{"id":6829,"date":"2021-11-21T05:52:21","date_gmt":"2021-11-21T05:52:21","guid":{"rendered":"https:\/\/researchwithfawad.com\/?page_id=6829"},"modified":"2022-01-20T15:30:51","modified_gmt":"2022-01-20T15:30:51","slug":"assessing-construct-reliability-and-convergent-validity-in-spss-amos","status":"publish","type":"page","link":"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/assessing-construct-reliability-and-convergent-validity-in-spss-amos\/","title":{"rendered":"Assessing Construct Reliability and Convergent Validity in SPSS AMOS"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"6829\" class=\"elementor elementor-6829\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-t3sswr5 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"t3sswr5\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t\t<div class=\"elementor-background-overlay\"><\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-d4acc06\" data-id=\"d4acc06\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-bee4950 elementor-widget elementor-widget-heading\" data-id=\"bee4950\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h1 class=\"elementor-heading-title elementor-size-default\">Assessing Construct Reliability and Convergent Validity using SPSS AMOS<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-6089ecd5 elementor-section-content-middle elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"6089ecd5\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;gradient&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-66 elementor-top-column elementor-element elementor-element-154929c1\" data-id=\"154929c1\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-78fe4a76 elementor-widget elementor-widget-image\" data-id=\"78fe4a76\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"1280\" height=\"720\" src=\"https:\/\/researchwithfawad.com\/wp-content\/uploads\/2021\/11\/SPSS-AMOS-Lecture-Series-Construct-Reliability-and-Convergent-Validity.png\" class=\"attachment-full size-full wp-image-6846\" alt=\"\" srcset=\"https:\/\/researchwithfawad.com\/wp-content\/uploads\/2021\/11\/SPSS-AMOS-Lecture-Series-Construct-Reliability-and-Convergent-Validity.png 1280w, https:\/\/researchwithfawad.com\/wp-content\/uploads\/2021\/11\/SPSS-AMOS-Lecture-Series-Construct-Reliability-and-Convergent-Validity-300x169.png 300w, https:\/\/researchwithfawad.com\/wp-content\/uploads\/2021\/11\/SPSS-AMOS-Lecture-Series-Construct-Reliability-and-Convergent-Validity-1024x576.png 1024w, https:\/\/researchwithfawad.com\/wp-content\/uploads\/2021\/11\/SPSS-AMOS-Lecture-Series-Construct-Reliability-and-Convergent-Validity-768x432.png 768w\" sizes=\"(max-width: 1280px) 100vw, 1280px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-39e0e443\" data-id=\"39e0e443\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-910eb57 elementor-widget elementor-widget-heading\" data-id=\"910eb57\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">IBM SPSS AMOS Series - 11<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-22dcafe0 elementor-widget-divider--separator-type-pattern elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"22dcafe0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\" style=\"--divider-pattern-url: url(&quot;data:image\/svg+xml,%3Csvg xmlns=&#039;http:\/\/www.w3.org\/2000\/svg&#039; preserveAspectRatio=&#039;none&#039; overflow=&#039;visible&#039; height=&#039;100%&#039; viewBox=&#039;0 0 24 24&#039; fill=&#039;black&#039; stroke=&#039;none&#039;%3E%3Cpolygon points=&#039;9.4,2 24,2 14.6,21.6 0,21.6&#039;\/%3E%3C\/svg%3E&quot;);\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3e52da2b elementor-widget elementor-widget-text-editor\" data-id=\"3e52da2b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>In CB-SEM, Loadings, Model Fit, and Modification Indices are critical to model identification. This post will discuss in detail all these concept. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-8r7lxra elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"8r7lxra\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-c927f23\" data-id=\"c927f23\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-bf8264c elementor-widget elementor-widget-heading\" data-id=\"bf8264c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Construct Reliability<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-66 elementor-top-column elementor-element elementor-element-61ca281\" data-id=\"61ca281\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-338057a elementor-widget elementor-widget-text-editor\" data-id=\"338057a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul><li>Once the factor loadings and model fit are assessed. The next step is to check the reliability and validity of the constructs.<\/li><li>Construct reliability assessment allows the evaluation of the extent to which a variable or set of variables is consistent in what it intends to measure (Straub, Boudreau, &amp; Gefen, 2004).<\/li><li>The essence of reliability is covered in the word <i>Consistency<\/i>. For example, if the same measuring instrument produces the same results with the same individuals on different occasions, then the measuring instrument is reliable.<\/li><li>Construct reliability is usually assessed using composite reliability and Cronbach\u2019s alpha.<\/li><li>Cronbach\u2019s Alpha can be calculated using SPSS, in AMOS we will focus on Composite Reliability.<\/li><li>Both values are interpreted using the guidelines offered by Nunnally and Bernstein (1994) who suggest 0.7 as a benchmark for a modest reliability applicable.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-6d8e596 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"6d8e596\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-239e5a4\" data-id=\"239e5a4\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-86be552 elementor-widget elementor-widget-heading\" data-id=\"86be552\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Composite Reliability<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-66 elementor-top-column elementor-element elementor-element-433cb6e\" data-id=\"433cb6e\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-2ef9157 elementor-widget elementor-widget-text-editor\" data-id=\"2ef9157\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Composite Reliability is calculated based on factor loadings. The formula is<\/p><p><img decoding=\"async\" src=\"data:image\/png;base64,iVBORw0KGgoAAAANSUhEUgAAA7IAAADHCAYAAAAkh28bAAAgAElEQVR4nOzdZ3cUZ773+\/tNnHXW3vc+e\/bMHo\/BYTz22MYk22CDyTiTTZLUrYAklFBCgMgZgQCTc84ZkXMGIYQEKIFQ6qjOubu+54FElpCwhUHm\/2HVA1rVVVdXdVXXr65Q\/wchhBBCCCGEEKIZ+T+vugBCCCGEEEIIIcSLkCArhBBCCCGEEKJZkSArhBBCCCGEEKJZkSArhBBCCCGEEKJZkSArhBBCCCGEEKJZkSArhBBCCCGEEKJZkSArhBBCCCGEEKJZkSArhBBCiN8u4MZcXY3V4SbwqssihBDijSFBVgghhBC\/gYLfY+bi7jWMilQRlzaR3VeLcQeUV10wIYQQbwAJskIIIYR4YYrfxu2sJfQflMHerPWMD\/mcb7r34MBdCxJlhRBCvGwSZIUQQgjxwlwGHWcXzedypYOAEkBbcgx1569ZnqPB\/6oLJ4QQ4k9PgqwQQgjxmrCY7BiNzlddjFoBXC4r5RpHnX9VAgpOu+fh\/22aq4z65nu23KmWICuEEOKlkyArhBBCvAa89ptMHrWMafNv1r7ix26zoNUZMBqNGI1GDHo9Oq0WnU735KTXYzAaMOirsVhdTdS010dhzmHCBk0i39TQME528g6vol\/ITirtviZZuxBCCPE8EmSFEEKIV02xs316PDEjZ5BjfVCfqWPDoiQ++tc\/adOuPe3ateOLL7+k41df07FjxyemL9u1o3Wrj\/nn+52IStmApTGrbMQ8fmsRezJD6ZOQhfU582nzTzLzl9FcrHA1YqlCCCHE7ydBVgghhHjFqrPnEqZWMWdbxWOvBqg8v4TYHv\/DW2+9R4u336Jb32FMWbKOBfPmMm\/ePOZlZrIgcy7jI0L5vtW\/+MdfPyIoZhHm56zLbzeQc\/IAB8\/dxNFgG2AFbX4WcT1+ZN5FU51zmIovsjZ5POeK7QC4rdX4FBnuSQghxMslQVYIIYR4lRQts9KG03\/ULEq8T\/\/Ny+U1M\/mhZUv+962\/0zskiWP3nPhR8Pv9Dyefx4Px5j5G\/9SFgZELqDtyAgEPuSc20PujD+kyMIgVN+0Nl89dwaG5Knr8uJDyx4Ov4sdafIZfgzoybMEOrufd5NKZgyyatIx7Tk+9ixNCCCGaggRZIYQQ4hWy3piPukcPxs6\/UuffFc991s+K4p133qZFyw8JSppPoauuGk8HO5ZlMEg1FV19KzNcI2vPBMauX8\/ihZGM2FfeiBIGKLm8gd6derM42\/joZZeR86si6NG5A19370nP7l3o9VN\/xu0rwNVQl1ohhBDid5IgK4QQQrwyAY7PjOOb7lGsyqm\/dtR1\/ygzor6k5dt\/562\/dyNh0lHqGlOp6MQxtsxcTlV9a3PasOr1eAMVXD63gcSN9xpVSufdy0zt1oHeU07xcExlRcHndmCzO7DbrFgsFqw2G24JsUIIIf4AEmSFEEKIVyVQyoyEvnSLnMxzciwQ4N6xxYR3aMFb\/\/sP3v9GxeyD93k6yyp+PwGvt+GBnLz3OX1wCQmbShtVTMVyj21J39Pt22mUvC5PBxJCCPFGkyArhBBCvCJKxVGSf2lH0NgFaBtKnwEzB1ak8+k7LfjH22\/zzbAE9hfU\/YzXhpizd5M+\/AeiD9bbCPlJXiNnloXxVffvOVjq\/k3rFEIIIZqSBFkhhBDiFfGWbCDm544kzNxNY4ZHCphzWZo+gPfeaUmLf7zHkISZ3Kmzv+xzlmEpYG3at7Tq2p\/lOc9Wryp11ue6yD08hw7de7Oj0PZC6xNCCCFeBgmyQgghxCviubWKmG+\/YNTsvY0KsgCWvCzGD\/yC\/\/nL\/\/Jp3wTW5r1AW9+AhVNrJvPJX\/\/G14NU7Hps7CZ8Zu7fOs2x86V1RFknN4\/OpEO379ieL22LhRBCvHoSZIUQQohXpHTPZAZ+8D4pmQcaHWQVezHLRg\/gP\/\/enZT553D6G1sjG6DkxHJ+ebc1X3UcyNRpaWSVPfZeTxFHlkQRGnqsjrI4yT82k4879mD03rKG++AKIYQQL5kEWSGEEOIVseSuJKxHexIbWyPrt3FmaRod\/\/IhYelr0foaHymdd08zbcB7tPs5ilWHDnJgZTI7ygAC+PwBPA4rJoMenfHph9nCgyD7yVc9GHugQoKsEEKIV06CrBBCCPGK+IvWEfdjB0bN2ktd8fGpubl\/eD4hrf6D3uGTuKbzN3o9Acd9tk0fyt\/+2Z3YlQUYLh5hxYDB7CowoM29QIHGzrmNS5g4YRpXDHUtwcXNo7Po0P1bdhT\/tgGmhBBCiKYkQVYIIYR4RTyFG4n58XNGTttOQ\/HQUbKftCEf8cEPI9mQa657JsWNzW5Aa35Uv6t4LVzYMJnWf3mXPhHzKPeDPfcI84Z24LuIKYxVz0KjBLh0aBFdev9CtqnOtXNj72Q6dP+OXSXPfU6QEEII8YeQICuEEEK8KpbLTAjrzM+Jcyl4TpWs33KbX5P7898fDmDSpkIC9cyn3D3MmkWJzLryaEAmR9FJFgz6gG8GRrM1tyYu+6vz2TShK5\/0GkbK7jJQKjm6Io3eg9ZQ55jErvscmf0t3\/SK57qlvrULIYQQfxwJskIIIcQrY2ZTejDfDEllX2XdcyjeavZlJPDvv3QkceYJfPUtyu\/m4pppJA0M41T1o16sit+L227B6fY8FoB92K1V3CvT4gyAv\/Q0K1O+YcCmu3i8zwZVjy6bRcGt6R20A8mxQgghXgcSZIUQQohXqGjzOL79bgizj9WRZANecjdNo2+L\/6TtgGjWXy6h6n4heXn55OfXTncKKLpXyLEtU+nT9p8MjZpPnd1cn0N37gBTvmxP6rYTXL39bLNl\/a0jRH7RidhdRTLQkxBCiNeCBFkhhBC\/gRe7SUdZ4S1uXLvG9Rs3KSrToNPr0FbpcANelxVdlQaD0YhBr0NTVUlllQadwYzbI3HoAcVwijHD+xI7eRtPPKE14KHq3Eriv23J3\/\/xDv\/698d82ro1n7VqRas6pk8+eof\/+cdXhI4\/0IiBo55Udm0\/QZ90I3jEFjRPP85HsXN930S+7j2QCxqpjhVCCPF6kCArhBDiBSh4HBpuHFjKqCG96fhNN3r27EnPHj3o8m1nPuzcjSHh8zDip\/jsGoZ1+or2bdrw+ecd6NarF927dqF7lwFMmX+EYkvjR939cwtwJiMZ1S+pHLz3aJso2vPMT\/2RD\/\/9KW3bt6d9u7a0ad2a1vVMbVp9yD\/bDSfl1zsvXgKfB4fJjNPxbAT2anNYMKIDfederLdvrhBCCPFHkyArhBCikQI4K88yJ+Fb3n3nc4aoJ3O2oAKr1YrVouP8pil0+OBr+ow5gx\/w++zkHFpA27+8Q+fvU7mmt2AsvsCSmG60+KgLPycfwyUVswAopgtMjYgkYfo+rLVZ0qkv4cKR3ezcfZCDBxsx7d3Nvqyz3CxzNV25fHay986nV78RXLc02WKFEEKI302CrBBCiEbxV+exMr03b\/2tLcNGbqLM9dSwQ9WXWf1rNL+svFfz\/4CBSxtG8PcPutB\/\/IUHS6HwzALe+t8P6NQzhcJ6Ry560yhoD89EHR9NxsWaxKgoCkpAQWls2G\/0jI0vkzn\/AvO\/G0DGqaomXrYQQgjx+0iQFUII0TDFweWdM\/nsL3+hV\/Aocsx1hCavnoLc42y9XDPUkL\/yBmuGv8tHXfuScbn2oS6Kmcsbx\/DW3z+mZ78FVPyBH+G1F7Bw7cRZzp0ufk0GVPJiqCjg4O6c+kdKFkIIIV4RCbJCCCEa5Cw+S8awj\/mv1j8Rv7X+PpiKohAI1MSwyuyDBP3vf\/PVd33YX2TAYtJwbs9CBnzwAe17D2T6iap6A5vHbSLn6hUuX7rMlStX6p4uX+LylWvcKLXi+7N03lQUFP\/r82EUReHpsZ+EEEKI14EEWSGEEA26sW8F3f7rP+g6PI5T+sa8w072oQzef\/cDWrXrTO\/u3\/Fjlza0av0NPwZP5sj1crzPCUja8rP06tmdTp0607Vr17qnbzrxdfdv6bXkOiZPU31SIYQQQjQHEmSFEEI0wMHZrdP49\/\/bkoGR87jfmBo6xz0OzvqB97\/8ntEbznNo4wRa\/Pe\/6fn9XKo8DY9W7PPaKCnKJ6+ggILCQgrrmgoKuJN\/iyKNo8FaQ51OR0VFBZWVlTK9gVNFRQUuV9MNgiWEEOLVkyArhBDi+RQNe5fH8H\/\/4xNUMaswN+ItjrvnmN3zv\/j82yHs1YK54CSR7\/2dr78bxHFNI1YZ8OG22XDY7dgdDhx1THa7HYfdjsvjf26fUr\/fj1qtpkuXLvTu3VumN2zq1asXX3\/9NefPn2\/sN14IIUQzIEFWCCFEA+yc2DiFlv\/POwyJWUDd49f6cFn0mD01kfLuxc30+uu79B40gbsAzvscmNmVFq26ofr1VoNrtFVmkxEyjLCwMCJGjGBEHVNEmJrQoGCm7ruNo4HRiGw2GxaLRaY3dDKbzfh8MmSVEEL8mUiQFUII0aDiI+sZ\/Lf\/S6vvgtnw9ANFvU5KT25kVvJw5uV6AAcXN6bwt\/e7MHTSpdraUg\/F55fS9q1\/0fPnKZQ2kCnshlymh\/bl5+HDCAoJIeSpSaVSETRsMD\/3782E3bdwNtxaWQghhBB\/IhJkhRBCNEixF7A9M5h33v4nvfrHkHWjlKqqKiorCzi+YhpDOg8gas1VHG43xrxjTO\/Xgrfa9iBucx7W2ufNOsuvM+fbt3i\/bRdS9tzC6q4\/fSpKAI\/LidPlwlXv5MTpdOD50wxZLIQQQojGkiArhBCiERQCtjK2ZaTyTbvP+aZbd7p37843nToyMDiSPZcrcPnBU5HDmoRefN2xAx07fEnPweEsO1tWs4hANVd2j+OL9u3oPiCENVcaNfyxqIfPZaPaaMLRiMGzhBBCiD8bCbJCCCEaTfF7MBs1FN++xc38fApLSzE7XA8HW1ICPtwOKza7E4fDjt1ux+V9VGOqBHw1AzjZ7bilJvU3CmDXFLNlwVTCVUGMy1zFlTIzEmeFEEK8SSTICiGEEM2I33GLVVOnEZG4kqxdMxna6W0GxY7ljvdVl0wIIYT440iQFUIIIZqRqgunObZxM0VuH4ri5\/CK8QwbqOKM7VWXTAghhPjjSJAVQgjxhgugqTTjdL0uj2fxYjCaMZg9df7V5\/bjdj1qSHx+\/TxiBsdxxfVHlQ8Cfi+V5WYCz3uA7x\/KSXmVBadHmqsLIcSbQoKsEEKIN5qt4CCD+6\/jXE51zQsBNwZjNVqdHoNBj8FgQKfVotVo0Gi0aDSamkmrRafXozfo0etMON1NFYStbMicz5hxGzG5nz+nz3WHpdMXkDDlEn9UhFMUO9f2rWfg4AOYvAFAwe91oNEZ0Otrtpder0er1TzaVrWT9sE20+sxGCx4\/U2VhMtICx3Dmr05eKWzsBBCvBEkyAohhHhz+QqY0P9nZm44i\/FBqLLnEB38HR9+9BFt2rajbdu2fNmhIx07fkWHDh0eTh07fEn7zz6jVauP+eD9n1m5P7eJwqSCp3ArqSOjGbvyNvXFY8Xr5MyKxWQkLabsD6uIDFB1ay9Rvfqz9YaxdpAvP5W3d9Htq9Z8+tlntG3bjnbt2tHhq6\/o0KHjE9uswxdf0ObTT\/j4k9Z82SGGS+XWJiuX5tRE+g6fxt6r1X9YqBdCCPHqSJAVQgjxxsrZOoSeQQvILnmsXa7i5\/LyofT89G+83eI93n33HYYlTWLukhUsyJxHZmYmmfPn82vGTOL7\/kTnf7\/H3\/\/agUW7Lz83QDnL8jm8fz+XCirxNVgR6efUwjGohqeSVVlHlPW6yN21mlXTV3DXrYDfhctmfukBLmAtZPOM3vw06jiexz6Dz3qPzclt+eSdFrzzzrt8\/Fkb4mctZcHCX5mfmUlmZibz589n7qR0Qrp8Ret\/vsfHn\/zCufvmJixdNctD+hI1fSeFztemzbMQQoiXRIKsEEKIN5Jiu0T\/77oydc81HE\/nHr+GtSMG07ZFS1q8+w4Rc7Zzz+5DUQL4\/X78fj8Bvx+v203h3ml816Yrc7ZdqDdIKl4ja6al8EXLt\/g5\/VcuGhsOWkrFAdKGDmbkpKNP1MoqPhdF26cSO\/R70nZe4l5RNjvXrGXDqv1YfuvGaBQfBZdW0rn79+wuebZDrv3+ZcZ0+pz3WrTkk3YdmLEvH4eioAQebTO\/z4fHVsGRWSo6fj6Ek\/eqm7SEpiuz+bnLCNYcLUGirBBC\/LlJkBVCCPEGUri9eQidvxzJiTxjnXMEKs8SOaQ7Ld5pyT8\/+ILpW85T98DA5cQMi2T6uqPU9wQcd\/5yMrYtZ8maicTOz2DVzcaMzGRlw\/Roeg9J4rThUSzzlp9jaep3fN7ha7r1+pYenTvyw4ixbMl3NCq8+Z0OdCX3qdI4GjH3I4qtiG1jO9P1h1\/R15nY\/ZRfWEPnLz7hnXf\/SftO\/diRo69zWfaqc\/zQTc3hYl3TBk7lLkkDeqOavY3SusfKEkII8SchQVYIIcSbR9GQ8XN3vknYRIG5\/ihVdmYmv3T\/iJYtWvDeuyo2HL9XZ63riTm\/cvjwBez1LMdn1GKyu1BcF8nctIf1FxrXpPbephn0+6ovafvKHr0Y8OF2ObA7HDhsNqxWK3anu96+tE+zZh9mfJvODEs690JNkY13jhL+RSeGrnhOX2CfibOrg\/nyk3dp2eIjOnSaQrbu2RGrfC4jO1Knc63CSNOOzaRwJnkgHX9K52ChPI9ICCH+zCTICiGEeOMENMf4uUdHYjecxfTcNOfgxKwQuv77bd5++z1aDZ\/LmRLnM7WIis9HwB9ouHZRf5QZK7aw7lLjBjly5G0jvlc3Isbuo4EBjBvNdmMfYzq3o3\/a+ca\/SXFx+3gm7Tv3ZFnO85sDe823WaHqwL\/fa8k7H7bj+wkH0NVRAR3wegko9W8xxWuj4u4t8m7lknungJKCAkwud4PbuHzvOL5t158VBwukebEQQvyJSZAVQgjxxtGdmUWvTm1YeDiXhlqgKq7bpIf15f2WLWj57nsMn76JQnN9jYifx8utZbEMjBrD+juNrAs1XGJq6Ff8FD2BoiZ6uo8jL4vxPTrwS\/qVxr\/Jo+fUkmC+6vEjJ8sabrNrKsyif+f2vPPOO3zUpiPjd+TgeIFH7fgd5RxdNIKfvu\/G+F8nkTjkR9p+2IXtOSUN1uA689YxqFsrUtccoSmHkhJCCPF6kSArhBDijVN6JI6eHX5k59XSRs1vvbWVsH5f8W7Llrz7biumbj6F+QXbxNpv7yLq+w\/pFJXBZcOTf1Nq\/z2rjPljf6FXxGjyGtOt9hkKgYCvdrClAIFAAHPOIcZ27cDAMRfwB2peeziAVX1Z01XGwYwf6PZtPAXPjIxV5xu4lTWVbz7\/hPfefZfWX37HtlwdjcqyAR37xvakR6dubD53Dz8+3NWnGfTzfE5nax77ZPUszHyR4X2\/JnbZPuru\/SyEEOLPQIKsEEKIN4zCvd2R9PjiJ3ZllzU8e+17CrdNpe\/XH\/PXv7ag+\/gt5L7AgLs+y21mRQ+mxX\/+XwbPXUv+4xWy9iIunD1Lzp26Fnif+emD6BE6hpu\/pctnwErJjaOsXLmajRs3sWX7dpbNGEvfNp\/SbVAS67ZuYdOmTaxbs4pNO\/dxy1R3OlccdzkwuQfdvk2k0NXImlWPgaPTgmj90fu0\/PALwjbnYW8w\/AcoOz6Fnh0\/5KdJm8mv0KHTmvEG3Ny\/WYHFWtPA2qO\/yqFj2VQZ62hwbT3P8L6diPn1AAZ5oKwQQvxpSZAVQgjxhnFyfNx3fPVRT3bnlDf6Xe7CAwz\/rgNvfRLF9osVjatdBPCZ2T8+lI7\/6Ez\/H\/owb\/N2bj0+KpR2G5NHJJO5tKiON5eyeMIv\/POHOHbc+S3DIpk5vTuDLzp0pGu3bnTv3p0uX3Xgs399TKs2HenSvTvdunXj645f0k8VxdHyutfh1lxnwc8f0q33KAoaXTPsp\/RQBp990oav+i4gr7rh\/q0EtKwY2pU2\/\/qYL7r2R6UayHffjuPkUyHffH0Kqn6ZnMmuo\/Gw5Rwh\/TvRKWk9t5r26T5CCCFeIxJkhRBCvGEU8rZF0LX9j+y61rga2YCzgPlBP9CqRWcWZ+XibXR3Tz+lWdPo3\/Zv\/Dh+LVk7Mtiwez3ZtppyeL0+XBYjOp0Zi72u6sOaIPvhT\/HsKvotQVbB63FhNBqprq7GbrNx99BaQt\/+gj5RB6my2zFVV2M0GrFYrPjqqcFUbPfYmt6dbt+OoqCRNbKWgiwiP29Dx85hnCptXHWy4rpNbI\/OfNR1Ckeziykvv0dxcRVO76PP7nO7cZj0aLQO3HXtiNog2yV1I3dMjVqtEEKIZkiCrBBCiDdOeVYMvTr8xK5r9xueOeDg1NSBdPrwH8QvOYruBQZdct87RsLAL\/j\/2saxI9dBwcwU5k9ayOWCIu4XF1NSUsbGKanMXbObijpzahnzxw2iV0QaeU30XFRnXhbpXb98scGeXPc5POvbmqbFzoaDrM+Yx4qhn9D+y84sP13R6EfsKK58Rnb5mg+7LeD2w37ELkpzbqGz1YwWXZV9mllJ8Ww8e6fuxx1ZL9T0kV1+AIMMWyyEEH9aEmSFEEK8ce4fSKBHh15sulD3c2Ef8VN0ZDzfdfwX34xey83qeub2m9FVm7A6H\/3dZy9iQXQ\/Wv7XZ0xadxYfULw8ibiIgXz\/QzIrlh4hoLiZnhSMKnUehrqW6y0hM7kfvUeM4VYTPX\/HlnuAcV3aM2D0hca\/yVnOwZk\/0KVXFLnPee4uAB4dWQv68lm7joSvu0ydFc1AwGOgwmjH+1gbbcV\/n4y+vfj47b4s3nedisoKrhxcwKA+Ezl9S1szj+MG\/b7tyfy9V+p+JJH2JMN++oq4FfuRlsVCCPHnJUFWCCHEG8eZv5ofurRn\/K7rPO+Jro5bexnauyMtuk3m2O36m8dajk9k9PK1HK949FrZ3qlEdHmHfmPXcNNYE9bMN9eTOqwNHw2dzu7bPnCdJjk0njHz6g6VStlOxg36gqCUdU0Wyiy5WYzt1JHBiWcbCPGPF8TCtW2j6ND1e7YWPq+TrIv8vTNp3epLvonaToWzvvnc3NoYTsz262iemEeh6sZuBvX5no6dvqFHjx50Uy\/g5G0jvtrA6769nG97juXg1ao6l2y+MJ6+X3dh3p4cfstDkoQQQjQPEmSFEEK8eRx5JH7fiX5zD1JWT02nz5jL+F968693h7PjfGW9i\/K59KxS9WHCrE3ce6wNbcDrwuWw4X6846niQK8po8JgxQ9YDo0hLCmSWZcc+H3P1nRqT88iuHNXUhbm\/cYP+ixHxU12rpzHuguNH+gKApRf20yfr7uQfKS+5tgKuqs76N+mPZ16TONWfbXXKFiqrjK5RxeWnLzLs0\/zUXDbLOg0Giqr9FjsT7apvvPrd\/SavIZz5X4CdawiZ+lAunwVxZ6L8vAdIYT4M5MgK4QQ4g3k4GDKd7QPmUtOVR1J1lPJtrh+fPGPv\/HD+JVcLLzHvaLb3LyZR15e7XSngIKC66yePJjP32vNvK2XeIHus4DClQmjiO87kDnH7lBRRznOLhjNj73VrL7l+K0ftI7VKvgDfvzKi3UgdZVfZeagr+kx8UidNZ2OivNM7v4Jn7RqR9y6UxTfL+HOrfxH2ys\/n1tFhdy4cogJIV\/yeet+nCh+0dGYHGz7qRsjx0xl84UK7M88z8fKyuHf80PsYrKN8uwdIYT4M5MgK4QQ4o1kzlnMj536s\/ni3SceCxPw2ri0TE3P1v\/gHy3f48NPPqVVq1Z8+umndUyf8NEHb\/M\/7w9l7fHiFyyBn6MLJ9LjnX5krMnBF3gqWPoKmJ3Sj4EJs6m\/PvgP5DdycUMiPXrEcNP2xBbDYyxmc\/JX\/Pv9lrz3zw\/4d6tWtGpV1\/b6lE8\/+Yj33n2fjzqPJ9dQb9vjuil6FqqG0LVNIodz9DyTxXWH+eXnLkzZdIYXXLIQQohmRoKsEEKIN5SWVYO\/J3zaAcoeG4nXmL2M4D5f8fGnrWnf\/nPat21LmzZt6p3afvZv\/tV1Ggeu1PFM0wb4XE4cZhse77O1h1XHZhE0rD\/TDml+16dsStVFpxjfvRfpe0ofveizcH1nCp2\/bEvrNu1o37497drWv73atG7FZ22+5OvhOyg3vegjhRQ8NisOqxP\/08EfuLqgPz0ipnKyqNEPuxVCCNFMSZAVQgjxxnLkb2BQz2g2nivGU5sldQUXOLx\/H3v3Z5GV1fB0cO9uss7cRmttuqasPlsJGaMjUKcv4bVqIauYuLF9Kj\/8MJ4cS03f1YDXScmVQ+zZc5CDBxuxzQ4e4OC+A5y4rsH9Ym2xn8tddYbh\/QeSue8SHnnsjhBC\/OlJkBVCCPEGC3B14SC+nbiWa5qaVKUoCsqL9B99wb6mjSnTzUUzGTN8NPvvv2iN5cvnNxSwfsy39Fl+DXftR1cCStNvhhfi4VB4MCnTtnPHLilWCCHeBBJkhRBCvNn85RzefIp7FS\/eNPjlsHL9wjUuXiht\/ONx\/lABLNp89m+8gM33mpQwoOXo3kuUllledUmEEEL8QSTICiGEEP7Ai9XCvlQKAQVel9LUR\/H5X6MyBvC\/PoURQgjxB5AgK4QQQgghhBCiWZEgK4QQQgghhBCiWZEgK4QQQgghhBCiWZEgK4QQQgghhBCiWZEgK4QQQgghhBCiWZEgK4QQQgghhBCiWZEgK4QQQgghhBCiWZEgK4QQQgghhBCiWZEgK4QQQgghhBCiWZEgK4QQQgghhBCiWZEgK4QQQgghhBCiWZEgK4QQQgghhBCiWZEgK4QQQgghhBCiWZEgK4QQQgghhBCiWZEgK4QQQgghhBCiWZEgK4QQQgghhBCiWZEgK4QQQgghhBCiWZEgK4QQQgghhBCiWZEgK4QQQgghhBCiWZEgK4QQQgghhBCiWZEgK4QQQgghhBCiWZEgK4QQQgghhBCiWZEgK4QQQgghhBCiWZEgKwSRwGYAACAASURBVIQQQgghhBCiWZEgK4QQQgghhBCiWZEgK4QQQgghhBCiWZEgK4QQQgghhBCiWZEgK4QQQgghhBCiWZEgK4QQQgghhBCiWZEgK4QQQgghhBCiWZEgK4QQQgghhBCiWZEgK4QQQgghhBCiWZEgK4QQQgghhBCiWZEgK4QQQgghhBCiWZEgK4QQQgghhBCiWZEgK4QQQgghhBCiWZEgK4QQQgghhBCiWZEgK4QQQgghhBCiWZEgK4QQQgghhBCiWZEgK4QQQgghhBCiWZEg+9rx47bacPr9eBwWzFYnPuVVl+l38Dmx2V14Aq+6IKLZUXw4THq0OgMmu\/dVl+Y5PNjNJqxOP4HmfKzWJeDBXq1DozNidvhedWmEEOIl8OOxGdFpdBitTuRMJ95MflwOB053A0eA34XD7sT1mhwoLyHI1lzUme1e\/I9d1CkeOxaTFacvwKOX\/bisJsw2D36\/B4fFjNXpxd\/0hapHAK\/LisVix92UK\/V7cZp1aA0W7O4XSXBe9PnHWDd3I2e1Gi4tSyRy3GYKX2IK9DosmKxOfC9yBR5wY2\/svqo8w8YV69l+qQrv81ah+PB6vfj8TZMEFJ8bm8WM7XU50ppQzT5zNNm2AkDx4LCasTg8Nfu0iffH0wLeB\/unnm9QwIPmxi5mR\/5C\/4ixZOwufCnlaApK4AKLY0OZsPkees\/LWosfl82MxeauPY78eD1efP6XeIfI76T0wgamhA2gX+Rklh+530QLVgj4PLi9r9HdLb8Hj\/fl34jwu+1YLFZcTfHZFT8+rxdvE93pVPxeHFYTFsfrfNPo1VB8XrxeHy\/pdPinFvC68T5x3fe68WMvOsHqCSr694lmxvrTVDTJcgP4fR48r9V5zo3HG6j3POexVWPQatBotWh1egxGIwa9Dp1Wi1ajQWO04fW4cFotf\/C1evOmBHy43b7X+BgA8OMqO8mypRs4cr3y+bPqr7B37RrWHC\/luRGniX+j6vMSguwdNqaFEz3nFGWWR69W7Z9OzNB4VuRU4nzwov86a9LCSFqcTWX5JVakxjBpWw5Vf9jeNnB5+3gSEhZzpLwJF2u6y+nMcAYOG8mE9dewNerzKNjKTrIgIYZpe+7htOm4tCCKoMR13HmJQfbqsgRC09dzR+Nu\/JvMJ1iY2Mh9FfBRciCT9ImL2Z\/vqPfE56+4yN6Nmzh4TcuLf9oAXrcNi9nGg1xkvXWcOUlRTNtZ9MJL+02UAN4\/qAY9e2UioePWcrvqBfZZQ+yXWDMuhrQVpygFAuWn2b5+G8fzDL9hfzTMcvMIMxOjmbmnpM6\/e8uvsXH0UFRpyzhZYsBobYKEGKi9cWb3NOkPihI4S2b4EFLXlaB7aUG2hD2zokmceYSbNsCRw961m9l3+T5N+C14grvoFEvjBxMxaT3ny0yY7U10U8ipI+\/walbuvokVIODHWbtfXg0nd4+uZv2RO2gcL3dN5Yd\/JT5hIjuvGH\/3shTDTY5vX8e20\/d\/wwVlAJ\/Lhtlif3gh4izLZdWYMFJW3\/jdZWvOFK8Tq9mC\/WGthI\/y8zvZsOUI13WvUShpFszk7lzJusP5aJ2v6WW8I59D86IZHD2DzZfKsVgdTVIjq9gruH5wNWv23cIBEPDjtpkx21yvJgAqVooOrWT9ibsYXXXPkr91CikjhhGsCiNMHcSQXwYxZHgI6rBwwlTBDJ+4i+LcA6waE\/8HX6s3Y4oH8639LFuWxR1r0157NCV\/9TlWjR\/F2BWnKWmoAk7xU3FqJVPHz2bTZUu9x4uiv8HRbevZcbbspX7nX0KQ9XJ+QSRDRi3n5sMLbRPHZkczqEdvUnYWoKv91ErhFsapVIzbW4jVfJ6FMaGkbbqOpukLVQ89FzalEBk5n6ymuQVXQwngKbvKgdlhDAyfwq7Shr+6ir2cc0sSiZy0l4oA4NJzceFIVCkbKHiJQfbSwkiGJK\/mju4FvmbW48wdGUraxmtUNOaoDNxm14wEkubs42Y9qd5yYzdzRyWx4GBezcXtC3Fw68h0kkbOZX\/tDQnLzcNMHRnC+K1\/UJD1WclZkUjEuK3cfcnXOteWjGRI0nLyKpowwrgusjw5jIRFx7kHOK5uZkZ8KstOFvIyru3tN7OYHK1i8va6948xbxcTwkcwPUvXdCt16Nk7NZSRCy80aThXAudYEDmcMRvuvsQge5edU1VETzzADQdw7yCL0pKYtSeHJtxCT6i4uIbksHh+PW1peOYX4Dfd5\/CcBFJm7OYugLWC7RNDiVt6tUnX03h32DEtlfErL1Nhf7lrqsiax4jI0Wy5aPjdy3IWnWZlWhzTNlzkxWNxNYX7pxOTkMmJ2t3rKr3OklFDiV2W87vL1pz5cjYyKS6NZacftEAwkb1jNgkpizh8+yXf6fjTucve2anEz86ixPia1t+VnmBxfDjJKy\/RlIe\/T1\/IvulxjJ57kHIARxkn58YyckYWv\/\/o\/y1y2TQxhUkbbqCvJ8i6TVqqyu9TVl5JVc4GxqpCGL36JDllOqoqyrivcxDQHKq5\/vtDr9WbMcVF2YHZJCfPZn+Z8\/WsxVYcXFkzloTxqzld2sjWE4F7HFs8mlET1nOxuu4rKmfhSZanxjNjyxWqm7TAT3opfWRNR2YwImwSu4qMNReM7rMsjI9HPeh7gjIOU2Co2UzVR2YTrR7LxlwDeM6yIHYE4zdfpthcjU6jQauvxubxP7lR\/S6s1Tq0Gg1agwnHw\/aqftx2CzabHZvVgkFnxOqsbSIV8GI36Wveo6\/G9rAdsZ6LW9KIjZnPviIH1ToNGq2eaqur5g6D4sFlMWGyuR7dcVB8eOxmTBZ7AzUhXrQ31pM8NIxxW4sbuHD2Ul24jQnqaDJO22pecurqCLJ+XFYjeq0GjVaHwfzYnUNFwW03Y7I5cFmqMei0aPVGzA4\/EMBlMaLTatDqjVgcjw6ly4tGMjxtDXnFOqr12trP736qvAoehxmDrubv5oos5sSOIH1zNpUPN78bW3XNNtZoDZidTzajqD6zhNT4dNadq6xzWwTcVoxaLdW2R+sOeB2Y9Fo0Gi06gxlHXU10fC5sxgKObUglIjidVZf1mOweTPnHmBkfzsQt+bjM+pplGC04nr4p4HNgNmgf\/t3ZQKs6r92EXqdBW7v9vQABB6bS2xyaGc6Q2EWcL9Nidj349F7sZgM6be13y+Z6+PkUxY3dbMbu8mA3GdFptegMJmyep04jXgcWow6tVou+2s75RTEMH72S\/MoH38AAbruppkmQRoveZH+sGbcPp9WMze7AajGh15sebgOf04pRp0Wj0WPSnmRJSiTJS05QHADFZcGg1WFy+lACLmwmPVUaDdoHTY4MBgzVVhwPjyUFr8P88LtZbXU\/dZfOj9tajV6rQas3cf\/iXqbEhTN1Z\/HT3wS8bgu3ji4jVR3J1B0F6I0WHmzOgMtSW+b69pcPR+321uoMmGubSQbcFjQFOaxM\/oXgSbspqzLysIJR8WAz6WvfY8TyWF\/Qmn1kwuZ0Yqk2ojfa8DzVLqvOIKt4Hu73mnI8e8\/S57Rg1D\/6LE+3sva77ZgMWjQaHUbbTbZMCSNuShY3LIDXRrVWi6H27n7AbcditeN02LFV62q+B9V23E+0hQzgdZhq9pHOiM1mx2G3Y3N6n\/rhCuBxmsjePZf4sATmHbiLwWTD\/XAmLw6ToeZ8qjNidjx6v+L34rCZsdgdOKr1GKotOJ\/+5fZ7cRi16AwWXG4Llbcus3jUL6im7adKV\/1ovxDAba2uadamM2CyuR+tp7bWzOG0YzLoao5Hkx2PAnhstecyHQaTjacPp6cpbgt6rR5zHduhwePK6cZutWDUatDU7ucnzjABT833UaNBa7RyZ3cG0THj2HapnktZvwf7g\/OoRou+2lZvtxfF68Cs02KwOB+dU\/wuLAYdWo0Wrd702G\/dY+\/zebAacjm1OpWQ0PFsvq2n2u7DXZbDspRgEpZdxW0x1BzrBhPWp7+YARdWY806dAYT9vo2sBLAbTdjtjlxmo0YDNXYHmxAvwurUY+29pz3ZBf4AC5rNXptzT40Wh67+PO7sJkt2J02zNU1ZdQbzTieaQbjxVH7m6fRGbE804XBi\/PB37UGzM6a30m31UjpwfmMUo9kxrbzaMxOvIqCx2ZEqzPhcLtx2UyYbe7HWt4E8DotmM32R8dwwI3NqENTe51ib+BmtOK1P\/yt01c\/almk+Lw1XT5stceTyYrLV3OtY7Y6cFoN6I3V2B5sP2\/N8aDR1v7ePbZdA34XNrMZm9OJ2WDAYLI\/0XpIUbzYzdU158zHXvfYzZisrtrmqH6clkfXIE\/sGzw4LCZsDicWUzV6gwW334OtWofWaMf7cMbfclx5n7pu8OGyGh\/+rpocTx27Ac+T13yeug8iv9uB8dImpkeHk7L4CKUGM3b3o2tKl8WIrvaaxmT3PCqD34vTZsViteOw6Gu+g0\/\/Fvk92AxadCYbbp8d452LbElXEZy6lly9EevDC8jHrs90hsdaDCkEvA4sZisOp43q2vOcsfa6Q3FbMeq1ta\/ZaahDQMBtRqcxYGlsE1dDFjMiI5iy586Twbv6KPMac61e+5v\/4Bh7Zh89zefCUnvM6AxmnA+\/FD7cditWmw2bzYROb8LucuN12rBabdjtZvT6aqwPfmgUL\/bHfp8sj53XFZ8Hu+2x46naiuuxL5birzkGnuheoQRqr\/kffdf9zkfXIfpq66PfuAct8ywOnLZqDAYjFqcfn92IVmvE9lhXoIDbRrX+wed98hzosVsw2Vx47ObnXJd7Hx2LdY4jEsBtq649VvVU2zz15hCl7ACzY0cyace1J\/Z1wOvAbKgpY83x7nrinGHL3szkhGQWZN2tc9mKx4FJp8Vgeey61+\/EYtDW\/EYZTNiboF\/nyxnsqWQzY1QjmHm8DAug3FhBYuQUlv+azIi0RRwtMKGgkLMyiZBRi7hY6gXXaTLjo0ibNZdZM8cTExrMsMFhjFl9imJ77Qf1Wrl1eAVT40NRq0IJDk0gY\/sFytx+oJKjy0eTEjuOsRPGExycxqqT98DvpuzsZjKSw1Cr1ASpYpiy6giFNh9g4NL2CSSEpjBp\/nLSR6oIGTKM8NEL2FPgxu8q4GhGDKHp67j8oD20MY+saZFETNpMXgNnA682l\/Vx\/QlOWcvN57VV8Rm5u30iqhHzOP3gTpnjUZAt9IIScKO9eZBFY2OJDA9FrQ4lPHEWe