diff options
Diffstat (limited to 'vignettes')
14 files changed, 6006 insertions, 561 deletions
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) 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ke2C8vfjgV/Y9kkRQOJIn/xM9INZSpiBnqX0Q9GlQPpPKAyO5y+W5NMPSRdBCUlmuxl40ZfMCnf2Cp044uI9WLFtCi4YVxKjuRCOBWIb4XbIsGdbo4qtMQnNOQz4XDSui7W/N6l54qOynCqD3DpWQ+mpD7C40D8BZEWGJX3tlAaZBMj1yjvDYKwCJBa201u6nBKE5UE+7QSEhCwrXfbRZylAaAkplhBWX50dumrElePyNMRYUrC99UmcSSNgImhFhDI4BXjMtiqkgizUGCrZ8iwFxU6fQ8GEHCFdLewwxYWxgScAYMdMLmcZR6b7rZl95eQVDGVoUKcRMM1ixXQtXNkBETZkVVPg8LoSrdetHzkuM7DjZRHP02tCxA1fmkXKF3VzfN1pc1cv/8lbTIkkYpqKM9VOhp65ktYk+Q46myFWBapDfyWUCnsnI00QTBQmuFjMZTcd0V2NQ768Fhpby04k2IzNR1wKabuGJqYWwSly6ocMFGTeeI+ejsWDYgEvr66QgqdcIbFYDNgsm0x9UHY6SCd5+7tpsLpKdvhahIDyYmEJQCqMqtCF6UlrE5GXRmbu+vtm3BFSxI6ND6UxIE7GsGMgWqghXxSnaRJuGFveTcK5ZVSPJyjUxe1dKgI6kNF7EZhIZs8y8FVqwEfbM0Xk2ltORVDKZZM40SD3qQoQe0orJEKwPfZwm3YPqwixhUMOndis6MhbmfvLBKjC8sKKIZKbJk8L11oNkCQzCgvjhyyEiQSuJcgCQSG4Mocfgc0Hkwcjal1UNgP0CBPikYqBIk9tONv4kLtBswH07vUCjEaHiFGlLf8MgXKzSgjp2HolRRccAOh0ILHz9qlGgIFkwAnzHJRjWFhlA7ROwINyB5HFj59PRZHFor6voq7l23EPNRwdWhgawqbivLSjRA4htEYUFkjESu67icTg5S0aW1sOkCiIysfJ9UnIWevOOLGpepcBxy1wEhd2WI3AZg7sr9WBmHWyasxMcvY/iOmsLtHSWNUWEGk9hScMPShasUA1AcHOtRZlqMeQ0OzYS9vQvYUjOLrzP07BUAFikcJNMi7gIxEw4pL1G54TcmmmoAQ5s7TGWErJZ2Io4yQ0ljRYhL8H5e62oDtLF8aDpnIvZ5R3GWJyAugdiiJW9hQAVTsnCBHhwu7rkBlBX6r3b7ejEY0k5GGeyKv66v+6dg7mcJTrWHbtMywbedYqCQ0FPwoytmSWsL8WTtChZCKKzEF7vP6De4x2BJkkniMgSdWhbeBSLtJZR9CTHetK1xb34AYIJ37OegYIoPVbXgJ/qDQK+bfCtxQRVKQu77WzOoM6SGL7MaZwCGJVk46aImai9fmam+WpHG+0BtQPWUgZ7RIAlPq6lkECUhZQ2gqWkMYKcYMYaIc4gYCDFHYa2d1nzp3+J1eCBay8IYZ0wQRKGAqvCuZ/UgbQPyllosq+XtfKIZOzmeJqRazpmmoP/76YfkjzV2NlXTDSBYB04SVlNQsFTbGPk1t/I4Jktu0XSgifO2ozFOiwd/0SssJDn0dn4xqk4GDTTKX73/wQyBLdqgJ+Wx6AQaba3BA9CKEzjtQYIfAsiYamapq80LAamYjinlKXUkxdpIDk0puXUEYzSalfRibAeDAKpNiqQ0FTwoxuGYzRnisyTotdVTclis1LHRQCy/qqL8oUaQzWRxilq5Mi0IJGtMY02cGLD69vGjkj3p6pGePKI8bkBv5evq8SjjyU04vJR2cQXQwSJyoinDsUJHCQ50jrFTT7yRdbdYQMB3MYCb6uBzJ9ewhXYPAIZSXfeEQBZZ3GPN3Nbhh/wkvAJLXnQMdi5NYYZ5GHE400GS5rXkOZSQsdZgIbzRnF9ueLnsfQ47wHAsirITnTlkCcuWWIUhJSbpM3wWhXNHvt2xUsKKMpdBSbJnBMcihkoDqAd1Zml/R4yrzow1Q2A5G+kzo/RhRxQS2lCSDRV8LlYLBOOoo1bF4jwJAwKMK1tWLHlu9i0j4Ig8qVm6wE1DxXwAwQwsaBWUg2pOOol2dHxyt6npwJEdLDDVYyRc2D0HbcbLUJQj8gPevQBUBOUHXPrsAPBERICpnYESeu2OHotpXQxRGlCCtLdIsu23MhZVEoJg8Qumj/UMMc34IBqTKLDTp76WzL/dMjCxK7MjhiGjeYAC/kj/jY/Rde7hpSM1xChrog6yZ7OWTuD56xBJnGFE+pT2ElSyCnJcwVzCjkqeNLfMEJqKW0G7OFIp0G+9mh50I9o8k1tpCY0xYqFNIALgIfc2me4n1bmJnRZ89oepgLPT0NTMLNZsvSCZAc3TXaNB07vail36/dBySis4m9/DR8izaLJW6bWCkVgm5T+ius3ZXq4xI+GnbveLbdRwF2mNtsrE0JjYc1AXknCOrLSu7Te/r4dPYMCl5qtiHNTn+TPbh1jCBHH+dMJNhwNgs3nT+OhQoQ0vYif56BMG6WowAcHR3DjQolxLzyVekHj00PBAaW7IIAF1EF+uRIWyXjQMAs2chdpaKPNaB+kSezYt0+CA04sOg5vx8Fr7Ofa9sUv87h7SLAUFSzbetCCZ9pmyLt6l6/TzoA1/ZBG9bIUVHLAbi/kdBFgYGyGwRQGBpkqCEg2ah9UD6EedEcEL3j4y0BQQCiExEnocA3SZboh+epgd3YsOkHskZwPuQ5OoyA0fTA5AXrHcUOQF+zkJHIA7PwCDk1gGVmGUZSSoPhNf+Tklauz98QofOlCIQ/tCD4dosHYPqtPCXB3agggQQIqQJsSkB+qn0rkQ1toJjON/OtCIB9RYv3PqRA4C4U68ZMlZn6BdgEvi2ziU+TQ6NIw3ej+AtDwMGEZk7e2IjxUWKdAxyaw9OCwSmeADTPPleyk6UhGDNXQb++W6Uk4q6F7/rg6WVTo82IoCxSIsFDrav4EPHphD3u4hR53WKVvYZUwNCCeM4PMBWzK+EfIthZOkuAwPo5C5jgoZgn6dUdvx5rIDmd58cXXdKNfw3l+wM2UjgrDJeQHhbD7HW2QDoZMCujgIUkk5Fg8VCsdyjOtnGRx8wgKRPZN5dR0zPUyfGZFVihbFRniXZFOZGKPnEQzU3AnD1KfR6weHW2XS6KbPJxUkOTZsAB9vTVp3Le1F8q5l+DMcLiIq78jxAImD2pGFw0VHfRatScGlK6SMu8leTmhUSMy8Uhdd6xBiH3Gdman4tjQGLboJfqz6fL2WKHTmrfsKZRYX6BTDjDldKMosaSTLdQS7oDisJNqAUhw1PfTlnacCO8vl8706Km1FROgLDmudzxg+EWTiArtHgLsRrAXYWdB0NmToNCJdKm0KWycZQqb+Mw76Qy29iQ5up/X7oyw8QZ75kP5F6iJAJz6KCmqxz8fEa/xnsMYcIO/vEkGRuMckhr4rIeLrKaXnmIzlNLxbFspOphkcnJdnz/Chp/Vlpj2P7jJQmQRwGnltkTV5dbF9fE3/fxoSqTROgq9wFUlbuYzYcasE0ouzBo+dDCDzxKAfhbAZYxQiHrLzV2iVexnDX/QnT1fsT/xuhu1ui5qIytgbGmRoQkeQooO8eJNNZsf0iALur8QxZFH0nCMnjerYQqG1pIfjyVZWxhVRznmmfLG00BcBWJE6hzQWRyFknuJnXuk8A5FRDCulwrWASSNoBtR+CtGdkPwYN2o7DOw/VGlCZPusRBFXODQdUM5zeHDIVuAJBLqbO/f9Qua+pDqEPk230Sob9lEZ8BHiCorjVghuI0lI4JDgHGRDD/prQ84B1pVGkIpVUAHCG+iz3Bn3qm2AVrYcYWhock4jso5+J7HfHVj4WMIQdGctq3psBCVVzupQOEioBGA2Bk+UILT7+VoX5mdxxA5fS42gISQVi/HTzrgMxu0fY6hE1ocUwwbsbWcezrY2n6S8/6cxXkOH4prpmPuFoikTzY7T85C4T2XYlbxLglSv2uLCgFv8Quk/wdesUdWPeHYIH0R729JIisN9Apdd4eB10aqwXrPt+Su9mA8k8n1sjMwnfsfF2j3jMUzXepSHmZ/BfqXvzgUNQQWOXO8YEuFBh4QTYCkOAPxywpYu1VxiDyJmKVcmJPGWk/gc3Pov02StyYDahwmzw3E1gYC9wkupyWfDqDSUMpCTH5e5N8B//lHiMuIkTNw4USHrJU67bjXGqNav6PBuQSoqTxc8avHoGmvqNtXzIaoyMIQIiiUHIM64cXieouplhNYln7qgc4wBVAYR104kO+CvKqsg4yIUlFNThVUAKZxZt1XA34h3TCUUiXVkZ0w8Hh2R0Z5L0b4LZvPd/p1gi/07h8qfwHrByuSxglc9cI4QIg2oqvC/qm0i7tjPLTgDhoWTAKDO2ONW5oe+/eKB9vZB8K6C25yCZ9RFVMnb6NRdRjyVK57CHHSkJBfnM2/j4ODUwRkqrtBBCrDsDpt8jhZdXoy/1BCqw3sSGhgGGy0a5Jw6BP/TExoCmNFYjZl248A0osgPyGEmRA+fAsqPVaNAfytu0vuQJ7rk3J4kTDTR2AlCHJ5cls26opZM4w3jMULh2YXKpcqGBtuleAlOZnaZGbD6DHzMd6i2oFeJ8z9XYmalg1Szd/ocZDc1C7Y6vcALJz2lYnTXiWEr2wawtoR4g3jvWUU2Ngjd1cewtFzEvM1NiHZPeLlIXFbBPawxNgMwwAlyNSuGF3zizVeOoC9bag1qRAQKQE/EZBWC2J8mnXAN2aTBboZ7HewnObE8CwROudZHmUM5oZ/Ugd/JZQK8lvAm43uDRAbyW8gZ+ZGq0EVerVGUKUSm/Idn8AQHdR4m7bue88WBwft9mSCeMOt1ncBwziOmJYI2ZR7ewNMPiCugmSsE4EyQ+QATJG6qORMGd4snEzc6B4shPIo4G1T7PgSm8PY5eUkPdF8JZ0VBtadbHXoJgnEhZQaODPj2gpODKJY5Yp4DOsLBFxWbvXN755KWylJm+oOd4zEL9Hpubuy2gyyfxh8oEfFutnYWdfB8PdESLWYvSqbElP9qo3u6KTmkhoacDauMNNjj0oy40DFV7Ql0aZj77xfGl7TJNHnIwgqOkenruYYNo6h724+zUQ7+vkCpZB+pGA562hYQiDxHVWOq0oDQl/QsoiY+cuI7iWq/ZIBtHcXJ7kks+h2fCNUPA82BzjnqktNts+RLdk1VSu+tqEn7QZCCsvEqk6FkfiOYkrsw092J8jsfIuEKypNjLxrKA9kiA19mxBD2suxQKCzwXGws7kEJvlhUiV9tArLIdZW0IORcxEzdzKmjtFhsjKy/44XYXdI5noQoRcvjZ1RMPACRqYg2V1+OwOepcOknRLLFdYgTkT5UApt/JhLM3jeFYprZV+Zow2g8fP+U68hkKFWJj2yBbKqsrp25xkZX1DAjUw52IMYWaOhab8Kp05VrdNftqwRrymWF4OQSjbdfzmRZirK8FMJELEgER2PHjEAN9pGfLhCUiTJFbd5LBkOBMaxLr/A1SY9dXFz4RjzoU9ExfJCmx/I9FKEGT3n2cmzl2X42L3Jh+AbQq6sA+Ss1kitoa4TAYgKHaoybHUDJ51oETdeI/9ThSmjWGkyLi5QAGWhL0BG1UsTyRGRJOldKBrYJeB8ljLJHfATWTEQBXBDnQexOHTB+Un44zExFE4vLytcu5NwpWrUxO/0ZICUGM7hGABXym0V6ZvDST0E370St9MIWQOTWngeoQHUTdCJUP04spMBMS8LSker9cReVQkULFDIZDFPrhTzBl6sed9wcZQTbL+BDqMyaN3RJPh/anbx+Iv+qgQdAa3M9Z5JmvYlh4qop+Ho1F1W5gbOE9YKLgAnWytXElU4G8GtW47lhgFE6gaSs+gs37sFvi0PPVvA5dnCBgILTwoKd/+DoL9F6inlM7H4rOTzD79KJgKlZO/Zgt22UsKhrAaXU5ZcLrAglTVKJEmNJvORGN1vqrcfSMizfpsgbIe9zno+gBoKVXgIL/VI8dB1O5o/R3Suez/gD7M781ShjKpIIORM/nxG+jjhhgPwsn2IoXsPGPqYHXA63zJ07M2GPEykQwJBYLK808qYxuIew4frk52nhCsnCYmXiR6CuapvE1IwRB4/QftDbEn+AucIr1oxrLabRj9q4ae0+fXkHnteAJwXRbVkR0mctVSwEbqhJiMSZUp9DNbEDMmjX22m3ABpkrPQQTP3S1sib5pD2VRKRd+eNAjLYyT0hGrdjWJZy24OYXRoWQAIhGBZRxuBFMjjZQhpgrWo8SiFYbojcHO8V5DyscJpLTHyx9Fimassyo5U6WNtquUMYgccaHY5amgR3PQzq3ToNM5ABnoB9kuxsebqmYZm0R9qxJbFXCQ1UPyFIbxoUraTJFDpCk0Wk9GaYJKz/6oHwEP0Q14lMtlddQsOAU9zlYdMVHiT7RQP3XCmWYDcHCGbVRHGnHuwzScA0BaSBOGkz3lM8CArjrBsyEoV6Ys4qgDK3ykQQPZ3hCRGNXQTNNXbEb6tDiTDLKOyMzRhCFT+mAUmiYbV3YQVqFVp9dorv+TsLeCykS2b5yyu8AV7IS9cxcL8z4Kfwp+xJyYLv1OsxQCZwTB4a8BZ/5EdxTBJthApqyfd9u3ifr/WILTqq5VqgwMT9SOxbSGWLQJUUWCVi4k9tho9nEsbUh7U6NUsLmkYFXOhZ0kmamaJLRNJzSj/qn4Mso6zb6iLLBXoaZ6AqeWCjHQm2lztnejYYM2eubnpBdKVLORZhudH3JF1waBJKA9+W8EhMj3Kzf0L4vi4k6RoHh3Z5YgmSZmk6ns4fjScjAoL8GoOECgqgYEBYUGFVO4FUv4/YtowhEmTs0vrvlD/CrisnoBNDAcUi/teY7OctFlmARQzjOItrrlKuPO6E2Ox93L4O/4DcgV/dZ7qR3VBwVQxP1GCieA4RIpweYJ5FoYrHxqRBdJjnqbsikA2Ictbb8vE1GYIo9dacK0REgDX4smy6GAkxlH1yCGGsk+tgiDhNKuKu3yNrMdxafmKTF632F8Vx4BNK57GvlFisrkjN9WDAtjsWA0ENT2e2nETUb/n7qwhvGnrHuf5bX6Vh/n3xffU3PeHdR+FA92i6ufT3AlyAREoNDh6chiMWTvjKjHDeRhOa9YkOQRq1vQXEMppAQVwHCuIcV2g5rBn6GmZZpTR7vnSD6ZmhdSl176gqKTXu5E+YbfL0adwNtHP7dT7t7b46DVZIkzaRJOM+S6KcrzYVg+T3wSRFRQashjfU18NutrKa/7PXbtuJvpIjbgPeqd+pjmRw6YKpnANFSQcpzTZgpSNJ6J7uiagAbir/8tNXJ/OsOnRh6iuIexxrmkIneAgz8QoLmiaJ8sLQrELVK2yn3wOHp57BAZJhDZjTBzyoRAuuZ4eoxHruY1pSb7qq79cIeAdOwin4GdgMeIMHeG+FZWYaiUQQyC5b50zKjYw97dFjAeY2I4Bnl105Iku1y0lMA1ZHolLx19uZnRdILcXKlZGQx/GdEqSsMRU1BIrFqRcV1qQOOHyxOLXEGcbRtAEsuAC2V4K3p5mFJ22IDWaEkk9ttf5Izb2LkD1MnrSwztXmmD/Qi/EmVEFBfiKGmftsPwVaIoZanlKndMZsIBOskFYpDOq3QUs9aSbAAtL5Dbokus2G4/asthNMK5UQKCOhU97oaOYNGsTah+jfCKsZnTRn5TbhFX8ghg8CBYt/BjeYYYUrtUZ5jVij/op7V5SsbA4mYTOwZ46hqdpbB6Qvq3AS2HHNkC15pTDIcDNGsMPXaBidXYPHc6PJAkRh29Vx8KcgX46LoUQBhRM+3SW6Opll/wgxxsPgKJKzr5QCmwkUxNbeg6Wj34SUnEzOemSuvS2OetRCO8Tyy+QbSKVJcqkia+GvDefFwMOmgnD7h81TUtMn+mRpyJJ349HhAnoWFTejhpYTL9G8N2nVg1qkXBeoS9Nw2fB27t7trm7d/QK7Cr4uoCeOQ7/8JfKT77KiDzLImESHw/0wf73QeHu74hxv7uihi4fTX+XEwAyQG3264dwv17aJ5N335Vt9sdrAXhPOAv8JFvzqyYXwfx8WYJaef1gMl98JRFyl5Mv5Uo/oVH5ww5OzLFsiTPDns7fS6EURSSWd/92BxMYQ8sBaH+j+wthQPdVgDGpTfi+JQIWMD8xKqULliRH01rTeyF8x8q/GBEEEBrAJMPf25UQwi0b8tmqRXY7kIvNkzrkvRWLnxoGYEJsz8u4oOyMp8cHyaybb1HdMCaLApUE+/7xLIZGP6H9xuSEXp1zLIdjk5nBaMuV/yTDRRP8Y2ww5RO6d2D94o+6ucWIqUAvgHIHXhZsmDhjVLczmZ3ca0Cb3PpKwt2UtHVQ0BgFJsqqTsnzZPlKahRUkEu4qmkJt+kqdae76ViWe3STan69yaF9+fESD2lcQshLHWVu4ovItXxO69bqC5p1nZLvI8NdQB9s9UNaJGlQ5mG947ipdDA0eTIw/A1zEdjWquIsQXXGIVEH0thC5M+W9pZe7IhAVnPJkYCCXN5a32HjN6nsvokEqRS44tGIs7s2LVTvcrHAF+RVmI8L4HUYk4x+67AxSMJKqCg8zrGOgvK9kNMdDrNiUtSWuHFpC8/p5qIQrEo/H+1l/0cAwQ2nKmpWxKcMIuHY44Y6DlkpO48tRuUGBWT0FyHwSKO72Ud+tJUfdaZ4CWNijzZtlRa8+CkmO/EwHYfPZFU/hzjFWH7vnzHRMo+aF9u8qHSAiEkA2HjoNQPEwHsDKOt6hOoK3Ce/+/9boMWDa44I6FrQhdgS7OnNaSzwxWKZMcyHi6LN4WC6sSj0qm2PSOGBTvDs/GWJS6SwEN/ULwpb4LQo9fYjUfSXRwZkynUazlSpvX9e+G2zor8l+YaMxSEomDdLHGcD6YVQPegTaA74H8+V4WvJkFUrjMLGLlvSZQWvi8/QA7yzQ8GPno//5SJHRP/OqKObPCo81s/+6WgLqykYpGAgQZhVDEBPXWgU/WzFZjKUhSFInufPRiMAUULC6T11yL45ZrRoB4DzOyJShKXaAJIBS9wzLYIoCEcJKQW8GVCx4fihqJ6mshBUXSw3wWVj3grrHQlGNGhIDNNzsxQ3M+GWn6ASobIWC+LbYOC6UpahVO13Zs2zOzZC8z7FmA05JhUGyBsF4tsG0drcggIFzgg/kpf3+CnAXKiMgIE8Jk/Mhpkc8DUJEUzDSnWlQFme3d0sHZDrg7LavtsEX3cHwjCYA17pMTfx8Ajw9hHscN67hyo+RJQ4458RmPywXykkVcW688oVUrQhahpPRvTWPnuI0B+SkQu7dCyvLRyFYlC1LG1gRCIvn3rwQeINzZQC2KXq31FaR9UmVV2QeGVqBHjmE+VMd3b1fhCynD0pQNhCG6/WCDbKPyE7NRQzL3BzQAJ0g09aUzcQA6mUp9iZFK6Sbp/YbHjo++7/Wj8S4YNa+ZdqAw1hDrKWFXv9+zaXpf8ZTDSbiqsxnwN/CzK5tPkOr4tRh2kY3Bn9JtalbIOI4b3F7F1vPQMfoDcdxMS8CW9m/NCW/HILTUVWQIPiD0j1A6bo8vsv6P1hCESl2abrSJWDrq5sSzUpwoxaCU9FtJyYH4QFMxDBpkkBR6kn0LMPO+5EJ7Z6bCiRoPedRZ/P0SSdii7ZnPAtVwwHUidcdyspwncz5uq6vvm4IEDbJVLUFCn/LvIHfooUBTkFO130FC7CmmcrKdgDJcid9mvVzsDSibOoXtIf9k6ABle3PmIxejodc4aob0QKS432srrCMndbfD454q52V01G4q913mC5HOsTzWF4h2No1av1VbcUgWAqyoZl+11PoFYnNv2HwAODeNRkHj+8SF1fcvVBu6MrehHAZK1Gm69ICcTKizykHgGFx7QdowTVAsYEF2tVc0Z6wLryz2FI1sc5By2znJAAmINndoJiB4sfPdPrTC8RnkW7KRCwxC6YvXg5ahMlQuMpoCSXjOlBy0Kij+bsCYPbGp8BdCBiLmLSAkEQRaieWo1SYvZIKJGj9Ur/eWHjiB7SOVdqMAVmpBvfRiebsFjger7DC+8kRFGtNrTrnnGD2GAJb8rQCWkUPYHhwXsjNBSkE6lGWUj5QNhK0DMNM2l+kXRZ0KLZaGsFSIdQz/HXDxf3/TE30+DgBKWGWdxElyLccJfEpjsnszECNoDGZpdwdRgCixeg9L4EPhH+RptvRMVRaahu4cySjS3P5wxAUCPkmn+rhyASpmiTaiDeggaIxYBmtLZDDhiWIJaBgzfCsAGUF1Q1SFZYyXDt9skCaxJsxK2Ms65dmdp5WAZyxik/zbrTQk5KmgxCg/f45L0jywebOWUYFJQAJia7XzCV0x89rpp/f3AVWhSPyTanqmik2SkD8A3Ml4NhIGLAjBXtPShwKYfi2eXtrDuKLk4QlSyTw1ftXgwqA2jUuopDl+5tfUWZNwBpEPXghzbBggYCw/dhy0ntds2yeHCDKkF/YxQjNIL/F/37jLPHCKBO9ibwYCmuxImIo0ijV2Wbg3kSN2psoe8IsABv3RNFaF9uMyCtCYtqcD+qNOhwMlfARQUdJ2tUX+MNJqOwIciWalZsmEjt07tfa8ma4cji9sqz+Q9hWfmMoKEbIHPOQORbhQRHIsrTYlnVTNvcq1imqmmPDdVDkJgRcTgB8Sb6epCQVmFZe+jGDiNJQLWnfx+drTKYjm0G8yH0ZAGMWzEJhUEQ4Maimgf/bkvo8PLVBsZl152y5S8+HRDfZIMCbYZ1WDp4yrdchOJw8k6R+/2pHmydK4NIK2PHdFPHtoLmHxRDwLFb7eB+M4zNZcB9NrAgjVyzLM7xyYSY13ykWfIEEd2n5/iYp3ZdrCf7fL+en+sIJu2W7E30MrAgZBD1rAAbZHPgeAMtKCg3NpSpYQUDWJu9bT3V7tOKv+NRiJc8JAKqqgCA/PNRBR7ChpiEulyQApMK1AyqcWnpSOmYh6yLiWkGJ2mklCSPIqN7UypWj3dGi5MvsHQ87MrB4VFgypJaFriaHivwcHIpmyi5LhNqtem4q0n8awM19Qk8BOS0EsqGscuuydYsIGsbT5GHnERUiMpKJl4ON7qjB4fEqlGN/hCky89232UQCiaeWpDYCJINXjT6xl4Gc7DxRCtgV0i1ma4RgWLsNtnEBRQFqZggCLiuyEydmFd7WlogpkCw5G1x4ft2psm3KAREwVwr1Gzl6RT7FDAqpVal34ewVm3VH4qn5mjGj+bYL1NgfLNeXDwtmYSpwzbruDKpTjOdgiIHDVQSb5/zBgSMbHLkxWWgghIh9QTFSDILixVwg0Eg1puooBiHAt7DzwJ7m8i8/i+jHvKf0QDnnHVkVTIqMvIQImOrzCJwhSR7qYB5gSwL6aWL9hERHCZc4G2+JrpgHNB8eCCmcIWIQ6rSdyPCyftXkDlErUkHafHRlkOIjxGbAktz75bnh50dU7YHk+Mz7wwstg6RFZb+TZuSOx1qqP5C66c0mptQmzIC2dlpte7vZrauAMm/7RfBYkGtXWGiaWTtwvAQiq2oD4YixPLXE2khB2FRaNRDTk+9sZ6K74Ia9VntCpN4BhJGJMT4Z5c5FhSepRCRWmBXqx+whVZC4me4saDs2iNqXMuCl6iAZflH8fscC1sTsy4PHeC+XYuqMBMUun5YezKbRKmEPwuK+CLzijPEQgfhahQswBBLfg/GBgBiI4QwAqzJkkyYAWtjzSg2ILgMAgqxYfwERRo3zruBL9WOryUArSD8sQOcD7fvIODJxKFS615KFPsb68USBEPPj1orNzFY2xoTtNBVTyzBhPbhFH0PI5AtlJBl2aSgNPYzxYLw7XTDBDinmVoENwiGzmngrMo8OmnRP0Z0i0Zrln9DDFcnmOoBZjABaQIbPOJYZGqX+RCMlDDbElcjaROLDoualmUIQ88Kekk3iM4OQrADcxi3rJguS4MOIBIgKgXrjd1WkbCdqxJk/4efRIFsavZA7KvvJQqp3Iid5Z0NFc5aiMRzGN3vrpBzaMy4JYde3wr96PjN90AYOIbyp6T4zj8LoE66OGcX1Ef4Z3KoWLAUF4BTg7ug/AbkG5UNQXAMkQezujSHeir2uTThgd3gpyzDrbnEdDRH2W7U6PeRvBX1ZFMP5RM+Zu6UUZZD8hDPHldVWntTCNk7To8IeOW9yn2wx0gmurwqC60AOde4r3ETi5pVMSDK8wxhoGAoEX9NLWHIR33VbrbMveii2jAJlrxwytTHbWNu8Y4N8vCCyZjAX/pcsfwXbLze2+D+u33OGBoJyAAL3jn3RuEcdp5If8O+a4NKWvxOTyDltG0IWoHhwVGe7dKkCWFT++tm+haBCikRUUMrMhYKZJKYoVuv/bsJzO8DwfVIInQq3g3BYypiz8baogH3r3GwqCwFtZnz4xMjAVOYnyOi5HWbFA8n0qz1OjSpHWFzpQOpvkNETZBGpxN8ybhtqV/DMUxd9uFZmBfKXMCn/SqkWJyKPnT6lq+4zBZni6fYRByJn6OK+OgPBGRAJluwGSk4wxjOOzyce/PKODwRlsgrVkdcsEiYrqYdXo0Er2GXi2GQZd0tNJT6c9pK1EEJG1zgDJBoTVuCXGAU8BKTvCO/cEQ1Wjk3Zzuy90JX4m3O5IlxVFhYkSUwuQB2up7jhvkm+bddRQu5F9s0XftGEJ9JSuSk+ZachCbdU45fEqbugzTIUokwoAKvpUQF/CvLbWW5BNQFqFkJg2f30E/48StNe5QwBg8zz3YAJ82FZoXBxXSv4QDooDo79NixyglO9AembuBcx5Re3CwOKTHebOPhkmFC7wNaWtoBhFuV4AkEuJ0J+1pT0tLkvFVZaNzfhs/Kd3+A9YsImlO4XK4vpCo/elHQi/9gkFg07xxnuXLt21unCIpDV+bbRxb7FC6nWYTsMFF8+1LUg4JFjVt3vqbuhHmDKbgQ4e+RGizRiO8ky05LQGMdL2IKLSNar0kNG7lHJMaXr5mLdG3nykgj6vB/KVijd1ARWkFEf3yiUw1v/WaQivVUpIDdSNrrKbjO5NPnxz6qTTGgYg03HgPhDrCFyYZTi3XQw3HXCva39mpLNFtz8AiEhxAJHpWX13gCTAwgm9YTvMeiqetdNQv6IU0hH0G+ZManTqDLPjyrOse7WiiwOJCG+J0pZYULhN8NILulmYYvmVcV2MjAfA39sGKqGdjpiPo86fecg65UPyXDIAOyOkCx5NQsLeD4gGVjTVDwOHWkbbBW0GeNjDkcSOn2Nq4cEssP54t9D749A7M1AIOBl0Fi0sSO5v3P7LCBrM6ZwFY6kp2FX6AcbGUdybnfChHPyu6WlRZ2Fwv9YM0RMI7kISRgR8HpQSJJOyTfXj/6gQKuihPtiUtlCQVPohUgzfezTg8o1b3n9pNZeco1QucaoXe40Fa5JYhqdTspFmxGtW9h5ezLFZs3j/N46f+S2rjYNC2JySXrnSAFhvAkz9a5L3pza8eYKHNoPrvBRESpxYPJdKVUxBE39nJ1chrAFpy4MMkf0qKgYALctGg1DQI1kIymyeS2AJNT4X240d3IFQb/0jQbaHJ2YRK8A+ls6WMhWmpCXYG5jqapGs5/eOJErxi2/2KWVHiPellTgh/fNl/2KYPKb7DUcAg+mCOPQFCiU9Mq/WLcU1xxC8aLePFZZlE+PCLzf7ey46INWRw2kcXySR9FDgByXzfxiNKwDFbUSMMhALPFSedyjEVM5442GZ4hTrsAEvZxIieSHGSgkwFh/nFNdrrFD4tBH4Il7fW6ur4J8Xaz7RW9jgtuPEXQsYk7gcMs2neu3zJwTyUerHKSh1iTBkj2YJh1SSOZL5pLuQbFFAvyO4k1Hxg2h99MTC6cTUkbONQIAnEfGsGkNFWRbuRyyaEZInM5pij73EA9rPIUfU4XoqQpHT9THZkW+oKFLvpyvTBMM69tN1Ydwv1LIEhHsC+ueVG+w+kyCPsvV3erRikcscHjZCkccx6VrBkBRusTDDd8847GA7p2Ucy0y0HdSRN6YIBciYa4vuXcAZbQAuSEmzw+H/AuOx+aH+tBL88H57D0MsqyiZxhOEQkF/8DR1d2hSPMj/sNOa5rxcUnBgH8ictv2J+cb4BA4v3MCShdZ2vtK30vAwkobnEWh7rsSyhmos3WC93Gn9C4nnAd/PjMMtQfyDNZsOPd6XcAsnBE/mRHtHEyJMzJfZFLE9OvQa0i9kUmToJ0ZxknTgdl/XPV8xoh0K7wNHHsnBdvFH3sv52lU7UFteseLG/VanIvcwycVA7+BE1Ulyb20BvwUWZcMTKhaCcmY3ROpvonVMV4N7yBXTL7IDtHzQ4CCcqF66LjF3xUqgErKzolLyCG6Kb7irP/MVTCCwGRxfrPGpMMGvPLgJ881PHMNMIO09T5ig7AzZTX/5PLlwnJLDAPfuHynSGhV4tPqR3gJ4kg4c06c/F1AcjGytKm2Yb5jwMotF7vro4YDLWlnMIpmPg36NgAZsGA0W1spfLSue4xxat0Gdwd0lqDBOgIaMANykwwDKejt5YaNtJYIkrSgu0KjIg0pznY0SCd1qlC6R19g97UrWDoYJGlrvCE05J/5wkjpkre727p5PTRX5FGrSBIfJqhJE/IS876PaHFkx9pGTH3oaY3jJRvLX9Iy3Edoar7cFvJqyUlOhAEiOSAyYgVEGkzHdug+oRHIEOXAExMiTSKU9A6nmRC8mp8iYhwWdP2U/5EkFAdPrZw03YA3gSyNUtMZeh7dDCu8pF5x0VORCTgKp07ehy7NZqKTpIC4UJJ89lnboyAfy5OyXzXtuDRbtAFjZRSyGFTpFrXwkpjSLIQIG3N0Vj4BtzK3wdlkBJrO18MNsgseR4BysJilI0wI6ZahLhBFA0XBmV8d4LUzEcNVb0xbLjLTETYN8OEVqNxkt10W614dd1FlFFVTIgB7/BQQp1sWlNolpIu4ekxUTBV7NmxOFKEBmmN+nA7pvF78/RII5ZHA09OAiE/66MF6HQ+qVEJCHxwymukkNvzqHEh52dULPbVasfQMgTDyBZzx4007YiKdBuUauQOt27Gmy8ISclPmEUCIcuLbkb1mzQSqIa3iE0PJh7UMYQbkpe+hXjTJKdldyt2mVPwywoODGJtBV1lJTgMsuSQBlDMwhEKIfrvsxGQjHPCEfNfMAY2oxvyKcKPUbQySkKG6tj9AQyEW3Q5rpaDJ5Sns9ScLKeizPRbvWYAw4bXkrZdmB7CQopCH8NAmqbuciZChHN8lVGaDbCnmddnqO1PQ4ieMYfcSiBE5zzMz+JV/4eyzrzTEShvqSGzgWimkNxLvUj86iAwcZuIkqdB0VaIB7wncLRmzHkiUQpPBIXbDDLHBlq7vp9xwuC9AiNkIptAYlG7Biyuk8ILdynuUM1cHWJgeB+K3wBP/ineogxkvBNNQ4AkW0hvpBOQGFfeptF2YTR75MexYDUy7Q/9uocGsx41O4IZhViw/2FvAEuGO5g2kyXBUijAggWM08bRhXg5ijgMwDJy40QeY/cQpUDZiIzmvskQpO5G1zyGZA8WByjIQU4jRoFJt56behxtHUUE/om7Rj2psYXGmq3llVOCgGYKNMo4pzwntITtapDqjvQtqpjaJwjHmDzSVGLxMt12gEXAdLi/caHSM3FPRGRf7dB7YC+cD2ho6oL2zGDCkjlf/DFoQVl8GS/56wur3rdV6ggtzZW60MRB3g+U1W8o8cvqIpMkctiGVMzXUFI7FacFLrgtdz4mTEr4aRAaQ2AFQaNeG7GX0yOJgMRYFziXdJf24kg/gBQIZMG/YcPEllRTVNoDYR6oSJ8wQNLuihfw81UpiKPm714bZX1KYjcXJdfclCUOOpvTxr9AAJevTY4HK/G7F3mUc3GOAKqh60zM0v34v+ELyhJZqhkaMA8UMMOU90f8RKEJFj7EqepBVwsRiLbwMo1J2zrE2UYJnsgIAscDmjPjnzI8a719Wxp757wqmSJBjXowhc46QN4RwKIxqEE6E5218OeK7RfcpGjWG1jD7qND+/GTk6M56Ig4yMsU6LUW1EWE+fIYycVV1thldSlbP6ltdC01y3KUfkobkt2q01YYMmxpKRvh1Z48uNKzP/IoRIZ/F6buOymSnW8gICitpJjKWBscSb9JJKaWkvEkqinAJ2kowKoqkqZftRqfRQlLtKoqvTRDi2vg/RrPD/d3a09J8JhGZlEkOM6znTsoMCsuvTmywxTCDhw5dd0GJOHCMPbsj3QLkTE3MInsZsimDQ3HkvthT7U9VA4s6G07sID0FW4SHJmRGwCl+Mu4xf0ezqeXD2PtPDnwMPo86sbwDV+9PWcgFcARUVYm3hrFQrHcgMElFGbSM2A1zUYA3baWfheJp2AINmTJLuoyYD/OwA4a6V0ChBN97E8YtDBerUECv0u0TlxR5yhJCXvJxgyM73Bb6pyq0jTFJDZ4p1Am1SA6sh8nADd1hAcGBMfq4d/UfwnmBqe0Jun1n1LzrgKuZMAnxA3NtCN7Klf4BH+14B7ibBmgt0TGUafVzI4uKlpF7v8NmgNjg90D6QE3tbx8AjSAC+OA1YJvclyPKgT27QpIEgVYpbPYGBsnyCNrGz9XUsCHkW1QAHgL2STZk12QGqmvAB0NFteERkvBIH7INDsNW9KKaAYyDMdBEMzJiWaJHZALqDxQDWRntumSDPcplyFiI1oDpT8wbwe01AHhW6+vAUUBoGhY3CT2tgwehdPqU/4Q7ZLYvhRl/ogOvR9O2+wkkPKW5vCTjD2fHRYXONCoIl4Jh1bZY0ZE1O94mMGn/dFSWBWzQ/VYk+Gezi46RgiDv3EshoTmMSlioUK6MQEN8qeyK6FRninyX8ZPeUWjjbMJChn0n/yJvrq5bh5UcCAcBYSafTFg7p0jDgrXo2QWLb3WpSOET/Hh4oSadBTvyDo10IufLzxiMLAnbZ1vcUmj3w7BQuIXjEZXifwukVxrGa9j+DXfpi12m1RbzYLg9J2wFergEwOxFyD0/JstNK06ZN2XdZSGWxcJODpQHOq4iKqjqkJUmPu1VczL5xTGUfCgLEYyNBCCbMBFT/cUP6pE/mujnHsSDeWxMbhrNilS5MyYR0nJyzanWXBeVcEQrRIhQeJA6Xt4f2eQESNeLwmC10WJVHqwx8SSyrtAAjpGjidcj1E2FYN0LObUcFQhafUKTiGmHWRHGsFCB+HEXgrzJEB5bp0QiF8ZHh11nFX8AboTD0PS4O1LqF8XBks2MpjsQnwKHF6HgaKCVLJtcr0XjqFMRGfKv8tmmykhLRzu+vqQ02+KpJBjaLt9ye1Ab+BbEBhy4EVdIJDrL2naV0o4wU8YZ2Lq04FG1mWCKC+UwkXOoAjneU/xHplMQo2cXUlrVNqJYczgYlaOEczVCs/OCgkyvLmTmdaBJc1iBLuKwmr6qtRnhowngsDxhzKFAi02tf8bmET8BO27ovJKF1plJwm3b0JpMh38+xsrXXg7U74QUM8ZCIMOpXujHntKdaRtsgyEZl5MClMVMMMZkZLNxH9+b8fH6+b8Lev30A9TuEVj9CqAdmwAAHBPbfOBFEATAPZ2CS0OH1Pj/0Q7PFUcC8hDrxESWdfgFRm+7vvWbkEppHB4T/1ApWnlTIqQwjcPl0VgS1yHSmD0OdsCVST8CQVwuiew1Y+g3QGFjNMzwRB2DSsAk26cmA8lp2wIU4p93AUBiUHFGOxOajAqD7Gm6NezNDjYzwLOaSXRBYcWipTSONHjUDXCY4mMI8XoVCR/Rrs/JLKXgEx+qkmeDlFOD1/yTQNDClRuiUyKYCllfMiQiyFkmuTz2vLsBNyRW+xz+5FElFxWB28VjYIGZ0Yd+5wIjkcoMaggxswbT0pCmckRAErbRlIlcOGdBo4djTNO8FAgQ+lT6vPS60BwTRSUAM3ddkEAZiwtEyArrkiDRnS7LJ+2hwbzd2YDQagSgACpsovmjil5wfPuXq3GuH0CyE7FK3M4FgRaFoIkaodORrPx1+JpI9psyNYIFuJogZa0/1AhOWdlHQxdAgbwacsHqPZo8u/ngAH2GmaTdhYnBfSDbBfh8CHq6Bx5bttP2+RdM+MAaYaZ0Y/ADkbNCZuAyAVQa2OcXOeICmDn9Q/eFkDeFQg5MgHEDXq/tVjj+jtd26nhaaolWxs1ixSUgOBwrDhRIGOLyOVk2/Bc0UxvseQCO2pQ2i+Krfhu/WeBovNb5dJxQtJRUDv2mCwYVpNl2efQM9xQHnK0JwLYt/U0Wf+phiA4uw8G91slC832pmOTCAoZXohg1fewCZqLBhkOUBofBWpMPsqg7XEXgPfAlDo2U5WXjtFdS87PIqClCK5nW6adCeXPkUiTGx0emOIDQqw1yFYGHEVx20xKjJVYe0O8iLmnQr3FA9nSIQilUKtJ4ZAdcTm7+ExseJauyqo30hs+1qSW211A1SFAOUgDlCGq7eTIcMAeyZkV1SQJ4j/e1Smbq4HcjqgFbLAGLyKxlMDMgZavK5NAYH19Olz3la/QCTiVelFnU6O/GCvykqS/wZJDhKN9gBtSOp/1SP5VRgJcoVj+kmf2wBgv4gjrgARBWiURYx8xENV3bEVUAAWWD3dYDKAIWk5opaCFCMR5ZjJExiCAw7gYiSZ2rkyTce4eNMY3lfGn+8p6+vBckGlKEXnA6Eota69OxDO9oOsJoy28BXOR0UoXNRaJD5ceKdlWMJlOFzDdZNpc05tkMGQtqeNF2lttZqNco1VtwXgRstLSQ6tSPChgqtGV5h2DcDReIQadaNRR6AsAYKL5gSFsCJMgfsaZ7DpKh8mg8Wz8V7H+gDnLuMxaWEIUPevIbClgap4dqmVWSrPgVYCzAoZHIa5z2Ocx1D/GvDOEqMOKLrMefWIbSWHZ6jbgA8qVBhYNHpx0P+jAgN5TB3haSifDcApp6yymEi6Ij/GsEpDYUgcHATJUYDUAmC1SCkJ4cuZXSAP2DEpQsGUjQmKJfJOvlC2x/pChkOyLW7KEoMYc5FDC4v2FGqSoRWiLsbPCiyg1U5yiHZVm1XLkHMMZL11/yxyw0UnGig3MFdZklN5FI/qiT65T+jOXOdO7XbgWurOAZR6Cv9uu1cm5LjkXX4xi6mWn5r5NjBS0gTliHhMZI2WNqSiSphEtiCAwnafS11JhseDGHYQ5+bqWiAYiAv6Jsf79/VUs4cIl+n6+WOjcgB/2l5TreoAV2717JzZbQIR0W1cl/dEqCy5kJ3ZSIHuU0vBoHooEpiHeQWVkkkOqRX27eD1FWw4BfO9CJDdKoSogQi3hAAwsPRFrN5RbX7bqLdBJ9JYMohWrgJKHSjVl1sy2xAG0E3sNyO0oCbSGOxCNBRRXTXenYKuwAoDLfnDcQaCwehUOIDiHAu5m5hMpKeKM4sIo3vxACakIxKoH2YWF2QM84e6F5C5hJU4g8uxuFOlAYnqtwxmHyNEawLW/PhoawJDrGAP0JYWHgAVUByo/bGdiv2T2EMg8gsS14/rAdzlOYazFE7w4OzxeKiWdm3nSOnQRRKXSlVo8HEAbBfyJMKqoq+SCcTSx5NDtbFwNlh8VhjGGDu7JG5/TAGAvniQSSUog0pNzTim8Owc6QTuSKSTXlQqwV3eiEnklS3LeSXYPXGK2VgeZBqNcHG6tZHvA3vTINhV0ELuQdp3t1y9+ogD8Kk/W7QoRN1UWPqM4+xdygkFDPLoTaumKReKiLWoPHOfY54m3qPx4c+4pgY3MRKKbljG8w4wvz8pxk3AqKsy4GMAkAtmRjRMsCxbb4Q2Ds0Ia9ci8cMT6DmsJG00XaHCIS+o3F8YVVeikw13w+OEDaCYYhC0ZE54kA4jpjruBr5STWeqQG6M74HHL6TZ3lXrd99ZX++7LhNatQaZosuxEf5yRA15S9gPeHskBIq3Gcw81AGb9/O53DYi/5CsQ51EmEh8Rkg4vOciClpy4d04eYsfr6fyQkBmtD+P8sNh6e+XYHJXT/lkXxT4KXU5F2sGxYyzfniMMQkb9OjDN2C8tRRgTyL7GwozH14PrEUZc6oz05Emne3Ts5EG7WolDmU8OB1LDG3VrpQxp+pT0KYV5dGtknU64JhabdqcVQbG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) 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@@ padding: 0; <style type="text/css"> .tabset-dropdown > .nav-tabs { -display: inline-table; -max-height: 500px; -min-height: 44px; -overflow-y: auto; -border: 1px solid #ddd; -border-radius: 4px; + display: inline-table; + max-height: 500px; + min-height: 44px; + overflow-y: auto; + border: 1px solid #ddd; + border-radius: 4px; } + .tabset-dropdown > .nav-tabs > li.active:before, .tabset-dropdown > .nav-tabs.nav-tabs-open:before { -content: "\e259"; -font-family: 'Glyphicons Halflings'; -display: inline-block; -padding: 10px; -border-right: 1px solid #ddd; + content: "\e259"; + font-family: 'Glyphicons Halflings'; + display: inline-block; + padding: 10px; + border-right: 1px solid #ddd; } + .tabset-dropdown > .nav-tabs.nav-tabs-open > li.active:before { -content: "\e258"; -font-family: 'Glyphicons Halflings'; -border: none; + content: "\e258"; + font-family: 'Glyphicons Halflings'; + border: none; } + .tabset-dropdown > .nav-tabs > li.active { -display: block; + display: block; } + .tabset-dropdown > .nav-tabs > li > a, .tabset-dropdown > .nav-tabs > li > a:focus, .tabset-dropdown > .nav-tabs > li > a:hover { -border: none; -display: inline-block; -border-radius: 4px; -background-color: transparent; + border: none; + display: inline-block; + border-radius: 4px; + background-color: transparent; } + .tabset-dropdown > .nav-tabs.nav-tabs-open > li { -display: block; -float: none; + display: block; + float: none; } + .tabset-dropdown > .nav-tabs > li { -display: none; + display: none; } </style> @@ -352,7 +358,7 @@ display: none; <h1 class="title toc-ignore">Example evaluation of FOCUS Example Dataset D</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">Last change 31 January 2019 (rebuilt 2024-12-19)</h4> +<h4 class="date">Last change 31 January 2019 (rebuilt 2025-02-16)</h4> </div> @@ -419,12 +425,12 @@ equations represented in R code can be found in the character vector (gcc) is installed and functional, the differential equation model will be compiled from auto-generated C code.</p> <pre class="r"><code>SFO_SFO <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"))</code></pre> -<pre><code>## Temporary DLL for differentials generated and loaded</code></pre> +<pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre> <pre class="r"><code>print(SFO_SFO$diffs)</code></pre> -<pre><code>## parent -## "d_parent = - k_parent * parent" -## m1 -## "d_m1 = + f_parent_to_m1 * k_parent * parent - k_m1 * m1"</code></pre> +<pre><code>## parent +## "d_parent = - k_parent_sink * parent - k_parent_m1 * parent" +## m1 +## "d_m1 = + k_parent_m1 * parent - k_m1_sink * m1"</code></pre> <p>We do the fitting without progress report (<code>quiet = TRUE</code>).</p> <pre class="r"><code>fit <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE)</code></pre> @@ -435,95 +441,101 @@ variables is obtained using the <code>plot_sep</code> method for <code>mkinfit</code> objects, which shows separate graphs for all compounds and their residuals.</p> <pre class="r"><code>plot_sep(fit, lpos = c("topright", "bottomright"))</code></pre> -<p><img role="img" 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5L056udHUwauJs28jBp4G7awMOkoYdKbVzzMAAAAAAAikQDCPLQlpamfvNDcdKdsvyC/8fefcdFcad/AH9m+2xhlwUEEQsCSu/2KCqxl9hiTM4YNeVSLsmlX8qd5mKKqb/kkksz9jMxgl2MvSsqvQsIgvSyhS0zW+f3xxpi1BhQ2FmW5/3yD5j9zsxnLbj77Pf7fPu89le24wAA2FQaQ3qO8Vw2XVLR3lSIQ4qE4cGi0MGCQQGCgf5cpaKb7u5oCcQP8Lv5IbveaGlottQ1WesaLXVNlromS32TtUVtbVFT2UWOMQSXy+/vJwjsLxgUIAgMEAQGcEhRN0VFCCGEEEIIIdTjYAEIsUO3/4SlpgEAjBfzqewiMi6crSS2Nr3h5EVDerbpUqWj7kMI+KKIEFHEEFFkiCCwP8HlsJXNgSMVC4MHCoMH/naIYSyNLZaqOvPVekt1nbm6zlLXZL5Sa75Se20AQfD7+QqDBgqCBwhDBgkG9iP4+I8dIYQQQgghhHovfE+IWGDT6TXbfgEAMj6CyipUbdjuHx3q/DqLqeyK7peThrNZjMUKAIRQQMaFS0bFkQmRHJHQyWHaGc5k8fv6CAb3v90gguD7+fD9fMQjYhwHGJPZXF1nrqwxV141V9aYr9RYahosNQ1w4jwAEHyeILC/cEigcGigaGhg981jamepa+TIJFyZtLtvhBBCCPUspksV2t1HlI/M4/XxYjsLQgih3gULQIgFmh/32g1GMnpon1efqHvxXUtNg+7AKY/pSc65O2OxGE5n6n45abpcDQDA4YgToyRJw8XxEazvxU5lFTZ/tpbrIe33nxWObeM7iBAKhCGDhCGDHN8yVpv5So25vMp0udpUdsVS22gqrTSVVsJeAACet6cwLEg0dLAwPFjQvy8QRNc+C3N1Xf2rq3m+3v6fvIGdiRBCCKF2jNXW8tVmS10TY7H6vvEU23EQQgj1LlgAQs5muVqvO3IWuBzPpfMJHtfz4TlNq7/TbN0nGZvQ3RNGGKtNf/iMZvsBm0oLAFyZVJo8Sjb5Hhf5CI6x2lTrtwOArU2v2bZfuXTeHV+K4HEdq8ZkAABgp2hTaaXpUqWp7IqptNLaoraeyjCcygAAjkQsCh0sDAsWhQcJggZ2yTws9fpUxmqz1Da2pR2Xz06++wsihBBC7qFt3zFLXRMAUFmFVFYhGR/BdiKEEEK9CBaAkLOp1qeCze4xc4JggD8AiIdFk7FhVE6xZmua12MLu+uudrv+5EXNz2nWplYAEAzu7zF9vGRMPMHnd9cdO0+3/4SlrpHn7WlVaXX7T8gmj+H7+3bJlTmkiIwJI2PCAAAYxlLTQJdcNhVfposvW5tVxswCY2YBAHBEQmHoYFF4iCgiWBA86M6KQcaLeVTeJUIoYExmbcov0qThXLmsS54FQggh1KPZtDpt6gEAEA+PNl7IU23Y7h8TSnBxqixCCCEnwQIQcipjeg6VW8KRihXzp7YfVC6dX/fS+7qDp2WT73FUhbr8puqf9jp6TvP79/VcNFM8PLrL1z3dJZtWp0nZDwBef33QeCFXd+iM6odtvv/8W9ffiSD4/fvy+/eVTboHAKytGlNROV1URheVW2obqZxiKqcYHMWg8GAycogocoggMKCDv12M1abeuAMAlA/PobKLjJkFmi17vJ56qOufBUIIIdTTaLbssRspcUKkz0uP1r3wnqW2Ubf/hMfMiWznQggh1Fv0ogKQVqstKSmpqKjQ6/UGg0EkEkml0sDAwPDwcE9PT7bT9QqMxarevAsAPB+azZFJ2o/zA/xkk+9p239CtS7Fb8VzXXhHa1Nr6/dbHXul8/y8FQtnSMcmulrpx0Hz4167gSITIsm4cEHQAMPZLCq3xAmTw3leCt7YRMnYRACwadrownK6qIwuLLPUNDhmpwMARyomo4aKooaKoofy/Xxuc7W2PUcs9c38AD/ppDFkbDiVV6I7ek46+R5h0IBufRYIIYSQizNX1uiOpRM8rucjcwkuV/nI3Mb3v9FsTZPck8hVeHTVXSwNzZaqWvGI2K66IEIIIXfi/gUgq9W6Zs2ajRs3pqenMwxz8wCCIEaMGLFkyZJHH31UIGC5B7B7a9t9xNLQzO/fV5o8+oaHFItmGE5n0vmlxgt54uHRXXAzu71t33H1T3sZk5kjFXs+NFuaPMplZ1mbr9Tojp5zvBwEAK6HVLFgmmrDdtXaFP/oUKf1UeYqPCRj4iVj4gHAptbSBaVUfildUGptajWcyzacywYAXh8vMmqoKDqUjB56fRUPAGwanXb7QQBQLp1PcLk8P2/ZtKS23UdUa1P6rnrBNetuCCGEkHOo1qWA3e4xM9mxvtvxkQ+VXaTZmub110VdcgvGYm1a9V9LQ7PPi8slo+O75JoIIYTciZsXgOrq6iZNmlRUVHT9QYIgJBKJxWIxmUwAwDBMenp6enr6Z599tnv37tDQUJbCujmbpk278xBcqw7c2FyGIxHLF05T/bBNtWE7GRd2l615zBVXW7750VxRDQCSexKVy+a7eBsa1dpUsNtls5Pbm/7IpifpDp2x1DXqfmFncjjXUy4ZO0wydhgAWJtaqbwSOv8SnVdqbWrVHTmrO3IWCEI4uL8oJoyMDRUOGUzwuOr/7bJTtHh4NBkb5riI4v5phpMXTZcqDGezHXUlhBBCqBcynMmki8q5cpl8we+XwOdd0h0+I5s0WjC4C6bKtu07bmloBgD1xh3ixChC4EKNDhFCCLmCLtjxx2VRFDVnzpyioiI+nz979uw1a9YUFxc3NzdbLBadTkfTtMlkKi8v37Bhw8KFC8VicVlZ2cSJE1taWtgO7p7Um3baKVo8MpaMuXWJzWPKWMEAf2tjS9veY3d+G4bRpOyve/0jc0U1r4+X75tP+/x9qYtXfwxns+iiMq5cdn1fpPbZQJqtaTZNG3vpAAB4fbxk947xeWF5/7Xv+3/0D8+H55DRQwkez3S5Wrv9QMO/Pr+69NWGf/2f/vh54HE8H54LAIzFAnY7hxQpFs0AAPXG7YzJzO6z6JEYRpOyX3/yIts5EEII3TnGbHEsgVc8NIsjJtuP8/v5yqYlAcO0rk2BW81S7xSbpk2b+gsAcJVya4tau+vwXV4QIYSQ+3HnAtCqVasuXryoVCoPHTq0a9euRx99NDQ01Nvbm/vrOiCBQBAUFLRkyZKtW7emp6cPGDCgvr5+9erV7MZ2S+aKav3JiwSf57n4vj8cxOEoly0AAG3qAZtaewd3sen0je99rflpHzDgMWtiv8/eJOPC7zizc/z2ovDBmRwJef1DjsnhdorWbE1jKd1NCEIQGCC/717ffz07YMOHvm894zE7WTDA306b6KJyYBiw2hvf/ar5P5uql7/esOJzYBhZ8mhh0ABrqwZfid4Bw+kMzU/7Wr/abKltZDsLQgihO6TdecjarBIMCpBNGHnDQ4r7p3E9pKaSCkN6zl3eRf2/3Y55uH1eehQIQrvjoLVZdZfXRAgh5GbcuQCUkpICAO+++25SUtKfDo6Kivr2228BYNeuXd2erLf59aMtj1nJt28hLIoaIh4WbadN6i17OnsTU2ll/Surqewirlzm+9YzykfmEcIe0NFJu+uwtalVMChANnHUzY86munoDp9xLGdzKYSAT8aGKZfM9f/0Da9H7wcAgs/jSEhrQ4vhxHmGouniy3Uvvd+275jHrIlAEI6Xv2yn7kkYk1n9v90AwNhsqh9+ZjsOQgihO9H+EYhy+Xzg3LwEnlQsmgkA6g13NVXWXHFVf/w8weN6PjxXOHSwZFQcY7aot+y+m+QIIYTcjzv3AKqqqgKA8ePHd3B8UlISl8u9evVqN2bqlQynM00lFQBgvJDr2GL8Nux6AwDoj5/3mJYkGNy/g7do23dcvWkHY7WJQoN8XlzGVSruMrNz2FSaa32RbvWiEK5NDh/XtveYam2q3zt/d80+yozZ4nhp6/XEImnScP2x9JavtzgeMlfXqdZvBwBCKGBoc8t/Nvq++XSPKMy5Au2OQ9YWtWBQP2uzisq75IQt4RBCCHU59aadjMksGRMvCg+55QDZpDG6w2fNFdXaPUcV13UI6gSGUa1LAYbxmDmR39cHADyXzDVmFhhOZ8omjxWFBd1NfoQQQu7EnWcAyeVyALh8+XIHx1dVVdlsNsdZqAuZr9Y7vrDUNJgrqm//y9rUCgDAMOaaho5cnLFYm/9vvWpdCmOze8xO9n37+Z5S/QEAleNF4eg/fFEIAIr7p3M9pHTJ5bufHN5NtNsPWlvUgsH9peNHAEHoDp0BAMWCqcLBAwBAMNCfK5cxJjMQQBeVVz/yauO7/21LO2FtwGZbt2NtUWv3HAGCUD62UHH/NABQrU1hLFa2cyGEEOoEU2ml4UwmIeB7/uWPl8AThHL5/GuLtlrUd3AXw+kMuvgyVyGTz5vsOMLz9pTPTgaGUa3ddvfdhRBCqGdjmNbvt2q27mM7h0tw5xlA48aNS0lJ+eKLLyZOnEiS5O0H22y2jz76CACGDx/ulHS9iOKB6ZKRsQCdef3B4wn69/3TUXaKbvrwezr/EkdMev/t4a7ZP95ZTKWVhtOZhIB/u75Iv04Ob/3uJ/WG7eL4CFebPvNbnWLZAiAI/bF0U3kVV6mQz51MxkfUv/mppa7J/9M37AZKvXEHXVTOWK1UdhGVXaRau43v70smRorjIoThQQTXSVvd9xSOtQCSscNEoUHCkEDdkXOWq/W6X056zGJhSziEEEJ3gmFUa1OAYeT33cvr43WbgaLQIPGIGGN6jubHPd7PLuncTcwWx3phz7/cd32Hafncyfrj582VNfrj56U39R5CCKHeQ3/igu7AKQAQhYeIooawHYdl7lwAeuutt9LS0g4ePJiUlLRy5cqJEyeKRKKbh1ksllOnTn3wwQeHDh0iCOL55593flT3RnC5HV/M1XE2ra7x3a/NFdVcT7nvW08LBvbr8lt0o19fFPJ8lLqDp/9ksN1OcLnWFnXb3mPy+VOckq+j1Jt2MCYzv39fm6ZNf+K8Y8GXZGSMMasQAEShg+niyy3/2eQxa6J04ihTaSVjtUmTRzO0icopttQ1WnY3tu0+wiFFZGwYGR9Bxke4+JZtzuHoBkoI+J5/mQ0ABJejXDq/8Z0vNT+nScYmchUebAdECCH05/QnL5rKqwDAkJ5jzCy8/WC7weg4RTYtSRg8sON3+d083OsQAr7nQ7ObP1+v/t8u8YiY62tDCCHUe9gp2lElB4DWH372/+QNguvOq6D+lDsXgGJiYtavX//AAw9cvHhxxowZYrE4MTHRx8fHy8tLJpPp9XqVStXS0pKZmdnWdm2b7XfeeSc5OZnd2KgjrE2tje98aalv5vv38X3rmdt/sOaCLA0tjheFltpGbe2hDp5lOJPpUgUga6vGcDYbACxX65s/+aH9eFvaCUg70f6tqbTy+kdNpZX9PnuTsdlNlyqorEIqq9BcXWc4l204lw0EIQwaQCZGiuMjBYEBrtnzqNv92jRdPm8yz9vTcYyMCSXjI6isQs3WfV5/fZDdgAghhDrCWt/k+MLy61r4P8cw1obmjheAbpiHe8OjknsSdAdP0cWXtdsP3n66MUIIuSvt9oM2tVYYMsim1VlqGvSHz8imjGU7FJvcuQAEAPfff79SqXzppZdyc3ONRuPJkyf/aGRUVNT7778/Y8YMZ8ZDd8Z8pbbx3f/a1Fph0IA+bzzVE+eM8Pv6eD/7sE3d1qmzROHB3ZTnznAVHh7Tk2wqLQAwtMmYWwx2RhQ1lCsVt48x1zRYrtYTYhEZHeZ4aSqKCQUAgssRhQeLwoM9F99nbVZRmQXGzAK6sMxUXmUqr9L8tI/npSDjI8nESDJqKCHgs/QUWeDY9I3n7Smf9btitHL5grq8Et3hs7JJYwSDB7AVDyGEUAcpFk4Xj4gFxt7xUwg+n9+BJfDtflsvfMtOzwShXLag7rUP2/YelU4cxffv0/ErI4SQG7A2trTtPQYEoXz0fluruumjNeof90hGx3NkErajscbNC0AAkJycnJOTk5ube+DAgYKCgsrKSr1ebzQaSZKUyWSBgYERERGTJ0+Oi4tjOynqEHNFdcPb/7EbKDJ6qM8rj3PIWyzr6xGkSSP+fJBrI7gc5bIFjq+bVn8HdkY6YaT3M4uvH8NYLLXPr7I2tZLRQ2WT77nldXg+StnUcbKp4xiTmcq/RGUWUlkF1laN7tBp3aHThFBARg0lEyLFCZFcpZv3aLdTtGZrGgB4PjLvhn5PfD8f2dSktr1HW9em9H3nhV46PQohhHoQDkcQGNB9l79hvfAtOZaG6Y+lqzft7PPaE90XBiGEXJBqw3bGYpFOGCkMHgjBA8mYUCq3RJOyv/0tTC/k/gUgh5iYmJiYGOfcS6fTPfHEEy0tHd3kqK6uDgBKSkq6M5SbsNQ2Nq76r91ASUbFeT+/lOBh52CXQOeXGi/mcURCz4dm3fAQwed7Pjyn+ZMf1Ft2S0bF3b7cTggF4sQocWIUAJgra4yZ+VRGgelytTEj35iR30oQgsD+4sRIcWKkILC/W1ZANFvTbJo2YehgycjYmx9VPDDdcDrD8YpfMgpr1ggh1Ivdar3wLXn+5T7j+VzjxTwqp5iMDXNaQIQQYhedX2q8kMcRCT0fvPYORbl0fu3L77f9clKaPFowwOw5BQgAACAASURBVJ/deGzpLQUgZ6qurt66dSvTyU03q6qquimP27A2qxrf+dLWpicTIr3/vhT3jXIVdrtqXQoAyOdP4XreYoaOZFSc3lFuT/1FuXR+B68qCAwQBAYoFkyzadqozEJjZj6VW2KuqDZXVGt+TuMq5eKESDIxyp0WiFkamnUHTgJBeC2/RSsHAOCQIsUD01u/ddEt4RBCCDmN7shZc0U1EATYGe3OP2kmyPfvYyqvUm3Y7h/1ei/vfooQ6i2uf4fy6xoCfv++snvH6A6cUq1L9VvxLKv5WNPbC0COlV8HDhzo06fL1kVHRERkZ2c3Nzd3cPx33323bds2Dw/c2ed2bFpd4ztfWlvUorCgPi89itUf19F24JS5uo7n6+0xc8IfjblWbt9/QjpxVGfL7VyFhzR5lDR5FGOx0AVlxowCKjPf2qLWHTqjO3SGEArI6KFkYpQ4IbKnb4+lWpvCWKy3b/Eju3eM7tBZc0W1ds9RxYKpzoyHEELIdRhOXAAAYBjNz2kdPMVytd5cebVTW4whhFAPpTt4+pbvUDwfnGU4m0XnXzJm5DuWHfQ2vb0AlJOTAwAWi6VrL9up5WYHDhwAAMId17N0FbuBanznK0tdk2Bw/z6vP+k2Mz7cgN1g1P68HwCUj8wj+H/458Lv31eWPFp38LR6farvv+6w3E7w+WRcOBkXDo8vNF+pMWYUUBn5psvVxov5xov5rQQhDBogHhZNJkYKBva7w+fDHiq3hMoq5JAixQO3bUVPEMrl8xv++X/aHQel40fcfto/Qgghd+X5yDzj+VzozHxzrsJDOLh/90VCCCEXYTcYHV01lUvm3vAOhSMVKxZMU61LUa1PJWPCCH6vq4e47RNmGMZu7+i2C1ar1WaztX/LxdklroQxmZve/8Z8pYbv7+v75tMcMcl2IvQbzdZ9Np1eEBggGOhvbbxd3yvphJH6Y+epvEtUZgGZEHmX9xUMChAMClAsmGpTa42ZBVRmAZV3ybGDmPrHPbw+XuLESDIhShQR0jMaRdntqrXbAIAQ8lu+2PCnwwkejzGZNT/u9X724e4PhxBCyOUIgwfiXB6EELolxzsUUdQQ8YhbTMuQTR2nP3LWXF3Xtv+EfHbyzQPcm9sWgD799NOXX365g4MHDRp0/bedbd+DuhHDtHy1mS65zPP29P3X33riju+dYqdNTR98KxwSeHM3ZRdk0+jaDpwCAHNlTc0zKzt4lubntLsvALXjespl946R3TuGMVuovBIqo8CYkW9tam1LO9GWdoJDisi4cDIxShwfwbluc3pXY9MZLA3NAGDT6CjNpQ6eZSq70o2ZEEIIIYQQ6mksNQ26A6eBw/mj3qMEl+O5dH7jv/+j3bZfOjbxlj1M3ZjbFoDk8t71B+mutLuPGM5mccSk7z//1htWu2i3H6ALSunCMnFipHBIINtx/gSHFJJRQy31HW13BQBAgCgmtDvCEAK+YwcxL2aR6XK18WIelVlgvlJrOJtlOJsFHI4oNEicGEkOi+b39emOAHeDK5f1+/h1q7qtU2fx+/l2Ux6EEEIIIYR6ItWG7YzNJpsy7jZNIcjooeKESGNmgeanfV5PPeTMeKxz2wLQY489xuVyn3vuOb1e7+Xl9c4779yyzfOCBQsA4Ntvv/Xy8nJ6RvQnqLxL6v/tBoLwfm5Jb3iva21sadtzDACAYVrXbPNf/YqL73ROCAW+bz3DdoqbEIRjYrzng7OszSpjRj6VkU8XltFFZXRRGWzcwe/nKx4WRSZGiYYEAsdVNkPh9+/L79+X7RQIIYQQQgj1VMaL+VR2EUciViyafvuRymULqLwS3dFz0kljetWKWrctAAHAsmXLxo0bt3jx4vT09JUrV65Zs2bWrFsvq5kxY0a/fj2va6x7sza1Nn+2Fux2xcLpvaRDu2rjDsZikYyKoy9VmCuq9ScvSJNGsB2qZ+P5KD2mJXlMS7JTNJVdRGXkG7MKLbWN2tpG7c7DXA8pmRApTowkY8NxS3WEEEIIIYR6MIZRb9wOAIzVWv+Pjzt6yqadfm8/373BXIk7F4AAICgo6NSpU6tWrVq1atXs2bMff/zxTz/9VCqVsp0L/QnGbGn66Hu7ziBOjFLcP43tOM5A55caz+dyRELlsgVUfknLfzapN+0UD4/hkCK2o7kDDimSjI6XjI4Hu50uvmzMyKcu5lsamvXH0vXH0gk+XxQ1RJwYJU6M5CoVbIdFCCGEEEIIdQ5jttja9ADAmMy3353metZWdXeGcjluXgACAB6Pt3LlyilTpixevPj7778/evToxo0bR48ezXYudDut32wxV9bw/ft4P7fExZdBdQ27XbU+FQDk86ZwlXLpuOG6Q2dMJRXaHYd6RDfonoTDEUWEiCJC4JF5lpoGY0a+8WK+qbSSyiqksgpbvycEgf3Fw6LEiVGCwAC2syKEEEIIIYQ6hBAKAv77b7ve0KmzOO6+y9AN3L8A5DBq1KicnJxnn312w4YN48aN+8c//rFixQo+n892LnQLbWnH9ScvckhRn1ef6CWbvusOnjZX1fJ8vT1mTQAAIAiv5QvqXvuobfdh6cSRfD+Xa1rsHvgBfvIAP/mcSbY2PZVZYMzIp3KKzRXV5opqzdZ9PG9PMiFKPCxKFBFC8HvLj0qEUC8RFxcHAAcOHLhlh0SEEEKoJ+JISI6kV7x/vGO96F2NTCZbv3799OnTn3zyyXfffXf//v2bN29mOxS6kbm6Tr1pJxCE9zOL+QF+bMdxBrvBqNmaBgDKJXOJX4uSgsEDpOOG6U9cUG/e1eflx1gN6P64HlLphJHSCSMZi4XOLzVezDdmFlhb1LoDJ3UHTnJIERkbRiZGkvERXBkuIEUIuYOcnBwAsFgsbAdBCCGEkPP0ogKQw8KFC0ePHr1kyZJjx44lJCSwHQf9DmOztfxnI2OxyibfIx4Zy3YcJ9Fs3WfT6UVRQ8QjYq4/7vnwHOOFPGN6DpVbQnbP1unoBgSfT8ZHkPERXgxjrrxqvJhvzMg3V9YYzmUbzmUDhyMaEkgmRomHRfWGbekQQj0RwzB2u72Dg61Wq81ma/+Wy+V2TyiEEEIIuQRX2QLZmQICAg4fPvzhhx9e/6IHuQLttv3myhqen7fnkrlsZ3ESS02D7sBp4HCUS+ff8BBX4SGfMwkAVOtTGVtHX82jrkEQgsEDFA/M8P/oHwHfvOP12EIyNozgcuiSy+rNO2uff6f22bdVG3fQRWX4R4MQcimffvoprwMcgwcNGnTzQYQQQgi5q176nz2Hw3nllVfmzJlTXV0NAN7e3mwnQmAqr9LsOAgE4f3MwxyRkO04TqLasJ2x2WRTxgkG9rv5UY/Zyfpj6Zar9fojZ2WT73F+PAQAPG9P2dRxsqnj7LSJzik2ZuRTmYWW+mbL7iNtu49wpGIyLkI8LIqMDeslLasQQq5MLpezHQEhhBBCLqqXFoAcQkJCQkJC2E6BAAAYs6Xly01gs3vMThaFBbEdx0mMF/Op7CKORKxYNP2WAwg+z3PxfU0fr1Fv2S0ZFceRSZycEF2PIxKKR8aKR8aC3U6XVlIZ+caL+ZbaRsOpi4ZTFwkuVxge7NhLnueLNWWEEDsee+wxLpf73HPP6fV6Ly+vd95555ZtnhcsWAAA3377rZeXV1fd2mazffHFF42NjR0cf/LkSQCwWq1dFQAh5B4Yk7nxva/5/r5ef13EdhaE3E2vLgAh16HessdS08AP8PN8cCbbWZyEsdrUm3YAgOKB6bdpLSweGUvGhFK5JZrUX25eJobYweGIQoNEoUGei+dY6pupi3nGzAK65DKdf4nOv6Ral8Lv39dRCRIOCQSCYDsuQqh3WbZs2bhx4xYvXpyenr5y5co1a9bMmjXrliNnzJjRr98t5p/emezs7BdffLGzZ+l0uq4KgBByD9pdh+nCMrqwTDw8mowLZzsOQm4FC0CIfXRRedu+Y8DleD+7pH0bLLfXtveYpa6JH+AnmzL29iOVS+fXvvx+2/4T0omjBAP8nRMPdRC/rw9/drLH7GS7wUhlFRkz8qmcIsvVeu3Veu2Og1wPKZkQKU6IFMWG9Z6FjQgh1gUFBZ06dWrVqlWrVq2aPXv2448//umnn0ql3buPYUJCwvfff9/a2trB8WlpaSdPnpTJZN2aCiHUs1hbNdpdhx1fq9an+kcPJbA/PUJdBwtAiGWMydzy5SZgGMW8qcKgAWzHcRK7gdKm/gIA1sbWq8tf78AJDDCM5se9fV57otvDoTvCkYglYxMlYxMZm81UdNmYWWDMyLM2tOiPpeuPpRN8nigiRJwYRSZE8nyUbIdFCLk/Ho+3cuXKKVOmLF68+Pvvvz969OjGjRtHjx7dfXckCOKxxx7r+PiWlpaTJ09i82mE0PXUG7czJrN4ZKylus5S26jbf9Jj5gS2QyHkPvA/XcQyzfYD1qZWQWCAfMFUtrM4D2OxAocDAIzFwlgsHT0L963rCQguVxQ1RBQ1RLl0nqWmwZhRQGXk06WVVE4xlVMMa34WDOxHJkSKEyOFIYNwgRhCqFuNGjUqJyfn2Wef3bBhw7hx4/7xj3+sWLGC32sm2yKEehbTpQrD2WxCwFc+Ms9cXdf0/jeabWmSccO4Ht07gRGh3gMLQIhN1oaWtt1HgSC8nljUq6Z3chWy/mveZcwdLf044CZTPQ4/wE8e4Cefc69Np6eyiqiMfCqn2FxVa66q1W4/wJXLyPgIcUKkKCaUQ4rYDosQck8ymWz9+vXTp09/8skn33333f3792/evJntUAghdBOGUa1NAYaRz5nE81HyfJRkXDiVXaT5aa/XE9gNGqGugQUgxCbVhu2MxSKdMFIYMojtLM5G8Pm9p+ER4sqk0qTh0qThjNVGF5VTmfnGzILfFojxuKKIEEe3INxBDCHUHRYuXDh69OglS5YcO3YsISGB7TgIIXQj3ZGzpsvVPC+F/L57HUeUS+fX5V3SHT4rmzxWMKjLOtYj1Jtx2A6Aei8qp9h4MY9Dijz/MpvtLAg5CcHjktFDlcsWBHy5st//veW5eI4oPJixM1RuiWptSs0zK2v/vkq9eSddVA52O9thEUJuJSAg4PDhwx9++KENFxQjhFyMnaI1P+0DAM8l8wihwHGQ389XNnUs2O2q9amspkPIfeAMIMQOxmZTrUsBAPn907gKD7bjIMSC9gVidr2RynbsIFZsqWnQ1jRodx7mSMVkbLg4MZKMDedIxWyHRQi5Aw6H88orr8yZM6e6uhoAvL1xyiFCyCVotu23adqEQwdLRsddf1yxcLrhZAZdUGpMzxGPjGUrHkJuAwtAiB26fccttY18f1+P6ePZzoIQyzjS9h3E7KaSy1RmgTGzwFLbaDidYTidAVyOaEggmRBJxkcIBvizHRYh1OOFhISEhISwnQIhhK6xNDTr9p8AglAuX3DD/hgciVixaEbr91tV61PJuPD2yUEIoTuDBSDEAptGp9m2HwCUy+YTvF7U+xmh2yO4HFFEiCgixHPJXGtDizGzgMrMp4vK6eLLdPFl9eZdvD5eZFyEODFSFBmCPaQQQggh5AZU61IZi1V27xhh0ICbH5VNGqM7dNp8pbZt7zH5/CnOj4eQO8ECEGKB+n+77BQtHhZFxoWznQUhF8Xz8/aYMd5jxng7RdO5JcbMAiq70NrUqjtwUnfgJCEUkFFDyYQIMj6S56VgOyxCCCGE0J2g8i5RmQUcUqRYNOPWIzgc5dL5DSu/0Gw/IEkazvP2dG5AhNwKFoCQs5nKruiPnyf4POUj89nOglAPwCFF4pGx4pGxwDCmy9VUZoExs9BcedWYkW/MyAcAwaB+ZHykOD5COGQQcLC1P0IIIYR6BsZmV69PhT/rCiqKHCIeEWs8n6P5ca/3sw87MSBC7gYLQMjZ1Ft2A8N4zJrI88Pekwh1BkEIgwcKgwcqHphhU2uNWYVUViGdW2K+Umu+UqvdfoAjk5CxYeL4SDI2jCOTsB0XIYQQQuh2dPtPmKvreH7eHtOSbj9S+chcKqtQf/KCdNJoUWiQc+Ih5H6wAIScii4qo/NLORKxfM4ktrMg1INxPeWy5NGy5NGMxUoXlVNZBVRWoaW+2XAqw3AqAzgcYcggcUIEGR8hGNjvhn6KCCGEEEKsY0xmR1dQa0NL1YN/7+BZmi17/f79fHfmQsidYQEIOZXmp30A4DFrIkdMsp0FIXdA8HlkTCgZEwrLFljqm6nMAiqrgC6+bLpUYbpUod6yh6tUiOMjyLhwUUwoRyRkOy9CCCGEEAAAY7cTQgEYjJ06i+DjG1iE7hz++0HOQ+dfoovKOTKJx4zxbGdByA3x+/rwZ07wmDnBTpvo3BIqu8iYVWhTaXSHz+gOnyF4XGFYsDgunEyI5PfzZTssQgghhHo1Dinq/90qtlMg1LtgAQg5j3rrPgCQz07mkCK2syDUM+iOnOV5e5IxYZ06iyMSikfEiEfEeDGMuaqWyio0ZhWaSq/Q+Zfo/Euwcce17eTjw0WRQwihoJvCI4QQQgghhFwHFoCQk1A5xaaSCq6HVPZnPd4QQg5UVmHr11sIoaDf5/+8w01PCUIwKEAwKEA+b4pdb6Ryi6msQiq76Lft5AV8UUQIGRdBxofz/Xy6+hkghBBCCCGEXAUWgJCTaLbuAwCPOZOwCwlCHcHYbKr12wGAMZnV/9vl8/zSu7wgRyqWjEmQjEm4tp18ViGVVWi6XE1lF1HZRbAW+H19yLgIMi5cFBFCCPhd8BwQQgghhBBCLgMLQMgZqKxCU9kVrkLmMWUs21kQ6hl0aScsdY08X2+bWms4nekxdZxw6OCuuXT7dvILp9u0OiqnmMouonKLLfXNlvrjbWnHcVoQQgghhBBC7gcLQKj7MYxj+o98zqQbuo3YaRNOCELoZjatTpOyHwC8HltoKq3UbNvfuuZn/w9f6/IN3blymTRpuDRpONjtpvIqKquIyi40VVz9bVqQnw8ZH07GRYjCg7FbUI/HMC1fbubIpcolc9mOghBCCCGEnA0LQKjbGTMKTJeruZ5y2eTfTf/RbNuv+TnN54VlktHxbGVDyDVpftxrN1BkQqRjQZb++HlzZY3++HnphJHddUsORzgkUDgkULFoxu+mBTU0W9JOtKWdIPh8UUQwGRtOxofz/XveJmK6w2e4cpl4WDTbQdikO3JWf+I8AJDRoWRs5zqLI4QQQgihng4LQKjbabbtBwD53MnXdxWxNrVqtx8EhlFv2C5OiMSZBQi1M1+p0R09R3C5ykfmAgAh4Hs+NLv58/Xq/+0Sj4jhiMnuDnDjtKDsIiq7yHS5msoppnKKYX0qr48XGRtGxoWLoob2iEl8VE5x6zc/Enye/2dv9tpFbXaK1vy0z/G1an2q/yevE1wuu5EQQl2uLe0ElVXg88IyjkTMdhaEEEIuBwtAqHvRReXmimquQiabNPr646qNOxiLBQjC2qrR7jikWDSDrYQIuRrV2lSw22Wzk9sn2kjuSdAdPEUXX9ZuP+i5+D7nRWmfFvTADJtOT+eUUNmFVG6JtalVd/C07uBpgscVhgaRsWFkbJhgYL8uX6HWJRibXb1xBwAwFqt6w44+rz3BdiJ2aLbtt2naRKFBNm2bpaZBd+CUx/TxbIdCCHUlS32zeuN2xmrTbN2nXH4/23EQQgi5HA7bAZCba9t7FABkk8cS/N+m/9AFpcb0HEIo8HlhGRCEdvdha1MrexkRciGGs1l0URnXQ6qYP/W3owShXLYACKJt71FLXRMrwbgyqWRsovdzj/Rf857/h696PjhLFBrEMAxdUKrevKvu5Q+uPv5my5ebDKczbDo9Kwn/iO7ASXN1Hc/Xm0OKjBfzqNwSthOxwNLQrNt/AgjCc9l8z0fmAYBma5qr/UkhhO6SesN2xmoDgLYDp8zVdWzHQQgh5HKwAIS6kbWhxZhRQPD5sus3/7LbVetTAUAxb4pkdLzkngTGbFFv3sVaSoRcRvu/BcWDsziS3y31EgzuLx0/grHa1Jt2spTuVwQhGDxAPn+K36oXBqxd7fPSo9LkUTwvhU3Tpj9+vvn/1l9d/nr9Pz7S/LSXLrnM2OzshrXrjY5VqMrlC+TzpwCAal0KY7Oxm8r5VOtSGYtVljxaGDRAnBhFxoXbDcb2FWEIITdA5RYbM/I5pEgyJh5sdvWGHWwnQggh5HJwCRjqRm37j4PdLhk/giuXtR/UHTpjvlLL8/b0mDkBAJQPzzFeyDOczZJNHSsKD2EvLELs0+46bG1qFQwKkCWPuvlRz7/cZzyfa7yYR+UUu0gHX46ElIyKk4yKAwBzdR2dU0zlFNHFFabyKlN5FaT8whGToqihjjViPB+l8xOqf9xj1xlEUUPFCZFMTJj+6DlLTYPuwGmP6UnOD8MWKu8SlVnAIUXti22Vj8yry7ukO3RGNvkewcB+7MZDCN095teKj3zBVFnyaCrvEpVbbLyYLx4WxXY0hBBCLgRnAKHuYjdS+qPpQBAeM8b/dvDXz5yVS+c7Gj9zlQr5nEnwa98TlsIixD6bSqPdeQgAlMvnA+cWP5y5Cpl83mQAUK1PdcE5LIIB/h6zk33/9eyA9at933jKY/p4vr+v3UgZz+e0fvtjzVP/qn3uHdXaFCqrkDGZnRPJcrVed/gMcDnKZfMBgOBxPR+eAwCarft6z+onxmZXrUsBAMX907gKD8dBfoCfbMpYsNtV61JZTYcQ6hq6X06aq+t4ft4e08dzpGLF/dMAQLUhlbFY2Y6GEELIheAMINRd9EfO2SlaFDX0+o+XNT+n2XR6UeQQ8cjY9oPy++7VH0t37Hwku3cMG2ERYp9q007GZJaMjr/NVDiPmRP1R85aahp0B097THPROSyEUEDGR5DxEQBgbWqlckuonGK64JKlrtFS19iWdpzg84Shg8mYbm8drVqfCja7x4zxggH+jiPiYdFkbBiVU6z9eb/y0V7RIVW3/4Tlaj3fz0f2+78wigemG05l0AWlxvM54hGxf3Q6Qsj12fVGTcp+cHy6xucBgGzKOP3hs+bqura04/L77mU7IEIIIVeBM4BQ97Db2/afAAD5zAntxyy1jbpfTgGHo1w6//qxhIDv2NhI8+Neu4FyclKEXIGptNJwOrP938IfIXhcz4fnAoDmp54xh4XXx0s2aUyfVx7rv3Z131UvKhZME4YMYmx2Or+9dfQbLV9s0J+4YNO0de2tjedzqNwSjlSsWDDt+uPKpfMJLreXdEi1642a1F8AQLns2tvCdhyJWPHAdABQbdjBWCzs5EMIdYXf1romXlvwRfw681Gb8otNrWU1HUIIIReCM4BQtzBeyLM2tfL9fR2zABwc61Zkk+8RDLqx5YRkdLzul1N0UZkm9RflkrnODYsQ2xhGtTYFGIar8NDuOPinwwkB324w9qw5LASXIwwdLAwdrFg0w643UnklVG4xnVtibVHrT17Un7wIBCEY6E9Gh4piwkThQdfvG3gHGKvN0U7b88FZHJnk+of4AX6yyfe07T+hWpfit+K5u3pWLk+9ZbddZyCjh5IJkTc/Kpt8j+7QGXNVbdueo/J5U5wfDyF09yw1DdevdW3nqAcZM/LVP+31fuovbMVDCCHkUrAAhLqFdu9RAPCYntS+uMOYWUBlF3EkpGLRzFueolw+v+7VD3X7jsvuHc3393VeVoTYZtPqTJerAcDa1Ko7dKaDZxmzCntQAeh6HKlYMjpeMjoeACw1DVRuCZVbTBeWma/Umq/UancfIQR8UViwKCaUjAkVDPC/gzVibbsPW+qb+QF+0lutKlUsmmE4nUnnlxov5ImHR3fBU3JJlqv1uiNngcvx/P2ky99wOMpl8xtWfqHdflA6fiRXKXduQIRQF1CtSwWb3WP6b2td2ymXzqdyi/VH02WT7hEGD2QlHkIIIZeCBSDU9UzlVaaSCo5ELJ0w0nGEsdocm1Mo7p/O9ZDe8izBoADZxFG6w2dUG3b4vv6k8+IixDauwsP3raetTapOnSUMGdQ9cZyKH+DHD/DzmDGesVhNJRVUbjGVV2KurKFyi6ncYjUAV+FBRg8VxYSS0aFczw5VKGwanXbHIXCse+LeYqUzRyKWL5ym+mGbasN2Mi7sLmcbuaxrLZBmTrj5bWE7UeQQ8YgY4/lc9Zbd3n972JnxEEJ3z3g+h8otbu/6fAOen7fHtCTt7iOqdal9V73QfQ3XEEII9RRYAEJdr23fMQCQTRrj2OcLAHRpxy11jfx+vrJp425zouLBmYZzWVRmAZVdRMaFOyMrQq6BjHGJbd1ZRPB5oqghoqghnnCfrU1P51+icorpvBJrq+baGjEAwQB/UXQoGRMqCg9u//FyM/WmnXaKFo+Ivc3vqseUsfpDZ8zVdW37jjs2InQzxvRfWyDNn3r7kcolc6msIv2JC7IpY92jqohQL/HbWtdFM29Y69pOfv80/akM06UKw9ksyZgE5wZECCHkcrAAhLqYTaU1ns0muFzZ1Gu1HptWp0n5BQCkSSMsVbW3P10yMk535Kxqw3b/6KEEl9vtcRFCrofrIZWMSXC8V7HUNFB5JXRuCV1YZq6uM1fXte09SvC4wqGDyZhQUXSocHB/4Pw2zcdcUa0/eYHgcW/fThs4HOWyBQ1vf6FNPSBNGt7BuUU9BsOoNu0EAMZirfvHRx08Rb1hh9+qF7o3GEKo6/y21nXSPX80hkOKFItmtH69Rb1xhzgx6jalc4QQQr0BFoBQF9MfP8/YbJJRcTxvT8eRtn3H7UYKANRbdqu37O7IRSw1DcazWZKxw7oxKEKoJ7i2Rmz6eMZmM12qpPNKqLwSU3k1XVhGF5bBlj0ciVgUNYSMDhVFD+X7+bSu2eZop922+/D112GsNoJDXF8qAgAOKbJTtHrLHu9nFjv3aXUvxmJljDQAMCaztbGlg2fZ2nrAvnIIIYc/XevaTjZxlP7QGVN5lXbXYcXC6c4KiBBCyBVhAQh1PacaSQAAIABJREFUKYbRHzsHANKJo9qPiUIH08EDGTvT8csQPC7/j5tWIIR6IYLLFYUHi8KDFYtm2g0UXVBK5ZXQeSWW+mZjeo4xPQcAeD5Ka7MKAKwt6o6306ZyS7oxNxsIAb/ff1faO1nQ4So8uikPQqjLaX7aa6doIIjW77b+6WC73ggA2l2HZZPHchWy7k+HEELIRWEBCHUluqTCUt/M81KQMaHtB8n4iOs3g0cIobvEkZDiETHiETEAYG1qpfIv0XkldF6po/oDAAAEz0vBD/Dl9/Pj9/UxnM2mi8sBQDIqXhQ99IarCYcGOjW9U3BIEYcUsZ0CIdRd7AYjAADDdHyWH9jtdorGAhBCCPVmWABCXUl/9BwASMaPuGGdBUIIdRNeHy9Z8mhZ8mhgGPOVWiq3hM6/RJdctraqra1qKreE4HIYGwMEAAPGvBLlEwu5slvvRYgQQj2FzwvLPB+eA0wnpldzpGKORNx9kRBCCLm+XlQA0mq1JSUlFRUVer3eYDCIRCKpVBoYGBgeHu7p6cl2Ondgp2jjuWwgCNmEUX8+GiGEuhZBCAIDBIEB8jn3Mhar6VIFlXeJzisxlVcDMMAAADAGY/2rH3rMnCiKGiLo3xc3RUYI9VQcDq+PF9shEEII9TDuXwCyWq1r1qzZuHFjeno6c6vPSQiCGDFixJIlSx599FGBADdHuHPGs9l22iQKD+H5ebOdBSHUqxF8nihyiChyiGFQv+ZP13JIkXh0PF1wydrYam1WqdalAABXIRNFDiWjhoqihuD7KIQQQggh5PbcvABUV1c3adKkoqKi6w8SBCGRSCwWi8lkAgCGYdLT09PT0z/77LPdu3eHhob+wcXQn9A52j8nj2Q7CEIIAQAwZot68y4A8FwyVzZpDAC0fP0//ZFzXLkMuBybSms4nWE4nQEAPF9vRyVIFDmEK8cGGQghhBBCyA25cwGIoqg5c+YUFRXx+fxp06bNnj17zJgx3t7enp6eXC4XAMxm89WrV8+cObNv3769e/eWlZVNnDgxLy/P2xsnsHSapa7RVFLBIUWSkXFsZ0EIIQAA7a7D1qZWwaAAWfK1danKJXOpC/k2ra7Py4/xA/zoglIq/xJdUGZtbNE1tugOnwGCEPTvK4oaIoocKooI5ohJdp8CQgghhBBCXcWdC0CrVq26ePGiUqncvn17UlLSzQMEAkFQUFBQUNCSJUvy8/NnzpxZXV29evXqjz76yPlpezr90XQAkIxJIIS4jA4hxD6bSqPdeQgAlMvnt7el50jEikUzWr/fqlqf2u/zf8qmjpNNHQd2u6myhs4vdXSPNlfXmavr2vYdBw5HOLi/KGqoKHKIKHQw/nBDCCGEEEI9mjsXgFJSUgDg3XffvWX15wZRUVHffvvttGnTdu3ahQWgzmJsdv2JCwAgnYjrvxBCLkG1aSdjMktGx4vCQ64/Lps0RnfotPlKbdveY/L5UwAAOBxh0ABh0AD5nHsZq81UWkkXlNL5l0xlV0zlVabyKu2OgwSPKxwSKIocIooaKgwZRPC47DwrhDqjurr63LlzBQUFfn5+SUlJkZGRtxxWVVW1c+dOAHj++eedGxAhhBBCTuXOBaCqqioAGD9+fAfHJyUlcbncq1evdmMmN0VlF9rUWn6An3BIINtZEEIITKWVhtOZBJ/vufi+Gx/jcJRL5zes/EKz/YAkaTjP+3e7QBI8rig8WBQeDAunMyYzXXyZLrhE55eaKmvoonK6qBx+TiOEAlFokCgyRBQ5RDB4AMHlOO+JIdQxVqv17bfffv/99202m+MIQRDz5s374Ycf5HL5DYNLSkr+/ve/AxaAEEIIIXfnzgUguVze1NR0+fLlDvZ1rqqqstls2ADoDuiPpQOAdCLu/o4QcgEMo1qbAgwjv+/eW27vJYocIh4Za0zP0fy4x/vZJX90GUIoIGPDyNgwALAbKLqojM4vpQtKzVfrqdxiKrcYADikSBQeLIoIEUUOEQQG4L7yyEUsWrQoNTUVAAiC6N+/f2trq8FgSE1NLS8vP3LkiJcXbnuHEEII9Ubu/LnluHHjAOCLL76gKOpPB9tsNsfKr+HDh3d7Mvdi0+qojAKCy5WOw986hBD79MfPm8qruEqFfO6kPxqjXDKX4PP1Jy/SJZc7ck2OhBQPi1YuX+D/6Rv917zn8+Jy2eR7+P6+doo2ZhaoNu6oe3V19dLXmlZ/17bvuLmqFhim654QQp2zf/9+R/Xnr3/9a2tra1VVlUaj+eGHHyQSSW5u7pNPPsl2QIQQQgixw51nAL311ltpaWkHDx5MSkpauXLlxIkTRSLRzcMsFsupU6c++OCDQ4cOEQSB8587y3DqImOziYdFcxW4dzJCiGV2ilZv2QMAyofvu03bZl4fL4/ZE7WpB1RrU/1Xv9KpmTtcuUwyOl4yOh4AbCotVVBKF5Y6thIzXswzXswDAK5MKrw2MyhE0L8vzgxCzvT1118DwLx587755hvHER6Pt3z58uDg4IkTJ6akpKSmps6fP5/VjAghhBBigTsXgGJiYtavX//AAw9cvHhxxowZYrE4MTHRx8fHy8tLJpPp9XqVStXS0pKZmdnW1uY45Z133klOTmY3do9jOJMFANIknP6DEGKfdvtBm1rL8/Xm+fuaK6pvM5KMCdXtO26uqNafvCBNGnFnt+Mq5dJxw6TjhgGAtUVNF5Q6fllb1MbzOcbzOQDA9ZCKIkIcv/gBflgMcn0mk+mOzxUKhV2Y5M6UlJQAwAsvvHDD8XHjxr388surV69+9dVXZ82aJRB08cZ2FEW9/PLLzc3NHRyfm5sLAGazuWtjIIQQQuiPuHMBCADuv/9+pVL50ksv5ebmGo3GkydP/tHIqKio999/f8aMGc6M5waszSpTeRVHJCTjI9jOghDq7ewU3bb3KABYG1vqX/uwg2dptu2/4wLQ9XjentLxI6TjRzgC0IVldGEZXVBqbdUYzmUbzmUDAFcuu9YzCItBLuyW84U7iHGB1X/V1dUAMGTIkJsfeuuttzZu3FhRUfHVV1/dXCG6S6Wlpf/97387e5bRaOzaGAghhBD6I25eAAKA5OTknJyc3NzcAwcOFBQUVFZW6vV6o9FIkqRMJgsMDIyIiJg8eXJcXBzbSXskw7lsYBgyIZIQ8NnOghDq7QiBQJwYZWls7dRZoojgLk/C8/WW+no7WuNbGxzFoFKqoMymurEY5FgphsvEUBfy9PRsaGiora3t06fPDQ9JpdIPPvjgkUceefvttxcuXNivX78uvG9MTMzRo0dbWlo6OH7z5s27d+/28PDowgwIIYQQuo07LADpdLpTp05duHChubm5T58+K1asqK2t9fHx6fLpxF0lJiYmJibGOfeiafqVV15pbGzs4PgePQXaeDYLACSjsXyGEGIfweX4vPQo2yluxPPzlvp5S5NHAYCloZkuLDMVltGFZb+bGYQ9g1xJRkYG2xHuSnR0dENDw8cff7x582bipr9LS5Ys2bx586FDh5YvX56Wlsblcrvw1hMmTOj44AsXLgAAh+POG5IghBBCLuVOCkBff/31G2+8odFoHN/GxsauWLHip59++uCDD15//fUXX3yxSxP2PCUlJV9++WVnz+qJU6CtTa2my9UckZCMw/VfCCH05/h+Pnw/H1nyaGifGVRURheWXd8ziCMVi8KCRREhovBgwaB+gG+PnS4hIYHtCHdl8eLFBw8e3LJli1Ao/Pzzz2WyG7do+P7776Oiog4ePLhkyZLvvvuOlZAIIYQQcr5OF4D+/e9/r1ixAgA4HM7gwYPLy8sdxzkcTktLy0svvVRbW/vJJ590cczucenSpezsbJqmY2JiIiIiumr6Umxs7JEjR1pbO7oGoedOgTak5+D6L4QQujPXzwyyNrbQReV0QRldVGZtVrXvJsYRk8KwIFF4sCg8WDB4AMHFYpDrysvLEwgEoaGhbAeBxYsXr1+//ujRo+vWrfvxxx+jo6OXLl361FNPtQ8YOHDghg0bFi1atGXLluPHj4eFhbGYFiGEEEJO07kCUEZGhqP6s2TJkk8++cTb27t9avHf/vY3iqLefPPNTz/99KGHHnKdT8+Ki4u//vrr3NxctVodGxv72muvRURE2Gy2xx57bMOGDe3NGhUKxTfffPPAAw90yU0nTpzY8cE9dwo0rv9CCKEuca1n0ISRAGBtaqWLyunCMrqo3NrYQmUWUJkFAMARCYVDBzvaBgmDBxJ89+/i14PY7fbx48cLBIKGhga2swBBEHv37n366ac3bdpE0/SFCxfi4+NvGDN37ty0tLQFCxbU1dXV1dWxkhMhhBBCTta5l4+ff/45AMyYMWPDhg03PMTn89944426urqvvvrqk08+2bJlS5dlvAvr169/+umnKYpyfJufn79z584TJ07s3r17/fr1joNcLtdms2k0mkWLFpWWlv7zn/9kLW6Pguu/EEKoO/D6eEn7eF3bTaxVYyoqo4vK6aJyS20jlVtM5RYDAMHnC0MGiSKChWHBoqGBhNBFG/C5E4ZhDh8+fOHCBb1ef/OjhYWFarX6brYP61okSa5bt+7tt9/euXNndXV1RMQt/qdOTk6+evXqhg0bfvzxx/Ly8o73LkQIIYRQD9W5AlB6ejoAvPzyy3804PHHH//qq6/y8vLuNldXuHLlylNPPUXTtLe399SpUyUSycmTJ4uLi5988smysjKhUPj+++//5S9/8fT0LC4uXrFixc6dO999992HHnooKCiI7ew9wLX9vxKjcP0XQgh1E56Xgjd2mGTsMACwadro4sumonK6sMx8tZ4uKqOLygCA4HIFQf1FYcHCsCBRWBBHImY7tRuyWq2LFy/eunXr7Yfdc889zsnTQQMGDHjuueduM0AqlT7zzDPPPPMMABgMBmflQgghhBA7OlcAunr1KgDcZn17//79AaCysvIuY3WJ9957j6bp6OjoI0eOeHt7A4DNZpsxY8aBAwcA4N///vcLL7zgGBkdHb1jx47k5OSjR49+/PHHX3/9NZu5ewjjuWwAkIzC9V8IIeQMXIWHZFSc46euXW+kiy/TRWWmonLTlRpT6RVT6RXYdRgIQjDAXxR+rRjE9ZSzndpNpKamOqo/w4YN8/X1PXTokMlkmjt3LpfL1Wg0p0+fpmn6zTfffOWVV9hOeuckEgnbERBCCCHUvTpXAJLL5U1NTVevXvXz87vlgKKiIgAYOHBgF0S7a1lZWQDw3nvvOao/AMDlcj/44ANHAWjZsmU3jH/llVeOHj2an5/v5Jw90bX1X6SIjAtnOwtCCPU6HKlYPCxKPCwKAOwUbbpUaSq5TBeWmcqrzVW15qpa2H8CAPh9fYShQY56EN/Ph+3UPdgPP/wAAI8//rhjz6xPPvnk5ZdffvHFFx1TftLT08ePH19cXCyXY8UNIYQQQq6rcwWg4cOH7927d/PmzcOGDbvlgD179gBATExMF0S7a2VlZQAQF/e7KSqRkZEEQfD5/H79+t0wPjw8HACKi4udlrDnurb+C/f/QgghtnFIERkbRsaGAQBjsZjKq01F5XTJZVNJhaW+2VLfrD+WDgBcpVwUFiQMDRIPi+Z5e7Kduodx7HnavpGWY6uH8+fPOwpAI0eOfO655z766KO9e/fOnDmTxZwIIYQQQrfRuZ2n/v73vxME8eWXX37//fc3P7pnz56PP/4YAObOnds16e6OYzLzDU0NeTyeVCoVi8Xt+5e1MxqNAGC1Wp2WsOfC9V8IIeSCCD5fFBYknz/F982nB2z40P/DV5VL54tHxHLlMptKaziTpfphW+PbX7Ads+epr68HgAEDBji+DQkJgV+rQg6PPvooAHz77bdspEMIIYRuhWFU61Ladh9hOwdyIZ2bAZScnPzqq6+uXr36iSee+Oabb+69914AUKlUn3322dmzZ7dv32632++///6FCxd2T9rOCQ0Nra+vP3HixA2TgHJzc285Pjs7GwBunhmEboDrvxBCqAfgcASDBwgGD/CYOQEALLWNjmlBgoH431yn+fr6VlVVGY1GLy8vAJBKpV5eXqWlpe0DgoKC+Hy+Y68MhBBCyBXoT15o23ccCEIYHiwMdokmLYh1nSsAAcCqVatIkvzoo4+ysrIcTXaqq6tffPFFACAI4uGHH/7iC1f5aDEpKenYsWNvvfVWQkLC2LFj248HBgbePFij0axYsQIApk6d6ryIPZPhbBbu/4UQQj0Lv58vv5+vLHk020F6pNDQ0KqqqvT0dMdmFwAQHBycmZlps9m4XC4A2O12q9XqmEqMEEIIsc5Om9T/2w0AwDCqtdv6vvsS3LQCBvVCnVsCBv/P3p3HRVWvfwB/zmwww4wMw+KGmGsuKJlmmqYGLqm5r5Viblm2mZZtds201Mq6bTf95QaaS4GCC4b7DoqmKCqhoOygMAuzMtv5/TFmhkigMGeY+bz/gu95zsznde3W+Mx5vl8igUCwcOHCjIyMjz/+ePTo0aGhocHBweHh4bNnzz5x4kR0dLRcLq+LoA9g/vz5LVu21Ov14eHh/fr1mzFjRqVl+/fvX7p0afv27a9evSqRSO5M+MP9GJLPE+a/AADAYzj2+pk/f/6pU6ccK926ddNoNL/++qvj14SEBJZlW7duzVlEAACAu2i27bUpNV6tQvh+vuUZN3THznCdCFxCjZ8AcmjSpMmnn35au1FqnVgs3rFjx4wZM5KTk48cOXLixInVq1ffWzZs2DCTyeSoX7dunWOwH+7Hpi4rz8xhvESODUcBAADc3rx589avX5+ZmdmjR4+4uLgRI0YMHz78xx9/nDFjxvnz5/l8/o8//khEAwYM4DopAAAAWYtLynYeJIZRzBhvySsq+XGjamO8pHtnnrcX19GAYw/YAKovOnbsePLkyfj4+JSUlD///PN+ZQqFYtiwYfPnz3ccBAZVMP5xiVhWHNoW818AAOAhxGLx1q1bX3zxxTufJQYOHDho0KDExMQvvvjCsdKsWbOPPvqIu4wAAAC3KaO3sxaLtN+TXm0e8WrdXJt4rPxadlncPvlEHFXp6dy8AUREDMOMHDly5MiR9yvIzc0NCAhwZqR6zfDHJSISP96R6yAAAADO07Vr10uXLmVmZioUCsfKtm3blixZkpCQwOPxnnrqqUWLFvn5+XEbEgAAwJSWYTiVyvP28nthOBERwyimjS386GtN/AFpeE9BkD/XAYFLNWsAPfHEE9WsTElJqXkYbqD7U32szWa68CcR4fwvAADwNHw+v23btnd+lUgkn3/++eeff85hJAAAgH+w25XrYonId/RAvsLXsebVtoX06W66oymqDXGB86Zzmg84VrMG0Jkz2DvKo5VfzrQbjKKQJugcAwAAgOtQro0xnr/SaNGbfD9frrMAAHBGu/e4OTtfEOTfYFj43et+k0YYTl/QJ52TXbrq3RGb3nqumjWAVq5cee8iy7LFxcVnzpzZvXs3y7JTpkx5/fXXaykeuBbDH2mE+S8AAABwJeUZ18v2HCGWVf2yI+D1yVzHAQDghl1vUG9NICLFlFGM8B8btvIV8gYjB6i37Cpd/WuTrz5g+DU+DRzcQ80aQLNmzarialJS0rPPPhsVFdWlS5du3bo9XDBwRUZsAAQAAJ7HLUfg3QfLKtfGEMsSke7IadmzfbxaN+c6EwAAB9RbE2xanXdoW8mTj9171XdEhO5gkiW3UHfwpGxAb+fHA1dQm5tA9+zZ8+uvv54xY8b7778/a9Ysb2/vWnxx4Jz1Zqklv5gnEXs92oLrLAAAAM6DEXhXpjt8qvxatsBfLnkyrCzhiHJtTOPP5hLDcJ0LAMCpLHlF2sRjxOMppo6ptIARCv0mj7y1Yo3qlx0+PR/nSSVOTgiuoJZPARs+fDgRmUyma9euhYaG1u6LA7cMZ9KISNylPcPnc50FAADAeTAC77LsRpNq004i8ps0QvxEZ33y+fKM67pjZ6R9qvvQFgCAe1BGbWNtNtmgp0XNm96vxqdnF23HNqZLV9UxvyteGu3MeOAiarkBpFAohEKhxWJp3hwP37ob4znMfwEAgCfCCLzL0sQm2lQar7YtfHp3I4bxe35YyY8bVRvjJd0787y9uE4HAOAkhjMXjecu83wk8olDq65UTB1bMH952Z7DsoiewmaNnRMPXEctN4DOnDljsVgaN24sk8lq95WBW6zZYrp0lRhGHIYD4AEAAP6GEXiuWItLynYfIoZRTBvrmPmS9ntSm3is/Fp2Wdw++cTnuA4IAOAUdrsqervjx+LF//vXckbAZ80W1cb4oA9eqeNk4HJqswGUlpY2Y8YMIurQAT0Cd2O8kM6aLV5tHuHL0doDAKivTBczGLEXtsitdRiB54Ry/TbWYpU+0+Pvf6QZRjFtbOFHX2viD0jDewqC/DkNCADgDPZys7VUTUR2vcGclVPNuywFxXUZClxUzRpAgYGB97tUXl6u1WodP8+bN++hQoHrMZ69RETirpj/AgCor8w38ooW/8ATCZt+9zFfIec6jlvBCLzzmS5mGFIu8MTefi8Mu3vdMQ6mP5ai2hAXOG86V/EAAJyGJ/YO/v4/NnVZje4SBKJF7olq1gAqKSmpukAoFC5fvnzw4MEPEQlckfH8ZSKSYAMgAIB6S7k2lux2u6lctTE+4M0pXMdxKxiBdza7Xbkuhoh8xwzi+/lWuKiYPMJwOlWfdE526ap3xzZc5AMAcCq+Qo6vdqA6atYA+v3336u4KpPJwsLCfHx8Hi4SuBxzToH1lpIvbyBq0YzrLAAA8CD0SedMl6/yZVK7qVx37Izs2T5ebVtwHcpNYATe+coSj5lzCgQNAxoMfebeq3yF3HfUAPWW3cp1MU2+eI94POcnBAAAcEE1awANGjSojnKAKzOeTSPH+V8Mw3UWAACoMdZsUW2IIyL5C8OsJUpNbGLp6t+aLH8X/1avJozAuxS73qD5dQ8RKV4azQgr/yjrO6K/7mCy+Ua+9sBJ2YDezg0IAADgomr5FDBwM9YSlXJdjLXoFmH+CwCg3tLE77feLBU9EiyL6MlarPojp81ZObojp6X9nuQ6Wv2AEXiXot6y26bVeXdqK3mi8/1qGKHQb/LIWyvWqDfv8nmqK89H7MyEAAAArqmqBlBmZuYDv26rVq0e+F5wHcp1MYZTqcQQw+d7h7XjOg4AANSYTanWxO0jIsW0McTjMV4i+fPDSr6PVm2MkzwZxhPjzPJ/hxF412HJK9LuPU48nmLq2KorfXp20XZoY7p8VR2zRzFltHPiAQAAuLKqGkCtW7d+4NdlWfaB7wUXYbqYYTiVSkTEEt9fjr8kAADUR8oN8Wy52adnF+8Ot3fDlfZ5QrfvhCk9U7N9r98Lw7mNVy9gBN51qDbtYG02IiqY+3k1b9EmHGnwXLjAH9ujAgCAp6vlXfFkMlloaGhoaGjtviw4H2u7fb6GsGEAEdmUGptSw3UoAAComfKM6/rjZxzjMH+vMoxi2hhimLIdByyFt7hLB1BjfD/fmu5dxZfLGD72gQYAAKjyCSC1Wn3vYlRU1Ntvv80wzNixY1944YWWLVsqFIqsrKzff//9v//9r9FonDNnzvTp0+ssMDiJbt9xx/kaJBQQEWu1qn7ZEfDGZK5zAQBAtbGscm0MsazviP6CIP+7r4hahkj7dNcdOaXaGB/07gyuAromjMC7Mv+ZE/xnTuA6BQAAQL1UVQPI19e3wsqpU6fmzp3r5eW1f//+p5566s56kyZNevfuPWXKlO7du8+aNevRRx/t3RsHLtRjdr1BvTWBiORjny353y+MSEgs6Y6elg54yrsdPtoCANQPusOnyq9lO47Evveq3+SRhtOphlPnjalXxGHtnR/PZWEEHgAAANxSzR6I/eKLL2w22+LFi+/u/tzRpk2bzz//3GazLV++vJbiATfUW2+fr8GIRMSy3h1aNxgWTiyrXBtL+GgLAFAf2E3lqs07iUgxeQTjJbq3gC+XORpDynWxrM3u7HzuBSPwAAAA4Ppq1gA6fvw4EfXv3/9+Bc888wwRJScnP2Qs4JAlr0ibeJx4PMVLY0xpGUTkHdrWd/RAvsLXnJWjO3qa64AAAPDvNLGJNqXGq+0jPr273a+mwfD+wsaBlrwi3b7jzszm4tSV+fbbb3k8Hp/PnzBhQnx8/MWLF/Pz848dO/bRRx/5+Pg4RuAvXrzIdXYAAACA+6pZA8ixK1AVjzebzWYi0mq1DxkLOKRcH8vabLKBvUXNm5rS/iQi79C2PG8vvxeHE5FqQ5zdaOI6IwAAVMVaXFK26xAxjGLauCp2zGUEfL9JI4hItWWXXat3YkCX5nuP9PR0xwj80aNHt2zZMnz48NDQUMf8+5IlS86dOyeVSmfNmuX4ngw4UfLjxtwZH1hLVFwHAQAAcF1V7QF0ryZNmty4cePQoUOPPfZYpQUHDx4kouDg4FqIBlwwpFw0nr/C85HIJwyxKTWWwls8idirZTMikvbprv39WPnVG5q4fX7PD+M6KQAA3JcyejtrsQiC/E1pGY5nOavA85HYdQb1b3sU08Y6J169U50R+NmzZy9fvhx7IHLClJahO5RMRKro7YFzp3EdBzyX8cKfAn+5sGlDroMAAFSuZg2gwYMH//TTT//5z38GDBhw76D7pUuXPv74Y0dZrQUEJ2KtNtWG7UQknzCEL5PqjpwmImGTILupnCcRE8Mopo4p/Ojrsh0HpM/0EDYK5DovAABUwm4qN5y+QETWm6WqjfHVvEt39DQaQPeDEXiXZrcr18U6ftQnnZNdyfRu/48DK4xn01giSVfs0AR1y3Qls3jxD3x5g6bffcwTe3MdBwCgEjVrAH366ac7d+7My8vr3r3766+/Pnbs2DZt2hDR1atXY2Njv//+e6PRGBwc/Mknn9RJWKhjZbsOWQpuCoMbyQY9TUSOL43Lr2WXfL8h6L2XicirbQtpnyd0R06rNsYHvYNjgwEAXBHP2yvajUpRAAAgAElEQVTwrSnmG/k1usu7w4MffeX2MALvyrR7j5uz8wVB/j69u2q27VWu+a3JF/OJd3uXA3NOQfEX/0csNfnqfVFIE26jgjtjWeW6GGJZm0qjiU10TNcCALiamjWAAgICtmzZ8txzz6nV6i+//PLLL7+sUODn5/frr7/6+/vXXkJwEptGq9mWSESKKaMZPp+ITBf/dFwypFwwpqaLw9oRkd+Lww2nUg3J5++sAACAq/Hp3a2KvZ+hpjAC77LseoN6awIRKaaMEnfpqD9+1nwjT3swSda/l6NAuSaGbHYiKv2/rY2XvM1lVnBr2oNJ5qxcfgOpTasv231I1r+XoFEA16EAACqq2SbQRNSrV6/MzMw5c+bIZLK71+Vy+XvvvZeVldWzZ8/aiwfOo9q0w24wSrp1EnfpQETWm6WOnRT5Mind3hnaTkR8hdx31MC7VwAAANybY7b9P//5T1pa2r1XXXwEXqPRnDp1avPmzT///PN///vflStXbty48cSJEyqVO+yXrN6aYNPqvEPbSp58jBEJ/V4cQUTqzbvsBiMRGVIumC7d3gOrPD3TcCqVy6zgvuxGk3rzLiJSTB8v7fcka7Eqo7dxHQoAoBI1ewLIQaFQfPPNN998801BQUFGRoZIJGrTpk1gIHaEqcfMN/J1B5OJyJxTUDD/CyKyqTRERMTwfGV2U7kltzBv9kK+r4yIyGolIktuoe5wsiyiku0wAQAA3El9HIG3Wq2rV6+Ojo5OTk6udHiNYZgnn3wyMjJy+vTpIpHI+QkfniWvSJt4jHg8xdQxjhWfXo9rE4+aLl/TxPwuf2G4cs1vRMTweMSyLMuWrvlN/HgHRijkNDW4IfVve2zqMq9HW/o81cW7YxtD8nnD6QvG81fEj7XnOhoAwD88SAPojiZNmjRpgmlqd2AtvkUsS0TWm6V0s/SuK6wlr9Dxk61UZSv9x7eF1oKbzosIAADAkXo3Al9QUDBgwIDLly/fvcgwjI+Pj8ViKS8vJyKWZZOTk5OTk7/55psdO3a0a1f/xrqVUdtYm002qI+oedM7i4qpYwve+6Is4TDLso5nmaWD+zDElO0+ZFOqy3Yc9B0ziLvI4IasRSXaPUeIYRTTxhLD8OUy39EDVb/sUK6PbbLiA8e+CgAALuKhGkDgNiRPPtb0vwtYs9nxq+GPS+otu4ko4M1IUXAjIlKu+dX05w1pr8cbjPjrDBSGJ8RmigAA4BkcI/CLFy9es2bN3Zs9y+XyWbNmvf/++3K5nMN4dzMajSNHjrx8+bJQKBw8ePDw4cN79eoVEBDg5+fH5/OJyGw25+bmnjhxYvfu3bt27bp69Wp4ePiFCxcCAurTliWGMxeN5y7zfCTyiUPuXhe1CJY900N74KR212EiYsRefmMHk4CvP3zapterY36XPtODr/DlJjS4I+W6GNZilUU85dUqxLHSYFiE7mCSJa9Iu/d4g8F9uY0HAHC3qvYAEgqFQqGwR48eFVaqo+6TQy0TBjcStQwRtQwRNm2kTTxORDyxt/TpJxyL/q9OYvh8XdJ5EggcK6IWwQy/xntIAQAA1FOOEfiysrL8/PxDhw6dOHHi5s2bKpVq2bJlrtP9IaIlS5akpKQoFIp9+/bFx8dPnz69Xbt2AQEB/L+eRBCJRK1atYqMjNy6dWtycnJISEhhYeHy5cu5jV0jrNWmit5ORPIJQxybFd5N/sIwRiBgWTsRKSaN5Ml8eGJv+aRhRMRaLKoNcc4PDO7KeOFPw9k0nthbPnHonUVGwPebPIqI1Ft227V67tIBAFRU1RNAVquViGw2W4UVcG/qbYmODYDEj3ckhnEsCoMbyQb2LttzRLkuptHCNzkNCAAAwCUXH4GPiYkhos8++6xv339/9KBTp06rVq0aPHhwfHz8vaNtNaJWqydOnFhSUlLN+ry8PCIqLi7u1q3iiXUMw3zwwQejR4++e7GoqOjFF1/UaDREZFOX2ZRqRigUXk+i/zAV6m2latZqvWUyzDmzv/x