[{"data":1,"prerenderedAt":1604},["ShallowReactive",2],{"page-config-/news/open-data-and-better-questions-engaging-new-yorkers-to-develop-questions-that-matter-in-the-age-of-ai":3,"news-post-open-data-and-better-questions-engaging-new-yorkers-to-develop-questions-that-matter-in-the-age-of-ai":38,"all-faculty-news-detail":60},{"id":4,"title":5,"body":6,"description":10,"extension":13,"hero":14,"meta":32,"navigation":15,"path":33,"sections":34,"seo":35,"stem":36,"__hash__":37},"pages/pages/news.md","News",{"type":7,"value":8,"toc":9},"minimark",[],{"title":10,"searchDepth":11,"depth":11,"links":12},"",2,[],"md",{"showHero":15,"title":16,"badge":17,"description":18,"image":19,"imageCredit":20,"backgroundImage":21,"links":22},true,"Recent News & Updates","Latest Posts","Stay updated with the latest news, events, interviews, and videos from our community.","/images/news.webp","Sebastian Pfütze","/images/news-hero-bg.png",[23,28],{"label":24,"to":25,"target":26,"type":27},"Subscribe","https://mailchi.mp/4337f0e3e319/9262kjy9ck","_blank","primary",{"label":29,"to":30,"type":31},"Contact Us","/contact","secondary",{},"/pages/news",null,{"description":10},"pages/news","ATWPyIzBfpLDxYvll1Bbm4TfPyhAWlstJxCQJDVTKxs",{"id":39,"title":40,"author":41,"authorAvatar":34,"authors":42,"body":34,"category":43,"date":44,"description":45,"duration":34,"endTime":34,"extendedContent":34,"extension":46,"featured":47,"heading":40,"image":48,"location":34,"locationType":34,"mainContent":49,"meta":50,"navigation":15,"path":51,"registrationLink":34,"seo":52,"slug":53,"startTime":34,"stem":54,"tags":55,"videoUrl":34,"__hash__":59},"blogposts/blogposts/open-data-and-better-questions-engaging-new-yorkers-to-develop-questions-that-matter-in-the-age-of-ai.json","Open Data and Better Questions: Engaging New Yorkers to Develop Questions that Matter in the Age of AI","Adam Zable",[],"blog","2026-04-20T12:00:00","On March 27, as part of NYC Open Data Week, The GovLab, Brooklyn Public Library, and the Alliance for Public Interest Technology at New York University convened a “Questions Lab” to explore how open data can better reflect and respond to the needs of New Yorkers. The session was supported by a group of volunteers and collaborators, including students and faculty from NYU, some of whose reflections on the event are featured below.","json",false,"https://cms.thegovlab.com/assets/1a4d596d-4172-4912-9a59-cfae7fcc6242","\u003Cp dir=\"ltr\">On March 27, as part of \u003Ca href=\"https://opendataweek.nyc/\">NYC Open Data Week\u003C/a>, \u003Ca href=\"https://thegovlab.org/\">The GovLab\u003C/a>, \u003Ca href=\"https://www.bklynlibrary.org/\">Brooklyn Public Library\u003C/a>, and the \u003Ca href=\"https://alliance.hosting.nyu.edu/\">Alliance for Public Interest Technology at New York University\u003C/a> convened a “\u003Ca href=\"https://blog.thegovlab.org/archive/the-100-questions-initiative-state-of-play-lessons-learned-and-next-steps\">Questions Lab\u003C/a>” to explore how open data can better reflect and respond to the needs of New Yorkers. The session was supported by a group of volunteers and collaborators, including students and faculty from NYU, some of whose reflections on the event are featured below.\u003C/p>\n\u003Cp dir=\"ltr\">At a time when cities are producing more data than ever, the challenge extends beyond access to relevance, demand, and use. Data may be available, but it is not always aligned with the questions people have, the needs they are trying to meet, or the decisions they face. Too often, there is a disconnect between the supply of data and the demand for answers, between what is published and what is needed to inform action.\u003C/p>\n\u003Cp dir=\"ltr\">Led by The GovLab’s Stefaan Verhulst in collaboration with Brooklyn Public Library’s Diana Plunkett and NYU Professor Manny Patole, the session focused on addressing a growing “question deficit”: the gap between available data and the questions needed to turn that data into meaningful insight and action.\u003C/p>\n\u003Ch3 dir=\"ltr\">\u003Cimg src=\"https://cms.thegovlab.com/assets/0055eb33-f9b3-472e-b681-a2dbfb6391e4.JPG?width=1000&height=667\" alt=\"Dsc F0201\">\u003C/h3>\n\u003Ch3 dir=\"ltr\">OPENING DISCUSSION\u003C/h3>\n\u003Cp dir=\"ltr\">The session began with a framing on the role of questions in shaping how data creates value. While open data initiatives have traditionally focused on publishing datasets, far less attention has been paid to how questions are identified, formulated, and prioritized. The central constraint is not the availability of data, but the absence of well-defined, actionable questions.\u003C/p>\n\u003Cp dir=\"ltr\">Verhulst positioned the Questions Lab as part of a broader effort to build a more participatory approach to question-setting, including through the 100 Questions Initiative, a process through which data-actionable questions are crowdsourced and prioritized. Rather than starting from available data, this approach begins with lived experience and translates real needs into questions that can guide data use and collection.\u003C/p>\n\u003Cp dir=\"ltr\">\u003Cimg 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7HYHuv5lL/5mMOZ881kJbh4aDURE4PLREsWb3Hx//jKieoXLQ9f1b1uw8I2/CetOCG7uPw5PD61Eic2q4+pdunSN6YSDHxTbPxG7dgafH+6Ee6LkVW1tNwQTE/6B9W+q6frmjZewWUbTrjv6/TYZ91SesBmPPF3TpHnd2T4xGxqDX4QK7z98gZeXfoGrbYJw421xePbxdjhyQrhzIQESIAHvBOiDBEiABEiABEiABEiABEigHghcVw8ym4XI4DYW1bzjcfz6n9lIX/oq/rDwVXwT/zjukGHduStif4HxU3+O7XNfwh/k51YZzyN9P3D1SBG+73Y/Eq9twXMZr+KVpS/hpbJhGN9HCQQYPzFbvQ2HbkzEr69kq35X/wF/ONITA7tpft3tfjEOc/vsRdrCNXhl9Rqkz30HN6Y+hd5SZ0OYdrETMCpCnRPp5kcScPUVoaPUdcl/40ivWNvncZ6uGcRph8ZPzF7FJ8LopF1w3P1sCMbf8WfM/Z2m4zthuOcORy/uzmxvW7nzQPcWQIBJIAESIAESIAESKz7ivAAAEABJREFUIAESIAESIAESaGgCrdZA1D743zyz7vYwnvu1fLMnBHekzEXa7Gfw3KIFeDrWPh+RubsmtlMcnl02F89NfQbPLngR6/4wF88qn40Jeakv4g8LhPvsuXj5mV+hnRLkV3hu24uYI/0r62j0u9Hgd+pzeHnRKJvhBjb9lMDo+uvFeC5WPb7x3meE3yl4duoUpC17DvdFqO5dfz0Fid3UYyAE/WauVMO0i8YTf3gRaTLeeQuQ9pBMt+bPwzVHeeFIfGUZFkkZ2nrfL4SM2Cl4ReEojvVF7JVP536v6zgB/X4mHC0s/36Dl3yzIINeSIAESIAESIAESIAESIAESIAESIAEHAnUi4HIMYqmeRaizCPUNHWjVu4JqPM/ub/OKyRAAiRAAiRAAiRAAiRAAiRAAg1HgDG1HAKt1kDUoe2/oe31LScjW0NK/l0Y9W76Gd8gag15zTSSAAmQAAmQAAmQAAk0GQJUhARIoJUQaLUGojbCzvAfoWLTSjK6JSTz5rDrcB2zrCVkJdNAAiRAAiRAAiTQpAhQGRIgARIgARIAWq2BSGZ+aPC/Ibz9v8lDrk2cgMynn/GNryaeS1SPBEiABEigyRKgYiRAAiRAAiRAAiTghUCrNhBJNj//939DVOfrID9fkudcmxYB+Rlgz5v+DTKfmpZm1IYESIAEmhYBakMCJEACJEACJEACJEACdSHQ6g1EEp58M6XnTdehRwfVECHnJ5IGI65QDGcNzeHGn/2b8mZXjxv/Db06XQf5ppfMJ64k0MoJMPkkQAIkQAIkQAIkQAIkQAIkUG8EaCDS0Mq5bXTDxK3CMHFbx+vAtXEY6Ia6G9v+G+RcUWg1PyaUBEiABEiABEiABEiABEiABEiABBqHAA1EDcmdcZEACZAACZAACZAACZAACZAACZAACbR8As0whTQQNcNMo8okQAIkQAIkQAIkQAIkQAIkQAKNS4Cxk0BLI0ADUUvLUaaHBEiABEiABEiABEiABEggEAQogwRIgARaFQEaiFpVdjOxJEACJEACJEACJEACdgI8IgESIAESIAES0AnQQKST4J4ESIAESIAESKDlEWCKSIAESIAESIAESIAELBGggcgSJnoiARIgARJoqgSoFwmQAAmQAAmQAAmQAAmQQN0J0EBUd4aUQAIkUL8EKJ0ESIAESIAESIAESIAESIAESKCeCdBAVM+AKd4KAfohARIgARJoUgQuX8bZios4W1XbpNSiMiRAAiRAAiRAAiRAAvVHoGEMRPWnf50lf/nmWkyesxYvF1y0LOtswRYlzOQ3T1oO02w9Hs1X07qqCGebaiJ+/AZbF4l8tKBj5dFdePHJWbj1V09p63SMmvMudpzy8Sao5hx2vLoWo+J0OWKfsBgvvn0SlT8GBtSViuPYuHyZYxy/Evo+uwXr9n2DKwGKJzDaNrCUiiK8LOqtrLsbjzZw3A0W3UXsWCXKtZZOmVazdd6ru7DjyLnWXR4aLE8cIzr14QYoefLGcccLbs7O7tyk+hd5aq3cam3bnA3I/caN0HpyPvXG73BrjGjXFhbVUwwUSwIkQAIkQAIk0GIJMGHNlkCrNxD948+7sPHNXdh67J+WM/GHY4VKmI1/Pmc5TLP1+O0xNa3/9yR+aJKJuIzCFavwRJbIR0861pzEuseTER4vjIE7T+JsuXgyrqzf4NM3tyBp6ATELCqyZNypLNiA2yMnI+n3u/Bp6UW7rCPCaDFjFsJ/9SLWHfPR4GRkW3UcqyY8hZti5mPyqkLHOMq/wae57+I/x0zHTSKeVQesGzaNUTT748snsVXUW1l3P/u22afGTQL+iZL/K8q1lk6ZVrN11e/XIilhMm66bTom57eCNskNrcZw7hF8GUqezP8IO7wabC/iow0fqP5Fnr74oYUHDKcK8bJs2968jLY/b4wUMk4SIAESIAESMCdAVxIggZZJoNUbiFpmtlpMVc1FfPn2BoxK/QCn0Ax/NeewcdI0xL3i5Ubrx2+w6pFZ+M89l4Hrb0bKisX46sstqD65BeVFy5AzLRpdUYuSrMWImlmISg8oKneuQtSEfEheXeMeRc4nG1Be+g6+O7oBX+VNQkpkEFBRjP+8bz5WlcL3X2k+Rt01H/MKpOEnCHc+Og5b8jJxsmiDuv5pMXIWxyO2sxAt4pn3yCTEvXoSV8Rpi1l+FHlR8C6eEMa8HS0mUXVLSOwz07Blrfn6+u+G495wUe5EOd+YOgtP7BTlvG7RMbRVAnEDMUbxW4T8w8qB+83lQ8jdZ7989v39KLGfmh6d3XdI9fPoQIy83tQLHUmABEigvghQLgmQAAmQQCskcF0rTDOTrBP4PBt3zcjHpyfr8LaLLqsB91eqTmLHq8sQEzUZkz+8iLDQdh5ir8WOOb/DvCPCS+hQbDm0EpmP9UKPm8QN9fVBCOt8K0bOfB5//eRR3Cm8VL67DCm5bm6wT32A+Cf3KAakO+csw183jcPI3h0QFgy0De2AHncMR+bOtdjyqNTnJOb9dhO+9PpWAey/qkI8kbgBn1YJpx7xyBFGrD+tfBSJd3RB184d1PXmXhj526dQ8OcNKHj2VuGxFoW/n4W4rAb+/kTEXG/LNx/g8QlbsPXoVfdR9HgURWXvoFqsr49w762lXOnxq6FIfNB8HfPMJGy3lYfL2PrkKmx1U4RbCo8mk47rYxCvlL/LeH+3F0P1vr9AMXjGxWCkTMA3hdhxSh64W2vx6Sfqp2sjRRh3vuhOAv4TYEgSIAESIAESIAEScCRwneMpz0igaRPYMePXuKnPLCT9vhAlPwYhdtrzKFkR415pYdSZ97a8W26HMSsnI/Emc69te4/Dlt/drFzcMWMTPv1ROTRshKFp5bv4UrpIv8I401YeO6/Xd0Di7ydjjDAaQRg6pr8h3wRy9mR2fhm58zOxVRqHhCErJ+8pjHSjqxJaxBMrjFRFv5NGIuDLRZnYWK5c4aZVEmiH2BkTMFGWOxThrU+al9G3+WZZEO69r5ei/tmdRfBk7ynco87lM/LRyRirGJW+wY59HtqHH4uQvxPi1wuJg4LE3o+FQUiABEiABEiABEiABEjABwI0EPkAy1+vV767qPwbTKW0U/grpMmFq0VlhUzXZfj8eVON9u843/lxE3tNggjCnY9OwPaiTSiYGY2wNtLNfC187QOUyEs3x2POiCB55Hbt8dtHkSiv1uzCujwn3Sp2YdW7l+VVJE55CD3g4dcuFnOe6aJ4KFz/kRq/cuZhc+wDzFPeXOqCOVumYWSoB7+GS1FPTcPzvaXDcTy3plge1PtqK881vkRlKC8uxjdf5Pjn16azmoX+CdH+1cm3ely/6XZIyPXRuDdedTldflY98LBVmXipv3pdrfDiz108fjFThan6XURlA5SzK1WyLRNx+VE+uo64G/dC/I4dwqcVYm+6nET+h1K4NPZ0sBmVPv3kENy2n38uQq6UNehuPNBZHrhfbaxkFO69ub+i5bNvZdu9OPuVWns/0Qj13q4Hj0iABEiABEiABEiABKwQoIHICiV//HxXjHXTp+OmiF/jpjufUv4NJjzq1wjpMwtPvF4Ml3+6OrIFd8l/1kp8F4pBwyTOHQuFHOFncr6T8UL3e+xdxInrt475AG6fZH+TjyTpR67T90P5HXsHSjjppqwb8CnMf1dO7cG8MZMREjEO4fIfbmKSRRrH4a45e3DK043cj3K+o7W4q884hEQmKzxuvVMcCzl3Tf8Ahd+Zx+fses+ibHxXtgV/WvkQ7u3s2eADnNRuyoCuDw9ElLMw5/N2A/HwCNUxt0B92q+eAVf2FUFlEoOH7/MWLxB1/0B0hfh5/YxE+BFL4eZ8nIL4xcTjmTvEXl28b6+/GdOmqG9QXXljt8tEuZ8uUstM0hueJi8uwjwl31/EOjdfql05VYgXUyeLvDaU50hRnn81H/M+PAe3N7nlhXApL7cKGfHLsM5hgm1dB6HvI+9ArQP7kaLoJdyUvVE/3b/RzQmXr3VQCX4O6ybI+DS5otwWvr4Md8m0RqnlVqnHv1oMjxOEW063EmnANm2vNxG1ZwOUf+1bVCQu1uLUh2uV9Kjt0gZ8Jlwdl1rhZwNG/UrUT72uavX89jEbkOvtH/9+vAhTZpHTRdt3HJWOkTmc1X85M0T34znkLpqPW2V57CPyXLRnSt4qepq00YagDoedf4mRmpE2d5+btvlUEXKl8ah3P9FuATaj0s6/4LMfHaTZTgrz9+OKOIuK+6XalohjhyUQ5VvIWCXqtd4mx73hpgFwiFg7Kc3HOO1fHG+fJAzxlzV3uWuk8i+j5koCJEACJEACJEACJOA/ARqI/GfnNmTlvg24/c4X8Z/vfiMG+EHoGtkLY0bciq6dhWGh6iS2LnwR4YmbUFhlEHH7LzHwO/EUu2iP+bwUPxbirdfF9fKL2PiueuNgCK0cnirYg0JxHTHRcPuGy4+XcVr4Uf7Fq0q/mamFcq67l1+Gma3n7M5V+F9DV2HVvnNo27kDuoaLVaYJtfjyzVW4/ZF3UWJ2s1NzEqsenYS7ZuzClzLO0A64c0Qs7o3sgLYy7LubEHfnLEuTOrcNbSfCwNqv4hgK5U2Z8H1vjPopljj0sAThzhj1MzN8fgzK52Sa7y8PHVOPevdGbDv10OP29t64F/L3DT79Uucsz81W4UdOoC0u3TvmbvObQXHN3dL2vliMVC4WuUyUW/PdRSVvT1/2MJ+PyIN/KHn/Pa6Y5F9lvsz3ZXg5/xyuIMhenkNFeS4/jlWTJuPW6YVwufE/9QHu+tUypbwg9GbcmzgcKY/H4E4R7srRQvznI9MMEyrXQtVB6Fuh86pFpaKXcFP2Rv10/0Y3BYKy8asOKiGv4sq3Mj4h98o3kOU2bmEhvrzWTi3vosyHSSNMuTBQOegP+8+ndNuD1f3oJPZ/rkr5eei/qwdye+WyUgbOfleLklfn4/ZJoh6aVXDpV050/fhTwk8+Pi0X+RDawVZXw66vxal9wigw9CmMy3VjcBThbcxEHG07i3wf0QtR4aLS1Hwj2r75iHo8H2btRMOUM5lIuYq8TZyMcVnHcRbtEDUoVpTN4Rh5h67ni14nrJdS1PVmjIxX3xjckfu5qCOqq3GrTzbddUQMlLa58y81o1KRS52F8jsJ9Y2jDkgcpLVJiru6CUj5PleIJ4a+iHmiXqtSrW+vyIcRI4SxsPQirgjj6XtrHkKUQKdIaLTyr8TODQmQAAmQAAmQAAk0YQJNXzUaiPQ8qqzG2QpxY2hlrdQDmexL30X8GPVtkB6PTcNXpVtwsmAxXn9tGU4WbcF3eyYhsbMId+QDxI37AMpbIxC/66MR/6DYw828FAX7sVVelmv+HihfI8lj23oRnxZ8I846IOUBD8YQw+S+1a8NFf7F0nscvipTJ/yVk/5Wl03TDA7imr6IQf/DT+7BlaHjUPDlO/hO/qvWnzcoaSrPfQhygmcc2YJJLnPuCIPWhFmYVyRvNnOikOUAABAASURBVKPxX/lbUH10A/702ixsL9iA70ozkflgBxHLScxLXIUdVeIwUEv5OfxVkXUzojwgUbxomx4R6o0eKr7HPzQ3QNwYf6M9Ho+6GT1g4Xd9F/SQ+Sy8ev3c58e/o6RUeEQ79L5NspDHPqzteuPe3tL/Zez/Hzc37vKyP+uBDYhK3QP5wVKPJ2bhpLE8H92C8vceRez1gJzcO/5VWf7skexYuQnSyNbjt8+j/OhKbH9lEjJfno8/Hd2Er1bEIAyXDRMqx+J1vQzuGQf1ba+hyNHdlP1KTLMC3986aFddOdrxu9+JcnsjUtZm4ruT2Tgpy7tYy7/OxOvx8m5Y6D9pk8t8Vb6lW4kqIJvK/HewqkKK6oXEOJNydDIXk35/El3jxmH7n7fAta5fxo6Zv8NkaawMjcaS9zY41NXyrzegYNqtIoLLyH12FiYXiDotzuyLGl6p651jkblHtHdFIt9fW4yiP2ej+sv5mCjyr3LPBjyywmlC5wYrZ5q2O99VJ66/eTi2f5mNoq2zRNmchJy8bLWNDoVSpp/JdU6jFt5pZ3tj0PSNoFqok013MLTN0nAm8+gyTCe31t846nw3Hr7DKbIAle/3X8zE1uqbRflepvwToywPRc+4GqOcYkdl0SbE3bcFhcKYHBY/Cf+zKR5Rog3Q/TVW+dfj554ESIAESIAEGpUAIyeBZk7gumauf8DUL3llvvrZU4z6qcGtHvZxr3zjJt6LWDdji3JTHPboLPxpxVD0CHb02rbHcGz5dBJGSucjm/CfBkvPPdo/1ZjNS7Ejb48I0QV3yifcKEL+PnFqXC4fQq50Cx6IeOcbCqM/f49rLqPyjnEo2CQMAjc5CgmLmYBXn+2gOBbuOYQrypG6ubIzG89JvXArluQ+j4l9gtQL+ja4i7hBeQFLpM5Ve/Cf651uHHV//uzPfq8YNmRQ009v5AWntW2IvPGXjidRYrPencU/zkg3sRpuhMSZhyUIHW5SL5eU/F09cLf95htIQwpwo82o5M6ruXtX/Ly7eqXm2lX1ICDbb7BqXj6kPVQpz4ti0dWpPIcNGIePhKExTMT35e83Gf496xuU/EU4CqNX4q+jhTFIHutrEHo8NhkvxMhzk7Isnf1e61YH7dF+g09FuR0jDLuZD3ZBW/sFQBj/xqycoNbhmkP47CvjxQZO94+1qPzmODbOmY7w1ELIuhf2xDhMDDfqpB0XHceXibPEDf2juDfcqR5KL/s2YZwyz5ZaV6cNUOu0vKSs13dA7Mxl+NOzXcTpZWycL9o6YSQQJ+piCx+D1z+dhZQeTnHcFIP/ensCYoXvU69scSgrDV3OTp1Q25mwB4fjXmEMEirZFtlGr5zZSzl3/tRUcTTb3NEXDwfLC6I8H5Z7w6pPNu3UNt85bCBkuTKb3Fp/46jtg32hGN9t4gJXvguLbsTzO1cKA/2tyj8x2qLwcFC5bwPuSvwAsr3q8cTzKMkajq7XGwM0cPk3Rs1jEiABEmggAoyGBEiABFoygetacuJ8SltwByifTIVb2Ae7kXzsI6wqktd64YU5sZA3zfLMZQ0djiXT5E0WsONN9e0MiJ/tUyHnp9Dy87J3hYebh+L1F4YqNxVb8wqFg2HZ9xfsEKdtH4tVbsDEYYCXLpjzh0cdnhQbI7jzTuUVFuDMRfzDdqEWuVv2QN60dn0mFdMibRecDm7GtOlDFbdTgoe8+VBOuLFIIAhtQyx69cXbkd1YeUwGiMYSYRxyV57bxj2KuUr2Fxn+PevfEdZRhr2MHfvMDKodMDH3HeUtltdHSH8BWutYBx20iH8Ka0e0c3CynbTrjYFKeb6IU/L1KtuFf6+XdG998tcIiTBZbx2H8LvmY/KbknEQolLno2RhtE0bx4MumDbFXbtUi62v71LqatvfTvBQV4E7nx2HRCn4m13YaDOG2MN3FdfHOBldpHdlDX8A0x+Tbew3+B+9ojdCOftZqFphKgsOmX7u1vWJxUrZrF4pzVmK5l420doboJdd3wg6XIxcGfrBaMe2uW8skmVfcuwQHCe3rtXeOAIShzrlZUDL96Me81mqbFzlJ4BRytuxQYj93TIULYqGa5tQP+XfqAePSaABCDAKEiABEiABEmi1BGgg0rI+auYLtk9I9E9J3O0LZt6shXLcnT1UDOWlkzsGYmQ4PP5snyTsK8Z+/Sl8u35IHCSDOT2F1j4v6/HoQEQN0G4q3t2PHXo4EaRwj2KZQmKc0w2FuBaQJTgG8b09SGpzg8nFYny2U3UeGNXF8yd83W7GndJrxVmcMqRLOvm9dmgH+YTep/C2N3D+Az1s2fzv2k2/T5Jw5ZrqPyryP9QDd9vOHXCLcu2fuOjXJ3bf4FSJIiCgm1OFxVBsH5G34n/VXPSQf/+OHr9U3xY5XaaEEHp0wNjHlVeEUPLydNz6yAas23cSgf+XJBGVYalzHTTIulfUJU/lp20bg2fbYQOnO7QDuobfipGp45CzZxOKFsZAmSPJpo/xQBi13NbhEuwvUP2OHeGlDbFN5n4ZnxVKw5QMZw//wK9ulQ5u1iAkrtgg2tpMLFHaOqAxylnXxAegGLmOvYsYOdH6G8Uo+a7Wjc7WnEcmqEZu5zeCvty9H9JIPlJ7Q9QmzfZZ8XE4TG6tv3GEoRgbZ/OtHASyfN/5q97wVL6VCLVNyZsvCuPjHlSiHe59eRkKnrnVTdgGLv+afr7vGIIESIAESIAESIAESMCMwHVmjnTzj8APVdVqwNu6oge8/MK74H8pXv6OU/o9FjrggcReAC47PIXeoX1eNub/kTde0eLGu53wswdvFYidspzUJjR1vaFQLgdi0+NGdPVZTi1+0MLkTn8Knj7buzV+C9QXCow8tMD+7gRj9X74G/zP19aEnDqlfQ4WfCN+bvt0ogPCb9bC/0UYY7RDz7u/a/MKAT+/6d89e23XBeqniBfxPyf9uEn98e/4H+VNH2Bg75s9x+XL1WuaLqUfIM7DJ5cyX8e9q/otKdX4iXjaJk7T5qwBzh7Ix3+OmYXwqF/jpqHzMfnVQpyqEZ4CvNS9DtoV6ioMd/Yz60f1ke4xr6lvW8l5YhzWo9LYsgw5Cx/FSOdPupxVFmVDrQ/OF+R5NS4q+XEzestmRjq5XYPQ42bZBkGUcT2/fQnvJLg+ypm3ctZuKF7NfRRy/ixUHMeq+S8i5s5xCImcjFFz3sUOb//U5pQE5XTQLzFSHhw7hB3l8kCu3+DTnRfFQQzG3qcaUcWJbdE/K95RoBr4lQv6G0cjfol7bG2QcgWBLN9RPbqoQr1tc1ciZk4xKiGMe6+swvbHPbcxbRuh3ntLAq+TAAmQAAmQAAmQAAlYI3CdNW/01VAEusYNhPyowfYUWv+8rPdwJGt3d7G/Hg5prLF9ZqZPaGpyQ9FQenuLp21n+aaDlfVW9OjsTZrF6zffioHBqt8vT9iscKqDm+2XwgCkXOp3M4xGvv/dTxruABz7BsesvOFU+jeoBq92Fiae1j9PAXZ8qL5tIGKyvvy5CLmK7xjc8yvlILCb69vB6ueXsZHGm852ypw13xUtRua0GNzZWb1BvnLqODb+fhluj5qOefu0yb/h269p+26t6a5jrjRwOQuLGYeCkg340wphXLujg/pGTM05fPrmFiQNnYCYRdIo4kOabG+AHkdugTQKibCnCpEtjbeDYnCvalMTjvalrXSXp4Y3Qgv/7x7IN47uva8frL7hI0XU29o3GomhUnotctd/gEKvbzmy/EtaXEmABEiABEiABEigORKggSiAuabPa4Gvz0L51AwefrZ/2PoP2D9lEv7DY/CwNASJp9DKvBTa52VRv46FzWBxx1CkSCOKdlNx5ctjkF8YjUy8G03ihgL6Lwg/0w7HKp+VbIC7z/bs7tOQ2A5aqLruorV5QYCSj4qgf/zkVuqPxfg0X70aGz9QMcKpZ0DXQf0QpZwUIf/PyoHHzdk9xZB5guCBSOzr0atyUX+TAPkfIbtccbK4sc/9gvih/rFzZ/BqE6TqcPujKPizlbzbgIJnXV8/adu5F1JmzsefiragujQbRVsnQJkA+cdvsGrM77HRp/SqKrnbBqQOuhPuo3tDpttH1Uy8h6CDYkwVBlB1/mYTP7pTLU5p/+pn/3xSr+tWwutytH1jlrPgDrjzsXHIyduA78q2oPyT5/Ffqb1E3a9FSdaLGPemZujRVPW8098ABT795BCkkUefbDoq7pdCpknozr/EyN7SXbQrh+X+pPY2aC88NqKDdHBYG6V8dx+OLXtmQZlX6sgHiBu3yYKRCGhe5d8BM09IgARIgARIgARIoNUSuK5xUt4yY+3aLxqKEefIfsMnBjD9lXy8XzVYDIrGwOuNXm7GyPguwkE8hd53GZ8qnx7cjOR442v9t+LhMR2Enz14q6AWnyl+eiFxkHZDL640jSUa94xQNflo33H1oIG3Ix8bDsVoVpSPV+WTfA/xV+b9X6xTPrPphbHxkq/Bc4/hmBgjzy9j3et7IG/+5Jnp+uNJvLpeTW/bx4bgXof8NQ2BtgnjMOdmee045v2+EJXy0MJ6pSAT0xWj1q1Y8jt1AnMLwRy92N5AcnTuERut3tQeOYxPvb414BgWly+rcxZVqZ+e2a4Gt0PUoIew5L1VUCenPo7Vudbe7rLJ8HAQmDroIQJvlxop3d7U8n49CgO1+W7e2lns2fvl/Xh/p/TSDvfEKoVWnETDVtf/7NnC9OUbazF5zlpsPCqCiaXBy5n857eKi6J8Xnaqx0EI6x2NiQsX439eiRGaCUPPa7vg1dgP+89mSFb+aKAWn34i24EOom3WOdn9qkc3I/HX8tplvL9bcNPfBu3dD/fKhwCqJ9u20cr3TbF43WgkSthg3iY02/JvQ8wDEiABEiABEiCBeiFAoc2FwHXNRdFmoWfvBzBNua84jhde9nCTX1WIl189pyRp5OND1Ztw5Uzd6BNY79i5CW+/fRmIGYaxiuVJvS63dz5wtxJua162eOIsXGIG4gGTGwpxpRGXICSOU40WZ1/d6PFNkS9fmYVbf/UURr0eOGOBkvBB4/CHQfLoHF5+7l2U/CiPTVaRJ9Pnq/OAhD0xDhPDnf10wMQF8QiTzvkbMGmnyBd5bLKWbMjEy0oyeuEPU6JNfJg4XX8rnl+tyr+SuwzxLx/3biQq/QAPPLlH8Xfn76ZhmlMZkbF0CG0ndyj58zGnm2HFGZBv8fx+l/m1O4ZhuvJ2QzHmLfdgNKjagydE3t1611rsUAxsQvafN0DOTXTryHegvEklnByXdoj91c2KU0mJPo+Nclq3TYDqoN9KNFa6/VZYDxiEMU+oxtQrb2zCqlLd3XVf8vq7yJXONw9HSl95IFdDXX9lC7a6MyhW7cIL83dh45tlgLSDy6ANXc6uF+VZmVNrFl52YzQOu7M3lDcG5SelUkera48YJCrFugj5wvCav1ME7Hw3Hr5D7N0sPQbFKG25/Ky4cN8hyPrSdUQMTKpO/rn/AAAQAElEQVQz0JjlWxqJ/jRLfZPoVD5G3bsKO5zzudmWf/BHAiRAAiRQVwIMTwIk0CIIXNciUtFkEiGMCOkPQQ7sK9+VN/nFqHQ2SHxXhP9MWIat8kb6jgn4r8R2cPnpn5Dlihsp4S/2YdUY5OBP9/NuPuRbL1EPqDcZDn68ndx2M6Kkn2N7sNXDDaH04u/adkSyZqA5jski3RtdJn+txam3FwuDyEmc/a43Jj6q3F35G51JuA5I+f04KP+QdmQL4ia8i8LvHL1dObVH0U25qQ0dio1z3Bh1BozDxkdlfl3G1idnYfKH5+DwJtGPF1H46ouI+/1JJYI7F05GSrhyaG0z4CkUvBwNaYT68pX5+N9C108rnN7AkZK0eG6N24TCHztg5Msr8adnzLnd2U+x8AA733Wd76fmJNZN+B3mHWmHsGAp2Hm9GdOWqEarytcXI04YiVzLczFeHLcKW8sv4sbx/w9G6nKGDsNEefzNu3jilZOOnGQ0wjC19R3FioY7+zh9lnbzzbhT+kER3sq/rBxZ3wSoDlqP0NFnXdLtKKnhzwZNwFrljb+TmJe4GGZ1teR1Ub4V62c7pCwW9crwdpy9rhfhiYQN2OFUzyDK28vjhBFRpEwaYZXPZMUx0NDlLAaP/badiFkajbegRLSx4sRhKflwD6ShBnfcCq0GOVx3f3IrHn64g7h8Ge8v3KIY0to+2Fcrz8LZbLmjLx6WdUW0w5NeU984SnnAqU7YwjVy+Q6NxevCSKTkXcUeJN21CrnGfG7O5d/GmAckQALuCNCdBEiABEig5ROggSjQeSyMPn/KUt8K+vKVFxEeNRmjnl2rfFKRlJCMkDsXY538ZqFHPLZvUY1JrircivgH5Q2MvNLL9XMn6QxxI6J8ZiZPbnb6BE26WVh7xCJZebL9DV6MG4e7nlT1fGLMJnyKQP2EgSZrMebIeCoKMXnoBIQnLFZ4TJ6zGHf1mYDbZxShErdiztuTtclQAxW3JifyUeRr/1hUuUcYie4ch/BfzULSs/MREzMONw1dJW6Ghd/OQ7FlzzSM1NELJ8elHUYuX4bXH5Q3gOewcdJk3BT5FGLGLENSwlMIv+0pYRwqRiWCEPu7ZShINTfaOMp0PIt6/HmUbIpHlLjxPluwBaOkfjHTtTK0DKPinsJNt6rxnBXxRMVFI/yTVZBvX8k1/NZf4wn51oImtm3iBCyR7HEO68Yki7TOxxNz1ir63hQ5C/+5Jwgpr/0Oc3toAZx3wmglP7cJQy0KVzmW5yfGiPTf+SJePgKExU/CR0Yj1fXReGHtUIQJeV++PEtwmo4kEa/8tGiy4H7rbdPxonx7QxjkXni8i/BlWK6PwdjHZCZcxtbUp3Cr4KuEe3IV1qk2JYNnk8OA1EETuVac6pJuK/Lr1U87jFm7DEtigoCqIse6KvLs9shxiFkoy3c73Pvy75EZJ/w56NMBKVnPY2IP4XgqH0mint1uy7vpogzMwouirOCOcShY6GSEbeBydu/cyeqbMEfeRYxIl972TZ4j6tivRDp1I9jMByCTAx9+dw4biLbC/9lj3+CK2CcOdUqrcHNcokV7L12+QYmsE8EDEa/UWelmsjZm+ZbqCCNR5qdaPlftwTjRftqMRM26/MvEtfqVAEiABEiABEiABFo5ARqI6qEAhMVPw1+FoWHaIHHjK/8VR74J9OYu7DhyGbi+CxKnzcfJgqdwb6j7yGPj1ZsMxAzEw+Hm/vTPzNA5Bvf6ehejiLwZ07Y8j2l95I1eLb7cuQsbhZ5b930Pk4fqSgi/NqG98HzuBhT8LgZdr69F5ZEiJZ6Nbxbhyyqga7y4YfxyGZ6PaeeXeCuBlH8sOvQ8/uvRm9FWGDsqy09iR+5xlMg3dESe3Js6DV/9aRoSb/IiTfgds3Ytvlobj3vDBbeaiyjZVyjy9qLytljbPrH4r/fWouCZW0U8XmS5uRwmjEBFJZnI+V0s7gwNwpWKb/CpUoYK8WnpRcibTjVoLUoK9mDjzpM4W35RWV3e8JFvZ7yXiS3aX1NfOXUcW0Ue7zhyEegzHFv2rEXmiBBVnJtt18T5OCnKc4osJ4byvHXfOVwJvhkpazNxMmu4yFtHAWEjphnCfYMdIl5ZvjYK7md/DFLzXch1NcgFYaQ0QChzcdXirOCrhBPpvPIjLP0CUQctRWTiyf90mwhraKfgWzHtXVF+F4myF1xrr6siz07VAG1Fmcn8JNP9X52HRuO/CjYgZ1ovUR5qccqWd8JYIupO4u+eR7kw1kZd75qwBi1nwsjxepFaL2R7oLd9G98Uday8FgiPUebJcjWCuert4iL/9cvmGIP4QbYTtwe2SeqljwejESv3Hlb/y7cHob5ckvmcZzQS2d84a9bl3xcG9EsCJEACJEACJEACLZDAdS0wTT4laeTKd1Bd9g6KjG8/eJHQ45mVSpjqle6H8W17DMWSrZnKvzadLNoAdc3GdyfFTclMaSjxEsmgSfhO6FWd+xC6uvMqniSflH6KJkD9JMedRw/uYqC/JH8LqkuyNR2FrkcnY6QeRNzkSz7VnzyKHvDw8+bv+g6IfUYYGk6+g+++FHFoTMpLtwjjwqOI9WaY8RA1vMWth70pGhNXrhR5sAXlWvwni9Q82b5wKHoE6x697YPQ48GnsP3PW/DdUUNaSkTa8mdh4oAO3gR4vx7cBSOfmYU/HRV5U2rIG5veerxCf1kGDKs6+bMhCiEr8WVRZg1yyhVdJyGxhzBySSPSJ7IerITZPEZSUltRnjNlOTHIOPml1G0lMh/sAvnGhPTnvJqGU5h7yffrheEpy6n+CCOiXb9YvK6k2bPOvtdBYTRVWLwDF44OifPsz+90+xCHg1f9xGxvtX7oYWVdfWIW/lT6jmNd1cpMSu92uk/zvQg/cuZinDzpWs+2PBONsOvNg0nXtg1ZzrR6ofx7maFelYt0Vv95vvpPe1IpX9fr9bIp69R8jPGCS4pvK4ywSjsry7SHfkX61VfJqv7KN+C1nxN9x3/tkWkU69H5SFHaElU7qZtLe2Gl3qvBuSUBEiABEiABEiABEmgkAq3eQFTv3IPboWvnDtrazu2NNBr7166dpqPQNVQaDVBvv7Y3iTg0JuZz39Rb1Krg64MQpsXftXPd8qRtqCEtFm4EVQV83BrKkL0s6fH6oL9BTpi/uhpkdL3Jh3JiDCeZezASONAxhvMlPqMQowwZt/FafR87x2013fWtl0X5DnXV5zJTh3pm5OZLvhvDyby2xNuoZwf4XTcsMg24N+c0BzyCOgh01s1SftQhPgYlARIgARIgARIgARKoE4Hr6hSagQNNgPJIgARIgARIgARIgARIgARIgARIgARaPoEml0IaiJpcllAhEiABEiABEiABEiABEiABEiCB5k+AKSCB5kWABqLmlV/UlgRIgARIgARIgARIgARIoKkQoB4kQAIk0III0EDUgjKTSSEBEiABEiABEiABEggsAUojARIgARIggdZCgAai1pLTTCcJkAAJkAAJkIAZAbqRAAmQAAmQAAmQAAkIAjQQCQhcSIAESIAEWjIBpo0ESIAESIAESIAESIAESMAbARqIvBHidRIggaZPgBqSAAmQAAmQAAmQAAmQAAmQAAnUiQANRHXCx8ANRYDxkAAJkAAJkAAJkAAJkAAJkAAJkAAJ1B+BpmIgqr8UUjIJkAAJkAAJkAAJkAAJkAAJkAAJkEBTIUA9migBGoiaaMZQLRIgARIgARIgARIgARIgARJongSoNQmQQHMkQANRc8w16kwCJEACJEACJEACJEACjUmAcZMACZAACbQ4AjQQtbgsZYJIgARIgARIgARIoO4EKIEESIAESIAESKB1EaCBqHXlN1NLAiRAAiRAAjoB7kmABEiABEiABEiABEjARoAGIhsKHpAACZBASyPA9JAACZAACZAACZAACZAACZCANQI0EFnjRF8k0DQJUCsSIAESIAESIAESIAESIAESIAESCAABGogCALE+RVA2CZAACZAACZAACZAACZAACZAACZBAyyfQ2Cmkgaixc4DxkwAJkAAJkAAJkAAJkAAJkAAJtAYCTCMJNGkCNBA16eyhciRAAiRAAiRAAiRAAiRAAs2HADUlARIggeZLgAai5pt31JwESIAESIAESIAESKChCTA+EiABEiABEmihBGggaqEZy2SRAAmQAAmQAAn4R4ChSIAESIAESIAESKA1EqCBqDXmOtNMAiRAAq2bAFNPAiRAAiRAAiRAAiRAAiTgRIAGIicgPCUBEmgJBJgGEiABEiABEiABEiABEiABEiABXwi0XgPR1WpUXKi0uFajRqNac0mGsZ9rzq1jd60WVYJZVXXDJ1fhfrHWHrGvR9Uy3ypRc83XgI3nv+ZSOY7u3YP8A+W28mdZG7O8aoYMLKeXHhuHgNaONqd65RaUVj+qrrr14f6CVt8qLtWhjXIvvWVfaWh2Wj4by6zSv4i+zejW9KGL/vjbEhTs3IN9Zc2o3JnwbxTWWrnzq743isKM1B2B5ll/3aWmAd31OmB1TG/mX6vPTa4eabrWT58s2l7RX1T4cE/SqGW0Xlk0YHllVA1KoMkaiOqdwp+y0P++GRbXLOzTFNr3sgxjP9ecW8fu2w+RJJglbStv8PQq3J/5EGV+xly2bYnI6yXY9K2fAho4WOkf09Dr7jSMmvYmpk5521b+LKthklfNjYHltNJj4xFQ2tHmU688gVLrxwws+JMnX26uafWt/8tFbjzQ2S2BBman5rNjmVX6l/sc3dzq2xQuXDuDrCenIjphOVLS38S4dc2n3JnxbxSkWrnzq743isKM1B2BZld/3SWkod21OmB5TG/iX63Pfvab9ZleTdf66ZOLsEDcC/X34Z6kUctovbIIfCZSYtMg0HoNRHel4uAnKxzWnCc7iVzphCmvObof/CQVg8SV1rFU4+h7mRg3I89vY0zr4FSPqbzwMdJWlCM0IRXFB9fi+P5piKvH6Fqz6IrPNiNlfBYKmgwE1r/6zQr3fNtGxmDMIzEY/HM/NAi5BQ+LsGNiZB/iR3gfgzS9cutjAozeG5idMWr9uEuMmveRIbpL095X5G5ExuEgJK5cjdOfr8XpjNimrTC1I4F6JNAk6u/ZQiyfkYa0vfWY0CYo2o9+s2FSUa/9SicMlv398FvQ1pgaD2WgIcqo23FBvbIwAuBxSyLQeg1EN4Sgc8cwxzUUyi+sk5N7xxAEK1daw6YS+94uwr6/X20NiW2aaSwpVd4YihsRC61INk09W4BWf9lZgIKvrL5f3RAJZv2rX8ru+XYe/BiWpk3GmN5+aNAxBlNE2KWP9PQjsO9Bml659T0NthANzM4Wr+GgzyOTRd4/hriOBscmfPiXo2eEdj2RdE8zsWgJbbm0FgINn84mUX9L9mPN7nJU/tjw6W/MGOvUb9an4vXar/TEGNnfp8agszENHspAQ5RRt+OCemVhBMDjlkSg9RqIApWL2ve3G0yfIQAAEABJREFU3r9F1b5ZvVAJv+fw0eIyC19zsVKZT8nbd8D6d7De9bUCyJAmb/YkTfeKC9Xe59PRv5e14ldR06CHn/f6PnHR5l0JDEMlAY6bH73PJ+GTvo7S3Z7Vh0yXyHwpB4bANt0slwk1sC2cD9+KqyGdtlqee6tfTqFcTzU5luqBa2j/XbR4zfT3hZHVdsZXRW06eJvDx1Z+Ki3NJ6boW9e81xKjyApA++293bC3Z3WaP8HWjoo+x1v7rKRRi9dbHih+fdlociU7S3qYyNbz3Upe6n59bCtMYnVwspz/PnN3iMbaiT4pohffis5mzPzRUeNaZdLHKvHUJX/1dBj1MolH92bfWy9behtjNn6yy1OPdL9e66pBX8/zV9n1tFan7f7N2mxVS31r8GuJmR7OvtfzzyUNtvR5GbdpZUPO6+kiwx6N/Uj3b1Y27b7sR776t4e0Help9Jr/Wl/pNe9tkn05CEBeKfOgivF+wNtpkQ5bflvrX0UIr4tl7maSfMx3JS5fy5TVfkIrF97ro1lCAuBmyBuvZdjX6HTOVlkI+Qpr2eb72eYIEVyaOAEaiPzNoEslyJo+FbcMlnMSiTVuEm4ZnY2Ci84Cq1GUvRz9B05CtPxmVazRg59C/+k5KLrk7NfxvGDBU7hlbA7KnOJSws8rQMU1oKo4BynxT6FXnNBByh40FSnvyaeLjrKkv4kPTUKvu1V//aW+8UuQVazX7nJkjRXx9U1DxjER9lgehvSV52KVOggn+1KLit2ZGG5Mk4x32wlX48/FImSMF2x0TvdNRa+BMzDRzO+1ShSsSEOvWJ2V9DsXGXtFZ2WP3HBUi9Jtrmx7jTfLB0Mww2FNaR5cuNwtGIg4Dd7Uw6snsGnGDNwSOxXK/FUaw02lJ1R2CwoVf1X5y3FL30lIO6CcOm1OYPlDgumMAlQ5XcFnq0Q4cW16sXIld7o4lnlg4O+TvooU7xtLMg9koZdM02EneV9tRn+hY0q+Xo606xc+xjij+7Uz2GqsL0o5mIvZu89rAcx3Lrpp4TIOOMXnHPxSMTJGuy/vSt2S+uXJgMVIEce3KOsq7XOzQkwV58Ozz6D0vSXoL8q3zHN9vgolvCFfpBR9Nb127TzyF4uyrcnpr6XDnn7v9a8se64oH7p+emzq3vWaZ/1lKO9tgvSlrlV7szHq7qfs7Yyoo8NXFKFKtEGqD9+2OqPSs3sw25hPd4u2wqwddWlHZoh2wrWts3HQ81+2i8o8AVb5zkVWmWNabJykLNnGmrXfZTkYrpSXcltguy4lrn2FaHs3lTobgmtd2rP+Go99Wl+hcBPxpLgttyJ6l3Z0BqJFfvWfkYdSJwONIk+U49JTH4t+RG13lTkbLhUgpb/I78VqWySkOiylmaId7L8cuZpeDhdhL3su/YTQw6XcBISd0ECWJZe+xnsbI0JC4dDXWLf0NJS79rGDJ8ElDVKIXsdFGm19vTgevniP0ldLL3VdVT2fgnP+D89Wy529zGntnyyzSvnXYvajbARi/KHF7n5nppdSzz5GqbGN0crK1M9geQzi3Ico46fpoi6YGdn0dsPtOElPQrU6rhNlQc/rXmJckJJd4tSve6/TqkS9vJ1xTZcsQ6ZtrabDQK3eumub1AgMWz0u17It0zAxX/bJUrbo90TcavrEWEy0WVtPGcTIQ5/b5RJLbaG9HFvzr9aLOtZfi+M7mWyH1d24TbTTspyqfmtd2nZZDq2MVZW0yTZatm8W+krdv1M3pqjh/lq10M+Y31r/ajZOVyTZN2pe+dlv2sUoR6oskY9O9zz6/Ypzn2n3X6yO94ztndZW6G2jEoHcuJRZUbZN70n0emI2BlSv3aKN+d2O3Q1lQGFv7GP0ciP8qGNPbbyvnAsGUle5WmgbVdmu/cItenwBY+HaZkSLNtC0P5S6c23WBGgg8iv7yrHsmVXI7piE7XnqfEW7XolFn5N7kPKMMOjYZFajIH0uklaeQNhDydilzXm065U43PaXPCQJvw6DH1s4w4G4sXaMKx1bngxHRf5mPP38Kox6ogB4KFWVnTcNCwZCxLkKy6WRRxNTszcTgyfkIT84Guu2qfoe3JaKKZHlyHhCGImUTj8cE16V12YKdxEwMg45mr4HX30QEcJJXyo/zsKwxZV4YMVCdQ6nvJlIHybiXbwcsz4z3LhfEo3orzORdaE7FqxPV/1+shDrHgoSN8tLkCRuvnWZgGAlbqBT/ngeEY+M17imY/u87siftgTLSuHyK81ehOGLHdl+sT4Bgy6IfPi1aGAvuQRxdDiVgyTR8TpwkQxjKpH1bBqmGtMi8iFLsEr7rBZx06fhC4XNQmx8CFj9TBbeMsQVOmIkJrSvxaZte+AyBj2wG+vLgjBhbBxcPh/T58VKj1L0jE+X+SFWnb8v+ioSLGysyoy5A/FtavHRFycchB7dVYQK4VKws9AhrTUHjmAfopB0dwjkr2jlcsz+IgxT1r+E4wc34PjnC7H07mpsnbEEy7+SPkxWcZM6UeZPG0O5Ffkzped5ZE3KRK6hqDmGrsTWeauQ9X1PLN22GscPi/jyp2FKxxOivItwl4BBc1Yo5XH1CBkyCquV/JRuTvON7d2IpKWVGDRlPDaun4apatbIQD6s57F1WhomvleNh9Nm2svO2CCRfuG+UybEev3zIWLAVX8luLU2QfEKxe+ze3C0awyWbk5XuH2x+TH0+yITSevVG1PVp4/bS0WYOv5NHOr7uNp+fSLq+wsxiJDtqGg3CiQWKfKaMKpOyETWWcd2ZOkw0eakO7Z10jtQifUzMvH+jQORvnQatrzYD13gJ9/D2Rj2RB72dR+CjVrb+cXmJPQrEe33k5tx1HjzqkbutC2HbL/XIQ5bbH1FHAb9eAJpYxc5GKOq8leJ9qwEbRPsfcUX6+OUfmVcutqWeC+31Sh4Pg0pW2SZ1dupFdi1NBphe4UR64kcxxtuqe21YqQ9lYOy/glY/YrgldITaB+Hpx8JQs17O1zrmfCf/VYlgh8ZicT2UoD5qvQT88+gX7q9n1j6SAhK/yj6o8VFqDEPZnC1zg5iADwqIRtbjX2N6JeWJtSKOrbIsS03xOD1sORtjE39Al0mGertbzopaUj6o7HslyNrwlxRx43cZXmOFgxFGXq+0Mlw4DVmUw/u8j9ndLjBv1n5l5f9KRtnIMuvfazj+/hDxuxtPZq5CCl/rES/KdO09lGU2ReiUPPZ20hZ59jnSFlVO1dZG4OIcqH08Ve7Y8orWjkUY5+xP+7AKHFzVymF2Vbv/YbutWiFHNedwW0TUu1t15ye+Mua5UjKtOtrpU7rMuXeep0ReZkudXAc//g0tnQu27Jv7V2N/HmZmLogDUlbgLGrVGbKuEq0WbOnbkapVFSuPrfLPtRnKR+++lcCOW6c0yjGnxvN6q8P4zvHCMSZu3GbGFNk3CWui6XOY1WrfaWIy5+l8t1MDP9vYOJatX9X7g8Giv518RI4tnMWpdep3/Q13921dya6XvLlnkQL72YMpV1VdxbKgOrRsNXDiHJimw937VDlXis0YSD6aV6ttI3u+gWP8+f6w8JqfdJ05655E7iueavfWNqfR9VAMWBMi0Ofbup8RZGDU7EsOQw4VoR8/Z+yjuUgTdwURjw5E7vmD0WkNudR5ODx2LI6DhFf5SEt13GI4pKi0mIUOcQVLm5WUzClG1CUV4KYpS9h4+RYVXa3aKTOiUMkKvHRfn3wegJrXi5CVUQctr81GfGRqr6dI2Mxc5W4ae5WjozFHys3+cEd1GthbQC0CbHPz9QhCMZfxbEwZOTMw8zB3VU/3aIw4YUkxKEWuZ+XaF6rkZuehdyL4uZ72zykDghX/Xbsjvj5L2H7k2E4utJwc3fMyErnGo4+iZOxfUU4zhkMXkoExzYjZWU5nNlGDEjCxm3jEXexGCnaTZXi32Ujbjqn5uFoNycukuHKl7B6mNBf3GTqRoia3W8j4ysgLuMlbEyORoSSl90RN3keds8UNwx6nst4hEEjeawoCzvzsKlMOuirkLmtEDURQ5A8QHcz7PV5sdqrvIPbh6nMFP5W9TXI83rog8w2sUiKF09t9x6BPUkn8FF+JWIGdAd2H0K+fkMv4t33RQkw4A4MUuxD5Tgk3/gZcT9mDuiEYFG+gtt3x5glKZjQTdgwis6IEK5LlYjrL12jsPRVQ7kV+TNzZiyCr5Vg72HXMKqLuLYXiHz8MYyJVOcPC+4ajZkvJWFQ13LsLaqFzjb0BhkiCKFKfkreqn/pKtdSoffTb4nykByHuAHRiOwqXX1ba3ZuxGyhT+LSdCxNjLKXnenzsDEByF+Towy6gy3WP/jwM9ffepsAOPodc3u4UiYjbo/D0k2p6HP+vA/aOHn9thw1j88ztI1qff8iKxahF4qwOk+TfewQdl/rhNRl8xzakTFzHsQgVGL3Ab2t0+WfwaGOj2P3+mRMGBGNQb27I1hcCvaZ7wksf34PKiLvR84q0aZobWfE7QlYl5WAyNICrN5dDc+/8yjtLnRZKcpetzCFndL+CwPbIIiB8BvFWvBKFHxWjs7DHkNO2lC1PRdlMmLAeGSMDhL1qxj7hM9grU1wV25rdmYiJa/aqZ0KQ+SIydj1Whxkn5PifMNdegbnZD5kJCFxsOAV2UnEBAx6dAgiRD1bvc2Rb83uHdh0KQxPPxqt+HO3qfiqFrLuLB3RXUl3Z9FPjElbgZyxIah670NsPesupO5ulV01cteItlwYpFdvM5QRJb50tS1P24wiXawP+9L8Mxj22grHejtD7X9LP95vawtrdm426R/U8rw9IwpVeRuxzG17ZV0hd/kfqrSzuhzz8u9f2ajr+EPXydO+BLv3i/b6yVRssfWtYYhMTMWCAUCZ6AfsfY4qpyC/1toYxFAubOOVSDn2mYlZbc6jQhWnbb33G4pHMfaY+sdqRE6ZiS362KujyOvR05AzRYwFXsvRjKqVluq0IlPbWK4zDuMle3uhtC0Wx5YuZVv2rb+LQ2ecEeO47lgn6pLOTI6rlv62k8iMYhTomeFzu2y1Pmsw4Kt/PZx975JGMf6MM6u/vozv7OLVI3fjNtF+K+20KC8pdRqrimis9pXCqz9LxUU1v23jdFlHhEF1qah/R9d/iH1eH4QYY61rv+lrvpu3d0aN1ONq3+5J1EAwH0NpF/WdtzKg+zPu9TCinCjz4XY4g9UL9qCsYzRWz4mF+gDZWtvorl/o3NFxPGuP3k8WFvtDezw8as4ErmvOyjee7t0x5fGeLtGHhoqBPGoBrTE9+nGRGECa+8XtQ8XNK7DvwBEXOY4OTuGViyEIay8Oug3BxGEOI0Mgohv6iEu25av9eEt06INSHkQfcWNuc5cHbXri4YdFp3/grzik6Sydva3Bo02eHIeEqA1ajUi/FFBdiJydgKlfcb3P4yOVm7u3PlaftpXuLVZYPf2YCdd7HsPU3iKQYfHIVjz9nipvqnYWOhgtDMEBT1wQgl/9wbAAABAASURBVMTfxCpGiJzP1RvAgt3C4NE+FlPjnXgDCB0xFIltxIFhiUx5GPFiALrMdgMoLlYfwvbdQMxvRwojnjj3ZfFRX0uifZQ56J4o4Ji4Ub2gST+2H+9/2x1JL4wU6S+Bzgooxu7PRTpHxIjBpvQbjn59Rd3Y+T5m7zxjnzumTQzS88QN42+EgQmuv9D4aTiYNxNjnIwyNcEhiBDer7idDDIKgwcApW9uRtaB8/Y3FSISIN/iWHqP0EWEt7QMGIaxPSz5dOOpGvn5suzEIMW5ropyFrdwBQ6+PhJdnD79cSPMd2cz/T3lu3ObcOoQPhLthyyzLu2HqA+z5E2D71ppIbrj6USTvO87Ek9HCgP4ziJUSJ+3P4bteS9hQV95Yl9rbghS2hzXOS+CMOaxoco1u28/jnROj4n66lS/EZGE7QUrkCHKmWfJbnTpej+SRwA1+fuhGi7CkLhkBQ6uuF+rM5rUa8KYGRIm+hStXdWczXd6WYs1badw+4OYIvQty9uPozD+3ORD7yTMEjqW/rfxBkHEsVOU577DkOzUJhslKsdmZU9ciBkzRLR/Z/DRXi8PRxBkno/O7Dz2NSFITIgGLp3A3lMicl+XASORfLtzoBC1/7X1mYJJvmAi6oN5/xCLRNRi32FzQ7iz9Lqfm3HzrKOnsuE61tHSb2X8YSkxUZiyeQV2TRH5ZPR/NQhtOwgHG2dxrC2m4wrnMci1Q9i1E+ZvurXpjtSnneITBkYr/YY+9jBruyJ+k47iHSmIU/T0o05brDO6Dq55IyK2OrY0K9vtRd4KEZ1HP4j49uLAsET0CDecicNAtcvO9VmIVhezciyuuPUvrjkvZmkU/a4yfjaUK1/Hd87ReDr3mFdWxqqK8O7W+krFr++b4IRhLvkNdMKYsdGi7SzCR95uU4xR1rnf9DXf3fg36iSPPfYTgPM9iQyirG7qpHItYJtqFDyfhU0XOmHmhmmIs9U939tGSyr5zWKkhf7Qkgb05A+BBg5zXQPH10KiC0OXjt6Tcu6sHACXI2PCDPRPcF6XY81JIePkeYj7L3HgbvEQl+jMQ90F090r1Kdk+9YsMtFhBsZulE/py1FmfANGD+tmH/FzYVRyc83mfOGikq5+t3W3OTkciCdukaIRrDgr4xcPpv4m92GIcDIGqGHCESluGNVjdauwbR+O29zkQ8RtcjBzHucuqP5dtgqXIETeKm6+XC4Kh+63oJ/YlWl5WCbzqlsndBZuLosYbEY76YeQoUh2+kSjIq8A+YhCSoIFfs6R+KSvc2A35z7KDB7cTwx8z+CjA6rRrHRXEcoGDMQD3dS3i2yfmX11BB9d6oRRA+3pjJk8Gam3VmPr7EXoFTsJQyatwvLcE97nsBE3yKV7c5AxbxGGJ0yF/Fa71/gC2F5zN01aGMbMfwxx159AxtNz0WvgVIyakY2sveV2Y5FpOBPHLmHmeW7i1dypEuf+Lq5E3aIYtcSR46I8ReoE5Umj45XAnJnpr+S7ME5baRNOlyusI7rY89KoWITzTYPxorfjbj3xy45mnrrjl9L48PcKnLNdrkXFVwVYnr4co0Q56CXnx7l7M/Jt140HYehuri58+imc3LcRwfKNpA4h8Pzrif4yLSae+kQJJS9VokytTqqP6jMoyM7G1Elz0V/OcdZ/Eka9dl695nXrpawhDLf1DAK+Fe2igyx3fUwIEkcLQ/mlQmTrb0qdLcBGcdMdPy7Oa73oHN3T3E+PWyAfYnz9d2/pssjugtrX1ORlmfZx/dOF8Qbn8fVph0RbOzGrPy4hNe7VRXjapZ8X/f5D25ArwpT+rVxsRV+XPVdpx2Rb5tM6NgdligRvmzCT8q/p6K4d8rlsALAy/hDerC41F0qwNTMTKXK8dPdTylx/U0VZMwtvaQwiyrk0hEb8h6hnZkJ+0R2RDu5hlvoNj2OPNkEI7dgJDm90+VCnrdYZRQfUcWzpoWyHOSTAAZLTiS/tssX6bIvBV/+2gPYDD2m0eyqHz+M7e2CvR0peta/DWFXG4FNfKQP4tg660/XBrCJBjN8jUYuys8ZOSrniflPnftPXfA+Dpf5e6yes3pNA/1kqQ7pn//ZVu7MwMa8afaZPxhSTB5K+tI2WNKhXFoXK/J0+9W3KvEtPwT5nl6VU0FM9E7iunuVTPMIxZvrDyHBZR2P1kmSsm94PXRqAUkyimQ7CLU3osFTcTHesHyUqq911LLWocXfJqirVtZAmODPvV8Q1M3dHNw861Fx2K9tRhvsz/RONjcqnMieQ/cYZ9WlmiPswnq/Uh74+yAyJwQMDgIIvioWaJ/B+fiUGxccqN4FxI6Khf2ZWdqAYFb1jER8hvOlL+2gs2LYaBzcnY8G4ngg9WYI1LyxBdEImCi7pnpz24sZ0dsIkDJ/xBY7iFoydNBpbtq0QMuKcBvZO4eRpj/uxMW8tdq1PwpS4Tqgp2o+MZ9PQ68kc1zlYpP8Arq5vtARQuBtR/sTZWG2CmyQ4ODukR84P8eRU9J/wNnZfDMGwlNFYtykdBwvGI9EhVPM6cUijUL3qs0z0v3sRUt49DUTGIm3+ZGV+k+2T3dzgijCmy+XLuGJ6Aai8auVNJENg8fT0aVGP87cUoEI4l767G0XtY5Hs8jacuGh1uVb31+Wc2cmoOw8eadLPPoyM+Y9j3dJkTIySvupx7RqFqS79vIh/uiivIv51j6s3YV3ueUzRR+rk0xqIsUIgy0YAUZb+UbTN9y1H2t5qdBk4DBlLZipzEa2OR8P+AtxvBKxOm9aZRh5bBrBdNqvPnjLeV/+eZDXINTEerQRMo7I2VjUNqjj6wsIXv4pw8YBO2TeRjc/6m+hdr/ckJvF5dTqVg7Gzi1Ez4DFkJ3d38V6fbWP9sOiJibK/82Ot9z7ahS4dPBGggcgTnTpei7xNHdh3v2Mo4ke4WQd2R3Ad4/EYXH9K9h//y70OI2IQ6bfRwk3s3bopn7SVnjlv7uFsOb6+BkSKJxTSQ8QvJKtK8aRCnjmv5Sh1emVEYXvtvBv/4imtjLdNOCK7OcvSzjUuX4unjJqL40483ZZR9lHekBBy+ojLpWfMjQvVJdh7TFx3XrRPNIre2IGjyuTU3uftcBZhO/dJX1sozwc+ywzDsHjRgX1+BEXy87Ky7nj4njA1jiEDtc/MyrFv73l0HnyHyRszQeh8+1CkzpiJ7flrcXzb/ehzvggTM4tVGU7bosyN2FodjXU7V2CLMKamPjgUg+Q8MOLJtZNX81PxNDdyQAJmLlmIXQVrUbwiGqGH8+B13i9zaRZdT+AvDq9ji7ITKYKWnFbKuzhyXM6WIH9nMcquOjr7dlaNo0fd1DMzQVq+w0qbcIvQX8goO2cuv+yU+laE8OL78q1gdcEsmHCXDP+js2I8r8jdiIzDYZj5zlpsXzEZM0cPRZycC8nr2ztmsn1wUzjV4i+lZmmvRun+PSg45s5ErcdzAgfN2gZUQ8mz9mGIUNreYixLK0LViGQUf7AQq2ckIXFEtDIXUegNuixve5FXsqyViXbxmplf4S7fhOzt/OaEmV/drSemTBGWlcO7kf1VMbLfqkTE2GEY1Ea/7n5fUXxCMSq5+Dh2BnI+pdv+Q7b5LlcNDhbZaX1NVfvuHvq4oejT1SA6oIca90shiBzmpp8fIdxvV9Mb3CPGo571M1bQdAxo2QgQxAsfI21FOSKnLMTxzTOxdHIC4gdHIaJjGEItlDO3WnTrBNltl4m+3NTP385A9vEu17z0G8rY41I5/uIyh5aQVC3K9s5CHL0ojuF7nbZaZxQdRBSNObb0vV22WJ9FutTFV/9qKN+3om7IguLr+M5iREpe1WWsKuOx2FdKr+7XE05jE7vPfV+qUz3YXdSjmhJZR4IQ0VXppFRHb9s695v1lO9aP2H1nsRbMgNyXRpZf5eHox2isdH583IZQb21jd18uj+TqlhfRbsr+zs/1vrro61rT592AjQQ2VkE/ChiWCwicQbL/ltYh52ln/0YExNmiCdmPj7RdZbj7TxiIMb2BorW5WCfy03oeWydMQNDXi5CjU1OCEK9frdm8+z+oE00kuKDUPPe28hymfuhGgVrdqAI3ZEcH67IiBwcLQwKZ7D+bdeOquqzt7Ha6SbLxnZNIaoUCYaNsMhnvFeL4EeGIc7dANPGZbPrGyyy0V5ZiBrxpHzsEFVuXHwsgq8VIyP7jOpg29aiaP2HKLCdGw9C1E80yr7A2HlCnpV5O4zBjcc+6msM6vbYD5mdB/dDzKUSLFtZBPl52bCOmnRtEuuCDzfjo6IwjB3eU7sgdqKTG9f3KSRtOy9O7Etw5FAkiRvamnMXDeVPv16Jr0+IutGtO/q0193UfdXRE+YDe/UycDgLvfpOQtoB3UHdhw4bCDk3RNk/7HqEaROCqz583JoMKKt2f4j13zrKGTQiGsGXCrF+p/Mrc7IerMLENUdQYzMChHipf+UoLXOUj1MfYvVuJzdPp7Z8t9Am9OiHByIAxch5zUmoSNOyN+wsna5aOD2DNW+a1HeNYYwwXHcWUsq+LhfbcPyyh9gZl69Koc7fY3T0duyNryG8zkkaeJ3TXrYDEydl460zQYYAZoe12PTHApM26kMlz4LjByJGBrsg8vUSEBEViVB5blurUXTElbG7cquXtYw/OrdTQNVnm5WJkmMeHSraWlsEXg+Ch43EhPaVWD81C5sudcfTj/b0GkbxcGAHsr9SjgybauS/8QUq0B0PDA4zuJsd1lpjZ+trcrDJqe5JqUVr5qL/pDw4Vxt5zafVg2ed+5r3ZFl18ng4G8MTFmFTfSrgFKXZqa5jIMuGWTw+u505jUMiUJ+e3cXWsFw7gUPSUGxw8umwTT8MHwExBtmBXFG3HMLKPn59sYOT1X5DH3uYjVXKtmVi3Lw9KJXtuY91WlHGYp3RdVjWiGNL39tli/VZASE3vvqXYfxb/RvfOcUVGmL6sNeWV/6OVZVorPWVile5sTg2kV7lWrPNvI6s31gCtI/BA3dIXxbXOveb9ZTvtn7ibUv3JBZT6+jNTRlw9KSfifHf4uXI+CoEY15MNcw7pF8Xex/bRnfjAiHJcWkIFo4x8qwZEqCBqD4zLSIJ66aHo2rbKgxPL8DRbytRcaESpXs3IyX5beR3GILkgd5uMOqqYDhSX0pAn4uFGDcmE1u/Kld0qCgtxPLpizD7sxAkj4kxdGxh6Pe/xcD9WAEycs+ofs9WmtzAe9MrBHFzUpDYoRwZTy3B8r2arG9LsDU9DSl51egzPQUTumpyeich/ZEQlL22HMMX66zE0zgx4Bo1oxxdhJFL86nudLZ5WRjlxHbcU6pFfp3zpJdqSG2rcylGygQnLtPURjsxfbz9SfmA8ViXEILSNYvQ/+lsbNq5B/k785Dx9AwkHeiJRGf9tFigfKJRi6oLgJV5O/Rgrnsf9XUVYOLih8yuMRjVuxL79lci7pEhkDfwuuC4xFgE7y1BQUgUht2uu4r0VL4lAAAQAElEQVR9xyEYO0wYGV5egtm5JSgTdUCpB7mbFcNfRM9uhvIn/CuLSTm8oJWHlWfgeBOtBLBv7hiGpyPEIGOeodyJwfq+NR8iV/jqc1u42KpLnxh5syue9K4pVvX69jxcDI6qV4etcrN1rRhTpwkji1Kvz6AgcwmGzT+PSKeyEHxPKmTZKVgwFynZWjwiLflKPQhC4owkYUjWxZukW6t/6kDzPDKmZiK/VLYl5Tiam4nhY79A8O2izuoivO71fC+00Cb0xJQ5MQgtK8CosfZ6UnogD7MnZOFop04usRUseEqZY8X79+QhqMxdgiHpedinpEdjOLsYNbcnYNkjqmyXPBJ5eXTnZox7rhCV7V2i9+Lgnq9rQI3Tt2raVeaVKDuQg3FP5KFU6DhrmLcnq0EI/XIzBj+7GflK26vn2cco7RgNWxvVsSf6RQClb2zGVoWFyN/SYmwSZST9S9c4XJho5VYva0dXLse4zEKUKnVNjXPUjGJUCZ11rq7pdeMiBpPJY8NQc6EaGBHnMmm8m1BAx1pkT5iB2duKVT2UPmcuJubXOrb9bgVYZIcQra85g7QJhjov+5rFc5H0WjUGjY2DwOs2prpe0LnvW7zEhfvwSXtwru9IPFyfClhIgK5jQMuGhXi9eukdpTzIyV2/GfuUtlTUsa/2YPm0Vciursv4KASJU8TYByWYOnqJbQxSpshejmXXOjn0X7Dab2hjD3Wsskct26JN2pe9BKNWnkefKY8hUVZZH+u0wslqndF0aMyxpUsbJBh4bpet1meFhNj46l8E8Xfxd3xnjM+lHJejQhom9bzye6wqI7HWV0qfvoxNpH+5xsRUY2rSEizfqY7Pyr4qwOyx4vyYqEPGcbD07HWta79ZX/mu9xMW70m8ptPEg7syYOK16rMsTH1P9KkDYzG4+pC4n5D3FPpahFJxCS7yPLeNLnVSGxe4Rh+i9Zn1yMI1Uro0MwLXNTN9m526kckL8cVScYMlbmhGJcxA//tmYPizX6Ds7mR88bq4MXT3hksgU9ojCTk5yRgTUozZ49MUHfqPzsKav0dhXc5CpDo9mY/8bSqmDKhF/gvCGCL07T9tN875o0/7WKzOmYkFd57Hmmc1WQnLMXunsJiveAnbk7sbpIoGa346Nv6mE8re24xRCqtFGLfmPB5+fSEWRBq8aoeRyek4uMKV7dd3Joj0ToP9nwC0AM47wWV7ngmXM92xYNNLWH2PHOXpgYR+L6YjZ340uny9H2mzszExfTeO9h6t5GO07s1l3xODpBFQsKjTvB1Srk/6ygAWVp9lhiPubmmMiELS3UY+AGIGYoy4YQ9OGAjlrQjhpC4hSFwyD+n3AFtfWI4hskyJdfgLJ9BldDJyJkojjerTuI2cOA1LRRhbObxPlIc3ajErezTkm0BH3X3e1KYnZm6ajNSuZ+zl7r40jMsWRq3pM7FshF3v4GEpWJ0QhqOvrVL1StiMQ0Yl3BzLm623pvdE8P48jNPKakpBGDK2LcQsl7IagjhRdjaOC8O+NVo8Ii0TlXqw0KmcAW7rnxhoviXKe2R5ESaOlm1JGkatrcTYtelYfb+PN1Ii3622CcGDJ2PvK0PR52wR9PZj+KQP8fWImciZFO6GkBXnnli2ORmDSj7EOCU9i5CSdQa4Jwm7DG1j8IjJ2CLaBVse3SfSnX4C/ZZNxtRuwqjytQhjJTrNj1u+2nWHneT0VhLia4o15jMw5OmPobQxr1ppv8Mw9b9mYmqwyDOl7RW6v1CEsp5DsfEdYxvVEzNXP4a460swW2Eh8nd0JlbXPIiP5vcUKpU7vDnmvtyKspbxEnKmd8fXm7IwXNSz/gqvYoQ+koyDm5IQ6UefEzkgShhxgzBh9FCxF+pYWQaOxu6lPfEXUeYVPWSfsz8I8fPnIceh7XcnzCo7EV60r6sFT5e+5vMwLHhN1A+Htlz4D/gSotRxV+4liJg0DXtfjPVs1A64PmYChY71UDbMYvLJLWQolq0disgTBVpbOgNDJryJvbdPRvZvw4BjZzy/MeopMtFm5sj6e4O9Lxgy/k281SEJe1+KhZBuD+1DvyHHdbtEvaz8INtWx8atPY9Bon95y1a2favTkD8f6ozUoTHHlr63yz7UZ8lC5I61tlPxXMeNqBuij/Z9fGeIVpTjjIxodLaV4zSkF6nX6zxWhbW+Usbm29hEhgAixs9Ezm+D8FaaOj4bMn4ztp7thNRX/Gw769Rv+lpO1DRY2sp+wvI9iSWJjp48lAFHj8ChneKBjXTcX4Cp8n7CYX0bBeKhsvyzG1/aRvfjAhmR01rfLJyi42nzI0ADkSHPIpJfwunDLyHVw5O+uIwNwo8Y2BvC6Yfm4YMQIW5wtn++AccLVuDgJytQXLgWu+YPRcQNekjzvfu4wpH6ltBDDHxcVRVGmcMbsCs53EFocMRQLN28Fqf3qjoc/Fwcb5uM+AiTG8v2UZi5XlzX/BaLmwolHjHY2mUiW41Ijfd0Rqx6qm+FrNQVK3C6cLWS9oMFQu7n6Vg6TH07QPem7NuEIW5GOo4LPsWCk+p3IWZGi85bcjdJb+dhk6GyVdMl2R5ckYSY9opE28Y8b8TlriZcPpiJVBGnuOq4CP1iRk/DdpkGweH05yuwZYbMx3J8XSq8BpuwrN6D7PdqLc/bIaSIG+RpooxtEIYD5cxxY1Vfk7wKCAOhTeRkkZ+HZ6pPScW5bWkTjXRZzudE25xsBzf0xARZDg6uhZK3In+Pi2NZDzq7u2Ft0x1jVq52LLMfTEOiYLBa8N/1G8cybotLHnSIwQJZ3vVy98lqHD+4GhuToxxv1Np0QmKGSI/mr3ivXrfdlGcpW1lDEJM8Dwf3aeXaUJ+UeutcVkXZcS3bbuqBqDOm9U/EK8v7rr0aQ1kO8+chtW8YIpNl26XrLjzCm/6AL21C6OBkh3om62jOZMHynmmirDq2mTL9OWPDEBYq9fC8Bou8NLZLskwcXJGASIe2MQSDZoj0aXlkbxei1HZQ3Hzrsbgt47oHuXfD113Y4MgErPtgLY6L+q633y5tjEl9k1Epawehpyz7mv7FhaIv2JyMuA7KVftGTpCbL64Z+omDGXHoPExj3M3uFW3clVvpR5bNmTi4Xysnsq6JNnW76HOc65rMq9OHjeVGhndeq5G7rRA1EUOQPMD5mqfzG4TuhvZZltf9K7ButDCsGoMFgp2U1yEaLn2NVj/kZX01y2dXDp7qj5v+V9TxmGRn7rLNia7bXDq64oa9q77qRbO0qVf0bYhot5x1XAvfyoab9CtRqNycxx/KJW1jpmPogGTsciqvsn2J0du1a3rgJPg6BjGtv7JeiZtYKWu1eAihSQes9hsIQuRoI0fRv8iynSzaRJswceBLnRbeAYt1RvHr79hSzSOXsZqU6akuurT1IZbbZSlaWa22hYpnsbHo37U+eEgj3JRfWX99Hd8JFY1L5/hp9nZXtHfGsiX7bitjVaM847G1vlKGkHV8nrWxiZbf8mGoQ9slxzNinL5gcJgUaF81/8b6bVafZQDTemcyNpd+XVaL+e4ubkWeia6Ku+j/XfoJkVbXexJPZcj9NXdlwLmMqufiPk6MZU+7rPYxleW2USbO3bigXlm4qU9SH67NmkDzMRA1a8yq8sEdwtC5YxhCHW5+0LC/EFWHzlbmXtH8BkTfG0KUtHfuEASvvzZBCBWcLPnVhAXXla2WVndcqvJXoX/CcvFUBa6/A4fwvhjAxg+McrlWkVeA/Gvdrc/b4SLBjYMXfd2E8uxcHzKdY9TzVuRvcBvni27O3ellJbxe7jqGINiNeMVZ8xcaopxZ32jh3JUbF0F6+q3UAy3dLvXPFxkuCpg4aPFYSUOwVs885t2lQmw8GIWkO2D9p+ngUa7O2pmdlXJgpokWpwtfM7/CLbh9mNKGWfUvgjgumv7ewgdrjF38maVTl2labrV21Je6BpPf2QJs3AnE/Hak4VNIE38enIJlmpzzDT789HRa6Ts1v770Hz5oYsFrgLhbiMl/L01RRw86mZV9HxPvU/3Vy5C3fkMYipSxihd/wbL8i3poqU5D/QXLMBbrjOLXTD4a4KezctbVU55pYVx4uFPXV//u5Lhx93d8Zy5OK8fOPDTPwTJf65JXWr/lsa+E+GnMrPTrwre2aLpbuT/QQnjb+VTvnIVpabBcTpzDezrXZNdPP6FxdFMGPKllfk2TJ8qNS76b1TMtbZbHs5r/+mFhniKfXOm5UQhc1yixMlISaGYEQu8eimE1JZidvAqblLlEKpX5mUr3ZiNpini6fnsCXOcjOYHsN874Nm9HM+NCdUnAmcC+bYcw6pVUxJgNXJw987zJEyh9dzeK2kQhJcHkrc8mrz0VJAESIAHPBPwb33mWyavWCdAnCZBA0yNAA1HTyxNq1BQJhMRg6TuTkdqxBGnKXCIzoM4ntR9V9z3mMGeKrn7NZx9ifVmQb/N26IG5J4FmSmDQk5MR37WZKk+1HQlUF2L1W5UIfmSk6yeljj55RgIkQAJmBJq+mx/ju6afKGpIAiRAAv4ToIHIf3YM2doI6PMTaHMzyflI5JwpuzLuh+OcKSqY4Hum4fjhtUj3ad4ONSy3JNCSCajf33ub+6Z5E/A4P0JzSVpILFbLecXmR/ugsQgj51Rwno/OBwktgp0P6W3eXql93QnUvc7UXYf6k+BrffbVf0A093F8F5A4LQhpDX2ljqFR8l2PnHsSIAEHAtc5nPGEBEjAOwHtG3A5n5TL98DeQ9MHCZAACTQfAtSUBEiABFoLAY7vWktOM50kQAIeCNBA5AEOL5EACZBASyfA9JEACZAACZAACZAACZAACZCAJEADkaTAlQRaLgGmjARIgARIgARIgARIgARIgARIgAS8EqCByCuipu6B+pEACZAACZAACZAACZAACZAACZAACbR8AvWbQhqI6pcvpZMACZAACZAACZAACZAACZAACZCANQL0RQKNSIAGokaEz6hJgARIgARIgARIgARIgARaFwGmlgRIgASaKgEaiJpqzlAvEiABEiABEiABEiCB5kiAOpMACZAACZBAsyRAA1GzzDYqTQIkQAIkQAIk0HgEGDMJkAAJkAAJkAAJtDwCNBC1vDxlikiABEiABOpKgOFJgARIgARIgARIgARIoJURoIGolWU4k0sCJKAS4JYESIAESIAESIAESIAESIAESMBOgAYiOwsetSwCTA0JkAAJkAAJkAAJkAAJkAAJkAAJkIBFAs3YQGQxhfRGAiRAAiRAAiRAAiRAAiRAAiRAAiTQjAlQ9YYgQANRQ1BmHCRAAiRAAiRAAiRAAiRAAiRAAu4J8AoJkECjE6CBqNGzIEAKXKtF1YVKVFyqDZDAAIupFroJ/WquBVhuAMXVXJI6VqMmgDJbgyiVWyWact56zocmXnc8K8+r9UVAa1OrqusrgrrLrbko2yyntan2AXVPbquW0Pzb2cBmn1L2m2tZv1qNijqME3w0OAAAEABJREFUh5SycLGJjvUsZHNAvJi0zwoXX7hq49KqqwHRiEJIgARIoMUQoIGopWTltx8i6b4Z6P9yUZNMUdm2Jeh/3xJs+rZJqqcote9lwe++LOxTzrixSkDl1rTz1nNairCgCdcdz7rzar0R0NrUpG3l9RaFv4KrDmRj+MCn0CtOtllOaxPtA/xNK8OpBJpRO6sqXK/bcmx6RpT75lrW/5RVp/GQUhae+RBl9cq4iQs3aZ8VLj6MM9Vx6Qws+FMTTyvVIwESIIEGJkADUQMDr7foQm7Bw4/EYExMp3qLwqvg6hPYmr4IE99q2Buqis82I2V8Fgq8Kig8nC3E8hlpSNsrjrlYJ+Ahb7vEiHInyl5kiHVxjeHTp3ISKAU9cAtUFK1RTqPkZVMBXb0HsybtQWnP+7G9YC1OH96gralIbBAdZSTVOPpeJsbNyGvdN6kSRQOtpu0s25cGot+Somm5ddfXOtI2Uh27DP55S8pfpoUESIAE6k6ABqK6M2waEjrGYEraZCx9pGfj6XPhCNa/dwalNQ2rwl92FqDgK4vfgZTsx5rd5aj8sWF1bPaxecjbPo+Icpf2GOI6Nu1U+lROApUUD9wCFUXA5TQDgY2Sl02Fy7ESFFwDEp9+DH06BDWSVpXY93YR9v2d32Y0VAaYtrNsXxoKfwuKp+XWXV/rSOfBj2GpGDeP6d2CspdJIQESIIEAEKCBSIeofYtcccHqHDTavCUXKuHx+2Wf5eoKed/XyPknfPkOXfvu3UoaFdne0uZdRfc+rOqifWcuv9dv8LlA9LzzythQFizaqWxgbOmzWu5sIa0d6GkwlGvTgFb9mQb25OgHG4tlQ59voMJr/njSz/yarfx7yU+bDt74mkfj1tUm12ratPwzqyO2tHi7l9dkWGkfpOKKXDP99PwzuyYDOq8+xusc3OO5povHNtqjgHq6qKXZZd4uW3vgoV8RmeyXDd6KbD25GjerZUEP5m1vK9de546pQ7thIluPV6DzpqK4rsdt0ibrXKyWbSHN8qLLDnBbYjl+tx51Hh7KpB5WK9eWyo0/5dEKd1/k6nrLvU13i/Pp+ZBfSlvp73jKlh6T8ij1dl59TYdzeO1crzOW8lILY9tpbFzbXUNZ8tK3wpZuUe68+bVFHNgDGwMv5c6qv8BqR2kkQAIkUH8EaCA6uwezx0/CLYNnoL+ch+S+qeg1cC5m7z7vSL0sB8P7PoWpnwEVuzMxfOAkRCv+ZyB60FSkbDvhOLnxtTPYOn2qF7mFmCpkDs8ud4xLOXO9VpY9F7f0XYWCS8XIGD1JnX9C/w5d0294drkSWm5s/q+dR/5iETZ2qj2N8UuwqdR1ksOqvdkYdfdTqmyRvujYSRi+oghVu1eJuOciy+yjdy3uWx7KQ6mIuHRlmvD7lLK6pM2qLtcqUbAiDb1E/DbOg59C/+kfo1Q8PRfRoGCBGkdKnjwrRopgeYuyCkbSybh+JvUX/qcXK66508Wx4lfNU8VR31wqQZYx7+JE+RidjYKLugd9X42i7OXoP9BQFhQdc1B0SffjZu+SPrXcZeytRL5M19gcw6cbhT6VEyVGq+XaWzm1kLdqPjgzr0XpNlc2vcZnu3D0uZzq5f/uGWp5lvkjynNWsfkoUtXvKVgpJ1XFOUiJN5T/wVr518qcwlZsakrzMPEhUQd1HbR2I+OApoMFbkKMyyLjd5DrJm1KmmQZcSqr0bL8zStAhdBXynJIi2yn3jvjEicslhVbPun84wR/vf2RUq+ewKYZM3CL3s5oum8qPYGssaK+LSiUvuyrxXjtAWChzhdqdeUMSt9bgv4izbJd1+eYsHEzCtWOPV2TXqryl4s2bRLSDsgz5/UElj8k0jijAFXOl+S5Vh6mflaNfSvmav2CaIP1OdmUdtGpvRNt3/DFe5S8lCLgrg1z5qp4NmysyNa96341bv21cm3vD8vVvOybhoxjItCxPAzR2tFbZHkUTqaLlte99Ppyt2xTM5F/1tl3bd3bDSn7yc1qGyzjlX2lFm+0Up+LHfPIljfOfbtsk9OwXLYrCheRb8ayLfqEfcZ23iDHOVUwuWarT86yJXPRnjn3z0r5lP2/FK7Jc9fnBqasOvNwM9aR+kjOVsZR0q+SXi9lXfqT69UTbtoUrZ2VfgyrUudFvbGNGcSxQx0y+LUdXixChoPuM8S4Q4zpnNpKX/NLync7nhLts7zucfUwRjA1EHtNR7mluuu1bzNV2nO7C/gwVnJJtyh3sl8zjP1MVRCOvtQR4R1qnrqOa2XfaaUfht4Xau1Lf63PczcWkXFyJQESIIF6IhBQsa3bQCQGWaMSsrH1QncsWJ+Og5+swMG8mViaUIutMxYJY5DrIKRq5yoMW1yJB1YstPlPHyZuXBYvxywx+Ndzp2jlcsz+IgxT1r+E4wc34PjnC7H07mohdwmWf6X78mdfifUzMvH+jQORvnQatrzYD108iqlGzvOLMOvbaGzcJtIn0vjF5iTE/3gCaU9mosCQxCpxAzL42T042jUG62x+H0O//VlIWm9mxNIi7vYgcoTcg6/FIVI4RT45U2Uj3HJGhwsXfalFzu+s6XI0cxFS/liJflOm4QshR+bNrheiUPPZ20hZd0IROGiOmp7VI+RpFFZr/g5+kopB0sm43pWq6pQepbjGp6thpdyMuxQnbVOOZc+sQnbHJGzPU/3seiUWfU7uQcozRqNNNQrS5yJp5QmEPZSMXVrcu16Jw21/yUOS8FvqdhAowi5OE+k7j4hHxmvxpGP7vO7In7YEy6SVDXX4+VCuvZZTy3nrqG9p9iIMX+zI5ov1CRh0QXD89SoUXHL0LweQ1sppJbbOW4Ws73ti6bbVOH5Y1K38aZjS8QQynshErotcwHI5Kf0QY1O/QJdJM7UytxAbf9MJpX/MRNIfDeX/UgEmipvh/DbR0OvJwTyhQ8/zyJokdJB1yg9uNXszMXhCHvKDDXK3pWJKZLlI2xJknXJiJox7jmU1HVueDEdF/mY8/fwqjHqiAHgoVS2bQr8FA0U7lb4Ky+WNvS7Kh7KiBnHT/ghdsp5YgrTPahE3Xa+zC7HxIWD1M1l4yzlffI5Xjd1yXu7diKSllRg0ZTw2rp+GqWq1V4X4uQ0dMRIT2tdi07Y9jg8DpLwDu7G+LAgTxsYhVJ67Wcs2L8e4nSFInZ+MLetTEddReixH1oS5mPie1FdnJ9qDF6JR8142hj1fqBo03LVhc2KkEDerRdlK6PPYOi1N6FGNh9MMdWBskOi3hPtOWbDDMeFV2S7OFOVSBIqMU9t+2f69+iAihJPLcvZjjEsU/Wx1NJZuTlfbYVGuU9sXY2Licoc6G4h2Y5foJ0IPF2Ds4lVISXgTh/o+prWxC7HukRBRn1dhlpIWR01d+vZt4xH/7+VY8/9mYepskT+Hu0PXf9fSGESIPmFcuklZcBTr5azacv/sIMhL+xKIsurCQ4yNzMY60vhlfRzlS3kUfvU2JVVrxz5R25RlT5q0KcXZSEo/gS7J03Bw7wacLlyNXS/0xLltwj1THTM4MJQn105g+YRMZJ01jAFFHEuHmbSV0j+s55fSnmvjKb3cfLFZjKe+EP2Jp/GUFk/B4jTrYwRL6bBQd630bYp+bjam7W61T2MlK2M/N7E7OnupI46e7WdKvlnqhyt9HovYY+ERCfhDgGFIoOEItGIDUTVy1+ThKKKwets8pA4IR+eOYejcLQpj0tKxepi4nrYZRU55UZBfi4yceZg5uLvN/4QXkhCHWuR+XqL5Lsch+SbBiPsxc0AnBLcBgtt3x5glKZjQDdhbdEbz58/uDA51fBy71ydjwohoDOrdHcEexZxA7g9J2PvKeMRFhik6R9yegNUzxR2TePrx0WE98AmsX1GMqog4bH9rMuJtfuOw9K2ZeKD6vO7Rdd8mCKGSXacQ9VqoGo/kGao5qRcqcfTnVnQpwe79QOSTqdiSHI0IKVuskYmpWDAAKNt7RHm7Jri9Gk/oDVK6poPw17ljiCuTG0KUtHduHwT508MqOirhpatcz6Nq4Ex8kRaHPt3ClDCRg1OxLDkMOFaEfP2J/7EcpL1XjQhhDNs1fygilXjDEDl4PLasjkPEV3lIy62UAl1Xh7B6POHokzgZ21eE45zxBt41tBcXUW4tl2sL5dRy3hrUOrYZKSvLXdhEDEgSRkpRDi8WI8XlxspqOS3B3r2ibDz+GMZEqvkc3DUaM19KwqCu5aJuub4Vp+e113JyrBrDXluBpYlRWpnrjrgZKZjSDSj9eL9S5mQqq0T5+0vXKCx91V5POncTOsyMRfA1oZ+sUz5zO4E1Lxe51L/OkbGYuUoYn7qVI2Pxx6iQCuhraTGKHMpquDCIqPoW5ZUgZulL2Dg5Vi2bQr/UOXHCgFuJj/brxi5fyooeqXn7U7P7bWR8BcRliDhtdVbwmzwPu2cKI5tebxQx/sSrBBTtqFonveVlqWh/n37rJaxOjkPcgGhEdlXD12krDILJY0U7sDMPm8qMkkR6thWiJmIIkgcY3V2Pi0rChFFxIRaMHopBA3pCFGHU7Nxswk5rDzKiUJW3EcsOC1nu2jCtTRM+XBbLskXImp0bMVvUrcSl6Y51YPo8bEwA8tfkKG+IBndQ8yCsDYA2IUobKdvRzqbzIQk2K97GvhuisW7TZIy5PVz1HxmLBa+kIPGGEix7Q7t5D1C7ESna0Qxxg1+TX4wyUf52zdfb2O6In/8YEoXe+Z+pb5LC8Ctw7tuF8UvvJ3P/fj92iX5R1z9yxGSkx4vAO4uwT+z8X05Y7J+dYvDWvgSgrLrwEGMj17GOyF/L/Q2sl3WRXIeyq7djHdU2Jef/BKHUoU0Byor+Kgyp0Zg6ORqd5bhD1JfIxJlYLersleIjStkVYh2XY4ew+1onpC6bZx8DijjGzHlQPGSqxO4D5Y7+YTW/Tji053q5ibg9Dks3paLPeQ/jKRmjr2MEi+kI9lJ3LfVtcP8zbXcd0uJtrGRt7OdeA8MVb3XE4NV+6Jhv8do42LwfFn29aC8jfRiL2ONxc0RnEiABEmgiBK5rIno0vBrVhcjZCQSPHonE9s7RhyAxIRq4dAJ7TzleM/UfEgLlqXGNfnMajn59g4Cd72P2zjOwzTPRJgbpeSuQ85vu8P8XhDGPDVXjsyREPNX+jetT7eCfCf1E+Cv6ZM2nDuEjcdMzKOVB9GkjLhiXNj0x5tFORhc/jy3qIox2UzavwK4pIg+MMV0NQtsOwsHtmzniWp2X7pjyeE8XKaGhkpfIXy3uox8XCYOBuV/cPlQYL4B9B464yJEOpXvFjQu64+nHTOK55zFM7S19+bn6VK7D66WcemTTPg5TRwuWOwuRL19IgP6zXjYGi5vw0jc3I+vAefubHBEJ2CLq1tJ7hGxdpK/7ASORfLtzoBCEtRduWr6LI4TGT4N803BMV3lmX2uCQxAhTm11ShxbXr7aj7c81L+HHxb178BfccigB0QZcrg2rPsAABAASURBVC2rIaq+3YZg4jB5l2TQIKIb+hhO4VNZ0QOatz8Fu0uA9rGYGu8UJ4DQEUOVm3JxqC5+xasGtbwdMAxje1j2bdljZMrDiG9zXhg1iu1hqg9h+24g5rcjhQHO7mx2FJwwDPHtjVeqkZ/viV0sElGLfYf9eajgi2zdbwxSnMsNQhC3cAUOvj4SXbzNY2VMmjzW89ol3eJiSCyWiTqbMyZcqceBazdCEPozIV/0I1PHOvW1bbojWr7mKi87rWZ9u95Pxol+MdKpX4y8TdRJJxm+nwZhgpX+2XfBqHNZNRsbOY919Pw18yvKjeM4Si9jsW7aCceyrrcpT49wbVMif/OgY5sCIOKOnuLBULEwpO9BqaFviZNvGq9PMq+btz+G7XkvYUFfIcCw1NwQhFBxbhu7iWN1sZhf2nhKtgnG8ZQiQ7STs37ruez4PEbwOR2KJi6bOvdtJu2ux3rtMlaKQuON/QQOn/rhKNTbWESowoUESIAEGpPAdY0ZeaPGfeGiuMEXT7TystA/YYbrmi4G7TiPr087ahnxc88du+47ZvJkpN5aja2zF6FX7CQMmbQKy3NPoMrhBk/37cs+DN2tqQD1F2bN/+lylAKQT4LFzmWJ6BHu4ua7Q5g1XaD+ai6UYGtmJlImiPy5+yllbpOpO9Vr9bcNQ5eO3qWfOyvfDipHhtTNpfwsx5qTQsbJ80oZE0cOS9nf5NPDMEQ4GRhUT+GIdHMDo173svWxXNdHOVXYtA/HbW44Rtwmy9J5nLtgTEuYxbIRhjHzH0Pc9SeQ8fRc9Bo4FaNmZCNrb7lyk4m6/LqEobPV8NdqUbo3BxnzFmF4wlTIua96jS9Q6pBVEQ7+Ks6jQjjsW7PItS0S5WvsxvPiajnKHJ6aeyir7UOUGxwRyP1y4aJSPmt8agPDTPJJ6CXLe7dO5vycb8r9ihe+/XzJS18khwxF8iNBqHlvB3K1m9CKvALkIwopCd4bZtf+oxLn/i4UqC7C0yKfXfqih7YhV1wu/ZvzmwzC0evii2zNb9QtipHTRfQNIejcsRPUN7dg/afldb/bupuGCVbeAg0RN/ZA4NuNIISGmEZr6uiaN3ZvoT+zJMgewPJRmEl9QmB+AS+rJmpp+VtjqQ3Rypilsm5vUyKcDHOKFm1MDH19x0N+Elz2XjaGD34KvZIWYWpmAY66zB2oSDBsalHxVQGWpy/HqISp6NVfhL17M/INPuyHYdbySxtPRXQxbxMivIyn/Bsj+JIOuP/VpW8zaXeVeg3fxkqNM/YTSHzqh+txLCJU4UICJEACjUngusaMvCnE3XnwSGRMf9h1nf841i1NxsQoP7VsH40F21bj4OZkLBjXE6EnS7DmhSWITsg0mX/FJI5rvj6qNZHRjJ1K/5iGXvctR9reanQZOAwZS2Yq88Kslq/1o6n8wjHGrOxMH43VS5Kxbno/dKlvVd2UE8vluq7l1F36qmshTWhml6+Ia2bult163I+NeWuxSzwVnhLXCTVF+5HxrCgvT+bA/bxPlqV793ipELMTJmH4jC9wFLdg7KTR2LJthajr8hMu78E9+YhJNGmLZBlLE+VpqTCMdfQU2r9rlsuKf+LdhmqseN0p5Pq2gLNP9XzQo0MQca1ElEFptDuB7DfOIPiRkUisiw2haxSmynx2WUcr/dA6k7caVW0sbOtTtoXoffIi2oZ6azd8UiTAnsVNd4AlWhJXL2XVJGaf6nK9lccQDJrxEo5/MlP0vUMxqP155L+2GaPum4rZhvkhHdSXc6c9ORX9J7yN3RdDMCxF1LdN6ThYMB6JDh6b+Emg0lFvfVu45bFSUxj7We6HG3ss0sSLJdUjARJovgRar4GoWzflU6qq9t0RP2Ko27VP17pkbhA63z4UqTNmYnv+Whzfdj/6nC/CxEzD5wnuxB87g7rNbeBOsBt38YS3j7h09JT5k+qjR/z5xEEI9Ge58DHSVpQjcspCHN88E0snJyB+cJQyL0yo2dNEf+KoYxj984Lud7gvO/EDuyPYJJ6IX8inipUoO2tyEeUola9ymV0yc3MuJ36V6yD/y6mJTgqba+fdpA8oOyNurNuEI7KbSWCrTm2CEDkgATOXLMSugrUoXhGN0MN57ud9siLXop+izI3YWh2NdTtXYIswBKY+OBSD5FwF7etgIfhFd/UTiP/4X27bovgRMcqcNRbV9O7Nr7JiJlbkpWw8Ss+YG+iqS7DXOK9WwOI108VftxP4i/kXoa4Ceydh1gig6I0dOKpMTh2Gpx+NdvVnyUWwixQeL4UgcpiHtuR22WYIfz4tvsjW/JacxtfXTCI5W4L8ncUou2pyzZOTlteHSsz7j4qv9iD/gPr2X4O0G550rcdrNSL9vjTrAVMloGXVRCstf62No7QyZqmshyPiVhHft6IfMSuPwiBS7AZocMcoxCcnY+Om1Ti+bx5m9q7GVpP5JIV0VORuRMbhMMx8Zy22r5iMmaOHIk7Ok9WhDm25FHyLSKvYl50TfZ3YOy9lbsZZuj9fxwiBSkd99G1KvRYJszRWauyxnz/9cCOORQRWLiRAAiRQLwRar4GoTTSS4uWnAjnY5PDZhsq5aM1c9J+Up3yCobr4sBWd3Li+TyFpm+PgIDhyKJLEzUDNuYsOn8OUfn3GSXg18t/4QvnkxOlC/Z12G4qxfYHS/34b+ZecorlYgGXZ7p7rGvyGtEMXBOB35jQOCTF9ejp9lnDtBA6Z3MSFeZikVYixLcpBqPo5g3Jch03EsFhxQ38Gy/672CEvFZFnP8bEhBlI21urnDpvIgdHIwJnsP5tbXJWg4eqz97GauPNtOGapXLiS7n2pZz6kLc2NmsKUWXQXzk8lYOM92oR/MgwxPlj7DuchV59JyHtgCLNtgkdNhBx4qzsH451TjjZFp/KiS2U80Elvj4h8rVbd/Rp73it6ugJ10/MrHKLGIixvYXRYV0O9l11lAucx9YZMzDk5SLXsubs1ZdzX8qKF7lx8XKC7mJkZDu3ZbUoWv8hCozhAxBvnfLSxJBVtftDrDfpB4xq249DkDhapLfsC4ydV4iavsOQLPLOft23o0EjohEsntyvec/EOH84G8MTFjlNim1dvi+ydb/rXf7hqxoFa1Zh4pojqLlBjzsEoXKSFv3U3d6W1zsc/q1M8V5diPRnspFxoBrSkF6v7YYSYcNsXB6yCGPG+o3yk/V6iN9r+xLYsuqSAlv+WhtH6WXMSlmPGxYFiHrhWh7FOOWPHyLXwXBUia1PP4VbnsxzHDfd0BMP399JyKk0NW6WfS3rXDh+2cMpZV+VwvkPSpx8eD7t0Q8PRIj2XBqRHfQUwUSalr1xXhy4X3wdI/iWDnd1t9K3vs29+g5XbPXayljJx7GfQ0TuTrzWEUNAX/rhOoxFDDHykARIgASaJIGWZCDyEXAI4uakILHDGaRNWILle8+g4kIlKr4twdbFc5H0WjUGjY0TN/I+ipXeOw7B2GFicPDyEszOLUGZlCvW0tzNys1/RM9uyoAYEEaqhCAgbzPGZRer/koLsXz6XEw800kYIKSwhlo7YcLCBPSpLsbEEXMxNbtAPDHeg9zsVRgen4MuiWKw5k2Vjj3RTwyKSt/YjK2lgqVIc8XFam+hXK/3jlKMB7nrN2Pft6qcMvGkefm0VciuFrycQvSJ6SlcirFsjcZQPHV0MUwIH8riIrscFc4GMcWjl01EEtZND0fVNsEnvQBHNT1L925GSvLbyO8wBMkDXXVVpIqnuukJISh7bTmGL9bDnsG+bZkYNaMcXVxuNqORZLmc+FCuLZdTobUveauzycvCKCc2457Kw9EO0VjnPAG5iMLScscwPB1Ri03zltjr7IVy7FvzIXKFgD7K/EbiwGTxqZyYhFedwtDvf4cBxwqQkau1GRe0vFt5Bi73zJa5hSP1JVH/LhZi3JhMbP1KlEtZf5T2YBFmfxaC5DExWruhalL3rQ9lxVtkA8ZjnSjTpWsWof/T2di0cw/yd+Yh4+kZSDrQE4kOZbru8fqbl8oN6rViTJ0mDHFKnT2DgswlGDb/PCIddPSS4AFqOay6AMSPi0NnL949XQ6+J1Vht2/xEozLLESpzHdRpo/mZmL4pD0413ckHo7wJMH9NV9k634LFsxFit4fibKdn56GlLwgJM5IMvRJJvXgbKWJAVPP6xJMHb0EWQf0cr0Hs8dnIfeGaKT/VrbfIg312W4I8fW+aDeXpWuWK39OIccTZV8VYPbYJVjfJrxOZcSt7lbalwCWVVc99Py1No7Sy5iVsh48YjwW3A4o5VGvF2J8li/qa9J/1zq9gRqGBx4RY5TDOUgy9DkVpaKdlsaYiHD0ucFVe5d2RNa7nWI89lwhKtu7+rfu0hNT5sQgtKwAo8ba2/PSA3mYPSELRzsJo5UnYXKM8Ij1MYJv6XBXd03cRf1XxiVmfZsn/Y3X9HptZazkMj4Thj0PYz9jNG6PrdQRW+Bw6/2wpbFIIaaKh8W39F3l+JDEFh8PSIAEAkCAIuqBQCs2EAma7WOx+p1pWHDneax5VtzY3DcD/RPE4O7zMCx4LR2r7/H3NWPx1G7JPKTfA2x9YTmGSLliHf7CCXQZnYycidqAGCHCSCXiF/72rVyl+hu9ER91GY0vXn8QfYSKDbr0SEJOTjJS+9Yif81mTJz9JhbsApLXpmPp3UEWVOmJp38fh5irJZg9WrAUaR6bW2khnJOXkKFYtnYoIk8UYFyCKmfIhDex9/bJyP6tvDk/g1JDkOBhKVidEIajr2kMEzZDvoFk8GI/FLIzMqLR2SY7DelF9su+HEUmL8QXS8UgUAwoR2l6Dn/2C5TdnSzyT9xMuX1DRuT7i+lQJ9TcDDXsIoxbcx4Pv74QCyKdtRD+5/hQTiyX6xAkWiqnUh/f8jYyOR0HV7iy+frOBFHGpiHO38F3m56YuWkyUruesdfZ+9KEgbUScdNnYpnJP95I7eXqUzmRAdyskROnYamos/kvLEJ/Ucb73yfy7o1azMoeDfkW01GHzwd84NZDrX9jQorFzXOaKnt0Ftb8PQrrchYi1fkptxv9fHK2XFa8SQ1BnCjTOfOj0eXr/UibnY2J6btxtLdsy5IQ7Ry8jvH6m5fyBvWt6T0RvD9Pa1sWIaUgDBnbFmKWS71zVtp43hODpAFYpCPZ5V+/jP6sHGvspnfH15uyMFwpU2nCuFqCiEnTsPfFWFfDoxWxih9fZKt+N44LEwZXrS0VZXvizhCMWbHQpT+M/G0qpgwQfYVeD6btxjklTqeNYLT6nclI7XhGGAz1cp2N90NisG6zY1tQb+2Gk0r1cxqO1FXJGKP9OYVsG4ZMyMG5Bydj7+9jIHqueojWSvsSyLJqkgQlf6dZHEepZSzHUlkXPF+X46ggFGRp9UKMzybmh4j6Ohlj2zvqIv+Ba9f8KFzJ0/tUMXYYvRn5N4pyljXeYNy0hwseMRlbftPJPnYQfcmo9BPot2wypnYDXN/x9ao+AAAQAElEQVTatYf1dhQ8WOT7K0PR52wRZo9Xy/3wSR/i6xEzkTMp3EtwwWm+9TGCr+lwV3d969u8JMFw2fJYSYzPfBn7GaLwcGiljhiCW+2H6zAWMcTGQ0sE6IkESKChCbRuA5Gk3SEaqStW4HThahz8ZAUOFqzF6fx5wkjiNJwTT0F2Hd6AXclmHXssVotrpzNipUR1vaEnJki5B9eiWMoV63FxvGv+UHQ2Gg7aRyF15Woc/1zELfwUF66F9BNxgyrTGF9E8ks4ffglpJo9TTbRz6P/e6YJWRvEoF9VV98GRwzFgrUrcPzgBnFd6L5pGib0DUNZ2XnhJQhoI3YeltDo8cj5fAOOF8j0rMZ2jZevuoQOSMau/SJ+wUTmy3HBJWdyFGIUBuKm4ppBiTadkJixwpaHxXvFdcNl58PO8dNwUJct8nu1uNmXfuIyNog0m4c11z8IEWKAud2W3hWw55+U6GFtE4a4GemQ6VLKh9Dj9OcLMTPajVHSh3KixGq1XFstp0Kou7x1x63zsMlwZnNwRRJinAb25mxFhHIxK6cdYrBgs6inep39RNSfg6uxMTnK842023IS61p/ZdzKKm5S3hLl4q0k2Kpdm+4YI+rs6b2yjIv1c6HLB9OQ2HWoImfXbxzbCHfcFPFOG1n/lsq0GWVvm4z4CFH3DH7dMQdM9LWFU9NpbFOUSxbLisd8koJEmY4ZPQ3bZVmW7aFo07bMGIqIG8rxtbToBjumARbjlaJdVr/yUkoJEW3IPBzcp7X3Mu80vgpTYz6btKlSgrJW70H2e7WIGDsMg7y0iYp/T7KkB8kueaa9XRLt3nFRvjcmRyPUWb5ZnZAyoOavQz8k3X2RLfy6tkviAcEwkzceRJs0c70o+1pZLd6UZK8jMl7jqtdZ3e9e0UdsFuW6q9GTehyIdkPJy8NmbblWP4x9tae8ccsaUOuDUxyiDVi6ba3W/4l+VM9DcdMpxw96XyNTqoZ305+bxOsuTV7bl4CWVTdlzJe6LMpYjNWyrvdPgqMcBxyU9VVpa7sjVbbLxnxEECJHyzokypYy/hD9sShnp2X97iqJm60hkBNbO4z/lH5YjMukfGGc1UP5ml8yXOjgZIc+UPb3chwTquSvm7yXAeUqOLnWRXWMoJQFY1sF6+mQouGu7vrYtymylI2bcqFckxsvYyXpRVstj/1M6q3CxaTeu6sj7vLUaj8MvV3Ty6fLWETjYqKTllzuSIAESKBJEqCBSM+WG0LQuWMYOncIQkB/bYIQKuWKNdh5oG+IKLh9mBJ/6A0GxwY9LMGa8TMwfE2xSaznUbC7XIyIoxDXzeSyiVNwB5meEMi5JeD3zwM7M5ZaHoa6sbE4qqHJDlB+ByvpDYPP+aeXD4t6+FxONCZey7Wuh5dyCvELVtJqPW9V/36wgZefnraO1nVRJGrhrJUTJYT5JkSWcbE6z4FlVjaFhGBfuLmTjXr8aVy8lhUTFaryVylvX249a3LxwCG8fw2IHxhlclE41SFeaGF9zkstXGfnvBPqWFkq8gqQf607nn60pxXvPvjR2iUL9dAHoZpXH2Tr7YGVdkkrq5baPt2vhTY6WKkv9dBuoP5/wZrunvp8BPgXrMTp2hbWX1mF60+vV07lxtWjdPGhPOpyLdbXYIWFKDsWyhnkT5fvrLebtlwG8WUN1vTxqzz4Uhd9TYdeH53HnZq7S/sYAB46C8/thYeyUQcd1Lhd6wjc/dxxcPavc/d1LOIsh+ckQAIk0EQIXNdE9KAajU4gCg8/EILS1zKRtLJQmwejEnJOpk3zFiHtcAgSHeagAH8kQAIkoBAIvXsohtWUYHbyKmzS50+6UInSvdlImlKImtsTMGuY1bs1RWQT3pxQ/toeI+IwpmsTVrPhVGNMTZYAy2qTzRoqRgIkQAIkQAJNlAANRE00YxpDrYjfLMSu+T1R9laWNg/GDOWtgLTDnTDllbrMydQYqWGcJEACgSFgQUpIDJa+I+eZKUGaNt+GnINl+LP7UXXfY9j1uqc5uSzIb0Jeaj77EOvLgjBh9NA6viHZhBJFVVokAZbVFpmtTBQJkAAJkAAJ1CsBGojqFW9zEx6kfcNvn/tH+eY/fyFmDnaak6m5Ja0Z6at8R+8wv0A9K0/xJBAIAvp8DNo8M3LOEGXetYz7Een8CUMg4mskGcH3TMPxw2uRPqCRFGC0JGCRAMuqRVD0RgIkQAIkQAIkYCNwne2IBy2WgO8JC7LNm+TyDbrvwhiCBEigNRHQ522ol3l0WhNIppUESIAESIAESIAESIAEfCdQlxDX1SUww5IACZAACZAACZAACZAACZAACZAACTQYAUZEAvVGgAaiekNLwSRAAiRAAiRAAiRAAiRAAiTgKwH6JwESIIHGIUADUeNwZ6wkQAIkQAIkQAIkQAKtlQDTTQIkQAIkQAJNkAANRE0wU6gSCZAACZAACZBA8yZA7UmABEiABEiABEiguRGggai55Rj1JQESIAESaAoEqAMJkAAJkAAJkAAJkAAJtCgCNBC1qOxkYkiABAJHgJJIgARIgARIgARIgARIgARIoPUQoIGo9eQ1U+pMgOckQAIkQAIkQAIkQAIkQAIkQAIkQAIKgRZtIFJSyA0JkAAJkAAJkAAJkAAJkAAJkAAJkECLJsDE1Z0ADUR1Z0gJJEACJEACJEACJEACJEACJEAC9UuA0kmABOqZAA1E9QyY4kmABEiABEiABEiABEiABKwQoB8SIAESIIHGJEADUWPSZ9wkQAIkQAIkQAIk0JoIMK0kQAIkQAIkQAJNlgANRE02a6gYCZAACZAACTQ/AtSYBEiABEiABEiABEigeRKggah55hu1JgESIIHGIsB4SYAESIAESIAESIAESIAEWiABGohaYKYySSRQNwIMTQIkQAIkQAIkQAIkQAIkQAIk0NoI0EDU2nJcppcrCZAACZAACZAACZAACZAACZAACZBAyyfgQwppIPIBFr2SAAmQAAmQAAmQAAmQAAmQAAmQQFMiQF1IIFAEaCAKFEnKIQESIAESIAESIAESIAESIIHAE6BEEiABEmgQAjQQNQhmRkICJEACJEACJEACJEAC7gjQnQRIgARIgAQanwANRI2fB9SABEiABEiABEigpRNg+kiABEiABEiABEigiROggaiJZxDVIwESIAESaB4EqCUJkAAJkAAJkAAJkAAJNGcCNBA159yj7iRAAg1JgHGRAAmQAAmQAAmQAAmQAAmQQIslQANRi81aJsx3AgxBAiRAAiRAAiRAAiRAAiRAAiRAAq2TQOsyELXOPGaqSYAESIAESIAESIAESIAESIAESKB1EWBqfSZAA5HPyBiABEiABEiABEiABEiABEiABEigsQkwfhIggcASoIEosDwpjQRIgARIgARIgARIgARIIDAEKIUESIAESKABCdBA1ICwGRUJkAAJkAAJkAAJkICRAI9JgARIgARIgASaCgEaiJpKTlAPEiABEiABEmiJBJgmEiABEiABEiABEiCBZkGABqJmkU1UkgRIgASaLgFqRgIkQAIkQAIkQAIkQAIk0PwJ0EDU/POQKSCB+iZA+SRAAiRAAiRAAiRAAiRAAiRAAi2cAA1ELTyDrSWPvkiABEiABEiABEiABEiABEiABEiABFo+AfcppIHIPRteIQESIAESIAESIAESIAESIAESIIHmRYDakoCfBGgg8hMcg5EACZAACZAACZAACZAACZBAYxBgnCRAAiRQHwRoIKoPqpRJAiRAAiRAAiRAAiRAAv4TYEgSIAESIAESaHACNBA1OHJGSAIkQAIkQAIkQAIkQAIkQAIkQAIkQAJNiwANRE0rP6gNCZAACZBASyHAdJAACZAACZAACZAACZBAMyJAA1EzyiyqSgIk0LQIUBsSIAESIAESIAESIAESIAESaCkEaCBqKTnJdNQHAcokARIgARIgARIgARIgARIgARIggVZBoJUbiFpFHjORJEACJEACJEACJEACJEACJEACJNDKCTD53gjQQOSNEK+TAAmQAAmQAAmQAAmQAAmQAAk0fQLUkARIoE4EaCCqEz4GJgESIAESIAESIAESIAESaCgCjIcESIAESKD+CNBAVH9sKZkESIAESIAESIAESMA3AvRNAiRAAiRAAiTQSARoIGok8IyWBEiABEiABFonAaaaBEiABEiABEiABEigKRKggagp5gp1IgESIIHmTIC6kwAJkAAJkAAJkAAJkAAJNDsCNBDVIcuu/AhcvPITzl3+CX+v+gknv/8JJ777CWcqf8I//vkTvvvhJ/zzah0iYFASaKIEqBYJkAAJkID/BC6LscH5anXscOqiOnYoFeOHv4lxhBxDlF/6Cd+LMUSNGGf4HwtDkgAJkAAJkAAJkIBvBGgg8oHXtX8BlcIgJI1BX1X8C38VqzQKyXNpJJLGoqqan3BBDPrk4O60GPR9feFfKD77L5QJo5G8LmX4EGVjeWW8JEACJEACJEACASTwvRg/yDHDl//4F46LsYEcF8ixgzQEybHDJTF+kGMMOYaQD5mk4UiONb469y98Ix5CSaNSANWhKBIgARIgARIgARLQCdj2NBDZULg/uPYTcPafP+Gv5/8F+XRPDuh8ear3ozAsySeFcmAojUpy4Fd7zX18vEICJEACJEACJNAyCMgxw1Fh5Dn1/U/KW8f/EmMKX1JWI8YLFZd/UoxK8qGTfNjkS3j6JQESIAESIAGADEjAGgEaiLxwUgZlwjD07aWfEAijTq0wFsm3i44JmTQUeYHPyyRAAiRAAiTQTAnIt4HkQyH5lvFVYeQJRDLkZ+vyYZP8nL26NhASKYMESKDFEGBCSIAESCAABGggcgNRvvUjB2DytW4515Abb34764Yi+TRQDiL9FsSAJEACJEACJEACTYqANArJN47rY/wgEyo/SZMPmuTbSfKca+sgwFSSAAmQAAmQQH0ToIHIhLAc0JWc/xfkAMzkckCd5KvjchApP0ELqGAKIwESIAESIAESaFACcp5B+XDJT8ONz7pKQ9SZiz5+s+ZzLAxAAiRAAiRAAiTQWgjQQOSU0/L1bflKeCA+J3MS7fFUTlYpPznz6IkXSYAESIAEmjEBqt6SCcg3g7/+rmEeLhk5XvjhJ0ijVODMRLWo+GoPslZkYuqkueifkIaU9Gxk7SxBxVVjzNrxsY8xOz1TXd87oTm2kt3V8zi6MwcZ6csxKmEG+k9YLjhsxqYDZyAfADZrCp+twi19n1LXsTkoa9aJofIkQAIkQAJWCdBAZCAl5wiQn3wZnBr0UM5N5P/nZiewVR+gOe0zsguQv7cEZZea+oQFxjR8jKMNSj9AkV0owhon/raBsxv3rccCFLcbMWXZc9UBnhzoLSh048sHZ7M0bnNzU3DtBDY5pzurCBU+RNesvFJZEiCBVklA/pnF34Rx6IdG6mblG89ybqI6wz+7B7NHT0X/8dnI+GMRcvefR8W35Sh4bw8yZi9Hthlu3wAAEABJREFU/7tnYGr+ecdo/vFXbH2vSF2LnK45+gzs2bVaYcgqEIar8sDKtSStGkXZS9B/0FyMmp2HrPdKcPTbSlQUlwgOBUh7ehF6xS3C8sOVlqQ1qqdG5dioKWfkJEACJEACJgRoINKgyNfC5d/OaqeNtvvb9z9BvsXkuwLnsVcfoDnts1ZuxsRnl2PI3ZNwy+hV2NRkByzGNPwV53yHUO8hvEZQfRrvO/G3DZzduO/9h1epTcuDWRpf/hAFZpOwHtmNDOd07zqNK00rRdSGBEiABOpE4LTouxt70mj5gEm+jex3Qk7lYFRCNraW6lauIIR2C0NnubbXpNZUIneeMIpkn9EcGmdXsTMT/QdPEoaszXjr24bWoRoFC+YiaeUJVOj9XnCIyqlbCILbQP1dOoM1T6Zh3HuNy0pVxnzbuBzNdaIrCZAACZBA4xK4rnGjbxqxy9eyT1cq/1LWJBQ6+f2/UNenkMFdtUGdGKw4JKq0GGlPioFN9gnUOFzgSUAItGmHCDmYNq76wFpGYBtEhmmDyTB0CZYXmvl6rQS7j7im4egXJSxnrljoQgIk0IIIyH85rayRI4nGT5Scz1CuPmtyTYwNnsrDUc3gETo4ATkFa1GctwIH5fr5WnzxQhRCNcFHV2Yiq0w7aYTdlW/PoKKRBjEV7y1HSl61luoQxL+QjuP7V6uc8lbj+L50rHskTLtejX3pmVhzSjttYjuPHKNGYt3SZHWd3g9dmpjuVIcESIAEAkSAYpwI0EAkgPzj0k+QT97EYZNY5D+oyc/N/FemE2ZlaYM6MVg5fXgDjn8yE0ttA5ZaFK1cgpT3GvBVcP8T07xCdrsfG+Vg2rDm/J9O9jTcN14bROr5swILBtov18dRl3seUwd4cqD3eM/ARtE+CKp9qxYffeH8mdkJfJRfqcYXIQxi6hG3JEACJNBiCPzz6k84+8+fmlR65L+vyk/mfVGq4r0cbLqghYiIw1urkhDTQTtXdkGISJyJt57UDR/nseyNYuWK+aYWVRcqUaGs1U4PCjxcu1qN0gN7kL+zALkfyn0xSo2fx4vriswqQ6xVejyVTvP+iHjEQzEHWbpdxxZc+FF0lDI0PS+eQIGM/4DJGEkY0lavtL8RNChtIdYlhmv9oCb0hnDEpwkjUXyQ5nAeyzP32BlUy7jUtcpZH0/XhLSasyVCtz2CTYFgVISjF/S3vcRF4yI41Znjjd3RL+YOde3jlEYZl3Mce0tQ4Zwe6c8kTTUXZDq0PD5Qbmcj/etr9Xkc3SvKwIfC306xdydf9889CbglwAskQAK+EGj1BqKaHyE6tKY1uJMZKJ9GBtJoFdwxCmPSVmDX9HApXln3rdyMApfOvBZVngZUho6+4qLrwKTmkjrokQM4h4GPPpDQO3p/JnDUZciBmxw4uhssGHRUdahFxVdFyNfi3leqDQIVCp436iBGDEy0sAVfnTcfyHgWY+mq48BvD0z1FAwkW2XVBs01p4rFQFEMoAQTRT/tCbAeaXCXnuoATwz0BvUwGKs0D2r4PdqAUwzGXcqE5tFs1y0K8b3VCxV7jzhOYll2BB9pr/7HjYiBfluh+nbcWuIcwHx1jJ1nJEACJOAfgbo9zPEvTm+hfhJDGvlWkzd/9uuV2L3TbvSIm5yEPm3sV41HfR4ehkHaG7Khp0pRarzocFyEBffNQH9lzcI+C9eqDmRj+N1TMfzpbEycvRlT07LFfpXqtqIYik3oT1mKzKTX7Mab0teWK27971uCTVqfU1Oah5T4SYgevUrI2GyXNXgShuuyFJ0c9Xz/s0whSzxAk/HnOj/0EAGO7MfWS2Ivl/axmJLo2qfKS0AI4iffj0j1BNhZiHytby3btkTEobJJ2lau+1D2bq9dLELG6EnoFb8cKbOzRXo2i3RlYtR9k9B/+scoNfT7AeOosVby8OUiRT91U4vSbcvR/26nvHpWuCl8i1Bl0McxTSXY9/IM9LpPpkPLl6fT0OvJHIc0lL4nGN09F6OezcbEtM2YKtIsp0qQcaaYfbKnKsYtCZAACZBAAAi0egNRRfVPkPMPBYBlwEVc0AYTgRQc+ZvxSO2mSbxUjLf22iOxNKAq+xBJyoBvBvo/tBH7DIMAoBgZ8l88lOuLsL5MxiMHEqKjH6QNJPSOXk7gOHguMg7Y45e+zVcpQww8/BmMvLUHWU/KCTczbYOMcaOnqoORq+axKa7XzmDrdOFPGcRk28KmjJ8rBmhLsDWQr4vLgd9454FfNhQ948WA1zYfhNDMYcD2BQrShY5J9gGwol9CJvIvCr/a4jg4MwxGZbxywKmEz8ZUkTcTZ4vBuBzgLd5jn1tBk2O+647hgzXTz7Fi7NOfQAvPFQeEsUnsge54YEiIcuSy8YGzQzr8zVcXBehAAiRAAv4R+P6Hn/ycM9C/+HwJJXW77KmPcxBWgr0HdIdOGNzHTXstvUQkYIv+huz6JLsBRF6ry3qtGMtm7EGp/GwsuBPiHonBmEeiMSgiSEitRekfRT9n9a3nUzlIGpuDgrMiqFiCu4Zj0MBOCA0WJxDjCSFr8MvF8sRpLUFaWpHHvq/siOHz/LvvwCA3hjRFcMQdeEAfb+EEDvr7hxSXCjH115nI0scCHTqJ9ISjs/b5esVnb2P4pI9RISMNJEcpz2QtzV6E4YtL7J/4tQlCsMJWepZ8MzH4+ULVoCedDGvZG5kY95b2ZrHBHYfzMEvP37PiOF2b36lrdyQqZSEKfWR6r1WLcc9ypB02BuYxCZAACZBAIAm0agORnFDy/GXxqC2QRAMoK9BvESmqtYnCqLvlgEs5w6ESzWBgdUDVeyAejlDD4lIRPjI+VDq83/5kre8wJPcG1IGE1tGLQUTn6O4YFBmiCqg5j6yn53rt6FUZ/g1GSjOzkXHY9U0nORgZtajQzdtAYgCyeDlmf6YZr9qHoM/A7ojsoHE7ewKzp2728ORUTZ6lrT7w+8qgY3stHilAxJU2dhGyzAxS+ZuR8p6mo/Srr2eLMPEZL39Jq8erDzhF2GBbvLXi6WA2hrkZ4AmvDkvMkCjt9fozeP8zfeBXjX17tSfSvcUg3/Qhq/+c/ctXB7V5QgIk0HQINEtN5AOmpqx4U9fPgd23pdinvZkT+fRkbEybjKVp07AlZybSFQNBDCIg+pd7pkF+Nv/FdHunEjk9XXE7ffglpEacx6ZFedpcSkFIXLoCx/PTsWXtSyjemYrEjmqsVW/lYJNmQFJd5LYWNVc7IXH6ePWz7Dp/kh2CsPZSrlxrUam8AiWPfVv3rdmIXO3hS+TkeThe8JJITzoOFizEgts1WQdysPqAOA4YRyHLbDn7MdLWaONGhGDQfKHPwbU4vn8Dil+JRUQbNVBV3kYsk/qop7ZtjUhHvJyz6fAGnD64AusStPGg8FH0pfbGVkkpisS5XBJnL8RqpSzMxPZtqZiilIUodKkWZUF64EoCJEACJBBwAq3aQFRV8xOarnlIzevvr6j7QG47/zzMJq7i7Hlx7MuAqieSf9tdhJFLLbbutD+FK/q4yGZwiR8Xh87yKdBKbSDRPhrrPlmLg5sWYsu21SheGY1QKQLV2LQmT33ypZw7beo4GMG1EHUCSWUwshbbp4TbIqjJEwPEMtup/eBqMT7aH4TO8jX66PuxvWA1tq9diF2fTIPt7auyYhSYhbVLsXRUlLnZNvBDj1hsLNiA05+vxenCeUgfrA2crpUjQwx4laeDRqnXgD5PTkPxQRHmsGC7VGcqPB3Lw7LPxN7N4hDv7fdjV+EGHJfxFiTbB9B5b2O9lSeed9yBeG1QuG+vVgaqD2G7Fn/nwXeIgb2JInXh7E++mqhAp4YiwHhIoGURkA+YrL+h0zhp//6Hn1Ar+onGid3HWEPaoQvUX+maJYiesBwZ2QUoOBaCh+dMFsYisT7SU/XgaXuhCB8d1jx0jEFSDLR5kCpRcTUKo4boD2DOYPdh5wcsQZiw5iWsTo5D/IihiL+9kyYoMLu21/sjR4xH8mu1gN2RHN/JPrfTxTAMGh6uXasV4xZhYAkUR02q865i7377m+N9R2L16J7aAyIgdHAqVv8mTAtSi035RdqxYTcsCasTw9UwbcIEZ5M8DQ1Rr4tgubMnYcikVVi+bY8wIEZhynxRDoTBaIr+5rLww4UESIAESCCwBFq1geiSMBAFFmfgpdWHEavGOGAMFoMlHwdUnRPE4EkzCNTk64OFE9j9uTaIaRONscNCULH3EPThQWjCUPS7JgZo2mSQNb+MQVyIxuvwX7HPeZymXQrsYCRIGFQmY0FvTTjOY/eBSv3Evr8hFkv1V+g3PYY+16rFALMcR/efwDm7rwAcFSPH9k8oYmA6PxVxHTSxN/TEhCVJiNNOcXg/3v9WP9H23eKwbEo0QpW8CELnEdOwOkG7Jnb5++3GO3FqWIzxhmHK7x5D5A3a5Q5DsWzDZPXp6dKHMShEy1PtsumuTSyS4rUrnx1RB48HipCvlLMgPDDEZAAovdeFs8MgM8havso4uZIACZBAAAhcvNLUHy+pibSmZzhui1T9Q/SLxX/Tj0321Wewb+ce5CtrifuHOyZBPTp1vB/pM+zGjqriEmSt3IyU8WmIHjQJoxzmDfIg6cxpHNIvXyhEivLJ+wzbnD8Tc+19WtlZ5/4/CoOFQUkPbrbv0tVgNDpX6Tn91SXYa3vIIhjfaibRi9uFcpRe0v2cQdpD9rTIuYFGrSnXL0J54BcojjapjgdlX9vjixwSjc6OlxHTt7vdpeQ0nJ+jRfaxG5TsHp2O+o7Huke0AeK1WpSJscyaxdkYN3oGeg2egYmcg8gJGE9JgARIILAEWq2BSP5T2D+vBhZmfUiT8yNVBXQgeh77vjhvU7XPrWJAZmFApQdQBlQhQ5H8iDAsSUf9M7Ov9uMtzYAR/MgwxAmjhXEgUfVWpm2AJgc1/e/LRq7NKHQe5y5IYa6rUUZgBiPhiLQNhIFzl2xKOEYujFlF21ZhVNwk3BI7VeiehlHP5iBXS6OjZz/PHAZ+JgPT9sKtty67HF+f1I+1/R3d0Uc71HeRt9kHrzXu0uYQb3f80haHKiW4R4x4qjdUWdX5H1R3T9tBQ6LUy9dKsPsIULS/RD1vH4Okvuqh6dZPzq6DzHBr+WqqBB1JgARIwDcC8uGNbyEax3dVjZV4u2Nwf61PF95z8/bY3gYWpw5Lzd63MW52NibKdZv2GbGDDwsn1dWm89NE/iYdx3NSseCR7ojUP+mW4oSR4OgfV2HsH+3GCelsut4Yhgj9gvysXb4J7GaN0GwQune5D24jt+7X4AF3YJB++cBuvO9sAdGviX3V54dQIPbK0ncgHu6mHHncVFXZDViKxw4/MxhhghDqJi3KG883qnkYEI5K5K6b0M5hNseyCmcDG1Dzg0H/n3ewvRVmCyTyxHbs9iAEcWkrcHDzeEwZFm6ba0nxXlOJ/PTlmG2YP1Nx54YESIAESCBgBK7TJLW6XXMZ3IGCOcoAABAASURBVMmMqbQ0wJM+Laxf7cB6/fVrdELSPeGAHwOqQSNitFeAa7F1dzGO7ipChRJ9GJ5+NFo5Mg4k0D4EygDGzeDG3aDMKMOfwUjlZcNgBVZ/5diUOhdJi0W65D+1RXRH4iNxSF+ahEQLAzxY/TkM/CpRedE5YDUqbU8Ow9DlRqfrV390O4h38ul46jVeR+9WzuyD5lp89EWe/W2yu++A+wey/nP2L1+tpIR+SIAESMAzAfmAyXgf7Nl3417951VrbzrFPH4/bM9Odm7DrN2VropfKsSslzXjv7g6KD7GYLwQDhYXB8OJFkb/B9Sq9tGYkLYQuwrW4nThanyxPs72j2pHP9zv8kaKElwYkJS93HQRDwvayAOxhsRg9QcrcFB/I9hpv3G0GP8Ibz4tHYfg6QTVEAOcR8bcHId/37LJurgHCxbrrEIw5v/EeWd17QQ+2unEvU03dLdZvMIx6zX36Tk4IwYB42hLiONBRIThIVSe/ga57qca+Tv1NAMRPbtr40T9urV9zcVKVMh/aek2BDNXpOPg5xtweu8K7HohCqGKiGps/ahYOeKGBEigTgQYmARMCVxn6toKHJvL4E5mRXWttQGe9Otplf9SNm5qgW2AFZyQhAkRIoQ/A6oBw/C0DCuC1+RtxtR8bVCjTU4tnGEcSATHp7odpB3Mm4cJbgwvRhk1fgxGKnbu1yarlBqJ9VqxMF6Ivbb06WEyQDz8oX1i68HjUfzBQqxOG48JI6Jxm8kTR02U7zuHgd8ZvLX7vKOMr/bbPytrE47bejhexmeHkH/J6HYeBZ/ZZUTe1t140X7sFO9HTv8kV5afjdnpmcq66bBFA1vHaAzrrUZRkZ2DNd+qx3FDVGOheua0rQNnv/LVKXqekgAJkIA/BH4IUJ/sT9y+hvmXGD5c+dFCqIgkrJuu94fVyJ0xF8MX5CD3wBlUfFuCgm2ZGD4iC/pkybg9AemJdmOBawxBaNtGdy3GshWFKL1QjqO5mRi1wG5E0H2UvbEI6tvFMzBLN5LcEIKImH4Y1FX3Zd9HROi6AqVvvo2tpdKocB5VGIqJo0NUj8KgNXFeAUqr1dOaC3sw9b6ncEtfsfZfjlyH/lP1430bgrg5Kba5+vBVHoYnLMHyvYKT8gn9GeyTrOKzNflB6JOagik3lgjjSZGqi+EtmtI3NmPTV+WoKC3E8mmrsMbljaSeSE7R+/IzSHtuM/Zd0Prl6hNYM16kRaan7wws/woIGEeNmTOP4GEjMaGj5ir4jkvNRoFkL8rI1vQ0TN2tXWvTHVMe7amd+LITRqbF2md0cWlYo/+RRkgYIof1Qz9fRNEvAEIgARIgAd8JtFoD0dVrvsNqrBC+TzJZi+K9+hwBYr9tM6ZOmoFeo3PEwEJLRdcYrJsTqz7dCfFnQCUGLfpk1ZfOo1QzCAx6cIjtKZlxIFGzLQsTc89Anf+oFhU7VyFaGdSIwc2MAjGo0/Ry2hllwJ/BSFkBxk7brA5gxAAsIzUTm/RBoXhSmTTAKUJxWnO+0v5mzvlvce6qcLx6Hvsys7C8VBwHbBEMbQM/oOjlRZi4rQRlYpBZunczjMa80NEjkaiNeW3RXyvB1AmZ2CoHl2JwtmneIsM/wnXC2GH2AbQtjHLgGG/BC0uQoQxuy5XB+7gFe7D1vSJszT2P4G76k1IloIdNOAbpk0ba6lZ3PDDAWWm7iDpx9iNf7THziARIgAT8J6D2Y/6Hb+iQV34UViILkUYmz0NOqt5v1KI0Lw9TnxaGm4TlSFksjBs1mpAesdjyahIibQYgzd1hF4OxuqFGuB/9YxaG35eGUS8UoTI+2j6/nrgml8jfPqYZXWqRO3sGbrl7KvonzED04OXI0sYXfR4ciAjpWa4DY6D/OQIulGD2aGlUWI6tF4CYyamYoD1Qqdq5GcMHi3GGGG/0kp+2i+tAEGJmjkdieynIj7V9LFZvHo943XB1VhhqnhWclPmOFmGckRVqcTQrE0PGZ2Li7LdRIOKPGDHU/vf4Qve08WnoPzoLa/aHITHe1ejWOTEFS/U/rfiqAOPumwTFyDV4CZYLo5BMQefR4zHldiCQHKVcl7VNNNI3JNje6sLhPUiR7EUZmf2e9qAQIYhfMhljdD4uQjw5hCBxsi7/PJaPFmmNE3mbMBW94jZrn+yFYMwD0Z6E8BoJkAAJkEAdCFxXh7DNOujVa9YGTE0hkfJ1dvkU0Loulch9ORvKHAGzxX5xAXL36x030PmeBORsm4w4w+Aoxo8BVeeEoY6DvDZRGBsfZldTDCRmLYnV/va0GvkvLEKv/nKgNgn9ZxerRqHgcKRPj9NeG7YHtR0JGXUZjHSOCEPV3gJ1ADM6C1mGN2IGTR9vnyjbFiEQ3D3cPgg9VoDhsULn2LkYl1WLSO0tGeA8vvZz+gVDVOj8yExs1P/m9Vo18hcvxxAxyBz+bIHdmHd7HN6aYjIYEmmLOFWE2XJwKQZnafn2R36hY8cjNcIYk+Oxw4CzphxZyuA2TRm8lykGniDETE/xaYDXZ0iUanDUo+odjUH6k0bdzbCvC2d/8tUQNQ9JgARIwG8CDfmAyW8lDQGtP2QKEcaVdBz/IBmpAw19uS6rfTgS509D8TupGGQYP+iXnfcxM2di6T0hBucgdE4Yj90vDnTt86XR5Z1pWDAsTPV/qRoV31ZCmUMpOAzx02fird+Eq9fkVjzYWvZ6HGLM9BAPf9K3pmPdb7qjc7D0bFi7dseUV15CzliDLMNly4dd47AubwVy5kejT3vLoVSPXe/HurVDEWnUrb3Q6/V5mBWlenHYtumOMavSzeMK7oTEF+Zh7/wYtf8NJEcHJQwnPZKwPW8ypuh5ZbgUfLswIG16CetGdDK4+ngo5OfkJGPM7doDKvnJ2bfV6gNGUQbHrFhoN5j5KJreSYAESIAEvBO4zruXlumjuQ3w6qpvcNdOGPRIAjbmrMXBlUmugyp/BlQhsUgaYS8fwY+4vuUSOiAVu0RHbzbY7DwgDhvz06F85mYX43okBgv+DkbCHk0VT0S7w2GOI2WA8RK2POJmANN7PHKWRDsOLMUgLPWVeUjvqw1YhJbKhN1iX7clBHEZL2HXCzGug0wxKB70m2Qc3DQefUJMYrnjYRM91YH03jkmBiWjCH3AOT3KMZ3ST0QUFrwmBtBicC1PLa99B2KMYaAc+YDhaa+ZkDpw9itfzXSgGwk0LgHG3gwJyD+PaE5q+/o8LDhiKBasXYHTB9ei+JMVOCjW4r0bcPrzdKweHY3QNk6pv2caTh8W1+WaEWu/KPuZlauV+WMUGYVi/JERh85tYrFa+lXWafYHTR2ikbpCxHvYHu/Bz9fi9P4VWJcc5WJUCo0WffXnG3C8QNXxoNDP9mDkhnDEz1iIg/uN14Ws/IWYqb/tqmjqRhflmpdNmzDEjJ6G7UIHOUeOTKPDKudRUtKosTn8ku3BTeiAZOzar6VT+vtc6BUdgojkl2wsdyUbjFiGuI5/rqVXhtv/ElYn9lSNQ7q6geLoLl9lPF1jMFPmlbGMFArWm6dhgkiH9KKvbtMkPbiJQ5bBpZtFfhnla2Vw6TA3YzcpjysJkAAJkECdCbRaA5F8K6fO9BpQgHd9jYMcfTBi3x/PFwaRtCTE9bAbOFzUtzyg0kOGIHGpIQ7xJE2/YtzLjt5lsCkGEgfXj0dcB6NPYxoMg0bpxYfBiPRuX8UAbvJCHBcDU2WgKwdUYhDpbYDROX4aDu7VBm+frMZxMQhbIAaVg+aIAYs24NvyiPak0x6Z6ZFxcHTaOHi2+Q5BZOJkZZDpOPBbgS0zhorBtM2j08ENUPTUB5mKnitcBtLG+F0GnMkzHQbQyk3ABzOR2tdD2iKSsEtjcPqtJPvbVhBPDuVAWbvmEJebMIr+fnH2L1+dADbAKaMgARJoaQR8e6O38VP/009+6tAmCKEdw9BZrKFmDymsig3RZNxgNYA93s7tg7wGCu4Qpujozq+36wjET0ujZGVbOwR5kayl06s/RzHB7cPU9HoNp8kX+eeOjVFysBeOMPsZy4jl/DUT5MbNKL8uZdCNeDqTAAmQAAm4ErjO1YkuJAAE+zNQgJefsaP3dyDhrww9nNcBlSENepiOIY5P5wxeEOBj6wM/Y8RB2iDefz2Dtfyu000A/PzVhbMe1pd89VNNBiMBEiABEiABEiABEiABEiCBlkzgupacuECkjTJIgARIgARIgARIgARIgARIgARIgARaPoHWnkIaiFp7CWD6SYAESIAESIAESIAESIAESKB1EGAqSYAEPBCggcgDHF5qngQiRs9TJtU8+MkK5Iw2TPLYPJPjqPVdqba0HZwT43ithZ+16Hxt4XnH5JEACZAACZBAwxFgTCRAAiRAAv4SoIHIX3JauKuXq/D99+pa138a00RyV1cChskiG2VOnbrq7yn8DSHq5JQWJ51ES/q15HxtSfnEtJAACfhF4GqVOpaQY4rLP/glovUEYkpJgARIgARIgATqhQANRHXAevX0NswdOxNPjJfrS/jobB2EMSgJkAAJkAAJkIBCoNVtyrbhOdt4Yibm5JW3OgRMMAmQAAmQAAmQQOMToIHInzy4VoVj77yEic/uxN+u+SOAYUiABEigVRNg4kmABHQCl/+MP8zdiW/0c+5JgARIgARIgARIoJEI0EDkE/gqHHotAxPFU745r/0N39M45BM9em5NBJhWEiABEiAB7wSq8fl/bcDn33v3SR8kQAIkQAIkQAIkUN8EaCDyifBlfFN0Bmcva4FuC8fN2mGr2zHBJEACJEACJEACdSLwzTsv4w/76iSCgUmABEiABEiABEggYATcGogCFkMLFXRj7Ci8vDgBv2ih6WOySIAESIAESIAE6pHA0Q2Y85o+11AY+g3qVI+R/f/s/Q9cFFea749/lMGedEbarBCDd0FnJYaJQ/Yn4sXrv1wwGtRsCJlVcZ0RNys6siPkDmg0C25WuMEQmA04P4zizogzrBhngsyqYTTKjX9YuRK8K6NBg0mEHYmCG8ENM8iSfJ9Tf/pvdXd186/Bp19VXVWnnvOc57zPqXOePlV1mlUzASbABJgAE2ACTMA9AR4gcs/ISuJh/OlzL2D77v8/fv73L+CJh61O8S4TYAJMgAkwgaEiwOkOLwJi3qHX/y/UB5L/7KWN+Ovw4ZUFtpYJMAEmwASYABMYeQR4gMijMg3AjLgleCrE36NYLMwEmAAT6CsBjs8EmMBIIdCKylet5h2avRLZfxkK9ixGSvlyPpgAE2ACTIAJDF8CPEA0fMuOLR9ZBDg3TIAJMAEm8AAQ+PdfvY2ff6xk9JEIZP2vGPADyQoP3jABJsAEmAATYAJDSoAHiAYNPyfEBJgAE2ACTIAJPMgEvqzdaTXvUCBW7diIGTw69CBXCc47E2ACTIAJjFgCwzNjPEA0PMtN0+qWQ7mIWloKPX+IUlOQjqgfHUeLpqaBDZTS1rCz7VQxliwlu2jNOd/BZL9yAAAQAElEQVT/NkjpDmSez5cS/1zsv9n/trvTOOB5c2eAL53vbUfV61lUFqIuaV0Prdj/Izo3kHXBl3j0uy0Kv4L6ftfsoLDfrinFZi5zB8QcMIgE/vB/sTu/Aeq8Q5gegQmfncW/npHXDz/pMRvz5ScXKbwe//4Hc9Cg7XR/dgb56WobSm3lsu3YXFqLlvuDZoJvJXTzONYMkF/ibUZHZp/P7bS39cFpvHuNKEnfIvtDvt7/+eB15pSr9QneZwIjkAAPEI2kQu3qQNvNu+jWkafuL0i23eym6ojhhcj9dlyurMVlu6hS2vZ2XixBbHoDTN9bg4r9GVg/zS6SJ4eu0h3IPHffJf4d6O71xNj+kZWYDmTe+sfMQdFSn78d66v8kfRaBt4rWYrvaqTa3T4I9V8j3ZESJPH7wvJjdsDy1Y/XlGQzXyMDVlSsWAeBL27iE+tu92I1fpK7H28o657/02FW8sX/OUzhv8KHX5iDBmGnCzVvpGNqQin2fG5C4poXkPPyC0iNMeJ3/1SCuUtycfCzQTDD15Lo/RItNztwu9tzw7o/q0XlRUu5eq5BO8Zw7/OdcfHFdhroQduValRf1S4L3w1tx8H0fOR8HIycwm14Lysaj/qIsZrl34frzEeyxWYwgRFDgAeIRkxR+mBG/rUMS147j9t2psXk7MWNi2mIsQpvuXQdnRPnIvOlCISMD0bQWKuTnu46SddTNSw/HAm04sOLXQhatgobZ1I9mhiIAIdsBCO5nOpgeQJCHM5xwMgkwGU+MsuVc9WfBJpKc7GyvAfxBQW4VpaBjGXzELdwHlanZOBY9Q4URbZi89oS1A/BTZD+zKedrgE8bMX+LSUoutQ1gGkMR9XOuPhqO12P7FVlqPh8uLG+jnMXgJiUZMSFmRA0wQSDT2TBWfn7hHFsBBNgAkRgNK28PAgE7neh7U6XrqeLIJ5EutOBTh0+TffdDv163XEea9T4Me8ukvfn9dneg05ioZudE3O67wlOxNTtI/pKenfdPJ2hszx1pdurpHnPdZoyLx1PSKn1x11eVTkd9cyCVbHVTV02BRgtUTzYc8tLZeWufPSmKZUj1Q1X7PXI2KWnu6zozqhUv93lR5SVOxk7G9DfrIR+YQddj/35lN6gl7mSB5ftqxdlLvDwqkXAh8ImLMUbZfn4uZP1x/Mstv7p8h+T3BYsnmAJG9C9W8eRtbMVYRszUBRrckzKLxDxWQmIuVOLohNOGm2p3mr4GVJ4B9pctXOOKboJUfoCT9sl0iraR8frT9Hnpm+h6LZLX/OmN74kp8HW1hqNIzlfne76Y42YzoNkne78IsG5rR/ba4/0qe2si3x7pM85DOmMN7rc9j2SZg++nNSRgIec+EOSvLvrUilrd9eZLl0e5EWIKjodr1Vx0mpVy9pJs2QlCfX3jYMPoehwCLeOrNjjsh0bCL/H2gbeZwKDRIAHiAYJ9JAlc7ceOas2YFJ0KqKeScXUqFSsKW10eKpHsu/WGWwWsnPSSTYdEXPWIurlCtTfk85KX9WZazEpsxadDRVYE7cWU2OELOmdtQU5F5TWuaUCC6aT3MsNFKcBa8S+WBMrpDmPJB3KPj4oxCQ6N/etduDqUcyl/UnTt6CkBah5neyeX4Iah7uV15H//FrM3Xmd9FstbtJVJdtc2a4KoQv1pfmImrUBEc9Y8rj5FNlplnG/03mhFEvmE6f5Qgcxjd6ABQUN6DRHbUVJ4losKG2GmINpgZpeDOU9LtfxcX695XmrGqnPb8BUp+kKA0QecxFFNkl5nE9pPp/vkGbTu4ID5UEq63RMpbq05t1moUBeJe5UZk3Xsf/lVExS6w/pjcqsRpt9+emoZ7Ji6+8eNB0SdliXRzrWU11WWUr1anoWcq4CTW9lSfVqklrPrFVBZi7qsTnYLS9iVbyd8q6kr1E+Uvp0bZh1KjstpVvIlkJUK8fSRpoXIF25LqluCPbLilF1Szorf/W2ozLTXqYUNVbXoyxo+XZbVkre9da37qajWE/1SCpTkWe6zjefasUfLUlq7HWhvj9ZiRTuNsjtmFK3plLdSihulK8jcd7t6gNlrqfee1HmbrPOAr5DwM8fDz8SgEecrA/7w/L5lkmSG+NnCRrIvZYTZ1CDcKQmhjpPZmwMdlcXoGi++oNTvq4WlDbSNU9tLvUNUc+UkB5FxX3qE9Lt2jDqY0oaFF9BElN1tEpH1l9Sm2rVhkvHmWfQea4UNn3l/O3It9FprYX2JT+jkNrXaqxX/JaEQ2p6Xd719fca5DZJyrPahlvaZ8lW+/6I/JvUD8gesejpA4Sc3j5fyFqvvR2oLsgin0/usyKiFX+O/J8Fio8liStsqqUDqy+pX18Ls73SqS5drDobypAg/B4Nn8E1F7ku2PTNlK4ob8mPMuvbgCWvn7H1LdR8CK52foitzwXyX53bR8lZLYo900tQSaGVL5NfS2Wo+qgUJNVFt7YJQevVnb8hsSefivxg62hALVIp/QWlat2V7XO8/uTwSfZ2q/6Jm7qrpungk84n/0r181Uhwdv+Grf3ZVRZ2roufxIQC/WDVa+T36RcW/JvkeNo8saXdFIvps5RfrMI+63qi/Bv11fZ+flCxm0e6dpw4/eIrPHKBIYLAR4gGi4l5Y2dvQ3I+stilHRFYPehAlyr24WG8lUIowZzc5WdwntnsD6+FO89sggH3t+FGxf34lpVGhLbjiLhhxW2DXPTESRuasSc7B2kcy9unNuGvPldKNlQjErh9018DhXvF6AuO5wSCUeR2Bfr2885vtLzP5JRR+cqXgoEwmLkeO9vxeqJwOzvzUXIvVqUnhJKSZW6XDiFPS2hWLd8ihoib/Wke7MaSa5slzXhMjX0CXQ3NTZ7m5zH2iKc3BqI99K3k8NkZ48Sx2FDd2TXbzgDLEtDXS1xurgLdXtmAb8sxCa7O7Adx0uweCew6ZdFuCbYv5+GjeOvY3NqGZpUxbrLswMHs8pQ9XgCzkrp7kXDz+bCcKAQ699tV7VBvE6QsLMLibvUctyBfXNasfkv81F5TxG7WIKE7GbM2KpwoDp08tVQ1GQXIp8GYhQp2vSg9Mf5KJ9C5XluL24IuZ9G4tGqMsS+Xo9ukpAWT+qZFAEQm6bS7VjwRivM5SH0Z0/B5Z35SCxtFiKY/QrVufczsDGMqtJLGVK9qtOqc5K09ZcOXhfLkFjShdUlBdK1caM2G3lPEStvXrfovY78l/KR88kU7K5QrrXTdA19uxHrVxWiWmHfVlmM1NPByPuNLHOjmurEmDNYmX4cbdbmq/u6ywrQVd+o/q6hH2c1odQmHKV6SczrfjYPHXklyG9SE9XY9icrSX0Hiv5XCVqWb0WDqM+1O3DslSloKqGy/2WrJOH51yCXuc5673GZe55xjsEENAk0NdK19MQURKpjP5pSgGGcCQ4PaH7wDpJOUFtVlk3tbjJmgz69zSj561xkWbVzwlfYPbMVORRe8hnJeLNcOowlf9+Gdea+MgPZs9qxU+h0+EFtnUAHdm+tQOfzyTgpfI5lwdJJ7/r6dhxML0QJ5lr8Jeqzk/2ofc4+I/V3mv0RpZvzPyhZnX0AdPf5pNNuqc/PwpoDXXghdxsaqE++Rn3Mm5Prkfh3Z9FhJ6v3UB+rBrz5t9XofDENDXV7qb/chbNZofgwOx9ZF6HdT6tcNAzpJH91zo8+hOlvrPyoXYtgOlWK2L+nm5U2cVrx5g+LcWp6Muooz5If8toU3LbxuVzbZ6MOwVj9dgHV6VWIoxNx2WJfrFslH9Uz20iBtHTo8s8kUb1fDtefE7tfiSSN7usuCUHkLTa9EWHk+8n9bhGObTTi4Los5F8RErTqrcckqi4urwtFqPr17cjpXYqTp3dJ9aduTwwePfsO1uy+rkjQRmefSpK02NUL82+WXPp9U4hjU5X6Qr7FbiroqswSHLxD0cSiN4/97veIxHllAkNHgAeI+sT+v+PHx/bgsLS+jnga1OiTun6O3FZZgf13QpH9dgrE+8cGunsZEBaNzJ+uQbydE1iz859R9a1olBYmYPZ4+TamYUIEMn6SgMgrR1F01sq4q12I/clWJM8MhMGPwo2hWJEyF2HUkNZdpWORzngTgsb604E/AsS+WMeJYwqyXsYYESTOiYli/IzyPh1Lep9IwKZYoOpANdqgfrpQeagW3QtjsGKCGqZs9aR7zx9JrmwXqm4dRWZJO2a/uhV5C0PlPI4xIiw+A7uX9aBy12+lJ6GEqKu1+2YPQuKX482NEQgaIyT9ETRzERKfAKrrrTo6OtV21YSs/aKcjDDQsWF8BDL+JgJouY4PlY5Kf3m24uNPgJBp4QiR0gUCpq9C6f4kJIkxO9KPO8eR9VYrYnK2IsNcjoGIeSUFGyc2oujdViGFlvYxWJyyBjnxCgc/f4S9OI+cpQ58eLlDkpG/OtDy+HKUi7yKuiXk5iSjPCccne8ewUHlyRiP6pmsGJKtNFg327o8hP6FKah4JZQGicqkztww1iTVH5OokwHyfpBWnVP1mrfueXU2t6MbwYh6yiTHGhOMFa9l4MArMxBgHv2ST7n7bnu3DDs/DUWeuC4n+0vihrF0DeUmY/X9BuSo7D9tByaEYkaILINxEXQ9ijqofYdff1kBeupb/b4K1BijsVu0CROpXhLzoCeXYnfJIrrWJbM1v/qTlZxAD0zxadhNdTBA1OcxgZi2LAPlL5lwec8RjScM5Viuvwe3zPXW+xYPy9x1HvnscCMwI0P1J/bgp38ZPIjmt6KpiZL79kSE0MZmkf5JMF3+F6SlytbuHwybLhmRQ/3XiieDqQ2mtoIUtB0pQ85V23YO5CvEZW1FNg2u5+Qft+rXKYLehQaBXijKwIowOR3D+HCszqXB84n0I/AXDS60NKNtehoOpEQjjHwMaZDL277+PvUHIZHI27bK4i9Rn520OBA43yj9MYdBqz8S6VIbprcP0N/n22Wb+vc3y7sQ+cpWyYcReRV9TMzLW7H7qS7vuOtldZfqEt3kmPZUBAL8hF3+5AdRX71rGRY/0gVXXIS07dqIomwq08RkHEiKgMWPSsCB3Gh0Hy1DEZ22xGlH56w0WVb1Q8gHW0c3jcw+lxv7LLrkPQMNiAaNV+qaUqZBVI4GP09tg/Jx3/cogro3WtefQctu4ZPrqLsADaJlNcD0knW/a6R+Nw05Czuw859rIT5667GQVVc95d/55wk4ljUPYcJe+JPfvAqpC4GW/3fdXHf19qlyunb1gtqhFWuiEdTbisvTUlCRQnXLSJJjAhG3Pgbit8y539ExLXrz2P9+DyXOCxMYQgKjBz5tTmGoCPyuvhmYOQsOcxjQj74Eamwtdl1HzbkeBCycgRBpTqEOtN1R1m+EYsZk4PJnrRZxGmR64UnLobTnJ37E9qBDfd9HCuzrlxHxidEwXDyFUjHwJNTdOYvyE/5YvWyeNJAigjxaddjeffEj1CMUi2f6WzgoPB4PnwJcbYbwDTh0RgAAEABJREFUpd2la5i+FHlZizCNBMW75i1X6lF16AgqbgLd9+yeQloYjfixJGi9SM5Vl3kuKP3lGY4lC41oKs5HQsFR1DR1QYxhBD05D3FPkgMr0vi3j1CDcCTMF70irD5TkPGbvTiZFCyFhSxMQl5yJILoSLxj33ThDCpLz6Cejm/b5SF+aQwCKNx6CZg/AzFoRp30V3Ye1jNVkbC1NxyJcYrtajhtg2JnYTYNTJ77NzrwenHPK2DWDMz2a8D6xEKUnGhEmyg+cjJmL4wE/U7xIOUu1JwX1yXp+0aHbf26G4zvzgSaPqbzpDEyNgIBTceRuK4U+y80Q5pDYkI44haGS+VBIjaLJ2UFt/WtFR9e7IFhKfGV6qFVUiGzkPik1bHdbkC/sVIVB2LFX9B1px4q22kLqF7eowHUFgroakbNiTOocliprOi04zKYZa6/3nta5o754hAm4A0BEx79bxTvD3JfQXuW5ZFgxM6aoqzBeFTMp2f/D4ZPz0DcWEsUsffheboJ8vQ8vDBBHFmvgVj8HA1yn/sIH/Zah+vc1+zDp2DxQhNE393kVI0JLyywbUeEvFd9/ZhwrM5KwYonKDExL8nNRlSfOIo9xzsA6hfpm044W/T3Afr7fLu0fif691AkxgbanTAiZnGEXZi+Q92sxkXiBerHKl/dglTyFS7f6qEE6AbbrHmYPdne36BTrpbPLqH6jglJf6FhM/m2K8Z2oZr6RouKQI2+wh/fpD7M7HP1l30e26ZaGe7eP1NF9W41rj+nUfXU3c+o/twzOVwvgBHxeXtxIyea1OuvxyTs0aLlS0o3jTu/xB8lTfr7VEkcGvVirBEmOvnC0xH07WzRn8f+93uc2TScwtnW4Uxg9HA2vi+2+1OH0Zf4gx3Xc3tb5TuCj5o0f0yGhVo7Du1opkGLzvJiRD2TbrcWouQzGtD4UvwiVnJNg0EGZXfANzNjsS6kA3t+Ld8majt1HjUhc5FEDohXaeuw/fatdlLdjKzn7VnQcXYjneuRf6zTnuulB02VhZDeT5+fjgU/LsObzuYw8qPbii6VeVKeQGRGNio2hkLcgVy5LBVTZ6VjZXE9OhWHvKVFDPj5O74qYG+DmMegeDuiZq3F1JgtSHjtMHZfEg6fvSAda11TRqM0aHRZSs/DekYqxeLSVrqTF0RCsn7a8XJxxwsTFmFfRRJWPHQdOZvzETWHeKwqxMEmJyyc2tGB27+nk+crMNfhWtuCzafo3P370oAepifj3M+WYkbbeWSt246I6A2I2lCG6rsko7V4VFbu6lszGmhQNuQx63ZCTTQYYd9W9zW2/cbKols4+JYjZU9y8Nrx8Q06vtuIPW8dRqbDWouP6bTWMnhl7kG997TMtTLGYQNOYPSoAU+iXxNwb68RIY/6A403HW9+PLEIeTQYIq+rkDBRw7SHjHY3bFrR8inJOYRTGC3iCQyAZMjvoEPPFid9eEAA2d9EOp1q84dprO3JvvT13TR4nyrmbBTzpCQWY9OeDyHGqm1T0DrS2wd41udbp9TymejfTXh0vHWosh9K7bey68lGPyv6Mf7TbOx+3h/Vu0qxJG4DJs3PQmrldblf8yTRG8QAjuUmqwhEKNXFpk9EXuUQ8a3ZV4gT5rWf7PPCNtUEt32PKqh36+Q6cxbdbd11mTdVawdu6/Vl1Ch6t37uBD3oUxVV2vUiEI/TWLUiorHp0O+vDYDfo2EQBzGBQSMwetBS8rGExrhtgHzL4DEelxQ5AWGUh9sdsLyeRcfK0tQsBkGUAwTjcZI1JKbhxsW9muvZFNs7b2rMgd9OwbqUcHS/+1tUdl1H6S+aEfmDZ0HmDljSISHBpDsUee9rs7hxMQPxY0jEzdL2bi4WZLcjtmCHNI/RtaoCnNy1RtvBdqMLVEZhItO6ypOU+ZkQmZSBs6f3Qsw9cCB9Clp+Vowlyjvcch47cFt5fY1iaC7SPAbvGpW5kXah4WgBjhXEIlJLWhl8sjnV1QXxUNk0ial39cylreLJLkpw2mRRZrTj7eKGl1BrCJmHvP1FEPMa1JUlYd0j17E5sZDqpTirdw1GiBhcmbkcdU6utRt5lqfjAqYnQJqnSMyBtec5xH5ejTWrrealskrWo7Kyiqe9G4qIJ4CWz9s1TpPTLn78aZxRg/qHlaoN+KNW3ZLu1Afi8UkkN3ER9lHdrHNYkzCbTmsug1jmnrSvnpS5Zr44cMAJfMPj/njATXKZgB57I2MjYbh5FuUXXKrSeVJp55z0V+IJZalPC9GpzlqstwfiiVjrILHf2dkDhFG64kDnGiL1S6Ge9/XS/GzvoGlWijzXzeki1B3ahuxF4pkEd4mTjbr6gGB41OdbJRsi9YdO+vdmar+tZF3tWp/ziNWYYMS9mo2G8+R/VGVgd6IR1a/lItVu7kVr/Zr7k4gBOmDjrpoF5UGCMNe/8M3SNjv9YV9fbNPR99jY258Heuquy7ypxuitx6p8f26DB+k3i2d57G+/pz+JsS4m4CmBYebmeJo95/L+7m+pOY88yGeEczfKizuW342kofEL5/GeMv+L2eyuWlScMB/RTigWx5jQffS843we0qsbtbjs7KkFiu1ucfm3ke4i0/mAhc9itbER+159B3tuhmPNUq2nGkjQbvE63VmRiPNrRrnG0z5tV86g6kKrpoNqlzx+J17xW7jIMsePJNCj/WNXOuf6S3d5XnkHS5bmo1IZ/BFzD8xeloJ9KYFoOX1Jvsv559/BbM08Xpf+IU7+l4xWfHixC2F/tdw834NkYe99aWP/VXm0WhoMsg5vqzqDaoQiSrxnR1uv6plSHnuOtFqrlvZbjpxCjV84FsyUDr37csurA5Vb07GgWDw9RknQHWzxul5G4TLE9zbipPUPKvsfRL3NOPye7SBLTGw4oHVdoh2XT5xBTQv90EEjdq5Kx/ojHZQgLWOMCJu5FHn/uAhhLQ2odrhV7VlZkUY3SzBmTPfXbhNazqP8Cpx8+peVnEg7Dv7LdXnX6rv+X2rRNnYKZnjzI3NQyzxUZ/vqaZlbweDdQSUw7G4wad8Qs2U28zlserIH+7cWospZf9/bgVv3bKM5O5LbuVM4rNFWHX6nGVgYaTN46zAYfe8MKk5raG+qxWH79qe3AeWVHTBM/45nN4+UvsXjvr7xI/KVIpCqzl2imOnU56BBLUVE2shsNHwzmz4A0N3nS1qtvr5L/Tu0fJguVL/XYCWo7joOJjWdqEWTelpsdbLqrCpE1LIyiNfQRTTDhHDEpWzFm3FA1Qd2adtxEfI26+QZWBzSg4O/rnXwubo/OIWD90xYPMuzm0Me2WdjDB1Y36jw1ja3fQ+lIy2OA2Odp86jSjrXhy89dXfyU4gZT9wd6koXKjevhfovc3rrsVNr3ZW/04h6+1SnCnSf0JfHDv0+ou6UWZAJDC2BB3aAaDg5eN7aGhSfgNXjm5H1w2JUSfPQ9KDzZgPyf7QP9eNs73SF/WA54sfUYmVyGWqkd8aB7lsNKNmcj5X7biJgnBcV9bFAhKCRHLdmCMepu0v88PVCj18EkhJNqP+AfiS++KzDBNsOGvuarnEeNm0MRv0b27G+sll+nex+F5pOFCNh9SGcum+CwSFRx4CQicT4XC0Ofibnu/teM6qyi5Hv5ukLR01yiO7yfOI7mNHdiMw3qkHFLke+24DDJ2mgIjQQj4qQ8YuQLeUxF5tPyOUDGgwU9u3sikDmi8LpMuFRGmNsOl6NmjtKHkSdSDuESo0fHIYLFViSfQZNd3vQ3dUh83q9GQEvPmeeUNyreqaUR9POXNvyqMzHkrfaMW3jcvd1QuTZ2eqWlwmzZ5rQ9LMy5F9oVxzVHrQcqaXBLxNCH4P0kZ35I8g+QTLkSErXT1o+3vOzHdA0xC5H5pPNyEoSr6h1SfrkupGLJW80oFuaJD4cc57qQdVbJeb6A/pxVv8vDWgifY6vDnhWVpLBbr4i1yRgNg0mr0+rkNsEcuY6m6qxPpmcdaeDMv3LSjYxEIZzhVhZ2oCWe1S37rWipjQXSeVdmLbuORrolKU8+h7kMtdX7z0tc3c55vMDReCb3/Dijs1AGaNDr0GXvcFIfjsZK77RgPVxNDhdfAaXb3Yo86S10uB1Gd2cyUfJnWBsXOFq3g7ZILmda0dOsm07d3BzLnKagpGZoj4pGUyD0UZ0H3oHO6+I9pD8lKYz2Ex9bVMI9aGw+4ztwp7UXJTQjZrO+yRLPk1JWgn23w3Gph+4t8tGm9K3eNzX2/kYUPyD9Xuoj7VJwK5dpja0m+6vyGzc9QGA7j7fJk06GD8X6140kg8j9+9i/rpu0W4WUx9aJfflJCUv4TSYRDeLcv7xjDy3Xle71L4mnvS3HWzTySqABuke/7QamTsb0EZ5FYl0t1Sjgm6khExU+0JtLkLWdp2CdekRMBzdh8Ri0tdFZ4lh24UyJKQ3wLB0OdY9QWEeLPrss1cYiFDq86qOHpd9Kqp33b1e2ua276G0J4ZhNg3Q7M9/B5epz1Pr15K8doRNpPN9WXTV3XCkZhH3QyVSvyuV431RLwqReSoYmWvFHESA3nrsaK7e8neMqYbo61NVae+3+vKoz+/x3gqOyQQGn8DowU/SN1J8eIxv2KHHCqO/l84oDaxk/yoFyUZy+MQ8NNM3IOL5Evzu6TQUfc/fNumx0SgSst1nsVK8Mz59LabGFWO34Vmc/HkCqG+0lddz9EQC8r5vQnX2dkyNIn0vHZGfXtET104m7H9GIggmrPueDgewH9INS9qGk6+E4sPc7YiIpjsm0alYkN2KuMJs5M0x2lmnfRi2Pg15kc3YnLABkwTPmFwUGVehKJ7knTyFQ2ecL3rLU8iVJWHxp+9gwRyyndKeFFOIPZiHA1mqUw6EJW1FBQ0SncqSy2fSHBoQuxCKorI0xEhzNRgRn5WG5N7zWPmMkof4faiLXYXMJ8hMctTo27zEkey6O4ewIGYDps5Jx4KtDQhYmYJzr0ZaBtS8rGea5ZHbjpjXKA9JNIpltsKLHR28gl7MIFZGlG/YgqmCJ11Lc3NbMZvynCFYULJB8SkoWupPd9hIRtT3pSU4NzMF5euC6azV4heK5J9vRfY0qhvSdUnXxvztSG18CvvoGoxRqldkxjbsjm03159JUelIOAQk70qRB8SsVAKelZVNVGcH4p36chokaj4utwlR1H58/7cwbc5A5lPOIgH9ykpKJhib3l6DaUeKMXc+1a35WVi5q136l8GK79uxleR1fA12meus956VuY58ssiAEHjIf0DUDohSMX+h7ptMVE/zfpONfauDcbm8FEuWpitzEmZhSVYtbs9fhWNV2ciIMMLtx0k7t/nTKcgr34bkyRYNkSkp2BjZjvxVqdS+inbmKLBxG4oWaYCeGIPyf5iI8o1Z1DeT7NJC5DSHIlvo9MJR0exb3PX1T6zCvqxQ/O51pe8k/2DJvoeR/VqklClxQ0zaEe1yynLE3D0j96HUhm76VxiwzK0AABAASURBVDrjhI19HwDRTlGfoMuHI7WWxYiYV7dh94v+OLhZ9mGmUruZei0S5VkRFjGxR+18UU4EDO+XSnPrTZqzBakXw7Hn7ecwTZy3WnWxIn27f74Ujx4tRJTwnai/nPr8O/jwz5dj3/opijYj4rW4KGetNwHkr5776Sx0l5M+4c8Qw6gNtTC8RL7FP0QjwFpYz74u++wV0WBQ1jyEnHtH9qmis7D/JuCVbaJMy9z4ZySz6R9jENlyHEuoz5tE9WvBHiCzNBkJkm9mb58HxzrrrsjbqYJwdPxToVyO0Vuw8p96kES+S/JkJT299VgRt2z0l78ljt0etVX9/pvFLgnpUGce9fg9kj7+YgLDhMADO0A01uDloMsQFOy4b+pLNCRpB25cTEOMtfi4SGSW7cKNcwWoe78A12qLsC8pHJFJJFueABt/SpUlGSHbULsLdQVLETbGojAmZy9u2McTp0MScPLiXhQ9LQ7U1YjZ6ZSOqm+/nJ6WDsl2Lb2KqprDZ9E2PRZJyo9xJdjJRn+6kgJN2/0RtiwDdeId+mpiV00MT2cjc45JiqL5RY7MjYs7kKxCpY5lxVtFVux34RjdDYvPIoa58xQVwUgup2PpXyGUIGUDe30iXC0jd+U5YR7yDpHNilzDub24VpaE2TbOhZHqgcjjLjRQ3ag7TfK/SUP8BJGQso6NQCbpuXaaGLxfhGvnC8jpjEZy2V6cfClUEVI2Y6ZgtcivUt7XqP4cS49EgJ9yXt2oeVDktOqZKmrZ+juWx/kdNNg2xTL4JAnLPNV/YZOCHL5kGfmfOJSTbnkZidVW1NUprMwsrBj4BSI+pwA3lHyp15pwtByuS8GqgGQVfWr5xIxT7BEb0hf3Kl0/ikydWgdnOvlxpqusNPIu0hKrRn0zhC3F7t/sgqX8dyAvNhBxoh3QqrNCD/0oiqTBx35hJdmUhjhRZ6geyu2YUg+X2Ze9lLiTL418D3aZizyItlipH5r13tMyd5JbDh5YAuIN9W9Z9YsDm1rftH9rjIe+zphgxKRkSPPXydcbtf2ibzhfhGOvxmCadRuFYOf9lzBbo527cSgNK8L8xVnLOjYcGXuo/5H6K3F9y+1MmJafAn8EzUnCSbVvFrb9JgOr7XVatEOzLzWf1+hb7Pt6DR8h7MWtqKM+TvSdavs9Oy5F8sHirPu8yYuwr4r6X8WPMPtIGmxEH23TBwgb1XZDYuPGhxPy6mrXlggb695KQKRN+cnCQXFpqDsv921mubHRKHLw6XSwIpUBEQlyniW/oQByW7cIYW65aNenACrvY9J8ilQXRd9bV4SKFDvfQuorrPwvskNeHHXqsk+ObP4OmJlEdU5mVEf1Q/XzdNlm1qLsuO17gICIVagQeRb1RtTxQymImxAqXW8W/8Yxb0oKykarDAG9dTcoNgUSd2GD5H9scxwc1luPFYvMG63rQuM6U+W1fjdAvTZc9anO6oWUlkZ9kcLtfsvoyqMOH1HNDG+ZwDAg8MAOEPmNAsZ9k76GrpB0pSzmH/pWfwxmGU0Q/xxisO6gnVkwxijJBvSXA9xXfV1nUPpuD+JWxiAIHnz6mq6SlGEcsRvnD68/nrDXm4henYpcgJMxBTk5fwSMNyForL98qPFtGGuiOmG0G4zREBRBCne3dU2R87SeGfpaHnDxcctLYTXeBQslX27zL8zwk/W5LB9FJkhnHfSorIQNOlbvdMp5C+ovVsJOqXxcsBcynq6SThOcl0E/50OpHy7rvYdl7mmWWb7vBAL6o1/uuxluNQQY3Io4F1CuDVd9g/PIVmeU+uz8GlNkpfT0X98G0Re46Lfg4ccg9OlsZyE+evNFsgZnuvXqkNiYoKtfgdVHr37I7ZzbMlJUG5zlB5aP3G9Q2+rClzTo0APlI+vTXz+UaE43sj7X9tlGlhlpXQ+yLg9tU8rUFXOD4NOPdRzqR3e9AAzCBnfXhQf6YPUx6NENNx89faobFbpO68qjUkdc+T26EmOhEUxgWGRt9LCwcoCMHNsXx2mAbLJXG0BOqO8PY9lb3b/HbUerUWWMRlKsy1GO/k2UtTEBJsAEmAATcEJg3EO+3zOLJ50eGQY3wpwg5mAmwASYwDAkwCYzgeFP4IEeIDKR4zTGz7cL8ZGHfNu+gbeuFR/+PhDJrz7n3WS0A28gp2CchBdejMScxxgFE2ACTODBICCe5PD1p5DHG0dh1KiRUx6PRkZixYJJ+ObIydLg5+Sx72AF9ddhfL/Ne/YckwkwASYwwgmMHuH5c5k9f8p9IDlQLoWG8OQjdIdSPEE0hCb4QNLBiHs5BZlxwT5gC5ugSWB8JDZmpWCFrvmhNDVwIBNgAkxg2BEIfNi3TQ7yYf/GG3LTXkxBXnIkPHrV3IuERnSUJxYhL2s5YsaP6Fxy5pgAE2ACTKAPBGiIpA+xR0DUoIdH4Zvf8M2M+PLglW8SY6uYABNgAkyACbgk0G8nx44ZBXEjp98U9qOiR8m3Mfiob9OP2WRVTIAJMAEmwASYQD8TeOAHiMQ7+mKQqJ+59lmdeDT8Wy4m9utzAqyACTABJjAiCXCmmMDgEQgeO8rnXuMSr84LuwaPAqfEBJgAE2ACTIAJjBQCD/wAkShI8aTOnzw0Suz6xGr0B/40wHfs8QkobAQTUAnwlgkwASbgIwTEXES+1l8Le8TNLx9BxGYwASbABJgAE2ACw4gADxAphTVp3CiIgRnlcMg2YkLJbz8yGg+yczdk8DlhJsAEmAATYAIeEhA3mXzlSeSJY0dB/AGHh1lgcSbABJgAE2ACTIAJSASGYoBIStgXv54IHPqBmanjR0M8Hu6LfNgmJsAEmAATYAJMwJGAeGpnqOcjEoNUE741ytE4DmECTIAJMAEmMLQEOPVhRIAHiOwK688fGz1kTxI9GTR0adth4EMmwASYABNgAkzAAwKTxw3dpNVicEgMUnlgLosyASbABPqRAKtiAkxgpBDgASKNkhRPEglnS+PUgASJyai/O2E0+B9HBgQvK2UCTIAJMAEmMCgExCCR+AexQUlMSUS8VsaDQwoM3gwcAdbMBJgAE2ACDwQBHiByUszC2XpsEB7VHvfNUZjyJ6PhzyUB/jABJsAEmAATGO4E/lvAKISaRg34XILidfQ/e2QU+uu1MvCHCTABJsAEmAATeOAJ8LCEiyog/iZWzAk0EPMKiKeGvk2OnVh5QmoXhcCnmAATYAJMoD8IsI5BJDDeOArhQaMxEP6DyIZ4Suk7pJ8npBY0eGUCTIAJMAEmwAT6iwAPELkh+fAYQDwyPuVPRkn/DDLKjby70w/7Q7qz+Pj40RBPD7mT5/NMgAkwAX0EWIoJMAFfImDwk/2HsPGy/9BX28TNJPH6uxgYEk8pieO+6uT4TIAJMAEmwASYABOwJsADRNY0XOwHGEZBPMo9bcJoTFImovTTSU/EFc6ccOqmBo6GuLPoIik+xQS0CXAoE2ACTIAJDDsCY8fI/oP4IwoxX5B4glhvJvxpkEk8hST8jgjyP8Tr79/8ht7YLMcEmAATYAJMgAkwAc8I6Bzi8EzpSJYWcwX9yUOjpKeKniJnTThsYlJrMXgUYhoF4fyJJ44eHz8a0x4djenBoyGePhKPg7tz6oaE283jWLM0HWsOtbpP/nwpopbmYv9N96I1BekkW4oa96JeS0hp/Og4WrzW4GVEhVnOeS/jD3i0Vuz/EfEfCjYDnjdOgAkwASYwPAmIP6IQ8wUJ/+D/R76B8B2EvyBuIIk5D8UqfAgxf1HYn4ySfIjvkh8hZITfMTKeGFL6p4L6wS9ED3yYwTduAFIcal/lQeM9AEXIKpkAExj5BHwxhzxA1MdS+QYRNPpDev0s0DhKmixS3O0TdwjFBJJ9VD/w0Xu/RMvNDrR0WSfVg7Yr1ai+ah1G+9130Uay3b2072bp/qKDZO+i242crtNdzaiuakCbnbCURvuXdqGDcKgwu90vmRsYe7vbif9QsBmY7LBWJsAEmMCIIiBeVxe+g/AXxA0kMeehWMUAknjKeKxhFIaFD+FFqUj90xc9XsTsY5Ru/T5MH1PyjehD7as8aLx9o9TZCt8jwBYxgWFHYPSws5gN7l8CIQk4eXEvTiYFW+mtR/aqMlR8bhXk4W5Mzl7cuJiGGA/jaYm3HCrGmn1N+KPWSQ7TIBCM5HLiX56AEI2zHMQEmAATYAJMgAkwASbQHwRYBxNgAkxgZBHgAaKRVZ6Dm5uuDrTd6UDnfS+S7UtcJ8l13xX2dLl+aul+l2Rz2z3P7l4K3Z02T1k5MQI96CQmbXfc2KFEF3rb7jra4ixciSZtJBlKS88TXVIEqy85rj4braLxLhNgAkyACYxwAnL/0AH3fYvS32n0YTaIRH/vTsYmguWg+x716w79tZKu235WlvPKR7GYAFjv98o63foQIs/UP+vzG6wSkHwUx75Zb5kIOY/TtEre2a7Q696vkdno4S3r6786JtUT4u2+zjrLIYczASbABJiASoAHiFQSw337QSEmTc9Hpc0gRgOy5q/F3OLrNrlre3c7Ji0tw2UR2lKBBdPXIvUDcdCKksS1pKcElXRY+bLYF+sWlFhP9HO3ESUvp2LSnHREPZOOiOgNWFDQgE6Koy7VmRQvscIyP5BkXyGq77mPq+qAFIfsf6sduHoUc8nOSWLNrDWLiJ22hgqsiVuLqTHCnlRMnbUFORdsQAAi3fR0TIpOlWyOmr8Bk5YVo+qW0OBs7UHToXxEzZJ1R8yhPIk4v3cc0AENDMmyGxBBTKKeSSU70rG+tNGRC9nfaW1zjLClFDX3AIfwuEJU2tnY9K7FJsF/KuVpzbvNVplolcuR0lEDpfKgYxv9ko0arNRIvGUCTIAJPAAEOIsyAb19y4LSZrSdKsaCWUp/J/qwuFwc/EzWo353Nx3F+uepfxO+gpChvnnzqVbXTwNLPgn5HA2NyH9pA6bOp379jXpFZRfqS0X/p6Sr9GGbT5GPoEhIm94OVBdkYWqULBcRvRZRL1eg/q501vKlpmXt30hna5FKvsaCUuu5GUXauYgif0fq44UP8Xy+Q55x6ww2r1LyTL6A8BuktKl/l1Q7fMn99QLyFeqLsxQfpcQ8f6PgvIT8ONm/SYfU35Ostb8Fs//hzldR07LOl2yQ5CNY+WzSsV6fQS9vSqq/65jEe5lST4j3VCqfJao/2iv7wFNfb6CU7ZarZeRTpiP/il04HzIBJsAEmAB4gGikVIKZTyEGjTh5wSpDF8/jIDklLVXn0WQO7kLNuWYY5j+FaeYwdScYq98uQN37qxBHQXHZYl+sW7F6IgVISweK/lcxTk1PRt25vbhRtwsnX5uC278sxKYTdoMykrz1Vyve/GGx/rj/g9J4vwAVLwUCYTGooP06sb4SaVF6sxpJmxoxJ3sHrtWRPee2IW9+F0o2FFsGy3qvk6OZj5xPpmB3xS7cuLgX106T3LcbsX5VIQ1aWdRZ77W9m4sFrzfj8Q1pOHstBRSsAAAQAElEQVSa4p3bgWPLe5D58nErnnKMptLtWPBGK2Kzt8l2CC7ZU3B5Zz4SS60Hb0i+6QgSrWy+VkG8vziDlT/MQuLf1uO7Sl6uvZ+E+P9qQGr2cbRRNGm5WIKE7GbM2GqVzquhqMkuRL79nFFSBKsvu3RvaLGyEuddJjBCCHA2mAATcEfAg76l43gJFu8ENv2yCNdEf/p+GjaOv47NqWWWvvHWcayhAYea0EU4cJTkqE+s+9k8dOSVIN/ikDi16tg/FuPwt5fj2NEC1Cl9/uXi7UjYadXP1hbh5NZAvJe+nW5yWfyP+vwsrDnQhRdyt6GB/BTR3785uR6J2Q1O03N3oqk0l9LuQuIu1dfYgX1zWrH5L/NRSX6WFP/eGayPL8V7j1Ce3yefQbCpSkNi21Ek/LACTa7mb/zgHSSdCEZeWTb5YMmYTQo7T9BgWHojTH+ThrravZK/VbcrGrft/ApPfBVSq3/R6TPo5t3fdeweDeStIt5UT04KH+3iLjTsfw6m9wqxZOd1wC8CSYkmdL/7W4s/qOS+5tdn0TI9FklPKgG8YQJMgAkwATOB0eY93hneBIzRSIgFqs5bHKD64/XA0kjE3azHYXXwgO6onPzAHysWRWjm1zDOhKDxRhgAGMaKfXk1+FGAtPTAFJ+GA0kRCDJSgJ8/wuKXY10YUF1PHTIFOV/a0TnLg7hjjGQLpR8A6uiN8v54Oh7rD/Pnnj+SfrIVyTMDIdloDMWKlLkIo0GhOiXPbe+WYeenoch7OwVxk+W4hrEkl5uM1fcbkPNuq1mdZacBRW81w7AsWcpriEjTGIhpy9JQsYEGrCyCwJ3jyCKndfarW5G3MFS2Q3BZmIKKV0JpkKgMB+9YRbjahVgrmw2TY5AabwKutGNGQTYylLwYxs/Dph9QWuc+wu+U6C3tY7A4ZQ1y4q3SeXEeDeh14MPLHYqUk41dutBg5SSmjwezeUyACTABJtAXAp70LW1XTcjaT/1pmOIrjI9Axt+QT9FyHR8qfV39vgrUkF+yuzABsyeSHPWJQU8uxe6SRdQ/u7O0HfWPJOBYVgymTTQhSPS/t44is6QdNv3sGCPC4jOwe1kPKnf9Vn5imfrjN8u7EPmK3B8HGEG+TChiXt6K3XH+7hLWPk86s95qRUzOVnP/DPIHYl5JwcaJjShSfIianf+Mqm9Fo1TkebyclmECsflJAiKvHEXRWW31IrTpkhE5xHTFk8Hk6xAv8tXezG5EQKLsgwSNISnBcOYqlEp+RQWqpQEnD3wVUuHRosdnIDZ6efd7HSveh0pjDMpzYxAm6gj8EUB1bN8r4WgpPSLxCfvBc9LN031H2y1Z7zqD0nd7ELcyBkGWUN5jAkyACTABhcBoZcubYU/AiNlzQtF9+hKkV8fQgIqjwIrvrcGS2A4cPqkM3ly6hCpjJBY/5W2GA7HiL6bYRfbHN/2A7nuWO3h2Asqhl3GV2JqbsGi88KTdGXKigB50SM9gd6HmfDMwcwZmf6NDnn/ojrK9G4zvzgSaPqbzdirQ0oQaGnxasZicXrtzIQujMc067N8+Qk1vOBLjaDDHOpz2g2JnYTYNVp37NzpQFw2bAwLImaS7XbFkjyqmtQ1ZmIS85EgE0UnxDn/ThTOoLD0DGgrEbXf8NdKFDStSygsTYAJMgAk8cAQ86luoD4wfa4eIfACgC/L8N6348GIPDEup/5PCrWRDZiHRvs+2Oq3uxlAaAeoBbbsvfkT9XCgWz/S37cepP388nHySq83y00u/o/4YoUiMDaRY1osRMRr9ubWE033RxyMcCfNptAnWnynI+M1e5U8+rqPmXA8CFs5AiDQfouJnkH1t3wjFjMnA5c+0bkYp+p6egThrplcv4b17RrwwN9Qhv5g8CSG9rWi6SXE98VVI3KNFj8/gAe/+rWPNOFfXg6CFszDNro4ZFmbgRl0aYkT42Bise9Ef9b/4rVw/CEBbVTX5wdFIirUvTzrJCxNgAkyACfjGK2ZcDv1DIOhpcsZuNqCmhfSJ18sQiYTpRsQtpLspymtml882onv+U5gtOk4S82YRg0HexBNx+hJXxHdYaYDD4BBoHdCB27+n4/MVmPtMujz/kHm7BZtP0bn79x0ntv5EOJsmhNr7mKDPxIkIo426tLQIp88f4k6lGmbeiiee6OCyJEM7YnFmc1iwjV4h6rCKd/2LtyvzIm1BwmuHsftSj4OYZoCzdDWFOZAJMAEmwAQeGAKe9C1+4nEWV2Sa0XAVCHlMqwOlfu7bruLK5wIesv3xfvuWeAKkGVnP2/fjdJzdSJF6pD/MaJEGYUx4dDwF2S+hlLZ9mI5jl328OX47mmnAprO82M7PIPueKUTJZ3QT7UsXN9Eovza+TFs72mjAbf+PRHy7dd0ZtIBuggl1HvgqZlP17ujwGVo84d2vdawVHzcBJk3HyzaDs783FyEtZ1F6QYS3470jzQhJjO2THyw08coEmEDfCHBs3yUw2ndNY8s8JjA+ArFPtOPUhQ6IgSDQ3btIUmKYE628ZtaKmvMdiHs6gkIflCUYIcIZnbkcdRf3SvMPiTmIbNa8edIrdTZE/iyUBms60NxuEyof3LxpvhMlAkJCgmnTgdvKo/V0YFnE3UM6mjZZyNBOHxfpXf93jZDnftiFhqMFOFYQS0OBfVTM0ZkAE2ACTOCBJdC/fUsoIp4AWj7X6kBb0fSp55jlfjYUee/v1e7HL2YgnsatQqS+1kl/3Expe5405LSd6DTrC8bjdOfIkJjmxL69OJsyxSztdmdSMPkg/lj9M2f5LUAGMYYHvorbNL0Q8IR3/9axYIm3dh2zy8gTCdgU24P9h86g++pvsediKNZ9z4OysFPHh7oJsCATYALDlAAPEA3TgtM2OxhxiwNRc+4dlFcB6/4iQhYzzpBfMztyBOeuhGPJHNs7c7KQxrf0frtG+FAE9fY4PuWj046Y2HDgwnm8Z/dvYEA7Lp84g5oWjSdwQsIwe2wPDr5nmdNJTa7lRC0uqwdiOysScX7N2HNEPEkkAixry5FTqPELxwI3r45ZYrjaE4/tdyHsr5ZjhTL3gyTde1/a8BcTYAJMgAkwAc8J9HffEowZ0/3RffQ8auz9iJbzKL8Czz9KP1tu/49lpKntyhlUXWiVfYTvfgez0QxHuS5Ua/TngOONoM5T50EuFGlWlj8nndTHO+q8jvzn10L+t7NQLI4xaee5qxk1wm+4q+jTs5k8A4tDejR9kO7P6lH1wXVIb9F74qso6ToMqtw7g4rTykmPNiSsm3crPrzYn/5LKObMMmry7j6Rj0lRhdIcRGQhLUbEJ0bDcKIa63edRcvCGKyYQMG8MAEmwASYgCYBHiDSxDJ8A0NmRiDoVC32IxKLn1TzobxmdqAW1TOfwmy340OBCA0Bqo4eR5N4hPk+Dc7YO3mq6kHYPjoxEGiqR/mFdskB7O7SGNBxYYchdjkyn2xGVlIhDlKGukm2+14zqrJzseSNBnQrk0lSsNUSgdSXQ9F9qAQrSxvQJjh0daCpshAJR3pAeCyyxnnYtDEYTTtzsb6yWXrMHfe7SDYfS95qx7SNyxHvlrlFnfM9Ex4NJRTHq1FzR2bQfasBJWmHUOnnPBafYQJMgAkwASbgnICTvqUPfUvkmgTM7qrF+rQK1Nyi/opu8nQ20Q/05Fp023Sgzq2yOaP0s/VvbLftZ08UI2H1IZy6b5KfBB6/CJsSjah/IxebT8j9cfe9VtQUU/98yQgxfx/Uz0S6ETS+B/vz38Hle2Sj6LdJ35K8doRNVIVoSzqzqY9XdXYLf4gGfaqyi7GzKwKZL8pPCIf9gPr6MbVYmVwm55miSn305nys3HcTAeMoQPcyBevSI2A4RPkrVnwQYth2oQJr1hajqMWIAElXhH5fBcE0cEeDKofewc4rXehGDzqbzmAz8WsKMcGrD7HRx3sA6ti6VYgn3lIdk3yiHkh83mgkvysB0hxEaqZmxmJdSDOqPwBWL9N4alyV4y0TYAJMgAmAB4hGWiV4chYSJwJBcbNgPZGyQbxmRnmdHRdt6yBRmONCjknWPIScewcL5qzFpOgs7L/pKDVYIWKAJ+/pLpSs24Kp09di6uv1niXtF4rkn29F9rRmbF6WKuuYvx2pjU9h369SEGPUVhf04lacfDUUH+8qRJTgMCcdC37pj5yfvoBIuyhhSdtw8pVQfJi7HRHRazEpOhULctsR89pWVCTRqI6dvHeHRsRnpSG59zxWPrMBkwSL+H2oi12FzCdIIzmP9M0LE2ACTEAXARZiAjKBAehbJizCvnIaJGo+jpVx1F9FbUDE938L0+YMZD4lp+rpd1iSRj+b3Yq4wmzkWT0ZHZmRjX0rjTi8Ve6Pp87PQuq1SJT/77mwGQbxi8Cmf4xBZMtxLJm/AVK/vQfILE1Gwlhb68KStqKCBolOZW3H1Cjq4+fQQNWFUBSVpSFGlR0bjSLyKZK7z8p5Fn10XDF2G57FyZ8nwNNxsYCn03Dup7PQXa74IMQwauNZBKwnW74fbDbQE18lMiUFGyPbkb8qlXwhUSZHgY3bULTI36zP0x19vAegjgneZUlY/AXVMckn2oCoDcfxxxczUJ5k73dNweKFVPohc5HUL090e0qJ5ZkAE2ACw4cADxANn7LSaekUZBzdi7qNdu9X09233XV7ceBF6iCtNYUk4OTFvSh62joQCJiZhJPnd6Hh/QLUnc5GsvBsyFm5cXGHvG8jHozk8r24kRNtDo3JoeNyK4fIg7hmJeoODfCseKsIN86RLWTPtX+Q03FIQ5XXytOYKVhdUIAbdXKeGs7txbWyJMSMUyNpbf0RtiwDdWYOu3DjUAriJsxDkQMzVZb0VpOd1SR7fgeK4qfIdzUV9c5sDknagRvWvBR5KfwiOaDKMcZGIPPQLlw7TWm8X4Rr5wuw+8VoJJftxcmXQhUpHeWhSEKLlXqOt8OdANvPBJgAE9BHwMu+xaxco483hC3F7t9Y91c7kBcbiDjhH1j5C2YdYsdln6TRz5J/kjnHJGJaVj8TYtKzca3W0t/XvZWAyIgE8ndsfZiAiFWoOK3026ep35b6+FDJpzmZFGzRCSMik+z8gd+kIX6ClYjYHReJzDLSU1uEOvJXGsiGuoKlCBsjTmqtjv21tVTAnCQcE/ZZ9/nLbP0KwF+/rzI2HBl7yD7JnxI+hFwmYXY+iDNfRdNn0Mt7AOoYyB/LE7yl/BRIZV6REq48XWVFsrcB5Yc6EPmDZxFmFcy7TIAJMAEm4EhgtGMQhzABlYA/AsS/cI31VwOGdms0IYjsMfTldSo/OU86/vjCKq9ynCCdHAzjyM5x/hjIj2GsCUHjjTaDTwOZHutmAkyACTCBkU9gIPqWAdGpp5/1oL83CH26+nid/sAYo+SvBDgdGIJHH30MddomUpb8qX72IXTy1pcXYaQHq5QfE5z5h92nfov9XeFYszTQA6UsygSYABN4MAmMfjCzPUxyZM9Y/gAAEABJREFUzWYyASbABJgAE2ACTIAJMAEm4CWBdhw80AjDi8/203yQXprB0ZgAE2ACegj4gAwPEPlAIbAJTIAJMAEmwASYABNgAkyACfQzgZsfofnb8/DmDyL6WTGrYwLeEeBYTMDXCfAAka+XENvHBJgAE2ACTIAJMAEmwASYgOcEJs5DZlYS4sVcmp7H9iYGx2ECTIAJDGsCPEA0rIuPjWcCTIAJMAEmwASYABMYPAKcEhNgAkyACTCBkUuAB4hGbtlyzpgAE2ACTIAJMAFPCbA8E2ACTIAJMAEmwAQeUAI8QPSAFjxnmwkwASbwoBLgfDMBJsAEmAATYAJMgAkwASbgSIAHiByZcAgTYALDmwBbzwSYABNgAkyACTABJsAEmAATYAIeEuABIg+BsbgvEGAbmAATYAJMgAkwASbABJgAE2ACTIAJMIH+JOCbA0T9mUPWxQSYABNgAkyACTABJsAEmAATYAJMgAn4JgG2ymcI8ACRzxQFG8IEmAATYAJMgAkwASbABJgAExh5BDhHTIAJDA8CPEA0PMqJrWQCTIAJMAEmwASYABNgAr5KgO1iAkyACTCBEUCAB4hGQCFyFpgAE2ACTIAJMAEmMLAEWDsTYAJMgAkwASYw0gnwANFIL2HOHxNgAkyACTABPQRYhgkwASbABJgAE2ACTOCBJsADRA908XPmmQATeJAIcF6ZABNgAkyACTABJsAEmAATYALOCPAAkTMyHM4Ehh8BtpgJMAEmwASYABNgAkyACTABJsAEmIBXBHiAyCtsQxWJ02UCTIAJMAEmwASYABNgAkyACTABJsAERj6Bwc8hDxANPnNOkQkwASbABJgAE2ACTIAJMAEmwAQedAKcfybgYwR4gMjHCoTNYQJMgAkwASbABJgAE2ACTGBkEOBcMAEmwASGEwEeIBpOpcW2MgEmwASYABNgAkyACfgSAbaFCTABJsAEmMCIIcADRCOmKDkjTIAJMAEmwASYQP8TYI1MgAkwASbABJgAE3gwCPAA0YNRzpxLJsAEmAATcEaAw5kAE2ACTIAJMAEmwASYABMADxBxJWACTGDEE+AMMgEmwASYABNgAkyACTABJsAEmIBrAjxA5JoPnx0eBNhKJsAEmAATYAJMgAkwASbABJgAE2ACTKAPBIbJAFEfcshRmQATYAJMgAkwASbABJgAE2ACTIAJMIFhQoDNHCoCPEA0VOQ5XSbABJgAE2ACTIAJMAEmwASYwINIgPPMBJiATxLgASKfLBY2igkwASbABJgAE2ACTIAJDF8CbDkTYAJMgAkMPwI8QDT8yowtZgJMgAkwASbABJjAUBPg9JkAE2ACTIAJMIERRoAHiEZYgXJ2mAATYAJMgAn0DwHWwgSYABNgAkyACTABJvAgEeABogeptDmvTIAJMAFrArzPBJgAE2ACTIAJMAEmwASYABNQCPAAkQKCN0xgJBLgPDEBJsAEmAATYAJMgAkwASbABJgAE9BDgAeI9FByIvPH/wLu/vFr3P7ya/y+82t8+sXXaPoPedvcIYd9/p9f4w8k50RFX4M5PhNgAkyACTABJjCMCPzXV0AH+Q7CP7hxV/YbrrZ/hUZar935Cp+QH9FCPkQb+RZf3h9GGWNTmQATYAJMgAkwgYEmMOD6eYDIA8S9ilMnBoOutH2Fj2gVg0LiWAwSicGie91fS4NGd7rkgaPWe1+jkeSE7E3a76TzHiTJokyACTABJsAEmMAwJ/DV10A7+QUf3/kaDbdoEIhuKAn/4D/+8DWE39DVA/yBVjEg1EF+gpD9d7rxJAaMLn3+FT6jgSQxqDTMMbD5TIAJMAEm4JYACzCBoSXAA0Q6+PeSY3frP7/GR3R37xNy6sRgULeHTwWJp42Ejut0Z1A4esIZ1JE0izABJsAEmAATYALDlIAYGBIDQWJQSDwV9J/3yaHwMC/CB/mCBpKE/yFuNonBIw9VsDgTYAK+RIBtYQJMgAn4MAEeIHJTOOIR72s0MCSe/unpdSOs87Rw9IRO8eRRf+nUmTSLMQEmwASYABNgAoNA4A4N6ly+/RXEq2RioKg/khQ3m8RAk3glTTx51B86WUf/E2CNTIAJMAEmwASGKwEeIHJScmKOAPG0j3jEWzhkTsS8Dhb3EMWTSB/f+Uqai8BrRRyRCTABJsAEmAATGEwCLtMS/buYW6j57tcQvoRLYS9PiqeQxZyH4ukkL1VwNCbABJgAE2ACTIAJOBDgASIHJIAYEBKTRQ7GfEHdvYB4bJwfGdcoCA5iAkyACQwJAU6UCXhH4D716R+3fwUxt5B3GjyLJZ5OEj5Efz2h5FnqLM0EmAATYAJMgAmMNAI8QGRXov95H9Lk04P96pd4ZFw4enbm8CETYAIDQYB1MgEmwAT6mYC4udR05yt82dPPit2oE5NXN/3HVwP2tJKb5Pk0E2ACTIAJMAEmMIII8ACRVWFKd/7IubMKGtRd8ai4cPQGNdERmhhniwkwASbABJjAYBEQN5U+oUEa8VTwYKVpnY7497NPv/jKOoj3mQATYAJMgAkwASbgMYHhOkDkcUbdReglv+raEA4OqfaJR8XFU0zqMW+ZABNgAkyACTAB3ybw2d2vMFSDQyoZ4Tt8dlfMgKSG8JYJMAEmwASYwIghwBkZJAI8QESghTt1o+NriDuAdDjki7gL+IdBfkR9yDPNBjABJsAEmAATGIYExJ9ZiMEZXzBd/Euq+AMMX7CFbWACTIAJeEaApZkAE/AFAjxARKXw+b2vfeqfxMS/ntwkm8g075Y79diZXYzNOtaDV71LYsTFcsMs/9AZ1HxmP2pXi9TpazFJWregpEWm0nbuHTP7nec65EBoyyonh/HmOg6a69lxXB7GOWHTmQATYAKeEhB/ZtH2pbjN5GnMgZP/fefX0p9teJLC5Xdd+Qxl2H+iHpfv2PeB7lIYnH6vpXSL0g9Tf5xZ684o5fzg2KYkxhuVAG+ZABNgAkzA5wk88ANE3f8FtHX5lnMnao1wOsWdQLHv8dp1A4ffrcdBHeu5zz3WPjIjuGG28/VSrEzYgKiXj6Op1zWCPzZZ2B9u6nItPOzPtuOcuZ59hNvDPj+cASbABJiAfgJi7kD90oMn6elNptv1ln7roLlNV8OqkbW5GEuecd8HDl4O9abUg86mWpRUNqJbbxSWYwJMgAkwASbwABN44AeIxOCQmH/IF+tAe78MXPkjYKIJQU7WRw2+mPOhtsmE2S9GYoW0RmB2mNFsUNsH72DN7uvmY95hAkyACTxABDirVgTuUB/d5elDNVbxB3JX/OHFvW4vb36NNdr6DOP8oX5EH7hgczU61QBf3l59B0vmb0DEshLkXOgAuzu+XFhsGxNgAkyACfgKgdG+YshQ2CEcu3YfezTcmoOY00A4oNZhnu+Ho+hoAeqcrJmzPNc48mOEYl1WCvKkNQ0HDhWhITfcnO2W6g/RJB1Nwfq8JOyW1uWIGS8FOvnyRNaJCg5mAgNOgBNgAkxAL4H+uYmjNzXP5dq8fIA17G+22voM1bvQsD8G0/wUG04dwZ6ryr7LzeD0e48+vVzph5Ow+6+mWCz6vBWX71kObfcGxzbbNPmICTABJsAEmIDvE3igB4jEa1xe3l8btJLt+OMgJdXVgbY78tqpOJXddxpRfaIalUfOoOpCq5PHs3vQdqUeVYpc9ZV2Sa77nqxL6Oy8b58HNc4Z0l1NcetdzG2gysp26NMPdN8StuvRb2+b9nHAtCkIU0/RyGK3tG9CSORTmCGt4QixPGgknbX90pbtvmvhJP8DjpLfI5TfE2dQ09Jjq0Y9ut+Oy+eoXCQ5lV8POpUybLvTJZWDJG5dtkrZSuHiy9W53h40XaA0lLKtOtEAz96YI3uaGqh81XKg/JACmZ1InFcmwASYwPAkIG7gUFfg08aLp4jkfqXvZgZErELqQlVPBz68LObXozbevs+5e132Gy60k7B2v0cn5OV+l20fc64RmoNaDv1UD1qob6o8Uiv1SYZHpyj98FOYPTkQoL5L6gvvWfWflJbwR8Qq+yRatvVY9aGWvlnEMa/WOkUuSK/UT0p9MfWXF5qH/N/shFm8MgEmwASYABPwlsADPUAknCdvwQ1WvM77X6PXg1Esb+1qOZSLqGfSpTXhUCNq3kjH1GfysWZzGVKzSrF+XRamvlRhO//OvQbkLEtF1KpirFfk1qzagqjMWhym+Kq+zH+1suqz41gTt0GJU0q6yyiuk7kNvNF/t55s2oCpccL2Utf6rcxyt9vyAQ2OqELTQpXBonpkKsyinilBjXpec6sl24r9P5SZRz2Ti/1XGpAl5jkSPLPKkLq5FCufT0XCL5ttNHZeKMWC+Vuw5EelWC/JCX7pSP3grKY9tmXbaqPL2TkpjTkbsGAdpaGU7frNhVggwgoa3L9eoJRdxLJCKl+1HCg/VF+mLitFjdO7ujbm8QETYAJMwCcJDAf/QYAbWDtt+7XDHxSTD5Er+w2V4lVs2/OWPpJuPhzKR9T8VNs+5kcUJvUx9ei0muvPpp8qP4OSl1Ixl/qm1KwKVN8BbM4foj7u5hEkiL45q1EgkNcTZWSb3N/KPkm9Rn9pHSbLqn6MeftGvawPIg/kN81W8iD1xdRfrtuOqXO2IOeC/d0YJRpvmAATYAJMgAn0M4H+VvfADhDdJ+fD1+/+icL+mgaHvJ5HQChAM/aY/2Wq2PzvWvI/nGn/61TLL4qxslzcHZQUWL4uHsWmd8VdQRHUipIfFqKkyeoO3TgTgsYCnUdLsLlKyNitXfXYvPYdVN+Sww0TgjF7ViAClIkBxNwGlvl9vNB/rxapf1lssWlcIOkPlmwSKQr9CzYcR5s4cLk2InUpOYfqSgNacwuUQZoJkdiXNW8A5jLoQNH/KsT+z+wN60F9wT7sV5ihpQKJG86gyfwYjj8CJhph8OtCZXoZKu2je3NskwbpDwvF7AiRhlBGTvEvC7HphGvnt2anWg7+CIuV53OKn2USCoCmM1iZrqccZHH+ZgJMgAn4GgHxBLKv2aRlT6e5r9A6qz+ss6EMRSdUeRNmTFPaczUIjcjKqkdbrznA6U5T6XYseL0Rbaptfv4wKH4AIPqYYsz5+1p0amho2ffPyLlo5XdoyAxokMFfUi/n4bqcX7I/KIL6SXW+wu52lKzbgqyLkih/MQEmwAT6SoDjM4FBJTB6UFPzocQG9q5a/2a0bw5eB2oc/pGkXvmHs480/3Wqm+7Ixb2WjWsX9+JGXQF2L7W8O1X/b+KuIOXvwhG8eYW2YvELxsb9RbhRXYC607twcmMwoOEkdl84j1MGkzT5ZeTGrbhWlY0Du3agoWQegoQeWltOX0ILbeGF/pqd+1BJtovoYSmkv3oH6c9GXfU2ZD4pQmm9UIGiC7R1ufSg82YH2tT1luKMjg9FRvYqxNAgmMvoXp2kNBGOvN/swg3BvToJ8eNVRc2oU26E1vziOC6rbJ+MQQXxbjhahGs1W22/1zsAABAASURBVJHxhCrft23TBw24PUEup/i8AjQc2oYDVL51WZa5HapIxnkqrbh8SWEWFoPdBSnSfE5Fuwpw7NVIefLvEGjWPec6+QwTYAJMwDcI9HwF/PG/fMMWd1b85/2v3Yk4nG/6p1xEqTdIxDZmAyJWV5v7noCly7HOob/pQff9QMS/vEqeD8h6LiDrFG4dR9bOViXEiNmvUl9dtwvXzu9Fw0+jEeInn+o8ug9vavTV3fd6EDRzHrJdzf0XkoCToh99K0JWJr6XJst9K4UXPS0CtNZoFNF5qQ+WtkXYZ+X/YHwE9r0cCdyim2VvKXkYG4Hd7+9C3f5t8nyFlGaApLoL+3ceRZu0z19MwB0BPs8EmAAT8B0CD+wA0XBx7kRV6erx3MET8eTVHwFO/sEsaOI47SdhYhNQFB8sn/MzIW6hZWBA1gnUn6o3z3FjeHE5MiLUQSR/hL20BhsnqpKWreHpFPPElxUvTYE0T9HNRlRfvmsRUvY819+A96qUQQmEIiku0DKXwF0TZi8IVjT34L3zyiCXEuK4IXnpH8wi5cGMF8MRJu5u3mlGPt0VTChXHEPHiH0KiclIwYoQf1nHuHlImCXvWr6t8+iP1eSIR45Vzo6Zgo1/F2MeaFNCvdqEfX+buZyKFhrRLeZJampAzacqX3dqjQiQPWSg6TgWxGRhTUEFKs81IiAuWRosystahGnu1PB5JsAEmIAPEvhDn/rkwc2QeAr5D3qbbtW0e12WGyTiRsldi4KguFU49Q/RUJt4NQpAfdLOHShKikHcwnmIezLQcspqr+3cedSoNzmmP4uiZVNkX4NkAuYko+j7JtoTSw/2V6mvc4ljZZ2egPf2JGG1SGNhJNSHdpSz/bppKs3FmqPq07JGrH4zTbpB1HbuQ6iWBSydhxm9HeY5HLu/G4kYo2LGxY9Qo0ZXgnjDBJgAE2ACTMDXCYz2dQMHyj5xB3CgdPe33h7VmfJKcTic/4tZEmZr6AybNsXssGmcloI66C6etENfIf/N3hGcghnT6YTWcrcB+zOzEDFrLabOF69x5WPNGw2wv8vmsf47rWgyz2vTjKznSbeYg0BZl5jvWAJtt9q1LLMKC4XlX8zE0y8ZOHliFWIkiR7U55ehst+dvkDMCFe9Sikhja8udJjzaELoY7D9PBmmWZ62QvqOulvOIGdDOqZGUTnFEMtlhVhf2qwvMkxY8epSRIpBNRHjbiuqf3kUqT/Kx9z5GzB1VSnPQSS48MoEmMCwJNCt8fSQL2ek28OJDA1Phis3RyKVbQyy81JwTDwpk0s3Ivyg8QnHnEiNYLuglo8tN1jC5kYgyO585PRQS0jjDfmJYksItOJYne633c4PCpGgPiVEWqe9nIHs6bRDi3UeOsvFvEvURyq+RtQzpVb+QTtuK081UzRemAATYAJMgAkMCwIP7gCRhw7TUJbmf30FfNWXh4g8Nd5PeYpFb7xey91FOUoHbt+W92y+W44iIa4QWUdb0dntj6BZEViRnIDdrzg6iTbx9Ogf95CVo+nv4qkpE4Ie8bdRr+tgbDjmPKFI9jbi3ADMLfBNP0W/zs0fe+0Exb/J2AW5O+zstC87oPtcMeYklKLkfAe64Y9psZFIfnkVdidZOe5w85mcgIpT27Dv5UjMtrvF233lDFb+sMLB8XejkU8zgaEkwGkzATOB4XSDSRjt6U2mkEWrlCc9U5TtKqxeGIlp4133nQYdfVhAkEmYJK0tbR3S1vqr2/pxp8fG4VHrk2LfU/9ExPF0/awCiekN5jmQApYmo9yq/7POA8Yapdfmg5w8qa2HCfjDBJgAE2ACTMCHCIz2IVsG1RQxSfWgJtjHxHzN3u9Ghppz1HS4GvX3zYfAZ7/FHo25A+r/+Qjqu2W5mNcKULcrDXkpSxE3KxgWl1E+77F+v4kIDZHjAsHY9DPSf9TJmq7jNqeqSt32tqP5pnowVFsapJqppt2Og4cboeCUApsqT8HyLzFSkOuv3ut474S9g96Bw6XqRKMmJP98F44VpCBTvDbwlH0pOVF/vwvSXwL/wYQZK1OkeRluXNyFhqNp2PikEudKLaqkyaaU4yHfsAFMgAkwAX0ExE0bfZK+IeVL98NCQixPHHcftXrdTELVhaoTjdKe+AqZEur2aWYhp2vttXZSXMQQf3ax9qh5viU8uRQVdq/UWefBEJdsfiW7zsHn2IrVGq/bu0idTzEBJsAEmAATGHICD+wA0XBz8Ly3twPnTpwhp8vJeqXdq0oYtHAeYvyUqC1nkPD8dqQWV6CkIB8LEo+jSTll2fSg5U6P+bDldqs0uNF9pwH526sd5D3XPwVJa0IV/c3I+nEZatT0uq5j56q1mDRdrOnIv6KIOd30WOYvEk/l3GzEwdf3Yb/6epdfOOZMdxp5AE+YsPjFcLP+ltJ8zFlXiPzSCuSkb8GCnU7K0uqOa9MvyrD/SivammqRn1aInQ6DNO1o+VxNogu3P++QDro/q8b6vAZp3+3XpxVIUB+3//sz6JSedPJHwMQIxM7UOcjkNhEWYAJMgAkMDQExr8/QpOxdqr5kryH2Wawer+SDBmNWJpeiuqkDbaKfzc5C6inlnF8oNn5vinLg5WZSMMLUqCeOI/8C9X13aFX7cvWcuu1tRskPS8x/dgHjFGT/71iYxDx8whcQ670eWOeh+1AJ1lc2o1vq53rQdqIQEZKvQf5GerX5KSQ1Cd4yASbABJgAE/B1AqN93UC99rGcMwLk8GwuxXpn6z9fdxbRdfjYGBQVRCBAlbrVjMqSo8j5ZSOavhWJFbHqCXXrj2mTLYMDTcW5mEpO1NRnaJCiy2Rx4ppa5cEij/UDQfFrkDfHKCd4pRorn9kgDwrNyTUPCgUtW2V5ikWW1PhuRKo6wCG2S/Ox+V15oEQIT9u4HPFKMuJ4MNeAuBRY/6tK24UG7HzrKEpOtSPgxUhlniRbi0JoMG+2Oph3pxFZq7IQtawEO8+bEB9nuZsrxwrG4+HyHtCDys3pEsOpCWWoHheIIPXUpzedvyL2RAKylX9+6a4qRUT0BvkfcWI2IKFU4fhkNOJCVGW8ZQJMgAkwgQeCgF8EsvcuxTS1T7p4BmuWpVMfkW/VzxoRl5uCFRP6SGTyDCxW+5neVuxcR33fM1nIVmeYtld/80OUW99AoptLWQlkm/AD1PUNikx52JQbrfzjWheqXtsuzdc3aTr1dZsb5EEhQzCyX46x+Ej2afExE2ACTIAJ9JkAKxgYAjxANDBcHwitAU+noe43SdgYG6y8gx+I2d9PwtljKVj8kCOCsPVbUbTUMkgkSYTNw763V2H2WOkI6G03T+roqX7QHccVhdmoeDUC01R9iloYAhH/2lacezXSy0fW/REQFo6Nu3bgWFKoqnUItkbE5BTgbF4MYiJMMvewUCTnZaMua4a2MzphEXbvmif/E5tq8dhQbPz5VmwKVwPUrRHxWRnYONNfDZC2QU8n4NhPYvG4dERfLe1oo432Qjb+A5XDy+EIMpBEL91VNf8Tjj+CYpei4u0EqH47SfDCBJgAE2ACDwqBydSfHE0h38HkkGPDkzSAtH8Hdi+0v3nhIKojYAoySlYhXv13UB0x9IoEzEzGyYokJM9yzEPQzBjsq8rGau7k9OJkOSbgjACHMwEmMAQERg9BmpzkQBMIScDJi3txQ8+aEy1ZE5K0wyx/MilYCjN/0UCQWZcir54zhMxDRgENTkjv3u/AgfR5CBlzHR+aJ3EOxOOTFGm/QMTT4MaNcwWoe5/W07tw41ASYsaRQ3hatXcbVoxX5GnjkX6Sh58JkcvScIz0XTtNaYh0qimd8ztQFO/i39ncMtuFhkMZyJhl7bRGo8jMOM389I42Sy3ZYCSXq/negWQ7ZzImRz23F0VPi8ypqz9CFq7Cvv2UP8H90DZkLgyG4UqTZQ6isGCbAZiAmUk4eZ7yoPI4vQ0ZEUZo2jo2HBl7dkHl13BuL+reWoqwiYtwQM3v6WTIMzlp5YvsFOWQlIG683vNeureL8K1i7tQV5CASPsBPIrCCxNgAkyACQwNAev+xsEHcGqSk/bfLO/i/IRI8h0KcKNO6Zeob2qo3YtrZWlYTX2TWQXtaPZTFK4uLs9PiEHRb3ZZ+iHyB+T+VMM2t34A9clWPpDwTzJ3Oeahbs8q8mtU63jrmgCfZQJMgAkwAV8jwANEvlYiw8WelgosmL5Wev1o0jO5yD/RiJY7HWi5cgb5Lxdi500lI2OnYM5kZV/dGE0IGk/rWH81xHHbF/2KNsNYk5zOOH8lZPhvWkq3yMyJfcSPylAl5hO604rLJ8qw5sfV5qd6DFHfsby2Z862PwIEd508VH4BRrMCr3ZUPUHjjV4+veVVshyJCTABJsAEhpqAu/T9lH6J+qaAMe6EvT9v7od09n8epTRIefDIJhZmAkyACTABJuAlgdFexuNogsD9Ltz6uB7/euasvF5qxpd/ECcegDXkWWS+qIwc3LmOnZvzMfeZdMxdVYqdH3QpAIyIz16lPGmiBOndDLR+vXb4mFzIsuXmJ6w6z1Vj/Sp5ToUlm6tRfUsxdnwEdqdEKAe8YQJMgAkMHAHW7DmB+1924osvXK/3ez3XyzGYABNgAkyACTABJtBXAjxA5BXBLlz91Q789fdexvq0t/FG7n553ZKDVcv/Fq/8ohFfeqV3OEUyIubVbOxLDkWQOtmktfkTpiDzZ9koeloZRLI+p2t/oPXrMsL3hIyRyPtVCjbO1OYa9PRSVPwqDTH8Cpfvld3wtIitZgJMoJ8JNOzKwF+vcrXuwHvqgH8/p83qmAATYAJMgAkwASbgigAPELmi4+Tcv//qDbzys0/whdYdvt4eXD3wE2T9qtlJ7BEU7GdCTMo21NXuQsPRDOzLS8LuPXRcvQs3qrYiebqpb5kdaP19s27oYo+LRMaeIoi5nM6WpWA3cT9wqADyXEEJiBzniWksywSYABNgAoNHoAtf/nHwUuOUmAATYAJMgAkwASbgCQEeIPKElpD95ACyf9Yq9qT1z5ZvxM8P78HhX2fiR/MsT3V8UvornPaF180kKwf4S7x/PzEcMQvnIW5mKIL6+x3/gdY/wHgGTL3RhJAnIxFH3GeHmdDXuYIGzE5WzASYABNgAgqBDnzxubJLm/mb8/HzMvt1CxZPoJO8MAEmwASYABNgAkzAUwJ9lOcBIg8B3qpvgPnJ72kvIGtNBB4REys+FIpnvj8Hf6rq621Fy7+rB7xlAkyACTABJsAEmMAf8J/md9ADMWVqAB55xHEdo/XqNsNjAkyACTABJgCAITCBgSTAA0Qe0n1k8RbL3b7XFuIRp/E7cOs/nJ7kE0yACTABJsAEmMADR6Adt8x3mfwBMRD0h0588UUX7j9wLDjDTIAJOCHAwUyACTCBISPAA0Qeoh/zsNWdvofJubOK/8Wlq7A8NBSMKZOtTvIuE2ACTIAJMAEm8GAT+KIDd6F+OnD47/4WL3xPTFh/Yg1sAAAQAElEQVT9MpYv+Vv8dc5hXDU/YaTK8XbkEeAcMQEmwASYABPwTQI8QNRf5fJlNX76drNF28RwzOA5BCw8eI8JMAEmwASYwINCwFk+//AlLA8Xd+GLmz1Wkj34ouYYXlm3Ex9+aRXMu0yACTABJsAEmAATGCQCPEDUH6Dvf4JDrx7Ah1b/ajbj+/GW+Yj6Iw3WwQSYABNgAj5DgA1hAl4RuPm55UljPyP+x9/+GLvL8vHTrZGYIF43E0q/aMAb+xrEHq9MgAkwASbABJgAExhUAjxA1FfcYnBo0w6UfWxR9HDsWvz4f1r+0cxyhveYABMYJgTYTCbABJhA/xN4arVlHsPyN/HK0nBMeCQAfzrvh3jleyaon/tV5/ChesBbJsAEmAATYAJMgAkMEgEeIOoL6M567NloNzgUtQxv/K//jof7otfLuC2HchG1tBQ1OuLXFKQj6kfH0aJDtr9FpLQ17Gw7VYwlS8kuWnPO93eq9vr6fizlwynDVuz/EeXF6fm+p69Lw83jWDNMeOrKz4gUUupKQf3A5e58KbUNudh/sw9JKHVpzaFWt0pcXxtuo7MAExi5BMYYLf9aZjeP4Z99J9SS795W/Lt5MmtL8MDudaGpshQrl1HfNX8DIqjvWJJZgeoW69fgBtaCB1a7B+3roDFSbBoYf0zp94baRxo0mM4TGmrfV+qvrXxy6VhXuTiWoUNcyfdIx8DUIedMB/TMgF4XA2o5K2cCugmM1i053AQH2N77N45h24a3ccxqhOXheSvx079fiD9VHxMfYBsc1Hd1oO3mXXQ7nHAM6P6CZNsHeJKD++24XFmLy3bJS2nb23mxBLHpDTB9bw0q9mdg/TS7SD54KOXDBcPu9kFg7I5L75doudmB23oqhTtdI/p8D9quVKP66tBkUqorXwzgj7Duu9Q2dKDb6jVYj3Oq1KWWLvcx3V0b7jWwBBMYgQT+0IxLZ87iX6W1Hv/+B1/KYxeqM7dgQe5HCHruOeRk/RXe3DAXYVfpJkPCdpR85ku2DmdbnPQ1HrSvg5Z7xaaB8h+kfs+FDzVo+exzQk7KVI9eH/B9pf7ayieXjnWWi30ZOsRVfA+bOuTkt4EeXD4hM8DXhU/kcTgbwbb3CwEeIPIC45d1e7HxR4dx6QtL5D9b/mPs2RqDR4ZqcMhiiu/s/WsZlrx2HrftLIrJ2YsbF9MQYxXecuk6OifOReZLEQgZH4ygsVYnh+VuMJLLKZ/lCQgZlvY/aEbXI3tVGSo+f9Dy7UF+QxJw8uJenEwK9iASizIBJmAm8FAHzhftxxu5Yn0bP6+1HW29VNdoFsXDIQgfzD+6uFqBrKP+SC7ZgaKkGMQtnIe45xJQdCgbeZGtyNl+FG0W63jPawJO+poHrn0dST6SkzLVUUd8wffV8sl1mE4iOsrw6TTy9/ei6GkSVxcnvw3U0+62fJ4JMIGBJzB64JMYWSl8cWoHVm37v7hlfSd+Qij+rPP/4OdFb+On1mvVJ76T+ftdaLvTpevpIognke50oNPWd9XMS/fdDv16NTVYBY41IsDqsK+73feEbZSP++409aCT8tt2180THJ4wdJckne8vdrKePj4dQvZoLwobvXVHVeJBHVKjuNqqZenuCRjdLHrlfLnTZ7ZJKnuqT/fc1BFzhL7syLbpq49e2qSUj9v86+Uk8fGkfdEnq7s8+4Kb4zKBQSMQgSXPm8ypfVjwKrJ/cRaf3GjEv/5iB944amlfHl4QjSfMkoOw83k7WhCKqCft0wrEisQI4OKHOHXH/pzOtkppb/T4FHDSlnjVFjjRZZ8Lj44lnW7aXaXddNuG605Y4ayzH5ZZuWhjvbVPyXunW59Kd8a0BM1hUj40/DJn4eaIyo4uv0EnC126lHQ93rjwfaW8kn/qsq/Wkwc9Mk4Ml2zQKAcn4kMf7Ele1Trt5veOYKCr/Rr63LMFTKBfCPAAkYcY7/9Hp2OMW814v6recf1du6PsYIfcrUfOqg2YFJ2KqGdSMTUqFWtKGx2e6pHMunUGm4XsnHSSTUfEnLWIerkC9feks9JXdeZaTMqsRWdDBdbErcXUGCFLemdtQc4FpYVtqcCC6ST3svgXlgasEftiTawgBxSQdCj7+KAQk+jc3LeI1dWjmEv7k6ZvQUkLUPM62T2/BDXWg3GSFdeR//xazN15XTqy/+q8UIol88m2+cI2ykf0BiwoaICl5FpRkrgWC0qbId79XjBrAyKeIdkYSi8uFwftH6X3hKHZGDkNwUoNkvLthl3boe2YFJWPSgWlGhfoQuVmYppebc5H5zkln1IZpGMq5XPJ62fQ5sDLokVi+nwZmixByp490y7Ul+YjSmUj6g6V8eZTVE5KDM2Nuzr0WQWWRK3FEmJvHb+zKp/ynWV5jUGqF4WoEuyXbcBUpSxFHhOKG80MVB2iHKUyN7OQ67mlzJV6l0l8qgoRRawinhFz8ijlNL0ElaSs8mVibFUH0duOysx05fqhOjKf6siyUtRYXRMUTV56a5FKeVtTZVt49W9QHHvmd45j5fR05F+Ro8rf9/XVx/vXsT/dzqbn81HSYJuurNPu+26D3B4o17gWT7me2nMiPcp1nfoB7auLKB/RZuhoX7qbjmL988RCSltuM0R9qlLaFFWl2OopTyHHKxMYbgT+9K824q8fV6zu7cKHB/bjxxt+gjcOfALzS98hc7EtiQZlFLFB2TwWiBA0oKj0uuONpFjxFMA2rBhvsURcozZ95/wsiw+giqntg3TNU19MPsXUVcWosplbSW6DF5BfUl+cpbS11O8rOkQ67tp2RdSyUdO1a5fqS7dgkup7CGmln6kW+9arVlt3rxEl9u3uMvu8dKG+eLvUFzv6FHI+Jznra7TSRA+aDuXb9cPpWF/aaNMHym22G79Myl+XC/skAe2v3mYctMp7BPWfUZnVZl9D8is89tUUHuQTqYlq5kP4ZUqfa+N3ivC4QlRa1yWlPHX5DWqeotz4f8KnsfNBlpj9SSUPzspUzZjWVrJ1LbR8XyGuq97ryoP7Mpe4W18XwgBa21RfWvhVgvf87ci38TOU/FuVIUWzXaR8bpF8eih1fJLWb4NbR5FAvlfCIUcfs/tEPvmH5Bdr+V3WqenhIdlA9nzWjqrXqT2Q2gi5bYp6+TiabPxn9fpbK/3WEb+JJolr/veWgXzr5HmfCYwkAjxANJJK0z4vvQ3I+stilHRFYPehAlyr24WG8lUIowZ7c5Wd8L0zWB9fivceWYQD7++SHgm9VpWGxDZqtH9YYdtoNh1B4qZGzMneQTr34sa5bcib34WSDcXywMbE51DxfgHqssMpkXAUiX2xvv0cOaAUZL38j2TU0bmKlwKBsBg53vtbsXoiMPt7cxFyrxalp+x++F44hT0toVi3fIq1Jnn/1nGs33AGWJaGulqy7eIu1O2ZBfyyEJtO2OrpOF6CxTuBTb8swrWLe3Ht/TRsHH8dm1OtBlA8YShb4PrbDbugpTGIQyPKqzps9dyqxr4T/lidGAPxlFUnleGcH30I099Y5XPXIphOlSL272ttnEdrRRLTlrMovWgdSvt2TC+Tk5uwsxWx2dvkMq4twsmtgXgvfTtSP7DlSLHlRU8dmpyA0ldDcXlniWUwiDrsxMxGzH41DcmTZVXydzNyVu9Dy/e3okGUZe0OHHtlCppK8pH4y1ZZhL47yXmISm+0sKB6XrcrGrd3kpzdQBRuVmNdfhcSC7dRvRP1LBir36a6+v4qxJGuuGyxL1ZxDmirLEbq6WDk/WYXblAduVFNdWTMGaxMP442krdZ/CKw4GkaiDrbYBXcgArxVEBLPQ5ftQR3X7iEmrHhiH3SEobzh3TUx2aU/HUusj6Zgt0Vik10/e2e2YocCi/5zEqfw24Hiv5XCVqWu+YpRXPgJIXafnlybdB1uYYc0JpQal+qhN3UFv1yKUBllHPJVq1H5WkblY98gQDb4JqAXyjif5KPN14KxyNj7ETHGPFnS1dj987VeOIhu3MDffhEAoq+b6K2ORdTl+Ujv7IRbU6aetH/xFKbG7Z1m9I2F+HYRiMOrstC/hXFUOq7U8n/OPgIXfOqT0F97KZHGrF+VSGq7yly6uaDd5B0gtrasmxqm5Mxm8K9agtctEupv/byh13vdeS/lI8cq3b32ultyPu2XV4uliGxpAurSwrk/qI2G3lPtWLz2hLU97ruayi7DktT6XYseKPV0g9T33YyewqVkUbf5sa3kJS7tE+S0Pyqfj0fpZPXoO4c+VTChp9G4tGqMsS+Xi8NJkp+BZW3R76aZkoUaJePaxXUN39Bfe4Ps5D4t/X4bvYOySe59n4S4v+rAanZ9n1xsy6/oT4/H5v/jeqbVT+aN63Z1v+jPKWuIr/428tx8rTSb+1/Dqb3CrFEukHpeZlSDuXFhe+rt97ryoOXZS58paSsNiT9TPWPM5A9qx07hZ9BN3DlTHj47eq3wYQYrFkI1P/it3Y3MNtx8EAjDC8+i/ixrtPTxUNS0YOKv9uOnN6l5nKt2xODR8++gzW7LTee297NxYLXm/H4hjScFeV/bgeOLe9BphhIkvTwFxMYuQR4gMjDsp3wl6/j8LE9+taM/+6h9v4Vb6uswP47och+OwVxYSYY/PwREBaNzJ+uQbzRNq2anf+Mqm9Fo7QwAbPH+0snDRMikPGTBEReOYqis1KQ/HW1C7E/2YrkmYGkk4KMoViRMhdh5ETVXaVjkc54E4LG+tMBpSn2xTpOHFOQ9UIOcZA4J0Y9/IyQ9unY4AeAHNZNsUDVgWqrH+NdqDxUi+6FMVgxgWTslu6bPQiJX443N0YgaIw46Y+gmYuQ+ARQXW9p+MWZtqsmZO0XbIwwUIBhfAQy/iYCaLmOD+9QAC2eMCRx94s7dsZ5SHrRHzVHzlrlmQYqTn+I+pC5SJopkmhEUXYDkJiMA3SX2ZLPBBzIjUb30TIU0Wkh6bA+8SzWTe/BwfdsBWpOkKOnMqU7OZkl7TRgsxV5C0PlMqZyCovPwO5lPajc9Vto+Qd661DQixnYF9eBHBqIu3y/GSVbjqIlLhm7X6RBQhuDO4CFadgdH4oAUZZjAjFtWQbKX6IfMXuOyE+W0Y+BN7MbEWDNwk+U+SqUvhJKjnQFqnutlF7swJyirciYEyrVNVHPDONMtK/UgbFiX17FuZZP24EJoZgR4g/pM47qyE8EB6t/G5JOiC8j4hbSoOjpS6gXh2K9eB4HEYEVCztw+KSl/tWcJYdn6SxEChl1vRvqvj4eKUPO1VDkiWt6smITXX9xWVuRTT9EcvLtnWVVudj2wBTvhqcQE6sGJxEsVnX15Nqo31eBGmM0dov2ZYKw25/aonmUj3kIsq5MnpanagxvmcBwIuAXgCf+8sf4+eE9eKc8Hz8vE+tbeOfwW/jJ387FhDFDkRkjItML0PCzBMSPuY6dr+Ujas4GRKwuxsGr1BabTWrAm1kNML1k3ZYYqW1OQw61czv/EMXPMwAAEABJREFUuVaSrN9ThkrY+RTUxyYXJmP1/Qak7mmU5NSvpktG5FB/vOLJYLk99rItcNUuRd61zoeasvtt27tl2PmpbbtrGBuKFbnJUl5y3m2VlHQ2t6MbwYh6yiQdY0wwVryWgQOvzEBAN2Bw0dfA/nPnOLLoJs3sV636YerbwhamoELq28pwUPFTpKjufAsScmcfiWgunX+egHLhUwm/UdgwJxnlOeHofPcIDooneLzw1TQTEoF2+TBMjkFqPPG80o4ZBdnIUPxOw/h52PSDQODcR/idiGdeO9z7DWjFbUM4Mv4hGSus+tEVz1P/beX/1RfvQ6UxBuW5MQhT/dknl2LfK+FoKT0i+RYGT8oUVp8xRqrnJgTZ+75ogD6fRl8evC1z3PNHEvk6K8IU32h8OFbnpmHjxFa8+Qtb/xF6P1R3Asi/1/5tYET8mhiEiBuYF6wUXv0t9lw0Yd33IqwCtXb18ZBjduDyYwk4ljXPXK5BM1chlQaoWv7fdbRJQg0oeqsZhmWynx0iyt8ofNA0VGygeifJ8BcTGLkERo/crLnO2ahRrs/72llvzP1dfTMwcxYWT7DLDf1QS6CG0BJ6HTXnehCwcAZCyIFqu9MB8/qNUMyYDFz+THaApDg0yPTCk9Ke5YsafqAHHdbv9FjOerlnRHxiNAwXT6FUDDwJLXfOovyEP1YvmweDOLZbDdOXIi9rEaZRuHhnvOVKPaoOHUHFTaD7nt3t0IXRjnck/CgiutCpiOpnKOLpWHWwmy2enLLOM66j9BfNiPzBswgTSXx2CdV3TEj6iwhxZLtSea8Y24XqC1T2tmeUo0CsWBmO7nd/Kz/tJUK7zqD0XahM0X3xIxrgCMXimf6WeqDUicfDpwBXm+3u8AglntQhI2JeXYMVXdVYMj8XObfoR8Q/RCNAqLFZyda/oPRswoBpCyIRdI8G8VroxNVLeO+eES/MDXWwFZMnIaS3FU1U9iQpLxMjsfhJeVfPd2RsBAKajiNxXSn2E1Np7oUJ4dJAUJCGAsPMpzD7XiNOKXfR64/TUNHSWGTTwFFL1XmFWwNOnfbHCtJto0JHffzwPA0yPT0PL9hf0wjE4udCJWf5w14brVYHOniq0jo4/U53+9KKDy/2wEADYrP91ASU7YRILHlS2RcbT8tTxOGVCQxjAmMCAvDII2I1YowP5COA+tCisl3Sk8EHXp2F795twObELUj4pdKnfPYRau6Z8MIC+7bZiPi8vbiRE025aMa5810Iio9FpP017xeBhKX+6Dx/SWkPSVwsT89A3Fixo6xetgX62yUlHbebLtScp7zPnIHZ37DyjUSfeDcY350JNH1M50lPwCyS8WvA+sRClJxQnsCiAfzZCyNBv7NJwoPl34hzbzgS4xx/jAbFzsJsuiF37t+s9OnwLby1L36p/OSyVWoImD8DMWhG3WURSmXvoa8mYmmuGvkICPAHqN7EEmvNODaBevq5YMS9nIKNc0xAr5jfqRk1J6pR8htRjqr/R3W4rgdBC2dhml0dNizMwI26NMTYhduY4e2B7nqvJw+At2UOjXKA3xQsXmiSfMQmb/PnKt6Tz0o3MPcfOgMaT5Uk6w+fRcv0WCQ9IR26+NLHQ1WgVafFTUF0fok/CqGWJmrn/LFicYQ4sllDyFcTvzFsAvmACYwwAg/sANGYYZbzMR53RK1oEi34oyYE2VRa+SAs1NrpaEcz/YjuLC9G1DPpdmuh9CpQ95fKiImIToNBBrEdjHVmLNaFdGDPr+U7Fm2nzqPG/CSNlgE9aKospIGHtdK8NQt+XIY3nc2b4+fOHfeEoZYtGmF62ElP+XSg/DgNBggVNFi052Y41ixVyuwG2QV/mKydaSEnrYEInUgO6yet0pHWlyH2Waw2NqLitFymbVXVqJo4V3k6Cbh9q52iNSPrefu6QMfZ4q4vOVT3ScRm8bAOGSOxbgXlp7sHs1OWO/6IUHR/U6vejzXChHZ8fIOE2trRhi7s/xHZZl93151BC2jQUs4mCdNCcR0Hoijc2TI9Ged+thQz2s4ja912SHMvbChD9V0nEcZH44WZHXjvguDfgIqjwIpFETDMiUbczXpIr5lduYT3usIRG2mnQ0d9bPmU4jxk1BwcFU/fAa1ouUkyTha3PNV4bjm1etC+NKOBBnhDHqPyVvWbt8EI+7b5APC0PK2i8i4TGCwCfqMGK6X+SWe0N/aKgY1lSTjwmyKcTDGhvmCf/MSKy/5HtbcVHzcBpgCjGmCzDXqMfpg3UVtlHWrfrnnVFnjSLlkn7mq/A7d/T+fPV2CufR/zzBZsPkXn7t+Xf9BOWIR9FUlY8dB15GzOR9Qc8kNWFeJgUw8Jeba0tIg+xB+aCMfLft1lSUbRq8e38NY+rX7YaJRu6pht8NhXU+y23zjLRxj1FfayTo519XN365G/IR2TxBxES/OR+tYpnOuyVui6DltL9uu+J/XebR7IMq/L3B8Gim6/SIN19teuvZDXxzS4RzcwQYN10pNpvQ3kQ/UgbmUMtH7HOCSjh4caSatOq+fE9pNmNJGnafNTSYSLdeJESDdrxT6vTGDYEPDM0NGeiY8caX93jYOPZdVze6kzFS3Y7Q7Ij0vaZqipWQwCqGHBeJxkDYlp8nvzYq4Vu/Vsiv3dQjXuQG+nYF2K+sTLddsnaTSSbns3Fwuy2xFbsEN+T72qACd3rUHCRA1ht0HBCCMu0MXQrTIPBOROsu3QKek1qpr36gHx/rXqa08iu9ABmyI0a5cHasIeDzWHOOzQnbikRBOq3xGv7rXjvSPNlqeTSDgkJJi+Q5H3/l4n9SED8WNIxGYJ9qwOfVaB1N1dmP10MGpeL0Gl/XwUiu4/aj0Nc6+Lch+IxyeRkMTCH6t/5szWAmS4vfNEelwsAdMTIM33I+Zh2vMcYj+vxprVZeQ8aEUyYfacQDSda0CbGAhCJBKmk5xxBpbEdkivmbXU051lGh1yeJqGxFwvwQgRgylO6qN46g8IRliIcy1ueTqPaneG0tF9bYQigsqgqVn84LFTg2b8jgaPzKGDUJ7mtHiHCXhJ4BvDzH/4hltPr1X644apbzRoEPFHWFw0/SBqxjnxLo90jXY46X/U6MFSf9DyebsaYLNt+7wDeCKUdNoE2x5I6XjatgcPQJ8dLLe7M5ejzs4vuqEe51meaDaEzEPe/iLcqNuFurIkrHvkOjYnFlqe2LXNpdOjEKkf7sBt69fIVGnx9BLtT5ss+mra8WDxyj6tfrirC52U7jTJTtqBZ76aiDFQq9t+jgYexPycpWOfk+egOV+EuqPZ2Jdo7TcFu6zDA2U79NZ7XXmQrfSuzHugPsUja5G/Ozt7ABqsc+FmyIJefhtiE7AxpJluCl9H96nfYj+ikRSrOr8ulHrAw4UWy6k/E+2Tk3bu5k0n/p8l+pDscaJMoB8JuHUb+jEtn1I1ZhjdAvR8cEhG/d1I6uwunMd74h1xOUj+7qpFxQl5V/4OxeIYE7qPnpcGJOQw5burGTUnanHZ2RMTipirjcu/53QVUTkXsPBZiCde9r36DmyepFHOW29+J157WbjI/J66fK4Hmg6DfNLlt36GLtV4fFJ6yge1KK08jtJ3jbbvX0+egcUhPTj461rYd+DdH5zCwXsmLJ7l2nEM+14sIi+eIv2/xZ5L4TA/nSQsnRWJOL9mlGs8edV25QyqLrQ6pAt4UIfExI9rjwIbM3DgrTTkRTYi1X4idGEH2nHwX65Le9Zf9f9Si7axUzBDeCgqC7s5lYR892f1qPrgOoQTK451rTaOcCN2rkrH+iMdctQxRoTNXIq8f1yEsJYGVLfIwfbfIbHRCLtwCUWHaWBvGXGWBIzSa2ktVaew52IrYmJnQOvunCTq4ismNhy4cAqHHdJuxeF3moGFkdLkrtoqdPDUjqgZqv/aCEbMfBNQecpxIJDq08EmK/X9XZ5WqnmXCfQXgTHDbIDIvb3yNdp99BSq7mlQkgbl/WEKoHOTn0LMeOp/HNrcLkj/tCn9o1Go4lOcQrXNUxkUn/yPfUd7EDL/KYgmnEK0Fy/bAv3tkpqs4yBME/k81s2S3O5q+FLUR10+cQY1LfSjmW5bVG5Nx4Ji8ZQt6fbzR9CT85BRuAzxvY04eYHCrBebvsb6hLKv9MN7jrQqAZZNy5FTqPELxwJdr1yp8TrgkX1qNNpWHq126Efbqs6gmvr9qGkkoCye+GpKlAHY6Ojnbjah5k4g1qXEKHPQKGbYlEko5swyavrF8r9qFcJmfkOhwia+CPBidVHvbXwaXXnwvszRVIvDyqvy5lzQIEx5ZQcM07/jenDXHMH1juZvA78pSPpBKFrK38H6A40ISYyFrptpuni4tsfmbEgYZo/VaueAFmofLtsI8wETGHkERo+8LOnLkb+fPjlfkHLv3GlbGRSfgNXjm5H1w2JUNXXRj/oedN5sQP6P9qF+nMkmUtgPliN+TC1WJpeh5pZwdoDuWw0o2ZyPlftuImCcjbi+g8cCyQFsRHllM0RH0N0l69UX2UpKeeKlnn7sw/pJGisRdTdkIuXrXC0Ofian1X2vGVXZxcj/VJXwbOsJQ880u5FW8lz1+juoeioWtu9fT8G69AgYju5DYnEDpH+a6e1B24UyJKQ3wLB0OdY94Ua/9I8RHdiZXY3b9kyN87BpYzDq39iO9VR20rw797vQdKIYCasP4dR9k+bghr46RHeqf1iCyglLUfR9GsBEIFYUJCG+5SjWlzbbGe2P24cKsbK0AS33etB9rxU1pblIKu/CtHXPKU6DwuIQ2WbDogJr1hajqEV+DN5OscZhIELp10rV0eOgSwW4T+n1hmPOUz2oeqvEXJ/Q24H6f2lAk18gHh2voUYEhTyFxRMbsf8QkGg1T4dBes2sFvs/oB9PM41C0uPVELscmU+2IydZvLYgrmm6TqmOH9yci5ymYGSmWO5kOyoPhOFcoRuejrGchXhybUh1YxwNBC7LRf65ZrTdacXlymIs+DHV14nWKfRXeVrr5H0m0L8EDN/w5p2t/rXBE23f1GFv2JpVWGFswHrzNdpB12kHWq5UY/PfVaNtfCQSnhKphiM1KwKGQyVSW9J2n8Lut1PbXIjMU9QGrRVzEAHSNT+mAWt+ZOtT5CdT+z8mAtk/cPdUsndtQVDcc1gxXtvvqewiW62X8O9QP9KMnH88I/ejXSIfuUg86Q/xgKQqKre7pDOpEAepgxA3ZmTfIhdL3mhAt/THHibMnmlC08/KkH+hnfwtEbsHLUdqaSDFhNDHoHy0+hrplO2X0g837cy17Ycr87HkrXZM20g+m0fdiF77bM0ATAj7fQWWZJ9B013qF7s6ZF/g9WYEvEisJ1jJK36LHl/NKlY/7/q79xvGByLMrx2HD5P/JOqv5D9VYGVeg40tketWSX7x+rQKGlASPqXwsyqw5o1G4p9gNQeRkzI9X4qopelYc8hxkM8mIZsDnfVeVx68LXMy6Al/vKCW71oAABAASURBVJeaixK6Idh5X/7tUJJWgv13g7HpB47z8lAM/Yub3wZBS2MQ13Ud1RdD6eboFLPemoJ04pmL/TfNQZYdXTws4u73IpD6cii61XZOtB2i7lcWIuFID0LcK2AJJjCsCYwe1tb3wfixY/oQeZCjjh3jpTNKnXX2r1KQLDl9qZg6fQMini/B755OQ9H3/G1zMTYaRUK2+yxWxm3ApOlrMTWuGLsNz+LkzxO8awyfSEDe902ozt6OqVGk76UjaLFNVfdR2P+MRBA5Kuvc/JNB2Po05EU2Y3OCkoeYXBQZV6EonpLqFZ4AbT1ZPGHoiV4dsmHiKR+6I6X1/nUAleG5n85Cd3khouasxaSoDYjaUAvDSyk4pznhs32CRsQvi4ah10QdcIT9SYQlbcPJV0LxYa6Yd4f0R6diQXYr4gqzkTfHiVfqtg71kPNcjJxbEdj3dgI5aEqyY+chJzsCt3fmI/UD0Qsr4VTeGwvXYNqRYsydvwFT52dh5a526d/VKr4frApBk8XGswhYvxXWcuYImjvklGXNQ8i5d7BA8IzOkpyQyIxt2B3bbq5Pk6LSkXAISN6V4vBPgBa1U7A4jgYq7Sd6Ns7AkliSipyFWGeDS3Ta5eIXiuSfb0W2+DveZal0TdN1NX87Nn86BXnl25A82VXsYGx62z1PVxpsznlybUh1Iw2Z09qxJ207op7ZjsRfA5tKU5A41kZrP5WnrU4+YgL9ScBI3afnvXJ/WqBf1xi6GeZPq9sYxkjk/SYbu+d3Kdco/Rh7Jh1zV72DU4/FYN+vks1zxYk291RBODr+qRBR0aJ/2IKV/9SDJGqbzG2QdM2nINnGpyhE+cNCVxpi7K57LftEOg79nLu2XeSjLAkrviEGu0QbucHs9+TF2aUyYRGKciJgeL9U7kfnbEHqxXDsefs5WD0YAzhpd1MbnyIuKYhRusSgFzNQsdGI8g1bpLZ5Evlcc3NbMTsrDRlPqGlr9zXqWeutZj+c246Y17aiIkncYLGWdr+vzz57Pf5IzEnDujuHsCCG+uE56ViwtQEBK8nXeDXS4WaRXl/NPpX+OzbBrd9Ag29v7poHCP9J1F/hP2Vfxwtbl0rlbn7iXNRhqkuLvziOlc9sIL9Y+FnH8Ucq53Ib/k7KtPsu2m7SIKu1W6Mjo7rqvc48eFfmZGTYUlT8w0SUb8yS5l6MWFqInOZQZAs/o6+jI+5+Gwhf6WmyYWEMVlgNQHZ/0SHxFDec6aztopOHbSTXR0EvbsXJV0Px8S5q54RfKOr+L/2R89MXEOk6Kp9lAsOewAM7QPQtwyh4NXEjBv8TQLbqSTUkaQduXExDjLXwuEhkSv9IUoC69wtwrbYI+5LCEZlEsuUJsGnnVVmSEbINtbtQV7AUYVaDaTE5e3HDPp5ILyQBJy/uRZFo1MWxtBoxO53SUfXtl9PT0iHZrqVX0gPUHD6LNj3/ZECO3Iq3inDjnJrfXTiWHoH4LLI7lxwCSV8wksvpWPq3FSnA8kUDLzcu7kCyNRiVi1mnC4ZmTY5paOVbEtdkR2cmUAdNTHcvNNKB4xIwJwnHTu/FtdMir0W4VleEipRIBFj/GHCmW6ibmYxrFwusHFcRqK7+CFuWgbrzpL+a9Ffvwo3T2cicY1IFtLcqK7XMbeoQ6XwpGzfepzpq9+NAOEQNF4uo/tjm9ZuBVH8PUdoSe8rj+QLsXjbFwSl1YKEh55S/kpOAmUk4eX4XGug6qaO8SnXALxBxr1IdrlPCVQ5ungCatrEAN46ukpxNRT1tjIjPo3q3ZxENdtKheXGsK+ZTWvVxzBSsLiD9ik0N50jnoTSsCKNfreaIdjuSnjTEifJxw9MpJ626JPTpbl8ikEx2X6sjey8Sz/0piJsA6fXPsD8LtjFYT3naRPCFA7bhgSEwinIqfAja+PzyrTHCWp1mjgmm9i6b+hKl3ae28Bq1M3W7ViFmnK2OoNgUuf8x9w/bkBFh235DbR/M/cFe1O2x1+Wi/aMkvWoLJsxDnrmds/g9j5I++yUoLo36OWqPKK+iLa17KwGRYmCA+l4bf0aj3b1WlmTHxUi+1VbUETOpH3lf6bNeDLVJVrOv0Wpf4e/YD5/fQTe8bPtA/W22PvvMxko2kT8URn2O8KuUcrxG/fqxdDtfQ4mk21eDY7k7y4cz/1AKt/d5yQ49foNtGZCP8ZsMrHg6AcfI/9s4mZSoi6hLNn3cLlSkhCNAPa9sbfVlyz6k6HcLIhAywbnfJOVBw/fVU+9t03SWB/dlbs9dOs6ZB2HDSdUHPC3rX23jZ+goQ8GAmEr+lMIKMGr+NjCfvlWNfaf8sXqZ7RPRwq7dcYFOn97WxUOq0/a/VeSUhX7b3zf+yvUntw91gsEh4bfMQ5F9+yCr4G8mMGIIPLADRMJlMn1TfPt2WYo7fw9bDdB4ba3RBPEvR9LfOLpTMsYoyQb0R7qgT1/1dZ1B6bs9+v/JgJKEJ/kV8nrWgdCpJ103MoaxomyNDoMmbqLpPm0YR/rH+cOjT1/L3D4xib37PPadhT8CxpsQNNbf1gI/f0jhnnKw1dK/R36yrZr/cuMuJZ083akxn5f0meCsfWkqTsek58twudccQ965cgYHm4yYPd32B5R8Euh7eaqaeMsE+pdAgKF/9Q2UNtM3vdNsEO0+tYXOrmkoH4OQc9cu9kN/4FVb4KZdgvnjQVuqq92V9QWNd9VnKTL2fY3ZJtsdgx7O0PtR0nZpn4YupRyd1glvfDWNZPotSCr/fioDSZfzPk62WeGqlun969j5SyBhvlE+7cW3+3pvl6bTNBQ5T8uc9BlE3VPzhH78KPXJ/rdG069PoT5kLpJm2qbV3fQOdiMacS5xKvnsV3sHQqdt3viICfgagdG+ZtBg2vOt/hoAGUCjAzy5+zeAdgyl6raj1agyRuv7JwPvDeWYTIAJDBABMR/JivvVWLJ0OzaX0vV84gz2F2xH1Opq/PHFVdg0fYASZrVMYIAIPDIMbjD5kYc3bhjYOUBFxGoHmQD7anbAb3VhRm6ai9fR7eT5EOhtQGl5ByJ/8KzNPGACze0vwrAnx/apIhHOKxNgAv1PgNyH/lfqmxodrTKR4zTGzzHcl0LGPeRL1gyFLa348PeBSH71Oej6J4OhMJHT7H8Cj30HK16MRJjLO0X9nyxrHCACY6Mh5jfZtzIQLUeOIPOtwyhtCsT6wmycy4p2eFx/gKxgtUyg3wiIp3sfeci3n0Ie7+P29Vth6FT0aGQkViyYBC8fqtKZyoMq5gO+mq/5DSERmD3hQa0P3uW7+1IzuhcuRdbSQAcFITMjEeTjv9kcjOYAHyDAJnhDYLQ3kUZKHH/KfaDRdx084XwG6Jx/aKSUiWM+ghH3cgoy44IdT3HIyCXwxCLkZS1HzPiRm8UHLmdjghGTlIIDhwpQd7QAJ3elIHlOMIbJmzoPXHFxht0TCHrYd/0HYb2v2ydsHMx12ospyEumH5mDmegDk5YP+GrsNwz72maYvpR8vwREenJzcNjnmjPABHyPAA2R+J5Rg2mRcKC++Y3BTFF/Wr48eKU/FyzJBJgAE2ACTGDkEXjYHxjvozeZHvvWKPj6E9Ijr0ZwjgaCAOtkAkyACTCBwSUwenCT873UxD+ZiUEiX7NMOJ3DYY4kX+PG9jABJsAEmAATGCwCwWNH4Rs+5kmJm17CrsFi0Md0ODoTYAJMgAkwASbgQwR8zK0ZGjLiSZ0/ech3HhU30l3JPw3wHXuGplQ4VSbABJgAExj+BEZ2DvzJi/K1/vpPTew/jOxax7ljAkyACTABJjBwBMi1GTjlw0nzpHGjIAZmhtrmUeTXffuR0RBPNg21LZw+E2ACTMAtARZgAg84ATFfoHilyxcwiMGqsWPIkfAFY9gGJsAEmAATYAJMYNgR4AEiqyJ7InDoB2amjh/N8wZYlQnvDj0BtoAJMAEmwARcExCvdImnkV1LDexZMUjli6/MD2yuWTsTYAJMgAkwASbQnwR4gMiO5p8/NnrIniR6MmhI0rYjwIdMgAkwASbABJiApwRCTKMwVINEYnBIDFJ5ajPLMwEmwASYABNgAg8cAZcZ5gEiDTziSaLBvAsnJqP+7oTRMHxDwxgOYgJMgAkwASbABIYFATFINNgDNUOR5rAoDDaSCTABJvDAEuCMMwHvCfAAkRN24j1+cUfOyel+Cx73zVGY8iejISa6BH+YABNgAkyACTCBYU1A+A7ffmQU/P0GNhsP+QOPjx89ZE8tDWzuWDsTYAIuCfBJJsAEmMAAEeABIhdgxV1AMSeQmIDShZhXp8RTQ8KBFCtPSO0VQo7EBJgAE2ACTMAnCYibP98JHJjBG/FnFsI/CSf9wpfwSQBsVJ8JsAImwASYABNgAkNBgAeI3FB/eAwwedwoTPmTUTB9cxRGuZF3d/phuuMXahol3fUTDqQ7eT7PBJgAE2ACTIAJDD8CfuRhide/xEDOeKOD9+BxhsQTSeLppO8+Ohpi67ECjsAEmAATYAJMgAkwATcEyH1xI8GnJQIBhlH4s0dGYdqE0ZhEA0biqSLh/Ekn3XyJuP8tYBS+EzQaU+mOX384im6S5NNMgAkwASYwpAQ4cSYgE3hIuTH0FPkP4gaRuDn0DZ3el4gr5kQUN6nEwJB4ckhvXDl1/mYCTIAJMAEmwASYgH4COl0U/QpHuqSYK+hPHholPVUknL0IcvjEpNZi8EjcKZw4Vj4n5gWYRnf5pgePlp4+evThUfgmT0I99NXjfCmilqYj5/zAmFKduRaTEivQMjDq+1lrK/b/KB1RBfX9p/fmcawZQL79Z6gXmqS6k4v9N5W4ejcjmYleBgMip9TfHx0fJtfbgEBgpcOIgLipJG4QiVfLhe8gfISwPxkl3XQS8x6KVQwgCX9CPHX054+NhtiKcHGjaRhltZ9NVa71/uyr+tnCIVV3rxEl6Vsk3ybK59tDLsshrSv9krhShp7UNcUPWnOotV8sYCVMgAkMLAEeIOojX3Enz+gP6fUz8fe2E741CuLpIjEvwJgBnqCyj6Y/mNG776LtZgdud1tl/347LlfW4rJVkNh9ENbu9g60fdHTf1nt/RIt9nz7T/vQauqW6053r4dm9IFJ92e1qLzY4WGCI0/cGQep/rZ/OfIyzDl6IAgIH2GsYRTETSfxlJBYxQCSeJ1dPDnE8xNaqoF0rdv1Vc7aBUssT/Z60HalGtVXPYnjC7LtOJiej5yPg5FTuA3vZUXjUV8wi2xwVj5aZUniI3QZrvXKdXFIZehJ36v4QS1drvXyWSbABHyDwIM8QOQbJcBWDC6Bp9Nw4+JeFD1tley/lmHJa+dx2yqId5nA0BOgu3RbSlB06UH3qJxxCEZy+V7cKE9AyNAXFlvABJjAoBJw1i54a0Q9sleVoeJzb+MPVbzrOHcBiElJRlyYCUETTDAMlSk26fZ3+dgoH0YHw7VeuULsRd8bkoCT5HufTAp2pZjPMYHBIMAcBQOvAAAQAElEQVRp6CDAA0Q6IA0/kR503ulA2103T4Z0kQzJder6/anovGev01m4I7Xuu5Sehk3Owh016Ay534U23fnSqXMgxXTaK3GifHn8BIuXtutNT8h5Uod0yXpUN5UMKhzbHOqoct56o+q/bx1ovy/X7U6XMvZx5GPdTDyxWVZt8919j64pqhMe2djHNG0MEAe9Mie33FXmOtob3fxE+hqrPi6K3Xe60K2hg4OYwANNQL1eXbZ//XsNiete9N2D1cd5XL4qEx1tmKpbX1ukSuvYKu2tPaOAh4zakXW296qd9nptlOrUZRPH7YFShzR8Q5uoXrCX4yv6HfwCZ+FyLPVbV53sdy6Kbe6YkJFSuTnkjU54wkux38GPcBZO6p0tkj2e+iSSMp15VmxyWU8hKbT9UuK59VOUWHK5u/EN9OhUrle3v8eUdHnDBHyFAA8Q+UpJ9JMdbaeKsWDWBkQ8k46omA2YND8LORfsvJm79chZRefmkAzJRcxZi6mrilF1y9qIVpQkrsWC0mbY6Jy/AVEvH0dTLxzCJ71Uhvp7Fh3SfDyZtehsqMCaOEojJl22aVkpakjOITyuEJXWNnxQiEnTC1FtUSnvtVRgwfS1SP1APoR0vAUln7Wj6vUtmBSdiiglX1GKrYokIOkkWTFJkBRvLSa93ECnG7CGdE4Sq5hD6NZRJNB+wqF2Ome7dJ/Ix6SofFRSHmzPaB81vZuLiOkbkFB6Xf4hKqVLNuixl1R2nivFkvlrMVXwo3xNjd6AJa+fQRuVAZ0GemuRGrUWa6psy7n+DSrj58vQJAkpX3eOY+X0dORfUY41NqK8bdNLxZrSRnTayPag6VA+ombJdok6NGkZ1aHf2w8gUqTeDlQXZGFqlFwvhWzUyxWop3qxYPoWlLSQjLrcOoPNdnVTknXF+l6DXJ+Vco+iOjpJqWOqWrncqS6JuRpeTsUkte4TywUFDbZ5s7c3ei0kG+6atTnZ8YCJG5ula2c6XbtXgaa3sug6oHpK9VGt850XlDoxP12u61r5sLdS5D093Xx9yJyozMzXXBeqM4nNM4KTVeTe68h/fi0ixLUsBbcqbcN1qgO5iKK0pfZGcH/e7hoW8nrKVLouC6kNqsZ6pa1IONRK9oh8O+Mg2zGJ7BLJSOutaqQ+vwFTXXLpQn2pqLtyfYx6JhVTZ23B5lOO17qkk7+YwINAQLkGq0U74a6NhNrWWV9D6Vhv1U+0Hdou95O23RKR7ELlZrqu06vN7a6+PoeiKour9lFKd/p27De3a0okOKYrn1HakeklqKSAypfJNmprJ1n3Tbp8Jopsvbhri1Q/wLr/k+JTf07pLyi1zNUi5TeT+vyqQqW9zcX+m07sVttDN32MlJT4Eu3zMkubKfkX9n2iqBMu+w6hyLJK9rrov2TJ+7Y+pPBX43Jx8DP5rPlb2GfvEwj/walPIHPpi+8q0nbrdwmh3nZUZtr3qbJ/C8h2THJVr4QOZZWYZZ6BSNfWh9+O/Aari0itNw2NyH9JKbc36hUttNFTV1Ud9j7obPL13m0GKF82vjT18QnF1j6gkje1rlGy0FXfrfx2hY9DOTmrB2q+FD9vKm2FX1pfusX1vJ/3r2O/fd19Ph8l1kzJfpl/re3vFcU3cPgNpet6ID+jeDvE9ST5R87yRWnzwgR8kQAPEPliqXhpUyc5eLHpjQjbug0NtXtxo7YIxzYacXBdlmVA4B45H39ZjIOPLMKB93dJr1tdez8Nmx5pxPpVhai263Q7jpdg8U5g0y+LcO3iLtTticGjZ9/BmuQsLM7rwTopfC+ulS/CtEvVSNrTaGt90xEkbmrEnOwduFZHchWrEPfFGaz8YRYS/7Ye31XD309C/H81IDX7ONpsNeg86kHF321HTu9SnDwt8rXLYuvu69o6Jj6HivcLUJcdTufDUST2xfr2cwiZEIM1C4H6X/zWdoAF7Th4oBGGF59F/FiK5mZp+mUWFmS34rtZW1GRNAWWR7/12SvKdM6PPoTpb9JQJ8pUlMGuRTCdKkXs31NnJtL3i8CCp4Hqsw3iSFkbUHGUBmta6nGYBhmUQHRfuISaseGIfVINsd120uBXFNUhc3p1u1C3Kxq3d+YjkQYLVek2GvRa8HozHt+QhrOC97kdOLa8B5liQE4VUrb1+VlYc6ALL+Qq9ZJki6Y3Yt3fnUWHIiNt7p3B+vhSvGddN6vSkNh2FAk/rJAGJSU5my8qj/RClGCuTX1O9qM6ln1GHpAzy7fizR8W49T0ZNSdo+uD8nbytSm4/ctCbDphccBs7CW5a6e34c3J9UjMbjBr0trRz8S9zbNfKUDd+xnYGAaEvZRB++K4ADn/g1K+dRzrN5wBlqVZ6sSeWYBdPkjSsohBnpfykfPJFOyuENfHXoh85X270eq6NyLmH5KxGnQdvqHULfpRVf16IXZ2RaP0H6IRYNGIjl/TYPThUBQdVfRVUNyxFJcGm6tVnB6VaQd2b61A5/PJOEnXYcWyYLjkYGWLvNuBg1llqHo8AWela2UvGn42F4YDhVj/brssQt+XyWlL2NmK2OxtUpsk2smTWwPxXvp2GnRWDSdBXpjAA0egVVcb2VS6HQvesLqGRFuaPQWXrfqJoKUxiEMjyqs6bCnSD8l9J/yxOjFGak+0+hytPsdaiat2QUrXrxl7fm3X79ula9EXjNVvF1AbS74JBcZli32xbsXqiRTgoc9EMWjR1xaRoP7lZjXW5XchsXAb2Spsc2L3K5Gk030fQ0KAyNsq6nO/vdzsNzXsfw6m9wqxZKfCT1ffIWkzf7kqH7PQ+UNWvuVeCD904/jr2JxqdVPLo/7DrFna6Yvv2km+tFu/i1JpqyxG6ulg5P1G7gNvVKdh4xjyPdKPow1Oyud9UXYUWWu5dBhL/r7N4leTD5A9qx07/zrX9kYaxT32j8U4TOV27CjVVanMKVCUp27/vsfWZ67dgQNJJlRnFyIheTsy7z0r1wm6to9tDER9STGKnLpAHbr6XrLQYbEtJyf1oLcBWZSvkq4I7D4kfouQ3NFkzLlA/H9Nfq6DViWgtxklxC7Lyu+5cW4bds9sRQ6Fl9gPRtr9XhGyefO7ULKhGJWqa6D3erhYhsSSLqwuKYCY1uJGbTbynmrF5rUlqO9V7OMNE/BhAjxA5MOF45lpDXgzqwGml9KwOz4UAWMo9hgjptGPyJyFHdj5z7UUANTvKUMlolFamIDZ4/2lMMP4CCQX0o+7+/Tjbk+jFKZ+tV01IWt/CuLCjDDAH0EzE7D+aaDloj9SS9OwQgoHDE8sx6Y4oPPCR7C5IXa1C7E/2YrkmYEw+AGGyTFIjTcBV9oxoyAbGWr4+HnY9INA4NxH+J2auEfbDlx+LAHHsuYhbKw/xfRH0MxVSKVBnpb/d506awqyX/z8ETDehCBFXtoXx+P8SdKI+DUxCGk5i9ILdKguV3+LPRdNWPe9CDXEybYL9TuzsOCtDqygfB54MdROTo+9jSjKph45MZk67ggEiTJVyuBAbjS6j5YpHbYRcQtpkOv0JdSrqVw8j4OIwAoq+8MnFUePztWcbYRh6SxE0r7DQp3wm9mNCLBOz0/mWPpKKDn/FajuFbEaUPRWMwzLZLtCBD9joFTXKjZQGQoRdb1zHG+WdyHyla3IW6jUS5KdnbQVRbO6bMqlZuc/o+pbdnVzQgQyfpKAyCtHUXRWVWq1vd+O7pBI5G1bZVOfkxaTHecbcdlKFGhH56w0maWRTlDewuKXY10YDa7VK4zs7SU5w9hQxLy8FbvjRL2geJqLB0x02GwYa0IQ1UWTHyUWIO+LY3Fdd9/sQQjZ/eZG6zqxCIlPWOWDolkvbe+WYeenoch7m67lyf7SKZGvFbnydZ/zbqsUBr8IZO9dipCqEqRWdaHzA9pWmpC5NxmRwhZZSvpuuxmMoreJ+wRF3+RoZNNxDN3pKzraLsl4VqbNaJtO5ZMSjTDKe4DE3uSUg5SAzVcrPv4ECJkWjpAx8omA6atQuj8JSXR5SCG3jiKzpB2zX90q1UfRJmGMEWHxGdi9rAeVu35r235JkfirnwmwOp8loK+NzKIB1tnW15BoSxemoELqJ8pw8A5l0DgPSS/6o+bIWZt2vu30h6gPmYukmSSju88hWavFVfsISjeVfui2lJ9CjdRfyRGbfn3Kkq4cZP42jBPtjPBxAFW3aG9F++CpzwTpo6MtkuQ8+LrYgTlFW5ExJ1RqE4VtBi27RX+so48BfeqL96HSGIPy3Biz3xTw5FLseyUcLaVHpP5ed99B+tRFZajVf6kyuBtq5VsS9/HU1/8N+VUt1/GhqD8k6Fn/QRGsFu99V71+F/nBn7YDE0IxI8RfTnkc5eEnoi+R/T2DVvlQ3ybKDlqfFuCFogyLXz0+HKtz07BxIg3c/oJ8QXOcdtQ/kkD+bgymTTQpPiw89O/tfNAxgZj9N7GIQQf5kc/iPbVO0LU97aXnEC9uFtUrfoLZDnXH+/puW07a9aCtsgL774SSf0H+i/qbg3xD8bsl5m6HaoTDtu1IGXKu2vo9MIYijm7YZtNgTU6+GMizimb3e0XIrkiZizAaFKq7KsvpvR46m8k3RTCinjLJEccEY8VrGTjwygwEdMtB/M0EfJnAaF82jm3zgMBnH6HmngkvLJhiF8mI+Ly9uJETTeHNOHe+C0HxsQ4/9sQPw4Sl/ug8fwlNJGleFkbD9kkZIwIeorNPRiBmAm3dLWHReOFJW6GAAH+I9GKFg2h7qk9H8UtjEGCnQeqIO7/EH+3CdR0++SzWTe/B/kNnoLbn9YfPomV6LJKecKWhE9VvZCGhFNj48x3Ii1U6CLsobu397BKq75iQ9BcRdjHpcOYsrBjbheoLzXQAGGY+hdn3GnHqinSI+uM0VLQ0Ftk0cNRSdV4p0wacOu2PFbEa+kS0q5fw3j0jXpgbKs3h1Hanw7zF5EkI6W1F000SbGmiukZ6FjvqCaH6Mo1EzMvvqF4iFImxgeYgdWf209bxr6PmXA8CFs5ACHX41mm3fSMUMyYDlz9rVaNatmPCsTorBStEeYj3wW82ovrEUew5Tk7DvS5ydSyiQCBW/IX99eGPb/oB3SQrSTq114gYjfxKccSXJ0w8slkot10N05ciL2sRBGfxvn/LlXpUHTqCCiobcz5sonSh5jzVk5kzMPsbljKVGN8NxnfpOmz6mM6rcSYnYPfLwajOTEdEegO++2oakom/etq8pbKOH2s+knfGRiPhaXJU6z+iY0/L1KTRfpEa3Us4liw0oqk4HwkFR1HT1CVdt0FPzkPck3L96774EeoRisUz/c11W+JAdf3xcKobV5uVa0V3oizIBEYQAR1t5L9Rm94bjsQ4+ZqyznxQ7CzMph9T5/5NDp39vbkIuXgKpcqPK+A6Sn/RjMgfPAsalweuXtLX58jqdH9PW04+zr1alJ5Sb/s3oLS8w5Kubk1e+EySbvdtkSTmydfESCx+UmcEXX0MvjuOqwAAEABJREFU5a2uB0ELZ2Ea9YHWmg0LM3CjLg0xfl2e9R3WStzta/Ufkh10Y0IqNk/7D7sEHfQboct39cDviiRfKqDpOBLXlWI/+WLSHD4TwqUbdkF25ug61PCX4TcFixeaIPoua988hvIXYKOUytND/97BBzUSI9I5LTYSntkf7rbvJbXaC+XDwY+wqQfA7+rJPyGfd7H9bw6/CMQu1FYrQj88fx14eh5esI+HQCx+LhTihvSHVoPI0ORPv1fQgw5pjgX910PALPK3/BqwPrEQJSca0SbqNA1OzV4YCRrjEubxygR8mgAPEPl08Xhg3I1W+mHjD9NYV3Fa8XETYBK35jXEgh4zAU2ttnfQ/ZRb8fby356IEPswrWO6+2DQCg8LhuQgap3zNkzqVLyNrBWPnOWV4cCJahwU8xnQ3U7x2lbcyhjXnWfTeWQd6kDAc89hXYRRS7Ec5s5el2UaiNCJVFyfKIMm46PxwswOvHdBHDeg4iiwYlEEDHOiEXezHtJrZlcu4b2ucFDfL6dv/93Wjja6S7T/R/K8NmIeJ/O67gzVC+okuwB8In5EmxAaCMfPxIk25doiDeqY8Oh4R1GEWteBdjTTAEdnebE0p4453WeELYUo+YwGcb4UiTvq6SYHLVXMURCdiqjEYmza8yHZ6ignQsRgkNg6W/Xba6fBAyYipic2C3nbtQdNlYXyvFTz07Hgx2V40+X8OR24/XvScL4CcyWegqm6bsHmU3Tu/n1pMIX2pCUkfhHiDD3AuHnYFK9V0CSm2TYYZSec7qy2wNMyddd+UZpulsiMbFRspAFOuuO4clkqps5Kx8rienQqTuDtW+2koRlZz6v5t9pmi6cneyA5+STFCxN4EAm4bSNbRB/jD003YrxJ6hsvSzJE74ln6SZLB8qP0w81OgQNFu25GY41S5U2RW+fI+J6siqviFcdqKY+jSJeOI+D1PeZ06UgfYsXPpOi2F1bpIjp34yltlW/NNz3Ma7zJifled8hx9Pxrdl/WMfztP+wjkv7zvS781098bumJ+Pcz5ZiRtt5ZK3bjojoDYjaUIbqu5S+N4sTfzlA3FS1880dJyV3XZ7a/r22kWEhwdonXIR6Xd+dlZM5rVY0NdHBo3LbQns2S5imIypEWtHyKW0fMsJAG/tFPCEIkAz5neZzTvibz9MtR92+1IRF2FeRhBUPXUfO5nxEzVmLqasKcbCJ/CqLQt5jAj5LYLTPWsaGeUZgkvix3YFm8fvHacxgPE6jMi2fawu1fd4BPBEKEnGq4UE7YYhNwMaQZmk+g+5Tv8V+RCMp1sWgjwAUtggVBZEwHClBbHYtpBsPItzT1WWZys5T2OOhilYTZs8JRNO5BrSJgSBEImE6nTLOwJLYDojXzFrq6S4GjQ7NdjYwJaXnj9U/2yu/M33RfluAjCdI55+F0iCQk7p28yZEX05S0hIyWTga7Wiy7oSlM/T1cbOVrFw3DYlpTtLei7MpUyiS3XLrONYkvoOmWSmQ5hU6XYS6Q9uQvYgGO+1E9RzK9pJTrDzibhOnmRwVmwCrAw+YoI82t72biwXZ7Ygt2CHNoXOtqgAnd61BwkQre2x2gxHybQqYuRx1DmWqlHHePFicqC5Uv3EIVZOnYDbOILVY+XFHKmyW3vs2h/IB3f39A+19O5AGkL0sU4ru9eJnQmRSBs6e3ivNsXQgfQpaflaMJco8ZCGS4xuKvPeVfDvwyEC8kzFxr23iiExgBBGQryEnbeSdDmlAZprU7otMB2IF3WRpO3RKet2r5r16QMzfp3ahevscocqj1Si/Ik4DUqVXu1B5qNY2Xd265DbMK5/Jz+SyLdJtgjeCuvoY13mTk/W075Bj9c+3bJ/HPkFfE5fqpBP/BvZ+FxAwPQHSvH61RTi55znEfl6NNavLYO0H6Tapt8fmRo0ar7OTBhXCqCzUAM2tzMuruqqpz8PAAavvwQgTP0o+vYkWDZMuN7ZrhIog4iX8nttymyRCrFfx5DBAukOsQ93tKzp1+lKGkHnI21+EG3W7UFeWhHWPXMfmxELLfEbukuPzTGCQCVgnxwNE1jSG8/7kpxAzvgcH32uwywU5R+IfQ6R/GwjF4hgTuo+egnkSWVW6qxb7jvYgZP5T9MNODfSFbQdu2/1YbzpR613n6yY73cpTBjZiflOQ9INQtJS/g/UHGhGSGAunAyxWEYOeTsGpXdH4ZmUfBokmz8DiECrTX9dCfcVNTaL7g1M4eM+ExbPEAIwcGhIbjbALl1B0mJzwZbE0RCTCjdLjzi1Vp7DnYitiYmdYDQSI81armp5DHQK6P6tH1QfXIQ12hYRh9liyS0OuhcrGZt6fmZGI82tF6a+vWyUkdqleHrWuq2rdPC/9kBAS5rWrGTVCr9ZducaPSD4CqSkRCFJ/dFBEzbKkcLfLd79DgyLNKHd4IqcL1Rr5NevzhImnNpPTaE6Hdn4nHrdeuMg8fxcF0dKDP2rVXzojlpjYcIDuor8nnoQTAea1HZdPnEFNCzmgSlhTaS7WnJ+CfW9vxYE3o9Hxs0LtyZupTBz+ye/WWZR/AERGfoe0eVmmFFNzsePgIHPlHSxZmo9Kpb0QcyzNXpaCfSmBaDl9SXYuZ4n6qFW+oIHVM6i60OpwrTmkwwFM4EEmoFxDe46IJ4lsQbQcOYUav3AsmGkJN8Q+i9WoRWnlcZS+a7Sdv09vn2NR57jnrF2QXhHvwJ5dhdh3wmSbrqMWS4hNOxrqnc+kpy2SUnQciOg8dR5V0rk+fOnqY0IxZ5aR/EHHPlf+p9ZCaQ4iT/oOTYudlY+msHWgyt7RPrjyCaxVeLOv1km3flcjdq5Kx/ojHXIqY4wIm7kUef+4CGEtDai2H82wqVdyFIfvplocvmIX2tuA8soOGKZ/h27M2Z2zOVR5DYF/r7u+2xis+2D20+S/XDmLg/Zs7lWj/IRzNXLdPYXD9mWBVhx+pxlYGEn+nvP4Wmdknefh2pfqQOXWdCwoFk8lkxY/f4hX3TMKlyG+txEnL1DY0CycKhPQTYAHiHSj8nXBcKRmRcBwqAQrSxvQJm7u329HTWkhMk8FI3OtmIMICPvBcsSPacCaH5Wh5pb8o7D7VgPyk0tQOSYC2T/QeEpjqLIeTj/W/ZqR849n5Pd3u0R+cpF40t9NR+mhwY8F0qBYI3XCzRADC91dMhdVS9DSGMR1XUf1xVByMvXzCZiZjGM/j8GjNEg0J/OM+TUXVa/77RSsS6cyPboPicVUpl0Ug5yttgtlSEhvgGHpcqx7gsLUJeQpLJ7YiP2HgESruagM0mtmtdj/ATkQM42qtMZWSe9QMRJs0qvAmrXFKGpRH3GPQOrLoehW65qwq6tDeu0p4UgPsbRSbZyHTRuD0fKzfCx4vRqXb3agjZyg/Je3YPfvldcMFHG5btZiZbJt3SzZnI+V+24iYJwiaL2xKzvc70LTiWKs3+PsrpJ1ZI398YuwKdGI+jdysflEs/S6Ufe9VtQU52L9JaP0+gQ0Px4w0W2zCY+GAk3Hq1Fzh+oklX03XdchE03AuVoc/IzCyJbue82oyi5G/qd04GQxxC5H5pPNyEoSjzh3QQw4yvFyseSNBnQrE9Z3nytGwlsdiM9KRsxYUjY9GRUbjajMKkHVPTq2XozXkbm6GFVNHei+34POpjPY/MN3UDMuEqnKKyRelal1GtK+NgfplPXXE9/BjO5GZL5RjSZRJ8W5uw04fJLqQmggHhXHRrk+1r+xHesr5fJV60zC6kM4dd8Eg5DjlQkwAW0CyjXUtJPaROtrqDIfS95qx7SN5GNYdzN+EUhKNKHq9XdQ9VQsbOfvmyL3cW77HC1T3LUL8tNL3XRjo97tvIFCfyBCQ4Cqo8fl9oPaNOEPyG1Yg2c+k562aGIYZtNNvf357+DyPWrLlb5rSV47wiYKe/qw6uxjItetIn+wFuvTKuQ+Bj1ou1CBNW80UjkmIMYP0Nt3OFrrrnwcY9iHyOw99AnslXh8rNRJt35XOOY81YOqt0rMfTF6O1D/Lw1o8qP+ZryasHa9Us/abMd2YU9qLkroRkUn1b/Omw0oSSvB/rvB2PSDCBtRrYOwHyyn8vSwrmop8jRMT333VKeVvFwHO7DzpS3YXNmIljsdaDpXhjXLTqFFPF1kJWu9K8drR06yrd9zcHMucpqCkZkyz+P+XtbpzpcyYfZME5p+Vob8C+2SvwW6tlqO1KIaJoQ+Bv4wAZ8nMNrnLWQDdRMIeDoNpwrC0fFPhYiKXotJ0Vuw8p96kPTzrZZJZsdGo+hXKUjuPouVcRswafpaTI0rRPnDMdj3qzT5R6HuFAdYcMIiFOXQAMn7pRDv706aswWpF8Ox5+3nMK0/k34iAXnfN6E6ezumRhGPl47ITxuoaYjXtJ6mg4UxWDGBth4sARGrUE6DRCFVpZiztRpteu4iWekXZXrup7PQXU5lOofKNGoDojbUwvBSCs79QzQCrGSBKVgcZwLsJ7MU9seSYOQsxJqdFjrWWDTT23gWAeu3ouL7weYYQS9uxclXQ/HxLsWuOelY8Et/5Pz0BUSapeSdsKRtOJsXAcOJd7BkaTqiXnoHH09PQfkGiz5JUrNuFmO34Vmc/HkCyHeXxGy+nliFfVmh+N3rctlNik7Fkn0PI/s12Qrh4NvI6zgQ79LvW2nE4a3bEUHX0dT5WUi9Fony/z2XunbnCnQz0W2zEfEpyxFz9wxWPrMBk6jsN/0rELY+DXmRzdicQGHTqb7G5KLIuApF8WSb5mtfFO4XimRqB7KnUbxlqZgq4s3fjtTGp+i6T0GM+EFH6Wz6+3qEvJyBoqdFAMWjJSwpBZkhDVj/wwo0Wdff+X+Fir/qQGZiOqZGb0DEslIcph+P+6h9kfRRXHhTpiKezarNwUZEHNAP0eyyJCz+9B0sENcK5XFSTCH2YB4OZM0zO4KiPp58JRQf5srlK+rMguxWxBVmI2+OJd9CJa9MgAk4EtC8hnLbEfPaVlQk0ai2XZSw78UiktoOrfn79PY5dirp0H27YJgzAzEkqZUuBdstNDBA7UTIOaX9iM7C/pskotmGFbr2mfS0RSSz6R9jENlyHEvmU1tOfdeCPUBmaTISxlK6WoveML19jMibaDO/OC73MdM3kH9xHH98MQPlajnq6Ts07XJfPprRrAOFfdSf2PqrbnwC6/he7mvWyQ2Ofldkxjbsjm0398WTotKRQDfoknelIN7clTipV1q2TYxB+T9MRPnGLIj5jCKWFiKnORTZ5duQrOkA2SnR5OWmrtqp8OqQ6rKevtcr3SKSVAezsft5f7yXmy/NpbgkvxVz3tyKTBcDRJDibYW937P50ynIE0wnC+Uerk502vhSpDKIrqEKurlWvmGL5G9Nomtrbm4rZmelyVM1kAwvTMCXCfAAkS+Xjhe2BcWm4JiYf6O6AHXVu3Dj9DZk2E+UTHf4M8voXG0R6t4vQEPtXtTtWYWYcdYJBiO5fAkqaCgAABAASURBVC/kfz+zDgdiclyElydA7cckOatjVUtI0g7ccBZ+MU1y6FTZoLg01J3fhQZh5zmy860ERIpO8OJe+hGrSIUk4KT1sRIsNg420CDajYs77DpbI2ank00qj/2WPAgduFWNfaf8sXrZPPOPTClc48shPZIRg0TH6vaiIS8GQX4U4Im9JB4wJ0ku09NUpu8X4VpdESpSIhEgdNF562XaxgLcOLrKbgCNHLU8KrM9i+yegNEuY4f0zhdg97Ipdnn3R9iyDHPZ1J2m+nQoBXET5qHIoSz8EbKQ6qWoj3TuBuVjd1I4AnqF5cEIsb5b6lA3d6GuYCnCXMwLE0aDVXW1ljpyrSwJs+NScIPqUpzKSLPc5fQd6rmfCTHp2bhmpbNO1LsIUc/s647Qoa7+upnoslmonbwI+6r24ppyPReJgUpyUFa8VYQb50R9KJDsPJYegfgsKuPceSKW9jpmClYXFEjvw6vXk2Blvu7HUdm9vxfH1B8GqhZKL7mMdJclIEzlKZ0bQ/ndiro6W/ZmfZIMfekpU6flQ/HFosUBGvWX6l/eIaqLCpsGajNEHmfb/OBSy8nC9cbpbGTOMYmUeGUCQ0ZgSBN2eg1qXGfQuIbO76BBavt+QsnRhKWooLZ/90KjEmC7cd/naNlAOjTbBQpXlrajZ1BN/oLbeQMV+YCZSTip+Bt11CaYf5Q7tGHkizj4TIoSdaOjLRK+QYXqr5n70FDJ9zqZFKxqkn0uDZ9JFojW6HMB3X2MsFP4g0qbKfq9ipRw25tP7voO2RDHb83ycVKWIrZWHXRgv8uNT+Bcv+Sf5chP04vk1FUKt+PrUCe1/C6/QMS9Sr6j0gfKPjf1JXZPajutV6oB5q0/guaIOrhX7vNFnfhNBlaH+Zsl4MJ/lIQceGnUVac6tOsSIIdb6qQGY1GPXPW9Dmlq6JAyQF9a9WBMMLHORsN58kWoLblWkYFk+m0j3QSU5jykeFqLRt29cSgNK6yZUjytOkDB0OStoVP4Gba+jxGRSRb/qE747sKXfjFUUstfTMDXCfAAka+XkJf2GcaZEDTOHy4/Y4wIGm9CgIsf3/CJjz8ChJ3avmX/WeiER9OvT6E+ZC6SZvZfUt5oMow1UXkZYfAmshdx9KUnl03QWH/tFLrOYH3UWqyp6rI734XKow3AzO9ghs2ggyLmpCyUs44bP9kOzX/WcZTWF+K1TtkWp0zU1D3Qb9C6no0mqg8mGLT4wcXHg3RdaLE6JefXLXtPy9QqBXXXoMUBGh+FjTubDHr1oV8+rIQJjDgChn68hvT1OXD4GDRtuI7SXzTrnjcQ5o/cnmm23960YTraIoOw31kfij58PGnrFTtd9iee6LMy2yDy584fhZuPN+zdqNRzWledVLi49rld1CsNQwyCWV/qxBDxglKP3PW98OAjz4mVD4c5D+9VS3MQzSY/0q06pYz60y7o0qmU+/jB893dsmABJqCDwGgdMizCBB5cAr0NKC3vQOQPnu3feY8eFKLGedj0cjCqxYR96WXYf+IMqo5UIHVZOlLPBSPzVfunmoYzGLadCTABJsAEfILAhVPY0xLq0byBPmE3G8EEmIANAWnen6euI3XhFqwpPooq8iMrSwuxYGEZqp9aiuz4QBt5PmACTKDvBHiAqO8MWcMIJtB9qRndC5ciS0y6O4LzOZBZC/t+NhrKl2MxGlH01mFk7qpHZ8xyHKvKRvLkgUyZdQ8MASPCFkRiRSQ7ZQPDl7UyASbQNwI9qL/egxfSEzyeN7Bv6XJsJuAdgUcjqU9dMAnf9C76yI7lF4rkkh04tjUCOH8KmeRHZp/sweKtW9FQYv/q+8hGwbljAoNFgAeIrEjzLhOwJ2CYvhR5WQmINNqf4WNPCAQ8EYOMgmzUHS2gNRv7UmIwbZwnGljWdwiYEJOcgrwXp/iOSWwJE2ACTMBMwB+RidRGfT9i0F7JNifNO0zACwLTXqT6mhyJIC/iPhBR/EyYFr8K+/YLH5LW/RnIiJ+iORfnA8GDM9mvBFiZIwEeIHJkwiFMgAkwASbABJgAE2ACTIAJMAEmMLwJsPVMgAl4SIAHiDwExuJMgAkwASbABJgAE2ACTIAJ+AIBtoEJMAEmwAT6kwAPEPUnTdbFBJgAE2ACTIAJMAEm0H8EWBMTYAJMgAkwASYwaAR4gGjQUHNCTIAJMAEmwASYgD0BPmYCTIAJMAEmwASYABPwDQI8QOQb5cBWMAEmwARGKgHOFxNgAkyACTABJsAEmAATYALDgAAPEA2DQmITmYBvE2DrmAATYAJMgAkwASbABJgAE2ACTGC4E+ABouFegoNhP6fBBJgAE2ACTIAJMAEmwASYABNgAkyACYxoAtIA0YjOIWeOCTABJsAEmAATYAJMgAkwASbABJgAE5AI8BcTcEaAB4ickeFwJsAEmAATYAJMgAkwASbABJjA8CPAFjMBJsAEvCLAA0ReYeNITIAJMAEmwASYABNgAkxgqAhwukyACTABJsAE+p8ADxD1P1PWyASYABNgAkyACTCBvhHg2EyACTABJsAEmAATGGQCPEA0yMA5OSbABJgAE2ACggCvTIAJMAEmwASYABNgAkzAlwjwAJEvlQbbwgSYwEgiwHlhAkyACTABJsAEmAATYAJMgAkMGwI8QDRsiooN9T0CbBETYAJMgAkwASbABJgAE2ACTIAJMIGRQYAHiFyVI59jAkyACTABJsAEmAATYAJMgAkwASbABEY+Ac4heICIKwETYAJMgAkwASbABJgAE2ACTIAJjHgCnEEmwARcE+ABItd8+CwTYAJMgAkwASbABJgAE2ACw4MAW8kEmAATYAJ9IMADRH2Ax1GZABNgAkyACTABJsAEBpMAp8UEmAATYAJMgAkMFAEeIBoosqyXCTABJsAEmAAT8JwAx2ACTIAJMAEmwASYABMYEgI8QDQk2DlRJsAEmMCDS4BzzgSYABNgAkyACTABJsAEmIDvEeABIt8rE7aICQx3Amw/E2ACTIAJMAEmwASYABNgAkyACQwzAjxANMwKzDfMZSuYABNgAkyACTABJsAEmAATYAJMgAkwgZFEQHuAaCTlkPPCBJgAE2ACTIAJMAEmwASYABNgAkyACWgT4FAmoBDgASIFBG+YABNgAkyACTABJsAEmAATYAIjkQDniQkwASaghwAPEOmhxDJMgAkwASbABJgAE2ACTMB3CbBlTIAJMAEmwAT6TIAHiPqMkBUwASbABJgAE2ACTGCgCbB+JsAEmAATYAJMgAkMLAEeIBpYvqydCTABJsAEmIA+AizFBJgAE2ACTIAJMAEmwASGkAAPEHkJ/8v7wO0vv0bz3a/x8Z2v8FHbV/jdbXkV+yLsBp0TMv9Jsl4mw9GYABMYQQQ4K0yACTCBP/4XcKfra/x759e4/h9fo9HKf7hCfsTV9q/w6Rdfo/Xe1+j449fo/YqZMQEmwASYABNgAkxgcAjwAJEHnMWgUEvH12i49RWu0aDQ78m5u/OHryEGgITD19MLiFXsi7D/oHNCRgwWXfr8K4gBo87urz1IkUWHGQE2lwkwASbABJiAA4H75B98/p9fSzeTxE2kZvIl2ugmk/AJ/kADRsJ3EGs3yXX1AHdpYEjIf0IDRZfI52iigSQxqOSgmAOYABNgAkyACTABJtCPBHiASAfM/7z/tXSX79qdr9BOd/3+y4u7eb00LiQGjMTdQnF38AsaPNKRNIswASbABJgAE2ACw5SAGBgSN5Yu3/5KeiJI3EDyJiv36OaSGFQSN6hu0UCTNzo4DhNgAkyACTABJuApgQdPngeIXJT5VzSoIxyyj+98DXGXz4WoR6fE3cHP7sqDTuLOoUeRWZgJMAEmwASYABPweQLiFXPxypi4sdRfxoobVDfvfQ2hVzxl1F96WQ8TYAJM4IElwBlnAkzAhgAPENngsByIp4bEvAAD+Ui3GHQSafSn82jJAe1dOY7N2cXKWobqWxQ2hEvbuXcUW4qx81zHEFoCXH5X5aJzW1KPtiG1mBNnAkyACTCB4UBADOKIV8PEK+Z0n2lATBavool5isTTSf2RgOs+sQz7T9Tj8p2ePiZ1HQfNPslxXNaj7aqVH/PudSVGK0oS12LSdHlN/UAJ9nrTjMrXVV+gFJUtLhS1nEGOmoef1aPThag3p1pKt5jzNSmz1hsVHEeDAAcxASbABJiAfgI8QKTBSrwKJp4aEg6Yxul+DxIOnpissn8Vd6Fy3zs4+G69slYj69eqc9W/KenV9scm1ZZ6HG7q0httQORu11tsOWhm5CLs5A38cUAsYaVMgAkwASYwUgj8gcZQxByFYnLpwciTuMH08Z2v0PNV31Jz3SeS/7C5GEue2YCol4+jqdfbtNpxztzffoTbetR8/pHiw1D/XN/uLEYfw0MRhAYlnTPIPuzcV2r6l8MoUfLwoV8wArxMufteM6pLj6P+vpcKOBoTYAJMgAkwgQEiwANEdmCFsyUmk7YLHvBDMVllv6bb9SGOnbI1u6XqPJpsgx7YI8MjJgRNtF6NNiwME6zP0X7gwzbn+YAJMAEmwAQGi8DwSOdLGhxq+o+v0P1fg2uv+FOMJhokEvMd9UvKY422/eM4f6iftg/ewYLN1f3+5Iyqf6i2sxdGwqAk3ka+kvbTTddxuEp9+jkUSXHBSgxPNtexc9UGTJ2/HWve+ggdYzyJy7JMgAkwASbABAaeAA8QWTEWTw6Jp3msggZ1tz/T7z5di6pe2fyA8Ypz11KPw1fksAf9e3Z6AeqOWq+rEG+GEohNJdbnaP+nixBiPs87TIAJ9CsBVsYEhjkBMfn0JzQ49F99fJLHWwxS+l98BfGHGN7qUOOF/c1W2/6xehca9sdgmp8iceoI9lxV9odkY0LMy0nYnSev68P7wYiZs7BirKLnZj3e08rf1fM4rL5+Nn0GFk9Q5D3atOPjKzSS6CTOo08vN+dr919NcSLFwUyACTABJsAEBo4ADxApbMUduH59gkfR6+lGPMHU938o6cDhdxvlpP3CkfNqpHJnrAN7/qVBDrf57kHnnQ60SWsXusW5rmbUnDiDyiPVqDrXiDb7N8LudynyFO+e7Ox0f9aAqhPVFOcMqq+0w+0rel0UV0qTtndlHSJpde2+S+HK+U779P8/9t4GrqrrXPd9Eoq0pII5QgzeDaaVqI0l+yeai9uvXDRalJwa0u1XbcWdI1rdEXKLMbFHbCqcmBjsDtiNUbytuMuJxtMg3cHQGOUmflROCN4TaooW2wj7SA3kJuANu4TN2fcdc8611lzfc8ECFvCs3/wcc4x3vOM/xhzjXe+cayxbJG3fg7YP6yVvQ9+gzJWgCXbbdH/SiBrhUi1cKmWvldMcy8xFdO90en3czLkDTtckXdN756QMOj+PzFU+JmY2JrpORrr3WvX6U3Fd1652XLkgeRi6V7/X7KyDFt+so9EWvtDTaW3hdANazGVybSe2ayY92/pVt5pS3JB67+C4AAAQAElEQVQACZBAyBL4938HPvrsf2GonEM2MOrnbQNlx0QlrUX2YltOHXj/Sgcg45ZuN8hYbdgBthg+r9kjyYHrGOJzrJf42hKJ+GkPYmayvibGaIFOm27NHjkn9ojYMDJu+f9VexJWr4g2ZIgNdea6cezYNZ2pR4txmroyFbHGsbYTFv7GcN2mMcZVLZFpvDX4Rdwz2V6uOffZCmaK94mRvrcHWn7aeF6PK59pAt02OgebfdCs22Wm8dlmR7glZAAJkAAJkMCoJRCyDqLBrBH938r+12Bm6TMv9Q8lt7vF4vQZy8fFW+dx7D3j+sIUpD082/5krLvqEi4abxYZMWRXj52P5GKWtpbi5DslmLVgN9ZsL0N2Xjk2PVko59nYflYMQomtLb8tNeJLuhfPoyY/G1MyirBpe7mkKcP6tc9iSnoJqj/TYnvetLyBDC1PkfFIESrNhmHXOWSbrp287VkEPnoL69M2Y9baEsm7TPIul31JEOZKcMmvtxnHn5IyPlKI9cJlk3DJlr1WzrQ9OP6REX8M8Lt/yLOzmbu71v4qfssvdyPJVqbss/hYexorRt6JQo3voo1lonu5lKFMZz53Mxbtq0enqb5aTuyxy8440YiLL+ZiiqZTuZ5uYx6mPFHhPEdEbwdq9kn4gmex7Mky2HTftFH0mZON9Seum5xK9U5toUb4rlmmp9PawvYizFuQh9KPxClXXSR674ZTO1lWhBpVV8GoWwMpdyRAAiQQygRaOv8dyjkTCjqquY/+/P/1w34IpBBOdkC9c0pf14yYH1uxNYy4zrtWHP2BshvUugdHb5quflaPghWbDXukTMbFchlXi7Boroynz59Dm2k8NaXSDqcvSba/Kez+k3zTz8vkwVvGAtvP0q2O4a2GzuWo1nJTm0aHnfOizs95jG9VkWT1MC5/S8qjbAbNFlE2Tza2v2Myomw2i8kuU2P+lHUVqD5mtiNseUg2XEiABEhgiAkw+9AgQAeR1INyyAz2nAGSrc9F/fuJzwg+Lra9+z50UwNIWzwTEWFJWJpm/Mzsdj3etF30KKMB23Pr3Y2o3i4cz92DUtvjM3Pa6nKsf91kmNiu3arHph9U2J+42YLt+6mz8Zjtd1u9jTh1wSGj+0Kt/SdyWJyKVZ5e5e6qx/YNr9n/nS1iQhzmzI5BlDGRQNs7r2H9QfengPb8LR90oeb5QofxNTYS02cnINE2L8Ot69ieXW7M7xSJ1J9sw7apuvDOqnLsVEbbRxXY9LJhiI1PwpFXMpAoDqKmst1Y9Hwj2rr1+AgLR4ShP9CDpl+WYO6Pa+1OJiOWtmv5pxKsOWZy2mmhsrlchadft03mKbr/OA/rf9mqPzmUy4iQPCRvdQip15rn9yCjrFk7dd5cx9PC9+InzqHobkXBhlws3NHg3k4+acD6F2uB/tatS5Y8JQESIIFQJPDpX/4dA/lvp30pc+vtf4eaD6kvab2l6WwoR/Fp29VozJxue9vGFhbovhF5efXuY4iMSV5tDX9Z3K5F9t+WoLTJ8UZyxFjD9lHj6YkyLPQynmqizeOW+km++Wdm5p+XqQdvhn+oP2O4lmfAGy/jsuKWV248AJRx32yzqDzEbolVNsuHVdhU0q5CuJLAUBBgniRAAsOAwKh3EH3+BaAmiA61uvrXfwP69lOzVpz8lfFlX55yLZurWzFz5tp+pN+Do5XyBd5XgaemoqLmMG5cPoxrb2di+Xhb5HYUHPSQtheY/kQOGupUmgOo25uEKFuSq1V46R3biet+MjK/n2APrD79vvEmSwdOnjB+IodwrFsx3/iJnD2qdtD93iWcjYiGmmw6eesOXKvOx6sHXkBD6XzEajGAlnc/8O6gMuL43X3RgDcvhWv5xCYtwamaYpw6sAtn3s5B1kQjdUsDalqM47AEbH0h3ZivoQuV+UVY82wVrggnIBLrXspBqprr4NZbyNvfaiSKxJwfSRnqDuDapcNo+FkK4g0nTmfVEbz0nhHNtOsWx03ac/m4JvV0o24fDqbrda2i1P8PwzF2uRzZVYbjLSwa6362DzcuSR6Sz5kfTbbX05WXj+DoLZXSvHahc4KU94KqV9FpX5KjHj4RmYvX4nytce25aY6ETTeFef/q1iGMRyRAAiQQugSUMyYUteuPXk3/1x7MSs91rKmbkbSuxhjDgKj0ldhoPATpe9l70B0foK3hJ7P6knJUyrioRXtgCc7I+HTt3QO4UeOwYzqrXvMxf9JkrPpOjJYcEDvE9DOzJtPPy5anGzZJQGN4HLKOqfEyyzTfYRKOqPFbrQUpRr7+dmpcNnE7scSwNSTd7eu4clP2t2pQbH9oF4m0PS/gxrvFqFNzSck4HtUrcQZsoWASIAESIIHhTmDUO4g+/nyQXsXuQ0tRDiL187eAkl49izLbU6+HZyLN5jOYNxvLDYcDqs/B6edcThlEY+t/XovkcXpgxPj5KM5L0k/U9t0P4PYC0sRUvLQ1CVGafHGkLM5BcbqKrK/Vlxr0Aw/b2PRUpGnp5OLpWlR3yd78E7n4ech8SMI8LBEPb7FPpFnxxGR03+5A281G1Fz5zEPsfgSNScFe24TWR1diujypa/ukFVcuXYfXv+m9LwPHxFEWobL95DouGnUy/altyJ+hAsUxecH0c78Z30Lxisl2B0zU3CwUfy9ajyhPPo9Wu1EHFmageHmcnkacP2mL3Se0rH+r3v72UcTj65E/1yYzHIkrcrBzrpEFmnHsdKvtxNjHYNt/kfIabShq4WykGVeABDydm4r4MXpAVPJkJOqH9m1/6tYuhAckQAIkEKIE1MOlUHv72IZK/Uxd/dzMdh7Q/naXjKVqPDVW0zxysWlrcfYnKYgKSKCnyH2wNTyJsYc1oML2MARK9kokGuMTxs3HS4e3GJM/P4Y5kY43jOzJjYP4tPlINo4dD5iuO/69bGwKVs/TI7Rd6OcYrosJcKvKZrLREucjI9FZRNuF92G3GMS2KEizOb2AqIVZKFjoHJ9nJEACJEACJGAmMKodROotnc/+EroOIvVvJMoANVeYv2PzU66IP7+PvPwSbFfr8++jZYKR2uXnXEaosUvAN6cah7bd1xMcX/7FcOywhdv2DyZguu3Y2Cfe7zBIuiWNEey+i5yPzMfDjfBGHKvugPkncsnf/5YjbyOW0+6zBhzdmYek2RswZYF64lmI9S82oM0pUhBOejtQf6IIy+RJ6qSUbMx6JA/LnqxApXpa50V81EJxyMw2XVyciWOZCfaAlj84HDKJ85Lsbz3ZIiTPcMRF4w3YXlCyXU+c7nAo2cKc9x34w3WHIZw22+To0yJGYs5Djnq68ieHPtplxOGb9+lH7ttoxE9wD3UK6W/dOgnjCQmQwGAQYB7WCag/lbAee/BjtqsHLn3INuKBaVj1eLJpTUX+3i049fYB1O1JRWwYgvDpg63hK1d5aNN02xbBXXbEfclIWzxfW+fE22wOW3zTfkIKMmYY51floZUaeE0/L4tIm405Rvn7O4YbuQS4cy+bq4C/mG2uiTEutkUkkh90jPuuaXlOAiRAAiRAAqPaQfTpv4auc8jWNP/fgHSUp1xVHbak6P6wEcdfr7ev9SZnRnV1LWzT3tgTaAc96PZ8QbvqcfPFv3mR5TG2W+Ccxcn6WzBy5eIbR5D/T8ZP5ORJ3dOP+zBkWqqQkVaEvKpWdHaHI3Z2ElZlZeDgM+7OFhHdj6UVR7OeRcbzDbiinqTGJ2D546liMGdg+UR4/3z0Gxy6ZLp89i3HZNYSHBUbLVt9aWlz1JseAnSbZz29dxzusV2w7cN8GLlanGhEj9cOtE3bZ+55dHY6HEix4xz6aAmCsOlz3QYhb4rwSICBJEACQSCg3tD5y78FQdAAiujs/nf0Rcf4JWuxN2+LaV2LdTJOTx/vb8xxFMZp/HIEm476YGuYUrsdjvuKyRHSgY4+v0gcg6WPJhji23HsbCtaLjUYD2jCsWqp40FLv8dwI5dAdxFhAaTo/cItctufO9zCGEACJEACJEACNgKj2kEUym8P2SpIGXddju/wtmDP+8tnccjmBIqIhJqbx3mNhN2wOHsOJ22/1Yf504iKd50fO7aJcdRkizLV9DaRLeyd91F923ai9u2oeaddHWhr4v02Y0s7dd88tBAb443gyw2oVE/s5DR+9UL7kzo5dVvq/+sbqDecWanP7UPdgRzs3ZKOtNlxCKqr4/IbKLhsVMLctWj49S4U5ymDOQn3Gz+/clPudi2yN7ylT1xtM+Z6W1GQXW6fxyE+3uH86q4yvaquCetC9WnbPExA/OQEuxNNu+x143whMd5B4uIb553frOq9jjdPOwzFmdPinBMH46yPdRuMrCmDBEiABAaKwGd/GSjJwZU7NHaO8/jluUR9sDU8C9JDwyYiIV4/BJrx5ntdthNt31Jdpr9NnV+Co5eN8Vy74r6JTZuPVCO46c3X8NKbhj0zcR5WzzAuyC4YY3j3AMwHFP/gZIe9cLoWx83OstvncKTKd/mlaFxIgARIgARGMYFR6yBSjpdQnTvAtT2qp4D2MB8HF9+st7/Jk/rcC/b5eeps8+dUFeOgfRKZZnkyZhg9LjJrduZhe2UjWj5pxZUTRVj6ovFGj8RLXDob8bJ3Wnobkb2uBMc/bIWaA+jojt3Iu2yLEYPVC/05HiY7TVatp0zAxu+4z6mjX1PbHtHPYeS0fNyqlb37kwYU7q7RHTMqWhDW7vYOTbYmqv0mPlYP5L5ox8WSUhTaPWfaVWPTitIflBqTZYZj+d59OLPVcAa11GC18S8qEQu/hXW2N3zEobQmqww1TR0aw+P5ecg+a4hTk177ZGHE87BL/M63HE62yxVYuqMKV25KHk21KMwpwn7DGYfxKdi00Ju3y4Ngy0F9qVvLwhmRBEiABIaEwO0vQv8NZAVGvemk9gOyfincIbb6DeRdaHYfvxwx3I5qArU13CSYA2SsWZ9gD6h5bg8KlD7KjqkswZqd5/S3qSvbETHRpLc9hekgMgUZi43zqw2oNOYQjE+fDfPP6fs2hsfhfvucQQ0oLmkQW6ZDVmeHlpF733YPLnQ8dBP7bHvas1i/rwKlJUXIWFGGSqcHen3LgqlIgARIgAQGkcAgZzVqHUSfDxPjTrUH9U9rau9z7W3Am9U2h8k0ZCzw/GV/zsOOf5uq/9U547VpmD7RiJ/YgePPFWKemmfn+Qa02Z5wiRPhpe95cPbES5qP6rF9bR5mpRciT5tpWhcZtXotstw8Svo18zZ24WzMMQcsTsUqn3PchGP6fY63Y5pK9mDKjA2Y8og4PbqiHfMWNbX221kUkRDncIpdrcGilA2YlPIs1pT2IHGqTel2/KFZP24qK0HBh/pxVPp6FCwUfTK3YOcDelhnVSk2vS7OubAk5B+2/duZXLt8DutX5GoMt79ue7MnEml7tvhhIWm9LROW4KD61xLjelt1BZapf6dZUYr9FwyDNCwOW4vXItn2ppMRHxpALQAAEABJREFUN1i7wOs2WDlTDgmQAAkEn0CPjInD5QHT/ycPNAbMlTV7vuMhR28rjj652xi/erA83dcDHlUnYjcEamuoZD7W2OXrsdf451Z0y4MapY+yY56rR4vUGRCO5KfWWxhPZdxd7LCV9Cyj8dgilzL1aQxPwNJUh+1y5edFYmvlYt4/NOjZBGMbNhnbik22RXc7an5ZhYLSBtS3x2FVuvHAKhh5UQYJkEBQCFAICYQSgTtDSZnB1EVNUD2Y+fUnr3/tsWDenT+Lo7eNXBbOdPx7mRFk20XMTXH8a9jV8zhuPBmzXQcSsLN0LZY7TeIYjtiF6aioyPLsRHjwMVTsSUJshEMKIqKR9tQ2XHgmyRTo43B8MpbaX90Ox7oV8x2vSHtJlrhpB4rTo52vJs7HkVfWYs5YI7i3HR97/Cmdcd3KbupaD+WLQdbPdiB/huNJZMutDnS+I0/oXjYme5YyFf8oBVEqj7AEZP0Xh8F28fkilH4kF+7LwKmqLdgqTiQ5c1oiHhAH0tEXcHBx/4y5qIdzcOHEWqx6wKGrnlE4YsXAP1iVj20PROpBA7EVDoHW7UCoQZkkQAIkEAwC//pvFsbkYGQUJBnm6eyCJFIXozlIViJ1gn6qbWXsX75nF4oX+xtTxNYoXom0QGwNLQMfGxlnVxXlo+Kpac72iEoSPw07f/4CKr7neMtIBXtbIxbOd/zzq4o0dR5WTVUHLmsfxnCPtouL2H6fKr3ezkH+4wlInBitTTkwPT0DFW/nY+v9/ZZOAYETYAoSIAESGDYERq2DqPvfhk0doed/AeofzXxqLE6AG5cPQ1v3pXp3rkTOx8E6I97lfdjmweCJmJCK4l8fwLV396Hu7X1oqD2Aun0ZSB7rTYMxiE3LQd2lA2iQ+HVvF+PapX04mDlNd44YyeIzX9D1Ez3PZMYZocbuVi0qbD9Li5+HzIeMcF+7sBgsL9iHGxd0PevePYAbJzKROk4cK+/ayrgLq8b7EmK7loJi0euGtr7g9taTVr4L5vK9gJ1zozHnGclTS3MYrz4eDeWMaTDOb7y9BalmG1kZbDb2dfnIus/Ie0Iytu2TctTZ5Cvmh3GtPAfrkswCAJ8MzW2gIMUQru+iElOxt1x0rS3W6lTV6zXJr+5AJtIm6HH0rZlDjn0eBr/X4jNwxlbuYxmON65Uwr7UrUrHlQRIgARCkMBwsh8Uvm4LDq3UAtuYeRhu47MS4m29bwmOVMt4VaPGYX3sL1Z/q+5xPHIeX9Ik7UF/toZHOXHIOmbT12W8DotGcuY2sUdsOsl4ekHi/nobsmZEeyuFe3iY6Gobr9XY5jqumVNoY7i1MVxLZrNdzOPxT/Qx2/MYL7ooHbTVdVz2wUJsoXV5u3DGmGbgVEE6ksd1of6Ddk0NtZke72KLqUCuJEACJEACo5rAqHUQ9fj1uIRWu1CvtA+2RhFj5anT+GhEjbGacziiJH7s+EjvDiovoq68dhb1xrW0rRmOn4gZYT53kbqesWPDfUbr98WwvpfPUt52+YEwtyTZEWlMJGK1Oop2TFiOgf30q24HVjVKJwESIAFrBEyxhmI8NmUf8KF6yBRwogATRIyLlrEl8LEf8gnc1pBEFpYITScZT52fs2DAPoGO4QM1HvfWInvWBkyaIeusXGw/0YCmT9Tcgw04+vxux/yGSMDcvx4wGhRMAiRAAiQwTAncOUz17rfa//a/+i1iUAUMM39WYGy6zqG4rENPMzYFmQMyWbIunttBJsC6HWTgzK6vBJiOBKwSsPBCjlVRgxKvd5jZO4MCZSRnEpaCp58y3gzq7cDx54uw6JFczFpRhLwTpreHnlpv8Q3rkQyLZSMBEiABEnAlMGodRP/uSiLUz4edwtaBtlXVoLpXjx+/eqHjX7f0IG6HMYEQqtthTJGqkwAJhBKBfx/B43EocaYufScQ/71dOPNcEhLNc0PaxEXEYNXefFRkWpuPyZaMexIgARIggdFBYNQ6iEZH9VotZTIKtLmD1BwCWc7/JuZNxN9k2eeyqXsm2VssS+FRi3Pssk5tcPmXEEsSVCSuoUiAdRuKtUKdSIAESIAERjaBcCQuz8EZbW7IXXh1byYO/mwbzmtzRL6AvYvjAp4KYGTzYulIgARIgARsBIaPg8imMfcDQCDcmDsogDkETL+d7+/cPxHGPAFqbhzr8x2Bn2FAIIJ1C35IgARIgARIYGgIKPsuAXMWz0fa3GmI78MckUOjN3MlARIgAQCEMCQE7hySXJkpCZAACZAACZAACZAACZAACZDAqCXAgpMACYQeATqIQq9OqBEJkAAJkAAJkAAJkAAJDHcC1J8ESIAESGCYEaCDqD8V1tuDz2804LfnzhtrA/7l857+SGRaEiABEiABEiCBUULgi85mfDCsbYhRUlEsJgmQAAmQAAmMEgJ0EPWxoj+vO4onv/P3WLt5P17cc9RY9+PJFX+Pv/vJSVz9vI+CmYwESIAESIAEQoUA9RgYAp31+EXO32Pl6gLscrMhTuFfvhiYbCmVBEiABEiABEiABHwRoIPIFx0v1z6v3Y+Nu857NeA+rT2FZ350Ere8pGcwCZAACYQKAepBAiQwyARunsauza+g8g+e3zj+tPYknnz6JP6ld5D1YnYkQAIkQAIkQAKjngAdRIE2gd4GlBc2wPaC0F3z1+BnvzqEk//8j/jp+jiHtD+cRuUVxymPSGCICDBbEiABEiCBkCHQisoXT+CDTw2F7k/F7qP/iJOn/hG/eCEVXw+D/vnDKZRf7NKPuSUBEiABEiABEiCBQSJAB1GgoBtr8bbNOxQ2DZueSsVffUWEhIXj699ZjAVyqC89aLzWqh8O6JbCSYAESIAESIAEhgWBD6pQ/gdDU7Ehfvj8GjwYEy4B4bj7wTX44fdj5FhffvtOLfhLM50FtyRAAiRAAiRAAjYCA7ungyhQvvd/FwfLC/ELbd2KBco5ZJPxRY/9zSIV9PV40xtFKoArCZAACZAACZDAqCVw9eJlh9Nn/v+OBXc5o/irlc/j5KlD+rozFWOcL/OMBEiABEhgNBBgGUlgCAnQQRQo/DGRuPvuKH2NCscXn3fi009lvfHfUb6nEu/b5N39v2PxDNsJ9yRAAiRAAiRAAqObQCsaf+eYd+ivvj4Z6O3EH8+dxC+KX8HPZC2vasC//OvopsTSk8BoIMAykgAJkECoErgzVBUbLno1HNiGv1sr6+bDOFFnzBcQk4QfFm3AVNtcAsOlMNSTBEiABEiABEhggAi04PofHaLjwxrx4t9tww/3nEJldT3elvXEP+7Hkyufwov/d7sjIo+GIwHqTAIkQAIkQALDkgAdRANRbZ9exzt1zQMhmTJJgARIgARIgASGnED/Ffht6av4rfIDRUXj7ig1D5Ehs7cLv91bgEP8owsDCHckQAIkQAIkQAKDRYAOon6STtpciF+U/wQ/3b0GK1IidWli3L1f/FMadzoNbkmABEhg+BGgxiQw0ATunoYnD/8jTh57Cb849o947cBix7+YoQun/uk0bH92NtCqUD4JkAAJkAAJkAAJKAJ0ECkK/VjH3KXmI4rD12elYu2Ps7Fiok2YGHdv/nfbyaDsL+7Lxawn30KLt9wulWFW+h4cvektAsOHNYGbb2F9ei4KLg3rUgya8kORUdvZEiyTOpola0D1NGrrthVHn/TTrw1WRQbQf/rtiwdLZ+YTYgRiMGGCQ6Wp312HRyY63hwaM2mF07+Y4YNGmH6R5kg4wEedV2tQmJsn9oLce9JXzVqxG9vLatEyXP9SbVj0nwPR1wUiM5C4QWqAw6Je+lnWAMaNfuYUhORGG9hXHwRZFkUE0gZcWRpp159otZiZp2hGmX19d/KUjGEkMMIJ0EEUYAXfqnoBf/d3T+vrP5x3/BuJJufrmDZNO9A3H93ELf1oULbdn3agrf1z73l1f4a2mx3o7vUeJbSv9KDtwxrUXLWk5QBFCgUdvBSt93O0SP1+3O3lugruakZNdQPa1DHXwSVwuRQLcxsQ/Z31qDi6DZumB5C9lbr1Iq77o1pUXu7wcjV0gr3p2d3e4btfG6widFvvP/32xYOlM/MJMQL3In6yQ6UJd8c4Toyj8CG1yrpw8cVcJK0ux6E/R2P1+sdQ8NSj2PQgcPZAKeYtyENpk2OSbUPlkNp57Ef60X8OZuEGoq/zJNMjIymop7gSPHDLoNaLddvNG58+gQhg3OiT/CAn0trAp4N4jwfSBlxZGmlbjOlf+4pCK7Ov7059FdzXdF+040plLfgL474CZLpgEBhSU6R/BRia1BNi7sKntzr09ex/xyUnf0w7bpk9Ql+9C2OGRs0Rmms98teWo+LPQ1m8UNCh7+VvOVGC9Uea8Je+i2DKPhJo+eA6OifOw84nkhA/Pg6xY/soKKBk8nTs2VIUf9BPCyqgPPsS2Zueccg6dhg3jmUgvi9imYYEQopAJGY/7HiK9O7ZGjiZEOjC1cZ2h8YTYnC342zAj5rK9mDNCWDdz/bhWvk2bFsxH2mLU5GVtwt17+5A/qwOFKzeg+NmO2fAtQokA2/9SCAyhiruQPR1nmR6Y+Qp7lCxGIh8rdpu3vgMhE6U2S8C8Rk4c/kwzmTG9UNMCLb735Zj2XOX8HE/SjWyk7J0g0HgzsHIZETlMSvd8TOy3kb89Iev4FRdI/7lD+dR/hPnSSUf/D9mDKpx12fOX3Sh7RN5Sn/b91OD7s8kjsQL5A2k7tt6mk6rr6b39qBT8mj7zLcuvsqq5emnLH7Tiw6BlNOXPF/XvOlqlbWK1xnM7/4W24KvMtmvdRl1b0U/I19/ZVHlVW3VUt3YZLq2PW/hdsX7dmBJt7GRiLIoXsnzx0MTZZSnra9tvr/pRQmlq996sd3bfdVT8nFdLOVrS2Rrj67twXZd7QNhYciz1BaVbGMNSGcjDXcjh8CYOYuxzOb1ufgqnvzJq3j3g2b8ywc1YkP8CD+96CjrX6X/H/i643Rgj25V4emXW5G4dRvy50a75zVmMtYVZWHduGZs33cO3e4xJMTq+G3E+6TLixwRZV+MuP2wCeyibAfGfe63fzXucb/xbHJt+wD6OtUfWJGv4vntY438VVwrMo3oQdmpPK3op+IFrJtRX77HOKOdWGpT/SxyX9uFytZI63fcMNqQWzxLLCQjq/GUPn29t1RasZUt2/eilt9F6R0MmQY/323GrzZaBNVm26y0q4HgoWnADQkMPgE6iAJlHvZ1rP3xMkwdYyRsqcehXT/FkzlHcaLW9E14+jJsXOz++riRKjR2ve2o3JmLSSnZmPVILmYt2IxJK8pw8bazetq8KQs2YEqqxJF4UyT++rJGdDpHczrrfK8My1SaBXqapJTNWLSvwUeaLtSX7MYUiZckecxKFV3S5EnlR0psK0pXb8CkGaWolNPKp9SxWp9FqZpwqaUCi2bIcUMjCp/YjCkqzxf131DX7JR4O2sllfPSUvasyCtCjTn41jlsXx0b/KgAABAASURBVGGkFx2ULsvsOvvR4Z0id3lKtqbbBmS/o05k1c496ypXYY11D5pOFGLW7A1anSTNlTKuKEH1//ThVNP024B5L8vT6atVmDdD0qjVxuZ2AwrWCnOpW89toQs1O7Mx6RFhZm4fvddR+O0NSBI59vbwWb0ua65R96LflLWin/mps43DR+2ofv5ZextUZZn11FtocvkZZOcFoz3Z2+BmLHv+HNrM8bzJnJON9a83A9LenfKStpZRYrRj+XKUITwyTggfVRGmtft0ISbNKkSludym635188he2oBquyY5+mEAdeunzrS2PyMPBVeBppfzpH3qdW5vi37S6/r43ja97miHqt1ofYNi7ZSsC/VlezBLeGv3tupnvl2I49q9DWlXSi9vehr3nbQvs0i/zFVkjbtqr40ofUrarq09ih5ufVEgLD5rcGrfqp+wtyOVr5fV2r3tJTGDRw6BsCRsfMFhQ3xaW4OfPluAJ5991c2GeDajP0/GA0PWUn0O9aLbzu/5yFOuZ29KAE7Xotowd7R+Zuc5qHty0ezN0O5xNX4v2I3CBiOSXZUu6QtUn2HEeyQbU2Y/i+1nzf2ufs8vKmvWxkMnmXabwC7QfqDp4au/UzFdxwAZmzyNN1C2gBoPbX2GFq8C9V7GAMCm83UZm137uiJUmsc+o1+qvlWDTWkbtDE8Q5tDRZcxqR99nT+Zvhm55l+LbBkTJ3laTTpa69d6hIuqd728apyf5M9mMeqrcqc/G9VfmzLK5c1+VPkYq28+EsmKbSPRPC4Wxg0t/51i11QXGeOlac7QL67jaK4LCxlHS13vMYtjWXdTFTZ9W2w+1cbV/ardh62+3y632VhNoovLmDprZ42zPWa0cycbW4HRZJhsYhWm1t5mHDeVT31ncJOp4plXj7JUe3C9Bx32hjm5fmy0D1Ob1uuhFp0NFVhv3KOzjL6q4D1Tn6blL7ZcsHho8sQeeqpBVGvAetu9t7rC+9yyEpMLCQwEATqI+kI1/jG8eHQr/m5ONMaEuQi4Kw4L/n4ryl94DH/les0l6lCftlWWIPvdOOz99QHcuHwYN2pysHXMOazJfcs+R02nfDmelduI6P+Ug7paiVN3AHUHUvDx/kKsFgPOYxluvYVNm88BK4w0lyXNodnAL4vw9GlT52pOfLkcq0u7sK50n65LbT72PtiK7RtKUd8bh3Wv7EPd22uRJmnS8tWxWndgnX1ScODUP5Tg5NdW4lSVXHsmWWIGsNwWY2htGd6U9GfeVTwOoOHoo4h+swjL9l8XQdZ0kIiWFk+6WmXd9voeLHq+GfdvzsF5peuFF3BqZQ92KseKt9z/Jkv47UPFEzFAYioq3hZGatU4teN4bhFKMQ+vvq3KfhjX3s5BVpi0hfxzxhPeSKT+JAvr0IDsF2Xg1PLpQs3zRdjflYKyn6RAezNGcfzbEhy/e4mTrKfvbsSmtUWocTKwe1Dxn3ejoDcdNuZ1h1Jxz/nXsP6gYq5lgk4xNOY++T7sbVC1pwNLEH22DAt/bNNFjwu4yKx9Aa9mRqMmvwgZWbux8/a39LykHZ/aGoP60hIUN0jaCalYvxio/6ffoElOHYuwebUREY9/C8vHOkJtR5Z088jeue3a5FmvW9Ertwi+6mzOM/ukzrdha6JU+RPb5Fid70PB36jc/KdXsXyul0uRkd+MmTt24Vqd3jec+VECLgrrwquOlOpnKxn7u7D6wAt6PGmvR+a2Yvvf6k4333o65NiOLDG3RZYvbi/9oARnZ0j7v2Do+NxkfOzUFwXCogPF/2cpWlbuQIPqD6V9nXpmMppKpT/8Zas9V9cDq/e2azqej1ACJhvCrYR3ReNvnhh8G6LpD+1A8jfwzTA3jZwCYqdPRiyuo850j+ODk1j24zZs/GUxrl0+LOPHNuTPbsf+v9uD0hZH8ivyEChjfysW5u/S+4LaYpzZEYM3c3fLQxRn26DjrVIs3Q88bZcp9sn469ieXe7SR+vyrfQjNc/7H29w+xw2LRdbwDyGVedgdVsVMn5Q4fbwQs9d33b8qgSLTiaguOqAZsdcq8jCurEyZsoDkhqn4nXg4I4KdH47C2fe3oeKFXG6AJdtYH2df5lWGDlUSEaB6FZnWtVYCkRj63dTtGhW+zXr45om1r6xYqP6b1PWbTeffAKybexFMA4CGDdu1mBjYRdWF+2SMXuHbuOK86RU7qW8P07GwQq9bd24sAsHH2pFgYSXGg9bgHYLtpyoJDb6enE6XExYgler5J4Ve6ju5/PRsbcUhU1y3efSg7IfFuLY5CzU2cbUnyXjnupyLHy+3rAXfQrweLHm+UKU3be+3zL92RseM/cU2PQGVj/diLn5NrtlF/Yu6ELp5hJUOt3LQeQx8VHdNs9XP0OehmLbvffKo4j3pCPDSGAACdBB1Fe4UUlYvvMlvPbP/4jy8kL8Qq0n/hEnT/wEP0xPwl1hfRU8eOla/iQG4YQEzIw3/kVlXBK2/XQbDq6QJ4RKjd4GvJTfiKjVWfIlOwmxYyQwLByxD61F2TMJuLK/AjW9EuaydN/sQfzylXhpq5EGKs0SrJ4K1NRfd4mtn3Y2t8vAEodZD0brAWPisOq5bXj1mZmI6gYixkUjdnwkIiDHY9WxvkaESYC2tKP+7gycykvF9IlybWy4Fmp1U19yBJWRqTi2JxWJWtpwRD2QjiPPTENL2RtaOSP86gCLHw+6WmbdgOKXmxGxQq+TeKVrZAymizOuYrM4f7xpMCZS+EUjVnlxwozj8XKu0n8h7OOTsXfXWswZHw71iRifhMylIu9SI+wT5YUlIf9wOuKrS5Etj48735F9ZTR2Hs5CslEP9YfKUYkUlBVlOMnKUj9N+EIM5UONSryxduDKvarO5tuZq7aVvRho+X+uo02L1YjifPHgmNsgwqUNZuDVPSnorirXHTxaXLVxkTkmBnP+00KkogP1+BbetNWvtOPpTzyK5RAnV32rJIzE8vWpiG85j7L35NS2XP0NDl2OxsbvJNlCTHuLunlh72i7NpEB1K2FOosw7pNoVTdR0Xr9S51HqfvYQnqbVt72Le1jsHTLehQsT4BWFmGa+Ph8ceJ24P0rHXqyT95C3sutSC3YgW0PxejxpL2mPrMFWycKv9db4VNPXYppK2kCag/t6Jydo/dfkSJG6Sh900Zxmtn7ooBY9CB6eQ4OSpk1jtK+pq/YhmNPROPKoTdwsVfycF0s39uuCUP1nHoFhYBhQ5x0syFewjN/O9g2RCuamqRU90g/ITufy9hIRKMHHZ2mWC3AY8XbsCoxEtoYPX4a1u3JkXu8FS/9k/TfKuqtKuwsbcecH+3A3sVGnzEmEonLlc3Rg8oDv4GIUTG1te1qNPKObkGaXWYStv0n6YdbruP9T7QoThsr/UjnX2eIjeBrvAEu7v+vqP6qyxg2QfL+aQaSP6xC8XmnbJ1O2m7GofgVGUcnhGvhEfelIF/OUz+rF6dRuxamb5rRNiMHr25JQaLqkyP1UOdtoH2df5lWGDl0CEeU6BZrW2+fRcEvOzD9qRxse0BiWe7XAhjXRKx58WujWmxTERZtN198ArNt4PLpsT5uXO7A3GIZL+cmaGO2Glvb3ihHwdUE7H1F7of79LaFyASk5e1AvjxILSh8C5q9ZHEsqz9SgYuRKTio7LSJcs/KuBj7QDoOli5BoqcxzKk0HWi5fyWOKftetVtJmzg3C8cKpqHz9Tf6PD+Zujf7LdOCveFUFF8nV7uw8Kc7kGW3WxKwass84XPd2TkutmXQeAhL7Z5TdrnYudqxuv/GGXXuS19eI4EgE7gzyPJGobhw3HV3FO5W613D6yZOXpiEqKa3sHpjGY6+1wztd8QTpiFt8TTEQj5XP8CbtyPx2LwEfY6iTzrse9w3CfG9rWi6KfFclogZ6dibtwTTJVzNsdPyYT2qT7yBConbfdvJ9S4x9CVq9kzMCWvAptVFKD3diDYVTQbAOYuTIfahHsnPNnWx8RaLn3jul5txoa4HsYtnY7r6Mm2KELF4G27U5SDVJdwUpU+HbrpaZd3ShIu3w7FqaZJbvvFSfsXc7YK/gDHTsC5vC1ZNlYjq9983G1FzugqH3uoApL5kKxeM5b4MHHwqDjU7c5GU24Bv/igHWfcZ1yAcL3UhdvlCu8PIdgXiXMpID0fnpQ/QZA8ElqenIsp0rg6VQYTOz/EXdfLRB6j5JBqZ/9G9vHhoNlaNFQePtF0V1ba6yYyM1PKYvjBZb9e2iK77B76FjTN6cPTEOXFW6hfrT55Hy4yFyJyqnztt+6CbU3rXk0DqNpA6c81HnftKb6vzW42oPn3Ofb3UrPGJX5yJvVk6U/Ub/ab3zqGy7BzqRf7HIkN2wP/4PS5iGjIWKEsSps9kbPv14cAnlwyYeQxW/cfJpnzVYTi+LPezvS+ywkIl01ZP8oDpi4TD7et4v0WL5Lyxem87p+LZaCEgXwqG3oaIQ/zXBPi/WpgTqLdHIgJf/pK20zeJKXjsAf3Qvg2bjKWLo9F9+fdan6/29UjA0ofC7XZEm2FT3D9N7tGrzVo8e3oZz9ze2pT7FuhCwHPYGELdxgYJdxpvcB0XL/QgavFMxBtzLtp0bPtSAmbeB1z5qFVSeVk86Tw2BRkPA/X1vzclisZji6TMphC3w4D7Ogsy3TKxGCDOoLwNVbgyNR0HMxP0RFc/sGYfBjKu6ZLtW382asBtyi450IPAbRvnHGI8jEPwPG5MTMZSl3vp/UvyYPXh+XhsgrNUIAZLH5X6uPB7vK8cO5bGsla8f7kHEemzxeZ2kRc/G6td8naJoZ16uo+iFsxEqtiAdfYnilpUy5ugyAymveGxT1Pf8Vyc41LCoOgucriQQCgRGLUOorA7Qqka/Otyp4Waiv96DCDGW7d/cXqMGVm48PN0zGy7hLyNu6H95ndzOWo+0y+jrR1tYowdfTIXan4Rp3XjOXnaJx1llxHXadeDpsoi2OYgWvTDcrzkNMeAU2T9ZMISHKnIxKqvXEfB9kLMmrsBU9YW4XgAf6kb9ZVIXVbA21b8QbwW0VF9TR9whnDT1SrrPyojOhoJUtVw/UyciETXMA/nnoK6xVGYreZcUHMQrS7B04fel/r1FBOIX74EaRHyJWHcfDy93KyIb46x90YDTa3OcjWD33M+WuiNVvnSEI7osdqZyyYGCRNF5B9dDHYvMhPj41zSu56KEbdmGnC6Rn8KJkZxRVUP0takenYs9UU31yzN5wHWbSB1Zs7Gduw3/Z9qsfPlk+7rLxv1f9fo7UBNyW7M0ubCehYZz53EwQ+kXdgykH1Li6qbcATt1uoDc+UMElV8Ln5ZmFJ7lKe9VdGOP9wwRbQdWr23bfG5D3kCYXeGvIpOCt5pwd6Jnyj983tN+hdNp9TOJ231jdInJ2CmdJX2K+LkirCfOA6iouQLldHnf3yrXS40I+/bHuyJfPVmaY/+kEpiaUvYGG0X1I2XscGRRzua5UFSVfaPAAAQAElEQVRW57ESd5vnkSKUfgR0f+7R6NFFeNQ5UsZ7ufyndtPYF+5lTJN4tiXgvs6CTJvsgPbyEObHpTiKJBx5JQPxtrRW+7UAxzWbeG3vx0YNuE1pQvuy6YNt45KN5XFDxpIop7RiM/1JAsS+9XSPxY6X+xYSR9qtxIL/sawZDVeB+HvNtptKqdY4JH5N7f2snu4j40HcFW3M95Pe0+UgyAyqveGlT/OkOoKgu0e5DCSBISRw5xDmPaRZf8nTDT2kGvnO/EsWaip6XKR8W27GFS/2S4t68hWWgET7CA9EzcjQf9Os5gI49CgW/rkG69eViwEo+kySwQLhWPfzw7ih5ihyW/dh21SJ57K0vb4Hi/LbsXCf/tvda9X7cObAemTIl3mXqE6nEfHzsfdoMW6o30OXZ2Lj3dexfXWRy+99nZIE6SQO9ycCLX9uD5K8PoixyvrrUn/oQLMnVW/e1Ost0Oy136O/hqbZW1CnflP+bjHqTuxC/hJleLgKE2PxxROovm8y5uAcskvkyZY9im+ObX/uAKYmBObE0rh4KS/aNWM+8X55gmbXoX8HEQszsDW+GYd+dR3dZ38jRnEKMhfKfeVJbLB1C6RuA6ozD8pbST87E3VqPi/X9WdLtC8J9YV5WP96JPQ5Qg6gQeKd2rcQyabs4jWnXAc+9vCTEFM064fBZq5ytsJCxTPWv6gntcaxfXe7S+7KGNw/yR7iONB0DrwfdQjgUagRsDIeh5LO4RbsnelLkhF/uxaHvM0TqBWoFSdfawZmzMTC8VqAvvHyYKqzUxzGiXFaf6H3BQnY+/ZhL/bENiwfo4sbuq0+hkWszvGi42Gc3+LjzZ/eLzyo3oXOf5Xgr8VoHOTI2qL1G4M39nlTqvNsKTZVAcvzspA61hRL089CvxbIuGYSbzv0ZaMOXpvS24U3G9GKbRPwuGEDALl/lNPm4w55YGsPtB+oN9wgcTS73tJYloCkqd7sXXkgp5xRduleDjyNgV3SziX6dG3Ml4NAlyDI1NtDR/DsDatlCILuVrNiPBLwQSColyy4HYKaX8gIGzOMXiFST//CLdRU7NyZ8uWsEcXHxIBzJd17Hcd/1Q4sTJIv9upiI/avzcWmN+RLuzpVcwE8lI69/7AEiS0NqFE/lbhvJpbG9+D4mw0qhtPa/VE9qt+5DvM0BLYIv6uX/Bcvccw5ol3ogccBUrvWgcoduVhUop4iSoB47mMfmI9tRSuwvLcRZ96TMPPiqTM2X3c9dh1Ye5tx8k1hYY+XgLmzI9FddcltDhH9H6yK4DbXkkcd3AemptO1aLLn4+PAKuv4RMwZ67lOWiQvS2/3uhrzjb+Xciche0sSYk2+kG4PZVQTAK6/NFmeJO7Aqy+loOPnRabJRROwNDVaOJ6F84ScUu6uWhyp6kH8ggcDM5JtXH5VC9c347rfOYvjt6OxdLa/N4Mkf6tL2GRkfj8BLcdew6ZXGxG/eqH7a9g2WcHWLZC6DaDONHWlzrW9bRNoels6+74V71/uQuJ3V9rnHdEuuX5B+utvCL9mHHN7g/C69u93i8rUG0ZaSn3jqqce6tgGm7mSHBCLdhz/Z7NTVAkA6v+5Fm1jJ2NmvH7utLXpHGA/6iSDJyFFYExYSKnjVxlL+k7NwM60cNQUljj/65Zdeg+afl6CgqvR2LotHbH2cDloqsXJD2VvXnobcKyyAxEzvqE/FJidjLQwT30B0PbhOVS/1+rWx5vFBXTsrx/xKizBGMPcbQF0NeOiGmNtb1h7kiHX3f7t8tZ5HHsHSE7+hqcU3sNs/cZAjX1WGH1UgdXbG3DPEzkofthkHCitbfr569cCGdeUXPva6N9G7Uub8mDX2LM0HzjxsbWLvto2fRg3TLqkLpwGvHcWJ5VdbgoHDIft4mTdrrc0lsVh5oxwsdM8tPGWSzjmeh/D/VNZVeNm+7dVn0MNEjBrujm+dZvYukyzfJfjQO0Nl+R9PbWuu3UeShd3G1yFciWBwSFgwe0wOIoMdi5Ov58f7MwDzM+yrhPS8dJTcWjaX4g1JeKYMH7f3/JhDbavLsT+jydjb+58RGj5T8PcB3tQ/XIpjn8kT/lUWG+HfNFpQFNYDO7Rng5OxsbcJEScKEFGSYM+L5AMmm3vVWD9hhIUt0QiSqVzWbVX1S/U2uV2325GdX4JCv/kEtF+Go05D0WL8VmOwvfaDSOxBy1v1MqAE42Ee2F8YpAgX8Cqq95Ck3pL6ose+OtAv5mcIAPrG8g/LXLFMOi+1YDSnEK8KWU0hGq75I1r5ellLTblVODiJ4pHD7RyvtiI6VszTHMQedFhmv5FuOAfzumcutpxsWwPVp8J141jLRdfG6usxZHzVAK6T5RiTZlRJ10dUD/py3ijx6/z5Z6JMUBTPY4ZnLu7pKz3xki6RjHmm3WeX3Sh6XQJNh0yO9GA7gslyHi5w/EkcUYWKrZGojKvFNXGv5Mlfn+lcGzA+ifLcfGWyJYiK+aFWaWoHJOE/O/7ePoqcd0Xg0vVEax2aoPlyMhtQET6Smyc6p6qPyGx6alI67qOmssJ2PgdX/oGW7ckZFutW4t1BkTjHrkFmt6q0du13L/dXwgdy+klrsfFRa7EUfVcmnMCleYvz+OXIH9rHOpf3IPtp432JV+0VH+wvysJOx+Pk5RqcZFn01NdclqDzVyEB8QiBhEXirR7r+W29D+3W7X7PPNYF6ZvfFScYSLPbTF0DrAfdRPDgJAh8OUvWfjNVshoC1jTNxJpP8rB1gmNyF7+LLLLatFktyHOofCpXCza34E5eTnQJik2l29sFw5l70GpOHk6ZVzuvNkg42wpjn4Wh6e/n6THjJyPp7W+YDc2VTbrPyczxpqMdSdw9otowzbRo/dtG+25vwtAmD6G1WJNlvMYVrpd7KojNxE1zoewyOvYua4E1U0d6FYcms5h+w9ew8VxychOl7HXR1L3S5N1+6sq2GOfRUbyIK30P1fhinqze2UM1Jsq9vUzNbYb+vnt1wIY15wgWLBRIwNpU15sN6c81YlnPnq7aOijbWNx3FDZe1gjFq7EzgfaUZBVhONi/KqHZcquPr59Dwqa4rBzi2HXWxzLktdnYI48tNPsXWWnyXjb2VSDTVm16BYb24MKTkER8h1gWf45NEk76Fb2p9iLGc83I+rxR7FqghE1IJs4Gon/swJ+ZRqive4s2xteJfTpQtB5uNSjZqeLZhf35WJW+h4cvSknXEhggAmMWgfRXeEDTDaI4u8Kt26MJmbuwpnnJqPjWCkWPSKdiazz1pbjpHw5P1i5w9F5i37J23bh4MJ2bM/YjEkzNmDSrFxknACyDmzBcuNhUdTDObjws9noPlaEWXNVnM2YtfU8ojbtQMX3bF/uRJhpSdyUg73JzXa5U1L3oDhyLYqXSyTXNwwkSC2xj2/TnA3HNj+LKUqXGZsxb0+rboxOVTHUKgZJ3nzEX3gNi5QuKXl+O8rY5VtQnB6Oyu0id9YGTEkvxYWHtuDYRhfdx6aguDwTSz99C2seUTyknJvfwl9Er2OZ8g1bZa+tXnSYsATFBUmIeLtM5zT3WWRfnoZDrzwKpwcqmgzPG6usYx/fgTM/SsAfDhQZeeVi0S/DUfCzx5DsWbQ9VBkaex/uQulG4SGcpzxfD0xdiyN5Cfjd87sxRRhNSsnGsiN3If85XVp3ryT/7Bye/nE94p/a5vQkMTFzC3bGN2CT7e9/Fcf/tgVZ3eexJk1xFOZpRTh2VyqO/Lcc51fURayVxSOXzbWIeGILLvwkxaOT0opcr3EiZ2LZw3J1carT/SIhbkuwdbNct1bqTNM2Esu3rESq1J/WrmdtxtO/lQuW00tcj4vIlS+KWb2XjPtF6nn5EdQtXIudUyWBGJyy1ZbEzB1yb8fhbJ7RvubKF8T3EuR+yzG1B5HnSU9NgvMm2MwttX+7CnF4+pX1mP5GCeYt2IwpC/Kw5kC79s9M3vpDldSjzlt996MqHdfQJBAp9oN6szc0tXPWKuJLgOWfxI2dhm1H90H9I+bFA2YbogyH2qZh74kX8Orj5vHQyGtiKo79ZCKObc3T5jJMSi9CQXMC8o/tQla8EUd2mn3yTALe37Nb4m2AGmsW5bcirSgfe+caRofE6/tivR/xmofHMawEByO+hTO/yJCHKV5TAgu+i4rvdmDn6lxMSdmMpBVlOClOjCMyJqZGAj5Serzksd/o99hnkdHN9/W3ST46J2O5bkvOEntSW3/wBlpEY4/6eejXLI9rItO8JFuwURMzd+GMpTblxXYzZ6gde+HjsV0UWbRt+jZuaOqoTVgCsn6xA/nTxa5eka3ZyFMW7Mb2P03GXnWP3aciyWp1XBd79cixDMxpFntX2WliFyR97zeI3r4NOx8UOX6WNBn7N35yAotSZQycK/bnjgZErRF77EfJDiev5GHdJg7H6oIc+JXpRy912Zq9oWIGbw06j6kZ2Pu9aNTk6zbTlCfe0O637k870HZTnM/KJg+e+pREAh4J3OkxdBQEfnXMHbhjmJTzqxGBKBqOxOU5OPXuYVx7dx/q3t6Ha3VyXL4FaRNc5ITFyBPDF7Q5fxokXl3NAdx4Nx87H3I21KLmZprkFePapX04uGKyYyBwEQsZzFa9XIwbF4z8aw/gVG4Slucdxo09811jG+eRSJYvknV1B6Dp8raRj4sxGvVQJs5cMuKIrprxGZ+BM5cPi+PCEGXeSRmXF+zDjdpinYXsj2ROgzJsblyWL6jmuBPmY2+5MDDpXbFF4prjyLFHHSQ8Ni0HdYZuDRcOo+7lDCQro8Ksmy9dRYY11uFIXLHNnlfdu6LzCVW/81FszkvkuS2udSMOFhUnUZxOdVJPir3S/Zo4y+akbYFilBYmMcaJ7LcP41RmgpyYFpGXVS71Wp6BRBVPXZKnpTsVR2Gt2l9DrbA4tBap49RFY/XBIbVA5InxYvpeATcudcWo2JKMKFueSqxXmeL888hFDz+TGadSO9ZbNThyNhzrVsz33sYdsa3pJvHjM+VecymXBLss1uvWb53ZJN+3BEeqpQ+okftR7vFi5fySa5bTS1yPy9gk7DxxwOhnbPdrClR7OPOEuZ1Eyr3t0l5/nYPlE1yketQzDlnHpD0UpDhFttQexLl94/ILTl9QdSHuMi2x0OTlIE21bym33r8Z5XbpDy21YX/9qK4styFKQNkQLqqF5GnAeoZFG/er9BmGDaH68GtiQ6xKjPRSxnDEip1w5pKkUf2MGpN+vQ3rEsNd4ocbY5cRT/ojzeaYG22K535/2i9q96Cne9oeA/DUj3gdGwBP9yrUPe40hh1A3b50JPqdI2mMlG8HbHaMbSx1Gvu8lsFzuYPd12mkPDGCS/4GM4/zT5rGMTf9PPZrtnq32W4WbZYwKzaqTfZhXFNtz2Ob0koNb7abftW09chHrru1i8Ooc7VtJJrTotV338cNu6wxk7Fun9iyho2s2taNEzlY5XKPWRrLRGhEWTfilAAAEABJREFUYjoO/vqAafx+AXsXCm9lf7mMtxLdeVG6KBvfsPGuie14KtfFHpMU1m1iuacTpXz+ZGosJa7NODTaqM2mkSxlsWhvSEx9cWn3EuixT5BweMxPLgSTh4gDIjEnV+xFg2/D0QzNMa30OpgWY/zCQ4vIDQkMGIFR6yC64w4g6suyGTC0wRGsNIyKUNvA5UWMjYb6hwPtb1x9JQ8LR9R4iTsu3Fcs6PIiLX1p1gRFRlvLX4ts2xi6jPeVjxFnbLgtkf/9mEjruljS25sOenh//7XJGms9r9hAOMD4eCqj0Q76q7uRA2Awj/JrVNtT+D2wxsWvGJ8Rmn51FvXx85D5kM9obheDq5vFug2gziLGRSPW9R4PID28fKyX21qZIjzpCc8f63l7Tu8U6peFU2xAu4d89VMu8Y3ToOpsyORu8AlEf3nw8+xLjtERfUmlp9HbajQC6cMj1P1rYUzS4rn2RwjeJ0Lp0V/5fR7D9L4uaGMpELj9JWn8LRHBYAT9o7cVf/2hziVgm8Xom93GLz1r+zbCUnms6+BVXp/bBfo8bkhKfTFY+GxbVuLo0vrXrgwOvr9j6Lx96mvoou0sydRi+tno+Qbc1vxI9XnZku66XpZ4GPJs/W9302s4iBSkefPT+1SOF0kgMAJ3BhZ9ZMW++8uhX567v3IHhsur7OCHBIYzAaV7bwPKjnUg+fvfsjh3lErElQRIYLQRGDcMHjCFi4UXPQz0HG1th+UlARIggUAJfPxpIg4VWHuzPVDZjE8CrgTEfHANGj3nyvkyJiy0yzuenuKgVRAFkYA/At0fNKN7cTryAp5Q1J9kXicBEhhJBNS8PuMj+/Z272BxGAz97klOxqpFkzAMnrcNIPZIJC4SDskxA5gHRZPAEBOInITHHk/G3HuHWI9QyX6QecQ/lIzYEP/OGipVQz36T2AkOYj6ROOeu0LXwFM/LQt4/oA+UWAiEiABRSBiRjr25mUgmY5ZhYMrCZCADwKxIewgUm8exw6CfTP98S3YmyVfXHxwGvmXopGatQV7H5888ovKEo5eAuOTsTVvC1apP6IYvRQcJScPB4uhPWLuA0Bg1DuIlAH1lS8NANkgiLz3q6HrvApC8SiCBEiABEiABIYtga+EA8qGCMUCKPtBveUUirpRJxIgARKwToAxSYAEBpvAqHcQKeATo0LPEaPebLoriJP7qnJyJQESIAESIAESCB6BiWPvQKj9VP0ucVxN4AOm4FUyJQ0sAUonARIgARIIKQJ3hpQ2Q6SM+ilXKBlTyrj730LQaTVE1cNsSYAESIAESCAkCaifcv1ViI3XfxUdWqZdSFYclSIBEiABEiABEvBIgFaEgUU9BQyFfyVRr4QnjGO1GNXCHQmQAAmQQGgTGPXaqX8KUzZEKICYNO4ORIaHgibUgQRIgARIgARIYDgSoCfCVGv33X0Hxo4Zup+bqSeRX7v7Tnz5SyaleEgCJEACQ0qAmZMACfgjoN5CVqu/eAN5Xb15/B++MnQ2zECWjbJJgARIgARIgAQGhwAdRCbOyqz6+n+4A+onZ6bgQTkMk5qYLHl/dcygZMdMSMBBgEckQAIkQAL9JqDeIhoqJ5FyDqm5C/tdCAogARIgARIgARIY1QTELTGqy+9WePUWj3LUjB/Ev69V/4Ry//g78dUBenvJrZAMIAESIAESIAESCDoB5SQazDmJ7pAnW/eNuwN0DgW9KimQBEiABEiABIYtgf4oTgeRF3oJ0XdAGXlie3mJEZxg5YiaKs6hr3wpOPIohQRIgARIgARIYOgIxN51BxLH3zHgPxdXP4mfFnMn7ubPyoauspkzCZAACQwNAeZKAgNGgA4iH2iVkfeN2IExvtRbQ1+/+w4oR5R6AuhDDV4iARIgARIgARIYRgSU80bZD/d+NfiPmcLDgL+Sh1iD4YQaRsipKgmMMAIsDgmQAAkMDQE6iPxwj/gSoF7fnjI+OI6iu8YA6l9G1FM/9c8nfrLnZRIgARIgARIggWFKIG7sHfjmPXdCzU2k/qW0P8VQD5bUm81KXuwg/gy+PzozrQ8CvEQCJEACJEACIUiADiKLlaIcO8pRlDThTsTLk7txX74D6imev+RqTiP1JFEZicoppBxN/JcRf9R4nQRIgARIgASGNwGb9spWUHMTKftBvTkcI86dL8vDJ9t1b/uwO4CxEXdA2Q9TY+6EsiHUm83e4jOcBEiABEiABEiABPpLgA6iAAmqJ4DKuPva3fpTQWXwTRHDTRl9yoGkVnXt/vF3Yro8Nfzre+/U5iJQr5mrp38BZjcqol/cl4tZ6WW4OCpKO5oK2YqjT0rdPvkWWvwV+1KZtIE9OHrTX0QL13vbUf18nsiTvIdju7r5Ftan56LgkoWyWoliyFt/otVKbMaxToAxSSBgAurNYfWQSf38TNkHyumj/hjjPrEplP2gbAn1IEnZDw8q++E/3AFlP0SGB5zVqEgQWvaDMebtqx8V7PtSyKDUVzDtBS+FCETPQOJ6yY7BJEACJBBSBOgg6md1KIfRXWK4KaNPTRSpVvV20VfHAGPC+il8pCXvakZNdQPaXMrV/WkH2m5+hm6X8IE97UHbhzWouTqwuQyodC88BzTPAIV3t0vdtn/uP1X3Z9IGOtDd6z+qvxj1hbuxqTocmc9tw5ul6fimvwRerw/Rhd7P0XKzAx93B55/90e1qLzc4ZzQkNfS5RzMMxIggaEloN4wVg+OoiLuwN1flvUrd0DZEuqNZdoP1upmaOwH77ppY96nPd4jjJYrXuyToNRXd/DsBW/V4VHPgSyTN0UYTgIkQAJDQIAOoiGAPlqzbDlRgvVHmvAXFwCpBYdx43IOUl3CB/a0Hvlry1Hx54HNZSCle+M5kHkGJjsOWcekbo9lID6whP2I3Yr3L3chdsVabH0oDrETYxDVD2nDK6k8vX62FMUfuHiC4jNw5vJhnMmMG17FobYkQAIk4IfA0NgPfpTiZXizT4ZLfXnSc7iXic2SBEiABKwSGNEOIqsQRmK87s860OnyPbG/5VQy2z7x/ZZH9+0OqDidX/Q3NyN9lyHPSlm+6NLzthLXEB+UXW8POoVL220/Tw0N/XzGs8n6zI8sS4oben3SFdjbWYaewXibx7uaum6W2omhj09uLhlFR0W6hDhOrbRjW2wV1/N9pOvvrpO3cJvEfu77wMJ3joa+VtuI7X4M1v3tWzleJQESGAoCtvvcylhqi+ujT1D9qLILgjamaP2gxXHNgn4KsaajvzFcRXRdlXxL43Ugfa0R100fb+HOSmllEZvEL2+lu8TzOw574W05H2f1vJ7Z7Ee/enuVIBc0XcVudGMn18yLVnaLbcicri/HWl4ebGdv4aY8bEx81pHNbvRXZpNcHpLASCbAsvWfAB1E/WcYQhJ60HSiELNmb8CU1Fwkzd2ASStKUH3rHLJnbED2OzZVW1G6egMWlbnPSVKzU9KsrnCaM6btbAmWLdBlznokF1NSsrG+rBGdNnFqf6sG2d/ejCkLcqHiJKVsxqJ9DXqcd4owSfKf93I7cLUK8+RYnU/aWatSwlOe+KweBWs3Y9JcQ56UZcpaVRYtib5pqcCiGc+i9CM158yzmCR6aXlL3FlPvYUmjz9XatXKPmlGKSpFSuVTUl5NHyWnAXmqnM83yBWX5Wq56J2Lwg9VuC5jUdl14b0Hs6SsScJl1gLR99tFqLyl4pjW240ozc2166fF0+rFFAddqC/ZLWw3Q5OVKrLS9uD4R+Y4xrEfnkCP6KXagSHrkWxMmZ2LTa51Zoiz72zMDY62eq4vE7bmNqHlX4Qae0LjQKsP93Zmq2ctVm8HavblYcosXbeklA2Y9VQF6j/TrjpvLHFzJNHa0Yw8FFwFml7O09rcJJPenRfKXNrxZix7/hzazO3EKFu1tOdNaRu0+yhDm7vHVufNUPfDotm6/qoubW3NNXzSE+Wov+3QD4Zs/9xMaWyHtxv0+8GoG5XvpBVluGjI91h2adfaPe9WL0qoxTZi01nVxVPZjvtR2rz9/lbiuJIACQx/AmIrbHcZd7X+2ehntAIG0Cd0NpQjQ42pYo+osVkbU15v1sRoG5ss7cS0ceuzbP1vo4yT0rdr/WAp1LyFWt9n6uft/aylPsvWD+p9vd6vKjvD1WYy6WYcdjdVYZPYPJqNosbr2c9i+9lWtzekgS7Ul7mOxyqu2EOGLMBWvn6MLyLL0hgn8eCBzaxcs81k08edt0re9Loqj8FMbB+netXqdAMCsvdUu1ux2W4/TpHxZZnNflQZWllVmfzaWYBTvWm2kV4X1cr2NWxSlZ1Tuwq0TFr8ItQonUzj5pS5z6LgPfG6uoZLe95UbW4PQOd7hr3iyaZWCmqraluuNmih3W5sO7Fb7KDdOOpqk0qbrNwutm9ujW6ja7KG1YbKkgAJDDABOogGGPBgim97fQ8WPd+M+zfn4Py7B3Djwgs4tbIHO588ifo+KtJ5WgyB3EZE/6cc1NUexo26A6g7kIKP9xdidZnN0OvA8bxyVN+fgfMqzuXDaPj5PES8WoRNr7cDf5OFurf3oeKJGCAxFRVyrM7rnkn2rNXtWmT/bQmO370Er74t5RB5197OwdN3N2LT2iIZdM3JelDxn3ejoDcdZ1SZL4t+h1Jxz/nXsP7gdXNE4zgO617ZJ/qsRZqEpOWrY7XuwLr4JGSujkb3679BpYzhctm+XPzVebTMWIjMB+xB6PhVCRadTEBxlaFjRRbWjW1Atjiyamzpe6+j8IlCFPxxMg5WGPHe3YW9X2t0Lsvlcqwu7cK60n24IeW9UZuPvQ+2YvuGUtT3OvLUjvzwbCrbjUUvtmJh/i5cqzus1dmZ/Mm44lRnmiTHplecY8K8tCsJB08U45rocK0qC3PfK0H2r4LxNpOeVX1hHta/2oXH9uxCw4XDuCYsXrqvHqvzG/QItq1Vbrb4sp/zzD6p123YmijN7Iltciznrzyq/bytUwy2uU++D3s7Vu3kwBJEny3Dwh/XuhhJHTi4owKd387CGWmrFSscP83qeKsUS/cDT/9SMTqAOltby8rD0r092KiFH8a1Y0sw/YMaZB5qFM36u7TjeG4RSjHP6X7ICjuHNfnntLfDPJZddC/4G895B9ZGWvHSD0pwdobcx1Jnqg8489xkfPzLIjx92tbQPefDUBIggWFC4PY5bFpehjfN4251Dla3VSHjBxUuD1ys9AkNeOnva9D5eA4a1Dgkfe75vAS8n1+IvMt9ZPLOa8g8HYe95fnSv2dhjlcxVvQDzDZTnerbaotxZiNQ4M9muvUW1q+uwMWEJXi1SsYCZRf9fD469paisMlZqSvy4Cdjf6tjPFZ57IjBm7m75aGdc//Z0Y/xxfIY19uM0h+ITdKWglerDZukej2W/vE1LNpe4zwWeuJ9uRQZ+c2YucNkX/woARfzi1B4Vcruxz6RGM6LsvfWSrv72kq7Dddw9FFEv1mEZfuvO8f1dtC+MS0AABAASURBVGbVXjDXm1b2A2j4ZTogtlHBB96ES3igZZIkEKef07h5YRf2LuhC6eY9cj8V4dQUYzytfQEHxRit3lmK459oCQHRc9Pmc8AKw+6We6fu0GzAZcxtKhNZ+7uw+sALuq0nNv+Rua3Y/reFqBSnbmx6KtLCmnHoVy4c5QHYkdPhWLc6FaPnJ/gGW+5IgAQsEbjTUixGGgYEGlD8cjMiVmTh1cwkxI8NByJjMF0GmIpHw53eCLJcGHEavJTfiKjVuszYMZIyLByxD61F2TMJ4nCoQI3mvGjFH/4IxE+fhngVR6JFzZA4RzOROU1OxkQidnw0YqMAhEXqx+pc6Qj3T/2hclQiBWVFGZgzPlyLEDE+CVlF4oD5Qhwwh8xfujtw5d4MnMqbj0RNnq5f9mKg5f+5jjYttfMmYly06BCJCAARY9WxvkaEAYnffxSpaMSRqna5aixd51D2eg/S1qQi1ghSu7abcSh+ZS3mTAhXp4i4LwX5cp76Wb04jfT0ba+XY/+fErD3lS1Iu8+INzYBq/ZkYZ2UpeD1Vi1tZ3O7fNGPw6wHo7VzjInDque24dVnZiKqWw+yb33x/OQt5IkxOudHO7B3cQJUmSB1lrh4Cyq0Oit3GCF2gWIoV1bg6CcJor/omWiwmaAzT/2swxSzH4ei20vHupD8jK6b+hVYhLBIfWqHGEg6G5t0q9xs8dXeVpfRYXIWFS11LOu4cDlpRHG+OKDM7Rjh0o4z8OqeFHRXlaNYLktEY5GnuDNy8OqWFCRKO1V6GhfQdjUaeUdtjHQZmx6WtnY5HNllOVhlYzd1JZ4Wo6/zvd/37d6zZaj2X0jbiE/G3l3S1kz3Q+ZScbheasQVieOx7Ep3436UKI5F6iGwNtKOztnCQ/qV2EgRo9rT8pXYKI64mnoXw1MucyEBEhh+BC7u/6+o/qrLuCtjwLafZiD5wyoUnzeXyUKf8FkrmuRL6vQHkxCl+mSEI365jEMHVmDp3c6OEbNkX8dNH0SiQPrfVQ/ESf+uj1Oe41vQD842k9a3jYmENlaujEaLZ8FaaP0RcQ5FpuCgslEmih7SJ8Y+kI6DpUuQ2KtF0Te3qrCztB1zzOPxGMlj+TYcXNGDygO/ccqn7+OL9TGurfIICq5Oc7ZdJojt8kIq4s/+BsdNBffEu6V9DJZuWY+C5Sb74vH5SEMH3r/SAUj5ArL3So6gMjIVx/ak2m24KGF55JlpaCl7w7AxdZzetlbtBad60+y2cEQlzhf7bD5iTeV2yyfAMunpXdpgZAJWrU9BbG8rrkzfgootSdDbXAzSNknZxcl14Xd6yu6bPXKvrMRLWyWONoYrW2MJVk8F7GOuGsdfbkVqwQ5seyhGt/XE5k99Zgu2TpT2oGzLyPnIzpS2fOwsLpraZdOvzqI+fh4yH9Lz45YESIAEXAnc6RrA82FKoKUJF2+HY9XSJLcCxC9OwXS3UAsBVz/Am7cj8di8BG1un7ZPOux73DcJ8TLQNd1UcqZh2eJINJUUImNfFS42dYmzA4h9QIyGB+RLrIpieW3GhUtdiF2+EMlhLonCkpCRHo7OSx+gyXRpeXqq21MQzTHS+Tn+Yopn6XBsKjY+Ho76f/qNPY+26hpUizGYuTDSWYRwXT7WOQhjU5AhDoP6+t/LhS5cvNQMPDQTc77kYKdx/CwO35TBuekPcl1iRs2WOGEN2LS6CKWnG9Gm7GcxKOYsTob4HCSGxeV//F4MgWlYnebOPXbhbMxRRsj/cJf1u3rR46HZWDrB5ZowXyjONpfQvp3+TnRDAlYvdNUtEqlO7dY6N0uKfPQBaj6JRuZ/dL83IGVeNbYLNe9J+e3CovHYosn2M6cDtzqPRNRXJMYDSUh1ZSfBQVnGTMO6vC1YNVWkqfkVbjai5nQVDr0lxvjtLjHLJTyQJeA2EoNV/9GVRzi+LPdnt+QfSNaMOzoIsJTDjcB1XLzQg6jFMxEvDwS0Mco23n8pATPvA658pD/M0EtmoU8Yl4zHZIyr/NGz4jw/hyu3eiSpOEdmz8ec+1zGUrliaXl4JtLGWokZ47/P8mEzxc5N8mEzteL9yz2ISJfxVPpAJ23iZ2P1A46Q7su/Rz0SsPShcLvtZGN7/zTpU6822+0MLVVfx5cAxrj31VjniePUtTh/+QVkxWua6BsP8eIXZ2JvVjJiJUa3tJWm986hsuwc6uX844DHA7H36noQu3g2pruwjFi8DTfqcpDqEi7ZuCxW7YVW7/U2IRnLTPXmkkEfTz20wbGRiBZpjz2cJFvvS8SMdOzNW6K1QTUHUcuH9ag+8QYqxN62j7lqHMc0ZCxwvZcmY9uvD9v/lGL6SrGlb9ei7KwyKiGfBpQd60Dy978FecYj51xIgARIwJ0AHUTuTIZnyB+VoRGNhBi4fyZO7NtA0NaONnTh6JO5UPMHOK0bz8mTrx50GGNO8rZ8VGwVR1JlBdasyIaa82ZNST06TU8t3BXzFNKKPzQB0ebXNkzRYu+V4bWpVfI2Bfo1IExxLRzO+c48xLecR9l7KnI73nyjGfGrF2KOaz5h2qMdFcm0RkJzGPypXXTswMf/Uy5dqsC8R1wZPovtZ+XaF19ozjRMWIIjFZlY9ZXrKNheiFlzN2DK2iIcb1JGtcSzuLS0KCM+HB7xjY/WjLorWhyzwFY0CXPco183X1HHiR4blboS2NqifcGIxj3jPaRLiDO10QC4eRDlFnRDyodwRI91uyIBMUiYCDT9UXGTU23xFlcueqxzCf/aRJjtagkJ6tLd9Bay1dwgKdmYtboETx96X9pX37LoSxtRziBTbjwkARIYUQTa0SxfPjuPlbiP9Y8UofQjoPtzY7A3yu2/T5AvyD/Lx8Fvh6PmQBmWpW3GpAV5yK68ro95hpyAdl+J1N78tZLGr359tpma0XAViL83xoMaMo59zRH88a12OWlG3rddx385z1dvQvfAafLhvo4vlse4VrT8SVSyytFTPDWPYMluY67LZ5Hx3Ekc/CAwO0U0MBbf9p4Ryc+uw6KdZb3e/GRo+bLnNhiD+xP8iehBU2URlqn5uxbkYtEPy/HSWdWWHOl8juOOaMCEVKyXh3zVr9aIPS8X3ruE413TsD7dU/uV61xIgARIQAjcKSuXkUDg6wnyBbsDzc5jiF6ymzedn1Lpof63k+JEZjjW/fwwblz2tO7DNvVWg5IUFo3kzG04/+5hbV6ZV3Mno+XnJVjmcR4glcDbGof75bFGy5/bPUZo+3MHMFWV1ePl4AROzcDTC3tw9MQ5dF/9DQ5dTsDG78jTPlfpvV+4hsh5Fzr/VXZfixGHQRzilbH40ErUeeR3GDf2zrcbvBHx87H3aLE8NTuAuvJMbLz7OravLoLrfEgi3esSHx8n18Rg+kR2rot6Iixh0+9TceTAvsQhUZjjTzcNp4P9gnZwpbFd2/d3E6/l60W3ZuXEseUQGDdbKq97rR17uTegfzFK9G+xeRU/4BduvYX1q19D0+wtqFPzZLxbjLoTu5C/RJylfci8b22kDxkxCQmQwDAhEKeNuxGrc7yM9YdxfouHMdBf6cbEIe1H+Wi4dBjXqrfh4OpI1Dy3B9mhMHeZZjO1w3iJ17kkH92A+umuc6DtLAFJUwHPNoqMY8oBY0TV+9oE7H37sBeu27B8jBG5PzvLY5wxtn7coTsL+pCnNo/g65GwzcPXULUPp/YtRHIfZAF6u/PM0qpAo0x+7Sy93prE1nCX3IzfidPPPXxoQtpe34NF+e1YuO8FbW6ha9X7cObAemRMdOijt60OfOzJ1nNEk6NILF+fivjLZ1F2tQuVJ2qBx7+F5a4vHklMLiRAAiRgI0AHkY3EcN/HJ2LO2B4cf7PBrSQtp2udjR0jhtugfPscKt41LqrdfTOxNN6zzO6P6lH9znVo/2T24WtYll6ISmOgUvPKzFmxBUe2xKDl3Q+cnQ69PXCdUkdl5VgTsDQ1Gt1VZ2Gf6Nl2sasWR6p6EL/gQXG+2AL7uff4hpMMqKtTEHG6BpsOnEfL4lSsmuAhH+GqJgJ0unLrPI69AyQnf0MLTl04DZAnNm+6/YtEO66cPoeLLerJWwcqd+RiUYl6oijJtPkM5mNb0Qos723EGe1NJgn3tLjynJ2MNDUp4RvmN2L0hC1vnMXFsGlY9JB+bt7OeVj0/PA8jn9oDpXj2zU4dlr2bou7YdIkPJrc4pkCvvkNzEEzjrk8CQO6UOPSbq1xM8n2dWhrx7+qdWt73e+cxfHb0Vg6O86XhCBe6wO3xt/jYm8Ssm1zFhjadHtsu3JR2oRsvS99bCPeBfIKCZDA8CZgG3cvSV/jUpKuZlyUvv3KZy7hfk47q4swa0W59tMjFTViwjSkbdmBl9KA6nfMdkof+kQlsL9r/INi3wDHX3eZmFnkXvnnWngfy+Iwc0a42CgeWLVcwjHzGGr0te5jHtD24TlUv9fqNiahL58AxriZs8XR58kmuVqOeepfYVt8KdCK9y93IfG7K+3z7WmxPT4skysyFvmz9+bOjvTIsvt0ISbNKoI+z6XI8rFYsxfikLpAHqpUntUmcHYSJ3Vx3HuFO0WF3zI5R+/LmfaT/8VLHHMLaUJ68BfzmP/XYk+Jrefetq6j8Nsb4PQvxQ98CxtndODQgSIcOR0tDzyTNInckAAJjDICARSXDqIAYIV21CRs3BiH7hOlWFPWoM9h09WhvaaacaDdRfU4MXBkUD7xGvZ/2CUGSg86m85h+7oTaIqXARS2z2RszE1CxIkSZJQYMmVwbHuvAus3lKC4JRJRKurUb2BmdyN2vliDJttb6J814OQZyTchBveoOLLeMzEGaKrHsffaJU+gu0s5R+SCy5L4/ZXyVK0B658sx0Vt3gKJe6sBhVmlqByThPzvi4Hjkibw0xgkxIuhWvWWrvMXPXD6wv3QQmyMb0bNO8C6FY63fJzyibyOnetKUN3UgW5JrzH8wWu4OC4Z2cbruxELV2LnA83IyyzCcYGjjKXu282ozt+DZS82oFubdDgacx6KRtPPy1FosAF60PJGLWoQjYR74fHjkWfkfDy9NQ5N+/dgU2Wz/vr6F13SDgqx7OV2TN+60uOTI13PDux/4llsr2xEyycdaLpQjvUrzqJFvV1k1mCabpgU/MM5o52142LZHqw+Ew7XqOZkGL8ET8sT5PoX92D7aV237tutuFgiun4QCTWnAYyPro8/bkZkvzujHVcdwWqndlyOjNwGRKSvxEZ5IuxXTH8j9JXbvTGIRyOOSX1qbVTV5+kSbDok95eTTtG4J0FusbdqcPETubfkXu329JJbH9uIU1Y8IQESGFEE9HG3FmuynMfd0u2FWHPkJqLGBVbcqBnfwP1/qsHO/WI7GP1Qd0sNKuSBR7yyBZS4vvaJKm2/18mafRN1thwLnypHjYzjbTcbcfz5Z5F5IdxpPHLNKnl9BubIA6tNORW6jSJ9bWdTDTZl1aJb7Ap7fKOvrX9xt/Pyiy1TAAAQAElEQVR4LP13hthbZ7+Itr9BbE/Tp4PJWlkiqo74HeNiH12r2yQ/ENvlIxknJL/uj8T+e7YGHekZWGXWX645Ly5jjFzsFtusNOcEKl1+gu/RPpH4rkvyxrVi79VCY6nGLbF9NBvzxUaxVzKQ6iLXNb06t2ovaG18XCOyV+xB4YVmtH3SiiuVJVj0Q7EDJipJvlerZfItxf/V+InRwIVaHLfVj2YzlqDwT6a045cgX2w9mz3VrZxH4sytzi/B/q4k7Hzc/NArBqvWTEO3PNStV//GOxj2jklVHg4eAeZEAsEiQAdRsEiGgJz47+1AxVNx+MP+Iqg5bCbNzcWiX4aj4OUlbl/ck7dswdbkdhSuzcaUGZuR9L0qYOsuFC8JdypJ1MM5uPCz2eg+ZsictRmztp5H1CbJ63vGABQmTpvyTCz902tYNHcDJs2QNbUIhzAfr+Y5nCtqEN/7cBdKNz4reW7AlOfrnfKyn4xNQfF/24Ks7vNYo+YtEHlT0opw7K5UHPlvOUgda4/ZjwMxqES3+AuGzil5OHrTLG4yli6WQdrXPz0s+C4qvtuBnatzMSVFGK4ow0kxCI+I7qmRhqywBGT9Ygfypzdj+wrFWsq9YDeyGx+UsmyBLV7s49tQsTUSxzbrbCZJnczb04o5eTmOn/EZIm07bzwTM3fhzDMJeH/PbiSlbMCklGws2tOO1OekzjLFg2ATYN5reuZDzRfx5p5Cbc6kZYWtmPvSDux09fpMWILigiREvF1mtLNnkX15Gg698iimm2V6OFZzVR1ZE4mTO3Tdpqg5Ka4l49h/mSeuMFMCTR//3EwpfB56bMebaxHxxBZc+EmK7uj0KSEIF/vKbepaHMlLwO+e340ps/T6XHbkLuQ/l6wppRmG2lEklm9ZidTPzmHNI5sxSe7Vp3+rXXDbJGbuCryNuElhAAmQwIgh4HHcLcHBiG/hzC8y4NNv4AmC9HcHf5GOe6qKMEuNQ2oc//ZreP+vV+LIpsl6ConT17FEF9C/rTYu/DwdM2+cx/oVuZj1eAnKkI43X0hxHo9csxG9jxwTJ1HzW7qNIn1t0vd+g+jt27DzQefIHvva/FakFeVj71yboeCcpi9nWllcbTVPY5waW18RPb9+HZsyZJxQ9ZLxX3F2+lqc9TsWyhgjNklW7yV9jFFplx9B3cK12KmcDuIos+nuzT6xXbfvVbtT9uOnwlKNW2L7zNr8Fv4iNtGxTC/2ij2xcaDKZMHOgspLbMid09txKGc3Zj2yG6t/BTxdtgWrxxqyfOwsl8mHDCuXEjflYG+y2Iy2+kndg+LItSheDsD0tlZi5g6xG+NwNs+wDeaKI/K9BBSX57jZyRFzZyJVMk9bk+rT+SlRuJAACZAA6CAaUY0gEskyYNTVHUDD2/tQ9+4B3DixBWn/W7h7KcdOw7ZDcv2CxHu7GNcuvYC9C2OQmPkCbojhYzYGo+Zm4pQ2t5At7j4cXDHZ+cnXhPnYe8Imbx8aLhzGtfJMzDEPujKIr3q5GDe0PPfhmhgjSrHUgsNueWJcMnaWi7zaYtRJWRpqD6Pu0FqkjlMpjDU+A2cuH0bxw8a5aedRpum6Oox6KBNnLtlY5Tv/e0dvA46d6PDzTw9jkLhiB2y8bWV20lFlNGYy1u3bp80tpOrFczyXutPqRDg/nqAkeF698ATCRa9tqFPzPtRIndUIR6nf4uUudeYq1TRfhJpz6lrFNmQlRepvVmlzKjkSxKbliHydnSpP3csZSFbGl1N9xCHrmNRtQYojYVg0UnPzca3WJW2SqkuXf1CxxM0hWj/S8zyTGaefmrZu7biuGBVbkhFlfkIpDtEbl1300GTocm+Yy6KFA1pb8xbuci9Z4uahXSc+Lu3MxEy7t9K24MblHKSZ9b9vCY5Uy71n1Lt2b3iQZ7mN9IGHgYU7EiCB4UbAbdw9gLp96UgcYypIAH1ClPTrWn/0roxD2jiu5MkDK1OfZalPhJ/+19zPBqCfKlXUjAwcrJAxUsauG5eKcepH8xEL9WZNDO6fpGJ4XiMS03Hw1wdwTSubw4ZKU/aM03jgYTx+Nx8750abBNvKl2IK0w8DGV8sjXFKrNh/WcomsdtXUi8FqYi114t3fTA2CTvF1nOUW9kpKcgqP4wzT5jsFS/2iVYec30pfZT9qOw9m20oY13FlmneH9x4qmOr9sK4JKiyX6sT2+Sy2CFHxUaeAO3nW4lfd9gNHvW0WiZP+qlyamOxB/tCCzfZsq75CI9TuUlYnic675mvJBlrpNj8ytaTcsj9pdn8v87BcimPEcG+a6s6hxqx0TIXRtrDeEACJEAC3gjc6e0Cw4czgXBEqX+sGhvuvxCR0YgdH+ns7PGSKmKshbiavGjP/6Jlk2vEibAbI7YLHvZjIkU/kTcGA/TxzKr77G9wtGuaxX960GV4/Ocws9ZhVuLpcazWiSbeB8+IcVJn48Lh76P/3r/Q/bf5t2u0OYjmPKTPqeQsR9fVb7mdE+lnlljoURFIXCOJr52lduxLQL+v9ZFbABwiLNY75BMRQFzwQwIk0HcCwyVlkMddvc/1NY73sU/sF8/r2lwt8/Zfd5NyRc1BNH4y5sa7XXIL0Mtm0YYapL7Wsk79qGfLefiwT9xgBhLXLbER4GOcbCrJxaRvl+NKrxHXttPmIIrEnBkmB5ftmqd9MPT0JNc1zHI++v0T69Xmv46yf2pG/OqFmGPF7nbVg+ckQAKjjsCdo67ELDAJ+CXQjuOvNiJiFP3Tg3p1eueD15G9+FmsL6lC9elzqCwrwqLF5ah5MB35y2P8UmMEEhjtBFh+EiCB4UJgMjbmJuMvZXswa2MJSt84J+NeFQo2ZmNZWQ9W5a1FMr9MD5fKtKSnmoNo1Rc1WJa+G9vLaqS+z+Hovt2Yta4Gf3l8LZ6eYUnM8Iv03lkcaknAxu8YP+0cfiWgxiRAAoNMgA6iQQY+JNlFTsJjjydj7r1Dkvvwy/Tm79H8tfl46ftJXnSPROKiZKxKHkFOk7AEZJW+gFM7pMyXzmLnyyeRf6YHS3fsQENpBhJpKKu2wJUESIAESGCEEIh6eAsuVGRhU3w7jh04KePeOVyJT8WRin3Y+3DkCCkli2EnMDYFe3+djyNrYtDyxhtS3ydR1hSDTUX5uJCX4v0nbXYBw/GgB/XXe/BYbobnf+MdjkWiziRAAgNOgA6iAUccAhmMT8bWvC1YNTUEdAlpFQzlJs7HzrxMLPf6enk0UrO2YO/jI+xpTFg0pi9fiyNH96GuStaj27Bt+WTnOXoMRNyRAAmQAAmQwHAnEBGfgqy8XTijxryqF/BqXgZS48OHe7GovzcCY+KQmrkFr54QG0fq/MyBLciaG2dpmgVvIkM7PBzJq8Ve/V7SCC5jaNcAtSOB4UhgdDmIhmMNUWcSIAESIAESIAESIAESIAESIAESIIHACDB2wAToIAoYGROQAAmQAAmQAAmQAAmQAAmQAAkMNQHmTwIkEFwCdBAFlyelkQAJkAAJkAAJkAAJkAAJBIcApZAACZAACQwiATqIBhE2syIBEiABEiABEiABEjAT4DEJkAAJkAAJkECoEKCDKFRqgnqQAAmQAAmQwEgkwDKRAAmQAAmQAAmQAAkMCwJ0EA2LaqKSJEACJBC6BKgZCZAACZAACZAACZAACZDA8CdAB9Hwr0OWgAQGmgDlkwAJkAAJkAAJkAAJkAAJkAAJjHACdBCN8Aq2VjzGIgESIAESIAESIAESIAESIAESIAESGPkEvJeQDiLvbHiFBEiABEiABEiABEiABEiABEiABIYXAWpLAn0kQAdRH8ExGQmQAAmQAAmQAAmQAAmQAAkMBQHmSQIkQAIDQYAOooGgSpkkQAIkQAIkQAIkQAIk0HcCTEkCJEACJEACg06ADqJBR84MSYAESIAESIAESIAESIAESIAESIAESCC0CNBBFFr1QW1IgARIgARGCgGWgwRIgARIgARIgARIgASGEQE6iIZRZVFVEiCB0CJAbUiABEiABEiABEiABEiABEhgpBCgg2ik1CTLMRAEKJMESIAESIAESIAESIAESIAESIAERgWBUe4gGhV1zEKSAAmQAAmQAAmQAAmQAAmQAAmQwCgnwOL7I0AHkT9CvE4CJEACJEACJEACJEACJEACJBD6BKghCZBAvwjQQdQvfExMAiRAAiRAAiRAAiRAAiQwWASYDwmQAAmQwMARoINo4NhSMgmQAAmQAAmQAAmQQGAEGJsESIAESIAESGCICNBBNETgmS0JkAAJkAAJjE4CLDUJkAAJkAAJkAAJkEAoEqCDKBRrhTqRAAmQwHAmQN1JgARIgARIgARIgARIgASGHQE6iIZdlVFhEhh6AtSABEiABEiABEiABEiABEiABEhgZBGgg2hk1WewSkM5JEACJEACJEACJEACJEACJEACJEACI5+AvYR0ENlR8IAESIAESIAESIAESIAESIAESIAERhoBlocErBGgg8gaJ8YiARIgARIgARIgARIgARIggdAkQK1IgARIIAgE6CAKAkSKIAESIAESIAESIAESIIGBJEDZJEACJEACJDDQBOggGmjClE8CJEACJEACJEAC/gkwBgmQAAmQAAmQAAkMKQE6iIYUPzMnARIgARIYPQRYUhIgARIgARIgARIgARIIXQJ0EIVu3VAzEiCB4UaA+pIACZAACZAACZAACZAACZDAMCVAB9EwrTiqPTQEmCsJkAAJkAAJkAAJkAAJkAAJkAAJjEQCdBA51yrPSIAESIAESIAESIAESIAESIAESIAERj4BltCFAB1ELkB4SgIkQAIkQAIkQAIkQAIkQAIkMBIIsAwkQAKBEKCDKBBajEsCJEACJEACJEACJEACJBA6BKgJCZAACZBA0AjQQRQ0lBREAiRAAiRAAiRAAiQQbAKURwIkQAIkQAIkMDgE6CAaHM7MhQRIgARIgARIwDMBhpIACZAACZAACZAACYQAATqIQqASqAIJkAAJjGwCLB0JkAAJkAAJkAAJkAAJkECoE6CDKNRriPqRwHAgQB1JgARIgARIgARIgARIgARIgASGNQE6iIZ19Q2e8syJBEiABEiABEiABEiABEiABEiABEhg5BKwOYhGbglZMhIgARIgARIgARIgARIgARIgARIgARsB7knAIwE6iDxiYSAJkAAJkAAJkAAJkAAJkAAJDFcC1JsESIAEAidAB1HgzJiCBEiABEiABEiABEiABIaWAHMnARIgARIggSAToIMoyEApjgRIgARIgARIgASCQYAySIAESIAESIAESGAwCdBBNJi0mRcJkAAJkAAJOAjwiARIgARIgARIgARIgARChgAdRCFTFVSEBEhg5BFgiUiABEiABEiABEiABEiABEhgeBCgg2h41BO1DFUC1IsESIAESIAESIAESIAESIAESIAERgABOoj8VCIvkwAJkAAJkAAJkAAJkAAJkAAJkAAJjHwCo72EdBCNX3fpJwAACWdJREFU9hbA8pMACZAACZAACZAACZAACZDA6CDAUpIACfggQAeRDzi8RAIkQAIkQAIkQAIkQAIkMJwIUFcSIAESIIG+EqCDqK/kmI4ESIAESIAESIAESGDwCTBHEiABEiABEiCBASFAB9GAYKVQEiABEiABEiCBvhJgOhIgARIgARIgARIggcEnQAfR4DNnjiRAAiQw2gmw/CRAAiRAAiRAAiRAAiRAAiFGgA6iEKsQqkMCI4MAS0ECJEACJEACJEACJEACJEACJDCcCNBBNJxqK5R0pS4kQAIkQAIkQAIkQAIkQAIkQAIkQAIjhoBXB9GIKSELQgIkQAIkQAIkQAIkQAIkQAIkQAIk4JUAL5CAIkAHkaLAlQRIgARIgARIgARIgARIgARGLgGWjARIgAT8EqCDyC8iRiABEiABEiABEiABEiCBUCdA/UiABEiABEigfwToIOofP6YmARIgARIgARIggcEhwFxIgARIgARIgARIYAAJ0EE0gHApmgRIgARIgAQCIcC4JEACJEACJEACJEACJDBUBOggGiryzJcESGA0EmCZSYAESIAESIAESIAESIAESCAkCdBBFJLVQqWGLwFqTgIkQAIkQAIkQAIkQAIkQAIkQALDjwAdRIHWGeOTAAmQAAmQAAmQAAmQAAmQAAmQAAmMfAKjrIR0EI2yCmdxSYAESIAESIAESIAESIAESIAEdALckgAJOAjQQeRgwSMSIAESIAESIAESIAESIIGRRYClIQESIAESsEiADiKLoBiNBEiABEiABEiABEggFAlQJxIgARIgARIggWAQoIMoGBQpgwRIgARIgARIYOAIUDIJkAAJkAAJkAAJkMCAE6CDaMARMwMSIAESIAF/BHidBEiABEiABEiABEiABEhgaAnQQTS0/Jk7CYwWAiwnCZAACZAACZAACZAACZAACZBACBOggyiEK2d4qUZtSYAESIAESIAESIAESIAESIAESIAEhisB6w6i4VpC6k0CJEACJEACJEACJEACJEACJEACJGCdAGOOSgJ0EI3KamehSYAESIAESIAESIAESIAERjMBlp0ESIAEXAnQQeRKhOckQAIkQAIkQAIkQAIkMPwJsAQkQAIkQAIkEBABOogCwsXIJEACJEACJEACJBAqBKgHCZAACZAACZAACQSPAB1EwWNJSSRAAiRAAiQQXAKURgIkQAIkQAIkQAIkQAKDRIAOokECzWxIgARIwBMBhpEACZAACZAACZAACZAACZBAKBCggygUaoE6jGQCLBsJkAAJkAAJkAAJkAAJkAAJkAAJhDwBOoj6XUUUQAIkQAIkQAIkQAIkQAIkQAIkQAIkMPIJjOwS0kE0suuXpSMBEiABEiABEiABEiABEiABErBKgPFIYBQToINoFFc+i04CJEACJEACJEACJEACo40Ay0sCJEACJOCZAB1EnrkwlARIgARIgARIgARIYHgSoNYkQAIkQAIkQAJ9IEAHUR+gMQkJkAAJkAAJkMBQEmDeJEACJEACJEACJEACwSZAB1GwiVIeCZAACZBA/wlQAgmQAAmQAAmQAAmQAAmQwKASoINoUHEzMxIgARsB7kmABEiABEiABEiABEiABEiABEKHAB1EoVMXI00TlocESIAESIAESIAESIAESIAESIAESGCYEOiHg2iYlJBqkgAJkAAJkAAJkAAJkAAJkAAJkAAJ9IMAk44GAnQQjYZaZhlJgARIgARIgARIgARIgARIwBcBXiMBEhj1BOggGvVNgABIgARIgARIgARIgARGAwGWkQRIgARIgAR8EaCDyBcdXiMBEiABEiABEiCB4UOAmpIACZAACZAACZBAnwnQQdRndExIAiRAAiRAAoNNgPmRAAmQAAmQAAmQAAmQwMAQoINoYLhSKgmQAAn0jQBTkQAJkAAJkAAJkAAJkAAJkMAQEKCDaAigM8vRTYClJwESIAESIAESIAESIAESIAESIIFQI0AHUfBrhBJJgARIgARIgARIgARIgARIgARIgARGPoERVUI6iEZUdbIwJEACJEACJEACJEACJEACJEACwSNASSQwegjQQTR66polJQESIAESIAESIAESIAEScCXAcxIgARIgAY0AHUQaBm5IgARIgARIgARIgARGKgGWiwRIgARIgARIwD8BOoj8M2IMEiABEiABEiCB0CZA7UiABEiABEiABEiABPpJgA6ifgJkchIgARIggcEgwDxIgARIgARIgARIgARIgAQGkgAdRANJl7JJgASsE2BMEiABEiABEiABEiABEiABEiCBISNAB9GQoR99GbPEJEACJEACJEACJEACJEACJEACJEACoUkgmA6i0CwhtSIBEiABEiABEiABEiABEiABEiABEggmAcoagQToIBqBlcoikQAJkAAJkAAJkAAJkAAJkED/CDA1CZDAaCNAB9Foq3GWlwRIgARIgARIgARIgAQUAa4kQAIkQAIkYCJAB5EJBg9JgARIgARIgARIYCQRYFlIgARIgARIgARIwCoBOoiskmI8EiABEiABEgg9AtSIBEiABEiABEiABEiABIJCgA6ioGCkEBIgARIYKAKUSwIkQAIkQAIkQAIkQAIkQAIDT4AOooFnzBxIwDcBXiUBEiABEiABEiABEiABEiABEiCBISZAB9EgVACzIAESIAESIAESIAESIAESIAESIAESGPkEhnMJ6SAazrVH3UmABEiABEiABEiABEiABEiABAaTAPMigRFLgA6iEVu1LBgJkAAJkAAJkAAJkAAJkEDgBJiCBEiABEYnATqIRme9s9QkQAIkQAIkQAIkMHoJsOQkQAIkQAIkQAJuBOggckPCABIgARIgARIggeFOgPqTAAmQAAmQAAmQAAkERoAOosB4MTYJkAAJkEBoEKAWJEACJEACJEACJEACJEACQSRAB1EQYVIUCZBAMAlQFgmQAAmQAAmQAAmQAAmQAAmQwGARoINosEgzH3cCDCEBEiABEiABEiABEiABEiABEiABEggJAgPqIAqJElIJEiABEiABEiABEiABEiABEiABEiCBASVA4cOfAB1Ew78OWQISIAESIAESIAESIAESIAESGGgClE8CJDDCCdBBNMIrmMUjARIgARIgARIgARIgAWsEGIsESIAESGA0E7jzwqWL4EoGbANsA2wDbANsA2wDbAOjoA3Q7qPdyzbANsA2wDbANsA24KUN/P8AAAD//5SsC/UAAAAGSURBVAMAPXjp9jWjbXYAAAAASUVORK5CYII=\" width=\"624\" height=\"352\">\u003C/p>\n\u003Ch3 dir=\"ltr\">GROUP DISCUSSIONS\u003C/h3>\n\u003Cp dir=\"ltr\">The 20 New Yorkers who participated then broke into smaller groups organized around key domains drawn from the Mayor’s platform: Affordability, Housing, Early Childhood and Education, and Labor and Small Business.\u003C/p>\n\u003Cp dir=\"ltr\">Each group followed a structured process to move from issues to data-actionable questions. Participants identified key themes, refined them into specific components, developed questions that could be addressed using data, and mapped those questions to relevant NYC Open Data and other datasets. This created a clear link between community priorities, available data, and potential pathways to action.\u003C/p>\n\u003Cp dir=\"ltr\">After deliberation, the full group reconvened and each subgroup shared the questions they identified as most important.\u003C/p>\n\u003Ch3 dir=\"ltr\">EMERGING QUESTIONS AND INSIGHTS\u003C/h3>\n\u003Cp dir=\"ltr\">Affordability\u003C/p>\n\u003Cp dir=\"ltr\">The affordability discussion expanded to capture the full set of costs shaping life in New York City, including housing, childcare, food, transportation, healthcare, employment, and leisure. Participants framed affordability in terms of thresholds and tradeoffs, focusing on what it takes to maintain stability, participate in community life, and build toward longer-term goals. \u003C/p>\n\u003Cp dir=\"ltr\">The distinction between surviving and thriving emerged as a central theme, alongside the cumulative burden of everyday expenses and the added costs of navigating life without shared support. Participants also highlighted challenges in accessing services, pointing to a persistent gap between service availability and discoverability. Existing programs are often difficult to find and navigate due to limited awareness and fragmented systems. This raised questions about how open data, and potentially AI tools, could improve visibility and access to services that support community connection. \u003C/p>\n\u003Cp dir=\"ltr\">In the shareback, the group centered on three questions: How do we ensure that those born and raised in NYC can afford to remain in the city over their lifetimes? What happens to the city when people with talent and knowledge are priced out? And how can residents more easily find and access free or low-cost public programs and services?\u003C/p>\n\u003Cp dir=\"ltr\">Housing\u003C/p>\n\u003Cp dir=\"ltr\">Participants described housing as an interconnected system shaped by legal, economic, social, and environmental forces. Discussions linked tenant and landlord dynamics, zoning and regulation, redlining and segregation, financialization, and short-term rentals, alongside concerns about affordability, homeownership, and access to shelter. \u003C/p>\n\u003Cp dir=\"ltr\">The group also emphasized neighborhood identity, public space, and community connection, as well as environmental risks such as lead exposure and air quality. These factors interact to influence stability, safety, and the ability to remain in place. Housing support, governance, and access to services were also raised as key components of the broader system. \u003C/p>\n\u003Cp dir=\"ltr\">In the shareback, the group focused on questions of both aspiration and constraint: What does ideal housing in New York City look like, and how far is the current system from that vision? What land is available for new development, and what is the condition of existing housing stock?\u003C/p>\n\u003Cp dir=\"ltr\">Early Childhood and Education\u003C/p>\n\u003Cp dir=\"ltr\">The early childhood and education discussion focused on how resources, access, and outcomes are distributed across students and communities. Participants raised questions about where students are located relative to schools and services, the availability and quality of childcare and aftercare, and how resources such as funding, teachers, facilities, and nutrition are allocated. Issues of equity were central, including language access, disability support, and differences in travel time and safety. Participants also expressed interest in how progress is tracked and how outcomes are measured in a changing technological landscape. \u003C/p>\n\u003Cp dir=\"ltr\">In the shareback, these themes converged around questions of distribution and accountability: How are quality, access, and cost balanced across the system? How is nutrition integrated into student outcomes? And how much funding is available, and where does it actually go?\u003C/p>\n\u003Cp dir=\"ltr\">Labor and Small Business\u003C/p>\n\u003Cp dir=\"ltr\">The labor and small business discussion surfaced a set of pressures affecting both workers and local enterprises, including healthcare, benefits, wages, regulatory conditions, and access to capital and support. Participants noted how these factors are interconnected, with changes in one area shaping outcomes in others. Concerns about cost-of-living pressures, retirement security, and aging in place underscored the importance of long-term stability, while discussions of unions and public programs raised questions about accountability and effectiveness. \u003C/p>\n\u003Cp dir=\"ltr\">In the shareback, the group focused on questions related to transparency and responsibility: What evidence is needed to assess whether union welfare funds are being spent effectively compared to other public employee systems? What obligations do different levels of government have in providing healthcare? And how much funding is available at the local level to support public service employees aging in place?\u003C/p>\n\u003Ch3 dir=\"ltr\">\u003Cimg src=\"https://cms.thegovlab.com/assets/d6120d58-2556-4235-a5fc-19c5c64ff791.JPG?width=1000&height=667\" alt=\"Dsc F0664\">\u003C/h3>\n\u003Ch3 dir=\"ltr\">KEY TAKEAWAYS\u003C/h3>\n\u003Cp dir=\"ltr\">Three themes stood out across the discussions. First, participants approached public issues as interconnected systems rather than discrete categories. Questions about affordability, housing, education, and labor consistently overlapped, reflecting how these challenges are experienced in practice, even as public data remains organized sector by sector.\u003C/p>\n\u003Cp dir=\"ltr\">Second, access and discoverability emerged as persistent challenges across topics. Participants focused on where public programs, services, and resources are located, who can access them, whether they are reaching people in practice, and how well they align with actual patterns of need.\u003C/p>\n\u003Cp dir=\"ltr\">Third, the session showed the importance of starting with lived experience. The strongest questions begin with everyday challenges and then connect to data. This highlights the need for stronger alignment between community priorities, the questions being asked, and the data systems used to answer them.\u003C/p>\n\u003Cp>\u003Cstrong> \u003C/strong>\u003C/p>\n\u003Ch3 dir=\"ltr\">LOOKING AHEAD\u003C/h3>\n\u003Cp dir=\"ltr\">The Questions Lab reinforced the importance of focusing on the questions that data is meant to answer. It also demonstrated a replicable model for connecting community insight to data-driven action: starting with lived experience, translating that into structured questions, and linking those questions to data.\u003C/p>\n\u003Cp dir=\"ltr\">The questions identified during the session point to opportunities to improve how data is collected, shared, and applied across New York City. They also highlight the growing role of open data in the age of AI. As tools make it easier to access and analyze information, the central challenge is ensuring that the right questions are being asked and that systems are equipped to respond to them.\u003C/p>\n\u003Cp>\u003Cstrong> \u003C/strong>\u003C/p>\n\u003Cp dir=\"ltr\">As cities continue to invest in open data, embedding this kind of participatory approach will be critical to ensuring that data is not only available, but meaningful and actionable. Strengthening the connection between community perspectives and data systems will help ensure that data more effectively informs decisions, guides policy, and supports more inclusive public problem-solving.\u003C/p>\n\u003Cp>\u003Cstrong> \u003C/strong>\u003C/p>\n\u003Cp dir=\"ltr\">REFLECTIONS\u003C/p>\n\u003Cp dir=\"ltr\">Manny Patole, NYU (Professor and Co-Organizer)\u003C/p>\n\u003Cp dir=\"ltr\">Open Data Week with The Governance Lab and Brooklyn Public Library was a meaningful reminder that engaged communities remain deeply interested in shaping how data informs public life. It was especially valuable to see students experience, in real time, how classroom readings and lessons translated directly into practice through the work led by Stefaan and GovLab. \u003C/p>\n\u003Cp dir=\"ltr\">The event reflected many of the principles we discuss in class: that community and question science are ongoing, relational efforts. Only through human interaction, conversation, and trust can we better understand what communities want, what they need, and develop questions and data practices that produce useful public knowledge.\u003C/p>\n\u003Cp>\u003Cstrong> \u003C/strong>\u003C/p>\n\u003Cp dir=\"ltr\">Claudia Duran Garcia, NYU Student Volunteer\u003C/p>\n\u003Cp dir=\"ltr\">The conversation in the “Labor and Small Business” breakout group sparked instantly. Engaging with fellow New Yorkers across generations with unique lived experiences was a highlight of the workshop. Hearing from a retired union teacher seeking data to defend healthcare benefits that were once promised underscored a vital truth: data tells us the “what” but people tell us the “why”.\u003C/p>\n\u003Cp dir=\"ltr\">This event reinforced my belief that data-driven and mission-driven decision-making must go hand in hand. I strongly resonated with the observation that we often prioritize data collection over the thoughtful formulation of the questions themselves. While formulating well-crafted questions is difficult, it is a critical step in driving meaningful change. The \"Questions Lab\" (Q-Lab) concept is a great initiative for researchers, activists, and decision-makers alike. I am eager to see how the Q-Lab framework fosters a more inclusive, responsive New York.\u003C/p>\n\u003Cp dir=\"ltr\"> \u003C/p>\n\u003Cp dir=\"ltr\">Chaitanya Kukreja, NYU Student Volunteer\u003C/p>\n\u003Cp dir=\"ltr\">Attending the Open data and Better Questions event during NYC Open Data Week was a grateful experience for me. This was the first time formally I sat in a room where real citizens/public (New Yorkers, regardless of background) discussed issues through the lens of their Lived experience. The energy in the room was welcoming, creative and genuinely collaborative. Everyone’s perspective was respected, and watching people organically choose the bigger categories(early childhood & education,housing, affordability, labor and small business) according to their interest before drilling into specific questions showed me what real community engagement looks and feels like. I am someone who values education and the conversation around childcare and education was really good. \u003C/p>\n\u003Cp dir=\"ltr\">People raised some questions like: where are quality childcare programs? How is the budget allocated, and where does it actually go? How to find quality teachers? These were not abstract questions they came from parents and people navigating their lives and finding good childcare/education for their children. One moment that made me understand more was discussion around school surveys that parents fill, where only few parents fill it, that partial response is rarely flagged making the data skew silently. It made me question reliability, consistency and trust: why should we trust data that doesn’t capture the full picture? - of course there will not always be 100% participation but how do we ensure the data is trustful?\u003C/p>\n\u003Cp dir=\"ltr\">What also moved me was realizing how many people still don’t know where data lives or how to access it and that is not just a technical gap but it is an awareness problem too. \u003C/p>\n\u003Cp dir=\"ltr\">This connects to something I now see more clearly through courses I take at CUSP and experiences I have had. A course that it connects to is Citizen and the City at CUSP, where we explore how communities use data, design thinking and appreciative inquiry to address urban challenges and co-creation of different community led projects. This event was that framework alive in a room. It also made me think about the AI dimension: How are education and childcare programs actually utilizing AI and how do we ensure it works meaningfully, rather than just existing or not used well? Also, I love to teach and love designing something meaningful for students that makes them grow and independent to think. This made me think about what the next generation (not just next but in fact the previous generations needed this too) truly needs: not just information delivery but development of creativity, critical thinking, and skills that prepare them for a future which we can’t predict fully.\u003C/p>\n\u003Cp dir=\"ltr\">Trust, reliability, human judgement, passion, engagement, and intuition are things AI cannot substitute and this event reminded me that good data like good education has to be built with communities not just for them. The conversation during the event was wide ranging and unstructured - but that is part of community engagement and I love it. Through human judgement I chose to highlight what I believe can bring change/impact.