{"id":176,"date":"2026-10-13T11:24:12","date_gmt":"2026-10-13T11:24:12","guid":{"rendered":"https:\/\/therisk.global\/food-nexus\/?page_id=176"},"modified":"2026-10-13T18:00:53","modified_gmt":"2026-10-13T18:00:53","slug":"spatial-statistics","status":"publish","type":"page","link":"https:\/\/therisk.global\/food-nexus\/stem\/spatial-statistics\/","title":{"rendered":"Spatial statistics for food security"},"content":{"rendered":"\n<!-- Nexus network page \u00b7 https:\/\/therisk.global\/food-nexus\/stem\/spatial-statistics\/ \u00b7 source template \u00b7 generated by integrations\/wordpress\/network\/build.mjs, never edit this file --><script type=\"text\/x-nexus\" class=\"_iub_cs_skip\" id=\"nxe-fig-engine\" 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auto;padding:0 16px;font-family:ui-sans-serif,system-ui,-apple-system,\"Segoe UI\",sans-serif;line-height:1.55;overflow-wrap:break-word}#nxe-n1t3duu7.nxe :where(*,*::before,*::after){box-sizing:inherit}#nxe-n1t3duu7.nxe :where(section,header,footer,nav,aside){display:block;margin:0 0 32px}#nxe-n1t3duu7.nxe :where(h1,h2,h3,h4){line-height:1.2;margin:24px 0 12px;font-weight:600}#nxe-n1t3duu7.nxe :where(p,ul,ol,dl,table,figure,details){margin:0 0 16px}#nxe-n1t3duu7.nxe :where(ul,ol){padding-left:20px}#nxe-n1t3duu7.nxe a{color:inherit;text-decoration:underline;text-underline-offset:2px}#nxe-n1t3duu7.nxe table{border-collapse:collapse;width:100%;display:block;overflow-x:auto}#nxe-n1t3duu7.nxe :where(th,td){padding:6px 8px;text-align:left;vertical-align:top;border-bottom:1px solid;border-color:inherit}#nxe-n1t3duu7.nxe svg{display:none}#nxe-n1t3duu7.nxe svg.nx-i{display:inline-block;width:18px;height:18px;vertical-align:-3px}#nxe-n1t3duu7.nxe :where([role=\"tablist\"],[role=\"toolbar\"],[hidden],button,.nx-theme-toggle,.nx-live-slot){display:none}#nxe-n1t3duu7.nxe img{max-width:100%;height:auto}<\/style><svg class=\"nx-defs\" aria-hidden=\"true\" focusable=\"false\" width=\"0\" height=\"0\"><defs><linearGradient id=\"nx-gi\" x1=\"0\" y1=\"0\" x2=\"1\" y2=\"1\"><stop offset=\"0\" class=\"nx-gi-a\"\/><stop offset=\"0.55\" class=\"nx-gi-b\"\/><stop offset=\"1\" class=\"nx-gi-c\"\/><\/linearGradient><linearGradient id=\"nx-giu\" gradientUnits=\"userSpaceOnUse\" x1=\"0\" y1=\"0\" x2=\"800\" y2=\"0\"><stop offset=\"0\" class=\"nx-gi-a\"\/><stop offset=\"0.55\" class=\"nx-gi-b\"\/><stop offset=\"1\" class=\"nx-gi-c\"\/><\/linearGradient><linearGradient id=\"nx-gp\" x1=\"0\" y1=\"0\" x2=\"1\" y2=\"1\"><stop offset=\"0\" class=\"nx-gp-a\"\/><stop offset=\"1\" class=\"nx-gp-b\"\/><\/linearGradient><\/defs><\/svg><section class=\"nx-hero\" data-layout=\"console\" aria-labelledby=\"nx-title-1\"><div class=\"nx-container\"><div class=\"nx-theme-toggle\" role=\"group\" aria-label=\"Appearance of this page\" hidden><button type=\"button\" data-mode=\"light\" aria-pressed=\"false\">Light<\/button><button type=\"button\" data-mode=\"dark\" aria-pressed=\"false\">Dark<\/button><button type=\"button\" data-mode=\"auto\" aria-pressed=\"true\">Auto<\/button><\/div><div class=\"nx-folio\" aria-label=\"Document reference\"><strong>G02 \u00b7 METHOD \u00b7 spatial-statistics<\/strong>GCRI dossier \u00b7 service level R0<\/div><div class=\"nx-hero-grid\"><div class=\"nx-hero-text\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">GCRI<\/span><span class=\"nx-eyebrow\" aria-hidden=\"true\">\u00b7<\/span><span class=\"nx-eyebrow\">Food Nexus<\/span><span class=\"nx-eyebrow\" aria-hidden=\"true\">\u00b7<\/span><span class=\"nx-eyebrow\">Established<\/span><\/div><div class=\"nx-accent-row\"><span class=\"nx-accent-rule\" aria-hidden=\"true\"><\/span><span>Method page \u00b7 Spatial statistics and geostatistics<\/span><\/div><h1 id=\"nx-title-1\" class=\"nx-hero-title\">Spatial statistics for food security<\/h1><p class=\"nx-tagline\">Spatial statistics turns sparse surveys, soil samples and market prices into maps with stated uncertainty. In food systems it estimates food insecurity for small areas, predicts soil properties between sampled profiles and tests whether price anomalies or malnutrition cluster in space.<\/p><div class=\"nx-actions\"><a class=\"nx-btn\" data-variant=\"primary\" href=\"https:\/\/therisk.global\/food-nexus\/contact\/\">Make an enquiry <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a class=\"nx-btn\" data-variant=\"secondary\" href=\"https:\/\/therisk.global\/food-nexus\/stem\/\">Back to STEM foundation of Food Nexus<\/a><\/div><\/div><div class=\"nx-hero-panel\"><div class=\"nx-register\"><div class=\"nx-register-head\"><span class=\"nx-eyebrow\"><strong>Method record<\/strong><\/span><span class=\"nx-eyebrow\">spatial-statistics<\/span><\/div><ol class=\"nx-register-rows\"><li><div><span class=\"nx-register-k\">Method<\/span><span class=\"nx-register-v\">Spatial statistics and geostatistics<\/span><\/div><\/li><li><div><span class=\"nx-register-k\">Steward<\/span><span class=\"nx-register-v\"><a class=\"nx-link\" href=\"https:\/\/therisk.global\/space-earth-observation\/\">Space and Earth Observation<\/a><\/span><\/div><\/li><li><div><span class=\"nx-register-k\">Applied in<\/span><span class=\"nx-register-v\">Food Nexus<\/span><\/div><\/li><li><div><span class=\"nx-register-k\">Data<\/span><span class=\"nx-register-v\"><a class=\"nx-link\" href=\"#nx-sec-data\">4 inputs<\/a><\/span><\/div><\/li><li><div><span class=\"nx-register-k\">Checks<\/span><span class=\"nx-register-v\"><a class=\"nx-link\" href=\"#nx-sec-checks\">3 validation steps<\/a><\/span><\/div><\/li><li><div><span class=\"nx-register-k\">Canonical page<\/span><span class=\"nx-register-v\"><a class=\"nx-link\" href=\"https:\/\/therisk.global\/stem\/mathematics\/spatial-statistics\/\">STEM Commons<\/a><\/span><\/div><\/li><\/ol><p class=\"nx-register-foot\">GCRI \u00b7 Established \u00b7 service level R0<\/p><\/div><\/div><\/div><\/div><\/section> <nav class=\"nx-index-strip\" data-nx-spy=\"true\" aria-label=\"On this page\"><div class=\"nx-container\"><a href=\"#nx-sec-brief-answer\"><span>01<\/span>The short answer<\/a><a href=\"#nx-sec-application\"><span>02<\/span>What spatial statistics and\u2026<\/a><a href=\"#nx-sec-terms\"><span>03<\/span>Terms used on this page<\/a><a href=\"#nx-sec-field\"><span>04<\/span>Food systems and trade routes in\u2026<\/a><a href=\"#nx-sec-canvas\"><span>05<\/span>The method as a run graph<\/a><a href=\"#nx-sec-data\"><span>06<\/span>What the method consumes<\/a><a href=\"#nx-sec-checks\"><span>07<\/span>How results are checked<\/a><a href=\"#nx-sec-connections\"><span>08<\/span>Topics that rely on this method<\/a><\/div><\/nav> <section id=\"nx-sec-brief-answer\" class=\"nx-section\" data-hue=\"food\" data-variant=\"split\" aria-labelledby=\"nx-s-2\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">01<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">In brief<\/span><\/div><h2 id=\"nx-s-2\" class=\"nx-section-title\">The short answer.<\/h2><p class=\"nx-lede\">Small-area estimation is a set of statistical methods that combine sparse survey data with mapped covariates to estimate conditions, such as food insecurity, for districts the survey did not sample adequately. With digital soil mapping and cluster tests, spatial statistics turns sparse samples into maps whose uncertainty is reported.<\/p><\/div><\/div><\/section><section id=\"nx-sec-application\" class=\"nx-section\" data-hue=\"supply\" data-tone=\"alt\" data-variant=\"stack\" aria-labelledby=\"nx-s-3\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">02<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Application<\/span><\/div><h2 id=\"nx-s-3\" class=\"nx-section-title\">What spatial statistics and geostatistics does here.