduAbL40vBZjwffPzBsNrM i4z+m16nUdUFBqH7pBqUGMm0Tu7rP3vu1t07t29GBcnzuRTN2HPr9rc3+21c2HtgLuHYfP3h 0NNrV6Uo6NXTwn8TRDIzn7pJ/+0oGReXZoca7SjDGgtldOZrRMZ3z4UCiKKwzMqqPAdDI16f /tfGcfwhY0JeDOR5vwyONbpUU9L2e00PcD6U0oms8qvTrO8NOYMBpD7SGdbCsIUzO0S7k2Wi yQmzGrafU8bcppMhdMMor11Yi0+1zeNEmYTR43hVagzj1Uj+PmkcOIpB1cdev96cr6Bn9eDt G7JoirXMllQwV7M52uurPzhDA4ePJ3ExkOc9EDnaFuXxHIBlNIL+FK9QrjP657cpkyC6QpSz kBwkHElx4V2PHj94fvf6hfnonEgaqnVdJq9oOkZnygjW3Qcz4mtHX58ady1mlTGKqSWbxIcD s3SK1vZFjnJTruWsNMnV9ymjdVCUF7rm+j9e1Zjn67q69rm7DvXtXrs5xpt2DOT5FEqN4HKo XHuZg8rBvHtG9ufFen/8oBzMsubQvkMnR5K/+HP8rIbuE/5C5Jo1DL5BPgp5fiQYN2qDNeFu 6aJwqf/C6XVlxWuGte8fd9StOu473Z823PdetDpN8zq/c3jvjlM+tN+6fNH3DP9iT7CJjzff YtN4NZK/TxoHCoU30li/QKqT/H4QlrfWauZNXqzoHTjn9c19OmPkPNpNGDDYJro4BE8ZN39C RHOJgTwfwbjRAXzPqhFFnpamujRyFqf5xz/mEmx6oZlKzwty4TxjjFi/XTCCgOCMQpl4lB2J QF5D4iB5zT1KT7E2Wm9U4UJ/bs5CTSBBzawRhJCPVr/c71tTW+QJn2+kEcxk8Z76DxfhBCrK NgyA/Fe8ZUumIcxw5zHLmR55nr6IHolk5fA58JaUIWueFth4BsWyq+aWCgKHeN0xNtNWb/zc uRQ5CZpc8vG83oO37ny/sasGiW1UcAzkeRfKjXLmfNGYUqyEEC9O9Wrw1+n98bbziebrL68e u+4Ta+zHxzOYqikPn7vuEQT+7CiCLdcReu8O2otAQPiOYBhftmpTvQgpXN9MIa7K4mzUOHIV emXDc8+OAIZsSpNvFbdcEIphBAQrx6mXPRZYsBIQuVL69IiBSRIn3VIOnShKZloC8dqjoPdP bjKIyvfJrUbCTpuKdu84tNpp5jQUVwAwx5NeHPjFMzZpnPiZR2tmrD4yCtTLEByNgTzvQ2l/ A8pgKV+iWrD2pXox4pfpVdQ8lZpcMrSRt183Dt7B2Fm5RxBNHKrZO5KHaRlpHNbq3xNhYGg7 xKBVkmJjMYlK3quD6eRiACrzpJSUdPdKeWxhOdFZmZWvvNN6dJJX26sCiG5atnJXMUzJGayy Ibhu18MLNSgpqUGPMVaqYPmlkSy0kRDp8aTrkvIcGcdiVfvnGI+hewoIXhsy91R6huyFtEik aO94jTiMVxXV8tmzyiuJLO+CPzR7oeVPIWWxok7uJwbyvAHn/OT5e4FpkUbjJROlncXLTD4J I8DvNlNfyJn0G0L/HqCxyytmr6ko9uc1Xrx7ECKTSYFX0gYClok+LxgliKBehls+DP6tYEZO JNdYzspxNO5QLkpLZJXaGghfIX16ka+X3mfncEzWmT3IJexAFo/RnC3KvVnqpWbk/R/TRyHZ WRQcyCyUGlUBj+dcJdxgO0PJjlRyKSez5q3s3tLWuX3XmYOyQ7dczhK8hnkzREWfmCkQuTGQ 5+UQ43fe7kYrvNbfFwKkZpAc5s1+J+ufVghcUEOU7YaI065lqHj3wM2U9Vemh7VS++mRlbhF CgAzbCGOyk5t56hUSb2Xw/Dmwo7l7SzOed+1BzHasfrywDlZLQJapEqnkCxCNXetIiBggJwN ZcvS/r2BJEA43WVuQpem8uT9OazlnJRKzUbpKW7D1m01K4/zOpqGvQ9HhTkgsMEu0nuQvB1j L8gYyPMaRNHxqaULFVTYPOmLmVrU41KD1xItV/yWiau23iB836KXt7hISpb3sv5VjAK8e0iA vO/Qk5QNiGAnickvoaeiBHb2ked5HEJQAsVGTvM3cKrFh8Z2PThR/zBjkrcfKJfN0lspH1FD S1aRCHAfG3JQEwk1qUKT5+1Inoq41n1ssx73fCxBOFrVDnWL7Eh4Uo4fpOXR/ecGrl978eJO O+l9UB9oj0aMqIyBPK+DSJ/z9lIK8j1BbEMI+Rp8gNX7u5N4Mn2iOAJNZh8UsTwXYZoh/IHo 3arDZ9Rbi8Frr9oEO4DUdJFGWZP9zqYH9+B3VkqcNbtzfkfbUq/Xj2eYJX39YlpTgLr+Kwur Vuj3Lj1QiLhNPQoVt1wYcIp75qAmki2vKHU4/Dkla8aZAxcrsppLWMymKXvKWBaee/b06VN3 jhxfYPDNSLbOqGMpkbRbBRT1JkN6TUdxkAwxpRjI82Y9PRKP1M+wdOaRG0+fDJ6WVWI/5sNs 8/Jlcx1w+SiYzGwt2WIQ6nYZlkS6FFVppdeABfrXnePtSDTB07mGPG3IWbikwt6Cw7I8f7bp pMNENt44Butz2Iz76vU6UqXXlhNV5fmJ3QAEON2ohoq9z0Qkc6MTVQ1XkU5Qrp+fOsbcH1U/ yUax4cD+GguGeDeCAoLjWdxkEmyYw5ntu87VcN3VR/dv3b+8Mwv3GlZfUhF7QYakOgbyfI8p GxmiysQykAmhONKYqqBM55hgnQHFxNHuE6WBvL59aN2GRSySu0AOdcblyGQoBo1mB+yncdVG eIRrlAyHR1jlvP3OCkuuIT0aBNIHnwtGBuR1uQdVFqRIXkBzENW3bR/DgBdCC6xep8/yrLTC DlN4yPmBVy5PH5vnMNlLrDjQb9F5U4rl4FsHB2+fu/323f1RsFBuyaNBLv1WuwZHdJ0aJ4tb hDTGQJ49p33SsW0vKZxZU2nOzmUFXDHmXpw901lTUjwmd2Ip7HPNDkD1vjDTp/o2Pdnb2rZ+ /d4zV09L+aEiLp+y8EzZ3/ZchigTv4/k5U4ecjpJOfc6k5LvAI8K1UQZ0gcH6vumWiKdr0Gq OUrxZ6lBJJX7XVFUIkrn4QXgavLGbBrrIBs+sfYRrV34jZI148mBaoP3BZ/jNdCOHjAD14T+ YZY3wvgs3BNTwmIhz/4ESWfvt57vD58Ner8PmJ9llvF8yJnsh8bBF4spH5TOtvKYe8XJva0O WNFn7TCJ8r/XaCm/O/Dmg/FFA0e3OFNHdv8fFsLrcg9qdE2XUIjflxLIvEs3L3RTrOz91onT ddd+3pqDmKQMdK8FeOT+HfksbHjkbZIt4sqBbUZNh9JPBg3kSac0b+QrnKFi6ZKaWR3ze/Jw NVdKjOiLvTyHS1ixaSRqOlCbTZMSLzpq5CUcakAQiKtQIH1OOlS30dz/yPJ83wGhNIIzPs6k H3mrbx8LUmWvtjsFCPABwyC2R/7xdQVnwtMFhJBDUH64TOFltgnMysyk/TncZ2yy6g0Y0JLy UrPFlaWqfwMM/J2mNDXTBQZyZ+dVOaycjY9x0FXi5NkNjc2a7MuGtfqI7c8xWL4dxTkzS0o3 aT1pJ6QzzUrq9YvNDhyb67e77vJzS701Z1iuOFDE8TyboTnbf+xkAYgJr27rzbz3uhUFyRnJ 83YUXIthkw+PxwA+ddTVzp5dU9Yyq2zESXvUiG8QTLMzDoncQWAN/fr+syeYt3O05CZMnn+K RGg17coUeUtMQPG60Aca+9kef5iVoPRdPrn/rA8O2fktXbN2aJUjwicbPYIbTQBPkkNqHLjb SROsfcmtSDkyjHr9QO9TaiLxSPsYv37C5PkljoW+RJ5KTZQ8j8+1hNaw6EO+cBJK2x80eJCn b7dANO+/uM3FqH6P4c9YFLS6b0VX+M1C1xwdBJAweX5GQqH9ww6nRRsgHCteTdF+LPpJAhKd ovOfHLKebws4zmaCk00VtzLZSBVZdlZziz6Ne+MxYj2ZhMnzUxINzZBNCTtvx5rHse3vl02X xOuNLUsj6K0MVeX8oDFSD0QRar45dPFg/8cXNfrYBj370J8web6Nk6HTqq2ZkJE2giX63apm 6EcidX432n93eoctZtX4+t4GPYtsNhlGA0EhJJJVSFkDAO5F1lmw2gmT5w80F/rYuBBOpoEI uGpOo6JrT5Y4cuDmumWr1q/bfnTovp+H7znMa51sNkR4HzzQ3lBYtvbQspUb9oMguziVhMnz d5IPvT/PSg3tPxYnhkz9btPZqU/j707hW7MBg0xpaSYStvpeC6+gGdrUlui8UPlTaLYJk+dh TAi9P89GIjYJTCG+xpEUGxfHzpNdKxyoYERRtIoih9C+bhLvbN443NmGS1OQWQmT58cYHfr+ sQidiFNkFKweXt93vHfZynVb9/nCco7X0bErVy4fv3T57ttJC0q2BfUQHo/W5O/jceDCJrXG YYtosVgtZoFQI9nV7+tZFW/nbsaUXIaEyfN7Cg+9//agk3x/bv0HN5sgBJqeogs/susEhaGY CUcIh03y5Tw2kUCL8R429XdLTpgVk9UmwIGZDM8LrMXJ+7vmrtYr0f1PW6EYI2dPgFafpgmT 59cC6Y8nNno+8xAAvTeZ5UDlnJlWgWMcsxeHn7NmFopisMXOEU4pTcYQNIIwGjfShREwwriN kez4JMuKFsHM+SISSFwpSZPChC4WU5ZJfjzDW5KEyfMlCi3ZszOEi/ZMYlT86PgTGtowb9Ga vsMn+3uXhI71CNbV1zYzsyi0mm5U0xKMX3X2/uOH995KmGnH07CoEmeNPzWfGkLcjJcRkfGn V84RzFaGwkYH3j4qT7VkZ7EIY9949OjQp18/B6eUd0/C5PkiThJWR+HtoI9FORKxl3MlQXA0 Z+V5Co4S1XgtyUR2a8/HfOPRjxupWDvYj+WPOSnPk/EuabZaRE4JiPMv6/Myc10Ua8KtPGtz ZmH2SB3F4kl9wuT5qolqvxbqyNJgivhAY6E4q8hxnGBh6Yh2WS+H+1E0ZCjj7dH7fpWxx2d5 LmKU9iladO1xF/sN5gOmMG7t37ZC/Ey04bHkaq5oEWnX6MwvauOLDqL05NPbR0+fP5I7Iwqo 9/BoiKJWwuR5SM+GxqBeAkd8dLXzVofNBvSSTkuU99jrNKpZFWU/gsEB6f8vNc+BrSJW5u9F UAv5JtYZpBU8ExAvmel2azHu4OPNZXO7vHFZg+DnBZVK1qsHDfXdkZ1DfjITAQeN4hGJV5M3 Ie9Tk3mKfRtgh61ABbPASvATavn44v7g+w8fPZ9eAXfb9zZRe3q3kT6K7s87FravOBgvjoXR b8Lk+blhJNlUQDr3UBFvsem83WJx2O12p90PKDUMPqhV7jM4ANSVyqksySevCD4M/l+OyaGd 1ypZkoRxb9Sj9F01nj6SywrgmBCyEe71PCtE4qgX0HCt1QQjlIro2sVkNAimFCnx0eBmJwQb SkMHgo+m3hJFqoDwOTBpNdelV48BV5y0wccd6JaTBbYql2xieXIFAItcy2J5WrQ40tPNzkHP sOBF3L1EeEEkPffKYQRFicxokhWPS1JYFRImz1+xzNAErqAjdmssN5utVpszHRgOI3WWV2k5 SOOq1v2cBKF93ywluRwmKQlS6KUDc9Q1F5sYn33XM5OiSpcs7FBh089BkOxkUA0LFW35CLZB 6bYZg82FLhxSEuxZCZrEy8sBImQ7buJt2VYDG4nB22OT3gS/fVnBwKb6KSvQn5qsZosAAWzH CzU21MBvedlkwjiWoGiMxJG7ntecV4gfcF774SUXkd2xrLeLNga4dk/OiiVMnochbzho4JnO hiPOCVPBms1mm82VYWajfOYPMJR63v5iIwGeGE2av3jOGcolEhcgTcAD9cMimts/QnEjxWCI 7n/U4a4glGSAXmSiQBTOs6UEKcO4DrH22afeXSmjURmk2WYWGNl2+dZO5J58+uasK2WcV5s/ g6xR6vom54EKc5Rn6ayFReomzQknTLK0aqsFThRZ/LbnXKazYVNXri0bwM3wLGsx8xwFEkN8 Ebzy/IjxuozV4qvkw9pyvQI7l4CSMHl+io7jX9MiRrw/l/Jge7bb3Q6bOUob7U3ae5xumXHZ s0CwA5zhNXLmnb2YVVHGL8dpbxaTL9kUmdvaXqni757HbQC/861IKxhEnYgg1VzAyJHer7tQ 2VOzzMwqOvHTuFnOMXCuKKITtN3rQpyA5yVGQ54roy0Wm4BHkcUkRhSE7OahjRYsApf9dpdF uUE/bcCBmpXnLGazRQRHsPu497z9hFTvUZ6zFg0LsBYOCjsUZ/ITJ8/QOJgrLX44e+Gw4S2w VQFdmCMrEwCthdNgbJ3jGK95eK2H2k5nmdPZJW8L7FKMXG2a6vn3y22UskTK5Ws+L/i4sV2A zcMgazSTpah6XgsoQLG4yDcplT+5dZI1s44yK2N8c/A5ZyLX+9i8j1J0c5wCrQ7xuGCxmm3c CA79FKBqhIQVDNiJRd610q7lermTTgtmkRGBjkwCwH9IeLM3PdDOgh9m0/YtCmTBpRhnvQif OQmT5zumcQ6Zs9hIYXXesLzZ4XRYc7Ns5ijPpGvxXO1Od4IUXYzFhtmacDklSYVJ0UiDDAGQ 9kb2fMom5bO4WvaYpFSbR6Fq9TNDAM1IvX6T+rHIJIHUOGEtxUknA3MZhXUD4S+XVNM+zkUl st4SUXtvBilYBYvNzBO8X5DSszEo7TEl7/TCBV2r1nVu6DsX2nTyIIcFbxurFWDIaxD4niaK F0TObKZRSX19F/YeqU4Qyv35RpOZIRlXn/T3Yyq2WWzC50LC5PmacZxtppqIVJ5fW51WoN62 5RZYBVf4LPCtuYrN0q50L4A3PjtnicDbUPm2OxfRQLezUS8y+KCZWunTvhehAQ7/dVoDYeUx cKBuNu1Rq+SmnQQXcBrVntrvc0QxgzbQjZEYq7/zUb6rouNIHFrdy2dFmyTPFouAp/t66Z9u jcNwI13mwiSE4zhJmhwa9E/A8RbzYCMGsgsU2l4omF6aBL7AgojK3jyPR17j+ynp0vCjTnpQ xAaHKEG33o9xFtjwuZIweX6AjSNyRUiksDrfQWp1hqMZs40lotRvd41k8vTkiZzjkadS4MkB iZ/LtW31rd3mhZd4JhI+JkrPZgwDl6tBRlVvfmYZoBDbCS9Q1yOfBJap92Z8BJrq2uHLvVk4 rMH6hrNuw8Rol+JwWk2lOrM43gyMijYQwWTm9ZPojZFHgFuxyHM86T0tB+LLaZETRLA/mzkC 88YMbeUEOyfYOFTGCXzLYjsldPwXv561G6Steus01+E1SJ3nilnq+hShneUmm+8Jk+fT8Dj6 sHwsMrMs4Nw8Ljsjt7Shp611lo8KOhKWdmFWr3dHO0dVADHmGKvsqPZAdCtJh7ahI9aI705p B/aWnQh+GezPqEuBIr6E2AA00HVC1X4/tkh/vLUQ/j7BD0vISLxf3jK/u79nBiVY7eZ0m2iz izZsElViG4E8WwSepRE/zPTRD8hsHNydBYanQWI5b26y9WZOFEDElZT+BJQ2E2IRndlUhhOB peNXIXLd81AAKqFOAqREaoqriuNmCPT2hMnzNuM4YX95dOTGjKtf3/ofXbUMI+HK9EqE8F6t DglSmtcLLFymtJ5LtJ4GEn0g2z0S6PnLjfjKcy9KA4fQV4L8yvZ4Ogyd0j91nBx28qFJLx20 X/PcFZWcHYr36DzaEcGB+zkZN/DmcLk0wXoD5SwwLAoAMECSrjDhzCc4ptK8FlihwC2YYEKa TgpJq40laRAKwBLeC0AZy/Ecy4lAPyKXvs6Vq7rmt27YvFjasE8bJZVJN9l9bwlDL5ytLHic ShuOB0fuSZg8b0gDm1+oUsDEgB8+vlth9XYEZrwoCz/zTZI2xJqmHpfPsAYqp75CYH1sEV/s el/LRG8qLOWxW0GZGtasnN8smuXgjj0I1dG/Z3EFyUh3xXcUprqsvEbwuRefPLpSgGn6s3Bo /ED+7udtz1CtIAB/DbAJsplSOs5JK+czgNeXhUdD8/vU2o0nj+86uGvttq2dyl3L82kNy9vM LE0H8Wvol6Ohf9SgFjvIcTqiX4nDzAZ4JMX3juc/RMLkeVtaWejJZiXEJt/VMkLVA1nuVtPa mWu7hRVQA1rnS/cCh9cWDb4+Lipn4aU0bDLAmFWV9TkWBoeMqEvxFK3TktJ6qmDY6eQYODsS FIQfVPhoC3F4nGLS5fVC0QzU2xzXFnFM7MTG7+SBmothlGyfWvkw+ODOhQePh+4GUJK1Orff vTK0PxvkxBBY4FDit/gjXZyC5TvY++UFAsPOVt0CJ0ZqkNYP6hge0ZLHj60TB3n+OWqNnl89 c1E7pYwQcGY8eXbGDXM8Qj6PhFTdO3Ogo3GT//T8gyk+qGiuu/NoBNUt1Ua62CHkNS1TQ4a+ 92lfv+8sLQY6l1LfV8L4xAlBMxiO33aq1HhUz3GimSqOEPJ2wuQ/BDYoAfdXQOwTKBxJQzAY xb0O9yMDrc8v5SAIJYG1ihR4PIjS5y6jGh1eVuHjnDsnNofvxRxrRX09jv37i4E8d1T53VJ/ tfvH863KEmjWVjt65Y7rx1GE5CRkf54Yt31av+zfWu8T3D06ubda8+vz6/cj2ZxBsx/0HyDP nqsuUaCx4PtMzNZhVEdZPM+h2/2+XI+yQOcNLCNMSkA7w/dLvTkswwkgEJcT8YHAlDVh6mu/ nfTvPcYT6SWBSwscOHeHNNSE5fnjYnyG111K6nEjBfnuN5tQmmJQ3Jg7Kpp534xx5DmPjjEr /pDuXiXMthlTBq62s3hRxBF0EyZhdTpDsf6nxQd2mmV5oCeji4Iojm/UsjghcBTFCLq5gUm4 Ymp87/ly6/p6a3TeuHcWL9u1bm5xzbwQumsw8hOX0Z3Bov73BV+KJirP/bUig2r2VanjnQ6C 1PS34GM/wyzZuH9pEw2PSnR7XOfrWBWAScVRumxOeMmneAe3MdV9dIrTOQ55X9v4+vhjuYwl YjYFj+ZfJQi14AUz7RgOTPMJF9ieeQFs0CIPzQkyLwuRUeiACdqkeR1Ftjw1LEqA1OKQvkxt 9/zBvTv3Xz9+/QZYRC56bS5rYfrQUhYKjqY1QXl+J6I0T/joKDssKM1KOl61lJgU/6lmiPW3 HR01jOMSVxxXG15k3J5Ktc+aVBPoVCIqGlouR2p8iGaQMW0OmYRRcXs3XQywSYPrcTCt90qE ZClQRXJECRYI+YOlQTwlb6PT9kZD5j2L2UJnzm12C4yiZVlEUBSJCThBg4AuGFONeg9czDbP bgYNjtI1QXm+V1NiYSjNnxHQUcIRHDtyI9yOWZRVe+I0+ntRbDeO49uXYYnAKBsND3/TNncU d/JkiY4DL9pGw+BX4RQQVyA1wSRxPcmQtFVkRYElgqFRbcVaTn76Mvx6hSm4LSkEwTd4M9UI jtqnsw0tcrUqjKEonKQIjoMB0p6EqgFKKwyutts4bCBoVxOUZ6CeWYtiXqd1cDZYKvLsyHm7 Tq8dbtoMPX5E7ND7AukFoI+LxGkqurX9LVtdHcd6+ltOKu5EB48FOEGD0zRFcjgfLNStA2Nx Ghy3eY4kffHEPp07N6wS/lpklDPxN5srmrm8TOfz5fNrO6Hev29cOHv6yOHe7Reube87rGLm 7LQigx7PJjPmp7DyG2/C8ux5h9B++Q7bUGoEjsluUCODPTsM/mbTQ3BIH1rPOzp53g74ZOyH J9FDMppn8zdrUw3RwLTMCjxAj3myKK8wJ6tzlFdhGcFTPDj+8hxBjbwXXrQ6SFQsUVz5D6Rp Rp0FaFcUNvWHNlx2JfQsZeGg/LvpYoXD947kc2SZj4bKv/rE5dmDwj77M/CGQg3q6wSEoeA6 TV93SefvWrNcH/pF9pAYB18sPo/Nj1cRoxDGh5CgvR7Dk4rCGLL8rpUGyi6zgy276LnuxiyF VeL/qrGQqr/xvUzeCmBJgLu5mRqJ4d1tgWu3n11GpclYYTuM6p3zXT1LuOeFzhQRiPoMVIay 8axgpg3Lf3y4fW/47Zsvnp/fn3z/rLinzoYpxmq3SkGbnCsI/mkM5BlD/MAYDutQ7/35DmPS dJgPR+ELdWDWA4Ovv3tUB4wxc/zGRhkiFe1a/6rNLc4rSBfJzIpaSf0wGG1HAI2mp7F9waqm qqaV9SHRf6Ma4TxSFlW7ZKOAHGhjwd7MWHKpXs9bV7akaPo+Wy/55M92sSJrdxcuLAOu3CRH AsM1S6Pa/nyRMsvefhdZORq9D+qTe3/XxDIsRWElEToV/HRiist3F5sqG3ZX8gQrgnCVotLm okIbJyvAVjjMeaILgNjaBC4/iIo7BvJMo37H+dOw2XvguMvhmrw+hTE/s18PiQIbgC3dHmw/ tE0yzPQwDYsEgWM0h0sRGEfJAM5CYcpEpx6GdEa9XgfPEN94vjRH31OAAY/pe8IkI1ltfA48 ywbKLslXzfjIs8+uGmaKU8EpOjsNK6uau23N6UaaY3iQCInjWSsmAUNKpUMLat8OScJ/EZKB TE7VAn008CSjoIzIbHGv7Iiywa+lU+Xb62zSWpFfku3KrMgvzrM6QdQeKPfO3jh26XQxT2Us Cxa/PXF5/kXii3z5dsNIKdGCoNxlEC1I6grG+9knFuF0e8+q1duUY0aAYp3ssIOjSzbNFAS6 bOnmgWGPp2V69P5pbgwTM2xOMymSQBfd9w8ZeaRY8CexT79z/Mf0r66xtKF5xd5LZ26duDM+ 3xfRAKOAZcwMiPabCSId5XIQXuzxpGPqEXoAHLYBOCAFPEUtGHgy5JLDqDv1ZysFZOuSHP17 QWQYAEhkNoMQrjCQVnxk8rEFV5y+1zFGeYiP94B7iufz08eeJ2fvPPa70C8UqOAO4hOX5y8G Usbv0ModhPCeBZ6LsOYCdMPE+dmftiO5SjhxsOIex98kHo/s+wqSG1A6no9EfX9/YSZBDMfX t29ffBkER5VDJvP4z1X4szmqj/x2Fn7vf0DN5WkYRpIkRMKIYzx0tjsixglmAEcCwGdeU5oe 6H1++rO7ZkILqswmaODqCYTaykDa4+zwArxXwFIQXaP5medXCW4x03l7dxewIqUlVgiPoXdZ FUFwBw8Fu4FqPXUx2KmgvU5cnt8iXvwceZRLJtprsP/Oz9AukK8Qf331Gl1L6KlmRRJEONLV 9bqJnG6bIE3ZtACO+n3yI5PxDVHejomRIieF4swxbFN4z8jfWmsTIoDYapZkWUgYT9XchrAc Z2VJMW2z5zEnAWh8efj27k6g62rAYE1qqnEKWLQkF26CUcNvvtu91poKo7R9fXKVHOohwbGc Aq2uu3lB52f0GW8tzuFG5Uh9gjGNR3MXSQX3xJm4PH+CpdPJSDkB04PeT4V6be8eMEm4iCOl UT/KAXTUlD+L0YUdVEPsBHRQdbBm9Z6LSgjLX6LyackmfXV5G2Ahlp5QW2VUhGQJyoFfGSCt gg3gc4pUwPgKn4bP3IwZwPCSvAh3ed5Zwa15sUvMdnAMA6IzZIgCqaxlMMJlpVGC1Hlj8t3e GK1KoxyucCnLzAGPE6ZKemI6OeuIkSecpXqUwSo7/zvruGbaDQQVvMuJy/MXgvXap6RxjsDE yKup16SRNzPN/5hYZvJGEwak7jkTlX77jZ2iJxAf3ObN2tGC0ctXZQi2xaq+buelE+uqy2VP 9acLqsrKbKWaluBsZ2VR217fI3U2PpICBXi042b1pnRj15J1Pt5JL/Yd3O0bunJlZedZqf9X BzfvPuYfh+nDos3GvnCekL+4zmaHhE8mity4otEPXDTBcZts6lgDztpFwDc7y4iwYCcG92AO cak8fMzhswduH+s/cWCuN1CxBFEf+hsWtdqXFTRvZtPlFTwgcLrIEK9uAaRIuZwNalrWVvRt W0s85fkNhPuRsEtHjAD5XUbhTfLgOzDef6trSg0dVzYUGrMt2Iy2ClaLPWLgMW9v3ZAWdV2L YTwLvO1Mdnm73wOhGJWZJ8W2vioxmgyIMcUFXOVBWU3P0KX9M6NgeISkMtz3bHEUZmQTxN06 NDVtuqFZAwTe6NATOr0ar7OuoK4Qm/Ef8NZ/uthiMOinW/zdY0c635DmZx78i+U22NQfZAs0 B8CyUf8T4PlZXhhHreUKHhy3AQKBcnFeDZ3zVBM0gE0BPtOiiHmvyO6xppZVcHrf9h27j1+u 9wYvfszmzIyzu3XLvitdIqMPquiN64pNfH9+o/fxBwO0HvRzFMmBbTveeoY3COgoCLYF00Jj LN3GQ/uPBeHKTIvVag6984fiZzOqyXMdTdesO3VpR5FB9oOpwWl2/mP5HjyXJLKaFhagkLxZ 7yXw3AWLi0jfVA/VFH/g3vmzg8pIFyiLtNledhrJrOJCQa9ECd5qpdjq1XWIqVW6Lz1gCATm CmZt9nzMpwmGc2cgCuLF2LJZp6lh4/pg/M6dN/EUAO8jRl3Yiqf7hzJuq65MZ4AXp5lVYKE8 b525u+azAAkJSDTFiiN55pzNY7hxXAROoASCcLTVe99tpUSGEzGa4mhBRLxGnknl5MTl+Yde 8FNUn9f5wj8cEDDcUWSGDaO9tdemhn6B3SJli16EZTMjWOzWICASYfQFXHDUY0QlZpHPwrsw QvqngaFVr6E9FNcBzs8PMigZkqIcngWkfChPIEbOHxU8QzPG6WnKkesAJNNTpSuT3FQuZKRJ t7rhAgML3nC/VouY1O8dIMMNslkAmO+rD1/98HFJWpBA2u269WFM5K+ucnsWyGImMP5Ig0dI FPHVC1/FCJwGIPkcRxOqV/bNCrsVB5FWLAegkARKuzS9x3wDghXO9gH9GMUgNAeNhBmsAp5b olXgeYa3cHJqo8kvE5dnT4fieaqVj2a/++t6KwpBeF7DaHyS1Wl+WrQxM3+eq6aXiYgndbxV tIpI1PCKS1HN6lytnpnvUTKufwtL9ymUVNOiFAL6s5JjwSv4ey4tRYveL+FMI1fhHI50uAVL tXJt3g9LSKY/nJBy49pklJTf6yhOdhT0NGIouKG/5NhMhUO36fQe+Y+5muf7KAZsNcUTnioi bk/ZynspRwE2Cj+2neFx3yPPfkiwiJzVauEYXAvw/X57LvD+FCgAV8KxJK+onD2nU8ZqIBeb KpcuWTjvaJ8jzQvpuwm4koA0PqBHs2AKAjQWb5bFQJ5Hk/jM3519oKNrk5/CTKnflhI68PsG HgXQ+hIzSABts5JRI1Svxb3yDKuvJQsm2SHnUoJyXX6XARD83LnF5TzKAQlcDJQneeDgxguE N+r7k512nfv4WvMQ6pczB2wwZKqMshFAzsvxQuXjkM0gyWcGrYWPplPONaF0Ir26v0Ke727Z uGrusg3bb1wJHrwfXDhmMjbU3+3mHHDqpNnhkSZHUSB9UtJInjMNeL8+IgK3EZqmwYmdNuQu aa3LLyng0iQSnj57oMQifJUUIPmiukGdoLx62zXg6s1brOk24E/C+yo64y3EPv3HQZ7Do75e F1ofdtnob98Kp9cH6dYM4LBuZinvOzOcZj51Orz67VItHYZZNgjWY9mKBvu61cUwmN4ImWBw gHjjoiwY8Ow0IrDXlOH5aKd8ITBOkFbQrgzTrr2zIADby1Ft6rB50ELwVzqpOc2uTscgvGBR 0OfhGKIGw0Y4td+r+s/MNB0EGQxpBr3OBx5jZBL3z197EtxSeyWTkK9JI6UFNwuCr4yfxgUz CxyzKZLVjYj+DYdZ4EHYA80LNNJ8aPOuuU3zFkkwZ0uBCnQ6Co7STgLEA3+0acvnyUS1l8Ri jKSB6wnPCGYy33/0m5MVE5cweS6DVHe6IM/ZBShyee5h7G6QL9Zq9pOnSB5kN6LdWssRdZO3 yiElTZDqfjrEiK41uzdtWb92HXicPhcKBbWr9q1fsKBgxIn9pYD4qqzWSzbt7y5U9t8HpQsD cOgCrbm95EObgDuCFT+g0bkhB8TNI5ZgMa57jbJJ5E8vBQSwIAsgqTrNjkKek2b+qRI4dfEO d0ZhjR823rcu9apbj/mrEy+ylEgzvsieBxGeBympJJg/w4jF4BQLDto4LeJA0O2+7ws3xHas Wbiwa1m383+Xei6avMszghPfBbmWN2VRGEZRxKjD5fd9k7ReCZPnutTQbot3NHkKnxFXbZwj Mz03x2VhLZpRKPzWck0HrhopvhfgqqY7F5I0la2QhgrC0r6Kul4tGayP//1PG+x79ujEpP25 DtP0BQ3UgMdjJlUgqke2VHAbecqQI7qar7e2LU2HsQCXFInEnZB6rYtwar9Z9X0c8AkRRTND BELgO47gADeDZGjC5Jedb61hWp880Tfn/OfbifIWkSXgEZPVURociwEOIE/SBu+71LOTd7NA ScZzYFPwdSLwZFCZaojiMd18zyPEi7FVb9ZG2oGBQIYnvYsXLynGyxLE7oTJc+10zYIeeOZ3 TRGzZMhCZNnFIreFwdJD+4YHZXYmpnpqvs8iFMn+lIlK+oBSVHusyrWMS/ckhfYzTg3rPuOj TXWZDvqMUEdYgbBvNdqVC/hFVnoAKmH1xXGEMQFz+V2C8GfHebd2IBhN6yb9ZL3rE/RIKsMO CbxZNIM0lEyg+/PPEh4ktLM4nCLpZyapY73vXT/qb7sYq8VloX2SIVwDjtvgbC3lbUdHbP39 OU4QIUUTGImZfO1dl0Tc5Khsld4SL3Qdni+OFrX/YafXDrHGm3hwhZxzMhElYfKcl+KHgjBm 7rdwH5TBMDnTt/vaxSNXTl08eyZaj5JyUtWK/sxR7VODOCmN7oK0C9N+hpoJuv+2DJPPVLPT ZEE/xPiEY2XpfA0jNRgD3gi/shVoyDfVsnvsKTsu3co8ngWEpAm9g/O31EleUrTiyy2jYSjV 3zsNwTxNwuTSb1KtHtx3gc8mrQmOP9mnbYIN5Ju1sKpaEdgNQIXTINSfDjS/DhqESNkFjqS9 t7wzQJFtAYpsM88TkrrlB8j1+fhoHgvMxwTuKKujOV+FbQcFO2aV6DGg6X6dCvxUVliVt7Nn x0j0wibEoj7TdaaoNbITXJ2EyXNJSuj781XE3ww2wXmG2Xy+mpzb4ykglAvYLZP8gJSlecWr GSfMLdVZsFG2YlxFkMaty4t4g+Q6phbn9PM+482HWGntd2KmWQcHNjpU3KUtgmHplQcDs0mX pO75QEHq9ePCdGvvec/70xVCEPv70lHAimHO7Ler9qDIbDaDSMbRhk51IjM5s81pMVNeVw+g lPCUEzynD2Cev2enzIKUH5I3lWuM2EwCeQZpngWWMfWBL6t1040cZi7KySagMtDZE9894QyH oEDo79VPa/QMSsi092jl/nVc4FTBBi4JVhyf9+Dbzb3tlH5egvidMHkuSw2Zs9MzBCVCnnek aVK0JEd1wK2T4XG30F5z+VCxi0R1Olz+Hki6yQQbppO1N0ZWsATVwt6l7w6ziifKaosBFXGD W3Uc6sFgziYYcfmq/YXXqTqZ97kUzRVncSghWbkDlEV/iTx7ymgAcu/nOevLjfUUcOwF8uh7 s+nnebMwWrUsNVoOAYROisRJXrtPAY0IYQa1OYCHy7Ag4OAWnL5u16nL774PX876Z8xW084i slnqkYBeP22SdG6H3AWtS+eX0xk+tsVclqLtxVaClU2ZCSkJk+estKMhJ3wnNbS/SZy4tdfr pjcqZlk7DoNxPx/sdgut2n33ySJ3Rm3HgC89g/7msh1qfMoRN+QSawe1midzERQRVd/UrV7v mSfrCgH2MusKIs6e7rTQL8I4MWbyuz0ggmSQZDBnuBvgpGwWcZcvXa0MgLHm0bFRVbuy7dbC qtwsl8OpierPfJwF3iQ2YIDmpMvuYXiV1tW2/46+Cl4XCTXaajlUO0e3Sap5MZt1kDarL7BQ nw0jKBLg7FKSCTIhJWHynK8LbXG/CyVEnsNchPe+2Elfx4ua1zp9se2MdCrUyuMDG9YGipK9 OKt5292gfS6b4X3wwiT2d61WRwokMxyM+gUML5oxX1f99SJQelkFMhCE1UvF9vTSe3BaTDic 9Zm5DoJwNEgxb7vSvIavy/8Z/WhuJ3hUuX69cwtWo/pKfvri/lf/yPYdOTaWIFGbZpqcfM4n TJ4zU30iBwPM+3wy7CDw07DKIG8Pf0HpoylMvdQEmO2LYlYUEV+n2FzOYgEppXk4jHick0TR /Zee4XfHCshNsi1k8z/e1+SR/xl9dFyPcLDifwAU67wuxJN7cV5NSWrixHni+eiifa6EcezP F4x/y2MbIQe7Uv0dGSNs/jtVL1YiWYKUzcD+jI1Yjj13WZB6EXjvm0cCo4I3LkxVN9mdjKL4 OJTmfQts///6RjU8R3CEkgZ8XzrPjeMAcFXvxeeZfGYnbH82w70hZ3te76vpmHzGTNkRF5v+ Bn9Pmf19YrDsb9Kv51Bdk++rrQ2yWgBACEGhhtvjrd53zW/IcwVSfPn6U7w+feumj/GfdwGo XufsOas3pCOk0c+DJcBIiiEyMSVh8mwxhr4GnkRC26cTw64pMOoSXRDHsSlA2+SS0OPvEbzM ZsUIksu3U0qka6jyCtLcQd+p+vBLRk3z9rHCMEZV20HQLIbpTSzqmDXb1xgJLFcRAYWNR9hE f0+YPFvx0Hh/p7FRHnsTnemf0n6RIYHHuSnNxIcXjx49+dLzQQmDClkepmj+178sSgDzefka M7got7QQN6QfPHJsy9b9xw9v61kke8t/qWEonsdQS4n/1v1lTR6P2Zf7ajnHGzq+vydMni0Y NnNmffB321ljtDFS8WVYwntfpfsr/Lfjy+dhg+bdMMQrd/RDM1Z77i93wTBB0QTKMTSJIAhJ mabplOdwhQOByfwN/nblH7NJlBEFzJIYcKEAPEqYPIuYpaF+cfCIt4Npk++m/OT8/WePXn59 c1PFAIzsmfr+6VcscbaDDb5Jn5TnyBYmUG2zpmmbM0NRXO/VZa3KZjAEIwSBJCmGZQgCzj20 1fW/PUr78wvXHfs2qiuAGSXmN7Q7ULx9QzB/gYnTGkkPCZNnqwK/FbT0p22KZB6xqLuENxIE ZmIZPQQCZSIs60H0gMNsz8zNcNf+8gzH77KwUR86kCVCun+36jLIvf8NNpop7J6hyN8mglEu yzdcFISRwF+Uw2kcBUCQBEFj5R8824zBAlcBkhRDMgxAJ2Jp0lg4NVYlYfLs1Ie+dOyEJtsN 6pMTJQBWDO50mS2Rx4LQJhiCTCajyWln73nWGKOGPBrv6eyaEQSIaLyGf8Tvu0UpSiIG3pTl aPuJi9vqUFGLbnm9nAaA+TSFuxszSZpniCV3b/UWt1UZ/QKs/Zg4G2Yoc+PiJfkAzCRu6x3Z siVMnjOnq6DUQejtNQXnY2RTDLf2RxfJbjhz7cKTL+99gpnDbP0OJ5yzl2/ZtefAy/fAkX/B P9Fl9whjtEWGCeQLCKP/KV3lZRZsjzC0/f6le68e33915+kOP7jeD7WoLjWF6x3xtH+fztkp ErFd8FzNIiiclDw5F2FYWvCDdCO4a0u79/V8FguNtjNpTE2YPFtTvZ7SASe71hTaPh2o0ZH1 izoX9J7btf1MhGsud3aXR6KALFPpeMUSvu+fRkP0XY2z9t1Y5G+bSXuc4j3QepBaPTJz3TqW srqs1gzOaTL6PxU3Ni1d73dwbyABNAkueX8XgQu0DCo55KLS1gSdVC2JZckqk1UUlEijsw+B CZNnXhda6bTIqOBfRlD2oQQO3qsCgcm5aiItL61w9JlkHjKor0t3PRYVeng4JHcEDPANp+Xv X+dVPkUj3ojnsOZTCkKrQMEBlsn/+gP4Ss3f+7rJr6EZHsv/DCKcnThBGl1ShVmYEhYbsFQS mE3+4a7ZGLnGJSzyI62UMHlmU0NjDqzVR3x/PgNgzEFuTw6E2PjmgwuXJZ9d8OgcJVc3HfaN fBzd09OdXk/eu4gEM+It+dgIIJx/XpBRffy4MxwufWq9Guiv3Z9/zgI5HkUkDPvyCE9tIB+s wIhAWylAo/NjnOsUuYLV3hUeYCkBXg6anhEBqAGJShfCpRhZMVqr7e28nMCUQKrLNmKKWKwS Js+8CaBchig7dH0RPugAtE+wAbxeS7rVEg2k7bAT8zORfdhQYjHg1kYlXv1Fl7r/NspAJZ+W tc2tthv02iv/Kgr7qrSrERVmfc+sdGv2yEmjr6E6v07zXrq0+9SmLBMp+RkeaynKKewJS9FV rftb/UmuzwYoIxYR8kIYhPOAmEGGKhaERvJ2UecfJ/G2g7NV1GaJvPdil4OJiLRWnSQjcpwc 1tFF8UTd5raq+St3rus9PWrAchJVHrSNPDl27w+HupjXSZg8szJsZvCyP4o0x7fSQUSr0+YU 2qKBD3vooAt8HAV/FCOQmJ6JIZmPJCq7/5Xe3OBGlSpnGltlMCBErsutOcRcIAjfE1eRSYYf ul+EwThOQw41JHctAUGQnmyRVYHXKAOBIzABfuuzwAhs1BmLw9h656RNHW+kmD+OITsswkF8 s8ATgn+QYmgiKgGeNgtgPAWHYNrkV3UmPFNSeQ1XpWme2xczKGPRwJl2lgZe4CzKAtSoOgwx VdTmcCwAaNbN8In+kLqqIzF73/VrZxZYSTq0NmjSGJUweea9md4Dz7XXEAUsbQ9ttjks7prB aPh3ETwq6SU55apMbzHBFceGHy3LN8mpeppVe/gJo6zKXETjwvo3n73+MAMgR3BBeXWb6vmb Q8r7c7mRrd2ydY0rNU12Dt6N6wrXdJTSugzp016UojJn9oBn5JmbYhubZ+XAM8Kwk3XD0YKj RcOU+LW5vKNn47Hj+zZfvRnGS0wmo5Ax2ziQ+BGLJKF9JQswf80kDlLY+CPeDKbVgqsyKPcY CX9VLhdKKQMQZAJg7AMTlEnYvMFMkUWg2qeHXcC/JHUUyt8WhoUMACScpY1y0oUpUBIlz28Z aDjk9DdDKjxqREzKZu0Oc/HeiNpola/wIkWZoVTVL7/aOFeW1gELJhmImgyKXuQaRkqOvosx 1C+/xxWAW8PDqE5F+5wle/kfxnn5Rv6oBJYO6W+zDI3SI7RZ1EkhUltwyq1Axd82U5L67Fkz GUbkbhsZmnFRzX3yG/WzJIyRJIFQliBAaaNp+pjLmy0iQPUksiLYoGtIkI+KEaWkkZgPZCPI 6oxoyu0W3Hv1+7aWQxmG5wQRQAVSRoImSFOrRMivpRRJYkj3sUs37ny53ag8m88zKEvz3IW7 hlbRCUpvM2bdEiXP9wgZ1jp42YAGTIswzoO3jAUeIfkRKUy8PZ4nGX7Z1T1qNOJ7C67KTdsM Kai+IU05T98wkZIn0DKceeVLywmWK17TXjK7T/myDZfkud2kasV2YJXg426kQFGttMmodBtp TnMiLmfKpE3qpw+oUdCJ1jhDKtjGYdCU+fk42AQFgH5tpvlAANsB6NxCCwCTG6SHoiMxZeYx glWwAdg/s0CZfE1PM6UlkUtvms85ei1Ngwx1HM9TADkIFmdnpkpr+Ly1dNmODIrAUQOMsOQ/ 6rt8OV4C1u3zjdogWKyTz+1EyfNnm9HXvjN24suR0dqHcJjzxC0IQk50btQXeFELogND9WMq nr7nECods+aqt68HKC8J8lp/5CrPAIeDbdtr4GzDJItZiQpq4XllkWJm5yKblDkch3jw/z0U rXmGLCLwvB5fjN8Qc62V2v7+5XshC8Cvwd2WYvvU2bweDjmtehQkriBJliP0ERj3i8AoQMUN ctDwBCKO7BGX7HZNr3hOzviplXyGZTEUnK6za4TUVs8JE7gcvc+3gpDqDpoVaLhhZw1jUvHB LvGm+lWdOUKJrGKZCiVR8uwR9KGn32KK5Jbk7Wu9SJSP0lqEy+bDPOPjqrDXa8N+IGM89xiU 2Pl7qFXaZLth+RjmLbcZyvf9NBsrAz/lIhpcTrX1qee5i1Tj7B8JAvh1PeJF3/hWhiEomz0v HCCyYqntH1BOpwNpFkFwkra3PXM7AlmGvL6dC4GftMvKZ5a4MoKBBAZgi0VKCStlnRN5Gk9T vfYu5DlskEloXrZ2wbIPnh2pvq/SGnCupmCIAlqS7ehmzzsUXJXW/iOpvbso2szjyz1vZnMa pMGD5YUMO3PyI4eCrn+i5Pk7qwv9TFYbooqXvGvFgvvPhx5xP+droLxIOdXqF+RMHVtNymvi uMkiHY1bRmVhPoH7bZsNsJRNstKgaU5LnC+APpvWXuO5ZvDrTg27H/z9bnO9lTDMyA4jsCuX ju74MeVeAfWsYLaZaVx7iS5GUscicwLAei/hw1evv3376t6X95FMJZ8DlmcOaMYdPIVBBqXt /H+JMhpGMrOKmX/yz1VxIN+BVp67Ebj8yNrZbpCW4zG9yfMcZLfxlKdKC1NDc1YRI+ZsaDWn jlzphn0aR0JYfOrGXp7fDt6/73c1fvYqgPXom4CPI8+m6MwyJWRk/oAjVGzGaB8//488rOpd N8iRYIf1yhlrDmaXdFoL/O5iHs9BRDuey7UaEEme2w3tSvenZX1XMa4aKR+LteDjyAlArnN/ YL4ZVeuHYk0Z7ndxj89jMRm97iQ5XjBjmeolZdhJQa6grhtRE1RM8gzNYCjOESxBmxT7YoNu ieewizwIztAYxsHemzT4qVEsNEpv4Qag4riAHvIM/dPl+WkmAGHv7AC/m0HY0gyGmDaSwS5q yuLScOLyvMPld9xYU2rn7dYS7bvPy5pEq6O6e7Sm5wk1kl814MRqseiCaJZ7gawj5dd+Sto1 vaU6RTEePS4ulk7SgyZ51V9lI6Qk5wv1/u6ofZCf8rQJliR2Z5pFyY3YaJJ8IDbA5YqFZPU0 SZ+yEfOet5Wj+o9iIow0KW5ySm0IkXLZp74bAObzhCYZPShDw7EPOmxAKJJ3z+7qbMmjaJRT zjbFOqCc2Z9RP/i2mcXRJp/j9lp69aBdWulV0JJj5eJ7z4vpIB4jkwPfHMUxoswG4MbuVGBp sg1zCpaJyvP3tWKKr65+PYXwLjuuJ1TfuqUYbEQpE0WPwjC9i1GhuVFNhPK0jAcnLxNe+ZK6 P4DwjbseftybqwCBf6MdK158OtUiYvLBek2av4PfRdxvPo0m6Vb4idXVSlfiDTgm1y5IaZD+ 2SGmSttEJ8a9VebxxjZHltE8MgzXp6w/5P4MbASMaCUy1O35fRGwLMPzYr6wi01igUP24nqU g6eqYlgkv5PP5FAOmIR8RXOAMW37lWsCBs9ThAm1SK8XAhytCjCgSNtipmw32kmwM521aoaL mJM70Q4nKM83KhhSTqimlpMUNvuS5/nadJ0SeHJJcM7Z1H9+ea7R7m9luYlBoUkvM44L6jbR uY9qf92g3ZiVH9pxGGWtRmS2InXzMxHWZhNoU7r0ac1//E9cVxD5a620zJCx6o7mIpbizgyI Ve7RV9xGV0VXjhGRj9VLDTb1WX5sQRzztu1oRZkwomgz/xh5flEg2nBtxl0ggzrHcGOQNSe6 yBsNy16XKarMBghRXXFylcPhj7U1JMT47BvfmzHMXkkZsLoFzaShSDJL/+L0NfOoae6lKzMI MuNSeYZk+86XVvvlxuqWFb6bzovX4XrGTHRSwdtPUJ5z9AzPyJuOUgoRxetigNfLtr56SAkX v+ok/EHIr9HjyHMDFo0/yYQYNXeUT/3BagfE5XjVa9sFiKmY0z13kzTIiXJ/rcyruX4Bc3t5 RXv2vJHSm+BWzV/hdrY+NTXNpsTK3iC8Vsu9DqPeaEgjFJfS0MU9VVyRxiN0/N+XgcAnVdUy xBEcz7KxDzPdnNLn2Zp/AKhwPpaoqQBBzjGv8nI36Rqh83opgjM0SxBQQzltgIFGDBSXPrOu UIBTDBgrUjZkFfjqWz6J5WczdpvbZZytXKHAu9uaZtJbFqhHrvEnH58aE5Tn9fWlHFXmJe0X pwUqNpskWKYHtLaZNI8KOztBIaEn1IxprtHxmXg4vf661XvYR7W3fMHpsOOqvcTv7e7tGxnr 8ZYFLRs16T45gp52IL+0MqdkTzhEFZPaExRO7djX2VAas0vuY86gBT8uo0EIFG8l+ShW/f3p U4MPrly6eOGgAo3vV7pmnPB8naUX5y9vpw3apa+I09wzrzIjl6wXDgOC40ANjlkGv63AVS/Q woLbni+n2tMJguJYhgFa2huLGIamFUyqOWl1sg7vQS1vLe1amKlzaT4FYXE+5ks5QXkGCiI3 lOMl/YTXQLMeEkHQwRZvdu0+tNhvgocInaIqClaaocGwOPLXVWpMrDwvI1Kp/lgxfVmdave5 baGtVt5iD5RMbtzBKozAO5vEMBJHfByC1GbVM6Rz3pZq0W7RGaX4C6kUG9p2HT1x9Z7nzWKW 8L5BuvUlrSLLUnlWqOlyNyYqurN+RZV7bkWViGMcWb21gqPBWYKglddyrlG+Wc1MTT8D/vnY Ouo5D0X8z31Ls0ZH6I472XEqTFiePVXwiFJ2/r+asajPiAL3m3adFov2SSD9rFaXmOmhYSob UCVOMVlGcaAhmtjumHGxV2AY2BX+PfHH2k1bgXL5wLn1faFomMcIFoEHezQ2sjmES/NXC85x PIDmYxlsrC1vEa4Q+/XzeYbUdDhVJkT/v/9NwXLdBhT4gKml0LR8j4BRa3+cEvUs+GOUw97j fCM/3w1zrvZ5No5mVN/RxTAPbtRHCUF5Vzy1h7sPPVpaiJtnR+eaHJw1E5fnMkiyrSolA9OU WK/NODh4lI7Y6SyQn37rOgsFOBz50FlPTpGIlXAfq8mql55Ieb7kJEGcAqJA3IZTajGUgDAT ySMzRqMJ+DR/ayF4s8ixHFA3RuQtIvXxgAeZY80AswDka4dnj36qelK96YGyFDARUPJIM2Pr aJ+9Yi7HY6z65TMr6rCQ7oy1nldlDEWTqNjh7wKx31Lr+fzkzU/Pg0LW6ylwjILAgz5PLxtB QJlnDCsb3cOVbrpxcxjuQ+Ew2afOxOW5GB2B4rAi2onGk6sHJuhc2Ou0aTf46W7P8VToeZfQ Ec7kL6n+nfE+lZM/4+MlFIhx4iidv7NrCEIySMFiFi0sQ00PAcPwGMDXcxROg2spEbHbwWtR FDne4bByokiaLPPK5vhueiuM2SfevB5+dO9JP1ShEZoBAPOb7t5+/+12qZk0Sv45rw6s6+Bw HF39dFteyUwLQ7IiQ0GMJqVKwzVOWZ39oNtCmDRXtsdmAnjxlpi0FDm7EL+4u8CseVnAZdZt jL3zjCcG+SWzjd7z9mtM7w2iyIFAOFou6d2UXf752fsp1FrhLKmuCIYkUkJN/uP6W4yY6Wcl n1SST7pJTuRF0UJi4YKvruZ5gBljFXkyZBjJ8gVWvqylAaXbo0i2lwlCLmyZLhvYpPE5Q18u N1gV+5RceimUcLgyGIxF9IrCGpTlMG6EBLGotlLkGMNpz8PlLqOYDm7gZnDJe1SAMAxLUVYe daBLfRk8TLON6+cVkBmLCgya2eOdmQMH+kJYu4VvN3SMvyYXM5bGybti4vtzDuaN8h5GYO01 5SlAb3h+ZkNeFrp1WnCgPF2A1VS5vXfHquXBdCtFkktOsozlQFHC4qv6HBgLErJyosBi5nDl rpwC+zNQXTPjAGCeBG/+H85o4g6/ZALYOIdDsNgFXrrkAdwQxVgKyq/b3QwI4wCA9wRe7XMf rkPwlVvmuSAUoxmYtrkqtg59PePAFI+9rSiN8/jMTaVsf7nJbwHOt5pT/mEWPPM88QZt32Qk P+Bqk2aOXYdEF30fo0d94vJcrThYSOU9iXqX2Y7f93zP0mto5Z581E+9cI3FQ/tn5/k7d8Ro un9ANw14gvw9L9pArCLPCwT4v4AC1+awygYSBF2YBWLkUhai2epo4g4BDoWFZy2CRbTQkHxa 78f0MqKT50EOTAC3bdzqZlHB9363D5W00ksYCMZwEoElozL4iGHyo9yOEjxPHvH06WY3TB8V FfR4+Qa56/kG9WA5F5FcYDZrt8mfJVRC3esnLs8tBin0QCnFhi3qX7dpASg2ivTaNeONhfSz T50ixrE/5yVgf766/c75/UcHb12LAP8irIc6lpXmJAp/exkBBMdqtvASWCad0hLmpNykFOaM Re1WP+4wj62MGXjhWgSKZxHFKFL/v4qyZrXBXCIgeNuqA0s5o9PHYHIDB5GsKwVX05o2niJs ihn4lctEgvPkFSeMpGcz9z1vAbLBf4PQfZnFpaPouzmE7KL62Vam3IZ38WJCH56Jy3O1hrED ZrMuTdN87kOlo0uzUfu8A8nyW5idsDH0QuVN+jXx6xzaKOE0280oPQiIuxdCgROQ9iGpVdxL ZaIUhasoWgRxxDwLLtAiY1SiVcYvvQzFsrg5GrSZ8TuXarzJMtutLG53WETOoBin9kyTQuIA