6En10+8OMJbeItZjp4G+i1Ss+XPRJFa9f0zyo98x61mZXrY8lIt9xg2+VG1+MGH13vaXwFms08o7ECgMVrpkf9ahHfTXr712vx9j7O3z48OHDh8+ePXtn5Xi1VfGyUME777xDRE2bNuU6CMuyrOWW8sYLb18f89r1Ma+V7f3Hn6NNp8956b3rY17Tn07lKh4AAHBIoVAQUWlpKddBoCpeXl5EdOXKlWrWGwwGPp/v7e39kO975coVfu1td/L2229XeP3Tp08zf30vVVW93V7w/pfXx7wWHz7uvtUP8/qoR/1f9Zpdh66PeS33tYV2s8UV8qAe9aivo3p3+vzDsJUdDAHO9O6773711VdNmzZ1fBXGrVsr1uiTzjEiAWu2Nv1+obDxPw53K9tzRLnmN0HDgKb/XcAIsYEUAIBn8ff3VyqVpaWljk9C7soxyd61a9fk5OS7V6rDYrHUVaxqa9iw4c2bN3ft2jV06NB/ryZKT09v3759w4YNi4qKHvKtb9y4UVpa+u91RET07bffbtiwISgoKCEhocIlHo/XqVMngaDiJ43MzEy1Wq2O2WM4fdG7Q2vFS6PvrdcfS7n1bRQRido0v3r+go7PC3pvBk8icVQqo7eb0q7yGOr10dvyZ3pU+vr3Rq06D+o9s55nMue9sciu1Qd98Iqka2il9Zq4ffqT58Rtmvf74XNXy4961KO++vUNGzZ0m88/tfN3eJZlc3JyAgMDJX/99xXqI1N6pj75PAkFrNnKV/hW6P4QUYNBT+v2nTDnFJTtPuQ7cgAnIQEAAOpUfR+B79OnT0xMzHfffRceHi4Wi6suttlsjsmv7t27P/xbP/LII4888kg1ixs2bEhEQqGwa9eu1bylVatW5qzcgmvF5N+w8ZszhU2C/r5WbraXm1mzRbl+GxExfJ7/yxN464Smy9dk6QX+Myc4qqzNW+a/voi1Wss2xDXo2YXn7VXh9auZBPWoL90ca9fqvTs96uj+VFpvb9c+7/VF9hK9+dxlwROd6zQP6lGPemfW118P0gA6ePDg0aNH586d26BBAyJKSUkZN25cdna2UCgcNGhQdHS0n59fbeeEuseyyrWxxLLiTu2Mf6SJOz1aSQ2Pp5g6tmjRd5rYRGnf7nw/HKIBAADu5vDhw0Qkk8nurBw/fpyzNDW3YMGChISEvXv39u3b95NPPgkPD/f29r63zGKxHDt2bNmyZfv27WMY5q233nJ+1Aeg2rKLWJZYtvCDr6ooY232wne/cPys3XfCd0R/QZA/EQkC/HxHRKhjE+1lurK4ffKJzzkjNLgdS26hdv8J4vMUU8dUUcbzkcjHDVGu/U25fpv4sQ54fB4AOFfjfw29/PLLP//8MxG9+uqrDRo0MBgMju4PEVksll27dvXp0+fcuXP3PlUFLk67/4Q5K0cQ4MfzEhCRV4fWlZZ5d2oreaKzIeWCatPOgNcmOTcjAABAnbt37+RevXpxkuTBhIWFrV+/fsKECSkpKUOHDpVIJN26dQsMDPT395fJZDqdTqlUlpSUnD17tqyszHHL4sWLIyIiuI1dTaKQxuV/Xq/0EmtnWZORWCKG4Un+fvSJr/Bl7nrSx3f0IO3+kzaNVrN9nzS8p6MxVIH1ZqlybYxsSD9x58q+DwOPV7r2N7LZhU0bGv+4ZPzjUlWlNjsj4FuLS8p2H/Yd2d9ZAQEAKlezNs327dsd3Z82bdqIRCIiiomJyc7Obty48ebNm8vKyqZNm5aWlrZ58+bJkyfXSV6oG3a9Ub15FxEJmzU2pqYTkSWvSBO3r9JivlxGRLrDp2TP9vFqFeLMnAAAAK7DZUfgx40bp1Ao5s2bl5qaajAYjh49er/KTp06LV26tJq7BbkCv0kj/SaNrPTSza9WG5LPE5H48Q6iZv84pq1sx4G7fxW1bm48m8babMr1sUHzX773pUrX/GY8m2a+kdf0248ZL1HtxQd3YL1ZarqYQUSW/GLVxvhq3qU7mIQGEABwrmYNoP/9739ENHXq1DVr1jg2yt65cycRvffee46vyz7//POXX355w4YNaADVL4aUC7YyHREZz112rJTtPPgv97Cs7sBJNIAAAMBD1K8R+IiIiPPnz6empiYmJqalpV2/fl2n0xkMBrFYLJPJWrRo0bFjx4EDB3bp0oXrpLWEZQ2nLzp+NJ69ZDxb5UMZfzGeuUh2O/F4/1i88KfxbBoRWUtUmvj98vFDaj0s1GuCQIVi2jibspL9Yqvg3bFNHeUBAKi+mjWAMjIyiGju3Ll3jkk7duwYET377LOOX/v3709EWVlZtZkR6p6kWyf52MGs1WrJLzakXBA09Pfp+fi/3MMw0r61sGEkAACA66unI/BhYWFhYWFcp3AShs+w9hrew6t4bj1rsynX/kZEPr0e1588p4nfLw3vKQhwodYecI9hGgypOCgKAFAv1Oxjys2bN4moSZPbT9VmZ2cXFxcHBAQ8+ujtAWm5XE5E+fn5tRqydmg0mvT09KysLJ1Op9frvb29pVJpixYtOnTo4FJf2XGCJ5XIJw4lIuX6WCKSPtNDPnYw16EAAABcAkbg6wGGkY8dbL6RV6ObhM2bVHj8R7vnqCWvSNgoMOD1SCJGf+KsakNc4NtTazUrAAAAN2rWAGrWrNnVq1eLiooUCgURHTp0iIh69+59p+DGjRtEFBQUdJ8X4IDVal29enV0dHRycjLLsvcWMAzz5JNPRkZGTp8+3fGpzpM5Nlb0atuS6yAAAACuAiPw9YLvmEEP+Qp2nUEd+zsRKaaOYYQCv8hRhjMX9SfOygY97X2fwzEAAADqEd6/l9ylffv2RBQdHU1ELMuuXbuWiIYM+Xs0etOmTUQUEuIq+8IUFBSEhYW9+uqrSUlJd7o/DMNIpVIvr9vnQbAsm5ycPHv27NDQ0PT0dO7Cco+1WM038ohhvFq7yp8gAAAA5zAC7yFUm3bYtXpx50fFXUOJSOAv9x3Rn4iU62Kosi8RAQAA6peaNYDefPNNIvriiy/Gjh07cODAY8eOeXt7Dxs2jIgyMjI+/PDDb7/9lojGjx9fF1lrymg0jhw58vLly0KhcPjw4atXr75y5cqtW7csFotWqzWZTOXl5deuXYuKiho/frxEIrl69Wp4eHhJSQnXwTljzsxhLVZRSJO7D08FAADwcPV6BB6qyZJbqD1wkvg8v5fG3Fn0HTlAEKgwX8/THUzmMBsAAECtqFkDKCIi4uWXX2ZZNjY2dv/+/UQ0b968Ro0aEdGGDRuWLl1qsVjatm07c+bMOglbQ0uWLElJSVEoFPv27YuPj58+fXq7du0CAgL4/Nsb/olEolatWkVGRm7dujU5OTkkJKSwsHD58uXcxuaQ6c8sIvJ6tAXXQQAAAFxIs2bNiKioqMjxa70YgYeaUq6PJZu9weC+opC/j5BnREK/F4eT4+Egg5G7dAAAALWgZg0gIlq1atWmTZsmTZo0YsSIlStXLlmy5M6lxo0bT5s2LSUlxdvbu1ZDPqCYmBgi+uyzzxzz+VXr1KnTqlWriCg+Pr7Ok7mq8ozrhAYQAADAP9W7EXioKUPyeWNqOk8qkY95tsIln97dvNu3smm0mm2JnGQDAHhgmvj9uqMpXKcAF/Igh5U+//zzzz//fIXFBQsWLF68uDYi1RrH4az9+vWrZn3fvn35fH5ubm4dZnJt5Rk3CDtAAwAA/NObb765Y8eOL7744tq1axqNpsII/Pr1611qBB5qirVYVRvjicjv+WE8mc+9BYpp4wrmLy/bdUga/pSwCZ7zAoD6wXD6gmpDHPF5Xi2bCYMbcR0HXEKNnwC6nzt7KrsOX19fIsrMzKxmfXZ2ts1mc9zlgaw3S20qDV8mFTYK4DoLAACAC6lfI/BQU2U7DliKbgmbNZb271VpgahFsPSZHqzVptqw3cnZAAAezN//yrLZS9f8xnUccBUP8gQQEWm12mPHjp0+ffrWrVtBQUELFy7Mz88PDAx0qWPU+/TpExMT891334WHh4vF/7Krsc1m+/LLL4moe/fuTknncm4fAP9oC/rriBMAAABwWLVqVb9+/RISErRa7eDBg2fNmnXnUuPGjQcPHvzNN9+4yAg81IhNXaaJ20dEipfGMPz7fjPq9+JwQ/J5Q8pF47nL4i4dnBgQAOBBlO06aCm8JQxuZFOVmS7+aThzUdKtE9ehgHsP8gTQTz/9FBISMnTo0EWLFv3vf/+Li4sjoi1btjRt2vTrr7+u7YQPbsGCBRKJZO/evX379k1ISDCZTJWWWSyWgwcPDh48eO3atQzDvPXWW07O6SJubwDUFhsAAQAAVOL555/fsGFDXFzc3d2fBQsWFBQUrFmzpkGDBhxmgwem2hBnN5okPR4Th7WroozvK/MdPYiIlFHbWJvNWekAAB6ETa3VxCYSkWLqGPmEIUSkXB/LWqxc5wLu1fgJoE8//XThwoVExOPxWrZsee3aNcc6j8crKSmZN29efn7+ihUrajnmAwkLC1u/fv2ECRNSUlKGDh0qkUi6desWGBjo7+8vk8l0Op1SqSwpKTl79mxZWZnjlsWLF0dERHAbmyumP7EDNAAAQM244Ag8VJ85K9exPar5el7B/C/+pdpqJSJLXpHuQJJsYO9/KQYA4I7ql3i70STp3lkc1t479FHd/pPmnIKyhMO+I/pzHQ04VrMG0JkzZxzdn8jIyBUrVgQEBDB/jQu9/vrrRqPxo48++vrrr1944YWuXbvWftiaGzdunEKhmDdvXmpqqsFgOHr06P0qO3XqtHTp0qFDhzoznutgy83m7Dzi80StcIIJAABA5erFCDxUn7VURSxLRNbikhrcdUtZZ4kAAB6WOStXd/gUI+D7TR5FRAyfp5g6pmjR95qY36V9nuD7eeiOt+BQswaQ45CLoUOHRkVFVbgkFAo//PDDgoKCH3/8ccWKFY7zUF1BRETE+fPnU1NTExMT09LSrl+/rtPpDAaDWCyWyWQtWrTo2LHjwIEDu3TpwnVSLpVfyyabXdSyGc8b32QCAABU4qeffvrwww/VarXj18cee2zhwoVbtmxZtmzZBx98MHfuXG7jwQOQPNG56Q8LWYOxBvcIBKJmjessEdRjdp2h8ONvvFoEB7w5hess4MFYVrk2hli2wbAIYeNAx5p3p0cl3ToZzlxUbdkV8OqL3AYEbtWsAZScnExE77zzzv0KZs6c+eOPP164cOFhc9W2sLCwsLAw57yX1WpdsWKFSqWqZr3juSSLxVKXof6FYwdo70dxADwAAEAl6tEIPNSIsFEg1xHATah/3W3JLbTkFvr06iruGsp1HPBQ+uNnTOmZfLnMd/TAu9cVL40xpl7RHUyWDejt1bo5V/GAczVrAOXm5hJRu3b33SSvWbNmRHT9+vWHjFWvpaamvv/++zW9S6fT1UWYasIO0AAAAPdT70bgAcDJLLmFZYnHHD8ro7Y3CWvPCPjcRgIPxJotql92EJHfiyN44n8cTCloFNBgSD9N/H7l2pjGn83Fuc8eq2YNIF9f35s3b+bm5jZq1KjSgsuXLxNR8+au1VPMyclJSkpKS0tr1KhR3759Q0Mrb8lnZ2c7TjR7yIPAHn/88Y0bN+bl5VWzPiEh4ejRo1yeHsKytxtA2AEaAADgHvVxBB4AnEkZtY1sdtnA3qYrmZbcQm3C4QbDPfRgGeCQZttea4lK1LKZtN+T9171Hfus7mhKecZ1/ck/fHrh6woPVbMGUPfu3Xft2rVx48Ynnnii0oKdO3cSkdOGrf6V1WpdtGjR0qVLbX8d2MkwzOjRo9esWePrW3H7q/T09Dlz5tBDN4AYhnnxxRqMVpaUlBw9epTP5+xbAktRia1Mx5fLBEH+XGUAAABwWfV3BB4AnMCQcsF4/grPRyJ//jnz9fziT79X/7bHp88TfDl33++C57GWqDQ7DxDDKKaOrfQBH57Y22/icyU//aKK3i7p1onxwvEFnohXo+o5c+YwDPPDDz/8/PPP917duXPnV199RUSjRo2qnXQPbeLEiUuWLLHZbAzDhISE+Pj4sCwbGxvbt2/f0tJSrtO5ivKMLCLywgZAAAAAlcEIPADcD2u1qTbEEZF8wlC+TCru/Ki4a6jdaFJv2c11NPAsqujtbLnZp3dX7/at7lcjDe/h1bq5tVStid/vzGzgOmrWAIqIiJg/f77dbn/55Ze7du363nvvEZFSqfzmm2/GjRs3cuRIu90+bty48ePH103amtmzZ09sbCwRzZo1q7S0NDs7W61Wr1mzxsfHJzU19ZVXXuE6oKtw7ACNDYAAAAAq5Xhq2NEGqpRrjsADgBOU7TpoKbgpDG4kG9TbsaKYOoYRCrQHTpZn5nCbDTxHeXqWPukcIxL6vTC8qjqGUUwdQwyjidtnvaV0VjpwITUbASOiJUuWiMXiL7/88o8//vjjjz+IKCcnx3H0KcMwkydP/u6772o/5gP56aefiGj06NErV650rAgEgmnTprVu3To8PDwmJiY2NnbMmDGcZnQJ5Rk3CBsAAQAA3Ee9G4EHAOewqbWabXuJSPHSGOav/RyEjQJlz/Yp23lQuS6m8eK3sdsu1DmWLV3zK7EsT+qjit7+r+U8by+70aT6ZUfgnJfqPhy4lho3gAQCwcKFC2fOnLly5cpLly5lZGSo1eq2bdu2a9du0qRJPXv2rIuUDyY9PZ2I3n777Qrrffr0eeedd5YvXz5//vxhw4aJRB49/chaLJbcQuLzvFo04zoLAACAK5ozZ87u3bt/+OGH0NDQmTNnVrjqgiPwAOAcql/i7Qaj5InO4sfa370uHz9Ef+yM46EMn6ce5yoeeAi7wWTJKSQim1KtTzpXzbvK0zPrMhS4qBo3gByaNGny6aef1m6UWpeTk0NEbdu2vffSggULoqOjs7Kyfvzxx3s7RB7FnF3A2myikCbYBgwAAKBSjhH45cuXv/zyyytXruzfvz/9NQJ/8uTJbdu2udQIPAA4h/l6nu7wKUbA95s8ssIlnthbPmFo6arNqujtkq6h+JgNdYrnI2687F1L0a0a3YWv/z3TAzaAqma1WgWCOnnlGvHz8ysqKsrPzw8KCqpwSSqVLlu2bMqUKYsWLRo/fnzTpk05SegKzJk5RCRqif//AwAA3Fc9GoEHAGdgWeXa34hlGzz3jLBJxb9rEJGs/1O6/SfKM3M0Ow7Ixw12fkDwKKIWwaIWwVyngHqgWptA22y2jIyMo0ePZmVlVV1pMpni4+PvbbhwonPnzkT01VdfsSx779XIyMgBAwZoNJpp06bdOSTeA5mv5xEaQAAAAFVyjMBnZGR8/PHHo0ePDg0NDQ4ODg8Pnz179okTJ6Kjo+VyOdcZAcB59MfPmq5k8n1lvqMHVV7x12ncmu17sdsuALiIf3lOx2QyffDBBz/99FN5ebljpUuXLitXruzevTsRXbhwISoq6uzZsxqNRqPR6PX6kpISu91e56mrZ9KkSXv37t20aZOXl9e3334rk8kqFPz888+dOnXau3dvZGTk//3f/3ESknPlWXgCCAAAoFrqxQg8ANQ11mxRbdpBRH4vDudJxPcr82rX0qdnF/3JP1SbdgS+9ZLz8gEA3EdVDSCWZSMiIk6ePHn34rlz5yIiIk6cOFFcXDx8+HCTyVTHCR/cpEmT1q9ff/DgwXXr1m3evLlz584vvfTSq6++eqegefPmUVFREydO3LRp0+HDh9u3b1/Fq7kl1mqz5BYSw4iae+4QHAAAQK1wkRF4AKhrjiO0hc0aS57obNcbqqj0HT3QcDpVf/xsg2f7eD3a0mkJAQAqVdXHlK1btzq6P4888sjMmTNbt25dVFT066+/njhxYsaMGSqVymQySSSSoUOHNm/eXCwWW61WuVzetGnTfv36OSl+lRiG2bVr1+zZszds2GAymU6fPv344xU34R81alRCQsLYsWMLCgoKCgo4yckhS04Ba7EKgxvxxN5cZwEAAHAtNpstMzOzqKgoODi4Zcuq/uZmMpkSExOnTp2qVGLQA8DN2XUGTfx+IrLkFuZMfa+ad6l+2dHo0zl1mQsA4N9V1QCKiooiog4dOiQnJ9+Zn3rjjTfGjBmzfft2ImrZsuXJkycbNmzohKAPRiwWr1u3btGiRXFxcTk5OR07dry3JiIiIjc3NyoqavPmzdeuXSsuLnZ+Tq6UX88lIhF2gAcAALhLvR6BB4C6xWMEgQqbqqxGNwkCFXUUBwCg+qpqAGVmZhLR3Llz7949h2GY+fPnOxpAH330kSt3f+4ICQl58803qyiQSqWvvfbaa6+9RkR6vd5ZubhnzsolIi9sAAQAAPCX+j4CDwB1iicRN/3vAq5TAAA8iKoaQNnZ2UTUunXrCutt27Z1/NChQ4c6isUhHx8friM4D86ABwAAqKC+j8ADAAAAVKqqBpDZbCYiX1/fCusKxe0nGMXi+256D66PtdnNOQXEMKJHgrnOAgAA4CrcYAQeAAAA4F7/flYFwzBOyAHOZ8krZM0WYeNAng8aeQAAALe5zQh8Tk5OUlJSWlpao0aN+vbtGxoaWmlZdnZ2XFwcEb311lvODQgAAABOhcNKPZdjAyDMfwEAANzNDUbgrVbrokWLli5darPZHCsMw4wePXrNmjX3Ptmdnp4+Z84cQgMIAADA3fG4DgCcMd8+AiyE6yAAAAAuxA1G4CdOnLhkyRKbzcYwTEhIiI+PD8uysbGxffv2LS0t5TpdPcDa7IYzF+16I9dBAAAAahMaQJ6rHE8AAQAA3Ef9HYHfs2dPbGwsEc2aNau0tDQ7O1utVq9Zs8bHxyc1NfWVV17hOmA9UBa/7+ayVSU/RHMdxFPY9caC+cuVa37jOggAgJtDA8hTsazlRj4RebXADtAAAADu46effiKi0aNHr1y50s/Pj4gEAsG0adMSEhL4fH5MTIyjPQT3Y1NqNNv2EpEh5aIx9QrXcTyC+rc95qzcskudLTsAACAASURBVD1HTJevcp0FAMCd/XsD6Omnn/a7RxWX7i4Al2XJL7abygVB/jyZBx17DwAA4PbS09OJ6O23366w3qdPn3feeYeI5s+f7xhzg0qpftlhN5XzG0iJSLkulrXZuU7k5iz5xdo9Rxw/K9fGkB3/gwMA1JV/bwBptVr1Paq4dHcBuCzMfwEAALilnJwcumvL6rstWLCgcePGWVlZP/74o9Nz1Q/lGdd1R08zQkGjT+cImwRZ8oq0iUe5DuXmlOtjWZtN2vdJQaDCfCNfeyCJ60QAAG6rqlPA3n//faflACdzHAHmhQYQAACAe/Hz8ysqKsrPzw8KCqpwSSqVLlu2bMqUKYsWLRo/fnzTpk05Sei6WFa5LpZYtsHwCGFwI7/Jo24uX6XemuDzdDe+TMp1OPdkPJtmPHeZJ/b2mzzSdPnqra/Xqjfv9HnqcZ6Pq2+1DgBQH1XVAFq6dKnTcoCT3T4DvgUaQAAAAJV4+umn+Xx+TS+pVKq6DFUtnTt3Lioq+uqrrzZu3HjvVtaRkZEbN27ct2/fnV2BOAnpmnRHT5dfvcGXy3xHDiAiyROdxI+1N56/ot6a4D9jPNfp3BBrsymjthORfMIQvlzm89Tj2t+PmS5fVcfsUUwZzXU6AAA3hE2gPRLLmm/kERpAAAAA91F/R+AnTZpERJs2bZo+fbpWq7234Oeff5bJZHv37o2MjNTr9U4P6KLspnLVLzuIyG/ySJ7Y27GoeGkMw+dr9x43Z+dzms49aXcfthQUC5s2lA3u61hRTBtDPJ424YiloJjbbAAAbqmqJ4DAXVkKb9kNRoG/nC+XcZ0FAADAtdT3EfhJkyatX7/+4MGD69at27x5c+fOnV966aVXX331TkHz5s2joqImTpy4adOmw4cPt2/fvlbet7Cw8Omnn1YqldWsNxgMROQ6HSjNtr02pUbUMkTap/udRWFwI9mg3mUJR5TrYxstfJPDeO7HptGqY3+nv7psjkXRI8Gy8J7a/SeUUdsbfvAKpwEBANwQGkCeyHzdsQN0CNdBAAAAXE59H4FnGGbXrl2zZ8/esGGDyWQ6ffr0448/XqFm1KhRCQkJY8eOLSgoKCgoqJX3NZvNJSUlGo2mRnfZXePIJ+vN0rKdB4lhFNPG0D/n5uQThuqPnTVdzDCcSpU8GcZVQvej3rzLrjeKu4aKu3S4e13+/HP6pD8cewNVuAQAAA8JI2Ce6K8NgIK5DgIAAAC1TywWr1u3Lisr69tvv503b1737t3vrYmIiMjNzf3hhx969erVsGHDh3/T5s2bFxcXK6vtjTfeICKZzCUeRlZGbWctFmmf7t7tWlW4xPORyCcMISJl9HbWYuEinRsy38jTHkxi+HzFlFEVLvF9ZfIxz9Jfp4NxkQ4AwG3hCSBPdPsM+FZ4AggAAMBthYSEvPlmVVNLUqn0tddee+2116iWRrG8vLy8vLyqX/zw71grTGkZhlPnGS+R/PnnKi2QDeyt3XvcnFNQtuuQ76iBTo7nlpRrY8lulw0LFzappPkoG9pPu/+kJb9Yu+dIg+fCnR8PAMBd4Qkgz8OyjhEwL+wADQAAAERE5OPjw3UEjtjtyvWxRCQfPUgQ4Fd5DY+nmDqWiDSxiTZlzWbc4F76k3+YLl/lN5DKxw6utODOk0Hq3/bYynTOTQcA4M7QAPI41ltKu87Al8v4Cl+uswAAAICzffDBB4sXL+Y6havQ7jthvpEvCPBr8NwzVZR5d2or6d7ZbipXbd7ptGxuiTVbVBvjiUj+/DCej/h+ZY69gex6o3rLLiemA1dRfvWGpfAW1ykA3BBGwDyOObuAiESPYAMgAAAAT7Rs2bKAgICPP/6Y6yDcs+uN6q27iUjYrHHZniNVF/N9GxCR7vAp2aCnvVo3d0Y+d6SJ32+9WSp6JFgW0bPqSsVLYwou/Kndd0LW/ykcXeJRzFm5hR99zW/g0/T7hTyxN9dxANwKGkAex5JTQESikCZcBwEAAADgkiHlgmPCyHjusvHc5Wrdw7K6g0loAD0Yu86gidtHROYbeTfGV7VB1d1Um3c1/Gh2XeYCV8KyynUxZLfb1FrNb3v8IituEw4ADwMNII9jzikgIiEaQAAAAODZJE90ko8bzFqsNbiHYaR9KzlVDaqDZe08sbet3Fyju3jerrJfODiB/uQ505VMno/EbjCWJRyWDugtbBzIdSgA94EGkMex5BYSkSikMddBAAAAALjE85HIJwzlOoUH4cukzVZ/znUKcF2s2aLaGEdEishRpows3YEkVdS2oPdncZ0LwH1gE2jPwtpsloJiYhhh00ZcZwEAAAAAALhNE7fPekspahEsfeZJvxeG8yRiw5mL1R3PBIBqQAPIg9jKdOV/XmetNmGjAMZLxHUcAAAA4ECPHj26du3KdQoAgH+wlqo18fuJSDF1LPF4fF+Z75hBRKSM2sbabFynA3ATGAHzFHadPu+VBWRjCRsAAQAAeLCkpCSuIwAAVKSK3s6Wm316dfXu0Nqx0mDoM7oDJy15RdrEYw2G9OM0HYCbwBNAnuLWf9exZqujfY4jwAAAAAAAwEWU/5mlP/kHIxL6TRpxZ5ER8B2ngKm3Jti0Ou7SAbgPNIA8QnlmjvF8+t+/C/HkFwAAAAAAuACWVa6LJZb1HTlAEKi4+4qkWydxlw52vUG9JYGrdADuBA0gj3Drm7VExPeVEY9HRNo9x7hOBAAAAAAAQNqDSeXXsgX+ct8R/e+9qpgymuHztfuOm7PznZ8NwM2gAeT+tAdOWotKiEjx8gRiWSKyqtTavce5zgUAAAAAAB7NbjSpt+wmIr/IUZUeUyMMbiQb9DTZ7cp1sU5PB+Bu0AByc6zVplofS0RerZoL/P2IZRkvIUOkjN7GWqxcpwMAAAAAAM+lifndptJ4tW3h89Tj96uRTxjCl0lNaRmGU6nOzAbgftAAcnPKtb/ZjeXEMIHvTLfkFBCROKwjwzCsyVy6+leu0wEAAAAAgIeyFpWUJRwmhlFMG0sMc78yno9EPmEIESmjt7MWixMDArgbbAbszmwqjXb/SSKS9u0uCFSYcwqJyKtNCF8m1h5I0h9Klo8bLAjw4zomAAAAAAB4HGVULGuxCoMbma/nma/nVV3MeImsxSVlOw/5jh7onHgA7gcNIHd266s1ZLfzRCL/WROJyJJbQETCkCYNhkXoT5y1m8y3vl7b+PN5XMcEAAAAAADPYlOXGVIuEpElr6h01eZq3qXdfwINIIAHhgaQ2zJduWb6M4uI/CYPZ4RCInI8ASRq1pgR8P0mjyr9eWt5xnXjhXRx53YcZwUAAAAAAE/C95UpIkdZCm/W6C7xYx3qKA+AJ0ADyG2VfLOeiPgBfrLB/YjIrjPYVBqe2FsQqCAi2aCnNfH7rTdLS76LbvbzZ1XM3AIAAAAAANQyhmkwPILrEACeBZtAu6eynYesSjURBb411bFizs4nImGzxnd6PYFzpxExNnWZJn4/VzkBAAAAAAAAwAnQAHJDrNmi2rKTiLw7tPZu39KxaM4pICJRSJM7ZV6tm3t3bENE6q272XIzF0kBAAAAAAAAwBkwAuaGSlZuYsvNxDCyZ/sYL/zpWDSeu0JExOffWSGiBoP7mK5cZS3Wkp82Bc55iYuwAAAAAAAAAFDn0AByOyxrOH7W8cOtr9dWuKhNPKpNPHrvTfoTZwPfmEx8vhMCAgAAAAAAAICTYQTMDTFi75rewpN4Ew//MAAAAADULbbcbPzjEtntXAcBAACPgyeA3A7DNFu7lDWV371mLVUXzP2cL/Vp+uPCym/y9sJBYAAAAAB1reSnTfrjZ+QTn5OPfZbrLAAA4Fk8qAGk0WjS09OzsrJ0Op1er/f29pZKpS1atOjQoYOfnx/X6WoTw+czPpK7V2wZN4hI2KIp75/rAAAAAOA05elZ+hNniUizfa/0mR4CfznXiQAAwIO4fwPIarWuXr06Ojo6OTmZZdl7CxiGefLJJyMjI6dPny4SiZyf0AnM2RWPAAMAAAAAp2LZ0rUxxLI8qcSuM6g2xge+NYXrTAAA4EHcfNuXgoKCsLCwV199NSkp6U73h2EYqVTq5eXl+JVl2eTk5NmzZ4eGhqanp3MXtg5ZcguJSNisMddBAAAAADyU7mCyOStH4C9v/Nk8xkukP37GlJ7JdSgAAPdXnpljySviOoVLcOcGkNFoHDly5OXLl4VC4fDhw1evXn3lypVbt25ZLBatVmsymcrLy69duxYVFTV+/HiJRHL16tXw8PCSkhKug9c+c04+4QkgAAAAAI7YjSbVll1E5Dd5pLBpQ99h4cSyyrUxVNnz6QAAUFsseUVFH64o/GiFrUzHdRbuuXMDaMmSJSkpKQqFYt++ffHx8dOnT2/Xrl1AQAD/r8PORSJRq1atIiMjt27dmpycHBISUlhYuHz5cm5j1z673ZJfTAyDJ4AAAAAAOKGJ+d2m0ni1beHTqysR+Y4aKAjwM2fl6g6f4joaAIA7U0ZtY202u96o3ryT6yzcc+cGUExMDBF99tlnffv2/dfiTp06rVq1ioji4+PrPJlzWYpusWaLIFDBq/nx8AAAAADwkKxFJWUJh4lhFNPGOs5dZbxE8heGE5Hql3i70cR1QAAA92Q8m2Y8d5knETN8vvZAkvlGHteJOObODaDs7Gwi6tevXzXr+/bty+fzc3Nz6zATFyw5hUQkwuM/AAAAAFxQro9lLVZpeA+v1s3vLEqf7ubdrpVNrdVs28thNgAAd8Vabcqo7UQkHz9ENrgv2e2lq3/18MFbd24A+fr6ElFmZnV318vOzrbZbI673IkZO0ADAAAAcMR08U/DmYs8sbffxOf+ceGvB4LKdh6wFN7iKB0AgNsqSzhsKSgWNm0oG9xHPmEIX96gPD1Ln3ye61xccucGUJ8+fYjou+++MxqN/1pss9m+/PJLIurevXudJ3Mux4bnwmaNuA4CAAAA4FlYm125LpaIfMc8y/er+C2jqGUzad/urNWm2hjHRToAALdl02g1Mb8TkeKlMQyfzxN7yycMISJV1Da23Mx1Os64cwNowYIFEolk7969ffv2TUhIMJkqn6+2WCwHDx4cPHjw2rVrGYZ56623nJyzrlkKiolI2KQh10EAAAAAPIs28ag5p0DQKKDB0H6VFvhNGskTextOpRpTrzg3GgCAO1Nv2mk3GCVdQ8VdOjhWZP17iVqGWEtUmp0HuM3GIQHXAepQWFjY+vXrJ0yYkJKSMnToUIlE0q1bt8DAQH9/f5lMptPplEplSUnJ2bNny8rKHLcsXrw4IiKC29i1jGWthbeISNg4iOsoAAAAAB7ErjOof9tDRIopYxhh5Z+6+XKZ76iBqk07lOtim6z4kOG787ezAADOYb6Rpz2UzPD5flNG/b3KMP7TxhZ+/I1m215pvx6CAD/uAnLGnRtARDRu3DiFQjFv3rzU1FSDwXD06NH7VXbq1Gnp0qVDhw51ZjwnsCo1dlM531fGk0q4zgIAAAAuoUuXLkSUmJgYFITvh+qQavMuu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width="768" /></p> +<pre><code>## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded +## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded</code></pre> +<p><img 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" width="768" /></p> <p>Confidence intervals for the parameter estimates are obtained using the <code>mkinparplot</code> function.</p> <pre class="r"><code>mkinparplot(fit)</code></pre> -<p><img role="img" src="data:image/png;base64,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" width="768" /></p> +<pre><code>## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded</code></pre> +<p><img 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width="768" /></p> <p>A comprehensive report of the results is obtained using the <code>summary</code> method for <code>mkinfit</code> objects.</p> <pre class="r"><code>summary(fit)</code></pre> -<pre><code>## mkin version used for fitting: 1.2.6 +<pre><code>## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded</code></pre> +<pre><code>## mkin version used for fitting: 0.9.49.8 ## R version used for fitting: 4.4.2 -## Date of fit: Thu Dec 19 10:28:45 2024 -## Date of summary: Thu Dec 19 10:28:45 2024 +## Date of fit: Sun Feb 16 14:25:34 2025 +## Date of summary: Sun Feb 16 14:25:35 2025 ## ## Equations: -## d_parent/dt = - k_parent * parent -## d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1 +## d_parent/dt = - k_parent_sink * parent - k_parent_m1 * parent +## d_m1/dt = + k_parent_m1 * parent - k_m1_sink * m1 ## -## Model predictions using solution type analytical +## Model predictions using solution type deSolve ## -## Fitted using 401 model solutions performed in 0.101 s +## Fitted using 389 model solutions performed in 0.398 s ## ## Error model: Constant variance ## ## Error model algorithm: OLS ## ## Starting values for parameters to be optimised: -## value type -## parent_0 100.7500 state -## k_parent 0.1000 deparm -## k_m1 0.1001 deparm -## f_parent_to_m1 0.5000 deparm +## value type +## parent_0 100.7500 state +## k_parent_sink 0.1000 deparm +## k_parent_m1 0.1001 deparm +## k_m1_sink 0.1002 deparm ## ## Starting values for the transformed parameters actually optimised: -## value lower upper -## parent_0 100.750000 -Inf Inf -## log_k_parent -2.302585 -Inf Inf -## log_k_m1 -2.301586 -Inf Inf -## f_parent_qlogis 0.000000 -Inf Inf +## value lower upper +## parent_0 100.750000 -Inf Inf +## log_k_parent_sink -2.302585 -Inf Inf +## log_k_parent_m1 -2.301586 -Inf Inf +## log_k_m1_sink -2.300587 -Inf Inf ## ## Fixed parameter values: ## value type ## m1_0 0 state ## -## -## Warning(s): -## Observations with value of zero were removed from the data -## -## Results: -## -## AIC BIC logLik -## 204.4486 212.6365 -97.22429 -## ## Optimised, transformed parameters with symmetric confidence intervals: -## Estimate Std. Error Lower Upper -## parent_0 99.60000 1.57000 96.4000 102.8000 -## log_k_parent -2.31600 0.04087 -2.3990 -2.2330 -## log_k_m1 -5.24700 0.13320 -5.5180 -4.9770 -## f_parent_qlogis 0.05792 0.08926 -0.1237 0.2395 -## sigma 3.12600 0.35850 2.3960 3.8550 +## Estimate Std. Error Lower Upper +## parent_0 99.600 1.57000 96.400 102.800 +## log_k_parent_sink -3.038 0.07626 -3.193 -2.883 +## log_k_parent_m1 -2.980 0.04033 -3.062 -2.898 +## log_k_m1_sink -5.248 0.13320 -5.518 -4.977 +## sigma 3.126 0.35850 2.396 3.855 ## ## Parameter correlation: -## parent_0 log_k_parent log_k_m1 f_parent_qlogis sigma -## parent_0 1.000e+00 5.174e-01 -1.688e-01 -5.471e-01 -1.172e-06 -## log_k_parent 5.174e-01 1.000e+00 -3.263e-01 -5.426e-01 -8.487e-07 -## log_k_m1 -1.688e-01 -3.263e-01 1.000e+00 7.478e-01 8.203e-07 -## f_parent_qlogis -5.471e-01 -5.426e-01 7.478e-01 1.000e+00 1.304e-06 -## sigma -1.172e-06 -8.487e-07 8.203e-07 1.304e-06 1.000e+00 +## parent_0 log_k_parent_sink log_k_parent_m1 log_k_m1_sink +## parent_0 1.000e+00 6.067e-01 -6.372e-02 -1.688e-01 +## log_k_parent_sink 6.067e-01 1.000e+00 -8.550e-02 -6.252e-01 +## log_k_parent_m1 -6.372e-02 -8.550e-02 1.000e+00 4.731e-01 +## log_k_m1_sink -1.688e-01 -6.252e-01 4.731e-01 1.000e+00 +## sigma 5.940e-10 5.021e-10 2.938e-08 2.342e-10 +## sigma +## parent_0 5.940e-10 +## log_k_parent_sink 5.021e-10 +## log_k_parent_m1 2.938e-08 +## log_k_m1_sink 2.342e-10 +## sigma 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. ## t-test (unrealistically) based on the assumption of normal distribution ## for estimators of untransformed parameters. -## Estimate t value Pr(>t) Lower Upper -## parent_0 99.600000 63.430 2.298e-36 96.400000 1.028e+02 -## k_parent 0.098700 24.470 4.955e-23 0.090820 1.073e-01 -## k_m1 0.005261 7.510 6.165e-09 0.004012 6.898e-03 -## f_parent_to_m1 0.514500 23.070 3.104e-22 0.469100 5.596e-01 -## sigma 3.126000 8.718 2.235e-10 2.396000 3.855e+00 +## Estimate t value Pr(>t) Lower Upper +## parent_0 99.600000 63.430 2.298e-36 96.400000 1.028e+02 +## k_parent_sink 0.047920 13.110 6.126e-15 0.041030 5.596e-02 +## k_parent_m1 0.050780 24.800 3.269e-23 0.046780 5.512e-02 +## k_m1_sink 0.005261 7.510 6.165e-09 0.004012 6.898e-03 +## sigma 3.126000 8.718 2.235e-10 2.396000 3.855e+00 ## ## FOCUS Chi2 error levels in percent: ## err.min n.optim df ## All data 6.398 4 15 -## parent 6.459 2 7 -## m1 4.690 2 8 +## parent 6.827 3 6 +## m1 4.490 1 9 ## ## Resulting formation fractions: ## ff -## parent_m1 0.5145 ## parent_sink 0.4855 +## parent_m1 0.5145 +## m1_sink 1.0000 ## ## Estimated disappearance times: ## DT50 DT90 @@ -536,12 +548,12 @@ the <code>mkinparplot</code> function.</p> ## 0 parent 102.04 99.59848 2.442e+00 ## 1 parent 93.50 90.23787 3.262e+00 ## 1 parent 92.50 90.23787 2.262e+00 -## 3 parent 63.23 74.07319 -1.084e+01 -## 3 parent 68.99 74.07319 -5.083e+00 +## 3 parent 63.23 74.07320 -1.084e+01 +## 3 parent 68.99 74.07320 -5.083e+00 ## 7 parent 52.32 49.91207 2.408e+00 ## 7 parent 55.13 49.91207 5.218e+00 -## 14 parent 27.27 25.01258 2.257e+00 -## 14 parent 26.64 25.01258 1.627e+00 +## 14 parent 27.27 25.01257 2.257e+00 +## 14 parent 26.64 25.01257 1.627e+00 ## 21 parent 11.50 12.53462 -1.035e+00 ## 21 parent 11.64 12.53462 -8.946e-01 ## 35 parent 2.85 3.14787 -2.979e-01 @@ -556,18 +568,18 @@ the <code>mkinparplot</code> function.</p> ## 3 m1 12.96 13.02400 -6.400e-02 ## 7 m1 22.97 25.04476 -2.075e+00 ## 7 m1 24.47 25.04476 -5.748e-01 -## 14 m1 41.69 36.69003 5.000e+00 -## 14 m1 33.21 36.69003 -3.480e+00 +## 14 m1 41.69 36.69002 5.000e+00 +## 14 m1 33.21 36.69002 -3.480e+00 ## 21 m1 44.37 41.65310 2.717e+00 ## 21 m1 46.44 41.65310 4.787e+00 -## 35 m1 41.22 43.31313 -2.093e+00 -## 35 m1 37.95 43.31313 -5.363e+00 -## 50 m1 41.19 41.21832 -2.832e-02 -## 50 m1 40.01 41.21832 -1.208e+00 -## 75 m1 40.09 36.44704 3.643e+00 -## 75 m1 33.85 36.44704 -2.597e+00 -## 100 m1 31.04 31.98162 -9.416e-01 -## 100 m1 33.13 31.98162 1.148e+00 +## 35 m1 41.22 43.31312 -2.093e+00 +## 35 m1 37.95 43.31312 -5.363e+00 +## 50 m1 41.19 41.21831 -2.831e-02 +## 50 m1 40.01 41.21831 -1.208e+00 +## 75 m1 40.09 36.44703 3.643e+00 +## 75 m1 33.85 36.44703 -2.597e+00 +## 100 m1 31.04 31.98163 -9.416e-01 +## 100 m1 33.13 31.98163 1.148e+00 ## 120 m1 25.15 28.78984 -3.640e+00 ## 120 m1 33.31 28.78984 4.520e+00</code></pre> diff --git a/vignettes/FOCUS_L.html b/vignettes/FOCUS_L.html index c341f104..2b970d1b 100644 --- a/vignettes/FOCUS_L.html +++ b/vignettes/FOCUS_L.html @@ -30,28 +30,28 @@ document.addEventListener('DOMContentLoaded', function(e) { </script> <style type="text/css"> -code{white-space: pre-wrap;} -span.smallcaps{font-variant: small-caps;} -span.underline{text-decoration: underline;} -div.column{display: inline-block; vertical-align: top; width: 50%;} -div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;} -ul.task-list{list-style: none;} -.display.math{display: block; text-align: center; margin: 0.5rem auto;} -</style> + code{white-space: pre-wrap;} + span.smallcaps{font-variant: small-caps;} + span.underline{text-decoration: underline;} + div.column{display: inline-block; vertical-align: top; width: 50%;} + div.hanging-indent{margin-left: 1.5em; 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/* SpecialChar */ +code span.ss { color: #bb6688; } /* SpecialString */ +code span.st { color: #4070a0; } /* String */ +code span.va { color: #19177c; } /* Variable */ +code span.vs { color: #4070a0; } /* VerbatimString */ +code span.wa { color: #60a0b0; font-weight: bold; font-style: italic; } /* Warning */ + </style> <script> // apply pandoc div.sourceCode style to pre.sourceCode instead @@ -146,26 +147,25 @@ code span.wa { color: #60a0b0; font-weight: bold; font-style: italic; } <style type="text/css"> - +/* for pandoc --citeproc since 2.11 */ div.csl-bib-body { } div.csl-entry { -clear: both; -margin-bottom: 0em; + clear: both; } .hanging div.csl-entry { -margin-left:2em; -text-indent:-2em; + margin-left:2em; + text-indent:-2em; } div.csl-left-margin { -min-width:2em; -float:left; + min-width:2em; + float:left; } div.csl-right-inline { -margin-left:2em; -padding-left:1em; + margin-left:2em; + padding-left:1em; } div.csl-indent { -margin-left: 2em; + margin-left: 2em; } </style> @@ -364,33 +364,25 @@ code > span.er { color: #a61717; background-color: #e3d2d2; } <h1 class="title toc-ignore">Example evaluation of FOCUS Laboratory Data L1 to L3</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">Last change 18 May 2023 (rebuilt 2024-12-19)</h4> +<h4 class="date">Last change 18 May 2023 (rebuilt 2025-02-16)</h4> <div id="TOC"> <ul> -<li><a href="#laboratory-data-l1" id="toc-laboratory-data-l1">Laboratory -Data L1</a></li> -<li><a href="#laboratory-data-l2" id="toc-laboratory-data-l2">Laboratory -Data L2</a> +<li><a href="#laboratory-data-l1">Laboratory Data L1</a></li> +<li><a href="#laboratory-data-l2">Laboratory Data L2</a> <ul> -<li><a href="#sfo-fit-for-l2" id="toc-sfo-fit-for-l2">SFO fit for -L2</a></li> -<li><a href="#fomc-fit-for-l2" id="toc-fomc-fit-for-l2">FOMC fit for -L2</a></li> -<li><a href="#dfop-fit-for-l2" id="toc-dfop-fit-for-l2">DFOP fit for -L2</a></li> +<li><a href="#sfo-fit-for-l2">SFO fit for L2</a></li> +<li><a href="#fomc-fit-for-l2">FOMC fit for L2</a></li> +<li><a href="#dfop-fit-for-l2">DFOP fit for L2</a></li> </ul></li> -<li><a href="#laboratory-data-l3" id="toc-laboratory-data-l3">Laboratory -Data L3</a> +<li><a href="#laboratory-data-l3">Laboratory Data L3</a> <ul> -<li><a href="#fit-multiple-models" id="toc-fit-multiple-models">Fit -multiple models</a></li> -<li><a href="#accessing-mmkin-objects" id="toc-accessing-mmkin-objects">Accessing mmkin objects</a></li> +<li><a href="#fit-multiple-models">Fit multiple models</a></li> +<li><a href="#accessing-mmkin-objects">Accessing mmkin objects</a></li> </ul></li> -<li><a href="#laboratory-data-l4" id="toc-laboratory-data-l4">Laboratory -Data L4</a></li> -<li><a href="#references" id="toc-references">References</a></li> +<li><a href="#laboratory-data-l4">Laboratory Data L4</a></li> +<li><a href="#references">References</a></li> </ul> </div> @@ -398,13 +390,13 @@ Data L4</a></li> <h1>Laboratory Data L1</h1> <p>The following code defines example dataset L1 from the FOCUS kinetics report, p. 284:</p> -<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="fu">library</span>(<span class="st">"mkin"</span>, <span class="at">quietly =</span> <span class="cn">TRUE</span>)</span> -<span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a>FOCUS_2006_L1 <span class="ot">=</span> <span class="fu">data.frame</span>(</span> -<span id="cb1-3"><a href="#cb1-3" tabindex="-1"></a> <span class="at">t =</span> <span class="fu">rep</span>(<span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">2</span>, <span class="dv">3</span>, <span class="dv">5</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">21</span>, <span class="dv">30</span>), <span class="at">each =</span> <span class="dv">2</span>),</span> -<span id="cb1-4"><a href="#cb1-4" tabindex="-1"></a> <span class="at">parent =</span> <span class="fu">c</span>(<span class="fl">88.3</span>, <span class="fl">91.4</span>, <span class="fl">85.6</span>, <span class="fl">84.5</span>, <span class="fl">78.9</span>, <span class="fl">77.6</span>,</span> -<span id="cb1-5"><a href="#cb1-5" tabindex="-1"></a> <span class="fl">72.0</span>, <span class="fl">71.9</span>, <span class="fl">50.3</span>, <span class="fl">59.4</span>, <span class="fl">47.0</span>, <span class="fl">45.1</span>,</span> -<span id="cb1-6"><a href="#cb1-6" tabindex="-1"></a> <span class="fl">27.7</span>, <span class="fl">27.3</span>, <span class="fl">10.0</span>, <span class="fl">10.4</span>, <span class="fl">2.9</span>, <span class="fl">4.0</span>))</span> -<span id="cb1-7"><a href="#cb1-7" tabindex="-1"></a>FOCUS_2006_L1_mkin <span class="ot"><-</span> <span class="fu">mkin_wide_to_long</span>(FOCUS_2006_L1)</span></code></pre></div> +<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(<span class="st">"mkin"</span>, <span class="at">quietly =</span> <span class="cn">TRUE</span>)</span> +<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a>FOCUS_2006_L1 <span class="ot">=</span> <span class="fu">data.frame</span>(</span> +<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a> <span class="at">t =</span> <span class="fu">rep</span>(<span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">2</span>, <span class="dv">3</span>, <span class="dv">5</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">21</span>, <span class="dv">30</span>), <span class="at">each =</span> <span class="dv">2</span>),</span> +<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a> <span class="at">parent =</span> <span class="fu">c</span>(<span class="fl">88.3</span>, <span class="fl">91.4</span>, <span class="fl">85.6</span>, <span class="fl">84.5</span>, <span class="fl">78.9</span>, <span class="fl">77.6</span>,</span> +<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a> <span class="fl">72.0</span>, <span class="fl">71.9</span>, <span class="fl">50.3</span>, <span class="fl">59.4</span>, <span class="fl">47.0</span>, <span class="fl">45.1</span>,</span> +<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a> <span class="fl">27.7</span>, <span class="fl">27.3</span>, <span class="fl">10.0</span>, <span class="fl">10.4</span>, <span class="fl">2.9</span>, <span class="fl">4.0</span>))</span> +<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a>FOCUS_2006_L1_mkin <span class="ot"><-</span> <span class="fu">mkin_wide_to_long</span>(FOCUS_2006_L1)</span></code></pre></div> <p>Here we use the assumptions of simple first order (SFO), the case of declining rate constant over time (FOMC) and the case of two different phases of the kinetics (DFOP). For a more detailed discussion of the @@ -414,68 +406,69 @@ like <code>"SFO"</code> for parent only degradation models. The following two lines fit the model and produce the summary report of the model fit. This covers the numerical analysis given in the FOCUS report.</p> -<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a>m.L1.SFO <span class="ot"><-</span> <span class="fu">mkinfit</span>(<span class="st">"SFO"</span>, FOCUS_2006_L1_mkin, <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span> -<span id="cb2-2"><a href="#cb2-2" tabindex="-1"></a><span class="fu">summary</span>(m.L1.SFO)</span></code></pre></div> -<pre><code>## mkin version used for fitting: 1.2.6 +<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>m.L1.SFO <span class="ot"><-</span> <span class="fu">mkinfit</span>(<span class="st">"SFO"</span>, FOCUS_2006_L1_mkin, <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span> +<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(m.L1.SFO)</span></code></pre></div> +<pre><code>## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded</code></pre> +<pre><code>## mkin version used for fitting: 0.9.49.8 ## R version used for fitting: 4.4.2 -## Date of fit: Thu Dec 19 10:28:47 2024 -## Date of summary: Thu Dec 19 10:28:47 2024 +## Date of fit: Sun Feb 16 14:25:37 2025 +## Date of summary: Sun Feb 16 14:25:37 2025 ## ## Equations: -## d_parent/dt = - k_parent * parent +## d_parent/dt = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted using 133 model solutions performed in 0.02 s +## Fitted using 133 model solutions performed in 0.106 s ## ## Error model: Constant variance ## ## Error model algorithm: OLS ## ## Starting values for parameters to be optimised: -## value type -## parent_0 89.85 state -## k_parent 0.10 deparm +## value type +## parent_0 89.85 state +## k_parent_sink 0.10 deparm ## ## Starting values for the transformed parameters actually optimised: -## value lower upper -## parent_0 89.850000 -Inf Inf -## log_k_parent -2.302585 -Inf Inf +## value lower upper +## parent_0 89.850000 -Inf Inf +## log_k_parent_sink -2.302585 -Inf Inf ## ## Fixed parameter values: ## None ## -## Results: -## -## AIC BIC logLik -## 93.88778 96.5589 -43.94389 -## ## Optimised, transformed parameters with symmetric confidence intervals: -## Estimate Std. Error Lower Upper -## parent_0 92.470 1.28200 89.740 95.200 -## log_k_parent -2.347 0.03763 -2.428 -2.267 -## sigma 2.780 0.46330 1.792 3.767 +## Estimate Std. Error Lower Upper +## parent_0 92.470 1.28200 89.740 95.200 +## log_k_parent_sink -2.347 0.03763 -2.428 -2.267 +## sigma 2.780 0.46330 1.792 3.767 ## ## Parameter correlation: -## parent_0 log_k_parent sigma -## parent_0 1.000e+00 6.186e-01 -1.516e-09 -## log_k_parent 6.186e-01 1.000e+00 -3.124e-09 -## sigma -1.516e-09 -3.124e-09 1.000e+00 +## parent_0 log_k_parent_sink sigma +## parent_0 1.000e+00 6.186e-01 -1.516e-09 +## log_k_parent_sink 6.186e-01 1.000e+00 -3.124e-09 +## sigma -1.516e-09 -3.124e-09 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. ## t-test (unrealistically) based on the assumption of normal distribution ## for estimators of untransformed parameters. -## Estimate t value Pr(>t) Lower Upper -## parent_0 92.47000 72.13 8.824e-21 89.74000 95.2000 -## k_parent 0.09561 26.57 2.487e-14 0.08824 0.1036 -## sigma 2.78000 6.00 1.216e-05 1.79200 3.7670 +## Estimate t value Pr(>t) Lower Upper +## parent_0 92.47000 72.13 8.824e-21 89.74000 95.2000 +## k_parent_sink 0.09561 26.57 2.487e-14 0.08824 0.1036 +## sigma 2.78000 6.00 1.216e-05 1.79200 3.7670 ## ## FOCUS Chi2 error levels in percent: ## err.min n.optim df ## All data 3.424 2 7 ## parent 3.424 2 7 ## +## Resulting formation fractions: +## ff +## parent_sink 1 +## ## Estimated disappearance times: ## DT50 DT90 ## parent 7.249 24.08 @@ -502,33 +495,44 @@ report.</p> ## 30 parent 4.0 5.251 -1.2513</code></pre> <p>A plot of the fit is obtained with the plot function for mkinfit objects.</p> -<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a><span class="fu">plot</span>(m.L1.SFO, <span class="at">show_errmin =</span> <span class="cn">TRUE</span>, <span class="at">main =</span> <span class="st">"FOCUS L1 - SFO"</span>)</span></code></pre></div> -<p><img role="img" src="data:image/png;base64,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" /><!-- --></p> +<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(m.L1.SFO, <span class="at">show_errmin =</span> <span class="cn">TRUE</span>, <span class="at">main =</span> <span class="st">"FOCUS L1 - SFO"</span>)</span></code></pre></div> +<pre><code>## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded</code></pre> +<p><img 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" /><!-- --></p> <p>The residual plot can be easily obtained by</p> -<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a><span class="fu">mkinresplot</span>(m.L1.SFO, <span class="at">ylab =</span> <span class="st">"Observed"</span>, <span class="at">xlab =</span> <span class="st">"Time"</span>)</span></code></pre></div> -<p><img role="img" src="data:image/png;base64,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" /><!-- --></p> +<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="fu">mkinresplot</span>(m.L1.SFO, <span class="at">ylab =</span> <span class="st">"Observed"</span>, <span class="at">xlab =</span> <span class="st">"Time"</span>)</span></code></pre></div> +<p><img src="data:image/png;base64,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" /><!-- --></p> <p>For comparison, the FOMC model is fitted as well, and the <span class="math inline"><em>χ</em><sup>2</sup></span> error level is checked.</p> -<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a>m.L1.FOMC <span class="ot"><-</span> <span class="fu">mkinfit</span>(<span class="st">"FOMC"</span>, FOCUS_2006_L1_mkin, <span class="at">quiet=</span><span class="cn">TRUE</span>)</span></code></pre></div> +<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>m.L1.FOMC <span class="ot"><-</span> <span class="fu">mkinfit</span>(<span class="st">"FOMC"</span>, FOCUS_2006_L1_mkin, <span class="at">quiet=</span><span class="cn">TRUE</span>)</span></code></pre></div> <pre><code>## Warning in mkinfit("FOMC", FOCUS_2006_L1_mkin, quiet = TRUE): Optimisation did not converge: ## false convergence (8)</code></pre> -<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" tabindex="-1"></a><span class="fu">plot</span>(m.L1.FOMC, <span class="at">show_errmin =</span> <span class="cn">TRUE</span>, <span class="at">main =</span> <span class="st">"FOCUS L1 - FOMC"</span>)</span></code></pre></div> -<p><img role="img" src="data:image/png;base64,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" /><!-- --></p> -<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a><span class="fu">summary</span>(m.L1.FOMC, <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div> +<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(m.L1.FOMC, <span class="at">show_errmin =</span> <span class="cn">TRUE</span>, <span class="at">main =</span> <span class="st">"FOCUS L1 - FOMC"</span>)</span></code></pre></div> +<pre><code>## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded</code></pre> +<p><img 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" /><!-- --></p> +<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(m.L1.FOMC, <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div> <pre><code>## Warning in sqrt(diag(covar)): NaNs produced</code></pre> +<pre><code>## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded</code></pre> <pre><code>## Warning in cov2cor(ans$covar): diag(V) had non-positive or NA entries; the ## non-finite result may be dubious</code></pre> -<pre><code>## mkin version used for fitting: 1.2.6 +<pre><code>## mkin version used for fitting: 0.9.49.8 ## R version used for fitting: 4.4.2 -## Date of fit: Thu Dec 19 10:28:47 2024 -## Date of summary: Thu Dec 19 10:28:47 2024 +## Date of fit: Sun Feb 16 14:25:37 2025 +## Date of summary: Sun Feb 16 14:25:37 2025 +## +## +## Warning: Optimisation did not converge: +## false convergence (8) +## ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted using 342 model solutions performed in 0.045 s +## Fitted using 342 model solutions performed in 0.242 s ## ## Error model: Constant variance ## @@ -549,39 +553,29 @@ checked.</p> ## Fixed parameter values: ## None ## -## -## Warning(s): -## Optimisation did not converge: -## false convergence (8) -## -## Results: -## -## AIC BIC logLik -## 95.88782 99.44931 -43.94391 -## ## Optimised, transformed parameters with symmetric confidence intervals: -## Estimate Std. Error Lower Upper -## parent_0 92.46 1.2820 89.71 95.21 -## log_alpha 15.08 NaN NaN NaN -## log_beta 17.43 NaN NaN NaN -## sigma 2.78 0.4569 1.80 3.76 +## Estimate Std. Error Lower Upper +## parent_0 92.47 1.2820 89.720 95.220 +## log_alpha 13.20 NaN NaN NaN +## log_beta 15.54 NaN NaN NaN +## sigma 2.78 0.4607 1.792 3.768 ## ## Parameter correlation: -## parent_0 log_alpha log_beta sigma -## parent_0 1.000000 NaN NaN -0.000772 -## log_alpha NaN 1 NaN NaN -## log_beta NaN NaN 1 NaN -## sigma -0.000772 NaN NaN 1.000000 +## parent_0 log_alpha log_beta sigma +## parent_0 1.000000 NaN NaN 0.000603 +## log_alpha NaN 1 NaN NaN +## log_beta NaN NaN 1 NaN +## sigma 0.000603 NaN NaN 1.000000 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. ## t-test (unrealistically) based on the assumption of normal distribution ## for estimators of untransformed parameters. -## Estimate t value Pr(>t) Lower Upper -## parent_0 9.246e+01 NA NA 89.71 95.21 -## alpha 3.555e+06 NA NA NA NA -## beta 3.719e+07 NA NA NA NA -## sigma 2.780e+00 NA NA 1.80 3.76 +## Estimate t value Pr(>t) Lower Upper +## parent_0 9.247e+01 NA NA 89.720 95.220 +## alpha 5.386e+05 NA NA NA NA +## beta 5.633e+06 NA NA NA NA +## sigma 2.780e+00 NA NA 1.792 3.768 ## ## FOCUS Chi2 error levels in percent: ## err.min n.optim df @@ -589,8 +583,8 @@ checked.</p> ## parent 3.619 3 6 ## ## Estimated disappearance times: -## DT50 DT90 DT50back -## parent 7.25 24.09 7.25</code></pre> +## DT50 DT90 DT50back +## parent 7.249 24.08 7.249</code></pre> <p>We get a warning that the default optimisation algorithm <code>Port</code> did not converge, which is an indication that the model is overparameterised, <em>i.e.</em> contains too many parameters @@ -625,21 +619,23 @@ sponsored by the German Umweltbundesamt <span class="citation">(Ranke <h1>Laboratory Data L2</h1> <p>The following code defines example dataset L2 from the FOCUS kinetics report, p. 287:</p> -<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" tabindex="-1"></a>FOCUS_2006_L2 <span class="ot">=</span> <span class="fu">data.frame</span>(</span> -<span id="cb13-2"><a href="#cb13-2" tabindex="-1"></a> <span class="at">t =</span> <span class="fu">rep</span>(<span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">28</span>), <span class="at">each =</span> <span class="dv">2</span>),</span> -<span id="cb13-3"><a href="#cb13-3" tabindex="-1"></a> <span class="at">parent =</span> <span class="fu">c</span>(<span class="fl">96.1</span>, <span class="fl">91.8</span>, <span class="fl">41.4</span>, <span class="fl">38.7</span>,</span> -<span id="cb13-4"><a href="#cb13-4" tabindex="-1"></a> <span class="fl">19.3</span>, <span class="fl">22.3</span>, <span class="fl">4.6</span>, <span class="fl">4.6</span>,</span> -<span id="cb13-5"><a href="#cb13-5" tabindex="-1"></a> <span class="fl">2.6</span>, <span class="fl">1.2</span>, <span class="fl">0.3</span>, <span class="fl">0.6</span>))</span> -<span id="cb13-6"><a href="#cb13-6" tabindex="-1"></a>FOCUS_2006_L2_mkin <span class="ot"><-</span> <span class="fu">mkin_wide_to_long</span>(FOCUS_2006_L2)</span></code></pre></div> +<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a>FOCUS_2006_L2 <span class="ot">=</span> <span class="fu">data.frame</span>(</span> +<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a> <span class="at">t =</span> <span class="fu">rep</span>(<span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">28</span>), <span class="at">each =</span> <span class="dv">2</span>),</span> +<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a> <span class="at">parent =</span> <span class="fu">c</span>(<span class="fl">96.1</span>, <span class="fl">91.8</span>, <span class="fl">41.4</span>, <span class="fl">38.7</span>,</span> +<span id="cb17-4"><a href="#cb17-4" aria-hidden="true" tabindex="-1"></a> <span class="fl">19.3</span>, <span class="fl">22.3</span>, <span class="fl">4.6</span>, <span class="fl">4.6</span>,</span> +<span id="cb17-5"><a href="#cb17-5" aria-hidden="true" tabindex="-1"></a> <span class="fl">2.6</span>, <span class="fl">1.2</span>, <span class="fl">0.3</span>, <span class="fl">0.6</span>))</span> +<span id="cb17-6"><a href="#cb17-6" aria-hidden="true" tabindex="-1"></a>FOCUS_2006_L2_mkin <span class="ot"><-</span> <span class="fu">mkin_wide_to_long</span>(FOCUS_2006_L2)</span></code></pre></div> <div id="sfo-fit-for-l2" class="section level2"> <h2>SFO fit for L2</h2> <p>Again, the SFO model is fitted and the result is plotted. The residual plot can be obtained simply by adding the argument <code>show_residuals</code> to the plot command.</p> -<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" tabindex="-1"></a>m.L2.SFO <span class="ot"><-</span> <span class="fu">mkinfit</span>(<span class="st">"SFO"</span>, FOCUS_2006_L2_mkin, <span class="at">quiet=</span><span class="cn">TRUE</span>)</span> -<span id="cb14-2"><a href="#cb14-2" tabindex="-1"></a><span class="fu">plot</span>(m.L2.SFO, <span class="at">show_residuals =</span> <span class="cn">TRUE</span>, <span class="at">show_errmin =</span> <span class="cn">TRUE</span>,</span> -<span id="cb14-3"><a href="#cb14-3" tabindex="-1"></a> <span class="at">main =</span> <span class="st">"FOCUS L2 - SFO"</span>)</span></code></pre></div> -<p><img role="img" src="data:image/png;base64,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" /><!-- --></p> +<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a>m.L2.SFO <span class="ot"><-</span> <span class="fu">mkinfit</span>(<span class="st">"SFO"</span>, FOCUS_2006_L2_mkin, <span class="at">quiet=</span><span class="cn">TRUE</span>)</span> +<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(m.L2.SFO, <span class="at">show_residuals =</span> <span class="cn">TRUE</span>, <span class="at">show_errmin =</span> <span class="cn">TRUE</span>,</span> +<span id="cb18-3"><a href="#cb18-3" aria-hidden="true" tabindex="-1"></a> <span class="at">main =</span> <span class="st">"FOCUS L2 - SFO"</span>)</span></code></pre></div> +<pre><code>## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded</code></pre> +<p><img 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/><!-- --></p> <p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error level of 14% suggests that the model does not fit very well. This is also obvious from the plots of the fit, in which we have included the @@ -659,22 +655,24 @@ kinetics.</p> <h2>FOMC fit for L2</h2> <p>For comparison, the FOMC model is fitted as well, and the <span class="math inline"><em>χ</em><sup>2</sup></span> error level is checked.</p> -<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" tabindex="-1"></a>m.L2.FOMC <span class="ot"><-</span> <span class="fu">mkinfit</span>(<span class="st">"FOMC"</span>, FOCUS_2006_L2_mkin, <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span> -<span id="cb15-2"><a href="#cb15-2" tabindex="-1"></a><span class="fu">plot</span>(m.L2.FOMC, <span class="at">show_residuals =</span> <span class="cn">TRUE</span>,</span> -<span id="cb15-3"><a href="#cb15-3" tabindex="-1"></a> <span class="at">main =</span> <span class="st">"FOCUS L2 - FOMC"</span>)</span></code></pre></div> -<p><img role="img" 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" /><!-- --></p> -<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" tabindex="-1"></a><span class="fu">summary</span>(m.L2.FOMC, <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div> -<pre><code>## mkin version used for fitting: 1.2.6 +<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a>m.L2.FOMC <span class="ot"><-</span> <span class="fu">mkinfit</span>(<span class="st">"FOMC"</span>, FOCUS_2006_L2_mkin, <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span> +<span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(m.L2.FOMC, <span class="at">show_residuals =</span> <span class="cn">TRUE</span>,</span> +<span id="cb20-3"><a href="#cb20-3" aria-hidden="true" tabindex="-1"></a> <span class="at">main =</span> <span class="st">"FOCUS L2 - FOMC"</span>)</span></code></pre></div> +<p><img 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/><!-- --></p> +<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(m.L2.FOMC, <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div> +<pre><code>## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded</code></pre> +<pre><code>## mkin version used for fitting: 0.9.49.8 ## R version used for fitting: 4.4.2 -## Date of fit: Thu Dec 19 10:28:48 2024 -## Date of summary: Thu Dec 19 10:28:48 2024 +## Date of fit: Sun Feb 16 14:25:38 2025 +## Date of summary: Sun Feb 16 14:25:38 2025 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted using 239 model solutions performed in 0.029 s +## Fitted using 239 model solutions performed in 0.167 s ## ## Error model: Constant variance ## @@ -695,11 +693,6 @@ checked.</p> ## Fixed parameter values: ## None ## -## Results: -## -## AIC BIC logLik -## 61.78966 63.72928 -26.89483 -## ## Optimised, transformed parameters with symmetric confidence intervals: ## Estimate Std. Error Lower Upper ## parent_0 93.7700 1.6130 90.05000 97.4900 @@ -740,15 +733,19 @@ order to explain the data.</p> <div id="dfop-fit-for-l2" class="section level2"> <h2>DFOP fit for L2</h2> <p>Fitting the four parameter DFOP model further reduces the <span class="math inline"><em>χ</em><sup>2</sup></span> error level.</p> -<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" tabindex="-1"></a>m.L2.DFOP <span class="ot"><-</span> <span class="fu">mkinfit</span>(<span class="st">"DFOP"</span>, FOCUS_2006_L2_mkin, <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span> -<span id="cb18-2"><a href="#cb18-2" tabindex="-1"></a><span class="fu">plot</span>(m.L2.DFOP, <span class="at">show_residuals =</span> <span class="cn">TRUE</span>, <span class="at">show_errmin =</span> <span class="cn">TRUE</span>,</span> -<span id="cb18-3"><a href="#cb18-3" tabindex="-1"></a> <span class="at">main =</span> <span class="st">"FOCUS L2 - DFOP"</span>)</span></code></pre></div> -<p><img role="img" 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" /><!-- --></p> -<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" tabindex="-1"></a><span class="fu">summary</span>(m.L2.DFOP, <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div> -<pre><code>## mkin version used for fitting: 1.2.6 +<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a>m.L2.DFOP <span class="ot"><-</span> <span class="fu">mkinfit</span>(<span class="st">"DFOP"</span>, FOCUS_2006_L2_mkin, <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span> +<span id="cb24-2"><a href="#cb24-2" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(m.L2.DFOP, <span class="at">show_residuals =</span> <span class="cn">TRUE</span>, <span class="at">show_errmin =</span> <span class="cn">TRUE</span>,</span> +<span id="cb24-3"><a href="#cb24-3" aria-hidden="true" tabindex="-1"></a> <span class="at">main =</span> <span class="st">"FOCUS L2 - DFOP"</span>)</span></code></pre></div> +<pre><code>## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded</code></pre> +<p><img 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/><!-- --></p> +<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(m.L2.DFOP, <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div> +<pre><code>## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded</code></pre> +<pre><code>## mkin version used for fitting: 0.9.49.8 ## R version used for fitting: 4.4.2 -## Date of fit: Thu Dec 19 10:28:48 2024 -## Date of summary: Thu Dec 19 10:28:48 2024 +## Date of fit: Sun Feb 16 14:25:39 2025 +## Date of summary: Sun Feb 16 14:25:39 2025 ## ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -757,7 +754,7 @@ order to explain the data.</p> ## ## Model predictions using solution type analytical ## -## Fitted using 581 model solutions performed in 0.08 s +## Fitted using 572 model solutions performed in 0.399 s ## ## Error model: Constant variance ## @@ -775,31 +772,26 @@ order to explain the data.</p> ## parent_0 93.950000 -Inf Inf ## log_k1 -2.302585 -Inf Inf ## log_k2 -4.605170 -Inf Inf -## g_qlogis 0.000000 -Inf Inf +## g_ilr 0.000000 -Inf Inf ## ## Fixed parameter values: ## None ## -## Results: -## -## AIC BIC logLik -## 52.36695 54.79148 -21.18347 -## ## Optimised, transformed parameters with symmetric confidence intervals: ## Estimate Std. Error Lower Upper -## parent_0 93.950 9.998e-01 91.5900 96.3100 -## log_k1 3.113 1.849e+03 -4369.0000 4375.0000 -## log_k2 -1.088 6.285e-02 -1.2370 -0.9394 -## g_qlogis -0.399 9.946e-02 -0.6342 -0.1638 -## sigma 1.414 2.886e-01 0.7314 2.0960 +## parent_0 93.9500 9.998e-01 91.5900 96.3100 +## log_k1 3.1370 2.376e+03 -5615.0000 5622.0000 +## log_k2 -1.0880 6.285e-02 -1.2370 -0.9394 +## g_ilr -0.2821 7.033e-02 -0.4484 -0.1158 +## sigma 1.4140 2.886e-01 0.7314 2.0960 ## ## Parameter correlation: -## parent_0 log_k1 log_k2 g_qlogis sigma -## parent_0 1.000e+00 6.765e-07 -9.004e-10 2.665e-01 -1.109e-09 -## log_k1 6.765e-07 1.000e+00 1.112e-04 -2.187e-04 -1.027e-05 -## log_k2 -9.004e-10 1.112e-04 1.000e+00 -7.903e-01 9.553e-09 -## g_qlogis 2.665e-01 -2.187e-04 -7.903e-01 1.000e+00 -1.545e-08 -## sigma -1.109e-09 -1.027e-05 9.553e-09 -1.545e-08 1.000e+00 +## parent_0 log_k1 log_k2 g_ilr sigma +## parent_0 1.000e+00 5.144e-07 2.339e-09 2.665e-01 -6.798e-09 +## log_k1 5.144e-07 1.000e+00 8.434e-05 -1.659e-04 -7.791e-06 +## log_k2 2.339e-09 8.434e-05 1.000e+00 -7.903e-01 -1.240e-08 +## g_ilr 2.665e-01 -1.659e-04 -7.903e-01 1.000e+00 3.195e-08 +## sigma -6.798e-09 -7.791e-06 -1.240e-08 3.195e-08 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. @@ -807,7 +799,7 @@ order to explain the data.</p> ## for estimators of untransformed parameters. ## Estimate t value Pr(>t) Lower Upper ## parent_0 93.9500 9.397e+01 2.036e-12 91.5900 96.3100 -## k1 22.4900 5.533e-04 4.998e-01 0.0000 Inf +## k1 23.0400 4.303e-04 4.998e-01 0.0000 Inf ## k2 0.3369 1.591e+01 4.697e-07 0.2904 0.3909 ## g 0.4016 1.680e+01 3.238e-07 0.3466 0.4591 ## sigma 1.4140 4.899e+00 8.776e-04 0.7314 2.0960 @@ -818,8 +810,8 @@ order to explain the data.</p> ## parent 2.53 4 2 ## ## Estimated disappearance times: -## DT50 DT90 DT50back DT50_k1 DT50_k2 -## parent 0.5335 5.311 1.599 0.03083 2.058</code></pre> +## DT50 DT90 DT50_k1 DT50_k2 +## parent 0.5335 5.311 0.03009 2.058</code></pre> <p>Here, the DFOP model is clearly the best-fit model for dataset L2 based on the chi^2 error level criterion.</p> </div> @@ -828,21 +820,27 @@ based on the chi^2 error level criterion.</p> <h1>Laboratory Data L3</h1> <p>The following code defines example dataset L3 from the FOCUS kinetics report, p. 290.</p> -<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" tabindex="-1"></a>FOCUS_2006_L3 <span class="ot">=</span> <span class="fu">data.frame</span>(</span> -<span id="cb21-2"><a href="#cb21-2" tabindex="-1"></a> <span class="at">t =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">30</span>, <span class="dv">60</span>, <span class="dv">91</span>, <span class="dv">120</span>),</span> -<span id="cb21-3"><a href="#cb21-3" tabindex="-1"></a> <span class="at">parent =</span> <span class="fu">c</span>(<span class="fl">97.8</span>, <span class="dv">60</span>, <span class="dv">51</span>, <span class="dv">43</span>, <span class="dv">35</span>, <span class="dv">22</span>, <span class="dv">15</span>, <span class="dv">12</span>))</span> -<span id="cb21-4"><a href="#cb21-4" tabindex="-1"></a>FOCUS_2006_L3_mkin <span class="ot"><-</span> <span class="fu">mkin_wide_to_long</span>(FOCUS_2006_L3)</span></code></pre></div> +<div class="sourceCode" id="cb29"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a>FOCUS_2006_L3 <span class="ot">=</span> <span class="fu">data.frame</span>(</span> +<span id="cb29-2"><a href="#cb29-2" aria-hidden="true" tabindex="-1"></a> <span class="at">t =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">30</span>, <span class="dv">60</span>, <span class="dv">91</span>, <span class="dv">120</span>),</span> +<span id="cb29-3"><a href="#cb29-3" aria-hidden="true" tabindex="-1"></a> <span class="at">parent =</span> <span class="fu">c</span>(<span class="fl">97.8</span>, <span class="dv">60</span>, <span class="dv">51</span>, <span class="dv">43</span>, <span class="dv">35</span>, <span class="dv">22</span>, <span class="dv">15</span>, <span class="dv">12</span>))</span> +<span id="cb29-4"><a href="#cb29-4" aria-hidden="true" tabindex="-1"></a>FOCUS_2006_L3_mkin <span class="ot"><-</span> <span class="fu">mkin_wide_to_long</span>(FOCUS_2006_L3)</span></code></pre></div> <div id="fit-multiple-models" class="section level2"> <h2>Fit multiple models</h2> <p>As of mkin version 0.9-39 (June 2015), we can fit several models to one or more datasets in one call to the function <code>mmkin</code>. The datasets have to be passed in a list, in this case a named list holding only the L3 dataset prepared above.