<description/> <author>CEE</author> <company>Parametric Technology Corporation</company> <keywords/> <revisedBy>Labuser</revisedBy> </userData> <identityInfo> <revision>1</revision> <documentID>8ACDEF3A-B5A4-4228-B4C3-700C7D22EC33</documentID> <versionID>E78A6A67-2D02-4D29-8F9F-C7448A6D0601</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="inherit" text-align="left" list-style-type="inherit" tabs="inherit"/> <inlineAttr font-family="Arial" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false" line-through="false" vertical-align="baseline"/> </textStyle> <textStyle name="Heading 1"> <blockAttr margin-left="0" margin-right="0" text-indent="inherit" text-align="left" list-style-type="inherit" tabs="inherit"/> <inlineAttr font-family="Arial" font-charset="0" font-size="14" font-weight="bold" font-style="normal" underline="false" line-through="false" vertical-align="baseline"/> </textStyle> <textStyle name="Heading 2"> <blockAttr margin-left="0" margin-right="0" text-indent="inherit" text-align="left" list-style-type="inherit" tabs="inherit"/> <inlineAttr font-family="Arial" font-charset="0" font-size="12" font-weight="bold" font-style="italic" underline="false" line-through="false" vertical-align="baseline"/> </textStyle> <textStyle name="Heading 3"> <blockAttr margin-left="0" margin-right="0" text-indent="inherit" text-align="left" list-style-type="inherit" tabs="inherit"/> <inlineAttr font-family="Arial" font-charset="0" font-size="12" font-weight="normal" font-style="normal" underline="false" line-through="false" vertical-align="baseline"/> </textStyle> <textStyle name="Paragraph"> <blockAttr margin-left="0" margin-right="0" text-indent="21" text-align="left" list-style-type="inherit" tabs="inherit"/> <inlineAttr font-family="Arial" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false" line-through="false" vertical-align="baseline"/> </textStyle> <textStyle name="List"> <blockAttr margin-left="14.25" margin-right="0" text-indent="-14.25" text-align="left" list-style-type="inherit" tabs="inherit"/> <inlineAttr font-family="Arial" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false" line-through="false" vertical-align="baseline"/> </textStyle> <textStyle name="Indent"> <blockAttr margin-left="108" margin-right="0" text-indent="inherit" text-align="left" list-style-type="inherit" tabs="inherit"/> <inlineAttr font-family="Arial" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false" line-through="false" vertical-align="baseline"/> </textStyle> <textStyle name="Title"> <blockAttr margin-left="0" margin-right="0" text-indent="inherit" text-align="center" list-style-type="inherit" tabs="inherit"/> <inlineAttr font-family="Times New Roman" font-charset="0" font-size="24" font-weight="bold" font-style="normal" underline="false" line-through="false" vertical-align="baseline"/> </textStyle> <textStyle name="Subtitle" base-style="Title"> <blockAttr margin-left="0" margin-right="0" text-indent="inherit" text-align="center" list-style-type="inherit" tabs="inherit"/> <inlineAttr font-family="Times New Roman" font-charset="0" font-size="18" font-weight="normal" font-style="normal" underline="false" line-through="false" vertical-align="baseline"/> </textStyle> </textStyles> </textRendering> <mathRendering equation-color="#000"> <operators multiplication="narrow-dot" derivative="derivative" literal-subscript="large" definition="colon-equal" global-definition="triple-equal" local-definition="left-arrow" equality="bold-equal" symbolic-evaluation="right-arrow"/> <mathStyles> <mathStyle name="Variables" font-family="Times New Roman" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false"/> <mathStyle name="Constants" font-family="Times New Roman" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false"/> <mathStyle name="User 1" font-family="Arial" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false"/> <mathStyle name="User 2" font-family="Courier New" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false"/> <mathStyle name="User 3" font-family="Arial" font-charset="0" font-size="10" font-weight="bold" font-style="normal" underline="false"/> <mathStyle name="User 4" font-family="Times New Roman" font-charset="0" font-size="10" font-weight="normal" font-style="italic" underline="false"/> <mathStyle name="User 5" font-family="Times New Roman" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false"/> <mathStyle name="User 6" font-family="Arial" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false"/> <mathStyle name="User 7" font-family="Times New Roman" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false"/> <mathStyle name="Math Text Font" font-family="Times New Roman" font-charset="0" font-size="14" font-weight="normal" font-style="normal" underline="false"/> </mathStyles> <dimensionNames mass="mass" length="length" time="time" current="current" thermodynamic-temperature="temperature" luminous-intensity="luminosity" amount-of-substance="substance" display="false"/> <symbolics derivation-steps-style="vertical-insert" show-comments="false" evaluate-in-place="false"/> <results numeric-only="true"> <general precision="3" show-trailing-zeros="false" radix="dec" complex-threshold="10" zero-threshold="15" imaginary-value="i" exponential-threshold="3"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="true" simplify-units="true" fractional-unit-exponent="false"/> </results> </mathRendering> <pageModel show-page-frame="false" show-header-frame="false" show-footer-frame="false" header-footer-start-page="1" paper-code="1" orientation="portrait" print-single-page-width="false" page-width="612" page-height="792"> <margins left="86.4" right="86.4" top="86.4" bottom="86.4"/> <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="false" optimize-expressions="false" exact-boolean="true" strings-use-origin="false" zero-over-zero="error"> <compatibility multiple-assignment="MC12" local-assignment="MC11"/> </calculationBehavior> <units> <currentUnitSystem name="si" customized="false"/> </units> </calculation> <editor view-annotations="false" view-regions="false"> <ruler is-visible="false" ruler-unit="in"/> <plotTemplate> <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="149" 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="139" left="618" top="6" width="532.5" height="361.5" align-x="618" align-y="6" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <picture> <metafile mapping-mode="8" x-extent="531.241" y-extent="359.8866" item-idref="2"/> </picture> <rendering item-idref="3"/> </region> <region region-id="40" left="6" top="30" width="562.5" height="249" align-x="6" align-y="30" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <picture> <metafile mapping-mode="8" x-extent="561.2598" y-extent="247.748" item-idref="4"/> </picture> <rendering item-idref="5"/> </region> <region region-id="41" left="18" top="290.25" width="419.25" height="132" align-x="18" align-y="300" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> <sp count="4"/>*<sp count="2"/>maximum flow=400 L/min <sp count="4"/>* minimum flow (corresponding to height of rectangular weir) = 40 L/min <sp count="4"/>* linear proportionality = 20 L/min/cm <sp count="3"/>1. Write functions in MathCAD that design all the geometry of the Sutro weir given these constraints. The functions must have the parameters that could potentially be varied as inputs. <sp count="3"/>2. Create a graph in MathCAD that draws the shape of the Sutro weir (including the rectangular portion). <sp count="3"/>3. Use the graph formatting options to hide the arguments and to add appropriate x and y axis labels. Change the display units to cm (by dividing the arguments by cm) and include the units in the labels. </text> </region> <region region-id="42" left="12" top="446.25" width="67.5" height="27.75" align-x="39.75" align-y="462" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="max">Q</ml:id> <ml:apply> <ml:mult style="auto-select"/> <ml:real>400</ml:real> <ml:apply> <ml:div/> <ml:id xml:space="preserve">L</ml:id> <ml:id xml:space="preserve">min</ml:id> </ml:apply> </ml:apply> </ml:define> </math> <rendering item-idref="6"/> </region> <region region-id="47" left="174" top="453" width="60.75" height="16.5" align-x="207" align-y="462" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="dmax">H</ml:id> <ml:apply> <ml:mult style="auto-select"/> <ml:real>20</ml:real> <ml:id xml:space="preserve">cm</ml:id> </ml:apply> </ml:define> </math> <rendering item-idref="7"/> </region> <region region-id="48" left="180" top="471" width="40.5" height="16.5" align-x="197.25" align-y="480" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="d">C</ml:id> <ml:real>0.62</ml:real> </ml:define> </math> <rendering item-idref="8"/> </region> <region region-id="43" left="12" top="477" width="57" height="16.5" align-x="50.25" align-y="486" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="Qratio">Π</ml:id> <ml:real>0.1</ml:real> </ml:define> </math> <rendering item-idref="9"/> </region> <region region-id="50" left="180" top="501" width="54" height="16.5" align-x="215.25" align-y="510" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="Sutro">Π</ml:id> <ml:real>0.1</ml:real> </ml:define> </math> <rendering item-idref="10"/> </region> <region region-id="44" left="12" top="507" width="91.5" height="16.5" align-x="39" align-y="516" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="min">Q</ml:id> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="max">Q</ml:id> <ml:id xml:space="preserve" subscript="Qratio">Π</ml:id> </ml:apply> </ml:define> </math> <rendering item-idref="11"/> </region> <region region-id="45" left="18" top="532.5" width="126" height="35.25" align-x="51.75" align-y="552" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="linear">C</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:div/> <ml:id xml:space="preserve" subscript="max">Q</ml:id> <ml:id xml:space="preserve" subscript="dmax">H</ml:id> </ml:apply> <ml:unitOverride> <ml:apply> <ml:div/> <ml:id xml:space="preserve">L</ml:id> <ml:apply> <ml:mult/> <ml:id xml:space="preserve">min</ml:id> <ml:id xml:space="preserve">cm</ml:id> </ml:apply> </ml:apply> </ml:unitOverride> <result xmlns="http://schemas.mathsoft.com/math30"> <unitedValue> <ml:real>20</ml:real> <unitMonomial xmlns="http://schemas.mathsoft.com/units10"> <unitReference unit="centimeter" power-numerator="-1"/> <unitReference unit="liter"/> <unitReference unit="minute" power-numerator="-1"/> </unitMonomial> </unitedValue> </result> </ml:eval> </ml:define> </math> <rendering item-idref="12"/> </region> <region region-id="46" left="18" top="574.5" width="187.5" height="56.25" align-x="94.5" align-y="594" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define warning="WarnRedefinedBIUnit" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:function> <ml:id xml:space="preserve">W</ml:id> <ml:boundVars> <ml:id xml:space="preserve" subscript="max">Q</ml:id> <ml:id xml:space="preserve" subscript="dmax">H</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:div/> <ml:id xml:space="preserve" subscript="max">Q</ml:id> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve" subscript="dmax">H</ml:id> <ml:apply> <ml:div/> <ml:real>3</ml:real> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:id xml:space="preserve" subscript="d">C</ml:id> </ml:apply> <ml:apply> <ml:sqrt/> <ml:apply> <ml:mult/> <ml:apply> <ml:mult style="auto-select"/> <ml:real>2</ml:real> <ml:id xml:space="preserve">g</ml:id> </ml:apply> <ml:id xml:space="preserve" subscript="Sutro">Π</ml:id> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </ml:define> </math> <rendering item-idref="13"/> </region> <region region-id="63" left="264" top="591" width="117" height="16.5" align-x="339.75" align-y="600" 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">W</ml:id> <ml:sequence> <ml:id xml:space="preserve" subscript="max">Q</ml:id> <ml:id xml:space="preserve" subscript="dmax">H</ml:id> </ml:sequence> </ml:apply> <ml:unitOverride> <ml:id xml:space="preserve">cm</ml:id> </ml:unitOverride> <result xmlns="http://schemas.mathsoft.com/math30"> <unitedValue> <ml:real>8.5841386231467389</ml:real> <unitMonomial xmlns="http://schemas.mathsoft.com/units10"> <unitReference unit="centimeter"/> </unitMonomial> </unitedValue> </result> </ml:eval> </math> <rendering item-idref="14"/> </region> <region region-id="64" left="24" top="645" width="126" height="16.5" align-x="53.25" align-y="654" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="base">s</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="Sutro">Π</ml:id> <ml:id xml:space="preserve" subscript="dmax">H</ml:id> </ml:apply> <ml:unitOverride> <ml:id xml:space="preserve">cm</ml:id> </ml:unitOverride> <result xmlns="http://schemas.mathsoft.com/math30"> <unitedValue> <ml:real>2.0000000000000004</ml:real> <unitMonomial xmlns="http://schemas.mathsoft.com/units10"> <unitReference unit="centimeter"/> </unitMonomial> </unitedValue> </result> </ml:eval> </ml:define> </math> <rendering item-idref="15"/> </region> <region region-id="65" left="24" top="676.5" width="319.5" height="70.5" align-x="104.25" align-y="696" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:function> <ml:id xml:space="preserve">y</ml:id> <ml:boundVars> <ml:id xml:space="preserve">x</ml:id> <ml:id xml:space="preserve" subscript="max">Q</ml:id> <ml:id xml:space="preserve" subscript="dmax">H</ml:id> </ml:boundVars> </ml:function> <ml:program> <ml:ifThen> <ml:apply> <ml:greaterThan/> <ml:id xml:space="preserve">x</ml:id> <ml:real>0</ml:real> </ml:apply> <ml:apply> <ml:mult/> <ml:apply> <ml:div/> <ml:apply> <ml:id xml:space="preserve">W</ml:id> <ml:sequence> <ml:id xml:space="preserve" subscript="max">Q</ml:id> <ml:id xml:space="preserve" subscript="dmax">H</ml:id> </ml:sequence> </ml:apply> <ml:real>2</ml:real> </ml:apply> <ml:parens> <ml:apply> <ml:minus/> <ml:real>1</ml:real> <ml:apply> <ml:mult/> <ml:apply> <ml:div/> <ml:real>2</ml:real> <ml:id xml:space="preserve">π</ml:id> </ml:apply> <ml:apply> <ml:id xml:space="preserve">atan</ml:id> <ml:apply> <ml:sqrt/> <ml:apply> <ml:div/> <ml:id xml:space="preserve">x</ml:id> <ml:id xml:space="preserve" subscript="base">s</ml:id> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </ml:parens> </ml:apply> </ml:ifThen> <ml:otherwise> <ml:apply> <ml:div/> <ml:apply> <ml:id xml:space="preserve">W</ml:id> <ml:sequence> <ml:id xml:space="preserve" subscript="max">Q</ml:id> <ml:id xml:space="preserve" subscript="dmax">H</ml:id> </ml:sequence> </ml:apply> <ml:real>2</ml:real> </ml:apply> </ml:otherwise> </ml:program> </ml:define> </math> <rendering item-idref="16"/> </region> <region region-id="58" left="24" top="764.25" width="183.75" height="27.75" align-x="33.75" align-y="780" 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:apply> <ml:neg/> <ml:id xml:space="preserve" subscript="base">s</ml:id> </ml:apply> <ml:apply> <ml:minus/> <ml:apply> <ml:mult style="auto-select"/> <ml:real>0.1</ml:real> <ml:id xml:space="preserve">cm</ml:id> </ml:apply> <ml:id xml:space="preserve" subscript="base">s</ml:id> </ml:apply> </ml:sequence> <ml:apply> <ml:minus/> <ml:id xml:space="preserve" subscript="dmax">H</ml:id> <ml:apply> <ml:mult style="auto-select"/> <ml:apply> <ml:div/> <ml:real font="0">2</ml:real> <ml:real>3</ml:real> </ml:apply> <ml:id xml:space="preserve" subscript="base">s</ml:id> </ml:apply> </ml:apply> </ml:range> </ml:define> </math> <rendering item-idref="17"/> </region> <region region-id="62" left="24" top="800.25" width="83.25" height="27.75" align-x="53.25" align-y="816" 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="d">H</ml:id> <ml:boundVars> <ml:id xml:space="preserve">x</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:plus/> <ml:id xml:space="preserve">x</ml:id> <ml:apply> <ml:mult style="auto-select"/> <ml:apply> <ml:div/> <ml:real>2</ml:real> <ml:real>3</ml:real> </ml:apply> <ml:id xml:space="preserve" subscript="base">s</ml:id> </ml:apply> </ml:apply> </ml:define> </math> <rendering item-idref="18"/> </region> <region region-id="59" left="42" top="864" width="229.5" height="404.25" align-x="42" align-y="864" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <plot disable-calc="false" item-idref="19"/> <rendering item-idref="20"/> </region> <region region-id="71" left="18" top="1286.25" width="389.25" height="24" align-x="28.5" align-y="1296" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> What fraction of the total flow goes throught the rectangular portion of the Sutro weir at the maximum design flow rate? </text> </region> <region region-id="73" left="528" top="1344" width="339" height="57.75" align-x="528" align-y="1344" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <ole clsid="{0002CE03-0000-0000-C000-000000000046}" type="static" item-idref="21"/> <rendering item-idref="22"/> </region> <region region-id="72" left="18" top="1319.25" width="350.25" height="48.75" align-x="49.5" align-y="1356" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="base">Q</ml:id> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:mult style="auto-select"/> <ml:apply> <ml:div/> <ml:real>2</ml:real> <ml:real>3</ml:real> </ml:apply> <ml:id xml:space="preserve" subscript="d">C</ml:id> </ml:apply> <ml:apply> <ml:id xml:space="preserve">W</ml:id> <ml:sequence> <ml:id xml:space="preserve" subscript="max">Q</ml:id> <ml:id xml:space="preserve" subscript="dmax">H</ml:id> </ml:sequence> </ml:apply> </ml:apply> <ml:apply> <ml:sqrt/> <ml:apply> <ml:mult style="auto-select"/> <ml:real font="0">2</ml:real> <ml:id xml:space="preserve">g</ml:id> </ml:apply> </ml:apply> </ml:apply> <ml:parens> <ml:apply> <ml:minus/> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:plus/> <ml:id xml:space="preserve" subscript="dmax">H</ml:id> <ml:apply> <ml:mult style="auto-select"/> <ml:apply> <ml:div/> <ml:real>1</ml:real> <ml:real>3</ml:real> </ml:apply> <ml:id xml:space="preserve" subscript="base">s</ml:id> </ml:apply> </ml:apply> </ml:parens> <ml:apply> <ml:div/> <ml:real>3</ml:real> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:minus/> <ml:id xml:space="preserve" subscript="dmax">H</ml:id> <ml:apply> <ml:mult style="auto-select"/> <ml:apply> <ml:div/> <ml:real>2</ml:real> <ml:real>3</ml:real> </ml:apply> <ml:id xml:space="preserve" subscript="base">s</ml:id> </ml:apply> </ml:apply> </ml:parens> <ml:apply> <ml:div/> <ml:real>3</ml:real> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:apply> </ml:parens> </ml:apply> </ml:define> </math> <rendering item-idref="23"/> </region> <region region-id="76" left="18" top="1376.25" width="87" height="27.75" align-x="48.75" align-y="1392" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="base">Q</ml:id> <ml:unitOverride> <ml:apply> <ml:div/> <ml:id xml:space="preserve">L</ml:id> <ml:id xml:space="preserve">min</ml:id> </ml:apply> </ml:unitOverride> <result xmlns="http://schemas.mathsoft.com/math30"> <unitedValue> <ml:real>125.41906569853195</ml:real> <unitMonomial xmlns="http://schemas.mathsoft.com/units10"> <unitReference unit="liter"/> <unitReference unit="minute" power-numerator="-1"/> </unitMonomial> </unitedValue> </result> </ml:eval> </math> <rendering item-idref="24"/> </region> <region region-id="80" left="24" top="1408.5" width="123" height="35.25" align-x="73.5" align-y="1428" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="baseratio">Q</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:div/> <ml:id xml:space="preserve" subscript="base">Q</ml:id> <ml:id xml:space="preserve" subscript="max">Q</ml:id> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>0.3135476642463299</ml:real> </result> </ml:eval> </ml:define> </math> <rendering item-idref="25"/> </region> <region region-id="88" left="504" top="1409.25" width="336.75" height="73.5" align-x="794.25" align-y="1446" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:apply> <ml:mult/> <ml:apply> <ml:mult style="auto-select"/> <ml:apply> <ml:div/> <ml:real>2</ml:real> <ml:real>3</ml:real> </ml:apply> <ml:apply> <ml:div/> <ml:real>1</ml:real> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve" subscript="dmax">H</ml:id> <ml:apply> <ml:div/> <ml:real>3</ml:real> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:apply> <ml:sqrt/> <ml:id xml:space="preserve" subscript="Sutro">Π</ml:id> </ml:apply> </ml:apply> </ml:apply> </ml:apply> <ml:parens> <ml:apply> <ml:minus/> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:plus/> <ml:id xml:space="preserve" subscript="dmax">H</ml:id> <ml:apply> <ml:mult style="auto-select"/> <ml:apply> <ml:div/> <ml:real>1</ml:real> <ml:real>3</ml:real> </ml:apply> <ml:id xml:space="preserve" subscript="base">s</ml:id> </ml:apply> </ml:apply> </ml:parens> <ml:apply> <ml:div/> <ml:real>3</ml:real> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:minus/> <ml:id xml:space="preserve" subscript="dmax">H</ml:id> <ml:apply> <ml:mult style="auto-select"/> <ml:apply> <ml:div/> <ml:real>2</ml:real> <ml:real>3</ml:real> </ml:apply> <ml:id xml:space="preserve" subscript="base">s</ml:id> </ml:apply> </ml:apply> </ml:parens> <ml:apply> <ml:div/> <ml:real>3</ml:real> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:apply> </ml:parens> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>0.3135476642463299</ml:real> </result> </ml:eval> <resultFormat numeric-only="true"> <general precision="6" show-trailing-zeros="false" radix="dec" complex-threshold="10" zero-threshold="15" imaginary-value="i" exponential-threshold="3"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="true" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="26"/> </region> <region region-id="86" left="564" top="1516.5" width="104.25" height="56.25" align-x="614.25" align-y="1536" 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:div/> <ml:id xml:space="preserve" subscript="max">Q</ml:id> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve" subscript="dmax">H</ml:id> <ml:apply> <ml:div/> <ml:real>3</ml:real> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:id xml:space="preserve" subscript="d">C</ml:id> </ml:apply> <ml:apply> <ml:sqrt/> <ml:apply> <ml:mult/> <ml:apply> <ml:mult style="auto-select"/> <ml:real>2</ml:real> <ml:id xml:space="preserve">g</ml:id> </ml:apply> <ml:id xml:space="preserve" subscript="Sutro">Π</ml:id> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </math> <rendering item-idref="27"/> </region> <region region-id="69" left="12" top="1532.25" width="427.5" height="84" align-x="21" align-y="1542" 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"> How could we estimate the maximum flowrate for a Sutro Weir installed in a pipe? The weir will begin to fail if the water isn't experiencing free fall (or when the weir isn't discharging into air). We need to calculate the average vertical velocity at the base of the Sutro Weir. Take the total flow rate and divide by the average velocity to get the minimum pipe cross sectional area. </text> </region> <region region-id="90" left="24" top="1616.25" width="87.75" height="27.75" align-x="35.25" align-y="1632" 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">h</ml:id> <ml:apply> <ml:minus/> <ml:id xml:space="preserve" subscript="dmax">H</ml:id> <ml:apply> <ml:mult style="auto-select"/> <ml:apply> <ml:div/> <ml:real>2</ml:real> <ml:real>3</ml:real> </ml:apply> <ml:id xml:space="preserve" subscript="base">s</ml:id> </ml:apply> </ml:apply> </ml:define> </math> <rendering item-idref="28"/> </region> <region region-id="98" left="36" top="1646.25" width="52.5" height="33.75" align-x="47.25" align-y="1662" 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">ρ</ml:id> <ml:apply> <ml:mult style="auto-select"/> <ml:real>1000</ml:real> <ml:apply> <ml:div/> <ml:id xml:space="preserve">kg</ml:id> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">m</ml:id> <ml:real>3</ml:real> </ml:apply> </ml:apply> </ml:apply> </ml:define> </math> <rendering item-idref="29"/> </region> <region region-id="127" left="636" top="1664.25" width="356.25" height="12" align-x="650.25" align-y="1674" 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"> vertical velocity of the water from any height when it reaches the bottom of the weir </text> </region> <region region-id="118" left="552" top="1669.5" width="66.75" height="18" align-x="562.5" align-y="1680" 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:sqrt/> <ml:apply> <ml:mult/> <ml:apply> <ml:mult style="auto-select"/> <ml:real>2</ml:real> <ml:id xml:space="preserve">g</ml:id> </ml:apply> <ml:parens> <ml:apply> <ml:plus/> <ml:id xml:space="preserve">x</ml:id> <ml:id xml:space="preserve" subscript="base">s</ml:id> </ml:apply> </ml:parens> </ml:apply> </ml:apply> </math> <rendering item-idref="30"/> </region> <region region-id="134" left="642" top="1688.25" width="197.25" height="12" align-x="657.75" align-y="1698" 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"> velocity through the weir in the vena contracta </text> </region> <region region-id="119" left="552" top="1693.5" width="48" height="14.25" align-x="562.5" align-y="1704" 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:sqrt/> <ml:apply> <ml:mult/> <ml:apply> <ml:mult style="auto-select"/> <ml:real>2</ml:real> <ml:id xml:space="preserve">g</ml:id> </ml:apply> <ml:parens> <ml:apply> <ml:minus/> <ml:id xml:space="preserve">h</ml:id> <ml:id xml:space="preserve">x</ml:id> </ml:apply> </ml:parens> </ml:apply> </ml:apply> </math> <rendering item-idref="31"/> </region> <region region-id="89" left="30" top="1687.5" width="313.5" height="45" align-x="81.75" align-y="1710" 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="in">M</ml:id> <ml:boundVars> <ml:id xml:space="preserve" subscript="max">Q</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:mult style="auto-select"/> <ml:apply> <ml:mult/> <ml:apply> <ml:mult style="auto-select"/> <ml:real>2</ml:real> <ml:id xml:space="preserve">g</ml:id> </ml:apply> <ml:id xml:space="preserve">ρ</ml:id> </ml:apply> <ml:parens> <ml:apply> <ml:integral auto-algorithm="true" algorithm="adaptive"/> <ml:lambda> <ml:boundVars> <ml:id xml:space="preserve">x</ml:id> </ml:boundVars> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:sqrt/> <ml:apply> <ml:mult/> <ml:parens> <ml:apply> <ml:plus/> <ml:id xml:space="preserve">x</ml:id> <ml:id xml:space="preserve" subscript="base">s</ml:id> </ml:apply> </ml:parens> <ml:parens> <ml:apply> <ml:minus/> <ml:id xml:space="preserve">h</ml:id> <ml:id xml:space="preserve">x</ml:id> </ml:apply> </ml:parens> </ml:apply> </ml:apply> <ml:id xml:space="preserve" subscript="d">C</ml:id> </ml:apply> <ml:real>2</ml:real> </ml:apply> <ml:apply> <ml:id xml:space="preserve">y</ml:id> <ml:sequence> <ml:id xml:space="preserve">x</ml:id> <ml:id xml:space="preserve" subscript="max">Q</ml:id> <ml:id xml:space="preserve" subscript="dmax">H</ml:id> </ml:sequence> </ml:apply> </ml:apply> </ml:lambda> <ml:bounds> <ml:apply> <ml:neg/> <ml:id xml:space="preserve" subscript="base">s</ml:id> </ml:apply> <ml:id xml:space="preserve">h</ml:id> </ml:bounds> </ml:apply> </ml:parens> </ml:apply> </ml:define> </math> <rendering item-idref="32"/> </region> <region region-id="120" left="552" top="1725" width="105.75" height="16.5" align-x="647.25" align-y="1734" 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:mult style="auto-select"/> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="d">C</ml:id> <ml:real>2</ml:real> </ml:apply> <ml:apply> <ml:id xml:space="preserve">y</ml:id> <ml:sequence> <ml:id xml:space="preserve">x</ml:id> <ml:id xml:space="preserve" subscript="max">Q</ml:id> <ml:id xml:space="preserve" subscript="dmax">H</ml:id> </ml:sequence> </ml:apply> </ml:apply> <ml:id xml:space="preserve">dx</ml:id> </ml:apply> </math> <rendering item-idref="33"/> </region> <region region-id="123" left="678" top="1724.25" width="432.75" height="24" align-x="681" align-y="1734" 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"> is the effective area of a differential weir element as a function of height given the effective of the vena contracta </text> </region> <region region-id="91" left="36" top="1767" width="93" height="16.5" align-x="87" align-y="1776" 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" subscript="in">M</ml:id> <ml:id xml:space="preserve" subscript="max">Q</ml:id> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <unitedValue> <ml:real>6.25349178131874</ml:real> <unitMonomial xmlns="http://schemas.mathsoft.com/units10"> <unitReference unit="newton"/> </unitMonomial> </unitedValue> </result> </ml:eval> </math> <rendering item-idref="34"/> </region> <region region-id="110" left="264" top="1779" width="72.75" height="16.5" align-x="324.75" align-y="1788" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="SutroSafety">Π</ml:id> <ml:real>2</ml:real> </ml:define> </math> <rendering item-idref="35"/> </region> <region region-id="111" left="360" top="1778.25" width="102.75" height="12" align-x="368.25" align-y="1788" 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"> Add factor of safety of 2 </text> </region> <region region-id="94" left="36" top="1804.5" width="153" height="35.25" align-x="90.75" align-y="1824" 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="out">V</ml:id> <ml:boundVars> <ml:id xml:space="preserve" subscript="max">Q</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:div/> <ml:apply> <ml:id xml:space="preserve" subscript="in">M</ml:id> <ml:id xml:space="preserve" subscript="max">Q</ml:id> </ml:apply> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve">ρ</ml:id> <ml:id xml:space="preserve" subscript="max">Q</ml:id> </ml:apply> <ml:id xml:space="preserve" subscript="SutroSafety">Π</ml:id> </ml:apply> </ml:apply> </ml:define> </math> <rendering item-idref="36"/> </region> <region region-id="114" left="264" top="1820.25" width="98.25" height="27.75" align-x="318" align-y="1836" 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" subscript="out">V</ml:id> <ml:id xml:space="preserve" subscript="max">Q</ml:id> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <unitedValue> <ml:real>0.46901188359890544</ml:real> <unitMonomial xmlns="http://schemas.mathsoft.com/units10"> <unitReference unit="meter"/> <unitReference unit="second" power-numerator="-1"/> </unitMonomial> </unitedValue> </result> </ml:eval> </math> <rendering item-idref="37"/> </region> <region region-id="96" left="30" top="1852.5" width="133.5" height="35.25" align-x="104.25" align-y="1872" 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="pipemin">A</ml:id> <ml:boundVars> <ml:id xml:space="preserve" subscript="max">Q</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:div/> <ml:id xml:space="preserve" subscript="max">Q</ml:id> <ml:apply> <ml:id xml:space="preserve" subscript="out">V</ml:id> <ml:id xml:space="preserve" subscript="max">Q</ml:id> </ml:apply> </ml:apply> </ml:define> </math> <rendering item-idref="38"/> </region> <region region-id="101" left="30" top="1899" width="165" height="33" align-x="102" align-y="1920" 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="pipemin">d</ml:id> <ml:boundVars> <ml:id xml:space="preserve" subscript="max">Q</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:sqrt/> <ml:apply> <ml:div/> <ml:apply> <ml:mult/> <ml:real>4</ml:real> <ml:apply> <ml:id xml:space="preserve" subscript="pipemin">A</ml:id> <ml:id xml:space="preserve" subscript="max">Q</ml:id> </ml:apply> </ml:apply> <ml:id xml:space="preserve">π</ml:id> </ml:apply> </ml:apply> </ml:define> </math> <rendering item-idref="39"/> </region> <region region-id="102" left="36" top="1982.25" width="138" height="27.75" align-x="48.75" align-y="1998" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve">Q</ml:id> <ml:range> <ml:sequence> <ml:apply> <ml:mult style="auto-select"/> <ml:real>100</ml:real> <ml:apply> <ml:div/> <ml:id xml:space="preserve">L</ml:id> <ml:id xml:space="preserve">min</ml:id> </ml:apply> </ml:apply> <ml:apply> <ml:mult style="auto-select"/> <ml:real>200</ml:real> <ml:apply> <ml:div/> <ml:id xml:space="preserve">L</ml:id> <ml:id xml:space="preserve">min</ml:id> </ml:apply> </ml:apply> </ml:sequence> <ml:apply> <ml:mult style="auto-select"/> <ml:real>10000</ml:real> <ml:apply> <ml:div/> <ml:id xml:space="preserve">L</ml:id> <ml:id xml:space="preserve">min</ml:id> </ml:apply> </ml:apply> </ml:range> </ml:define> </math> <rendering item-idref="40"/> </region> <region region-id="136" left="204" top="1992.75" width="94.5" height="29.25" align-x="241.5" align-y="2010" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="plants">Q</ml:id> <ml:apply> <ml:mult style="auto-select"/> <ml:matrix rows="2" cols="1"> <ml:real>100</ml:real> <ml:real>1000</ml:real> </ml:matrix> <ml:apply> <ml:div/> <ml:id xml:space="preserve">L</ml:id> <ml:id xml:space="preserve">min</ml:id> </ml:apply> </ml:apply> </ml:define> </math> <rendering item-idref="41"/> </region> <region region-id="138" left="348" top="1992.75" width="71.25" height="29.25" align-x="385.5" align-y="2010" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="plants">D</ml:id> <ml:apply> <ml:mult style="auto-select"/> <ml:matrix rows="2" cols="1"> <ml:real>3</ml:real> <ml:real>8</ml:real> </ml:matrix> <ml:id xml:space="preserve">in</ml:id> </ml:apply> </ml:define> </math> <rendering item-idref="42"/> </region> <region region-id="104" left="54" top="2046" width="299.25" height="241.5" align-x="54" align-y="2046" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <plot disable-calc="false" item-idref="43"/> <rendering item-idref="44"/> </region> <region region-id="147" left="48" top="2289" width="53.25" height="16.5" align-x="89.25" align-y="2298" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="elbows">K</ml:id> <ml:real>3</ml:real> </ml:define> </math> <rendering item-idref="45"/> </region> <region region-id="115" left="48" top="2320.5" width="147" height="37.5" align-x="93" align-y="2346" 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="e">h</ml:id> <ml:boundVars> <ml:id xml:space="preserve" subscript="max">Q</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:div/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="elbows">K</ml:id> <ml:apply> <ml:pow/> <ml:apply> <ml:id xml:space="preserve" subscript="out">V</ml:id> <ml:id xml:space="preserve" subscript="max">Q</ml:id> </ml:apply> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:apply> <ml:mult style="auto-select"/> <ml:real>2</ml:real> <ml:id xml:space="preserve">g</ml:id> </ml:apply> </ml:apply> </ml:define> </math> <rendering item-idref="46"/> </region> <region region-id="116" left="48" top="2373" width="85.5" height="16.5" align-x="92.25" align-y="2382" 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" subscript="e">h</ml:id> <ml:id xml:space="preserve" subscript="max">Q</ml:id> </ml:apply> <ml:unitOverride> <ml:id xml:space="preserve">cm</ml:id> </ml:unitOverride> <result xmlns="http://schemas.mathsoft.com/math30"> <unitedValue> <ml:real>3.3646374698341419</ml:real> <unitMonomial xmlns="http://schemas.mathsoft.com/units10"> <unitReference unit="centimeter"/> </unitMonomial> </unitedValue> </result> </ml:eval> </math> <rendering item-idref="47"/> </region> <region region-id="149" left="36" top="2408.25" width="367.5" height="12" align-x="43.5" align-y="2418" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> The design constraint of preventing flooding of the Sutro Weir dominates the problem. </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" content-encoding="gzip">H4sIAAAAAAAA/+y9DXwbx3nnvwOQhCgCoCARJC3RokwQlEhKJEWKpExZokiKki3TpCRLpCzZ ll8kWXb9FkeSacsJ6TjxS9omTpq0ub6d0ybXNEnrJte66V1byW3Tpml79bVJmH+aXOW24blp rlUS5+pzbfP/zOwCWAC7wM7gbR/w+fIzxC6wu8/s/va3M7s7O8u0ak3zfutHVVqN9r/f8mia Vr9K09qqarWWDTM37R1jGodpX23kn9fyfxs0bfbVKm2W+TT+6/2Qfhlm/I0KTfv0Gk3bAvPX 12haFQxvP6xpL+yBWcZv2suXwud5A2byaclcWg3/rtK0v4CPWpby21Ve7UhLhQYL0DZBaoK0 b88NYzyvv1alaR+DdCaoab8f1Ke/BtIKSHdB8ou8a1qrkYBdpkXHh/myNkJqNKbnvPfeJfEZ NS1njWl4hxHnCKRbTQtdMoDNoP3D3u9t+629n9j/r3t+6lT9wE+dmu/f//D1A99/8J/6N5+r Gtl8bv6e58/9yiPPn9v1SM/9ux5pfHjXI8dOtZw+dmrork9d77l7sv86I8YuI2+/Bev6S5CO wLp+MZicn1g+gXnT+o6YshYf9jjcJicgbTeGtxvfb49PwLSfNQZ/MDammYnlxWMaroR8xbbV p2EdPgrpBKzDH0DKx7aKxYF9a152W2wy5bfFGOYa8n2uztCA73Ne+LTaD6vhh2cg8Esw4fPG zq26H2bb5rEtfbovaZPbbvMZI6YP8vgBUx75dO8fn77udOP0daXcdpvgh9+EfeBG2B9+N0cP F2rbtUEeP2/k8UtBfbqu3ufP3XHV8+dKue2+DJn8NZjpIOTrt/zu3HZ/DCP/Bb48BHn8gl+f 7o7eY6f2dh07Vcptt1Srad8PaFo4pGk/DjjZdvOmbTcvte2uUdx270Ae/wXyVg95/L8Bfbrg 7pcHR/a8PJh9283vNuU3PpyPbfdn4IHfhtQU0o/fbtx2X4Z8/Vcjj39oeHbzjleHju15daiU 2+6L4IMo6Prf4fNKrZNtV3zPfgHyFoG8/b6RR5Hv/r2PfbH/8FwpPbsTdOyE/OwLunfbXQd5 azflkU/nf2T2Mf8jpd12XNOHID/XQ74+b2y72DJZHrYjs9qOq5lYZqbtyOeL1b09KdOat/Ua 0/BxSHdA+m1Yp0dgXcZgnT4Dn3zd+aEc9NAqjKQNjj9x8WGenn1CG+Rp/InRwa73fLH/NHz+ 8gU+/MTDv3zB/8jpJ554WB+2q1eb9ZPRjC9HRbOHVull5+fh80qeyig7nSpStn0Ms07Z9HgE 8vnr8OVnVullq5UeraOewdbRw33jIvFhz+AnRi8PbBv9zOArI5cH+PArI+f6to3+Ru8nRs/1 8WE5PbIfu1X1eId7B2baUavXA7PrUVjfZNOD1er1wsFavby20uPl2bGzL89+63TFYzzx4bGz n31MO/sPs8+fWzurieG1s033/MPsT5367GNN9/Bht/jjG7AiPwXB6oy6pdv1+Bbk98OQ31rI 72/4rfV4qjtw8qnub+z8U5H4cODk/T1fvftT3cdOfbvrq3fz4W93Ne76VPez193f07iLD7tF j/srdD1+vcLp8aq0ejxUoevx6Qr741V3q/fB7tZv7HxEJD7sfbAxeumBA60HHno2cukBPvxs pHHXgdZnr2uMNu7iw27Row5+eBqCHfTiOF41QD6fg/ze5LU/Xr3rqrGz77rqUzt/WyQ+PHb2 2Frt7EfhXPyrjZoY/mrj4s6PXjV93bG1izv5sFv0mF6paZ+Dlfmdlfk7Xy+UHhWwEscgn78J +f3CSv141Qzf/7yhRykS17/68LFT1YcPnJwQiQ/z9Ozd1Yffc9eESHyYp9+5s/rwZ++YEIkP 8/QPJ6oPf/P2CZH4ME+rbq8+XHnbhEh8mKedt1Yf7j0+IRIf5um+Y9WH77hlQiQ+zNMnjlYf /umZCZH4ME9fnq4+/N+OTIjEh3n64eHqw6/dPCESH+Zpw83Vh8OHJkTiwzzddLD68OiBCZH4 ME+PT1UffnhyQiQ+zNNnbqo+/EsTEyLxYZ6+fmP14a/unxCJD/Pk2V99+I3rJ0Tiwzz1XF99 OLpvQiQ+zNNte6sPHxqfEIkP8/TBPdWH58cmROLDPH1ptPrwb4xMiMSHefru7urD3xqeEIkP 87R6uPrwil0TIvFhnnbvrD7cf92ESHyYpwd2VB++a2hCJD7M089fW334+e0TIvFhnr4yWH34 DwYmROLDPP24v/rw97ZNiMSHeWrZVn24sW9CJD7M04He6sPjWydE4sM8vaen+vAj3RMi8eH3 9HznULC74/B7t8weCXbzYbccM54E7z0FM313taa96uiYUbpzgI/BSjwF+X0avnwV8vsdvz79 lxS8XoxjSbjjpd5fEGmhNyzSS71LkN7fydMCDC+I8ddg/IHNPC3A8IIY/58wPrOFpwUYXhDj /x3Gd3fxtADDC2L8UzDe3s3TAgwviPGfhvE1PTwtwPCCGH8Uxt8SaQGGF8T4HVtf6v2uSAsw vCDGb+h9qfevRVqA4QUx3tf3Uu+XRFqA4QUx3rTtpd5PirQAwwtivLL/pd6fFGkBhhfE+L9B OjvA0wIML4jxb8L4bYM8LcDwghh/Gcb3bedpAYYXxPjnYLznWp4WYHhBjH8Mxq8a4mkBhhfE +Htg3LuDpwUYXhDj98D490VagOEFMX7gupd6vy7SAgwviPFrd77Ue1GkBRheEOORXS/1fkak BRheEOP+4Zd6PyLSAgwviPEfQ7qwm6cFGF4Q438P4ydHeFqA4QUx/hUYv2mUpwUYXhDjX4Dx wTGeFmB4QYz/Aoxv2MPTAgwviPH3w3j1OE8LMLwgxh+A8R+JtADDC2J8Zu9Lvd8WaQGGF8T4 7n0v9X5ZpAUYXhDj7de/1PuiSAswvCDG19zwUu8nRFqA4QUx/hakJ/fztADDC2L8v+7f0Hvh xj+Fzwf6+LBbzpv5/eE/gePCyzDBVUYmzNezcr1nG7tuBFkSn1bX6GD589Pw2WPk548hP5eM /PDjwY/q1k//qO6P79DW88SH10/ncr0u23Y6BOlhpq9rQyzjBs/HbkLv1lYf1u7VHtBOae/W NmiT8PkofB7SHoLv7tQehEnO/tUHtY8b07/5B4s9fPxTf/NBbcr4Lc4PVvsC2mrf7//uGpG/ v/zCGu2vGj09W2D4zxr45xox2T/zf92rfV/q/LuKK+eO9Pzln31QTP8WTH+PMd2FkYoe/pt1 WwGdeFuBl/W2Audg3lhW+LyvfXGN9idnbhTfvQ7D/8vPf2fN5nzcuNLTw2Npf3yk5wMLrIfn nbMZ8sSHv1IB87xhve0+9TtrNPO2+2fzRLAtJuFjFcTly+GzbGlkzSfP69Vmc3lXbwzz/Wy1 6ftGYxjKSs9h+OTp72BDvQ771ftA4P9Xo0935Mgzu7X5Z3bz375sfDehJRNrM3AAhq+H9BlT XHN+4JRo3uxFK6/9DOzMtZCHJ6s07WSNPr+qv7L56KMQK2iKxX308c1nD3x881XH2Tae+PDZ A1h81Gjho0kt2Uff3CXho4FkH3GsfOQmb/D1K4Q3XoGdYxfsK3tgXxlL8Qb/rdaBN85AOqep e+NlmKg6APtord72hFMob1wyxbo9aNQ7z19/pvL8/3nkk0/wxIevP4PFG98yJs/kjW8PS3jj F/B5g69fIbzB7ytcC/vKUdhXdgWTvcF/WxnM7o13a/o5mKo3fiGkaT+GCT4Amf3POd7Tz+aN 1FjcG/fv/Xrf/XsfGwrt54kPf70vuzfs66yFyueDexf6Htx7YWjNfp748EJO+bTaxqFEXjQ4 ZIr2cTGqY+NwArvbp0/Hx38RZvq/MFJjjPP99tdZYvl2eTTvL42m4au0+LFFHFf4ML8Waz6m rNKS23L+vlER93k8LTdrj8E+eRZc9ICmytprrvnQVdpvse+1/vDRr779P3suQtmx6cNrtA/d 87c9/Pf3P3apin9+6VdervoYpN5X/2fPPIxv0/TrGtc9v0YLG8M/V1PRU6clrn/8k3Gx4zWY 5jN/d2rjLAtrb8N4z+c+W7li1UeegC33oVXGUeG3f/D23FsfWaOdgWmv0T7yxKwvrO3U+DRQ Xjb8q5D44WtW+1o8/zG385M/ePQaSBDlX77o0a9hh41t9+LV7FOxdetqrTyVWNPP/mUdLPuX Nv7w0c88fN9Gvt5WxxTzccSsT0xD/l22/aVN033vNX5vM00bi8V/W2fMt1vTz5MGjPGVHn3c vK/Epn0XpF/0JPYRsYnftj7O+jzaqmzljhOegO3W2ffDR/nni9EfPsoXb95v+DRvfWiNduVK oEf7BkvaOF+BeUI/84NHv//RHzz6eZjmJEv81viX/60ydj1sb8vKfzkM034dpv0OJNMiBHy/ 5/uNZsTm0zvxh5ajP1L3EZ7fWJnyEhwPfghfroGi73cCRplyy/nhv90+OxybP1uZ0su/qErW 95tVCX3FChdY31Sc6h3z+CvP/SdvkG0J8U/NmLZXc6Y3nCg++pUP6XWHT4DfvwLL+NiH9WPA a1X6MWCVsWye+DZKbduubwvnentMw6n68u0dK8dTtf5eQNf6N4JqWotLviatfx4yMl+R3ctM K5zWR03bfhP7b5Wtxra/3jj+/vmalT/XA9+btX4kRes+zZn2/DOb1mdNWse050f32PKTtdev p5+BZXLtb4Vlaf/8/BO7YT6+zfn8utZh8d/KyzGteeZiWm+DQuBO0BqKI61VUWteV+Rax8qC Sottb97nYvsAL2xXaYUt81Mx7wOzjfduTN0H7o2s/Jt5+N68D3wjZR94t+Zsn+Bk2we+mbIP nE3aB1L3icQ+wJHdB/j2Nvvdrg7XYwzXmr7ndf4btURxZz52eLXkfWuTKZ7ddWLzuWbs+0DK eYV5GpXrxGegsvJN/kzISv05Ik6hzkF4rAVTLL4dXojevuOF6Bd2/7CDJz58+45c6vbFPD9f Zfgw0/n5gRMS5+efxnd+ztevEOfnn4Z9xVer34v1r0o+P+e/fbM2+zGXtw/+tqZ+fr7PaKN0 c9Dp8wqKbUcctH02308C38TbPu+t0J9XOBxMPK/w8CPve+jWlvc9lMt1rlzvP/0e/PDrMNFp yGyPP7dtl+248iUj1ikjFj+ufPnq/eNfvvrCnW2dPPHh/eO5bI985DN1m/B8/vnVk+N/fvXc nR2dPPHhyZzyabWNY9+t13SvfcmUj61+vSSMlXfmYbPWhycPHKiChR2E4dk/TaxLbJvHxmO8 CNM+B98dMNZ9HtIbX9Z/e1r7V+034fdnvfp+E5uWxe9kWZNyuCYIwm1szz6JJU1lHu+J4sdz 1Fbgk3pbgWLnTYk3ihvPqxiPvaU2X61ivAbFeBfV4rEltXjdS0rx2Am1eDVLavFOLCnFg3BK 8WaWlOJ5ltTi8XNehXginEK88JJavItq8SqXJONdNodzHo9d0sPNqcXrXlKKx4xwRyTjGeHe dH48Y1f4/xP6fLsljp88Xo0Rbt758VrEM8K9InO8hngr9dnemZcoj3i8hng4iXhvGE5YeucF TS6eEe51TSreO8baLfFwEvGWZkzhJOLFLp98RpOMp/O2Puq8bmXM95o+tkk23iXHgZLi/dtl ydmMePvU4r1ZqxbvgE8p3jvzavFe0ZTigfGU4n1NU4v3gqYUjztBJR6EU4knnKAQ701NLd4l xXjzivE0tXjvaGrx3lKM94ZivCt5ibfCfsKM8ZwfPy8nxZMq/4oa71JSPLn6BIZ4uxXjzSfF k63vysfT8hFP+vzWiCd9Pm3E8ynG61aMd0Itnkf6fEWP1yB9fiTieS6qxauUPx8T8S7Kn4/x eJUK53883osK538QT5wjKcTrVjnf9F3ST8nk43Uqnd/6Lp2QPN/coMf7F8nzTdEEEeItqJ3f 1qqd38bOkWTjGadkS5+TjBc2zpEkjmci3sXYKZnc+a1xceLHsuXRxfg5klQ84+IEr4jKnN/G rhbwUzKZ+oRxceLt3ZLxjHCi3us83tuxSy88nMz57UVTOPnzW9GsQOH8ll+cUIn3ij66SjKe Ec45RrzXJWeLxXtBdjamGE6P9xnp2VjMCSrxXpOfjSmG4/G+pzAbizlBPt6bKrMxxXCa/K5p xFPYV0S8y0qzMcXVM86n5eMp3sExzqeLF0/1jtgVJPEuI4l3qbzjGfXPPLDys/NK8zWoHuBU igtxQL2iMh/UtJUOAc2plWxHbQWelGorsKSWN1GRVJjPu7R0USWeb+lrlSrxapde8KrEa4ai bE5hvuHX1A73c5fU4vGibFfWqdIQRYtCgwsvr4gorJ849CrEE9c0FIoXfk1D5XBfOw/reFl+ Pt4BnMrhl0snfd1b089tVJoD8XOb4Xn5+R7W1Nrn8HgqddeHFUtriFdzWf7Mg8fzjSusIMQb flHBDxBvbk7heLadH0CV4vmUjte7+HUNhXhXH1ErH4Z5cfSH8vPxtfuabGPB7fo1sBdkj59N oj3Q68IUcvE84rxY9njWxC+5vS1/fNnuEefFNZdl44XFebH08WXXRXFeHJyXnG9OPy+uyDph CsOK58VziufFw4rnxXOxK0SSKIbT5r6mNJsmf4VI56zabJpiOIIgCIIgCIIgCIIg3E3lbkgH 5Ofj18hVHrxsvqJpvisUj+JRPIq3rOI5aitwp0VbgTLcFhSP4lE8ikfxKB7Fo3gUj+JRPIpH 8fIYT/XaHkEQBEEQBEEQ9qxSm83zJbX5Kovbb3GlYr/FLyr2WzynFq9brR9hdqK4/RafkOqn JymcSrwZuX6BYqj2W9yQ6FxGPpz8o0NhU+cyMlyU7O3FuEtbae5cxgne+UQ4mS4u9A7O9L56 ZHrw0ON1xzsikopn9ES0Wzae0RPRvGw87oSlf5faN/nDwEY3yVL7Jo8nwr3d/IpkPL0j4deG L0vGE054e7fcE+S1lw0neB21FRjR2wrIPQ3qu2SYVLIYqX1Tz9q85JWo5liXTM2X5OLFumS6 +IJCvFekH94X8d55QZPtnKDZMGnlFbn5ho0eoGQv7DUbxwTJzSni8eewZR82bza6ZJLtC2FY PwR5ZJ9VbtbDSXe1M6z3ACV9nXROP+JJX5cd1osruQMDjyfCScunDc2LD+meklYZYfWPyt1y c8fc8KJk9yyGfGzuitx8hnzdso+46/LJv/NCj1cj/Uh9s4h3QvqRerG7eOS7eBG7izhwK8S7 KB+P936iV3fk5uO7p6gQyMaL1T8k9YN4eoVALh7viFtUCCTjeZfm9RL6RbnDDFvSS923JQ8z 3qXv6fUPyT6AYx0h7pbsitu7ZNTEJbtAYbFCXikeFPKSpRKLFfLNUrMZ8V6Q7jGHGYU8k3yy 3mvUxGW7cI51hCjdk/qSfhrVfVlyvjm94i+5OXmp++Y8fOyal5zPp5+1Sb9NzKP3S+a4h3iC IAiCIAgXsFKhNzyOSssxjurrMSkexaN4FI/iUTyKR/FyiFd5BJj5Mf9/QCrUUhypoM2J+eTu L1M8S1T104O6f//EHc9RW4FrpN5BgHZbUDyKR/EoHsWjeCWIp3ptjyAIgiAIolSsVJxP5c1n HP5AhwrS7dQMVN60RvEonlvjHVCMFzstkm12e0UmXuJcyCsTrzLx1r/4aZh1vMqkse7EuV5t 5njJjZvnEpfQmy3imV6CFv+Z4zVdQh+2iNelpf/M8S1dSoS2iHdCS/9ZLMT05MdSejyP6eek eCdMdwgs4pmfApsz30wwrZ7XIl6t6WnNYVNramZ6PpLFX86biGfehuZ4XtMrx2st4l1M/JwU z7dkHk6Pxx9kiA+bpq01b6/0eF7zczbmeM1L5uG0eMz8vIR5WvMyltLj1ZobpJunNW+jufR4 PrNk5nhJ8qXHG7aNdyU+yN5Oj5dkAZt4tW9YxDPvkqaVTewisC4/SI+XZAGT1ubdZfhyeryk F9jbxGt+2Tbe7kzxluZt4+n/TY8QJrk93Q9M/9k4rlrHg6WlxfM6iFf5VrZ4pp21NhEPDp62 8d7IEK/5ilS8+djg8GWreGLAmyne3KX0eD4H8WBIJR4szDaezyLeZWOIL8xue8bKKat44Gz7 eMbTdQnzJ+Jxp6nEa75kH685Qzw+YBfPOLAkbU8jBJuzKh+MnzPEE0pZxHvDHC9xsInH45vM Np5RMCTFM5bRzRdpF28pabdJiscPlFbxeAhmPFVnuspcG3uATSzYIt474r8er9Ycz9hUXCgr /70lJkraTRML5E8+XraOxyPVLhm7zWVTPD3rDUuadTz+GFHzkiHjJVM8Pet6LcYq3mXx8JIu YyIeM7I+95ZdvNfhQGd0GWIq673608c1eqcSFsfrpXdeePEd47FBU1nP9IerG0Qki/rL3NLS 15euzC3pq68l0PuTuKhvMKt4fOWGxWqaqx76g1eV+lpax4Pf9A3akBxv6Xvve9HYqhblLZ/v 8rD+AL2peHfUVoAZbQV48BmjiYlF3kQrFPH/cHIfMcN6ExMR1qLuKFqhzPP/71xMaodiNGu5 YhPPJ5q7+EwTJce7bBPPKx6K85omMi1wydjBLOIxEYaZJjKvQMz7FnVVEcZrmsi0wKW4YdPi 8SdSNUPyt02zGYsyyiaLeN3GvreU+hisWNQrtvEq9Y4EUjensbe+YBtP++wLsa2Q/Fh4cyLn VnXxWNSl1F5Gapfivb9Y1f11xFPTyd9VxlcvQzy+WVM6UfHEVy9DPK1zKe39yyfiS7Ko+8dg Rw6nfrXys7ElDceXKXfuPRffI+TO9RO96kjFYwmFpOJ5EwpJxTMVTXLxXogPyq1fYrBcrw1R PIqHIZ7qvQPVexUEQRAEsXypU5tt5R+qzTcj2Y+UQadsP1I6lYp9yNaq9ekav+ClEO+yYrxL xYzHZtTiaSvV4sGJ9hW1+bqV9FNv8CvbsVMM6e4NY8j6ofKI/vlE5snS6DauU+ySnC92j1Iy njd26UoyHr+kJgYkz1pqY1eAJOP5YpcCJeMNxy7gScbjV9ReV4wnNmhQbj5xxVAhXvxap9z6 saUfGxtUNt6vGBtUMt5bsWO1XDzvO3yPeUc+3pXYHirXfSocVrz6BpXs1/CKkPCSrP+8l8Uu 84ZKvGZ+SVvyeMbj8Q0q25kwu6Rf9D0gWT7weOI2gGT5zuPpnpCPp6Xdb3AQj4dpTr3f4DBe ber9BofxfDHPS8A3o1c+nr5bzsl3SS92y4vy1TNhuwb56pm4mFUjX10S/YuzubezTZcWT1ym 7/is7HzNS2n3GhxRK9P9fVI8tYr8sPSekls8+T1TwOaWvqYSzst7DleJp/DyAhFP9LqpEG+3 0mzEcsRRW4FXK2X6FUDBQUgVb1Zqv+fVtF9foWmnKjWtx6/Fx2O8CBvjOa/eCPv9Hk37GqSe f9cbJf+p9qz2m/D7s/D7JtO0zKLr2p8zDfOSuLL7hFYdvUFjW09p3t67tYqe27XKLUe1qvaD WmXHzVr1xpu0muZBTes7rTFIFT0ntMquY1pl582ad+udWnX7FAzPaBXdt2ue3pPw23GtcvO0 5oVlVMMyaiLDmq/zCPzGl30H/H4rxLobvrtZ83XPaJ6td8FyITZ8VnTfBvPOQKx7NB8stwKW pfHfIF5F962Qn0OQrynNC8ur7DwMMaZhOTOQ/32Qzxsh37doXohRBdNVR0a0mmt2wvLv1LyQ t+pr+PiwVrEV8rD5CCzzdlj+rTAtX487YPoxmPeE5mu7QfPBOlfB8n1t+7WajbDsllGtZl0f fHczzAPrvuUYxNwL40c0H3z62m/WPLAdfZCPSp43iOeD/Pjgu0rIt3cr38bj8N0U5P8AbM9r IS7M23YjLGMaYo/DMo9rvi0zkNcTWhV8VnYc1Kqbd2gVsE4eyJ8Xtk0NjFd3HtSqIH/VsC5V sI29ML8Ppqnh6w/DlTBv9cYbIO5tEGsSptsJ+TsI630n5AdicG1gu9bw9YX194H2FbCcClin KtieVRC3Cqapbr8J8jsJ63W7VnP1oOaD6atgvLLzFvj9iFYN28bLlxfZC9sT8sH14dr03a0x AhOkFy5IL1yQXrggvXBBeuGC9MIF6YUL0gsXpBcuSC9ckF64IL1wQXrhgvTCBemFC9Irzs/W 1xdgqY7aCrystxXIc2jSNkHA31HqLGSF9IrT4feXOgvZIb0S+MlfmNhC/sLEdT3kL1T4yF+Y 2BAlf6GilfyFiakA+QsRm9r95C9MhP3kLzTU7Bv3k78QwUsv8hcS9h/V5SJ/oWAdq9PlIn+5 n+vYVF3Eb1DqzGRnuevV1TMQV4v85Wr2s+Nz9U1+M6XOUnaWq147WYiliJUffzlqK/DJgrQV SGVZaruNVY9Gw6nCkhddyYOss38sYiEWlXVuY571V48eSztikr/cyE52nNkai/zlLniJlclY 5C8XkanEIn+5irXZSizyl3u4ls2y886MRf4qNX1scLC+NSAhFvmrZKxm588HrU6IyV+Fon/6 40rzrWWnFIxF/sqRj4xFFOYSJZaKschfuSK/8ZRKLH+4tao9GoqQv3JD9kEglRIr0HqsZys4 8jZ2kg2Qv3LD73/E4ZQ1rFfBWOFgw7lZtt1YxtlRakuVE+CvM/c6mnKWDUuXWE3HRtmplOVQ W8Wc8Pt7sk6zh4XmpI0VGevvZA+yiZRlXdhM/soB8JfPm3GK1eyckrGq2TbL5c2Rv3LB7w/a /gYlFlMz1nG203apYSV/OWor8GRR2gqk4lZtwYvhQZvfVEq3cHSU9WeMeDJEXswB2MYNt6Z9 q1a6haY62Ya00i2VKJV16nRwT6R8p1S6haM902ylg4jb+slfOcC3dcvMBjG8lnmZp1r+fIwb KxQ/H8vGNPlLnQ7DHHOsA/56fFWV0heloj3spETEx7eQv3JAUp1kAq31c1Bi1UhFbCR/qdOh LlY4OHwOyjp51veo3e4pJm7VKwd/NUEtUoVexyVdCXGrXir+Cut1x1JnvaC4VS9Zf4kSq0kM ur/SkANu1UvKX+Hg+fOixCJ/lQwJYw0Osj4xTwf5q1Q48xcYi82ya+Nzkb9KRnaxmo6NDp5i a03zkL9KRhZ/GTcoUyF/lYzMxrK+QUn+Khl2/gJjMfsblOSvkmElVriuZ7qfzdvOQ/4qGen+ aoIT4nVZ5iqCvxy1FbizJG0FUil1fFtSXBidc3C5l7xYMsxeDIR62AZHc1FZVzISag3sOJm1 nYYO+atkxP01tENiLvJXyTDMNdklMQ/5q2Tomz4wdUhqLvJXyRCbflJOLvJXyRCbfkjmYMgh f5UMfp3qScl5yF8lg2/6qPRc5K+SAZWN+v2S8+Tqrz7lOYuGW/WCTR+Sacmrk5u/VrXdrzxv sXCrXrDp26Xnyc1foTCCQ6lb9YJN33C39Fy5+QtDVcWtesHGk63M5+qvRzBUVdyql9oLfnPx 171nyF85oLLxcvMXvaM2B4rvr7428lcOBFrlK9e5+WuzpdSO2gqMuKKtQCpuy0+MaTarMFcu XqR+B3LCvnWVPbl4ce35CJV1xSYXf02iuKxcVnrl4q/zYRSXlctKrxz8dUMUx22bstJL3V8f 7Q/guG1TVnop++tofwTJbdGy0kvNX/PMF3s/akFylVfKSi8lf/Wy9nhfV+SvoiLvr9XsY0HT S0MKlrO8UVZ6SfprNUt9Pyr5q6g499cE25AmFvmr6Djd6P3M+p2n5K+i4sRfO9lx+zfIFSmf OVBWemX1V7ZXM5K/ikomf01A9SLrqxmLnF8FykqvDP46xQbTqxfkr9Ji7a9r2azT9/GUJNdS lJVeFv6Se9FVh8O2Ate4sq1AKm7PnxTJXpyQfylWSXPviLLSK8mLvUy+E3Yq63JArodtTsxf +9mswtsNyF85Ma0wj77RQ0z1/ajkL2VCEdV2w2H196MWYD3yjFv1Umk33KvQEXugMri5fXPs 2En+UkZu4ymVWLz74WoPuwB/56PU1iMnZPy1XaXESnkTxXPUlio3/P4zjqZbyaZ76hRe4ziY 8iYKaquYE+CvnuuyTrWBdU6FMl/uTcOul9R28lcu+P2+jL/Ps5PTPZY3KLMYq9q6l9T5TvJX DoC/gr22v/ISS8VYVv16xwiFyV85ADWCausrHLzEkjZWOGr74uEYIfKXOvzU9ymL75VKrNAU +NH+xcPGkqPkrxzgnjiW9M3DSiUWf+fpSUdPnvWTv9QRl5ZaThuvcbuV9bJzq+oVjNXpsEdh xvZMk79yQN/gwZ6trGPrmYb6oPw7T+scvkw4BvU7kAM5vEPTL640MecvE47hD1QUZF3yiVv1 yuEdmvxK0yxz2meco7YCLLWtwP1TUwVZ67ziVm0VvShe42h/jpcZmQNtqXCrXipeVH7xMCLc qpesv/TXOKq9eBgTbtVLyl/8/ajm1ziWMW7Vy7G/uLHYqVJnt2i4VS9n/jJePOzsDS9lgVv1 cuCvpsSLh5cPbtUri7/4fZTjy6PESsatemXyV9OxUdZf6gyWCLfqZecv4wblMiqxknGrXpb+ cnCDstxxq17p/mqqn8t+g7LscateHanOYsdLnSVX4Fa9zP4KtLatcnxtvsxxq14JfwUGdiy7 syx73KpX3F9NB+8sdVbchFv1ivlrnN1a6qy4CrfqZfhr7iEqt5Jwq14dhrvyjqO2Aq9WYOhX IBuuzb8o5+4hJ6bgVr24FwMH5d9YVu64VS/ur4Gjpc6E+3CrXtxfq0qdCRfiVr34cy9PL9sr 9va4VS/wV926UmfChbhVr6zPbS5T3KqXs+eilx9u1Qv81VjqLLgRt+ql9r7N8seteqF42XYJ cKte5C9r3KoXa6pV6aiv7HGtXuzZUmfAlbhXLxdRw65iHtbBIqxX6d25eYT0ys4s61l/NX+O PlAZ7C9xK2TSKwsf8k0ldQEYGHhSocfivEF6ZWbHQFpfIpFSOqzs9XLUVuDl3NsK9O8YsOp5 JDB1qGRuLHttc+BJS7W4YD9TsjyRXjZsGGy374EpfL5U2SK9rNnHhmzVAmpL1QcI6WVJ7IXf oi4Sirad2drBOrYmOituLVXGSC8r7oi98Jv3XMZOxp8LWMcGDA2bS5Qz0ssCX+xY2NK2al3S mdnZ0XH9l1JljfRKp75FrwUOsPstfm0hf7kLQ66W0+w2i1+9Piq/XMU9Qq7wU0/b/N7If27f VNQ8JSC9UhjkNYpAcOp9tlNwveaKmKMkSK9k+vi7XMINGaZ4hB8ri5afVEivJI60QUV+6FiG fkO294D9ri9Zh4CkVzJhfyA6mnGKKr9//IYi5SYd0stETdeQPzCVWYz6iD9cW6T8WEB6mXnK H+k/lHGKfeP+QMkq84z0SuJ8OND/0YxTHPUFAiXtnZv0SnBhc2Aqs7t4+TZSjKzYQnrFmfdF dtyZcYrembB//LG1xcmONWWnl6O2Ap/Mua1Aezjbo1EtgZEclp8Pyk5bdZ7OeEoNp91j4cDU 3pI6kfRKcO+Zpoy/H2oIZ/Vf4SG94kxmvODbNRJomilli0Yd0itG/0CmFx6/a8A/VMJT6jik V5xwt90Vxnk21hQYWSGG17LHqd2wG+jI8OBhNBBmFxh/i1KIbdmypeuni5etFEivOH4bf60b a/IPTL1PvJr1ar0x1RC1VSw5zZEtlt/76gKRgWfuYkmvZh0oVdt80itOa1V6xy/7pidBpchA NOXVrIES5E9AesXY1B5J+06oZQW1pSo99YGW+sTrHGtYv6/d9uXUoVJlkvRKEPW3sJNsO1TY vWyWNYxZPRsRbm3lIvq8Jcoj6RVn/mCLPxDq79mypccXbbV47CgcPN8IQrZG/OGS9YlAeplp sTv+GW+g1tvchwPPlCyHy1CvtY1hm1/6nrF+mE9/R2esj+LTgWhfyXp8XIZ6sYbQSYtvr4VD 3aSFXk0p7+isbylAz+GOWYZ6bWoPpX3XxwYH69PLLPHGs+R3dNa3DIyWsDfwZagXC6b4azU7 fz6YXnVvOmbxxrP1JXVXGejlqK3Ak6ptBULhREO3teyUtQlDU53H096OVsPGKqMrStovP3pt FfCHz4lPXrpZmTAc5c9qWtFfWeq70ctQr9v9/pEL7IJ16RZonepkG2zuhq2pPVbqu9HLTy/+ IF9goH2zlbGCw+dYph4FVhctl3YsP71Y1PJ8DE6e50KsZM+zOGX56XUylC4WnDxDWYaB5acX C6cZa3DwFCtxG0SnLD+9GpPq7WPnz7ugVHLOstNrz3TyyTOyV68uO71Yv2GsfpZ+Qux+lp1e 6+rECfF0f6l7A1Zj2enFZsQJMVaWnV63ZDwhdj3LTi/kkF64IL1wQXrhgvTCBemFC3R6OWor cGfu7yAoA5b7+mOD9MIF6YUL0gsXpBcuSC9ckF64IL1wQXrhgvTCBemFC9ILF6QXLkgvXJBe uCC9cEF64YL0woXr9XLUVmCE2gpYQNsDF6QXLkgvXJBeuCC9cEF64YL0wgXphQvSCxekFy5I L1yQXrggvXBBeuGC9MIF6YUL0gsXpBcuXKeXo7YC11BbAQfQ9sEF6YUL0gsXpBcuSC9ckF64 IL1wQXrhgvTCBemFC9ILF6QXLkgvXJBeuCC9cEF64YL0wgXphYuS6+WorQCjtgIK0PbCBemF C9ILF6QXLkgvXJBeuCC9cEF64aK89OoQf9l+zzSF2ykrvfw6tr93GBMgFqyc9MouRzZB3U85 6UX+wgX5CxnkL1SQv5BB/kIF+QsZ5K/846itwKveQrcVIC8ig7yICvIXMshfqCB/IYP8hQry FzLIX6ggfyGD/IUK8hcyyF+oIH8hg/yFCvIXMshfqCB/IYP8hQryVx5w1Fbg5YK3FUiDvIgK 8iIyyF+oIH8hg/yFCvIXMshfqCB/IYP8hQryFzLIX6ggfyGD/IUK8hcyyF+oIH8hg/yFCvIX Mshf8jhqK/DJ4rcVSIG8iAzyIirIX8ggf6GC/IUM8hcqyF/IIH+hgvyFDPIXKshfyCB/oYL8 hQzyFyrIX8ggf6GC/IUM8hcqyF8OcNRW4MmStxVIg7yICvIiMshfqCB/IYP8hQryFzLIX6gg fyGD/IUK8hcyyF+oIH8hg/yFCvIXMshfqCB/IYP8hQryFzLIX+k4aitwp/vaCqRAXkQGeREV 5C9kkL+Q0SH+sv2OWK4y06v8Ib1wQXrhgvTCBemFC9ILF6QXLkgvXJBeuCC9cEF64YL0wgXp hQvSCxdZ9XLUVmDE9W0FliOkBy5IL1yQXrggvXBBeuGC9MIF6YUL0gsXpBcuSC9ckF64IL1w QXrhgvTCBemFC9ILF6QXLkgvXKTp5aitwDXUVgABpA8uSC9ckF64IL1wQXrhgvTCBemFC9IL F6QXLkgvXJBeuCC9cEF64YL0wgXphQvSCxekFy5IrwTbSp0BB2iO2gowaiuQYII9yDr7x0qd jeyQXpx+NnqsCUVn68ter2vZ7PnzYxEsLzNY3nr1scHB+taA34/mZQbLVq8JtpqdPx8M+82U OlPZWa56nWJJxiJ/uZf9bPZcQ4qxyF8uZQ8LMQtjkb/cyGp2btjaWOQv19HL5uyNRf5yE/Ps 5HRmY5G/XMN2KLGmQpHsYvkjYx8pdWazUuZ6rWTTPVEHxvL7m46NVveXOrvZKWe9NrBOh8YK TXWyB0udXUeUqV7zrN+pscLRnmnwIRLKUa+d7DjrH3NoLCjdtpc6wxKUnV7bWLV+b8SRsU6C E1HhVr0eV5pL3HR0Yix/ePMclG/4cKleR7YMSs6xlvU7NZY/EOpZcaEg+c4/jtoKvOqxaivg Tm1/sstf6ZGY/lo2y847MyEUcHXsZMFyXmjcqRdr8wcdT5t6gzKLDweeZPsLmPMC4069Lmz2 jzg7uFncoMzIEENwAp0Bd+rFgn5f1mlq2Cmnxgq36pKG7+vCWCEx4U69QmF/z3VZppllNjco Uw+ArfXs2KyYcqi+KLkvJK7Ua6Lb79+S4fc9LJT9PopuqODwudm+548NwXCkfUXR1qBguFIv djvoZXe2lvUGpclYc6wX5njsPj75ENtUzFUoEK7Uy+vz+89bfF/j6AZl3FggLWdqgIsXHUVe cum4Ui8W9fsH0utxJ5ljY7EQHDR1RFWjCUGzbEe4Uq+TIShtTveavoESy+F9FFFixc+wNoxG wY2B4LtKsBYFwZV6MW6JlviYTIk1yMwyH2W8ohFuuKH4q1AgXKnXI2LrD9VfYNslzrGC58+D tBOm5Rz1iYpGfV/J1iTvuFGvx7cYCmye7ul3bKxBlirLXXWgc2SyJOtQKNyoF/M5kCjZWLPs 2pRl1LCZFm6uHTBURrhRrw1Rx1qJEuuU5VL4sRBq8UXOe6Fxo16s1bmxUkqsGL3iWBhuLYtz LjNu1KvZSb296dhoeokVo4+JY2FRc10cXKjX2sZsWkXG+juPs522SzjUEOYVja7bipjrIuFC vdh0NmNluYfVNQLHwqbTRcptnnHUVuBly7YCqbhQ2239GU3IHrQs3RLcw68tDtUWKbdFxoV6 sTpbE1Y76KdDVPnH7yhCPkuBC/WatahKZivdYtSIS8GBOtlWW2hwoV4stSqZvXRLMMUvBU8d LWj+SokL9UqqShqPOGQusWLsf2xEyHWowDksIe7Ta0+iKin9iMO4v8zlcqFerDZuLLlHHDYc LH+5XKhXb1AYS6G97lD5y+VCvdgMlFgbpK/A99bzerw/Wj63MS1xoV6KD+UJucbVmrfVsNuY V2nOYuNGvRTYf5AfDP1DB6XnrIHaZ3VXW3u7rzOp2YFLKRO9RM3QH5Zv6zvLeo7Fmia0ZJ+8 5JSFXhMHRvj2DrRKzbWfHe+cSmpGEh67pUA5zBtloRdrE1s9KjEHf3a6vin1sle4YDnMF2Wh 13rRfqfpLqfPOO9kgzaPeLYXNJ95oBz0GhR1jcDBux1NzcWqs2ug1Tpb4LzmShnotaldbOoB Jxd5t2cSi4te8NzmSBnoZVzPX5V1urvZdE8msThu7/mtDPQaEBu6qSvzNXwQqyH70+vFr3A4 aivwSUdtBVLBr+1uvfbenumpMWfCAkG3n16j16tft2KGQ+dKds6ZWEBDEXOuBHq9mHFybPPr SjY15bgPCTpXKzyxNuFWlQo5sQCHnVOUEOx6Dcbqhmm/rHYmVqD1WH+sghm2erjXXSDX694z se2eXH6tdvb8dCQ0NcUeZOvGdMGeKtFaSIBcLzYZ2/QDz8W/WwdiNWU/+EXGGmItSfj9b1hE qVZCAuR6nQrGj2uTT7Pb2M0s1NhW5eDRQSFW4mLXZviq5Z6zJVwThyDXi1UmJGiJtrdHr3by mGc4mCQWYytaMNQNOcj1qpCp/Rli1Y0OsuTLyEfvQ3GfjINcL6YiVnoT8LA/4MPRfhi5Xh0y YjXVz4UsxOo9FsbTZA65Xsxxl4pN9ey4zTJa8MiFXa/eYHal+DnxVOdxm95Kb5lp8Uf6sciF XS/W4EQsZn+HuQ8cGmb7ipjj3ECuV0115gNiZKx/ajZDP/f8CWokNUMd5HqxW5+xr9HDOXGW Z8xWjARQtFBMgF0v1mfTaQs/J+7P2GJ/gtUP+f0D97j9FmUS6PViD42kOwxOs2x60zHzYajH jxQ+g8k4aivwpFJbgVTwa8sunG5JOmKG6ufYOgdPel7Y7A+3srVFyGEeKQO9gLn6qysDgcpQ cHMbaza6os3OXcemCpqrQlAeeoFEHhZhJ9kFKb/geKVcEmWi17KB9MIF6YUL0gsXpBcuSC9c kF64IL1wQXrhgvTCBemFC9ILF6QXLkgvXJBeuCC9CoyjtgJ35qWtQCqkLS5IL1yQXrggvXBB euGC9MIF6YUL0gsXpBcuSC9ckF64IL1wQXrhgvTCBemFC9ILF6QXLkgvXJBeecZRW4GRgrQV SIW0xQXphQvSCxekFy7KWq+NrEP8bSx1RvJHWesV62G41NnII2WtV6yHYbe/NE6CstaL/IUL 8hcyyF+oIH8hg/yFCvIXMshfqCB/IYP8hQryFzLIX6gohr8ctRW4pihtBVIpa23Ji7igsg4Z 5C9UkL+QQf5CBfkLGeQvVJC/kEH+QgX5CxnkL1SQv5BB/kIF+QsZ5C9UkL+QQf5CBfkLGQXw l6O2AqwkbQVSKXX8gkJeRAaVdaggfyGD/IUK8hcyyF+oIH+5hA6HfzF/OZ3e/aDUy18oSr1i 2cGoV0f2Da+I+x2GUS/yFy7IX8gomF6lXrHsYNSL/IWMgulV6hXLDkq96PyrPKHrG8hAc5hz TFnrlQ9/OWor8CpzQ1uBVNyWn/xCXkQFlXXIIH+hgvyFDPIXKshfyCB/oYL8hQzyFyrIX8gg f6GC/IUM8hcqyF/IIH+hgvyFDPIXKshfyFDwl6O2Ai+7sq1AKm7PX06QF5FBZR0qyF/IIH+h gvyFDPIXKshfyCB/oYL8hQzyFyrIX8ggf6GC/IUM8hcqyF/IIH+hgvyFDPIXKjYa3atszDCN o7YCn0TRViAVbPld7pBeuCC9cEF64YL0wgXphQvSCxekFy5IL1yQXrggvXBBeuGC9MIF6YUL 0gsXpBcuSC9ckF64WHZ6OWor8CTKtgKpYM//coP0wgXphQvSCxekFy5IL1yQXrggvXBBeuGC 9MIF6YUL0gsXpBcuSC9ckF64IL1wQXrhgvTCRdnr5aitwJ1l0VYglXJbn3KnrPXifYCUGWWt Fyuv/nU4Za1XmfVfxSlnvcqt/ypOGevVUW79w3HKWC/yFy7IX8ggf6GC/IUM8hcqyF/IIH+h gvyFDPIXKshfyCB/oYL8VVTuV7vZ6KitwEhaWwHyYhGpb8nVR+TFInJXS+7eIH9ZsJZdxTz5 yEkSg+O5e4P8lcZ2FmIN66+urMhTbmLoci0/f32IRbJMkYNe29l0TzRciI3y0Hg+3jOGz1/1 A5FA6+l9mSZR12tFz1hE36zhun7lpVhxaDI/7xkrnL82FGSpM2LnD0wdyjCNol7rpjaHjY3a 0rZqndpCbDg0FcjLe/wK5a/9bCr6rrwvlbGbmvS1DviO2k+kptdUXSSm1nSXYvbsSMjlTn+t Y7Dy4afzvtxt/bHVDmeYSkGva25qj6kVbuvao5xBa4764nK50F9rjV116KGJvC6X0xpf7wH7 ieT1usAM3/oDwakcsmdHOJ5t9/nrViZ21Uh7XpeqU5HYTwP1tlNJ69U1Eltu2JexJqPELWMm uVznrxsOil01PAY+yz+mFW/fZDeRrF73DCSOshmKRWXMcrnNX+8Kil21ZX0el5mgw7TiTbZT SerFr+oZi5y5JccMppPsLnf5q4ad1suB8bvytMRUzOv+iN1Ecnol5MpUh1EnWS53+atfr2SN D+ZticmcCprWfNXDNlNJ6TUVlyvSmn939c6kyOUif+0frNOL7aGDeVmeFZOmNbe9ACGj18Gh +PLen/+qBmMt/hRyXF4+/WVcIWvx5Wl5VpjWfPMFm2kk9Nq7I764gZ/OSwbN9IpjbSRiyrRr /LXfuP7sD8/kYWl2GOWjIGg3kXO9jrTFTxDCtXnJYDJcrkD/+13pL0Mu/315WZod7Yk1D520 mcaxXmdrE6XLZJ4ymEB3V2Dq0COy/nLUVuCabP0K5MuLNbFbC/6hrpqcl2bP/vrE2XWr3UTO vTgeX1gh6pGGtElnLG7xYuz4kO0+Vq6cDMXXPNJsM41jvXYnxJ/Md8Uk7kSWVEC7pKzbESt/ o4W55ZKgLfuu6lSv/vg1EH/kfJ6ylyAhl/v8NRirQUcKUcInkyie7HYzh3qdXZ+wV1Xesqdj dpf7/LUvfgqV4Qp7nrg5mjd/VSW24pbH85S9GGa53Oev+D7fs11qPrvaYCbm8uWv6cQ5VKRa ISP26NcXEzfM3eavfsX1rg6FFKI15cdfdzckNqJKNjKRLJfb/NXVpLbeD69S2VU2xU/XcvSX 6bql/X0cBVLd5TZ/Pb5Fdb1rlXaV+AHRbgJneoVMV20bFLJhRx9Llctt/qpTXG9+ZV5lVzE2 dG7nX2sbTRvxzL0K2bDmpxpS5XKbv0w7atdPSs0JM3bLtwdZZ+wfOV7fMJ3I2d92k0ZvNZUs l9v8lchMt9R8/MCm0srMqN7kdq3xwub8bcQERy3kcpu/HFxxsERUHKbl25ntmdaD5XgtP5i3 jZhAv1uYKpe7/GXeUeXWu8mf4QZlBrr0YLndK5ttzd9GjHFQXD9Ob6jsLn8FFdd7FZ+jTr6N 9ES3HizHe9H5a5AWo3bIWi53+cu8o8qUX2vEzqhyH8NYf9tDqSO9kjZiYz7a6c20WMvlNn+Z bo1L1A/31+rzdSpENDay3c/O/GXeiD0KmUhlzPapDXf5y5wbmfMv47pt/zbFiPZ32uT9pZCJ ZOZv8Am5wq1W99Hc5S9TZiSubxj2Urp0J2ZsnbX7Wd5ftlVNx0TFvRnro3tWfzlqK8Ck30GQ Xy+GHM81HWvwFJB/bkG/irHZ9nd5L4Zza4jZa7QUD49Zt4x0rRcDTm/p3pa4bS99XdY4V7O/ zSNfl8ztXtlRo/WiXT3LvWWd3+d1OFei+ad8bVLcSQ2cs/1d/lwtp3vRR/ViztZdbvOXeUdt 6XJ27fA+0zxtkvH05jkh+wmc+WuXeSvm0ObhLqO1tP1+5y5/mVo3AdOO5klqrd6yQjKiqNVk qP450mtD1Jxt1TZFNcYZWuZnZtzlL/NFO3/LqIM57kpurf7U+6Ti+fg8maopzvzlS8qDapu9 +4w9ryXDNO7y18rapBUfsbsCmGBwPGkOyRKsS4gdyjCFI71O3J6ciYHn5NvEHq2PFcMt9b0Z pnOXv1YlrXcg69M+qXJlqpqnYzTRaOuzn8SZv6oDyfmWf/qjK2aujO5ym78YS17xbG5Jl8sf OS0RrUkEydQIyOH5Zyg5E5LPf6xlT8bruEO+TO5ym79Opqx4y/r3ZJj6rnS5YBbnz3mOiBky PiTvTK99kymZaLJ/Mj6dPhbvWMI/nmVat/krdcUzPQ1+usUfGRjyp7LjToexWjNcI4rh0F9z qZlocS5YV1u8hhsYeWx/lqnd5S/z8yWGYPdZNwnYtyYa8Qf6n/tgml6ZOy5KYJwIZH6Swen1 uLRnKlucHhJPJ2aNZL876zZ/pR4QgaEd7O6UqXayNZN8a48wiy0Fgu11EMl45jyc+SzPoV5e X1ouwq1Ze7eYYLM7BgLmObLXK80hSu8vxnrSVtwfCDVMs5XG2tewq9jgaFAc8McfWxtvrZE0 w0j20+ZYFwFZbvE49df5iEUu2K0Z53mWTbeaDieOHOk2f5mvBpqIjNVOsw746+5qWH+1cbzX y+audIOBJ7MVHzNNxoRZpnN8fyJqkYumg/uY3TW1u5mvtsk88cA9juKYZ3GDv0xX27MwdFAv my0MBseWyWn7cunsPuOyqj9cf1vm7Di/n5TaiQMnEOphqy2mXcmmG8aSHBkYcdZyz33+utlq T7UgcV5p6UiumE2EdSxo7BOBZzIfsST0SqvSGzTVz7FTcGDUS6Ya5mWzrHMqlHL4DLdmv5Sj Y57LDf5i7Cbr7Z/CwD2x88rbnkwvO/SNsLl+ls2nLH2nqdND/8iBbJlx7q9Rq+Oyno9gfcMZ 42je44u2pmc321E5QdJz7I7nsiYfy+AkH9etMZ9Xbmq3myrQOsVCcPSJVbu2xasq+kIey5oX ifvrp212m6xA9SjLUTnOvWfMM3JvOGor8Kom21Yghfz1AZLW+U/a1oiuMJ+DPp3JveFg7XRX N7dCY09/0LzkAQf3CmS2x/tt85CRpjGJGFvMc0rMZ0U+lqETq+nZSZB67bU+m8BWDH3AQU4k 9Er0byFDoM5ZHVLn8SS93FHWcWozOQZOalLPQU/LCzZ+0ElG5I43tsWdLeEGJ9cBEpifsHGP v6Ckj9qdAgSiBy2m9zk9Y4jL5azJk5RevfWSggWiazLc97HgtqRi3T3+YuyI3r9pGkM7rO7M 15h6rna0oSYfcpYN2fJcSrChHZJLTzmFl547mXwsI8GtbDL9INdif2WwQeKQGGZOu7aR1EvG YeGn5B+ES2615SZ/8Tt+076ktY8M9HTZXguayFZJSRAITmW6A5eEfH3ZYaUj0s7kmqXoJJ1f KMxvJh/LSOZR1umrquR5DO861jPNrss49bsGrDZMGk0HJXIgrdf+x9odnLNF6qaU+rFOqh66 zF86XuYRlxHezbJ3y3K0LfsxMTx5k61HLVA5H92R7WJOpI6pvSZkXVXScpSWkSAfy8iNPhbN vHM39TsuuXSUrh8MWpS85jysYdnuOFsTex4rhhv9Jcc8W9PfZLtbj0H5J3kUUrves8+qrqTn ITjK1B9kSrlsrrwcndL7i7MNzt0sNlY4CuWfXE9XHMXrc2vZip5gWi54HnpljsYppFwEt+2M xBlu8Bdngm1gjQ31QaOPkEhlqKp/1daQglgsl3eQbWeDK3xVeiYCeh5yfDdXl7lcDLRO5dof tjv8pXMr62UhUVFpZidzOALl9E7GB6BaoWfCA0N23ew75ZDpblx4cz1bmePy3OKvvOKed9Qm ruDwMtDprdBMuMlf+cI1eh015OINlXIpAxOQvwrIPfpTZE21Sv19WkP+KhhTTaJ6OXc8rS2E OuSvgnFbO794Ku4AOWor8HKubQXSIC8WjPf61mS50CoPebGAPFqAZZK/UEH+Qgb5CxXkL2SQ v1BB/kIG+QsV5C9kkL9QQf5CBvkLFeQvZJC/UEH+Qgb5CxUbyV/YIH/lHUdtBT6Z97YCaWxk ZeZEF2hLSEF64YL0wgXphQvSCxekFy5IL1yQXrggvXBBeuGC9MIF6YUL0gsXpBcuSC9ckF64 IL1wQXrhouh6OWor8GTh2wosA2j74YL0wgXphQvSCxekFy5IL1yQXrggvXBBeuGC9MIF6YUL 0gsXpBcuSK8EexTf7FRMSC/G37LSx97DGqf7qypLnZVskF6rWYh1tUWfCKN47H0Z6/U4HP6a +cuLEu96CkdLnalsLE+9+ti72dYtxuswEwzUljpjWSm4Xo7aCtzp3rYCjW3Rqy1e+TY+WuqM ZceV27PAdKRLxWm5q9QZc8By1ItZ69VW6mw5YTnqZeOvUmfLEctRL9ax9cyWts3BykCSXm6v 