SJS+dyifkr3ullCGvpH+LhpbPcuhymHwzVwS1W4whzhj4ZElWQTGZDjd4KcemkkJ5lG+K50Q MvN5uFlxMdqCFfe98wyt5uzBLMrP9k+GA/PE5bnAIAPYyuUGZlF9nuYgkpZsD0yrh8N6k78v 40YMDr1aDi9IW3irqtUaltYoqrIFRWHECBswkUPAbegxnjHivxVWhzl8hA3C6nV0pUIibCe1 qPoP3qiKkQEI7DaeY4issPXQraLZjPkpT2JL10MzbUsXcd4MjgGE4uRwepqCYDJL9+qDs0D+ 80W6ThJRtVzDmTYCRESCsh2BKe2ZKSOAUwqAnRLYNMmJ+YmdTA2q+Hu1KlcgKr2H+G2ckbMR cFkw97CVok4Dt4jt9P17m7g8syblXSgXu1Ehej9hkDWDdhXK5TSPn/MbuAsfJ/7ZqQJYRzj5 uxm2aL2XZhPEzOPXHt58PPxaelYPpFLKJSzc8gPTR7qjh9u1T72vdr/AzCh6iLrJQoLhxXRr uo2laBWVPJy+ekFYJBna2SCcboLWeceRNjvPA/M1i6qbwO7/Ko5ihcibfqOiiHsmEjM05ybw shZp3KGYtfYhhHbl3eNgQII5RkIzMUo+9q/sqN4/7CAEofe3b991LcgT83htITE5CSgnLs8O LQhNmutsI1q3/tbNFbZUl3zAWgoxyx++ebIxC3P6a3HW4Ub/gKvRnHL5xSuFu+DXMnHEGh0Q AlCHsL6QZbtNYoQGQpyPsEG4s/KrxydAsaAQ0FpcCsCsKZanoJnhe4h5XtgF2g/sYcy0I3Yh 8e8hm2AZHLPkpPMml/LLkumKXcWKfl2aovw5BCC0RwKHvqYzRLmi0FnFcIiyqR5mcJKCRE7g SA6W1F3nedgSAzCY291mrPxA6Mc9qkchQKOJy3Ppf3z25yE3ZoIYxkCSSrj/p1wdxGc6ISx/ lH1jPQaFfvgJdWkimuiXmYwZhNQCy3cUZQsmWR+95bh/BOT4Hb4U7JOheS5OGD7JyvfD+QCh AwBxkRGFNebB0Fi/al9+RoP86NN+jcs2s3XusqUbytUj9UHN5yYTfd+Zek+uehYTjCOOjB8E GBVyy2rzGgpRE4ko9upaI0G6itoLMYxhkMIvnvcNJBROxNs4j0YrTdjCNAaM/5CNV2OsPP98 8/qt52M4yHRK3xeafI0MLzvttJBRskZF1vHcr3EhAGd6xWhn7FU4EZo0SxT2qtdzQH4jAEmV H5Vc7cPFYR+aTkLaCWHweN/mIGE/3x9evfr0nawe/TSLpOZ5WfF18L6v3eLNns0D4y1FmL+X 4eGvTZhdhl9tgwBAgghGDkEIu5xObwm77oQqnuWlnfZDn1VLW1KPf1unphPZCdNpEu6IWpZl kpDJ7HaXZpSXU5BDkvmvOQQuguP16xbcJlBw5uxckN8wrJC3kES/pFpuT4ZeRSFilDz/vNRm ExjKQYsFhYtUNMoIWfzqur+m5N2R47fGbsVrSc1tNkj3OZHrtx9nAy9gs1kUvZCREZG+hqB9 Z3wNFmXlyqM647TU6ZDsD3TgrNrj8075jLiWN6AsoiMkt8C1BM0gpGLJfNUopupMGcpr/6wb YNHpU/6rRIZPuGQlzJ9EIn1rHmmI+CGPky/UWFbO1lO1bel6r6tDmc5zFVVEcj/N6n11W++r jQuVI/DNbNroXL7rRCdNk7I55gFr5VkUAwGSlEncoL2WH5/bu0Vb/ogWcVLjEPzl+UqVBcHM BMVzLJpmsDWpcA4RkR9e5eX+4D5jG6WHvnIFGOVeOcODbHQ24GAsRhOiO5vA5z4dMR72GxRH 6UqYLWjKROS0cSUWRbf81pkqHUC6TXq6fL4D0RPg+HHWQXBEjaz1O5pndFbXCXpUPq3NBq95 wlFbEVFgbHAuFrrC43Ccaj0tlTMkT8nyoRQ1YTSF4svVA1oFAM0ozpUlcj+L+jPuDi1j3L1d WooTFKJHK7MBXKCM+fgCIHbzLDA+k5B7JiPKBqhPTRiMwaRbQXqbusVXnl/usBuF/O7DG3ov nL+6IcdJ6rkubyaQGE+hxzTihRKw6+xIYXVOZlK8TRRAShSQU9QexYWlCWcxhmK0iPpeo6xh P8sQy795bregKAg16QJeQ1JZmCrdBlaR3EKg0T5tZ8zSbr2VZKpl7ckhm8EBrvA7wUWsB3yc yYrcwtjxMd/fkB/jdRm/u6+TjhszPk1qjQUYRRIEI0VCz5G/qtVdupdvyAMK0o+VJlyBjPGW anKP59NiNwS5Ggt5qOjlg2zCIKuCgBKX5Sgsu9oMju+PKiUkji+NbFPv+fOHydxEGQvDZIKP PG/NoanKY96j/vD5jVWkUZgfn7P/OsTr9R2Y0qJI7VWFuODKzrA4MoH7v5V2B8AIHoclKzkL aTKZ9LuVeqsh+QbfjNuku/OtDADs6LlP5krfXaP00uHZZZwljzKTJiRxHUIIxXu90MjL2r8n uQgHNu45LKdYOmNTCsPynIzNWL9XL9+APzbYaHEyI0uvG5Ror/zHmo1CaWzr8nKDj5uEMq0b ZlNtEZSaarvquVM4HSxoO26UsAE/W21LM1hSOPpjNQxOYSeN4KQ4F1KwqObrR5wtQjHnioZv O9kcHJHnFRyVOTqEcZebhcrjItCLjBKgbYjC+oHnhsGWOoZzOMFhO88FkhxR47wtAvXXS1K2 zhM3NLfUpZB03n6QaRbn7ti+o9UMSXFwzUYJhWEbYh0GikACVd74czFKQiV6T1gVK44Nb1H6 78IwcONaTYthwPyFMUOlSrqPW0TYjaZgxesxv8y9dxKIvbTOhc1awxrkW/OVPKC9M2M6BIVS i6Xn+OGBA7v3aVqS2+kGCLJ1SvfbxxzwOunA4Wrw92uy52cxSwLlWT+WdQokneNuPrOopq63 BdT4ZH88OFNJL5qA4pXnJzDaPjiGgOcAW1aaY8zLgnFCaD8bIvUPuzqT5TOsDrvdYraaneGG //hMrBdjfU/pK+QUVNsJC8PgehiGEOlld4tpBf/fjEjOMitRQgEXW05w0kvgC29WFCwCpobA XhRAggXPbCqmMPHWiBULMV+8BHW4ec6Wfcs7Fu08dTGwd8ozM4keBMei+ZZsTM3u63ly6/rb S9fePj1xCIjtjxXEDBozojLyFyjXbViTqrxt0J/xtOBIGfj2LnbEU0rS2afPNtKQuafHjLQ3 mzTlx5I0ReMZtHw9UsNDZH1EBr0Y8tMrz/ez5gc0ne/LMUfgOxA2ZYv1GnJg4CY/IVfYfakV bzcwZl60WW1uwa2FhUTSx1Lc5WvzXwhJZ+v9BI+zudkF7tw5Eht+FknSVMlIgrySoBTO7GJk h/7XjKIQ/0DCCoCn5zOFAUIaRvJURUJOsLrpkSv+YzFswvt4P8/0jwnWzUhNTdEb/cKSNdL2 EwQi6b4+1FMQ7A3s8yG8KxVZ9Oz9wDxU85gogjXQlAPwHM8qDK8BtU+DPO41DAALc+UzJlsR hZE87s2Is88wKxQnhudlUHqmOTSUVjxZOb4/yVcfhMvYUbIIVtxqg5WfeGh5D9TubZ1otdht NpdTlafIyG1C5MuxVmr1ErDPDZ6qlPzNNIvbemTby7mw/Dz08+qh6jRrldTi70lBVpb8YlH1 3vKWY4Bz0lwqpjdeRyLxhiLjaCxqX+4orurp3X1gdRlsyuyxmDIr29ucaWwgMMlKjDOWrQOC /K0cRf1F6s365av6elCdcvHZME19389CNX31Q6bA002RGTtffJxtbNoLUk2L3MlrmekPPB0s wOklNRjeK3goXcirAlisnJeovVma2vjyHItFGdtHh7ElZMdvoCge2wcu3mYRWCE6S28l4QdB Wi1D072xYeAIN1IemsUsDB6UvnhK48phbQlrla3R6bJaDPyLS/ZoULbC/DBIdoPEUp7f8n5v nfisztTp9ZPdBDKs6HR6AD1AbX6cXSs5Ju3F9b5JLa7vOPvk+7efm4CbpsmcR9W8kiKZfY/k r1aIKSmpekTHKLqg9wyl2CXXpmqGzTtQnacbYVwmLFuAMYICuCWlnuFMcLvaKxDpGTO0O3cf Hurhet65PvaQYBEtRqLkuV2nWBSClddpkQM9AtGhAHYGVxOdTaGZcPqa/stNsk5tsRph8lDd cxsoTj6VgSIYlKihctotPx6NhKIfmw1ZZTP061xIqlCHjgCgRrQ2ASu/xUMfbCY+wpTq4SVH myWgfZBlneXmdguyBeG9g1RdrqVPl4RUmCvJy+cJlrbd9Ay321cXkIgsqB/Onjt5/u7KTBIo vSk7jVnUY1ZjqgJT8ArToj4PIfs8r0oRFiYxnBAAwiBHmrfMRVw7z8+h4Q3HoGaVK02YFyFv SvFJI2aUPH/+pjgvfo2LUtuHAx360NAyn9Eo/D0991pdHFsediSf/4p0kRw4hnmVCEv1hdLv L3hswe2vz/aK+kG5+imOFlTMihacXf/45eetLOmQj2P70WzZc/xhPpFx6MvbvS59hXQhb5Zz 08WqfDT7A5nHqt/49XN6la+DaoQhK09AJnYJFAVAolhoHlc1WW4CHjn2rjPh3Vsu7svGgC7b Babx0UUSJCz7hGfPSAOp03RGzEnhOXuyUGj3e/nB3qZlcM9SDzvvi6Tr3Y+9W3acPbCtd1E+ eHsAJyACQWCExIynPXWYcn4/LASLh44f/yLq2U+eny/Jyc7OW3j9+bJ8R9X83ng6Diz2vvGC kGuXpSni0mth+yJupDTYQVJ8faPbnZ8/SzrNdf+jKJ4adToqU0Cma+CQmYZsdRt/VmhEzYKb QVBSjpP7lQORsu/ULYcJc7kZoyAH2/WkxNL87EmP5uASJUti0qzq3wkEP3+wg4RKLIATJCiB IfSqO3UxhYxEma1KkR+VLbwgEnLuzZ1OjoEl18wfQkr+7p3Verb6TBXYP9pMBtoqx2Qc0q1U prY+TYmmbEn1gyHYXi4QBEESeoM+DcVT9nrumalVd+9fWsBXxUM5HBM2K534yfMsAKdEYJjo Qk0w8LJRczDFcLSRrhbrxrkfk1G6Qd2MOtL2TiaMojrYqNel/B+wyptp5Sz2pctphFBRslPJ 5WCtV9VyqpqCdBCZUzxX+WkFnKqTgzWPlVAmFLEokdh9+PpYstASqeNcLAePoq+bdpSM/lL5 zUVxgsCClOwALp/XHLPLWELOECmXtamyr8ceANtLGYGl8PMcDqD/SYL9ioaBtfgokvvVsy+l 0XMYJNAQZLS+1bC2hi3mXQ8urMpHRmEN3G+STui5uZ2HT/XQEvL0ThJFEQPvp0tRx/8wP6br GwWLfZr4yvMlHHL2bKigSAvJLDvYIEDm+Hmrrh2FPzRmFraMiU0sitaftvZ2li+Z09bR0XzL t/mH/uPngxwTT3eWLLk3UvfKHs062dtU3ah570d5/g88g5/WSdmfVwYJKIucrfUoow8DkjpY x8UAQ8EG/nMA5DIBUtyufuQCgJRBrcUyJXVvv8XG04ghoySDpHFUYZJFAsG5aCgDl+w0cOnJ phSc4V9ZI8aT5Rg63Tx3jIHpVyWNtct93HVI0Z7vyxh+9f4AgSU7mni9+jqPnDexb+Erz+dg EkQEvSmleV46juxi0DWxH1DtsRejQl/RLSPBqnEj4nfs+Jc9ltryoByI2Zt8kOUs+ARIdpNm h9NudthtZiunbqPbWXD87tFoX6CTdV9X2IZZwH0bR0kLW7hPCVwVJSyXc5LX1xbpPFgHSQ6c nm8dJlUDAj68Ov4k4Pt2h6Akw/qwiCXJls5KGnH6BsAqg7/sqzUZhFF+4Ql9qnzl+TjGScu4 l1YVuOXYODETE6B8A4ktOrw7BKCPkJTngOx9EkWGpwms04SbrqQEM6+ks4+q5OO8OzsnPTsv IyeTgbOkU/ZSFmRpnjezSL1ZrTLJu+szsf9nI0ODcBxnrmqdvsNL3j+PRcvZva5UoA3ZTGOz Xzw+Wpym7LygnFhQ2RlYYX3ZilvW9B/bmE/CpBFqW7VliTVvVGDZ63k8jM9cE/UFLyp+jNPI V553o/Kd9bMoeTWBMgdRHaDiMHAHgbCoKYSixOFNThKH0X/jLt8yUbimJ26+d+yU1WwmojFW KEQX4Wy6UxAyc/IKC2kELz13bhaGQhQ4eK92KrgmS3WK+5BzlWee2doxUxA1d6IFFLHy+s3l NIQDtRB4nhZAOC0YpyOakWpPHsyK7AzHmgCekQOsANRhpn/z9paQRtladZL8v37K7ecuvYGO ynMpjsvhK89LIAVdV4AVb9WDNOULPRJTKhbi1M5roXqnymI63h/T2T08tKFvik10PieIHMfo AymSwiI1nxWsNlbMcjmtVnCUNvE8RtCKcWQOfUr6p9WoiFmXpcJh4coceVKkulQWgAxUKEGT MA6ADBnh0OH0OYePbu89o90lPjnTFr/+8TAXgrN2nQAOI7KlVisbaOC3j6LOF55l5L+KYnMt RPpa3i7NXLtZCaibQsVXnhuU1Jm/KIPisXaZx310PbElulPG8w1ePo1kxYrtwL97byf1s3+j KfSLADHGxgnkCBRfZNS/tmEMCfRcvItmkZIFc/PsNg4ImqwXu0uSiw/2r8/G0nfektAnd/EU jrjmSx5kcnEzPFG3+uCRg11WDGchIUNGKxgpm03ZkppzJY1jaQZXQQaCcFqkhmfQjdNccTYK 7NMdiKAG3zZMUxBnpnDxlecaRQPwkUCU184uWvTyJtZTqDeG3ma+YVH4e8aayKnY301o1FM5 FYn00lSNA1Bsm2Dh6WidGQqnw0RBsaViUUsPUNL+3F2QTuOkCv/rMhkBZDpFkQxD1r3xDNhw vmNEafXaAsD/lQd4HsVQRN1ogJh8qE/6cQPwPYMqLg3d6GFSNVvgt1rgkpb//udiYu6RdJPm 4rl1mnZSn7JM95XnXJPsyPRAQBUj+yzcEZ3nZBizrU1tCVnrje53c4MKY9KxqPJWSbDye5SV BAcsxwJvFZVHa6Tc6JrZsH9g267tZ/1OuQGmdfWFD5zvwVYHa7UKRkUzVYBlrcjgODNLMbDO WpaNcL6W4NcMzakHzGaWgWVLlW+5jSiP2AraTLPy1e+CA1LP4nMJiiEy73sO21AOMmsk7jYo F9IpXHzl2Y07T3x4d6WTI+rfer7/uOvkXHGjvFmn3XICD/EmJZZOknGbxuR3/BT3DUWY/PEj GfFnBsFZWJYBqN0I5udTskeETDodlJKWNkMYgWwbr+8vy01wruDkyx28fBHMpnIWu4CbiY2h cMxkyOvxi3oHrmVEmnlB797dS4AzCjoG5PsKqpjF1whWRnX7rCSVDWwrguNceqYrxwx8BnGv EC9D8ZlbxyMysb/7ynMjzphzy7JFluZzqkqKSwnaGjc37kZ9aKeat4b6xDJmqo7+lo72Kjr5 M+pDQJZvgrA5aFRn9xPbdpThRIvNYuZxg4rvND5516vQaTVX5vLQvlsWyfpxxkwBDE7RnZlp EwkGNo9ygfkmIlY7RWCUTcBwaiwqxyCqmFdWc1lUnTJ6nepF2g4yaa65/OxALkqzPKbtakP5 QEEmX0mnbvGV56sZFPCjg1CQIICirSAnqCV+MjXfJDveBS0P9GVTl2mJpOyeaoRIJA3hjn2z 59SJoXOnbty4f7xfSS6jlTkUuFcDJBngI6LfFGZ39x0mKP2tZ7gB67qZBzI8D5cgJCVsuf35 5+tbiwkaVmXS29sjHD/5+sHdOy9+3SgxZWgBzD6DZStHwDbMAqlnniJaRrS4l87ydumQPWCh OYFA+qQv32/JBW7Qqt93mCRPfjU//+3jy9YvmF3qbNqwru/g6a3bOjYPx42gWbrRWGX+Qz3U xzSIIW7zmPSOL039O1wYPOmkLVablNwORFooPpzjlyUsjUixooOFZAZvIpprGJwlFUxlz+si Bh19QXtg8iKt7cMDWVPqRBnWt4ak1cxWP5yw9Nq5WweyvCNzJD+RBhSg/0Lswv5jXbkpxpxK ryPK+OQmpkai4p+rUgNCxniZcN/wW7lNTN7iDRh+n/N2cK6sYFlONIP/7Nbx5PnVvMZFc9dv 6G3kcURx7NhEAQWbuO7t/RKSVMHUXpcSmCq9X7bUckwLMBRfhb2aw7NoII/TXXAduC4PuzFS DWj/aiOk+A0nzPIMzhDAur2XBZCAN1tQXdq/aa4oMKAn77lQR0qUPM/SbQo516GUWIIATDpb 4zfgQOroc2X8xoppz28fPXz8+O0vz+0bQNY6URbETDHAlMUi40QnzZk+DVQ1Z3AIheuUTSAL 52HpPr6coQkgkCfunGsQgM1JiVXN/B9rbobRtWxnNeK9Lp5i8baO5qJaLUJGndhauODEUAcG 52huIhnolqsr7QgFNObupQSwmO4jaSlg7ta2xR37AziRxZRBYXQ2vjorUfLcaNgQkv4b8YEV DYNnU7zKFf3vqfg/k2ElYYyx5VFG/oanm5YSE7GsBaRaDq1Iees2FD78/v3roy6OIg1KJFM9 yeoADvavepCWZvW5Bt5uZliWMBANG18+K5hR8trzfnexkSJMWTdfynFxn1spU4opEyR9L/d3 kFpjwqwohmthR18zIQtIEEPgHI661tvxlXcaiZap8zwMLszOnVVfVJxdExyhzE+eny09/+jO 49d3Tt97/en7j7fhGxIin3OjfknIRoO638qtMXIGRNvioj4kwGRU3d6MqlVkjfL/wais1tba YsE4rQHolO0WniOBLYsLjD9xdvXyK9++vhr8sEpQ0RXv2ICVWbEY1+GUHhx+jzAsjRI8RmIs wooYamNSimYSLjmo8W05w9C2+posx9wNi8uBljsbNPjUYiDnH9hx/rYXx/XCvkXpJKWpcu6C /M8cZFuWQeAs42QxOpOmBiObZjxrL8RhHKNAPp/U4LENfvKcmYJyKGMVEdaRlV1gsbXHD4yh UrkKBS2PFN/TZBnNgYdkzP1JXkcBVq6Q1RQIHC8/IOJ8zrSO2/IGcacWQKvvFSxmB9CHAS8s w+IAi/xN+OdfzIbhcB5NIuogIHiCIM+Dym8yER0EQvNajRjHoTDGkyCmiiXcN3owhIRrnj06 /+KrZz2NZe++eG5m7bL+7U04KshH7Y9FxrR//0saRA3eD3x3wEI6exZtObJ30/YaiqJEvA1A I1AMzQLaCGgqHYYeLmuqyuIYjvc6rI3lnJ88F5lRggIaPQLHLRyDEob4xT8XjxPj/kb3m14T 4/0GuoFOHX/PtToowPY1xhFLZkluinbUvYCx3846ec4K7KIMzyk54EaVIZpsvHr7woWLgzuc qBo5e0okKFjs3n95DpLKS+k+y1GE40kcB0hfnIOHZ3lOCBSFsvmUgU9vKyFhrzO2p8aoDtJA YlTf202YcwTAYD2DQ7ABZnAE1iGwkC9dw+eTPC+yIAt0bgAEg3ivcMj+X6/KFJgQUNb+9+fL vbs6OSpj+aZ129ctsVNRZRLs23TCDxz58rHtl8eie+TrAgGej0zlXsqfoMaNw9LvnaaBwceh 88i6fO2AdDPDbZKh03yHH8Dsh4sCTskqbiBMgTyuHjCUFpZcA2tR+M0MwB8xpRJsTpUkjjed OA9cOoFTCfDozrYjCzynnCSJ04il+8iBNgwzqmBjoOoKw4BM5xYLbZJigm9VMWrA18BcmrWa CTzz8N3LB1tYeP3jDJDo5qyZtYJ4LpYMfSkMd+6xrPergeVDwPWP0YddYhAVHWYzw0RuPX9V a9QZubJj2hTOVSKYEcvaODo9XI4pdKjZs6kE4hLL9ZhoX/tSQzvKTrT/CNovIRkKCTeaX5yh 9XwDEr4/L8FRcEzOdMNGID4B9mfW68/VrkTlg3LQJtAoCrl6h6VPP2eieD44gVsZAsCSZIrQ Js+SHBbsv7SkMP9ZTiByNKVcmlJlETjnpDgpDhpgECFI086Lt9/s4I3Lrzw4XEsDX21w6S5g HnjWmM55DouUCBR2DDNrY7FoyV0tg8JNjfKomA51ch0jz92Y+ESh/D1Lhb7jBpjg6WKUtKUL MKfGeV+ymEhrTi4G5Yw6uGTqQ6ekeQJNIVCmqbGSChX9aO8UIedZOmkW4VFAekFpsxu1n57K iXgH7zx788vzdm9gqMCDJq+XeidplvV1H8+U4+B0jRIKioFnE4rA3e21RTaKohnShNIt6x0u XgSqL/lVsJkQR0KtFuhXfjy5dr4FoXiTbIketECplJViMZ3s83k3M82xGyjJ6kBajecOKMtB 8BkgtSEYzZjT3FkNpcX5sPi1b9fmRZ2z25cdHE8H/TKPD5lrcYw8lylpj6VipyNVpQ5ZjGT3 0PPTDTA2IHdRauDa+p+/WkBoHnVa34Vj4138HoUho+Xk0I93L14Cq9+HtzJA7tM3nu9D75/c Onf4iRQFo/jXv33/y/Pm5u2rN9+9HR6+Bd5EEkPevPv46sPwy89vv9x88fr1h3uvXw9e3r6t d/v+y/deD166fPD00M3zx/r3H96+9tC58xd3nhm4uXXtpiMnzx7d2LXx0Krth/eeHDy7Z9/R k6tXbjhy+tTJ2/cGTnZt2LKu99j5l+9vHhl6eL5/45pNJ4+fOLN/18DFWw9e3zz28O7ZY1tW rj+yo+/a7fvnL7149vjemzc/L186sX7TvptXTx/cfHj78v7Tg59ePRx8/ODa+QvnB4Fr0pt7 L7553vtg+D+46aeABAfUX2OMnp3G0I51flyM64dFNMgXBqsOWuON9J37R6vyAhl/mzg+kn60 g0KFQjf4j8dAWCSBYHKuApCn3YSQICpqeD4jAWVjNYfLKISnBBokyZBkdBuuZi+R6h6HTPQM 1gFwDQSdLO0bGST9yNX1HCzId8MNImpIxdedysZAPqteGKU4cybPOG04JOMKHnP+H+m+Hr+y Qm9IQ9A0SD89XRnk1rFt23afOHvs2MCFUzvO+ISg7cEtIhbihDZGnjshXqPbQobOYTF2fh2w gnj+2GmUg1IuMtBy6d9PbtrqD0ZSjoeGlHqOG2nObeMpR/qsTLcrKz8rnebsdsIsULiB5d1l OUJGYZEjS7RnFFhNJIwAtFYXR6e70925bpDQnaOtNos7CzLzjmIYRMCTBkQPmVCUwRBMB7PA 1QgxYCYdjGLWFBRCYZ0J0+tho4FE9CYS4a24ETKRhpQZBgSFKQrDU/U6XUoa5LBh0xEc1ukM BlOaDoaAtz4jiJgBJ0wkYUozpBh4AoMwliJQC2nBccyoS8Mx3Aib9AZYRwgiDVCdCYPRiJAW i4UghQzRllldmOnMzc0tcCAw67RZKtxmp8uRl5NhtRdYxfyK8vx0V25hTXtLQ0t543wmJVz/ yPg9fnLP92wgbEog1Ryb4w32gp+uVfmOjn/0Om7welbOES0YQQD4Y7AH4wSKG62zqhrA8S8b g+V8YXesJGGlKl56WijExtpIhEMNPY+OFOixkZfjDbD+7S+flWEMp5PUde9K0RRJrV5F8KDS 1zmYiwcgJTyF6Oufe95lEmDDp8u2X1tEqrD9/TrzeBOc0O+LSQItmNfclU5j8sX0PUcCKxwN 9NI4y5r01pHI7fMEC2LVgr8Qx8gz0D6qt4XPZibCo93XTCRfcWGZb8IkGjoM6vtmOYf6myVy dOMEqhyqL87Iy55VVZ9dVdW8pKO+pqo4v7Jm3ooNK6orG+b1tJa01eTNzC6b2VRVv2rx8sZZ a1rmLy+x5cxqrK90VXfP78prWd62ZX3rtt6t53tmt3bXVpSXt6zrXbp0zqJZeY1dKxbNWzl/ zYEdB3ceObJ07/ZNh3r371o2b/X2nQeXd+w9fOrSmQ0r1qxZvmXDzu0Ht29cvnLVmhW7Dxza sHjhmrW7tm5dsXDR+q29vb3bd2zds3v3lh1bNu3as33p2qUbdhzctvfQmnmdnfWNre0r6qtr 5zV2r2pbvGLxnCUtzd2L53Y2NM9ft6S9u6ljydy2rq7W/CKnyGXYhdKKlpp8s8XqyMqvKMot ryyta23vac4szCty5uSWZLqtBTmsw51nzykpzXbXjnckm9CTFX7jlYxgFi1mNLxkYT/sOi1g cdAgv+NDloMj+3OrwyaKHJmeK6m3aScKCTl5ttStnpk0kfUTRFlcBfdnJ8jPPOAkCKfZDOC6 KaMBM1sRNfZRGuc0yJoDTFQNhMVskFQ7CxlSJ+nbmmgcXLLXEo2D3USb56qZwpDCs49KWIJy SaegrQzGKGQ6IPVUMB7h0f3eAULEJUvcBhA7JvXwBYJpDiS2JHkMFwkT56Ok2N1WaaFnnAk2 zlj/sCK4TI6FedSG2SO0Pz8gCPVccgaBpftxKaQ68w1njkrilKv9Eh0DgrSaAi55Ec7nK7Cj hkN1gPCgCEeKcfVLNornQU4p1BICpNVnzAxD542H3949fHGi7J/Qpg1ZAAmvu28lSrEcZu+6 tJLFoZ6bLscguO/SZR9qKEI+A57nSIomnQcbgVoM/IHUdFXgxqxD1/NGMmh4tjEkMvv4Op6k WR14Qt8Da6yMRNIOS9FqpenvPSeZHo8nT2QQPe6iSGOpLL7dBJWmaOUbUyI9qkbE7naBlu/x r6wwIAOUrd27draVV3UfOHTkxPa1o9zB1oi6oFHYY+X5NIO7F/duXZuLoZEiVT1DkU3KPG5j FDCrvBcYTQ1WiwK8VJ9SoHkJRDTvZOXEcOBy95hLeyNMCyCTKnDCDM9+Vm9AMYwWKAHcawIl e/Wf2HFUQs6WSy1F0qSp+5vnazEGNtdmyRr9mi8ftCF65PCL4deLSApHEXO9GcOgzPmlwjXP q3ISeMgU+gTotcFAAQ4yuQs4KYHrX2M5Xn62t4N70bOnIvDQ2YRV/TonkLjFTaMkaldUabNJ croCWLIrZfw7/wSWpoMzKshkZXgYKqtPWfqgPggB/Ldn2QmBw0mEnjM+30fNIQtRXwG9pKQb v0hkaBU2kho0k/KNG5nyoWcTWJ8/rGkfqYdHKYTuCBTIEGfLNAsEOn6UAOBHf6boNKNkaXN2 URief1dZdNGtoZtvPU8OufD0bjMvaV9qYHjx1Szndc/7NUjNPTOKGo0QRhggjMTnHLq6wGW0 XvZcoLd5PpawV3+uy/JxU8sBKTQe9R84eWcJD9OHBmowlpPl5xiJwxdPwfV397kwusTl4tEl L3bbKFyO7Xtz1E4wiqHLcxIK5MUWs2Vewar5MmdiisRc2bax79DApWvPhu8/7huxu6kD1huC uq0FkOe351ps6VxO+Qqvl2vYdHcZOflk/6YQlQwO/ZBVi1w5T/oveyZm23/lxqlLU83/Juyp /kUVPxUSHK5kivGWo0AJDAHdDI5DqUFyAY3m0LMvr47e+OEJS/o/W43/6FP+xR08BZmqX9bI u3U9hvNZCJxeWyjCmXtpatvuNauWbd48jzPJ6d07TdIhosR+bhlj3dxK+CJjF04bUKlZANRM NoJMt+RI14RBG4Pt3UURjgyKp/Tlg7UksJAsxgnrA8+hUpeI8oLxltxwKRa1T2ywB+XH1ZFf 1jGGQflTMS47zzxwgDRyGEoRQqbbzcKjrWXLkKDJJgLHV334CBgfRflcYLS1X3i8s57nYKAn OcVQD9Ve3loRPwCnDJKy2FgD4VYB2KIYLNlkcjjwqpECcVAmv+TYIHNq/8rGZrOeKCpqiQcZ fbu6N29euXfvzgX2tHkgs4O0P5fjUpJEq1nAEZxOh8u0cVcYZWP1A0yS5xuZHAbBot3vRLp6 hoJwCU7YJMgFjVDZZqgAKKB+FtKoIx1jSVemg+beeNokd7c7YOe3OfTIgiVmACgoO2OcN8l2 q5iWdxLEsFq6cVxRVRUpKWluuEAaPoK247wVg2GjzcemKf3ckhr0cBvjeMlzwIGW5HGaFfFd 4JSCwJrT7n0z6qvSeWnDHVtO7lw2EC2Ma0xZ+xd2Fr6SfLCI4kH8MY+XjdWOPmwNqmiNGU/X pC7xzCFAjqm7meAmDRy+zSKIaASmq/xhdYxVKrZkFdP78PuDWhTj0yXnEJ/yhFW90H7NpQgK q9lYzBFGfd2RLSKOYiAM2zDvyUaBe3lHlLPGVtI0DYGzu2e+wENzP3g+77Kj8QXO76RQhdoS SrHn3xs6//T64x+P7j8+ffrEHsXnZhOwjSulLDWoM1aM5dnTwRAEY3FznAAk9S5Fae4/O0nC 93H45MbHN0LG7JFIduTlwM2OCJlxLA/nQKiyRWRwBT5vsstZQ8P3Rh4oqDoYBKdojGzMwggS xxA0vXnhojkLe2otqX0yUZ8aU8T0dApvHei/O4rKHEy+L99uBjYgqPqr55SbxtP0IAuOYJm9 qaPIeMNzwowUOfSg3UA7R/A84X7wfk8Ggpn0luocLEvOeBe/0oFjSucuNqh/zhEIUX3Ot1NU 0FOtV55vBlOB7wicryvY5I6tXrDh4mmXDPDyzooq1w+Ppxrzup1JH38VwRHatuPHzL+q50ur IpvuMYYQOTPP8haRpBKCJn8bZttdhvbLG9IpIMck5HzXD+5qGMzUlBWymBE4gBb1aHMqhzGS IWYuyRHK9r/xnehilGnunj/TBjxTEFiyQJ+pYmEc4pb2S84WfWz1/kYK1udsfn1vAYaASBGW 1NMOZmZbEY4b2Yw1Yd35I2OsX+2lOKR8dhJB5fmJGeJaevbuW1pjxoNjdXnleXFKp3Y29h1q uDsqoM0cvazfr09VtRJvHZjfFf5X9jjxGBNgTrJpDDlQjQM/O4toFgSLSJFx3qYC0n1FBwFn TIqDQCwFY6YY2/NrIptHK47Xn4c+v/W5x7WhtNXMEXq+Lg9DmuTn+fjGjhU778zC+ZxSGHgD cgxuWijtbp9356LwJnXE0xWkXYD4O57jdqK2UuB5VsBaDgNRH7LD+efHQ/yfOLtX61W/lWJD cBtxGckgMEoCRVkIO/LI/kwQGWO3zENVnANchMMuqqrznmiQ1aEbFO93EBpDsAf8OsmHfgd0 tbDn/adWHBI4m0UE4AMcx5t5IjRGVHyYcMWoBxEX6QRioQmURghdbRUKs6hB8qcaVZbjFrBn 86iku37elAaS3dxrARAmiF7ADTl7D5ZSOEFLOa7ssoPGvWxkBJjl1u21wizPxXxs89fddhwn GfXBLYBirtgOwKZTonr7vLg0uAvXzfosAjguo2xdiOPCyP355ELCWDbsN9jxZhitUKOtwlqs w1yhdGl/OReRM315XliUlLBPi6Ay/w5yDH1h9ZislFgOzLPwZuAOzwq8maMzNIeEgBnQ40Xo gS23n74Y2tRYIoisGc0omcnpIRtB+6iHlZG/zYIKB76fyBAwgC0G1NSZqQWNZm7H0we7RZTC NoGQKxzkjAQvJsooyk/nTMpvHksqV+ajeTfPOF3VNpK2Kp1WIxH6SEbHBT/bT9AuXm7YfHBT l3qLDlzLVx+2MhfJXe+NXH61o5wlhJ6Irg5PzanC3MWzsnBIVcBVGNGylXt7CmFmlP+r25CI s1t0zP6rW7WyjGi1iDZRIG3e6P5Ijmyx4t63rRkUbSsAZ+U2dPmNLDkWw68s+FcKCXjr4Bix 90bfqRVuDDekyfe9mSDdDfhjJVJYwgM3GIZOkR7Pd45RvlgLGYzC00XLdc9qFk+TPeLeWeIb iBEr5nj78dNvv+1yAKSh0oLuZSvqKiwYXboz0hfxISuEI3qUylPtzueyIAgB0Wx1o0/XhUl5 jvlaxqfD5SIrChy4PTvmhreLxIcMYATmgYNFxS/Pu3L9uff2MRHJnx24dHx4Z2FQBMK5IqA8 4xWooo8uXNSD+vVIbSsHztIUi0huD/OJ0ca2Nhg1FW4CirQBmlQCfBfrEnHFmAAHR9mrbi2v Am6uQAZNjK3Ta++KoP9DDTmMc/MVr/nv+aJcnihaNcbVLEPXH0GvyaoJ5EAxKgCwDsalmSom l5T7vd4LX5WJgHW5tZmYLrOCG4ODcAWTQzheMiB8gxB33JzNkhwsa/OHBW6hg/7xORdmQNAS wYokaTDPyUqRwzl8yxs3rlybvxQQsPX+p33lIZA0J5cN4Y421v58ZFkmSMfXcinaiJ7X10dw ISQqnuzePvbM/tFJhAtUE+5MkvVCcEBz04uGSRdcLDijKrnBJ72crjYWaKJ7MsOFgJhzCAGQ ld7wKy9FA7Cc5eJHBvCAIPKA83YdzahAmFX05gLj6xdOjBEpfPHpgRKgIxZLAuD4t2thXTtJ 1FRWwVsWhO93E4A1Ph6dk8W4QP4kj48cGVH8xYeQH3bKH98gPsMke40FB/aDJMj2fP+EcrHo d/w+3rRyBIayLVrNx7vXb27nEYxh7WNk5R4A2pVKDsXxLFm081IxwxoVtfFGvIA2ff6ZS7AM KsGTnGEc6wOCCi8huY3XAXKMZx9madx+6t4ELxh9488w1jVi7R8WJn0/7OQY/WSYTZPVJp0D i03EwklR8/rP7O7SXIxkacqo8zO4LqcRxG0WF44O5bEpEblAnjkSRTEbOHcjipb6mQUAFXk8 8ygOdcgaITGI/3M3DfxIXR37+92EVy886dye0ICJkmcrosK6TYj6ZOPJ4cDSSmk7i+MOfa0l W0h3N+9YvWqupkS/nIFDJggBwRUkkua7063ATMU3nl0oY7L9AwnL9McBkT+yOZagKTwNRhBw 8lYcIoo5nHr1A0QJKaasPVRgxNsLGQRLUYiJYOIb7hzHRUuQPL+04oG80eI40WTXU5kD+f9O p83kv//+m5b6PyoMfksaZuLaF2xe0lgvpPmC+pamtsnBf1tmIMO+c9qN5Q2CmD6Amo2BNOyW XDvOsOTam9ce3dopMiZ9XTXA5sZrPe+ONjKBAfW+5+ELt7gQhCQJ44hr69WjY1RvoTmphQgn hN8Jkuc7PJo8bydkwafkoB9shqJbH573NjfOLdCrqq5imKKVmIK7GbDq3SF/LODUk3aFmrZZ m9Jymuycl01yVOXegVtPv50DCThEi40ScDR3SanFTDusDJyWa8XFIJBne+BGz0M3wTktBKT6 X77c3khMJ1qkK/XvURIkz5coMhkp+Xs8IZNB5VsHpblvHiNUD45cmlKB9D9ZMGrk+n4TK1NJ 2qH3i/EBppQmdyFJiPwmucILO2fOYCgjalM02eDo/Xz3opaeU8Gks5K47Wm1r7n56ZATTTsh NbnlMrkqaphp9j2TwYVYjJEgeb5MkFMo50AsGDkV+9gcJnJIwml/RGOa59Igq+Zld6KIKs8f LTA9QuKhGRLogFQ+MIbRZ9uXX7s5BhEkU/m9Dg5Dq9afPRW2raYE37gUlTVlPTQkAdM+zUyb DUa4V2/8b3gopglnpCdB8nyHpAcTP/k/nQJln/oNyj3W61Z52FimEJxNUTbFcWGY06syLn06 mtKqzSjbMnZuZ3jCAQvlPQ0WVMdEhoJfbGBpqFx6B67kKAKAkG4w5Sh35xY9HDn4VkL4niB5 fkDRE3FxSAir/tJBR2HdxIULT0kZge+k+MJzyKiKax3JoopD5kNR55MX7mJauUrDJ3MAhIWv udTJGyt5A2SEQIa6cMq19d3lBZuAGJfjJEi0ULj1i6cDoY0gS9RMRD51g3O6698RSPtw+kxY nQTJ8z0C/U0YlLCV+ZsGfmiSs8R1pd7yHNGpURJ5abheyby2lYJ97EsPYafKmhWpgRyw6jGw U9wqh4xwu5xrYryyiJhmdULT8o6ecXECQdQUQe42lum0Znx/NhKM0Tajb7x+psbvQeX5bF88 Az/vs3jS33NqPAFTgYqXiBdv7zxSplCUn7poLt66/+iaIoAz5EtkBSnH6r1ZQ6o1/Wdw2Z1z 4PXtLogNDzG7CZYy0X0EmLYsgG6Q0lquRQlknqfMOLNS7722z5ruD286FZgWkIax8jxUAAwG T2czmCknMqShSOb4kIZHQzxF0jxZd/I58Kglo7iuuLm+trZawX6PYbnPqqovgNkOFyod1+jv eFbQ//yjM+Gwaj1SfhhKx8vbSrOQNHtgF4YPFdP0qa72wbDoOwOR6oNYCrLQAVySY5/W8RRa e8oB0ZwJlTxUpFIF/SbXwzHy/LEqNeObpxaHCARqCYsl0VS6hqHjJz2JpuNkm3hxYJEJ5OAz QIguNRUOmm4lysFfWLz782aTGgk5Uw/SXB22c0XtGUrOZm/5tCk/NUXI3u4DKf39sW/WnbMr FoTrrbTTkKf2u5PkRZanXMUgQTxKOdjqHVuscLYaUlnin90lymlOQrMx8rwCEg549qHWqhVu hArotB4Lqs4TTGLQImNB/N/ZRwOcvfHu00dDe3pK9NoNNlacuCfAXUPvvjw+P7QvW69mimrQ sT29nQJdtzKb98XGl8e85mOEerp3UY5NZDNzqxzs3rApen7uomSHbtetVZtcIQFEGsj/iVMA w4TApbjnXh4mN4F/7zeTvws61hh5zoWaQPotCHjE7cPknHJxKYcBknLLVo2VcRnir+00Tgrp GpN2zP5kVbOGqiz+efvJw/een2+Go2X557eOf/87LcWg/+/0GakGdZj2aSm6FJPOCENIesi7 WYEBQgkCxQDaX5rqKzpCyIu+VQ0LDo0JJrlYmvrPNPK0543bpOnUjqIgrTBDkTxIyUUiilJu BZqGnPH0O9OATPweZYw8Z0uZemoM4DT8gkLXxGsSfSTDCdU5yTN3HBgcEz3m7jGhguVG1XgD ko/5ul96TjgJFuUyRZ52lEU7nQcNrfPrGlorqmdWa4qnL0u3nD5xuu/siR2HA6PkvFFeXAMk axZ4hsAAiJ+hdxQBz1wzjEYyjRiVobZXwPPWz8PMs8qw1LNqk7UEjwpZFgIkhBfQAvUN0Enp zUUmhwyzP1IAgslULWPkuUiy2uWlXQIE0/DqeJG9E2c6BuPVebLfeHCg0OhdsEw/fXM7jNMo RhIkjRpCXtDur16yfvvZY/tunz404XQTnxflwkjRamA1XoEzNEmlu4pnWSnj6LyLTSm5hx48 35+R0ufLkmsMDw7mx2wEReBe3OganLYeGL5VT9O8BdPCMb6VwWTRgrhdO2O/TmPkeU5avmeA M4H7yVcU88fYjeHoe0h81Dsvhp0nu4oHB6pJLV7yl4XzHaCVAiloWADRbaaRkFrgIqNe948u 7X9NUNp/J4go+GKmLlUsaiHNi3ozaRZh598G/lsHGHQU0Hwf6ZDDfi6yKvyuQnf99E0gD3wu LdAgYboEcADKOaecEtVzRKAZbiSbZAtcFSmGXjyYH3afY+T5AUvMLEGyAXpiH2SN28FiN4RG g04W9rSSFWPOgXpcS7JyjUR8e+9gBbNZEG0CQLIPFTT3FkZKZ9V0LlzVXUenlHsmElb4vSyF mg+OxEMLaRihEYuS8PyDA/a3pN21KmEVHs/CNB/Xz8Oo+Av4ZuO7Xq4XKAyXvciOWsARw3Xm o2efwLAs7D2fl6d5jxLhqsxjzvlIOhxrf24GFxESzGcvk9YSSU8R1b3CsgMRNUhWTjQHZuPD KgnHMaMvMUsYMy9wvCgCeQ6FGfiV1gIdPSvgAs9ENMZvOViNxzrE44RXbZtl8lfILANnTaVc w4mRLBerU0H65M4UAOD5wo5VFuiyN23ZkEkQ4tJqPVKQTfBWkdZCPh6ZLV6r2MEJog9NygKO led+C1G0HtxL5s8oiF9y5uusNZkodlIWOGaDNFIarONFmvLtdSXN84IAdmiLiGhRj4FGfYd5 wyfOoZrAREfeEMtrDfNILksF7XvnMigbtVY2QZu0P9P/M3IJXpyy1PMukwQOio8E8/0zLIxZ BZLny0AARl1uVSbNczSjOJJ8nAPFTSUc3czHaxXA3/PmPjlA9GJ7/NzDPP1G6xg89PFITf6e UA7MhDSP+yOYn7/WfIbjOQ5ggPIMNCprgh/B91GvWvwU5HefjXheV1hy/1nlwN5A0pwaentT NPhf4g7ourxin6bl9gCKMONazxUyG/z0wQz8WDKQkmvnnBxFSYaBXz+3WFhKwGjpw6mZmOM3 C+tNUDzGEUL8fTAfIn7cEtGgNtJEsJES2WTKO3Px4j3PzzvtkOZTJfcxk5DS1dksAseiXpNW gN6vmLy5E/v0/hbsSGnZSxG6/yqyWkuyph6l/SrB4I8Ldgp23VZ++mBXvUilD3ewVs8RStKD HUfBxbKBW+4ZsIIUsfY+8NXXeSzTcnglL9bNKWF0JQMHfi+cuwTJ8zHM8ZsElEb6qCWq/sl4 q2GXpqSgIGjJKSA6vSpAylyzgAuGyDrMPE+HTN52V0dqzDkKFUyET8MzKdQ0WzE/l1IcTN2X xZnFUfkPbznLoLbqfdLHFWm+uW6rmANtCPAhfegQAddKULEyL91OkTCR112XSxkk2l6szucy 2hf/9LwKGw5hIjOKWdsA8vxcuid9OBoABT9mo/7/7L2FX1tL1z/6t937u+/vfZ5zWiS23Xc8 Ibi7uxVKqUDdW+reUqPeUqEu1L2l7m3ubAtJiOxAIBQy53MKIaNrZo0s+S73fi0/Eflm9DoS r0ktBd7Pr6uvXb5w3oKu7u0+b6UcAkiEGZOFMzN8KBnwM4MJ3GdFyJQL+gkYXB0pMaIE5ZTO gz0swdB6prShKoXAScInlMOzRpRE9LqUpfuXM2ZvYdbLfIZEqG370qhBEDg2lUKQPff3p/E0 RcEGBDbJBjkTgtJXS9Vo5xvLz9c54F91tojRZnuMbaLPelt08ft2tKcyVvW1AX4G0JkmcN3m Q0FhPsKylS4OjzH8UN/51br/W9BhRawCu33qQbWoAUYMCXOT9ASh94IxAZdpi4FJoSEcAjE1 kv3CUHWziAE8vMHZ/XUtTRDCW2WlEQNyAAbnA8TNUN+7WOccy88lmlL3xxQEQJySh+XeRT+U xTaDQ5WzeazJE28/PAUuWmgrX1hEIxDrZ1bpW/Yl6ZFvP0bHH+bNagLeGadZTWFVTR5n4LtW thfn263pPOwwG33UVc5/2x/+2psHIlaRSKv/MA6WWiks6/aHrS6GgTPF2E69Fhjc3bP/Gui/ gDMzhp+vw+wl92EdvXYJg4ogMJOSDmknr+5J6fBMrnSikoynD76MAFTd47vB7TVEuo8q5h7f eufMHzdBh8WDYEc2Rf5LNZ2XtMPgFv/xntvXEeUFiQl37E9belJRh5drpdLwm5VOzEwm5R5a T6ZK1tqvWnmCjE3UvXFTw7/gGH5ebACqhBUwkOY3EbQsHYxaa56KBpCJKSCj36N4jZNIgT2C UOouAiywbJn5Zs6Ka8YB9/H70p5+rz5++PMwJHzpI7xFzn2YMgbgZxCKct+aheA0vo665K+/ VKBilNi/OI3hZycKyNCOlLrdh2lk0pTpx7QTU0D+xRSfjV0vEFBwv2SzIFBrMghiw2aO8WsM R5UfLzdwif/80+YJHhuugHsYVVAXzlNUKNOuG7jHnbsAnzSPhbD9jU6GMfzMo0A4kK8HnlWX iHEYx2xoKippqVw0avj94Ni6XoCY6JeO6D0AM9EZx2ytxRuWY/rSQLK1/HbrzoOBKzeG7o34 3fCfHtu2cdfRqyeuDx0K7Mp0pcnJJpHVVRnJaH5BaelWNbGML+jk0/z3QhJTJEGBSPTIxspi 8ROwZWxo4+lL1UA9G8PPpXrHxX08Dyi2AicjdqXtArJGCNEmWeUN+HIJpdfpYVOVn+PNLr09 bh/2d62Ucfc2fPgnlyFxbqIW1mmT5mBjw6b8OVpBaHVQwwjoQRNKQHO1LAx89MOlYVgEBPyw owjFtEwI6+KPzkQx7Lv7Y2nS5nC1Tvfvx/DzEEuYrVQFkDFkohJcagTpNAcZU3LnNadhGjGg wIhLx+a3L+rEkwt9a9luoH2jvkfQSDxrWAqMKy7GwvyGtUfPDJw4ssXXCjpsaxPN8IaEy3cP Xb93dmUGpfEPFfegi9IzJoblRDzYSpSl4d0v69S4CjlgYNp1PBsyGChWkxZ8TFcdKNn4zv15 rdn117NzgPgYTSDkLgq8y3ZDuALdoHrC6mHzwafg4H1VjbqEQstJ1zawM/7uQA2+Wr0NBvZv 8FZRPfBplfHnImNqXk5VQ2dr86JRr6JwXVyOaA16DNXDhrlsuLwqv/eBx759aGDw7puREYC0 61f8OmySz/A72ZDN9wWxlNOln3h6zEnjC8FQzvG8CwNQmx+s/wm/NtfomXktwBIb12Zu3ULS QUxdnp2soThMa8pMSbb/5bJtgaxj9c9PXSkcDexgKxCfKNqqptAFyeCMG2CBn39mQFJggx+Z cJpPBZsgv1BiqqqPZ1JFgVXaZNoMo7yJpv6R5yNAud8CBI1XqqGMZp6mWZpjfdwtVDUZOJN0 jZXSRTQxQYfAyVoT468+OgUr+J0P8mhdprfhap0+bS14ql1LIw0Gvm2JAzky7ELA7WPjv3IU yhC9u+4iSRLnGawBXAU3JhefuHZko78lxdtuG9jBcEKHl3V2hH8YTIAWU1Q0gL3nsTXLBd/U c+MQ9dUJYIJCajMIAKxXKEJeNJtJD7qF+P0J6fyOp8mgQKc258avh+A4e36MwgJqagK12s6Y rDaryWQx0vQkSIW2Juvyuno29CxcVqD/94xPB05o5NfYcAGB6eYgmR6vit+1ko1iD4ubl2/K Bi/nV+7yucCT55vRYwsejIK3awGgGGM2srxwlP/KhJP+k5CEEjXeN5aH+Vq8eGlPJoao0589 f/D03tC4HjOTMc+B6oyuP8YhnF8HdNY3l5nhLtDaSkjxarlupnxc6U4Qfwug8VRNRBTbqYc9 W7Fdqxrts56x2G1Wo9lqIulJsN3brFXWwie7wRdW97hOstP8mWegEWLt/jTSD4vqNPC0EACK qlBN77kMTJCklurCxA/fmcLN29FMshxD5B548WsoFUeKT19ZR/7j9Dqjyw3zhcv/0yZC1eE1 DyMYBs3cMXm4ABNeBdHlZ7cd3HDSXQzATxW2YLteMet7bfKgvYpdPoLb4/Kw8U9eaJ+fdr3H ZjFVo/oBXcUBbjZZrFYLy6jeBNQPYW2SxyIwz8+a+jQq4ZGt1vM8BjTVZzCNrx9ziYGGBIvN wxYC50hEEG3VyK6RQ929+wJpr24TycD49I2D5UgsGTIyACO/QFhxx83JYkRYEAUWfDC0SL+P OKlQnp7yKJfgsNXCWSEd5S8BUE+Gyc4ZkJ+fbNt3YnxdLuVMNAlTDC16yBpRj9SixDemwl4i AujzyabBTKu/QSf6CAoplVA9uAXgvm1x2ExmE+cD96e6AjnjSGAV8vYEz4t3/lxf2L5TuGRc VErtbhHRvHL+8cHpfEUZMjEc2GQuN7IA1wgTAPfzIcF2cXch9p//mWP09oWUe7FSgBMCwNM0 x8H2QzutAGVMMo7qwUedtxdrFHCwBl3qcLiBbicI4+Ezx6+00Frh8jk9UwB+PlLqNMCotX0c KKVXHQAMOSvVjKNWAa7ABHnA1NoNPlO0iWCtFfOqSmvUaAjOd3nWZ3giXhn0yRNolvyQFE57 Ffgk3eK8hv4XwiKVGTyHTWbYZ6Zn8G0swCSwAVQ/E+sDrx2e5L459gQOwnDk3xIl37IEb0EZ EJVh4lX8ApnzdgVGr2m/sD7ZJ8DNeW3Jp52Q6/hBC8HSFIvC8/vXoW2gxCod1nF8Vyc7p3CM WU0pIgrXu3iOJ8EbfA3LoVKQjYt2wjO7+XNkyJXTuQxsXzoGdd9nYC9clClTWA0/6zHzJAgY IqVz4Pxj+bmfgmjMRsFJvt4qqpprhsna4w8e7iknkjsFfjZ4ImDM0/lsohtxY1ZVa5aRVeGW 0ctotCojoN3vcOq1eds8/p3fqpGxe3evyccNqO2f0TAdr226raDfaxI9aJBd5BQrY1XROXSm ulFYvBxPnHS5yPeglCzAzRYTYzEB2CCP/WMUOqNUcWaOR13Smugbv2YQ5cGL9EsZjOU48GRN 5acHulTvlq8j4Dzcx84xIARNExSDgkhaSWBTHjSQYkXXiuYW+/c0DREfuauAa7agfF3NENA5 4Q9X69lkD+JI3j8XxXL7TRROFXFijJugaStpNJJdwq3gPGOQyk3DNIaff2QaLMu23RpYbtNb Ig3peoaEukTTzh9legIIMLINHvP2VoOPBHELwp0A2d6+Cf+8Ww9hfNFYq6FAtDyehwKfdgIp UjQPyzFszIZxjvVdLbmaUVPydSgMDNfdhVpgTyOl4qTItXaxnuZmneeIzFKvempljeDtbLeY LRSAs416uvqv55LaPFeSWitpGE8qXLu5gqBojoZ4IAt7DvlISx+R1kNv3QcsADafJVGOJSkG F1wvm/6V72032WQFG1yu85sFYCeAtIthaa1r/TwnkOuIxo69mJempTURLEJwG8tkGaT3z700 rQduLMDwK80pFpp31t5yv83UT5pjw0TpPoafd0GUJE25ZI3YZ6JLa5H5cwCGgANVm75c7t+P TG2/d1e3+PJUqFFUIqyaSzmo4p4FIUuXrSnGkFzp9T/PzGC+em8A2ViM4T73yQKM8YiXclGD