24aa34EvA5OZUaiTpvBiiljSYoKJSgohJEz93Ej\/wQrpycTrQ4hJDiYuFkHuGWqOelcWRJLcOIUlk2fSHpcOMHBwwhLW8mJYkvtD5eCp+oia6fEETp8OKqweCZnTGBkUBiTt+dQBQRcBvIOLGFyQgTq0DDUQWoS5u3iaqXn0Y+c6QzLk2MZs+ZsnbW43rxdzElOIfPAHXyAz13G2S2zGaUKQq1WERyWxKxN5yh7qs9roPwau2dF8Mug\/vT7uQ\/9gtP4dV8BVXfPMjdRRfykpWyaFkuoajhD1QlMXXcJQ81dCgKuKm7snU9ydChhqhDCY8aSuSuP6rp+ERQ\/jrJrbJsaQ1iYGlVIKJGjZrA5+z4Gw3HmR0cQPLAPP\/UZgDo2hRWXvfj8DiqubGV6UhQRoaGo1eGMTF\/Oybt2Agr4XaeYHx3KmIWbyBiXQkxoEMODwhi79gqVD5ph+fRc3zKD+LAQglURxIybz9RRw\/klbR13Kt2AB0vRcZZPTSRSrSZUpUYVN40tl8pw+BWggG2TR5I6ejpj0lIYHjqd\/bf0eJ232JWRTPiwYYSExjFx9njiVGGMWXmKe4CSs5Epo8aw9Ewl2K6xY04cg4aFEBYWwYgwFUEDf6Bn6HS2XTYAftz3L7A9M5VwtQp1kIqRk5Zz4Lal5sdV8WG+fZilY8IZOjSYiIRJjB87hqiokczY9XTTYgf3b2xi7KAB9OvTh34D1SRNX8sFqw+3NoesJWlEh4WhVoegjh5N5q48jLX7S1FcaG\/sZW5SBGHqEEJUI0iYsYUbGhPGvA2MiQhlSP+f+KnfMKITZ7KvLAABBxXn1zExfgRh6hBU4ZEkzthNrq5moT7nSTIj1aRNX0DKyGhCk1Zzw\/zkLawngqwXCDipOreeaUlRqFQqQkPDiJ+1i6sVzocXgw5NNjszxxEXoSY0TE1weAqLs\/LQ+2pO935HCUeXpTMyZBjD1dGMnjeNlPDBxM0+Sq4dqDrIwvhkph3IwQ5oDy8mJSmVWTPn8+u4SIKHD2Zw+ARWHS\/GGahZp+f+adZPj2HY4CCCo9JYkDGVyelTmL41mydv0Ju5c3Ylyf360qdvP\/oPDCUtczvZTlD8dkovbmV2fDgqtYrgkBEkz1rPJU1NyHTey2ZFeggjxs0mMzqEqLRfOf50ZjOXcnJBMkkztnA9ey2p4WqG9KvZLzGj53KwAiCArfQ0ayYkMCJchSoskoRJqzlf7kHBhz1vLalhsUxdMI\/05BhCQ4IIiRzPmrMFXNw6m7TYcEKCQwiOGM+6q1W126AuCmW7pxAdn8nJ+4\/XPLuxFB1nxYPjSq16eFzZa4+r7VOiSZm0mNmzZpIyIoThg4cQPWsX2VW1YTbgpuzUWqbEhTB0WAiRqRlMTY1iWMxkdl1+NsgqXjvFx9YyLUZNcGgYYaEhhIwcz5J9NzG4nv0A\/runWDVuFBPXX8MF+D06rh9YRGrYg3NmHBOXHaLQ+uQJzVmWz9aJwxg2eAB9f+5D\/8ixzDtYhl+Tx6q0IMLHL2PbzAQiQoMZropi9K9HKX9wg8ZrovjYCsbFRRCmUhEWlcS0DVeodD97Ug84qjk8Jwz1mJksTIokKmIsGwtc+P027h1dSvqoEahVKsIjEpiy7RpVTgWFALb7p1mWHsuIkBDUoWFEJGWw54au5gaSdj8zYmNJnzWN9DFJjFAFMSwohunbr1Hpqq1JUDwYs3cyPz0WtUpFqCqC5MVZ3NDV7hfFg+H6VjLTIgkJUaMKCiE+Yzc3zMWc3TyL+AH96Nu3PwMGDiR12WmKPFC0ewZJaSs4W3CHs8tGMXLCJi4ZHtxdu8fxZcnEpa\/nYjUoPjMFx9YwNSYMVWgIweGJzNpwpvY65dnvn99WyqWts0kYoSZUFUxY\/CSWHSrE5Qf3vWusGBdCZO3xNHLiGk4U3uPE\/HhCE6YxLz2E0PgJbMpXCDjKuLp1BrERYahVIYRFJTF941U0HgXwYa7czXS1mjGzFpIQEcnIiTu4+9jNYZ+9iG3p4URmnMRUu78D7ip2T45gZOZp9HY71Xl7mZsWw4jQUELVoUSMXkjW7WpqTluXWZYQxuhJ80gbFYcqdjEXNYUcmp9I4tyjFFcDeLAUn3jyuIqdxpaL9x8dV1NHkjJpEbNnz3rsuNrJtapH+6\/65i4WpUcREqxGNTyY2JmbOVtaO0iTz0nJqQ3MSqq55gtWxzJt7XGK6+gaUZ17jCUJffj55370GzCQ0HFL2HvTC34vmux9LEoLR61WExIcRvyUlRwtqak8UCqz2TE9lojEacwZM4zQpNnsenoYB0MBh2Ynkrp4H\/ll+5mhCmbowD781Kc\/ESlT2JwHKH5MxcdYMTaO8DAVqrAokqZv5IrGA7iovraCJHUcMxbNY1xiNOrgINQjJ7PxQgHnN04jZWQYIcEqVNGT2ZpreM5NOx\/FW8cTlbiYi7pG1jvrDjI9MoLJu2892eqn+hiZCU9dqw95cK1e+31SvNhuHWLF1HhC1SpCg0NJyNjOhTJ3nTWSAY+e3P1LmRAdSkioipARqWTuuEKVJwCBAg4vGUdCXCqTpyUyKHIGO46f4drmqcTFpDB5ZjKDw8ax7FgFit\/F3XObmBkbhipUTXDwCFIzNnNVX3NzwFFyhWXjQogcN4t5USFEjV3CyceqCr3aPLZNjCB2ztGHNYh+YxF7p0YSk3ECU8CPo+Iau+elEB0RTqhKRVh0GgsOF2LzA24TOauTUcdOJXNSJBFRcSw6aUR\/eD6xIzM4YnGhoOAxFnBixUQSoiJQh4YSGhbLlK1XqXIFUAhwcXEcoWmZrM+YwJi4MIKHDyMkdQXnSm211xBeqnN3Mi9tJGEhKkJUamKnb+NSmaO2H78PZ8lJVs9IYoQ6hDB1KLFTNnK2xFZHP3+F8qw5RMdNYlf2o43hd5dzYcsckiNCUIeFEaYOYUTqPLZdLMf54FCyZ7Ntcjyj5h2kuI7fWH\/xCZaNGcXkjdm4Ab9HQ\/b+haSqgwhVqwgOi2fyiiMU2X7fuAwvJ8gGrrIkbjhJK\/PReBTKto9hxLiN3Ly+i3lxqSw4fQ+7omXf5HBGzjzMHTNgP8mCxOH8ODCRpVnZ3DVUcGHVWNS\/JLDiQikB\/JTtn0tKTCpLs26gsVkoOrqENHU0s\/fk48LIyaWJDPhmEAmzV7Ll2DWKjS6qLqxnUkwss7ddosJup\/T8JqZFRpC+6hJWxU7OnikM7zqAuLnbuXBPT\/mV3SyMGUTQ2LVcdvopOTyLyKBUll+qSbKW\/ENMjwwmbW1uA00EfOhv72TMj135PmgUi87X05YDUCxlHJsVgTp9O\/cffMkeBtlNlAQCeOzXWT06lpQ5B7mts2DW3mD3pAiCYpZwzRkAxc3xaUMYMCCYiVuvUVx6n\/wDM4kaMoB+g6OYsfMShaUV3MmaRcywYUzcV4Y5ADnLYggaMIj4+Qe4VlRO2Y1tTB85mCHpu7ilD0CglF3TohkSPI4Np\/KoqLrDseVjUf04gHE7c9ECjgurmRyfQMbuK1Q4fFTnbmXqiCEkLDtL8cOmlnfZPyeakek7qKt7mCdnM5Mjo5m+8zbg5+buGUQMTmD+\/luYbJVc3j6XmPBUlh6599SdVz9eVyUXd6YzMmwKm\/OduL0BzHfOkBnalb4RU1h7shBNRQ6HliUwXDWONTcCEDCSf2AGUVETWHuxAqv+HrkHMkmITCDjiO6Zu0sBUxmHpoYwPG4eR0v0aO5d5+CceEYkZHLK5sNtquDs\/GiCk1aRY3bi8Qdwag4xJyGB8SsvUGKwYLp7juXJakLS91Dp9qH4TzJj2AD6\/5LE4uM3uXu\/iEvLkwkaEsfynGrcKFiOziY6KIjEFae4eVfD3fNrGB\/Rn+\/i11Oo9QI32TAlkVHTdnDhrhmXqYgDs2MYEjWfM8V2oJDN48Po2zOEcUs2svNkHlpLMUcWpzB8yCiWZl2nQlvC2Q1TifipD6NWn+E+oFxdRVp4HJmHiwkofqz6Ckrvl1Gu1aO5vIkZkf0YOG4jFwwKftNFNk2JJXraJs6X27CXXmLbzGjCxy7jhD6AorvE1nFD6B87h+3XtBhKLrN7XiQ\/DIxh1jN9ZBUCfjf3zq9jbHg0M\/dX4HR58Nnvc211GpGp89hXYMRhLuPytglEqBNZfMELBLAUHWJmRDBxc3aRfa+cwqv7mBkVRNjYzRR5PNi1JWwaO4ywmUcw2V14\/R6MV5cQFxJG6rIz3Ll\/n7vZ25gSGUz4pAOUunz4PSeYPrQ\/\/QcnMnfLfrLOF1L91N39B0F27MZSDD5wX11CcqiKxGXHuXm3ioob25gZG4R60j5uGQOAjmOLxhKXvIRDuVocbi1X14wldGgaG3MqAR8568cTPkjNjO3nKdRryN07n6RB3QmZc5w8O1C+m1lhUYzbeRUboDm0iMT+nRmQuJT9V8vRl55k5bggBscv5VgZ4Mphx\/RQBoRPZ+OZUnRl2RxcNIr+P6iZuuX6U0FWwe9zcfvIryRFJLHohB6n24tf8VJ+eSNpw4JJ+fUgd\/Q6Ci\/uIiNyGJETN5DvBXdpNksju\/JjUAIzVh7g1IVcKp5ubm0q4ciMcFTpOyn3e7CV32JN6jAi5p7A7nDjDSg4tVdYnhhB4vzD3NFpqSq9wIb0aMLHbafE6cSRv5yo\/gMYlrScM7fvUXrnLMuTh9F\/wCDCx63mZN497heeZ2WKiqEJq7hRX6ew\/5+99\/xrK8vyfv+1++Lez8zzTOqZ7q6u6qqyCQJlASKanDPY5GBsgxM2GYPJGGzAGEzGJmdEFBmUpfO9LxQQwVXVM9NP+HxqvTzn6OjsvVf6rbX22hzQkS7DO6aauX03Y2qZoDI1jJC0OgZXTzCcXMrVh6ULYInqRDnf\/4uIhNcf+LK9x1JLDsF3RcTX28u9LTN1ZKl+xju5kg\/zWvZm2imL9uCfvFNpGrsJZM1f35Atv4N\/bhNfTkwYT9d4\/zwVVeh96sduFmZZl7opCZET\/ngEGwKr\/WUE31GS8noC7fku051PiFAEkVU3e0VnCjYrJsMqo9XxyFXpNGsMGM02DFszPAv8N\/7iG0lJ61c2t+b4WBOHxDuQ\/AGjI+hTQrAiguKeFQ72t1juf0qMn4rExjWuY1mb7oi2xL\/y\/Y8iIgvraHs3wurJBft9BQT5xfDwwzxbe7usDTwhSiknvX2DE9MiryLFiMNfMLi4hVYzwavkACTRr5k7MMHBW5JkP\/GdJIPG4QW2NNO0FIbh\/b2K4k\/rnAtgmasiNTiIpFf9zO2dcjTTTE6wLyEP+1gxClgWa8kM9CUgp4FPK1p2R6vJ9P8JcdYbJo4N6IeeEKUMIa9lmnOjBRsGJl4loAx4QMe6lf3eQtR+0Tz\/ZF9DYa2L0mA\/IiqGORUsrHU\/IkoZTmHLVw6NWubeVxArDyKtaoIb20StW3yuTkKlzqR6bA\/dwRKfazMI8I\/myagZs2aCMuU\/8iefMLIqWhkYW2Zre4Wu1J\/4t7\/KiSp4Tc\/HcVa39piqjMBLEk5R9yI7mgVGmjMJ8FESX7WIBStnuw1E\/ek7fpbGUfKmk97Pq5y7V5dYztmsi8NHep+eY7unat5pIMFXxoOefU6PBimLCia6uIeZ3VPOdiZ4GSfDK7yShSMzMEKR0oMf\/hJAxosGWgbmOTCs0ZQqQ57WxuIRwDSv0tzlapm2B4F4+OW75KomSc73\/ywi\/tV7l1yF3PUmrm6CPcCy3EROiC\/KzCoGojEOWAAAIABJREFUlg7Ym2ggJ\/AnRCm1TJybOflYQbw6mJyGUbYvzlkbeEGKQknSy0\/sXTPsgtWCfvoNOUEqossHODQYMdtsHE42kSW7izqrhpHtIzRjbTwKEyGOKaPvxAZ7E9RG\/YV\/vhNAYkk17z+Ns3JybW21szQnS5GltrBnM2PY\/srbNAWyhFpW9AZMVhvnG\/0UByuIKO1l5XAfzdIHKqJVqJLfsmXWcTxWhPS7HxHHPmdoaYPVr72UBt\/h+x898Ut+xcDcOmtzHygN9UEU+Yr5829kTIQdmhN9ESU2sX78GzNg3wSyPTd89cGKGIevvoYZsK61kBeuJqKsnandc86WuimLluB\/v5nJ4+uLYGTmTR5hfrE86Z7nyLjPdFsR96Rh5LasYBVW6S7x59\/\/2ZuI++XUffrK5uYUg89C+OO\/eBKW+Yja\/nEWD4\/YHnpJhKcvUaXtLBxoWfhUR5bCC7+0aubMYFz9TIniH\/mT7z3uP22jb+gL2+72ybLHTG0iEmUmnUcANo6WGkmWyMnoOUO42OdTaSjqmBJ6Vo45P9pg+FUUUt97PJ8HwXTGVKmMv\/zghX\/GU1q7+5jfOuOoNQ0fUSotpyYEzMy\/TiQ4NI36CQ3HZ0csdGQR4C0nd+AQnQCf8yXc+f6vBOV3ML6kYW3sOTHiO\/iVfGZHB1ZNE+lqGYFZ9QwtbKH5Uk92oCfeiU3MHgjYzvsojgwgrKCV8fUjdJsfeZ4gR55az9jO9fU\/YaDsHorwJ\/SvX66NprOIIG9f0l4Psa83cr79mZoHwQQkv2bA1fdxhw\/l9\/APeUzvzk0Wsi50UhAkJ6piDAGBlQ8lBN1RkV4zxcH5LlPtj7inCCa7Ye6\/tHf473T8zjHd2SrkKU0sH63TmaEi6tEI24Yd2lJVRJePsbPZR1GQkqT6r+wBnPaQF+hL2ONPrDkZa7GWZIkvEXVfOLPOUhUnwz+lho\/re2gPDtjfHeJprDeKuLfMWk\/pL1MjC6qgd8PqiIjP8zYnAHn4IzqX7b\/R7k3RkKNAGviUIZ2J6YYY\/BQ5NK84F9fCam8pAT6BlHw8h80P5Ad4E1Y6jBkj8x1Z+EsTeT33y9MuGDSMvYjmzvfeSBS+BDx4x\/E3njVpV6iM8UZV8PEybX8lI2sDjBzvbLCxrWVfq0W7t87Cqzi8vNNoPzNjtRnoyZIgiq9h48QxFtMCL8O9+TG0kpVDh4KzzfDingi\/vEG2L2DqsRrve2VMbDpTzgK7LanIPaN4OXeAdaWaWB8ZSa1zrn1YwkEnOQFy4qrG2QYwHLGr2WR7Zx+t9gDt3jDPYkX4p7Qx6Rr0MR8fhyMPfsi75ZtzZ5qqIS0gkKw3C8AFY6+jkUlSefn5kJNzHYajTWaHPzG2uH9LSfcZX9qSCZJn8WbdfuV05h2Z\/t7EPJ9wzenexGsiRHLS2vYQdid4EuGNX3Yrmj0tB1ote7P9VESIkKa1ob32D5bDFeriPfGJq2b54IRznY7T3RmGB0ZYPgVMJ4yVBuIb+Yol5x5w0xn7WxtodrVotVq026v0FarxUj9m+sKIxfKe+xIREc8WOHBGwHebSBJ7EVe\/xbF5n5YkCd6xdSwcXPLncKkarwhnabGZ410Nmu1d9rRatNo95mqTkEkSqJ8+AJapjhOjTGxl6sCehReW60mTiwmvHHaVhgunAzwKl3PvYa89Izv2jDhVKIVdi1cDNrZNukvD8FFl82bqAhs2ltoLCJKHUti6yK72gAPtHl+bC\/GXqnn4cYOD4ScE+gSS17XjUlbHE29JDpDdUlrsWKupGhIVgeT2OLJkNjO6w202N7bs\/K\/dZ\/5TJbFSOcmtR2DeZ+FFJF6q+7w7df7LBTuDT0mOK+H9MQgX+zQmeqPI7bfrB\/0eHRlifKNfs+Qqtzdw+KkAf+8Qyr+ccq57zwOJiKgXyxwZhVuVrQvIvl7nwHxOT5Ycn5hqZvacwMjGRV8+aq9QHo1r0AP6w202Nbvs7tvHsjdQQqhHILmf1rBaP\/MoxIuAgl4WnFvybAvUpUpQp7YzdQFsNfNAqSbhzZgdyLYXoJYFU\/5e4wrCLDZlI5dGUT2hxzxVRZRcQfabOdd6mmaauR+oIvHV9YysndYHnhCpjOTJkOPuxTzv84MQhT\/ik845E0Y2Bsu5J1ZT8NGAVTNKcbAHwaWD18qa3eh4le5sFbLEN+wAwskGVbHe+BUNOSb0jK23qfj4xPJyVuPg6w3mWtKReobzevGEo+kKwnyDKB05wS46Zs470xDdlZEzeOAYj4Wj1jQk3gnUbx3f3pDCPESR2pvwZzNc6Z1mM3C8q2Fze+eGXNVNHQAr1MQ75cpZPTHK02gvVFnvWbbamH4RhUiRydsv5y4+WGvJQeUfe2tpsbDSzaMof6JKWpjRatnfO+Do5IwLvQGjyXIjcGpdfsfDeyqiyscQMLHQlY2\/1z2KerUcn12gP91jeewTw1+3LhssukjL3JtkFAH36XLoaMPmFI\/C7qIu6HNlsE\/XOkmTiAh7uYjlaI2GZB\/ECdUs7uyj1WrZX52mMUmMZ2TVlewegO3ikJYUET5xtWjOrQgCWPXL1ET7IklpZHbN\/g7t1hLVcV54JDWiOfxERbA3fjl9zG7bS0zP1iYZ\/DiNRmeGvbekyOWkNC+6SuAE02fKQj3xzx1kw2jkc2kQvsEFdI5vsa894GB\/hw+FgdwJyGVwc4epZ5FIQkroXXJyvY21zlISsp4wqAG+viLO7x7FPauO+wYmXifhH5RD6wqg7aE0VE3MsyHOgO3uUiJDEqmcNtsDRol+SBKfMbizj\/ZAy\/7WINUpSqRRzxi5Zrwu5nvICxIRWtTD9p6WA+0+OxPtFAT5EPxwlJ2tcYqDPQktG8LklKeDeVpSpEjjali02ACBs\/UP5Pr5EFo+dVmmq99m8lEw3n7FDFvNnOzUEe8pJaVZi1G4TZfZ0G+\/JVkq5\/77UywWK\/stScjUxXw6MiBg5Gh7A83OnsMH2ebz41C8lXl83L0ARijx9yakeALNhXOf3TbNKXKUGe0sHgKYrsnVPnN1yVfkqjZBgjKhhUk3uXoW44Uqs4clh1xJ1Xm0zTqVo8BmbznJWY8Z+DJMV34giqgK3q04fb4JarNkyEKf0rd3Y9AIS50UhvoTXzlh1yXCJgNP7yFV59KucRVEsj9aR5xcRlrzFsLhJK+jxKiyWti0fKPcQzvH21QFyvQ2O6+erdGdpUKR8tbu81oPWK5JQCROpHpp1z6n+ytM1Sci9o6lYf0M7Ugpgb5hVHxxlLUKOg6a4vHw8Kdk\/NxRTmxgtyEBkU8arYe620s8dX3kqETEvF7h8Lf2cfiFjOwNX32hhmSJDxG1Exxh48vLWGR+aVR\/XGdPe8DB\/i7DT2MRKeKpH70WlNvv52mEEmV2DaM7+xwcaNnf6KE8Sooi4y2rBg09Bf5IoyoYOHSUL1uX6SkJQhJWSu++49r5LF331fhEVfDZFTEysNpXRog4mJIhE9aNIfIDPQl7NIz5Vvtk43ChmXSliszucxCOWHqbjtI\/n34D9sq8g202Nnbsuku7w+LEYyI8peQMCFgNp0yUqhGpSxk+MyEIgE3PSVs6vr7ptJ7Yt4GYTnbZ3Nhib9\/uG26vN5Dq60Fcwy7HJvhcpMQ7rIzJHaeOOqIjQ4ZH1CuWD4zMvQxHFJDHx1WnbRHYe\/+QxNRyPm7qWG5OQaaMo6Z\/gb19u082V52I2DeaypHrDYFWeJMpR53UwoQbSNEOPidOHUxe\/Qh7Wi37+4ecnJ2j0xsxuXj+gsm6ZPxUmdTfUnZqXeyiKFRF7LMJBIzMd2Tg7x1Oad8Bx2c6DCe7dhs1c5uN+u30dztHVtOYhNw\/h56xGrJCo3nYt8Y5sF4dTWBGPZNvS4jzj6diTGNXHsfvyAtUEF9tL1cFYLmedLknQa+nOVnpoST0Ln\/4qxdShQK5TIZMLsPH4weCCj+wZDmh72EAATE1DDkR124fL+NF\/Ot3Hkiu\/cYvo4FxnZGphmgC1EV0uK3tyeRbkgIUZL7dATbpKw1GHPmUUc0GfQVBKBKrmP2V8MH56gD5\/hIiHtQyWB2DSJlJ2\/7tPzJpV3gZ7YWqcNDO+HAFyC6YsZdDTb8hLzoIlVKBQiFD6vEzP3mm06WzYLXp7UA25S0HF04DsMLrKF\/uxNSzc+p0g9apj5Pin9PH1jlMPQ7EO\/oFC3tu2m3qCaHe4Tz8sstGXx5q3xiqvrp1Ez7tIT9I4QKygvmAyaYSkoIUyOUKFEopd7\/7V+Q5PUy7BMPA6PNYVCGFdC7cjAheAtkZbIBl5T0vkxR4+foTFJvJo4YPfN08d9s34U7HTLYkESjPpMGR4Dv92k1mkJRkt2ZCe1O1RHjJSHkzx8VqF2me\/873HmKUcjkymQy51BdPbz8iC7pvaaCjY\/dzBZFyCQplCLGZj6ju\/8L2saOcSX\/A5xI1vhEvmXd0fBOsF6wPvCA90h+V3L5mvnd\/4ueAcr7qjVjM73kgFZHYuG8vSwEEywD5ChFx1ZscGMd5GOBF6KPZK02Exh8F4R3pBLIWTmY7eJQWgp9chkKuQO79V\/54N4W3s0fAMlWxvgTnfGTRYffP+ooI9Q3l4ce1S+deP0R5lOqXgaxg5Gt9FiqPAAra5x0BhX0+1STh+6\/fcVesQCGXIZPJkfl68IMqi7qxaebbk1FKsq6UXp1Od5Ie9C0gK7AzXkWCQk1Ot8PoCVZ022M05UcRKFciVyiR+fzMj17+pHecwNk677IUSBIa2Lz+Npv9uAHL8RZ1Cd7Ic\/qwAOaTUUrVPoRWzFxpIW867yDD15f09kOOzt6TI\/MhuemAi2\/IvB3IioirWkdrnuJxoBfh5XO4ixT7raQpFKS2LHKIgGl\/mOrcONQKGXKFAqX4Lt\/9WwAPxzXYtt6QIlaR3DnjFvxapTFDTuC3gGxbPgEBsdQMX5Y77PQ8JNg\/guqJHba785FLY6kcvkwbWOfbyAvx+waQtbHa94gIZQSPBu2laMLGR55Fyggq\/IB78kG\/PEhesA9xtRvYNGMUhfkQ8fzr7ZMFV4DsNmA5XOVVjLdd\/wEYDxkvUfD9D3cQyeXI5TJkMhlSXxESVRKNK2ccTVUQJg6l\/KtjnzpmDO8y8fXyp3ji1AFurZj7clB4x1GtOb51D5kwVUGoj5qCIQ3nV26YOZnt4HH6TblqnjkEVqiOE9vlylWpt0x9qhR1Wi9LNi2dOXIU0Q2MuUXFttoLCAi4Hchi1rHS8YgE2c\/clUjw8QyjoHn8m00yLoHsMEZA2BmnKdsfkY8f6sgUiqo6GF89udkXAIBdvjYkofDPpsPxKYaNSR6Fiwh9ctns6XTjHekSEcHlo1zsDlMo+44f7vggd+hMmUyCj0iGOqWRtWv7VG0XB7Sk+iDO7OTcIQsmXS\/ZPj\/z408+SGVyu02WyfD19UWc1cnBuYHdrjwi\/WRIVPeIy3nM28EFdk5N9pK47Tcky1Vkda25ycYB7ZkqFCktLJ3N0pii4uc\/\/YyP1KGP5HJkPh78GPGSL7vjtGYqCEjrYuZaDzPBZkMQQP\/5KbGqMIq6lxy67xLItswDbPOuNJSAuNdMnx4w+CSa0JQqJq3AajePQ3\/gD3\/1RqZU2O2KXILoZy\/CcmsZv+atbY3WEvfTH\/jBU4pS4bBDEh88vNWkVU6wpRmjKMyXqJezlz\/SztGSpkSZ3uawUyZ2Zp8Q4ammeMj97Rfs9ufhJ46jYdvI0XYdiSIFuX2WW8EOgFW\/TWuKDEVuH\/unW7SnKQgqHeHICAgmdkZqeBAbgEouR6FQIPH4ke9lhQxrdTiBbPTLVbQuw3IdyFo4mbtNrpJdclUTLyb4QT8LLoFcpiFNhjq1h0XrAZ0P5PglNDN+TTAEmw1hq4eKGC\/+9S\/XfL67P+CfXc\/QLZ3xrAvtFIT4Efdi1O5MH43RlOCPLKuJK\/VCmiGeRckJezTGydEUVbEKggs\/fLs5pRuQ1QIcL9OZqUSe3GT3cS+2+ZQn4c\/f38FHcSkLEpEIWVAGLRvnaD+XEiSOpHLJWVat56Q1GS9RCOVzzt4tJi66MpH4pNB0cHFLpaAN80gpalEIZRPaW4OWt9I3gWz3TV99qc7uq78Y5wAtXfkhePzhr3hJ3XwCHw9+CC6ia+rqwlmmG8gL+J5\/\/1F0RWa8f\/Ym8vF7NnSbvMsPwi+5inGnTbUt8a44DFXsMz47BiSs9fP4nozQhwNX1uRi4QP3g3xJbNBg2xgmP9SXmMq5bw7bdrhIS4YS\/5w+tMcrtGb6oS74ZOcNwcb51gg1mfdQK5UoFEpk4p\/42UNG3qC9h8d4WSC+916y4Kzzdgeyx3YFYDmZp700gTA\/BXKFAoXcm59\/+CvxzVpOTTBcpMQ7rpr1o0twOFISgCj6OUv7q3RlqpCltbFyLTtm12HH9OSFIPr+e7x85a75l4u9+Fmaxdvp3WtBrGUaM2Sok1uZdDPsNsMOA89TCfS+g49UjLcyg5qh68e7mJlpyiIwIJXqyZsRkksgO2K3UdujNGb54e2jIiAqleLqTia+aaN+O\/3dgCxzL4gQR1NQEIYkvpyBVQdrfXlK1L08CpJUyGJKGVhyXHcA2Tj3BkJLdaS5gGw3eX5i1GUdTO0fsre1iUazzc7uLofnFgS26CwKICC6mk9Ox2G3l\/J7YsSZNXzePWJ\/a5NNzRY7O7scnJoQOGS4JpoAdTGdbscmHU80k+AnI6NhDRBY739ElDqOjIe1PIxTk1q\/\/MtpcOGMlQ8PUHmH8\/SrnuOlaqI9pMTXrtxqQMzaFV7FeqPM67+MpjqBbORrVmxm9PvdZMt8Cc7pZEarQ284Qduciq9vOh3n5ksgm9zE\/rkDSJmXeBXpy53oOrZOnGnCNeripPjnXgWyi+5ldROPCPEKpWRqh83+AgKl8dTOuHljZ70UBCmJq5pgD4Ht1vsESf3IaRhn+1yPwThPXZqMwJR2t4zsOUMVUfiFPeTdyi9lZGexYMWs13GhO2Z75TOdLwtIUfvgoUrhWf\/2LYraCWSzaFy1X3EC2aRaJ3oS2J2sIdwBZM8XW0jw9SHq6TD7+ztsbWrQbG2xs3vI0enN9viCzYTh4oLzYy1Lnzt5VRJPiFyCKvIxI2eA8YiREjW+kY7AAxYuZl4S7qsg9nk\/q8d69BfHTD4JxTfoiT0j6wSyDbucOhPm5n7yFSLiazY5ME7xSO3NvYoFDtyX53EQosgKZnfMcP6B\/BA5fhnVDG+cojcY2W3Lwl+R5MrIVsX6Evygn3mHY3A+UEKoLJxHHzcu+dEwTMUvAlkrp6OvSJb4EPl8AI1rCff5+CISiU8qlZ92Od7fYnNTw9bODjtaPTZBy8SbBPzkWbxxw6xnX7rI+BuArHC0SEe2DFFwPu2zh+j1BnZmm0lVKkhrP4HzdXqzFYjj67h62qkNs9GI2eYGZB0ZWcvJOGWBIoKffLlSFmk+bSPN15f0Tjcg+2afs2813bkCZL9SHuRF2OOZq0B29y2pcgVpbcuc2dZoTFEhCymlfWYPnd6Aaeo5MT4h5A5vYNtpJlUSQFrXHJe+9hpvMn8NyMZc6Ya7\/e4hwQERVI0fsN9bTKBfHK9HL+GabaGd\/L8FyG4O8iJKRmBB75XqEt3yADlBPsTVaVxANvzZl9snC74NZIs+2e8btHwuVOKhyqFzdYvtnS22NBq2tu2NfYxWI6eTjwnxDeXJ9LkDtJrQdWfg6+VP0dixI8hiwfQhB4V3PDW3AlmBjbp4JMosutav1QUe9pAfIkeVXsXQ+lW5cmaOquPEV+QKlqhPlRKY3suS7ZDuXAWquEbG3bZSbHUUEqC+HcjqvzTxIEhCZHE7s0c6dDoDZosVm82K9ZaDH92BrB4bFoOOC90p+5pJ3tc+JDtUjpc8ivzWlVuy0U4ge59Ox\/c5gWzI4wnXUyfr3aRJfAgpH+Vie4BchRfqwl42trfZ3tpCo9lie0\/L4bHuxrFjLiCb0cGpg8GM591k+ngSXPyJhY0ddrc1aDRb7Oxp0Z7osZqM6C8uOD\/RMDP0lsr78QRLvZGmNjF\/bEHQviVFriK7a90tmLJPW4YSRUobS2ez1MZLkEbXMDC3x8HuFhqNhu3dPfaODMAidSlS\/FM7rwJZwYrJZMJiA8Pnp8Sq7lH8ztmk0Q3IztlncvfDY6IjM6iureF1RizJrx0ytNBGYYAIdX4zM8cH7Gjsfsr27h5HpxfX9jDa0Ay9JFwkI7lqkkPtDppNDZqtbXb2DrkwCZytDFMQ6kPkC7fAkBPIprU6usaa2J19SqSnP4WD7h7GBbsfclCJ42jcvQSyOR9M3z5exmbgoPs+fn7ZtM41kuYXxqORE8yAba2OeJmMew+7mNvXozcYWHwVhdi\/4EpGNvrFspvucwLZDpaOgfP3FITKrsjVXns2\/orEK5UOwfc\/MOdCI0s0pMkITOth0XpEd44CVfwbrlbbWzGbTFg3OykLESPLaWB0z+nzbTt8Pt2tjdNuANnjcZqT\/JFlNHLFOm1+oiJCRtjjcU4dQDao4D3XK4rd1+lWIJvSbF+38036smV4qovo1eywva251HNHZ5itOg6Gi1GLI3i54Og3YtNx1JKElyiYJzPOIJ6R885MJD6pNN8GZAUzy68iEfvl0b\/zN\/Rl\/hUge8VXX6y1++ovxjlgn45sJeLAR7RP7XO4t8WmRsP2jv0cWv010GKaqiFDLib8URcLTpnZ2mZnd49jnRXBPE9bbhB+Sa8ZcxoqqwPIxjxl2BGgt633Ux4uI6Sk78qanC\/0khXoS2LjtgvIRrsHhm7QMSuduYSGpFM13ElOUBCFn+x\/bD1a5k2SD+LwUj4snaDXX7C\/XkW8t5y8j2B1AdkXzDk7trkB2bYzEzbhjMFCf3wD0mgc3+ZMb0B\/3s0DmTeJDXscu4BsFatujt\/nkgBEMS9Y2l\/nXZYKaWrLNSBrlwGL7ZDuBwGI1Pl0jK1xuG\/XK1s7u+ztn6C70fhslaYsBQGJ7sEhG5tdpUQognhQN8bOhQ6d3oTFasVms7jZIwPTDWkEBmTS8PUXMrLPRzFgxaK326i9zQl6a0rJDJHhJY+hsG31v3Tk1d8PyBo\/UhQYiPTOH5EUfWDNOeGWzzwOj0J+5w\/45HUw75SQXwKylZMc6WapifdFnFjDZ62rBxm6kyNOzo3Y2KKz8BqQtczRcl+JKPQh3ZrLxTOeOzuQHTBcG4NKkkn9nBO86JhtvY\/cK4TiD\/Y6FJtmiOoMMX\/6ixchkQ9o2QQEK2ajAf0tHeCEo3naksWIwsuZAjhepiVJhCi0gqnbVutMw4cHKsQxNaxeB7JR1axadJxMleF\/V03p2Jnj\/87Q1MQj8kml7fS\/CGRDihhaPnMYbRNLVTGIPaKpmj9C2G4mWSIjvn7Ctb\/EvN1Clp+UuOopDrDwuTgQcUgJH9YcH2\/6wusEH1QpbUy6BGOZ5mw\/1EmNjN1SY+0Csm+XAA0fK8soqR+2H0WEgHmpjSKFiIDCrlsOKz9m8m0i\/pJ06h2W59tAVkJSwzrC3hRPQu6gTGthy6UrrOhPjji60WHWyvneEDV5hdSOHjvK1i\/Y+pyP3x0p+cNg0x8zWuyP6N5zZu0pWo5akvHwTKBh16F9TYcMFasRqR8x+YtA1pv4Gg2HpnPeZ8vxinzJ5I7DM7Dq6C\/wwyPiKXO7Fph\/SrCHmpyPK47SDDMbb1JRiBOo+waQZaud+35SQsr7WHf8r+XgPcUhUsLK3t8CZAXM+\/08jZMiTa5l\/Mr62VjuKMBfFEh++\/qlETVdcHJ0ht4C24PPiZSqyGxec2XKtCP1xPnJSf+NQNawNkJR0B0CSj873iGwN11NnERBcssBWA5YropDJM+k3dX96Zzt0QYeFlQzdgaWkx3q4jyR3e+1v8N4QG+OFNG9Cr4cO5nggp2ebBSicF7OnXGh6+WB9LcAWWdpsZ6BXCXe4U8Z0zitrpnDziz8vCOo+LKPVdfLfYUvkS\/GHY6ogHHkMeGeanIG17AIX3kR6YN\/dhvTjjJpwfiV14k+BKR1\/I1A9h6VI3qss\/XEK+WkVk87ss8C+qkGMvxVJL3+bUAW3TIDxaGIggvpdUaKBR3LvSUE+wZT+tmEdXP0PwVkK6M8UeYPOMrnzth5m4xIFEbF14vLYIvpjKOjc0w2I2f\/LUD2gPZ0OT4JDSwdXJP5qccEe6i5P7DsOH7K4pKr2l8Fst3MmwWW6xKQKpKpHT1y6dbFxixkqjjqbmn2tNVVjL\/En+LuSw0naOf5+K6HoYXDG3bGBWSfjSNwwHRTOQUvelh2yvT2EFX3fPBNrOPmIVe7fG1IQKbKpN1hL78NZL0JejKF5WidhngvfCJfMW+4tMHG0yMOz2+ej3gVyNrvWgyLVEWJ8ImtY9Ftr57p9IhjnRHTcjeP8sppW3Ssl2Bm900ivj9F8HrlEOtRO2lyGQn1My57ZLvop1DtgbpwGI1Rz+fSYLyVOXTNXSJV68Uxh2d6zLYLPpdHIAnKp3NO5\/hmM5t9lRSW1zO+B0w8J0YRTEH38s2MrAPIcvCRyvtp+PlKCU1\/QO2Ew5s+GqchUYJX7FOGz9xPGzjm6ORmgPR88T25fncJyuu5zGIKZnTHR5wZbOhWhn4DkIULzSDFahHq4iGc2ySFszWGCwPwVj9mXDBzvF1HgvevAFkEjMeDFCslJCWF4x1SwfipvQeB8X0WvncjebF87GiqpWe6IgwfRR79e78CZDM7WDrBYa8CrsjVZlMqCnG8S66+DWS7mLcIzFbHIgvIpmnq3CFXZrY\/1VBS0cjkwmc6c1WIwst5v3MJmIznxxyfXtw67htAVthm6GUkMlUGjctOvrawNfiKKKmCrI5dhIMJXv1ngGy6HGlio92HsR2xUhODp28UrxbcOrIbTzk6usAs\/DcBWds2TYkSxKmtaE6tYDXZ+07cEhy7Qv8pIDvKPgJfK2PwlSRR\/VlAkjQOAAAgAElEQVR7OS7dCUcn55eNgpy0N8DzCB\/EabVMukqCLehPjznRmcAyT2vOrwNZLhbpzQtCFFZKn8vfvWC+q4BAcRiPxyxY14fI+1UgC6frA5Tfu4sqJo2gkFKGnbtrNH3kyDwJez7nsDsW9ucriPCSkdNv+xUgm0H7uQGTeYmXIR4ocvrYdWw1MB68JU3sSVzdDie\/AGS9o1+wtG9mqToan4D7vF88dekw7VAVRWVvmNg3sdiUgkwUzsvhrUt+MJxydHyG\/kaH9zM+PY5Aca+MD6vOp88ZrohErMikafbyG85WP9PV08+Mq+R5i\/ePwwi4V86H26odnEC2cgrQMtVYTsGLXlacNkozSGWoCHFKw9Xqh7+R\/n5Aln1aU2X88D\/\/SHzbKpdHiF3wsTCYu\/\/0PwiumsF1gshRF\/dVYqIqRy+72i5Wk+T7E35Ph9lBYLurmAgfb0Ly6+j7ssrKeCMFsZFk14xxwCHv8hUowl\/x0TWhAruDr0iW3kWV9pzuqVXWpjuoSIsm5VEPGnRMNiZy9\/\/9M8r0l3R\/WWam5wVZyjvI06oZc9YSCntMNSXyw\/\/3V4Jz2+1K6HiZgZoyHrfOXKvttqKdridG5E3kyxkHk52z0fcA5c9S0ptWOLqegbcesd6UilR+nx6nUOr2GMxX4BVWybLVhG6nhWSJCGncKwampxjpKCNa\/Fe++z6IyjUTZouBd+ne3I1vZM8FZBd5EebJD+HVaFyCvUp1lAh59ns05zBZ5sfdv\/wFv8xa+iZmmRkoJ1bhiSy7h9VjAdimPS8Ub897lL39xNz8KM3FEXj921+IrptGC6y8uU\/AXW8SnnYxPj9F++MkFN\/9A99FVzPsTN0d9FIaFkR85adbzx4zTb4mSaEirXEegTPG69JQeoeRWzPE4vYyww2FhIv8SK8dv6WUR8dibwEBd5UkVU5wdGHmaLabND8R8VULLl7YGX9NyM\/eRL1aAuGY+bYMZJ5KYko7mVpbYKKvjvzYaLLfLN8wBubDBRqTpEhC7tM8ssjizCgdD0OQKhOoWQHMZyxWhuPlHUHF6Dy7FybOx58Q4iNCndPG8NQ0g\/UZBHl+z59\/zqD3TIfR0kuW6C6xtTtuQPYDOeI7RL3a4NAChsFiwsSe+Be8ZXBqkamuh0RLvuOf1c9Z2DPDQSc5Id54R5bTNjzPl94npKl\/4l\/+KZDy8S1srPA60hP\/zA\/MuRxuLX1PYvG9E0BuzQdml6boqEhA\/O9\/JOjJB9YBYaScKGkgeZ2rYNvhXXEg\/\/Fnfx5UD7KyusjszAxfphfYPDZi0Y5QnybjriKZ8o5JVte+0P0sk5ikElqWQTieojnNlx9VyVS8X2R1qpeqLCl\/+MGPjG8A2e3RSnvnc0e6yKad522yNx6qJF4MzfB1qJVHYZ78xw8iQp\/OICBwvvGBwkApgalVDC7MMvGhmiy1gqDMFjQCCLp93mWJ8VRl0z6zzonZxNmXF0TJpQTntTHydYapvlck+8kJfNDDjtmKRd9FurcH8fV7vwBk+8mT3iHyxRpaC1i\/PCdB5Ys6r4nByQXm+itJCxTjn9PN8okAwhSViQruSjKp\/jDF3OgbiqLF\/PkffyS5dYYjBBbqM\/G\/403C0w5GVxYZrL5P8A\/\/gHe2o2ux5g2ZEiUx9SOcAzstOSgUEbwavKyc2Ooqxl8RTHn\/CQjLdJYEcdc3joctX1ie7ac2N5Af\/iQno2byViC78r6UUEkopQPOKL6V3ckGksW+hGRVM7K8zETvK7L9JQRnN7BkA+PKEHmBHoSUT98+WQBHK3SmS\/GJrWcLsJ1u0ZYqwjMgl3fzG5xYwLA\/wpNQX8TqTOo\/zTH3dZx3T5O4F\/+SydMLTqceofYMpGzKDch2puD5s5z8kaNLINubhfhONK83j24CWX0\/uUoP7j2bdTkUl5PXRk6IN14RT2gdnufL+3KXXD0ZtctVVZTXNblapCZRhCq5g2kdCCstFAbdwSO6jLcjKywPN1B470f+4W4SjSPXd+GDcbaNokAPJImP6ZjZZHd1mNq8MLykKVQN3dzcZ13qojhYyr3HI9gwsviukEDvAJKffmBOs8J45zOSJDKiH\/Xdcm7lMcs92Sg9lKS\/WWT33IJxc4LSkLuoH465njpZ6yTJ+w6K4lEQdOx8KiFQJCXk\/hs+L8zzZaSDxwnhJFZOu7ZHOMl2oaU50QOvlDYXkEUwsfchnxCxhJD8Jj59mWV2vIOH8WGk1s1yqB3lWYwcWcQT2kdnWFweozE9ELEql\/c753DYTpriLj8rM6l+P8Hs0gTND4Lx9bzH84lte\/fmWXtzNXHME1qHZlhZHKY6K5yoog5mj8G20Up+mAR1dhXvptaZ739OeqAPgYVdzOmB5UayFD6oc6sY1uoxC2YmX8Uh98uiedbphR8yWpPK3f\/nf6AsaGHWZdONLL8vJ9zDk7AHVXxc0LA49IaHyTGkP\/94c8uKZZux2gR8PNQkV\/TydWOR0c6XZMckUPb+EN36MLmBHoQ9dQsM7c\/SnCRBkvT2srTTpGWuLh6JJJjMxnEWZsd5X5VBoDyIjKYNBKycaF4TfUdMVu8vAVkQTCcM5oj4\/k9\/QVXxlTMLgA3TXCXRMm8UaQ30T8ww0pJLhO8P\/Puf42nePsXCZ4rkdwh\/usSuaz62eJPgiyTV0exJ20muu1x9qCBd\/RP\/8k9ql1xVR3vhn9HLrMvQL1Kb5IMqqY3pCxC2OimJlOCX+pzOyQ0WP74iO0SEf04bM3obx0MvSBB7EJBVSc\/0KmtTbTxKiSKtooeV2zqrzreSq5YR9fSzw5cTOJnppMDfE7+kcnrm15n52EBhuBS\/5Bd24LQ9zItIMX65Pd8GsvuzNCWKkSS32PfEXmwyXByAhzSRmpkNDk0CF5oBitXeSENzaBpZYGF6mPZHCYQnVzOrP+dguBCVVyjP5i+B7GFzHD\/f9afsqxuQbU\/F2yORN9qbQNZ20k2GxIPo6nWOzaAfrya\/uJEx7a+cibzXQopUbA8auV8\/7Lzpqy9UOXz1ITSAsNFOSYQPXiH51PV9YXVlnDcFsUTer+VGdSo6ZloLCborIqq4kaHFLRY\/VpMfF0tOwzRGVmjL9kMW95IRF5BdpKsgCGnEEz65hmFle6yGBF8xYQ9qGVlZZuLdSzJVEkJzm1mxgWFpgPtqT8Kfzfzy2M81DBV48z\/\/6E3Ik0lXMNV4uEhVpBci\/zTqPn9lcrCJghAP\/vKDB9FvtFh1x4yV+OEZ\/JRZdyDbkoynZzItp0YstmN67qvw8b3Hk3djTI\/38DRRyZ0\/\/weKx1851NkYKpByJ6qSFe2l1RoqVHAnvIKFPQuCtpu8e3L8Mqr4MD7P\/GAVmcESVDldLB2DcNLPoxgpHn4PqHk\/weLGVzpL47mX9opPazd3o+5256EOTKRu1NV5AE1vOTE+nkQUv2F0bYeNLy2Uxknxjqjg\/Yrju05HeJ0YTFhRF+u3TKN1oYP8QCmRFeMIGJjvykftHUDqs37mt1YYa39KgkRGzJOB\/8RZ6Jf0dwSyMF+bTIgiiTfLB1eciIPeYqLlEZRPbOLiwdMBHsdFkN30hT2ncKy1UBAeQHzdODsCYLtgqec5GaEKpBIJUoWa5PIevmqtCOzyviKGmIwmxtxnRDCy+bmB\/AgVMokEqcyP2KI3jGgs2DhmojWH5LBkHpSWEx8oQSwSE5rxkg+bVzOtO6OVRIhVZLXapdC6OcSzBH8iKiauddHVstiagTo0k4alyzumg1nqk8Qo72XReCM8buF4pYkMhYoHHxxekeGAsacxhKQ2YA+SGNkeekFasBKpVEFQZAZPXhUQHRTDi2kDRpORj6WhBOV2cuBogS5Y1mhMC0aV0cLumRPIbtKSHUb0oyF2L2D2dTzBqTk8u59GQrACsUSKOrOBCbfOJ7aLWZoKYlGLfZDKw8h4+ZC08DhKWr6yCwjWLQarsglX+uIrURFd+ILSjBjupb1gcNUECBz2FhMZmkLV51u6LQDm2WYKYuIo6VjAAtiMGoYb8ghXSuxrrYymoGEEzTcaK1j2pmjOVXJHFEFx6xLajSFK4sPIfbvqZAT2Z5pJUYeS\/caRpTUe8LW9hFi1DKlUgswvguwX\/awZb\/kPQUC\/O0FjZjh+CilSqRx19ANqRpwt723ot\/soDZUiC4igfMyCxXLBSnchMX5yxFJ\/QuNyaCzPJDAoh9b9cwyWT5SFBfGgbR\/n8tgswzyJCCS7aYtjCyAYWOkoIS5QiliiJCjpERV5UQQ+aGRxzwTYOJyoISfSD4mvDHloBrXPHhAdlMjD4Q2MbPI2O5iEh8Msu1UVCcZV3pWnECwWIZYEkfq0hIzYePJefbJ3LZ6uITsqiYrBXTgfpTbTnx\/u+CCTK1AqFPZ9X3fDKHg7xhECtq1Rmoui8ZNKkEilqKLzqRvacGX\/DBvD1GSpEfv4Ig9J48GDXDIyUilqXbtlNQX2vjTxICKWsv4T1\/yfrw3yKlmNRCJGEZpCZv4rCpMDiav47NAvFk6WeiiLD0SlkCJRBhNX2MTMsfN8OyMnUzUk+ElRhmTRsmUDLBxNNXA\/KgCFTIJUFUR8UTfLjmyKRddHSWgwuR0HNxx119faRqiICiSzYcNxtJOF44lG8uMCkUkkyBUBxBR3MevqsmHDsNbD09QQ5GIxMv84SipLSY0II6t+ii0rYNPyufoBkXJffCR+xBY\/IS8hjIzyD8xcALtdPIyM5UHHNBfAfs8TYmIyaXKrt9sbeEFCTAo1Qw7QdDhJS0k0cpEYaVA8GUnRBCmCSX09diuQ3RiqJDUylcpRN4dHMLH3pZWSKH9kUgkSWRDxpW\/4cmKfL\/36OBVJwaTUfHsPEqcb9JdEce9Bh71ZnGDgYOwVMXIxfhF5dOw6eGZzkMqMcPzkMqQyJUEJhbz9eoZVsHAx+4rE4CRez1649sjq+4sIVkfzdPrUFS03fyojIvA+zTunN533uWqSgmN5PLbDjYI7wcrhRA25kf435Kr00zoGNLTcD7kmV2u05ocRV9jH7Ll9Do8n3lAUo8TXR4wqqpCy3GQi0ktonbjFbAtWDqbbeJSoRiqVIZWJkYc+oKpvDf0tjUmsawO8SIkivWocPSBYtXztekSMnxS5VIJUEU7m8\/cs33J0DwgYd6doTJNyRx5NfrsG294cr1KDSHx1mf070\/RTEBpIzDNHltZ2zmrfU9LClMilUqSqEJJLO5g7u36GpL1rcXd+MCFF7zlzr9236VjvLSf1nh8yqRSZKoSEsi6WjswI2DBtDVCRHoafTIpUqkAdU0Lb\/IF9281OMylKFXE590mJj0AllSBRRvLw3TJHbueUHk+\/5WFSEHKpFIncj5DM1wyunDiOnrBxPt\/Kk5Qg5GIpUokfMWWdTGsdYxB2Ga66T9DdvxL6qJ9lCyy35BAdX0b30iUXHY\/VkxEWTmnn0tUtQ8IpK\/2VZIYqkUikSOSBJD1u48vB7ed3CrptxhpzifSXIpVKkAfEUlA7wr5FwLgxTnlSMGl185c\/OFyiuyCSyIJ3V4GxcZ\/Zt4VEBiiRSiUog+MpbJrFLpoWTneauR8YQdkn8y+X8QkmzgdLCVMn8Xrh\/NJ3s+nZ7H9MUqACiURJQNR9Gp9mERKYQe3qESameREdSEbdGpf+9x5deeFEFvWyemKf+8PJWpdcyZxyFXwpV633Q0go\/cSSK0C0RlvBPeIK3jvArQ3dUhfPM0KQiaVIJUoii1sY33WcNSsYWB+sJTdcafcdZH7ElbQwtn373mDrSi\/lidFk101d6kHBxtF8L89S1Paj86T+ROXV8GnbvobC7iSNWRHElw9+e4\/s4RJd+RFEFvY4mkeaOVpo5b7KC\/m9NKpnAaxcrPbxNDUUpVyKVKoiJKWMjgU9VsHI8fgzYkNSqV8xO4CsnpPuHNRBCbxe0F\/ukf1QTGhwPl1HumtA1oZl+gVxQfG8mDnGBGw2ZxAWlEuX5uSXT9446KEoKoK8jsWrgbCT\/pu++urbq746Ni4W3\/EiIxSFVIJEqiAguZx3M1puc+EE2yFzXRUkB8vtMqMMJv1FLwtnVhCW6CmLJzrnDdNOt9S6xsDTZKIyq6\/uOxeM7Ew2UxThh1QqQSILJKmshVmHPdetjvA4MZiM+utnJV2nc7aGS4kMTOT1jPtBsxbOVt\/zJCEAmUSGKiyerMfPyYkKJrluGYvuhK8v4wlOqWPJWcduM3DWU0hwcCHvHIcHW7WT1D4Ix08qQRYQRurj1xTFBhH5eBjtuYXJZ9EEZjex4bZHduJZDIHpNazsmwGBi6UOHqWEopJIkMv9iSps48u+0dFnR8C4PsDrBxH4yyRIZDLU0aW0fdnDcFsmfqebgqBgkutHOXTGHG0XrPS+IDNUgVgmRSaTEZj8lK6ZY5zH4Z59riQlLIqSns1bdZt1tY+nSVFkOYLlgnWf6Y4yYvwkdjymiCD75QdWbrVRv53+rkBWsJoxmSzYhGtDtFkwm8xY3a8LNixmE2ar7XJCBCsW0\/VrNntJr05n31vmMvACVosZs9nKzXUSsJgM6PWO37gkScBmNWMxW7BYLBgd940W29VFEU6Yas4mIiib9i0AK3ujDaQGqsnvuR5ZF7BZTBhNN5WmzWzE6Nivd4N0u4w+i8EvvooFneB4jxmT2d1BsDnGocdgNGG1WTGbzC7FYLOYrj0vYDU7rglXr5kdY7RZzJgsVqxmE0a93jH+mywpWM32+zoDJosFi9mMxX1dbBZMBj06nR6j2YrVYsFstmAVQDDOUp8ZQVxxpz3ifRvZrK53cuOdOkcpzDd+6xiXzWLEoLcfJSG4+MltNmz2PQRm9\/8QrJiNevQ6HXq98Rud7Nw\/yYRBr0On12O4scYCVpP9G1zbEJzv1+sxGM3YrBZMLt63OfhbuLbG1665vlGP3mix87nFnc9tLv7WG0xYrRbMZrNjLFfX+8rX2pxrqsdosTjkwPGczYrZbLaXHwlWLGYTRqMRg2ON9Xqda61dEmg1YXC8z2C8hf8tRvvcGYyYzBYsFjOWb8y34OLtaw1kzPZ36A32znlWiwmzxd0cC1idc3FF1i\/nymwyYNC7y6Hb\/OmNmK7wv9W1Ht+mS167KavO77gx+5fzoTdisdr5\/8o6ucmUwTlfFkdHdsHxvNXZWMy+5u57KQWb2zWbgYuTQ7RHZ+iMZswmPftDlSQolaTVfb29EZLj9zflzqFPdY6xXZGnSz3+TRJu121mowGD4ap+vC5v7nPjrvtc83WdZ2zu8naNHLJoue2e45v+NrkSXPxoc9e3zrkymLFYLJgtllv3vLqeNxvt9u0bcnT5qO0mzwhWTIa\/RZ8ZMRjsOtO1dm6TKggOfXRloq0uG3w7b1+dD5PlJsh1bc1x2eSrT1gdcq7TXVt3Z7On9kX2jAaHTjTdejai1Wx001PXZ1HAar7Nj3DctVkwuel4wWp22TP3MViuydwlfctP+dZUuds64yVf3yZPguCyATeHfakzrttMQXDuofvlT3GOzXyLD+Nae70evcOeXerp2\/SgU07cfZD\/JrlyysmtPGhz6SjdrXbg6ljddemV0V75j6t67lt29fKZy+92H4vFZMBgMGB2M1sufr+m5wSXr+z+uWaHnnP\/UAsmk+UWOQAczzvv2SwmjEbT7Trxyvdbb\/rkjrH\/Jl8dwWWv7XJ8C79e+79LmXHXXbZL39793TeuXd67tE\/Gq7L3W+yT81HB7gPdNqf29dKjNxgxWy954Xa\/3W6fr6+P3bbpXTbUaja75MT1jivPX792qeO+pYed\/3FDj94gE9PVKdyLLaNr0XQ5p1fw1jV\/z7RKZ0kcURm1jH0rmuMmW9dtlFPXmX+txP030N8VyP7fTzaM+h2+tpeTrlYT+3jA0UFSy5f2J2Tl1DP\/XzvH9wrp98Z4FhdMUuU0p\/+1AMX\/OWTVM1ufQ3z6U3qWbu6j+p1+p9\/pfwFdfKG1JJ6wtGe8+7LJ5kw3z9KUiEKLaZv\/jecK\/k6\/0\/9u2qon3ldKavvqt0s6f6ff6Xf6nX6nv4kE4xS1WVHElHQyd\/ErrZesBlY6ykhJLuLNtP5\/u1\/\/O5D9RTpl8dMTwn\/yIbKgkYnjy+yvzWK+FqX+7yAbZxufaSyo5JPuV6JX\/7fQdj9Vz+tonbrZrOR3+p1+p\/9FJFjRrQ9QmxOOSiJFKpUSEF9Gy9ThLRUsv9Pv9H8o7XWQGx5F8fv134Hs7\/Q7\/U6\/038j2Q5HeF1ZT+\/kzi8\/qB2h5fX\/3959flWV7\/ue\/zf6WT\/qMbrv7dujz7g9bp97xj2j995np6p9zs67kpaBsDI5JwEBBXMEERUBCWXOOSdERVGSIEFJKxBWznPNdz8AFBGtqnNKq7j7+xpjPVkL5\/yt3wz+PusX5kEONZvfPWLoI5Ig+17Tw4T93m8ZmvIDi7xjOMOiFAlPDwn6scshxN+9ucOZfQS+baiXED816syUgzeGrwohhPghKOEwyrcNvVYVwmHlJ5NTJMgKIYQQQgghhFhUJMgKIYQQQgghhFhUJMgKIYQQQgghhFhUJMgKIYQQQgghhFhUJMgKIYQQQgghhFhUJMgKIYQQQgghhFhUJMgKIYQQQgghhFhUJMgKIYQQQgghhFhUJMgKIYQQQgghhFhUJMgKIYQQQgghhFhUJMgKsZBImIDbjt3lR\/lxC4LfNcW4xYLN4Scc+VELIz6wSMCDw+7CF\/pxz7oPRg3jc7rwqe\/9I5SQB6cr+LFKJYQQQohFaNEE2UgoSDCkoLy3AfRTMNMIszvxhhb4LOzFOeXA89ZnP66Qx8GU00v4R6tghYDXgcPpJfhTCGv2AS5vSSah7AxjP2IxQrYW9hcmoIuKIW3fQ8zub\/83Ya8Tu9NDMPyTv1i+F1UJ4XHacb59Yf1Htkok5MFhd+KdqS9VCRIIKqgfpPpCeJ12nJ7Qgvcyx81dZCWVcOjxGB\/u6IXxux04XD5CAEqIUDDEBz9dVD+jreep3d7EfV8Yv8OGZdyOyzc\/tPuwPz\/Lrs3HaJv8id0ohRBCCPGT8dMMspEAHocdpzc40xsWYOByEweO3aHH\/gFbW+EAHqcDpzf4H2hEenn5eDe5+lXUd8z\/LID5WR0Fukz2PvoPlfQH97g6E11BHd3mwEfZnxry4XI4cPvDM+9YuFmTTXpuA7etH6UI7zfVx4V1BrRFJxj90Qrh5PrWRDRxZZxo6WXMGUSZH\/Ijftx2Oy5\/mNmPupoKMORW8mDA81FKGQl6cToceAIfthfR+\/Ixe\/L05B5o\/wG3GsDxpJosYy71PUGUSAhzcwN7mpoZeytg\/RDaOZCrJ7eqjZfetz91XNtGkj6fhoejHy7Ihp9xfkcWaeuO0gU42y9wqP44d0c+4PFTI0z2nGVjajrbLlsJe6y07k1m5UojWVV3mHzjvFZR3aPc259P5pZLDPs+XLGEEEIIsXj9NIOsr5k9OcmUHHqCDYBx7tSuJa2wnpZB\/4fb79At9q1KpbixjX9\/P0CQ4UflpK\/MYP\/T+Z8FGOvaR9aKJCpb\/0Ml\/cE9qkpiRdY+Osc+TpBVOo6wLi2HiisDM++McXVPMvFptdyyfZQivN9UPxc3xGEoOfkjBtlHVCVFkVndM6+hP4f9CltSUth0aRDXzFsddVlEpW6nZfDjHEt7yzfkZ+RQc3vig+4nOPyIXRkrSat+8gNuVcHdVkVydBo1z4KEwj6e1BWQlb+fVveHGNr6lJr0laSVtzK4UJC9voNUUyFNrR8wyNLDuc1JxBUfpluFFzf2kJe+icOt9g+2x4i9h0vb0kneeJnxCIBKyNLJzcpkvo7No6F3\/gmu4hm7TnlKPKWnRvk4Z7IQQgghFpMPGGTD+JwTWC1mzNZx7J7wvI99uCZtWMxmLNYJHP7p3gDFY2f8+THWxWnI2HWeVpuTQDhMwDmOddyBLwQQxOt04na7cTmnsFrMWGyTuIOAGsZrn9mubQp3IPxGg1AJuJm0mTGPmbGM2\/GGpj8Neu2Y7x5mQ5KOtO3neDHhwPuqyAo+xzhWsxmzdQKHN\/RmI1MN4baPYzVbsE6O0tFcTlZsNrVvdRwFGOveT15MCpX3\/DjGrVgsFsanXPjndYaoQTdTNitmsxnL+BTuwLxmrRrANWXDYpmpv7fGKofw2Men68Y6gWN+71LQi2Niev+2KTfNlclE5+2n651BNkLAPYXNYsZssTLh8M2ZOxrG57Lj8vrxuJxMvDrmId7OX2F8zkmGLlaSrY9nTeMtxqa8BFUrN6rTSMmu4+oLD1NWM2aLjUmXf96QR5Ww1864xYzZbGXC6SP81j5ADflx2adw+eZ8Ggnjc01hd\/lnKxn31PS5YraMM+UOvC7vqyB7ijFUlIB7eu7iqy8dQfG5mLJ7CM4pX9hrZ9xqmakj74JlmyvkcUz\/vdnKuN3Nq+gU9jBpOcdGwwqyqh7SNTSJJ6y8cd4FXBPYOhrJ12hZ1XCHzgkXIQW6G\/KIzaqgudOMfdzy6ni9WY8Rgp6p6Xq0WJl0Bd47F1gJuJmyWTCbLVgnHExXaxivc5yOEztJMsSz6fATxh2v6yMSnP0309ea59WY8QghvxuX043H58Numy7DuMMzb1i5SshjZ9xqxmKdZLSjmYqsGDJr5vxCFPHjnLS+vo+8GnasogSnh\/h7fC4mxseZcM6e27PXhgXruJmRlt2kajKp6\/YRVlUCDhsWm4NAJELIPTl9D7NYsFgsWG3jTExOMuUOEpmtz0jg9b3MNolr3rUa8jmZtE0fh0n3ffZmxJBV+YgX3zXIKn5cs9\/RNokrMP+qCuN3Tkzfn8xWJpzzrxlQXh0LK5OTrRzflEbSmkM8CULIa8dmncDpVwGFgMeFy+XB63NNX8sWG1Nu\/5vnshrCMzWOxWzGOunA5fHicS00nQIgwNjjOlbp0tn9cO6PAxEcA6cojokls67z7WslZKf\/YAG6jP20ed8z3yDkmb6XztSPc16goMUAACAASURBVP7NlCAe++v\/i6bcr0fcRPxOpiamcAWVD\/jDgRBCCCE+hA8UZEM4u85RXpyKSafFYDCSvukEj8Z800Eh7KX\/QiXFqXpitXoMGiNZu87TaQ\/x8vJu1iR+xReff8GSZctZXtjEU7OFjqZi0ooaaB4FlFbq12SStWot6zcVk2qMYflyPSX1N7l35wL7SxLQaTWsWG6kqPYa\/b4IoOCf7OLC\/lJS9FoMWi2xhiw2H3rIVMBNz8295C5dypKvvuDzL78iZWMDN0aASAjr0\/NUFcZj0OmI1SSwavthHo1PN4ZUxcPLuw2sSYgmOtZIcn4+hWvSSdTnUbdQkO05QP5KHfnljZRmxmGIjUYXn8eua4N4VUCNEJ7q41Z9CWlxJnR6HTpTCmvrmhmeWSFFjbgZvVdHcUoceq0WncFERtlRHll8M\/P6Aoy3HmXjqqTp+jcmkFt+ka7JmQAetvH44HrSDLHEak0kF+ykLCOar\/PqebZgkA3h7bvC3rVpGDVadHoNhqxdnHtqnVl8qI\/jG5LJKqpg0+bN5MVpiF6+nPh1R7g\/Mj\/Mmrl7aDNpX33JF19+yVdLlpBdeY2eoJuW+hxSU9awvnwvRQkaNCuWYyjYy8Ue70zIUgiOPORkeR4mvQ5drIbE1ZWc7Jh6I0wC+PpvUZVtIn\/\/fWb78AND96nNjyevphV\/yMnzGw2sSzViNOrQaI2krq3hzoh\/uryzQXbtOSZwMXpzB6nxazk5MrOx8BQjR0vQJVVwd2ayY8DawakdOaSYdGi1RhJyNnO6c5KF+\/Ui+KwPObw+mwSDFo3WgD6lhG8ejE4vhGO+yPY8E0v\/8le+XK5FZ9jEuZGpN7bVfWQ9q+KW8Plnn7NkRTQxpSfon4gweHg1+rQ1VG1ez\/osE7GxK4lK2crZ9vGZgKPgH7pL09Zc4nRaDDotiUX7ufzMvsBiUiohXz9XastI1USj02mJNeaw40Q7Lu8L7jSuRb9kCV98+QVffaWjeP8lnikKflcvVw+sJ9Wgw6DTEqPPYH3DbYb8CuCk88pWck05lO1poDRZiyZqObGp62h8MDkdaNQIvqFmmtYmEB0VizE5n\/zCNWQk6sip7QBUwgEr7Rf2UJBowKjTodXFk73lyMy8Sg\/m5t2k61Io21GMwZBI5r5WVNWP7fFh1iTEotEYSEzNYe2GPIwxWTT0hlDCPp41rCIhv5FOu4sXZ9aRGqdFo9NjMJowxnzNV18uJanmKVN+QPEyfLOW0uwEdFodBn0Sq+vv8MIZQQXCU085viUDU2wMGmMyBTvLyIz5mux9TxYeWjwvyKpBBx1ndlGcokWr06PVxFNYfZmuqfCre6nl0TF2FSai1xrQaWIwrarkzNPxVz8KhL39XNlfQnzUCmJ1yRSUrSY7KY709cd4Boxc301eRhkHn6rAC27tLyItfTXbq7eTHaclank08YV7uDwUmLnn+Xh5r4nSuCiiYvUk55exfuN61uYX09S9wKkeGKO9Lh9d6h4ezg\/YjkFOrlpGVHIlD94abBPCMfAN+ZpU9jxa6AcrFcU9wsOjG8hJnL5Xag0J5O++Sp9jJpiqASbaDrMhKxG9RovOoCdpdT13XrhRVHDd3kmGIZN9rWYWOBxCCCGE+An7IEE2PHqWDalJFNbeomd0CufANXbn6ojfepV+l0rkcTU5Wh3539yh1+bE3FJLSbyO4qPtjAaDBMYvsS3FSF7Nbfp8IRTVxd3dGRiyqrk1BCgPqVsdwx8+z2TPhceMTbzgRu1a4r\/6A3\/VFbHvXBsj40PcOVCCdkU6u6+OEGGSB0fWkZa2lUMPbQSCE7Qf20x8bBoV5wZQwkG8HefYkR5H7t5bTAZChNUIjs7z7MgwUbDvOi+cHsbaL1GVbSJ9w2mGFBXHs4ts1H6JsfgAzSMTvGy\/QnXWEpboV\/H2VL4A1ucNpP\/pC2KyKjj9dJjR562c3mAiSr+KAz0hCLkYOlVGckYZTW2jOFwTPL++kzSNkbILk0SI4B89SqFGQ+buu\/SbzYx2nGRdipH0vY9wBSOEBg+zOiGFsiOPeGkZZ7zrHFvSdaTufYjNp+C6voXEmBiy9t+i64WFweY6ikxf8ae0enotbwdZxXyBbRmx6FY3cqNjlMmRZg6siWN53A4u93pQGeR4WTxffBpFYcN1OszjDFzcSeaKKPIPtjI2r5dNCQWwN9dSGJ\/CphNtuINhIli4XZ\/Psk+XkVV5hkfDE4zcbaBUu5LUyksMqhCZauXYhlQS1jZwZ8SNd+QRp7anYszZycXhN3th1MAgt6qyMGTt4Z5\/uu6HW6rJ0WdR\/dSNY+A8m5LSKa1vxer3YXt2kcpkDXFrTzICYB\/g0pwgO3xtEyZNIUeGZk\/ySYa+yWeFfht3UFE8w5xfH0fimkYevhzHNvqMKxUZGBO2cWMi8FbPtOLp4EC2Hm16OZfbXzAy2sW5raloYldxqHOKUCRMwHeT7XFR5NV2M+QIEI68mQIioQD+lydYazJRcvwpI4EQERW6GgsxfvU34jYeo7nHjKXnJJuTlqHfcJluB6j+e+wvSiZt0zHuDzoImB9yeH0CutUN3Hk5vzfLQUtjEbqVeey7+hynZ4TmxnXExxXRcHuYcCjA0JVqMhJTqDg\/QCAYJoKNR6c3kZ68kYZmM\/7gFN1ny0nWJLP55CDgpevaZmJ\/+VcMaw9wfcDG2JPzVKWtQFNQR6sCqv0ZVzbq+NJYRF3zCOMv27lancXSJXpyD3QBAcwdDRQm5rL1VA\/jHifmtlOsi9eSUt5CGA\/W5m3E\/OUrNBlbaLx8g3t9FjwjV9loWIkuv5Z7fcO87L5Jbb6WZSvSqXseRgn76KxOJTplL23OAGHvOGOjI4yMmrEM93BjezzLVqay++EEAVR896vITMxhx6V2hmyTmFu\/oThew+qjPUyGvNytSCU2KpWqK+28sA3xoKmUxCV\/ILG6neH3BtkxIEzf8Y2kRBlYf+QuvROT9F\/eR4FmJen7rtMfVmH4CtWrU8mpOkv7ZIiA9QH1xRo0qxq4PQaoDloPlaJdYmLDobsMT4zQdnYXmUv+hnHjCXqAl5e2kWIqoO7RbJBN48+fLiNn1ynabJP03qynOGoFqTsvY0PF3nmOdbFfEb+2kZbRCV4+ucie9M\/5Sl9AQ+fb3yky3sPJNQZM687z5qwBBefQRdYu+T1\/XhbHpmuOt\/6tb6KFiiQNOQ3PeWsKdsTP+JVtZKatprq5n3GXk5GHNeTpYsg\/MoQ7AjgvstEUS+LmS7QPW7H1XWZHloGE7TcYc6moY3f4pvYkLSOuH3l1ciGEEEJ8Xx8gyEZ4UpNBdOIGLj4exGqzMW4187gmiyhdGRcH3bjuV5ISk0bFlXYsDje+wCQDj5u53zGCMwJ4b1GRbqKgsRUzAG7uVmURl1vD7WFAecD+PBNpOy\/TOzuUbegiFYlLid1yju7ZbqvB8+xMMJFd38wEEHLYGB0eZsxqw2azMnqnkbXxcRQ1Ppr+tX\/wGpVZ8eTXPZyek6WOcL9xFYa4Mg63m7GOj2Oz9nKjKReDdh1nnjvpOZ6PxlDC0cHZqOJhoGUH6THvHlqcszKJnXeCr8KNe\/gGm43RpFQ\/IRKJEHaPMzI0gsU2Xc6h7gtsio8mo6GPcCSMr6ee7Gg9pWf7GLO78Pm9jHQ20\/xkCKfPTfOueKLSd9HcM4LVZsNmGeFuRRIr4su5Z+7mRJmJmIKj9E7ONt0U7u9OJip3oaHFEXoPFqCPK+Vs1+Trd0fOsD5ey6qmdiYY5XSZHlPhSR7ZZsKW8oSG1THElZ3n6dvtU0JPvqEkIZ1tF57PDOkzc706HWPCbi7O1qXyjDOb9cTk1PHAD5bbe8jUJ7P11DPMtnHGbRa6LuwlTW9k05mheXsIM3yvhlVx2ex+oIA6QktNAQl5B3gKEHIxMTrM8JgNm82G9WUrVzenEJ1ezRMAx5tBduT6FhL0RRwbnt38JMOHComOq6Al4mWqs4aM6Dg2Xx5k1GrDZhuj\/2k1WV9Hs\/6KC\/8bGTSE+\/YO4mIyqW6fejX\/L+J5QnV6DPE77mL1ATxgV2I0BU0vmXxXK3viAuvj4yg718fsDMfO+jw0Ceu40m2fqVs7N3amsDy5kvujESaubCbemEPN5U4sVhvjNgvPT24gLjaDPTdezBtiaeHW3nT08Vs52jaB3e3FPzFIR8t9ngxOATB1p57s5HT2XJ9dpSuMz2ljdGRk5lqzYb5\/lI1JJvL2P0LFT9fFUhJiimh8Ojv0f5wnJ\/LR60s5bQZ75xEKNHqKjgy8uk68Ay2UZ8SQWTN9YYX9dqwjQ4xYZo7h84ccKTagKz6DDQ\/mO1uJj82itmPmWlOcjBxfTZSmkCNDszeJAFMPK0nRZFL3bLpHtqsmA01aNU9c84bBPqklR69n9eE+HOHpc\/Z0iR5NYROtz80z19owp0o1LMtrpP\/JUbYkxZLT2IF59j6lPKI6I5qMXd8ytPjxOCht1OZqSd5+mZ5X63YpdDaVoDet4WTbFKgBpswjjIyYsdls2KxDXK3KxpCyjXP9gP0ONVlaTNvO8moaarCT0xuTSVxzhG5g6PIO0hNWU\/94Oshe352KLqOSa7aZf+Dv5erWBKJzD\/DE76LzWAF6YynHX7y6g9F7ewtpmlXUd739nfzDT9mbHUPavsdvfhCaoPdYESu+0mKMi8aw9iyWeT22QfsgjatiMG69OTOtZA41QtgzwdjQCOaZ82zs5R0qU6JI3P0YewAYO0mxJoaCg20MTjrx+gNYelq429rLhE8BVCIRVYYVCyGEEIvQBwiyZk6vMbL8q6XE6oyYjAYMBgMGbQwrE8q5NeQCBjizo4B4jYakzBK21JyluceMKzAzHMxxg\/I0E\/n1DxhRYeEgG0f2nhsMzLZARi6zO3UFhoor9M02+i3XqU5LIqPuFuOohKztnNtbRIrJgMloIk67jL99kcCGw4+nF3fqu8KuzHhW1dyfHmbm7OTadiO\/\/+vXaEwmjAYDBqMBbdRSjEX7uPZkhIc7k4nKq6Pt1ff38fLxLrLeE2Rzo1OpmrNqcWjyBU35UcTtuIuqRlCcz7m6r5iM+DhMJhMmfTRfL4sms+H5dDmDVm5X5pJoNBKXWciWvce51TmGM6AQCQ7wTc5KlixZgVZvxGicLrNeo0GXtZ9H1svsTNGQta8b25x2+uO9qUQvGGTHubAhHkPRcbrG577fy8FCDambm3kRMXNmnY7Ujdfpnl1xiBec2mAkqfgsbQusIeN\/1EhxQhpbzz2bGTJo5vq+NJKyDnBrNg8pPZzfrmVleh333GGeXViP7t\/+xjKtaea8MmLQRLEkroR9l+cHWQgP36M2P57cmqc4bY+oL0wi\/0AnKhAJT9J7bT\/FKfGY4kyYTFqiP1+GLrtmOuh+1yCbsIv7fgfjF0r4+sslrNAZpuvcYMCgi0FrTGTHLR9vTJsM+3hWl0lsciUPHHMOgqpwZ7uJqMLDDNvDwH0qEqLJbxh441i9wXqOdXFxlJ7pYXappc76XGIzynkwOLvcq4NbFeksNW6nZdjC04ZidJ9\/xnKN8VU9GnVRfK0p5fD9obd6j309F6hebSRKk0T66o3UnLrDs1E3\/pmJmNZbdWQlpVF1dfZBRRFCE91crl1DapyeOKOJOO0KPvvCxJr6x6j46bxQSopxPSdm1\/rCTc\/NTaRpSzk5GmGoZSvJK3OpmZN9fC8fszt7do6sSsRv4emZcvJTTMSZTMQZNSxfsgLTmrPYcGO+vZ1EXcHrXnS\/jZadCcSk19LxapxqENeTvdNzZN8TZIMTN9geH4ux9CwDrpkairRQHreSpV9FoTPMXGsGIzptLDFrTzF8YTvZukzKH4\/yeuHddmqzYsj8tiD7ZBJGT1KqS6L0UhfOucejpZo8Uya7r7+ASBDz\/SOUr0rCqDdhNMURu\/QvfJlexdWXwPPjrDPGsebok9dDZ5VnnNua9o4gO8i1ykzi82u5PzvUVx2iuSYXY14dbWPDtGxPJCqvhtZXJ4qX\/vs7yHxPkN2TFUNaddub71sfsy9NQ0rZER6eKkGrL+bovJEVYaeF48Ux6DZdfTvIoqJ4hrjbsJ7cxHiMJhMmo4YVS5cQX9XG9KXlorWumHSTDn36KtZXHORq20vGvSEikl6FEEKIRe2DBNmTxXqis2u41z3ChM3M6MgoZosN24SLQDhMKODF7fXgHOrmzulatqxOIXalic2nuxhXANd0kC1oeDizYuzCQTar6jr9s42p4UtUpqzAUH6Z57ONVPM19qUlkVl\/H486wrVdmcTGr+fIozH8\/gD+jtNsS0uguPHRG0E2v\/bBdMPT2c6FdSaWZVdxbWwK29gII6Nj0wuqeEKo9j7u7kghJq+e15nVz1Bb5fuDbEwqe+Y00ENTAzTmrSRuZwthzwRt+1OJiV\/LkYejuP0BXNYWdiVryG7oxa+oKAEfHrcH53g39881srM4HZPGRP6hJ0y4+jiYG4Om6AjtA6PYLGOMjo5itk4wafcSCN1jV2I0WTU9TMxpGLbtSyVmwSA7yeVNCegLj9DxRpDtpik\/ltSt93g5G2Q3XKXzVYt7kJMbjCSXvC\/IprPtfM\/MkL6ZIJtZx03LzB+Fn3Fu22yQDdF5shB9VBG1dyzYbWOMjIwyZrZgmXDifmuBF0Ad40HjGrKKyjl84yjr0\/M50KkCfoYf7idrhZGSpkeM+f0EnP082p1FTMb+hYPstc3E64s5MbuEccTO2NHV00HWN4XlTDErY\/KoezrMmMXM2Ogoo2MWbJPTi\/+80WYO++itzyI2sYJ79rmt8xC3thqJKjz6vYNs2dleZvvLp4PsTlr6Z6OLnZvls0HWzNPaPGLj1nO8ZRD7hGW6Hi0WbOMOvMF5MxEjIQJeDx6vnZG+Fi7UbqM4IZrliRs59MBGBLDdqiMrKZ0916bHT6AMcGNfDjGmNTTeH8brDxDouUxlZjyr9s8JsoZ1HO+f3ZGLZzdeB9nhlh2kxeRSO2ddJ\/9QG1XZMWTWdoPq4sX1bZhiUtlytoNxv5\/AeD+XNsVjmBdkD7+YqX2\/jQe7kojNPED3q68Zwv10H2kLBtmZY+N9wfl1ccQmV3Db+jqSqkozO0xRJG6+TuegdeZaG8Nim2DC5SfSWkVaTAa7n5h5Pf2zg7qs2O8WZM2nKdMnsuZ8J3MHNXju7SXHmMm+ZhuBvpNsSNaSUX6eTpsff8DDw8ZiElO3cf4F0H+S9QkJlB5rf73yb6SH89u+JciuqqFltnyRl9zdn4spr4422xj3d6WjXXXgjR\/vBh7sfGeQDYw8ZV92DKl7Wl\/\/SKJ6GWutJCU6iYr7Llyj5ymN0ZBV1\/3GPPCwY4jDhTGYtt1+O8iGvPR+k4vWmE\/NzQGm\/H78nk4OZGlI3t3KpA8iQT8etxfX1HMeXzlCVVkOCbEa0vbeYdTzU3hgtRBCCCH+vT5AkFV5WptJjKaEMz2vh6KqHjsTTi8hxUfPub3sqLtE7\/h0a1IN9XCoMJbo\/CO02YDATXam6smtffDDBNmmR\/j9rTRmGDFtv8DQTLvW++Qo6+JMFDW2Tjee+i5TkW4kZ\/9Mjywj3G\/IIdawhoPdr1e6DPucTE25CTJB17lSjJpVNHTNLBKkOum9uYmUdwXZZzVkL49n6xXHTC9dhKnnpyiKiSazqZfg1AjHCpYTu+4Szpls5hm9ypa4WDLrnxMK+rHerWfbrlOvGtqqz8r1TXpWpFTzZMLOvapEok1buTXy+jmiinuKSaefsDLOlQ1GonLreWKeaTIqXm7tTGBFTg3dbwVZlcETJRgMhRx+ZJkJnSqB50coNuopOtLFFKOcKv1+QTbwqIGiuGQ2ze+RXTDI7ue2A6x395AWncCmU\/2vH48U9OCYmsL5jqBnfXySiqwVGDLXklvcQKcKYKXj+Go02rWcGpk5qq7n3NmSTHTGvgWGFnsYvb2VuJhcGntmTq6ghWc1OUTFVdCieJlq3096lJ7SC2Ove98ifuwTk3jnLyFLCN\/93SRGJ1Pe8nqRmeDkPSqSYkje\/YBxP0DLtwdZ21nKjAZKTn2XILuV5pcK49e2ER+dQtX1wdf16HcyNeXEE5pXVlcXl2p2s+dMG9aZx6YEu46yMTaWzNo72IGpm7VkxidTOdsjO9nCwTwjug2nXl2f\/q6zbE4ykLf\/EZH3BtkSjg+Dp\/cc600acus78c\/s19l7ky2pMWTW94BipqM6g6\/jy7kze8pOdHOiRI+h+DSWhYJsxI3l4ga0MTnUP5udE+nFcnsHibEZbwdZdxgibgaPFmE05LD\/sf3NBYciI5xeoycq8wBt5tcHKOScZNIdRLHfYFtCNBn77jM0c1Io3nvsTl1JeuXjbx9arHbRVKAlYd0pnthnh9v7ad2fjzZhHZf6Faau7yDOmMmBuzOzTyN2mmty0Sdt4+xzFbyPaCwwYlh3lKczqx1HvG0cLU0gYe3R7xdks2t45PPQc7oEk76Qb55NL\/5ExEHX1fWkaPMXDLLqZC9nS00Y155mts9edQ1zc5ORlcnltIQAn4Wbm\/REJ+zi3px68Y7fZWeillUHB9+aIxvxObhYGsWKgiOMuKffCzseUJUaQ2JlK3ZfBPfjw+zceZh7Fs90WRU3DyqTWK7fzK0RN6gh\/L7gW\/PPhRBCCPHT90EWe4qMnGdrhpbYjEpO33tKT99jTm3LJWvzGZ5Nqrju7WGVUc+qfedo6RrgeUsjpckxpFbepd8NqG3UF2jQZO\/lxOMpAmEHzbvT0GXu4+YQoLSwN0tH6q6r9M0G2aELlMcvIXbbRXpnW5tjV6hKNJFS14KbQa6VJ7FUU0jV+U56Hp+jrlTLn3+7nJx916cXIRm7R1Oehpiccq4MT+ENqzifXaYiaQW63HJOPnjOQMdNjpQXUbDhIO0qeAZvU56wFF32Ts539NPefIrylM\/5IjaLmgWeI2vprSP5d39mefImvrnTQdfDKzQWaolJWs\/pMRV8k7RUJBCtzaTiyiOePr7BNyVxLP3iS2K2tRAKB\/E9ayLfaCBj5xnud3bT9fAc29IMmNadw+wNERw8xpp4DYb8Wi4\/6qC7+x6HN2SRs+sGIy4FX8sesrQrSdhxmuYnvTy5tJtVus\/41+Q6esxvL\/akTtxkb4EBTfYeTt3pZKDjCntXJ6DJqObOoB8Y4FBhNAmll+l4FWQHOFaqIa7wNI+mFjhJnp9hW0IM8RvquWuZfvzO9apETCn7uT7TsUe4mzOboliaVMUVK+Bs4\/TmOJYbVlN17hH9Ax1cbdxCftEOTnS948m\/tg7Or\/2CX\/wlnrJjfTM\/Rrh4+aCK5K+1ZO24wNPuVq42lWH6\/C8si9\/EVS9g7+dCqY7YwpNYieAYOUtZ9Eri1hzlQXcHj67Wkh\/9Ncs0W7mrguId4uL6eGKNeVRdbKWr6wnNp3awKnMjZ1763lpIJhJ4ztGSRHSJGzh2+wmdXS0c25CCIb6MM\/0zIUu9w3bDMnLq+rG+K8h67lKZHoWh+CCXO+2EFOg6kMWK5K00980mgimub0viM81Gbj4Pgr+V+rUmlhvLOHCpledD3VytXkNOcQ3XXk\/GnDn4Y9yuX40+No9dJ+7zfPQ5d5s2khqdyo7zXYQAd9sxSg0rSdtyjA6Hh6C\/j5t7Ulkak8fO0+30PLlE4wYDf\/3t16SXX2eKAJ3niomPXcORvtkduei+to7EqHya+gFPP3crElmqzWLHuQ762ps5VZ7CF1\/EklHbDTh4eWk92hUmCmru0N5xnwvVOcT89QtWpNbSpboZu7kZY3Qu3wzOhhQVn7WZiqQYYtN2cLG1g\/b759iVHc2SJUns751Z7GlfMiuT9\/HU5cXZXk3q8s9YWXKc5rZn9DzrorOzk67nFjxhBefDavIMGhI3HOLG4y56nt5gf0k6qxseY\/X5eVyXj2GFkc1Hb9H2vINr1asxfvYpcXufLrhqsf3qFhK0edQ\/GAVUhi\/sIjtWS9H+czx43k\/rqXLyDHoKG+8xokKw7RDrDEsxrq\/nancPD47vpiDq9\/w6ppRjrS7AT\/uZrcQv1bJ6zzna+7q4fWQLSZ\/\/GV3ZMbqBlxe3kGRYRW2rCgxwpTwFffY+ml8F2Rfc3puJNrWSuyEIDN5id\/LX6HN3cbGrn\/bbx9iR+DeWmIpoWiDIErTQ1bQaXXIF9xSACI6+S6zVriC5amZ9AvzYHlWRsiyGgm86sfgAQjh6D5CryaC2PbDA43m8tNdmootOYNPpZp60N3NiYxpRX\/yFr9ZcYtKroA6fpixZT+L6w9x60k1v+zWqckxoCw7xfDJA5OVl9u6o5\/qg\/VsflSWEEEKIn5YP9PgdFXfvFfaXZZBg0KM3xpFeUseNATshFYj4GLxWw\/qsePR6AwZjKgX7rtAz+3gYIgxfr6Y0aSVL876h3WKl68h68tYd4v4YoDzh4Po81jY083I2yI7d5MDqJLJrbjE42yKx3aWpuIA1Rx4wiUpk7D7Ht2dhiNWiSSxkz+6dVKxZTUH1ZV4AqC76btSwKuorEtY3cmsMQGGy5xo1xamY9Dr0BhNZGw5w7flsOAkz3nWB8iwDWo2RlKJ1bN61nY1F6zj8bH69BLH2H2Vzciab9h1iXW4iJp2OhKwNHGybmmlIqQQnuzi7NZs4gw5jci7F249TvS6ZzPIr2MOAGsDysImN2UnE6fUY4pIp2H6ap5OzIVRhsv00FUUpxBv06OMSydlwiPtmz\/S8MDXA4KVKVqca0OrjSSvZR+32VaRuOcHzBVYtBhVl7D4Ht+WRaNChNxpJLj7A7X7HzPEa5Oy2TIp23aHn1RzZIS7uyqFw29xe2rmbtPHo2DbSl39JWuV1nod9PD5STMGaI9ybXdo03Me1fRkklzRxxzJdjrC1nQtVBSQZ9ej0BuJzt3Lozou3VzR9ZYLuC5vJytrG2TnTaNWAjc5zO8g16dAakilYv4291RvIy93KBQvgCccnGgAADeJJREFUGuLmrjyyt13ECqDY6bu4jQyjHp0+joxVJWzfW0FRXg0PZx6\/E7b3cqGyiLQ4PXq9kaSc9dTfGcWjLNzbE3Y940JFMWlxBnT6OBJXbeVs95zH9URaqV2VysZjQ28MA39TiL6zO8iPjyaq5CQDkypDZzaSVrKfxy9n+4ad3KspIS5nLy0DHkAlNPaQExX5JBt16A0GEnN2cvLxKN63eqZUIv4X3Dq4iUyTDr1OjzGxiKoL7Vhn\/zYwRPM36zAsjWV1zRX6UIlMPOZMRQ6mGC2axHwqKnZSVVZEbuUlRgjRd2Mnq7PLOf9ydj9u+u7tpjhjC6f6p\/erTHRzsSIbo1aDMaWIss272LGxiNLD0894Ub2jtB4uI02vR2tKp3jdTg5VlpJSWMvDgAfb\/X3kZ27izPAbE5RxDFylIiceo85IUlYR25p2U5pexvGBMErYT++hEjLWHKbb4ebF6bUk6mOI0U3PwzWZjBgMJuLym2i3BwEFy71GtuQlYTDo0celsqriLF1W\/\/SjasJDXK9eQ7pBi9aUQvHeWrbnp7LpcCfDPt7iaq6mKHcTJ55aZno7AwzeqGdTjgm93oDelMHGwy288Mz2Mvt4caOe9Wl6YnRakksOsG\/HRvILyzjcMv2LkBqx0npiJzn6GDTaZIp27WTTxjI2lZ\/lOTB6q5qSVZs51qECQ9w9sIa8DYd4PFu+yAgPD64jd20D970AESafXWRXthGtNo7U4mJK1qWRbCigfoFViyGItaOJEmMqux6EADejN7eTnlZCY+ecp1G7XnB+YzzxqUU0PQPCDnoaczHkNtH11rNzpynuQa5VFpJs0mFISKdg60lqN6aRtukUI24FVIWpzhPsLEidvlcaE8hef4j7Zi+qCq67VeSn5FPXZpHH7wghhBCLzAcKsgAqkXCQgN+Hz+cnEJr3wHk1Qjjox+\/z4fMHCM1v7KsK4aAfXyCEoqqoSphQWJlZoCOCEg4RViKvt6lGUEIhQgu8N\/fvVGWmTP4g4bBCZGa7c\/9NKODHHwi98UzNSCiI\/13fZeaRMn6fD38gSEhRUMJhFsovqqoQDoVQFIVQwD+zvfBbC+yoSmimnAGCYZVIOPRmOYkQDgZe1V\/wreGrM2Xy+\/D5\/QTnPyB0pn79Ph\/+YBhlth7eM8LuzeM5d3sqSjhEKDyn7ue8965Re2pEIeSf3n8EiCihOcd4ehuRcIhQSHljG6\/qZqbs3zYqUI0ohBeoY9Q3j4ESUQiFZo\/bAnU+8\/d+nx9\/IEg4ohAOKW9sV1VCBP3T2\/QHQt\/6SI\/X32W6DPMesDNz\/n7LqqqqQmjmWomoTF8rofCb9aiEZt6bc4Tm1+P79hEJz3wv3\/T3mn+5znwemLOduefw\/GtNjYTn1PXrbcx\/j7nXQUhBUcKElbkXZphgwD99jofCRGa2EXnX9l5t9vX1Gp45P2b\/Tn11Hk6fA8FgkGDAP3P9+xaogwjhV9fa29ei+qru\/ATDytv3rnn1HAqFUd44qV\/fK\/3+4ALP+n19L\/AHFRRFIRwOvbmNeed6WFEIzz8WM0O4Z4\/T693MeU8FJeDBPm5jwuUnFA4R9I\/x6GQx8dqiOT3sb1Kdg9yqyiG59ARDARWU0PTxmv934SDBQICwouIavs7O9BS2XZl4x7OYZ7\/azDXnDxAMR17dR9Q55\/\/s8Z5\/L1SV6eMblpWLhRBCiEXnAwZZIYQQ\/7OZenaNytw01tbc5NlwP23XmigzaMjYdo4X70mDrqE77F2Vzvqjz+csfrUw1TnA9V355O++y\/g7RyMIIYQQ4u+ZBFkhhBDfmRpx8\/L+EbbmJGAy6tEZUijafZJOx7etAqww1XuXY5WHaX1vOPVh7zvP3ooLPJeVhYUQQgjxDhJkhRBCfE\/fMjXkff8yFP6WIffq9HDnt6ZLCCGEEEK8JkFWCCGEEEIIIcSiIkFWCCGEEEIIIcSiIkFWCCGEEEIIIcSiIkFWCCGEEEIIIcSiIkFWCCGEEEIIIcSiIkFWCCGEEEIIIcSiIkFWCCGEEEIIIcSiIkFWCCGEEEIIIcSiIkFWCCGEEEIIIcSiIkFWCCGEEEIIIcSiIkFWCCGEEEIIIcSiIkFWCCGEEEIIIcSiIkFWCCGEEP8hit\/F+PgELm\/oxy6KEEKIvxMSZIUQQgjx76NGCDj7OHFgC3FaI9kF+7j3zImi\/tgFE0II8T87CbJCCCHEIvaxM6Maeb1HxT3MhSojS7ee4OzxWvT\/8ht++5tS+jzSMyuEEOLDkiArhBBCLDZKEMdwP729vYz5Ix9nn5EAk103OHH8Ct326aBqt\/ZTveEsFq+Cqgbpu13Dr3+l5d649+OUSQghxN8tCbJCCCHEohHG45qi7dQ+En7\/3\/iTKY8Hjo+x3wjul0\/Y+Zf\/l3\/69b\/y+40tKCqoqoo3NNtDq2Btq+U3f0nj6YTvYxRKCCHE3zEJskIIIcRiEbFy9eQB0vQ6lv36\/+L3+kxa3R9jxy46758lalUTJ45s4U\/LT2EPzxvU7B\/lQnUCmkNtuGVksRBCiA9MgqwQQgixaKiEQ0H8\/kFqy6L5bXQKjz5GkFWDBLw2xoNhhgdaSVt2FHto7pBmL13Xj7I84TgvfbLSkxBCiA9PgqwQQgjxTmEmzYN0dbTT0dZFT08nw5Mugh9pWuq7WajbqPl4QfYVheHOKyR\/PTfIBul\/eJaCpFoG3Qqg4Bh3EolIoBVCCPHhSJAVQgghFhLxM3Svhq+X\/JGluesoW6Xlj\/\/9P5Nbc5lx5btvRrG\/oKftDnfu3uPevXe\/7txqpm\/YTPg7bXWUmg2xHz\/IBvo5vm0lf1h5EkcYVCVA391viPrlL9hx7CptbQ+5cqaR4oq7OPzfo5KEEEKI70mCrBBCCPGWCC9u7OCzf\/sFhj0XGA1BJOymVh\/HoeM38ACgokZU1G\/pePS1l7Ja\/\/\/wzz\/\/lE8++eSdr3\/+p3+irKae75ZLv2uQVYkoYcLhMIqivOcVRol8Szez6ufpqfX8l3\/8Ob8uvUMYCE52Urn83\/jkl7\/iX\/\/4R373ySf87culnOmx86N3WgshhPifmgRZIYQQYh5l8j6aJb\/lf\/lZFqebBxkbs+D0h7G\/7Gd0wk4AIGLlzuVLtA+Mvv9ZrsFJzC976Ol9zuDgIAMDA2+9BgcH6HnWi3l86js+F\/Y7BtmIk\/4nl9i7t5r6hgYaFnzVsr+2iVNXenh3H6qK7fEJEv\/hf+c\/\/Y\/f8Yfakem\/VRWCPh\/+QJCA34\/X68Xn80uIFUII8cFJkBVCCCHeoNJ3OJs\/\/ewf+Id\/+iUajYY\/\/2EJ2861vdlbGuwkL3kZVWevvCcAghoO4fW4cTpduFzvfjkdHnz+8A8bZJni9qlN\/OwXv+TT3\/2O3y34+hU\/\/9nXJCTceOew5qDtITuM\/4V\/\/v\/+mfSSVPKODyJTYIUQQvyYJMgKIYQQbwhzu\/gzfv1\/\/oztx5sZsVgZGhhkyht8FVhVJYDP62DMOo7bF3zv1vx9V9iYFccXSzQYjQYMhrdfRmMsn\/8tlV3fPOT9W5s1Ru1GDZ\/EpNLmfd\/fqYRCAex2Ow6H4x0vO3a7E7c78I7qsHFhl47\/+7\/\/DtOuCzRf2UpyYx8RIKSogILH1sWFc9cYD8q8WCGEEB+HBFkhhBBinluFf+Ff\/o9f8c39oVfvOYae0DM2gUeF0GQXp7YXkr\/3Mt1T79+WOnaVopQYfv+nZRiNenQ63byXAaNhCZ\/+OZbNDffe27v7mpm69Sv4bWwmTz\/kM1vVIM+uVPI\/\/vN\/5Y8rKhhx9HG7\/iu+bnrK2GgL13udgIK5\/RuiY6Jod\/g+YGGEEEKI1yTICiGEEPO0V8byh3\/4XzFuqqfj5TBD3ddJjVlC+ckbuAAIcFj7JfFF+2l\/b48ooCqEgkECgSDB4LteAQKBIKHwt88uDXmdmB8fI3\/lf+U\/fRrFjhtDTDr933FI8vegKtg6z5Pw3\/43\/vHTLznY44fgKNf2rOAf\/5pEQdJnNHY4CAW9uFxTjNmmUGS4sRBCiI9EgqwQQggxj2p\/zu64aH7\/81\/wyb\/9jk9+baLmTDvu8MwcVnUQw9efU9Z0kY\/bBxlh8OZBsj7\/Hb\/6zW\/4zW9+yW\/+pGNz04MfvhxBGw+akvnFp5+TuPcR0x2\/Pnov7+DnP\/8jXxlPYAsGsLSfobyoiO3XhglLkBVCCPGRSJAVQgghFhAJ+bBPjmOxWHA6vW\/0NkaGaljxWRkHTr4gonzcNXoj4RB+rxefP0DA78fn9REIKT98jywqSiiALxAiNOcrKsEA5pFRJgPTe3QOtlDy699Qeu0lH3KUsxBCCDGXBFkhhBDiexo6ZuQrUxJF+1uYmHL92MX5EUV42X6MTz\/5jLvj\/h+7MEIIIf6OSJAVQgghvqfehnSiP\/0ZG4+04FL\/jsfTqhM8PZnPv\/75ICNTH2CerhBCCPEOEmSFEEKI70lVfHgDHoIfeVjxT43qfMHptb\/ik\/T9nL\/ZQ0geLiuEEOIjkSArhBBCiH8XxfGS46n\/wudLTVwb8PzYxRFCCPF3RIKsEEIIIf591AjhkBtPIMDfd9+0EEKIj02CrBBCCCGEEEKIRUWCrBBCCCGEEEKIRUWCrBBCCCGEEEKIRUWCrBBCCCGEEEKIRUWCrBBCCCGEEEKIRUWCrBBCCCGEEEKIRUWCrBBCCCGEEEKIRUWCrBBCCCGEEEKIRUWCrBBCCCGEEEKIRUWCrBBCCCGEEEKIRUWCrBBCCCGEEEKIReX\/B7wre3DdaEdsAAAAAElFTkSuQmCC\" alt=\"\" \/><\/p><p>\u00a0<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-b7716ab elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"b7716ab\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-317472e\" data-id=\"317472e\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-5c84ab9 elementor-widget elementor-widget-heading\" data-id=\"5c84ab9\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Construct Validity<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-66 elementor-top-column elementor-element elementor-element-c52a727\" data-id=\"c52a727\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-244633f elementor-widget elementor-widget-text-editor\" data-id=\"244633f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul><li>Construct validity is the measure of how well the items selected for the construct actually measure the construct.<\/li><li>For example, Life Satisfaction in this case is measured using 5 indicators, construct validity will help determine how well these five items measure the latent unobserved construct of Life Satisfaction.<\/li><li>Construct validity is established through two forms of validities, convergent validity and discriminant validity.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-334b3ac elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"334b3ac\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-38478d3\" data-id=\"38478d3\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-abb48b9 elementor-widget elementor-widget-heading\" data-id=\"abb48b9\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Convergent Validity<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-66 elementor-top-column elementor-element elementor-element-ea85bf8\" data-id=\"ea85bf8\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-eb68b36 elementor-widget elementor-widget-text-editor\" data-id=\"eb68b36\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul><li>Convergent validity refers to the degree to which multiple measures of a construct that theoretically should be related, are in fact related (Gefen, Straub &amp; Boudreau, 2000).