\u003C/p>",{},"/blogposts/open-data-and-better-questions-engaging-new-yorkers-to-develop-questions-that-matter-in-the-age-of-ai",{"title":40,"description":45},"open-data-and-better-questions-engaging-new-yorkers-to-develop-questions-that-matter-in-the-age-of-ai","blogposts/open-data-and-better-questions-engaging-new-yorkers-to-develop-questions-that-matter-in-the-age-of-ai",[56,57,58],"data","ai","opendata","evcyaFHFhxy7eos3ZVzsuC_kV1zNDK_5fFVPZKez8Lo",[61,73,87,102,116,129,141,153,166,178,192,204,216,228,240,252,264,274,286,300,313,326,339,350,362,376,391,404,417,430,443,456,465,478,490,502,515,528,540,552,564,576,589,602,614,626,639,650,661,675,688,702,713,726,737,750,762,774,786,799,811,823,837,850,863,875,887,899,911,923,935,947,959,972,984,996,1010,1022,1034,1045,1059,1072,1086,1098,1111,1123,1135,1147,1159,1169,1181,1193,1205,1218,1230,1242,1257,1269,1281,1295,1307,1321,1335,1348,1361,1373,1386,1398,1410,1422,1434,1447,1459,1472,1483,1494,1508,1521,1533,1545,1557,1568,1580,1591],{"id":62,"title":41,"affiliation":34,"bio":63,"body":34,"category":64,"cohort":34,"description":34,"expertise":34,"extension":46,"headshot":34,"image":65,"legacy":47,"linkedin":34,"meta":66,"name":41,"navigation":15,"path":68,"photo":34,"role":69,"seo":70,"stem":71,"topic":34,"website":34,"__hash__":72},"faculty/faculty/adam-zable.json","Adam Zable is a Research Fellow at the GovLab. His work and research interests focus on the intersection of emerging technologies, data governance, and democracy. In his previous role as the Director of Emerging Technologies at the Digital Trade and Data Governance Hub, he spearheaded the development of the Global Data Governance Mapping Project, assessing national-level data governance efforts across the globe, and organized conferences on the international policy implications of extended reality and on data governance in the age of generative AI. He holds a Master of Public Policy from the Willy Brandt School of Public Policy and resides in Freiburg, Germany.","author","https://cms.thegovlab.com/assets/1646f23a-0139-43d8-b0b2-51a455000728",{"slug":67},"adam-zable","/faculty/adam-zable","Research Fellow",{},"faculty/adam-zable","5bR3wydz1eXMWjD7PSD8j4im9GZM7_x4k8iz5TN85Dw",{"id":74,"title":75,"affiliation":34,"bio":76,"body":34,"category":77,"cohort":34,"description":34,"expertise":34,"extension":46,"headshot":34,"image":78,"legacy":15,"linkedin":79,"meta":80,"name":75,"navigation":15,"path":82,"photo":34,"role":83,"seo":84,"stem":85,"topic":34,"website":34,"__hash__":86},"faculty/faculty/adrienne-schmoeker.json","Adrienne Schmoeker","Adrienne Schmoeker is the former Deputy Chief Analytics Officer for the City of New York. During her tenure with the City of New York she led the team at the NYC Mayor's Office of Data Analytics, grew the NYC Open Data Program, co-founded NYC Open Data Week and was part of the founding team in the inaugural NYC CTO's Office. Prior to working for New York City government Adrienne led new market expansion and the development of the enterprise business for the social-impact company Catchafire. Adrienne is currently advising urban technology startups, helping design new data programs and advising on government technology programs working to close the digital divide.","instructor","https://cms.thegovlab.com/assets/d359af3c-47d4-4da8-a3b1-acc521906ceb","https://www.linkedin.com/in/schmoeker/",{"slug":81},"adrienne-schmoeker","/faculty/adrienne-schmoeker","Course Advisor",{},"faculty/adrienne-schmoeker","z_7IPuXoKOHDOgpFTsSqBY_T1QYYDk0kAWowjYGC3ow",{"id":88,"title":89,"affiliation":90,"bio":34,"body":34,"category":91,"cohort":92,"description":34,"expertise":34,"extension":46,"headshot":34,"image":93,"legacy":47,"linkedin":94,"meta":95,"name":89,"navigation":15,"path":97,"photo":34,"role":34,"seo":98,"stem":99,"topic":100,"website":34,"__hash__":101},"faculty/faculty/alex-hutchison.json","Alex Hutchison","Forr Data","guest-faculty","DS Berlin 2024, DS Berlin 2025","https://cms.thegovlab.com/assets/6c6e0f38-0cae-48d9-9c3d-acea65f0eb5c","https://www.linkedin.com/in/alex-hutchison-62517412/",{"slug":96},"alex-hutchison","/faculty/alex-hutchison",{},"faculty/alex-hutchison","Impact Measurement & Work of Data for Children Collaborative","Q6zJ9wxOc_kYKt4uhkMYd9u3Ptug0EKTJWUmU75-NX4",{"id":103,"title":104,"affiliation":105,"bio":34,"body":34,"category":91,"cohort":106,"description":34,"expertise":34,"extension":46,"headshot":34,"image":107,"legacy":15,"linkedin":108,"meta":109,"name":104,"navigation":15,"path":111,"photo":34,"role":34,"seo":112,"stem":113,"topic":114,"website":34,"__hash__":115},"faculty/faculty/alex-pompe.json","Alex Pompe","Freelancer, Open Mapping and Nature Photography","DS Turin 2024","https://cms.thegovlab.com/assets/049e41fa-e266-495e-80e1-0096c00bca85","https://www.linkedin.com/in/alexpompe/",{"slug":110},"alex-pompe","/faculty/alex-pompe",{},"faculty/alex-pompe","Data powered collaborations for social good & Meta's Data for Good Work","NxXESqPg21h7BH-7afZnYJ00dZxFvjnMPJZFkxAyNPY",{"id":117,"title":118,"affiliation":119,"bio":34,"body":34,"category":91,"cohort":120,"description":34,"expertise":34,"extension":46,"headshot":34,"image":121,"legacy":15,"linkedin":122,"meta":123,"name":118,"navigation":15,"path":125,"photo":34,"role":34,"seo":126,"stem":127,"topic":34,"website":34,"__hash__":128},"faculty/faculty/alison-paprica.json","Alison Paprica","University of Toronto","DS Academy 2020-2022","https://cms.thegovlab.com/assets/22da179a-53e4-4752-aa1a-1f70bb07ffaa","https://www.linkedin.com/in/p-alison-paprica-4468326/",{"slug":124},"alison-paprica","/faculty/alison-paprica",{},"faculty/alison-paprica","GdVZUxEJDplSSVWpxCQFaDliddUXHhtXXbk2BbtBQeA",{"id":130,"title":131,"affiliation":132,"bio":34,"body":34,"category":91,"cohort":120,"description":34,"expertise":34,"extension":46,"headshot":34,"image":133,"legacy":15,"linkedin":134,"meta":135,"name":131,"navigation":15,"path":137,"photo":34,"role":34,"seo":138,"stem":139,"topic":34,"website":34,"__hash__":140},"faculty/faculty/amen-ra-mashariki.json","Amen Ra Mashariki","Bezos Earth Fund","https://cms.thegovlab.com/assets/a4701d40-6533-4230-8874-871ac94b3f34","https://www.linkedin.com/in/amen-ra-mashariki-1452885/",{"slug":136},"amen-ra-mashariki","/faculty/amen-ra-mashariki",{},"faculty/amen-ra-mashariki","B9EIq50RRHe6ou-4B7T6sCncR2ahTJxjNa8CKUzSMHg",{"id":142,"title":143,"affiliation":144,"bio":34,"body":34,"category":91,"cohort":120,"description":34,"expertise":34,"extension":46,"headshot":34,"image":145,"legacy":15,"linkedin":146,"meta":147,"name":143,"navigation":15,"path":149,"photo":34,"role":34,"seo":150,"stem":151,"topic":34,"website":34,"__hash__":152},"faculty/faculty/amy-regas.json","Amy Regas","Tetra Tech","https://cms.thegovlab.com/assets/4b28666d-dde5-4d03-bd08-d7aa6009b6e3","https://www.linkedin.com/in/amykimregas/",{"slug":148},"amy-regas","/faculty/amy-regas",{},"faculty/amy-regas","YdWm44wcLD4bmciE5cbyAEjZTvsqHUtaxZLa3Hsy1I4",{"id":154,"title":155,"affiliation":156,"bio":34,"body":34,"category":91,"cohort":106,"description":34,"expertise":34,"extension":46,"headshot":34,"image":157,"legacy":47,"linkedin":158,"meta":159,"name":155,"navigation":15,"path":161,"photo":34,"role":34,"seo":162,"stem":163,"topic":164,"website":34,"__hash__":165},"faculty/faculty/andrea-zaramella.json","Andrea Zaramella","Vodafone Business","https://cms.thegovlab.com/assets/6bb956bb-52af-469e-8cdf-2dff4a4a4321","https://www.linkedin.com/in/andreazaramelladott/",{"slug":160},"andrea-zaramella","/faculty/andrea-zaramella",{},"faculty/andrea-zaramella","Use of TELCO Data for Impact Evaluation","VkhRswUzIdWPshGjj7Js6ub9aLPKoPWBg3C4mMWCB68",{"id":167,"title":168,"affiliation":169,"bio":34,"body":34,"category":91,"cohort":120,"description":34,"expertise":34,"extension":46,"headshot":34,"image":170,"legacy":15,"linkedin":171,"meta":172,"name":168,"navigation":15,"path":174,"photo":34,"role":34,"seo":175,"stem":176,"topic":34,"website":34,"__hash__":177},"faculty/faculty/andrew-collinge.json","Andrew Collinge","Jacobs","https://cms.thegovlab.com/assets/6a9b4a0a-d447-4c44-aeaa-ab2155274359","https://www.linkedin.com/in/andrew-collinge-0708b91b/",{"slug":173},"andrew-collinge","/faculty/andrew-collinge",{},"faculty/andrew-collinge","CJI_-FFIpM7E761MupRMJsu5qZDTThFIU9aiNR1X2s0",{"id":179,"title":180,"affiliation":34,"bio":181,"body":34,"category":77,"cohort":34,"description":34,"expertise":34,"extension":46,"headshot":34,"image":182,"legacy":47,"linkedin":183,"meta":184,"name":186,"navigation":15,"path":187,"photo":34,"role":188,"seo":189,"stem":190,"topic":34,"website":34,"__hash__":191},"faculty/faculty/andrew-j-zahuranec.json","Andrew J Zahuranec","\u003Cp dir=\"ltr\">Andrew J. Zahuranec is a Research and Partnership Manager at The GovLab, where he oversees projects focused on how advances in science and technology can improve governance and help society better address systemic challenges such as climate change and migration. Andrew has coordinated and managed various initiatives, including the #Data4COVID19 Africa Challenge—a collaboration between l'Agence française de développement (AFD), Expertise France, and The GovLab to accelerate responsible data innovation to tackle COVID-19 pandemic and its effects across Africa—and the Responsible Data for Children initiative—a collaboration with UNICEF to promote the more effective and responsible use of data for and about children in settings afflicted with challenges such as forced migration, climate change, and disaster response. \u003C/p>\n\u003Cp>\u003Cstrong id=\"docs-internal-guid-ccdeb17b-7fff-01a5-cb71-4ee622a7f2b9\">\u003Cbr>\u003C/strong>In addition, Andrew conducts significant research on data collaboration and data stewardship, especially as they relate to ongoing, dynamic crises. He developed publications including \u003Cem>The #Data4COVID19 Review, The Use of Mobility Data for Responding to the COVID-19 Pandemic, and What Is Mobility Data? Where Is It Used?\u003C/em>, all of which examined the role that non-traditional data could play in understanding patterns of human mobility for pandemic response. He is a lead for the Data Stewards Academy and has facilitated courses on data stewardship for the State of Maryland.\u003C/p>","https://cms.thegovlab.com/assets/d7d6b2e8-0e2e-4f85-b1b7-2074e8ab6932","https://www.linkedin.com/in/ajzahuranec/",{"slug":185},"andrew-j-zahuranec","Andrew J. Zahuranec","/faculty/andrew-j-zahuranec","Course Facilitator",{},"faculty/andrew-j-zahuranec","yzaZQzcLZDC1c1DhsU9KAioBGaCIWd8NOlZVK5ui2Vw",{"id":193,"title":194,"affiliation":34,"bio":195,"body":34,"category":64,"cohort":34,"description":34,"expertise":34,"extension":46,"headshot":34,"image":196,"legacy":47,"linkedin":34,"meta":197,"name":194,"navigation":15,"path":199,"photo":34,"role":200,"seo":201,"stem":202,"topic":34,"website":34,"__hash__":203},"faculty/faculty/andrew-young.json","Andrew Young","Andrew Young is the Knowledge Director at The GovLab, where he leads research efforts focusing on the impact of technology on public institutions. Among the grant-funded projects he has directed are a global assessment of the impact of open government data; comparative benchmarking of government innovation efforts against those of other countries; a methodology for leveraging corporate data to benefit the public good; and crafting the experimental design for testing the adoption of technology innovations in federal agencies.","https://cms.thegovlab.com/assets/25da5949-b5b6-4d38-9536-d1c14254d987",{"slug":198},"andrew-young","/faculty/andrew-young","Knowledge Director",{},"faculty/andrew-young","ES3foDZEbla4rnSjm-O-j2YKaeuV7UJT-wYzo3nsWHM",{"id":205,"title":206,"affiliation":207,"bio":34,"body":34,"category":91,"cohort":120,"description":34,"expertise":34,"extension":46,"headshot":34,"image":208,"legacy":15,"linkedin":209,"meta":210,"name":206,"navigation":15,"path":212,"photo":34,"role":34,"seo":213,"stem":214,"topic":34,"website":34,"__hash__":215},"faculty/faculty/astha-kapoor.json","Astha Kapoor","Aapti Institute","https://cms.thegovlab.com/assets/e6b4a35a-9c6b-4cca-a83f-ec6a030191cf","https://www.linkedin.com/in/astha-kapoor-020b4760/",{"slug":211},"astha-kapoor","/faculty/astha-kapoor",{},"faculty/astha-kapoor","1dBPf4BnttnxR4o4dPzmhz1cd59hs20Kcoz01SZiY50",{"id":217,"title":218,"affiliation":219,"bio":34,"body":34,"category":91,"cohort":120,"description":34,"expertise":34,"extension":46,"headshot":34,"image":220,"legacy":15,"linkedin":221,"meta":222,"name":218,"navigation":15,"path":224,"photo":34,"role":34,"seo":225,"stem":226,"topic":34,"website":34,"__hash__":227},"faculty/faculty/audrey-tang.json","Audrey Tang","Ministry of Foreign Affairs, Taiwan","https://cms.thegovlab.com/assets/69c84800-8eba-4fc4-b536-4971a87729bb","https://www.linkedin.com/in/tangaudrey/",{"slug":223},"audrey-tang","/faculty/audrey-tang",{},"faculty/audrey-tang","guDc8SZ1teF85fU2ZpyjBo5kT7eFEi6aqz0KnMI3ZQc",{"id":229,"title":230,"affiliation":231,"bio":34,"body":34,"category":91,"cohort":120,"description":34,"expertise":34,"extension":46,"headshot":34,"image":232,"legacy":15,"linkedin":233,"meta":234,"name":230,"navigation":15,"path":236,"photo":34,"role":34,"seo":237,"stem":238,"topic":34,"website":34,"__hash__":239},"faculty/faculty/bapu-vaitla.json","Bapu Vaitla","City of Davis & Data2X","https://cms.thegovlab.com/assets/55cfe9b5-222f-4521-8416-b6dca6f2969c","https://www.linkedin.com/in/bapu-vaitla-3389b94/",{"slug":235},"bapu-vaitla","/faculty/bapu-vaitla",{},"faculty/bapu-vaitla","xY4K5eu3CdK8TcDvRERRonurgCaRe7vfqME3ptSPXoQ",{"id":241,"title":242,"affiliation":243,"bio":34,"body":34,"category":91,"cohort":120,"description":34,"expertise":34,"extension":46,"headshot":34,"image":244,"legacy":15,"linkedin":245,"meta":246,"name":242,"navigation":15,"path":248,"photo":34,"role":34,"seo":249,"stem":250,"topic":34,"website":34,"__hash__":251},"faculty/faculty/barbara-ubaldi.json","Barbara Ubaldi","Tony Blair Institute for Global Change","https://cms.thegovlab.com/assets/6a3cc3f2-b0d1-46d0-94fe-088d6607976f","https://www.linkedin.com/in/barbara-ubaldi-416146/",{"slug":247},"barbara-ubaldi","/faculty/barbara-ubaldi",{},"faculty/barbara-ubaldi","OqdWXuIUfhBjhmn9X4-G4P0L3YgggPM_hGvvy9C8Iho",{"id":253,"title":254,"affiliation":255,"bio":34,"body":34,"category":91,"cohort":120,"description":34,"expertise":34,"extension":46,"headshot":34,"image":256,"legacy":15,"linkedin":257,"meta":258,"name":254,"navigation":15,"path":260,"photo":34,"role":34,"seo":261,"stem":262,"topic":34,"website":34,"__hash__":263},"faculty/faculty/bart-rousseau.json","Bart Rousseau","Digital Vlaanderen","https://cms.thegovlab.com/assets/1aa4a2bb-f3e7-4509-89e5-0605a0a92423","https://www.linkedin.com/in/bartrosseau/",{"slug":259},"bart-rousseau","/faculty/bart-rousseau",{},"faculty/bart-rousseau","FzXJOFe3JOVPVIw_CuvcAR_rOIaEScwzutzZ2svmUoI",{"id":265,"title":266,"affiliation":34,"bio":34,"body":34,"category":64,"cohort":34,"description":34,"expertise":34,"extension":46,"headshot":34,"image":34,"legacy":47,"linkedin":34,"meta":267,"name":269,"navigation":15,"path":270,"photo":34,"role":34,"seo":271,"stem":272,"topic":34,"website":34,"__hash__":273},"faculty/faculty/begona-g-otero.json","Begona G Otero",{"slug":268},"begona-g-otero","Begoña G. 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University","https://cms.thegovlab.com/assets/e8770782-607b-4f62-8076-7980c7a7173f","https://www.linkedin.com/in/julia-stoyanovich-b184851/",{"slug":918},"julia-stoyanovich","/faculty/julia-stoyanovich",{},"faculty/julia-stoyanovich","Gw1KbXq3FyB0quxq7VTlz_EgLKDJlJrlDiEl2LQ0J0c",{"id":924,"title":925,"affiliation":926,"bio":34,"body":34,"category":91,"cohort":120,"description":34,"expertise":34,"extension":46,"headshot":34,"image":927,"legacy":15,"linkedin":928,"meta":929,"name":925,"navigation":15,"path":931,"photo":34,"role":34,"seo":932,"stem":933,"topic":34,"website":34,"__hash__":934},"faculty/faculty/kersten-jauer.json","Kersten Jauer","United 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America","https://cms.thegovlab.com/assets/b5553bc1-fcf2-4012-b359-3e6d08b95202","https://www.linkedin.com/in/lcoral/",{"slug":979},"lilian-coral","/faculty/lilian-coral",{},"faculty/lilian-coral","PFVGbRvqYy1KzELxWJoua5jrbJnMNUzlngzC92NPKF4",{"id":985,"title":986,"affiliation":987,"bio":34,"body":34,"category":91,"cohort":120,"description":34,"expertise":34,"extension":46,"headshot":34,"image":988,"legacy":15,"linkedin":989,"meta":990,"name":986,"navigation":15,"path":992,"photo":34,"role":34,"seo":993,"stem":994,"topic":34,"website":34,"__hash__":995},"faculty/faculty/lisa-moretti.json","Lisa Moretti","Freelance, Digital Sociologist","https://cms.thegovlab.com/assets/2ee04896-2db7-436c-b2b8-1de20e12c578","https://www.linkedin.com/in/lisataliamoretti/",{"slug":991},"lisa-moretti","/faculty/lisa-moretti",{},"faculty/lisa-moretti","EV8XLcbAFAoKtBzuzfT85iXeQ49CT2LH-2tTPcZln8I",{"id":997,"title":998,"affiliation":999,"bio":34,"body":34,"category":91,"cohort":1000,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1001,"legacy":15,"linkedin":1002,"meta":1003,"name":998,"navigation":15,"path":1005,"photo":34,"role":34,"seo":1006,"stem":1007,"topic":1008,"website":34,"__hash__":1009},"faculty/faculty/ludovica-paseri.json","Ludovica Paseri","University of Turin","DS Turin 2024, DS Milan 2026","https://cms.thegovlab.com/assets/f7c1cd4c-45fe-44b5-8d73-7ba5b03f394d","https://www.linkedin.com/in/ludovica-paseri-616880149/",{"slug":1004},"ludovica-paseri","/faculty/ludovica-paseri",{},"faculty/ludovica-paseri","Legal Implications to Data 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Innovation","https://cms.thegovlab.com/assets/453663e1-2ae9-4767-b18d-40c689edd62f","https://www.linkedin.com/in/lynn-overmann-325bb432/",{"slug":1017},"lynn-overmann","/faculty/lynn-overmann",{},"faculty/lynn-overmann","T_4VBcHNBpT91i3OLMPlturz60ufyjOayd4Wu5Y0s5E",{"id":1023,"title":1024,"affiliation":1025,"bio":34,"body":34,"category":91,"cohort":120,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1026,"legacy":15,"linkedin":1027,"meta":1028,"name":1024,"navigation":15,"path":1030,"photo":34,"role":34,"seo":1031,"stem":1032,"topic":34,"website":34,"__hash__":1033},"faculty/faculty/marc-lepage.json","Marc Lepage","Asian Development Bank","https://cms.thegovlab.com/assets/0b25f882-fdb8-4cc3-bbf2-5d7a9f174c7a","https://www.linkedin.com/in/mlepage/",{"slug":1029},"marc-lepage","/faculty/marc-lepage",{},"faculty/marc-lepage","JCDw1BHIBhdOfzo1coSP7TDfQgIgSwdNw8P2txPefxY",{"id":1035,"title":1036,"affiliation":579,"bio":34,"body":34,"category":91,"cohort":120,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1037,"legacy":15,"linkedin":1038,"meta":1039,"name":1036,"navigation":15,"path":1041,"photo":34,"role":34,"seo":1042,"stem":1043,"topic":34,"website":34,"__hash__":1044},"faculty/faculty/marcus-bartley-johns.json","Marcus Bartley Johns","https://cms.thegovlab.com/assets/a36a2963-d9c0-4f73-a66c-ca96ae2dad3e","https://www.linkedin.com/in/marcus-bartley-johns-57a84422/",{"slug":1040},"marcus-bartley-johns","/faculty/marcus-bartley-johns",{},"faculty/marcus-bartley-johns","6ezX8H4I7u6nV0D1iKncL_l-M-af7CYPz9NXFU1iaAk",{"id":1046,"title":1047,"affiliation":1048,"bio":34,"body":34,"category":91,"cohort":665,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1049,"legacy":47,"linkedin":1050,"meta":1051,"name":1053,"navigation":15,"path":1054,"photo":34,"role":34,"seo":1055,"stem":1056,"topic":1057,"website":34,"__hash__":1058},"faculty/faculty/martin-pompery.json","Martin Pompery","SINE Foundation","https://cms.thegovlab.com/assets/ed8cc1b5-1ca9-4bfa-9ae2-e954ddf739b6","https://www.linkedin.com/in/pompery/",{"slug":1052},"martin-pompery","Martin Pompéry","/faculty/martin-pompery",{},"faculty/martin-pompery","Data Commons (Governance and Technical Infrastructure)","tjuo0EubXDpXzFAIg_QIbXCZltPEmfV5O6BB2OiGxgk",{"id":1060,"title":1061,"affiliation":34,"bio":1062,"body":34,"category":64,"cohort":34,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1063,"legacy":47,"linkedin":1064,"meta":1065,"name":1067,"navigation":15,"path":1068,"photo":34,"role":34,"seo":1069,"stem":1070,"topic":34,"website":34,"__hash__":1071},"faculty/faculty/martin-stewart-weeks.json","Martin Stewart Weeks","\u003Cp>A strategic thinker, organisational consultant, policy analyst, facilitator and writer, Martin’s work draws on over 35 years’ experience spanning government, the “for purpose” or social sector and the corporate sector.\u003C/p>\n\u003Cp>As well as his own advisory and research work, Martin has also been a senior advisor to Deloitte’s public sector team in Australia and with The Impact Assembly as part of PwC’s social impact practice.\u003C/p>\n\u003Cp>He is an ANZSOG Practice Fellow for Digital Government Strategy and Leadership and the founder and principal of Public Purpose Pty Ltd, working at the intersection of public policy, strategy, leadership and technology. He has led the development of new programs exploring the public leadership implications of digital, data and AI, including the role of ethics and ethical frameworks for public leaders. He co-directs ANZSOG’s Deputies Leadership Program with Kathryn Anderson, that integrates a significant focus on the evolution of leadership responses to the opportunities and risks of digital, data and AI capabilities and platforms.\u003C/p>\n\u003Cp>From 2001 to 2013, Martin led the Asia-Pacific public sector consulting and innovation team in Cisco’s Internet Business Solutions Group (IBSG). He led strategy and design work on digital transformation and public policy and public sector reform projects in government, education, health, human services and urbanisation in India, China, South-East Asia, Australia and New Zealand.\u003C/p>\n\u003Cp>Prior to his role in Cisco, Martin held various policy and management roles in the federal public sector, including Chief of Staff to a Minister in the Federal Government, a federal public servant (Communications, Sport, Recreation and Tourism) and as a research and strategy lead in the Office of Strategic Planning in the NSW Cabinet Office. For the past three years, and again in 2026, he has been a Learning Guide and facilities for the NSW Leadership Academy (Band 1 and Band 2 senior executives).\u003C/p>\n\u003Cp>He chaired the former NSW Digital Government Advisory Panel and the Expert Advisory Group for the Welfare Payments Infrastructure Transformation program (WPIT) for the former federal Department of Human Services (now Services Australia).\u003C/p>\n\u003Cp>He was a member of the Government 2.0 Task Force established by Federal Minister for Finance, Lindsay Tanner, in 2009. In 2017, Martin was one of 3 members of a review for the NSW Government, chaired by former NSW Premier Nick Greiner AC, of regulatory policy and strategy across the State.\u003C/p>\n\u003Cp>He was an inaugural member of the NSW AI Advisory Group which crafted the early versions of what has now become the NSW AI Assurance Framework which is also being adapted as a national AI assurance framework.\u003C/p>\n\u003Cp>Martin writes and speaks extensively on government, service design, digital transformation, the impact of AI in government and on public leadership for the digital age as well as on different dimensions of policy reform. Together with former Finance Minister Lindsay Tanner, he wrote Changing Shape: Institutions for a Digital Age (Longueville Press, February 2014).\u003C/p>\n\u003Cp>He is also co-author with Simon Cooper of (Are We There Yet? Digital Transformation of Government and the Public Sector in Australia (Longueville Press, July 2019).\u003C/p>\n\u003Cp>Martin holds an Honours degree in English from the University of York, a Masters degree in Social Science and Policy from the University of New South Wales as well as graduate qualifications in applied economics from what is now the University of Canberra.\u003C/p>","https://cms.thegovlab.com/assets/ae57bd4e-ccca-4e57-90ae-ccdea38ab424","https://www.linkedin.com/in/msweeks/",{"slug":1066},"martin-stewart-weeks","Martin Stewart-Weeks","/faculty/martin-stewart-weeks",{},"faculty/martin-stewart-weeks","nuHbo3Pfrch0Q_uq8-nDe3bEo8ZnLVP1A8Psh91GOYc",{"id":1073,"title":1074,"affiliation":1075,"bio":34,"body":34,"category":91,"cohort":92,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1076,"legacy":47,"linkedin":1077,"meta":1078,"name":1080,"navigation":15,"path":1081,"photo":34,"role":34,"seo":1082,"stem":1083,"topic":1084,"website":34,"__hash__":1085},"faculty/faculty/martin-wahlisch.json","Martin Wahlisch","University of Birmingham","https://cms.thegovlab.com/assets/7615dae2-797a-4f6f-9597-c7c7ed0e8b92","https://www.linkedin.com/in/martinwaehlisch/",{"slug":1079},"martin-wahlisch","Martin Wählisch","/faculty/martin-wahlisch",{},"faculty/martin-wahlisch","Institutionalizing Data Stewardship, data hubs and digital transformation of public organizations","cZUQPYrsyAC_ng4hHzqnYTmfJC5j8FWC-2Zq7Uc4moc",{"id":1087,"title":1088,"affiliation":1089,"bio":34,"body":34,"category":91,"cohort":120,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1090,"legacy":15,"linkedin":1091,"meta":1092,"name":1088,"navigation":15,"path":1094,"photo":34,"role":34,"seo":1095,"stem":1096,"topic":34,"website":34,"__hash__":1097},"faculty/faculty/michael-khoo.json","Michael Khoo","Asia Travel Tech, Digital Platforms Industry Associations","https://cms.thegovlab.com/assets/2fd51780-b7e0-4f6a-9479-ee706f588580","https://www.linkedin.com/in/michael-khoo-/",{"slug":1093},"michael-khoo","/faculty/michael-khoo",{},"faculty/michael-khoo","vRl_-AO0A8xraN5t_Y_XgR1EMzL-G9WpJKI9LRhnKSM",{"id":1099,"title":1100,"affiliation":1101,"bio":34,"body":34,"category":91,"cohort":120,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1102,"legacy":15,"linkedin":1103,"meta":1104,"name":1106,"navigation":15,"path":1107,"photo":34,"role":34,"seo":1108,"stem":1109,"topic":34,"website":34,"__hash__":1110},"faculty/faculty/michael-p-canares.json","Michael P Canares","Australian Department of Foreign Affairs and Trade","https://cms.thegovlab.com/assets/67c98b20-e643-4314-8e6c-3fb94387560e","https://www.linkedin.com/in/michaelcanares/",{"slug":1105},"michael-p-canares","Michael P. Cañares","/faculty/michael-p-canares",{},"faculty/michael-p-canares","6BbSAcLd0R0TuJaYMZ_bcmUHcwp5VrQCMy2iTB6QXlU",{"id":1112,"title":1113,"affiliation":34,"bio":1114,"body":34,"category":64,"cohort":34,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1115,"legacy":47,"linkedin":34,"meta":1116,"name":1113,"navigation":15,"path":1118,"photo":34,"role":1119,"seo":1120,"stem":1121,"topic":34,"website":34,"__hash__":1122},"faculty/faculty/michelle-winowatan.json","Michelle Winowatan","Michelle Winowatan is a Research Assistant at The GovLab, where she focuses on exploring the ways data and collaboration can improve policymaking. Previously, she has worked at several international nonprofit organizations, such as Search for Common Ground and Human Rights Watch, implementing projects that focused on conflict and human rights issues. She is a Fulbright awardee, through which she obtained her MPA in Public and Nonprofit Management and Policy from New York University. She also has a BA in International Relations from Universitas Pelita Harapan, in Indonesia.","https://cms.thegovlab.com/assets/3b47fcf8-abb4-4ef9-b85f-3ce0f73497e3",{"slug":1117},"michelle-winowatan","/faculty/michelle-winowatan","Research Assistant",{},"faculty/michelle-winowatan","ntoprYtYdb69qzeOoDH0OIZRyYocJVD0uWd0qtFkRIA",{"id":1124,"title":1125,"affiliation":1126,"bio":34,"body":34,"category":91,"cohort":120,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1127,"legacy":15,"linkedin":1128,"meta":1129,"name":1125,"navigation":15,"path":1131,"photo":34,"role":34,"seo":1132,"stem":1133,"topic":34,"website":34,"__hash__":1134},"faculty/faculty/natalia-adler.json","Natalia Adler","Mitiga Solutions","https://cms.thegovlab.com/assets/57496f31-6ebb-41af-8fd7-eb98ff828199","https://www.linkedin.com/in/nataliaadler/",{"slug":1130},"natalia-adler","/faculty/natalia-adler",{},"faculty/natalia-adler","x3XYVJctiWzq4vPbSnpRVYZqJf4wJGw9XvT1lhOUHyk",{"id":1136,"title":1137,"affiliation":1138,"bio":34,"body":34,"category":91,"cohort":120,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1139,"legacy":15,"linkedin":1140,"meta":1141,"name":1137,"navigation":15,"path":1143,"photo":34,"role":34,"seo":1144,"stem":1145,"topic":34,"website":34,"__hash__":1146},"faculty/faculty/natalia-carfi.json","Natalia Carfi","Open Data Charter","https://cms.thegovlab.com/assets/bd0e0761-0de4-45cd-a475-bd0ee12a36d9","https://www.linkedin.com/in/natalia-carfi-8683029/",{"slug":1142},"natalia-carfi","/faculty/natalia-carfi",{},"faculty/natalia-carfi","DWP3TEtP8jvPaTnVAcRhfUa81L0oknuD_Ov4ueMyB0s",{"id":1148,"title":1149,"affiliation":1150,"bio":34,"body":34,"category":91,"cohort":120,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1151,"legacy":15,"linkedin":1152,"meta":1153,"name":1149,"navigation":15,"path":1155,"photo":34,"role":34,"seo":1156,"stem":1157,"topic":34,"website":34,"__hash__":1158},"faculty/faculty/natalia-domagala.json","Natalia Domagala","Freelance, AI Strategy, Ethics & Transparency","https://cms.thegovlab.com/assets/0702cbe7-3553-436c-b8cf-11c79af4cfe0","https://www.linkedin.com/in/nadomagala/",{"slug":1154},"natalia-domagala","/faculty/natalia-domagala",{},"faculty/natalia-domagala","z4UoxPAvcyt_19R6WsXAwqQ8KG3FlKWI1Df9ceQ2OAk",{"id":1160,"title":1161,"affiliation":34,"bio":34,"body":34,"category":64,"cohort":34,"description":34,"expertise":34,"extension":46,"headshot":34,"image":34,"legacy":47,"linkedin":34,"meta":1162,"name":1164,"navigation":15,"path":1165,"photo":34,"role":34,"seo":1166,"stem":1167,"topic":34,"website":34,"__hash__":1168},"faculty/faculty/natalia-gonzalez-alarcon.json","Natalia Gonzalez Alarcon",{"slug":1163},"natalia-gonzalez-alarcon","Natalia González Alarcón","/faculty/natalia-gonzalez-alarcon",{},"faculty/natalia-gonzalez-alarcon","5_O4v0eHO9xJcxtyU5BUk1_2r3V-ybOjkdqGq_DXddQ",{"id":1170,"title":1171,"affiliation":1172,"bio":34,"body":34,"category":91,"cohort":120,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1173,"legacy":15,"linkedin":1174,"meta":1175,"name":1171,"navigation":15,"path":1177,"photo":34,"role":34,"seo":1178,"stem":1179,"topic":34,"website":34,"__hash__":1180},"faculty/faculty/nick-hart.json","Nick Hart","Data Foundation","https://cms.thegovlab.com/assets/3112fb04-06e4-4c67-89ba-ba1344d58bfd","https://www.linkedin.com/in/nickrhart/",{"slug":1176},"nick-hart","/faculty/nick-hart",{},"faculty/nick-hart","nr_yHL5OvAPmgt58TYw04NVjf19AvCLixDQ-ZMwuRko",{"id":1182,"title":1183,"affiliation":1184,"bio":34,"body":34,"category":91,"cohort":120,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1185,"legacy":15,"linkedin":1186,"meta":1187,"name":1183,"navigation":15,"path":1189,"photo":34,"role":34,"seo":1190,"stem":1191,"topic":34,"website":34,"__hash__":1192},"faculty/faculty/nicolas-schifano.json","Nicolas Schifano","Fastcatalog.ai","https://cms.thegovlab.com/assets/833ce985-df9a-44d7-9066-0b1e1c7777d9","https://www.linkedin.com/in/schifano/",{"slug":1188},"nicolas-schifano","/faculty/nicolas-schifano",{},"faculty/nicolas-schifano","4rKkBJGscyHEf8wdKvX5iTd--IdVRMqrSf7EtvF_M6g",{"id":1194,"title":1195,"affiliation":1196,"bio":34,"body":34,"category":91,"cohort":120,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1197,"legacy":15,"linkedin":1198,"meta":1199,"name":1195,"navigation":15,"path":1201,"photo":34,"role":34,"seo":1202,"stem":1203,"topic":34,"website":34,"__hash__":1204},"faculty/faculty/nigel-jacob.json","Nigel Jacob","Barr Foundation","https://cms.thegovlab.com/assets/fecfd38e-9969-4fcf-b16e-06041d6aeaf6","https://www.linkedin.com/in/nsjacob/",{"slug":1200},"nigel-jacob","/faculty/nigel-jacob",{},"faculty/nigel-jacob","2p7Bz3gycQDE6ffGBxPzAkMwBrWHVA5ZrOR6huMWsIs",{"id":1206,"title":1207,"affiliation":1208,"bio":34,"body":34,"category":91,"cohort":380,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1209,"legacy":47,"linkedin":1210,"meta":1211,"name":1207,"navigation":15,"path":1213,"photo":34,"role":34,"seo":1214,"stem":1215,"topic":1216,"website":34,"__hash__":1217},"faculty/faculty/oksana-riba-grognuz.json","Oksana Riba Grognuz","Swiss Data Science Center","https://cms.thegovlab.com/assets/495267c3-3d55-4af6-ad6a-febd9d5af866","https://www.linkedin.com/in/oksana80/",{"slug":1212},"oksana-riba-grognuz","/faculty/oksana-riba-grognuz",{},"faculty/oksana-riba-grognuz","Creating a Loop for Sharing and Reuse of Processed Data (\"Data Products\")","vCN9ptAja28aOGuJB1BVKp9zxT4VSmIZx-ZtON_As4Y",{"id":1219,"title":1220,"affiliation":1221,"bio":34,"body":34,"category":91,"cohort":120,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1222,"legacy":15,"linkedin":1223,"meta":1224,"name":1220,"navigation":15,"path":1226,"photo":34,"role":34,"seo":1227,"stem":1228,"topic":34,"website":34,"__hash__":1229},"faculty/faculty/paul-ko.json","Paul Ko","Grammarly","https://cms.thegovlab.com/assets/36f4d186-588e-4f9e-80b6-f1da1aae6e5d","https://www.linkedin.com/in/paulhko/",{"slug":1225},"paul-ko","/faculty/paul-ko",{},"faculty/paul-ko","xQQ0r2in4ZYZPu4O9cD4VvGB4veSoNYrx0eo5AZTaCk",{"id":1231,"title":1232,"affiliation":34,"bio":1233,"body":34,"category":77,"cohort":34,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1234,"legacy":47,"linkedin":1235,"meta":1236,"name":1232,"navigation":15,"path":1238,"photo":34,"role":188,"seo":1239,"stem":1240,"topic":34,"website":34,"__hash__":1241},"faculty/faculty/paulina-behluli.json","Paulina Behluli","\u003Cp>Paulina is a Program Manager at the Data Tank in charge of the data stewardship program. She is a digital governance specialist, specifically, public sector innovation & transformation. At the Data Tank, she leads the Data Stewardship Program focusing on Data Stewards Bootcamps for senior executives across Europe. Paulina is also involved in fundraising and partnership building. In parallel, Paulina has supported in building the She Shapes AI initiative as a volunteer, and additionally served as an Expert Reviewer. She holds a Master’s Degree in Public Policy from the Hertie School in Berlin. Prior to joining The Data Tank, Ms Behluli has managed several projects at Open Data Kosovo - a Forbes 30 under 30 listed organization. She has led Kosovo’s membership in the Open Government Partnership leading a close collaboration with the Office of the Prime Minister. Ms Behluli has co-authored two reports measuring the openness and transparency of the Kosovo Parliament and the Office of the Prime Minister. She was also engaged in the Global Data Barometer study, where Open Data Kosovo acted as a regional hub responsible for monitoring data usage for public good in Kosovo and Albania. In Berlin, she worked for the Berlin Innovation Agency helping startups accelerate tech for social good. She also served as a student advisory board member at the Center for Digital Governance of the Hertie School for 2 consecutive academic years.