9guWpV6MV/wvMA8brG8NkL/QMAEn1lPtsZpirq9jLQrLWi/Bpti7r0P5exNh4SC92Fo2NcD1 Cqw/W+qsZIf04jw2yYuxqlJnwwGkl0C8/DiCoAQjvXRuiILBanN9pXjhIb0Mbhry+5u68vGe 6oJSXnr1sVnWAX8e5pWedwccEbcUIE/5pYz02sPOt1W18rp5pLLKNyX9GlWoJEY3FCRneaR8 9OqabjVfXwq0+s5Jzf/wKr8/XKC85Y8y0evsDde3pF3ADU9O90ksYxCs2ViwHOaJMtGLBQNp cnHF2h6SWYbf3yYjcCkoC71umUk3l8FQPatxuBSvDwqwguYzD+RdL0dtBUZK2FbAoiFA3I73 OV7KFr9/17sLmMt8UAZe7J3JIBcw8K5rHC2n5hEElZMy0IvZHjgNmtg+R8vh96jdfoOzDPTa kUUuOHebdFZF8bv/Bid+vU5b1iJTGHfSTor8VQS6sh0NdVpmsi5popv8VXD4FQxdkKkV3ayD NRxrshYs3Jp1WV0wVajwWc4J7HqxcaFG00/czx5g/L7yg109Q5aCBSYf2p9xSX1tfn+oKHnO Aex6TYuW9eNJ33XdZ13BH7dZRoyg3x/sLVxW8wJyvbb3CCEGk51ztN7aYi0zRzMsi19rdP2D FMj1Yu1cBovvR6yvPfrsBdszDROcubdwWc0LyPU6BcewwOn0g9jaQw2Wx8RA3T02S1o7ChXN SHVhs5s7yPVilbZtDG2uFzeN2SyJO3WgcBnNE8j1qoDDXr/Nbz6bemL0rlstpm7lFZeGuwuZ 2XyAXC9+Cen2Eza/3VFnfemj6eCdadOKNgdh1x8OsevFLyF12/56dMpasMDADnababrtXZPi tGBz4TOcK8j1ynKJdp/dnbHAADvO9MPiHhY7w3b9zRSGXq/mSOZLtBnuUjcda2hkHaxxur7J +GbS2W2XkoJcL9aa9RKtTSGWztBgUXKcGznr5aitwDVu6FdgXZ3fvyrjFPuP+jLfqo4RXl+k POcEdi9OOzjk2R8/TQSyX+t3A8j1qqkOW166SuZgNOvxMzBVhNzmAeR6sZujfv/hrFMdsb6S hU8u9Hqx2oiTSkXflHVDVYNI/yGnrR9LDHq9GBhs5ICD6Q422coVtrv+6D7Q67UfDBZouzHr dBPspknrg2IgiOVgyMpAL7ZmnN8D2+tgyn2s38JjLdffUPA85g/8erHdUDIFRpycGK9lXaN1 SSaLjPV07Sl4DvNIGejFQnzLD+1g1zmYdicbHKwNir4Xw7uiw8zuxo1bKQO9tvXrxdBAzyDb 7mD6a1kva2YdLMQ2OH4mxjWUgV6sM17Pq+vZCmL04ZPBMeWgV1I/ieHg5rYtXd1sY6kzVRjK Qa9OvwWlzlSBKAO9NrVb6eX2hxwUKQO9rO83lzpTBQK9XtfUp90tibRG2xqp/HIl61jSJYvw 1esboLLu+u5UlEGu15pEy4BwsL6hMcSuYmtLnalCIq2Xo7YCzA1tBdIQZ9WVwdpVcKq8utSZ KQJu2/6SVFRW+VZUe9jKMj6dTgK5Xkq9wWEGu17LDdILF6QXLkgvXJBeuCC9cEF64YL0wgXp hQvSCxekFy5IL1yQXrjQ9mqaVgdpBaTDkwcO+JimtcCwH9ISYDdsnm/fnhvGvPDJv9sEqcn4 zgOfP1qtad+o0bR/XKNpWwL6PTWreQ9Bepjp990aYPmaiedjN+N2a6sPa/dqD2intHdrG7RJ +HwUPg9pD8F3d2oPwiRn/+qDWqMx/Zt/sNjDxz/1Nx+EafXfYsxrq30BSL//u2s0ns+//MIa 7a8aPT1bYPjPGvjnGjHdP/N/q1f7vtT5dxVXzh3p+cs/05fxFkx/jzHdhZGKHv7bORiPLZ+P v/bFNdqfnLlRfPc6DP8vP/+dNZuXfeNKTw+fX/vjIz0fWGA9PD+czRCHD3+lAuZ5w3p7fOp3 1mjm7fHPpmn4+vF1XgVx+XL4LFsaWfPJ82Kza1FDS/59vTHcyFfV9H2jMXy6T/Mc5vsHpK2Q PR98+Wn49Af06Y4ceWa3Nv/Mbv7bN/36dxNaMksGB2D4MUjnTHHN+QlC1jeZ8mC1T129UtPe UwHTwH71tUp9fjfsU+9nEvvUDL59iq9fIfapY6Dnb4Ce/wM+v1iZvE/x395bmX2fmtVy26dm V2laG+zPPx3StMqgPn9sHv77NZq+b/2NprcV+Hlm3VZg1hjhbQXuMsVsNRJkZVdiDRLDPMYJ SNuN4e3G97FPbTXTKoxB2HZJxNaTz7fR2MYsZVqzNmtM0x/X9O36cVj/fab1579VQ+L24nEb 9ga2f2P0U/3fG32uv3Pvjwb5+M+N9gwcG+3rv30s2s+H15hixOLxlYwN+2DYtB12m7ZDfJjH NW/3bGVO0PiOs8oY5vn1shUi7zWez8OezIf8nkvs5z18nvU8J9pbsPj33rtkTL+F6Z9VUCby 5b/p0Q8s/LCh/860G2Ga7ZVM+1XtCHsbUogdZlF2M7uWHWI3sQPsTjbFfoJNsrNsgr2X3cie YTew59k+9gk2zj7JRtmvs93st9hO9iU2xP6QDbAvsz72F6ybvcI62ddZG/v/WCv7O3YN+w5r Zn/Pmthltpa9yhrYP7I69k8Q7Z9YkH2X1UBawRZZhUj/AunfIP2Iedm/Q3oT0tuQNI+XeSFV QVoBaSUkP6RVkNZ4Klg9pKsgNUHaACkCaSOkdkidME0XpK2eKkjVkPyQaiGthlQP6SpIV0OK QuqCNAhpN6T9kA5DOgHpXkhnIc1B+jjr9Xwe0u9C+iNIfwlpAdKrkP4F0uuQ3oZU5en1BCGt hhSGdBWkJkjNkFogRSFtgrTR0wWpExL/a4XUAqkZ0tWQ1nnaYM42TwOkOkghSEFINZBWQPJB qoTkhaRBeoe1ef4D0huQfgzph5D+FdL3IH0Xtsx3YQt9l633/BNb5/lH2Gr/AFvvVVbnucxW e/6e1Xq+wwKeb7Maz7dYteebzOf5Oqv0/C1jnv/B3mFfAUX+CJT5ffY6+x32A/Yb7F/Zp9n3 2S+x77GPs9fYT4GKT4Gyj4PKD4Pa94Dqt4H6h9n/YvthT9gNaZB9m22BFIG0FlII0gpIS2I/ 1PfWhJf0vdUT35tj+7peAOq/euNzcT/8YGzM+L7CmJolLatSux6+iHp/hfZ72u9dsd+b93G+ N/PPK7D38s+NbGlJP8rH9nFmlIOxfTy2V/Pfo16PNgn73jntGpbfpf5A6/U8Dim/S/2h9l32 95Dyu9QznnrPRyDld6nXevo8H4WU36V62B6Ph92Z56VGPePgsuxL1cf14+b6pGNvYula/Jir 177MMatg2bwuub3SC7US/l01uxkczNNhOObwdLNnJZuBdeSfU2L4as8U5O5m+NSHY8d2cy5j ub5i1GTSt0FqLjPn6z1w7HgPHKXeKxIfDnqe9ATgM+x5DD758GOe77P3wDHiSfjkw8XI17hn M2j1fbZXJD682XMDHBP3wvFxFD758Kjn/8Bvi+wG+OTDxcjXd7W/Z9/VNrFFkfjw37N/1r4D n99lr8InH35Va4fxFhhuF8PFyNdvakHPb0KeXhSJDwc9X9QCnhe1sOdz8MmHPwd5eRHy9UX4 /FyR8vW4thmOjxvZEyLx4c2eOa3TcwGOmY/CJx9+FPJ8AY7Mc/DJhwuTrxlRr2+GfKn/VWgf g/rJx6AU+1mR+DBP34a0AOM8fdsY/1tI/wPGefpbY/wvIP0pjPP0F8b4H0H6Axjn6Y+M8d+D 9NswztPvGeNfgPR5GOfpC8b4ZyD9Kozz9Blj/D9D+nkY5+k/G+M/C+kj8J+nnzXGPwTpORji 6UPG+AcgzcM4Tx8wxp+A9CiM8/SEMX4W0kMwztNZY/wnIJ2GcZ5+whi/C9JtMM7TXcb4LZAO wzhPtxjjByFNwDhPB43x6yGNwThP1xvjw5CGYJynYWN8AFIvjPM0YIx3QWqHcZ66jPEopA0w zlPUGG+C1ADjPDUZ43WQamGcpzpjvAZSFYzzVGOMeyC9rf2sSB5j/P/B/vE6jPPEh3n6AaTv wzhPPzDGX4P0jzDO02ti/MNwpPg4+5b2C/DJhwvjgf8kPBDNyQPJfvg47P8/L9Ii1Ph4+i77 ZUi/KtIiDC+K8V+D9HmRFmF4UYy/COm3RVqE4UUx/iVIfyDSIgwvivGXIf2pSIswvCjGvwrp r0VahOFFMf43kBZEWoThRTH+d5Aui7QIw4ti/J8g/bNIizC8KMb/D6QfirQIw4ti/MeQ3hRp EYYXxfg7kLwenhZheFGM+2DcL9IiDC+K8VWQwiItwvCiGL9K1LN5WoThRTF+DaQ2kRZheFGM d0DqFmkRhhfFeB+k7SItwvCiGL8O0ohIizC8KMb3QLpBpEUYXhTjN0E6JNIiDC+K8WlIx0Va hOFFMX4C0kmRFmF4UYyfgfSASIswvCjGH4Z0TqRFGF4U449Beq9IizC8KMbfB+kZkRZheFGM /ySk50VahOFFMf4zkD4h0iIML4rxX4T0SZEWYXhRjH8a0mdFWoThRTH+m5D+q0iLMLwoxl+C 9N9FWoThRTF+EdKfiLQIw4ti/CuQ/kqkRRheFON/Dec934Dhv4Z6Bx926sNYDTL5ypP5bDz2 ycTVrFZvpXZK+0V2Svtr9rxIfPgXLWv8fPn8KuZ/eL5jXM01ruPyq9r3PnDq3RsmTz264dBD D9z5oDY8PAw/DAum4NN8BOhijeLTA3ng172qPfz6LL/+17vyj9iDovZ8/beuMqbVr5z+h8dj XGX8fXF8YR5Py82PvfvsqQc0W/SYVVryNrHbCgvak2xB+232pkh8+Mk8bIXx+FaYlNgKv8Yu i7U9861QfCvIrc12z4/Ao22eoyLx4R/lYW2+pbQ2V3lGxDWed38rqLg2/8Vzhf0XT4/nz0Xi w1cs10Z1Xj26OQ/Jy+U51teKb5pKsf/FrtVWsICxh8auY+l7qM/xHlqRtOUSS+5iVyWdUSbm 8Ikp6zwhz2fEleM1MPQlT3IO6zw+z2c9es4Wk7T1ebRVqdpa52yFpivIl8Rn7f3WNmPZm3Ne drWhfSzGZiNGXzxGo+cP0mIwyfzrMaqNGHyJ+p64zeZKZnWOWq6Mr5X1/hnb433xnOmfKy2P 7Al3JufH+dEvFif5mkKixmbnmvvgiHEf+wnPL4nEh7dbXq1QLRNkjh+3e94tdJ/51jVZjh9O r79s9NR7Pq05vQJlt40+rE2wD2t/w14RiQ9POD4qOZk39ai02bjWU1/xt8Y8/FufkV/+ec3d mjZS87T2r9pz3heFDs9qI9qNMCW/y8W/ec7Lt05sb7Im5eY1QRBuY3v2SSxpKvN4T1A8S94o bjyvYjz2ltp8tYrxGhTjXVSLx5bU4nUvKcVjJ9Ti1SypxTuxpBQPwinFm1lSisdPsFTi8VZ9 CvFEOIV44SW1eBfV4lUuSca7bA7nPB67pIebU4vXvaQUjxnhjkjGM8K96fx4xq7w/yf0+XZL HD95vBoj3Lzz47WIZ4R7ReZ4DfFW6rO9My9RHvF4DfFwEvHeMJyw9M4Lmlw8I9zrmlS8d4y1 W+LhJOItzZjCScSLNRD9jCYZT+dtfdR5fcmY7zV9bJNsvEuOAyXF+7fLkrMZ8fapxXuzVi3e AZ9SvHfm1eK9oinFA+MpxfuaphbvBU0pHneCSjwIpxJPOEEh3puaWrxLivHmFeNpavHe0dTi vaUY7w3FeFfyEm+FYjznx8/LSfGkyr+ixruUFE+uPoEh3m7FePNJ8WTru/LxtHzEkz6/NeJJ n08b8XyK8boV451Qi+eRPl/R4zVInx+JeJ6LavEq5c/HRLyL8udjPF6lwvkfj/eiwvkfxBPn SArxulXON32X9FMy+XidSue3vksnJM83N+jx/kXyfFPbrcdbUDu/rVU7v42dI8nGM07Jlj4n GS9snCNJHM9EvIuxUzK581vj4sSPZcuji/FzJKl4xsUJXhGVOb+NXS3gp2Qy9Qnj4sTbuyXj GeFEvdd5vLdjl154OJnz24umcPLnt+LBSYXzW35xQiXeK/roKsl4RjjnGPFel5wtFu8F2dmY Yjg93mekZ2MxJ6jEe01+NqYYjsf7nsJsLOYE+XhvqszGFMNp8rumEU9hXxHxLivNxhRXzzif lo+neAfHOJ8uXjzVO2JXkMS7jCTepfKOZ9Q/88DKz84rzdegeoBTKS7EAfWKynxQ01Y6BDSr 3eQU13sVZhOVQ4X5vEtLF1Xi+Za+VqkSr3bpBa9KvGYonuYU5ht+Te0QPndJLR4vnnZlnSoN UVwoNKLw8sqFwvqJw6lCPHGdQqHI4NcpVA7htfOwjpfl52vW1A6pXDrpa9mafr7ik59NnK8M z8vP97Cm1uaGx1Opjz6sWAJDvJrL8mcTPJ5vXGEFId7wiwp+gHhzcwrHs+38AKoUz6d0vN7F r1UoxLv6iFr5MMyLoz+Un4+v3ddkG/Vt169rvSB7/GwSbXxeF6aQi+cR57qyx7Mmfhntbfnj y3aPONetuSwbLyzOdaWPL7suinPd4LzkfHP6uW6FbLxhxXPdOcVz3WHFc9252FUfSRTDaXNf U5pNk7/qo3NWbTZNMRxBEARBEARBEARBEO6mcjekA/Lz8WvkKg9TNl/RNN8VikfxKB7Fo3gU j+JRPIpH8SgexaN4FI/iUTyKR/EonhWq1+sIgiAIgiAIgrBnldpsni+pzVdZ3P6FKxX7F35R sX/hObV43Wr9/bITxe1f+IRUfzpJ4VTizcj13xNDtX/hhkQnMPLh5B8HCps6gZHhomSvLMa7 LSrNncA4wTufCCfTFYXeEZnep45MTxt6vO54h0FS8Yweg3bLxjN6DJqXjcedsPTvUvsmf8DX 6M5Yat/k8US4t5tfkYynd/j72vBlyXjCCW/vlnsqvPay4QTJp959lwzjSRYNtW/q4eYlry41 x7pDar4kFy/WHdLFFxTivSL9kL2I984LmmwnAs2G8SqvyM03bPS+JHuxrtnwueTmFPH489Ky D4U3G90hyfZZMKwfVjyyzxQ36+Gku7kZ1ntfkr72OacfxaSvtQ7rRZCc2Xk8EU5aPm1oXnxI 91K0ygirf1Tulps75oYXJbtGMeRjc1fk5jPk65Z9FF2XT/59E3q8GulH35tFvBPSj76L3cUj 3xWL2F3EgVsh3kX5eLyXEr0KIzcf3z1FIS8bL1ankNQP4umFvFw83gm2KOQl43mX5vVS90W5 wwxb0kvdtyUPM96l7+l1Csn+d2OdEO6W7Abbu2TUriW7KmGxQl4pHhTykqUSixXyzVKzGfFe kO7ZhhmFPJN8At5r1K5lu0+OdUIo3Yv5kn5q1H1Zcr45vTIvuTl5qfvmPHzsmpecz6efiUm/ ncuj9wnmuHd2giAIgiAIF7BSodc6jkprMI7qqykpHsWjeBSP4lE8ikfxcohXeQSY+TH/f0Aq 1FIcqaDNifnk7hlTPEtU9dODun//pHgUj+JRPIpH8SgexUtC9XodQRAEQRBEqVipOJ/KW8c4 /MELFaTbnhmovOWM4lE8t8Y7oBgvdlok25T2iky8xLmQVyZeZeKNe/HTMOt4lUlj3YlzvdrM 8ZIbLM8lLos3W8QzvYAs/jPHa7osPmwRr0tL/5njW7qUCG0R74SW/rNYiOlpjqX0eB7Tz0nx Tpiu+lvEMz/ZNWe+QWBaPa9FvFrTU5XDphbSzPQcI4u/7DYRz7wNzfG8pld411rEu5j4OSme b8k8nB6PP5wQHzZNW2veXunxvOZnZ8zxmpfMw2nxmPkZCPO05mUspcerNTcyN09r3kZz6fF8 ZsnM8ZLkS483bBvvSnyQvZ0eL8kCNvFq37CIZ94lTSub2EVgXX6QHi/JAiatzbvL8OX0eEkv hLeJ1/yybbzdmeItzdvG0/+bHgtMcnu6H5j+s3FctY4HS0uL53UQr/KtbPFMO2ttIh4cPG3j vZEhXvMVqXjzscHhy1bxxIA3U7y5S+nxfA7iwZBKPFiYbTyfRbzLxhBfmN32jJVTVvHA2fbx jCfmEuZPxONOU4nXfMk+XnOGeHzALp5xYEnankYINmdVPhg/Z4gnlLKI94Y5XuJgE4/HN5lt PKNgSIpnLKObL9Iu3lLSbpMUjx8oreLxEMx4Us50lbk29lCaWLBFvHfEfz1erTmesam4UFb+ e0tMlLSbJhbIn2a8bB2PR6pdMnaby6Z4etYbljTrePzRoGbjLfem51ZjWddrMVbxLosHknQZ E/GYkfW5t+zivQ4HOqNrD1NZ79WfKK7RO3+wOF4vvfPCi+8YjwKaynqmPzDdICJZ1F/mlpa+ vnRlbklffS2B3u/DRX2DWcXjKzcsVtNc9dAfpqrU19I6Hvymb9CG5HhL33vfi8ZWtShv+XyX h/WH4s1lPV/gjNEUxCKeaC0i/h9O7p9lWG8KIhZlUR8UrUXm+f93Lia1FzGan1yxiecTzVJ8 pomS4122iecVD695TROZFrhk7DQW8ZgIw0wTmVcg5meL+qcI4zVNZFrgUtyEafH4k6OaIePb ptmMRRnljUW8bmN/Wkp9XFUs6hXbeJX6A/+pm9PYA1+wjad99oXYVkh+fLs5kXOr+nUs6lJq Dx+1S/GeV6zq8zri6ebk7yrjq5chHt+sKR2YeOKrlyGe1rmU9j7jE/ElWdTnY7Ajh1O/WvnZ 2JKG48uUO5+ei+8RcufviR5tpOKxhEJS8bwJhaTimYobuXgvxAfl1i8xWK7XeygexcMQT/V+ gOr9B4IgCIJYvtSpzbbyD9Xmm5Hs78mgU7a/J51Kxf5ba9X6U41fxFKId1kx3qVixmMzavG0 lWrx4ET7itp83Ur6qTfile2AKYZ0N4QxZP1QeUT/fCLzZGl0G9cpdknOF7vvKBnPG7t0JRmP X1ITA5JnLbWxK0CS8XyxS4GS8YZjF/Ak4/Eraq8rxhMbNCg3n7hiqBAvfq1Tbv3Y0o+NDSob 71eMDSoZ763YsVounvcdvse8Ix/vSmwPlevmFA4rXn2DSvY/eEVIeEnWf97LYpd5QyVeM7+k LXk84/H4BpXt9Jdd0i/6HpAsH3g8cRtAsnzn8XRPyMfT0u43OIjHwzSn3m9wGK829X6Dw3i+ mOcl4JvRKx9P3y3n5LuDF7vlRfnqmbBdg3z1TFzMqpGvLom+vdnc29mmS4snLtN3fFZ2vual tHsNjqiV6Xo+KZ5aRX5Yek/JLZ78nilgc0tfUwnn5T18q8RTeHGAiCd6x1SIt1tpNoLAzf9+ y6ON1Pyp9qz2nPdFcfPvWW1Eu5F9WNsEw/yb57z8rmB6B7A/Zxrm5WRl9wmtOnqDxrae0ry9 d2sVPbdrlVuOalXtB7XKjpu16o03aTXNg5rWd1pjkCp6TmiVXce0ys6bNe/WO7Xq9ikYntEq um/XPL0n4bfjWuXmac0Ly6iGZdREhjVf5xH4jS/7Dvj9Voh1N3x3s+brntE8W++C5UJs+Kzo vg3mnYFY92g+WG4FLEvjv0G8iu5bIT+HIF9TmheWV9l5GGJMw3JmIP/7IJ83Qr5v0bwQowqm q46MaDXX7ITl36l5IW/V1/DxYa1iK+Rh8xFY5u2w/FthWr4ed8D0YzDvCc3XdoPmg3WuguX7 2vZrNRth2S2jWs26PvjuZpgH1n3LMYi5F8aPaD749LXfrHlgO/ogH5U8bxDPB/nxwXeVkG/v Vr6Nx+G7Kcj/Adie10JcmLftRljGNMQeh2Ue13xbZiCvJ7Qq+KzsOKhVN+/QKmCdPJA/L2yb Ghiv7jyoVUH+qmFdqmAbe2F+H0xTw9cfhith3uqNN0Dc2yDWJEy3E/J3ENb7TsgPxODawHat 4esL6+8D7StgORWwTlWwPasgbhVMU91+E+R3Etbrdq3m6kHNB9NXwXhl5y3w+xGtGraNly8v she2J+SD68O16btbYwQmSC9ckF64IL1wQXrhgvTCBemFC9ILF6QXLkgvXJBeuCC9cEF64YL0 wgXphQvSCxekV5yfra8vdRayQ3olCPg7Sp2FrJBecTr8/lJnITukVwI/+QsTW8hfmLiuh/yF Ch/5CxMbouQvVLSSvzAxFSB/IWJTu5/8hYmwn/yFhpp9437yFyJ46UX+QsL+o7pc5C8UrGN1 ulzkL/dzHZuqi/gNSp2Z7Cx3vbp6BuJqkb9czX52fK6+yW+m1FnKznLVaycLsRSxyF9uZRur Ho2GU8Uif7mSB1ln/1jEQizyl9uYZ/3Vo8fSjoLkLzeykx1ntsYif7kLXmJlMhb5y0VkKrHI X65ibbYSi/zlHq5ls+y8M2ORv0pNHxscrG8NSIhF/ioZq9n580GrE2LyV6Hon/640nxr2SkF Y5G/cuQjYxGFuUSJpWIs8leuyG88pRLLH26tao+GIuSv3JB9uEelxAq0HuvZCo68jZ1kA+Sv 3PD7H3E4ZQ3rVTBWONhwbpZtN5ZxdpTaR+UE+OvMvY6mnGXD0iVW07FRdiplOdT+MCf8/p6s 0+xhoTlpY0XG+jvZg2wiZVkXNpO/cgD85fNmnGI1O6dkrGq2zXJ5c+SvXPD7g7a/QYnF1Ix1 nO20XWqY/KUO+Cs8aPObSokVjo6y/owRT4bIXzkA27jh1rRv1Uqs0FQn25BWYqUSJX+p08E9 kfKdUokVjvZMs5UOIm7rJ3/lAN/WLTMbxPBa5mWeavlzLG6sUPwcKxvT5C91OgxzzLEO+Ovx VVVKX2iK9rCTEhEf30L+ygFJdZIJtNbPQYlVIxWxkfylToe6WOHg8Dko6+RZ36N2C6eYuFWv HPzVBLVIFXodl3QlxK16qfgrrNcdS531guJWvWT9JUqsJjHo/kpDDrhVLyl/hYPnz4sSi/xV MiSMNTjI+sQ8HeSvUuHMX2AsNsuujc9F/ioZ2cVqOjY6eIqtNc1D/ioZWfxl3HRMhfxVMjIb y/qmI/mrZNj5C4zF7G86kr9KhpVY4bqe6X42bzsP+atkpPurCU6I12WZi/xVMlKcFZ1zcAmX /FUyzP4KhHrYBkdzkb9KRkKtgR0ns7a90CF/lYy4v4Z2SMxF/ioZhrkmuyTmIX+VDH3TB6YO Sc1F/ioZYtNPyslF/ioZYtMPyRwMOeSvksGvPT0pOQ/5q2TwTR+Vnov8VTKgslG/X3KeXP3V pzxn0XCrXrDpQzKtc3Vy89eqtvuV5y0WbtULNn279Dy5+SsURnAodatesOkb7paeKzd/Yaiq uFUv2Hiylflc/fUIhqqKW/VSe7luLv669wz5KwdUNl5u/qL3w+ZA8f3V10b+yoFAq3zlOjd/ bSZ/qTPNZhXmysVf1D9ATti3grInF3+tPR8hfxWbXPw1ieJScVnplYu/zodRXCouK71y8NcN URy3YspKL3V/fbQ/gONWTFnppeyvo/0RJLc6y0ovNX/NM1/sfaMFyVVeKSu9lPzVy9rjfVKR v4qKvL9Ws48FTS/sKFjO8kZZ6SXpr9Us9X2j5K+i4txfE2xDmljkr6LjdKP3M+v3jZK/iooT f+1kx+3f3lakfOZAWemV1V/ZXotI/ioqmfw1AdWLrK9FLHJ+FSgrvTL46xQbTK9ekL9Ki7W/ rmWzTt+FU5JcS1FWeln4S+4lU+SvopLsrwn5l0yVNPeOKCu9kvzVy+Q7QCd/5YBc79acmL/2 s1mFNwuQv3JiWmEefaOHmOr7RslfyoQiqu17w+rvGy3AeuQZt+ql0r63V6ET9EBlcHP75tix k/yljNzGUyqxeNe/1R52Af7OR6n9Rk7I+Gu7SomV8haI56h9VG74/WccTbeSTffUKbwWcTDl LRDU/jAnwF8912WdagPrnAplvoSbhl0Ppe3kr1zw+30Zf59nJ6d7LG86ZjFWtXUPpfOd5K8c AH8Fe21/5SWWirGs+tSOEQqTv3IAagTV1lc4eIklbaxw1PZFvjFC5C91+KnvUxbfK5VYoSnw o/2LfI0lR8lfOcA9cSzpm4eVSiz+vtGTjp4m6yd/qSMuLbWcNl6hdivrZedW1SsYq9Nhz7+M 7Zkmf+WAvsGDPVtZx9YzDfVB+feN1jl8kW8M6h8gB3J4f6VfXGlizl/kG8MfqCjIuuQTt+qV w/sr+ZWmWSbbtxtj909NFWA98oxb9VL0l3gtov15W2ZkDp6lwq16qfhL+UW+iHCrXrL+0l+L qPYiX0y4VS8pf/H3jZpfi1jGuFUvx/7ixmKnSp3douFWvZz5y3iRr7O3q5QFbtXLgb+aEi/y XT64Va8s/uL3Ro4vjxIrGbfqlclfTcdGWX+pM1gi3KqXnb+Mm47LqMRKxq16WfrLwU3Hcset eqX7q6l+LvtNx7LHrXp1pDqLHS91llyBW/Uy+yvQ2rZK4Xp7WeJWvRL+CgzsWHZnWfa4Va+4 v5oO3lnqrLgJt+oV89c4u7XUWXEVbtXL8NfcQ1RuJeFWvToMdxHJuFUv4a+me8hdKbhVL+6v wEH5N4CVO27Vi/tr4GipM+E+3KoX99eqUmfChbhVL/58ytPL9iq8PW7VC/xVt67UmXAhbtUr 6/OVyxS36uXs+eXlh1v1An81ljoLbsSteqm9v7L8cateKF5eXQLcqhf5yxq36sWaalU61Ct7 XKsXe7bUGXAl7tXLRdSwq5iHdbAI61V6b20eIb2yM8t61l/Nn3cPVAb7S9yymPTKwod8U0ld 9QUGnlToWThvkF6Z2TGQ1udHpJQOI70y0L9jwKrXj8DUoZI5jPSy50lLtbhgP1OyPJFeNmwY bLfv/Sh8vlTZIr2s2ceGbNUCakvVVwfpZUnsBdqifhGKtp3Z2sE6tiY6Cm4tVcZILyvuiL1A m/caxk7G2++vYwOGhs0lyhnpZYEvdixsaVu1Luls6+zouP5LqbJGeqVT36LXAgfY/Ra/tpC/ 3IUhV8tpdpvFr14flV+u4h4hV/ipp21+b+Q/t28qap4SkF4pDPIaRSA49T7bKbhec0XMURKk VzJ9/D0q4YYMUzzCj5VFy08qpFcSR9qgIj90LEP/Htt7wH7Xl6zjPtIrmbA/EB3NOEWV3z9+ Q5Fykw7pZaKma8gfmMosRn3EH64tUn4sIL3MPOWP9B/KOMW+cX+gZJV5RnolcT4c6P9oximO +gKBkvaiTXoluLA5MJXZXbx8GylGVmwhveLM+yI77sw4Re9M2D/+2NriZMca0itBezjbI0wt gZFiZCQDpFecpzOeJsOp9Fg4MLW3pO4ivRLce6Yp4++HGsJZ/Vd4SK84kxkv4naNBJpmStny UIf0itE/kOkFwu8a8A+V8DQ5DukVJ9xtd9Vwno01BUZWiOG17HFq3+sGOjI8IBgNhNkFxt9g FGJbtmzp+uniZSsF0iuO38Zf68aa/ANT7xOvOr1abyA1RO0PS05zZIvl9766QGTgmbtY0qtO B0rVhp70itNald5By77pSVApMhBNedVpoAT5E5BeMTa1R9K+E2pZQe2jSk99oKU+8SrFGtbv a7d92XOoVJkkvRJE/S3sJNsOFXYvm2UNY1bPO4RbW7mIPm+J8kh6xZk/2OIPhPp7tmzp8UVb LR4lCgfPN4KQrRF/uGR9F5BeZlrsjn/GG531dvThwDMly+Ey1GttY9jml75nrB/Q09+PGetL +HQg2leynhmXoV6sIXTS4ttr4VA3aaFXU8r7MetbStlr9zLUa1N7KO27PjY4WJ9eZom3jSW/ H7O+ZWC0hL12L0O9WDDFX6vZ+fPB9Kp70zGLt42tL6m7lqVeoXCiQdpadsraWKGpzuNpbxur YWOV0RUl7RN/GerF/OFz4pOXWFbGCkf5M5VW9FeW+g7zMtTrdr9/5AK7YF1iBVqnOtkGmztc a2qPlfoO8/LTiz9wFxho32xlrODwOZbpyf/VRculHctPLxa1PMeCE+K5ECvZcydOWX56nQyl iwUnxFCWYWD56cXCacYaHDzFStyu0CnLT6/GpHr72PnzLiiVnLPs9NoznXxCjOxVpstOL9Zv GKufpZ8Qu59lp9e6OnFCPN1f6p541Vh2erEZcUKMlWWn1y0ZT4hdz7LTCzmkFy5IL1yQXrgg vXBBeuGC9MIF6YUL0gsXpBcuSC9ckF64IL1wQXrhgvTCBemFC9ILF6QXLkgvXJBeuCC9cEF6 4YL0wgXphQvSCxekFy5IL1yQXrggvXBBeuGC9MIF6YUL0gsXpBcuSC9ckF64IL1wQXrhgvTC BemFC9ILF6QXLkgvXJBeuCC9cEF64YL0wgXphQvSCxekFy5IL1yQXrggvXBBeuGC9MIF6YUL 0gsXpBcuSC9ckF64IL1wQXrhgvTCBemFC9ILF6QXLkgvXJBeuCC9cEF64YL0wgXphYvy0qtD /GX7PdMUbqes9PLr2P7eYUyAWLBy0iu7HNkEdT/lpBf5CxfkL2SQv1BB/kIG+QsV5C9kkL9Q Qf5CBvkLFeQvZJC/UEH+Qgb5CxXkL2SQv1BB/kIG+QsV5C9kkL9QQf5CBvkLFeQvZJC/UEH+ Qgb5CxXkL2SQv1BB/kIG+QsV5C9kkL9QQf5CBvkLFeQvZJC/UEH+Qgb5CxXkL2SQv1BB/kIG +QsV5C9kkL9QQf5CBvkLFeQvZJC/UEH+Qgb5CxXkL2SQv1BB/kIG+QsV5C9kkL9QQf5CBvkL FeQvZJC/UEH+Qgb5CxXkL2SQv1BB/kIG+QsV5C9kkL9QQf5CBvkLFeQvZJC/UEH+Qgb5CxXk L2SQv1BB/kIG+QsV5C9kkL9QQf5CBvkLFeQvZJC/UEH+Qgb5CxXkL2SQv5DRIf6y/Y5YrjLT q/whvXBBeuGC9MIF6YUL0gsXpBcuSC9ckF64IL1wQXrhgvTCBemFC9ILF6QXLkgvXJBeuCC9 cEF64YL0wgXphQvSCxekFy5IL1yQXrggvXBBeuGC9MIF6YUL0gsXpBcuSC9ckF64IL1wQXrh gvTCBemFC9ILF6QXLkgvXJBeuCC9cEF64YL0wgXphQvSCxekFy5IL1yQXrggvXBBeuGC9Eqw rdQZcADpJZhgD7LO/rFSZyM7pBenn40ea0LR0fmy1+taNnv+/FgEy4sElrdefWxwsL414Pej eZHAstVrgq1m588Hw34zpc5UdparXqdYkrHIX+5lP5s915BiLPKXS9nDQszCWOQvN7KanRu2 Nhb5y3X0sjl7Y5G/3MQ8Ozmd2VjkL9ewHUqsqVAku1j+yNhHSp3ZrJS5XivZdE/UgbH8/qZj o9X9pc5udspZrw2s06GxQlOd7MFSZ9cRZarXPOt3aqxwtGcafIiEctRrJzvO+sccGgtKt+2l zrAEZafXNlat3xtxZKyT4ERUuFWvx5XmEjcdnRjLH948B+UbPlyq15Etg5JzrGX9To3lD4R6 VlwoSL4Ljjv1+skuf6VHYvpr2Sw778xYUGjVsZMFy3mhcaderM0fdDxt6k3HLN4aeJLtL2DO C4w79bqw2T/i7IBlcdMxI0MMwUlxBtypFwv6fVmnqWGnnBor3KpLGr6vC2Mlw4Q79QqF/T3X ZZlmltncdEw9ALbWs2OzYsqh+qLkvpC4Uq+Jbr9/S4bf97BQ9nsjuqGCw+dm+54/NgTDkfYV RVuDguFKvdjtoJfdGVjWm44mY82xXpjjsfv45ENsUzFXoUC4Ui+vz+8/b/F9jaObjnFjgbSc qQEuXnQUecml40q9WNTvH0ivx51kjo3FQnDQ1BFVjSYETa0d4Uq9ToagtDnda/oGSiyH90ZE iRU/w9owGgU3BoLvKsFaFARX6sW4JVriYzIl1iAzy3yU8YpGuOGG4q9CgXClXo+IrT9Uf4Ft lzjHCp4/D9JOmJZz1CcqGvV9JVuTvONGvR7fYiiwebqn37GxBlmqLHfVgc6RyZKsQ6Fwo17M 50CiZGPNsmtTllHDZlq4uXbAUBnhRr02RB1rJUqsU5ZL4cdCqMUXOe+Fxo16sVbnxkopsWL0 imNhuLUszrnMuFGvZif19qZjo+klVow+Jo6FRc11cXChXmsbs2kVGevvPM522i7hUEOYVzS6 bitirouEC/Vi09mMleUeVtcIHAubThcpt8XFhXpt689oLPagZYmV4B5+vXCotki5LTIu1IvV 2Rqr2kEfGaIaP35HEfJZClyo16xF9TBbiRWjRlzeDdTJtq5Cgwv1YqnVw+wlVoIpfnl36mhB 81dKXKhXUvXQeBQhc4kVY/9jI0KuQwXOYQlxn157EtVD6UcRxv1lLpcL9WK1cWPJPYqw4WD5 y+VCvXqDwlgKbXCHyl8uF+rFZqDE2iB9Vb23ntfj/dHyuTVpiQv1Unx4Tsg1rtZkrYbdxrxK cxYbN+qlwP6D/GDoHzooPWcN1D6ru9ra232dSU0JXEqZ6CVqhv6wfPvdWdZzLNbcoCX75CWn LPSaODDCt3egVWqu/ex451RS05Dw2C0FymHeKAu9WJvY6lGJOfgzzvVNqZe9wgXLYb4oC73W izY5TXc5fRZ5Jxu0eRSzvaD5zAPloNegqGsEDt7taGouVp1do6vW2QLnNVfKQK9N7WJTDzi5 yLs9k1hc9ILnNkfKQC/jev6qrNPdzaZ7MonFcXsPbWWg14DY0E1dma/hg1gN2Z9Id32FA79e u/UaeXump7uciQUE3X7KjF6vft1eGQ6HK9k5Z2IBDUXMuRLo9WLGCa/NryvZ1JTjvh7cfzjE r1es7bZVRUFOLMBhJxIlBLteg7H6Xtovq52JFWg91h+rNIatHsJ1F8j1uvdMbLsnl1+rnT3n HAlNTbEH2boxXbCnSrQWEiDXi03GNv3Ac/Hv1oFYTdkPfpGxhljrEH5PGxZRqpWQALlep4Lx 49rk0+w2djMLNbZVOXjET4iVuIC1Gb5quedsCdfEIcj1YpUJCVqi7e3Rq508jhkOJonF2IoW DHVDDnK9KmRqf4ZYdaODLPnS8NH7UNz74iDXi6mIld6sO+wP+HC0CUauV4eMWE31cyELsXqP hfE0g0OuF3Pc9WFTPTtus4wWPHJh16s3mF0pfk481XncplfRW2Za/JF+LHJh14s1OBGL2d81 7gOHhtm+IuY4N5DrVVOd+YAYGeufms3Qxzx/0hlJzVAHuV7s1mfsa/RwTpzlubEVIwEUrQ4T YNeL9dl0rsLPifsztsKfYPVDfv/APW6/RZkEer3YQyPpDoPTLJteb8x8GOrxI4XPYF7Brxe7 cLol6SgYqp9j6xw8kXlhsz/cytYWIYd5pAz0Aubqr64MBCpDwc1trNnoBjY7dx2bKmiuCkF5 6AUSeViEnWQXpPyC4xVtSZSJXssG0gsXpBcuSC9ckF64IL1wQXrhgvTCBemFC9ILF6QXLkgv XJBeuCC9cEF64YL0wgXphQvSCxekFy5IL1yQXrggvXBBeuGC9MIF6YUL0gsXpBcuSC9ckF64 IL1wQXrhgvTCBemFC9ILF6QXLkgvXJBeuCC9cEF64aKs9drIOsTfxlJnJH+UtV6x3n1LnY08 UtZ6xXr3dftL2CQoa73IX7ggfyGD/IUK8hcyyF+oIH8hg/yFCvIXMshfqCB/IYP8hQryFzLI X6ggfyGD/IUK8hcyyF+oIH8hg/yFCvIXMshfqCB/IYP8hQryFzLIX6ggfyGD/IUK8hcyyF+o IH8hg/yFCvIXMshfqCB/IYP8hQryFzLIX6ggf7mEDod/MX85nd79oNTLXyhKvWLZwahXR/YN r4j7HYZRL/IXLshfyCiYXqVesexg1Iv8hYyC6VXqFcsOSr3o/Ks8oesbyEBzmHNMWetF/kIG +QsV5C9kkL9QQf5CBvkLFeQvZJC/UEH+Qgb5CxXkL2SQv1BB/kIG+QsV5C9kkL9QQf5CBvkL FeQvZJC/UEH+Qgb5CxXkL2SQv1BB/kIG+QsV5C9kkL9QQf5CBvkLFeQvZJC/UEH+Qgb5CxXk L2SQv1BB/kIG+QsVG41uUDaWOiP5o6z1KkNIL1yQXrggvXBBeuGC9MIF6YUL0gsXpBcuSC9c kF64IL1wQXrhgvTCBemFC9ILF6QXLkgvXJBeuCC9cEF64YL0wgXphQvSCxekFy5IL1yQXrgg vXBBeuGC9MIF6YUL0gsXpBcuSC9ckF64IL1wQXrhgvTCBemFi7LWi/e/UWaUtV6svPq24ZS1 XmXWdxSnnPUqt76jOGWsV0e59c3GKWO9yF+4IH8hg/yFCvIXMshfqCB/IYP8hQryFzLIX6gg fyGD/IUK8ldRuT/nm43kryJS35KrN8hfReSulty9Qf6yYC27innykZMkBsdz9wb5K43tLMQa 1l9dWZGn3MTQ5Vp+/voQi2SZIge9trPpnmi4EBvlofF8vLcLn7/qByKB1tP7Mk2irteKnrGI vlnDdf3KS7Hi0GR+3ttVOH9tKMhSZ8TOH5g6lGEaRb3WTW0OGxu1pW3VOrWF2HBoKpCX9+IV yl/72VT0XXlfKmM3NelrHfAdtZ9ITa+pukhMrekuxezZkZDLnf5ax2Dlw0/nfbnb+mOrHc4w lYJe19zUHlMr3Na1RzmD1hz1xeVyob/WGrvq0EMTeV0upzW+3gP2E8nrdYEZvvUHglM5ZM+O cDzb7vPXrUzsqpH2vC5VpyKxnwbqbaeS1qtrJLbcsC9jTUaJW8ZMcrnOXzccFLtqeAx8ln9M K96+yW4iWb3uGUgcZTMUi8qY5XKbv94VFLtqy/o8LjNBh2nFm2ynktSLX9UzFjlzS44ZTCfZ Xe7yVw07rZcD43flaYmpmNf9EbuJ5PRKyJWpDqNOslzu8le/XskaH8zbEpM5FTSt+aqHbaaS 0msqLlekNf/u6p1JkctF/to/WKcX20MH87I8KyZNa257AUJGr4ND8eW9P/9VDcZa/CnkuLx8 +su4Qtbiy9PyrDCt+eYLNtNI6LV3R3xxAz+dlwya6RXH2kjElGnX+Gu/cf3ZH57Jw9LsMMpH QdBuIud6HWmLnyCEa/OSwWS4XIH+97vSX4Zc/vvysjQ72hNrHjppM41jvc7WJkqXyTxlMIHu rsDUoUfc56+a2O0C/1BXTc5Ls2d/feKMudVuIuf+Go8vrBB1Q0OupLMQt/gr5vls96Zy5WQo vuaRZptpHOu1OyH+ZL4rG3F3saRC1yX+2hErU6OFuY2SoC37rupUr/74dQ1/5HyespcgIZf7 /DUYqxVHClFqJ5Mocux2M4d6nV2fsFdV3rKnY3aX+/y1L35alOGqeZ64OZo3f1UltuKWx/OU vRhmudznr/g+37Ndaj67Gl4m5vLlr+nEeVGkWiEj9ujXDBM3wd3mr37F9a4OhRSiNeXHX3c3 JDaiSjYykSyX2/zV1aS23g+vUtlVNsVPwXL0l+lapP29GQVS3eU2fz2+RXW9a5V2lfgB0W4C Z3qFTFdiGxSyYUcfS5XLbf6qU1xvfrVdZVcxNnRu519rG00b8cy9Ctmw5qcaUuVym79MO2rX T0rNCTN2y7fxWGfsHzle3zCdyNnfSpNGbwmVLJfb/JXITLfUfPzAptJyzKje5Hb98MLm/G3E BEct5HKbvxxccbBEVBym5duO7ZnWg+V4fT6Yt42YQL8DmCqXu/xl3lHl1rvJn+GmYwa69GC5 3f+abc3fRoxxUFw/Tm987C5/BRXXexWfo06+3fNEtx4sx/vL+WtkFqN2yFoud/nLvKPKlF9r xM6och/DWH/bQ6kjvZI2YmM+2t7NtFjL5TZ/mW53S9QP99fq83UqRDQ2st3Pzvxl3og9CplI Zcz2SQx3+cucG5nzL+O6bf82xYj2d9rk/aWQiWTmb/AJucKtVvfR3OUvU2Ykrm8Y9lK6dCdm bJ21+1neX7ZVTcdExb0Z66O7e/0VcjzXdKxhUkD++QL9ysRm29/l/RXOrcFkr9GiOzxm3YLR tf4KOL1Ne1viVrz0tVbj/Mv+1o18/TC3+19HjVaGdnUn9/rL7/M6nCvRTFO+hijujgbO2f4u f/6V0/3lo3rRZesut/nLvKO2dDm7HnifaZ42yXh6k5uQ/QTO/LXLvBVzaMdwl9Gq2X6/c5e/ TC2WgGlH8yS1Km9ZIRlR1FQyVOkc6bUhas62ajuhGuOsK/OzLe7yl/lCnL9l1MEcdyW3Kn/q fVLxfHyeTNUUZ/7yJeVBtR3efcae15JhGnf5a2Vt0oqP2F3VSzA4njSHZAnWJcQOZZjCkV4n bk/OxMBz8u1cj9bHiuGW+t4M07nLX6uS1juQ9amcVLkyVc3TMZpdtPXZT+LMX9WB5HzLP6XR FTNXRne5zV+MJa94Nreky+WPnJaI1iSCZGrY47D9Rig5E5LPaaxlT8bruEO+TO5ym79Opqx4 y/r3ZJj6rnS5YBbnz2OOiBkyPszuTK99kymZaLJ/gj2dPhbvAMI/nmVat/krdcUzPbV9usUf GRjyp7LjToexWjNc94nh0F9zqZlocS5YV1u8hhsYeWx/lqnd5S/zMyOGYPdZ3+bftyYa8Qf6 n/tgml6ZOxhKYJwIZH46wWn73rRnH1ucHhJPJ2aNZL/j6jZ/pR4QgaEd7O6UqXayNZN8a48w iy0Fgu11EMl4Njyc+SzPoV5eX1ouwq1Ze6GYYLM7BgLmObLXK80hSu8vxnrSVtwfCDVMs5XG 2tewq9jgaFAc8McfWxtvgZE0w0j20+bYo/xZbts49df5iEUu2K0Z53mWTbeaDieOHOk2f5mv BpqIjNVOsw746+5qWH+1cbzXy+audIOBJ7MVHzNNxoRZpnP8/FfUIhdNB/cxu2tqdzNfbZN5 4oF7HMUxz+IGf5mutmdh6KBeNlsYDI4tk9P25dLZfcZlVX+4/rbM2XH+fGVqZwucQKiHrbaY diWbbhhLcmRgxFlrPPf562arPdWCxHmlpSO5YjYR1rGgsU8Ensl8xJLQK61Kb9BUP8dOwYFR L5lqmJfNss6pUMrhM9ya/VKOjnkuN/iLsZust38KA/fEzitvezK97NA3wub6WTafsvSdps4J /SMHsmXGub9GrY7Lej6C9Q1njKN5jy/amp7dbEflBEnPmzuey5p8LIOTfFy3xnxeuandbqpA 6xQLwdEnVu3aFq+q6At5LGteJPrfOG2z22QFqkdZjspx7j1jntEd/kq5RWK5htEV5vPKpzM5 Mhysne7q5rt3Y09/0LzkAQfX/2X6t3m/bR4y0jQmEWOLeU6J+azIxzJ0YrU3OwlSr6fWZxPY iqEPOMiJhF6JfihkCNQ5qxfqPJ6kl1v8FWveagOcqKSeV56WF2z8oJOMyPXPZluE2RJucHJu n8D8JIx7/AWld9SuWh+IHrSY3uf0LCAul7NmTFJ69dZLChaIrslwL8eC25KKavf4i7Ejet+i aQztsLrbXmPqNdrRhpp8yFk2ZPsXlRJsaIfk0lNOy6XnTiYfy0hwK5tMP8i12F/ta5A4JIaZ 0y5oJPWScVj4KfkH1pJbYrnJX/wu3rQvae0jAz1dttd3JrJVUhIEglOZ7qolId8/tsNKR6Sd yTU10Uk6Z1CY30w+lpHMo6zTV1XJ8xjedaxnml2Xcep3DVhtmDSaDkrkQFqv/Y+1OzgPi9RN KfUhnVQ9dJm/dLzMIy4NvJtl7z7laFv2Y2J48iZbj1qg8n6HHdku0ETqmNorOtZVJS1HaRkJ 8rGM3Ohj0cw7d1O/45JLR+l9HIMWJa85D2tYtrvI1sSem4rhRn/JMc/W9DfZ7tZjUP5JHoXU 3p+yz6qupOchOMrUHzhKuRSuvByd0vuLsw3O3Sw2VjgK5Z9cj1QcxfcTrWUreoJpueB56JU5 GqeQcmHbttMQZ7jBX5wJtoE1NtQHjb48IpWhqv5VW0MKYrFc3v+1nQ2u8FXpmQjoecjxvVhd 5nIx0DqVa1/U7vCXzq2sl4VERaWZnczhCJTT+xAfgGqFngkPDNl1ce+UQ6Y7bOHN9Wxljstz i7/yinveD5u4gsPLQKe3NzPhJn/lC9foddSQizc+yqUMTED+KiD36E+GNdUq9ctpDfmrYEw1 ierl3PG09g3qkL8Kxm3t/OJpvt/8Q/4qGO/1rcly8VQe8lcBebQAyyR/oYL8hQzyFyrIX8gg f6GC/IUM8hcqyF/IIH+hgvyFDPIXKshfyCB/oYL8hQzyFyo2kr+wQf5CxUZWZu4qc73KENIL F6QXLkgvXJBeuCC9cEF64YL0wgXphQvSCxekFy5IL1yQXrggvXBBeuGC9MIF6YUL0gsXpBcu SC9ckF64IL1wQXrhgvTCBemFC9ILF6QXLkgvXJBeuCC9cEF64YL0wgXplWCP4luVignpxfgb TvrYe1jjdH9VZamzkg3SazULsa626BNhFI+nL2O9HofDXzN/cVDiPUvhaKkzlY3lqVcfezfb usV4FWWCgdpSZywry1Kvxrbo1RavUBsfLXXGsrMc9epIl4rTclepM+aA5agXs9arrdTZcsJy 1MvGX6XOliOWo16sY+uZLW2bg5WBJL3cXpUXLEu9GK/MX2AeNljfGiB/oWECTpan2mM1xVxf b1oUlrVegk2xd0mH8vdmv8JBerG1bGqA6xVYf7bUWckO6cV5bJIXY1WlzoYDSC+BeJlwBEEJ Rnrp3BAFg9Xm+oruwkN6Gdw05Pc3deXjvc8Fpbz06mOzrAP+PMwrPe8OOCJuKUCe8ksZ6bWH nW+rauV180hllW9K+hWmUEmMbihIzvJI+ejVNd1qvr4UaPWdk5r/4VV+f7hAecsfZaLX2Ruu b0m7gBuenO6TWMYgWLOxYDnME2WiFwsG0uTiirU9JLMMv79NRuBSUBZ63TKTbi6DoXpW43Ap Xh8UYAXNZx4oC72Yxc39uMXuc7yULX7/rncXMJf5oAz06p3JIBcw8K5rHC2n5hEEFY4y0IvZ HgwNmtg+R8vh953dftOyDPTakUUuOB+bdFbt8Lv/piV+vU5b1gxTGHfS9on8VQS6sh0NdVpm si5popv8VXD4VQldkKkV3ayDNRxrshYs3Jp1WV0wVajwWc4J7HqxcaFG00/czx5g/F7xg109 Q5aCBSYf2p9xSX1tfn+oKHnOAex6TYsW8ONJ33XdZ13BH7dZRoyg3x/sLVxW8wJyvbb3CCEG k51ztN7aYi0zRzMsi18/dP0DD8j1Yu1cBovvR6yvJ/rsBdszDROcubdwWc0LyPU6BcewwOn0 g9jaQw2Wx8RA3T02S1o7ChXNSHVhs5s7yPVilbbtBm2uATeN2SyJO3WgcBnNE8j1qoDDXr/N bz6bemL0rlstpm7lFZeGuwuZ2XyAXC9+Cen2Eza/3VFnfemj6eCdadOKdgRh1x8OsevFLyF1 2/56dMpasMDADnababrtXZPitGBz4TOcK8j1ynKJdp/dnbHAADvO9MPiHhY7w3b9zRSGXq/m SOZLtBnuPDcda2hkHaxxur7J+GbS2W2XkoJcL9aa9RKtTSGWztBgUXKcG8j1Wlfn96/KOMX+ o77Mt59jhNcXKc85gVwvNu3gMGZ/TDQRyH793g0g16umOmx5OSqZg9Gsx8TAVBFymweQ68Vu jvr9h7NOdcT66hQ+udDrxWojTioKfVPWDUoNIv2HnLZSLDHo9WJgsJEDDqY72GQrV9jumqL7 QK/XfjBYoO3GrNNNsJsmrQ+KgSCWgyErA73YmnF+X2uvgyn3sX4Lj7Vcf0PB85g/8OvFdkPJ FBhxcrK7lnWN1iWZLDLW07Wn4DnMI2WgFwvxLT+0g13nYNqdbHCwNij6PQzvig4zu5sxbqUM 9NrWrxdDAz2DbLuD6a9lvayZdbAQ2+D42RXXUAZ6sc54Pa+uZyuI0YdPBseUg15J/RmGg5vb tnR1s42lzlRhKAe9Ov0WlDpTBaIM9NrUbqWX2x9cUKQM9LK+h1zqTBUI9HpdU592tyTSGm1r pPLLlaxjSZcswlevb4DKuuu7PVEGuV5rEnf7w8H6hsYQu4qtLXWmCglyvURVPlAZrF0Fp7+r S52ZIoBcr4rKKt+Kag9bWcanyEkg10upJzbMYNdruUF64YL0wgXphQvSCxekFy5IL1yQXrgg vXBBeuGC9MIF6YUL0gsXWoWmaRvZ0lLqZxN8/ofnO0ua4HkmPnZrqw/f+8Cpd2+YPPXohkMP PXDng9rw8Dj8MCyYhE8+/xZWIZbbxRrFpwfiVMJnted0n+aphqHeld9n/+jhC33sWyFjWk3T 560Sn1dgGYXOw99oH2J8obPfWm2bB/1T07zwuV5bAf/f9Oj5geUYU2uaDz7f8ujfxb7V561i Hq0RPrdXeiB+hfarnkbPUx7meb9H8/yqp8HDx5/0+DxznrfZs5D4sD4fM5bjNT498bzENPKK fPz/AgAAAP//AwDr7dFTtOcNAA==</item> <item item-id="3">iVBORw0KGgoAAAANSUhEUgAAAsYAAAHiCAYAAADrvQoIAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA Q3hJREFUeF7t3b/PJ0ed4HGDMQhd5L9gdV7drbwsIHvCTUDMaqULQLt3w3JrlhULjOSIzJYI LprAEYEjO0IitOQYCUdkSDh2NhKSMweWLJE/N/WYHvfzne/329VdVd3149XSyDPzdP16f6qq 31Ourv7SzZPrORcCCCCAAAIIIIAAAqMTCGLsQgABBBBAAAEEEEBgdALPjQ5A+xFAAAEEEEAA AQQQuN1FAQMCCCCAAAIIIIAAAggQY30AAQQQQAABBBBAAIFbAlaMdQQEEEAAAQQQQAABBIix PoAAAggggAACCCCAwOcErBjrCQgggAACCCCAAAIIEGN9AAEEEEAAAQQQQAABK8b6AAIIIIAA AggggAACTwnYSqEzIIAAAggggAACCCDwhAAx1g0QQAABBBBAAAEEECDG+gACCCCAAAIIIIAA Ap8TsGKsJyCAAAIIIIAAAgggQIz1AQQQQAABBBBAAAEErBjrAwgggAACCCCAAAIIPCVgK4XO gAACCCCAAAIIIIDAEwLEWDdAAAEEEEAAAQQQQIAY6wMIIIAAAggggAACCHxOwIqxnoAAAggg gAACCCCAADHWBxBAAAEEEEAAAQQQsGKsDyCAAAIIIIAAAggg8JSArRQ6AwIIIIAAAggggAAC TwgQY90AAQQQQAABBBBAAAFirA8ggMBoBB7ee+7m0q9TFn/+6MPoe0fjqL0IIIBAjwSsGPcY VW1CAIGrBD764wc3v/zOi3ek95OPH19M84f33729N6T50+/fQxcBBBBAoFMCxLjTwGoWAghc JxBEeC7H18Q45BTuXboHcwQQQACBtgkQ47bjp/YIIJBAYFoJnlaDL4nvr1+/T4oTOEuKAAII tEKAGLcSKfVEAIEiBN5548HTLRW/+v5Lz5Tx/ttvkuIi5GWKAAII1EeAGNcXEzVCAIGdCQQh nl7IC6vD0/XbRw/tKd45FopDAAEEjiRAjI+kr2wEEKiCwOl+47BKHLZZ/O43b1VRP5VAAAEE ENiHADHeh7NSEECgcgLhtIn5MW5hi4ULAQQQQGAsAsR4rHhrLQIIXCEQtk5MchxOoXAhgAAC CIxFgBiPFW+tRQCBKwROP+gx328MHAIIIIBA/wSIcf8x1kIEEIgk8Oi1ezd/+ezTO+cb22cc Cc9tCCCAQAcEiHEHQdQEBBBIJxCkeDrH+HTlOHwpz4UAAggg0D8BYtx/jLUQAQQWCJw7li2s FM/3G4eVZBcCCCCAQN8EiHHf8dU6BBBYIBCOZQvHs527wh7jSY7DirILAQQQQKBvAsS47/hq HQIIXCEQtkwsvWAXTqeY5PiSQIOMAAIIINAHAWLcRxy1AgEEVhKYPuqxtEXCfuOVYN2OAAII NEyAGDccPFVHAIFtBIIUh60RYSU4bKW4doWfzz/8EVaQw8dAXAgggAAC/REgxv3FVIsQQGCB wFx0p9+fJpm/fHfu/vB3LgQQQACBvgiY2fuKp9YggAACCCCAAAIIbCRAjDeCkwwBBBBAAAEE EECgLwLEuK94ag0CCCCAAAIIIIDARgLEeCM4yRBAAAEEEEAAAQT6IkCM+4qn1iCAAAIIIIAA AghsJECMN4KTDAEEEEAAAQQQQKAvAsS4r3hqDQIIIIAAAggggMBGAsR4IzjJEEAAAQQQQAAB BPoiQIz7iqfWIIBAAgEf7UiAJykCCCDQAQFi3EEQNQEBBNIJzL9ul56bHBBAAAEEWiRAjFuM mjojgEBWAjGfiM5aoMwQQAABBKokQIyrDItKIYDAngTOibFtFXtGQFkIIIBAHQSIcR1xUAsE EDiIwCUpJsYHBUSxCCCAwIEEiPGB8BWNAALHErgmxdPPjq2h0hFAAAEE9iRAjPekrSwEEKiG QIwUk+NqwqUiCCCAwC4EiPEumBWCAAK1EVgjxrZV1BY99UEAAQTKECDGZbjKFQEEKiawVoqt HFccTFVDAAEEMhIgxhlhygoBBBBAAAEEEECgXQLEuN3YqTkCCGQk4AMfGWHKCgEEEGiUADFu NHCqjQACeQkQ47w85YYAAgi0SIAYtxg1dUYAgewEiHF2pDJEAAEEmiNAjJsLmQojgEAJAsS4 BFV5IoAAAm0RIMZtxUttEUCgEAFiXAisbBFAAIGGCBDjhoKlqgggUI4AMS7HVs4IIIBAKwSI cSuRUk8EEEAAAQQQQACBogSIcVG8MkcAAQQQQAABBBBohQAxbiVS6okAAggggAACCCBQlAAx LopX5ggggAACCCCAAAKtECDGrURKPRFAoCgBL98VxStzBBBAoAkCxLiJMKkkAgiUJkCMSxOW PwIIIFA/AWJcf4zUEAEEdiBAjHeArAgEEECgcgLEuPIAqR4CCOxDgBjvw1kpCCCAQM0EiHHN 0VE3BBDYjQAx3g21ghBAAIFqCRDjakOjYgggsCcBYrwnbWUhgAACdRIgxnXGRa0QQAABBBBA AAEEdiZAjHcGrjgEEEAAAQQQQACBOgkQ4zrjolYIILAzAVspdgauOAQQQKBCAsS4wqCoEgII 7E+AGO/PXIkIIIBAbQSIcW0RUR8EEDiEADE+BLtCEUAAgaoIEOOqwqEyCCBwFAFifBR55SKA AAL1ECDG9cRCTRBA4EACxPhA+IpGAAEEKiFAjCsJhGoggMCxBIjxsfyVjgACCNRAgBjXEAV1 QACBwwkQ48NDoAIIIIDA4QSI8eEhUAEEEEAAAQQQQACBGggQ4xqioA4IIIAAAggggAAChxMg xoeHQAUQOI7AZ9/73nGFKxkBBBBAAIHKCBDjygKiOgjsRSBI8fRrrzJrLsce45qjo24IIIDA PgSI8T6clYJAdQTmYvz/Xr+5Gf3XXIwnFtUFrbIK+T8OlQVEdRBAIJkAMU5GKAME2iRAjO/+ Y+CcGI/+j4Vr7fd/HNoc92qNAALXCRBjPQSBQQkQY2KcIv7EeNCJQ7MR6JwAMe48wJqHwCUC xJgYE2PzAwIIIHCXADHWIxAYlAAxJsbEeNDBr9kIIHCRADHWORAYlAAx9sIhMR508Gs2AggQ Y30AAQTuEiDGxJgYmxUQQAABWyn0AQQQeEKAGBNjYmwqQAABBIixPoAAAsT4mXObHde27h8K TqUwjSCAQI8E7DHuMarahEAEASvGXr6zYhwxUNyCAAJDESDGQ4VbYxH4ggAxJsbE2IyAAAII 2EqhDyCAgK0UtlIkfgbcVgrTCAII9EjAinGPUdUmBCIIWDG2YmzFOGKguAUBBIYiQIyHCrfG ImArxSUZ9PKdl+/MDwgggAAx1gcQGJSAFWMrxlaMBx38mo0AAhcJEGOdA4FBCRDjdSukKRLZ Y1p7jAedODQbgc4JEOPOA6x5CFwiQIyJcYqwE2NzCwII9EiAGPcYVW1CIIIAMSbGxDhioLgF AQSGIkCMhwq3xiLwBQFibI8xMTYjIIAAAncJEGM9AoFBCRBjYkyMBx38mo0AAhcJEGOdA4FB CRBjYkyMBx38mo0AAsRYH0AAgbsEiDExJsZmBQQQQMBWCn0AAQSeECDGxJgYmwoQQAABYqwP IIAAMb45lUJfvlt3Sofj2kwjCCDQIwF7jHuMqjYhEEHAivE6EUxZXe0xLTGOGGRuQQCB5ggQ 4+ZCpsII5CFAjIlxirAT4zzjUC4IIFAXAWJcVzzUBoHdCBBjYkyMdxtuCkIAgUYIEONGAqWa COQmQIy9fEeMc48q+SGAQOsEiHHrEVR/BDYSIMbEmBhvHDySIYBAtwSIcbeh1TAErhMgxsSY GJslEEAAgbsEiLEegcCgBIgxMSbGgw5+zUYAgYsEiLHOgcCABOZSHH6fIki9pHWO8bqXEZ1K MeDEockIDECAGA8QZE1E4JSA1eJnJZAYE2MzBQIIIECM9QEEBiRAjNdJYC+r4jnbYcV4wIlD kxEYgAAxHiDImoiAFWMinFOKQ17E2LyCAAI9EiDGPUZVmxBYIGDFmCinijIxNs0ggECPBIhx j1HVJgSI8eoXCu0xXvePBWJsmkEAgR4JZBXj+YPF75+7wQCDWvqArRTL0keMlxnNV5mJcY9K oE0IIJBVjE9xzh80vaIObez9GqGNvceQGC9LHzFeZkSMR5sptBeB8QgUtbrexXhqX8/dZoQ2 9hy/S22zx9hxbfYYjzjytRkBBJYIFBPjc/8Leakyrf28d/EP8Rihja31uxz1JcbEmBjnGEny QACB3ggQ440RPRX/jdlUnWyENlYdgIKVI8bEmBgXHGCyRgCBZgkUEeNrLxw1S2pW8RFWw09X i22p6KHnftEGYkyMiXFfY1prEEAgD4HdxbiHF7kuiX+ekNSRywhtrIP0MbUgxuteNEuVyB7T O5XimLGrVAQQKEsguxjHHE9Vtkllc+99NXyiN0o7y/aWenMnxsQ4VdaJcb3jW80QQGA7gUPE uOVV4yXx3x6KelKO0MZ6aB9TE2JMjInxMWNPqQggUDeB7GI8b25vJxosCWPLwn8pbrZU1D2A t9aOGNtjTIy3jh7pEECgZwLEOCG6o7yQNko7E7pCc0mJMTEmxs0NWxVGAIEdCBDjBMijCOMo 7UzoCs0lJcbEmBg3N2xVGAEEdiBAjBMgjyKMo7QzoSs0l5QYE2Ni3NywVWEEENiBADFOgDyK MI7SzoSu0FxSYkyMiXFzw1aFEUBgBwLEOAHyKMI4SjsTukJzSYkxMSbGzQ1bFUYAgR0IEOME yKMI4yjtTOgKTSWdS3H4faogST/m0W/OMW5q2KssAghEEiDGkaDO3TaKMI7SzoSu0FRSq8Vj imzuf8AQ46aGvcoigEAkAWIcCYoYF+0qCVGQdC0BYkyMc0gyMV478tyPAAItEChqO7194OM0 oKOspI7SzhYGbI46EmNiTIxzjCR5IIBAjwSIcUJURxHGUdqZ0BWaSkqMz4vx/B/yOcSx9zxO 96o3NQhUFgEEELhAgBgndI1RhHGUdiZ0haaSEmNinEvabadoauirLAIIRBAgxhGQLt0yijCO 0s6ErtBUUmJMjIlxU0NWZRFAYEcCxDgB9ijCOEo7E7pCU0mJMTEmxk0NWZVFAIEdCRDjBNij COMo7UzoCk0lJcbEmBg3NWRVFgEEdiRAjBNgjyKMo7QzoSs0lZQYO5WCGDc1ZFUWAQR2JECM E2CPIoyjtDOhKzSVlBgTY2Lc1JBVWQQQ2JEAMU6APYowjtLOhK7QVFJiTIyJcVNDVmURQGBH AsQ4AfYowjhKOxO6QlNJibE9xsS4qSGrsgggsCMBYpwAexRhHKWdCV2hqaTEmBgT46aGrMoi gMCOBIhxAuxRhHGUdiZ0haaSEmNiTIybGrIqiwACOxIgxgmwRxHGUdqZ0BWaSkqMiTExbmrI qiwCCOxIgBgnwB5FGEdpZ0JXaCopMSbGxLipIauyCCCwIwFinAB7FGEcpZ0JXaGppMSYGBPj poasyiKAwI4EiHEC7FGEcZR2JnSFppISY2JMjJsasiqLAAI7EiDGCbBHEcZR2pnQFZpKSoyd Y0yMmxqyKosAAjsSIMYJsEcRxlHamdAVmkpKjIkxMW5qyKosAgjsSIAYJ8AeRRhHaWdCV2gq KTEmxsS4qSGrsgggsCMBYpwAexRhHKWdCV2hmaRzKQ6/zyVIPeQz9fPw3x7as0cb5v2pmUGg oggggMAVAsQ4oXuMIoyjtDOhKzST1Grx5dViYrx+JZ0YNzP0VRQBBCIJEONIUOduG0UYR2ln QldoJikxJsY5V5JP/w9EMwNBRRFAAIELBIhxQtcYRRhHaWdCV2gmKTEmxjnFOORl1biZ4a+i CCAQQYAYR0C6dMsowjhKOxO6QjNJiTExJsbNDFcVRQCBAwgQ4wToowjjKO1M6ArNJCXGxJgY NzNcVRQBBA4gQIwToI8ijKO0M6ErNJOUGK9/wSy3SPaWn60UzQx/FUUAgQgCxDgCkq0Uz90E OXa1T4AYE+PcYk6M258XtAABBL4gUNR25scf9Qh9lJXUUdrZYx89bRMxJsbEeISRro0IILCV ADHeSm6gdMS4n2ATY3uMiXE/41lLEEAgPwFivIHpz159/uYHL399Q8o2kxDjNuN2rtbEmBgT 437Gs5YggEB+AsR4A9P5FpER9t4S4w2dpNIkxJgYE+NKB6dqIYBAFQSI8YYwXJPhuTT/r//5 tQ2515eEGNcXk601IsbEmBhvHT3SIYDACASI8cooP/jGedn9+atfvj254fRX+PvWL2LcegS/ qD8xJsYlxTj0LxcCCCDQMgFivDJ6v3jlS8+kuLa14hf3nr1/ZZGH306MDw9BtgoQY2KcW4x9 Fjrb8JQRAghUQIAYbwjCtJXiBy9/9c4K8b9/6ysbcqs/CTGuP0axNSTGxJgYx44W9yGAwIgE iPGGqJ/bNvGjb351Q05tJCHGbcRpqZZzKQ6/LyFI8hzznGQf+VgafX6OAAKtECDGCZEKwvij b76QkEMbSYlxG3FaqqXV4jGldY9/rBDjpdHn5wgg0AoBYlwwUmFrRQ/HuRHjgp1kx6yJ8XUx nr8rsIdM9lQGMd5xICsKAQSKEiDGBfD2ds4xMS7QSQ7IkhgT41IyTowPGNCKRACBIgSIcRGs fWVKjPuIJzEmxsS4j7GsFQggUI4AMS7HtpuciXEfoSTGxJgY9zGWtQIBBMoRIMbl2HaTMzHu I5TEmBgT4z7GslYggEA5AsS4HNtucibGfYSSGBPjPcTY1+/6mC+0AoFRCRDjUSO/ot3EeAWs im8lxo5rKyXGvn5X8cBXNQQQWEWAGK/CNebNxLiPuBNjYkyM+xjLWoEAAuUIEOMMbHs4q/ga BmKcoZNUkAUxJsbEuIKBqAoIIFA1AWKcITzEOANEWRQl4HPQpLikFNtKUXT4yhwBBHYkQIwz wCbGGSDKoigBq8XLYuzLd8uMrsm1j3wUHcIyRwCBnQgQ4xPQW7YNEOOdeqtiNhMgxsvSR4yX GRHjzUNQQgQQaIRAt2J8+lnm8Odf3PtSVFjWynEJMQ51reXT0mt5REF2064EiPGy9BHjZUbE eNdhqzAEEDiAQJdifE6K53/3w3944SrqtSK4Voz/49tfOZHeu8K+VP+9+8laHnvXT3nLBIjx svQR42VGxHh5rLkDAQTaJtC9GP/g5a/fidAXK7GXV4/XiuAaMX5Wip97KsmhokurxA//upK8 Z7dby2PPuikrjgAxXpY+YrzMiBjHjTd3IYBAuwS6E+Pp4fbzV798NSo/feXLtxJ67lorgmvE eMr7v56Uf3otSfF0/4+/9ZWb15782utay2OveiknngAxXpY+YrzMKFaMff0ufmy6EwEE6iLQ rRjHYj4ntWtFcIsYX6pfbNlryoxlkVqn1HKkL0eAGKdJX+mjznrJ38kU5cawnBFAYB8Cw4tx wHwqmbFyOoVojaQu5f3Pf/ffLq5kz7vEmjJTu9JSnVPzl748AWJMjPeQb2JcfiwrAQEEyhIg xn/lOxfNtSK4RlJj8o7JL+aeXF0nps65ypJPGQLEmBgT4zJjS64IINAXgQ7F+PNjzrZcU7q1 IrimvLV5n2vH/375a5vbuJXLmjZuKUOacgR89S5Oiu0xjuMUu8+4XI+WMwIIIFCOwDaDjKzP /EETmSTLbSnyuaXOa6Ux3H/u5bvYxqe0L7aM+X17l7eljtJcJmC1OE74iHEcJ2JstkEAgZ4J dCnGIWCfP+TiPuhxGuC1IrhWjKfytp4ssbZ+qR147/JS6yv9XQLEOE74iHEcJ2JshkEAgZ4J dCvGU9CWPuZxKbhrZHfNvS12JmLcYtS+qDMxjhM+YhzHiRi3PR+oPQIIXCfQvRjv0QGI8R6U lbGVADGOEz5iHMcpVoydZbx1xEqHAAJHEiDGGegT4wwQZVGMADGOEz5iHMdp6XQLR7YVG8oy RgCBHQgQ4wyQiXEGiLIoRoAYxwkfMY7jRIyLDVUZI4BABQSIcQVBqL0K9hjXHqHr9SPGeYRv SQj9/HPOVozbni/UHoHRCRDj0XtARPuJcQSkim8hxsR4T2knxhVPBqqGAAKLBIjxIiI3EOO2 +wAxJsZHibEX8NqeO9QegREJEOMRo76yzcR4JbCKbvfVu3gptsc4ntWSaFs1rmgSUBUEEFhF gBivwjXmzcS43bhbLY6XPWIcz4oYtzsnqDkCCFwnQIz1kEUCxHgRUbU3EON42SPG8ayIcbVD XsUQQCCRADFOBBiS9y6OvbcvQxeoNgtiHC97xDieFTGudsirGAIIJBIgxokAiXEGgLIoRoAY x8seMY5nRYyLDVkZI4DAwQSIcYYA9L6i2nv7MnSBarMgxvGyR4zjWRHjaoe8iiGAQCIBYpwI 0IpxBoCyKEaAGOeTvSUZ9PMvWDuVotiQljECCBQmQIwzAO59RbX39mXoAtVmQYyJ8RHCfnpM YLUDRMUQQACBEwLEOEOX6F0ce29fhi5QZRbOMCbFR0jxVKZV4yqnBZVCAIEFAsQ4QxfpXRx7 b1+GLlBlFlaL14mxPcbreC1JNzGuclpQKQQQIMb5+sD8wbnm9/lqcExOxPgY7qmlEuN1okeM 1/EixqkjVHoEEKiRgBXjDFHpXRx7b1+GLlBlFsR4negR43W8iHGVw16lEEAgkQAxTgQYkvcu jr23L0MXqDILYrxO9IjxOl7EuMphr1IIIJBIgBgnAiTGGQDKIjsBL96tlzxivJ7ZNTm2xzj7 sJYhAgjsQIAYZ4Dc+4pq7+3L0AWqy8Jq8XrJI8brmcWKceiPLgQQQKAFAsQ4Q5R6F8fe25eh C1SXBTHOK3lL2wb8/Dxvq8bVTQ0qhAACCwSIcYYu0rs49t6+DF2guiyIMTGuQdaJcXVTgwoh gAAxLt8HehfH3ttXvofsXwIxJsbEeP9xp0QEEGifgBXj9mNYvAXEuDji7AUQ4/VibI/xemZL 8m3FOPvQliECCBQmQIwLA5Y9AkcQIMbrJY8Yr2e2Roy9gHfETKBMBBBYS4AYryXmfgQaIECM 10seMV7PbEmMw8+tGjcwYagiAgg8JUCMdQYEOiPgDONtgkeMt3FbkmNi3NkEozkIdE6AGHce YM0bj4DV4m2CR4y3cSPG480xWoxAzwSIcc/R1bYhCRDjbYJHjLdxI8ZDTjMajUC3BIjxxtD+ 7NXnb372ypcupv7xt1/YmLNkCKQRIMbbBI8Yb+O2Roy9gJc2tqVGAIHyBIjxBsbzB+j0+3// 5rMi/PNXv7whd0kQSCNAjMsI3pIA+vll7vYZp41pqRFAYD8CxHgl63/75tduggyH60fffP72 9+d+/dPffv32nunelcW4HYHNBIgxMa5N0onx5uEsIQII7EyAGG8Afkl2L0nyhiIkQWATASdS kOLapNiRbZuGskQIIHAQAWK8AfyPv/XCzQ+/8dWLKf/17796ZxV5QxGSILCJgNXi7WJsj/F2 dksybsV403CWCAEEDiBAjM9A/0nEi3O2SBzQWxW5SIAYb5c7Yryd3Rox9gLe4jB2AwIIHEiA GJ+B/x9PxHj+kLwUHy/XHdhzFf0MAdso0sSOGKfxWyPHhi8CCCBQKwFifCEyl/YLP7z37BFt 5/6u1oCrV78ErBaniR0xTuNHjPudW7QMgZEIEOML0f6/s+PXXnvy+0ui/NNXnr/NwdaKkYZN nW0lxmliR4zT+BHjOucFtUIAgXUEiPEFXv/y8tcukgxbKEY4geJPv3/v5tev3yf968bUIXfb RpEudcQ4neE1OT7to4cMFIUigAACCwSIcWIXCSvLlyT5u389yzixiF2Tf/Lx45vfPnp488vv vBi1z3rXyinsIgGrxWWlbmk11M/j+DudwiSGAAK1EyDGmSN0TpIzF1E0u0ev3btdJSbGRTFn z5wYx4kZgT2WEzHOPvRliAACmQkQ48xA59mFl/Ja3Xv80R8/sGJcsG/kzpoYpwufrRTpDJf+ 4WE7Re6RLz8EEMhNgBjnJtpRfjFH1nXU3GabYn9xHqEjxnk4rpHjZgediiOAQLcEiHG3oU1v GDFOZ7hHDlaL8wgdMc7DkRjvMeqVgQACpQgQ41JkO8iXGLcRRGKcR+iIcR6Oa8TYV/DamGPU EoGRCBDjkaK9sq3EeCWwg24nxnmEjhjn4bgkxuHnXsI7aLJQLAIILBIgxouIxr2BGLcRe2Kc R+iIcR6OxLiNeUMtEUDgPAFirGdcJHBJjH/3m7dufvX9l25PrQjHuoVzj13HEPDiXT6ZI8b5 WC7JsRXjY+YLpSKAwDIBYrzMaNg7TsU4fPxjEuLT85rD+ceu/QlYLd5P5pZkz8/jY+HYtv3n CiUigEAcAWIcx2nIu+byO0lxWC2erj9/9OGdD4GET0i79iVAjONljLjWxcqq8b5zhdIQQCCO ADGO43T2rl882UrQ8zUX47BSHOT49AoyPN0Xvpjn2pcAMa5L9sh3fDyI8b5zhdIQQCCOQFGz 6/nlrSDF575q1+qX7s51l9MV43P3/OWzT5+Kcdhv7NqPgP3F8RIWI6z2GOflucScGO83VygJ AQTiCRDjeFbP3DmSGF/D1PM/gBK6R/GkVovzihwxzstzjRg7z7j4dKEABBCIJFBUjCPr0Oxt xPjz0BHjY7owMc4rcsQ4L88lMXae8THzhlIRQOA6AWKc0EOIMTFO6D7JSYlxXpEjxnl5EuPk IS4DBBA4gAAxToBOjIlxQvdJTkqM84ocMc7LkxgnD3EZIIDAAQSIcQJ0YkyME7pPUlIv3uWX OGKcn+mSHDvPOGkakBgBBAoQIMYboY52KsU1TPYYb+xECcmsFu8vcUuS5+fbYuJ0ioSJQFIE EMhOgBhvREqMvwBHjDd2ooRkxHibhJHX+rgR44SJQFIEEMhOgBhvREqMifHGrpMlGTGuT/BI 97aYEOMsU4JMEEAgEwFivBEkMSbGG7tOcjL7i7cJGHGtkxsxTp4SZIAAAhkJEOONMIkxMd7Y dZKTWS0uI3hevivDdekfJF7AS54SZIAAAhkJEOMEmD2fSvHJx4/vfLgjfPr50jUXij9/9GEC UUljCBDjMgJHjMtwXRJjH/qIGfXuQQCBvQgQ4wTSvYpxkOJHr927I8bhz+fk+P2334y6LwGz pDMCtlGUkzdiXI7tkhzbTmGaQwCBWggQ44RI9CjGczm49PsJ2dK9CWglvUDAanE5eSPG5diu EePQx10IIIDAUQSI8VHklYvABgLEuJy8EeNybJfE2HaKDZOBJAggUIQAMS6CVaYI5CdgG0VZ cSPGZfkuybHtFPnnDDkigMB6AsR4PTMpEDiEgNXiY8VtSez8PC0+Tqc4ZFpRKAIInBBYJcZL e0qXvoCWml70EBiZADFOEy/iWj8/q8Yjz3DajkAdBFaJ8VTlcGrBO288uHMaQfhz+PuYKxzp lZI+pgz3INATAdso6pc64p0eI2Lc06ylLQi0SWCTGIemhqO75ivA1865PYcmNX2buNUagW0E rBanS9eSuNpjXJ7xUgxsp9g2P0iFAAL5CGwW41CFpa0TS9VMTb+Uv58j0AsBYlxe2ohxecZL Yux0il5mLO1AoF0CxLjd2Kn5QASIcXlpI8blGRPjgSYtTUWgUQLEuNHAqfY4BOwv3kfYiPE+ nJfk2HaKceY2LUWgRgLEuMaoqBMCMwJWi/cRNmK8D+clMbadwvSHAAJHEiDGR9JXNgIRBIjx PsJGjPfhTIwjBr1bEEDgMALE+DD0CkZgmYBtFPvJGjHej/WSHNtOsTw3uAMBBMoQIMYFuE4P 2B+8/PUCuctyJAJWi+uRtSWZ8/O8sXKm8UgznbYiUA8BYlwwFvMVqP/zjRfulPSzV58vWLKs eyFAjPPKFnlthycx7mUW0w4E2iJAjHeI1+mnsP/rlc+l+If/8NUdSk8vYqp/ek5yWEPANop9 Jc5Win15L/0jxXaKNbOFexFAIBeBbGJ8Kn9r/5yrQbXl87NXvnSnSj/59ldu/9ySbLZU19ri n1Ifq8X7ihox3pf3khg7nSJl9pAWAQS2EsgmxlsqMMKX7/7xb9rfZ0yMt/Tu9DTEeF9RI8b7 8l4rxmE8uBBAAIHSBIhxacJ/XR3eoZhiRRDjYmgvZmwbxf6SRoz3Z75WjvcfiUpEAIHRCBDj nSIeHro/+Luv3Skt/F0LFzHeP0pWi/eXNGK8P3NivP/cokQEELhOIMnMUrdCpKZvLbin+65/ /uqXm2gCMd43TFaLjxE0YnwM9yU59hLevvOP0hAYnQAxPqAH/Oe3X7j5t2/cXT0+oBrRRRLj aFRZbrRaXKegLQmcn5eLm6PbskwtMkEAgQgCxPgCpFa2OUTEOPkWYpyMMDoDq8Xl5Iq4tsvW qnH0FOJGBBBIJECMifFiFyLGi4iy3ECK2xU30l0+dlaNs0wzMkEAgQUCxPiKGAch/Of/0f5x a6mjgBinEoxLbwtFebkisO0ytmocN4+4CwEE0ggQ4wUxHu0FwXM4iHHaIItJbbX4eGHz8t3x MVj6h4tV45jZxD0IIJBCgBivEOP5g/Nf/76NzzmndI4pLTHOQfF6HlaLj5cyYnx8DNaIsQ9+ lJ+XlIDAiAQ2i/FfPvv06WeNwwMl/HnNlZp+TVlb7g1t+u7ffv3mn55spXjtWy/caevpsWs/ feX5i0V870kerV/EuGwErRbXIWTEuI44kOOy843cEUDgOoFNYvzJx49v3nnjwR1ZDH8Ofx9z hfsevXZvc/qYMlLvuXYqxU++/ZWrovzjJ8exTVcrZxVf40WMU3uT1eIl2anh58S4DTEOfcWW irJzktwRGJnAKjE+XSm99udzUFPT1xao0J4ffuP6anIPx74R43I9z2pxPTJGjOuJxdI/lLyI V25OkjMCoxNYJcajwzpt/6n0/uDlr59dSW6dGzEuE0FSXJeIEeO64rFGjsuMULkigMCIBIhx QtSXVoN7Ecpe2pEQ6iJJvXDXlogtiZqf7xtPq8ZFpiWZIjA8AWKc0AWWxDhkbY9xAuCOk1ot 3leiSGufvMlxx5OkpiFwEAFifAF8jPTG3POPf+NUioP6dtXFWi3uU9QI+P5x9SJe1VOdyiHQ HAFiXFiMm+sRZypsK0XeKFot3l+eYoTVHuM647IUO6vGeecnuSEwOgFifEWMl1aEl37eS+ci xnkjabW4TgEjxnXGZUmMT49v8+GPvPOV3BAYjQAxXhDja1JIjEcbLunttVpcr3wR43pjs1aO 00eqHBBAYFQCxDhSjOcPzemFOmI86rDZ3m6rxfXKFzGuNzZrxdiq8fY5SkoERidAjCP2GKd8 ErqHDmYrRb4oEuN65YsY1xubGDH2Rbx885ScEBiZADGOEOP5LT995fmrn4MOD9d/+cYXJ1H8 +Mnno1u/iHGeCNpGUbd4EeO64xMjx06oyDNXyQWBkQkQ4wvRf/DkU88x18N7X1oU5Zh8ar6H GOeJjtXiusWLGNcdn7VibDtFnnlLLgiMRoAYZ4z4j7/9gk9CZ+TZU1ZWi9uXrhgxc8/xcbZq 3NPMqS0I7E+AGBdi/p+zLReFitgtWyvGaahJ8fGyRFjHiYFzjdPmK6kRGJ0AMS7cA3wSujDg yrMnxe0Ima0U7cRq6R86Vo0rnxhVD4GKCRQV4/mDpmIGRav23f/uk9BFAVecOSluS7SIcVvx uibHVo0rnhhVDYHKCRDjygNUQ/VspdgWBS/btSVaxLiteK1ZNfYi3rY5TCoERiRAjEeM+so2 E+OVwJ7cbrW4Pckixu3FjByvn5ukQACB6wSIcUIPGUUYR2lnQle4k5QUtylYxLjNuJHjXDOX fBBAIBAgxgn9YBRhHKWdCV3haVJS3K5cEeN2Y7dGjnOMc3kggEC/BIhxQmxHEcZR2pnQFW6T kuJ+xWpJvPy87th7GS91dpMegXEIEOOEWI8ijKO0M6ErkOLX6xYj4io+5DhlhpMWgXEIEOOE WI8ijKO0c2tXsFJMuoh3G32AHG+d5aRDYBwCxDgh1qMI4yjt3NIVSHEbQhQjrvYY9xNLZxxv mc2kQQCBQIAYJ/SDUYRxlHau7QqkuC+RIsZ9xZMcr53R3I8AAsQ4sQ+MIoyjtDO2O5wKcfhz zIqke+oWL2Jcd3xyjx/bKmJnPPchMBYBK8YJ8R5FGEdpZ0xXIMX9yhMx7je2l6SaHMfMeu5B YCwCxDgh3qMI4yjtXOoKpLhvcSLGfcc3Ro6X5gA/RwCB/gkQ44QYjyKMo7TzWlewn7h/aSLG /cf4nByf+wdvwmNBUgQQSCTwu9+8dTOfj09/H35+ep1Lc+6+mKoR4xhKF+4ZRRhHaee5MFsl HkeWiPE4sT4V5HPjPPydCwEEjiHw0R8/uPnld168I8jvvPFgsTKPXrt3m+7PH324eO+lG4jx ZnQ3TwOWkEWVSa/9S20kSSbF44pS7he95Fd/XyLHVT6OVGpgAkFu5z4SswL869fv3/zp9+8l USPGCfh6lcQlMU5A1kTSSw9IclO/3IiRGKX2AYLcxDStkoMQ+O2jh0/lOGbFOKwWp17EOIFg r2IckFyT4wRk1Sa99DB0FBvRShUt6dvsQwS52ulaxQYi8JfPPr3jI+HPl64/vP/uzftvv5lM hxgnIBxRjBNwVZn0mhCT4jaFZquI2mM8Vrxj+snS/FDlpKZSCOxMIMydJa/5qvE18Q37i6+J c2wdi7Zm/qCJrVBL9/UsxpdWjVuKz1JdrRITobkcEWP9IeZINyvJSzOrn49GoLQLzfcaX9oq 8cnHj2+CGOe4iHECxdKdIaFqWZKebqfIkunBmZBh8nNJfoixvrG0iry0guwki4MneMUfQmCP RdDwUt1UzrmX8MJKcupLdxM8YpzQjXoX49NV4wRUuyeNeYBN9yw9DP18DGEixmPEOdd4jplj dp/4FIjAAQTOvZOUuxrh+LapnF99/6Vnss/x0h0xzhC1kcQ4A67sWcQ8mJbuyfWQlE/7UkWM 24/hUeNwaZ7xEZHs078MKyJw6WX93FUMQjyVNV8dDtIc9iHnuqwYJ5DsUYyXjmpb+/MEvE+T bnno2DJBctZK0lLfvrQf+ZJQX8tvyuvSPTFlhbQx951yWCpz6ec56p6rTlsYxMQl1C+VwzQH XcrndNtFjFzE3HP6f/rO/W/uawzmc3ZMeTnzSqn76bMmpe5r88rJIFdeufKZWFzjGVtWqg8E GT63ahyOcQtynOsixgkkexTjBBzFkm4V47Vi5H4ynVOaYvKKka+YfHKI3BrhjS0vpn0xeeVk kDOva3Unxnf1IkZUiXH8UalLPGNldSmfVDEO+ee85l/Dm75ud25rRUqZeWt8UpNz/1JNqWxt aYnxPhGx+ktY/aNFH+ilD8zns31m0POljPT86t1FjuxH1/4xk1uKQ1nhxbspnuGFvFxnF88Z EuOEHjXSxJKAKTnp/EHSy8NRO4iePjBmH6hNjEvIS/KknzGD0xXRjFnL6q8EYledcwGbrxqH 3+c4u5gYZ4oOMc4EciEbYjymQBBHce+xD9QgxiPJ4jlp2+fJNU4pezOef/Aj19nFxDhTfyXG mUAS4zsvUfUoA9pEcvWBz/tAjWLc66rx3iuZ+zwR6ytl739ozT8TnevsYmKcqV8R40wgiTEx fiIMxAmDEfrA0WI8kizGvoC2z5Os31KO2MM9rRqXoGqPcQJVYpwAb0VSWykI0wjCpI1j9PNa xbi3VeNrUtxbW1c8TovceoQLhZfucp5dbMU4U9c4ojNkqnpT2RDjMYSBGIrzCH3gSDEeSRaX 2kqO82nAESzD2cXTcW35WvJ5TlaME4gS4wR4K5ISY8I0gjBp4xj9vGYxPkJwVjwKkm71vE7C V1XisMc499nFVowzhdhAywTSHmP7a+0x1gcG6QNHivF8qh3t+TVae/d5Oh9TSthGEc4zLnVZ MU4ga6AlwFuR1IrxGCtpVkzFeYQ+QIxXTP4Zb/W8zgjz4KzCEW25zy62YpwpqAZaJpBWjK0W DrJaOIL4aeP1f+AQ432eG6eleF4fwz13qWFfcdhfXPKyYpxA10BLgLciqRVjK4lkSx/opQ8Q 4xWTf8ZbPa8zwtwpq/fffvP288/h089BiMMqcenV4tA0YpwQYAMtAd6KpMSYFPUiRdqhLxPj FZN/xls9rzPC3Cmr05NFwuefP/rjB8VLJ8YJiA20BHgrkhJjMkEo9YFe+kAtYrxiCu7iVs/r 9sIYvmoXZDjELmyf+OTjx7s0ghgnYDbQEuCtSEqMSVEvUqQd+jIxXjH5Z7zV8zojzM6zIsYJ ATbQEuBFJp0/RMLviQWx0Af0gZb7ADGOnPwz3+Z5nRlox9kR44TgGmgJ8CKTWi0mQS1LkLrr v6d9gBhHTv6Zb+vpef3hhx/ePHjw4ObFF1+8ee65527/G/4c/t6VToAYJzDsaaAlYCialBgT C3KpD/TUB4hx0UfGxcx7eV6/9957tzJ86Vf4uSuNADFO4NfLQEtAUDwpMSZFPUmRtujPxLj4 Y+NsAT08rz/99NNbIQ6rw48ff/Ei2gcffHBz7969p7Ic/uzaToAYb2d3+6Zkz9+WT0CTLSkx JhJkUh/oqQ8Q42yPh1UZ9fC8fvjw4c277757sd2THIf/urYTIMbb2RHjBHaxSYkxKepJirRF fybGsbN/3vt6EOOwUnztCivF0xaLvPTGyo0YJ8S7h4GW0PxdkhJjIkEm9YGe+gAx3uXR8Uwh ozyviXF6/yLGCQxHGWgJiJKTEmNS1JMUaYv+TIyTHwubMhjleR3E+P79+5sYSfQ5AWKc0BNG GWgJiJKTEmMiQSb1gZ76ADFOfixsymCE5/X0cp6TKTZ1kaeJiHECvxEGWgKeLEmJMSnqSYq0 RX8mxlkeDaszmZ7Xa/67upCDE4QX81566aWDa9F+8cQ4IYbEOAFeZFJiTCTIpD7QUx8gxpGT f+bb1ghxq6dNBSl2VFt6xyHGCQyJcQK8yKTEmBT1JEXaoj8T48jJP/NtvYvxW2+9dXu+sSud ADFOYEiME+BFJiXGRIJM6gM99QFiHDn5Z75t/rxekuTMRRfPLnzswxaKfJiJcQJLYpwALzIp MSZFPUmRtujPxDhy8s98W6wYZy52l+zCBz3mX8LbpdCOCyHGCcElxgnwIpMSYyJBJvWBnvoA MY6c/DPfFivGre0vDlL84YcfZqY1dnbEOCH+xDgBXmRSYkyKepIibdGfiXHk5J/5tjVi3Ioc Byl2NFvmjvIkO2KcwJQYJ8CLTEqMiQSZ1Ad66gPEOHLyz3zbkhhPxc33H2euQtbslqQ4HN3m 2kaAGG/jdpuKGCfAi0xKjElRT1KkLfozMY6c/DPfdk2MT4s6fTkvc1WSswtftrt0LFvYVhGk +eHDh8nljJoBMU6IPDFOgBeZlBgTCTKpD/TUB4hx5OSf+bbT53XM87vG1eMgveGzz0u/nGe8 vQMR4+3srBgnsItNSoxJUU9SpC36MzGOnf3z3rdFjOf/Z7iGfcexUuzotrS+Q4wT+MX8izMh e0mfECDGRIJM6gM99QFifMyj7ZwYx9ak9q0Vse1wXxwBYhzH6exdxDgBXmRSYkyKepIibdGf axHjP/3+vZtfv37/6f/5fPTavZvf/eatm7989unNnz/68Pbve7pyPK9r3FrRU4xqaUvRnt97 J8ox0GrpCLXWgxgTCTKpD/TUB2oQ498+enjzy++8ePPRHz94OvV/8vHjm3feePBUlInx+adi 715TqwvsWS9inECbGCfAi0xKjElRT1KkLfrz0WL8h/ffvZXfIMLnrrBq3OOzLWebbK2IfIA3 ehsxTghczoGWUI2ukxJjIkEm9YGe+sDRYvyr77+0uE0i5p7WHjwlntdWj1vrBXH1JcZxnOwx TuCUkpQYk6KepEhb9OejxXiSubDH+NI1rSqnzN21pS0hxqGNvchx+CBIOM0iHAMX/jt9Ue/B gwe1hbJ4fYjxCsSn//vk3J9XZOfWCALEmEiQSX2gpz5wtBiHvcXh2XW6x3g+HYcX8FrfY7z3 87plQX7rrbduhTj8N1yffvrp7UdCwt+N+KEQYhwhZ/NblgbbyuzcvkCAGJOinqRIW/Tno8X4 /bffvLPKGU6mCKdQ9HYtPatLiH+rcvziiy/eSvD8Ch8ICX83rRz31j+utYcYr4z2tcG2Miu3 RxAgxkSCTOoDPfWBo8U4TLvzY9qmZ1qPgnzE87pFOZ6+ohc+Jz2/wt+H1ePRLmK8IeKXBtuG rCSxYnzT00NfW0isPnC9D9QgxmHaDadPTNsq5s+0ngT5CDGeHmktCXLYLhEkOKwcu25uiPGG XkCMN0DbmMSKMdEiWvpAT32gFjGepuRLgnzt5byN0/khyY58FyiHHE+ruSn/jQE/bacIe4tH v4jxxh7gHMON4FYmI8akqCcp0hb9uTYxnqbk073H4Rk3/wDIyqm7mtuPFOMAIYcc7wHz8ePH tyvGQcDv37+/R5HVlkGMN4aGGG8EtzIZMSYSZFIf6KkP1CrGYWoOL+HNt1eEz0T3cNUgpzXU YSmW0wt38xMqltL0+HNinBDVFjp6QvOqSEqMSVFPUqQt+vPRYhyeW+E4tktX2ELR27OtlvbU Uo9rD/fp6LZwlvGoFzFOiPzUyROykHSBADEmEmRSH+ipD9QgxuEDHteuFgRu7cOzxPFsa+tQ 09aKcNrE6RFtU3um/cxb2tdDGmKcGMVaBltiM6pNPn+IhN/39IDUFsKnD4zXB2oQ4/DJ5xgx fueNfr56Vtuz+uh/fITziYMAh+0Tp9fo+4yJ8YXZIeZwcPc8+SrOk/8t5xcG+oA+oA+s7wNH rEhMcfrto4dniw8v3E337P3hj9b6UGr8jpTj+RFt8/OLw9+Hl/DCy3ijXsR41Mg31G7bKcZb VbOSKua99oEaVownIQsv181Pngi/Dy/fhV+9HNdW+6PuKDmePvUcVo6nzz8HIQ5/P+JHPeb9 hBjXPmrU74YYk6ReJUm7xuvbNYjxJx8/vgm/whFt86/ghS0WYSX52st5HkllCNS21aNMK9vI lRi3Eaeha0mMx5MHwijmvfaBo8V46IeJxiMQQYAYR0Byy7EEiDFJ6lWStGu8vn36QvGxs6vS WyQQ9gQ/ePDg9uW56VPOtkDkiyQxzsdSToUIEOPx5IEwinnPfcCqcaGHxQDZTqdJnPtE9Ogv zeUKPzHORVI+xQgQY5LUsyRp23j9mxgXe1x0nfH02ebwEY7pBbnw3+mjHKMfs5Yr+MQ4F0n5 FCNAjMcTB7Io5j33AWJc7HHRdcZh+8SlY9Tefffdp1sruoawQ+OI8Q6QFZFGgBiTpJ4lSdvG 69/EOO2ZIPV5AtN+Y3zSCBDjNH5S70CAGI8nDmRRzHvuA8R4hwfHgEUEMZ7OJx6w+dmaTIyz oZRRKQLEmCT1LEnaNl7/Jsalnhbj5hteygsv343+cY4cPYAY56Aoj6IEiPF44kAWxbznPkCM iz4yhss8HN8WpPiDDz4Yru0lGkyMS1CVZ1YCxJgk9SxJ2jZe/ybGWR8Rw2YWRDhsnZiObnvp pZcuvpw3LKQNDSfGG6BJsi8BYjyeOJBFMe+5DxDjfZ8hPZZ2//79p0I8P9PYdor0aBPjdIZy KEyAGJOkniVJ28br38S48ENjoOzDqvGpJHsBL60DEOM0flLvQIAYjycOZFHMe+4DxHiHB8dg RYR9xvNPRA/W/KzNJcZZccqsBAFiTJJ6liRtG69/E+MSTwp5zr+Ah8Z2AsR4OzspdyJAjMcT B7Io5j33gRrE+OG9J2fePvnl6odAOKptWjXup1X7t6ToqJgGnsG3f2B7KpEYk6SeJUnbxuvf xLinJ1RdbQliHD4d7dpOgBhvZyflTgSI8XjiQBbFvOc+QIx3engMVsy0Yhz2G7u2EyDG29lJ uRMBYkySepYkbRuvfxPjnR4egxUTVoqdSJEedGKczlAOhQkQ4/HEgSyKec99gBgXfmh0mv29 e/du9xAHAQ6fgJ6ux48f3/7dm2++2WnL921WdjGe7yu+9vt9m6m0lgkQY5LUsyRp23j9+ygx jnk+t/ys6L3u4dSJ8HW7+Qc9whnGQYjDNgpXHgKHiHGeqstlFALEeDxxIIti3nMfqFWMR3mm aCcC1whkF+NQ2NK/SoUEgVgC8wdI+H3PD0ttI4P6wBh94CgxXno+x87L7kOgZwK7i3HPMLUt PwGrxWOIAiEU55H6QI1inH/2liMCbRIoIsaX/lXaJiK1PpIAMSZMIwmTto7R348UY8/nI59o ym6BADFuIUoD15EYjyEKhFCcR+oDtYnxwI8YTUfgGQLFxPj0X6XYI7CFwOkeY3/+3g0G6xiM JFza2sY/ME7H8Ja5MTWNL9OmEpS+VwLEuNfIdtIuErhOAvHCSx9orw8cMV1PYnxE2cpEoGYC RcX40l6mpVMr/Py5xZM9WmCUq+N70Lf3oBczMdMH4vtArrlybT7hOeJCAIG7BIwKPQIBBLol QM7i5QyrY1jlGnwtLJb0WMdc8ZNPPQSIcT2xUBMEEEAAAQQQQACBAwkQ4wPhKxoBBBBAAAEE EECgHgLEuJ5YqAkCCCCAAAIIIIDAgQSI8YHwFY0AAggggAACCCBQDwFiXE8s1AQBBBBAAAEE EEDgQALE+ED4ikYAAQQQQAABBBCohwAxricWaoIAAggggAACCCBwIAFifCB8RSOAAAIIIIAA AgjUQ4AY1xMLNUEAAQQQQAABBBA4kAAxPhC+ohFAAAEEEEAAAQTqIUCM64mFmiCAAAIIIIAA AggcSIAYHwhf0QgggAACCCCAAAL1ECDG9cRCTRBAAAEEEEAAAQQOJECMD4SvaAQQQAABBBBA AIF6CBDjemKhJggggAACCCCAAAIHEiDGB8JXNAIIIIAAAggggEA9BIhxPbFQEwQQQAABBBBA AIEDCRDjA+ErepnAw3u66DIldyCAAAIIIIBADgKsIwdFeRQjQIyLoZUxAggcQMCcdgB0RSKw ggAxXgHLrfsT8BDZn/meJYb4Xvv169fv36nO737z1tX7p7zOtSHkda2sPdutrHEJmNPGjb2W t0GAGLcRp2Fr6SHSf+g/+uMHN7/8zot3pPW3jx5ebPhfPvv05p03HjwjuacSfSmDP7z/7tO0 odxQvguBvQiY0/YirRwEthEgxtu4SbUTAQ+RnUAfXEyQ0/lq7p8/+nCxRqcrwDFppkx/9f2X bstbk2axQm5AIIKAOS0CklsQOJAAMT4QvqKXCXiILDPq5Y5JVkPMY1Zxg9SulemJVUj36LV7 vaDTjoYImNMaCpaqDkmAGA8Z9nYa7SHSTqxSa/r+228+Fd1rWynm5cxlOuw/jrk++fjxbTlh S4ULgb0JmNP2Jq48BNYRIMbreLl7ZwIeIjsDP7C4+XaKILwx11ym1+4xDnuVXQjsTcCctjdx 5SGwjgAxXsfL3TsT8BDZGfjBxc1fwgsru0vX6XaKpfvDz8MWCtsoYki5pwQBc1oJqvJEIB8B YpyPpZwKEPAQKQC14izDFopp33BYDY655tsp/vT79xaThPxjt10sZuYGBFYSMKetBOZ2BHYm QIx3Bq64dQQ8RNbxav3uILaTGMeu6s5XmZfSTPnHrEa3zlL96yRgTqszLmqFwESAGOsLVRPw EKk6PEUqNz9pYmkf8PxM4indtTRhRTqItAuBowiY044ir1wE4ggQ4zhO7jqIwLWHyCRCB1VN sYUIzM8nXjo5ImyjCKI7T3NtC0a4N/bEi0LNk23nBJbmJWLceQfQvOYJEOPmQ9h3A849ROYr ih4y/cV/vgocvnB36Zq2RYT9wvMtGJdWhKdj2mL2IfdHVYv2IrA0P5mz9oqEchDYRoAYb+Mm 1U4E5g+R0wfO0srMTlVUTGYCYSvEPNaXsp9WiaetE/O9xuc+EDId7Za5urJD4A6BpXmKGOsw CNRNgBjXHZ/ha3fpIRMjTsPDaxhAeIluivE5yZ2OaZtvi5ifaXxupdkxbQ13iIaqbs5qKFiq isAZAsRYt6iaQMxDxj3P3VlhrYFHaqda+gpeEN/QzvnpEtNWiXP/J2Fahc5xTFsNfNWhvj6/ Jiap40N6BBAoR4AYl2Mr5wwE1jxs3FuPLKSGfi65p3uGJ8k996W7+Urz/MW9aQ9yWGlOvfSz evpZq7FI7YPSI4BAOQLEuBxbOWcgYI9xBoiNZjH/cMd8ZXj6CMg5yZ2/uDc/0zisMDumrdGO 0Fi17TFuLGCqi8AJAWKsS1RNwKkUVYenaOUufQUvCG6Q5kvXXEwmoQ5prp1wUbQhMh+KgFMp hgq3xnZIgBh3GNSemnTpDW4v3/UU5fNtCS/dTXGeVn/DHuHwd9eOXJv2H4f7wl7l6UU9x7T1 32dqaOHS3ORUihqipA4IXCZAjPWOqgksPUSWfl5141RukcBcMsLe4rBSfG21OGQ4F+qwUjy9 yLf0Fb3FyrgBgQgCS3PS0s8jinALAggUJECMC8KVdToBD5F0hi3nMF/9nV6sizlZYn6mcehD 8/3GLfNQ9/YJmNPaj6EW9E2AGPcd3+Zb5yHSfAiTGjD/ol3oC7Ev0M2Pe5u2VCRVRGIEMhEw p2UCKRsEChEgxoXAyjYPAQ+RPBxbzmW+nSIIb8x1+vW8HMe0xZTrHgSWCJjTlgj5OQLHEiDG x/JX+gIBDxFdZH428Zp9wlO62FVmpBHYg4A5bQ/KykBgOwFivJ2dlDsQ8BDZAXLlRUxnE88/ /xxT5WkbhmPaYmi5Zy8C5rS9SCsHgW0EiPE2blLtRMBDZCfQFRczfQVvy3aIpaPdKm62qnVK wJzWaWA1qxsCxLibUPbZEA+RPuOqVQiMSsCcNmrktbsVAsS4lUgNWk8PkUEDr9kIdErAnNZp YDWrGwLEuJtQ9tkQD5E+46pVCIxKwJw2auS1uxUCxLiVSKknAggggAACCCCAQFECxLgoXpkj gAACCCCAAAIItEKAGLcSKfVEAAEEEEAAAQQQKEqAGBfFK3MEEEAAAQQQQACBVggQ41YipZ4I IIAAAggggAACRQkQ46J4ZY4AAggggAACCCDQCgFi3Eqk1BMBBBBAAAEEEECgKAFiXBSvzBFA AAEEEEAAAQRaIUCMW4mUeiKAAAIIIIAAAggUJUCMi+KVOQIIIIAAAggggEArBIhxK5FSTwQQ QAABBBBAAIGiBIhxUbwyRwABBBBAAAEEEGiFADFuJVLqiQACCCCAAAIIIFCUwP8HXURd9vmh DDQAAAAASUVORK5CYII=</item> <item item-id="4" content-encoding="gzip">H4sIAAAAAAAA/9xcC3BVRZr++9xnbt4P8hA2XiAiPoIhQc1SRCIwbFgDBhRBcByCCSEzCcmQ hIQxjlmEERUERRRBV7akaqwax1Wq1h3FcjI462YdGPExJetrWJ+AzM4NxTDKK/v/p0/f06dz Tu7NvYGwc6i+3ae7z//339////0MDBIAXNcVASRDdhrgQ7/jvKkwJrhg9szpjLKAwUv3Ueyi nyBA6dUYKikfoAHDzZh41QPwXSbAfycDPOQG+H4uwNtzAdZNxk++N3sm1aZvbse6AbA+3Rn4 kwfwO4xSmVKW54L5Y9yABGA8hlEY/mHGrOkaxqcSsRz53o58+3y8/mgMfgxLMSQBb6NdXrqR pnxqT5rEM8F4DyKznX5ej95PI7/fIL9E4/1O5FmmmfQvM2hidpdI49OVbaYhV0rnGem+vj7I MtLYfTAPQzPj7Uzj9MLPaxqPfZo25hZYDS3QCrXQCLE+WvLojXnwIjtacLz9rbPvFP3ai/28 KRMernuviMo79r+mw76taUxuK4bi/3mnqAvfqXAShimbMyHTSFPTMoz0BqTxyrm3ikqvWTqR 6u66+0f56426XRNn+A+Vb83HntuYBpt1xLfBI52591yashzrjMZ0h28ElGFMZUHMp5i+GaOd vnc2pkObdEqdRdeM0PMrP97c2Z30T53jEdgspDEKA2FCH+ZjfsMfN3dS/tOXH28vT7suheSu WaVXCePGjPaLtIyPwJDyIunLOOB6akClyywewYvKRhrfFSDBOrSZ64z3x5HgWK9Jx2WDm6Dj kuhsSAX4wGvS+RDfn5Xet+JPvt98X4J9VSLx3TGSv18OXE/dDjzdUhrJdd2GcQW9I5H3iB8K /LGP28m61P3eP6f+zLs1rds3Nz3o3jGSwkx3U/pM93Np+f41GN5M7fZuSf3MK8skZPcYvMfZ tMMjyX4MX7pdpiwHR/J32d5E3Yewf2/2m3aGTYbXzyHI0rPZ8EM+DdJuhXq0sFq0tSDMwbgd 43nQhHnVsMIGG7unE3XvqpLj7RT/62XH24m8bHtU58zDmRAKJRfBzJkWBevBb9K39LYfe7S3 /XmsUyP5yG17S/wCp56xgexbse4fsO4nGCQS0PMw10Kys+4RpSmPbeJ21uPndtaDeYQXxaqP gRh8TJKUVu2M+vtWDEsw3I0G+s+oK+tRV8/4uN09tW/j1J//fMNU8T3pQ6VCv894iA7h0IHx SQwbMKzVTKy3o44vcplYky+dfp6xVp9osaf8LxDM0mmTUv6XtXgpBqMulzEy9gC97TLWRUhD YJ1r+NTjBm0KKvaC9mCwl/2TijX193iw4r4QwxNpAH+PGbsRnz1ee9xJDZ1wr8I09chejH/i MvH+PXLf47Ha9oXGe6HU/10rpqcUGP1fYfT/6LGBt9ZjvsD7YM6olIVai5diXW7Em2SLBn+K ZbwPIA2B92Ev57fIoE1B4A8wwqCv4s90/74caRL+d9A4e2RzZznWo36n77l7GKH/2tm2wJuw F3jPSuFj01a089sd7DwS3iVQDPcwK95raLz3We0bzg4f3lX3XetX8e4aG0hfiPky3q8reJNs 0eBPTyS8uxW8CX/Ci9NX8TfxJvwHi7edfVN4BMH5AjPG4oTqDQe8KW8gvItRZiIqsJ6QZvXl TrbNLhDWDra9T7XtApcV62KIHvtIWI9zWbHuVrC2Ym9iTfSHwrYplKZxXz56AF8eCWvCRLbr 7cj5I1/kcftCYe1g1xmqXa9XsG6E6LGPhPWDCtaEvYx1twPW9D4Udk1zrClIuQYL30RyXznY tW8ArO8gXvjvFmjDmcVKRAigAcfrRTgn/xR4uAfDL8Hq5/OR7xbNavt2fn6Ybf/3su2/OGNS yjHEiGKBt1X2gfVD1ofnZpjzOLE2PmHQpiD0Q9YHq35E1gfPAPrgZPsvpPK9n3K0/SditP25 ABY/fwzzu9yRx/Rhtv0s1faXuq1YzoXosY9k+zVuq+2fMLDl9FXsh972KZzESeJkzFifDJAT 45i+QMH6Wq91/naR2vXb6pj+poL1AogOe0DsI2Hdo2BN2Mt2bcU+Pqzt7HoJhq2I8ZdY+BjO 3/bGuC6vgOlQh/F+9A8TfVbc1+PH0dg4dR/hTmuRRliKSDfEiHL/Jwobz5ZtnOZMV3qs63Iu Y3R6EGldPsGjrMsV3K16YOJO9O1w5+vywdn4YcwowIyfIIfsGG28LJRpwfqDLG7jgsePcax4 G8tdWO03MfKoCvGGi31Crw2+so6LPU2/Qx3KF7oo9nPP5767+kThgw4IH3QlcB/0oseqa1Wh 6HUzkg/a7bH6INJNy56Bgy7G4oOov2Uf5HSOUmSkU6V8ZN51E5jbpR6JlialXVLaLaXl+rIv 9EvpBKlNIPGm9o2B8BlBn8hH19klviW85Dpyms60sgxedKZFbbQ751pHNomNOItCXm8cng3l ORdVCrrNc677MXFZgnnO1YZl6exv55zr1kcy4f6S4+3JSr6cHgcPMnpPhPX6KNSAOl2ReV1K 6SZuN08hje7t93XC9i5dx88+2dW59UxvO40XXA6mn3PxIcw451rR2x4s/0s75T+PdIj+aORT uidfP4/KQd6Svb9bjvkUACb5S09N8nv9Ld5ADpfn+c2ZwCqOt+MYlUNdtQ/b80718fYg1hPn XOOvOj/nXEJf7M5WqrG1H7msvnS4z1ZsxvUcwokCvUdztvKrgsDlTmcr9JC+Eq5g8Kb60eh1 LGcr8mPnS8UcrhKZf4gZi9K536D2uP+0cepD+UNztmJ/V8Ckq98VSDLuCmAn1El6QY2+0Ps5 0eqJmO8dLtzk+gzHTIoBojuHEXqinsMcQhrqfO8rgzYF6k9ZT8Rjpyc7lvS2U/jt5rP3QghD v8fdTy/U8YywbcbwfbTXZzDjJDZgtIOO0LjopCO0t2+uaWv67eE84LLu45Rj2KlZ/cNwrvfI 99rMtd4jvyv0gNKVrMXLfTHXA1nmwZzPEL/HFH7zDNoUhF7QXEnonVUvIp/PcF2MPNeiAnE+ 8zhmjMbCPgTpXIK9HkQ6n2nBFd/vaNCQ5v5Xa1a89cYM4zrv0NRp/fD+a0Yg4zDmW85nmHVu TbJFgz89Ec9nmHVuPc84j+H0VfxNvOkZ7Nzazu4J7zyc/CRixvdwMn02RrzLQlWhSqrkvXjW Tg72/Adhz2LtVKCcv5Es0eBNu9cRz2SU87duZl3HW/E28SW841k7DTQ3L4LBrZ2Ge81zJy4+ foThTZyovpvMGzGUa55yZFaeYK55liCvhkRzzbMMBV32N3S3L5Y1DxvlLnGX4PwtwpqHfCff V4l7zfOBtOZJmTJtUsqnLlzzZHF5xJoH/XWWvOahPQmx5gldf37WPEJfxH5TglFudy+NysT4 dwaNe6d0x+64j7+L8mXJ1vKnUq3l63Kt5Ysv4e+iHep+ltwOv9SOn6GQj3pMOgfwvVh634Dr SibdDVyIfHeA+b7tEv4u9tHU/TZ5r83ubuB0NMpNaFwLEIR7k7mddWY/6Pkq2+3ZkDPXV5G7 HnlQeAHqc1+AZ3KYfzWGPdnVns3Z2z2yTHbrznOoIz5pP5l82fm+9zPYdSfpLdkKhWjXnT1j A7lDcacv0nqBjeL3fn+xqrcd7X4SfUdNorR7Eq81BctvuP64Xq7aF/W32N/9IzYyDTN7EPNH kzidWf+xcWrWA9Hv704E67phP+pMp7R+1PvnAt/zGez6kcb7o8o8ciJEh7e6frSbZxxT5pHW e1yDx5t8qsBbxVfdMxVYn0VOO/HjTQjSvTFiXUwZ0pphFTrQJR6rLQ/nna7DH93Q72wIbXlE CPNlrOdoA9/1cMKe4khYVylzymPKnNLpDh89g51TyvNAokM412D4MJnu0gO8jnZ91gFrqj/Q ftJEnNMQKi9j6w/iexURkbB/A5n/yje8di5jX9SaAyr2n14SGF+K+fK54G7NevYi5BTvTrpA T6RzwZc167lglXLHy6oLke/zDXQu6GTnn2HGKxhakcO25KGx8y/dVp/uZOcXai/Iwc5zVDvP t7nPFy32kew8qNzxelmznrtVDeEdLzs7p72B5zDjX9CZtCK5nzrYeaS9gQXQAX/JtOLdCVa/ 7mTbFwpvB9u+QrZt6tc1LvWuRwdEi38kvNcqeAeVO1xW/OPD28m2f4xYX4qZmWjXZ2Icw2mH Wsb6UK7Vj1+ktp2r2vZhBevVED32kbA+qmC9VsE66HCHK5a7una2rftxHLtfxcxVSO6JGP14 i4J1XXJ0Yza7QHu8DnZ9lWrXlW51jzd67CNhPUe5w3VUwXrtEN7Xc7Lrz5HyLsxsQ5AejxHr 5hBYsE6Mfp19QbB2sOs8YdfhuzIK1s2h6LGPeFdGwXqOcm/r6BBi7WTXOxEcN2Y+QPssMfrw wpDytzXIOTW6Nfdw2vXVwq4F1lnKvajCUPTYR8I6W7kXtVvBes4Q3ouys2uan21HW25K5H9v Xx3j/OxI6LvQbbQhKtn2No3PzwSfFKSdgOFQKsDpGPmUhQpD9LccxV5+V2BRkv1dgVJjQ4/u CshtIh3s81hlL07ishc4+LRIbToaOtVPduLzit/K5+sk8/80iIXPN6HTYT7q3zPLj2zbYn9V /Ztv+W+jhT1qMLxnbQ6+d6Tqe99V7JH6JVr7jGSP7yv2mO2x2uPuIbRH+T6hG4b2rM3u3qF8 H1GuL99l1GB4z+kykEgKNjQnAFBrKKLdt/K5V06f/X4OlENGpHUAA+dnZ88W/F06kXhXnHmr aNdLmeH/x+D9ikOuNbOh6NiyTQn0Pi9XK8oy8kNt84u+3bsF+WGfI4Mz+N25HK1oWhbLKIRM uPtGdxHVacM8wYveD+/OhN8uv0nPO4HpT5OonH9DzxEMNwW0Ivoe3phftO4DVvTav/OyhciP 0j1u/OZb+/7Y9W+ZIPfHEanOpJOL86m9aciX6NAn1F67cy9xvkV9IZ+BiXOUZSWgkd87APz/ crmB9uZQKacbd7Pnz7+/HLruL6eyVB+n5+T3OnSsa+EuYw1cB204G6iGldj+pRi3YFkQUa2H GqyxXD9TqjPCjUbcJoVyI49knWzk0ftiI6a850h2qX6rJLPcFymK7tvp8w+wIA9lfwL1udm4 azu8+syf0r8uzk+GvnzCmnDbhzq6H3W4ENP/mUOxqXMwri9f1mt6SKfrjHoXoz6TfEOtz7S/ /DQW/CPiuRixvDlg1WcqG4l5dKXWSZ9pzyqyPi/H33osW461gkOi023S9/J3sep1LhYkYYWn Ud67jPsUF4Ned64nvd6q6/UlEEGvZ2216DXxuNj1muQbar3+AsNtWFCGmT9APKclW/WaylKS +X0GJ73uwnSVfn9tGba8QddiSgdR09swfxXm1OBbM+auRK2ux3gFvq8BrrdtRnwjmDo+Wcqf DKbei/riXdSjMFui0WrkRaPjNDk0roXocypK07juYn59rpSofQxX6Kkk7Vr2a410Ox/kX+r+ U+K/aDK+LmR8Nn1G4zdDRC6fN04w9jsS9K8oxTSqRWVulqzb22lNaD6fievz8NUtrbUDTME5 D02PNeRlpTyB5YXbyixfuPSaWZqflWiUytTy2ATNXi63UTfIfmjUzWeNRqqQtRopL+sMp77Q RE/IdDxGW/g6RrSxkHl16y1wJcIPkccK7e+Q5hVEDelQ8LF7MLTpvApZg3YptmM0E22zl89n tJmxBKNVjH2lWRHI0q5hhRrv+ROGdXK71HeV6xtrW4JzatuD85oaq/vvI3M+fl2rigNEiXSj sbrDFcDZd57B4Rzca3D4c4wcEvQ4hJwCOieiSCJUd/hgrMFlBBsXJxe/wSXB4EIUiUtJcWqY y1j2U4PLZ3HLkqBzIYpEorhySri/mvtJwgbFw5QkweivZp1HY6WQ40pWECcPU44MnQdRJI2v qbmlrXVlE1wPdyJ38iYmRifhjn69F59kRJEozA33XgLbx+LjoSJEFInCgsqysBYkDZkc3HaI ItlOxfQ612SYBaY0vf2kAbFfuLpxadMAW4VO0vTqbS0L99hY9owmdPEZvTerUFJ7P+gHO68W iNN3J4Z7Q4wiYtywo8uipitkdxmx24g9RuwL9w2PA0acaBm3RHsuZ3198bVHtEML0xNx//FV 7YnhHUHVUWMta4lz1HAbmk+U7EaNLnZFnB7KE0Y9TedEFGlPSPdPNcI3FYS9Uwf7Mk7PYeoX H0GIInVaS0Vj2KI72LdxWrQpl+Dyrc6lrCoHyuLUAauFqPpnWpTb0UL+v2u6/VxKzP8+Z4cZ n0t9zryayCvTRN4t4bxfhvO6HeaUHoPmMfakUfcIe8pIebRnjdQe9otwaq/tnNLXbw75JNJ8 WvuaPaudw69fxe8ovMaex7CL09Z2aN8g3z8xuS2yhX/CHox7PsVHFaJEn04Mzz4OshvjnEUJ Ty14EEXrLOobNiJOHuaokKzzIIqUM7G1esUM1JOUsE1rWsjOpuOQhyha5TnIvo7TO5nyBIw+ +1r3Gws6/gvKwr22J04uqiREkSisrjR766Uhmj0Fwr31ko5+i8SjzI7HILxsf0TKdB7NYR6f sC47CxkED1UOokgkCsOW8r7NTHNwPEw5OOpEkVA/8t0rYNrKAcZLKaWPJYULwqW9+AUv7TW+ PXrK/DZJE6WUotJvTlPpYOaPiXH68yTL/C3SqOU8/xPz0KSYRzcuxycWL6Dv9yl6bd3fmzpV HpcmsFzL+JSg0c7VeL2PU1ixvr8zr/Yu9EJ1bQ3VK4NLq1tqg+31Na3LcyEV5zoJGDIgHQOl koBWaKnordIwlYdakIEhPTzKWntIg/MnwZW6BPexG/Qd434SLK+tr1veimVp2NoA/qbrUlAq ESXhMqQaqYBeGhgGKQp1KU6wEn1/sGpl07L6htpg07LgXW0rV9XWBJubVrbWN63QsrDn0zGQ t6VWUzoBUxl6PkmXoMtHNXjKSRZ+lv1/AAAA//8DAIjRH7xsYAAA</item> <item item-id="5">iVBORw0KGgoAAAANSUhEUgAAAu4AAAFMCAYAAACUITmyAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA UNVJREFUeF7tnb2uJbdytvVdgm5Bt6AbcKDYmSLnAhw58wCOT6DIgSMrMuBwgBMb8ETODFjx yQYw4MyBAAPO96d35NKp4fCn2GSz2b2eBgZ7z178qXrIXv2SXST/39uv11dcEIAABCAAAQhA AAIQgMDeBCTcuSAAAQhAAAIQgAAEIACBvQl8tbd5WAcBCEAAAhCAAAQgAAEIfIqSAQMEIAAB CEAAAhCAAAQgsD8BhPv+bYSFEIAABCAAAQhAAAIQYMadPgABCEAAAhCAAAQgAIE7EGDG/Q6t hI0QgAAEIAABCEAAAi9PAOH+8l0AABCAAAQgAAEIQAACdyCAcL9DK2EjBCAAAQhAAAKPJPDL L7+8/fjjj2/fffedztX5/d+333779u7duzd9zgUBI4Bwpy9AAAIQgAAEIACBxQQ+fvz4Sax/ /fXXbz/88MPbzz///JkFHz58+F3MS9hzQUAEEO70AwhAAAIQgAAEILCQgGbSNbv+/fffN2uV qFdaxHsT1UskQLi/RDPjJAQgAAEIQAACOxBQCIyE+E8//RQ2R7PyykPYTBjZYxMi3B/btDgG AQhAAAIQgMBOBI6IdtlvM/T6yfXaBBDur93+eA8BCEAAAhCAwAICCouJhsek5ijeXXkl/Lle mwDC/bXbH+8hAAEIQAACEDiZgMJiJLwV8nLk0sJV23HmSH7yPIcAwv05bYknEIAABCAAAQhs SMBi1N+/f3/IOhP+Eu9cr02AHvDa7Y/3EIAABCAAAQicSMB2hfnmm28O12Ix7iNlHK6cjFsR QLhv1RwYAwEIQAACEIDAkwhYiEvPLjKp/7aoNbJ95JPY4cuXBBDu9AoIQAACEIAABCBwAgGF xozGpmsLyBni/wT3KPICAgj3C6BTJQQgAAEIQAACzydgO8nohNSjl4XJsI/7UYLPyodwf1Z7 4g0EIAABCEAAApsQUEz66KmntrBVsfJcEEC40wcgAAEIQAACEIDACQQsxOXobjI//vjj72Ey nJp6QgPdsEiE+w0bDZMhAAEIQAACENifgAl37cPee338+PF30S4BzwUBEUC40w8gAAEIQAAC EIDACQRsN5gjM+6WdyQ+/gSXKPJiAgj3ixuA6iEAAQhAAAIQeCYBW1iqnz2X7f0u8c4FAU8A 4U5/gAAEIAABCEAAAicQsK0ctcA0eiHao6ReMx3C/TXbHa8hAAEIQAACEFhAQAcvKda9dXiS RL6Fx/TO0C9wgyo2IYBw36QhMAMCEIAABCAAgWcSsJAZxat/+PDhMyf1f5tl1+dHFrI+kxpe 5Qgg3OkXywj87//88vbPf/jh7Ydv79nt7m7/soZeXNG//NOPn/qUfs64zmjnM8qc4StlvH0S UbbzR/rTwhtMVJXSafcPuyS8culSsQb71yMgQa6+ZPuyq5/od/UZzcqz3ePr9YkjHncpKD0c e/79zV98/fb3f/3dJ7H2H//6/oh95HkAgf/8089v//i333/Wd+7k1t3tvxPrI7b676Qj+S3P Ge2sMvUdOMvGEf/IWyfgT6eUuEpFlP5vIQ8SXLXDcPze2/odQUbv8wRshv0oFRsYWv7W/9N6 1B/tRFflVXiODSzTQacGG/5vR20m3zwCXcLdqv3v//r4xcNIDyeJc80s+QehzbDqwSUh/6d/ //wV0TxXKGlXAuobf/eX39xWvPzhr769tf279otZds2acT+jnc8ocxY3yvmSgAmUmtBORVKO o+2/fWQLQNrl2QRMNI96qX6oq/UzV4+d5qp+asLcL561N0wS8xqs6o2Af6s0ajv5xwgcEu6q UgI9OoskoS/RbunvNvtuvo6hfu3cPf1lR1J3t39HpjvapO+q6Pea/x6s+ULf2bGlv7QpsvuH n32seaXZTAkeLgikBFoLVHPENAD0+Vpi3T4v0dcMu8S7XbnBqC2S9elozT0IHBbuMr/nAZc+ EPUK+S7Xv/3xp9vGZe/EuKe/7GS32XJ3+3dkuqNNPe0c/W7oKXNHJq9gk4ny2mE3NlNZS6NZ yiPi7BUYv7qPCsfqnblW+lQ8t0JjWsLd2kEDAhPoaZ7I/fDq7XmV/8uEuxz0cc76/S6XhXnc xd5d7by7eLm7/bv2i93s6mnn6HdDT5m78XgVe2yRakmUK1bdFp62xP2rMMPPOAGJ5DR+XP1I YSl6O5PuJCPBrj6nEJZU7Kciu/X/1EobDEi0l94i+b+zy028nVekXCrcLRbV4t1XODhah8J6 7KE7Wtar57+7eLm7/a/e/6L+R9u557shWmbURtLNJ2ALSnP7ZyuMRrOelkY/c5dm2tk9Zn7b 3L1ECWW/k0xpd6KcsM+tt2gJ9daMu705Mq65UBnbtlJ2Ey6zVw+8TLjfYUvANDZ/r6a7nzV3 Fy93t/9+PeYaiyPt3PvdECnzGm+p1QjURLntrV1LQ4gMfalEwC9SlhDXDLZm2TVIVN/yol4i WbPwpVnuqGiviXcT6hpQ2EJq/U2/2yFQVr/NvNd2UaLl1xK4TLjrFfPOl2Lw/YLaOww0duYp 2+4uXu5u/+79Yxf7Wu185LuhVeYuvr+yHbY9Xrqo1G/dZ2lyu8Uo7IALAmcTiAj31oy7he1o kCCxbotVc1s/pm8BzvaP8tsElgp3vyWgtomMXnolrZh4L6S1xaAWhkUu25/Z51d5pa0p0z3H c3vXq/70ytmp7eD++A/vPtsms2Rz6ZAWhRgZO/nQyy7dRzrCLudzzu5oul7hfgZL25pUDI/s bJQTXypHbWyfiW10y9NRH3P9QjPB6u+RgebofZXrD2lf88z8AUm1dLmF6z7Mzsq08o4cbpR+ J6hPWHk1kX30u6FU5si9HfnuI02cgMWvpzOdPkyglEYzpzN3kYmGUtTSxT0nJQQgcCcCy4R7 Gt/u93uvCVmJIv2zh7nySQjbg1CCVmKldEkgpELNdoJQGS0RHJ0pM1Go+sw3PzMnG0p2lg5+ Ufp0/3OzR0xalwlK8bJLdXmhmQpvX6aP4a0JwVRUley6kmXO5xa/9PPU/pqIa/Wrkf4iu9L7 Qn9Tv/aD0zPvqxo79Zv0bVXuftffUgHfGoxrUKSy1Y+PHphk3x9+QG33RdpPan5G+3Nu0Dp6 b/f2XdK3CVjcrxfuqSDPpVH62mLVds1fpkC4H6FGHgi8BoElwt0L7ZqATZErbSmkxosmpctd 9hDOzeD5/DWxEHk4e+Ga2uHFb2knndwhLfZg97alr+hrdpvvuTolmLywqm3NGfE/J0xy7REp a5Sl+ks62BEDm2G3vtgS1i37JThVrg3GxDQV8n6G2Zc36qPlz4lhCVvjXPoKG72vIl+N0UGf yor2RaVVudZfjxxuZAOmUtv0HBQW6c/Gyqe1e9u/9Unv7SNvhCLtQpoygXSBnoUQ+Byl/a45 GZWeBQEIrCJwinCXoJCAkEhKw1MiM+1y3mbiSg+wdF/49EFsIqok0Lx4qsXbRx7OtVlOf/hK