</p> -<div class="sourceCode" id="cb22"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1" tabindex="-1"></a><span class="co"># Only use one core here, not to offend the CRAN checks</span></span> -<span id="cb22-2"><a href="#cb22-2" tabindex="-1"></a>mm.L3 <span class="ot"><-</span> <span class="fu">mmkin</span>(<span class="fu">c</span>(<span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span>), <span class="at">cores =</span> <span class="dv">1</span>,</span> -<span id="cb22-3"><a href="#cb22-3" tabindex="-1"></a> <span class="fu">list</span>(<span class="st">"FOCUS L3"</span> <span class="ot">=</span> FOCUS_2006_L3_mkin), <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span> -<span id="cb22-4"><a href="#cb22-4" tabindex="-1"></a><span class="fu">plot</span>(mm.L3)</span></code></pre></div> -<p><img role="img" src="data:image/png;base64,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" /><!-- --></p> +<div class="sourceCode" id="cb30"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Only use one core here, not to offend the CRAN checks</span></span> +<span id="cb30-2"><a href="#cb30-2" aria-hidden="true" tabindex="-1"></a>mm.L3 <span class="ot"><-</span> <span class="fu">mmkin</span>(<span class="fu">c</span>(<span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span>), <span class="at">cores =</span> <span class="dv">1</span>,</span> +<span id="cb30-3"><a href="#cb30-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">list</span>(<span class="st">"FOCUS L3"</span> <span class="ot">=</span> FOCUS_2006_L3_mkin), <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span> +<span id="cb30-4"><a href="#cb30-4" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(mm.L3)</span></code></pre></div> +<pre><code>## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded +## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded +## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded</code></pre> +<p><img src="data:image/png;base64,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" /><!-- --></p> <p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error level of 21% as well as the plot suggest that the SFO model does not fit very well. The FOMC model performs better, with an error level at which @@ -857,11 +855,13 @@ as a row index and datasets as a column index.</p> <p>We can extract the summary and plot for <em>e.g.</em> the DFOP fit, using square brackets for indexing which will result in the use of the summary and plot functions working on mkinfit objects.</p> -<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" tabindex="-1"></a><span class="fu">summary</span>(mm.L3[[<span class="st">"DFOP"</span>, <span class="dv">1</span>]])</span></code></pre></div> -<pre><code>## mkin version used for fitting: 1.2.6 +<div class="sourceCode" id="cb32"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb32-1"><a href="#cb32-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(mm.L3[[<span class="st">"DFOP"</span>, <span class="dv">1</span>]])</span></code></pre></div> +<pre><code>## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded</code></pre> +<pre><code>## mkin version used for fitting: 0.9.49.8 ## R version used for fitting: 4.4.2 -## Date of fit: Thu Dec 19 10:28:48 2024 -## Date of summary: Thu Dec 19 10:28:48 2024 +## Date of fit: Sun Feb 16 14:25:40 2025 +## Date of summary: Sun Feb 16 14:25:40 2025 ## ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -870,7 +870,7 @@ summary and plot functions working on mkinfit objects.</p> ## ## Model predictions using solution type analytical ## -## Fitted using 376 model solutions performed in 0.047 s +## Fitted using 373 model solutions performed in 0.258 s ## ## Error model: Constant variance ## @@ -888,31 +888,26 @@ summary and plot functions working on mkinfit objects.</p> ## parent_0 97.800000 -Inf Inf ## log_k1 -2.302585 -Inf Inf ## log_k2 -4.605170 -Inf Inf -## g_qlogis 0.000000 -Inf Inf +## g_ilr 0.000000 -Inf Inf ## ## Fixed parameter values: ## None ## -## Results: -## -## AIC BIC logLik -## 32.97732 33.37453 -11.48866 -## ## Optimised, transformed parameters with symmetric confidence intervals: ## Estimate Std. Error Lower Upper ## parent_0 97.7500 1.01900 94.5000 101.000000 ## log_k1 -0.6612 0.10050 -0.9812 -0.341300 ## log_k2 -4.2860 0.04322 -4.4230 -4.148000 -## g_qlogis -0.1739 0.05270 -0.3416 -0.006142 +## g_ilr -0.1229 0.03727 -0.2415 -0.004343 ## sigma 1.0170 0.25430 0.2079 1.827000 ## ## Parameter correlation: -## parent_0 log_k1 log_k2 g_qlogis sigma -## parent_0 1.000e+00 1.732e-01 2.282e-02 4.009e-01 -9.671e-08 -## log_k1 1.732e-01 1.000e+00 4.945e-01 -5.809e-01 7.148e-07 -## log_k2 2.282e-02 4.945e-01 1.000e+00 -6.812e-01 1.022e-06 -## g_qlogis 4.009e-01 -5.809e-01 -6.812e-01 1.000e+00 -7.929e-07 -## sigma -9.671e-08 7.148e-07 1.022e-06 -7.929e-07 1.000e+00 +## parent_0 log_k1 log_k2 g_ilr sigma +## parent_0 1.000e+00 1.732e-01 2.282e-02 4.009e-01 -6.814e-07 +## log_k1 1.732e-01 1.000e+00 4.945e-01 -5.809e-01 3.193e-07 +## log_k2 2.282e-02 4.945e-01 1.000e+00 -6.812e-01 7.661e-07 +## g_ilr 4.009e-01 -5.809e-01 -6.812e-01 1.000e+00 -8.680e-07 +## sigma -6.814e-07 3.193e-07 7.661e-07 -8.680e-07 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. @@ -931,8 +926,8 @@ summary and plot functions working on mkinfit objects.</p> ## parent 2.225 4 4 ## ## Estimated disappearance times: -## DT50 DT90 DT50back DT50_k1 DT50_k2 -## parent 7.464 123 37.03 1.343 50.37 +## DT50 DT90 DT50_k1 DT50_k2 +## parent 7.464 123 1.343 50.37 ## ## Data: ## time variable observed predicted residual @@ -944,8 +939,10 @@ summary and plot functions working on mkinfit objects.</p> ## 60 parent 22.0 23.26 -1.25919 ## 91 parent 15.0 15.18 -0.18181 ## 120 parent 12.0 10.19 1.81395</code></pre> -<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1" tabindex="-1"></a><span class="fu">plot</span>(mm.L3[[<span class="st">"DFOP"</span>, <span class="dv">1</span>]], <span class="at">show_errmin =</span> <span class="cn">TRUE</span>)</span></code></pre></div> -<p><img role="img" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAASAAAAHgCAMAAAAytUAGAAADAFBMVEUAAAABAQECAgIDAwMEBAQFBQUGBgYHBwcICAgJCQkKCgoLCwsMDAwNDQ0ODg4PDw8QEBARERESEhITExMUFBQVFRUWFhYXFxcYGBgZGRkaGhobGxscHBwdHR0eHh4fHx8gICAhISEiIiIjIyMkJCQlJSUmJiYnJycoKCgpKSkqKiorKyssLCwtLS0uLi4vLy8wMDAxMTEyMjIzMzM0NDQ1NTU2NjY3Nzc4ODg5OTk6Ojo7Ozs8PDw9PT0+Pj4/Pz9AQEBBQUFCQkJDQ0NERERFRUVGRkZHR0dISEhJSUlKSkpLS0tMTExNTU1OTk5PT09QUFBRUVFSUlJTU1NUVFRVVVVWVlZXV1dYWFhZWVlaWlpbW1tcXFxdXV1eXl5fX19gYGBhYWFiYmJjY2NkZGRlZWVmZmZnZ2doaGhpaWlqampra2tsbGxtbW1ubm5vb29wcHBxcXFycnJzc3N0dHR1dXV2dnZ3d3d4eHh5eXl6enp7e3t8fHx9fX1+fn5/f3+AgICBgYGCgoKDg4OEhISFhYWGhoaHh4eIiIiJiYmKioqLi4uMjIyNjY2Ojo6Pj4+QkJCRkZGSkpKTk5OUlJSVlZWWlpaXl5eYmJiZmZmampqbm5ucnJydnZ2enp6fn5+goKChoaGioqKjo6OkpKSlpaWmpqanp6eoqKipqamqqqqrq6usrKytra2urq6vr6+wsLCxsbGysrKzs7O0tLS1tbW2tra3t7e4uLi5ubm6urq7u7u8vLy9vb2+vr6/v7/AwMDBwcHCwsLDw8PExMTFxcXGxsbHx8fIyMjJycnKysrLy8vMzMzNzc3Ozs7Pz8/Q0NDR0dHS0tLT09PU1NTV1dXW1tbX19fY2NjZ2dna2trb29vc3Nzd3d3e3t7f39/g4ODh4eHi4uLj4+Pk5OTl5eXm5ubn5+fo6Ojp6enq6urr6+vs7Ozt7e3u7u7v7+/w8PDx8fHy8vLz8/P09PT19fX29vb39/f4+Pj5+fn6+vr7+/v8/Pz9/f3+/v7////isF19AAAACXBIWXMAAA7DAAAOwwHHb6hkAAAZcElEQVR4nO2dCVwV1RrAh6coIq4IKosKibhQmaBiWVGYC2BaWplh2SvLtBTNfUvDp1m2aM9c0jC1TCVzBbeCyizLsiyLEpcszdx4JIqy3O/N3HvBK97hu2fmnDlzuef/+3mZy8z3zekfc++ZmXO+kUBQKRLvBpgdIQhBCEIQghCEIAQhCEEIQhCCEIQgBCEIQQhCEIIQhCAEIQhBCEIQghCEIAQhCEEIQhCCEIQgBCEIQQhCEIIQhCAEIQhBCEIQghCEIAQhCEEIQhCCEIQgBCEIQQhCEIIQhCAEIQhBCEIQghCEIAQhCEEIQhCCEIQgBCEIQQhCEIIQhCAEIQhBCEIQghCEIAQugg73eXQRj/1qgYugsd/B7Tz2qwUugi5Z/rgP2SS7ryu/UuP8UbIGVQKfz6C1ff5CtnBF0JLIxsmXri6MC2n6PByLjZwHMCyHUkM5Cdoy2nLtL4rs/66+c0HQvhZ/5SdML1/YHnMpL3Lb09tKOuT/OoxeWw0WNDpiL6xr/Xhi/2Tr2/lhLUeX7h44YIryD2aGh421vgOrDevKHh8BxHxqXawoaP0rAMuSyxe+yATot37ca0fbXnr0BL0WG/0X9GGPS80zy95k33y6YMDM3fVyQPm3LTL/ctcVypKyqq9tZdogOByeZV20CurV3MoBW4JzHT9yWPgwIely/tPddnwzkWKDjRZkiXisd/mbqS3i4tol7r4TQPk3bjLAgsHKEiiCbCv/17ho1gzb4vVH3aqWqxwXTq672eq+f96mHmMuU2qw4Z9Bc2ocuro8G6Awf3eSLEj+N1Y+tBYNUpZAEWRbCX22tz9iW7QK6h5s5Qd5sXTQvbaPetvC+j0Arw6V325/+WyH36e+Sqm9hgsa4H/1/+3+1qcvJ2SUCcpsc+HK7WnlgmwrYXXcXfbtrvsMilc+6i05xbaFN+/Kz4+Xv8Es9xb+Hm9ZO4lSe40WtLtp71VX3y26Ifg5KBMEqS3Dny8tF2RbCQW+y+3bVRQ0qoafn99jl6VjtoXSkc1DhhcDfLAU4IU2t5+i1GCDBZVGp2VFFxu7T30YLGhZjAWSQ44Yu1NdiLN5BCEIQQhCIBZ05cx5Fu0wLWSCjk8K85Kkmi0nuNPHrD6IBH3nG/rMvJUr5j8b3uB7Vg0yG0SC4npdsi0UP9yNRWPMCJGguullS5/Xp98Uc0IkKKb8QtT0zvSbYk6IBKV79Vq255ecr1bcXy0d37pqQPYttuUuScHr7gxGzTEfpP2g8z/t3HngnOrq0dFuQldXz/YpdxRj0/a5BxE/MBGEdhRjvyTKx4+bmAjCO4oeLgjvKHq4ILWO4qfhZVRzl0EJbASpdRRLDpdR+22SfBxhIwjvKPpZBZ3/H0lWLrARhHcUFUEbIhvUi8pU2cAsMBKEdhRlQe+1/BRgR7ONhIkNhpkgBFlQ2NfKwq4b6SamDT9BZ/wBfjsKpbUu0c1MGX6CztW3wNRUKPGhNXqADWwEbRxzFedbyIdY24/hxamwweQXjNgI2tlJ8mlpx/kWsqCMoA9SR7/b5FOSxMbD6BArub1r5RsoX/N74mrWuGcfUV4b455izzv2fbH6DPqvC4IA3nyGLKudagsXsyblHvu+WAn6A7mUaBO09AmyrHaqlWgKI2EHa0EYNkErBmkK9hxBax7UFOw5gj7qoynYcwRl9NIU7DmCdt2tKdhzBH2OdAZU8BxBeztqCvYcQd/frCnYcwT93FpTsOcIOhyuKdhzBP0RrCnYcwSdDtAUzE7Q/m/sCyYRlF9XUzA7Qcll0zxMIqjQR1NwRUGnngz1jd1UaYjleqdXihzf2eaFmE1Q6b80BVcQdDJ08vGLma3nqm6fK2U282o9R176Z2RLn/BUC0CTNWMaHIPl0b7tlCtjIQu6SfUeOAexkiTZ7mWaRBB4FyHbOaWCoBETlNc/Gqjeps2Vag3ZMMbrBYD7G8zdNFJ6XxYU02dd4XzvaRkpXm/Jgho8kv1y9eFwrl/PU7ZpxGYRVLtAS7Ai6OurV/9CJ1l/tE4p/807106kypX6ya9j6/wD/ZQxE5HjZEE3WaDAP1V+NyRQFtRe1tL7NvMdYtBQ/d5rJSiC1l69fNywv/VHaM/y34y69l5brqSMEPhR2iu/Xti/1HuMLGi8fKIj7Tl79uxK6TiEKLN7x8SaUFATrG6AUyocYo9ap53m+6vmypV2y695sqbdN3kF9QpSBL0GsEaycQBCXgGTCmp+TEtwBUEHA9+3wLFuz6pub/sL+ln64rz3sL8BYhVBbwB8Ip22bxCifMCbUlDEb1qCK37N77u1fniD2eoTOHMl5dLuJN+8HVKu3Ldoahd0uuYy+ddT480sKOpHLcHXdxTzjpRWsn2u5DNs68R/TYJj3g98vrFz7YSzVkEwvsbMjLFe8xwEPd5uny23WQR1+FZLMGlPOlf6KLF+q1nyN9UHkbU7bX63wTSbIMvcKN+2yvdauaCssDr51hCzCOqyR0swuSDi+7dmEXRntpZgDxLUfbuWYA8SlLRZSzCpoOJjV0h3YRZB/TTNlvKcC2bw8Ptagj1I0OA0LcEeJOipxVqCPUjQc/O1BHuQoOfVLwNWggcJmvQfLcEeJGj6C1qCPUjQrAlagqt9zXyq6gL2giqf1GsX9Opo0rQKPQyY7TzFvi9GgtBJvXZBbw4nSssBXpN67YKWDCFJywNek3rtgpY/SpKWB8ZO6r2KXdDqASRpeWDspN6r2AV9iNXT5g7fSb2wJZEkLQ94TuqV2Wn6AlXGTuo9WX73vOYC6y8+vYMwreEw7UmXHqoweCPn6bK7595vWH/xlcnnG7I7xPonpcFif8lnpsX5BvZD7LtbiNJygI2gtVLHBO9hfqmZU7yXOd/CLuhgW5K0PGAj6JZnABZKs+WliSp/InZBh1SmtJoHNoJ8twKcsY45yaztfAu7oOOhJGl5wEZQxBz581daLi/NUxlLbxd0qjFJWh6wETTbJ2VGcMegHWc3+k92voVdUJ7p61CyEVQ8JShgaNGTclcx8aLzLeyCLvqSpOUB037QLyv2qq2yCyqppiGtoXC+5GrE5WV98BZk8tov/AVpGwdsILwFhR6nm5c6vAW1/pluXurwFhTzNd281OEtKC6Lbl7q8BakbQyegfAW9NBqunmpw1vQE2YvWcpb0Mg36OalDm9Bk2fSzUsd3oJm0XxQJQt4C5r3HN281OEtaNnjdPNSh7cgjVXMjIO3oK0JdPNSh7egbLPfe+YtaF8Hunmpw1tQTiTdvNThLehPbRWEjIO3oLx6dPNSh7eg4up081KHtyDwKaSbmDbcBfmfoZuYNtwFNT9KNzFtuAtqp6k4hXFwF9T5K7qJacNd0N276CamDXdB926gm5g23AUNXEU3MW24C9I2Mdw4uAsa9SrdxLThLmjqDLqJacNd0Evj6CamDXdB/x1W6Xbc4S7oXW0PZzEM7oK2mPyqPXdBX2l7MIJhcBd0uAXdxLThXHkB4B8/0sTGwrnygoyPuUdKc668IBNi7oHAnCsvyLTXVK3UMDhXXoDSj8Ie/4IktdFwrrxwIub2m3tHDCSuA2kcnCsv9JgBI1+/kpRKktxY+FZe+LthMaROgh9aESU3FGMrL2RJ5ShPuYD97QEWDYFLtQiTGwjTnnTeb6p1wm1/Qaf8i5UCMD+ZeHI4I0EruxyAU0mSVEdtGLT9M6jbbPisa9F904mSG4omQcexm33zpNtPQffghZljq69wvkXZvPmbuo1t1LafiZ+orkHQtiaSBPGV1hgNmygfPtJn8tKEyisvQPHq4bU+cbEJXCAXtLL6UyslmOpV2d0I/w+VB6Eoj6XJqON8i6s96VJv9ec9mAByQW1HwVn5zdioSjbum3AFiuso90wnVt5RVAg45WITuEAuyDfDKihDpSiHlV8Co17Omhn0TtZEb5XpTg6C2v7kYhO4QC6owwtWQS/eVNnWvz3T0NrbabdGZQMHQd00Ff43CnJBad6pe6TTy2q8Vvn2RUe+3LpH7WrQNYKeWuhiE7ig4Vtsvr/8p1FzgkppKRdxEDRnjK5MjNHSDyrYu+YTvQPnHASlm7qWIveL9vIJWaWfZrwhF5RUhq79Ogj6x0/f0coWckGDFfoG/kv9eWeu4CAIAjQ9ps4gtB5iBd313fBzFBS7W1cqtmj+DMouf/6dJhwFPfKunkyM0SxoeS1dnxyOgqZN05OJMeSCVlmZ3jRO134dBb37iK5UbCEX5GPFN/YXXft1FPRFJ12p2GKCfhAU1K5aF8yo4CgI2qsWy+MPoaBGDuja7zWCnn74sQdfvqArHzMIBS1atOgNv+CU1yZHhm7RtV9HQedbNF2+ZnDIQV0JWUF+iA29U/nEKOn5tK79Ogp6dmC4/LrsVl0JWUEuKNh2M3lDkK79OgoKOtLwFEBpwEldGRmhQZDtVtf8Zrr26yio5uWeG+UfNx7QlZER5IKG1FPKjm2pR+8Qa/PNXDlZQb18XRkZQS7oQpzUMKqhdHeBrv06Cnqj6/7GJVeeMOdTbLT0gz55KeWVz3Tu11GQZXyTBr3D+v+jMyUbTNFRhCP3d1MZy8gdQkFLd8LSMnTt91pB8GPzyp4XzxNCQX4DoH4ZuvZbQRB01NfvZIc5DjGAd3vQzU8NjYJyMnXWga4o6EoTfZdPmEEu6Pg9Y+DDalIjfZ+qFQXBNJM+zI9cUN+gDdC+x5F7qN32sZEXYM4/IXJBDZfASWknvB+ga7/XCYJZ5nyaH7mg+h/AOz6FsFnfLJ3rBRUEm7L2Nrmg7rfuurEPFCTG6Nrv9YJgZbQZHyFBLuj7xlLd76BVDX2Vsp0IssS9pSslGzR8zV/aJ3/Fr/1N336dCIKDASacGcVmGDCOM0EwM958wxjYDAPGcSqoONp8hTzYDAPGcSoIfm70q66sDGAzDBjHuSDLo40SJ5lrMAybYcA4TgUV3nPbzf3GBpqqJhWjYcAoTgXN6Ws5E7IjO7TIyTpeMBsGjOBUUJdsgKygEx3NNImV3TBgFwsLOBKZI7/MvDNxk4ttMgJGw4AJCgs4kKCMyy/tUddM5/Wkgi5sX/O7/G1z4mB2ZQ9CJyks4MDmlkcBSlJ8zFQYj1DQz6Hy4fXMD82UCbuVbExSWMCRBYF9/x3ZPTPQRL0hQkG9/VceWB0Q3OWDHXsru3HoemGBCpxev2wfwOIo84yFIRQUoExWniMdQzZ2ubCACk/2N81JGaEgSfkY/RC9w+FyYQEVLsf+x8VmMYdUkPLfuwG/BeRiYQFVToSY5cFsjASpFBY4NL6MGsjVgK8CTTLgjJkgKxsqjMY//lIZNf+LhK644ayre2EKqSBvHx8fb8k6VNqVINXzTuQQkxkfZ4pTMkJBUxyoZOP1yTak+ORk51vggkr7POFi05jC5t78Vl8pOlZGahMb63wLXBBcuOVlkn0ygtHghZzoTkpvWM8hBvBnqEofwUhYje4oGu+3UK8g2B/Iv+A9u+Ev2aG9/tIpCDKa6Ly7pB+G44PyHmqkVxAsidA1bY8CTAdQrU5RPS13URBM6XxRw44pYpYRZmpYHk/kWxzG7IKgODH59X6J0yu7fMsU0wuCY76R6VuGN+U1T8H8gv49MmqOfGqm0uFkjvkFNTr5d+QiKG3EqQyT+QVVL4YjIe9B65/pNsBVzC+o9dcAB5u+V4/T1735Bb0ZKx9cu30qu8vEEvMLskwPvO+hoKSAnXQb4CrmFwRwcv0Hh+DzwCy6LXARdxBkI5uPIfcRBFlcDLmRINkQh6FV7iQIPgvMpNsKF3ArQfBl4410m4HjXoJgXxOV0qfMcDNB8FPwEroNwXA3QZAbZuzNILcTBH+2G2fk2Bj3EwTnugw28CqsGwqCiwmJ+upikOCOgqB4cGfD7ga5pSCwTIk4RKslCO4pCGBpU4OG47urINgWaEyX0W0FwQ/NZxjxde++guCvzgMLKbQEwY0FQeHATif0Z0FwZ0FgmR3CfACRWwsC2Bz4DpU86ri5IPil9TC2D0h0d0GQf1+XPymlcorbCwLLrKCPaeVygvsLAtjVdCa7EnFVQRCcuKMns5PXKiEIiieGZNHM50DVEASwI3gam6toVUUQnOpx21HKKa1UGUFgmV53yD7aSVkK0lBYQBevBcbXr9ON+oc1I0HaCgvoYV3bP6BwZJ1oymkZCdJYWEAP8dZqBDurPUy56jIbQVoLC+igxVHrj7t7hdEdAsJGkObCAtrp8KX1x437tjV/6n8U87IRpLewgAZefFC5ALs9vATyh4ZSHAPCRpDewgIauBR3x4pNoxpbS8l/2qo/tUdQMPoW01tYQAOlKwfdO/Vv23LhlMC3KJ2/MusHOS0sUHq+jNrUBV3LwTs6fUslEStBJUdsdxwKr5like3fwI7XHLJ8xFjSmjxLYw4VG0HFU2tJtcYptWuXq8TRP8Su4/ywJkv1H2dsBL1c/fn0UdUfB66C5P5q1xjdN6jZCGo1UX5ZJW3kLAgs74U+/Lu+FGwE1d6qvCaHFXIWBHBxeqNJuk4+2AjqOFp5/TtwOHdBAH8ObrJAR5kUNoLmSyN2XAbYWu3R8dwFAXzfPWKt5nEOjL7mU+tKufKPzUGSCQTJJ/kxMTs0hrLqB105bH00aPHHi5yvN1YQWNa1iftcU2TVueSKUJIW3v1LDXEeIwigaEnznnuUhd+27Hf9+QseJEg+7heHdcv6vUezxKi2LncgPUqQ/FeUFuH7WDHApkBX+48eJkhW0/qWG1cWwbjJLm7vcYJeGQPb45vNXXWfi9t7nKCFSm29bwf6tT3q2vYeJ+hwY+WqY3H0vY3uz3Jle48TBLPC077feFvf0oKFbW5ajE+K8TxB8NmDNyetUM7NLDv7+o/AysN7oCBHjk8NuvO9y87W7Blx3zilApuHC5J7RundA0ZeX95qXItX109vvEQIUjg6rVnMgmsv8H/cSnky99GA34UgK6XbB9R/cLPDZbXhtseqPLlACCojb3HXwOfKT/cH2OZavTBdCHLg8Iutb5j8k3Vxou1M5P6VQtC1fDumWdSL8jf/r4HKaL6Pgv8Rgipi+SIlJGr6jxuCeo+Ia/W1+BZzhmX3qOYRKbPfyFCmyQhBzvl2yo23WheEIFVsoy+EIAQhCEEIQhCCEIQgBCEIQQhCEIIQhCAEIQhBCEIQghCEIAQhCEEIQhCCEIQgBCEIQQhCEIIQhCAEIQhBCEKoOtVfGFFlqr+wospUf2FFlan+wgpjq7/88EAZ3q+T5OOIsdVfzq0to9knJPk4wqv6S6yW2X884FX9xdMFqVR/uYoQhCAEIQhBCEIQghCEIAQhCEEIsRMWyzzXJ1mFvklqa/olqK15sIfamgH3qK0ZGK+25pEEpYWLgzkJevsphYjANioENlRbE1RPbU2on9qaFrXU1oTXUFvTqrq1iSPy+QiyMek/amvmPq+2Jm2w2ppNvdXWfHGr2pqcSLU1ZxqprXGOEIQgBCEIQQhCEIIQhCAEIQhBCEwETVN9sPW88WprVj2ptiZTtSbQ3jvU1uS2U1uT10RtjXOYCLp4SW1NoWpdliLVrm1Jntoai/qlzbMa1jiFiaCqhBCEIAQhCEEIQhCCEIQgBCEIQQhCEIIQhCAEIQhBCAwEpXesd9d+spCt68kCi1+KqN1hHVHMTtvDyd7s7Bf5SjFBIH1BW7yeWdez9nGSkBzfZLLAKTVnbxkqbSWIKe00RvmRKo3eMqH6VIJA+oLu6glwKXQSQURRtJRMFhiUIr907O9yzB8L7pAUQVfqjpBfn69V4vrOqAs6Ly2TX4eGEYSM7xSdTBYYMEN+SUhyOSaja1cfRdBhSXkOR7p0xPWdURf0k6Q8omCe1xWXI7L9fo1NJgscGrz37GLvNJKYloqgy7lKBdxRtQpdD6QuaJekVCpeKZ1xNSCv2UJQBJEEFsVIkjSMKMYqyMqq6mMJAqkL2inlyK8rJJcfI/dQL7AKIgkcEpS2e1adJSQxZYJOD5IGFxMEUhd0QPpKfp1f09XtV/v/ZRNEEJhj/ShJ9S8liLEL2hoYtoGoldQFnfNaIb8+d4Or26dINjYQBK6QlCvvmdIhghiboK3VhhWStZL+1/zd98sduXDV2zsV+XWXTJv4XacJAvcqD8mDKT5FBDFWQcVBg0hbSV9QRrUZuwc2UJtQ5hzlECMJTPCfv22ydypJjFXQx9K45QqFrgcyONVY16lePOGphlUQQeDFia1qt3/bQhJjFbTIfjyfcj1QnKwiCEEIQhCCEIQgBCEIQQhCEIIQhCAEIQhBCEIQghCEIAQhCEEIQhCCEIQgBCEIQQhCEIIQhCAEIQhBCEIQghCEIAQhCEEI5hPU3357WJoJfkt5N8aMgr5MT08PvU1++QUG7OTdGDMKUogawLsF5ZhaUH35EAt5vYtv+Fsnk+o3f0/+zfJo33bvGNoU8wvynvZxklfI6xmdfC7AfO9pGSlebxnZFPMLekAZkzgSYIv0Y4F/qvzbIYFGNsX8guYAlEjvK5a+3yvtOXv27EqJaJ6DTswvaK4iKN0qaI29A3DAwKa4laBPpNOGN8WtBJ2uqcywmBpvZFPcShCMrzEzY6zXPCOb4l6CLHOjfNsuMrQp5hRkIoQgBCEIQQhCEIIQhCAEIQhBCEIQghCEIAQhCEEIQhCCEIQgBCEIQQhCEIIQhCAEIQhBCEIQghCEIAQhCEEIQhCCEP4P1CtmUU9s8ycAAAAASUVORK5CYII=" /><!-- --></p> +<div class="sourceCode" id="cb35"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb35-1"><a href="#cb35-1" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(mm.L3[[<span class="st">"DFOP"</span>, <span class="dv">1</span>]], <span class="at">show_errmin =</span> <span class="cn">TRUE</span>)</span></code></pre></div> +<pre><code>## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded</code></pre> +<p><img src="data:image/png;base64,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" /><!-- --></p> <p>Here, a look to the model plot, the confidence intervals of the parameters and the correlation matrix suggest that the parameter estimates are reliable, and the DFOP model can be used as the best-fit @@ -962,99 +959,106 @@ parameter <code>g</code> is quite narrow.</p> <h1>Laboratory Data L4</h1> <p>The following code defines example dataset L4 from the FOCUS kinetics report, p. 293:</p> -<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" tabindex="-1"></a>FOCUS_2006_L4 <span class="ot">=</span> <span class="fu">data.frame</span>(</span> -<span id="cb26-2"><a href="#cb26-2" tabindex="-1"></a> <span class="at">t =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">30</span>, <span class="dv">60</span>, <span class="dv">91</span>, <span class="dv">120</span>),</span> -<span id="cb26-3"><a href="#cb26-3" tabindex="-1"></a> <span class="at">parent =</span> <span class="fu">c</span>(<span class="fl">96.6</span>, <span class="fl">96.3</span>, <span class="fl">94.3</span>, <span class="fl">88.8</span>, <span class="fl">74.9</span>, <span class="fl">59.9</span>, <span class="fl">53.5</span>, <span class="fl">49.0</span>))</span> -<span id="cb26-4"><a href="#cb26-4" tabindex="-1"></a>FOCUS_2006_L4_mkin <span class="ot"><-</span> <span class="fu">mkin_wide_to_long</span>(FOCUS_2006_L4)</span></code></pre></div> +<div class="sourceCode" id="cb37"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb37-1"><a href="#cb37-1" aria-hidden="true" tabindex="-1"></a>FOCUS_2006_L4 <span class="ot">=</span> <span class="fu">data.frame</span>(</span> +<span id="cb37-2"><a href="#cb37-2" aria-hidden="true" tabindex="-1"></a> <span class="at">t =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">30</span>, <span class="dv">60</span>, <span class="dv">91</span>, <span class="dv">120</span>),</span> +<span id="cb37-3"><a href="#cb37-3" aria-hidden="true" tabindex="-1"></a> <span class="at">parent =</span> <span class="fu">c</span>(<span class="fl">96.6</span>, <span class="fl">96.3</span>, <span class="fl">94.3</span>, <span class="fl">88.8</span>, <span class="fl">74.9</span>, <span class="fl">59.9</span>, <span class="fl">53.5</span>, <span class="fl">49.0</span>))</span> +<span id="cb37-4"><a href="#cb37-4" aria-hidden="true" tabindex="-1"></a>FOCUS_2006_L4_mkin <span class="ot"><-</span> <span class="fu">mkin_wide_to_long</span>(FOCUS_2006_L4)</span></code></pre></div> <p>Fits of the SFO and FOMC models, plots and summaries are produced below:</p> -<div class="sourceCode" id="cb27"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1" tabindex="-1"></a><span class="co"># Only use one core here, not to offend the CRAN checks</span></span> -<span id="cb27-2"><a href="#cb27-2" tabindex="-1"></a>mm.L4 <span class="ot"><-</span> <span class="fu">mmkin</span>(<span class="fu">c</span>(<span class="st">"SFO"</span>, <span class="st">"FOMC"</span>), <span class="at">cores =</span> <span class="dv">1</span>,</span> -<span id="cb27-3"><a href="#cb27-3" tabindex="-1"></a> <span class="fu">list</span>(<span class="st">"FOCUS L4"</span> <span class="ot">=</span> FOCUS_2006_L4_mkin),</span> -<span id="cb27-4"><a href="#cb27-4" tabindex="-1"></a> <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span> -<span id="cb27-5"><a href="#cb27-5" tabindex="-1"></a><span class="fu">plot</span>(mm.L4)</span></code></pre></div> -<p><img role="img" src="data:image/png;base64,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" /><!-- --></p> +<div class="sourceCode" id="cb38"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb38-1"><a href="#cb38-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Only use one core here, not to offend the CRAN checks</span></span> +<span id="cb38-2"><a href="#cb38-2" aria-hidden="true" tabindex="-1"></a>mm.L4 <span class="ot"><-</span> <span class="fu">mmkin</span>(<span class="fu">c</span>(<span class="st">"SFO"</span>, <span class="st">"FOMC"</span>), <span class="at">cores =</span> <span class="dv">1</span>,</span> +<span id="cb38-3"><a href="#cb38-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">list</span>(<span class="st">"FOCUS L4"</span> <span class="ot">=</span> FOCUS_2006_L4_mkin),</span> +<span id="cb38-4"><a href="#cb38-4" aria-hidden="true" tabindex="-1"></a> <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span> +<span id="cb38-5"><a href="#cb38-5" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(mm.L4)</span></code></pre></div> +<pre><code>## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded +## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded</code></pre> +<p><img src="data:image/png;base64,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" /><!-- --></p> <p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error level of 3.3% as well as the plot suggest that the SFO model fits very well. The error level at which the <span class="math inline"><em>χ</em><sup>2</sup></span> test passes is slightly lower for the FOMC model. However, the difference appears negligible.</p> -<div class="sourceCode" id="cb28"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1" tabindex="-1"></a><span class="fu">summary</span>(mm.L4[[<span class="st">"SFO"</span>, <span class="dv">1</span>]], <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div> -<pre><code>## mkin version used for fitting: 1.2.6 +<div class="sourceCode" id="cb40"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb40-1"><a href="#cb40-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(mm.L4[[<span class="st">"SFO"</span>, <span class="dv">1</span>]], <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div> +<pre><code>## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded</code></pre> +<pre><code>## mkin version used for fitting: 0.9.49.8 ## R version used for fitting: 4.4.2 -## Date of fit: Thu Dec 19 10:28:49 2024 -## Date of summary: Thu Dec 19 10:28:49 2024 +## Date of fit: Sun Feb 16 14:25:40 2025 +## Date of summary: Sun Feb 16 14:25:40 2025 ## ## Equations: -## d_parent/dt = - k_parent * parent +## d_parent/dt = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted using 142 model solutions performed in 0.018 s +## Fitted using 142 model solutions performed in 0.096 s ## ## Error model: Constant variance ## ## Error model algorithm: OLS ## ## Starting values for parameters to be optimised: -## value type -## parent_0 96.6 state -## k_parent 0.1 deparm +## value type +## parent_0 96.6 state +## k_parent_sink 0.1 deparm ## ## Starting values for the transformed parameters actually optimised: -## value lower upper -## parent_0 96.600000 -Inf Inf -## log_k_parent -2.302585 -Inf Inf +## value lower upper +## parent_0 96.600000 -Inf Inf +## log_k_parent_sink -2.302585 -Inf Inf ## ## Fixed parameter values: ## None ## -## Results: -## -## AIC BIC logLik -## 47.12133 47.35966 -20.56067 -## ## Optimised, transformed parameters with symmetric confidence intervals: -## Estimate Std. Error Lower Upper -## parent_0 96.440 1.69900 92.070 100.800 -## log_k_parent -5.030 0.07059 -5.211 -4.848 -## sigma 3.162 0.79050 1.130 5.194 +## Estimate Std. Error Lower Upper +## parent_0 96.440 1.69900 92.070 100.800 +## log_k_parent_sink -5.030 0.07059 -5.211 -4.848 +## sigma 3.162 0.79050 1.130 5.194 ## ## Parameter correlation: -## parent_0 log_k_parent sigma -## parent_0 1.000e+00 5.938e-01 3.387e-07 -## log_k_parent 5.938e-01 1.000e+00 5.830e-07 -## sigma 3.387e-07 5.830e-07 1.000e+00 +## parent_0 log_k_parent_sink sigma +## parent_0 1.000e+00 5.938e-01 3.430e-07 +## log_k_parent_sink 5.938e-01 1.000e+00 5.885e-07 +## sigma 3.430e-07 5.885e-07 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. ## t-test (unrealistically) based on the assumption of normal distribution ## for estimators of untransformed parameters. -## Estimate t value Pr(>t) Lower Upper -## parent_0 96.440000 56.77 1.604e-08 92.070000 1.008e+02 -## k_parent 0.006541 14.17 1.578e-05 0.005455 7.842e-03 -## sigma 3.162000 4.00 5.162e-03 1.130000 5.194e+00 +## Estimate t value Pr(>t) Lower Upper +## parent_0 96.440000 56.77 1.604e-08 92.070000 1.008e+02 +## k_parent_sink 0.006541 14.17 1.578e-05 0.005455 7.842e-03 +## sigma 3.162000 4.00 5.162e-03 1.130000 5.194e+00 ## ## FOCUS Chi2 error levels in percent: ## err.min n.optim df ## All data 3.287 2 6 ## parent 3.287 2 6 ## +## Resulting formation fractions: +## ff +## parent_sink 1 +## ## Estimated disappearance times: ## DT50 DT90 ## parent 106 352</code></pre> -<div class="sourceCode" id="cb30"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1" tabindex="-1"></a><span class="fu">summary</span>(mm.L4[[<span class="st">"FOMC"</span>, <span class="dv">1</span>]], <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div> -<pre><code>## mkin version used for fitting: 1.2.6 +<div class="sourceCode" id="cb43"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb43-1"><a href="#cb43-1" aria-hidden="true" tabindex="-1"></a><span class="fu">summary</span>(mm.L4[[<span class="st">"FOMC"</span>, <span class="dv">1</span>]], <span class="at">data =</span> <span class="cn">FALSE</span>)</span></code></pre></div> +<pre><code>## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded</code></pre> +<pre><code>## mkin version used for fitting: 0.9.49.8 ## R version used for fitting: 4.4.2 -## Date of fit: Thu Dec 19 10:28:49 2024 -## Date of summary: Thu Dec 19 10:28:49 2024 +## Date of fit: Sun Feb 16 14:25:40 2025 +## Date of summary: Sun Feb 16 14:25:40 2025 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted using 224 model solutions performed in 0.027 s +## Fitted using 224 model solutions performed in 0.151 s ## ## Error model: Constant variance ## @@ -1075,11 +1079,6 @@ negligible.</p> ## Fixed parameter values: ## None ## -## Results: -## -## AIC BIC logLik -## 40.37255 40.69032 -16.18628 -## ## Optimised, transformed parameters with symmetric confidence intervals: ## Estimate Std. Error Lower Upper ## parent_0 99.1400 1.2670 95.6300 102.7000 @@ -1089,10 +1088,10 @@ negligible.</p> ## ## Parameter correlation: ## parent_0 log_alpha log_beta sigma -## parent_0 1.000e+00 -4.696e-01 -5.543e-01 -2.456e-07 -## log_alpha -4.696e-01 1.000e+00 9.889e-01 2.169e-08 -## log_beta -5.543e-01 9.889e-01 1.000e+00 4.910e-08 -## sigma -2.456e-07 2.169e-08 4.910e-08 1.000e+00 +## parent_0 1.000e+00 -4.696e-01 -5.543e-01 -2.447e-07 +## log_alpha -4.696e-01 1.000e+00 9.889e-01 2.198e-08 +## log_beta -5.543e-01 9.889e-01 1.000e+00 4.923e-08 +## sigma -2.447e-07 2.198e-08 4.923e-08 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. @@ -1115,7 +1114,7 @@ negligible.</p> </div> <div id="references" class="section level1 unnumbered"> <h1 class="unnumbered">References</h1> -<div id="refs" class="references csl-bib-body hanging-indent" entry-spacing="0"> +<div id="refs" class="references csl-bib-body hanging-indent"> <div id="ref-ranke2014" class="csl-entry"> Ranke, Johannes. 2014. <span>“<span class="nocase">Prüfung und Validierung von Modellierungssoftware als Alternative zu ModelMaker diff --git a/vignettes/mesotrione_parent_2023_prebuilt.rnw b/vignettes/mesotrione_parent_2023_prebuilt.rnw deleted file mode 100644 index f8a7a097..00000000 --- a/vignettes/mesotrione_parent_2023_prebuilt.rnw +++ /dev/null @@ -1,7 +0,0 @@ -\documentclass{article} -\usepackage{pdfpages} -%\VignetteIndexEntry{Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione} - -\begin{document} -\includepdf[pages=-, fitpaper=true]{prebuilt/2023_mesotrione_parent.pdf} -\end{document} diff --git a/vignettes/mkin.html b/vignettes/mkin.html index bda45abb..3e5a004e 100644 --- a/vignettes/mkin.html +++ b/vignettes/mkin.html @@ -40,27 +40,27 @@ display: none; </style> <style type="text/css"> -code{white-space: pre-wrap;} -span.smallcaps{font-variant: small-caps;} 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-margin-bottom: 0em; + clear: both; } .hanging div.csl-entry { -margin-left:2em; -text-indent:-2em; + margin-left:2em; + text-indent:-2em; } div.csl-left-margin { -min-width:2em; -float:left; + min-width:2em; + float:left; } div.csl-right-inline { -margin-left:2em; -padding-left:1em; + margin-left:2em; + padding-left:1em; } div.csl-indent { -margin-left: 2em; + margin-left: 2em; } </style> @@ -372,27 +372,26 @@ code > span.er { color: #a61717; background-color: #e3d2d2; } <h1 class="title toc-ignore">Short introduction to mkin</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">Last change 18 May 2023 (rebuilt 2024-12-19)</h4> +<h4 class="date">Last change 18 May 2023 (rebuilt 2025-02-16)</h4> <div id="TOC"> <ul> -<li><a href="#abstract" id="toc-abstract">Abstract</a></li> -<li><a href="#background" id="toc-background">Background</a> +<li><a href="#abstract">Abstract</a></li> +<li><a href="#background">Background</a> <ul> -<li><a href="#derived-software-tools" id="toc-derived-software-tools">Derived software tools</a></li> +<li><a href="#derived-software-tools">Derived software tools</a></li> </ul></li> -<li><a href="#unique-features" id="toc-unique-features">Unique -features</a></li> -<li><a href="#internal-parameter-transformations" id="toc-internal-parameter-transformations">Internal parameter +<li><a href="#unique-features">Unique features</a></li> +<li><a href="#internal-parameter-transformations">Internal parameter transformations</a> <ul> -<li><a href="#confidence-intervals-based-on-transformed-parameters" id="toc-confidence-intervals-based-on-transformed-parameters">Confidence +<li><a href="#confidence-intervals-based-on-transformed-parameters">Confidence intervals based on transformed parameters</a></li> -<li><a href="#parameter-t-test-based-on-untransformed-parameters" id="toc-parameter-t-test-based-on-untransformed-parameters">Parameter +<li><a href="#parameter-t-test-based-on-untransformed-parameters">Parameter t-test based on untransformed parameters</a></li> </ul></li> -<li><a href="#references" id="toc-references">References</a></li> +<li><a href="#references">References</a></li> </ul> </div> @@ -411,36 +410,42 @@ this guidance from within R and calculates some statistical measures for data series within one or more compartments, for parent and metabolites.</p> <details class="chunk-details"><summary class="chunk-summary"><span class="chunk-summary-text">Code</span></summary> -<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="fu">library</span>(<span class="st">"mkin"</span>, <span class="at">quietly =</span> <span class="cn">TRUE</span>)</span> -<span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a><span class="co"># Define the kinetic model</span></span> -<span id="cb1-3"><a href="#cb1-3" tabindex="-1"></a>m_SFO_SFO_SFO <span class="ot"><-</span> <span class="fu">mkinmod</span>(<span class="at">parent =</span> <span class="fu">mkinsub</span>(<span class="st">"SFO"</span>, <span class="st">"M1"</span>),</span> -<span id="cb1-4"><a href="#cb1-4" tabindex="-1"></a> <span class="at">M1 =</span> <span class="fu">mkinsub</span>(<span class="st">"SFO"</span>, <span class="st">"M2"</span>),</span> -<span id="cb1-5"><a href="#cb1-5" tabindex="-1"></a> <span class="at">M2 =</span> <span class="fu">mkinsub</span>(<span class="st">"SFO"</span>),</span> -<span id="cb1-6"><a href="#cb1-6" tabindex="-1"></a> <span class="at">use_of_ff =</span> <span class="st">"max"</span>, <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span> -<span id="cb1-7"><a href="#cb1-7" tabindex="-1"></a></span> -<span id="cb1-8"><a href="#cb1-8" tabindex="-1"></a></span> -<span id="cb1-9"><a href="#cb1-9" tabindex="-1"></a><span class="co"># Produce model predictions using some arbitrary parameters</span></span> -<span id="cb1-10"><a href="#cb1-10" tabindex="-1"></a>sampling_times <span class="ot">=</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">28</span>, <span class="dv">60</span>, <span class="dv">90</span>, <span class="dv">120</span>)</span> -<span id="cb1-11"><a href="#cb1-11" tabindex="-1"></a>d_SFO_SFO_SFO <span class="ot"><-</span> <span class="fu">mkinpredict</span>(m_SFO_SFO_SFO,</span> -<span id="cb1-12"><a href="#cb1-12" tabindex="-1"></a> <span class="fu">c</span>(<span class="at">k_parent =</span> <span class="fl">0.03</span>,</span> -<span id="cb1-13"><a href="#cb1-13" tabindex="-1"></a> <span class="at">f_parent_to_M1 =</span> <span class="fl">0.5</span>, <span class="at">k_M1 =</span> <span class="fu">log</span>(<span class="dv">2</span>)<span class="sc">/</span><span class="dv">100</span>,</span> -<span id="cb1-14"><a href="#cb1-14" tabindex="-1"></a> <span class="at">f_M1_to_M2 =</span> <span class="fl">0.9</span>, <span class="at">k_M2 =</span> <span class="fu">log</span>(<span class="dv">2</span>)<span class="sc">/</span><span class="dv">50</span>),</span> -<span id="cb1-15"><a href="#cb1-15" tabindex="-1"></a> <span class="fu">c</span>(<span class="at">parent =</span> <span class="dv">100</span>, <span class="at">M1 =</span> <span class="dv">0</span>, <span class="at">M2 =</span> <span class="dv">0</span>),</span> -<span id="cb1-16"><a href="#cb1-16" tabindex="-1"></a> sampling_times)</span> -<span id="cb1-17"><a href="#cb1-17" tabindex="-1"></a></span> -<span id="cb1-18"><a href="#cb1-18" tabindex="-1"></a><span class="co"># Generate a dataset by adding normally distributed errors with</span></span> -<span id="cb1-19"><a href="#cb1-19" tabindex="-1"></a><span class="co"># standard deviation 3, for two replicates at each sampling time</span></span> -<span id="cb1-20"><a href="#cb1-20" tabindex="-1"></a>d_SFO_SFO_SFO_err <span class="ot"><-</span> <span class="fu">add_err</span>(d_SFO_SFO_SFO, <span class="at">reps =</span> <span class="dv">2</span>,</span> -<span id="cb1-21"><a href="#cb1-21" tabindex="-1"></a> <span class="at">sdfunc =</span> <span class="cf">function</span>(x) <span class="dv">3</span>,</span> -<span id="cb1-22"><a href="#cb1-22" tabindex="-1"></a> <span class="at">n =</span> <span class="dv">1</span>, <span class="at">seed =</span> <span class="dv">123456789</span> )</span> -<span id="cb1-23"><a href="#cb1-23" tabindex="-1"></a></span> -<span id="cb1-24"><a href="#cb1-24" tabindex="-1"></a><span class="co"># Fit the model to the dataset</span></span> -<span id="cb1-25"><a href="#cb1-25" tabindex="-1"></a>f_SFO_SFO_SFO <span class="ot"><-</span> <span class="fu">mkinfit</span>(m_SFO_SFO_SFO, d_SFO_SFO_SFO_err[[<span class="dv">1</span>]], <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span> -<span id="cb1-26"><a href="#cb1-26" tabindex="-1"></a></span> -<span id="cb1-27"><a href="#cb1-27" tabindex="-1"></a><span class="co"># Plot the results separately for parent and metabolites</span></span> -<span id="cb1-28"><a href="#cb1-28" tabindex="-1"></a><span class="fu">plot_sep</span>(f_SFO_SFO_SFO, <span class="at">lpos =</span> <span class="fu">c</span>(<span class="st">"topright"</span>, <span class="st">"bottomright"</span>, <span class="st">"bottomright"</span>))</span></code></pre></div> +<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(<span class="st">"mkin"</span>, <span class="at">quietly =</span> <span class="cn">TRUE</span>)</span> +<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="co"># Define the kinetic model</span></span> +<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>m_SFO_SFO_SFO <span class="ot"><-</span> <span class="fu">mkinmod</span>(<span class="at">parent =</span> <span class="fu">mkinsub</span>(<span class="st">"SFO"</span>, <span class="st">"M1"</span>),</span> +<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a> <span class="at">M1 =</span> <span class="fu">mkinsub</span>(<span class="st">"SFO"</span>, <span class="st">"M2"</span>),</span> +<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a> <span class="at">M2 =</span> <span class="fu">mkinsub</span>(<span class="st">"SFO"</span>),</span> +<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a> <span class="at">use_of_ff =</span> <span class="st">"max"</span>, <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span> +<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a></span> +<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a></span> +<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a><span class="co"># Produce model predictions using some arbitrary parameters</span></span> +<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a>sampling_times <span class="ot">=</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">3</span>, <span class="dv">7</span>, <span class="dv">14</span>, <span class="dv">28</span>, <span class="dv">60</span>, <span class="dv">90</span>, <span class="dv">120</span>)</span> +<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a>d_SFO_SFO_SFO <span class="ot"><-</span> <span class="fu">mkinpredict</span>(m_SFO_SFO_SFO,</span> +<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a> <span class="fu">c</span>(<span class="at">k_parent =</span> <span class="fl">0.03</span>,</span> +<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a> <span class="at">f_parent_to_M1 =</span> <span class="fl">0.5</span>, <span class="at">k_M1 =</span> <span class="fu">log</span>(<span class="dv">2</span>)<span class="sc">/</span><span class="dv">100</span>,</span> +<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a> <span class="at">f_M1_to_M2 =</span> <span class="fl">0.9</span>, <span class="at">k_M2 =</span> <span class="fu">log</span>(<span class="dv">2</span>)<span class="sc">/</span><span class="dv">50</span>),</span> +<span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a> <span class="fu">c</span>(<span class="at">parent =</span> <span class="dv">100</span>, <span class="at">M1 =</span> <span class="dv">0</span>, <span class="at">M2 =</span> <span class="dv">0</span>),</span> +<span id="cb1-16"><a href="#cb1-16" aria-hidden="true" tabindex="-1"></a> sampling_times)</span> +<span id="cb1-17"><a href="#cb1-17" aria-hidden="true" tabindex="-1"></a></span> +<span id="cb1-18"><a href="#cb1-18" aria-hidden="true" tabindex="-1"></a><span class="co"># Generate a dataset by adding normally distributed errors with</span></span> +<span id="cb1-19"><a href="#cb1-19" aria-hidden="true" tabindex="-1"></a><span class="co"># standard deviation 3, for two replicates at each sampling time</span></span> +<span id="cb1-20"><a href="#cb1-20" aria-hidden="true" tabindex="-1"></a>d_SFO_SFO_SFO_err <span class="ot"><-</span> <span class="fu">add_err</span>(d_SFO_SFO_SFO, <span class="at">reps =</span> <span class="dv">2</span>,</span> +<span id="cb1-21"><a href="#cb1-21" aria-hidden="true" tabindex="-1"></a> <span class="at">sdfunc =</span> <span class="cf">function</span>(x) <span class="dv">3</span>,</span> +<span id="cb1-22"><a href="#cb1-22" aria-hidden="true" tabindex="-1"></a> <span class="at">n =</span> <span class="dv">1</span>, <span class="at">seed =</span> <span class="dv">123456789</span> )</span> +<span id="cb1-23"><a href="#cb1-23" aria-hidden="true" tabindex="-1"></a></span> +<span id="cb1-24"><a href="#cb1-24" aria-hidden="true" tabindex="-1"></a><span class="co"># Fit the model to the dataset</span></span> +<span id="cb1-25"><a href="#cb1-25" aria-hidden="true" tabindex="-1"></a>f_SFO_SFO_SFO <span class="ot"><-</span> <span class="fu">mkinfit</span>(m_SFO_SFO_SFO, d_SFO_SFO_SFO_err[[<span class="dv">1</span>]], <span class="at">quiet =</span> <span class="cn">TRUE</span>)</span> +<span id="cb1-26"><a href="#cb1-26" aria-hidden="true" tabindex="-1"></a></span> +<span id="cb1-27"><a href="#cb1-27" aria-hidden="true" tabindex="-1"></a><span class="co"># Plot the results separately for parent and metabolites</span></span> +<span id="cb1-28"><a href="#cb1-28" aria-hidden="true" tabindex="-1"></a><span class="fu">plot_sep</span>(f_SFO_SFO_SFO, <span class="at">lpos =</span> <span class="fu">c</span>(<span class="st">"topright"</span>, <span class="st">"bottomright"</span>, <span class="st">"bottomright"</span>))</span></code></pre></div> </details> -<p><img role="img" 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" /><!-- --></p> +<pre><code>## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded +## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded +## Warning: In subset.data.frame(fit$fixed, type = "state") : +## extra argument 'type' will be disregarded</code></pre> +<p><img 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/><!-- --></p> </div> <div id="background" class="section level1"> <h1>Background</h1> @@ -618,7 +623,7 @@ parameter estimators.</p> <div id="references" class="section level1"> <h1>References</h1> <!-- vim: set foldmethod=syntax: --> -<div id="refs" class="references csl-bib-body hanging-indent" entry-spacing="0"> +<div id="refs" class="references csl-bib-body hanging-indent"> <div id="ref-bates1988" class="csl-entry"> Bates, D., and D. Watts. 1988. <em>Nonlinear Regression and Its Applications</em>. Wiley-Interscience. diff --git a/vignettes/prebuilt/2023_mesotrione_parent.pdf b/vignettes/prebuilt/2023_mesotrione_parent.pdf Binary files differdeleted file mode 100644 index a7ecfe3c..00000000 --- a/vignettes/prebuilt/2023_mesotrione_parent.pdf +++ /dev/null diff --git a/vignettes/web_only/mesotrione_parent_2023.html b/vignettes/web_only/mesotrione_parent_2023.html new file mode 100644 index 00000000..00e229fb --- /dev/null +++ b/vignettes/web_only/mesotrione_parent_2023.html @@ -0,0 +1,5427 @@ +<!DOCTYPE html> + +<html> + +<head> + +<meta charset="utf-8" /> +<meta name="generator" content="pandoc" /> +<meta http-equiv="X-UA-Compatible" content="IE=EDGE" /> + + +<meta name="author" content="Johannes Ranke" /> + + +<title>Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</title> + +<script>// Pandoc 2.9 adds attributes on both header and div. 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) 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e={item:this.active};this.active.has(".ui-menu").length||this.collapseAll(t,!0),this._trigger("select",t,e)},_filterMenuItems:function(t){var t=t.replace(/[\-\[\]{}()*+?.,\\\^$|#\s]/g,"\\$&"),e=new RegExp("^"+t,"i");return this.activeMenu.find(this.options.items).filter(".ui-menu-item").filter(function(){return e.test(String.prototype.trim.call(V(this).children(".ui-menu-item-wrapper").text()))})}});V.widget("ui.autocomplete",{version:"1.13.2",defaultElement:"<input>",options:{appendTo:null,autoFocus:!1,delay:300,minLength:1,position:{my:"left top",at:"left bottom",collision:"none"},source:null,change:null,close:null,focus:null,open:null,response:null,search:null,select:null},requestIndex:0,pending:0,liveRegionTimer:null,_create:function(){var i,s,n,t=this.element[0].nodeName.toLowerCase(),e="textarea"===t,t="input"===t;this.isMultiLine=e||!t&&this._isContentEditable(this.element),this.valueMethod=this.element[e||t?"val":"text"],this.isNewMenu=!0,this._addClass("ui-autocomplete-input"),this.element.attr("autocomplete","off"),this._on(this.element,{keydown:function(t){if(this.element.prop("readOnly"))s=n=i=!0;else{s=n=i=!1;var e=V.ui.keyCode;switch(t.keyCode){case e.PAGE_UP:i=!0,this._move("previousPage",t);break;case e.PAGE_DOWN:i=!0,this._move("nextPage",t);break;case e.UP:i=!0,this._keyEvent("previous",t);break;case e.DOWN:i=!0,this._keyEvent("next",t);break;case e.ENTER:this.menu.active&&(i=!0,t.preventDefault(),this.menu.select(t));break;case e.TAB:this.menu.active&&this.menu.select(t);break;case e.ESCAPE:this.menu.element.is(":visible")&&(this.isMultiLine||this._value(this.term),this.close(t),t.preventDefault());break;default:s=!0,this._searchTimeout(t)}}},keypress:function(t){if(i)return i=!1,void(this.isMultiLine&&!this.menu.element.is(":visible")||t.preventDefault());if(!s){var e=V.ui.keyCode;switch(t.keyCode){case e.PAGE_UP:this._move("previousPage",t);break;case e.PAGE_DOWN:this._move("nextPage",t);break;case e.UP:this._keyEvent("previous",t);break;case e.DOWN:this._keyEvent("next",t)}}},input:function(t){if(n)return n=!1,void t.preventDefault();this._searchTimeout(t)},focus:function(){this.selectedItem=null,this.previous=this._value()},blur:function(t){clearTimeout(this.searching),this.close(t),this._change(t)}}),this._initSource(),this.menu=V("<ul>").appendTo(this._appendTo()).menu({role:null}).hide().attr({unselectable:"on"}).menu("instance"),this._addClass(this.menu.element,"ui-autocomplete","ui-front"),this._on(this.menu.element,{mousedown:function(t){t.preventDefault()},menufocus:function(t,e){var i,s;if(this.isNewMenu&&(this.isNewMenu=!1,t.originalEvent&&/^mouse/.test(t.originalEvent.type)))return this.menu.blur(),void this.document.one("mousemove",function(){V(t.target).trigger(t.originalEvent)});s=e.item.data("ui-autocomplete-item"),!1!==this._trigger("focus",t,{item:s})&&t.originalEvent&&/^key/.test(t.originalEvent.type)&&this._value(s.value),(i=e.item.attr("aria-label")||s.value)&&String.prototype.trim.call(i).length&&(clearTimeout(this.liveRegionTimer),this.liveRegionTimer=this._delay(function(){this.liveRegion.html(V("<div>").text(i))},100))},menuselect:function(t,e){var i=e.item.data("ui-autocomplete-item"),s=this.previous;this.element[0]!==V.ui.safeActiveElement(this.document[0])&&(this.element.trigger("focus"),this.previous=s,this._delay(function(){this.previous=s,this.selectedItem=i})),!1!==this._trigger("select",t,{item:i})&&this._value(i.value),this.term=this._value(),this.close(t),this.selectedItem=i}}),this.liveRegion=V("<div>",{role:"status","aria-live":"assertive","aria-relevant":"additions"}).appendTo(this.document[0].body),this._addClass(this.liveRegion,null,"ui-helper-hidden-accessible"),this._on(this.window,{beforeunload:function(){this.element.removeAttr("autocomplete")}})},_destroy:function(){clearTimeout(this.searching),this.element.removeAttr("autocomplete"),this.menu.element.remove(),this.liveRegion.remove()},_setOption:function(t,e){this._super(t,e),"source"===t&&this._initSource(),"appendTo"===t&&this.menu.element.appendTo(this._appendTo()),"disabled"===t&&e&&this.xhr&&this.xhr.abort()},_isEventTargetInWidget:function(t){var e=this.menu.element[0];return t.target===this.element[0]||t.target===e||V.contains(e,t.target)},_closeOnClickOutside:function(t){this._isEventTargetInWidget(t)||this.close()},_appendTo:function(){var t=this.options.appendTo;return t=!(t=!(t=t&&(t.jquery||t.nodeType?V(t):this.document.find(t).eq(0)))||!t[0]?this.element.closest(".ui-front, dialog"):t).length?this.document[0].body:t},_initSource:function(){var i,s,n=this;Array.isArray(this.options.source)?(i=this.options.source,this.source=function(t,e){e(V.ui.autocomplete.filter(i,t.term))}):"string"==typeof this.options.source?(s=this.options.source,this.source=function(t,e){n.xhr&&n.xhr.abort(),n.xhr=V.ajax({url:s,data:t,dataType:"json",success:function(t){e(t)},error:function(){e([])}})}):this.source=this.options.source},_searchTimeout:function(s){clearTimeout(this.searching),this.searching=this._delay(function(){var t=this.term===this._value(),e=this.menu.element.is(":visible"),i=s.altKey||s.ctrlKey||s.metaKey||s.shiftKey;t&&(e||i)||(this.selectedItem=null,this.search(null,s))},this.options.delay)},search:function(t,e){return t=null!=t?t:this._value(),this.term=this._value(),t.length<this.options.minLength?this.close(e):!1!==this._trigger("search",e)?this._search(t):void 0},_search:function(t){this.pending++,this._addClass("ui-autocomplete-loading"),this.cancelSearch=!1,this.source({term:t},this._response())},_response:function(){var e=++this.requestIndex;return function(t){e===this.requestIndex&&this.__response(t),this.pending--,this.pending||this._removeClass("ui-autocomplete-loading")}.bind(this)},__response:function(t){t=t&&this._normalize(t),this._trigger("response",null,{content:t}),!this.options.disabled&&t&&t.length&&!this.cancelSearch?(this._suggest(t),this._trigger("open")):this._close()},close:function(t){this.cancelSearch=!0,this._close(t)},_close:function(t){this._off(this.document,"mousedown"),this.menu.element.is(":visible")&&(this.menu.element.hide(),this.menu.blur(),this.isNewMenu=!0,this._trigger("close",t))},_change:function(t){this.previous!==this._value()&&this._trigger("change",t,{item:this.selectedItem})},_normalize:function(t){return t.length&&t[0].label&&t[0].value?t:V.map(t,function(t){return"string"==typeof t?{label:t,value:t}:V.extend({},t,{label:t.label||t.value,value:t.value||t.label})})},_suggest:function(t){var e=this.menu.element.empty();this._renderMenu(e,t),this.isNewMenu=!0,this.menu.refresh(),e.show(),this._resizeMenu(),e.position(V.extend({of:this.element},this.options.position)),this.options.autoFocus&&this.menu.next(),this._on(this.document,{mousedown:"_closeOnClickOutside"})},_resizeMenu:function(){var t=this.menu.element;t.outerWidth(Math.max(t.width("").outerWidth()+1,this.element.outerWidth()))},_renderMenu:function(i,t){var s=this;V.each(t,function(t,e){s._renderItemData(i,e)})},_renderItemData:function(t,e){return this._renderItem(t,e).data("ui-autocomplete-item",e)},_renderItem:function(t,e){return V("<li>").append(V("<div>").text(e.label)).appendTo(t)},_move:function(t,e){if(this.menu.element.is(":visible"))return this.menu.isFirstItem()&&/^previous/.test(t)||this.menu.isLastItem()&&/^next/.test(t)?(this.isMultiLine||this._value(this.term),void this.menu.blur()):void this.menu[t](e);this.search(null,e)},widget:function(){return this.menu.element},_value:function(){return this.valueMethod.apply(this.element,arguments)},_keyEvent:function(t,e){this.isMultiLine&&!this.menu.element.is(":visible")||(this._move(t,e),e.preventDefault())},_isContentEditable:function(t){if(!t.length)return!1;var e=t.prop("contentEditable");return"inherit"===e?this._isContentEditable(t.parent()):"true"===e}}),V.extend(V.ui.autocomplete,{escapeRegex:function(t){return t.replace(/[\-\[\]{}()*+?.,\\\^$|#\s]/g,"\\$&")},filter:function(t,e){var i=new RegExp(V.ui.autocomplete.escapeRegex(e),"i");return V.grep(t,function(t){return i.test(t.label||t.value||t)})}}),V.widget("ui.autocomplete",V.ui.autocomplete,{options:{messages:{noResults:"No search results.",results:function(t){return t+(1<t?" results are":" result is")+" available, use up and down arrow keys to navigate."}}},__response:function(t){var e;this._superApply(arguments),this.options.disabled||this.cancelSearch||(e=t&&t.length?this.options.messages.results(t.length):this.options.messages.noResults,clearTimeout(this.liveRegionTimer),this.liveRegionTimer=this._delay(function(){this.liveRegion.html(V("<div>").text(e))},100))}});V.ui.autocomplete;var tt=/ui-corner-([a-z]){2,6}/g;V.widget("ui.controlgroup",{version:"1.13.2",defaultElement:"<div>",options:{direction:"horizontal",disabled:null,onlyVisible:!0,items:{button:"input[type=button], input[type=submit], input[type=reset], button, a",controlgroupLabel:".ui-controlgroup-label",checkboxradio:"input[type='checkbox'], input[type='radio']",selectmenu:"select",spinner:".ui-spinner-input"}},_create:function(){this._enhance()},_enhance:function(){this.element.attr("role","toolbar"),this.refresh()},_destroy:function(){this._callChildMethod("destroy"),this.childWidgets.removeData("ui-controlgroup-data"),this.element.removeAttr("role"),this.options.items.controlgroupLabel&&this.element.find(this.options.items.controlgroupLabel).find(".ui-controlgroup-label-contents").contents().unwrap()},_initWidgets:function(){var o=this,a=[];V.each(this.options.items,function(s,t){var e,n={};if(t)return"controlgroupLabel"===s?((e=o.element.find(t)).each(function(){var t=V(this);t.children(".ui-controlgroup-label-contents").length||t.contents().wrapAll("<span class='ui-controlgroup-label-contents'></span>")}),o._addClass(e,null,"ui-widget ui-widget-content ui-state-default"),void(a=a.concat(e.get()))):void(V.fn[s]&&(n=o["_"+s+"Options"]?o["_"+s+"Options"]("middle"):{classes:{}},o.element.find(t).each(function(){var t=V(this),e=t[s]("instance"),i=V.widget.extend({},n);"button"===s&&t.parent(".ui-spinner").length||((e=e||t[s]()[s]("instance"))&&(i.classes=o._resolveClassesValues(i.classes,e)),t[s](i),i=t[s]("widget"),V.data(i[0],"ui-controlgroup-data",e||t[s]("instance")),a.push(i[0]))})))}),this.childWidgets=V(V.uniqueSort(a)),this._addClass(this.childWidgets,"ui-controlgroup-item")},_callChildMethod:function(e){this.childWidgets.each(function(){var t=V(this).data("ui-controlgroup-data");t&&t[e]&&t[e]()})},_updateCornerClass:function(t,e){e=this._buildSimpleOptions(e,"label").classes.label;this._removeClass(t,null,"ui-corner-top ui-corner-bottom ui-corner-left ui-corner-right ui-corner-all"),this._addClass(t,null,e)},_buildSimpleOptions:function(t,e){var i="vertical"===this.options.direction,s={classes:{}};return s.classes[e]={middle:"",first:"ui-corner-"+(i?"top":"left"),last:"ui-corner-"+(i?"bottom":"right"),only:"ui-corner-all"}[t],s},_spinnerOptions:function(t){t=this._buildSimpleOptions(t,"ui-spinner");return t.classes["ui-spinner-up"]="",t.classes["ui-spinner-down"]="",t},_buttonOptions:function(t){return this._buildSimpleOptions(t,"ui-button")},_checkboxradioOptions:function(t){return this._buildSimpleOptions(t,"ui-checkboxradio-label")},_selectmenuOptions:function(t){var e="vertical"===this.options.direction;return{width:e&&"auto",classes:{middle:{"ui-selectmenu-button-open":"","ui-selectmenu-button-closed":""},first:{"ui-selectmenu-button-open":"ui-corner-"+(e?"top":"tl"),"ui-selectmenu-button-closed":"ui-corner-"+(e?"top":"left")},last:{"ui-selectmenu-button-open":e?"":"ui-corner-tr","ui-selectmenu-button-closed":"ui-corner-"+(e?"bottom":"right")},only:{"ui-selectmenu-button-open":"ui-corner-top","ui-selectmenu-button-closed":"ui-corner-all"}}[t]}},_resolveClassesValues:function(i,s){var n={};return V.each(i,function(t){var e=s.options.classes[t]||"",e=String.prototype.trim.call(e.replace(tt,""));n[t]=(e+" "+i[t]).replace(/\s+/g," ")}),n},_setOption:function(t,e){"direction"===t&&this._removeClass("ui-controlgroup-"+this.options.direction),this._super(t,e),"disabled"!