wP/powBZaVrf2IYTpfdUlK/XH1Wacfh4+IdsvJ3ibCmpNrPJRPof6tHo9KUEj4tkn8bPXLOR QGkCxjmGRXIFXdUZrXC78ySAk89Y1vTwPMsihCXNhBMEJrjnpRiVLGX/FRnTKzmM4u15NcWy FMXABEejkilEG2ryXN+26sSLfxvYRwQ0sf2UeDsLnI7wRqvRyOI6auk6W5IKe9NoEC3yOsbw c4ZB8WRcirJq7N692myFlXvSFxYFFtybDYrsYYjGfCi+bzSAd5g+f3QwajmqAzUKN6Yv9SQm hhPbquOsjBcOnNjSIs7M+fh2FRtJ5Z71J8XBCKsuA/eUspELIydqjEvU4R5aW/2HH7xrvQYE J804QmIa9YVGqwsnbbmn9VgP7zUonk9K8ed3nx9PK8lmkCLxnO1K9Abn3uMCTEmCixdDEwXH f7mv7l9gRmzAs9monBburrkeQY1cZWmS+KhaiHNc/uZTjQCClMgQNSrroV5Pp++SBLiEHzJy pCQNWwUF38mqaKPVYrObOQr0RKdKvRWLZeDPz3dJSPFluwh7wlmr7Fk3opyGPy0IMPC8RLGy sm8Xxvvg1W0THKxVpQOsiMSoIu1i5cfyLTsj7iuHi7pMjGDX4pWOmjgz6wNPmUOTymlwmbeR wss5Bfe8x+y4f0gkFT2ZpCwjKutdBKeduPHe/W3ozibx+aAuvWuqLMmo6JjfW5KzS10Jn1xj JFJf3371Fjw+RG2yFuxBpSGQ7eWH9wctevFRtgL2eiT9bGFgEJiGYFnOSOEy25ZBOrf7PeNS etD4r4SXPZoOw4jwRqsiCBoou7cArEIGzjt2/myf3dvxfi9r3LY3hySNkrzkWx4cDFXxotls t5rMqS6LyWKm6VHw33GQajKLjOVng3IvumuAfO14wvajETbKViNDBCpsYXkQ8LsUUjFW7VN6 DTJGghG49suZPO9Q140m0iAr+otIo2wdXsr4ymoPWI123ktjARpN52jlVrGEtuDCk6xhlJ9d klf88tqqpvnNC9b52UiGpUhUM6iFgj4Kw1QSm4VqIK1uPKwZhU5/riYoCLOmlWdmFWc4U/e6 3bdhTVpjU9f8+RVUcjBb364E55b9B9q1OV6bwxIYxyiaZUiCYynpUex2l6MG8G+WVjENydH4 AnuCLyuw1B1Hl5p5iv7ofpjBGnGLSa+DknSrfUY3WGIC0WEh5UFXA/mYnHtl3cJZUh28OcUJ sIlNRh9xXRTIFb0qxty3KwyKK9ROxBShndB6A1Yp6ndeNSLEPfBzvp4VePFZL+LwNYGYj6q7 RL/MJqwm3KWqHyUcJR8INR50716a9/b7eJKD1m1OQ3zO53SGJ2V7pTzaSgry314qTTaTHLKS 4u2AwxiaYjDMOq2N/ZRl0di0vKioqbKutVZ0GYxB2gKhLEun0Yh+TrIGTsJAF7rQhH//bwLK zv23zPO89+vZp2pyzv/zD17ixc7vUw06OiMzC8EwlqdkbfpxF50ILs8Vc2Unn5dWeMz14NeB zCSdFiII4oV7MWLQW1p7M1FMN0ZzfHl1DmJUbM0yA7hrSn1sMFrMHM/bzLwFBMblBBO1aZnG 8PMFnJB3qxIPZ6vt+ZsMjLCn59XlpeCSIfdNh8GUV1dkhnGPxZJUV69eJQDzItYkCSuU9G1R QU5eYdm89sai2rL0Iq8DcwmDyOYrlbgiH12B+tzrO/SOx+4K379lWW2c9BDbzJpMuHDja6HJ ynW7Bw5ubnfSqPhdKgZm08hb0sdnZqOWgJOWL/oIraG72oowPJZ75tLp0xtBaCscFzbVl3fv XL/zYeSM9zXjuj8fPtx+3IfE61B6yaOf7reLHCxj5AjRVmAJgyE4+HnXSok6qCMZ2jrw48mR beJHJX3OgHrv3t+dXVlJaqgucM58rNdBAZ75V0wuucgD3Psy7l3XfjNj5nAKZznOaOFJD1Dp pE3YeCseq38u1pOLjj15cqwaRQNbBoRo6noGAtBT4UTY3CbZYRy1AXBjFDL6GhC43esMKh92 P/JNrE/h7XO0eg1mSDRodFpd4lwvF+2bTkxSjz3MpFLkTi6AvPn5Ko8ATUkD4oONksvwNCzq w0p4EMdI8Nsu5cxGkrZAegNuZkqErw5VrT6wwELZfA2Px0v0GVPudf6zwGPpQ9JTDZIrczWX 4xL5eUx6t6c+WXSTD5GciuB0g91kNKGO3WdXFiMIRYvPtztpcEF7jUPDLv/lvlmq/e///V8b uNl70kHxMH63loPwfvGPn9P0gZ5u5Qbpa3cj6rMheFVVSvNGJm9zb4OVAcE0sZJpO4Nj+fl+ mg5FHNkM5FAEYxF0/tnm5s5la+bv9Nyun+5p7F64cYz8Yzmk9ibYzjI+yueRSmdWVkdPR09T Q0dzVpc3gkorS60FmtNL1UaPBHsBo2C9gUG8LGGFXaSK9tFhZbIpKRpBl/HQaLTyhHDfLuHM JfM3H1jTt9hl4gRDJDGdTCFC6ScjoNKMyfqwNMj747ihsQmShEzZZN8STHh8+aXXS2y6am85 diCqnIJReWu+bOZZEkNNLIw0LqJ4SU7zbVm21dTQDaRTR4xJhX2bmznNWI3Tm1S0Va57mYxE 4ttUnz4VrNcvh4qRoAeYiTFaGSAx/9PLsWYTMn11HmP52T0wvwg1UK6aYK+caKzGWtijawhT 3UoM8z0D/jz+4llEvmZ3bzspIiUnxcJQlFJ7GeJlmdBM8eWf3Y8zKbtg5aOkYqqt3yhw+DFL exONCYLSHCrrvKSqO8yzgrWwmBqojNAWgdEgzAyp46i+oReWXrfV6Opu7RikgL0FPFYe0h5L LLwcp8lhiSa5OFuaQ1EYSS1c7m38L0o6PuZDoiHgy2JurEQrD1Nwuc/gARHC11uwzEKr3hnc rDiD581U9q0HT1awIJws0jdtpykAP4OYXFtWHA5vuDWRMZUa1Bo69OCYvxO6OIGBWv/VhEAQ xrFGWvahcVdivOc59sXIcGzxvCLeSKSvGH1H5aJLftbA4H1fnPv4rkUvvNAypEczSL9shGIq tp2zeF8GJjL6mV/2sp5PT5QsAlfrK7ISPR7N8tA7kpIqw53NQtYeA64VPDRAKoVb3a9WpMIJ /2vLkUxDvNJGvXyM30EElCHfVGNQmhqiA8v7PzSmWMpXhvD4Wwii0lKoI8WVAkJd47AKZ6wY zXFAfpb78mTyrFTrVeufWxHJ8FZJr2prS0uKs0w59fM6GhYe9iXbl+W15cs7HSysbMPlTJpH FPnJSDI0StCsEZgZjV7LsoiV7lNk6vVV2iXu3xkaQXCXhiqH8nuOkFn7mJFSaUQezZkc7rIW 7n/+aeSJW5WIP5pNT6iuI/rqJp206pdTm6sRP3PMn/n5Hs+7kO2s0FMYVLfz9Mit5aRekH3e 21XbHcD92KJRBCnVc8cgl2zXKfqodq2/nlrlKM8TZQ9qUBDUxwTiUJIRy5VUthKFbCH4+aUJ 8XY9ikJjo1UUG1TKt93thIA1Opou6JP1JEkA/QWB4f/1CygoZVvPMor/bJm3rvnSxpULupbl szRT0Td6c7brO9zfsiAzh913f7Mk9YEK8gyKP8Zvi2zN8jBV52ODGFVqBK/MDBmS58xJ+u9c SmubPvHRAl2YfMewSdO/RiediquZU8sVRWLEVDuFUaXpiXMTKAvm2WMDVUJ4dvC9yWO8Sb6Z ZfuU44R+VE/9ct2itX2qd8nc1O+PKjiTzUwQ6b7W5xGPaVILhODn68kh7dMn1K1iWKU9iXsJ 5KccGDp+cXDrrqM3Hr8aftjvs8t/H5T6tHnUQLsEUyTdnu6+zmVSvfz1v9sMwBh3CWfiBEsy h0HYyms8OAx/aLpeKPmjGnfFwHPyM0QVdy9Ze3BHb4frn4BPvwnNwngLh1faLTN0tiHS86QR 6ypT7ECCN3imr2+Ht1RDzvkGYzY/Xp8FDFEN5aFaNaLKhet08liXvXWCWsP952i6YdS55kT2 PxrdP5lbVBoU7GObLgJrQ2AM3jlG1T1eMk5GuRD8fEOHTZo8t95HjxRqYH1+SIIh8vbABcIS upetoBKCNzFhG+Oq2ol5vKeEytIFG7YtFEcKzz0nLig88kbt8o2S8rvHbI0YvDgK0/ULTldO kH0xuR+MdwzLEdYkBFoRnJcwDg3jUP14mTPRoJ2bHAAmwIxlLN9dTZK51QFE5J7e3eQ9hq2b 5gZAN1jIpi1eZDE4Ru3GV0JYxY57xyv0VpVYLPtdNiOCmJao8/QbL90mXC4EP5+DcUmDOAmp weDv9xSskZ1aVm379UmYsbV3US6CeY7tEoQf4/vXmygeuUpKF9BvThCEGez/n+kk4W1kS1T0 zH9I0TD1rsVot3VsWbpxigUhz5JKlG5uVQM5rZZSk55vFQSCOmJZ1WvbS2CERElA23vtZDAd ZWZSVu/p013mub5+0aCXl10oizOcLsVfoOY9gh8HiwkCl+XamZpA6F6HMpOTmc5R/98RThSU uH/2iKYoatLxxiZbmZ9ZlJpyU5snBD8P4gKyw+SkctUq+VOoWs53X0nhCYxEYeOoC8X6AO2c wX2CGzr0gqV5HSQaoGamCcqxQr1Hkl0nhhrtJUkz8L5FEGJ4cugRpNYHcz2mL4eTfS3gp7Qf ETd2jTVSSZg+KVlHMhBJ0rvOLjUaioOcbVsQ0R3O/crO+5L3RBnweDbDEK73t0fy6tDw6nwS YThIFG983wAHUZy8eOv9Vt6cJF+3fufoJB+PH3cuDIYb5lRb2YXrT4DvQ/DzNZwIryAcR5NC kVqdOvttkPVoMBP5sU0/O9CUWV6+2Fssf94PXEgoNOyjRr4gCf2Oi5P1Qfzq66ix+Zfjghhq W4qxoNTlsmfUqXxujZMs/sXuJXv4+YjWY9gSpcontZrrt04vOX742MmLj1+dKidQYi4+L5i2 5IUdl8WX6xN9pCqnqeSSY8+frzHq0WBv5/e3NnFaGCdpIAZv3buvI4Mda6EdaJylCYqueXWC wNm/NqYY9MnO9dP6baxmwkLdt9HJ4+f6ZJVOkGrGMAV53lx78+vzh6llZjCst1qPuWp/YoDX 5RSMPApNtOv0GfOD+0f365Tn9UsMAkx/7bgQUwWkIkR6721Gk4Ooht+ACxlKshRB0gSkRRgI piD7mAdWgCFkJsiOgO4hgna7H5cm/0uUV/D/GAE6yl+dQvDzEULxzIj+CNvi8dxDE1W6lHzD PMrUi8myM1H0J2Nya/y2ETOGfLf16T02fLmwpaGKpGGiHZhwnsAUzUZjUhC18WsCYA/xAGmA B36RFE1CmAnWmrrDy9/rkhXDkhEHaL0mMe84sFD61aHTehuATy5hJqV2b36+vL5v8OPNO+9e Pbr7ePjJvVqKU6f0H0fHFuvHaP3HUcuML/Ie9ojuDgc0PZ7uFHh8rDsLH4X3D9jdI6MAbnk0 Mqfn8pMrjXPoQfdSPPe9+7lgfHAWDqZcsNCckcYoM8dRJIUibM/QqYO5/7EGtB70brwHUh4y ffo691XMKT+uCxPHarumO419+ufNz2kQQefzljS71eq0ObJYKlK8IfUjX4SotSdRX+cMzPkF 8RixbBfw4WOddteWl9UvWt0+v71SDVDCUA6Gz9EbjMMh+/0cUVB+vlpJSHyH/WpFLd0OFMvI SrVXbjo8L0k+wZ+/EI1qPKqrWzzwX+RIOG9RPUOyWLkEv9s1N6zMeg+dKJ1V7/ONN9ybPBge F0aR48CXX44uLG2eNJuqSZlMb37O4Z2mPNoM4FQQFGfM1pSwZBl3l3oIu2IjP+46ZkHBd4Yc xRarW9MfkwG/8ULmek/DOtpgwHETnCQq9kKnu7yh4/yR7S0kMNgR040jx6/df3zv0skbPnZZ OVq5rmMMnio73xxgCeCKobcvazVjGKoRECi+dvMUTAMR7UfRtx6kW0U4ywIDzJwR97dOjDYo NiXZWiE6Toj0sMuBZwuYQW8b9Yvdb9MwRZP22pA6WmyDC8dInb7pb/LC8ebnL3deP3h48sHV Y4d61uzZcfLaJAr72kjMVF0xfd3Owi3VKfr+G6UjyruXrFy+uIZMFtRqU59GvFSuHwik9Oih g0NvPu2zzg2toABWPG9yNdIh3pQo/9T8f//zP//zzz+ahH8xxSBX+H4Tyosm+jczDQblPjJi JQDmFwY2k68raUzXCy7dGf9Qdhv+31KPnd4asyAMM/Ok4H0zzHOwYnW/aq6kAAuc7i3KdxlJ VMtXNOaZdal3n6w144T8QH+BjlojrkU1uTtPrHYkmH2xdaZ+DiJoMYQ8LIJaIs7aitB0qi0U 1SOuckYWWOtEdQRmQAmTM8x1++wULLo3KHNfJnO3LtRD8+hC4Cy8OlE+9T6kGUWZM51sX9TR 2Lr6bK9rjjeEaC+N0UVVuWbSxBhkvDngg07zFgbtBKKtDyCI7FH3w9Q5DUC/MFKT5JIx+waw 8m0OnuVBGBrgqfHEylNcm2RLco0OYCOmrI+7lmRDwcIFVYQhGU5tyS2ss6XQGMJJ+quLsHL2 u59bkS7Bl+eaSwl/+TessBjxcwtm6/8byBP7Pr588On+gw9jjFZj0rFnSQKMvZg2hnB4vVCl 1dXfc5djChjIWp3g83jdjCnaoJsk5xnAx04jRuCwQatf9HQb5jEFKiZZniP1dPPujVYcveFu TraKb+cPKf/KZ3iV8cnPfBBvi+Mxcvu9RWbKYoFMJQteut0vuFHDzjGEqtPWipeDpRADbjwD DL1icB6No1idcP8u+V+PFmGQ4yRYsb1mKER1MZmI4I3GiJ8r/PA+pxlVwnfn66Qgtnpws8N3 IBY5Rgwe36W9uiAONe/Xu5LJdsEHOYuQxFNu91qIWry41Ikp0cDdX3iPFdfjIp11+cDpFk7w OX/G1yjjymeMBAHBkA5mSLLYfY2l5Yf8imQJzvFparX7YqrTaObALdyYQtA2m5UhsMSMX+5j VHBE2P1IgYT1+I4VRLJdenAgj1QRRsJgXts/z0t73QMpI+xR7awfi0nxbTNG/JwnhCSKJ38K TB8Zoff71tPL92KYGTHdgQO/n3ehMDVPcnAtTVB0uTtR7VwNCmN6hVs/GRSPq+s0CsC/QNrO FoJ/s1nZ7uQ8CClVuqs5CwYxq0hDt3ufTlFwPpFDnjy12XtdfArFGx02C0livA0AVeAMllx5 KtfHQH+UyG/v/niQq5NgFoCJL/LC/cc0V+jkl04Thup1yento94Zi7TK1fsiTk6iKCm6XBAj fjbpZdSJ6I4mXlu0KDAGIEio+D7miTBzX4hnFCClkv2KfVZXkqLT2gQxZQcu5dKwPl1ydPls UMRODXMUn598IQrODm3GsJDhtAUhBNfVtz2ZNDim97sPaFrk1n6Y5BAJRag2rX+PC0TJAcpn I7ieUxTPMxwIV4co+LveHRyopFnU4TCgCnzVcm2f+xOBSEbZR/MIAOWqSAeEv6zTKd6FuzEi zs+hF5ZZgnuKp7+KApeTFE2x+7IY6Gts+j1qbXkQVvxSK3DA2cPpFEsZkFphp/hDKGFL7EmK IegAyoNvqhPZ/I7la1w64zybeD4uNtKQ+Y37dLKyf3wwyTeDHpIYBBdmlhaMwwA6OkGbCBgj WM7gubR79W8/g5VtWltMILQiH+9KXOl+x4NAG2K6mwcZfaxKD8GKE0gfJvTs70gxOp/ToDEQ UFGk17MLN17cvnbhwRh767MHd24/cmyvYrurNBnWnCiKfYtuVReuRre+0LV9QD3a2bN6Hyfy gOXSdZI8YDsFFJMDaThK05herxO00bRyezZ5dohPrBDz4kuXw07a52eA99hKZvUn9692GtYu cLuHULP07HUPIPLjuA/jAMjdzwaGBzYlJpZkl109tKKCIVgogGbvGp0qzvpmllPO51agQn9B Q0rXt+sktGclPbSWSL9+rUMnDQcg6rMXI362weuiPhRPha8JCAeWDzBhPOzbyGlaawD/JWh9 /cZazX+XDZDXoN5O6UXwC29SbqSnVUzgVg229Oa3oXZL8gn3x/QkBDY28ZZuo/61+6tJPt1H aJsymi+sXvz1lxtoibbAxSNrkQRTQbWDIS1gv32fbpBWzNs8xdr7rt0g6Ma3AcB+C4CAlE3p DmUwOlGM/nLgxPAopWoTZYeqcnE/AekbgwJReIaHn5cljrpHixlqadGI/nYblTvljjjjZo4Y 8bMxMHDquIfhU/C3kyBZmuRZnZ8TVz1Bg6cWCel8DFn6aG1LdBqe6bV8ZHW2hcu3HzxyoCdr roDOFybtLtEk8ZQWBjgFhxFulTHnbGqFe69hj/sFrzi1O7TKjvSRc41WNwDrXRaIzWNQM4MI N4GtRhhrWr1m7SKLw+N3WYWZG9oXZZIcRgPUAwVopINJBuZm31vJpGQsX8Gcfsey8vE+HyHn C9Zpb1ppQTW1RitL/npYfz/7cxS17PTVTSZ9wUi4gU6f7wPz8zs1PmcTGYQDVWwAJ1JLsLLP 923euiSPGuO/bcNdS7dv3bvZB2f5gRXH/yrX4smgmMo6Lfpk3b//0ejmJCcQSpHX/VnBTV0O dbU2NXOJ/e5tmi53PZOHOD8PkrlnliKKoLxFq0zGPtjr1vSQRUncePbTeoLCkLrvQ/NQvdGg S0qem1wtW5OA5reTKIYYnCv7F5gJjpP37ueZKLgOPM9A67fuWleIr5e6eZeQ5a93cggKTWs5 vDVDL0ame28rEKXxPZraMbgmZ51JSaTF0TyldyCVMxEs21h+/rShLtdZ2d7SvHhwgnWHKG6C ZEJPXhOnrLi/yMZuCoCgvZBNoSfzMT95I5z6mgc3rOnbsmPfzsbyJskM9N0mEC6S9ZdH+HWs N7nZvSFpOQilTuFIbTmJ8Lgek0/Zq8qT/JIrzxvAtJZkSOD7/RTYbumxDCOTs/X0YjMEEd5O o/eNIBB7KpCnvcrCWSZF5Mv3S0jU/uRrPiaezPesegmN+4JBWm/vK2iWxmENjetk47V+2NjS 1ZWBpwcQorze3Lx1muOF+a+BMfz8CogAUfCfAdawAbA9orSG7Ia+KNUUtJpzLOWnzv1gN3s8 czzFtht5EzEKUDTZvZpR9V/tNv7D2XMuhEG9fWcznWzUrHC34AB8CIQzgwnWqOfk02I/Vnb6 yceHq5hmn0fqAZanyeKFRSDuMwXr20SvlBs5mlIf+s1jGUIQe+9gOBMN5x548e1ekxFj97iP igZpIF0zGkWN8mXZOKSbYjmaT+FxlFc6PZCDG7iqC9PDAi/k8ni/qru7t2fvSKhMY/i5UUe6 5ne2VxQUG9WGpBnHGrV48FXVFY78/v+nBPd3ybxpZlc9efPNZ+YGzazDRMQNydVNg2+ujkQo s+Xa6fYCmz4F6IqDpyrMySAlp7NwxGEkCTj7xPNLrUmQdHK6d9D//b9z/+MSTCpPep7G7q0M jeN6GEYwgsKdsiJpP1Ti08hVO0VwXT3O5OpLfVbSoDebGBTTmz+5O+cosq2iZCAcB1I0jhLe z328s9i45Pm3+02ka7SmVzFAYh4Hvc+mQrBOY9CHRPXz5+fdHN4syzDvTOKWxeEbVI5oV8/y zvbitOK6CP0NtmF0lt9OdhijgS1Rir3AC0ayVl8OXl+jsMwqexXbbC+Al0IUU3h4/MCN7dgL GOFeHcJnp7JJKUHn5/dKE8TiJOVC9FX3NjAQL0rSajTKE/pKVmrvOlFa1T+qZ2gCqKA4m1vN EziFSU9cIGomJMcOJX1ORTFcSzvBJfx7OsRgKMQBAEHAwFn/KlNfgJjF2W4Romu8SCdsqSnC 5nDXnBAA7TuKNI1+Vc9yMYw3G208gj0OXrs/PzdSo2C30e+Tp0bM46sappGrwN0WRfSIHnL6 hMm4sXzV9kUbt3ctXNXft+FQgGdxHs5hhb6wU9sQkiBhFNXq25T4K5tJ5splC/732NtPxpyI zorjTRW6XKAD/7BYZw0SOtZdlYA3729ANXo9ee53D0dJVmNXHXMUEKFAl68KgjK0g9dRL8XQ JCPrf9+whCyllnubixQdOiBZ0mdoOntLOautRTAadyXKUJzvXBiKFgILz/uWprcf21CwOUhR i+xzYuN+Ol4yAw8YHMs/fuH4Zhftpyj3qdKfn2tQZPzivJEFTR0rDh/fvvHAUUVY+OvmgzvD YwdBY1vUjewybmSMzT1b6iidx5VOKGmZqwHhmTGDVodBCUlzxzrXLwC2+Um+z61rOJreWLt6 YT6GZEuPtb024zr3jxxy2sMqqyNWDHKdp0okk+fOxCDoF4fQFMGTYkcqhMKF8y20YpvRp+kL 0d9VNCZer9bywA0ahRqkRVnkp1Oq9LhwudMFw5TrF6Src4FWKAtSL24g0i0J1Z/cG1hbBgXk Z7LfQEZCIJvQGBBQdZN1Blr0b9luIrDgN2d/fl5IIaMhflS3JWes02gNOsBkGhiiBddRt3t/ KssSTOpSv9hLv0lcsYoP08Y9II8UDQl6fIGV23gAh0RyJI8zRhvnCnADudOZR+h94RoPH5Gm uYMEsQpB+uTAgWngj3wQkW7mpVvbzz8aefj518O7vmfahEb6692nt6+uXL9/WzmMiyBZD/3G jgSuOUd2O3SvplGMh1OBEYeYLkKhojWsJ8R4VSMOkreYwdbsEC4Rv7I93slSHc0JSh2vGG+Z 5iKdhL/fTqKpp9xfF0DOF+67G1zgMZ4txU39ZaUmRIcYFM6DZPvTAosu+NvTn593mbDxe0oc LMkpyynOK8o1YhqRbteMOiDPANjIuRIZPQlDVPLzAERIc3iaJn1qGLo8cPHs7fsPzj7+8uVJ 4BX7tgYOjEF/1SKaCrpXkBx4OR83j3rpTeE8TTI4+1vmf/4z95//+a/2f/8PujxKw/peCBn0 2v/+v3N0eiRD4kscV+qu/j8BI8e8HY1sVAiuw4RVcTS9gYeCpzlBYpnP3K97cZSkaWb5rQwa 3Ph2YX6asbMG2Y/6RaN92GuQ12iTYCbSi8OV4jG8JjlX+LHDpuCxDVGKAXmUSDOxak6durC/ uyK/LdSd4ZriTFvGhIgY48PPX0berTAztoC+cpF0+H09Ll6M3hSj5p4de3bV07Cf/JNXez6f whnJXPeZM/AoRsB30l1gbNpOoIEt9XJIDLzNrvEEl9Zcm8GY+KoVyrERyTgnkvf5JDuk7NVj 7du7l+08vIzWpgTq6DiUkcsR1KDPa2mr7esy/CtdfVBKqbvun4C76qV/FZ+sty6jgzWQil3Z Ct8wYn5d3A4jupRcuy7BlEbi6PwXFcmNh9dSHjRyJXd7YjvQO71fbUk571PBQYRcNLDWgnDS BvDZphcP7JPmclFNNZytkZ7REaX3K0pcJb43vojKB818hEQIBDIgWpvs431r3YpVm7atWLZ1 1/kDW3eP+BR8Zg/i2ibm8uLnV8X2dBvwPqPsJW3zWzsa6nzCwkTU8wW4+KBdaXCIKuBHxajv THwlMJ/XcPDK9yBmiSXf2w3eD/vN5fVLensL87OcxXXZ6QGMwY8Cv9brRs1YhbNQWQ9FAyH+ MZwiMZKiWJOJNsy0K/darRLT/pA1YHjHiPn58RICRtJWSHNVqpMYwqRXTuXqfwKqoT/o5HvV +UoIowh9siTTeL2Qg5GSdUEltecIiCuidQby8ONiWINlguWumVtww7NQ+uc3dwvm1atJU02D K8HpL63el8nZaNQgPqtAap4jHSgrCNeiQxuW2Ch5GJGs6mN5eh2l1VYMR1JIVd5GkqeYxqZ2 KyTtvPcdMLD/QBAMhzHY8K8vtPUqXA7cGbBqL34eAOo+A1AAMCScmJio083RBmaG8F18Zc0V j8wMVObaG0ZS1jbKhS2cStf9fTg9Ipa5RHh353SCTqtNFl7rOgTWJGkGx3RKuE/fY/W+Em4l 11rSLizDGo7PrstngOespWG8Yw1Pjdjk6PEgxrtL8Cg4kD1dzREomqNgXbYBey8h9cDSSxUE /aICD9TBCef2iyrKoMcWNFB6DdAuXWp16BCK1s/RtwZ73JfiC/7s5dEet7scrltbRJMopWxQ QHFV+N//JCdoC8GiulPH5zTWB7hdvb6zJgVSpK6rEmT74k7Tf/7zv3BZGIs2z0i8Nr2NFIRs vrOvLJGMOjRNGwhJvhoQYjubIG5wb9KcDh5HAL1RLYwiPtrUlUY8lKm093171c728oKs/EW7 li9cVNdWXKlSBD12GjcgR4Q/XkbMyvRbUF8TLFzyXQmfNpOEtCnfZXSj1gbuj9XlGTkFZWUl BYW5eXmm4sAVXeU93nggw8PFnudJJVwolPhxC+jARip5Os13twnfremfY2lin9LJgqQQCkuV I9nD52/anwV5TKwr/gWcBtITXBZRHEYVd2G/GhfAwkneAuFwMkDFX8tAWG4+B8LUU2aaqGjl i4IYly3Bc09s4PAe9w0TwAv7sRwrGBUCHbLo8/qfna6GRauk4MLeWkiR0izVKVe4D9u27FZn yvB5fXY6W6xEFP3gQIzCMvvRqI26nVUnS5YIVPuYrpGOuc/vrl05ubd/1byNi3ed9oGW2M4h paGsDwL7Y6ic5GDZ7Fbxm40axbXNnY2IHVbSNwgXOT586sZQCYXqCQWPWuL7lfN5JN/KL1dw buZDkpHuN1HdvOEfRdtxl0W87lsXrLxoQzSz0hKNR+JYqJ34iXJnF+C7jUl9CpEWJMp6p3xE tAy6nOZ7Kxwl5n07u/1aN4ngOgrM+eMsUg8whFA47/LNQnyPe+PclsB0X4hiFpqE+Y5cEYBg Gzz6QAPTJ+Frd0L+PlG+dS0QQnsL6VOFaSTS6T2ayVhSLTgqGz4tQFOkI+iZRRNtc4UalhJf L19d0OgdJGB/j9kxzxUpYIbJ4OcTMpbECsiutNmL+fpGIKTKB8wSxCAdy89JX/l20Ok5odHS gnDk0+1WHBHLDhVYBNngBT3aI97LbpZAxV5v8bdp6PhF+pGukinLvwS4JcqpPGH03enjWhZp Z64kea5sy5Nlft6WnJDVUJUOlwQzJ3HfdDEMeP1CjGBIsgFEpYGzyjEciMX2o/ngTaYLWLDf ghhxHYqhFCzAed4uRk+7z7Xm5S4ddH9t1cr2ud+ykrxM/caOZi8iSQ4eNesDo52FIMDzVGTZ i2/vdmDJ0r2jRKuAktXpoiEb/+Elyy5mUNE+9bOTlN3Bf4+8+vT7x/uHfuLck7l6OrSvfih+ Hq/IN98yLJJgm85z3d3jF68KJc2L1y5YsES5zQQlbGcyLX/H5Khcfu0MwWXm5NlZHJJuRiVw kigX6SQhxulyOmlNurfXzCurbtwvC5VdikG2hTrPKVLmwfQB+BwTOaqH//U83dZ40HgOd2Yk kAXefk9jRvukEoZSTotmYDUQSbNnHuWRgrVfXsIp935tIOe2LRjSOrC0ModFaTT90PYFKTmL dhYYdARAFTOmUTrl+btY0xiKtL9KtLXH397uYfXzI7ZdroekTX4bAokGGXXJvXJT8+DUKM9n MQuJd89XdkKqutRopRibzchZUlIsWX1Kc0MFiE3BTA3SBX9+fjdqTXKIAO+dcaQ9kCwr3qX3 wEH2I+KD1ZNwBrbwGBYEhMorYz+rqJBb9qvtSwMFLHkhVI+mDIpF0jBIeuxtcgEAWAMMZfg+ oNZXA2H3TEsLR+NRVuh8TGXHP9LXczwq49U6z/Hvdp/xlTqO3L7k98R7ZFFiV2x2cjjwVF5G CSK6fdrl4B1VEqBDOQkSox51ILghiUrrHLmTqsve8erV1SwDzuiV+duUHPote9qlmQMlz8nw xaNRQ4D3DCZDOc3XCIYt7poEBd2wQ37FqalmTB4/wyrp+1xGCnD9yoqIR/91jiRwvT4ZXGUM FJygvFqPpeuZcIIeP37+UWH0PGwrE9SeiL69tvKygf8ej8m9e6deua2Ieb/ARPqp4ds3r4c3 XbovWfBFkkY2Z6WlYGT2NvmCcW2xcgScLSxu7u3ZMnHxUCTdiU3edXqF935l6RVz9ci6crtO 2gZH0+irqTwxGHLZyzUuCmMX+DR51SYFstlUmsXSELDt2IQIa+Sqvt39gQ4ERJYNyXreoQwc zroMtCWb9IUjQhWHjDit6Ze7tN0Q8l4AJMXrKwpSihTIwQiG/5ixyyZQRxGXUG69RrnEFUKK fCaC+kJmrWb1Yhc/OSGnmHHnxpbSiqqOtYt7lh5c3CuLjh9nUURYLa+vPcmXDRzpEu+in08u ZDD/6VTV/yvKy8p9NUnqHUh18itE/ngH5iYRPww08ubZFWERzOK0zkD0HD+289rbkcWEYRy+ B3f7imiH1xkskPIPjS0ZvPD09uN7mwh9kIv7Xguf2r4oXYt6c1qlTgu49sZCWkcQBGP+6F6r FZwxTmva3c/oQGvUiSpH8FoSFXXLeXr56b+DNxoUK88OWK3iKdKVcIeW9ORAU25hR4QfRIb4 YHCvRzMDGylF2sRo/hIakfwdskHkgKBpIUH5XnID5fTi58EyV76JNRE55cV1TbmYnmvwM9JU 1+HGBGUq3uk4RX9nS/RxgTquQ1Xae6prM55rDAVupsz9z//8v/8nQYf9o51LR2g88nRzDqzP 2iYtYK9kTdRp/lf7TyLxzz9BxNk/LdoewH8/9xIaL4HPQAPG1FfQBhQlAEaQ9pj7hF64xB4k ar70WgK4a762aRUH5p2SvehjHlUeRfkcLqN3X+EjFnOpXSnPeY+uNVsjaozma0suvX93sQfR hhX6qG1EyVdGQ5JSqhAKzrFbU1i6TLyshrIm8OLnhZCOM1ImBgePTz1ENProvVR38ZCXZSwP y1x7i0J99obzOjLMu151e/GMQSjwcuO63Uc3HRw4uvPUadWiB6mulZCWbA6E9/fn0OHdG3e2 z9++OdiaXquVRWZ7dT7mhbudWtiAs0BRBQGpybvnNrzvx8lsA5JJ9gfo/4jFcz6vgkQR9j3K qYi0amkGnnf3m/vNXpvHGDz6q6AQli1Ir7BmqeX1HM5ZYIqN/kk0D8mQBpA5R4BbCZg2cLyd 49JTUpx2s1PxXhyb0/u+vWXl1oUMbq8qrCoo7Q0tFg9Kv69Oj4EukHogGZLOuFnvuXmLn6/q 2Nnwho3+KpuaGheWbhunBO2HHZUn9iuNFa/ZN+iRND5KY0gcY8sbc3mDIWu5E9KnmyFdMhV4 q8lVIlD9KcBFYdsLC6/cMWpIGph0m1h4DjGJfq7rEFkp1qARlSMg/bywtrplUL0fjbd9csiJ u1gnu4JvalSuJWPy1+CUEUUBZgNKInNECV3A5CcP+92Svfv7D/eP8HKqIPWdMQDppZLus7qO YffP00tpva952IEEKvgWMzVrNt7KZFDgg96jorTAmn/+maOvVx6bTTiqd4CH/I/dRsyAG5Ln ICyT3RPk2KhJkpfRRkxWspTKplMA/A947IHYNihaKnnqTE66QZMCAOidhaRpfBfVKHfrzqb5 yxpratILKprrWxQY4rFtRNueZBl93auRxThiyiugCX2O7wZxDWKCmh9EmQ7Torqgu+606F0U O/GblkwDgc6FNFgaN3fZ5ipKkjYEk8K2u3sYzJBVu2jgUUDljZjluEPjWrFv8/wUpFQ+lgfp QvE5/7nBYODMZhobpzZV9WAXWQzO+gZzcnqE7KyYOKtuKKoZo83PV339yTaZYJ0e5gv8XsuD Gnw2nc+PbFFwiIh82n+MFxjMr6kxYrEQXcnRyhZbZVpWcP57nv1/BBtrkFYaJMRr4LVI6C3h RK0fW/X/+8//B9eN2n81JWRtuP/2QFaipv7I2YE2UtcbOUUiKvFiHg0TpvKgVsZBKguAfRVR sxPLHG1+9u/NzS3Le/rG+C5d1TERG+xMbJyxLR2heDlanY1Er/JVfsL5t32+NtNiyVV9s+1L wET3+f1YkmQQelXBM9gNK9D5F62oZFcfMu1fuX2jN1TO9Vy9lkkhYUzi4+2wK1wNE/7+++tI mXnCTU60An9+/tZdXdW7qqNy8fJtW8PtoeNv+6HOYzk2/kriJaNHgcG+zIBO0t+bcSilIi0x nN3GaE/yRRyx7/V6WQXjrpKtM9/aNZKFy4t2Dh4PYsq3jrTsLMaQIe9SDm1X9MY/Y2ry5+d1 yQDk12BIBoj6ggnAJKU7SSEgUyapzXi1QSlwNGUO2hlQ4dCYkC148C3DsUGV9Dub/E/TyKlG RqdYYi5OlhVXHQay89jNqytTUcgwTu2J+1uHbLrrdi/UB9yBVHZzpmbz5+fNNMulZKZnOkwc xUdyX4uIQNeSRPyueJoOFFiTiuibAoszjqN28eV/NwcDrlDq0npOY0Ygwiy6tj260W+FZKOP TQgFYxQJZZbS9nE/QFYaZP9F92mUj/mbbRyGpOqIOO5c/vy8myMKrn16+354M0sRkybF6U+e 7Pu2/4J5eeH6SAAi/bx17vrQ5XCKwsEd+84MDZ4/P+miy0gkT8Gm/GV/RIvhTJEByw9mb9Ks ES7GD5elEYQmtIHucQWtDtjablxRjRtTgZXY2azERJIgeEmOfRcAdeIESRw+zXn7m78eieSR ujRR4efLaDTcFiOi1ZjM6oARJtZGZKX9+bmZwA5LNVTQE0DuDdOJIQM/udbV6xp8zIt/rXEw TErRQn8EyrtFNM0D2N/mwLeFTQWiKfwqEidQnmPotO7hyKgbi9y/I9OvNGTMF0/SgMnFAF5b kMHgDAeTqmViAIs33WysOtZLmOtqjBTwj5Qq32bDSJpD0nJSR8FprtdRFG5doNpIo1/bJvd0 v3Lux4LI07ZNf37OZRSDgB0U7GeOH71BXJ/kt89dMtnj2Qd6fbwMxggEOFHm+h4FP0C8FAKj KViXG9C7wCRZNbgwjmF4EkN0EBcJSf5+k1Ye/+pepi27/OFBLUOwanFf32wpcxkxnMXYYfev aow0KA/dPhuFO0z65aPvuBWsgc0tIxOy1NqjfWFJycDhVwns47IXvcUZtiZ/7MGwBaYwgx8/ v7agEsQgYAIakfWF0e/OZZ2MwxD9qoUaXzRQqGKkBz4eIyC8e8uq+fm0wfcVuM1A53Rt2Lu7 idJ7WbWN9smKCUKdX3aOzx969XSw18EgCnXUdHwyoF3VtBu9PEX4uQEyS7i8LMdZvbevwMPz 90dAONdATa1l/ku2LXXAJG0693wxndKcMkfxwX3T37P/qJejZbumRAjsOGD3i2QSYgQNCS7h uH8wn8IzAoM9Rm/4f2FNfvz8wkRz8iNuA2GIvuW5TKHtyTIO+qRQbFs2y2Bem3cPyon6ka/l FOqzBFoxu+Rv14oH5FMT1SJ8m8pSkoj29TwOGn0nTkrfp1elu4lUByQiQ1UihfY5g0rvjtex etbizIQUH3+vbi9P1lWDd+VegmcRym6f99jdL+NPjRnbIEYMi388yell5Omw4/9USSGu/GIr AkRrxUF05mErmbkZ/O/bdbSMjP2lknKGkxONmyybdb6BAsddUcCCaQxjJL1M1svhHOl9vJXW +sCK5sCy4915UyBguR82sZbPNkIJMXCeR2aE0vOOSknn82yKogRXqhNMwYM1cxVR1Ho4Ob9t QyeN/jMW0/ObUStd6zJICkpfIzjTD0MeXEjf+VoyV9BVC6lbpzyLwy6FDx04Bl5JGMU7YXx5 kIf3h/EHbQrbgWmdwZ+fr6VCoqbhRjMCq4TsG8f41gab4WB1fX59S/3mMrQ720R48fNCBZjh psngcwcuQFOlBk9yvnCFIOTG1Zu31/CEIIh9ZcJa5H6dNZKTgMMwaWrBwNR8sDoXZVTiAu7k KKL9w8e9lqyr7qMJskziqlEzT5iNQ+Z/FcyO0Zb6dfKbZg1FSQHU3B/JALvlyAgILwWCvEtp SAHCU7GYfq+2k7C2fPfJoc38P65Ago+LFSyb6i1AUVHrDMkyxt7zBIM7W5pKeATKnAh0XGjy bNLLDp9qqHigLCPfzjHmrGrV76VBK+7t0KW4owxRell4L7W7hSGFI+fD7iyDLyjdl9VpFMPR DIB/BuoaHlHMmY7z9KCaLk/fPMO9Wbgm2aCXApiET608gXKYPhvIqzbpZBiuEo3s89SbLPsU etXTmCRLXe4alZjpv4z+yusX62sYqnIZp1Gm4w0biQLzqkMvGYzesiYoMCKjXXhYSBhYWp/o CoaIFH7Uf2+OsfbbbQatHqDyuxZEpveIiAR7k0IDJ3tV9rQeBz7kNEUjMOElYH3YUFq/eOnC Tat7+/Yu69750695O9wSoEOrIMJ3j8qnmfSCFBuD8T6WAddKoWSAEc1YEMEG+bEFa5Iq+1SF I5EoSyOiyZRk3sjNmcM2rlrbRQRghEA9GOk2JWPNotCgQiMR6Q5JypzyHGXGlMmb2yf/LR81 SKKYkxrfzeN5EzwXs5vAKtMr5/sLJhJh9edUWkYg3oYYxuiA8+Yyqx4NL0mHw4LnTQnJp7aR sfz8sb00157Vo1aBMK7uHkiWL7rhS5cjmCXHAYLwOLd563/KANwFgkEgwg9Mwjqtv4M3KwSC 9U8PUw1+l7BOnGZxjLGwvgdIAUKkrd5Zw3FiENLrZsY4f/2SzsbqPI4Oih8RfiSxz3G/gsCR dNFp8TCnnKPhujUfkx7JNyn5sXuR8FyuLLC/Rt+dr1Ve2V14kiht/F6G+my3v/OT6dXD3z/v shKI8n7eQQ2G64fX9+8syoP8TzXsc+MCmfaRKaJG69NiTXoEdc6QrIH9q1RfbMdJhYOQWeV+ 0Y9gpee+PW6hSW+bIhCxm3UCiGKaphgKp2je/1VrIbz0VUonm2DLfd8O1/G8HdRDkz63to0E K0SD+F5K0UIt53kzTxAgcB3L2Y0RGFWMkzaTWOxBBs5aZV/sPp1RXUvH6HkvQc7BckQ+E4dw EdFLSFVjkUNXYYrDcy8D14MD+lGzwVfW1a+VMeEHU2BWQir8WRmJHtD9gVO8rN2rkv21gqXJ MvD4WzMk+VvPpjTZ/pKBabkZJhc/fikDNIUkdyOULSg575tQOR6DnPvz1cdDm7dsWLd+x7E9 /efGOGQaA0SxXkFa/fBpNpmwnN03zy3NIgkvwc5rCyWdSFsYXuDnixyHUxYLizMghvwkItxM wbJ7kE16eNEJh9y1Px5ZsVba/VanZ+dnsElzORl+4C7pOZ9rPXzl6fz3TES2tS3B4GS6wK7H WnwFfm1zFGX/Qgw2Hfjx88vuoio1++TZhrrOvcJR/451Ke31JvmV/OOkleaKDWPFdVNA5Jg2 4cPPTzctXjI0bqihCMbRZyAYl50ID7b6yZYgebQX6JWrmapmeGKMXmkrB/f6lj1PEbmiy//L Ngjt93y3H3JKK/ItTQjX68cW2nX67cvrR/cvS8WCaF5UdWoaZNoLe/CAKqExu+BoB38uTtUa EKRANAS5Oy/PXlJf76FQEyf7Yp03Uzue3nrnO64tUL34zt6G0Y5MY0ZLj7/EoeYfRWv91Egi aLqJ1zeq8KzoySOSMCTRKNiaZzIyVMPvPNKv+eO8x3WvMWkCgaae7e5pVqkEmAbz6umCFz9/ 25mNQIjZKeNJTGYv18N0y8BBFUgOW5FEaTkshAvGdMhzZR+LW8Oh/uqKtbSh3i/fehSTLWYe OuHR+3kbLF/+flCY4Br01kkpXx6m9eH3oMkk3ETrfkt55Mj5+uD2qN8rNcbivee7XUljBdhu 9x6DJF2+nY3DCKlBi3wuWj/qDEjTlr5qc+WVwHhCtf94RFhOMquju7xzt4ph7dVr01YMHc3U opuBdYtmnlRkF+0bwO3BeieDK1BWxXCIHStMk2s5jTYRzlBr4qpiAFOTxYuf57MQakqj9abJ 01MpYzqgc/pLpAMPdz4KS19sgop9cmwvryhPraupbe2sqshytPo773KUoGnySltpQ7X/8lpA 8opdRaVXTKJGWG7qDY4KL7/HNoOsn3F/tRpGAXCmZoIm3Mrd1d42FzZc+ZQGewfx8m1mwb/l IkWfZc4JsLt/T0vKWrr72kAODVPNPeVmnQimPZr2OP79P//DBT0bO/9RIOa+Wxgf+55QY61N zhee8W+XwwDKdzWkWwT2+ZFtVvuIV6Hny6lkgNivAAVnUOMm3iIWMTfNL0Oh0sjDs4y70WgU HOXnp1bKtf7pn90YrEQfiEb9ges4mDT2tA2Ycx0tz0mXnwLKJoi2gZUQAkGQNinBH78GJ5b6 1PfAhvm+v4VvWwmXkqkN5z35F8Iy+77AESF2w1u7x4f+BUdOB3vPCO4Ip+ux/xHsg5RUaJAl ALvQENJflpW323OY573tVcmXNSn//FdP6BFEvGLdcP3rF3bmzesQrqUH5yoMdwjx23WDr7jT hFHG529LqHf/6WT1CQgKmzK8X8/dNGLsaE8jDVIgiK1yOMJxLOP9lGGJUOxaXvJf9sAa5ecD GC3ygBMNH1VjHCTyKdKT4G+PFaTGW5wUTvRdql/Q3a0NmYW5+S6LK7eoqrH3oP/yIXzv2wN5 UOPYFhahHnFOJzoaaWGLPlV6P38zk2fBjwc8IklhgSaVMk+H/RrcOFWkBzdPdwIsEDTZ+169 NJkWdqSBEiLFT9bvVeN9wsPrhWjglg5s3NpOKkF/t2gjWfUX9ES/WOnz7LHGIMHG1edRSV6D BCPdbyc6e1rX+ShJNmnpfiBPf1Zp0Lk2HV9XhOYGdBdRQTl3G2aUlsCztDmRx7JT08Jk5Rnl 5yUEIb4m69BAW3J0229KzFVZYbYUVm0FmuYvMhE+f3j9WfYe8a3uF0kqZ4DwxUAq5fDBupSQ Yg9RlMydv0vh0RPmhhVrFkWk+2lYWPu3OFy5ZK+MmY+eSnKNZrtYxPIsSrG8Ccv3EnF+7kag gmVNpDZF2aMC1DyAe8x9cvCgLV/jTLKE/JERiwBKtI9Eucpjry8tzeQFLCN16Tik8NVnNoht Ya5W2ucGzRiGIhDZOn7JbjmioCVtUR5f6roZ81yj/FyLcuJjplc/+Wr4ctVXoR6YO/79znzW qH7qhUGwhNf1eoHFaHLJylOR4H10mvCKHHEaigVrhO+DVZi3ncgWO1J0aHBgmZMghWV6nqLy h96+e3ujf206EQzJY7qJQofSMIpjOKOZMxEJPp2+VJ9CUXkh9W6H9B4ZsVn2WAmwUB9SHqVx sW6MldbYAj8uL8iyZiyqYnQUloDwmKZcRSGlmv0eRMLXWF5ArrlCWmXjltU8zdSIniBj0it1 sqE8vWL/OwDZY86jkXRglJ8bCOnMXAaNVSpGUqOavOUGle9n9zAO0w4qkVdpbqw0jmpHPX9e ojzP4baixtrswnrx9p6pTRCNjFfTEJRSkGEnEjkfxPv5BgzYnqGQnhgBuV6YgCV3it1CAun/ X2Ie9nGBDWcZEqZIwrqlRb/Xb06Gg1+1xZwXyST5NjSEB1f5vGM9QYy6ksPqKoaLrBqtIUGr h+HKI8cWVBE6YyQQ7JcMipnKXX0gmTu4hBHKvfKxhQrmud9crWZ5uis8hqjHKdWWjKpqnuxM o/xcIr+b16KTvyOV65X7TNjx9Zhh2lgWKDhaqKJ1aaMlrupohiUBdxr0OinyTxNOSyKlA3YE h3SQs91XdfN7k5PhGT4tT5IMNrEcTSKs2Vq9Ztw4dmHHGT5D8GgSY8rWITRLYNasrnICPbBX H+xSEbTNXN2g9F21v3rXu0iOUVEPVFCC7DlU+pT1T2rPjSuVJEdZxLxrkbHH7Hs/XbJ3hRVJ 8qNnHhzY6/kwovDzU8oVrCs/Aj7PxuTu1inqzu5JRLkNP+WR5xjl5zJMOjN30c5Jx96sRFtU d/XV1v5xOLN6myQtaFm5dn5Vc1VddtsOkSl+DCmKyftrNyxu6xs73tdnjh0c9Uc5sKyhYfHO 47KAVXXPo5xRUauGr/anCeOplD3CsMp17dsSIjZq24k5F6zZfGhThU7WRH071llX1bLH19u4 RbkDn0dldXDwrt2nYUGXe9PMULRExzzfEIXuoTYnCnNdJ7duOHI2gFfzMdQqyrI3YKLkeWw6 7wHYv0H5KqXDEuz39Ue+fH4ekzu312iazuhCY0c2ys/NpE0k9DrWMhyWAhPMUAlF4k4zwcZm X/GPHHhgSDYwJ/Al/YqKSj0hztlpg04LY7gStrXWMEenm5tY4CMx3qo3igLm+wUZI+HqPi3Z D9w2sYxWssLs0flI5I7zeqeL1sMcptUmmQM8r4p1ZFplVY7WLxSap+HvDp1s3rkN9fV+Dde3 gwWYMc3XrsCRvPHjj68Pd7g0U2BdFa5/kXw/ys/1FFV15ub5JRk01/DI/f2NSgiLSBrz5K30 waIaVxXxQgEocFcSAj8lCCspKeAfU0sPaNTawHje2YspkjWgLIlhUiPrDfbtL55ty0y2ed+I fxUlu/r2Li/mmKGws7EJEiu/TNJWrXR6bvvfTq9SV8zExhefVpkYI0XmVlIGb/d1KduvZiQp ycBVBW1rB2oRz9LTVj64pczYjj6uJjgnq8ULvKUMa1Os9twMFuNU+/WGpcDUZBjl56U4g+Mk MNLgWcxUkGk1FvvIiKLanRJtRP40UW175lb2rJtIEmG4PpLODkS6KV8mFm82qLVaVLbwjxlQ /dqsNefXWBNFlccd2C6qB361zvVR439bqP1fLDmpWoWR1wKdaBR2jzNWMJIl/mnIW2hVNFcQ YK5hGFqwsT1k0gSwAT0/cOBuiBe2u5nC2ncty8RwSff4Sx2+TrW+9qH7qN2IJXhrmj8tISA0 a9mFCPRw02JhjfLzwwprWk5KipnlzSaGwxD93JWT1sOMZL/H06S1NLkVv1VZ/VQg954tNbNV 0hb8na/eDUlOiseQruWI2m4qo9kGy86pGyVLzoUa2QzrvdNP9/Fg1+3rqpb8siTJsC6TtBIt 4m8HNYJbxpWGjNT0hu2LMFTg1Ey0rsQg6BD2ysIclfSVsr3v0CfptZosNX57norPcZnAcnS7 1crqHT62QjeOXFThJRJRB6cgs5f99o9bzz+9HTmxZPvpWxcPdRcXL5684aTpZsb7WeWOd3PS QQT/bCiE8YWKkPkn2XwVKhFXzwBS24lGaCf1J52VfaK+uUxCJYUejUe7fpwuxX1ipA23ez5B MRKUZ4flMQBWhhKA3wMCIwlisDSb5dNVVvTLTkWCL/4PW5rbtwd09rzZv7GrRaVnvVx/G9b1 /WS9Ka3ShSEF6rTTU8CW427Ci5/HXYdvweu7tvnIoz8E2BYc0F8mZogSbSarmtf5/zd5/uij 8SFce13Wxq6i0rOY0EqaU/532DukxzqgARa+dHjAXnrg8E3zsNIAAF82SURBVE/lgIM8rpfU U3s4lhf4+Xu7Gaisq5OsO6/cPLmep2BRz+2sdn+yiB6d1UHsTAH2wQrz3H8T/yW6IybmuZ6S 8vn+b48CtGSF07runftUCgOptYqIuOkpKxBlfv51NJuhEHOnrAF6uaU0y5Iyf8x1064d6x0x ZUOegQ2dySrwXqeDicWnWaLz4dX9K8wkh4pnbATpyqh1QAcp7AVWZM3JleUNlRvWGclx2q9/ NiUuBAfnizqMRPJ3HdtYKgCl9uO89D5PMaGC58t3crn7k2SRlgsF6fDLWpQs76zJxhJ7vXMo kI/BhzlShyM6A043+0DFuSsp1iKhpDSyXAiXswjoF8us0eXnb000RjAMqpM8lEBYQpg1W1AK zJ1Pcqq294wlbf7atq/qiu+kERCL61GSJrQKOqnq8dg9Ut060V26GCIQc359PqbT2tUZZIxt arBUa3DmMbiBtpHaOWSTcM4364AY+2t/VyZCi04D36nC94O4IHL7RAcLuNCD0AIguPsgoZOd 18W2wr9oag1ZNQBihoZoH2ewOtbMpf0WqlhEsXoZ+FE1paZdxujy81IGMaWUL6mEEkTpRyFB oZ0nzzQSrJ+0MlXv68087cjyd3foOWzZ6SQpHtxteSxTnZjXe8RVBlkw8MgoHpZLDTgqaIT7 ODqYH83xBS3pxSdFvvBK77f3HfY8t9a6iNyuPZvOvjy1rley1anR193cWkCgNjtJiCj8BWhx tk5YK/1Yb+ApOGkiZHfeGih/JIJp2oBm37qRasq0URihIBYKxdewdpMkzCmhLFRWBDVOy6xR 5ecXNrj8miCOWGgTBK0P9WTzMsGTcTnE+dr3ZkJ//UY4SbN5ct6izvbSmtLC1p0RSrG8OvQa 0TkpRIgIB+vYIA/evhBGYwOcXnAUBXh/yaJO6QLJ0QLsXi9FWAIqMfdnQBiPzzXkeRtT/T5R wyRrErmlQe20G3GK0hTsfO1+VsZiRN/IrTSSZBggcbuZXxtYGNufgROyB/oAaTCqX0UfOKzf vYRacGaLjSJxr9v5RY7ERePOC0xKHh4JCvgkrYCJVRtVft5MmWQ3JvGVtVpB2X5uh31twgsQ ydphRqfXkbmEibR4jM/VaGDUbDEk/qMPebDe6eF8UUG8aXl4xc5N2eXlVeUVTSHcIoNTf3Gi 4+wH99UGbYpoN3uXYDC2bmk+SVBaqVN3OpyZLUcUh8QdGmbzjY+POgxzvdzah/KgpIoN2xaW JqKCE1ug1EvwsKSMXsgYCT1rR8D7AHItqjWmBHqmP1qVSSEMVSfZi35KJWuHVS+gK3Dep32o YMmymOYJbwieVlKIOvhsh5Pcu40qUV3hNM0YVX5OJ1NHvMaZnazIRatJ1MdGOkvvfeWZpqQJ 2i1/cKNgGSPxH5LruKC3HhHP5fuXWkLFBLrYQMAFk2fv87taZzCacKJLuiOch6hSO5bGcuW5 mOCg9LrNzGdnmjCLpKX4Y8ckLdZWo2FU9FmXlCZdypZpgwnkjjuMtLTp1eFGhsrp7lq2Y02G JS3vUCDjxIMmfVZNCkVjJtG3/U+KOQK7/stw6oFcUhCFfXLwJtwLz+ZWCkvlFGaaqYZvq9G/ eVmKhIwmP4+AOHBNS7pK6iR/np8mvYJctJDAfPg5XbP+b2Nir/6O/yIcdtADBo9D332zI5hD /sVylGyPAHRotNnnaveiQ/OqbRWDcsFTetujO1t3FRldFgEp9zeIgQH8Wl4vQXDxfn1Oky9n XDga2OALQsiX7++pSUGQgX9nsbS4Vk7zlBXPHpFqCYIsd5LFV3x1D2WxHAkL/ho3mEicV+8S RhcK+g6SkWZJ7x3mQjFJMQxD1u5z8pPuiRR2BUwwQzT5eRBjGYMe0iQaxHfNB5NBlESCdJjS yRYK0ucc1B/wa4LDmCnF9yZ5bqw/04LgSnxYZ9S1+pBzckY/urbPIVBn37xSjmSB4eDhH5uU 2euZIyqBd85V3Ktuj4qlL2ltSrdKE4PtgL0cltP/7NIyjl9Xh4VxoihPEiGhVgB5tGg7uli+ FKgb/A+jxYSVitsFj3EY5r1p3C/CeIbFMNolO3Soq3Ja5oomP2/Eeb588ballXSS8Cp6ymgV Vd8dFvJZgBmEcd3WnUNnFKDHaUmaWHRqjReyYWpgT/ofZn718NT2bQtCUzhKYGY7a0LpDIKR XxKvHSKK4jFIUV98NHsESgPJHlF4TVIw356POQyG0ph58U33FiqwW7My0g9OWpRiPU/hOagF WGgygUQwb9ZlpZbMux+APPWchRYFDpvRlh2Er5vFm/ZUgsJcyyKzLJvaOVDZWjT5eRVFilZ7 7iMmHdhs3xl1yrq7yWMyFozUrXwKQ3QomoSq9RRQOZq/PtvaxFFxtMPPVloZ3OZAyzXEyL8t STPaaiZgQv6niOBwHMMxCwuVliIIXKm01q4R3vAXR7UVDs8d+DHsudM2aoP2bruLIfNXC3t9 axjR8iMal3aFbpqn0MoOayBp4UGrnqZ0Gi6AEPBJDktxG87tquQbf1xFe/169PrQwdDbyd+y sqLJz72EEna1HAaz85OFFYPic5SvOW4hnrNoz6kTvStChlz5W2jo288JPcG2zR2liCM6PivL 0g0G3pHMXBw3OU8RhIXhKYIEUu4HP6sITPbWcLvr9cL285LA5evrfkpQa4npF0vI8v0/LtEi O3BqJPLEnX6PKTCw0XzFBPMdr5dW0/f5HI4658laK59a9/DU1lsDacA/sHfse+SshSIok5lM ve/uMEWAXDZussWkYDT5eTkOy0KaLkQDrtJORLlnbaJIn9EVGbxte2Iy8Mlq9M+ELm335npU o78skeMmvxirtV2VaN546fmv3SwfMUqJQqJWTVErTdKoyWFgvriPEFCr8k2mRtQdLUyQ7E/6 ea8nQh2eJRmG78NDKN52E/yaMzcO1frFufDMTp/HtNCW1C//tRJuCgjv/dam63J/32zhCFjP eTtjSuU2l2cBuFPG3tvkihiAabJWS9TrjSY/X0ANMj+vJvTAMLcQVs6ENtboI6st1AXaXqM+ uL+vwkeIB1v7DR2hDd36Ghdhafe7an53iDg/bhCIER/vzcFiufWoUJO28sIZGwX00U5KuW8/ JiXc7Xs0uuzeh1vtjIjtLad9Vl2FsLftt4XEZl6EzPlHTziDqahH68tPUOhRKb3qxqSduOM5 sCF1LWgx0wwR4P58L8/MElxq5QTeHtN9SUWTn92cXnabmo8KSLfNCQqqWrVvQFZ3sXYcthYx JqUiq496N/Z41XgZUkLruC8SkqmFkG6WhVeMNiShFgLHDd6oH+BtayiR66iFgkN1hhzTHw7Y Vu9IFrz9qyBwMOYiRvmo70Xkbi0ldCxv0Bb60OhciYEryMlmCkPjrp1YWrf8rgqcxUNWba8g J79chgcBR16KVrqPwlngqr2fBZFDAwzq1f6ulVeD6MOiPq8xqTCq/JyPygYkJYhgD7YzUbT+ BcYRNsTHBt5dMsd7Ecdk4NOz0SMGtGV3/6ajjy5vyjPINLqwutpSEdbteLkm78LnBy+OWjU+ b8MT+h55pGdkhIPIB24FVs2XRCCtCj0AJUzDIN3KoQcj1+dJhh1CurZjTUPzGB+nrSXFhfMi eLeHRjA9wicTaSWVlsRgXufdcJe7WIxS/S2N59CwJIucEtO/RFT5+TBBCmb7I6sIEZHtJUOI r5g/82Hcd49OSeif/qSJRQ+vEklJyZo5iTRvSHKJHbjaQszNDH+b+cpbJSXvWqTXu+PHEEWn +ogZL4aESRC02zPAP8UAl3cf1jjPpRNACNAoC0HuCz5WIdLhfP2/qC01qJ56M5K5miwRK8hh OcQHGikWkxmLNqPKz+4eiq7ctDwNhqSLdiuO5uzbt7YA4/xc9hza8P6qsaBG7Nu8uWPlgiXr Dx+5OHxLkPu+a6BNJZtUBG4Z0MkynusM7S3bPQYpl9PrZBD7lLCDrtGDM3aptvvDkAVdu1oU RD9dVNl9WT2AcNgmpAxhxVTP74eSAVzjKZMUp+wCYa/ihA1o1qXo8vPvWoC4atCSMjrjixoK olBUQ/vb3Tj14U+cWTcVgQY84Fw0CgMeiiJLFIXgZ/PcXV4Zr8DyffvVQtKu4pUaqI2rhGXQ vYskUlKgRNI+jT1pqlgzL1isfSgnjjzOCOZBPaMXVnT52f1jvY1Nn3daIdmnbitKmDPGaErs Wl//yRlN4gkNTsXRLNbfmKw0k5LkzXGfjKJ0+evGLIbBe8fZla1JWEkmgqM4nzrg7+I8zion pdgmkmZz7rrv1cPz3d9SZyWEbJT52e1+ctdnCQ4fOn7HxzRMnEiHxsPykzKxs6/SRf8owqTF czwqL4EMS1AgmL5SSOCG7DK60BuL83x9bnqt5OccLm1NgXVkzeJDj7zDjoQrFPr7sYtiYvUJ pT8XkQRqKjARS367B2nRWnu2pajzsyoC2nRxQ09VhFKdadm/iur3eNKwT6lyvqTYjBoy5j9x 92EpHoOTLfkYxtshjUc/FrKpP1cPqTWpuqTEEgpZ4aPM8WrDQ1X7tINEUA4BWpb3eeaIYHtV E3qaZ4wNP5s1f8v7OXoH0uQuhBX/KFgd9S6/llZgEGUuHxT+WpOg6KAv8dqVwNrjS4cA4TWh dHH9hVH7jAstxiTE2qbCwnyStEm78kkWLxt+UqmdnRrRmPDzH7s2fj4HY6KTTXYV7OBf+pmV 6hf+9r7e4DGhVvIUIU6Ze7bqFFutGq2En/sl7d/gjghnV6/atOPO/eEty44E85Da5EhKwObY Zbz9bUyysb63FcKCWXi+DwYn+PLgloMi1udE08sWAFuIcq7eiVb0d5aPCT/fpQ1T4L87HSck rIPo1gzUFcT8KfR4btvQdXefnSsLgLS4W4pyAdItRvZiesdZr0p/2p0QDLZobw6GExoNxREU R5lLAt1U7jfrmaUnDjUnQyJDH2YMRcJtey9FBLHffBXYAefjsjRdwtxEe1QU2m921MzbK5mO z74UE34+RugnEeNjOk9iaOXTq63ZCLUltJVU0NFdy0Og/Mz0NWM55gGkaPvfWVxS+SG9Ym3x CQ1kGAmyrCO0NXeerGJ5GjnqftY4tyFAy4XaOhHzZz5k23v99jrO0Cdl6kj2gBkIH8M+lYuS cWddW67G4A/sPJ3nclr2LSb8fIPTq8W9mZZEm6ROrUI0piUREcZXRrV79wMg7np5rTvd5lg2 PNrJN8mK/v8kIftgXtYoz+ZveGBvxgs83QiMtc/SLEWAqj6XJY81YH9NWiX52ps0DCUZHGbk a/kVeo63QWBBGJ+zrZBdNCVZgTAz0IF2klZL4Gpjws9frB7kkikd7PRu7LuV2hAYpTaSfq83 UWRmcwZiVrDbQGFXi1xDs152jboB9cp/esb7Qq8qbfVAIohQE2ImdcJ7/oTOg2Lg6c5Jnexw M+QiaYYFaEQyvKs7PUG+zot5w70ysnSyCUw57GvnH3jgf4uMMpJpi1bemPDzRzM0DuzLaA15 2tbzXIC5m2B670SXC7QdbExu81S1mZIepgPGLOX8y1Fu2dsoaunBbbsU5AlPmWJE5MgMdt+C BPHwLEfG5On7V2K/46kYQbOskYEzZTZeMScCg97blF1+YxzGQvpWCm1dqU+lmFK/+AwTJNoM Kh4Tfn5OIuM0PZxBlJ+coewFPoNi+p4Oewx7PrjQyiNDOztp1qNWaNP2i9k+F1GIVp/4D9Xs Z+HjhEXMgJxS9y2tKDFfkTBGxbQxSeKrAoJnyPbVhUYWlqTm7k1JZ90/1Yap3eXxbXxuZ0Pf UL43k3OSKDYhuXVyyPfX1xoTfr6LM3894SIZwBSCN1TrFRnxZim2u5gGGJ0OmNXXSjwspNO0 5QLg96vFOGxad/veiTX5sK+cu4wUOcte6r6CiLCOyxPGGGjs/Fc0LT1FciYDAN+8ZmIYQ5tg EPo1WxcQQSQw0XbplVP5dxYcWlfXpjUuOvnmaSfvY9QayVzM8Lwx4eeL2Ky0lZ+KpZQjPnaF dGyul7D4yYGV3X0K2qr4dR+ip1z5lB6xSPrqD8X6V94dXA2JFTmZQy0a0ZGqCR3xH8ALQoz3 tIw0MrhgIFRHc2SyAC62SHp8q0wDqHyqu5+Z+ZCis/0UJO2NA6x2XPjjKnv092aLCT9fIciw Koy/l6QR9Pzb1rIoL8ssjRIp5rwm9CNzb3VJdU8B5TH9WOkrwD4HiWAFJWSOUSeOKDtArLZi 3V7wTQVjpUSsswETzWAF3929RGlEMqsMUpaGb9GHtlZrg3iZuvUJYwHCIiD8jM0aE35+YSRm qf7ZZx2NLHcyOXIQiWgtsLIk5Xw+kawCgfZZGqp4ZBxJ8o1ZkkMAYdf3as7IiWDVO0Bs5jHp aRG3YOtSs5GHW8TvNphMFFFWTFWMRDSedcmSserrDDg0NlGeTgbAca9KGC86Q0Q9++syx4Sf 31jQ5RFt4H8dWVV0+NtiFq68GDzj93tHznq7NpxWZfLUrVVAAearklEUsspD96ZmiU9n7qQl OoqKGZynzcN/rtViQiy3sel4A0+gnBFaLH51N89CISiWG5orx9TyloWrjj17sS8D9yABB6ZL hU7xBd2drAD5qiD1LMoSE37+ZMH5hpaguDGzgfzve7NNyxVtbYABn6y3mEgUz1V3fH+7/Ux+ d55BZdnScVswO06f1mop5Si9luxnnPV9fwmCQY5cLJnisdSA7g2CBP36kSyaFYK8grSPtdQW AwV4TU1HRA43K7hkPcHqaB9PzwBUWeCJj3UWUzW82bCUfMYYE37+bkaNPQuVrXbW0VwYcAnV HopVqwx085FzW1KT01Scyg87bChGL5XqK4fnXb386No8c5kqIPB5OuW+fUgBT/eaj+uXTj14 sn1BUfe+gH4U/UCoDdIGiqTE1+zBNGTVvfkmLiU3h8UaItGmH21KNdtLw/pcrTUoavOVmp2z ct2EG3RM+NlthifqpRduXNH/PrrAHKtCYkBvSeYEYEX3OcxPixRgWD/mW/V4TjanJ8ULz5MC E6UzwGynOgnFAa1ilz1vrPw6HBHPStjAbx0USdZsWVZCaVPfnuANG9//dA9XzBmFGw5Xj/D9 hwcqOnyZEAVwAFctjYhbJAUia0z4+TWJ/XX+VV+nUiJfkig/RpaTlnAmoDUJJQICzKfFMCQ9 t0fW1lU1qV3t7zjDZsG259cR0qWG7QLm6WVIksAIA1R5312S3CvmuaVjx11f8IJLdZZzN3++ 211MSHwdT34UiAk/D1Gy2158OgJTIE8vmzx/yQsHIz0Iu2RjydokAQsv0lSnT6Yd9jQroZEF afd7M7isdnUohEpjbQSMwgTR+Ml9GUuV/+iaEyAqXKS9G5O/BSYJEzoX98Y8nHClM6iCmPDz cdg8g0joOxRVaDvhRp9BK4/PleEifa1VYPfdV+kgno8hW6tFOUexhcIgSb38tMFoSNIkJvBr wvXR5/sjWcb8LYLJ9vpExRJzvcYTmj6iqkJnHul0cqSzeSiKVc6oqmLCz7vhEBEH/3Lyiv7A AdPzDT4GWKHG6WKU1/pmKAwodaNBgcH+bpaiSUWUTmEssGgZeb5ZQjr4XgzpqYymbKN+jHtk mOet/CxYkKDw82NjGHj8iPo5mvnno2vBQE7GWeNMKhYTfu5H8JlEQ1VjGV7L6mVUnvD50ygF 36cVDhM8rRwfUepzBvZ8DNlc/lzZzqpDI0iXj+JQ8cl37h/7TTp/ffMGVVy0JFG0PgHpLRNh PL3wZInnCEuB2PAziobt2IzK8GFjNknU3VU9pgIFz/idM5wDYQOq3AhGrJL6KJL0klb2gKuQ cD0uNKRL53AuREVSjyfvHj0uG6hcYwJboIyr2nghlRSICT/vgGmV3ZsR2e5W2NGs9kgEsgs1 5eLIn1ej4RB4ujAlx2EiRJxlX0I+7WuQZGeX9IoM7RsOfGS+M3i/lHOpQXtY+BmplvelLUkW gzWEe/nPiLmdboOICT9vhGaVfLtV2xTYHiPoYhg0aTI2n7vUZdb4Rn8dW+BYKmSWXDo+55jG AA4EbmColkueqy0WYAcPJMsRft0jKJBpfEAV3K9v+Rpxd/CGGVG1dpcllwsX85/rtbMyHpwq Gk1ippjwc59hGhnfqn7UjnsWjqs8mg/0eZr4cjyD1COILXTY5zcrXBSNGoiyPS/uH6hkVB7P qyiILlixskyf/9U9kCQZXgM7FI0gTeNJ0alyx233eiQii01Pz7/bdfm7jlTaNAUqLNvGTdJ4 wSAUiA0/Q6XxCQlHgetbtx8NbcNyzfLftD1Xd3dZkw2Yq3CZyngQG7XMATHYzLyEhe7reiXK 07Yk4YqfrxPFY1lN7gW4WpMUv4FsZhLnJCdoLKqF+eEIEf8+AgrEhJ9X6koi6GI8axAKLIPy ROHVOgYrVI+LWQLJhvNHMSDFKIClY/i2CxLevT0Gp+D4dvnIwRRV7lmBenZuWUtj46p4gLKo r9vbbeFvYDHh5+WaoqgP9u+tMAJkHp9Bjth5SbT9wRWJ6MmouyJV8zY9+ab7stkGHhwjB4oM 7W/m33DfsRgWPPnt3lVh0iuexmLeWe/dOg2W15k0WBcW/SK6/PxIlY7SvVEriW+na3oW1tEn XM9/3VMLhue+PF6sypMeV4ryfyMA67AiioJrVWIvgAVLQvNyyKT/Wl+7hcmr1yPWrHwWhTIj AgIPR4/49xOmwMVsnNKNibzsX21U+bnL6MpOzy4rXelp5dP1Hat2jHGc26CNXFM6YXpEUMGO LnWZg0l8Pm20p4Qx61LXQMhcS/5VdoKDiSo7LNSXASlxojbDzhH3GxcMWco2H5PtVB+UmxAM R6CMcJjZURhAvIoIKLDXyfHG5LBB9qLJz8MMDuLvItpkrXyjG1lghvSIPtcfi3mDLlDwlAgG N62z9meb0aopkAbV/DMo0+F28lr1FGmGFF+GZpTIbltpdB70ceG61tTZ0S36XMXTNKKAhbbx VijsxTGa/LwPQyz13T1NdQpqTDuGYZmVNMT6GfdvSppG+qooz9mRJcaEtC1RrjRgdcVaJQLN of+K3tLqUj8iX47upSE4ybKT4jahrivxXDIFvr54/fjtcEhByr7da0ycIawfSjT5uZtglPDC Yj9P2gl718OP6y2QyRcrY6NWhIOciekUxJQsH6+EKzKCZKLKLaeWisSdvJg+4353ZOXqfMi4 aPDlLtooaq/iKXYU6E2zp1ttrKvUS2YxVlX5M5PThUCokrofTX6upHmfdVWsTROVIX006nvv X6qfsfftKuO4or1GvpbebbUSSPOwUHAPpibqk6eJ1RiTacK1KM6KXV0C1NDxFEsK7CMIEidJ EtXJkMgr8gvy7HnFmfnlzUt72vqVvtVwos9MyBRNfs4jSNHmV07DBCVjU1UqQVjkb5YZ/maw 1fuhCPoqWODzcBMR4fcrU0jEgWsc2y6cXWZKi0idVEsYcNzO8pT06DkFm9RpJSLsYTy7Wgo0 kDSXtWDdmmICEuXX20kYxTEchQ16hCASs5V6ynGNAvYWtOpo8nM2T6WuPXx++KfU2hatosLc hVh9ZNwb0NnqSqcKo0/FOjhIoiU7nu61wRBs4NvDQRKNVgiiS/Si6Qu2LymgGEIWYmT9O4Xx eFSMbdZlqeDZrEEw6gdWVLwqbYJJu4UjKdpmtpjSKA/UXgOtDRvwPor8/JIjabC+UDZFekXX J/fJU3ODonwu/uuxALEWZsU0Romfn9mNohfTuTbMkD42LnMoUn6jUMF066qVJszS66gvcbwa 8FkxZZM/yGKWEn3kPllg6emzbOfN2xe3HDp+49q1k0OjV+xeRh/Wpj6K/Ox2GMyFFTUOUmdo EUQsmbACQvWK1vlsLCvgbE/sw8kn12S38GPIW9c8uHFh5fxjUeLbYF3vnKsEq8jWqnD28BZ4 Dc6VxNvNFGuQ4K4va45MNoni9YeiQKGREOXWHxk4pDvdYlo/GI6S0eTnnuzDgE1HLi0lkwWs 1iLEI67LgH2CpSymjYvPvR88ORyud3/D96czmRQPFs+nJhS15NhRajzQfOpH69A9kTMvmRvh Zfn4v1IYjAGM0kurZ58uYqdI9T2N5wxPgTKcFgUgIyZCitl9qLu6bc2enb1dbT0r1vZ43szt rHZK5WFu5ZE8HyWA0KgS8kDlVCA+kQ/aGZKy5NM647Hwg/0+amwWPnOYHJ+ifxBd76QcJzyX jS/FBvuuj+73221Qs1dfgmOKjXNIjAevaW9ShPL0y8mSMPwDTWGSlqHWoNJvepydjRcLQ4EC wiTmuEFhYvDu07gehlAMg1EQOwhNSFeKVzMKbE3wCqN5PntaGTJC4EFQm+i5CpZBPk+0Hpaq 37yjuXmnGsHqrSiuh0jUtCqa/TFQb6RzvWCE5usrJXS+gyzpZQyqAileRWteWVhPvN1T2ghR cQcT+6SKclkyVfi5C48M+D6yns663CqeP/40KaYkfr5HwaKc+Gy61ZmeAv63Wm0OEvHodsto JAyYXHT1z55+vrDDPW53+1yP1VKm3ge3pg1LlzGjp8F0j18Y9CoHtW+46T0El0b5WC/7IU7O AGuSlU1kZUJYkadvFx5q5P1+A8uhx7/fXUXFPZUnPkmjy1k2dY6kygzGKmZ/a6ZlP6Vv391f 3v/8/enN27cDirU9YCiOCAuLPinn82enHtyvN2uUq/IbK+4DS92E+DjjRTL2qOe9mD5u0dzD jqO+plU/Wb3y5FgEjVPFfnFpafaicGZ9O+dUyYQoTBr0JcndcMZpOXMkWcYyEgS0sAKYwqjf HqI+R9O/wgl536TL5/OImfB+pY0ZdTuDTal92EvFrOEORwAlymNcserc6QmaIPWxUh+y31M7 fVE0AHlNe0AAmvR4WN1CgHF+2kTqdHoELQjt3vTaRIjBml+2GjzWBmqpNmgiNr95ObCBMxZW pdR2xP2o1BJu0vKVohJIzGeTIWTM1VJSdzFcJ6J4Ph9xNsrXjq0QL4DVpCMiGJXb3eTBoJT5 WTdDseIslELvFjh/PHg9FVD2tk27VmdD9pGQM7eOQGo2b9udjZYraPrhJnr0+w0uLc9pNawK YaT6SuM5x0+B1rmSedVPB+RtXzmmwqUQHNI2USgQRX7epEMlVcgFJyzetFcmSwjMh4lU3+CM zYaW8Y9+OpfM1SmRzDtSxxP45iBSJV7Y31RrwrhonSojkrHUhnGdrj/W1WZX7Qp3L5/OdJ5Z fbvbJT/b7oQWd52GfLS+AYkQRX6+wJJkYUvb/EJTshR45SFj6Dh4aNuWVE2vb9vdsKRn+wvT vZDi9s2aGslx+CAzrh2ripeP26+5YRFc7hy4NO6X/19I+HiX3W4V6tYo8rO7y4QTCArDpmLZ xmEtidtYlNCZh31no0vfOpXTEypwemT9uNnOK4KogAU/ZyFWAN7wbSGTNZ6T8yurBItxd/BR fNdHNsh4bnd0Y31PJUGjyc/uvuI8S3pmnWAlJqWN5UUMVdh2yW9ELQbpYv6XpZc9KWy9YpgV uO93y2DalZ1j6hiXicYLjwTR3a6RZdyXr8ShQqZ8nbye8haj1WBU+dn988PdT76a5S+DAUzU 8pDwwKPRGmDU6vm1JgWvCIuv+ONYmy1rhfpIVT7d+20RLTyEVA8JF/s7q53aBLwkMo+LqI14 tlV0Vb2f2rQlTXT5Wd0wP5vRpepyxijXhf6xDW9PM5SpRKwff6+zPbfsTKDC+FaJ2eat39hB ET7WsuOvPl4yEAWibo0bSzLHgp/fm/R9sRzzeNreTSAA0TZoitApIlg960g5/Ey/YJnfZ8gW hdAHKHzyg/KMhyh/cZmH41E//AXjjQU/A8fKCJ0IpgEhr9+ciltvDSWAb/7aakSA3WBBkmwl Oz/JIyebBpT4i7uwIU+Nx8BfPMBo6p/VkyHbEBYXXH1lMyrn8zIyvWOJHU8F6uc/DCULFp+l EOMxTplRlJnIYDwm0G3TG/d9ImOUy8bkfE7Th46bGIVxgdvq2FquhjQzjbLz1TgH0Z8KOVO2 C85ZvykhwrqYKnVTgOc9zg5P12K/Kj16yllkCRcTfnbqJ1++/TRz7EI7qgCDB1yDY4VO41I6 TXR9//bIZ+wN7m+SwW6FNq60ipSuN3UDkRaZAfljws85iAfjbPJIONEb6u+tBaaw7mmT131Q czbw7pC8qDOQSW3o7688kN/RrIxwGRN+LjFMb30VWN9vNmRAGVGERhkPy7QlyG54l7GK8ZSf RWWcESGQz2DCxISfCxXB7TQl7PsleRySvy7WstCbRqNoNdqXbQ2LSzFNKTlV3bo7ffAxpmrI gduJCT9Xav0tQGNIhLFXtUcZutTlYS3BpqDL23A6s3FjpaEutJHpFPRkOjWxunY69Waa9SUm /FyUPA5Ulkki3IBdhv8frf9x7Wov8K/Q7b69sGfjslWTNZy9RYwWITvjrhlu91MPsNMmEcoh ngJSICb8nKkGNDp6ExZS2f1hQi+v9SkAnxczTJ49+pG1lyJHLIge6WJekwd56fBUmPPEfLgT 7kCM+HlKt9jJO9524ljZrpf39lmQGWUEPOFVFbUKHiDDUatrVlQUE37OQUSc4clIUysXcenm i4M4bJ2xATMnY5IiqPNgBHnjWaOKN6SenMU0U9y4bJrdI8cB9nEZcclIMeuhceAuqyfY7MgZ MtjL7CDBhEcZk/O5jKJJq2tCGKcTHPhXv3DZz3szpZM2orRT2yvnP6UNiw8UUc2zKPPoaygk GN4soshEhhoTfs7Fss9MJzi64z0p6UsCXhcGQ9J2h06JU3+djCtRIlqGNzwBhF9MD7v5iHo/ fTPHhJ8L9OGBCidMMtXWu6fyNfjGIBHRPRE+AnZnjQeF8z7s8Z2YcMdnRQVlgl9oPEWdAjHh 5xR9hEFaxjHsVypDxr+aZzVkjQf6HvRplywOAyGHQkf6HEf/Z2SR5x4Ugbh/yeRMcEz4OV07 BdZXgeRbATRXCxzzNoxXKH4TN8uBnsuhKCGUTM4sx7bWbdEMKRjboUz71mPCz9lklN9MKvEY d5WOnY9HAcI3Dai1r+xIrhf2grtN9F8LKD55K9RjSHtrkqPbT94Q/r6aY8LPGdhITCj1RKVb g2+UuVBdXcBmVzZlOu2T/36ICcEib/TQqIv5OEFOI28zXmKUAjHh56yZY0411GqldAtn/dL1 vFie+wQGjnPaVFMgJvych4QLiDq5ZLgCYlhELb1Re+pHrcXpUtGt0bM4lrYE04Uc06IfseDn r9aY8vOFDrZ1WtD+b+9Ef+rbv30IM67/seDnVzg0WQ6GAefHW7r1Z6CVNgPwzHgaNwXOPFaK xkEKx03EySoYC35+gmFTig8/Cu94elG+NWe5Smn4ZJH876xXvYzw7xzfDOl1LPj5BQfHxlR3 gIFbzvorT1RDF8yQGY8PYyZTIBb8/IGCo+KcHjG+V4dxLMLf0LKZPL3jG9uX+EV6fISLfalY 8PNbCvdELJhaCvh5VYmNexwDprYr07m1Z6pt36fzKGZl36LPz3sKFKcj9+/9re1NCzf5o1g/ ZagHUSX2twA4+ResMyD6Z1SpFK6ykTgXhyPR9P8+6vx8w5TklIf9tJLRYToEyRBRZ0fTcxzx +8tE6RQgoMm1oolWOkvKr/aMc/JwmWYJKafBMKPOz/MZgpfH1awjyrsLrBSS6fvS/UiSUxet cyTgVeDsdHK/jsUyGPE0Gpf2x4L+k9VmtPl5t5VjWamzL1h4ldv981Qzpe3x6f5bhJ0iC/2v Gytp/dTtHZM1SZNQ7xRNwCT0PF5lKApEmZ/POhieYqXjuFcvY/AsghW3Qqknr1BqagyLTufC VGYcaGD0LJ5StX+c8WJAgSjzcwlk4UlU9Hf9bjbIXkcf7ZCPfe8HjlDrkDiWIgOqgboO9TDU 8oveNZxYN34KTzP0wkgGctwTODWSUvG8fyMFosvPeynTxkLaIPrYXIfpRzJF8pJ9BNBfbNT4 HZIO5HnIHBLj4nYDaizw87vYMwEv5bHRZKf5dEfV6WSajzXePYUCUeXnKzZ8j7uFlLA69iVm K40sgnwO1e9WagLy7VGkr5AQuTloxflZOM2PRpVO4wAgnoUEm2FDjiY//87TAiXRQpIW73fr tJ6X62aDzZtsHxl066ST8f79aDTx96HWjf/mEw16xeuIMQWiyc/bWBy8mHsIRlQRLdVUKWPb BXHed+PPVipj2861C1o2TMngr03g6RtlXKQpGW+8kVlMgSjy89kUht3i/tZAUmJ0qr6EfIWu h2Gf8/k5x8E2B5+S0hglwn8btYkIUOOG2IInRGmM8WriFFBBgSjyczlMYpnVJWYSo4uAffax xDSl/R2aTO+uDFNk5dCrRz+CYF6r6LVfliGjXNWQJ6ho5JV4lZje8uBLYwLcTmiw8cIziQJR 5OcFJoIgOIpkScIwz+2+ojErZmF18BJvmj3jmAkA/gQSpUk36mNN1MwFHom7dc4ktpu0sUSR n93n+vu3HVnJUWjdWWAK/BrXX5K6/ZhEPS4awudXFB3xk9bjkHWtOTAtLtRkoVTdKO7JjYgh reLX8klbZfGKp4oC0eRnqc/tNCUZcdTrO6S/7IdMw97j+UhSEzA3DHjbvNTB6SzNp71a2S83 rp6QEe8x6qsed84ncevqcdNuVhaMPj/3YWbJzfgkRkj2hW2on02XDQsAYh85+T1eHiPdNNF0 cXz+kXHX/cgJHy8xbSkQfX5elWSSR5ujc21/5365gdXu9h2/FboaDYJ4cOnqtVnbx1thet94 S0a73Nt47PJok3T21Rd9fj73j0sm40oIJbKKnDiU5xsg6gOJRdfn6frWN+OeudOxjow2KuiK i7zGPYvxgjIFos/Pv1I83pEdNhjDoIxtfvHeXpJRxieJfDYvxlGGIidavMT0p0D0+dntpVY+ s3rZ5q1jLBBvQ+TDSClzNLrWyP2xfjZHjGUYKcHi+WclBSaBn8PS8ayBiJifRy/UG6X6B5eM Ygp+UnlTnYBUPeyg4hniFJgGFIgJPyezKq+7npCjo5Q6kbQCfLhewWjWev64UCVWWN2maUDx eBfiFJg8CsSCn29ofcy5IxvckCH/wqGKNEuNl+vTDZXOyZdGImsrnjtOgb+MArHg5yeQZQJU MhM8nXf4pfoaJuBrrb6ReM44BaYDBWLBzy+wCZzPv0lt9q6ITEf2RVeUNh1mLd6HOAUCUyAW /DyCmVVOx6jZyWMPOHRZ+/TD2r08rHJA8WxxCkwqBWLBz89hlfftTwWea3XXYYUMv9XR48q4 LcbU1Q9yPfMBG1RdLJ4xToFJo0As+PmRnlapfp2A7dbrqJiUBqD7hel3PZi01RGv+G+jQCz4 +S7kCEmm0CA/5ZdjS+Pp6IYVW4rEW58+FIgFP99H00MSYAxuyc29RzwFvmbFKDbl9JmzeE/i FAhGgVjw8wsjq85/4kszOAzv7G+hyIidmeMzHqfAbKRALPj5MUEHCsQ8lvy/NJmrWjnCkiMC egdPnyLGIgldX1SgfmfjaoqPOdYUiAU/3zIY1fGz2wijRNnue8A/K8aP5lhPU7z9OAVUUSAW /Hwb4sMcuErXXaYFMgaZqsGMJ1Otp1Dc7GQ89IuXmVYUiAU/X4EIS1lVi4oYk+smPyBilK/q 02py452ZdRSIBT8PGnCuoj3f1676VVcAl+T+APOhYhuYdbMYH3CcAhIFYsHPNwlurPFW37+e v3nAiH6YxhqU3Koc/9Qt84ENHn898ZJxCkxTCsSCn1/ijrEOFUeS+mQSfR99yIqBpP1SxOZZ Qx60g0diYK14ilNgxlIgFvz8ngxgH3ZZL0aNfrlnSUEgMN/hSGfg4YhS4nY8qFykxIvn/1sp EBN+ZmWEbm+ivXWB6O+/96USRfXKQe39dcTBK/Ln/61TEu93nALjpkAs+HmEQsbi9f7J33Fv RyVfe8jtjoo5x4W4Wei4F0W84F9LgVjw87tA8W7OYITJ2njcl5CL1NH1R5x31REqnmumUyAW /PwAs4x1Yj4MU5Wj0eRksqs08Xh4bKZPU3x8cQqookAs+Pk+ziv4nh8KFEPO/XOzFE/EkBrm d3EQAVUTG880KykQC35+gNCKXvlcknKlftN5QdUELM1WlS2eKU6B2UiBWPDzfdSDHzZkUOkJ CcRkUno4OBunKT7mOAVUUSAW/HxDZ1T6tpVoU34N8AZ+pJLZVY00nilOgZlPgVjw8yM9Lj2R 3292Qq2eg3cssZ8tm/kTEB9hnAJRpEAs+PktSQIF05+DLTk4iXQpg3kSxVHFq4pTYHZSILr8 /EeV89Nzlmq+sCINRanMEmShQvcls3MC4qOOUyCKFIgqPy8tMOfWdq5YKoeAdLs/nek/e3uM j9Q9isF5xlG39/ybj839URxMvKoIKHB4AmDIETQTzzqlFIgmPw8hEE6SKIHo1kljWF+WwvP2 3LN+I7pKcI4tV1Ud5VNKi1nV2KuEllk13lky2Gjy89cKY3q6mTTyiHR3PleMkKY0I2T2c6a4 BNOrZwl5p+8wTybkT9/OxXs2XgpEk59BBJgvr/ZazQwvGn29LarK6Nw5tLdM5/Lt3X3CeGe8 /Y2XixIFds4tjlJN8WrGTYGLj8ddNEjB6PIzaOR7KsVtEBtbqskVY868TdNt8Wn9KW7yQAxE ezwh6hvF5B9/oxcirUQdLulA6B69Dvp14DfwURUDPJW0QEWueJbJpMAVqjHa1Uedn49SaKfY yY9mbY/U29WwbzyMx7jKeHRRHewvMgzXqGnNiqvJNZpnn/68igJb4JAcuCY3WB1dWYG+2ZYw dtfZJ03KaHqmkXbdqU0eM7+JNDvZkK8T6VtEZXdoJwCeFbilqPNzC2U4IDZ1TMs9l9p8xPiu 6rd0akTDjk7mL8j6iVdkhCKrY/O/B1UUqJ0TUp6QqwtWR15yoG/Wztk05s+ZSX6H/NsEj+Zf RQ+jlGUoUc3VIUxjK/SnIuvOfjWeAfd2R1ZpNHIv02VGoxrvOqLNzw9sNDFP9J5amuiBtrZr N3u3OYJlRHsYKuobIfaqyBUmSw4fWR37aDXAKosNIa/xdVSwRguRQN9sQcdeCjJRvyfOnTkx UPgf/J/FkdEvUO55/10bWSWUGq5ZYAqALzvaTrHHLtm/7Za+QL05G2CH/uM/y92jjgyRjSh4 7mjz8wmUYskMYa+rTe5Vmi3Vz/PuwRvCHvxFGK2Bjannk+3cxOsui5Cfdxlvqmh0NRwyvkA1 E6yOioDX/+30WHFjuckvJMnDpF0qehblLCfm+Gzs46t9RXKElajimorE0yG68x4K9kS8rbUF Khfo5rQ0we9isQzFPo6PBkFLRZuff3U4SFhvAUCa5VqP6Vdbko8s9acRaz169uixA5cv37h0 7dDO03fvPLv38PndofOD1248efbm46fPd15+e3nu2Qf3z18/vrs/vBj5/UkcwdsbT/8EDh39 Pixdzpg8+0vQvK9ejwUe9dmJKtjIJPPzUBVh5T9UYWtCdP+EjQjy8v+ZQQXaLtpxj0GPUu0b CzXs28QRNAbv50GdCnKEm8ll+m3hsvh8/xFPUZG/JDnUnvoYCXbGX4UDPh4tiWPbLPjX74De iERdkBRtfgYInZvKaBg4Ri1OKleG1Klp8RldAUQRNEmSPMfTPEUwFsZopO1mkmI52mgx52Y6 aaPLZbM7XTZnqjPLZnRmWTKb6vMrMhk+LbMgqygzr6KyqqCmqaikqqW9sTijoMiZXtPSUpuX VVpamFNSXdNYUZKXW764s6qsKKeivDS/pH5RNkkXVLQvri/pXNbV0lG3sLekqbopLye1PCM7 p7KwpKu+rinDbHHVNdW219Qvbm9pbiisaKgqq2upLCkqzi7t6Ohd3JpvIXPXLWpubZ3fuWhR fWtXX19dRlHpoiXFC+pL53Wv7du4uL21OrewujC7MrOqJqeuzsGkVnX29PRuXFDfurynrXlJ Q01dY2VJdWdzZcui9s7OspbSuqoaM5PZVF2eX790TWt9Y2dvS21jeXNLQ/28BW2LetqKHZwx bfGyzt4dG1d1N9XV16Y580s6Fy5u7Fxcx/KFSzraehcv6qxr3bS6p75s5apVvY1Ok6Wld+XC jnmNi7Zt27dz54F1axp4pr5/z/7zJ/r7Hl/c2Xdk79IcwnXk/PWLO46fG7x45sjAlSfPLx6+ 9OT+0Knhc8fOnLly+eCVm8/uHz517eGzkc/vb915dmjH8Milj++fvHty68rRo0NX794fOP3w 6+t37z5KG+wb+aT58+fDu9+frrz7/RZssQBfZsRbBL+VKAGoj273h3sKoIUKTvPP0o743PbC 1vAwEJysf6mfhUQofr6GBePnt8a8QD3ISBx77lRo/DaiI9EXDEefn93uV7y+yu3elVSqDLQ9 2ffZtNDe3VyebXWZTCiZl26lrWYTb3TwCE5yDIlCOESZIIRAIZJCURjFdRhKojSqRQiCgBCY 0ut1umQtrdMmQ7A+WQNpkxI1Oi1kgJJ1Br2BMOg14Gs9LvyENRAEIxjEYRBKUIgWcmEIRpHJ NKLVaw0agwGBMBzDMBSD9JABoUgchzADrNPoDXoEgQiEMGAoBGOYDoe0MIwxFM9CFIbokmhI j0GQwQDDEGgUhSGdFlxMDIjBgOpQBAUJQWAcWLYaMEij0ev0kAb0m8LBiHACRSAI5AElSYJE SYpxYhiJQTCk10MoaBkDLUI6CAwHgnGOZhkE0SSDWrQ6BEZREtGBZqXekJAGfIBgWA/a00Kg XhglTSRoALSIGIQKYQRBGbBtouAPOoPOzmMGFMEYBgejI3UIhtJGFid5GkEpjuVwI8/TNKgA Z1JcGM1yZrvNQrFmBGVNlNVMWY0ojMCM2cTpIMJE253WlMyC3OxCq8mZXlLb0JLtsDNOh8GU YTalumzZKWZjZl5phi27uj4nu8xKIumNZXl5Niatorq4PD2zori0KsfhzCooqm6pKiuf31GW UdrS3lDfUlGcmZVV2drbs6hnQXtLS9eydYu27N66Yd2OtQ02il964sLQobWL91w+cfzYpYtH Bg4dH756ZvDptcdfvjx8+Objs6M3r767d/7e/advvn18voy1H7pz7/67x8NPfnx7A7YXcNcD eFcgyKEnvXu828oslz6OhcICfzxEB9PWv0srCsTPmejYv5YRfg//XWhB2N0owgyTwc/uLATw 89lkjzIl2+AncAW2nj9fPbh7ZnDH2XdPhm++evtk+Omnmwf3b94z0L98+YHDlx4fOXpmYGv/ wKG+Hdu2rjwzeOb4xa0r95w9vKGqe/GKhRs2bVu++OC8RZ3rVnTU1q9Z2dW+eGvfphXzWpe0 LNxxaNe+TSsWdi9YA9bB8vVtS4oLG3esXdlQWVyybGPXhoub1y5bvX3e+hWLmhd01HT2LmvM TC9oy3Vk5be0Nc5rqsqubGyr7VjU0ruio6q0paa4uiSnuHFFR3nDge09vfV5ZRVNZdU9HSs3 tXRU5TgzG0tzCqpaF5Y3txbXNZWXlqe78rIL8zMbSmoLwb2iuLi8oXVJZUtndnFFQ9vSpfMK 8xYuyE1z5jrY9Ix0F2+vri2tyM60VnS1NBeUF+WkZmXnZabmZtns1TVVWZmVJaVZpcVVlfkp Ji63rqq7oji3vCwrpaSAM2blZthNRpbm03PyKuvLip1OhxlY1jo5trCuPCuzxEib7Zl2Rypn NzKsiWVdOWl2Z0oKR7F22m61EKZ0p8lsNHMpLtwCTHKNNoZkWBIwstHM0EaT0WZ00RTBp/IW M7g78ZSZ420miwnHCRpDCNpuIWmaoCmKQkke7DXCzoWTONjewMZCMIgBR8H2RBMYAvZHHAc7 BgTpEQNC8xxJEDAONgsMbKNgBzJAKEZQOIzoCQzV68GOjWAERqCgCEkSOIIQCIlAoBoCN6A4 RhAUYwRbCY6QLIHAoAs8Z+IJHCU4xmGz0pzTbrY6Um2kibEaKYbnjKnpaSxvspuNRicH8jis rqIMe2aKJbvQllmYmp3XVp5TXFGYbk+1mLn6Rc0t9VVlzcWVi9qLc4rrFvR2t1csb1u9eN6i Ct6x7OSlm+cGjx7u33N1YFPf0b1btp/fs2fvwR6TZd2li8M3L9y/fPXQ4cvbdl6+fvXSg01m CsDJv//w4829e2++fHB/fuj+uS2VbwKXmN/nn35z33366u23+/VE1G16JoWf82CgMX2AEfJj 9AsF74twm5nC7D9+CBdAlQFsffr16qd/N397qvnz+a33AeCV8e3w92v9X39/fHtTkjj/kS+k 70aE6r6C3nwShAF/PMW/3zwj2uVICYCsDV/89unL4wcPzxw+OyJ0HlxqX43cO3H+zferp8Hn 37/cZ04+f+/++uHb4JVdW/aeO3H60uvvrx7cuXnqwOHhHW0bBg4PPb1+4+TxSxeffj597fLZ bb1btu8/sGTB4aEbt49uO3Dx8KZVa452L1+/89jGgdXNXWs2rOhdsmzp6iNrWxetO7hp6abV u3cv27Rh+cFtfa35LUt6Nq6e19S7c/v2nkXzKootWZ19vd0tZTWdtWt7uxfvWNxU6SztWV5T Ch4fOaWp1rTKedU9K1vzMytc6bVLF7Y48wpSwCupuX1xSWNORW19eU5VWY6JSc3Ndzkd9uy8 Aps9ty7LzBXmOak0G0XYs2w4m27Nycgrz3KkcDzgYYfRbOFSwIZDg4sMYG2W4yjeBC4+CE0Y zSRJszRrxHGK5MFdhKQJHGMxnErlORpnULDbUCxF0pzJTFNmHONoUA5BwXOQxoQNBFznhC2J pRiQyWaiQAmEBTsVa8QQM4mBbZCiGYYxsYzVajXiDG62Mzhp4ymSyM2zO8HNxJaWYU+xEem5 KVaTOa+gvJDkU4o43mhJdXBQwLN9Ios/ivz82XNTyUNBAIzfVr0cce4GSanRAE5kGPGysaHA GADWsdGIvl2S9hywRbnfXFNe07/c70WHHKDoeCN9e/+ruIOBzD8vHwN/e3772vBv969HL8Cp 8OD2xzev739+0uwa+HC8dvODOx8+/XH/ePX63Ilbx08+uj90dOflwfNADHBw8451O470H915 9OS6LVvXb9t55Or+Xdu39mw4v2vtqvnLawtry3Oyymt7y3MySmyuspzirJyW+tba4pKcjIrK wsquFHul3ZaVaSstMfN5VmNWdpaLddqdGbZUs4MygctKWnMKg4FHmj7FxrlsNhuJsQzYSwgO 3DH0mBE8pgjaoIdtZgI8+8ClBMZhA3gGgZcZeBVhGAXrYOktBnYKkqHwqCu9o8fPHx25cmCZ G1a4DsxMhU7G+tsOkcOxWW7xVmcYBSQ1xxSkX0AbLe4vH6UH9btvH55+eSlI8T7cPX/3yPbh h0/e3Hn14dShqzef/3h8/sGjG8e2HT25Yd2uY8dXrzt69cyhMye3Lt/c1b1t++Z9S9Yc2rR+ /ZYNB7b0bd6zf3lnWcn8pS0dlbVLa9qjPpLo8fOjZG2xqD25Wcw6hLjNK3WWl8Ln82n6qD8T ok6HeIVxCswECkSPn91pOJE1f/+J1RmEQ5TjvcnQVR48eWF7FpQTP55nwlqJj2H6UyCK/Hyl 1kSTrJkyOMVIkW73bphgGQuNWQanPx3iPYxTYCZQIIr87H65oYEAms4qj49ggx01QAZn1B/9 M4Hw8THEKTAJFIgmP4Pu3Th6WTQAktOzI2t6tseBOydh3uJVxikQiAJR5ufpS+S3F45deh7Q 9kd9n6/sOrSr/2xkwGffw8sO7u3YePVqKHS+V89UBuZTP5R4zplJgdnCzxszzBRpLXo6kVl8 a9LRGE5EZKP3qTo9HETJ4jQeoXWFwXr2Zm+9zZQW1F1vIgOa4rKfbp4YujUU2J9GdVdeHTp+ 5uaI6uyzLeMs4edPKQRuS8EN/igdkU13T0E2xyD2SAqtxvRhsA+PUBzDZtrnB6n1YzEwYSLh pKgge0TS8+jnrbNgJG+eoM61EkIRwhoZVtKPw+EG8+387v3bQrlt/R5+qpjFhKsrpt/PEn4+ hZI5Ry8WUGkTJPb1HAsRyYI8bzXhS0O3uZI1GzvHWI56ynRDqKmoKtV2cYI9j33xowwOUxSS 2DqhrlQTNEPCCccjqORtuyaMvfHnIhJFtXOCeq0+WZhF0+YwExlBjyYv6yzh5xGbETBEJ63G EzYUsd8Cb7AIYMjeFjJWZowzsm8DNbTZ1BL89VzEsZ1vfr8WLXP+7rRATxRtb6d0Eb1Xxgz5 1+EKnmd5NbAvctlfBZS2OTTpVmGckbHW3guS61uxHiIIHQ68+qd7miX87L7bD2aiA82Z4Hzc BP7aEfiWLARm99Sq0G2m8zYjlbsyyFr64OBMVaeP3Z1gv6dD8bVcHbDursSCohuq7OSzDJb2 QFmpKLMDY4kwAFd1wGvLLAeBCFDjBgJ11OYxiBpsRxUdmswss4WfBRo+ziYmqgp/yrF41kO1 E3LdabHZ2ODrRKzHgVIMDWn5kwFrvW808sCXxzQTAhCIwsgmuEEt+YLke5vOmyJ5P+cxVjo0 P/9xsWYr0x20X4083et+dCQyaJoJjnKcxWcRP98owCMDtghE0j0FDK7Wye1DAW+zmblgki65 +jbLsjs31mUgroDKtFMAxIUVHPGE+8UMSLtNyRPFIbxAUUyat5VDaLKs5CwW1gOtETDvVwsP nKrJpiDajx9pFjp/62bV23gsp2n28PO9TF2R7Jo3EYIPp9FseLAysYWFlNFutxv5UOBgINuI kHcPh/qH+RLrOAg89tuPrrATQfVZExnMlJfdaoOdIXE01fSojGNh1cF6dpnBm8cUEOPL09Z7 jkZZGw67AotGznMWG8+ihbfVdC7GeWYNP79rRIqDyTsimoNGI3lfXYEGmndYzWY18FXuUyY0 4Al8jndmA1CLOpaSnVHVNT1Nc20xwVxY3VHYvt9vMZMalQf0oMtoNppMRGh1X605f8tAEwsZ A7a9FACd0BYOKQwNFhm231ORYbbw8/d6jp8glvunFeIJ2kCoPhsW2ecVGlk29Nv3qShfO2oM jEf3zOoQrvd1vBEAk/zt6YhZ59oThUF8rzf5hVAKWmm73sibbHbSFhohWrRJbmWQgLtEOcux rVsaGDjq0WmiQAu/KmYLPzcSnDlCGHZ/Yjdr2FX9/atpRH09x9wPXXjomCa/UpJbn7lfNKFp gX31HTS3a2CDFS2J/txPdY33sxlmwhjo4jWlx6RRecxvQViet1lYWoXCegcHBXzz1HFG12v3 1zLcMNUUi7y9WcLPq0neyhpLuiaCId9KUAL+KFYbkS64zhBapv6Fwbm0TJfBKkUJGpOKKMZs 5VHHlcjndpqVuFfKstyeV0Fg1VR2toBZ/v73UC5pVAtEv7OmkDcxTOhJWNwtzOkmjg9om7+R 5S3P3Z+KWf+YQSr7PJXZYsLPoXwPJmfwzaTdDFyzuYnonx+0ZBAk5SiLTKh2DECRh0xtqSYA RWk+FiTTdouZt5LGYF9PDrkmpdYSwmjlbGarjEI1rja+0AiRkZNC8hHEInvfY8NC68iu8lju xsEDeWjgCzXQGZJrz8/jiexx9XlKC8WAn991ZAY5iyZv5C92bG9s27l6y8SsJvcX5DecjLpc 6vSaxT3bgztt7D2w6+jmSWTnsQh+kzQNmQDenCBQrW48WKpKnyqBNTuEY/kRiabq4ND3sptG lqStDEwGCSNWycHWLA63RmAZOEk0DFvt1PPz2yociuOJhZ2YScrwbLMMGy/Vf6o705LbvC0i 7hhvzw4VZOTWVOZZJ2bvuTE71Vy+ODLLywdy4OJgPf8xz04TCO0IZpn7cRkHcMfz/gZH/inn 58ulAKR4om4R411Ss77cq3rkXy/VzQIKA7E+DFDBlDC0pGn/PIFQN0L5Xz+eTtDlMsAqOLV9 15bjIW4qO1fmFQ//Datnyvk5l2H5MOZ3fwPd/s4+3q/gWGhUY3SawUD0sIZcXDcT3Kun/5S8 9nWju9u/Z5P3Q+rdib39E9VKTjk/t/RWG8mJiKWm/7RN2x5+zMN5Hh9Vw7fgTMvhEffXFgIT AJbjaVIp8GOB0VugdqvRQSEw3CZrSz70VTspGLc2iZeYcacp52e3+zqPxN/P456wiRS8hAoR YTxCn3tGuFpUel9x6sO8MCfSarysSIFPHWbkn1HLwt9ZOGVPtaGGNOlZ3gm8w1kcRw25EwLF igE/u014SXyOY0KBHbuKacRjWXEcN8kinkbYE9s3Jv2aBY3eqwMRrpJHA5HtwMiuux+GVzOI iO9wzkG4Vp3uX+pkkAkpf2LBz1Z6oi6ws2D+J2mIBxnYA6uzVa/sqz0GfiJKpEnq64yqthIB Plyj/Pw+G0sXQR4bSEh4AFXhuCjX2O5Aqycy7ljwsy1oMN2JjCReVg0FLhkJOdqB292mV2AB 9hqgcKiFaiqP5wlOgb7qOosVfa5kOIDhkp5hM64rA7FknJQEnQPg4sJgL4Qmciz42YqBEcRT TCgwZEI9thWZHmiBQQRXaQ8dk07PjEb/VFphj69oN+yStG7POIz+5D5lpBdLo9xO4RHZE/vR Jhb87KArZsYM/YWjGCL0yvvsNAUptqgn9Nimv3Awf1mXO42EBzEhFVGwNdI46Ia7lUT3SqM5 xeATMQaMBT8b4fj5HKuleJnQb5DbHjRqFJi8y+joLTxWPZv57S5jCMVY+AOPK7ByHSx46zTQ kOx5NsxSkQDI+lMtFvzMxfk5Zov3qhFeoTSeAyluCjcwXIU/Ycw6PUMa7mVxxWT0La2cx+5d NHPbXUtR8tH9ycRmTmC8seBnGxk/nycwZRMqeoVFPXijTZAyD5tg8m8Au5vQyGNfuInUK0/j 9yx2UO7QRhS9684mWNkM9iNLcRPoaiz42Y6FhmebwHDiRcNQ4LnJ4NFXDUA2Ofd8g2lqLLhn 9fzMIzUKpuB3K6xsqz0089xdTuMKKJ2N+dvO5wzkLwBumaErD/CzR779xUbKj7YqJH5jmvwJ b8cMHmfbQs9bpwwj37q7cViBWHeiEwFAjcX57NJPxKV98uk+k1u4YdSP4oE3G5rEsZ43YhEE CZjJ5JnUsfUwnvu2ezGcJbeVjZjd7kMUriComIiJuFnHgp9r5+6YVLrFKw9OgSsMstTz7QGM bD7958XubI/yJE66SaRAlxc/n6EoWTbmEgzCXtlx+Q36GOUn0oVY8PPNsshCKE9kfPGyvhQY gnRecOCVkJZMNaLJ9sdxOk0+BdqpRI875Ds7LLnADHO4YLBXhcPSHGzVTki4FAt+nnzKxVsI SoGCDC8c0VscRaEojqtHLI1TdvwUaGKRUTDEQogX4OB/1RtE/NcNhAQbez49eUKWenF+Hv/8 /JUlv/tYE55e1V5Uu/MviLP2V9Lar9PtmH4UAmUzA9kbVq+vJ8gTYrZynCzbuXdhGsZOyDMm zs8zYaXEx/A3UGApnTfazV+1ZoNGj2C6ZdLfjrIGmCFQXUqoqPLhRxnn5/A0iueIUyAqFPCF hj1fZEvJrvQYd+7PYhhjVdME4WPj/ByVmYpXEqdA5BQQHaA96dex/RMPmhDn58inIV4iToHp SoE4P0/XmYn3K06ByCkQ5+fIaRYvEafAdKVAnJ+n68zE+xWnQOQUiPNz5DSLl4hTYLpSIM7P 03Vm4v2KUyByCsT5OXKaxUvEKTBdKRDn5+k6M/F+xSkQOQXi/Bw5zeIl4hSYrhSI8/N0nZl4 v+IUiJwCcX6OnGbxEnEKTFcKxPl5us5MvF9xCkROgTg/R06zeIk4BaYrBeL8PF1nJt6vOAUi p0CcnyOnWbxEnALTlQJxfp6uMxPvV5wCkVMgzs+R0yxeIk6B6UqBOD9P15mJ9ytOgcgpEOfn yGkWLxFzCpzwAhEf25k9F2PewVh1IM7PU0/5Uys8sOpT37jY4uvXMWo4Ss1uZ+beDF7VLYIc jekTpRb/lmri/Bx4plZlZaU7XFZHTnZBqRcAfTSm9QmXxLwTKvJw9bf746l3b0n+wq2rqzvW X1ZCE6qt5aGFeaA2ryffvYGdA/cmKbDJ1VTrYAQd2s4iJaGy10L6axFUN5Oyxvk58GzmswxJ Cv+bUk1ypO1oTftxCIYEdrrm6JSr7KCVON8RtPHDiSMoASfqMVPaygjKgayb4OSlkZVw95fw JEbyrrI9ERYMmf3cfOnrViSpQn2976xQrhJ4NWCpB2n6cvXVzaiccX4OPJ2vLh3d2sSyaVtu vx6J8oS/y8LbhCo7k3Lkmh3/2Rt5G2cIkjDaCZeLQvVwZMfROQvUF1mDTx0oziAYiiGEzIKR lQ+SO32uFLy2HtVFELB2ocZyPXTzh3lKCAo1C1Ocn4NP+udUtmEylsQbifva0VK59nR0e+Tt XOax4isvzv/8dGIeow0pHhpb983+CNtbQuEpLc2t7R12/b9XIywbPPsHo04iwb3y7GHVtb6w wGHvI5V68yQ9DVR3MzYZ4/wcgu4uavEkzsoCokjhZ6Mnxrr69s6zkCL1KYE9URbUl48oZyFL SBGiH2/IfxRRyVCZX5ioYvn70UBtYWtfgWSGlSdet2pWhK1oJmaI83OIWU0nvfj5/cOncii3 W6cev/juXezN7RujH3+/fvDc+8t3P6VPf/74tvQyi2KXiHLm3RyTu+2HsrA/vvrsnfF7sJV+ mzMop3IbIl7gxfTeJ95ctJZsqZEbDaUWrUrdl+2mjIgr+5WHbghfqAuzzcoDOs7PwdfGLzNW K337pLRiXo7JZBHX9JYswmLOrOs6JXzoW7J9abaVw9pkBt9TmpvG8d3SGXa47+T++pSMkqNA Q7Qw35ladEH88xlRDraa5AhDHfjllpHhCb0YNdTtXpXmsOS1nxR/f3Fg6EJbCrcocA/v8dhy +Zs6mZ9f9DbnpeQfVvJf68woWuaJHXmrt7Kgcqv03S9BIv5e2SnuSbqfpztbWntlcdfP229v 7po/77RSVRmrhB/3dOb1trV7Tt6SP74f2bztvI8e4N6VoRGvjn+9t/au+PH91dPXhuRAL4so I+l/df79+LZPCKcPt8/6MuYZSi+RJ2TaSkILw+WJ2vfTSPsX5+fgs/oFV/h5pYEkMTrd9QVk 7jbDiJGCSK35N1A5MRBFmIxmRFsj1jOfRgiGRhFekL/2wzBJ2axmiK6Zn4WnsRRqFOXYeYm7 wb8rcY6mheCCj1Io3pjSKnxzu4llIIQmyP3Cp1zEZse59CCyshtGQtgN3O7fRxxQl/DLqRyE 5kwENU/888s6K0QSCFkjdNrtXmbDaRZHGoRopB/zbIChO1MkNdlnKy/82J9CEjgCl4jnez1m NOFwsrydud1rGLLF59rg3uTEEAQ1LRFyf6uw2giGsNVJd3K3+36vi6Ioc3aG+CJY7UjP4In/ Ngu/L3fwCE5npooPhHTGTurFe/H8LKkvx+scFobrkT58be4ZWJxixEzi6JTUghFK9PkV3Tt3 zW9f0DQfbEgXy+uLc9Z4rk3XeLbQu9Rs+T3OzyFm2kzLWpQagjbWHXwoHBzz9ETp0oPd6Uaa AKvutZkyFZ25cn2jkRwGX7620abuLetbLXN6wKedKMuUnrp+soLBUHb103OdvHgev+C0wjK/ lMIbe6VFzBtbRab7WoCzDY21GSYmF3z6YeFNttr9ftd0T3dPkBRdtGBTX282g6MCI31JI4mC 9moKpgaFTPU6JLVnoROZIwYkXYASmM3JkfoO8GEHahgCf0qSZMpbDNnCv04E5xkcg9OEv9kY I8fnNymM475jJfHUNq9zcbsN5VgSZN8Mcp/DMZxMzWQ1yVLw040mHUSYWZo00MLRlU2iBFte DZjuzyIKJQmGxAyckK+Bd7A5Au9eh/9ZD378ahYoRcJ67Izw9Rqd3gAxLjNG3fGM2u3OIM3y p1e4DoLITCsEmUuqU22VhbDWIyX/ZubMyhPGq/CM/zXOzyGmmKbFM8XtbqQVydgFDlkoPIgf 5tMMWO1fHFTmbSFHlV643l1luBLhGDuQJhyqAzSbIqzEiykWhxDV930WZgM/XprgTUKRCj5f uqH+5FmBK8CBzvPpQNSz1mphwOL/7WBTdsiP79/PHt64fmXo4vCfkff3bosRv3dQQFuFkxRP s+mrhT/sM5mK37p/1rOUsJkc5MlG0PjVBZigYLpiYsw1t+5uTqdMI273LhIGD/7zFCm2WmkA 79Hv6SZT5f6DCxw8Dlj9rdVqKTvwxos0a1wEbmDrt9+T/vY9g83atnNVtRnLBJ8u8BbXiivP 9+QQ5mHh23TcvuT0ja05RhMFRAm/nSZL7XGx2AqeSV1/4Ei3meZBN9yneT5FlDxcxvUCBbbR hKVg5/pqo0Go1d3NMOzaczfPtaHdoz35bKft8qdh3s5Y1t89lcPRKNV65vXVAoKULvUgpXDM OJT6XiP+O3+N83OIecMw+aa3goHlhbwYsn8US6wnWLB23lmwBeLH5Qbh0vozhUnd67mXnmBZ SQJdyImHnnspTYMj/nUatVb4NJ+U7stut5XdIf6cb0rJGPryuMdmg8GT253iUZe9ybbZOLOR pyxp9lwLbBe4fDdjLCutLzaxZI20cJtpthEcSZ1WPhWc6TVUmtTRB0J/Vhrtpvo7n6+W8MJR forBhU0oK1k4Bh9ZHK/c7mO82bz+94+LRSZswO1+ZWRd8oDlLrrvLWnI5WDYuliULe9m6RLw CN7jYnFgbTPAZooDuJeHCAq+WxQnfnxcyhHDgJ+trPGSWM3nDDxL1HY18Yygs/vpIiR91Qsj ugr8qEVd4sF82IQK7L2Sp1uEjw+txKhM/ZedEa4TQhpiLTbhtn4um2OzhIvAy2xELCCkfAvt NwLlmxn9M87PIaaXo2SZygr5KBMOM/kKfhpjARt9ToWlN2M/hAo/GhAEd1UKzAjSIcYq2nW6 G+h08ecxEgdH5tMUQhTQtuGKdjuFkWRYFVYLa02zmcwWQvAoSCOFRS6kuySKEeBBaiJwFNYn ibXto7FFX91feozkFimTy8jyBYU5PEvx79z37LhifSZ8V8xZLSZHVqGJMfQBfuYY4d7QCwuP /nWIwBPdnNWYWVdWZKeF8/m5kQ6keb/aa4f0YuMVRjO/sG9lkc3Eg83gKM1KV/ENLAp2jzZM frouN7PCazyVc0nXjPMmQtrgdjKMwOHfrKQk0ntnxYSHeDosXGNAykVLwL9beUwSyGVAo3qG 51aTouY7xVskdVcvT0u7aivmkZfnWZg4P0vUjCeZAgTaIv22hDTKfypBxLstEB8hJnAKfs3E Losfn9CQ8AS+t6DSiiFki6ix2odJbOzuJqVfzlE2IEN76KBFdXMjpYibsghpHWcbaZPDSZto s/DKdbuwg8pUDB8+uqVhwbETSxZePbVplSgAPsJiQvjv39WYLBrnrSYTgRE0bQFMcslkPKEU Bj/TGN7CsyRJUTTo7wUjKgjn3zr4L+4/GbTwgi3nOI4BcnaCLADjeGalAlu4XC3mEbDHfUkx GikSpTHeLLDuCYqSjExepgFLtc+paJPU9AaWEbqaRsjP2j2cfLkZ4ERm+2HBJPH2NxcF+Pmb DZdF2824CVz2N5ho8WXhLqXkIYLfv2fwBfLAjhmpRvHX5ZRwqQCpk3Aqg7bRZul+MrtS/HwO Md8cLp/Pi4gUOVsJLCt9N0LCM+69HZVkRt+MBkmt8udMURavF+/XmxCXVGo+Kh0bRzHhqvjS joirr5lSzsBUQpAGgYXPOpYN3dy8a7vEig5TKCuLa0adKPC5aKUktjcZTa3zKus37BfekOd4 EzhnPSmDY3MX9axe3LtEuE4M8pDYg1LsiPskWi/82sTwRSW1XU3ltYIK6raJlhhkTLqZi9Gf 3D9SKLagqLirIZMTrhDbCUw2GkuHr7pHUjBJLObeRwq38a92SGI790ZCkB+ANMBRw8LPFFIa +BcXDrRy3yyUJIt3t9DsCNgGWZk7i3DXaEeqKIWfBxhcMkPfQOOSyV0DzitMbKSUKRstOgt+ i/NziEnm5esheOrKJ617My4uf7e7BxH4+ZOVAgsPpPc0Jv0CTpCRvWa9oOjdgMul2uVr4F5E WKAvUyjxQr6GVDQqdl7SSeUzPpoZh6hHCpYGTRA47EGqgG2iPtdIj1qVuG+Y2ANeJfNZSfAk pX5GPNqBDKD6WLle/HUNw3vZcV6jafEhGyCtZfTgPM+lc9+4wXbyVryd9Jvws2LWZylaUEsm Kt9h9pLkiNv9xq6IIdYyrFTjGZN0GbahveLnNzZIuPSnKgPuYExAVNhCyJKvYsY62pMeTjGT vW/h+sW/r6NY6WCvJGmFn1M4UaY221Kcn0PMuI0WJMUgNRHKgnrsZKS7cR0t8OYbMy8Jox4w pHC2rJV8sVoxQeG0DJetGRdg0llxkOKB3vdDKiE+l4+zRsm+xF1ESny8kiyVHpqHxdVu9FrF UkbvdIpLksTjR80a8ZQrxeRr7QWBz0vxXK/7Zh9vlYzWRGH8IYYUD+/3aWYbViL+/ZqJ9iin wJNAZvjR9mQ5O5CN8wTQrbfR3g5M/Twt8fNODgWeY+vhLKngUs4GbhgvbLhse3mB00nmLXtN lCiIzoQkXfmwDTsEfqRB8p5ShAv0a6Rk/s6mFJE2+OsmRjl4H7HkMbH4IoqWLkf1JA6e82JK YfJHez97fovzc4i5tuLyedmEyxdFIDfWWdcDRjnqIAVuA4JZaYVeRUnwnL6R5BgAT75rBZBw is9H5SXVLf9yDCGBHOqtFRKX4bCNKh8RCy/lzOKV+V26oQowwJN2vcCgPyhTqHV4jE2UOXA3 KgilgFQKXg/68HYhJqjDTls0pcIOcyVTYI2PWVi7oMgeEHeOw5x8nnbTnChJBqlAXybopx5V CrfZ+xwr2YwpabdJfnm8L0N4kK+PxEQR9vdmQZW0n5EEh+8rqCrw4wuPiwf0+2yTsOd9NiOy xO5nGlosSv/bWbHH7m5U2oLepHCD4EejoVi8cF9jOcGgpphEpPbzFUGE8GEvwcoahCs0K4gN AT8zpOTP3USbZF/wjzZGEZtJdcySFOfnEBONIbKfxGLJykJIQzxrzq4odFowYeG/5YSLIUhn ky3g329WA59Tme8iWeEVukqXJ5VZniwdQ4MIC5bxK17iZ3cBzeSK5+VRnjGLEtzjZjinrjwb c4pKIbvAiUHTOZZUzqLyhBaQ7WcRTBV1lGcgKULj7p02rKC7udasE1/pvRBevGp+tTNZ4L/T tKzLuc0wjHjdFsTlSM6yvesydSXgwyse99iJit9uNGCFJwZvXO9vxM2iQD8Xt3Rd/nCiGhZq 76M5dvvnz9cq8CrxFlBK0ts+ui+3GKUHsMMj19vEYiVnPvwZSDcaRUnXRQsryt0+WzChvbMu LH/hpVd7yqVreCEJSX0rwbx485GNkiUDR0hOcoNezOglqWQlpsgtB0yoRPNZluL8HGLCS7Mk ZnUfTxU1xmLa4EARhLKmUuK6ni/fyN+WiqbRp9NoikX5TNFgZDhb/AHMS2wS01zBS8C/vwpS JTXWHhufJLLs52aOgEUe32ImEAwzHhG/L5WEv0HSA1qWMgG2sIkGF/cKgFYLJ3jZgHstjaMM ZlkknmZv6lBgIYYgZcKIHnGKZL0KEvREYlpI4ziGGfIEafcbDvX1ML6exqI4zrA4TkuU2MsR GJfB6AuEl8E2xkxQNieP5UrH5EgDRtsKGYSgRSvYNMizOTSymMlZZKEZYfcDqQZCSsCJ+gIX 7unARsZKoqYUHrGI1+dKjJBylUl7kpzKEEkK5j5BmKXTeDUMSR5XFbByF68l8+S58yo5C36N 83OISf7leYFKDCili3XNnVtvvvMSH3l9ObRmQUPJLuk1N5qEm6yQroq//JQe2UAfu6ZUuon+ KGMLpTIHlhQWVMqSrDD6FvlgFUrJ3XvSmGEyVUhPWZC2lKVwueeUT8ucLGurGxE/rhWYVkjD dR5fgk+LUzDGIdumrJZZRinsfrY1z4zjCFUjbxbu1RkojOhLRT7axwNTF1xPtXh8sBbyegPb 3Nsu3od35EhqJyFtyyQRCrN0y7f8q5lwkmDKXSjfZJbZYRhzpewU827iZLXzJp0sYJP+TNmk UQyTLqnS84qgYp1WvkdtYhhvdZ2n+Rn/S5yfp8cUy4qaiXbm9ytlr5BqUvhW+H3k/muPVCtg O68vh3IU+n19za6z4k1eSu+2LFor88wukllw7XSnN9OdWz3gsZPzaez9iZ2HzgtWJlIa6dsh bGSi5k1Ib3Zu3T0sa6HdHo2bz+X/RTokabncZ6TrABA0Km0tlOxaPmURPpoCnx7M6A9xfp7R 0zs1gztBmhTOmooG16AOL1l8oBY3UrKkbCq6M63aiPPztJqOv7Mzm3CL94NksgdxA5cA2IKm R9mwtzptsvszneqP8/N0mo2/tC+tBlm8NUX972VwwYM8WPpUTRq9HgZT1Knp0Uycn6fHPPzV vViOePyOp2YcSxEuRENbaQpYj87OFOfn2Tnv0R31/9/OHZsACENBAF3VoVzBJRzCmSy0sZEr QsjBs/7E432uMng9l8/mPcffp7xz298fGs0LtMqb9HmVTcgxTOB7F2bYsQ0H6XPDlmQkkAno c+ZkikCDgD43bElGApmAPmdOpgg0COhzw5ZkJJAJ6HPmZIpAg4A+N2xJRgKZgD5nTqYINAjo c8OWZCSQCdxRPRdFdgoqLQAAAABJRU5ErkJggg==</item> <item item-id="60">iVBORw0KGgoAAAANSUhEUgAAAicAAAFECAYAAAD82J60AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA rD5JREFUeF7t/Ym7NFV5L37/3j/tnFyeqIA4xORkNolJnKNR44izRCMGFUWUOAMiiqggCuKA iBOoDCoOqEQBQRRExAm13/OpPPd27fWs6qruru7dvfd9X9e+9t7dVavW+lbVWt91j/+/2f+T /y8lEUgEEoFEIBFIBBKBbUEAOUlJBBKBRCARSAQSgURgWxD4/7alI9mPRCARSAQSgUQgEUgE OotOwpAIJAKJQCKQCCQCicA2IZDkZJvuRvYlEVgQgfPOO2/2yle+cnbqqafOvvnNby549vjD f/jDH87uvffe2Ve/+tXZPffcM/7EPDIRSAQSgSUQSHKyBGh5SiJw0Ahceumls9e97nX7uvHA Aw/M/vzP/3zfZ//3//7f47r66Ec/etRncdB11103++u//uvZ3XffvXfej370o9kXv/jF2ckn nzw788wzl4LjBS94weyss8467twHP/jBS7WnL7/85S+XOjdPSgQSge1CIMnJdt2P7E0iMIjA Rz7ykdmjHvWo5nFve9vbZh/+8If3vvunf/qnfcd96EMfmv3FX/zF7Ac/+MHe59/97ndniEKf 3HjjjbMnP/nJza//4z/+Y9YiO0OD+O1vfzv713/919mFF164EFGa1+6f/dmfzb797W8PXTq/ TwQSgR1AIMnJDtyk7GIiUCLwl3/5l93CPkY+9alPzd7ylrfsHXrKKafM3vzmN89OO+20vc/O Pvvs2Sc/+cne5m666abZk570pOb3X/jCF2b/8A//MPvpT3963Pc/+9nPZnfeeWfzvHPPPXf2 93//97PnPOc5x32PPC0jyElKIpAIHA4EkpwcjvuYozhCCFi8r7rqqlEj/v3vfz97ylOesnfs 3/zN33R/P/KRj5xdf/313d9/93d/N7ctfiYISEue/vSnz/72b/9231e33HLL7OUvf/mMSekf //Efu9/6/Pa3v33vuCc+8YmzxzzmMbPHPe5xs5e+9KX7zke+EKhnPvOZs8c+9rHd+RdddNHg eP/0T/908Jg44Morr5whMy984Qt7CZRj9RMR/MAHPjC79dZbj2v/N7/5zV5fv/71r4++fh6Y CCQC8xFIcpJPSCKwQwjcf//9s7/6q79aqMdBSDiyhqYCWQhSgqjMk6uvvro79pJLLpl97GMf m11wwQWd5gXpecITnjBDRkKQJv0744wz9j67/PLLZ0972tNmz3ve8/Y+o2l5/OMfPzvnnHOO u7RrPfvZz97nP+J8WqB5MgaXT3/60x0p0e/LLrtshqQgPwhRKUiV417ykpfM/vM//3P24he/ uCNY73jHO/YO+7d/+7fOpAUHvjPI1iMe8YjZ+9///oXuTx6cCCQCxyOQ5CSfikRgxxB42MMe 1hGEsYKcvP71r+80HL/73e+605hx/uRP/qTTCrzvfe+b2xSH2Fo70ndC7ZAbx1nkaUJKobVo XbvVBq3LkCkLmRhyiO3Trrz73e+ePfe5z93rHo0Of5377ruvFxt+ODRKHJFTEoFEYFoEkpxM i2e2lgisHYG3vvWtvQ6xLl4TFz4ltBHISIhF3GLfiuapB/C5z32uM2+Mkb7F/9WvfvWM82yI Bd3iLuqollYb73rXuzoiIEqoT2gxfvGLX8ztJmLXEqStdjL+6Ec/2uGmP7Q8H/zgB4879eKL L+60J3BEbmhjUhKBRGB1BJKcrI5htpAIbBwBZgakoRZRN6J5Svna177WLZ7MEKUwUfRF/ZTH ieapzR59A6YhYcap5b/+679mp59++r6PaU5oZWoRtvzf//3f+z4WhYQgzNNSjCFaYeKqr8k0 1ef069hPfOITM/41fGH65E1velOnbSlJ2MYfjLxgInBIEEhyckhuZA7j6CHA/CHXCe2DXT5N RN/iKXdIi8yMCQPm6MnnY6zQNJT5S5CjZz3rWTMROqXcdtttTZ+TllmHH0dfOHO0iZy0nFbL ayJ1tbZGqDTSUkY1tTQgt99+++xf/uVf9pprmaS+973vjSZyY/HM4xKBo4hAkpOjeNdzzInA BhCgxXnGM54x41D75S9/uXlFeU6QmVJTwmxVC3PNkEMsswoND8LFOZajLzPOgx70oH3RPhxz kZGTTjqp+6GVqYUZSjsnnnhi187DH/7wrq1SHKMdDrG0LkKj/aQkAonA6ggkOVkdw2whEUgE jjgC3//+9+c6zx5xeHL4icDCCCQ5WRiyPCERSAQSgUQgEUgE1olAkpN1opttJwKJQCKQCCQC icDCCCQ5WRiyPCERSAQSgUQgEUgE1olAkpN1opttJwJrQEC+D06kMpZy1nzta1+7l1ytvBzn TM6gpXDiLD/j6NmqDDxFtyUwE2Hzyle+srvGZz7zmaWaff7zn7/UeU5SBDESzy3dSJ6YCCQC G0cgycnGIc8LJgLLIyApWB0KK/9GnbpdrpNW3g+ZXu+99969DgjnXVeGU3lVRLOcf/75Xe4V xAipKtPYDyEh/LmMkpE+vwznrc+XLO7//J//s/exaJsx4dJD/Zj3/Q9/+MN99YtWaSvPTQQS gf9BIMlJPgmJwI4goMpvK3uqPB3quihOR+Q8QULqlPOyrI5NQz8FJE996lO72jS1qD+ziJRZ Xz/0oQ91ydj6RKHARQoALtKP1rEK/7nmJnFdtc95fiKwCwgkOdmFu5R9TAT+HwLnnXdel4G0 JYrTRQE/GUppUkptito6coAgMTKzhiiwJwy2FCYYGg/p7VvVjx2vH0PmFtd5zWtec1x3V9Fk SMXfqpD87W9/u7uOfvVlga07omLzqnLXXXfN5Grpqym0avt5fiJwVBFIcnJU73yOe+cQYB7p IwQyk5ZmHKSDCaSUj3/843uViONzWgaZT4lKwornlcKsooIwkUgNuYk0+DK/IgJ9WgN95Rfj J7QLfqv4GyJRm9T2pWjz17/+9d5HEqWF8CEpCwgiOjQ05L3vfW83vrLKsu/DDKS/+n/qqafO 1MSREVa9obK43zXXXNMRDd9Lra8AoHT6Q8J3JyURSASmQyDJyXRYZkuJwFoRkEa+jwjccMMN nU8HsbAjBHffffe+/jCJ1FoH2hQZXImFW7bTPrHI00w4zo9FXH/qmj1xvqypJZk655xzumP1 LYTZp64FhCCVWo3SDCSNfZCTM888c6Y+UClPeMIT9hEsbb3sZS/rDqFdqTUcp5xyykybIQhe 6ZMztq7QmBpFa304svFE4JAhkOTkkN3QHM7hRQDpaJlE7rzzzplF+Rvf+EY3eA6uCICFtZTP f/7zxznOIhlhEqHRYMrpE5E3r3jFK2b8LMhQFAzn1HLhj3ZLcsJMo1+lIEw/+clP9j4qCdlF F120VyHZmOsCga5Zan9oUd75znd2bYkWqv1dTjvttD2Cc8cdd+wjTtGBsppzHzZjjjm8T2aO LBGYHoEkJ9Njmi0mAmtDwE5fDRnmB8LngW9JSSo4ztIQxDHRGaaKOoKHWSeIwJVXXtlpRPrk iiuumL3hDW/Y9/VNN93Ue/xznvOc4yKLHFxqL/jRvPGNb9zXRklefHHCCSfsfa/yr3o9ROG9 utAhs1Hpc6Kt0CjR0NSaE5WSw5FYmy3yN8afJMnJ2h75bPiIIpDk5Ije+Bz27iKAiMhhYkG0 cNYaEiOjVWhpNviVlEKTcP/99+99xE/FAs134+STT+6uUQrTju+Zf5CieWYgjrn6wadE9WS+ HsxKtB8htDaucfnll3dEA7GoyUCpCXnVq141408Tov98Tj772c92UUr8Q8rzmVuCwNCy1MSH aeg973nPXntMUaUTLz+WMdE/adbZ3fcpe76dCCQ52c77kr1KBBKBkQj89re/nfHHGSPf+c53 Bg+79tprO+3M0572tK7a8BhyQoOVkggkAtMhkORkOiyzpUQgEThkCNCs9Dn8HrKh5nASga1C IMnJVt2O7EwikAgcNALKATANCSPmo8NRNiURSAQ2i0CSk83inVdLBBKBHUCAnwqH25REIBE4 GASSnBwM7nnVRCARSAQSgUQgEehBIMlJPhqJQCKQCCQCiUAisFUIJDnZqtuRnUkEEoFEIBFI BBKBJCf5DCQCiUAikAgkAonAViGQ5GSrbkd2JhFIBBKBRCARSASSnOQzkAgkAolAIpAIJAJb hUCSk626HdmZRCARSAQSgUQgEUhyks9AIpAIJAKJQCKQCGwVAklONnA7br311r2y9K3L/epX v+rK3N9zzz3Hff373/9+Az3MSyQCiUAikAgkAtuDQJKTNd8L1V1TlkOgJGa/+MUvZnfeeedM Zdnvfe97s1tuuaUjc1/60pdmN9xww+z666+fffnLX+5I4Be+8IWuQu0HPvCB2bvf/e7ZpZde OlPMTXG4r3zlK7NPfOITs8suu2z24Q9/eHbBBRfMPvnJT84+9alPzd7+9rd357zlLW+ZveQl L5m97W1vm7361a+evfjFL56dfvrpM1V2ffeyl71s9oY3vGH2vOc9b6ZC8HOe85zuRw2Wxz/+ 8bOnP/3pXTVeFXyf+MQndt/5XwE5VXlVxvX/3/7t33bVhR/zmMd0FX597hgVblXXlTrdZ498 5CO73//8z//cneO7xz72sV2KdeeryqsfL3zhC2evfOUru6q9+piSCCQC4xAwZ3ivVMB+1rOe NbvmmmvGnZhH7SFgTlbh+9xzz529//3vXxnDJCdrfrhOOumkNV8hm08E9iNw5ZVXdsQmJRFI BIYRUKnapuCSSy6ZXXXVVbMLL7xwlvP2MG71ETZZNuOqedtI+X8VSXKyCnojzj355JNHHJWH JALTIkATk5IIJALDCFhQaUJLQVRSFkMAGXnRi1602Elzjt5qckI1NE+wXQ8WtfY//dM/db+p 90uhpsfinvzkJ08K3Ng78LCHPWzsoXlcIjAZAsxLKYnAYUPgzDPPnH3wgx+cnXLKKZ0Jxv+r CnPOddddt2ozO3n+fffd15nIf/jDH67U/1/+8pedqdl9ueuuu2Y//elPV2rPyVtJTjiHsskP qYWo437yk5/MPv/5z89e+tKXdsd/7Wtf2wOF3ZBdHiu+6KKLZmeffXanvvvtb3+7MnBjG/AC pSQCm0Tg/vvvnz360Y/e5CXzWonARhD4m7/5m84/y5xu3v/Lv/zL2Ve/+tW9a992222zhz/8 4Z1Z01wfflveB//z3XrGM56xr69hjvAdny7tf//739/IeA7qIkiEccPzSU96UkcsrLmlfPe7 3+387Z773Od2/nR8cfjlWWtf97rX7SM0yN3jHve4ThEQ/nSw59+3rGwlOYnBAG6McFrEfjlE hnhwnY/RlcKZ8gUveMGYZic5Jm3/k8CYjSyAwB133DFLjd0CgOWhO4EAB3jzPG14yM0339xt QBHyZcXCbJNbigWbo/xhlJ///Oeds/5TnvKUfcND9KbeuCN88F1GtpqcAGuMMOlgbP/4j/+4 d/hZZ53VnKA9xGNJz5hrDx2T0TpDCOX3UyMgskmET0oicJgQYHown/7gBz/YG5boPRtATqyE iYIJn4nfTl60nF2/H59bKD/zmc/sgwXhqcUG9rBGvCEnsKg1SLRGv/71r/dBQSNSukpYP+++ ++7Rj5V78NCHPnT08eWBh4KcxIBe+9rX7rG0N77xjb2q7TEqb4wPsMIz2e+H/F/60B8yTS11 1/KkRGAOAjQnVNspicBhQuAb3/hGpzm5/fbb94blWfdZ6cDKzPPNb35z9rnPfa7zJREmLOXA 1VdfPZNzql6AaV6kGCjl3//935ee87cdc+SOZuhVr3rVcSRN6oUQqRNs+K2DfofJxpomdcFH PvKRwaFSHJRKg8ETigO2mpz8yZ/8ySJj6Y6NKIWPf/zjveRkyA9EPo1lVVF1h5GclERgkwjI 55JmnU0intfaBAIIh4WyNj3QsH/2s59dugu0LrQvr3/962cXX3xxt3DLYXRY5Uc/+lE3xte8 5jX7hoh0SEOwjNA00VLJFxXyjne8o7tfNfEb2/5Wk5M+s47Jt6Vy48VNfUd4C2N6fE9KEcd+ xhlnDOJDxTWFpM/JFChmG4sgICndsqrURa6TxyYCicDhQYC5ZxWhEOCo/IpXvKL7KX2Dlml3 q8lJn90cg0ZcnvnMZ3ZZPoGCpVHPlfZEIU3IwVvf+tYuI6jsnjy2h0R41VQaj2W0P0P9y+8T gXkIeO4zv04+I4lAIrDLCGw1OeHcNE8QEravU089dfbOd76zeSg10/Of//yOyb3pTW8ada9o ZuqwqlEnNg4aQ4aWbTvPSwRaCFCjPvjBD05wEoFDgcDPfvazQzGOHMRiCGw1OVlsKNMdzfFq qiRWadaZ7r5kS+MQEF75iEc8YtzBeVQikAgkAluIQJKTxk256aabujjwKWQq89AUfck2jgYC nNpOOOGEozHYHGUikAgcSgSSnDRuqxA0IcRTSOY5+R8UxcarFLxIjPwU+B/FNuR+yMJlR/HO 55gTgcODQJKTxr2UFr9OULPsLeeke5RFrDzTlnh34dnCuP2cd955WwWLdNXyJUSaa33U7zJB 0xR1PDYxaCUcUnOyCaTzGolAIrAuBJKcNJBls5fEbQpZNgHNqtd+3/ve18Wyq4dwkCLvzEte 8pLjunD++ecfZLeOu7Y8CX//938/kyK7JfIf/MM//MNW9bmvMxxiM5R4J25VdjIRSAR6EEhy 0gBGwaM6e96yT9DYFPzLtl+fJwuiSKOyGBaSoljTQQiz1lgt1Kc//enZFVdcMfv973/f7KoQ b577inuVIqWyxEJ98pvf/KbLFDmvmJfv5pnghKxPlZhv3ffh3HPPzWiddYOc7ScCicBaEUhy 0oBXThTqfNntVtU8TJXMbexTwBTBZ6YWVSjrtM1j21z1uBtvvLHTOsBCJFQtwq3D/KUitdBv jsRIIkFa3IsytTIioUbS2972tu4Y+W1oaZ7+9KfvNe972hDpmhWFRC5adTScQHPC9CRttUKS SF4pnglthSB/+s2P5kMf+lDXlzJCRh0Q13va057WEUMh7+6NsYWE6UiyQFU+pxI4nXjiiVM1 l+0kAolAIrBxBJKcNCCXafZFL3rRJDdDSt9NigVW/pdalL4+aKERsVhbyGORpg1BKiTSKwX+ v/vd77qPkJPI/BvH+D9KFcRnyEOQwW9961udJgT+Ko6qrcHE1meaueaaa7rvzj777Nm73vWu fbU6tK92R7SN7Kjo6TkJUe1ahFfUm9D/un/aL4tOShddEp6p7o+y5klOpkIz20kEEoGDQCDJ SQN1C4ykbVOIHf8mxYJZFxuUIXesaWUTfaV1CE0J4mDBrs0yNBgh/FPq/v/Xf/1XlyW4FFqR IBCS7yEjqpOqk4F0SNZXalbKc5l15iXeQ5CibSaeVkFH2puI8kKM6pLkCKKxhl8Lzck6zG3M Oln4bxNPcl4jEUgE1oVAkpMGspxJTzvttEkwp87ftDBhSJvP18QCyd/ioIS/R0vK/C/MIxdd dNHeYbQQTB1MPIQmoy7E9cpXvrIbYykITyTPQ3ZoKsry6vMwUI+mJhPl8ciJPpEf//jHHZG5 99579zUpMimKaYXZqTzAM1WOGzkpq6lOdY+YulJzMhWa2U4ikAgcBAJJThqo8zeYImz0V7/6 VWfGOMqixosF+fTTT59Jbnf55ZfPnvOc5+zzvWAOsVB/7GMfmzHHMIeUWhH+GLVZh8bhUY96 1D5onVNqqlyX2SQcYfmS1OdEAwpEziMn55xzzj7NCqJRRmLRmJTaCu25flmRU62okmQhELU5 a4pn5c1vfnNWJZ4CyGwjEUgEDgyBJCcN6D/5yU/u8ydY5e4c5tLbi+BCG8Ixlkmjr1aGgo5j NR2LXFutpFVKqvdd68477+xI08tf/vLZHXfc0TyMcyqCpOhkLZdeeukiwxh9rEizLPw3Gq48 MBFIBLYQgSQnjZvCoZBPw6ryy1/+sov8SEkENonA61//+vQ52STgea1EYEEEmNq9p6JB+Yil HI9AkpPGU8Fm/9rXvnaS52WqdibpTDZyJBBgQsskbEfiVucgdxABDv7cBmQiJ9IR8E0MH7sd HNJaury15MSiHg6IrZELDeWnIHRWynE/cnmUItMrHwTHiJLgk9DKs1G3z8chHBtXQZ3mhJNk SiKwSQRMfGnW2STiea1EYDwC1q1akJMyT5LcSdY0UYGc7znYc/AX5LArmarHI9I+cuvIiZsi 6gKp6EuYxdlQQqtbbrll36jK4y+88MIuSVYt2ha+Ok/e9KY3zd7znvesim3niDllcq2VO3RA DXAmdT+jVs0m/HA4t3JIdU3E1N9eaveWeD4Oq1xwwQWpOTmsNzfHtfMItBzvmXbKnEdKfnzi E59ojrW1ru08KI0BbB05iT5yFqyTWA3dgPJ4TLOV4MrCKAfGPBHtIJfFqvKFL3xhsnwpY/vi mlLA0yx9+9vf7lSHImDmpXcf2/Yyx73gBS/oiIEoHZqsn/70p12EyrrZP6Iq5wtNmSq9IoVK Z9vDHGrLzwkpS0kEEoHNI3DllVd2qSgiWvC5z33u7L3vfe9eR8xNYcIRuSh/Eq1+ZJOmHaEx qTff0QCNylGQrSUnFrMym+bQzcAyS7MOtbZcGLV4SIbq5ggLnaLwH4IwVabZofHH9wgaZ97b b7+9IyQWZ2Mp82uMbWuK47B8PhC1eAGRqHXIz3/+870kb33tH+YkZcKsj8oEto7nJ9tMBFZB QMqAN7zhDXtN3HXXXbNnPvOZ+zQhNmdyMlnjWusUwtIXudjnT6Y8ieShtPX8WESd0qKWmaxX Gdemz91aciLb5tjd9cte9rLjFiOLj/wZtYjEGTIrCA+ldcF4+b1Qw0Va8kVu0LXXXrtxs86T n/zkZhf7Pl9kPIseK18M+6oEZ7Uwm4Wmyw7By+iHfVUuEosrghUilb2XDMny49g+kqkQ4FDB xZqc8J63W3EeQqUKcS1XXXVV12fX58dUCgIoJT+zpORwrTEvit+yx3teh8a/bNt5XiKQCPQj IDFjmZNJigFzmjpb5g2ZrhEXOZL8RKbsukVzkTxZLenzJ3v2s5/dRf984AMf6LTmtDNIiszY Q2veNt7TrSUndtWtFOE1iMwGNCZyWZRi8bE7r0W20Xe+851z74WHa8j0M+Zmyiq6adbal569 hcWYMaxyDKyRjEjXXraFkITpgRqUGa5M5Y5EltoeC65dhl0Icd/7NGtIDXKJQCBlMLn77rv3 DcXuJoQPEhLFHHLeeed1L7S+lcX/TCo+893111/fvfDl9fXfToiaVj6XgxRYleM7yL7ktROB w4BA1PkaGot5rUzgKbADGfniF7/YldIIUVLE/NS3xtkE+TEnKuVh/qGF54vSN8f3BZC4dpTV GOr/Nn2/teTEzrrMwNkCzeLFl6AlEmO1TBl1yvPWuWx/UsCvKnbRm45hbzlbUffVO/1Vxzbm fL4eFv3I0Fqewzcmatnw8bHQl8nZ7DjCe50mxM7Dj/ZCe9KnHZDMbagadGSK/e1vf9tpx844 44x9Q1L/J54VO5LWDkefI7kb8jd0zTGYTXGMCe2gzHhT9D/bSAR2GQHzWpTRsHFmAahJiKSM EWnaGmsfAYlj+e3JPs1sb8PGt7Bvs8Y8tIspLbaWnLQK2MWNoZGwU3XDmX8sZErUqywbYtGh areDFi2CvVpAxviAYKlTZCq1y25VCF7ni2dBp1WwoBMPLRtkn/pwnX3RtgWeqrEWJpmzzjqr +xiJs0soa9UIwy53/74fW++IpmbImbo069Cu0NSU4nkKcoLwtXw4kFg2XaKNbSEEyNRh9qlZ 9zOb7ScCUyBAu2oD9f73v/+45mhYOcbacJmrmJJtxh1vLlHKo0+4MfAtue2227qNnx9uB31z nlpgLb+WKca4zja2lpwYdB9BEEmDkdKOUHMhE0wxbq7deil2xG624/kEjBGLe59GZsz5cQzT 1Dqqzs7rA40EmyOmrIAhvxekDYk7CLED8NKU6d1ppUpNDpxq1n/rrbfuIyeIQKmZ8GLyCeqT IaJQ1tgpKw5HeyYWizz57ne/2/W3JL8+Ny4vPqF92ZYQvyc+8YkHRkYP4hnLayYC24oAIiFC sRYOssua2pmHSn+8aNtcW89RvhOYIbHorslWk5ODAhOJqX1YlumLSrsIwlEX2iNkgfaGWYaa E/kIQahaL+qDH/zgvWPsDhwT7dCc9RVnpDUKtWof9rVmwU4mcqLYvdRh6HxW9BshCUe2MqKL 8/W2kBNEri9H0FF/FnP8icAmEWB6MWfZHNv0yveERESqg2X6wmJAs1yLOVEyN5t1m2JzYDjj LnOdgz4nyUnjDliA+mLMF7lhHJFKp8pFzs1jE4FlETAB9lVfXrbNPC8RSARms8suu6zbnPzn f/5nF5FnU4IUbFL6/AcPwq9wmXH//ve/H3VakpMGTEwMnCpXFZqTJCeropjnL4qA3VJG6yyK Wh6fCAwjQAtyySWX7DtQZCfN66YECUGOuDAgSMzSnGIP24YkyUnjiaI2+8UvfrHys3b22Wfv M1+s3GA2kAiMQICPVSZhGwFUHnJkEZA1W0LOd7/73d1vm0im43khw3zl+szFY6JApwJbwlE1 24QHc8iXskE5jigkONV1DrqdJCeNO8DnYApywhlXMpyURGCTCNCcjE1guMl+5bUSgW1B4MUv fnFnjqGFQDj85hM3L7eWVAd1cdkYzybJybZguO5+JDlpIMymKDHYqsJhM5KGrdrWLp4vkkpE lZc/KmoK2V0m2+4i46d6RTD5Xohc4XxW5kRpVQVdpP1tP9bzG5FG297X7F8icFAIcMTn+G6e EG03JrISOSmrBwueEEV4lOf5dd2/JCcNZDnEWtBEh8wLVx26KXKqtLyqh85b5Xv99mMHgBSI oxeFwov7oITqcZNOY0Op25k8yoRvB4XLuq5rot3FdNXrwiPbTQT6EAityaLRbXI3SV+BmCA4 1gxzLa2laBxz0BR+i0f5ziU5adx9jkVyW6wqQmg3XQ3YCxIhZHYE/g4NwqrjWfZ8WQxbeUdK ++73vve9veblDhGbX4paN1SvY9IwD2lGWqpbIX8InBwxfcK2q6jiPHnTm950XPg4+7CMkJsS RHAXM0JuCp9duE4rN8Yu9Pug+7gIIWDaiRo3EnX2Ca2I+UlBUcLkr+QFQUZaUTKcZvvymNiw 8keUHHOKMikHjfm6rp/kpIHs0M577M2Quj4e6LHnrHqcXbMfpIRjJGLiZ5Pe5PUYvICt7IWB M0e0crcvWqpMuOYl9yMrIjXqkBbGuXIKmAAiC23ZJ+SlrH8jUZJJyoLO7lzff57xPtNHoYPO N7GEwBrh0W/assgI6Ri5TyKdtR2W8PJ1i+uVxcfWfb1sf1oEPN+0nbuY1XNaJNbXmjpasXGL zUzUzLKRssmzSXJc5D0yr/jbex4Zt5mrWxmwJRCVaK0W8w4tjXkpNjvpvN6+z0lOGrgMZRcd +8qcfvrpGycnpeaEv4VF3aJ6kD4IPONb5AQ+atjQNiAHsQPhXCb1P5HGufaQt8tBBrRbi0qe JhGLM3LA76WWMrNrq/iWWj9lXSeLRVlrifakfEZocxCPMr2+TI2uIxoghKlFEa91i/teFhlb 9/Wy/ekQ8CzFhsIz2OeAOd0Vj15LEkCaP0JrImmZ+abWntB4eocREPNq+Kb4HZsb37fqmSl/ YVNTi3NrMSfIn5KyH4EkJ40nYioma0feV/Z6HQ+iLKa0JRZzpMQOmsbES2jHf1BigW4VsqIu 9bJa2F//+td3/dRnRR9D1EQ6//zzj+s6ckAzUgv161ABPrueKHNgkmppV0J7YoK5/PLLj7sO 8sOhjmjDLqgWJcuRH0QB4UJexiYgWuVemUgP0sdolb4f1XMlffQeeP69ByVBaS1+JU6vec1r Dh1s3i8aiqjO629ayLq6+DID9354L0PLHP+32mLmdW0ExX0x75T+XKEtrc9VmC+IpTpvtL6u 43ykx/UjBT3NsXIZKUlOBp8BKtUphLZiigKCi/TFSxS1hrwETBWK7HlZDkpoGvryA+gv2y+x c6GlKD3f4VcvtOy5Zer4clwIwZBZDjmhsSEmi5q42RVFxWT+MrWJxKRS7oD8PVS7yG7NREar sW6BYTrErhvladtHYC1+ng9kxPMX/gxDz/O0PdmO1phGg6T5jbhNUbxUegekBFHQblQ77yua p/xIEBPH1z5n5lekyRxgY2hj5Le+xqZGUEX0PTSytLPhhMt0dNikpdVedIypOWkgVoaKLQpo efxB+nkM9busADx07KrfW7j7/EQQOMUJQ1rYm5i89Gy4iExd96bsH03VUKZEdXVCc8IvSNuI 3HnnndepYu1qyvIFJhbHyMQoFBrJKMfjuzr03A7PeTQ/VLxf+9rXOtJEm7Zu0b8xYZHr7ke2 vxgC1PsWL4unxc7C6VnfpDP1Yj1e79EIGyyCREzx7vD5Qnw+97nPdaYcPzY7yAKn+FJocEOb 5fe8HCjzkODTEtpgWhQbIiTU/TV3tDTA60V2va3D0dw3z8l4TA92mpzMy+YXgxctY/GL6rFj QJmKnGDQKYsh0Fdw0T2UGZFp5Ne//vXcRofCt1tOypzavFDSvr/zne88rn1pok2Sdjl1Jkba mpbcd999nWOunRdiMsVuYgya+jm2AveY9vKYzSHACdbzgiBbuO68887NXXzLrkTbiBR4nvs0 G2WX4zjzt3eZBnzMedEGLYnNEvwRCiQ/TD5j+9CCMAryld/ZPCzSty27Nb3dMRfCj6Z8XuTj mPFsHTmxO7WDcEM5SvaJRF7UYnakLdGONoTzMmlYVPr8C+rzp6pLkgvEmEcwj5kaAUToMKqK p8ZpW9tjKrAoHuZcPGOxZ4ZB0sY8z5zfw0yDTJjv77nnnrGX2jvu85//fEeKwhFWW/E337hl hCaMJogvoHWLKajlS7dM24uew7/F+CIfFiLW0jbb2NMg84lhyg5/H79bKTLks0IIaZG137fR HNvfrSMngONIhFi0HBENzPceFj98AloSGULL76jXxzi7jjlmDMAexk04QI7pSx5zdBCwI+Nn lJII7DoC/M/G5uzx3IemxdqwrO8gDYr1JxJZRpuLJmoL7OVosh5xoGd2trgjJ6uaPZa9twgc c1lEF4os5KcoejKEZkefEUO4+m38cI0kpfX14a1OEXFcbSZbtL9bR05iANRyGNs8YbvrS7oj OoL2pJYxNRCWfajray1ro1z0JubxiUCJgOc3Q1DzmThKCPBHifDgIBORi+itb31rl+mbzxrf kiERzh0aGItyROvMiwIUMeU45jj5UkphZqpTGrAKWL+GTNRDfV3me47WtUmJKXuVdU9QAQ3T VVdd1QUHICdjEmbO6//WkhOsbSj2m/mlpTnhE4CZttSiQ9lDgeXa/BswXDa03/zmN8s8A3Md N5dqcIdOojES5cKXg2Owl/ZpT3taZ7KjNiVlVtiDGpqdzNve9rYuJ4qQZzsGKuRdToCF2K86 MRzU/cjrJgKLIkCzgpD4ofGItPLasWjefPPNe00yE5W5h3xvbuKgisBwTg0yYoENvxNtt/Jf 8UVDWi699NK9awQ5ig+sOZ/85Cf3vrcuRV8jsVufBWBRLMYczxeyjgAThSRQIERwQDhkwwah EQEoYABOdWFR5ipzfWDJ5yRqfC1r3tlacuKhwHjnCfMLU00tUs+boHlG1+KmDEWqYJWxWLEL RsbPMTe+PKYvfHbRdnb1eP4+Cv+F/Rc7p+oLO/BJJ5104EMTpuyZ8FJ5Aak7PXt+iEnF361n 6cA739MB9uN1F1fc1rFnv7YLAe+8BU0yRU7tqrTXDuWr9pjGJCoM++0nsrOKxqul1BC0wrTN AeGUHKTHHNY61nXqSEQO+eb+8NFwvUhlT6vi+IhC8tvcw2TSypW0Kjat8/m/1JoT5pjSneH+ ++/fF7F4xx13dH4n1lv3z/fzBF4XXnjhSt3fWnLihgrbnCeO6dt9m6DLZF7RDo3KkAyFog6d H9+3ai6MPXfV46g554Xcrtr+IudLQNTK71HuRDaZrK7sO1trEJHWmLxgXrQhQrsIHus+1vO7 qYlu3WPJ9ncbgQgHDuIfUUhTjcrmIshEmGKsCzQBNBRhYgkNCZIQBAYBafkXcuxERJyDZJin 6vQB0X/Xb+UUYsYpo/PkTtLP0KrQ0IY/IlOT64xxOZgCN/ekXhtgNZTsb5FrL+ufU15ja8kJ VVhfJE4MgFknCjDVwHmgahsa1ZsHeEhK9dbQsfO+n7fordLumHMjgdGmk8C1+iZPQyttswnA /QhHK8SRmjSEQ7T7SPVJZRjJ2txzk4rjachOPvnkWV0ozW7K88He+7CHPazXa5/5Y15I39VX X31cYjhkS7+0bUJphbSbBJEEfZDrpBS7FG0Yv77F5IaMa88PrdJQYre+50C/hkyiY56hPCYR WBUB809EuyAmiEErI3N5neuuu+44v42+fpg/IjhC22XOJOeEGT8iU0pfEv3pS3DXKnvR6gMT kXWGhtX6In8Kk3DLf4O2QV/105zIT+WKK67oiJTPptoUr3rPtuX8rSUnblRf/oiSnPQByWck YtNN/h6Wscx018nJBz/4wb1sk32VMTf5AEY4YH3NOveHhEgl45YYreVUjKyY4EJtaidQZpFV fK+MVkFs+pg8wmPCch2Ex8JeZtP1HJUY2vEgNOE3Y2dW5sVhsvKcfehDH+qGa3dkEixr6gQZ K22xPObtrDiU+dxEtmy+HYRIxEFKIrANCHgXIy3/UNFOhLwsrNfXf3NHRJEE+fEO33bbbftO ob2gtY1IHu9ikBm/W06u8hPNS2NRXgAp0UZoY/QBIerLBO2aUZi1zFAbfdqG+7UtfdhacjIG oDEJiixiF1100Zjm9o6xE59CDiLJDj+PyDBpQjhI7U1gSE3aMnG1dhflTobGIdI9R1vUjxy6 IrGRXDKxe4pjIkcB01bkPugjJyYRE4rCfq26HSK+yvT2LYIL5zgXMeIwVopstOV5yFSrHopJ jilySGM49GwiJ6vae4eukd9vPwIIMn8575HnQWTjqs/WsqP2vtI2DvlCed8jjHdejhJtlYt7 vPN94avmnzCpeN8jO6u/OdB6HyVgizQV5cbBOhN1cGL8oS11Tj232bj0RfbEtWmSmXKiRIH+ bCpJ47L3cNPn7TQ5WRdYU2lOpqgFscgYvVAYuJ0++yFnrSggNiab7iLXWuRY/akL/9GKtchb GU1lwmjl6xgK0R5juov+mxzmhdCFPTiOb6mBaXhiUkQM60RNdnPlM2XylUa7JRwGpdN2/5at qQLDqB20yH3KYw8XApGp0xwQJpVFfQHMYc71TtnoePeYU+cVlrQZOfHEE/dFqNRm1xbS3rWo /otIzHsv9SMIR/TPZzSdLYlimEhDaFqQg3nhwdqxkRBlWEsk6oRPq7xAX7RfvNMcS2VQZcq1 wcrouuPvWpKTxpO8y5oTvgY33XRT569gAfzSl7608eKDNaS0CeE9X+48apLBubnU9NBYtBIw WdxFAYU/DedP/4cwI5V+HjIW9jknI03zMgJLCuh64Z3OgbYkHybdchKFeU26qIj5mIT4Hgmp pdbctPx0xiyftDTL+quMaT+P2Q0ELJARFeL5976VibbGjIK50WKOxMdGp9YMaqfUTrumaw1t IurrB+HQV9c0F/Ql4vROhuYkTCUICJ+PEPMgks5vLGrjRBIxZMA7WYfEln2idUdebK5oVMrN Qmw2RAPVUZl8T1phx9pGZmq/GP3IQp1JTsa8j/t2uaNO6DloTE6VVdrflXO9rK2S4DVhMZ6y 6ie1dCtii5kEcTF5IRYtDYzdXfgZOa4vWZ9Jp28i0R8+IKW/iM9M0giASdTvOpxd3hTXjmqm df9MorXDKju3idA5dlJ+L7rLjeeB70ySk115O9bbz0gotkq9Jc+lBZgJ1DP/qU99qrfTzKBR 8XcRk3JUJEdQIiupdmwGWuLdiOuEqQRhicSdyD/tIz82BIWmlpnIZqSP8NTXqSsh04CaK+Qk KaM+bX7kchI2bSNG29JnPoscLLD0nhuHNse4KKz3Sdm+1lNz0rgnU9XWSXKy/gd+XkHHX/zi F2vrgLTa9Q6ovBgbNRUzFfciwowlL4RcPcuKCKAy0dSy7eR5hwOByNuzTJ0ZCND8RR6RIVMj 8oLIRBKuPsfQFrL1u9z3btuw0GbQDFnsLfB+mFdDotpwfR1EZYxvx4c//OGO/NQbF+YsRK8V gmxOGJPx1aYsattov0zgdjieuGlGkeSkgeNUPidT1eiZ5lZnK0cFAc/vkOPhUcEixznrduir Ru2JAERy5pEThDpMMshJJBeb8h4wKdXzs2e9jgKizWhVF0c6ymytfX3jIB/ay9JURBuDtEy1 8czw4f6nI8lJA5upomyGdhlTvrTZViIQCDA1raJ5SSQTgWURkKeIj0doPeool2XbjfP4A0Zx ufhMuH7tnyVXShB0vmw0LC94wQs6EzCCwez52c9+dqYoH/LBCV1Gcj56IeFvhoxEIdmhaCfJ JKUucA6tEb+XlOUQSHLSwM2DSEVIfThGTdcH/bJ5Kpa7ldt5lkmCHZi9FpZUqq1y25voPTPP UagSbTeW0TqbeKLyGptGgLNtrQXioM6UWYp0+fxL+KyYz/0w/YSjLU1LmFb4h9G08GP74z/+ 4+OGpN4OIjNG+MvQGAkxVgKF2cbvDO0fg97+Y5KcNDCbStU2lepv8du6HWfIR2JCoOKlIvV/ 5GDZdA85sLkffvztx87mlltu6bpymExwJupWRe5NY57X2z0ELKzqiZFtddLkdGrBp6UR6aLP LQdwTrZhmgkn29NOO22vmB+CIi8U51mmLwkWV5kHRAlG3RzX0bbrctjlA5OyGAJJThp4TeVz MqaOz2K3a/homUGpJzmxsZuysR5UjhOqzTLEd7j36zlCuvuhyJex2YPX08NpW7VLbFXknvYq 2dphQ8DcEcnJNjE25hnai3CgFeViA2PeHJqzRN/xKaGZnSfhDMycE87pwnYjq2uQBhGAPltF 