aYAhf9NDWnKhOCZcfFhGrqN40VQS5X4wVduaM+L/TOF+BstZW496FqVBkz8pWOlz/X3Ux9YW hHbv5PrG6H3V88XkRXBNiHpmtQGVWKb3as+BSTaoqd3vPQOO6L2R3h8j93YPf9L2EUhFee60 yDSNFquWdpjpq53UEIAABGIEpgn3XKyz/qZZMYmcqGCX2Raj24rT9XX60BH/MC/FG/s6avVE Hs6tNK3Pc7NypeaLnMLoX/eXyvH+1wYUM22PlNVK0/o8x7Kn79Vum2jdXrD6MKVoO7fqsc9L gwcTn6kvo/dV7Cvlz6l8SFpJrCq1v19rfVGD7dxMeYuXWWTtUntTFbm/ou3oeUVsjA7ye9uB 9G0CtijP4tltxw+fMz0aPjcj365prxQzQnIo46sv9meHCUxm9wH/zTFNuFuhaRhG7UFc+gpL Zy1Lg4L071aen02ufU3arHvptbnyRh64rXIiZUTriqSbWd/qslaz7HmMRlm0BOuoj35gkBOh JgBT30bvqx5Wlta/Xait8fBsS7Pz8js3CIu0i59Jr62JidxfZwn3nrqPtAV5ygTSUyJz+1an abQTR+8pmNE2mPHQj9ZFOghA4F4Epgt3ue/jbPVQ7Q1ViMwY1zCP5vdlR0RByRaJqjMWu7Vs an3eIzyuKCvH8yyWPbdrlEX0bU5ad9THdDGwBG0kJnrmfRHlFgmD0feDT5dbeC2mpVn7SLv4 8lu2R8rrFdlnlNnyg8/jBDTDLrFsJ1nmtnv0afT7mSEyCPd425ESAq9G4BThLojp7F50i7ze B2KuwaIPyUhj95al2TzzXT97YnCjdbXSeYEWWXhaC2No1XXGIMDKXMEy0gd6fOxNe9TH3Ox5 S8BH27KHSSttKwzGx63XZuflb2lwEvHLv6Vo2Rwpr/d76owyW37weZyAHawk4V7aIcbSaC93 9myPsyUlBCAwl8Bpwl1m+oelHsrRWGP/kKsJzxIKnz9aZ6SsGnq/q0ga1zz7od0qz8/I5mKs 5YcXVLXZ2lZdPUI1WtZKlj23U9T+iKib4aPNQnu79LsGbrlwkNH7qoeVT+u3fEz7mvqn/a02 Oz+6gHxm2/X0+d60PXYebQ/yfUlAp0LaLHdphxifpnSqJWwhsCOBGW9wKOOauP1cfzpVuKez zbWZXW+cf3i19nXOOeXzR0IIajda5EFq+1bb/tJpeZEyImKvRwT4A5tygxcTSa394GfaHilr NcueL9mI/Wkb5QTnLB+trpyAzw2UR++rHlY+rY8vT3n4/5dm55W/tttMpF16BvOR8nru1560 0bqPtgX58gTsYKVa+Iul0d7uXBC4EwFE9zWiewb35cJdFabxuBEh7mfqazNt5pCEqRegPlQk El8vwVASsK0HqY9nHnmVP/vhbj7ZDKx/c2Ft0hLts23akWXPl2/L/tzgM+1/M/tLarsdTmR2 pmJ39L7qYZWmze3VrvslfSPk7127n8QwEvIlv0uXr78Vthdt52i62ffRSDuQN0/Ajnyv8VGa r7/OnxkCVwjsTEACkus5BIZaM/rgSk9HbO3qkMbvtsS+hKgXoWn+SH0lgd/yMbLgr1WGdafZ 6SRQJNbs9FgrX/+PvomYaVOrrCtY9tzKLfutrJo4n+Gj7CiFgPkdbVIhO3pf9bBK0/qdnmxA kdslxs/Oi1Vu7/a07Ei7+O+g1uFbkfJ6xHhP2mjdI21B3i8JSNh8+PChiiaSBrbnEkCAHuML t2Pcds21RLinW0S2ZnrTXWlap63q8/SEUf8ArNVnr+ePzpZHHrSRNLMf7hrMtAY8kU5ZOyzI 54/42ErT+nw2o4j/vT4qvYnU3NuiGT6qjCPrEkbvq15ePn26P7psKQ2WfZ+TyC6t0+gZ8KYD mtral0gb9fTFnrTRukfagrwQuCsBBGh/y+VO++0vhRw7EVgi3OVweiJhK4Ql3UZRDzSJUf/A VZkSR7m94tNZfqXzr8hVTi1/VBT4B21OTJ1xEmPr4W4i5cjC3rRz1hYWKq04pqxLoqhl9xUs e27Glv1WlgnPXEjGDB9VRmSxZm7AOnpf9fBK09ppxrK/tB7ED3yMVWuBeW+7KH1pMJ8K/Nrb umi9CPeRXkPeJxOwQ6xMXGoBcOnKiXbl1577+kxhTKw/+JIewv15d9Bh4Z7OoLUerkLnH9x6 6LVOU/Qzb/4hmf6eE8x64Ebz12JevdCxGWz9NNtLYkT1K00qaiWmxSr3uj4qBFrpon6bgJIt kdALletZaSAlAZkuQtbfcsKoZfcVLHtu6ZboVlkWjlKaEZ/ho9mROzisFUPfc19Ew6miDP2M f23g4b9bWm/nekRx+sYhHcyLZ27AX7K19d3gubT6fnSiIMqadBDYnYBEt/bD/73v/9/OPqXt OHPC3YS6dgLSGgSlQbx/3vII993vhH77Dgl3v/DRHkh64LViyWWeXyBnwlEPzNzssMpL00dE u2Fo5ZcQbYmTdHGt2Wy+pqLV22fCKudzyiqtp3SaayRdbla1NvBJfUq7Uak8E+2peMotJozY vZpl7+0iH/ygSH5aO9r2jungJq1jho++LWWDDbp0D1lb1cJLWveFym/dF73sLL3dC63yTUC3 0kX6lbc1fQPmWdogwf9NdpTeXLW+G6zeqI3pbH/tROej/MkHgV0IeNFuNtVEZirc0x2A7GRb FhAj3Hfp42fZ0SXcW+IvMquUniqZPiRzjqYnSurhb4cbRcDY4lWrSw9oCZvIWwKV7wWbF0pW txdMqsMLOqWx7f/0WW52u8bV+xdNpzzp241I29XCl2S3iVbxT0VFybdU1Kd2pO23imWk35TS qD3Fyot49anomoIZPqrvqhy1ix8YtnZg8T6N3ldHGKrOXGhbWpYJ7FodPfeDL8cOvfL92Q90 an05taf13RC1MZruCHPyQOAOBDRrXpsxj8S3M7v8ZUtHuN2hf2Djnwl0CXfA3YdA7aTJVMQo hMBmau/jIZZCAAIQgMATCGi2XKEupTCXqPhUOp1sy/UbAQYyz+wJCPcHtqtmAVuLf3Nua9aP CwIQgAAEILCKgIW4SGT2xLen9r1///6TUOVUWzcz+yuP6KBnVXv7emYcUFQr4wqfVtSJUltB eWEdFkMdDQPypkUWAi50haogAAEIQOAiAjVBlBPYLRFWc8MvLs3NmEfEp2bsa7vSjGCUfYrJ l9+KoTdf7S2BFtrqUhrtcrPL1Zpx12BHvH3bycfc+oMzfVK7eRt66/J5z+oDvTadmR7hfibd C8q2RX29VWuWvrUQsLdM0kMAAhCAwH0JSLDadosSRy1Bl6ZPF5DWSEj85oRmRLTLLono2Zf8 kRCUWJcv6Wy+hK/ns9uuNjXhLoFeG2zJLxuQzOaaK2+WcF9h69V1INyvboHJ9dsit9bpkL5a W2w52RSKgwAEIACBmxMwgRedybT0R7ZlPCLcbS/42Zgl1GVPxG8v3lsn8M62s1ZeadCjWXYb jGhwYpcGIv6Ngn73n59pO8I9ThfhHmd1i5T+WHvtNqLdMnLb2dkBVLbX/C2cw0gIQAACEFhK wPZHjwo4E7HR9OaM7SqThsrUZtwl2tOwHZsFH4FkoRsqK3JZfH3k7UCkvFlpcvborYEEeWk2 XX/34n3VYl+Ee7zVEe5xVrdJmdtfurTdHHtF36ZZMRQCEIDAUgImpqNhKBbuEkkvcehns5Un N8NbEsOyzQtML/yOzPYbWBPtR8J8VoncSCcohcnIv9YCXj8QWTUYQbhHWvW3NAj3OKtbpdSM up3w6vcb1++aZddnRxaw3goCxkIAAhCAwGECJuCiQlix5tE4b5VpYk2z9LmQlJpotDcBuTjt o+EqZn9pd5sayMgagMMNcSBjSbhHfRsR0gfMnbY49Ujdd8uDcL9bi2EvBCAAAQhAYAEB23Gk NUNrppiYjqZvubBqtld22NsF1XlkUaby9YYHtfwf+by2MDVSruWPvD2JlNdKMzJQGMnbsmvH zxHuO7YKNkEAAhCAAAQuJiBBpHCU6NWbvlXuSuFuITKRxagtu3f4fJSdieHWTkLmqwYteouS vgnR4C8ykBsR3yN5d2irXhsQ7r3ESA8BCEAAAhB4OAGFm0gQReO2Lawmmj6Cb1R8RupQGj/b HhGZ0XKvSjc6225tHx20WdiTwnD82wqVk1uHkAtlGhHfI3mvaqORehHuI/TICwEIQAACEHgg gfRQnFwsee5v0Z1YWshWiXbZYbHtUaHasv3qz0eFu4VIRWbbW28q/Mm4ZlcupGhEfI/kvbqt jtSPcD9CjTwQgAAEIACBBxPo3dbRZlZ3ivOONk/vXvXRcq9KNyLcbWegyCJWe8vSOi1W9kTS HLUb4X5VT6NeCEAAAhCAAAQuJ9CzraOMtVnVVQsZZwOyQUfPFpCzbZhZ3lEBLBsk2KMHL1k8 e+0tiw9Dqvk4Ir5H8s7kvqosZtxXkaYeCEAAAhCAwA0I9GzrKHcsxjm6beRuCEz4Hd1Gckd/ jthk7R6N848MEKJhSCPieyTvEU5X50G4X90C1A8BCEAAAhDYiEDvto696Tdy9ZMpJvyignU3 +709ETGds9/esvSsUYjUZSFXrbcZI+J7JO/ObVmyDeF+x1bDZghAAAIQgMBJBCSEogs1LRQi mv4kk4eKNXHZI1qHKjwxc0RMp9XbKbS9/rf2+beFq5EQqhHxPZL3xKY4rWiE+2loKRgCEIAA BCBwLwK92zr2pi/OIv46WFi5k4y3wwTmqlCfXnHdk74nrTGQsD6yf71m6TVg0z8v+hVyZAt+ I4tcZceI+B7Je6+78zdrEe53bDVshgAEIAABCJxAwGZRo7OvvelrJl8l3G1xbWvnk5ztEq9H RG/UVy9KI3l6hftR0W4s5L+JdKtbHMWkJ/RoRHwfyZvLE+F7wi3XXSTCvRsZGSAAAQhAAALP JGA7rES9602/o3CXTSY+W7HY3n4TrVFWPl1EJKaiPZonak9EtEcGJmImsT6yuPeI+DY/e/Om HHvzR/melQ7hfhZZyoUABCAAAQjciICFvURikuVWb/pdRbvZZYtsIwcPWTjIkebtFeDRWfee 2faIaFf7amBWG8zYjkJHOKQDmR77R/Lm+EfapHfgNcqklB/hfhZZyoUABCAAAQjchIA/nl5i rTV72pu+haFXOLXKO/q5hf5oBl7C1R8opd/1N32mdEevI75GZoWjwtcGKLkZ/dzfaodqWXqJ e83OH7nSU3p73nqkeSNhSwj3I61EHghAAAIQgAAELidg+2yXRFwq2lrpow6lQvSImI3W1ZtO sdkSgKnA1f81w9wrUGf4Gpl1jwj3XtHeGqCk8e0a9CmP+knrFN3IwKE46/x/i5lrZdTavdbf ShxLdVk9Pl+tLSKDsKrtvR2a9BCAAAQgAAEIQOAogVTU7CTaj/rUEphe3B2toyX4IsL9aN25 fJoZ10BGIl1i3dY7eDsjM+Azbeopqzbz3pqVL/XZVLyn9qT5jvR9QmV6Wpm0EIAABCAAAQgc JtASRIcL3jDjbF9bs+5HROARbLYYNffmQX/T535mP7ol5BFbRvNE28ini4j2nF3Rulo+Idxb hPgcAhCAAAQgAIEpBGaJlynGnFzIGb6WZt1XzbZLlKuuyFaPtnh51YDiaHNGZsGjwr1kQ03s 99qNcO8lRnoIQAACEIAABLoJlITs7sKu29FfM5zpa068rxLuvdt/yq4j++MfYR7JExlM5YQ8 wj1ClzQQgAAEIAABCDyGQEQ0PcXZM329UrhLhKv+yAFdtpC5tUPRyjaPtEtOpJ8h3I8OWJlx X9ljqAsCEIAABCDwogRqM5lHRcyuKM/2NRXvq2bcFcNus+6501H9lpmRbUVXtl9kwNMj2iNi 3vxrzeL3cEC499AiLQQgAAEIQAAChwlcJTgPGzyQ8UxffdmrRLtHYTvJ2Ay82aBFqdphJjIj P4D2UNZUaNfCmXJpU+beiMjAMzdwOOIIwv0INfJAAAIQgAAEIHAaARNCXpTWxFEqXtN8EWF1 mjMnFVwTkidVSbEbEEC4b9AImAABCEAAAhCAwG8EajPVrRjlVqjD0xjPmsV9Gpcn+4Nwf3Lr 4hsEIAABCEDghgRK8cOlmfOSgH3iTLtvToT7DTv3oMkI90GAZIcABCAAAQhAYC6BHuFeWyT4 dOHu31DMbQFK25UAwn3XlsEuCEAAAhCAwIsSmCHcfZz8kzG+wuDkye3X6xvCvZcY6SEAAQhA AAIQOI1AK8a9tRtILf9pRlMwBBYRQLgvAk01EIAABCAAAQhAAAIQGCGAcB+hR14IQAACEIAA BCAAAQgsIoBwXwSaaiAAAQhAAAIQgAAEIDBCAOE+Qo+8EIAABCAAAQhAAAIQWEQA4b4INNVA AAIQgAAEIAABCEBghADCfYQeeSEAAQhAAAIQgAAEILCIAMJ9EWiqgQAEIAABCEAAAhCAwAgB hPsIPfJCAAIQgAAEIAABCEBgEQGE+yLQVAMBCEAAAhCAAAQgAIERAgj3EXrkhQAEIAABCEAA AhCAwCICCPdFoKkGAhCAAAQgAAEIQAACIwQQ7iP0yAsBCEAAAhCAAAQgAIFFBBDui0BTDQQg AAEIQAACEIAABEYIINxH6JEXAhCAAAQgAAEIQAACiwgg3BeBphoIQAACEIAABCAAAQiMEEC4 j9AjLwQgAAEIQAACEIAABBYRQLgvAk01EIAABCAAAQhAAAIQGCGAcB+hR14IQAACEIAABCAA AQgsIoBwXwSaaiAAAQhAAAIQgAAEIDBCAOE+Qo+8EIAABCAAAQhAAAIQWEQA4b4INNVAAAIQ gAAEIAABCEBghADCfYQeeSEAAQhAAAIQgAAEILCIAMJ9EWiqgQAEIAABCEAAAhCAwAgBhPsI PfJCAAIQgAAEIAABCEBgEQGE+yLQVAMBCEAAAhCAAAQgAIERAgj3EXrkhQAEIAABCEAAAhCA wCICCPdFoKkGAhCAAAQgAAEIQAACIwQQ7iP0yAsBCEAAAhCAAAQgAIFFBBDui0BTDQQgAAEI QAACEIAABEYIINxH6JEXAhCAAAQgAAEIQAACiwgg3BeBphoIQAACEIAABCAAAQiMEEC4j9Aj LwQgAAEIQAACEIAABBYRQLgvAk01EIAABCAAAQhAAAIQGCGAcB+hR14IQAACEIAABCAAAQgs IoBwXwSaaiAAAQhAAAIQgAAEIDBCAOE+Qo+8EIAABCAAAQhAAAIQWEQA4b4INNVAAAIQgAAE IAABCEBghADCfYQeeSEAAQhAAAIQgAAEILCIAMJ9EWiqgQAEIAABCEAAAhCAwAgBhPsIPfJC AAIQgAAEIAABCEBgEQGE+yLQVAMBCEAAAhCAAAQgAIERAgj3EXrkhQAEIAABCEAAAhCAwCIC CPdFoKkGAhCAAAQgAAEIQAACIwQQ7iP0yAsBCEAAAhCAAAQgAIFFBBDui0BTDQQgAAEIQAAC EIAABEYIINxH6JEXAhCAAAQgAAEIQAACiwgg3BeBphoIQAACEIAABCAAAQiMEEC4j9AjLwQg AAEIQAACEIAABBYRQLgvAk01EIAABCAAAQhAAAIQGCGAcB+hR14IQAACEIAABCAAAQgsIoBw XwSaaiAAAQhAAAIQgAAEIDBCAOE+Qo+8EIAABCAAAQhAAAIQWEQA4b4INNVAAAIQgAAEIAAB CEBghADCfYQeeSEAAQhAAAIQgAAEILCIAMJ9EWiqgQAEIAABCEAAAhCAwAgBhPsIPfJCAAIQ gAAEIAABCEBgEQGE+yLQVAMBCEAAAhC4C4Gvvvrq7cx/d+GAnRDYjQDCfbcWwR4IQAACEIDA JgR++OGHzwR8r1le/Kusp121wc133333hbutwdDT+ODPfAII9/lMKRECEIAABCDwGAJebPY6 NZK3t66r0v/yyy9v33zzze8DnJ9++qlqSpr+xx9/vMp06r0hAYT7DRsNkyEAAQhAAAKrCIyI 75G8q/ybUY9m1+Vr9K2CpX/37t2M6injhQgg3F+osXEVAhCAAAQg0EtgRHyP5O2188r03377 7Sfhrtn0yGUz9NH0kTJJ8xoEEO6v0c54CQEIQAACEDhEYER8j+Q9ZOwFmSS+5afEe+T6+PFj V/pImaR5HQII99dpazyFAAQgAAEIdBMYEd8jebsNvSjD+/fvPwnxaNiLYuB70l/kFtVuSgDh vmnDYNbb2//+zy9v//yHH95++Pa+3fQJPqzqizux+pd/+vFTv9PPVddO/q/ymXruQWBEfI/k vQedt7fvv//+kxD/+eefQyZbWE00fahQEr0MgVMVkR58Pf/+5i++fvv7v/7uk1j7j399/zKN gKOfE/jPP/389o9/+/1nfedujJ7gwyrmYqX73n9XrKq7VM9KW3b0/2r+T67/w4cPv+9Aojhn zdbqym0duAuHEfE9kncX/1t2yMevv/66lez3z3vThwsm4UsQOFW4G8H//q+PXzyY9aCWONcs k116gNkMqx6cEvJ/+vcPL9EQOPlnAuobf/eX32wl5Hrb5w9/9e3tfej1+Wj6HVmtnHHf0f+j bUm+OgELqbDt/zTjKsEnIYdwv2fv0UBM7adZ98hlfSCaPlImaV6LwBLhLqQS6NFZLAl9iXZL f7fZd/P1tbrSXG97+svcmueV9gQf5tGol/TqrM7wn++hVb03Xo/tJOJzWLxzz17erUN8Ip/H rX6bdgBTT513SZseUBVhrzT2puUufmLnPgSWCXe5HBXuSivx7tNrNv4u17/98adbx2Xvwrmn v+xic2rHE3xYxfbVWc32n++hVT03Xo+JOr8FoO1Iopnb6BUVh7V00bqUbiTcZSRvj41Xpe3d 1tHesLAN5FUtdv96txXuQuvjnPX7XS4L87iLvbvaOVvIXOHnE3xYxe3VWc32n++hVT03Xo8t SkzDYnr2/47XNi/liPgeyTvPg3NK6t3WUaFRPdtGnmM1pd6dwNbC3eJMLd79DrAV1mMP4DvY u7ONs4XMFb4+wYdV3F6d1Uz/+R5a1Wv76rF4aIm36AmbfTWck3pEfI/kPcebeaX2buuo7SLZ BnIe/1ct6TbC/Q5bAqax+a/aqWb5PVPIzLKpt5wn+NDr89H0r85qlv98Dx3tgWvy+ZhoCb87 XCPieyTv7mx6t3XsTb+7/9h3DYHbCHe99t35Ugy+X1B7h4HGzjxl2ywhc6WfT/BhFb9XZzXD f76HVvXWsXpMwB0NkSHGfYz/rNw92zraWoaebSNn2Uk5zyKwtXD3WwJqm8jopdfEion3Qlpb DGqxVvSyfbh9GSoztz1luud4bu961Z9eOTu1Ndwf/+HdZ9tklmwuHdiiECNjJ/t72aV7akfZ 5fzO2X4kXavdzmBpW5OK4dGdjXJiTGWpne0z8Y1uezrqZ65vaIZW/T0y2Jxxb5XasiRcR/vz ke+CI4chic3Re6c2UI36f/R7qHVv8fk5BGyR4pGQGYT7OW3SU2rvto696XtsIe1rEdhWuKfx 7X6/95qQlSDSP9uFRvkkhE0USNBKqNQuPXxTsWY7NKicmhCOzpqZKFRd5pufLVP9JTtLh/so fbr/udkjJq3LxKR42aW6vMhMRXduMBJh4Nu3JBgj5aj+M1jmfG7xaw1S9HlNXLUGWCN+qu70 3tDf1K/94PTse6vGMG3vkf6se+rId8HRg7Nm3Dsz/Y/eO0f6NHn6CNhMa5rLhNwdZmBHwl1G 8vaRXpvaTkuNbuvYm77mjTFd6zG17UJgS+HuhXZNwKYQlbYUUuMFk9KVLnsA57af9GWUZu8j D0wvWmvCt7STTu7AFhM53q70tXntjYP5natTIsiLu9bWnBEG8ruVrvW5yhhlqf6SDnbEwGbY rS+2RHWpP3kfNEhT2TYgE9dUyMuf3DXqp+XPDYA12292lvyYcW+1vvQ8K+vP/k1H2p9rb0GO 2nvkMKRZ985M/yP3Tqs9+HwOAVuQqh1I0usuAmxEfB/Jm8ujv+102RuTqE296Vvl7sajZS+f zyOw9E4oPUwkJiQeJJLS0JTITLtw2Cvq0sM83Rc+J5BMRJVEmhdPpQFC5IFZm+H0B7HUBhjp gS25UBxx8btLRNKURLkfTLW25owwmCXcz2DZ8q/n9vMsSgMnf1qw0uf6/Kifra0B7f7J+Tbj 3oowSwc5uTyR/jxqb/pdUbPd2zN678zyP3JvRdqDNHMI6GAliaz0gCWbib/DCZpHxLfR682b CtLe/HNarV6KvS3RWoXI1Zs+UibCPULpmWkuE+65OGf9TbNXEjhRwa5msfjcVoyurzMNHfEP 61K8sa9nJLyjJWxbn1tXjKSLnMjow0JK3dz7XhtQ9IiGlv2tzyN1RcpIy+npe62vhWj9ftbf hypF27pVj31eGjyYAE39mXFvtRhFfVS61sB2lr0tnmbzzHsnUmfL/x6W0XYh3RgB7dluixh9 WIUEu2Zhdz+IJz0ZtOeE1zRvJJ4/J0h7RWpv+p4W1hsUmz3Xz9bBWb3pI7ac6V+kftJcS+Ay 4W5up2EYLVGYw5XOWJYGBenffVl+RrnWJDbrXgppiDx8Z5QREa3Rh3jE5pn1zbRrNcve2zXK 1q+hyL0ZGfXTDwxy4t0EYerfjHsryizKqpZulr0zbPF+R8qLpIneh9Gyom1DuuMEbEZdot12 k5Hgk4jdWbRHFsCWqIzmTcvtFaq96SOta3u2l3xL27KVPlJnje9IfvLem8Dlwl34fIytHji9 oQqRWa9WM80oI/pQLdkiQZUuiqzZHX04t9K1Po8K7dnponblGJ3FstWP0s+jPkTe5oz4mS4G lpCP7JQz676IcIuyqqWbZe8MWxDukVYnDQS+JFAS3qU1AZY+J6qtdJ+3trZgx9Cc1KYzBib0 w/sQ2EK4C1c6UxbdHm9ULPeKzVbTRh/4Vo5CdMx3/eyJr43W1UrnxU5t4amVU4qV72XZsqv1 edoWK1i22v+ocO/px0f9zM1GtwR8bxv08ukVty1Os+yNljPz3onWGUkXSTPSVuSFwNkEcrHu qjMSShMR/iPlnO27Lz8dZCDaV9Lfs65thLvw+Nf5CpmJxhr7h1Rrx5NSM/gyovXmyoo+MP2O ImlMc7SMWen8bGwuvlp++gFFa6Z2ll3Rclay7L2Noz60BKk+n+Gn7o90r3HZKAGa2350xr0V ZRZlVUs3y96oLTPvnWidkXSRNNF2IR0EriJQEu+psK393z6rzbJHBwSrOUQGF6ttor7rCWwl 3NPZ5tbMruHzD6meQ5Y8fl9GS5jWmi3ywFT5GpjoX26gESkjIvRyfEq2+wObcgMXm62N7Ac/ y/5IOatZ9t6yER/SdsrtWDTLT6srJ+Bzg+UZ91aUWZRVVLgf/S7oubeUdta9M8P/nns+2i6k g8CVBLx4jQjZ2mx7yY8jeVYwifi7wg7q2IvAVsJdaNJY3MjD18/Ul7Zp9NjtcBb/N//KOxJj r0FGTsS2Hr4+lrk0QGiV0ftwjpRn/tjsqx9QWJtERHuP6GnZ1fr8Cpa9t2/Lh9zgMe1/M/1M 7VdImt9qMt0Kdca9FWUWZVVLN8veqC3ybda9E60zki6SJtoupIPAKgItoZqbgY/Mysv+WojJ jsK9xIJQmVW9cd96thPuQpW+ym+ddJrG7rbEvoRoKkLTMiJ15gR+64EZWTzXKuMM4a4yJeIk fOzkWLND/+95CzHL/lY5V7DsvZVbPlh5NXE+w0/ZUQoB8zvaKJ2/ZtxbUWZRVrV0s+yN2mK+ zbh3onVG0kXSRNuFdBBYRaBHuPsFqd6+IyK8Ve8q/1t+INqvaIn96txSuKdbRLZmetNdaVqn rerzVNynu3rU6rSQnpyYbT0wW5+ri0TSzE6nwUxrwBPtvrXDgnwZLT9HP5/NKOp/j4+W1rYj zb0xanGI+KkyaoOvUh0z7q0ot4ifLV9n2Ru1RfbMuneidUbSRdJE24V0EFhBILebS7oTjNmR E+21RZwtwRuZyV/BoCbcSyyUx/MoDWg8u5q/rbUAqzlQ35cEthTuMtOfSKiHUCt8Jd1GUXn0 QPWzjCpTwqi0V3w606+0fncbldUqo/XA9J/nhFTqd63TtuqyvK10NuN6dGFvaqPnmPNRHFPW udnglt1XsOz9Emn5YOXZYCe3m9IMP1VGLYzM6sgNWGfcWxFuUVatdDPsbdVh/sy8d6J1RtJF 0kTahDQQWEUgjWUvictculS4RsNnUqG8m2hNBzM5+2ppahxKn7UGOav6A/WUCSwT7ukJnpGd WyTW/QOotlhVs+B+ptfnS38vzTz2lFHartKLBpvB1k+z3fvkF6eqbqVJBa3EtFilscdq0ujD uZUuyk3lKK1sqbWfD71Qes9KgykJyHQhsv6WisaW3Vew7P0yaYlulWfhHaV+OcNPsyN3cFgr hr7nvugJqUpZtto7OhCdYW/Ulpn3TrTOSLrW91BvPyY9BCCwL4F0MGOW1uLkdwwP2pfwXpYt Ee5+8ZY9dCRQW3HkQuUXm5lwlPjIzQ6rvDR9VLRbs7TK0IO6Jk7SxbVms/maClZvn4mqnM8p q7Se0kmukXS5GcrawCf1KdelS2WaaE8HHhKnuQWxNcG5muWRW1f8vbiTn9aWtr1jOrhJ65nh p29P2WADLzG3tiptBSp7WveFyh8R7ZF+KjvSePxSvx+xN2qL7Jl170TrjPrf+h460pfJAwEI 7EkgKtxL6WpCf0+PX9uqU4V7S/xFZo7S2PPIDHx6aqbEoh1uFG1uW8Bq9ekBLWETeVPgxZoX SVa3F0sq34s5pbGt//RZbna7xtX7F02nPOnbjUjbtcKXZLuJVrVBKrJG/ZPdq1hG+00pndpU vLyIV5+KriuY4af6rspRu/jBYTpoqvk6495Ky4/202g6X36vvUfqGL13onVG05n/re+h0T5N fghAYA8CM4R7KzZ+D0+xQgROFe4gvg8BibnIjKlmMhX6YjON9/EQSyFwDgHunXO4UioEINAm 0Ipxr60VKC1qJc69zf3KFAj3K+lvUrdm5lqz5zlTNQPIBYFXJsC988qtj+8QgAAE1hNAea1n vlWNFj8dCQFKDW9t07mVoxgDgckEuHcmA6U4CEAAAhBoEkC4NxE9O4HtYtPrpWYaI6E1veWS HgJ3IcC9c5eWwk4IQAACzyGAcH9OWx7yxBa85babLBVoCy0PVUgmCDyEAPfOQxoSNyAAAQjc iADC/UaNdYap/oh47TSinXNyW23a4VO21/wZtlAmBO5EgHvnTq2FrRCAAASeQQDh/ox2HPIi t+dzaeu50r7ZQwaQGQI3JcC9c9OGw2wIQAACNyWAcL9pw802WzPqdsKr32tcv2uWXZ8dWcA6 207Kg8BuBLh3dmsR7IEABCDwXAII9+e2LZ5BAAIQgAAEIAABCDyIAML9QY2JKxCAAAQgAAEI QAACzyWAcH9u2+IZBCAAAQhAAAIQgMCDCCDcH9SYuAIBCEAAAhCAAAQg8FwCCPfnti2eQQAC EIAABCAAAQg8iADC/UGNiSsQgAAEIAABCEAAAs8lgHB/btviGQQgAAEIQAACEIDAgwgg3B/U mLgCAQhAAAIQgAAEIPBcAgj357YtnkEAAhCAAAQgAAEIPIgAwv1BjYkrEIAABCAAAQhAAALP JYBwf27b4hkEIAABCEAAAhCAwIMIINwf1Ji4AgEIQAACEIAABCDwXAII9+e2LZ5BAAIQgAAE IAABCDyIAML9QY2JKxCAAAQgAAEIQAACzyWAcH9u2+IZBCAAAQhAAAIQgMCDCCDcH9SYuAIB CEAAAhCAAAQg8FwCCPfnti2eQQACEIAABCAAAQg8iADC/UGNiSsQgAAEIAABCEAAAs8lgHB/ btviGQQgAAEIQAACEIDAgwgg3B/UmLgCAQhAAAIQgAAEIPBcAgj357YtnkEAAhCAAAQgAAEI PIgAwv1BjYkrEIAABCAAAQhAAALPJYBwf27b4hkEIAABCEAAAhCAwIMIINwf1Ji4AgEIQAAC EIAABCDwXAII9+e2LZ5BAAIQgAAEIAABCDyIAML9QY2JKxCAAAQgAAEIQAACzyWAcH9u2+IZ BCAAAQhAAAIQgMCDCCDcH9SYuAIBCEAAAhCAAAQg8FwCCPfnti2eQQACEIAABCAAAQg8iADC /UGNiSsQgAAEIAABCEAAAs8lgHB/btviGQQgAAEIQAACEIDAgwgg3B/UmLgCAQhAAAIQgAAE IPBcAgj357YtnkEAAhCAAAQgAAEIPIgAwv1BjYkrEIAABCAAAQhAAALPJYBwf27b4hkEIAAB CEAAAhCAwIMIINwf1Ji4AgEIQAACEIAABCDwXAII9+e2LZ5BAAIQgAAEIAABCDyIAML9QY2J KxCAAAQgAAEIQAACzyWAcH9u2+IZBCAAAQhAAAIQgMCDCCDcH9SYuAIBCEAAAhCAAAQg8FwC CPfnti2eQQACEIAABCAAAQg8iADC/UGNiSsQgAAEIAABCEAAAs8lgHB/btviGQQgAAEIQAAC EIDAgwgg3B/UmLgCAQhAAAIQgAAEIPBcAgj357YtnkEAAhCAAAQgAAEIPIgAwv1BjYkrEIAA BCAAAQhAAALPJYBwf27b4hkEIAABCEAAAhCAwIMIINwf1Ji4AgEIQAACEIAABCDwXAII9+e2 LZ5BAAIQgAAEIAABCDyIAML9QY2JKxCAAAQgAAEIQAACzyWAcH9u2+IZBCAAAQhAIETgq6++ eov+CxVIIgg8gMCO9wTC/QEdCxcgAAEIQAACIwSiAkXpuCBwBYGePno0bepXtJyVPLgDV9Km LghAAAIQgMCGBEygbGgaJkHgE4GoiB5J14v6ivsG4d7bSqSHAAQgAAEIPIzAFQLkYQhx52QC O77tueK+Qbif3NEoHgIQgAAEILA7gSsEyO5MsG8vAgj339oD4b5Xv8QaCEAAAhCAwHICCPfl yKmwkwDCHeHe2WVIDgEIQAACEHgmAYT7M9v1SV4h3BHuT+rP+AIBCEAAAhA4TADhfhgdGRcQ 2LV/XmEXoTILOhxVQAACEIAABHYmcIUA2ZkHtu1FYNf+eYVdCPe9+ibWQAACEIAABJYTuEKA LHeSCm9LYNf+eYVdCPfbdmMMhwAEIAABCMwhcIUAmWM5pbwCgV375xV2IdxfocfjIwQgAAEI QKBC4AoBQoNAIEpg1/55hV0I92ivIR0EIAABCEBgAoEPHz4UT4H8+uuvP9Xwww8/VE+K/Pjx 4++WfPfdd9m0qid6XSFAoradke79+/dv33///WfcxPGnn346ozrKHCSwa/+8wi6E+2BnIjsE IAABCEDgCIF37979Lhwl1H/55ZfPitH/JSRNHChN6frxxx9/T6ff07Ja9l0hQFo2nfV5aaBj DL755ps3PzA6yw7KjRPYcStIWX/FfYNwj/cbUkIAAhCAAASmErAHf01oR8SBhKbSaSb5yBWp 40i5u+XRLLveaqSDG3HT342Dfu8d/Ozm65PsQbj/uTUR7k/q2fgCAQhAAAK3ISBhKEFi4TE5 w3/++efQrJ4E6UiYxysId7EU69Jsuv7uxbuYcu1BAOGOcN+jJ2IFBCAAAQi8LAET5QrdKF0K 25BoqaVRLPuoyHwF4a5QIzGvXZp5Nxa7isVXvGF2bYsr7htm3F/xDsBnCEAAAhC4nIAtUi2J coVzWDx2S9yPOnOFABm1uTd/jaEvC+HeS/bc9Dv3zStsQ7if298oHQIQgAAEIJAlYAtKtUg1 vRRGo9l2S6OfuUsz7T27x5Sa4goBsmu3MBbffvvtria+lF07980rbEO4v1T3x1kIQAACENiF QE2Ua3ZYYR21NDNCZIzFFQJkl3ZI7TAWI2sGdvXtjnZF+qbtwKSBrAa8ucveXtV2Z+rlE7Gt t8xWeoR7ixCfQwACEIAABE4gYPuIpwJRgtzEhaXJ7RYzc0b4CgFyAtLhIi18qbZgeLgSCugi EOmbPrxJv3txni46nhkvH7Gty9lAYoR7ABJJIAABCEAAArMJ2AxgumDSzxiW0ii8ZuaM8BUC ZDbPGeWVBlMzyqaMYwR6+qYdXOYHXvb2ygT86EJu70WPbce8/zIXwn0WScqBAAQgAAEIdBCw HWO8cE8FeS6N0kcXWkbNuUKARG1blc72wp/NdpX9T62nt2/alp66T3Q/tXYSGuHWa9tIXZYX 4T6DImVAAAIQgAAEOgmkD30JxzT8JScMlGb24UBXCJBOXKcnl2Dn4KXTMXdX0BvaYrPumlnP LfzuNqCS4Yr7BuE+swUpCwIQgAAEIBAkkD70JRzTw4HSNFqsWtphJlhtNtkVAmTE3tl5FXYk BmfOzs62+VXK6xXutk6hN98RnlfcNwj3Iy1FHghAAAIQgMAAATt8yeLZJRzT2UFLY/G6uRn5 ARM+y3qFAJll+2g5FiKTWwA8Wjb5xwkcEeCr+vOqejxFhPt4n6IECEAAAhCAQBeB9NTU3BZ2 aRqFyKQz8l2VVhJfIUBm2T5SjkKONDBCtI9QPDdvr3DXfWNx7me36xX3DcL93P5G6RCAAAQg AIEvCFhohsJjFJObExg+jX4/I0TGDLtCgOzQLTQYmrmv9w4+zbbB75G+epvMI/1S95Sdf3B2 2x6xb7R9EO6jBMkPAQhAAAIQ6CRgwkIio7SLiaXRIruZe7bnTL1CgHQim54c0R5DKk76d0Uf 6a1TQl0x7hb+dPZAo9e+GPF6KoT7DIqUAQEIQAACEOggYDtf6MFf2iHGpzl70eQVAqQD1/Sk EdEu8Xf2jO10x04s8Io+0lOn3lr5Pdr9tpDCovts9i4zPfbNahqE+yySlAMBCEAAAhAIErCD lWrhL5Zmtth49Rn3iGiXCJTwOzM8KdhVtkl2iUj9dWBbinGXSFcbaYCl9krXidjA19pQ99PM Q8vUMJcw2aZHYAgEIAABCEDgRQhIPOYWpHr3lebsV/1W3xUC5Iqm9iEf5nPt5+z98q/weVad V/SRWp2+3UzAe18l5pVGn+leO+PtySVMZjUo5UAAAhCAAAQgECOgB75icWtXJE2stnaqUQEi X2yGs13bNSl6RbsPu7jG4r1qHe0jR7yp1enXiZR2W7JDtc56c3IJkyMgyQMBCEAAAhCAwHMI HBEgEksK47FY4iNlPIfg8z25on1LYTK70L6EyS7OYwcEIAABCEAAAtcQOCJANJsp4Z7OZF/j wX612s4mu1kWCRHK2Xykj4z6jnD/kiCLU0d7FfkhAAEIQAACNycwIsoUB+7F4M1RTDN/99Ch XkdH+khvXZYe4Y5wP9p3yAcBCEAAAhB4LIFRUYZw/7xr2ImsuwvPng492kd66kK4l2kx497o Sf/yTz++/eGvfj1Z7duvPv37u7/85u2f//DD2//+zy+fcupz/V0/a5fSK5/SPv1qMXu6/3f1 75X66Ow2in4PzK6X8iAwi8CoKEO4f94StpXnU4S7f6uyaqed0T45696olXOFjd0q0gRs9KdE 79//9XefhO1//9fHFRyn1PGnf//wSaT/559+/lSeRM0//u33vwt4+f8f//r+s//nKlb+NN8U AzcsJMpsQ9Nf2qRX6qNnNbT/PjyrDsqFwJkERgUIwv3PreMPznqCcC/FxJ/ZH1X2aJ88276r bOwW7gZCIjwVpBIANhNtYlfiVsLdHmz6ffdLAtSEeWqr98Vm2msz7kqvAcDTH+w9zHZv/1ez T4PrV+ijZ7YrM+5n0v2ybM34aY9mWxhZ2urNBJTSctUJjIokhPtvfP1Me07wpifgpot7tUOP tqGsbRWq/p/Gzyu9LRLWnuVP6POjfXLFPX+FjYeFuwnzqCD9tz/+9Lt4/Zu/+Hrr2XfZJ79K bwhMvP/xH96F+oUGM1FOoQI3TDSb2YYuPtok9fWn99EjDWj37pG85DmPQCqISocUWbpVhxid 5/H5JY8KEIT7523U4qEdZ2wbTRt4SpD7HXrSUz4l+iXq07LTGX77vHVOwPm9aqyG0T45Vnss 9xU2Dgl3udXzsLdZKYsVj2FZm8oPMGbW3MNpZr0ryjqL2QrbqePPBJ7cR4+2s/Xto/nJdy4B zVbagzMXdyvhY+KoZUkpHKDn7606dv58VIC0hOrOvp9hW4tHKQZe4tzypifr2mm7vmyJdv/G yYt41XHna7RPrvD9ChuXCndBtJnZyILOFdDTOnwozMz6nyyKzmI2kz9ltQk8uY+2vc+nsBCi o/nJdz4Be3CmIQhWs/4eETA9Av2qmN8zaY4KkJZQPdP2Hctu8ah9Xvss3XozDYl50tac4rD7 NXrfHPFvmErvw952VlE+xdbudvmBxUzbejnNrPvsss5idrbdlP85gSf30SNt7RefH8lPnjUE WsJdM5BPiPc9m+aoAGkJ1bPt3638Fg+bGdfP9GrlHf18N1YlexDueTLLhbsPl9lxa8SzxMtZ 5e5wAz7Ztx34rrKBdvwzacX8MyBd1fPG6rGQg9wCVYsZHqvhNXJfIdyf/JajJa7TXqW+qtCu yCm0rbJbn9+lRyPcEe6hvnqWeDmr3JBTJyd6sm8no9uqeNrxt+bQ7lhetO84wbBVx7nYmJpw 12elEJqrzJ4hVkfKqM1ujgilI2JxxI/WQGNG2SMhUVEeFsqltRhas6FFq628o59f1fd76y31 x9lt22uXT9/qhyNlF+/V0UJ7H/atBaq5Q2AsvEYPU72+Ll2W129tZwcm2X7subzeh9rvubzR Q2t6Oaku+aotN72IUDy5FszNuq5g1mu77TPuOYiLtqCMtudIOuWN9kvZWutD6UFd6Ruo2tqP Gf0hZSmmZtORPlq7J1S2Xele8QqTq93LtXJ7728rK9KG6Ra3ubbMbWkb/R5IbTnqS6lP6u9q TytXbaDvzydftstGOuOu2cvSNpE5HjPEwJ05jwqQlpi8M5sjtrd4SKCXBp2tvKOfH/Hnijwj A8lV9o7eN0fsXB4q4xcy+u0US4fA+FNL7SGac1Rl6XN/qqkepvZ3faaHcuuKipfeQ2ui5doD WX7rnz8AyvuiB/PogVarmbXY5z5Xf0kHbH4Xm5IoiRyOZSKn1jZH+mXusC61V+0yP3MDTJU3 oz9Ye+unnbeg+qzsnj6a80Vl+TUsNlPt2ysVwlFROdJXj7Sh/Ivw6P0eULkjvih/qU59H6T7 8ZsPO64nOvJ9kMsjca6Hp1+AqrCDyIJUXx7C/atPHI9eLTF5tNy75qvx8FtBpls+yt8Wy9HP 78D0CkF8hMsVdh6/S//Pw8jDzWD42UiJMX9Ykx446UNHQttm5fzDLoVrYqG0r7oXDq0HWNSf 3kNrouXKN7EpCT0/I+hnNHs73BXMem20QVtOzHoOpTcQUea1dCP9sufgMfmTzsYbrxn9wdq7 VMeMA5hy94Tu39oAtPaGwfwf7atH2zDSf3q/B0Z9EZNcnSba/b2QhvzMfFPXey+fmT4n3CXa JY644gRGBUhLTMYteUbKGg8fx57ztsVy9PM7EB7tj6t8vMLOZcI9XeyVe02eHlQUmSE3Ud4S saWZ/rRxIw9ry9NzaE20XLOzFEaQ1lkSYrVOeyWz6M1kwrw0I9sKuVI9UeatdEf6peq302RV fqt/luKoZ/QHs6M26x99Q9Fqv5RV6aRk//ZIvvtBvK9jVl890oatftH7PTDLF9Ub5ezb9Q6n Vrf6V+5z7RjjZ9wVJ8wuMv0kRwVIS0z2W3TvHDUe/rPc+QMtlqOf34HsaH9c5eMVdk4V7jmx KaEpkWXxyfpZik1OxVbpYe4bxMptifw09rhUdvRhbTZE00fSeRtrnc6X1XqDkCvnamatG8oP Tkp9JW3PXJkR5lGB78uK9Euzx8fll3zRfZMboMzqDzabXptxnXm6b5S7Z1OybWZf7W3DqB/R PjTTl2idM9u1dd9e9bk/sEYnRea217vKtjvVOypAWmLyTixm2GoHf4mLhcPop94G+c80+27i XQPOdFcZfabBqL9arFufz/DvaBnRcKzR/njUvt58V9g5Vbin8av2fwkHH/YSFaUtgH42KTLz 7MMBSmE1PQ/r6MMzmi6NDy7xTP/e4uQ/34FZy14/G1tLa7PupbaPtmUkXSRNzlbvS+ntgQZf OVE/oz/49m6tiTjqY+p3tBzPJjcbPLuvRu3qHZRH7u/ZvkTqPOJH697c8XMv3Hvj2nf05yqb RgSIhStZGT2Lgq/y9+x6/QmmxkWCXSFcKS/7XAutdXlhr8/8G6QWa38/KO9ObdHTx3rSnt2W tfKvsHOqcJ8Br+fh6sNfIsLdC4XSTHVP/bMfnn4h7gyWuTJ2YNbybRaHaFtG0kXS5PxqzXjq 81IIywwOXvy3uB/18ahwb701md1Xe/3rSd9KO9uX2d89rb6x8+d2UqTETi7sYGfbd7LtiADx M7ul33fycbUtEu8mwiXKff+UoP7mm28+hXnpp1+kauFf+rvfzrTG23zbtR163wJEZ+ZXt2la 35H7ZtTmWwv3SBiCBxSJ4209gI+KlEi5kTSjDb4Ds5YPszhEy4mki6Qp+eUFeBoSogHnrLc/ ufr9W6bduLfE5+y+2tuGPelbaWf70mLn27plW6tf3OFzPTx326/9Dty8jVcIkLsxwt5jBHKD iVZJCPcyoVsLd/9Aqu3Tbu63Zvh6HoZWZvShGEnX60+r4+c+763jDGYtu72NPfHkuw6q/IAx fdNT29azt61a7d3DvZW29nmkr0fun17/W321x67e74JW2bN96bGvZdtIW++QV7OauS31drDt TjYg3O/UWvex1fernll3hPsLCPfIVmfpdpSjQmf2w9M/YCP+HLl1e+s4g1nLbm9j7yE9vuyo YImki6Sp+eVnXC3W3PZQL+XrbatWf24NgkZ9jIjx0uAqt+tOr/+tvtrrX0/6VtrZvsz+7mnd k7t+rpACFqPOaR2E+xyOlPI5AS/AEe5zesetZ9x9CEIp3MBj8g/20i40rQdwSXgo3+gsZHqK YquJ7WCeVjr/+Q7MWvZ6G1u7BaksCeHcmoVoW0bSRdLU/PKx5rZIVT9rA7QZ/aEnRGPUxxHh nlucOruv9vrXk76VdrYvCPe3Twv2tAMH1xwCCPc5HCmlTCAq3O/UF6+w9dbCPbLY1Hchv692 aSa39QA+U7inu4i0Zt0VH927HeQOzFpfbCmH1m4oSp8T+NG2jKSLpKn5lZsNbu3tPqM/+EWR rVNKR33sFe5+289cX5/dV3v960nfSjvbl1cU7rZgT7HsWtj3KqJdfmthoxc92j1ndnjQFQKk 9Szg8+cRiIj3O/XFK2y9tXBPd+xoxbnbw7MmmFoP4DOFux9YyA7ZWROt+rwl7lN7d2DW+ipK Y5VrgxMTf7mBmJ9tHn0b0tsvcvX5GXQJ6tbbhBn9wZ8aLB9q4TIzfOwRlD72P2fX7L7a619P +lba2b70cG7Z1rofd/ncP/BfRbRLoNd2EtGuI7NOiL1CgOzSt7BjHQGE+zjrWwt3ud/aC9oj ihxE0/uQi6aPpvOv1C2PZta9sJHgkS+tGdtS97iaWaTb+plicZC/fq9z8Whx8GWUTupN65l1 MFfOR3/aq3yqHURm+Wf0Bz+AKQ2CUoHfessRHQjVyjH2tTC3mX01eg/2vjmIiuiZvkTr7EkX uS+vTKNZZ22t9yox7eav3i74bQQ1A+/3+Z61DSbC/cre/Tp1I9zH23pIuKezSK3FbxFzex+u KtOLm9J+7iaaauEC/tW97GjN4Pc8FKN+yQYvtHy+9PeRhZtXMov0gx4OJQHsxWh6Yq/6g+3o 4rnqbyPx8jXf/P0SHXT1cCj1h3TmPh0EiUVuoFTaX77Vfp5n6a2CzbZHQr1m9dXoPZgbNNmb Lf3MxeNHy57lyxnfPa125fN1BBQOZIf15GrVLLsX73Zwz4iFCPcReuTtIdAS73fqi1fYeli4 S1DooewfWHqgjczUpTOSkUOVrLN4W/S7DSL008qtzezZQk/vjx6ytcFI1N5oOvNFDH1YRU68 j4j2K5n13NwtDhK/LQ65GWubwbe+mgrNdMDW2341H62ftuLNfRktDrK/xSE9wyDt56kQ1L0c GbjmfPVlq43ks/9eMJ4R0T6rrx5pwzRPKXytt+zR7yoxidYZTddzX5L2fAJ6q9Dal95i/mcJ h1nlnE+HGu5OAOE+1oLdwr02A5x+1mNardxoORIaeiimoQES7NHY3qgds9PlfNTsnheeEvMS fCODo7Selcyi7Zims0W4xlxMWm3qyxAz6xNimA4IVa7S5PpItJ2jvpmAPtKGo/1BdaYs/GC2 xiHqXzoA0P9VZxrf3xpo5Oo7o6+2/FJfsb7jJwQs39H+cdSXlG/tO/eobS0mfH4+AcW2R66W AIqUYWkQ7j20SDtCoNVv77KHuxhccd90C/eRxiIvBCDwfALRsJHnk8BDCJxLwETDjMW6VwiQ c+nUS3/37t1n4Ua1RcD6TKFJXPMI1Pobwr3OGeE+rx9SEgQg8CsBhDvdAAJrCJj4mbE15CsJ dw10WkI9/fxVFkWv6bl/nqnOifQZwv3Dhw9v2nVJZemnQst0Rd9mRTlccd8g3KOtQzoIQCBE AOEewkQiCAwRkDCZORN8hQAZAnAw8xHRLjaztt08aPbjspXCZWb0Q1v/oR2ZdNlib5WNcH9c V8IhCEBglADCfZQg+SHQJmCHMs2YbVdtMwRT2+prU1h4jM2+aptNm5E1y3J/u9bq59ae63Mz +qHNtHtyuk9Uton5WVRn2NtrCzPuvcRIDwEIVAkg3OkgEDiXgGZ/Z88eXiFAzqX0eemadU3X AkjIy28/+DGBp8+4ziWQm3Wf0Q+tDH/+gQ3I9KZq5jXD3l57EO69xEgPAQgg3OkDELiQgF73 zzp4ydy4QoBciPBT1WKYLjq1UBpCY85vnbOEu7VhGhaj+ryYn+HhFfcNwn1Gy1EGBCDwiQD7 htMRIHAuAZsRbu3z3mvFFQKk18aZ6RUyIZ/9olPCZGYSjpWV9rsZ/dDWf6TtG7OoL9UMe/tq /DWsrTcD6SEAAQjkCLBvOP0CAucSsBAZi9GeWdsVAmSm/b1l2cmzfgaWMJleiuPpzxDuskoD Mit71jqQnLdX3DcI9/F+RwkQgAAEIACBUwlIYEpsniHaZfgVAuRUYJXCbbZdC3z9RZjM+hZJ w2Vm9kO/g9DsEBkjNdPeKH2Ee5QU6SAAAQhAAAIXEZAIOXMv8SsEyBUo7a2F/PXhRoTJXNEa v9Xp+55+n3nZm5Wz7p0r7pu5hGbSpiwIQAACEIAABD7thnKW8Lhy5nB109pbi9yOPBYmY5w1 Kz9768DV/t6lvhnC3QZeqc+2p/tZJ98i3O/Sy7ATAhCAAAQgsIBARLRrFnlU2F8hQBbg+6wK HzqRLu7VDiRioG0gxfMsobfa5zvU58Nljs6424LU3G5AZ/btM8sutR0z7nfo1dgIAQhAAAIv RyAi2jWjKJE5Ojt8hQBZ2aBetOcGOal4HOW50re71zVDuNu6hbTdbCY+Xc8wi9kV9w3CfVbr UQ4EIAABCEBgEgEvNFNhk/v/6OK7KwTIJFTNYiwMRj6mhzBZZs803f+7WQEJhgmki1R7C7Q3 JukCbgn22WceeNuuuG8Q7r29g/QQgAAEIACBEwn0ivYZs4lXCJATEX5WtMU51wS5bR/Iiamr WuXzekaFu90Damu7fyTY1a6jg9oakSvuG4T7NX2UWiEAAQhAAALbELhCgGzjPIZcTmBUuF/l wBX3DcL9qtamXghAAAIQgMAmBK4QIJu4jhkbEEC4xxsB4R5nRUoIQAACEIDAIwkg3B/ZrLdy 6o598AqbEe636tYYCwEIQAACEJhP4AoBMt8LSrwzgaNbQV7p8xX3DcL9yhanbghAAAIQgMAG BK4QIBu4jQkQGCJwxX2DcB9qMjJDAAIQgAAE7k/gCgFyf2p48OoErrhvEO6v3uvwHwIQgAAE Xp7AFQLk5aED4PYErrhvEO637zY4AAEIQAACEBgjcIUAGbOY3BC4nsAV9w3C/fp2xwIIQAAC EIDApQSuECCXOkzlEJhA4Ir7BuE+oeEoAgIQgAAEIHBnAlcIkDvzwnYIiMAV9w3Cnb4HAQhA AAIQeHECVwiQF0eO+w8gcMV9g3B/QMfBBQhAAAIQgMAIgSsEyIi95IXADgSuuG8Q7ju0PDZA AAIQgAAELiTgj5xv/X6hmVQNgaUEWvcCwn1pc1AZBCAAAQhAAAIiEBUodzzdkhaGwFEC0fvi aPlH8jHjfoQaeSAAAQhAAAIQgAAEILCYAMJ9MXCqgwAEIAABCEAAAhCAwBECCPcj1MgDAQhA AAIQgAAEIACBxQT+P8cqCIdWGyDWAAAAAElFTkSuQmCC</item> <item item-id="6">iVBORw0KGgoAAAANSUhEUgAAAFoAAAAlCAYAAADC3P4WAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA AaBJREFUaEPtmOEOwjAIhPX9H1rnkhmGFHqsdI25Jv5wUko/8Mr6fG3jwVFP4AOao57Ao34J rrCrBjHMIUDQIOdNzD9n2v5BBmaNeP5jWxQypSNZDASdBIdOI2iUWNKeoJPg0GkEjRJL2hN0 Ehw6zQIdwWd7B1KWfTTSU/+A1o7AOG43l5XVAoEAGrWhE+jMX2JUICP8yDc2vZfje+v5iPU9 H1/QnsZE+lMdZI9/DZKgHWpXEkrQPeWobNADR+vyfregLn0oHYlEyCko0B6NbiU6+/x0qbSS RiMV7W3eSkgP6Iu5N6f/VdchJQOt9Aq4p0TrBaI+WmpdS/eODVs9rfXbEcOVw9DysWwf7Wmf BBTBtg4nr9qqq2kF/+4ruFUREURZWcf8KIErgKiOAb7r6AHdsrHaruoNVvvvlbupoD1NrwZy t38ItJYSKQ1aJrwWzZKUu0FUrw+Brg5mNf/Roa+l0LL/nlmrbW6VeKJ/r25Tw8ZhlY2tGMfI g5/S4WSYoCeVP0FPAB1pdHSZ9TN/QsxcYiNAjZ5UBgRN0JMITFrmDVH/lQH9hqKoAAAAAElF TkSuQmCC</item> <item item-id="7">iVBORw0KGgoAAAANSUhEUgAAAFEAAAAWCAYAAAC40nDiAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA ATRJREFUWEftmAEOwyAIRbv7H3prm9awP0AUYkiHyZLVKsITvm6v9962aj4CB8RqPgKbb3rN Piu5MPgJNIi7KBzaeH7uxvX5l1xrQYohMravTKQAKci1YcethvHcz1L/7MqPhohQ/h4iVyWj mWOFKEnbdR1sktfsUUfo5EjNGA1WGz/rI2o9xs3JF4VOz4uf75IxqyZGZEgkZMnWDEQraJcm 4mm+CgaXjb3s1ZJFA6xlaCvvp2ciB0g7nSWgWv/UPRE1AXdLeo/3T+5Zyu4Z2dAyVtN89AHH qppoKUfcEav4WsriLg+LH5nGDP/s85YCvSZotjJB6vmyHKL1lOw5num9CyKnFdayxdKlspAJ kMWXYYi0HCmIrvgKf2xo8ywBZBgzBTGD45l8KIgBu1EQC2IAgQATH9zFPhXstc2vAAAAAElF TkSuQmCC</item> <item item-id="8">iVBORw0KGgoAAAANSUhEUgAAADYAAAAWCAYAAACL6W/rAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA APRJREFUWEftlskOhTAIRfX/P9qhsQ0iZaqwqDZ5mxcK95TJdTvOMuM5wWY8y4xQpQo/A3a0 29lz5Xf1Xxo7js0FhhqrzptueLnC1P/w5UhCKnYvnmRbdEMIyhF2EgUniZV04gSJYB4Qz2No wGB7cDFUGfOA4VKGvdMTpAWDdpSvBm9JMYb0ZMbbN9QgGwLjXlgCw5nihpE2Y9Kwa4nSGkYP llEwXKKPBS3tktuuuHbdWyXai/0QDfYs3mHNh2U4UAEs9zNtTZ9UmnLJFM9+mViEfAJM6kXL g0XYmkoRNiq1VyIEen2awbyBsu/9YNkvPhpv2oztq/5fg05soAsAAAAASUVORK5CYII=</item> <item item-id="9">iVBORw0KGgoAAAANSUhEUgAAAEwAAAAWCAYAAABqgnq6AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA AUlJREFUWEftlwsOgzAIht1VvIJX8LRe0g1TTKXQUsTpujYxS5U++Ar032v9tKE3PQEA1pue wKA37ZZbNnYMdQR2YMuyrNM0QT3bnnmetwf74ziuYNNSQ9/gN9fi74klwsIJEFpLoMJFd3BJ goZQ0bgDCyRyUdZchJVSissOOuavgO3pEupvXJtyqRaDvByYZlOWumeJFo91LgXGTW511DpO SjN6kLdHmPYktKfuCUy7Zs0tSW1FHQaaCx48MdBo2Jc2drhNgrah77g+N46rS5p1a6FR2ZAA iuqiSelrIowThBKUElBtYa4Bdca2+q+RBhgX8vguEYJEZZei7YyzHmO/BkwTYVw6/HyE5aIn 51wJGH5vLsIkkUhhScWU1rfYTqp93Fwe6WWZozol6SJ3yQKLsx5jTgOTirnH5p44hwuwJzp2 1Z46sEqyb7Gb1GKae7QrAAAAAElFTkSuQmCC</item> <item item-id="10">iVBORw0KGgoAAAANSUhEUgAAAEgAAAAWCAYAAABjadrAAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA ATJJREFUWEftlgEOgyAMRd1VvAJX4LReUldiDda2tDLNSEpCprEweP18+lm/bYomEwBA0WQC U8DRCQSghkIOQMuyrCkl8KPSc86l4/s8zyvEjNxwL/Crtfr7JRLh4AQIaWQw+0V02oIECSFi cABiMj+8glpHhFM7HaPNMTygQ/67X9beoh2dGtyjgKjZ3cno2572moK4P7ICssZZ4VHlUIPV FPOYgriJrRu3xlkBeeK6FUTrIHiHjlmBGgnrIClTlyty9wi8Zrnv9Aq21Cp3QUtzn0y58rWy Nk8WaCwHSsuUthBPrdKzZu/YLkD1bcI9c+rw+MJdpXghqFW1d7KWB1kVZFHMsICsEKRyAMe3 jHNYQJLp0gJOMmBtvMWkvarvif+JB/Us4N/HBqBGhgJQAOo7xBt416aCBqD+NAAAAABJRU5E rkJggg==</item> <item item-id="11">iVBORw0KGgoAAAANSUhEUgAAAHoAAAAWCAYAAAAPb4jFAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA AdVJREFUaEPtWduRgzAMzLVCC7RAtTTJRZmIM4qsl32DIfIMHxALrXeth8PP9hyPHPdnAITO cX8GHvdfYq7wlbWThu9g4EPoZ7GCmr1fV6DhTMzrum7zPO98LcuywYWYpmnaYM7Z4yA0gKOD e3Y26NL/KJgBBwiMA8UehatdWUnQUcUeCXMKbdzSkc2UQhvJLZuxUUij9RbvuSWNgvn9X8T3 pe5IdNr35t/Ms4Su9QWWGv1fjaPG+XA1+s4R3bNx1IT9aKpH7GA90d2TPI9f7nQiRXTv7NMk NNYa6RyNDsraSZ2W99x8b73VBNDSoQVzuXb0R9fIkUvP0XAPF9rCGRvva+uo8cXhoBqVc2jQ HvBbnFOHEnEcOdxCNPEiv9eiuyZYDZdGfC9sVCQuwKwYJY5f75VAtziWdlqEKKtNFHMtUvC5 1b8ncDiOLJuVw9QktJZirbtN6wNaSdTsvTilUqT56i20BTsVnsPv/qhhcaztrihZUbsWzGWZ ivqvZTctMiUBKS6Nc5fQNC3W6nE5T6vZreRp9hbMXA8i2Wk+reWQjbz3ByWaBS3roGIfuG8B nbZxBrhaHH+bbumKaP11OcPDANc4euw9c1NoD1sXnptCX1g8D/RfvoNZorwdhDEAAAAASUVO RK5CYII=</item> <item item-id="12">iVBORw0KGgoAAAANSUhEUgAAAKgAAAAvCAYAAAB3/oHUAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA A3FJREFUeF7tm9Fy6zAIRNv//+i2aeOOQhaEFCETs5m5L64swXKCgMz9/Pr5fPBDBbIqcAOU HyqQVYGPrIbRLirwe7tTBiqQWYHSgP6UXbf6+/9f5kBVta0soDcw5Qc9qwpGFr9LAmqBSEiz oPlnBwEV8SCgBPR0BZhBTw+B2wBmUGZQNyxoYXSTSUAJ6EuA/taJoOF8edP7BiUB1USNFHpV wDLuE6lbWUAPSK0r6hD+WGOB3QZJro8MYAZgI/0rDegRXG0mqoEmYUT7tHBngCjSBgIaqe5R 64BflDwgykzcmhoZuA2SuI+I9JMZ1AiDB1BtTXTz4KZnw0ICukFkbYRiXd8SQgRrZPBOkuXp 2MifjZlBlSjL5slqfNq11ntZgFpph+X7inOeAEUCrziIe8QooE0hogfoMd487/oAqEzVbdbY ZRDP8SuA4oVq33cuM/4B1Zx4Z+f8ob7GSm209c4x7AKaKXTvLPQOHQnoDpUdZ0QX5g4TwpbM +nbVcddQBrVECIvYgo21oJ/5fIFbD1t4AdX6jDO1MKcgaM7Xet5eG1mu2NkssxqKiP1mfNOa pV5sI+xfvWe3i0ewrjaC+80rgDJnD9j50/a/2Z2DaoBqwqAsq42r0HOt0Efjk/1y5ToRZds2 a15hTDj0SxK67luR0JWi1UYW4FlKiVw41rRmClCZzawrRX7LZUaW33LCWRNEzestgKLDPZl1 Zai0TH+Fa3ClTtZeZySPrYB6oIwUQauPdwWY54wr4AbUMyc7rn50bVvNk1Xsj7ukv0FAV6q5 Zy83oHvMiT2FgPa/vG1y6fUWbULqRc5qps0zextf6e/aLXAlH2d8kfAgYOSzkebWM7HRIGUG 7fyf7siaeAamqHdG+wNPdkVjRznF6Y4moxzOuO/oFV+pw18JqIy99iV3nZkRpCibRgE9aqwo ezLt64KluW2sDGoBOnxOJpEibdGKdJQl1XroHiCrqEeNA6rfIn0d3btXg2q1+wEb0hZBKuHU dHl4PurM1dcjEVvoZFBklu1liCo17SpOSjVJHtG8xb8GIoK51yx47Kq6hoCKyL8KaA/cqqDN +k1ADUBRbdYdiyiNRFsazAar4nsEFEQdNUGeRqKFFzUWqIGqCN2IzwR0RC2u3a4AAd0uOQ8c UYCAjqjFtdsVIKDbJeeBIwp8A9NbEDRE0LtbAAAAAElFTkSuQmCC</item> <item item-id="13">iVBORw0KGgoAAAANSUhEUgAAAPoAAABLCAYAAABZeprtAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA BuxJREFUeF7tncFRLDkMhtkQSIHrHrnukVhIgeseSYEUSIEUSIEUSIGH2O0pj5Bs2VbL7vbf VdR7M+O2rE/+bdnd0/PX1/dxgwMEQODcBEjoOEAABM5N4Obc7sE7EACBn6wdGEAABM5PAEI/ f4wP7+H34pn2kS5/h3dogAMQ+gDoMGknQALnh/SevcY1S0Loa8b9EF7nBA2x14UQQq/jhdKB BCB0P9gQuh9L1ORMAEL3Awqh+7FETc4EIHQ/oBC6H0vU5EwAQvcDCqH7sURNOxDArrsPVAjd hyNq2ZFA6Tr6Nhhs5agp2gCRvs/Ln3knH0LfsYOial8COfGWxM4FvrUsPc+3tXPV5ib0t7e3 uTybsDVg1B8U6Q65kohTUaezOBd7f+vmrcFF6O/v71/0hyNPgBh9fHwAkzMBi9C1Mlqa79zE 4dW5CP3p6Wm4I0dpAFj5R6pH6EjdjfH4/Pz8enx8NJZGMRI6McPhQ4Cn8rkNNr6pt+RmnAaM r2l4udfX16+Xl5dfUSvtlPqEOV+Ltp6T1mkR7SEbtE4nZjhAIJLAVepuvWaZlqPZnK/PrfVE ODpTW8hfYoUMKCLysJES6Bb6w8PDVSqauxY54jrlbEKntJ2Y4QCBSAJZoUsbFVw4pddXo4rw 3eK9nZ1N6OTv7e2t6vaIwXDvGKD+8QR+7bpLO5g1lyZmnNGlTZiR6C1izm0cjWw7bB+TgCr0 kuA3d884o1uE2BPuPerXBga8f/0YqhV5UF+F0JliI3bkLULHjN4zlOJcTkC8YaZmXSutN2vO 3zskLW2xCLGn3bk1ek+9OBcENALdQqcdZOm2Tj4j/Rph/t+YS2dQTZR8GZFuEmqf/aQrwpND pffSstv/+dLEs525Xfe9BxlIYU0C3bfAStfRU5Q58UqC1Tb+WvcMLGEtDSSe7cR1dEtEUMab wEXoN/9ea976mu7yen5+zrZLm1mlDT1pc09aN7ek5Gpawy771Qw2qQ+WduLOOO8ujPosBH7U TaJOhV3zmm4Aufm7PjGoFZM0AFgctJTJ1e3dThoUV7zXXVsyWeKDMv0ELkLfBN/07z+xQk9T 6X4E108j4R2yR+hSO0vZj4c/s9Wx5yA9m6+ztuemdSZPMwHajKv5ooYkptIGW3qOVrYHsrTZ 5t1OfG//vwhhw7Gnp7adeyX0dDa/rD2Na3c8PaUcgJrBsFzbMUtA5GPiVp9zj2knrJ6AgLRZ eQK3DuEChH6IMJ2rkZjV4+MJocczX94ihB7fBSD0eObLW4TQ47sAhB7PfDmL0hWT5SAMdhhC HxwAmAeBCAIQegRl2ACBwQQg9MEBgHkQiCAAoUdQhg0QGEwAQh8cAJgHgQgCEHoE5UVt8N12 6fWiaMLdhtDDkcMgCMQTgNDjmcMiCIQTgNDDkcMgCMQTgNDjmcMiCIQTgNDDkcMgCMQTgNDj mS9pEc+MGxt2CH0s/yWs45lx48MMoY+PwXItwNdU40MOocczX94ihB7fBSD0eObLW4TQ47sA hB7PfGmLM4nccovu0ctsnQ1CX1p2sc7PJHLyfLb27BkNCH1Puqj7QkD7xZuRiCD0kfRhu5mA dK16huvXrd9ak34ppxmOcCIffHqeO08/znF/f3/5qW76aWz62+q8u7ur+jUjTz9/shfvClHf WALSLHXEmUu69u7pRy7D6LFD55LAt2MT+9heAaGP5u9u/wxC14TWI0AOOldXjx0I3b1Lo0KJ wJmF7hnxGqHXLD0gdM8ooS6VgHY56EjIemZUq5/WrEFaQuRsQOjWCKBcF4GWGT1CWCWncmvm 0rk/m03fa+PtsPjjIXSNdWmNng7GvO0WX1vKYDOuhdrE59QKfet0M7lkFWEqbIu4Ux9rOHFh 9szoUoZgbbu1nLikmynAaEs/gZoOXDMD9resroaWlNlqocSoNjvgA0huRi/ZLg0iVh95Oczo reQmPE+aebTZKJ3JpY6tfa7Norx8z+zDbeRm+O2zGnvarFril8t++HV0ek1/2zl0jZ1eb6m6 Jngei9Q/bl/yXY33hP0VTdqZgLUzlQYDvr5sEV2rq5oPlvp6BgXus8WeVkYaOHKZjORzLvu4 +qynoTj3mARqOpOW3ucyhQgqOR94Km0tK7Vbm3k9fTSLNdlwlAacbFw9G4y6jkGgV+jWjrkn jRrx1pTVxK4NbLU+lgYOa2wg9FryC5bPpYCaiGver0mNW/GX0lheb25wam1Dy3mldN0SG22J hBm9JSInPyftcGnH4e/zz9JUPp3ltPP2xCj5kFsPa8uQPduoDTi5DEHzS2IsiVscTCKdhC0Q GElAEsDI9kTaxuW1SNqwNZQAhD4UP4yDQByBiP2DOG/sljCj21mh5AkIrCr0Pxac93a8/sUc AAAAAElFTkSuQmCC</item> <item item-id="14">iVBORw0KGgoAAAANSUhEUgAAAJwAAAAWCAYAAAA1k/XzAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA ArVJREFUaEPtWgvRwjAM5peAJyxgAQtYwAIWsAASsIAFLIyFY1zI8mxz/GNr7zi20WbJly+P dfx1/VgljMvlstpsNgmS5iuiYdT7FghXO67XawefNnQEAKPb7bZomFIIt9/vFw1ixPilY1VN uPv93u12uwjmi54LhAPMljrehIPqOnxefd37HFdeOu90OnXH43GEH56XVLnDPqK6UrvCAhMW nM/nDjDLGpyNnOxSLLJ995HhOOHWNchutH+z1mSB7ZEzJV1AX8AqqyJQ2zRyWHMlnCZHuP7J 9KNERIz2EKZ2ztQIB+UUMMsYFomGe+DKha9hHSRZXyXccDNaUj2KSmk9A+iIjKkRDnRfr9cR E9S5nI+kBRFSeeVKpRq3L8Px85sqx5Hr1wlH+8nsqI2yx6oCUX0j9niyouZvKdlgguKMOjqW CGcRz5uatWwYdVTJ/JIMF3Fglk4lcj4yh3NL1fKrRkgPWbFO3LGY4SzF5ko4rt8pJYO3tNGg 9GY4LwG4oNfKa+n9OU5QHrH7cJGswPUjkfXZztRaBClIPGsy9czq4aKEi7RGHqwkedr1asLB Exf3uoZGiVa6cf3n5o2i5FU+aDaSzrn1nLM4PWhvQssEdoxXz6yn1EEXLitTvaUkYGV0q72w MMe/P49rI5fbh7P6NkxGDJpV/2nUZWVSiygW6axIx79n7sPV+u4/1lcTDnbND4eDqjslmJdY ngi2yO0BVStNFplohrPKXPabBo99U5pTTTjYyNxut2GbPI4s6TnCivQLagkX0ROCs71LLfES WlPyD4gawmk9X4kpWo+XradVDUr0/6U11RkOjIWHhsgLaauHkwigrasFnTa3XDkfNcBo7wvr JunZ/jfIvGkodRz0Jm3oCESCcq5YpmS4uYLT7MpHoBEuH9MmUUGgEa7R46sIPADMaKyu7ILo 7wAAAABJRU5ErkJggg==</item> <item item-id="15">iVBORw0KGgoAAAANSUhEUgAAAKgAAAAWCAYAAABUi9exAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA Al1JREFUaEPtWAluhDAM3H5lv8AXeC2fpBtUS15jx5OEhBQZCbVLrvF4fMDP/rlecQUDszKQ BBpXMDArA69ZgQWuYOCo7kFDMDAzA48R6LZt+7IsqZ8+7nVdj5t+v9/vPc2Z4SJM6S9d2rMZ sCIYemJ/jEC5o5Mw6SKRIkSPnMPFybGPxHDFWdIOza6Wc0KgLew1rH2KQCUF3QXaM12j/mwx ksp7ZFCU7Wvneb6z2pvjheivPaP/Ty9Jsie6Fnr5btwYNHD+k0A1+8pZun5FDe9cVBYiTV/S r1KkXyWeD/JDvKi4nqL6HRGBaqTUn4itRHo1j2dvHEPSb5Zno4XfSoyHnzS4JxWzt81+5p13 rolkT6Aaiajj0XkIR9peuf2t5IGcVTqnlnfvnGaB5pTsHT7LeKlAkdJEtt0p0BKco31h6Ubi QPQl56gl3krVuRbAygjaGi8b1AhBfgdNv9NNZ6VvpPQdFLVDqyQaySdSWbOfq1DIOolBBos1 zudp55Dga7iW7Z/MuLkAkdzztRLTMYZEm7apllVqIgQ5v9ccK3is/tsTlBXYtfg9YdG4J2KZ ffm6Wmyj1sECtZzGVW8Rmou4UYZ6UW2VcSvotJLbU6CWyHLPLd/M3C6c2gJEIDnicw7Uykhr SUHwomL0qgDifC9wW/DW8I5UsRDohwGEqDvE6pX1XBmXpdHLmK32IVhqMuvjSnyuVMgx2Rdp TtJE0pJpStaW4kP7QJ6Nr7RP6y/pmdWDWsLOrSvhcORcqAcdCSjOCga+2qagIxiYmYHIoDN7 J7Bh30GDp2DgLgZ+AZZRhjr2olrgAAAAAElFTkSuQmCC</item> <item item-id="16">iVBORw0KGgoAAAANSUhEUgAAAaoAAABeCAYAAABy+1BNAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA D7dJREFUeF7tXYF1HCsMzC/BLaQWt+AW3EJacAtuwS0kJaQFt+AW7puXrIOxQBIgEDD3Xl58 B0ijETALu8f9d3t/fcMLDDQy8OvXr2/39/eNVvZuDo72zi+iM2QgCBVeZzDw3o3CRUn3YH// /n0L//AqMxA4en19BU1gAAwoGeg/aykBoPo4BqyE6sePH+OCWNwTuFo8gYA/hQEI1RTa5zi1 EKq3t7fb4+PjnIAW9BqEKnCGFxgAA3IGIFRyrpavWRKqq+zaGozfx9uFab2Xl5fb8/PzF25y 7UeSmGINvqnPRmL6+fPnLXBW+7LYuq3F4qXdqpxwuLlyL/yPwAGhGsGyEx/ciooaGNxnYTWV 3p/i2oykwxOWEHfgqnYFionra89ZnRMOP1c+cizN9AWhmsn+YN8WQvX+pN+nrazSwJox6LwJ Vdj2C5xpXzO402IcXV/DSbrCn/0+5oqLgysfzfsMfxCqGaxP8qkVqngb8IKcDhruvWZAWtDi TahCjHd3d6pQMVG1r6S8c8jh48pVHWrByhCqBZNWC5kTqmA3HhC7CBV19VzLYY922klHW78H Ru82tJxo64+On8PHlY/GO9ofhGo04xP9aYSKEyzpCmvFFZX1pKCxr6nbu2vFAt/bdou9Gk5q 2rRgrGnLYeTKa3yu0gZCtUqmOuCEUP0hsTTgJRy1pkIz4WjqtuIqXVTMwkHFVIOlpk1PPiW2 OIxcucTHqnUgVKtmrgK3dBKmBkRukFD3WzTtK8JQNanBYj0haO5RWWORkukFB3ehkYvHE/5a jCvEIO1P2noQKi1jC9e3EKrwBBt1LFC8bVQSixhTrl78eVo/V3ZNaGnMMa50BXHZSm3GtnIT Zc5P2ja8lz7152li8oKlFkdtu1If6WEznU44m1z5wtNTETqEatfMEnFJhUpDCfU9qtIWEiUg ObGiBCMWiprVEhUbJ4SxgJXELBUxqq7me1ReJiUvODyspqicasYLV5fjmivn7K9arhaq8M16 vPoxMJJPC6EKpyw8PT2Vr4b+HobLCQ81EZVWQNqr0RzIdPBb4tScTOFlUvKCw4NQ1WKQzhgc 11y51M9q9VRChVOy+6d35InaFkIVvsD68PCgJkYrBiUxUTtPGrQKVS4WalILoi4968/DpOQB A7dC5/LfO4be9jTxWfrmeJxZrhIqnPxsk6pRvFoIVWCkBn+LUPXefqG2/mq2+CRbf9zqUzNp 2fTGf1ZLAmztW7r6leDoObn37nvaXYGesUi481JHLFQ4JdsuZaNO1LYSqvAwheag1XQ7L8aV Yozrltq1ZifFcIlODlsNTu2OxMxJieK9leMe7Ws4qWlDYS310x6xUStwrZD1wuHNjliocqdk awJKO7+mba+66WRHTUi9fEntaO5bSG1yA63FDtV25L223thH2dOIuWTSktYZFd8IPzWiU9Nm RCy5MVryXRNL2oaaA0fFW+tbLFTc011coBTBNaRzfiTlnrAEvJonwSTx5epYrahaMKFtngFu fKyWTy4eSV+osVHTRoLFog6HlSvnMMXtW21xvtJySjClNsRClZ6SLXXAXfWNJiuHZwaOi8Pa E7U1ObjinhmnFu/p9SW5ktTxwmMPrDU2atrM4ozDypVzuFvbc+KjWQ1qsHwIVboko97HIOKr Oe7KrgRIA5ZLgrTc24oq4C6dVtCLIy5PUv5QbwwDkrxL6oxB+9lL2tfS+eSqTfXJK6ZSmSYm rxxRMXBYufKSzTgHEjvSOV46r6Q+JRg++kkqPrn3pcmdc+hRqNKkcTFoBkZNXYl/CrOkXWlS qMGKNmMYkORWUmcM2n9eYky5v0NtqowStJINSWweOcrh5rBy5dSKp4W/+KKB45rDZiJUUqMc uLRDUkRyBPQuL4lubQdqxSjhsYePXOw5EcTn3z79fH1vPqQXirX9UntxI42vhPsa85et+EIp N5FqBU0zFqi5TBqnRb0Sdm4e4Mop2y1Cxc3fnDC29u+PvpMTDgjV5yvEmg7SMphyHY4aOFI/ 6cQhbYd6cxiQ9DlJndHocxNjaU4ZKVSj+dD44/LJlc8UKg6bVFPIGKRCVTolmwOYU2VJO02S pXU9rqg0J2pL49Rc7dTaRDs7BiTjQ1LHDiFtmROqq5yr93E1/X4EF/W3NC6PHOWwc1i58t5C ReWqdl7pKlQ5UUlPyY6vztMr9dyVe7oaKIlkiaCSPw5LjCHu/BTmNMZ0sOTKU7vU+9Rf6UTt ms6Z67C9bEknCdSrZ4DLFdWX6731bUmN9dIYS+vH458bZyXkHId9o263xuHlynMiws293HxR mtM1Udf2WfLxdIoM7feoSoRSZWkAnEikgsoJm4ZMynZp4FC+4xipv+PP8D0qbXbOqK+dlE5g RcuJtv5sDjm8XPls/Fb+vwhVjgjJKdma5TmlrNzkLlkB5VZpWgJTHiTYqDa5q8gYzw4nU2j5 RX2egVMnpZ4rpNU45PBy5XyvWrNG9ntUaTi1p2RraNGKQUlMNH5zy9748xZsHE7NidotcVGi 2WJvdlvqYmc2pp7+T52UIFR5Bk7tE+KTKQJ1NadkawZuDzHolcgUSzzJ98apOVFbwye1uuzF TwuOHm058e/hY7aNXXLVk0ctJ9r6PbHW2OLwcuU1PldooxIq7SnZGgLSq+NUGHLCUWqn8Z9b VV1+4/9z2NIVS4wth1N7onZLTLutqHIr3haOPLU9dVLCigorqi8X2dqBiVOytYyV62tP1G7x DqFqYW9821lCFbb5cxdZ4fNwcTXrpeWEq59eQM6K6/IrwTsb4wz/qhXVDIDw2Y+BXYWKG9z9 GBxraVZc4asS4QIqPix5FpYvV9bRd6ok2SjhTndpJPas63A8c+XW+GbZh1DNYn6CXwjVBNIb XM6alK4VUxCr6/t9s7BYCxUV18xYOd9ceUN3c90UQuU6PX3B7ShUOw/c2bGFh6diofKw7a/l RFI/HhezxwiHlyvvO2P4sQah8pMLcySzB2HvAD1u3fSMcfakFPxfT/p+//79Fv7Nfmk5qdn6 0/royQnnmyvvicWTLQiVp2wYY9lJqK5Y4v+N6RtufvakFPxf24BhNTUbT0iAFgMnVNSYuNrk xgv3eepTM+64+Ljy4Z10kEMI1SCiPbjRDBgPeIFBPzGfwJlmstbUvbhLx0lu5d7yOZUnDitX vnPuIVQ7ZzeJDUK1XrJPnpxy2cpxUlodaTJPrYji9tQqXrLK4nLZWq6JcbW6EKrVMtaAF0LV QN6kptzkNQnWVLfcdl68MqoBWhKq3CpKshrjsHC55so5+yuXQ6hWzp4SO4RKSZiT6idPUNpV Vah/8VXLW41QabcB07g4rFy5k65qBgNCZUatP8MQKn85kSA6fZKiOJKsqlp4o7b30lVTPJ4k 9Uu55rBy5ZJ+tHIdCNXK2VNih1ApCXNU/fSJqkasVuGMw8mVO+qmZlAgVGbU+jMMofKXEw0i TFhf2cpxskpf53LKlWv6z8p1IVQrZ0+JfZXBqwzrqOqYuHRi5blzcLnkyj3H1hsbhKo3o47t QagcJwfQujOAib47pdMMQqimUT/eMYRqPOfwCAbAQDsDEKp2DpexAKFaJlUACgbAQMQAhOqg 7gChOijZCBUMbMQAhGqjZHKhQKg4hlAOBsCARwYgVB6zYoQJQmVELMyCATBgygCEypReX8Yt hcrDj+r5YvsrGnDkPUPA55UBCJXXzBjgshKq8JtF1+8WGcDexmTg6PX1dZt4EAgYGMUAhGoU 0w78WAnV9SuwDkJ0DwFcuU8RADpkAELlMClWkCyE6u3t7fb4+GgFeTu7QagCZ3iBATAgZwBC Jedq+ZoloUpPf47fx9/wT+u9vLzcnp+fv3CTaz+SxBRr8E19NhJTuE8VOMMLDIABOQMQKjlX y9fkVlTUkTPcZ2E1ld6f4tqMJNITlhB34Aor0JE9AL52YABCtUMWhTFYCNX9/f2nrazS+Woz zl7zJlRh2y9whhcYAANyBiBUcq6Wr6kVqmuST7f+YiJSIYBQ8d3k7u6Or4QaYAAMfDAAoTqo M3BCFaigRGl1oUrvl81Y2ZXE/aAuiFDBQBUDEKoq2tZspBEqTrAuBnZcUVkLmbX9NXsnUIOB PAMQqoN6B4TqT7K57UlrIbG2f1CXRqiHMAChOiTR1wQtmSQ1DyBQ91s07a3pr8Ei4agFN+5R tbCHticyAKE6KOuSFVVuxZGbvMMTbNSxQOl9oZTm+EEN6qGNeGsx3Yak2lJCHGNI7VFblqWH R2LucuInxYmn/g4adAi1CwMQqi40rmFEKlSaaKjvUcXtS5M6J1bUfbJYSGtWS1RsnMD0xInv UWl6F+qCgb/b9SDiHAYshCqcsvD09FQkMbeyiVc51N/xKolaAeVWadqMlmxzYilZqcV4cDKF NjuoDwbe7yuDhHMYsBCq8AXWh4cHNYkSAcjViVdVasdEg1ah0uAMoo6z/npkDTZOYgBCdVC2 LYQq0FdzIniLUJXuadWkk9r6K92vSoWSiyUu51afNfjRBgzszgCEavcMR/FZCVV4mEJz0Gq6 FZg+qJATjlK71jRSD0v0xonf7WrNEtqfygCE6qDMWwlVoBC/Xst3JI2Y89b4GtTDJnwrfY1R fvTI0GIXBiBUu2RSEIelUAnco4oxA7mnF43dwjwYMGcAQmVOsR8HECo/ubBAInky0sIvbIIB awYgVNYMO7IPoXKUjAYoucf90/zGD4RQ23NUf0jbcO9DGJRA5mxjm7Ah8Qc3hVAdlHwI1frJ Lq2acoJxRS15OjEndqkg5USQ80EJ2/pZQQTWDECorBl2ZD8nVPEVOv7+9unn6q350HYPrVDF 9ql7WCVhKokTZzeNK+VRGzfqn80AhOqg/GNFtX6yewoVxUbOftx3JPfCOAFcPxOIYCQDEKqR bE/2BaGanIAO7iVCFW/LlVY+3JZga7lkG7ADJTBxAAMQqgOSHE84uJm9fsLjbbRUiLiHH6jt v7R/SMQw1yaH7dpGRP9bv//NiABCNYP1ST6xoppEPNyCATDQxACEqom+tRpDqNbKF9CCATDw hwEI1UE9YTehKm0zHZRWhAoGtmcAQrV9iv8FuJNQSZ48Oyi1CBUMbM0AhGrr9H4ObiehStOG m/QHdWSEehwDEKqDUg6hOijZCBUMbMQAhGqjZHKh7CpUWE1xmUc5GFibAQjV2vlToYdQqehC ZTAABpwwAKFykogRMHYUKqymRvQc+AADcxmAUM3lf6j33YQqd0TPUFLhDAyAAXMGIFTmFPtx sJNQxd+h2ikuP70FSMCAHwYgVH5yYY4EE7o5xXAABsCAAQMQKgNSvZqEUHnNDHCBATBQYgBC dVD/gFAdlGyECgY2YgBCtVEyEQoYAANgYEcG/gfyTMkOoKL2CwAAAABJRU5ErkJggg==</item> <item item-id="17">iVBORw0KGgoAAAANSUhEUgAAAPUAAAAlCAYAAACaj69KAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA Az5JREFUeF7tmuFSKyEMRvX9H1ptnXUQAwmYQJaeztwfd1ghnC9fyNK+f3x93vhAAALnEHiY mg8EIHAOgbdztsJOIACBZ+cNBghA4CwCmPosPdnNYgJfL+KPO6nnvyyfPJFkIUIcEDASqI2c xdiY2iggj0FAI4CpNUKMQ+BmBDD1zQQjXAj0CGQxNLff5CkEnAhgaieQTAOBDAQyGZqTOkNG EMOtCZSGzmJubr9vnVIEv5NA+R11pu+qMbWQFdYfFGSpzDsT27q2xNTK2boGz30TwNRVJlh/ UJCpMt8lmaUiSGH0Vw9TT5r6WRET/TTQPzX8Z8TU/kylGX9MXZ48rVMoKokztWHWk7pn6lar ef3NtYY3z0wcxWQTiqA3gzW2GVtltS6/TmprskkXBLPiZLs9/K+ppf3UotYFdCxF5KezcWyZ OuvlkocGWiGb9chobH/ab++FtQKwuivQAHmaulyrZboWb41bvY9sHLUEv8a1fNPGNT2t47O8 tSK1Q5cpU48CsICtT69VYkrmaJlRe1ZryaVE9t5nFo4epm4ZwpJP2Z5ZqctU++0JbPQE81zb kng900ljlv1EtMqWdaPZafNrvCx6aGtkG9+hy/aLsvLUl07IXvvSShLpb0bm0WK6TmTrnPV8 EVVbi9kaa9lNjHLstdRSfL2YL20lU9T8tFeRCN7W4rFDl7RfadUJZal4o89Y3+usAmZ8biVH j/3XGpbmbplzRHfpIPCIe3SOSF1Sm7r3btur/K3K3Tp5RgW50/N1Ekv/lxJM64KizNGL12Le XhcVFfNMPkTqcktTt8StW8dXOIm1hJoxyU6OM/GOmF3jNTreaq+1eWb2adXlKFNbxM1UrTXh PcZnkmcnR6n9LltwqVCPxOupv3ba9vSL1CWtqa2XUdJ7Vq919DDK3ebonSblWGke6VWl1CSS Qa2plAuS7vVJ1ttbRPyjBSNKl9SmjgDPnBCIIDBq6IgYfopa5OTMDYFXINDqanbtnZN6F3nW PY5AltMaUx+XWmxoFwFMvYs860IgiACmDgLLtBBYRUC6XV+1dm8d2u8MKhADBBwJYGpHmEwF gQwEMHUGFYgBAo4EMLUjTKaCQAYCn8RkkKJiNTLuAAAAAElFTkSuQmCC</item> <item item-id="18">iVBORw0KGgoAAAANSUhEUgAAAG8AAAAlCAYAAABMBrdqAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA Ag9JREFUeF7tmFluwzAMRNP7H7pNjLqQZa4T0xmhNJCfmJKpeVwkfX0/n0c/ayrwgtfPmgo8 1nS7vd4qZsuwrgINL8ju2RRfe4Ptx/LweMKiiODHDIwFYMMDgqbhAaKxDGl4LCSSfrCA691m EtzvhQYwqmZI97yErkxZ15kHgmOB2JkXADie8ZjOen/wpEMoejDVIpMlYgO8ljA5ZJ4kblZw z957v4RqJE5eCi8KJmpXqRGDD++u7zJ4Wtbu/8+9whIPEXacP9KXkG94YqNtxptXe3+ChzZn r88h94OSLxHoETARm4yo43xXzx2GNxt6joyZpX5EuIn35s0IN9p6cJGAiIzRsr1qnadznlb6 LCiV8CKiZYNtt68SNVu+0SB9G954XVRRNrMLiwRSFTytbFYFyQGe1Gy1BjxHl3bnZ0WhV94Q cCM8TzTvPfp9rbdbZVWreNKYg6aIk2P0Io36auGya7jTfgYQydCoTfp6TIusaDn6T+CkiiTp JwH2snEbk41CD55WQq3/sz58wl5rIZ4vll5e1bKy9rRh8RyZAaALinyHySYSsNaOXDvKRMuj VNWgzNsBjpsDJqHv8CVb+q0gH9/NGy6rdEKZd4c4zN/IgqtcS7rnVTrDPre23f+U3w0PUJ4l +xpewwMUWHhIZ95C8KQdIYP7XTYZKIA+NDxQOIZhDY+BAuhDwwOFYxj2AwGwXOJ69MV7AAAA AElFTkSuQmCC</item> <item item-id="19" content-encoding="gzip">H4sIAAAAAAAA/5RUS1MTQRDuyWM3CYFglGfAIKCAvJeHPFQGCIiKWMKBIxWSBUIZg2Gx8Jbi B3jz5MGLP8Hybnnz4B/ygN90b0KwvDhbvTPT8/XXr52NE5GC7EBivA7iHXHfbruHhdKbMJkx DYnmSznRhVg3b2DF3Mv9YzfnsYa22T6At3V6dLJSOhf1JsSGYrsGfAGZg1RAdQzvn6yrvRmL MoEqEOTokjXny55XLuyfea5ixAIkQfUjtNB7bR9mgpBXdt0UKxxI4wkvLafjO7yGbeczZm1F ZLajMkdiMkcbnACcvEN9bGVNIvUxyBQeijv2bzDEGp2Pz4GMUxNDvzQieHVqgnOsnwDEm1mf Txg97G4IDBIM5KjIPUiyt0r0pmNSWknC+hYvL5IcbYsT+4HzSCv71C1tTvIX5tZ2oW5DlsEN hJYn6nA+IMNKG3WKnw7jltuREkVrzTF1SQot3ULXdVvouuvo0kLXTT1ina7R3RFF1xUdUa/k YfdJ9Xr7Ze67K1XsN8Zqguge77/V9jQghn2DUoABqIakAANMfF820t9hAVsjwj48KvPImHgZ RXdVeBrxO+gVej7OHboYpQkGfB3/G0A0KZQjjvifhGpKXE7KNZAGDM9IA6ZnpWIzD6RisyaR 9/iSpVqzNC/4uQWe9fyi4Bb86j2U2mFrhV8hjiJlyRT1kehxwWxLGlA9IXosZzNX9V7ivCrT pLU0ckkvS1wYK+JRL/uh6VWJDQqdkej0ql6T+HRGr/v4NT9E/UT8GcX1IPWGf5L5V5j6qX+q 6z8M0s/475DIO3uH5ezJ0d5BqVzMerZ83uYnw+pM1suKQad/iYPVl7nLaf8PoXgXy54XTteZ R/4e+BZpS/lYM9p3qYDoPDqiHirRAd47dIZ9Gbse2iUX52WsBslEOkR0iYdU1zWWjg3GHYLF +0+ezXoeX1sN3/bK2ZzrZDhDSlVTVM2kUgq4FO5GbRVQqSCM0yqoUiHQpFUIZ3SJRVilAhWN I8tgDIPNGCwiBmPAUWDYPAYMmzcAw+ZxYNi80WCMVRNjsEgYjAFLwImts6JbLuSyr6XoEQ58 A5JH+gXukvkjx6ptuz7i9Zs/AAAA//8DAEWIG8Z9BgAA</item> <item item-id="20">iVBORw0KGgoAAAANSUhEUgAAATIAAAIbCAYAAABojRKmAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA LuNJREFUeF7tnYt24ziuRfv+/0ffqdjlRFbJIg4eIsTs6lVrptuACG0AJyAtO//3/3/+/Mcf CEAAAncm8CVk/IEABCBwZwL/3Tl4YocABCDw2FWCAQIQgMDdCSBkd88g8UMAAkxk1AAEIHB/ Akxk988hdwCBX08AIfv1JQAACNyfAEJ2/xxyBxD49QQQsl9fAgCAwP0JIGT3zyF3AIFfT2AJ IfvzyYqvj1k9/m7/fPrvvz7rAIDAYgRuL2RH4vWVo0//fbH8cTsQgMBXv69G4SVgCNlqmeV+ IPCZwK8Usu2Wk///sy2HBSxm1kBEqH+tkEWgVfn+aaOqS4euWxLX13nm7kxTDTIcV0IMRzGH 41JBGO27xnV0FGS8pW+znp2j3sVf++128mxruX/NuVy6W9dCK4krQUTCcSXEgJDltEG0J5cR stGZ2JnI5aQifpVwY8ZDOLxCSVwJIhKOKyEGhCyn6BCyr3csNlsUy2F/FFpO6v69SrgxiwIr iStBRMJxJcSAkOUUXbQnbz+RHR1OvtCePV+Wgz/3KuHGzA1nc/5QUCYJIhLmlRADQpZTdL9e yDwYo9A8a1p8wo1pWcRhUxJXgoiE40qIASFzFNSBS7QnC37U5txY5VWi0KpiCzdmUWAlcSWI SDiuhBgQspyii/YkQpaTh5SrhBszJYqLzu4SRCTMKyEGhCyn6BAyB8coNMeSJpdwY5pW0Y1K 4koQkXBcCTEgZHo9HTILP1OYE8etroKQaekKC8Zx5fZ5IDbYRPvbK+GlpezQumtcX8FGe5Kt ZUKBZF2ia6Glx/WahIICkhJXwVSWEldWUW2u0zUuhMyZ7Kj6O5cdunUttPS4ksQjJa6kWLbJ TYlrWC26Qde4EDI9lw8PhEwDl94ASeKREldSLAiZVlP/bMfD03ls/Vt6I2Ra2lIE463T4x8Y f/xAyviQPUKmFUORdbQnOSMrSoznsimN6Vl44JMeV5J4pMSVFAsTWazwEDIHvyg0x5Iml5TG NK2kGaXHlSQeKXElxYKQaTXF1jLGizMyB78UwWBr6SCf65Kex8TwosMFW8vEZEQv1bXQ0uNK moJS4kqKhYksVv0ImYNfFJpjSZNLSmOaVtKM0uNKEo+UuJJiQci0mmJrGePF1tLBL0Uw2Fo6 yOe6pOcxMbzocMHWMjEZ0Ut1LbT0uJKmoJS4kmJhIotVP0Lm4BeF5ljS5JLSmKaVNKP0uJLE IyWupFgQMq2m2FrGeLG1dPBLEQy2lg7yuS7peUwMLzpcsLVMTEb0Ul0LLT2upCkoJa6kWJjI YtWPkDn4RaE5ljS5pDSmaSXNKD2uJPFIiSspFoRMqym2ljFebC0d/FIEg62lg3yuS3oeE8OL DhdsLROTEb1U10JLjytpCkqJKykWJrJY9SNkDn5RaI4lTS4pjWlaSTNKjytJPFLiSvqSR4RM qym2ljFebC0d/FIE47VuonCkxZUkrN+3mPH1Qo48jVzSeI0WcrweHS7YWjqgV7l0LbTUuBJF Iy2uxJi+aiMtruRC6xrXgxlfrKhnOwpNX9Hm0bXQUuNKFI20uBJjQshstc7W0sfpzQsh0yCm CcbzR2/4tyelb+ESY0LItNr6OXGIbQ5j3r6Yp3shZFoKELKJvLSlT61T85gYF1tLJ0yETAOX 2gCJ009aXIkxMZFptcVE5uP18ELINHhpgsHWUgOfbJ2ax+zYOOzXiSJkGrPUBkicftLiSoyJ iUyrLSYyHy8mMge3NMFgInPQz3NJzWNeWCk9yWF/ckIil+taaKlxJU4/aXElxsRE5uuA6C4J IfNxL/FKa8zk6FLjShSNtLgSY0LIfMWHkDm4RaE5ljS5pDWmaTW7UWpciaKRFldiTAiZva62 ltGeZCLzcS/xSmvM5OhS40oUjbS4EmNCyHzFh5A5uEWhOZY0uaQ1pmk1u1FqXImikRZXYkwI mb2umMh8rL69EDINYJpgPLq88UeUgs8yvaim8tJSdWrdNa5nWcQ2hzHvRMhXXioKrSrWroWW FlfiV/ikTz4dBTa50NLymBwXQuYEipBp4NIaIFEsEDIth+m89OXPp0UmMp0oQqYxQ8gm8dKW HVqn5XG4km4Q7Um2ljrzMo+uhZYWFxNZWe1YLpyWR8tiog1CJgLL2I87ljS5dC20tLgQMlMd VBml5bEgQITMATUKzbGkyaVroaXFhZCZ6qDKKC2PBQFGe5KtZUFSvJfsWmhpcSFk3tJI8UvL Y0o07xdByBxQo9AcS5pcuhZaWlwImakOqozS8lgQYLQnmcgKkuK9ZNdCS4sLIfOWRopfWh5T omEiC2OMqn84gA8X6FpoaXEhZFWlY7puWh5Nq2lG0Z5kItN4l1p3LbS0uBCy0voZXTwtj6OF HK8jZBOgOZY0uXQttLS4EDJTHVQZpeWxIECEzAE1Cs2xpMmla6GlxXUHIQt+VOYr0Wm8TFVj N+oa14NZkDtbS3sdlFt2LbSUuJI/MF4iGElCm8KroNq6xoWQOZMdVX/nskO3roWWEleSSGwh psT1dsGcrxhKj2tYOTaDrnEhZLb8/WOFkGngUhoAIdOgF1in5LEgLoTMCRUh08ClNABCpkEv sE7JY0FcCJkTKkKmgUtpAIRMg15gnZLHgrgQMidUhEwDl9IACJkGvcA6JY8FcSFkTqgImQYu pQEQMg16gXVKHgviQsicUBEyDVxKAyBkGvQC65Q8FsSFkDmhImQauJQGQMg06AXWKXksiAsh c0JFyDRwKQ2AkGnQC6xT8lgQF0LmhIqQaeBSGuBOQhb+uEzPD8yk5FErHbN1tCd7Ejffvs8w Cs236tira6GF4yr4eNLjp/iff9L/JAhuSVwJN9o1LiYyZ3IRMg1cuAESxOEo4nBchxeNf0yp JC4tZYfWXeNCyJzJRcg0cOEGQMg04EXW4TwWxYWQOcEiZBq4cAMgZBrwIutwHoviQsicYBEy DVy4ARAyDXiRdTiPRXEhZE6wCJkGLtwACJkGvMg6nMeiuBAyJ1iETAMXbgCETANeZB3OY1Fc CJkTLEKmgQs3AEKmAS+yDuexKC6EzAkWIdPAhRsAIdOAF1mH81gUF0LmBIuQaeDCDXBHIQs8 3R/mpaXHbN01LoTMnMJ3Q4RMAxdqgKKn+h/FX/Fk/7Ornn+df8ricsbzcusaF0LmTCxCpoEL NUBQFM4iDcV1emGETKuQuHW0J/0/duKxT7tCFFpV4GWNGQw4FBdCFqSf5x7KY14Yh1eK9iRC Vpwg5fJdCy0UF0KmlECpbSiPpZHxC3pdeKPq71rU4NS10EJxIWSGzF9jEspjcYjRnmQiK06Q cvmuhRaKCyFTSqDUNpTH0siYyFx4o+rvWtTg1LXQQnEhZIbMX2MSymNxiNGeZCIrTpBy+a6F FooLIVNKoNQ2lMfSyJjIXHij6u9a1ODUtdDccRU+Q/aF0x3XKBfBuMviGsU9eL1rXI9cBp7b e9bCL/wThVaFrGuhueMqnMZKhezZWe6HYt28qgrr73W7xoWQOROPkGng3A0QEANLhO64TBdH yCyYsmyiPclElpWJhOuUNmYgPndcCFmAer6rO4/5ofxzRYTMATkKzbGkyaVrobnjQshMeb/K yJ3HCwKM9iQT2QVJsi7RtdDccSFk1tRfYufO4wXRIWQOyFFojiVNLl0LzR0XQmbK+1VG7jxe EGC0J5nILkiSdYmuheaOawUhczwW4OZlLRSnXde4vm4HIXMkNQrNsaTJpWuhueIKPotlAeaK y3Lhl41TiMvjUu5hY9s1LoTMm1DHT1nnUpJb10JzxeUUAQWYKy5pAd8jGOVxKfeAkDlp3cCN iUxLkqsxETIN8gXWrjxeEBcTmRMyQqaBczUAQqZBvsDalccL4kLInJARMg2cqwEQMg3yBdau PF4QF0LmhIyQaeBcDYCQaZAvsHbl8YK4EDInZIRMA+dqgJWETHxzyMVLS4nLumtcCJkrnfFn VpzLDt26Fpoc1wWPXjyK/4ovb3EI8iVxDavpX4OucSFkjmRmQHMuO3TrWmhyXI7mH8I5MJDj ci2iP4JxSVyOe+kaV0ZP8mS/oyCqXLoWmhwXQlZVIqHrynkMraY5R497EDKNd6l110KT40LI SuvEe3E5j96FHH4I2QRojiVNLl0LTY5rRSETDvxlXqbqiBt1jYutpTO3UfV3Ljt061poUlwX HfQ/iv+Kw/5nl0lfe31ZXMOKejfoGhdCJibyZY6QaeCkBhCbXotkUmOK9yTxigAQfbvGhZCJ iUTIfMCkBhCb3hfR00uKK7QQE1kEn8U3Olxw2G+hfJHNZY0p3o8UF0J2ncBW5lG8dtQcIXMQ jEJzLGlykQTDdMUcIymulYXMeOAv8cpJkekqXeNia2lK379GCJkGztwAFx70X7q1fHaa+cDf zEtLQ9i6a1wImTO1CJkGztwAQrNrERxbm+NKWQwhy8D46RrRnuSMrDI74rUvbUwhNnNcCNmD qpmXkIMM065xMZE5sxtVf+eyQ7euhWaOCyFDyIZV/mG6Np4/fpzonOve2g0h09KHkG3OyAwN Z+alpSFs3TUuJjJnahEyDZypAS4+6J+yhTNOnCZeWgpSrLvGhZA504uQaeBMDWBscm3lc2tT XKkL2g78L4/LeI9d40LIjAncmyFkGjhTAyBk31BNvLQUpFh3jQshc6YXIdPAmRrgNwnZ4JzM xEtLQYp117gQMmd6ETIN3LABJpyPTTkje3bc8MHYIS8Nf5p117gQMmeKETIN3LABDM2trWiz HsZlu4xmZbjXKXEZ7qJrXAiZIXlHJgiZBm7YAIbm1la0WQ/jsl1GszLc65S4DHfRNS6EzJA8 hMwJaeM2bABDc8ej+PcKw7hKFv27tTw5J5sSl+Feu8aFkBmSh5A5IVmFbNL52KP4r/qG2D3C gXBPi2uQ6q5xIWTOHmVrqYE7bYBJ0xhCpuVwKi9DqNGe5EPjBshXmXT9iYmQ7SpgMIXeMo9X FfmHdRAyRwKi0BxLmlxu1wATt5XTJ4yTSfR2eTRVZ61RtCeZyGrzI139dg0wcVuJkEml9TDu Wl+ckem5fCbU8A0GzkuH3LoW2se4ELLDh2Nvl8dQ1eY4R3uSiSwnDylXuVUDTN5WtpgwPgj5 rfKYUrnxiyBkDoZRaI4lTS63aoDJ0xhCZiqpN6Ou9ZWxS2Ii0+uhzKNrof0TV4NprJWQ7Y4q bpPHskrWLxwdLhAynXmZx20aoME01kLInqPEP+dkt8ljWSXrF0bIdGYc9ovMPk5k4nWyzVsI xsF02iKuA9hd42Jr6eyMqPo7lx26dS20t7iabCvbTGQHU9kt8jisxmsNoj3J1vLafJ2udosG aLKtRMj0wu1aX0xkei4fHlH1dy47dOtaaIcT2fBu6g3a8NpNqW3i2qWga1wZPclEVt9v5hW6 Ftp3XI22la0mst32sn0ezRV5nWF0uEDIrsvVcKX2DdBoW9lWyP4wap/HYSVeb4CQOZhHoTmW NLm0boBm01g7IdtOZXwEzlTvW6NoTzKRycjrHLoK2fezUs0atCWvZlPrm1jM+iJKQ8sgZAZI e5MoNMeSJpeWjbk7/zHdyEVGLXk1nFxf6WjJ629w0Z5kIruo6SzLtCw0GtOSunebplNZy/pC yPT6+v7J1GyL1PonZtOmbHlG9lNgpt9/6a9gnydC5uPW1is6xlbdWLtCazyNdRayRx4b/gBo V1+bRor2JFvLKlVyXLddof1txnZxvbYjTQ+v34Ss0fTfNY/PY9iYFMW8Hc3awSUKreoeWhXa ZhprFdf2p3hnIdu+SRJs0qx665pHhMyZYYRsAI6P3Dgr6+l2+JGuBmKGkIXS2s8ZITvJCV9L Ey7YfwSjyVkjQhZOba8LIGQf8vGh4bo2wK3ianD435UXW0unPiJkAyHbvdy1AW4VV4OprCsv hAwhcxI4cDtptK4NcLu4JotZV14ImbONmcj2I9f5c09dG+CWcU0Us668pgnZlxAc/XXqyuVu CNn2GYbxw5tdG+C2cU0Ss668Lheyl3h9Up7R65cr1sejoJ6Pz11aaNtmGjwacGlcQpHcOi6B v4Dk1LQrr0uFTJliFNusJCnX6RrfJYXmaKBL4lIS+Nd2ibgc+XCgerh05XWZkHka3+PjTZDq 1zW28kJzNk15XGoCVxKyZxe//3XyGLl1zeNlQjYCdLfXf52QBRulawMsF1cwT6M+7MqrnZB1 FYh9grvGWVJoCc1REteo6wyvLxlXQr4+nmE3/WzqVCG78zuXv0LIEhtiScEwCKXXJIVXYv5e 95ESlxfKwC/ak6637z4tGg2miNE/l+0aZ0qh7Rsg4cPKKXEVJHf5uJJz2ZXXtImsqxBYe6Vr /HKhHRV6wTNKclzWRATtfk1cSXnuymuakH1auKtA3P6MbFTIBduQLbOuDfDr4grWQVde04Xs rk/3txBctSiTtxnKMNS1AYjrbxZvVEsf34gIHoFwRqZ0dJatp/Cy1nZcB8HQoLXgpdaYdovp 1tHhIlXI0u+u6IJRaOGwXkW2u1CLBji4OeLSMn4rXh9qUbvjuHW0J1OFLBpMHIftCtPjRMhs iRpY3UowUu44dpFDXr9dyHiOLFBUCFkA3o8rQqZhRMiOthsHh3PTJx1jXqfHiZAZM3VuhpBp GBEyjVd7a4RMSxGCsTCv37y1/ErrXgymi4NQa9NjZSITsvXZFIHVMDKR7d9d+/DMx3SBMOZ1 epwImTFTbC1TQP29CEKGkGXW0893T+25Nv12AiYfLf234vWbt5Z3+dD49p3VbSkykS3cmNqt lVgjZDrWaE+6niN7nZHtH8HQw6/zODvDi0ILR83WMozwUYNMsBJHtpYSrh7GCFleHhAMjeWt eP2mraVngvH4aOUyOAjevSGxjecxSU785/Ud7TNjYG0IPAT3r5BNp3HVh8YVYVJsM8Xr7Bxs L2RV65quy9bShGlkdKvJZ3QzF7zO1vL19u3XJHOinK8zswtyMlyCreUQkdkAwTCjehjeitdv 2lru03iHz1kiZFrznVnfqjHzbtt9pVvx+s1C5s7whY4IWR7sWzVm3m27r3QrXgiZO8+XOfIc WQ7qWzVmzi2HrnIrXghZKNdTnae/GcFhf0r+byUYKXccuwiH/TF+7bynC9kHIjSmVirwWoPX 4w2Sqx6/0JD1to5Cq7o7GlMjC681eE0Tsq5CYE1r1/hpTGsGn3bwWoMXQqbl8dsaIdPAIRjw 0gjo1tGedH1o/C7ffvEJZxSaniabB4Jh4/SygtcavKZOZHd4KBYh0wr9Iy++ZUICicBKuJ7H BLMO+48Wjgaj377Po2ucNICWT3itwWuqkGkIe1kjZFo+EAx4aQR062hPus7I7rZl28cbhaan yeaBYNg4cUamcerO67KJ7Ohzi5yR+YrpzAsh05jCaw1elwnZES7OyLQisljTmBZKPzbwWoPX VCHTEPayZmup5QPBgJdGQLeO9qT7jGz/JYrRQPRb93t0jRXB0HIKrzV4TZvIXkJw9p1fGuJr rREyjTeCAS+NgG4d7UnXRIaQ6YmyeCAYFkqckWmU+vNiInNmNKr+zmWHbgjZENGbAbzW4DVN yF4L7x/B0LDOs0bINPYIBrw0Arp1tCddW0s9zF4eUWhVd4NgaGThtQavyyayro2vpXFzVhD8 gKp33ZEfjTki9P46vNbghZBpefy27irMNKaWUHitwetSIdueh2n4+lkjZFpOEAx4aQR062hP ms7ILJ+11EOf5xGFVhU5gqGRhdcavC6byCy49k/6W3xm2SBkGnkEA14aAd062pOmiewsrDsJ 2Os+otD0NNk8EAwbp+888s21ErCu9XXZRHa2tZRINjFGyLREdG0A4lojj5cK2f6wv6sYWFLb NXYa05K9Hxt4rcHrUiE7QnbXdzIRsjUaACFbI4/ThWyL8U5nZQjZGg2AkK2Rx8uE7BMuJjKt kEbWNOaI0Pvr8FqD12VCtp1g7ipe++lRK4FrrGlMjTO81uB1qZDdaes4Si9byxEhJh+NELwi vC4VsmignfwRMi0bTD7w0gjo1tGeND0QG11Ev61aj673g2BoeYfXGrwum8g0XP2tETItRwgG vDQCunW0J00TmR5Wb48otKq7QzA0svBagxcTmZbHb2uETAOHYMBLI6BbR3uSiUxnXuaBYGho 4bUGLyYyLY9MZF5efMuERA6BlXA9jKdOZHd9ODYKTU+TzYMGsHF6WcFrDV5Thezoq300rPOs ETKNPYIBL42Abh3tSdcZ2adFo8Hot+/z6BongqHlE15r8Jo2kSFkWgFZrWlMK6mnHbzW4DVN yI4W7jrlHKW6a6w05hqNSR61PE4Vstfid/wwOUKmFRqNCS+NgG4d7UnXGZkeZi+PKLSqu0Ew NLLwWoPXtImsqxBY09o1fhrTmkHOyDRSvXkhZJ5sfh0S/9dzEEXItITCaw1e7YSsq0Ds0901 ThpzjcYkj1oepwrZ9qn+uz3hj5BphUZjwksjoFtHe9K9xzpaOBqMfvs+j65xIhhaPuG1Bq+p E9ldtmtHqUbI1mgAhGyNPLYSsoxgtLT4rREyjR2CAS+NgG4d7Ulpa/npXIwzMj1xh5MiX5cj gURgJVxtP9KVMQRJQvbCFlVPDX++ddf4aUwt1/Bag9c0IdPw9bNGyLScIBjw0gjo1tGedE1k dzpAv1OsCIbWAPBag9e0iezsrExDO8c6qv5VUdOYGll4rcFrqpDtEXYVByYyrdh5EwJecQL6 FaL6kba1zFBV/fZ9HlFovlXHXkwYY0ZbC3itwStDOxAyrRZKrWlMDS+81uA1Tcg4I9MKyGpN Y1pJPe3gtQavqUKmIexlzdZSyweCAS+NgG4d7cnUraUe/hyPKLSqqBEMjSy81uB12UR29hGk 12sa0rnWCJnGH8GAl0ZAt472pHkiG31tTzQQ/db9Hl1jRTC0nMJrDV6XTmR7ZCNh0xBfa42Q abwRDHhpBHTraE+aJrL9Ip8WjQaj377Po2ucCIaWT3itwWvaRIaQaQVktaYxraSedvBag9dl QrZf6M7bygxoWvnYrWlMOyuETGPVmVdGT5q2li9kn96h5IsV9aI68kDINI7wWoPX5UKmYetr zRmZlhsEA14aAd062pPSRKaH19MjCq3qrhAMjSy81uDFRKbl8dsaIdPAIRjw0gjo1tGeZCLT mZd5IBgaWnitwevSiSyqmBryWuuu90JjanmH1xq8LhOyro2vpfHHuuv90JhaRuG1Bq9pQsYD sVoBWa1pTCuppx281uCFkGl55LDfy4tfHCyRQ2AlXM8fSv/FjuvN3tuHXs/+v34L13tEoVVF TANoZOG1Bq9LhWyLjK2lVkBWaxrTSoqtpUaqN69pQuaB2MmHiUzLBgILL42Abh3tSfPW8ii0 u33G8nUPUWh6mmweCIaN03ceObuTgHWtr6kTmfU7yiTSFxkjZBrorg1AXGvkcZqQcUamFZDV msa0kup95kMetTwiZDqvhwcTmQaOxoSXRkC3jvak+4yMraWerJEHgjEi9P46vNbglTFcuIXs tfinL1vUEF9rHVX/qmhpTI0svNbgNV3INIx9rBEyLRcIBrw0Arp1tCdDE5kebg+PKLSqu0Aw NLLwWoMXE5mWx29rhEwDh2DASyOgW0d7kolMZ17mgWBoaOG1Bi8mMi2PTGReXjxBL5FDYCVc D+MpE9nRotFA9Fv3e3SNlQbQcgqvNXi1ErKMYLS0+K0RMo0dggEvjYBuHe1J6YzM8p1k+i1c 7xGFVhUxgqGRhdcavDKGIEnIXti6CoE1rV3jpzGtGXzawWsNXtOETMPXzxoh03KCYMBLI6Bb R3vSNZF9CjMajH77Po+ucSIYWj7htQavqRPZp/MyDe0ca4RM445gwEsjoFtHe9I1kfF9ZHqi LB4IhoXSjw281uA1bSKLqqeGP9+6a/w0ppZreK3Ba5qQfVq4q0Ds0901ThpzjcYkj1oeLxMy y/Njd/peMoRMKzQaE14aAd062pPmMzLLQhYb/RbzPbrGiWBouYbXGrwum8g0XP2tETItRwgG vDQCunW0J80T2Ta0s0XvsMWMQtPTZPNAMGycXlbwWoPXtIlsf2b2XVj//ehiV7HIgKaVj92a xrSzeuSRrxeSgHXlldGTKRPZS7S24oWQSTX2MO5aaMSl5RJeGi+ETOf1FIzN5Oi8RIkbDaBh hdcavDJ6kolMq4VSaxpTwwuvNXhNE7LXwtuD/f0hf9epJwOaVj52axrTzoqtuMaqM6+MnnRN ZGcIeddSL7CXB0KmsYPXGrxaCpmGdo5112mRxtTqAV5r8GonZF0FYp/urnHSmGs0JnnU8niZ kO0b/+yzl/otXO+BkGnMaUx4aQR062hPus/IjhaOBqPfvs+ja5wIhpZPeK3B67KJTMPV3xoh 03KEYMBLI6BbR3syNJHd5Ul+zsj0wtp6IGQaP3hpvKZNZEcfScoIRr99n0dU/X2rjr1ogDEj BFZjdAdeGdrhmsgQMn8xnXkiZBpXeK3BCyHT8vhtzUSmgUMw4KUR0K2jPemayF4K+unrfPTb uNYjCq0qWgRDIwuvNXhNm8g0fP2sETItJwgGvDQCunW0J90TmR5qH48otKo7QTA0svBag9dl E5m18a12Gv58665x0pharuG1Bi+ETMsjh/1eXnyltEQOgZVwPYyjw4Vpa3n22co7HvhHoelp snnQADZOLyt4rcHrUiHbI+OzlloRWaxpTAulHxt4rcELIdPyyNbSy4utpUQOgZVwXbe1PAqr 6/bMgrBr7DSAJXtMZBql/ryYyJwZRcg0cAgsvDQCunW0J82H/ZyR6clRPRAMjRi81uB16URm fedSQzvHOqr+VVHTmBpZeK3B61IhsyDrKhCWadJyf9U2NKZGGF5r8ELItDx+W3cVXBpTSyi8 1uB1mZBpuPpbI2RajhAMeGkEdOtoT5oO+/WwentEoVXdHYKhkYXXGryYyLQ8srX08uKBWIkc AivhehhHhwsmMp15mQcNoKGF1xq8EDItj0xkXl5MZBI5BFbCxUSm43p6RMdY77ojPxpgROj9 dXitwSujJ9laarVQak1janjhtQYvhEzLI1tLLy+2lhI5BFbClbJLYiLTmZd50AAaWnitwYuJ TMsjE5mXFxOZRA6BlXAxkem4nh4c9mvkaEx4aQR062hPsrXUmZd5IBgaWnitwStjuEDItFoo taYxNbzwWoMXQqblkTMyLy/OyCRyCKyEK+W4h4lMZ17mQQNoaOG1Bi8mMi2PTGReXkxkEjkE VsLFRKbjenpE3yHxrjvyowFGhN5fh9cavDJ6kq2lVgul1jSmhhdea/BCyLQ8srX08mJrKZFD YCVcKbskJjKdeZkHDaChhdcavJjItDwykXl5MZFJ5BBYCRcTmY7r6cFhv0aOxoSXRkC3jvYk W0udeZkHgqGhhdcavDKGC4RMq4VSaxpTwwuvNXghZFoeOSPz8uKMTCKHwEq4Uo57mMh05mUe NICGFl5r8GIi0/LIROblxUQmkUNgJVxMZDqup0f0HRLvuiM/GmBE6P11eK3BK6Mn2VpqtVBq TWNqeOG1Bi+ETMsjW0svL7aWEjkEVsKVsktiItOZl3nQABpaeK3Bi4lMyyMTmZcXE5lEDoGV cDGRvXB9Hd6//m4Rnv13HXW9Bw2gMYbXGryYyP4Q2L8D+fr3T/89A5pWPnZrGtPO6pFHJkUJ WFdeGT253BkZQibVtsm4awMQlyl9P0cqTYUfITvIo1XIvpqAfyAAgSYE/hwPRf7EvCMrF/la haxo+dBlmTA0fPBag9fSE9n2oP7Tof0+jdtzMc7ItCI/s0YwNJbw0ngtLWQqijPh2oPiI0oa XRoTXhoB3Trak0tsLY8mMSYyvZg+eSBkGkt4abyYyP4QONqCvjDyHJleUEceNKbGEV4aL4RM 5/XwiI6xzmWHbjTAENGbAbzW4JXRk0tsLbV0ImQyr6bPHyFkWia78kLItDx+WzORaeC6NgBx rZFHhEzLI0Lm5cVEJpFDYCVcKcc9bC115mUeNICGFl5r8GIi0/LIROblxUQmkUNgJVxMZDqu pwdnZBo5GhNeGgHdOtqTbC115mUeCIaGFl5r8MoYLhAyrRZKrWlMDS+81uCFkGl55IzMy4sz MokcAivhSjnuYSLTmZd50AAaWnitwYuJTMsjE5mXFxOZRA6BlXAxkem4nh7Rd0i86478aIAR offX4bUGr4yeZGup1UKpNY2p4YXXGrwQMi2PbC29vNhaSuQQWAlXyi6JiUxnXuZBA2ho4bUG LyYyLY9MZF5eTGQSOQRWwsVEpuN6enDYr5GjMeGlEdCtoz3J1lJnXuaBYGho4bUGr4zhAiHT aqHUmsbU8MJrDV4ImZZHzsi8vDgjk8ghsBKulOMeJjKdeZkHDaChhdcavJjItDwykXl5MZFJ 5BBYCRcTmY7r6RF9h8S77siPBhgRen8dXmvwyuhJtpZaLZRa05gaXnitwQsh0/LI1tLLi62l RA6BlXCl7JKYyHTmZR40gIYWXmvwYiLT8shE5uXFRCaRQ2AlXExkOq6nB4f9GjkaE14aAd06 2pNsLXXmZR4IhoYWXmvwyhguEDKtFkqtaUwNL7zW4IWQaXnkjMzLizMyiRwCK+FKOe5hItOZ l3nQABpaeK3Bi4lMyyMTmZcXE5lEDoGVcDGR6bieHtF3SLzrjvxogBGh99fhtQavjJ5ka6nV Qqk1janhhdcavBAyLY9sLb282FpK5BBYCVfKLomJTGde5kEDaGjhtQYvJjItj0xkXl5MZBI5 BFbCxUSm43p6cNivkaMx4aUR0K2jPcnWUmde5oFgaGjhtQavjOECIdNqodSaxtTwwmsNXgiZ lkfOyLy8OCOTyCGwEq6U4x4mMp15mQcNoKGF1xq8mMi0PDKReXkxkUnkEFgJFxOZjuvpEX2H xLvuyI8GGBF6fx1ea/DK6Em2llotlFrTmBpeeK3BCyHT8sjW0suLraVEDoGVcKXskpjIdOZl HjSAhhZea/BiItPyyETm5cVEJpFDYCVcTGQ6rqcHh/0aORoTXhoB3Trak2wtdeZlHgiGhhZe a/DKGC4QMq0WSq1pTA0vvNbghZBpeeSMzMuLMzKJHAIr4Uo57mEi05mXedAAGlp4rcGLiUzL IxOZlxcTmUQOgZVwMZHpuJ4e0XdIvOuO/GiAEaH31+G1Bq+MnmRrqdVCqTWNqeGF1xq8EDIt j2wtvbzYWkrkEFgJV8ouiYlMZ17mQQNoaOG1Bi8mMi2PTGReXkxkEjkEVsLFRKbjenpw2K+R ozHhpRHQraM9ydZSZ17mgWBoaOG1Bq+M4QIh02qh1JrG1PDCaw1eCJmWR87IvLw4I5PIIbAS rpTjHiYynXmZBw2goYXXGryYyLQ8MpF5eTGRSeQQWAkXE5mO6+kRfYfEu+7IjwYYEXp/HV5r 8MroSbaWWi2UWtOYGl54rcELIdPyyNbSy4utpUQOgZVwpeySmMh05mUeNICGFl5r8GIi0/LI ROblxUQmkUNgJVxMZDqupweH/Ro5GhNeGgHdOtqTbC115mUeCIaGFl5r8MoYLhAyrRZKrWlM DS+81uCFkGl55IzMy4szMokcAivhSjnuYSLTmZd50AAaWnitwYuJTMsjE5mXFxOZRA6BlXAx kem4nh7Rd0i86478aIARoffX4bUGr4yeZGup1UKpNY2p4YXXGrwQMi2PbC29vNhaSuQQWAlX yi6JiUxnXuZBA2ho4bUGLyYyLY9MZF5eTGQSOQRWwsVEpuN6enDYr5GjMeGlEdCtoz3J1lJn XuaBYGho4bUGr4zhAiHTaqHUmsbU8MJrDV4ImZZHzsi8vDgjk8ghsBKulOMeJjKdeZkHDaCh hdcavJjItDwykXl5MZFJ5BBYCRcTmY7r6RF9h8S77siPBhgRen8dXmvwyuhJtpZaLZRa05ga XnitwQsh0/LI1tLLi62lRA6BlXCl7JKYyHTmZR40gIYWXmvwYiLT8shE5uXFRCaRQ2AlXExk Oq6nB4f9GjkaE14aAd062pNsLXXmZR4IhoYWXmvwyhguEDKtFkqtaUwNL7zW4IWQaXnkjMzL izMyiRwCK+FKOe5hItOZl3nQABpaeK3Bi4lMyyMTmZcXE5lEDoGVcDGR6bieHtF3SLzrjvxo gBGh99fhtQavjJ5ka6nVQqk1janhhdcavBAyLY9sLb282FpK5BBYCVfKLomJTGde5kEDaGjh tQYvJjItj0xkXl5MZBI5BFbCxUSm43p6cNivkaMx4aUR0K2jPcnWUmde5oFgaGjhtQavjOEC IdNqodSaxtTwwmsNXgiZlkfOyLy8OCOTyCGwEq6U4x4mMp15mQcNoKGF1xq8mMi0PDKReXkx kUnkEFgJFxOZjuvpEX2HxLvuyI8GGBF6fx1ea/DK6Mlfu7X8EjP+woAa6FMDmizvf6hFvPGF AAQg0IDAr5zIGnAnBAhAIJEAQpYIk0tBAAJzCCBkc7izKgQgkEgAIUuEyaUgAIE5BBCyA+6v d7LmpOR81U6Pjmzf8ZvFqkMMR/feNa5trJ1qaR+XGhtCtqtCFeCVDdxJYPecZnDrEMMnEesu GJ1qKYMVQrahOKMZrUL4iq1rjDPi6ipk+5zOYHNWV11rKcIJIfub8W1yI0CtwqTadS2+133M YIaQqVX0tO9YS9H+Q8g2QrZtjBmN+aksu8aVsSXwteJ7Q84UU0v81NKY0n6rqzJbXsi2h65n B7BX/3S/e1yzRWw7WXQWMrUhxy3vt7i6xpVIo7EtL2RWmFGQ1nVUO6vgqdfNsp/ZqF1z1lVY O9dSNJcI2e6MrGsRdoxr9pY3WvxZYn50ndlsLPc284fQPr5oLhGyDdGzraelMK6w6VJ8Rz/d r7j/owbYn6/MiGO/1d7zmR3TSGw7xBfpP4SsQwaJAQIQCBFAyEL4cIYABDoQQMg6ZIEYIACB EAGELIQPZwhAoAMBhKxDFogBAhAIEUDIQvhwhgAEOhBAyDpkgRggAIEQAYQshA9nCECgAwGE rEMWiAECEAgRQMhC+HCGAAQ6EEDIOmSBGCAAgRABhCyED2cIQKADAYSsQxaIAQIQCBFAyEL4 cIYABDoQQMg6ZIEYIACBEAGELIQPZwhAoAMBhKxDFogBAhAIEUDIQvhwhgAEOhBAyDpkgRgg AIEQAYQshA9nCECgAwGErEMWiAECEAgRQMhC+HKcj37jzqffwvPJ9iySs9+8lP1bmSy/CWdv kx2DNStR7l7mo/gyeWReaxT3zNcRspn0d2sfFZ31v326jbNflZb9a9Qsv5vwyGZ2s1kZXxFn