==t?this.refresh():this._callChildMethod(e?"disable":"enable")},refresh:function(){var n,o=this;this._addClass("ui-controlgroup ui-controlgroup-"+this.options.direction),"horizontal"===this.options.direction&&this._addClass(null,"ui-helper-clearfix"),this._initWidgets(),n=this.childWidgets,(n=this.options.onlyVisible?n.filter(":visible"):n).length&&(V.each(["first","last"],function(t,e){var i,s=n[e]().data("ui-controlgroup-data");s&&o["_"+s.widgetName+"Options"]?((i=o["_"+s.widgetName+"Options"](1===n.length?"only":e)).classes=o._resolveClassesValues(i.classes,s),s.element[s.widgetName](i)):o._updateCornerClass(n[e](),e)}),this._callChildMethod("refresh"))}});V.widget("ui.checkboxradio",[V.ui.formResetMixin,{version:"1.13.2",options:{disabled:null,label:null,icon:!0,classes:{"ui-checkboxradio-label":"ui-corner-all","ui-checkboxradio-icon":"ui-corner-all"}},_getCreateOptions:function(){var t,e=this._super()||{};return this._readType(),t=this.element.labels(),this.label=V(t[t.length-1]),this.label.length||V.error("No label found for checkboxradio widget"),this.originalLabel="",(t=this.label.contents().not(this.element[0])).length&&(this.originalLabel+=t.clone().wrapAll("<div></div>").parent().html()),this.originalLabel&&(e.label=this.originalLabel),null!=(t=this.element[0].disabled)&&(e.disabled=t),e},_create:function(){var t=this.element[0].checked;this._bindFormResetHandler(),null==this.options.disabled&&(this.options.disabled=this.element[0].disabled),this._setOption("disabled",this.options.disabled),this._addClass("ui-checkboxradio","ui-helper-hidden-accessible"),this._addClass(this.label,"ui-checkboxradio-label","ui-button ui-widget"),"radio"===this.type&&this._addClass(this.label,"ui-checkboxradio-radio-label"),this.options.label&&this.options.label!==this.originalLabel?this._updateLabel():this.originalLabel&&(this.options.label=this.originalLabel),this._enhance(),t&&this._addClass(this.label,"ui-checkboxradio-checked","ui-state-active"),this._on({change:"_toggleClasses",focus:function(){this._addClass(this.label,null,"ui-state-focus ui-visual-focus")},blur:function(){this._removeClass(this.label,null,"ui-state-focus ui-visual-focus")}})},_readType:function(){var t=this.element[0].nodeName.toLowerCase();this.type=this.element[0].type,"input"===t&&/radio|checkbox/.test(this.type)||V.error("Can't create checkboxradio on element.nodeName="+t+" and element.type="+this.type)},_enhance:function(){this._updateIcon(this.element[0].checked)},widget:function(){return this.label},_getRadioGroup:function(){var t=this.element[0].name,e="input[name='"+V.escapeSelector(t)+"']";return t?(this.form.length?V(this.form[0].elements).filter(e):V(e).filter(function(){return 0===V(this)._form().length})).not(this.element):V([])},_toggleClasses:function(){var t=this.element[0].checked;this._toggleClass(this.label,"ui-checkboxradio-checked","ui-state-active",t),this.options.icon&&"checkbox"===this.type&&this._toggleClass(this.icon,null,"ui-icon-check ui-state-checked",t)._toggleClass(this.icon,null,"ui-icon-blank",!t),"radio"===this.type&&this._getRadioGroup().each(function(){var t=V(this).checkboxradio("instance");t&&t._removeClass(t.label,"ui-checkboxradio-checked","ui-state-active")})},_destroy:function(){this._unbindFormResetHandler(),this.icon&&(this.icon.remove(),this.iconSpace.remove())},_setOption:function(t,e){if("label"!==t||e){if(this._super(t,e),"disabled"===t)return this._toggleClass(this.label,null,"ui-state-disabled",e),void(this.element[0].disabled=e);this.refresh()}},_updateIcon:function(t){var e="ui-icon ui-icon-background ";this.options.icon?(this.icon||(this.icon=V("<span>"),this.iconSpace=V("<span> </span>"),this._addClass(this.iconSpace,"ui-checkboxradio-icon-space")),"checkbox"===this.type?(e+=t?"ui-icon-check ui-state-checked":"ui-icon-blank",this._removeClass(this.icon,null,t?"ui-icon-blank":"ui-icon-check")):e+="ui-icon-blank",this._addClass(this.icon,"ui-checkboxradio-icon",e),t||this._removeClass(this.icon,null,"ui-icon-check ui-state-checked"),this.icon.prependTo(this.label).after(this.iconSpace)):void 0!==this.icon&&(this.icon.remove(),this.iconSpace.remove(),delete this.icon)},_updateLabel:function(){var t=this.label.contents().not(this.element[0]);this.icon&&(t=t.not(this.icon[0])),(t=this.iconSpace?t.not(this.iconSpace[0]):t).remove(),this.label.append(this.options.label)},refresh:function(){var t=this.element[0].checked,e=this.element[0].disabled;this._updateIcon(t),this._toggleClass(this.label,"ui-checkboxradio-checked","ui-state-active",t),null!==this.options.label&&this._updateLabel(),e!==this.options.disabled&&this._setOptions({disabled:e})}}]);var et;V.ui.checkboxradio;V.widget("ui.button",{version:"1.13.2",defaultElement:"<button>",options:{classes:{"ui-button":"ui-corner-all"},disabled:null,icon:null,iconPosition:"beginning",label:null,showLabel:!0},_getCreateOptions:function(){var t,e=this._super()||{};return this.isInput=this.element.is("input"),null!=(t=this.element[0].disabled)&&(e.disabled=t),this.originalLabel=this.isInput?this.element.val():this.element.html(),this.originalLabel&&(e.label=this.originalLabel),e},_create:function(){!this.option.showLabel&!this.options.icon&&(this.options.showLabel=!0),null==this.options.disabled&&(this.options.disabled=this.element[0].disabled||!1),this.hasTitle=!!this.element.attr("title"),this.options.label&&this.options.label!==this.originalLabel&&(this.isInput?this.element.val(this.options.label):this.element.html(this.options.label)),this._addClass("ui-button","ui-widget"),this._setOption("disabled",this.options.disabled),this._enhance(),this.element.is("a")&&this._on({keyup:function(t){t.keyCode===V.ui.keyCode.SPACE&&(t.preventDefault(),this.element[0].click?this.element[0].click():this.element.trigger("click"))}})},_enhance:function(){this.element.is("button")||this.element.attr("role","button"),this.options.icon&&(this._updateIcon("icon",this.options.icon),this._updateTooltip())},_updateTooltip:function(){this.title=this.element.attr("title"),this.options.showLabel||this.title||this.element.attr("title",this.options.label)},_updateIcon:function(t,e){var i="iconPosition"!==t,s=i?this.options.iconPosition:e,t="top"===s||"bottom"===s;this.icon?i&&this._removeClass(this.icon,null,this.options.icon):(this.icon=V("<span>"),this._addClass(this.icon,"ui-button-icon","ui-icon"),this.options.showLabel||this._addClass("ui-button-icon-only")),i&&this._addClass(this.icon,null,e),this._attachIcon(s),t?(this._addClass(this.icon,null,"ui-widget-icon-block"),this.iconSpace&&this.iconSpace.remove()):(this.iconSpace||(this.iconSpace=V("<span> </span>"),this._addClass(this.iconSpace,"ui-button-icon-space")),this._removeClass(this.icon,null,"ui-wiget-icon-block"),this._attachIconSpace(s))},_destroy:function(){this.element.removeAttr("role"),this.icon&&this.icon.remove(),this.iconSpace&&this.iconSpace.remove(),this.hasTitle||this.element.removeAttr("title")},_attachIconSpace:function(t){this.icon[/^(?:end|bottom)/.test(t)?"before":"after"](this.iconSpace)},_attachIcon:function(t){this.element[/^(?:end|bottom)/.test(t)?"append":"prepend"](this.icon)},_setOptions:function(t){var e=(void 0===t.showLabel?this.options:t).showLabel,i=(void 0===t.icon?this.options:t).icon;e||i||(t.showLabel=!0),this._super(t)},_setOption:function(t,e){"icon"===t&&(e?this._updateIcon(t,e):this.icon&&(this.icon.remove(),this.iconSpace&&this.iconSpace.remove())),"iconPosition"===t&&this._updateIcon(t,e),"showLabel"===t&&(this._toggleClass("ui-button-icon-only",null,!e),this._updateTooltip()),"label"===t&&(this.isInput?this.element.val(e):(this.element.html(e),this.icon&&(this._attachIcon(this.options.iconPosition),this._attachIconSpace(this.options.iconPosition)))),this._super(t,e),"disabled"===t&&(this._toggleClass(null,"ui-state-disabled",e),(this.element[0].disabled=e)&&this.element.trigger("blur"))},refresh:function(){var t=this.element.is("input, button")?this.element[0].disabled:this.element.hasClass("ui-button-disabled");t!==this.options.disabled&&this._setOptions({disabled:t}),this._updateTooltip()}}),!1!==V.uiBackCompat&&(V.widget("ui.button",V.ui.button,{options:{text:!0,icons:{primary:null,secondary:null}},_create:function(){this.options.showLabel&&!this.options.text&&(this.options.showLabel=this.options.text),!this.options.showLabel&&this.options.text&&(this.options.text=this.options.showLabel),this.options.icon||!this.options.icons.primary&&!this.options.icons.secondary?this.options.icon&&(this.options.icons.primary=this.options.icon):this.options.icons.primary?this.options.icon=this.options.icons.primary:(this.options.icon=this.options.icons.secondary,this.options.iconPosition="end"),this._super()},_setOption:function(t,e){"text"!==t?("showLabel"===t&&(this.options.text=e),"icon"===t&&(this.options.icons.primary=e),"icons"===t&&(e.primary?(this._super("icon",e.primary),this._super("iconPosition","beginning")):e.secondary&&(this._super("icon",e.secondary),this._super("iconPosition","end"))),this._superApply(arguments)):this._super("showLabel",e)}}),V.fn.button=(et=V.fn.button,function(i){var t="string"==typeof i,s=Array.prototype.slice.call(arguments,1),n=this;return t?this.length||"instance"!==i?this.each(function(){var t=V(this).attr("type"),e=V.data(this,"ui-"+("checkbox"!==t&&"radio"!==t?"button":"checkboxradio"));return"instance"===i?(n=e,!1):e?"function"!=typeof e[i]||"_"===i.charAt(0)?V.error("no such method '"+i+"' for button widget instance"):(t=e[i].apply(e,s))!==e&&void 0!==t?(n=t&&t.jquery?n.pushStack(t.get()):t,!1):void 0:V.error("cannot call methods on button prior to initialization; attempted to call method '"+i+"'")}):n=void 0:(s.length&&(i=V.widget.extend.apply(null,[i].concat(s))),this.each(function(){var t=V(this).attr("type"),e="checkbox"!==t&&"radio"!==t?"button":"checkboxradio",t=V.data(this,"ui-"+e);t?(t.option(i||{}),t._init&&t._init()):"button"!=e?V(this).checkboxradio(V.extend({icon:!1},i)):et.call(V(this),i)})),n}),V.fn.buttonset=function(){return V.ui.controlgroup||V.error("Controlgroup widget missing"),"option"===arguments[0]&&"items"===arguments[1]&&arguments[2]?this.controlgroup.apply(this,[arguments[0],"items.button",arguments[2]]):"option"===arguments[0]&&"items"===arguments[1]?this.controlgroup.apply(this,[arguments[0],"items.button"]):("object"==typeof arguments[0]&&arguments[0].items&&(arguments[0].items={button:arguments[0].items}),this.controlgroup.apply(this,arguments))});var it;V.ui.button;function st(){this._curInst=null,this._keyEvent=!1,this._disabledInputs=[],this._datepickerShowing=!1,this._inDialog=!1,this._mainDivId="ui-datepicker-div",this._inlineClass="ui-datepicker-inline",this._appendClass="ui-datepicker-append",this._triggerClass="ui-datepicker-trigger",this._dialogClass="ui-datepicker-dialog",this._disableClass="ui-datepicker-disabled",this._unselectableClass="ui-datepicker-unselectable",this._currentClass="ui-datepicker-current-day",this._dayOverClass="ui-datepicker-days-cell-over",this.regional=[],this.regional[""]={closeText:"Done",prevText:"Prev",nextText:"Next",currentText:"Today",monthNames:["January","February","March","April","May","June","July","August","September","October","November","December"],monthNamesShort:["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"],dayNames:["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"],dayNamesShort:["Sun","Mon","Tue","Wed","Thu","Fri","Sat"],dayNamesMin:["Su","Mo","Tu","We","Th","Fr","Sa"],weekHeader:"Wk",dateFormat:"mm/dd/yy",firstDay:0,isRTL:!1,showMonthAfterYear:!1,yearSuffix:"",selectMonthLabel:"Select month",selectYearLabel:"Select year"},this._defaults={showOn:"focus",showAnim:"fadeIn",showOptions:{},defaultDate:null,appendText:"",buttonText:"...",buttonImage:"",buttonImageOnly:!1,hideIfNoPrevNext:!1,navigationAsDateFormat:!1,gotoCurrent:!1,changeMonth:!1,changeYear:!1,yearRange:"c-10:c+10",showOtherMonths:!1,selectOtherMonths:!1,showWeek:!1,calculateWeek:this.iso8601Week,shortYearCutoff:"+10",minDate:null,maxDate:null,duration:"fast",beforeShowDay:null,beforeShow:null,onSelect:null,onChangeMonthYear:null,onClose:null,onUpdateDatepicker:null,numberOfMonths:1,showCurrentAtPos:0,stepMonths:1,stepBigMonths:12,altField:"",altFormat:"",constrainInput:!0,showButtonPanel:!1,autoSize:!1,disabled:!1},V.extend(this._defaults,this.regional[""]),this.regional.en=V.extend(!0,{},this.regional[""]),this.regional["en-US"]=V.extend(!0,{},this.regional.en),this.dpDiv=nt(V("<div id='"+this._mainDivId+"' class='ui-datepicker ui-widget ui-widget-content ui-helper-clearfix ui-corner-all'></div>"))}function nt(t){var e="button, .ui-datepicker-prev, .ui-datepicker-next, .ui-datepicker-calendar td a";return t.on("mouseout",e,function(){V(this).removeClass("ui-state-hover"),-1!==this.className.indexOf("ui-datepicker-prev")&&V(this).removeClass("ui-datepicker-prev-hover"),-1!==this.className.indexOf("ui-datepicker-next")&&V(this).removeClass("ui-datepicker-next-hover")}).on("mouseover",e,ot)}function ot(){V.datepicker._isDisabledDatepicker((it.inline?it.dpDiv.parent():it.input)[0])||(V(this).parents(".ui-datepicker-calendar").find("a").removeClass("ui-state-hover"),V(this).addClass("ui-state-hover"),-1!==this.className.indexOf("ui-datepicker-prev")&&V(this).addClass("ui-datepicker-prev-hover"),-1!==this.className.indexOf("ui-datepicker-next")&&V(this).addClass("ui-datepicker-next-hover"))}function at(t,e){for(var i in V.extend(t,e),e)null==e[i]&&(t[i]=e[i]);return t}V.extend(V.ui,{datepicker:{version:"1.13.2"}}),V.extend(st.prototype,{markerClassName:"hasDatepicker",maxRows:4,_widgetDatepicker:function(){return this.dpDiv},setDefaults:function(t){return at(this._defaults,t||{}),this},_attachDatepicker:function(t,e){var i,s=t.nodeName.toLowerCase(),n="div"===s||"span"===s;t.id||(this.uuid+=1,t.id="dp"+this.uuid),(i=this._newInst(V(t),n)).settings=V.extend({},e||{}),"input"===s?this._connectDatepicker(t,i):n&&this._inlineDatepicker(t,i)},_newInst:function(t,e){return{id:t[0].id.replace(/([^A-Za-z0-9_\-])/g,"\\\\$1"),input:t,selectedDay:0,selectedMonth:0,selectedYear:0,drawMonth:0,drawYear:0,inline:e,dpDiv:e?nt(V("<div class='"+this._inlineClass+" ui-datepicker ui-widget ui-widget-content ui-helper-clearfix ui-corner-all'></div>")):this.dpDiv}},_connectDatepicker:function(t,e){var i=V(t);e.append=V([]),e.trigger=V([]),i.hasClass(this.markerClassName)||(this._attachments(i,e),i.addClass(this.markerClassName).on("keydown",this._doKeyDown).on("keypress",this._doKeyPress).on("keyup",this._doKeyUp),this._autoSize(e),V.data(t,"datepicker",e),e.settings.disabled&&this._disableDatepicker(t))},_attachments:function(t,e){var i,s=this._get(e,"appendText"),n=this._get(e,"isRTL");e.append&&e.append.remove(),s&&(e.append=V("<span>").addClass(this._appendClass).text(s),t[n?"before":"after"](e.append)),t.off("focus",this._showDatepicker),e.trigger&&e.trigger.remove(),"focus"!==(i=this._get(e,"showOn"))&&"both"!==i||t.on("focus",this._showDatepicker),"button"!==i&&"both"!==i||(s=this._get(e,"buttonText"),i=this._get(e,"buttonImage"),this._get(e,"buttonImageOnly")?e.trigger=V("<img>").addClass(this._triggerClass).attr({src:i,alt:s,title:s}):(e.trigger=V("<button type='button'>").addClass(this._triggerClass),i?e.trigger.html(V("<img>").attr({src:i,alt:s,title:s})):e.trigger.text(s)),t[n?"before":"after"](e.trigger),e.trigger.on("click",function(){return V.datepicker._datepickerShowing&&V.datepicker._lastInput===t[0]?V.datepicker._hideDatepicker():(V.datepicker._datepickerShowing&&V.datepicker._lastInput!==t[0]&&V.datepicker._hideDatepicker(),V.datepicker._showDatepicker(t[0])),!1}))},_autoSize:function(t){var e,i,s,n,o,a;this._get(t,"autoSize")&&!t.inline&&(o=new Date(2009,11,20),(a=this._get(t,"dateFormat")).match(/[DM]/)&&(e=function(t){for(n=s=i=0;n<t.length;n++)t[n].length>i&&(i=t[n].length,s=n);return s},o.setMonth(e(this._get(t,a.match(/MM/)?"monthNames":"monthNamesShort"))),o.setDate(e(this._get(t,a.match(/DD/)?"dayNames":"dayNamesShort"))+20-o.getDay())),t.input.attr("size",this._formatDate(t,o).length))},_inlineDatepicker:function(t,e){var i=V(t);i.hasClass(this.markerClassName)||(i.addClass(this.markerClassName).append(e.dpDiv),V.data(t,"datepicker",e),this._setDate(e,this._getDefaultDate(e),!0),this._updateDatepicker(e),this._updateAlternate(e),e.settings.disabled&&this._disableDatepicker(t),e.dpDiv.css("display","block"))},_dialogDatepicker:function(t,e,i,s,n){var o,a=this._dialogInst;return a||(this.uuid+=1,o="dp"+this.uuid,this._dialogInput=V("<input type='text' id='"+o+"' style='position: absolute; top: -100px; width: 0px;'/>"),this._dialogInput.on("keydown",this._doKeyDown),V("body").append(this._dialogInput),(a=this._dialogInst=this._newInst(this._dialogInput,!1)).settings={},V.data(this._dialogInput[0],"datepicker",a)),at(a.settings,s||{}),e=e&&e.constructor===Date?this._formatDate(a,e):e,this._dialogInput.val(e),this._pos=n?n.length?n:[n.pageX,n.pageY]:null,this._pos||(o=document.documentElement.clientWidth,s=document.documentElement.clientHeight,e=document.documentElement.scrollLeft||document.body.scrollLeft,n=document.documentElement.scrollTop||document.body.scrollTop,this._pos=[o/2-100+e,s/2-150+n]),this._dialogInput.css("left",this._pos[0]+20+"px").css("top",this._pos[1]+"px"),a.settings.onSelect=i,this._inDialog=!0,this.dpDiv.addClass(this._dialogClass),this._showDatepicker(this._dialogInput[0]),V.blockUI&&V.blockUI(this.dpDiv),V.data(this._dialogInput[0],"datepicker",a),this},_destroyDatepicker:function(t){var e,i=V(t),s=V.data(t,"datepicker");i.hasClass(this.markerClassName)&&(e=t.nodeName.toLowerCase(),V.removeData(t,"datepicker"),"input"===e?(s.append.remove(),s.trigger.remove(),i.removeClass(this.markerClassName).off("focus",this._showDatepicker).off("keydown",this._doKeyDown).off("keypress",this._doKeyPress).off("keyup",this._doKeyUp)):"div"!==e&&"span"!==e||i.removeClass(this.markerClassName).empty(),it===s&&(it=null,this._curInst=null))},_enableDatepicker:function(e){var t,i=V(e),s=V.data(e,"datepicker");i.hasClass(this.markerClassName)&&("input"===(t=e.nodeName.toLowerCase())?(e.disabled=!1,s.trigger.filter("button").each(function(){this.disabled=!1}).end().filter("img").css({opacity:"1.0",cursor:""})):"div"!==t&&"span"!==t||((i=i.children("."+this._inlineClass)).children().removeClass("ui-state-disabled"),i.find("select.ui-datepicker-month, select.ui-datepicker-year").prop("disabled",!1)),this._disabledInputs=V.map(this._disabledInputs,function(t){return t===e?null:t}))},_disableDatepicker:function(e){var t,i=V(e),s=V.data(e,"datepicker");i.hasClass(this.markerClassName)&&("input"===(t=e.nodeName.toLowerCase())?(e.disabled=!0,s.trigger.filter("button").each(function(){this.disabled=!0}).end().filter("img").css({opacity:"0.5",cursor:"default"})):"div"!==t&&"span"!==t||((i=i.children("."+this._inlineClass)).children().addClass("ui-state-disabled"),i.find("select.ui-datepicker-month, select.ui-datepicker-year").prop("disabled",!0)),this._disabledInputs=V.map(this._disabledInputs,function(t){return t===e?null:t}),this._disabledInputs[this._disabledInputs.length]=e)},_isDisabledDatepicker:function(t){if(!t)return!1;for(var e=0;e<this._disabledInputs.length;e++)if(this._disabledInputs[e]===t)return!0;return!1},_getInst:function(t){try{return V.data(t,"datepicker")}catch(t){throw"Missing instance data for this datepicker"}},_optionDatepicker:function(t,e,i){var s,n,o=this._getInst(t);if(2===arguments.length&&"string"==typeof e)return"defaults"===e?V.extend({},V.datepicker._defaults):o?"all"===e?V.extend({},o.settings):this._get(o,e):null;s=e||{},"string"==typeof e&&((s={})[e]=i),o&&(this._curInst===o&&this._hideDatepicker(),n=this._getDateDatepicker(t,!0),e=this._getMinMaxDate(o,"min"),i=this._getMinMaxDate(o,"max"),at(o.settings,s),null!==e&&void 0!==s.dateFormat&&void 0===s.minDate&&(o.settings.minDate=this._formatDate(o,e)),null!==i&&void 0!==s.dateFormat&&void 0===s.maxDate&&(o.settings.maxDate=this._formatDate(o,i)),"disabled"in s&&(s.disabled?this._disableDatepicker(t):this._enableDatepicker(t)),this._attachments(V(t),o),this._autoSize(o),this._setDate(o,n),this._updateAlternate(o),this._updateDatepicker(o))},_changeDatepicker:function(t,e,i){this._optionDatepicker(t,e,i)},_refreshDatepicker:function(t){t=this._getInst(t);t&&this._updateDatepicker(t)},_setDateDatepicker:function(t,e){t=this._getInst(t);t&&(this._setDate(t,e),this._updateDatepicker(t),this._updateAlternate(t))},_getDateDatepicker:function(t,e){t=this._getInst(t);return t&&!t.inline&&this._setDateFromField(t,e),t?this._getDate(t):null},_doKeyDown:function(t){var e,i,s=V.datepicker._getInst(t.target),n=!0,o=s.dpDiv.is(".ui-datepicker-rtl");if(s._keyEvent=!0,V.datepicker._datepickerShowing)switch(t.keyCode){case 9:V.datepicker._hideDatepicker(),n=!1;break;case 13:return(i=V("td."+V.datepicker._dayOverClass+":not(."+V.datepicker._currentClass+")",s.dpDiv))[0]&&V.datepicker._selectDay(t.target,s.selectedMonth,s.selectedYear,i[0]),(e=V.datepicker._get(s,"onSelect"))?(i=V.datepicker._formatDate(s),e.apply(s.input?s.input[0]:null,[i,s])):V.datepicker._hideDatepicker(),!1;case 27:V.datepicker._hideDatepicker();break;case 33:V.datepicker._adjustDate(t.target,t.ctrlKey?-V.datepicker._get(s,"stepBigMonths"):-V.datepicker._get(s,"stepMonths"),"M");break;case 34:V.datepicker._adjustDate(t.target,t.ctrlKey?+V.datepicker._get(s,"stepBigMonths"):+V.datepicker._get(s,"stepMonths"),"M");break;case 35:(t.ctrlKey||t.metaKey)&&V.datepicker._clearDate(t.target),n=t.ctrlKey||t.metaKey;break;case 36:(t.ctrlKey||t.metaKey)&&V.datepicker._gotoToday(t.target),n=t.ctrlKey||t.metaKey;break;case 37:(t.ctrlKey||t.metaKey)&&V.datepicker._adjustDate(t.target,o?1:-1,"D"),n=t.ctrlKey||t.metaKey,t.originalEvent.altKey&&V.datepicker._adjustDate(t.target,t.ctrlKey?-V.datepicker._get(s,"stepBigMonths"):-V.datepicker._get(s,"stepMonths"),"M");break;case 38:(t.ctrlKey||t.metaKey)&&V.datepicker._adjustDate(t.target,-7,"D"),n=t.ctrlKey||t.metaKey;break;case 39:(t.ctrlKey||t.metaKey)&&V.datepicker._adjustDate(t.target,o?-1:1,"D"),n=t.ctrlKey||t.metaKey,t.originalEvent.altKey&&V.datepicker._adjustDate(t.target,t.ctrlKey?+V.datepicker._get(s,"stepBigMonths"):+V.datepicker._get(s,"stepMonths"),"M");break;case 40:(t.ctrlKey||t.metaKey)&&V.datepicker._adjustDate(t.target,7,"D"),n=t.ctrlKey||t.metaKey;break;default:n=!1}else 36===t.keyCode&&t.ctrlKey?V.datepicker._showDatepicker(this):n=!1;n&&(t.preventDefault(),t.stopPropagation())},_doKeyPress:function(t){var e,i=V.datepicker._getInst(t.target);if(V.datepicker._get(i,"constrainInput"))return e=V.datepicker._possibleChars(V.datepicker._get(i,"dateFormat")),i=String.fromCharCode(null==t.charCode?t.keyCode:t.charCode),t.ctrlKey||t.metaKey||i<" "||!e||-1<e.indexOf(i)},_doKeyUp:function(t){t=V.datepicker._getInst(t.target);if(t.input.val()!==t.lastVal)try{V.datepicker.parseDate(V.datepicker._get(t,"dateFormat"),t.input?t.input.val():null,V.datepicker._getFormatConfig(t))&&(V.datepicker._setDateFromField(t),V.datepicker._updateAlternate(t),V.datepicker._updateDatepicker(t))}catch(t){}return!0},_showDatepicker:function(t){var e,i,s,n;"input"!==(t=t.target||t).nodeName.toLowerCase()&&(t=V("input",t.parentNode)[0]),V.datepicker._isDisabledDatepicker(t)||V.datepicker._lastInput===t||(n=V.datepicker._getInst(t),V.datepicker._curInst&&V.datepicker._curInst!==n&&(V.datepicker._curInst.dpDiv.stop(!0,!0),n&&V.datepicker._datepickerShowing&&V.datepicker._hideDatepicker(V.datepicker._curInst.input[0])),!1!==(i=(s=V.datepicker._get(n,"beforeShow"))?s.apply(t,[t,n]):{})&&(at(n.settings,i),n.lastVal=null,V.datepicker._lastInput=t,V.datepicker._setDateFromField(n),V.datepicker._inDialog&&(t.value=""),V.datepicker._pos||(V.datepicker._pos=V.datepicker._findPos(t),V.datepicker._pos[1]+=t.offsetHeight),e=!1,V(t).parents().each(function(){return!(e|="fixed"===V(this).css("position"))}),s={left:V.datepicker._pos[0],top:V.datepicker._pos[1]},V.datepicker._pos=null,n.dpDiv.empty(),n.dpDiv.css({position:"absolute",display:"block",top:"-1000px"}),V.datepicker._updateDatepicker(n),s=V.datepicker._checkOffset(n,s,e),n.dpDiv.css({position:V.datepicker._inDialog&&V.blockUI?"static":e?"fixed":"absolute",display:"none",left:s.left+"px",top:s.top+"px"}),n.inline||(i=V.datepicker._get(n,"showAnim"),s=V.datepicker._get(n,"duration"),n.dpDiv.css("z-index",function(t){for(var e,i;t.length&&t[0]!==document;){if(("absolute"===(e=t.css("position"))||"relative"===e||"fixed"===e)&&(i=parseInt(t.css("zIndex"),10),!isNaN(i)&&0!==i))return i;t=t.parent()}return 0}(V(t))+1),V.datepicker._datepickerShowing=!0,V.effects&&V.effects.effect[i]?n.dpDiv.show(i,V.datepicker._get(n,"showOptions"),s):n.dpDiv[i||"show"](i?s:null),V.datepicker._shouldFocusInput(n)&&n.input.trigger("focus"),V.datepicker._curInst=n)))},_updateDatepicker:function(t){this.maxRows=4,(it=t).dpDiv.empty().append(this._generateHTML(t)),this._attachHandlers(t);var e,i=this._getNumberOfMonths(t),s=i[1],n=t.dpDiv.find("."+this._dayOverClass+" a"),o=V.datepicker._get(t,"onUpdateDatepicker");0<n.length&&ot.apply(n.get(0)),t.dpDiv.removeClass("ui-datepicker-multi-2 ui-datepicker-multi-3 ui-datepicker-multi-4").width(""),1<s&&t.dpDiv.addClass("ui-datepicker-multi-"+s).css("width",17*s+"em"),t.dpDiv[(1!==i[0]||1!==i[1]?"add":"remove")+"Class"]("ui-datepicker-multi"),t.dpDiv[(this._get(t,"isRTL")?"add":"remove")+"Class"]("ui-datepicker-rtl"),t===V.datepicker._curInst&&V.datepicker._datepickerShowing&&V.datepicker._shouldFocusInput(t)&&t.input.trigger("focus"),t.yearshtml&&(e=t.yearshtml,setTimeout(function(){e===t.yearshtml&&t.yearshtml&&t.dpDiv.find("select.ui-datepicker-year").first().replaceWith(t.yearshtml),e=t.yearshtml=null},0)),o&&o.apply(t.input?t.input[0]:null,[t])},_shouldFocusInput:function(t){return t.input&&t.input.is(":visible")&&!t.input.is(":disabled")&&!t.input.is(":focus")},_checkOffset:function(t,e,i){var s=t.dpDiv.outerWidth(),n=t.dpDiv.outerHeight(),o=t.input?t.input.outerWidth():0,a=t.input?t.input.outerHeight():0,r=document.documentElement.clientWidth+(i?0:V(document).scrollLeft()),l=document.documentElement.clientHeight+(i?0:V(document).scrollTop());return e.left-=this._get(t,"isRTL")?s-o:0,e.left-=i&&e.left===t.input.offset().left?V(document).scrollLeft():0,e.top-=i&&e.top===t.input.offset().top+a?V(document).scrollTop():0,e.left-=Math.min(e.left,e.left+s>r&&s<r?Math.abs(e.left+s-r):0),e.top-=Math.min(e.top,e.top+n>l&&n<l?Math.abs(n+a):0),e},_findPos:function(t){for(var e=this._getInst(t),i=this._get(e,"isRTL");t&&("hidden"===t.type||1!==t.nodeType||V.expr.pseudos.hidden(t));)t=t[i?"previousSibling":"nextSibling"];return[(e=V(t).offset()).left,e.top]},_hideDatepicker:function(t){var e,i,s=this._curInst;!s||t&&s!==V.data(t,"datepicker")||this._datepickerShowing&&(e=this._get(s,"showAnim"),i=this._get(s,"duration"),t=function(){V.datepicker._tidyDialog(s)},V.effects&&(V.effects.effect[e]||V.effects[e])?s.dpDiv.hide(e,V.datepicker._get(s,"showOptions"),i,t):s.dpDiv["slideDown"===e?"slideUp":"fadeIn"===e?"fadeOut":"hide"](e?i:null,t),e||t(),this._datepickerShowing=!1,(t=this._get(s,"onClose"))&&t.apply(s.input?s.input[0]:null,[s.input?s.input.val():"",s]),this._lastInput=null,this._inDialog&&(this._dialogInput.css({position:"absolute",left:"0",top:"-100px"}),V.blockUI&&(V.unblockUI(),V("body").append(this.dpDiv))),this._inDialog=!1)},_tidyDialog:function(t){t.dpDiv.removeClass(this._dialogClass).off(".ui-datepicker-calendar")},_checkExternalClick:function(t){var e;V.datepicker._curInst&&(e=V(t.target),t=V.datepicker._getInst(e[0]),(e[0].id===V.datepicker._mainDivId||0!==e.parents("#"+V.datepicker._mainDivId).length||e.hasClass(V.datepicker.markerClassName)||e.closest("."+V.datepicker._triggerClass).length||!V.datepicker._datepickerShowing||V.datepicker._inDialog&&V.blockUI)&&(!e.hasClass(V.datepicker.markerClassName)||V.datepicker._curInst===t)||V.datepicker._hideDatepicker())},_adjustDate:function(t,e,i){var s=V(t),t=this._getInst(s[0]);this._isDisabledDatepicker(s[0])||(this._adjustInstDate(t,e,i),this._updateDatepicker(t))},_gotoToday:function(t){var e=V(t),i=this._getInst(e[0]);this._get(i,"gotoCurrent")&&i.currentDay?(i.selectedDay=i.currentDay,i.drawMonth=i.selectedMonth=i.currentMonth,i.drawYear=i.selectedYear=i.currentYear):(t=new Date,i.selectedDay=t.getDate(),i.drawMonth=i.selectedMonth=t.getMonth(),i.drawYear=i.selectedYear=t.getFullYear()),this._notifyChange(i),this._adjustDate(e)},_selectMonthYear:function(t,e,i){var s=V(t),t=this._getInst(s[0]);t["selected"+("M"===i?"Month":"Year")]=t["draw"+("M"===i?"Month":"Year")]=parseInt(e.options[e.selectedIndex].value,10),this._notifyChange(t),this._adjustDate(s)},_selectDay:function(t,e,i,s){var n=V(t);V(s).hasClass(this._unselectableClass)||this._isDisabledDatepicker(n[0])||((n=this._getInst(n[0])).selectedDay=n.currentDay=parseInt(V("a",s).attr("data-date")),n.selectedMonth=n.currentMonth=e,n.selectedYear=n.currentYear=i,this._selectDate(t,this._formatDate(n,n.currentDay,n.currentMonth,n.currentYear)))},_clearDate:function(t){t=V(t);this._selectDate(t,"")},_selectDate:function(t,e){var i=V(t),t=this._getInst(i[0]);e=null!=e?e:this._formatDate(t),t.input&&t.input.val(e),this._updateAlternate(t),(i=this._get(t,"onSelect"))?i.apply(t.input?t.input[0]:null,[e,t]):t.input&&t.input.trigger("change"),t.inline?this._updateDatepicker(t):(this._hideDatepicker(),this._lastInput=t.input[0],"object"!=typeof t.input[0]&&t.input.trigger("focus"),this._lastInput=null)},_updateAlternate:function(t){var e,i,s=this._get(t,"altField");s&&(e=this._get(t,"altFormat")||this._get(t,"dateFormat"),i=this._getDate(t),t=this.formatDate(e,i,this._getFormatConfig(t)),V(document).find(s).val(t))},noWeekends:function(t){t=t.getDay();return[0<t&&t<6,""]},iso8601Week:function(t){var e=new Date(t.getTime());return e.setDate(e.getDate()+4-(e.getDay()||7)),t=e.getTime(),e.setMonth(0),e.setDate(1),Math.floor(Math.round((t-e)/864e5)/7)+1},parseDate:function(e,n,t){if(null==e||null==n)throw"Invalid arguments";if(""===(n="object"==typeof n?n.toString():n+""))return null;for(var i,s,o,a=0,r=(t?t.shortYearCutoff:null)||this._defaults.shortYearCutoff,r="string"!=typeof r?r:(new 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i=this._get(t,"dateFormat"),s=t.lastVal=t.input?t.input.val():null,n=this._getDefaultDate(t),o=n,a=this._getFormatConfig(t);try{o=this.parseDate(i,s,a)||n}catch(t){s=e?"":s}t.selectedDay=o.getDate(),t.drawMonth=t.selectedMonth=o.getMonth(),t.drawYear=t.selectedYear=o.getFullYear(),t.currentDay=s?o.getDate():0,t.currentMonth=s?o.getMonth():0,t.currentYear=s?o.getFullYear():0,this._adjustInstDate(t)}},_getDefaultDate:function(t){return this._restrictMinMax(t,this._determineDate(t,this._get(t,"defaultDate"),new Date))},_determineDate:function(r,t,e){var i,s,t=null==t||""===t?e:"string"==typeof t?function(t){try{return V.datepicker.parseDate(V.datepicker._get(r,"dateFormat"),t,V.datepicker._getFormatConfig(r))}catch(t){}for(var e=(t.toLowerCase().match(/^c/)?V.datepicker._getDate(r):null)||new 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Date));t.selectedDay=t.currentDay=e.getDate(),t.drawMonth=t.selectedMonth=t.currentMonth=e.getMonth(),t.drawYear=t.selectedYear=t.currentYear=e.getFullYear(),n===t.selectedMonth&&o===t.selectedYear||i||this._notifyChange(t),this._adjustInstDate(t),t.input&&t.input.val(s?"":this._formatDate(t))},_getDate:function(t){return!t.currentYear||t.input&&""===t.input.val()?null:this._daylightSavingAdjust(new Date(t.currentYear,t.currentMonth,t.currentDay))},_attachHandlers:function(t){var e=this._get(t,"stepMonths"),i="#"+t.id.replace(/\\\\/g,"\\");t.dpDiv.find("[data-handler]").map(function(){var t={prev:function(){V.datepicker._adjustDate(i,-e,"M")},next:function(){V.datepicker._adjustDate(i,+e,"M")},hide:function(){V.datepicker._hideDatepicker()},today:function(){V.datepicker._gotoToday(i)},selectDay:function(){return V.datepicker._selectDay(i,+this.getAttribute("data-month"),+this.getAttribute("data-year"),this),!1},selectMonth:function(){return V.datepicker._selectMonthYear(i,this,"M"),!1},selectYear:function(){return V.datepicker._selectMonthYear(i,this,"Y"),!1}};V(this).on(this.getAttribute("data-event"),t[this.getAttribute("data-handler")])})},_generateHTML:function(t){var e,i,s,n,o,a,r,l,h,c,u,d,p,f,g,m,_,v,b,y,w,x,k,C,D,I,T,P,M,S,H,z,A=new Date,O=this._daylightSavingAdjust(new Date(A.getFullYear(),A.getMonth(),A.getDate())),N=this._get(t,"isRTL"),E=this._get(t,"showButtonPanel"),W=this._get(t,"hideIfNoPrevNext"),F=this._get(t,"navigationAsDateFormat"),L=this._getNumberOfMonths(t),R=this._get(t,"showCurrentAtPos"),A=this._get(t,"stepMonths"),Y=1!==L[0]||1!==L[1],B=this._daylightSavingAdjust(t.currentDay?new Date(t.currentYear,t.currentMonth,t.currentDay):new Date(9999,9,9)),j=this._getMinMaxDate(t,"min"),q=this._getMinMaxDate(t,"max"),K=t.drawMonth-R,U=t.drawYear;if(K<0&&(K+=12,U--),q)for(e=this._daylightSavingAdjust(new Date(q.getFullYear(),q.getMonth()-L[0]*L[1]+1,q.getDate())),e=j&&e<j?j:e;this._daylightSavingAdjust(new Date(U,K,1))>e;)--K<0&&(K=11,U--);for(t.drawMonth=K,t.drawYear=U,R=this._get(t,"prevText"),R=F?this.formatDate(R,this._daylightSavingAdjust(new Date(U,K-A,1)),this._getFormatConfig(t)):R,i=this._canAdjustMonth(t,-1,U,K)?V("<a>").attr({class:"ui-datepicker-prev ui-corner-all","data-handler":"prev","data-event":"click",title:R}).append(V("<span>").addClass("ui-icon ui-icon-circle-triangle-"+(N?"e":"w")).text(R))[0].outerHTML:W?"":V("<a>").attr({class:"ui-datepicker-prev ui-corner-all ui-state-disabled",title:R}).append(V("<span>").addClass("ui-icon ui-icon-circle-triangle-"+(N?"e":"w")).text(R))[0].outerHTML,R=this._get(t,"nextText"),R=F?this.formatDate(R,this._daylightSavingAdjust(new Date(U,K+A,1)),this._getFormatConfig(t)):R,s=this._canAdjustMonth(t,1,U,K)?V("<a>").attr({class:"ui-datepicker-next 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ui-helper-clearfix"+y+"'>"+(/all|left/.test(y)&&0===m?N?s:i:"")+(/all|right/.test(y)&&0===m?N?i:s:"")+this._generateMonthYearHeader(t,K,U,j,q,0<m||0<v,l,h)+"</div><table class='ui-datepicker-calendar'><thead><tr>",x=o?"<th class='ui-datepicker-week-col'>"+this._get(t,"weekHeader")+"</th>":"",g=0;g<7;g++)x+="<th scope='col'"+(5<=(g+n+6)%7?" class='ui-datepicker-week-end'":"")+"><span title='"+a[k=(g+n)%7]+"'>"+r[k]+"</span></th>";for(w+=x+"</tr></thead><tbody>",D=this._getDaysInMonth(U,K),U===t.selectedYear&&K===t.selectedMonth&&(t.selectedDay=Math.min(t.selectedDay,D)),C=(this._getFirstDayOfMonth(U,K)-n+7)%7,D=Math.ceil((C+D)/7),I=Y&&this.maxRows>D?this.maxRows:D,this.maxRows=I,T=this._daylightSavingAdjust(new Date(U,K,1-C)),P=0;P<I;P++){for(w+="<tr>",M=o?"<td class='ui-datepicker-week-col'>"+this._get(t,"calculateWeek")(T)+"</td>":"",g=0;g<7;g++)S=c?c.apply(t.input?t.input[0]:null,[T]):[!0,""],z=(H=T.getMonth()!==K)&&!d||!S[0]||j&&T<j||q&&q<T,M+="<td class='"+(5<=(g+n+6)%7?" ui-datepicker-week-end":"")+(H?" ui-datepicker-other-month":"")+(T.getTime()===b.getTime()&&K===t.selectedMonth&&t._keyEvent||p.getTime()===T.getTime()&&p.getTime()===b.getTime()?" "+this._dayOverClass:"")+(z?" "+this._unselectableClass+" ui-state-disabled":"")+(H&&!u?"":" "+S[1]+(T.getTime()===B.getTime()?" 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l,h,c,u,d,p,f=this._get(t,"changeMonth"),g=this._get(t,"changeYear"),m=this._get(t,"showMonthAfterYear"),_=this._get(t,"selectMonthLabel"),v=this._get(t,"selectYearLabel"),b="<div class='ui-datepicker-title'>",y="";if(o||!f)y+="<span class='ui-datepicker-month'>"+a[e]+"</span>";else{for(l=s&&s.getFullYear()===i,h=n&&n.getFullYear()===i,y+="<select class='ui-datepicker-month' aria-label='"+_+"' data-handler='selectMonth' data-event='change'>",c=0;c<12;c++)(!l||c>=s.getMonth())&&(!h||c<=n.getMonth())&&(y+="<option value='"+c+"'"+(c===e?" selected='selected'":"")+">"+r[c]+"</option>");y+="</select>"}if(m||(b+=y+(!o&&f&&g?"":" ")),!t.yearshtml)if(t.yearshtml="",o||!g)b+="<span class='ui-datepicker-year'>"+i+"</span>";else{for(a=this._get(t,"yearRange").split(":"),u=(new Date).getFullYear(),d=(_=function(t){t=t.match(/c[+\-].*/)?i+parseInt(t.substring(1),10):t.match(/[+\-].*/)?u+parseInt(t,10):parseInt(t,10);return 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t&&t<e?t:e},_notifyChange:function(t){var e=this._get(t,"onChangeMonthYear");e&&e.apply(t.input?t.input[0]:null,[t.selectedYear,t.selectedMonth+1,t])},_getNumberOfMonths:function(t){t=this._get(t,"numberOfMonths");return null==t?[1,1]:"number"==typeof t?[1,t]:t},_getMinMaxDate:function(t,e){return this._determineDate(t,this._get(t,e+"Date"),null)},_getDaysInMonth:function(t,e){return 32-this._daylightSavingAdjust(new Date(t,e,32)).getDate()},_getFirstDayOfMonth:function(t,e){return new Date(t,e,1).getDay()},_canAdjustMonth:function(t,e,i,s){var n=this._getNumberOfMonths(t),n=this._daylightSavingAdjust(new Date(i,s+(e<0?e:n[0]*n[1]),1));return e<0&&n.setDate(this._getDaysInMonth(n.getFullYear(),n.getMonth())),this._isInRange(t,n)},_isInRange:function(t,e){var i=this._getMinMaxDate(t,"min"),s=this._getMinMaxDate(t,"max"),n=null,o=null,a=this._get(t,"yearRange");return a&&(t=a.split(":"),a=(new 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i&&!s&&this._mouseCapture(t)?(this.mouseDelayMet=!this.options.delay,this.mouseDelayMet||(this._mouseDelayTimer=setTimeout(function(){e.mouseDelayMet=!0},this.options.delay)),this._mouseDistanceMet(t)&&this._mouseDelayMet(t)&&(this._mouseStarted=!1!==this._mouseStart(t),!this._mouseStarted)?(t.preventDefault(),!0):(!0===V.data(t.target,this.widgetName+".preventClickEvent")&&V.removeData(t.target,this.widgetName+".preventClickEvent"),this._mouseMoveDelegate=function(t){return e._mouseMove(t)},this._mouseUpDelegate=function(t){return e._mouseUp(t)},this.document.on("mousemove."+this.widgetName,this._mouseMoveDelegate).on("mouseup."+this.widgetName,this._mouseUpDelegate),t.preventDefault(),rt=!0)):!0}},_mouseMove:function(t){if(this._mouseMoved){if(V.ui.ie&&(!document.documentMode||document.documentMode<9)&&!t.button)return this._mouseUp(t);if(!t.which)if(t.originalEvent.altKey||t.originalEvent.ctrlKey||t.originalEvent.metaKey||t.originalEvent.shiftKey)this.ignoreMissingWhich=!0;else if(!this.ignoreMissingWhich)return this._mouseUp(t)}return(t.which||t.button)&&(this._mouseMoved=!0),this._mouseStarted?(this._mouseDrag(t),t.preventDefault()):(this._mouseDistanceMet(t)&&this._mouseDelayMet(t)&&(this._mouseStarted=!1!==this._mouseStart(this._mouseDownEvent,t),this._mouseStarted?this._mouseDrag(t):this._mouseUp(t)),!this._mouseStarted)},_mouseUp:function(t){this.document.off("mousemove."+this.widgetName,this._mouseMoveDelegate).off("mouseup."+this.widgetName,this._mouseUpDelegate),this._mouseStarted&&(this._mouseStarted=!1,t.target===this._mouseDownEvent.target&&V.data(t.target,this.widgetName+".preventClickEvent",!0),this._mouseStop(t)),this._mouseDelayTimer&&(clearTimeout(this._mouseDelayTimer),delete this._mouseDelayTimer),this.ignoreMissingWhich=!1,rt=!1,t.preventDefault()},_mouseDistanceMet:function(t){return Math.max(Math.abs(this._mouseDownEvent.pageX-t.pageX),Math.abs(this._mouseDownEvent.pageY-t.pageY))>=this.options.distance},_mouseDelayMet:function(){return this.mouseDelayMet},_mouseStart:function(){},_mouseDrag:function(){},_mouseStop:function(){},_mouseCapture:function(){return!0}}),V.ui.plugin={add:function(t,e,i){var s,n=V.ui[t].prototype;for(s in i)n.plugins[s]=n.plugins[s]||[],n.plugins[s].push([e,i[s]])},call:function(t,e,i,s){var n,o=t.plugins[e];if(o&&(s||t.element[0].parentNode&&11!==t.element[0].parentNode.nodeType))for(n=0;n<o.length;n++)t.options[o[n][0]]&&o[n][1].apply(t.element,i)}},V.ui.safeBlur=function(t){t&&"body"!==t.nodeName.toLowerCase()&&V(t).trigger("blur")};V.widget("ui.draggable",V.ui.mouse,{version:"1.13.2",widgetEventPrefix:"drag",options:{addClasses:!0,appendTo:"parent",axis:!1,connectToSortable:!1,containment:!1,cursor:"auto",cursorAt:!1,grid:!1,handle:!1,helper:"original",iframeFix:!1,opacity:!1,refreshPositions:!1,revert:!1,revertDuration:500,scope:"default",scroll:!0,scrollSensitivity:20,scrollSpeed:20,snap:!1,snapMode:"both",snapTolerance:20,stack:!1,zIndex:!1,drag:null,start:null,stop:null},_create:function(){"original"===this.options.helper&&this._setPositionRelative(),this.options.addClasses&&this._addClass("ui-draggable"),this._setHandleClassName(),this._mouseInit()},_setOption:function(t,e){this._super(t,e),"handle"===t&&(this._removeHandleClassName(),this._setHandleClassName())},_destroy:function(){(this.helper||this.element).is(".ui-draggable-dragging")?this.destroyOnClear=!0:(this._removeHandleClassName(),this._mouseDestroy())},_mouseCapture:function(t){var e=this.options;return!(this.helper||e.disabled||0<V(t.target).closest(".ui-resizable-handle").length)&&(this.handle=this._getHandle(t),!!this.handle&&(this._blurActiveElement(t),this._blockFrames(!0===e.iframeFix?"iframe":e.iframeFix),!0))},_blockFrames:function(t){this.iframeBlocks=this.document.find(t).map(function(){var t=V(this);return V("<div>").css("position","absolute").appendTo(t.parent()).outerWidth(t.outerWidth()).outerHeight(t.outerHeight()).offset(t.offset())[0]})},_unblockFrames:function(){this.iframeBlocks&&(this.iframeBlocks.remove(),delete this.iframeBlocks)},_blurActiveElement:function(t){var e=V.ui.safeActiveElement(this.document[0]);V(t.target).closest(e).length||V.ui.safeBlur(e)},_mouseStart:function(t){var e=this.options;return this.helper=this._createHelper(t),this._addClass(this.helper,"ui-draggable-dragging"),this._cacheHelperProportions(),V.ui.ddmanager&&(V.ui.ddmanager.current=this),this._cacheMargins(),this.cssPosition=this.helper.css("position"),this.scrollParent=this.helper.scrollParent(!0),this.offsetParent=this.helper.offsetParent(),this.hasFixedAncestor=0<this.helper.parents().filter(function(){return"fixed"===V(this).css("position")}).length,this.positionAbs=this.element.offset(),this._refreshOffsets(t),this.originalPosition=this.position=this._generatePosition(t,!1),this.originalPageX=t.pageX,this.originalPageY=t.pageY,e.cursorAt&&this._adjustOffsetFromHelper(e.cursorAt),this._setContainment(),!1===this._trigger("start",t)?(this._clear(),!1):(this._cacheHelperProportions(),V.ui.ddmanager&&!e.dropBehaviour&&V.ui.ddmanager.prepareOffsets(this,t),this._mouseDrag(t,!0),V.ui.ddmanager&&V.ui.ddmanager.dragStart(this,t),!0)},_refreshOffsets:function(t){this.offset={top:this.positionAbs.top-this.margins.top,left:this.positionAbs.left-this.margins.left,scroll:!1,parent:this._getParentOffset(),relative:this._getRelativeOffset()},this.offset.click={left:t.pageX-this.offset.left,top:t.pageY-this.offset.top}},_mouseDrag:function(t,e){if(this.hasFixedAncestor&&(this.offset.parent=this._getParentOffset()),this.position=this._generatePosition(t,!0),this.positionAbs=this._convertPositionTo("absolute"),!e){e=this._uiHash();if(!1===this._trigger("drag",t,e))return 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this.helper.is(".ui-draggable-dragging")?this._mouseUp(new V.Event("mouseup",{target:this.element[0]})):this._clear(),this},_getHandle:function(t){return!this.options.handle||!!V(t.target).closest(this.element.find(this.options.handle)).length},_setHandleClassName:function(){this.handleElement=this.options.handle?this.element.find(this.options.handle):this.element,this._addClass(this.handleElement,"ui-draggable-handle")},_removeHandleClassName:function(){this._removeClass(this.handleElement,"ui-draggable-handle")},_createHelper:function(t){var e=this.options,i="function"==typeof e.helper,t=i?V(e.helper.apply(this.element[0],[t])):"clone"===e.helper?this.element.clone().removeAttr("id"):this.element;return t.parents("body").length||t.appendTo("parent"===e.appendTo?this.element[0].parentNode:e.appendTo),i&&t[0]===this.element[0]&&this._setPositionRelative(),t[0]===this.element[0]||/(fixed|absolute)/.test(t.css("position"))||t.css("position","absolute"),t},_setPositionRelative:function(){/^(?:r|a|f)/.test(this.element.css("position"))||(this.element[0].style.position="relative")},_adjustOffsetFromHelper:function(t){"string"==typeof t&&(t=t.split(" ")),"left"in(t=Array.isArray(t)?{left:+t[0],top:+t[1]||0}:t)&&(this.offset.click.left=t.left+this.margins.left),"right"in t&&(this.offset.click.left=this.helperProportions.width-t.right+this.margins.left),"top"in t&&(this.offset.click.top=t.top+this.margins.top),"bottom"in t&&(this.offset.click.top=this.helperProportions.height-t.bottom+this.margins.top)},_isRootNode:function(t){return/(html|body)/i.test(t.tagName)||t===this.document[0]},_getParentOffset:function(){var 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t=this.element.position(),e=this._isRootNode(this.scrollParent[0]);return{top:t.top-(parseInt(this.helper.css("top"),10)||0)+(e?0:this.scrollParent.scrollTop()),left:t.left-(parseInt(this.helper.css("left"),10)||0)+(e?0:this.scrollParent.scrollLeft())}},_cacheMargins:function(){this.margins={left:parseInt(this.element.css("marginLeft"),10)||0,top:parseInt(this.element.css("marginTop"),10)||0,right:parseInt(this.element.css("marginRight"),10)||0,bottom:parseInt(this.element.css("marginBottom"),10)||0}},_cacheHelperProportions:function(){this.helperProportions={width:this.helper.outerWidth(),height:this.helper.outerHeight()}},_setContainment:function(){var 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i=i||this._uiHash(),V.ui.plugin.call(this,t,[e,i,this],!0),/^(drag|start|stop)/.test(t)&&(this.positionAbs=this._convertPositionTo("absolute"),i.offset=this.positionAbs),V.Widget.prototype._trigger.call(this,t,e,i)},plugins:{},_uiHash:function(){return{helper:this.helper,position:this.position,originalPosition:this.originalPosition,offset:this.positionAbs}}}),V.ui.plugin.add("draggable","connectToSortable",{start:function(e,t,i){var s=V.extend({},t,{item:i.element});i.sortables=[],V(i.options.connectToSortable).each(function(){var t=V(this).sortable("instance");t&&!t.options.disabled&&(i.sortables.push(t),t.refreshPositions(),t._trigger("activate",e,s))})},stop:function(e,t,i){var s=V.extend({},t,{item:i.element});i.cancelHelperRemoval=!1,V.each(i.sortables,function(){var t=this;t.isOver?(t.isOver=0,i.cancelHelperRemoval=!0,t.cancelHelperRemoval=!1,t._storedCSS={position:t.placeholder.css("position"),top:t.placeholder.css("top"),left:t.placeholder.css("left")},t._mouseStop(e),t.options.helper=t.options._helper):(t.cancelHelperRemoval=!0,t._trigger("deactivate",e,s))})},drag:function(i,s,n){V.each(n.sortables,function(){var t=!1,e=this;e.positionAbs=n.positionAbs,e.helperProportions=n.helperProportions,e.offset.click=n.offset.click,e._intersectsWith(e.containerCache)&&(t=!0,V.each(n.sortables,function(){return this.positionAbs=n.positionAbs,this.helperProportions=n.helperProportions,this.offset.click=n.offset.click,t=this!==e&&this._intersectsWith(this.containerCache)&&V.contains(e.element[0],this.element[0])?!1:t})),t?(e.isOver||(e.isOver=1,n._parent=s.helper.parent(),e.currentItem=s.helper.appendTo(e.element).data("ui-sortable-item",!0),e.options._helper=e.options.helper,e.options.helper=function(){return s.helper[0]},i.target=e.currentItem[0],e._mouseCapture(i,!0),e._mouseStart(i,!0,!0),e.offset.click.top=n.offset.click.top,e.offset.click.left=n.offset.click.left,e.offset.parent.left-=n.offset.parent.left-e.offset.parent.left,e.offset.parent.top-=n.offset.parent.top-e.offset.parent.top,n._trigger("toSortable",i),n.dropped=e.element,V.each(n.sortables,function(){this.refreshPositions()}),n.currentItem=n.element,e.fromOutside=n),e.currentItem&&(e._mouseDrag(i),s.position=e.position)):e.isOver&&(e.isOver=0,e.cancelHelperRemoval=!0,e.options._revert=e.options.revert,e.options.revert=!1,e._trigger("out",i,e._uiHash(e)),e._mouseStop(i,!0),e.options.revert=e.options._revert,e.options.helper=e.options._helper,e.placeholder&&e.placeholder.remove(),s.helper.appendTo(n._parent),n._refreshOffsets(i),s.position=n._generatePosition(i,!0),n._trigger("fromSortable",i),n.dropped=!1,V.each(n.sortables,function(){this.refreshPositions()}))})}}),V.ui.plugin.add("draggable","cursor",{start:function(t,e,i){var s=V("body"),i=i.options;s.css("cursor")&&(i._cursor=s.css("cursor")),s.css("cursor",i.cursor)},stop:function(t,e,i){i=i.options;i._cursor&&V("body").css("cursor",i._cursor)}}),V.ui.plugin.add("draggable","opacity",{start:function(t,e,i){e=V(e.helper),i=i.options;e.css("opacity")&&(i._opacity=e.css("opacity")),e.css("opacity",i.opacity)},stop:function(t,e,i){i=i.options;i._opacity&&V(e.helper).css("opacity",i._opacity)}}),V.ui.plugin.add("draggable","scroll",{start:function(t,e,i){i.scrollParentNotHidden||(i.scrollParentNotHidden=i.helper.scrollParent(!1)),i.scrollParentNotHidden[0]!==i.document[0]&&"HTML"!==i.scrollParentNotHidden[0].tagName&&(i.overflowOffset=i.scrollParentNotHidden.offset())},drag:function(t,e,i){var s=i.options,n=!1,o=i.scrollParentNotHidden[0],a=i.document[0];o!==a&&"HTML"!==o.tagName?(s.axis&&"x"===s.axis||(i.overflowOffset.top+o.offsetHeight-t.pageY<s.scrollSensitivity?o.scrollTop=n=o.scrollTop+s.scrollSpeed:t.pageY-i.overflowOffset.top<s.scrollSensitivity&&(o.scrollTop=n=o.scrollTop-s.scrollSpeed)),s.axis&&"y"===s.axis||(i.overflowOffset.left+o.offsetWidth-t.pageX<s.scrollSensitivity?o.scrollLeft=n=o.scrollLeft+s.scrollSpeed:t.pageX-i.overflowOffset.left<s.scrollSensitivity&&(o.scrollLeft=n=o.scrollLeft-s.scrollSpeed))):(s.axis&&"x"===s.axis||(t.pageY-V(a).scrollTop()<s.scrollSensitivity?n=V(a).scrollTop(V(a).scrollTop()-s.scrollSpeed):V(window).height()-(t.pageY-V(a).scrollTop())<s.scrollSensitivity&&(n=V(a).scrollTop(V(a).scrollTop()+s.scrollSpeed))),s.axis&&"y"===s.axis||(t.pageX-V(a).scrollLeft()<s.scrollSensitivity?n=V(a).scrollLeft(V(a).scrollLeft()-s.scrollSpeed):V(window).width()-(t.pageX-V(a).scrollLeft())<s.scrollSensitivity&&(n=V(a).scrollLeft(V(a).scrollLeft()+s.scrollSpeed)))),!1!==n&&V.ui.ddmanager&&!s.dropBehaviour&&V.ui.ddmanager.prepareOffsets(i,t)}}),V.ui.plugin.add("draggable","snap",{start:function(t,e,i){var s=i.options;i.snapElements=[],V(s.snap.constructor!==String?s.snap.items||":data(ui-draggable)":s.snap).each(function(){var t=V(this),e=t.offset();this!==i.element[0]&&i.snapElements.push({item:this,width:t.outerWidth(),height:t.outerHeight(),top:e.top,left:e.left})})},drag:function(t,e,i){for(var 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s,i=i.options,i=V.makeArray(V(i.stack)).sort(function(t,e){return(parseInt(V(t).css("zIndex"),10)||0)-(parseInt(V(e).css("zIndex"),10)||0)});i.length&&(s=parseInt(V(i[0]).css("zIndex"),10)||0,V(i).each(function(t){V(this).css("zIndex",s+t)}),this.css("zIndex",s+i.length))}}),V.ui.plugin.add("draggable","zIndex",{start:function(t,e,i){e=V(e.helper),i=i.options;e.css("zIndex")&&(i._zIndex=e.css("zIndex")),e.css("zIndex",i.zIndex)},stop:function(t,e,i){i=i.options;i._zIndex&&V(e.helper).css("zIndex",i._zIndex)}});V.ui.draggable;V.widget("ui.resizable",V.ui.mouse,{version:"1.13.2",widgetEventPrefix:"resize",options:{alsoResize:!1,animate:!1,animateDuration:"slow",animateEasing:"swing",aspectRatio:!1,autoHide:!1,classes:{"ui-resizable-se":"ui-icon ui-icon-gripsmall-diagonal-se"},containment:!1,ghost:!1,grid:!1,handles:"e,s,se",helper:!1,maxHeight:null,maxWidth:null,minHeight:10,minWidth:10,zIndex:90,resize:null,start:null,stop:null},_num:function(t){return parseFloat(t)||0},_isNumber:function(t){return!isNaN(parseFloat(t))},_hasScroll:function(t,e){if("hidden"===V(t).css("overflow"))return!1;var i=e&&"left"===e?"scrollLeft":"scrollTop",e=!1;if(0<t[i])return!0;try{t[i]=1,e=0<t[i],t[i]=0}catch(t){}return e},_create:function(){var t,e=this.options,i=this;this._addClass("ui-resizable"),V.extend(this,{_aspectRatio:!!e.aspectRatio,aspectRatio:e.aspectRatio,originalElement:this.element,_proportionallyResizeElements:[],_helper:e.helper||e.ghost||e.animate?e.helper||"ui-resizable-helper":null}),this.element[0].nodeName.match(/^(canvas|textarea|input|select|button|img)$/i)&&(this.element.wrap(V("<div 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t(t){V(t).removeData("resizable").removeData("ui-resizable").off(".resizable")}var e;return this.elementIsWrapper&&(t(this.element),e=this.element,this.originalElement.css({position:e.css("position"),width:e.outerWidth(),height:e.outerHeight(),top:e.css("top"),left:e.css("left")}).insertAfter(e),e.remove()),this.originalElement.css("resize",this.originalResizeStyle),t(this.originalElement),this},_setOption:function(t,e){switch(this._super(t,e),t){case"handles":this._removeHandles(),this._setupHandles();break;case"aspectRatio":this._aspectRatio=!!e}},_setupHandles:function(){var t,e,i,s,n,o=this.options,a=this;if(this.handles=o.handles||(V(".ui-resizable-handle",this.element).length?