2y2qz7tXFgnlmG5DxGcnZTEEkpw08JqKVEwV9bPILa1zEnjxD0qlaKJRO2Ke6FuE5uprnayp DC2OdsrcJ0w1/pcNFsFoab0kp5uX0Em7rYR5Imf0iV26JRzrXLfMYRLHKfQlXFj+B89TWR1V rgke+0KO16Hh8Nylz8kib00ea9dPyxAJ20SMrVvKQnjIg+tHuPJQBItCnI5XfoL5hvZEUrNa vGe+K99hhCXS2JfzAnKCnHmfzSlvfOMbZ+edd17n4yLfy5DQ6NTRPTL0ul6UuxhqI7//AwJJ ThpPw9BCNvYBOojduAWPGjGSJrXyi4zt/6rHIUo0FpGttg7hk8cE1uy5whwlU6uJYb2T8ZKX jsa0RCY2uz6lyls+QrK3mnjkIjCBtBK71TkZqHfthExqbNG0aWW9GsQDCZBunqOdv+38QqJg oeyS7gmhJheKaLdo8jdxGn8rw+Qq2GtzGwo+rjKGo3wufyGLs4WNs6Zdt3dnndow5ghzRpgl NuHMr7SEdwEhQlS8x1FhWLbYeWJe8L6X0le3x/tpLjYHeC8j1X2YXMxTNkn+Nw9Eu7feemuX d8kcNkabbj7TtmvRlvhtLoblz3/+86P8SC819iQnFWx8EKbyFZmX3GupuzXyJIt8+HlM7S0/ sgvdYV50OxH9aSU+MinVuRcQh1JbUpMT6tySSGh7qJgYQmOXhUiwVdsl1lJGaDmGQ1spFouo VIqY0LT85je/2TtEv0tiZVJqZZ9EjspU1UPq60XwjmNprI6C4+8y2OzCORbDqANjh2/B9h7V 1a2nGotF1QId5hW/Pb8tTcRU19SOqBbvbtSYiUyv+jKkvW5VDdbfvvT1TDrxTkbStjBhhROr jURJQpTPQGy8++GzgnAg/+7PQx/60OY8EiQSyakTOE6J32FvK8lJ4w5P5RA79IKt8+EyqVkI D1K8/PO83FvkzY6/TI9da5+QxzJddJQenzfOm2++ebCGRrlTFHbYMifFdSM7bXlN0UhlkULk pZVbAqFxLWMXSXDmmWdOfovqYomTXyAbXCsCTAi0je5j5A159atffdw1aeIWzV/ScnIVjWKx 9Vx7Zy3S/l53ZWubk6h147r6EHVybBaYTEtRAdx4aXjMrbSPSIcNCs0EEtFXs0o7tMg2Nq5p A+o34ZdHO4VUxJyk3dIU5NjQ7nC0ZX5ubT5i/ooSG2t9UA554ztNTsSvR7rivvvkZZS6eKxY PKbKc3KQ5ERa9rHhb2OxWfQ4i/A8j38TUq2qpl4ty5XXRFHYYJl10rE0IvNEuu2hOjWlzwnN iZ9SLBh2UYRnf/2M0FCV9XtMcnaGfeK5ZdM24dXXWhTn+nh94wScsrsIyNER5Q+8R7WGjcaR hswiW2rwdmnE3iNkwUIeVYsjuob2qJ4/pXcQAUOQp0ijH6HC88zxome8n2U0TmSURXqEEnOK DW2I9zJwNUd5p8xX5gkkiglXH+syFLuE/7b3devICQcnD+q86Aos1gPJ6VDODLsMNv/SS9qu w8vLIZFHt92wB3NI3c22O5VZZ5WwtG1/cMb0b4zPjZc+7MDwqk0uFnATgt2SHypZ9zsEMRma ICROKs9p9b3Ok+B5QWjYvj0PJqRStKm/nle+I/pYkgxj199SEBht2v1J+kRtbOxThxmm5mTM 07n9x/CR8gy1no8wTVjM5xWv2+ZR8qnxnih4WWc09s7VflPeyVLM97Qhjp1n2qVhsV7Y1MTa YVPjc9fnW0KsDbDUpuP4h4kIQkSQKOuNvyVaI0y/Ma9IOkfTUzq+t7Cvi3tu8/056L5tHTlR L8EONVRuNUAeoNaO1IMmTp14qFsTtGOoAOcJ/4RW5MYyN2oTTmXL9GvbzpFngL24LqAX/aRG pblYh39GXKOlgZPIaZ7dHdkwyZlka/nCF77Qm8DPpMZmTrs1tgjZIvesdOpb5Lyjcqz5xf3Z dvF8tQi+Oaz0RXG/hzTI6xirYpXMKOZbC7e8I/OKAtZ9YDqNTYHIOGPyriP5rXELAW45liIm feYtn5vPQ/MRmV5pRVoVhZmUmYEREP1APmyUkRbXiYyvfN/8H9WXOeaH8JMrI/jgw/eEWdiY w2wGr3VL7TS87utN2f7WkZMYXJ+Kzk66Za4Q1hn2Qg9Va1ERdjpUKdNLdtJJJ02C8UE5xE7S +WxkZxHoq+S6swOasOM2LRYV88s6o190OfwWaANp4OQdGlPXZWi4+k8DZywWRyaIVoXvoXZW /V4fLNrMHaV5ZqxZPCLtyn5YTGm6W/O0zSeiwdT7kIc8pNOG0G6YZ/uiaZiGbAAca8NL24IE hcNx3ya47BOSSEtq7Ylq49aY0OTWWhuZxR0XGpbITFu2SYszlYa+dR9pfNwHz95BBkWs8oxt LTnpW9ixTyWrWxL+CdR/bIgtGTI1mEDGPLBjQDdxpCQCm0SAf1VtTtrk9bf5WjRVFtJYVNed h8gCG2GyflvEbHxWyUFjoYmokwiF9b8FcNMZXS3y4cBKQxHF/mLcnL35iPUJzYVFn7aauYNT Oe2p3D/MpbUwqYQGA4Y0N7QcyJlrhp+V36J2zjrrrG6BtpbccccdnSnVM+BeOB5BcU+QBIRi njAfayOENkQ7yE9dedw90id9I45Rb6gUprqpNPTRLtMSfGhmWtrcbX43W33bWnLS50zqobrq qquaOAdjl5Snlc7YSUPaDGq7scx/6GbzeUlJBDaNgOf3/vvv3/Rlt/Z61OfmjdjlR6guomI+ 6NvIrDogzt0WKWTR76Esxctez6J+EBJEL7QQFms/tUaA1qL2taDpgUf8IDbRzrwEbO4X0z/h HOta2vfMW+xlZC3Fd6W/GY2Ha4a2x7FIKufYsvpwH57MTmVSS9qUesMr5LskHr6v/YaQpKk2 wQdx7zdxza0lJ327Gtn6wrekBEgm0rA7lv4n5TEeesRlnnDMmsqRddEwv03c8E1ew64JlpFC mirT5FJqlFbZRS46Ft74IrwOu6TGbv8dtmCVuS0iZDW0GVMnwSuvbmduEXJNBOUwibnUO21s fpvvaFLqxZpZpYzAgwEtt/eR/5UFPwRW87Tb5hOO5M4pc50gBK2NJ+LpHJtVZAYxcU9sHJmE iCSJPls2rB/x0C6NBdJj/Sn94+SRQZLCpKd2Ebw+/vGPL/U4LHveUhc7wJO2lpzMC8P1sJV5 KNwsDwc7WwinpFLdxqHSg8NsM0+0O5XmpAx53fQ9pgJdR2r0RcbhnqitY0fCI5660w4q1M9U r5vcPZggpsphswgOmzxWhtwypHmT197ma8Vu2S7aD22GZ0+I6TqF7xti5J5wojxMQhMFR/Nq pIOPWjnlOEXT0TCUQrNgfqo3gtpxr/oc0X0Pz3rjZ3PqvDqEHjEKghjJ7PTRdeU3Ic5BbPgb rlNcD5FZJmLH3MlZ1xh3wZl7Chy3lpw8+MEP7h0fxmuhiRCy2JmXJ/A7oX3x4mDJjq+zfrYu IPHQVI5KYvE3LZywEIJQIw8Vxlpn/2Be18opr4e0DIX4Ttk/DoQHSRinHMu8tjLPyfHo8E/w /sdCWqr1131fRBdONaesu6+LtM8PAzmhyRBBZHMo8WNoJKKtVpgvMsBJuHZkNWd4TyOnUN0f 33E29VumZdhatLVnHahDkkNzK9KHSUf7+oMcRbJHJp1tvj9RjmSRe3MYjh1FTjBK6bk9EOGE hcFG2NVBAdHnGFv2hzpPqOpYoW2pc1qMPbc+btNmHew6dojIiXt0kOHM+hJOYS0M+ffUu3wh eCeeeGI3aZUTBi3Ll770peOaKWtwuHfhR2Dycf1wdIv01SamOhLAzkk/oj5OOcG5ZuQmiSiB ZZ+HTZ0Hg5Q2AhYzZoOptKOJ8/EIeE+Ezloz/N3K2updU5CzNsVYiM2/nFf7BNFs5awSpeZz Jh8aCnOD9ko3AI6s4d/i2uYIaxpNPV+SVvJC2nammZTNIjCXnHBacuOE7rYKqslSGFUXEZda dbfZoUxzNT4tnJWmkE3u0hFF98ALyqSDGLl+1MmodzNTjG+oDWpfRMCL76dOlsb2WjoNs9WW kSZIZRAUeRxKUoHYeDYjbw0tlVwJZTbgiGKIfgZhKfttMisXc9ECtG6R2IrfjL8PuhTAENbl 92M0hIu0l8cmAmMR8B5a9JGS0GbwTal9CGViNVc5hgme9sU7hji2CnPW12ciM6fQiNjI+EF2 vvOd73QRUTEvIKGl6c58SMODAJlPRNGYh6JqMJNJZJM295RRL9a3MX0bi1V53FD19mXa3PVz eskJ1dkiabU5m9p91vUQdg0gjltelFVFvH5ZA2bV9obO92JGWmXXRUy8ZP5GAPoinIbaXeV7 Wpt5kRBCCcuEeuGsZkIxuXkGS693ZCXujVoY5XcmuTqJWyRPijEgIbVToj6anEoR3RFOpfIE mHB3KUX4UKr+Ve5pnns4EUDCLZDCb+Ufoe1YxkfG+1VqQyzoSEhdwBOK3mfab5r5e++9tyMI kWRNPhLvb1T1bTm7OrdVIoOZJnzLzCVloc3QJCMm2uaLiLwgIhxj/ZhXbJRaSdKMo44IWuaJ sNmn1Q/zoozTKfsRGGXWWQS0viyfi7RxkMd6MaWNXlV4a8+rrbJq+63zveAIYpATWoiDDGce iuOn+i0ThnlRSw/8FiHQpnh+xKWMAGiZMmrnPBNSmTlYtmGTXh26CMewk4sC2DVNRCZhW8fb tfttfvWrX+3IBzNHVPC1cHuXRJogtX57B2kILMR33XXXQgO3+NNG0kBHOgcakaEUDi7C38/C Hwk16/Bb5t5aWu1yvo/3l3aVJtk4hNfrm8zPsVGhFaWFDd83+Jg3nGceqAXZWVWLan0xT8M5 SUn/4zU5OVnoSd7Cg2mL5iUOGttlHtkHkbXRLsCLZRH24q0z5fsQFnY+rXTTcZ6XvDSh8Q+p qwHXxMGuKOpflNc33tL/xHd2RqU5CzZ1rgk7plLD5b4x60RbfGCojndFTKhTkOtdGW/2czwC 3ptI4GYBLv9GwuuSH5H6ve8K4ZzqHQ5nY+2GaZbZnwbX81hvVPhyMaFw2D/vvPM6E4uF39zl HCSn3px4f+tcJC2fOtrauB7H2jDhOLb0yYMHTY+x047qi7kfWePKQHtSizbG+DqOvyt5ZB8C g+Qkkty4qW6Mm8jW5wb6/7A531kgp9p5DuVUWedjqYDhQYtdmJ2NH88LVevJJ588+6M/+qOu a0wzdY4J5MTuxnPXFxJL49ISJkWTG/u2yU7VUxNPKUxJ7NNRI8N3JiLn6afdYxmCbRe37tLx U9+nLPw3NaLLtxe5fRQoPWgxd8fGJRKe0TD42zxeO67Sqpj3+9L8l5E2QnYjY2wZHmyxD+IR 4+d0WpYnQSSYOZiVwj/EOT6n5QhhfqX9qQXBoGVRXRhJqjc4Ldxf/epX7/nExGbE+Jl1wiTE MV4/zAt8YRapG+Sah8EH8yCf2bnkhJdy6YSIhXqIMd0QD/dQyfqDHOCi16bunCr3Rl843KJ9 yuMTgbEIMFXtmhlq7NjK45j2tl0k/LM7R3i34Z7QIprPw1ne78heq5/1oo60ICcW7JaUTq7M E47nlB5kJMg+Z/06i2258QiiIiImNoY0liWBoUXpy32F7NCoLJq+n1a21OjQ8jLr6//pp5/e mV1olxYxjdvM6CcyI8IwZXkE5pIT0RXl7p+TZ61Gw4ynWsyXH8Z0Z7I5emAjDn+Vllv1IVZp L89NBIYQYNaZKtps6Fr5/XwEIlpunanrF7kHzJNMJ/wraNdoNiNPiEXYdzakfiKPVESR0Fpw Hi2ljsCJRdx4zaPyLbW0n3zFkB7mERreuLbPYs50jP7oJ01My3+NtgaBiUJ+yBZH2rHyla98 pWufpgShgAVNDXJSmnSYnx70oAfNbZZmTDuZnXks+sPHzSUn7Ptl5IpdWZ1amJrtMOUM4Gsw hY+BCshTtDN8C/OIROAPCJhIRRilHCwCTIPhdEk7YRE0nx6k6AfSEZEq+hd5RRBaJlCEynE0 4vW87jxmb2Nj6tQW04c09CHadB4TUp9ZQ9uICMLDVOKH35fzmHNhdfbZZw9ChVCUfikR2WON cn2OsEgNbUgttFo0Sc5h/i0da51TpiRwrntXJ3gb7GAesBICc8kJW1xp35fApvaOpsbbdLKx lUY8cLLxUumtKjKjeuhTEoFNIkBzsmyNkE3287Bfy+JLg8BcYrG1+M4ryRF4CN+lcbAY2tnz pWglH1wWPykHJDFrCZJgLo8otzq1PMdWmvSoDmx8tOa0F8bHR8XCPs9Hi8N5aErKPtBUIC3z kq+Vx0vuWM+v2vVTmpD4obSyv0byNb/lUJJqwdoWTrI1PqwIrZpuy96HPG8YgbnkxIPs4XMD wzGozBIb2TijSuTw5bb/CGy/dMJatseIXBlfv2w7eV4isAgCt9xyS06iiwC2hmOZKjhX0ihE vhAL4JhaV+Gwap7lqxIE56KLLlpDT/+nSX1DnCKcVpQNrYhxhHOozxAP/eMbghyIBJSrxPqA oPATGRLEgYYESVH0U9I0GnmaG6RnEakd48MUVbdRm1qM09pVJsmEdRDJ2oRkvOHUv0j/8tjV EBiM1lmt+d07GzsuCwguOwIvfNr+l0Uvz1sWAbvtgzYfLNv3PG/WaS0sklFIDzmhlZgqgyjn URExTDjStfvbQh2Jx+IeIENRh0jOkvBlqks/OJ7jP2JQZ6FW5wuBoBFh5iY2vC3NHu3Qok7D CFGkSghCdeqpp+57jEQBlekD+MXAOMxW8Z0NqTHQDCFlkbn2hBNO6ExEKZtHIMlJhbmX1Uu7 qlDP7nq23FUxyPM3j4Coib5Q6833Jq9YI0A7gGxYWC2QnDDLEgp2+QiJH4uoHT0tyhTaXAQD gRA9w6fCwi1CJ8pM1GH3iEjp9EpDrk98RTjNhk9NRPXw9UCMRVIxAT3sYQ/rNCTMSEwwoYVH AOpoK76NUeaCo642xyQ7cw48kYmW76MxMuvAMcpXGK/jkQ7aE9Gn5v0pzPlH9YnnI8QNhJsH X0tRV6smmBskJ/JSxM30ELjB7JIeMA8g56m+OPhdvFHCx1rJdxYdC/XlFGnwF71uHn+0EZAd c1d9TpYpJT/13Y6aLGPatdNnvl1EZDkVyUKrEIJMRqABIoKYmGMjCgVhiQKWY6+lHbt+2gA/ yJB2EI56jAhSJCqL9vnMmetLfw0kKtYC7YVmhTaCBgLxifTvvqfFMA9GltlwWOW/gdwYt758 +MMf3ovMKcdnkZOl1jj0uy/vkXOE7TrG9ZlhZKel/dEHpMi5QV78jRiGM7Df21yVeOw9P8jj 3EdWB7wAtp6NRd6lVt8HyUmcpP5BK5xLR8q8J5sAiGrSg43Ve1kk0arFw49oxMupgFPtgd3q q6yuikGtKmLu653Iqm3m+YnAEALU/xLNpSyOgMXNQrZMTZmxV6NNqMX1LI7mJ6Zgu/pY+C34 tWPqmGv1VVaXAbuVHNIm0yLt2voi31MI0wknXYt7kBgb1fBFpOlwDjLwjne8ozvGvAvLcI7V huP7SlpIilYmWDN/O187ZUoG5/sROcPvRa6SqESMKFoX9IdZnZSlKOTj0i7n1yAsiKA1zI6f dgdxoQEokzSOwTuP+QMCcBzjfzSE2WhywrmzVWXXZ27spoSTmYfRROJvsfeRrbbsg50I4sIJ DZnxYoxhx1SeYz3G541Z36gS/aZZsnuIGPy6ZoNdionJsWoTeYHKGkW1d338zzbKhuu3Ntld 47zYmWlbTQkY+F6uGt85x05V4iG//ZgcjN1Oxt+cK4Xc+RvO+ifFM0y94BdccEFX2M9nJgv+ Dtr1uZ0LOzJSSHXsOO1STwt39Z2F1N/ILVOaa/jfdbXlPLsgE6VrUAVrv+yHCcc5ioZ9/OMf 76IbFOwyUTkWceV457frmCxNaCYfExjS7d7cfvvtM1iZRI1XeyZbbeiXSAZjVo7d/3CBqd+u pX8iA/xPtGUcQh37oiMc59x5msewqXMaHCNC4euU/2POO+rHCDeNHV9ZGXtKXLzjqvHW4vkb qkO1aD/6CA2tDQISVcyRH0TI2CMra53LymLDfBL+JuYRgQPISmQO925EtI6FP7Q0ZYSS4/vS Tkii5ljkDblxnD7ZgJbmcZ/ByvziXTb3l/V/zCOR0VUbHJLL8The/5ASJEZ77ne9NtAG8bU5 bGLjYm2kkQoiqopzOEPHeM2X1gJaLnO1/83J8Lz22mt7YZF7xrrXKiuwKJajyYmJt8XG3eSS ZS/agUWP90BRp7IlhnhRPNhRRdILWINtUUFWhtSjNCdTpR1mo2VjZn9D4PxP9cVspI/xv/FQ P1LHe7m8jGy3VJRMQ4pwUb1qxwRgl+FcY2Er1TZ7qTF60YzB5zRH2jIeC71jtOWl1m4s+vJi +N+ug53Xcb7jGCzJnva1pw2TuEUeKbAIWrRNFI5FPizozkUSjMdniIu+ISYeboTAOLXhc387 RlvIjPYVAPMiUdf6nk3T8dq9+OKLu//1xd+u5zr+9nJ4ofRNXxzvO4RJOz730iFL+mcxh6Xx ux684GC8+gBjP3CAi3T7NGLOcx9NYHaj7ol76js4ac99Ncl57pB4joN2ge51ODpS3zuOChv2 jrHr0Kb2LSLuvXbs6Dz/dq1Mqt5Hi4u23XPn+r80GSz6fh3F4z1vFin3xMQ6pkjdMjjZnFhs a7KK0Nb5o5Zpvzynj5x4Ls0RNATIhmfK30EoYsHyPDnOO0VrgmzQuMTGyqYIMfFcRthzmHci xNhzr11j8xlS0XKo1W+E3zVoQ4g23QvnhmbcO1/7wTgn0u57x7233tFwdHVdC6X3yCJLM+U7 /iZB4o2xtZgiLDaKh0nMUchoWU/M/yU5M3+EGdD9g7ljYORZsdbO0y5aY9y7lpZwESxHkxON eoFNfhYDN9SDva4XuW8QrleH1XmgARdVatlb77jjjuOasOgM5WQx6a9qK1vkBuSxiUAiMD0C tF9jNUgxESN7sVCXFa+n7J3F1hxmvjJPRYbSCNmd6lp987I50GLjJ+pa2ejpTxS7i7IbtKkI M5IS5hRaCp/bGFi4XCcceCOU12bIuGgnaReDkCAIfU6uFjSkAf4IB3JF8wGvSMkQNYDKJIPR B2b08EmxPugLbaN+aCcSrulLbQHQns9rJ13+llP4H051T6doBzmBbZlQDr5TJ1IVnbXqM70Q OcGa7R5pJbBiD9pYdfMUwGojvMtL1ZKH3ksSRaOwwAhdK69L9T5EpuxMvYw8jWOXP1Xfs51E IBFYPwLefQudCXfIRMu/zJwQkSSxmClqV1fFnarnFlKL9jp9W+yMmZAsEBxKbcqM0dxIO1iH 3EYRVyQEWUNiLPbmQ2TBuTSStIkKdvqxBpTa9NKkSEOBBNjo6QuNDQ1mn5jDmQ1qQRr0zZzs bzvystp7ZL2lldXHGKedvrHTSpXaGs9EaA1oT2lQ3H/3Xc6WUlyXuWoXhWarJTS9CF+Ynx3D bFeSE6bmMNeF71CUO0DyyppH87BZ1VS5EDnZlpuE/UUK5Eie40WR0Id4SFtVQLHFMua9Ho9J DfjpyLotdzr7sesIeFc3JXb+JtnI8mluaKVh31R/tvE6TBtMJjZyCBKJemLhgGoxZ8IJghe1 xmgRaEyYV5ic4avQXSnaRWpsYLWHlIxJPqeN2rTieqHlCb+T0IDwmYhgB/2g/QitgLb4jEVk KZ8zgvx4NhCXiC5iYmN61xYyE+TnICvKr/O5gVGNM4LW50A9pi8Ib12xGRF1/1eRXnJCpcWh b6xwkqImPIisqJh0qULiQ1CzdFofrHpepWBaIH4dU0TrjMUtj0sEEoFpEEBM7KQtMBbIyLIa /hXTXGW3W6ERtpjYPYcmJUhJRK7AkEnGgkVrQiMSwqHeT+3UX6JCE0E7EQUPkQF+U8gC36wQ TrB8zPh62Vi6f+E7FUEOQS7LWjuuz6WgtYPnuxKmG+PUrusgI7RBngn98bvUjHCy1x7/L+0f VpGjBqal0Jys4vNkzdcm/MJkuVaHWBEQbugYsoHh6tQmqvBSs1EBctAl7IQe+ii17TN2UU47 5UvF4XBMtI6Xo94NHNYHNceVCBwmBCLdOzIS5CTSv7dSozOvHEURAccnz4JM08EEAy9Ywcn/ NAzmS2tAWdhvHl7MYM6hoaC5CI1EnIMoMBPx63MtPkH6wVwUi1ocG9E6+hGOtXWek6GdubBm zs7aFnXHFYCWIMKmS6dQ17WOHKacXZt8tq3DTH00KFwvppBBs44LRQgYrQPtCKYrosBkgFlv ulqjh8pLEOpGrFzyqVKo5eIYx+kvG+OQID6IWUoikAjsHgKRuCy0JuGHMC+ce/dGOW2PwxcP EQkfQv6EIlVa6SNaV6dtoJmOsF6LfEQ+mYcRDLtp16C5KDXYUh1YY0rttzmbSSjWHvfRZ+G0 yul2Xkhr9JGjbYRORyAF8kS7Um5WReRNkRl82jtztFsbJCc1PMJGsdDDKEL9vIxlVcvDOM4c UyJwmBGQ78aC5F2WvyZl1i304eTY519Ag2H3K4wdEaCqH0tOuADUER+00OE0S3MthQATguvX fn3OtdFFciJhm+vXOU6kQVg0Ky8iwnz0jW98Y+9RQJSQJ+b/RdwXNvEspeb+f1BemJxs4uYc 1DWQE1oZdsuURCAR2F0E5N1ZdBHbldGKmKQZ4nTKpM6PwGLPjFGKcGqmFRoCPxHqa7HmUDpP HItIMH2FT0HkE+k7z3H8O4i5lKk9/FaQDz4JQXrqwISIGOGk69zIq1Fea8iM0+oX879rOdcP cz/Sc9hChHfl2V2kn0lOCrTYRL0UY22siwCdxyYCiUAisCoCMjpzXhT26m85p/iN8OOo80pY 8COHS3ld0StD4aA0HrWMSaoVDrSISZQDEOarv3xSmIqi6jLfD4QBceHgytTDF0UbURzR5/6e V1dnHqYxTlE+kTTO79oplLkIjr7zw9SUcrAIJDkp8LfT8GBmXYWDfSjz6tuNANV/ysEgwOTc ioSQt6I218jYyrxVh8VaeOeRE755ssLWYkFv5Y+qj7PQ801EQmhKIn8I80mEssqULax5SBuz KsqIDy0aU1WIMGiapMgizimXJinGpp8SlWUyzlXRX+38JCcFftTAmHztXLsaxHl2IpAIJALT IGAhFU0TwnmfEypCUaesFwRAe1EnnjS/SaPfJ8qAtDK50mqMFVlnaT2idhZtj/4hBevKvssf Rb85vkYCvShWiCQJdpCaHUGKLLTGg4iJLipFnizJ66Ikythx53HTIbAQOaE6JF4QSW8w+DJb 33TdOpiWvLQe2kjaczC9yKsmAonAVAjQNPDRYOLwIweSfBelo+VU19pEO4pVthKEWURrbYgs sGHqMa/JGIuURJjuvP7SKkStJws7zcIyjqPSS+iDEN4yK+mUWCE8rZo9SFk45ErQRzPCbO9z GhvRRXDhhxK+MmW/tJsb1Snv1GJtjSYnwoc5YZFIsuMzD+5hcTwTUsesU+ZHWQzOPDoRSAS2 CQHZUC3aojUitwe1Pu0Ds8euCYJgPFHtXDSSv/l41D4nxo6MGK85zUJMm+LYeankS0zmRWby JbE5jTT3NCUWfJ+vU2g1So2GMSr6V0uku9e/yHbLNIWwIGgROsxvB6ZlzTYOs2NST6xznEe9 7dHkxAMdFX2p6IQUE2YQ1WIPgwjh8kB7eO2sYoyHYWw5hkRgUQS2ua6IsNcxYvffV6G3TsJV tkfNj7zw4wgHTrvuocKhY/q06jFIhzwfiIYQYXOVn7qOEBMGfwvRh374eBjXMvf1pz/96b5u yxUCGxtWCz4temhkxlSpFwXEwda9MefOKysSF9aua8hrpaI4M5MIIXWQjK0WbYbPjb+RM/fS Z3WyNeQNudMmTYpw9JSDRWA0OVEyXmpfD6WbF4zajVbW/jDI5Zdf3r0o+WAehruZY0gEZt2i 3VL5SzbWikgJzCz6Nl4WK4uw3xZiC9wUqn6Lsh2/nz/+4z/u5lSmk49+9KNbddv4Y1j8aR+Y x+CAJMj0Chc/qsKHICoI0ZC4J2VUJE2I9WWenHTSSXtf09K4H1EYsDbTiQryXYQM22gGkerL FE57wlE3ZTsQGE1OdFfSGsl0wtkIew8/lO0Yzmq9kKTHy1aXzl6t1Tw7EUgEDgoB4aGt/BgW w3l5M6Q9R0SiDouFLdLiM/+uIpE9tW7DgtqKklnlWqucizwYfy00F1EcsP6Ob4n6PfOE+aQ0 ocSx/Fz6XARo7ZEe2CFGUdHZfeGHI1ssckEbpm/6HaVXaJiQDuf53H2tfUxsvg+LBWCVe75N 5y5ETrap4+voC3adMe7rQDbb3HYENlk9eJNYKDRHK2HxCj8F+YyEllpk+wRJMBeExsTGzEJo 81JHdiw6Hou3HB+1MCtHeOuiba7jeL4jMHjIQx6yr14Kh2IEAK61wEhKhnnCGRWJqWVeNlr3 jDbLHF2SyjISKTRhCEpUHo5sq6KHmIJodpi1fI9sIiU+44PEApAJONfxJC3X5kLkhCNSiARA Y52qluva5s/yMrI7jonl33zv8oqJQCKwKAKq51qU+GnYeYsypPEVzipNep8gLhZmZMTihZxY /Py9TNRKeZ0f/vCHe0nFQgvte5qTsmrvomOd8nhaBws/IhD1dqJ9mIQPBzxe9apXdaTKZ0iL LK/zhDbLcUxb/BdDKzIUqiwU2HmCMBQvpAnhZkCkvY/6O+6PuRwB8VlkpvW79rfhl5OynQgs RE4iNt6DGGmIW/bcXa1N44G3G3jggQe2825lrxKBRGAjCDDdICR24UKQmQLMeX11aRbplE2e tmvhy7INxeeMOTaexmv8NElM+OZH5O6cc87pMrrSdsi+auHnLBwRnX14cDKmtZKiXnvIA1ID 2zHCH5CTs/NECjHz0MIgSfqAjIaIUvIZUslHhU9hyu4gMJqcqE4cDktuuOgd4sEqHaJ2Z+jH 9/Tqq68e/ZLs8jiz74lAIrBZBEpHVyYK0S0WVfMojUGEt9YRN5voJQ3SPEdcBID2pNTyLNMv /iR1cUDtfOpTnxokNa3rIRtl2geaHqSnFFrwOgndMn3PczaPwGhygq1ixh4I6rJgqB62ZULT VhlqqOl4XTPDYPpvfetb9zVJLYldUxs6HvMf8rK/9NJLO1XjPHXvKv3OcxOBROBoIVCawqcY Oc2FqBXznnnN7xNPPHGG8IyVT3/6092m0tzIJHPPPfd0pzIp+Tzm1aH6O2OvF8dJwla2ifQg Y8iDcawaiEAr8+53v3tft6xbctyk7B4Co8mJoTHhUG++973v7Ub6kY98pMmE1wkD27GdBvVe kAiZEEt2zLZsV8JMQxzn+5YDV9lX43LMfffdt84hZNuJwM4gwMRJVU8Fj+Crfssx8eyzz17r GLzDFizvIy2DTUMszGu98ASNf/3rX+9tRZ0Zjpd8JfhmBDGo84i0GlDzq1UAj8mCg+3dd989 t/e0MwiJbKk2lFGkj78Gsek0b8LbRg55ePCDHzwBIn9oAnlgghGW7Dr64D6r12R9Wcafhw+P 6BubUW3CiEZKaHbU9Zl0ENnYRhBYiJx4oMsYdh7wmxbpp2unsde+9rX7iIeH9MYbb9zXNezZ wy+jYp94aVMFuOk7mtfbZgSQEc6h1OWxiMm06h2rVehTjoNfgkWGVjSq0kaeEZEXuyoWTASB 34bNELOE9AzI11ARPPMT/49SaCNCC0KDEub2Gh/htKXQNEdRPH4utDHhT/OSl7xkryrvkA/J IvcBUYh6PwhKRD9FAjXPE82KvCoStJmLRQbFc9e6FidZ4+D/YkzIybpMY0KrlTYZikZaBJM8 th+BhcjJtgCJZPjhiOUhjzLX0b9WRkgqQ5MdbU+fSFfsQR/yNt8WHLIficA8BOalHh+LHJLQ ciR86lOfOnfRGNt+33G0NLQBQUz85kRqARUpuKti4S0L98U4aKaGnGEV0asTx5nraDyi6i6C M6+oX1yPFqpMRsbvwzyqfXNr5CpBDurFWKQTZ9PS+XTofiAmiBlHWsJFIK7lc0QF4UVIkLUQ WnLft0RYMQ1MKTRR2lk13Lu+nnXBM6jPfiOYY7RdQ7jk9xOREzedGs6k4W8v2qZTvMv45+XB wDFZjlxeqvIB7jPfCCNUyrtPVLX0EPICp7oOZj/lA+SlNnnMI0mrXI9aVOy+ScuE1wqLFj7H FDYPi7IP+hyTSl/fFBVrCVLoeaG2JdS6J598cvPYVuTXKljkuasjQFPZesfn5Qgpr7rsc26B sjAhJDYgNhYWYX/vstBumIdqMd8MpWZgCoqkaN5rRfVoCiz05j/zCuJm4ZwnzEM0NwTZ0yfR MuZ2c2mQBO2Y40PMq2Ufma/KrK3zrinsl8YtRF22yDAbbgLuLcJUauSZ8R3Xyj9ifqsT4nlO zOHGgEgMEb4xz5J5yVoQmDmHtmfITWBM23lMPwKjNSecReuMitdff/1xxabWDTb7ZK3mE2UT Tq+u39Kc8DSn8oykPK1+Sss89GKvOj6TwLoctKgcte9lesc73tGpOv1fM3yTvonAhDEk999/ fzcBzqtDog3HtHbqIrzshmK3SNvVlz66Llw21Lf8fv0IWBw4UNay7hozNAEWF8SWlsYza+HZ dQLLXCHvSi0ypkbtsnl3NfKLmO+ilo13vNRuDL1HfF68k4gfUwjCgwBox2fmBW27VpmxtmXi QZCYm4bE81LWs5GBN8hJ5CKJujfmrhAme31tbbJseEoHWMdGXbQ4X9uxMRrqY+t72nTjjo2q 9UGbssmWxG2ZtvOc+QiMIic8wU0KdUSMpr0krXLT6wK+FQ/Pac9LRfNBPDx1rhXkygKq4FSf cKCd2kO9vNYVV1xxHMGbEicve60NkYfA5B5CfWsHYKKnIRoSx1kYhnbKdl38E2ox8VlgYnck R04rnNB5fZ8P9fGofx9OlevAgeOrxUppB2IRlDpgne+J6yDU6qWIupN63ILu76HncB0YjG3T polTKkLfJxbaOjssDbCxLuIMaqFnVjHvlT4W73rXu5pmI/0xTzKDuHcW20gyZ0E3Z9rklYJ4 xGbNtVpZXSVUGzP/m3fM3RFVFBl4zRvExjA0Y5EsjbbE/DUvpb85z7ytEGRoOMox0BIv+6zS ypj79ME8aB6Fd8yJ0fexz0cetxgCo8iJJk0QtTOWzz1wm0xaRpXmRS7Dzrwg+kFzQExkHtpw MOPN7fuh3Z7z1uUQK6TQZODBbr3ki9229tEmmXpiZDN2zfpzpppW3YxoGbmzEFDrW5yGFgWa GJNDXS7dC0zrEup91205U1r06l1xePNH1dMh7c0UGGYbxyPAAZ3my2/PAxNEFFRLvP4HAdrA 0AR4X8w3zCVMLRZHzzKxMTDHnHDCCZ3Tp3P8jQgsI8w43pv/9b/+14xpNerJtNqyuJo/vZO0 yPqoX/pQa8Wdb54qTbAtE453cihKKPpic+a6CA+8Sl9BfaHx9myZI2A0VoutLAFNjDbKYoJx 3cB+UXyFWSNxCEpssvxGUPyeVzhy0Wvl8ccjMJqcONVL56GxyAtf84C1tCnrBppZxORI/YdQ tMwJonL4o3DeCo3KUL9EIvWZHIbOHfM9VfWQzdwuWLw+rQZs+Y9YFPz2WWn3rK8ZlVTPP//8 7itmNy9Xq9qnWip9JAmRMmHJEUDsXIZMQI6nFSmd8Vw76lwEOTF5eqn9dh+pZSX405e+l127 jklJBNaNQDzzi17Hou89recPxIHJZpEF0saLBtTizKwwhXAQNXfXtXti4dVP33uPkakomCil finm3HBSNrcSfWTisHn1TnuPFzGlIE3mrlr6fNP68IBzqSVmvhqbebbVZkQUxZwdlaxhZMNX JoCb4h5lG/sRWIicOFUYH9UhNd1hExPTOp2cItRtEdwWzbligvRCmiRNEghCePKX16V96tOc OMe5XkLOe1FEi79IX9VQExsNGk1NRHcgRdS4dhoiDcjTn/70pnZKGKGdVC0XX3xxM9X3Ihjm sYnAuhGw6/d+1Auqz2mdxy60Fnnv5lSkJMYdocO1P4o5L8we5nV/0+gwPZW+H/Pwa/n4mYPG mlPMGa3xLmM2Ye5CwMw5Q078Q88EwhYmLxtyG6ioUs0EPXVE0FB/jtr3C5OTGiAMMhaeXQeP A+k6yQmSMM+UAj87FZ73XoyoOWHX5Ud10EXNTggGDUot1Mh9phrHe7FpV0xQUfzMJFs6tZVt xgTFxhvEiA3d/yaKKJEe9Tnq/kiUV6uWlUIfq9rd9WdvqP92uJ5NpgILmGcBXq3qtkNt5ffT I0BjSAtYP6923Raxltlk+l70txh9QEYQJQTEe2k+oZVdRVokhA9IXwhwfS1zRB1eLUKsRXri XA6pNMTrdCmwuaKJsSEL5+wwRfGRSVkvAiuTEy/jYVG5i0xYp1nHAj1kHlnldtsdCcGMcgJ8 cPrGg4iNXdg4uiIm86S8jom49B9hpjJZEeSkj3DUOyULcp08ahV8dvlcC0BrIk7yth13labA Rq3cPFjUbCoiN8YUPbVZQC5oQPiAzDPz1tfTF5pLJlbvp8U3/PSW7ds3v/nNpoYkEruNbZeJ xNzIARtp6vMPtGky15iP9N1vZqZ15b7hQ2eejPu7SJmAsWPP49oIrExOqLcOC4uUGXER2/Ci D5XdyzrJiay4YYIx+VjQ+tKMX3DBBXOd58qxcSxuhT+Wx5S7HE6TpYMfJzhOyYRdum9HVNqH OcVFuQRkx3cts8+i92BXj+/LJzF2d7qr496VfodTKa2W5zu0nxbSeI5FnzCZ0oAts8iZZzlp liKSRJbYeSLUVx/0EeHXp3laiTGYu6bsrN5L5KuM1GP6N/cwcy0qnGvnaUPgW29YaIBrki51 RO0vs2hfyuNbocyrtJfnDiOwMjmxYKwrb8dw96c9Qpz8UI6Aaa+YrSUCbQTq+ix9pDnJyXY/ QZzx3btVN3C0rn2EAhFAWjiuRyK1u+66qwMGMRG8UMuyOToiSRtzVUTi2RD5OzSmxhtFCcNp dqq75HmXFqIUQQShaeG/QjsbiQP5siEuobmdqh/ZzvoRmEtO7PI9jBiyB9D/VGjU9FRdokc8 kIukMV7/kJa/Ai3DKt7dy185z0wE5iPQ91wmORn/5Nhx0+h5z4We0gZyPp1ahMVboPkH0ZJM UZ9GdtXWMyC3Cq1IOQ4LMvMSYmDR1hdzNS2L4wQztMjJvBxQgVGE0BqT6yICzOGhmaF9LoXZ iSZ1KkGCaudi61Ng3PIZpEGZlwqBVoRZSQToYfGfnArvg2xnLjlx04S1cUyUaEe9BeFZHL+E 6fpsbIz7QQ5y7LVpTrxkbMdDPhZj28zjEoEpELAJsFGw4LC7C3u3S0xy8gd05yWj48htcZft VkVzBIKfgs3WsjlGyvsaxQjV5apr24igq3f7jnnQgx7U/chP4n/5TlrO63Edfhy1Qz2TaWtB hoXnJbQakWANmbHAhxmEqYkfSjhXc7TuC6dmEopwY3+XfmVBWlrOtWNT3M97TxBK0TIR8ux6 wp31g9MqMxkHWW4GdVI4pKulPXI958BeWn6EFZnTZsrBI7CyWefghzBdD6KM97wU99NdLVtK BBZDQP4ZC6pEU31RU4u1eHSO5vPE0bIWZpdlfCOiHbtypmDaA0Jz0KouXPpEqG3TMtNxLu3L 9cOnI7K6hsmEmUc+pz6TX2gztOlYmgHOsAhZOO4it2VywwjFrbM9MxlJzEhLERlSEQTX4Jhq QUdwIoOqSD/PK1k1WRnNS+1nwm/GRpmEiUntIcTL2GASpi05lfpyXelbnbjNWKQwSDlYBJKc FPh76VZ1FDvY25lXTwSOBgKq3C4iwtpb5hWfz0uP3ncNi7KkXLUwY9RaEsdYTGloaEZEybQc RmVxbu3wmYdqQRJCQyN9Pe1H6RiLJNCchFaFhpuWJXxSRKHI2FpX9XUdzqR1aQuhvkiYjVsk TAwn4Kg7g6wgKKFFQRpcv5W1tQ9XGh6mJ23TYji/lT6B5pAfDrJOI1Y6rNKOMTcZa21mqq/b inZD4I6y8/0i79U6j01yUqDLp2adocTrvJF9bdsxqotCtcnJcuxEQd1L/W0HITR5jOf7ognj DgKPo37NMffxMGJEI1BmD40xes6nNuHWC553KLKJRlE9C6fdvc1QWfBPKoBaWmYGi7IFPAQp spgjTXxpwj+FCZC5gsiz5Lrh9M+0pW0+KqUgCLX2BxEIDYhoGg64kZAsCjLqD9O/ZG6IiQV+ kfnU/OQelc+oNluaFxll1dQxr/VdY0yYfaskBk3NYUmPscvvcpKT4u7ZARy24nOcl0O9G7/H OAFG9dPwwPeiz0vXbPIy6bV2Yrv8gmTfDwcCiHM8w8gC4k1zQEMgBH9KsfOm1bB4eydoSfg0 cES10J577rmdg2aQJdoBPnwW2laqgVYEofHMM5e4Jg0HDUokUYxq5EHGpJiPuj8IDP+LeTlG kCmkhP8HLYzrIzvab+Ua4X8SPlF8csw/HHG106p0rZ2aKAXRKlMZ8OspcyLBp/bVkfNpTNqG a6+9ttP2cITl04NotQjilM9HtjUOgYXIicqyVJIWNyycDZOd77CIB3MRpr9r45ZnZSz5gkUk cxszThOLCWNsyusxbeYxicCUCJi/kGhmECUWOFRyVi1FNWSLt0W11Eys0g85e8yVrs8cQZgq vIt8KSzaNB3+b/mPtMwaiAfTRZ9oN4pxOoZvSmhrXMNGDEGyKNMSRCG+eeOMshaRNdWxyF3U 1SqjNtUGMi4RQpxw63EhM7RWpdSOvdoL8gMbhM/vWiPC1IRk2RjRIDlmET8iQR/uzzK5Z1Z5 LvLc+QiMJide5FAZIiZeYFlGh2x6u3QDIp3zJvrcslev+7ocw+ZNaHF9jnkmHDs8u5SIROjr nx0ZlTJtS50kat1jyvYTgakQEKJKI8C8YI6zg2Z2mSKPk8UZWfAOIgQW0DDDWFj5mggXZq5A FOqF2yItkkbkS1mws45MCSxoRZzjOsYQGhLvqM+816XwQZlX0dixTDa0DDQmSACt7KmnntoR PmsDchHamTBZMT9xrjWn1IIAcpwN0W6pOaF5ssYgRbDhH+Q6fQVLwwF2quch2xmHgGfVM4s0 IpCet1Xz+rjyKHIiZBF734awYQ8zdSG7pkQ7vLCpCD28pXjIvRA333zzOIT/31Gxsxh9woIH mhRMUFi6m8e2OWQXRZjihnvhTQBRcpyKlN11rLi2NoYqr5qc3W+7Dzsw5hzn2d1xlguR2Ei+ G3UufG7iiKJ/Y/uUxyUC24aABbhOh464LJOvo64ETOtgIkcWmCpaxe0s9nU48qIY2VhEfpM4 12IfocX6UM/nY8wgfZFBpV9M7deE5NVh3rRJ5jJkyRyH5BCbInNKSYLKsXP8VRcoZXsQcB9v uOGGvQ4xm7qvQ5vaoRGMIie8z6n5p872N9S51vfYfRStowVgZvLClC+zxdUC7nMPud+Oo16c J7EAL9OvMeeYEGo18pjKm278ddddNxOhYPeAqfJOF9q3qJiQTIwxGYw9367JJFLauaNqpzYQ RRMfInvU5bCmukZuH/zgB3e7d/k52Pr/6I/+aOFilAfxfJgoI207DcT//t//u5sjEO4IeY1+ tUyf3hdjXUQiQqZ1ThSSazlkcmAfa37t64/MsOYzmiCCDCBKkUqfeclCH5s6c/wYZ+lWThVa 9Xkp9M3H1o8QfTEHlc75cmeZo0OY3lpJ58zndSTRIvfkqB/LtO8+05Sxfsjttap41moxV6wq c8kJlZrdsxfJxTwYyAHGT/1IhT9Uc2XVDo4534tcahC8QJG+2PkKcHkx40XtaxPbW2dtnVrz oB9ssvM0UlTKcih4odWlEUJInWoXt4hd1bUihTM16TI7M05mQab4o4SzrAnP3yYgP+us7Dzm echj1oNAyzFTronWAjuvB0JmNym33nrrXoVvGtdSaBE5sJbSSieAnEiWtoiIECoTlTn3S1/6 UuezZ54x39RJ9Pg/mG8t3uZafV9GOHpGdXDvrHeSQ672wsRjPvK5uaDPCZQ/B01vuTGNRJXm f1qMMdppGtUgZOZi60ctdci0e8WEwwGXvw6NSn3/lsFm0XPMdZ4Jc5x3YOrorkX7s8rxMVdb z22UvbtPecpT9jXJ3BjPht9RjiA090MOw7R2U2Ran0tOXCRsr9iqhwTj9tviiLxMYVtaBWwP 7ZVXXrnXBC1DK/RujD8JkjNGk7Fsfz0YdkWleEhoRVrCI9+kaIfjgaAxMmEhKbRG5W5kkT7R cMzLgtgXRmdSde2Qb33rW3ulC3wHuzGq4UX6msduDwJ12vDomWdz2UV0naMTAYPMi8Iwf/ld i4UG6S6lpbWwoNrdLyqIQJAfDpva9lkkFQtTK62BxRtZsAh6P5EXi7gcI8uIa2nXHBljMv9E gjVzuXvnPW6JOdM8Ix0BPxn95suyqhg7zUgtLRM3gmfjWRPIVfsQ50duGeYkWoVaIrOwtY4w X9MU7aqvpXGWvkXWGOS03MzDhLZLFJsNLR7AfcK6MJTen0kHMeFYvqqMMuvMu0itEl21Q4ue X2d99CC3Fl4T1VCCtVB7ugl8W3ictx7YRfsYx7dSjU+h/urrD2JpgqMx4plPS2OSql8sk05Z a8PkZdelb3YuNDUe4HmaGs5oJpeDJqvL3ps8bxiBPv8opJl2siXb4qTI7FDmE4m+Ihwt0uEd sCmwoejLLjqM2B+OQN69Uyb30hneZxGRAsNS04IYmH+o4BeNgrPLRWzKpGaRPZW2y/xmThDi jKCEiStqASFILTPPMhrXGif+gDQQFkRaY+TQ3Lyqj8Ii98OxNNKlRsjaYWFVriXEJpzWuhZz 5i4KclLWUPJ+WiPKHEC0iTQqUdKApt06S8PiN5+oWuTMgR3t1lQyipzYXfQVrzpI5yQA1n4X SEgrTbWXcIiceODcKBMC/w5ppuu4+1WA5y+yaWEWos3wYJn4Wg60yFg9+fFroSWh0jV5bOPO eNNYHvXrtd4f7wethFDVgxDv6VhH1ZYGAlHnT7WMIDXIOyJQRp202nKN2txpEbBYICjhb1Gq w51jg2H3vqgpoZUSAdmyOXG/fve733XddE9LbSjiYq5HZFplPKYy2YoWMibEaFnN0DL3LM4x n9l81X6IgivKaKALLrigueHa1ZQTnrdyI8Hx21hsQEOslbR8nk/Zk+W/sQYg99aKumI6LBEZ x00pc8mJHRG1qAExIWDa1FtsVV4mD/ZBho62iBF1Uis5EUbXp5YOQO0eWjkFpgQ820oEdhUB BJdDLGdYzqHeF34YdsHrNIfWeCFC5iLzD7I9Nizf+2/nr7/6bSOyqB9J9MUGyIQd/hbU39ru y8BMg4HE2K1blCMtuw2Dz8OfpySAEsVxMLdzX8T5nd9Kyz/IolMuqhYV811r02QOrVPpU/NP USRxG55/jsDM260aVbV2KJK8WVvccwRySo36JvHwntalHzwTJUFdtD/mBe167kKmyBY+l5xQ 0bMxYpgYNxblB6NqqboWHdSyx2O7mF5fZkcPT13+20tfssPWtU22UyVeWnZseV4ikAjMR8D8 Uyb82jRe/A5am5hIRUCj0peckhYz5lNzDf8FiySyQqNhjjJXMaFaNJiCmXUWFTvZWmgpSn8a c6RrtxyUkaaoAKyfoVVetB/bfLwUCPXm2v8tZ10mLv5/7usuR+O1tIyewVYemjH3Dnn2rHkf wn/K39bgVoXqMW3GMaPMOos0uIljmSDm7dSonoATqYjtkOy0hkw04WexiTHkNXYLgWVf3t0a ZfZ2DAJMIq0IpUhEZrKXpEzIdS1CN81NSEEpduSRl0TAAU3Lr371qzHdaR4j4oJ5QnQL067d LVJXExHzaLlr9r0FOhKqRaHAuirw0h3bshNpwBBCY0YEF42AXGU4WVl8PnqjyImXhOMQrQnP Xc5dCACV6lDukFVu3qbPpTlp+atsuh95vUTgKCPA4W7RSDTOlLQCFmFmEyacKRxZ++4Dc3e5 C2WusbmpVebmlNKxlAMox/SyThUNBt+Sg3KyNL/T6NDcICV8U0pBdFpmoqP8jObY14/AKHKC eWPQ7JAiP/x4YP1f7wDW3+X1XUHEyrxiWuu7cracCBxtBJhCRAwwadTZnscgQ5Vc2rnVSVln ziJ9MlfwT6AJoZ0tUwJwOuRQirDUfhpCmiM5JG2L+ZRWA2k5aEFOhB6XIsIIvpuOpjloLPL6 B4vAKHIiYsPLJ4lPKXYoUyRbOVgI/nB1L+ZhIlsxspZttcacyQvhZDfkPV+n727dIwuAYyMU 0mTtWVlU2MJLD3ATtl2wCTEqn9LczctCueg18/hhBGotwPAZyx0RyRz78v2MaTVC8kXNRFSK Z8ez9MUvfnFME0sfo9/SF9AsS9hlrqRViaiXlh8bB/3IsExrwexcR0FEh7SFzHgnhqKClh7E sRNpSOqsoXxedjU6ZVU88vyDQ2AUOelLYCZO/TCREyrYKeO0D+62/uHKVMbhsNTXH5OpyQcB YLajskbU/J4njkFM2NdFK4jici1mv0XEDjdyryBJoZETOy8yg/O1xWcTz5oIiZTdQ8DCzU+D aSJMzZKMIQbzUgjIaSFapa79MgYBoZNR+VeYJbNMZE+l/fFuIOtMTWWiyGibZsXCPy+UmWam NAvRrgzVxhrT975jIutrEDrRR0GM6mKBq1wnz00EhhAYRU4weirXMrmZTIMWi4NIJzw0qGW/ t0DTHmxCOJ4JvVp3MUWZFdnvEZA+QQ7qLJlj0tDTbJTJe7SvSuqYvAWcCjmZUufrW+wIJQXS H3H2pci7YidKC5SyWwhY4M4777xOE3bFFVespfPCZC2ikRcEWY4aLHJXlD4eOoAQWPjL98/8 NjaJIGJRk3fPsj7UzvrMTYtkTg6iZCyt+bVOib8OQN0v16FJMYd4r5n3hV4vmliPhh1JrO/B OvqdbR4eBEaRkxiunXKU3zbRrFvFuGmY+ZwsMoks2j9aADu8mMD8Dnt1X1t2dnZKHJC93OzV NAkmX5qOBx54YLAbdluLOtshavMIjYuanE3EPNztDiOUrC9bqHMQXePhGa/90IhEyny5F5CT ukAilTns6hDxwcHnAQeGAIdU9VIiZJamwT1f187f++C58vyUxcgsjuUzKTeJhbfWWNB2jM3Y LHtrK1NmaB1L0C3sQ9XDXTtq3tgYxDzbIktj+7jqjbfxqMttGId1oC/lfX1NEUu0sUyy5glj tNFISQSGEFiInAw1tuvfW5Dn1ZxZdXyIiImZdzzNgIV9iODxxxDqFoTGOZEnwd9jNAnMckNE oxwbYuCcoYy2bOF8dJAfoZX6M7Sr0w+LVZAqeDgvcgsojOYeaNex2nO8a/RlKV71vuT50yNA W4JMtkJu+9LgT9ELz9IZZ5yx1xRzYW0OZC7sq/o7lEU6Grbg8hOpJSruRmI4Zp2hrKq0MDQU dd0ShL/2V/He1IXapsCt1UZfcAACOMb86d1Wa6UUmqr0X1nXHTtc7Q6SEzt1NWa80IddTCIW RPkBFlnMx+DCwbTOaIt4jE29PeYafcfEQj+mDUn3xmhZkAgYlWXO1VniTNeXZZd9HdEqcy2Y rLRDM0JoTiww7oHvlBBwnl3cuqMvxuCTx4xHgHavRfb5JH33u98d31DjSJo2Czri4af06eCo TbOhwJkQ31YadgS8LsngmbYJoOEcU7jMIqv9EFoFzyjTMCJtAzJG0yeRVStTrX5EIjTaFO+x HCpTCnMurGiN4ajfkfRNFFGdmZY5Vp+G8OGP2BdcUNbxmnIsi7aloKHxr8vUuGh/8vj9CMwl J1i7hYapw4KxaO6BXQPbjm4oi+yyYzrzzDOPW/QRv6h22deunQe1tMXZJCYZkqJkPjMJjql5 YwfUyhhZXxMxQdDGJH+i8TH5tNIU95EbE2s9Xk52nrGIEDAefaiTPnFytAvnW5CyPQjwTZiX MbOloTCfIBDLimevLl5pIV2kYq6wWIskR1amRs605jjviQULqTIfDGW5VMvrhBNO6FL611qC sePzPrfIiT6u24RDC1L6znAi9v4y27i3MAiCxRfGOzjPeZh5CobOs+kw79UyZi4ai92yx1nb kDEkVag3olmmX1+23TxvOgR6yQltSa09OOx1ZyRuKjUB08H8Py2VKmOObmNzB0R+ARkFvUBj a4lE/yMVdd94TMzIz5AzcGm3VyDMJFO/0HZWfeSE/drOsgw3tktzfPgImBQtaDUJOeecc0Zp dKa+Z9neHxDg6IyQmtjde3+7n/OiuviBmPhpCS0EtB2enVWkzyTT9/mYayEorc0XTcIidW3G XKs+hu9KhBULRb7++us70rTutAa0GzZItdBs1onYxozL3MlpthQkzzOCCBhjKzX8mLanPAax