xRoV12zUNo9QELJGGalqqLNfFptZ5KNrfXp95PdKkdVOTWkV94w41Guc/UDLulbH6yBkzbLi mWpGt9BdyEbxVwvZ4yf6f++tYBU3a+wWuyqhPro/Szx3skHImmVr1ECj17dN/7Ld/u/2XGhb 4Mo288h2e92zhlS2ulvbo+vv72ubSuV+PjX62Q+APeczITyLcyTS+3zt7ff3OeLbrNzTwkHI 0lDmXOioIUYT1aiJFP/RVDC6lpXCp4Yb3ctIQPaiNLqfT0JyJBD7e/sU6ycBPmNj+QF19APp 6AeXRZitebqLHULWMFNHk9TZT+7R5KCIz2iaOmtmq2jsJyfrv5+JnJWPRUz2/M+E4WhS9Iip RcisnBCyhk39G0M6a6RRwauvW8TBKhIeIRs1vSLC0QaOcI8K/Chv1mnQmqvV+oqJrGFGIw01 aoiRcFVOZJ+ubRWrUewI2U8xj+qgYdmHQkLIQvjqnJVCPGvw7dZn2+hHYvlJCD5taSzCsvcd +exF7RW/JfajaUSdEjO5n01HownrKBdWwY8Kel1V110ZIatjG7qytaH2QrVtnk+T3fa/74Vi +++fbuDIZhvHaPL6FHMk9iNhUEXMIwBH97JnYWF6tvbe/+iH0yiPHhahAr7YGSG7GDjLQeDs B0QVHYSsiizXhQAE/iFQITgV1+yWOiaybhkhnl9PIFN4Mq/VOTEIWefsEBsEIGAigJCZMGEE AQh0JoCQdc4OsUEAAiYC/wNPMGghCZS9YwAAAABJRU5ErkJggg==</item> <item item-id="21" content-encoding="gzip">H4sIAAAAAAAA/+wYW2xUVXDua7fdbruP0vKqsFSKpaUFtwqWgHbZbqGJfdA24cOQst1e2yXt bulupYsfNGCqMSRFE0gkGqMh8cNAkA98ftT4YYyi+GdCJPjhh4EPgmt8AF1nzjn37u0KdCug MXZ25965c+bMnDln5sy51wkAEmI3ooPRKl6Lgh1DenM80prUh4FBCaJ9FqcIsbA/HunSB6Lx mMp4jYgFw5GOvr16JMnFuphSGa+2xODItvg4Zz9N+pDRZQq2IU6TnB3gBVTXZs8+E7TyG6qS FTZkr2k8kEyORvvGkrrEJDYjusAK6ubKWc/l5dztApPDeiL3wjeey2+9t+wHyIEnQYGZTCHY LDzJ6EfgBuYkPc9kMhmDnVmA/xTcQiwQa0gxrVHgiiAsFPyZf3mMC/DgoAvi+EuCD0IQw/so pHK3grtCOUaMoYv2A+VrmfGneXOLVfaa64llTy3+QlKQbioy9pQOGAJ9Xjat4ABZsvqTb79F YNgPov/DMILj6IO987bvRfu0B1Lu5Guf5KOCloX/nTjzOiRgA/vlD0tQw3z9p/n/SJSCGbFu t8t/EqE9gOokLZcTsRh4baR640b0AM0BQCnwOS0DVlZgMRsbwFLEZYjLESsQH0JcgbgS0Ye4 CpFq1cOIqxGrxFgewXs14lrEGsRaxHWibQHuH0i8jM8CP/RDA/4aYRM8ilFJEenH7OjH50b8 bWS4CVsaMJZnMoqDx35u7lMctIWTgz2pEd23sX6DL7RvLJzE8wuLJLMl1MKiyWisb+5u63kM 0o1n9+URy8wu8PyTxLPh0Pv1GC8YyOMF1IaRrGxRKaqvsFaKdJ67REkydmRUvbRU8IpkQ84p N9n31FCMr7G5MI7JCogMmEb6DKIPw/SajWcKSTlNDwH2SJRLf6C+NXxoMo23TuLaF8nTyqBG VClSfrshC7NkJSFbo54s5rK/KtcFpWunSjj1pjTuMihfKWXxTa5J5nNUJ8nA9WvmWY7zFZan q5UieKPYp9pLLiqnShxa1HUE9RCelV50nZWmS3TtBOKl4lL13eLHVevYCsmSzEd5Tr28iPL9 huy++TkzdJQfH2Vwd6eG++JDaLfYcWDkkhc+OP3z/k8RfU2/7D9f/qwYj8r2Hj9tPmyk1Slj ts6pXy2fS/dkmeOVO+vW2P3aX2ystdioqMxj/M/fbfzchnYXP5xVefjx6r34cUo+X8FtBGf4 ah+VRDy4e6LDesLXru/3dcWHw7E5/XCbNqhCNKz34/8KvAOn7XQ9oHCLr8uvadwi3Pp7Fu/k lT8FNm5jQj3hujcbd1qdrI3vbA/KjwbTxnbt7cLc1ZHuix/9KWM9JtQKyPVjfjayfnhNG7Sz 7AzuGhhMDMIh+3X5Y9u3arP6W4Hh2WXlTOltZi83fw5uuTh3/jhMq7TB/PR7U1k2DlTFabZS HdhaW3esouyq3ZCotp0pnd3/yg3qTzWAdukvpcDISKKtJRiU4CSOkLBZT0QHYr7uSFSPRfR1 vtZYpB40CaOeStNG3Fx3RWOBoaFt4UQ0Eoz3653hAT0BHi13Oj2KcNWjBeNjo1F9lBrBo7b1 +ELjydEwTmhBZUdoqnl9oDPtdm3vDXReX7kC6TJXANKhdFV6alsvUm5XkG5ToXRN7470FOM0 4f3gDiQCNb3NvaE0/QMup4RzLMkI6KyEC63wTxwOmvnDO7GYTG6FrQpVKeK85Bf3BjY7h4Mo j0e1Iibdj20SOJDahWyH2QMZAySDbQooZABRYa2HB8lCLdQimSCNx6vpspYWp4L1EdZkOLRe 2EcL6qS3ss6qJM+eNLjjz5DcbhZj2drLzzbtjJtTT82otlRHk2sTueIWn2E+Ye04oZXdqQT7 FkQRSmfiVfAySyOydORHr+hvN3NFgdtDe6CdfdrJUE9mTYbdn1Hc9cD30DnZxaSOIP1PhFwI 9sEYhPHNL4rvQTE8k7eLp+fyeivz4RlKgex3nzy6sDPQtDSnWN5gPc/eP63/D/sLW869bTlO MY9VgkacMGjcQSaqBS1ZZFQLn7aXJRY9SwVNrzFeiwy9cxvfLNyCLoR4shU+lKOg43uDBhcG PPj2Qtp47WuBq9lzuYUy3gOI8ycAAAD//wMAmqAQchAXAAA=</item> <item item-id="22">iVBORw0KGgoAAAANSUhEUgAAAcQAAABNCAYAAAA4lxS7AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA EMVJREFUeF7tXY3VHacOzCvBLbgFt5AW0oJbcAtuwS2khbTgFtKCW/jisc/4KQQWIWAXLnPP ycn1d1kQI6ER4mf/9/b985s+QkAICAEhIARORwCEqI8QEAJCQAgIgdMR+O10ANR/ISAEhIAQ EAI/sqWCQQgIASEgBISAEBAhygaEgBAQAkJACPxAQDNEGYIQEAJCQAgIARGibEAICAEhIASE wE8Ejp8h/vnnn29//PEHjp78+u/3339/+/Lli2xECAgBISAEDkLgaEIE8VkiTL+/f//+7e+/ /z7IHNRVISAEhMC5CBxLiJgVvnv37u3z589v3759+2UBmDHi7yRHfLe/n2sq6rkQEAJC4LUR OJIQv379+oP0SrM//N2SIshTHyEgBISAEHhtBI4kxI8fP76BFK8+mCnaFOprm4F6JwSEgBAQ AkcSItYOPR8RogcllQECV2vR6W9CTAgIgbkIeMdjKsWRhOhVBUH98OGD9xGVOxQB7wBEOX2E gBCYi4B3PIoQG/RAUHUEowG0Q4vSVg7tvrotBLZCoDReFa4W1PjXX3/9SINhc40+QqCGgAix htCav2MHOY5X6fM8AnfqQoTYqG8e1tfssBG4zuLeVMdT5UrdEyF2Kn7Q49ghjiUO6gMb6K4+ GN+fPn36VxHUAZJkQJz+PkjUl69mZV2IEBvMD4oEYN7NNw1Vq2gFgaeIztuuCHFdE8a4tQEs yLA2jkGe6fErEiBmLCRXkWKb3lfXhQixQZ8gQh3IbwBsYNFdZ1q7yj1QdY9Xlcvm1PSSLong og77wfEsLZ20q3Z1XYgQnTqFIgFW7ZyiszoVa0Sg5sAaq7ut+K5y3wbQAw1hhge9lGZ3OGtc S6lCbOm2X3mr6UKE6NApU6UYKPo8g8CuzmdXuZ/R8vxWEdAi3XmV6sQ+AWyeq32gW91WVUOp /PuKuhAhVvSJCAbpE5Fh3PBHPLkrsewq9widrVYH05y19UP8XvvwxipljGpI5X9fVRcixIo+ EU160icxs9BTHgTsxhZP+ZXKzCBEBGna2PVTy60zNLshJvcsZoaeOnfzC7NtxoNZOi5X1IUI 8cJ77mb0KxHBSFlmkMpI+a7qGi07HHZuB+Rd/VmtHR6FaHkdG5dAcjNBBL+1bBD2E+x0S9Ud NhPRA2xpNV2IEAsj3EOGUKZmj/Nd5GhSmS/x/1sYKTtfQXan/Du0FXHGJb3ULtzgGbodcIGM d9pMRA+QcSVdiBAzlu0hQxpauh17l4Gyk5wjSeXufo+SnZF0y0zo7r4+2R7Howcf7mxM03w1 ssPvaaoa7a56o80TNtOiB9jLaroQISaj2N5mYdeuSt/1kuD5bnAUqcyX9L8tjJIdM5fWQ+DY uICADVkM2LVn5+QTGI1qEwSXIydgZzM5wCJ3nhj4lm6g4ua6nB9o1cuo/tbqecpmSnqAvKvr Ygghwlgw8BA9WYPh9uZdSKOVDCMLyakRAxsMQh76J37EjhEvykQjUd6/mhvMTBHx9o4S8dvI O9Uzn5nhcGuEgoiU1+mxLORb5Wq9mvw1p4bfqRtPWZYBBrxmbIQMLW0/VbZ0pg2ERQyASWmZ A7+VfNWVb5hh970YPmkzV2cLV9dFFyEyhUDWT7cgw1DoPJVa/LeJw2hgtMAO2KTYMRVjCao3 ErXGiLbTwU9yZptX66OQmeXwfVbQc+XMS8RsnZ8nhdbrfK6e7yUjbk+PrlXzQokRwdtMnEbV zeDoab2P6k+knhVsZlc9hAmRztUz0BitiBR/mjfJxOPkbJQ/IhKlwq8IzOPE77qsoCQL7I7B hO0L1zD43NNX7XmwvHJ6JP2o7jn2ajsnI453xWd4PvDkYykr2MyueggRItMHLWkpOCY0Nmsm seLgzMnU6qBoWMCu98NUxtVOOntg9qo9EFKL/qOy5wwUMqIPpVkA/k57w/OeoC0qX+25HkKk vnp07wmAan3Y7fcT+0wdrWQzO+qhmRAjZAhlcUbZm/bbbXBaeSMzZc7ERjh1kt1V9MwZ6VUZ 7+HlEbrKkQFwrN0QYgOJHkLp7UMPIXLMRGc7XDve6cxcL954/uQ3UaxkMzvqoYkQmReOOOdT BycHONdyIs4NShoxG6MOSjLYjVE10hzhuDx15MjMi6Fdf/W0NaNMDyEyOIkGkTOD0J5+zcA5 F3hGN6HNlm9m/SvZDCcAO+nBTYh06LWDqyVle1NxM43lqbptGiOy2D8q1cy1y5yD5VupWaa0 3uu9+HgE1r1Ol88/OUOK9sHe4BFde6dzrM2mI7qK9ivSVuszdsNXZLy1trdK+dVsZkc9uAmR azLRxXkS6pPpq6cMl5GSZxPNTBmvyA6zLp5bg45yTvjOVClw6HW6fL5ldo3AAAFDus0egUCE WKJ9sOMl0u7VejF060mNX9litF9XdcK+KBf+T1/jzQiwbnvMqEX3M8feHXWvZjM76sFFiCOm vkzf7DR9HmHEdnYYcWwjZGAdTHmnTgKGS7JmmVzgc/dMq8fpcjC2ZDTs+oudWaAuu0mHcnl2 fkb7YM+FRuymdNwid54uMgON9qtkz1zzpSzcOIV2WgnRZqOeDkJHjt9aXavZzI56cBFiJNJO lceB6F1/tOs/0e81A7rj995U80gZuR07dbA2SCmVubrFY6SMtq6ebEKJ/Euy1mbxdnDTHj07 pqPEYc9YRvDNBTYYg6iXZM8xuQIhcmZo+8qx0yqfTR22kmkE61WeWc1mdtRDlRBHbPu3syRv CiNKgitspLADhEa6QqSaW1NKiS5XBmTwhGOJEiIHoldm2ngtewF5amVS5xglRDsjjTjclLRB fimxkBA9M91R/Sr1JRdk0G/0yNeqrwjWqzyzos1Qr7vooUqIjDS9ziVnHPaGFE9UvYqBjZCD Rtoa5Y5ou+bEcpcZ5wwCjvNuvUWJBH3mNXhemUkMV+vj0fNd0X70BHbpju7cK42i/aFNRftV smvqIPUzaMerxzS7MFrGGWNyZJ0r2kyPTCOx8dZVJUTOGHocOklhhVlSDpgRs9EikN8HNH6L RLleJXrLpTLa9FnJ0UHvPbr3ylYjb289TLO1rLt5HGc09e2pu2aT3r6zHANQ6A12l9Of50q3 3nHRIrfdgDHCT6zqiHsxvcqa9PR5lM2UxnE029NiQyPKVgmRBaK7S+3W20ikN6KTV3WMMNAr Q+RvLQ56Rp+5BsbUBRxievyCZbgRpfY6nBlyWmJurT96nZyHtKKBoafu0YRIWe1mqbQNz6y4 d2y06s9uCvEurZTa6CGHVrlbyvdiOosQR9nMMYQYceg952JmGk6LAfeWpaFFA4re9vl8ektN LqeflnnyzeytESVfzxPBmcsCJRuno47ssr2bEO1xi9KB/t50KWwq2q+aPdtdsD0B9KqEWOt/ z+/RPs+0mahMPTj0PFudIXoiyZIApXUBj8CvQoh0ptHbRjxYecrYm3Jya0qow5bB95Gp0taB 0UqInpc6l3Di3aeYGVtCtW9ria6hR4nDXuru0S/L2NRu6VC6J11aazPar1q9+H3EEgvl22Uz hweXWpkVbWY3PVQJMXr9U09UXVP8Tr+nqcoW2eHQWtdTSgpl6hqO/erqNjyPGVNkNpTrWymw qeHQQog9ZEg5+CozKy/fnRfJjrDeKHHYLfQtt61wtnsVzETvI7Y6i/bL1sGZSWoL3PXbcoY0 rYPyRQOZmn2u+PuKNrObHqqE6HlDQmocIsN/I0JDbZlx0UFHBl6OTOz6TCkV1Xuwt+SUUnKo kV2Ls/WQoTew4A0uIzdAtfTF4hfVRa09my7F9xayHU2I3EiTk6HWj6txseOB8Mg4L/ldYNcS xNWwjtrMjnqoEiJA96ZYANyON5yPMMZaHS1ROVN1tTpLv+cIx0PKLDMzvetJndYIk/32kCHf j1gLRpgJiWJ+pQtvf2wd9houL0F7LtDn7AvY4bv3ooxasBPBrXSVIB1wVDbriHs350T69dQz q9nMjnpwESIUbK+1Sgcod7OhMt6J+ZRRrNyuPdMJZ2RnaviOvwG/qCNA30vOFw6wtp6CMj1p Kg/2owgxdwVZKT2Lv9c2aPBZOOnorCnX/1r0XcIssiHNbp0v1WttsNfOIkRv5WIAlq7d8uXP NZ2V+miJYaQuPfb9ZJnVbGZHPbgJEYoG4yOVY29EwHcYNjofNeAnjejutolh6tDxbzi03gFc clL4e22m4SkzAq8aKdYcbSsZehy/XX9B+7BrvgS5x66jhAicuUnCu47M8lc2RNL01jl65mvr o14QCFKnwB2y9WB+6r3Jq9nMjnpoIkQa89X5phEOU3X4EUjJpUYm/prnlewlxNGSYVbIK+w4 O0lnm1EC6SFEOpRRG5xG47ZqfXek/lft+0o2s6MemgkRkZsn4l7VYF5JrlR5O5Ah8S+RYg+B tOqWm2hyMyr8Db/b2Whkx2JPf0acF2zF5BXKE/OeWeauOKxkMzvqoZkQI2TYs3i/q2HOljtH fq9CiLOxQ/3c0OHZjddzwX0PIULOq9dx3YHTbm1wY1EkeNmtryV5V7CZXfXQRIiRNS5E2rXN HK9iiHf2Y3dCBFa5WeJdpM51cK/OIFfEjnsJsfXNHd7+vGo5HlfxBDqvisEKNrOrHtyEaKNk PoQoDB3HhprUAJlyguPp3Shyt+Ei7YB+2Zsfcq/PuVsum25M2+51vE/2xZLgXYTYcqUed8vV NiXlMByhF64LnZgCbLVL4B1d721ta+XyT9vMrnpwESKvtko3GtT+DcLcbRDX+rrCBodXmB1a crdGeBchWj3DgaYBnT0Gg6AuQoZ2FtzrPEHgkeWK3nZ3eh4kMPvY0E54PGUzO+vBRYj2fkc4 CjgPvi2B755jRSOuu3rK6HhBNBykndXiO/Py6OfMg+uevqek8QSheOT0lkkDK+9zI8rBjqFb mw2APAh88PfIZeFWrhEzRNRHAu+VZwRmK9bBNOFu2aiZWD5hM7vrwUWIM5W2Ut0g9yuHw7z4 ClFouv42yvE+oY/cWuITcsxoc6ReuB4vp/9fTaWH+2focsc677aZ3fUgQjRWXkvvrrSlecfB eSXzSOJYCZvR/aKDO3nTiNUv8YimtFeylVmy3GEzr6IHEWKjFQIwreU0guYsftf6oVOcIcVG EyKFkg3+RGLHfQpDDCtQyUybeRU9iBAbDItrjEpZNYB2eNFZhHg4rOq+EJiCgAixAVYc5tam hgbAVHTam+UFrRAQAuMRECE6MQURigydYKnYLwQ0Q5QxCIF9EBAhVnTFdxMSqKePXOxjWpIU CIgQZQdCYB8ERIgXukpfCUSwVjicv4+JnS2pCPFs/av3eyEgQnToK50lArST3sTtgEhFCgiI EGUaQmAfBESIDbqy97lqltgA3MFFRYgHK19d3w4BEWKjynhbzSuemWuEQsUdCIgQHSCpiBBY BAERYqMicEOInFwjaAcXt9fS1b4fDJO6LgRuQaA2BkWIATUANKVMA8Ad+Ih3ACrjcKBxqMu3 I+Adj6lgv90u6UYNAlQc0tdHCAgBISAEXh8BEWJBx9hxusLbLl7fBNVDISAEhMAaCBxJiHzn 4dXhe6RK9aaBNYxUUggBISAE7kDgSELEzI85Zrww1r5ShmcRdbH3HeanNoSAEBAC6yBwJCGC 7NLbaUCMI96cvo5qJYkQEAJCQAi0IHAkIbYApLJCQAgIASFwBgIixDP0rF4KASEgBIRABQER okxECAgBISAEhMB3BESIMgMhIASEgBAQAt8R+Afl0JvMKOXQqwAAAABJRU5ErkJggg==</item> <item item-id="23">iVBORw0KGgoAAAANSUhEUgAAAdMAAABBCAYAAACKJ05RAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA CpFJREFUeF7tne2RFDkMhu9CICdSIAVSIAVSIAVSIAVSIAVS4K636CqP17Y+LLkl+52q/cG0 bUmPZL3unt3h3z//v/7BCwRAAARAAARAQE/gElO8QAAEZAT+33HXIfTlR7YCRoMACGQk0Nr7 b+9lDAY+g8DTBK7NgxcIgAAI/H26CzFFKYCAhgDEVEMNc0BgTwK4M90zr4hqAQGI6QLIi02U j+8Wm4a5oAS4NQExDZpAuBWfAMQ0fo4kHtb5RH4l9PYcK6kJiOmeNYCoFhBAs10A+UETyO+D 8IOaHtUExDRo0uBWfAJotvFzpPUQudWS23ceVRMQ031zj8icCVCby9k8lncicH9G5rQ8lk1I gFMTENOEiYXLMQhATGPkwcsL5NeLbN518Zg3b+7geWACaLaBk2PgGvJrAHGzJSCmmyUU4cQg gGYbIw9eXiC/XmTzrgsxzZs7eB6YAJpt4OQoXCv/nhC5VQDccIqkJvCZ6YYFgJDWEEDDXcMZ VkAgAwGIaYYswceQBCCmIdMCp0DgEQIQ00eww+gOBCCmO2QRMYCADQGIqQ1HrHIgAYjpgUlH yCDQIQAxRWmAgJIAxFQJDtNAYEMCENMNk4qQ1hCAmK7h7G2l/o3N1r+9fcD6sQhoagJiGiuH 8CYRAYhpomTBVRBwJgAxdQaM5fclADHdN7eIDASkBCCmUmIYDwJ/CUBMUQogAAI3AYgpagEE lAQgpkpwmAYCGxKAmG6YVIS0hgDEdA1nWAGBDAQgphmyBB9DEoCYhkzLlFPlb3FOLYTJ2xDg 1gTEdJuUI5DVBCCmq4n72qvzifz68s6wuqQmIKYZMgofQxJAsw2ZFjOnkF8zlNssNKoJiOk2 aUYgqwmg2a4mvtYe8ruWdwZrENMMWYKP6Qig2aZLmchh5FeE64jBENMj0owgVxOQNFvJ2NVx 7G5Pw14zR8NxlR2NbzvP0XCn5uAx784Vg9hcCVCb6zbOHefq7OGLS3IgGTuDdZWdGR93nivh zxkLMd25WhCbKwHuBnN1AouzCUjzxRnPNl4N9Fxb69OJ8zh5KMcc/5j37cTw9+fEgkHMPgSo jUhd9/Hq2VWjx0w2w6JXeMXite6zmW9bz9B7rWpi+zvTGtRJhRxxc+3kE1VL1PWdWFyxZDiw RshJBB9W1F6W3muVj+3FtC4aK3ArihE2YhOgTrSxvffxLsP+etLHJ237ZJy/auTYLXyDmPJr ASNB4IUAxPR9QVg0Je8ye9LHJ217c6XWjxy7hW9HiakFMKpgcP0cAhBTiKm02k/tQdHjtvAP YirdDY7jf/z44bj6HktHYtTbgBYbM2u2ssT+hJ9P2IxSRxlin/XxGDGdBeVdlD9//vxz/eA1 JnAx+vXrlxrTW8ErfloGIaY570wvr5/oB0/YVG8Uw4lZ4p718wgxLSHNAjOssZelvnz54rX0 duvOsLLMP8QUYirZXJa1J7H75NgMvffmM5uf7cW0dRfyZHG1bP/+/fvP58+fo7kV1p9LTC9m mtfshiltQkxfM1DuNU1uVs6xrAOu30/Y5PrmMS5D7+XsZy6bppjWELiLZR5XNwJN4bfWaDWY +r3v37//+fbt2zt8EfLA8X913q/PTS9m0led09nmrxHTiDylHHcYr9nfs3GPbKIuZunOz5+t iXdi2lpw1sh8mL4r9JqsxiqXXznuuiutPy/lrqPxUTonki+X7xcrzZ18GUcr5xZcrjWo/RKN pzTuHcZTOfKIkbKJuvCgzl+Tyg+10ouYUicnarGM1zV3F6M4uRuiHPfx48eXx5bR8sCNaVX+ r0e8FzPpy5qrtnai8exxnG0u0vysHP9EbJTNLHWxMk8rbVH5oXxJIaazQUrFj4ImWe/2XXJX ZN30Z+Lp3Wl55oTj74cPHzjDXsZIuL5tDOJ7WiGm4hS8MJXPtpvxRP1SNk8V03Kf2WVYvhKV H2rFFGJ6B9FqcBYAKEjS6y3hzC6mHHGRcpoZr8k7V/zqcdx5ZZ1SB65oPFv+ahhTcXMZzdQG Z651bBY2Pfobx68nx4z64mq/ZmtCJaazRldB6hXnbAKp+fd1SlR7jUVyB7WCpebE7F0jI8G7 816ykTCl1qYEgYo9Ik/qwErx4whBKy+9Jx/edU3liGOfEzO3BnscKD+p65w4OGM0sXLm7FQT YjHtBc9JyOwYTnKkNnrFOHqfKuDTxXRFjXBzoBW+stZ6NSWtnZEvHLGS1vbseIqxdv2yPlbU yoq7bg4Liqf0kPUUO06s0jE71MSLmHJPR1RRSEE+Pb6Oh4qPus7leMfd+vxPurE8GWp84TCa 8ZnzmWnpAxUDNVbSkKnYKV8ktmYYUnOpOKj59fUeY2s7HL8i2sxSFxy+3DE71cQ7Mb2FYHQy vwH0TkbU+y3xGhX3isIfxVsy6QnlqHFQd0fXb6a2viKv9Gm00epTXcuXumh7Oaxz1+LSY9Xz o2WrFw/XT85v844EsmbbssupCanwReUpjYPbLMtxo7qp91hrXq+upf2Eu4c1MY7mUD2Os/dG vchjn1kzkOQwW000xZRq/q2kt+b0mhnnfe8kStbvNXjJGqOxrb8zrZsJVYSUkNUNpNx4nIMA FSslgtRGp2qivC75O9NRnL2YekLP5TRqmhTH3j6q+Vny5PrkNe6JfmKRIykPC5sr95k0Psvx GWtiKKblyYA65VMn0BpO2TSoOwDLJGnWap18Nev05lzf5vP169fhkr07GuoQ08thLa7UAYqK d8SIEspWLYwOE5JvQOrV3SieaGLaOwiN3pfmncqv53Vqf/Vqv5cnTj+xEDYpEwubK/eZND7L 8RlrghTTFiBNQuuGOdu8JYnjbC5Jc7XYFKW960sIPn36JAnpbSxHpCSHILEDxQRNTXD8b4n+ dfCQfDevJl9lzfTmS9+X8F3JU+IXtU8sDi/cuigPDNJ+oqmJWU4WNrPVhbb3auKc1ZjZ/CwT U84GmQ1GKvzczVH7rmkYlC3N/4QiZdoab8V8xMjaT+ouvmZtFSN3XQt7K3lStcm5Pmp+1HxN 45TWFDd3lK8z10+ri9NqQiWm5YmwVSCtU/3ohOIhTr2i1xZ06aN2jdFGvH4BSfLl7TXP2r9e Mx7Nm2kU9V3Cbd/az0j/72uvDqzqo1Vz1jxnc261zzj9oVXjo/5D3UV7xW7FZLROub9a/ThC P5g9wGSrCbWYri5EC3tWTc7Cl9Ya12eBeI0JSA4c3iy9xdTbf6/1o++zW3y84vcW09V+W9g7 oSaOEdOVd78WxYc14hMYNYgMzcODcIZ99mRunrTtkW/OmqfUxDFieif9xGLmFDzGyAlATPvM Iu+zJ3170ra8wm1nRI7dwjeIqW29YLWDCEBMIabScrdo2lKbUcZHjt3CN4hplEqDH+kIUBuQ up4uYIHDUWOP4FcEHwSpNBsaNW4rv7YX0/t5fYbn9mZVi4WWEKA2IXV9iZOLjGTZZxFyEsGH FWVxWk1sL6YrigY2ziTAaYqcMWfSWx91pFxE8mV9JuJYtMwDxDROXuFJMgLcjcgdlyz8VO5G zEFEn1IlddJZa/4Q08mEYPq5BOrHWKPNab1xz6Uujzwy+8i+yUnnmTHLvbX3IaZ58g9PQQAE QAAEAhP4D/CtlEcYuwpmAAAAAElFTkSuQmCC</item> <item item-id="24">iVBORw0KGgoAAAANSUhEUgAAAHQAAAAlCAYAAACTSM11AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA Ag5JREFUeF7tmAluAyEMRdP7H7pNRqGiHoyX2C6CP1KkJAPenjGGr+/n88CzTwReQPHsE4HH Pq7Ak6vaIgx7RQBAF+X53NRfvc31sTy20RbJGPtxBKwwUXI/DnmuAADNjW+5dAAtD3muQgDN jW+5dAAtD3muQgDNjW+59BFQCTKOLeWYdAr7c6jlTHoDSgXp1K81ists7qBuChg56Ft1ZUfq D1DPEs820Cp/BI36Jf3mdFLZHl1Wf6zjf4HOarNUt61Ks8dL9vbvtddrbY6UDNL7dN+bglOB avy/rtTepVYCJr0/HqinOfAmJzePrugeigRQqhbRgM0lt9pAj8ORQD3AuMZyJItLWM//VyXR lJy+3FQDjVyhku2j95J+b/J4ElUzx9zlSkHRKM0ew4GZJa/WL2nFahZIpv/mc6i0WltGU6P7 TKd7kDaYmkCM9IxWWWt0ZvaO9NH9lM7n5GlsjxjD3hTNGgSuSYj6P8KxU2VMr/64bJe6PG3W cs3DqTAi/Dbf5c72EG6F9vuKNCbCqR1laLelEqDWUrwjkCqfzEA1zQQt1VxD1Muqcnh3PS6g uwel2r/RyUGztY06agCtpkf0cdWsVS9axaRGFUD/GWgPzvP9NmcBf443wdo0Tsvx8dFcIAAA ugCEKBOkPXR0bdk3Q7f5UYZBzhoRQFO0BocwKwA0LJRrCALQNTiEWfEDwiCvq/PGjCEAAAAA SUVORK5CYII=</item> <item item-id="25">iVBORw0KGgoAAAANSUhEUgAAAKQAAAAvCAYAAABtwmFaAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA Ay9JREFUeF7tnNlOxDAMReH/PxqmhY6C8Zq4TpTeSjxQJrZzfcbOIvH59Xo+8ECBVRQ4gMQD BVZR4GOVQBAHFDi7NWSAAispACATsvFafh3r8PdPgsnHmgCQg6k/QKQP927QzWOGA8iBVGvg Aco+YQFkn27nKAA5IJ4wFEAOaAogB8QDkDeIx6wfLy8trGjffu1RIf1ahTYvF4TX7nvAzaOG AsjBdHt22aiQfpEBpF8r8ZPWOaRVLaUqKp1t7lx1AWQCkNy6sTVLAZLWlyPvE6cx1RSATJaf q2q0ZXO/c9BKy4Gdb4UAZDKQnDkNSGs3rlXXgtDLXQDIAsl7gIy274JplLgAkCUy/9zqeDYv dAOkte2i0Evd/APS2jGWRgdnpQp41qbWF0sL2HP89QdIz5laqUJwVqaAtfE6ArGWHhpw3qOq N5CWsTJl4GiKAh4gaWDeMe0yxJrc44H0tBFLxB3+7oVLO3OVtASQHYTQtbO3xXS4KhsSmVME SG1zplVRz5c/XCE9RssUJ44qY5OSPeN9ht4RIKUqadnw5CcE5GpVwzNBb7Ii1cRrc/bnInOy YOLmYo2J+H9D3jriEmw5nSl6JpAz57GC7548R8d48hU+h2wXqBLA2ntuEtLE2ve0OrffPq2F WFXdI9IKwFTEwGnaHvfQikeLWYbW4k2NlCgODA4ICpP3MxZoml3pb62oFYmFj34F1KtDCw4u 0dIYq2pqMF1+tC8DjSXaTvolxMhMBcJ32VqivVBJLdaze/NWwRlAai0tM2k72yoBsrd9R9r8 7AopdYCd4bljbmEgpfbZwkPbtrRYbm1x46VF85X8toVrS4W7Ny6a/bt93wHFTJtdQM4MeEXf ADIvKwAyQUsAmSDirwkAmaAlgEwQEUAmiuj8DxZ5Hve1hAqZlFvssnOEBJA5Op5WrHNI6XSA hsBdAHBjE0NfxhSAvCEV1q0UPe6KnNPufowEIG8AUjqr9YCnnfPuDuM595vyAbOMAh4gvVej uwoMIAszOwJku4YsDLncFYAskly6TqXrSW5zxF2RFoVd7gZAlksOh5oCABJ8LKUAgFwqHQgG QIKBpRT4BhmCP+hPrHtSAAAAAElFTkSuQmCC</item> <item item-id="26">iVBORw0KGgoAAAANSUhEUgAAAcEAAABiCAYAAAAoRGgcAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA DERJREFUeF7tnQty3DoORWe2ki1kC1ltNpmXzpTe0AwpACIkAtBxlauSND/AwRWuqHY7//31 ++s/fEEAAhCAAATeSOBjgnzVJfBb05+bnC/fdbMlMwhA4CAwuvY//8bXVwIQKa4IRF+8wKQH AQMB+sHfsDBBg4AyDkX0GatGzBC4hwD9ABO8R1mBV0X0gYtzMbT2MdfFJZhWjIBWE/QDTLCY 9OV0EL3MKNOIvp7UN1P17onVogn0ggneo8LAqyL6wMVxCI36OkAstsSZJtALJlhM7nI6iF5m lHUEtc1aufviljQhvX5fZHFX5gdj4tbGJTJE74Ix3CLHe0DhAiOgbQQ0mqAfcBLcJtBdGyP6 XeSf2Zf6PsM50y48DrVVi5OgjVe60TTJdCUzBUx9TbheMRgTtJUZE7TxSjeaJpmuZKaAqa8J 1ysGY4K2MmOCNl7pRtMk05XsNOD282DUtlZtr2Zj0QSa4T3BqzpLOw/Rpy0dgUPAnQD9ABN0 F1X0BRF99AoRHwSeI0A/wASfU1uQnRB9kEIQBgQCEKAfYIIBZPhsCIj+Wd7sBoHIBOgHmGBk fd4SG6K/BSuLQiAlAfoBJphSuCtBI/oVenHm9j8BOPp7nGiJ5AkCVzRBP8AEn9BmqD0Qfahy EAwEthKgH2CCWwW4Y3NEv4M6e0IgJgH6ASYYU5k3RoXob4TL0hBIRoB+gAkmk+x6uIh+nSEr QKAKAfoBJlhFy+o8EL0aFQMhUJ4A/QATLC/yPkFEX6/k7U8F1suOjK4Q0GqCfoAJXtFX6jmI PnX5/gq+ryf1rVXfK9lYNIFeMMErGks9B9GnLp8YPPUVEb1uwJkm0AsmyAXxOgK1E6ap1a7v lewwQRs1/j9BG690o2mS6UpmCpj6mnC9YjAmaCszJmjjlW60pUlaxqYDETzgK+yvzLmC4al9 rsRWec4V7tIc6fXKPGe5YYLFq64VvXZccVxb07PUwDJ2Jamn9lmJsfJcC3/NWM2YyjxHuWGC xSuuEb1mTHFMYdLT1KIdoxl/Nbk7174a0xvnaeqg1YRmrbcxHprgB9TxXR1IdVFI+UmvV6x/ 9Jyl93Ta6/OuXO5aN6KeMvQ7L028qa5arf1lgj2kytDeYPRS/aTXtULKMi5DzSPUJEIMT2gq S7/zqofXOk/U5qk9xMeh1aG9Ob/quU/fCP/9pCP6187a7Nx7d10i5+4Rm8cau2vkvb/YDapD e3N+1XPHBK+1i7fq4kMrcu4esXmscU1VcWedmuAbgFXPUXovIa4074ssQ813xrhz7/uqLq8c PW+P+DzWkEnmGoEJJng0tiKpmejffDFkyX1HnDv2XNG359wMua/GuDrfk3eUtaYm+BZY1fPE BP++1LLUfEecO/aM0Ayz5L0a5+r8CLXyjmH6EYljo+rQ3ppf9bzPLpQsue+Ic8ee3k3Nul6b c/T8V+NbnW9lm2H88CMSH1Dtd4ZErsT4lhxHbN56MWSq+Y4a7djzyrXrNafvddHzX41vdb4X 90jriD8dGilYYrETuPI4dGQUmczDTinmjB0N62xPdLFfJ6uaWJ3fPiE89DCjctYzRnFI48/2 60/zUmxtzJjgfl3fGsEVE/wENBPprcGy+BcCXg3LglXaE11YaPqPleoj7bg6f9QbNJoYmdQX I+p+QLFfU3Nzpo2tZ4QJSqpJ/np1E/S4qKOWeEdu0p6ahheVZ4W4pPpIOa7Ov2w0gsn9ZUzN eM0J8MjLYp7/nmolaLyemwAmaK/f2WMZ+2rXZ3g0LOvu0p5vNcEqmpDqq9GL1WismrH0rJH5 zQxxlhsnQU3VE4+xCKp/PNFe+Gd3YzvxeFzUs8cy3mtbOe3YX9pzpAlpjjXvaOPb/Hbnurr/ WT/QXu8WE5z1DSmOqWF1J8R/T3PGk+aXa17T+KSAZxcG//71p2zv5GFt5NLFZL17Gz0muauZ nXE8u3g0Ddx60d6VoxdPTc4z7Yxyy6gLNPF/AtJ1r9GzxQSvmpR0834Ww/GaNldOgpqqJx4j iUlzxzUT8ugG6mlUWqFb42rNcGaM1jWt4+/K7SwOaU+rCe5iZ2WtGV9BE9YDjeZGSNLM6IZO miP1Le2NsLTPn9g0xWdMXgKSmLxM0OvkYiWtEbllzXa92Z8t662M9c5NE4u0p9UEd+lCk6t2 TCVNSPXVMNl1EjyLfXb60+SLCWqqnnjMFRNs77LaU+Dorr6/O+5PjbPX+3VHf9ecIjQit5Rv lHvbyGcxSf8+ahxS7NLrlry0Y6VG0+d5xmvUmNp/mzWug3dvPqO52rxWxlXShJemtEzOesao vlp99fWcraWpOyaooZR4zBUT1KYrNSmp4fXzRwaqjeXucaOLs4+3P/VI+UmnJK+GZWHjsedb dJFREx71tegpw1hMMEOVFmJ8ygSvGEB7xz86KS2k7T5Vim90Zzy6C+5zlk5e7okIC3o0yTNW mhuDLLrIqAmP+j6tybv3m/4C7dkFfHdAnuvPjuyee0RfK7IJzhqidEJaYX5VE1cae3tSPMt1 ls+OhuWx5xVWFnNcqf9o7ps04VFfb/671xv+Au02qKzQpLu03eCf2v8pE+wbiaapSWO8tbei ibsae/WTYHRdvE0T3tfUU33szn3Ex6FVoFXJwyqGO02wfWzVnt5Gje+Io3/C0I49m2fNWzPe qomzE0Ofx9kjvZ5btZNgZl1U14Q1P811lH3MK/5n+TcX/m4TzHoBZNDEjhh37BlFQxlyX41x dX6UWnnGcfo/y1cA1p88POFlWCva47YIzDJoYue1t3PvXfp4iybeWFtJUzwOlQglfx0TnBcw ckPYGdvOvXdfbpFz94jNY43dNfLeHxP0JhpsPUwQE7RK8s2NMnLuHrF5rGHVU/TxmGD0Ci3G J4leen1x+9DTo+YeIa4IMewQT9S8veLyWmdHbe7ac/gRieP5eGZgbQ6Z81gtvJS79Prq/pHm Z9FEhJpEiOEJ7bxNE2+pq0U74knQshhj4xHQiF4zJl5mNSOKVItIsdSsti4rzzp4rqWLPv4o TDB+jZYi1IpeO24pGCafEohYg4gxvUlG3vy916tQC0ywQhVPcugf95xdBFwg+8QQmX3k2PZV 7P6dV7mPrv3VNe/P+vkdMMHnmbMjBCAAAQgEIYAJBikEYUAAAhCAwPMEypng7BFAhZ94fV4e 7AgBCECgNoFyJli7XGQHAQhAAAKeBDBBT5qsBQEIQAACqQhggqnKRbAQgAAEIOBJABP0pMla EIAABCCQigAmmKpcBAsBCEAAAp4ESpsgPxHqKRXWggAEIFCPQFkT7H8zAr8poZ54yQgCEIDA KoGyJtiDwQRXpcJ8CEAAAvUIYIL1akpGEIAABCCgJIAJKkExDAIQgAAE6hF4hQmuPgrV/Cq2 nWPqyZKMIAABCDxDoLwJrhrgpwweazxTTnaBAAQgAAELgdIm2JrXipGtzLUUg7EQgAAEIPAs gWUTHH0WL8Ln8yz/mayE/DDBnz9//vr+/fufk+Hn+8ePH3++j79/+/bt12cMXxCAAAQgkIPA sgnOHhdWOT2N8jgM8CjxYYQ5Sk6UEIAABCBwEMAEBS1gglwsEIAABOoSwAQxwbrqJjMIQAAC Uo/3IDT7eIDH2rvX4CS4uwLsDwEIQOA+AttOghHeM9TEcNUE+x8O0ux1X5lZGQIQgAAERgS2 mOBhEBlKcsUER7+8W2uC2nEZ2BEjBCAAgegEtpjgB0qGZj+LUfrp0JlxasSQgYsmD8ZAAAIQ yEBg2QQtnxNsT4Btsz/+PHv9ANmfIPvx3gbSr9d/TvDz98/3EcfnM4TH5wRnp11NDjM2PQfv fDMIlhghAAEIeBJYNkFtMH1j741PMsKRMbQnyjsMwWPNkRmOHpe2BndmdmdztbVgHAQgAAEI /I/AFhPsH4dKBiedgJ4yqxXRzHI8Y9G/pvn7SozMhQAEIPA2AmlMUGsiKwX0OmVJ7wlqT4Ia 0/O4AVhhxlwIQAACmQlsMcH+fUTNSVAa42UGZ2arLbT0CHT0aLh9HNyan2TMXnlrc2McBCAA gUoEHjPBo7GPmn1rGrM/t/NbEx29l7hSIC8T7OPtY+rz7B/5ns3vbyJW8mUuBCAAgTcTeNQE s4AeneSyxE6cEIAABCCgJ4AJDlhhgnoBMRICEIBAZgKY4KR6vNeWWdbEDgEIQEBHABPEBHVK YRQEIACBggT+AdEDLh4weUACAAAAAElFTkSuQmCC</item> <item item-id="27">iVBORw0KGgoAAAANSUhEUgAAAIsAAABLCAYAAABA8XRLAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA BCFJREFUeF7tnWuWIiEMhXu24hbcgqt1k444BxuZvIASkuLWOf7oEopw85GksLT/PJ7HDw4o YFEgwYIDClgU+LE0Qhso8MpAkAEKWBXYBpZnSk612ftlFQjtfhXYApYESX1Q5wCGrMDpYZGg ADBtywOwtOm1dWvAsrX72yYPWNr02ro1YNna/W2TPz0sr80k3A21UcG03gKWDIy0z5KBym0k yEr46vZnvsPaBpa8WLgowzm9BoO6TgnaIUvY6UW2g6WOMpLzqejCRaczR5S3Rk4hnm5WSwTh IJpu9OQBt4wslMYjsCANTaZ25XB1apGK1voDSRS4Kz2Hsd0qgDTk1jX+DAMs/nzi1iLA4tY1 /gwDLP584taiD1i4Sh/nPx/J3FGP196SW4xhmDsFAIs7l/g1CLD49Y07ywCLO5f4NQiw+PWN O8tCwYIvia3lJwws9fMiOzw/shaN/0cPA0ttOmCZj1JIWADKfFBCbsqVz4+skWzfUUNGlhfl xNc79nXjnJkDljk6n2IUwHIKN86ZRBhY6k9658iDUUoFwsACt61XALCs90EYCwBLGFetNxSw rPdBGAsASxhXrTfUPSyW513Xy7iHBe5h2cMNMWYJWGL4yYWVgMWFG2IYAVhi+MmFlYDFhRti GBEKFjyDuxaqMLDgGdy1oKTRw8BSS4WHn+bDA1jmax52RMAS1nXzDQ8Ji6cUZPk4InqbjGU4 WDyB8ir6NnpwPBQs3G/Vzg/IvyNuDQu1l+Fhf4MK5RZIqN+4tfSztqkBHvle0/1+f1yv1/c/ /bzdbo/0yte8XC6P1GbVQUYWarVEXEHU3syR85Ai3cg4qW+CJB8ZmFWQiDXLGWDhnDXixJa9 npFxAMvkZTHiLKup0hhcVLOk9HCw9NYIVqG/3W4lLBQo5Xw128LB0hJyvdxCjt4ttfa3pjoJ Hi7lazXLtwt3ajEfUuCO3AF8K8JYHfku3p5FpbbiLQtIGteqkxZZRgr31jl+RESSoI5/QDli xCxgNBu197VUwkWmluvmKC1FFi4aWXRstUWEhSrAuKKsXCmUUNz73Gqu249MrB5DW/GtqZRb 3Zp+UnSp91nS3+mV+6Q9mLzPwl2nPk9pyvlK06x7B7ceMP9di6UBVTupvI5lpYy04eZguWYL yCORQLOFgoYCuQRBgkLsqxnDvd9iEGVohsQy2V4btX7SHOqUY217VFrXbJfqJ6tvqGjqEhYq FHJQtQpnbd8CQEtbDhgqRVlt7a2XuMgtLWBurEPTEJVCOCi08y1hvkdwSkSpntBE77Whp58W jan0WvuGS/dfiSxlGimF1GoWbiJSvx5BrX24mkpLJzNg1koAKVJx86rPc+CR562iot0/BbTo c2adutPQmUWR5gZYdvV857xXpqBOkw/phsjSIeOusPwFW0ONzK3OFMkAAAAASUVORK5C YII=</item> <item item-id="28">iVBORw0KGgoAAAANSUhEUgAAAHUAAAAlCAYAAAB8iqZLAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA AfVJREFUeF7tmNtywjAMROn/f3QLmUnHCF2QI7mLu5nhBRxF2qNb+Pq+XzdeeynwgMprLwVu e4XDaI7OSxn2U4BQLzK9D+PHTnJ8UC4cT1AUSfghQaKAJdQExOgooUYKfeDvhPqB0DyXUYBy +y1MLEItFBPBFBJQVmpBRoxAUeBy+70AdnxHRXpXfYLamWnaSzrii/sFxjC3/kJdkWla0nQm EozKix1ZVqnHAFf+SiPUeuIq1KhqZ0H8V6irx8wL1Mw2py0KHvDs+focXm8xo2eVd277na1I y7mZSq32wfMts81aCSr9tbpeZ1yXoGYrLws1GgNVmb3CzhhLd1yXoGbFyEK1lqvsc//yvNV+ l1SqHObecJ9xSLNnPUNm9QnlfK71+3hOiqnduwK2F+OZtFaye997bR7uH6UIRgQ2qoyZhOyA L1tw5LfsWt55aKiZQEbho+rogJS1qVVaFIM1i192m6wz3ee9YK9kc9d89saUp9VMnN54eUqI bkhZ+1r7HVuunK/Zaq5sv1G1VUN9N6nh2q9cHuQMtWaqlQyykqwWlk0+7Xw2Ybwqly111CVa rCChVgi82kYWaKd/hFqgbmf1z7hHqDOqGfegVCuhEmqhAhuaYqVuAFXbUBHCYvtFoFDsA6EW C4pgjlARKBT7QKjFgiKY+wHtyoPlgdTgAwAAAABJRU5ErkJggg==</item> <item item-id="29">iVBORw0KGgoAAAANSUhEUgAAAEYAAAAtCAYAAAATHR0dAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA AdNJREFUaEPtWtGVhCAM3GvFFmzBWqzFWmzBFqzFFryFg3sxJAS5E1cIP/vUxcBkZiA8v/Z3 e2kLETDAaAsReCkoNAIKDMOM6oF5m0eWKPJ6ZYUq38mAosBwklDGMCYKgMEM8tfw17+laim5 PZqdK5aUeF1e+WUjcj6jwDgpUSZMSag5KWE5SatV1R6DGQFlFTNeC2JZxYcm6DNJeQFH9ZgE UufDSesWKXGTh5PxA+bMUTLNRwJzZtm8GhjM1CAeRHjbtr3run0YBrvum2uqScYVy5qU8VKM kZh18BgzqGmabJ9xHC1I0iQlE8P9HwvMsix2LvM8ZxdgEpiUp/yaHrPvkJiE30klLPVesCrB bKYAwwW6GxhJJinPAymt65ospZQA1UjJsMCYb9/3/26+Z/clZ/+fkyiuT8AYIyFtaOdrMqTA /NAiYIxflVphDSvXVgCg5hnbUxUvIj85ERAoBcZlKlorfXI2rxwbWfVfGfBp71YpMRlTYBSY uJhxEXyo0J/mA6XGq8s1J6tSGbgjDjzc4g66kqrrOwZ/VUxcA8G9SsqZddVSOiy/6KsHKSEK TMseY89XlDHuoMl9ZsYZsOQzVUtJ8pHYcwWmRY9RxvwFAabvNyxTgJPRpDfFAAAAAElFTkSu QmCC</item> <item item-id="30">iVBORw0KGgoAAAANSUhEUgAAAFkAAAAYCAYAAACRD1FmAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA AgBJREFUaEPtmVuxg0AMhnsk4AkLWMACFrCABSxgAQtYwAJtOl0msye3DQuUDsz0BbKXfJv8 CfRveV2P+9qXAEC+r30JPPadXp59GIYzl99t7dgvE+RXLoGk3D8vA8txAuDcV9M0uaf8qvmw fyZ6uSHP87zUdf1VUHJvBiCDn+/GwjI5hoxlwzKWsun7fum6zjv8EuNAl8FPE+QYMPbQG+EQ xeM4XgKWd5PgX8hWNZIlkF7IZVmuqeRxwruuZy3vGJAK8DM5kuMFY2epDoTaJDfuvaFPBZec S4GcQ968oIui2AaZAmWVEgpSuGcBaLEJBxb2ZB3jBSoFk1suUiBrthYQUp/OgeGy4ijga+Bo JydFHSUfW9Ld6rzVDh8ezpTU8Roj6ZBVTdYAS52HtLGgVZS8WABYbCS5sI73wg3jTJqcUtis RQ82AFV3mqbVBxz9WzLBmllaVEsyQz3j7E3dRcqJa1GPAZzdJ8dQuIzccp/sk7kTsqZMCmR4 E2rb1jp1djutEOOspHQ9ljnKnn3j0xbXvI0lg7OHRr2qKm263Z5Lfmp1RsoCvGEIIvbbhbZI Ls/P/ArngZwqHThT//XJR0GGwhc+oOQ6uJR5qBSPpYEqyJws4vugx/jbDPkyYqnwKQ5xtr/6 z0gcPKdCznFQV5iDfa1Oad+u4OiZe7whH0D/CZZZoVAvyfYlAAAAAElFTkSuQmCC</item> <item item-id="31">iVBORw0KGgoAAAANSUhEUgAAAEAAAAATCAYAAAAgcwuHAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA AR5JREFUWEflVtEOhDAI0///6NNdnGEIa2GeibclvjjWQSmw9bOvZeZVCJh5LTMH/1U/Q8Be IaVM/vNjCWDs3mhDK+CNwTE+QwKK9OWSpcBc0LOR2PqeUWzrvHVHiACLjKyjOvgnCDhGfptQ FEDPsazTZiaU0pBfI/sN+QgoQoA1KTS+h1f/VwzkF7MvsTTueR8CQg7X82x59PB+0RMksbqX Ne8Ai/lo9q0LdN1lCZVEM0rzfNEJa5rgXVmMEBdVEFIs3f2PnnOZAkiG3j7bEEcVECWALgEr E5YitPx60oxkw2pYLKkeKekm6LHGPmo8WaP/0exm7eEYzIwiK1uZXpANij13UXVPPiyoVwbo /KjEET7zBnGfwk87Fw3mLvsNq5rEECrReIAAAAAASUVORK5CYII=</item> <item item-id="32">iVBORw0KGgoAAAANSUhEUgAAAaIAAAA8CAYAAAAkEL7oAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA Cb9JREFUeF7tnYtxVDsMhrkl0AIt0AIt0AIt0AIt0AIt0AIt0EJayMW514yjyNYvP4/2/DuT mWSPH9InWb/PYzf/PP95veOLBEiABEiABE4RSELE13UJ/MmLtFF49XNda2kZCZAACdQJaPXs 5T1CuzaBFCS+SIAESOBRCVCIAkSWQhQgSDSRBEigmwCFqBvdvo4jQpRPg/dZy5lIgARIwEeA QuTjdaT1iBAlg0f7H3Gak5IACdyGAIUoQKhHhWS0fwBENJEESCAwAQpRgOCNCknuPzpOAFQ0 kQRIICABClGAoHkEpHw0MrtW3ifyjBUADU0kARJ4AAIUogBBRMWjPPMp+9R+D+A6TSQBErgB AQpRgCB7hUi6RCEKEGSaSAI3JkAhChB8RIhabShEAYJME0ngxgQoRAGCPyJE8p4RP1cUIOA0 kQRuRoBCFCDgI0IUwD2aSAIkcHMCFKIACUAhChAkmkgCJNBNgELUjW5fRwrRPtaciQRIYD8B CtF+5u4ZKURuZOxAAiQQiACFKECwKEQBgkQTSYAEuglsF6KfP392Gzva8eTcI7bfXYiixs2K +aP6Zfm98zgZz6W9iudWIfr169dz+jn1SnP//v371PTd895JiMrHzfn72//Me5pJdxIf6Hi6 3hxwefmUq2roViH6+vXrclDWBFewwbJRHr+bEJX+R4yXJ76P7p+Hxey2ZDub6H/jreC6TYie np6ev3z5soaMY9QEMdkS6XVXIbpKzqzMlYj5uJLHrLHvkDuzWHnHWZGzr4RIfgpf25n3fjL/ x48fz9+/f3/js7zU4IXibZ+ucSZbZr8sdiPz3VWIajkzwvJqfT35uPurmpC8K3lq3+LRw3vG WpqRO7trk8ZKYzGDT09ccp/enG3N+eaMqCU0vSKUDEhnQ/L+kJbo3uT3Ak02zD4zkzbP9gEZ D2njZbW7vfRBy5ndNq2eD81HKUK74o3Oo60BtK8Us9bfaDxGc+dEbar5diVbko2enEVzQBWi NFmtuKIDS6ifPn16dUmsNU7vHEiSplP2ZMvK12z7kfGQNit9njG29EHmjHeOCEyQfDxdiCyO teNWPySevWOM5M6p2hRFiJCczb6g8dsmRJ6zBtR4JJG1Nu/fv692nTF3bXf4Avv/H82AFOAP Hz68CGVql+9lITYhbXp57epn5UjJrsXRuwjyxgsZcwWLnnzMrGba3OJ7qji3RE6zt+Qiz6xQ Zqd8jSJEyU4rZ2UcrLV7SyFCinYpGpaAeC4tWEn+7du3l+HSpYUkStrZqZawiE8riujMMS0h Klkg/iJtJF+0z0q/W/lUimxp6yy7W4XcUyhn8LF8auWL1rfmm7TVWqMzfPOM0VuLPHN427Y2 CFaO1mLzrpb4WuCQYoEUypPBthLcG5ReIdJY5g+MpZut6MJBxWrErx190dyycse7cMvdGiIC s1l4C6oW71Zh0Hi0fGgV8bLIaL+jbBARtfKhNQZSKHuEdWXt8Nhj2WEdR+PksQnJy2aejggR 6pCVVDsLABIkbzFriUE5FloAKERv71N6Cx8SZ5l3pfh7+6NrQbazhHV0Y+e1CxEiZL1a4oiu BaQdWl+QmPbEw8vY074nHoifHhvQnLXicFyItGuKXsAj4Mq+reubvXPUdmeehEht85OFd7s0 p3Fq5QzCFWkjNxDITr03R2r9rOvtO4WoFGJEbGqbA4s9WuzRcWS7Fblj2TI7L1obL8sW6/io rbWclesnb8LLdVar+38vzeVOraJaDlxbxLJNapduwGtfr1PO2RImzaHabjbbVS6qcuzWU3M9 AZQ+1IKhcdF8SPZ9/PjxVg8raNxlzsgcsGJlHS8XuhabWv7IftpusTZezc9aYagu2uKhF23d 9hSaFl+EpWWHtYbLdWuNZYmkJ3dq6/JEbdLqK/KeVvOkkFn8a/PIsXM9b+VsuXasevz3eE/S 1gaXRTiPbT3X71lwNQFsCWO2A30GvpeJVpSQ97Lt2odt0SIwy+YT42g+Wjmz2k5tYcrF7c1F 2R7JRyT+K1nMmF/bnM2yeUbutHzcVZu03EIZ1fhKQa+JUa1uS0FJ9iA5i9pd+vzqYYWeAWoA S+dSgc1PhFlKakGRi7kURZk08m/Pp4K9LLT2tSSutb2DEGk7UI0TkjMzYtTKx/KYllutXaTs K4tCOo7m4wwx6GE1a15rXfbYptWBPI4ndxAftdghdcpTm1r+WHxafGfbieasZfOb9eHpINvW nJRQ02diPn/+7J4KgYjakCZPYrj7u+bKJLZ2XtrXrKMLxQ33YAekMPXmzCy3ehZ3uSlr5WVu 58lHJA9m+T5SFJEN2agvmjCU8+7Indm1aSR2PbmK2K/lgSdnUZ9e4ok2thLMKi4939qKwLLa lMets7IRFqv6IosWabPKvt5xkULdkzO99rQ2WXJBWjmHto+Yjz18Ja8sJD1joX1W586MHJi1 blt8Z9u5ImeHhEju9Mvk0nYs6WEFzxeOyjG08eUOVNqUC0J6P+r/J0GSFWmDLuBd7RAh8ubM bNtbO+9WrmnxkMU3aj72Mpbrt3cctN/K3Jldm1CfWu00vrPtXJWzQ0LUA2/Vf/hDbPGIIDLe rjaIyCBtdtnrmQfZGZ/MGY8v3rZR89Hr58n2j5o7p5iuytntQnQKYOR5EZFB2lyRASJEV7Sb NpEACcwjQCGax3LZSIjIIG2WGTg4cGTbB11ndxIggT8EKEQB0gAp1Eibq7oa2farMqVdJBCJ AIUoQLSQQo20CeAqTSQBErghAQpRgKAjIoO0CeAqTSQBErghAQpRgKAjIoO0CeAqTSQBErgh AQpRgKAjIoO0CeAqTSQBErghAQpRgKBbImMdD+AiTSQBErgxAQpRgOBbQmMdb7mYP8czMkYA hDSRBEjgwgQoRBcOTjZNE4kZAlKOSyEKkAg0kQQelACFKEBgvUJUilRLsPKxUvAoSAESgiaS wIMRoBAFCOhqcSgFafVcAXDTRBIggc0EKESbgfdMt0octEtzq+bq8Zt9SIAE7kGAQhQgzqvE Qbtsl+eSl+0SptZ7UtRW2RwgXDSRBEjASYBC5AR2ovnOoq7dN8oiVN5L8rx3ghnnJAESiEOA QhQgVruFqESinekgZ03a2VMA1DSRBEjgAAEK0QHo3imvIETa/aTyDKkUJ69/bE8CJHBvAhSi APG/qhBZDzvstDtAGGkiCZBAhQCFKEBq7C7o2kMM+Z5QecxqFwAtTSQBErgAAQrRBYJgmbBb iCx7eJwESIAEZhKgEM2kuWgsCtEisByWBEjgEgQoRJcIQ9sI7XJYALNpIgmQAAm8IVC7vP+O rEiABEiABEjgJIF/AcIfwejbMNOkAAAAAElFTkSuQmCC</item> <item item-id="33">iVBORw0KGgoAAAANSUhEUgAAAI0AAAAWCAYAAAD9/x8lAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA AtNJREFUaEPtWouNwjAM5UZgJ1ZgBVZgBVZgBVaAEViBFVihhxGpfK7/SWk5GgmddE2c52fH z6T8dI+xmvm4XC6rzWYzc5SfA6+aT0iaOY/r9drBZxntGAA+b7db2uAqvfJNC/f7/Zt2+q5t anidddLc7/dut9t9VzTf5C0kDfCbGYOkeSgz9DjPz6vfydjtbRQ7GSOn06k7Ho+Zpf0a7E8N lhoQlNPCK+a5xn5m7fl87oBfbUh8/UkaOinrFGcn4xhUmZp+hnN6ysShHEyFBXAAr1oV12Lf J42YVa+Kkwl6WZMl5/GNKV9CFdxZPK05mAJH8QGkCfitqjRjOqAlJJVCTRq5uZLTmj9j+hrB MwUOjG+9Xg/gcvwPeK+tBtbps4jRpEyTl6hdjNNaa/mUeU57q6z0Z/b2JjLmheIrz57/r0ka ugkHzupvNBs1kvkfKs3YyR2JTWnen3+tpLGkJaOJUkNoOWFh9VaTsYPhOTw4CNL8sXFafEvP zW9P2aTwVCGaBBSkpLkW4VpyjR0IrxR4fBgbK+WXkycsSwWzeU/DVQVqSJqDddzSYo4g6O7x dTfWWU5zORu0l9CqnOaXtp+FheNB4ob6yB2sVji5b0/c/gPerWBKJZ8SFbHjLd/Re5poH0MD ZwWMVgcreFFOuJPen25y4crtLVV3bq51T6MqTMQxSeMiNrwJA/PgxvJwOLjMe0q5dOI5OeN8 5Q6KVN1coMkkjV8rIbAPHpyeG2FRaiPOtU4aqUQXTHABtd1uIxDDcz3BkOZ4+pIIoNqkieCE w9js3ZNaltAtqxXwCFna3Jq3sR4MNUmjyZNnb6m/kiTJqoiWL/i5t4KzyhB1TtL9qB3vfGiE rRdrXltckAZNHnpRy/UY+LDgpPHIowcnxy89oBJmmmzSutrfKM36pxGFZNDfZbRjoPYQfkTS tKNrsdSCgSVpWrD4ZTaWpPmygLdw9xeRsBYNAyj7mQAAAABJRU5ErkJggg==</item> <item item-id="34">iVBORw0KGgoAAAANSUhEUgAAAHwAAAAWCAYAAAACcfiCAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA AmBJREFUaEPtWe1xgzAMTUdgp6zAClkhK7BCVsgKZARWYAVWoFGutEbo03ZsesF3+dEaW9J7 epIxX/NznAqNx+NxOp/PhaytzdS0XSVgzigQXmIMwzDDr9YA2+M41jK/G7unUp5cr9dSplg7 e/ChNghFCJ+mab5cLrVjnYFw8OWTxy/hz5IPvfz1o4Y2L4F4v9/n2+22eSTck7Obk5y+72fw JfeIxY3CVMKZsuPlZcWuxfEYsEDduH9TBL+bdPAhd6WRfMZzqX//HLBJ4Vh52RBObbo4GkvI 82S+KqUekKyBWJ6Dcg6+5BpePMLnJXEt/nGi0BJHiq8I4R4HvSB6yWuaxruEbXGLOKw+e6qa 1FqxKK32X+vCaDglpyp8T4Rr1QWfKyTgsWJFZTFnI66iaoSH65IJ5zbzEEclklSqLHM5pOkB x0Ogtq+WaJQtrSLECJFUuJVwKwGeRNGAs9rkntOA9yhcSmrrHKVwrYfjvT2YFSGc6pta9qYS y63P3cO1yqSVfS0BcRypuInv4djZJfupIMPygp2CkzF1rYnVxAUX2uUCpnylDlW5T+kYE1wd qRjD/1FqpfYMY/FUzA2msUrCJEjJQL2HayUPg8KRzimI6m/veA+Pxa/WuqSrValchXNwu9V1 nRgjlfUamWGlkZS2PPeum7Za5MXYLUI4XHi0bev2z0K4NenAOCTdcZfupuFvgQfsmC9VKYRT JV2rMglQ/Jul0QrHhxGtp8OhzfPhQuvh3IGSW1f7e/xeMiKa8JgAoIfWGp5kq+VjCbtFCS8R 0GFDRuAg/MMy5CD8wwj/BgVxQ5m5Cde6AAAAAElFTkSuQmCC</item> <item item-id="35">iVBORw0KGgoAAAANSUhEUgAAAGEAAAAWCAYAAADQIfLaAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA AZpJREFUaEPtWAGuwjAI1avsCrvCTrtLzt9GTOU/KFDXacKSxWi6Au89Hqv34++65XUtAoWE vK5F4HZt+IxenShhuB6BFwn7vh/rupb5UO9t2+pN35dlOcqavMYRIEzLJ+wEIoBCERHjoXOH 50vQGxCVEA5NkjBXLEnCIN5kJyPbJAkj6DXPth7P/V4LMTQT/g2W54D5UE1wm2hMDyhn5o/2 DpPAW5CKtBQQbd9ozPY5LXY0L6o50glvuXkHM0rYWoR1HcrJ8ltkDXpjsQhqZA0Xh3hOKGeC chPL5QxB5wRJ/fz39juyhVc7NnYm2Uev46TYWk5EgJanZmcRUcGuGWUUWUW7p2QJGiltmyN1 azE1C9JykXKe0Skf+dsiUrh0cNHA6AGFVM+f8ZIfUbtX2G4SejPBqlIPCZ6YVkH0yEZW6QXX uj5EghVorjpemNXK0DywWIsnTy6KrycBDTTU9qiwtjgJXMlWekMSDj12htFiRt+urIqX1rk7 YTTgLz0/Yx5Uof4SKDNznUVAkgBY1SzvLBFkJ5yFrGPfB+eB17O2OZ+ZAAAAAElFTkSuQmCC</item> <item item-id="36">iVBORw0KGgoAAAANSUhEUgAAAMwAAAAvCAYAAACv8iXeAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA BSxJREFUeF7tXYtRIzEMzZVAC7RAC7RAC7RAC7RAC7QAJdBCWqCFXJQZB0eRLNsrr/bzdoY5 krUt6a2eJdle7t/pfB1wAYEKBL6/vw/Pz88VLf2bRMq+sYYIgwsIWAj8/Pyc6CfqItnH4zFK /FXuIVwDKLAKBN7e3sL1XIIOIEy4Gyxfgd/f39Pr62u4okQY0iXyAmEi0XeUfc6zqRa9/EiX db+kyufn5+nj4+OuST6mJtfRxNPX19eJdIm8QJhI9J1l1xCmRyRFF16/SAQZTRrSITrSgTA9 HrTQPslhueNq39eacV4Zu0mFSsQYSRpKx0iXyAuEiUTfWfYowmgE1FI/Z7Nuhnt4eBg5vDk2 CGNCtJ4GeyDMyAhW86RBmBqUVtImdyaJPC2RIje5pd9ohx49vvWoQRgLoRXdbyVMrWkgzB9S IEyt16yg3SjCSHVDxCoZPQLUMCtwxDWoKO2zcALxZWeNYJwMtDIlHUux9mHytLC0UifpJfWl 54BVsjV44wZ1zJ3UIpu0D1OqcegeH5PLS/0l0qb++b/0++73YWjnNuqKlB1lM5erOSt3VNpd f39/L6ptkc6SxaMM14E+73qnH6df42ljOXHSkDYMX15emhW2ogcnhbW4QKTd7VmyJZw8XYIO zV7o2KGWMCSyB6sphJFqHivKOUKjDhWySobTr3M82rIMXrBbNQ0V/S0HH60aRluQ0PpFZyTX misvvizQvB4zTr96ITnvOJF1XwtZR6JyE2GsHNJLEZx+9UIS48yNQAhhcPp17scMeV4IhBCm JZLxtl6Gp3FKO8ejZXvbgvHGI3BX9Jd2ZL3UWRJhakhRKpC9MME460AAhFFe6Z3y+DSC4fu/ 16jXiMVl34g7xogIY0WU0ixfEwGmOrfVHxHGQmg/98V9GG8n5ePh9Ot+HGxrljYThm8sXcIU +2sl1n7OWk6/ek8cW3OePdrTtNNfSq1a7uH06x5dbRs2hxAGp1+34Tx7tCKEMDj9KrsaX1yY 0yFpEnt6erqm15Q200/S6fHxseks2Zy6zylrCGGkA3Sp1knG4fTr7WOW6qWIGopk5m81JtLM 6ZRLltVEmLzA1x6wtCzNFwVoHJx+/XOLyGV1aVsBhNEp20wYT/Zv/fQrpZ6UyqRZWnv5CYTx 9KqxY4USZqxp8aMTEdJLT7QySOSRLhAm/lnVagDC1CLV0Y6IkKIoFdU5MbTfpRSpQ3RVFy2t tlIyrUatEtrZqFemtG/YqcKlGwgzBT2jb+6QnDB51zVFGGm/rXZxoradNWlINbEVub0wBmEG Eyb9NxGllCwtpliOMlDV69DWKtmU1Twvwmh49eLXohcIM9AL00xIKQ7tcVh/8cTah8lXIKXV yGQKn4Hzz9rszPdh6HOKitSH9E+vCWtj1MiVUlH+nTS+FVU02SWdEvEkfDjW188D/WX3QxPI Pe+ia7O45viSE/JZuESw3gelObaWblrE4HaXcKiVMbXdHY69YKGfjUAvYaSZTyOAlJ7kkaq2 VrKt0VtohC3prOldq69ELima8PEkbEpEvbs3BSj0LSNAYHvuNbVEkhqH7Hl+2qyfp4M1s3qN fqWo2IqFJE/TOW8LwvR4yUL6tDqJ1F5y+BbzrDSslHZxAlgpGK8jaojYE+0kYmpkRdHf4i2B bXkqkTuulIrk7UttW03iTmzVGRKBtJTTSkW1VJP347ZLaZn1nRaRQJhWj0H73SAgTga7sR6G AoEGBLTUFRGmAUQ03T4CpbTvkqZtHwJYCAT8EPgPTH3MTGGK43YAAAAASUVORK5CYII=</item> <item item-id="37">iVBORw0KGgoAAAANSUhEUgAAAIMAAAAlCAYAAABh2FqVAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA AtlJREFUeF7tmoFxwjAMRdsR2IkVWIEVWIEVWIEVWIEVWIEV0oo7c64iW1IsRw5V7nrtNbYs fz3LipPv6ff6iisUAAUAhrhCgVdSCBlCgaRAwBAsvBUIGAKGgCEYmCsQmSGoiMzwaQykB0P4 nf+tmWdkBo1ag7ZNALQC4QrD7XZzk9dz7B6Tzo+MSn9z47rBcL/fJ/jxumDsx+PhNbz5uJuG 4XQ6mQuiNTiCD1qfS+03C8Pz+ZyOx6OVDovtAAzgy9YvrmaQvnV4bxMlg1JDGkGv1+t0uVxm XXIfeoyLB4S6AXyxurCGnF1qjvnTAPTX2uTGrN3/UzNg53oFBLICrhdKwrRMjusLPlhlKK12 OOgp8LnPWpvcfLn7LjDs9/s/6bkGXS8gQRjYIsAXi0sTOOocQLIYemrxgtGDxCXCWQSMsrHb 7UxML5kThoLaImrxMXE8MzJ7tKSoNR8UfUbhlRmo1IzFx3VMyVcpDFTVX4JAatMqPgGD0fc9 ksCV2tT6Yhhx4ClYpQDPbFHGuRWjJZET6r9khlrguO2gd70wqxmSQ9YDY3vUPi0poLQQStp7 1AxY55bFIpmjtA15HM2tVGqPw3thXnvg9lDBU0fBkpSYslattqn5R/kiFYtrl/vPrXScfam+ 2Fdu/Nb7qncT3N5WEgD3o84ZOPGwWLlQGEQqw1HwWJ4ztAZihP4uMMCp3/l8rs6/tFJqgc7v UasKQ2l9AjlCQFt8cIEBDnsOh4Pab00GoIpgDAMA+QnvJtRCFjp0gYFK6Tg4S94YtsBAbRNc drISeSt2VDDkxVup+qdEp1I2FJCal0RczUAVsFRBmv7n/T3FiICoYbCchOfXRhoQLefc21bp iUYyrisMEgejjVyB0jYqtRAwSJXaQLvWc4mAYQNB1rq4FIqAQav0wO1jmxg4OGu71lI8vp4U 13Y4xhtXgYBh3Nis7lnAsLrk4w4YMIwbm9U9+wFzNc8vzerBMwAAAABJRU5ErkJggg==</item> <item item-id="38">iVBORw0KGgoAAAANSUhEUgAAALIAAAAvCAYAAABHcpD1AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA A85JREFUeF7tm4ttQjEMRdsR2IkVWIEVWIEVWIEVWIEVWIEVaF01VWqcj/MhTrhPQmof+TjX J4kTt5+P7+cDDxSYXQECGQ8UmF2Bj9kHAPuhwE9UARmgwAoKAOQVvNhwDN+hMp2Z/j4Nm+7a FEDuKu9cjRPA/JHeWRwVQLbolQE2xYCdAWaAPAAai10CZItegU1qBQCyWjJUsKgAQLboFdik VgAgqyVDBasK4NbCqmdgl1qB1D2yg92Vow5CE8B/z8u3vgnBrYXa1e9RIQZnCmYOsFPMr9da xbcA+XK5tNYtu72RfWcbGSgoZfhSkPrQ+qswh7nWNl5/eZCv1+uDPqMe6vt2u43qvnm/OSCH yoTCkBZGLg/y4XBooVNVGxZsqBqAV7kGZIQWhV643++P/X5fWLtdNQKZbJn94aFG7ADHD43D DnutT5UjnHg+nx+n0+mp69TJvLWtFCeTLXj6KRAMLVYAmVZjHh+PuCslGyzsDP0wGt+yCHLP WOaVQ95ut/+29FHZKworyBY8/RRYGmQO7iiQyX2bzSboxRV2v36I5rX8BLLG+XldjCulGUtv mHLajx2Qxqk4R88iyDWHoRyH1UqT28dsIGt1CYH/bu9Jt38ghwDJBUfriN7lZwMZK3I5EUuD LMWlI24tUjFyufvG1ByZdg/1/Qcyv+x2Eknv/VuN2KoXK/ezHfz+t67vDqlOaR90UyClh1Oh U44Nkv3+ePjYYrcWM+14VlP+6hR1Ltixcg4Cf7L4YMRAkmDxQfB/lu6RpUnD34WADPUjvfff rXSPbCHdLtmgBplDGIIyp1wJ7LH++HeUTTsej9H9t9QGaeLxiel+XyWzZznlPxRkiTDNqpea LCT8brdTB5ItbaDOaTL1+lsLPhFDIaJaBKGC5ZR/Mci5zs4tJ61m2roS2CVbobbfVGiR2hVq IYudIWrb9utbTvkXgxw77PgxbuhQl1ufx8v8oJaKmemwp/mDndAKl7I3VO8Vh6NXgWw55V8F cmq2WzmNj7wu0kyilJ6h718Fsqaf3r7nV6tvAXIpIDPV47tgD9stgfxki3bAuYeJ3HLa/lFe VgAgg4wlFOgBcmoFjoUPvUOL6hV5Ca8vOojW8PD2LKf8i2LkRTmYflipFdK/4Yndwvjf+W1a TvkD5OnxTQ8gFiJovrOc8gfIaQ6mL6GBVUruOAEsp/wB8vSYpgfQCmTLKX+AnOZg+hK5IEvZ SRcvOxGspvwB8vSY5g0gdq/P0/yuRX44pPdWU/4AOY8DlPIUsJjyB8hAdAkFAPISbsQgADIY WEKBL7m9q1OJKy56AAAAAElFTkSuQmCC</item> <item item-id="39">iVBORw0KGgoAAAANSUhEUgAAANwAAAAsCAYAAAAOyNaYAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA BONJREFUeF7tXIFxciEM7j9CV3AWV+gKruAKruAKruAKrtAVXME2vZ8TYwJBCATMu+td+4SQ fOR7CXmp/26/14dfjoAj0AcBIJxfjoAj0AeBjz7L+CqOQH8Ezudz/0X/r8it7YQbtiVtF/7N h+Bo4D/WMWi77S5tFAJANr/uCOz3++FwUDr4Lg3fljYKOOHuOF6v19tut2sDbIUUIBzoEl9O uApALU3NES73ebBFOs6S7ViX0+l0Ox6PTyrilFvbBjjHgS5OOG2kB8hPESU4mkStFQgH0e1y uTw6OpFya9sKOuBI6xFO4oUTjOGcJ9yXOFfJWMuQbLfbh1Qu9zDSsgXSSdDFI5wWwgPlOuHu 4GMsRhEONPr8/HTCDeSFytI5ssGiuQhX4qQqRjQUWmJLDpdatZ50qRXo88cjQDlNyum48TVF BW3HlTw0wk444cb75NIaSAkkiYQP541J3+054ZZ29/HGSaLLK+cYidzx1j9rgM9NXHTsYZ+f 4Sx6SKVOEsdJRTfqtUGcXuJUjRsvGRecn4tCsWxJWkzpApXB7+/vJ1RzKXNYL6UDpT8eH+vt VcpK57Y4XUK4Wr2lBEyNw5EGO3jK4Smnju2Of6few+VSZaw3RzpuzVj3sNZbvYez2Cle6/Tc /B6Eo9KynPNRRQwuykhkcWOwbtDdcTgcknC/8mCI7clFeRj7Np0m8GTBnQZazk7JhbWplEZL h9kIR+HQknDwwvnr66sY7pY6wOJA+rfopbTaKV7sAcIJPQkndUrpOC4Kltynou8rPtBC51gG FWWXa+2y3Cku5E/xsJ6ESxUN8DmGSruk86nzHU5HuTMgrAEZBm4cTgGbO8NhMuZ04bKsJOF6 bWSxhyUmWO4Ub2lnLKvXPknXkY7TwiPIHXmO58jOEo57OmmDVCvfcqd4rW3c/F4OLl1HOk4L D8tyl4twljvFtRyhh4NTVT2u+DHrw1prfx6yEbxIDFaPjWxtJNY5ZYO2fVTHA1UMqMFA24Ya 3XzuMwIPEY46GM4GmiXCSchAHb4l81oTd7Z9nlVflnChmiQxrMRBJPK4VEUydzbCSWxKjaGw 50js98d+q9kfpx7yS9Qd3oNItQ5HpcQpm6SftdBLgp9HuBZIzyMjm1JKnMaSuZY7xTVwmm1/ NDCYSeZTlZIrmoSNpSpQ+OwX0tFUukNFmlTBRrqG5U5xjejqhJuJbiil5FSnSsIUAVPj8JkQ z+fkYfLm1rDcKa7hGk44DVT1ZIpbu1LFCCr6BJWpyieOktx8yX1MZMud4hrb6ITTQFVPZnfC UaZIiCUlteVOcY1tdMJpoKons4hwUmJIx3FRsOQ+jnDwt9VOcY1tdMJpoKons4hwVDEEn6lS xRXpfOp8F6+Diys4RbXaKa6xjU44DVT1ZBYTLqeKFQew2Cmew+6Vz63g/Yru1Bw4ElAP13Bv 5D8Wt7BxWcK1AGcGGasRDl7rQOEr/prwlWwUEY4qxXPFD5zezeC0s+hIYbuSM8I+hAgGpAvf eLWSjSLCzeKQ76Bnqlq7kv1Q+IoJN/KI0BJXJ1xLNDvJiotKnZbsvgzYGKrNm83mBj8rXE64 CXfxXQgX0kuIbquklU64CQlHvV6Z1Iy3U9sJN+mWe3Fqzo1zws25b39ar5JmTbwFxao74Yoh szPBCWdnL6Sa/ADcyc9Rms3h3AAAAABJRU5ErkJggg==</item> <item item-id="40">iVBORw0KGgoAAAANSUhEUgAAALgAAAAlCAYAAADxyyNaAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA AtRJREFUeF7tmoFuwyAMRLv//+itqZaJMsD2nSHEdaRJVYLB93wlQPf1/bweeSWBqAQOg+eV BKISeEQVlrqSwGt1khiSQGQC4Q3+XFoee4zX312vCBpY9iiD+1bdQOzO5j5lRtBgKFmzKcIg Dc5SXxSPFHdRasuGQRikwZeVhxsIKS434n7RCIM0+H51dHs930SaOs00eAcVAkZNfVHDCBpY VAiDnMFZ6ovikeIuSm3ZMAiDjzU4AmtZJRsDtfK9mwaWH8IgvMHL81P0LJUtDBsfQcNVDP4Z vIbJJtaL730bWz/K7GjMXk7W+7P4ov161GUnBm8GR14BCMieicu+zlzqnHZ4Lfdyst5H2M2M 8ajLbgz+DD4yzgxTScbd2eC1yaRcJa0zTWvtW8oV1Sr1a81T254yOGN8SbAEUitwRTspV0nr ihy1Y0i5olqlfrX5WdtRBj8HK9dc2vWyJFgCaRU6q32pY7fXM6KZrctuDFwMfgXIer3e+5Ih 9y16vAyO5DmKsWho7X3KycuyN2IMPoOBi8E/dQZnZzvUhDPjWE2MwWfouuQU5RDCgpwBw9Jn a+berbgWPdKMXT+3apXqjeSqiaHOwdFNZm+dbr2vEejR5sxrtOdoPestozxysvYxWkqhuVvr pd2fWbWN2nd/yUTN65ncTn0lj52qoc9l+FP9Fd84ferrWqa517H2Hin8/6LUwCKYNYIG1sha Bh9ncBZsxt+LQBr8XvXKbI0EQhn8fG2Vpx6j46lWeyM/9+YRNLBQPBmEMXi9IW6ZvL7XOv5j i8PER9DA6D9ivRmEMfgJpzzTtXyu49lCofG982rN/V00oNot9Rqd6789Y5PZKV5jAi2Yq3RF 0MCy82SQM3hRDe3RE1vAUbxncWfmObNvTwZhDC6t3crn9efW2m9mAXt9R9DAcvNmEMbgLNiM j0kgDR6zrqnql0AaPK0QmkAaPHR5U9wP5jQHTlxJx70AAAAASUVORK5CYII=</item> <item item-id="41">iVBORw0KGgoAAAANSUhEUgAAAH4AAAAnCAYAAADJovy3AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA Ar1JREFUeF7tW4tuwzAI3P7/o7e+UlEKNgeYOIkjTVojIOc7wDjrfv9u18+6rsfAXfh1PRm4 qV9OxR7PfKy1fKWTPnAvAXZLuEl1KIW1p+jbQqsxXL7iEcIl2/u97Ydmq3a/ldEIlmhlLOGN +7omLhdbat1WQa12UdEvv8ejRHN77XPPLrPqPZ1lCW+sdm0fnkF473B46VZ/hopfwoMbHyq6 Ze/eYkZavUdI11pAvk5j7iKLbQ0jWv0SfnCKLeEHEzxreFR49LzunbZXxQ/OGFT4wXA+wqPY tBdLzWNj5YJmehZKbiV2BBvtLEiX+TrO8UCVC658FkJuJS5Pq/fg+xDe0zI8D53BZwn/UqFF xMwkeZNo5jVVYHtX/FGF95Lk9fMmGuJXge3wwtP36NKgoxFeQS4iNrWtwJYqfAVgL5ncb2as 0htBbXr33H8MkLRiItWxAYgIExEDPdZEnhVZo8W3AlvqVB8FHPW3kGpJdCTOCNsKHuBz/AZK qnCtRUn7F/enFcvtW93ES5LXb4TQe2xD6t/jJWIkcahd73fEn7/IyBYqO15mQlRga34RoydU T5ws/0xSV6t/MgB/A0dq5xKZverv+UjtPjMBrFVl7XxbEWhbIDL8WrFJfFh9XcJbRLXYaB2D zhGjKtRCkHWO4eugn1uFEjlFRYvAJTzNbipMa2CjbZ8KS31a/tGFegeonnBSkp5a+GwhquNZ Kr5VybwTaQnSSxykXfOC6eGT7N+4UcKthKFx97C3rKUnXHbFa5j4oMw7Je/C3P5rW0UIl4Ih /rPZHkl4bR6y3g8JP5twUTxL+CiDB/bviV/Z6hEsllNTCzs81R9YYxF6i2xta8u6j5w0ens8 PzVp9u+Z5GxCetbTqzRPTNSnGsPlK54fy1DBMuyrRX8MehnAzxJjFwHA/9jN4noJn8XkweL8 A3dPmjHPctWSAAAAAElFTkSuQmCC</item> <item item-id="42">iVBORw0KGgoAAAANSUhEUgAAAF8AAAAnCAYAAABpPZRZAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA AjNJREFUaEPtmdtuxCAMRNv//+h2N1VWhPoyY7DIxSv1CWPgeGxw+v3z+n3Vbw2BN/z6/RF4 RQBGwdhqTvHV4G1d0zACMzKnpVPwScX30hoJwOPhW/DeY/uflc/RABR8pc73QL0gRYrto+Ez ivVsvXEpOAUfkCwCFrH5d18Aa9/WBAGG1Hz2mboDfazyEfCHZyHQA9A+bytr52A0qII/TyoF fx5L2pMHv33je7afGg5kR3W4g12t+q2m4GNJgKoZ88Z/mNteSG3K9KmWsUHmMJm2GWdjuuID fO2tmrHJTKio74xzsT4P73xtMusUBbDSLuNMrM/Lw2cPHH2ZIEKxys4+1nbMl4cfvbOiQbOC 0Po8QBY+TW/jSAuNbhS1Q1SUbZOxV0T5B7HMgo9+gEKVw8KXXmoW4EvAZzbJ2EpwR+czActY K6x8VDnSxaFdYlI2aPPb9ftstLIqCjE6D81ci+dnjFVLD6K/ZKRA9MCROX3fMRvWbH9an2QG i4EvLaCB3G2tYHmAZ9wj2vluDX+mujOCgMDXSuGsgNL/yRopO2jmtGUqqyny4FuX51L4bUmR mhyv8dHuACmwGapH6vOp4bN3xdnsPeW3AUJskYD2DEJl52wgo/tBoCI2EfDbHGbj7AXE+F5h 64GV7p5Z9Z6GvwJQ5poWfLbme4GUzkEpPxPEKt8aNAZ+BPzjle/VarTMFvyB1InC84Lnbenx ZWekiRsJWpWdTpoMTMZWfSF5qVHjeQR+Acx1DN4pejQOAAAAAElFTkSuQmCC</item> <item item-id="43" content-encoding="gzip">H4sIAAAAAAAA/2xUy1ITQRS9PZPMi4SECEYy0SgvoyKElvBaIUVRLlAK/ABqSEYSi5AYBotl yg/wF9y48AMs95Y7y3LrP/gLLuJ9DOFRdOpO3z597rl3bk8nBQAK7TWax76JTyd8txceNtvH SaCxiObW2zXBEoytEq1V2zl4G9YiRmCP4w18WieNzkb7TOBtNBuBvQHxJdoLtC+Y8Q9m/zt0 sabRiIsywDDZyQ2SP4+ibvPgNAoVM9fQMnB5JNYmrqyTLJCIumHoM6DR0h12LT3+HaCXtPUn nNctR2bbldnxZHaHtIFJ3mN1lkpqWII5bMkSQIrxb+f4HGKLsEL6HNhzhzVVt5GmKtn9kObE We39wH1nRNv/MEE2p3O/cR65xYL1HJ6FW0e9DjTRQmjhfAwwqj9i4b0cjDHv8yi2SO2S4G0B RgBMg6mQZ+le9o5I5vEEnU2WPIIAGRGcAIxLWP4iDKAgtdu+vHyhKLN/V5pQpJyqAnBPXn6w hpIE+vflpUsIPZCXLrHwhCwKvJgUsjUl6pPTMk/NaOsnBk8/ZPVohtUXAMo6+Qv5M48EL2PF ylgAzPuYka9lZtKXqp5IZdN47MokDlU3Kwmnnkp1swjNSUGzXNC8HMlkRY5kfkH6VolbrKWd lWcCa4Lxw16UBuIyYcaHBFAV7vyScKvU+t3rrV8WUnVFSMux4KoILl8RpO+crlWmrvcPu0Gn sf+m3W0Fkc17Y3Q7Gd4MokD4hfjrpyi+0XQJsCFQZJ9WXnDWPNliHbnRtLWjYq6MPv5AFW9G t29Az8XtqBvUQr1p8pYfE0sqC8pXffKUIrRfdJUylG9icEmZyk+gTEklkAV9dJLKN3rruGUR h+Js5qDjEIfILnI43EMOhw8hh8NTyOHwNHEoapg56GSIQ2QpOPPqtBV2m7XgSFricOH0p1TH U2hyD+mPxov7eW2kLi/+AwAA//8DAHhRAu5UBQAA</item> <item item-id="44">iVBORw0KGgoAAAANSUhEUgAAAY8AAAFCCAYAAAAJ7FGNAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA KQpJREFUeF7tnYt24zgSQ3f//6N3O0k77SiSgAKpB8U7OXOmp40CKVSxIFK289///fnnP/yD AiiAAiiAAhUFPsyDf1AABVAABVCgosB/KmCwKIACKIACKPB5YoUMKIACKIACKFBVAPOoKgYe BVAABVCAnQc1gAIogAIoUFeAnUddMyJQAAVQYHoFMI/pSwABUAAFUKCuAOZR14wIFEABFJhe Acxj+hJAABRAARSoK4B51DUjAgVQAAWmVwDzKJbAn0/v/4r4+LvXv+8vbv19cUjgKIACKHA7 BTCPQkq2DGJpGB//vzSZNdMpDA0UBVAABW6lAOZRTIcyhdfrClccFjgKoAAK3EoBzKOYDmUK jnm8H2fx539HfmiBFtRAXgPFVtYMxzyKEvYyj+Kwp8P/lPDhY/YYI+GoxLhYhdt7feu1UY46 1bW3FlIP/pSjEudgFSapk7Vj8lbNnfjjO4Qzi4EwmEe/ZKmF5IyUcFRiXKzCJU0B8/iqAKXt UXVSHduZp8IkdYJ5OBVwAwzm0S8JaiE5IyUclRgXq3BJU8A8MI/3NbBbQyvvAnXWTwuGnUdR PcyjKNgOXDVcZ6SEoxLjYhUO83CyuY5R2jrMKUclzsEqTFIn7DycCrgYU/08xx7+4kuRw6si lwQGoMcYCUclxsUqXNIU2Hmw82DnYTSSmSCfpsIPCqAACvRSgGOrOSxkhDtKdSfdI1M9xkg4 KjEuVuHYeeQVo7R1mFOOSpyDVZikTji2cirgIRjM49rjCLWA3aOCCi5pCiPUyWfjOvht3T34 U45KnINVmKROMI+HGINzGSM0BVXkznUqTI8xEo5KjItVuKQpjFAnmMe/Klc14GiV1AnmoTrN g14foSk4C6E1JT3GSDgqMS5W4ZKmMEKdOA1x1DqpXpuqAYcvqRPMo7XCBorngfnHEuEHBVCg mwI8MB/IARqmOsIdpXMX1SDBZ2iPMRKOSoyLVbjkjnKEOumVx71aUto6dZhyVOIcrMIkdcLO w6mAh2BGaAqqyHukoscYCUclxsUqXNIURqgTzINnHj16ARymAiM0BdUMzUvdhfUYI+GoxLhY hcM88opR2jrMKUclzsEqTFIn7DycCngIBvP4SqRaSE66E45KjItVuKQpjFAnvfLIsZVeE7s1 xDMPp12MjxmhKahm2CMLPcZIOCoxLlbhMI+8YpS2DnPKUYlzsAqT1Ak7D6cCHoLBPPRdlptq tRjXeCoxLlbhkqYwQp2w8+CZh7tWwXVQgLfqfrRSflAABbopwLFVh848AMUId5TqTrqHzD3G SDgqMS5W4dh55BWjtHWYU45KnINVmKROOLZyKuAhGMyDY6v3Ut5qGCPUCcdWHFs9pC2PcRkj NAV1h9RD6R5jJByVGBercMkd5Qh1gnlgHj16ARymAiM0BdUMzUvdhfUYI+GoxLhYhcM88opR 2jrMKUclzsEqTFInHFs5FfAQDObBsRXHVv5iVg3XYUo5KnEOVmEwDyebE2MwD8wD8/AbgGq4 DlPKUYlzsAqDeTjZnBjDW3U/lgg/KIAC3RTgrbpzOAo7D3Ye7Dz8ta7u1h2mlKMS52AVhp2H k82JMZgH5oF5+A1ANVyHKeWoxDlYhcE8nGxOjME8MA/Mw28AquE6TClHJc7BKgzm4WRzYgzm gXlgHn4DUA3XYUo5KnEOVmEwDyebE2MwD8wD8/AbgGq4DlPKUYlzsAqDeTjZnBiDeWAemIff AFTDdZhSjkqcg1UYzMPJ5sQYzAPzwDz8BqAarsOUclTiHKzCYB5ONifGYB6YB+bhNwDVcB2m lKMS52AVBvNwsjkxhg8JfiwRflAABbopwIcE53AUdh7sPNh5+Gtd3a07TClHJc7BKgw7Dyeb E2MwD8wD8/AbgGq4DlPKUYlzsAqDeTjZnBiDeWAemIffAFTDdZhSjkqcg1UYzMPJ5sQYzAPz wDz8BqAarsOUclTiHKzCYB5ONifGYB6YB+bhNwDVcB2mlKMS52AVBvNwsjkxBvPAPDAPvwGo huswpRyVOAerMJiHk82JMZgH5oF5+A1ANVyHKeWoxDlYhcE8nGxOjME8MA/Mw28AquE6TClH Jc7BKgzm4WRzYgzmgXlgHn4DUA3XYUo5KnEOVmEwDyebE2MwD8wD8/AbgGq4DlPKUYlzsAqD eTjZnBjD15N8LBF+UAAFuinA15PM4SjsPNh5sPPw17q6W3eYUo5KnINVGHYeTjYnxmAemAfm 4TcA1XAdppSjEudgFQbzcLI5MQbzwDwwD78BqIbrMKUclTgHqzCYh5PNiTGYB+aBefgNQDVc hynlqMQ5WIXBPJxsTozBPDAPzMNvAKrhOkwpRyXOwSoM5uFkc2IM5oF5YB5+A1AN12FKOSpx DlZhMA8nmxNjMA/MA/PwG4BquA5TylGJc7AKg3k42ZwYg3lgHpiH3wBUw3WYUo5KnINVGMzD yebEGMwD88A8/AagGq7DlHJU4hyswmAeTjYnxmAemAfm4TcA1XAdppSjEudgFQbzcLI5MQbz wDwwD78BqIbrMKUclTgHqzCYh5PNiTGYB+aBefgNQDVchynlqMQ5WIXBPJxsTozBPDAPzMNv AKrhOkwpRyXOwSoM5uFkc2IM36r7sUT4QQEU6KYA36o7h6Ow82Dnwc7DX+vqbt1hSjkqcQ5W Ydh5ONmcGIN5YB6Yh98AVMN1mFKOSpyDVRjMw8nmxBjMA/PAPPwGoBquw5RyVOIcrMJgHk42 J8ZgHpgH5uE3ANVwHaaUoxLnYBUG83CyOTEG88A8MA+/AaiG6zClHJU4B6swmIeTzYkxmAfm gXn4DUA1XIcp5ajEOViFKZvHx7us/vx7RU/5MzL/nK3AFYmuXqMq8irfGr7HGAlHJcbFKly5 KfwRbIQ6+ciruvbWWunBn3JU4hyswth18tc0PozjMwe8Vbe1zMaIvyLRVWVUkVf5MI/t+7Qt rUeoE8zjX2U7a0ZhpHm8TGOxoK6oldLO4/PDbRc4XEujWpvv6zrWrueM6ztjjBbNzmgIvcZQ i7HVtFx+hZNNYWWiI9RJrzzu1avS1qn1lKMS52AVZvX1xS5jtaYv6Msl87hqe+QUhyvolpm8 xx+9aI/mT/X6ocGfw4ij/1ELyRk/4ajEuFiFwzycbK5jlLYOc8pRiXOwCvP9+oph7NYQ5uGU gY9ZNum9pv3+2tHN/Wh+X6FtpCryu4yRzLMS42IVDvPIK0Zp6zCnHJU4BysxG8dSaod3RU+x bi/fj3eumKRTHEuMs8PY220ceZ1HcidatR7tpGPKhWQQJxyVGBercJiHkcwNiNLWYU45KnEO dhXztstI6uSqEyFpHss78hEa35aY7s7j6GSMoKGzEJxFu4fpMUbCUYlxsQqXNIUR6kTdEbfW SC9+lZ+teVbiHOwPTPVoauco+YpaKZlHpakefTGKn51H27J1FkLbCH3e4pnMsxLjYhUO88ir RWnrMKcclTgH+4lJj6ZmMQ8noUdiMI82dZ2F0DYC5vHSb0trdYPUqn+v+KNrpQd/ylGJ28Ua 75hSu6zdG5A7PjCvPHTuVYw9eLYWnmsqRy7cI7l7aKeK+E5jVBa3atZr1+XyKxw7j7xqlLYO c8pRiVvFLnYZii+pk8qJkKOVi5HHVq+JvR6aL//7/vr34nz7PMjyYfuaGW09V9l6B9QWp2t0 a9fwLtjRzf1ofjf5ezhV5HcZI5lnJcbFKlzSFEaokzNuNJS2Ti2mHJW4b+zOLkPxJXVya/PY Ss57E19ewN6D9tdrLeZQNZzVu8qNrd7Ri/ZofmcxKYwqchXvvN5jjISjEuNiFS5pCiPUCebx Vuk7zzLcXW9SJ8Oax9Yd+5YxvF/o1g5gz1Tedzfqz46o73Nw8E5TVJgRmoJqhuoandd7jJFw VGJcrMIlTWGEOpnePMy32WIei46wd0zkmofaEbQYidPArsB8GhY/KIACwyrwesfUbdbxHR+Y 756LL77ryjmGWu481O7hqeZxhWlVxlR30hWuLWyPMRKOSoyLVTh2HnnFKG0d5pTjV1z4Nlt2 Hhs7j7VnH8uH2i/TcB+Yvz8XWf55edy19QDdKaorMCMcR6SLraJnjzESjkqMi1U4zKNSGT+x SluHOeX4jOvwNlvMQxxbbd5hXrClcgrqKgzm8aV8uqDf85ZwVGJcrMJhHvlqU9o6zBGH8QC8 WotqHkmdvJ/mOFr0wlhv1W01hRGaZS9BHZ4R9FBF7lynwvQYI+GoxLhYhUuawgh10usmYK9W lLaqzkpzXP6SpcK3SzvzVJikToYzj+W7lPYMZnms5ST7yZgRmoIq8h756TFGwlGJcbEKlzSF Eeqk1JjDolHaOrSSY+uXLGEem/I27TycpIH5rcAITUEutg6J7TFGwlGJcbEKh3nkBaO0dZhX OYxnGZWxHazCJHUy3M7DSRiYdQUwjy9d1EJy6ifhqMS4WIVLmsIIddIrj6cdWxmG8T4Xldcq VvEldXI789h6R9Osf+80MhczQlNQRe5e62lNoTChyrW5WIVLmsIIdTKKeex9k22vGlU14GiV 1MntzKOwFoEWFRihKTgLoXjZv+A9xkg4KjEuVuGSpjBCnTgN8bI6KX76e22eKq/sPFqzS3xJ gRGaQmXRlC7+DdxjjISjEuNiFQ7zSKskPN5cfDxA5WdrdpU4B6swSZ2w88hra7hIzOMrZWoh OYlNOCoxLlbhkqYwQp30ymPz0dH7s4yVz5Wp/GAezmr7ieHdVnXNmiNGaArpYquI02OMhKMS 42IVDvOoVMaySe20KfODfCo/mEc9P5hHXbPmCMyDnYdzVj5CnVyy8yi+Y6pljhXTcbAKk9xk cGz1fhb+8K8zGaEpqCJvdlCOrb4l3NJ6hDppacxuDX3rY+4y1njTeq7EOViFwTzcqpgUN0JT UEXeI3U9xkg4KjEuVuGSpjBCnZxhHunbbJ3dnapjldfqGIovqRN2HiqLD3p9hKagirxHOnqM kXBUYlyswiVNYYQ6OdQ8/u40lLZOLaYclTgHqzBJnWAefytg6+vVR1lIViEPcCynity5ToXp MUbCUYlxsQqXNIVRal5du6qDH6+vPM/owZ9yVOIcrMIkdYJ5bDzzWJpJqRBvCh6hKagi7yFt jzESjkqMi1W4pCmMUCfNOw/xFttm/tdNaeELDqtHUS+8qgHnWpI6wTx2zOM7OQPcsTtNdYSm 4CwE51r3MD3GSDgqMS5W4ZKmMEKdOA1xtQYKD7+Vtk4dphyVOAerMEmdYB6Yh7MGTsOoIu8x kR5jJByVGBercElTeJx5BG+xjc1pUaAqP1v1XIlzsAqT1AnmgXn06MfdOFSR9xioxxgJRyXG xSpc0hQeYx6FXcZaXSltnVpMOSpxDlZhkjrBPF5nk38Kbe2be18CjbKgdo9rBjh+U0XuLFiF 6TFGwlGJcbEKlzSFUWp989oaTaPyLOGoWlN5fR/XwSpMUieYh8r+g14foSmoIu+Rjh5jJByV GBercElTGKFOVo+VOpkG5vFzle3W0AU3pHw9SY8uWOQYoSmoZli85FV4jzESjkqMi1W4Kcyj s2lgHphHjz7zKA7M4yudquE6SU84KjEuVuEeax7hQ3Ant5gH5lGpkymwmAfm4ZyV37ZOFoah jLN1UffgTzkqcQ5WYZKbDJ55tFbYQPG3bQpvGqoi7yF3jzESjkqMi1W4pCncqk52dhjq2ltr pQd/ylGJc7AKk9QJ5tFaYQPF36opbOimiryH3D3GSDgqMS5W4ZKmcIs6MZ5jqGtvrZUe/ClH Jc7BKkxSJ5hHa4UNFH+LpiD0UkXeQ+4eYyQclRgXq3BJU7i0TgzT6PlMYq+elLZOLaYclTgH qzBJndzHPJbfNXPE/zvZHgyzttDfP6/y44z7grfVVeVURV7lW8P3GCPhqMS4WIVLmsLp5hE+ /FbX3lorPfhTjkqcg1WYpE7uYx6tmZ4w/v3LG7/vxhYG8d4ITm8KQU5UkQeUv0J6jJFwVGJc rMIlTeG0OinsMo66CWDn8aVAUieYR49udCHHcqHv/f9pTaFBD9UMG6i/Q3uMkXBUYlyswiVN 4dA6CXcZmMd25asaUOagXt+toQtOM/iQYI8u+HHHsLPTWN4ZrH39Cn/3Z2lsfDUNf99Hm+/f yIfOj6y1Tq3MpsE8bKn2gVXz6DTsYTTOXVTr4D3GSDgqMS5W4S7deTQeS6k8q2tX8er1Hvwp RyXOwSpMUiccW6kKuvnrmEc9QWohOYwJRyXGxSpc0hSajq06HkupPKhrV/Hq9R78KUclzsEq TFInmIeqoJu/jnnUE6QWksOYcFRiXKzCJU0hMo+Ddxk88+CZx0sBjq2cDmVgMA9DpAVENVyH MeGoxLhYhTvUPE7cZWAemAfm4XQmE7P3eQ7nbbzmMKfCVDPsMZkeYyQclRgXq3CHmMcFuwzM A/OwzWPrnS49msesHJ+a8oMCoQKvd01RQyjwrcBd36q79enpWZt/63VHZ9mtgxbj1Z10kW4V 3mOMhKMS42IVrsvO4yY7jWUy1bW31koP/pSjEudgFSapkw99r+gp1jOPrYldMeHWQrxD/Ai6 qSLvoWOPMRKOSoyLVbikKfx4lHHBnaWbY3XtLs8Wrgd/ylGJc7AKk9QJ5tFaYQPFYx5fyVIL yUlpwlGJcbEKV20Ka18p5+hxBUZde+ucevCnHJU4B6sw1Tr5fv5wwc0FO4/Wyg7iMQ/M471s vhvGX8fY+y7SoNwOD1ENsXUCPfhTjkqcg1UYzOOtWtbebdRaTKPH88D8Y4nw89ko3pziW5Gf f/0OQTUUWFfgqTuP9E47jbu7uYxwXeoOqYfGPcZIOCoxLlbhfr3+9vB7+Ro7j5/VpbR1ajHl qMQ5WIWZYudRaYAV7I/t/AVu6hRiKybVo3XcSrwq8grXFrbHGAlHJcbFKtz36yvvmFqL5ZnH v6pR2jq1mHJU4hyswjzKPJzPebwfTakPxS1fX8a+PwDawo7QfPcKeoT5qyJ3FqzC9Bgj4ajE uNhdnPgE+FbsxR8cV+n7ft3VyCZcAHvwpxyVOAerMI8yDzfha43+3Qg+/vzeNJd/fv3/HmbJ 587tbjjM4ysjaiE5eUs4KjEu9hdu0fmTpjBCnfTK4+7N1sezocZ/3Dwuh6nEOViFSepk2Vsb pbLD27Pyd6hloSujqJrEkx68j9AUVJHbFbYD7DFGwlGJcbHfuI0P8iVNYYQ6wTz+FbhTKwqT 1MkU5pEYxq87gb+/yKZH87qKY4SmoIq8h3Y9xkg4KjEW1jhfSprCCHWCeWAezb2g9dhq7UjK MZvmiV9A8KkVP8MrwHdMUcW3UeCCNxd1P7ZSD7m3HsC/P/PY+/Mod2O7Z7gXJLrqkdYdd5V0 ge8xRsJRiVnFmu+Yer9cdh55sVTytTVKylGJc7AKk9TJ446t8lJ5fuQIBqiKvEeWeoyRcFRi fmB3vphQcSZNYYQ64diKY6vmXjBKoTdfaAeCEbRSzbCDDEO82+rHx7sbHv5jHnnF9KjFlKMS 52AVJqmToXce70dReYnME4l5fOVaLSSnIhIOGbP4ypAe80iawgh10iuPu8e8vFVXrpcraqXb Mw9ngYH52zR55iEXg1sr0ghWiDZjgmcZL3o1D8zDzehvnNLWYU45KnEOVmGSOhl65+EkD8zb 2SjmcR/zCD/9vaznI5rCFXeTyTpV155wvsf04E85KnEOVmEeYR5774q602uthXlFPG/V/Vgi 1/2sfpPthfO5TglGfowCF9yQcmx1gXuMcEep7pB6yNZjjBLH6/dlFM7QXX6FS+4oR6gTnnm8 nSgYdXVEnXBs1aMbDcIxQlNQRd5D6h5jWByLZxlWzN8LdLEKh3nkFaO0dZhTjkqcg1WYpE4w D6cCHoLBPL4SqRaSk+5Njp1nGZVxXazCJU1hhDrplce9XCttm+pEBFfGdrAKk9QJ5uFUwEMw IzQFVeQ9UtFjjF8cOx/ke825Mq6LVbikKYxQJ5gHx1Y9egEcpgIjNAXVDM1L3YX1GOObwzAN zKNH1n5z9MgjOw+9G9+9AXniA/MRGuUxS2qbdQRNjm4IXe5YjW+zXctC5dpcrMKx88hXmdLW YU45KnEOVmGSOuHYyqmAh2AwD32Xtb9l+fMmwb93WmoxYh7HL5okB5VZ9eBPOSpxDlZhMI9K ZUyIxTwC89jYZajFiHkcv8CSHFRm1YM/5ajEOViFwTz+Vsb717Mv/1wpnqdh+ZDgxxLxfvid GZ5OoCZX4OnPPDCQv3fcFyS6asDqDqnKV94BmA/Ak3lWYlyswiV3lCPsULs8uxLFpLR1ajHl qMQ5WIVJ6uSxzzzeF8DWn53kPwkzQlNQRd4jH7/GCB6AJ/OsxLhYhUuawgh1gnn8WwmqBhyt kjrBPHp0o0E4RmgKzkJolft7DHOXUd69bEywcm0uVuGSpjBCnTgNsVudNBCp/GxRV+IcrMIk dYJ5NBTGaKEjNAVV5M2aB7sMzKNZ9UMIjq6VHvwpRyXOwSoM5vG3RLe+fffllCM00SNW2wjX rYo81uVtl9FjjISjEuNiFS5pCiPUCTsPjq3iXkBgXYERmoJqhqWr7vg22+W4yTwrMS5W4TCP UsX8ACttHeaUoxLnYBUmqZPHHls5iZ0NM415iGcZaiE5dZFwVGJcrMIlTWGEOmHnwc7DWadg OikwQlNQzXBXCvMBeNMYr6NR43cotOxW3DkqHOaRLx6lrcOcclTiHKzCJHXCzsOpgIdgHmke wQNwtZCcdCcclRgXq3BJUxihTth5sPP4tU6dXtAL4zSJJ2FGaAqqGX7nw9xlrOXPHmMn+QlH JcbFKhzmka9gpa3DnHJU4hyswiR1cqudx7spbPWGXhgn8U/DjP71JPwO8I8lzg8K3EiBC761 4tfvMF8zhaWB9MI8zRTc6xl259Gwy2Dn8Wup/du8bTy3GaFOOLbi2OrXKcTezgLzcG1iHTdC U/ixfe5sGi9V1BbeUTnhqMS4WIVLjiNGqBPMA/PAPJxO1QkzRFNwHmg16qEarkOfcFRiXKzC YR5ONjdutoJ31C2ZVH62ZleJc7AKk9QJzzzy2hou8tbm8dc0VJH3EL3HGAlHJcbFKlzSFG5d J28FoK69tVZ68KcclTgHqzBJndzKPL4m8+/fTVfuhGktrtHib9cUVpKtiryH5j3GSDgqMS5W 4ZKmcLs62Ui6uvbWWunBn3JU4hyswiR1cjvzaE048dsK3KYp7DzLUEXeI789xkg4KjEuVuGS pnCbOhHJVtfeWis9+FOOSpyDVZikTjCP1gobKP7SpuBsKT92nh3OmVVKeoyRcFRiXKzCJU3h 0jpRyePY6pdCqgY+m7xYV0mdYB6FYh0deklTKL5jShV5jxz0GCPhqMS4WIVLmsIldRIkVl17 QPkjpAd/ylGJc7AKk9QJ5tFaYQPFn9YUzF3GmnSqyHvI3WOMhKMS42IVLmkKp9VJYzLVtTfS y7t1hz+dYyXOwSpMUieYh1MBD8Ec3hSKuwzMY7uw1GJ/RSpc0hQOr5NO60lde+swPfhTjkqc g1WYpE4wj9YKGyi+Q2//fbUNuwzMA/NoWT6qIbZwfzbGDs/fUo5KnINVGMyjtVoeHt+1zx/i RH0WrEqjWkgqPm0slXFdrMIlTYGdx1cFKG2PqpPq2M48FSapE3YeTgU8BLP8epfosg4yDfcY JprzIkgtJGeMhKMS42IVLmkKmAfm8b4GdmvoDl+M6CxYMG0K/PpuMPMbWvk224/lww8KoMAv BTCPtqY8SnR553HwLoNnHjzzaFk7atfVwl09OtoaK51jJc7BKkyyQ+XYqrXCBoq3nnlYoOMu WhV5j5F7jJFwVGJcrMIlTYFjK46tOLbq0WkexLG7kbhgl8HOg51Hy/JSxtnCzc7jn3o882it pAfE/7qjvHiXgXlgHi3LCvPwd0hKq2SHyrFVS/UOFvttHjfZZWAemEfLElINsYWbnQc7j9b6 eVb8jU3jJfTRDeHKplC5NhercMkdJc88/Dt61SBUfrbiK3EOVmGSOmHnobL/oNdHaAqqyHuk o8cYCUclxsUqXNIURqiTXjcBe/WktHVqMeWoxDlYhUnqBPNwKuAhmBGagiryHqnoMUbCUYlx sQqXNIUR6gTz8I6V3B19UieYR49uNAjHCE1BNcMeUvcYI+GoxLhYhUuawgh1gnlgHj16ARym AiM0BdUMzUvdhfUYI+GoxLhYhcM88opR2jrMKUclzsEqTFIn7DycCngIBvP4SqRaSE66E45K jItVuKQpjFAnvfLIMw+9JnZriK8ncdrF+JgRmoJqhj2y0GOMhKMS42IVDvPIK0Zp6zCnHJU4 B6swSZ2w83Aq4CEYzEPfZbmpVotxjacS42IVLmkKI9QJOw+eebhrFVwHBUZoCqoZdpCBY6u/ Im5pPUKdYB6YR49eAIepwAhNAfOo7Y6UXuw8zMWxAlPaOswpRyXOwSpMUiccWzkV8BAM5lFr zHtpV4uRY6vjF02Sg8qsevCnHJU4B6swmEelMibEYh6Yx3vZc2y13wRUw3VaSMpRiXOwCoN5 ONmcGIN5YB6Yh98AVMN1mFKOSpyDVRjMw8nmxBjMA/PAPPwGoBquw5RyVOIcrMJgHk42J8Z8 mAc/KIACKNBNAT4kOIejsPNg58HOw1/r6m7dYUo5KnEOVmHYeTjZnBiDeWAemIffAFTDdZhS jkqcg1UYzMPJ5sQYzAPzwDz8BqAarsOUclTiHKzCYB5ONifGYB6YB+bhNwDVcB2mlKMS52AV BvNwsjkxBvPAPDAPvwGohuswpRyVOAerMJiHk82JMZgH5oF5+A1ANVyHKeWoxDlYhcE8nGxO jME8MA/Mw28AquE6TClHJc7BKgzm4WRzYgzmgXlgHn4DUA3XYUo5KnEOVmEwDyebE2MwD8wD 8/AbgGq4DlPKUYlzsAqDeTjZnBiDeWAemIffAFTDdZhSjkqcg1UYzMPJ5sQYzAPzwDz8BqAa rsOUclTiHKzCYB5ONifGYB6YB+bhNwDVcB2mlKMS52AVBvNwsjkxBvPAPDAPvwGohuswpRyV OAerMJiHk82JMZgH5oF5+A1ANVyHKeWoxDlYhcE8nGxOjME8MA/Mw28AquE6TClHJc7BKgzm 4WRzYgzmgXlgHn4DUA3XYUo5KnEOVmEwDyebE2MwD8wD8/AbgGq4DlPKUYlzsAqDeTjZnBiD eWAemIffAFTDdZhSjkqcg1UYzMPJ5sQYzAPzwDz8BqAarsOUclTiHKzCYB5ONifGYB6YB+bh NwDVcB2mlKMS52AVBvNwsjkxBvPAPDAPvwGohuswpRyVOAerMJiHk82JMZgH5oF5+A1ANVyH KeWoxDlYhcE8nGxOjME8MA/Mw28AquE6TClHJc7BKgzm4WRzYgzmgXlgHn4DUA3XYUo5KnEO VmEwDyebE2MwD8wD8/AbgGq4DlPKUYlzsAqDeTjZnBiDeWAemIffAFTDdZhSjkqcg1UYzMPJ 5sQYzAPzwDz8BqAarsOUclTiHKzCYB5ONifGYB6YB+bhNwDVcB2mlKMS52AVBvNwsjkxBvPA PDAPvwGohuswpRyVOAerMJiHk81JMGtGgXlgHpiH3wBUw3WYUo5KnINVGMzDyeYEmA+TwDy2 E60WklMiCUclxsUqXNIURrjJ+MiRunYnj3uYHvwpRyXOwSpMUiefOfjTa87+5/wRz77Ci8fD PDAP1WC3GsYVDSFZLqohJpzOzqzCm86xEudgFQbzqGT14dgt83jtSvjv1+6Mf9GAGmirgbNb KTuPgxUf5e7xYBmgRwEUeJgCmMfBCcU8DhYYehRAgUsUwDwOlh3zOFhg6FEABS5RAPM4WHbM 42CBoUcBFLhEAczjQNnfHwAeOAzUKIACKHC6ApjH6ZIzIAqgAAqMrwDmMX4OuQIUQAEUOF0B zON0yRkQBVAABcZXAPMYP4dcAQp8KsAzNgqhqkDLG3owj6ra4FFgAAVamsIAl8cUOyiw9d17 LjXm4SoFDgUGUQDjGCRRF08T87g4AQyPAndSoLUh3OlamMtxCrxuMFpuNNh5HJcfmFHgMgVa msJlk2bg0xRYfgllMjDmkahWjFHfrPtOx0PPorgDwquNvVI/LzmqYwwo4xRTruaxWitV/h+9 aooMXHiRa8cIy4RtbSFbEnvhJTP0jgLVY6Vq/VT5SdZ9FajmslIrPW4y2HmcUDtbZrFMoMKd MFWGOEGB6k2Bqosq3wmXyBCdFKjm9sxawTw6JXmPxk2owp0wVYY4QQGV59bXT7gEhjhJgdZa UPEtl4F5tKhnxqoEcmxlCvkQ2Na59Mfl7b22ddRQvTt9iIxTXMadawXzOKEEMY8TRB5oiK1m 7/69qqeBpGCqQgG3Jtwbi543GpjHCeWrFjs7jxOScKMh7nw3eSOZmIrYiV69S8U8TihRzOME kQcawq2HK+4mB5JxiqneuVYwj4NL8CP5r3/fh6r+/cHThP4kBbbyvjU8dXJSYm44zN1rBfO4 YdEwJRRAARS4uwKYx90zxPxQAAVQ4IYKYB43TApTQgEUQIG7K4B53D1DzA8FUAAFbqgA5nHD pDAlFEABFLi7ApjH3TPE/FAABVDghgpgHjdMClNCARRAgbsrgHncPUPMDwVQAAVuqADmccOk MCUUQAEUuLsCmMfdM8T8UAAFUOCGCmAeN0wKU0IBFECBuyuAedw9Q8wPBf4o8P49Rz2/Vhtx USBVAPNIlSMOBU5SwPnq7ZOmwjAo8K0A5kExoMCNFdjbZbADuXHiJpga5jFBkrnEcRXAPMbN 3dNnjnk8PcNc39AKYB5Dp+/Rk8c8Hp1eLu4IBc58eN1qHmu/iOwITeCcTwHMY76cc8UNCpz9 8LrVPD4uNX02ksY1yEvoQApgHgMli6leq0CPRp5cQathpSaQxiXXSMx4CmAe4+WMGV+kwFXm 8do9qN9n/o57l+h93stjrPf/X8Opv7soFQx7AwUwjxskgSmMocCV5vFSaGsOa6awjFkawfvr r9f2MGoOY2SRWfZSAPPopSQ8j1fgDuax3IVsNXRlFFWT4MH748u7fIGYR1kyAmZV4C7msab/ cm5r5pAYxnIsTGTW6v993ZgHtYACBQVaH14XhipBW4+t1nYwjtmUJgn4UQpgHo9KJxdzhgLv D67v8o6k92cWew+5l3NfPoRfPkBf8t7les/IM2PsK4B5UCEoECpwp0Z6p7mEchI2mAKYx2AJ Y7r3UmB5537V7DCPq5Sfd1zMY97cc+UPUeAuBvYQObkMUwHMwxQKGAqgAAqgwD8FMA+qAQVQ AAVQoKwA5lGWjICnKbD3DqQjXtvT74jxzuB8Wk1wPVoBzENrBAIFUAAFUGChAOZBSaAACqAA CpQVwDzKkhGAAv0U4Os++mkJ07kKYB7n6s1oKPBLAT6jQVGMqADmMWLWmPNjFMA4HpPK6S4E 85gu5VxwiwKjfSBvtPm25IbYcxXAPM7Vm9EeoMBou4XR5vuAEpniEjCPKdLMRfZUYLRmPNp8 e+YKruMUwDyO0xbmhyowWjMebb4PLZvHXRbm8biUckFHKzBaMx5tvkfnD/4+CmAefXSEZSIF tn7D3l0lGG2+d9WRef1UAPOgIlCgoMDa90QVwk+Hjjbf0wViwFgBzCOWjkAUQAEUmFcBzGPe 3HPlKIACKBArgHnE0hGIAiiAAvMqgHnMm3uuHAVQAAViBTCPWDoCn6pA71+edLROo833aD3g P0cBzOMcnRkFBVAABR6lAObxqHRyMSiAAihwjgKYxzk6MwoKoAAKPEoBzONR6eRizlZgtK/+ GG2+Z+eT8XwFMA9fK5AogAIogAJ/FcA8KAUUWFHg/XeLL//8gi/f5fTx91fd2Y82X4pufAUw j/FzyBUcpIDbkF+m8TKOuxvIXeZ7UNqgPUkBzOMkoRlmPAW2vo127e/v8M21o813vIpgxu8K YB7UAwpsKDBaMx5tvhTe2ApgHmPnj9kfpED6qe3lkdBB0/tFO9p8z9KFcY5TAPM4TluYUQAF UOCxCmAej00tF4YCKIACxymAeRynLcwogAIo8FgF/g/dXTIgclLGnAAAAABJRU5ErkJg gg==</item> <item item-id="45">iVBORw0KGgoAAAANSUhEUgAAAEcAAAAWCAYAAACSYoFNAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA ARdJREFUWEftlwsKhDAMRN37H3rVYksS8q+wtJuCIFq085xM4+d7jaMGT+CGU4MncBQYmUDB Udwx4FxFd2dPO/rgru3sNKoXOQeCeYJ6ZxZIG6ddhEMn/w2lR2hzERTdgawOZnb9gwOFQ3Nn ZcfADPHmJ9T/WlnRIF8Zas/bVwIZ0SY73i8hZZyDdmouc6xrnOCtnWP1OVz/I0GEwc6Fo/au YWngwGj/lQlk6rK2jqjtJYdo8DRXeZ8H7c6dR3V45qfgSF9eWvQMHLY5A128R2R2TgpONHOi cGjAS+/LlE8E1BQcT0nQ3xDOCVZ2Re5HxFtzw3BgYKJtL/DTqjVk1q7nbeYs4Z77KTieB+8w p+AoX7HgFJxckZ/khHayWNxjGQAAAABJRU5ErkJggg==</item> <item item-id="46">iVBORw0KGgoAAAANSUhEUgAAAMQAAAAyCAYAAAADbXRvAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA BHJJREFUeF7tXe1xIjEMvSshPdECLdACLdACLdACLdACLdACiY/ZOa+QtJKN1nb2MZMfCf54 ftazJFsz+fv8+fzBBwyAgRcDSRD4gAEw8GIAgoAlgIGMAQgC5gAGIAjYwNYZSNnC9JNzAQ+x dcvY4Ppp6pz/DkFs0CCw5DkDEAQsAgzkeUN22QoPAdMIZ+B6vYbPIU1gmRseotn2bG/i2+32 TD+tPmnu+/0uTv+WT7QCinm3wcDxeGy+UAkD9y6NkKn5dv1eAI/H43k4HJovMAkiYZldr+Z5 A3II3x5xd9bSPbZv5PVbU9yR67hcLs/z+fy2yHzONaqHUh6RsEwfOj9yiAI71O6uC4Zr2mWt tSTvQPMHNkwJLqlLGKyeCiGT0TRnp0jwBhohFTdbSxC73W4WqmjeINJTpHApYbF8IAgLSz9t pg2L3DgjlOpmawnCM080r19fXybeIAgTTS9BcLUvxu7dNVtD4D0Jwio4CMJoqjUhU01fIzy1 mRa3Ww2lBAcEUcLaIH08m0uv9/LTONIAPVRGeIgljlrlEIkXK+/wEEYrqrkdae0hpCVajcRI 0ZvRcXF7DY9WHFw75BA17JG+XP6Q/40avHZSLnkLba7ppMtzGZrXeHKdpRNbGnuih8ORj5lu driyCYm7fNx8rdIJr+Gj3+GW6YOCsA4leQJNMJr3sI5HDckTInBrswjaMif3DkHDSTo/FTQV 3dK8XCjY5B3CUlVoNSxvu5Zzc/kCd3JxG0kN12qI3KbTE9XLoWaoJaJN46XX4dPppEKRPOKS 4XPeUuKYvlRrgD6SQ/Re0VhjHJ6+UghSalBcP+nE5ITpwR4hiPQgtt/v3TBqPCPnHZMoaS2T BOojgui5otG9GxUdvBvp8RBLbS3fW5fm8VScQPP+Jbbh5VE6OKb1Lnmp2WFgJUlq13NFY+3a SvpLiR6XR3AehQshLOGDFkLUrEPDyImQtk9JdV5Yt4RlKYeQeJT6eaOXmYeQXL62iF4rGpeI x/frMdAyx/OI8Z/A89OnRBAjVjSuZwqYaTQGqj3EiBWNo20S8K7HACsIGgdrcLQEjPYr8UAe KrTXyOi5PTjRtl8G3gShXRFyy+hJEBajz5MvLYHtd8uALJIBNWSyGlgOUOtjGa9msRHjSwLC 3/+Xw/8WLmZJNb1C436Hh6iRK/qOwEC1hxixonGEjQHGNgy8XbtOYQcXX3PJ9igVjRHhVJst w6yRDJhLN6Rke8SKxkhCMfbYDLgEwXmNESsax94yoI9kwCUIDsiIFY2RhI42Nq6e5ztWJAga j49W0Tia0Ubh9bwhRWHobVyzIKZrWC45Ha2isbdN6AUPLh4++F9IR6po7MUAe8PBeQz66NYb 5k/jcXmIT0+O8fphYCl82or3gCD6scmmSCCIF/0QRFMz7GNy6fTf4g0UBNGHTTZDIT24biVE osRDEM1Msf3EXJXqhEr7rj3yOAQQRBy3Q4/MeYgteA0IYmizjQMPQcRxi5EHZWBrbxC4ZRrU UAE7joFviFgnDWblqKQAAAAASUVORK5CYII=</item> <item item-id="47">iVBORw0KGgoAAAANSUhEUgAAAHIAAAAWCAYAAAAcuMgxAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA AlFJREFUaEPtWYuNgzAM7Y3QnViBFbpCV2AFVmAFGKErdIWuQPHpgoLPiZ/zEVyPSJUq6tjP fv6E9Gte1qXAmqbp0jRNAU12FXvatqOttIOIzF2Px2Omz16LbD+fz73MH8LupQSK+/1eQk2W jiNgyHIgc3M2ka/Xa77dbpkw8rcTkYTlv64NkUv3NsdhGIa57/tf+0iX/zErNm4Yx3EmLKUW gl2Tcb/7mLQ9qfhX5iSjiFKqRj4fpYRISRLEvpMhDKU6A8eK+IPs+TlYWtyCZbMrcjmpblpa jLCaZFJbJSw1FoLblwnJpxYL4pNIpMUgmok1s9E5er1eEZ9NMqkkxmKI6uQ63D7/+fpM6t/u GWqQ6whFCtFnijIT1roBn9saHiShpWCj1YnEiZMXIjPaWjVHpSrTgplDlLYXwavpkH5H9PoB RhI7RnasNUtF9p1MMaMWB5AqRvSlBBq1ba1IRC+XQUdNSsKHyC9CpDSXJJC1SaSA1piR6Gy3 VKQWixhhakX62erAa/2f5OikKF2P8eznlRbq9ZIcd0za67DkVLQfJB4PqfJiMqHYaSRyDBIm n5/1O+p4KEuk90htRoSShme/lpn+7yXfI9GYHEkOvsoJVRjdpnRdF/VJylyNpFAmhuZW6Zud I5GEYDERKSmkF/G2bRFbGxmEyJCMNLcomc67VoCGWFBT/nnIIVI6WGhdAXDxT4vAFckHrO81 HXYsF9bajJQON1Jrd8/2/j/0CBlgIjIGmGbUXsuSRHthrG23GJG1gZ764xE4ifyQDDmJ/BAi 32awT2NoZSceAAAAAElFTkSuQmCC</item> </binaryContent> </worksheet>

