{"id":33538,"date":"2022-06-21T19:43:24","date_gmt":"2022-06-21T19:43:24","guid":{"rendered":"https:\/\/ccm-swiss.com\/?p=33538"},"modified":"2023-02-23T10:40:02","modified_gmt":"2023-02-23T10:40:02","slug":"multiscale-analysis-a-general-overview-and-its","status":"publish","type":"post","link":"https:\/\/ccm-swiss.com\/index.php\/2022\/06\/21\/multiscale-analysis-a-general-overview-and-its\/","title":{"rendered":"Multiscale Analysis: A General Overview and Its Applications in Material Design Simcenter"},"content":{"rendered":"<div id=\"toc\" style=\"background: #f9f9f9;border: 1px solid #aaa;display: table;margin-bottom: 1em;padding: 1em;width: 350px;\">\n<p class=\"toctitle\" style=\"font-weight: 700;text-align: center;\">Content<\/p>\n<ul class=\"toc_list\">\n<li><a href=\"#toc-0\">References<\/a><\/li>\n<li><a href=\"#toc-1\">Alphanumerical scales<\/a><\/li>\n<li><a href=\"#toc-2\">Camera Model Training<\/a><\/li>\n<li><a href=\"#toc-3\">Multiple-scale analysis<\/a><\/li>\n<li><a href=\"#toc-4\">Concerns with Single Item Scales<\/a><\/li>\n<\/ul>\n<\/div>\n<p>In this section, we introduce the original and adaptive threshold CSS corner detectors, and analyze their drawbacks. First we quote the definition of curvature k of the contour in CSS. Sarstedt and Wilczynski 2009 questioned the approach used by Bergkvist and Rossiter using measures of customer satisfaction and customer loyalty. They still found that while single-item measures are not appropriate for complex constructs, they \u201cperform acceptable with regard to reliability\u201d for simple constructs.<\/p>\n<p><img decoding=\"async\" class='aligncenter' style='display: block;margin-left:auto;margin-right:auto;' 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\" width=\"309px\" alt=\"multi-scale product analysis\"\/><\/p>\n<p>However, with the decrease in the spatial scale range, the driving force of landscape index and socioeconomic factors tended to increase. At a time scale, the drivers of soil conservation service are changed over time as impacted by regional land use, economic and social, ecological restoration project, etc. Multiple-scale analysis is a global perturbation <a href=\"https:\/\/wizardsdev.com\/en\/news\/multiscale-analysis\/\">multi-scale analysis<\/a> scheme that is useful in systems characterized by disparate time scales, such as weak dissipation in an oscillator. These effects could be insignificant on short time scales but become important on long time scales. Classical perturbation methods generally break down because of resonances that lead to what are called secular terms.<\/p>\n<p>The most efficient solution is to use multiscale FEA to divide and conquer the problem. To accomplish this, a local scale model of the material microstructure is embedded within the global scale FE model of the part. The purpose of this example is to show the features of multiscale principal components analysis provided in the Wavelet Toolbox\u2122. In mathematics and physics, multiple-scale analysis comprises techniques used to construct uniformly valid approximations to the solutions of perturbation problems, both for small as well as large values of the independent variables.<\/p>\n<h2 id=\"toc-0\">References<\/h2>\n<p>This is done by introducing fast-scale and slow-scale variables for an independent variable, and subsequently treating these variables, fast and slow, as if they are independent. In the solution process of the perturbation problem thereafter, the resulting additional freedom \u2013 introduced by the new independent variables \u2013 is used to remove secular terms. The latter puts constraints on the approximate solution, which are called solvability conditions.<\/p>\n<p><img decoding=\"async\" class='aligncenter' style='display: block;margin-left:auto;margin-right:auto;' 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BvRgtOIFPAtR+8nihC2A2gbimm9jikNQtgN4oCredtNOnkQdwIHSgLZ5gUiYaY0qZAEAcUjum4kQPpT040EySI+FN923APbqaWTD8kOxtshtRCd+1U\/nJPrUZ2np8aujHE+hVU7nQArKT3qZgy+si3LSPW6KKWmKULTBmilp2969LPPBKob7UWrfaj1J7iiSI4rjhM6mOBRC0yKVrEmilI3oZLaC2JNBB2oD6DoPwpaUUQ8nY0zJC7IRmlubdz4Gsp+MtuA29sODWtczN\/gLkcg1lnxlt5Q7A23oILUiRWzGma2ki8VtwrmoBj2eHMuXq7BNmFIUgpLgX6gSnkfCamviZ5rDqrpOJKtmmpUpKR\/me0wYrP97dOXdw4txalhaidzvzVinwOe4Ut+6unFrW444jVKiST8CaA5ahsNnVOr8wPSlybRaXrb7unWbqEpSRMqBiP4Ufc2v3fU04AShRQUkflghQjv1rkxPI1ut6FBCgfwzBnqOn8aG4yS1Ck+onY94H\/FKH2vOuFDQIIT7bcD9KVYi1cM+Sy8j1Mp8sgDYETt8a7YqiMjY2IkcGjXW0qabcR1EGO+9GWdqhy4QhU6CrSVAT3pbaYc7c26wz+4HVmT+6lIP1pdiAFFIwthnyvUHiQsgSZAkTyQIH1PekroW8FuqUSpSokmTzUju7RLtph1qhhLP3cuJU6v0lcpClBXeNQA+NLkYKwnA8IQizKL27uitYWnSCgR5agTyDrVPwTRHEVvWgHltjYBRJA47UUG1pCQUEgFMiN57frVjOZDW5b6lNpFwpZSUlW7gGolaY5G4G3b3poxDBEWmGjEEqWGnSHE6o3KfTt3gyPkaFpipEbdSryXCoidRCipRmT1+n8aSG1UlKQR6lbhI6CnVNlL+hwExpU4T2iVT9KAA55Ll48ChV2tQTA4TO5A7D+QpBfca3WW7dRSV69v3T+ZXb4US4kggHYq3gdKcUW3nhDhICAnSgcdz\/AAH8K+cw58OHUjSdyTEQImKQ7Q3M2jz6w3boK1KBMAdAJJ+AAoTjHlOBKlFXG4M04FgsrbWlnSC2QJPyk0JdipSfMQEkJkAdREbx7lQj4GuO7RsTpQ5IQUkHrzU78K\/EHMXhpm+wzRgT67d6xfbuFBSykOoCgSgwQdJH1qK3FiLS2Q6tJ1OglOpMA7xI\/vvSLziNlCR1k80M4qacZeGLHcGpLye7eQc04dnnKOGZrwrE7S\/tsSZS8h62\/JuPy8mCngg7yOBxT24ztqFYJ\/w3fHS1RdXPgpiPlo++LXiFgsmJc0jzEfMJBA9jXoGtraP41icrHePa637F1Xb6kVJDFctc8U03LRG5EE1I7tmSRTPcsn6VX2ImVsj7qCkxJNEFBI24p0eZ3jT8NqTqtwTt+tRm2h1rY3LRIII+FJXWSR7U8Fgx+UUncYJmB+tNS5BS0NKrcBXBr4sbgR86cFMGZPbrQwwmIG4NNtDiGw23txtXSwNM6RTmbaRtweIoKmI3I2oGh1MZlsBJmKJca6fwp2etwN6SLakb0A6mNDzaoMgU23bZjcCOtP77RA3FNV41tJ4Fc3wGmQvHGiGjAPFUznRHrJP+qrxx5oFtZjiapfOyCFSlO2qpWE\/rI9\/g9aFiRRaxFHKFFrG0V6ieeCdadqTqR1pYRIolQ3NccJVJoBTShSd6ApArjgmBRD6PSSKWaKLdQSk7U3JCohmY2tTC\/gZrMXjHbSy7t0NaozA1LK9qzJ40gN275CeATtTSX1D9Z50eO12q3u129pduBCnClxpQmPgem9U9aN6nU+Z+UHc1OPF\/E3Xs13dotDqC28pUOIAO\/bmfrUTwy2RcOeWsgFQ5B2qf7D68i+6tXnLb7u2PNQwrzm1cHR+9A+Y+ho99g3YRcKJOzZdSJGo+oBRHAPH1mnBjDLryUtvsOIdZBTHOpHSPbn4yYnenjC8EKFsOqZ0nyxoJEhRSQeB0IJoHLQ9GDY0YfhIexhhvUEoaaR5jizu2QSAfhEHtRGMWBu37jFW0EMLWSEo2DY1EBBH\/AEipk\/gbjTdy2EqBXqSHEbpImQArr1HypLb4Ld3jQs2rUn704okBJGjfYGduvypFIN164GzLuVmmHnGrzQtNuw\/cKUlXpJDStG449RH6022OCP21yWykvoDgR6CCCtQACZ99Q+hqU2drjAV92DQT96Uq2JT\/ALRCo6dSflQTYLtje26HlMi2KHh\/ucS4hKSI7ajNO93A04NCBnB9VkcUdZQ8zd3ht0J1etLZIJUBzylInsYqQWlslhnA7V50vpLtxLVyjUhCwlCU6T0BSVaexRRdy2lttltltKQy1upk8EgqUk9\/yk+xCaS2rF09e2yUP+WMOtSlCVInU5BWr47qUBS96OcGibZcRhzKfvdwkujD23HmXVK49S5R8IKDHaKZMdbwK\/wJrDEsBD7TjbxB\/eK9AIHb1LJ+VNNiziFxYONYfcy8VKW8AFej0hIB6biB8UGnHMivPvG719sNqcs1I0tiPxEmEq+ekH50vemKoNoYU5YecvMUfQEoaudTbPbSVBKle2kkj5UXmLB7Wzt2MPYsyl9Fpo0lMqBCSpSoHcJn50IXd4xhTSCFqNyw4l3ckpK3iqB2MJH1qQ2Flf5gxC8uxbr+83SipSVJklSmy24Qe3qUflQOS0FGsijWSjb3SLaQsWzX3pWrjTKAJ\/8AqEjpv2r6xwVF0991UgIV5Kg4FmAHACP6\/IGp2zl28u2XL67VrDqErVp2JbCtQHvKtAj3ijsNyw82ze4hcW6fx\/w0lYO2ognb4fpt1pp2L2HVS15KmvsObQ6UsSUJVKtPToBRdzZrUG7jSpCVnbVtp07k\/rAqxcUyq455rVuhXlgyXCJUpW4+X9+1Ib7AH3LZFum3KnCCUdgI35+AoVNJhSpK\/wAVYadaQEAkp\/MUpJ2jZPxqPPgJ\/IggDaFc1O\/2DcWhXcrQ2NB2ClpSB8dRFRDFkBL7iFbHUTIUD\/DanU9kacWhfkDN2O5GzPh+ZcAvDYX1hcIfYukoBU2oHn4cyOor3H8Pswu5oyThGOv39neu3dslblxZqllxXBUn2PMdK8G7cpDoJkgHqqIr2F+xH+0m\/ADA1318m7Zd1LtlaSFIRxoVPOkiAY4Aqj61Umo2e\/gkYU3twL4uUggztTRcJ1TO1Or6wRTY+fVBrM2IuKhucZBJJnaiVskbgSKXaJM7bmvvLkH01GlAkbG1TaYgiKIWzMzTmtr9KIW3E7bfCmHHR3kbfu\/qgSZoSbeeRzSwtgSOaEGgPj3mg0KuBGGT2+tcUwdPHtFOAQJgAUBxoQdvjXdoSlsaHW46UgeYJMiO+9PL6DxApA8jkA\/GmJLQ4mNNw2ob\/wAqbLtI9\/hTy+NuJprukqIJ4+NNscUiJY6j8NUg1SeeUgKJgfm4NXhjwPlKSOKpLPaTrIJ\/eqTh\/jGbntHrEUzRLid6UGinB6q9TPPNhBG9AKZ2ijSN6CrvXHbCCnfigKTR6h1oswa4VPYVp9qA4mQRR+kUBwbGuYpGMfRLauRtWY\/HVX3bCL640T5bS1c9hWpcdbllfuDWbfHPDg9l3EwtoOAsOemJnY7RtTKX1D9bPIjP961iWZ7u6aQpJU4dWpRJmeTNF5aslXl0kBxKVJM7gwP513OrXkZrxJt1kNKTcLSpKQEgb9gTH1pzyDKcS0toJ81OgHTPX+NSn4JMVtlhZZy3dYrcN2WpLalI0DTyRO3erbt\/Bi\/Yw5tm7ZMEyhQXxtsZ4p48HMpanlXNy0NTJChqOpSfqP0rQmEYYhSEqdbSoAcRVLmZvpT7Ua3pfS1fV3yMvPeGdzaNKsLtKmnVq8xpSRqSud4PzkdIJ7b0bh\/h5eqceYS2424kqWiG5KXAJgDbn5R1rUlxky0xNEKYbCZESI\/7UosvDllD6LoJLh1J8wKTuojgn+v\/AHpmPUoyRMn0Np7Mi\/8Aga+++MPCzQgNPq8wtp5Cpgf\/AHbmOoFNr3h3id4gXNthhddt0oS\/uSFFRJIkcnpHG1bgufCiyvLs+UwoNXMhZSPyq7\/Hj5gVJMK8HbFTK22rRDQWEwFJB2BJ222g8VKh1CLRBu6NKBg\/LngveOWaTdWoSohpYU4gjTqnrxyUbSJkjeYp\/R4J4gW37tmwUltLf4YCI1En0gyZM6eOYHxrb2G+DzNo+0w80g2rbiFgK3KglBSmfgelTJrwywjykuN2+pSCCAePnSvM+AYdNWl3HntZeC17l\/AVBzBz96uGywFpMStaySDPPQif1kUBfgRiL7jambJ24eDqEqn8gSlCgNPsdI+teglz4d2LzqHHrJB8pZWmOJI3P8PpSvDcjYdZuKuHLVtCtJShsRG4gztz\/Wg++sdj02HgwEPs\/OtOvLXZBIQpTwSU6QEpQHCrrO5Hxmm3LXhViS23MQbZ0spZT5Sp9SVlGo7\/ABXHyr0Gv\/DzCnwVJZmWy2ARwDzHaaarTwmwfDbNNow0kISNwQAVD5e23zoXm8Bw6ZGUtGJbvw9u7bDGLNDStTyAQFESETP8hSprw\/dW22wi0UhCEfiLVE6o\/LPfnf5VrS+8O8MDjjy2tZnf\/jtTDiOV7GxbIbZCtQnfiokupJeCzq6LF\/iM2J8K03TKWHWwhKN29Pud5Pfim3GvDJgJNom2SykpjUgmZjc9DV63tmhLmhHpk7GOKj2P2jjQ3321A96YjnzlNckizpVUINJGR845Obw924tQ4pslJhwNhxJPuSRHyk1RWZrA2l24FOpUQo7pG1bazzg37Rt13Nqy394QnSpBTusbwBWPPENpq1xh9pphxoAn0uGTM7\/3tWixrPUhsw\/Ucf0LHEhASPMTsomdgOtevn2FbxN59nzBEotFslgutKKl6tRCzBHbaNtt5ryFTr1gpBHUkCvWP\/DqzHb454ENYYlnQ\/ht2604oN6QveRvqOowRvA6bdTE6ut0r8yJiPVj\/I0o+FEQDFIlpUfSdvjTw9bg9KSLt4OqKy9keS4rkI0MH60PyCRNKkMkdKEW5HBmmHEebG1xkxJHFJVs7+5p1cRsRB96SLSATqB+lMyWxUxCps6htE8V95REEfKlYb\/Wvi3vABNNdrF2xIWzBj50At9DvSstfumfjRa0TsB70OhVLQ3PNATtTe+1ClEdqeHkSDzvSB9ABMimJoNTGO5BI2HwppupE+nbin99EExTLiAAkCo0g4y2yH49OhU9RVJZ73Uf+r+dXbj8ltfNUlnoSsj\/AHU\/hP6xLOUesc780BQHIrpO9cJJ616seehS6KUYobioNFLIIMVzO5OEgpkUE19JHU0EHeaTZyBUFYkV2Qdq4relD2M2MIBZPwqgvGJlLmC37IQklbDgAWYBMH6VoHF0gtqHNUN4sJSbC41EhIQZ+EUK8jkPKPG\/xPtrq3z3izF2W\/MD6t2jII6fpTn4YOMW+NsOqYW75SgVIKylKh14ps8SnbNWfMcFmXCz99cKS5+b8x2NPXhepwYsxoCFFKgQSIPXYHtTs+EWFS3JI3L4YvWz2BpumbJLAf30xEbe9WvhQCmNBTASJgd6qTw\/vEOYJbwrdcSnseKt3AyFtpSgbEjUe9YrqEm7Gem9Lio0xSHyxaUoo3kE7CpthNk0G0kxuNvaoxh7IW8mQExFTXDUjywgqGw561XQnplpPwO+FsNW5KgkEqJnapDbNW\/lhQEKA+Qpit21AhsQD3p1Y8wjQlXWp1VrS0Vl8O4W+SHJVCdtu0UYi5CEhAB67UFA20qWR0oSm2ykb8e9SnY9cEPtW9M55wJmN+ntQVnVsUjjtXxSmYJgihpKP9QI\/hSKz5CcUvAncIZ3USSdtqR3C1ls8SDEGl135REzMnimx8BW0kDjambLdcIeqjt7GTEyEpUNgedutQvGW\/MSY57RUyxYApBB2mDt071GL5gk6SlQE9qrbZvZbVcLZXeKWBSvzkJO5iBUfzIwHbIpJEt7gRvxVhXts06VJA2AIE96iOYLRKWFgiCTERsR3o658oWS2tMozNZmwd8wkAclME+3NZJ8WWw5jSyl4rRBSlS20zz1UCZPPStj5msg7Y3KOolI9IMj5\/Csi+KNkf2o+E2KWdAAhoqTx7KKpPfjetd06e46MH1yvUip1NFolRIPbavTT\/Cweu3sh5qs1YgldszftrRbFJ1IUpG6gZiDHbkV5mv+hzQdW3OrvXot\/hQ5jK0Z2yubdeiWLxtzT6Rykpn9af6mm8ff9UZ2jiZve4YidqQuMgTT3ctRJptdbJrL2Isq5CLyyelfKRtMUoKYBmgLT0EzUdoeUtiFxMGkriRyRFLnU7xSZaTJpiSHosThB6iulPYUYqgE0IrloKWOBRCyD0pSsAiY35pMocmaZkB3Cd6N6QXA2604vQUztSJ0EpjeajzDTGi5TAmOlMmII579akNygmZO3xplv2pSY3+dRZIeg+SEY+D5S4k1Smd2wVmR+9\/Orxx5v8JQ424qmc7MErJgc\/zp7D4mHY+D1OVXJ2r7mg78V6ueeoKd3IosjajViTQDxxQtihZEUAiDRhHWgHvSHAQexoU7ySa4BvXSIFccNmKf5auxqj\/FFsm1dA6girxxL\/LI9qpbxORqtnZI67Uq8jsGePv2hbC0svFXF2bd9bilOhbuoAaVHeBTVkRlz9ooeRHlIISpSjET\/Gpt9q+3Z\/8Aio8q2TpLjCSs91bzULyK+4zeNoCFlqJOmN1bfTp+lHb+Essf8SNr+FjbTWDsMtEcApWEng+5q+cuIhhKPUFDg1QXgqtD+GtKUFLKQBKlFRHHVW9aEwFC2\/WFFKFTsodqxXUH9bPSelv9yiT4c2tTmoyCDMEbE8VLLJSAQNQ0kRUWQ4pLRXASRyZ9uBTzh9ylzcKgDbfrVQ56fBbdu0Sy1WFfm2UT7807W7wIAkJ27io9aPSBJ35p5tnEiNxAHXpUuqZCuh8jk28YO4gcmK4V6j3HwolC44GwPShKQSSop+tSXJsjKKTB6gJB3IrqfSCQfrSZBWCUg8dJrsqB0gdOlApBOAe6uUEkCeRTa8ptOoqiT3NKVJCpJKj3BpJchCTAiew6UM2w64pcDPiCtaNgCD7VGrw7KLiTq5FSW8J0wOYqNXoUVH1AdBUGx8llUtrkZHgG9Z0H1cjt8Ki2Oo+8NKbE7bn4VKr0eWhSe\/WajmIlGgq0yqZG1LW2FLSKezZhks3KEkalAj83FY88Wbe+Zv3RcalJUtKNSiQQmOCo7x17b9q3BmO3SsPekaiDM8melZS8XMLedffZuEo0aSWDATBHw3rUdKt9jI9fpeu5Gb8Tbb86PVIESTMx1+FbN\/wrMZRZ+MmN4O4+8g3+EKKUJILayhYO45ncx86xdeB1D62nUFC0KIIjg9a0T\/h9ZoRlj7TmWi7ei3ZxQPYesEGHC4g6UGO6gmPeKus6O8aX9zFwerD2UuW4E02vIEwaen0xPFNdwjcmOKytqJ8GIVJMbUUpEcHalCxvMUBSZHFR5DyfIkcRzNI3RpkxO8Uvc454pG6obxUaQ7GQkVJPFfCSJPNDIHTmvgk7780ATkElO3FJ3B0pYpBAkUQtA703KPuCmI1gq6UldSoGKXLRHtvSZ5MzTEohp6Gu5QI+FNF4zyN6fXkkgzse9NlylIkk8bVGmh2LITjjBIXtAiqczsxCyfervxtAKFbxVN51T+IfjS461Mcm+D0vBrhP1oKD1rterbMAgJ52oBTNG1wiuCClJ9qAU0cRNBgcUjZwUE10pGnehhMV8dhQ7OGnEh6DtVNeJiJtXZG0GroxFMoNU74lN\/8AlnfgTSKWmOR8nlr9sWwLWa7C9S0ElxpxBUByQRz9apnKDr4uggXCm0uH1JQgqUsDsB\/OtFfbHalizVtKbpUSf9prM+XjcffkpDqUDncx\/KpE+YlhS9NG5vs\/Oqcs1OPJJKkhMkDYDjitIYItP3WQdwo\/Ej+zWc\/AG1u05dTcvEwtCdI34\/hWgsvLCmwSd4gxWJ6lza9HpPSXqiOyStuLUgN7ESTHUzTtYhYcE7gkbdaY7VbQUNTsjbc1KbNFuEIVrG25B5qpdMnyi4VsUPmGon8w9h7U82Z0r3nf6Ujw1pryw55gJiIFOqG20AFJHYbzUiEJQ8kayyMnwK2UqIEbdRtRp779vlRDNz6tAST3NKlFCk6yd+1S46a4IUtp8icpTJO9cVP7qSegpRCCNSTvXCG4PWk1oXuCFJIEg7fWkjjZWsiaclGW4SaRKW0nWp5cA0jrcvAcJjFiTBbBKSSZ33NRy7QEL1LO3SN6dMy5jw3DWiXrxlso9XqUNxVIZs+0BlizU7ai7IcCtCFRtq3PPyNDHAsufCHpZ9dEfqZNcwXrVo0pa1cA+3xqIXGIs3DZKFpM7wCKzp4ofaAxC5LrWGPuLQDMtrBMdeD8KiGUvHzEbG7U\/igdftkwk6jpKSTxJ5PPAj3qeukOMNryV663XKztfj5NDYxcBN0pCiPWO81QXjZl64+7qxa3SVc+YACTEcwKtLB88YDnizN\/hNwCtB0usq2W2fcdfjxQcfw1rErF21dkoWgghQ5kUmM3j2LYWbCOZV9L\/I8+ceSBiLykCApalAfEzxVnfZNt7S8+0Bkth\/EGbNX7SbcZdcWUAup9SUah+UqIgHuRUa8VspP5azNdW6jrb1akQOh6f9qTeEWOOZX8RMEzGygKew+8bfb1CUhSVAgkdRzxvWot\/eUPXujz91yV\/Z77PfwIhhA16oSN++1IbhszMSKzhlX7SuaLm8ssQxVplWG3KUamNASdMbqSe\/UVpRl9m+tWru3XqafQlxCu4IkVlp6fBPycO7C16vuIFNbmKIcRE804uNA8UmdRGxBqO4tDCmNb0iQKROAnnmnG4RyI4pEobxFRZrkfixNo7Cu6CBvRoG+1fET0NNtCuQUtIg+1JVp5EbUtWN96SuABRIpA4sSqTA3pG6DJ4NLlk87Uie2JNM2DqELw5nkU1XhBBn5U5XCiBPfvTTdLMEVCmx2KI5jR\/DVNUxnhXr3j83WrjxpctqgbxtVO53TK9JMb0eMtzDs\/Cek6TJ3NGUSkiYnejAa9TRgkwQr4mK+oKprmKcJjauHiaERXxHtSaFAz2FfHcV2K+IIpNHDbfpGg1UXiS3Nq6R0Bq4r5MoJqmfFy5XYYLcXCAJkJKj+4D1NNWNQTk\/Yfx65X2Rrj5b0edv2scDViWGP3gUomxJcSkfHc\/Sso5WGvHLdhSo1rAk\/GvQbxX8PbjMGVL7ErdwvksqUoDeawTlvCiM629i8koLd1pUkjf83A967HyoZEH2+xd5HTrsCUY2+\/weg3hbaotsp2TQ0CW5JCYmY\/lVhMYxaYPZuXb7yUIQNyeKiWT7Q2mD2zU+lLSRMb8bfzqvfGHF7p5bOBWrq0pcIUsoWoc8DbY9NqzPp+vc0\/k3EbFi46fwhwzJ9o22Zx5Npg\/mOoaOnVoOkqBOoDudvhG9PNh9o5x4M\/eULaWUal7TO3Yfw\/WqlwPwxxHE22SxaALbSQqPVqO+\/sfrv7b1KMN+z9m\/HWRZsAWtwqRqgy5B5Pb\/vVuseiCUdFHLMypzckXHl\/7VmXbEJaxLEGQoyYTJ39+v0BqysE+0RkjFlBKsUQ0qRsVek\/AkbH2rF+OfZm8VMJW6zfYCu6aiPMZB3TO\/PwppY8JM7YOnQhN4nSvVqWevIBHcECo9uPjzWtkunKyYvfbs9GMG8Q8FxS4WxaXTainckGpRbYs06AoEb8GsF+H13mrAMat13y3y2pIS6SogEjrFahyvmS5vGGkqdUtSIlXcVR5VLxuYPaL3GmslaktMt\/72NjPNEC+HmwV7g0zW18t1kKIM9jSO8ubkfiNoMyevIqP63c0PRpXJLHb1ppkvKV6QDM1l7xq+0Vd5efu8Nwa4IKTpSpIJOrtMbVZuas3PDDnbYr8owQTO\/yrOl54cM5mxF66vJWhbhWEk7JE8zVzjTrX4yvvqtSfY+Sjc2eKviDmNx1D+LrXpcGlIC1GAepAj5CRTPl3w88T883rf7Mw+\/fQ6ZUpTaktyBySdhI+PFaIvLbwq8N2\/MxPD\/v90VD8JtnUSvoPrSO2+2lhmS8RbssJ8J8QfCnRbpS2kBReMaW4SD6z\/p5q1hdZJfuYcFFfiQg932ckUtvsmZ6Ww09fsISsJgpW2FGY68jaT9OKj+cfAi7wG3RaW1im4vEJAUoelEkHYHrEb7Rx871wX7dYzr97s2vDm+Q9aQXGW3AXUpGypBAgAmDSe88dsi5ofawu5Yew28cP+Tct6DqPPsfl7UkrroLco6OqxabH9Mlv8zNOWl4hkDMAbUwp1tZSh5ZGgJJHQDc9\/pV82Cm8Qw9u7bXrSvcRwD1HemHPWXLG6fWuzGtTygpuN9J7\/32FSzAsFewvB27Z31LQkAqnYnqR7VAy5RaU15LTBhKDdUvBmn7TeUVKdtcZtiE629K5EDY8mPjVceBWR38254s7bytTbD6HHieAgGT+g+sVpnx0wRGJZPfWhGpxg60nt7VFfsX4bYu5ixBLpGsaFKAG4QCdvjMTU+GT24Dn8FPZh76rGHzyaYytlO3uMZbdW0CphsNssK2SkD271rLJK1uZWsAuJbb8sgf7SRWecPQMNzcwtCiUPCUj+NXz4cXou8GeZTwy8Y+f\/M1UQmrIplp9osdrFU\/h\/7JG4maTOpkccUsUOZpO4mZFJJGMiNlwkneKQOJIO006vp1AiKbnhvvUOaJUGJFCuEkUNUd6CQdyd6YY4uQpajxFJnFSdqPcFJ1jbehcRyPAnePp7GkLpkHelriZNJHUgE0xOLHUxtuDsTPFM95Jkinm5TsZHNNN0mJqHKL2PQZFcWBIJIn5VU2dEAk9IPWrexYehW23xqps5pB5333qRjQ+oW2XGj0XQsEAjrRqVdaaMPu\/PZSTvtTmg+1enowEJdy2KE12gJNDBBrgjp7xX2xr6a5XHHQK+KQelfCuniuFQ33whBmqo8S7ZF1h1zbrQFJcQUlJ4M1bV\/Hlkiqtz6QGVE8fypqyPA9RLssjL4aKIVgruHtLw5LIVZ3KSlTc\/lMdK88bnLQw3x8fwxxAhOKkBPtq\/oa9O8YLbLFutQTPmwI6iJrB\/iXgf7M+0wm\/W0lsXNwm4SCJ2UOfjM1TYclC+cV7o9G6zV30V2LwpL\/ACaew628uzCEAbJAA3\/Sq8xzC3b3Mo+9aFMhf4YKANEd+vYc1aOFabi2SBJ2HXf2\/hQMdymi7ZTftMEXCNxpH5k\/1qspuVdj2WeRT6laSDcoYfl7C7dV3fuBvVClkr0mB0J4Mb77Hf50\/Yj9pLw8y7buPYPZqxZVuhRUu2RKQE8gq46TWfM+5V8Qc5KThlq7c2NgFBLulyNSesgcj2q+\/BPwpyllPB3sGxa2OJLuWvLcW+2CACIMRt3qxxoxvmnOWkV+TCVMH6UdtFMYz\/iU4niWIW+C5Z8PrJlV08m2aucVxBLTaCogBTnp9KRMlROwmkOSvtF+Knine3ykZCwy4YsFD7wqySpcJKikLSdwU7Ez2FDzB9kxdjj2LJwTBsBx3DLi2fs7d69WoKZ8zZLiAlSYeR0JlPWDWg\/s5eHFt4NYbePLsrW6u8RZRblLIKm20JmQZ5M6gSNqlSpxlxJc887\/ALaIlcs1y3DXtpa1+e9r\/pXllithjrSU32GLsnjyoJJB+cRUtypaX1rej7tcFTY6HqKtp7w5w\/Gr031uu1w8rKlS2xPIH5hICht1HU0LF8o4Xg7yV4c2AoIOrT+U\/AdKqMitVxbfKNFC1bUV5JJlWyau2EIeKVSBI6iluP4dZWbalNgJj1RFMmQr1S1BsGdJ0707Z4dX+znFpSBpEE8VWV9sqnLXKDkpq9R3wU5mlNvdXD6ymW2t\/iajl1hOMGwS95SMOs3VJbS6U+pZPRAPJPvUmwxtOLOu28y5q4NXFgjNi\/hdqi8sGnXGABC07JI6gVYYzjZHTAuk6p+CnfDfwnydfYqzjOYlsjyUKNuw76iFEfmWSN1dayX44+A+MYHnPF2\/vuNs2zdy\/ieFv4Tb+Yh26UfwVKOtOiNW6xKhp2SZkejLuFi5Kg6wgNK20pTED+FNWKZXsdEhp0JTAATttO\/14mrSrOVEe1cae9lVl4EMi12P3Wtf\/OTFv2SfCHFcFzavN2P2i0WlpauNLN0ghVw44RsQrngknvFPXjn4R5XxvGjjVhrbvX3wdDJhtPIJgda0XfYQGXX3mmYW4RKlKJ2HA7VD8Syw5iFz510ZKSSkAQJqPb1KCr9OPP5j2P02MJu2XllU5WyBdWSrY3NyX2mR6Ssyr51KcYs0MpbbCVHYwTU3tMIRbNkOI2G4JH1qOZpYDbLi0rTCeJ6bVUTucy1qp7eSm8+Mi6wm6tQQdbahsKrD7OC8dylnm9v28HBsr5RaUsoOiCeh+tWjj7wcLgJHpnrVn\/Z5wCwxzCv2C5aocJDq4IAI0yoEfLarOqTeO6v5mVllcPvsbpfwpkuxNhq3\/Z+YWhpQy4jWJkBKjB\/lVyeFDQ\/YTt2EwHnNvkP+arJvCR93ucDuFgtFCkok7jtVy5DsFYZlHDmHAPMUyHF\/E71Dx4uEnW\/Yb+0lyjiKv+Zr\/A8OxNJ3OTShZmkzpjpUiRh4iR47GKb3xBMUvfPMU3vRPNRJokwEqokk9KLKoG1DcIJkUSpXSaa7R5chaztxRCviaOcPvRKqJQFCHOwG9JHeDvSx0SI4gUhuDApqdQaY33RgcimW9dG4pyvXwgEzFRLGcUSylRLke9RnTsdjNIQ4zdJCFgkVUmcr5snnr\/On7M+bmmAoecJFU3mrODTizDn7229SsbGk3vQFtiZ6l4MlP3dEKHFPKCBVX4NnVhxKUpc7daluHZgQ+BDgM+9byPjR57j5lVkUkyUJUI5owKB60hbfS4AQob0elXaiJ\/kUBVCkUQle+9DCga44MrpIigBVdkVxwkvfyHeqs8Qp+6OH2q070SgxVXeIQiydJ6A0LW2LspxOIDGPJtmwZs9XmxvKhwfpWUPtRYa5g3iDg+cHFAec4LdCI6JMhRPudX0rUnh9cpRmbFrJek6khYSetRn7SuRms\/ZCNrhlg0Ly0uEXXmcGUBW30Ur61n3FUZ8pN8fqel4zln9Gh7tL\/KOZEvUX+G2joIV5iEn9KtrBsJZvLWHuD0HWs4+FN6\/ZYLhzFyoFTQ8pZPOxrReAYp6G9JEaY2qmzI+nY9eC7xZO2pNeRPiWXGWbxBt2QUK2UIG4qRYVhAZCRb3Cm0wBHYUcwgPoKlCSeCacLVtYUITI71HjkzXgeljJ8sMs8tWKVz5GsemAdhIMjjsaem8KabR5PlpSgdABFctH9RCT8aVykgyIg1Kje2ttkd1yi9CYMpRIQfTTPjDqPJWo9EkCnp7caU7e3eonmN8oQpud4qPkXyku1eCTTSpPbOZCei8VxzImpRnZJXgjkQCrqKhmSnD9\/LcSOYqZZnCncNVAMwdo7DiioX7iSDuhrJiylMNuThuNBSVEbwoVcOB4kh61Q6n4bVSmLh20xMOFMf6pFWRku\/8ANtkoB2IBkGo\/qOElok3VRktvyTw3S0p1pGoH++aJXfKcToU0fea40hZTO+kcmhKQvTM7n61I7rPkrvTh7oZr+1DySQkGeBFR65wk69eo7cipXcOFOyhB+P8Af9ime+dShsqV1mIFRJtsl1R0RnEmm20BsCF7wU8\/OqtzteFplTQMggie9WBjd7DiwBvpiquzY8HgoqUCZJ2M0ePtyRJmlGOyr8RWtzWniTzVmfZ+xk4IrEsTC9PktKbQFdFLgbfKq6urZRCzH5uSKnHhLh7NwxiKnFAKQ4gJT1O3P6VeSm66u5exQ1pTyF3eOSz7XEL1T\/7TuW1Bt1XltT+8tRgCtJWrItrNhhPDTaUfQRWZspoex3HMLw9xZIavm0pQBMBKpUT8ga064YEA01R3Pc5eWUv2ou9SyuC8JMIXRDh2o1wmeKIc7mDTjRmUJLjg03vfGnB+DIHNN7sAzTLjsfhwI17TNEqJ5pQ5ANJnCAN6Dt0SEwtRjaJookDpNfLX78UWFSr27UqBk9AV9TA+NIbockinEweKQXe6TUyjElb7EOzKVfki2NPFpCoJFVFnfHTatOALnarVzCFFtYneKobxHaeUhZEwJirWvocpLeiDLqiT1spfO+dnG1uDzT161UGL5ydddP4p571IPEBt4Ou7nkzVSXq3Q8QSZmrTH6XGr8SO+\/ep4Z6e4Bnk6kRcbfGrXytnVqElx8b+9YPyt4mBXlpW9uOd+at3LviEhxKFJuI271HalU9M8hxrr8WevJtnDc3MuhIS6PrUmsccQ8ACsVkTAfEd1txKC9qAj96rUy94hsPpSC6Bx1o1L3NXT1KUUvU4L8auUOCUqFHJcmq9wjNbLiRDv61JLTGG3Y9YNEpItasyFq4ZIkuRRiVyIpqRfJVwoRR6bkHcGl2iSpph12RoNVln9IVaOhXY1YlzceggVXGfHZtXCT0NJ7nSlwZWxDNgyRnxjFHSE27i\/Kek7aSefl\/WrGxZy4vbRN\/bFp61u0ynQqZBqifG4jznlAzE0+fZ1zy5j+DqwG9utblivSEKO4SeCKr+tYLnWsmvyvJs\/sl1f0pPCn4fK\/6hC04rD8WctgkAJdVAHQzxVwZKxBLjOpZ4A61V+e8PFhmzEWkSgJdDqB3ChIqSZMxJSW0Nlf5u9ZnKXfBSNxhSUZOJeWG3ja0pIPqI1QfepLbLYKUkmClM\/pVfYLdSRxvG1S+wdK9xPq4E7mqyKe9FhOO1wPTcFZKFDbtS5plcpnk88UksbdcSoz0p3ToZaAUIJ\/SpcIt+SFZLt4QnuwlpoLk7VXmZL5jzlhK0kgQr41J8exoolpO87QDVfOWLty09dqV+Ykpk9KjXTU5aiTMapwj3SHzJqj98CgBBVIFWZiVut6xKfzEp4ielVhksEutpM65k\/CrfcaK7KOCUxNWWDFSrkmQ+oT7LIsz9n2wU28XA3vzNNOTM0qwu4U08r0agkzxHerDz5hC7lpzy0AGNttuOKrLBcGSHLkXKRzsJqJZV5RZQsU4Jsv3BMYssStklDyVBaZlNLn2Ej1NKJ7zVK5UzDc4ZihwkrUUxrbmSFJ7fHerVwnHUXjEOqEyOtdC3jssIV2PKt90PB9eJkEkQelR7FgW2l7wak1yNQKjwduajeOhIalPIkGgnEdplsq\/M9wpsu7HYQZqtsWui+tYVyAT7VPc4KCUvwshR6d\/7mq0v3lqQtZ4iSak4tXGwcqzS0MzoBSpSkiJPWpD4a3D9qMSdbBKUqQZ+VR+R5HmEdJqe+DVra3WH402\/p1qdRpM9ADU22LlW0itx2vWTZY\/gTYftHPjuJHdu0t1ugTwpXpn6TWiHDzVQ+A+XXsOfxrGViGH1Jt2D0UEyVEfMxVsLcHWnseLjXyZDrtqtzpKL2lpAVK5pO4T8q6p3eiXFiOd6ORWJhD6ueKQOnfvSl9wxzSB1zaaZl4HYMKeWIPvSN1YJPWjXVzyaSurn2pl8jvdoLUv5zXAYotZAPJ3opTsb6hUzExXbIh5OSoIUqcAEnptSC7cGmTXztyEAmaab7EEpBBUK2\/Tum6S2jLZmd\/UacbWkpUap3PSG3G3CYiDVhZixxtpCvxP1qjM\/ZuYaS4C9361qqcSMI8mcsyp2T0ikPEe3ZBdOwO9UTiykJuFARzVj+IWbm3luIQuZnrVQXt+XXSomZ71W5ajHwaDBU35JbhuN39s4kJJJ5BqzMtZzvWEoD7hSPjVQrWtmHATSu1xm4aUPUSO1RHhRs8jUqKW+5xNM4Ln1wqSS\/v8AGrPy1n7SlAD+\/wAax3heZ1oI9ZEVOsDzqUKT+MRHvUO\/D7VpFbnRhKOkbcy94gOgJIfkfGrHwTxBSvSFvRWK8tZ7KggB7jpNWFhWeikp\/EI+dU04uD0ZKzOsxJ\/SzZGG5vafCQl0dOtSOyxxLoELBrK+Vs8OPqSPNPMc1ceV8ZcuUIOqg7jQ4HVnkaRabl+FIO\/SoBnp8m2djsak7DpW1vUQzukrtnP+k053Gh721sxt41uEPPE8Sap7wlzv\/wCDfEe2U+95drfKDDhnYEn0n61cPjOwsuOgpgbzWV8zW7yLkuNKIUlUhQ2IIq0rhHIplVLw0ScXJnjWwuh5i9m5M+FN5iFpiql6hcsBBM8lJ2\/RX6U34C55Dq2kq49SZqH+FXiTh\/iH4bNWd6+j9uYMUofbUoBSkjYLHcEfqKldmoN3TKwYSr08VgraJ0t0zXKPZsfJryFG+p8SSf8A+\/ItfLuKeptBUDr6TwasnCHp0k7jpEb1R+B3hYfA1hW+29WpgF\/5jSNSpATI9qqpx7JbLmD7oliWj6WxCZJjeKS4viS0NqI5iQAaQ2t0V+oKITO88mm3GbhakqSkkmNp3mhnY+3QNdSc9sZ7u8ubgvPAHZJAPt1ou2vWV4c2AtBBHE9Kc2sLXb2qFughREwR3qq\/EjB85Ya4q+yHoeC5Ltm8uIPdB+uxpKae5Ds7km0W3k7ydXmhSQEmBFWWi\/Qu3JKgIFYnyb4x5zwHEl2GbMt3FklJ3eTKmvn1FW2nx6wNduU\/f2t0yRrAirKpSojpFdeo5T3vwWTma\/tFMulahxVL4hj+H2t06628ANRneo5nvxkXiFsq0wE\/erhwEIbZMk\/0FQHLvh1m3NNwL\/OuYXbVha9X3S1GnYniTJoo0uUXKfAayI1tVx5LvyY8MaxC4xYkBhlOlCh\/q6gf31qe4VfqbuDrk9tMwaguXba0wGyZwrDUBq3twAE8\/Enfk1LMMebfQVmUGRChuPjUC6r4JMblLyTJ27lAG5ncbx86jOPXultZGqPh1peLlpNvDkApPpUo\/wB+1RXHH5DhBG2w35ppy8bDrS9ivc43illQSAIBE96gGLkt2kJ6jcVLswuF14pESZJ24M1EcaUS0E6ZJEc1aYq4K\/NkM6FFVtpCoJSN+lXr9lfL2AYzheO3mK4ah51m7bQhayfy6Zis\/puNLKkwdh8YrS32YU\/cfD+5vF7KvL9xXxSkAD+dWlFLsloy3Wsp0Y+4vTL1bVa2LKba1aQ00gQlCAABRS70d6YLrFwmfWBTcvHkzBUKkW48o+xkI5UZe5KVXaepoh252kEVGxjIMesH511eKAiZ\/WocqpfA+roju7cgggmkbtynqoCmp\/Ewf36Qu4oJPqFMSpm\/Yc9eK9x3XcJP71Jl3Se9Mj2KiTCjSJ3Fx1VEU5TiTnLwNWZaS8j85dIG2qaSu3aUjmKYl4tvJVSS4xYBJhZ9962nS+naW2jMZ+drfI53uJpbB9cVDsdzK20lUuQQJ5pDj2YPKSr1ztVRZyzgUJch0gweDWwox1Wtsy9uRK6WkHZ6z42y24A9B361mXxD8RVOrdSl2TuNjSrxCzo6suJS4Z3jeqMxrEX7p5SlLJkmo+Xldi0i16fheo9sLxfGnbx1SlrJnvTOXio7k0FwqKqClJmIrPWWSm9s1tVUao6iWBcNgEoVAjmRSJCUp3Jk9pp9OUcxXSiU2pAPUmjEeG+YnNygD61Md8fkpnVOXgZRcBBkH\/il9piqkEerrTuz4WY+sesgf+2lH\/wrxNgeZcXCkp7hNMzyYv3I9mHKa5Q4ZfzBchxKW3CY96trKt\/e3yAFhVVjl7w7dcfSlm9fJmPywDV+5EySvD7VCHVKWTvJ5qiy7opmQ6r09VPuWtkzyW2+HEatUTNaMyKhZbamqgyzg6WXEejrV4ZPYCG0CI4qtdu2SOj4jg1ssGzT+CJ7c1HM3Ma7dYHY1KrRH4QjtTTjtmXWlEiaKVukbOFO0kZD8V8BXcrdhMzPArNuZcnOLdWdER2FbszjlVNyVlTczVRY54ftrcXDAE8bUVfUVWtFti4LkuUZHs8DxbAcRZxHDnXWXW1BUoJTqAMkH2rUGDYoMQwm0vUyStAX84ppvfDtAUT5HvxSnDcPfwRgWhB0BRKRzHtUXPvWWk\/dGr6KpYsnB+H\/ALJzbXGppDqd9JExVhZbxlBQEqcMgSADVUYNeAteXGxMbbGalmBuKQ6IhTexnt7Vncivg3GNami4rTFE6UhBbjSQog+1K7dKbl9DrhACPypNQrCbrQEBJIb2G\/f5RUltcSbWdalgqRsIMCocIOT0P229q4JLcOoWyoEAGIRO81DMWZdKlFCSY3pff44lhSQVTqG8Hgf3FDw99i8EpKVhW3zqYkoogObZDLrCHLxQS7aB2eZTNVtnrwbVjF4XsItRauL\/ADadgo1o+3s0qJhtJ+Qr64wRSXg55YJkbAd\/4U5C6UVwNOrv4ZQ+SvC05Rs4etA5dK\/M5pn5Cn1y0eQqNB7SBEVcycDDjZUtBCTuNQ3FMOI4XZJWpYKEwoyTuKF3S\/iHYVaX0orxFw8lQBKthEH+NS3AL9LjKW\/L6yf7+lMWYG7azSotlGpW4EzSDCMw29rJuHENFOw1Ko+JoR91bJ0\/fG3UkuOkb7AiQO3wphxd1x1tzWoQBII+PEfOme4xh67uoYuEqCjOwmT\/AGadb9tTeGofc2JHVPBioltWnsl03bK\/xdHlOKVH5jz7fyqF4y4godPCQdvjUvx1+XlrEmBsD155qDY6+W2CFH81WOKmQsyXljCFSggyConrxWnfCW+Yw3w5wplpX5kLWo9yVmsutpK21GBCQeO9W1lvNKcIyzZ2anQCwgpInjef51sOgURuyHCXx\/1Hm32yvnThRnH+b\/jLcxPMQAJDsUwrzINeznWqxxPxAbJKfPBT8ab7XNqLhwfi9e9a+\/pEGvB5ti9WnJ8l12uNlYHr5penEioSFVW2DY0lwA65+dSNnFUlIhVVz6NF+xZ\/tUkT+IEj83ypvuL9W0L3\/Smt7FGyDCunekFxiiIkqFcuix+AX1bQ5v4go8KP\/NIHcQVuSqR8aansVQOVRTa\/iyInXJJ70\/V0iEX4GbOqtof14ks7aqR3mJHQfVuKYXMYG8KG1Nl3jKSCnzOelXmPixrRR5OXK1ifMmJuFKglW9U1m+4uHQv1GTO9WLjOIocQoax8KgWLobfKialyS1wDjb3sojNmHXDziyQqe9V\/eYO8FH0EmtA4rhTDpUEp59qil9l1gkwgdTVTkY6n5L\/GyZ0+Cm14S4DugzQRhykndJqzLrAWkSSkCKZrvC2UKjSAarbMaMFtFvTnTm9NF\/YXmHBCgedZtKO1SSxxjL7kAWbQrP7WKusn0OmBtzT1h2ZXEqAJPxBqsuwpeYslK2S9y\/2rvAQPTZt0F1OEXpCPuST2iq0wfMhWUha5BgVZeV\/udyUurdAPMGqm7vpK3qGZfXHVPLHjA8sWKSHUWwQZkTU6wmwbaACUgGO1J8Ot2FNpLSgRAp9smwkg9KrLJyse2Zp12ZNndb5H\/B7YJUkhI6VaOVU6Eoqu8GbBWk7RVkZeGgAHmmG9MtMaCpkT6yI8sddq5esBTZJPwouxeASNxxSl5YIiunLaNNizjLRCMbwxKkqOie9QPFsHSCohA2k\/GrUxRAWDAqG4qzuZHFV0298GuwoxaKzv8HaTILYk+1RnF8FD1u4hpI1jdHsQKsbErZSiSE80w3FkpSvyDahjKS5LSMdcoqnCbrRcm3WYKTMdjNTrLd+G3k6\/STEiajGdMCXhN8nFrZpQQ8r1aRwrr9aUYXeFxKLhLk8TsaK+KlHuXhl1h27WmWvZteYZbglQ6dDSG6xNzDEErBUpBkJ23NLMoXrV1oBHqA361L3cnYZiz4cumUEKO4PX5VBqai9Mm3c8lFYzn3Mb2JM2uHYFiFwtZJCWkTqEb704HxXxPKbQTiGTMdS7yCm21pPzSSK0XY5cwPDGEosbBpBCdMhMmm27w+zt7gr8tI1GTqTU2Lqk9NDNe1zNcFJtfaEzCq3Tc2mQMZLZ3n7qTqO3Sn3BPtSYQ2RbZnwDEcLWvbXc2TiE\/MxA+tW3aW+EJcKnba2KeUkIiDz\/ABJ+tIMRxTBGnvI\/ZFuqN50D+\/8AvU2NNPsiWrsXxL\/ZVWYvtF3GMrOHZHwq+xAqMeaywSn4hSto+BqNXLfjjjziVtWn3ZtwTDjpJB9tuavC2zBgKX1MpwS2bUkBOvQJJjkmJml72P4ekqLriQ2ANOmYEnYUfZVFfh2zpZNCX0f7M82Phn4n390V5rzQ\/b2og+VaNJ8xfsVqmB8ADVgZc8CMtvvocv2bpxs7qL126sq991VNmMRbxXEVJQyfKbUQTE6qeV3gQiE7dNjUG\/I7XpDHarvqIojw9wTA7g\/ckBDSeN9Xy3+FIszMhNkUp9OkbSZmpHeX5eEIUlQ1CTE9Nqi+Z3U\/dnFOOAEpMbwKgTsdr5CrgqSpsccIdWkDTIA5kAn+\/wBagOZX1PXSLNmSdW4HSpjjN4UKddT02+FMOXsG\/amIOYg4mUhWlPvBq1xo9vJXZc+59q9wiwwNQZTqB9RG3eq9z74iKwTNd7logoDQS40eJkcfpWg2sFSoJUE7IkyetZI+07lm\/wAuZzw\/MapNvj1spTSoI0qbWUkVpPs\/N\/eXr4\/6jLfaTGrsw1Gfu1\/pjfeeJjofLfmkEHeD71IctZ8U86krdnfmapV9FviretayzcpgBfRXxFfYde32EXHlPk7HYjcGtrPMnH8R5wujwi\/pNi5fzqgNph0dOtShGeGkJnz0\/I1lLCc6ONtD8QzA607\/APj5zSAH+nQ0q6jHXJHn0ZtmkH89tAf5ye+5FNtxn1sAkvCPjWdrnPrw\/wDXJB9zTZcZ9dM\/jEfOufUIix6I\/c0PdZ8aM\/ig+0013GfkK9PnbjcVnu4zw+on8VXtvvTe9nZ8yUvGT2NA+pfAf7F\/oaDuM+oAJ84duabbnP7agR53PY1QLuc3lb+YT86RvZsfMnzjPeaF9SaO\/Yq+C8L7PCFTDs\/OmG7zk0qfxAfiap17M76iYWf60idzA+STrM\/GhfUpMdh0hLwWreZqaVJKxzTRdZmaImRVbrxt4n\/MUJ96IXirxJlRpqWc2So9N0Ti7x9Kvyr5pkucUStU6iajar10\/mV+tAD61nc1GnkuZKrw1Dlk7W2QsqUaTi\/U09IOw7Upu3IaK\/jA6zTUpOhHrJMc1PaXhjaj7ktwfMTTbiSFEGeDVk5aziELSnzuI2mqC80o9QXBG43pwscwvW6wQ4dveoOTixsW0MXxl28I2TlfOLaggF0QferHscbtrhpJQpM7TvWKcu5+eaWhBePTrVw5Xz0taEa3TtHJrOX4jiyjqpundtrSRqfAb5CimFA\/CrGwW7ToEKFZtyrnJtbiZdHI61bWA5kQ42k+YJ+NVWRHs5G8y9UsuKzvwAI3pcbxJH5h7b1AbHGkkJ9f0p1Ri407qFQVZsLA6onJLY8Xr6VJJkTvUav3EKBJI5oF9jiEAnVUZvsxNgmVj50TrUj0rpWUrICu7S2okbGmty1SSTMdqRPZgZUQAuRRJxtlZ2IntND6Roo2bQZimB22KWD1hcpGh1MSOUnoaqc2N1l3E3cLvR6NWkK6exHxqz3MabAkuDfrVdZyzplu7zdYZRfuEjErtla0hJ4SO\/v2oq65T3FIkV3KEltkoyjizlpdItysgdJ6irjwXFUutJC1Ekp71nu2NzZPoafP47f5VD99Pf8A4qyspY+i5Q2hbobWkDUB3qsvqcHsvabO+JbTFxOkpIJ67188BcAtlPqmZpotLwFSSsmVDb2p+tFIcbDik6SelNRbXgeetcjE\/gzhJLTikz+YTsRXGsJfVKQgq6DaYqThllawVJBHbnel1swyDpICd4G\/UVOotkRrIRS5RCxl5Rbg25+Y52oC8uhZAWhS9vjU8uENb7kkbCBtTbdIKU6lJCQvmDvR3Sm15ApjH4GFjDRasBDYTKuSnrSbE3PJSE7gCQYmaeHn0NNa3VBWw07cVEsXvHFXidSSEAkLVPWarH55J8Wkghy6bbbU8sAgCAfeoLnDHEKbU2lQSpQjuT8frRmcMxGyPlJc0gcAH9arHH8zJaQXrh31KHoTxJ6AVJooc2iLdcq02xLibjt3dJsGlHW4YnsOpqxMp5dat7RDZRCUpA4iaiGQsIexG7F7coAUrif3U8xV14bhzaEJRp2EE7VYz+hdiK6vdku9jTd4Yhi1ccA0+mAY9qrv7U3hJ\/4u+znh+O2lqTieX2V4m1CfUporJWn5pM\/IVb2JtebbuMI3UkQNupqfZnwW1Xg1jl26bSu3esDarQeCkpitD9mY7tnZ8LX9zP8A2pnqmute7b\/t\/wDTxPbe8tSVFW3WnTDsXLRClpSoTuFCdqX+J2UnciZ8x3K7ySBh9642gRH4cyn9CKizbiUuGCQD0rZNezMcnvkmbl1hlwBpw8p1byyuP0NELZs3YSzdvNGOHG9vqKYLa8daVJkiNvanRm789KSFxFBKiufOg1No7cYNihQV262n090OCfoaZLtvELafvFs6iD1SRUkQ+ylyVDfp8e1DVco0aHN9XQGmJYUf4WOK75RBnLp2YMgDmaIVcr71OV4fhrzn4tumVRueo\/rQF5VwF9Q3cbHtTLxLF4YfqxfkgqnlHf3osulXO3zqYPZDZWgqtb\/cnYK4ppxDJWNWqQtpkPp7oNNSosj5QqlFkfWszPO9FKWY22ijri0urZWm4t3Gz\/uSRSRSo5pl8BaRxSt\/60ErVsJrhPaK5O\/FDsUFrUdqGgxvNFAnmjE8Vwmif3DwcUEaoj1RFJXBrQvfeY32FDQsSVqG06aC4rT2ir5rZWLgQvoDDZUoao7namwrWSVJAAJ+VOV8NaghPMyqm18Bs6Qoc01YvgVvgW2N2tl1KySdJnY1PcFza41pSVxHvxVapd8tUE9vlRqb9SCCFVHlXGS0yJY5PwaOy1n\/AMlSAXeAOv8AOroyl4iNuIQkvidhzWHLLMbzBADhj41OsteIL1spH49Z\/PwtrgyvVsW2cdpG\/cFzk29H4+596kreZgtHpdkneRWQMpeJylJQkv7nrP8AOrVwbOv3pCfxR6u1ZyWM65clL03DyHYuC08VzGoJKtW0cCoTimZXSSEqVzNBVeru0SDMzwf79qa7qwW6qfVvMbUveonr\/RMSyMFsMTjrzxALhmZP6UrZxh0kArPWmtvClhZO\/wAhSXM+M4Xk3BXsaxh5KGmUEgE7rPYUCs75KMFts1ka3CPdJ8II8QPE6yyPgrt\/dupNwpKgwyVQVqisveF2bcSzj48YDjuNurdVd4o02oatkIUqIHyNRTxGz9i2fcZexK7UoMSUsMg7IR0+dH+DT33bxIyrczpH7YtAf\/7Uitt0zp0cWv8AeLcn5\/QzPUM6VrfY9JeP1PTPHMgkeZh72rW2PMZdTyUHhQ71AbpOLZTv0C9SUgH0upnS4P6+xrUNrgqcz4Ci0SUoxKyTNus\/vJ\/0n2qFYtlm3xO2csL+yGtJ0OtOJkA1mOrdOeFa+Nwfj9DWdI6ms+lPeprz+oxZTzixetIbU4d+\/Sp1aYqlA0pUSNgNJ2I71SuL5JxfJ94rEMEWt60G5YUZUj\/pPX5094HnJhy2DRehUwUnlJ+FZ2ylwe48o0lVynxLyXXZ3zTp2VHYz0pzQ+tUeTEp78n4e9VVYZjSypAU9Inr1\/pUvs8z2savNQZ9QAVvS1y1wFZHfJJfvgS96wpAgj5\/wpHfXjQSVACBuDO8Uw3+ZmUII81MzM9IqL4xnFi2bCvNmDtHHwp1ybWtDcI6exwxvGtCCwHPVpgCRtUKxXMRYacecf3SokCajuMZwUtbruuFGdIKt\/b9Kr\/MWblLSq3tnC++vZKQdkmghizsYdmVCqJ9m7NgeunH3V6yknSgdTTJgWC4hj2IoxHEtWkf5aP3UielLMvZTu8Sd+9XsqUv90irRwPL7dqhADe4Owq0UY0x1HyU0pTyJ7l4HfJeCi2ZSrR14O1WAzbpaSAJ39qacEtNDadQiOB3qSJQS0PRBioknt7ZYRiorganEld\/h1igeq8u2kbf6dUn9Aan+dDGJWaExKEEVDcsspxLxGsLMAlGHsLulCOCdk\/zqXZtUVY00rpuK2n2cp7KHP5ZhftNf35Ma17I8xftxYM3hXja\/epRpTilm0\/twVCUms8L31QBpjrAPNbR\/wAQnKn\/AMu5yaQdLTrli6oDopOpP8DWLTBWpEQfhzWkfkz8OUAU+tJCeg7UstrhRJTE7jjn4U2OqSlWsKiODQ2HVAlRUdvrXb0FokLNw24dzB2MUaq6bEAiRPzppbUYSSRI6z1o4uRsVHURMd67YrHT7wkwJJCTuaUNPgKhWwPczTMl4hqNXIHFKEulOnylFUz0o0IPgUdMpEAHnpSu3fcUI6cCKYWH3SZJ39jS1q6UNzsNyBRCDjc+XcpU3cMNOIHRSQTTJdZPwC8BcVbFknYeWevwpzS+helRBEjr1oXnII0gxvyf60E6oT\/EhVJrwQnEPDp+dWHXzax0SsFJHzqPXmWscsSrz7B0pTtqSJFWxrKhqIIINdXdqCQRuFfmBFRZ4UJfh4HVa15KUUlbZ0rQpJ6yKGgnYE1cVxh+GXSQLqwZcKhIOnpTNe5JwS6VrYStlUkwk7VFnhTXjkNWoZ\/vDqpDbayJ2hNGJtsReVKLN0\/LatLWfg9boISbZO3+2nm08JLVBBVbDt+WgfUR6PTZPyzKgy\/jr6itFgrfqaKPh9mO6XrLaUz2FbItvC+zQN7ZO3SnJnw0w9JEW6B14puXUh6PTV7mKk+F2YVGSr9DShvwlx1yNTp3\/wBhrbjXhxh4ibdG3+2ljPh7h4glhJ\/9lMPqOxf2ZExG14NY47v5qhH+ynK08FcfQoKFy5z0RzW27bIFgP8A0E\/DTTnb5Ew8EKNuIB\/00xPMUvJz6TCS1rZkLL\/hfj9ktJLzxE8RVwZRyjiduUIWtZj2q8LXI1incW8T\/tmnuzyrbMQUtbDsKg3Ku0Gvo1NL3GJC8JwJ0NjUgydxT2nAPSCUbcz0pLnrxRyF4b2yv2vibarkCU2rKgpxR+FZY8TvtRZyzcl7Dcsj9j4co6SpH+ase56UNPRJZXMVpFgsyrCWvf4Ls8QvFXIPh82tF7iCbq8SNrdlQJn3PSseeK\/i1jfiRif4yixYNk+RbIOwHv3NRrEH3Li5U\/cXjr7qt1rWokzTSUS4pWrrNXeD0bHwZd6W5fP6Ffl9Tty12+I\/ABzXo0hau0R0p9yfdjDcyYPfgx92vrd34aXEn+VMiTrhB3PFKW1lASU\/mbIUCB1FW6KufMWj3Hy8ViztMTtidflpUY6giadsxYC3mC1GLYUlKL9tMqRMB0f6T79jUe8K7xOK5FwS9SrUH7FlyfigGppYhTLhU0SPam8qiGTB12LaY1iZNmLNW1vTKnF5ZXqnLG8ZLL6DocZcEKSfh1qA518PkuOKv8MJYdnUCnYH4960TmzIuFZuY+9ISLbEW0w3cIEE+yu4qoMXt8xZVfVh2NMlSCYQspJQsex\/lWEz+m24Mu7zH5\/U9D6b1SnqEdLifx+hSFxi2ZcJcVb3jktzsQDt8KJts\/XqSEG6KVoGkSDv+lWRjuGYZi7ZKmw0sj5VWeO5SctnS62kFM7QdjUWHpy8xRPm7YeJMVXuecbuWZW8gnTEJBimO9zJir+5WrbjaP60oscMTIS8jYH4RTmnAPNPo3TwJp9KGtRSI7tt92yDXDuMYi4U61AKO9SvK+TW0rFxcty4eJ5p9w3K4YVJZTJ3kmal+F4KoDUUAe560EuBYxcnthGFYOhlEhAAI6c1IbGwAUPTEUY1ZFBAQDA69KcLG3Tr3SSJ69aZmTaopDnhtvpSCpPwFONw55DC3VkRB9uK+tGDoHUx1qHeMmaE5ZyfdLaVNy6nymkjkrVsAPmabhW5vSClNR8kp8A9WNXeYM4OgeXcXarS2PTymvTt\/wC7VUmzL+LjqWwTshRo\/wAGcrDKvh3hGEkQ4i2Sp093FDUo\/Mkmk+Nf\/NLQn92D869H6fSqKIw+EeYdQveRkSs+WZ++2DlQZm8EsecRb63cOLV8gxunQoaj\/wDSTXmKpXrCoH04r2XzpgzON5SzDhFw0HG7mwuWFIPUKbNeOeI2qre9uLMkhTLqkAfA1OfKTItb5aGq4AMGJ680JhJiSgiefejX2EqUCQSRvFdZQfSkTp3HzrkmO7FDLeqE8HmlXkIUkOJV6utAYShao3UTxSxpJMN6E78yKJRB2EJQkmYMnrxR6ULDggAAbb11xBKvOBGkn1AAwPehJDaxJIkbnfc0jWmKGJCkbCClPO079qPSYXskAmigdJHpBjmf+KPQrUQJBMSTRo5vQOdJ3MJHA4g1xsgCVbiaCpaUDZQ2MUFS0xsTv2MUTB8eBQHlEagr27T8a6XSU6SDvzHWknEaSZPJB5oTbqjCZgJ2A2\/hQi7FiHULT5alHYRB6UKV6ZkATHfakZcBUQFKCiOm\/wDzQzcJKZnZMCVDr8qTQrZv9vLrhn0QJ6ClbOWpgqST8alo+7o7GuLWgqgJn5V51602btUQRH2stNgbpmlrGXmQd2\/0p1LmlO8J2pMcQKCBM137yfgWUa4eT5vALcH\/ACxSpnA2eCkCiE4mQRBo1OJK29UfOh9OwDvqF7GCsdEjalzeDMJ2CRTQvHG7RtT908httA1FSiAAKp\/xK+1flvKrTthllxGKX4kakqltJ9z1o6sPIyJdta2JPKopj3SaRduO4llvKOGuYtj1\/b2ds0kqKnFQT7DvWTfF37V2I4sp\/BfDycPtAClV4ofiLH+3tVJZ78Vs3+It4q9zBizzzRVLbGqG0D2FRLzSok6yO8GtR0\/okcf6733S+PZGfzurO76KFpfPv\/6O395cYjeOXuIXjtw+6okuOKJUTSC5WENKKPjvyTSla9c6jyKR3Wn0NgEhO5NXrSS0im5b5Gt1PLhncTuaRFoypQHWnZ9KFoO2mdqSONDQf3SN9qZcQt6G4CFRtAPejxvGmSCO1dUhISQkb8bfChIbVB2O253pO0Q9ivsq4z+2fAvKN6V61fs1ppZ\/3IGk\/qKuSxUC6TNZB\/w6s6ftvwkustPPAu4FfKbQmdw2v1D9ZrXNootuieDXTXJBjwtfA76VNnUJrmJYVh2P2K7HErZt5CxBC0zRzY1oB5oaEqSr0g0zKKmtSHoTlBqUXplJ5z8JL3DQt7BEm4Y3IaUfUke3eqjxaxurRxaFsq9JKVNLG4Pwra7Vom5TpcSCD3phzL4R4BmNtS7i30PEbOo2V\/zWbzOix330PT+DWYH2glpV5K2vkxa3bWV66WWgUOjlB2NOtlglwhXqSoCeDIqzs9fZ8xrBULxHCgm7Yb9WytDifgOD8qhFtmW+wlf3O+tFa2fSoKQUqHxmqO2i2l6mtGjpuqyVup7HPCsD4UpKRT81haBsUlUb7cCkeFZrsLshJUGXONxtPxp7XdJCZMAd+lNJP3HnpCc2qUpB0gniBSixtvWJTt7niiA+lxxIQdgeelPNlb+gEjmkcGxxWJLQqASxblc7ATVEZtuLrxD8Z8tZIsVLUzaXH327CDwhHE\/ExVu5vxtrCMLfuFqgNoJ3+FQXwEVg2S8IvfHXOVu+43mO+XbWjjbetTTDayge4BUFHbsKtOlYruvS0VPV8xY2NKTfL4NW2GGps8PbZSPyIA4qt83oLOYm3htqRIHwqwcsZ6yjnuxU7lvFWbgpRqU1OlxI90ncfSoHnqEYhbvq4SopJ+NbatOLcZLTPPpNS+pchbDKLl59JSCl5O47yK8hPGPLQy54rZnweAn7tiTxQOySokAfI1664epbbzKh2KTXmh9r3CU2P2gcwhLZQH\/KfJHUlIqRFbTR0PxFCOsqmIV6ASaISgIUIUAomAKd79iAFJmDsR\/xTaloJJKiQAfh9KNINsNZhKQQn1TuR1NKE7xJOxIE0Un0I1EHYkknr\/OjGnQAlW3PQ0ukcKitH5FpAAjVtuRQHEpbMgAIXugEyY7V8k6laFxuN5nigr8ssBLm6SRBVyk96RraCTDZSgwpQkiJ4o5SnEbaIkCJEmkbK9P4LqkhcjeeR0NHlTiyVFJUpPBNCL5DNS1Df1A\/WukrSkEaUkn4UEJcIGpQKTvHMV9MJ2nUOev8aJAnClUCZlW0xFcIWFhAkHkiOd+Y4oRVJKUHaIjuaL8xRVvJ0j4\/oaTwcGTsFcbz\/fai1OKJCiRqjvP618STtAKQBBnj+lEurWgFQSJ4+NIKeox8lvkD5UQ9fJQdKIim125ccXsqvk27rm5VzWGhir+I208n+UMdunXSQATRYauHN5ijE23lDWt0JSOSVVC87+MWT8jsKFxfpuboDa3aUCZ9z0qXXR3cQWyDdf2rumyZ+UGklbrkQN94iq9z5435QyS0to3qby8HDLSpIPuelZ+z99ojNGaC5aYe8bC0Vwhv8xHuaqK6unbx1Tt0+pa1GSVHk1a0dL7vqt\/sVVvUPassLxG8cM3Z4dUwLpVpZT6WW1QI9+9VwXlElSlGSZJImiPM0qIKiRzRbjxIMCSBG3NXFdcKY9sFpFbOcrHuT2w5S+FJ3jbbg0T5hkgyKKDpXBKjt2\/nXwUeUwe+3FFsDQclwTKz8KJQCpxbilbflrrzpQ3I7d660gsspC0ydOo\/GhfISCVo0AJnmZ96JUhWkAiY\/KQaOcUFGAiP1orUVk7RBihFE6k7kaZ\/rR7TZKZJTI96+AUd1KPeYpQwCf3O8jrS62J7mrf8OrMy8L8V8WyuskM4vYeYkTt5jSp\/gT9K9K1sQkKAjavH77NWcm\/D7xtytma6UU2bV6li7PRLLoKFE+w1SfhXs23aN3Vs280Qpt1AWhQOxBpu3jRF7dTa\/wDJzCkB9iCNxzTj92GnVHFNWHLVZ3ZbXISakaUoWySneN6jSeg4o+s0AAGnLzG22\/MWQEpBJJ6AU0tO6VRMUx52xz7vYIwttyHb3mD+VvrQem7JaQfeoLbI9i+cbXMWNG1Ye\/8AKWyoQgf+oofvHuO1SlVllnGMNaRjWFWt16An8dlKo+oqtvu2GMvNhaA0rUEpWNiCTAq37QW7OGfdrVbLn3dISSs6lrPU9qPNVdMIwa2DiTtnNzi9FLZ98AMNvbdzFci3ItLlMr+6qVLa\/ZJP5T+lVTgN1f2uIqwfGmVtutqLakuCClQ6Vri2LTpUUNgFB3A7VUPjNlVlh1nOFraDzULDdxpESnor5Hb51Q5nToOHq0rX9DV9K6tZOxY2Q978P338DDb4W3s42yN+sU6ptlst6yiQO1JsvXbWJ2yPLBCoHPNPeJtGyw5xxZIAB571SuBoJScXplH+KSL\/ADjilpkDAFlN3ibgZUocIR+8o+wEml3jhY3OCZHwTJuXmlN4JgzLVqn0\/n8sRqPxMk+5qZeHuXF2asT8Qr5tBvMScNjhuv8AcaBhxz5nb4J96f8AN+AKzHgNxhjTaVBLerWocQK2PQceNCV015MV9osp3zdEHxH\/AGZ7ymu8wFKcx2l27b3LIlpTaiCDHtzV1YbnQ+I2XHLu5QhGLYYU\/e0oEBxB\/fA\/jVM3ra7ZhnDkpMBUERTr4eY69l7MzOIFBVbuqLNy3H521bH6c1p74+vByfn2Mhjz9Cel4Lpwh8vW4JO4IIFYC+3jg6sO8Z2cVCVFOI4ay5sP9JKf5VvlNt+x8YXaJVqtnh5luvuk7j+lYu\/xBVNKzrlR0Ea1YW4k7b7OH+tV0OWy4T5TMlvaS3GyRpnfimZwJQojZRJ2PQe21Oz5LRMJCkdNJiKb3yuJVAHEk7xTnA6J0Ohc7HgCY4Hzo0awNLYhJ22IFJ1JhXpUkqiD12o1pXpH5pI4gCkOQe2SjdS59tVHq1Foa9x0AINEjQmEQrcTO0UZ6i3qPA2Ow3rhRMowlIEpWkyn0fUTSpi6S8krJ0kGCODRKoOyFJM7xJG9Fayyouxx+caud+aFioXo0pUD0mZ\/5oaiCSoggHgkT+tEtPNuo1pImZjihySkKO2\/wpEcd1TIESkcjeKKUJkbn353oRK9Mnnjc9PjRSlEqg\/EHmfpStie4YtQJ0k7noeDQCrV8TyTQDIKN\/bUo0KU6TvsAN6TYrPTAoZt0FbhAA3k7AVAM6+OGTMmpW0u9Td3YGzLJnf3NZy8Q\/tBZqzc45aWDxsLE8NtmCR7mqodunnll19xTijvqJkzVXT07f1WstLuob4rRaPiD9oPOObVrtbS5NjZKMBtowSPc1WFxf3F0ouP3C3FnclZJJ+dJ1uAesEe80nU8YOr0+4qyrrhUtQWiunOVj3Jhy1qUdQnfkUU47uADBA6jmizcemSokgUQ484TPXpG1E2DoMXcAiJlRosrKoSUzydjQAZSAob0IawAlEb8TXCMMBiCkAK25PNdSJ9QO\/MUEalAqUkwOK7q0\/lURHNJsQJcSXXm2QAATJA343pa4SpGngg7z1FJLbSXFvJGogwme1KVuFQA2mOg35pPIoWomRJA964pI3JWRPcc11bcrMqUDtzwK+UQtIRpmD1MUovgJKEatOwMQZNKGPRp17xtzyKLhIUsR7dxQ9KysbRBkb70oI95cxD9n4tb34CXC06FFKuCJ3HzE17MfZqzMvHPDrCbS8u1XDf3RC7F9RlS2o\/KT3Tx9K8V2QpATP5zuTXqN9gDNLmZvCcYO85N1g7vnW56lsnStP1H8KSxKVemMT4mmjV2JYfB1gepPNcsblba\/LUdlDrTg08L1nSf8xI+optumyz+InoYqAueGG1rlH3nguFJOxME9h1NVvjT+J4xmO6KHmkFBAZadBH4cbEH3qaXD0B4qOxhkfPc\/pUdzHgr1w0jEMOMXVrumP309U1Kp+l7GLV3Ih+Pqu23k2+IW62FJg6gNSSenFS3LOP5husPTb2jrikD0rV93lSh2nvR2Ev2WZ7JCH2x56PSrb1JUKkuF5NUpBU3ev2xPRogD6GnLZVyWrF4ArhJPug\/Idgrl62worZhxZkhxWkxSbNTNli+AXuF3eIWbbr7K0pTJUQqNv1pcch2ylhy7xjEFgf7wP4ClDOQcuJPmeU66e7jhNRZeg1y\/7IlVu2uSlFcrkorLVjc4LfIQ+SmTBCu3tU1xK1GNtIw5lYT5my19Ep6n6U\/eI+VbDD8vOYrZW4SuxBWoDko6\/SoPkjEW8fy\/eXyLt1qXBbpW0RJ6qTuD0jis8sGP3hUp8M2dnUndhPL1prh\/mOCmm8SvrfBsKbKLGyQGkEDYAdfidz86klxh7DCGsPZTpDo0qPU96My\/h3lNnyLYpSNkyNz70rfZcTfp81BSUpJE+9aaKVa7V7GJlJzk5P3K0zX4RYdiL637QaHEoKgAOsVWGX8m39li\/3a\/tlAoUeRsa0y20t9a1ASOKcF5Rw7EWgt5hKXANlgb08sx1rtkMyxoze0VveYYp7A7V4JJdsAI7lvqPlzWKPt94DcqcyxmZST5epVoFexSVf\/jXoVeZcvLJ1DCWlONrlIUkd+9ZR\/wAQvLPkeGVm00wScLeZuVOhPGpZQR\/99DTNTlpe45pxS\/o0ecDqwAUH1AkCIpseQUKOogBWwJ6Clzx\/GVpG3HvSe5QSACAU7iTTjJKEDqSdwRCRAMGhgBSQfUTuDCRIrsEEpKjtASYigKStKwV9OYH6UqezvAaPUANJgdIG1GGFhQAIjf8AL0ohKdCSTJH\/AEQZoYWmComVK4JFKcfbGOnaDB+hr5aTJ07dtgRXD+XVJBIj0n+tDREbkEdyII+dDs4KZUq2WUaVeUTz1BpbpC5bgdx0pvu3W0NleowAZnpRlpdrdtWnCPVHqV0\/uKTwL5D3HHEktoG\/JkxFEoWZLSx6hPH9aMLgQlR0kFR2n+NN13cuqctSEhMqVxse38qFy0drYvBTpJMnSdp4r4HUNQIFF6iEAKEyO9c9QBjiaVeTtCxT2kg6iPnQFvkABCpnekyoO8xPTpQFORCVJiON6VsUML51nUdM9OlAW+rcAyBtPSk63pMFRn4UAKXESCJ7UOxQSnJH5gP60NKipZWYKh1ii1bmTtuK6lxSRM\/pXCNhkgkH1GhkpJIT8xRQd2iSO5NDSDwDPtXex2w4OagAOB70S84A2QhRkkBPxNDGyoj\/ANomiQhbl2lOj0o3Irt8CC1lpVs0GHU\/lHJrklKgVK36dhQXHN4Kt\/nQQ4rSdzPPFKmcw4JVI3mdhtRcBZJCZAO5rqCpUqV+TsTxtXYSJAUACYImCaUQ4dZB2G5iuNpWp1OpMid\/jXyhpGgAQNyDRjJKFakmCJjb9K4UUFTgJISCEwBNbl\/w485jDsVusGddAFvdpbcE\/wDo3CYH0cQn61hpBWVpP7s6jIq+fsb5hVg3jC3hwe0JxexeZTJ\/9VuHUH4+g0uu5dvyMW+N\/B7C4hbuWoTeWxgoM+0UnduG7pvzm\/yqHrH+lVLsCvW8ZwW2u9im5YSr5kUyXDD1hiBDaSUr2Kehquhzw\/KCl8obb8lLqGCNkgqV\/wBR\/wCIoKFlsid0mjn2nFuLDyYcJJIo3DMOcu3\/ACyDoHNSu5RjyNa2xjXgt1YYw3j2CoCgpQ+8sDhQ\/wBQ96s7Dblq4t0uITp1AEjsaT2WGMWUAJkUc4x93X94tk+n95IqJbYrOB6EOzkXFKVDSoUlcadt1a25KO1HNOh1IWmjQUrERTKfaOa2Ib9i3xLDLmzuEhTb7Sm1g9iINUt9nWywtnBsbydegHFMu4tcNPtq\/N5a1amnI7KTAn\/aavF23gEoGxG4qhceytmXLXj\/AIDnXKzJNljrLmG443Hp0oBWhw++xAPsB1o4x\/jj5X+mP1zbhKmT4fP\/AJX\/AK2Xo03bWbUNtpSB7VEcSuvvN488k7E6E\/AU8Y3fli0IbUNSxCfnUebAU620mYMA0\/VHX1MhzfsSS0w5Ddm1tusAmndCEoQAOgpPACG0jYBIpQTAiajSbl5HUtHy4ke1Uf8AbCwBnHPs+Z3bWyhTjeFLfSopkjylJc\/\/ABq7VkSKg3jThgxnwqzThh\/\/AJOE3bX1aVTmO9WRBs\/CzwvfQErOwkDakbmmOojp09qWXYCVLK0HsBFILgpJhKhp7R1qzYqfwAB1qhRTPsKL3bXKxrTOxPT6dK+BmSuVKG3aPpXdYEJWADsZB3NAgmcKZkqA1HpBgUGAHNege3qIoa21pWFIV6V7kUBMncFQETBMkUaYLR961HQ0SpPfYivgoAiB1jY\/yNcc1KkdeeNzSZ5Tl0fu9pAjZ1zt7D3\/AIUMpaOS34Cn1Lv3DbNEBpKvxVgQVH\/SKcEJCEBsDSlOyR8KAyyi2bSjQAEiBH9967uJJO8TJFAt+WF4OvK9OqSesDpSF9IVd2iQkgpSTued6UlatOlR55pOpRcxFoGDpanb3rmcL4gKI3B7n+FfFSimBEn2oCYA9LaoBgRXAXCJjn+FObBCS7oT6o9tqJeUowCSZ3gjmuqaUpJ342NBCYElXcGd4oQz6FaIPQzvXQAEgkgCevSgJh0kgyEDauKUVK0gQRzSHA5BOytuZr6N9R+org2mSSa6Dxq32pUCwaF6kx1BO5o5vnaB0ohBUoFIERRqdSiNJ9Vds7yCUstalKVGkTIr63B8vzHSD5hKgSrcUQ6pbim2QZCvUT7Cj1Oa3IbMAbVzEBBUggr6bCa6CkCdUkCKClREpjed9+9GJTpBCpme\/FcjjqDMyoSBzQ1JKSCIgc0AKUZUSontNfKJZBB5O5BNEcCKFwYJ3PA2o1CQkbgQmSPlRCFNr9A2VE9aMa9bg1SOBHSa4RsUohKON+87VLfCjMKst+JeWcbCylNtiLPmH\/apWlX6KNRBa0g6RvtG45FCaeW0pLqBC2lpWD2g80fgCS7k0e7XhDiovMqtMlcm3Wpv5TI\/Spq40grDhQCfcVn77J+a149lCzdWpRN1YsXG4\/eKQFfqK0GNxBqsvXbYxa33R2Ib7C7a5UHSgT7Utw+3trdvQ0gA18CIg1zSUKkU25NrQ5pb2KyBxQAS3vyOtfIdCkya6R0NN+AglxosH7wyCUHdSR0o5t0KHmtmR1FfNHRKeUnpXHGVNfjNRo6ppd74B8CpCgtMpG1NV7Za3nHGxukUubc0J8xElJEkGkj9+hq2cunCQkgrO3SihuL4Oen5IxjOtTluhSj6ApRH8P50ms\/VdNz\/AKq+urhd08t9X75kDsOgrlkCblB95qc12xI29yJigzB9qP5FJ2IKAT8KPOx3NV7JSAq3Me1MWc7c3WVsTtonzbdxH1QR\/OnxROvim\/GEhWGXKD1QaKviSYkuUzwTzDaqscVvbJRhVu+ts9SCFRTK\/wCUvhQKgZqZeKlqLHxCzJZECGsVuh\/\/AKKioW4hQEECFe399xVvMGHhBRkbiExsCD0oJGoJJbCiYJPWgKX3PO0xXUELXsocnp70zscFCFnYL47ztRTyEgyVqkbyK5r5KVEDme1BdVpJlRncUojCnitQSA5pB2JEz8qMQlppCUthKegHbfmvgSANUGYiP0ooafOCSkyRPNd\/VnB6QsrjnadqCApKVQBA611tQElUgDbY\/WgqIIVA2\/71xwW6oKBI54ohPrxIaiDDQ34H0o5wBKFAJHG8UmbX\/wDqKpSNmwNvhQPyjmOSTvp2CZnnigk7CCPj3oIKgnUoQBz7mgKXqHO3Ap3aBP\/Z\" width=\"304px\" alt=\"multi-scale product analysis\"\/><\/p>\n<p>Then, they generate a relationship between all relevant variables that match the observed outcomes. However, this introduces possible <a href=\"https:\/\/wizardsdev.com\/\">https:\/\/wizardsdev.com\/<\/a> ambiguities in the perturbation series solution, which require a careful treatment (see Kevorkian &#038; Cole 1996; Bender &#038; Orszag 1999).<\/p>\n<p>The materials in question are heterogeneous in nature, meaning they have more than one pure constituent, e.g. carbon fiber + polymer resin or sedimentary rock + gaseous pores. Npc is the number of retained non-centered PCs for approximations at level 5, and npc is the number of retained PCs for final PCA after wavelet reconstruction. As expected, the rule keeps two principal components, both for the PCA approximations and the final PCA, but one principal component is kept for details at each level. Alternatively, modern approaches derive these sorts of models using coordinate transforms, like in the method of normal forms, as described next. This term is O and has the same order of magnitude as the leading-order term.<\/p>\n<p>The simulation results on the Matlab platform show that the algorithm has a good effect of enhancing details of images and suppressing noise signals meanwhile. Corner detection is a crucial problem in computer vision and image processing, since many applications rely on the successful detection of corners, such as feature matching, object tracking, robot navigation, and image registration. Over decades, a large number of impressive corner detectors have been proposed [1\u201312,19\u201337].<\/p>\n<h2 id=\"toc-1\">Alphanumerical scales<\/h2>\n<p>Some of these techniques aim to homogenize the properties of the local scale; others attempt to capture nonlinear behavior via curve fitting and progressive damage approaches. Many of the most famous techniques, such as those evaluated in the World Wide Failure Exercises, are related to the analysis of unidirectional composites. The key is that the user must be very aware of the assumptions and bounds of their model when employing one of these  techniques.<\/p>\n<p><img decoding=\"async\" class='aligncenter' style='display: block;margin-left:auto;margin-right:auto;' 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\" width=\"300px\" alt=\"multi-scale product analysis\"\/><\/p>\n<p>The weighting of different criteria therefore not only shows the difference between options but also how relevant this difference is. For example, safety might weigh less heavily on the buyer\u2019s mind than maintenance costs, because he considers it less important. This effect, the relative importance of something, is something the car buyer also notices when he has to make a choice between cars. This is similar to comparing temperature scales such as Celsius and Fahrenheit. Both scales may concern temperature, but a difference of 1 degree Celsius is greater than 1 degree Fahrenheit.<\/p>\n<h2 id=\"toc-2\">Camera Model Training<\/h2>\n<p>Consider transferring direct quotations to Wikiquote or, for entire works, to Wikisource. Making statements based on opinion; back  them up with references or personal experience. You look at the image in frequency domain and divide its data into octaves. Stack Exchange network consists of 181 Q&#038;A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. One technique used to account for microstructural nuances is to use an analytical equation to model behavior. Engineers develop these equations empirically by witnessing controlled experiments.<\/p>\n<div style='border: black dashed 1px;padding: 15px;'>\n<h3>BILL.COM HOLDINGS, INC. MANAGEMENT&#8217;S DISCUSSION AND ANALYSIS OF FINANCIAL CONDITION AND RESULTS OF OPERATIONS (form 10-Q) &#8211; Marketscreener.com<\/h3>\n<p>BILL.COM HOLDINGS, INC. MANAGEMENT&#8217;S DISCUSSION AND ANALYSIS OF FINANCIAL CONDITION AND RESULTS OF OPERATIONS (form 10-Q).<\/p>\n<p>Posted: Fri, 03 Feb 2023 21:09:13 GMT [<a href='https:\/\/news.google.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?oc=5' rel=\"nofollow\">source<\/a>]<\/p>\n<\/div>\n<p>The present work generalizes the family of Curvature Scale Space descriptors in order to increase the shape information quantity to tend to the completeness property. For this, a more pragmatic criterion is introduced in this paper which we call the almost completeness. We define it as a pre-completeness for a given resolution of features. Such descriptors are formed by the curvatures on the set of curve points obtained from the antecedents of different curvature levels.