<\/h2><p class=\"nx-lede\">Spatial statistics and geostatistics analyse data that vary across space, accounting for spatial dependence. They estimate values at unsampled locations and test for spatial patterns. In food and agriculture, trade, resources and supply chains and climate and hazards, spatial statistics produce continuous hazard and exposure surfaces with uncertainty. They reveal geographic clustering of risks and vulnerabilities across sectors. The canonical description is on <a class=\"nx-link\" href=\"https:\/\/therisk.global\/stem\/mathematics\/spatial-statistics\/\">the STEM Commons<\/a>.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-cols\"><article><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\" data-size=\"lg\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M14.106 5.553a2 2 0 0 0 1.788 0l3.659-1.83A1 1 0 0 1 21 4.619v12.764a1 1 0 0 1-.553.894l-4.553 2.277a2 2 0 0 1-1.788 0l-4.212-2.106a2 2 0 0 0-1.788 0l-3.659 1.83A1 1 0 0 1 3 19.381V6.618a1 1 0 0 1 .553-.894l4.553-2.277a2 2 0 0 1 1.788 0z\"\/><path d=\"M15 5.76v15\"\/><path d=\"M9 3.24v15\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Small-area estimation of food insecurity<\/h3><p class=\"nx-p nx-mt-1\">Household survey results are combined with satellite, census and market covariates to estimate food insecurity for districts the survey sampled too thinly, with an uncertainty for each estimate.<\/p><\/article><article><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\" data-size=\"lg\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"m12.83 2.18a2 2 0 0 0-1.66 0L2.6 6.08a1 1 0 0 0 0 1.83l8.58 3.91a2 2 0 0 0 1.66 0l8.58-3.9a1 1 0 0 0 0-1.83Z\"\/><path d=\"m22 17.65-9.17 4.16a2 2 0 0 1-1.66 0L2 17.65\"\/><path d=\"m22 12.65-9.17 4.16a2 2 0 0 1-1.66 0L2 12.65\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Digital soil mapping<\/h3><p class=\"nx-p nx-mt-1\">Soil properties measured at sampled profiles are predicted across the landscape from terrain, climate and spectral covariates by regression kriging or machine learning, with prediction intervals mapped.<\/p><\/article><article><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\" data-size=\"lg\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M19.07 4.93A10 10 0 0 0 6.99 3.34\"\/><path d=\"M4 6h.01\"\/><path d=\"M2.29 9.62A10 10 0 1 0 21.31 8.35\"\/><path d=\"M16.24 7.76A6 6 0 1 0 8.23 16.67\"\/><path d=\"M12 18h.01\"\/><path d=\"M17.99 11.66A6 6 0 0 1 15.77 16.67\"\/><circle cx=\"12\" cy=\"12\" r=\"2\"\/><path d=\"m13.41 10.59 5.66-5.66\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Clustering of malnutrition and price anomalies<\/h3><p class=\"nx-p nx-mt-1\">Spatial autocorrelation and hotspot tests show whether high malnutrition or price anomalies cluster, before any cause is proposed.<\/p><\/article><\/div><\/div><\/div><\/section><section id=\"nx-sec-terms\" class=\"nx-section\" data-hue=\"climate\" data-tone=\"alt\" aria-labelledby=\"nx-s-4\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">03<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Key terms<\/span><\/div><h2 id=\"nx-s-4\" class=\"nx-section-title\">Terms used on this page.<\/h2><p class=\"nx-lede\">Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Term<\/th><th scope=\"col\">Definition<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Term\">Small-area estimation<\/td><td data-label=\"Definition\">Estimating indicators for areas with few survey observations by borrowing strength from related areas and auxiliary data.<\/td><\/tr><tr><td data-label=\"Term\">Digital soil mapping<\/td><td data-label=\"Definition\">Predicting soil properties across a landscape from sampled profiles and covariates such as terrain, climate and imagery.<\/td><\/tr><tr><td data-label=\"Term\">Spatial autocorrelation<\/td><td data-label=\"Definition\">The tendency of nearby places to have similar values, which statistical tests must account for.<\/td><\/tr><\/tbody><\/table><\/div><\/div><\/div><\/section><section id=\"nx-sec-field\" class=\"nx-section\" data-hue=\"food\" data-tone=\"alt\" aria-labelledby=\"nx-s-5\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">04<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Geography<\/span><\/div><h2 id=\"nx-s-5\" class=\"nx-section-title\">Food systems and trade routes in West Africa.<\/h2><p class=\"nx-lede\">Plains, deltas and basins, the corridors that carry food to ports, and how far each division sits from one, shown for West Africa as an example geography for this subject.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-canvas-wrap\"><div class=\"nx-map nx-cmap\" data-nx-map=\"ctx\" data-cmap=\"west-africa:food\" data-ramp=\"orange\" data-basis=\"case\" data-focus=\"0 0 2000 1000|1200 305 1838 838|584 486 918 835|756 249 1182 561|243 366 638 677\"><figure class=\"nx-fk\" data-fk=\"map-west-africa-food\" data-ramp=\"orange\" 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ErNIe+ANSG8YhP9Qe2LqjTXIBQ2I2OnDeHXAgqnyakvf0zAEk0g442YW+nlwBJx4qgXJqaDpVtcYhCLcFw9w6RDa6SH8dHhY4fqEss425mBySmNFnAXFSAXDgFPvm7iLkuHlD3jXGpMv4hdhe6FesAnQrRWbJ9fNU+Fg1zUVG5lXjENYdHtmnt93zzdvDuPDJdFzYaVTUKrJvo0RR4zj7ellB6cu4nQ9DTwMvufSII2ZZ2iYl+w6uSBJ5eAWcn8k4QHrKbczqXxSuJEn0UwtHHUEOwjEFv1hN3MHfzj8vLm0J1InWuM9ZBdNCm6EaxTUnRexb1A1mRlKXAdKekTYkYymu6KJqmDhWyEXzMwHg8gE\/7LBBw6MesReBfiKiGyaKi4xarBlHnLXIJAeyTg4otBfjlkacVk0Reft4sbNXMxvt\/uXDx\/ejx\/e\/8b86e3h9lfzzWFzd39mC5ML15my4xYGk3weIUYBNSCkqipKGAgwJIxiPC4FJWb88DrHbK\/raspBTxecQmOM5VCgCNjbIuG2nIY4qfBXmlzQwLJLMct3a+aCfr\/Zv5bK6yeteUxu3RZIOs+lEf5IQsAIhRwYgrqg5RTmfcU9QY46w8X0yBsmzcdT2Wa6AYwacRMhV7gwyVDtyOIrJCJCWpBWZFDyaBDlbaqkF\/cmzt1zCUX2l1zYFPJKASkmpTzimCUKhsmT3A5zu3J2Dc8JQxLhq9HEBVQ\/UCmEuoJpslRIA\/OGia6d142O2OrKZJJooIZin+goxCpJy2AoQaCKt2iO3MzP\/Xr760XHPpS2UmaaSS2MUrNBxQahCnLpkitOkpJjWojJUdoX2ZCGrZFXNk2HW\/xME0la4VJ\/wNGbRIJY1RWSSwhsWSVYloyZI\/z15ufd\/uU12iPUuvrq5BDGyER20\/yJuCkNT14l6w1zjIJAQbQNHwd3A3EQissDMsvMwNIb1uhOqswI5aPGTUmrJ2LvUC3zUDtZ7iTNVqCUFRTW4L3IYyzbqJkn\/O3uH8MfH3b35sfD\/n7zcr+9u2bTrg4Kjzcqmxz8CL\/NMyZk5QrxL8O8ZJgeZJo7wd1ry0G6q3M7e0UUu04FRKnWBTdGXNgEvQY9lQxjcmbiKtxI\/IreGI7aol7BtCgytQAeoNhamD1FIhcpY6ajoGBRkWVxw\/M+SL7cSDJaPlkqA7gTPbUVHoELnrUXPImFc14hOix9+Z5bl6yIw0WErV7c2Zlf+z1CyZcSTf603W9fbsZztyiv3F\/rjU9xVC\/O0B+T9QXTnVtvtKRuGetJBQLVabkmDW4c8R+wJPGYEHXwiqB8sFMWuyyVkMJKs0MtM+C\/DDiQbGI2Jg6MYVjVjMhHBLpFwJmgWNJr3vCkNPGryUI8ByLRXopFss2jKAb0AbECLAJbZqtcFXXocHrcCLEtaTEXN\/O8\/7aZnm9eHB72N5uLR2Tbqog\/hGZCbqOUgRBrMTilE4nQClbNI46qrAuwOIKkM2V40FoWYzzmzcNAdxN5Ek1doYZYlzM4fuZUf4\/F3l688DmsijX6aoeWmENFeQZ4KtzANDDlLwpLisvqrXgWTKmB6YpnrX1r+kaDM6b0ouqHBPuA0ixkennd87TrYbx8xq66lbnIYkKNY6bNxU1jJge1wqoFBZ6QLCwExkAsTCSU7xJK\/okVUykni1+3uKAnudVxd8lFOQ0YPyWyUdFhgk+LOY6QwsKqasLFrY+JR025sDIXiFxxnnXfBmn2zGDRoXVweO3RgHt4tA23FE4OLnVVD8cx1oKvF2AhKsJMlNwpFwTSWF4NgDsaLWAzfE5aEWQOoQoGRgwZWCkihBwqG+q244uS5lHrQIuSoVeQTUXtPwMTgbry4tnMHOvvt8+f735j\/rx7+YnQNbsThRIvC5vzsZgYwgjkhuk6UEvvjj6eldpkQRCGMi9LxATgIUmDy\/eI7IDf3ag\/WOKk0vdeQi+YSCrg5cLGzGv+atw9P9zufmO+3u53+3PBRFzn+jifikk2jLGnEhTlJClSQ4CFbATch57ct3D1I7QDYJKDwtMU74WiNN7SEIdmjd5zR4SxbCdC6Zo6nIg9mT6FRDo4441qSwFKgisBPkqlMCBEC\/gElka7\/8PEgmjGBsuMUoXvh5ngssNxhyAig4APpBPiCWsiustE5Ghw1gWiELgMR+yjFroZZqWBMDbH8nbG\/WFqHUkHpNL1ehE0q\/iqRDeM4kKbvege+1kcgRtw6SLEslYksjOxSRKH9TgLvzgRAKJawsIZwE8G9fOalt0LK7xZUCHQS9wRhu0B1WTAF2GOHLWfRUrCYhuQ5QS6B9COc5UTPwsUvj7cwg\/81N0IJa\/ciCjZrDhWgE2QzBMFSz0r9wMLhGYIzNsCoAr9qJtSNXWQtbwLPIM4E4xSA+UA9w4OJWBagY64AhGpfzX34xJyHZDFQmRlD08DEVnJsNTsoehh6FlNdx1pGY3XBXmjSaLWswcOFrUsp1X9LK75\/hf4OZ8WvuDdqvqc8zabnOv4GIg4r6hMmjBIhIPWybiUvpexuMsZKUAgQ4nfCr14LNuhqVmv4QliljYw88M4CuE+NkwOkSmAipxbHLSUq3gdRTXAU\/fM7yqC1FnmSJjztADGegUpWgVeIMvphuVCqJ\/FOX95+bC\/tNHNX1c4edxnj7S97g1xMUgI+GOkhlueVf8W4J4iF+Y1ICHgm3g9ZhkWzXeYRQV\/HTf3282Fy3oawl9RCHLeWpOjH3FrgFpmdgxWOUPlolBPVz96hd5l9a1EVjKunldNzLvN4pc9l\/ALM+f\/m8MoPU93n16Za22V7\/+4NFEVR6hzIcag0QInBvH071Bs1HydUzAQgeENACuoKKh2MVBwkCnFvXoaBLbmEc0TIpeR2El4bTtmCBOTPV4hQQysYF8Xs+hhFjH85dfDJZmWha+pbUKoi8+jG7KeJpLntqMbWSkIimFC3enogwHQISYZjmgc1A1RjCQVKaBMx5jbw8E5Bz4Ks3Dih83Lw91FSx3X3uFgircj4d+MaIGRUucd4pLga2XW+0UwlnElMwf7Lw8f3k9S3vi0Ob3uaXv0U0oxwabRFxPESgpmOBuKI9MgEpxh4xGTsmjLwABoY98xzobZCJi\/KEjyQgvIa86uBuBMxMrR3XKRuCNi95mDiXwxhQSQHDohgCIjD07z6KUzpmnMzdxXL4WzZkxAuCaIKCl0lJh484Y5z8XMeEhP3Jj9xWvRQrgusjnufAsmpjIqIFSaouCgdxUfmB5whH9kDepQrm2DpgiYkiLwp9BfkOCgSXxpUbUFNrQB+yYVJ3yF4JwtnBX2saCWUGhJK\/RJI6DE6ynx0iF4IiyF7gufNGs226O0sbibM\/f4q5vNtPnloo+SU10XKRbrTE4CeWNvUlFVQy8W9oSSFpDyhjpBcxDce8IqiY10x3J20a4hJgjRvuGIJW7YSLrPGd0VCTkCdkdItgquCG46nExum9cjLUCFQ38D3So+NmDJWVqjiK+rSAE5Gg6LohsipiJfFIALd8CZ1zNmsMyzfreb5xd3PQa\/zhtP1Zri7Aiz1RiDBaZIPGHc6SRLFCBQGYhwVIIQdkTDZAYrzBUH4h6bgDIrhAPzvmjGSXCxXdEAT5oMxcRm5nRxtjAqhXFyJMKPGZfMoL8qUJfBMz0qgPXdsgzXea1s\/7C9aChzWZU8dal4U1weFQ2D7A4bojSYxhaiqKjPi3AY+lqhDlSgx4YX1skY+ToNhQbmsz1LRpGvHIL23iRtTySGGV+HBhzHS2M1C4vgKw\/UDP0Ry6BYG\/j4SDAtV6vDzJH+5vZwd2f+bff2ctTs7JVeWleyIVYTnBsDvKiE6COgwMbWl8y+g0hXybL8GtlzqJhJIpqlO0QdFCvGrVIx4uIipykS6XEMrOizzobqAaFKAC4C4AWnfjjJ3zgagMj4FR2Di0BMO8cPvT7cXzRL5bTIco1ZilJYj3Vs0EDIz8CbLcB356McWuBGKrOe7OuxKDU2who8ytLI3KAlKaJ6xb7LSilk2AX3lXKVEen6zCiS3l9grKyJySOWyTHcychALW7WLAj4+2b6+YrAuLiVu5V9NcG3MUAsPN1WPBQkyfLONaKIA3sgiLWi9+sS7ZKjg4\/4zmlDpmWjrmN9hJgRr3k6FzQAkM7BwqIT+0FwImyLY1AiKe2ihqeyY8e3ZVxonMUC\/3F4flHH1VpWildxJoQiFSI0SzuvQMyKKwmPBfVL9JhKtZoNr2i18yZ0sBGao9H7b6LmQNNAeHPmrUbej\/6PsVpXa\/jsgtQB7mtVpB\/cBHxhhAsrGlAsUmNPlMM3SevP0q49wd7c3+32FyWtupUbl6SNKIdRkhq0bfD5MvEeUTGKFSg1gE8IpE6a2kPSmO1VASGnZc2FPoaBz+SJAKOXBOOZGbFY2tljeTOzBxOwlALnhSgECF7RGl5CwyX7mAsR2ZqXSUxvSTaosGUWjSdSM\/HI7zQkgRcTjHEWEv1vu5dXuJJ17a121Zvo\/QiwMvCD2pwDLK7mRitSFSx0ab4wDMSBOTRjWm0MZU+QYlHZHdiQT0a+Mhm\/vNA0997eHDa\/MV8\/3L7e7TfmD5u7wzm\/OayssDhJLMc20kOWXKiCBJnvR75t9tBB++4R8RGIIPQQ7ASC90B97rtCIxaiQ3i1H8wZJgrp2VjFChfNyKIl8EyNM87DifvN64No9k9FxbHZdW6tC9bEWABhJgZ5oA+LG4FMZ0LeMmhDOoL93BFZemGUNCBoeJuhvrF9TTH\/vMCommTtNlrWzGVe153e7Lb7l9dJRKpp3eJtrCbmOjKZSqS8ag64gJ64hczyp2NQyuoUo\/ZiyC+R9LIwW957cFnF0V5OYmg1ZgrnklRx5n3\/tHm7QULvb4eXZxdt3dpFJxNzGh+x3oEhcGB5lxhNTzQjMtSsbqP7whDL5xTWEZVHhDEQkaFFu1kL887oQdHMStHmKgoEEenBsPXDLSf948yD\/j\/evNneGmlQ+I357tVmfxbUmMvaTWkm1jYqXo9mIrET\/4hPY+ER0S7NJHGLrsONmMRBdY+tWAofjobGXmrYECENejSlBDvlNbjwuiEOIAh3BC0l5piSbi07JLNSVmjJEyUfwqYqY8bIajHw6ygTG9ZHo2JxAotlrChloDBoSRP5LISChBwsRRFZjshfpxQyjLOgARnFJmQCOsAHzmtwvZ1QgUUEfSgjCxwh9JOypz6qbCTdS3GkoTipRmHKVeEudjSd6br8lLTE0ya1q6SlJICkm2YsaDo9ar4SrGovHThlFPHgCAdy2l0ZtHfPI45INBAo0ETsuyNniyL+IjvAefUgjHXQJqjeC2Z6J5hbBEalOUwIEcInlEqoq3dE8K9xJPNFgVvjVCyKJqUAdLLsR\/GVTf+GbdSGveiQF8tMaByUlygegbLKFrNY40kzj\/6b7f7+dvvJJfpry7E9aWFrZdqN1C4IcTq4RTuIHdx0oA7TQCQh8QMNrcOayHfdskTiy2FDkt4gcvlEpIg0pZHw69hzHYPikYblgw4Lta5\/fHInXLLrKkK2FpNjGB0pO7KiIYpi1BXuRV+dyAbWQ0B5oSn3zEYJdCkRAZ+IblCb4wev6O+icAbypyDxpqk1Ij5Q9YT3lZUtqRAJRIYIBNvIXihdAl1Z9mCVobOHDk4LpkGJV5Jm6YJiKQPbuuAhNKaDkyaFCgEgi\/Yrzdz4fz+M95tPKyNX1nXUOSdMIdGNll6FNiWAZWsomldMj0bnCKFByKgJ16h9KkFxmKJgJP5aVrFpjlS73d3vpk9ftlhWNYw2W01KaXRaK2cfYx56VEHIe7QswSPHjJgYZF5kvQAGgZUygoC0kauXVrSD8NhjJ\/qlMH01EKoYNLHIJjAItd7LqL49dzqo5WYInvAsUaGRneRJLSatPio5DWXRCrfbsQAklLiPdAokDIJjgVdocRwdIounkudpoP07aYy5AuK\/MmJoks7zdQxUWsQ+ODLaiD0ElAGUIJpoYHqHbakAnkSNNTRBpExWilrp\/BH0Fpxj5RQvbgiXK+GUiv4TY5sVLQDqFMYj5LYjGsUFvb2VpCFUGOz2U\/Q0rfExZqe\/lwnpsLDIRfs1vJbuijpndcgKGCdQO6pCah2b4CAK5K046S1EXwOBPUjywB2Sny4e7RxrdHvYvzRfvdr8fPi0JvF1XRdzrdnk2sZB2T2SJk08aC+q+mWdJ6PggvjOXmZcb8rVTnRYAKdKlKbrsbkO2h2bx1sFv1D90k7eQVIKBumSt1mW+jpHQEyH7TVC78o6k1erM0XAmZF5uqRZHUeGRPb5eDZBsNcEyD9KXVbvtSkxGxZW2NdVBjItOrZeEjjhSPLmUJtEb692zxCZhSTgED3KoD1eCGRAUMArWzNTdw\/ZKazR2XIWJs3iLCFQveg2Ox9XWaq+iSCjA1Oi4tcY7XijUahnbjipu+OUvaLraraIIRll6HAHo0Qs6HD1HRZStObjFXavMMuhvJuCUF02Ao8WeQPmBYiHm0FE67qOS3cKG7kCmVCy0IvaEb6KctAgOmOCkx5zYTPgIybSKerZmUV3OM97bT+8Z030mrSas6vSaiVlk6wfM8nj2H1MOh+viBd2G4MPjF1eatmi1vmIlWJwRHsIWVBIeYASKuiyiIMaG3m7V3SRx5cFwoUMqQyLZrOQ2AVMTsvcYNDzg+IZiR4rLMIGbahX1wGxcNDH9IxRBm3AUWg8bLRn+Q9PncjdJQ5rUVrNyFKGAs8R0CiJHQOHCnIHeqqBT6zRjknq1wDdqSugLu0xgsJBnAoHf2N1PxgnZ42e0cGPQnDShEJPWZJ1YRnwm\/1iPuqKvmO7Du1aSkXTre+t0w4YENehNWxFbpAlUgPDXyjsE4r04pX\/lO21itDMqklpn7OSZ\/QEfEXQADINBUzAQ4NUQYH2TTcdNUlCMQpUpyZz6Euld3fGPcvhSV4T2uSapGZe18BdU2MPvNYTw0D0XNT24tQpRMmXmrWhAr3r7K8gmLhoM2Zk8KAdcyTzyurXeewrKwXuDGQ8zwKgvx72uyuXvS6tWWszKYYRJ0LJdspvoYpB0bsJd4KKKRkCEEiVtYhMy2meT3qYnu+243VLuJbKqldjLKj72uiNIt19VDCaZ0xAQKxDLT1otxjrpgJ8QvDjkJVYjNtynoPsQMJ\/1UpaXgf5aikZX90o\/rUDGBYUW1V54+jGEyobWSTw6ODRRoZqyHeUqZcTGwqDIvbNOVVVnpCOTNetLa5cW7QmWDsqbpfsYIU+my5IIVcNi8nsoJSLV5VgrCzDmPKTvt3b65eQ1i3ByhLqiKdBmtmqoYg0F015wGhrq\/Zwo28RiYTG+g5pEZyWCRsZGQVb0zrzeM9u1bMUcXnmd\/7H5v7qak9zddWqa0sm+Dp6bTp1dETIbYuKrxJYaye+ck9Z0ynEyWM7MDYl8AF5KbZjgQLSOPUXFiW0PIG1PNyiZ+TyUkW5rVpqFOxBGoN2N7fOx8qlyrNriSdqLwP9SuYpSLOVtBWVCZGgQQ8ZOBWTqenndOZoy5PW1Lu761R\/jStX64MJRTISPZgoWPMxJRSPUUZUPsqiafOs8LljJA4uZkagUftjmL55LI2wQ5\/pWGJ5WAeGfl5UvMXP2Rf2v1wn4aW4dRthvYkujABP2mNYpOUdepSgaXdHGrLOfaMtnZGsAozKlT4vKa+S0lmTphxZFsfWQ8N6X4IJ8iCD6WW9rP1DPaPTlHYVu7+4VTM36Y+Hh+39dVFLXqcCS8smujKyozeQnNqzJZnZTzQlkEeISSdmShmEZfItKjGMY\/HTMX9RjthBx94p4MCKZpzJvnhOQcQn\/AL7R4qBazahXSkvmtVITbizyhiIe3AKFyc7PmY1kPdcfT\/XO59Zn+LeBCX7YX04ssJBQu1IMnvPPj0tAVvl6QiDpv+IvVzMnZe577Wbnl9kjHJ1HYtWqtUU58eMjCV9XaexUdSsXB6UkIgk5mQWBUqKnhmNJYZOSG7Hi22g802MjNHWON9REUNv48ha4PC9hYCZX8tsNRFKy2XxMvPlvtm83Yzjr1eRk9i6coMioLrF0Jh4bXGNRP2RPZbs8FFJeNgL20NjzMRwvWYO\/GfW7LpXKgDFoZH0txGx20sy6RxGtJSPif6+295sX10UkdJWpa1ycqYISMRzjAla1cl77jX9RCZd9rIjtodz68nI15ijtsyPVFLptqFXsyPZIEkGXxSa64C3pcPlBIXPaLMxDCAOi12g2tYXlnGOZQ7m3twNP23u7jb7G6n\/XhaWU31yRRdcjs2Ugs5djjKA6Pc0BXaMzY1BkQZZI33eFcaXUYcJnJH7mbsIKLX5bru9eOZxXdNbDtVUK5FYOg6noNHSZA7AldpNkQkPI2sXKD8WlXu1T8pmv46b\/c1lWsCwirQiCb2KLSMulXp4g45riLDkni5B78qlrvO9192ytALOgUaaWsEzNC1M69mxx+tYEAvDsZymvC+LDmF18x14sd3fX+YmsquKGCkIJWceK5O+lQ4vge7Ia3jJI5IiumgB6ASMk7REGHrSXEsVXllbFoWyPkETX0GL7NfVH\/qqgnIbUbn4YwY9GYUjaPEwaUpLB++Q4CcSkoDVEUGAqQW9LdiRXN\/piZL1u5Iqi7g49kaxbObPsK+GeT+Z0IxdEHCf1wk4YBhIl9NLb8SeoTqnk0cie0nLEYUEsV583pnH9ed3H97ffnj\/WoiQL51gXSeXQdzs2kb04zHfRsyE1eQx826O1Xkwuiowzyr+DPFqZTs9k5lJm+m1N5A8eRFud9RYzSvAipFAOHo4GL5wzAsuS\/XM+\/puc3jYjBfFep1zEbw1oZZRqYGcetvQT8d5YWzFd53iPw4aYsDZ1s4KzTxw1Yk+G5GsYETg2KReWA6a3JftX1z4nDV68\/zD\/9vXbb7e3d1tHs6Jcl7HXetrMdG7kSzAnFoDeExmTp6cdeyHYyju+hATdgMfEXWeLOGK9gOij0VWp3wBWcnUQcGtblXWu95z2Xw1+uiNjheCWmEbKfvWZESG9KPqlVr2OuoTbozX20uE+MmtcslkbF4UktKkXaHgpC1E5GYdsAQQD3mMtVfOq2rz5NYW3Ii2DXhFZxdwIZM8CNVddNv5MwZ95lz97TA9PyFt+rSk1HV4mJQimKga7HRmOfyED82RfxVagXC7xA6iADid4+STyqBVvJao0E6BLaLdrcGoVOKdk1GxQXFkWW\/OGZ53Lx92t5cIq8Q1WUWDJ0ORrBtJ7+nJYgFRJnAmMLxi7cFrh0xFlQgpvEXZbPYJX9rzi+MagltJ3ycAa5vHqIaaPEGc\/UOKU23\/getLCa69gb7yAmJUFckeM2oAdvkcmpvL4INQA19aj19XUYpZ5l+VUbtzke6uuHqFPTke202APCtKDYpZi2KKLWMnfIUDqT3zgIAMOmNQQZFFGaYb0kQRlxMTYQZOcGjnsqttzpK824tMbn69pHuiXde86lNDe5InOj5omwRbrZShEbiWxP7QgaEwFablHAfPuR06mDGo+UsmHIEwhJaRPsF3elf0wupMo8WqfwtPaF439x\/+zyttl83rVHBklxZYOIIyctF+gcmL4G0GgkSwZ80QARgFiCanLz3S2HvlzbBkwVpe4pwpYne4bnnZruK26KtjlTMoJ5fDEKFiOKggacojHXNfnsuHg+KVlkDhUU7x3Z6QNGs42Ug9eRZiifUA80c0yh7klrtk2sw3++nD+5tXu8s8pDWs2wRBzJQ0YkibVZyzVpWJwCBHBiU5K9C0shG4qDdHWgrX7zabGYc+64NEvfjkQrLP80yX7UnK68Wr7cN1hx\/rOmKT1kwo5EnUs8KIPCDz4TQbNncU7QaB2Fvlcnk3ybDLKH11PGnPmQJt6OxNSvepQ8mIJXAkb4IXE4ziv5D8IWU4MQjlTJG6zRyuv4s13r94dbi9yHpaV+V3MKk4BnQOyShDzd2CeQA8HBn\/KzpgCXjGSDs2qGdKtghL\/k8CmTgusxBLTh4KuRVs0Yx9TkpVnEJl2dUfmaVx6YjoVQg38XCL+\/Sku2q3vx9+ODzs7i5Sh6a2KkgNsj2FPVakjlE12MnqlPMvEeJlko6MBBOGcnrrjByrjRORCGin01XhupFYBsNPLWBFzegsYCIDYu\/QUciAuviOWCTacJLOoDlPhDXq0MXF7Wvz\/M39Zrq0cb6kldaV1XWlFyXCMSO9nIkHbhjUOXDWwkAfEHQvJmsF3nNuwfJkDvtRm8hm7JQM322m559IxK00kFkuicxk85hMRgwY54yq6+qPqLQ+BNoprUDA1GTBjCL+0iyAfCDuCDkOl6nu7ZNOn59\/vt3tt5fO6Vq+s744YVFyfmTvmgPzvs7sRczBMeOsnD72OimCgO5tOpNMc9Z\/1MFlvt7sX184n+RWrqCaYNvIGWiWTB99UBryouKKAyvmM1P\/zWjCsBNyKgkGaYuW1\/KkY\/4wbl68vkwXvk4r12CCqyNmPoOsQmH1ykCtU9TY3p6WHzN+3GR5zZ3wrq10GpMJLo5EKXriWBMgkEppqGhxIrZAghdJwnlG2NMcMvPLlQ9e3UqPQMTdjtC0OjQyEDEKB88pjjJRrQ991FMhaJmT2qx2D9PwV46wPNoBkl5lmbgKImlCTllfrSRINgSOclwa+NVgAOOghC\/oZOizfyDE7MT3fQwpJj8vT5ixeT6q8X53WU7TSp9aUtG+kS4upiMRDxBd5OBlcjQpTEWBwZqaQelCsbiBNEbLKylPgewscV2QiOjX0cnZHI2vcXT2kWnCJSVgLuoPk+E8U8AHTsuJvSeysMt8eQyFrU8cfOFluwY7U9ZhZ5yt1gSXx6TU6+04ZqYPckY2NmuvczjO382Kr7Q6jJcgAPVEnA5xPnNrn2C+Xr66Dg9T7crFSVAbUh2tCR1LTzBbVoLbdAL8xRQeIMQZnipFMsN5pbNUVk+SjJ8ZteLsU6Tv\/uY\/N7fXYZ\/KWnI9W6KJqLlmdRAHqpmmeiorBaAML2N5Ph2nuUb66svLeDpYYHzz4f14eHmdHKZWV64DVAwyd0rdZTZfKuCw6HRIMjiDDY160CoUPikimaj7qC2B3jCFydkRlnhf9vfrzAynDcYZmhIVJNP0xJe3ZY7lOjyMV6NzvF\/JV+ILuiC1Tz9T5buo7TESdTELr6wCTiskhlTllvBUvLyQmuCRQlLExTlWU5aHyMwHbX17ICzzU4SccV1FzLkYTM5+tGrQMEWjcdg4Q2pmzrQaxmiwKbuWxObLzsx8CtbfL9FyO7eS5dVFb3KD76vxrxcuGY55UNLIcuQkhsqQmGXZQZxPpvr+8PCzdPV98nntOrPrXBQEjwyp1BH2mTT0kVw1HMKUybdLesK6\/KgzF+HHw\/7Cg+a4yqK2JopM4iVw7rA4qcRhpKhw2jJetbvD9dnVnYkVvlkbzgwfnPkFf968OjxcqZOzX4kQL9XE4sehkD2naFckyed763kiGSmZPDqdK1JtjmoNQGQsNqIkyMbTDDep9xspwEoScsuLnrsRu7u73XjloktYpa1asbBDyOsjjQWx6vS6phPmFp1rx+HMTB+izGgNh9wWc0QgYMSgUnEFxRZ6wunBZ4L6Xj133gvEMeINXiCOqX5Vz0azhVLLAVeJjN5toGvhtNwIBk\/HDjKlQsraakMEB9kM4pkoZz7V6E+Hazv5UlgFB3XBV1OSH3UoCsnmIvGKaieLMp51ZlKv\/csVJA4wM0D5kZFP+fGb8uigWZYov9i3ypMIP5MT0iknBikKwJcKTJjpnKmsirik0zBQWkJTSV1OlLv5fKS\/7G8u035fO3T0uG0pmgKDQHKcRE2kDMdUrkNQUELp5KXavNiO3AIc+qKlaCJFlVoA+87h5+oSKa02iww6xFZ5d5LOISk9RuU2abRRUTMmfynT9BwxxZCbDY1IQAIylsjK6nsDHUmvog45buqUddYP9DdrecopT94ZgX4y0BSkGBcIUXNedyrRB1PJTt44\/EUrkQR1td6hinC06oBC5ZpWY5KgpjmZq3JSu9KCWLRxKlIX2WqU2CUVq3R3Sh8WCA8uOp0yccwR8VPEKXklggAAO4LV0Z+X5icFvl+3493lrVtZ2nMxF1OdmGLNYDRSsnREL2JZFoOA8dS5c2y\/5jRP4qcZv1etc7IcSpbkdPTIg46jiycc\/07RO0Upafu8AMvud3K5ZKWZw45pAVbJyZzSnQROYHUc0hGPXcOVc8Xs0OntGpvymaGv7yatMXC8V9JwREi\/J+genZKMRijCJpZLVm4+OAqZqcs0\/OuqdC7ZZqqtYzAk1yBTYSUyrhPPAhYWydhYFFyOoiWpzavKbNRsrjtzb+cToV7\/sjF\/en6YLjKc23XDNLJQhsfRAUnvkqb2XSG3PlDUOhsBVSioNW\/UL1A1B6eK\/\/CEGgSSsSkpmWKrtUecCRqOB5XgohFNRxB2MzoWkexmZ25mnmciH15enke3MnsqjrwPaZSNqJ18gt0px+5W22dwIPtH3hyOik8EB8Y+eI\/t9QSOZh3b4zpLB+JsSYmfWeyccP3V7sP\/fbGEVlaKtSxWYJ4Nw16tclPmDp3HfJ3lh5v5vN9uXm8u1kH9yuk12SHjjgYXZTjoGYszeCk3H6nz9Wb\/nw\/jxYrT6nJ9JEG9VwiKU8KVTGC3lp50HmtQWQg9\/SoW\/t3ERBIlHoxGcCKWR3HP\/NKvd4fp+ZVwjBBXFmuciTaNA+uj7VzDkJsPxOEjXPdE5bdrH4kNZDqLSCdEsjWMHXdQRUgKRJ0MGoY+ya3SKfA6fZEDVJthT37UTwoKigo6HpQcQr6HUyeUJ65TJpHHUZ\/GHSmsvCIMyPrglQUJwcegPkgnTjl31DOP7X9\/2O1PpoxeQF+0dRsbHDaWncRKwt5zz7xiArEQ7dBdqhMIbBILje3uxGdis5WLU5urOk8jawsgiSKW4d1E\/cl2laRNWvHdhHDEGmXf0N8wDPJyY9j4xpob3xzeTb4iggJdBBYhj+3ZjaEYCVmJztlVNgUUg95NrvMtaHwjp\/VuCnYgdwf75si7VM\/U9MITJP64ma48sBJXmesg417lJgSGbpze3qlLB6I40jHLoaaXsm\/I2OV1gHBvfusNtXVIStOn7TWWURIDEvVb0vlk7Xys0NeHn3++hPVc6ytj8UF6jxsNp3aL9xAvaH8oUxaWYySjcrGD3gTtUIXskMoOzZ8lHV7teoS4vMSZS\/btw3PpX7i0yrzOsgThug5thDyeqxfMZ998c9hvXt\/+egl7+dt1uczgudt65TtxW4Pg5WM11RFD39u9Nexefuoyf+pfN68uoZjXlY5DsiY7P5LGlQyfnGmZFOpPBNCywz0fxnJk5fppt7293Zgftof99mwWY9VjxpxMcWDC5nSkPnLpeN\/Iu+jYLgQZVd4xry1EZ8Ri7vccpt2lhqC4zhmLJZri6zh0Gl1lNdbESYbyiCR8UcSNjvfUmCdzLC5uolRMFpcxn0jy42F\/fydHINmkT68mrLtqsTZTgh8HMmNFhLvdN6+d342JU+eUV5VA86aoRLa\/MODrjRBnDic+aci7fbl7fvF8cl61ouSDKZg6XZizxRxBDtH1Rxhsn8HD8ix9nM6Wx+GbWevsSBuSxAFpGdczziy3dHAGWIaXo5f58A92Dn8tbbGXVp7WeTIpWlOqJyVRUoejsAQAxHDo4Ebm0yKBwEWRjvbIjsZ5H3QvgFKq8AvOgY7mYzrYI\/vN9u7u0vJKW2X3U5EeWfGAwdnlCmdASnahdQrDor2CnIDCXsug7LO9wb4PQyZ8lB2ZEsIvry3OCxr7wxW9v+u6srJPpopC8WihRUKF06BJm47AegDi1XHo8xmVkT4Ws+9vHy63p6973JKqKS2NR8T1MXOVdWwskvkcwNZ0uB1hn56lJpQAC3uXSbNWOy0rkgyW6Up4bctOyHxQhdAlfr3Z38DhvIIvMa5zuUoOpjQ76kyNIWj7gVdceek2QFsqFRKqE96H9m5qlX5Zf4ciYp2StGofB3FpTsMFUnzStrikuJCikVePr4tR9jgl\/cUg1Nhp4ZsO1YIUNWWTcYplyDrErbBXK4HDX6c5emXGwtA4Or+mEggQmGRGwt1bpRXl1HTmrTK7ggle8WIhlz2N+dyNHzYvH64k+isrwfVW5myUNAKY0gBOK+yPAMzIMRpuyBQzHLNK28hZ75qqqzQmUclFSOINrVJVixAkFpSF3CtsUwCI7HY\/I8p1ASD86ZrhyrYKZ5s3JQuaEJlqNuhlmkKOXouKMs2K1Kmdyw4DVMlZ7pV59oxlb3Pls93cb2\/NVy9e3F6gHw9pJbqgBrpf3mqBG5PXXdEkhSfbVOx9rItPOx\/a8NX9uNnf7\/5LBg9+EnDSVj5qs6b4Mg7lOGHwzOPMyVN39\/cHCMEnH+a3ZXX+yEehyPNaDglD7++SNlZCZKvRoVTeKt1CZAsABgcxRoxoC1eap3OFo\/SkOe7wcPtcaAwvNAytS9znYk1OeQQvu6gbr2ECks1eC5Po1HB96IdzWjWCBg3aAIIcTOwj7YWzGGNmMABMZ1JVxXx00DDMHzNGHERIUgN1CpMCb5ejwPnQhD9uHu7X8aT4tM6EVSm0hziyvpRlo5D2I1ZN8a46\/yQcW80wDI40Dr1TrqhVg+\/laOMkgbS4yJnr9NXL\/Yf3z6+iDGpplRQIfVtBxFt1DKk1ysSO8inNbVASEW001xHXsSct0QcRVZ9A01clnCNAgNO4EOiDustxMAz7L62WNsg8XI9sw4nM\/djgTq\/dqXExpUJp4bNyVQUVTeRxl\/d0zlD1397tVhBFy\/zsNU1FJcnGupHQEsRK1BfiGzTOjXdJGUGLgi84QprVIoCqAERQ28IWNs4NtMBDZp1fRFNqle04Io0tXuFyxDGfPfDj5lbQVVduQl5HUl9Swhhe8pwTNaIBhcc6Yw+SnXbPBZ3DHXUmRqOHhXlJw9EQcD4AoHFnuJTcnIb\/D7eHafNSOg2uoZuvq066hmxySGMH7iseTGfoaJIwkHPSFWU4QB9A506uSqrveS+igs444oTNqC7pZLvemKpZ7U5ORl4zpSGL3a9V9cOxiH2aQdJuUK\/TGgNkKSj2pMtZVI77QFXXG2iC2r6gEa47To3hRyw7OHPi\/3\/fjOOH91tz82BWRB4hrhsBJKSBRXARQXnOtO0z0G8TDt6sTfwMGqLOaKogf43KmsRWPTcc8TnAOGvM77120nujHhP8v0xu9aKTCUUn1XNEU27O5f\/D5sVVjrx3ZZ06EqOccuet195E0UMyA4n8DJ0pFsBlbkfRFzUWyLxR+DUmVQdV7qTyjorQ8dqHoH30sj9NJYxk8YB31E6DoTN5GB1WnXCmVJ0Yq7S4aXPK\/\/9+uD105sXLW3c1h0Dn8xB3PzRy5jujJh+zwXScrjLhKCBOtLbn\/CK0+\/Rh9p5wJXIkwyQYMh61M20+87EA3wsetK\/y05QRYeUCYzWpCCQu40wwNuOk5UknhHptfK0D22giHZqq4MLlvMqckv77D+9vDw+b\/WXWi+bXETalbGJyY+Idh3FBNoHKrWBMInmzQFDvXWch0rnQVvmnXGd8pOrF7FfOXXT0jJdXOXNJ\/\/bh\/bibPrwfLy7TZbeOgkmGomYHACunAyp+9RjDOc0+Z\/XG4rnQec70\/oN0izxMF\/ndruW30+eNtpoYpV+panVdab\/Y54YUfu6DHGCavJp6neL9iNEkPUE8EwTOid+\/3YwYoHGB\/qqsXEvCWkg5rFwnRNgWveeoSBDj5O3ZMtGc2f2HzfPL9GVrxaQl3AaSCDrUY8H\/zTELlQVOzyl+4QgDZedOGcjdQ\/KxxESP0gwhXXPmBGb+1l93F7kCXYvrqORUDUOT5oEj+irco6KVCbYZZf1tUOAm8bicexCVJZuzyQt8b3iW5PWoRJ0pB2PExikJQOrcCZUcoSSkdietgfK3pvMtlreoPmkfuT08oJDx6bKrW6kKBX4jRCCOmbbMDiSwmWIulKLqlt3mOcP7H24v8i1d3ZFznEcojURSaHFBB3WR76DC1BB577RupApMtRmRbn3IdZ9JaoemA7uijgDJyjZY+jwlHfRFYtfAdAS0ZyLEBoau6dAwl3QyObCMva8SExHJAIBXek3pBSb86D4HjdTQn1bOhF9zXvmv7l5J+uzC7KbmV+1xq8WU2EY6VpyZ4jQTXkgNQz4nhuNFmg0xG9drz0pR2uSsdKNl8MoTbpUjqLEyNPQ+y8okGL8oHqdKe46y6k63azjCdBZ4N2ehl9HBlwdbrQZM12RKqmNkxQTMARbpWqK7lYOUueiGbH4ldRncBZ0lhhZ99J8iLmrEjJE4mPDVZc91Ti\/\/FQjNLsyJX8f74pItpliZ3IXxKb5PzmQ7JuqzmrZJ6DVUthfy8qEm0q+a0hvDwnE4CwF9WQvXZ9c4c4d+\/\/yw\/3V3Efce68qWAZd5jGRYRH\/DgJ5uttBWzRSRKYUpVz2YI8ELB0ooRoIzBVQa7BnjPSd9\/2q\/mZ7fXtEOYdeurTF29RpVBO0wccfprQVqqCmSiT0oEObCYVkkJGBcV4BtQw4qaDDH9jWnTQJBKcG9Vnob1EbqGVlYQ7aSMEtXdBwP62xFSx+xk6ACJHNG+80ctd9f0eATrnTTem+y8FoDdoRLK35kwHxtrrFoQiTqUDDwfyHuVE5WLSImnc9llaCUA\/ocOk847RfqUj4kaK+yszqmyrEB3eksB44RYzMQxq9wlpnTcYxC0s8Y2Cso3gednsrwsHIUSOkcY46sdUTbY6Ymx\/FVhDpA\/YBUJPRJn8sHkecQ54cXry7e0ZzbuqZoqc6nGMfEvBKTW8hygbzSKbLWsrnJd+4sMqGAL0lx\/1an84BtCwNZLQcYCOBO29M0e806i\/NaMSf2nNOMHBOGXudl+CFor3o4v01lYULzxaaQtm4clkslmyz8NlFTwEPn4nP0iCAzlRUmeK4kCEcbDPn4K\/8IbLp2lao8a0dPYitYY8UPRR2MpDdEOBLuqeg2B83CaWx9ZK+jLxDpDffOHIzKRi8OR0S5Qj685Z2s85beu7vN7eaXi7oz2nU9ni4nz\/uflTcZuisj466MAxgwzfI1StNEbjq9NLi5TTs9reY5EWC7zAhbG3cwCOuMHzOv6B6m59uLl+t0ivs1DaAxiyMdxtz5pqiFSQRejFO\/zbk+dU8HPSm9oXM6eY0eGaccAvwKh475YohgZvCwnNqdE+X\/fv\/w8uL1cC2tivxcLM7kYEcHhgSk\/YvREUBK\/4EIMBGmBdc9EAEzaIsei5xIA6EApxwo9FKLBhPa8AkOmKwsME79Q\/Te6DyVoliHRj0l+pcTU2hu2CfXZcnz+tQjeKpylo8Txe\/PDCFy9Qmhx8uLyjn4lYwOvnqThc\/bKdE0uMDyEepm2MmFoWM6XEX8d9OOw5lyJ7RwxzpDQDq9cfQM+v7SmSsy5+H\/y93D\/qLc1LWevusjLmBtHetH9XH4uFNaEVJuNMA7hkYW2+HMtFk3Z83\/03TRhaltFX+0CylB3OHrtSPikkPQOG4k0sonbTDO4KMb1IltylYMkBRa8ZfXMXNlf\/96d39RxvwpTcQ1yjj5ZKJMqfbI\/iUlmEPhErMcAQEqpvVOCY7sBJu1ypHnsINyrKWRdFm7yxUcp14t+qrRtEr\/TYe9IQFfyEADTyNq44LXURjEodZjIzdp2Xv5B2UiTnMRuWm9tMcZMMVULaVxRp0OQz8Zlo7GbNdn5lYyfemg+EZA6Rlyezen9f9+c\/Owv2gxU4vr2HxSsibGNLIS27Tvkm24iUvNff6uV4gGZowIE6jmY0jD0zO55EHjiCSFSWmmoU\/JTRikXVlQYZKFncZFBwpbEMxYNWP8UwfHOqW0hpO8vG15vm37i3e0pHWDlF0MwWRp4DvOHHRDdATPQN1XNQ5WJzH1WAtgcJROS8d+n7mf8wr29mYr7JffbN7s7jej+dv29nZ3f5CujQsO1GlLV7hm8mIx0cUxHGuXnMdadXoWSpkkBLGEcWOJFANcOEIDofw5XLNqxkGbsDiXlhoNpcLGjqqhKFOCOweUq08mJI1XjuUtK0lhamvaLziwIT3p9KbUzf5Acns2LChEGq8rAE9pAwNEPHd9EXsPhnFadOcMH2XYSB0wgNl8nKGsEKllj\/oJrf9GONSv3ZBq122IFJ4CgByYGKeZT6AOMgMrTJhAWixrLhWAWiW2qNrZzpibA3s4D8VzXNs5JqD5CIC\/Pfx6HZvKqatwzfpKMlEI0klHoOkCjjNX4gmO34ksjidFAygIuSMmFeMKz2h5Ne4JIdlLITa6liLG55WTYx1GAQzECDWdKM1bGNWb5ShZZouQ3BySzlUNGhYqS7iC9hQJotk17cwJOsqbGSBXTWeaoIBXhT5iAvjyvvh5x86D+ePhYX\/l3M2V5s56udwyWhaWujfQg5iOTBIoKDCboo6LNxwBgXmlnGwA12vZarfwtNCzu35Sbm6r1yPHbMfUediUvJoTCY1TRaW05MQwd8afqE14vZ2R43b9WQjdnND\/u83+3eHh9lpOveLyypXZbKKX9BGmTCgqiV5j0jvnWCRKA6dYBJ1+SmiZwjAZlZ3rKpzT8\/\/98LC\/+eXD+2u5E3Ncx53YqjMxulHHYzuvszKS9n\/FY22Swwt5yTIRlLYbErqyzGqlcw21cw7+v+9ur1xRW6lmgjOhHpkPUczzrDz6k3EDCQeo+WzS+MgvERzHI1StavHBHzvEnNPoXeT1zAmWpzSPr3fb6eZaW1hWDuSW6roNI4KxelJYBogEDr2Ox+JQWtKi9\/7\/Lrle8yuOvDZiK5d92TkN\/vebm921onm1T\/t435IJjf2MPaBnrZ21o9pp0YIO2iPHUmLBHdEB8uIKkMrIoAWyhskKz43kc3Ou+r88PF+jMMtpD9bV7KVe8E4FKOKslWAAnaVzE0oGQ0KRDGH+PqlJJABXx8xaAt9xwIm8a4MyLRFPkXU2NYfs6iQd7NiiT+fnlPffHR5upyvNRl69C400HbhXOonNs6aZWPIJDH1Jk5E1n9jnHESmHSur9ZmD3bymiTHlIXHl6JlcXuzMG\/puv3m5v\/LI80oVJTGNrSMHHgXFBLDdK2lqnTQIEQ5c00F+lrPDyatczx3Zk4Hh7z68v1Zy3TpT31IwocWxc3WRS9ejPpt0cmAwHcHr1a7To3GV6gh5jjPuqbcfOS4rLGFZazckyVgKesuUUT1pwZLNIhx3mRTVUZV1Piv1ZD6Oj3Cdm4wDMdn8vry++QDJza+b8Uoy6+DXrU18soK8F\/rakaRm2qqRuaxjXOiHeR3CHrXKXrTImDs+ntDLpAYFzGBIyS4vc+bR\/HVzd8cZmVctdB1rd4vWhNTG\/qRZAe1OXRcdmol48Dh4na5AVSbCc\/6Ln9Pc\/\/2wvzlcqQxryf+aD61GDrcFcQ6UttfHVBLFgsJYM1WLY1HDpMjZw1VhM85qiezMIT2huJ22++ttXrVp5QLl0aobFY\/DqknT2oY\/ZjdQQwSc0XXmtaoM68CPJJmvkdkw3gklSDeJ6MPRDhbCYMLyup+M9dlPmxXr9is1pkzDI2A2cYgOoyB28nNyMAe4VBxjpuYkjCHpSM9KIAzLyWcO8wlh2HSlmC46aJ+9\/PLzl7PljHf68ePm1+3tF89kFvXdi82b7bOFtY93w5txr42LVhC46IIABL+YAFA3YQzRDTHLnzJIKKBGb022nFyO4Dejh80PNQ8pD7ENRexOBllSifLWKqWyBtANOsjEkibUqQvaeQrHhZOvTwDjCEHDkDACUwrU3kSkx0MS2Dzm4AJfwY54CVTBd53RdyhpYYRGqFVGADSapNGbNzGysc6QcE+mjg4B5KHRon03kyrNI18j4IIin+MxxSlzcJggNISpUH5a3FCCiVauTJEKd0aBsTbSMIrKwKYFyaMkxJmxyDa4DMUgXoQ1NUsnt\/NyQZyPH5dRx7vfzZy+n7764+9\/+OcP\/Yj\/+ddxs9vfGWEkGA+\/4OgXZX68G55v7k7O3Qs7cCAxgEWfZbWYVuLkJAU5LrIxxGrkVJvJkfU8KYjT918Aj493v3uSw7oB0t18u31zu7272x32J8\/+9eauP\/vbzThuf7386K7KI1srxEFtKGEoDTXpMsRkahxKE1Eo1SRkGgHGtcaXpX2deWbf\/OXP3\/3FfP3VT3\/68\/\/cExYcvuytM74NTSx7LZpa8UWY5kV4q05HEJy7z4oWKkNGzBvIjO0yKVKRjpG8OZrLC3rGhTXNcU4qUD4E\/GjdUpAHkihfWPTMh\/vq8PD6w\/91+y8t+GY79oqkNGPHNvoyKF8niAxkbl5hFYujiBPB8eKisCcxc81wP8EG6chop+QJgJcAnl1ZcgJjrHwcgEJaoasomXu0OUTN7DBaY9sw6v6ceQwYEQbZBfC8po\/zWOPd7+ZeoFRPDGiNT3YJ\/747o403r7dPFPItyGK7NlYxbiaEPPoj+TNJ45mOG9yR79ErBy6HjCWTlDszGI5mjUew4kd1InmSufL45tXm5p9\/2G7uH263\/\/xh83p7PFx2+zqSkhIQOXC+GbAxOv6Lw6S1owd+DilZxauOi18\/0wbffnj\/\/HD++12w2SSLoYQkS+NsYOO0YuIUpMWqqA4gYxOIV2pEdtY\/Uhb7QUeGoJablLJYJ5M\/8ktp5VJXeuTjRyVjODanKjHy8krnrUyb3f4\/d8el\/tv2bnv79rC7na\/XFm+y8CkF4nA67yOh5ZwExYG0HDRVCB4kXiErcpcpBd9za5gVb4rm6tmn6HSiCpubCJLT0gUHr3u2bmhzPURLt57M9kmTU16HsygFqc7y4\/isbDoVY98tr4SBXmkBgwamLJpZLZ\/o\/DwWc4\/DNJ2OlA1KzJKUmFPPWekk86D43ayoP6eDJo7TuYJSLPbBOcjPaMmOFTRmokhxSAJDfxyg3p+ZE7xIfe005gS1qiZmg84qZ\/mEEdnjrvQ4x2khONPfxJrKQEbz3JEEODpCjrKOHgCp78AgkLAI3tDC7l8tnnC4OR62KcMop1c5q1RAOk6OxJYc6BD1Enl2p+LokpLjECFMsGHRbkbgq+gVn3CX6ojLgbVaJBE0FURitqh5HxePw+gVVww9EvTGkibSDZ1YMmohmPAqp81Ux1EM2pekpNBaHGbhLR\/J9x0r6jqREyGlgtipUknW1ZkGsLdkX+PiTThSN6Clh6RYyiZApgGCESgdXu+AUzQH0JPvJqmHJXJa9IGeSmGHIim0ezzyDS10P0DBzCz3vx\/EIn1avwi7Us5l1LlznTFh6HQAOgaMbEvHpm\/NeJVB8ba8yMrYapVmiQnezmLLpVutHyqrrQ588loD7Vc\/6k\/IaowDh4ofdBjMMS9A+tvEArLKknbW2c6soZg5jB7UhRTl2KXKckdOEUqsIjGM7+y6bExf3O84z3bd3R0eLmx4LtJUnkcd9uCcIgLBUKXB9MCJTvHI0u66TOIyRZ1J7LQlmI9rueE6\/Ir1hDp0xkzXxyuxqILckPx7cVWz5M\/XD78eltf0sV9zu3v7Ka9m9tJjU6Q3sSaMsyRvjPYZsK2rdCokp+i7qvNRRegqQiXLnjxIVyPuPqsPktRDwfaSYQv8XI4YooE0GUN9sgm3u7dzn+jrzc+7\/cvHXZBVLfq8fVWu1GpyABwgE4Dujl2emfNfI3LGrI3UTuNxZOqunNdFDhi4tMYvPOY8kpK5gQ8f3q94UOkHRiTiQFsI2ZcAuo\/96z08AbcMEHpOwQBSw2vBlEpWh7cBHAugobfK+ILB7wSQ49a5yTfBfAcdveoCql6cdlJh8BJjfOXTRd+Zssl4TcFIOtcubIl\/OkpRsunjmsPLrZkUZVPgCABo5chfCIvO9DFnf0TDYMMRyuOKDulExi+AliTAEEnBY+Fp57TN2\/3DdsWTtuxMtpAymn1i6aNx0+CD1OgJwyeMkOqmQg0q4ZTDxJeoMcUxZcmqU+pDZQyoUshp\/BEUhAQyjnOtyCPrs9F2wMzZR1rpoblNbAuWW9yUWiPopyag\/wMH8vnOQRuO48B686zV9rGk9cOsgU9QnPTCPs+poWUur3liHy\/LhbWy3W4UijMvq0QO3QWFdyBChTdz2gUJBz1NlsP70HyjW58VbrHwtOkTdvz8c3ZkYU2NVVjUCjOQAEqg54Y4uSZtCiDV8+QqJcle7UMjqmYXqIMAoPVHRC2JaQsz25wq5Jj4lWm5iUp2SVXlJ1Hm7W7Nmvre66xR+d5pSHbwGYxjFY4liQuIKA+aTMasVu02A8tYoBkdyKCIP5VgNxCvSlqkSEgZezFRYWGDhAXjpI7HkvCaVA\/w8KJKJVm9q2GtNxPt59gg6qzS+ILNb2Gf5gNtX+3ut2atBGRpd6jAbQHvCuUaJznTZpXw09PTcqZANj02Cc3qmAmTFGxde9NeFab9KJeWheqgCETcS6T8tB0Q4VY1aWFpTxL3Srm2TrRlGqMdVUfoFAUqt9InIzunJMroa9Khn7H3ImYNAziiSQkWFx52zqpzmFY9ZpWUqBuzYa+ry4POM1QdrSR5JHIjHRy1g1MogGHue8nk2znU4dVm3aMlZ0rIozVe4dPYuaDA48ep5xxUXRSBl498LscWLqwoQ5w0XKT5Kdo4V1jlV\/46Rz5x2orSJxEhC5cWheXJRNrNzYf3H\/7HmpWW4k1pVBnoaouEpiPDyKGqciN6ER\/GW0ySxZiJRPbcpKgpwNqXntI\/KUZtXq4S6BayaVn8Xx3X1rRJg1wRYXlrwlPc7qtVXxqqN7FlqeUFP2ifIMg70KEnzsDSt8aPWwnWfKvwdRQvNIh+AhOq79cyZDX8GIIpTzVZpBKd6CbR1NlwgG8QSWxHvaTdQ4FYmmqCV0Mb+d9B\/u37B8lHLa0rzS\/Uh\/fTdT50Z8bKEsLUx5BOKfUCOXstSWnUijgN7CrWixvEQayGlLYKSBi0fYq9IuQn1BGDQYfNsYmXTdRNe6nSIsGaU\/asiJv6MV2icywJoLmT5o90TZFN715b3plZPMLPaAHoAYqP3sdyYd2eN3\/JwM0ny34PAsU12y3NTc2Oll10JzPlnZLnuTSJabcSM4jZSkPIOoW0AkfGgSpsTpmOI0udtmeiXxveGx3ipfBwPl5WSehWLCL4bIJQ\/rHbhz0Qj1CVqCNfI0cLI0Gq05zIaQY4BJPVFck\/1oBIl0m\/vOrM3GDi5JKYGcKrG\/sEwMOcuIMFxVzPYMuwh52jTJwhlisb9fz6TEoHrxJfxEFt4k0sbVSd430xAGvFYUslN+UxD9o4gAjGK8sOYwGvSj0f+yEs+1Z1aFLP1FvTWUOV2MCkU0q5AL2CMHdpHW3hwM3X42b\/YpVNstnkgvKFCKJGMjQwrhgtc3ml8rYEgbCt2sMtaPBCBRuxFPrOx1Y8X+cZlBhMS2h2rDqDwiCYCcrk2MjvQeB+INl5MW3pQWa2+6sP\/2O\/zn31aCDqw0UVYoFMfOSkHTm2+jgQehpEwItDHxnbhbwFoJutgO34Lp20xD4cclVxGq\/X2ZDk8IHbg4RD1StHPs2kw34Wozb\/BMd3eBhXaTZmjZIboXKLalJwyKGnnt61t8OSgM5nhf77btWOhxhMTGxXQrd+Rv2HHXuO0wGRme+Eo4V9fpkDAzydQzIHFNURadHYzkdkfvfh\/3m+yoeIUjEWvelAvMfB9tpNzVZ9ohkH1sXR1NqIPSIxXJIFekn0R6stGUv6fT768qfD68P9YdVzCiYkWs5ULSCSagxAmjqUqAEsfvVTdt3dOo+yFAE1VET\/i1I6M11\/ervb36xaWm3JZF\/HqhcmsTvWw9dHgkOHXqPIxPQXR4agy1iOpYFykP1cclkzk4Xsni8DB4W4RfGp86jspYxEWqV\/m2mljkwyk5Gfs0DJf4P0MwwriPmSNBm35fvW5rp2t9+tyCXV6I0P0jofsOA8xdJMsJL89AUVj2mIsnsuKagVDq5E5gMjLc4A9BJM+U5TCaOIvzCV5vucBIcAzJFXVj05ZqIITMzqbHpyceCm0KUupCLApG9xbXX4QlUQh+88Pi7rxL2g7iFGpsAsM88SlUBF+v8dQkHHrlBmJvPjekm81vlykY5W913cenqYXsfOe6VYQ10MgIOq9DyZLdwymC+imjAJ\/ieAr0e7SiozMrFTafOvMCaZ\/QuNNs4rPJOzJ9B3KCnWKFvaWJREmiGzedUFsrpCgbPZi+wHyoNE2hq2d4eGAQqNIYzvbwARZMTQ4sw4ArwKFqmC0DldUOBtcNzZhw4u9Slmb3xLhuAI8nc4RWIoyT5oW5i0JOLNchQR0l34RnEgrVGuACCkYp8dE60OJwn+ZDw4K4On48FxleQbpqFWoa8+aoyIwjPAOln7OQHQMQT+Kvv4wPjHE0mPKSjEXkQdEuzR9xSmmpMJVt1CJaizEN3Afjb5iDJ1LKIm+FD7Q8wE17DHmmmSmw\/qAJH3paS9\/Qj\/syqAKSYIuZ6yPWp4p5ETG0QAV8ClnwZB\/jXOWFJaPE0gg0bQL6a7n0xG3Swpp4\/rd88PtzfzAp785CkuSSgZqvR9RCPRlS0yAoQD7hhRFqZVp6F4yLbX7qWAwkSKkMIwYYw3K0NhCBOMtfhdck4g0JF9mAblTHYTYtw8lEkI+1hH0DsTkNqEdMh7OWLPTYNyOFk2MU50VUQPsMwx4T2QhAkcWmnwkwuc4eqMasg6NOXLmBxArcZOwgIDaZvkejaOJJjgwZvIR4XHOg2xmJLZQqg3ndFq4jUUhqXJCWRaK\/ASsHrBnRY2zXaptJMAXFsv+CKemTjxzk1NMiikCFNmZfF69Z1uqpK3Jb7JMSbuv6Gy4qZkNpJP2hs5oYM8DNxGblxgJUyHboiGnipO3XJmajK5vy0IosFntsQOqH\/I2yaywvupt3JPnF2Lg7PI8+hcbnHjIRMg\/pI3sBV84rSMMAHCwucrUoKD\/+fFdhTlrAKWCWk+xKvaGVkmYULD9PiJwJE0kXWAZkbUgUxkheX34XH\/gQakDYKemKxRwcTsEwKbivJ3OPksD0hSr\/1MIAHVveDuW9SlhUt68pn4NX0EyUu44yPJ9+fHf59kxuxUMEe2TZwpayeCnlU65L+eKWYVCCTXPn6F2K54XL6glsUT4SuC4V+a\/iDqv2N\/QTb87HJy0LUE2WUxeRzqMYlGpvkGL5JkDjC9F+3REMUAqDxzr2lo0AMBojv1YfDIW4SJLjTuolzi4HufHGZ+wTzyF24u3IBCJHlXMA2FbJTsMmBDAmyerGmWnxtNqizFgxKq516kt2PKVoDQABWHMMkQ42DFQKfjMWBjvGhID864Bj\/P9UurUnA8X3axTglLSP1zjr\/28889OX6rEy6dXImAJhr9KdD4UxJnu\/8IGiuD1ntwEybDFQoozmxwrRjf4MfwCzhtsU6pDhjQok\/vJ4R6+eTGMNaK+kXyaY7myj6+pIJOyx7XUhqCbKeLkGNNxCA+ri+yaSkff0DMRjgRX8WCPV4dHe32+CHapGKPl0cUeOU4LhLQZJy3Vzgr8gNuShoL990PUyhy1SvFF0RA+is1JrOdnl0zq2faN7F+dMZuEkB6RL9B4c+qaI1qCKmYlFX1+I2F4zyObyfsJRxFBj5DoCriOxzw5ppKyaLlpPUAODoSmUU1DvL4YqwcgvzI3B\/Dtqakkz1qwxegagKEeZgkPEQxWNSnxFAO7EfS1D2JBUPTGuTF6l0mIanF39AjKvvsgva8FtJpBnybOzFewIiIqxiapm519BAtOOw5OXrtMfp3mLkhrxk0n06+A7TrDAxziPpC8bf2lLJVGuPWqae0Cw\/YNDR7ObrqSmJGsn+nbZackwnyXx1bRQIuCCpoxFEI08RWwnahZOWDstMVQ\/CJZ4wturGUIbYoClLcpNiUOaTCqGaSMYoPZhxSNzhcne0iaJTUCV3FcS8sUyI14cVXcpxmTxcByI8phqb+AnWr+mqhAB4CUB0YDhtbmiWAlVODVxVyheMfJ8Wlw2qCR2JS\/xvKWbY9Tr7\/wB8dNf\/Ru3z3GxQLp3\/hTwBJ1L\/ITzLcxSR2Q+EoIj\/simAJyg91CpAHxEkiF9OQ0eklOSaR10CyBE439Xg\/oiIXJ58y8tLdn5l9OQqidGY9YQIn\/ubgc2ak7dQj5hwJcVPAho2fE6CKjcGh1OMuWHCFo3zmTJqkH4sTIqfCepOFj0pOQ4my\/TEJAIeVZzuEiA\/wxXgxWU1EYu6eEeotLm13rxXiOmnrKV5zdOosX8Pg0E9aiFo8wGCeuIJOK6\/0oXEkT1xFgninxBqUZCgqojsSCU3SHGZ1pJEEswKHYp+Imhpnp5BABOvnDpVq4+7ihKPJU7XaDaNqq9L1N2qORC6fGCadEKovyFMmBdqJzUCSoluuMLm+b\/3Eld1sUlqA\/u15IiWWf9SGCpyehoQWhiGIjZbuLNJz44a3oyWFgW6SZT51KsoTW1uRex6IX4LNDZxMhLP2OFoH11xSRyi2Sop4AsPK8Oi0uH74LHPxLeq9ETY8OeK6jT\/ZYVFGlVamDEXNZhQrFpLOuxZz4HyXIxR6uvT5SSp8cPSrISkBm9YkGJAPSe14Y8tANn8nVjSz5U8BtmwqF1fhaBlDFE8WI19xCrljFNuJcFRhesK20sBk5mBYa5oYLgXEgNNAEB4ueIgnjopULspMMCWnJ5qFbnkvdQGCE2GPpsj+kAn4V\/HHCLoj\/GKCc5WoHww7JI\/4n3DiZg5O7G0FSHVyZCJ59CPoNJwIX5pFFG5ypEd+jEFPJYL\/Lie\/F0nDGbjHVxQGNm7u8faHkJUGZJH7lYR0WoY6rruU8SRxqFeu6njjUzcKyuTocTpayS5vYn0ncWjgME4K9Ie3FU+8L5dPlix3ubFxUqoQjxtVFdqu3+6fxGL6MY9eHu7gLBBoZFLPE3pBgX+I01BEcbN9gbyBieT8GIbH7AFcnaiOl5hZUlREkRWHOcIQF7ylIS3mmasQdQRYirTFTkMUY+U9jUkR6us4MKs6Oz+wRCeN7bI8nWPlGc0hkjkAhmGSwBMhHknIu26Qe5wcavNs4PDqjGQ6VpAowFInIBU1aHMkzmT4JuXdKbChALesKwryllFlZNPpZzigOE7R91\/zLllkjn1g4qIR0gezrndWepJ14ph4mBOm4PF5Sc3JfjhY9WNVD15JpE+kqHbNisCAAemB1zhqzPjo79iu7\/qiA9PodRp0Mprt+RLeTcP7g3LHRGJhkmM86qtq0Aud+4y7oskMQMLtLPrKuNxgLzuxZFn84NhRIUVnMYq3ry+KkzZpTNpiZyfFswxq4VA4nDI7O9QGDt1lMOpWyF3AlGSRncZ2GYSyolwnFAHFhZqyDsu1cnujzpyekvIKiXEmEmwiPX6eFIPuYRpQ0ZhYGRLzpJsdVIrwNgAe7SxCdz2wP6pVrFrOfgJfBw\/wURfK88w1G3Mpxj1JEXjS7OGuSp87LJaa8jgxHaeItqMH459EoJFrtCeaPHjTmd36CmSN2ss5RVwQq6MMAbo\/UXmnHy+C2EzKRA9L5HX8Dsn4IvN2kjBgnp6dKji2xqr9zPvS5Bk9o4KsRX70KZCgPzVCjVtwmrFzx0BcPDgnPfbV6sSqPPf0joG7RphOt1crCJjnMvlMapLH1Bn04uyGZMwAOb5b5InEQPousAUgU9o47dkdjUHR9I32+fFDC0dDhyk3JGH8MQlz6qFC\/Ilr0TxD5PiDviNyEhlpevG25tnGdEzLPB6vnYbGIT6iamwF7UHSkVhk60IQhBIEfdmgQDXlzZfpIgWvaGqkoMvS1CtpXUl5xJOWiTmPWCgNVbWDxA6quLPRdF7qbmRwnGGAsAZGqk9Yt5Ovmfl6Ks\/QaaEZkMozE9AKC5I11yU3ib1gc\/HuVOxtarCv\/E74noEVjEzjTiI2udX6ofKcks5g0ho1JcRDrqtxdjegNjiF2j3loEawaIuhaHQOiBVjP7i+f4DjMFwXNw3fWvSDm+kpUk5YIGnVBHA3lGXRjU1E86FXNk46ph5+u7c0TNoiiSPQBv6JwxBhpppp+gsUf0XPIBsmv8D19arxWRw4Olegg59AZMzxMU7He2YsxYlfzCV3W1c0kj8Wc8LwWD+Ic\/GQFh3XB9Qij6nur3xSTnWIBaUAR10epiihBaFvEYUUMpZyWjUIb1DT8JIpzJCXIAnVABoMEr\/x0qHR2IrmC0KRguNrVgC2YSi1e3cVaQR0ZOdHezBPCxaTpijquR3zpLiPNNGPKc8Yk0nFUUMMaariYRe5PiWCW2TIcoyhDgUc71UdxTYIDzVbaDyLJpLRq0hveAu\/R0QiWfBFQERQzqpVCdXhZLiE8khjagPRN9KwkpcABBPA9mmokJl69HC7ugrHQByzxqZYyaV2kpsWv7GdxAPg0Q2nRRt\/khhGOCCQCjS7Wx3m8ZifHxJHL\/pjpIAqQN93ZFkCSSPizFD4x3w11MpjU1uKAJoef49r8Fip4zFBdZ7EWWjuPTHITk4B2UJV8xHJanNM9Fox7M74yl690m2PvNkzfiQ1\/XEbhFQEG\/C4kNqrJ5Yu3sS6YT56dFbSvx5NQhq+YEOzUP3QgS0AeLB5QbySioJQ0M5N3YLM+TlwnBLUpJewUuyvbnN5khX3ZZKPcgjVXS+j2KnJWWR3PB4JacAs3m9Ok1gJZb98NJwWII0hnNT2ZIfh0MwEyc82jHnb7qwg8Jli0jnrj6bfkmJBz4n2ElANjoOdGqcpaJm1HP3t4x1nodHNUjCDy8FERW0U0yTGgqKM\/Bg60Ji9p8SSYl0a0jLiMyuJGvKPMk4ndaFX0i3HrGYySQoulY1OnDUgP5xiTw88BhwOzAFInxbUS44S7mauE+sfDnUdlpofRZCk\/XaWTDu9u8LK45AwVhlNU7NJqHswIMt5kmuWDixybSoMHiY2qxZEkAEAJQlMg5LDTJyw6exjIbYRTxvRywTvU5S0PcloBHF8A0Op2JkBYw8vcs8yPUkuk47KHXeHGJo4VWmIxCnOSz5d9\/knNaPHf\/tpOC1lyQ8IkHnUF1Ag+XgtEpGxTmP\/INGsEG0xPSskPKhjiauSj6pNypEtDOVUEZGMYRpaAIvS8Xbo4mBY6iyXgQh+VqJGP1b32dNCChU9tKfJjf+vvWtJjtxIslcJw2rGjGAhPvhpimmmKqm7NVMqaVQya+vegZkoEiI+HCSSJab1ou\/QN5idTjD70U36JGP+CTDxycpIspiz4UIlMplAeHh4uHt4RLyHd19srY8KJkQD2afIuMP7sOhQo33UoDJw3AKXGtaYYKWo8aqSvzPxgp0sXWIKo3eyPTx+52vANUPuLDw5jlfFCGA4xkU\/357bOYyRDJZ\/dNpBVpqReokuMyS6K77PQgft4N4dVOEBUgszRgVnfgZlC7I3vNtWaZtOEbwFtZbg6a1Q6LCa1AiIYy6u8Fwvpj8EEwnHiCrL+4LLY97gMPB5jNt34AwSQG\/TdDcBKudViKBKmNoxU1LA2LYQruigIOwlEK5GykcKYY+fwTfiiPICTdkqVkpiu4FMR9zo2nBkOwf1GbDIxFfxgwc3\/ayyC6MII3eAoHT0EUzdFM8C7xTfFVVBHpacfkr49dIuwNFnVlIHCCvQb21zNdzvb\/XyEZlB6V8GCdV5JPJW8zVKgzql8g+ypio4JIIFRonXyCkkK0aPh40LOImCa4U9mAtNNjqzBZ9YdKnm48cLr2s3+eAEF1yajaIU7gAOz4SgX9UY1WHbF9cFsJdIZyd5eYXFBkW1Dkn7A8Q5F+KpH9wDjHD2G0rpeXgSOLhFdQSw6pTOBUdwacsEuBROKE82Pp4qVXGVEJA+TMZAEt+AXd4nlVZ4ywVMtsJiO\/Ibsh9Uyi62ZbhHcVlRjhQHnwxOusEQwQVCOjuEwDqwhgad+GkFW2Z4XljpSkKEixhVMKLkRflppXWKK4wA08\/I7gAjBySdipaWMy2iIohKETU69iM4QFVplSCom2GyMAIYgiBs8OAyHOdKowQ2OiRSUOHJMli\/pfZwjoYVd6L9lBDw8AR8AEtnOLwiKGPAC9R8iNvg1Wk4vlRFAPhnzw4rOgSs6HRxjPevTYLlNqzF4O40ron53BBfKQp8OkaALcqYANwV3tai48SIDCER907hHQ9OdROL3m5AEwHdjoRqXgSJKRGl0z0VgytJNDxQm4QTdLBLAmsSHVURnD4N8JAD7fDjoULkjMJcjbIFehmeCVIBoc1h+dKPDB2nJqZMKD8imE0VqRRiKiothZMVpHYguQ8TujCBJzlxqxGOAKkIa454017PmyXgeg7NEj6BE5jLol2W+fDLnSeWv154EdBfLO8vvAQuabYXnr2+sPOjPR4KrxvhRWa1\/2O+ah+uAXwgfFGQ8DOtyiAMqdkYyNsdmh2cSv0537ky59ZiCIi+2KBJnRoc3sjZLIEGuj62myHco8BWgRDBodXBVZy3WZldZu2xjQKiPbSpgB\/Roc3R5dwGIC6ObRMI3aBNHYVObQ55L4qrTVEvr5ujuwp8eNAscvs6NDu4IPOhE++aTbE+tlGlre3CzSmHVgfXZn4uavE2q8V3a4BTPbLtRCfcdOJmxcmI8Xv1yyOMOOH+JsbNiAf3W75pNll57GxNI543CUA2u7iHIVVTdtO02ao4tqdAAY9WLB2d0vis\/C+b8mjtptzVNHLs6uiuYHesck1kXSFcUXBpUQ8Zkuq2uSuObVVJbtXRQwwxA943m+xmed103bH6NXbKJNLNHQ4v9f\/YtJ34U9Yum017dNtSWU+h3abr8IL7u6bKj2xSS\/aIoXEc24FzetvU2U17f\/TQcsQxgeO8GXLpFet1tjlWt5qnjXJ0SsNr2N9kN0cHVm0tKUzdZurwUvUf2jzvmk9Hu18kY6Tp6qbd4RXqt03X1M3m6EBjLTdxU+\/wBvNnIs1M3jpaTN2W2TLfl7aWSyugsQJS3hqd74j48EsvZDnMXFnCM\/HdXdPei7dNtu7+9mNzuymzDnDE9XkiqqIs4ecPXdZt1n9j+st9mrOSSZVYy3SVTE5M80x8yOsc0Od2ZFLn0WNlCgz7hThWjkINB3S5bLMz8UfIgociyceKFPVpRagdRRrEob9kQJeWn4m3WZW3TVPvyiUfryoT8YoodFaVGVrW5pcdotwvIlRkA4mOE0eZwhErRHbTnInvs7IYShQ+UiItQxtoAkeJoplIcyb+uCnqPPsyQkmp5DDgHpYqHkfcM\/Fzc9V8GYHSmG1Jp7GjPMkQUii7ylZI1Tli9RiI99g5aKLRSuaweOlcKngm3hWXU2sPHjuMScgJhU5ch3G4AHi\/+gVIbbIzgaD0O1KlSXoWBMHRhmVzZq1dNTVcHLwvsiq3hMkDgWT4CIG0jm0u4qwhNZeNnIkPRd62mXiXAyDCjmCJih+lKVtQipVz+NMzCcuZeJPXxcCpx5F6hER9dhq5uvN9Sw\/wn8CnmtVDS4\/N4+RKhlmzg2DhNG+2HlRwGr0jlwn0I+TCihwOYCBd5YqGgblqNgBofQOsn3vyKxVEj7GuUPM8TCJn64qHqChldtmciW\/\/a5N1TVtk5VwECh8lXBzY8kHkKlsyLSCciZ+vc8F4V7ujedRgapuMmmC42I92RJoRaIhdmV0169lUJn0IiXtFUH0RlIOy4RW42dWLmc09g2FNsm5mpdAPke+gFCri6RZFqasUAw\/+3WW2ggXDjBw7iflhbdjlXGJCVznUtKC2L+1NnQUxtgRvUu0qiJ7iOO7JdI2zHDqyOZtyVogZYV9DTbXo7vfIotx10heLuO7oIMv+ctEecYKD4sh+JRna0EXi6F1x9Kw4A1f8w9VlUzXr6\/npk4YSXMpBMeKwr+84izFyusVqc7Vpi1kxkjRyEkNb\/xrHgasYA\/\/656zdJ4IO3ESwVQcdRq4ipFM8sFkZYq2cZDCRrXxwlfqwDEPUmW\/rzdVmVoSIkuNDIsSJtKVc7SrBiLhtk93ke3ODMIqdNBEFdjfEuBqEHhXPq7xe7fGlxoRusyO2ZUHeWXSQQg+hLdtms08I7aSKRHF5x3CS6yDD0Ic2l434pmg28PZm\/1pTR24TJbEFC8M7cw4ChSP0ur1K0cpNCCRnp8jibB\/R2Js3\/vvmrplZjejATYrQ5vmRcVbFyHvebNrVvONSqXYSIrU1ydjZRpOZbeH5smScuDkubYsh2nk40pFNZL0eBhJoNy2EvQTSVQITDHeq61XezkrgapK8fgkjV9dp5JBRuc1uZlfH0s15h7Gx5ugawszAab7f5m1+07T5bO0ucrOF1PqHMHHWw2gXclOvQA\/jAo8MnTxmbKsCWjurYeAxP+RYlJtUVJWbKRpbGZTu7Q8c5NdX2SrfzpiilG5DEIScTkn3IYiGcWuuoixjx0yK+69CV69ohucosmsMEDP9d0onw9CW+QNnXzByifUNrAdnJoE0bvmLrbSkgWsmZwYO8dvLpmw+7YmSiZsZSFvIj5w9UjjcSeuym9tsrm6eOC0vjAltbHIWYETwcJ+vp5YYu2VOKbtD6ZzRh2p4nqe63Nx0M64gcvLHiewPnbimbuHAE37zS17X+bR5t5iojRweoXJofnRgLIeYPJcXOK1nQhPZAXAWYOgI75tNLfJSfN21zWa9r0jb2+Icqe6lsD\/c7tmL7vJfOwHDBWEID\/RZK9heeNCV4fuW3oJ3mF+\/gkcfXiAhuUO\/d+gFuBE8eTyA53DfOzgkAOzZTp6PwOCxgCoPPM8brJM3GDgmhaH7oASwFTp+PtIkgE6iA4\/TruX4ea3o+bB3x\/ue5z3GSQfw7AguG+MDb4D9wPHjaULdN0F44Omd3bvxS0xMnYj1oVGwe2yTXiRQ0LRbi599hd0NmyoC5vw9O4HPvwK3ryZDAXMXQkdwSAS73TQRQQWSFaEOjibuC43foBSpIT5kjA\/7ONNX0JQ08tCI0h7L+Pk4jliE9OCUetgamagiNDQaSXxIFbSJMXkBrO3ueYIcmFiw0TAdC0MviFXqLbD2P5VRBayqweRbQIl++r6Y9BrFA\/NYUCV9+u6U1AjnQRZU5Z76nognfyBH7UMtesbX8kzvKy709YeS8bQFQ0IncuCeFoPK7rSr7FUjPXyqL8BOB4unr5ERDCgXSGd6wGMSDxWOtczptzk66WhgAgsqO854cg5mKvQWWBacmHZKCoyG0WJBFbxpcJExe9ahtFxomyohoe+H4dBCqCQ2\/nqiKfSZ4VxfDItXU0fJAcckQyP4frYRqQKezSqhUacK0MxUYOsOx6\/Fcs3EyQQc95N4ZLlYWZmxQ3KMWidjqbPp22Vovz6amFSxmOkk+ZowHo4rlxfG38dVOvZ1aFd9LWDSQMpKD1OQmRbrE+Ni163NyFrzmYgpDcddOfo2LYRn8iSaODKVI9NqZqYNvVpFw6GkNeZEGRHnP8HEB97MuLWIo0M6jC4LXrxNgzLnN9FoaOw6a5JKhIYNa+wS7\/OJA08DGkg5chB2FTOZcuzvtRpOOVp0TPKBkL4tRzMN1wgTNYYhizL88iift499Pm0fHSGFqxXrZXabe7uKTch9KNiSgU\/9DC+dXHhVsVqV+dAGv\/7Tt++mriSlV0hQx6FXvGlWv\/9W\/v6b+Ca\/bfP1umimxpHw4OGJp0MvfPvD+z\/+IN58\/eG79xNlciYmIwfBvm42N7\/\/99Qp6FQ+rG0OvQTrLuKbvOyy3SGaDtN6mfWP3lSbC0+dB3Boe+e6pAlEGgXXysQDxnP4OAzuVPC9SmL+Eb7Qu9SUpjjcnFiEQSBuqoEo6zv4d1XcLV6vijsrVP0rrCWr7NbvmqZce6JtyvzCu2qbza0nsrYAK7rMS6g334q7Iv\/0sAKtsttNwddixXWxWuX14vXlpuuaWnT3t\/mFR788PJEtuwtv2zSVX9TDt\/+1aSpR1N7i9fruCtt50\/x64SH2F9zc5W9TK7bNj81ys84uQeCPWbkeXc4V4Z00AxXClc5r+ozVQQI6S91suhmx4dMvJveTZWzzdT6SEujmO9Fd5zSAX0xWuDKaMVoivORc++H5yGqFuTPXc90hUwRJHkxxCYZYNktvLB1chlVHyTfwjc1yTZwhYSBMGSPcZ0DUhUTILVIkxkzxJ2JxIW4QScBlfsQct4SJjyjEAEOIl6QJW4XY3iMEqAfmdfiJyP8QZJ940iRjkfopgSAEfrSdJTkpm2XZc2CJSJWI7YiIlQHyuUk\/3CKmiSIsMWIrQDyDLUGTEMs4IvkQIA8CXxMthURcb4YZRiAIwhcghj\/6jThS8La9YJQoAhJElHy8+4\/YzAHhd\/CziqjLEdXD+IrJ1gJCO\/aZP5AlIzweJLyHC93bCojeESkNXgvdIMiGLWA2IEBVhB+n9LGPX5KCKUnxl5BIbRD9kTHW6AnivKEeq21lWYgCAohjrlDGxCCkUEGdDYjPij8