Mathcad Professional 14.0 <description/> <author>CEE</author> <company>Parametric Technology Corporation</company> <keywords/> <revisedBy>Labuser</revisedBy> </userData> <identityInfo> <revision>1</revision> <documentID>8ACDEF3A-B5A4-4228-B4C3-700C7D22EC33</documentID> <versionID>E78A6A67-2D02-4D29-8F9F-C7448A6D0601</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="inherit" text-align="left" list-style-type="inherit" tabs="inherit"/> <inlineAttr font-family="Arial" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false" line-through="false" vertical-align="baseline"/> </textStyle> <textStyle name="Heading 1"> <blockAttr margin-left="0" margin-right="0" text-indent="inherit" text-align="left" list-style-type="inherit" tabs="inherit"/> <inlineAttr font-family="Arial" font-charset="0" font-size="14" font-weight="bold" font-style="normal" underline="false" line-through="false" vertical-align="baseline"/> </textStyle> <textStyle name="Heading 2"> <blockAttr margin-left="0" margin-right="0" text-indent="inherit" text-align="left" list-style-type="inherit" tabs="inherit"/> <inlineAttr font-family="Arial" font-charset="0" font-size="12" font-weight="bold" font-style="italic" underline="false" line-through="false" vertical-align="baseline"/> </textStyle> <textStyle name="Heading 3"> <blockAttr margin-left="0" margin-right="0" text-indent="inherit" text-align="left" list-style-type="inherit" tabs="inherit"/> <inlineAttr font-family="Arial" font-charset="0" font-size="12" font-weight="normal" font-style="normal" underline="false" line-through="false" vertical-align="baseline"/> </textStyle> <textStyle name="Paragraph"> <blockAttr margin-left="0" margin-right="0" text-indent="21" text-align="left" list-style-type="inherit" tabs="inherit"/> <inlineAttr font-family="Arial" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false" line-through="false" vertical-align="baseline"/> </textStyle> <textStyle name="List"> <blockAttr margin-left="14.25" margin-right="0" text-indent="-14.25" text-align="left" list-style-type="inherit" tabs="inherit"/> <inlineAttr font-family="Arial" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false" line-through="false" vertical-align="baseline"/> </textStyle> <textStyle name="Indent"> <blockAttr margin-left="108" margin-right="0" text-indent="inherit" text-align="left" list-style-type="inherit" tabs="inherit"/> <inlineAttr font-family="Arial" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false" line-through="false" vertical-align="baseline"/> </textStyle> <textStyle name="Title"> <blockAttr margin-left="0" margin-right="0" text-indent="inherit" text-align="center" list-style-type="inherit" tabs="inherit"/> <inlineAttr font-family="Times New Roman" font-charset="0" font-size="24" font-weight="bold" font-style="normal" underline="false" line-through="false" vertical-align="baseline"/> </textStyle> <textStyle name="Subtitle" base-style="Title"> <blockAttr margin-left="0" margin-right="0" text-indent="inherit" text-align="center" list-style-type="inherit" tabs="inherit"/> <inlineAttr font-family="Times New Roman" font-charset="0" font-size="18" font-weight="normal" font-style="normal" underline="false" line-through="false" vertical-align="baseline"/> </textStyle> </textStyles> </textRendering> <mathRendering equation-color="#000"> <operators multiplication="narrow-dot" derivative="derivative" literal-subscript="large" definition="colon-equal" global-definition="triple-equal" local-definition="left-arrow" equality="bold-equal" symbolic-evaluation="right-arrow"/> <mathStyles> <mathStyle name="Variables" font-family="Times New Roman" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false"/> <mathStyle name="Constants" font-family="Times New Roman" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false"/> <mathStyle name="User 1" font-family="Arial" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false"/> <mathStyle name="User 2" font-family="Courier New" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false"/> <mathStyle name="User 3" font-family="Arial" font-charset="0" font-size="10" font-weight="bold" font-style="normal" underline="false"/> <mathStyle name="User 4" font-family="Times New Roman" font-charset="0" font-size="10" font-weight="normal" font-style="italic" underline="false"/> <mathStyle name="User 5" font-family="Times New Roman" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false"/> <mathStyle name="User 6" font-family="Arial" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false"/> <mathStyle name="User 7" font-family="Times New Roman" font-charset="0" font-size="10" font-weight="normal" font-style="normal" underline="false"/> <mathStyle name="Math Text Font" font-family="Times New Roman" font-charset="0" font-size="14" font-weight="normal" font-style="normal" underline="false"/> </mathStyles> <dimensionNames mass="mass" length="length" time="time" current="current" thermodynamic-temperature="temperature" luminous-intensity="luminosity" amount-of-substance="substance" display="false"/> <symbolics derivation-steps-style="vertical-insert" show-comments="false" evaluate-in-place="false"/> <results numeric-only="true"> <general precision="3" show-trailing-zeros="false" radix="dec" complex-threshold="10" zero-threshold="15" imaginary-value="i" exponential-threshold="3"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="true" simplify-units="true" fractional-unit-exponent="false"/> </results> </mathRendering> <pageModel show-page-frame="false" show-header-frame="false" show-footer-frame="false" header-footer-start-page="1" paper-code="1" orientation="portrait" print-single-page-width="false" page-width="612" page-height="792"> <margins left="86.4" right="86.4" top="86.4" bottom="86.4"/> <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="false" optimize-expressions="false" exact-boolean="true" strings-use-origin="false" zero-over-zero="error"> <compatibility multiple-assignment="MC12" local-assignment="MC11"/> </calculationBehavior> <units> <currentUnitSystem name="si" customized="false"/> </units> </calculation> <editor view-annotations="false" view-regions="false"> <ruler is-visible="false" ruler-unit="in"/> <plotTemplate> <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="149" 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="139" left="618" top="6" width="532.5" height="361.5" align-x="618" align-y="6" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <picture> <metafile mapping-mode="8" x-extent="531.241" y-extent="359.8866" item-idref="2"/> </picture> <rendering item-idref="3"/> </region> <region region-id="40" left="6" top="30" width="562.5" height="249" align-x="6" align-y="30" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <picture> <metafile mapping-mode="8" x-extent="561.2598" y-extent="247.748" item-idref="4"/> </picture> <rendering item-idref="5"/> </region> <region region-id="41" left="18" top="290.25" width="419.25" height="132" align-x="18" align-y="300" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> <sp count="4"/>*<sp count="2"/>maximum flow=400 L/min <sp count="4"/>* minimum flow (corresponding to height of rectangular weir) = 40 L/min <sp count="4"/>* linear proportionality = 20 L/min/cm <sp count="3"/>1. Write functions in MathCAD that design all the geometry of the Sutro weir given these constraints. The functions must have the parameters that could potentially be varied as inputs. <sp count="3"/>2. Create a graph in MathCAD that draws the shape of the Sutro weir (including the rectangular portion). <sp count="3"/>3. Use the graph formatting options to hide the arguments and to add appropriate x and y axis labels. Change the display units to cm (by dividing the arguments by cm) and include the units in the labels. </text> </region> <region region-id="42" left="12" top="446.25" width="67.5" height="27.75" align-x="39.75" align-y="462" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="max">Q</ml:id> <ml:apply> <ml:mult style="auto-select"/> <ml:real>400</ml:real> <ml:apply> <ml:div/> <ml:id xml:space="preserve">L</ml:id> <ml:id xml:space="preserve">min</ml:id> </ml:apply> </ml:apply> </ml:define> </math> <rendering item-idref="6"/> </region> <region region-id="47" left="174" top="453" width="60.75" height="16.5" align-x="207" align-y="462" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="dmax">H</ml:id> <ml:apply> <ml:mult style="auto-select"/> <ml:real>20</ml:real> <ml:id xml:space="preserve">cm</ml:id> </ml:apply> </ml:define> </math> <rendering item-idref="7"/> </region> <region region-id="48" left="180" top="471" width="40.5" height="16.5" align-x="197.25" align-y="480" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="d">C</ml:id> <ml:real>0.62</ml:real> </ml:define> </math> <rendering item-idref="8"/> </region> <region region-id="43" left="12" top="477" width="57" height="16.5" align-x="50.25" align-y="486" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="Qratio">Π</ml:id> <ml:real>0.1</ml:real> </ml:define> </math> <rendering item-idref="9"/> </region> <region region-id="50" left="180" top="501" width="54" height="16.5" align-x="215.25" align-y="510" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="Sutro">Π</ml:id> <ml:real>0.1</ml:real> </ml:define> </math> <rendering item-idref="10"/> </region> <region region-id="44" left="12" top="507" width="91.5" height="16.5" align-x="39" align-y="516" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="min">Q</ml:id> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="max">Q</ml:id> <ml:id xml:space="preserve" subscript="Qratio">Π</ml:id> </ml:apply> </ml:define> </math> <rendering item-idref="11"/> </region> <region region-id="45" left="18" top="532.5" width="126" height="35.25" align-x="51.75" align-y="552" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="linear">C</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:div/> <ml:id xml:space="preserve" subscript="max">Q</ml:id> <ml:id xml:space="preserve" subscript="dmax">H</ml:id> </ml:apply> <ml:unitOverride> <ml:apply> <ml:div/> <ml:id xml:space="preserve">L</ml:id> <ml:apply> <ml:mult/> <ml:id xml:space="preserve">min</ml:id> <ml:id xml:space="preserve">cm</ml:id> </ml:apply> </ml:apply> </ml:unitOverride> <result xmlns="http://schemas.mathsoft.com/math30"> <unitedValue> <ml:real>20</ml:real> <unitMonomial xmlns="http://schemas.mathsoft.com/units10"> <unitReference unit="centimeter" power-numerator="-1"/> <unitReference unit="liter"/> <unitReference unit="minute" power-numerator="-1"/> </unitMonomial> </unitedValue> </result> </ml:eval> </ml:define> </math> <rendering item-idref="12"/> </region> <region region-id="46" left="18" top="574.5" width="187.5" height="56.25" align-x="94.5" align-y="594" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define warning="WarnRedefinedBIUnit" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:function> <ml:id xml:space="preserve">W</ml:id> <ml:boundVars> <ml:id xml:space="preserve" subscript="max">Q</ml:id> <ml:id xml:space="preserve" subscript="dmax">H</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:div/> <ml:id xml:space="preserve" subscript="max">Q</ml:id> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve" subscript="dmax">H</ml:id> <ml:apply> <ml:div/> <ml:real>3</ml:real> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:id xml:space="preserve" subscript="d">C</ml:id> </ml:apply> <ml:apply> <ml:sqrt/> <ml:apply> <ml:mult/> <ml:apply> <ml:mult style="auto-select"/> <ml:real>2</ml:real> <ml:id xml:space="preserve">g</ml:id> </ml:apply> <ml:id xml:space="preserve" subscript="Sutro">Π</ml:id> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </ml:define> </math> <rendering item-idref="13"/> </region> <region region-id="63" left="264" top="591" width="117" height="16.5" align-x="339.75" align-y="600" 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">W</ml:id> <ml:sequence> <ml:id xml:space="preserve" subscript="max">Q</ml:id> <ml:id xml:space="preserve" subscript="dmax">H</ml:id> </ml:sequence> </ml:apply> <ml:unitOverride> <ml:id xml:space="preserve">cm</ml:id> </ml:unitOverride> <result xmlns="http://schemas.mathsoft.com/math30"> <unitedValue> <ml:real>8.5841386231467389</ml:real> <unitMonomial xmlns="http://schemas.mathsoft.com/units10"> <unitReference unit="centimeter"/> </unitMonomial> </unitedValue> </result> </ml:eval> </math> <rendering item-idref="14"/> </region> <region region-id="64" left="24" top="645" width="126" height="16.5" align-x="53.25" align-y="654" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="base">s</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="Sutro">Π</ml:id> <ml:id xml:space="preserve" subscript="dmax">H</ml:id> </ml:apply> <ml:unitOverride> <ml:id xml:space="preserve">cm</ml:id> </ml:unitOverride> <result xmlns="http://schemas.mathsoft.com/math30"> <unitedValue> <ml:real>2.0000000000000004</ml:real> <unitMonomial xmlns="http://schemas.mathsoft.com/units10"> <unitReference unit="centimeter"/> </unitMonomial> </unitedValue> </result> </ml:eval> </ml:define> </math> <rendering item-idref="15"/> </region> <region region-id="65" left="24" top="676.5" width="319.5" height="70.5" align-x="104.25" align-y="696" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:function> <ml:id xml:space="preserve">y</ml:id> <ml:boundVars> <ml:id xml:space="preserve">x</ml:id> <ml:id xml:space="preserve" subscript="max">Q</ml:id> <ml:id xml:space="preserve" subscript="dmax">H</ml:id> </ml:boundVars> </ml:function> <ml:program> <ml:ifThen> <ml:apply> <ml:greaterThan/> <ml:id xml:space="preserve">x</ml:id> <ml:real>0</ml:real> </ml:apply> <ml:apply> <ml:mult/> <ml:apply> <ml:div/> <ml:apply> <ml:id xml:space="preserve">W</ml:id> <ml:sequence> <ml:id xml:space="preserve" subscript="max">Q</ml:id> <ml:id xml:space="preserve" subscript="dmax">H</ml:id> </ml:sequence> </ml:apply> <ml:real>2</ml:real> </ml:apply> <ml:parens> <ml:apply> <ml:minus/> <ml:real>1</ml:real> <ml:apply> <ml:mult/> <ml:apply> <ml:div/> <ml:real>2</ml:real> <ml:id xml:space="preserve">π</ml:id> </ml:apply> <ml:apply> <ml:id xml:space="preserve">atan</ml:id> <ml:apply> <ml:sqrt/> <ml:apply> <ml:div/> <ml:id xml:space="preserve">x</ml:id> <ml:id xml:space="preserve" subscript="base">s</ml:id> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </ml:parens> </ml:apply> </ml:ifThen> <ml:otherwise> <ml:apply> <ml:div/> <ml:apply> <ml:id xml:space="preserve">W</ml:id> <ml:sequence> <ml:id xml:space="preserve" subscript="max">Q</ml:id> <ml:id xml:space="preserve" subscript="dmax">H</ml:id> </ml:sequence> </ml:apply> <ml:real>2</ml:real> </ml:apply> </ml:otherwise> </ml:program> </ml:define> </math> <rendering item-idref="16"/> </region> <region region-id="58" left="24" top="764.25" width="183.75" height="27.75" align-x="33.75" align-y="780" 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:apply> <ml:neg/> <ml:id xml:space="preserve" subscript="base">s</ml:id> </ml:apply> <ml:apply> <ml:minus/> <ml:apply> <ml:mult style="auto-select"/> <ml:real>0.1</ml:real> <ml:id xml:space="preserve">cm</ml:id> </ml:apply> <ml:id xml:space="preserve" subscript="base">s</ml:id> </ml:apply> </ml:sequence> <ml:apply> <ml:minus/> <ml:id xml:space="preserve" subscript="dmax">H</ml:id> <ml:apply> <ml:mult style="auto-select"/> <ml:apply> <ml:div/> <ml:real font="0">2</ml:real> <ml:real>3</ml:real> </ml:apply> <ml:id xml:space="preserve" subscript="base">s</ml:id> </ml:apply> </ml:apply> </ml:range> </ml:define> </math> <rendering item-idref="17"/> </region> <region region-id="62" left="24" top="800.25" width="83.25" height="27.75" align-x="53.25" align-y="816" 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="d">H</ml:id> <ml:boundVars> <ml:id xml:space="preserve">x</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:plus/> <ml:id xml:space="preserve">x</ml:id> <ml:apply> <ml:mult style="auto-select"/> <ml:apply> <ml:div/> <ml:real>2</ml:real> <ml:real>3</ml:real> </ml:apply> <ml:id xml:space="preserve" subscript="base">s</ml:id> </ml:apply> </ml:apply> </ml:define> </math> <rendering item-idref="18"/> </region> <region region-id="59" left="42" top="864" width="229.5" height="404.25" align-x="42" align-y="864" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <plot disable-calc="false" item-idref="19"/> <rendering item-idref="20"/> </region> <region region-id="71" left="18" top="1286.25" width="389.25" height="24" align-x="28.5" align-y="1296" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> What fraction of the total flow goes throught the rectangular portion of the Sutro weir at the maximum design flow rate? </text> </region> <region region-id="73" left="528" top="1344" width="339" height="57.75" align-x="528" align-y="1344" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <ole clsid="{0002CE03-0000-0000-C000-000000000046}" type="static" item-idref="21"/> <rendering item-idref="22"/> </region> <region region-id="72" left="18" top="1319.25" width="350.25" height="48.75" align-x="49.5" align-y="1356" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="base">Q</ml:id> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:mult style="auto-select"/> <ml:apply> <ml:div/> <ml:real>2</ml:real> <ml:real>3</ml:real> </ml:apply> <ml:id xml:space="preserve" subscript="d">C</ml:id> </ml:apply> <ml:apply> <ml:id xml:space="preserve">W</ml:id> <ml:sequence> <ml:id xml:space="preserve" subscript="max">Q</ml:id> <ml:id xml:space="preserve" subscript="dmax">H</ml:id> </ml:sequence> </ml:apply> </ml:apply> <ml:apply> <ml:sqrt/> <ml:apply> <ml:mult style="auto-select"/> <ml:real font="0">2</ml:real> <ml:id xml:space="preserve">g</ml:id> </ml:apply> </ml:apply> </ml:apply> <ml:parens> <ml:apply> <ml:minus/> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:plus/> <ml:id xml:space="preserve" subscript="dmax">H</ml:id> <ml:apply> <ml:mult style="auto-select"/> <ml:apply> <ml:div/> <ml:real>1</ml:real> <ml:real>3</ml:real> </ml:apply> <ml:id xml:space="preserve" subscript="base">s</ml:id> </ml:apply> </ml:apply> </ml:parens> <ml:apply> <ml:div/> <ml:real>3</ml:real> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:minus/> <ml:id xml:space="preserve" subscript="dmax">H</ml:id> <ml:apply> <ml:mult style="auto-select"/> <ml:apply> <ml:div/> <ml:real>2</ml:real> <ml:real>3</ml:real> </ml:apply> <ml:id xml:space="preserve" subscript="base">s</ml:id> </ml:apply> </ml:apply> </ml:parens> <ml:apply> <ml:div/> <ml:real>3</ml:real> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:apply> </ml:parens> </ml:apply> </ml:define> </math> <rendering item-idref="23"/> </region> <region region-id="76" left="18" top="1376.25" width="87" height="27.75" align-x="48.75" align-y="1392" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="base">Q</ml:id> <ml:unitOverride> <ml:apply> <ml:div/> <ml:id xml:space="preserve">L</ml:id> <ml:id xml:space="preserve">min</ml:id> </ml:apply> </ml:unitOverride> <result xmlns="http://schemas.mathsoft.com/math30"> <unitedValue> <ml:real>125.41906569853195</ml:real> <unitMonomial xmlns="http://schemas.mathsoft.com/units10"> <unitReference unit="liter"/> <unitReference unit="minute" power-numerator="-1"/> </unitMonomial> </unitedValue> </result> </ml:eval> </math> <rendering item-idref="24"/> </region> <region region-id="80" left="24" top="1408.5" width="123" height="35.25" align-x="73.5" align-y="1428" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="baseratio">Q</ml:id> <ml:eval placeholderMultiplicationStyle="default"> <ml:apply> <ml:div/> <ml:id xml:space="preserve" subscript="base">Q</ml:id> <ml:id xml:space="preserve" subscript="max">Q</ml:id> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>0.3135476642463299</ml:real> </result> </ml:eval> </ml:define> </math> <rendering item-idref="25"/> </region> <region region-id="88" left="504" top="1409.25" width="336.75" height="73.5" align-x="794.25" align-y="1446" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:eval placeholderMultiplicationStyle="default" xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:apply> <ml:mult/> <ml:apply> <ml:mult style="auto-select"/> <ml:apply> <ml:div/> <ml:real>2</ml:real> <ml:real>3</ml:real> </ml:apply> <ml:apply> <ml:div/> <ml:real>1</ml:real> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve" subscript="dmax">H</ml:id> <ml:apply> <ml:div/> <ml:real>3</ml:real> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:apply> <ml:sqrt/> <ml:id xml:space="preserve" subscript="Sutro">Π</ml:id> </ml:apply> </ml:apply> </ml:apply> </ml:apply> <ml:parens> <ml:apply> <ml:minus/> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:plus/> <ml:id xml:space="preserve" subscript="dmax">H</ml:id> <ml:apply> <ml:mult style="auto-select"/> <ml:apply> <ml:div/> <ml:real>1</ml:real> <ml:real>3</ml:real> </ml:apply> <ml:id xml:space="preserve" subscript="base">s</ml:id> </ml:apply> </ml:apply> </ml:parens> <ml:apply> <ml:div/> <ml:real>3</ml:real> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:apply> <ml:pow/> <ml:parens> <ml:apply> <ml:minus/> <ml:id xml:space="preserve" subscript="dmax">H</ml:id> <ml:apply> <ml:mult style="auto-select"/> <ml:apply> <ml:div/> <ml:real>2</ml:real> <ml:real>3</ml:real> </ml:apply> <ml:id xml:space="preserve" subscript="base">s</ml:id> </ml:apply> </ml:apply> </ml:parens> <ml:apply> <ml:div/> <ml:real>3</ml:real> <ml:real>2</ml:real> </ml:apply> </ml:apply> </ml:apply> </ml:parens> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <ml:real>0.3135476642463299</ml:real> </result> </ml:eval> <resultFormat numeric-only="true"> <general precision="6" show-trailing-zeros="false" radix="dec" complex-threshold="10" zero-threshold="15" imaginary-value="i" exponential-threshold="3"/> <matrix display-style="auto" expand-nested-arrays="false"/> <unit format-units="true" simplify-units="true" fractional-unit-exponent="false"/> </resultFormat> </math> <rendering item-idref="26"/> </region> <region region-id="86" left="564" top="1516.5" width="104.25" height="56.25" align-x="614.25" align-y="1536" 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:div/> <ml:id xml:space="preserve" subscript="max">Q</ml:id> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:pow/> <ml:id xml:space="preserve" subscript="dmax">H</ml:id> <ml:apply> <ml:div/> <ml:real>3</ml:real> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:id xml:space="preserve" subscript="d">C</ml:id> </ml:apply> <ml:apply> <ml:sqrt/> <ml:apply> <ml:mult/> <ml:apply> <ml:mult style="auto-select"/> <ml:real>2</ml:real> <ml:id xml:space="preserve">g</ml:id> </ml:apply> <ml:id xml:space="preserve" subscript="Sutro">Π</ml:id> </ml:apply> </ml:apply> </ml:apply> </ml:apply> </math> <rendering item-idref="27"/> </region> <region region-id="69" left="12" top="1532.25" width="427.5" height="84" align-x="21" align-y="1542" 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"> How could we estimate the maximum flowrate for a Sutro Weir installed in a pipe? The weir will begin to fail if the water isn't experiencing free fall (or when the weir isn't discharging into air). We need to calculate the average vertical velocity at the base of the Sutro Weir. Take the total flow rate and divide by the average velocity to get the minimum pipe cross sectional area. </text> </region> <region region-id="90" left="24" top="1616.25" width="87.75" height="27.75" align-x="35.25" align-y="1632" 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">h</ml:id> <ml:apply> <ml:minus/> <ml:id xml:space="preserve" subscript="dmax">H</ml:id> <ml:apply> <ml:mult style="auto-select"/> <ml:apply> <ml:div/> <ml:real>2</ml:real> <ml:real>3</ml:real> </ml:apply> <ml:id xml:space="preserve" subscript="base">s</ml:id> </ml:apply> </ml:apply> </ml:define> </math> <rendering item-idref="28"/> </region> <region region-id="98" left="36" top="1646.25" width="52.5" height="33.75" align-x="47.25" align-y="1662" 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">ρ</ml:id> <ml:apply> <ml:mult style="auto-select"/> <ml:real>1000</ml:real> <ml:apply> <ml:div/> <ml:id xml:space="preserve">kg</ml:id> <ml:apply> <ml:pow/> <ml:id xml:space="preserve">m</ml:id> <ml:real>3</ml:real> </ml:apply> </ml:apply> </ml:apply> </ml:define> </math> <rendering item-idref="29"/> </region> <region region-id="127" left="636" top="1664.25" width="356.25" height="12" align-x="650.25" align-y="1674" 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"> vertical velocity of the water from any height when it reaches the bottom of the weir </text> </region> <region region-id="118" left="552" top="1669.5" width="66.75" height="18" align-x="562.5" align-y="1680" 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:sqrt/> <ml:apply> <ml:mult/> <ml:apply> <ml:mult style="auto-select"/> <ml:real>2</ml:real> <ml:id xml:space="preserve">g</ml:id> </ml:apply> <ml:parens> <ml:apply> <ml:plus/> <ml:id xml:space="preserve">x</ml:id> <ml:id xml:space="preserve" subscript="base">s</ml:id> </ml:apply> </ml:parens> </ml:apply> </ml:apply> </math> <rendering item-idref="30"/> </region> <region region-id="134" left="642" top="1688.25" width="197.25" height="12" align-x="657.75" align-y="1698" 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"> velocity through the weir in the vena contracta </text> </region> <region region-id="119" left="552" top="1693.5" width="48" height="14.25" align-x="562.5" align-y="1704" 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:sqrt/> <ml:apply> <ml:mult/> <ml:apply> <ml:mult style="auto-select"/> <ml:real>2</ml:real> <ml:id xml:space="preserve">g</ml:id> </ml:apply> <ml:parens> <ml:apply> <ml:minus/> <ml:id xml:space="preserve">h</ml:id> <ml:id xml:space="preserve">x</ml:id> </ml:apply> </ml:parens> </ml:apply> </ml:apply> </math> <rendering item-idref="31"/> </region> <region region-id="89" left="30" top="1687.5" width="313.5" height="45" align-x="81.75" align-y="1710" 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="in">M</ml:id> <ml:boundVars> <ml:id xml:space="preserve" subscript="max">Q</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:mult style="auto-select"/> <ml:apply> <ml:mult/> <ml:apply> <ml:mult style="auto-select"/> <ml:real>2</ml:real> <ml:id xml:space="preserve">g</ml:id> </ml:apply> <ml:id xml:space="preserve">ρ</ml:id> </ml:apply> <ml:parens> <ml:apply> <ml:integral auto-algorithm="true" algorithm="adaptive"/> <ml:lambda> <ml:boundVars> <ml:id xml:space="preserve">x</ml:id> </ml:boundVars> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:apply> <ml:sqrt/> <ml:apply> <ml:mult/> <ml:parens> <ml:apply> <ml:plus/> <ml:id xml:space="preserve">x</ml:id> <ml:id xml:space="preserve" subscript="base">s</ml:id> </ml:apply> </ml:parens> <ml:parens> <ml:apply> <ml:minus/> <ml:id xml:space="preserve">h</ml:id> <ml:id xml:space="preserve">x</ml:id> </ml:apply> </ml:parens> </ml:apply> </ml:apply> <ml:id xml:space="preserve" subscript="d">C</ml:id> </ml:apply> <ml:real>2</ml:real> </ml:apply> <ml:apply> <ml:id xml:space="preserve">y</ml:id> <ml:sequence> <ml:id xml:space="preserve">x</ml:id> <ml:id xml:space="preserve" subscript="max">Q</ml:id> <ml:id xml:space="preserve" subscript="dmax">H</ml:id> </ml:sequence> </ml:apply> </ml:apply> </ml:lambda> <ml:bounds> <ml:apply> <ml:neg/> <ml:id xml:space="preserve" subscript="base">s</ml:id> </ml:apply> <ml:id xml:space="preserve">h</ml:id> </ml:bounds> </ml:apply> </ml:parens> </ml:apply> </ml:define> </math> <rendering item-idref="32"/> </region> <region region-id="120" left="552" top="1725" width="105.75" height="16.5" align-x="647.25" align-y="1734" 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:mult style="auto-select"/> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="d">C</ml:id> <ml:real>2</ml:real> </ml:apply> <ml:apply> <ml:id xml:space="preserve">y</ml:id> <ml:sequence> <ml:id xml:space="preserve">x</ml:id> <ml:id xml:space="preserve" subscript="max">Q</ml:id> <ml:id xml:space="preserve" subscript="dmax">H</ml:id> </ml:sequence> </ml:apply> </ml:apply> <ml:id xml:space="preserve">dx</ml:id> </ml:apply> </math> <rendering item-idref="33"/> </region> <region region-id="123" left="678" top="1724.25" width="432.75" height="24" align-x="681" align-y="1734" 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"> is the effective area of a differential weir element as a function of height given the effective of the vena contracta </text> </region> <region region-id="91" left="36" top="1767" width="93" height="16.5" align-x="87" align-y="1776" 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" subscript="in">M</ml:id> <ml:id xml:space="preserve" subscript="max">Q</ml:id> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <unitedValue> <ml:real>6.25349178131874</ml:real> <unitMonomial xmlns="http://schemas.mathsoft.com/units10"> <unitReference unit="newton"/> </unitMonomial> </unitedValue> </result> </ml:eval> </math> <rendering item-idref="34"/> </region> <region region-id="110" left="264" top="1779" width="72.75" height="16.5" align-x="324.75" align-y="1788" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="SutroSafety">Π</ml:id> <ml:real>2</ml:real> </ml:define> </math> <rendering item-idref="35"/> </region> <region region-id="111" left="360" top="1778.25" width="102.75" height="12" align-x="368.25" align-y="1788" 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"> Add factor of safety of 2 </text> </region> <region region-id="94" left="36" top="1804.5" width="153" height="35.25" align-x="90.75" align-y="1824" 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="out">V</ml:id> <ml:boundVars> <ml:id xml:space="preserve" subscript="max">Q</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:div/> <ml:apply> <ml:id xml:space="preserve" subscript="in">M</ml:id> <ml:id xml:space="preserve" subscript="max">Q</ml:id> </ml:apply> <ml:apply> <ml:mult/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve">ρ</ml:id> <ml:id xml:space="preserve" subscript="max">Q</ml:id> </ml:apply> <ml:id xml:space="preserve" subscript="SutroSafety">Π</ml:id> </ml:apply> </ml:apply> </ml:define> </math> <rendering item-idref="36"/> </region> <region region-id="114" left="264" top="1820.25" width="98.25" height="27.75" align-x="318" align-y="1836" 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" subscript="out">V</ml:id> <ml:id xml:space="preserve" subscript="max">Q</ml:id> </ml:apply> <result xmlns="http://schemas.mathsoft.com/math30"> <unitedValue> <ml:real>0.46901188359890544</ml:real> <unitMonomial xmlns="http://schemas.mathsoft.com/units10"> <unitReference unit="meter"/> <unitReference unit="second" power-numerator="-1"/> </unitMonomial> </unitedValue> </result> </ml:eval> </math> <rendering item-idref="37"/> </region> <region region-id="96" left="30" top="1852.5" width="133.5" height="35.25" align-x="104.25" align-y="1872" 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="pipemin">A</ml:id> <ml:boundVars> <ml:id xml:space="preserve" subscript="max">Q</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:div/> <ml:id xml:space="preserve" subscript="max">Q</ml:id> <ml:apply> <ml:id xml:space="preserve" subscript="out">V</ml:id> <ml:id xml:space="preserve" subscript="max">Q</ml:id> </ml:apply> </ml:apply> </ml:define> </math> <rendering item-idref="38"/> </region> <region region-id="101" left="30" top="1899" width="165" height="33" align-x="102" align-y="1920" 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="pipemin">d</ml:id> <ml:boundVars> <ml:id xml:space="preserve" subscript="max">Q</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:sqrt/> <ml:apply> <ml:div/> <ml:apply> <ml:mult/> <ml:real>4</ml:real> <ml:apply> <ml:id xml:space="preserve" subscript="pipemin">A</ml:id> <ml:id xml:space="preserve" subscript="max">Q</ml:id> </ml:apply> </ml:apply> <ml:id xml:space="preserve">π</ml:id> </ml:apply> </ml:apply> </ml:define> </math> <rendering item-idref="39"/> </region> <region region-id="102" left="36" top="1982.25" width="138" height="27.75" align-x="48.75" align-y="1998" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve">Q</ml:id> <ml:range> <ml:sequence> <ml:apply> <ml:mult style="auto-select"/> <ml:real>100</ml:real> <ml:apply> <ml:div/> <ml:id xml:space="preserve">L</ml:id> <ml:id xml:space="preserve">min</ml:id> </ml:apply> </ml:apply> <ml:apply> <ml:mult style="auto-select"/> <ml:real>200</ml:real> <ml:apply> <ml:div/> <ml:id xml:space="preserve">L</ml:id> <ml:id xml:space="preserve">min</ml:id> </ml:apply> </ml:apply> </ml:sequence> <ml:apply> <ml:mult style="auto-select"/> <ml:real>10000</ml:real> <ml:apply> <ml:div/> <ml:id xml:space="preserve">L</ml:id> <ml:id xml:space="preserve">min</ml:id> </ml:apply> </ml:apply> </ml:range> </ml:define> </math> <rendering item-idref="40"/> </region> <region region-id="136" left="204" top="1992.75" width="94.5" height="29.25" align-x="241.5" align-y="2010" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="plants">Q</ml:id> <ml:apply> <ml:mult style="auto-select"/> <ml:matrix rows="2" cols="1"> <ml:real>100</ml:real> <ml:real>1000</ml:real> </ml:matrix> <ml:apply> <ml:div/> <ml:id xml:space="preserve">L</ml:id> <ml:id xml:space="preserve">min</ml:id> </ml:apply> </ml:apply> </ml:define> </math> <rendering item-idref="41"/> </region> <region region-id="138" left="348" top="1992.75" width="71.25" height="29.25" align-x="385.5" align-y="2010" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="plants">D</ml:id> <ml:apply> <ml:mult style="auto-select"/> <ml:matrix rows="2" cols="1"> <ml:real>3</ml:real> <ml:real>8</ml:real> </ml:matrix> <ml:id xml:space="preserve">in</ml:id> </ml:apply> </ml:define> </math> <rendering item-idref="42"/> </region> <region region-id="104" left="54" top="2046" width="299.25" height="241.5" align-x="54" align-y="2046" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <plot disable-calc="false" item-idref="43"/> <rendering item-idref="44"/> </region> <region region-id="147" left="48" top="2289" width="53.25" height="16.5" align-x="89.25" align-y="2298" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <math optimize="false" disable-calc="false"> <ml:define xmlns:ml="http://schemas.mathsoft.com/math30"> <ml:id xml:space="preserve" subscript="elbows">K</ml:id> <ml:real>3</ml:real> </ml:define> </math> <rendering item-idref="45"/> </region> <region region-id="115" left="48" top="2320.5" width="147" height="37.5" align-x="93" align-y="2346" 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="e">h</ml:id> <ml:boundVars> <ml:id xml:space="preserve" subscript="max">Q</ml:id> </ml:boundVars> </ml:function> <ml:apply> <ml:div/> <ml:apply> <ml:mult/> <ml:id xml:space="preserve" subscript="elbows">K</ml:id> <ml:apply> <ml:pow/> <ml:apply> <ml:id xml:space="preserve" subscript="out">V</ml:id> <ml:id xml:space="preserve" subscript="max">Q</ml:id> </ml:apply> <ml:real>2</ml:real> </ml:apply> </ml:apply> <ml:apply> <ml:mult style="auto-select"/> <ml:real>2</ml:real> <ml:id xml:space="preserve">g</ml:id> </ml:apply> </ml:apply> </ml:define> </math> <rendering item-idref="46"/> </region> <region region-id="116" left="48" top="2373" width="85.5" height="16.5" align-x="92.25" align-y="2382" 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" subscript="e">h</ml:id> <ml:id xml:space="preserve" subscript="max">Q</ml:id> </ml:apply> <ml:unitOverride> <ml:id xml:space="preserve">cm</ml:id> </ml:unitOverride> <result xmlns="http://schemas.mathsoft.com/math30"> <unitedValue> <ml:real>3.3646374698341419</ml:real> <unitMonomial xmlns="http://schemas.mathsoft.com/units10"> <unitReference unit="centimeter"/> </unitMonomial> </unitedValue> </result> </ml:eval> </math> <rendering item-idref="47"/> </region> <region region-id="149" left="36" top="2408.25" width="367.5" height="12" align-x="43.5" align-y="2418" show-border="false" show-highlight="false" is-protected="true" z-order="0" background-color="inherit" tag=""> <text use-page-width="false" push-down="false" lock-width="false"> The design constraint of preventing flooding of the Sutro Weir dominates the problem. </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" content-encoding="gzip">H4sIAAAAAAAA/+y9DXwbx3nnvwOQhCgCoCARJC3RokwQlEhKJEWKpExZokiKki3TpCRLpCzZ ll8kWXb9FkeSacsJ6TjxS9omTpq0ub6d0ybXNEnrJte66V1byW3Tpml79bVJmH+aXOW24blp rlUS5+pzbfP/zOwCWAC7wM7gbR/w+fIzxC6wu8/s/va3M7s7O8u0ak3zfutHVVqN9r/f8mia Vr9K09qqarWWDTM37R1jGodpX23kn9fyfxs0bfbVKm2W+TT+6/2Qfhlm/I0KTfv0Gk3bAvPX 12haFQxvP6xpL+yBWcZv2suXwud5A2byaclcWg3/rtK0v4CPWpby21Ve7UhLhQYL0DZBaoK0 b88NYzyvv1alaR+DdCaoab8f1Ke/BtIKSHdB8ou8a1qrkYBdpkXHh/myNkJqNKbnvPfeJfEZ NS1njWl4hxHnCKRbTQtdMoDNoP3D3u9t+629n9j/r3t+6lT9wE+dmu/f//D1A99/8J/6N5+r Gtl8bv6e58/9yiPPn9v1SM/9ux5pfHjXI8dOtZw+dmrork9d77l7sv86I8YuI2+/Bev6S5CO wLp+MZicn1g+gXnT+o6YshYf9jjcJicgbTeGtxvfb49PwLSfNQZ/MDammYnlxWMaroR8xbbV p2EdPgrpBKzDH0DKx7aKxYF9a152W2wy5bfFGOYa8n2uztCA73Ne+LTaD6vhh2cg8Esw4fPG zq26H2bb5rEtfbovaZPbbvMZI6YP8vgBUx75dO8fn77udOP0daXcdpvgh9+EfeBG2B9+N0cP F2rbtUEeP2/k8UtBfbqu3ufP3XHV8+dKue2+DJn8NZjpIOTrt/zu3HZ/DCP/Bb48BHn8gl+f 7o7eY6f2dh07Vcptt1Srad8PaFo4pGk/DjjZdvOmbTcvte2uUdx270Ae/wXyVg95/L8Bfbrg 7pcHR/a8PJh9283vNuU3PpyPbfdn4IHfhtQU0o/fbtx2X4Z8/Vcjj39oeHbzjleHju15daiU 2+6L4IMo6Prf4fNKrZNtV3zPfgHyFoG8/b6RR5Hv/r2PfbH/8FwpPbsTdOyE/OwLunfbXQd5 azflkU/nf2T2Mf8jpd12XNOHID/XQ74+b2y72DJZHrYjs9qOq5lYZqbtyOeL1b09KdOat/Ua 0/BxSHdA+m1Yp0dgXcZgnT4Dn3zd+aEc9NAqjKQNjj9x8WGenn1CG+Rp/InRwa73fLH/NHz+ 8gU+/MTDv3zB/8jpJ554WB+2q1eb9ZPRjC9HRbOHVull5+fh80qeyig7nSpStn0Ms07Z9HgE 8vnr8OVnVullq5UeraOewdbRw33jIvFhz+AnRi8PbBv9zOArI5cH+PArI+f6to3+Ru8nRs/1 8WE5PbIfu1X1eId7B2baUavXA7PrUVjfZNOD1er1wsFavby20uPl2bGzL89+63TFYzzx4bGz n31MO/sPs8+fWzurieG1s033/MPsT5367GNN9/Bht/jjG7AiPwXB6oy6pdv1+Bbk98OQ31rI 72/4rfV4qjtw8qnub+z8U5H4cODk/T1fvftT3cdOfbvrq3fz4W93Ne76VPez193f07iLD7tF j/srdD1+vcLp8aq0ejxUoevx6Qr741V3q/fB7tZv7HxEJD7sfbAxeumBA60HHno2cukBPvxs pHHXgdZnr2uMNu7iw27Row5+eBqCHfTiOF41QD6fg/ze5LU/Xr3rqrGz77rqUzt/WyQ+PHb2 2Frt7EfhXPyrjZoY/mrj4s6PXjV93bG1izv5sFv0mF6paZ+Dlfmdlfk7Xy+UHhWwEscgn78J +f3CSv141Qzf/7yhRykS17/68LFT1YcPnJwQiQ/z9Ozd1Yffc9eESHyYp9+5s/rwZ++YEIkP 8/QPJ6oPf/P2CZH4ME+rbq8+XHnbhEh8mKedt1Yf7j0+IRIf5um+Y9WH77hlQiQ+zNMnjlYf /umZCZH4ME9fnq4+/N+OTIjEh3n64eHqw6/dPCESH+Zpw83Vh8OHJkTiwzzddLD68OiBCZH4 ME+PT1UffnhyQiQ+zNNnbqo+/EsTEyLxYZ6+fmP14a/unxCJD/Pk2V99+I3rJ0Tiwzz1XF99 OLpvQiQ+zNNte6sPHxqfEIkP8/TBPdWH58cmROLDPH1ptPrwb4xMiMSHefru7urD3xqeEIkP 87R6uPrwil0TIvFhnnbvrD7cf92ESHyYpwd2VB++a2hCJD7M089fW334+e0TIvFhnr4yWH34 DwYmROLDPP24v/rw97ZNiMSHeWrZVn24sW9CJD7M04He6sPjWydE4sM8vaen+vAj3RMi8eH3 9HznULC74/B7t8weCXbzYbccM54E7z0FM313taa96uiYUbpzgI/BSjwF+X0avnwV8vsdvz79 lxS8XoxjSbjjpd5fEGmhNyzSS71LkN7fydMCDC+I8ddg/IHNPC3A8IIY/58wPrOFpwUYXhDj /x3Gd3fxtADDC2L8UzDe3s3TAgwviPGfhvE1PTwtwPCCGH8Uxt8SaQGGF8T4HVtf6v2uSAsw vCDGb+h9qfevRVqA4QUx3tf3Uu+XRFqA4QUx3rTtpd5PirQAwwtivLL/pd6fFGkBhhfE+L9B OjvA0wIML4jxb8L4bYM8LcDwghh/Gcb3bedpAYYXxPjnYLznWp4WYHhBjH8Mxq8a4mkBhhfE +Htg3LuDpwUYXhDj98D490VagOEFMX7gupd6vy7SAgwviPFrd77Ue1GkBRheEOORXS/1fkak BRheEOP+4Zd6PyLSAgwviPEfQ7qwm6cFGF4Q438P4ydHeFqA4QUx/hUYv2mUpwUYXhDjX4Dx wTGeFmB4QYz/Aoxv2MPTAgwviPH3w3j1OE8LMLwgxh+A8R+JtADDC2J8Zu9Lvd8WaQGGF8T4 7n0v9X5ZpAUYXhDj7de/1PuiSAswvCDG19zwUu8nRFqA4QUx/hakJ/fztADDC2L8v+7f0Hvh xj+Fzwf6+LBbzpv5/eE/gePCyzDBVUYmzNezcr1nG7tuBFkSn1bX6GD589Pw2WPk548hP5eM /PDjwY/q1k//qO6P79DW88SH10/ncr0u23Y6BOlhpq9rQyzjBs/HbkLv1lYf1u7VHtBOae/W NmiT8PkofB7SHoLv7tQehEnO/tUHtY8b07/5B4s9fPxTf/NBbcr4Lc4PVvsC2mrf7//uGpG/ v/zCGu2vGj09W2D4zxr45xox2T/zf92rfV/q/LuKK+eO9Pzln31QTP8WTH+PMd2FkYoe/pt1 WwGdeFuBl/W2Audg3lhW+LyvfXGN9idnbhTfvQ7D/8vPf2fN5nzcuNLTw2Npf3yk5wMLrIfn nbMZ8sSHv1IB87xhve0+9TtrNPO2+2fzRLAtJuFjFcTly+GzbGlkzSfP69Vmc3lXbwzz/Wy1 6ftGYxjKSs9h+OTp72BDvQ771ftA4P9Xo0935Mgzu7X5Z3bz375sfDehJRNrM3AAhq+H9BlT XHN+4JRo3uxFK6/9DOzMtZCHJ6s07WSNPr+qv7L56KMQK2iKxX308c1nD3x881XH2Tae+PDZ A1h81Gjho0kt2Uff3CXho4FkH3GsfOQmb/D1K4Q3XoGdYxfsK3tgXxlL8Qb/rdaBN85AOqep e+NlmKg6APtord72hFMob1wyxbo9aNQ7z19/pvL8/3nkk0/wxIevP4PFG98yJs/kjW8PS3jj F/B5g69fIbzB7ytcC/vKUdhXdgWTvcF/WxnM7o13a/o5mKo3fiGkaT+GCT4Amf3POd7Tz+aN 1FjcG/fv/Xrf/XsfGwrt54kPf70vuzfs66yFyueDexf6Htx7YWjNfp748EJO+bTaxqFEXjQ4 ZIr2cTGqY+NwArvbp0/Hx38RZvq/MFJjjPP99tdZYvl2eTTvL42m4au0+LFFHFf4ML8Waz6m rNKS23L+vlER93k8LTdrj8E+eRZc9ICmytprrvnQVdpvse+1/vDRr779P3suQtmx6cNrtA/d 87c9/Pf3P3apin9+6VdervoYpN5X/2fPPIxv0/TrGtc9v0YLG8M/V1PRU6clrn/8k3Gx4zWY 5jN/d2rjLAtrb8N4z+c+W7li1UeegC33oVXGUeG3f/D23FsfWaOdgWmv0T7yxKwvrO3U+DRQ Xjb8q5D44WtW+1o8/zG385M/ePQaSBDlX77o0a9hh41t9+LV7FOxdetqrTyVWNPP/mUdLPuX Nv7w0c88fN9Gvt5WxxTzccSsT0xD/l22/aVN033vNX5vM00bi8V/W2fMt1vTz5MGjPGVHn3c vK/Epn0XpF/0JPYRsYnftj7O+jzaqmzljhOegO3W2ffDR/nni9EfPsoXb95v+DRvfWiNduVK oEf7BkvaOF+BeUI/84NHv//RHzz6eZjmJEv81viX/60ydj1sb8vKfzkM034dpv0OJNMiBHy/ 5/uNZsTm0zvxh5ajP1L3EZ7fWJnyEhwPfghfroGi73cCRplyy/nhv90+OxybP1uZ0su/qErW 95tVCX3FChdY31Sc6h3z+CvP/SdvkG0J8U/NmLZXc6Y3nCg++pUP6XWHT4DfvwLL+NiH9WPA a1X6MWCVsWye+DZKbduubwvnentMw6n68u0dK8dTtf5eQNf6N4JqWotLviatfx4yMl+R3ctM K5zWR03bfhP7b5Wtxra/3jj+/vmalT/XA9+btX4kRes+zZn2/DOb1mdNWse050f32PKTtdev p5+BZXLtb4Vlaf/8/BO7YT6+zfn8utZh8d/KyzGteeZiWm+DQuBO0BqKI61VUWteV+Rax8qC Sottb97nYvsAL2xXaYUt81Mx7wOzjfduTN0H7o2s/Jt5+N68D3wjZR94t+Zsn+Bk2we+mbIP nE3aB1L3icQ+wJHdB/j2Nvvdrg7XYwzXmr7ndf4btURxZz52eLXkfWuTKZ7ddWLzuWbs+0DK eYV5GpXrxGegsvJN/kzISv05Ik6hzkF4rAVTLL4dXojevuOF6Bd2/7CDJz58+45c6vbFPD9f Zfgw0/n5gRMS5+efxnd+ztevEOfnn4Z9xVer34v1r0o+P+e/fbM2+zGXtw/+tqZ+fr7PaKN0 c9Dp8wqKbUcctH02308C38TbPu+t0J9XOBxMPK/w8CPve+jWlvc9lMt1rlzvP/0e/PDrMNFp yGyPP7dtl+248iUj1ikjFj+ufPnq/eNfvvrCnW2dPPHh/eO5bI985DN1m/B8/vnVk+N/fvXc nR2dPPHhyZzyabWNY9+t13SvfcmUj61+vSSMlXfmYbPWhycPHKiChR2E4dk/TaxLbJvHxmO8 CNM+B98dMNZ9HtIbX9Z/e1r7V+034fdnvfp+E5uWxe9kWZNyuCYIwm1szz6JJU1lHu+J4sdz 1Fbgk3pbgWLnTYk3ihvPqxiPvaU2X61ivAbFeBfV4rEltXjdS0rx2Am1eDVLavFOLCnFg3BK 8WaWlOJ5ltTi8XNehXginEK88JJavItq8SqXJONdNodzHo9d0sPNqcXrXlKKx4xwRyTjGeHe dH48Y1f4/xP6fLsljp88Xo0Rbt758VrEM8K9InO8hngr9dnemZcoj3i8hng4iXhvGE5YeucF TS6eEe51TSreO8baLfFwEvGWZkzhJOLFLp98RpOMp/O2Puq8bmXM95o+tkk23iXHgZLi/dtl ydmMePvU4r1ZqxbvgE8p3jvzavFe0ZTigfGU4n1NU4v3gqYUjztBJR6EU4knnKAQ701NLd4l xXjzivE0tXjvaGrx3lKM94ZivCt5ibfCfsKM8ZwfPy8nxZMq/4oa71JSPLn6BIZ4uxXjzSfF k63vysfT8hFP+vzWiCd9Pm3E8ynG61aMd0Itnkf6fEWP1yB9fiTieS6qxauUPx8T8S7Kn4/x eJUK53883osK538QT5wjKcTrVjnf9F3ST8nk43Uqnd/6Lp2QPN/coMf7F8nzTdEEEeItqJ3f 1qqd38bOkWTjGadkS5+TjBc2zpEkjmci3sXYKZnc+a1xceLHsuXRxfg5klQ84+IEr4jKnN/G rhbwUzKZ+oRxceLt3ZLxjHCi3us83tuxSy88nMz57UVTOPnzW9GsQOH8ll+cUIn3ij66SjKe Ec45RrzXJWeLxXtBdjamGE6P9xnp2VjMCSrxXpOfjSmG4/G+pzAbizlBPt6bKrMxxXCa/K5p xFPYV0S8y0qzMcXVM86n5eMp3sExzqeLF0/1jtgVJPEuI4l3qbzjGfXPPLDys/NK8zWoHuBU igtxQL2iMh/UtJUOAc2plWxHbQWelGorsKSWN1GRVJjPu7R0USWeb+lrlSrxapde8KrEa4ai bE5hvuHX1A73c5fU4vGibFfWqdIQRYtCgwsvr4gorJ849CrEE9c0FIoXfk1D5XBfOw/reFl+ Pt4BnMrhl0snfd1b089tVJoD8XOb4Xn5+R7W1Nrn8HgqddeHFUtriFdzWf7Mg8fzjSusIMQb flHBDxBvbk7heLadH0CV4vmUjte7+HUNhXhXH1ErH4Z5cfSH8vPxtfuabGPB7fo1sBdkj59N oj3Q68IUcvE84rxY9njWxC+5vS1/fNnuEefFNZdl44XFebH08WXXRXFeHJyXnG9OPy+uyDph CsOK58VziufFw4rnxXOxK0SSKIbT5r6mNJsmf4VI56zabJpiOIIgCIIgCIIgCIIg3E3lbkgH 5Ofj18hVHrxsvqJpvisUj+JRPIq3rOI5aitwp0VbgTLcFhSP4lE8ikfxKB7Fo3gUj+JRPIpH 8fIYT/XaHkEQBEEQBEEQ9qxSm83zJbX5Kovbb3GlYr/FLyr2WzynFq9brR9hdqK4/RafkOqn JymcSrwZuX6BYqj2W9yQ6FxGPpz8o0NhU+cyMlyU7O3FuEtbae5cxgne+UQ4mS4u9A7O9L56 ZHrw0ON1xzsikopn9ES0Wzae0RPRvGw87oSlf5faN/nDwEY3yVL7Jo8nwr3d/IpkPL0j4deG L0vGE054e7fcE+S1lw0neB21FRjR2wrIPQ3qu2SYVLIYqX1Tz9q85JWo5liXTM2X5OLFumS6 +IJCvFekH94X8d55QZPtnKDZMGnlFbn5ho0eoGQv7DUbxwTJzSni8eewZR82bza6ZJLtC2FY PwR5ZJ9VbtbDSXe1M6z3ACV9nXROP+JJX5cd1osruQMDjyfCScunDc2LD+meklYZYfWPyt1y c8fc8KJk9yyGfGzuitx8hnzdso+46/LJv/NCj1cj/Uh9s4h3QvqRerG7eOS7eBG7izhwK8S7 KB+P936iV3fk5uO7p6gQyMaL1T8k9YN4eoVALh7viFtUCCTjeZfm9RL6RbnDDFvSS923JQ8z 3qXv6fUPyT6AYx0h7pbsitu7ZNTEJbtAYbFCXikeFPKSpRKLFfLNUrMZ8V6Q7jGHGYU8k3yy 3mvUxGW7cI51hCjdk/qSfhrVfVlyvjm94i+5OXmp++Y8fOyal5zPp5+1Sb9NzKP3S+a4h3iC IAiCIAgXsFKhNzyOSssxjurrMSkexaN4FI/iUTyKR/FyiFd5BJj5Mf9/QCrUUhypoM2J+eTu L1M8S1T104O6f//EHc9RW4FrpN5BgHZbUDyKR/EoHsWjeCWIp3ptjyAIgiAIolSsVJxP5c1n HP5AhwrS7dQMVN60RvEonlvjHVCMFzstkm12e0UmXuJcyCsTrzLx1r/4aZh1vMqkse7EuV5t 5njJjZvnEpfQmy3imV6CFv+Z4zVdQh+2iNelpf/M8S1dSoS2iHdCS/9ZLMT05MdSejyP6eek eCdMdwgs4pmfApsz30wwrZ7XIl6t6WnNYVNramZ6PpLFX86biGfehuZ4XtMrx2st4l1M/JwU z7dkHk6Pxx9kiA+bpq01b6/0eF7zczbmeM1L5uG0eMz8vIR5WvMyltLj1ZobpJunNW+jufR4 PrNk5nhJ8qXHG7aNdyU+yN5Oj5dkAZt4tW9YxDPvkqaVTewisC4/SI+XZAGT1ubdZfhyeryk F9jbxGt+2Tbe7kzxluZt4+n/TY8QJrk93Q9M/9k4rlrHg6WlxfM6iFf5VrZ4pp21NhEPDp62 8d7IEK/5ilS8+djg8GWreGLAmyne3KX0eD4H8WBIJR4szDaezyLeZWOIL8xue8bKKat44Gz7 eMbTdQnzJ+Jxp6nEa75kH685Qzw+YBfPOLAkbU8jBJuzKh+MnzPEE0pZxHvDHC9xsInH45vM Np5RMCTFM5bRzRdpF28pabdJiscPlFbxeAhmPFVnuspcG3uATSzYIt474r8er9Ycz9hUXCgr /70lJkraTRML5E8+XraOxyPVLhm7zWVTPD3rDUuadTz+GFHzkiHjJVM8Pet6LcYq3mXx8JIu YyIeM7I+95ZdvNfhQGd0GWIq673608c1eqcSFsfrpXdeePEd47FBU1nP9IerG0Qki/rL3NLS 15euzC3pq68l0PuTuKhvMKt4fOWGxWqaqx76g1eV+lpax4Pf9A3akBxv6Xvve9HYqhblLZ/v 8rD+AL2peHfUVoAZbQV48BmjiYlF3kQrFPH/cHIfMcN6ExMR1qLuKFqhzPP/71xMaodiNGu5 YhPPJ5q7+EwTJce7bBPPKx6K85omMi1wydjBLOIxEYaZJjKvQMz7FnVVEcZrmsi0wKW4YdPi 8SdSNUPyt02zGYsyyiaLeN3GvreU+hisWNQrtvEq9Y4EUjensbe+YBtP++wLsa2Q/Fh4cyLn VnXxWNSl1F5Gapfivb9Y1f11xFPTyd9VxlcvQzy+WVM6UfHEVy9DPK1zKe39yyfiS7Ko+8dg Rw6nfrXys7ElDceXKXfuPRffI+TO9RO96kjFYwmFpOJ5EwpJxTMVTXLxXogPyq1fYrBcrw1R PIqHIZ7qvQPVexUEQRAEsXypU5tt5R+qzTcj2Y+UQadsP1I6lYp9yNaq9ekav+ClEO+yYrxL xYzHZtTiaSvV4sGJ9hW1+bqV9FNv8CvbsVMM6e4NY8j6ofKI/vlE5snS6DauU+ySnC92j1Iy njd26UoyHr+kJgYkz1pqY1eAJOP5YpcCJeMNxy7gScbjV9ReV4wnNmhQbj5xxVAhXvxap9z6 saUfGxtUNt6vGBtUMt5bsWO1XDzvO3yPeUc+3pXYHirXfSocVrz6BpXs1/CKkPCSrP+8l8Uu 84ZKvGZ+SVvyeMbj8Q0q25kwu6Rf9D0gWT7weOI2gGT5zuPpnpCPp6Xdb3AQj4dpTr3f4DBe ber9BofxfDHPS8A3o1c+nr5bzsl3SS92y4vy1TNhuwb56pm4mFUjX10S/YuzubezTZcWT1ym 7/is7HzNS2n3GhxRK9P9fVI8tYr8sPSekls8+T1TwOaWvqYSzst7DleJp/DyAhFP9LqpEG+3 0mzEcsRRW4FXK2X6FUDBQUgVb1Zqv+fVtF9foWmnKjWtx6/Fx2O8CBvjOa/eCPv9Hk37GqSe f9cbJf+p9qz2m/D7s/D7JtO0zKLr2p8zDfOSuLL7hFYdvUFjW09p3t67tYqe27XKLUe1qvaD WmXHzVr1xpu0muZBTes7rTFIFT0ntMquY1pl582ad+udWnX7FAzPaBXdt2ue3pPw23GtcvO0 5oVlVMMyaiLDmq/zCPzGl30H/H4rxLobvrtZ83XPaJ6td8FyITZ8VnTfBvPOQKx7NB8stwKW pfHfIF5F962Qn0OQrynNC8ur7DwMMaZhOTOQ/32Qzxsh37doXohRBdNVR0a0mmt2wvLv1LyQ t+pr+PiwVrEV8rD5CCzzdlj+rTAtX487YPoxmPeE5mu7QfPBOlfB8n1t+7WajbDsllGtZl0f fHczzAPrvuUYxNwL40c0H3z62m/WPLAdfZCPSp43iOeD/Pjgu0rIt3cr38bj8N0U5P8AbM9r IS7M23YjLGMaYo/DMo9rvi0zkNcTWhV8VnYc1Kqbd2gVsE4eyJ8Xtk0NjFd3HtSqIH/VsC5V sI29ML8Ppqnh6w/DlTBv9cYbIO5tEGsSptsJ+TsI630n5AdicG1gu9bw9YX194H2FbCcClin KtieVRC3Cqapbr8J8jsJ63W7VnP1oOaD6atgvLLzFvj9iFYN28bLlxfZC9sT8sH14dr03a0x AhOkFy5IL1yQXrggvXBBeuGC9MIF6YUL0gsXpBcuSC9ckF64IL1wQXrhgvTCBemFC9Irzs/W 1xdgqY7aCrystxXIc2jSNkHA31HqLGSF9IrT4feXOgvZIb0S+MlfmNhC/sLEdT3kL1T4yF+Y 2BAlf6GilfyFiakA+QsRm9r95C9MhP3kLzTU7Bv3k78QwUsv8hcS9h/V5SJ/oWAdq9PlIn+5 n+vYVF3Eb1DqzGRnuevV1TMQV4v85Wr2s+Nz9U1+M6XOUnaWq147WYiliJUffzlqK/DJgrQV SGVZaruNVY9Gw6nCkhddyYOss38sYiEWlXVuY571V48eSztikr/cyE52nNkai/zlLniJlclY 5C8XkanEIn+5irXZSizyl3u4ls2y886MRf4qNX1scLC+NSAhFvmrZKxm588HrU6IyV+Fon/6 40rzrWWnFIxF/sqRj4xFFOYSJZaKschfuSK/8ZRKLH+4tao9GoqQv3JD9kEglRIr0HqsZys4 8jZ2kg2Qv3LD73/E4ZQ1rFfBWOFgw7lZtt1YxtlRakuVE+CvM/c6mnKWDUuXWE3HRtmplOVQ W8Wc8Pt7sk6zh4XmpI0VGevvZA+yiZRlXdhM/soB8JfPm3GK1eyckrGq2TbL5c2Rv3LB7w/a /gYlFlMz1nG203apYSV/OWor8GRR2gqk4lZtwYvhQZvfVEq3cHSU9WeMeDJEXswB2MYNt6Z9 q1a6haY62Ya00i2VKJV16nRwT6R8p1S6haM902ylg4jb+slfOcC3dcvMBjG8lnmZp1r+fIwb KxQ/H8vGNPlLnQ7DHHOsA/56fFWV0heloj3spETEx7eQv3JAUp1kAq31c1Bi1UhFbCR/qdOh LlY4OHwOyjp51veo3e4pJm7VKwd/NUEtUoVexyVdCXGrXir+Cut1x1JnvaC4VS9Zf4kSq0kM ur/SkANu1UvKX+Hg+fOixCJ/lQwJYw0Osj4xTwf5q1Q48xcYi82ya+Nzkb9KRnaxmo6NDp5i a03zkL9KRhZ/GTcoUyF/lYzMxrK+QUn+Khl2/gJjMfsblOSvkmElVriuZ7qfzdvOQ/4qGen+ aoIT4nVZ5iqCvxy1FbizJG0FUil1fFtSXBidc3C5l7xYMsxeDIR62AZHc1FZVzISag3sOJm1 nYYO+atkxP01tENiLvJXyTDMNdklMQ/5q2Tomz4wdUhqLvJXyRCbflJOLvJXyRCbfkjmYMgh f5UMfp3qScl5yF8lg2/6qPRc5K+SAZWN+v2S8+Tqrz7lOYuGW/WCTR+Sacmrk5u/VrXdrzxv sXCrXrDp26Xnyc1foTCCQ6lb9YJN33C39Fy5+QtDVcWtesHGk63M5+qvRzBUVdyql9oLfnPx 171nyF85oLLxcvMXvaM2B4rvr7428lcOBFrlK9e5+WuzpdSO2gqMuKKtQCpuy0+MaTarMFcu XqR+B3LCvnWVPbl4ce35CJV1xSYXf02iuKxcVnrl4q/zYRSXlctKrxz8dUMUx22bstJL3V8f 7Q/guG1TVnop++tofwTJbdGy0kvNX/PMF3s/akFylVfKSi8lf/Wy9nhfV+SvoiLvr9XsY0HT S0MKlrO8UVZ6SfprNUt9Pyr5q6g499cE25AmFvmr6Djd6P3M+p2n5K+i4sRfO9lx+zfIFSmf OVBWemX1V7ZXM5K/ikomf01A9SLrqxmLnF8FykqvDP46xQbTqxfkr9Ji7a9r2azT9/GUJNdS lJVeFv6Se9FVh8O2Ate4sq1AKm7PnxTJXpyQfylWSXPviLLSK8mLvUy+E3Yq63JArodtTsxf +9mswtsNyF85Ma0wj77RQ0z1/ajkL2VCEdV2w2H196MWYD3yjFv1Umk33KvQEXugMri5fXPs 2En+UkZu4ymVWLz74WoPuwB/56PU1iMnZPy1XaXESnkTxXPUlio3/P4zjqZbyaZ76hRe4ziY 8iYKaquYE+CvnuuyTrWBdU6FMl/uTcOul9R28lcu+P2+jL/Ps5PTPZY3KLMYq9q6l9T5TvJX DoC/gr22v/ISS8VYVv16xwiFyV85ADWCausrHLzEkjZWOGr74uEYIfKXOvzU9ymL75VKrNAU +NH+xcPGkqPkrxzgnjiW9M3DSiUWf+fpSUdPnvWTv9QRl5ZaThuvcbuV9bJzq+oVjNXpsEdh xvZMk79yQN/gwZ6trGPrmYb6oPw7T+scvkw4BvU7kAM5vEPTL640MecvE47hD1QUZF3yiVv1 yuEdmvxK0yxz2meco7YCLLWtwP1TUwVZ67ziVm0VvShe42h/jpcZmQNtqXCrXipeVH7xMCLc qpesv/TXOKq9eBgTbtVLyl/8/ajm1ziWMW7Vy7G/uLHYqVJnt2i4VS9n/jJePOzsDS9lgVv1 cuCvpsSLh5cPbtUri7/4fZTjy6PESsatemXyV9OxUdZf6gyWCLfqZecv4wblMiqxknGrXpb+ cnCDstxxq17p/mqqn8t+g7LscateHanOYsdLnSVX4Fa9zP4KtLatcnxtvsxxq14JfwUGdiy7 syx73KpX3F9NB+8sdVbchFv1ivlrnN1a6qy4CrfqZfhr7iEqt5Jwq14dhrvyjqO2Aq9WYOhX IBuuzb8o5+4hJ6bgVr24FwMH5d9YVu64VS/ur4Gjpc6E+3CrXtxfq0qdCRfiVr34cy9PL9sr 9va4VS/wV926UmfChbhVr6zPbS5T3KqXs+eilx9u1Qv81VjqLLgRt+ql9r7N8seteqF42XYJ cKte5C9r3KoXa6pV6aiv7HGtXuzZUmfAlbhXLxdRw65iHtbBIqxX6d25eYT0ys4s61l/NX+O PlAZ7C9xK2TSKwsf8k0ldQEYGHhSocfivEF6ZWbHQFpfIpFSOqzs9XLUVuDl3NsK9O8YsOp5 JDB1qGRuLHttc+BJS7W4YD9TsjyRXjZsGGy374EpfL5U2SK9rNnHhmzVAmpL1QcI6WVJ7IXf oi4Sirad2drBOrYmOituLVXGSC8r7oi98Jv3XMZOxp8LWMcGDA2bS5Qz0ssCX+xY2NK2al3S mdnZ0XH9l1JljfRKp75FrwUOsPstfm0hf7kLQ66W0+w2i1+9Piq/XMU9Qq7wU0/b/N7If27f VNQ8JSC9UhjkNYpAcOp9tlNwveaKmKMkSK9k+vi7XMINGaZ4hB8ri5afVEivJI60QUV+6FiG fkO294D9ri9Zh4CkVzJhfyA6mnGKKr9//IYi5SYd0stETdeQPzCVWYz6iD9cW6T8WEB6mXnK H+k/lHGKfeP+QMkq84z0SuJ8OND/0YxTHPUFAiXtnZv0SnBhc2Aqs7t4+TZSjKzYQnrFmfdF dtyZcYrembB//LG1xcmONWWnl6O2Ap/Mua1Aezjbo1EtgZEclp8Pyk5bdZ7OeEoNp91j4cDU 3pI6kfRKcO+Zpoy/H2oIZ/Vf4SG94kxmvODbNRJomilli0Yd0itG/0CmFx6/a8A/VMJT6jik V5xwt90Vxnk21hQYWSGG17LHqd2wG+jI8OBhNBBmFxh/i1KIbdmypeuni5etFEivOH4bf60b a/IPTL1PvJr1ar0x1RC1VSw5zZEtlt/76gKRgWfuYkmvZh0oVdt80itOa1V6xy/7pidBpchA NOXVrIES5E9AesXY1B5J+06oZQW1pSo99YGW+sTrHGtYv6/d9uXUoVJlkvRKEPW3sJNsO1TY vWyWNYxZPRsRbm3lIvq8Jcoj6RVn/mCLPxDq79mypccXbbV47CgcPN8IQrZG/OGS9YlAeplp sTv+GW+g1tvchwPPlCyHy1CvtY1hm1/6nrF+mE9/R2esj+LTgWhfyXp8XIZ6sYbQSYtvr4VD 3aSFXk0p7+isbylAz+GOWYZ6bWoPpX3XxwYH69PLLPHGs+R3dNa3DIyWsDfwZagXC6b4azU7 fz6YXnVvOmbxxrP1JXVXGejlqK3Ak6ptBULhREO3teyUtQlDU53H096OVsPGKqMrStovP3pt FfCHz4lPXrpZmTAc5c9qWtFfWeq70ctQr9v9/pEL7IJ16RZonepkG2zuhq2pPVbqu9HLTy/+ IF9goH2zlbGCw+dYph4FVhctl3YsP71Y1PJ8DE6e50KsZM+zOGX56XUylC4WnDxDWYaB5acX C6cZa3DwFCtxG0SnLD+9GpPq7WPnz7ugVHLOstNrz3TyyTOyV68uO71Yv2GsfpZ+Qux+lp1e 6+rECfF0f6l7A1Zj2enFZsQJMVaWnV63ZDwhdj3LTi/kkF64IL1wQXrhgvTCBemFC3R6OWor cGfu7yAoA5b7+mOD9MIF6YUL0gsXpBcuSC9ckF64IL1wQXrhgvTCBemFC9ILF6QXLkgvXJBe uCC9cEF64YL0woXr9XLUVmCE2gpYQNsDF6QXLkgvXJBeuCC9cEF64YL0wgXphQvSCxekFy5I L1yQXrggvXBBeuGC9MIF6YUL0gsXpBcuXKeXo7YC11BbAQfQ9sEF6YUL0gsXpBcuSC9ckF64 IL1wQXrhgvTCBemFC9ILF6QXLkgvXJBeuCC9cEF64YL0wgXphYuS6+WorQCjtgIK0PbCBemF C9ILF6QXLkgvXJBeuCC9cEF64aK89OoQf9l+zzSF2ykrvfw6tr93GBMgFqyc9MouRzZB3U85 6UX+wgX5CxnkL1SQv5BB/kIF+QsZ5K/846itwKveQrcVIC8ig7yICvIXMshfqCB/IYP8hQry FzLIX6ggfyGD/IUK8hcyyF+oIH8hg/yFCvIXMshfqCB/IYP8hQryVx5w1Fbg5YK3FUiDvIgK 8iIyyF+oIH8hg/yFCvIXMshfqCB/IYP8hQryFzLIX6ggfyGD/IUK8hcyyF+oIH8hg/yFCvIX Mshf8jhqK/DJ4rcVSIG8iAzyIirIX8ggf6GC/IUM8hcqyF/IIH+hgvyFDPIXKshfyCB/oYL8 hQzyFyrIX8ggf6GC/IUM8hcqyF8OcNRW4MmStxVIg7yICvIiMshfqCB/IYP8hQryFzLIX6gg fyGD/IUK8hcyyF+oIH8hg/yFCvIXMshfqCB/IYP8hQryFzLIX+k4aitwp/vaCqRAXkQGeREV 5C9kkL+Q0SH+sv2OWK4y06v8Ib1wQXrhgvTCBemFC9ILF6QXLkgvXJBeuCC9cEF64YL0wgXp hQvSCxdZ9XLUVmDE9W0FliOkBy5IL1yQXrggvXBBeuGC9MIF6YUL0gsXpBcuSC9ckF64IL1w QXrhgvTCBemFC9ILF6QXLkgvXKTp5aitwDXUVgABpA8uSC9ckF64IL1wQXrhgvTCBemFC9IL F6QXLkgvXJBeuCC9cEF64YL0wgXphQvSCxekFy5IrwTbSp0BB2iO2gowaiuQYII9yDr7x0qd jeyQXpx+NnqsCUVn68ter2vZ7PnzYxEsLzNY3nr1scHB+taA34/mZQbLVq8JtpqdPx8M+82U OlPZWa56nWJJxiJ/uZf9bPZcQ4qxyF8uZQ8LMQtjkb/cyGp2btjaWOQv19HL5uyNRf5yE/Ps 5HRmY5G/XMN2KLGmQpHsYvkjYx8pdWazUuZ6rWTTPVEHxvL7m46NVveXOrvZKWe9NrBOh8YK TXWyB0udXUeUqV7zrN+pscLRnmnwIRLKUa+d7DjrH3NoLCjdtpc6wxKUnV7bWLV+b8SRsU6C E1HhVr0eV5pL3HR0Yix/ePMclG/4cKleR7YMSs6xlvU7NZY/EOpZcaEg+c4/jtoKvOqxaivg Tm1/sstf6ZGY/lo2y847MyEUcHXsZMFyXmjcqRdr8wcdT5t6gzKLDweeZPsLmPMC4069Lmz2 jzg7uFncoMzIEENwAp0Bd+rFgn5f1mlq2Cmnxgq36pKG7+vCWCEx4U69QmF/z3VZppllNjco Uw+ArfXs2KyYcqi+KLkvJK7Ua6Lb79+S4fc9LJT9PopuqODwudm+548NwXCkfUXR1qBguFIv djvoZXe2lvUGpclYc6wX5njsPj75ENtUzFUoEK7Uy+vz+89bfF/j6AZl3FggLWdqgIsXHUVe cum4Ui8W9fsH0utxJ5ljY7EQHDR1RFWjCUGzbEe4Uq+TIShtTveavoESy+F9FFFixc+wNoxG wY2B4LtKsBYFwZV6MW6JlviYTIk1yMwyH2W8ohFuuKH4q1AgXKnXI2LrD9VfYNslzrGC58+D tBOm5Rz1iYpGfV/J1iTvuFGvx7cYCmye7ul3bKxBlirLXXWgc2SyJOtQKNyoF/M5kCjZWLPs 2pRl1LCZFm6uHTBURrhRrw1Rx1qJEuuU5VL4sRBq8UXOe6Fxo16s1bmxUkqsGL3iWBhuLYtz LjNu1KvZSb296dhoeokVo4+JY2FRc10cXKjX2sZsWkXG+juPs522SzjUEOYVja7bipjrIuFC vdh0NmNluYfVNQLHwqbTRcptnnHUVuBly7YCqbhQ2239GU3IHrQs3RLcw68tDtUWKbdFxoV6 sTpbE1Y76KdDVPnH7yhCPkuBC/WatahKZivdYtSIS8GBOtlWW2hwoV4stSqZvXRLMMUvBU8d LWj+SokL9UqqShqPOGQusWLsf2xEyHWowDksIe7Ta0+iKin9iMO4v8zlcqFerDZuLLlHHDYc LH+5XKhXb1AYS6G97lD5y+VCvdgMlFgbpK/A99bzerw/Wj63MS1xoV6KD+UJucbVmrfVsNuY V2nOYuNGvRTYf5AfDP1DB6XnrIHaZ3VXW3u7rzOp2YFLKRO9RM3QH5Zv6zvLeo7Fmia0ZJ+8 5JSFXhMHRvj2DrRKzbWfHe+cSmpGEh67pUA5zBtloRdrE1s9KjEHf3a6vin1sle4YDnMF2Wh 13rRfqfpLqfPOO9kgzaPeLYXNJ95oBz0GhR1jcDBux1NzcWqs2ug1Tpb4LzmShnotaldbOoB Jxd5t2cSi4te8NzmSBnoZVzPX5V1urvZdE8msThu7/mtDPQaEBu6qSvzNXwQqyH70+vFr3A4 aivwSUdtBVLBr+1uvfbenumpMWfCAkG3n16j16tft2KGQ+dKds6ZWEBDEXOuBHq9mHFybPPr SjY15bgPCTpXKzyxNuFWlQo5sQCHnVOUEOx6Dcbqhmm/rHYmVqD1WH+sghm2erjXXSDX694z se2eXH6tdvb8dCQ0NcUeZOvGdMGeKtFaSIBcLzYZ2/QDz8W/WwdiNWU/+EXGGmItSfj9b1hE qVZCAuR6nQrGj2uTT7Pb2M0s1NhW5eDRQSFW4mLXZviq5Z6zJVwThyDXi1UmJGiJtrdHr3by mGc4mCQWYytaMNQNOcj1qpCp/Rli1Y0OsuTLyEfvQ3GfjINcL6YiVnoT8LA/4MPRfhi5Xh0y YjXVz4UsxOo9FsbTZA65Xsxxl4pN9ey4zTJa8MiFXa/eYHal+DnxVOdxm95Kb5lp8Uf6sciF XS/W4EQsZn+HuQ8cGmb7ipjj3ECuV0115gNiZKx/ajZDP/f8CWokNUMd5HqxW5+xr9HDOXGW Z8xWjARQtFBMgF0v1mfTaQs/J+7P2GJ/gtUP+f0D97j9FmUS6PViD42kOwxOs2x60zHzYajH jxQ+g8k4aivwpFJbgVTwa8sunG5JOmKG6ufYOgdPel7Y7A+3srVFyGEeKQO9gLn6qysDgcpQ cHMbaza6os3OXcemCpqrQlAeeoFEHhZhJ9kFKb/geKVcEmWi17KB9MIF6YUL0gsXpBcuSC9c kF64IL1wQXrhgvTCBemFC9ILF6QXLkgvXJBeuCC9CoyjtgJ35qWtQCqkLS5IL1yQXrggvXBB euGC9MIF6YUL0gsXpBcuSC9ckF64IL1wQXrhgvTCBemFC9ILF6QXLkgvXJBeecZRW4GRgrQV SIW0xQXphQvSCxekFy7KWq+NrEP8bSx1RvJHWesV62G41NnII2WtV6yHYbe/NE6CstaL/IUL 8hcyyF+oIH8hg/yFCvIXMshfqCB/IYP8hQryFzLIX6gohr8ctRW4pihtBVIpa23Ji7igsg4Z 5C9UkL+QQf5CBfkLGeQvVJC/kEH+QgX5CxnkL1SQv5BB/kIF+QsZ5C9UkL+QQf5CBfkLGQXw l6O2AqwkbQVSKXX8gkJeRAaVdaggfyGD/IUK8hcyyF+oIH+5hA6HfzF/OZ3e/aDUy18oSr1i 2cGoV0f2Da+I+x2GUS/yFy7IX8gomF6lXrHsYNSL/IWMgulV6hXLDkq96PyrPKHrG8hAc5hz TFnrlQ9/OWor8CpzQ1uBVNyWn/xCXkQFlXXIIH+hgvyFDPIXKshfyCB/oYL8hQzyFyrIX8gg f6GC/IUM8hcqyF/IIH+hgvyFDPIXKshfyFDwl6O2Ai+7sq1AKm7PX06QF5FBZR0qyF/IIH+h gvyFDPIXKshfyCB/oYL8hQzyFyrIX8ggf6GC/IUM8hcqyF/IIH+hgvyFDPIXKjYa3atszDCN o7YCn0TRViAVbPld7pBeuCC9cEF64YL0wgXphQvSCxekFy5IL1yQXrggvXBBeuGC9MIF6YUL 0gsXpBcuSC9ckF64WHZ6OWor8CTKtgKpYM//coP0wgXphQvSCxekFy5IL1yQXrggvXBBeuGC 9MIF6YUL0gsXpBcuSC9ckF64IL1wQXrhgvTCRdnr5aitwJ1l0VYglXJbn3KnrPXifYCUGWWt Fyuv/nU4Za1XmfVfxSlnvcqt/ypOGevVUW79w3HKWC/yFy7IX8ggf6GC/IUM8hcqyF/IIH+h gvyFDPIXKshfyCB/oYL8VVTuV7vZ6KitwEhaWwHyYhGpb8nVR+TFInJXS+7eIH9ZsJZdxTz5 yEkSg+O5e4P8lcZ2FmIN66+urMhTbmLoci0/f32IRbJMkYNe29l0TzRciI3y0Hg+3jOGz1/1 A5FA6+l9mSZR12tFz1hE36zhun7lpVhxaDI/7xkrnL82FGSpM2LnD0wdyjCNol7rpjaHjY3a 0rZqndpCbDg0FcjLe/wK5a/9bCr6rrwvlbGbmvS1DviO2k+kptdUXSSm1nSXYvbsSMjlTn+t Y7Dy4afzvtxt/bHVDmeYSkGva25qj6kVbuvao5xBa4764nK50F9rjV116KGJvC6X0xpf7wH7 ieT1usAM3/oDwakcsmdHOJ5t9/nrViZ21Uh7XpeqU5HYTwP1tlNJ69U1Eltu2JexJqPELWMm uVznrxsOil01PAY+yz+mFW/fZDeRrF73DCSOshmKRWXMcrnNX+8Kil21ZX0el5mgw7TiTbZT SerFr+oZi5y5JccMppPsLnf5q4ad1suB8bvytMRUzOv+iN1Ecnol5MpUh1EnWS53+atfr2SN D+ZticmcCprWfNXDNlNJ6TUVlyvSmn939c6kyOUif+0frNOL7aGDeVmeFZOmNbe9ACGj18Gh +PLen/+qBmMt/hRyXF4+/WVcIWvx5Wl5VpjWfPMFm2kk9Nq7I764gZ/OSwbN9IpjbSRiyrRr /LXfuP7sD8/kYWl2GOWjIGg3kXO9jrTFTxDCtXnJYDJcrkD/+13pL0Mu/315WZod7Yk1D520 mcaxXmdrE6XLZJ4ymEB3V2Dq0COy/nLUVuCabP0K5MuLNbFbC/6hrpqcl2bP/vrE2XWr3UTO vTgeX1gh6pGGtElnLG7xYuz4kO0+Vq6cDMXXPNJsM41jvXYnxJ/Md8Uk7kSWVEC7pKzbESt/ o4W55ZKgLfuu6lSv/vg1EH/kfJ6ylyAhl/v8NRirQUcKUcInkyie7HYzh3qdXZ+wV1Xesqdj dpf7/LUvfgqV4Qp7nrg5mjd/VSW24pbH85S9GGa53Oev+D7fs11qPrvaYCbm8uWv6cQ5VKRa ISP26NcXEzfM3eavfsX1rg6FFKI15cdfdzckNqJKNjKRLJfb/NXVpLbeD69S2VU2xU/XcvSX 6bql/X0cBVLd5TZ/Pb5Fdb1rlXaV+AHRbgJneoVMV20bFLJhRx9Llctt/qpTXG9+ZV5lVzE2 dG7nX2sbTRvxzL0K2bDmpxpS5XKbv0w7atdPSs0JM3bLtwdZZ+wfOV7fMJ3I2d92k0ZvNZUs l9v8lchMt9R8/MCm0srMqN7kdq3xwub8bcQERy3kcpu/HFxxsERUHKbl25ntmdaD5XgtP5i3 jZhAv1uYKpe7/GXeUeXWu8mf4QZlBrr0YLndK5ttzd9GjHFQXD9Ob6jsLn8FFdd7FZ+jTr6N 9ES3HizHe9H5a5AWo3bIWi53+cu8o8qUX2vEzqhyH8NYf9tDqSO9kjZiYz7a6c20WMvlNn+Z bo1L1A/31+rzdSpENDay3c/O/GXeiD0KmUhlzPapDXf5y5wbmfMv47pt/zbFiPZ32uT9pZCJ ZOZv8Am5wq1W99Hc5S9TZiSubxj2Urp0J2ZsnbX7Wd5ftlVNx0TFvRnro3tWfzlqK8Ck30GQ Xy+GHM81HWvwFJB/bkG/irHZ9nd5L4Zza4jZa7QUD49Zt4x0rRcDTm/p3pa4bS99XdY4V7O/ zSNfl8ztXtlRo/WiXT3LvWWd3+d1OFei+ad8bVLcSQ2cs/1d/lwtp3vRR/ViztZdbvOXeUdt 6XJ27fA+0zxtkvH05jkh+wmc+WuXeSvm0ObhLqO1tP1+5y5/mVo3AdOO5klqrd6yQjKiqNVk qP450mtD1Jxt1TZFNcYZWuZnZtzlL/NFO3/LqIM57kpurf7U+6Ti+fg8maopzvzlS8qDapu9 +4w9ryXDNO7y18rapBUfsbsCmGBwPGkOyRKsS4gdyjCFI71O3J6ciYHn5NvEHq2PFcMt9b0Z pnOXv1YlrXcg69M+qXJlqpqnYzTRaOuzn8SZv6oDyfmWf/qjK2aujO5ym78YS17xbG5Jl8sf OS0RrUkEydQIyOH5Zyg5E5LPf6xlT8bruEO+TO5ym79Opqx4y/r3ZJj6rnS5YBbnz3mOiBky PiTvTK99kymZaLJ/Mj6dPhbvWMI/nmVat/krdcUzPQ1+usUfGRjyp7LjToexWjNcI4rh0F9z qZlocS5YV1u8hhsYeWx/lqnd5S/z8yWGYPdZNwnYtyYa8Qf6n/tgml6ZOy5KYJwIZH6Swen1 uLRnKlucHhJPJ2aNZL876zZ/pR4QgaEd7O6UqXayNZN8a48wiy0Fgu11EMl45jyc+SzPoV5e X1ouwq1Ze7eYYLM7BgLmObLXK80hSu8vxnrSVtwfCDVMs5XG2tewq9jgaFAc8McfWxtvrZE0 w0j20+ZYFwFZbvE49df5iEUu2K0Z53mWTbeaDieOHOk2f5mvBpqIjNVOsw746+5qWH+1cbzX y+audIOBJ7MVHzNNxoRZpnN8fyJqkYumg/uY3TW1u5mvtsk88cA9juKYZ3GDv0xX27MwdFAv my0MBseWyWn7cunsPuOyqj9cf1vm7Di/n5TaiQMnEOphqy2mXcmmG8aSHBkYcdZyz33+utlq T7UgcV5p6UiumE2EdSxo7BOBZzIfsST0SqvSGzTVz7FTcGDUS6Ya5mWzrHMqlHL4DLdmv5Sj Y57LDf5i7Cbr7Z/CwD2x88rbnkwvO/SNsLl+ls2nLH2nqdND/8iBbJlx7q9Rq+Oyno9gfcMZ 42je44u2pmc321E5QdJz7I7nsiYfy+AkH9etMZ9Xbmq3myrQOsVCcPSJVbu2xasq+kIey5oX ifvrp212m6xA9SjLUTnOvWfMM3JvOGor8Kom21Yghfz1AZLW+U/a1oiuMJ+DPp3JveFg7XRX N7dCY09/0LzkAQf3CmS2x/tt85CRpjGJGFvMc0rMZ0U+lqETq+nZSZB67bU+m8BWDH3AQU4k 9Er0byFDoM5ZHVLn8SS93FHWcWozOQZOalLPQU/LCzZ+0ElG5I43tsWdLeEGJ9cBEpifsHGP v6Ckj9qdAgSiBy2m9zk9Y4jL5azJk5RevfWSggWiazLc97HgtqRi3T3+YuyI3r9pGkM7rO7M 15h6rna0oSYfcpYN2fJcSrChHZJLTzmFl547mXwsI8GtbDL9INdif2WwQeKQGGZOu7aR1EvG YeGn5B+ES2615SZ/8Tt+076ktY8M9HTZXguayFZJSRAITmW6A5eEfH3ZYaUj0s7kmqXoJJ1f KMxvJh/LSOZR1umrquR5DO861jPNrss49bsGrDZMGk0HJXIgrdf+x9odnLNF6qaU+rFOqh66 zF86XuYRlxHezbJ3y3K0LfsxMTx5k61HLVA5H92R7WJOpI6pvSZkXVXScpSWkSAfy8iNPhbN vHM39TsuuXSUrh8MWpS85jysYdnuOFsTex4rhhv9Jcc8W9PfZLtbj0H5J3kUUrves8+qrqTn ITjK1B9kSrlsrrwcndL7i7MNzt0sNlY4CuWfXE9XHMXrc2vZip5gWi54HnpljsYppFwEt+2M xBlu8Bdngm1gjQ31QaOPkEhlqKp/1daQglgsl3eQbWeDK3xVeiYCeh5yfDdXl7lcDLRO5dof tjv8pXMr62UhUVFpZidzOALl9E7GB6BaoWfCA0N23ew75ZDpblx4cz1bmePy3OKvvOKed9Qm ruDwMtDprdBMuMlf+cI1eh015OINlXIpAxOQvwrIPfpTZE21Sv19WkP+KhhTTaJ6OXc8rS2E OuSvgnFbO794Ku4AOWor8HKubQXSIC8WjPf61mS50CoPebGAPFqAZZK/UEH+Qgb5CxXkL2SQ v1BB/kIG+QsV5C9kkL9QQf5CBvkLFeQvZJC/UEH+Qgb5CxUbyV/YIH/lHUdtBT6Z97YCaWxk ZeZEF2hLSEF64YL0wgXphQvSCxekFy5IL1yQXrggvXBBeuGC9MIF6YUL0gsXpBcuSC9ckF64 