{n:".ui-resizable-n",e:".ui-resizable-e",s:".ui-resizable-s",w:".ui-resizable-w",se:".ui-resizable-se",sw:".ui-resizable-sw",ne:".ui-resizable-ne",nw:".ui-resizable-nw"}:"e,s,se"),this._handles=V(),this._addedHandles=V(),this.handles.constructor===String)for("all"===this.handles&&(this.handles="n,e,s,w,se,sw,ne,nw"),i=this.handles.split(","),this.handles={},e=0;e<i.length;e++)s="ui-resizable-"+(t=String.prototype.trim.call(i[e])),n=V("<div>"),this._addClass(n,"ui-resizable-handle "+s),n.css({zIndex:o.zIndex}),this.handles[t]=".ui-resizable-"+t,this.element.children(this.handles[t]).length||(this.element.append(n),this._addedHandles=this._addedHandles.add(n));this._renderAxis=function(t){var e,i,s;for(e in 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s=this.originalSize;return{top:this.originalPosition.top+i,height:s.height-i}},s:function(t,e,i){return{height:this.originalSize.height+i}},se:function(t,e,i){return V.extend(this._change.s.apply(this,arguments),this._change.e.apply(this,[t,e,i]))},sw:function(t,e,i){return V.extend(this._change.s.apply(this,arguments),this._change.w.apply(this,[t,e,i]))},ne:function(t,e,i){return V.extend(this._change.n.apply(this,arguments),this._change.e.apply(this,[t,e,i]))},nw:function(t,e,i){return V.extend(this._change.n.apply(this,arguments),this._change.w.apply(this,[t,e,i]))}},_propagate:function(t,e){V.ui.plugin.call(this,t,[e,this.ui()]),"resize"!==t&&this._trigger(t,e,this.ui())},plugins:{},ui:function(){return{originalElement:this.originalElement,element:this.element,helper:this.helper,position:this.position,size:this.size,originalSize:this.originalSize,originalPosition:this.originalPosition}}}),V.ui.plugin.add("resizable","animate",{stop:function(e){var i=V(this).resizable("instance"),t=i.options,s=i._proportionallyResizeElements,n=s.length&&/textarea/i.test(s[0].nodeName),o=n&&i._hasScroll(s[0],"left")?0:i.sizeDiff.height,a=n?0:i.sizeDiff.width,n={width:i.size.width-a,height:i.size.height-o},a=parseFloat(i.element.css("left"))+(i.position.left-i.originalPosition.left)||null,o=parseFloat(i.element.css("top"))+(i.position.top-i.originalPosition.top)||null;i.element.animate(V.extend(n,o&&a?{top:o,left:a}:{}),{duration:t.animateDuration,easing:t.animateEasing,step:function(){var t={width:parseFloat(i.element.css("width")),height:parseFloat(i.element.css("height")),top:parseFloat(i.element.css("top")),left:parseFloat(i.element.css("left"))};s&&s.length&&V(s[0]).css({width:t.width,height:t.height}),i._updateCache(t),i._propagate("resize",e)}})}}),V.ui.plugin.add("resizable","containment",{start:function(){var i,s,n=V(this).resizable("instance"),t=n.options,e=n.element,o=t.containment,a=o instanceof V?o.get(0):/parent/.test(o)?e.parent().get(0):o;a&&(n.containerElement=V(a),/document/.test(o)||o===document?(n.containerOffset={left:0,top:0},n.containerPosition={left:0,top:0},n.parentData={element:V(document),left:0,top:0,width:V(document).width(),height:V(document).height()||document.body.parentNode.scrollHeight}):(i=V(a),s=[],V(["Top","Right","Left","Bottom"]).each(function(t,e){s[t]=n._num(i.css("padding"+e))}),n.containerOffset=i.offset(),n.containerPosition=i.position(),n.containerSize={height:i.innerHeight()-s[3],width:i.innerWidth()-s[1]},t=n.containerOffset,e=n.containerSize.height,o=n.containerSize.width,o=n._hasScroll(a,"left")?a.scrollWidth:o,e=n._hasScroll(a)?a.scrollHeight:e,n.parentData={element:a,left:t.left,top:t.top,width:o,height:e}))},resize:function(t){var e=V(this).resizable("instance"),i=e.options,s=e.containerOffset,n=e.position,o=e._aspectRatio||t.shiftKey,a={top:0,left:0},r=e.containerElement,t=!0;r[0]!==document&&/static/.test(r.css("position"))&&(a=s),n.left<(e._helper?s.left:0)&&(e.size.width=e.size.width+(e._helper?e.position.left-s.left:e.position.left-a.left),o&&(e.size.height=e.size.width/e.aspectRatio,t=!1),e.position.left=i.helper?s.left:0),n.top<(e._helper?s.top:0)&&(e.size.height=e.size.height+(e._helper?e.position.top-s.top:e.position.top),o&&(e.size.width=e.size.height*e.aspectRatio,t=!1),e.position.top=e._helper?s.top:0),i=e.containerElement.get(0)===e.element.parent().get(0),n=/relative|absolute/.test(e.containerElement.css("position")),i&&n?(e.offset.left=e.parentData.left+e.position.left,e.offset.top=e.parentData.top+e.position.top):(e.offset.left=e.element.offset().left,e.offset.top=e.element.offset().top),n=Math.abs(e.sizeDiff.width+(e._helper?e.offset.left-a.left:e.offset.left-s.left)),s=Math.abs(e.sizeDiff.height+(e._helper?e.offset.top-a.top:e.offset.top-s.top)),n+e.size.width>=e.parentData.width&&(e.size.width=e.parentData.width-n,o&&(e.size.height=e.size.width/e.aspectRatio,t=!1)),s+e.size.height>=e.parentData.height&&(e.size.height=e.parentData.height-s,o&&(e.size.width=e.size.height*e.aspectRatio,t=!1)),t||(e.position.left=e.prevPosition.left,e.position.top=e.prevPosition.top,e.size.width=e.prevSize.width,e.size.height=e.prevSize.height)},stop:function(){var t=V(this).resizable("instance"),e=t.options,i=t.containerOffset,s=t.containerPosition,n=t.containerElement,o=V(t.helper),a=o.offset(),r=o.outerWidth()-t.sizeDiff.width,o=o.outerHeight()-t.sizeDiff.height;t._helper&&!e.animate&&/relative/.test(n.css("position"))&&V(this).css({left:a.left-s.left-i.left,width:r,height:o}),t._helper&&!e.animate&&/static/.test(n.css("position"))&&V(this).css({left:a.left-s.left-i.left,width:r,height:o})}}),V.ui.plugin.add("resizable","alsoResize",{start:function(){var t=V(this).resizable("instance").options;V(t.alsoResize).each(function(){var t=V(this);t.data("ui-resizable-alsoresize",{width:parseFloat(t.width()),height:parseFloat(t.height()),left:parseFloat(t.css("left")),top:parseFloat(t.css("top"))})})},resize:function(t,i){var e=V(this).resizable("instance"),s=e.options,n=e.originalSize,o=e.originalPosition,a={height:e.size.height-n.height||0,width:e.size.width-n.width||0,top:e.position.top-o.top||0,left:e.position.left-o.left||0};V(s.alsoResize).each(function(){var t=V(this),s=V(this).data("ui-resizable-alsoresize"),n={},e=t.parents(i.originalElement[0]).length?["width","height"]:["width","height","top","left"];V.each(e,function(t,e){var i=(s[e]||0)+(a[e]||0);i&&0<=i&&(n[e]=i||null)}),t.css(n)})},stop:function(){V(this).removeData("ui-resizable-alsoresize")}}),V.ui.plugin.add("resizable","ghost",{start:function(){var t=V(this).resizable("instance"),e=t.size;t.ghost=t.originalElement.clone(),t.ghost.css({opacity:.25,display:"block",position:"relative",height:e.height,width:e.width,margin:0,left:0,top:0}),t._addClass(t.ghost,"ui-resizable-ghost"),!1!==V.uiBackCompat&&"string"==typeof t.options.ghost&&t.ghost.addClass(this.options.ghost),t.ghost.appendTo(t.helper)},resize:function(){var t=V(this).resizable("instance");t.ghost&&t.ghost.css({position:"relative",height:t.size.height,width:t.size.width})},stop:function(){var t=V(this).resizable("instance");t.ghost&&t.helper&&t.helper.get(0).removeChild(t.ghost.get(0))}}),V.ui.plugin.add("resizable","grid",{resize:function(){var t,e=V(this).resizable("instance"),i=e.options,s=e.size,n=e.originalSize,o=e.originalPosition,a=e.axis,r="number"==typeof i.grid?[i.grid,i.grid]:i.grid,l=r[0]||1,h=r[1]||1,c=Math.round((s.width-n.width)/l)*l,u=Math.round((s.height-n.height)/h)*h,d=n.width+c,p=n.height+u,f=i.maxWidth&&i.maxWidth<d,g=i.maxHeight&&i.maxHeight<p,m=i.minWidth&&i.minWidth>d,s=i.minHeight&&i.minHeight>p;i.grid=r,m&&(d+=l),s&&(p+=h),f&&(d-=l),g&&(p-=h),/^(se|s|e)$/.test(a)?(e.size.width=d,e.size.height=p):/^(ne)$/.test(a)?(e.size.width=d,e.size.height=p,e.position.top=o.top-u):/^(sw)$/.test(a)?(e.size.width=d,e.size.height=p,e.position.left=o.left-c):((p-h<=0||d-l<=0)&&(t=e._getPaddingPlusBorderDimensions(this)),0<p-h?(e.size.height=p,e.position.top=o.top-u):(p=h-t.height,e.size.height=p,e.position.top=o.top+n.height-p),0<d-l?(e.size.width=d,e.position.left=o.left-c):(d=l-t.width,e.size.width=d,e.position.left=o.left+n.width-d))}});V.ui.resizable;V.widget("ui.dialog",{version:"1.13.2",options:{appendTo:"body",autoOpen:!0,buttons:[],classes:{"ui-dialog":"ui-corner-all","ui-dialog-titlebar":"ui-corner-all"},closeOnEscape:!0,closeText:"Close",draggable:!0,hide:null,height:"auto",maxHeight:null,maxWidth:null,minHeight:150,minWidth:150,modal:!1,position:{my:"center",at:"center",of:window,collision:"fit",using:function(t){var e=V(this).css(t).offset().top;e<0&&V(this).css("top",t.top-e)}},resizable:!0,show:null,title:null,width:300,beforeClose:null,close:null,drag:null,dragStart:null,dragStop:null,focus:null,open:null,resize:null,resizeStart:null,resizeStop:null},sizeRelatedOptions:{buttons:!0,height:!0,maxHeight:!0,maxWidth:!0,minHeight:!0,minWidth:!0,width:!0},resizableRelatedOptions:{maxHeight:!0,maxWidth:!0,minHeight:!0,minWidth:!0},_create:function(){this.originalCss={display:this.element[0].style.display,width:this.element[0].style.width,minHeight:this.element[0].style.minHeight,maxHeight:this.element[0].style.maxHeight,height:this.element[0].style.height},this.originalPosition={parent:this.element.parent(),index:this.element.parent().children().index(this.element)},this.originalTitle=this.element.attr("title"),null==this.options.title&&null!=this.originalTitle&&(this.options.title=this.originalTitle),this.options.disabled&&(this.options.disabled=!1),this._createWrapper(),this.element.show().removeAttr("title").appendTo(this.uiDialog),this._addClass("ui-dialog-content","ui-widget-content"),this._createTitlebar(),this._createButtonPane(),this.options.draggable&&V.fn.draggable&&this._makeDraggable(),this.options.resizable&&V.fn.resizable&&this._makeResizable(),this._isOpen=!1,this._trackFocus()},_init:function(){this.options.autoOpen&&this.open()},_appendTo:function(){var t=this.options.appendTo;return t&&(t.jquery||t.nodeType)?V(t):this.document.find(t||"body").eq(0)},_destroy:function(){var t,e=this.originalPosition;this._untrackInstance(),this._destroyOverlay(),this.element.removeUniqueId().css(this.originalCss).detach(),this.uiDialog.remove(),this.originalTitle&&this.element.attr("title",this.originalTitle),(t=e.parent.children().eq(e.index)).length&&t[0]!==this.element[0]?t.before(this.element):e.parent.append(this.element)},widget:function(){return this.uiDialog},disable:V.noop,enable:V.noop,close:function(t){var e=this;this._isOpen&&!1!==this._trigger("beforeClose",t)&&(this._isOpen=!1,this._focusedElement=null,this._destroyOverlay(),this._untrackInstance(),this.opener.filter(":focusable").trigger("focus").length||V.ui.safeBlur(V.ui.safeActiveElement(this.document[0])),this._hide(this.uiDialog,this.options.hide,function(){e._trigger("close",t)}))},isOpen:function(){return this._isOpen},moveToTop:function(){this._moveToTop()},_moveToTop:function(t,e){var i=!1,s=this.uiDialog.siblings(".ui-front:visible").map(function(){return+V(this).css("z-index")}).get(),s=Math.max.apply(null,s);return s>=+this.uiDialog.css("z-index")&&(this.uiDialog.css("z-index",s+1),i=!0),i&&!e&&this._trigger("focus",t),i},open:function(){var t=this;this._isOpen?this._moveToTop()&&this._focusTabbable():(this._isOpen=!0,this.opener=V(V.ui.safeActiveElement(this.document[0])),this._size(),this._position(),this._createOverlay(),this._moveToTop(null,!0),this.overlay&&this.overlay.css("z-index",this.uiDialog.css("z-index")-1),this._show(this.uiDialog,this.options.show,function(){t._focusTabbable(),t._trigger("focus")}),this._makeFocusTarget(),this._trigger("open"))},_focusTabbable:function(){var t=this._focusedElement;(t=!(t=!(t=!(t=!(t=t||this.element.find("[autofocus]")).length?this.element.find(":tabbable"):t).length?this.uiDialogButtonPane.find(":tabbable"):t).length?this.uiDialogTitlebarClose.filter(":tabbable"):t).length?this.uiDialog:t).eq(0).trigger("focus")},_restoreTabbableFocus:function(){var t=V.ui.safeActiveElement(this.document[0]);this.uiDialog[0]===t||V.contains(this.uiDialog[0],t)||this._focusTabbable()},_keepFocus:function(t){t.preventDefault(),this._restoreTabbableFocus(),this._delay(this._restoreTabbableFocus)},_createWrapper:function(){this.uiDialog=V("<div>").hide().attr({tabIndex:-1,role:"dialog"}).appendTo(this._appendTo()),this._addClass(this.uiDialog,"ui-dialog","ui-widget ui-widget-content ui-front"),this._on(this.uiDialog,{keydown:function(t){if(this.options.closeOnEscape&&!t.isDefaultPrevented()&&t.keyCode&&t.keyCode===V.ui.keyCode.ESCAPE)return t.preventDefault(),void this.close(t);var e,i,s;t.keyCode!==V.ui.keyCode.TAB||t.isDefaultPrevented()||(e=this.uiDialog.find(":tabbable"),i=e.first(),s=e.last(),t.target!==s[0]&&t.target!==this.uiDialog[0]||t.shiftKey?t.target!==i[0]&&t.target!==this.uiDialog[0]||!t.shiftKey||(this._delay(function(){s.trigger("focus")}),t.preventDefault()):(this._delay(function(){i.trigger("focus")}),t.preventDefault()))},mousedown:function(t){this._moveToTop(t)&&this._focusTabbable()}}),this.element.find("[aria-describedby]").length||this.uiDialog.attr({"aria-describedby":this.element.uniqueId().attr("id")})},_createTitlebar:function(){var t;this.uiDialogTitlebar=V("<div>"),this._addClass(this.uiDialogTitlebar,"ui-dialog-titlebar","ui-widget-header ui-helper-clearfix"),this._on(this.uiDialogTitlebar,{mousedown:function(t){V(t.target).closest(".ui-dialog-titlebar-close")||this.uiDialog.trigger("focus")}}),this.uiDialogTitlebarClose=V("<button type='button'></button>").button({label:V("<a>").text(this.options.closeText).html(),icon:"ui-icon-closethick",showLabel:!1}).appendTo(this.uiDialogTitlebar),this._addClass(this.uiDialogTitlebarClose,"ui-dialog-titlebar-close"),this._on(this.uiDialogTitlebarClose,{click:function(t){t.preventDefault(),this.close(t)}}),t=V("<span>").uniqueId().prependTo(this.uiDialogTitlebar),this._addClass(t,"ui-dialog-title"),this._title(t),this.uiDialogTitlebar.prependTo(this.uiDialog),this.uiDialog.attr({"aria-labelledby":t.attr("id")})},_title:function(t){this.options.title?t.text(this.options.title):t.html(" ")},_createButtonPane:function(){this.uiDialogButtonPane=V("<div>"),this._addClass(this.uiDialogButtonPane,"ui-dialog-buttonpane","ui-widget-content ui-helper-clearfix"),this.uiButtonSet=V("<div>").appendTo(this.uiDialogButtonPane),this._addClass(this.uiButtonSet,"ui-dialog-buttonset"),this._createButtons()},_createButtons:function(){var s=this,t=this.options.buttons;this.uiDialogButtonPane.remove(),this.uiButtonSet.empty(),V.isEmptyObject(t)||Array.isArray(t)&&!t.length?this._removeClass(this.uiDialog,"ui-dialog-buttons"):(V.each(t,function(t,e){var i;e=V.extend({type:"button"},e="function"==typeof e?{click:e,text:t}:e),i=e.click,t={icon:e.icon,iconPosition:e.iconPosition,showLabel:e.showLabel,icons:e.icons,text:e.text},delete e.click,delete e.icon,delete e.iconPosition,delete e.showLabel,delete e.icons,"boolean"==typeof e.text&&delete e.text,V("<button></button>",e).button(t).appendTo(s.uiButtonSet).on("click",function(){i.apply(s.element[0],arguments)})}),this._addClass(this.uiDialog,"ui-dialog-buttons"),this.uiDialogButtonPane.appendTo(this.uiDialog))},_makeDraggable:function(){var n=this,o=this.options;function a(t){return{position:t.position,offset:t.offset}}this.uiDialog.draggable({cancel:".ui-dialog-content, .ui-dialog-titlebar-close",handle:".ui-dialog-titlebar",containment:"document",start:function(t,e){n._addClass(V(this),"ui-dialog-dragging"),n._blockFrames(),n._trigger("dragStart",t,a(e))},drag:function(t,e){n._trigger("drag",t,a(e))},stop:function(t,e){var i=e.offset.left-n.document.scrollLeft(),s=e.offset.top-n.document.scrollTop();o.position={my:"left top",at:"left"+(0<=i?"+":"")+i+" top"+(0<=s?"+":"")+s,of:n.window},n._removeClass(V(this),"ui-dialog-dragging"),n._unblockFrames(),n._trigger("dragStop",t,a(e))}})},_makeResizable:function(){var n=this,o=this.options,t=o.resizable,e=this.uiDialog.css("position"),t="string"==typeof t?t:"n,e,s,w,se,sw,ne,nw";function a(t){return{originalPosition:t.originalPosition,originalSize:t.originalSize,position:t.position,size:t.size}}this.uiDialog.resizable({cancel:".ui-dialog-content",containment:"document",alsoResize:this.element,maxWidth:o.maxWidth,maxHeight:o.maxHeight,minWidth:o.minWidth,minHeight:this._minHeight(),handles:t,start:function(t,e){n._addClass(V(this),"ui-dialog-resizing"),n._blockFrames(),n._trigger("resizeStart",t,a(e))},resize:function(t,e){n._trigger("resize",t,a(e))},stop:function(t,e){var i=n.uiDialog.offset(),s=i.left-n.document.scrollLeft(),i=i.top-n.document.scrollTop();o.height=n.uiDialog.height(),o.width=n.uiDialog.width(),o.position={my:"left top",at:"left"+(0<=s?"+":"")+s+" top"+(0<=i?"+":"")+i,of:n.window},n._removeClass(V(this),"ui-dialog-resizing"),n._unblockFrames(),n._trigger("resizeStop",t,a(e))}}).css("position",e)},_trackFocus:function(){this._on(this.widget(),{focusin:function(t){this._makeFocusTarget(),this._focusedElement=V(t.target)}})},_makeFocusTarget:function(){this._untrackInstance(),this._trackingInstances().unshift(this)},_untrackInstance:function(){var t=this._trackingInstances(),e=V.inArray(this,t);-1!==e&&t.splice(e,1)},_trackingInstances:function(){var t=this.document.data("ui-dialog-instances");return t||this.document.data("ui-dialog-instances",t=[]),t},_minHeight:function(){var t=this.options;return"auto"===t.height?t.minHeight:Math.min(t.minHeight,t.height)},_position:function(){var t=this.uiDialog.is(":visible");t||this.uiDialog.show(),this.uiDialog.position(this.options.position),t||this.uiDialog.hide()},_setOptions:function(t){var i=this,s=!1,n={};V.each(t,function(t,e){i._setOption(t,e),t in i.sizeRelatedOptions&&(s=!0),t in i.resizableRelatedOptions&&(n[t]=e)}),s&&(this._size(),this._position()),this.uiDialog.is(":data(ui-resizable)")&&this.uiDialog.resizable("option",n)},_setOption:function(t,e){var i,s=this.uiDialog;"disabled"!==t&&(this._super(t,e),"appendTo"===t&&this.uiDialog.appendTo(this._appendTo()),"buttons"===t&&this._createButtons(),"closeText"===t&&this.uiDialogTitlebarClose.button({label:V("<a>").text(""+this.options.closeText).html()}),"draggable"===t&&((i=s.is(":data(ui-draggable)"))&&!e&&s.draggable("destroy"),!i&&e&&this._makeDraggable()),"position"===t&&this._position(),"resizable"===t&&((i=s.is(":data(ui-resizable)"))&&!e&&s.resizable("destroy"),i&&"string"==typeof e&&s.resizable("option","handles",e),i||!1===e||this._makeResizable()),"title"===t&&this._title(this.uiDialogTitlebar.find(".ui-dialog-title")))},_size:function(){var t,e,i,s=this.options;this.element.show().css({width:"auto",minHeight:0,maxHeight:"none",height:0}),s.minWidth>s.width&&(s.width=s.minWidth),t=this.uiDialog.css({height:"auto",width:s.width}).outerHeight(),e=Math.max(0,s.minHeight-t),i="number"==typeof s.maxHeight?Math.max(0,s.maxHeight-t):"none","auto"===s.height?this.element.css({minHeight:e,maxHeight:i,height:"auto"}):this.element.height(Math.max(0,s.height-t)),this.uiDialog.is(":data(ui-resizable)")&&this.uiDialog.resizable("option","minHeight",this._minHeight())},_blockFrames:function(){this.iframeBlocks=this.document.find("iframe").map(function(){var t=V(this);return V("<div>").css({position:"absolute",width:t.outerWidth(),height:t.outerHeight()}).appendTo(t.parent()).offset(t.offset())[0]})},_unblockFrames:function(){this.iframeBlocks&&(this.iframeBlocks.remove(),delete this.iframeBlocks)},_allowInteraction:function(t){return!!V(t.target).closest(".ui-dialog").length||!!V(t.target).closest(".ui-datepicker").length},_createOverlay:function(){var i,s;this.options.modal&&(i=V.fn.jquery.substring(0,4),s=!0,this._delay(function(){s=!1}),this.document.data("ui-dialog-overlays")||this.document.on("focusin.ui-dialog",function(t){var e;s||((e=this._trackingInstances()[0])._allowInteraction(t)||(t.preventDefault(),e._focusTabbable(),"3.4."!==i&&"3.5."!==i||e._delay(e._restoreTabbableFocus)))}.bind(this)),this.overlay=V("<div>").appendTo(this._appendTo()),this._addClass(this.overlay,null,"ui-widget-overlay ui-front"),this._on(this.overlay,{mousedown:"_keepFocus"}),this.document.data("ui-dialog-overlays",(this.document.data("ui-dialog-overlays")||0)+1))},_destroyOverlay:function(){var t;this.options.modal&&this.overlay&&((t=this.document.data("ui-dialog-overlays")-1)?this.document.data("ui-dialog-overlays",t):(this.document.off("focusin.ui-dialog"),this.document.removeData("ui-dialog-overlays")),this.overlay.remove(),this.overlay=null)}}),!1!==V.uiBackCompat&&V.widget("ui.dialog",V.ui.dialog,{options:{dialogClass:""},_createWrapper:function(){this._super(),this.uiDialog.addClass(this.options.dialogClass)},_setOption:function(t,e){"dialogClass"===t&&this.uiDialog.removeClass(this.options.dialogClass).addClass(e),this._superApply(arguments)}});V.ui.dialog;function lt(t,e,i){return e<=t&&t<e+i}V.widget("ui.droppable",{version:"1.13.2",widgetEventPrefix:"drop",options:{accept:"*",addClasses:!0,greedy:!1,scope:"default",tolerance:"intersect",activate:null,deactivate:null,drop:null,out:null,over:null},_create:function(){var t,e=this.options,i=e.accept;this.isover=!1,this.isout=!0,this.accept="function"==typeof i?i:function(t){return t.is(i)},this.proportions=function(){if(!arguments.length)return t=t||{width:this.element[0].offsetWidth,height:this.element[0].offsetHeight};t=arguments[0]},this._addToManager(e.scope),e.addClasses&&this._addClass("ui-droppable")},_addToManager:function(t){V.ui.ddmanager.droppables[t]=V.ui.ddmanager.droppables[t]||[],V.ui.ddmanager.droppables[t].push(this)},_splice:function(t){for(var e=0;e<t.length;e++)t[e]===this&&t.splice(e,1)},_destroy:function(){var t=V.ui.ddmanager.droppables[this.options.scope];this._splice(t)},_setOption:function(t,e){var i;"accept"===t?this.accept="function"==typeof e?e:function(t){return t.is(e)}:"scope"===t&&(i=V.ui.ddmanager.droppables[this.options.scope],this._splice(i),this._addToManager(e)),this._super(t,e)},_activate:function(t){var e=V.ui.ddmanager.current;this._addActiveClass(),e&&this._trigger("activate",t,this.ui(e))},_deactivate:function(t){var e=V.ui.ddmanager.current;this._removeActiveClass(),e&&this._trigger("deactivate",t,this.ui(e))},_over:function(t){var e=V.ui.ddmanager.current;e&&(e.currentItem||e.element)[0]!==this.element[0]&&this.accept.call(this.element[0],e.currentItem||e.element)&&(this._addHoverClass(),this._trigger("over",t,this.ui(e)))},_out:function(t){var e=V.ui.ddmanager.current;e&&(e.currentItem||e.element)[0]!==this.element[0]&&this.accept.call(this.element[0],e.currentItem||e.element)&&(this._removeHoverClass(),this._trigger("out",t,this.ui(e)))},_drop:function(e,t){var i=t||V.ui.ddmanager.current,s=!1;return!(!i||(i.currentItem||i.element)[0]===this.element[0])&&(this.element.find(":data(ui-droppable)").not(".ui-draggable-dragging").each(function(){var t=V(this).droppable("instance");if(t.options.greedy&&!t.options.disabled&&t.options.scope===i.options.scope&&t.accept.call(t.element[0],i.currentItem||i.element)&&V.ui.intersect(i,V.extend(t,{offset:t.element.offset()}),t.options.tolerance,e))return!(s=!0)}),!s&&(!!this.accept.call(this.element[0],i.currentItem||i.element)&&(this._removeActiveClass(),this._removeHoverClass(),this._trigger("drop",e,this.ui(i)),this.element)))},ui:function(t){return{draggable:t.currentItem||t.element,helper:t.helper,position:t.position,offset:t.positionAbs}},_addHoverClass:function(){this._addClass("ui-droppable-hover")},_removeHoverClass:function(){this._removeClass("ui-droppable-hover")},_addActiveClass:function(){this._addClass("ui-droppable-active")},_removeActiveClass:function(){this._removeClass("ui-droppable-active")}}),V.ui.intersect=function(t,e,i,s){if(!e.offset)return!1;var n=(t.positionAbs||t.position.absolute).left+t.margins.left,o=(t.positionAbs||t.position.absolute).top+t.margins.top,a=n+t.helperProportions.width,r=o+t.helperProportions.height,l=e.offset.left,h=e.offset.top,c=l+e.proportions().width,u=h+e.proportions().height;switch(i){case"fit":return l<=n&&a<=c&&h<=o&&r<=u;case"intersect":return l<n+t.helperProportions.width/2&&a-t.helperProportions.width/2<c&&h<o+t.helperProportions.height/2&&r-t.helperProportions.height/2<u;case"pointer":return lt(s.pageY,h,e.proportions().height)&<(s.pageX,l,e.proportions().width);case"touch":return(h<=o&&o<=u||h<=r&&r<=u||o<h&&u<r)&&(l<=n&&n<=c||l<=a&&a<=c||n<l&&c<a);default:return!1}},!(V.ui.ddmanager={current:null,droppables:{default:[]},prepareOffsets:function(t,e){var i,s,n=V.ui.ddmanager.droppables[t.options.scope]||[],o=e?e.type:null,a=(t.currentItem||t.element).find(":data(ui-droppable)").addBack();t:for(i=0;i<n.length;i++)if(!(n[i].options.disabled||t&&!n[i].accept.call(n[i].element[0],t.currentItem||t.element))){for(s=0;s<a.length;s++)if(a[s]===n[i].element[0]){n[i].proportions().height=0;continue t}n[i].visible="none"!==n[i].element.css("display"),n[i].visible&&("mousedown"===o&&n[i]._activate.call(n[i],e),n[i].offset=n[i].element.offset(),n[i].proportions({width:n[i].element[0].offsetWidth,height:n[i].element[0].offsetHeight}))}},drop:function(t,e){var i=!1;return V.each((V.ui.ddmanager.droppables[t.options.scope]||[]).slice(),function(){this.options&&(!this.options.disabled&&this.visible&&V.ui.intersect(t,this,this.options.tolerance,e)&&(i=this._drop.call(this,e)||i),!this.options.disabled&&this.visible&&this.accept.call(this.element[0],t.currentItem||t.element)&&(this.isout=!0,this.isover=!1,this._deactivate.call(this,e)))}),i},dragStart:function(t,e){t.element.parentsUntil("body").on("scroll.droppable",function(){t.options.refreshPositions||V.ui.ddmanager.prepareOffsets(t,e)})},drag:function(n,o){n.options.refreshPositions&&V.ui.ddmanager.prepareOffsets(n,o),V.each(V.ui.ddmanager.droppables[n.options.scope]||[],function(){var t,e,i,s;this.options.disabled||this.greedyChild||!this.visible||(s=!(i=V.ui.intersect(n,this,this.options.tolerance,o))&&this.isover?"isout":i&&!this.isover?"isover":null)&&(this.options.greedy&&(e=this.options.scope,(i=this.element.parents(":data(ui-droppable)").filter(function(){return V(this).droppable("instance").options.scope===e})).length&&((t=V(i[0]).droppable("instance")).greedyChild="isover"===s)),t&&"isover"===s&&(t.isover=!1,t.isout=!0,t._out.call(t,o)),this[s]=!0,this["isout"===s?"isover":"isout"]=!1,this["isover"===s?"_over":"_out"].call(this,o),t&&"isout"===s&&(t.isout=!1,t.isover=!0,t._over.call(t,o)))})},dragStop:function(t,e){t.element.parentsUntil("body").off("scroll.droppable"),t.options.refreshPositions||V.ui.ddmanager.prepareOffsets(t,e)}})!==V.uiBackCompat&&V.widget("ui.droppable",V.ui.droppable,{options:{hoverClass:!1,activeClass:!1},_addActiveClass:function(){this._super(),this.options.activeClass&&this.element.addClass(this.options.activeClass)},_removeActiveClass:function(){this._super(),this.options.activeClass&&this.element.removeClass(this.options.activeClass)},_addHoverClass:function(){this._super(),this.options.hoverClass&&this.element.addClass(this.options.hoverClass)},_removeHoverClass:function(){this._super(),this.options.hoverClass&&this.element.removeClass(this.options.hoverClass)}});V.ui.droppable,V.widget("ui.progressbar",{version:"1.13.2",options:{classes:{"ui-progressbar":"ui-corner-all","ui-progressbar-value":"ui-corner-left","ui-progressbar-complete":"ui-corner-right"},max:100,value:0,change:null,complete:null},min:0,_create:function(){this.oldValue=this.options.value=this._constrainedValue(),this.element.attr({role:"progressbar","aria-valuemin":this.min}),this._addClass("ui-progressbar","ui-widget ui-widget-content"),this.valueDiv=V("<div>").appendTo(this.element),this._addClass(this.valueDiv,"ui-progressbar-value","ui-widget-header"),this._refreshValue()},_destroy:function(){this.element.removeAttr("role aria-valuemin aria-valuemax aria-valuenow"),this.valueDiv.remove()},value:function(t){if(void 0===t)return this.options.value;this.options.value=this._constrainedValue(t),this._refreshValue()},_constrainedValue:function(t){return void 0===t&&(t=this.options.value),this.indeterminate=!1===t,"number"!=typeof t&&(t=0),!this.indeterminate&&Math.min(this.options.max,Math.max(this.min,t))},_setOptions:function(t){var e=t.value;delete t.value,this._super(t),this.options.value=this._constrainedValue(e),this._refreshValue()},_setOption:function(t,e){"max"===t&&(e=Math.max(this.min,e)),this._super(t,e)},_setOptionDisabled:function(t){this._super(t),this.element.attr("aria-disabled",t),this._toggleClass(null,"ui-state-disabled",!!t)},_percentage:function(){return this.indeterminate?100:100*(this.options.value-this.min)/(this.options.max-this.min)},_refreshValue:function(){var t=this.options.value,e=this._percentage();this.valueDiv.toggle(this.indeterminate||t>this.min).width(e.toFixed(0)+"%"),this._toggleClass(this.valueDiv,"ui-progressbar-complete",null,t===this.options.max)._toggleClass("ui-progressbar-indeterminate",null,this.indeterminate),this.indeterminate?(this.element.removeAttr("aria-valuenow"),this.overlayDiv||(this.overlayDiv=V("<div>").appendTo(this.valueDiv),this._addClass(this.overlayDiv,"ui-progressbar-overlay"))):(this.element.attr({"aria-valuemax":this.options.max,"aria-valuenow":t}),this.overlayDiv&&(this.overlayDiv.remove(),this.overlayDiv=null)),this.oldValue!==t&&(this.oldValue=t,this._trigger("change")),t===this.options.max&&this._trigger("complete")}}),V.widget("ui.selectable",V.ui.mouse,{version:"1.13.2",options:{appendTo:"body",autoRefresh:!0,distance:0,filter:"*",tolerance:"touch",selected:null,selecting:null,start:null,stop:null,unselected:null,unselecting:null},_create:function(){var i=this;this._addClass("ui-selectable"),this.dragged=!1,this.refresh=function(){i.elementPos=V(i.element[0]).offset(),i.selectees=V(i.options.filter,i.element[0]),i._addClass(i.selectees,"ui-selectee"),i.selectees.each(function(){var t=V(this),e=t.offset(),e={left:e.left-i.elementPos.left,top:e.top-i.elementPos.top};V.data(this,"selectable-item",{element:this,$element:t,left:e.left,top:e.top,right:e.left+t.outerWidth(),bottom:e.top+t.outerHeight(),startselected:!1,selected:t.hasClass("ui-selected"),selecting:t.hasClass("ui-selecting"),unselecting:t.hasClass("ui-unselecting")})})},this.refresh(),this._mouseInit(),this.helper=V("<div>"),this._addClass(this.helper,"ui-selectable-helper")},_destroy:function(){this.selectees.removeData("selectable-item"),this._mouseDestroy()},_mouseStart:function(i){var s=this,t=this.options;this.opos=[i.pageX,i.pageY],this.elementPos=V(this.element[0]).offset(),this.options.disabled||(this.selectees=V(t.filter,this.element[0]),this._trigger("start",i),V(t.appendTo).append(this.helper),this.helper.css({left:i.pageX,top:i.pageY,width:0,height:0}),t.autoRefresh&&this.refresh(),this.selectees.filter(".ui-selected").each(function(){var t=V.data(this,"selectable-item");t.startselected=!0,i.metaKey||i.ctrlKey||(s._removeClass(t.$element,"ui-selected"),t.selected=!1,s._addClass(t.$element,"ui-unselecting"),t.unselecting=!0,s._trigger("unselecting",i,{unselecting:t.element}))}),V(i.target).parents().addBack().each(function(){var t,e=V.data(this,"selectable-item");if(e)return t=!i.metaKey&&!i.ctrlKey||!e.$element.hasClass("ui-selected"),s._removeClass(e.$element,t?"ui-unselecting":"ui-selected")._addClass(e.$element,t?"ui-selecting":"ui-unselecting"),e.unselecting=!t,e.selecting=t,(e.selected=t)?s._trigger("selecting",i,{selecting:e.element}):s._trigger("unselecting",i,{unselecting:e.element}),!1}))},_mouseDrag:function(s){if(this.dragged=!0,!this.options.disabled){var t,n=this,o=this.options,a=this.opos[0],r=this.opos[1],l=s.pageX,h=s.pageY;return l<a&&(t=l,l=a,a=t),h<r&&(t=h,h=r,r=t),this.helper.css({left:a,top:r,width:l-a,height:h-r}),this.selectees.each(function(){var t=V.data(this,"selectable-item"),e=!1,i={};t&&t.element!==n.element[0]&&(i.left=t.left+n.elementPos.left,i.right=t.right+n.elementPos.left,i.top=t.top+n.elementPos.top,i.bottom=t.bottom+n.elementPos.top,"touch"===o.tolerance?e=!(i.left>l||i.right<a||i.top>h||i.bottom<r):"fit"===o.tolerance&&(e=i.left>a&&i.right<l&&i.top>r&&i.bottom<h),e?(t.selected&&(n._removeClass(t.$element,"ui-selected"),t.selected=!1),t.unselecting&&(n._removeClass(t.$element,"ui-unselecting"),t.unselecting=!1),t.selecting||(n._addClass(t.$element,"ui-selecting"),t.selecting=!0,n._trigger("selecting",s,{selecting:t.element}))):(t.selecting&&((s.metaKey||s.ctrlKey)&&t.startselected?(n._removeClass(t.$element,"ui-selecting"),t.selecting=!1,n._addClass(t.$element,"ui-selected"),t.selected=!0):(n._removeClass(t.$element,"ui-selecting"),t.selecting=!1,t.startselected&&(n._addClass(t.$element,"ui-unselecting"),t.unselecting=!0),n._trigger("unselecting",s,{unselecting:t.element}))),t.selected&&(s.metaKey||s.ctrlKey||t.startselected||(n._removeClass(t.$element,"ui-selected"),t.selected=!1,n._addClass(t.$element,"ui-unselecting"),t.unselecting=!0,n._trigger("unselecting",s,{unselecting:t.element})))))}),!1}},_mouseStop:function(e){var i=this;return this.dragged=!1,V(".ui-unselecting",this.element[0]).each(function(){var t=V.data(this,"selectable-item");i._removeClass(t.$element,"ui-unselecting"),t.unselecting=!1,t.startselected=!1,i._trigger("unselected",e,{unselected:t.element})}),V(".ui-selecting",this.element[0]).each(function(){var t=V.data(this,"selectable-item");i._removeClass(t.$element,"ui-selecting")._addClass(t.$element,"ui-selected"),t.selecting=!1,t.selected=!0,t.startselected=!0,i._trigger("selected",e,{selected:t.element})}),this._trigger("stop",e),this.helper.remove(),!1}}),V.widget("ui.selectmenu",[V.ui.formResetMixin,{version:"1.13.2",defaultElement:"<select>",options:{appendTo:null,classes:{"ui-selectmenu-button-open":"ui-corner-top","ui-selectmenu-button-closed":"ui-corner-all"},disabled:null,icons:{button:"ui-icon-triangle-1-s"},position:{my:"left top",at:"left bottom",collision:"none"},width:!1,change:null,close:null,focus:null,open:null,select:null},_create:function(){var t=this.element.uniqueId().attr("id");this.ids={element:t,button:t+"-button",menu:t+"-menu"},this._drawButton(),this._drawMenu(),this._bindFormResetHandler(),this._rendered=!1,this.menuItems=V()},_drawButton:function(){var t,e=this,i=this._parseOption(this.element.find("option:selected"),this.element[0].selectedIndex);this.labels=this.element.labels().attr("for",this.ids.button),this._on(this.labels,{click:function(t){this.button.trigger("focus"),t.preventDefault()}}),this.element.hide(),this.button=V("<span>",{tabindex:this.options.disabled?-1:0,id:this.ids.button,role:"combobox","aria-expanded":"false","aria-autocomplete":"list","aria-owns":this.ids.menu,"aria-haspopup":"true",title:this.element.attr("title")}).insertAfter(this.element),this._addClass(this.button,"ui-selectmenu-button ui-selectmenu-button-closed","ui-button ui-widget"),t=V("<span>").appendTo(this.button),this._addClass(t,"ui-selectmenu-icon","ui-icon "+this.options.icons.button),this.buttonItem=this._renderButtonItem(i).appendTo(this.button),!1!==this.options.width&&this._resizeButton(),this._on(this.button,this._buttonEvents),this.button.one("focusin",function(){e._rendered||e._refreshMenu()})},_drawMenu:function(){var i=this;this.menu=V("<ul>",{"aria-hidden":"true","aria-labelledby":this.ids.button,id:this.ids.menu}),this.menuWrap=V("<div>").append(this.menu),this._addClass(this.menuWrap,"ui-selectmenu-menu","ui-front"),this.menuWrap.appendTo(this._appendTo()),this.menuInstance=this.menu.menu({classes:{"ui-menu":"ui-corner-bottom"},role:"listbox",select:function(t,e){t.preventDefault(),i._setSelection(),i._select(e.item.data("ui-selectmenu-item"),t)},focus:function(t,e){e=e.item.data("ui-selectmenu-item");null!=i.focusIndex&&e.index!==i.focusIndex&&(i._trigger("focus",t,{item:e}),i.isOpen||i._select(e,t)),i.focusIndex=e.index,i.button.attr("aria-activedescendant",i.menuItems.eq(e.index).attr("id"))}}).menu("instance"),this.menuInstance._off(this.menu,"mouseleave"),this.menuInstance._closeOnDocumentClick=function(){return!1},this.menuInstance._isDivider=function(){return!1}},refresh:function(){this._refreshMenu(),this.buttonItem.replaceWith(this.buttonItem=this._renderButtonItem(this._getSelectedItem().data("ui-selectmenu-item")||{})),null===this.options.width&&this._resizeButton()},_refreshMenu:function(){var t=this.element.find("option");this.menu.empty(),this._parseOptions(t),this._renderMenu(this.menu,this.items),this.menuInstance.refresh(),this.menuItems=this.menu.find("li").not(".ui-selectmenu-optgroup").find(".ui-menu-item-wrapper"),this._rendered=!0,t.length&&(t=this._getSelectedItem(),this.menuInstance.focus(null,t),this._setAria(t.data("ui-selectmenu-item")),this._setOption("disabled",this.element.prop("disabled")))},open:function(t){this.options.disabled||(this._rendered?(this._removeClass(this.menu.find(".ui-state-active"),null,"ui-state-active"),this.menuInstance.focus(null,this._getSelectedItem())):this._refreshMenu(),this.menuItems.length&&(this.isOpen=!0,this._toggleAttr(),this._resizeMenu(),this._position(),this._on(this.document,this._documentClick),this._trigger("open",t)))},_position:function(){this.menuWrap.position(V.extend({of:this.button},this.options.position))},close:function(t){this.isOpen&&(this.isOpen=!1,this._toggleAttr(),this.range=null,this._off(this.document),this._trigger("close",t))},widget:function(){return this.button},menuWidget:function(){return this.menu},_renderButtonItem:function(t){var e=V("<span>");return this._setText(e,t.label),this._addClass(e,"ui-selectmenu-text"),e},_renderMenu:function(s,t){var n=this,o="";V.each(t,function(t,e){var i;e.optgroup!==o&&(i=V("<li>",{text:e.optgroup}),n._addClass(i,"ui-selectmenu-optgroup","ui-menu-divider"+(e.element.parent("optgroup").prop("disabled")?" ui-state-disabled":"")),i.appendTo(s),o=e.optgroup),n._renderItemData(s,e)})},_renderItemData:function(t,e){return this._renderItem(t,e).data("ui-selectmenu-item",e)},_renderItem:function(t,e){var i=V("<li>"),s=V("<div>",{title:e.element.attr("title")});return e.disabled&&this._addClass(i,null,"ui-state-disabled"),this._setText(s,e.label),i.append(s).appendTo(t)},_setText:function(t,e){e?t.text(e):t.html(" ")},_move:function(t,e){var i,s=".ui-menu-item";this.isOpen?i=this.menuItems.eq(this.focusIndex).parent("li"):(i=this.menuItems.eq(this.element[0].selectedIndex).parent("li"),s+=":not(.ui-state-disabled)"),(s="first"===t||"last"===t?i["first"===t?"prevAll":"nextAll"](s).eq(-1):i[t+"All"](s).eq(0)).length&&this.menuInstance.focus(e,s)},_getSelectedItem:function(){return this.menuItems.eq(this.element[0].selectedIndex).parent("li")},_toggle:function(t){this[this.isOpen?"close":"open"](t)},_setSelection:function(){var t;this.range&&(window.getSelection?((t=window.getSelection()).removeAllRanges(),t.addRange(this.range)):this.range.select(),this.button.trigger("focus"))},_documentClick:{mousedown:function(t){this.isOpen&&(V(t.target).closest(".ui-selectmenu-menu, #"+V.escapeSelector(this.ids.button)).length||this.close(t))}},_buttonEvents:{mousedown:function(){var t;window.getSelection?(t=window.getSelection()).rangeCount&&(this.range=t.getRangeAt(0)):this.range=document.selection.createRange()},click:function(t){this._setSelection(),this._toggle(t)},keydown:function(t){var e=!0;switch(t.keyCode){case V.ui.keyCode.TAB:case V.ui.keyCode.ESCAPE:this.close(t),e=!1;break;case V.ui.keyCode.ENTER:this.isOpen&&this._selectFocusedItem(t);break;case V.ui.keyCode.UP:t.altKey?this._toggle(t):this._move("prev",t);break;case V.ui.keyCode.DOWN:t.altKey?this._toggle(t):this._move("next",t);break;case V.ui.keyCode.SPACE:this.isOpen?this._selectFocusedItem(t):this._toggle(t);break;case V.ui.keyCode.LEFT:this._move("prev",t);break;case V.ui.keyCode.RIGHT:this._move("next",t);break;case V.ui.keyCode.HOME:case V.ui.keyCode.PAGE_UP:this._move("first",t);break;case V.ui.keyCode.END:case V.ui.keyCode.PAGE_DOWN:this._move("last",t);break;default:this.menu.trigger(t),e=!1}e&&t.preventDefault()}},_selectFocusedItem:function(t){var e=this.menuItems.eq(this.focusIndex).parent("li");e.hasClass("ui-state-disabled")||this._select(e.data("ui-selectmenu-item"),t)},_select:function(t,e){var i=this.element[0].selectedIndex;this.element[0].selectedIndex=t.index,this.buttonItem.replaceWith(this.buttonItem=this._renderButtonItem(t)),this._setAria(t),this._trigger("select",e,{item:t}),t.index!==i&&this._trigger("change",e,{item:t}),this.close(e)},_setAria:function(t){t=this.menuItems.eq(t.index).attr("id");this.button.attr({"aria-labelledby":t,"aria-activedescendant":t}),this.menu.attr("aria-activedescendant",t)},_setOption:function(t,e){var i;"icons"===t&&(i=this.button.find("span.ui-icon"),this._removeClass(i,null,this.options.icons.button)._addClass(i,null,e.button)),this._super(t,e),"appendTo"===t&&this.menuWrap.appendTo(this._appendTo()),"width"===t&&this._resizeButton()},_setOptionDisabled:function(t){this._super(t),this.menuInstance.option("disabled",t),this.button.attr("aria-disabled",t),this._toggleClass(this.button,null,"ui-state-disabled",t),this.element.prop("disabled",t),t?(this.button.attr("tabindex",-1),this.close()):this.button.attr("tabindex",0)},_appendTo:function(){var t=this.options.appendTo;return t=!(t=!(t=t&&(t.jquery||t.nodeType?V(t):this.document.find(t).eq(0)))||!t[0]?this.element.closest(".ui-front, dialog"):t).length?this.document[0].body:t},_toggleAttr:function(){this.button.attr("aria-expanded",this.isOpen),this._removeClass(this.button,"ui-selectmenu-button-"+(this.isOpen?"closed":"open"))._addClass(this.button,"ui-selectmenu-button-"+(this.isOpen?"open":"closed"))._toggleClass(this.menuWrap,"ui-selectmenu-open",null,this.isOpen),this.menu.attr("aria-hidden",!this.isOpen)},_resizeButton:function(){var t=this.options.width;!1!==t?(null===t&&(t=this.element.show().outerWidth(),this.element.hide()),this.button.outerWidth(t)):this.button.css("width","")},_resizeMenu:function(){this.menu.outerWidth(Math.max(this.button.outerWidth(),this.menu.width("").outerWidth()+1))},_getCreateOptions:function(){var t=this._super();return t.disabled=this.element.prop("disabled"),t},_parseOptions:function(t){var i=this,s=[];t.each(function(t,e){e.hidden||s.push(i._parseOption(V(e),t))}),this.items=s},_parseOption:function(t,e){var i=t.parent("optgroup");return{element:t,index:e,value:t.val(),label:t.text(),optgroup:i.attr("label")||"",disabled:i.prop("disabled")||t.prop("disabled")}},_destroy:function(){this._unbindFormResetHandler(),this.menuWrap.remove(),this.button.remove(),this.element.show(),this.element.removeUniqueId(),this.labels.attr("for",this.ids.element)}}]),V.widget("ui.slider",V.ui.mouse,{version:"1.13.2",widgetEventPrefix:"slide",options:{animate:!1,classes:{"ui-slider":"ui-corner-all","ui-slider-handle":"ui-corner-all","ui-slider-range":"ui-corner-all ui-widget-header"},distance:0,max:100,min:0,orientation:"horizontal",range:!1,step:1,value:0,values:null,change:null,slide:null,start:null,stop:null},numPages:5,_create:function(){this._keySliding=!1,this._mouseSliding=!1,this._animateOff=!0,this._handleIndex=null,this._detectOrientation(),this._mouseInit(),this._calculateNewMax(),this._addClass("ui-slider ui-slider-"+this.orientation,"ui-widget ui-widget-content"),this._refresh(),this._animateOff=!1},_refresh:function(){this._createRange(),this._createHandles(),this._setupEvents(),this._refreshValue()},_createHandles:function(){var t,e=this.options,i=this.element.find(".ui-slider-handle"),s=[],n=e.values&&e.values.length||1;for(i.length>n&&(i.slice(n).remove(),i=i.slice(0,n)),t=i.length;t<n;t++)s.push("<span tabindex='0'></span>");this.handles=i.add(V(s.join("")).appendTo(this.element)),this._addClass(this.handles,"ui-slider-handle","ui-state-default"),this.handle=this.handles.eq(0),this.handles.each(function(t){V(this).data("ui-slider-handle-index",t).attr("tabIndex",0)})},_createRange:function(){var t=this.options;t.range?(!0===t.range&&(t.values?t.values.length&&2!==t.values.length?t.values=[t.values[0],t.values[0]]:Array.isArray(t.values)&&(t.values=t.values.slice(0)):t.values=[this._valueMin(),this._valueMin()]),this.range&&this.range.length?(this._removeClass(this.range,"ui-slider-range-min ui-slider-range-max"),this.range.css({left:"",bottom:""})):(this.range=V("<div>").appendTo(this.element),this._addClass(this.range,"ui-slider-range")),"min"!==t.range&&"max"!==t.range||this._addClass(this.range,"ui-slider-range-"+t.range)):(this.range&&this.range.remove(),this.range=null)},_setupEvents:function(){this._off(this.handles),this._on(this.handles,this._handleEvents),this._hoverable(this.handles),this._focusable(this.handles)},_destroy:function(){this.handles.remove(),this.range&&this.range.remove(),this._mouseDestroy()},_mouseCapture:function(t){var i,s,n,o,e,a,r=this,l=this.options;return!l.disabled&&(this.elementSize={width:this.element.outerWidth(),height:this.element.outerHeight()},this.elementOffset=this.element.offset(),a={x:t.pageX,y:t.pageY},i=this._normValueFromMouse(a),s=this._valueMax()-this._valueMin()+1,this.handles.each(function(t){var e=Math.abs(i-r.values(t));(e<s||s===e&&(t===r._lastChangedValue||r.values(t)===l.min))&&(s=e,n=V(this),o=t)}),!1!==this._start(t,o)&&(this._mouseSliding=!0,this._handleIndex=o,this._addClass(n,null,"ui-state-active"),n.trigger("focus"),e=n.offset(),a=!V(t.target).parents().addBack().is(".ui-slider-handle"),this._clickOffset=a?{left:0,top:0}:{left:t.pageX-e.left-n.width()/2,top:t.pageY-e.top-n.height()/2-(parseInt(n.css("borderTopWidth"),10)||0)-(parseInt(n.css("borderBottomWidth"),10)||0)+(parseInt(n.css("marginTop"),10)||0)},this.handles.hasClass("ui-state-hover")||this._slide(t,o,i),this._animateOff=!0))},_mouseStart:function(){return!0},_mouseDrag:function(t){var e={x:t.pageX,y:t.pageY},e=this._normValueFromMouse(e);return this._slide(t,this._handleIndex,e),!1},_mouseStop:function(t){return this._removeClass(this.handles,null,"ui-state-active"),this._mouseSliding=!1,this._stop(t,this._handleIndex),this._change(t,this._handleIndex),this._handleIndex=null,this._clickOffset=null,this._animateOff=!1},_detectOrientation:function(){this.orientation="vertical"===this.options.orientation?"vertical":"horizontal"},_normValueFromMouse:function(t){var e,t="horizontal"===this.orientation?(e=this.elementSize.width,t.x-this.elementOffset.left-(this._clickOffset?this._clickOffset.left:0)):(e=this.elementSize.height,t.y-this.elementOffset.top-(this._clickOffset?this._clickOffset.top:0)),t=t/e;return(t=1<t?1:t)<0&&(t=0),"vertical"===this.orientation&&(t=1-t),e=this._valueMax()-this._valueMin(),e=this._valueMin()+t*e,this._trimAlignValue(e)},_uiHash:function(t,e,i){var s={handle:this.handles[t],handleIndex:t,value:void 0!==e?e:this.value()};return this._hasMultipleValues()&&(s.value=void 0!==e?e:this.values(t),s.values=i||this.values()),s},_hasMultipleValues:function(){return this.options.values&&this.options.values.length},_start:function(t,e){return this._trigger("start",t,this._uiHash(e))},_slide:function(t,e,i){var s,n=this.value(),o=this.values();this._hasMultipleValues()&&(s=this.values(e?0:1),n=this.values(e),2===this.options.values.length&&!0===this.options.range&&(i=0===e?Math.min(s,i):Math.max(s,i)),o[e]=i),i!==n&&!1!==this._trigger("slide",t,this._uiHash(e,i,o))&&(this._hasMultipleValues()?this.values(e,i):this.value(i))},_stop:function(t,e){this._trigger("stop",t,this._uiHash(e))},_change:function(t,e){this._keySliding||this._mouseSliding||(this._lastChangedValue=e,this._trigger("change",t,this._uiHash(e)))},value:function(t){return arguments.length?(this.options.value=this._trimAlignValue(t),this._refreshValue(),void this._change(null,0)):this._value()},values:function(t,e){var i,s,n;if(1<arguments.length)return this.options.values[t]=this._trimAlignValue(e),this._refreshValue(),void this._change(null,t);if(!arguments.length)return this._values();if(!Array.isArray(t))return this._hasMultipleValues()?this._values(t):this.value();for(i=this.options.values,s=t,n=0;n<i.length;n+=1)i[n]=this._trimAlignValue(s[n]),this._change(null,n);this._refreshValue()},_setOption:function(t,e){var i,s=0;switch("range"===t&&!0===this.options.range&&("min"===e?(this.options.value=this._values(0),this.options.values=null):"max"===e&&(this.options.value=this._values(this.options.values.length-1),this.options.values=null)),Array.isArray(this.options.values)&&(s=this.options.values.length),this._super(t,e),t){case"orientation":this._detectOrientation(),this._removeClass("ui-slider-horizontal ui-slider-vertical")._addClass("ui-slider-"+this.orientation),this._refreshValue(),this.options.range&&this._refreshRange(e),this.handles.css("horizontal"===e?"bottom":"left","");break;case"value":this._animateOff=!0,this._refreshValue(),this._change(null,0),this._animateOff=!1;break;case"values":for(this._animateOff=!0,this._refreshValue(),i=s-1;0<=i;i--)this._change(null,i);this._animateOff=!1;break;case"step":case"min":case"max":this._animateOff=!0,this._calculateNewMax(),this._refreshValue(),this._animateOff=!1;break;case"range":this._animateOff=!0,this._refresh(),this._animateOff=!1}},_setOptionDisabled:function(t){this._super(t),this._toggleClass(null,"ui-state-disabled",!!t)},_value:function(){var t=this.options.value;return t=this._trimAlignValue(t)},_values:function(t){var e,i;if(arguments.length)return t=this.options.values[t],t=this._trimAlignValue(t);if(this._hasMultipleValues()){for(e=this.options.values.slice(),i=0;i<e.length;i+=1)e[i]=this._trimAlignValue(e[i]);return e}return[]},_trimAlignValue:function(t){if(t<=this._valueMin())return this._valueMin();if(t>=this._valueMax())return this._valueMax();var e=0<this.options.step?this.options.step:1,i=(t-this._valueMin())%e,t=t-i;return 2*Math.abs(i)>=e&&(t+=0<i?e:-e),parseFloat(t.toFixed(5))},_calculateNewMax:function(){var t=this.options.max,e=this._valueMin(),i=this.options.step;(t=Math.round((t-e)/i)*i+e)>this.options.max&&(t-=i),this.max=parseFloat(t.toFixed(this._precision()))},_precision:function(){var t=this._precisionOf(this.options.step);return t=null!==this.options.min?Math.max(t,this._precisionOf(this.options.min)):t},_precisionOf:function(t){var e=t.toString(),t=e.indexOf(".");return-1===t?0:e.length-t-1},_valueMin:function(){return this.options.min},_valueMax:function(){return this.max},_refreshRange:function(t){"vertical"===t&&this.range.css({width:"",left:""}),"horizontal"===t&&this.range.css({height:"",bottom:""})},_refreshValue:function(){var e,i,t,s,n,o=this.options.range,a=this.options,r=this,l=!this._animateOff&&a.animate,h={};this._hasMultipleValues()?this.handles.each(function(t){i=(r.values(t)-r._valueMin())/(r._valueMax()-r._valueMin())*100,h["horizontal"===r.orientation?"left":"bottom"]=i+"%",V(this).stop(1,1)[l?"animate":"css"](h,a.animate),!0===r.options.range&&("horizontal"===r.orientation?(0===t&&r.range.stop(1,1)[l?"animate":"css"]({left:i+"%"},a.animate),1===t&&r.range[l?"animate":"css"]({width:i-e+"%"},{queue:!1,duration:a.animate})):(0===t&&r.range.stop(1,1)[l?"animate":"css"]({bottom:i+"%"},a.animate),1===t&&r.range[l?"animate":"css"]({height:i-e+"%"},{queue:!1,duration:a.animate}))),e=i}):(t=this.value(),s=this._valueMin(),n=this._valueMax(),i=n!==s?(t-s)/(n-s)*100:0,h["horizontal"===this.orientation?"left":"bottom"]=i+"%",this.handle.stop(1,1)[l?"animate":"css"](h,a.animate),"min"===o&&"horizontal"===this.orientation&&this.range.stop(1,1)[l?"animate":"css"]({width:i+"%"},a.animate),"max"===o&&"horizontal"===this.orientation&&this.range.stop(1,1)[l?"animate":"css"]({width:100-i+"%"},a.animate),"min"===o&&"vertical"===this.orientation&&this.range.stop(1,1)[l?"animate":"css"]({height:i+"%"},a.animate),"max"===o&&"vertical"===this.orientation&&this.range.stop(1,1)[l?"animate":"css"]({height:100-i+"%"},a.animate))},_handleEvents:{keydown:function(t){var e,i,s,n=V(t.target).data("ui-slider-handle-index");switch(t.keyCode){case V.ui.keyCode.HOME:case V.ui.keyCode.END:case V.ui.keyCode.PAGE_UP:case V.ui.keyCode.PAGE_DOWN:case V.ui.keyCode.UP:case V.ui.keyCode.RIGHT:case V.ui.keyCode.DOWN:case V.ui.keyCode.LEFT:if(t.preventDefault(),!this._keySliding&&(this._keySliding=!0,this._addClass(V(t.target),null,"ui-state-active"),!1===this._start(t,n)))return}switch(s=this.options.step,e=i=this._hasMultipleValues()?this.values(n):this.value(),t.keyCode){case V.ui.keyCode.HOME:i=this._valueMin();break;case V.ui.keyCode.END:i=this._valueMax();break;case V.ui.keyCode.PAGE_UP:i=this._trimAlignValue(e+(this._valueMax()-this._valueMin())/this.numPages);break;case V.ui.keyCode.PAGE_DOWN:i=this._trimAlignValue(e-(this._valueMax()-this._valueMin())/this.numPages);break;case V.ui.keyCode.UP:case V.ui.keyCode.RIGHT:if(e===this._valueMax())return;i=this._trimAlignValue(e+s);break;case V.ui.keyCode.DOWN:case V.ui.keyCode.LEFT:if(e===this._valueMin())return;i=this._trimAlignValue(e-s)}this._slide(t,n,i)},keyup:function(t){var e=V(t.target).data("ui-slider-handle-index");this._keySliding&&(this._keySliding=!1,this._stop(t,e),this._change(t,e),this._removeClass(V(t.target),null,"ui-state-active"))}}}),V.widget("ui.sortable",V.ui.mouse,{version:"1.13.2",widgetEventPrefix:"sort",ready:!1,options:{appendTo:"parent",axis:!1,connectWith:!1,containment:!1,cursor:"auto",cursorAt:!1,dropOnEmpty:!0,forcePlaceholderSize:!1,forceHelperSize:!1,grid:!1,handle:!1,helper:"original",items:"> *",opacity:!1,placeholder:!1,revert:!1,scroll:!0,scrollSensitivity:20,scrollSpeed:20,scope:"default",tolerance:"intersect",zIndex:1e3,activate:null,beforeStop:null,change:null,deactivate:null,out:null,over:null,receive:null,remove:null,sort:null,start:null,stop:null,update:null},_isOverAxis:function(t,e,i){return e<=t&&t<e+i},_isFloating:function(t){return/left|right/.test(t.css("float"))||/inline|table-cell/.test(t.css("display"))},_create:function(){this.containerCache={},this._addClass("ui-sortable"),this.refresh(),this.offset=this.element.offset(),this._mouseInit(),this._setHandleClassName(),this.ready=!0},_setOption:function(t,e){this._super(t,e),"handle"===t&&this._setHandleClassName()},_setHandleClassName:function(){var t=this;this._removeClass(this.element.find(".ui-sortable-handle"),"ui-sortable-handle"),V.each(this.items,function(){t._addClass(this.instance.options.handle?this.item.find(this.instance.options.handle):this.item,"ui-sortable-handle")})},_destroy:function(){this._mouseDestroy();for(var t=this.items.length-1;0<=t;t--)this.items[t].item.removeData(this.widgetName+"-item");return this},_mouseCapture:function(t,e){var i=null,s=!1,n=this;return!this.reverting&&(!this.options.disabled&&"static"!==this.options.type&&(this._refreshItems(t),V(t.target).parents().each(function(){if(V.data(this,n.widgetName+"-item")===n)return i=V(this),!1}),!!(i=V.data(t.target,n.widgetName+"-item")===n?V(t.target):i)&&(!(this.options.handle&&!e&&(V(this.options.handle,i).find("*").addBack().each(function(){this===t.target&&(s=!0)}),!s))&&(this.currentItem=i,this._removeCurrentsFromItems(),!0))))},_mouseStart:function(t,e,i){var s,n,o=this.options;if((this.currentContainer=this).refreshPositions(),this.appendTo=V("parent"!