FdRluj5roZaVZAOre9lwfzPFBcIvbivDGEOUGviOIfSoi5A38OiN1I0p3gCqhrgkaXxRwW+EAWAUgTZNtGOVWBFMjGGnEglf04\/wgNOBmxMyC0xM8aiT+USw28az2jyHCWECI+Wh0CISHSK+E\/4kwPAhnhRrZVsRUQUyp1DPWo\/Yl41pLRFQPEeRIExKZLxkOVRKfLaGFbitJGG\/EuS2ZkIDYzgg7Aq+UM8yi7OVRhBeHiJFWdwbfbaVS24opNtB2eXYILawpEGo2khVsqyggwg1iF0XYR+hrTM1Dl7YAUUFzlueLbzkYe4EIX1ngmAI2RsTyI\/wkYb1Q09uK2FYlDblvCLeE0O1oXtKjPkOkkHdA8H22PYBEQIR3VKOvLWYgQnWk0H5ghGHId5xLBGtOAHrMXs787TyOgkFo7SyhcTW9XyC\/waDV5FfI7GP+GH0wjhxqDyZ2wiS5sQV6IZh063xIhUzCwwYegLQRAfoLazz4EDJPBr22FAOfEtYgTVi1BQhwAn4kTNotgWnjQwgti13ZEuYd9NEgXxYh6YHDTciWe2cjieYAwfiQukMSbi04Cdtn1DA5DEmdjzWhfpETjmniEm82McWyy5XYDYmctIqAwIV1yb1Ohd6ZN4bGidWCUpML9vvme5NMCQWb6N55EqBKJaLPEAdYwNEgZPZidAtEwIud41EnxyZoqtL3jO2C4KlJZD+EksruBRHD6D38zbBXAf3fcLyRBDDno11JtFcWYsvw3TZGENwif8yuL0VqNNl76H52hYoAcwx7ZU2xxZdhSJCQpH92pxoGNkUaCx6CLcFbJUwwjf2ACWwIHVCQg+AxAOQxEg0YHglJk0D3EauHdclOa0dS\/SAxDl6KjkM9TA38rQ9jPFlI7NAaBwebGCNHKhKSLWBXF1keAlY14ZuCGScSkKsQ9p1ZCiUz3XN64Pd+mEFikVKUZAd4LLMFkgikMFO2OxDrkFKFqZEN5wohofHitCfjp14yAUbfV4lCYLDySaCYSbwVehSAP9E8Nqn16sxwojl\/Upw\/IZAkWq5BbgGDcDHpw3SBHihixAIXj7OH+LF4UJCmQzP\/OP6L+UxMkRWdgbG+APx7YH03\/ClgNfBE4eyCsT\/hS1tAzUt8Trw4MWDaBPYOAfPyEe8fuzpte0q6BPVvCViZ5eaMQjH3h95WOvYRWkr2+Ypms6O0EtO8beWHQDZMv9GwYZBRRNvOI8dBUiEDGztksxMYAso2fHIeNCuIM9lGdWT4CHiUFOL9BxzaFThpnMyYHRBiGZIRCG2pnTB0xmyp7MpIDTzBkKGOJizTY5LsmLvZ6UBUR9JaHZEfoqgY\/Ajel\/4Qw4Mh0oUbnloUjVOyPh5SCDqh4LSPKDu3hNermQeSVC3JdNilsvukcC5txkZZF3XbELUIGowiujw2LAh3IRoWpkV9Rk3zSW8ttYTsIyZag6Rpr\/jtlD8gDhEHG01Z77aiJJzp6fBRorVnrGrD0cDmyMambIKGiWjvOdRQeOBG2PqQsBp\/C7fAvBaxV00oqlHgACOJfOCA5kSYRhmjueScmghSFWGuIZMppKugsZj6Bb3fVkSESSru8yfJoSHklRamraRNiEhoq4qh2VU\/pXGSaYynTMGCk1NathKO18HOKEsiyODlmRa89OEwZMHUff2QT2A6rtD1RDS5\/YgjaIhxllShKfUFilnF2ZyhKMc5lxK9tQmcBkD5xF67N0jOMmy0lKT3dHcpQyS5nPEqnuq8jNgi5mxsaVzYWUjKrXE2kR0hKpi0UYDTfM1uWLGwNh\/WRGhL6z1Mlxi5n8U2gsYLXTHGRMnjSKkozQkmN+WkTPYJGHFfhlsCf9a85uxzG0SqAzM05F8pLloRA+tPSXvkQcjlkXdAGkha8BoK63au2xUxUk8wpa5itWuOaCwPrxUSfFCz7eLciohinH1DP6ns4lORVSc0UIKmemBdD\/IY2\/pAb4GU+SmeCwGR6Ng5DfOFI7O26xGe2+Rrd+oL5Cg4leLiheQYowhpmxbBAecT\/SJg50OfGo373Fhy\/OyTIOsNthVxbgX2C6BaXqJxvHsYJ1BiQkkojguv46W1RkE9tdmW9ROKMxKaxjb0y4c4RtkcCW2lI8diyXqlQEPSBPdKORtYDX7fRmsQMUhEpOzSDNeAkgyZHJbaEqkqr5ZxiaJsL\/qx55FMEOadzFb7xGVifK4GkeWQdxPEdIGIdhHRBttchBNQux4H8wO+FSpa0fSIicydDQWx7ziDJBoMIrolE9W81pJsuCEvSIipWtspTT\/RpEBsT2MrAP2iQ3MthyH40e9BWopIolwvAWUhfSBnh5zkBrZYQ9xR1va0lRIfNIS0LdmfxEP\/zB6KqL45P8e5k9hag49+C0Ako4cwTMbD3haNFciXUlYmGbqi6M3LE\/yNCNBoLtDKA2dyyLFMcnXGFgTQHdvVIX4fUwcy78j4JuldTF8L861ph8y\/TeJJohPA0QpBWoLE1xx\/KB6Sr0DPxOTx1AegTQxDDp+alzWSV+YQiwIwJEnxmmuMVHzDepJG6yDSJgx14A\/AwnCViYtn0D+SNwlCFFSYSiDAc4BVqYifpAkRc1+J\/SOyq3B2npLqjxSTNOGuYwGN\/LJBR08apZAUEGd7QKzOvS+JbfqNqwIefPbmKRDXUx4nha1mcK1qbyX5I1eSg1gYcx6W5OfPSS\/qnM19z3\/nYV\/oJG8r8QH+d+6nvb8HvCR0\/u+htOvjW+y\/u7\/75+HuB7t\/2u5sO+Buw+1w22vd2n2S4i4H6J6y6PKHPZVm0114sCPR5tnKW7x+dWvfs2cbbd1lyxvYXsmXcMpy+MfbrM4BFnXyFP7Bv6Y28MXXZu4bXdGVubd4l1\/l9er1q2szlGZTDh8q8Xve4nVZzP3B77zFN8W6y+plLrpGZGJNsERnuHFYFvDgnk2nwetu8ntW2fLCq3Zo3rvm6gq2ZZalRdG9u\/BuJKscN4HzVY+qW8xs7ixevyoW+jz+59\/\/AXyTr9e32Uipy2ZTd6MRw8++upHewqSvX8Ezu7tOj+2YOtQxdWzHZBD98+\/\/UJE5smPKW4TRl+uYPtQxfWzHVGT++fd\/hOmxHdPeIlRfrmPmUMfMsR0LU+hYGukjO2a8RajnO\/ZqUz5q6r6Dox3rLzrqeFpkRz8fi7xcHaui7+pVsQQsiS8wgP1gra8zfKT59bDUD4ddjpT8nX1w\/XTRP2Vd3g6FL4s6Pyx9W9zh78eJ\/hM+9XSxi\/pmKPQqW187CI0I4EeKnBXlp+z+ywitHqVqgncfim2PEOyV+\/vsl6YV+OyzmElb1FeHZSco6yNVzrCD\/48qv2za1SPM+w099hz23XQOyqZbL0cK\/bboiokrQW8\/SQCXTd21cPhqMRdWunLf537pLf4Ax19saJl+a51ffeZIFz7sHThZtM67Cw9P2TyoJJjXxp+vmzIXbX6Fh\/uczi3NvV3umZV84\/sJb1Z73rwDvfCEt+s9b9+FD3jC682e19OVyx1L4zyD\/0dLBF6SPPPa5KesvslXYlXcFXDAc02rlNu5J9abS2\/xc3MLzALNR6FkIP73f8Q6L\/NlJzLRNp9gVfKxqFei6AQo6joXVXZ7eBXWwWmwT21mL6+tl21Tlva8mNUwHDC78PC7X\/XywgFZ+GTmhTt+gk6bPTw0mFOzaypxeS8+Fu2688v8Li97BUF7oNzF666FH8V62YA5LBt7hHfZlBeePSu5btruwqubOj84aembsMSjlnbMo7v+TFN3\/Eu9qazGdppe5etlXq8gRjkJcOctYDHp1vTlrJ9bt97ipxyuLd7lYl1s896wP\/uym8\/2w12FN97iLYgjXplRP17BkL2yw3fZrO5hFIfXM1fF3VeAldhll3Dz4Ff0nOSO2+aTFQltfjf76FYQWIZq+DRc2eyJOj9sVlmZ1a\/XVVaWI8dDn71+BW\/vVhP10Fy5gwPE2lukkR5+8zJr+4aGhzY\/3vjFZdaOzmzKIBCR24lNAR4tb+\/yr9e3+bL7Ca6o9mPUgj9ACVBSKLAI0uM9\/vupWHXXcMMh8MR1Xlxddwjl1sIRbij\/DJ5HMens9YVXXA5Vesxbuah0SJXGWxj6FlrLrHkkJzSPd03bbKqnWYcJvUVqwhfr8NLkXD67ecQnNI8PTzcOcB3mxXUA2PF5+tzGASBRJzOON01bN2wdfSbubBgaDEO\/GAaE1+c3DAD1PJlh\/CXr8voqe5rjkIm3SGXyYh9eGp7r57YPuDJ8yqjSNpsnmUeSpN4iSdIX8\/BS9fzmARBnp3MfzWX+2LCSJDHYRfxiF2AXz56MyuCUi5UPv\/\/WJxyPdBsxuI34xW1AgH1+t4FYESczj7829aqpsicaSAQGEr0YiJfK5zePU2alb7LqiaYRgGkEL6bhpcEJMlJ5Qtv4j2a9Lp5mHQYii3mJLJCDnavndx2nrHT8CMBJ7e+\/Pc1CNFiIfrEQcKXnJwgu5oQW8j67Bzy2p9mHAvtQL\/bhJdG5eX77OOnSJaurL1AVS5TxFokyLzbiJeF59OxlD33KHCTr1kWdPbryIaHyIV8qH5CLnSfPbhqnjC7\/Xlxln55gGbCmlS9rWrSM+PlrYqfda7m6fmJMCSAvDV7yUjCP58864pNmpVVef4GsIwjBQl5OeECS\/vxZhwpOmnY0m\/bT7781V0+rrMdp5C3i9CXIQJJ+giCTpKddvVxtiifWP4CRYREnL\/VTyMie3T7S9KSHTC\/b4ulxJo5jbxHHL0sYCLkn8CFpfNLTYpvypsir1VPdSByCkbwkI2gk4fMbySmPfnzfbMSfmk39RAsxYCEvRTK0kGdf0sTqpJu49aenBhkJ1iFfrAOs49kTEXPag4XI4\/LIOlkcwRImelnCwGruy0aWV3wd6hVelZveQdy9vje6HFg3Hdz0zLpNm5Xi26ztroX8SgaVyOqVkF+FQSX+5XZzWRZLAcd\/ivpfz8X3eVaL1c7FO7grWOdZm687uDN4m6\/sTbxz8Z+brO0Ke8cvX\/+buGzz7GYtsjYXMDLFx2JJLynW8PS5eJdVl3nbiWxbVJvuOitF\/l+brPSzNs\/OxQegdBCXWStAKyIjpP1lXndtDlcb+eLiOd5cnN5WLG5nzcfyKaCW\/g8=\"><figcaption class=\"nx-fk-cap\"><span>Plains, deltas and basins, the corridors that carry food to ports, and how far each division sits from one.<\/span><span data-honesty=\"true\">Indicator derived from Natural Earth at map scale; a screening view, not an assessment.<\/span><\/figcaption><\/figure><div class=\"nx-live-slot nx-live-slot--map\" data-nxf-slot=\"map\" data-nxf-kind=\"map\" data-nxf-resource=\"nexus:\/\/gcri\/G02\/stem\/spatial-statistics\/#map\" data-nxf-title=\"Food Nexus live map\" data-nxf-height=\"560\" hidden><\/div><div class=\"nx-map-hud\"><span class=\"nx-eyebrow\">GCRI \u00b7 Food Nexus \u00b7 field<\/span><span class=\"nx-eyebrow nx-map-hud-right\">DESIGN FIGURE<\/span><\/div><\/div><\/div><\/div><\/div><\/section><section id=\"nx-sec-canvas\" class=\"nx-section\" data-hue=\"supply\" data-tone=\"alt\" aria-labelledby=\"nx-s-6\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">05<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Canvas<\/span><\/div><h2 id=\"nx-s-6\" class=\"nx-section-title\">The method as a run graph.<\/h2><p class=\"nx-lede\">Data from the source register feeds the methods; calibration and an independent test gate the estimate and its limits; the decision stays with the competent authority.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-plate\" data-wide=\"true\"><div class=\"nx-plate-label\"><span class=\"nx-eyebrow\"><strong>Method canvas<\/strong> \u00b7 Spatial statistics and geostatistics as applied here<\/span><span class=\"nx-eyebrow\">Design, not a record<\/span><\/div><div class=\"nx-figure-frame\" data-scene=\"true\"><svg class=\"nx-figure nx-ui nx-dag\" viewBox=\"0 0 1200 564\" data-nx-dag=\"true\" data-w=\"1200\" data-h=\"564\" role=\"img\" aria-labelledby=\"nx-dag-g02-stem-spatial-statistics-gis-chain-t nx-dag-g02-stem-spatial-statistics-gis-chain-d\" data-nx-z=\"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\"><title id=\"nx-dag-g02-stem-spatial-statistics-gis-chain-t\">Spatial statistics and geostatistics in Food Nexus: from layers to a published map<\/title><desc id=\"nx-dag-g02-stem-spatial-statistics-gis-chain-d\">Layers are ingested with their licences, reprojected and harmonised, analysed (overlay, zonal statistics, indices), checked for uncertainty and published as a map with its passport. Design of the process, not a record of a run.<\/desc><\/svg><\/div><\/div><p class=\"nx-caption\"><strong>Fig. 01<\/strong><span>Nodes carry their type, metrics and state; connectors show what feeds what; the gates are the validation steps; the timeline is one application. Design of the method's use, not a record of a run.