\u003C/p>","https://cms.thegovlab.com/assets/92b175dd-c3ad-40bb-b67d-1bc77c854f4a","https://www.linkedin.com/in/paulinabehluli/",{"slug":1237},"paulina-behluli","/faculty/paulina-behluli",{},"faculty/paulina-behluli","KzMq7h-4JXW4j4bYqqTkIXwnl5QhtGb3qLT3UDEYy8Q",{"id":1243,"title":1244,"affiliation":1245,"bio":34,"body":34,"category":91,"cohort":1246,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1247,"legacy":47,"linkedin":1248,"meta":1249,"name":1251,"navigation":15,"path":1252,"photo":34,"role":34,"seo":1253,"stem":1254,"topic":1255,"website":34,"__hash__":1256},"faculty/faculty/peter-bjoern-larsen.json","Peter Bjoern Larsen","Smart City Insights","DS Turin 2024, DS Berlin 2025","https://cms.thegovlab.com/assets/635b6215-76f6-4db0-bd1b-05f209453bec","https://www.linkedin.com/in/peter-bj%C3%B8rn/",{"slug":1250},"peter-bjoern-larsen","Peter Bjørn Larsen","/faculty/peter-bjoern-larsen",{},"faculty/peter-bjoern-larsen","Local data collaboratives for developing new data products and encourage more re-use of data in urban development","Lp-yGgDtRiumjQDA7V0pCK9eKgypPaVB_AQ2vCJurf4",{"id":1258,"title":1259,"affiliation":1260,"bio":34,"body":34,"category":91,"cohort":120,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1261,"legacy":15,"linkedin":1262,"meta":1263,"name":1259,"navigation":15,"path":1265,"photo":34,"role":34,"seo":1266,"stem":1267,"topic":34,"website":34,"__hash__":1268},"faculty/faculty/peter-lovelock.json","Peter Lovelock","Access Partnership","https://cms.thegovlab.com/assets/4a42ed27-8592-4d51-8dc4-7c06676d11e8","https://www.linkedin.com/in/peter-lovelock-24b234/",{"slug":1264},"peter-lovelock","/faculty/peter-lovelock",{},"faculty/peter-lovelock","T-0rMxQjEpVL0gBDJT3nu7rEeXXiZmVcF30aB_OECzk",{"id":1270,"title":1271,"affiliation":1272,"bio":34,"body":34,"category":91,"cohort":120,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1273,"legacy":15,"linkedin":1274,"meta":1275,"name":1271,"navigation":15,"path":1277,"photo":34,"role":34,"seo":1278,"stem":1279,"topic":34,"website":34,"__hash__":1280},"faculty/faculty/peter-rabley.json","Peter Rabley","The Open Geospatial Consortium","https://cms.thegovlab.com/assets/f7df3113-939b-4ec7-b527-f50622634e74","https://www.linkedin.com/in/peterrabley/",{"slug":1276},"peter-rabley","/faculty/peter-rabley",{},"faculty/peter-rabley","DJ0hn_3mdyNHFgwu8wiVLPcziiRfljHClrWpRzW-3J4",{"id":1282,"title":1283,"affiliation":1284,"bio":34,"body":34,"category":91,"cohort":380,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1285,"legacy":47,"linkedin":1286,"meta":1287,"name":1289,"navigation":15,"path":1290,"photo":34,"role":34,"seo":1291,"stem":1292,"topic":1293,"website":34,"__hash__":1294},"faculty/faculty/petra-keller-gueguen.json","Petra Keller Gueguen","Federal Statistical Office FSO","https://cms.thegovlab.com/assets/13164688-806d-4e0f-ad7e-2a3ceb3e4c86","https://www.linkedin.com/in/petra-keller-gu%C3%A9guen-1b58264a/",{"slug":1288},"petra-keller-gueguen","Petra Keller Guéguen","/faculty/petra-keller-gueguen",{},"faculty/petra-keller-gueguen","Agriculture and voting results: 2 use cases for successful data stewardship","Urak9ekI5LPju5tXbCLNHMy4FFgSvEZjhMxaGoK48U4",{"id":1296,"title":1297,"affiliation":1298,"bio":34,"body":34,"category":91,"cohort":120,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1299,"legacy":15,"linkedin":1300,"meta":1301,"name":1297,"navigation":15,"path":1303,"photo":34,"role":34,"seo":1304,"stem":1305,"topic":34,"website":34,"__hash__":1306},"faculty/faculty/pieter-de-leenheer.json","Pieter De Leenheer","DNAnexus, Collibra","https://cms.thegovlab.com/assets/4b3efc00-c8b9-4f27-9bc4-b5aa3bc7b28d","https://www.linkedin.com/in/pieterdeleenheer/",{"slug":1302},"pieter-de-leenheer","/faculty/pieter-de-leenheer",{},"faculty/pieter-de-leenheer","nAiKM_c4vT7YgMB6xtyozZ0sSP1dSe2FBYyadFwzBYI",{"id":1308,"title":1309,"affiliation":1310,"bio":34,"body":34,"category":91,"cohort":366,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1311,"legacy":47,"linkedin":1312,"meta":1313,"name":1315,"navigation":15,"path":1316,"photo":34,"role":34,"seo":1317,"stem":1318,"topic":1319,"website":34,"__hash__":1320},"faculty/faculty/prof-dr-maximilian-von-grafenstein.json","Prof Dr Maximilian Von Grafenstein","Humboldt Institute for Internet and Society (HIIG)","https://cms.thegovlab.com/assets/199a4fc2-a59e-40cd-9129-e1c500724065","https://www.linkedin.com/in/max-von-grafenstein-208a8632/",{"slug":1314},"prof-dr-maximilian-von-grafenstein","Prof. Dr. Maximilian von Grafenstein","/faculty/prof-dr-maximilian-von-grafenstein",{},"faculty/prof-dr-maximilian-von-grafenstein","Regulatory component to data sharing & pre-conditions for data governance and re-use","yw8vqvESuFtOZy5-U5JYfG2vp-D9avyR5X8pvXyC7s8",{"id":1322,"title":1323,"affiliation":1324,"bio":34,"body":34,"category":91,"cohort":366,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1325,"legacy":47,"linkedin":1326,"meta":1327,"name":1329,"navigation":15,"path":1330,"photo":34,"role":34,"seo":1331,"stem":1332,"topic":1333,"website":34,"__hash__":1334},"faculty/faculty/prof-ingmar-weber.json","Prof Ingmar Weber","University of Saarland","https://cms.thegovlab.com/assets/955b4d7d-da9f-45df-b55b-343053d0b05d","https://www.linkedin.com/in/ingmarweber/",{"slug":1328},"prof-ingmar-weber","Prof. Ingmar Weber","/faculty/prof-ingmar-weber",{},"faculty/prof-ingmar-weber","Computing for Society/Social Good","ePpzAhfnIE9mCjxcBmbJGDxpIVRb0-hToWFYEWasY5s",{"id":1336,"title":1337,"affiliation":34,"bio":1338,"body":34,"category":64,"cohort":34,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1339,"legacy":47,"linkedin":1340,"meta":1341,"name":1343,"navigation":15,"path":1344,"photo":34,"role":34,"seo":1345,"stem":1346,"topic":34,"website":34,"__hash__":1347},"faculty/faculty/professor-brian-head-fassa.json","Professor Brian Head Fassa","\u003Cp>Professor Brian Head is a leading authority on public policy and governance, with extensive experience working across government, universities and the non-government sector. He is currently the President of the Scientific Advisory Committee for the UNESCO social sciences program and is a Fellow of the Academy of the Social Sciences in Australia.\u003C/p>\n\u003Cp>His work focuses on evidence-informed policy, policy expertise and complex or ‘wicked’ problems, with a long-standing commitment to bridging research, policy and practice. Professor Head has advised state and federal governments, contributed to major policy reviews, and delivered keynote addresses at national and international forums, including those organised by the OECD.\u003C/p>","https://cms.thegovlab.com/assets/78d98826-ff3d-4072-8992-ae5ec530b99c","https://www.linkedin.com/in/brian-head-377b7b15/",{"slug":1342},"professor-brian-head-fassa","Professor Brian Head FASSA","/faculty/professor-brian-head-fassa",{},"faculty/professor-brian-head-fassa","ISkmG6eQk4OVvOmFtV0GLkG0lVzQklOWBOHA0Rio7h8",{"id":1349,"title":1350,"affiliation":34,"bio":1351,"body":34,"category":64,"cohort":34,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1352,"legacy":47,"linkedin":1353,"meta":1354,"name":1356,"navigation":15,"path":1357,"photo":34,"role":34,"seo":1358,"stem":1359,"topic":34,"website":34,"__hash__":1360},"faculty/faculty/professor-tahu-kukutai-phd-frsnz.json","Professor Tahu Kukutai Phd Frsnz","\u003Cp>Tahu Kukutai is Professor of Demography at Te Ngira Institute for Population Research and Co-Director of Ngā Pae o te Māramatanga Centre of Research Excellence. Tahu specialises in Māori and Indigenous demography and data sovereignty and has received a number of awards for her contribution to these fields. Her recent publications include \u003Cu>\u003Ca href=\"https://press.anu.edu.au/publications/series/caepr/indigenous-data-sovereignty\" target=\"_blank\" rel=\"noopener\">Indigenous data sovereignty: Toward an agenda\u003C/a>\u003C/u> (ANU Press), \u003Cu>\u003Ca href=\"https://www.taylorfrancis.com/books/oa-edit/10.4324/9780429273957/indigenous-data-sovereignty-policy-maggie-walter-tahu-kukutai-stephanie-russo-carroll-desi-rodriguez-lonebear\" target=\"_blank\" rel=\"noopener\">Indigenous data sovereignty and policy\u003C/a>\u003C/u> (Routledge), The \u003Cu>\u003Ca href=\"https://academic.oup.com/edited-volume/37077\">Oxford handbook of Indigenous sociology\u003C/a>\u003C/u> (Oxford), \u003Cu>\u003Ca href=\"https://www.routledge.com/Indigenous-Statistics-A-Quantitative-Research-Methodology/Walter-Andersen-Kukutai-Gabel/p/book/9781032002507?srsltid=AfmBOoqPLZnF318mBsuzlutgUWnCLEk1KjuEMjPAfaD6IfyzsMPYfn39\" target=\"_blank\" rel=\"noopener\">Indigenous statistics: From data deficits to data sovereignty\u003C/a>\u003C/u> (Routledge) and the \u003Cu>\u003Ca href=\"https://www.kahuiraraunga.io/maoridatagovernance\" target=\"_blank\" rel=\"noopener\">Māori Data Governance Model\u003C/a>\u003C/u> (Te Kāhui Raraunga). Tahu has undertaken research for numerous tribes, Māori communities, and Government agencies, and provided strategic advice across a range of sectors. She is a founding member of the Māori Data Sovereignty Network Te Mana Raraunga and the Global Indigenous Data Alliance.\u003C/p>\n\u003Cp>Tahu is an elected Fellow of the Royal Society Te Apārangi and a Life Member of the Population Association of New Zealand. Her affiliations are Ngāti Tiipa, Ngāti Māhanga, Ngāti Kinohaku and Te Aupōuri.\u003C/p>","https://cms.thegovlab.com/assets/0de27b97-53c6-46ba-b2df-87d124b6fe35","https://www.linkedin.com/in/tahu-kukutai-phd-frsnz-167810/",{"slug":1355},"professor-tahu-kukutai-phd-frsnz","Professor Tahu Kukutai, PhD FRSNZ","/faculty/professor-tahu-kukutai-phd-frsnz",{},"faculty/professor-tahu-kukutai-phd-frsnz","Lzmn4cLNPFj29qN6t0_uVa3e7Lmqof_E9_rDqxsznIs",{"id":1362,"title":1363,"affiliation":1364,"bio":34,"body":34,"category":91,"cohort":120,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1365,"legacy":15,"linkedin":1366,"meta":1367,"name":1363,"navigation":15,"path":1369,"photo":34,"role":34,"seo":1370,"stem":1371,"topic":34,"website":34,"__hash__":1372},"faculty/faculty/rachel-wells.json","Rachel Wells","Datakind","https://cms.thegovlab.com/assets/f8e45cd8-70a5-44f7-a129-241c854ead8b","https://www.linkedin.com/in/rachellaurynwells/",{"slug":1368},"rachel-wells","/faculty/rachel-wells",{},"faculty/rachel-wells","Zd9QUrN8U6ZrsSjhy-DLWFIY0ZYcw7MdP2uG5IdoZDs",{"id":1374,"title":1375,"affiliation":1376,"bio":34,"body":34,"category":91,"cohort":366,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1377,"legacy":47,"linkedin":1378,"meta":1379,"name":1375,"navigation":15,"path":1381,"photo":34,"role":34,"seo":1382,"stem":1383,"topic":1384,"website":34,"__hash__":1385},"faculty/faculty/ramy-hcini.json","Ramy Hcini","Think-It","https://cms.thegovlab.com/assets/8405f27d-b2e7-498e-aeb2-34b15fc92119","https://www.linkedin.com/in/ramy-hcini/",{"slug":1380},"ramy-hcini","/faculty/ramy-hcini",{},"faculty/ramy-hcini","Data Spaces in the context of matching demand and supply and security","-rWrvlNEWCn3XBHQmb9CWYvT0ahjPATj9z7NS8LhpXg",{"id":1387,"title":1388,"affiliation":1389,"bio":34,"body":34,"category":91,"cohort":120,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1390,"legacy":15,"linkedin":1391,"meta":1392,"name":1388,"navigation":15,"path":1394,"photo":34,"role":34,"seo":1395,"stem":1396,"topic":34,"website":34,"__hash__":1397},"faculty/faculty/richard-benjamins.json","Richard Benjamins","OdiseIA","https://cms.thegovlab.com/assets/1b4d1a3a-4bc9-46bb-8f4c-59001cee0bdb","https://www.linkedin.com/in/richard-benjamins/",{"slug":1393},"richard-benjamins","/faculty/richard-benjamins",{},"faculty/richard-benjamins","qVN1VHgFK92frM8UkhDTLNHl1e7fKik9zKgXft5EHhY",{"id":1399,"title":1400,"affiliation":34,"bio":1401,"body":34,"category":64,"cohort":34,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1402,"legacy":47,"linkedin":34,"meta":1403,"name":1400,"navigation":15,"path":1405,"photo":34,"role":1406,"seo":1407,"stem":1408,"topic":34,"website":34,"__hash__":1409},"faculty/faculty/roshni-singh.json","Roshni Singh","Roshni Singh is a Researcher at The GovLab. She holds a Master’s degree in International Affairs from The New School, with a concentration in conflict and security, and a Bachelor of Arts in Liberal Arts, where she designed her own major titled 'The Politics of Injustice: Examining the 'Other' in International Law and Security.' Roshni has worked with organizations such as UNHCR, the International Rescue Committee, and Generation Citizen, focusing on research and policy analysis. As a passionate advocate for human rights and refugee protection, her work at the Ritsona Refugee Camp and other initiatives highlights her commitment to making a positive social impact.","https://cms.thegovlab.com/assets/d04e315a-34dc-4939-abb2-9c276d703a30",{"slug":1404},"roshni-singh","/faculty/roshni-singh","Researcher",{},"faculty/roshni-singh","kUaO0OcbTVbPEwRHOjfHOS7QHtVgeVjEWIuGL18ywrM",{"id":1411,"title":1412,"affiliation":1413,"bio":34,"body":34,"category":91,"cohort":380,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1414,"legacy":47,"linkedin":34,"meta":1415,"name":1412,"navigation":15,"path":1417,"photo":34,"role":34,"seo":1418,"stem":1419,"topic":1420,"website":34,"__hash__":1421},"faculty/faculty/ross-purves.json","Ross Purves","University of Zürich","https://cms.thegovlab.com/assets/760a7335-c0af-4597-9f96-96e39bae928d",{"slug":1416},"ross-purves","/faculty/ross-purves",{},"faculty/ross-purves","Teaching with and about (Open)(Government) data","y1f3pdZUzV6y2h-35-8fSldW2Z6ct7TajTj1ikGqh8c",{"id":1423,"title":1424,"affiliation":1425,"bio":34,"body":34,"category":91,"cohort":120,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1426,"legacy":15,"linkedin":1427,"meta":1428,"name":1424,"navigation":15,"path":1430,"photo":34,"role":34,"seo":1431,"stem":1432,"topic":34,"website":34,"__hash__":1433},"faculty/faculty/rudi-borrmann.json","Rudi Borrmann","Open Government Partnership","https://cms.thegovlab.com/assets/efeb6c92-a5bf-4d70-8af0-5c3f568e70ba","https://www.linkedin.com/in/rudiborrmann/",{"slug":1429},"rudi-borrmann","/faculty/rudi-borrmann",{},"faculty/rudi-borrmann","S95VUc9mtByKc1NUVAzZHo4KBPBdauoJdxLVaVn6Mbw",{"id":1435,"title":1436,"affiliation":1437,"bio":34,"body":34,"category":91,"cohort":380,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1438,"legacy":47,"linkedin":1439,"meta":1440,"name":1436,"navigation":15,"path":1442,"photo":34,"role":34,"seo":1443,"stem":1444,"topic":1445,"website":34,"__hash__":1446},"faculty/faculty/sachit-mahajan.json","Sachit Mahajan","ETH Zürich","https://cms.thegovlab.com/assets/2a8243e0-6507-43ca-8eb5-641cb710de93","https://www.linkedin.com/in/sachit-mahajan-9052b745/",{"slug":1441},"sachit-mahajan","/faculty/sachit-mahajan",{},"faculty/sachit-mahajan","Designing Routing as Civic Infrastructure: Open, Interoperable, Reusable","h2TFPUORfSpJrqXTOYHtmXLzbLYs7bNnHgPb79YXW6g",{"id":1448,"title":1449,"affiliation":34,"bio":1450,"body":34,"category":64,"cohort":34,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1451,"legacy":47,"linkedin":34,"meta":1452,"name":1449,"navigation":15,"path":1454,"photo":34,"role":1455,"seo":1456,"stem":1457,"topic":34,"website":34,"__hash__":1458},"faculty/faculty/sampriti-saxena.json","Sampriti Saxena","Sampriti Saxena is a research intern at The GovLab. She recently completed her master’s degree in International Social Public Policy with a specialization in Development from the London School of Economics. At The GovLab, she supports the Data Program research team across a number of their projects. Sampriti also holds a bachelor’s degree in Economics with a double minor in Global Studies and French from the University of California, Berkeley.","https://cms.thegovlab.com/assets/7d058427-033e-421d-9960-6fa6dada36bb",{"slug":1453},"sampriti-saxena","/faculty/sampriti-saxena","Research Intern",{},"faculty/sampriti-saxena","1lFtbfZZp9b1-kPQmsAfiyWL97jl9yyGknrizGeyyQQ",{"id":1460,"title":1461,"affiliation":1462,"bio":34,"body":34,"category":91,"cohort":380,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1463,"legacy":47,"linkedin":1464,"meta":1465,"name":1461,"navigation":15,"path":1467,"photo":34,"role":34,"seo":1468,"stem":1469,"topic":1470,"website":34,"__hash__":1471},"faculty/faculty/sebastian-sigloch.json","Sebastian Sigloch","Switch","https://cms.thegovlab.com/assets/ba59d107-03c4-4ef2-bf4d-55c2f6377c76","https://www.linkedin.com/in/sebastian-s-3889b024a/",{"slug":1466},"sebastian-sigloch","/faculty/sebastian-sigloch",{},"faculty/sebastian-sigloch","Switch Data","0kYV6nwaqMpwvxd2A511FYmQqP7CvEYqhjs2h4ZyaTA",{"id":1473,"title":1474,"affiliation":826,"bio":34,"body":34,"category":91,"cohort":120,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1475,"legacy":15,"linkedin":1476,"meta":1477,"name":1474,"navigation":15,"path":1479,"photo":34,"role":34,"seo":1480,"stem":1481,"topic":34,"website":34,"__hash__":1482},"faculty/faculty/shanna-crumley.json","Shanna Crumley","https://cms.thegovlab.com/assets/bfe62fac-d24c-4f90-9698-f67638348b00","https://www.linkedin.com/in/shannacrumley/",{"slug":1478},"shanna-crumley","/faculty/shanna-crumley",{},"faculty/shanna-crumley","iXUsWMnMY6mPvKHDLhgAZyhxocsHbNxH-_5w1E3Ffww",{"id":1484,"title":1485,"affiliation":826,"bio":34,"body":34,"category":91,"cohort":120,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1486,"legacy":15,"linkedin":1487,"meta":1488,"name":1485,"navigation":15,"path":1490,"photo":34,"role":34,"seo":1491,"stem":1492,"topic":34,"website":34,"__hash__":1493},"faculty/faculty/smita-jain.json","Smita Jain","https://cms.thegovlab.com/assets/3dbab9bf-ab59-4ff4-8a3c-56310b628071","https://www.linkedin.com/in/smita-jain1/",{"slug":1489},"smita-jain","/faculty/smita-jain",{},"faculty/smita-jain","g6qNp1x4R9a3MCXfGu-OOgoj8Dze1jUxqKdMuaw29M4",{"id":1495,"title":1496,"affiliation":1497,"bio":1498,"body":34,"category":77,"cohort":34,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1499,"legacy":47,"linkedin":1500,"meta":1501,"name":1496,"navigation":15,"path":1503,"photo":34,"role":1504,"seo":1505,"stem":1506,"topic":34,"website":34,"__hash__":1507},"faculty/faculty/stefaan-verhulst.json","Stefaan Verhulst","Data Stewards Founder","\u003Cp dir=\"ltr\">\u003Cstrong>Dr. Stefaan Verhulst \u003C/strong>is Co-Founder of the DataTank and The GovLab and the main lecturer of the data stewardship academy. In addition, he is a Research Professor at the Center for Urban Science and Progress at the Tandon School of Engineering of New York University; and a Senior Advisor to the Markle Foundation where he spent more than a decade as Chief of Research. He is also the Editor-in-Chief of the open-access journal Data & Policy (Cambridge University Press); the Research Director of the MacArthur Research Network on Opening Governance; Chair of the Data for Children Collaborative with Unicef; a member of the High-Level Expert Group to the European Commission on Business-to-Government Data Sharing; and of the Expert Group to Eurostat on using Private Sector data for Official Statistics. In addition he is also a member of the UNESCO Information Ethics Working Group; Researcher at the ISI Foundation (Torino, Italy); Senior Researcher at SMIT (Studies in Media, Innovation and Technology) at the Free University of Brussels (VUB) . In 2018 he was recognized as one of the 10 Most Influential Academics in Digital Government globally (by the global policy platform Apolitical). Previously at Oxford University, he was the UNESCO Chairholder in Communications Law and Policy and co-founded and was the Head of the Program in Comparative Media Law and Policy at the Center for Socio-Legal Studies. He was the Socio-Legal Fellow at Wolfson College, and is still an emeritus fellow at Oxford. He also taught for several years at the London School of Economics and was Co-Founder and Co-Director of the International Media and Info-Comms Policy and Law Studies (IMPS) at the University of Glasgow School of Law.\u003C/p>\n\u003Cp>He has published widely - including seven books- and his writings and work have appeared in the Harvard Business Review, Stanford Social Innovation Review, Project Syndicate, Wall Street Journal, and The Conversation (among many other outlets). He is asked regularly to present at international conferences including, for instance, TED, Collision, and the UN World Data Forum. Numerous organizations have sought his counsel on a variety of topics including data and AI governance - including the WorldBank; IDB, CAP, USAID, DFID, IDRC, AFP, the European Commission, Council of Europe, the World Economic Forum, UNICEF, OECD, UN-OCHA, UNDP, UNESCO and several other international and national private and public organizations. He is also a Linkedin Learning instructor seeking to democratize the practice of data stewardship globally.\u003C/p>","https://cms.thegovlab.com/assets/5f5b52b5-44d9-4840-88f3-8ac6e9cb5ae2","https://www.linkedin.com/in/stefaan-verhulst/",{"slug":1502},"stefaan-verhulst","/faculty/stefaan-verhulst","Course Lead",{},"faculty/stefaan-verhulst","OU0_J9qbLAdOggqbBTXHGlhgyazJ7eKPpwEn4eIiPdA",{"id":1509,"title":1510,"affiliation":1511,"bio":34,"body":34,"category":91,"cohort":380,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1512,"legacy":47,"linkedin":1513,"meta":1514,"name":1510,"navigation":15,"path":1516,"photo":34,"role":34,"seo":1517,"stem":1518,"topic":1519,"website":34,"__hash__":1520},"faculty/faculty/stefan-metzger.json","Stefan Metzger","Beyond Civic","https://cms.thegovlab.com/assets/0da4a628-95f3-434f-8281-043a04c9db29","https://www.linkedin.com/in/stefan-p-metzger/",{"slug":1515},"stefan-metzger","/faculty/stefan-metzger",{},"faculty/stefan-metzger","TriRegio Data Space / Data Space Concept","Si9xi0nfhdPCS4LYK1_0Zl3GtlwKsgmkY3IkaVGxIjM",{"id":1522,"title":1523,"affiliation":1524,"bio":34,"body":34,"category":91,"cohort":120,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1525,"legacy":15,"linkedin":1526,"meta":1527,"name":1523,"navigation":15,"path":1529,"photo":34,"role":34,"seo":1530,"stem":1531,"topic":34,"website":34,"__hash__":1532},"faculty/faculty/stephen-chacha.json","Stephen Chacha","Development Transformations","https://cms.thegovlab.com/assets/a07ea099-6032-439c-b046-5ccb7f7c31f6","https://www.linkedin.com/in/stephenchacha/",{"slug":1528},"stephen-chacha","/faculty/stephen-chacha",{},"faculty/stephen-chacha","XFQOVAoxxF-67x3MvbGeTd21mOgiVjsQQPfmiv5hsA0",{"id":1534,"title":1535,"affiliation":1536,"bio":34,"body":34,"category":91,"cohort":120,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1537,"legacy":15,"linkedin":1538,"meta":1539,"name":1535,"navigation":15,"path":1541,"photo":34,"role":34,"seo":1542,"stem":1543,"topic":34,"website":34,"__hash__":1544},"faculty/faculty/stuart-campo.json","Stuart Campo","IOM UN Migration","https://cms.thegovlab.com/assets/f0c094cc-6f8b-4ed2-a06f-9e11b0992bc2","https://www.linkedin.com/in/stuart-campo-102182a/",{"slug":1540},"stuart-campo","/faculty/stuart-campo",{},"faculty/stuart-campo","xXK07w7YG_SxEIXVQ7xX6hefQP1-rtKW0CU4ggTVc_w",{"id":1546,"title":1547,"affiliation":1548,"bio":34,"body":34,"category":91,"cohort":120,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1549,"legacy":15,"linkedin":1550,"meta":1551,"name":1547,"navigation":15,"path":1553,"photo":34,"role":34,"seo":1554,"stem":1555,"topic":34,"website":34,"__hash__":1556},"faculty/faculty/tyler-kleykamp.json","Tyler Kleykamp","Connecticut Foodshare","https://cms.thegovlab.com/assets/93f81f60-f2fd-48bf-a4ba-edf9a5f76ed1","https://www.linkedin.com/in/tyler-kleykamp/",{"slug":1552},"tyler-kleykamp","/faculty/tyler-kleykamp",{},"faculty/tyler-kleykamp","DOVnyGWVqDjwZO2jmL5sZtThIYSc2L4-qLXFkUbHh6I",{"id":1558,"title":1559,"affiliation":34,"bio":1560,"body":34,"category":64,"cohort":34,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1561,"legacy":47,"linkedin":34,"meta":1562,"name":1559,"navigation":15,"path":1564,"photo":34,"role":1119,"seo":1565,"stem":1566,"topic":34,"website":34,"__hash__":1567},"faculty/faculty/uma-kalkar.json","Uma Kalkar","Uma Kalkar is a research assistant at The GovLab and a dual-degree candidate for a Master of Public Policy specializing in Digital and New Technology at The Paris Institute of Political Studies (Sciences Po) and a Master of Global Affairs at the University of Toronto. She is also the Innovation Director of 18by Vote, a youth-led non-profit that helps 16, 17, and 18-year-olds understand how to vote, when to vote, and why to vote. Her work focuses on digital inequities, open data initiatives, and civic organizing in digital spaces. Uma was a 2019-2020 Presidential Fellow at the Center for the Study of the Presidency and Congress (CSPC) researching the political effects of the urban-rural digital divide in the United States. Uma holds a B.Sc. (Honors) majoring in Peace, Conflict, and Justice and double minoring in Mathematics and Biology from the University of Toronto.","https://cms.thegovlab.com/assets/32ba2a39-f9a4-4a58-8488-957ccdd54b41",{"slug":1563},"uma-kalkar","/faculty/uma-kalkar",{},"faculty/uma-kalkar","19ZAaVczDlFV-CVrOsdscP-9-93mnw4uUTgdHt78-6I",{"id":1569,"title":1570,"affiliation":1571,"bio":34,"body":34,"category":91,"cohort":120,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1572,"legacy":15,"linkedin":1573,"meta":1574,"name":1570,"navigation":15,"path":1576,"photo":34,"role":34,"seo":1577,"stem":1578,"topic":34,"website":34,"__hash__":1579},"faculty/faculty/valentin-muresan.json","Valentin Muresan","ABQ.Institute, Equal 1, Primăria Municipiului Timișoara","https://cms.thegovlab.com/assets/48264b87-4afd-423b-bc22-60ea1553179f","https://www.linkedin.com/in/valmuresan/",{"slug":1575},"valentin-muresan","/faculty/valentin-muresan",{},"faculty/valentin-muresan","bMedh5cgp7_cnJQAqbyIyK6naTlb14-9oRlJ539yl5Q",{"id":1581,"title":1582,"affiliation":1376,"bio":34,"body":34,"category":91,"cohort":366,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1583,"legacy":47,"linkedin":1584,"meta":1585,"name":1582,"navigation":15,"path":1587,"photo":34,"role":34,"seo":1588,"stem":1589,"topic":1384,"website":34,"__hash__":1590},"faculty/faculty/wassim-kallel.json","Wassim Kallel","https://cms.thegovlab.com/assets/7fc3249b-36ae-4bf2-8a24-f17915701918","https://www.linkedin.com/in/wassimkallel/",{"slug":1586},"wassim-kallel","/faculty/wassim-kallel",{},"faculty/wassim-kallel","vHzByAWe3IfptF8--3FBseOaOdIP_uSWNAH37IgoZbs",{"id":1592,"title":1593,"affiliation":1594,"bio":34,"body":34,"category":91,"cohort":665,"description":34,"expertise":34,"extension":46,"headshot":34,"image":1595,"legacy":47,"linkedin":1596,"meta":1597,"name":1593,"navigation":15,"path":1599,"photo":34,"role":34,"seo":1600,"stem":1601,"topic":1602,"website":34,"__hash__":1603},"faculty/faculty/yannik-sassmann.json","Yannik Sassmann","Federal Ministry of Economic Cooperation and Development (BMZ)","https://cms.thegovlab.com/assets/39964c54-c032-44ee-855f-ea9a59546eb5","https://www.linkedin.com/in/yannik-sassmann/",{"slug":1598},"yannik-sassmann","/faculty/yannik-sassmann",{},"faculty/yannik-sassmann","BMZ Data and AI Lab","ZlegG-Md_hy5eQodCby9lZL8eA_6iQMH6KvMupVtYeA",1781873986234]