==o.appendTo?o.appendTo:this.currentItem.parent()),this.helper=this._createHelper(t),this._cacheHelperProportions(),this._cacheMargins(),this.offset=this.currentItem.offset(),this.offset={top:this.offset.top-this.margins.top,left:this.offset.left-this.margins.left},V.extend(this.offset,{click:{left:t.pageX-this.offset.left,top:t.pageY-this.offset.top},relative:this._getRelativeOffset()}),this.helper.css("position","absolute"),this.cssPosition=this.helper.css("position"),o.cursorAt&&this._adjustOffsetFromHelper(o.cursorAt),this.domPosition={prev:this.currentItem.prev()[0],parent:this.currentItem.parent()[0]},this.helper[0]!==this.currentItem[0]&&this.currentItem.hide(),this._createPlaceholder(),this.scrollParent=this.placeholder.scrollParent(),V.extend(this.offset,{parent:this._getParentOffset()}),o.containment&&this._setContainment(),o.cursor&&"auto"!==o.cursor&&(n=this.document.find("body"),this.storedCursor=n.css("cursor"),n.css("cursor",o.cursor),this.storedStylesheet=V("<style>*{ cursor: "+o.cursor+" !important; }</style>").appendTo(n)),o.zIndex&&(this.helper.css("zIndex")&&(this._storedZIndex=this.helper.css("zIndex")),this.helper.css("zIndex",o.zIndex)),o.opacity&&(this.helper.css("opacity")&&(this._storedOpacity=this.helper.css("opacity")),this.helper.css("opacity",o.opacity)),this.scrollParent[0]!==this.document[0]&&"HTML"!==this.scrollParent[0].tagName&&(this.overflowOffset=this.scrollParent.offset()),this._trigger("start",t,this._uiHash()),this._preserveHelperProportions||this._cacheHelperProportions(),!i)for(s=this.containers.length-1;0<=s;s--)this.containers[s]._trigger("activate",t,this._uiHash(this));return V.ui.ddmanager&&(V.ui.ddmanager.current=this),V.ui.ddmanager&&!o.dropBehaviour&&V.ui.ddmanager.prepareOffsets(this,t),this.dragging=!0,this._addClass(this.helper,"ui-sortable-helper"),this.helper.parent().is(this.appendTo)||(this.helper.detach().appendTo(this.appendTo),this.offset.parent=this._getParentOffset()),this.position=this.originalPosition=this._generatePosition(t),this.originalPageX=t.pageX,this.originalPageY=t.pageY,this.lastPositionAbs=this.positionAbs=this._convertPositionTo("absolute"),this._mouseDrag(t),!0},_scroll:function(t){var e=this.options,i=!1;return this.scrollParent[0]!==this.document[0]&&"HTML"!==this.scrollParent[0].tagName?(this.overflowOffset.top+this.scrollParent[0].offsetHeight-t.pageY<e.scrollSensitivity?this.scrollParent[0].scrollTop=i=this.scrollParent[0].scrollTop+e.scrollSpeed:t.pageY-this.overflowOffset.top<e.scrollSensitivity&&(this.scrollParent[0].scrollTop=i=this.scrollParent[0].scrollTop-e.scrollSpeed),this.overflowOffset.left+this.scrollParent[0].offsetWidth-t.pageX<e.scrollSensitivity?this.scrollParent[0].scrollLeft=i=this.scrollParent[0].scrollLeft+e.scrollSpeed:t.pageX-this.overflowOffset.left<e.scrollSensitivity&&(this.scrollParent[0].scrollLeft=i=this.scrollParent[0].scrollLeft-e.scrollSpeed)):(t.pageY-this.document.scrollTop()<e.scrollSensitivity?i=this.document.scrollTop(this.document.scrollTop()-e.scrollSpeed):this.window.height()-(t.pageY-this.document.scrollTop())<e.scrollSensitivity&&(i=this.document.scrollTop(this.document.scrollTop()+e.scrollSpeed)),t.pageX-this.document.scrollLeft()<e.scrollSensitivity?i=this.document.scrollLeft(this.document.scrollLeft()-e.scrollSpeed):this.window.width()-(t.pageX-this.document.scrollLeft())<e.scrollSensitivity&&(i=this.document.scrollLeft(this.document.scrollLeft()+e.scrollSpeed))),i},_mouseDrag:function(t){var e,i,s,n,o=this.options;for(this.position=this._generatePosition(t),this.positionAbs=this._convertPositionTo("absolute"),this.options.axis&&"y"===this.options.axis||(this.helper[0].style.left=this.position.left+"px"),this.options.axis&&"x"===this.options.axis||(this.helper[0].style.top=this.position.top+"px"),o.scroll&&!1!==this._scroll(t)&&(this._refreshItemPositions(!0),V.ui.ddmanager&&!o.dropBehaviour&&V.ui.ddmanager.prepareOffsets(this,t)),this.dragDirection={vertical:this._getDragVerticalDirection(),horizontal:this._getDragHorizontalDirection()},e=this.items.length-1;0<=e;e--)if(s=(i=this.items[e]).item[0],(n=this._intersectsWithPointer(i))&&i.instance===this.currentContainer&&!(s===this.currentItem[0]||this.placeholder[1===n?"next":"prev"]()[0]===s||V.contains(this.placeholder[0],s)||"semi-dynamic"===this.options.type&&V.contains(this.element[0],s))){if(this.direction=1===n?"down":"up","pointer"!==this.options.tolerance&&!this._intersectsWithSides(i))break;this._rearrange(t,i),this._trigger("change",t,this._uiHash());break}return this._contactContainers(t),V.ui.ddmanager&&V.ui.ddmanager.drag(this,t),this._trigger("sort",t,this._uiHash()),this.lastPositionAbs=this.positionAbs,!1},_mouseStop:function(t,e){var i,s,n,o;if(t)return V.ui.ddmanager&&!this.options.dropBehaviour&&V.ui.ddmanager.drop(this,t),this.options.revert?(s=(i=this).placeholder.offset(),o={},(n=this.options.axis)&&"x"!==n||(o.left=s.left-this.offset.parent.left-this.margins.left+(this.offsetParent[0]===this.document[0].body?0:this.offsetParent[0].scrollLeft)),n&&"y"!==n||(o.top=s.top-this.offset.parent.top-this.margins.top+(this.offsetParent[0]===this.document[0].body?0:this.offsetParent[0].scrollTop)),this.reverting=!0,V(this.helper).animate(o,parseInt(this.options.revert,10)||500,function(){i._clear(t)})):this._clear(t,e),!1},cancel:function(){if(this.dragging){this._mouseUp(new V.Event("mouseup",{target:null})),"original"===this.options.helper?(this.currentItem.css(this._storedCSS),this._removeClass(this.currentItem,"ui-sortable-helper")):this.currentItem.show();for(var t=this.containers.length-1;0<=t;t--)this.containers[t]._trigger("deactivate",null,this._uiHash(this)),this.containers[t].containerCache.over&&(this.containers[t]._trigger("out",null,this._uiHash(this)),this.containers[t].containerCache.over=0)}return this.placeholder&&(this.placeholder[0].parentNode&&this.placeholder[0].parentNode.removeChild(this.placeholder[0]),"original"!==this.options.helper&&this.helper&&this.helper[0].parentNode&&this.helper.remove(),V.extend(this,{helper:null,dragging:!1,reverting:!1,_noFinalSort:null}),this.domPosition.prev?V(this.domPosition.prev).after(this.currentItem):V(this.domPosition.parent).prepend(this.currentItem)),this},serialize:function(e){var t=this._getItemsAsjQuery(e&&e.connected),i=[];return e=e||{},V(t).each(function(){var t=(V(e.item||this).attr(e.attribute||"id")||"").match(e.expression||/(.+)[\-=_](.+)/);t&&i.push((e.key||t[1]+"[]")+"="+(e.key&&e.expression?t[1]:t[2]))}),!i.length&&e.key&&i.push(e.key+"="),i.join("&")},toArray:function(t){var e=this._getItemsAsjQuery(t&&t.connected),i=[];return t=t||{},e.each(function(){i.push(V(t.item||this).attr(t.attribute||"id")||"")}),i},_intersectsWith:function(t){var e=this.positionAbs.left,i=e+this.helperProportions.width,s=this.positionAbs.top,n=s+this.helperProportions.height,o=t.left,a=o+t.width,r=t.top,l=r+t.height,h=this.offset.click.top,c=this.offset.click.left,h="x"===this.options.axis||r<s+h&&s+h<l,c="y"===this.options.axis||o<e+c&&e+c<a;return"pointer"===this.options.tolerance||this.options.forcePointerForContainers||"pointer"!==this.options.tolerance&&this.helperProportions[this.floating?"width":"height"]>t[this.floating?"width":"height"]?h&&c:o<e+this.helperProportions.width/2&&i-this.helperProportions.width/2<a&&r<s+this.helperProportions.height/2&&n-this.helperProportions.height/2<l},_intersectsWithPointer:function(t){var e="x"===this.options.axis||this._isOverAxis(this.positionAbs.top+this.offset.click.top,t.top,t.height),t="y"===this.options.axis||this._isOverAxis(this.positionAbs.left+this.offset.click.left,t.left,t.width);return!(!e||!t)&&(e=this.dragDirection.vertical,t=this.dragDirection.horizontal,this.floating?"right"===t||"down"===e?2:1:e&&("down"===e?2:1))},_intersectsWithSides:function(t){var e=this._isOverAxis(this.positionAbs.top+this.offset.click.top,t.top+t.height/2,t.height),i=this._isOverAxis(this.positionAbs.left+this.offset.click.left,t.left+t.width/2,t.width),s=this.dragDirection.vertical,t=this.dragDirection.horizontal;return this.floating&&t?"right"===t&&i||"left"===t&&!i:s&&("down"===s&&e||"up"===s&&!e)},_getDragVerticalDirection:function(){var t=this.positionAbs.top-this.lastPositionAbs.top;return 0!=t&&(0<t?"down":"up")},_getDragHorizontalDirection:function(){var t=this.positionAbs.left-this.lastPositionAbs.left;return 0!=t&&(0<t?"right":"left")},refresh:function(t){return this._refreshItems(t),this._setHandleClassName(),this.refreshPositions(),this},_connectWith:function(){var t=this.options;return t.connectWith.constructor===String?[t.connectWith]:t.connectWith},_getItemsAsjQuery:function(t){var e,i,s,n,o=[],a=[],r=this._connectWith();if(r&&t)for(e=r.length-1;0<=e;e--)for(i=(s=V(r[e],this.document[0])).length-1;0<=i;i--)(n=V.data(s[i],this.widgetFullName))&&n!==this&&!n.options.disabled&&a.push(["function"==typeof n.options.items?n.options.items.call(n.element):V(n.options.items,n.element).not(".ui-sortable-helper").not(".ui-sortable-placeholder"),n]);function l(){o.push(this)}for(a.push(["function"==typeof this.options.items?this.options.items.call(this.element,null,{options:this.options,item:this.currentItem}):V(this.options.items,this.element).not(".ui-sortable-helper").not(".ui-sortable-placeholder"),this]),e=a.length-1;0<=e;e--)a[e][0].each(l);return V(o)},_removeCurrentsFromItems:function(){var i=this.currentItem.find(":data("+this.widgetName+"-item)");this.items=V.grep(this.items,function(t){for(var e=0;e<i.length;e++)if(i[e]===t.item[0])return!1;return!0})},_refreshItems:function(t){this.items=[],this.containers=[this];var e,i,s,n,o,a,r,l,h=this.items,c=[["function"==typeof this.options.items?this.options.items.call(this.element[0],t,{item:this.currentItem}):V(this.options.items,this.element),this]],u=this._connectWith();if(u&&this.ready)for(e=u.length-1;0<=e;e--)for(i=(s=V(u[e],this.document[0])).length-1;0<=i;i--)(n=V.data(s[i],this.widgetFullName))&&n!==this&&!n.options.disabled&&(c.push(["function"==typeof n.options.items?n.options.items.call(n.element[0],t,{item:this.currentItem}):V(n.options.items,n.element),n]),this.containers.push(n));for(e=c.length-1;0<=e;e--)for(o=c[e][1],l=(a=c[e][i=0]).length;i<l;i++)(r=V(a[i])).data(this.widgetName+"-item",o),h.push({item:r,instance:o,width:0,height:0,left:0,top:0})},_refreshItemPositions:function(t){for(var e,i,s=this.items.length-1;0<=s;s--)e=this.items[s],this.currentContainer&&e.instance!==this.currentContainer&&e.item[0]!==this.currentItem[0]||(i=this.options.toleranceElement?V(this.options.toleranceElement,e.item):e.item,t||(e.width=i.outerWidth(),e.height=i.outerHeight()),i=i.offset(),e.left=i.left,e.top=i.top)},refreshPositions:function(t){var e,i;if(this.floating=!!this.items.length&&("x"===this.options.axis||this._isFloating(this.items[0].item)),this.offsetParent&&this.helper&&(this.offset.parent=this._getParentOffset()),this._refreshItemPositions(t),this.options.custom&&this.options.custom.refreshContainers)this.options.custom.refreshContainers.call(this);else for(e=this.containers.length-1;0<=e;e--)i=this.containers[e].element.offset(),this.containers[e].containerCache.left=i.left,this.containers[e].containerCache.top=i.top,this.containers[e].containerCache.width=this.containers[e].element.outerWidth(),this.containers[e].containerCache.height=this.containers[e].element.outerHeight();return this},_createPlaceholder:function(i){var s,n,o=(i=i||this).options;o.placeholder&&o.placeholder.constructor!==String||(s=o.placeholder,n=i.currentItem[0].nodeName.toLowerCase(),o.placeholder={element:function(){var t=V("<"+n+">",i.document[0]);return i._addClass(t,"ui-sortable-placeholder",s||i.currentItem[0].className)._removeClass(t,"ui-sortable-helper"),"tbody"===n?i._createTrPlaceholder(i.currentItem.find("tr").eq(0),V("<tr>",i.document[0]).appendTo(t)):"tr"===n?i._createTrPlaceholder(i.currentItem,t):"img"===n&&t.attr("src",i.currentItem.attr("src")),s||t.css("visibility","hidden"),t},update:function(t,e){s&&!o.forcePlaceholderSize||(e.height()&&(!o.forcePlaceholderSize||"tbody"!==n&&"tr"!==n)||e.height(i.currentItem.innerHeight()-parseInt(i.currentItem.css("paddingTop")||0,10)-parseInt(i.currentItem.css("paddingBottom")||0,10)),e.width()||e.width(i.currentItem.innerWidth()-parseInt(i.currentItem.css("paddingLeft")||0,10)-parseInt(i.currentItem.css("paddingRight")||0,10)))}}),i.placeholder=V(o.placeholder.element.call(i.element,i.currentItem)),i.currentItem.after(i.placeholder),o.placeholder.update(i,i.placeholder)},_createTrPlaceholder:function(t,e){var i=this;t.children().each(function(){V("<td> </td>",i.document[0]).attr("colspan",V(this).attr("colspan")||1).appendTo(e)})},_contactContainers:function(t){for(var e,i,s,n,o,a,r,l,h,c=null,u=null,d=this.containers.length-1;0<=d;d--)V.contains(this.currentItem[0],this.containers[d].element[0])||(this._intersectsWith(this.containers[d].containerCache)?c&&V.contains(this.containers[d].element[0],c.element[0])||(c=this.containers[d],u=d):this.containers[d].containerCache.over&&(this.containers[d]._trigger("out",t,this._uiHash(this)),this.containers[d].containerCache.over=0));if(c)if(1===this.containers.length)this.containers[u].containerCache.over||(this.containers[u]._trigger("over",t,this._uiHash(this)),this.containers[u].containerCache.over=1);else{for(i=1e4,s=null,n=(l=c.floating||this._isFloating(this.currentItem))?"left":"top",o=l?"width":"height",h=l?"pageX":"pageY",e=this.items.length-1;0<=e;e--)V.contains(this.containers[u].element[0],this.items[e].item[0])&&this.items[e].item[0]!==this.currentItem[0]&&(a=this.items[e].item.offset()[n],r=!1,t[h]-a>this.items[e][o]/2&&(r=!0),Math.abs(t[h]-a)<i&&(i=Math.abs(t[h]-a),s=this.items[e],this.direction=r?"up":"down"));(s||this.options.dropOnEmpty)&&(this.currentContainer!==this.containers[u]?(s?this._rearrange(t,s,null,!0):this._rearrange(t,null,this.containers[u].element,!0),this._trigger("change",t,this._uiHash()),this.containers[u]._trigger("change",t,this._uiHash(this)),this.currentContainer=this.containers[u],this.options.placeholder.update(this.currentContainer,this.placeholder),this.scrollParent=this.placeholder.scrollParent(),this.scrollParent[0]!==this.document[0]&&"HTML"!==this.scrollParent[0].tagName&&(this.overflowOffset=this.scrollParent.offset()),this.containers[u]._trigger("over",t,this._uiHash(this)),this.containers[u].containerCache.over=1):this.currentContainer.containerCache.over||(this.containers[u]._trigger("over",t,this._uiHash()),this.currentContainer.containerCache.over=1))}},_createHelper:function(t){var e=this.options,t="function"==typeof e.helper?V(e.helper.apply(this.element[0],[t,this.currentItem])):"clone"===e.helper?this.currentItem.clone():this.currentItem;return t.parents("body").length||this.appendTo[0].appendChild(t[0]),t[0]===this.currentItem[0]&&(this._storedCSS={width:this.currentItem[0].style.width,height:this.currentItem[0].style.height,position:this.currentItem.css("position"),top:this.currentItem.css("top"),left:this.currentItem.css("left")}),t[0].style.width&&!e.forceHelperSize||t.width(this.currentItem.width()),t[0].style.height&&!e.forceHelperSize||t.height(this.currentItem.height()),t},_adjustOffsetFromHelper:function(t){"string"==typeof t&&(t=t.split(" ")),"left"in(t=Array.isArray(t)?{left:+t[0],top:+t[1]||0}:t)&&(this.offset.click.left=t.left+this.margins.left),"right"in t&&(this.offset.click.left=this.helperProportions.width-t.right+this.margins.left),"top"in t&&(this.offset.click.top=t.top+this.margins.top),"bottom"in t&&(this.offset.click.top=this.helperProportions.height-t.bottom+this.margins.top)},_getParentOffset:function(){this.offsetParent=this.helper.offsetParent();var t=this.offsetParent.offset();return"absolute"===this.cssPosition&&this.scrollParent[0]!==this.document[0]&&V.contains(this.scrollParent[0],this.offsetParent[0])&&(t.left+=this.scrollParent.scrollLeft(),t.top+=this.scrollParent.scrollTop()),{top:(t=this.offsetParent[0]===this.document[0].body||this.offsetParent[0].tagName&&"html"===this.offsetParent[0].tagName.toLowerCase()&&V.ui.ie?{top:0,left:0}:t).top+(parseInt(this.offsetParent.css("borderTopWidth"),10)||0),left:t.left+(parseInt(this.offsetParent.css("borderLeftWidth"),10)||0)}},_getRelativeOffset:function(){if("relative"!==this.cssPosition)return{top:0,left:0};var t=this.currentItem.position();return{top:t.top-(parseInt(this.helper.css("top"),10)||0)+this.scrollParent.scrollTop(),left:t.left-(parseInt(this.helper.css("left"),10)||0)+this.scrollParent.scrollLeft()}},_cacheMargins:function(){this.margins={left:parseInt(this.currentItem.css("marginLeft"),10)||0,top:parseInt(this.currentItem.css("marginTop"),10)||0}},_cacheHelperProportions:function(){this.helperProportions={width:this.helper.outerWidth(),height:this.helper.outerHeight()}},_setContainment:function(){var t,e,i=this.options;"parent"===i.containment&&(i.containment=this.helper[0].parentNode),"document"!==i.containment&&"window"!==i.containment||(this.containment=[0-this.offset.relative.left-this.offset.parent.left,0-this.offset.relative.top-this.offset.parent.top,"document"===i.containment?this.document.width():this.window.width()-this.helperProportions.width-this.margins.left,("document"===i.containment?this.document.height()||document.body.parentNode.scrollHeight:this.window.height()||this.document[0].body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top]),/^(document|window|parent)$/.test(i.containment)||(t=V(i.containment)[0],e=V(i.containment).offset(),i="hidden"!==V(t).css("overflow"),this.containment=[e.left+(parseInt(V(t).css("borderLeftWidth"),10)||0)+(parseInt(V(t).css("paddingLeft"),10)||0)-this.margins.left,e.top+(parseInt(V(t).css("borderTopWidth"),10)||0)+(parseInt(V(t).css("paddingTop"),10)||0)-this.margins.top,e.left+(i?Math.max(t.scrollWidth,t.offsetWidth):t.offsetWidth)-(parseInt(V(t).css("borderLeftWidth"),10)||0)-(parseInt(V(t).css("paddingRight"),10)||0)-this.helperProportions.width-this.margins.left,e.top+(i?Math.max(t.scrollHeight,t.offsetHeight):t.offsetHeight)-(parseInt(V(t).css("borderTopWidth"),10)||0)-(parseInt(V(t).css("paddingBottom"),10)||0)-this.helperProportions.height-this.margins.top])},_convertPositionTo:function(t,e){e=e||this.position;var i="absolute"===t?1:-1,s="absolute"!==this.cssPosition||this.scrollParent[0]!==this.document[0]&&V.contains(this.scrollParent[0],this.offsetParent[0])?this.scrollParent:this.offsetParent,t=/(html|body)/i.test(s[0].tagName);return{top:e.top+this.offset.relative.top*i+this.offset.parent.top*i-("fixed"===this.cssPosition?-this.scrollParent.scrollTop():t?0:s.scrollTop())*i,left:e.left+this.offset.relative.left*i+this.offset.parent.left*i-("fixed"===this.cssPosition?-this.scrollParent.scrollLeft():t?0:s.scrollLeft())*i}},_generatePosition:function(t){var e=this.options,i=t.pageX,s=t.pageY,n="absolute"!==this.cssPosition||this.scrollParent[0]!==this.document[0]&&V.contains(this.scrollParent[0],this.offsetParent[0])?this.scrollParent:this.offsetParent,o=/(html|body)/i.test(n[0].tagName);return"relative"!==this.cssPosition||this.scrollParent[0]!==this.document[0]&&this.scrollParent[0]!==this.offsetParent[0]||(this.offset.relative=this._getRelativeOffset()),this.originalPosition&&(this.containment&&(t.pageX-this.offset.click.left<this.containment[0]&&(i=this.containment[0]+this.offset.click.left),t.pageY-this.offset.click.top<this.containment[1]&&(s=this.containment[1]+this.offset.click.top),t.pageX-this.offset.click.left>this.containment[2]&&(i=this.containment[2]+this.offset.click.left),t.pageY-this.offset.click.top>this.containment[3]&&(s=this.containment[3]+this.offset.click.top)),e.grid&&(t=this.originalPageY+Math.round((s-this.originalPageY)/e.grid[1])*e.grid[1],s=!this.containment||t-this.offset.click.top>=this.containment[1]&&t-this.offset.click.top<=this.containment[3]?t:t-this.offset.click.top>=this.containment[1]?t-e.grid[1]:t+e.grid[1],t=this.originalPageX+Math.round((i-this.originalPageX)/e.grid[0])*e.grid[0],i=!this.containment||t-this.offset.click.left>=this.containment[0]&&t-this.offset.click.left<=this.containment[2]?t:t-this.offset.click.left>=this.containment[0]?t-e.grid[0]:t+e.grid[0])),{top:s-this.offset.click.top-this.offset.relative.top-this.offset.parent.top+("fixed"===this.cssPosition?-this.scrollParent.scrollTop():o?0:n.scrollTop()),left:i-this.offset.click.left-this.offset.relative.left-this.offset.parent.left+("fixed"===this.cssPosition?-this.scrollParent.scrollLeft():o?0:n.scrollLeft())}},_rearrange:function(t,e,i,s){i?i[0].appendChild(this.placeholder[0]):e.item[0].parentNode.insertBefore(this.placeholder[0],"down"===this.direction?e.item[0]:e.item[0].nextSibling),this.counter=this.counter?++this.counter:1;var n=this.counter;this._delay(function(){n===this.counter&&this.refreshPositions(!s)})},_clear:function(t,e){this.reverting=!1;var i,s=[];if(!this._noFinalSort&&this.currentItem.parent().length&&this.placeholder.before(this.currentItem),this._noFinalSort=null,this.helper[0]===this.currentItem[0]){for(i in this._storedCSS)"auto"!==this._storedCSS[i]&&"static"!==this._storedCSS[i]||(this._storedCSS[i]="");this.currentItem.css(this._storedCSS),this._removeClass(this.currentItem,"ui-sortable-helper")}else this.currentItem.show();function n(e,i,s){return function(t){s._trigger(e,t,i._uiHash(i))}}for(this.fromOutside&&!e&&s.push(function(t){this._trigger("receive",t,this._uiHash(this.fromOutside))}),!this.fromOutside&&this.domPosition.prev===this.currentItem.prev().not(".ui-sortable-helper")[0]&&this.domPosition.parent===this.currentItem.parent()[0]||e||s.push(function(t){this._trigger("update",t,this._uiHash())}),this!==this.currentContainer&&(e||(s.push(function(t){this._trigger("remove",t,this._uiHash())}),s.push(function(e){return function(t){e._trigger("receive",t,this._uiHash(this))}}.call(this,this.currentContainer)),s.push(function(e){return function(t){e._trigger("update",t,this._uiHash(this))}}.call(this,this.currentContainer)))),i=this.containers.length-1;0<=i;i--)e||s.push(n("deactivate",this,this.containers[i])),this.containers[i].containerCache.over&&(s.push(n("out",this,this.containers[i])),this.containers[i].containerCache.over=0);if(this.storedCursor&&(this.document.find("body").css("cursor",this.storedCursor),this.storedStylesheet.remove()),this._storedOpacity&&this.helper.css("opacity",this._storedOpacity),this._storedZIndex&&this.helper.css("zIndex","auto"===this._storedZIndex?"":this._storedZIndex),this.dragging=!1,e||this._trigger("beforeStop",t,this._uiHash()),this.placeholder[0].parentNode.removeChild(this.placeholder[0]),this.cancelHelperRemoval||(this.helper[0]!==this.currentItem[0]&&this.helper.remove(),this.helper=null),!e){for(i=0;i<s.length;i++)s[i].call(this,t);this._trigger("stop",t,this._uiHash())}return this.fromOutside=!1,!this.cancelHelperRemoval},_trigger:function(){!1===V.Widget.prototype._trigger.apply(this,arguments)&&this.cancel()},_uiHash:function(t){var e=t||this;return{helper:e.helper,placeholder:e.placeholder||V([]),position:e.position,originalPosition:e.originalPosition,offset:e.positionAbs,item:e.currentItem,sender:t?t.element:null}}});function ht(e){return function(){var t=this.element.val();e.apply(this,arguments),this._refresh(),t!==this.element.val()&&this._trigger("change")}}V.widget("ui.spinner",{version:"1.13.2",defaultElement:"<input>",widgetEventPrefix:"spin",options:{classes:{"ui-spinner":"ui-corner-all","ui-spinner-down":"ui-corner-br","ui-spinner-up":"ui-corner-tr"},culture:null,icons:{down:"ui-icon-triangle-1-s",up:"ui-icon-triangle-1-n"},incremental:!0,max:null,min:null,numberFormat:null,page:10,step:1,change:null,spin:null,start:null,stop:null},_create:function(){this._setOption("max",this.options.max),this._setOption("min",this.options.min),this._setOption("step",this.options.step),""!==this.value()&&this._value(this.element.val(),!0),this._draw(),this._on(this._events),this._refresh(),this._on(this.window,{beforeunload:function(){this.element.removeAttr("autocomplete")}})},_getCreateOptions:function(){var s=this._super(),n=this.element;return V.each(["min","max","step"],function(t,e){var i=n.attr(e);null!=i&&i.length&&(s[e]=i)}),s},_events:{keydown:function(t){this._start(t)&&this._keydown(t)&&t.preventDefault()},keyup:"_stop",focus:function(){this.previous=this.element.val()},blur:function(t){this.cancelBlur?delete this.cancelBlur:(this._stop(),this._refresh(),this.previous!==this.element.val()&&this._trigger("change",t))},mousewheel:function(t,e){var i=V.ui.safeActiveElement(this.document[0]);if(this.element[0]===i&&e){if(!this.spinning&&!this._start(t))return!1;this._spin((0<e?1:-1)*this.options.step,t),clearTimeout(this.mousewheelTimer),this.mousewheelTimer=this._delay(function(){this.spinning&&this._stop(t)},100),t.preventDefault()}},"mousedown .ui-spinner-button":function(t){var e;function i(){this.element[0]===V.ui.safeActiveElement(this.document[0])||(this.element.trigger("focus"),this.previous=e,this._delay(function(){this.previous=e}))}e=this.element[0]===V.ui.safeActiveElement(this.document[0])?this.previous:this.element.val(),t.preventDefault(),i.call(this),this.cancelBlur=!0,this._delay(function(){delete this.cancelBlur,i.call(this)}),!1!==this._start(t)&&this._repeat(null,V(t.currentTarget).hasClass("ui-spinner-up")?1:-1,t)},"mouseup .ui-spinner-button":"_stop","mouseenter .ui-spinner-button":function(t){if(V(t.currentTarget).hasClass("ui-state-active"))return!1!==this._start(t)&&void this._repeat(null,V(t.currentTarget).hasClass("ui-spinner-up")?1:-1,t)},"mouseleave .ui-spinner-button":"_stop"},_enhance:function(){this.uiSpinner=this.element.attr("autocomplete","off").wrap("<span>").parent().append("<a></a><a></a>")},_draw:function(){this._enhance(),this._addClass(this.uiSpinner,"ui-spinner","ui-widget ui-widget-content"),this._addClass("ui-spinner-input"),this.element.attr("role","spinbutton"),this.buttons=this.uiSpinner.children("a").attr("tabIndex",-1).attr("aria-hidden",!0).button({classes:{"ui-button":""}}),this._removeClass(this.buttons,"ui-corner-all"),this._addClass(this.buttons.first(),"ui-spinner-button ui-spinner-up"),this._addClass(this.buttons.last(),"ui-spinner-button ui-spinner-down"),this.buttons.first().button({icon:this.options.icons.up,showLabel:!1}),this.buttons.last().button({icon:this.options.icons.down,showLabel:!1}),this.buttons.height()>Math.ceil(.5*this.uiSpinner.height())&&0<this.uiSpinner.height()&&this.uiSpinner.height(this.uiSpinner.height())},_keydown:function(t){var e=this.options,i=V.ui.keyCode;switch(t.keyCode){case i.UP:return this._repeat(null,1,t),!0;case i.DOWN:return this._repeat(null,-1,t),!0;case i.PAGE_UP:return this._repeat(null,e.page,t),!0;case i.PAGE_DOWN:return this._repeat(null,-e.page,t),!0}return!1},_start:function(t){return!(!this.spinning&&!1===this._trigger("start",t))&&(this.counter||(this.counter=1),this.spinning=!0)},_repeat:function(t,e,i){t=t||500,clearTimeout(this.timer),this.timer=this._delay(function(){this._repeat(40,e,i)},t),this._spin(e*this.options.step,i)},_spin:function(t,e){var i=this.value()||0;this.counter||(this.counter=1),i=this._adjustValue(i+t*this._increment(this.counter)),this.spinning&&!1===this._trigger("spin",e,{value:i})||(this._value(i),this.counter++)},_increment:function(t){var e=this.options.incremental;return e?"function"==typeof e?e(t):Math.floor(t*t*t/5e4-t*t/500+17*t/200+1):1},_precision:function(){var t=this._precisionOf(this.options.step);return t=null!==this.options.min?Math.max(t,this._precisionOf(this.options.min)):t},_precisionOf:function(t){var e=t.toString(),t=e.indexOf(".");return-1===t?0:e.length-t-1},_adjustValue:function(t){var e=this.options,i=null!==e.min?e.min:0,s=t-i;return t=i+Math.round(s/e.step)*e.step,t=parseFloat(t.toFixed(this._precision())),null!==e.max&&t>e.max?e.max:null!==e.min&&t<e.min?e.min:t},_stop:function(t){this.spinning&&(clearTimeout(this.timer),clearTimeout(this.mousewheelTimer),this.counter=0,this.spinning=!1,this._trigger("stop",t))},_setOption:function(t,e){var i;if("culture"===t||"numberFormat"===t)return i=this._parse(this.element.val()),this.options[t]=e,void this.element.val(this._format(i));"max"!==t&&"min"!==t&&"step"!==t||"string"==typeof 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i.parents[t]}),n.closing=!0,this._trigger("close",t,{tooltip:e}),n.hiding||(n.closing=!1))):s.removeData("ui-tooltip-open")},_tooltip:function(t){var e=V("<div>").attr("role","tooltip"),i=V("<div>").appendTo(e),s=e.uniqueId().attr("id");return this._addClass(i,"ui-tooltip-content"),this._addClass(e,"ui-tooltip","ui-widget ui-widget-content"),e.appendTo(this._appendTo(t)),this.tooltips[s]={element:t,tooltip:e}},_find:function(t){t=t.data("ui-tooltip-id");return t?this.tooltips[t]:null},_removeTooltip:function(t){clearInterval(this.delayedShow),t.remove(),delete this.tooltips[t.attr("id")]},_appendTo:function(t){t=t.closest(".ui-front, dialog");return t=!t.length?this.document[0].body:t},_destroy:function(){var s=this;V.each(this.tooltips,function(t,e){var i=V.Event("blur"),e=e.element;i.target=i.currentTarget=e[0],s.close(i,!0),V("#"+t).remove(),e.data("ui-tooltip-title")&&(e.attr("title")||e.attr("title",e.data("ui-tooltip-title")),e.removeData("ui-tooltip-title"))}),this.liveRegion.remove()}}),!1!==V.uiBackCompat&&V.widget("ui.tooltip",V.ui.tooltip,{options:{tooltipClass:null},_tooltip:function(){var t=this._superApply(arguments);return this.options.tooltipClass&&t.tooltip.addClass(this.options.tooltipClass),t}});V.ui.tooltip});</script> +<style type="text/css"> + +.tocify { +width: 20%; +max-height: 90%; +overflow: auto; +margin-left: 2%; +position: fixed; +border: 1px solid #ccc; +border-radius: 6px; +} + +.tocify ul, .tocify li { +list-style: none; +margin: 0; +padding: 0; +border: none; +line-height: 30px; +} + +.tocify-header { +text-indent: 10px; +} + +.tocify-subheader { +text-indent: 20px; +display: none; +} + +.tocify-subheader li { +font-size: 12px; +} + +.tocify-subheader .tocify-subheader { +text-indent: 30px; +} +.tocify-subheader .tocify-subheader .tocify-subheader { +text-indent: 40px; +} +.tocify-subheader .tocify-subheader .tocify-subheader .tocify-subheader { +text-indent: 50px; +} +.tocify-subheader .tocify-subheader .tocify-subheader .tocify-subheader .tocify-subheader { +text-indent: 60px; +} + +.tocify .tocify-item > a, .tocify .nav-list .nav-header { +margin: 0px; +} + +.tocify .tocify-item a, .tocify .list-group-item { +padding: 5px; +} +.tocify .nav-pills > li { +float: none; +} + + +</style> +<script>/* jquery Tocify - v1.9.1 - 2013-10-22 + * http://www.gregfranko.com/jquery.tocify.js/ + * Copyright (c) 2013 Greg Franko; Licensed MIT */ + +// Immediately-Invoked Function Expression (IIFE) [Ben Alman Blog Post](http://benalman.com/news/2010/11/immediately-invoked-function-expression/) that calls another IIFE that contains all of the plugin logic. I used this pattern so that anyone viewing this code would not have to scroll to the bottom of the page to view the local parameters that were passed to the main IIFE. +(function(tocify) { + + // ECMAScript 5 Strict Mode: [John Resig Blog Post](http://ejohn.org/blog/ecmascript-5-strict-mode-json-and-more/) + "use strict"; + + // Calls the second IIFE and locally passes in the global jQuery, window, and document objects + tocify(window.jQuery, window, document); + + } + + // Locally passes in `jQuery`, the `window` object, the `document` object, and an `undefined` variable. The `jQuery`, `window` and `document` objects are passed in locally, to improve performance, since javascript first searches for a variable match within the local variables set before searching the global variables set. All of the global variables are also passed in locally to be minifier friendly. `undefined` can be passed in locally, because it is not a reserved word in JavaScript. + (function($, window, document, undefined) { + + // ECMAScript 5 Strict Mode: [John Resig Blog Post](http://ejohn.org/blog/ecmascript-5-strict-mode-json-and-more/) + "use strict"; + + var tocClassName = "tocify", + tocClass = "." + tocClassName, + tocFocusClassName = "tocify-focus", + tocHoverClassName = "tocify-hover", + hideTocClassName = "tocify-hide", + hideTocClass = "." + hideTocClassName, + headerClassName = "tocify-header", + headerClass = "." + headerClassName, + subheaderClassName = "tocify-subheader", + subheaderClass = "." + subheaderClassName, + itemClassName = "tocify-item", + itemClass = "." + itemClassName, + extendPageClassName = "tocify-extend-page", + extendPageClass = "." + extendPageClassName; + + // Calling the jQueryUI Widget Factory Method + $.widget("toc.tocify", { + + //Plugin version + version: "1.9.1", + + // These options will be used as defaults + options: { + + // **context**: Accepts String: Any jQuery selector + // The container element that holds all of the elements used to generate the table of contents + context: "body", + + // **ignoreSelector**: Accepts String: Any jQuery selector + // A selector to any element that would be matched by selectors that you wish to be ignored + ignoreSelector: null, + + // **selectors**: Accepts an Array of Strings: Any jQuery selectors + // The element's used to generate the table of contents. The order is very important since it will determine the table of content's nesting structure + selectors: "h1, h2, h3", + + // **showAndHide**: Accepts a boolean: true or false + // Used to determine if elements should be shown and hidden + showAndHide: true, + + // **showEffect**: Accepts String: "none", "fadeIn", "show", or "slideDown" + // Used to display any of the table of contents nested items + showEffect: "slideDown", + + // **showEffectSpeed**: Accepts Number (milliseconds) or String: "slow", "medium", or "fast" + // The time duration of the show animation + showEffectSpeed: "medium", + + // **hideEffect**: Accepts String: "none", "fadeOut", "hide", or "slideUp" + // Used to hide any of the table of contents nested items + hideEffect: "slideUp", + + // **hideEffectSpeed**: Accepts Number (milliseconds) or String: "slow", "medium", or "fast" + // The time duration of the hide animation + hideEffectSpeed: "medium", + + // **smoothScroll**: Accepts a boolean: true or false + // Determines if a jQuery animation should be used to scroll to specific table of contents items on the page + smoothScroll: true, + + // **smoothScrollSpeed**: Accepts Number (milliseconds) or String: "slow", "medium", or "fast" + // The time duration of the smoothScroll animation + smoothScrollSpeed: "medium", + + // **scrollTo**: Accepts Number (pixels) + // The amount of space between the top of page and the selected table of contents item after the page has been scrolled + scrollTo: 0, + + // **showAndHideOnScroll**: Accepts a boolean: true or false + // Determines if table of contents nested items should be shown and hidden while scrolling + showAndHideOnScroll: true, + + // **highlightOnScroll**: Accepts a boolean: true or false + // Determines if table of contents nested items should be highlighted (set to a different color) while scrolling + highlightOnScroll: true, + + // **highlightOffset**: Accepts a number + // The offset distance in pixels to trigger the next active table of contents item + highlightOffset: 40, + + // **theme**: Accepts a string: "bootstrap", "jqueryui", or "none" + // Determines if Twitter Bootstrap, jQueryUI, or Tocify classes should be added to the table of contents + theme: "bootstrap", + + // **extendPage**: Accepts a boolean: true or false + // If a user scrolls to the bottom of the page and the page is not tall enough to scroll to the last table of contents item, then the page height is increased + extendPage: true, + + // **extendPageOffset**: Accepts a number: pixels + // How close to the bottom of the page a user must scroll before the page is extended + extendPageOffset: 100, + + // **history**: Accepts a boolean: true or false + // Adds a hash to the page url to maintain history + history: true, + + // **scrollHistory**: Accepts a boolean: true or false + // Adds a hash to the page url, to maintain history, when scrolling to a TOC item + scrollHistory: false, + + // **hashGenerator**: How the hash value (the anchor segment of the URL, following the + // # character) will be generated. + // + // "compact" (default) - #CompressesEverythingTogether + // "pretty" - #looks-like-a-nice-url-and-is-easily-readable + // function(text, element){} - Your own hash generation function that accepts the text as an + // argument, and returns the hash value. + hashGenerator: "compact", + + // **highlightDefault**: Accepts a boolean: true or false + // Set's the first TOC item as active if no other TOC item is active. + highlightDefault: true + + }, + + // _Create + // ------- + // Constructs the plugin. Only called once. + _create: function() { + + var self = this; + + self.extendPageScroll = true; + + // Internal array that keeps track of all TOC items (Helps to recognize if there are duplicate TOC item strings) + self.items = []; + + // Generates the HTML for the dynamic table of contents + self._generateToc(); + + // Adds CSS classes to the newly generated table of contents HTML + self._addCSSClasses(); + + self.webkit = (function() { + + for (var prop in window) { + + if (prop) { + + if (prop.toLowerCase().indexOf("webkit") !== -1) { + + return true; + + } + + } + + } + + return false; + + }()); + + // Adds jQuery event handlers to the newly generated table of contents + self._setEventHandlers(); + + // Binding to the Window load event to make sure the correct scrollTop is calculated + $(window).on("load", function() { + + // Sets the active TOC item + self._setActiveElement(true); + + // Once all animations on the page are complete, this callback function will be called + $("html, body").promise().done(function() { + + setTimeout(function() { + + self.extendPageScroll = false; + + }, 0); + + }); + + }); + + }, + + // _generateToc + // ------------ + // Generates the HTML for the dynamic table of contents + _generateToc: function() { + + // _Local variables_ + + // Stores the plugin context in the self variable + var self = this, + + // All of the HTML tags found within the context provided (i.e. body) that match the top level jQuery selector above + firstElem, + + // Instantiated variable that will store the top level newly created unordered list DOM element + ul, + ignoreSelector = self.options.ignoreSelector; + + + // Determine the element to start the toc with + // get all the top level selectors + firstElem = []; + var selectors = this.options.selectors.replace(/ /g, "").split(","); + // find the first set that have at least one non-ignored element + for(var i = 0; i < selectors.length; i++) { + var foundSelectors = $(this.options.context).find(selectors[i]); + for (var s = 0; s < foundSelectors.length; s++) { + if (!$(foundSelectors[s]).is(ignoreSelector)) { + firstElem = foundSelectors; + break; + } + } + if (firstElem.length> 0) + break; + } + + if (!firstElem.length) { + + self.element.addClass(hideTocClassName); + + return; + + } + + self.element.addClass(tocClassName); + + // Loops through each top level selector + firstElem.each(function(index) { + + //If the element matches the ignoreSelector then we skip it + if ($(this).is(ignoreSelector)) { + return; + } + + // Creates an unordered list HTML element and adds a dynamic ID and standard class name + ul = $("<ul/>", { + "id": headerClassName + index, + "class": headerClassName + }). + + // Appends a top level list item HTML element to the previously created HTML header + append(self._nestElements($(this), index)); + + // Add the created unordered list element to the HTML element calling the plugin + self.element.append(ul); + + // Finds all of the HTML tags between the header and subheader elements + $(this).nextUntil(this.nodeName.toLowerCase()).each(function() { + + // If there are no nested subheader elemements + if ($(this).find(self.options.selectors).length === 0) { + + // Loops through all of the subheader elements + $(this).filter(self.options.selectors).each(function() { + + //If the element matches the ignoreSelector then we skip it + if ($(this).is(ignoreSelector)) { + return; + } + + self._appendSubheaders.call(this, self, ul); + + }); + + } + + // If there are nested subheader elements + else { + + // Loops through all of the subheader elements + $(this).find(self.options.selectors).each(function() { + + //If the element matches the ignoreSelector then we skip it + if ($(this).is(ignoreSelector)) { + return; + } + + self._appendSubheaders.call(this, self, ul); + + }); + + } + + }); + + }); + + }, + + _setActiveElement: function(pageload) { + + var self = this, + + hash = window.location.hash.substring(1), + + elem = self.element.find('li[data-unique="' + hash + '"]'); + + if (hash.length) { + + // Removes highlighting from all of the list item's + self.element.find("." + self.focusClass).removeClass(self.focusClass); + + // Highlights the current list item that was clicked + elem.addClass(self.focusClass); + + // Triggers the click event on the currently focused TOC item + elem.click(); + + } else { + + // Removes highlighting from all of the list item's + self.element.find("." + self.focusClass).removeClass(self.focusClass); + + if (!hash.length && pageload && self.options.highlightDefault) { + + // Highlights the first TOC item if no other items are highlighted + self.element.find(itemClass).first().addClass(self.focusClass); + + } + + } + + return self; + + }, + + // _nestElements + // ------------- + // Helps create the table of contents list by appending nested list items + _nestElements: function(self, index) { + + var arr, item, hashValue; + + arr = $.grep(this.items, function(item) { + + return item === self.text(); + + }); + + // If there is already a duplicate TOC item + if (arr.length) { + + // Adds the current TOC item text and index (for slight randomization) to the internal array + this.items.push(self.text() + index); + + } + + // If there not a duplicate TOC item + else { + + // Adds the current TOC item text to the internal array + this.items.push(self.text()); + + } + + hashValue = this._generateHashValue(arr, self, index); + + // Appends a list item HTML element to the last unordered list HTML element found within the HTML element calling the plugin + item = $("<li/>", { + + // Sets a common class name to the list item + "class": itemClassName, + + "data-unique": hashValue + + }); + + if (this.options.theme !== "bootstrap3") { + + item.append($("<a/>", { + + "html": self.html() + + })); + + } else { + + item.html(self.html()); + + } + + // Adds an HTML anchor tag before the currently traversed HTML element + self.before($("<div/>", { + + // Sets a name attribute on the anchor tag to the text of the currently traversed HTML element (also making sure that all whitespace is replaced with an underscore) + "name": hashValue, + + "data-unique": hashValue + + })); + + return item; + + }, + + // _generateHashValue + // ------------------ + // Generates the hash value that will be used to refer to each item. + _generateHashValue: function(arr, self, index) { + + var hashValue = "", + hashGeneratorOption = this.options.hashGenerator; + + if (hashGeneratorOption === "pretty") { + + // prettify the text + hashValue = self.text().toLowerCase().replace(/\s/g, "-"); + + // fix double hyphens + while (hashValue.indexOf("--") > -1) { + hashValue = hashValue.replace(/--/g, "-"); + } + + // fix colon-space instances + while (hashValue.indexOf(":-") > -1) { + hashValue = hashValue.replace(/:-/g, "-"); + } + + } else if (typeof hashGeneratorOption === "function") { + + // call the function + hashValue = hashGeneratorOption(self.text(), self); + + } else { + + // compact - the default + hashValue = self.text().replace(/\s/g, ""); + + } + + // add the index if we need to + if (arr.length) { + hashValue += "" + index; + } + + // return the value + return hashValue; + + }, + + // _appendElements + // --------------- + // Helps create the table of contents list by appending subheader elements + + _appendSubheaders: function(self, ul) { + + // The current element index + var index = $(this).index(self.options.selectors), + + // Finds the previous header DOM element + previousHeader = $(self.options.selectors).eq(index - 1), + + currentTagName = +$(this).prop("tagName").charAt(1), + + previousTagName = +previousHeader.prop("tagName").charAt(1), + + lastSubheader; + + // If the current header DOM element is smaller than the previous header DOM element or the first subheader + if (currentTagName < previousTagName) { + + // Selects the last unordered list HTML found within the HTML element calling the plugin + self.element.find(subheaderClass + "[data-tag=" + currentTagName + "]").last().append(self._nestElements($(this), index)); + + } + + // If the current header DOM element is the same type of header(eg. h4) as the previous header DOM element + else if (currentTagName === previousTagName) { + + ul.find(itemClass).last().after(self._nestElements($(this), index)); + + } else { + + // Selects the last unordered list HTML found within the HTML element calling the plugin + ul.find(itemClass).last(). + + // Appends an unorderedList HTML element to the dynamic `unorderedList` variable and sets a common class name + after($("<ul/>", { + + "class": subheaderClassName, + + "data-tag": currentTagName + + })).next(subheaderClass). + + // Appends a list item HTML element to the last unordered list HTML element found within the HTML element calling the plugin + append(self._nestElements($(this), index)); + } + + }, + + // _setEventHandlers + // ---------------- + // Adds jQuery event handlers to the newly generated table of contents + _setEventHandlers: function() { + + // _Local variables_ + + // Stores the plugin context in the self variable + var self = this, + + // Instantiates a new variable that will be used to hold a specific element's context + $self, + + // Instantiates a new variable that will be used to determine the smoothScroll animation time duration + duration; + + // Event delegation that looks for any clicks on list item elements inside of the HTML element calling the plugin + this.element.on("click.tocify", "li", function(event) { + + if (self.options.history) { + + window.location.hash = $(this).attr("data-unique"); + + } + + // Removes highlighting from all of the list item's + self.element.find("." + self.focusClass).removeClass(self.focusClass); + + // Highlights the current list item that was clicked + $(this).addClass(self.focusClass); + + // If the showAndHide option is true + if (self.options.showAndHide) { + + var elem = $('li[data-unique="' + $(this).attr("data-unique") + '"]'); + + self._triggerShow(elem); + + } + + self._scrollTo($(this)); + + }); + + // Mouseenter and Mouseleave event handlers for the list item's within the HTML element calling the plugin + this.element.find("li").on({ + + // Mouseenter event handler + "mouseenter.tocify": function() { + + // Adds a hover CSS class to the current list item + $(this).addClass(self.hoverClass); + + // Makes sure the cursor is set to the pointer icon + $(this).css("cursor", "pointer"); + + }, + + // Mouseleave event handler + "mouseleave.tocify": function() { + + if (self.options.theme !== "bootstrap") { + + // Removes the hover CSS class from the current list item + $(this).removeClass(self.hoverClass); + + } + + } + }); + + // only attach handler if needed (expensive in IE) + if (self.options.extendPage || self.options.highlightOnScroll || self.options.scrollHistory || self.options.showAndHideOnScroll) { + // Window scroll event handler + $(window).on("scroll.tocify", function() { + + // Once all animations on the page are complete, this callback function will be called + $("html, body").promise().done(function() { + + // Local variables + + // Stores how far the user has scrolled + var winScrollTop = $(window).scrollTop(), + + // Stores the height of the window + winHeight = $(window).height(), + + // Stores the height of the document + docHeight = $(document).height(), + + scrollHeight = $("body")[0].scrollHeight, + + // Instantiates a variable that will be used to hold a selected HTML element + elem, + + lastElem, + + lastElemOffset, + + currentElem; + + if (self.options.extendPage) { + + // If the user has scrolled to the bottom of the page and the last toc item is not focused + if ((self.webkit && winScrollTop >= scrollHeight - winHeight - self.options.extendPageOffset) || (!self.webkit && winHeight + winScrollTop > docHeight - self.options.extendPageOffset)) { + + if (!$(extendPageClass).length) { + + lastElem = $('div[data-unique="' + $(itemClass).last().attr("data-unique") + '"]'); + + if (!lastElem.length) return; + + // Gets the top offset of the page header that is linked to the last toc item + lastElemOffset = lastElem.offset().top; + + // Appends a div to the bottom of the page and sets the height to the difference of the window scrollTop and the last element's position top offset + $(self.options.context).append($("<div/>", { + + "class": extendPageClassName, + + "height": Math.abs(lastElemOffset - winScrollTop) + "px", + + "data-unique": extendPageClassName + + })); + + if (self.extendPageScroll) { + + currentElem = self.element.find('li.' + self.focusClass); + + self._scrollTo($('div[data-unique="' + currentElem.attr("data-unique") + '"]')); + + } + + } + + } + + } + + // The zero timeout ensures the following code is run after the scroll events + setTimeout(function() { + + // _Local variables_ + + // Stores the distance to the closest anchor + var closestAnchorDistance = null, + + // Stores the index of the closest anchor + closestAnchorIdx = null, + + // Keeps a reference to all anchors + anchors = $(self.options.context).find("div[data-unique]"), + + anchorText; + + // Determines the index of the closest anchor + anchors.each(function(idx) { + var distance = Math.abs(($(this).next().length ? $(this).next() : $(this)).offset().top - winScrollTop - self.options.highlightOffset); + if (closestAnchorDistance == null || distance < closestAnchorDistance) { + closestAnchorDistance = distance; + closestAnchorIdx = idx; + } else { + return false; + } + }); + + anchorText = $(anchors[closestAnchorIdx]).attr("data-unique"); + + // Stores the list item HTML element that corresponds to the currently traversed anchor tag + elem = $('li[data-unique="' + anchorText + '"]'); + + // If the `highlightOnScroll` option is true and a next element is found + if (self.options.highlightOnScroll && elem.length) { + + // Removes highlighting from all of the list item's + self.element.find("." + self.focusClass).removeClass(self.focusClass); + + // Highlights the corresponding list item + elem.addClass(self.focusClass); + + } + + if (self.options.scrollHistory) { + + if (window.location.hash !