rcmt9s1DQ5vFKfuRbc1HoJecCCmrF+rDbiu0YzeJrktMnrE7M/EMOclN1Q8kqC/DI1OMvvjR PwsQrYWU1qXK2s5UG3ZTIcbimWADN5khswjevFTvdlJwNlk5NxxvYzdmlxa5TaJEd6juw/Qz FS7ZzjgEaMeE2CMlFpgImbWLZroY66cx5MM0rjezrg91QjBm52U1F67LTNHSmgqTRximEu8X 3LxLzLzh+MrU5TqecVqHTfhXIUEtvxlarUULutrgtEzhUbF2GfyWyZk05jq0zi1HY4S7jj4c 014esx4EeskJVVe9UFuMDlO6+hpSi3gdwjo17FTPInA2LYqV9Ykx00wgDiYEfjDMLJdccsne Kc5v+R1ZcGSUdPwiz4Y8Cn1OdWzA6ieZIGlQ+iq9bhrDo3q98HdAFst7bBFFsFvVwdeJlWcC kRC6Swtj999ysrXYe5Yt+MwvQ9JyXJ1SW+xdQeyQAoLEM9uYV8tnnA/IkHnZuxj+Xci8iCBz C9+bRQrTGV/ptOxdXsa3i79OS2NKWyokelPCR21Is6yvNb78ozznUWV6U/3N6/Qj0EtO7Jox SYuIm8nM4yUKRm3nMkVZ5G26OXYxq5SO3qaxZF8SgWURqFX9dsQWMKaGUswPiGRdYn7Z67bO s2jUwhk2iuyFNqdeUC+44IIuwosmjuMjzd9QgkVaDY7Yrkm9b9GfMhAAruV4brjhhm5OpUUp F3CE74//+I97YVSN3T0Rqs3R3qJK0+J+0Hb7rXjgGJH00IaE/w1yhsgtO2abmbo2medmFf+i MWNwDA0NTJAOeNICt56daA/R1jd4eYaybMlYpDd33FyHWPZ/Dz+VO58AalOf2a2wJXuBD1O9 Bbuv0pSxuduQV5oCAdqfdYgdLvX+YRW7ZbteeXSY+WqJnCCiX0phbuPMOaV2YQhju+JWOnik opRWnRoE4Nprrx26xNq+51vC4bSUyPXDLBLCCTcW+cjYasFlMnWfak2VY+VsKXFZ1TzBvw1p WcZUJjrK+7JuLXTgRYOEnFqLzN8crpnJfLZOM/3aHpRsuENgMJR4G3GS8fHf/u3fuh0Ch6va nGCCFWMvQySGbIcyRkwAkQxszPF5TCKw6wjQjA5N4BZFOTgspEySFkIO3WWY66ZwoDVoOTNa 0Et1PuLE1OFYCzwCRrtwkBEZHE1hGZlSaXXCWZRTMTOUeStyF1nka6dY81lJBi3ICI9NZBm2 vaxjv7mTz5l7i3iaZ/VZ/pY+MR9PJTbCTHHuX5THGPI9QUSsBUha6WwtOpBGZBOam6nGn+38 AYGdIydUyx5cqkwTjwfTTqLMCGkS9QJ7uUy+/peCeUiYddJbewil/P6oIWC3zs/DgmWxjNpH FtaDKF/R8g2pHUgdUy7itBZ1Wvmx91FUCy2ADKcIzxihcagLpcZ5kSmXdoGpByH4/Oc/fxwB kDrAQs2XJOY0GiF/l47pxolElH43y/iN6F+r1IV+cFxfpv7QGKzKY2IsfFX0hSkO8ZiXd4rv Ea1RHW1F04ewRBmDRfuSxx8sAjtHTjyodUrlcjfkpa1T0Edq6KGS5EhPbVc/2NuTV980Ahai TQt7uTTffDdMznbAFle5L6ZY/OXtGMrXMW/MFlnkxAJoQ8AvQcTDqiHBy+JMuynsnemBFodm pM6aajGzQFvcvdOIlbmjxhMu/FGQD/4XtbgnwmP51wmld1/6akfx1YiEZjQ1NCS0DsuavWgr kME6USMnWuOzmTKnaT8i5/iaLKMhkvEWPtr2u3TQpZVhJqpzDy17//rOoxWDn+uVmLnfcJiX Z0uIchAyPkb8X9xz/ju1OXLqfmd760Fgp8gJ5u7lkS+Bg66HLrIZBjweYC9aLTFZzYPRRMSR LiUR2CQC/A1OPfXU4y4pusMi3Fo0x/QPkTBBl1Vtx5w31TE2BesSxAhmSNJXvvKV4y5j4UYm aB0uvfTSbsG2iw7fFPl4EMJIOGiDY3NSRmvwT2nlcOkLy2/VyWJ2sbAvU9FXH2kEyjmOeQ15 4PCJLK4qNEGuEaSNliGcjCPqx2Zw3cngjIP5xX1yj2ofojGlMWi9g2DpLzLFQXaRKMJV8czz p0Ngp8gJ0lHXbhE6R50p9JR4uBWZqiUSRs2DzoONfWsPc/cTYX/TQZ4tJQL7EaDm74uQaNWm GcLPTn+qnCJD17K42yh476j/t0H4ntiMWMC9z34jJkwTfDqQiL5EgWXk0XXXXXdcAUrj60tW qM3W7n6VlPPMQxZaJKk05dRRMcvibtGvk9cJdjBfmv9olhbV3kWdLfMpPBfRsMm7EtEzyCGz vb5IZTBWA+V6Y2r/LItZnrcZBHaKnERq5Roak01MKphyK8TZizJUlI7KtsztsZlbkFc5CASG ciFssk/qzLScCmlMDiK0fWwEns0CE5SQVloSfl3rzmo65r5Q8TO9WHj9WNQQitj9Iy59CSXr sOg6MoaTal8kDPOLhb6sO3P55Zf3FhkcMxbH6C+/Cv5zFt5lfWdaRUL5dLRq3SAFdWXwMf3V Hl8azxCSzD8JaVskVNd5ocFxz5iu9KXWko/pTx6zuwjsFDkBcysbpckniAfP7vCGL2+L74dK tVPXHoTPwe4+PuvvOUfBwyDzkoCJJmvZ8+3c+wpDCm/m1zCV8NvwHtmRW2CHKum6busY41gm /HSqcSCdsRDaaJQ5W4KQiGSxoUEcSuHjUWPKX4UmFeky3iGyaLcfmaaZXRw/RRJB/fAsTB15 guzQdJTROJ7HZUsBhHYDIQlHVKSCRqmVjXbefXee9AD8ilKOHgI7R07sIsq073ZvXogw6/B8 rz27ZT71sg1NEnZJrQqmR++xyBFvEgGTr92rImwnnnhi50RpMax3tI7zbHPgnJJER1HFcsxM TfPCRx1rga+Fn8dB5woqTStljRjaU8LUw9TCryEqG1tMd1Fr6lmYQpiNFDCk+VglR4pngvmp dkKNQnurmLimGGe2sTsI7Bw5AS2nVS+R0D4OYrXw9rYbiMiHeofUd3tMVFPW0didxyB7epQR 6FswompvHzZ1+C7zDjW8jcAQsVk33vpugbRY6o8fiy4n05T1IUBTxexES0LLw2QZ/j3wr6Oq 1teTbHnXEdhJcrIu0Klv0wF2Xehmu9uKAK1jmSco+mmBbzmXl+Og4UECIvyZlpLIK9K3ENml 0144l6llXSGqNE0yWq9LLLxTC83TrkeXcKJFCm0OQ7t27733ds+IUigpicAYBJKcHEOJrZrm JMnJmMcmj1kHApz+NhVlU/ZfXogwecTnTDNjIx44n7YK1bWK8YnokcuiFHmJxiY3a+F+zTXX dOkDmGXgh1AhXMvW/uI8y8SLQLVyI9EO0RrRDpX5NRZ9JizYywjTtaq6Y+/PMtdY9Rx9lDsG hkhKHRq8avt5/uFHIMnJsXvMnMPZ9vbbbz/8dz1HOAkC3/72t1du56677torxRA5N1ZudIkG vv71r3e7XJoMfgyiQ8YKX65WtFEr3LXl0M4Mu6izpL6JPrGhQHhuu+22LgSVSSF268xVUah0 7Fi0V1YNpwliQlbt1v3mbEsrVApNwVBRwbHXHzoOEWSiisrhootaUThD7eT3icC2I5Dk5Ngd knDIxEStmpIIbAIBflE0FmNDdzfRp7gGv64IvxcBZ8FvJTeM41tF3lpEpBXCS3MzlL25Nfa6 LX0UlVdqbPwtLHaM0N7UkX7yz2iTtgT58bvOUK3tOuR4zPWWOaaljerL2bJM+3GOvCIpicBB IpDk5Bj6nLc2kQXxIG92Xns5BFr+GMu1tDtntYiF/BNl9dxyND63cAqjtZj3+ZFwYKdRiay3 /EJoAkTY0ADw46C1GbPg1nmLpAqQAr2MIpIbaSxxkKK/LF8hjJ2mpAyZ1k/zhEq4IXxEWqRh 6rst022roJ+Q3SnMgcbEXCUFvoy2apO1cqBMPa5sLxFoIZDk5BgqJjWT3a47o+Vjvl0ISJ9+ UOnjV0GiTkamLc6MYwpoDl33sssu65xopZ9XEblVpE5+i6EcN7XmxPtrcS2JglwjixR+K8+V Gp92q8xtgoQxHelzaLyEd29K09BKJNkikkP3oPV9S6vF96jMTLtMu3lOIrAMAklOjqFmp8Bx y04rJRFYBQG+I0wAdv8SqS0rFj/lGTh89kkrlH7Z65XnterHRCTOFO2XbcCqFknmpMWfJ+rg yAnDgVVdFRoYxEFUCAd32pvXve51C3eXn0pUAUZ2FJ4rhUMvk5f5AkkZyp+0cAfmnAArhReZ wu68886ZPC5nnXXWJJdoaX+QUQQyJRHYNAJJTo4hbiEw2ZiMWhlmN31j8nq7iQCNwDILYj1a pgS+HjKOisyQoK1Miz4FOhw/OXJaYOtq3IoGnnHGGV12Tn5Y/n7xi188xWWPa6O1KMpcuojG SZQdkiA7rcVafZhFSxTUCRhli6WpKDUHImSYjZifmHiYfZhakAakJorlrQWoY42K6kLA+Ol4 PqaSVsZfGizmrpREYNMIJDk5hjjHt6mKaW36Jub1DhcCduaiQ0qx6I0tfDaEhiq+Qjvr6CD+ BRb1TYvIE+SL1sOCy5TSMisJ3aUpYKaZIlJqzDj5YdCA+aFNiYR1LVMUTGX43WUxToQM8VK1 mZ9LSiJwEAgkOTmGOvVlnfHyIG5IXnMzCJThoote0Y5aCPC6xCLXymTaV6xu0X5YYJkFarEb 76u4u+g1FjneQh9aknvuuacrGFduFGg1jZ0pBTnhR8LPYsgnRR/WlRqg5WR76623Hoo55IEH HpgJLU9JBA4SgSQnx9Cn0p1qZ3qQNzSvvR4Efvazn3WLot3z1MXX6h6z8de+JEw6rUiNZUbL /NAiZzQW6zLd9PVTsq5WGHFJxDikKlVRCizqxHHLYLHsOciRZ6IUC3pG/C2LaJ6XCOxHIMnJ MTzUBUmzTr4eLQQ4h7LzL+rDsCyazC7MGjQIfCmUoG+ZOZZtX40TWpLSp+P+++/vVPnIwjqE 2ZR/BlMB59WQK664YiaiqZZyo9DaNDBJTYnJomOWgZZfTjwTtA2ekyxstyiSeXwi0EYgyckx XGR9lCchJRHYFgT4nXDwbGVfXbSPdvXIt1TxnFwRHpoSTpDMSFHVe9F2xxxv0aZRkAk2hFmE 47kxtmrwlPk11OIpnVIjNFh0zkGKMYkW4i+DKK3T1HeQ48xrJwIHgcDOkZN1ZXClObETSlkv AnwdpowwmKq36yjiVvZtivwgq4wVQWilcj/hhBNWaXbUuaJbajOSbLMWdiJ8n3aEIzBnzIc8 5CGzH//4x/s0VcwoyBRiIjKG2YePR0oikAgcLALM3Uysl19++aQd2SlyoqCX3VLkIDDhtvIx CCU0eZnQ/IzZeXJyXLQOx6R3IhvbOAL8jJg3LIyHfdf70Ic+dMZcVItd/7rFu1hHfQiThruJ TbiqwoFDheyY1mQtFVHEDJWSCCQCB4sAbax0BDYOU286d4qcmJSGHM7Ux0BeqK3t1tQIMTkO VQClOSkzQR7sLc+rrxsBz4ScJLVT47qve1DtIwIt8r0Jp1I+JsKEiaRyTDTwFx2HMCEu/i7N PgeFU143ETjMCDDrci5HJmgg57kyjM2Wzt+KP1mr5tQqWO4UOQm177wBIyZ1Rk2Fv4aKf7G5 S9Wcsv0ITFFH5L777tv+gU7QQ1oJphIhwrSOfnOE9bxzgN1E2nWZWr2Xri8y57GPfexxWUfl 19CflEQgEVgPAjYnNglMo6wLalh5L0tzDB8376J3VYi/TQ2/NKZZx4Yptu6h4+dlsl5mRDtF Tkys1PBqXiiTbtIFXLnjahUMo7KXhfKSSy7pxehd73pXp2KOgmTLgJnnbA8CFt2PfexjvR2y Y+eQKSX7VVddtT0dn7AnTCCtcgwtU+iEl93XFJzr8GTaGu+taKSQG2644UCjb9Y1/mw3EdgW BFgGEIxSWCLME6sK0tMyG6/S7k6Rk9ZALS4Aj910X6IqzK+cDOu2pBxHdkycogOQmakXrSgU tsoN29S51O+7Kl42RLaPaDIz2MHTnjD32S3MS3C2SsK2g8SwzwS6yUqzSAeTaSlROK80M9Gu tNKnHyR+ee1E4DAhIH9SrZ203pVWBfmV+GmaD6N2lN/xd1+Fb3PN1HW+dp6cKH5FW/LWt761 e47kUWgJwOfViJCmmi2uTul9mB7OwzwW2jEvENKhaNw8aeXHYNL7zGc+c6ggol1sJYzrm2DW MXjErk6gxvmcjdr9QthF7nCsO+xOyevAN9vcTQRuvPHGjXdcdmW+JqWwRNRRqrEGKpnBn4Rl 4u677+5++jIHe5+nLimxU+SklSBKZVKak0hl3cp9gJRIPDVPsEcRBCmHGwEhqi2NgkiQg8qb 4eWntaOto+GzaNe1dZa5K8Zaq3Ff85rXdObLTYrdmn6o/H311Vd3xISKWXVdGpMpxrrJ8eS1 EoFdRIDza51TyLs5RY4j89cqFdhbeO4UOTF512XUhSBy+gvxd10V1sTYqlVSAoKYnH766bv4 zGWfF0SgVQX39a9/fVdp9iAEISmr2VrEp8p+irR7/jm3IegHRQT4ACEiCMlRiZA6iGcpr5kI 9CFAi1pHxNncTyFT1f0q+7JT5ETH+QtQAVMVq2ba8itg4z7llFO6HbIdmhTgQ4KY1MRn6Jz8 fjMI0Jhh/ELfbrvttpUv6iVlCpQZ9VnPetZa07YPdZZZshXCjmBL+V4K/43M7zGEaH6fCMxH gEYxZfsR2Dlysi5I2d7e/va3r6v5bPcYAsJYJT8bK+LyN12MbmzfpjpOaG0tz3jGM7qP2H+Z HCU6krOHhocmZJ5z91T9ynYSgUQgETgoBJKcHENedIciZCnbhcCQc+t29Xa53nBcjQyqNIEK 4TH1EB72dXSNCLVNRtwsN6o8KxFYDQF5rVKOLgJJTo7dezt0UQSHWRQ3HCvpHDwWqWmOo01i t2W+EtocItTv1FNPPe4itCkZ3TIN9tnKZhBAvIeSYW6mJ3mVXUAgycmxuySVeZp1NvfIcvoU 2uYnpR8BIe516C/fEw6uKYnANiBQ57HZhj5lH3YfgSQnx+4hRj/GcXb3b/nBjgAZES3Tylx6 sD3b3qt/6EMf6tJOn3baad3OkyP4FI7B2zvi7NlBIyCyKiUROEgEkpwcQ58D4mWXXXaQ92Jn r71NoaGcRuUJEdEVNSF2Ftiq4+eff/7shz/84WEZTo5jCxCI/FBb0JXsQiKwD4EkJ8fgEFKa 9tDdfjuEBtc5TD71qU81q/Hu9kiz94lAIpAIHG4Ekpwcu7/Pfvazu8yZX/ziFw/3HV/j6KR/ V0ZADppFd/hTFI1CTD760Y8eN0LZC1MSgV1AoK/svHw4KYnAUUIgycmxuy1KYl7V4sPwUEhg tw4RNaJmg1LcB1mfBjkRZltLq1L1OnDINhOBRRBoZSTOJHuLIJjHHmYEkpwcu7u0Jrk7+cOj rg7DWPnBD34w9tCutsq6nDklKHvEIx6xV7xRJlg1c0S8pCQCm0Cgpfn41re+tYlL5zUSgUOF QJKTY7fTrl8F1ZTdR0BxO/lB3vzmNzer8u7+CHMEB43AvArnB923vH4icBgQSHJy7C4yS9S1 TA7DDZ5iDFLOq2tj0U9JBA4rAkOVyw/ruHNcicA2IpDk5NhdUcZdZEfK/yCgaCJfjWuuuSYh SQQSgUQgEUgENopAkpNjcDMDfP3rX98o+Nt8sV/+8pfb3L3s2xFGgN9SSiKQCBxuBHaWnHA8 axU/u/3227tdv/TeojcUULvpppsG7+Jf/dVfjTpusKEtPuCUU07Z4t5l1xKB/Qjcd999CUki kAgcUQR2kpx85CMf6UJX+UHU4rNzzz23+/iBBx6YKWD3qEc9avD2IjH33nvv4HGH4QBaEfUw pERvhTMehjHmGLYTgaPyjm0n+tmrRGB3ENg5ciLF/D/90z/tLa6l1zwNiQW3FqW3zzrrrLl3 5QlPeMJWRHZcccUVzX5Okf/gVa961ewf//EfZ5/+9KcHn9AMvx2E6MgdcOONNx65MeeAE4FE 4GAQ2Cly8rvf/W6fFkTmz5tvvnkPufe85z3N8vLvf//7Z8jHPGEK+uAHP3gwd2FDV73llls2 dKXFL3PppZcuflKesTYE5IhJSQQSgUTgoBDYKXIii2uYcn71q1/NkBOF3kIuuuii2XOf+9zj sBSFM0ROROuITnHcYx/72JlrbbvAYFV573vfu2oTeX4ikAgkAolAIjApAjtFTphz/vqv/3qG SIiuQVT8f8YZZ3SgUDv/8z//83EAKTH/lre8ZS5w2hxj7pgU/Ykbu+qqqzpcMh/JxMBmc4lA IpAIJAIbRWCnyEmNDP+S6667bt/Hj370o48D8M/+7M9m3/ve9+YCSwuzi856119//UzadpFJ nH/nieNSdhOBdaX83000steJQCJw2BHYaXLy0Ic+9Lj7c84558z+4i/+oitCJ3RWiDCH2CFB TnYtcoUD8E9+8pOhoa39+y984Qtrv8ZhvMBPf/rTwzisHFMikAgkAisjsNPk5De/+U0TgC9+ 8Yuzd7zjHTOOsGMd+2hXNl1bZxfNSCoQp/Qj8Pvf/z7hSQQSgUQgEVgRgZ0mJyuOfd/pqtn+ 4he/mLLJvbZalUr7LvTZz3529q53vav7mmbkpS996eyRj3xk83CF7dYlF1544bqaznYTgUQg EUgEEoG5CCQ5OQYPzcm2CCdf/jSijH70ox+ttVtjsueutQPZeCKQCCQCiUAiUCGQ5OQYIMKI 1yXPe97zmiTjjW9847ou2Wz34osv3uj1NnmxH/7wh5u8XF4rEUgEEoFEYI0IJDnZQs3JDTfc sMZbnk0nAolAIpAIJALbjUCSk2P3R1r3dUW+CPGNnf3HPvaxLhdJhvVu94uRvUsEEoFEIBE4 OASSnBzD/k//9E9nCuKtQxAfZiO+JIoWRmbX1JCsA+1sMxFIBBKBRGDXEUhyUph1Hve4x81e /OIXz04//fRJ76uaP8KbD6vQBqUkAolAIpAIJAJTIZDk5BiSkrX9+te/Ho3rwx/+8OOO7Tv/ W9/61uh2N33gV7/61eYl//u//3vTXcnrJQKJQCKQCCQCHQJJTo49COr0LGLW+Zd/+ZfuTJWS L7/88s5k8x//8R8LPVZ9xGChRhY4+Mwzz1zg6Dw0EUgEEoFEIBE4GASSnBzDfdHaOn/5l385 U4jwtNNOm33mM5+Ze/fOPvvstdzdvgy5a7lYNpoIJAKJQCKQCGwIgSQnx4Bm1mlliP3Zz342 azmunnTSSXsROFPeq6y3MiWa2VYikAgkAonALiKQ5OTYXVPVtyUf/OAHZ6997WuP++qqq64a fb8zbHg0VHlgIpAIJAKJQCKQPifxDDDrfP3rX+/+VbxNxA5tis9bmVyzhH2+PYlAIpAIJAKJ wHoQ2DnNiVTwz372s2eveMUrugJ573vf+45D5uc///nsk5/85Oyaa66Z3X///aOQkxgNGXns Yx87e9KTnjS77LLLuqRs9913X9dWSiKQCEyDgJpRKYlAIpAIzENgp8iJaBoJzf71X/919s// /M/d33/+53++b3zXXntt9xln1ac97WkzBf3GRMVIknbeeefl05IIJAJrRuBhD3vYmq+QzScC icCuI7BT5KQEW04R2o73vve9++7BIx/5yNnXvva1vc/uvvvuWZ8/SXkirUk6o2734/zlL395 uzs4ondXX331iKPmH/LjH/946TbGahKXvsCIE1s5gkaclockAonAAgjceOONXTTprspOkhMJ wp7whCd0uUXKCJu3v/3tnbmnlic+8Ymz97///XPvkdDglNnsnnvu6Uxa2yjPetazNtYtuWvG aNwW6dB//dd/zZRJWFWQ8mWl1jQOtfOBD3xg9qUvfWnosO77ZzzjGaMI/oknnjiqvcN+0IMe 9KCtHeJDHvKQjfVtlee5r5NnnXXW7K677lppDC94wQuawRBjGl00yzgXhEU091deeeVgN970 pjd1FoZdlZ0kJ7/97W+7wnmcVR/96EfPIpvpS17yktmb3/zm4+6FaBsP2jxhInrNa14z+/Sn P939XHHFFV0I8Te+8Y3OUfbDH/7w7Dvf+U4XPvzd7353dscdd3SL11e+8pXuew6yfFT4u9x8 882zH/3oR91PFPx74IEHZpGX5M477+yStzk2hIbHtTjjLir33nvvoqf0Hi/N/tve9rbJ2jNB rLLTj47A9qUvfWmH7SbkjDPOmF188cWTXurlL3/5bFUS/NnPfrYj5ctoYDyDiy4Er3vd65p+ XS1glH/43ve+N4jZX/zFXwwecxQOGKPRPSgczKubElrrqeWFL3zhzLuyiiDyCPeiQnPPTWAR 8e6cfPLJo0854YQTBo/lj6ndXZWdJCcl2BxYTfrk0ksvnb3oRS867l6YYO1a5wlSg2mqIPxv //ZvMxlg+a2YSJ/znOfMHv/4x3c/PgunWQ8vgvQP//AP3W/+LX4c47dFxG8LgnY8sF5EC5Tj feZHO47142/HmxxMXo94xCNmj3nMY2aPetSjura04TN/U48zYzkuru1vu/P48b3jne/a0a52 oj398bff+uP6cT2fRX+1o3/aKK/h3BhXHB/HaMtY43vX1Uff+4mx+63NwEk7MS7n+P/v/u7v Ouyjr86BQfzvtzZibNGHuKZ++Dv6XuJf/u2YcJB2XRiWOEffy/vq3mlDH/Qr/nZsfOd4Ywg8 A3/HRN+dGxiW99Qx+uVZk81YO451T/StvK+ubazwc04Lv3hOog/xHLrv2vV5tKMtn8X9iO+j TzFe98ZYYzyO9+PzeLbiPsazHm27lufc2Hzm73gWfRbO6tqK73z2lKc8pXtXfQYbWlJaVVjb NWrD+fG/dzieMbg4xnn6qU/aefKTn9y1a2HifP/85z+/e6d9btF71ate1c0RruU+cKD3vbb8 1h/f+18b5o+nP/3p3WeySNMAquFlnPzn/O0Y/eYnd+qpp3abKd8h5OY057jOM5/5zNkpp5zS RROa92zIbNS07zhznWs4z7Ud/7KXvazbcEgG+da3vrU7xvnveMc7us9ox6RMUAPs/PPP78YH L5poptRPfOIT3ecf/ehHuwVfGoXrr79+ZvfuO4TUpiY2arTZ3//+97sfAQU2exZsmzpauFtu uWUv2MAmz/3Rju+Z1m+99dZus6dAapghy43cvHnc+Yqrus/vfOc79x1qQ7iIeBaGNrWt9vTB fVxE3KtFSLtndUishwJIdlV2npx4qU0ghAalddO8aNT0KetDoMxWu0gZgKl6tIgKt9U/Giva q2XE5Ex7JqrLBKid2n+p9vXwPU1GSOBXasGienXZJ75WFoLSr6r8PjR1+uR8C4X3wjmiZJjt SvG/H4sO7ZT2v/CFL3SHGAstpXMtIkRSQu2yZ1vUOKA712LlXNf65je/Ofv85z/fXe+SSy7p /MJoemgab7rpptnnPve57vjrrruuW3xgR639oQ99qGtTIcm3vOUt3fnO+fa3v91953ifnXvu ud2CZ/Gh0bRYat/G4t3vfnd3PYuxxZeq3CJKK2rxvfDCC7sfn1vE/K2dj3/847OLLrpo9tzn Pre7tl2nY1796ld316BFs7DTqFmwLPqOdx0L/b//+793JmWLuE2OcTsXoUEaXMPvV77yld2P vy2giAMyom19tEghN4gJcqBtcxyy5HcQECSJPwESEoQHWUFiECZzonZcH4kK8uNv10eckC7f mx/DTG5c/kYY9A/pQYB8rg9BDhE62mY7c587BzGzoLu+hdZc7Djt+87/QTyd628/rhUENDYz QXi157wg7L5/6lOf2rWLnLpObHyC2Pou+mKczo12ynZd23fOD7Ic//ssNhOOC8Ib13Mt5/gd gRnleOJcWMRmAp6O9Zl+uReuE207333zufvscz/uAYxdz3Phb+NzT5EP99F9QVr9do3AyGe7 KjtDTn7wgx90u3kTX4i/3fiy4q8dEHNKCKa+SRXlrj4I2e9EIBFIBBKBRGBbENgZcgIwO5NQ /WKe1PN2NaXYDWGY2ClWjwlTN6YkAolAIpAIJAKJwG4gsFPkJCCl0m3Vu4nvqbapd6lKM5Pr bjyI2ctEIBFIBBKBRCAQ2ElykrdvPQiws9NIsX2yVd5+++3rudCCrUauGlozNtpNOXnxBZhH ghcZBods9uBwLF3k3PJYYd5h32/VfFq23TxvswgwSfOnmTpcfdVR8N3inMpUXvsnrdp2np8I LIJAkpNF0Drkx3L24lDH8ZBD16Ie5+uGhzMlU51og3ULXyVOZ5wSp5BwnHvDG96wdHMcLd0j xJHzI5KSsnsI0PyGwyWyyhF2WwQhD7O4aKbPfOYza++azYeIsymEg3VEQvJRpEFfRmjcOQHb EO1yrpBlxr4t5yQ52ZY7sUX9EMpnguK/s01Ck2ORN2msU4IEWfxFeEwhSAU/qAgLF7GyqPDC D298vlSLJG1a9Fp5/GYQENEjdHfbRFQIcr4JifdiimuJUEImRMYg88uK9yzyPY1JeLbsdfK8 fgSSnOTTsQ8BuQZEPAkr3UYRImuBnzLxXDlO+W5ojYTz+TFJTSGlily+iEVyGsT19UuYpJ23 opdjch1M0fdsYz0IyC1CO7FIGPx6enJ8q3Kv2AgIrV6XyMMRNdKmIkLej8htQzuFrCwqtKY0 tN59GAjVTdk8AklONo/51l5RnopY/LapkxbiUta9KEtMJZ8FTYXJaQop8+wgF8uMwW6wzFtg 8oy8JlP0MdvYHAJ24zYBiOq2ivwp68xiK8WDdwwxieR5U2NBKyMNxSIir4t5kLlN3h1t7HKN mkXGvk3HJjnZprtxwH1hq41EQW984xu7jLvbIJG8KTLxtuonraOfklVNYdqSCM0ujg+PJFtC 4JVHWFQQExOlexRZihdtI48/eARoJ5Fez/O2aU08XxLGScAnIRjysC6RBkLyOs+y607xrnE8 L/NcwXjR5IoCA/QpEg9KupfFKtf1FPS3m+Tk/2HDuVC67pREIBGYFoGhgpsyyUaaeBleae8O u4iGkfXTwhxZPrdlzBbmSL0/VPJjyj5L+zCF0HhGNli/jWcZkV3Y+Xy73KOUzSNw5MmJ3YFd jAcxJRFIBKZDQGp171af6UKafJM/zRinUO/iOs0I040sW0oEDhaBMcVUldNQnkF5hz5ResJx 22gePvLkxE0TnbFoKfmDfTTz6onAdiOgdk7UWWlFOzBtMBkIXS1FrZepdtHbjVD2LhFYDgGF Hq1XCii2hEmOGTn8eDgI12khpDRwTNQ0ckzUqFuuV9OfleTkGKapOZn+4coWEwG+BIr21SJS pVXzCjGZKkIq0U8EDhsCCkV6PzgRt4p/imJklhMKXgp/Qo74hBsDYlJqNBW2XCaCcJ34Jjk5 hi71ckoikAhMi4BJtOVYzc9EGG0t6mCZOFMSgUSgjYAK6pziW87Ucrt4rxxTCsfg2IDTWLY2 41IVlM7EB41/kpNjd6A1UR70zcnrJwK7joCJsA4FNyaZiFtO6N/4xjeSnOz6Tc/+rx0B5phW 3Tiko6UBsUGgxSRIiJ9abCRsGrZFkpwcuxOpOdmWRzL7cZgQMOG1zDpXX311k5yceeaZG8tM ephwzrEcLQSYab7//e8fN2galdZGm7k0yEnUKKtPlhE7yckWPkct+/cWdjO7lAjsFAI0J30l AIQOS99eiuPLhHU7NdjsbCKwIQQQjZYJRvh3i5xEeHhoTlqmUxsJm4ZtkdScHLsT0jWnJAKJ wLQIMN3MK77me7tApIQ6WoRBSiKQCMxHgFmmFf7705/+tHuH6og3PieS3pFwiFVDrRSFErdJ kpxs093IviQCiUAikAgkAgMIzIsu/e1vf9vVLFLpWcgxlwVJ5UoRuSNTNQ2MTYH8Qi3z60He iCQnB4l+XjsRSAQSgUQgEVgDAkjKPPH9xz72sTVceZomk5xMg2O2kggkAolAIpAIJAITIZDk ZCIgs5lEIBFIBBKBRCARmAaBJCfT4JitJAKJQCKQCCQCicBECCQ5mQjIbCYRSAQSgUQgEUgE pkEgyck0OGYriUAikAgkAolAIjARAklOJgIym0kEEoFEIBFIBBKBaRBIcjINjtlKIpAIJAKJ QCKQCEyEQJKTiYDMZhKBRCARSAQSgURgGgSSnEyDY7ZyxBFQebeVTvqIw7Jv+D/+8Y8Tjh1C 4OKLL56ddNJJK/X4W9/6VleeICURWBSBJCeLIpbHT4LAc5/73C51sokrfis85WfX5DOf+UxX bEudmJAnPelJsze84Q37hvL6179+Ni/t9LrG/Zvf/Gam0JdU1n/91389O/nkk7uU1rA+55xz 1nXZ49qF0dvf/vaNXe+Tn/zk7LGPfWz3jPn5l3/5l9lHP/rRjV1/igup0lzKa17zmtnDH/7w KZqe24YaLZ7nVuXbRS/+tKc9bfaSl7xk0dPy+COOQJKTI/4AHPTw77///tnf//3fz171qlcd dFdWuv7Xv/71feerV/GiF71o32f/+q//2tWx2LTccMMNHRl5wQteMLvzzjtnDzzwwOyzn/3s 7IwzzphtstjXN7/5zY0OXXG0pzzlKbPTTz99ZlFHDlUf/9rXvrbRfix7sZ/97GddzZNSvvvd 785UmF23PO5xj5uUuML97rvvXne3s/1DhECSk0N0M3d1KHa1Sn235N57753dfvvtx331+c9/ fqaq5q9//eveYf/kJz+Zffvb3+79npnhjjvumAubhbyU3//+94MwK2VuYUS6SlOGyqC0RK0C W7/61a9md91119y254113okwUOSrVR2YNqUP98GBbvkBKh1b4HdVkMqD0CSquTI1iX7jG984 e8ITnpAEZVcfxgPod5KTAwA9L7kfAaaOl770pfs+/Pd///fOBPLKV75yn2rZbp9pgnng4x// +Iw2wnG33HJLdz4ygBiccsopsxtvvHF20003zSxS3/ve9/baN0ma9D/wgQ90O2q7xCgxrqy4 iRmJQIA+97nPdX/TPCAbiBJzDXOBvhHaCBqI97znPd3/119/ffe965YSn/3iF7/Y+/iXv/xl Z3KhOXrFK17R9cv1QvTNefq8bJEuWhKYwOmss86aXXTRRd3uOyqSxrWo3xHF5z3vebNTTz21 M1U5L+Q//uM/OtPQU5/61Nmb3/zm7u+y2qm23Rt9ffKTn9y1RVsRAtevfvWr3b9MEy9+8Yv3 vnMvEKgQ58PF2P3Wl1L0HdljHnz1q1+9/4E69t/jH//47lz9dR9bwq9Cv7Slv+7z+9///r1D nR9tPOMZz+gqvdYVXt0zJhCVYD0j2inNd7B2Dd/BNTRIZ599dvc5zOIZ048QzwTz4LOe9ay9 zzzPD33oQ/f+RyTiPmofTsZQCu1LVKBl2vL9zTff3MTDh09/+tO7sdQCB+M0PljEmI3JPX/Z y17WXb+8r9EG8m1srXZ7O5JfHGkEkpwc6du/HYM3WdpZlcLBtF6QfG9CvO+++/Yda3L/zne+ 033GVl5Pzu985zu7RS6Eqt+CxaRUi4XcJB9Ew/fPf/7zu4m1FOQoFqAgROeff/7eISbwIC/x ocVS26XwKdAfizZNkEnej5LmxHf1OT6nFXKcfiFBxofIPPGJT+x+wy60Ph/5yEe6YywaCIbF VP8QwFKMx7X4qBD91W5oixCVGvsSQ9fQtoXP5y984Qv37cAtoqHJeuYznzl7xCMesXd5fbYQ k6uuuqpbxC644ILZ7373u9l1113XtXv11Vd339Mw6ScTx5C85S1v6YgfnGBSa+i0o59RwdXi bREO8f0ll1yy979rltomDp/1s8Gc55kk+g/HmqjCx3VKExOSVRJT9y/aiQ4gwqVWA6HV51Jg VRIs95BpK4QfSd1ufAcHz1DLdAQLz1zItdde243dvQyhNezTxrnHB+FzNfSM5PfbiUCSk+28 L0eqVybbcvI0eItKPaH7vDXx+SwWUJqImpzcdttt+5xVLTB2eY6zSJfXvvzyy487/01vetNx EQcm5pioTfYm3UsvvXTvvlnILRylmMTrRcEi4FwLs99+yt1l7HRbD8SnPvWpTvtDY2FBh5nF 7sILL5yde+65e6dceeWV3ZhokmLB9D8MSrGIWtj00Y++WFAIbVDrfpTnW1gdH9qD0NbEMc6n jSL8D4yZFsm9K7VfnCfDBKZNfXGfQuMkKqrU6Ix9WWBjoab1IdrTJ22FtsP3yGiI72ofFRom PkYIBpJba27e+9737iMZjq+1CRxzkYBSkK+SnCCJ9bNMA1GO3TOIsJTyn//5n/vMQe6p96KU PvyYDmlOaM9qgdVpp512XDtBGuOLPuJTP9tj71sedzQRSHJyNO/7Vo3aZFZqKnSORoHJppZy VxvflYTl5z//+XELF7+VepJ3ron4sssu68jABz/4wa45C0t9jde97nXHne88pgcSPiah7fCZ xa5c5HxmMSo1OD5jlqi1RuWYLeD1OYvevK985SsdObOLD2FGo0kIjYHPLR6vfe1rm81/4xvf aGpwyoOREsf1iYU3CJJjaEaYBJCR8nO+MfPGjBgsuwOn3eKcGWZA7dBChdROmxb2a665Zt+Q aLOCsDBvnHfeefu+92xE+Ky23ff6HutHrRH74he/uI+caLT2/dBeGRWGHNXClFb6qrTIQkmC 6vOZOGtnbscgniKgSkFyanJk7LWGzTkIJ7xSEoExCCQ5GYNSHrNWBJCLUuvgYtTvdpy1MH+Y 1GkcLLAIQkk8LC61E6HFJTQA2gtzBc0BzYTJlHqe0DjUERLMQqUJwnEW/FhcmBksGLQXIXae Fr5nP/vZe58hLxbd2u7uf34FFmljCdLjRAtb2cYyN4KWxxhbDrcPe9jD9prkp4Ow+K0v7ouF OIQ/jv4Zm925fpdRVm9729s6YgdD5NI4aJ1C4FGbYiySrYXSsTDh34G4lvfEOFoktcaGnwWC yFSHNFlYnUc7FgJbzwbywHkWWSrHFKYuWhI/nrmXv/zl+y4F23By5sRKw1DeQwSTf1QtYVJy XdojZLzWTukbv5gQfSifd8+2/2mDaLdoZGpC08oz0iLrcY0f/OAHTc2UdurQYvfOmEtptU27 ol98xlISgTEIJDkZg1Ies1YEWruseRfkg2Bx5JjKuXCZ0FC7c4sQUjQmxLHcXUffys9aEUVf +tKXjtuBUtVblOprMslY9K+44op9Q18Um0VvFB+dUpjAqPVbGgPHMSMxUdgFIz21IBPG56f2 uSl9cuI8jsqt5Gw0YIiRhc7irl+ljMmXwvzznOc8pyNRSIldPlNYLcZr4UTGao0BMhlOuTQu tfkx2nIMMmTcSJpnNITPSm1Wie9oElxX3xCR0lfEMZ5thJH2LsSYSoG5Y/wg20hkKbVPjO9q Al5joj/33HPPvo9bJKdFElvh6UgMkpiSCIxFIMnJWKTyuEQgEThyCFiQ//u///vIjRt5Dwfl VQf/vve9b9AkuOo18vzDh0CSk8N3T3NEiUAiMBECZXjzRE3uTDO0HSK9VhEasNJsukpbee7R QiDJydG63znaRCARWACBL3/5ywscffgOXbW8Af+eMtfN4UMoR7QuBJKcrAvZbDcRSAQSgUQg EUgElkIgyclSsOVJiUAikAgkAolAIrAuBJKcrAvZbDcRSAQSgUQgEUgElkIgyclSsOVJiUAi kAgkAolAIrAuBJKcrAvZbDcRSAQSgUQgEUgElkLg/w8la7XLYEhipgAAAABJRU5ErkJg gg==</item> <item item-id="61">iVBORw0KGgoAAAANSUhEUgAAAF4AAAASCAYAAADSSGl5AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA AWRJREFUWEftmIERgyAMRdt5XKJLOEWXcJ7O032oUvFi/DEBigc9euf1VATy+PlB785N7tZ/ 1xNYwPfjega3Dv166N5lOvhKwM9mt3h+9fZzNk90zxpXTL+hLeWFriFx7xTfAvB1M7AJg88Z xcDBaFkes3DaeNJYfwVeEk7V4HmK0FVHqpJSkvZTKoMkkLWC50z8OU0FCbDWJty3pL1v+364 YR58fKUXtjM7OJuHRQyhjfTPeSCwGjMVPPeomKD0IJ9uGr7F/HCMo1jgLYq3tBH9d91cWMFr jNAiJIGX7MSs+MyXNgtUSxsp82IVbwW/m5OWEtZO+W4DnR8VVrfitR2UFCPK9IMoc8FLyrKB T/N4i5otbTSr+RV4WDtRoaDFUlo9VNzCRKkVweAKFVc6PspUveb4D4bbQcGjmFKubc9oLxM5 9y2B5vTf8rNFv9V08LKVFgOv2k3mzqZltXsLaz2AVuf/AabyifPUJc/FAAAAAElFTkSuQmCC</item> <item item-id="62">iVBORw0KGgoAAAANSUhEUgAAACoAAAASCAYAAAAt6kybAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA ALZJREFUSEvtVIkJgCAUtXlcoiWcoiWcp3na5+eRYp7flEQwkOgX+nrXBsCBzHBJoDMsMgNI pfoCWrCUiIXMRtJ25r29j2DUBZgC68+HS18N1JWiJEtPFaqBPn368k3SQ9cOVPiLnW3VhvVo kPrAFxmza1YP4FSHIliMFfs5B9RVTe3tD3LPf0geO6MD0O+MYtm0lhzBaG09RRn1N4n+eWOY fD8bsmIZMbPhPYr1/QKKZQr73TSM3j5DErSJq2+PAAAAAElFTkSuQmCC</item> <item item-id="63">iVBORw0KGgoAAAANSUhEUgAAAR0AAAAoCAYAAADDqi7RAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA BExJREFUeF7tnQFy4yAMRbvn6Xn2Pj3Pnqf3ySbtuksVSUiAsU1fZzrTiUGCJ/ERdpL+ut1/ XviBAAQgMIvAQ3T4gQAEIDCLwMssR/iBAAQg8HGyAgMEIACBksD9lNUE5NFv67v9Xb62GW2z 3jQkOkEAAiMW95kploJl/n3mCTA2CKxMoLWiODMTROfM0WFsSxOQR42PexnG8ePr2CGua33k a1pfD2x57LH8auOsBcuyW/rgeFWjyHUIDCBQ3uOQC7BWFWjXrT7a4pbDj/b1xqwh8ebBPZ0B SYQJCEQIyJ0/IiAtfWS14h3ZouIwUnSodCLZQhsIdBKICMx2TPIqlFF21IX/7wmV56NWGXl2 vfnx9KozwegOgcgxJiog5WKN9ukRB0SH/IXAIgQiRyWtGmi5z7PZ0e6faIIo21ljjYqZ5r+0 KY98VDqLJDnTgECGgHfvJ2OnpS2i00KNPhA4MQFZZciKpVVwPLsZHIhOhhZtIQCBbgKITjdC DEAAAhkCiE6GFm0hYBDQjh689vkB0KdfsggCEIDATAJUOjNp4wsCEOD7dMgBCEBgLgEqnbm8 8QaBKQSe3pBXfIJ9ygAcJ4jO0RHAPwQGE9Decby5aH2PzsghIjojaWILAichUPs4xZHDRHSO pI9vCOxEANHZCSxmIQABnQCiQ2ZAAAJTCSA6U3HnnWVvrmXb50d0jR4jOXhftyC/emH2TVFt npGvkTgyikuLzsjEOzJIGd8j5jzCRmbMV2irPeaV4858x8uIOWviMnsMLfNAdFqonbTPKLEY ZeekmJqGlRWdh5MZHL1xzfCfhalVhmeqzJ6eXlll7hZgqaBeWVyWwVu/jH2tfyQAJXTPn5xT 2dYKkpVkmXl5SaHx9dhr187IzYqbjFXZTmMdEaZIjmTaXE10MnM7oq35yNxKfrm7eKVmxkak ZI3sKtGk9MrP6Jy0gGXmbN2byNi4CrcaK61q8TYPi903P+9vt9f7O3F//+lbWohOHz/Z26x0 oguvpZ0nXLXkq02/JjpWlRERAK+cr9ntEWtr92+Zq1dxeFVGbX61sbSKzlPCiv+zHdmIbrf3 29ur8TULAUVCdGqrLnf9m+h4u38k8b2dKRO46DiyiRyxW1s8tZK/RYR/ArdsrCyBr8Unl/6x 1pncjVn82a2Gi07LThoNamxX+//vW1vs1pJ6L9FZnduxokOlcyaZ21V0Wnb9zDEkksjRCq3n eBWpoDLzWpFbJFY1wbeO3nsvqOjmtfc4VrHvPr3aYG/lf7kwvYUm22/JIvtk7Jdta/DlcUUK j7RVjjcz14xdi4FkqiX4Cty8Sq6Ml8ZDu25tEt/8DLiRbPnWcryWl1z/JLDcBz5rx5/ewEeP eL1+Zvffm9te81k1HnvxOoNdRKchCism+hVFZ8U4NKTj5bpcUnTk8ck6Eu1VAl812Y/gVvPZ s2KuGoeeOa/Q95KiswJ45gCBn0rgL/wQiG7uiIbuAAAAAElFTkSuQmCC</item> <item item-id="64">iVBORw0KGgoAAAANSUhEUgAAASMAAAAoCAYAAAC1kJxaAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA BGhJREFUeF7tnY1x2zAMhdN5Mk/3yTydJ/u4dlq1PAQ/DxRFgdLzXe9SmyKAD+ATKFv2j8fz 8cYHCZAACZxN4CVGfJAACZDA2QTeznaA9kmABEjga4dGDCRAAtcj8NxxvS6/fAW2/d0+VzFi ilHFrNCnEgS2xVzCmQ4nWv+tvzumPewQitFhaDnx6gQoRnMzSDGay5vWFiEgtzZyy6OF4Y2R WyS5jWq3U9vcyBjLD81eO+9mT9o9cytHMVpkcdDN+QS0rU3ULUXHtIIlxUEKhCceFg1va6YJ TaWtHMVofo3T4iIEpPBEQtSKSVZYpEhpF5177Vt+oc/PShfFaBZp2lmOwNliJIFRjJYrITpM AmMIRFsu77pR1BnJrgTpUihGY/LKWUhgSQLtdkle7LXEyLo2E3Va2utSEFFBkhe/ZRze9SpN KGckj9u0GZRp4zYEELG4DYxkoBSjJDAOJ4G2Q5IXmme9Na7ZXT0zFKPVM0j/SeAiBChGF0kk wyCB1QlQjFbPIP0fSsDadt31+aFwg8koRjNp0xYJkIBJgGLE4iABEihBgGJUIg10ggRIgGLE GiCBQgSiDz4WcnW4KxSj4Ug5IQn0EZCfUbLuqO+bvf5RFKP6OaKHNyJQ6Ss9ZmOnGM0mTnsk 4BCgGLE8SIAEShCgGJVIA50gARKgGN2kBryvg7C+cqFFc/c7srULrCNu2JzFVbMz68ZWdIld SYzS6+1ui836Tpnou2ZenJAxaNGtOg5hkBWX7PgedproVHy36kpi5K0Z9SMMFKP/P3TnsbDO qj0LY+VjjhAjrWiPYOT5PkMQo5hkJ7FxqeBb5Lv1eubk/+/dNK3d3ibSIEnj2jbHGiPV37Lj bQu2uaVdBFq0oBDhuRsvqwY8VlZuEL5IHrNjqotRNp4zx+/JrSlQVjcQLVgNhCYerWhotiI7 rVB5vmbOHqjNqGuUohqN9wRcdgeZM0qWq8yTV9QIKynM0h95svHad9WXz4/H+/Onmn/+2rf8 KEb7+KENCFIzas16C8gSgmxL5hUf6vjoQpKxRbEiZ/NoDi2Z2ZPBbF6oPSSOveL9eHw+Pt7/ /Ib8t3+AUo2uobFLe73ZrHx6a8Vad18aEZ3NR3UcSCG4jioFaHVdSFqPECNNdHu7jiq8aokR kll7DMJ0n4ULH610p1PFaMaZvl3AkRihHRlSEkeI0RV51RIjdkZIbc8aM02MoiLUAs6ceaL5 M69nujdkW4rEJrugyF9kzsw2JrKXyUVv92Z1gkiRRv4fsaBGMTnCtxXnRPKcWW/f7k2Te/Gt 4Lbno8Jtj5djo9eQLaPmB+JbVgw03605ZFxenDLGiIkVG7KYrbmzvLSakHWB2GrtSh/cE8qA C9iIfysKwpk+W/nsrdmhN8r2dihnAkVtHxHbEXOi8VQbRxbVMjLfH4pRgvnoBTN6vkQopYaS Q6l0nOZMWoxky679/7Ro/hq2fNzrV8+iuTMvlHcPV3RujluHQFqM1gmNnpIACaxE4DfNEkxj f2JfRgAAAABJRU5ErkJggg==</item> <item item-id="65">iVBORw0KGgoAAAANSUhEUgAAA/EAAABCCAYAAAAfd1bTAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA FmJJREFUeF7tnc115MYORvVWLwlnoCScROfgrTNQAI5EOzsSL7Tx0hk4g37dnscZqgQUPhSr +HvnHB/PiEUUcIEq4iPZrf/cH39e+AMBCEAAAhCAAAQgAAEIQAACEIDA/gk8RTx/IAABCEAA AhCAAAQgAAEIQAACENg/gZf9u4iHEIAABCAAAQhAAAIQgAAEIAABCDwJIOKpAwhAAAIQgAAE IAABCEAAAhCAwI4JPN7x/+4dIn7HicI1CEAAAhCAAAQgAAEIQAACELg2gaeAR8RfuwaIHgIQ gAAEIAABCEAAAhCAAAQORAARf6Bk4SoEIAABCEAAAhCAAAQgAAEIXJsAIv7a+Sd6CEAAAhCA AAQgAAEIQAACEDgQAUT8gZKFqxCAAAQgAAEIQAACEIAABCBwbQKI+Gvnn+ghAAEIQAACEIAA BCAAAQhA4EAEEPEHShauQgACEIAABCAAAQhAAAIQgMC1CSDir51/oocABCAAAQhAAAIQgAAE IACBgxCYfsXcJOT5PfEHSRxuQgACEIAABCAAAQhAAAIQgAAEEPHUAAQgAAEIQAACpyQwPbl4 BvfpNcQX2p9TJpygIAABCFyEAFexiySaMCEAAQhAAAJnJDAX6lZ8pZD//ioiQv6M5UBMEIAA BC5BABF/iTQTJAQgAAEIQOB8BJSn696Y+c/PR4aIIAABCEDgzAQQ8WfOLrFBAAIQgAAETkwA EX/i5BIaBCAAgQsTmH+Rnfn3C7MhdAhAAAIQgAAEDkwAEX/g5OE6BCAAAQg0E+BJfDM6ToQA BCAAAQhAYGsCymfiJx8V0b91PMwPAQhAAAIQiAgg4iNCHIcABCAAAQhAYJcEFFFe+2I7Phe/ y7TiFAQgAAEIBAQQ8ZQIBCAAAQhAAAJDCURPy8vJLXFt2VBE/NDAMA4BCEAAAhDYgAAifgPo TAkBCEAAAhC4CoGs0M6I9aztqzAnTghAAAIQODcBRPy580t0EIAABCAAgd0QUF9fL8fVxHr2 Kf9uYOAIBCAAAQhAoJEAIr4RHKdBAAIQgAAEIJAjMELE5zzoOPr9dn95ud3fO5rEFAQgAAEI XIHA+/328nK/CReQ6bpZ3rBGxF+hTogRAhCAAAQgsCGB7NPyzJP4LcJ6v73cX5TuawvnmBMC EIAABA5B4HkteX37CH213kZDxIfYGAABCEAAAhCAQA8CZ3gS//H2Kj096cELGxA4OoHsDbyj x4v/EMgSeF5TIiGPiM9SZTwEIAABCEAAAt0IfBLxH2/3V+d1wh5P4ifxEP0/FdzTZ57Ap5Ax +LoE+OLJ6+aeyHME3m+v99oDeUR8jiejIQABCEAAAhDoSODYT+I/7m+vfAa+Yzlg6kIE1LV/ ISSECoEfBJ43iF/f7t6L9Yh4igUCEIAABCAAgVUJtDyN6/EkvnuQjybrJnx2sfu8GITACQgg 4k+QREIYSqD2NB4RPxQ9xiEAAQhAAAIQsAhkPhc7f/19bitjY0QWPt5u1dcdR8yJTQicgQAC /gxZJIbhBB6/8ST6bPyna+Jwh5gAAhCAAAQgAAEIHJoAr9IfOn04vxkBBPxm6Jn4cAQev3au 8kp9GQ7fTn+4BOMwBCAAAQhAAALrEng0V3yh3SrIs6IvO36VIDaYpCeHL7+P+vEFlNYXRH56 KvgY80VkGD/bAM2upyzfPJqzt/5e+6jRrgMd5FxLrSqu9FxPynzfxuRuFiPidbKMhAAEIAAB CEDgigQO8nn4bRrPbQuiR8w9bGxLof/siliMvu8iOq54fYXceKyzOXjyvAKv6GaRxSDLJTte qWVlTPQt9Z9unCkGGQMBCEAAAhCAAAQuSyD5WcWtOG3VeB493qtxU/LVQ0CWT++VeSOB1mJj 7+e0ivgeYnXvbBT/Wmq1xa5yztIxme9e4Un8UtqcDwEIQAACEIDAIQiUr17KTj9E/BZv03uv ij79rr1GOsVVvqpbnpexX9pU2dVew1bn9/LmiW/V7pxH+dS4xYaVlz1y83JXE92KYOx9M8Sq nWkOq7atGwDleG+MlX+rPjLrbjpfWSuREFX5e+vtjLy8mGqs5vkrc27VhpK7nmM+3l7law0i vid5bEEAAhCAAAQgsEsCiwTGRiJ+LgA9Meg1ojVRYAkRxf48sQrPSJh48am+RD54wqwUV7Vm