<\/span><\/p><\/div><\/div><\/section><section id=\"nx-sec-data\" class=\"nx-section\" data-hue=\"climate\" data-tone=\"alt\" data-variant=\"split\" aria-labelledby=\"nx-s-7\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">06<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Data<\/span><\/div><h2 id=\"nx-s-7\" class=\"nx-section-title\">What the method consumes.<\/h2><p class=\"nx-lede\">Datasets and records used by spatial statistics and geostatistics on this platform, with the role each plays and the label its values carry. Sources are listed in <a class=\"nx-link\" href=\"https:\/\/therisk.global\/food-nexus\/research\/sources\/\">the source register<\/a>.<\/p><\/div><div class=\"nx-section-body\" data-lv=\"register\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Data<\/th><th scope=\"col\">Role<\/th><th scope=\"col\">Label<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Data\">Household and nutrition surveys<\/td><td data-label=\"Role\">Response variables<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OBSERVED<\/span><\/td><\/tr><tr><td data-label=\"Data\">Soil profile databases<\/td><td data-label=\"Role\">Point observations<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OBSERVED<\/span><\/td><\/tr><tr><td data-label=\"Data\">Satellite covariates<\/td><td data-label=\"Role\">Predictors<\/td><td data-label=\"Label\"><span class=\"nx-chip\">MODELLED<\/span><\/td><\/tr><tr><td data-label=\"Data\">Market price monitoring<\/td><td data-label=\"Role\">Spatial price fields<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OFFICIAL<\/span><\/td><\/tr><\/tbody><\/table><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"data\" data-nxf-kind=\"data\" data-nxf-resource=\"nexus:\/\/gcri\/G02\/stem\/spatial-statistics\/#data\" data-nxf-title=\"Method record\" data-nxf-height=\"520\" hidden><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"evidence\" data-nxf-kind=\"evidence\" data-nxf-resource=\"nexus:\/\/gcri\/G02\/stem\/spatial-statistics\/#evidence\" data-nxf-title=\"Evidence\" data-nxf-height=\"520\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-sec-checks\" class=\"nx-section\" data-hue=\"food\" data-tone=\"alt\" data-variant=\"stack\" aria-labelledby=\"nx-s-8\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">07<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Validation<\/span><\/div><h2 id=\"nx-s-8\" class=\"nx-section-title\">How results are checked.<\/h2><p class=\"nx-lede\">Predictions are tested using spatial cross-validation that respects dependence. Uncertainty maps are compared with independent observations.<\/p><\/div><div class=\"nx-section-body\" data-lv=\"ledger\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Check<\/th><th scope=\"col\">How it is done<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Check\">Spatial cross-validation<\/td><td data-label=\"How it is done\">Blocks of locations are withheld in turn so spatial dependence does not inflate apparent skill<\/td><\/tr><tr><td data-label=\"Check\">Uncertainty maps<\/td><td data-label=\"How it is done\">Every published map has a companion map of its prediction error<\/td><\/tr><tr><td data-label=\"Check\">Scale and zoning tests<\/td><td data-label=\"How it is done\">Results are re-run at different aggregation units to check that patterns are not artefacts of boundaries<\/td><\/tr><\/tbody><\/table><\/div><p class=\"nx-note nx-mt-3\">Results depend on the scale and zoning of analysis. Sparse or clustered sampling limits prediction in unsampled areas.<\/p><\/div><\/div><\/section><section id=\"nx-sec-connections\" class=\"nx-section\" data-hue=\"supply\" data-tone=\"alt\" aria-labelledby=\"nx-s-9\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">08<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Connections<\/span><\/div><h2 id=\"nx-s-9\" class=\"nx-section-title\">Topics that rely on this method.<\/h2><\/div><div class=\"nx-section-body\"><ul class=\"nx-list\" data-nav=\"true\"><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/food-nexus\/topics\/agricultural-production\/\"><span>Agricultural production<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/food-nexus\/topics\/food-security-and-access\/\"><span>Food security and access<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/food-nexus\/topics\/soil-and-land\/\"><span>Soil and land<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/food-nexus\/topics\/food-trade-and-markets\/\"><span>Food trade and markets<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><\/ul><\/div><\/div><\/section><section id=\"nx-sec-stewardship\" class=\"nx-section\" data-hue=\"climate\" data-tone=\"alt\" data-variant=\"split\" aria-labelledby=\"nx-s-10\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">09<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Stewardship<\/span><\/div><h2 id=\"nx-s-10\" class=\"nx-section-title\">Who maintains the method.<\/h2><p class=\"nx-lede\"><a class=\"nx-link\" href=\"https:\/\/therisk.global\/space-earth-observation\/\">Space and Earth Observation<\/a> stewards spatial statistics and geostatistics for the Consortium and reviews how it is applied across platforms.<\/p><\/div><div class=\"nx-section-body\"><p class=\"nx-p\"><a class=\"nx-link\" href=\"https:\/\/therisk.global\/stem\/mathematics\/spatial-statistics\/\">Canonical page: Spatial statistics and geostatistics<\/a><\/p><\/div><\/div><\/section><section id=\"nx-s-11-sec\" class=\"nx-section\" data-hue=\"food\" data-tone=\"alt\" aria-labelledby=\"nx-s-11\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">10<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Connected<\/span><\/div><h2 id=\"nx-s-11\" class=\"nx-section-title\">Where this page leads across the Consortium.<\/h2><\/div><div class=\"nx-section-body\"><ul class=\"nx-dest\"><li><a class=\"nx-dest-card\" href=\"https:\/\/therisk.global\/stem\/mathematics\/spatial-statistics\/\"><span class=\"nx-dest-top\"><span class=\"nx-dest-rel\">Canonical method page<\/span><span class=\"nx-dest-host\">GCRI<\/span><\/span><span class=\"nx-dest-title\">Spatial statistics and geostatistics<\/span><span class=\"nx-dest-why\">Platform application links to the canonical method page.<\/span><span class=\"nx-dest-go\" aria-hidden=\"true\"><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><\/li><li><a class=\"nx-dest-card\" href=\"https:\/\/therisk.global\/space-earth-observation\/stem\/spatial-statistics\/\"><span class=\"nx-dest-top\"><span class=\"nx-dest-rel\">Steward platform<\/span><span class=\"nx-dest-host\">GCRI<\/span><\/span><span class=\"nx-dest-title\">Spatial statistics for EO<\/span><span class=\"nx-dest-why\">Space and Earth Observation stewards this method.<\/span><span class=\"nx-dest-go\" aria-hidden=\"true\"><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><\/li><\/ul><\/div><\/div><\/section><section id=\"nx-s-12-sec\" class=\"nx-section\" data-hue=\"supply\" aria-labelledby=\"nx-s-12\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">11<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Questions<\/span><\/div><h2 id=\"nx-s-12\" class=\"nx-section-title\">Questions and answers.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-faq\"><details><summary>What are the limits of spatial statistics and geostatistics in this field?<\/summary><div class=\"nx-answer\">Results depend on the scale and zoning of analysis. Sparse or clustered sampling limits prediction in unsampled areas.<\/div><\/details><details><summary>How are results checked?<\/summary><div class=\"nx-answer\">Predictions are tested using spatial cross-validation that respects dependence. Uncertainty maps are compared with independent observations.<\/div><\/details><details><summary>Are the values on this page real?<\/summary><div class=\"nx-answer\">The data names, roles and checks are real descriptions of practice. 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