IL1wQXrhouh6OWor8GTh2wosA2j74YL0wgXphQvSCxekFy5IL1yQXrggvXBBeuGC9MIF6YUL 0gsXpBcuSK8EexTf7FRMSC/G37LSx97DGqf7qypLnZVskF6rWYh1tUWfCKN47H0Z6/U4HP6a +cuLEu96CkdLnalsLE+9+ti72dYtxuswEwzUljpjWSm4Xo7aCtzp3rYCjW3Rqy1e+TY+WuqM ZceV27PAdKRLxWm5q9QZc8By1ItZ69VW6mw5YTnqZeOvUmfLEctRL9ax9cyWts3BykCSXm6v 9guWpV6MV/wvMA8brG8NkL/QMAEn1lPtsZpirq9jLQrLWi/Bpti7r0P5exNh4SC92Fo2NcD1 Cqw/W+qsZIf04jw2yYuxqlJnwwGkl0C8/DiCoAQjvXRuiILBanN9pXjhIb0Mbhry+5u68vGe 6oJSXnr1sVnWAX8e5pWedwccEbcUIE/5pYz02sPOt1W18rp5pLLKNyX9GlWoJEY3FCRneaR8 9OqabjVfXwq0+s5Jzf/wKr8/XKC85Y8y0evsDde3pF3ADU9O90ksYxCs2ViwHOaJMtGLBQNp cnHF2h6SWYbf3yYjcCkoC71umUk3l8FQPatxuBSvDwqwguYzD+RdL0dtBUZK2FbAoiFA3I73 OV7KFr9/17sLmMt8UAZe7J3JIBcw8K5rHC2n5hEElZMy0IvZHjgNmtg+R8vh96jdfoOzDPTa kUUuOHebdFZF8bv/Bid+vU5b1iJTGHfSTor8VQS6sh0NdVpmsi5popv8VXD4FQxdkKkV3ayD NRxrshYs3Jp1WV0wVajwWc4J7HqxcaFG00/czx5g/L7yg109Q5aCBSYf2p9xSX1tfn+oKHnO Aex6TYuW9eNJ33XdZ13BH7dZRoyg3x/sLVxW8wJyvbb3CCEGk51ztN7aYi0zRzMsi19rdP2D FMj1Yu1cBovvR6yvPfrsBdszDROcubdwWc0LyPU6BcewwOn0g9jaQw2Wx8RA3T02S1o7ChXN SHVhs5s7yPVilbZtDG2uFzeN2SyJO3WgcBnNE8j1qoDDXr/Nbz6bemL0rlstpm7lFZeGuwuZ 2XyAXC9+Cen2Eza/3VFnfemj6eCdadOKNgdh1x8OsevFLyF12/56dMpasMDADnababrtXZPi tGBz4TOcK8j1ynKJdp/dnbHAADvO9MPiHhY7w3b9zRSGXq/mSOZLtBnuUjcda2hkHaxxur7J +GbS2W2XkoJcL9aa9RKtTSGWztBgUXKcGznr5aitwDVu6FdgXZ3fvyrjFPuP+jLfqo4RXl+k POcEdi9OOzjk2R8/TQSyX+t3A8j1qqkOW166SuZgNOvxMzBVhNzmAeR6sZujfv/hrFMdsb6S hU8u9Hqx2oiTSkXflHVDVYNI/yGnrR9LDHq9GBhs5ICD6Q422coVtrv+6D7Q67UfDBZouzHr dBPspknrg2IgiOVgyMpAL7ZmnN8D2+tgyn2s38JjLdffUPA85g/8erHdUDIFRpycGK9lXaN1 SSaLjPV07Sl4DvNIGejFQnzLD+1g1zmYdicbHKwNir4Xw7uiw8zuxo1bKQO9tvXrxdBAzyDb 7mD6a1kva2YdLMQ2OH4mxjWUgV6sM17Pq+vZCmL04ZPBMeWgV1I/ieHg5rYtXd1sY6kzVRjK Qa9OvwWlzlSBKAO9NrVb6eX2hxwUKQO9rO83lzpTBQK9XtfUp90tibRG2xqp/HIl61jSJYvw 1esboLLu+u5UlEGu15pEy4BwsL6hMcSuYmtLnalCIq2Xo7YCzA1tBdIQZ9WVwdpVcKq8utSZ KQJu2/6SVFRW+VZUe9jKMj6dTgK5Xkq9wWEGu17LDdILF6QXLkgvXJBeuCC9cEF64YL0wgXp hQvSCxekFy5IL1yQXrjQ9mqaVgdpBaTDkwcO+JimtcCwH9ISYDdsnm/fnhvGvPDJv9sEqcn4 zgOfP1qtad+o0bR/XKNpWwL6PTWreQ9Bepjp990aYPmaiedjN+N2a6sPa/dqD2intHdrG7RJ +HwUPg9pD8F3d2oPwiRn/+qDWqMx/Zt/sNjDxz/1Nx+EafXfYsxrq30BSL//u2s0ns+//MIa 7a8aPT1bYPjPGvjnGjHdP/N/q1f7vtT5dxVXzh3p+cs/05fxFkx/jzHdhZGKHv7bORiPLZ+P v/bFNdqfnLlRfPc6DP8vP/+dNZuXfeNKTw+fX/vjIz0fWGA9PD+czRCHD3+lAuZ5w3p7fOp3 1mjm7fHPpmn4+vF1XgVx+XL4LFsaWfPJ82Kza1FDS/59vTHcyFfV9H2jMXy6T/Mc5vsHpK2Q PR98+Wn49Af06Y4ceWa3Nv/Mbv7bN/36dxNaMksGB2D4MUjnTHHN+QlC1jeZ8mC1T129UtPe UwHTwH71tUp9fjfsU+9nEvvUDL59iq9fIfapY6Dnb4Ce/wM+v1iZvE/x395bmX2fmtVy26dm V2laG+zPPx3StMqgPn9sHv77NZq+b/2NprcV+Hlm3VZg1hjhbQXuMsVsNRJkZVdiDRLDPMYJ SNuN4e3G97FPbTXTKoxB2HZJxNaTz7fR2MYsZVqzNmtM0x/X9O36cVj/fab1579VQ+L24nEb 9ga2f2P0U/3fG32uv3Pvjwb5+M+N9gwcG+3rv30s2s+H15hixOLxlYwN+2DYtB12m7ZDfJjH NW/3bGVO0PiOs8oY5vn1shUi7zWez8OezIf8nkvs5z18nvU8J9pbsPj33rtkTL+F6Z9VUCby 5b/p0Q8s/LCh/860G2Ga7ZVM+1XtCHsbUogdZlF2M7uWHWI3sQPsTjbFfoJNsrNsgr2X3cie YTew59k+9gk2zj7JRtmvs93st9hO9iU2xP6QDbAvsz72F6ybvcI62ddZG/v/WCv7O3YN+w5r Zn/Pmthltpa9yhrYP7I69k8Q7Z9YkH2X1UBawRZZhUj/AunfIP2Iedm/Q3oT0tuQNI+XeSFV QVoBaSUkP6RVkNZ4Klg9pKsgNUHaACkCaSOkdkidME0XpK2eKkjVkPyQaiGthlQP6SpIV0OK QuqCNAhpN6T9kA5DOgHpXkhnIc1B+jjr9Xwe0u9C+iNIfwlpAdKrkP4F0uuQ3oZU5en1BCGt hhSGdBWkJkjNkFogRSFtgrTR0wWpExL/a4XUAqkZ0tWQ1nnaYM42TwOkOkghSEFINZBWQPJB qoTkhaRBeoe1ef4D0huQfgzph5D+FdL3IH0Xtsx3YQt9l633/BNb5/lH2Gr/AFvvVVbnucxW e/6e1Xq+wwKeb7Maz7dYteebzOf5Oqv0/C1jnv/B3mFfAUX+CJT5ffY6+x32A/Yb7F/Zp9n3 2S+x77GPs9fYT4GKT4Gyj4PKD4Pa94Dqt4H6h9n/YvthT9gNaZB9m22BFIG0FlII0gpIS2I/ 1PfWhJf0vdUT35tj+7peAOq/euNzcT/8YGzM+L7CmJolLatSux6+iHp/hfZ72u9dsd+b93G+ N/PPK7D38s+NbGlJP8rH9nFmlIOxfTy2V/Pfo16PNgn73jntGpbfpf5A6/U8Dim/S/2h9l32 95Dyu9QznnrPRyDld6nXevo8H4WU36V62B6Ph92Z56VGPePgsuxL1cf14+b6pGNvYula/Jir 177MMatg2bwuub3SC7US/l01uxkczNNhOObwdLNnJZuBdeSfU2L4as8U5O5m+NSHY8d2cy5j ub5i1GTSt0FqLjPn6z1w7HgPHKXeKxIfDnqe9ATgM+x5DD758GOe77P3wDHiSfjkw8XI17hn M2j1fbZXJD682XMDHBP3wvFxFD758Kjn/8Bvi+wG+OTDxcjXd7W/Z9/VNrFFkfjw37N/1r4D n99lr8InH35Va4fxFhhuF8PFyNdvakHPb0KeXhSJDwc9X9QCnhe1sOdz8MmHPwd5eRHy9UX4 /FyR8vW4thmOjxvZEyLx4c2eOa3TcwGOmY/CJx9+FPJ8AY7Mc/DJhwuTrxlRr2+GfKn/VWgf g/rJx6AU+1mR+DBP34a0AOM8fdsY/1tI/wPGefpbY/wvIP0pjPP0F8b4H0H6Axjn6Y+M8d+D 9NswztPvGeNfgPR5GOfpC8b4ZyD9Kozz9Blj/D9D+nkY5+k/G+M/C+kj8J+nnzXGPwTpORji 6UPG+AcgzcM4Tx8wxp+A9CiM8/SEMX4W0kMwztNZY/wnIJ2GcZ5+whi/C9JtMM7TXcb4LZAO wzhPtxjjByFNwDhPB43x6yGNwThP1xvjw5CGYJynYWN8AFIvjPM0YIx3QWqHcZ66jPEopA0w zlPUGG+C1ADjPDUZ43WQamGcpzpjvAZSFYzzVGOMeyC9rf2sSB5j/P/B/vE6jPPEh3n6AaTv wzhPPzDGX4P0jzDO02ti/MNwpPg4+5b2C/DJhwvjgf8kPBDNyQPJfvg47P8/L9Ii1Ph4+i77 ZUi/KtIiDC+K8V+D9HmRFmF4UYy/COm3RVqE4UUx/iVIfyDSIgwvivGXIf2pSIswvCjGvwrp r0VahOFFMf43kBZEWoThRTH+d5Aui7QIw4ti/J8g/bNIizC8KMb/D6QfirQIw4ti/MeQ3hRp EYYXxfg7kLwenhZheFGM+2DcL9IiDC+K8VWQwiItwvCiGL9K1LN5WoThRTF+DaQ2kRZheFGM d0DqFmkRhhfFeB+k7SItwvCiGL8O0ohIizC8KMb3QLpBpEUYXhTjN0E6JNIiDC+K8WlIx0Va hOFFMX4C0kmRFmF4UYyfgfSASIswvCjGH4Z0TqRFGF4U449Beq9IizC8KMbfB+kZkRZheFGM /ySk50VahOFFMf4zkD4h0iIML4rxX4T0SZEWYXhRjH8a0mdFWoThRTH+m5D+q0iLMLwoxl+C 9N9FWoThRTF+EdKfiLQIw4ti/CuQ/kqkRRheFON/Dec934Dhv4Z6Bx926sNYDTL5ypP5bDz2 ycTVrFZvpXZK+0V2Svtr9rxIfPgXLWv8fPn8KuZ/eL5jXM01ruPyq9r3PnDq3RsmTz264dBD D9z5oDY8PAw/DAum4NN8BOhijeLTA3ng172qPfz6LL/+17vyj9iDovZ8/beuMqbVr5z+h8dj XGX8fXF8YR5Py82PvfvsqQc0W/SYVVryNrHbCgvak2xB+232pkh8+Mk8bIXx+FaYlNgKv8Yu i7U9861QfCvIrc12z4/Ao22eoyLx4R/lYW2+pbQ2V3lGxDWed38rqLg2/8Vzhf0XT4/nz0Xi w1cs10Z1Xj26OQ/Jy+U51teKb5pKsf/FrtVWsICxh8auY+l7qM/xHlqRtOUSS+5iVyWdUSbm 8Ikp6zwhz2fEleM1MPQlT3IO6zw+z2c9es4Wk7T1ebRVqdpa52yFpivIl8Rn7f3WNmPZm3Ne drWhfSzGZiNGXzxGo+cP0mIwyfzrMaqNGHyJ+p64zeZKZnWOWq6Mr5X1/hnb433xnOmfKy2P 7Al3JufH+dEvFif5mkKixmbnmvvgiHEf+wnPL4nEh7dbXq1QLRNkjh+3e94tdJ/51jVZjh9O r79s9NR7Pq05vQJlt40+rE2wD2t/w14RiQ9POD4qOZk39ai02bjWU1/xt8Y8/FufkV/+ec3d mjZS87T2r9pz3heFDs9qI9qNMCW/y8W/ec7Lt05sb7Im5eY1QRBuY3v2SSxpKvN4T1A8S94o bjyvYjz2ltp8tYrxGhTjXVSLx5bU4nUvKcVjJ9Ti1SypxTuxpBQPwinFm1lSisdPsFTi8VZ9 CvFEOIV44SW1eBfV4lUuSca7bA7nPB67pIebU4vXvaQUjxnhjkjGM8K96fx4xq7w/yf0+XZL HD95vBoj3Lzz47WIZ4R7ReZ4DfFW6rO9My9RHvF4DfFwEvHeMJyw9M4Lmlw8I9zrmlS8d4y1 W+LhJOItzZjCScSLNRD9jCYZT+dtfdR5fcmY7zV9bJNsvEuOAyXF+7fLkrMZ8fapxXuzVi3e AZ9SvHfm1eK9oinFA+MpxfuaphbvBU0pHneCSjwIpxJPOEEh3puaWrxLivHmFeNpavHe0dTi vaUY7w3FeFfyEm+FYjznx8/LSfGkyr+ixruUFE+uPoEh3m7FePNJ8WTru/LxtHzEkz6/NeJJ n08b8XyK8boV451Qi+eRPl/R4zVInx+JeJ6LavEq5c/HRLyL8udjPF6lwvkfj/eiwvkfxBPn SArxulXON32X9FMy+XidSue3vksnJM83N+jx/kXyfFPbrcdbUDu/rVU7v42dI8nGM07Jlj4n GS9snCNJHM9EvIuxUzK581vj4sSPZcuji/FzJKl4xsUJXhGVOb+NXS3gp2Qy9Qnj4sTbuyXj GeFEvdd5vLdjl154OJnz24umcPLnt+LBSYXzW35xQiXeK/roKsl4RjjnGPFel5wtFu8F2dmY Yjg93mekZ2MxJ6jEe01+NqYYjsf7nsJsLOYE+XhvqszGFMNp8rumEU9hXxHxLivNxhRXzzif lo+neAfHOJ8uXjzVO2JXkMS7jCTepfKOZ9Q/88DKz84rzdegeoBTKS7EAfWKynxQ01Y6BDSr 3eQU13sVZhOVQ4X5vEtLF1Xi+Za+VqkSr3bpBa9KvGYonuYU5ht+Te0QPndJLR4vnnZlnSoN UVwoNKLw8sqFwvqJw6lCPHGdQqHI4NcpVA7htfOwjpfl52vW1A6pXDrpa9mafr7ik59NnK8M z8vP97Cm1uaGx1Opjz6sWAJDvJrL8mcTPJ5vXGEFId7wiwp+gHhzcwrHs+38AKoUz6d0vN7F r1UoxLv6iFr5MMyLoz+Un4+v3ddkG/Vt169rvSB7/GwSbXxeF6aQi+cR57qyx7Mmfhntbfnj y3aPONetuSwbLyzOdaWPL7suinPd4LzkfHP6uW6FbLxhxXPdOcVz3WHFc9252FUfSRTDaXNf U5pNk7/qo3NWbTZNMRxBEARBEARBEARBEO6mcjekA/Lz8WvkKg9TNl/RNN8VikfxKB7Fo3gU j+JRPIpH8SgexaN4FI/iUTyKR/EonhWq1+sIgiAIgiAIgrBnldpsni+pzVdZ3P6FKxX7F35R sX/hObV43Wr9/bITxe1f+IRUfzpJ4VTizcj13xNDtX/hhkQnMPLh5B8HCps6gZHhomSvLMa7 LSrNncA4wTufCCfTFYXeEZnep45MTxt6vO54h0FS8Yweg3bLxjN6DJqXjcedsPTvUvsmf8DX 6M5Yat/k8US4t5tfkYynd/j72vBlyXjCCW/vlnsqvPay4QTJp959lwzjSRYNtW/q4eYlry41 x7pDar4kFy/WHdLFFxTivSL9kL2I984LmmwnAs2G8SqvyM03bPS+JHuxrtnwueTmFPH489Ky D4U3G90hyfZZMKwfVjyyzxQ36+Gku7kZ1ntfkr72OacfxaSvtQ7rRZCc2Xk8EU5aPm1oXnxI 91K0ygirf1Tulps75oYXJbtGMeRjc1fk5jPk65Z9FF2XT/59E3q8GulH35tFvBPSj76L3cUj 3xWL2F3EgVsh3kX5eLyXEr0KIzcf3z1FIS8bL1ankNQP4umFvFw83gm2KOQl43mX5vVS90W5 wwxb0kvdtyUPM96l7+l1Csn+d2OdEO6W7Abbu2TUriW7KmGxQl4pHhTykqUSixXyzVKzGfFe kO7ZhhmFPJN8At5r1K5lu0+OdUIo3Yv5kn5q1H1Zcr45vTIvuTl5qfvmPHzsmpecz6efiUm/ ncuj9wnmuHd2giAIgiAIF7BSodc6jkprMI7qqykpHsWjeBSP4lE8ikfxcohXeQSY+TH/f0Aq 1FIcqaDNifnk7hlTPEtU9dODun//pHgUj+JRPIpH8SgexUtC9XodQRAEQRBEqVipOJ/KW8c4 /MELFaTbnhmovOWM4lE8t8Y7oBgvdlok25T2iky8xLmQVyZeZeKNe/HTMOt4lUlj3YlzvdrM 8ZIbLM8lLos3W8QzvYAs/jPHa7osPmwRr0tL/5njW7qUCG0R74SW/rNYiOlpjqX0eB7Tz0nx Tpiu+lvEMz/ZNWe+QWBaPa9FvFrTU5XDphbSzPQcI4u/7DYRz7wNzfG8pld411rEu5j4OSme b8k8nB6PP5wQHzZNW2veXunxvOZnZ8zxmpfMw2nxmPkZCPO05mUspcerNTcyN09r3kZz6fF8 ZsnM8ZLkS483bBvvSnyQvZ0eL8kCNvFq37CIZ94lTSub2EVgXX6QHi/JAiatzbvL8OX0eEkv hLeJ1/yybbzdmeItzdvG0/+bHgtMcnu6H5j+s3FctY4HS0uL53UQr/KtbPFMO2ttIh4cPG3j vZEhXvMVqXjzscHhy1bxxIA3U7y5S+nxfA7iwZBKPFiYbTyfRbzLxhBfmN32jJVTVvHA2fbx jCfmEuZPxONOU4nXfMk+XnOGeHzALp5xYEnankYINmdVPhg/Z4gnlLKI94Y5XuJgE4/HN5lt PKNgSIpnLKObL9Iu3lLSbpMUjx8oreLxEMx4Us50lbk29lCaWLBFvHfEfz1erTmesam4UFb+ e0tMlLSbJhbIn2a8bB2PR6pdMnaby6Z4etYbljTrePzRoGbjLfem51ZjWddrMVbxLosHknQZ E/GYkfW5t+zivQ4HOqNrD1NZ79WfKK7RO3+wOF4vvfPCi+8YjwKaynqmPzDdICJZ1F/mlpa+ vnRlbklffS2B3u/DRX2DWcXjKzcsVtNc9dAfpqrU19I6Hvymb9CG5HhL33vfi8ZWtShv+XyX h/WH4s1lPV/gjNEUxCKeaC0i/h9O7p9lWG8KIhZlUR8UrUXm+f93Lia1FzGan1yxiecTzVJ8 pomS4122iecVD695TROZFrhk7DQW8ZgIw0wTmVcg5meL+qcI4zVNZFrgUtyEafH4k6OaIePb ptmMRRnljUW8bmN/Wkp9XFUs6hXbeJX6A/+pm9PYA1+wjad99oXYVkh+fLs5kXOr+nUs6lJq Dx+1S/GeV6zq8zri6ebk7yrjq5chHt+sKR2YeOKrlyGe1rmU9j7jE/ElWdTnY7Ajh1O/WvnZ 2JKG48uUO5+ei+8RcufviR5tpOKxhEJS8bwJhaTimYobuXgvxAfl1i8xWK7XeygexcMQT/V+ gOr9B4IgCIJYvtSpzbbyD9Xmm5Hs78mgU7a/J51Kxf5ba9X6U41fxFKId1kx3qVixmMzavG0 lWrx4ET7itp83Ur6qTfile2AKYZ0N4QxZP1QeUT/fCLzZGl0G9cpdknOF7vvKBnPG7t0JRmP X1ITA5JnLbWxK0CS8XyxS4GS8YZjF/Ak4/Eraq8rxhMbNCg3n7hiqBAvfq1Tbv3Y0o+NDSob 71eMDSoZ763YsVounvcdvse8Ix/vSmwPlevmFA4rXn2DSvY/eEVIeEnWf97LYpd5QyVeM7+k LXk84/H4BpXt9Jdd0i/6HpAsH3g8cRtAsnzn8XRPyMfT0u43OIjHwzSn3m9wGK829X6Dw3i+ mOcl4JvRKx9P3y3n5LuDF7vlRfnqmbBdg3z1TFzMqpGvLom+vdnc29mmS4snLtN3fFZ2vual tHsNjqiV6Xo+KZ5aRX5Yek/JLZ78nilgc0tfUwnn5T18q8RTeHGAiCd6x1SIt1tpNoLAzf9+ y6ON1Pyp9qz2nPdFcfPvWW1Eu5F9WNsEw/yb57z8rmB6B7A/Zxrm5WRl9wmtOnqDxrae0ry9 d2sVPbdrlVuOalXtB7XKjpu16o03aTXNg5rWd1pjkCp6TmiVXce0ys6bNe/WO7Xq9ikYntEq um/XPL0n4bfjWuXmac0Ly6iGZdREhjVf5xH4jS/7Dvj9Voh1N3x3s+brntE8W++C5UJs+Kzo vg3mnYFY92g+WG4FLEvjv0G8iu5bIT+HIF9TmheWV9l5GGJMw3JmIP/7IJ83Qr5v0bwQowqm q46MaDXX7ITl36l5IW/V1/DxYa1iK+Rh8xFY5u2w/FthWr4ed8D0YzDvCc3XdoPmg3WuguX7 2vZrNRth2S2jWs26PvjuZpgH1n3LMYi5F8aPaD749LXfrHlgO/ogH5U8bxDPB/nxwXeVkG/v Vr6Nx+G7Kcj/Adie10JcmLftRljGNMQeh2Ue13xbZiCvJ7Qq+KzsOKhVN+/QKmCdPJA/L2yb Ghiv7jyoVUH+qmFdqmAbe2F+H0xTw9cfhith3uqNN0Dc2yDWJEy3E/J3ENb7TsgPxODawHat 4esL6+8D7StgORWwTlWwPasgbhVMU91+E+R3Etbrdq3m6kHNB9NXwXhl5y3w+xGtGraNly8v she2J+SD68O16btbYwQmSC9ckF64IL1wQXrhgvTCBemFC9ILF6QXLkgvXJBeuCC9cEF64YL0 wgXphQvSCxekV5yfra8vdRayQ3olCPg7Sp2FrJBecTr8/lJnITukVwI/+QsTW8hfmLiuh/yF Ch/5CxMbouQvVLSSvzAxFSB/IWJTu5/8hYmwn/yFhpp9437yFyJ46UX+QsL+o7pc5C8UrGN1 ulzkL/dzHZuqi/gNSp2Z7Cx3vbp6BuJqkb9czX52fK6+yW+m1FnKznLVaycLsRSxyF9uZRur Ho2GU8Uif7mSB1ln/1jEQizyl9uYZ/3Vo8fSjoLkLzeykx1ntsYif7kLXmJlMhb5y0VkKrHI X65ibbYSi/zlHq5ls+y8M2ORv0pNHxscrG8NSIhF/ioZq9n580GrE2LyV6Hon/640nxr2SkF Y5G/cuQjYxGFuUSJpWIs8leuyG88pRLLH26tao+GIuSv3JB9uEelxAq0HuvZCo68jZ1kA+Sv 3PD7H3E4ZQ3rVTBWONhwbpZtN5ZxdpTaR+UE+OvMvY6mnGXD0iVW07FRdiplOdT+MCf8/p6s 0+xhoTlpY0XG+jvZg2wiZVkXNpO/cgD85fNmnGI1O6dkrGq2zXJ5c+SvXPD7g7a/QYnF1Ix1 nO20XWqY/KUO+Cs8aPObSokVjo6y/owRT4bIXzkA27jh1rRv1Uqs0FQn25BWYqUSJX+p08E9 kfKdUokVjvZMs5UOIm7rJ3/lAN/WLTMbxPBa5mWeavlzLG6sUPwcKxvT5C91OgxzzLEO+Ovx VVVKX2iK9rCTEhEf30L+ygFJdZIJtNbPQYlVIxWxkfylToe6WOHg8Dko6+RZ36N2C6eYuFWv HPzVBLVIFXodl3QlxK16qfgrrNcdS531guJWvWT9JUqsJjHo/kpDDrhVLyl/hYPnz4sSi/xV MiSMNTjI+sQ8HeSvUuHMX2AsNsuujc9F/ioZ2cVqOjY6eIqtNc1D/ioZWfxl3HRMhfxVMjIb y/qmI/mrZNj5C4zF7G86kr9KhpVY4bqe6X42bzsP+atkpPurCU6I12WZi/xVMlKcFZ1zcAmX /FUyzP4KhHrYBkdzkb9KRkKtgR0ns7a90CF/lYy4v4Z2SMxF/ioZhrkmuyTmIX+VDH3TB6YO Sc1F/ioZYtNPyslF/ioZYtMPyRwMOeSvksGvPT0pOQ/5q2TwTR+Vnov8VTKgslG/X3KeXP3V pzxn0XCrXrDpQzKtc3Vy89eqtvuV5y0WbtULNn279Dy5+SsURnAodatesOkb7paeKzd/Yaiq uFUv2Hiylflc/fUIhqqKW/VSe7luLv669wz5KwdUNl5u/qL3w+ZA8f3V10b+yoFAq3zlOjd/ bSZ/qTPNZhXmysVf1D9ATti3grInF3+tPR8hfxWbXPw1ieJScVnplYu/zodRXCouK71y8NcN URy3YspKL3V/fbQ/gONWTFnppeyvo/0RJLc6y0ovNX/NM1/sfaMFyVVeKSu9lPzVy9rjfVKR v4qKvL9Ws48FTS/sKFjO8kZZ6SXpr9Us9X2j5K+i4txfE2xDmljkr6LjdKP3M+v3jZK/iooT f+1kx+3f3lakfOZAWemV1V/ZXotI/ioqmfw1AdWLrK9FLHJ+FSgrvTL46xQbTK9ekL9Ki7W/ rmWzTt+FU5JcS1FWeln4S+4lU+SvopLsrwn5l0yVNPeOKCu9kvzVy+Q7QCd/5YBc79acmL/2 s1mFNwuQv3JiWmEefaOHmOr7RslfyoQiqu17w+rvGy3AeuQZt+ql0r63V6ET9EBlcHP75tix k/yljNzGUyqxeNe/1R52Af7OR6n9Rk7I+Gu7SomV8haI56h9VG74/WccTbeSTffUKbwWcTDl LRDU/jAnwF8912WdagPrnAplvoSbhl0Ppe3kr1zw+30Zf59nJ6d7LG86ZjFWtXUPpfOd5K8c AH8Fe21/5SWWirGs+tSOEQqTv3IAagTV1lc4eIklbaxw1PZFvjFC5C91+KnvUxbfK5VYoSnw o/2LfI0lR8lfOcA9cSzpm4eVSiz+vtGTjp4m6yd/qSMuLbWcNl6hdivrZedW1SsYq9Nhz7+M 7Zkmf+WAvsGDPVtZx9YzDfVB+feN1jl8kW8M6h8gB3J4f6VfXGlizl/kG8MfqCjIuuQTt+qV w/sr+ZWmWSbbtxtj909NFWA98oxb9VL0l3gtov15W2ZkDp6lwq16qfhL+UW+iHCrXrL+0l+L qPYiX0y4VS8pf/H3jZpfi1jGuFUvx/7ixmKnSp3douFWvZz5y3iRr7O3q5QFbtXLgb+aEi/y XT64Va8s/uL3Ro4vjxIrGbfqlclfTcdGWX+pM1gi3KqXnb+Mm47LqMRKxq16WfrLwU3Hcset eqX7q6l+LvtNx7LHrXp1pDqLHS91llyBW/Uy+yvQ2rZK4Xp7WeJWvRL+CgzsWHZnWfa4Va+4 v5oO3lnqrLgJt+oV89c4u7XUWXEVbtXL8NfcQ1RuJeFWvToMdxHJuFUv4a+me8hdKbhVL+6v wEH5N4CVO27Vi/tr4GipM+E+3KoX99eqUmfChbhVL/58ytPL9iq8PW7VC/xVt67UmXAhbtUr 6/OVyxS36uXs+eXlh1v1An81ljoLbsSteqm9v7L8cateKF5eXQLcqhf5yxq36sWaalU61Ct7 XKsXe7bUGXAl7tXLRdSwq5iHdbAI61V6b20eIb2yM8t61l/Nn3cPVAb7S9yymPTKwod8U0ld 9QUGnlToWThvkF6Z2TGQ1udHpJQOI70y0L9jwKrXj8DUoZI5jPSy50lLtbhgP1OyPJFeNmwY bLfv/Sh8vlTZIr2s2ceGbNUCakvVVwfpZUnsBdqifhGKtp3Z2sE6tiY6Cm4tVcZILyvuiL1A m/caxk7G2++vYwOGhs0lyhnpZYEvdixsaVu1Luls6+zouP5LqbJGeqVT36LXAgfY/Ra/tpC/ 3IUhV8tpdpvFr14flV+u4h4hV/ipp21+b+Q/t28qap4SkF4pDPIaRSA49T7bKbhec0XMURKk VzJ9/D0q4YYMUzzCj5VFy08qpFcSR9qgIj90LEP/Htt7wH7Xl6zjPtIrmbA/EB3NOEWV3z9+ Q5Fykw7pZaKma8gfmMosRn3EH64tUn4sIL3MPOWP9B/KOMW+cX+gZJV5RnolcT4c6P9oximO +gKBkvaiTXoluLA5MJXZXbx8GylGVmwhveLM+yI77sw4Re9M2D/+2NriZMca0itBezjbI0wt gZFiZCQDpFecpzOeJsOp9Fg4MLW3pO4ivRLce6Yp4++HGsJZ/Vd4SK84kxkv4naNBJpmStny UIf0itE/kOkFwu8a8A+V8DQ5DukVJ9xtd9Vwno01BUZWiOG17HFq3+sGOjI8IBgNhNkFxt9g FGJbtmzp+uniZSsF0iuO38Zf68aa/ANT7xOvOr1abyA1RO0PS05zZIvl9766QGTgmbtY0qtO B0rVhp70itNald5By77pSVApMhBNedVpoAT5E5BeMTa1R9K+E2pZQe2jSk99oKU+8SrFGtbv a7d92XOoVJkkvRJE/S3sJNsOFXYvm2UNY1bPO4RbW7mIPm+J8kh6xZk/2OIPhPp7tmzp8UVb LR4lCgfPN4KQrRF/uGR9F5BeZlrsjn/GG531dvThwDMly+Ey1GttY9jml75nrB/Q09+PGetL +HQg2leynhmXoV6sIXTS4ttr4VA3aaFXU8r7MetbStlr9zLUa1N7KO27PjY4WJ9eZom3jSW/ H7O+ZWC0hL12L0O9WDDFX6vZ+fPB9Kp70zGLt42tL6m7lqVeoXCiQdpadsraWKGpzuNpbxur YWOV0RUl7RN/GerF/OFz4pOXWFbGCkf5M5VW9FeW+g7zMtTrdr9/5AK7YF1iBVqnOtkGmztc a2qPlfoO8/LTiz9wFxho32xlrODwOZbpyf/VRculHctPLxa1PMeCE+K5ECvZcydOWX56nQyl iwUnxFCWYWD56cXCacYaHDzFStyu0CnLT6/GpHr72PnzLiiVnLPs9NoznXxCjOxVpstOL9Zv GKufpZ8Qu59lp9e6OnFCPN1f6p541Vh2erEZcUKMlWWn1y0ZT4hdz7LTCzmkFy5IL1yQXrgg vXBBeuGC9MIF6YUL0gsXpBcuSC9ckF64IL1wQXrhgvTCBemFC9ILF6QXLkgvXJBeuCC9cEF6 4YL0wgXphQvSCxekFy5IL1yQXrggvXBBeuGC9MIF6YUL0gsXpBcuSC9ckF64IL1wQXrhgvTC BemFC9ILF6QXLkgvXJBeuCC9cEF64YL0wgXphQvSCxekFy5IL1yQXrggvXBBeuGC9MIF6YUL 0gsXpBcuSC9ckF64IL1wQXrhgvTCBemFC9ILF6QXLkgvXJBeuCC9cEF64YL0wgXphYvy0qtD /GX7PdMUbqes9PLr2P7eYUyAWLBy0iu7HNkEdT/lpBf5CxfkL2SQv1BB/kIG+QsV5C9kkL9Q Qf5CBvkLFeQvZJC/UEH+Qgb5CxXkL2SQv1BB/kIG+QsV5C9kkL9QQf5CBvkLFeQvZJC/UEH+ Qgb5CxXkL2SQv1BB/kIG+QsV5C9kkL9QQf5CBvkLFeQvZJC/UEH+Qgb5CxXkL2SQv1BB/kIG +QsV5C9kkL9QQf5CBvkLFeQvZJC/UEH+Qgb5CxXkL2SQv1BB/kIG+QsV5C9kkL9QQf5CBvkL FeQvZJC/UEH+Qgb5CxXkL2SQv1BB/kIG+QsV5C9kkL9QQf5CBvkLFeQvZJC/UEH+Qgb5CxXk L2SQv1BB/kIG+QsV5C9kkL9QQf5CBvkLFeQvZJC/UEH+Qgb5CxXkL2SQv5DRIf6y/Y5YrjLT q/whvXBBeuGC9MIF6YUL0gsXpBcuSC9ckF64IL1wQXrhgvTCBemFC9ILF6QXLkgvXJBeuCC9 cEF64YL0wgXphQvSCxekFy5IL1yQXrggvXBBeuGC9MIF6YUL0gsXpBcuSC9ckF64IL1wQXrh gvTCBemFC9ILF6QXLkgvXJBeuCC9cEF64YL0wgXphQvSCxekFy5IL1yQXrggvXBBeuGC9Eqw rdQZcADpJZhgD7LO/rFSZyM7pBenn40ea0LR0fmy1+taNnv+/FgEy4sElrdefWxwsL414Pej eZHAstVrgq1m588Hw34zpc5UdparXqdYkrHIX+5lP5s915BiLPKXS9nDQszCWOQvN7KanRu2 Nhb5y3X0sjl7Y5G/3MQ8Ozmd2VjkL9ewHUqsqVAku1j+yNhHSp3ZrJS5XivZdE/UgbH8/qZj o9X9pc5udspZrw2s06GxQlOd7MFSZ9cRZarXPOt3aqxwtGcafIiEctRrJzvO+sccGgtKt+2l zrAEZafXNlat3xtxZKyT4ERUuFWvx5XmEjcdnRjLH948B+UbPlyq15Etg5JzrGX9To3lD4R6 VlwoSL4Ljjv1+skuf6VHYvpr2Sw778xYUGjVsZMFy3mhcaderM0fdDxt6k3HLN4aeJLtL2DO C4w79bqw2T/i7IBlcdMxI0MMwUlxBtypFwv6fVmnqWGnnBor3KpLGr6vC2Mlw4Q79QqF/T3X ZZlmltncdEw9ALbWs2OzYsqh+qLkvpC4Uq+Jbr9/S4bf97BQ9nsjuqGCw+dm+54/NgTDkfYV RVuDguFKvdjtoJfdGVjWm44mY82xXpjjsfv45ENsUzFXoUC4Ui+vz+8/b/F9jaObjnFjgbSc qQEuXnQUecml40q9WNTvH0ivx51kjo3FQnDQ1BFVjSYETa0d4Uq9ToagtDnda/oGSiyH90ZE iRU/w9owGgU3BoLvKsFaFARX6sW4JVriYzIl1iAzy3yU8YpGuOGG4q9CgXClXo+IrT9Uf4Ft lzjHCp4/D9JOmJZz1CcqGvV9JVuTvONGvR7fYiiwebqn37GxBlmqLHfVgc6RyZKsQ6Fwo17M 50CiZGPNsmtTllHDZlq4uXbAUBnhRr02RB1rJUqsU5ZL4cdCqMUXOe+Fxo16sVbnxkopsWL0 imNhuLUszrnMuFGvZif19qZjo+klVow+Jo6FRc11cXChXmsbs2kVGevvPM522i7hUEOYVzS6 bitirouEC/Vi09mMleUeVtcIHAubThcpt8XFhXpt689oLPagZYmV4B5+vXCotki5LTIu1IvV 2Rqr2kEfGaIaP35HEfJZClyo16xF9TBbiRWjRlzeDdTJtq5Cgwv1YqnVw+wlVoIpfnl36mhB 81dKXKhXUvXQeBQhc4kVY/9jI0KuQwXOYQlxn157EtVD6UcRxv1lLpcL9WK1cWPJPYqw4WD5 y+VCvXqDwlgKbXCHyl8uF+rFZqDE2iB9Vb23ntfj/dHyuTVpiQv1Unx4Tsg1rtZkrYbdxrxK cxYbN+qlwP6D/GDoHzooPWcN1D6ru9ra232dSU0JXEqZ6CVqhv6wfPvdWdZzLNbcoCX75CWn LPSaODDCt3egVWqu/ex451RS05Dw2C0FymHeKAu9WJvY6lGJOfgzzvVNqZe9wgXLYb4oC73W izY5TXc5fRZ5Jxu0eRSzvaD5zAPloNegqGsEDt7taGouVp1do6vW2QLnNVfKQK9N7WJTDzi5 yLs9k1hc9ILnNkfKQC/jev6qrNPdzaZ7MonFcXsPbWWg14DY0E1dma/hg1gN2Z9Id32FA79e u/UaeXump7uciQUE3X7KjF6vft1eGQ6HK9k5Z2IBDUXMuRLo9WLGCa/NryvZ1JTjvh7cfzjE r1es7bZVRUFOLMBhJxIlBLteg7H6Xtovq52JFWg91h+rNIatHsJ1F8j1uvdMbLsnl1+rnT3n HAlNTbEH2boxXbCnSrQWEiDXi03GNv3Ac/Hv1oFYTdkPfpGxhljrEH5PGxZRqpWQALlep4Lx 49rk0+w2djMLNbZVOXjET4iVuIC1Gb5quedsCdfEIcj1YpUJCVqi7e3Rq508jhkOJonF2IoW DHVDDnK9KmRqf4ZYdaODLPnS8NH7UNz74iDXi6mIld6sO+wP+HC0CUauV4eMWE31cyELsXqP hfE0g0OuF3Pc9WFTPTtus4wWPHJh16s3mF0pfk481XncplfRW2Za/JF+LHJh14s1OBGL2d81 7gOHhtm+IuY4N5DrVVOd+YAYGeufms3Qxzx/0hlJzVAHuV7s1mfsa/RwTpzlubEVIwEUrQ4T YNeL9dl0rsLPifsztsKfYPVDfv/APW6/RZkEer3YQyPpDoPTLJteb8x8GOrxI4XPYF7Brxe7 cLol6SgYqp9j6xw8kXlhsz/cytYWIYd5pAz0Aubqr64MBCpDwc1trNnoBjY7dx2bKmiuCkF5 6AUSeViEnWQXpPyC4xVtSZSJXssG0gsXpBcuSC9ckF64IL1wQXrhgvTCBemFC9ILF6QXLkgv XJBeuCC9cEF64YL0wgXphQvSCxekFy5IL1yQXrggvXBBeuGC9MIF6YUL0gsXpBcuSC9ckF64 IL1wQXrhgvTCBemFC9ILF6QXLkgvXJBeuCC9cEF64aKs9drIOsTfxlJnJH+UtV6x3n1LnY08 UtZ6xXr3dftL2CQoa73IX7ggfyGD/IUK8hcyyF+oIH8hg/yFCvIXMshfqCB/IYP8hQryFzLI X6ggfyGD/IUK8hcyyF+oIH8hg/yFCvIXMshfqCB/IYP8hQryFzLIX6ggfyGD/IUK8hcyyF+o IH8hg/yFCvIXMshfqCB/IYP8hQryFzLIX6ggf7mEDod/MX85nd79oNTLXyhKvWLZwahXR/YN r4j7HYZRL/IXLshfyCiYXqVesexg1Iv8hYyC6VXqFcsOSr3o/Ks8oesbyEBzmHNMWetF/kIG +QsV5C9kkL9QQf5CBvkLFeQvZJC/UEH+Qgb5CxXkL2SQv1BB/kIG+QsV5C9kkL9QQf5CBvkL FeQvZJC/UEH+Qgb5CxXkL2SQv1BB/kIG+QsV5C9kkL9QQf5CBvkLFeQvZJC/UEH+Qgb5CxXk L2SQv1BB/kIG+QsVG41uUDaWOiP5o6z1KkNIL1yQXrggvXBBeuGC9MIF6YUL0gsXpBcuSC9c kF64IL1wQXrhgvTCBemFC9ILF6QXLkgvXJBeuCC9cEF64YL0wgXphQvSCxekFy5IL1yQXrgg vXBBeuGC9MIF6YUL0gsXpBcuSC9ckF64IL1wQXrhgvTCBemFi7LWi/e/UWaUtV6svPq24ZS1 XmXWdxSnnPUqt76jOGWsV0e59c3GKWO9yF+4IH8hg/yFCvIXMshfqCB/IYP8hQryFzLIX6gg fyGD/IUK8ldRuT/nm43kryJS35KrN8hfReSulty9Qf6yYC27innykZMkBsdz9wb5K43tLMQa 1l9dWZGn3MTQ5Vp+/voQi2SZIge9trPpnmi4EBvlofF8vLcLn7/qByKB1tP7Mk2irteKnrGI vlnDdf3KS7Hi0GR+3ttVOH9tKMhSZ8TOH5g6lGEaRb3WTW0OGxu1pW3VOrWF2HBoKpCX9+IV yl/72VT0XXlfKmM3NelrHfAdtZ9ITa+pukhMrekuxezZkZDLnf5ax2Dlw0/nfbnb+mOrHc4w lYJe19zUHlMr3Na1RzmD1hz1xeVyob/WGrvq0EMTeV0upzW+3gP2E8nrdYEZvvUHglM5ZM+O cDzb7vPXrUzsqpH2vC5VpyKxnwbqbaeS1qtrJLbcsC9jTUaJW8ZMcrnOXzccFLtqeAx8ln9M K96+yW4iWb3uGUgcZTMUi8qY5XKbv94VFLtqy/o8LjNBh2nFm2ynktSLX9UzFjlzS44ZTCfZ Xe7yVw07rZcD43flaYmpmNf9EbuJ5PRKyJWpDqNOslzu8le/XskaH8zbEpM5FTSt+aqHbaaS 0msqLlekNf/u6p1JkctF/to/WKcX20MH87I8KyZNa257AUJGr4ND8eW9P/9VDcZa/CnkuLx8 +su4Qtbiy9PyrDCt+eYLNtNI6LV3R3xxAz+dlwya6RXH2kjElGnX+Gu/cf3ZH57Jw9LsMMpH QdBuIud6HWmLnyCEa/OSwWS4XIH+97vSX4Zc/vvysjQ72hNrHjppM41jvc7WJkqXyTxlMIHu rsDUoUfc56+a2O0C/1BXTc5Ls2d/feKMudVuIuf+Go8vrBB1Q0OupLMQt/gr5vls96Zy5WQo vuaRZptpHOu1OyH+ZL4rG3F3saRC1yX+2hErU6OFuY2SoC37rupUr/74dQ1/5HyespcgIZf7 /DUYqxVHClFqJ5Mocux2M4d6nV2fsFdV3rKnY3aX+/y1L35alOGqeZ64OZo3f1UltuKWx/OU vRhmudznr/g+37Ndaj67Gl4m5vLlr+nEeVGkWiEj9ujXDBM3wd3mr37F9a4OhRSiNeXHX3c3 JDaiSjYykSyX2/zV1aS23g+vUtlVNsVPwXL0l+lapP29GQVS3eU2fz2+RXW9a5V2lfgB0W4C Z3qFTFdiGxSyYUcfS5XLbf6qU1xvfrVdZVcxNnRu519rG00b8cy9Ctmw5qcaUuVym79MO2rX T0rNCTN2y7fxWGfsHzle3zCdyNnfSpNGbwmVLJfb/JXITLfUfPzAptJyzKje5Hb98MLm/G3E BEct5HKbvxxccbBEVBym5duO7ZnWg+V4fT6Yt42YQL8DmCqXu/xl3lHl1rvJn+GmYwa69GC5 3f+abc3fRoxxUFw/Tm987C5/BRXXexWfo06+3fNEtx4sx/vL+WtkFqN2yFoud/nLvKPKlF9r xM6och/DWH/bQ6kjvZI2YmM+2t7NtFjL5TZ/mW53S9QP99fq83UqRDQ2st3Pzvxl3og9CplI Zcz2SQx3+cucG5nzL+O6bf82xYj2d9rk/aWQiWTmb/AJucKtVvfR3OUvU2Ykrm8Y9lK6dCdm bJ21+1neX7ZVTcdExb0Z66O7e/0VcjzXdKxhUkD++QL9ysRm29/l/RXOrcFkr9GiOzxm3YLR tf4KOL1Ne1viVrz0tVbj/Mv+1o18/TC3+19HjVaGdnUn9/rL7/M6nCvRTFO+hijujgbO2f4u f/6V0/3lo3rRZesut/nLvKO2dDm7HnifaZ42yXh6k5uQ/QTO/LXLvBVzaMdwl9Gq2X6/c5e/ TC2WgGlH8yS1Km9ZIRlR1FQyVOkc6bUhas62ajuhGuOsK/OzLe7yl/lCnL9l1MEcdyW3Kn/q fVLxfHyeTNUUZ/7yJeVBtR3efcae15JhGnf5a2Vt0oqP2F3VSzA4njSHZAnWJcQOZZjCkV4n bk/OxMBz8u1cj9bHiuGW+t4M07nLX6uS1juQ9amcVLkyVc3TMZpdtPXZT+LMX9WB5HzLP6XR FTNXRne5zV+MJa94Nreky+WPnJaI1iSCZGrY47D9Rig5E5LPaaxlT8bruEO+TO5ym79Opqx4 y/r3ZJj6rnS5YBbnz2OOiBkyPszuTK99kymZaLJ/gj2dPhbvAMI/nmVat/krdcUzPbV9usUf GRjyp7LjToexWjNc94nh0F9zqZlocS5YV1u8hhsYeWx/lqnd5S/zMyOGYPdZ3+bftyYa8Qf6 n/tgml6ZOxhKYJwIZH46wWn73rRnH1ucHhJPJ2aNZL/j6jZ/pR4QgaEd7O6UqXayNZN8a48w iy0Fgu11EMl4Njyc+SzPoV5eX1ouwq1Ze6GYYLM7BgLmObLXK80hSu8vxnrSVtwfCDVMs5XG 2tewq9jgaFAc8McfWxtvgZE0w0j20+bYo/xZbts49df5iEUu2K0Z53mWTbeaDieOHOk2f5mv BpqIjNVOsw746+5qWH+1cbzXy+audIOBJ7MVHzNNxoRZpnP8/FfUIhdNB/cxu2tqdzNfbZN5 4oF7HMUxz+IGf5mutmdh6KBeNlsYDI4tk9P25dLZfcZlVX+4/rbM2XH+fGVqZwucQKiHrbaY diWbbhhLcmRgxFlrPPf562arPdWCxHmlpSO5YjYR1rGgsU8Ensl8xJLQK61Kb9BUP8dOwYFR L5lqmJfNss6pUMrhM9ya/VKOjnkuN/iLsZust38KA/fEzitvezK97NA3wub6WTafsvSdps4J /SMHsmXGub9GrY7Lej6C9Q1njKN5jy/amp7dbEflBEnPmzuey5p8LIOTfFy3xnxeuandbqpA 6xQLwdEnVu3aFq+q6At5LGteJPrfOG2z22QFqkdZjspx7j1jntEd/kq5RWK5htEV5vPKpzM5 Mhysne7q5rt3Y09/0LzkAQfX/2X6t3m/bR4y0jQmEWOLeU6J+azIxzJ0YrU3OwlSr6fWZxPY iqEPOMiJhF6JfihkCNQ5qxfqPJ6kl1v8FWveagOcqKSeV56WF2z8oJOMyPXPZluE2RJucHJu n8D8JIx7/AWld9SuWh+IHrSY3uf0LCAul7NmTFJ69dZLChaIrslwL8eC25KKavf4i7Ejet+i aQztsLrbXmPqNdrRhpp8yFk2ZPsXlRJsaIfk0lNOy6XnTiYfy0hwK5tMP8i12F/ta5A4JIaZ 0y5oJPWScVj4KfkH1pJbYrnJX/wu3rQvae0jAz1dttd3JrJVUhIEglOZ7qolId8/tsNKR6Sd yTU10Uk6Z1CY30w+lpHMo6zTV1XJ8xjedaxnml2Xcep3DVhtmDSaDkrkQFqv/Y+1OzgPi9RN KfUhnVQ9dJm/dLzMIy4NvJtl7z7laFv2Y2J48iZbj1qg8n6HHdku0ETqmNorOtZVJS1HaRkJ 8rGM3Ohj0cw7d1O/45JLR+l9HIMWJa85D2tYtrvI1sSem4rhRn/JMc/W9DfZ7tZjUP5JHoXU 3p+yz6qupOchOMrUHzhKuRSuvByd0vuLsw3O3Sw2VjgK5Z9cj1QcxfcTrWUreoJpueB56JU5 GqeQcmHbttMQZ7jBX5wJtoE1NtQHjb48IpWhqv5VW0MKYrFc3v+1nQ2u8FXpmQjoecjxvVhd 5nIx0DqVa1/U7vCXzq2sl4VERaWZnczhCJTT+xAfgGqFngkPDNl1ce+UQ6Y7bOHN9Wxljstz i7/yinveD5u4gsPLQKe3NzPhJn/lC9foddSQizc+yqUMTED+KiD36E+GNdUq9ctpDfmrYEw1 ierl3PG09g3qkL8Kxm3t/OJpvt/8Q/4qGO/1rcly8VQe8lcBebQAyyR/oYL8hQzyFyrIX8gg f6GC/IUM8hcqyF/IIH+hgvyFDPIXKshfyCB/oYL8hQzyFyo2kr+wQf5CxUZWZu4qc73KENIL F6QXLkgvXJBeuCC9cEF64YL0wgXphQvSCxekFy5IL1yQXrggvXBBeuGC9MIF6YUL0gsXpBcu SC9ckF64IL1wQXrhgvTCBemFC9ILF6QXLkgvXJBeuCC9cEF64YL0wgXplWCP4luVignpxfgb TvrYe1jjdH9VZamzkg3SazULsa626BNhFI+nL2O9HofDXzN/cVDiPUvhaKkzlY3lqVcfezfb usV4FWWCgdpSZywry1Kvxrbo1RavUBsfLXXGsrMc9epIl4rTclepM+aA5agXs9arrdTZcsJy 1MvGX6XOliOWo16sY+uZLW2bg5WBJL3cXpUXLEu9GK/MX2AeNljfGiB/oWECTpan2mM1xVxf b1oUlrVegk2xd0mH8vdmv8JBerG1bGqA6xVYf7bUWckO6cV5bJIXY1WlzoYDSC+BeJlwBEEJ Rnrp3BAFg9Xm+oruwkN6Gdw05Pc3deXjvc8Fpbz06mOzrAP+PMwrPe8OOCJuKUCe8ksZ6bWH nW+rauV180hllW9K+hWmUEmMbihIzvJI+ejVNd1qvr4UaPWdk5r/4VV+f7hAecsfZaLX2Ruu b0m7gBuenO6TWMYgWLOxYDnME2WiFwsG0uTiirU9JLMMv79NRuBSUBZ63TKTbi6DoXpW43Ap Xh8UYAXNZx4oC72Yxc39uMXuc7yULX7/rncXMJf5oAz06p3JIBcw8K5rHC2n5hEEFY4y0IvZ HgwNmtg+R8vh953dftOyDPTakUUuOB+bdFbt8Lv/piV+vU5b1gxTGHfS9on8VQS6sh0NdVpm si5popv8VXD4VQldkKkV3ayDNRxrshYs3Jp1WV0wVajwWc4J7HqxcaFG00/czx5g/F7xg109 Q5aCBSYf2p9xSX1tfn+oKHnOAex6TYsW8ONJ33XdZ13BH7dZRoyg3x/sLVxW8wJyvbb3CCEG k51ztN7aYi0zRzMsi18/dP0DD8j1Yu1cBovvR6yvJ/rsBdszDROcubdwWc0LyPU6BcewwOn0 g9jaQw2Wx8RA3T02S1o7ChXNSHVhs5s7yPVilbbtBm2uATeN2SyJO3WgcBnNE8j1qoDDXr/N bz6bemL0rlstpm7lFZeGuwuZ2XyAXC9+Cen2Eza/3VFnfemj6eCdadOKdgRh1x8OsevFLyF1 2/56dMpasMDADnababrtXZPitGBz4TOcK8j1ynKJdp/dnbHAADvO9MPiHhY7w3b9zRSGXq/m SOZLtBnuPDcda2hkHaxxur7J+GbS2W2XkoJcL9aa9RKtTSGWztBgUXKcG8j1Wlfn96/KOMX+ o77Mt59jhNcXKc85gVwvNu3gMGZ/TDQRyH793g0g16umOmx5OSqZg9Gsx8TAVBFymweQ68Vu jvr9h7NOdcT66hQ+udDrxWojTioKfVPWDUoNIv2HnLZSLDHo9WJgsJEDDqY72GQrV9jumqL7 QK/XfjBYoO3GrNNNsJsmrQ+KgSCWgyErA73YmnF+X2uvgyn3sX4Lj7Vcf0PB85g/8OvFdkPJ FBhxcrK7lnWN1iWZLDLW07Wn4DnMI2WgFwvxLT+0g13nYNqdbHCwNij6PQzvig4zu5sxbqUM 9NrWrxdDAz2DbLuD6a9lvayZdbAQ2+D42RXXUAZ6sc54Pa+uZyuI0YdPBseUg15J/RmGg5vb tnR1s42lzlRhKAe9Ov0WlDpTBaIM9NrUbqWX2x9cUKQM9LK+h1zqTBUI9HpdU592tyTSGm1r pPLLlaxjSZcswlevb4DKuuu7PVEGuV5rEnf7w8H6hsYQu4qtLXWmCglyvURVPlAZrF0Fp7+r S52ZIoBcr4rKKt+Kag9bWcanyEkg10upJzbMYNdruUF64YL0wgXphQvSCxekFy5IL1yQXrgg vXBBeuGC9MIF6YUL0gsXWoWmaRvZ0lLqZxN8/ofnO0ua4HkmPnZrqw/f+8Cpd2+YPPXohkMP PXDng9rw8Dj8MCyYhE8+/xZWIZbbxRrFpwfiVMJnted0n+aphqHeld9n/+jhC33sWyFjWk3T 560Sn1dgGYXOw99oH2J8obPfWm2bB/1T07zwuV5bAf/f9Oj5geUYU2uaDz7f8ujfxb7V561i Hq0RPrdXeiB+hfarnkbPUx7meb9H8/yqp8HDx5/0+DxznrfZs5D4sD4fM5bjNT498bzENPKK fPz/AgAAAP//AwDr7dFTtOcNAA==</item> <item item-id="3">iVBORw0KGgoAAAANSUhEUgAAAsYAAAHiCAYAAADrvQoIAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA Q3hJREFUeF7t3b/PJ0ed4HGDMQhd5L9gdV7drbwsIHvCTUDMaqULQLt3w3JrlhULjOSIzJYI LprAEYEjO0IitOQYCUdkSDh2NhKSMweWLJE/N/WYHvfzne/329VdVd3149XSyDPzdP16f6qq 31Ourv7SzZPrORcCCCCAAAIIIIAAAqMTCGLsQgABBBBAAAEEEEBgdALPjQ5A+xFAAAEEEEAA AQQQuN1FAQMCCCCAAAIIIIAAAggQY30AAQQQQAABBBBAAIFbAlaMdQQEEEAAAQQQQAABBIix PoAAAggggAACCCCAwOcErBjrCQgggAACCCCAAAIIEGN9AAEEEEAAAQQQQAABK8b6AAIIIIAA AggggAACTwnYSqEzIIAAAggggAACCCDwhAAx1g0QQAABBBBAAAEEECDG+gACCCCAAAIIIIAA Ap8TsGKsJyCAAAIIIIAAAgggQIz1AQQQQAABBBBAAAEErBjrAwgggAACCCCAAAIIPCVgK4XO gAACCCCAAAIIIIDAEwLEWDdAAAEEEEAAAQQQQIAY6wMIIIAAAggggAACCHxOwIqxnoAAAggg gAACCCCAADHWBxBAAAEEEEAAAQQQsGKsDyCAAAIIIIAAAggg8JSArRQ6AwIIIIAAAggggAAC TwgQY90AAQQQQAABBBBAAAFirA8ggMBoBB7ee+7m0q9TFn/+6MPoe0fjqL0IIIBAjwSsGPcY VW1CAIGrBD764wc3v/zOi3ek95OPH19M84f33729N6T50+/fQxcBBBBAoFMCxLjTwGoWAghc JxBEeC7H18Q45BTuXboHcwQQQACBtgkQ47bjp/YIIJBAYFoJnlaDL4nvr1+/T4oTOEuKAAII tEKAGLcSKfVEAIEiBN5548HTLRW/+v5Lz5Tx/ttvkuIi5GWKAAII1EeAGNcXEzVCAIGdCQQh nl7IC6vD0/XbRw/tKd45FopDAAEEjiRAjI+kr2wEEKiCwOl+47BKHLZZ/O43b1VRP5VAAAEE ENiHADHeh7NSEECgcgLhtIn5MW5hi4ULAQQQQGAsAsR4rHhrLQIIXCEQtk5MchxOoXAhgAAC CIxFgBiPFW+tRQCBKwROP+gx328MHAIIIIBA/wSIcf8x1kIEEIgk8Oi1ezd/+ezTO+cb22cc Cc9tCCCAQAcEiHEHQdQEBBBIJxCkeDrH+HTlOHwpz4UAAggg0D8BYtx/jLUQAQQWCJw7li2s FM/3G4eVZBcCCCCAQN8EiHHf8dU6BBBYIBCOZQvHs527wh7jSY7DirILAQQQQKBvAsS47/hq HQIIXCEQtkwsvWAXTqeY5PiSQIOMAAIIINAHAWLcRxy1AgEEVhKYPuqxtEXCfuOVYN2OAAII NEyAGDccPFVHAIFtBIIUh60RYSU4bKW4doWfzz/8EVaQw8dAXAgggAAC/REgxv3FVIsQQGCB wFx0p9+fJpm/fHfu/vB3LgQQQACBvgiY2fuKp9YggAACCCCAAAIIbCRAjDeCkwwBBBBAAAEE EECgLwLEuK94ag0CCCCAAAIIIIDARgLEeCM4yRBAAAEEEEAAAQT6IkCM+4qn1iCAAAIIIIAA AghsJECMN4KTDAEEEEAAAQQQQKAvAsS4r3hqDQIIIIAAAggggMBGAsR4IzjJEEAAAQQQQAAB BPoiQIz7iqfWIIBAAgEf7UiAJykCCCDQAQFi3EEQNQEBBNIJzL9ul56bHBBAAAEEWiRAjFuM mjojgEBWAjGfiM5aoMwQQAABBKokQIyrDItKIYDAngTOibFtFXtGQFkIIIBAHQSIcR1xUAsE EDiIwCUpJsYHBUSxCCCAwIEEiPGB8BWNAALHErgmxdPPjq2h0hFAAAEE9iRAjPekrSwEEKiG QIwUk+NqwqUiCCCAwC4EiPEumBWCAAK1EVgjxrZV1BY99UEAAQTKECDGZbjKFQEEKiawVoqt HFccTFVDAAEEMhIgxhlhygoBBBBAAAEEEECgXQLEuN3YqTkCCGQk4AMfGWHKCgEEEGiUADFu NHCqjQACeQkQ47w85YYAAgi0SIAYtxg1dUYAgewEiHF2pDJEAAEEmiNAjJsLmQojgEAJAsS4 BFV5IoAAAm0RIMZtxUttEUCgEAFiXAisbBFAAIGGCBDjhoKlqgggUI4AMS7HVs4IIIBAKwSI cSuRUk8EEEAAAQQQQACBogSIcVG8MkcAAQQQQAABBBBohQAxbiVS6okAAggggAACCCBQlAAx LopX5ggggAACCCCAAAKtECDGrURKPRFAoCgBL98VxStzBBBAoAkCxLiJMKkkAgiUJkCMSxOW PwIIIFA/AWJcf4zUEAEEdiBAjHeArAgEEECgcgLEuPIAqR4CCOxDgBjvw1kpCCCAQM0EiHHN 0VE3BBDYjQAx3g21ghBAAIFqCRDjakOjYgggsCcBYrwnbWUhgAACdRIgxnXGRa0QQAABBBBA AAEEdiZAjHcGrjgEEEAAAQQQQACBOgkQ4zrjolYIILAzAVspdgauOAQQQKBCAsS4wqCoEgII 7E+AGO/PXIkIIIBAbQSIcW0RUR8EEDiEADE+BLtCEUAAgaoIEOOqwqEyCCBwFAFifBR55SKA AAL1ECDG9cRCTRBA4EACxPhA+IpGAAEEKiFAjCsJhGoggMCxBIjxsfyVjgACCNRAgBjXEAV1 QACBwwkQ48NDoAIIIIDA4QSI8eEhUAEEEEAAAQQQQACBGggQ4xqioA4IIIAAAggggAAChxMg xoeHQAUQOI7AZ9/73nGFKxkBBBBAAIHKCBDjygKiOgjsRSBI8fRrrzJrLsce45qjo24IIIDA PgSI8T6clYJAdQTmYvz/Xr+5Gf3XXIwnFtUFrbIK+T8OlQVEdRBAIJkAMU5GKAME2iRAjO/+ Y+CcGI/+j4Vr7fd/HNoc92qNAALXCRBjPQSBQQkQY2KcIv7EeNCJQ7MR6JwAMe48wJqHwCUC xJgYE2PzAwIIIHCXADHWIxAYlAAxJsbEeNDBr9kIIHCRADHWORAYlAAx9sIhMR508Gs2AggQ Y30AAQTuEiDGxJgYmxUQQAABWyn0AQQQeEKAGBNjYmwqQAABBIixPoAAAsT4mXObHde27h8K TqUwjSCAQI8E7DHuMarahEAEASvGXr6zYhwxUNyCAAJDESDGQ4VbYxH4ggAxJsbE2IyAAAII 2EqhDyCAgK0UtlIkfgbcVgrTCAII9EjAinGPUdUmBCIIWDG2YmzFOGKguAUBBIYiQIyHCrfG ImArxSUZ9PKdl+/MDwgggAAx1gcQGJSAFWMrxlaMBx38mo0AAhcJEGOdA4FBCRDjdSukKRLZ Y1p7jAedODQbgc4JEOPOA6x5CFwiQIyJcYqwE2NzCwII9EiAGPcYVW1CIIIAMSbGxDhioLgF AQSGIkCMhwq3xiLwBQFibI8xMTYjIIAAAncJEGM9AoFBCRBjYkyMBx38mo0AAhcJEGOdA4FB CRBjYkyMBx38mo0AAsRYH0AAgbsEiDExJsZmBQQQQMBWCn0AAQSeECDGxJgYmwoQQAABYqwP IIAAMb45lUJfvlt3Sofj2kwjCCDQIwF7jHuMqjYhEEHAivE6EUxZXe0xLTGOGGRuQQCB5ggQ 4+ZCpsII5CFAjIlxirAT4zzjUC4IIFAXAWJcVzzUBoHdCBBjYkyMdxtuCkIAgUYIEONGAqWa COQmQIy9fEeMc48q+SGAQOsEiHHrEVR/BDYSIMbEmBhvHDySIYBAtwSIcbeh1TAErhMgxsSY GJslEEAAgbsEiLEegcCgBIgxMSbGgw5+zUYAgYsEiLHOgcCABOZSHH6fIki9pHWO8bqXEZ1K MeDEockIDECAGA8QZE1E4JSA1eJnJZAYE2MzBQIIIECM9QEEBiRAjNdJYC+r4jnbYcV4wIlD kxEYgAAxHiDImoiAFWMinFOKQ17E2LyCAAI9EiDGPUZVmxBYIGDFmCinijIxNs0ggECPBIhx j1HVJgSI8eoXCu0xXvePBWJsmkEAgR4JZBXj+YPF75+7wQCDWvqArRTL0keMlxnNV5mJcY9K oE0IIJBVjE9xzh80vaIObez9GqGNvceQGC9LHzFeZkSMR5sptBeB8QgUtbrexXhqX8/dZoQ2 9hy/S22zx9hxbfYYjzjytRkBBJYIFBPjc/8Leakyrf28d/EP8Rihja31uxz1JcbEmBjnGEny QACB3ggQ440RPRX/jdlUnWyENlYdgIKVI8bEmBgXHGCyRgCBZgkUEeNrLxw1S2pW8RFWw09X i22p6KHnftEGYkyMiXFfY1prEEAgD4HdxbiHF7kuiX+ekNSRywhtrIP0MbUgxuteNEuVyB7T O5XimLGrVAQQKEsguxjHHE9Vtkllc+99NXyiN0o7y/aWenMnxsQ4VdaJcb3jW80QQGA7gUPE uOVV4yXx3x6KelKO0MZ6aB9TE2JMjInxMWNPqQggUDeB7GI8b25vJxosCWPLwn8pbrZU1D2A t9aOGNtjTIy3jh7pEECgZwLEOCG6o7yQNko7E7pCc0mJMTEmxs0NWxVGAIEdCBDjBMijCOMo 7UzoCs0lJcbEmBg3N2xVGAEEdiBAjBMgjyKMo7QzoSs0l5QYE2Ni3NywVWEEENiBADFOgDyK MI7SzoSu0FxSYkyMiXFzw1aFEUBgBwLEOAHyKMI4SjsTukJzSYkxMSbGzQ1bFUYAgR0IEOME yKMI4yjtTOgKTSWdS3H4faogST/m0W/OMW5q2KssAghEEiDGkaDO3TaKMI7SzoSu0FRSq8Vj imzuf8AQ46aGvcoigEAkAWIcCYoYF+0qCVGQdC0BYkyMc0gyMV478tyPAAItEChqO7194OM0 oKOspI7SzhYGbI46EmNiTIxzjCR5IIBAjwSIcUJURxHGUdqZ0BWaSkqMz4vx/B/yOcSx9zxO 96o3NQhUFgEEELhAgBgndI1RhHGUdiZ0haaSEmNinEvabadoauirLAIIRBAgxhGQLt0yijCO 0s6ErtBUUmJMjIlxU0NWZRFAYEcCxDgB9ijCOEo7E7pCU0mJMTEmxk0NWZVFAIEdCRDjBNij COMo7UzoCk0lJcbEmBg3NWRVFgEEdiRAjBNgjyKMo7QzoSs0lZQYO5WCGDc1ZFUWAQR2JECM E2CPIoyjtDOhKzSVlBgTY2Lc1JBVWQQQ2JEAMU6APYowjtLOhK7QVFJiTIyJcVNDVmURQGBH AsQ4AfYowjhKOxO6QlNJibE9xsS4qSGrsgggsCMBYpwAexRhHKWdCV2hqaTEmBgT46aGrMoi gMCOBIhxAuxRhHGUdiZ0haaSEmNiTIybGrIqiwACOxIgxgmwRxHGUdqZ0BWaSkqMiTExbmrI qiwCCOxIgBgnwB5FGEdpZ0JXaCopMSbGxLipIauyCCCwIwFinAB7FGEcpZ0JXaGppMSYGBPj poasyiKAwI4EiHEC7FGEcZR2JnSFppISY2JMjJsasiqLAAI7EiDGCbBHEcZR2pnQFZpKSoyd Y0yMmxqyKosAAjsSIMYJsEcRxlHamdAVmkpKjIkxMW5qyKosAgjsSIAYJ8AeRRhHaWdCV2gq KTEmxsS4qSGrsgggsCMBYpwAexRhHKWdCV2hmaRzKQ6/zyVIPeQz9fPw3x7as0cb5v2pmUGg oggggMAVAsQ4oXuMIoyjtDOhKzST1Grx5dViYrx+JZ0YNzP0VRQBBCIJEONIUOduG0UYR2ln QldoJikxJsY5V5JP/w9EMwNBRRFAAIELBIhxQtcYRRhHaWdCV2gmKTEmxjnFOORl1biZ4a+i CCAQQYAYR0C6dMsowjhKOxO6QjNJiTExJsbNDFcVRQCBAwgQ4wToowjjKO1M6ArNJCXGxJgY NzNcVRQBBA4gQIwToI8ijKO0M6ErNJOUGK9/wSy3SPaWn60UzQx/FUUAgQgCxDgCkq0Uz90E OXa1T4AYE+PcYk6M258XtAABBL4gUNR25scf9Qh9lJXUUdrZYx89bRMxJsbEeISRro0IILCV ADHeSm6gdMS4n2ATY3uMiXE/41lLEEAgPwFivIHpz159/uYHL399Q8o2kxDjNuN2rtbEmBgT 437Gs5YggEB+AsR4A9P5FpER9t4S4w2dpNIkxJgYE+NKB6dqIYBAFQSI8YYwXJPhuTT/r//5 tQ2515eEGNcXk601IsbEmBhvHT3SIYDACASI8cooP/jGedn9+atfvj254fRX+PvWL2LcegS/ qD8xJsYlxTj0LxcCCCDQMgFivDJ6v3jlS8+kuLa14hf3nr1/ZZGH306MDw9BtgoQY2KcW4x9 Fjrb8JQRAghUQIAYbwjCtJXiBy9/9c4K8b9/6ysbcqs/CTGuP0axNSTGxJgYx44W9yGAwIgE iPGGqJ/bNvGjb351Q05tJCHGbcRpqZZzKQ6/LyFI8hzznGQf+VgafX6OAAKtECDGCZEKwvij b76QkEMbSYlxG3FaqqXV4jGldY9/rBDjpdHn5wgg0AoBYlwwUmFrRQ/HuRHjgp1kx6yJ8XUx nr8rsIdM9lQGMd5xICsKAQSKEiDGBfD2ds4xMS7QSQ7IkhgT41IyTowPGNCKRACBIgSIcRGs fWVKjPuIJzEmxsS4j7GsFQggUI4AMS7HtpuciXEfoSTGxJgY9zGWtQIBBMoRIMbl2HaTMzHu I5TEmBgT4z7GslYggEA5AsS4HNtucibGfYSSGBPjPcTY1+/6mC+0AoFRCRDjUSO/ot3EeAWs im8lxo5rKyXGvn5X8cBXNQQQWEWAGK/CNebNxLiPuBNjYkyM+xjLWoEAAuUIEOMMbHs4q/ga BmKcoZNUkAUxJsbEuIKBqAoIIFA1AWKcITzEOANEWRQl4HPQpLikFNtKUXT4yhwBBHYkQIwz wCbGGSDKoigBq8XLYuzLd8uMrsm1j3wUHcIyRwCBnQgQ4xPQW7YNEOOdeqtiNhMgxsvSR4yX GRHjzUNQQgQQaIRAt2J8+lnm8Odf3PtSVFjWynEJMQ51reXT0mt5REF2064EiPGy9BHjZUbE eNdhqzAEEDiAQJdifE6K53/3w3944SrqtSK4Voz/49tfOZHeu8K+VP+9+8laHnvXT3nLBIjx svQR42VGxHh5rLkDAQTaJtC9GP/g5a/fidAXK7GXV4/XiuAaMX5Wip97KsmhokurxA//upK8 Z7dby2PPuikrjgAxXpY+YrzMiBjHjTd3IYBAuwS6E+Pp4fbzV798NSo/feXLtxJ67lorgmvE eMr7v56Uf3otSfF0/4+/9ZWb15782utay2OveiknngAxXpY+YrzMKFaMff0ufmy6EwEE6iLQ rRjHYj4ntWtFcIsYX6pfbNlryoxlkVqn1HKkL0eAGKdJX+mjznrJ38kU5cawnBFAYB8Cw4tx wHwqmbFyOoVojaQu5f3Pf/ffLq5kz7vEmjJTu9JSnVPzl748AWJMjPeQb2JcfiwrAQEEyhIg xn/lOxfNtSK4RlJj8o7JL+aeXF0nps65ypJPGQLEmBgT4zJjS64IINAXgQ7F+PNjzrZcU7q1 IrimvLV5n2vH/375a5vbuJXLmjZuKUOacgR89S5Oiu0xjuMUu8+4XI+WMwIIIFCOwDaDjKzP /EETmSTLbSnyuaXOa6Ux3H/u5bvYxqe0L7aM+X17l7eljtJcJmC1OE74iHEcJ2JstkEAgZ4J dCnGIWCfP+TiPuhxGuC1IrhWjKfytp4ssbZ+qR147/JS6yv9XQLEOE74iHEcJ2JshkEAgZ4J dCvGU9CWPuZxKbhrZHfNvS12JmLcYtS+qDMxjhM+YhzHiRi3PR+oPQIIXCfQvRjv0QGI8R6U lbGVADGOEz5iHMcpVoydZbx1xEqHAAJHEiDGGegT4wwQZVGMADGOEz5iHMdp6XQLR7YVG8oy RgCBHQgQ4wyQiXEGiLIoRoAYxwkfMY7jRIyLDVUZI4BABQSIcQVBqL0K9hjXHqHr9SPGeYRv SQj9/HPOVozbni/UHoHRCRDj0XtARPuJcQSkim8hxsR4T2knxhVPBqqGAAKLBIjxIiI3EOO2 +wAxJsZHibEX8NqeO9QegREJEOMRo76yzcR4JbCKbvfVu3gptsc4ntWSaFs1rmgSUBUEEFhF gBivwjXmzcS43bhbLY6XPWIcz4oYtzsnqDkCCFwnQIz1kEUCxHgRUbU3EON42SPG8ayIcbVD XsUQQCCRADFOBBiS9y6OvbcvQxeoNgtiHC97xDieFTGudsirGAIIJBIgxokAiXEGgLIoRoAY x8seMY5nRYyLDVkZI4DAwQSIcYYA9L6i2nv7MnSBarMgxvGyR4zjWRHjaoe8iiGAQCIBYpwI 0IpxBoCyKEaAGOeTvSUZ9PMvWDuVotiQljECCBQmQIwzAO59RbX39mXoAtVmQYyJ8RHCfnpM YLUDRMUQQACBEwLEOEOX6F0ce29fhi5QZRbOMCbFR0jxVKZV4yqnBZVCAIEFAsQ4QxfpXRx7 b1+GLlBlFlaL14mxPcbreC1JNzGuclpQKQQQIMb5+sD8wbnm9/lqcExOxPgY7qmlEuN1okeM 1/EixqkjVHoEEKiRgBXjDFHpXRx7b1+GLlBlFsR4negR43W8iHGVw16lEEAgkQAxTgQYkvcu jr23L0MXqDILYrxO9IjxOl7EuMphr1IIIJBIgBgnAiTGGQDKIjsBL96tlzxivJ7ZNTm2xzj7 sJYhAgjsQIAYZ4Dc+4pq7+3L0AWqy8Jq8XrJI8brmcWKceiPLgQQQKAFAsQ4Q5R6F8fe25eh C1SXBTHOK3lL2wb8/Dxvq8bVTQ0qhAACCwSIcYYu0rs49t6+DF2guiyIMTGuQdaJcXVTgwoh gAAxLt8HehfH3ttXvofsXwIxJsbEeP9xp0QEEGifgBXj9mNYvAXEuDji7AUQ4/VibI/xemZL 8m3FOPvQliECCBQmQIwLA5Y9AkcQIMbrJY8Yr2e2Roy9gHfETKBMBBBYS4AYryXmfgQaIECM 10seMV7PbEmMw8+tGjcwYagiAgg8JUCMdQYEOiPgDONtgkeMt3FbkmNi3NkEozkIdE6AGHce YM0bj4DV4m2CR4y3cSPG480xWoxAzwSIcc/R1bYhCRDjbYJHjLdxI8ZDTjMajUC3BIjxxtD+ 7NXnb372ypcupv7xt1/YmLNkCKQRIMbbBI8Yb+O2Roy9gJc2tqVGAIHyBIjxBsbzB+j0+3// 5rMi/PNXv7whd0kQSCNAjMsI3pIA+vll7vYZp41pqRFAYD8CxHgl63/75tduggyH60fffP72 9+d+/dPffv32nunelcW4HYHNBIgxMa5N0onx5uEsIQII7EyAGG8Afkl2L0nyhiIkQWATASdS kOLapNiRbZuGskQIIHAQAWK8AfyPv/XCzQ+/8dWLKf/17796ZxV5QxGSILCJgNXi7WJsj/F2 dksybsV403CWCAEEDiBAjM9A/0nEi3O2SBzQWxW5SIAYb5c7Yryd3Rox9gLe4jB2AwIIHEiA GJ+B/x9PxHj+kLwUHy/XHdhzFf0MAdso0sSOGKfxWyPHhi8CCCBQKwFifCEyl/YLP7z37BFt 5/6u1oCrV78ErBaniR0xTuNHjPudW7QMgZEIEOML0f6/s+PXXnvy+0ui/NNXnr/NwdaKkYZN nW0lxmliR4zT+BHjOucFtUIAgXUEiPEFXv/y8tcukgxbKEY4geJPv3/v5tev3yf968bUIXfb RpEudcQ4neE1OT7to4cMFIUigAACCwSIcWIXCSvLlyT5u389yzixiF2Tf/Lx45vfPnp488vv vBi1z3rXyinsIgGrxWWlbmk11M/j+DudwiSGAAK1EyDGmSN0TpIzF1E0u0ev3btdJSbGRTFn z5wYx4kZgT2WEzHOPvRliAACmQkQ48xA59mFl/Ja3Xv80R8/sGJcsG/kzpoYpwufrRTpDJf+ 4WE7Re6RLz8EEMhNgBjnJtpRfjFH1nXU3GabYn9xHqEjxnk4rpHjZgediiOAQLcEiHG3oU1v GDFOZ7hHDlaL8wgdMc7DkRjvMeqVgQACpQgQ41JkO8iXGLcRRGKcR+iIcR6Oa8TYV/DamGPU EoGRCBDjkaK9sq3EeCWwg24nxnmEjhjn4bgkxuHnXsI7aLJQLAIILBIgxouIxr2BGLcRe2Kc R+iIcR6OxLiNeUMtEUDgPAFirGdcJHBJjH/3m7dufvX9l25PrQjHuoVzj13HEPDiXT6ZI8b5 WC7JsRXjY+YLpSKAwDIBYrzMaNg7TsU4fPxjEuLT85rD+ceu/QlYLd5P5pZkz8/jY+HYtv3n CiUigEAcAWIcx2nIu+byO0lxWC2erj9/9OGdD4GET0i79iVAjONljLjWxcqq8b5zhdIQQCCO ADGO43T2rl882UrQ8zUX47BSHOT49AoyPN0Xvpjn2pcAMa5L9sh3fDyI8b5zhdIQQCCOQFGz 6/nlrSDF575q1+qX7s51l9MV43P3/OWzT5+Kcdhv7NqPgP3F8RIWI6z2GOflucScGO83VygJ AQTiCRDjeFbP3DmSGF/D1PM/gBK6R/GkVovzihwxzstzjRg7z7j4dKEABBCIJFBUjCPr0Oxt xPjz0BHjY7owMc4rcsQ4L88lMXae8THzhlIRQOA6AWKc0EOIMTFO6D7JSYlxXpEjxnl5EuPk IS4DBBA4gAAxToBOjIlxQvdJTkqM84ocMc7LkxgnD3EZIIDAAQSIcQJ0YkyME7pPUlIv3uWX OGKcn+mSHDvPOGkakBgBBAoQIMYboY52KsU1TPYYb+xECcmsFu8vcUuS5+fbYuJ0ioSJQFIE EMhOgBhvREqMvwBHjDd2ooRkxHibhJHX+rgR44SJQFIEEMhOgBhvREqMifHGrpMlGTGuT/BI 97aYEOMsU4JMEEAgEwFivBEkMSbGG7tOcjL7i7cJGHGtkxsxTp4SZIAAAhkJEOONMIkxMd7Y dZKTWS0uI3hevivDdekfJF7AS54SZIAAAhkJEOMEmD2fSvHJx4/vfLgjfPr50jUXij9/9GEC UUljCBDjMgJHjMtwXRJjH/qIGfXuQQCBvQgQ4wTSvYpxkOJHr927I8bhz+fk+P2334y6LwGz pDMCtlGUkzdiXI7tkhzbTmGaQwCBWggQ44RI9CjGczm49PsJ2dK9CWglvUDAanE5eSPG5diu EePQx10IIIDAUQSI8VHklYvABgLEuJy8EeNybJfE2HaKDZOBJAggUIQAMS6CVaYI5CdgG0VZ cSPGZfkuybHtFPnnDDkigMB6AsR4PTMpEDiEgNXiY8VtSez8PC0+Tqc4ZFpRKAIInBBYJcZL e0qXvoCWml70EBiZADFOEy/iWj8/q8Yjz3DajkAdBFaJ8VTlcGrBO288uHMaQfhz+PuYKxzp lZI+pgz3INATAdso6pc64p0eI2Lc06ylLQi0SWCTGIemhqO75ivA1865PYcmNX2buNUagW0E rBanS9eSuNpjXJ7xUgxsp9g2P0iFAAL5CGwW41CFpa0TS9VMTb+Uv58j0AsBYlxe2ohxecZL Yux0il5mLO1AoF0CxLjd2Kn5QASIcXlpI8blGRPjgSYtTUWgUQLEuNHAqfY4BOwv3kfYiPE+ nJfk2HaKceY2LUWgRgLEuMaoqBMCMwJWi/cRNmK8D+clMbadwvSHAAJHEiDGR9JXNgIRBIjx PsJGjPfhTIwjBr1bEEDgMALE+DD0CkZgmYBtFPvJGjHej/WSHNtOsTw3uAMBBMoQIMYFuE4P 2B+8/PUCuctyJAJWi+uRtSWZ8/O8sXKm8UgznbYiUA8BYlwwFvMVqP/zjRfulPSzV58vWLKs eyFAjPPKFnlthycx7mUW0w4E2iJAjHeI1+mnsP/rlc+l+If/8NUdSk8vYqp/ek5yWEPANop9 Jc5Win15L/0jxXaKNbOFexFAIBeBbGJ8Kn9r/5yrQbXl87NXvnSnSj/59ldu/9ySbLZU19ri n1Ifq8X7ihox3pf3khg7nSJl9pAWAQS2EsgmxlsqMMKX7/7xb9rfZ0yMt/Tu9DTEeF9RI8b7 8l4rxmE8uBBAAIHSBIhxacJ/XR3eoZhiRRDjYmgvZmwbxf6SRoz3Z75WjvcfiUpEAIHRCBDj nSIeHro/+Luv3Skt/F0LFzHeP0pWi/eXNGK8P3NivP/cokQEELhOIMnMUrdCpKZvLbin+65/ /uqXm2gCMd43TFaLjxE0YnwM9yU59hLevvOP0hAYnQAxPqAH/Oe3X7j5t2/cXT0+oBrRRRLj aFRZbrRaXKegLQmcn5eLm6PbskwtMkEAgQgCxPgCpFa2OUTEOPkWYpyMMDoDq8Xl5Iq4tsvW qnH0FOJGBBBIJECMifFiFyLGi4iy3ECK2xU30l0+dlaNs0wzMkEAgQUCxPiKGAch/Of/0f5x a6mjgBinEoxLbwtFebkisO0ytmocN4+4CwEE0ggQ4wUxHu0FwXM4iHHaIItJbbX4eGHz8t3x MVj6h4tV45jZxD0IIJBCgBivEOP5g/Nf/76NzzmndI4pLTHOQfF6HlaLj5cyYnx8DNaIsQ9+ lJ+XlIDAiAQ2i/FfPvv06WeNwwMl/HnNlZp+TVlb7g1t+u7ffv3mn55spXjtWy/caevpsWs/ feX5i0V870kerV/EuGwErRbXIWTEuI44kOOy843cEUDgOoFNYvzJx49v3nnjwR1ZDH8Ofx9z hfsevXZvc/qYMlLvuXYqxU++/ZWrovzjJ8exTVcrZxVf40WMU3uT1eIl2anh58S4DTEOfcWW irJzktwRGJnAKjE+XSm99udzUFPT1xao0J4ffuP6anIPx74R43I9z2pxPTJGjOuJxdI/lLyI V25OkjMCoxNYJcajwzpt/6n0/uDlr59dSW6dGzEuE0FSXJeIEeO64rFGjsuMULkigMCIBIhx QtSXVoN7Ecpe2pEQ6iJJvXDXlogtiZqf7xtPq8ZFpiWZIjA8AWKc0AWWxDhkbY9xAuCOk1ot 3leiSGufvMlxx5OkpiFwEAFifAF8jPTG3POPf+NUioP6dtXFWi3uU9QI+P5x9SJe1VOdyiHQ HAFiXFiMm+sRZypsK0XeKFot3l+eYoTVHuM647IUO6vGeecnuSEwOgFifEWMl1aEl37eS+ci xnkjabW4TgEjxnXGZUmMT49v8+GPvPOV3BAYjQAxXhDja1JIjEcbLunttVpcr3wR43pjs1aO 00eqHBBAYFQCxDhSjOcPzemFOmI86rDZ3m6rxfXKFzGuNzZrxdiq8fY5SkoERidAjCP2GKd8 ErqHDmYrRb4oEuN65YsY1xubGDH2Rbx885ScEBiZADGOEOP5LT995fmrn4MOD9d/+cYXJ1H8 +Mnno1u/iHGeCNpGUbd4EeO64xMjx06oyDNXyQWBkQkQ4wvRf/DkU88x18N7X1oU5Zh8ar6H GOeJjtXiusWLGNcdn7VibDtFnnlLLgiMRoAYZ4z4j7/9gk9CZ+TZU1ZWi9uXrhgxc8/xcbZq 3NPMqS0I7E+AGBdi/p+zLReFitgtWyvGaahJ8fGyRFjHiYFzjdPmK6kRGJ0AMS7cA3wSujDg yrMnxe0Ima0U7cRq6R86Vo0rnxhVD4GKCRQV4/mDpmIGRav23f/uk9BFAVecOSluS7SIcVvx uibHVo0rnhhVDYHKCRDjygNUQ/VspdgWBS/btSVaxLiteK1ZNfYi3rY5TCoERiRAjEeM+so2 E+OVwJ7cbrW4Pckixu3FjByvn5ukQACB6wSIcUIPGUUYR2lnQle4k5QUtylYxLjNuJHjXDOX fBBAIBAgxgn9YBRhHKWdCV3haVJS3K5cEeN2Y7dGjnOMc3kggEC/BIhxQmxHEcZR2pnQFW6T kuJ+xWpJvPy87th7GS91dpMegXEIEOOEWI8ijKO0M6ErkOLX6xYj4io+5DhlhpMWgXEIEOOE WI8ijKO0c2tXsFJMuoh3G32AHG+d5aRDYBwCxDgh1qMI4yjt3NIVSHEbQhQjrvYY9xNLZxxv mc2kQQCBQIAYJ/SDUYRxlHau7QqkuC+RIsZ9xZMcr53R3I8AAsQ4sQ+MIoyjtDO2O5wKcfhz zIqke+oWL2Jcd3xyjx/bKmJnPPchMBYBK8YJ8R5FGEdpZ0xXIMX9yhMx7je2l6SaHMfMeu5B YCwCxDgh3qMI4yjtXOoKpLhvcSLGfcc3Ro6X5gA/RwCB/gkQ44QYjyKMo7TzWlewn7h/aSLG /cf4nByf+wdvwmNBUgQQSCTwu9+8dTOfj09/H35+ep1Lc+6+mKoR4xhKF+4ZRRhHaee5MFsl HkeWiPE4sT4V5HPjPPydCwEEjiHw0R8/uPnld168I8jvvPFgsTKPXrt3m+7PH324eO+lG4jx ZnQ3TwOWkEWVSa/9S20kSSbF44pS7he95Fd/XyLHVT6OVGpgAkFu5z4SswL869fv3/zp9+8l USPGCfh6lcQlMU5A1kTSSw9IclO/3IiRGKX2AYLcxDStkoMQ+O2jh0/lOGbFOKwWp17EOIFg r2IckFyT4wRk1Sa99DB0FBvRShUt6dvsQwS52ulaxQYi8JfPPr3jI+HPl64/vP/uzftvv5lM hxgnIBxRjBNwVZn0mhCT4jaFZquI2mM8Vrxj+snS/FDlpKZSCOxMIMydJa/5qvE18Q37i6+J c2wdi7Zm/qCJrVBL9/UsxpdWjVuKz1JdrRITobkcEWP9IeZINyvJSzOrn49GoLQLzfcaX9oq 8cnHj2+CGOe4iHECxdKdIaFqWZKebqfIkunBmZBh8nNJfoixvrG0iry0guwki4MneMUfQmCP RdDwUt1UzrmX8MJKcupLdxM8YpzQjXoX49NV4wRUuyeNeYBN9yw9DP18DGEixmPEOdd4jplj dp/4FIjAAQTOvZOUuxrh+LapnF99/6Vnss/x0h0xzhC1kcQ4A67sWcQ8mJbuyfWQlE/7UkWM 24/hUeNwaZ7xEZHs078MKyJw6WX93FUMQjyVNV8dDtIc9iHnuqwYJ5DsUYyXjmpb+/MEvE+T bnno2DJBctZK0lLfvrQf+ZJQX8tvyuvSPTFlhbQx951yWCpz6ec56p6rTlsYxMQl1C+VwzQH XcrndNtFjFzE3HP6f/rO/W/uawzmc3ZMeTnzSqn76bMmpe5r88rJIFdeufKZWFzjGVtWqg8E GT63ahyOcQtynOsixgkkexTjBBzFkm4V47Vi5H4ynVOaYvKKka+YfHKI3BrhjS0vpn0xeeVk kDOva3Unxnf1IkZUiXH8UalLPGNldSmfVDEO+ee85l/Dm75ud25rRUqZeWt8UpNz/1JNqWxt aYnxPhGx+ktY/aNFH+ilD8zns31m0POljPT86t1FjuxH1/4xk1uKQ1nhxbspnuGFvFxnF88Z EuOEHjXSxJKAKTnp/EHSy8NRO4iePjBmH6hNjEvIS/KknzGD0xXRjFnL6q8EYledcwGbrxqH 3+c4u5gYZ4oOMc4EciEbYjymQBBHce+xD9QgxiPJ4jlp2+fJNU4pezOef/Aj19nFxDhTfyXG mUAS4zsvUfUoA9pEcvWBz/tAjWLc66rx3iuZ+zwR6ytl739ozT8TnevsYmKcqV8R40wgiTEx fiIMxAmDEfrA0WI8kizGvoC2z5Os31KO2MM9rRqXoGqPcQJVYpwAb0VSWykI0wjCpI1j9PNa xbi3VeNrUtxbW1c8TovceoQLhZfucp5dbMU4U9c4ojNkqnpT2RDjMYSBGIrzCH3gSDEeSRaX 2kqO82nAESzD2cXTcW35WvJ5TlaME4gS4wR4K5ISY8I0gjBp4xj9vGYxPkJwVjwKkm71vE7C V1XisMc499nFVowzhdhAywTSHmP7a+0x1gcG6QNHivF8qh3t+TVae/d5Oh9TSthGEc4zLnVZ MU4ga6AlwFuR1IrxGCtpVkzFeYQ+QIxXTP4Zb/W8zgjz4KzCEW25zy62YpwpqAZaJpBWjK0W DrJaOIL4aeP1f+AQ432eG6eleF4fwz13qWFfcdhfXPKyYpxA10BLgLciqRVjK4lkSx/opQ8Q 4xWTf8ZbPa8zwtwpq/fffvP288/h089BiMMqcenV4tA0YpwQYAMtAd6KpMSYFPUiRdqhLxPj FZN/xls9rzPC3Cmr05NFwuefP/rjB8VLJ8YJiA20BHgrkhJjMkEo9YFe+kAtYrxiCu7iVs/r 9sIYvmoXZDjELmyf+OTjx7s0ghgnYDbQEuCtSEqMSVEvUqQd+jIxXjH5Z7zV8zojzM6zIsYJ ATbQEuBFJp0/RMLviQWx0Af0gZb7ADGOnPwz3+Z5nRlox9kR44TgGmgJ8CKTWi0mQS1LkLrr v6d9gBhHTv6Zb+vpef3hhx/ePHjw4ObFF1+8ee65527/G/4c/t6VToAYJzDsaaAlYCialBgT C3KpD/TUB4hx0UfGxcx7eV6/9957tzJ86Vf4uSuNADFO4NfLQEtAUDwpMSZFPUmRtujPxLj4 Y+NsAT08rz/99NNbIQ6rw48ff/Ei2gcffHBz7969p7Ic/uzaToAYb2d3+6Zkz9+WT0CTLSkx JhJkUh/oqQ8Q42yPh1UZ9fC8fvjw4c277757sd2THIf/urYTIMbb2RHjBHaxSYkxKepJirRF fybGsbN/3vt6EOOwUnztCivF0xaLvPTGyo0YJ8S7h4GW0PxdkhJjIkEm9YGe+gAx3uXR8Uwh ozyviXF6/yLGCQxHGWgJiJKTEmNS1JMUaYv+TIyTHwubMhjleR3E+P79+5sYSfQ5AWKc0BNG GWgJiJKTEmMiQSb1gZ76ADFOfixsymCE5/X0cp6TKTZ1kaeJiHECvxEGWgKeLEmJMSnqSYq0 RX8mxlkeDaszmZ7Xa/67upCDE4QX81566aWDa9F+8cQ4IYbEOAFeZFJiTCTIpD7QUx8gxpGT f+bb1ghxq6dNBSl2VFt6xyHGCQyJcQK8yKTEmBT1JEXaoj8T48jJP/NtvYvxW2+9dXu+sSud ADFOYEiME+BFJiXGRIJM6gM99QFiHDn5Z75t/rxekuTMRRfPLnzswxaKfJiJcQJLYpwALzIp MSZFPUmRtujPxDhy8s98W6wYZy52l+zCBz3mX8LbpdCOCyHGCcElxgnwIpMSYyJBJvWBnvoA MY6c/DPfFivGre0vDlL84YcfZqY1dnbEOCH+xDgBXmRSYkyKepIibdGfiXHk5J/5tjVi3Ioc Byl2NFvmjvIkO2KcwJQYJ8CLTEqMiQSZ1Ad66gPEOHLyz3zbkhhPxc33H2euQtbslqQ4HN3m 2kaAGG/jdpuKGCfAi0xKjElRT1KkLfozMY6c/DPfdk2MT4s6fTkvc1WSswtftrt0LFvYVhGk +eHDh8nljJoBMU6IPDFOgBeZlBgTCTKpD/TUB4hx5OSf+bbT53XM87vG1eMgveGzz0u/nGe8 vQMR4+3srBgnsItNSoxJUU9SpC36MzGOnf3z3rdFjOf/Z7iGfcexUuzotrS+Q4wT+MX8izMh e0mfECDGRIJM6gM99QFifMyj7ZwYx9ak9q0Vse1wXxwBYhzH6exdxDgBXmRSYkyKepIibdGf axHjP/3+vZtfv37/6f/5fPTavZvf/eatm7989unNnz/68Pbve7pyPK9r3FrRU4xqaUvRnt97 J8ox0GrpCLXWgxgTCTKpD/TUB2oQ498+enjzy++8ePPRHz94OvV/8vHjm3feePBUlInx+adi 715TqwvsWS9inECbGCfAi0xKjElRT1KkLfrz0WL8h/ffvZXfIMLnrrBq3OOzLWebbK2IfIA3 ehsxTghczoGWUI2ukxJjIkEm9YGe+sDRYvyr77+0uE0i5p7WHjwlntdWj1vrBXH1JcZxnOwx TuCUkpQYk6KepEhb9OejxXiSubDH+NI1rSqnzN21pS0hxqGNvchx+CBIOM0iHAMX/jt9Ue/B gwe1hbJ4fYjxCsSn//vk3J9XZOfWCALEmEiQSX2gpz5wtBiHvcXh2XW6x3g+HYcX8FrfY7z3 87plQX7rrbduhTj8N1yffvrp7UdCwt+N+KEQYhwhZ/NblgbbyuzcvkCAGJOinqRIW/Tno8X4 /bffvLPKGU6mCKdQ9HYtPatLiH+rcvziiy/eSvD8Ch8ICX83rRz31j+utYcYr4z2tcG2Miu3 RxAgxkSCTOoDPfWBo8U4TLvzY9qmZ1qPgnzE87pFOZ6+ohc+Jz2/wt+H1ePRLmK8IeKXBtuG rCSxYnzT00NfW0isPnC9D9QgxmHaDadPTNsq5s+0ngT5CDGeHmktCXLYLhEkOKwcu25uiPGG XkCMN0DbmMSKMdEiWvpAT32gFjGepuRLgnzt5byN0/khyY58FyiHHE+ruSn/jQE/bacIe4tH v4jxxh7gHMON4FYmI8akqCcp0hb9uTYxnqbk073H4Rk3/wDIyqm7mtuPFOMAIYcc7wHz8ePH tyvGQcDv37+/R5HVlkGMN4aGGG8EtzIZMSYSZFIf6KkP1CrGYWoOL+HNt1eEz0T3cNUgpzXU YSmW0wt38xMqltL0+HNinBDVFjp6QvOqSEqMSVFPUqQt+vPRYhyeW+E4tktX2ELR27OtlvbU Uo9rD/fp6LZwlvGoFzFOiPzUyROykHSBADEmEmRSH+ipD9QgxuEDHteuFgRu7cOzxPFsa+tQ 09aKcNrE6RFtU3um/cxb2tdDGmKcGMVaBltiM6pNPn+IhN/39IDUFsKnD4zXB2oQ4/DJ5xgx fueNfr56Vtuz+uh/fITziYMAh+0Tp9fo+4yJ8YXZIeZwcPc8+SrOk/8t5xcG+oA+oA+s7wNH rEhMcfrto4dniw8v3E337P3hj9b6UGr8jpTj+RFt8/OLw9+Hl/DCy3ijXsR41Mg31G7bKcZb VbOSKua99oEaVownIQsv181Pngi/Dy/fhV+9HNdW+6PuKDmePvUcVo6nzz8HIQ5/P+JHPeb9 hBjXPmrU74YYk6ReJUm7xuvbNYjxJx8/vgm/whFt86/ghS0WYSX52st5HkllCNS21aNMK9vI lRi3Eaeha0mMx5MHwijmvfaBo8V46IeJxiMQQYAYR0Byy7EEiDFJ6lWStGu8vn36QvGxs6vS WyQQ9gQ/ePDg9uW56VPOtkDkiyQxzsdSToUIEOPx5IEwinnPfcCqcaGHxQDZTqdJnPtE9Ogv zeUKPzHORVI+xQgQY5LUsyRp23j9mxgXe1x0nfH02ebwEY7pBbnw3+mjHKMfs5Yr+MQ4F0n5 FCNAjMcTB7Io5j33AWJc7HHRdcZh+8SlY9Tefffdp1sruoawQ+OI8Q6QFZFGgBiTpJ4lSdvG 69/EOO2ZIPV5AtN+Y3zSCBDjNH5S70CAGI8nDmRRzHvuA8R4hwfHgEUEMZ7OJx6w+dmaTIyz oZRRKQLEmCT1LEnaNl7/Jsalnhbj5hteygsv343+cY4cPYAY56Aoj6IEiPF44kAWxbznPkCM iz4yhss8HN8WpPiDDz4Yru0lGkyMS1CVZ1YCxJgk9SxJ2jZe/ybGWR8Rw2YWRDhsnZiObnvp pZcuvpw3LKQNDSfGG6BJsi8BYjyeOJBFMe+5DxDjfZ8hPZZ2//79p0I8P9PYdor0aBPjdIZy KEyAGJOkniVJ28br38S48ENjoOzDqvGpJHsBL60DEOM0flLvQIAYjycOZFHMe+4DxHiHB8dg RYR9xvNPRA/W/KzNJcZZccqsBAFiTJJ6liRtG69/E+MSTwp5zr+Ah8Z2AsR4OzspdyJAjMcT B7Io5j33gRrE+OG9J2fePvnl6odAOKptWjXup1X7t6ToqJgGnsG3f2B7KpEYk6SeJUnbxuvf xLinJ1RdbQliHD4d7dpOgBhvZyflTgSI8XjiQBbFvOc+QIx3engMVsy0Yhz2G7u2EyDG29lJ uRMBYkySepYkbRuvfxPjnR4egxUTVoqdSJEedGKczlAOhQkQ4/HEgSyKec99gBgXfmh0mv29 e/du9xAHAQ6fgJ6ux48f3/7dm2++2WnL921WdjGe7yu+9vt9m6m0lgkQY5LUsyRp23j9+ygx jnk+t/ys6L3u4dSJ8HW7+Qc9whnGQYjDNgpXHgKHiHGeqstlFALEeDxxIIti3nMfqFWMR3mm aCcC1whkF+NQ2NK/SoUEgVgC8wdI+H3PD0ttI4P6wBh94CgxXno+x87L7kOgZwK7i3HPMLUt PwGrxWOIAiEU55H6QI1inH/2liMCbRIoIsaX/lXaJiK1PpIAMSZMIwmTto7R348UY8/nI59o ym6BADFuIUoD15EYjyEKhFCcR+oDtYnxwI8YTUfgGQLFxPj0X6XYI7CFwOkeY3/+3g0G6xiM JFza2sY/ME7H8Ja5MTWNL9OmEpS+VwLEuNfIdtIuErhOAvHCSx9orw8cMV1PYnxE2cpEoGYC RcX40l6mpVMr/Py5xZM9WmCUq+N70Lf3oBczMdMH4vtArrlybT7hOeJCAIG7BIwKPQIBBLol QM7i5QyrY1jlGnwtLJb0WMdc8ZNPPQSIcT2xUBMEEEAAAQQQQACBAwkQ4wPhKxoBBBBAAAEE EECgHgLEuJ5YqAkCCCCAAAIIIIDAgQSI8YHwFY0AAggggAACCCBQDwFiXE8s1AQBBBBAAAEE EEDgQALE+ED4ikYAAQQQQAABBBCohwAxricWaoIAAggggAACCCBwIAFifCB8RSOAAAIIIIAA AgjUQ4AY1xMLNUEAAQQQQAABBBA4kAAxPhC+ohFAAAEEEEAAAQTqIUCM64mFmiCAAAIIIIAA AggcSIAYHwhf0QgggAACCCCAAAL1ECDG9cRCTRBAAAEEEEAAAQQOJECMD4SvaAQQQAABBBBA AIF6CBDjemKhJggggAACCCCAAAIHEiDGB8JXNAIIIIAAAggggEA9BIhxPbFQEwQQQAABBBBA AIEDCRDjA+ErepnAw3u66DIldyCAAAIIIIBADgKsIwdFeRQjQIyLoZUxAggcQMCcdgB0RSKw ggAxXgHLrfsT8BDZn/meJYb4Xvv169fv36nO737z1tX7p7zOtSHkda2sPdutrHEJmNPGjb2W t0GAGLcRp2Fr6SHSf+g/+uMHN7/8zot3pPW3jx5ebPhfPvv05p03HjwjuacSfSmDP7z/7tO0 odxQvguBvQiY0/YirRwEthEgxtu4SbUTAQ+RnUAfXEyQ0/lq7p8/+nCxRqcrwDFppkx/9f2X bstbk2axQm5AIIKAOS0CklsQOJAAMT4QvqKXCXiILDPq5Y5JVkPMY1Zxg9SulemJVUj36LV7 vaDTjoYImNMaCpaqDkmAGA8Z9nYa7SHSTqxSa/r+228+Fd1rWynm5cxlOuw/jrk++fjxbTlh S4ULgb0JmNP2Jq48BNYRIMbreLl7ZwIeIjsDP7C4+XaKILwx11ym1+4xDnuVXQjsTcCctjdx 5SGwjgAxXsfL3TsT8BDZGfjBxc1fwgsru0vX6XaKpfvDz8MWCtsoYki5pwQBc1oJqvJEIB8B YpyPpZwKEPAQKQC14izDFopp33BYDY655tsp/vT79xaThPxjt10sZuYGBFYSMKetBOZ2BHYm QIx3Bq64dQQ8RNbxav3uILaTGMeu6s5XmZfSTPnHrEa3zlL96yRgTqszLmqFwESAGOsLVRPw EKk6PEUqNz9pYmkf8PxM4indtTRhRTqItAuBowiY044ir1wE4ggQ4zhO7jqIwLWHyCRCB1VN sYUIzM8nXjo5ImyjCKI7T3NtC0a4N/bEi0LNk23nBJbmJWLceQfQvOYJEOPmQ9h3A849ROYr ih4y/cV/vgocvnB36Zq2RYT9wvMtGJdWhKdj2mL2IfdHVYv2IrA0P5mz9oqEchDYRoAYb+Mm 1U4E5g+R0wfO0srMTlVUTGYCYSvEPNaXsp9WiaetE/O9xuc+EDId7Za5urJD4A6BpXmKGOsw CNRNgBjXHZ/ha3fpIRMjTsPDaxhAeIluivE5yZ2OaZtvi5ifaXxupdkxbQ13iIaqbs5qKFiq isAZAsRYt6iaQMxDxj3P3VlhrYFHaqda+gpeEN/QzvnpEtNWiXP/J2Fahc5xTFsNfNWhvj6/ Jiap40N6BBAoR4AYl2Mr5wwE1jxs3FuPLKSGfi65p3uGJ8k996W7+Urz/MW9aQ9yWGlOvfSz evpZq7FI7YPSI4BAOQLEuBxbOWcgYI9xBoiNZjH/cMd8ZXj6CMg5yZ2/uDc/0zisMDumrdGO 0Fi17TFuLGCqi8AJAWKsS1RNwKkUVYenaOUufQUvCG6Q5kvXXEwmoQ5prp1wUbQhMh+KgFMp hgq3xnZIgBh3GNSemnTpDW4v3/UU5fNtCS/dTXGeVn/DHuHwd9eOXJv2H4f7wl7l6UU9x7T1 32dqaOHS3ORUihqipA4IXCZAjPWOqgksPUSWfl5141RukcBcMsLe4rBSfG21OGQ4F+qwUjy9 yLf0Fb3FyrgBgQgCS3PS0s8jinALAggUJECMC8KVdToBD5F0hi3nMF/9nV6sizlZYn6mcehD 8/3GLfNQ9/YJmNPaj6EW9E2AGPcd3+Zb5yHSfAiTGjD/ol3oC7Ev0M2Pe5u2VCRVRGIEMhEw p2UCKRsEChEgxoXAyjYPAQ+RPBxbzmW+nSIIb8x1+vW8HMe0xZTrHgSWCJjTlgj5OQLHEiDG x/JX+gIBDxFdZH428Zp9wlO62FVmpBHYg4A5bQ/KykBgOwFivJ2dlDsQ8BDZAXLlRUxnE88/ /xxT5WkbhmPaYmi5Zy8C5rS9SCsHgW0EiPE2blLtRMBDZCfQFRczfQVvy3aIpaPdKm62qnVK wJzWaWA1qxsCxLibUPbZEA+RPuOqVQiMSsCcNmrktbsVAsS4lUgNWk8PkUEDr9kIdErAnNZp YDWrGwLEuJtQ9tkQD5E+46pVCIxKwJw2auS1uxUCxLiVSKknAggggAACCCCAQFECxLgoXpkj gAACCCCAAAIItEKAGLcSKfVEAAEEEEAAAQQQKEqAGBfFK3MEEEAAAQQQQACBVggQ41YipZ4I IIAAAggggAACRQkQ46J4ZY4AAggggAACCCDQCgFi3Eqk1BMBBBBAAAEEEECgKAFiXBSvzBFA AAEEEEAAAQRaIUCMW4mUeiKAAAIIIIAAAggUJUCMi+KVOQIIIIAAAggggEArBIhxK5FSTwQQ QAABBBBAAIGiBIhxUbwyRwABBBBAAAEEEGiFADFuJVLqiQACCCCAAAIIIFCUwP8HXURd9vmh DDQAAAAASUVORK5CYII=</item> <item item-id="4" content-encoding="gzip">H4sIAAAAAAAA/9xcC3BVRZr++9xnbt4P8hA2XiAiPoIhQc1SRCIwbFgDBhRBcByCCSEzCcmQ hIQxjlmEERUERRRBV7akaqwax1Wq1h3FcjI462YdGPExJetrWJ+AzM4NxTDKK/v/p0/f06dz Tu7NvYGwc6i+3ae7z//339////0MDBIAXNcVASRDdhrgQ7/jvKkwJrhg9szpjLKAwUv3Ueyi nyBA6dUYKikfoAHDzZh41QPwXSbAfycDPOQG+H4uwNtzAdZNxk++N3sm1aZvbse6AbA+3Rn4 kwfwO4xSmVKW54L5Y9yABGA8hlEY/mHGrOkaxqcSsRz53o58+3y8/mgMfgxLMSQBb6NdXrqR pnxqT5rEM8F4DyKznX5ej95PI7/fIL9E4/1O5FmmmfQvM2hidpdI49OVbaYhV0rnGem+vj7I MtLYfTAPQzPj7Uzj9MLPaxqPfZo25hZYDS3QCrXQCLE+WvLojXnwIjtacLz9rbPvFP3ai/28 KRMernuviMo79r+mw76taUxuK4bi/3mnqAvfqXAShimbMyHTSFPTMoz0BqTxyrm3ikqvWTqR 6u66+0f56426XRNn+A+Vb83HntuYBpt1xLfBI52591yashzrjMZ0h28ElGFMZUHMp5i+GaOd vnc2pkObdEqdRdeM0PMrP97c2Z30T53jEdgspDEKA2FCH+ZjfsMfN3dS/tOXH28vT7suheSu WaVXCePGjPaLtIyPwJDyIunLOOB6akClyywewYvKRhrfFSDBOrSZ64z3x5HgWK9Jx2WDm6Dj kuhsSAX4wGvS+RDfn5Xet+JPvt98X4J9VSLx3TGSv18OXE/dDjzdUhrJdd2GcQW9I5H3iB8K /LGP28m61P3eP6f+zLs1rds3Nz3o3jGSwkx3U/pM93Np+f41GN5M7fZuSf3MK8skZPcYvMfZ tMMjyX4MX7pdpiwHR/J32d5E3Yewf2/2m3aGTYbXzyHI0rPZ8EM+DdJuhXq0sFq0tSDMwbgd 43nQhHnVsMIGG7unE3XvqpLj7RT/62XH24m8bHtU58zDmRAKJRfBzJkWBevBb9K39LYfe7S3 /XmsUyP5yG17S/wCp56xgexbse4fsO4nGCQS0PMw10Kys+4RpSmPbeJ21uPndtaDeYQXxaqP gRh8TJKUVu2M+vtWDEsw3I0G+s+oK+tRV8/4uN09tW/j1J//fMNU8T3pQ6VCv894iA7h0IHx SQwbMKzVTKy3o44vcplYky+dfp6xVp9osaf8LxDM0mmTUv6XtXgpBqMulzEy9gC97TLWRUhD YJ1r+NTjBm0KKvaC9mCwl/2TijX193iw4r4QwxNpAH+PGbsRnz1ee9xJDZ1wr8I09chejH/i MvH+PXLf47Ha9oXGe6HU/10rpqcUGP1fYfT/6LGBt9ZjvsD7YM6olIVai5diXW7Em2SLBn+K ZbwPIA2B92Ev57fIoE1B4A8wwqCv4s90/74caRL+d9A4e2RzZznWo36n77l7GKH/2tm2wJuw F3jPSuFj01a089sd7DwS3iVQDPcwK95raLz3We0bzg4f3lX3XetX8e4aG0hfiPky3q8reJNs 0eBPTyS8uxW8CX/Ci9NX8TfxJvwHi7edfVN4BMH5AjPG4oTqDQe8KW8gvItRZiIqsJ6QZvXl TrbNLhDWDra9T7XtApcV62KIHvtIWI9zWbHuVrC2Ym9iTfSHwrYplKZxXz56AF8eCWvCRLbr 7cj5I1/kcftCYe1g1xmqXa9XsG6E6LGPhPWDCtaEvYx1twPW9D4Udk1zrClIuQYL30RyXznY tW8ArO8gXvjvFmjDmcVKRAigAcfrRTgn/xR4uAfDL8Hq5/OR7xbNavt2fn6Ybf/3su2/OGNS yjHEiGKBt1X2gfVD1ofnZpjzOLE2PmHQpiD0Q9YHq35E1gfPAPrgZPsvpPK9n3K0/SditP25 ABY/fwzzu9yRx/Rhtv0s1faXuq1YzoXosY9k+zVuq+2fMLDl9FXsh972KZzESeJkzFifDJAT 45i+QMH6Wq91/naR2vXb6pj+poL1AogOe0DsI2Hdo2BN2Mt2bcU+Pqzt7HoJhq2I8ZdY+BjO 3/bGuC6vgOlQh/F+9A8TfVbc1+PH0dg4dR/hTmuRRliKSDfEiHL/Jwobz5ZtnOZMV3qs63Iu Y3R6EGldPsGjrMsV3K16YOJO9O1w5+vywdn4YcwowIyfIIfsGG28LJRpwfqDLG7jgsePcax4 G8tdWO03MfKoCvGGi31Crw2+so6LPU2/Qx3KF7oo9nPP5767+kThgw4IH3QlcB/0oseqa1Wh 6HUzkg/a7bH6INJNy56Bgy7G4oOov2Uf5HSOUmSkU6V8ZN51E5jbpR6JlialXVLaLaXl+rIv 9EvpBKlNIPGm9o2B8BlBn8hH19klviW85Dpyms60sgxedKZFbbQ751pHNomNOItCXm8cng3l ORdVCrrNc677MXFZgnnO1YZl6exv55zr1kcy4f6S4+3JSr6cHgcPMnpPhPX6KNSAOl2ReV1K 6SZuN08hje7t93XC9i5dx88+2dW59UxvO40XXA6mn3PxIcw451rR2x4s/0s75T+PdIj+aORT uidfP4/KQd6Svb9bjvkUACb5S09N8nv9Ld5ADpfn+c2ZwCqOt+MYlUNdtQ/b80718fYg1hPn XOOvOj/nXEJf7M5WqrG1H7msvnS4z1ZsxvUcwokCvUdztvKrgsDlTmcr9JC+Eq5g8Kb60eh1 LGcr8mPnS8UcrhKZf4gZi9K536D2uP+0cepD+UNztmJ/V8Ckq98VSDLuCmAn1El6QY2+0Ps5 0eqJmO8dLtzk+gzHTIoBojuHEXqinsMcQhrqfO8rgzYF6k9ZT8Rjpyc7lvS2U/jt5rP3QghD v8fdTy/U8YywbcbwfbTXZzDjJDZgtIOO0LjopCO0t2+uaWv67eE84LLu45Rj2KlZ/cNwrvfI 99rMtd4jvyv0gNKVrMXLfTHXA1nmwZzPEL/HFH7zDNoUhF7QXEnonVUvIp/PcF2MPNeiAnE+ 8zhmjMbCPgTpXIK9HkQ6n2nBFd/vaNCQ5v5Xa1a89cYM4zrv0NRp/fD+a0Yg4zDmW85nmHVu TbJFgz89Ec9nmHVuPc84j+H0VfxNvOkZ7Nzazu4J7zyc/CRixvdwMn02RrzLQlWhSqrkvXjW Tg72/Adhz2LtVKCcv5Es0eBNu9cRz2SU87duZl3HW/E28SW841k7DTQ3L4LBrZ2Ge81zJy4+ foThTZyovpvMGzGUa55yZFaeYK55liCvhkRzzbMMBV32N3S3L5Y1DxvlLnGX4PwtwpqHfCff V4l7zfOBtOZJmTJtUsqnLlzzZHF5xJoH/XWWvOahPQmx5gldf37WPEJfxH5TglFudy+NysT4 dwaNe6d0x+64j7+L8mXJ1vKnUq3l63Kt5Ysv4e+iHep+ltwOv9SOn6GQj3pMOgfwvVh634Dr SibdDVyIfHeA+b7tEv4u9tHU/TZ5r83ubuB0NMpNaFwLEIR7k7mddWY/6Pkq2+3ZkDPXV5G7 HnlQeAHqc1+AZ3KYfzWGPdnVns3Z2z2yTHbrznOoIz5pP5l82fm+9zPYdSfpLdkKhWjXnT1j A7lDcacv0nqBjeL3fn+xqrcd7X4SfUdNorR7Eq81BctvuP64Xq7aF/W32N/9IzYyDTN7EPNH kzidWf+xcWrWA9Hv704E67phP+pMp7R+1PvnAt/zGez6kcb7o8o8ciJEh7e6frSbZxxT5pHW e1yDx5t8qsBbxVfdMxVYn0VOO/HjTQjSvTFiXUwZ0pphFTrQJR6rLQ/nna7DH93Q72wIbXlE CPNlrOdoA9/1cMKe4khYVylzymPKnNLpDh89g51TyvNAokM412D4MJnu0gO8jnZ91gFrqj/Q ftJEnNMQKi9j6w/iexURkbB/A5n/yje8di5jX9SaAyr2n14SGF+K+fK54G7NevYi5BTvTrpA T6RzwZc167lglXLHy6oLke/zDXQu6GTnn2HGKxhakcO25KGx8y/dVp/uZOcXai/Iwc5zVDvP t7nPFy32kew8qNzxelmznrtVDeEdLzs7p72B5zDjX9CZtCK5nzrYeaS9gQXQAX/JtOLdCVa/ 7mTbFwpvB9u+QrZt6tc1LvWuRwdEi38kvNcqeAeVO1xW/OPD28m2f4xYX4qZmWjXZ2Icw2mH Wsb6UK7Vj1+ktp2r2vZhBevVED32kbA+qmC9VsE66HCHK5a7una2rftxHLtfxcxVSO6JGP14 i4J1XXJ0Yza7QHu8DnZ9lWrXlW51jzd67CNhPUe5w3VUwXrtEN7Xc7Lrz5HyLsxsQ5AejxHr 5hBYsE6Mfp19QbB2sOs8YdfhuzIK1s2h6LGPeFdGwXqOcm/r6BBi7WTXOxEcN2Y+QPssMfrw wpDytzXIOTW6Nfdw2vXVwq4F1lnKvajCUPTYR8I6W7kXtVvBes4Q3ouys2uan21HW25K5H9v Xx3j/OxI6LvQbbQhKtn2No3PzwSfFKSdgOFQKsDpGPmUhQpD9LccxV5+V2BRkv1dgVJjQ4/u CshtIh3s81hlL07ishc4+LRIbToaOtVPduLzit/K5+sk8/80iIXPN6HTYT7q3zPLj2zbYn9V /Ztv+W+jhT1qMLxnbQ6+d6Tqe99V7JH6JVr7jGSP7yv2mO2x2uPuIbRH+T6hG4b2rM3u3qF8 H1GuL99l1GB4z+kykEgKNjQnAFBrKKLdt/K5V06f/X4OlENGpHUAA+dnZ88W/F06kXhXnHmr aNdLmeH/x+D9ikOuNbOh6NiyTQn0Pi9XK8oy8kNt84u+3bsF+WGfI4Mz+N25HK1oWhbLKIRM uPtGdxHVacM8wYveD+/OhN8uv0nPO4HpT5OonH9DzxEMNwW0Ivoe3phftO4DVvTav/OyhciP 0j1u/OZb+/7Y9W+ZIPfHEanOpJOL86m9aciX6NAn1F67cy9xvkV9IZ+BiXOUZSWgkd87APz/ crmB9uZQKacbd7Pnz7+/HLruL6eyVB+n5+T3OnSsa+EuYw1cB204G6iGldj+pRi3YFkQUa2H GqyxXD9TqjPCjUbcJoVyI49knWzk0ftiI6a850h2qX6rJLPcFymK7tvp8w+wIA9lfwL1udm4 azu8+syf0r8uzk+GvnzCmnDbhzq6H3W4ENP/mUOxqXMwri9f1mt6SKfrjHoXoz6TfEOtz7S/ /DQW/CPiuRixvDlg1WcqG4l5dKXWSZ9pzyqyPi/H33osW461gkOi023S9/J3sep1LhYkYYWn Ud67jPsUF4Ned64nvd6q6/UlEEGvZ2216DXxuNj1muQbar3+AsNtWFCGmT9APKclW/WaylKS +X0GJ73uwnSVfn9tGba8QddiSgdR09swfxXm1OBbM+auRK2ux3gFvq8BrrdtRnwjmDo+Wcqf DKbei/riXdSjMFui0WrkRaPjNDk0roXocypK07juYn59rpSofQxX6Kkk7Vr2a410Ox/kX+r+ U+K/aDK+LmR8Nn1G4zdDRC6fN04w9jsS9K8oxTSqRWVulqzb22lNaD6fievz8NUtrbUDTME5 D02PNeRlpTyB5YXbyixfuPSaWZqflWiUytTy2ATNXi63UTfIfmjUzWeNRqqQtRopL+sMp77Q RE/IdDxGW/g6RrSxkHl16y1wJcIPkccK7e+Q5hVEDelQ8LF7MLTpvApZg3YptmM0E22zl89n tJmxBKNVjH2lWRHI0q5hhRrv+ROGdXK71HeV6xtrW4JzatuD85oaq/vvI3M+fl2rigNEiXSj sbrDFcDZd57B4Rzca3D4c4wcEvQ4hJwCOieiSCJUd/hgrMFlBBsXJxe/wSXB4EIUiUtJcWqY y1j2U4PLZ3HLkqBzIYpEorhySri/mvtJwgbFw5QkweivZp1HY6WQ40pWECcPU44MnQdRJI2v qbmlrXVlE1wPdyJ38iYmRifhjn69F59kRJEozA33XgLbx+LjoSJEFInCgsqysBYkDZkc3HaI ItlOxfQ612SYBaY0vf2kAbFfuLpxadMAW4VO0vTqbS0L99hY9owmdPEZvTerUFJ7P+gHO68W iNN3J4Z7Q4wiYtywo8uipitkdxmx24g9RuwL9w2PA0acaBm3RHsuZ3198bVHtEML0xNx//FV 7YnhHUHVUWMta4lz1HAbmk+U7EaNLnZFnB7KE0Y9TedEFGlPSPdPNcI3FYS9Uwf7Mk7PYeoX H0GIInVaS0Vj2KI72LdxWrQpl+Dyrc6lrCoHyuLUAauFqPpnWpTb0UL+v2u6/VxKzP8+Z4cZ n0t9zryayCvTRN4t4bxfhvO6HeaUHoPmMfakUfcIe8pIebRnjdQe9otwaq/tnNLXbw75JNJ8 WvuaPaudw69fxe8ovMaex7CL09Z2aN8g3z8xuS2yhX/CHox7PsVHFaJEn04Mzz4OshvjnEUJ Ty14EEXrLOobNiJOHuaokKzzIIqUM7G1esUM1JOUsE1rWsjOpuOQhyha5TnIvo7TO5nyBIw+ +1r3Gws6/gvKwr22J04uqiREkSisrjR766Uhmj0Fwr31ko5+i8SjzI7HILxsf0TKdB7NYR6f sC47CxkED1UOokgkCsOW8r7NTHNwPEw5OOpEkVA/8t0rYNrKAcZLKaWPJYULwqW9+AUv7TW+ PXrK/DZJE6WUotJvTlPpYOaPiXH68yTL/C3SqOU8/xPz0KSYRzcuxycWL6Dv9yl6bd3fmzpV HpcmsFzL+JSg0c7VeL2PU1ixvr8zr/Yu9EJ1bQ3VK4NLq1tqg+31Na3LcyEV5zoJGDIgHQOl koBWaKnordIwlYdakIEhPTzKWntIg/MnwZW6BPexG/Qd434SLK+tr1veimVp2NoA/qbrUlAq ESXhMqQaqYBeGhgGKQp1KU6wEn1/sGpl07L6htpg07LgXW0rV9XWBJubVrbWN63QsrDn0zGQ t6VWUzoBUxl6PkmXoMtHNXjKSRZ+lv1/AAAA//8DAIjRH7xsYAAA</item> <item item-id="5">iVBORw0KGgoAAAANSUhEUgAAAu4AAAFMCAYAAACUITmyAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA UNVJREFUeF7tnb2uJbdytvVdgm5Bt6AbcKDYmSLnAhw58wCOT6DIgSMrMuBwgBMb8ETODFjx yQYw4MyBAAPO96d35NKp4fCn2GSz2b2eBgZ7z178qXrIXv2SXST/39uv11dcEIAABCAAAQhA AAIQgMDeBCTcuSAAAQhAAAIQgAAEIACBvQl8tbd5WAcBCEAAAhCAAAQgAAEIfIqSAQMEIAAB CEAAAhCAAAQgsD8BhPv+bYSFEIAABCAAAQhAAAIQYMadPgABCEAAAhCAAAQgAIE7EGDG/Q6t hI0QgAAEIAABCEAAAi9PAOH+8l0AABCAAAQgAAEIQAACdyCAcL9DK2EjBCAAAQhAAAKPJPDL L7+8/fjjj2/fffedztX5/d+333779u7duzd9zgUBI4Bwpy9AAAIQgAAEIACBxQQ+fvz4Sax/ /fXXbz/88MPbzz///JkFHz58+F3MS9hzQUAEEO70AwhAAAIQgAAEILCQgGbSNbv+/fffN2uV qFdaxHsT1UskQLi/RDPjJAQgAAEIQAACOxBQCIyE+E8//RQ2R7PyykPYTBjZYxMi3B/btDgG AQhAAAIQgMBOBI6IdtlvM/T6yfXaBBDur93+eA8BCEAAAhCAwAICCouJhsek5ijeXXkl/Lle mwDC/bXbH+8hAAEIQAACEDiZgMJiJLwV8nLk0sJV23HmSH7yPIcAwv05bYknEIAABCAAAQhs SMBi1N+/f3/IOhP+Eu9cr02AHvDa7Y/3EIAABCAAAQicSMB2hfnmm28O12Ix7iNlHK6cjFsR QLhv1RwYAwEIQAACEIDAkwhYiEvPLjKp/7aoNbJ95JPY4cuXBBDu9AoIQAACEIAABCBwAgGF xozGpmsLyBni/wT3KPICAgj3C6BTJQQgAAEIQAACzydgO8nohNSjl4XJsI/7UYLPyodwf1Z7 4g0EIAABCEAAApsQUEz66KmntrBVsfJcEEC40wcgAAEIQAACEIDACQQsxOXobjI//vjj72Ey nJp6QgPdsEiE+w0bDZMhAAEIQAACENifgAl37cPee338+PF30S4BzwUBEUC40w8gAAEIQAAC EIDACQRsN5gjM+6WdyQ+/gSXKPJiAgj3ixuA6iEAAQhAAAIQeCYBW1iqnz2X7f0u8c4FAU8A 4U5/gAAEIAABCEAAAicQsK0ctcA0eiHao6ReMx3C/TXbHa8hAAEIQAACEFhAQAcvKda9dXiS RL6Fx/TO0C9wgyo2IYBw36QhMAMCEIAABCAAgWcSsJAZxat/+PDhMyf1f5tl1+dHFrI+kxpe 5Qgg3OkXywj87//88vbPf/jh7Ydv79nt7m7/soZeXNG//NOPn/qUfs64zmjnM8qc4StlvH0S UbbzR/rTwhtMVJXSafcPuyS8culSsQb71yMgQa6+ZPuyq5/od/UZzcqz3ePr9YkjHncpKD0c e/79zV98/fb3f/3dJ7H2H//6/oh95HkAgf/8089v//i333/Wd+7k1t3tvxPrI7b676Qj+S3P Ge2sMvUdOMvGEf/IWyfgT6eUuEpFlP5vIQ8SXLXDcPze2/odQUbv8wRshv0oFRsYWv7W/9N6 1B/tRFflVXiODSzTQacGG/5vR20m3zwCXcLdqv3v//r4xcNIDyeJc80s+QehzbDqwSUh/6d/ //wV0TxXKGlXAuobf/eX39xWvPzhr769tf279otZds2acT+jnc8ocxY3yvmSgAmUmtBORVKO o+2/fWQLQNrl2QRMNI96qX6oq/UzV4+d5qp+asLcL561N0wS8xqs6o2Af6s0ajv5xwgcEu6q UgI9OoskoS/RbunvNvtuvo6hfu3cPf1lR1J3t39HpjvapO+q6Pea/x6s+ULf2bGlv7QpsvuH n32seaXZTAkeLgikBFoLVHPENAD0+Vpi3T4v0dcMu8S7XbnBqC2S9elozT0IHBbuMr/nAZc+ EPUK+S7Xv/3xp9vGZe/EuKe/7GS32XJ3+3dkuqNNPe0c/W7oKXNHJq9gk4ny2mE3NlNZS6NZ yiPi7BUYv7qPCsfqnblW+lQ8t0JjWsLd2kEDAhPoaZ7I/fDq7XmV/8uEuxz0cc76/S6XhXnc xd5d7by7eLm7/bv2i93s6mnn6HdDT5m78XgVe2yRakmUK1bdFp62xP2rMMPPOAGJ5DR+XP1I YSl6O5PuJCPBrj6nEJZU7Kciu/X/1EobDEi0l94i+b+zy028nVekXCrcLRbV4t1XODhah8J6 7KE7Wtar57+7eLm7/a/e/6L+R9u557shWmbURtLNJ2ALSnP7ZyuMRrOelkY/c5dm2tk9Zn7b 3L1ECWW/k0xpd6KcsM+tt2gJ9daMu705Mq65UBnbtlJ2Ey6zVw+8TLjfYUvANDZ/r6a7nzV3 Fy93t/9+PeYaiyPt3PvdECnzGm+p1QjURLntrV1LQ4gMfalEwC9SlhDXDLZm2TVIVN/yol4i WbPwpVnuqGiviXcT6hpQ2EJq/U2/2yFQVr/NvNd2UaLl1xK4TLjrFfPOl2Lw/YLaOww0duYp 2+4uXu5u/+79Yxf7Wu185LuhVeYuvr+yHbY9Xrqo1G/dZ2lyu8Uo7IALAmcTiAj31oy7he1o kCCxbotVc1s/pm8BzvaP8tsElgp3vyWgtomMXnolrZh4L6S1xaAWhkUu25/Z51d5pa0p0z3H c3vXq/70ytmp7eD++A/vPtsms2Rz6ZAWhRgZO/nQyy7dRzrCLudzzu5oul7hfgZL25pUDI/s bJQTXypHbWyfiW10y9NRH3P9QjPB6u+RgebofZXrD2lf88z8AUm1dLmF6z7Mzsq08o4cbpR+ J6hPWHk1kX30u6FU5si9HfnuI02cgMWvpzOdPkyglEYzpzN3kYmGUtTSxT0nJQQgcCcCy4R7 Gt/u93uvCVmJIv2zh7nySQjbg1CCVmKldEkgpELNdoJQGS0RHJ0pM1Go+sw3PzMnG0p2lg5+ Ufp0/3OzR0xalwlK8bJLdXmhmQpvX6aP4a0JwVRUley6kmXO5xa/9PPU/pqIa/Wrkf4iu9L7 Qn9Tv/aD0zPvqxo79Zv0bVXuftffUgHfGoxrUKSy1Y+PHphk3x9+QG33RdpPan5G+3Nu0Dp6 b/f2XdK3CVjcrxfuqSDPpVH62mLVds1fpkC4H6FGHgi8BoElwt0L7ZqATZErbSmkxosmpctd 