PmMjEgWqqBvNzVqg0ZyeOMzWRHZzUHMT3ZiIam2eG4WFlctarURxq3NGvK/AS2FV1mvJzbq5 E9mNctjjOCK+B0VsQAACEIAABCBwGgKR4KsGupGIt540ReLcEis10Zo5No0tbwLUBFRNdETx RU21l9PI7hIR78Wj+LoXbq0ivjwvirnH5uHNoa5ndb2MEPHZ+OfCsuaPt8ZrNxbOxCuqOytW 7+bG0pse2RxH4xHxESGOQwACEIAABCBwOQJqI/sFzAYiXmk6IzHqPY1qETaR2M8KQyW+Uc36 1bllc2WJw5pglDeWxxdGvj6+wb72UZVIkEVzLa11T1hP8atrLPLTEu3l3NFNFDVPNV+OwGvU vhDZVXK4dAwifilBzocABCAAAQhA4JQEmoT8rz/df/p1XRyKyI3EqOfx0kbdEwueOLSeQCvx RU11JPDUp2y1cfOYzsJtNyJeWFIec3UdL81ZJOLVNSaE+v2b5Wtz1uqxdmPlTLxG7QuRXSWH S8f8/vN/5WsNn4lfSpvzIQABCEAAAhA4DAG1mf0U0EGfxHsN/1Jho4r4GuujiPiWGwGtN1em fI3mdlQR3yKyWmu9Joi9PKk3g2qbZSbGzE2s1jWr1v+avCJGGS6Z+Na4yPEkfg3KzAEBCEAA AhCAwGEIWE+DZec3EPFTU2w9lSsbz5ogtuIufzb/d+2Y51MkSiabVvNdi0/xxROkcPvxnE6t /XmerBrzjis3H+S1Zgy05vVqqjxd8dnjE4nF+Xqo1XYmdmXOiPcVeC2pVW+/s/abTO56jG0W 8SUQ605GDwdVG5E/1vFosXqLO+uTZUe1ccZx3oU2m48sG68G5naiMVGdqT6pF0nVXtksZc7L jI383nIfaJk7iifDJjPWmtdqMqJ6jObcaq1Ffh3xeEt9eXGOyEtUK2V9RY3JFG/PuI+Y9yaf NxLxTb7u6CSr1nrWX09bO8L2/bXqspfZk4+WL3vMxx596pXHEbGNsNkr3rXtbMGiWcRPomFP m0YJcP7v2jErll7CW7kobZH4tYs74qlw6uGzMk+2VrL5y47PxD3S9uSHOoc6LhPfiLFb+VkK OW8fiuoxYqKcP5rBaPsRgz0eH5GXaH/z5sxcO/fIcnc+IeKbUhLVb5PR4qQz7kVrcOvBfk96 wYvnjPUxkvvZeal1vxWHy4j4SERGiWpN0IhGLfL1CMe34qJc7KJcK77XchDZX5K/kbYR8Usy 8/XcVhGf9UKp19F1M9p+lskexo/IS7S/tYh47+bSHhju1gdEvJuaad8r/z+/vsyP9c7xUfei LbhFcy7JzZZ58OIaWXdZVqPYt3C/Mi81by1cVdu1ccNFfNmozv9tNbFTw6Bs8JEwr0HNAPcW tup/7S5YbaEqG0qWZ3mhLHnP/21dVL3zlWL0cuo1iVOOyhizYrLG35p7XhtRU5xtcMuc9ogt 4uqtwZKvuiasvJR1s2btWjVr1XVZB4qP1jnznHv5VFjOOdZqOqpHa+1FNRHtndF6zuzrFufa z2p7kJfr2s+9WKy919rfvNruMWd2b1LyEuW2rM3o39k9LvLxEscR8ZdIM0FCAAIQ2JLAcBFv NadWA+g1sbUGNmo+ao200mRHDZYXW+28SBRGMdUa9ow/0TyWwCibuTKPtUJW5ouEhyUeW/JY m2femHvxRbFEC9o6vzW2mi+txxT/vXWZFZxL9oLW+Jbkz6qJVnvWGvP2h3nNKvmJ9qBoDURz LNnXo7zVakupL2VPUHO21JcoD+XxpXkpz/fqKeNXKOKF392s1tNpxiHiT5NKAoEABCCwVwKL RXzUXFrHoybOazCixivTACmNXiTis7F5zZAXl+pjC0+FpeWv0kR7NxqWNI6KaFQWWbap7SGq Sr/UfCn5V21ZN2Ci+vZ4qnOu6X8mvqj2MzejSjGrxOwJYGV/UOxH8SlrIFpL2b2vtm/0ODax m/xS/I/2o9KW6mfLulFyH8WUsTHF4v0/2rNsXz7ub68v/37J1pf/bu+K++cZg4g/Ty6JBAIQ gMBOCSwW8VZDWmtSrUZDbY6s5sBrxKJmNzoeNTGqqIwa6kzj1dIcKoKr1sjVmGcYRhwiYaHy jtZZNI+Xj9Y6s/xRcqL4oaylMn9WHJk8KnPW9oTsuvL8j/KoMlY5KzEt4ViuQa/eovqO8tN7 v8ns3XOGal5b7Uec1PpQ9x0l90v3wCimTG69Pd+LQ4lP8c/bD63rzJF+9iWuQMQfKTZ8dW5M WTer+Jl9Ew8ucKEGFtWAd33tJuKj5lFtxiY704Uj08xGTVLGVlZseE2+4pM3Rm2c1Ia0zJHS yNV8UP2LakNpPtVmOmokI/GniDolpzU/1HwpfFVbXvOsxNtrLdR8sNZPtvbUPWZJ/iKRo+Ss tlco9ZmtcSXeVr9bmGfy2tt+eQ2IrjPqvqPwi/IwIvfR3updA6I1b9cgT+K/c+FJfLRNcRwC EIAABBYS6CriPZFY+3l0rCa8o6aoJlCUpkudW200o4ZKEbtLhVDELHM8wzBjNxJ0Nd7RelAa 5aW+Zn1QhULv3LcI+KhGs7nJ3ISIbKvrMMpvLX8t9ePZs/xQ7C+pL2UPythvYd4r5z3XjbfX q3Moe2FUdyNyn7kG1mJQ4ovq5lLHf/3p/tOvl4qYYCEAAQhAYGUCv//8X/la81I2Os8Le9SI zsfM7/qXzZH175p9b97In6mpsWxHzXvNdsZ/T4xNPpWcogZqfp7XCHrxRo1ljVcrw/K80v+o rsqa+hzD+/32rEvjM5iZeTymZY7mNxyyfljn1mOzK7SWf89f60bJZ+s+x0xNjKrdqEaidV5f M3ENlblbsmY9ARnFoOxXLWvtm10t/3OR6M1l1Zq6jmrroTZfVHclW2Vvz/ji5UbZV+prNq7N 6Hrgrf0asypPvtjua7p5Er9yK8t0EIAABK5HYNGT+BG4rGZBachG+DLC5ohYRtgcEfuaNt/f 3u4fa07ozLUXP1pRjPZ/z7U7OvbWnKx53lkZHP06s3Ze9rxO11wP8lyIeBkVAyEAAQhAoI0A Ir6N26KzejdEve0tCm7zk799LnP7L0Peix+tCVnH/33W7jqxt2ZmnfPOzeC4In79vGyxRkfO ab3F031NdRTxPVmsEvtCmD3jnbuSib02VrGjjMn6Vr6h470Z5L3J471tVHtzpzxWxmUdb0l/ lpcyxwib5bzRHKNqWY1fGZetw6xNZbzF0arjqB6juZT6zeYs8ql13ap+7E7EP5NQ21SiJC05 HiVjie1yoWRteb55G3bWfo/xa/Hr4euRbByd6xFqd0Q9jMzbSNsjWGxhM2I04joTzbkFh6Vz qs3E0nmm86PGeMk8q8XSUcQvidc6V2WgjvP8W3p+77in3jKyq/hdG6Oc35qX0vb0b+/nUd9p +RrZyh7vwTuysWUNqvlWx7XG2uu8rfy09v2W+ow4RPWr7hOZtdWybtU87FLER0ngOAQgAAEI QAAC5yNwFhH/+raHD3R9rQ+1OVTHbSmgstWvxLR0jHL+2iLe86mHSFJEUS1PrbyU3I+0Pc2v zqGOU+IaOWYrP1tFfJaFUq9ZBtE6ahHx6s2Ej7fbXb3UfPpiuyw4xkMAAhCAAAQgAIFI+GWb KIXo1CROtudNo9VkeTcT5nZcPx9f9ncLOitv/rnNcozlezZ2JdaS1dRQKkyisaW/XoxzkVQy j86Z+zCfL6orK27LVpQjj7EXu8dVEftLREkkPkohkR0f1aa6JqO8lTmO8mjlr1xb6pzemrRq tGYzU9O1+qvVvrJ+M3HPY7fWvZeHWl2XsVlrZr43RDVW+rVkvUTXrdpeE82rrC1r/vfbKyJe KQLGQAACEIAABCAwjkDZFFoz/fPPP18+cjdvFv/++2/XQUVc1ZqtqBH7MfHjtwgIX8ziNe5e I1s24UrD3RpPrZlWjrX4FolGT7yU53nNdItPUXOt18S3j4p6AkTxzYpTmd+zPV83rQJPmb+2 Y9TWZLk+op2nR63XaskTn7X1mhGctfUdxRYdV9eEWodzcZwRr5EQrq2RSJB7+Yn8K9dBVGfl cXUdRX5Yduu+PL8j5/b4XULaH57Ea5wYBQEIQAACEIBAgkDUvE2mfvvtt/uff/55f4r5598z TbciOFqb4c+has1VtvlX/I8aQVVItoyrNalKg13mckku5o11VkhFcUx+RQJ/Pm9NoGVqeD62 9KMm1lv4R/mYjlus1aWv5lgRl6qtKC5lLq+mlDXtsenlfya+lj0lWk+19aHErq4vJU9RfOoa rtWzasNbr9l1+8OXx83iV/03cSHi1V2JcRCAAAQgAAEIyATcpyHF76GfnrQ/hXxVxBu/vz5q 6CxRoorZMlDls4pKw986vyoyPO7lvMq4SPwqQlKNVxU8NXuKP5bAronWFr+OIuIjXoqoKm20 8op8sYSsu8c8BrcIT682rLWnsIlYZNZgj3UUbd61mJR4vf1CFfGRf1F+vHW3xHdv353P5Yn5 aG18iffxBaqZ715BxCsVwxgIQAACEIAABJoJKE3UH3/88UnE//XXX+F8qoj3DCnnfz9X/Fx8 1PCrzXiLz2oTro7zmvIoMVGMk3ip2c/eEFF5ZfITiYYozohT6ctSMWDlNVXjD4ey4yOhouRR zV3mBs4RRLwad1mH0c2BHuvWsqHs49b6mvxX6jNaM1F9jpjD2gfmP1u6bqeYM5+H/3f+CBbH IQABCEAAAhCAwBICSvP3HPMU8s8/v/zyy7//RX+ihs5qvtqFV/xKvSJY2uf/RqO1gVfn9ewr ObQaeFV4qXGp9jwxEflYMm7xy2v6a/Ws1HImJm9sNM9SEdTKy2Kj2vJ4Z2pW4bW09lrXYGtN ZutQyb3K1MqdYn/pnj9ijhrHaD3N46mz07535ZO9CBbHIQABCEAAAhCAwBICSuP3HDO9Wv98 Cq+eUzbGXhP3/Hl0LJzz8brjS+UL7uZzZP8+NYqhD/8X8pN9r1mOmlnv/LkfmQbV87/kPp+3 ljvvvHlD7eW0bJyjGonmqgkSy7biV7meMqyVOrZ8KNnXclNjeH989dbtuZ6MtaCyVBl59VKr 0+hmR83/qP7LfNf2xZrvmXmstaWu36+1ks/dZKM2p1UvUU2r9qy1Yp0b1fcPOzGDKHdejVlr M67HR0UmvpX+u70lF2XOhQAEIAABCEAAAmchUGvAphg/3l7vwhfVnwVJ1zg88dl1kgsYU+p0 DQzvb/qXcK3hT3aO0f7vJU8Wl9GxZ3Oxxfi1Gbj1kPwsPCJ+i2phTghAAAIQgAAEdktAbbrf by8I+YYsIuIboDmnqLXab8a5pedHS468Btbxf9sceZlfJ/YxddfL6voMagK+9nZXLWI+E9+r HrADAQhAAAIQgMBhCWRf7XwK+ZcX/Xf6ZsDor4VmrPYZu9Q39TXVjLdLfcrMlR07yrd9CsQs nW/f8WD9l7e0zRme/9n9ZBvv22cdmbeRttsjXnZm5mMN6kyIeJUU4yAAAQhAAAIQgAAEIAAB CEAAAhsT+B9LdHS+oMuNrgAAAABJRU5ErkJggg==</item> <item item-id="66">iVBORw0KGgoAAAANSUhEUgAAA8kAAABCCAYAAABkU5QfAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA Fc1JREFUeF7tnc1148YSRvlWTmIyUBJOQjl4OxkoAEeinR2JF9p46QycAR85MjRQq36+anST IHnnHB9LRKO66lZ1oz4CpP53PP078A8CEIAABCAAAQhAAAIQgAAEIACBw+EskvkHAQhAAAIQ gAAEIAABCEAAAhCAwOkmMhAgAAEIQAACEIAABCAAAQhAAAIQeCeASKYSIAABCEAAAhCAAAQg AAEIQOChCZyeMv+IH5H80KVA8BCAAAQgAAEIQAACEIAABB6bwFkgI5IfuwaIHgIQgAAEIAAB CEAAAhCAAARWBBDJlAMEIAABCEAAAhCAAAQgAAEIQOA/AohkSgECEIAABCAAAQhAAAIQgAAE IIBIpgYgAAEIQAACEIAABCAAAQhAAAKfCXAnmYqAAAQgAAEIQAACEIAABCAAAQhwJ5kagAAE IAABCEAAAhCAAAQgAAEIcCeZGoAABCAAAQhAAAIQgAAEIAABCHwhsPwJqOWRa/5OMkUCAQhA AAIQgAAEIAABCEAAAhD4jwAimVKAAAQgAAEIQOBqBJZ3788OfPrSlAMtytWSwsQQgAAEHpwA V6AHLwDChwAEIAABCMwmsBbC1lytUP543A2hPDs12IcABCAAAYMAIpmygAAEIAABCEBgGgHl 7rA3Zv36NAcxDAEIQAACEGgIIJIpCQhAAAIQgAAEphFAJE9Di2EIQAACEOgksP6iLvPnTruc BgEIQAACEIAABFICiOQUEQMgAAEIQGBnBLiTvLOE4A4EIAABCEDg3ggon0leYlZE9b3xIR4I QAACENgXAUTyvvKBNxCAAAQgAIG7IqCI3uiLu/hc8l2VA8FAAAIQuAkCiOSbSBNOQgACEIAA BOYSyO72trNb4tWyoYjkuZFhHQIQgAAEIFAjgEiu8WI0BCAAAQhA4O4IVIVsRQxXbd8dXAKC AAQgAIGbI4BIvrmU4TAEIAABCEBgHgH18eZ2XCSGq3ep50WHZQhAAAIQgEBOAJGcM2IEBCAA AQhA4GEIzBDJV4P3+nw8HJ6Pr1dzgIkhAAEIQODyBF6Pz4fD8VnY/JdrXvtmLiL58lljRghA AAIQgMDuCFTv9lbuJF8j2Nfnw/GgdEjXcI45IQABCEBgOoHzdeDp5S2dx3oSCpGcYmMABCAA AQhA4HEI3MOd5LeXJ+kOwuNklUgfmUD1DbBHZkXs90fgfD3IhDIi+f7yTkQQgAAEIACBoQQ+ ieS3l+OT88jaiDvJS/Oe/b8U4Nln7iCXkDH4fgnwxXn3m1si0wm8Pj8doxvKiGSdJSMhAAEI QAACD0ngtu8kvx1fnvgM8kMWLkGnBNS1nRpiAARujcD5zdOnl6P34DUi+dYSir8QgAAEIACB CxDouds04k7y8NBOjdCz8Pmz4fNiEAI3QACRfANJwsVpBKK7yYjkadgxDAEIQAACELhtApXP La4fj15HXbExg9bby3P4SN2MObEJgVsggEC+hSzh41QCp792kH02+dP1bKozGIcABCAAAQhA AAIXIcCj1hfBzCQ3RwCBfHMpw+EpBE5/Fip45Lqdkm+3npIEjEIAAhCAAAQgcFkCpwaIL+wa grwqqqrjhzi5QyMjOXz5m62nL9CzvuDu052v05gvjb7x2g7RXdWl9smYNXvr5+ijJlcN5EqT 99Sq4urI9fQ+X+2NVESykiXGQAACEIAABCCwbwI7+Tzy+MZu39jP3o2IeYSN/ZOqeaiIsez7 BLLjikePkBuPdTUHo9aDkpc9jenhlPk/o+6yb7n+9KZT5iDHIQABCEAAAhCAwO4JFD9vNiue GY3dLF9H2B0V7yg7I2Lai40e4WGd432HgBrnI+SmVyRbbB6BV1s7PbWq1N9olpXvreBOspIh xkAAAhCAAAQgcBEC7aN78qQnkTzjaWvvUcKzX9FjhovflkDxHudsbXosqozWPkTxRPNHvlg5 gtt7i701V2u2iiAbLSqs2lnmUMR3VOvVNdLOWzlf2Ucyoafy99bbPfLyYopYtTWxzmMmvpU8 RmPeXp7k6wQieSttzocABCAAAQhAYAiBTQ3+JJFsNXCWAPTGeSIns7Fm4XFReGWNf+Z35Oci AqPkt3F4cUXxVmzcCjfvjYVIFEdvdkRCY+viVHPjzZPVuhVzVrdrwayusYyDOmfljYvMplcH yjpZr79sntG8lPnaem25rX1S/MvypxxHJCuUGAMBCEAAAhCAwK4IKKLPdXiSSLbulEQNoioo VBuWELV8igSKKrwywez5EjX6PTx62VQa96zwe2ypsW7JVXtu5mcWp3Lcm0Ndrz35zOJSRJXq XyTSvXmsteK9ltmo5FTNRc+4Ci81P97eU30TQKlTZQwiWaHEGAhAAAIQgAAEdkeg0qh9cn6C SO5p5NpzvLspPcKhFakKK3Wenlgt0Rz5WPHl3rl5byxkb2hUBJW0uE9fePd0+gbs6KMKVp0p tZeJxuxNl1ZcWmKzrZPojQqFhzJnlCNlTWR+qOvEW2teDMt4dW1FfiKSsyxyHAIQgAAEIAAB CAwmUGnAP6b+/u347ftYR3qEo9qkb22EvWbcE19LY+w1+D2xKoIg4qHOqQpHlalaX2sxMZrb bkSysGQ8rhWOCr9e0eeFoPpn+RYJzapItsR9rwBVajwTySN43apI/uPXX+TrBJ9JFjYHhkAA AhCAAAQgcDkCPc3tcad3kreIg+XcHjFpndtmULE7qxn2BJElvHuEdsW+IliVPKp+qiIlY5+x GrViZ8aVxbjleNc+coKWzVkRyRVb3pqt8M/mU0S2UjfVedT1mNlVfIvG8Lj1VoKcDwEIQAAC EIDAxQksd+66Jp4gkpfGzroz0zaukeC04mpfW/8eHfN8ipgt9jI/1nFaP7dzWyJt3ejD7ee9 KLW2vVy1r3v2eoVhtuYsv9av9dbfuqYs3xXR5PmhMm99V+b0BG1b/9a6znIUrdc98dpSq95+ Z+19WW1WjneLZGsBViYePTbzx9swosUy2sdbsudddLPFuDVGZWPPxmS1oPrYu2Eqm7/qQ8+4 zO9sw+2ZUz2nZ+4sHnXu6jhrXmu/yOoxm/daay3z6xaP99SXF+eMvGS10tZX1hgs8Y6M+xbz 3uXzJJHc5cuOTrJqaWR9jbS1I2xf7iYu4mRPPlq+7DEfe/RpVB5nxDbD5qh4L21nNItukWxt AKOdq8Jt51//Hh1TYrl2bFUWI8ZnzGZdBJSLdOZbdjzjMzPfM20vcalzqOMyXrOPX8vPVih5 NT+63pQ1MJr5tRiPjmOkPSWvVW5Zbr05K9e3kQzu1hYi2UxtVp8j6qG6ZkbMOdvGJbiNjmGv edirX6P4j45vtL1RcV7azgwODyOS22RFMG9xs5tRjDMaRMVPhX+2GBTfI18y+0oc3piZthHJ WzLz9dxekVz1QqnX2XUz236VyR7Gz8hLtr/1iORZb1juIQfTfHhgkbzsa+3/19eP9bHRObjV veYa3LI5t+Tmmnnw4ppZd1VWs9j3cH9kXmreerhmtqeL5LbJXP9uNaDLxV7ZvDPhWxXCnsCI FoqyoKsxtxeqlsn6d+ui5p2fFYPFfn1O1Ny1MVbFmjqPZTdrOqsNZJvTEbF59WzlNsq3ugks 45T1l60Tz1a19qz1Hq2fiJlXy1auvHwqLNexRzW9tqXY3bLWlHUc7RHV/aONrVJb1h6t8lGv BdF+acWa8cvWanbdUexnNlpG2e/VPS7z8SGOP7BIfoj8EiQEIACBjQSmi2RP1HhNZdQMZI1C djwTYr1NsNL0WWLFm89qzK0mSBlnCZNIYESM2ubR4m010Aqfdt5MICnH1XxbPEbG1lvTlXrO xIi63jwW6vmeoPL2AS9HI2OP1k62f1ZE8romVbvRHpDVeDbHlr03q9moHrxj1fWo1sBWX7I9 r7o3ZXnJ9lGlXq09Ndxnhb9tmvl9d8cRyXeXUgKCAAQgMJLAZpGcNYbW8awB622cK01l1FAo DYgnUHsEqMcjaxI9H5QmVRWGWWOriKeweTMcqfC3Gsrltaw2o4Wk1qgSm2qrjSXL/635X4lv ZOytWFRy5gnMqN48wTtjrSkXgS1776yarazJrAay+LLzr5mXdu5oz2v3ea9+tbp+O748HX58 ydCX/55flbK6nzGI5PvJJZFAAAIQmEBgs0iOGkNFQFUb50gIV8ThVpHsNcsjRfJ6jqhRshqe SsPujVWaTCXHWvP2k1xVJGfNbnV+K7dKnL25V2q6GoMqchS7mS3P/yyPmd01T8VPpeYrdry9 KYtL2aeztbV1DrVeo3FqXqN9V/UjW8PRdUKdQ8n97Lwob7K0dRzt/b1rRKnR1rZ1nbml177E nIjkW4oNX503fqw3g3jNfpMMLnB54BrwronDRPIWYWE1JsumX2kCsgZHtaU0qG3jkjUdvYLA a5CixlSNs/U545dxUZvVKqustqoNdjZ/Np/KXnnzIWvcq3VWnTObv8Iiq6dI6FSOZfnzYuph 6e1NSqyRn1vXWoXBaBG7dS/zhKK3jrNrgbrvbK31isCt5D6zO1Ykcyf5IzfcSc62EY5DAAIQ eGgCQ0Vy1Oj2NGpZI5o1mlEzFjVMFTGoNny9jWUmUrLjSmOoCKuskVNzr6y2jH8Ws+Jr5oea L4WvamuE30ouZ4r8zHbvPqBw9mJX6sWrB0TyO5kRectqI9vv18cVYaxcH7I5FRuV2qyucWX+ qO6zfe6hj3//dvz2/aEJEDwEIAABCAQE/vj1F/k6cWibFO8d/raBsd4J98YsTcRi22tSq68r vmfNxtqndUwWB6v58mJqY642btH5im8eGytHFoM1t5bL2ref87wen8+PdRifgWvtRzXm+ZLV TsWPemz2alO4ec2234T7HCs1kTX5ke+VeaxaUNfEVx/zGmpzt2XNegItW7vR1Uep34jP8ajl f/Ex4m/tfeo6Wq/Rdr1Gv2d1p14LvPgyX7zczMyLsr9ltRb57dYbX9z1FQ13kqPtiWMQgAAE Hp7ApjvJM+hZjZPSTI30ZcZ8M2yOjPkatl5fXo5v15i4mXMvfvSimO3/nmt3duy9ObnkeffK YA/Xgi15vHRe9rxOt3Ccdi4ieRpaDEMAAhC4BwKIZCeLoxuO0fZuu/jePxd3/S9T3Ysfvdm8 jP/7rN3LxN6bmcucd98MblckXz4v11ijM+dsnwKYsp4GiuSRLC4S+0agI+Ndu1KJPRpbsbPM 3z454z0hEz1hY8XiPZlyHps9WWId70ldD49snhk22zmzOWbVYRb7kjtlXG99V21H4y2OVh1n 9Zj51M6z1V51jazHez8vNaPWzu5EsgUlS8yM4ypAZVP0NtUZfmc2RxRtNscjHr91rp7/e6rd GXU1M28zbc9gcQ2bGaOowez1N5uz1+41z+u5Xm3xN2tet9recr587kCRLM8pDlTzqY7zpt16 vhhOaZji06gxrWOtXa+htubvfc2bc/EtO57BVVhlNq5ZP6r/6rjeWEeddy0/rT1bqdmqv0q9 brW56MRsjUS+qD7sUiSPKkbsQAACEIAABCAwlsC9iOSnlz184OdrbtQGTh13TZFTrTwlplFj Rolkz58RIkQRHRFjhVU1R5446bUzwv+ZcY6M61p+9orkauxKvVYZZOuo980txY+3l+ejepn4 9MVdVXCMhwAEIAABCEDgtgksjYXSYFQjXRq59RzefO3Y9VzrY66fpy8ze066n3VjubbT/uz5 W2EU+WzFqr62FjRtPFbjbOXMOi/iHeXROq99LRNNXsOv5CiryazJzxr2LJbIvmK7t6bOfin1 rPDx1p5Sk9G69Wqv9d2KJavlrIbXNqP1bfninWutPTV/Vq68daXYXO9Ra79a5t4aUtdktn4W VlmdbVlH1hzK2rJ8en1+QiRXksVYCEAAAhCAwKMSsJqtlsW///7rft7yfP4///zj4rOarKzx iho7v4E8fTu88MUYXnOtCrJKA2s1r6qoUsetG8ge39oGNJtXabqVMVHTnDXASpyecMhqL2r6 16LGE3HtmEi0jBAYSj17izPLtZcjhWE0Z7TWIsFnsVRsWTW+h9hV8WexrIjkdU1m17kst8oa UObYuk6yPcL24fz9Is+nvyOi/eNOssaJURCAAAQgAIG7I6CKmd9///34119/Hc9i+fxzpbnP mq6sUVTOf0+M1gApTXXERRFoIxrwio2qsIiEW2Vea2wrHrfyamNT7HnxtcLCs1V5vcJrqfWK aIlyZYm/LbWg1r2+Jr9umSovJc+qrYiTxavXbnaByOpKiTnyNxOOiv0st9kcGQPv2hHZ3bpu f/p0eiP1Sf8rPIhkJZuMgQAEIAABCNwhgXWz/qlJaf4O83Kn+CyUQ5Fs/P3mrOmyhIParLcp UT5v5tlWXlffHMiabI9764Mybs1AaYKtGFTeWVyK4LCWkdJ4jxCWW5tt1c9oq4hYK1tMTw48 u5ktd384GWxZKr5Xak+p5V7/szxmdnvWXLZ3KPF680a52GI3y5e13pU6yPi3dreu2w+fTl/u WPneCkSykk3GQAACEIAABO6cgNJM/fnnn59E8t9//51SUUVyTyP/5Rzxc8mZmFNFY4/PEWdV QHk2lBxmsS+N8SKQIkHg+avGkTXZiv20AA1Blwm8Cl+lvlWGlViyPCq1UBGDrW8Zw61rY6b/ mUircFH8VOt8iy1rvWbC3MpRVs8Zu54a9vz0xHFlfa79qXwe+YdPSjCMgQAEIAABCEDgvgko Ddp5zFkon//99ttvP/7L/mVNl9Ug9YvU/JFrRXj1z/9Oo7fJVuf17Cs53CKu1Lhmi+QZzf+6 jitNeFbf2fFqLD052CpYMxGT7QERW2U9jvY/E3oq4625y/aKiKvlYxaXkqesXmfMEXHM/FHW 7fsY7TsrPtlTgDEGAhCAAAQgAIH7JqAIrPOY5dHr811k9Zy2EfYarfPr2bF0ztMjdYfgC7zW c1R/Xpq51If/hPJi32tos4bTO3/tR6WJ9Pxvua/njXLnnbduer2cts1tViPRXMrKrHBSatCK q+UWcY3i/9HQn9eCUccZ88V3hXtbR5U6jQWi739Wu20dZEKxd414sS7zqXa/1ko9d23Osv0l 2lesusrstYyz9e8d/2knZ5DbeLe2dd0uPlXvIv+YW9lYGAMBCEAAAhCAAAT2QEBp+N5eno7C F13vIZzd+eAJxN05OtEhpcYmTv9h+vVF/5KhS/hTnWO2/3vJk8VlduzVXFxj/KUZuPVQ/Czy xxsl14DGnBCAAAQgAAEIQKCHgNoYvz4fEModgBHJ79DUOutALJxy/tjALdfvZfy/bo68NF4m dqGIrjjk8gwigRw9WRRB4k7yFUuIqSEAAQhAAAIQ0AlEj0BaVs5C+XDQ/y6m7sm7iLL+q9iY NXarb+qjkBX/t/rkzTXTbiW+vY6dxedS8Xr+V/eCS/k7ap6ZeZtpe1T8VTuVx95V24hklRTj IAABCEAAAhCAAAQgAAEIQODuCfwfkQJwnsrVjc4AAAAASUVORK5CYII=</item> <item item-id="67">iVBORw0KGgoAAAANSUhEUgAAAaoAAAASCAYAAAAKYZpaAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA BVFJREFUeF7tXAtSXDEMo+fpebgP5+E8vc+Whb5tMLYlOXkfWu9MZ4CXxLIsy9ll6I/b2+up X81AM9AMNAPNwFUZuA+qfjUDzUAz0Aw0A1dl4OmqwBpXM9AMNAPNQDPw/qlf09AMNAPNQDPQ DFyZgU+D6u3jyfvvqz79OxM8wuM9z/Dfn/0rryyXjYMt14gnlYsKf0xstAbpoJJHJRcljq2B 3bt3/PdbaEHvCLeXh40z1usKGlTqdvZatWarfUCNv+ks61Grh1Fj3tcbhhHLF+f2RHdm8TI8 CKv6/Mw81dhIUCj3ipGhmFEO3j6ET32+mj/1PG89yxe7bgUm5gwVjzfcKjXfA1tF5wyOq6xB tUJ9ZPlB51V7PMLB+vu3HlTKLZVpnKuID+FgxKQKFMUcb8bs2mwPg2+Mo65HGBkO0RnoORuD XYfirXqu4qkOKhWvd9NmzlDzYc68whomL6ZvmDUoX+SvlUE1DtHSoLLCHL/3RLsF3J5F+1Gy 6HaUFc4jyuIZG8HDyuQ2mrN33siFXTtitJxmpu2dw4jP4mNuV0xzMBeIDN+sDrJb31hXyynS ZbQX3TKZfrFnz2jN05jtQa9Gan72ImL1m+lgRkdRL6u9lfUb+8zr90hXmQ4sdtZTPe1FPhv5 J+MVs4PKi83EfXgUMhXW/L0iWCF73yOw6Dlj4EyBkHFGTRjtQ7jHIRHlkJmVWjcrXrbZUR5I wNYgGQP2NDeLA9U3qwGrscj8ke4jU0QXh2yfurea4xiHGVSzwzDzFKZHkQ6yM1B+CueM3jJP ZTwtGg7ZIIvORX2O/GWXQYXE5D1HAmAKwxCbEcYOVTRAkSlmgmUGUNTcSOgR7lkRZHERF0jA lYaKBhXSpaINpoYMrytirsCChnu1P9X6RvpntR3FQzpk82PXIbzeOepgQT6q5szoFelkZlDZ 2mWxrE4i736syw5HSa1qsGzCZzEYYhRzX2GqHt6sKDZ3Jl9UF0aw7HBCzaIaGbokMEYViXpm aDAaVOOypqj0UYQTaYLFUjGqTPPRBZXRjd3L1MjTl8XHcqjUBcWt9BvqPVTzig9U6s/65rhu 6aBCibKF3M7ZBKKIFxWLPUspKorJ5h0VgxlGmWCUXNhBHWFCXDCGU8GLLiF7DQ0vH1tHJudq 7ygDBWlf1WmmFfUSwNS8yuOqoaBojK2Ltw75XrX3GI6Z/mXWoFoxWHYdVJWbQLZHEYcyqdFN ABHJChHlhoquPI/MGOWCTBI1OmtwSLzskGTwMjmrZhrhm43FnFsxJ0Wjik6RJlGdI3NGvY7O RYOT1WllXZW/LFZ0sahogfVGprbMGlQrpWeUeNva8A9+LamWzMcBf/6o0N4aou+j20Xl5x5G xdy3Ym+xx5xGPOPzqCAefsaALYaMd6ZxbS5ejhnX43r7dXwZeL093/9Q/Pn1i54td6jOCH/O T4wjqvXIaVTDOO/7Ez4m6imEUdVK1oPeEPG4/1jH1Tcy4gz33z1+jAhT1LO2nl7PoDO9M1h/ s1rJvMN6DNN71oe/NNzbD6KYTC/u1eNIG14eI5ZD/quGyNwjcHv8PBpgTKyZvcz56poz8WSx X19ebr/UZHZYfwaOVTHPrC0qxaocszhHxEB5rnq+t+/tpZUr9fjjzcOqomTn7F0wNodqYav7 WFyVdWdgimP+ur38fLo5b6gqqU3sOQPH2phn1BUTvjZHP94RMXCmK1cc4Xur9XKlHv/07nFl YdCwij9aOAZFpahnY0ZviY9h7iNKhb8j8X33WFfV2nfn9Uz82Ud/K3Ct7snV583keMqgmgHc e5uBZqAZaAb+XwZ+A/QTpNKikf1pAAAAAElFTkSuQmCC</item> <item item-id="68">iVBORw0KGgoAAAANSUhEUgAAAVsAAAAnCAYAAAChWOvjAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA BjZJREFUeF7tXcGR4zgM9Fbte36OQkk4CT02Br8mA8cz8Uw+WskezWhokGhApEyCuKur3bMp Emg0WhBEWX9u0206+T+OgCPgCDgCZRFYxLbLfz+G+SQzTGOf3rvXjkCFCIzTcDpNw4dNRTrZ dCvt1TieptPoMttj7N3n+hFY8vN8u1Z4MtiHXXdie72dzZ4591HBj3YE6kFgyVNrgtuX2H5e prNXtOYqhnokwi3JicA4nqfLZ84ZXztXR2J7nS6D92hfSzdf3REQILAUR8NlstJQ6Eds58AN BvtAAup6ResINIeApeq2G7G93gZTlyQuss8IzPt2lm2MzQmKxzKBwLxryErvthOx9RaC9YR2 kbUa4Xk7mJFWQidiOwfMb4yZrvhcbK2KrZ1CqQ+x9X6taaFdZcYF16bgWunb9iG2hvo+NtMp n1cuuPmwrGUmK/dbuhFbq48A1pIQtdjhYltLJPLZYeVBJBfbLi6w8xG/1pl8J0Ktkdlvl0mx XQm7/XM/VPoZOHuo78PP7qvPbQSkstUkLHJMbAz1OWk/eEJA1+FspuJfCyf0bCp/5J7YcdaF FfvetagrgFr5U0JsUV+5uEi+f6psqaBKJsw9NmUPZ+v394DYai4/t8fEjo+NQT6XYInMh9ib utmkwUjiQ+tjS+ETOylr8eLmk3KJsiMm5hqbc4ttDv80fjQttqHDUbK/v01v73F4tEmCiJc0 sFpbtt5J1yydLBpitnhMjtjF/OYKCyleaBGDcKk0f/5d/ibzd4/vSJEknT8aQ06wkMuN7ZmS K8/X78Mgpo6LCQln+/f3TGWrTRIrYstVOrFqdxvLWPxyEbWFebQ8QnyrWWxL8+eoyjalM6HG pa4As4ottVAIeEqI0O+WdSQki5IdEFuNWJQSW40tKUGMxStFGEpIuViUFBtEkF49hiokctlE Yf9KnnDcz8mf3GK76oq0qo0VlSjvyTYCR5pYdYpcyoYCiiQwZw97lgHElp2DuEnFES7lq/RY NGlTeFJrSgiXIhVKONSP1sflxiM39lKexLh8BH/iYrs8Xfb4PYyn/xJPjHK5h/jEzUHxN9mz pRaNLYIGjwpaCBQq2iXaCJIkQQBH8EKCi4iPNF7bMzWCZS47t5UFmShU8rzoMwR3MrGgH8TB xaJGsT2KP7kr21Teojgjuf+UU1ySoSKaGhcmV0xMEdKiYPyaq5PKViq2XBuB40Z44tQKk8Xj JCdtxH8V7xNbBjV5jRQOOTgV4nFkZYviXERstZfCqDGISCNJn0x8wW4ESZIgPiIERcZwCYnM ITmjc8JN4S3BjvOnxe8RPmj9QsURnR+dD+EVygVNri9zH7UbgYsfikUsBr/aCOtlAQVKuND6 /9s/Y2Ooy8awul3XlnyOjL2PAffZ0mLxeONn7AWRMbsp26iTRkzUpLZsY8fFbxuPbSUSxpKK Z+wzCofb/O7iFHaoMNQ1Ts6Hh/16LNC8/MEJsxHhCcWV4/jz8Ch3G4Hzieby42Y9pXex8U/5 fgSRkSqpqB2A2HLrj7d6Xs9Rky0cbneZqQg7xF5kjNYn7XGITeGYI9fS2IceU0Js0bVzjjvk txHaFtvHTQzkcd+cgaHnqskWxFvMXrQyiFUkR3z+4y3m0zM62uMQnMMxR66lsU92jIst+Lz+ CmvqMlcGvWJ0hspWsaoQoT5X4PpkW1SQnlmJMX1Gph6vXWxbkhL/PdsmokX3qemkLyGqEuGv R4rsW+K/Z9tE+n4R0d/U0ES0XGztC6fGQ39TQxPpu4bW30GmIfmRx2iFdu3XbttVuf9+JA6+ FtV/Hua9HO0jc8gNstfDZOelca/HsowFLrZlcG1/Vn+7bnPnGSt9n/aT59kDUmjn1s953tcY 7gLhdraU6OVaxLwZnwzdb+mksp2p5X3bak+Qe6pabyM0I5sq/lnp1955ajtUW++8lVBjrCU7 AEpUrcicNeLWh0227rV0JLYzPedLkthjt32Qtz4vJfuvU2ORRyy33kvH14ecfYssVbWdVbYP clrZIG0/1dzDrhEw1Ktd49hXZfvVNRrHWh6/7TqdVD08R6wDBIxegXYptgtdF8E9nWzs3+sg /VyYu0BA/8toLeRAt2LbQnDcRkfAEbCDwH/lxnP+5het6QAAAABJRU5ErkJggg==</item> <item item-id="69">iVBORw0KGgoAAAANSUhEUgAAAXIAAAAnCAYAAAASEMP5AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA BqFJREFUeF7tXU2ynDwMnFRlnd2cgkvMJVh8Z3ir3GDOk/PkPnyQKRIGZKtlCzCazqvUqwf+ kVpSWzY2fHsOz+HGf0SACBABInBdBCYi548zAr+6cXDshp7IEgEi0AgC/dDdbkP3yznWG9Hu FlOt87Tq+9tw60nh51mAPROBNAJTfN6fX43Qr5+lSOSOJv163sOO+H4ux5aIwLkITHEajcxJ 5F5E/vsx3JmJe6HJdojArgj0/X14/D53QPHsnUTu4i5fw6PjmrinY7ItIrArAlPi1T2GKIss JHIPIh+dogu47rZrIHngzjaIQAUCkbJyEnmFI8xE9/XsQk3TSOBbBMZ9adM2XQdvIbrNIDDu LouyVk4irw5NLqs0E5jVtpQ1IYFHtfC4JTHI8gqJvDr4R2fgQ85qFFumChJ5y9apkS1OEkYi r6Ugro/XIniJ+iTzGsJst26UdXISeS2NBFpnazfc2pCMZN6GHTyliPJ8i0TuQORRj/16BkyE tkjkEaz4rkOUQ3wkchJ5LQLh63PHSjwC/7fjLMZp7Dcinx12+ftME2rySPfX1zT5rUG6KT8u rSAZubWfSW6kTqqMdN2KzRI7tB9NZsm3WvE3zVfOvF9jO03u9Uyjti9p5tKq/+yRkaO6anax 3N9k5JJRLQ16l83Jo8mqTYW1+yld3uoBRF7Sz7JOqn6qDHLdYiekPUTeuc9UoFtk+rSyJT6E YJQa8JG6UhmtPasvpfpYXy/Fx5vIPfQrwf7SRF5rzFLjv9X7+WP48TMNvUcfZxP5OjOXCJlE XhJ+eJ1SP0J60BIipI2Uj8wzS4vPaLp6JgL/Pb5n49equzVWatp/60sjQ2SatByFtWnFfH8d +Ll6OSdB70l6prKHnM6bOkpGrjklkvVfmci1DC2VpS/9JOUbXkFwhXZK/QjRrWUi39t/vDPy EiJf82cqJnK2rMrIJTLWRt7cCL2+h/xdSuRS24gRNvUAIi8hIiTDtU7jpEEUCfQc2Wr2RgZF JFj3JDILBmeVrbVdlgRWrx6o7Ss3MJT4bC4JS8mKDk57ETmSAK/5Zq6TInYzkWuGlO6jxish ck2ekhEsReRW3Z4AkZfItweR1wx62qAqyYvMJBBsPp3EJSLzHFBy+JZgj3KBRupH+E+ayKdT n6/362z+Aye5UQxKEiHJ9tmMPDWqaJ1byFoCCs2Max0cBTs7un8IkVt9YZlVIHayBK1GYmLw SQF58jVNj9R9jFxxImqRyI/ynz0yckusaFyK2Xrc4aYFmQvZLbbSZUlR2JFsKY8qXbNUULK0 gmSdOTsgJKdlN5qdNVKxOKc2m9DaKsFLkz/Sfaufa7q3SOQ5H/D0nz0yck0+y33U1iqR57Lr XMBqwYyQaW75Q5xeGF8z6jJIGXatoEbRZjSWUdxK8BquSHva4Ks5smR3C3YacV3xPhpPJbqh cYC2jbaH+BLqCyU+N7XtvWsF0Unz/xJbJw8ELY02NbxufP57+TtVZjbG3I4E+rqPdZ2cPLmy OefL9SmRqSg/uI9cJqLXl71TH2tOyadhscRDysStsiz1TtnO4i8pf5L8J+UzzyGPHUo6bZWz +8NL/nIstJjc4oPJiPhJyk81vvHxn5dm3ksr1lhZJmUSl0ocICZbRziyNgIdIcNufQBErvXd P9v55FRLsmi4/aGwhrBD5EXKlOpUWg+RaV3myL5K5EPreBM52q93uUPetUIiT5nt9UAKOeLv bfhtey3JgmiLyYtmNCWzmFTb1uv/tMV0Otd2pTIiNj2+DInc+Kqk3JRjT/Ot+7UENiSXQ0YO 9WPEm22+3lWznLrmMEmV3fs67XQuAiRyEssLAb6P/BKeID8XkElkb/K2yHIuzcXvne8jv0T4 HuCI/ELQJTzBQp4k8gPiphGv4ReCGjHE+S7Hb3aeb4O8BKUkPq+bS8szXmTfOnax5eM3O0nj fxGI4wxRg5ZEHtWytXqNSVjXzo6xGm0O2bVSI+AV6kZZZ7sC1lYZRRIfl8Pu44PQ9W4hbXeV VxZuGVis+rK8AYFAz7dI5B5zC66Te6C4SxsW0iSRG0hwF2sd23+U9fE/S4DHQhe1Ny6vtGhZ bj9s0SqtyBTr2RaJ3CuzGKdpqaP2rbjup8lhObtQcrTaevDH/QyDl+9+YDuRsnFm5M4OHOVw wacRPvX9MAQCrY3/3VH1YSZ0pu4ten3fypF7WpYIEIENAkFnzlxa2YHaJzK/3brxnXQMJCJA BNpAoPwNkW3In5eCRE6yJQJEgAhcHIH/AfTPOfnBjN1XAAAAAElFTkSuQmCC</item> <item item-id="70">iVBORw0KGgoAAAANSUhEUgAAAT4AAAAnCAYAAACWliTXAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA BdZJREFUeF7tXcuV2zAMVN7LeW+uQk24CR1Sg0/bgevZerYfRbJXa1kmiAFE0hSB5OXtxuYH mAGGICXZf67jdez8jyPgCDgClhCYhc/k369+Evx+HGx67147Aj8IDGPfdWP/ZUsFOlvu3r0d hm7sBpc8i9y7z2EE5pw4XS9mlgNzwne5nsytbp7sjgCCwJwbVsTPlvB9n8eTV3pmVnUk2b3N MwLDcBrP3+2jYkj4LuO59zO99kPaPdyFwFwc9Oex9U2vHeGbCO0NnWHsCn6vCU0jYKHqMyN8 l2tvooQ/iuBNd07Mt1GZFphquZrueGj9rM+I8Pk2t6Ykc8GriY2QLdMtLo1vd40I30SkX9So prpy4atd+NovFGwIn5/vVSN6S8q7+NUtfq2f89kQPgNnFnWnUdg6F796WWv9TNyM8Fl7JKfe lHpY5sJXL0ut3+jvwlfdJrDeZEhlmV/RTYVkvnFMCd8SkOuf+aDlR+bsCb2/fe02y7TVRSo+ TUKifTTVDTX29nXOhhCftXDMR4GuRTAOEi1yWy61c8X6vZvjnMJH4bdmWospGi0vFV/IKHSw HO1i9nC2/r4PCJ9WmJDDek6YQrit7ZH+zo2H2JyDy5JjavhE7AtxqZ2L6ofwnZvjXMIXEvRQ PGoxRTic2xxa+LZOkmB9fowfnzQkWpCpAEWDEiZpdaNvDUmB2v3OdlpOEZu5BRcZ45Z8xA3c NXD87/w3mjOoj6EclfqtnSvWTyV8sTKcqm62peuW3Fi/bQlMOUQGO1PxaZOkVuFDq5JQAm95 yhF0JcbUcorYVoPw5eY4R8W34LZX+Na+S3LwSUe4qikWQCHxQsrWmLFcUKH2PPkFCB8lvNFV g6jEUlZ8MTwklUFo4dmDNSIQ72yz9je1HbEFQzIXZSPCaygPUnJcUvgoEY/l5Fb8OFF90Tmq FEUmRQUMTd5Q+S+pQEhRBIQvJNhcEEtWm5hgo+K6xSc0P7KaIr5q7eUwe8f7qX1RLb7MhRWJ 2JXgmBa++amO+3PWL/8iT0chuaL1CxkbEr5YYiAExRI09F5M3CROtSZ81KpOVdXcYsVV94gg xo4ZqIoj1eta0cSED0/odwpfKY5TV3xIAVON8FEVmHQ7u4wTIo0Lyli1iCYydzuLRFzXc0r6 cX6i22Nk4eEEE13YtEJTWz8N9pIqnIoJCQ5SXnNznLriQzCqSviQ7ZVEIDmxQoROAtBtPsFV XUmS5BS+vYmgwRHpI0nmd7aVcCO1M4ZTivhBuOdyjlrcUI5zXdXV6Akn8hqun67qrsvRrUJv B1/+v/5JtdlWfCHwqWow9nrIxmDggffxhYP2/i1U1JcTUfZhttFjr7mg8JJwtLUzxPV2zlc8 4lhIBWR/ezk39zn1fqA58vBNZyMS9+s2HJ9rIYzF1tIu9VY3VvGFfF1rRihOQr5zfZ5s2B98 /AicYvMj7GwBCB83w3DN93HcOcfm/NK8X5u9Wnu0/VrAjPMhp/Bxc5d4v8izuscWvvuhN/LI m5ywnGPLreF78PZKK9XYKk1VAg87eXvCPmn78Qi9tig5l8Y+AqHGv42wiPCtg5sP5nTk/Y6U oOLLYFWiJ0frsix8XPD8lAJ1JoO8Xpe37VrjFV8L6emfx1eMRRe+NsTQP4+vWMpkDBj/BOZi LLrwZYzjYixOl38a/37dYlvd94aDf+dGKfxjVz0XG5AtLdWmlB+25/Hv3Ci4xuQMtfaJzIme duw9AufCp0U9RT//lrVGhO86tn5mkSLcU4/hwpca0ULjGTgTN7LVnQLGz/mKL2IufIWEKjGz rZ/v3e4yOSY1Gqt9u6tBTdonh9hRF0yktnl7BAEb5+GGhG8ifSrhqUfPkJDwNhgC0keQpO0x K7yVBgEL1Z6xiu8eBq3fmKkJdu/jCNwQMHC2tzBtq+L7OQsZhlyPoHkCOQIHRcDYbsik8M2h OYtf1/XT53QcNFDdbkcgCQL6T6o5cuaYFb4jk+a2OwKOwD4E/gPm31iDsbMPjgAAAABJRU5E rkJggg==</item> <item item-id="71">iVBORw0KGgoAAAANSUhEUgAAAVUAAAAnCAYAAAC/kdtQAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA BjpJREFUeF7tXVty5CAMnK3a7/zNKXyJuYQ/9gzzlRv4PDlP7uP1ZOIUYYAWIDCG3tRWKjYP qSW1BH79WdZlvfAfESACRIAI6CDwIFX+KCPwMW2JalpnIksEhkZgXqfLZZ0+lOOrcUwvY6lb Xtt5vqyXmXRaHmnOcBYEHjFxXe6NU6EemiRVRVPfl+twWVnPFTlSzwg8YmMUYiWpapHq5229 skLVQpPjdIjAPF/X22fPqeOpG0lVxXnv623iHmr/4UINsxB4FB7Tbe19I4CkqkGqm7NMA+0Z ZQWWBt4c47QIjFCtklQV3PO+TEMsa85Cptt9MY/bBBUsexaNTyTndmdM73urJNXs0OPSv6WQ Jpm2ZA2XLNttVp1vAZBUs0l1cxJeoMpGUYsKSKpaSJYap/8ihKSaSwfcT81FUL0/ibUUIeqM 2/u+Kkk1N6QH2CPSCaW6o5BY6+IdM1vv1yBIqgqkOtpjeDEBdFRbkupRyON5e39IhqRKUs1F oKn+vPKPSe3oFkOR6u6Q5u8jDYDkcZ23jyH5Y4Pwpf22/JdUqrHzfD2ZIbw1KKUq841tH0cy uHylFf9Btk89H+tjMfPYtkydK9TvaBuXIFWpP++2QH4dYzO77Uul6jJqzgS5fUPyIFkR2aDz Ptl/9ROQaso8Zp9Q/xTn8I0tOe7CxCVfis65vlKrfyndXLZMncvXrwUba5OqRCdJGy3/OTWp vmSIyBu+VRz2/W19e/ebQ2MONAY6H3IWibOh8UmqWuH4XJ2YoyHsRYnfGFFi79KJ89/tbzBm ctDUTiYpsiSRqp1Rzb99ldN+3Pxtl+KS4ERVmxQElxyhJbct/888oFLVCAo0BjqvSarSaspF Dl4Mm9qVxR6UgzcavQVSLW1j7UpVkoSkycTmMpOjkO1+2qJqT0piLqL1CeRTcCe1EEhSeSQA SMcKOrqAVH2JJoXspFVEiv4Sx/MlvlDSlNhVIm8rbXwJWUO+UDKKGT9UNLjiMhSTZrERSozS hFCCVEMxFpIr5M92kSVNps5KFTmN63yM4LGkiuRJySauQLedR5IUFgGppsiHnFySnVEQSm22 t/M5lc8xffNLnRPJ38J5bV2kiT5G95hkWcPGflJ9PG31fG/Dy3/hU4sSX0SxJcErWBCFKlUk oJQcUfBKsx9yYHQ+piqX6nZWUo21bUwl4EtYoeOoancGmiv4Eo/FkFR8QpOTxZGkWsvGJSpV UQH0vc10KKmiZZuYeIzbg0IEK1naajodInuRoU5aqcaSaqjaRmOlVOqpJFe7X2wiR/Jp+nfM Mj+GaGJ9wda5ZqWKfDPmvNTW8EKVTazS0hgZSURY32SMKszUoFUh1Yir/1KjhDCXJJ6YwJXY EyVC5JgoOSN5Wzsv9e0UuaU+icaOtWtNG2tf/c/RFfluiq1/kaq5xLKXOPbg+9/mb1+bPajQ +C7D7n1C8tjjI4czSdg3p4vYnPIL71N1E+rza5O+DwX6dHdh8Tq+f2xTj5D+PpxMuVyYoGMu vZbt27MhLKQ21WsXb5vn3Ol6oPh41S1NRklMlbHxU4MSy/+QThJ9ffziwgH5WJXHVFE2QEI2 fV5Aqkj+eSn3iYmSYyO9Us63Jm+qPKn9esAM6VCCVNGcNc+TVHPvkcwi1ecFDMljrvFOUXLs eGlwDyxvbIVtrmDs+d3VstkKy+PWKbUfRui1Rc25UuTzINT5V4erkKrp3NiZ9Yznmld9/ixS 1dW199HcWyi/n0Ly7YFJjveOXyv6sVLNreR678/3qVazMEm1FVrMk4PvU60WMnmGOqw33/xf zUNIqod5uaqN+eZ/VTj7cIrfWvAbVbWsGro6vssgWeb72tTSY+x5+I0qUipEoH8naZEEcsiT pHqkRfk1VUgpR5qnlbl73yNqBWdTDpJqi1YRyDTANYhqV/8FcJ+XwLmvWt12JNVzRlTv+6lf dxyd0zStSc0tgBoWKUGkvotfNfQZb44xrj+QVLVqrG1Z43vcdLzgKaex7z5jrePlJOfII1Sp rFS1CPV7nN5vaiYtEIFkBAbYS/25AyUZJGVC6kWOeS712GkvCFGP4RAYbBXH5X+B5PAg1stl 2t5XNFz4UGMiYCCQ/sauM0cOSZVBQASIABFQROA/hoIcfxQJpbwAAAAASUVORK5CYII=</item> </binaryContent> </worksheet>