<\/li><li>Hence, the multiple indicators measuring the same concept through convergent validity are assessed to whether these indicators converge to measure the underlying construct.<\/li><li>This will ensure uni-dimensionality of the multiple-item constructs and will help in eliminating any unreliable indicators (Bollen, 1989).<\/li><li>Convergent validity is assessed using Average Variance Extracted (AVE). The AVE indicates how much of the indicators\u2019 variance can be explained by the latent unobserved variable.<\/li><li>An AVE greater than 0.50 provides empirical evidence for convergent validity (Bagozzi &amp; Yi, 1988), as the corresponding latent variable explains more than half of the variance in the belonging indicators.<\/li><li>AVE is calculated by taken sum of squares of the factor loadings and dividing it by the no. of items in the unobserved latent variable.<\/li><\/ul><div><img decoding=\"async\" class=\"aligncenter\" src=\"data:image\/png;base64,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\" alt=\"\" \/><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-fb8f7b7 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"fb8f7b7\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-02eae4f\" data-id=\"02eae4f\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-5379dfc elementor-widget elementor-widget-heading\" data-id=\"5379dfc\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Supporting Argument for Low Average Variance Extracted<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-66 elementor-top-column elementor-element elementor-element-bc8fb7c\" data-id=\"bc8fb7c\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-893e949 elementor-widget elementor-widget-text-editor\" data-id=\"893e949\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>According to Hair et al. (2016), if the average variance extracted is greater than 0.4 and composite reliability is higher than 0.6, the convergent validity of the construct is still acceptable (Fornell and Larcker, 1981; Lam, 2012).<\/p><p>Fornell, C. and Larcker, D.F. (1981), \u201cEvaluating structural equation models with unobservable variables and measurement error\u201d, Journal of Marketing Research, Vol. 18 No. 1, pp. 39-50.<\/p><p>Hair, J.F., Jr., Hult, G.T.M., Ringle, C. and Sarstedt, M. (2016), A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), Sage publications.<\/p><p>Lam, L.W. (2012), \u201cImpact of competitiveness on salespeople\u2019s commitment and performance\u201d, Journal of Business Research, Vol. 65 No. 9, pp. 1328-1334.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-bef1ac7 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"bef1ac7\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-dea29b3\" data-id=\"dea29b3\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-236cb34 elementor-widget elementor-widget-heading\" data-id=\"236cb34\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Video Tutorial<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-66 elementor-top-column elementor-element elementor-element-dcab2d0\" data-id=\"dcab2d0\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-995d3d9 elementor-widget elementor-widget-video\" data-id=\"995d3d9\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/youtu.be\\\/5eD8tJUPIHY&quot;,&quot;video_type&quot;:&quot;youtube&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-wrapper elementor-open-inline\">\n\t\t\t<div class=\"elementor-video\"><\/div>\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-a5522af elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"a5522af\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-b093d8f\" data-id=\"b093d8f\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-90aaf6a elementor-widget elementor-widget-heading\" data-id=\"90aaf6a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Excel Reliability and Validity Calculators for SPSS AMOS<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-66 elementor-top-column elementor-element elementor-element-6291c4b\" data-id=\"6291c4b\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-f15b56f elementor-widget elementor-widget-text-editor\" data-id=\"f15b56f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Right Click <a href=\"https:\/\/researchwithfawad.com\/wp-content\/uploads\/2021\/11\/Reliability-and-Validity-Calculator.xlsx\">Here<\/a> and Select -&gt; Save Link As to Download the File<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-7242eb5 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"7242eb5\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-c928b26\" data-id=\"c928b26\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-9f36351 elementor-widget elementor-widget-heading\" data-id=\"9f36351\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Additional SPSS AMOS Resources<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-66 elementor-top-column elementor-element elementor-element-2297dff\" data-id=\"2297dff\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-fb86496 elementor-widget elementor-widget-wp-widget-ccchildpages_widget\" data-id=\"fb86496\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"wp-widget-ccchildpages_widget.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<ul><li class=\"page_item page-item-7107 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/basic-first-structural-model-in-spss-amos\/\" class=\"menu-link\">Basic\/First Structural Model in SPSS AMOS<\/a><\/li>\n<li class=\"page_item page-item-6713 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/building-a-basic-model-in-spss-amos\/\" class=\"menu-link\">Building a Basic Model in SPSS AMOS<\/a><\/li>\n<li class=\"page_item page-item-7029 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/common-method-bias-in-spss-amos\/\" class=\"menu-link\">Common Method Bias in SPSS AMOS<\/a><\/li>\n<li class=\"page_item page-item-7064 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/common-method-bias-using-latent-common-method-factor\/\" class=\"menu-link\">Common Method Bias using Latent Common Method Factor<\/a><\/li>\n<li class=\"page_item page-item-6757 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/confirmatory-factor-analysis-and-analyzing-spss-amos-output\/\" class=\"menu-link\">Confirmatory Factor Analysis and Analyzing SPSS AMOS Output<\/a><\/li>\n<li class=\"page_item page-item-6687 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/first-measurement-model-in-amos\/\" class=\"menu-link\">First Measurement Model in AMOS<\/a><\/li>\n<li class=\"page_item page-item-7247 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/full-structural-model-analysis\/\" class=\"menu-link\">Full Structural Model Analysis<\/a><\/li>\n<li class=\"page_item page-item-6909 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/how-to-assess-discriminant-validity-in-spss-amos\/\" class=\"menu-link\">How to Assess Discriminant Validity in SPSS AMOS<\/a><\/li>\n<li class=\"page_item page-item-6415 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/ibm-spss-amos-lecture-series-basics\/\" class=\"menu-link\">IBM SPSS AMOS Lecture Series &#8211; Basics<\/a><\/li>\n<li class=\"page_item page-item-6443 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/ibm-spss-amos-series-1-what-is-structural-equation-modelling\/\" class=\"menu-link\">IBM SPSS AMOS Series &#8211; 2 &#8211; What is Structural Equation Modelling<\/a><\/li>\n<li class=\"page_item page-item-6538 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/ibm-spss-amos-series-4-introduction-to-amos\/\" class=\"menu-link\">IBM SPSS AMOS Series &#8211; 4 &#8211; Introduction to AMOS<\/a><\/li>\n<li class=\"page_item page-item-6638 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/ibm-spss-amos-series-factor-loadings-and-fit-statistics\/\" class=\"menu-link\">IBM SPSS AMOS Series &#8211; Factor Loadings and Fit Statistics<\/a><\/li>\n<li class=\"page_item page-item-6561 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/introduction-to-confirmatory-factor-analysis-cfa\/\" class=\"menu-link\">Introduction to Confirmatory Factor Analysis (CFA)<\/a><\/li>\n<li class=\"page_item page-item-7330 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/mediation-analysis-with-multiple-mediators\/\" class=\"menu-link\">Mediation Analysis with Multiple Mediators<\/a><\/li>\n<li class=\"page_item page-item-7820 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/moderation-analysis-with-categorical-moderator-in-spss-amos\/\" class=\"menu-link\">Moderation Analysis with Categorical Moderator in SPSS AMOS<\/a><\/li>\n<li class=\"page_item page-item-7370 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/moderation-anlaysis-in-spss-amos\/\" class=\"menu-link\">Moderation Anlaysis in SPSS AMOS<\/a><\/li>\n<li class=\"page_item page-item-6944 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/reporting-measurement-model-fit-indices-reliability-and-validity\/\" class=\"menu-link\">Reporting Measurement Model &#8211; Fit Indices, Reliability and Validity<\/a><\/li>\n<li class=\"page_item page-item-7354 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/serial-mediation-analysis-in-spss-amos\/\" class=\"menu-link\">Serial Mediation Analysis in SPSS AMOS<\/a><\/li>\n<li class=\"page_item page-item-7080 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/spss-amos-assessing-normality-of-data\/\" class=\"menu-link\">SPSS AMOS Assessing Normality of Data<\/a><\/li>\n<li class=\"page_item page-item-7300 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/spss-amos-mediation-analysis\/\" class=\"menu-link\">SPSS AMOS Mediation Analysis<\/a><\/li>\n<li class=\"page_item page-item-6782 menu-item\"><a href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/understanding-assessing-and-improving-model-fit-in-spss-amos\/\" class=\"menu-link\">Understanding, Assessing, and Improving Model fit in SPSS AMOS<\/a><\/li>\n<\/ul>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Assessing Construct Reliability and Convergent Validity using SPSS AMOS IBM SPSS AMOS Series &#8211; 11 In CB-SEM, Loadings, Model Fit, and Modification Indices are critical to model identification. This post will discuss in detail all these concept. Construct Reliability Once the factor loadings and model fit are assessed. The next step is to check the [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":6468,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"site-sidebar-layout":"no-sidebar","site-content-layout":"page-builder","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"disabled","ast-breadcrumbs-content":"","ast-featured-img":"disabled","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"enabled","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"class_list":["post-6829","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Assessing Construct Reliability and Convergent Validity in SPSS AMOS - ResearchWithFawad<\/title>\n<meta name=\"description\" content=\"SPSS AMOS Lecture Series - Assessing Model Fit in AMOS. The session will discuss in detail how to assess model fit in AMOS.