9hDOzeD5/DWxEHk4e+Ga2uHFb2knndwhLfZg97alr+hrdpvvuTolmLywqm3NGfE/J0xy7REp a5Sl+ks62BEDm2G3vtgS1i37JThVrg3GxDQV8n6G2Zc36qPlz4lhCVvjXPoKG72vIl+N0UGf yor2RaVVudZfjxxuZAOmUtv0HBQW6c/Gyqe1e9u/9Unv7SNvhCLtQpoygXSBnoUQ+Byl/a45 GZWeBQEIrCJwinCXoJCAkEhKw1MiM+1y3mbiSg+wdF/49EFsIqok0Lx4qsXbRx7OtVlOf/hK aYAhf9NDWnKhOCZcfFhGrqN40VQS5X4wVduaM+L/TOF+BstZW496FqVBkz8pWOlz/X3Ux9YW hHbv5PrG6H3V88XkRXBNiHpmtQGVWKb3as+BSTaoqd3vPQOO6L2R3h8j93YPf9L2EUhFee60 yDSNFquWdpjpq53UEIAABGIEpgn3XKyz/qZZMYmcqGCX2Raj24rT9XX60BH/MC/FG/s6avVE Hs6tNK3Pc7NypeaLnMLoX/eXyvH+1wYUM22PlNVK0/o8x7Kn79Vum2jdXrD6MKVoO7fqsc9L gwcTn6kvo/dV7Cvlz6l8SFpJrCq1v19rfVGD7dxMeYuXWWTtUntTFbm/ou3oeUVsjA7ye9uB 9G0CtijP4tltxw+fMz0aPjcj365prxQzQnIo46sv9meHCUxm9wH/zTFNuFuhaRhG7UFc+gpL Zy1Lg4L071aen02ufU3arHvptbnyRh64rXIiZUTriqSbWd/qslaz7HmMRlm0BOuoj35gkBOh JgBT30bvqx5Wlta/Xait8fBsS7Pz8js3CIu0i59Jr62JidxfZwn3nrqPtAV5ygTSUyJz+1an abQTR+8pmNE2mPHQj9ZFOghA4F4Epgt3ue/jbPVQ7Q1ViMwY1zCP5vdlR0RByRaJqjMWu7Vs an3eIzyuKCvH8yyWPbdrlEX0bU5ad9THdDGwBG0kJnrmfRHlFgmD0feDT5dbeC2mpVn7SLv4 8lu2R8rrFdlnlNnyg8/jBDTDLrFsJ1nmtnv0afT7mSEyCPd425ESAq9G4BThLojp7F50i7ze B2KuwaIPyUhj95al2TzzXT97YnCjdbXSeYEWWXhaC2No1XXGIMDKXMEy0gd6fOxNe9TH3Ox5 S8BH27KHSSttKwzGx63XZuflb2lwEvHLv6Vo2Rwpr/d76owyW37weZyAHawk4V7aIcbSaC93 9myPsyUlBCAwl8Bpwl1m+oelHsrRWGP/kKsJzxIKnz9aZ6SsGnq/q0ga1zz7od0qz8/I5mKs 5YcXVLXZ2lZdPUI1WtZKlj23U9T+iKib4aPNQnu79LsGbrlwkNH7qoeVT+u3fEz7mvqn/a02 Oz+6gHxm2/X0+d60PXYebQ/yfUlAp0LaLHdphxifpnSqJWwhsCOBGW9wKOOauP1cfzpVuKez zbWZXW+cf3i19nXOOeXzR0IIajda5EFq+1bb/tJpeZEyImKvRwT4A5tygxcTSa394GfaHilr NcueL9mI/Wkb5QTnLB+trpyAzw2UR++rHlY+rY8vT3n4/5dm55W/tttMpF16BvOR8nru1560 0bqPtgX58gTsYKVa+Iul0d7uXBC4EwFE9zWiewb35cJdFabxuBEh7mfqazNt5pCEqRegPlQk El8vwVASsK0HqY9nHnmVP/vhbj7ZDKx/c2Ft0hLts23akWXPl2/L/tzgM+1/M/tLarsdTmR2 pmJ39L7qYZWmze3VrvslfSPk7127n8QwEvIlv0uXr78Vthdt52i62ffRSDuQN0/Ajnyv8VGa r7/OnxkCVwjsTEACkus5BIZaM/rgSk9HbO3qkMbvtsS+hKgXoWn+SH0lgd/yMbLgr1WGdafZ 6SRQJNbs9FgrX/+PvomYaVOrrCtY9tzKLfutrJo4n+Gj7CiFgPkdbVIhO3pf9bBK0/qdnmxA kdslxs/Oi1Vu7/a07Ei7+O+g1uFbkfJ6xHhP2mjdI21B3i8JSNh8+PChiiaSBrbnEkCAHuML t2Pcds21RLinW0S2ZnrTXWlap63q8/SEUf8ArNVnr+ePzpZHHrSRNLMf7hrMtAY8kU5ZOyzI 54/42ErT+nw2o4j/vT4qvYnU3NuiGT6qjCPrEkbvq15ePn26P7psKQ2WfZ+TyC6t0+gZ8KYD mtral0gb9fTFnrTRukfagrwQuCsBBGh/y+VO++0vhRw7EVgi3OVweiJhK4Ql3UZRDzSJUf/A VZkSR7m94tNZfqXzr8hVTi1/VBT4B21OTJ1xEmPr4W4i5cjC3rRz1hYWKq04pqxLoqhl9xUs e27Glv1WlgnPXEjGDB9VRmSxZm7AOnpf9fBK09ppxrK/tB7ED3yMVWuBeW+7KH1pMJ8K/Nrb umi9CPeRXkPeJxOwQ6xMXGoBcOnKiXbl1577+kxhTKw/+JIewv15d9Bh4Z7OoLUerkLnH9x6 6LVOU/Qzb/4hmf6eE8x64Ebz12JevdCxGWz9NNtLYkT1K00qaiWmxSr3uj4qBFrpon6bgJIt kdALletZaSAlAZkuQtbfcsKoZfcVLHtu6ZboVlkWjlKaEZ/ho9mROzisFUPfc19Ew6miDP2M f23g4b9bWm/nekRx+sYhHcyLZ27AX7K19d3gubT6fnSiIMqadBDYnYBEt/bD/73v/9/OPqXt OHPC3YS6dgLSGgSlQbx/3vII993vhH77Dgl3v/DRHkh64LViyWWeXyBnwlEPzNzssMpL00dE u2Fo5ZcQbYmTdHGt2Wy+pqLV22fCKudzyiqtp3SaayRdbla1NvBJfUq7Uak8E+2peMotJozY vZpl7+0iH/ygSH5aO9r2jungJq1jho++LWWDDbp0D1lb1cJLWveFym/dF73sLL3dC63yTUC3 0kX6lbc1fQPmWdogwf9NdpTeXLW+G6zeqI3pbH/tROej/MkHgV0IeNFuNtVEZirc0x2A7GRb FhAj3Hfp42fZ0SXcW+IvMquUniqZPiRzjqYnSurhb4cbRcDY4lWrSw9oCZvIWwKV7wWbF0pW txdMqsMLOqWx7f/0WW52u8bV+xdNpzzp241I29XCl2S3iVbxT0VFybdU1Kd2pO23imWk35TS qD3Fyot49anomoIZPqrvqhy1ix8YtnZg8T6N3ldHGKrOXGhbWpYJ7FodPfeDL8cOvfL92Q90 an05taf13RC1MZruCHPyQOAOBDRrXpsxj8S3M7v8ZUtHuN2hf2Djnwl0CXfA3YdA7aTJVMQo hMBmau/jIZZCAAIQgMATCGi2XKEupTCXqPhUOp1sy/UbAQYyz+wJCPcHtqtmAVuLf3Nua9aP CwIQgAAEILCKgIW4SGT2xLen9r1///6TUOVUWzcz+yuP6KBnVXv7emYcUFQr4wqfVtSJUltB eWEdFkMdDQPypkUWAi50haogAAEIQOAiAjVBlBPYLRFWc8MvLs3NmEfEp2bsa7vSjGCUfYrJ l9+KoTdf7S2BFtrqUhrtcrPL1Zpx12BHvH3bycfc+oMzfVK7eRt66/J5z+oDvTadmR7hfibd C8q2RX29VWuWvrUQsLdM0kMAAhCAwH0JSLDadosSRy1Bl6ZPF5DWSEj85oRmRLTLLono2Zf8 kRCUWJcv6Wy+hK/ns9uuNjXhLoFeG2zJLxuQzOaaK2+WcF9h69V1INyvboHJ9dsit9bpkL5a W2w52RSKgwAEIACBmxMwgRedybT0R7ZlPCLcbS/42Zgl1GVPxG8v3lsn8M62s1ZeadCjWXYb jGhwYpcGIv6Ngn73n59pO8I9ThfhHmd1i5T+WHvtNqLdMnLb2dkBVLbX/C2cw0gIQAACEFhK wPZHjwo4E7HR9OaM7SqThsrUZtwl2tOwHZsFH4FkoRsqK3JZfH3k7UCkvFlpcvborYEEeWk2 XX/34n3VYl+Ee7zVEe5xVrdJmdtfurTdHHtF36ZZMRQCEIDAUgImpqNhKBbuEkkvcehns5Un N8NbEsOyzQtML/yOzPYbWBPtR8J8VoncSCcohcnIv9YCXj8QWTUYQbhHWvW3NAj3OKtbpdSM up3w6vcb1++aZddnRxaw3goCxkIAAhCAwGECJuCiQlix5tE4b5VpYk2z9LmQlJpotDcBuTjt o+EqZn9pd5sayMgagMMNcSBjSbhHfRsR0gfMnbY49Ujdd8uDcL9bi2EvBCAAAQhAYAEB23Gk NUNrppiYjqZvubBqtld22NsF1XlkUaby9YYHtfwf+by2MDVSruWPvD2JlNdKMzJQGMnbsmvH zxHuO7YKNkEAAhCAAAQuJiBBpHCU6NWbvlXuSuFuITKRxagtu3f4fJSdieHWTkLmqwYteouS vgnR4C8ykBsR3yN5d2irXhsQ7r3ESA8BCEAAAhB4OAGFm0gQReO2Lawmmj6Cb1R8RupQGj/b HhGZ0XKvSjc6225tHx20WdiTwnD82wqVk1uHkAtlGhHfI3mvaqORehHuI/TICwEIQAACEHgg gfRQnFwsee5v0Z1YWshWiXbZYbHtUaHasv3qz0eFu4VIRWbbW28q/Mm4ZlcupGhEfI/kvbqt jtSPcD9CjTwQgAAEIACBBxPo3dbRZlZ3ivOONk/vXvXRcq9KNyLcbWegyCJWe8vSOi1W9kTS HLUb4X5VT6NeCEAAAhCAAAQuJ9CzraOMtVnVVQsZZwOyQUfPFpCzbZhZ3lEBLBsk2KMHL1k8 e+0tiw9Dqvk4Ir5H8s7kvqosZtxXkaYeCEAAAhCAwA0I9GzrKHcsxjm6beRuCEz4Hd1Gckd/ jthk7R6N848MEKJhSCPieyTvEU5X50G4X90C1A8BCEAAAhDYiEDvto696Tdy9ZMpJvyignU3 +709ETGds9/esvSsUYjUZSFXrbcZI+J7JO/ObVmyDeF+x1bDZghAAAIQgMBJBCSEogs1LRQi mv4kk4eKNXHZI1qHKjwxc0RMp9XbKbS9/rf2+beFq5EQqhHxPZL3xKY4rWiE+2loKRgCEIAA BCBwLwK92zr2pi/OIv46WFi5k4y3wwTmqlCfXnHdk74nrTGQsD6yf71m6TVg0z8v+hVyZAt+ I4tcZceI+B7Je6+78zdrEe53bDVshgAEIAABCJxAwGZRo7OvvelrJl8l3G1xbWvnk5ztEq9H RG/UVy9KI3l6hftR0W4s5L+JdKtbHMWkJ/RoRHwfyZvLE+F7wi3XXSTCvRsZGSAAAQhAAALP JGA7rES9602/o3CXTSY+W7HY3n4TrVFWPl1EJKaiPZonak9EtEcGJmImsT6yuPeI+DY/e/Om HHvzR/melQ7hfhZZyoUABCAAAQjciICFvURikuVWb/pdRbvZZYtsIwcPWTjIkebtFeDRWfee 2faIaFf7amBWG8zYjkJHOKQDmR77R/Lm+EfapHfgNcqklB/hfhZZyoUABCAAAQjchIA/nl5i rTV72pu+haFXOLXKO/q5hf5oBl7C1R8opd/1N32mdEevI75GZoWjwtcGKLkZ/dzfaodqWXqJ e83OH7nSU3p73nqkeSNhSwj3I61EHghAAAIQgAAELidg+2yXRFwq2lrpow6lQvSImI3W1ZtO sdkSgKnA1f81w9wrUGf4Gpl1jwj3XtHeGqCk8e0a9CmP+knrFN3IwKE46/x/i5lrZdTavdbf ShxLdVk9Pl+tLSKDsKrtvR2a9BCAAAQgAAEIQOAogVTU7CTaj/rUEphe3B2toyX4IsL9aN25 fJoZ10BGIl1i3dY7eDsjM+Azbeopqzbz3pqVL/XZVLyn9qT5jvR9QmV6Wpm0EIAABCAAAQgc JtASRIcL3jDjbF9bs+5HROARbLYYNffmQX/T535mP7ol5BFbRvNE28ini4j2nF3Rulo+Idxb hPgcAhCAAAQgAIEpBGaJlynGnFzIGb6WZt1XzbZLlKuuyFaPtnh51YDiaHNGZsGjwr1kQ03s 99qNcO8lRnoIQAACEIAABLoJlITs7sKu29FfM5zpa068rxLuvdt/yq4j++MfYR7JExlM5YQ8 wj1ClzQQgAAEIAABCDyGQEQ0PcXZM329UrhLhKv+yAFdtpC5tUPRyjaPtEtOpJ8h3I8OWJlx X9ljqAsCEIAABCDwogRqM5lHRcyuKM/2NRXvq2bcFcNus+6501H9lpmRbUVXtl9kwNMj2iNi 3vxrzeL3cEC499AiLQQgAAEIQAAChwlcJTgPGzyQ8UxffdmrRLtHYTvJ2Ay82aBFqdphJjIj P4D2UNZUaNfCmXJpU+beiMjAMzdwOOIIwv0INfJAAAIQgAAEIHAaARNCXpTWxFEqXtN8EWF1 mjMnFVwTkidVSbEbEEC4b9AImAABCEAAAhCAwG8EajPVrRjlVqjD0xjPmsV9Gpcn+4Nwf3Lr 4hsEIAABCEDghgRK8cOlmfOSgH3iTLtvToT7DTv3oMkI90GAZIcABCAAAQhAYC6BHuFeWyT4 dOHu31DMbQFK25UAwn3XlsEuCEAAAhCAwIsSmCHcfZz8kzG+wuDkye3X6xvCvZcY6SEAAQhA AAIQOI1AK8a9tRtILf9pRlMwBBYRQLgvAk01EIAABCAAAQhAAAIQGCGAcB+hR14IQAACEIAA BCAAAQgsIoBwXwSaaiAAAQhAAAIQgAAEIDBCAOE+Qo+8EIAABCAAAQhAAAIQWEQA4b4INNVA AAIQgAAEIAABCEBghADCfYQeeSEAAQhAAAIQgAAEILCIAMJ9EWiqgQAEIAABCEAAAhCAwAgB hPsIPfJCAAIQgAAEIAABCEBgEQGE+yLQVAMBCEAAAhCAAAQgAIERAgj3EXrkhQAEIAABCEAA AhCAwCICCPdFoKkGAhCAAAQgAAEIQAACIwQQ7iP0yAsBCEAAAhCAAAQgAIFFBBDui0BTDQQg AAEIQAACEIAABEYIINxH6JEXAhCAAAQgAAEIQAACiwgg3BeBphoIQAACEIAABCAAAQiMEEC4 j9AjLwQgAAEIQAACEIAABBYRQLgvAk01EIAABCAAAQhAAAIQGCGAcB+hR14IQAACEIAABCAA AQgsIoBwXwSaaiAAAQhAAAIQgAAEIDBCAOE+Qo+8EIAABCAAAQhAAAIQWEQA4b4INNVAAAIQ gAAEIAABCEBghADCfYQeeSEAAQhAAAIQgAAEILCIAMJ9EWiqgQAEIAABCEAAAhCAwAgBhPsI PfJCAAIQgAAEIAABCEBgEQGE+yLQVAMBCEAAAhCAAAQgAIERAgj3EXrkhQAEIAABCEAAAhCA wCICCPdFoKkGAhCAAAQgAAEIQAACIwQQ7iP0yAsBCEAAAhCAAAQgAIFFBBDui0BTDQQgAAEI QAACEIAABEYIINxH6JEXAhCAAAQgAAEIQAACiwgg3BeBphoIQAACEIAABCAAAQiMEEC4j9Aj LwQgAAEIQAACEIAABBYRQLgvAk01EIAABCAAAQhAAAIQGCGAcB+hR14IQAACEIAABCAAAQgs IoBwXwSaaiAAAQhAAAIQgAAEIDBCAOE+Qo+8EIAABCAAAQhAAAIQWEQA4b4INNVAAAIQgAAE IAABCEBghADCfYQeeSEAAQhAAAIQgAAEILCIAMJ9EWiqgQAEIAABCEAAAhCAwAgBhPsIPfJC AAIQgAAEIAABCEBgEQGE+yLQVAMBCEAAAhCAAAQgAIERAgj3EXrkhQAEIAABCEAAAhCAwCIC CPdFoKkGAhCAAAQgAAEIQAACIwQQ7iP0yAsBCEAAAhCAAAQgAIFFBBDui0BTDQQgAAEIQAAC EIAABEYIINxH6JEXAhCAAAQgAAEIQAACiwgg3BeBphoIQAACEIAABCAAAQiMEEC4j9AjLwQg AAEIQAACEIAABBYRQLgvAk01EIAABCAAAQhAAAIQGCGAcB+hR14IQAACEIAABCAAAQgsIoBw XwSaaiAAAQhAAAIQgAAEIDBCAOE+Qo+8EIAABCAAAQhAAAIQWEQA4b4INNVAAAIQgAAEIAAB CEBghADCfYQeeSEAAQhAAAIQgAAEILCIAMJ9EWiqgQAEIAABCEAAAhCAwAgBhPsIPfJCAAIQ gAAEIAABCEBgEQGE+yLQVAMBCEAAAhC4C4Gvvvrq7cx/d+GAnRDYjQDCfbcWwR4IQAACEIDA JgR++OGHzwR8r1le/Kusp121wc133333hbutwdDT+ODPfAII9/lMKRECEIAABCDwGAJebPY6 NZK3t66r0v/yyy9v33zzze8DnJ9++qlqSpr+xx9/vMp06r0hAYT7DRsNkyEAAQhAAAKrCIyI 75G8q/ybUY9m1+Vr9K2CpX/37t2M6injhQgg3F+osXEVAhCAAAQg0EtgRHyP5O2188r03377 7Sfhrtn0yGUz9NH0kTJJ8xoEEO6v0c54CQEIQAACEDhEYER8j+Q9ZOwFmSS+5afEe+T6+PFj V/pImaR5HQII99dpazyFAAQgAAEIdBMYEd8jebsNvSjD+/fvPwnxaNiLYuB70l/kFtVuSgDh vmnDYNbb2//+zy9v//yHH95++Pa+3fQJPqzqizux+pd/+vFTv9PPVddO/q/ymXruQWBEfI/k vQedt7fvv//+kxD/+eefQyZbWE00fahQEr0MgVMVkR58Pf/+5i++fvv7v/7uk1j7j399/zKN gKOfE/jPP/389o9/+/1nfedujJ7gwyrmYqX73n9XrKq7VM9KW3b0/2r+T67/w4cPv+9Aojhn zdbqym0duAuHEfE9kncX/1t2yMevv/66lez3z3vThwsm4UsQOFW4G8H//q+PXzyY9aCWONcs k116gNkMqx6cEvJ/+vcPL9EQOPlnAuobf/eX32wl5Hrb5w9/9e3tfej1+Wj6HVmtnHHf0f+j bUm+OgELqbDt/zTjKsEnIYdwv2fv0UBM7adZ98hlfSCaPlImaV6LwBLhLqQS6NFZLAl9iXZL f7fZd/P1tbrSXG97+svcmueV9gQf5tGol/TqrM7wn++hVb03Xo/tJOJzWLxzz17erUN8Ip/H rX6bdgBTT513SZseUBVhrzT2puUufmLnPgSWCXe5HBXuSivx7tNrNv4u17/98adbx2Xvwrmn v+xic2rHE3xYxfbVWc32n++hVT03Xo+JOr8FoO1Iopnb6BUVh7V00bqUbiTcZSRvj41Xpe3d 1tHesLAN5FUtdv96txXuQuvjnPX7XS4L87iLvbvaOVvIXOHnE3xYxe3VWc32n++hVT03Xo8t SkzDYnr2/47XNi/liPgeyTvPg3NK6t3WUaFRPdtGnmM1pd6dwNbC3eJMLd79DrAV1mMP4DvY u7ONs4XMFb4+wYdV3F6d1Uz/+R5a1Wv76rF4aIm36AmbfTWck3pEfI/kPcebeaX2buuo7SLZ BnIe/1ct6TbC/Q5bAqax+a/aqWb5PVPIzLKpt5wn+NDr89H0r85qlv98Dx3tgWvy+ZhoCb87 XCPieyTv7mx6t3XsTb+7/9h3DYHbCHe99t35Ugy+X1B7h4HGzjxl2ywhc6WfT/BhFb9XZzXD f76HVvXWsXpMwB0NkSHGfYz/rNw92zraWoaebSNn2Uk5zyKwtXD3WwJqm8jopdfEion3Qlpb DGqxVvSyfbh9GSoztz1luud4bu961Z9eOTu1Ndwf/+HdZ9tklmwuHdiiECNjJ/t72aV7akfZ 5fzO2X4kXavdzmBpW5OK4dGdjXJiTGWpne0z8Y1uezrqZ65vaIZW/T0y2Jxxb5XasiRcR/vz ke+CI4chic3Re6c2UI36f/R7qHVv8fk5BGyR4pGQGYT7OW3SU2rvto696XtsIe1rEdhWuKfx 7X6/95qQlSDSP9uFRvkkhE0USNBKqNQuPXxTsWY7NKicmhCOzpqZKFRd5pufLVP9JTtLh/so fbr/udkjJq3LxKR42aW6vMhMRXduMBJh4Nu3JBgj5aj+M1jmfG7xaw1S9HlNXLUGWCN+qu70 3tDf1K/94PTse6vGMG3vkf6se+rId8HRg7Nm3Dsz/Y/eO0f6NHn6CNhMa5rLhNwdZmBHwl1G 8vaRXpvaTkuNbuvYm77mjTFd6zG17UJgS+HuhXZNwKYQlbYUUuMFk9KVLnsA57af9GWUZu8j D0wvWmvCt7STTu7AFhM53q70tXntjYP5natTIsiLu9bWnBEG8ruVrvW5yhhlqf6SDnbEwGbY rS+2RHWpP3kfNEhT2TYgE9dUyMuf3DXqp+XPDYA12292lvyYcW+1vvQ8K+vP/k1H2p9rb0GO 2nvkMKRZ985M/yP3Tqs9+HwOAVuQqh1I0usuAmxEfB/Jm8ujv+102RuTqE296Vvl7sajZS+f zyOw9E4oPUwkJiQeJJLS0JTITLtw2Cvq0sM83Rc+J5BMRJVEmhdPpQFC5IFZm+H0B7HUBhjp gS25UBxx8btLRNKURLkfTLW25owwmCXcz2DZ8q/n9vMsSgMnf1qw0uf6/Kifra0B7f7J+Tbj 3oowSwc5uTyR/jxqb/pdUbPd2zN678zyP3JvRdqDNHMI6GAliaz0gCWbib/DCZpHxLfR682b CtLe/HNarV6KvS3RWoXI1Zs+UibCPULpmWkuE+65OGf9TbNXEjhRwa5msfjcVoyurzMNHfEP 61K8sa9nJLyjJWxbn1tXjKSLnMjow0JK3dz7XhtQ9IiGlv2tzyN1RcpIy+npe62vhWj9ftbf hypF27pVj31eGjyYAE39mXFvtRhFfVS61sB2lr0tnmbzzHsnUmfL/x6W0XYh3RgB7dluixh9 WIUEu2Zhdz+IJz0ZtOeE1zRvJJ4/J0h7RWpv+p4W1hsUmz3Xz9bBWb3pI7ac6V+kftJcS+Ay 4W5up2EYLVGYw5XOWJYGBenffVl+RrnWJDbrXgppiDx8Z5QREa3Rh3jE5pn1zbRrNcve2zXK 1q+hyL0ZGfXTDwxy4t0EYerfjHsryizKqpZulr0zbPF+R8qLpIneh9Gyom1DuuMEbEZdot12 k5Hgk4jdWbRHFsCWqIzmTcvtFaq96SOta3u2l3xL27KVPlJnje9IfvLem8Dlwl34fIytHji9 oQqRWa9WM80oI/pQLdkiQZUuiqzZHX04t9K1Po8K7dnponblGJ3FstWP0s+jPkTe5oz4mS4G lpCP7JQz676IcIuyqqWbZe8MWxDukVYnDQS+JFAS3qU1AZY+J6qtdJ+3trZgx9Cc1KYzBib0 w/sQ2EK4C1c6UxbdHm9ULPeKzVbTRh/4Vo5CdMx3/eyJr43W1UrnxU5t4amVU4qV72XZsqv1 edoWK1i22v+ocO/px0f9zM1GtwR8bxv08ukVty1Os+yNljPz3onWGUkXSTPSVuSFwNkEcrHu qjMSShMR/iPlnO27Lz8dZCDaV9Lfs65thLvw+Nf5CpmJxhr7h1Rrx5NSM/gyovXmyoo+MP2O ImlMc7SMWen8bGwuvlp++gFFa6Z2ll3Rclay7L2Noz60BKk+n+Gn7o90r3HZKAGa2350xr0V ZRZlVUs3y96oLTPvnWidkXSRNNF2IR0EriJQEu+psK393z6rzbJHBwSrOUQGF6ttor7rCWwl 3NPZ5tbMruHzD6meQ5Y8fl9GS5jWmi3ywFT5GpjoX26gESkjIvRyfEq2+wObcgMXm62N7Ac/ y/5IOatZ9t6yER/SdsrtWDTLT6srJ+Bzg+UZ91aUWZRVVLgf/S7oubeUdta9M8P/nns+2i6k g8CVBLx4jQjZ2mx7yY8jeVYwifi7wg7q2IvAVsJdaNJY3MjD18/Ul7Zp9NjtcBb/N//KOxJj r0FGTsS2Hr4+lrk0QGiV0ftwjpRn/tjsqx9QWJtERHuP6GnZ1fr8Cpa9t2/Lh9zgMe1/M/1M 7VdImt9qMt0Kdca9FWUWZVVLN8veqC3ybda9E60zki6SJtoupIPAKgItoZqbgY/Mysv+WojJ jsK9xIJQmVW9cd96thPuQpW+ym+ddJrG7rbEvoRoKkLTMiJ15gR+64EZWTzXKuMM4a4yJeIk fOzkWLND/+95CzHL/lY5V7DsvZVbPlh5NXE+w0/ZUQoB8zvaKJ2/ZtxbUWZRVrV0s+yN2mK+ zbh3onVG0kXSRNuFdBBYRaBHuPsFqd6+IyK8Ve8q/1t+INqvaIn96txSuKdbRLZmetNdaVqn rerzVNynu3rU6rSQnpyYbT0wW5+ri0TSzE6nwUxrwBPtvrXDgnwZLT9HP5/NKOp/j4+W1rYj zb0xanGI+KkyaoOvUh0z7q0ot4ifLV9n2Ru1RfbMuneidUbSRdJE24V0EFhBILebS7oTjNmR E+21RZwtwRuZyV/BoCbcSyyUx/MoDWg8u5q/rbUAqzlQ35cEthTuMtOfSKiHUCt8Jd1GUXn0 QPWzjCpTwqi0V3w606+0fncbldUqo/XA9J/nhFTqd63TtuqyvK10NuN6dGFvaqPnmPNRHFPW udnglt1XsOz9Emn5YOXZYCe3m9IMP1VGLYzM6sgNWGfcWxFuUVatdDPsbdVh/sy8d6J1RtJF 0kTahDQQWEUgjWUvictculS4RsNnUqG8m2hNBzM5+2ppahxKn7UGOav6A/WUCSwT7ukJnpGd WyTW/QOotlhVs+B+ptfnS38vzTz2lFHartKLBpvB1k+z3fvkF6eqbqVJBa3EtFilscdq0ujD uZUuyk3lKK1sqbWfD71Qes9KgykJyHQhsv6WisaW3Vew7P0yaYlulWfhHaV+OcNPsyN3cFgr hr7nvugJqUpZtto7OhCdYW/Ulpn3TrTOSLrW91BvPyY9BCCwL4F0MGOW1uLkdwwP2pfwXpYt Ee5+8ZY9dCRQW3HkQuUXm5lwlPjIzQ6rvDR9VLRbs7TK0IO6Jk7SxbVms/maClZvn4mqnM8p q7Se0kmukXS5GcrawCf1KdelS2WaaE8HHhKnuQWxNcG5muWRW1f8vbiTn9aWtr1jOrhJ65nh p29P2WADLzG3tiptBSp7WveFyh8R7ZF+KjvSePxSvx+xN2qL7Jl170TrjPrf+h460pfJAwEI 7EkgKtxL6WpCf0+PX9uqU4V7S/xFZo7S2PPIDHx6aqbEoh1uFG1uW8Bq9ekBLWETeVPgxZoX SVa3F0sq34s5pbGt//RZbna7xtX7F02nPOnbjUjbtcKXZLuJVrVBKrJG/ZPdq1hG+00pndpU vLyIV5+KriuY4af6rspRu/jBYTpoqvk6495Ky4/202g6X36vvUfqGL13onVG05n/re+h0T5N fghAYA8CM4R7KzZ+D0+xQgROFe4gvg8BibnIjKlmMhX6YjON9/EQSyFwDgHunXO4UioEINAm 0Ipxr60VKC1qJc69zf3KFAj3K+lvUrdm5lqz5zlTNQPIBYFXJsC988qtj+8QgAAE1hNAea1n vlWNFj8dCQFKDW9t07mVoxgDgckEuHcmA6U4CEAAAhBoEkC4NxE9O4HtYtPrpWYaI6E1veWS HgJ3IcC9c5eWwk4IQAACzyGAcH9OWx7yxBa85babLBVoCy0PVUgmCDyEAPfOQxoSNyAAAQjc iADC/UaNdYap/oh47TSinXNyW23a4VO21/wZtlAmBO5EgHvnTq2FrRCAAASeQQDh/ox2HPIi t+dzaeu50r7ZQwaQGQI3JcC9c9OGw2wIQAACNyWAcL9pw802WzPqdsKr32tcv2uWXZ8dWcA6 207Kg8BuBLh3dmsR7IEABCDwXAII9+e2LZ5BAAIQgAAEIAABCDyIAML9QY2JKxCAAAQgAAEI QAACzyWAcH9u2+IZBCAAAQhAAAIQgMCDCCDcH9SYuAIBCEAAAhCAAAQg8FwCCPfnti2eQQAC EIAABCAAAQg8iADC/UGNiSsQgAAEIAABCEAAAs8lgHB/btviGQQgAAEIQAACEIDAgwgg3B/U mLgCAQhAAAIQgAAEIPBcAgj357YtnkEAAhCAAAQgAAEIPIgAwv1BjYkrEIAABCAAAQhAAALP JYBwf27b4hkEIAABCEAAAhCAwIMIINwf1Ji4AgEIQAACEIAABCDwXAII9+e2LZ5BAAIQgAAE IAABCDyIAML9QY2JKxCAAAQgAAEIQAACzyWAcH9u2+IZBCAAAQhAAAIQgMCDCCDcH9SYuAIB CEAAAhCAAAQg8FwCCPfnti2eQQACEIAABCAAAQg8iADC/UGNiSsQgAAEIAABCEAAAs8lgHB/ btviGQQgAAEIQAACEIDAgwgg3B/UmLgCAQhAAAIQgAAEIPBcAgj357YtnkEAAhCAAAQgAAEI PIgAwv1BjYkrEIAABCAAAQhAAALPJYBwf27b4hkEIAABCEAAAhCAwIMIINwf1Ji4AgEIQAAC EIAABCDwXAII9+e2LZ5BAAIQgAAEIAABCDyIAML9QY2JKxCAAAQgAAEIQAACzyWAcH9u2+IZ BCAAAQhAAAIQgMCDCCDcH9SYuAIBCEAAAhCAAAQg8FwCCPfnti2eQQACEIAABCAAAQg8iADC /UGNiSsQgAAEIAABCEAAAs8lgHB/btviGQQgAAEIQAACEIDAgwgg3B/UmLgCAQhAAAIQgAAE IPBcAgj357YtnkEAAhCAAAQgAAEIPIgAwv1BjYkrEIAABCAAAQhAAALPJYBwf27b4hkEIAAB CEAAAhCAwIMIINwf1Ji4AgEIQAACEIAABCDwXAII9+e2LZ5BAAIQgAAEIAABCDyIAML9QY2J KxCAAAQgAAEIQAACzyWAcH9u2+IZBCAAAQhAAAIQgMCDCCDcH9SYuAIBCEAAAhCAAAQg8FwC CPfnti2eQQACEIAABCAAAQg8iADC/UGNiSsQgAAEIAABCEAAAs8lgHB/btviGQQgAAEIQAAC EIDAgwgg3B/UmLgCAQhAAAIQgAAEIPBcAgj357YtnkEAAhCAAAQgAAEIPIgAwv1BjYkrEIAA BCAAAQhAAALPJYBwf27b4hkEIAABCEAAAhCAwIMIINwf1Ji4AgEIQAACEIAABCDwXAII9+e2 LZ5BAAIQgAAEIAABCDyIAML9QY2JKxCAAAQgAAEIQAACzyWAcH9u2+IZBCAAAQhAIETgq6++ eov+CxVIIgg8gMCO9wTC/QEdCxcgAAEIQAACIwSiAkXpuCBwBYGePno0bepXtJyVPLgDV9Km LghAAAIQgMCGBEygbGgaJkHgE4GoiB5J14v6ivsG4d7bSqSHAAQgAAEIPIzAFQLkYQhx52QC O77tueK+Qbif3NEoHgIQgAAEILA7gSsEyO5MsG8vAgj339oD4b5Xv8QaCEAAAhCAwHICCPfl yKmwkwDCHeHe2WVIDgEIQAACEHgmAYT7M9v1SV4h3BHuT+rP+AIBCEAAAhA4TADhfhgdGRcQ 2LV/XmEXoTILOhxVQAACEIAABHYmcIUA2ZkHtu1FYNf+eYVdCPe9+ibWQAACEIAABJYTuEKA LHeSCm9LYNf+eYVdCPfbdmMMhwAEIAABCMwhcIUAmWM5pbwCgV375xV2IdxfocfjIwQgAAEI QKBC4AoBQoNAIEpg1/55hV0I92ivIR0EIAABCEBgAoEPHz4UT4H8+uuvP9Xwww8/VE+K/Pjx 4++WfPfdd9m0qid6XSFAoradke79+/dv33///WfcxPGnn346ozrKHCSwa/+8wi6E+2BnIjsE IAABCEDgCIF37979Lhwl1H/55ZfPitH/JSRNHChN6frxxx9/T6ff07Ja9l0hQFo2nfV5aaBj DL755ps3PzA6yw7KjRPYcStIWX/FfYNwj/cbUkIAAhCAAASmErAHf01oR8SBhKbSaSb5yBWp 40i5u+XRLLveaqSDG3HT342Dfu8d/Ozm65PsQbj/uTUR7k/q2fgCAQhAAAK3ISBhKEFi4TE5 w3/++efQrJ4E6UiYxysId7EU69Jsuv7uxbuYcu1BAOGOcN+jJ2IFBCAAAQi8LAET5QrdKF0K 25BoqaVRLPuoyHwF4a5QIzGvXZp5Nxa7isVXvGF2bYsr7htm3F/xDsBnCEAAAhC4nIAtUi2J coVzWDx2S9yPOnOFABm1uTd/jaEvC+HeS/bc9Dv3zStsQ7if298oHQIQgAAEIJAlYAtKtUg1 vRRGo9l2S6OfuUsz7T27x5Sa4goBsmu3MBbffvvtria+lF07980rbEO4v1T3x1kIQAACENiF QE2Ua3ZYYR21NDNCZIzFFQJkl3ZI7TAWI2sGdvXtjnZF+qbtwKSBrAa8ucveXtV2Z+rlE7Gt t8xWeoR7ixCfQwACEIAABE4gYPuIpwJRgtzEhaXJ7RYzc0b4CgFyAtLhIi18qbZgeLgSCugi EOmbPrxJv3txni46nhkvH7Gty9lAYoR7ABJJIAABCEAAArMJ2AxgumDSzxiW0ii8ZuaM8BUC ZDbPGeWVBlMzyqaMYwR6+qYdXOYHXvb2ygT86EJu70WPbce8/zIXwn0WScqBAAQgAAEIdBCw HWO8cE8FeS6N0kcXWkbNuUKARG1blc72wp/NdpX9T62nt2/alp66T3Q/tXYSGuHWa9tIXZYX 4T6DImVAAAIQgAAEOgmkD30JxzT8JScMlGb24UBXCJBOXKcnl2Dn4KXTMXdX0BvaYrPumlnP LfzuNqCS4Yr7BuE+swUpCwIQgAAEIBAkkD70JRzTw4HSNFqsWtphJlhtNtkVAmTE3tl5FXYk BmfOzs62+VXK6xXutk6hN98RnlfcNwj3Iy1FHghAAAIQgMAAATt8yeLZJRzT2UFLY/G6uRn5 ARM+y3qFAJll+2g5FiKTWwA8Wjb5xwkcEeCr+vOqejxFhPt4n6IECEAAAhCAQBeB9NTU3BZ2 aRqFyKQz8l2VVhJfIUBm2T5SjkKONDBCtI9QPDdvr3DXfWNx7me36xX3DcL93P5G6RCAAAQg AIEvCFhohsJjFJObExg+jX4/I0TGDLtCgOzQLTQYmrmv9w4+zbbB75G+epvMI/1S95Sdf3B2 2x6xb7R9EO6jBMkPAQhAAAIQ6CRgwkIio7SLiaXRIruZe7bnTL1CgHQim54c0R5DKk76d0Uf 6a1TQl0x7hb+dPZAo9e+GPF6KoT7DIqUAQEIQAACEOggYDtf6MFf2iHGpzl70eQVAqQD1/Sk EdEu8Xf2jO10x04s8Io+0lOn3lr5Pdr9tpDCovts9i4zPfbNahqE+yySlAMBCEAAAhAIErCD lWrhL5Zmtth49Rn3iGiXCJTwOzM8KdhVtkl2iUj9dWBbinGXSFcbaYCl9krXidjA19pQ99PM Q8vUMJcw2aZHYAgEIAABCEDgRQhIPOYWpHr3lebsV/1W3xUC5Iqm9iEf5nPt5+z98q/weVad V/SRWp2+3UzAe18l5pVGn+leO+PtySVMZjUo5UAAAhCAAAQgECOgB75icWtXJE2stnaqUQEi X2yGs13bNSl6RbsPu7jG4r1qHe0jR7yp1enXiZR2W7JDtc56c3IJkyMgyQMBCEAAAhCAwHMI HBEgEksK47FY4iNlPIfg8z25on1LYTK70L6EyS7OYwcEIAABCEAAAtcQOCJANJsp4Z7OZF/j wX612s4mu1kWCRHK2Xykj4z6jnD/kiCLU0d7FfkhAAEIQAACNycwIsoUB+7F4M1RTDN/99Ch XkdH+khvXZYe4Y5wP9p3yAcBCEAAAhB4LIFRUYZw/7xr2ImsuwvPng492kd66kK4l2kx497o Sf/yTz++/eGvfj1Z7duvPv37u7/85u2f//DD2//+zy+fcupz/V0/a5fSK5/SPv1qMXu6/3f1 75X66Ow2in4PzK6X8iAwi8CoKEO4f94StpXnU4S7f6uyaqed0T45696olXOFjd0q0gRs9KdE 79//9XefhO1//9fHFRyn1PGnf//wSaT/559+/lSeRM0//u33vwt4+f8f//r+s//nKlb+NN8U AzcsJMpsQ9Nf2qRX6qNnNbT/PjyrDsqFwJkERgUIwv3PreMPznqCcC/FxJ/ZH1X2aJ88276r bOwW7gZCIjwVpBIANhNtYlfiVsLdHmz6ffdLAtSEeWqr98Vm2msz7kqvAcDTH+w9zHZv/1ez T4PrV+ijZ7YrM+5n0v2ybM34aY9mWxhZ2urNBJTSctUJjIokhPtvfP1Me07wpifgpot7tUOP tqGsbRWq/p/Gzyu9LRLWnuVP6POjfXLFPX+FjYeFuwnzqCD9tz/+9Lt4/Zu/+Hrr2XfZJ79K bwhMvP/xH96F+oUGM1FOoQI3TDSb2YYuPtok9fWn99EjDWj37pG85DmPQCqISocUWbpVhxid 5/H5JY8KEIT7523U4qEdZ2wbTRt4SpD7HXrSUz4l+iXq07LTGX77vHVOwPm9aqyG0T45Vnss 9xU2Dgl3udXzsLdZKYsVj2FZm8oPMGbW3MNpZr0ryjqL2QrbqePPBJ7cR4+2s/Xto/nJdy4B zVbagzMXdyvhY+KoZUkpHKDn7606dv58VIC0hOrOvp9hW4tHKQZe4tzypifr2mm7vmyJdv/G yYt41XHna7RPrvD9ChuXCndBtJnZyILOFdDTOnwozMz6nyyKzmI2kz9ltQk8uY+2vc+nsBCi o/nJdz4Be3CmIQhWs/4eETA9Av2qmN8zaY4KkJZQPdP2Hctu8ah9Xvss3XozDYl50tac4rD7 NXrfHPFvmErvw952VlE+xdbudvmBxUzbejnNrPvsss5idrbdlP85gSf30SNt7RefH8lPnjUE WsJdM5BPiPc9m+aoAGkJ1bPt3638Fg+bGdfP9GrlHf18N1YlexDueTLLhbsPl9lxa8SzxMtZ 5e5wAz7Ztx34rrKBdvwzacX8MyBd1fPG6rGQg9wCVYsZHqvhNXJfIdyf/JajJa7TXqW+qtCu yCm0rbJbn9+lRyPcEe6hvnqWeDmr3JBTJyd6sm8no9uqeNrxt+bQ7lhetO84wbBVx7nYmJpw 12elEJqrzJ4hVkfKqM1ujgilI2JxxI/WQGNG2SMhUVEeFsqltRhas6FFq628o59f1fd76y31 x9lt22uXT9/qhyNlF+/V0UJ7H/atBaq5Q2AsvEYPU72+Ll2W129tZwcm2X7subzeh9rvubzR Q2t6Oaku+aotN72IUDy5FszNuq5g1mu77TPuOYiLtqCMtudIOuWN9kvZWutD6UFd6Ruo2tqP Gf0hZSmmZtORPlq7J1S2Xele8QqTq93LtXJ7728rK9KG6Ra3ubbMbWkb/R5IbTnqS6lP6u9q TytXbaDvzydftstGOuOu2cvSNpE5HjPEwJ05jwqQlpi8M5sjtrd4SKCXBp2tvKOfH/Hnijwj A8lV9o7eN0fsXB4q4xcy+u0US4fA+FNL7SGac1Rl6XN/qqkepvZ3faaHcuuKipfeQ2ui5doD WX7rnz8AyvuiB/PogVarmbXY5z5Xf0kHbH4Xm5IoiRyOZSKn1jZH+mXusC61V+0yP3MDTJU3 oz9Ye+unnbeg+qzsnj6a80Vl+TUsNlPt2ysVwlFROdJXj7Sh/Ivw6P0eULkjvih/qU59H6T7 8ZsPO64nOvJ9kMsjca6Hp1+AqrCDyIJUXx7C/atPHI9eLTF5tNy75qvx8FtBpls+yt8Wy9HP 78D0CkF8hMsVdh6/S//Pw8jDzWD42UiJMX9Ykx446UNHQttm5fzDLoVrYqG0r7oXDq0HWNSf 3kNrouXKN7EpCT0/I+hnNHs73BXMem20QVtOzHoOpTcQUea1dCP9sufgMfmTzsYbrxn9wdq7 VMeMA5hy94Tu39oAtPaGwfwf7atH2zDSf3q/B0Z9EZNcnSba/b2QhvzMfFPXey+fmT4n3CXa JY644gRGBUhLTMYteUbKGg8fx57ztsVy9PM7EB7tj6t8vMLOZcI9XeyVe02eHlQUmSE3Ud4S saWZ/rRxIw9ry9NzaE20XLOzFEaQ1lkSYrVOeyWz6M1kwrw0I9sKuVI9UeatdEf6peq302RV fqt/luKoZ/QHs6M26x99Q9Fqv5RV6aRk//ZIvvtBvK9jVl890oatftH7PTDLF9Ub5ezb9Q6n Vrf6V+5z7RjjZ9wVJ8wuMv0kRwVIS0z2W3TvHDUe/rPc+QMtlqOf34HsaH9c5eMVdk4V7jmx KaEpkWXxyfpZik1OxVbpYe4bxMptifw09rhUdvRhbTZE00fSeRtrnc6X1XqDkCvnamatG8oP Tkp9JW3PXJkR5lGB78uK9Euzx8fll3zRfZMboMzqDzabXptxnXm6b5S7Z1OybWZf7W3DqB/R PjTTl2idM9u1dd9e9bk/sEYnRea217vKtjvVOypAWmLyTixm2GoHf4mLhcPop94G+c80+27i XQPOdFcZfabBqL9arFufz/DvaBnRcKzR/njUvt58V9g5Vbin8av2fwkHH/YSFaUtgH42KTLz 7MMBSmE1PQ/r6MMzmi6NDy7xTP/e4uQ/34FZy14/G1tLa7PupbaPtmUkXSRNzlbvS+ntgQZf OVE/oz/49m6tiTjqY+p3tBzPJjcbPLuvRu3qHZRH7u/ZvkTqPOJH697c8XMv3Hvj2nf05yqb RgSIhStZGT2Lgq/y9+x6/QmmxkWCXSFcKS/7XAutdXlhr8/8G6QWa38/KO9ObdHTx3rSnt2W tfKvsHOqcJ8Br+fh6sNfIsLdC4XSTHVP/bMfnn4h7gyWuTJ2YNbybRaHaFtG0kXS5PxqzXjq 81IIywwOXvy3uB/18ahwb701md1Xe/3rSd9KO9uX2d89rb6x8+d2UqTETi7sYGfbd7LtiADx M7ul33fycbUtEu8mwiXKff+UoP7mm28+hXnpp1+kauFf+rvfzrTG23zbtR163wJEZ+ZXt2la 35H7ZtTmWwv3SBiCBxSJ4209gI+KlEi5kTSjDb4Ds5YPszhEy4mki6Qp+eUFeBoSogHnrLc/ ufr9W6bduLfE5+y+2tuGPelbaWf70mLn27plW6tf3OFzPTx326/9Dty8jVcIkLsxwt5jBHKD iVZJCPcyoVsLd/9Aqu3Tbu63Zvh6HoZWZvShGEnX60+r4+c+763jDGYtu72NPfHkuw6q/IAx fdNT29azt61a7d3DvZW29nmkr0fun17/W321x67e74JW2bN96bGvZdtIW++QV7OauS31drDt TjYg3O/UWvex1fernll3hPsLCPfIVmfpdpSjQmf2w9M/YCP+HLl1e+s4g1nLbm9j7yE9vuyo YImki6Sp+eVnXC3W3PZQL+XrbatWf24NgkZ9jIjx0uAqt+tOr/+tvtrrX0/6VtrZvsz+7mnd k7t+rpACFqPOaR2E+xyOlPI5AS/AEe5zesetZ9x9CEIp3MBj8g/20i40rQdwSXgo3+gsZHqK YquJ7WCeVjr/+Q7MWvZ6G1u7BaksCeHcmoVoW0bSRdLU/PKx5rZIVT9rA7QZ/aEnRGPUxxHh nlucOruv9vrXk76VdrYvCPe3Twv2tAMH1xwCCPc5HCmlTCAq3O/UF6+w9dbCPbLY1Hchv692 aSa39QA+U7inu4i0Zt0VH927HeQOzFpfbCmH1m4oSp8T+NG2jKSLpKn5lZsNbu3tPqM/+EWR rVNKR33sFe5+289cX5/dV3v960nfSjvbl1cU7rZgT7HsWtj3KqJdfmthoxc92j1ndnjQFQKk 9Szg8+cRiIj3O/XFK2y9tXBPd+xoxbnbw7MmmFoP4DOFux9YyA7ZWROt+rwl7lN7d2DW+ipK Y5VrgxMTf7mBmJ9tHn0b0tsvcvX5GXQJ6tbbhBn9wZ8aLB9q4TIzfOwRlD72P2fX7L7a619P +lba2b70cG7Z1rofd/ncP/BfRbRLoNd2EtGuI7NOiL1CgOzSt7BjHQGE+zjrWwt3ud/aC9oj ihxE0/uQi6aPpvOv1C2PZta9sJHgkS+tGdtS97iaWaTb+plicZC/fq9z8Whx8GWUTupN65l1 MFfOR3/aq3yqHURm+Wf0Bz+AKQ2CUoHfessRHQjVyjH2tTC3mX01eg/2vjmIiuiZvkTr7EkX uS+vTKNZZ22t9yox7eav3i74bQQ1A+/3+Z61DSbC/cre/Tp1I9zH23pIuKezSK3FbxFzex+u KtOLm9J+7iaaauEC/tW97GjN4Pc8FKN+yQYvtHy+9PeRhZtXMov0gx4OJQHsxWh6Yq/6g+3o 4rnqbyPx8jXf/P0SHXT1cCj1h3TmPh0EiUVuoFTaX77Vfp5n6a2CzbZHQr1m9dXoPZgbNNmb Lf3MxeNHy57lyxnfPa125fN1BBQOZIf15GrVLLsX73Zwz4iFCPcReuTtIdAS73fqi1fYeli4 S1DooewfWHqgjczUpTOSkUOVrLN4W/S7DSL008qtzezZQk/vjx6ytcFI1N5oOvNFDH1YRU68 j4j2K5n13NwtDhK/LQ65GWubwbe+mgrNdMDW2341H62ftuLNfRktDrK/xSE9wyDt56kQ1L0c GbjmfPVlq43ks/9eMJ4R0T6rrx5pwzRPKXytt+zR7yoxidYZTddzX5L2fAJ6q9Dal95i/mcJ h1nlnE+HGu5OAOE+1oLdwr02A5x+1mNardxoORIaeiimoQES7NHY3qgds9PlfNTsnheeEvMS fCODo7Selcyi7Zims0W4xlxMWm3qyxAz6xNimA4IVa7S5PpItJ2jvpmAPtKGo/1BdaYs/GC2 xiHqXzoA0P9VZxrf3xpo5Oo7o6+2/FJfsb7jJwQs39H+cdSXlG/tO/eobS0mfH4+AcW2R66W AIqUYWkQ7j20SDtCoNVv77KHuxhccd90C/eRxiIvBCDwfALRsJHnk8BDCJxLwETDjMW6VwiQ c+nUS3/37t1n4Ua1RcD6TKFJXPMI1Pobwr3OGeE+rx9SEgQg8CsBhDvdAAJrCJj4mbE15CsJ dw10WkI9/fxVFkWv6bl/nqnOifQZwv3Dhw9v2nVJZemnQst0Rd9mRTlccd8g3KOtQzoIQCBE AOEewkQiCAwRkDCZORN8hQAZAnAw8xHRLjaztt08aPbjspXCZWb0Q1v/oR2ZdNlib5WNcH9c V8IhCEBglADCfZQg+SHQJmCHMs2YbVdtMwRT2+prU1h4jM2+aptNm5E1y3J/u9bq59ae63Mz +qHNtHtyuk9Uton5WVRn2NtrCzPuvcRIDwEIVAkg3OkgEDiXgGZ/Z88eXiFAzqX0eemadU3X AkjIy28/+DGBp8+4ziWQm3Wf0Q+tDH/+gQ3I9KZq5jXD3l57EO69xEgPAQgg3OkDELiQgF73 zzp4ydy4QoBciPBT1WKYLjq1UBpCY85vnbOEu7VhGhaj+ryYn+HhFfcNwn1Gy1EGBCDwiQD7 htMRIHAuAZsRbu3z3mvFFQKk18aZ6RUyIZ/9olPCZGYSjpWV9rsZ/dDWf6TtG7OoL9UMe/tq /DWsrTcD6SEAAQjkCLBvOP0CAucSsBAZi9GeWdsVAmSm/b1l2cmzfgaWMJleiuPpzxDuskoD Mit71jqQnLdX3DcI9/F+RwkQgAAEIACBUwlIYEpsniHaZfgVAuRUYJXCbbZdC3z9RZjM+hZJ w2Vm9kO/g9DsEBkjNdPeKH2Ee5QU6SAAAQhAAAIXEZAIOXMv8SsEyBUo7a2F/PXhRoTJXNEa v9Xp+55+n3nZm5Wz7p0r7pu5hGbSpiwIQAACEIAABD7thnKW8Lhy5nB109pbi9yOPBYmY5w1 Kz9768DV/t6lvhnC3QZeqc+2p/tZJ98i3O/Sy7ATAhCAAAQgsIBARLRrFnlU2F8hQBbg+6wK HzqRLu7VDiRioG0gxfMsobfa5zvU58Nljs6424LU3G5AZ/btM8sutR0z7nfo1dgIAQhAAAIv RyAi2jWjKJE5Ojt8hQBZ2aBetOcGOal4HOW50re71zVDuNu6hbTdbCY+Xc8wi9kV9w3CfVbr UQ4EIAABCEBgEgEvNFNhk/v/6OK7KwTIJFTNYiwMRj6mhzBZZs803f+7WQEJhgmki1R7C7Q3 JukCbgn22WceeNuuuG8Q7r29g/QQgAAEIACBEwn0ivYZs4lXCJATEX5WtMU51wS5bR/Iiamr WuXzekaFu90Damu7fyTY1a6jg9oakSvuG4T7NX2UWiEAAQhAAALbELhCgGzjPIZcTmBUuF/l wBX3DcL9qtamXghAAAIQgMAmBK4QIJu4jhkbEEC4xxsB4R5nRUoIQAACEIDAIwkg3B/ZrLdy 6o598AqbEe636tYYCwEIQAACEJhP4AoBMt8LSrwzgaNbQV7p8xX3DcL9yhanbghAAAIQgMAG BK4QIBu4jQkQGCJwxX2DcB9qMjJDAAIQgAAE7k/gCgFyf2p48OoErrhvEO6v3uvwHwIQgAAE Xp7AFQLk5aED4PYErrhvEO637zY4AAEIQAACEBgjcIUAGbOY3BC4nsAV9w3C/fp2xwIIQAAC EIDApQSuECCXOkzlEJhA4Ir7BuE+oeEoAgIQgAAEIHBnAlcIkDvzwnYIiMAV9w3Cnb4HAQhA AAIQeHECVwiQF0eO+w8gcMV9g3B/QMfBBQhAAAIQgMAIgSsEyIi95IXADgSuuG8Q7ju0PDZA AAIQgAAELiTgj5xv/X6hmVQNgaUEWvcCwn1pc1AZBCAAAQhAAAIiEBUodzzdkhaGwFEC0fvi aPlH8jHjfoQaeSAAAQhAAAIQgAAEILCYAMJ9MXCqgwAEIAABCEAAAhCAwBECCPcj1MgDAQhA AAIQgAAEIACBxQT+P8cqCIdWGyDWAAAAAElFTkSuQmCC</item> <item item-id="6">iVBORw0KGgoAAAANSUhEUgAAAFoAAAAlCAYAAADC3P4WAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA AaBJREFUaEPtmOEOwjAIhPX9H1rnkhmGFHqsdI25Jv5wUko/8Mr6fG3jwVFP4AOao57Ao34J rrCrBjHMIUDQIOdNzD9n2v5BBmaNeP5jWxQypSNZDASdBIdOI2iUWNKeoJPg0GkEjRJL2hN0 Ehw6zQIdwWd7B1KWfTTSU/+A1o7AOG43l5XVAoEAGrWhE+jMX2JUICP8yDc2vZfje+v5iPU9 H1/QnsZE+lMdZI9/DZKgHWpXEkrQPeWobNADR+vyfregLn0oHYlEyCko0B6NbiU6+/x0qbSS RiMV7W3eSkgP6Iu5N6f/VdchJQOt9Aq4p0TrBaI+WmpdS/eODVs9rfXbEcOVw9DysWwf7Wmf BBTBtg4nr9qqq2kF/+4ruFUREURZWcf8KIErgKiOAb7r6AHdsrHaruoNVvvvlbupoD1NrwZy t38ItJYSKQ1aJrwWzZKUu0FUrw+Brg5mNf/Roa+l0LL/nlmrbW6VeKJ/r25Tw8ZhlY2tGMfI g5/S4WSYoCeVP0FPAB1pdHSZ9TN/QsxcYiNAjZ5UBgRN0JMITFrmDVH/lQH9hqKoAAAAAElF TkSuQmCC</item> <item item-id="7">iVBORw0KGgoAAAANSUhEUgAAAFEAAAAWCAYAAAC40nDiAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA ATRJREFUWEftmAEOwyAIRbv7H3prm9awP0AUYkiHyZLVKsITvm6v9962aj4CB8RqPgKbb3rN Piu5MPgJNIi7KBzaeH7uxvX5l1xrQYohMravTKQAKci1YcethvHcz1L/7MqPhohQ/h4iVyWj mWOFKEnbdR1sktfsUUfo5EjNGA1WGz/rI2o9xs3JF4VOz4uf75IxqyZGZEgkZMnWDEQraJcm 4mm+CgaXjb3s1ZJFA6xlaCvvp2ciB0g7nSWgWv/UPRE1AXdLeo/3T+5Zyu4Z2dAyVtN89AHH qppoKUfcEav4WsriLg+LH5nGDP/s85YCvSZotjJB6vmyHKL1lOw5num9CyKnFdayxdKlspAJ kMWXYYi0HCmIrvgKf2xo8ywBZBgzBTGD45l8KIgBu1EQC2IAgQATH9zFPhXstc2vAAAAAElF TkSuQmCC</item> <item item-id="8">iVBORw0KGgoAAAANSUhEUgAAADYAAAAWCAYAAACL6W/rAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA APRJREFUWEftlskOhTAIRfX/P9qhsQ0iZaqwqDZ5mxcK95TJdTvOMuM5wWY8y4xQpQo/A3a0 29lz5Xf1Xxo7js0FhhqrzptueLnC1P/w5UhCKnYvnmRbdEMIyhF2EgUniZV04gSJYB4Qz2No wGB7cDFUGfOA4VKGvdMTpAWDdpSvBm9JMYb0ZMbbN9QgGwLjXlgCw5nihpE2Y9Kwa4nSGkYP llEwXKKPBS3tktuuuHbdWyXai/0QDfYs3mHNh2U4UAEs9zNtTZ9UmnLJFM9+mViEfAJM6kXL g0XYmkoRNiq1VyIEen2awbyBsu/9YNkvPhpv2oztq/5fg05soAsAAAAASUVORK5CYII=</item> <item item-id="9">iVBORw0KGgoAAAANSUhEUgAAAEwAAAAWCAYAAABqgnq6AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA AUlJREFUWEftlwsOgzAIht1VvIJX8LRe0g1TTKXQUsTpujYxS5U++Ar032v9tKE3PQEA1pue wKA37ZZbNnYMdQR2YMuyrNM0QT3bnnmetwf74ziuYNNSQ9/gN9fi74klwsIJEFpLoMJFd3BJ goZQ0bgDCyRyUdZchJVSissOOuavgO3pEupvXJtyqRaDvByYZlOWumeJFo91LgXGTW511DpO SjN6kLdHmPYktKfuCUy7Zs0tSW1FHQaaCx48MdBo2Jc2drhNgrah77g+N46rS5p1a6FR2ZAA iuqiSelrIowThBKUElBtYa4Bdca2+q+RBhgX8vguEYJEZZei7YyzHmO/BkwTYVw6/HyE5aIn 51wJGH5vLsIkkUhhScWU1rfYTqp93Fwe6WWZozol6SJ3yQKLsx5jTgOTirnH5p44hwuwJzp2 1Z46sEqyb7Gb1GKae7QrAAAAAElFTkSuQmCC</item> <item item-id="10">iVBORw0KGgoAAAANSUhEUgAAAEgAAAAWCAYAAABjadrAAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA ATJJREFUWEftlgEOgyAMRd1VvAJX4LReUldiDda2tDLNSEpCprEweP18+lm/bYomEwBA0WQC U8DRCQSghkIOQMuyrCkl8KPSc86l4/s8zyvEjNxwL/Crtfr7JRLh4AQIaWQw+0V02oIECSFi cABiMj+8glpHhFM7HaPNMTygQ/67X9beoh2dGtyjgKjZ3cno2572moK4P7ICssZZ4VHlUIPV FPOYgriJrRu3xlkBeeK6FUTrIHiHjlmBGgnrIClTlyty9wi8Zrnv9Aq21Cp3QUtzn0y58rWy Nk8WaCwHSsuUthBPrdKzZu/YLkD1bcI9c+rw+MJdpXghqFW1d7KWB1kVZFHMsICsEKRyAMe3 jHNYQJLp0gJOMmBtvMWkvarvif+JB/Us4N/HBqBGhgJQAOo7xBt416aCBqD+NAAAAABJRU5E rkJggg==</item> <item item-id="11">iVBORw0KGgoAAAANSUhEUgAAAHoAAAAWCAYAAAAPb4jFAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA AdVJREFUaEPtWduRgzAMzLVCC7RAtTTJRZmIM4qsl32DIfIMHxALrXeth8PP9hyPHPdnAITO cX8GHvdfYq7wlbWThu9g4EPoZ7GCmr1fV6DhTMzrum7zPO98LcuywYWYpmnaYM7Z4yA0gKOD e3Y26NL/KJgBBwiMA8UehatdWUnQUcUeCXMKbdzSkc2UQhvJLZuxUUij9RbvuSWNgvn9X8T3 pe5IdNr35t/Ms4Su9QWWGv1fjaPG+XA1+s4R3bNx1IT9aKpH7GA90d2TPI9f7nQiRXTv7NMk NNYa6RyNDsraSZ2W99x8b73VBNDSoQVzuXb0R9fIkUvP0XAPF9rCGRvva+uo8cXhoBqVc2jQ HvBbnFOHEnEcOdxCNPEiv9eiuyZYDZdGfC9sVCQuwKwYJY5f75VAtziWdlqEKKtNFHMtUvC5 1b8ncDiOLJuVw9QktJZirbtN6wNaSdTsvTilUqT56i20BTsVnsPv/qhhcaztrihZUbsWzGWZ ivqvZTctMiUBKS6Nc5fQNC3W6nE5T6vZreRp9hbMXA8i2Wk+reWQjbz3ByWaBS3roGIfuG8B nbZxBrhaHH+bbumKaP11OcPDANc4euw9c1NoD1sXnptCX1g8D/RfvoNZorwdhDEAAAAASUVO RK5CYII=</item> <item item-id="12">iVBORw0KGgoAAAANSUhEUgAAAKgAAAAvCAYAAAB3/oHUAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA A3FJREFUeF7tm9Fy6zAIRNv//+i2aeOOQhaEFCETs5m5L64swXKCgMz9/Pr5fPBDBbIqcAOU HyqQVYGPrIbRLirwe7tTBiqQWYHSgP6UXbf6+/9f5kBVta0soDcw5Qc9qwpGFr9LAmqBSEiz oPlnBwEV8SCgBPR0BZhBTw+B2wBmUGZQNyxoYXSTSUAJ6EuA/taJoOF8edP7BiUB1USNFHpV wDLuE6lbWUAPSK0r6hD+WGOB3QZJro8MYAZgI/0rDegRXG0mqoEmYUT7tHBngCjSBgIaqe5R 64BflDwgykzcmhoZuA2SuI+I9JMZ1AiDB1BtTXTz4KZnw0ICukFkbYRiXd8SQgRrZPBOkuXp 2MifjZlBlSjL5slqfNq11ntZgFpph+X7inOeAEUCrziIe8QooE0hogfoMd487/oAqEzVbdbY ZRDP8SuA4oVq33cuM/4B1Zx4Z+f8ob7GSm209c4x7AKaKXTvLPQOHQnoDpUdZ0QX5g4TwpbM +nbVcddQBrVECIvYgo21oJ/5fIFbD1t4AdX6jDO1MKcgaM7Xet5eG1mu2NkssxqKiP1mfNOa pV5sI+xfvWe3i0ewrjaC+80rgDJnD9j50/a/2Z2DaoBqwqAsq42r0HOt0Efjk/1y5ToRZds2 a15hTDj0SxK67luR0JWi1UYW4FlKiVw41rRmClCZzawrRX7LZUaW33LCWRNEzestgKLDPZl1 Zai0TH+Fa3ClTtZeZySPrYB6oIwUQauPdwWY54wr4AbUMyc7rn50bVvNk1Xsj7ukv0FAV6q5 Zy83oHvMiT2FgPa/vG1y6fUWbULqRc5qps0zextf6e/aLXAlH2d8kfAgYOSzkebWM7HRIGUG 7fyf7siaeAamqHdG+wNPdkVjRznF6Y4moxzOuO/oFV+pw18JqIy99iV3nZkRpCibRgE9aqwo ezLt64KluW2sDGoBOnxOJpEibdGKdJQl1XroHiCrqEeNA6rfIn0d3btXg2q1+wEb0hZBKuHU dHl4PurM1dcjEVvoZFBklu1liCo17SpOSjVJHtG8xb8GIoK51yx47Kq6hoCKyL8KaA/cqqDN +k1ADUBRbdYdiyiNRFsazAar4nsEFEQdNUGeRqKFFzUWqIGqCN2IzwR0RC2u3a4AAd0uOQ8c UYCAjqjFtdsVIKDbJeeBIwp8A9NbEDRE0LtbAAAAAElFTkSuQmCC</item> <item item-id="13">iVBORw0KGgoAAAANSUhEUgAAAPoAAABLCAYAAABZeprtAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA BuxJREFUeF7tncFRLDkMhtkQSIHrHrnukVhIgeseSYEUSIEUSIEUSIGH2O0pj5Bs2VbL7vbf VdR7M+O2rE/+bdnd0/PX1/dxgwMEQODcBEjoOEAABM5N4Obc7sE7EACBn6wdGEAABM5PAEI/ f4wP7+H34pn2kS5/h3dogAMQ+gDoMGknQALnh/SevcY1S0Loa8b9EF7nBA2x14UQQq/jhdKB BCB0P9gQuh9L1ORMAEL3Awqh+7FETc4EIHQ/oBC6H0vU5EwAQvcDCqH7sURNOxDArrsPVAjd hyNq2ZFA6Tr6Nhhs5agp2gCRvs/Ln3knH0LfsYOial8COfGWxM4FvrUsPc+3tXPV5ib0t7e3 uTybsDVg1B8U6Q65kohTUaezOBd7f+vmrcFF6O/v71/0hyNPgBh9fHwAkzMBi9C1Mlqa79zE 4dW5CP3p6Wm4I0dpAFj5R6pH6EjdjfH4/Pz8enx8NJZGMRI6McPhQ4Cn8rkNNr6pt+RmnAaM r2l4udfX16+Xl5dfUSvtlPqEOV+Ltp6T1mkR7SEbtE4nZjhAIJLAVepuvWaZlqPZnK/PrfVE ODpTW8hfYoUMKCLysJES6Bb6w8PDVSqauxY54jrlbEKntJ2Y4QCBSAJZoUsbFVw4pddXo4rw 3eK9nZ1N6OTv7e2t6vaIwXDvGKD+8QR+7bpLO5g1lyZmnNGlTZiR6C1izm0cjWw7bB+TgCr0 kuA3d884o1uE2BPuPerXBga8f/0YqhV5UF+F0JliI3bkLULHjN4zlOJcTkC8YaZmXSutN2vO 3zskLW2xCLGn3bk1ek+9OBcENALdQqcdZOm2Tj4j/Rph/t+YS2dQTZR8GZFuEmqf/aQrwpND pffSstv/+dLEs525Xfe9BxlIYU0C3bfAStfRU5Q58UqC1Tb+WvcMLGEtDSSe7cR1dEtEUMab wEXoN/9ea976mu7yen5+zrZLm1mlDT1pc09aN7ek5Gpawy771Qw2qQ+WduLOOO8ujPosBH7U TaJOhV3zmm4Aufm7PjGoFZM0AFgctJTJ1e3dThoUV7zXXVsyWeKDMv0ELkLfBN/07z+xQk9T 6X4E108j4R2yR+hSO0vZj4c/s9Wx5yA9m6+ztuemdSZPMwHajKv5ooYkptIGW3qOVrYHsrTZ 5t1OfG//vwhhw7Gnp7adeyX0dDa/rD2Na3c8PaUcgJrBsFzbMUtA5GPiVp9zj2knrJ6AgLRZ eQK3DuEChH6IMJ2rkZjV4+MJocczX94ihB7fBSD0eObLW4TQ47sAhB7PfDmL0hWT5SAMdhhC HxwAmAeBCAIQegRl2ACBwQQg9MEBgHkQiCAAoUdQhg0QGEwAQh8cAJgHgQgCEHoE5UVt8N12 6fWiaMLdhtDDkcMgCMQTgNDjmcMiCIQTgNDDkcMgCMQTgNDjmcMiCIQTgNDDkcMgCMQTgNDj mS9pEc+MGxt2CH0s/yWs45lx48MMoY+PwXItwNdU40MOocczX94ihB7fBSD0eObLW4TQ47sA hB7PfGmLM4nccovu0ctsnQ1CX1p2sc7PJHLyfLb27BkNCH1Puqj7QkD7xZuRiCD0kfRhu5mA dK16huvXrd9ak34ppxmOcCIffHqeO08/znF/f3/5qW76aWz62+q8u7ur+jUjTz9/shfvClHf WALSLHXEmUu69u7pRy7D6LFD55LAt2MT+9heAaGP5u9u/wxC14TWI0AOOldXjx0I3b1Lo0KJ wJmF7hnxGqHXLD0gdM8ooS6VgHY56EjIemZUq5/WrEFaQuRsQOjWCKBcF4GWGT1CWCWncmvm 0rk/m03fa+PtsPjjIXSNdWmNng7GvO0WX1vKYDOuhdrE59QKfet0M7lkFWEqbIu4Ux9rOHFh 9szoUoZgbbu1nLikmynAaEs/gZoOXDMD9resroaWlNlqocSoNjvgA0huRi/ZLg0iVh95Oczo reQmPE+aebTZKJ3JpY6tfa7Norx8z+zDbeRm+O2zGnvarFril8t++HV0ek1/2zl0jZ1eb6m6 Jngei9Q/bl/yXY33hP0VTdqZgLUzlQYDvr5sEV2rq5oPlvp6BgXus8WeVkYaOHKZjORzLvu4 +qynoTj3mARqOpOW3ucyhQgqOR94Km0tK7Vbm3k9fTSLNdlwlAacbFw9G4y6jkGgV+jWjrkn jRrx1pTVxK4NbLU+lgYOa2wg9FryC5bPpYCaiGver0mNW/GX0lheb25wam1Dy3mldN0SG22J hBm9JSInPyftcGnH4e/zz9JUPp3ltPP2xCj5kFsPa8uQPduoDTi5DEHzS2IsiVscTCKdhC0Q GElAEsDI9kTaxuW1SNqwNZQAhD4UP4yDQByBiP2DOG/sljCj21mh5AkIrCr0Pxac93a8/sUc AAAAAElFTkSuQmCC</item> <item item-id="14">iVBORw0KGgoAAAANSUhEUgAAAJwAAAAWCAYAAAA1k/XzAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA ArVJREFUaEPtWgvRwjAM5peAJyxgAQtYwAIWsAASsIAFLIyFY1zI8mxz/GNr7zi20WbJly+P dfx1/VgljMvlstpsNgmS5iuiYdT7FghXO67XawefNnQEAKPb7bZomFIIt9/vFw1ixPilY1VN uPv93u12uwjmi54LhAPMljrehIPqOnxefd37HFdeOu90OnXH43GEH56XVLnDPqK6UrvCAhMW nM/nDjDLGpyNnOxSLLJ995HhOOHWNchutH+z1mSB7ZEzJV1AX8AqqyJQ2zRyWHMlnCZHuP7J 9KNERIz2EKZ2ztQIB+UUMMsYFomGe+DKha9hHSRZXyXccDNaUj2KSmk9A+iIjKkRDnRfr9cR E9S5nI+kBRFSeeVKpRq3L8Px85sqx5Hr1wlH+8nsqI2yx6oCUX0j9niyouZvKdlgguKMOjqW CGcRz5uatWwYdVTJ/JIMF3Fglk4lcj4yh3NL1fKrRkgPWbFO3LGY4SzF5ko4rt8pJYO3tNGg 9GY4LwG4oNfKa+n9OU5QHrH7cJGswPUjkfXZztRaBClIPGsy9czq4aKEi7RGHqwkedr1asLB Exf3uoZGiVa6cf3n5o2i5FU+aDaSzrn1nLM4PWhvQssEdoxXz6yn1EEXLitTvaUkYGV0q72w MMe/P49rI5fbh7P6NkxGDJpV/2nUZWVSiygW6axIx79n7sPV+u4/1lcTDnbND4eDqjslmJdY ngi2yO0BVStNFplohrPKXPabBo99U5pTTTjYyNxut2GbPI4s6TnCivQLagkX0ROCs71LLfES WlPyD4gawmk9X4kpWo+XradVDUr0/6U11RkOjIWHhsgLaauHkwigrasFnTa3XDkfNcBo7wvr JunZ/jfIvGkodRz0Jm3oCESCcq5YpmS4uYLT7MpHoBEuH9MmUUGgEa7R46sIPADMaKyu7ILo 7wAAAABJRU5ErkJggg==</item> <item item-id="15">iVBORw0KGgoAAAANSUhEUgAAAKgAAAAWCAYAAABUi9exAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA Al1JREFUaEPtWAluhDAM3H5lv8AXeC2fpBtUS15jx5OEhBQZCbVLrvF4fMDP/rlecQUDszKQ BBpXMDArA69ZgQWuYOCo7kFDMDAzA48R6LZt+7IsqZ8+7nVdj5t+v9/vPc2Z4SJM6S9d2rMZ sCIYemJ/jEC5o5Mw6SKRIkSPnMPFybGPxHDFWdIOza6Wc0KgLew1rH2KQCUF3QXaM12j/mwx ksp7ZFCU7Wvneb6z2pvjheivPaP/Ty9Jsie6Fnr5btwYNHD+k0A1+8pZun5FDe9cVBYiTV/S r1KkXyWeD/JDvKi4nqL6HRGBaqTUn4itRHo1j2dvHEPSb5Zno4XfSoyHnzS4JxWzt81+5p13 rolkT6Aaiajj0XkIR9peuf2t5IGcVTqnlnfvnGaB5pTsHT7LeKlAkdJEtt0p0BKco31h6Ubi QPQl56gl3krVuRbAygjaGi8b1AhBfgdNv9NNZ6VvpPQdFLVDqyQaySdSWbOfq1DIOolBBos1 zudp55Dga7iW7Z/MuLkAkdzztRLTMYZEm7apllVqIgQ5v9ccK3is/tsTlBXYtfg9YdG4J2KZ ffm6Wmyj1sECtZzGVW8Rmou4UYZ6UW2VcSvotJLbU6CWyHLPLd/M3C6c2gJEIDnicw7Uykhr SUHwomL0qgDifC9wW/DW8I5UsRDohwGEqDvE6pX1XBmXpdHLmK32IVhqMuvjSnyuVMgx2Rdp TtJE0pJpStaW4kP7QJ6Nr7RP6y/pmdWDWsLOrSvhcORcqAcdCSjOCga+2qagIxiYmYHIoDN7 J7Bh30GDp2DgLgZ+AZZRhjr2olrgAAAAAElFTkSuQmCC</item> <item item-id="16">iVBORw0KGgoAAAANSUhEUgAAAaoAAABeCAYAAABy+1BNAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA D7dJREFUeF7tXYF1HCsMzC/BLaQWt+AW3EJacAtuwS0kJaQFt+AW7puXrIOxQBIgEDD3Xl58 B0ijETALu8f9d3t/fcMLDDQy8OvXr2/39/eNVvZuDo72zi+iM2QgCBVeZzDw3o3CRUn3YH// /n0L//AqMxA4en19BU1gAAwoGeg/aykBoPo4BqyE6sePH+OCWNwTuFo8gYA/hQEI1RTa5zi1 EKq3t7fb4+PjnIAW9BqEKnCGFxgAA3IGIFRyrpavWRKqq+zaGozfx9uFab2Xl5fb8/PzF25y 7UeSmGINvqnPRmL6+fPnLXBW+7LYuq3F4qXdqpxwuLlyL/yPwAGhGsGyEx/ciooaGNxnYTWV 3p/i2oykwxOWEHfgqnYFionra89ZnRMOP1c+cizN9AWhmsn+YN8WQvX+pN+nrazSwJox6LwJ Vdj2C5xpXzO402IcXV/DSbrCn/0+5oqLgysfzfsMfxCqGaxP8qkVqngb8IKcDhruvWZAWtDi TahCjHd3d6pQMVG1r6S8c8jh48pVHWrByhCqBZNWC5kTqmA3HhC7CBV19VzLYY922klHW78H Ru82tJxo64+On8PHlY/GO9ofhGo04xP9aYSKEyzpCmvFFZX1pKCxr6nbu2vFAt/bdou9Gk5q 2rRgrGnLYeTKa3yu0gZCtUqmOuCEUP0hsTTgJRy1pkIz4WjqtuIqXVTMwkHFVIOlpk1PPiW2 OIxcucTHqnUgVKtmrgK3dBKmBkRukFD3WzTtK8JQNanBYj0haO5RWWORkukFB3ehkYvHE/5a jCvEIO1P2noQKi1jC9e3EKrwBBt1LFC8bVQSixhTrl78eVo/V3ZNaGnMMa50BXHZSm3GtnIT Zc5P2ja8lz7152li8oKlFkdtu1If6WEznU44m1z5wtNTETqEatfMEnFJhUpDCfU9qtIWEiUg ObGiBCMWiprVEhUbJ4SxgJXELBUxqq7me1ReJiUvODyspqicasYLV5fjmivn7K9arhaq8M16 vPoxMJJPC6EKpyw8PT2Vr4b+HobLCQ81EZVWQNqr0RzIdPBb4tScTOFlUvKCw4NQ1WKQzhgc 11y51M9q9VRChVOy+6d35InaFkIVvsD68PCgJkYrBiUxUTtPGrQKVS4WalILoi4968/DpOQB A7dC5/LfO4be9jTxWfrmeJxZrhIqnPxsk6pRvFoIVWCkBn+LUPXefqG2/mq2+CRbf9zqUzNp 2fTGf1ZLAmztW7r6leDoObn37nvaXYGesUi481JHLFQ4JdsuZaNO1LYSqvAwheag1XQ7L8aV Yozrltq1ZifFcIlODlsNTu2OxMxJieK9leMe7Ws4qWlDYS310x6xUStwrZD1wuHNjliocqdk awJKO7+mba+66WRHTUi9fEntaO5bSG1yA63FDtV25L223thH2dOIuWTSktYZFd8IPzWiU9Nm RCy5MVryXRNL2oaaA0fFW+tbLFTc011coBTBNaRzfiTlnrAEvJonwSTx5epYrahaMKFtngFu fKyWTy4eSV+osVHTRoLFog6HlSvnMMXtW21xvtJySjClNsRClZ6SLXXAXfWNJiuHZwaOi8Pa E7U1ObjinhmnFu/p9SW5ktTxwmMPrDU2atrM4ozDypVzuFvbc+KjWQ1qsHwIVboko97HIOKr Oe7KrgRIA5ZLgrTc24oq4C6dVtCLIy5PUv5QbwwDkrxL6oxB+9lL2tfS+eSqTfXJK6ZSmSYm rxxRMXBYufKSzTgHEjvSOV46r6Q+JRg++kkqPrn3pcmdc+hRqNKkcTFoBkZNXYl/CrOkXWlS qMGKNmMYkORWUmcM2n9eYky5v0NtqowStJINSWweOcrh5rBy5dSKp4W/+KKB45rDZiJUUqMc uLRDUkRyBPQuL4lubQdqxSjhsYePXOw5EcTn3z79fH1vPqQXirX9UntxI42vhPsa85et+EIp N5FqBU0zFqi5TBqnRb0Sdm4e4Mop2y1Cxc3fnDC29u+PvpMTDgjV5yvEmg7SMphyHY4aOFI/ 6cQhbYd6cxiQ9DlJndHocxNjaU4ZKVSj+dD44/LJlc8UKg6bVFPIGKRCVTolmwOYU2VJO02S pXU9rqg0J2pL49Rc7dTaRDs7BiTjQ1LHDiFtmROqq5yr93E1/X4EF/W3NC6PHOWwc1i58t5C ReWqdl7pKlQ5UUlPyY6vztMr9dyVe7oaKIlkiaCSPw5LjCHu/BTmNMZ0sOTKU7vU+9Rf6UTt ms6Z67C9bEknCdSrZ4DLFdWX6731bUmN9dIYS+vH458bZyXkHId9o263xuHlynMiws293HxR mtM1Udf2WfLxdIoM7feoSoRSZWkAnEikgsoJm4ZMynZp4FC+4xipv+PP8D0qbXbOqK+dlE5g RcuJtv5sDjm8XPls/Fb+vwhVjgjJKdma5TmlrNzkLlkB5VZpWgJTHiTYqDa5q8gYzw4nU2j5 RX2egVMnpZ4rpNU45PBy5XyvWrNG9ntUaTi1p2RraNGKQUlMNH5zy9748xZsHE7NidotcVGi 2WJvdlvqYmc2pp7+T52UIFR5Bk7tE+KTKQJ1NadkawZuDzHolcgUSzzJ98apOVFbwye1uuzF TwuOHm058e/hY7aNXXLVk0ctJ9r6PbHW2OLwcuU1PldooxIq7SnZGgLSq+NUGHLCUWqn8Z9b VV1+4/9z2NIVS4wth1N7onZLTLutqHIr3haOPLU9dVLCigorqi8X2dqBiVOytYyV62tP1G7x DqFqYW9821lCFbb5cxdZ4fNwcTXrpeWEq59eQM6K6/IrwTsb4wz/qhXVDIDw2Y+BXYWKG9z9 GBxraVZc4asS4QIqPix5FpYvV9bRd6ok2SjhTndpJPas63A8c+XW+GbZh1DNYn6CXwjVBNIb XM6alK4VUxCr6/t9s7BYCxUV18xYOd9ceUN3c90UQuU6PX3B7ShUOw/c2bGFh6diofKw7a/l RFI/HhezxwiHlyvvO2P4sQah8pMLcySzB2HvAD1u3fSMcfakFPxfT/p+//79Fv7Nfmk5qdn6 0/royQnnmyvvicWTLQiVp2wYY9lJqK5Y4v+N6RtufvakFPxf24BhNTUbT0iAFgMnVNSYuNrk xgv3eepTM+64+Ljy4Z10kEMI1SCiPbjRDBgPeIFBPzGfwJlmstbUvbhLx0lu5d7yOZUnDitX vnPuIVQ7ZzeJDUK1XrJPnpxy2cpxUlodaTJPrYji9tQqXrLK4nLZWq6JcbW6EKrVMtaAF0LV QN6kptzkNQnWVLfcdl68MqoBWhKq3CpKshrjsHC55so5+yuXQ6hWzp4SO4RKSZiT6idPUNpV Vah/8VXLW41QabcB07g4rFy5k65qBgNCZUatP8MQKn85kSA6fZKiOJKsqlp4o7b30lVTPJ4k 9Uu55rBy5ZJ+tHIdCNXK2VNih1ApCXNU/fSJqkasVuGMw8mVO+qmZlAgVGbU+jMMofKXEw0i TFhf2cpxskpf53LKlWv6z8p1IVQrZ0+JfZXBqwzrqOqYuHRi5blzcLnkyj3H1hsbhKo3o47t QagcJwfQujOAib47pdMMQqimUT/eMYRqPOfwCAbAQDsDEKp2DpexAKFaJlUACgbAQMQAhOqg 7gChOijZCBUMbMQAhGqjZHKhQKg4hlAOBsCARwYgVB6zYoQJQmVELMyCATBgygCEypReX8Yt hcrDj+r5YvsrGnDkPUPA55UBCJXXzBjgshKq8JtF1+8WGcDexmTg6PX1dZt4EAgYGMUAhGoU 0w78WAnV9SuwDkJ0DwFcuU8RADpkAELlMClWkCyE6u3t7fb4+GgFeTu7QagCZ3iBATAgZwBC Jedq+ZoloUpPf47fx9/wT+u9vLzcnp+fv3CTaz+SxBRr8E19NhJTuE8VOMMLDIABOQMQKjlX y9fkVlTUkTPcZ2E1ld6f4tqMJNITlhB34Aor0JE9AL52YABCtUMWhTFYCNX9/f2nrazS+Woz zl7zJlRh2y9whhcYAANyBiBUcq6Wr6kVqmuST7f+YiJSIYBQ8d3k7u6Or4QaYAAMfDAAoTqo M3BCFaigRGl1oUrvl81Y2ZXE/aAuiFDBQBUDEKoq2tZspBEqTrAuBnZcUVkLmbX9NXsnUIOB PAMQqoN6B4TqT7K57UlrIbG2f1CXRqiHMAChOiTR1wQtmSQ1DyBQ91s07a3pr8Ei4agFN+5R tbCHticyAKE6KOuSFVVuxZGbvMMTbNSxQOl9oZTm+EEN6qGNeGsx3Yak2lJCHGNI7VFblqWH R2LucuInxYmn/g4adAi1CwMQqi40rmFEKlSaaKjvUcXtS5M6J1bUfbJYSGtWS1RsnMD0xInv UWl6F+qCgb/b9SDiHAYshCqcsvD09FQkMbeyiVc51N/xKolaAeVWadqMlmxzYilZqcV4cDKF NjuoDwbe7yuDhHMYsBCq8AXWh4cHNYkSAcjViVdVasdEg1ah0uAMoo6z/npkDTZOYgBCdVC2 LYQq0FdzIniLUJXuadWkk9r6K92vSoWSiyUu51afNfjRBgzszgCEavcMR/FZCVV4mEJz0Gq6 FZg+qJATjlK71jRSD0v0xonf7WrNEtqfygCE6qDMWwlVoBC/Xst3JI2Y89b4GtTDJnwrfY1R fvTI0GIXBiBUu2RSEIelUAnco4oxA7mnF43dwjwYMGcAQmVOsR8HECo/ubBAInky0sIvbIIB awYgVNYMO7IPoXKUjAYoucf90/zGD4RQ23NUf0jbcO9DGJRA5mxjm7Ah8Qc3hVAdlHwI1frJ Lq2acoJxRS15OjEndqkg5USQ80EJ2/pZQQTWDECorBl2ZD8nVPEVOv7+9unn6q350HYPrVDF 9ql7WCVhKokTZzeNK+VRGzfqn80AhOqg/GNFtX6yewoVxUbOftx3JPfCOAFcPxOIYCQDEKqR bE/2BaGanIAO7iVCFW/LlVY+3JZga7lkG7ADJTBxAAMQqgOSHE84uJm9fsLjbbRUiLiHH6jt v7R/SMQw1yaH7dpGRP9bv//NiABCNYP1ST6xoppEPNyCATDQxACEqom+tRpDqNbKF9CCATDw hwEI1UE9YTehKm0zHZRWhAoGtmcAQrV9iv8FuJNQSZ48Oyi1CBUMbM0AhGrr9H4ObiehStOG m/QHdWSEehwDEKqDUg6hOijZCBUMbMQAhGqjZHKh7CpUWE1xmUc5GFibAQjV2vlToYdQqehC ZTAABpwwAKFykogRMHYUKqymRvQc+AADcxmAUM3lf6j33YQqd0TPUFLhDAyAAXMGIFTmFPtx sJNQxd+h2ikuP70FSMCAHwYgVH5yYY4EE7o5xXAABsCAAQMQKgNSvZqEUHnNDHCBATBQYgBC dVD/gFAdlGyECgY2YgBCtVEyEQoYAANgYEcG/gfyTMkOoKL2CwAAAABJRU5ErkJggg==</item> <item item-id="17">iVBORw0KGgoAAAANSUhEUgAAAPUAAAAlCAYAAACaj69KAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA Az5JREFUeF7tmuFSKyEMRvX9H1ptnXUQAwmYQJaeztwfd1ghnC9fyNK+f3x93vhAAALnEHiY mg8EIHAOgbdztsJOIACBZ+cNBghA4CwCmPosPdnNYgJfL+KPO6nnvyyfPJFkIUIcEDASqI2c xdiY2iggj0FAI4CpNUKMQ+BmBDD1zQQjXAj0CGQxNLff5CkEnAhgaieQTAOBDAQyGZqTOkNG EMOtCZSGzmJubr9vnVIEv5NA+R11pu+qMbWQFdYfFGSpzDsT27q2xNTK2boGz30TwNRVJlh/ UJCpMt8lmaUiSGH0Vw9TT5r6WRET/TTQPzX8Z8TU/kylGX9MXZ48rVMoKokztWHWk7pn6lar ef3NtYY3z0wcxWQTiqA3gzW2GVtltS6/TmprskkXBLPiZLs9/K+ppf3UotYFdCxF5KezcWyZ OuvlkocGWiGb9chobH/ab++FtQKwuivQAHmaulyrZboWb41bvY9sHLUEv8a1fNPGNT2t47O8 tSK1Q5cpU48CsICtT69VYkrmaJlRe1ZryaVE9t5nFo4epm4ZwpJP2Z5ZqctU++0JbPQE81zb kng900ljlv1EtMqWdaPZafNrvCx6aGtkG9+hy/aLsvLUl07IXvvSShLpb0bm0WK6TmTrnPV8 EVVbi9kaa9lNjHLstdRSfL2YL20lU9T8tFeRCN7W4rFDl7RfadUJZal4o89Y3+usAmZ8biVH j/3XGpbmbplzRHfpIPCIe3SOSF1Sm7r3btur/K3K3Tp5RgW50/N1Ekv/lxJM64KizNGL12Le XhcVFfNMPkTqcktTt8StW8dXOIm1hJoxyU6OM/GOmF3jNTreaq+1eWb2adXlKFNbxM1UrTXh PcZnkmcnR6n9LltwqVCPxOupv3ba9vSL1CWtqa2XUdJ7Vq919DDK3ebonSblWGke6VWl1CSS Qa2plAuS7vVJ1ttbRPyjBSNKl9SmjgDPnBCIIDBq6IgYfopa5OTMDYFXINDqanbtnZN6F3nW PY5AltMaUx+XWmxoFwFMvYs860IgiACmDgLLtBBYRUC6XV+1dm8d2u8MKhADBBwJYGpHmEwF gQwEMHUGFYgBAo4EMLUjTKaCQAYCn8RkkKJiNTLuAAAAAElFTkSuQmCC</item> <item item-id="18">iVBORw0KGgoAAAANSUhEUgAAAG8AAAAlCAYAAABMBrdqAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA Ag9JREFUeF7tmFluwzAMRNP7H7pNjLqQZa4T0xmhNJCfmJKpeVwkfX0/n0c/ayrwgtfPmgo8 1nS7vd4qZsuwrgINL8ju2RRfe4Ptx/LweMKiiODHDIwFYMMDgqbhAaKxDGl4LCSSfrCA691m EtzvhQYwqmZI97yErkxZ15kHgmOB2JkXADie8ZjOen/wpEMoejDVIpMlYgO8ljA5ZJ4kblZw z957v4RqJE5eCi8KJmpXqRGDD++u7zJ4Wtbu/8+9whIPEXacP9KXkG94YqNtxptXe3+ChzZn r88h94OSLxHoETARm4yo43xXzx2GNxt6joyZpX5EuIn35s0IN9p6cJGAiIzRsr1qnadznlb6 LCiV8CKiZYNtt68SNVu+0SB9G954XVRRNrMLiwRSFTytbFYFyQGe1Gy1BjxHl3bnZ0WhV94Q cCM8TzTvPfp9rbdbZVWreNKYg6aIk2P0Io36auGya7jTfgYQydCoTfp6TIusaDn6T+CkiiTp JwH2snEbk41CD55WQq3/sz58wl5rIZ4vll5e1bKy9rRh8RyZAaALinyHySYSsNaOXDvKRMuj VNWgzNsBjpsDJqHv8CVb+q0gH9/NGy6rdEKZd4c4zN/IgqtcS7rnVTrDPre23f+U3w0PUJ4l +xpewwMUWHhIZ95C8KQdIYP7XTYZKIA+NDxQOIZhDY+BAuhDwwOFYxj2AwGwXOJ69MV7AAAA AElFTkSuQmCC</item> <item item-id="19" content-encoding="gzip">H4sIAAAAAAAA/5RUS1MTQRDuyWM3CYFglGfAIKCAvJeHPFQGCIiKWMKBIxWSBUIZg2Gx8Jbi B3jz5MGLP8Hybnnz4B/ygN90b0KwvDhbvTPT8/XXr52NE5GC7EBivA7iHXHfbruHhdKbMJkx DYnmSznRhVg3b2DF3Mv9YzfnsYa22T6At3V6dLJSOhf1JsSGYrsGfAGZg1RAdQzvn6yrvRmL MoEqEOTokjXny55XLuyfea5ixAIkQfUjtNB7bR9mgpBXdt0UKxxI4wkvLafjO7yGbeczZm1F ZLajMkdiMkcbnACcvEN9bGVNIvUxyBQeijv2bzDEGp2Pz4GMUxNDvzQieHVqgnOsnwDEm1mf Txg97G4IDBIM5KjIPUiyt0r0pmNSWknC+hYvL5IcbYsT+4HzSCv71C1tTvIX5tZ2oW5DlsEN hJYn6nA+IMNKG3WKnw7jltuREkVrzTF1SQot3ULXdVvouuvo0kLXTT1ina7R3RFF1xUdUa/k YfdJ9Xr7Ze67K1XsN8Zqguge77/V9jQghn2DUoABqIakAANMfF820t9hAVsjwj48KvPImHgZ RXdVeBrxO+gVej7OHboYpQkGfB3/G0A0KZQjjvifhGpKXE7KNZAGDM9IA6ZnpWIzD6RisyaR 9/iSpVqzNC/4uQWe9fyi4Bb86j2U2mFrhV8hjiJlyRT1kehxwWxLGlA9IXosZzNX9V7ivCrT pLU0ckkvS1wYK+JRL/uh6VWJDQqdkej0ql6T+HRGr/v4NT9E/UT8GcX1IPWGf5L5V5j6qX+q 6z8M0s/475DIO3uH5ezJ0d5BqVzMerZ83uYnw+pM1suKQad/iYPVl7nLaf8PoXgXy54XTteZ R/4e+BZpS/lYM9p3qYDoPDqiHirRAd47dIZ9Gbse2iUX52WsBslEOkR0iYdU1zWWjg3GHYLF +0+ezXoeX1sN3/bK2ZzrZDhDSlVTVM2kUgq4FO5GbRVQqSCM0yqoUiHQpFUIZ3SJRVilAhWN I8tgDIPNGCwiBmPAUWDYPAYMmzcAw+ZxYNi80WCMVRNjsEgYjAFLwImts6JbLuSyr6XoEQ58 A5JH+gXukvkjx6ptuz7i9Zs/AAAA//8DAEWIG8Z9BgAA</item> <item item-id="20">iVBORw0KGgoAAAANSUhEUgAAATIAAAIbCAYAAABojRKmAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA LuNJREFUeF7tnYt24ziuRfv+/0ffqdjlRFbJIg4eIsTs6lVrptuACG0AJyAtO//3/3/+/Mcf CEAAAncm8CVk/IEABCBwZwL/3Tl4YocABCDw2FWCAQIQgMDdCSBkd88g8UMAAkxk1AAEIHB/ Akxk988hdwCBX08AIfv1JQAACNyfAEJ2/xxyBxD49QQQsl9fAgCAwP0JIGT3zyF3AIFfT2AJ IfvzyYqvj1k9/m7/fPrvvz7rAIDAYgRuL2RH4vWVo0//fbH8cTsQgMBXv69G4SVgCNlqmeV+ IPCZwK8Usu2Wk///sy2HBSxm1kBEqH+tkEWgVfn+aaOqS4euWxLX13nm7kxTDTIcV0IMRzGH 41JBGO27xnV0FGS8pW+znp2j3sVf++128mxruX/NuVy6W9dCK4krQUTCcSXEgJDltEG0J5cR stGZ2JnI5aQifpVwY8ZDOLxCSVwJIhKOKyEGhCyn6BCyr3csNlsUy2F/FFpO6v69SrgxiwIr iStBRMJxJcSAkOUUXbQnbz+RHR1OvtCePV+Wgz/3KuHGzA1nc/5QUCYJIhLmlRADQpZTdL9e yDwYo9A8a1p8wo1pWcRhUxJXgoiE40qIASFzFNSBS7QnC37U5txY5VWi0KpiCzdmUWAlcSWI SDiuhBgQspyii/YkQpaTh5SrhBszJYqLzu4SRCTMKyEGhCyn6BAyB8coNMeSJpdwY5pW0Y1K 4koQkXBcCTEgZHo9HTILP1OYE8etroKQaekKC8Zx5fZ5IDbYRPvbK+GlpezQumtcX8FGe5Kt ZUKBZF2ia6Glx/WahIICkhJXwVSWEldWUW2u0zUuhMyZ7Kj6O5cdunUttPS4ksQjJa6kWLbJ TYlrWC26Qde4EDI9lw8PhEwDl94ASeKREldSLAiZVlP/bMfD03ls/Vt6I2Ra2lIE463T4x8Y f/xAyviQPUKmFUORdbQnOSMrSoznsimN6Vl44JMeV5J4pMSVFAsTWazwEDIHvyg0x5Iml5TG NK2kGaXHlSQeKXElxYKQaTXF1jLGizMyB78UwWBr6SCf65Kex8TwosMFW8vEZEQv1bXQ0uNK moJS4kqKhYksVv0ImYNfFJpjSZNLSmOaVtKM0uNKEo+UuJJiQci0mmJrGePF1tLBL0Uw2Fo6 yOe6pOcxMbzocMHWMjEZ0Ut1LbT0uJKmoJS4kmJhIotVP0Lm4BeF5ljS5JLSmKaVNKP0uJLE IyWupFgQMq2m2FrGeLG1dPBLEQy2lg7yuS7peUwMLzpcsLVMTEb0Ul0LLT2upCkoJa6kWJjI YtWPkDn4RaE5ljS5pDSmaSXNKD2uJPFIiSspFoRMqym2ljFebC0d/FIEg62lg3yuS3oeE8OL DhdsLROTEb1U10JLjytpCkqJKykWJrJY9SNkDn5RaI4lTS4pjWlaSTNKjytJPFLiSvqSR4RM qym2ljFebC0d/FIE47VuonCkxZUkrN+3mPH1Qo48jVzSeI0WcrweHS7YWjqgV7l0LbTUuBJF Iy2uxJi+aiMtruRC6xrXgxlfrKhnOwpNX9Hm0bXQUuNKFI20uBJjQshstc7W0sfpzQsh0yCm CcbzR2/4tyelb+ESY0LItNr6OXGIbQ5j3r6Yp3shZFoKELKJvLSlT61T85gYF1tLJ0yETAOX 2gCJ009aXIkxMZFptcVE5uP18ELINHhpgsHWUgOfbJ2ax+zYOOzXiSJkGrPUBkicftLiSoyJ iUyrLSYyHy8mMge3NMFgInPQz3NJzWNeWCk9yWF/ckIil+taaKlxJU4/aXElxsRE5uuA6C4J IfNxL/FKa8zk6FLjShSNtLgSY0LIfMWHkDm4RaE5ljS5pDWmaTW7UWpciaKRFldiTAiZva62 ltGeZCLzcS/xSmvM5OhS40oUjbS4EmNCyHzFh5A5uEWhOZY0uaQ1pmk1u1FqXImikRZXYkwI mb2umMh8rL69EDINYJpgPLq88UeUgs8yvaim8tJSdWrdNa5nWcQ2hzHvRMhXXioKrSrWroWW FlfiV/ikTz4dBTa50NLymBwXQuYEipBp4NIaIFEsEDIth+m89OXPp0UmMp0oQqYxQ8gm8dKW HVqn5XG4km4Q7Um2ljrzMo+uhZYWFxNZWe1YLpyWR8tiog1CJgLL2I87ljS5dC20tLgQMlMd VBml5bEgQITMATUKzbGkyaVroaXFhZCZ6qDKKC2PBQFGe5KtZUFSvJfsWmhpcSFk3tJI8UvL Y0o07xdByBxQo9AcS5pcuhZaWlwImakOqozS8lgQYLQnmcgKkuK9ZNdCS4sLIfOWRopfWh5T omEiC2OMqn84gA8X6FpoaXEhZFWlY7puWh5Nq2lG0Z5kItN4l1p3LbS0uBCy0voZXTwtj6OF HK8jZBOgOZY0uXQttLS4EDJTHVQZpeWxIECEzAE1Cs2xpMmla6GlxXUHIQt+VOYr0Wm8TFVj N+oa14NZkDtbS3sdlFt2LbSUuJI/MF4iGElCm8KroNq6xoWQOZMdVX/nskO3roWWEleSSGwh psT1dsGcrxhKj2tYOTaDrnEhZLb8/WOFkGngUhoAIdOgF1in5LEgLoTMCRUh08ClNABCpkEv sE7JY0FcCJkTKkKmgUtpAIRMg15gnZLHgrgQMidUhEwDl9IACJkGvcA6JY8FcSFkTqgImQYu pQEQMg16gXVKHgviQsicUBEyDVxKAyBkGvQC65Q8FsSFkDmhImQauJQGQMg06AXWKXksiAsh c0JFyDRwKQ2AkGnQC6xT8lgQF0LmhIqQaeBSGuBOQhb+uEzPD8yk5FErHbN1tCd7Ejffvs8w Cs236tira6GF4yr4eNLjp/iff9L/JAhuSVwJN9o1LiYyZ3IRMg1cuAESxOEo4nBchxeNf0yp JC4tZYfWXeNCyJzJRcg0cOEGQMg04EXW4TwWxYWQOcEiZBq4cAMgZBrwIutwHoviQsicYBEy DVy4ARAyDXiRdTiPRXEhZE6wCJkGLtwACJkGvMg6nMeiuBAyJ1iETAMXbgCETANeZB3OY1Fc CJkTLEKmgQs3AEKmAS+yDuexKC6EzAkWIdPAhRsAIdOAF1mH81gUF0LmBIuQaeDCDXBHIQs8 3R/mpaXHbN01LoTMnMJ3Q4RMAxdqgKKn+h/FX/Fk/7Ornn+df8ricsbzcusaF0LmTCxCpoEL NUBQFM4iDcV1emGETKuQuHW0J/0/duKxT7tCFFpV4GWNGQw4FBdCFqSf5x7KY14Yh1eK9iRC Vpwg5fJdCy0UF0KmlECpbSiPpZHxC3pdeKPq71rU4NS10EJxIWSGzF9jEspjcYjRnmQiK06Q cvmuhRaKCyFTSqDUNpTH0siYyFx4o+rvWtTg1LXQQnEhZIbMX2MSymNxiNGeZCIrTpBy+a6F FooLIVNKoNQ2lMfSyJjIXHij6u9a1ODUtdDccRU+Q/aF0x3XKBfBuMviGsU9eL1rXI9cBp7b e9bCL/wThVaFrGuhueMqnMZKhezZWe6HYt28qgrr73W7xoWQOROPkGng3A0QEANLhO64TBdH yCyYsmyiPclElpWJhOuUNmYgPndcCFmAer6rO4/5ofxzRYTMATkKzbGkyaVrobnjQshMeb/K yJ3HCwKM9iQT2QVJsi7RtdDccSFk1tRfYufO4wXRIWQOyFFojiVNLl0LzR0XQmbK+1VG7jxe EGC0J5nILkiSdYmuheaOawUhczwW4OZlLRSnXde4vm4HIXMkNQrNsaTJpWuhueIKPotlAeaK y3Lhl41TiMvjUu5hY9s1LoTMm1DHT1nnUpJb10JzxeUUAQWYKy5pAd8jGOVxKfeAkDlp3cCN iUxLkqsxETIN8gXWrjxeEBcTmRMyQqaBczUAQqZBvsDalccL4kLInJARMg2cqwEQMg3yBdau PF4QF0LmhIyQaeBcDYCQaZAvsHbl8YK4EDInZIRMA+dqgJWETHxzyMVLS4nLumtcCJkrnfFn VpzLDt26Fpoc1wWPXjyK/4ovb3EI8iVxDavpX4OucSFkjmRmQHMuO3TrWmhyXI7mH8I5MJDj ci2iP4JxSVyOe+kaV0ZP8mS/oyCqXLoWmhwXQlZVIqHrynkMraY5R497EDKNd6l110KT40LI SuvEe3E5j96FHH4I2QRojiVNLl0LTY5rRSETDvxlXqbqiBt1jYutpTO3UfV3Ljt061poUlwX HfQ/iv+Kw/5nl0lfe31ZXMOKejfoGhdCJibyZY6QaeCkBhCbXotkUmOK9yTxigAQfbvGhZCJ iUTIfMCkBhCb3hfR00uKK7QQE1kEn8U3Olxw2G+hfJHNZY0p3o8UF0J2ncBW5lG8dtQcIXMQ jEJzLGlykQTDdMUcIymulYXMeOAv8cpJkekqXeNia2lK379GCJkGztwAFx70X7q1fHaa+cDf zEtLQ9i6a1wImTO1CJkGztwAQrNrERxbm+NKWQwhy8D46RrRnuSMrDI74rUvbUwhNnNcCNmD qpmXkIMM065xMZE5sxtVf+eyQ7euhWaOCyFDyIZV/mG6Np4/fpzonOve2g0h09KHkG3OyAwN Z+alpSFs3TUuJjJnahEyDZypAS4+6J+yhTNOnCZeWgpSrLvGhZA504uQaeBMDWBscm3lc2tT XKkL2g78L4/LeI9d40LIjAncmyFkGjhTAyBk31BNvLQUpFh3jQshc6YXIdPAmRrgNwnZ4JzM xEtLQYp117gQMmd6ETIN3LABJpyPTTkje3bc8MHYIS8Nf5p117gQMmeKETIN3LABDM2trWiz HsZlu4xmZbjXKXEZ7qJrXAiZIXlHJgiZBm7YAIbm1la0WQ/jsl1GszLc65S4DHfRNS6EzJA8 hMwJaeM2bABDc8ej+PcKw7hKFv27tTw5J5sSl+Feu8aFkBmSh5A5IVmFbNL52KP4r/qG2D3C gXBPi2uQ6q5xIWTOHmVrqYE7bYBJ0xhCpuVwKi9DqNGe5EPjBshXmXT9iYmQ7SpgMIXeMo9X FfmHdRAyRwKi0BxLmlxu1wATt5XTJ4yTSfR2eTRVZ61RtCeZyGrzI139dg0wcVuJkEml9TDu Wl+ckem5fCbU8A0GzkuH3LoW2se4ELLDh2Nvl8dQ1eY4R3uSiSwnDylXuVUDTN5WtpgwPgj5 rfKYUrnxiyBkDoZRaI4lTS63aoDJ0xhCZiqpN6Ou9ZWxS2Ii0+uhzKNrof0TV4NprJWQ7Y4q bpPHskrWLxwdLhAynXmZx20aoME01kLInqPEP+dkt8ljWSXrF0bIdGYc9ovMPk5k4nWyzVsI xsF02iKuA9hd42Jr6eyMqPo7lx26dS20t7iabCvbTGQHU9kt8jisxmsNoj3J1vLafJ2udosG aLKtRMj0wu1aX0xkei4fHlH1dy47dOtaaIcT2fBu6g3a8NpNqW3i2qWga1wZPclEVt9v5hW6 Ftp3XI22la0mst32sn0ezRV5nWF0uEDIrsvVcKX2DdBoW9lWyP4wap/HYSVeb4CQOZhHoTmW NLm0boBm01g7IdtOZXwEzlTvW6NoTzKRycjrHLoK2fezUs0atCWvZlPrm1jM+iJKQ8sgZAZI e5MoNMeSJpeWjbk7/zHdyEVGLXk1nFxf6WjJ629w0Z5kIruo6SzLtCw0GtOSunebplNZy/pC yPT6+v7J1GyL1PonZtOmbHlG9lNgpt9/6a9gnydC5uPW1is6xlbdWLtCazyNdRayRx4b/gBo V1+bRor2JFvLKlVyXLddof1txnZxvbYjTQ+v34Ss0fTfNY/PY9iYFMW8Hc3awSUKreoeWhXa ZhprFdf2p3hnIdu+SRJs0qx665pHhMyZYYRsAI6P3Dgr6+l2+JGuBmKGkIXS2s8ZITvJCV9L Ey7YfwSjyVkjQhZOba8LIGQf8vGh4bo2wK3ianD435UXW0unPiJkAyHbvdy1AW4VV4OprCsv hAwhcxI4cDtptK4NcLu4JotZV14ImbONmcj2I9f5c09dG+CWcU0Us668pgnZlxAc/XXqyuVu CNn2GYbxw5tdG+C2cU0Ss668Lheyl3h9Up7R65cr1sejoJ6Pz11aaNtmGjwacGlcQpHcOi6B v4Dk1LQrr0uFTJliFNusJCnX6RrfJYXmaKBL4lIS+Nd2ibgc+XCgerh05XWZkHka3+PjTZDq 1zW28kJzNk15XGoCVxKyZxe//3XyGLl1zeNlQjYCdLfXf52QBRulawMsF1cwT6M+7MqrnZB1 FYh9grvGWVJoCc1REteo6wyvLxlXQr4+nmE3/WzqVCG78zuXv0LIEhtiScEwCKXXJIVXYv5e 95ESlxfKwC/ak6637z4tGg2miNE/l+0aZ0qh7Rsg4cPKKXEVJHf5uJJz2ZXXtImsqxBYe6Vr /HKhHRV6wTNKclzWRATtfk1cSXnuymuakH1auKtA3P6MbFTIBduQLbOuDfDr4grWQVde04Xs rk/3txBctSiTtxnKMNS1AYjrbxZvVEsf34gIHoFwRqZ0dJatp/Cy1nZcB8HQoLXgpdaYdovp 1tHhIlXI0u+u6IJRaOGwXkW2u1CLBji4OeLSMn4rXh9qUbvjuHW0J1OFLBpMHIftCtPjRMhs iRpY3UowUu44dpFDXr9dyHiOLFBUCFkA3o8rQqZhRMiOthsHh3PTJx1jXqfHiZAZM3VuhpBp GBEyjVd7a4RMSxGCsTCv37y1/ErrXgymi4NQa9NjZSITsvXZFIHVMDKR7d9d+/DMx3SBMOZ1 epwImTFTbC1TQP29CEKGkGXW0893T+25Nv12AiYfLf234vWbt5Z3+dD49p3VbSkykS3cmNqt lVgjZDrWaE+6niN7nZHtH8HQw6/zODvDi0ILR83WMozwUYNMsBJHtpYSrh7GCFleHhAMjeWt eP2mraVngvH4aOUyOAjevSGxjecxSU785/Ud7TNjYG0IPAT3r5BNp3HVh8YVYVJsM8Xr7Bxs L2RV65quy9bShGlkdKvJZ3QzF7zO1vL19u3XJHOinK8zswtyMlyCreUQkdkAwTCjehjeitdv 2lru03iHz1kiZFrznVnfqjHzbtt9pVvx+s1C5s7whY4IWR7sWzVm3m27r3QrXgiZO8+XOfIc WQ7qWzVmzi2HrnIrXghZKNdTnae/GcFhf0r+byUYKXccuwiH/TF+7bynC9kHIjSmVirwWoPX 4w2Sqx6/0JD1to5Cq7o7GlMjC681eE0Tsq5CYE1r1/hpTGsGn3bwWoMXQqbl8dsaIdPAIRjw 0gjo1tGedH1o/C7ffvEJZxSaniabB4Jh4/SygtcavKZOZHd4KBYh0wr9Iy++ZUICicBKuJ7H BLMO+48Wjgaj377Po2ucNICWT3itwWuqkGkIe1kjZFo+EAx4aQR062hPus7I7rZl28cbhaan yeaBYNg4cUamcerO67KJ7Ohzi5yR+YrpzAsh05jCaw1elwnZES7OyLQisljTmBZKPzbwWoPX VCHTEPayZmup5QPBgJdGQLeO9qT7jGz/JYrRQPRb93t0jRXB0HIKrzV4TZvIXkJw9p1fGuJr rREyjTeCAS+NgG4d7UnXRIaQ6YmyeCAYFkqckWmU+vNiInNmNKr+zmWHbgjZENGbAbzW4DVN yF4L7x/B0LDOs0bINPYIBrw0Arp1tCddW0s9zF4eUWhVd4NgaGThtQavyyayro2vpXFzVhD8 gKp33ZEfjTki9P46vNbghZBpefy27irMNKaWUHitwetSIdueh2n4+lkjZFpOEAx4aQR062hP ms7ILJ+11EOf5xGFVhU5gqGRhdcavC6byCy49k/6W3xm2SBkGnkEA14aAd062pOmiewsrDsJ 2Os+otD0NNk8EAwbp+888s21ErCu9XXZRHa2tZRINjFGyLREdG0A4lojj5cK2f6wv6sYWFLb NXYa05K9Hxt4rcHrUiE7QnbXdzIRsjUaACFbI4/ThWyL8U5nZQjZGg2AkK2Rx8uE7BMuJjKt kEbWNOaI0Pvr8FqD12VCtp1g7ipe++lRK4FrrGlMjTO81uB1qZDdaes4Si9byxEhJh+NELwi vC4VsmignfwRMi0bTD7w0gjo1tGeND0QG11Ev61aj673g2BoeYfXGrwum8g0XP2tETItRwgG vDQCunW0J00TmR5Wb48otKq7QzA0svBagxcTmZbHb2uETAOHYMBLI6BbR3uSiUxnXuaBYGho 4bUGLyYyLY9MZF5efMuERA6BlXA9jKdOZHd9ODYKTU+TzYMGsHF6WcFrDV5Thezoq300rPOs ETKNPYIBL42Abh3tSdcZ2adFo8Hot+/z6BongqHlE15r8Jo2kSFkWgFZrWlMK6mnHbzW4DVN yI4W7jrlHKW6a6w05hqNSR61PE4Vstfid/wwOUKmFRqNCS+NgG4d7UnXGZkeZi+PKLSqu0Ew NLLwWoPXtImsqxBY09o1fhrTmkHOyDRSvXkhZJ5sfh0S/9dzEEXItITCaw1e7YSsq0Ds0901 ThpzjcYkj1oepwrZ9qn+uz3hj5BphUZjwksjoFtHe9K9xzpaOBqMfvs+j65xIhhaPuG1Bq+p E9ldtmtHqUbI1mgAhGyNPLYSsoxgtLT4rREyjR2CAS+NgG4d7Ulpa/npXIwzMj1xh5MiX5cj gURgJVxtP9KVMQRJQvbCFlVPDX++ddf4aUwt1/Bag9c0IdPw9bNGyLScIBjw0gjo1tGedE1k dzpAv1OsCIbWAPBag9e0iezsrExDO8c6qv5VUdOYGll4rcFrqpDtEXYVByYyrdh5EwJecQL6 FaL6kba1zFBV/fZ9HlFovlXHXkwYY0ZbC3itwStDOxAyrRZKrWlMDS+81uA1Tcg4I9MKyGpN Y1pJPe3gtQavqUKmIexlzdZSyweCAS+NgG4d7cnUraUe/hyPKLSqqBEMjSy81uB12UR29hGk 12sa0rnWCJnGH8GAl0ZAt472pHkiG31tTzQQ/db9Hl1jRTC0nMJrDV6XTmR7ZCNh0xBfa42Q abwRDHhpBHTraE+aJrL9Ip8WjQaj377Po2ucCIaWT3itwWvaRIaQaQVktaYxraSedvBag9dl QrZf6M7bygxoWvnYrWlMOyuETGPVmVdGT5q2li9kn96h5IsV9aI68kDINI7wWoPX5UKmYetr zRmZlhsEA14aAd062pPSRKaH19MjCq3qrhAMjSy81uDFRKbl8dsaIdPAIRjw0gjo1tGeZCLT mZd5IBgaWnitwevSiSyqmBryWuuu90JjanmH1xq8LhOyro2vpfHHuuv90JhaRuG1Bq9pQsYD sVoBWa1pTCuppx281uCFkGl55LDfy4tfHCyRQ2AlXM8fSv/FjuvN3tuHXs/+v34L13tEoVVF TANoZOG1Bq9LhWyLjK2lVkBWaxrTSoqtpUaqN69pQuaB2MmHiUzLBgILL42Abh3tSfPW8ii0 u33G8nUPUWh6mmweCIaN03ceObuTgHWtr6kTmfU7yiTSFxkjZBrorg1AXGvkcZqQcUamFZDV msa0kup95kMetTwiZDqvhwcTmQaOxoSXRkC3jvak+4yMraWerJEHgjEi9P46vNbglTFcuIXs tfinL1vUEF9rHVX/qmhpTI0svNbgNV3INIx9rBEyLRcIBrw0Arp1tCdDE5kebg+PKLSqu0Aw NLLwWoMXE5mWx29rhEwDh2DASyOgW0d7kolMZ17mgWBoaOG1Bi8mMi2PTGReXjxBL5FDYCVc D+MpE9nRotFA9Fv3e3SNlQbQcgqvNXi1ErKMYLS0+K0RMo0dggEvjYBuHe1J6YzM8p1k+i1c 7xGFVhUxgqGRhdcavDKGIEnIXti6CoE1rV3jpzGtGXzawWsNXtOETMPXzxoh03KCYMBLI6Bb R3vSNZF9CjMajH77Po+ucSIYWj7htQavqRPZp/MyDe0ca4RM445gwEsjoFtHe9I1kfF9ZHqi LB4IhoXSjw281uA1bSKLqqeGP9+6a/w0ppZreK3Ba5qQfVq4q0Ds0901ThpzjcYkj1oeLxMy y/Njd/peMoRMKzQaE14aAd062pPmMzLLQhYb/RbzPbrGiWBouYbXGrwum8g0XP2tETItRwgG vDQCunW0J80T2Ta0s0XvsMWMQtPTZPNAMGycXlbwWoPXtIlsf2b2XVj//ehiV7HIgKaVj92a xrSzeuSRrxeSgHXlldGTKRPZS7S24oWQSTX2MO5aaMSl5RJeGi+ETOf1FIzN5Oi8RIkbDaBh hdcavDJ6kolMq4VSaxpTwwuvNXhNE7LXwtuD/f0hf9epJwOaVj52axrTzoqtuMaqM6+MnnRN ZGcIeddSL7CXB0KmsYPXGrxaCpmGdo5112mRxtTqAV5r8GonZF0FYp/urnHSmGs0JnnU8niZ kO0b/+yzl/otXO+BkGnMaUx4aQR062hPus/IjhaOBqPfvs+ja5wIhpZPeK3B67KJTMPV3xoh 03KEYMBLI6BbR3syNJHd5Ul+zsj0wtp6IGQaP3hpvKZNZEcfScoIRr99n0dU/X2rjr1ogDEj BFZjdAdeGdrhmsgQMn8xnXkiZBpXeK3BCyHT8vhtzUSmgUMw4KUR0K2jPemayF4K+unrfPTb uNYjCq0qWgRDIwuvNXhNm8g0fP2sETItJwgGvDQCunW0J90TmR5qH48otKo7QTA0svBag9dl E5m18a12Gv58665x0pharuG1Bi+ETMsjh/1eXnyltEQOgZVwPYyjw4Vpa3n22co7HvhHoelp snnQADZOLyt4rcHrUiHbI+OzlloRWaxpTAulHxt4rcELIdPyyNbSy4utpUQOgZVwXbe1PAqr 6/bMgrBr7DSAJXtMZBql/ryYyJwZRcg0cAgsvDQCunW0J82H/ZyR6clRPRAMjRi81uB16URm fedSQzvHOqr+VVHTmBpZeK3B61IhsyDrKhCWadJyf9U2NKZGGF5r8ELItDx+W3cVXBpTSyi8 1uB1mZBpuPpbI2RajhAMeGkEdOtoT5oO+/WwentEoVXdHYKhkYXXGryYyLQ8srX08uKBWIkc AivhehhHhwsmMp15mQcNoKGF1xq8EDItj0xkXl5MZBI5BFbCxUSm43p6RMdY77ojPxpgROj9 dXitwSujJ9laarVQak1janjhtQYvhEzLI1tLLy+2lhI5BFbClbJLYiLTmZd50AAaWnitwYuJ TMsjE5mXFxOZRA6BlXAxkem4nh4c9mvkaEx4aQR062hPsrXUmZd5IBgaWnitwStjuEDItFoo taYxNbzwWoMXQqblkTMyLy/OyCRyCKyEK+W4h4lMZ17mQQNoaOG1Bi8mMi2PTGReXkxkEjkE VsLFRKbjenpE3yHxrjvyowFGhN5fh9cavDJ6kq2lVgul1jSmhhdea/BCyLQ8srX08mJrKZFD YCVcKbskJjKdeZkHDaChhdcavJjItDwykXl5MZFJ5BBYCRcTmY7r6cFhv0aOxoSXRkC3jvYk W0udeZkHgqGhhdcavDKGC4RMq4VSaxpTwwuvNXghZFoeOSPz8uKMTCKHwEq4Uo57mMh05mUe NICGFl5r8GIi0/LIROblxUQmkUNgJVxMZDqup0f0HRLvuiM/GmBE6P11eK3BK6Mn2VpqtVBq TWNqeOG1Bi+ETMsjW0svL7aWEjkEVsKVsktiItOZl3nQABpaeK3Bi4lMyyMTmZcXE5lEDoGV cDGRvXB9Hd6//m4Rnv13HXW9Bw2gMYbXGryYyP4Q2L8D+fr3T/89A5pWPnZrGtPO6pFHJkUJ WFdeGT253BkZQibVtsm4awMQlyl9P0cqTYUfITvIo1XIvpqAfyAAgSYE/hwPRf7EvCMrF/la haxo+dBlmTA0fPBag9fSE9n2oP7Tof0+jdtzMc7ItCI/s0YwNJbw0ngtLWQqijPh2oPiI0oa XRoTXhoB3Trak0tsLY8mMSYyvZg+eSBkGkt4abyYyP4QONqCvjDyHJleUEceNKbGEV4aL4RM 5/XwiI6xzmWHbjTAENGbAbzW4JXRk0tsLbV0ImQyr6bPHyFkWia78kLItDx+WzORaeC6NgBx rZFHhEzLI0Lm5cVEJpFDYCVcKcc9bC115mUeNICGFl5r8GIi0/LIROblxUQmkUNgJVxMZDqu pwdnZBo5GhNeGgHdOtqTbC115mUeCIaGFl5r8MoYLhAyrRZKrWlMDS+81uCFkGl55IzMy4sz MokcAivhSjnuYSLTmZd50AAaWnitwYuJTMsjE5mXFxOZRA6BlXAxkem4nh7Rd0i86478aIAR offX4bUGr4yeZGup1UKpNY2p4YXXGrwQMi2PbC29vNhaSuQQWAlXyi6JiUxnXuZBA2ho4bUG LyYyLY9MZF5eTGQSOQRWwsVEpuN6enDYr5GjMeGlEdCtoz3J1lJnXuaBYGho4bUGr4zhAiHT aqHUmsbU8MJrDV4ImZZHzsi8vDgjk8ghsBKulOMeJjKdeZkHDaChhdcavJjItDwykXl5MZFJ 5BBYCRcTmY7r6RF9h8S77siPBhgRen8dXmvwyuhJtpZaLZRa05gaXnitwQsh0/LI1tLLi62l RA6BlXCl7JKYyHTmZR40gIYWXmvwYiLT8shE5uXFRCaRQ2AlXExkOq6nB4f9GjkaE14aAd06 2pNsLXXmZR4IhoYWXmvwyhguEDKtFkqtaUwNL7zW4IWQaXnkjMzLizMyiRwCK+FKOe5hItOZ l3nQABpaeK3Bi4lMyyMTmZcXE5lEDoGVcDGR6bieHtF3SLzrjvxogBGh99fhtQavjJ5ka6nV Qqk1janhhdcavBAyLY9sLb282FpK5BBYCVfKLomJTGde5kEDaGjhtQYvJjItj0xkXl5MZBI5 BFbCxUSm43p6cNivkaMx4aUR0K2jPcnWUmde5oFgaGjhtQavjOECIdNqodSaxtTwwmsNXgiZ lkfOyLy8OCOTyCGwEq6U4x4mMp15mQcNoKGF1xq8mMi0PDKReXkxkUnkEFgJFxOZjuvpEX2H xLvuyI8GGBF6fx1ea/DK6Em2llotlFrTmBpeeK3BCyHT8sjW0suLraVEDoGVcKXskpjIdOZl HjSAhhZea/BiItPyyETm5cVEJpFDYCVcTGQ6rqcHh/0aORoTXhoB3Trak2wtdeZlHgiGhhZe a/DKGC4QMq0WSq1pTA0vvNbghZBpeeSMzMuLMzKJHAIr4Uo57mEi05mXedAAGlp4rcGLiUzL IxOZlxcTmUQOgZVwMZHpuJ4e0XdIvOuO/GiAEaH31+G1Bq+MnmRrqdVCqTWNqeGF1xq8EDIt j2wtvbzYWkrkEFgJV8ouiYlMZ17mQQNoaOG1Bi8mMi2PTGReXkxkEjkEVsLFRKbjenpw2K+R ozHhpRHQraM9ydZSZ17mgWBoaOG1Bq+M4QIh02qh1JrG1PDCaw1eCJmWR87IvLw4I5PIIbAS rpTjHiYynXmZBw2goYXXGryYyLQ8MpF5eTGRSeQQWAkXE5mO6+kRfYfEu+7IjwYYEXp/HV5r 8MroSbaWWi2UWtOYGl54rcELIdPyyNbSy4utpUQOgZVwpeySmMh05mUeNICGFl5r8GIi0/LI ROblxUQmkUNgJVxMZDqupweH/Ro5GhNeGgHdOtqTbC115mUeCIaGFl5r8MoYLhAyrRZKrWlM DS+81uCFkGl55IzMy4szMokcAivhSjnuYSLTmZd50AAaWnitwYuJTMsjE5mXFxOZRA6BlXAx kem4nh7Rd0i86478aIARoffX4bUGr4yeZGup1UKpNY2p4YXXGrwQMi2PbC29vNhaSuQQWAlX yi6JiUxnXuZBA2ho4bUGLyYyLY9MZF5eTGQSOQRWwsVEpuN6enDYr5GjMeGlEdCtoz3J1lJn XuaBYGho4bUGr4zhAiHTaqHUmsbU8MJrDV4ImZZHzsi8vDgjk8ghsBKulOMeJjKdeZkHDaCh hdcavJjItDwykXl5MZFJ5BBYCRcTmY7r6RF9h8S77siPBhgRen8dXmvwyuhJtpZaLZRa05ga XnitwQsh0/LI1tLLi62lRA6BlXCl7JKYyHTmZR40gIYWXmvwYiLT8shE5uXFRCaRQ2AlXExk Oq6nB4f9GjkaE14aAd062pNsLXXmZR4IhoYWXmvwyhguEDKtFkqtaUwNL7zW4IWQaXnkjMzL izMyiRwCK+FKOe5hItOZl3nQABpaeK3Bi4lMyyMTmZcXE5lEDoGVcDGR6bieHtF3SLzrjvxo gBGh99fhtQavjJ5ka6nVQqk1janhhdcavBAyLY9sLb282FpK5BBYCVfKLomJTGde5kEDaGjh tQYvJjItj0xkXl5MZBI5BFbCxUSm43p6cNivkaMx4aUR0K2jPcnWUmde5oFgaGjhtQavjOEC IdNqodSaxtTwwmsNXgiZlkfOyLy8OCOTyCGwEq6U4x4mMp15mQcNoKGF1xq8mMi0PDKReXkx kUnkEFgJFxOZjuvpEX2HxLvuyI8GGBF6fx1ea/DK6Mlfu7X8EjP+woAa6FMDmizvf6hFvPGF AAQg0IDAr5zIGnAnBAhAIJEAQpYIk0tBAAJzCCBkc7izKgQgkEgAIUuEyaUgAIE5BBCyA+6v d7LmpOR81U6Pjmzf8ZvFqkMMR/feNa5trJ1qaR+XGhtCtqtCFeCVDdxJYPecZnDrEMMnEesu GJ1qKYMVQrahOKMZrUL4iq1rjDPi6ipk+5zOYHNWV11rKcIJIfub8W1yI0CtwqTadS2+133M YIaQqVX0tO9YS9H+Q8g2QrZtjBmN+aksu8aVsSXwteJ7Q84UU0v81NKY0n6rqzJbXsi2h65n B7BX/3S/e1yzRWw7WXQWMrUhxy3vt7i6xpVIo7EtL2RWmFGQ1nVUO6vgqdfNsp/ZqF1z1lVY O9dSNJcI2e6MrGsRdoxr9pY3WvxZYn50ndlsLPc284fQPr5oLhGyDdGzraelMK6w6VJ8Rz/d r7j/owbYn6/MiGO/1d7zmR3TSGw7xBfpP4SsQwaJAQIQCBFAyEL4cIYABDoQQMg6ZIEYIACB EAGELIQPZwhAoAMBhKxDFogBAhAIEUDIQvhwhgAEOhBAyDpkgRggAIEQAYQshA9nCECgAwGE