<\/p>\n<h2 id=\"toc-3\">Multiple-scale analysis<\/h2>\n<p>This likely includes customer satisfaction, job satisfaction, likelihood to recommend, advertisement favorability, brand favorability, and perceived ease of use. One of the principal criticisms of using single items is that internal consistency reliability cannot be computed . While Cronbach\u2019s alpha is the most common measure of reliability, it\u2019s not the only measure. To see whether the conventional wisdom that single items are insufficient, I examined the literature for studies on single versus multi-item scales. There are a lot of papers advocating for multi-item scales , but quite a few showed times when single items are sufficient. In this section, we will define the concept of multi-scale curvature product.<\/p>\n<ul>\n<li>A new image denoising method is proposed by using the dependencies between the non-subsampled shearlet transform coefficients and their neighbors.<\/li>\n<li>For convenience, we refer Harris, SUSAN corner detector, the original CSS , Adaptive threshold CSS , the proposed method as Harris, SUSAN, OCSS, ACSS and MSCP, respectively.<\/li>\n<li>More difficult examples are better treated using a time-dependent coordinate transform involving complex exponentials (as also invoked in the previous multiple time-scale approach).<\/li>\n<li>Among them, the contour-based corner-detection techniques are a crucial branch and our approaches belong to this category.<\/li>\n<li>The method is sometimes attributed to Poincare, although Poincare credits the basic idea to the astronomer Lindstedt .<\/li>\n<\/ul>\n<p>Experimental results show that MSTPD is a promising corner detection scheme compared with the other seven impressive corner detection methods based on two common evaluation criteria, that is, average repeatability and localization error. Inspired by the work of Mokhtarian and Suomela and others , we define the multi-scale product with respect to the curvature of planar curve, and a simple and efficient corner detector based on multi-scale product is proposed. According to CCN and ACU criteria, it is easy to verify that the detection performance and localization accuracy of the corner detector based on multi-scale product can be improved a lot. Moreover, a number of experiments demonstrate the good results of multi-scale curvature product. Xu et al. proposed a wavelet-based spatially selective filtering technique by multiplying the adjacent scales.<\/p>\n<p>Image corners have been widely used in various computer vision tasks. Current multi-scale analysis based corner detectors do not make full use of the multi-scale and multi-directional structural information. This degrades their detection accuracy and capability of refining corners.<\/p>\n<p>With Impressive CAGR, this market is estimated to reach USD million in 2029. Procedure such as nearest grid-point relocation or binning, due to the use of continuous basis functions (so-called &#8220;grid-less&#8221; analysis). Has the highest power especially for fine-scale features such as the 20-km wavelength. There are more references available in the full text version of this article. We are grateful to the referees for their comments on earlier drafts of this paper that helped the quality and presentation of this paper.<\/p>\n<h2 id=\"toc-5\">Available localization Algorithms<\/h2>\n<p>The NSST is well known for its approximate shift invariance and better directional selectivity, which are very important in image denoising. Then, the blend of the proposed multivariate shrinkage function and its method noise thresholding using NSST is proposed for image denoising. Experimental results demonstrate that the proposed method is very competitive when compared with other existing denoising methods in the literature.<\/p>\n<p>Here, we also have the true noise covariance matrix covar and the original signals x_orig. These signals are noisy versions of simple combinations of the two original signals. The first signal is &#8220;Blocks&#8221; which is irregular, and the second one is &#8220;HeavySine&#8221; which is regular, except around time 750. The other two signals are the sum and the difference of the two original signals, respectively.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Content References Alphanumerical scales Camera Model Training Multiple-scale analysis Concerns with Single Item Scales In this section, we introduce the original and adaptive threshold CSS corner detectors, and analyze their drawbacks. First we quote the definition of curvature k of the contour in CSS. Sarstedt and Wilczynski 2009 questioned the approach used by Bergkvist and [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[6302],"tags":[],"class_list":["post-33538","post","type-post","status-publish","format-standard","hentry","category-it-vacancies"],"_links":{"self":[{"href":"https:\/\/ccm-swiss.com\/index.php\/wp-json\/wp\/v2\/posts\/33538","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ccm-swiss.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ccm-swiss.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ccm-swiss.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/ccm-swiss.com\/index.php\/wp-json\/wp\/v2\/comments?post=33538"}],"version-history":[{"count":1,"href":"https:\/\/ccm-swiss.com\/index.php\/wp-json\/wp\/v2\/posts\/33538\/revisions"}],"predecessor-version":[{"id":33539,"href":"https:\/\/ccm-swiss.com\/index.php\/wp-json\/wp\/v2\/posts\/33538\/revisions\/33539"}],"wp:attachment":[{"href":"https:\/\/ccm-swiss.com\/index.php\/wp-json\/wp\/v2\/media?parent=33538"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ccm-swiss.com\/index.php\/wp-json\/wp\/v2\/categories?post=33538"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ccm-swiss.com\/index.php\/wp-json\/wp\/v2\/tags?post=33538"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}