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/assessing-construct-reliability-and-convergent-validity-in-spss-amos\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Assessing Construct Reliability and Convergent Validity in SPSS AMOS - ResearchWithFawad\" \/>\n<meta property=\"og:description\" content=\"SPSS AMOS Lecture Series - Assessing Model Fit in AMOS. The session will discuss in detail how to assess model fit in AMOS.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/assessing-construct-reliability-and-convergent-validity-in-spss-amos\/\" \/>\n<meta property=\"og:site_name\" content=\"ResearchWithFawad\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/ResearchWithFawadFB\/\" \/>\n<meta property=\"article:modified_time\" content=\"2022-01-20T15:30:51+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/researchwithfawad.com\/wp-content\/uploads\/2021\/11\/SPSS-AMOS-Lecture-Series-Construct-Reliability-and-Convergent-Validity.png\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@ResearchCoach83\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"4 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/researchwithfawad.com\\\/index.php\\\/lp-courses\\\/ibm-spss-amos-lecture-series\\\/assessing-construct-reliability-and-convergent-validity-in-spss-amos\\\/\",\"url\":\"https:\\\/\\\/researchwithfawad.com\\\/index.php\\\/lp-courses\\\/ibm-spss-amos-lecture-series\\\/assessing-construct-reliability-and-convergent-validity-in-spss-amos\\\/\",\"name\":\"Assessing Construct Reliability and Convergent Validity in SPSS AMOS - ResearchWithFawad\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/researchwithfawad.com\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/researchwithfawad.com\\\/index.php\\\/lp-courses\\\/ibm-spss-amos-lecture-series\\\/assessing-construct-reliability-and-convergent-validity-in-spss-amos\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/researchwithfawad.com\\\/index.php\\\/lp-courses\\\/ibm-spss-amos-lecture-series\\\/assessing-construct-reliability-and-convergent-validity-in-spss-amos\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/researchwithfawad.com\\\/wp-content\\\/uploads\\\/2021\\\/11\\\/SPSS-AMOS-Lecture-Series-Construct-Reliability-and-Convergent-Validity.png\",\"datePublished\":\"2021-11-21T05:52:21+00:00\",\"dateModified\":\"2022-01-20T15:30:51+00:00\",\"description\":\"SPSS AMOS Lecture Series - Assessing Model Fit in AMOS. The session will discuss in detail how to assess model fit in AMOS.\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/researchwithfawad.com\\\/index.php\\\/lp-courses\\\/ibm-spss-amos-lecture-series\\\/assessing-construct-reliability-and-convergent-validity-in-spss-amos\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/researchwithfawad.com\\\/index.php\\\/lp-courses\\\/ibm-spss-amos-lecture-series\\\/assessing-construct-reliability-and-convergent-validity-in-spss-amos\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/researchwithfawad.com\\\/index.php\\\/lp-courses\\\/ibm-spss-amos-lecture-series\\\/assessing-construct-reliability-and-convergent-validity-in-spss-amos\\\/#primaryimage\",\"url\":\"https:\\\/\\\/researchwithfawad.com\\\/wp-content\\\/uploads\\\/2021\\\/11\\\/SPSS-AMOS-Lecture-Series-Construct-Reliability-and-Convergent-Validity.png\",\"contentUrl\":\"https:\\\/\\\/researchwithfawad.com\\\/wp-content\\\/uploads\\\/2021\\\/11\\\/SPSS-AMOS-Lecture-Series-Construct-Reliability-and-Convergent-Validity.png\",\"width\":1280,\"height\":720},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/researchwithfawad.com\\\/index.php\\\/lp-courses\\\/ibm-spss-amos-lecture-series\\\/assessing-construct-reliability-and-convergent-validity-in-spss-amos\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/researchwithfawad.com\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"All Courses\",\"item\":\"https:\\\/\\\/researchwithfawad.com\\\/index.php\\\/lp-courses\\\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"SPSS AMOS Software for SEM\",\"item\":\"https:\\\/\\\/researchwithfawad.com\\\/index.php\\\/lp-courses\\\/ibm-spss-amos-lecture-series\\\/\"},{\"@type\":\"ListItem\",\"position\":4,\"name\":\"Assessing Construct Reliability and Convergent Validity in SPSS AMOS\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/researchwithfawad.com\\\/#website\",\"url\":\"https:\\\/\\\/researchwithfawad.com\\\/\",\"name\":\"Research with Fawad\",\"description\":\"Learn to Research the Easy Way\",\"publisher\":{\"@id\":\"https:\\\/\\\/researchwithfawad.com\\\/#\\\/schema\\\/person\\\/82ab6f2fbc72d2168c64dba5ee751972\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/researchwithfawad.com\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":[\"Person\",\"Organization\"],\"@id\":\"https:\\\/\\\/researchwithfawad.com\\\/#\\\/schema\\\/person\\\/82ab6f2fbc72d2168c64dba5ee751972\",\"name\":\"Khawaja Fawad\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/researchwithfawad.com\\\/wp-content\\\/uploads\\\/2023\\\/06\\\/Research-With-Fawad.png\",\"url\":\"https:\\\/\\\/researchwithfawad.com\\\/wp-content\\\/uploads\\\/2023\\\/06\\\/Research-With-Fawad.png\",\"contentUrl\":\"https:\\\/\\\/researchwithfawad.com\\\/wp-content\\\/uploads\\\/2023\\\/06\\\/Research-With-Fawad.png\",\"width\":1080,\"height\":1080,\"caption\":\"Khawaja Fawad\"},\"logo\":{\"@id\":\"https:\\\/\\\/researchwithfawad.com\\\/wp-content\\\/uploads\\\/2023\\\/06\\\/Research-With-Fawad.png\"},\"sameAs\":[\"http:\\\/\\\/researchwithfawad.com\",\"https:\\\/\\\/www.facebook.com\\\/ResearchWithFawadFB\\\/\",\"https:\\\/\\\/www.linkedin.com\\\/groups\\\/8948539\\\/\",\"https:\\\/\\\/x.com\\\/ResearchCoach83\",\"https:\\\/\\\/www.youtube.com\\\/c\\\/fawadlatif\"]}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Assessing Construct Reliability and Convergent Validity in SPSS AMOS - ResearchWithFawad","description":"SPSS AMOS Lecture Series - Assessing Model Fit in AMOS. The session will discuss in detail how to assess model fit in AMOS.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/assessing-construct-reliability-and-convergent-validity-in-spss-amos\/","og_locale":"en_US","og_type":"article","og_title":"Assessing Construct Reliability and Convergent Validity in SPSS AMOS - ResearchWithFawad","og_description":"SPSS AMOS Lecture Series - Assessing Model Fit in AMOS. The session will discuss in detail how to assess model fit in AMOS.","og_url":"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/assessing-construct-reliability-and-convergent-validity-in-spss-amos\/","og_site_name":"ResearchWithFawad","article_publisher":"https:\/\/www.facebook.com\/ResearchWithFawadFB\/","article_modified_time":"2022-01-20T15:30:51+00:00","og_image":[{"url":"https:\/\/researchwithfawad.com\/wp-content\/uploads\/2021\/11\/SPSS-AMOS-Lecture-Series-Construct-Reliability-and-Convergent-Validity.png","type":"","width":"","height":""}],"twitter_card":"summary_large_image","twitter_site":"@ResearchCoach83","twitter_misc":{"Est. reading time":"4 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/assessing-construct-reliability-and-convergent-validity-in-spss-amos\/","url":"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/assessing-construct-reliability-and-convergent-validity-in-spss-amos\/","name":"Assessing Construct Reliability and Convergent Validity in SPSS AMOS - ResearchWithFawad","isPartOf":{"@id":"https:\/\/researchwithfawad.com\/#website"},"primaryImageOfPage":{"@id":"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/assessing-construct-reliability-and-convergent-validity-in-spss-amos\/#primaryimage"},"image":{"@id":"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/assessing-construct-reliability-and-convergent-validity-in-spss-amos\/#primaryimage"},"thumbnailUrl":"https:\/\/researchwithfawad.com\/wp-content\/uploads\/2021\/11\/SPSS-AMOS-Lecture-Series-Construct-Reliability-and-Convergent-Validity.png","datePublished":"2021-11-21T05:52:21+00:00","dateModified":"2022-01-20T15:30:51+00:00","description":"SPSS AMOS Lecture Series - Assessing Model Fit in AMOS. The session will discuss in detail how to assess model fit in AMOS.","breadcrumb":{"@id":"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/assessing-construct-reliability-and-convergent-validity-in-spss-amos\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/assessing-construct-reliability-and-convergent-validity-in-spss-amos\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/assessing-construct-reliability-and-convergent-validity-in-spss-amos\/#primaryimage","url":"https:\/\/researchwithfawad.com\/wp-content\/uploads\/2021\/11\/SPSS-AMOS-Lecture-Series-Construct-Reliability-and-Convergent-Validity.png","contentUrl":"https:\/\/researchwithfawad.com\/wp-content\/uploads\/2021\/11\/SPSS-AMOS-Lecture-Series-Construct-Reliability-and-Convergent-Validity.png","width":1280,"height":720},{"@type":"BreadcrumbList","@id":"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/assessing-construct-reliability-and-convergent-validity-in-spss-amos\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/researchwithfawad.com\/"},{"@type":"ListItem","position":2,"name":"All Courses","item":"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/"},{"@type":"ListItem","position":3,"name":"SPSS AMOS Software for SEM","item":"https:\/\/researchwithfawad.com\/index.php\/lp-courses\/ibm-spss-amos-lecture-series\/"},{"@type":"ListItem","position":4,"name":"Assessing Construct Reliability and Convergent Validity in SPSS AMOS"}]},{"@type":"WebSite","@id":"https:\/\/researchwithfawad.com\/#website","url":"https:\/\/researchwithfawad.com\/","name":"Research with Fawad","description":"Learn to Research the Easy Way","publisher":{"@id":"https:\/\/researchwithfawad.com\/#\/schema\/person\/82ab6f2fbc72d2168c64dba5ee751972"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/researchwithfawad.com\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":["Person","Organization"],"@id":"https:\/\/researchwithfawad.com\/#\/schema\/person\/82ab6f2fbc72d2168c64dba5ee751972","name":"Khawaja Fawad","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/researchwithfawad.com\/wp-content\/uploads\/2023\/06\/Research-With-Fawad.png","url":"https:\/\/researchwithfawad.com\/wp-content\/uploads\/2023\/06\/Research-With-Fawad.png","contentUrl":"https:\/\/researchwithfawad.com\/wp-content\/uploads\/2023\/06\/Research-With-Fawad.png","width":1080,"height":1080,"caption":"Khawaja Fawad"},"logo":{"@id":"https:\/\/researchwithfawad.com\/wp-content\/uploads\/2023\/06\/Research-With-Fawad.png"},"sameAs":["http:\/\/researchwithfawad.com","https:\/\/www.facebook.com\/ResearchWithFawadFB\/","https:\/\/www.linkedin.com\/groups\/8948539\/","https:\/\/x.com\/ResearchCoach83","https:\/\/www.youtube.com\/c\/fawadlatif"]}]}},"_links":{"self":[{"href":"https:\/\/researchwithfawad.com\/index.php\/wp-json\/wp\/v2\/pages\/6829","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/researchwithfawad.com\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/researchwithfawad.com\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/researchwithfawad.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/researchwithfawad.com\/index.php\/wp-json\/wp\/v2\/comments?post=6829"}],"version-history":[{"count":0,"href":"https:\/\/researchwithfawad.com\/index.php\/wp-json\/wp\/v2\/pages\/6829\/revisions"}],"up":[{"embeddable":true,"href":"https:\/\/researchwithfawad.com\/index.php\/wp-json\/wp\/v2\/pages\/6468"}],"wp:attachment":[{"href":"https:\/\/researchwithfawad.com\/index.php\/wp-json\/wp\/v2\/media?parent=6829"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}