rEMWiAECEAgRQMhC+HCGAAQ6EEDIOmSBGCAAgRABhCyED2cIQKADAYSsQxaIAQIQCBFAyEL4 cIYABDoQQMg6ZIEYIACBEAGELIQPZwhAoAMBhKxDFogBAhAIEUDIQvhwhgAEOhBAyDpkgRgg AIEQAYQshA9nCECgAwGErEMWiAECEAgRQMhC+HKcj37jzqffwvPJ9iySs9+8lP1bmSy/CWdv kx2DNStR7l7mo/gyeWReaxT3zNcRspn0d2sfFZ31v326jbNflZb9a9Qsv5vwyGZ2s1kZXxFn xRoV12zUNo9QELJGGalqqLNfFptZ5KNrfXp95PdKkdVOTWkV94w41Guc/UDLulbH6yBkzbLi mWpGt9BdyEbxVwvZ4yf6f++tYBU3a+wWuyqhPro/Szx3skHImmVr1ECj17dN/7Ld/u/2XGhb 4Mo288h2e92zhlS2ulvbo+vv72ubSuV+PjX62Q+APeczITyLcyTS+3zt7ff3OeLbrNzTwkHI 0lDmXOioIUYT1aiJFP/RVDC6lpXCp4Yb3ctIQPaiNLqfT0JyJBD7e/sU6ycBPmNj+QF19APp 6AeXRZitebqLHULWMFNHk9TZT+7R5KCIz2iaOmtmq2jsJyfrv5+JnJWPRUz2/M+E4WhS9Iip RcisnBCyhk39G0M6a6RRwauvW8TBKhIeIRs1vSLC0QaOcI8K/Chv1mnQmqvV+oqJrGFGIw01 aoiRcFVOZJ+ubRWrUewI2U8xj+qgYdmHQkLIQvjqnJVCPGvw7dZn2+hHYvlJCD5taSzCsvcd +exF7RW/JfajaUSdEjO5n01HownrKBdWwY8Kel1V110ZIatjG7qytaH2QrVtnk+T3fa/74Vi +++fbuDIZhvHaPL6FHMk9iNhUEXMIwBH97JnYWF6tvbe/+iH0yiPHhahAr7YGSG7GDjLQeDs B0QVHYSsiizXhQAE/iFQITgV1+yWOiaybhkhnl9PIFN4Mq/VOTEIWefsEBsEIGAigJCZMGEE AQh0JoCQdc4OsUEAAiYC/wNPMGghCZS9YwAAAABJRU5ErkJggg==</item> <item item-id="21" content-encoding="gzip">H4sIAAAAAAAA/+wYW2xUVXDua7fdbruP0vKqsFSKpaUFtwqWgHbZbqGJfdA24cOQst1e2yXt bulupYsfNGCqMSRFE0gkGqMh8cNAkA98ftT4YYyi+GdCJPjhh4EPgmt8AF1nzjn37u0KdCug MXZ25965c+bMnDln5sy51wkAEmI3ooPRKl6Lgh1DenM80prUh4FBCaJ9FqcIsbA/HunSB6Lx mMp4jYgFw5GOvr16JMnFuphSGa+2xODItvg4Zz9N+pDRZQq2IU6TnB3gBVTXZs8+E7TyG6qS FTZkr2k8kEyORvvGkrrEJDYjusAK6ubKWc/l5dztApPDeiL3wjeey2+9t+wHyIEnQYGZTCHY LDzJ6EfgBuYkPc9kMhmDnVmA/xTcQiwQa0gxrVHgiiAsFPyZf3mMC/DgoAvi+EuCD0IQw/so pHK3grtCOUaMoYv2A+VrmfGneXOLVfaa64llTy3+QlKQbioy9pQOGAJ9Xjat4ABZsvqTb79F YNgPov/DMILj6IO987bvRfu0B1Lu5Guf5KOCloX/nTjzOiRgA/vlD0tQw3z9p/n/SJSCGbFu t8t/EqE9gOokLZcTsRh4baR640b0AM0BQCnwOS0DVlZgMRsbwFLEZYjLESsQH0JcgbgS0Ye4 CpFq1cOIqxGrxFgewXs14lrEGsRaxHWibQHuH0i8jM8CP/RDA/4aYRM8ilFJEenH7OjH50b8 bWS4CVsaMJZnMoqDx35u7lMctIWTgz2pEd23sX6DL7RvLJzE8wuLJLMl1MKiyWisb+5u63kM 0o1n9+URy8wu8PyTxLPh0Pv1GC8YyOMF1IaRrGxRKaqvsFaKdJ67REkydmRUvbRU8IpkQ84p N9n31FCMr7G5MI7JCogMmEb6DKIPw/SajWcKSTlNDwH2SJRLf6C+NXxoMo23TuLaF8nTyqBG VClSfrshC7NkJSFbo54s5rK/KtcFpWunSjj1pjTuMihfKWXxTa5J5nNUJ8nA9WvmWY7zFZan q5UieKPYp9pLLiqnShxa1HUE9RCelV50nZWmS3TtBOKl4lL13eLHVevYCsmSzEd5Tr28iPL9 huy++TkzdJQfH2Vwd6eG++JDaLfYcWDkkhc+OP3z/k8RfU2/7D9f/qwYj8r2Hj9tPmyk1Slj ts6pXy2fS/dkmeOVO+vW2P3aX2ystdioqMxj/M/fbfzchnYXP5xVefjx6r34cUo+X8FtBGf4 ah+VRDy4e6LDesLXru/3dcWHw7E5/XCbNqhCNKz34/8KvAOn7XQ9oHCLr8uvadwi3Pp7Fu/k lT8FNm5jQj3hujcbd1qdrI3vbA/KjwbTxnbt7cLc1ZHuix/9KWM9JtQKyPVjfjayfnhNG7Sz 7AzuGhhMDMIh+3X5Y9u3arP6W4Hh2WXlTOltZi83fw5uuTh3/jhMq7TB/PR7U1k2DlTFabZS HdhaW3esouyq3ZCotp0pnd3/yg3qTzWAdukvpcDISKKtJRiU4CSOkLBZT0QHYr7uSFSPRfR1 vtZYpB40CaOeStNG3Fx3RWOBoaFt4UQ0Eoz3653hAT0BHi13Oj2KcNWjBeNjo1F9lBrBo7b1 +ELjydEwTmhBZUdoqnl9oDPtdm3vDXReX7kC6TJXANKhdFV6alsvUm5XkG5ToXRN7470FOM0 4f3gDiQCNb3NvaE0/QMup4RzLMkI6KyEC63wTxwOmvnDO7GYTG6FrQpVKeK85Bf3BjY7h4Mo j0e1Iibdj20SOJDahWyH2QMZAySDbQooZABRYa2HB8lCLdQimSCNx6vpspYWp4L1EdZkOLRe 2EcL6qS3ss6qJM+eNLjjz5DcbhZj2drLzzbtjJtTT82otlRHk2sTueIWn2E+Ye04oZXdqQT7 FkQRSmfiVfAySyOydORHr+hvN3NFgdtDe6CdfdrJUE9mTYbdn1Hc9cD30DnZxaSOIP1PhFwI 9sEYhPHNL4rvQTE8k7eLp+fyeivz4RlKgex3nzy6sDPQtDSnWN5gPc/eP63/D/sLW869bTlO MY9VgkacMGjcQSaqBS1ZZFQLn7aXJRY9SwVNrzFeiwy9cxvfLNyCLoR4shU+lKOg43uDBhcG PPj2Qtp47WuBq9lzuYUy3gOI8ycAAAD//wMAmqAQchAXAAA=</item> <item item-id="22">iVBORw0KGgoAAAANSUhEUgAAAcQAAABNCAYAAAA4lxS7AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA EMVJREFUeF7tXY3VHacOzCvBLbgFt5AW0oJbcAtuwS2khbTgFtKCW/jisc/4KQQWIWAXLnPP ycn1d1kQI6ER4mf/9/b985s+QkAICAEhIARORwCEqI8QEAJCQAgIgdMR+O10ANR/ISAEhIAQ EAI/sqWCQQgIASEgBISAEBAhygaEgBAQAkJACPxAQDNEGYIQEAJCQAgIARGibEAICAEhIASE wE8Ejp8h/vnnn29//PEHjp78+u/3339/+/Lli2xECAgBISAEDkLgaEIE8VkiTL+/f//+7e+/ /z7IHNRVISAEhMC5CBxLiJgVvnv37u3z589v3759+2UBmDHi7yRHfLe/n2sq6rkQEAJC4LUR OJIQv379+oP0SrM//N2SIshTHyEgBISAEHhtBI4kxI8fP76BFK8+mCnaFOprm4F6JwSEgBAQ AkcSItYOPR8RogcllQECV2vR6W9CTAgIgbkIeMdjKsWRhOhVBUH98OGD9xGVOxQB7wBEOX2E gBCYi4B3PIoQG/RAUHUEowG0Q4vSVg7tvrotBLZCoDReFa4W1PjXX3/9SINhc40+QqCGgAix htCav2MHOY5X6fM8AnfqQoTYqG8e1tfssBG4zuLeVMdT5UrdEyF2Kn7Q49ghjiUO6gMb6K4+ GN+fPn36VxHUAZJkQJz+PkjUl69mZV2IEBvMD4oEYN7NNw1Vq2gFgaeIztuuCHFdE8a4tQEs yLA2jkGe6fErEiBmLCRXkWKb3lfXhQixQZ8gQh3IbwBsYNFdZ1q7yj1QdY9Xlcvm1PSSLong og77wfEsLZ20q3Z1XYgQnTqFIgFW7ZyiszoVa0Sg5sAaq7ut+K5y3wbQAw1hhge9lGZ3OGtc S6lCbOm2X3mr6UKE6NApU6UYKPo8g8CuzmdXuZ/R8vxWEdAi3XmV6sQ+AWyeq32gW91WVUOp /PuKuhAhVvSJCAbpE5Fh3PBHPLkrsewq9widrVYH05y19UP8XvvwxipljGpI5X9fVRcixIo+ EU160icxs9BTHgTsxhZP+ZXKzCBEBGna2PVTy60zNLshJvcsZoaeOnfzC7NtxoNZOi5X1IUI 8cJ77mb0KxHBSFlmkMpI+a7qGi07HHZuB+Rd/VmtHR6FaHkdG5dAcjNBBL+1bBD2E+x0S9Ud NhPRA2xpNV2IEAsj3EOGUKZmj/Nd5GhSmS/x/1sYKTtfQXan/Du0FXHGJb3ULtzgGbodcIGM d9pMRA+QcSVdiBAzlu0hQxpauh17l4Gyk5wjSeXufo+SnZF0y0zo7r4+2R7Howcf7mxM03w1 ssPvaaoa7a56o80TNtOiB9jLaroQISaj2N5mYdeuSt/1kuD5bnAUqcyX9L8tjJIdM5fWQ+DY uICADVkM2LVn5+QTGI1qEwSXIydgZzM5wCJ3nhj4lm6g4ua6nB9o1cuo/tbqecpmSnqAvKvr Ygghwlgw8BA9WYPh9uZdSKOVDCMLyakRAxsMQh76J37EjhEvykQjUd6/mhvMTBHx9o4S8dvI O9Uzn5nhcGuEgoiU1+mxLORb5Wq9mvw1p4bfqRtPWZYBBrxmbIQMLW0/VbZ0pg2ERQyASWmZ A7+VfNWVb5hh970YPmkzV2cLV9dFFyEyhUDWT7cgw1DoPJVa/LeJw2hgtMAO2KTYMRVjCao3 ErXGiLbTwU9yZptX66OQmeXwfVbQc+XMS8RsnZ8nhdbrfK6e7yUjbk+PrlXzQokRwdtMnEbV zeDoab2P6k+knhVsZlc9hAmRztUz0BitiBR/mjfJxOPkbJQ/IhKlwq8IzOPE77qsoCQL7I7B hO0L1zD43NNX7XmwvHJ6JP2o7jn2ajsnI453xWd4PvDkYykr2MyueggRItMHLWkpOCY0Nmsm seLgzMnU6qBoWMCu98NUxtVOOntg9qo9EFKL/qOy5wwUMqIPpVkA/k57w/OeoC0qX+25HkKk vnp07wmAan3Y7fcT+0wdrWQzO+qhmRAjZAhlcUbZm/bbbXBaeSMzZc7ERjh1kt1V9MwZ6VUZ 7+HlEbrKkQFwrN0QYgOJHkLp7UMPIXLMRGc7XDve6cxcL954/uQ3UaxkMzvqoYkQmReOOOdT BycHONdyIs4NShoxG6MOSjLYjVE10hzhuDx15MjMi6Fdf/W0NaNMDyEyOIkGkTOD0J5+zcA5 F3hGN6HNlm9m/SvZDCcAO+nBTYh06LWDqyVle1NxM43lqbptGiOy2D8q1cy1y5yD5VupWaa0 3uu9+HgE1r1Ol88/OUOK9sHe4BFde6dzrM2mI7qK9ivSVuszdsNXZLy1trdK+dVsZkc9uAmR azLRxXkS6pPpq6cMl5GSZxPNTBmvyA6zLp5bg45yTvjOVClw6HW6fL5ldo3AAAFDus0egUCE WKJ9sOMl0u7VejF060mNX9litF9XdcK+KBf+T1/jzQiwbnvMqEX3M8feHXWvZjM76sFFiCOm vkzf7DR9HmHEdnYYcWwjZGAdTHmnTgKGS7JmmVzgc/dMq8fpcjC2ZDTs+oudWaAuu0mHcnl2 fkb7YM+FRuymdNwid54uMgON9qtkz1zzpSzcOIV2WgnRZqOeDkJHjt9aXavZzI56cBFiJNJO lceB6F1/tOs/0e81A7rj995U80gZuR07dbA2SCmVubrFY6SMtq6ebEKJ/Euy1mbxdnDTHj07 pqPEYc9YRvDNBTYYg6iXZM8xuQIhcmZo+8qx0yqfTR22kmkE61WeWc1mdtRDlRBHbPu3syRv CiNKgitspLADhEa6QqSaW1NKiS5XBmTwhGOJEiIHoldm2ngtewF5amVS5xglRDsjjTjclLRB fimxkBA9M91R/Sr1JRdk0G/0yNeqrwjWqzyzos1Qr7vooUqIjDS9ziVnHPaGFE9UvYqBjZCD Rtoa5Y5ou+bEcpcZ5wwCjvNuvUWJBH3mNXhemUkMV+vj0fNd0X70BHbpju7cK42i/aFNRftV smvqIPUzaMerxzS7MFrGGWNyZJ0r2kyPTCOx8dZVJUTOGHocOklhhVlSDpgRs9EikN8HNH6L RLleJXrLpTLa9FnJ0UHvPbr3ylYjb289TLO1rLt5HGc09e2pu2aT3r6zHANQ6A12l9Of50q3 3nHRIrfdgDHCT6zqiHsxvcqa9PR5lM2UxnE029NiQyPKVgmRBaK7S+3W20ikN6KTV3WMMNAr Q+RvLQ56Rp+5BsbUBRxievyCZbgRpfY6nBlyWmJurT96nZyHtKKBoafu0YRIWe1mqbQNz6y4 d2y06s9uCvEurZTa6CGHVrlbyvdiOosQR9nMMYQYceg952JmGk6LAfeWpaFFA4re9vl8ektN LqeflnnyzeytESVfzxPBmcsCJRuno47ssr2bEO1xi9KB/t50KWwq2q+aPdtdsD0B9KqEWOt/ z+/RPs+0mahMPTj0PFudIXoiyZIApXUBj8CvQoh0ptHbRjxYecrYm3Jya0qow5bB95Gp0taB 0UqInpc6l3Di3aeYGVtCtW9ria6hR4nDXuru0S/L2NRu6VC6J11aazPar1q9+H3EEgvl22Uz hweXWpkVbWY3PVQJMXr9U09UXVP8Tr+nqcoW2eHQWtdTSgpl6hqO/erqNjyPGVNkNpTrWymw qeHQQog9ZEg5+CozKy/fnRfJjrDeKHHYLfQtt61wtnsVzETvI7Y6i/bL1sGZSWoL3PXbcoY0 rYPyRQOZmn2u+PuKNrObHqqE6HlDQmocIsN/I0JDbZlx0UFHBl6OTOz6TCkV1Xuwt+SUUnKo kV2Ls/WQoTew4A0uIzdAtfTF4hfVRa09my7F9xayHU2I3EiTk6HWj6txseOB8Mg4L/ldYNcS xNWwjtrMjnqoEiJA96ZYANyON5yPMMZaHS1ROVN1tTpLv+cIx0PKLDMzvetJndYIk/32kCHf j1gLRpgJiWJ+pQtvf2wd9houL0F7LtDn7AvY4bv3ooxasBPBrXSVIB1wVDbriHs350T69dQz q9nMjnpwESIUbK+1Sgcod7OhMt6J+ZRRrNyuPdMJZ2RnaviOvwG/qCNA30vOFw6wtp6CMj1p Kg/2owgxdwVZKT2Lv9c2aPBZOOnorCnX/1r0XcIssiHNbp0v1WttsNfOIkRv5WIAlq7d8uXP NZ2V+miJYaQuPfb9ZJnVbGZHPbgJEYoG4yOVY29EwHcYNjofNeAnjejutolh6tDxbzi03gFc clL4e22m4SkzAq8aKdYcbSsZehy/XX9B+7BrvgS5x66jhAicuUnCu47M8lc2RNL01jl65mvr o14QCFKnwB2y9WB+6r3Jq9nMjnpoIkQa89X5phEOU3X4EUjJpUYm/prnlewlxNGSYVbIK+w4 O0lnm1EC6SFEOpRRG5xG47ZqfXek/lft+0o2s6MemgkRkZsn4l7VYF5JrlR5O5Ah8S+RYg+B tOqWm2hyMyr8Db/b2Whkx2JPf0acF2zF5BXKE/OeWeauOKxkMzvqoZkQI2TYs3i/q2HOljtH fq9CiLOxQ/3c0OHZjddzwX0PIULOq9dx3YHTbm1wY1EkeNmtryV5V7CZXfXQRIiRNS5E2rXN HK9iiHf2Y3dCBFa5WeJdpM51cK/OIFfEjnsJsfXNHd7+vGo5HlfxBDqvisEKNrOrHtyEaKNk PoQoDB3HhprUAJlyguPp3Shyt+Ei7YB+2Zsfcq/PuVsum25M2+51vE/2xZLgXYTYcqUed8vV NiXlMByhF64LnZgCbLVL4B1d721ta+XyT9vMrnpwESKvtko3GtT+DcLcbRDX+rrCBodXmB1a crdGeBchWj3DgaYBnT0Gg6AuQoZ2FtzrPEHgkeWK3nZ3eh4kMPvY0E54PGUzO+vBRYj2fkc4 CjgPvi2B755jRSOuu3rK6HhBNBykndXiO/Py6OfMg+uevqek8QSheOT0lkkDK+9zI8rBjqFb mw2APAh88PfIZeFWrhEzRNRHAu+VZwRmK9bBNOFu2aiZWD5hM7vrwUWIM5W2Ut0g9yuHw7z4 ClFouv42yvE+oY/cWuITcsxoc6ReuB4vp/9fTaWH+2focsc677aZ3fUgQjRWXkvvrrSlecfB eSXzSOJYCZvR/aKDO3nTiNUv8YimtFeylVmy3GEzr6IHEWKjFQIwreU0guYsftf6oVOcIcVG EyKFkg3+RGLHfQpDDCtQyUybeRU9iBAbDItrjEpZNYB2eNFZhHg4rOq+EJiCgAixAVYc5tam hgbAVHTam+UFrRAQAuMRECE6MQURigydYKnYLwQ0Q5QxCIF9EBAhVnTFdxMSqKePXOxjWpIU CIgQZQdCYB8ERIgXukpfCUSwVjicv4+JnS2pCPFs/av3eyEgQnToK50lArST3sTtgEhFCgiI EGUaQmAfBESIDbqy97lqltgA3MFFRYgHK19d3w4BEWKjynhbzSuemWuEQsUdCIgQHSCpiBBY BAERYqMicEOInFwjaAcXt9fS1b4fDJO6LgRuQaA2BkWIATUANKVMA8Ad+Ih3ACrjcKBxqMu3 I+Adj6lgv90u6UYNAlQc0tdHCAgBISAEXh8BEWJBx9hxusLbLl7fBNVDISAEhMAaCBxJiHzn 4dXhe6RK9aaBNYxUUggBISAE7kDgSELEzI85Zrww1r5ShmcRdbH3HeanNoSAEBAC6yBwJCGC 7NLbaUCMI96cvo5qJYkQEAJCQAi0IHAkIbYApLJCQAgIASFwBgIixDP0rF4KASEgBIRABQER okxECAgBISAEhMB3BESIMgMhIASEgBAQAt8R+Afl0JvMKOXQqwAAAABJRU5ErkJggg==</item> <item item-id="23">iVBORw0KGgoAAAANSUhEUgAAAdMAAABBCAYAAACKJ05RAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA CpFJREFUeF7tne2RFDkMhu9CICdSIAVSIAVSIAVSIAVSIAVS4K636CqP17Y+LLkl+52q/cG0 bUmPZL3unt3h3z//v/7BCwRAAARAAARAQE/gElO8QAAEZAT+33HXIfTlR7YCRoMACGQk0Nr7 b+9lDAY+g8DTBK7NgxcIgAAI/H26CzFFKYCAhgDEVEMNc0BgTwK4M90zr4hqAQGI6QLIi02U j+8Wm4a5oAS4NQExDZpAuBWfAMQ0fo4kHtb5RH4l9PYcK6kJiOmeNYCoFhBAs10A+UETyO+D 8IOaHtUExDRo0uBWfAJotvFzpPUQudWS23ceVRMQ031zj8icCVCby9k8lncicH9G5rQ8lk1I gFMTENOEiYXLMQhATGPkwcsL5NeLbN518Zg3b+7geWACaLaBk2PgGvJrAHGzJSCmmyUU4cQg gGYbIw9eXiC/XmTzrgsxzZs7eB6YAJpt4OQoXCv/nhC5VQDccIqkJvCZ6YYFgJDWEEDDXcMZ VkAgAwGIaYYswceQBCCmIdMCp0DgEQIQ00eww+gOBCCmO2QRMYCADQGIqQ1HrHIgAYjpgUlH yCDQIQAxRWmAgJIAxFQJDtNAYEMCENMNk4qQ1hCAmK7h7G2l/o3N1r+9fcD6sQhoagJiGiuH 8CYRAYhpomTBVRBwJgAxdQaM5fclADHdN7eIDASkBCCmUmIYDwJ/CUBMUQogAAI3AYgpagEE lAQgpkpwmAYCGxKAmG6YVIS0hgDEdA1nWAGBDAQgphmyBB9DEoCYhkzLlFPlb3FOLYTJ2xDg 1gTEdJuUI5DVBCCmq4n72qvzifz68s6wuqQmIKYZMgofQxJAsw2ZFjOnkF8zlNssNKoJiOk2 aUYgqwmg2a4mvtYe8ruWdwZrENMMWYKP6Qig2aZLmchh5FeE64jBENMj0owgVxOQNFvJ2NVx 7G5Pw14zR8NxlR2NbzvP0XCn5uAx784Vg9hcCVCb6zbOHefq7OGLS3IgGTuDdZWdGR93nivh zxkLMd25WhCbKwHuBnN1AouzCUjzxRnPNl4N9Fxb69OJ8zh5KMcc/5j37cTw9+fEgkHMPgSo jUhd9/Hq2VWjx0w2w6JXeMXite6zmW9bz9B7rWpi+zvTGtRJhRxxc+3kE1VL1PWdWFyxZDiw RshJBB9W1F6W3muVj+3FtC4aK3ArihE2YhOgTrSxvffxLsP+etLHJ237ZJy/auTYLXyDmPJr ASNB4IUAxPR9QVg0Je8ye9LHJ217c6XWjxy7hW9HiakFMKpgcP0cAhBTiKm02k/tQdHjtvAP YirdDY7jf/z44bj6HktHYtTbgBYbM2u2ssT+hJ9P2IxSRxlin/XxGDGdBeVdlD9//vxz/eA1 JnAx+vXrlxrTW8ErfloGIaY570wvr5/oB0/YVG8Uw4lZ4p718wgxLSHNAjOssZelvnz54rX0 duvOsLLMP8QUYirZXJa1J7H75NgMvffmM5uf7cW0dRfyZHG1bP/+/fvP58+fo7kV1p9LTC9m mtfshiltQkxfM1DuNU1uVs6xrAOu30/Y5PrmMS5D7+XsZy6bppjWELiLZR5XNwJN4bfWaDWY +r3v37//+fbt2zt8EfLA8X913q/PTS9m0led09nmrxHTiDylHHcYr9nfs3GPbKIuZunOz5+t iXdi2lpw1sh8mL4r9JqsxiqXXznuuiutPy/lrqPxUTonki+X7xcrzZ18GUcr5xZcrjWo/RKN pzTuHcZTOfKIkbKJuvCgzl+Tyg+10ouYUicnarGM1zV3F6M4uRuiHPfx48eXx5bR8sCNaVX+ r0e8FzPpy5qrtnai8exxnG0u0vysHP9EbJTNLHWxMk8rbVH5oXxJIaazQUrFj4ImWe/2XXJX ZN30Z+Lp3Wl55oTj74cPHzjDXsZIuL5tDOJ7WiGm4hS8MJXPtpvxRP1SNk8V03Kf2WVYvhKV H2rFFGJ6B9FqcBYAKEjS6y3hzC6mHHGRcpoZr8k7V/zqcdx5ZZ1SB65oPFv+ahhTcXMZzdQG Z651bBY2Pfobx68nx4z64mq/ZmtCJaazRldB6hXnbAKp+fd1SlR7jUVyB7WCpebE7F0jI8G7 816ykTCl1qYEgYo9Ik/qwErx4whBKy+9Jx/edU3liGOfEzO3BnscKD+p65w4OGM0sXLm7FQT YjHtBc9JyOwYTnKkNnrFOHqfKuDTxXRFjXBzoBW+stZ6NSWtnZEvHLGS1vbseIqxdv2yPlbU yoq7bg4Liqf0kPUUO06s0jE71MSLmHJPR1RRSEE+Pb6Oh4qPus7leMfd+vxPurE8GWp84TCa 8ZnzmWnpAxUDNVbSkKnYKV8ktmYYUnOpOKj59fUeY2s7HL8i2sxSFxy+3DE71cQ7Mb2FYHQy vwH0TkbU+y3xGhX3isIfxVsy6QnlqHFQd0fXb6a2viKv9Gm00epTXcuXumh7Oaxz1+LSY9Xz o2WrFw/XT85v844EsmbbssupCanwReUpjYPbLMtxo7qp91hrXq+upf2Eu4c1MY7mUD2Os/dG vchjn1kzkOQwW000xZRq/q2kt+b0mhnnfe8kStbvNXjJGqOxrb8zrZsJVYSUkNUNpNx4nIMA FSslgtRGp2qivC75O9NRnL2YekLP5TRqmhTH3j6q+Vny5PrkNe6JfmKRIykPC5sr95k0Psvx GWtiKKblyYA65VMn0BpO2TSoOwDLJGnWap18Nev05lzf5vP169fhkr07GuoQ08thLa7UAYqK d8SIEspWLYwOE5JvQOrV3SieaGLaOwiN3pfmncqv53Vqf/Vqv5cnTj+xEDYpEwubK/eZND7L 8RlrghTTFiBNQuuGOdu8JYnjbC5Jc7XYFKW960sIPn36JAnpbSxHpCSHILEDxQRNTXD8b4n+ dfCQfDevJl9lzfTmS9+X8F3JU+IXtU8sDi/cuigPDNJ+oqmJWU4WNrPVhbb3auKc1ZjZ/CwT U84GmQ1GKvzczVH7rmkYlC3N/4QiZdoab8V8xMjaT+ouvmZtFSN3XQt7K3lStcm5Pmp+1HxN 45TWFDd3lK8z10+ri9NqQiWm5YmwVSCtU/3ohOIhTr2i1xZ06aN2jdFGvH4BSfLl7TXP2r9e Mx7Nm2kU9V3Cbd/az0j/72uvDqzqo1Vz1jxnc261zzj9oVXjo/5D3UV7xW7FZLROub9a/ThC P5g9wGSrCbWYri5EC3tWTc7Cl9Ya12eBeI0JSA4c3iy9xdTbf6/1o++zW3y84vcW09V+W9g7 oSaOEdOVd78WxYc14hMYNYgMzcODcIZ99mRunrTtkW/OmqfUxDFieif9xGLmFDzGyAlATPvM Iu+zJ3170ra8wm1nRI7dwjeIqW29YLWDCEBMIabScrdo2lKbUcZHjt3CN4hplEqDH+kIUBuQ up4uYIHDUWOP4FcEHwSpNBsaNW4rv7YX0/t5fYbn9mZVi4WWEKA2IXV9iZOLjGTZZxFyEsGH FWVxWk1sL6YrigY2ziTAaYqcMWfSWx91pFxE8mV9JuJYtMwDxDROXuFJMgLcjcgdlyz8VO5G zEFEn1IlddJZa/4Q08mEYPq5BOrHWKPNab1xz6Uujzwy+8i+yUnnmTHLvbX3IaZ58g9PQQAE QAAEAhP4D/CtlEcYuwpmAAAAAElFTkSuQmCC</item> <item item-id="24">iVBORw0KGgoAAAANSUhEUgAAAHQAAAAlCAYAAACTSM11AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA Ag5JREFUeF7tmAluAyEMRdP7H7pNRqGiHoyX2C6CP1KkJAPenjGGr+/n88CzTwReQPHsE4HH Pq7Ak6vaIgx7RQBAF+X53NRfvc31sTy20RbJGPtxBKwwUXI/DnmuAADNjW+5dAAtD3muQgDN jW+5dAAtD3muQgDNjW+59BFQCTKOLeWYdAr7c6jlTHoDSgXp1K81ists7qBuChg56Ft1ZUfq D1DPEs820Cp/BI36Jf3mdFLZHl1Wf6zjf4HOarNUt61Ks8dL9vbvtddrbY6UDNL7dN+bglOB avy/rtTepVYCJr0/HqinOfAmJzePrugeigRQqhbRgM0lt9pAj8ORQD3AuMZyJItLWM//VyXR lJy+3FQDjVyhku2j95J+b/J4ElUzx9zlSkHRKM0ew4GZJa/WL2nFahZIpv/mc6i0WltGU6P7 TKd7kDaYmkCM9IxWWWt0ZvaO9NH9lM7n5GlsjxjD3hTNGgSuSYj6P8KxU2VMr/64bJe6PG3W cs3DqTAi/Dbf5c72EG6F9vuKNCbCqR1laLelEqDWUrwjkCqfzEA1zQQt1VxD1Muqcnh3PS6g uwel2r/RyUGztY06agCtpkf0cdWsVS9axaRGFUD/GWgPzvP9NmcBf443wdo0Tsvx8dFcIAAA ugCEKBOkPXR0bdk3Q7f5UYZBzhoRQFO0BocwKwA0LJRrCALQNTiEWfEDwiCvq/PGjCEAAAAA SUVORK5CYII=</item> <item item-id="25">iVBORw0KGgoAAAANSUhEUgAAAKQAAAAvCAYAAABtwmFaAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA Ay9JREFUeF7tnNlOxDAMReH/PxqmhY6C8Zq4TpTeSjxQJrZzfcbOIvH59Xo+8ECBVRQ4gMQD BVZR4GOVQBAHFDi7NWSAAispACATsvFafh3r8PdPgsnHmgCQg6k/QKQP927QzWOGA8iBVGvg Aco+YQFkn27nKAA5IJ4wFEAOaAogB8QDkDeIx6wfLy8trGjffu1RIf1ahTYvF4TX7nvAzaOG AsjBdHt22aiQfpEBpF8r8ZPWOaRVLaUqKp1t7lx1AWQCkNy6sTVLAZLWlyPvE6cx1RSATJaf q2q0ZXO/c9BKy4Gdb4UAZDKQnDkNSGs3rlXXgtDLXQDIAsl7gIy274JplLgAkCUy/9zqeDYv dAOkte2i0Evd/APS2jGWRgdnpQp41qbWF0sL2HP89QdIz5laqUJwVqaAtfE6ArGWHhpw3qOq N5CWsTJl4GiKAh4gaWDeMe0yxJrc44H0tBFLxB3+7oVLO3OVtASQHYTQtbO3xXS4KhsSmVME SG1zplVRz5c/XCE9RssUJ44qY5OSPeN9ht4RIKUqadnw5CcE5GpVwzNBb7Ii1cRrc/bnInOy YOLmYo2J+H9D3jriEmw5nSl6JpAz57GC7548R8d48hU+h2wXqBLA2ntuEtLE2ve0OrffPq2F WFXdI9IKwFTEwGnaHvfQikeLWYbW4k2NlCgODA4ICpP3MxZoml3pb62oFYmFj34F1KtDCw4u 0dIYq2pqMF1+tC8DjSXaTvolxMhMBcJ32VqivVBJLdaze/NWwRlAai0tM2k72yoBsrd9R9r8 7AopdYCd4bljbmEgpfbZwkPbtrRYbm1x46VF85X8toVrS4W7Ny6a/bt93wHFTJtdQM4MeEXf ADIvKwAyQUsAmSDirwkAmaAlgEwQEUAmiuj8DxZ5Hve1hAqZlFvssnOEBJA5Op5WrHNI6XSA hsBdAHBjE0NfxhSAvCEV1q0UPe6KnNPufowEIG8AUjqr9YCnnfPuDuM595vyAbOMAh4gvVej uwoMIAszOwJku4YsDLncFYAskly6TqXrSW5zxF2RFoVd7gZAlksOh5oCABJ8LKUAgFwqHQgG QIKBpRT4BhmCP+hPrHtSAAAAAElFTkSuQmCC</item> <item item-id="26">iVBORw0KGgoAAAANSUhEUgAAAcEAAABiCAYAAAAoRGgcAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA DERJREFUeF7tnQty3DoORWe2ki1kC1ltNpmXzpTe0AwpACIkAtBxlauSND/AwRWuqHY7//31 ++s/fEEAAhCAAATeSOBjgnzVJfBb05+bnC/fdbMlMwhA4CAwuvY//8bXVwIQKa4IRF+8wKQH AQMB+sHfsDBBg4AyDkX0GatGzBC4hwD9ABO8R1mBV0X0gYtzMbT2MdfFJZhWjIBWE/QDTLCY 9OV0EL3MKNOIvp7UN1P17onVogn0ggneo8LAqyL6wMVxCI36OkAstsSZJtALJlhM7nI6iF5m lHUEtc1aufviljQhvX5fZHFX5gdj4tbGJTJE74Ix3CLHe0DhAiOgbQQ0mqAfcBLcJtBdGyP6 XeSf2Zf6PsM50y48DrVVi5OgjVe60TTJdCUzBUx9TbheMRgTtJUZE7TxSjeaJpmuZKaAqa8J 1ysGY4K2MmOCNl7pRtMk05XsNOD282DUtlZtr2Zj0QSa4T3BqzpLOw/Rpy0dgUPAnQD9ABN0 F1X0BRF99AoRHwSeI0A/wASfU1uQnRB9kEIQBgQCEKAfYIIBZPhsCIj+Wd7sBoHIBOgHmGBk fd4SG6K/BSuLQiAlAfoBJphSuCtBI/oVenHm9j8BOPp7nGiJ5AkCVzRBP8AEn9BmqD0Qfahy EAwEthKgH2CCWwW4Y3NEv4M6e0IgJgH6ASYYU5k3RoXob4TL0hBIRoB+gAkmk+x6uIh+nSEr QKAKAfoBJlhFy+o8EL0aFQMhUJ4A/QATLC/yPkFEX6/k7U8F1suOjK4Q0GqCfoAJXtFX6jmI PnX5/gq+ryf1rVXfK9lYNIFeMMErGks9B9GnLp8YPPUVEb1uwJkm0AsmyAXxOgK1E6ap1a7v lewwQRs1/j9BG690o2mS6UpmCpj6mnC9YjAmaCszJmjjlW60pUlaxqYDETzgK+yvzLmC4al9 rsRWec4V7tIc6fXKPGe5YYLFq64VvXZccVxb07PUwDJ2Jamn9lmJsfJcC3/NWM2YyjxHuWGC xSuuEb1mTHFMYdLT1KIdoxl/Nbk7174a0xvnaeqg1YRmrbcxHprgB9TxXR1IdVFI+UmvV6x/ 9Jyl93Ta6/OuXO5aN6KeMvQ7L028qa5arf1lgj2kytDeYPRS/aTXtULKMi5DzSPUJEIMT2gq S7/zqofXOk/U5qk9xMeh1aG9Ob/quU/fCP/9pCP6187a7Nx7d10i5+4Rm8cau2vkvb/YDapD e3N+1XPHBK+1i7fq4kMrcu4esXmscU1VcWedmuAbgFXPUXovIa4074ssQ813xrhz7/uqLq8c PW+P+DzWkEnmGoEJJng0tiKpmejffDFkyX1HnDv2XNG359wMua/GuDrfk3eUtaYm+BZY1fPE BP++1LLUfEecO/aM0Ayz5L0a5+r8CLXyjmH6EYljo+rQ3ppf9bzPLpQsue+Ic8ee3k3Nul6b c/T8V+NbnW9lm2H88CMSH1Dtd4ZErsT4lhxHbN56MWSq+Y4a7djzyrXrNafvddHzX41vdb4X 90jriD8dGilYYrETuPI4dGQUmczDTinmjB0N62xPdLFfJ6uaWJ3fPiE89DCjctYzRnFI48/2 60/zUmxtzJjgfl3fGsEVE/wENBPprcGy+BcCXg3LglXaE11YaPqPleoj7bg6f9QbNJoYmdQX I+p+QLFfU3Nzpo2tZ4QJSqpJ/np1E/S4qKOWeEdu0p6ahheVZ4W4pPpIOa7Ov2w0gsn9ZUzN eM0J8MjLYp7/nmolaLyemwAmaK/f2WMZ+2rXZ3g0LOvu0p5vNcEqmpDqq9GL1WismrH0rJH5 zQxxlhsnQU3VE4+xCKp/PNFe+Gd3YzvxeFzUs8cy3mtbOe3YX9pzpAlpjjXvaOPb/Hbnurr/ WT/QXu8WE5z1DSmOqWF1J8R/T3PGk+aXa17T+KSAZxcG//71p2zv5GFt5NLFZL17Gz0muauZ nXE8u3g0Ddx60d6VoxdPTc4z7Yxyy6gLNPF/AtJ1r9GzxQSvmpR0834Ww/GaNldOgpqqJx4j iUlzxzUT8ugG6mlUWqFb42rNcGaM1jWt4+/K7SwOaU+rCe5iZ2WtGV9BE9YDjeZGSNLM6IZO miP1Le2NsLTPn9g0xWdMXgKSmLxM0OvkYiWtEbllzXa92Z8t662M9c5NE4u0p9UEd+lCk6t2 TCVNSPXVMNl1EjyLfXb60+SLCWqqnnjMFRNs77LaU+Dorr6/O+5PjbPX+3VHf9ecIjQit5Rv lHvbyGcxSf8+ahxS7NLrlry0Y6VG0+d5xmvUmNp/mzWug3dvPqO52rxWxlXShJemtEzOesao vlp99fWcraWpOyaooZR4zBUT1KYrNSmp4fXzRwaqjeXucaOLs4+3P/VI+UmnJK+GZWHjsedb dJFREx71tegpw1hMMEOVFmJ8ygSvGEB7xz86KS2k7T5Vim90Zzy6C+5zlk5e7okIC3o0yTNW mhuDLLrIqAmP+j6tybv3m/4C7dkFfHdAnuvPjuyee0RfK7IJzhqidEJaYX5VE1cae3tSPMt1 ls+OhuWx5xVWFnNcqf9o7ps04VFfb/671xv+Au02qKzQpLu03eCf2v8pE+wbiaapSWO8tbei ibsae/WTYHRdvE0T3tfUU33szn3Ex6FVoFXJwyqGO02wfWzVnt5Gje+Io3/C0I49m2fNWzPe qomzE0Ofx9kjvZ5btZNgZl1U14Q1P811lH3MK/5n+TcX/m4TzHoBZNDEjhh37BlFQxlyX41x dX6UWnnGcfo/y1cA1p88POFlWCva47YIzDJoYue1t3PvXfp4iybeWFtJUzwOlQglfx0TnBcw ckPYGdvOvXdfbpFz94jNY43dNfLeHxP0JhpsPUwQE7RK8s2NMnLuHrF5rGHVU/TxmGD0Ci3G J4leen1x+9DTo+YeIa4IMewQT9S8veLyWmdHbe7ac/gRieP5eGZgbQ6Z81gtvJS79Prq/pHm Z9FEhJpEiOEJ7bxNE2+pq0U74knQshhj4xHQiF4zJl5mNSOKVItIsdSsti4rzzp4rqWLPv4o TDB+jZYi1IpeO24pGCafEohYg4gxvUlG3vy916tQC0ywQhVPcugf95xdBFwg+8QQmX3k2PZV 7P6dV7mPrv3VNe/P+vkdMMHnmbMjBCAAAQgEIYAJBikEYUAAAhCAwPMEypng7BFAhZ94fV4e 7AgBCECgNoFyJli7XGQHAQhAAAKeBDBBT5qsBQEIQAACqQhggqnKRbAQgAAEIOBJABP0pMla EIAABCCQigAmmKpcBAsBCEAAAp4ESpsgPxHqKRXWggAEIFCPQFkT7H8zAr8poZ54yQgCEIDA KoGyJtiDwQRXpcJ8CEAAAvUIYIL1akpGEIAABCCgJIAJKkExDAIQgAAE6hF4hQmuPgrV/Cq2 nWPqyZKMIAABCDxDoLwJrhrgpwweazxTTnaBAAQgAAELgdIm2JrXipGtzLUUg7EQgAAEIPAs gWUTHH0WL8Ln8yz/mayE/DDBnz9//vr+/fufk+Hn+8ePH3++j79/+/bt12cMXxCAAAQgkIPA sgnOHhdWOT2N8jgM8CjxYYQ5Sk6UEIAABCBwEMAEBS1gglwsEIAABOoSwAQxwbrqJjMIQAAC Uo/3IDT7eIDH2rvX4CS4uwLsDwEIQOA+AttOghHeM9TEcNUE+x8O0ux1X5lZGQIQgAAERgS2 mOBhEBlKcsUER7+8W2uC2nEZ2BEjBCAAgegEtpjgB0qGZj+LUfrp0JlxasSQgYsmD8ZAAAIQ yEBg2QQtnxNsT4Btsz/+PHv9ANmfIPvx3gbSr9d/TvDz98/3EcfnM4TH5wRnp11NDjM2PQfv fDMIlhghAAEIeBJYNkFtMH1j741PMsKRMbQnyjsMwWPNkRmOHpe2BndmdmdztbVgHAQgAAEI /I/AFhPsH4dKBiedgJ4yqxXRzHI8Y9G/pvn7SozMhQAEIPA2AmlMUGsiKwX0OmVJ7wlqT4Ia 0/O4AVhhxlwIQAACmQlsMcH+fUTNSVAa42UGZ2arLbT0CHT0aLh9HNyan2TMXnlrc2McBCAA gUoEHjPBo7GPmn1rGrM/t/NbEx29l7hSIC8T7OPtY+rz7B/5ns3vbyJW8mUuBCAAgTcTeNQE s4AeneSyxE6cEIAABCCgJ4AJDlhhgnoBMRICEIBAZgKY4KR6vNeWWdbEDgEIQEBHABPEBHVK YRQEIACBggT+AdEDLh4weUACAAAAAElFTkSuQmCC</item> <item item-id="27">iVBORw0KGgoAAAANSUhEUgAAAIsAAABLCAYAAABA8XRLAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA BCFJREFUeF7tnWuWIiEMhXu24hbcgqt1k444BxuZvIASkuLWOf7oEopw85GksLT/PJ7HDw4o YFEgwYIDClgU+LE0Qhso8MpAkAEKWBXYBpZnSk612ftlFQjtfhXYApYESX1Q5wCGrMDpYZGg ADBtywOwtOm1dWvAsrX72yYPWNr02ro1YNna/W2TPz0sr80k3A21UcG03gKWDIy0z5KBym0k yEr46vZnvsPaBpa8WLgowzm9BoO6TgnaIUvY6UW2g6WOMpLzqejCRaczR5S3Rk4hnm5WSwTh IJpu9OQBt4wslMYjsCANTaZ25XB1apGK1voDSRS4Kz2Hsd0qgDTk1jX+DAMs/nzi1iLA4tY1 /gwDLP584taiD1i4Sh/nPx/J3FGP196SW4xhmDsFAIs7l/g1CLD49Y07ywCLO5f4NQiw+PWN O8tCwYIvia3lJwws9fMiOzw/shaN/0cPA0ttOmCZj1JIWADKfFBCbsqVz4+skWzfUUNGlhfl xNc79nXjnJkDljk6n2IUwHIKN86ZRBhY6k9658iDUUoFwsACt61XALCs90EYCwBLGFetNxSw rPdBGAsASxhXrTfUPSyW513Xy7iHBe5h2cMNMWYJWGL4yYWVgMWFG2IYAVhi+MmFlYDFhRti GBEKFjyDuxaqMLDgGdy1oKTRw8BSS4WHn+bDA1jmax52RMAS1nXzDQ8Ji6cUZPk4InqbjGU4 WDyB8ir6NnpwPBQs3G/Vzg/IvyNuDQu1l+Fhf4MK5RZIqN+4tfSztqkBHvle0/1+f1yv1/c/ /bzdbo/0yte8XC6P1GbVQUYWarVEXEHU3syR85Ai3cg4qW+CJB8ZmFWQiDXLGWDhnDXixJa9 npFxAMvkZTHiLKup0hhcVLOk9HCw9NYIVqG/3W4lLBQo5Xw128LB0hJyvdxCjt4ttfa3pjoJ Hi7lazXLtwt3ajEfUuCO3AF8K8JYHfku3p5FpbbiLQtIGteqkxZZRgr31jl+RESSoI5/QDli xCxgNBu197VUwkWmluvmKC1FFi4aWXRstUWEhSrAuKKsXCmUUNz73Gqu249MrB5DW/GtqZRb 3Zp+UnSp91nS3+mV+6Q9mLzPwl2nPk9pyvlK06x7B7ceMP9di6UBVTupvI5lpYy04eZguWYL yCORQLOFgoYCuQRBgkLsqxnDvd9iEGVohsQy2V4btX7SHOqUY217VFrXbJfqJ6tvqGjqEhYq FHJQtQpnbd8CQEtbDhgqRVlt7a2XuMgtLWBurEPTEJVCOCi08y1hvkdwSkSpntBE77Whp58W jan0WvuGS/dfiSxlGimF1GoWbiJSvx5BrX24mkpLJzNg1koAKVJx86rPc+CR562iot0/BbTo c2adutPQmUWR5gZYdvV857xXpqBOkw/phsjSIeOusPwFW0ONzK3OFMkAAAAASUVORK5C YII=</item> <item item-id="28">iVBORw0KGgoAAAANSUhEUgAAAHUAAAAlCAYAAAB8iqZLAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA AfVJREFUeF7tmNtywjAMROn/f3QLmUnHCF2QI7mLu5nhBRxF2qNb+Pq+XzdeeynwgMprLwVu e4XDaI7OSxn2U4BQLzK9D+PHTnJ8UC4cT1AUSfghQaKAJdQExOgooUYKfeDvhPqB0DyXUYBy +y1MLEItFBPBFBJQVmpBRoxAUeBy+70AdnxHRXpXfYLamWnaSzrii/sFxjC3/kJdkWla0nQm EozKix1ZVqnHAFf+SiPUeuIq1KhqZ0H8V6irx8wL1Mw2py0KHvDs+focXm8xo2eVd277na1I y7mZSq32wfMts81aCSr9tbpeZ1yXoGYrLws1GgNVmb3CzhhLd1yXoGbFyEK1lqvsc//yvNV+ l1SqHObecJ9xSLNnPUNm9QnlfK71+3hOiqnduwK2F+OZtFaye997bR7uH6UIRgQ2qoyZhOyA L1tw5LfsWt55aKiZQEbho+rogJS1qVVaFIM1i192m6wz3ee9YK9kc9d89saUp9VMnN54eUqI bkhZ+1r7HVuunK/Zaq5sv1G1VUN9N6nh2q9cHuQMtWaqlQyykqwWlk0+7Xw2Ybwqly111CVa rCChVgi82kYWaKd/hFqgbmf1z7hHqDOqGfegVCuhEmqhAhuaYqVuAFXbUBHCYvtFoFDsA6EW C4pgjlARKBT7QKjFgiKY+wHtyoPlgdTgAwAAAABJRU5ErkJggg==</item> <item item-id="29">iVBORw0KGgoAAAANSUhEUgAAAEYAAAAtCAYAAAATHR0dAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA AdNJREFUaEPtWtGVhCAM3GvFFmzBWqzFWmzBFqzFFryFg3sxJAS5E1cIP/vUxcBkZiA8v/Z3 e2kLETDAaAsReCkoNAIKDMOM6oF5m0eWKPJ6ZYUq38mAosBwklDGMCYKgMEM8tfw17+laim5 PZqdK5aUeF1e+WUjcj6jwDgpUSZMSag5KWE5SatV1R6DGQFlFTNeC2JZxYcm6DNJeQFH9ZgE UufDSesWKXGTh5PxA+bMUTLNRwJzZtm8GhjM1CAeRHjbtr3run0YBrvum2uqScYVy5qU8VKM kZh18BgzqGmabJ9xHC1I0iQlE8P9HwvMsix2LvM8ZxdgEpiUp/yaHrPvkJiE30klLPVesCrB bKYAwwW6GxhJJinPAymt65ospZQA1UjJsMCYb9/3/26+Z/clZ/+fkyiuT8AYIyFtaOdrMqTA /NAiYIxflVphDSvXVgCg5hnbUxUvIj85ERAoBcZlKlorfXI2rxwbWfVfGfBp71YpMRlTYBSY uJhxEXyo0J/mA6XGq8s1J6tSGbgjDjzc4g66kqrrOwZ/VUxcA8G9SsqZddVSOiy/6KsHKSEK TMseY89XlDHuoMl9ZsYZsOQzVUtJ8pHYcwWmRY9RxvwFAabvNyxTgJPRpDfFAAAAAElFTkSu QmCC</item> <item item-id="30">iVBORw0KGgoAAAANSUhEUgAAAFkAAAAYCAYAAACRD1FmAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA AgBJREFUaEPtmVuxg0AMhnsk4AkLWMACFrCABSxgAQtYwAJtOl0msye3DQuUDsz0BbKXfJv8 CfRveV2P+9qXAEC+r30JPPadXp59GIYzl99t7dgvE+RXLoGk3D8vA8txAuDcV9M0uaf8qvmw fyZ6uSHP87zUdf1VUHJvBiCDn+/GwjI5hoxlwzKWsun7fum6zjv8EuNAl8FPE+QYMPbQG+EQ xeM4XgKWd5PgX8hWNZIlkF7IZVmuqeRxwruuZy3vGJAK8DM5kuMFY2epDoTaJDfuvaFPBZec S4GcQ968oIui2AaZAmWVEgpSuGcBaLEJBxb2ZB3jBSoFk1suUiBrthYQUp/OgeGy4ijga+Bo JydFHSUfW9Ld6rzVDh8ezpTU8Roj6ZBVTdYAS52HtLGgVZS8WABYbCS5sI73wg3jTJqcUtis RQ82AFV3mqbVBxz9WzLBmllaVEsyQz3j7E3dRcqJa1GPAZzdJ8dQuIzccp/sk7kTsqZMCmR4 E2rb1jp1djutEOOspHQ9ljnKnn3j0xbXvI0lg7OHRr2qKm263Z5Lfmp1RsoCvGEIIvbbhbZI Ls/P/ArngZwqHThT//XJR0GGwhc+oOQ6uJR5qBSPpYEqyJws4vugx/jbDPkyYqnwKQ5xtr/6 z0gcPKdCznFQV5iDfa1Oad+u4OiZe7whH0D/CZZZoVAvyfYlAAAAAElFTkSuQmCC</item> <item item-id="31">iVBORw0KGgoAAAANSUhEUgAAAEAAAAATCAYAAAAgcwuHAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA AR5JREFUWEflVtEOhDAI0///6NNdnGEIa2GeibclvjjWQSmw9bOvZeZVCJh5LTMH/1U/Q8Be IaVM/vNjCWDs3mhDK+CNwTE+QwKK9OWSpcBc0LOR2PqeUWzrvHVHiACLjKyjOvgnCDhGfptQ FEDPsazTZiaU0pBfI/sN+QgoQoA1KTS+h1f/VwzkF7MvsTTueR8CQg7X82x59PB+0RMksbqX Ne8Ai/lo9q0LdN1lCZVEM0rzfNEJa5rgXVmMEBdVEFIs3f2PnnOZAkiG3j7bEEcVECWALgEr E5YitPx60oxkw2pYLKkeKekm6LHGPmo8WaP/0exm7eEYzIwiK1uZXpANij13UXVPPiyoVwbo /KjEET7zBnGfwk87Fw3mLvsNq5rEECrReIAAAAAASUVORK5CYII=</item> <item item-id="32">iVBORw0KGgoAAAANSUhEUgAAAaIAAAA8CAYAAAAkEL7oAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA Cb9JREFUeF7tnYtxVDsMhrkl0AIt0AIt0AIt0AIt0AIt0AIt0EJayMW514yjyNYvP4/2/DuT mWSPH9InWb/PYzf/PP95veOLBEiABEiABE4RSELE13UJ/MmLtFF49XNda2kZCZAACdQJaPXs 5T1CuzaBFCS+SIAESOBRCVCIAkSWQhQgSDSRBEigmwCFqBvdvo4jQpRPg/dZy5lIgARIwEeA QuTjdaT1iBAlg0f7H3Gak5IACdyGAIUoQKhHhWS0fwBENJEESCAwAQpRgOCNCknuPzpOAFQ0 kQRIICABClGAoHkEpHw0MrtW3ifyjBUADU0kARJ4AAIUogBBRMWjPPMp+9R+D+A6TSQBErgB AQpRgCB7hUi6RCEKEGSaSAI3JkAhChB8RIhabShEAYJME0ngxgQoRAGCPyJE8p4RP1cUIOA0 kQRuRoBCFCDgI0IUwD2aSAIkcHMCFKIACUAhChAkmkgCJNBNgELUjW5fRwrRPtaciQRIYD8B CtF+5u4ZKURuZOxAAiQQiACFKECwKEQBgkQTSYAEuglsF6KfP392Gzva8eTcI7bfXYiixs2K +aP6Zfm98zgZz6W9iudWIfr169dz+jn1SnP//v371PTd895JiMrHzfn72//Me5pJdxIf6Hi6 3hxwefmUq2roViH6+vXrclDWBFewwbJRHr+bEJX+R4yXJ76P7p+Hxey2ZDub6H/jreC6TYie np6ev3z5soaMY9QEMdkS6XVXIbpKzqzMlYj5uJLHrLHvkDuzWHnHWZGzr4RIfgpf25n3fjL/ x48fz9+/f3/js7zU4IXibZ+ucSZbZr8sdiPz3VWIajkzwvJqfT35uPurmpC8K3lq3+LRw3vG WpqRO7trk8ZKYzGDT09ccp/enG3N+eaMqCU0vSKUDEhnQ/L+kJbo3uT3Ak02zD4zkzbP9gEZ D2njZbW7vfRBy5ndNq2eD81HKUK74o3Oo60BtK8Us9bfaDxGc+dEbar5diVbko2enEVzQBWi NFmtuKIDS6ifPn16dUmsNU7vHEiSplP2ZMvK12z7kfGQNit9njG29EHmjHeOCEyQfDxdiCyO teNWPySevWOM5M6p2hRFiJCczb6g8dsmRJ6zBtR4JJG1Nu/fv692nTF3bXf4Avv/H82AFOAP Hz68CGVql+9lITYhbXp57epn5UjJrsXRuwjyxgsZcwWLnnzMrGba3OJ7qji3RE6zt+Qiz6xQ Zqd8jSJEyU4rZ2UcrLV7SyFCinYpGpaAeC4tWEn+7du3l+HSpYUkStrZqZawiE8riujMMS0h Klkg/iJtJF+0z0q/W/lUimxp6yy7W4XcUyhn8LF8auWL1rfmm7TVWqMzfPOM0VuLPHN427Y2 CFaO1mLzrpb4WuCQYoEUypPBthLcG5ReIdJY5g+MpZut6MJBxWrErx190dyycse7cMvdGiIC s1l4C6oW71Zh0Hi0fGgV8bLIaL+jbBARtfKhNQZSKHuEdWXt8Nhj2WEdR+PksQnJy2aejggR 6pCVVDsLABIkbzFriUE5FloAKERv71N6Cx8SZ5l3pfh7+6NrQbazhHV0Y+e1CxEiZL1a4oiu BaQdWl+QmPbEw8vY074nHoifHhvQnLXicFyItGuKXsAj4Mq+reubvXPUdmeehEht85OFd7s0 p3Fq5QzCFWkjNxDITr03R2r9rOvtO4WoFGJEbGqbA4s9WuzRcWS7Fblj2TI7L1obL8sW6/io rbWclesnb8LLdVar+38vzeVOraJaDlxbxLJNapduwGtfr1PO2RImzaHabjbbVS6qcuzWU3M9 AZQ+1IKhcdF8SPZ9/PjxVg8raNxlzsgcsGJlHS8XuhabWv7IftpusTZezc9aYagu2uKhF23d 9hSaFl+EpWWHtYbLdWuNZYmkJ3dq6/JEbdLqK/KeVvOkkFn8a/PIsXM9b+VsuXasevz3eE/S 1gaXRTiPbT3X71lwNQFsCWO2A30GvpeJVpSQ97Lt2odt0SIwy+YT42g+Wjmz2k5tYcrF7c1F 2R7JRyT+K1nMmF/bnM2yeUbutHzcVZu03EIZ1fhKQa+JUa1uS0FJ9iA5i9pd+vzqYYWeAWoA S+dSgc1PhFlKakGRi7kURZk08m/Pp4K9LLT2tSSutb2DEGk7UI0TkjMzYtTKx/KYllutXaTs K4tCOo7m4wwx6GE1a15rXfbYptWBPI4ndxAftdghdcpTm1r+WHxafGfbieasZfOb9eHpINvW nJRQ02diPn/+7J4KgYjakCZPYrj7u+bKJLZ2XtrXrKMLxQ33YAekMPXmzCy3ehZ3uSlr5WVu 58lHJA9m+T5SFJEN2agvmjCU8+7Indm1aSR2PbmK2K/lgSdnUZ9e4ok2thLMKi4939qKwLLa lMets7IRFqv6IosWabPKvt5xkULdkzO99rQ2WXJBWjmHto+Yjz18Ja8sJD1joX1W586MHJi1 blt8Z9u5ImeHhEju9Mvk0nYs6WEFzxeOyjG08eUOVNqUC0J6P+r/J0GSFWmDLuBd7RAh8ubM bNtbO+9WrmnxkMU3aj72Mpbrt3cctN/K3Jldm1CfWu00vrPtXJWzQ0LUA2/Vf/hDbPGIIDLe rjaIyCBtdtnrmQfZGZ/MGY8v3rZR89Hr58n2j5o7p5iuytntQnQKYOR5EZFB2lyRASJEV7Sb NpEACcwjQCGax3LZSIjIIG2WGTg4cGTbB11ndxIggT8EKEQB0gAp1Eibq7oa2farMqVdJBCJ AIUoQLSQQo20CeAqTSQBErghAQpRgKAjIoO0CeAqTSQBErghAQpRgKAjIoO0CeAqTSQBErgh AQpRgKAjIoO0CeAqTSQBErghAQpRgKBbImMdD+AiTSQBErgxAQpRgOBbQmMdb7mYP8czMkYA hDSRBEjgwgQoRBcOTjZNE4kZAlKOSyEKkAg0kQQelACFKEBgvUJUilRLsPKxUvAoSAESgiaS wIMRoBAFCOhqcSgFafVcAXDTRBIggc0EKESbgfdMt0octEtzq+bq8Zt9SIAE7kGAQhQgzqvE Qbtsl+eSl+0SptZ7UtRW2RwgXDSRBEjASYBC5AR2ovnOoq7dN8oiVN5L8rx3ghnnJAESiEOA QhQgVruFqESinekgZ03a2VMA1DSRBEjgAAEK0QHo3imvIETa/aTyDKkUJ69/bE8CJHBvAhSi APG/qhBZDzvstDtAGGkiCZBAhQCFKEBq7C7o2kMM+Z5QecxqFwAtTSQBErgAAQrRBYJgmbBb iCx7eJwESIAEZhKgEM2kuWgsCtEisByWBEjgEgQoRJcIQ9sI7XJYALNpIgmQAAm8IVC7vP+O rEiABEiABEjgJIF/AcIfwejbMNOkAAAAAElFTkSuQmCC</item> <item item-id="33">iVBORw0KGgoAAAANSUhEUgAAAI0AAAAWCAYAAAD9/x8lAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA AtNJREFUaEPtWouNwjAM5UZgJ1ZgBVZgBVZgBVaAEViBFVihhxGpfK7/SWk5GgmddE2c52fH z6T8dI+xmvm4XC6rzWYzc5SfA6+aT0iaOY/r9drBZxntGAA+b7db2uAqvfJNC/f7/Zt2+q5t anidddLc7/dut9t9VzTf5C0kDfCbGYOkeSgz9DjPz6vfydjtbRQ7GSOn06k7Ho+Zpf0a7E8N lhoQlNPCK+a5xn5m7fl87oBfbUh8/UkaOinrFGcn4xhUmZp+hnN6ysShHEyFBXAAr1oV12Lf J42YVa+Kkwl6WZMl5/GNKV9CFdxZPK05mAJH8QGkCfitqjRjOqAlJJVCTRq5uZLTmj9j+hrB MwUOjG+9Xg/gcvwPeK+tBtbps4jRpEyTl6hdjNNaa/mUeU57q6z0Z/b2JjLmheIrz57/r0ka ugkHzupvNBs1kvkfKs3YyR2JTWnen3+tpLGkJaOJUkNoOWFh9VaTsYPhOTw4CNL8sXFafEvP zW9P2aTwVCGaBBSkpLkW4VpyjR0IrxR4fBgbK+WXkycsSwWzeU/DVQVqSJqDddzSYo4g6O7x dTfWWU5zORu0l9CqnOaXtp+FheNB4ob6yB2sVji5b0/c/gPerWBKJZ8SFbHjLd/Re5poH0MD ZwWMVgcreFFOuJPen25y4crtLVV3bq51T6MqTMQxSeMiNrwJA/PgxvJwOLjMe0q5dOI5OeN8 5Q6KVN1coMkkjV8rIbAPHpyeG2FRaiPOtU4aqUQXTHABtd1uIxDDcz3BkOZ4+pIIoNqkieCE w9js3ZNaltAtqxXwCFna3Jq3sR4MNUmjyZNnb6m/kiTJqoiWL/i5t4KzyhB1TtL9qB3vfGiE rRdrXltckAZNHnpRy/UY+LDgpPHIowcnxy89oBJmmmzSutrfKM36pxGFZNDfZbRjoPYQfkTS tKNrsdSCgSVpWrD4ZTaWpPmygLdw9xeRsBYNAyj7mQAAAABJRU5ErkJggg==</item> <item item-id="34">iVBORw0KGgoAAAANSUhEUgAAAHwAAAAWCAYAAAACcfiCAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA AmBJREFUaEPtWe1xgzAMTUdgp6zAClkhK7BCVsgKZARWYAVWoFGutEbo03ZsesF3+dEaW9J7 epIxX/NznAqNx+NxOp/PhaytzdS0XSVgzigQXmIMwzDDr9YA2+M41jK/G7unUp5cr9dSplg7 e/ChNghFCJ+mab5cLrVjnYFw8OWTxy/hz5IPvfz1o4Y2L4F4v9/n2+22eSTck7Obk5y+72fw JfeIxY3CVMKZsuPlZcWuxfEYsEDduH9TBL+bdPAhd6WRfMZzqX//HLBJ4Vh52RBObbo4GkvI 82S+KqUekKyBWJ6Dcg6+5BpePMLnJXEt/nGi0BJHiq8I4R4HvSB6yWuaxruEbXGLOKw+e6qa 1FqxKK32X+vCaDglpyp8T4Rr1QWfKyTgsWJFZTFnI66iaoSH65IJ5zbzEEclklSqLHM5pOkB x0Ogtq+WaJQtrSLECJFUuJVwKwGeRNGAs9rkntOA9yhcSmrrHKVwrYfjvT2YFSGc6pta9qYS y63P3cO1yqSVfS0BcRypuInv4djZJfupIMPygp2CkzF1rYnVxAUX2uUCpnylDlW5T+kYE1wd qRjD/1FqpfYMY/FUzA2msUrCJEjJQL2HayUPg8KRzimI6m/veA+Pxa/WuqSrValchXNwu9V1 nRgjlfUamWGlkZS2PPeum7Za5MXYLUI4XHi0bev2z0K4NenAOCTdcZfupuFvgQfsmC9VKYRT JV2rMglQ/Jul0QrHhxGtp8OhzfPhQuvh3IGSW1f7e/xeMiKa8JgAoIfWGp5kq+VjCbtFCS8R 0GFDRuAg/MMy5CD8wwj/BgVxQ5m5Cde6AAAAAElFTkSuQmCC</item> <item item-id="35">iVBORw0KGgoAAAANSUhEUgAAAGEAAAAWCAYAAADQIfLaAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA AZpJREFUaEPtWAGuwjAI1avsCrvCTrtLzt9GTOU/KFDXacKSxWi6Au89Hqv34++65XUtAoWE vK5F4HZt+IxenShhuB6BFwn7vh/rupb5UO9t2+pN35dlOcqavMYRIEzLJ+wEIoBCERHjoXOH 50vQGxCVEA5NkjBXLEnCIN5kJyPbJAkj6DXPth7P/V4LMTQT/g2W54D5UE1wm2hMDyhn5o/2 DpPAW5CKtBQQbd9ozPY5LXY0L6o50glvuXkHM0rYWoR1HcrJ8ltkDXpjsQhqZA0Xh3hOKGeC chPL5QxB5wRJ/fz39juyhVc7NnYm2Uev46TYWk5EgJanZmcRUcGuGWUUWUW7p2QJGiltmyN1 azE1C9JykXKe0Skf+dsiUrh0cNHA6AGFVM+f8ZIfUbtX2G4SejPBqlIPCZ6YVkH0yEZW6QXX uj5EghVorjpemNXK0DywWIsnTy6KrycBDTTU9qiwtjgJXMlWekMSDj12htFiRt+urIqX1rk7 YTTgLz0/Yx5Uof4SKDNznUVAkgBY1SzvLBFkJ5yFrGPfB+eB17O2OZ+ZAAAAAElFTkSuQmCC</item> <item item-id="36">iVBORw0KGgoAAAANSUhEUgAAAMwAAAAvCAYAAACv8iXeAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA BSxJREFUeF7tXYtRIzEMzZVAC7RAC7RAC7RAC7RAC7QAJdBCWqCFXJQZB0eRLNsrr/bzdoY5 krUt6a2eJdle7t/pfB1wAYEKBL6/vw/Pz88VLf2bRMq+sYYIgwsIWAj8/Pyc6CfqItnH4zFK /FXuIVwDKLAKBN7e3sL1XIIOIEy4Gyxfgd/f39Pr62u4okQY0iXyAmEi0XeUfc6zqRa9/EiX db+kyufn5+nj4+OuST6mJtfRxNPX19eJdIm8QJhI9J1l1xCmRyRFF16/SAQZTRrSITrSgTA9 HrTQPslhueNq39eacV4Zu0mFSsQYSRpKx0iXyAuEiUTfWfYowmgE1FI/Z7Nuhnt4eBg5vDk2 CGNCtJ4GeyDMyAhW86RBmBqUVtImdyaJPC2RIje5pd9ohx49vvWoQRgLoRXdbyVMrWkgzB9S IEyt16yg3SjCSHVDxCoZPQLUMCtwxDWoKO2zcALxZWeNYJwMtDIlHUux9mHytLC0UifpJfWl 54BVsjV44wZ1zJ3UIpu0D1OqcegeH5PLS/0l0qb++b/0++73YWjnNuqKlB1lM5erOSt3VNpd f39/L6ptkc6SxaMM14E+73qnH6df42ljOXHSkDYMX15emhW2ogcnhbW4QKTd7VmyJZw8XYIO zV7o2KGWMCSyB6sphJFqHivKOUKjDhWySobTr3M82rIMXrBbNQ0V/S0HH60aRluQ0PpFZyTX misvvizQvB4zTr96ITnvOJF1XwtZR6JyE2GsHNJLEZx+9UIS48yNQAhhcPp17scMeV4IhBCm JZLxtl6Gp3FKO8ejZXvbgvHGI3BX9Jd2ZL3UWRJhakhRKpC9MME460AAhFFe6Z3y+DSC4fu/ 16jXiMVl34g7xogIY0WU0ixfEwGmOrfVHxHGQmg/98V9GG8n5ePh9Ot+HGxrljYThm8sXcIU +2sl1n7OWk6/ek8cW3OePdrTtNNfSq1a7uH06x5dbRs2hxAGp1+34Tx7tCKEMDj9KrsaX1yY 0yFpEnt6erqm15Q200/S6fHxseks2Zy6zylrCGGkA3Sp1knG4fTr7WOW6qWIGopk5m81JtLM 6ZRLltVEmLzA1x6wtCzNFwVoHJx+/XOLyGV1aVsBhNEp20wYT/Zv/fQrpZ6UyqRZWnv5CYTx 9KqxY4USZqxp8aMTEdJLT7QySOSRLhAm/lnVagDC1CLV0Y6IkKIoFdU5MbTfpRSpQ3RVFy2t tlIyrUatEtrZqFemtG/YqcKlGwgzBT2jb+6QnDB51zVFGGm/rXZxoradNWlINbEVub0wBmEG Eyb9NxGllCwtpliOMlDV69DWKtmU1Twvwmh49eLXohcIM9AL00xIKQ7tcVh/8cTah8lXIKXV yGQKn4Hzz9rszPdh6HOKitSH9E+vCWtj1MiVUlH+nTS+FVU02SWdEvEkfDjW188D/WX3QxPI Pe+ia7O45viSE/JZuESw3gelObaWblrE4HaXcKiVMbXdHY69YKGfjUAvYaSZTyOAlJ7kkaq2 VrKt0VtohC3prOldq69ELima8PEkbEpEvbs3BSj0LSNAYHvuNbVEkhqH7Hl+2qyfp4M1s3qN fqWo2IqFJE/TOW8LwvR4yUL6tDqJ1F5y+BbzrDSslHZxAlgpGK8jaojYE+0kYmpkRdHf4i2B bXkqkTuulIrk7UttW03iTmzVGRKBtJTTSkW1VJP347ZLaZn1nRaRQJhWj0H73SAgTga7sR6G AoEGBLTUFRGmAUQ03T4CpbTvkqZtHwJYCAT8EPgPTH3MTGGK43YAAAAASUVORK5CYII=</item> <item item-id="37">iVBORw0KGgoAAAANSUhEUgAAAIMAAAAlCAYAAABh2FqVAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA AtlJREFUeF7tmoFxwjAMRdsR2IkVWIEVWIEVWIEVWIEVWIEV0oo7c64iW1IsRw5V7nrtNbYs fz3LipPv6ff6iisUAAUAhrhCgVdSCBlCgaRAwBAsvBUIGAKGgCEYmCsQmSGoiMzwaQykB0P4 nf+tmWdkBo1ag7ZNALQC4QrD7XZzk9dz7B6Tzo+MSn9z47rBcL/fJ/jxumDsx+PhNbz5uJuG 4XQ6mQuiNTiCD1qfS+03C8Pz+ZyOx6OVDovtAAzgy9YvrmaQvnV4bxMlg1JDGkGv1+t0uVxm XXIfeoyLB4S6AXyxurCGnF1qjvnTAPTX2uTGrN3/UzNg53oFBLICrhdKwrRMjusLPlhlKK12 OOgp8LnPWpvcfLn7LjDs9/s/6bkGXS8gQRjYIsAXi0sTOOocQLIYemrxgtGDxCXCWQSMsrHb 7UxML5kThoLaImrxMXE8MzJ7tKSoNR8UfUbhlRmo1IzFx3VMyVcpDFTVX4JAatMqPgGD0fc9 ksCV2tT6Yhhx4ClYpQDPbFHGuRWjJZET6r9khlrguO2gd70wqxmSQ9YDY3vUPi0poLQQStp7 1AxY55bFIpmjtA15HM2tVGqPw3thXnvg9lDBU0fBkpSYslattqn5R/kiFYtrl/vPrXScfam+ 2Fdu/Nb7qncT3N5WEgD3o84ZOPGwWLlQGEQqw1HwWJ4ztAZihP4uMMCp3/l8rs6/tFJqgc7v UasKQ2l9AjlCQFt8cIEBDnsOh4Pab00GoIpgDAMA+QnvJtRCFjp0gYFK6Tg4S94YtsBAbRNc drISeSt2VDDkxVup+qdEp1I2FJCal0RczUAVsFRBmv7n/T3FiICoYbCchOfXRhoQLefc21bp iUYyrisMEgejjVyB0jYqtRAwSJXaQLvWc4mAYQNB1rq4FIqAQav0wO1jmxg4OGu71lI8vp4U 13Y4xhtXgYBh3Nis7lnAsLrk4w4YMIwbm9U9+wFzNc8vzerBMwAAAABJRU5ErkJggg==</item> <item item-id="38">iVBORw0KGgoAAAANSUhEUgAAALIAAAAvCAYAAABHcpD1AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA A85JREFUeF7tm4ttQjEMRdsR2IkVWIEVWIEVWIEVWIEVWIEVaF01VWqcj/MhTrhPQmof+TjX J4kTt5+P7+cDDxSYXQECGQ8UmF2Bj9kHAPuhwE9UARmgwAoKAOQVvNhwDN+hMp2Z/j4Nm+7a FEDuKu9cjRPA/JHeWRwVQLbolQE2xYCdAWaAPAAai10CZItegU1qBQCyWjJUsKgAQLboFdik VgAgqyVDBasK4NbCqmdgl1qB1D2yg92Vow5CE8B/z8u3vgnBrYXa1e9RIQZnCmYOsFPMr9da xbcA+XK5tNYtu72RfWcbGSgoZfhSkPrQ+qswh7nWNl5/eZCv1+uDPqMe6vt2u43qvnm/OSCH yoTCkBZGLg/y4XBooVNVGxZsqBqAV7kGZIQWhV643++P/X5fWLtdNQKZbJn94aFG7ADHD43D DnutT5UjnHg+nx+n0+mp69TJvLWtFCeTLXj6KRAMLVYAmVZjHh+PuCslGyzsDP0wGt+yCHLP WOaVQ95ut/+29FHZKworyBY8/RRYGmQO7iiQyX2bzSboxRV2v36I5rX8BLLG+XldjCulGUtv mHLajx2Qxqk4R88iyDWHoRyH1UqT28dsIGt1CYH/bu9Jt38ghwDJBUfriN7lZwMZK3I5EUuD LMWlI24tUjFyufvG1ByZdg/1/Qcyv+x2Eknv/VuN2KoXK/ezHfz+t67vDqlOaR90UyClh1Oh U44Nkv3+ePjYYrcWM+14VlP+6hR1Ltixcg4Cf7L4YMRAkmDxQfB/lu6RpUnD34WADPUjvfff rXSPbCHdLtmgBplDGIIyp1wJ7LH++HeUTTsej9H9t9QGaeLxiel+XyWzZznlPxRkiTDNqpea LCT8brdTB5ItbaDOaTL1+lsLPhFDIaJaBKGC5ZR/Mci5zs4tJ61m2roS2CVbobbfVGiR2hVq IYudIWrb9utbTvkXgxw77PgxbuhQl1ufx8v8oJaKmemwp/mDndAKl7I3VO8Vh6NXgWw55V8F cmq2WzmNj7wu0kyilJ6h718Fsqaf3r7nV6tvAXIpIDPV47tgD9stgfxki3bAuYeJ3HLa/lFe VgAgg4wlFOgBcmoFjoUPvUOL6hV5Ca8vOojW8PD2LKf8i2LkRTmYflipFdK/4Yndwvjf+W1a TvkD5OnxTQ8gFiJovrOc8gfIaQ6mL6GBVUruOAEsp/wB8vSYpgfQCmTLKX+AnOZg+hK5IEvZ SRcvOxGspvwB8vSY5g0gdq/P0/yuRX44pPdWU/4AOY8DlPIUsJjyB8hAdAkFAPISbsQgADIY WEKBL7m9q1OJKy56AAAAAElFTkSuQmCC</item> <item item-id="39">iVBORw0KGgoAAAANSUhEUgAAANwAAAAsCAYAAAAOyNaYAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA BONJREFUeF7tXIFxciEM7j9CV3AWV+gKruAKruAKruAKrtAVXME2vZ8TYwJBCATMu+td+4SQ fOR7CXmp/26/14dfjoAj0AcBIJxfjoAj0AeBjz7L+CqOQH8Ezudz/0X/r8it7YQbtiVtF/7N h+Bo4D/WMWi77S5tFAJANr/uCOz3++FwUDr4Lg3fljYKOOHuOF6v19tut2sDbIUUIBzoEl9O uApALU3NES73ebBFOs6S7ViX0+l0Ox6PTyrilFvbBjjHgS5OOG2kB8hPESU4mkStFQgH0e1y uTw6OpFya9sKOuBI6xFO4oUTjOGcJ9yXOFfJWMuQbLfbh1Qu9zDSsgXSSdDFI5wWwgPlOuHu 4GMsRhEONPr8/HTCDeSFytI5ssGiuQhX4qQqRjQUWmJLDpdatZ50qRXo88cjQDlNyum48TVF BW3HlTw0wk444cb75NIaSAkkiYQP541J3+054ZZ29/HGSaLLK+cYidzx1j9rgM9NXHTsYZ+f 4Sx6SKVOEsdJRTfqtUGcXuJUjRsvGRecn4tCsWxJWkzpApXB7+/vJ1RzKXNYL6UDpT8eH+vt VcpK57Y4XUK4Wr2lBEyNw5EGO3jK4Smnju2Of6few+VSZaw3RzpuzVj3sNZbvYez2Cle6/Tc /B6Eo9KynPNRRQwuykhkcWOwbtDdcTgcknC/8mCI7clFeRj7Np0m8GTBnQZazk7JhbWplEZL h9kIR+HQknDwwvnr66sY7pY6wOJA+rfopbTaKV7sAcIJPQkndUrpOC4Kltynou8rPtBC51gG FWWXa+2y3Cku5E/xsJ6ESxUN8DmGSruk86nzHU5HuTMgrAEZBm4cTgGbO8NhMuZ04bKsJOF6 bWSxhyUmWO4Ub2lnLKvXPknXkY7TwiPIHXmO58jOEo57OmmDVCvfcqd4rW3c/F4OLl1HOk4L D8tyl4twljvFtRyhh4NTVT2u+DHrw1prfx6yEbxIDFaPjWxtJNY5ZYO2fVTHA1UMqMFA24Ya 3XzuMwIPEY46GM4GmiXCSchAHb4l81oTd7Z9nlVflnChmiQxrMRBJPK4VEUydzbCSWxKjaGw 50js98d+q9kfpx7yS9Qd3oNItQ5HpcQpm6SftdBLgp9HuBZIzyMjm1JKnMaSuZY7xTVwmm1/ NDCYSeZTlZIrmoSNpSpQ+OwX0tFUukNFmlTBRrqG5U5xjejqhJuJbiil5FSnSsIUAVPj8JkQ z+fkYfLm1rDcKa7hGk44DVT1ZIpbu1LFCCr6BJWpyieOktx8yX1MZMud4hrb6ITTQFVPZnfC UaZIiCUlteVOcY1tdMJpoKons4hwUmJIx3FRsOQ+jnDwt9VOcY1tdMJpoKons4hwVDEEn6lS xRXpfOp8F6+Diys4RbXaKa6xjU44DVT1ZBYTLqeKFQew2Cmew+6Vz63g/Yru1Bw4ElAP13Bv 5D8Wt7BxWcK1AGcGGasRDl7rQOEr/prwlWwUEY4qxXPFD5zezeC0s+hIYbuSM8I+hAgGpAvf eLWSjSLCzeKQ76Bnqlq7kv1Q+IoJN/KI0BJXJ1xLNDvJiotKnZbsvgzYGKrNm83mBj8rXE64 CXfxXQgX0kuIbquklU64CQlHvV6Z1Iy3U9sJN+mWe3Fqzo1zws25b39ar5JmTbwFxao74Yoh szPBCWdnL6Sa/ADcyc9Rms3h3AAAAABJRU5ErkJggg==</item> <item item-id="40">iVBORw0KGgoAAAANSUhEUgAAALgAAAAlCAYAAADxyyNaAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA AtRJREFUeF7tmoFuwyAMRLv//+itqZaJMsD2nSHEdaRJVYLB93wlQPf1/bweeSWBqAQOg+eV BKISeEQVlrqSwGt1khiSQGQC4Q3+XFoee4zX312vCBpY9iiD+1bdQOzO5j5lRtBgKFmzKcIg Dc5SXxSPFHdRasuGQRikwZeVhxsIKS434n7RCIM0+H51dHs930SaOs00eAcVAkZNfVHDCBpY VAiDnMFZ6ovikeIuSm3ZMAiDjzU4AmtZJRsDtfK9mwaWH8IgvMHL81P0LJUtDBsfQcNVDP4Z vIbJJtaL730bWz/K7GjMXk7W+7P4ov161GUnBm8GR14BCMieicu+zlzqnHZ4Lfdyst5H2M2M 8ajLbgz+DD4yzgxTScbd2eC1yaRcJa0zTWvtW8oV1Sr1a81T254yOGN8SbAEUitwRTspV0nr ihy1Y0i5olqlfrX5WdtRBj8HK9dc2vWyJFgCaRU6q32pY7fXM6KZrctuDFwMfgXIer3e+5Ih 9y16vAyO5DmKsWho7X3KycuyN2IMPoOBi8E/dQZnZzvUhDPjWE2MwWfouuQU5RDCgpwBw9Jn a+berbgWPdKMXT+3apXqjeSqiaHOwdFNZm+dbr2vEejR5sxrtOdoPestozxysvYxWkqhuVvr pd2fWbWN2nd/yUTN65ncTn0lj52qoc9l+FP9Fd84ferrWqa517H2Hin8/6LUwCKYNYIG1sha Bh9ncBZsxt+LQBr8XvXKbI0EQhn8fG2Vpx6j46lWeyM/9+YRNLBQPBmEMXi9IW6ZvL7XOv5j i8PER9DA6D9ivRmEMfgJpzzTtXyu49lCofG982rN/V00oNot9Rqd6789Y5PZKV5jAi2Yq3RF 0MCy82SQM3hRDe3RE1vAUbxncWfmObNvTwZhDC6t3crn9efW2m9mAXt9R9DAcvNmEMbgLNiM j0kgDR6zrqnql0AaPK0QmkAaPHR5U9wP5jQHTlxJx70AAAAASUVORK5CYII=</item> <item item-id="41">iVBORw0KGgoAAAANSUhEUgAAAH4AAAAnCAYAAADJovy3AAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA Ar1JREFUeF7tW4tuwzAI3P7/o7e+UlEKNgeYOIkjTVojIOc7wDjrfv9u18+6rsfAXfh1PRm4 qV9OxR7PfKy1fKWTPnAvAXZLuEl1KIW1p+jbQqsxXL7iEcIl2/u97Ydmq3a/ldEIlmhlLOGN +7omLhdbat1WQa12UdEvv8ejRHN77XPPLrPqPZ1lCW+sdm0fnkF473B46VZ/hopfwoMbHyq6 Ze/eYkZavUdI11pAvk5j7iKLbQ0jWv0SfnCKLeEHEzxreFR49LzunbZXxQ/OGFT4wXA+wqPY tBdLzWNj5YJmehZKbiV2BBvtLEiX+TrO8UCVC658FkJuJS5Pq/fg+xDe0zI8D53BZwn/UqFF xMwkeZNo5jVVYHtX/FGF95Lk9fMmGuJXge3wwtP36NKgoxFeQS4iNrWtwJYqfAVgL5ncb2as 0htBbXr33H8MkLRiItWxAYgIExEDPdZEnhVZo8W3AlvqVB8FHPW3kGpJdCTOCNsKHuBz/AZK qnCtRUn7F/enFcvtW93ES5LXb4TQe2xD6t/jJWIkcahd73fEn7/IyBYqO15mQlRga34RoydU T5ws/0xSV6t/MgB/A0dq5xKZverv+UjtPjMBrFVl7XxbEWhbIDL8WrFJfFh9XcJbRLXYaB2D zhGjKtRCkHWO4eugn1uFEjlFRYvAJTzNbipMa2CjbZ8KS31a/tGFegeonnBSkp5a+GwhquNZ Kr5VybwTaQnSSxykXfOC6eGT7N+4UcKthKFx97C3rKUnXHbFa5j4oMw7Je/C3P5rW0UIl4Ih /rPZHkl4bR6y3g8JP5twUTxL+CiDB/bviV/Z6hEsllNTCzs81R9YYxF6i2xta8u6j5w0ens8 PzVp9u+Z5GxCetbTqzRPTNSnGsPlK54fy1DBMuyrRX8MehnAzxJjFwHA/9jN4noJn8XkweL8 A3dPmjHPctWSAAAAAElFTkSuQmCC</item> <item item-id="42">iVBORw0KGgoAAAANSUhEUgAAAF8AAAAnCAYAAABpPZRZAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA AjNJREFUaEPtmdtuxCAMRNv//+h2N1VWhPoyY7DIxSv1CWPgeGxw+v3z+n3Vbw2BN/z6/RF4 RQBGwdhqTvHV4G1d0zACMzKnpVPwScX30hoJwOPhW/DeY/uflc/RABR8pc73QL0gRYrto+Ez ivVsvXEpOAUfkCwCFrH5d18Aa9/WBAGG1Hz2mboDfazyEfCHZyHQA9A+bytr52A0qII/TyoF fx5L2pMHv33je7afGg5kR3W4g12t+q2m4GNJgKoZ88Z/mNteSG3K9KmWsUHmMJm2GWdjuuID fO2tmrHJTKio74xzsT4P73xtMusUBbDSLuNMrM/Lw2cPHH2ZIEKxys4+1nbMl4cfvbOiQbOC 0Po8QBY+TW/jSAuNbhS1Q1SUbZOxV0T5B7HMgo9+gEKVw8KXXmoW4EvAZzbJ2EpwR+czActY K6x8VDnSxaFdYlI2aPPb9ftstLIqCjE6D81ci+dnjFVLD6K/ZKRA9MCROX3fMRvWbH9an2QG i4EvLaCB3G2tYHmAZ9wj2vluDX+mujOCgMDXSuGsgNL/yRopO2jmtGUqqyny4FuX51L4bUmR mhyv8dHuACmwGapH6vOp4bN3xdnsPeW3AUJskYD2DEJl52wgo/tBoCI2EfDbHGbj7AXE+F5h 64GV7p5Z9Z6GvwJQ5poWfLbme4GUzkEpPxPEKt8aNAZ+BPzjle/VarTMFvyB1InC84Lnbenx ZWekiRsJWpWdTpoMTMZWfSF5qVHjeQR+Acx1DN4pejQOAAAAAElFTkSuQmCC</item> <item item-id="43" content-encoding="gzip">H4sIAAAAAAAA/2xUy1ITQRS9PZPMi4SECEYy0SgvoyKElvBaIUVRLlAK/ABqSEYSi5AYBotl yg/wF9y48AMs95Y7y3LrP/gLLuJ9DOFRdOpO3z597rl3bk8nBQAK7TWax76JTyd8txceNtvH SaCxiObW2zXBEoytEq1V2zl4G9YiRmCP4w18WieNzkb7TOBtNBuBvQHxJdoLtC+Y8Q9m/zt0 sabRiIsywDDZyQ2SP4+ibvPgNAoVM9fQMnB5JNYmrqyTLJCIumHoM6DR0h12LT3+HaCXtPUn nNctR2bbldnxZHaHtIFJ3mN1lkpqWII5bMkSQIrxb+f4HGKLsEL6HNhzhzVVt5GmKtn9kObE We39wH1nRNv/MEE2p3O/cR65xYL1HJ6FW0e9DjTRQmjhfAwwqj9i4b0cjDHv8yi2SO2S4G0B RgBMg6mQZ+le9o5I5vEEnU2WPIIAGRGcAIxLWP4iDKAgtdu+vHyhKLN/V5pQpJyqAnBPXn6w hpIE+vflpUsIPZCXLrHwhCwKvJgUsjUl6pPTMk/NaOsnBk8/ZPVohtUXAMo6+Qv5M48EL2PF ylgAzPuYka9lZtKXqp5IZdN47MokDlU3Kwmnnkp1swjNSUGzXNC8HMlkRY5kfkH6VolbrKWd lWcCa4Lxw16UBuIyYcaHBFAV7vyScKvU+t3rrV8WUnVFSMux4KoILl8RpO+crlWmrvcPu0Gn sf+m3W0Fkc17Y3Q7Gd4MokD4hfjrpyi+0XQJsCFQZJ9WXnDWPNliHbnRtLWjYq6MPv5AFW9G t29Az8XtqBvUQr1p8pYfE0sqC8pXffKUIrRfdJUylG9icEmZyk+gTEklkAV9dJLKN3rruGUR h+Js5qDjEIfILnI43EMOhw8hh8NTyOHwNHEoapg56GSIQ2QpOPPqtBV2m7XgSFricOH0p1TH U2hyD+mPxov7eW2kLi/+AwAA//8DAHhRAu5UBQAA</item> <item item-id="44">iVBORw0KGgoAAAANSUhEUgAAAY8AAAFCCAYAAAAJ7FGNAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA KQpJREFUeF7tnYt24zgSQ3f//6N3O0k77SiSgAKpB8U7OXOmp40CKVSxIFK289///fnnP/yD AiiAAiiAAhUFPsyDf1AABVAABVCgosB/KmCwKIACKIACKPB5YoUMKIACKIACKFBVAPOoKgYe BVAABVCAnQc1gAIogAIoUFeAnUddMyJQAAVQYHoFMI/pSwABUAAFUKCuAOZR14wIFEABFJhe Acxj+hJAABRAARSoK4B51DUjAgVQAAWmVwDzKJbAn0/v/4r4+LvXv+8vbv19cUjgKIACKHA7 BTCPQkq2DGJpGB//vzSZNdMpDA0UBVAABW6lAOZRTIcyhdfrClccFjgKoAAK3EoBzKOYDmUK jnm8H2fx539HfmiBFtRAXgPFVtYMxzyKEvYyj+Kwp8P/lPDhY/YYI+GoxLhYhdt7feu1UY46 1bW3FlIP/pSjEudgFSapk7Vj8lbNnfjjO4Qzi4EwmEe/ZKmF5IyUcFRiXKzCJU0B8/iqAKXt UXVSHduZp8IkdYJ5OBVwAwzm0S8JaiE5IyUclRgXq3BJU8A8MI/3NbBbQyvvAnXWTwuGnUdR PcyjKNgOXDVcZ6SEoxLjYhUO83CyuY5R2jrMKUclzsEqTFIn7DycCrgYU/08xx7+4kuRw6si lwQGoMcYCUclxsUqXNIU2Hmw82DnYTSSmSCfpsIPCqAACvRSgGOrOSxkhDtKdSfdI1M9xkg4 KjEuVuHYeeQVo7R1mFOOSpyDVZikTji2cirgIRjM49rjCLWA3aOCCi5pCiPUyWfjOvht3T34 U45KnINVmKROMI+HGINzGSM0BVXkznUqTI8xEo5KjItVuKQpjFAnmMe/Klc14GiV1AnmoTrN g14foSk4C6E1JT3GSDgqMS5W4ZKmMEKdOA1x1DqpXpuqAYcvqRPMo7XCBorngfnHEuEHBVCg mwI8MB/IARqmOsIdpXMX1SDBZ2iPMRKOSoyLVbjkjnKEOumVx71aUto6dZhyVOIcrMIkdcLO w6mAh2BGaAqqyHukoscYCUclxsUqXNIURqgTzINnHj16ARymAiM0BdUMzUvdhfUYI+GoxLhY hcM88opR2jrMKUclzsEqTFIn7DycCngIBvP4SqRaSE66E45KjItVuKQpjFAnvfLIsZVeE7s1 xDMPp12MjxmhKahm2CMLPcZIOCoxLlbhMI+8YpS2DnPKUYlzsAqT1Ak7D6cCHoLBPPRdlptq tRjXeCoxLlbhkqYwQp2w8+CZh7tWwXVQgLfqfrRSflAABbopwLFVh848AMUId5TqTrqHzD3G SDgqMS5W4dh55BWjtHWYU45KnINVmKROOLZyKuAhGMyDY6v3Ut5qGCPUCcdWHFs9pC2PcRkj NAV1h9RD6R5jJByVGBercMkd5Qh1gnlgHj16ARymAiM0BdUMzUvdhfUYI+GoxLhYhcM88opR 2jrMKUclzsEqTFInHFs5FfAQDObBsRXHVv5iVg3XYUo5KnEOVmEwDyebE2MwD8wD8/AbgGq4 DlPKUYlzsAqDeTjZnBjDW3U/lgg/KIAC3RTgrbpzOAo7D3Ye7Dz8ta7u1h2mlKMS52AVhp2H k82JMZgH5oF5+A1ANVyHKeWoxDlYhcE8nGxOjME8MA/Mw28AquE6TClHJc7BKgzm4WRzYgzm gXlgHn4DUA3XYUo5KnEOVmEwDyebE2MwD8wD8/AbgGq4DlPKUYlzsAqDeTjZnBiDeWAemIff AFTDdZhSjkqcg1UYzMPJ5sQYzAPzwDz8BqAarsOUclTiHKzCYB5ONifGYB6YB+bhNwDVcB2m lKMS52AVBvNwsjkxhg8JfiwRflAABbopwIcE53AUdh7sPNh5+Gtd3a07TClHJc7BKgw7Dyeb E2MwD8wD8/AbgGq4DlPKUYlzsAqDeTjZnBiDeWAemIffAFTDdZhSjkqcg1UYzMPJ5sQYzAPz wDz8BqAarsOUclTiHKzCYB5ONifGYB6YB+bhNwDVcB2mlKMS52AVBvNwsjkxBvPAPDAPvwGo huswpRyVOAerMJiHk82JMZgH5oF5+A1ANVyHKeWoxDlYhcE8nGxOjME8MA/Mw28AquE6TClH Jc7BKgzm4WRzYgzmgXlgHn4DUA3XYUo5KnEOVmEwDyebE2MwD8wD8/AbgGq4DlPKUYlzsAqD eTjZnBjD15N8LBF+UAAFuinA15PM4SjsPNh5sPPw17q6W3eYUo5KnINVGHYeTjYnxmAemAfm 4TcA1XAdppSjEudgFQbzcLI5MQbzwDwwD78BqIbrMKUclTgHqzCYh5PNiTGYB+aBefgNQDVc hynlqMQ5WIXBPJxsTozBPDAPzMNvAKrhOkwpRyXOwSoM5uFkc2IM5oF5YB5+A1AN12FKOSpx DlZhMA8nmxNjMA/MA/PwG4BquA5TylGJc7AKg3k42ZwYg3lgHpiH3wBUw3WYUo5KnINVGMzD yebEGMwD88A8/AagGq7DlHJU4hyswmAeTjYnxmAemAfm4TcA1XAdppSjEudgFQbzcLI5MQbz wDwwD78BqIbrMKUclTgHqzCYh5PNiTGYB+aBefgNQDVchynlqMQ5WIXBPJxsTozBPDAPzMNv AKrhOkwpRyXOwSoM5uFkc2IM36r7sUT4QQEU6KYA36o7h6Ow82Dnwc7DX+vqbt1hSjkqcQ5W Ydh5ONmcGIN5YB6Yh98AVMN1mFKOSpyDVRjMw8nmxBjMA/PAPPwGoBquw5RyVOIcrMJgHk42 J8ZgHpgH5uE3ANVwHaaUoxLnYBUG83CyOTEG88A8MA+/AaiG6zClHJU4B6swmIeTzYkxmAfm gXn4DUA1XIcp5ajEOViFKZvHx7us/vx7RU/5MzL/nK3AFYmuXqMq8irfGr7HGAlHJcbFKly5 KfwRbIQ6+ciruvbWWunBn3JU4hyswth18tc0PozjMwe8Vbe1zMaIvyLRVWVUkVf5MI/t+7Qt rUeoE8zjX2U7a0ZhpHm8TGOxoK6oldLO4/PDbRc4XEujWpvv6zrWrueM6ztjjBbNzmgIvcZQ i7HVtFx+hZNNYWWiI9RJrzzu1avS1qn1lKMS52AVZvX1xS5jtaYv6Msl87hqe+QUhyvolpm8 xx+9aI/mT/X6ocGfw4ij/1ELyRk/4ajEuFiFwzycbK5jlLYOc8pRiXOwCvP9+oph7NYQ5uGU gY9ZNum9pv3+2tHN/Wh+X6FtpCryu4yRzLMS42IVDvPIK0Zp6zCnHJU4BysxG8dSaod3RU+x bi/fj3eumKRTHEuMs8PY220ceZ1HcidatR7tpGPKhWQQJxyVGBercJiHkcwNiNLWYU45KnEO dhXztstI6uSqEyFpHss78hEa35aY7s7j6GSMoKGzEJxFu4fpMUbCUYlxsQqXNIUR6kTdEbfW SC9+lZ+teVbiHOwPTPVoauco+YpaKZlHpakefTGKn51H27J1FkLbCH3e4pnMsxLjYhUO88ir RWnrMKcclTgH+4lJj6ZmMQ8noUdiMI82dZ2F0DYC5vHSb0trdYPUqn+v+KNrpQd/ylGJ28Ua 75hSu6zdG5A7PjCvPHTuVYw9eLYWnmsqRy7cI7l7aKeK+E5jVBa3atZr1+XyKxw7j7xqlLYO c8pRiVvFLnYZii+pk8qJkKOVi5HHVq+JvR6aL//7/vr34nz7PMjyYfuaGW09V9l6B9QWp2t0 a9fwLtjRzf1ofjf5ezhV5HcZI5lnJcbFKlzSFEaokzNuNJS2Ti2mHJW4b+zOLkPxJXVya/PY Ss57E19ewN6D9tdrLeZQNZzVu8qNrd7Ri/ZofmcxKYwqchXvvN5jjISjEuNiFS5pCiPUCebx Vuk7zzLcXW9SJ8Oax9Yd+5YxvF/o1g5gz1Tedzfqz46o73Nw8E5TVJgRmoJqhuoandd7jJFw VGJcrMIlTWGEOpnePMy32WIei46wd0zkmofaEbQYidPArsB8GhY/KIACwyrwesfUbdbxHR+Y 756LL77ryjmGWu481O7hqeZxhWlVxlR30hWuLWyPMRKOSoyLVTh2HnnFKG0d5pTjV1z4Nlt2 Hhs7j7VnH8uH2i/TcB+Yvz8XWf55edy19QDdKaorMCMcR6SLraJnjzESjkqMi1U4zKNSGT+x SluHOeX4jOvwNlvMQxxbbd5hXrClcgrqKgzm8aV8uqDf85ZwVGJcrMJhHvlqU9o6zBGH8QC8 WotqHkmdvJ/mOFr0wlhv1W01hRGaZS9BHZ4R9FBF7lynwvQYI+GoxLhYhUuawgh10usmYK9W lLaqzkpzXP6SpcK3SzvzVJikToYzj+W7lPYMZnms5ST7yZgRmoIq8h756TFGwlGJcbEKlzSF Eeqk1JjDolHaOrSSY+uXLGEem/I27TycpIH5rcAITUEutg6J7TFGwlGJcbEKh3nkBaO0dZhX OYxnGZWxHazCJHUy3M7DSRiYdQUwjy9d1EJy6ifhqMS4WIVLmsIIddIrj6cdWxmG8T4Xldcq VvEldXI789h6R9Osf+80MhczQlNQRe5e62lNoTChyrW5WIVLmsIIdTKKeex9k22vGlU14GiV 1MntzKOwFoEWFRihKTgLoXjZv+A9xkg4KjEuVuGSpjBCnTgN8bI6KX76e22eKq/sPFqzS3xJ gRGaQmXRlC7+DdxjjISjEuNiFQ7zSKskPN5cfDxA5WdrdpU4B6swSZ2w88hra7hIzOMrZWoh OYlNOCoxLlbhkqYwQp30ymPz0dH7s4yVz5Wp/GAezmr7ieHdVnXNmiNGaArpYquI02OMhKMS 42IVDvOoVMaySe20KfODfCo/mEc9P5hHXbPmCMyDnYdzVj5CnVyy8yi+Y6pljhXTcbAKk9xk cGz1fhb+8K8zGaEpqCJvdlCOrb4l3NJ6hDppacxuDX3rY+4y1njTeq7EOViFwTzcqpgUN0JT UEXeI3U9xkg4KjEuVuGSpjBCnZxhHunbbJ3dnapjldfqGIovqRN2HiqLD3p9hKagirxHOnqM kXBUYlyswiVNYYQ6OdQ8/u40lLZOLaYclTgHqzBJnWAefytg6+vVR1lIViEPcCynity5ToXp MUbCUYlxsQqXNIVRal5du6qDH6+vPM/owZ9yVOIcrMIkdYJ5bDzzWJpJqRBvCh6hKagi7yFt jzESjkqMi1W4pCmMUCfNOw/xFttm/tdNaeELDqtHUS+8qgHnWpI6wTx2zOM7OQPcsTtNdYSm 4CwE51r3MD3GSDgqMS5W4ZKmMEKdOA1xtQYKD7+Vtk4dphyVOAerMEmdYB6Yh7MGTsOoIu8x kR5jJByVGBercElTeJx5BG+xjc1pUaAqP1v1XIlzsAqT1AnmgXn06MfdOFSR9xioxxgJRyXG xSpc0hQeYx6FXcZaXSltnVpMOSpxDlZhkjrBPF5nk38Kbe2be18CjbKgdo9rBjh+U0XuLFiF 6TFGwlGJcbEKlzSFUWp989oaTaPyLOGoWlN5fR/XwSpMUieYh8r+g14foSmoIu+Rjh5jJByV GBercElTGKFOVo+VOpkG5vFzle3W0AU3pHw9SY8uWOQYoSmoZli85FV4jzESjkqMi1W4Kcyj s2lgHphHjz7zKA7M4yudquE6SU84KjEuVuEeax7hQ3Ant5gH5lGpkymwmAfm4ZyV37ZOFoah jLN1UffgTzkqcQ5WYZKbDJ55tFbYQPG3bQpvGqoi7yF3jzESjkqMi1W4pCncqk52dhjq2ltr pQd/ylGJc7AKk9QJ5tFaYQPF36opbOimiryH3D3GSDgqMS5W4ZKmcIs6MZ5jqGtvrZUe/ClH Jc7BKkxSJ5hHa4UNFH+LpiD0UkXeQ+4eYyQclRgXq3BJU7i0TgzT6PlMYq+elLZOLaYclTgH qzBJndzHPJbfNXPE/zvZHgyzttDfP6/y44z7grfVVeVURV7lW8P3GCPhqMS4WIVLmsLp5hE+ /FbX3lorPfhTjkqcg1WYpE7uYx6tmZ4w/v3LG7/vxhYG8d4ITm8KQU5UkQeUv0J6jJFwVGJc rMIlTeG0OinsMo66CWDn8aVAUieYR49udCHHcqHv/f9pTaFBD9UMG6i/Q3uMkXBUYlyswiVN 4dA6CXcZmMd25asaUOagXt+toQtOM/iQYI8u+HHHsLPTWN4ZrH39Cn/3Z2lsfDUNf99Hm+/f yIfOj6y1Tq3MpsE8bKn2gVXz6DTsYTTOXVTr4D3GSDgqMS5W4S7deTQeS6k8q2tX8er1Hvwp RyXOwSpMUiccW6kKuvnrmEc9QWohOYwJRyXGxSpc0hSajq06HkupPKhrV/Hq9R78KUclzsEq TFInmIeqoJu/jnnUE6QWksOYcFRiXKzCJU0hMo+Ddxk88+CZx0sBjq2cDmVgMA9DpAVENVyH MeGoxLhYhTvUPE7cZWAemAfm4XQmE7P3eQ7nbbzmMKfCVDPsMZkeYyQclRgXq3CHmMcFuwzM A/OwzWPrnS49msesHJ+a8oMCoQKvd01RQyjwrcBd36q79enpWZt/63VHZ9mtgxbj1Z10kW4V 3mOMhKMS42IVrsvO4yY7jWUy1bW31koP/pSjEudgFSapkw99r+gp1jOPrYldMeHWQrxD/Ai6 qSLvoWOPMRKOSoyLVbikKfx4lHHBnaWbY3XtLs8Wrgd/ylGJc7AKk9QJ5tFaYQPFYx5fyVIL yUlpwlGJcbEKV20Ka18p5+hxBUZde+ucevCnHJU4B6sw1Tr5fv5wwc0FO4/Wyg7iMQ/M471s vhvGX8fY+y7SoNwOD1ENsXUCPfhTjkqcg1UYzOOtWtbebdRaTKPH88D8Y4nw89ko3pziW5Gf f/0OQTUUWFfgqTuP9E47jbu7uYxwXeoOqYfGPcZIOCoxLlbhfr3+9vB7+Ro7j5/VpbR1ajHl qMQ5WIWZYudRaYAV7I/t/AVu6hRiKybVo3XcSrwq8grXFrbHGAlHJcbFKtz36yvvmFqL5ZnH v6pR2jq1mHJU4hyswjzKPJzPebwfTakPxS1fX8a+PwDawo7QfPcKeoT5qyJ3FqzC9Bgj4ajE uNhdnPgE+FbsxR8cV+n7ft3VyCZcAHvwpxyVOAerMI8yDzfha43+3Qg+/vzeNJd/fv3/HmbJ 587tbjjM4ysjaiE5eUs4KjEu9hdu0fmTpjBCnfTK4+7N1sezocZ/3Dwuh6nEOViFSepk2Vsb pbLD27Pyd6hloSujqJrEkx68j9AUVJHbFbYD7DFGwlGJcbHfuI0P8iVNYYQ6wTz+FbhTKwqT 1MkU5pEYxq87gb+/yKZH87qKY4SmoIq8h3Y9xkg4KjEW1jhfSprCCHWCeWAezb2g9dhq7UjK MZvmiV9A8KkVP8MrwHdMUcW3UeCCNxd1P7ZSD7m3HsC/P/PY+/Mod2O7Z7gXJLrqkdYdd5V0 ge8xRsJRiVnFmu+Yer9cdh55sVTytTVKylGJc7AKk9TJ446t8lJ5fuQIBqiKvEeWeoyRcFRi fmB3vphQcSZNYYQ64diKY6vmXjBKoTdfaAeCEbRSzbCDDEO82+rHx7sbHv5jHnnF9KjFlKMS 52AVJqmToXce70dReYnME4l5fOVaLSSnIhIOGbP4ypAe80iawgh10iuPu8e8vFVXrpcraqXb Mw9ngYH52zR55iEXg1sr0ghWiDZjgmcZL3o1D8zDzehvnNLWYU45KnEOVmGSOhl65+EkD8zb 2SjmcR/zCD/9vaznI5rCFXeTyTpV155wvsf04E85KnEOVmEeYR5774q602uthXlFPG/V/Vgi 1/2sfpPthfO5TglGfowCF9yQcmx1gXuMcEep7pB6yNZjjBLH6/dlFM7QXX6FS+4oR6gTnnm8 nSgYdXVEnXBs1aMbDcIxQlNQRd5D6h5jWByLZxlWzN8LdLEKh3nkFaO0dZhTjkqcg1WYpE4w D6cCHoLBPL4SqRaSk+5Njp1nGZVxXazCJU1hhDrplce9XCttm+pEBFfGdrAKk9QJ5uFUwEMw IzQFVeQ9UtFjjF8cOx/ke825Mq6LVbikKYxQJ5gHx1Y9egEcpgIjNAXVDM1L3YX1GOObwzAN zKNH1n5z9MgjOw+9G9+9AXniA/MRGuUxS2qbdQRNjm4IXe5YjW+zXctC5dpcrMKx88hXmdLW YU45KnEOVmGSOuHYyqmAh2AwD32Xtb9l+fMmwb93WmoxYh7HL5okB5VZ9eBPOSpxDlZhMI9K ZUyIxTwC89jYZajFiHkcv8CSHFRm1YM/5ajEOViFwTz+Vsb717Mv/1wpnqdh+ZDgxxLxfvid GZ5OoCZX4OnPPDCQv3fcFyS6asDqDqnKV94BmA/Ak3lWYlyswiV3lCPsULs8uxLFpLR1ajHl qMQ5WIVJ6uSxzzzeF8DWn53kPwkzQlNQRd4jH7/GCB6AJ/OsxLhYhUuawgh1gnn8WwmqBhyt kjrBPHp0o0E4RmgKzkJolft7DHOXUd69bEywcm0uVuGSpjBCnTgNsVudNBCp/GxRV+IcrMIk dYJ5NBTGaKEjNAVV5M2aB7sMzKNZ9UMIjq6VHvwpRyXOwSoM5vG3RLe+fffllCM00SNW2wjX rYo81uVtl9FjjISjEuNiFS5pCiPUCTsPjq3iXkBgXYERmoJqhqWr7vg22+W4yTwrMS5W4TCP UsX8ACttHeaUoxLnYBUmqZPHHls5iZ0NM415iGcZaiE5dZFwVGJcrMIlTWGEOmHnwc7DWadg OikwQlNQzXBXCvMBeNMYr6NR43cotOxW3DkqHOaRLx6lrcOcclTiHKzCJHXCzsOpgIdgHmke wQNwtZCcdCcclRgXq3BJUxihTth5sPP4tU6dXtAL4zSJJ2FGaAqqGX7nw9xlrOXPHmMn+QlH JcbFKhzmka9gpa3DnHJU4hyswiR1cqudx7spbPWGXhgn8U/DjP71JPwO8I8lzg8K3EiBC761 4tfvMF8zhaWB9MI8zRTc6xl259Gwy2Dn8Wup/du8bTy3GaFOOLbi2OrXKcTezgLzcG1iHTdC U/ixfe5sGi9V1BbeUTnhqMS4WIVLjiNGqBPMA/PAPJxO1QkzRFNwHmg16qEarkOfcFRiXKzC YR5ONjdutoJ31C2ZVH62ZleJc7AKk9QJzzzy2hou8tbm8dc0VJH3EL3HGAlHJcbFKlzSFG5d J28FoK69tVZ68KcclTgHqzBJndzKPL4m8+/fTVfuhGktrtHib9cUVpKtiryH5j3GSDgqMS5W 4ZKmcLs62Ui6uvbWWunBn3JU4hyswiR1cjvzaE048dsK3KYp7DzLUEXeI789xkg4KjEuVuGS pnCbOhHJVtfeWis9+FOOSpyDVZikTjCP1gobKP7SpuBsKT92nh3OmVVKeoyRcFRiXKzCJU3h 0jpRyePY6pdCqgY+m7xYV0mdYB6FYh0deklTKL5jShV5jxz0GCPhqMS4WIVLmsIldRIkVl17 QPkjpAd/ylGJc7AKk9QJ5tFaYQPFn9YUzF3GmnSqyHvI3WOMhKMS42IVLmkKp9VJYzLVtTfS y7t1hz+dYyXOwSpMUieYh1MBD8Ec3hSKuwzMY7uw1GJ/RSpc0hQOr5NO60lde+swPfhTjkqc g1WYpE4wj9YKGyi+Q2//fbUNuwzMA/NoWT6qIbZwfzbGDs/fUo5KnINVGMyjtVoeHt+1zx/i RH0WrEqjWkgqPm0slXFdrMIlTYGdx1cFKG2PqpPq2M48FSapE3YeTgU8BLP8epfosg4yDfcY JprzIkgtJGeMhKMS42IVLmkKmAfm8b4GdmvoDl+M6CxYMG0K/PpuMPMbWvk224/lww8KoMAv BTCPtqY8SnR553HwLoNnHjzzaFk7atfVwl09OtoaK51jJc7BKkyyQ+XYqrXCBoq3nnlYoOMu WhV5j5F7jJFwVGJcrMIlTYFjK46tOLbq0WkexLG7kbhgl8HOg51Hy/JSxtnCzc7jn3o882it pAfE/7qjvHiXgXlgHi3LCvPwd0hKq2SHyrFVS/UOFvttHjfZZWAemEfLElINsYWbnQc7j9b6 eVb8jU3jJfTRDeHKplC5NhercMkdJc88/Dt61SBUfrbiK3EOVmGSOmHnobL/oNdHaAqqyHuk o8cYCUclxsUqXNIURqiTXjcBe/WktHVqMeWoxDlYhUnqBPNwKuAhmBGagiryHqnoMUbCUYlx sQqXNIUR6gTz8I6V3B19UieYR49uNAjHCE1BNcMeUvcYI+GoxLhYhUuawgh1gnlgHj16ARym AiM0BdUMzUvdhfUYI+GoxLhYhcM88opR2jrMKUclzsEqTFIn7DycCngIBvP4SqRaSE66E45K jItVuKQpjFAnvfLIMw+9JnZriK8ncdrF+JgRmoJqhj2y0GOMhKMS42IVDvPIK0Zp6zCnHJU4 B6swSZ2w83Aq4CEYzEPfZbmpVotxjacS42IVLmkKI9QJOw+eebhrFVwHBUZoCqoZdpCBY6u/ Im5pPUKdYB6YR49eAIepwAhNAfOo7Y6UXuw8zMWxAlPaOswpRyXOwSpMUiccWzkV8BAM5lFr zHtpV4uRY6vjF02Sg8qsevCnHJU4B6swmEelMibEYh6Yx3vZc2y13wRUw3VaSMpRiXOwCoN5 ONmcGIN5YB6Yh98AVMN1mFKOSpyDVRjMw8nmxBjMA/PAPPwGoBquw5RyVOIcrMJgHk42J8Z8 mAc/KIACKNBNAT4kOIejsPNg58HOw1/r6m7dYUo5KnEOVmHYeTjZnBiDeWAemIffAFTDdZhS jkqcg1UYzMPJ5sQYzAPzwDz8BqAarsOUclTiHKzCYB5ONifGYB6YB+bhNwDVcB2mlKMS52AV BvNwsjkxBvPAPDAPvwGohuswpRyVOAerMJiHk82JMZgH5oF5+A1ANVyHKeWoxDlYhcE8nGxO jME8MA/Mw28AquE6TClHJc7BKgzm4WRzYgzmgXlgHn4DUA3XYUo5KnEOVmEwDyebE2MwD8wD 8/AbgGq4DlPKUYlzsAqDeTjZnBiDeWAemIffAFTDdZhSjkqcg1UYzMPJ5sQYzAPzwDz8BqAa rsOUclTiHKzCYB5ONifGYB6YB+bhNwDVcB2mlKMS52AVBvNwsjkxBvPAPDAPvwGohuswpRyV OAerMJiHk82JMZgH5oF5+A1ANVyHKeWoxDlYhcE8nGxOjME8MA/Mw28AquE6TClHJc7BKgzm 4WRzYgzmgXlgHn4DUA3XYUo5KnEOVmEwDyebE2MwD8wD8/AbgGq4DlPKUYlzsAqDeTjZnBiD eWAemIffAFTDdZhSjkqcg1UYzMPJ5sQYzAPzwDz8BqAarsOUclTiHKzCYB5ONifGYB6YB+bh NwDVcB2mlKMS52AVBvNwsjkxBvPAPDAPvwGohuswpRyVOAerMJiHk82JMZgH5oF5+A1ANVyH KeWoxDlYhcE8nGxOjME8MA/Mw28AquE6TClHJc7BKgzm4WRzYgzmgXlgHn4DUA3XYUo5KnEO VmEwDyebE2MwD8wD8/AbgGq4DlPKUYlzsAqDeTjZnBiDeWAemIffAFTDdZhSjkqcg1UYzMPJ 5sQYzAPzwDz8BqAarsOUclTiHKzCYB5ONifGYB6YB+bhNwDVcB2mlKMS52AVBvNwsjkxBvPA PDAPvwGohuswpRyVOAerMJiHk81JMGtGgXlgHpiH3wBUw3WYUo5KnINVGMzDyeYEmA+TwDy2 E60WklMiCUclxsUqXNIURrjJ+MiRunYnj3uYHvwpRyXOwSpMUiefOfjTa87+5/wRz77Ci8fD PDAP1WC3GsYVDSFZLqohJpzOzqzCm86xEudgFQbzqGT14dgt83jtSvjv1+6Mf9GAGmirgbNb KTuPgxUf5e7xYBmgRwEUeJgCmMfBCcU8DhYYehRAgUsUwDwOlh3zOFhg6FEABS5RAPM4WHbM 42CBoUcBFLhEAczjQNnfHwAeOAzUKIACKHC6ApjH6ZIzIAqgAAqMrwDmMX4OuQIUQAEUOF0B zON0yRkQBVAABcZXAPMYP4dcAQp8KsAzNgqhqkDLG3owj6ra4FFgAAVamsIAl8cUOyiw9d17 LjXm4SoFDgUGUQDjGCRRF08T87g4AQyPAndSoLUh3OlamMtxCrxuMFpuNNh5HJcfmFHgMgVa msJlk2bg0xRYfgllMjDmkahWjFHfrPtOx0PPorgDwquNvVI/LzmqYwwo4xRTruaxWitV/h+9 aooMXHiRa8cIy4RtbSFbEnvhJTP0jgLVY6Vq/VT5SdZ9FajmslIrPW4y2HmcUDtbZrFMoMKd MFWGOEGB6k2Bqosq3wmXyBCdFKjm9sxawTw6JXmPxk2owp0wVYY4QQGV59bXT7gEhjhJgdZa UPEtl4F5tKhnxqoEcmxlCvkQ2Na59Mfl7b22ddRQvTt9iIxTXMadawXzOKEEMY8TRB5oiK1m 7/69qqeBpGCqQgG3Jtwbi543GpjHCeWrFjs7jxOScKMh7nw3eSOZmIrYiV69S8U8TihRzOME kQcawq2HK+4mB5JxiqneuVYwj4NL8CP5r3/fh6r+/cHThP4kBbbyvjU8dXJSYm44zN1rBfO4 YdEwJRRAARS4uwKYx90zxPxQAAVQ4IYKYB43TApTQgEUQIG7K4B53D1DzA8FUAAFbqgA5nHD pDAlFEABFLi7ApjH3TPE/FAABVDghgpgHjdMClNCARRAgbsrgHncPUPMDwVQAAVuqADmccOk MCUUQAEUuLsCmMfdM8T8UAAFUOCGCmAeN0wKU0IBFECBuyuAedw9Q8wPBf4o8P49Rz2/Vhtx USBVAPNIlSMOBU5SwPnq7ZOmwjAo8K0A5kExoMCNFdjbZbADuXHiJpga5jFBkrnEcRXAPMbN 3dNnjnk8PcNc39AKYB5Dp+/Rk8c8Hp1eLu4IBc58eN1qHmu/iOwITeCcTwHMY76cc8UNCpz9 8LrVPD4uNX02ksY1yEvoQApgHgMli6leq0CPRp5cQathpSaQxiXXSMx4CmAe4+WMGV+kwFXm 8do9qN9n/o57l+h93stjrPf/X8Opv7soFQx7AwUwjxskgSmMocCV5vFSaGsOa6awjFkawfvr r9f2MGoOY2SRWfZSAPPopSQ8j1fgDuax3IVsNXRlFFWT4MH748u7fIGYR1kyAmZV4C7msab/ cm5r5pAYxnIsTGTW6v993ZgHtYACBQVaH14XhipBW4+t1nYwjtmUJgn4UQpgHo9KJxdzhgLv D67v8o6k92cWew+5l3NfPoRfPkBf8t7les/IM2PsK4B5UCEoECpwp0Z6p7mEchI2mAKYx2AJ Y7r3UmB5537V7DCPq5Sfd1zMY97cc+UPUeAuBvYQObkMUwHMwxQKGAqgAAqgwD8FMA+qAQVQ AAVQoKwA5lGWjICnKbD3DqQjXtvT74jxzuB8Wk1wPVoBzENrBAIFUAAFUGChAOZBSaAACqAA CpQVwDzKkhGAAv0U4Os++mkJ07kKYB7n6s1oKPBLAT6jQVGMqADmMWLWmPNjFMA4HpPK6S4E 85gu5VxwiwKjfSBvtPm25IbYcxXAPM7Vm9EeoMBou4XR5vuAEpniEjCPKdLMRfZUYLRmPNp8 e+YKruMUwDyO0xbmhyowWjMebb4PLZvHXRbm8biUckFHKzBaMx5tvkfnD/4+CmAefXSEZSIF tn7D3l0lGG2+d9WRef1UAPOgIlCgoMDa90QVwk+Hjjbf0wViwFgBzCOWjkAUQAEUmFcBzGPe 3HPlKIACKBArgHnE0hGIAiiAAvMqgHnMm3uuHAVQAAViBTCPWDoCn6pA71+edLROo833aD3g P0cBzOMcnRkFBVAABR6lAObxqHRyMSiAAihwjgKYxzk6MwoKoAAKPEoBzONR6eRizlZgtK/+ GG2+Z+eT8XwFMA9fK5AogAIogAJ/FcA8KAUUWFHg/XeLL//8gi/f5fTx91fd2Y82X4pufAUw j/FzyBUcpIDbkF+m8TKOuxvIXeZ7UNqgPUkBzOMkoRlmPAW2vo127e/v8M21o813vIpgxu8K YB7UAwpsKDBaMx5tvhTe2ApgHmPnj9kfpED6qe3lkdBB0/tFO9p8z9KFcY5TAPM4TluYUQAF UOCxCmAej00tF4YCKIACxymAeRynLcwogAIo8FgF/g/dXTIgclLGnAAAAABJRU5ErkJg gg==</item> <item item-id="45">iVBORw0KGgoAAAANSUhEUgAAAEcAAAAWCAYAAACSYoFNAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA ARdJREFUWEftlwsKhDAMRN37H3rVYksS8q+wtJuCIFq085xM4+d7jaMGT+CGU4MncBQYmUDB Udwx4FxFd2dPO/rgru3sNKoXOQeCeYJ6ZxZIG6ddhEMn/w2lR2hzERTdgawOZnb9gwOFQ3Nn ZcfADPHmJ9T/WlnRIF8Zas/bVwIZ0SY73i8hZZyDdmouc6xrnOCtnWP1OVz/I0GEwc6Fo/au YWngwGj/lQlk6rK2jqjtJYdo8DRXeZ8H7c6dR3V45qfgSF9eWvQMHLY5A128R2R2TgpONHOi cGjAS+/LlE8E1BQcT0nQ3xDOCVZ2Re5HxFtzw3BgYKJtL/DTqjVk1q7nbeYs4Z77KTieB+8w p+AoX7HgFJxckZ/khHayWNxjGQAAAABJRU5ErkJggg==</item> <item item-id="46">iVBORw0KGgoAAAANSUhEUgAAAMQAAAAyCAYAAAADbXRvAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA BHJJREFUeF7tXe1xIjEMvSshPdECLdACLdACLdACLdACLdACiY/ZOa+QtJKN1nb2MZMfCf54 ftazJFsz+fv8+fzBBwyAgRcDSRD4gAEw8GIAgoAlgIGMAQgC5gAGIAjYwNYZSNnC9JNzAQ+x dcvY4Ppp6pz/DkFs0CCw5DkDEAQsAgzkeUN22QoPAdMIZ+B6vYbPIU1gmRseotn2bG/i2+32 TD+tPmnu+/0uTv+WT7QCinm3wcDxeGy+UAkD9y6NkKn5dv1eAI/H43k4HJovMAkiYZldr+Z5 A3II3x5xd9bSPbZv5PVbU9yR67hcLs/z+fy2yHzONaqHUh6RsEwfOj9yiAI71O6uC4Zr2mWt tSTvQPMHNkwJLqlLGKyeCiGT0TRnp0jwBhohFTdbSxC73W4WqmjeINJTpHApYbF8IAgLSz9t pg2L3DgjlOpmawnCM080r19fXybeIAgTTS9BcLUvxu7dNVtD4D0Jwio4CMJoqjUhU01fIzy1 mRa3Ww2lBAcEUcLaIH08m0uv9/LTONIAPVRGeIgljlrlEIkXK+/wEEYrqrkdae0hpCVajcRI 0ZvRcXF7DY9WHFw75BA17JG+XP6Q/40avHZSLnkLba7ppMtzGZrXeHKdpRNbGnuih8ORj5lu driyCYm7fNx8rdIJr+Gj3+GW6YOCsA4leQJNMJr3sI5HDckTInBrswjaMif3DkHDSTo/FTQV 3dK8XCjY5B3CUlVoNSxvu5Zzc/kCd3JxG0kN12qI3KbTE9XLoWaoJaJN46XX4dPppEKRPOKS 4XPeUuKYvlRrgD6SQ/Re0VhjHJ6+UghSalBcP+nE5ITpwR4hiPQgtt/v3TBqPCPnHZMoaS2T BOojgui5otG9GxUdvBvp8RBLbS3fW5fm8VScQPP+Jbbh5VE6OKb1Lnmp2WFgJUlq13NFY+3a SvpLiR6XR3AehQshLOGDFkLUrEPDyImQtk9JdV5Yt4RlKYeQeJT6eaOXmYeQXL62iF4rGpeI x/frMdAyx/OI8Z/A89OnRBAjVjSuZwqYaTQGqj3EiBWNo20S8K7HACsIGgdrcLQEjPYr8UAe KrTXyOi5PTjRtl8G3gShXRFyy+hJEBajz5MvLYHtd8uALJIBNWSyGlgOUOtjGa9msRHjSwLC 3/+Xw/8WLmZJNb1C436Hh6iRK/qOwEC1hxixonGEjQHGNgy8XbtOYQcXX3PJ9igVjRHhVJst w6yRDJhLN6Rke8SKxkhCMfbYDLgEwXmNESsax94yoI9kwCUIDsiIFY2RhI42Nq6e5ztWJAga j49W0Tia0Ubh9bwhRWHobVyzIKZrWC45Ha2isbdN6AUPLh4++F9IR6po7MUAe8PBeQz66NYb 5k/jcXmIT0+O8fphYCl82or3gCD6scmmSCCIF/0QRFMz7GNy6fTf4g0UBNGHTTZDIT24biVE osRDEM1Msf3EXJXqhEr7rj3yOAQQRBy3Q4/MeYgteA0IYmizjQMPQcRxi5EHZWBrbxC4ZRrU UAE7joFviFgnDWblqKQAAAAASUVORK5CYII=</item> <item item-id="47">iVBORw0KGgoAAAANSUhEUgAAAHIAAAAWCAYAAAAcuMgxAAAAAXNSR0IArs4c6QAAAARnQU1B AACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAA AlFJREFUaEPtWYuNgzAM7Y3QnViBFbpCV2AFVmAFGKErdIWuQPHpgoLPiZ/zEVyPSJUq6tjP fv6E9Gte1qXAmqbp0jRNAU12FXvatqOttIOIzF2Px2Omz16LbD+fz73MH8LupQSK+/1eQk2W jiNgyHIgc3M2ka/Xa77dbpkw8rcTkYTlv64NkUv3NsdhGIa57/tf+0iX/zErNm4Yx3EmLKUW gl2Tcb/7mLQ9qfhX5iSjiFKqRj4fpYRISRLEvpMhDKU6A8eK+IPs+TlYWtyCZbMrcjmpblpa jLCaZFJbJSw1FoLblwnJpxYL4pNIpMUgmok1s9E5er1eEZ9NMqkkxmKI6uQ63D7/+fpM6t/u GWqQ6whFCtFnijIT1roBn9saHiShpWCj1YnEiZMXIjPaWjVHpSrTgplDlLYXwavpkH5H9PoB RhI7RnasNUtF9p1MMaMWB5AqRvSlBBq1ba1IRC+XQUdNSsKHyC9CpDSXJJC1SaSA1piR6Gy3 VKQWixhhakX62erAa/2f5OikKF2P8eznlRbq9ZIcd0za67DkVLQfJB4PqfJiMqHYaSRyDBIm n5/1O+p4KEuk90htRoSShme/lpn+7yXfI9GYHEkOvsoJVRjdpnRdF/VJylyNpFAmhuZW6Zud I5GEYDERKSmkF/G2bRFbGxmEyJCMNLcomc67VoCGWFBT/nnIIVI6WGhdAXDxT4vAFckHrO81 HXYsF9bajJQON1Jrd8/2/j/0CBlgIjIGmGbUXsuSRHthrG23GJG1gZ764xE4ifyQDDmJ/BAi 32awT2NoZSceAAAAAElFTkSuQmCC</item> </binaryContent> </worksheet>