== "#" + anchorText) { + + window.location.replace("#" + anchorText); + + } + } + + // If the `showAndHideOnScroll` option is true + if (self.options.showAndHideOnScroll && self.options.showAndHide) { + + self._triggerShow(elem, true); + + } + + }, 0); + + }); + + }); + } + + }, + + // Show + // ---- + // Opens the current sub-header + show: function(elem, scroll) { + + // Stores the plugin context in the `self` variable + var self = this, + element = elem; + + // If the sub-header is not already visible + if (!elem.is(":visible")) { + + // If the current element does not have any nested subheaders, is not a header, and its parent is not visible + if (!elem.find(subheaderClass).length && !elem.parent().is(headerClass) && !elem.parent().is(":visible")) { + + // Sets the current element to all of the subheaders within the current header + elem = elem.parents(subheaderClass).add(elem); + + } + + // If the current element does not have any nested subheaders and is not a header + else if (!elem.children(subheaderClass).length && !elem.parent().is(headerClass)) { + + // Sets the current element to the closest subheader + elem = elem.closest(subheaderClass); + + } + + //Determines what jQuery effect to use + switch (self.options.showEffect) { + + //Uses `no effect` + case "none": + + elem.show(); + + break; + + //Uses the jQuery `show` special effect + case "show": + + elem.show(self.options.showEffectSpeed); + + break; + + //Uses the jQuery `slideDown` special effect + case "slideDown": + + elem.slideDown(self.options.showEffectSpeed); + + break; + + //Uses the jQuery `fadeIn` special effect + case "fadeIn": + + elem.fadeIn(self.options.showEffectSpeed); + + break; + + //If none of the above options were passed, then a `jQueryUI show effect` is expected + default: + + elem.show(); + + break; + + } + + } + + // If the current subheader parent element is a header + if (elem.parent().is(headerClass)) { + + // Hides all non-active sub-headers + self.hide($(subheaderClass).not(elem)); + + } + + // If the current subheader parent element is not a header + else { + + // Hides all non-active sub-headers + self.hide($(subheaderClass).not(elem.closest(headerClass).find(subheaderClass).not(elem.siblings()))); + + } + + // Maintains chainablity + return self; + + }, + + // Hide + // ---- + // Closes the current sub-header + hide: function(elem) { + + // Stores the plugin context in the `self` variable + var self = this; + + //Determines what jQuery effect to use + switch (self.options.hideEffect) { + + // Uses `no effect` + case "none": + + elem.hide(); + + break; + + // Uses the jQuery `hide` special effect + case "hide": + + elem.hide(self.options.hideEffectSpeed); + + break; + + // Uses the jQuery `slideUp` special effect + case "slideUp": + + elem.slideUp(self.options.hideEffectSpeed); + + break; + + // Uses the jQuery `fadeOut` special effect + case "fadeOut": + + elem.fadeOut(self.options.hideEffectSpeed); + + break; + + // If none of the above options were passed, then a `jqueryUI hide effect` is expected + default: + + elem.hide(); + + break; + + } + + // Maintains chainablity + return self; + }, + + // _triggerShow + // ------------ + // Determines what elements get shown on scroll and click + _triggerShow: function(elem, scroll) { + + var self = this; + + // If the current element's parent is a header element or the next element is a nested subheader element + if (elem.parent().is(headerClass) || elem.next().is(subheaderClass)) { + + // Shows the next sub-header element + self.show(elem.next(subheaderClass), scroll); + + } + + // If the current element's parent is a subheader element + else if (elem.parent().is(subheaderClass)) { + + // Shows the parent sub-header element + self.show(elem.parent(), scroll); + + } + + // Maintains chainability + return self; + + }, + + // _addCSSClasses + // -------------- + // Adds CSS classes to the newly generated table of contents HTML + _addCSSClasses: function() { + + // If the user wants a jqueryUI theme + if (this.options.theme === "jqueryui") { + + this.focusClass = "ui-state-default"; + + this.hoverClass = "ui-state-hover"; + + //Adds the default styling to the dropdown list + this.element.addClass("ui-widget").find(".toc-title").addClass("ui-widget-header").end().find("li").addClass("ui-widget-content"); + + } + + // If the user wants a twitterBootstrap theme + else if (this.options.theme === "bootstrap") { + + this.element.find(headerClass + "," + subheaderClass).addClass("nav nav-list"); + + this.focusClass = "active"; + + } + + // If the user wants a twitterBootstrap theme + else if (this.options.theme === "bootstrap3") { + + this.element.find(headerClass + "," + subheaderClass).addClass("list-group"); + + this.element.find(itemClass).addClass("list-group-item"); + + this.focusClass = "active"; + + } + + // If a user does not want a prebuilt theme + else { + + // Adds more neutral classes (instead of jqueryui) + + this.focusClass = tocFocusClassName; + + this.hoverClass = tocHoverClassName; + + } + + //Maintains chainability + return this; + + }, + + // setOption + // --------- + // Sets a single Tocify option after the plugin is invoked + setOption: function() { + + // Calls the jQueryUI Widget Factory setOption method + $.Widget.prototype._setOption.apply(this, arguments); + + }, + + // setOptions + // ---------- + // Sets a single or multiple Tocify options after the plugin is invoked + setOptions: function() { + + // Calls the jQueryUI Widget Factory setOptions method + $.Widget.prototype._setOptions.apply(this, arguments); + + }, + + // _scrollTo + // --------- + // Scrolls to a specific element + _scrollTo: function(elem) { + + var self = this, + duration = self.options.smoothScroll || 0, + scrollTo = self.options.scrollTo, + currentDiv = $('div[data-unique="' + elem.attr("data-unique") + '"]'); + + if (!currentDiv.length) { + + return self; + + } + + // Once all animations on the page are complete, this callback function will be called + $("html, body").promise().done(function() { + + // Animates the html and body element scrolltops + $("html, body").animate({ + + // Sets the jQuery `scrollTop` to the top offset of the HTML div tag that matches the current list item's `data-unique` tag + "scrollTop": currentDiv.offset().top - ($.isFunction(scrollTo) ? scrollTo.call() : scrollTo) + "px" + + }, { + + // Sets the smoothScroll animation time duration to the smoothScrollSpeed option + "duration": duration + + }); 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btn-default btn-xs btn-secondary btn-sm dropdown-toggle" data-toggle="dropdown" data-bs-toggle="dropdown" aria-haspopup="true" aria-expanded="false"><span>Code</span> <span class="caret"></span></button> +<ul class="dropdown-menu dropdown-menu-right" style="min-width: 50px;"> +<li><a id="rmd-show-all-code" href="#">Show All Code</a></li> +<li><a id="rmd-hide-all-code" href="#">Hide All Code</a></li> +</ul> +</div> + + + +<h1 class="title toc-ignore">Testing covariate modelling in hierarchical +parent degradation kinetics with residue data on mesotrione</h1> +<h4 class="author">Johannes Ranke</h4> +<h4 class="date">Last change 12 September 2025 (rebuilt 2025-09-12)</h4> + +</div> + + +<div id="introduction" class="section level1"> +<h1>Introduction</h1> +<p>The purpose of this document is to test demonstrate how nonlinear +hierarchical models (NLHM) based on the parent degradation models SFO, +FOMC, DFOP and HS can be fitted with the mkin package, also considering +the influence of covariates like soil pH on different degradation +parameters. Because in some other case studies, the SFORB +parameterisation of biexponential decline has shown some advantages over +the DFOP parameterisation, SFORB was included in the list of tested +models as well.</p> +<p>The mkin package is used in version 1.2.11, which is contains the +functions that were used for the evaluations. The <code>saemix</code> +package is used as a backend for fitting the NLHM, but is also loaded to +make the convergence plot function available.</p> +<p>This document is processed with the <code>knitr</code> package, which +also provides the <code>kable</code> function that is used to improve +the display of tabular data in R markdown documents. For parallel +processing, the <code>parallel</code> package is used.</p> +<pre class="r"><code>library(mkin) +library(knitr) +library(saemix) +library(parallel) +n_cores <- detectCores() +if (Sys.info()["sysname"] == "Windows") { + cl <- makePSOCKcluster(n_cores) +} else { + cl <- makeForkCluster(n_cores) +}</code></pre> +<div id="test-data" class="section level2"> +<h2>Test data</h2> +<pre class="r"><code>data_file <- system.file( + "testdata", "mesotrione_soil_efsa_2016.xlsx", package = "mkin") +meso_ds <- read_spreadsheet(data_file, parent_only = TRUE)</code></pre> +<p>The following tables show the covariate data and the 18 datasets that +were read in from the spreadsheet file.</p> +<pre class="r"><code>pH <- attr(meso_ds, "covariates") +kable(pH, caption = "Covariate data")</code></pre> +<table> +<caption>Covariate data</caption> +<thead> +<tr class="header"> +<th align="left"></th> +<th align="right">pH</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="left">Richmond</td> +<td align="right">6.2</td> +</tr> +<tr class="even"> +<td align="left">Richmond 2</td> +<td align="right">6.2</td> +</tr> +<tr class="odd"> +<td align="left">ERTC</td> +<td align="right">6.4</td> +</tr> +<tr class="even"> +<td align="left">Toulouse</td> +<td align="right">7.7</td> +</tr> +<tr class="odd"> +<td align="left">Picket Piece</td> +<td align="right">7.1</td> +</tr> +<tr class="even"> +<td align="left">721</td> +<td align="right">5.6</td> +</tr> +<tr class="odd"> +<td align="left">722</td> +<td align="right">5.7</td> +</tr> +<tr class="even"> +<td align="left">723</td> +<td align="right">5.4</td> +</tr> +<tr class="odd"> +<td align="left">724</td> +<td align="right">4.8</td> +</tr> +<tr class="even"> +<td align="left">725</td> +<td align="right">5.8</td> +</tr> +<tr class="odd"> +<td align="left">727</td> +<td align="right">5.1</td> +</tr> +<tr class="even"> +<td align="left">728</td> +<td align="right">5.9</td> +</tr> +<tr class="odd"> +<td align="left">729</td> +<td align="right">5.6</td> +</tr> +<tr class="even"> +<td align="left">730</td> +<td align="right">5.3</td> +</tr> +<tr class="odd"> +<td align="left">731</td> +<td align="right">6.1</td> +</tr> +<tr class="even"> +<td align="left">732</td> +<td align="right">5.0</td> +</tr> +<tr class="odd"> +<td align="left">741</td> +<td align="right">5.7</td> +</tr> +<tr class="even"> +<td align="left">742</td> +<td align="right">7.2</td> +</tr> +</tbody> +</table> +<pre class="r"><code>for (ds_name in names(meso_ds)) { + print( + kable(mkin_long_to_wide(meso_ds[[ds_name]]), + caption = paste("Dataset", ds_name), + booktabs = TRUE, row.names = FALSE)) +}</code></pre> +<table> +<caption>Dataset Richmond</caption> +<thead> +<tr class="header"> +<th align="right">time</th> +<th align="right">meso</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="right">0.000000</td> +<td align="right">91.00</td> +</tr> +<tr class="even"> +<td align="right">1.179050</td> +<td align="right">86.70</td> +</tr> +<tr class="odd"> +<td align="right">3.537149</td> +<td align="right">73.60</td> +</tr> +<tr class="even"> +<td align="right">7.074299</td> +<td align="right">61.50</td> +</tr> +<tr class="odd"> +<td align="right">10.611448</td> +<td align="right">55.70</td> +</tr> +<tr class="even"> +<td align="right">15.327647</td> +<td align="right">47.70</td> +</tr> +<tr class="odd"> +<td align="right">17.685747</td> +<td align="right">39.50</td> +</tr> +<tr class="even"> +<td align="right">24.760046</td> +<td align="right">29.80</td> +</tr> +<tr class="odd"> +<td align="right">35.371494</td> +<td align="right">19.60</td> +</tr> +<tr class="even"> +<td align="right">68.384889</td> +<td align="right">5.67</td> +</tr> +<tr class="odd"> +<td align="right">0.000000</td> +<td align="right">97.90</td> +</tr> +<tr class="even"> +<td align="right">1.179050</td> +<td align="right">96.40</td> +</tr> +<tr class="odd"> +<td align="right">3.537149</td> +<td align="right">89.10</td> +</tr> +<tr class="even"> +<td align="right">7.074299</td> +<td align="right">74.40</td> +</tr> +<tr class="odd"> +<td align="right">10.611448</td> +<td align="right">57.40</td> +</tr> +<tr class="even"> +<td align="right">15.327647</td> +<td align="right">46.30</td> +</tr> +<tr class="odd"> +<td align="right">18.864797</td> +<td align="right">35.50</td> +</tr> +<tr class="even"> +<td align="right">27.118146</td> +<td align="right">27.20</td> +</tr> +<tr class="odd"> +<td align="right">35.371494</td> +<td align="right">19.10</td> +</tr> +<tr class="even"> +<td align="right">74.280138</td> +<td align="right">6.50</td> +</tr> +<tr class="odd"> +<td align="right">108.472582</td> +<td align="right">3.40</td> +</tr> +<tr class="even"> +<td align="right">142.665027</td> +<td align="right">2.20</td> +</tr> +</tbody> +</table> +<table> +<caption>Dataset Richmond 2</caption> +<thead> +<tr class="header"> +<th align="right">time</th> +<th align="right">meso</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="right">0.000000</td> +<td align="right">96.0</td> +</tr> +<tr class="even"> +<td align="right">2.422004</td> +<td align="right">82.4</td> +</tr> +<tr class="odd"> +<td align="right">5.651343</td> +<td align="right">71.2</td> +</tr> +<tr class="even"> +<td align="right">8.073348</td> +<td align="right">53.1</td> +</tr> +<tr class="odd"> +<td align="right">11.302687</td> +<td align="right">48.5</td> +</tr> +<tr class="even"> +<td align="right">16.954030</td> +<td align="right">33.4</td> +</tr> +<tr class="odd"> +<td align="right">22.605373</td> +<td align="right">24.2</td> +</tr> +<tr class="even"> +<td align="right">45.210746</td> +<td align="right">11.9</td> +</tr> +</tbody> +</table> +<table> +<caption>Dataset ERTC</caption> +<thead> +<tr class="header"> +<th align="right">time</th> +<th align="right">meso</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="right">0.000000</td> +<td align="right">99.9</td> +</tr> +<tr class="even"> +<td align="right">2.755193</td> +<td align="right">80.0</td> +</tr> +<tr class="odd"> +<td align="right">6.428782</td> +<td align="right">42.1</td> +</tr> +<tr class="even"> +<td align="right">9.183975</td> +<td align="right">50.1</td> +</tr> +<tr class="odd"> +<td align="right">12.857565</td> +<td align="right">28.4</td> +</tr> +<tr class="even"> +<td align="right">19.286347</td> +<td align="right">39.8</td> +</tr> +<tr class="odd"> +<td align="right">25.715130</td> +<td align="right">29.9</td> +</tr> +<tr class="even"> +<td align="right">51.430259</td> +<td align="right">2.5</td> +</tr> +</tbody> +</table> +<table> +<caption>Dataset Toulouse</caption> +<thead> +<tr class="header"> +<th align="right">time</th> +<th align="right">meso</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="right">0.000000</td> +<td align="right">96.8</td> +</tr> +<tr class="even"> +<td align="right">2.897983</td> +<td align="right">63.3</td> +</tr> +<tr class="odd"> +<td align="right">6.761960</td> +<td align="right">22.3</td> +</tr> +<tr class="even"> +<td align="right">9.659942</td> +<td align="right">16.6</td> +</tr> +<tr class="odd"> +<td align="right">13.523919</td> +<td align="right">16.1</td> +</tr> +<tr class="even"> +<td align="right">20.285879</td> +<td align="right">17.2</td> +</tr> +<tr class="odd"> +<td align="right">27.047838</td> +<td align="right">1.8</td> +</tr> +</tbody> +</table> +<table> +<caption>Dataset Picket Piece</caption> +<thead> +<tr class="header"> +<th align="right">time</th> +<th align="right">meso</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="right">0.000000</td> +<td align="right">102.0</td> +</tr> +<tr class="even"> +<td align="right">2.841195</td> +<td align="right">73.7</td> +</tr> +<tr class="odd"> +<td align="right">6.629454</td> +<td align="right">35.5</td> +</tr> +<tr class="even"> +<td align="right">9.470649</td> +<td align="right">31.8</td> +</tr> +<tr class="odd"> +<td align="right">13.258909</td> +<td align="right">18.0</td> +</tr> +<tr class="even"> +<td align="right">19.888364</td> +<td align="right">3.7</td> +</tr> +</tbody> +</table> +<table> +<caption>Dataset 721</caption> +<thead> +<tr class="header"> +<th align="right">time</th> +<th align="right">meso</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="right">0.00000</td> +<td align="right">86.4</td> +</tr> +<tr class="even"> +<td align="right">11.24366</td> +<td align="right">61.4</td> +</tr> +<tr class="odd"> +<td align="right">22.48733</td> +<td align="right">49.8</td> +</tr> +<tr class="even"> +<td align="right">33.73099</td> +<td align="right">41.0</td> +</tr> +<tr class="odd"> +<td align="right">44.97466</td> +<td align="right">35.1</td> +</tr> +</tbody> +</table> +<table> +<caption>Dataset 722</caption> +<thead> +<tr class="header"> +<th align="right">time</th> +<th align="right">meso</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="right">0.00000</td> +<td align="right">90.3</td> +</tr> +<tr class="even"> +<td align="right">11.24366</td> +<td align="right">52.1</td> +</tr> +<tr class="odd"> +<td align="right">22.48733</td> +<td align="right">37.4</td> +</tr> +<tr class="even"> +<td align="right">33.73099</td> +<td align="right">21.2</td> +</tr> +<tr class="odd"> +<td align="right">44.97466</td> +<td align="right">14.3</td> +</tr> +</tbody> +</table> +<table> +<caption>Dataset 723</caption> +<thead> +<tr class="header"> +<th align="right">time</th> +<th align="right">meso</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="right">0.00000</td> +<td align="right">89.3</td> +</tr> +<tr class="even"> +<td align="right">11.24366</td> +<td align="right">70.8</td> +</tr> +<tr class="odd"> +<td align="right">22.48733</td> +<td align="right">51.1</td> +</tr> +<tr class="even"> +<td align="right">33.73099</td> +<td align="right">42.7</td> +</tr> +<tr class="odd"> +<td align="right">44.97466</td> +<td align="right">26.7</td> +</tr> +</tbody> +</table> +<table> +<caption>Dataset 724</caption> +<thead> +<tr class="header"> +<th align="right">time</th> +<th align="right">meso</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="right">0.000000</td> +<td align="right">89.4</td> +</tr> +<tr class="even"> +<td align="right">9.008208</td> +<td align="right">65.2</td> +</tr> +<tr class="odd"> +<td align="right">18.016415</td> +<td align="right">55.8</td> +</tr> +<tr class="even"> +<td align="right">27.024623</td> +<td align="right">46.0</td> +</tr> +<tr class="odd"> +<td align="right">36.032831</td> +<td align="right">41.7</td> +</tr> +</tbody> +</table> +<table> +<caption>Dataset 725</caption> +<thead> +<tr class="header"> +<th align="right">time</th> +<th align="right">meso</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="right">0.00000</td> +<td align="right">89.0</td> +</tr> +<tr class="even"> +<td align="right">10.99058</td> +<td align="right">35.4</td> +</tr> +<tr class="odd"> +<td align="right">21.98116</td> +<td align="right">18.6</td> +</tr> +<tr class="even"> +<td align="right">32.97174</td> +<td align="right">11.6</td> +</tr> +<tr class="odd"> +<td align="right">43.96232</td> +<td align="right">7.6</td> +</tr> +</tbody> +</table> +<table> +<caption>Dataset 727</caption> +<thead> +<tr class="header"> +<th align="right">time</th> +<th align="right">meso</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="right">0.00000</td> +<td align="right">91.3</td> +</tr> +<tr class="even"> +<td align="right">10.96104</td> +<td align="right">63.2</td> +</tr> +<tr class="odd"> +<td align="right">21.92209</td> +<td align="right">51.1</td> +</tr> +<tr class="even"> +<td align="right">32.88313</td> +<td align="right">42.0</td> +</tr> +<tr class="odd"> +<td align="right">43.84417</td> +<td align="right">40.8</td> +</tr> +</tbody> +</table> +<table> +<caption>Dataset 728</caption> +<thead> +<tr class="header"> +<th align="right">time</th> +<th align="right">meso</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="right">0.00000</td> +<td align="right">91.8</td> +</tr> +<tr class="even"> +<td align="right">11.24366</td> +<td align="right">43.6</td> +</tr> +<tr class="odd"> +<td align="right">22.48733</td> +<td align="right">22.0</td> +</tr> +<tr class="even"> +<td align="right">33.73099</td> +<td align="right">15.9</td> +</tr> +<tr class="odd"> +<td align="right">44.97466</td> +<td align="right">8.8</td> +</tr> +</tbody> +</table> +<table> +<caption>Dataset 729</caption> +<thead> +<tr class="header"> +<th align="right">time</th> +<th align="right">meso</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="right">0.00000</td> +<td align="right">91.6</td> +</tr> +<tr class="even"> +<td align="right">11.24366</td> +<td align="right">60.5</td> +</tr> +<tr class="odd"> +<td align="right">22.48733</td> +<td align="right">43.5</td> +</tr> +<tr class="even"> +<td align="right">33.73099</td> +<td align="right">28.4</td> +</tr> +<tr class="odd"> +<td align="right">44.97466</td> +<td align="right">20.5</td> +</tr> +</tbody> +</table> +<table> +<caption>Dataset 730</caption> +<thead> +<tr class="header"> +<th align="right">time</th> +<th align="right">meso</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="right">0.00000</td> +<td align="right">92.7</td> +</tr> +<tr class="even"> +<td align="right">11.07446</td> +<td align="right">58.9</td> +</tr> +<tr class="odd"> +<td align="right">22.14893</td> +<td align="right">44.0</td> +</tr> +<tr class="even"> +<td align="right">33.22339</td> +<td align="right">46.0</td> +</tr> +<tr class="odd"> +<td align="right">44.29785</td> +<td align="right">29.3</td> +</tr> +</tbody> +</table> +<table> +<caption>Dataset 731</caption> +<thead> +<tr class="header"> +<th align="right">time</th> +<th align="right">meso</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="right">0.00000</td> +<td align="right">92.1</td> +</tr> +<tr class="even"> +<td align="right">11.24366</td> +<td align="right">64.4</td> +</tr> +<tr class="odd"> +<td align="right">22.48733</td> +<td align="right">45.3</td> +</tr> +<tr class="even"> +<td align="right">33.73099</td> +<td align="right">33.6</td> +</tr> +<tr class="odd"> +<td align="right">44.97466</td> +<td align="right">23.5</td> +</tr> +</tbody> +</table> +<table> +<caption>Dataset 732</caption> +<thead> +<tr class="header"> +<th align="right">time</th> +<th align="right">meso</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="right">0.00000</td> +<td align="right">90.3</td> +</tr> +<tr class="even"> +<td align="right">11.24366</td> +<td align="right">58.2</td> +</tr> +<tr class="odd"> +<td align="right">22.48733</td> +<td align="right">40.1</td> +</tr> +<tr class="even"> +<td align="right">33.73099</td> +<td align="right">33.1</td> +</tr> +<tr class="odd"> +<td align="right">44.97466</td> +<td align="right">25.8</td> +</tr> +</tbody> +</table> +<table> +<caption>Dataset 741</caption> +<thead> +<tr class="header"> +<th align="right">time</th> +<th align="right">meso</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="right">0.00000</td> +<td align="right">90.3</td> +</tr> +<tr class="even"> +<td align="right">10.84712</td> +<td align="right">68.7</td> +</tr> +<tr class="odd"> +<td align="right">21.69424</td> +<td align="right">58.0</td> +</tr> +<tr class="even"> +<td align="right">32.54136</td> +<td align="right">52.2</td> +</tr> +<tr class="odd"> +<td align="right">43.38848</td> +<td align="right">48.0</td> +</tr> +</tbody> +</table> +<table> +<caption>Dataset 742</caption> +<thead> +<tr class="header"> +<th align="right">time</th> +<th align="right">meso</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="right">0.00000</td> +<td align="right">92.0</td> +</tr> +<tr class="even"> +<td align="right">11.24366</td> +<td align="right">60.9</td> +</tr> +<tr class="odd"> +<td align="right">22.48733</td> +<td align="right">36.2</td> +</tr> +<tr class="even"> +<td align="right">33.73099</td> +<td align="right">18.3</td> +</tr> +<tr class="odd"> +<td align="right">44.97466</td> +<td align="right">8.7</td> +</tr> +</tbody> +</table> +</div> +</div> +<div id="separate-evaluations" class="section level1"> +<h1>Separate evaluations</h1> +<p>In order to obtain suitable starting parameters for the NLHM fits, +separate fits of the five models to the data for each soil are generated +using the <code>mmkin</code> function from the mkin package. In a first +step, constant variance is assumed. Convergence is checked with the +<code>status</code> function.</p> +<pre class="r"><code>deg_mods <- c("SFO", "FOMC", "DFOP", "SFORB", "HS") +f_sep_const <- mmkin( + deg_mods, + meso_ds, + error_model = "const", + cluster = cl, + quiet = TRUE)</code></pre> +<pre class="r"><code>status(f_sep_const[, 1:5]) |> kable()</code></pre> +<table> +<thead> +<tr class="header"> +<th align="left"></th> +<th align="left">Richmond</th> +<th align="left">Richmond 2</th> +<th align="left">ERTC</th> +<th align="left">Toulouse</th> +<th align="left">Picket Piece</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="left">SFO</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">FOMC</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">C</td> +</tr> +<tr class="odd"> +<td align="left">DFOP</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">SFORB</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">HS</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +</tbody> +</table> +<pre class="r"><code>status(f_sep_const[, 6:18]) |> kable()</code></pre> +<table> +<colgroup> +<col width="10%" /> +<col width="6%" /> +<col width="6%" /> +<col width="6%" /> +<col width="6%" /> +<col width="6%" /> +<col width="6%" /> +<col width="6%" /> +<col width="6%" /> +<col width="6%" /> +<col width="6%" /> +<col width="6%" /> +<col width="6%" /> +<col width="6%" /> +</colgroup> +<thead> +<tr class="header"> +<th align="left"></th> +<th align="left">721</th> +<th align="left">722</th> +<th align="left">723</th> +<th align="left">724</th> +<th align="left">725</th> +<th align="left">727</th> +<th align="left">728</th> +<th align="left">729</th> +<th align="left">730</th> +<th align="left">731</th> +<th align="left">732</th> +<th align="left">741</th> +<th align="left">742</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="left">SFO</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">FOMC</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">DFOP</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">SFORB</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">HS</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +</tbody> +</table> +<p>In the tables above, OK indicates convergence and C indicates failure +to converge. Most separate fits with constant variance converged, with +the exception of two FOMC fits, one SFORB fit and one HS fit.</p> +<pre class="r"><code>f_sep_tc <- update(f_sep_const, error_model = "tc")</code></pre> +<pre class="r"><code>status(f_sep_tc[, 1:5]) |> kable()</code></pre> +<table> +<thead> +<tr class="header"> +<th align="left"></th> +<th align="left">Richmond</th> +<th align="left">Richmond 2</th> +<th align="left">ERTC</th> +<th align="left">Toulouse</th> +<th align="left">Picket Piece</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="left">SFO</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">FOMC</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">DFOP</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">SFORB</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">HS</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +</tbody> +</table> +<pre class="r"><code>status(f_sep_tc[, 6:18]) |> kable()</code></pre> +<table> +<colgroup> +<col width="10%" /> +<col width="6%" /> +<col width="6%" /> +<col width="6%" /> +<col width="6%" /> +<col width="6%" /> +<col width="6%" /> +<col width="6%" /> +<col width="6%" /> +<col width="6%" /> +<col width="6%" /> +<col width="6%" /> +<col width="6%" /> +<col width="6%" /> +</colgroup> +<thead> +<tr class="header"> +<th align="left"></th> +<th align="left">721</th> +<th align="left">722</th> +<th align="left">723</th> +<th align="left">724</th> +<th align="left">725</th> +<th align="left">727</th> +<th align="left">728</th> +<th align="left">729</th> +<th align="left">730</th> +<th align="left">731</th> +<th align="left">732</th> +<th align="left">741</th> +<th align="left">742</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="left">SFO</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">FOMC</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">DFOP</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">SFORB</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">HS</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">C</td> +<td align="left">OK</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +</tbody> +</table> +<p>With the two-component error model, the set of fits that did not +converge is larger, with convergence problems appearing for a number of +non-SFO fits.</p> +</div> +<div id="hierarchical-models-without-covariate" class="section level1"> +<h1>Hierarchical models without covariate</h1> +<p>The following code fits hierarchical kinetic models for the ten +combinations of the five different degradation models with the two +different error models in parallel.</p> +<pre class="r"><code>f_saem_1 <- mhmkin(list(f_sep_const, f_sep_tc), cluster = cl) +status(f_saem_1) |> kable()</code></pre> +<table> +<thead> +<tr class="header"> +<th align="left"></th> +<th align="left">const</th> +<th align="left">tc</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="left">SFO</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">FOMC</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">DFOP</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">SFORB</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">HS</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +</tbody> +</table> +<p>All fits terminate without errors (status OK).</p> +<pre class="r"><code>anova(f_saem_1) |> kable(digits = 1)</code></pre> +<table> +<thead> +<tr class="header"> +<th align="left"></th> +<th align="right">npar</th> +<th align="right">AIC</th> +<th align="right">BIC</th> +<th align="right">Lik</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="left">SFO const</td> +<td align="right">5</td> +<td align="right">800.0</td> +<td align="right">804.5</td> +<td align="right">-395.0</td> +</tr> +<tr class="even"> +<td align="left">SFO tc</td> +<td align="right">6</td> +<td align="right">801.9</td> +<td align="right">807.2</td> +<td align="right">-394.9</td> +</tr> +<tr class="odd"> +<td align="left">FOMC const</td> +<td align="right">7</td> +<td align="right">787.4</td> +<td align="right">793.6</td> +<td align="right">-386.7</td> +</tr> +<tr class="even"> +<td align="left">FOMC tc</td> +<td align="right">8</td> +<td align="right">788.9</td> +<td align="right">796.1</td> +<td align="right">-386.5</td> +</tr> +<tr class="odd"> +<td align="left">DFOP const</td> +<td align="right">9</td> +<td align="right">787.6</td> +<td align="right">795.6</td> +<td align="right">-384.8</td> +</tr> +<tr class="even"> +<td align="left">SFORB const</td> +<td align="right">9</td> +<td align="right">787.4</td> +<td align="right">795.4</td> +<td align="right">-384.7</td> +</tr> +<tr class="odd"> +<td align="left">HS const</td> +<td align="right">9</td> +<td align="right">781.9</td> +<td align="right">789.9</td> +<td align="right">-382.0</td> +</tr> +<tr class="even"> +<td align="left">DFOP tc</td> +<td align="right">10</td> +<td align="right">787.4</td> +<td align="right">796.3</td> +<td align="right">-383.7</td> +</tr> +<tr class="odd"> +<td align="left">SFORB tc</td> +<td align="right">10</td> +<td align="right">795.8</td> +<td align="right">804.7</td> +<td align="right">-387.9</td> +</tr> +<tr class="even"> +<td align="left">HS tc</td> +<td align="right">10</td> +<td align="right">783.7</td> +<td align="right">792.7</td> +<td align="right">-381.9</td> +</tr> +</tbody> +</table> +<p>The model comparisons show that the fits with constant variance are +consistently preferable to the corresponding fits with two-component +error for these data. This is confirmed by the fact that the parameter +<code>b.1</code> (the relative standard deviation in the fits obtained +with the saemix package), is ill-defined in all fits.</p> +<pre class="r"><code>illparms(f_saem_1) |> kable()</code></pre> +<table> +<colgroup> +<col width="6%" /> +<col width="44%" /> +<col width="49%" /> +</colgroup> +<thead> +<tr class="header"> +<th align="left"></th> +<th align="left">const</th> +<th align="left">tc</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="left">SFO</td> +<td align="left">sd(meso_0)</td> +<td align="left">sd(meso_0), b.1</td> +</tr> +<tr class="even"> +<td align="left">FOMC</td> +<td align="left">sd(meso_0), sd(log_beta)</td> +<td align="left">sd(meso_0), sd(log_beta), b.1</td> +</tr> +<tr class="odd"> +<td align="left">DFOP</td> +<td align="left">sd(meso_0), sd(log_k1)</td> +<td align="left">sd(meso_0), sd(g_qlogis), b.1</td> +</tr> +<tr class="even"> +<td align="left">SFORB</td> +<td align="left">sd(meso_free_0), sd(log_k_meso_free_bound)</td> +<td align="left">sd(meso_free_0), sd(log_k_meso_free_bound), b.1</td> +</tr> +<tr class="odd"> +<td align="left">HS</td> +<td align="left">sd(meso_0)</td> +<td align="left">sd(meso_0), b.1</td> +</tr> +</tbody> +</table> +<p>For obtaining fits with only well-defined random effects, we update +the set of fits, excluding random effects for degradation parameters +that were ill-defined according to the <code>illparms</code> +function.</p> +<pre class="r"><code>f_saem_2 <- update(f_saem_1, no_random_effect = illparms(f_saem_1)) +status(f_saem_2) |> kable()</code></pre> +<table> +<thead> +<tr class="header"> +<th align="left"></th> +<th align="left">const</th> +<th align="left">tc</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="left">SFO</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">FOMC</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">DFOP</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="even"> +<td align="left">SFORB</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +<tr class="odd"> +<td align="left">HS</td> +<td align="left">OK</td> +<td align="left">OK</td> +</tr> +</tbody> +</table> +<p>The updated fits terminate without errors, and the only +ill-defined</p> +<pre class="r"><code>illparms(f_saem_2) |> kable()</code></pre> +<table> +<thead> +<tr class="header"> +<th align="left"></th> +<th align="left">const</th> +<th align="left">tc</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="left">SFO</td> +<td align="left"></td> +<td align="left">b.1</td> +</tr> +<tr class="even"> +<td align="left">FOMC</td> +<td align="left"></td> +<td align="left">b.1</td> +</tr> +<tr class="odd"> +<td align="left">DFOP</td> +<td align="left"></td> +<td align="left">b.1</td> +</tr> +<tr class="even"> +<td align="left">SFORB</td> +<td align="left"></td> +<td align="left">b.1</td> +</tr> +<tr class="odd"> +<td align="left">HS</td> +<td align="left"></td> +<td align="left">b.1</td> +</tr> +</tbody> +</table> +<p>No ill-defined errors remain in the fits with constant variance.</p> +</div> +<div id="hierarchical-models-with-covariate" class="section level1"> +<h1>Hierarchical models with covariate</h1> +<p>In the following sections, hierarchical fits including a model for +the influence of pH on selected degradation parameters are shown for all +parent models. Constant variance is selected as the error model based on +the fits without covariate effects. Random effects that were ill-defined +in the fits without pH influence are excluded. A potential influence of +the soil pH is only included for parameters with a well-defined random +effect, because experience has shown that only for such parameters a +significant pH effect could be found.</p> +<div id="sfo" class="section level2"> +<h2>SFO</h2> +<pre class="r"><code>sfo_pH <- saem(f_sep_const["SFO", ], no_random_effect = "meso_0", covariates = pH, + covariate_models = list(log_k_meso ~ pH), center_covariates = "median")</code></pre> +<pre class="r"><code>summary(sfo_pH)$confint_trans |> kable(digits = 2)</code></pre> +<table> +<thead> +<tr class="header"> +<th align="left"></th> +<th align="right">est.</th> +<th align="right">lower</th> +<th align="right">upper</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="left">meso_0</td> +<td align="right">91.35</td> +<td align="right">89.27</td> +<td align="right">93.43</td> +</tr> +<tr class="even"> +<td align="left">log_k_meso</td> +<td align="right">-3.29</td> +<td align="right">-3.46</td> +<td align="right">-3.11</td> +</tr> +<tr class="odd"> +<td align="left">beta_pH(log_k_meso)</td> +<td align="right">0.59</td> +<td align="right">0.37</td> +<td align="right">0.81</td> +</tr> +<tr class="even"> +<td align="left">a.1</td> +<td align="right">5.48</td> +<td align="right">4.71</td> +<td align="right">6.24</td> +</tr> +<tr class="odd"> +<td align="left">SD.log_k_meso</td> +<td align="right">0.35</td> +<td align="right">0.23</td> +<td align="right">0.47</td> +</tr> +</tbody> +</table> +<p>The parameter showing the pH influence in the above table is +<code>beta_pH(log_k_meso)</code>. Its confidence interval does not +include zero, indicating that the influence of soil pH on the log of the +degradation rate constant is significantly greater than zero.</p> +<pre class="r"><code>anova(f_saem_2[["SFO", "const"]], sfo_pH, test = TRUE)</code></pre> +<pre><code>Data: 116 observations of 1 variable(s) grouped in 18 datasets + + npar AIC BIC Lik Chisq Df Pr(>Chisq) +f_saem_2[["SFO", "const"]] 4 797.56 801.12 -394.78 +sfo_pH 5 783.09 787.54 -386.54 16.473 1 4.934e-05 *** +--- +Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre> +<p>The comparison with the SFO fit without covariate effect confirms +that considering the soil pH improves the model, both by comparison of +AIC and BIC and by the likelihood ratio test.</p> +<pre class="r"><code>plot(sfo_pH)</code></pre> +<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAIAAAD17khjAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nOzdZ1wTWdcA8JtGAiSQQAgdUapiF7ssKhYsWFB0Vexlxd52bSgooq66rG0tq4i9F3TtAta1o7IqvfeeAKElmcz7YfaZzUszQAQN5//zw8ydO/fejENOph4KjuMIAAAAAOqF2twDAAAAAIDqQYAHAAAA1BAEeAAAAEANQYAHAAAA1BAEeAAAAEANQYAHAAAA1BAEeAAAAEANQYAHAAAA1BAEeAAAAEANQYAHAAAA1BAEeAAAAEANQYAHAAAA1BAEeAAAAEANQYAHAAAA1BAEeAAAAEANQYAHAAAA1BAEeAAAAEANQYAHAAAA1BAEeAAAAEANQYBvHhiGbd++vUuXLtra2lZWVhs2bKioqFBtFzNnzvT391dJU6mpqRQFXbp0IcqPHz9uZ2dnaGi4ePFiiURS2+rR0dFpaWkqGUmDDR48mFKNjY2NkqtnZGRQKBQMw77qINWPubl5lW0eGBiIENqxY0e7du10dHRcXFw+f/6Mat/HQH0FBgZW2ebm5uYIobCwsK5du2pra9vb2589e5aonJiY6OrqyuPxLCwstmzZ0qwDB18BDpqDh4eHg4PDzZs34+Pjr1+/bmdnN3fuXNV28fDhw/Dw8NqWXrhwYdKkSUo2FRYWZmNjE/0/SUlJOI6/fv1aS0vrzp07UVFRHTp08PX1xXH89u3b1tbWAoHAx8eHWFcikfTs2bOwsFD5kadJZU/LKxRLYiSS8IpK5VuorrCwMDs7Ozs728vLy9XVlZjOy8tTcvX09HSEkEwma8wYvn2YXPZW9PxA8o4XwscSeaM2OCE+Pp7cbR4/fszn81NTUw8cOMDn80NDQzMzM+fMmWNlZSWRSGrcx0ADiESiaAWenp7Lli0rKCjgcrm///57RkbG/v37aTTa58+fKysrzczM5s2bl5aWFhYWpq+vHxgY2NzDB6oEAb4Z3Lt3T1dXVzHmvX37lkaj5ebmqqqL8vLyuiscP37c1dVVydb+/PNPd3f3KoWzZs1asGABMX39+nUTE5OysjKBQPDx48f8/PwePXqEhobiOB4QELB37956DT5FKnXKyL5RWkbMfq6U9M3Ievz/Q36DrVq1asyYMfVdqyUEeLGsZEP00p3xG+ZGjPst0Xd15E+FknwVtu/p6blv3z4cxwcOHLh9+3aisLy8nMFghIeH17iPtRBf74grMjLS2tpaLBbfunXL1taWLG/Tps2xY8eePHnC4XAkEglR6O3t7ebmptoBgOYFp+ibwfnz5z09PXk8HlnSrVu3mJgYNpuNEJJKpWvWrGnVqhWfz58wYUJOTk54eDiTySwqKiIqb9++feDAgSUlJfPnzzc1NWWz2U5OTsR5Ti8vr507d06aNGnUqFHkKfrqNW/evLl06dKwsLDevXsjhHAc/+2332xsbIyNjadPn56fn19lwPHx8UlJSTY2Nlwud/To0US0+/jxY58+fYgKffr0yczMFAqFCCEqlUqj0Wg0GoZhBQUFFy9enD9/fr22jwWdfkKgHyAqvlFaHi2V/pRf4Mvj/sBiNmhjf8Hbt2/79evH4XAcHBxOnz6NEEpISOBwOGSFYcOGEWeVG7DKr7/+amFhoa2t7ezsHBcXRyxNSEhwdXXlcrl9+/YlVv9GXMk61Z7TeZXVZjqFsaDVLwP4w05n/Kmqxp88eRIREbFgwQKE0LRp00aPHk2UFxUVyWQyKpVa4z7WElAolDpmG2nRokVbt27V1tb+4YcfHj16RBRmZWUVFBQ4ODgYGRnt3r2bwWAQ5YWFhVQqRAS1Qm/uATS/pKSkrl27ikSixjdFpVL/+OOPL8azz58/T506tUqhlZUVMbFly5bbt2//9ddfPB5v+fLlkydPDg0NbdWq1V9//eXp6YkQOnfu3KpVq3bt2vXixYv79+9zOJyff/55/fr1wcHBCKH9+/cfPnzYxcVl3rx5RIM11tyzZ8/58+fv3LmDEDpy5Mgff/xx6dIlU1NTX1/fsWPHPnr0iEajkWOLj4+n0WhXrlzR1tZetmzZuHHjXr58mZWVpa+vT1TQ09OjUChFRUXHjh0bNWpUSUmJl5fX4MGDlyxZsn79evIbRFEWhp0Vl1Yv78FkOrGYLArFSZPpKxRhCDlrMiMkkgiJhFxaZd0BLFZXpkbd27xGhYWFQ4YMWb9+/dWrV1+9ejV58mQLCwtTU1OVrPLw4UN/f/+7d+9aWFgsXbr0559/Dg4OrqysHDx48JQpU06dOvX58+fJkyfr6+sPGzasAYNvmLD8O4XSvCqFDIqGm6FHeNFLI6axf9wvEnnlzoQNCEep5ckXs44zKSw3Qw8qhaa4rhaNPVzgrny/GzZs2LBhAxE/pk+fThRKJBIvL69evXp17tzZz8+v+j6m2mjXXP755x/ymreiMWPG9OrVq3r5mjVryKVV1p06daqDg4OS/T58+DAnJ2f8+PEIITabzWazi4qKBg0aFB0dvWbNmh49eiCEyNtQHj58ePr06XPnztXzw4FvGgR4RKPRWCyWSppiMplM5pcPNDEMq+ObKygo6ODBgx07dkQIHT582MDAID09fdKkSZcvX/b09IyKikpKSho3blxRUdHixYv5fL5IJDIxMXn79i2xer9+/VxdXRUbnD9/fo01SQcPHly1alW3bt0QQvv27dPR0fn48WPnzp3JCpcuXSLu1kEIBQYGGhoaxsbG4jhe5VPIZLIRI0aMGDGCmI2JiYmNjd27d2+NH5OGkC6lhsMFFoVCLNWkUBAF0XBExylkTXKp4rrMhoaB4OBgKyurlStXIoTc3Nxmzpx58uTJtWvXqmSVyspKuVyen5/frVu3CxcuSKVShNDdu3crKys3btzIYDD69+/v5eV1+PDhpgzwmjRNLTm7SiGDwkCIQqPQWFQthBBCFC2athxHCOHaNI4GRQMhSpV1tWhaynf6+PHjzMzMcePGKRY+f/583rx5AoHg7t27qJZ9zM7OrqEf9BuioaGheLqOVNt3BVGZWFpl3Rp/K9dm8+bNq1evVvwj1dbW3r59e2ho6L59+1xcXIifF+Xl5T4+PgcOHDhx4sTw4cOVbx98+yDAIwsLi6ysrKbssV27dlFRUVUKhw8fvmDBghEjRmRmZlpbWxOF+vr6PB4vNTV10qRJO3bsEIvF586dmzhxopaWVkZGxooVK6KiooyNjRW/KVq1alWlZbFYXGNNUmJi4saNG/38/IhZLpdLXg4gKJ64EwgEenp6WVlZhoaGhYWFRKFIJMJx3NjYWHGt1atX//rrr+Xl5bNnz75z5469vT1x1/2/7dBoc3SqRhpSoVx+u6x8hx7PikGflVvQX5M1SlvzvzHUua7y0tPTyU2NELKxsfnrr7+q1MH//yVS5VdxdXXduXOnv7//pEmTHB0dN27c6OLikpiYWFBQYGFhQVZ2dHRs/AdRXm9e/9oWOer2xnDM0+ynhR8nL7Rccy/vOpvOHiEYp8y6dTty5Iinp6fiXrRly5Zdu3b5+fktWLCAOFdU4z6mHgHe3t7e3t5e+fqrV69u8LqkuLi4N2/e3Lx5k5glzlByuVwXFxcXF5eYmJgzZ8706tUrISFh1KhRxsbG4eHh6rG1gSK44tIMRo8eferUKcVL3a9fv75z5461tTWFQjE2Nk5MTCTKhUKhUCg0MzMj/s5v3rx5/vz5mTNnIoSmTJnSqVOnqKiop0+fEiUExVPrhNpqkoyNjc+fP5+VlZWVlZWRkREcHExcmyfgOO7i4vLw4UNitrCwUCgUWllZde7c+dWrV0Thq1evTE1NyTP2CKHQ0FADA4NOnTr9/vvvurq6mZmZXl5eXl5eymyfJJlsTl6BD487SJPVmk4/aqC/q6g4tFzFjxEihExNTclNjRBKTEw0MzNDCGEYRsb17Ozshq2SmJg4YMCAV69e5ebmjho1atq0aRiGGRsbOzo6Zv3Pq1ev9uzZo/LP1TDjjKemVSRviftFiksDEje/FT2fYjq38c2KxeJr1679+OOPZMmJEycOHz787NmzxYsXE7trbftY43v/9uHVbrJTSbOnT592c3PT1tYmZvfu3UteGUEImZqaVlRUSKXSYcOGubq63r9/H6K7WoIA3wzGjRvXr1+/fv363bx5Mykp6dq1a+PGjfP09CR+qk+fPn3dunXR0dFZWVleXl7Ozs7EAd+kSZM2bdpEoVCIW9uEQiGDwZDL5Z8/f96zZ09ZWVlt3dVWs7i4mPg2mTZt2qpVqyIiIrKyslasWDFjxgzFM4EUCsXQ0HD58uUvX75MTU2dN2+em5ububn57NmzT548+e7du9zc3M2bN8+dO5c8GSiXy318fIhTAlKplEajUalUTU1NmUymzPbRpFAC9HmDNP+9bmLFoJ8U6BvQVL+vjh49OiYmZu/evUKh8O7du4GBgdOnT+fxeBUVFcePH6+oqDh69GhERETDVnn48OHw4cM/fvxIfJMSt5K5urrGxcUFBATk5+c/e/asR48er1+/VvnnahgmlbXWets4I88+POcRAvdNtr/r0LmNb5a4VVsxfgQEBMycOVNTUzPhfyQSSY37WON7/y4o3vasqjbv3LnTv39/cnbUqFEhISFXr14VCoUPHjw4efLkmDFjbt68WVBQMG/evKSkJOI/oonPZYKvrilv2QekiooKb29ve3t7TU1NW1vbDRs2lJWVkYtWrVplZmamp6c3YcKE7OxsojwlJYVCoWzbto2YvX//vo2NjY6OztChQ8PCwkxNTdesWTN//nxvb2+iwowZM7Zs2VJbzYSEBEtLy44dO+I4LpVKN2zY0KpVKzab7erqGh8fX2W0ZWVlCxcuNDMzMzAwmDlzplAoJMqPHz9ua2tLvOhG8RGyo0ePEl3jOC4WiydMmMDj8Xr27BkZGfk1Nma9VHlM7uXLl71792az2W3btj19+jRRGBAQYGBgQKVSu3XrNmrUqKNHjyo+JqfkKlKpdMGCBQYGBlpaWr17937+/DlR8/379z/88AObzW7VqlVAQEDTfvpmUGWDy2QyOr3qlcG3b9/Wto+BBigqKqLRaB8+fFAsvHHjRo8ePYgX3RDPu/v6+lb5jxg5cmQzDRl8FRRcdb8ZAQAAAPCNgFP0AAAAgBqCAA8AAACoIQjwAAAAgBqCAA8AAACoIQjwAID/J0sMWXEBUAcQ4JuHeuSDr061qd/zAzPSf45FctU86NG9e3fyI3A4nEGDBhHvE0xJSanjDaB///238mnjVdhvEyuT4UWV8qJK+ac8ifPZ3CdpFcRsqbRRG1/5fPC11QT1VVs+eFJqampSUpJiSVRUVI1vywffOwjwzWPSpEmnT5/esmXLP//88/vvv1+6dGnJkiWq7WL69Ol1vOT84sWLkydPVrKphIQExVzd165dQwjduXPHxsbG0NCQfJpWKpXOmDGDyInXeHlH0rMDksv+KUlZGKWqGL9582YiE/yzZ88qKio8PDwQQvr6+n/88UfjG+/Ro8e7d++avl+VcL2Q1+9U7oCzeR7BhQinzLsjGnA2z+l0bs8TOZi84c0+evSoSj74IUOGHDx4cOfOnfv374+JiWnTps3o0aOlUmmNNVX3+b51yeMXJY9fpJKmxo8fXyUfPJFvhlBaWuri4nLy5EmyBMOwzZs3P336VCW9g29Lcz+I3xKpQT748vJylaR+r03esfSPbZ9VJJZhpbLYEe+Sf/qMY/JGtuno6Lh7925y9vbt2wihzMzMutd69uyZtbW1Mu1bWlq+fPlSVf02sd1vilc/FKUXy7oFZeeXYT+cznmbVfnbq+J1j0Wq6qLufPA11mw5ksYtTBq3UOXNkvngyZLZs2czmUxfX19i1t/f38jICCE0f/58lfcOmh0cwSOEUFlZmbAW9apfJUdLbdQjHzxSRer3GuUHZWTvTLa51ZXZWpOqRbO62FGSVqHC43hFUqlU8VT5y5cve/XqpaOj07t37zdv3ijWlMlkHh4eI0eOrKysrJ7TvX///mlpacOHD79y5UoD+q0xSXyNg/lK6eRndWSHplT8+rLE00FLX5M6rzN771vxuaiy+V1Ucz7mi/nga6zZQpDH7qo6iCeR+eCJ2atXr0ZFRSme2Js2bVpISAiRhxqoH8gmh2JjYzt37lxeXl7j0lWrVu3cuVP5+r/99tuKFSvq7lE98sErn/q9RpVJ5Tm/pyCEdFz0NDtyiGmEUGVCWWl4MXcYv/yfEoQQUc6fbpJzID2q12vtXrqKjei46HFHC5TsESEkFosLCgoQQjk5Odu2bbOxsTE3N09NTSWW5ubmDh06NCAgYPjw4YGBgURmP2KRTCabMmVKeXk5Eb+r53R/9OhR69atz58/37Nnz/r2W2OS+G7dulUfDIZhjUwnf+i9OK0EY9Eoa3pzAiNK00r+u59OX4t6N7H872mCQ+/FKcWy11mSfmbMW/HlinUQQsS6DGr9UvR+MR98jTXVQ2V8ijj0OUJIu283CotJTNem4lNsjXW0+3ZjtbetV7+K+eARQhkZGcuXLw8LCyPyzRPMzMzMzMz4fL7K7wEC3wII8Ih4K3hOTk71RTQarXr21TrqMxgMU1PTL/aoHvnglU/9XiMah6bVmYMQ0jBjkdMIIao2rTS8hGHGVCyn6dBl+RLtbjpkNYKGGUv5HhFC3t7e3t7eCCEGg+Ho6HjlyhXF/4hz585179599uzZCKF169bx+XzirAkR3W/dupWdnc1kMq9fv17fnO5191tjkvgBAwZUH8yzZ88amU7emkfnMqkMGoVGoRDT5KKYQhmVgsqkyJpHZ9GoCFWUS/EqdRBCxLrK94iUywdfR83vHU2HrdHGAiFE4+pQNBjENKng8DnF2WzfvWYHNlWpQ6xb334V88HjOD59+nRvb+8WkqMPECDAIxMTk+rZ2VVYvzr1yAevmB+s7tTvNaLzNfiz/vsxpDitM1g/eeYnnSF8oo40RxI3/B1/honRKss6GlTG7t27ly5dWtvSlJQU8oZ5KpVKXm5ITk7u06ePsbHx3r17161b14Cc7nX3W2ODNQ6m8enkB1myapzOEmM7X5V4Omgf+SDe8oPu3rfiEVbMFxlSgRZNsVrDKJMPvraaaoAu0OcM7kvOKk7XeE4+fYGP5eX9jey0Sj74ffv2USiUSZMmlZaWymQyiURSVlampaXVyF7ANw4CfDMYPXr0rFmzfH19+Xw+UULkgw8ICCDzwRPRkcwHb2FhQeaDP378OEJoypQpQ4YMuXr1KoPBOHPmzKFDh4imaswHX2NNkrGx8f79+wcNGoQQksvlb968UXwQDsfxQYMGeXt7DxgwANWUq5tM/b5161Yi9fulS5e8vLzCwsIatn10BupZBrVP8vzY+nQHlq123PB3epOMGh/dv8jU1DQ0NJSYxnHc19d32rRpCCEzM7NTp06FhoaOHj36xx9/JHK6P3v2jKiZmpqqZBrc2tTY4LVr16oPRuVdkw6+F7tYMsfaanr+VTjIknX8Y+n+wTyBduUf78SHXXlfXr92RD54xecLyHzw7du3r7um2mt8IK9NlXzwb968CQkJ4XD+PQEWHBx88uRJFT7RCr5NavVL+XuhHvngiaWNTP1eG52BepbH2idN/RQ76K3+VOMmiO4IIQ8PjydPnpw5cyY/P/+3337bs2ePvr4+QojFYlGp1MGDB48ZM2bBggV15HRX8i7LKmpssMbBfL108tEFstdZkoUPhDgFn3unUCrH1z0R3U4oTy6SyRrxmBxSLh98ZWVljTVBg1XJB3/q1Cnyzurx48f7+vpCdG8RmuXefaAe+eDxr5z6vfhhQV5QRuPbIVR5XI2UnJxMp9OJ6bCwsK5du2ppaXXp0uXRo0f4/39MLjs7m8vlnj9/vsac7qtXr2az2RcvXmxAvzU2WH0wtdVUoYIyrFtQdoJQqqoGlcwHX70maLAa88GTiACvWLJs2TJ4TE4tQT54AMD/I5MjOpzaA+D7BwEeAAAAUEPwQx0AAABQQxDgAQAAADUEAR4AAABQQ/AcPGgRsrOzY2JiFEuYTGavXr0QQlKpNDIyksPhtGnTpplGp7ZevnxJPAJHsrOzI7Kb5OTkZGRk2NraKqYfTEhIEIvFbdu21dDQaOqxqos6dvUawf6vzpr7Nv4WKj09fejQoTQazczMjEaj9evXLzU1VbVdkI/J1aioqCgtLU3JpjIzM3v37m1kZNSqVSsHB4eUlBQcx2NiYqhUqtH/uLi4EJXT0tI+fvwol/+X/O3y5cufP39u+CdRhTNnzhgp0NXVbd26NY7j169fZ7PZVlZWRG7y0tJScpUHDx74+Pg024jVQpcuXRQ3O41GO3HiRElJybBhw7S1tW1tbXV1dYkHC4uKipycnFgsloGBgZmZ2evXr5t77N+r2nZ10t27d8PCwojpOvZ/oAYgwDcDmUzWsWPHqVOnEq+aKSgoGD16tJOTk2p7ycrKys/Pr21pvdLFTpgw4ccff5TJZDiOr1q1atSoUTiO37p1y9HRsUpNX19fc3PzTp06DRgwoKKiAsfxvLy83r17S6Uqe65aJdzd3Q8fPlxYWMhisU6cOIHjuFAo7NChw8aNG3Ecl8vlnz596tq1a4t6OPhXYdGy/MJl+YVuWblL8wuX5RduLBRiqmv/3bt3NjY2ZWVlv/zyS6dOnYidPzAwkMPhFBUVbdiwoV27diUlJTKZzMvLq3v37qrruUUjdnVyNiEhgcPhEM/B17b/A7UB1+CbwdGjR0tLS4OCgog3R+rp6e3fvz8pKYlML4bjeFJS0qdPn6RSKUIoLy/v+fP/skvl5eW9fPkSIYRhWFxc3Nu3b4uLi4lFcXFxGRkZEokkPDy8qKiotLSUKK9SMz8/Pzo6urCwUPE9aBkZGVXO7JEePHgwf/584iW4K1euvHnzpkgkio+Pt7OzS09P//DhA3Eatri4eP/+/bGxsR8+fOByucR7sH18fDZs2FD93Sa1wYR3y17qlb3kVf8nTduq9Dauy+3bt3Nzc+fOnfv27VsdHR3ifbRcLnfixIkvXrxACC1cuLB///6RkZEq6e57cbm07AcWc6gmK1UqHcBiDtVkBZeWS1T0GK1cLvfy8vrjjz80NTWfPXv2008/ETv/jBkzKisrP336lJmZ2a1bNzabTaPRnJ2dyTx+LcSsiDGzIsaovFlyVydmMQzz9PQk319b2/4P1AZcg0cIodzcXLFYjBCytLRUTHRR33IlhYWFTZgwQfGl8WZmZuSbI7Ozs8eOHZuYmMhmsysrK4ksrk5OTomJiUQimW3btmVnZ/P5/NGjR+fm5nI4nNzc3JMnT7q7uwcEBHC53Fu3bjk5OZWVlVlbW69fvz4+Pr5KTVNT04cPH6ampp49e7ZHjx4lJSWTJ09++fKlvr5+eXn5tWvXunbtqjhgNpsdERHh7OxMDE8ul6enp8fHx4eEhHTt2pXJZFZWVl64cMHZ2VlXVzcoKMjCwiIiImLz5s2RkZHJycn1SndG0+1PYZoxrQ9TOb3JQlwmrIjoRdMfW99NXZ1MJlu6dOnZs2cpFIqjo6PiC/M/fPhgYmKCEDpw4MCBAweWL1/e0nJoDtJkcahU70LRIC1NbQplbaFIVS2fPHmSz+cPHjwYIfTnn38S2xkhFBUVJZFIjI2Np0+f7uHhsWfPHoFA4O/v36LywZOhfVbEmGOdglXVrOKuTpT4+fm1a9eOzHhZ2/4P1AYEeBQZGdmpUyfi3ekLFy7cv39/w8qVFxUV5eLiUtvSX375xdjY+OnTp3Q6fdu2bdOmTYuOju7du/fVq1eXL18ul8svXrwYFBR04sSJzp07nzlzBiG0adOmQ4cOubu7I4TOnDnz9u1bgUAwc+ZMosHqNe/fv+/l5XX+/Pndu3cjhPz8/EQiUXp6OpPJPHXqlJubW1xcnGKmqblz5+7atYvP59NotF9//ZVKpYrFYi0tLU9Pzx07dlCp1I0bN06dOjUhIeHBgwf+/v4hISGHDh1q3769m5vbr7/+WuPHlJfHSdO3IVTTASLDsDJmMlXXGaF/v5jkpRGIxpFm7KipJQrdYDKNO0iJDY8QQqdOnbKxsenevTtCiMfj8Xg8hJBcLvf393/8+PHjx4+VbOc7FVBUnCbDalxUKsfdcnIpiFKG4yOycykIVeL46kJRjT9g9alUb56ukp3KZDI/P7+zZ88Ssw4ODsREfHz82LFjFy5c2Lp1a0NDw3bt2gUEBOjq6ubl5Xl4eNT3o32z4kqjQvJv1rb0jehvxdlZEWO6c/vWWJOKqIMN3NpoKZsVXnFXRwi9ePHi7Nmz4eHhs2bNIkpa4P7f0kCARwYGBt27d8/JyaHRaIrJN+tbrjwul0ueVCdJJBIajUaj0e7cuRMcHEyc0165cuXGjRsTExMnTZp09uzZ5cuXP3nyhEqluri4DB48WCqVfv78OT4+/vnz5+Xl5UQ7w4cPFwgEii37+fnVWJMUHBzs4eFBnPY3NzcvLCyMiIjo3fu/A2hvb28+n3/y5EkdHZ3du3c7OzsbGBhs376drLB+/fpt27ZFRkZ26dLl6NGjROH9+/dNTU3bt29fWVn5+fPn1q1bE98mBApDQNcbVcsWwiVp2yl0fZqOE0JILsmUi8IYrfwpNHaNtama1rW0U4Nt27YdOHBAsSQ6OnrGjBlCofDZs2e2tsp+e36nnFisAqzmAB9WXuHCZDEolLPiUhcWi4HQGXHpIBaTUVP2d3Z9TlxduHDB2Ni4Z8+eZIlcLt+5c6efn9+KFSs2b96MEJo9e3br1q1DQkKoVOrZs2f79euXkZGh/JWdb5mBhqGjbp/aljrq9jmYspOc9Wr1cx1N6WsYKN+v4q5eUlIybdo08rKgoha1/7c06vD300gGBgaKV7gbXK48BweHN2/eKJbIZDKBQHDo0KGJEyeKRCI9PT2iXENDg8PhFBQUeHh4LF++PCMj4/z589OnT6dSqdeuXVuwYEHXrl27du1qZ2f3/rydgNkAACAASURBVP17YhUyBS2ptpqknJyc8PDwxMREYnbMmDHkVToChULx8vLy8vJCCMXFxdHpdGNj48rKSg0NDeLsH5PJ5HA4ihntMAzbtGnTtWvXUlJSBg0axOfzk5KS/vzzz1Gj/g3qFLouTb/Wi45MVpvK6IkMCx8KTUcWNY5hsZEu8FRq49bp2bNnYrF44MCBZMnt27cnTpy4atWqtWvXtoRHs7oza/2MG4VFy7g6HCr1cmnZCq6ONoVyobRssJYmq6YAXy9BQUGenv/990kkEjc3t6ysrGfPnnXu3JkovHPnzp07d4gLXpMnT/by8goPD1f8TfD94jL0ajsoJ3Tn9iXO0qvw/HyVXX3Tpk0WFhZFRUW3bt3KysrS0NAIDQ11cXFpaft/SwMBvhnMmzevV69er169Ir+/zp8/X15ePmDAAAqFYmtr++rVq7Zt2yKEYmNji4qKbGxs9PX1BwwYcPHixStXrhCH2r/88svOnTuJ780jR46QYZtS7eu4tpokW1vbGTNm/PjjjwghkUh0+PDhKik7582bZ2xsvGnTJoTQpUuXBg8erKWlJRAItmzZMm/ePIRQTEwM8fgyucrRo0dHjRolEAh+/vnnBQsWLF++PCIiYvr06WSArxtVuxONO0iW8TuV3RWXZNAF05XbtF9w9uzZsWPHkrdNCIXCiRMnBgUFjR8/XiXtf9coCK0tFDEolEqE1haKaAhJVXGHXXZ29qNHj8jz8wihzZs3FxcXP3/+XPEJeB6Pl5SURJw3ysvLE4vF5M/clkCFoZ1QZVfX09OTy+W7du1CCMXGxmZkZEil0q5du8L+r94gwDeDLl26rF27duDAgV5eXg4ODh8+fDh06JC/v7+hoSFCaM2aNcTtXbq6ulu3bv3pp5+IrOSTJ09etmxZp06drKysEEICgeDKlSsCgSAuLm7v3r0YhqWkpNTYXY01NTQ0kpOTX79+3aNHj7Vr186ZMycnJ0cgEBw8eJDH461evVqxhZEjR06ZMkVbW5tCoezYseP69esIoWXLlnl7excWFpqYmGzbtm3lypXkN3JxcfGxY8eI63l2dnZnzpzp1KnT9evX27Vrp/xWYlhsrIjog/IvaVj9gSiq2VEfPHhAnBAmBAcHa2hoiESiwMBAosTc3HzIkCEq6eu7s5fPE2JyhJA9g96KTkcIjdVWweF7aGiolZWV4mWjU6dODRw48MKFC2TJ6NGjf/755+XLl4tEIh6Pt3v3bjc3Nxsbm0Z23ZJV2dXXrVu3bt06YtrDw6N9+/Y+Pj5BQUGw/6s3CPDNw9fX18nJ6fLly5cuXTI3N79z5w55Mm3q1Kk8Hu/ChQtlZWULFiz46aefiPKxY8eeOHFiyZIlxOzZs2f9/f3379/fp0+fFy9eLFmy5M6dO8SbQ4gK9vb2xO2yNdacNGnS/fv3//zzzx49eri7u+vq6p46daqgoGDEiBFkF6RRo0YFBgaeO3fOyMjo3r17xImHdevW2djY3Lhx4927d2vXriUetiHcvn173bp1LBYLITRnzhypVPr77787ODgcPnxY+U1EYQgYpssx8VuarnMDtnB1xcXFZmZmffv+d7IUx/GOHTsStx8SevfuTX7BWVtbSyQSlXT9XejJZH6NZgsKCqZMmULOYhhmZWWVnJycnJxMFjo5OS1YsMDMzOzixYulpaU//vjjwoULv8ZgWojqu7oiBwcHS0tL9KX9H6gBSBcLAAAAqCF40Q0AAACghiDAAwAAAGoIAjwAAACghiDAAwAAAGoI7qJvBiKRKCIigpzV1ta2s7Mj3zAVExOjqalpYWFR47rx8fF0Op24CfZrw3H8yZMn/fr1U3xt/neqvvngIUm2StQrHzyO44mJiTKZzNbWtvrrHAAA9dacqexaqpCQEAqFYm9vb29vb2dnZ2BgwGazz507RyytO4/7okWLvL29m2acRKoVkUjUNN19VfXKBw9JslVF+XzwmZmZvXv3NjIyatWqlYODQ0pKSnOPHYDvHjwm92XysvKSO08kqZk0Loft0kfDorEJl0JDQ0eOHEm+Ex7H8VmzZgUHBxcUFFCp1OzsbAaDQbzcprrFixdzuVw/P79GjkEZlZWVLBZLJBKRz9Y3jYSEhA8fPii+5qyiosLIyEiFLy4dN27c0KFDPTw8TExMDh8+PG3aNJFI9MMPP4wdO3bZsmXVC4m3+Km3jPVxPA8j0Y3cyoRyDQsWz8OoICjDPMAOqehY+v379xMnToyIiPD19b13797Tp085HM6xY8eWLVuWnp4+d+5cKpV6+vRpGo32888/x8bGEu9TAgA0GFyD/wJ5SWnmqu2y/EKtHh1pXJ2czfvLwj+ptgsKhULEkry8PISQYh53HMfj4+Pj4+OrrCKVSiMiIshX18XHx2dkZKSnp79+/bqkpAQhVFZWFh4eTjRINqWYY55cq6Cg4PXr12QqeoQQhmGfPn3KyspS7cdUnkQiKS8vZzKZHTp06NChg0AgKC4uJrdJ49WdD77FJsmmMKkxzm9k+RLuKANcjsf88BoTy1QV3b+YD/7Bgwfz588nLgatXLny5s2bIpHKktUC0DLBNfgvEF29p927C2/qv2lRWB3s8nYd1erWXrW9VFRU6OvrE6+q3b59O5HHPT09fcyYMTk5OaWlpVZWVn/99Rdx8TImJsbR0ZFGo338+NHHx8fb23vPnj3R0dHh4eE8Hi83N3fbtm379+/HMCw9Pf3+/ftOTk7Vc8z37t17z549QqHwyZMn+vr6//zzj6+v74YNGyIjI0eNGkVkux85cqRqP6aS6HQ6kRgjIyPDwMAgOjrawcFBVanZv5gPvsUmya6ILdMdaVAcWmgwzzxnb6rOYH1Jsmq2OVIiHzybzY6IiHB2dkYIZWdny+Xy9PR0LperqgEA0AJBgEcIIdHF25XRCf/O0Gj8hZ40rg5RLg57wTDi52zeR5QzrVvhMqzwZLA0Oa16feVhGHblyhWEEI7j+fn5e/fuDQgIqFJn2bJlHTt2DAwMxDBsxIgR+/fv37JlC0LoyZMnHz58MDIyOnTokJ+fn7e3N0IoJiYmMTGRy+UOGDDAx8cnMTFRV1d3xIgRly5dcnJyqjHHPEIoJCSEaCooKGjNmjUbNmyYO3du//79//zzT4TQ1KlTG75Nv0QoFL5+/ZqctbS0JDLcCIXCT58+SSQSHR2d6OjoqKioESNG5OfnFxUV3bt3r3r9+vpiPnj1TpKdtiKmIu7fpH9UTWqbUx0oTCpRXnQvX7uTDoVGjerzij/ZxGy33T+tnqQtj66IL69ev16UyQc/d+7cXbt28fl8Go3266+/UqlU4lcmAKDBIMAjhJB2n65MeytimkKhUDlssrz8nxjNjnZMeyuiHMcwvFLC/sER69y2en3lYRi2f/9+YjolJaW0tLTKbfNyufzWrVtv376lUCh0Ov3cuXPEuXeEkLu7O3Eo379//7Vr1xKF48aNIw53unXrZmlpSVw17969e0ZGBkKoxhzzCKGxY8cSTTk5OZWVlZWUlDx//vz8+fNEEqq1a9cqJgFTLQ6H06FDB3JWR0eHLDczMxMKhQYGBoWFhSwWKzExUUdHh8FgKEZ0sn59KZkPXl2TZOtPN8EK/71GQ9GgktFaf7pJcVgB180g73iGZgdO8dPCio9imi5df4ZpjfXrRZl88N7e3nw+/+TJkzo6Ort373Z2djYwqEfucwBAdRDgEUKIYWbEMDOqsVxnqFPRjRCO6w80HTaSywtPXWd1tNewNGtkjxoaGg8fPiRnlyxZsmrVqrdv35IlpaWlxHl7YlZPT4/M1UYWKj5KRGR2IQrJ29OICjiO15hjHiFEngIlahYVFSm2T+Sq+UrodHqNp77pdDqXyy0oKIiOjnZ1deVwOPfu3SssLORwOI0/Va5kPng1TpKt1YlTW7meh1HWr0l6k43Nd9nm7EmNHRmu525UW/16USYfPIVC8fLy8vLyQgjFxcXR6XRjY+PGdw1ASwYB/gu0nRylWbkZS/0YpoZYXiHD3Ji/VDW5yRW5uroqZs9ECBHxLDw8fMSIEQiho0ePRkRE7Nu3rwGN15ZjvnpNU1NTHR2dp0+fDh06FCH06tWrhnyYRhOLxdnZ2Xw+n/jFQ6fTc3JyMAxrfMvK5INvsUniSx4Jma1YwkvZ5R+KKxLKWdbaJX+LEI4aeZ+dkvng582bZ2xsTDytcOnSpcGDB2tpaTWqYwBaPAjwX8adMFxnRH9pejaNp0sX1Pz0WiPx+XyhUCiVShkMBlm4du3a+fPn+/n5lZSU+Pn5/fbbbw1uv7Yc81VQKJRffvll5syZvr6+FApl37595ImBpmRnZ6erq6vYtUwm09bWbnzLyuSDz8jIaJlJsi322bPstWV5koqEMqaFJsOEWRFd2vi76JXMBz9y5MgpU6Zoa2tTKJQdO3bAM3IANB4EeKVQtbWYdip7oxmPx/vhhx8US9q1a9e3b9/79++PGDGCzOO+aNEiQ0PDK1euEBfsJ0yYgBCysbEhj3u0tLT69etHFGpqahKFVlZWZBZzS0tLIi7WmGNecS1NTU0nJyeE0Pr1642NjW/fvs3j8c6fP79u3Triyn1T0tLS+hrvj1MyH7y1tXXLTJLNstdGCNENNNgGGooljaRkPvhRo0YFBgaeO3fOyMjo3r17KnznAQAtFrzoBgAAAFBD8KIbAAAAQA1BgAcAAADUEAR4AAAAQA1BgAcAAADUENxFr6zPJR9s2e0YFBW89kQN8sFXT+b9jatvPviCgoLExEQzMzN43Upj1JYPHsOwuLg4iURib2+v+CqhrKwskUhkb28P+eABUIFmS1T7XUkvT5kd4X4j+4JKWvuu88HXmMz7K7l06dLKlStV0lS98sHv2LGDxWLZ2NhQqdT58+erZAAtU4354FNTUx0cHAwMDPh8vpmZWXh4OI7jMplsxowZ2traRkZGHTt2TEtLa+6xA/DdgwCvlJ0JGy9kHl/8aWqhJL/xrYWEhLBYLHJWLpfPmDGDy+ViGIbjeFZWVn5+rb00e4D/5ZdfOnXqVFxcjON4YGAgh8MpKipSbb8vX75cu3atXC4/cuTInDlzcBw/ceJEUFCQCrtwd3c/fPgw8a77EydO4DguFAo7dOhAvKWfxWK9efMGx/F//vmHQqE8fPhQhV1/syrjfqqIHFMRObos3L7i88iKyDEV0T/icpmq2n/37p2NjU1ZWdmsWbPc3d0xDJNIJJMmTXJ2dsZx/OrVqxYWFvn5+XK5fNy4cXPnzlVVvwC0WHAN/sveFb0USQvHG3kO0He9lHVC5e1/X/nga0zmrZLtQGrXrt3jx48XLFiA4zhC6NixY+vXr1d8QU0j1Z0PPiYmpn///o6OjgihDh06tG3bVvGVLGoME92lG86haLXHpbmIaU43WYoVPUS4RCWNK+aDz8jImDdvHpVKZTAYY8eOJTbvyZMnp02bpq+vT6FQli1bdubMGZW8nBiAFq2Zf2B886Ry6Zoor0/F73Ecr8QqV0XOiRF/bmSbVY7gcRy/ffu2vr4+MU2eok9LS+vWrZuZmRmPx3N0dMzKysJxfNGiRR4eHh07duzSpQudTvfz8yMKBw0axOPx2rRpw2az9+3bZ2dnZ21tzWKxnjx5guN4VlZWr169BAJBmzZtTE1Nnz9/Tqw1ZcoUc3Pzzp07U6nUzZs34zj++fNnKysrQ0NDQ0PD2bNno2pH8J8+fSosLCSnEUKJiYmN3CDVicViZ2dnJycnJycnY2PjyMhIVbUslUqtra1fv36N43hhYeGnT5/IRePHj58+fbriGG7cuMHj8b7GB/wGlb1pJZfklb3rLM09W/bGSi4Vlb4yxrEylTQeFBQ0YsQIxRKpVJqenu7m5kZcBOnUqRN5uYdIepSdna2SrgFosSDA4ziOX8g8viX2F6GkoPr0ysjZAQmbyPLQvJu+MStqq6+kkJAQBoNx+fLly5cvX7p06eDBg23btiVOFOMKAX7cuHEzZ86Uy+VSqXTIkCHr16/HcZx4fy0R7A8ePGhiYkIUmpubC4VCHMf79++vp6dHROXhw4cvXrwYx/GpU6eOHTtWKpXiOL5161Zra2uZTKbY1LFjxwQCAY7jffr0mT17NoZhGIZNnjy5eoAnxcXF2djYLFy4sEGbHH/37p2jo+Pu3burTK9bt05LS8vc3Jz4BYMQMjIy6tatW9u2bQUCQfX69XXs2LFhw4ZVKcQwbPPmzQYGBuQvifz8fCInzY4dOxr2Ab9NlXE/lX8ahmPl1adLX/IlGfsqosZVxv1U9tZGkra19JVxZeycGuvXi1QqbdOmzcuXLxULlyxZghAyMzMrKCjAcdzQ0PD+/fvkUhqN9uHDh0Z+WABaOAjwOI7jBZK85LJ4OS6vMp1SlrDw4+SsinSyHMOxbXFr7+YG11hfSSEhIVQqtf//tG7dWiAQkBd6iQCPYRiLxSKPLwsKCpKTk3EcX7RokZeXF1EYFRXF5XKJwmXLlhGFK1eunDFjBjHt4+NDXMPm8/nPnj0jCisrK+l0emxs7KJFi8g7yOLi4thsdnFxMUIoNTWVKPz48WONAR7DsO3bt2tra2/YsEH5T11FRUXF27dviaM0xen09PRTp07dv39/48aN2traHA5n2LBhr1+/fv78+Y0bN6rXry8bG5sHDx4olkRFRfXs2dPW1jYmJkaxvLKy8t69e3p6ehcuqObmym+BvDwRK/1c43TZa7Oy8I5Y8Qt5eaKs8H7ZG6vSV0by0sga69fL6dOn+/btW708OTl5zpw5bdu2xXHc0tLy5s2bRDmRTCE6OroBfQEASPCYHEII6TH4egx+9enQ/Nt6DP67ov+ypn4uidDTMLibG9xffygFUarUV973mw++xmTeDcBkMrt161Z92tTU1NPT89ixY0eOHFm9enViYmJSUtKff/55+PBhMs2rYv16USYffHh4uFQq7dWrl4aGxpAhQ8aPH3/79m0i048aoLBaU2qZRgijaLamcnohhGis1jTeUFn+RQrLkkLVrF6/XhTzwUul0osXL44dO1ZLS6tVq1abN282MTFJTk42MjLKyckh6uTm5iKE4AFFABoJbrKrSxst24463cowseI/PYa+k56LHJersCNXV9e0tDTFEjIfPDF79OjRxYsXN6xxMh88MatMPnhitsZ88GQy78ZE97o9ffrUx8fn8ePHxsbGdDr9r7/+ioyM/P333xvfcm354H18fMinsZ88ebJy5UpyFbFYTP4MUm84JpGXPC97bUr8k+VfQbis8c0S+eDd3d2JWQaDsXDhwr///puYFYvFCCFdXd0BAwaEhIQQhaGhoV26dNHR0Wl87wC0ZHAEXxdn/SZKEvod5YOvMZk3n1/vcxh16N69+/v37/l8/pMnTxBCHA4nJCSkrKys8S0rkw/+xx9//O2335YsWTJgwIC3b98GBwe/efOm8V1/+1htr8ilOf+viKaD/nf43mDV88GvXLnSy8vL29tbQ0Nj586dRC7juXPndu/ePSAgwNzcfM2aNY3Z2wEABAjwzeD7zQdfWzJv1QZ4FotF/LAYOnRoly5diOGRQ20wJfPBDxky5MmTJ4cPHw4MDDQ3N3/z5k27du0a2fV3garr/DVO6FXJB48Q2rBhg5mZWUhISEVFxZw5c4i9sXXr1mFhYX/88cerV6/2799PHvEDABoM8sEDAAAAagiuwQMAAABqCAI8AAAAoIYgwAMAAABqCAI8AAAAoIbgLnplSeU4g6qaHNUFBQXVE7QQebJrSxVfXFz8/v376k0RL3AlpkUiUVxcnJGRkbm5uUrG2fzkcrlMRlXIF95g9c0HD1SivvngAQCq1Lwv0vuOjL2Sl1qkmtSZwcHBNBrN/v+7dOkSXnuq+PDwcKLQ1tYWIWRpaUnM3rhxA8fx9PT0oUOH0mg0MzMzGo3Wr18/8nWz3zEMy1z3W+rM1VixuPGN1SsfPOnu3bthYWGN773FUj4fPGn06NF5eXnNNWAA1AkEeGUNvZAbWyBVSVPBwcF8Pr/GRXWnisdxnHjzl2IeDplM1rFjx6lTpxI52gsKCkaPHu3k5KSSoTYbDMv9PSh9uX/Gcv+MldtUEuMV1ZEPnqyTkJDA4XB8fX1V23WLVXc+eBzHxWLxnj17EOSRA0BF4BT9N41IFX/8+PG8vDxDQ8Ma6xw9erS0tDQoKIhGoyGE9PT09u/f37t379TUVAsLi6Ydr4rI5fn7T8nFpRxXJ2lCGpXLyd6019BnMY3DVknzZD74kJCQKvngHz9+TNTBMMzT05N4TVDLERQUVKVk5syZKmm5Sj745cuXU6lUKpU6duzYn3/+GSGUmJjYq1cvyAEPgApBgG8eUqmUjCUER0fHGsMJkXKmtuiOEAoLC5swYQIR3QlmZmZV3mz/PZHL8/efworFgtXzxE/eIIR4k9wQQjmb9qkkxstksqVLl549e5ZCoTg6OoaFhZGLPnz4YGJiQkz7+fm1a9euxnQ7oAFOnjzJ5/MHDx6MELp79y5CSCaT5eTknDp1atiwYQihNm3a5ObmFhUVtZA3/wPQBCDAI4SQz9Oi5xmSU256Rto0n6dFj9MqEUI4Qnmlcn0tKo2ChBVysRSfdrOQSUfCCnmFDPFYFBadwqBSyLXIFpTpUSwWz58/X7Hk2rVr9vb2CCEMw65cuYIQwnE8Pz9/7969AQEBdTQVFRXl4uLS8A/fTCrjU/J2B3EG9tF1H0JMIxwhhDBRMS6V0g300hduwkTFFCq1/FMsLpVhwqL0OetofD2EEMflv7WIFpTv99SpUzY2Nt27d0cI8Xg8Ho+HEJLL5f7+/o8fPyZ+db148eLs2bPh4eGzZs36Gp+9GYWGhpaUlLi5udFotNDQUKFQSJTT6XQ3N7fq9S9fvqxYgViLbEHJTmUymZ+f39mzZxULV65cuXfvXjMzs+PHjzf88wAAagcBHiGElvfgzJHgRGxe3oMzp9O/h4n55Rhfk4YQKpbIF9wXbunHteTSiiXyUgku0KLRqAhR0H9r/a8FZfB4vKioqBoXEW+eJ6ZTUlJKS0vrPtPO5XKJPO6KJBIJjUZT/iu46THbmBttWETVYZPTRLk0Oz//4GntXp05g/qWhDyXZefxpo2tjEsuOHKR5zlKs6M9QkhxLWJaedu2bTtw4IBiSXR09IwZM4RC4bNnz2xtbUtKSqZNmxYUFMThcFTzUb8lffv2xTCM2DH69u0rlUqJciqVWuPeMnTo0CoVFFtQ0oULF4yNjXv27KlYuGfPnhUrVmzZsqVfv36RkZEN+zgAgDpAgEcIIS6TymXWMG2uQ36L0TTpFCM21VyHhlANX22KazXSF1PFV+Hg4FAl3ZlMJhMIBIcOHfrxxx9VM6avgUqlG/KrT9MN+cZbVmb77qGytekmArm4TFZYVBh0RfDzHFZ721pbUI4y+eA3bdpkYWFRVFR069atrKwsDQ2N0NDQ7/EcSY0U0wOSSX3qUP1XzhdXqU6ZfPCWlpb1bRYAUDcI8N86V1dXxdys1c2bN69Xr16vXr0ij5DOnz9fXl4+YMCAJhmg6tH5PCPfpdm+ezQsTZFUnrfrqMHymVWje4PUlg9+/PjxZB09PT25XL5r1y6EUGxsbEZGhlQqVZsA3/SIfPDk+XkiH7xAICCux5P54JtziACoKQjw9UBRzXtuEEKosrKSuNBOatWqlaOjY/WaNaaKV9SlS5e1a9cOHDjQy8vLwcHhw4cPhw4d8vf3r+O+vG8fEeMzf/kVL68w3LCI1c5aJc0qkw9+3bp169atI2Y9PDzat2/v4+Ojkt6/faq6Z16RkvngVd4vAAACvLK2/qBrqauazcXn87t160ZeaCe4uro6OjrWnSoeIUSj0fr370+mhCf4+vo6OTldvnz50qVL5ubmd+7cUTwL/Z2i83lGPotluQWqiu7K54MnZx0cHODUcSMpmQ+eQKfT+/fvD++2A0AlIB88AAAAoIYg2QwAAACghiDAAwAAAGoIAjwAAACghiDAAwAAAGoIAjwAAACghiDAAwAAAGoIAjwAAACghiDAAwAAAGoIAjwAAACghiDAAwAAAGoIAjwAAACghiDAAwAAAGoIAjwAAACghiDAAwAAAGoIAjwAAACghiDAAwAAAGoIAjwAAACghiDAAwAAAGoIAjwAAACghiDAAwAAAGoIAjwAAACghiDAAwAAAGoIAjwAAACghiDAAwAAAGoIAjwAAACghiDAAwAAAGoIAjwAAACghiDAAwAAAGoIAjwAAACghiDAAwAAAGoIAjwAAACghiDAAwAAAGoIAjwAAACghiDAAwAAAGoIAjwAAACghujNPYDGwjDsxo0bMpmsuQcCQKOw2exhw4Y19yi+dfD3DtRD0/y9U3Ac/9p9fFVRUVG9evUaOnRocw8EgEa5evWqWCxmsVjNPZBvGvy9A/XQNH/v3/0RPI7jZmZmFy9ebO6BANAoWlpa3/uv7SYAf+9APTTN3ztcgwcAAADUUPMcwYvF4pycnJKSEoFAIBAI6PTv/kQCAAAA8E1p6iP406dPd+7cmcPhWFtbOzo6mpqa8ni82bNn5+XlqbwveSkmzZWovFkAAADg29ekAf7KlStLly5duHBhSkqKRCKRyWTFxcUhISEymWzIkCGq7QsrkcWNeh/d61VFTKlqWwYAAAC+fU0a4C9cuODv7z937lwLCwsGg4EQ4nA4PXv2PHHiRGVlZXR0tKo6wkpk8aM/aLZjm+2wjRv5vjxSrKqWAQAAgO9CkwZ4LpebkpJSvVwsFotEIi6Xq5Je/o3uDmyLvfa88YZmv9rEj/4AMR6Ab8HGjRtV+FMeAFCHJr27bcmSJX369ImLixs6dKixsTGLxSoqKvr06dPp06eHDx9uZGTU+C4UozuiIIQQz90QIRQ/+oP19c6a7diN7wIAoKTHjx9XKbl48aK+vn5OTk7nzp11dXWbZVQAtBBNGuDb1J6KJgAAIABJREFUt2//+fPngwcPnjx5MjMzs7S01MDAwNra2t/ff/z48SrpIu9AGiaUmu+2QxSUU4rllsk7GDB47obFoYXpq+Ns/uqikl4AAF8kl8snTZqUlZWlr69PoVCIQpFI5OPjw2Awrl+/3qdPn+YdIQDqramfTzM3N9+6devXe0xOsNii5IkwdVFUqz/aPk2rfJMt/bW/bsHJzOL7BTY3IboD0HSoVOrHjx/nzJkjlUoDAwMNDQ0RQt27d9+1a5ezs3Pd64rF4u7du0skVZ+Cqaio+BpP3ACglpo6wJ8+fXrXrl0REREIIRqNhmEYm82eMGHC9u3bDQwMGt8+VYtmdalTgkdEysIoyuJWCKGCk5mZfok2N7uw7LQb3z4AQHn6+vrXrl07cuRIz549AwIC3N3dlVyRzWbfv39fKpVWKQ8LC1u4cKGqhwmAemrSAE88Jrd9+3biGjyDwSgpKYmMjDxw4MCQIUPev3+vkl6oWjSri50Sxkfw/RPbmGtmBefY3u3KtNJSSeMAgPqaO3eus7PzlClTbty4UVqq7GOr5ubm1Qs/f/5Mnu0HANStSQM8+ZgcWUI8JtezZ8927dpFR0fb29vXtq5UKj1w4EBFRUWV8szMTMVTdsIKeUYJhhBCh9rJp3xs91SIX+kcp8NAeVJdFtWcQ1PxRwIAKMHW1vb58+c+Pj4lJSVMJrO5hwNAi9CkAb4xj8lhGJaXl1c9TWRGRoZQKCRn7ydVnI0sI6ZL5ljgYplOqgSlShBCXQwZvv3grl0AmgeDwdi6devWrVubeyAAtBTfzWNyLBZry5Yt1cv/+uuvmzdvkrMT22pNbKuFEKqMTjz/PPO9BtvfBNfu1RnBaT0AAAAtibo9JkcovhFafPcJtZ8bFdcsvnmr9Fm4YNVsiPEAAABajuZ5TO6rdiERFmXfeWy1daVmDoORLTWetCx94+6Ct5/0u3f4qv0CAAAA3w41zAf/Oj17weJpeTrsVElESsX70pwCH49hJ8rLmntcAAAAQNNp0iP4ioqKoqKi2pbq6ekRGWgayZFOH/sxZjJX24pyVqCTNzt5IUckd7/2QGJhqmFp1vj2AQAAgG9fkx7Bv3z50t7e3qgWz58/V0kvzDbmo8Ne2Be++IfllWnsk28oWXsjhOfqnLvjiLyiUiVdAAAAAN+4Jg3w/fv3T0xMbNOmzY4dO0TV9OvXTyW9UJga0mXDKSXneSVYAtJ2jL8eO81E130ww9igMiZRJV0AAAAA37imvsmOx+O5u7traWl91URSlzUe5Oiual2CjZNgZxx+ERbt6icroXF1MFHJ1+sUAAAA+HY0dYBHCK1bt+6rvmzylejFXaqzPcNgzV9Xzf1WMMVlB+SLTmVed4tK0B0z+Ov1CwAAAHw7muEueh6PV/dL6xpDhksDc8PaaprttbbU0NTM2X54fGT8dGH5/eLCoj4mDHPjr9QvAAAA8E1phiP4r+pzSYSs/I2s/M3cbIRGEmV3EY54GPo8cIxD8w4OAAAAaCrqFuA76Tge6xTc3KMAAAAAmpm6BXiEkFz8VpK8GuFyHCtB8goKwwAhRBdMoxvObO6hAQAAAE1EDd9kJy+Pp9D5Gm0CEI7kWCHDYgONO0he+qG5xwUAAAA0HXU7gscRSiiWPH/hil58QGghQgh9SkHIpE/n9DZynEGFfDMAAABaBHU7gg9Nrrj24t+34T7tev1p1+vEdE506c6X8BA8AACAlkLdAryTObPMkIEQIkM7MSES0MfaaTbnyAAAAIAmpG4BnkmjOFRIiaBenCLOeJWHEHra9bqJUNxWXwWZbAAAAIDvgroFeIRQG1m007vRCKGr7mH3f3peGF3k9G60ISu5uccFAAAANB01DPAfqBMQQhv9/s0MS/klc9asWRcT1zXroAAAAIAmpYYB/l45e9asWQghHboGQuhVfiZCKJRjVfr0rejCLXHoC0gaCwAAQO2pW4AXVcolsdykcQsRQq7GVgihnPLSj6Pmtn6NPXqZjKjU8oiozGVbZDn5zT1SAAAA4CtStwAfHp6Sqvfvh5pn2wUhJMfxxzmpP+cdjTS353oMM1gxS3fskIKjF5t1mAAAAMDXpW4Bnnb8YPCj34jp1hwug0FDCN1Ii0UIOV/aR5Sz+/esjE5EcnlzDRIAAAD42tTtTXb9/9xx8ZrPM6PPxUkYTUvGjjUWfkx/kpt2eqJw3chAog6OYYhKRV8zJz0AAADQvNTtCB4hNGTEUglFPuG2btc8ax1rY4RQhUymcUNCZTGJCsU3QjU720OABwAAoMbU7QgeIfTh+T+5f1eELdZkvktOvRFOFMa8Sc5YtoVp10aamoljmKH3wuYdJAAAAPBVqVuAl4vf9jbc09udGS8THy8vxzE5QkiXTfupcze6UQzTdiDbyZHlYIOoanjqAgAAACCpW5yjaJhiJa9p+mNbCYfNZbRDCCEKKhJjrx2YFBqbYWLI6mAH0R0AAIDaU7dQR9EwZpguw4oeyrItBIZD2k2aqGWqjxCKeRQlKbKTZuY09wABAACApqBuAR4hxDBeLBe/1zDLqYxL9hi+2sitJ0IoXBgRl5Uvyy1o7tEBAAAATUENAzyisjQsfKnsY5hIZPp3BaejDUIoJrHwplWHin9imntwAAAAQFNQxwCPEE1/DKLr8hfrc4zT9Du2RggJizGz1gX8xdOae2gAAABAU1DPAI8Q0mi1FcsL6DT8CMoTahtr4zh+bM1Wx8GDcg6fa+6hAQAAAF+d2gZ4qnZHGm/YlXOxYWM2MXnaCKHkwvx/oj6FXb0OV+IBAACoPbUN8AghDcutMSJ3hBDSYiOENFnaCKEb5QUl958278AAAACAr02dAzyiajla2CKEkDYXIcQkAvz718IHf+OVkuYdGgAAKEovwZKLZM09CqBW1DrAIzRuxPABbCf9yVMoVEp2VraFKbtMXPyCUi5+8qa5hwYAAP+5GV9+Obq8uUcB1EpTB/jY2Nhly5Y5Ojqam5tzuVxbW9thw4adOnUKw7Cv0Z3QQtfbZJXRkF48Kx2ZXGakL0UInc9MLrnz+Gt0BwD4pjworwgrr1AsOS0u/SyRNtd46oA39wCA+mnSd9G/e/fOyclpzJgxS5YsMTY2ZrFYIpEoMjJy27ZtDx48OHnypMp7vCZ5MrCCSpNlCzoZFcYVCUsNKJSMe0/D0PTZCMchoRwA6s2CTpuTVyBD3CGaLITQweKSW2Xlwww0m3tcADSFJg3wBw4cWLFihZ+fn2Khm5vb4sWLra2tMzMzTUxMVNhdVmV6gjjcRT6AicUJOreLvhyTlJLd2czgfVruY1zs9ugVe0AvFXYHAPjW2DEYRwz05+QVSHHdbAz7q7T8uEBfn/atXJoskeCiCjkxXVQpL5OgtOJ/z2XqMCm6zG9lnOA71aQBXiwWGxsbVy/X0tLicrklJSWq7e5h/h2pXORzSCr9HI3a2yKE5Ljcuhd6n4aO3Qjux7FltrViGBmotlMAwDfFnsE4aqA/JTefTaFeMuQL/o+9845r4nwD+Ht32TskhLCHIIIIggO3FhXce+DeWke1Dqptf61bq622WvesW1y4N4i4FRUQlA1hjwSyd+5+f4RSRKooI2jv++GP3Ju7930uIXnyPhNBGmihS+mage4fZxs4kKCMSK3wu8v1KIZCMXkVDoVgF8oPnVj1LCLOf4xGVfDjxo2bOHEiiqIhISFmE71MJktMTNy5cyedTvf09Kzf5UbZTWnDVw/sH5Ke+7TD7a0UFlkr1zkwbLv3pDuHLGDQZMqb97kTh9bvojg4OE2NB1odF4bVGPZMp+9PaxD7vM6ELb0j+1gFv6Atc0FbpvnxzpdKlR5bEshsAOlw/qM0qglo4MCB+/fvP378uKenJ5PJJBKJfD5/6NChVlZWN27cqPflCBDBmsggyPWYCS0tIFq3tAIAXLuj2j66s5bI5QwLYfbpVu+L4uDgNCn2K5RnlerjAv4Ba956qeyKGo9Ux/mv0Kg7eADAsGHDhg0bZjAYioqKVCqVtbU1h8NBamE0UyqVffr00Wq11cZlMpnRWHPyaEKJ4acHMhqDD8qArlzP97XKfVSYnpf9Sjd56Zvd8tCtSGIqZjQxg7vUw43h4OA0Pc6q1BEq9WEBn4/AAgTZZ82bUSqxQxB/MsnSouHgNDiNreAvXrx48uRJFxeXn3/++eHDhz///LNIJOrWrdvu3btdXV3fcyGDwdi+ffu7ujwmJmbZsmU1XuLJI6ztwh6/4uvOFzF5QEs+2hyAJD6D7+jNzZXn77hWuMXfuXjVNsZXgRCRWG93iIOD02ToTCH3pFI4cIWpsgWRGC6wZsL1kz5jQsHTQj2KYQAAA4qhGHiQpzM/RSZAATakj1qHRoBQtF7kwsGpoFEV/LVr18aOHRsaGhoTExMSEpKXl7dp0yYnJ6cdO3YMHz78+fPn0Hvz1vz8/N4dLCgo+LeryAjkY00Mt+oomNq5C6yjtGwHIxfECrFMQW1ud/dh4VJtH0eSi73q/nM8nB4H54tE+I510I5Qb0F2hSrTrpdKc/46igEThu2OU5mfgiGwKYjDo36ED3SCDx1PhcepXxpVwR85cmTJkiUrVqwwmUx2dna//PLLkCFDAAC7d+92cHDIyMhwd3ev90W5OXpVnpY6gC6nuTu2tBcl5G7dGX5m2bgOyRlXM60HD+pVdvAMo0cgnhOPg4PzUTgwkUMDrMyPdSbM/0Dx4b8PP4F6Mivg4PxDowbZaTQaKysrAACCICwWi81mm8cRBGEymRpNgwS/3KGZlGK9A/u6luRi428FAIiOjtZ5jSCm/jRnQMcSNgUiIJqXrxtiaRwcHBwcHEvRqDv4Pn36rF+/nkqlvnr1SqVSbdy4sVOnTgKBYO/evUqlst7T5MwkMgCSnUIRv6R6BBNb0QAADCI96sbR3Jdx5ZnqX/7Y/tvwUbILt6kBLRtidRycJguKohj2r1bh2oS+1hIMw97vfcPBaXwMBcW6N5kQiUhp1RzhfJklBxp1Bz99+vSxY8euX78+MTHx2bNn3bt3d3BwoNFoP/3005EjR0ikBolrNbHA+OhxNyYeLQWYRwtHAIBCpTx++uWsIQIAwIljx8jt/WiBrRtiaRycpkzPnj0J/87du3Xt14Bh2OrVq+3s7KhU6tChQ2UyWeVTnp6e9+/fr+P8TQoCBLmwG6qEDk69Izt7o2jFVl1qlvp5YsHi9eqnCfW+hElhzBgeV362uN5nrj2NuoNHEGTdunXr1q0zH27YsCEsLEwkEnl7e1OpDVUdGhGQTZhJViB3UKtteE58WytxYdm9h4/Cjyc4r2khKi2Iir7Tq3sX+dVoVr8eDSQDDk4T5PTp0zqdrsanpFKpm5tbHef/66+/tm/f/scff/B4vD/++GPQoEE3btygUCgAALVa3UD9pSwFAoOro/CymJ8HutQsRdQj+00/wEw6AECfnV+8ZjvF2x1m0OprCZPCmD4kjuREyVuaBgDgDrepr5k/isZOk6sGn8/n8/kNugTBmmwFc0tMpQRJkYDjb+drIy4s0ym1+WLd8EGCzQcUf+79q3f3HrJzN6h+XkR7y7wNODiNT9WPnkgkSk9PNz9GUXThwoWHDh1q06ZNXea/cuXKypUrQ0NDAQDdu3cPCgpauXLl+vXra3l5QkKCwVC97Vt6evp73Ao4OLVB+yqV0bWtWbsDAEgu9iRXB11aNtXfu17mR1WmjBHxVG+G0xZPbbo6bcBLzIBZhQrrZfKPwsIKvhGgsIjD1q1UFMZgDoie7GXdmgVuABZg3boRMbmv8veD0PVLETLNDlbfHoqb96ymjLC0vDg4jc2tW7f69evH5XKVSqWTk5NIJOrcubO3d12/7MhkcqUyJpFIe/fuDQwMHD58eNu2bT94rVKpnD179rsGBplM9oVt/XEaH8xkAvBb7mkIhrF6qkKAqkzpw+IozelOWzwBDFGa0z0u+6cNeAkAaHwd/+V3K2LB0ABR17AFC6FSUbSixCqQDQBQk9SnjkTYKZu1tbU16rQnDh1hDenNHt7H0sLi4FiAgwcPLl26tLCw0NnZOTk5+cGDBwwGo+5es5CQkOXLl1+6dEksFgMAvLy8Vq5cOWTIkKioqA/uwhkMxoMHD2LfYfPmzQTCl78twWlQKD4eqvvPUW3Fz0dDQYkuXUT2cKmXyTNGxFM8aE5bW1QmPlKa090jWuctTVVEl9XLErXny1fwHBhmPZRr5kmJ5YWmNBdvz5Y8ayupUhbSJwT0ODm5hzsA4MDGTQBFNc8Tyw5HWFpeHJzGRiqV+vr6IghiZ2dXUlISEBBQWFiYm5tbx2knTpw4d+7cCRMmTJkyxTyycOHCb7/9dujQofn5+XWWGgfnE6F4e9DatypYtK78cIRkz8min37nThqGsBj1MjmtHUsdpzBK3/IuqZ/LIQpMcm6oULN/48tX8ANRErPYwJEjtPjWxXYU9mXY3ckaAKA0GgoU0pFzutGIpNgCUf69x1R/b2XUI5O0nrvW4uA0cby8vM6fP6/Vaps3b379+nUAQElJiXnbXUd+/vlnqVQaHh5eObJkyZLCwsLHjx+3atWq7vPj4Hwa3PFDBEumwRwWycnOdt1iRrd2H3U5ptPLr90tO3BaduG2Sa6s+pT9KndWb15a/5fGsgodLzlSWLAq0+OCP9kVV/D1iklhzB8ST/fHXticcG/prOIQ7Vg+Vq42AICrBy7/8ccfTGbMyp6t7ActLi3HEA6L0bWd/HKUpaXGwWlUFi9efO/evUOHDk2ePHn27NktWrRAEKRly3qrDEGj0aodBgYGmmte4eBYCpKbE3tQT2afbgSbjwv0NkkV+QvX6lKzCbYCY4mkYNFafU5B1RPsljdjh/DSBrw0lhkkRwoLVmZ4XPantKDXq/i14kv2ZqEaNH1IHPBl/JV/f92Z/X7aOGRzeOEAMgvQwRlQUFJYHi0jbt4/dN42dV6rS7C9PwCswb0Kwn5hD+ldj/kSODhNHDs7u6ysLJ1OR6fTY2JiXr58OWjQoAaqS4GD87lTfvwio0cgZ1Q/8yGlhVvZvlPCVd9WPcdueTNMj6Z0f4bq0ebXA8jullEoX7SCVxn1OVpkgq08hwYAkOVL7AxyJdOB7k8mE0iF+iJmAbO4xIGvaz/K/q+Jkj7LTBiJz6UHtpZfvsMJ7W9p8XFwGomrV68WFhZWHkIQdOnSpf79+wuFFkjswflkMEMxqn4DAMCM5QAmQzANAAAzAiDkyyzTZil0qVnswb0qD2kd/CW7T2ImFELesojbr/UguVJZX1mRm1lsu/glK3gCn9T8esCbfi+cnPkAAINc35atxtKbWUmtnLwc0l5lukLC4zMXTpvilfFIZ+tffCub3b8ZhT0suHDpr6zBPWEqxdJ3gIPTGNy9ezchoaKSl0KhePbsmZ+fX9euXXEF/3lhKPjTVH4DJjugqpcApsFUT1STSrRfQrCZYmnRvihgChnVaCsPMb0BEBCopmZB1tMdGlGuGviSFTwAgNyM5nLFP7D70/nssexxVEV2ypsC62ZUg7OfY9qrTAqBeVZbOr9nkDtB3ebp1ZO2Dl/Zou2DuseeOAfh9kmc/wwbNmyoepiQkNC3b99qjnOczwGUaDMVonkZsr/DjOUk182Gwu0A4E3m6xlqGx/ZuRvWi6ZBBARgmPTUFVqbVjX2I93wWDHai+rCtpie/cKD7AAAHHf66SXCyci0HjkdDeK0ZH+s1Enr3sUOAJAtyElMTDQwx7mFrEtK39ScodfpdAUFBZRWzbPW/rlozlxLy46DYwF8fX0dHR2zsrIsLQjOp2DI20h0WEawmW4o2GxpWeoMBnLmJ6f0jDXJjZYW5R/Yw4IhIjH/m5UlG/fkL1ijz863mjq8xjPjS/TFKkv+wPrCd/BmxF4UYjO6lcFD7xijI1H9yJ1zO6fSaLSSLHFzH49nL5J69OjX3In1x4iWEV5+AIDU1NTIqKiBnQItLTgOjgVITEyMj49v6BrSOHXhaaFeQIPf3Rqi2nRglCC8IbBJqY1rg7B7WEK6egIDOfOTtckqmi8zffBL9wv+CKtJKCwIQawXTjHkFxvyiwl8LsnN0dIS/Stf/g4eAIDySUadye9oV0BUMzBxoGBKGSYO7BCIoRijJamopBBVvZzSz41sQF8+uKMzGHv16sUb1d+LgJsocf4T9OvXj/k3DAajVatWffv29fLysrRcOG+RITVezdBezdBeTNN8fb18/i2p+fBqhlasqdgmotIoosNSAGAIYRFspqOq+m+S1khgIHdRivaNsllEa8fNnvR27LT+L4zl1XsTWBCivQ2tvW9T1u7gP7KDB45kU0TzkydOAg/gaUVJ1muFkI3GDYAo4HLdRdNHEzph8cYZCQPsXPamxOkJdAzDNh7Y8ysAdncv/nX+LJfLtfQN4OA0IPv27dNoNJWHFArF3t7egvLg1Mj1DO2BV6pO9iSR3ERCQIbUePK1mkWGbmZp9/ez6upIxnR5qKEQ1eWgBVsAAACgqDYdM0gsLPcngIHcRSmaV4pm5/0RBgIAcNjQPG9pavrAl+6X/AlcoqXlex+bnyok2orfW1lS474E1cV0DQAAgcAIT5qvoFGF/1cFn5WVtXXr1uTkZA6HExISMnHiRBj+XLf7W/jcbf2/X/v8187fdpQPZpU7RXfRIuTmVADAC9Mr79SWw8bNLBfYDQzadDAtwSgthpgO69at49Po3N0RLAIebYfzZbJ9+/bKDnLvMm/evGbNmjWmPDjvZ5wP7cAr1eL2rEmXJbv7WCWUGO7l6gZ7UEVyUxdHMgAAJjuhMM1YsLXyEgjhQMTPr0Nm8Z85iuiyFvfbw3SkYggCDhuaZ01JzJnzxu2Er0Wl+wAteES5rkLBP8nXubEJruyKu+BSGluH1rxeSkqKr69vYWFhnz59WrRosXLlyq+//rqRJatHhAgCckwAAKgENSlRObDVUTG2P5FMJWUrch6ee3zr+k0Gy3bV9fhgOzcAMEePlgsWLLBzb8bq0lZ+KdLS4uPgNAgkEolCoVAoFBKJdPDgwVu3bul0OqlUun//fqVSWfdmMzj1C4cMj/GiLYuWunMJfgLiaC9qktjw2xPFgrYMcwA30XkNtW3m238ZBJtJVSfJHjHPIsJ/FJyB1qgOLQsvqhxBNdriTZGya4UkxxxDXtF7rrU4/ZpRQr1p5j8BHQlyJlceOrKQD19fr9Ss4Lds2TJr1qyTJ08uWLBg+fLlT548OXnyZGlpaSMLV1+cU6l5VnYAAIqa7sW/BZGaT3NbqKcp2/i1wTDMrlD4+OGTAQMGnA4YMNa1JQAgKfHVyJEjR44cyR7RR3HrAV6dHueLZMaMGevXr1+/fr29vX1wcPCrV6927Nhx8ODBZ8+e3bhxw9ra2tIC4lRnciv68yL9yBY0AAAJgbo7kYo1aE+XL61iB9mV2vx6m+LfRaV78wAAqEqdM3V70W+o7RIGoz2haMUWzYskS8v4eVCzgs/NzQ0M/CeGXCAQuLi45OTkNJZU9UymwUi/NO6HH36Y9e1MLjFVgppIRCsd2eDp6QYAUAt1OTk5p0+fBgB0Eji4MjiykvyWfgHHjx8n8DiMbu1kETcsfQc4OA1IVFTU0KFDob8TeT09PRkMRlIS/h3a5Lgt0tozkUf5FX1OkyRGBAJJpbUNPTNv3z+LTTzJieJxJaB4S07p3ryS32/Lov1cDvjbhHXkjOgj+G6mZG/4h6fA+TcF36pVqxMnTphMJvNhbGxsVlaWp6dnIwpWnzBhmHda/v2AsF6dehOIRUyT7qz4nhulWbeRXQAAz9QvgoKCWKv2AAAgAMY38wEArNn0p6OjIwCAPTREGfPMKC637C3g4DQcjo6Oly9frjxMTk7OzMx0cLBwES6cahhRsOuFckUX1uUMbZHKdEek0xmxOf6MHS+UH774bb3+2ej4S/7Fm0VFf1DsVtmw+1XkbZKbu2IG42dhWB3kTnVhN7ZZvio1K/jvvvsuNTXVw8Nj1KhRISEhXbt23bBhA4NRP+1yGx8WDMF5Ok2Ckk3kAkjXkgY0BDt3q1acADafz8/JyenYsWPlySOcW9AIxDePow1GIwAA4TBZfbopox5ZTnwcnIYlLCzs1q1bHTp0+O6776ZNm9a+ffuFCxfiefBNjYhUtR4FRUq0FZ+4JFK28r6srZAEQ+C2SJtS1oTqwNQjZls9f1wR3ddUOYiZUEynh2mfgWMi1JtmQ296Cp7D4cTFxW3cuNHX13fgwIEvXryYM2dOI0tWj7BgWM5DjGI9BCABWWgLIEB0c6M3z9Kk9ejRAwCAnf7HCM8ikvd36r+7Y9/80IruQJxR/ViDemL6JpSCiYNTjzg5OaWkpIwYMUIikVhZWZ0/f379+vWWFgqnOmwy/JUz+ZXYwKPBz4p0MgOGApAuNY5sQTOh2PuvfXfL/lls4gEAJGcKZ7iX9OwNk0IJAAAYJj1xkeLTHCI16WS5CrAPvC8Nzb+myREIhBEjRpgfGwyft25jwnAGFyl4UxB59KF1B+HRN3leLeCpfI+90k3NXVwBAPdLCRHyR2u2r+/V1RmV3/e6f6nE98EbhO9ivh6C1E/i1U8TBGHTLXkbODgNBpfLXbJkiaWlwHkfwa6UYNeKbasPn+jGIXR3IltWpMaB0a2dsaCkYP4aoqPQWCIh2Aqsv51saaE+gPZ1evnhCL0oH6ZQGF8Fckb3h8gWyLiuWcErlcqwsLDx48e7urr2798/OTl506ZNn+8mngVDEivCpuitJ/adXrznGy//olc6K6URUAwEtgsTAPCg8OEhp5/9Pb1gqiNM9XS3T7a9sLVw4lqpDuWQYQAAvZO/9MQlXVo22cPFwjeDg1NP9OrVy8bG5tixY7169bp79261ZyMjI7t162YRwXA+yBRf+ked73JmWwNJ0jhwQvuz+vfQ5xYiXDbRtqnndxhyC0s3H+DNHktt7YVNawyTAAAgAElEQVTKlGUHz0j2neLPHd/4ktRsot+4ceOjR4/s7e137drl7u5+5cqVpUuXyuXyRhauvuDAcDkXphhIAABTIRjpTDTALBimNUNciEKDE5srM6qVYvqbo+eKMrMwQynJsXnxg/KI2IJdf0evQEQib/ZYgOJ9mXC+HI4dO7Zlyxbzg/R3qJpHg4NjcWAmneLt3vS1OwBAEfmI1b8HrY0PhCCIFZs/f6L6aULVDrONRs0KPjY2NiwszMXF5e7du9OnTw8KCnJ2dk5NTW1k4eoLFwJhaQu+ELIBAFC1jB6cQY4EsgxiN6O3QDTZXf0CAADPqC9PPnp2Yfsek7TUWLQxjZLX7umRMykarbHCiUL180LVWl0q3mIL5wvBxsbGHElnY2PjXAU7OztnZ2cy+T9h/m2CYLocTJf97h8wqSwtWm2ZGj9kavwQS0thMYzFYqKDsPIQIhIJ1lbGYnHjS1KzgufxeCKRSCwWP3/+vFOnTgaDQSwW0+kfZxRqUth7Mhff/X7VqlWzZ8++mCnRStHokhT+2eRcB51fn54AgOucBwUEZUTSyyOXn5xM+EEWPKqVqPCXLhR5eVnlJBiKSnadwPfxOF8YSqVy9uzZDx48KCgo8Pf3Z7FYO3bssLRQ/1FQ9RvNSz/t64Ha10Pf+kvoqsv4PJyklar9P6vjifY2unRR5SGq0hiLxURbQeNLUrOCnzlz5qpVq1q2bDlgwAAmk9mtWzeBQPD55sEDAMIk5WUL8v73/f8cHR3vG5YY5NrMB8nceJmSbugel45AcEZGho+Pz/PnzysvgZ1zt8wbbu/okJaeYR6htfGBmXTFnccWugkcnAbhC3PJfdbANC+E05so/Jrq//KfP9/7AGES7eZbWroPU02p/zd1vCqkqyz6qeJ6jFFcrkvNKtmwWz04CLVEkF3NCr5r164vXrz4888/Dx48CAAYNmzYnTt3PtNmM/vkSrEJfazTqROVxhK9FsNQmKNVyjIDvcj2QmeT3fPlU+lu/kajUaFQIAgiElX88ppsv+i5qMSo04at3lQ5m9WU4dITly3iTcHBaSC+MJfc5w7J5RdD/u+YoaRyxFCwCWH3gBltLChVLTngd/49h/8RDhGQX36ao3idVvS/zZJ9p1737DStY0CJyfThK+ubf9XZBALh4sWLwcHBIpEIQZDPt8oNCsCkUjEdhiE7klpumCMuAwQbmr6ghMAXrphvn4JCRdFU7x4AgMTExDZt2lRu4jvz4gb6UiAIuhz+l0RS0XKR5OJA9fWUX8Q70OB8OXx5LrnPGojihliHGnIrShFgumxj8WGi48+Wlar2VCr1/6Z2BwAs47LpVMrKCUOsd64qWL3wZ3enzXyuLWKBijc1K/jExMRu3brxeLzi4mKtVnv69OnQ0NBGlqy+mMliDKPTigymolPe06SvRYf+CqI7drQpUWOmJJK4dfCEIs2r9s6eAIDExMS2bdvGxsaaL9RyQtf2aNHd092k06zduqdyQs7YQfLrMaYyqWXuBwenvvnyXHK1BMXQU4WHdGiTM8gRHZaapNdR1UsAgF70E8F2LkQSfvCqSsoOnSs7dK7BpPswB/zO17t2z9ZkPCy/U79zNhAIABt5XAYMD85P/aZUvInPDbRQyGrNCv7YsWOzZ8/esmWLvb09AODOnTu3bt2qtF1/dkxjMuwI8J2dWWlL1kSFLRE9yHfhi+1g9SnxI3eBX6HQMF9bwGAwSkpKKBSKyWTKzc0FAMwM4LMHDu3kRQYA7N3xp16vN89G4HOtJg9HtXpL3hIOTv3xJbnkPop7Zbdvll68XHzG0oJUB0KYRIel+uzvTfIYVJVItJ1d+2sV12OUUY+VUY8V12MaTsKPJcNgzDK+VU/3td6Qb6yt1RoD2F+524/k7RbrixtAuvoHAaAzQZyLUkloSWuSBbzvZmr+DL9588bb27vykEKhuLi4VJqp645SqczIyIiLiysoKDAaG7yKshbDFBjGk6NUHRUAoMhTFWgLXDQkcqmd4doTvoH5ujsLbhkMANCcl80JmlVooGw09OWqL2L6O08Ksp2c7JXiwm0HjlZOyOjeXp+Ro88paGjJcXAahy/GJVd7NCb1heKT812+v1t2s1j31md5vVR2q0qcjQJFZ5VKihrXh0qwngBQtT51Msl5FYBrW3ddcfuB7MJtRvf2jK86yC5Fya9Wr19kKQpMpkklkpS/i6I+1elmlkqktc5IipHcIsPk/oLh4QV/NZSI9coznW61DFpEKyajkslFWToL1aytWcEHBgYeOHBAKq2wQj99+jQ3N7dFixZ1X+/o0aOtW7dmMpnu7u5t27a1t7fncrnTpk1ruGbzWgybIy6jQbC3Pb0VzRkAoJCbClW5yfEKmZFrFJc5pYE4jrFdey8AQIzy/FjQ/LnrsBCbpAOvNKjs/q7NCxa08QQA7NyyGavyJmFGo2T3SYuXGsbBqTtfkkuu9lwoPtma1c6X1TbYetCZwiNVnxpKp60sl15RawAAChSdXlrmQiTYNLIPFUJILr8i3BDEamAtr1DcfiA7e0O4Yj7MoME0inDlAvnVO01Ex3elkP/HZU8rkSTpDc91+kXi8t943Ja1qyevRTUXik+OsZvWRzAkV5udpIhraGnryGOtbm5pcTfj1an8tpt51sXarG8lEoMllEXNCn7RokUGg8He3v7ly5dTp04NDg7esmULjUar42Jnz55dsGDB3LlzRSKRXq83Go1yufz27dtGozE4OLiOk/8bSyTl1jA8gE7VWBPnOw4btfHXhGFjPNIZHZ2sEx050Lgebl3GSOnSxS0xCIYfpGWuXhDur0kJtc66Gd+mnNS5Wbtvh9oI7QWC9OSki9duVk7L6BEITCZdCl73Buez5wtzydWGEl3hw/LowcJQAEAf6yF5WlFVtdGCSNxnzVsvlZ1VqaeXlrUmE5dx2FCjCwkzA0nNdtby5LK/zpYfvUDv3Eb16KUuOVOXnKm6H0vv4C89daXsr7MNKmctCaZSfrbiTCsRzy0t+43H7UCprVv6QtFJP1ZbF5o7ASKOtJ14suAAilkgIr32xOvVLVV/fSvsCwHIh+k7CDw16XMkliigUrOCJ5PJt2/fvnr16urVq6dNm/bixYvJkyfXfbHw8PC1a9fOmDHDycmJSCQCAJhMZmBg4KFDh3Q6XXJyct2XeJcZLMY6Hncak9HNiUkpJ4WHLVmh1E8WB33fxjrfhLyQxWYpfRisLMK6Xu5kL6PBUPRMbOiyd/XKiEknxTcNSzF9bpk7NNLRFQBwPqGK+weChGsWER1tMRNe9wbn86ahXXJNkOMF+wYIRrAJXAAAASKMsJ1QTW20IBK38LgryqVsBKq7docgCIIa9hcCptVDAGBaLapUY3oDpjegSjWm1UIANJ1OmDwYRiEIAxiz1hEeJbrCB+V3hgjHmA/bsDtyiFYxZbcaTMZ6gKu62I1m5UrzMB+OtZ9CKf+DilqgsMS/vsoGg8Hd3X3YsGEhISFkMjk/P78yyuyT4XA4NW4LlEqlVCrlcDh1nL9G/EgkBAAaBDFtyIZiPQDAT29QIbpksdQWgWlU32k+9gIjgRL8nDHFBADIvVcMALDZSGw2KmO8n4M+fU6zoUFj7LzuRz9Lsg0xVGnLCBEQ6Zlr0vDLDSE2Dk6j0XAuuaZJkiKuSFcQxO9bOWJWG3erqA0Fim6UKvrQqK/1hqtqTV2Wq1TtDarmeV+PYQ/vo37xmhncheLrSfH1ZPbtrolLZg3qeRqV/vHHHw20bu15rtMvEJdt5XFX87izSiVJtfvZcbxgf3/BcPNPMTOhdlPPF51QGhUNJmmdKNEX3S+LHCYcVzkiIAm7WvU8V3Ss8YWpuZtcZGTk0KFDFYq3XsHo6Oju3bvXZbH58+d36tQpLS0tJCTE1taWQqHIZLLExMSjR4/269dPKPyIPJCPJcNgvEbX99CYAAAUr2aPXu9/kKZnC4cXs+mYpNwlE85GCI5dhS93JOfdLwYYABCIyczrAAHgvJpEtHYfchuRPNrdx5cIv/X5ZA/sWbB4PSOoI1H4GbRAwMGpkUWLFkVGRtrb2xOJxKlTpyYlJdWLS67JEl7wFxEi7s15S+epTaqIouNdrXoRIILZ7262zKcYDNNLJQCA/jTqJ6zV0Bv3qrAGBgEAilZspbVrBSA4K2x9Gpfcb1iIeM0anU4HANi2bZuzs/PAgbV16tcjcXr9AnHZZj63PZkMAEC57Nnisr+seW7Ef21ZDgBIVb1OkMciEJKpfqvskgkz3Sy9MMz2U/qzZUqNEamaxe2Zn3BtbThfdAIG0N7ct/67dCZtpjq1r2CokGzfQOvWSM0v7rZt24YPH75lyxYWi1WPi/n4+CQlJe3cufPw4cMFBQUqlcra2trd3X3t2rWVveffQ0FBgVZbPWO1uLhWWRMKDL1PNs190fHFixe3bt3q2KEDQfoCVXa/WP689e5o7wkt4wfGbKWRbgsoqiJNWarM+7w4slj1rT6MxOkFAGAPGZebsNGHP3fmtfJtwRwSUvGhRazYrKG9yw6etfn+6498MXBwmgpxcXFnz56Ni4tLSEig0+k9evRwc3OztFD1yWtlQrTkhsIg68rr2ZHbY5z9DLmxhjoWFIRKgAgAgNXlskq/u9kfP6NU0oZMElqiVslHwRoYhKGo/PxNAACtV6cla394ziGaf2T8+uuve/fuvXPHMqnkGAb+5Fv5/12utQ+NSoPhDzrSHSjOc1y+w94JT2vL7uRIdfk0SYpUprjiBvRZDLIZ1YUbVMMTEMQn2TTcujVSs4JnsVhDhw6tX+1uxtHRceXKlWYHPIqiSUlJSqXSz8/vg0m3CoUiKCjIYKj+xqhUqncH34UNwzIUzZyUuEqx8sLViycuHwOOBq2IVsLlmFaM9iGkXMiLUYf7jPKN/eu2ttlv6kHdnfddjTJ/MFBVHICQ5Zte9370vclv6bkUTaj3P5ub8sMRRAeh+tkrWrtWH/1y4OA0AbZv3z5gwIBRo0bV0UTXNLlUfOpRebQLzR0F6P2yqGfSB/Ndf4TA+zbWq6045Co77xZE4m1bG3Ij7sXrAntwL0NhKQCAP25IdO9OQUFBNjY2CIJkZ2ffuXPHHEfZ+Pi/U4m9Wy2C7GgIvS27U8NI1FAIyfaNvE1/DzWr1QULFmzatCkyMlJShbonrGu12lmzZpkrZMlksq5du/r6+gYHB7u6ukZGfqD4K5PJTE5OzniHvXv3mn8uvB8ODEtR1Fis4xDYAABprkwNSYvlBBXJ0V7ozSMwiCZYnODUP4gAALhTksV/095JQK34tYuZDDk/L5wyo128bK4fdVecslpcHW/aqLKDZ5pOJAsOzkexcuXKM2fOPHnyRK1Wm/7G0kLVD/mP794SnZl6zM7xmVqA8pa4rVSZlC9lT95/VaUu3yZTBOYXmf/a5BUF5hd1zi96qNXVXoBqu893N6Ofxvv7sfK/HnNQkde7d+9x48bxeLy7d+9GRkYKhcLJkyf37t177dq19SLDF4YRM2LgS0t7rnkHr9fr4+Pje/XqVXWw7j74X3/9NSYmxlwta926dXQ6PS0tzd3d/dChQ+PGjcvJySE1WMUfFgwrUJTkSnNDXAEASqXKmiSY5qeMQDiFKOKEYW5qMIPxR8oPJQCAl6JSD9HydZP8tm/f3rFjRyazDcnjkJ+PY3nZAHdGnvzm0aWlHr8tGA8AyB4xDwBQtHIro2dHzasUWhufBpIfB6fhmD9/flRU1OnTp6sO1v3zbnHkl+88KoogtKPcGGMqVeXLi8t3Qb9oMe2D8qgAdofazFBkMn3DZg6iUVeXy/zJpAE06opymfgjE2fqS6lXUrUf679VhB01apS/vz8A4MyZMy9fvoQgyM3Nbfz48QAAd3f3+pWn6RNfYjiVrDY/LlaZMqTGH2Nk5kMhHfmmDQMD2G8ZPzene3+aX7/JUvMOfsuWLQMHDszPz1dWoWvXrnVc7NmzZ4sWLTJ/a7x48eKbb74x/6tNmjSJw+G8efOmjvO/BwQABgyzd3gu/uO7w4cPz5o1y5bs0NZB3IKIHCy5g2HAk6CwbcevesncsNaTJk3KyckBAMA0LwhhUL7qsvnX2fFH1vweNj2/sKjqyfzZ40jO9iYp3mET5/Pj8OHDmZmZRW/TqdNnZhoFAOiz8+RXoxU37xuLxQDDpKeuuvcdTqOw2tv1amYbwCdYeyVR+ESBHdnp3Wt1qDZREVeqK6o2ToUgFgwTIejvB41yJ/9OLfuxOjs79+rVKyEhISoq6uuvv542bVpsbOzDhw979erl4uJSFwHMu5rPCzYZasUnmv9c2QQG8Z9DNw4BAPBUel9lUkaX3Sz9TErh1pKad/AqlWrMmDF2dnb1u5ifn5+5HyUEQZ6enunp6eZxhUJRXFzcoFH0AAAuDJdeKOEUGCYsnAAAsFM5vpaKrEgtH6pLVc9TB898POh4dwCBSiMNmWAcOnRo5a9vXfJoknDB60tnvmrfPvLx43k//fK79B+fRfaIefy545XRT4Qr5oPPxFeHg2OGy+V++KQmj/TkFWX0Y1p7P8xglIZfYQ3uCTPp3rYd9CnHYAh2obnr6BJepi6redoI2wnVro2WXD9XdNyIGYiAZE91nOG0kEvkAQBSDIaras1GqVyDYdfVml+kcg2G2iPvC/xuaA74na+q1N/T0yUiImLXrl137tw5ePCgTqe7fft2UFCQh4fHmDFjGkXSJoQLm+DCrnjXHubrkiXGqnFUelR/pvDwDKeFaarX4QUH57kss5CY9U/N/6kzZ85cs2aNt7d31VxYEolUx5SPBQsW9OjRo3PnziNHjmzduvWPP/6oVCrt7Oz27NkzYMAAG5uGjTDkIbDWhKkTKnL/AtiB18oL0gpNPNcehqxTJRHkSWIjtxmrPL1iF16Wm1KSHrfj4q6dO3fCMAwz2wFd6v869EgtlkdBT26c2FcWNNqK/E/mjHj7UXIzJ9Wjl/ROAQ16Izg4ONXQpWYp7z+z2/wDTKMCAFiDexb9uBnT6YlGeI7zd7tzNhtRvd6kTuiKjrOba0dxrHptsvLV1ZKInz1+PZi7vb/N8HRV8m7R5mXuawEAHkTiYDrtbRO91JnQqArepDTBVBhC/vnurdTx+30jTpw4QaVSK4OUzfET5qSknj17PnnypPLXm1AofPToUR2FMW/fs0fMczmzrY5T1R2RSOTs7Fz3ea6VnHOntWhO93ajNf8pZX6SIq4ls3Xdp20K1Gyi3717d2xsbOvWrSlViImpa28iPp//8OHDyZMn37x501x44eDBg4cOHZo+ffqBAwfqOPkH2cG3cnOmG/IrAmScqc1CXTuPsadnmGD1a2uEN6Lwmdi2/T9W+mvXWxMOG8PDw69cuQIAINotll2kkvQMLyK3T5t2GrVqT2r1ksjWYTPI7vXwD4eDg/NRaBPTaAE+hrwis5uMKLQmt3AjuTuLdx5zghzWeP45mTBuwA3r1czlGnK7DhVBc4Wt8woD84tCy5ka5qgSXZHKpMxRZzajeRbocsv1YgAAXIOJvlENdPoc7ZsOT7ImJGKGtxz55n6sKIrqdDq9Xt+mTZvOnTv7+flptVqNRgMwo7FoF1V5gKE5ZCj4Y1AX5bCv9IaCP2iqg3TDp9eAq2qcbwqG+vbt21dWZ/pkyg2SSMnVEbYTwd81DU8U7G/ipXBrT80/RY8dO/Zu7hmbza77eiwWa+bMmTNnzqz7VB+9NAzr7MiGQl10dPStW7dWrFiRb0jq7+T3W4HpcpDtsCNWPzDIL0fJvdotSroR+fP1iJsiRZuH6sH9B508ebJPSIj88HmDKJvWzjYvMX5WufN16Nk+UfKKe9eFgrdK3KhjX8ku3ObNGN34N4iD898EM6HqpwmGnAJdushYWEpt05L39ViAYszeXbSJqbnTf4BIRDaZ5DJpMr2FV4lS/RWV8j2HdValzjAYv+OwpmefzIZYV0vPSfSlT2T3EpVxRsyQp83lkvg0GFpTLtsolWv/MdFjvaifUvHm/ejTZxrFp98egxD+pbQhkPVMB+W9cphUsRmrFrIHw7C3t/fjx4+7dOny5MkTX1/fuLg4AFBDwTaY5gXTfAAAXm5UAAAwykzyGAhhEvgfLjrSlOnSpcuSJUuGDBliNlc8e/ZszJgxKSkpSO2qFLiyCf2b/dOdL7zgYE9+Px6p4mu8DbtjtORGtORm1UKHny//mgffyHI0AhdUahIbExboNm789dq1az179oyyj/AjzHel0R97sPufz/GbMuAKd7+jhNqNwgcAXLt5bUXfJSNdh35zbNGrI6dtM4usl3ylS5ggsAmT5Ub3dGx2Oyd94c/rT+zaXHUVaivPsgNnNAkpVF9PC90oDk6t2L59e2UczLvMmzevWbNmjSnPJyM7ex0iIDCbafP91xCJWLr1cNm+U7q0LN7XY+idA6ymj8LUGphJrzyfBAEWDFMgiARBLBh2pzpnG01LHFf+mvHzAJsRfJLNqtTFXsxWAIDv2Kx5LCYAwIhhyN97d1at66jXHsykIHueADBJnzYTIvIovg818SPz//dCMHeIYLajcME/dkEIgqrpeB8fH61We/369YCAAA8Pj7i4OACRSE4rDYVbiU4/VZppMaPMWHqc1CL80yR8d8tuKUP9tm3b+vbta34REhISxo4du2vXrlpqdwCALQOpdMCnqd68VsQPtBlVNbYuiNf3YN72QG5XOvLZN022ZLRIIyNG0TIYtWcg1mw+ACArK8vWzeFhQRZD0I4q8hIsCVHGxLL8EAgUC2eP9Xl4JbGkIMEjJSC5pVGuvJOSNMW7jeJqIaZ1R8Rar57YMo+QyN/TL/y1o2jFd1XDAyEyiTdjtGTPCftNP0Dv1HbAwWk6kEgkCoUCAEBRdPfu3Q4ODt26ddNoNGfPnh09ejS1AfapDYT6cZz1winqZ6/yF6+jtWkFYUAR9dD62ykImwkAgBAYqqLdMw3G8yr1dbVWj2EoAFfUGgPmRjeJDuT+qTDKX8qexsmfjrSbZC5pR4AgViOa5A15v5FcNxqK92tTT6mfy9kDrHnjHd89DYIgs09z4sSJAAC5XJ6Zmenk5JSamurkVJEggPCHG0oOGEuOEwQVeV+G/A2IVX+Y/tl7l1u3bn3t2rW+fftqNJrQ0NA9e/YMHjz406ZKU72hIrStWdULA1Bhao4m04vhW2dhLcx/SMFzYTjDYCS705o7eQAAtFqtA8VZzCwsKMeKeNaUls2pAT4+GSpZYYqYCPo6N08sKbieFzn4l6FjZ3e6H3NvWtsuEImoju+K2NEJNp0d8mOCHd1v5Kb/tPv03uXfVF2I6u9Nae5aHn7FauJQC90rDs6HmTFjhvnB1q1bg4ODw8PDzXvUZcuW9e7de8eOHRaV7iMwyZUwk84eFkzr4KdNSgcYqnmV/G6sq0mmgOk0VyKhH436PYddaaI/rVLnGDyExvxMdarCJJvu9G1zuneNCzUoqDoBGMsQ3hBA4CnvzyPw7Bk9atXh4vr1635+fp6enomJibdu/eNiJzmv1yWPQniDIISFatJMpeEUv1oF2RmKSuWX7xiLxQQBnzXgK6KtNQCgKUTVSaVSOzs7jeaf3j9qtXrIkCEAAAiC4uPjW7X6uHKi/QTD+gmG1bOUABiL9pgUjwAAmDYdItkBmAYATHJeC5EaNlPsXerf1tRk4cJwOYp6RrZdtGLJxYsXp0+f7kB1RgmiF5mZaQ7W1xL+AgC0su4g9YLdlMZBPYIAABcvXYSdiN8c2H43KU7y8DmzV2eioy29nZjezg/K5s5p2+GbH1Yl2IToTNULWXCnDFfdi9WlZTf6XeLgfDRRUVFDhw6tjB7z9PRkMBhJSUmWlar2kJs5qWNfAQCIdjbM3p0hCoXs8Vasq/pJfN7XPxUsWpczKUx15zHRZKpqoqdAEBEi9BMMX+P55yynxRbR7gAAoziC6PAdADDC7k7g2WH6YtWzmutqVA1JNplMEASlpKRcvHgxMzPTZDJVhk/BdF+E29uYvxkAYBD9QLRfDBEFAABUZTKU6DETpktXaxIUmA5FVSajuKJZqD4rr+jHzQQrDqtfDwKPU/TjJn1mTsPeea3hcDhqtRrDsOfPnwuFQjqdLhAIzp07h2EYiqIfq90bDlP5VZjaHGEGoqrXwKgkWA3G1ImYLqvxJanVDt5gMNSmHGwTh4vAZSa0cF0WpTlt4IiBAAAH4FxiTJbLJ3Fh+YPS9I4nLjk68XKp+eqrtzusDPO+cvL169dRUVEhISGLly3L4Qopq/4kCa1NKpH+QQRl5Ahy7NWta4/MvlF+NEk9zZdedS2EybCaOkJ68rLNT5aPNcXBeT+Ojo6XL1+uTI9OTk7OzMx0cHCwrFS1hzthSNHyLcZiCbmZky4zV3n7gU0Vo5ouXSTZf1rw3QyyuzOm01Nv3T+v1t7ILzJgmAmAK2qNHsPGMejvmb/umMt3VmXKlClVDzGDGBjLIbo/qhUBAMjNJkNgceH6bMwoZvflYxhWY/A+giD9+vUjvJ22V7XMMNHxJ218J4hki+lEBGGFwUb9SpEZ+gphIQCCYDpiLNHDVFgwz8l6lgMAoPzoBavJw+hd2wEAqP7eBBt+2ZHzwuXz6+FVqCdevHjRv3//Xbt2TZs27dSpU6GhoQCAoUOblrkUZnYylZ4g2n9rLDkK0VoCAs8iYtSs4JVKZVhY2Pjx411dXfv375+cnLxp06Y5c+Y0snD1CxeGpSgKIKBNVplHtKhWh2q29SH+Zsy38gsF0XnGZ6mOgdbK5QMJAt7IkSNXrlx5+vRp973CJb8sjM64V05jsKJjtQlE3twfue36CjxPmWRRi9p1G39JMroFjUF66xNI7+hPa+tjFJcT+F9CFRGcL5iwsLC2bdt26NChW7duEonk9OnTCxcu5PP5H76yaUB0ENr9ulR+PUZ5P5Yo5NtuXFr1Q6e6+5Q9uJc5f/+xeVMAACAASURBVBUik0b37eH3zQq7jUshGhUFwByaRX9v3Nzhw4dhGK5UsRiGYRg2YUL1ajlmsqcmWU0Qlp8p0WdpyB407lBB2ckiEPihezCpMWOJNr7NPyMQEH7Pzxj92nmXt+qp7HXHJ49n1GBTeX/5EIgoINh9o89eRm5xCkAVmzSaLxPAwFhmbH4zgGRPSekVqxdpWb2szM/qs3Ip306qnIHq7yXZfeJD0jcqYWFhe/bsGThw4LRp03x8fK5cuTJhwoRBgwbVPs6uEcD0hSZZFKX1CwDTjfmbLCVGzQp+48aNjx49Wrp06a5du9zd3Tdt2jR48ODx48d/1tH1ZhM9pTlN/aLC8HWyYD+fZO1qJe+tcb4ty54zojcCIa0ktCdJV8SvNKNGjVq5cuW5c+f+N3ux5EihopWC0SOQWqqgtHSntesBACDah52btfGrVc7dnXh74pWL2lVvMAwRCIXf/yZYOrNacnwTKROBg2PGyckpJSVl//79b968sbKyOn/+fFBQTf0umzCIFYc7dlCNTxnF5ZQqKS0wAnOoVJpESqr1rh3DMARBunfvzufzy8rK7ty5Y26vXiMwC2QMjuNPYdl866S4r0gfHCdc/OHaGBS/BzWOu4VLM4bHk5ypHpf9H1/4FKcJUfg1RLRGOL0rR9QvFRQPmvUsh/SBcQiPwOrJAwDIrokF85wAAAiHZSqXI8yKAHJTuRzhNK2v/crOZAiCIAgSEBDQBN1JRy5rAPgeJJwGgAtAIACBk2qoj9zg1KzgY2Njw8LCXFxc7t69+8MPPwQFBTk7O6emprZt27aR5atHmDCMAYwzXMgdbnPjxo094TtbLHCc6Dq7XMWjlpNUKHIy/CQJISko5YkOOcTXuXkF+S1btkxKSkp0T3Neb/VI8Sg+Pn716tXm2bSvupNbRGiM267uPDnhh+8mXymbG8AgI2+b0SDIauoI8bYj9r//WFm/tikUiMDBqUZpaWlcXJxIJDp69OjZs2c7d+5MJn+4m2dT455WF0gmkapYs6M02tZOttqkdFq7iohoU7nMWFpGtPu4upldu3aNiYlp27ZtbGxsp06d/q2ruurBC9n5LJoPKDttMGZHK9+054YKVXEKUEMsfK1gdOC0uNeewCMibEI1q35tgckE67FVB4xiPYFPYvXkQfQMvUjLm2gruyY2iis89/Qubcr/Omu9eBpMp6EqTdlfZ+nd2gIAygxiLpH3/ja7/wZmMJpkCoIVG9RrhmFsbCyHw6nHCeuLHH1FvdR7ARcAAF1fDAYA5BtNn/pf8OnU/HLzeDyRSCQWi58/f96pUyeDwSAWi+n0hnVTNTQQAFG2NsZ8XWqv2B07d5w7eMEpzcuH5a/Xs56laqVUioqAduzeYdbwuRAR1ZPVCIKMHDkSABBx/wLJnjzKe9i+ffsSEhIAAPkKUzHULlH02CYsbKvb6Cl7HgxC4rcdv/QgT6d6u+AUvaM/d9zgd6vT42oep+mQmJjYrVs3Ho9XXFys1WpPnz5t9mt+dlxRa74Rl+v/ThPfKJVvlysY/b9SP3ohPXVVly5SP0soXrODPSwYIn5cApGdnZ2fn9/9+/dbtmxZmYpWDWOxuOzgGZPayuPWEMEcT+ndABIvVRgm1MQr6nJTZDcqwq7PdCeaH1P1WJba/wWnn7XzTq/0wXHScyW0gIptOntIb6KTXd6c5QWL1uXN/ploa80eGqI0ypenfBspvvKxa2EGg2RveM7k74r+tzln6jL5leh6vBFHx8bXmLXCqiwX/K3dKx9o8woaX5J/rUXfu3fvLVu2DBgwgMlkduzYUSAQmPu4f9YwYdhERzRvVKY+OgCAJk+vR3Um6v3vO3T6pjhL5s+7e/euzFvOUdoAUzaKtQoNDV2xYsW5c+fWH1glMBFcXFwOHz68YcOGY6/V4W/CaIo8lM4zEkxqK/etv4wue/3Qbv6JPyd1GRbwVnQSrV0r8fajjK86ULzdcb2O0wQ5duzY7Nmzly9fbv79eufOHT6fX1+FvgEASqWyuLhYoVAIBAKBQEBosFrua604yyTlc8Vl2/hWO+SKR1rdQQGPBMO268OkZ2+U7TsFM+ns4SGf0C1CIpHEx8f7+fklJSX9m9tb8yqF1q6V9C5W+MNpcYQVoyNV/cpJfjGDaNu0bCEIhwgAZszTSp2pBg1K4BE1ySqKx99lD2DYavJwajtffVo2ycOF2tIDAHA+/4QH3ety8ekOnO4MQnVf5HsoP3LBJFU47lsHUynGYnHJhj0Ih0Xv/IHX/4MxiRYHM5nUj14aCkoIfC6tUwBM+ect3vBYAcnDkv/W7mbuBVy4WvjHr05Gl3r9rfZBal6sa9euL168SExMHDhwIABg2LBhU6dOhRugflMjs0km70WjYEYM2BsAAMdz9yUkRAMImLThNoZJdwVF3azzQBzUrmPnJMVlu1LM09PT39//5cuXj5Hng4YOmq2evW3btqysrIk+ruGv1d/7yH9/lT2Smff8MTHeJ7Ds9UPJudX9f61eoB4AQO/SVrz1kFFcXnUQ98TjNBHevHkzbty4ykMKheLi4iKRSOqu4I8ePfrbb7/Fx8cDABAEMZlMDAZj1KhRv/zyi7V1rZK8PwoEgF943GWS8oFFJVQIPiTgcWAYAIBYsXkzRn3ytBiGRUZGdurUycnJSSAQREVF1XyaRqdNSiMLkZKjAv44IoH2GFNb568mCBe5fpR+wkwohDTUl22RypT5UIqMFqLN6XHHilgYcB8pxOhI3B3pm8fVauWC3n/GEITWhoV9Y6UP13huu1gcfr74+Hj7WbVdDMOUMU8d/lwOUykAAIINnztukPx6zAcVfBMHVamLfvoD4XHIHi6auDfSU1eFKxYQhBVBqdY0OOdu+65lhHtVdHzXF4N38chCemOHAf7rvxGBQLh48WJwcLBIJEIQhMH47Iv2AQCkJjTVYCTaksPHnr9///799S8OtD7PJfJaYIttZUoCHOhS1qJr167SBFWOo5ZAQQAAo0ePBgCcPHkSADBq1KjMzMzo6GghHenXjPJIT9kf8L+NP6y5feVnVbspJJ69Lu91+MkaIk6pfi2obWvI0cQ39DhNgcDAwAMHDlT27Xj69Glubm7VTpKfxtmzZxcsWDB37lyRSKTX641Go1wuv337ttFoDA4OrrPUNYMAICQgMhSzgiFq/RWhMxgM9+7dO3bsWHR0tMFgqFYs1gxERIwlZYYye0ZHTtlptPxaJ1W8Lc2XKL1SWpslULVGsvtkzsSwnAmLC3/4TZfaIGnTp5M1c4tN2zvxVtGIu8fYrxltu9GNvsOBNpFQg+7BdHqYQjqWsGmwcAyDwBwsDI2VPsrVZgMAyjRoWpnx3UveuiOdHphQmPFPY1bE2sokqWt7GItTfvwypbWXzY9zOKP6WS+ayh4eItn7TwHgcS1pDik68LfrvfLBGAymEBq7k3jNO/jExMRevXqNHj366dOnZp/cvXv3IiIiGlm4escaQcQmE8mOjBYbO3fvbB50oroWlOfaS8tvIV7CtnwnJycKhRKXd6+MUgIACA0N/f777y9cuKBQKIw31JMnT75169bkyZOf/jk3PWgDXGLH4+oS32T5ZT/n9ltUfGTx9DnfsDmcwYOqB/Qqrte1Fx8OTgOxaNGiyMhIe3t7IpE4derUpKSkLVu20Gi0D1/5XsLDw9euXVtZLw8AwGQyAwMDAwMDvb29k5OT3/MbQqvVrlmzxmisrkLS09ONRuOyZRUdu93d3adPnw4AUKlU69atM5lMrzt0LHZyGZsYn9d/4Dxx2QYqedP69QYUgzG0xvP/bZ6q4+PHj1cqlZs3b64cd3V1NT+oer6f1OBDI3G7PGT1D9DKdYXh9/V++vgunsYy7NGBJHt7ewCATqcrKCjIzs5+d93Yuf/TwOCxkBxw05+h0wsGPQHEfII9+T7xmdMbm+ghz1EC+mnyVx3XI1St/UT/ooc3WcHbgznxhepT9xI58gQqvyd4p9bJBaapZ0IizU6RkZr71TRARxh9uEPW3fsfO9JBxPAqpjiM5WTFxcV9/fXXrq6u764LU8iASduy5PvCv2cOIrADXB0+KKe7u3s1ST75fhtiXPcmnfF16I8//mgeJ2Bgikhjg6IAhs3nc3kDy6Na93xYCLdc0jJBH96dO+iWLHzRpWXL0ivnqf5aNwz/2k2uQX1ylsIagdMMRqI92ZCvrRx0orqV03PpeohqMCQlafMfnyBABCtrmzJWsVqtdnZ2NgfQRpyLCPil2ZTfJy34bSGBQOgX2uqIWCxzCGFRVjFoMMRzsvLsSr+3PDNbfuRq5LsK3uXMNn1mbvHaHbYbvsMz43GaFGQy+fbt23fv3k1ISKDT6T169LCxsTGZTHVMLOZwOCKR6N1xpVIplUrfH/+MIIi1tbVWq602bjYlVvY45/Eq6ocQCAQ+n//Iu2WZrd2AyFtOLi7zeNwlkvKlKq1A4P6I0HqA8ua75+v1+hrnqTZOIBCoVCqXy33/+YhJZYApvLEDNM+SIYDFt2U6l8gxAgL4KJPJbNWqlVKpfPbsGYlEeld+SK4S6NFLvnZUCMi8VQ6X3Gi9DcyvFCa6k//C5ibHwmFinZJC0BIonyB/tfEWhszb1AA6Ee7sQG7JxXbG2mvJ3Hb6xHffBZ0d71y316GnuVk9Kmy93a1CLtLOEFtgedefoC4orxkvOTm5uLjY3d3dHKNgTqWuXJc9dmDwzmOv7FhKCtFaoXWTlLLnTf+gnO9KUpf7rf9xBIYxUDlOMKEA1pojqc3nv3GEZ3xfntDF6vc/HT1T9EvGZeV5UDhWCNNU/X1vaKo3JjIzZMiQcePGjRw58quvvtq1a5enp6ePj8/hw4cDApqc7+TSpUsjR45894ugRiI12giV+qejCoI1qayvdZRICwAog57lGO/eej6rVU8CRVUmzo/swu8wpRVne8LyzpkDOvYKioiImDVrVu/evQ9132WSGh1+bV4qlky4oZzOW/9D6uKZ0tkiFeux00plTITAFonZ8j+EQk9JSWnmZP+uALKIm5hOzwkdUM+vAs7nD41Gk0gkFmnxMmbMGCKRuHv37srV+/btu2zZsu7du9dl2sTExE6dOgUHB4eEhNja2lIoFJlMlpiYePTo0e7du+/bt+8T5nz/5/2UUt2HRqns9mYC4JhC1UpDWn1fdm5Yg9ftMZVJC77bSJ0eevrelZT2b4gspEU8iUbv5urYTCKRdOjQITIy0my6GDGiesNW7ZsM6fGLwtULAQBZExNJLhTxgRxOiERxX0Bgp+tLXT2vuegycmTnbtj8OJvkVqeU6hI12uVI8W9BnEEeVADA8nvycynquKnCw4eqh7a1T8uQ6SWCMgqiNOqX9DN52QEAUlRJT8pjQHTPP3+cd+/2te+++27ZsmVnzpxJSkq6cePGu4YfbXKG4lqMUVJOcrRjD+1NEHxYtzXxILvyoxdM5TL+vAlmpS49ddWQW2i9eFrlCX999YBtQDhik6vPibKYEc+9Ge6pSso+1479/tkhN87nveYdvNkn17t3RW2E+vLJWRw+ApeaUJtvnQEAN69Eb93w66DFvzJsnfVEEQIBihxLRZStSK2ey16EkcboyRhBUywSiUaPHv3tt99GRkZqV2OKUUV2q92PX7nTHLY7cVHFEL4hd1s4Vr8WlsO0juJThEU9et6KjrwbOnfZs0tH3hWAPTQYMxh0qVnk5q6Nfvc4ODVTVFQklUq7dOly/vz5ekw98vHxSUpK2rlz5+HDhwsKClQqlbW1tbu7+9q1a9/VcPXCKMZb2gUBYCKTHq8xNMRa74JYcVgTx24UbbWh052TeIiMrAohpWjvh3YYey/m3pUrV/7P3nmHNZF1cfhOyaRDEgihIx1UBFRUxN5772tva3ftfe31U9ey9oJlcVddy6or2BsqooBSlV4DIaRAepmZ74+wiIgdYVfzPnl8kpk7d86MJGfuvef8TvPmze3t7V++fFnlQIIkEHu+Pr+I0OpgGlX7SmW7uB5RmigO5TEbFjr/2iK1hxh1dKH6uCEWLNmpy4Ll07/EzqeFeh4NThAbTA4+VWaAIZAhr2ZBPdtRK0K1aAYVY8P3WI/VRZhGTwcAUIGXVfN27efu7tq9h4Oj05ZfdpXJpDeuhVe7rEPzcaf5fFrd4X+VO38bzpAexZsPCOdtxDxcDPmFpN4gWP46miqmSH9goGAM8/FKXetLNwGvzZ/RXQe9NOqzFfC5Wje1egf/ldbk6hw+jIhxXPFAVnK44Jn15eQHVyf06zin75xp8Xo/gb6dfXyGzm2BGxIt1/5d/Ke/ZbMSXbIm275p06Y9evQ4d+7cuQcX+jXpVHatZMvqpe1+XId2WO+mfbn7lR+TPODIpFvaD2xSkDl45OxHkVHP/j6VKtrnJagmMpHQ6Io3H/ykx3BTLJ455N7M12P37t337t1r3rz5n3/+2bJly5rq1snJacOGDTXV278cXG58cPoFrR9rSvN5F3WX8EeQ79KGx1YfeVEcU1RUJBAI0tPTbWxs3j7wRP4+I2ns37Kx+H+HOMN7UwRwyYEo6RmK9SSB5Jhem8qBaFKEhQAAaH5e0uPnv8RIEoC9scqlLS1XRZZOCmAVKIwFCnxyIHNfnPKXceMAQZTsCcOlMnozf9nx89yRfXXpOTi1VLBmWgAVO5moPhKvBACkn1imkU7ASQC49V7ERSMY3TWow4QJExAEmTp1aps2baqclFCpcWkpKrCGsP98TRMAAIRRBCtm6F5mGoQiZqum9IaelQV8jsar1Bg2dfzorIHlz2GHJ7cP2feqUIa+khq9ef+CNLm31+Tc3Nxq06yvhDUCGwFAmIguU+0a5AoASE1NhQDUi7MsFGCJkj8BZ5MtQzCQ7rri1cwBtiPvezzzypQnTV7STQmfAyDst99+uj0bpsJzpk1+8eLy6UVDDAXntryQ0nCPiU0LMeGsJ9lejIvWYSdOnn2WtS3OcKBbNTYgFizexCElv/5mv31pbV+/GTPvAIKgZcuWNWjQoHfv3lu2bKlrc76U+3m6lQ9KTauPehzItHi7U8WmXXwGcrZfza+A4nJjWu/Y1MWvRHaiFZo1KlwJBUFPvZ6oKYoLyadHNf7Rw8MjKirqwYMHVQ7M1mS8UDyDAdxueBf+baHk4GlSzy455sUf7+S4raEh7W72VCpvUHnmvbFEhlp9kXbbtUytTEtY0aFmdtjayNLcMrybG82TS9kTo8poYnTnoNbTfyjZ85vmWQJEoejSc3BpqWDZNIiKAQBGNWSMasgAANy0GRqRJEqVGvRP/ribjaEoNKFniLu7OwDA9G8FpE4vOfC7OiYR4XFwqdyid0fOoOp+Fv+DUH3cqD7VuMWfQyz+0sRXTiXMGjjddar3/ZeFntzars9e/fmuXr1aWFgIAGAwGCRJ3rlz586dOz179rS1re1ytjULBkE37WxgmhGxQIOCgiAIMmlu2FA8RNpXurQFjVtRb6qEQywcuvH7x4oeFFoquzExx40Lhis1C3yvPn/xIqXgpZ+f34QJE9zd3SMiIrp1nT5X3BKrt1mXPhW26h0ybcrtqCXtUKzvpgW/xijfZQYzOJDq7kyoNDDzwwswFal05rx5M1+bfv36ubm59enTp7i4uCJS/b9IC3vsRK9yL54iMe56Vrava3kxFezrpCLnzX9FEVCDW3YEMnSU44/hxRfuSiIAC1ioeDaF9hm6jIyMDACAWq2uKOcKACAB+UfBkQG2PyAQekp4eEWfrZZ9OqZ2i+UNp5XelJfY3IVgX7q3rOwWhdASpLpMevQsq32Lag04XxQm1hX96DLv/XZqjKQHFz3wXKXHyViRgQIDCyr8UmIMssNkWgIAAGDYevpI8e6ThFaHy8sEy6e/Pezu1KlTqaP61uJZTKUyODi4S5cuO3bsuHz5clBQUJWW0mPnAABOhzZAGAWXloo27Uetuax2b9Te0SQqMRcaocTVLxQoh8JozNYkq2heDJjxLyoe8/EImIgzVVMxfK/AiwPBtZ0l9w4Hbxq7m94rFIqnT5/6+/u3bt36v+7gAQAYBAEexeNCAITBqampJtVJKRljb381O2t6bwp2WhoViJR25ve+m3XeDhIYejpbcnnACgwb+cP+QwePHT267ZdfuFzusGHDQkND/f39bZxX6dMnwBRbhOmPsnxaL3QrOXCT173rJGXSrsdek4O41eY+ojZWonV7GM0D2J1D3mOtOVHeTC2wbt06X19f0/tGjRo9ffp0/fr19vb2dWvVl4AhkJNFuXuQaonKH78SdktcU3vEOl50KGpRkKR4flcSYdpexpR28+lFF7zW+a5c+PWJ7IGW0LbidQQA3JNceyS9E8Lr4PlXAESFAQCEGodpkOzUZcXVy8KfrhMqjUXfjuyurd8+e7G+6K7kGhWmpqqSvJgN3mNnfy96f6/yccUvTxVuHLSv51vDDBjmzxwFYajV+MHVTKoTWlx+4/L2Y+qspLuXVw0YtbGpj2Hf1vG9e3aJfnje2bP965YkqXoU67hvjakThGfJHdGn7PLtKg5efkVccrSA1JPMIAtDsd4o1BE6wudhM+y/6eABAD6Hr1TZ8ra/rx2qd/CbN2+u/DE+Pr579+7fwBo8AGCVTD6IyUTaxHpcCKhIRnyoPM2xLJjb6YZQbSFG3B5Ib8aXxbBVqJSrjH5x3u1mHGzJHtGq3f5DB8PCwjZt2PIqMHrmHzPadG4bExPTq1cvRBaOCsbBrKYAAHqTxRLfnrm/7naMzk1oPmI/5PtTi9dTaqQuDwAcAEDiau7IlqLVpzA3a6r7x2oAmwfxZr4GISFvPGXy+fwdO3bUlTH/UajuDK+rjVN7xI5ZMWmb37rKu9wDPas9RE/ozxWdnOQ8x1TBZbjDxJ1Z6xpbtqBTy39pTUNY7si+nOG9cFkZwrV8l8LdaWFoN34/PiY4VXD4Z89tMPRRQnhz3iqA+RoYtp76Q7V7CF2uLnXsDx0DJg0LoOmukQYxrnjUI9ju4k4Ox3gOgNcOntQbAEGaZOxMKG5GGgqKq3TIDLQQq/MojlSXg/VV0WXZ4xJRLgVzpIFvC830taDWf70/6u+gUaNGTk5OWVlfRVmpltEQZKbBCDMRfc7rTJv2Vl0BBFFh1AMmFIDDweoxEJaP0rHpQyzNokjbqkFpI3e36FQvSyuRWBx+I5zRxML6mUXz5s0vXboklUox9z0m724Q7oAQNmrRivogheJi/xNI/z1GqleVn4hQxWteBGmT+2sTumgT2uGycayOT8X/+4XQVJ/zU+3w3TymN1NTdOrUyaRQ26lTJ8pb3L//jUgz1dq0qMnHk+t0m5++/h0/6n/xXe3Di897MHy8mPVNH+vR3RuwAsKLq4mhgxAEtea+y7unKOPzNdld+L2DOCF0hPFAevP9dupzhSX7wopW75Yc/MNQ9FEqe5WB6V4of3i74DbN2/1B9Qz9cfYvgV2PYy7r/D0RttuiN8ymYgjXQvsy0/RR/meEOiYRl8t1admVm5VGlNitdGO34b5q+zRnSpLn5UCAQroszaca9u8hzt4iW1V20o31wFJ63o16tCRLbNSBpZM+fGRN81Fr/omJiS9evKhWf+A/Bx9BxATuZ0/VC7VMYGna2N6q+1P5I8TgGZ7sHtiYUKOefTlepUKdKkqVHUSLT7ludGgWbMEc7Fp//fMHR48ePTk3NG/uq4XbFs6aPUskEvF45ct7hDJWFbWZE0srbZeBJ/I8Jg5ZfuzUz4MS1p87hjDogNTDDD9agwhtfEuAMCgO86ju7gbhgZI9v9nMm/B2xTnzYN3MVyUsLMwkZRMWFvZ2Zvk3sB5nwtsKXRJcSxXNqe4Mz78D03rGzcMWbju55T3eXaIXXy+5NLPeUrFeVLGxrVWXXzLXtLbqzMfKo+oiIyPd3Nzes1xCkMTvBUeG2Y9HIQoAYLj9xF8y1wRxQhhI9cU/tSkZ4m2HLft1YbVqqkvPKVq2XbByJub8acsxFOeftfEhNJuRgOZuEvPWpY1HbSdD1Ko5lrxxg8Tbj1r276zPLlBHx0MAYnVqWbz5gM3CyRWpwrjSiLBQdgerktACzImGudERNoIrPqCD+69FcuiPID0SOaV+BudhLgsliQxKEzpfGYLsO6Nbbk11qUYf5etRvYPv0aNHRbQnSZIqlap///4Vq3T/afgIXIgTmBNNn6fLzMwcPHjwsmXLBgwYUI/hoSGyooROE4MZl6XZgaiOL5Fxf+jjmxGRbZUnkJYagxoOFpf8Lynq77//Vu7VAggEQn6TJk2KioqKior6p/v2nWNeWE+dxMd8RU8fq3Jyt0deu/swUjKCu//kNpMHN0ouAJRDdV6nS59AddvN7iaRn5KVXb5t0adj3d0VM98jf/75Z3p6+rv2zpgxo0pE9H8UKgIF2WG1dzp3hvedphAKHeVX792VBLlKJi/Wy/JZk8ZIcBaRAABgE2Jv/V0AABu1eKlMII0Qm82m0+n79+/v3r37Dz/8IBKJOBwOlVq1MN0dSTgbtQy0LF/Vdqa7BlgGXRadGWpffSq57OQFqykjGE39AAA0P2+EYyEPu2SzZMonXSNEsUHtputzV1K9fgMAEIpoQhFNdd/zdkt6YH3BkimSg6f1eYWstkG4tJRW34PR1K94y8HXPl62r/jAWEOuxiu8ifRMUWrnGINYT/f9r1YnL7x2L9te84Bb2uEoM70fXu86VNiQPMS/Mk4mePH7+WaLZ9amMdXP+Rw+fPj5P7x48SI/P//8+S9Kvvz3wEcQMY5jzjR9jiY+Pj42NtakmuTG8CzSp1lSIQc9qsYaWmM2CM8SoJR2gxekNcTt6YxiJ2uuHu/Vs6fRaDx2/JjjZi8IhRgMRuWYWAAAocSkR/8o2ZuB41DewVODGAIAwLGbZ+J/u2RqYBRupzguhdlBMM0Tl9+BUJK/YNK3kR5q5r8FhmE0Go1Go2EYFhoaeuPGYI6+ZgAAIABJREFUDZ1OJ5fLjxw5olQq60RT79uAYkdF+e98pJAQeLROP8LSaYxVEI0iWCAIGWXdAmd23Ox7wPRqzet0+PDhrl27KpXlmTgJCQmBgYEJCQlVulLhykui031th6lwZcWrK79vpPRWka666uMkqc8R0hu9liyj+/vqsvI/6xqnEeqXuPwWAIQ+ezHmsgbA1f/BlF29a5RIOUN6YK5OuKxUE//KKJYyWzYRrd2jjooztdG+KKM40VXPSmEWasjVIpYooa9GYvU/AWX5lPTWcAOpy2+4WEEx/oXnWFD9ZS6ErEegz7QxtWzMGyP4PXv2fPNP9OUO3oVedkPi5eUFAEhJSQEAuDG8fss/0MQWU5WQxWyMTrGhtm1WsuuEjbcrn2qrLn5ejBudG/lMatDx/MWLhw8fXjh3HCE6AF45b9q0aeTIkRXFJ2AahfvDKXqTR8LlB39P/33Wqv1n0uLvZ6eNPH8hYdRC0iiFKNaIZVsAAMVxsS51JER3R6047G5txL8c5Y7qb5apN1NrVJSB2bVrV5cuXU6fPm0K8F68eHHnzp337t1bp9Z9yzAgqBuDnmkwnlGpuzHo6QbjOZW6coMlS5bk5+d369bNwcFBKBQuWrTof//7X9OmTav0k6x4QZDE7qxqpISelz7pZtO/6lYIQizZuLS0orapUSpHuZ+1fgFhmPNqQ84yUjcZwBTE6q1z/YM+uwBCUYNQBMEQoVQbi0sgCBBqLUkQ+uyC4q1HAACcjucthixXPy1DOKj3/aDS6xKi1GjS9vnP4R3gH4pRtaI83x84aivCZZRrXlmcgQLRBzexYNfSUlEFbzh40xM9AIAgiAMHDjg6OrZp00aj0Zw7d27o0KHfxhO9Sa2WXt8C4VE8PT0dHR2trKxwALYr0HjGSGsP3RNSR4PAyKKs1vZW00b0Ea351bupLl0g6SdvYDVlRBc61cXFJTMz81bEbyGCvwCY2bJly6tXr06fXp4FwZ8/QxH+gjBchFHmjOFdpTvO7Jo8q9WGJck3z+7+vckk/wKKzUhCEWlqDCEs0lAeU4q5OYu3HrZd+xNEMY/mzdQqt2/fHjp0aEX6lre3N4vFSkpKCggIqFvDvkk0JFmCE3MkMiVBFuO46Y2UICoaJCcnC4XC/v375+fnh4eHX716dcaMGQKB4ObNm/7+/nw+v6JlECckiPO+PNu3YbUJkhw9y589FmbS8VKF7Nh5Zttmn3chCK+nUXRYn72E1vB6lVhGkQoX/FP73G7zAvG2I7is1GbBpJLdJxgtAjBXJ9GqnbzR/dldW8v/LM8n5A2x5Q0pD/ug+fxX5+dNSKOUBpJcl9z0QJvn7aN5iLvfIcu/GVJL8O6sha9E9cVmdu3aFRkZWfFE/+rVq86dO2dkZFD+fb7nk4rNAADUJDmgSBxhZ4MrjAgb1el0MAzrELS1sGg1h45D2FpZaScGLVaZUQbbRjs6ApIsLs5cL179S4NQGEIAAGvXrv35558H9m0b+iP9z6Iher1+4cKFixYtsrOzAwCM6v0DqZUWrdkNoSgEA6U+TYbZP3KznzdvHsuan3GGZNEBAIAgSQAADEEQw4fe6AEAAJCk7ORFVudWFDv+e+w3861Sh8VmZs6cKZVKw8LCTB9fvnwZEBCQn5//L4yr/dTv+5cz/kU/8N5g+E8ly2gcWyxZwrEQ4XioQrWYY1GEE6eUyut25YF1M2fONOnVq1SqqKgoCoUSEhJiCoccPXr0qFGjvuTsJI7Ljp9X3nmCWHFwiZzdoy13WK+3I3w/EkKThsuvUexmAAB0OKk1kgCAMh3R68+SNa0s27lQAQAYAtEgQrztCGnEYQyj+rqXXbppOaAru2vrKjlB30xYcUZB+oGy/zWmN4l7dd1F5VxQXx9IaTHAdQRcSdG2dr7v76wmN3To0OHDh1dsqV+//qlTp/6FT/Sf/YV/3uAh7WKgjycDAKAkyPaFRU8d7MoIolNh8RFrq7nSYiUBP3awMzVen76on2B4A3YAAEAoFLq4uEAQSD7V7oFiBADgypUrIpFowoQJ4J8yCYa8HFnYZNhitKJpYdGaRO9Vc3vNmvL48eMeg0dOGjGwzKNz5vWTSmH61q1bb9++jaJohXqzNiXDUFDE7vRpT+VmvgHq0MHn5uY2bdrUzc2tTZs2Eonk7Nmz06dP37hxY+1b8kFq2cGbvLuJmvLxOUbjj2JphJ1NpsE4UyL929Ym3WCcI5Fetn1Dpj4lJaVz584uLi4MBkOj0YSHh7PZNTYAJHV6Y4kMteHV4Hzh4IuS5BIDFYG0RtJAkABAbAwiSFJlIB+PFlhRSPG2I7r0bGDEuSP7sToGv53x+804eACA0qgIF5+/V3izHsu1i22fRhZVl1dq5/tefZCdk5PTlSuvtXhevnyZmZnp6Oj4VU2pZcqYSGRkqek9AYCOJKeKhcPznypxcl1RqQiHNZWefJpZtoqM+/3WlauFhYX29vZ9+/Y1GIzHLqQP8VjTdKPN/PgfU57HKuTC7vqz+jwtAIDi6MDuKuGNcndtNtd5iNj44NLYn3ciKCX8z1PTTkSviSyNoLVO5QTN/SNm7J5rjFvxuKI8moYisC49G66OqaY2sxkzX4nCwsLo6OhBgwZJJBIej3fx4sV/p3evZSp797c/fjYQACIcHy+WLJPJC3F8vFiyQiqH35ziTkhI6Nix47Zt21xdXceMGePj49OzZ0+VSlUjBgAAICpGcRC8x7srCTJCrYlQay6o1McUStP7dMP7Utf6etKDHbB7P9gwKND1ofwG1uj/OlgubmHRypFqTYchFOHPm4BwLdldWrM6Blfbw7ck8sFC2YPtxvza+OR8rzVve/dao/o0uQULFjRt2rRFixYVT/Rz5sz5F87XfR4LJLIJFix5cw75jzQwDAACQd3p9FDpQwrDuTGBGVEy3aCTGSRcihUAoBmn1SVWWEBaSVpamp2d3ZQpU86dO3f8Rv7cUQsRJo9eoBti3fdmxOlmw9px9QQAAEAorf5FgHIAgO0GrC1NGJ5UMsylz6zM89tISwFZmCIReJJ6JP72E0anWdDDX0VrfrVdPRtm0BGepc2iyaIN+9CfPzk51YyZz2PPnj29evWaP39+XRvy7+Ko/8WvMYJ3RNEDfB5OAhKAQiNujyIAAKs3RWyioqJ27do1aNCgy5cvQxB08ODBpUuXpqen+/v714gNHyRKp9skLwukYkU4LsJxfwwrNOKOKLLV6p1RwEN86PtilZsel3Vwobpy0KmNWTueKhU6YmvHcilPCEXst7xWwvmWxuv/Wqp38M7Ozq9evTpy5EhKSorpib5Dhw41cj4cxy9fvhwZGSkSiRQKhY2Njbu7+7Bhw1xcXGqk/4/BCEC2wVgw0l6vI0mSbNmypb2TM/rLjj4sziOoKB8iF9WzOKJQ7pDrEhSxbXidAQCWFK4d3UleGFNCQwwGQ8eOHb09HV6lFVy9f6LX8sswA1miwQPGJSf3H9PEqfyhGKI6AgAAoYVZjXOVbVLWjGaMP9WTpXsZNMAx62ritesKjl0vOzuyMI6uV2TpKWDVLtufZ8IsBubmzB09QLzlkN3G+TCbCQAwEmD0FcmGtpb1LGu7GJGZ74HVq1cvWrTIxcXFz8+vItPatOj7nVPh4z/VuyfpDblGY3fG6wnYSK0OBaAFjQoD0OytdPYqVCQ42NjYcLlcGIY3bdr0iba/RvUwVnHtAS4vozjbcQZ3xz5Ca4UEoBFG+cWKG6HWXNNof7HiXlVrbr1Dc9MEhkBj/ZjbnirCB1sDALq40tZGlnFpcFPb2hMhMFOFdzoMLpdb40/0YrG4VatWWq22Y8eOzs7ONBpNLpffvXt3zZo1W7dunTp1as2e7m1EKlysJiADiNcbuBElNgnKeE/PZzExxNOnPhs2zZHIMhlDtYCcVCQ1wCQB4MYWrws3NeO3S/G75GNkZmVleXl5TZs2dfac5ftOa7s6eGKOVLaBuD3DXiqDZUUyG+d/1tJwleZ5Y1qjSP+OO+eEdLskT3sePLUFSLXQJ8dEXxYsvhaFoOPgG0g6QnQJpolURWt2m3w8q02QIVcYvXzfgQ6jcQQVqfAsuXH4JYknlwIAQCCwuzOXhdV6ZSIz3yizZs26ffv22bNnK2+8e/du27Zt68qkfw+fN3C3hOEt8jI1SQ5kMgAAtzXan6XyQ/xPLlO7ffv2zzh7ZRQR98uuPeCN7o8KrLRJ6aK1v9qumk1x/IBMoYYkC4z4GaU6Sa/PM+JnlOpEvV5RKdS/+qMMBAIBpYEEABAkMJJAg5NkLaoFm6nCGw6+U6dOAoEgLCysU6dO9+7dq9L01q1bFbFgn8emTZv8/f1PnTplKtJaQUpKSnBw8MiRI2swiqRazqdqIjK1UjucQIGnAeqeof75kYpu56HITWkck9O1t+0rnLDWxpdmBwUIKJlUg4iks/45thmn1UWXk11fEQiClGiI0eNmrVi25VFmySsw0z3RGbWiNJhhU/J3kSW9UqYjwqTYzyZ1uRCrSZeVPzNvnrwh2TjZMHLFA8x//nmmpkRIwp7i/TQoyE/zlOLTy1BUXLjkf4I1P6FcS+4PfcpSQz3Fea26Nlx4Rz67KfvgC2U/T1qmHL+Vo2VQzF8ZMzXGiRMn9Hp95S1yubw2J9W+PRxR5JiN1dhiiZ4kHVD0Z5l8L5/nWxd6VvKz4bbr51Js+QAAir2ANBhLL96wnlFNKH5FniRJkiqCKCOJJIM+DzeWEUSSQZ+P49rqIrIrUBvIsGT1+EbMvbHKfV25R6MK2LpSrYG25UyyL60UAKDT6VgsVvfu3b/CVX4yhFpTevGGNikdwlBGUCN21zbvkvr/T/PGJYWFhe3cudP0Jv0tmjdv/o5OPpaMjIyePXtW8e4AAF9fXw8Pj8zMzC/s/4NMDWT9NdB6rj+7rQe1VUsL51LjXwOt544bYm1tvTHEoxuDPtzSkas8151B1+SBDnTawZInqapk07EWqKWrhU9pV4G7u/vKB6WxcmzcpHEAgKMxJ+yWubFacUkDeVp5Yc7Kufn5r5WhULtpMKsJAADhdjoH+sHilJWRvS5s2+ylkuZYOMIc24zUQNwA61Mt1Y/jSJ3BWCw1ZBcAAAAENVw45q7BUnM3yotHmd6ENb4RM7rQEF2on9GEXft1hc18w3C5XK1Wm/gP8fHxAwcOTEpKqmu7ahV9nlb7ssai2AAALih6zMZqd6lirkS615rXCKuDmWpcoQQkafLuJqjeroa8wrdbVi5iC0GQNYL4UiiruZwhTGYDjLKayxnEZNi9d9XmeKKqsS02qiEjTqR/ItSdTIODsbzWrOJHwCskJMTV1VWv14v1okRFXA1e4OdBGoxFK3cSChVvdD/L/l01MYmSfWF1bdRX4Q0HLxAITJF0AoHApRL29vYuLi5vyyB/Ku3atdu2bVti4hsh4lqtdv/+/fn5+Q0avK+McQ1iErPT2WAUuYHQEqtWrRKLxaYcARuqnZE01heURQn1rWm0VIKXpHhecWAzuPGjvHBcocRJgBPk7NmzEQQJDQ2FIIjhx6a60H/44YfTp0+fP3/+jfEQqdfnLBcq9A9VLfom7+e2Hu3dspWajUOasmZoaeYPXSE6xWraOHbPdob8IsHy6fTA8upS9iykpTvL8ubNBYYkAMD4RqyILI1EQ3R3+9YKKZqpW27cuOHh4TF8+PDevXtPnz69T58+tra29evXr2u7ag9dlia1S0xqt1hVdGkNdptjxEmIpAEoSW/4cOuvAMJikkacULx+cDEKi1GbqisFUHV58Pk4fkapfqwtn6J/rNW9/1zRQv0Tob7b6RKFnhx5WVakARd0Da6UOWWXGrPFZcnJyc4NHaWE+Lf8A0aybu5GBarHcQiPY/XjcKq3G72Rt83iH7VJaYYC0YeP/K9R/aSEUqmcOnXqw4cPhUJhYGCghYVFjehWzpo1q3379s2bN3dwcGjatGmrVq38/Px4PN7u3buvXr369sj+K8FHYDFOABjSCTB9dtWihM04rWzYapmW8MDRfMDpaDMYAICXKUVrdjtse/JK9zJtwc9W+S9IknB1dcVxvPKxzDzqtGnToqKi3rgWiEKqXtx98acVHbWaMJtvVI48lfqCUT97XYfjS3pGFPnJmTa69Gzx/w7zfxpH8/Oq3KGTgL0scCg3KgoAwMYgFgbbsWDz8N1MzRIaGrpo0aLCwkIXF5eXL18+fPiQxWJ9G8qVH4M+R5vWO04w16XekQYZg18oH8prpNv7Wt1SqeyAtVWYwPpgmfJ3ZU1OD3wsEMTuHCLedRyXlgIAdKlZslOX2F1af/A4bwrqh1GSDHolibNhkGTQwxDoRH/f0CK0Jy92nCB2nODZWAGfAf8STG6rl/xiov1Gh4TEJ/c7duoYpb3rQHN2pLtcF1+usQv8LAy5Qprva9l1iEKhetbT5xTUTOc5K7QJ7at9kdp3KsF/Jar3qVu2bHn8+PGiRYv279/v4eGxbdu2vn37jhw50sLii6R0YRjeuXPnqlWrYmNjhUKhSqXi8/keHh4fk/thNBr37dunVqurbE9JSSE+FPpRBTsEQSFgSYX1TnRdurqKLOIIh4kAgGUtNZYUuCGGxejw9nSK5MDvWD1HwfLpgfk7Exum8vEiRdwz4FY1IoHZxFImk3l5eaWnp5uE7gEAAEBUn9O2OYYBTAQA/zb8P0+WdfXlQUZXj/S4R4rf5qMNAyR7wvjzJtAavuHdcRL8kaJ28XI432rqxGcJiQSLAjFfSY15CtyJbY5wNlNjyOXyRo0aIQhib29fXFzcuHHjwsLCvLw8J6eq1T+/PfQ52tSesYLZzvxJjgCAekcbZo6IdzvViBXC+ZJuY3X6pRLZfr5VQ4wCADCtx/MR5P0+8mvAGd5bfvZqwZz1pNGI8iy5o/tXGUVUizOKruZ+5h2go9DdEXxxkTCpCOTk5CgUCgcHh1uPbyB8hEuxamffbm3aghBue0vKJ9TdwEsVZZdv6XOECMeC3TmkotTs54FYcaqsUxiLJTVVBwRi+JLSK1Svo5WDC/HSu8biMIjqXCOn+HiqH8E/e/ZswYIF9erVu3fv3sSJEzt06ODi4pKamlojp+RyuR07dhw1atSUKVMGDhy4dOnSSuVW34nRaBSJRLK3UCqV1YrxvQcLGP5TwB/ZgOE3XAD/k3hW+dFBbpD296JzqHAbGjVM9vJa0UVt/KuJzFbuB0UHHvqfCaTcA+7P5GXu+6tZymJq6HPnzp0/f/7jx49fb4UZHVwtJ/nk/xjIXjGwTcfEY6Mebvh7by8mAxXFXo3IjLeeMaqKdwcAXErTcGnwqlYWxxNVagNJ/fXQIjf9MF/GgTjlJ12vGTPvx9fX9+LFi1qt1svLKyIiAgBQXFxcUlJS13Z9dcq9+0/l3h0AYNGRZ/LxXziOd0KR3wTWDf+JqnNB0VMCa++6kPqGUIQ7vLfz8S3ORzc57F7JDA58u02Vn9BP/UV9GwyBAAAajebJkyddunQJad9SSOY6Cb1IkuRjgja8TueKfvv43nB5WeHCzYAEFj3aUT1cxNuPqh7Gfol5zOb+6qcJ6qcJAABAEKXnr5M4jrnXjPdF+cMhihWhSYeZAeUvRkNcfBqrtx5AtR2HUf0I3srKKicnp6SkJCYmpmXLlgaDoaSkhMn80gIAJEmuWbOmysbnz58fOHDg2rVrY8eOfU/gLo1GW7du3dvbL1++XFl075OwGmlvLNEDADZs2LB69eqHDx+a6jWtSZu/ymv7jQxqGzfa0TLrIs2D+ih6biAfAEAC2wWJv0mMud6IIn6UBevNzD6jzIBw0FmzZu3evfvu3bve3t48Hq9irz51LMVlHcLptHqqQZly3p7C3LV91YQpy1fG3QsElE5vmXcsQSVWEzNvyAAAHVNsOrm2Gr7/YFS7cQl6+sLmbAvqNxjzaaZOmDdvXvPmzY8fPz527NgOHTps2LABQZBai4mpQ0pOCFFL1Hr0G4pSFh14rJYc8aH8LxnE8xGkSkmJ90eo1QIQ9X3e5cudehU0Go1MJqPT6bdu3VLhCg5prdFrIbwUANBLMHjZyxlZ6jRXhmf1plaK5wcAlJ6/zmrfgjOsl2kj1cdNtG4vM6TxxxuTPWhGZV0dhMexWTBRcvB0yZ6TwIhTfd1tFk6Gauw/CMJcNurSxqK8ngBmAACMRYcgzBbhdKmh/j+B6h385MmTO3fuvHPnzl69erHZ7ODgYBsbG29v7y8/3+PHj69du9ahQ4cKz6fRaLKyspRKZQ0KMX6QyWLJAo6lXbSycH2m59XGIpFIr9ffvHnT5ODr0T1eKZN2PPU8acujwZRgxykQfZ32ZQbNxx0CUEvrjpcdXqJCt4cPH8bExDRp0sTU565dux7GP2rcuDGLxVq9enV4eHhgYGC3bt0qToq57wWEFgAgaLjCwnhRmGc7ctjM63cSTp8+PXDw0Oz4KC7rjem7XZ04Cj0JABAq8RnXZfX6tGEnkpvjftfPm2H27mZqEHt7+6ysLJ1Ox2Qy79+/HxcX16dPH6wuor5rGbvFrtoUZcawePffG0Gm7xQJ8hak6vO1npc/wX98F5BGQp0EAAlIgsTlEMoDAECYHUQRVNvcw8ODx+NRKBSpoWR39qa5rivYqKXJc9Ngen/bEaeEh5d6bILeSpKvEs9PkqQ+O58zvHfFRszFAUAAl5YiPMvPvhqql6v91sWESg2h6PsffT4DmB2EWIQYCnZQnJaSRplB+AvV96+aPcVHUr2Db926dWxsbGJiYu/evQEAAwYMGD9+fOVKOJ8HBEHh4eG7du3avXv3kiVLOnXqBAAICgpavXp1LUtqoBCUZzS6uNC0qWoAQGBgIAAgISHBtNeb1eCVKul/7YP4DKQ9Sbul1gyZ3Fe89bBF93aojVWDNOXlhskcmx/KCp4VFxc/evTo3Llz27Zt27dv38KFC93d3VEhNHHixJEjR0ZEROTm5jo7l8/8wKyKnwyY5vvX7bVDvM4sPnDgwLNnzzIyXoyaNvfKiTciGV3+WT5oyKf81seqiQBDmvSUkjrdgcPkihk1/kdp5ntjz5496enVR/0kJSXNmDHD3d292r3fDBAFcj3ulzUmIWN4vPvvjSAMzluQqnoi97zcGOF8y6qRysfy3Okv64U2YPh/rPQIXvZQnzoKormThmLSIIEZPiSugOk+VO93JpiZRnEX88OUFNnOorWVd5GALNEXv1Qm+LIaffDUphr2r481GEmNzqTy+TGYJO6rDOJNwEzGR3byqVCcV2vjW6M2Iw3CHYj1IJjh+5VO9H7e6bNRFL106VKXLl1ycnIQBGGxWO9q+UlAEDR79uwLFy7MnTt39uzZGk3VIPbawRZBRDiOOdAsuloBALp169a0adP27dub9nqzGr5SJrZwwJgUqD2NFq6SnbC4Yrv6J1ylVscm2vLcEOBYws9lsVhubm5CoXDRokVcLjclJUUoFFIolPR+z9VxChqNJhAI3s4t1KdPJlQJENW59/rxZbxESzb19OnTVCr175P7th0//y6Dm9lhJhkG3pj+VHdnXVr2V7ozZr4fMAyj0Wg0Gg3DsNDQ0Bs3buh0OrlcfuTIEaVS+Z1E0Zt8PEyDM0cn5M17pY4p9fz72/fumcMTOP1tMgY8V79QfPRxOMwKojW4CkgcprujtpMwlw0A4B88bJDd6LXeu+e7r6n8WuC+drPvAW9mwyqNq03YY7ZqKj971VgiAwCQBqP06Fl604YQ5aP+j+qqgA2E2aF2P+rSJ+HSvykOC+vEBvAuB5+YmNimTRsrKyuRSKTVas+ePTts2LAaPKufn190dDQEQU2aNCkqKqrBnj8SWwQRqQzKR3Lb+fUIDWFra/v06dOJEyea9jrTXOUGaZmxdHu0ohFKKSIxGuZEcRDwRvfnzx5r2a/zIOeuEuguAMDJycnb2/vGjRtdu3YFAJjEPm3n1ROuyQAAbN68+fz5qj4b4XQ1lvwBALD1mdxluoshf1OTJk22bt0KANhy4nJyyYcyRCGIN24Qxdlecug0afzwt8uMmXcxadKkjRs3bty40cHBoUuXLgkJCXv37g0NDX369Om1a9f4fP6Hu/gmMPl4CIHUcWUefwUiFt+0d4+SZ45IcA1tYL/CzXlv/Yz+z9WxZR9/uEF0GLYIwVx/MRRsA+RH/f7QEQYfE1T7gqGqDqjaUABGc39251bC+RuFczfkTVxKKNVWk4Z+vM0V1LKzp9jNJPWFFMdFEPpFGRlfQvV/ymFhYVOnTl25cmV8fDwA4M6dO9bW1jk5OTWoXkmj0Xbs2BEREREWFiYQVL+K8/WwN0Dw2NT0ZypafZbNVCfesDeUmWEI9mD6pKqSooTezeyx1nSaNe313xOh1bWj+V+VHHMosnqiUDq7ON9scKYHPjA8PPz58+fR0dE9R/cU7chR3Jdt2LChU6dOtra23bt3p9HK19cR64GI9UDTe8xtx9+b2lq/KJh+am+TJk3EFh5Tr8kuDrTm0j6wGoKwGLhcIf7lqM28CeCLl07MfOfcvn176NChFYMnb29vFouVlJQUEBBQt4bVGhAFcvvNjyQAhH7LKhPKKHnm8ATXow3Y7XkAAMuuVs776mcMeuH+pz+j8UekQJOEUfgr1fcCzKgPU13wsgdf2+AKf2/Rsx27SytDYTHK48Csj51Xr+P6szCN5vcAQr8ot/xLTah2a0pKSmUdKxqNVq9ePYlEUuOn79at28mTJ318fGq85/dA6gj7SalyC6T+0xb6TLXkjyIAgAZXnyk8Pi954k9JY8MKDrkyvFIU8W2dqPdzdZ3ptGtqdZoqBZfKRRv3509cKp631e85Lds6k2Ik9lM3AADuNDrXuXNnAMCuXbsgCmT/s3vR5qz69ev37t37/Pnz2dnZVWzA5TcJTSqEWrWftL1AW1i4NzQ4OLhPA34vD/qsG3L8gwGtMMyfMw7l83BVVWEAM2Z1n4inAAAgAElEQVQ+FScnp8qpKC9fvszMzDRpO35HwNC37d31udqMfs/rHazPbs8r0hWE5v36U9KY077HyV209H7PjdIPq8uR+nzYohXMqA8AoDguwSUXAKjh2HsAAFmJytshCoo523+8d6+WWnb5EGpZt6V2qh/BN2/e/OjRoyaPBQCIjo7Oy8urZTf8lSB1ROYPCXQWengdf4Qz3W6Zu3Blumh/7onOh0EJWp8dAFsCCEAx8ofdbQaynKnzbsvnBrN/lsm3Fm9bcsaLU7+BzcLJEAJ3Tny4Sbsr1SmjomfKDBXtCu3mzZuxsbGNBzWmB7ABAGvWrGnYsOGqVauqmmGUGfI30xreYAq69Nr+SLwtTvXgKbN10Lxm7Inh0m3RioXN2QAAgKv0eWsBUY1IJEz34I2drol/pY5+YTV+kHkcb+azWbBgQdOmTVu0aNGmTRuJRHL27Nk5c+aYVKvNfDNQBBgzmCP5rVDesmx79qruNgOi5ZEuFPfD3F39Rw9CPliKmtQRuhzUsq1BuLN8A2kk9cKvbvcXYC45X71XmDt3rsFgcHBwiIuLGz9+fJcuXXbu3MlgfK2Aw1qD0BDpg17ATMTraEMeFQEAMFtYUhxpz29GSXNLVnXYvH7wVibCHuEw0YLCxWCsAZ9SpiPUWrI5lepOH0Oo1JyhPU1Fh+o1DLHWclo9fa1OQ+Ng/fv3J0ly5cqVAAI0TwYAQCAQLFy4cPbs2Xl5eZUV91DrwZj7HtN7qsdyi4HC7J1HlKVlMAS2d+BcTdc8EeoBAABCcdlVAIwwy7/yi1AnEupkAADN29UoFJX8ehJ8opyfGTMVODs7v3r1atCgQRKJhMfjXbx4cePGjXVtlJkaBqLC7qcbEWr8VMT+QYLRXfl9YQh2WmjXJ3zAg1H3IeQDA00IokEIC5ecNwp3ml4QIABmVzvGf5D7ebpTyebpzKpA79E3uHfvXnx8PJPJbNeunZubW22a9fFcvnx58ODBWq32Yxor7skyBj2vHxOMOZeviBMqPN71QfSJmGjewxPtLhu0xtH3+lpwWQZC78byHmY3zgKux6HCl9WasKJ0n9ytK9sdq0jcjLwfestwdzp1xkL2egDAUf+LJSUlbm5uCoUiKiqqefPmgCD1BTrYDm3SpMmAAQMGDRpUVTyEUJNGGYQ5kPqCGUMae4WMnL1gGwCgoFR37fKFTp06O9ta4ZILBuEvtIZ3AFSuw0DqsrUJnWiNIiHMFgBAGozirYchKmY9e+w3WfHwO4HBYEgkkrqKXb969WpKSkrlLUOHDv0XztJ/0vfdzNuQeuLH+KE8OY/hyMzVZvOLbWgujFxt9iH/P+F3J1X9SyB1uYaCXyo+JisEp/MCAAAkgK6JfLUE1tODRkUgCIBOVnjAi0jOiD51Z+wHqJ3ve9X/0cjIyPnz5y9cuPDOnTtt27bt0aMHDMN//fVX3759364Q/5+D3ZZrv9I9tXuMPkdrIEkSAJiJwAzYfq/ABXNrHNAEkKCe1HuT735ftr8txe7v4nMcKgwACMbIJApTRMPvFr4uk+AZQyi4pLIJ76j/xaP+FwEA1tbWM2bMAACY5uQNJYaXrZ8CObF//34KhRIfH19FzIdQJWmTegBcBWEOu0KPTe54SxX1CADw7G7EhFHDG7bqXCBVIVb9AWJhFL9WdtTnrEDtppu8OwAAoqD8+RMIjbZk5zESN8fVm/lk9u7d269fvzt37jyvhFJpVkT+BoEw2J7t1PRxi+CZLVEjOqbFjL52wy0xzr/fuwMAAMzAJechhG2ayIRoXueFft5WGNDnW1J0jfgUuYbws6bczNJknL5RevFm6cWbdW1xHfPGusvNmze7dOlia2vbuHHjY8eOzZ8/f+XKldbW1q6urvXq1asQbPlPYzPDGQCQ2iPmxh9ebm7MtjeUhI5o+VPnp4xnXWe2Y7sw6vk6P5LdKdTmDXMbty5tIUHiQy7KBjS95UR6GKnN/iw4ESB0oDM4qocxxpzCtrY975ZEjHWaXtH//Pnz9+7dGxER8eDBg9atW/OG2RauywreERwcHBwXFxcdHf1WwN1PY/2MEAAItzsij85dc9SYLmzTs6Obm1tmRlxIv1FZ985hLht1LwchvH4QaomXPSBUCVSPQ5W7gCgUm4WTxduPSPaesp45qhZuo5lviatXr27dunXWrFl1bYiZ2qCddZcHQ2794DoOoaF8muBE/r423M51bdRHAVGsUccFRFkk1WUNAMDfBoRkSBGq1XMFbU17O3cuve+5kpEeKEVR1tNKR7jYK+9GARy3HNi1rg2vM954ajt27NjEiRPz8/OvXLkSExOzYsWKKVOm5OXl3b9//8SJE66uX1TA59+DzQxn/o9OzYe/MhwqzJv3yv1UI6Yz+yfXFYJgnusSq8faO0mK53PdVgqo9lYYP1OdZsUwhucnDrV0xByH+JKef+WclJ+6BNNpduvntbPpHlP6WPzo0fO454WFhQAAHo83d+5cAMCSJUsAAHaLXeWXirUpKgDA/fv3TbOg4ysByiMtAQAAc12RGZgxac1cLpd75coVDoeT8+DCz6tWwUw/hNvdWLAVkLghewnmshbAVWtSQRSUP38iu2sro6iE+FDlZjNmKsNkMh0cHOraCjO1RFurrgGcoG0B63S4dvmrGXY0xz6CIXVt1MdCsZ1MajNxefnQfFYT5o5YOoUmaO3McmAjHRwpK66LJjCKuT3bQghiu2qW8sHT0nPX6tbmuqRyTkL79u1PnjxZ8dHLy+v69evkv5tLly5RqdTPOPDW/9IfOd1TxyskpwozfohPaR0t+aMQJ3EDbqhoc1Z44kLhqT1Zmy8XnSnF8aD8wiK9fFbiqEJtQUWbo7m7fz81N+XqzStXrpi2KBQKU2b/X3/9RZJk8YG8jOHxJEkeOHAgJCSk2v8CdVxjXP2KJEnCIE2PaKBN/R1XqiIiIlAUBRA0Zs0hQl+sfuahy16mTer9/uuSn4sQLtpiLFN8xj0xU4fQ6XS1Wl0np46KimrRosXjx49VKpX2HwiCqBNj3s9nf9/NVMFIGLPVmQZCX9eGfDJGaYTmeRBJ6EmS1Bce8DuYtTqylCRJg0Qatvyk+z5hYUqO4m5UwU/rdBk5mrjEvB+XS46fr2urq1I733e4iqehVCpoiGHYN1xwwnKm055HPnQ/FmqFloWXsFpYFixPl58uRuHXyxYN2YFPSx9madKCLXtbwHBjKnZKnlGf7X+28HhFm078XtH1xYzbT3Q6nUmVj8ViLV26FACwZMkSo9HIn+TouMUTADBx4sR3la7HHJcSqucAAAjlurb6vfTmoZT5q7t06rxt2zZAkifXztj+VzJqP9tYtJ9S7wPhzZYDutIa+UgPnv7iO2Tme2HdunWxsbHBwcFMJpP2D/fv369ru8x8RRAIcaG7olAdVLD9QhBuV4jqbBQdIY3yiMSnNkz0RpbWQJCq+zGXaK71tcWhF5Ll528YiyUl+/+QnvqbJAjl9a+uyfPv5L8QWPF1cEKRHJgwFOvzl6azO/Act3h5Xm2ctSJl/7Rf5fLyUtDuDG+RTtjRutfiu4rwrNJONOgZzpEbpDnqjCTF8/J+aPUc2G6J9Uq8OdYvXrwwbZwyZYqHh0dycvKhQ4cABDBHGgAAhuG9e/dWawxiPRC1Lp8lg5l+jF6jEtNfxq7cPGvWrGnTphEG3ZIJg/7Obk/1OQszPlzEkzuit/XssaqHsQZh8RfeJTPfA2FhYUKhsKQS6enpLVq0qGu7zJipHorLBkPBdn32okPF05eEWLty0IupmqyWbfMEzotzbv0pCKBOGUVxsrPfspDdOQRCUfvty+ra5LqhqrjBX3/9VREFJhaLT506FRUVZfo4YsQIJyen2jTuq8JHEFhseDUh1nqwgNmSgyuMNE/G80kZUxfOjM9O2Hv1AACgUJcHA/hS0e8ipGBjnHND7/Px7GVsrZhCap/KHzZglwt5duP3+63l7sa3UyVBXiRJQhCEYdjmzZsHDhy4cuXKESNGWFpaAhIon8gDWgR07949PDy8WpOMRQcBykOtB7GcxjZZ8gg5nV32952dO3dmZmZGRESMnz4v+tYVj4+7OghFAEkUrdxps3AS1bNejdwxM98qFhYWOTk5FZXlCIKYM2fO8ePHK0ohmzFT52hifEiDqPIWfcn5NNXPt7N1NBRa+6iMjcGOfMbFZj0hifpMGj4AAMWNh6UXrtuumoXaWNWV2XXLGw7ez88vLi4uLy/P9NHT0zM5OTk5Odn0sWvXrt+Sg4cAWDIul+xjY7vINXNEPG+ILaefjXewDwDg9s3bykgZqxXXme62vX4oBaaUuWPdTot3+vZcLpM34v0ylElFode3rgE7gMq0FI1u0IYfDAD4I1ndzY02YMCAjh073rp1a/369Vu2bCF0RNbIRPfTjc6dOxcYGPjq1SvwVmUF2LK9PmMKaj0IAODRfo+aNkIY8Sc9wOvMmTOLFy9m+7Yaf1X692A+G/so7UNmq6Ywm1W86QBvwmBmS3NxazPv5MaNGz169OByuUql0tnZOScnJyQkpLJYtRkzdQ6JlzGaCQ35W4zFJxGrvpjzGvUzl1UhbBKgAIBsubFEQ/R2p6GwU/2kNLebt3CtvPTCddvVs1E+r65trzu+9iL/1+ZLgm7OL02Ka/bYUKIXrssQrs0gSVKj0dApdAyi6EXaKo0HXyi5l6uN1GgHFhbNT56IE8bKe5/IHmxIW0wYjMYyRf9z4uciPUmScXFxMAxjGJaWlkaSpORUYUqrJ4SRiIyMdHJyOnPmTGFh4XvMI4xlR9Z51Pe2NxrLzxWZp1XqCY3hE6KfdDkFeVNWlP599+MPMVMn1GGQ3fDhw5ctW2Y0Gn18fEiSjImJ6du3b51Y8kHMQXbfLaondoSuQP3UHVclqJ+6EdpcVZQ1SZTHRL+UGCLzXv9oy05fyRkxx1AsqSNjP0wdBNl9b/RfX1/Q2ya1SwzmSFMnKgEApXtF25zWHD9wjGLzuo77aWFomiplWmMWFYGCaVQJAXjMYJlBWrmrxkiAXJx3f/mM3J82uBbFaF9mAAACAgJGjRql1+sXL14MAOANt0Us0JLDBSEhIY8ePWrZsmVkZKTBULXGA4krDTkrAIlDCHvc/Jubp1JFS1fjpQoAQIgj9W6ubsQlicrwsTUeMGd72zU/Ka4/KD1//QtulZlvGblc3qhRIwRB7O3ti4uLGzduXFhYWDGTZ8bMvwRj4T7EegDMaIgKxlRI4pvw5qEhjq9/tDlDejqHbf+ux+4AgO85yA4AQADAXFqP29+maFuOOq5MtD1HclI48eHMYZNGqA2kgSh3olyKVaT0VjtnanN7DAagN5OOWQ6ywt6oli399bf2RQ2jBmIMXzc3hkx87ZY+Kx8AsH79eiaTee7cuWvXrgEAnH7xFu/PAwA4OjpaWVkZjcYnT55UsQpCGIQmxSj5EwAAANyhhYLTf7cuxVkdxVVHcduL7dbYTpx9U/bxl4nyeXbr51F93Q3CYlKn/+zbZeZbxdfX9+LFi1qt1svLKyIiAgBQXFxcUlJS13aZMVMZ0ij+nWL/EwAAtZuBSy7UtT3/Ab5rBx+v108vkdotd+MOsDEIdSUnhJ7XmlBsqQCAbdGKcy81pmZNOS3jyqIJEo8vNmTJjf0YjEsqTa42V0eUC2LjpYrrUorMa1oOpj4BaWKBe4GX7clbOX8kqyPLuHMXLwcATJ8+XaPR0LyY9eOCTUcVFBTMnj07JSVFKpW+aRdM9f4DtRoEACBxJUz3xNz3xaSQOiMNc99H87vrwZb35RWfOHNR/+Gysv/0yKTTfN1VD2MKl20ziqUfPsDM98S8efMePHhw/PjxsWPHTp061cfHB0GQqnUTzJipW0gjYtUHwuwBABDKQwVjAGmusPUBvmsH74AgWQYjAMB+lTvNnWG/1oNiU573j5Ok4Z8/Hh7Fmo8JXqqS4sWGX2OVbhTUEUVCS15EiC+WN5bIk63qJUpIqqZHevOCeI01ThpzDbqEEkO82NB//OyAgICMjIwNGzaUXhFnjkgoWJyWHPhY21801uOHY2uOGM+pqloGoQBCcPktQvMSAADT3ALqc0RJ9bQpPwPCCAD49efZY4b2Dxoxt2Ka4WPgDO7OatdcdvLil9w0M98e9vb2WVlZI0eObN68+f379+fPn//o0aNvWAPDzH8UoyjUNJGpjuKWV52BvmsX9kG+67vDRxAdIEsJAgDg/KsPu0W5ZGxaWlr4joWy4sKKlk0sg2PkjwZ40Qd5MwAAA5mMfCzIkeZi2ktxEExJCl/jD/+veW9LntQRS6qnhrpYlKxvY7m+jWWgPf3AgQMwDG/ZsqXAqVj5SCa7IOL049c72GA0PkQr0hzOO5mVlVWNBg4EG3KWAAAM+Rvpbutd27UkZKDo6iYAwIwZMygUSvyZHS3HLDF+ylOsRa8O/LnjFTcfKu9Efe5tM/OtcfPmTbVazWQyAQBNmjSZOHFifHy8TPYJy0BmaoEsozHNYKy85ZXBkGs0vqv9NwajuZjRQvbmS/Kdu7AP8r3fHVcUzTIaAQBUT6bibvkv2pkzZ6LPHbxxal9Fs2ackJjSKBpKBjtgAIAeDHqiATiympn2QlTMok9H0fq9htiXXaH2TM/7lPTioJbBpsg4AECzZs0mTJig1+snj51MsacyAi3kF4qzxiSiTPSXtpt37t4RHh5eIZJTAWLZHvM8DggNoctBrYdinpsTFTZ0bqRWLOndu/cff/yBouiz3za3mbgC/8SZKlp9z9ILNySHTpNGc/W575r09PQnT54sWrQoPDz8yT88fPhw5syZGRkZdW2dmTcQ4/i44pIEfXlY7nO9fnyxpORTv/xmvieqCt18b7iiaJbBGIBhuNwQvyRtjhIAAMqk7gCAW3fvNQ4t11UY68e0tOCkq1N4sO+RF6pFLdg9GfQ/lGUeuls9bQYBADiDulFs+WVX73lrVEhfsVjAKt56hNRoKfYC6xmjUFvrTZs2/fXXXw+fPwrveGfOiUXJgY8NIh3/R0cLNW+rw6Zlm1ZVqwwKIRakQYxwewAAsrJzx60tjjqEInqZsUQ6YMCAY8eOjRkz5nHoulYQcv/QSgr8UfnxAACKvY3d5gWSvWGFS7fZLJhojjX9btm1a9fly5eLiormzZtXWabay8urUaNGdWiYmbdpRqVutOJOLZHsteYRAEwTSzfwOI2p5pUUM+/ma+fhfW2+MC92b2nZNnkpSZIkTjy3uyst1sm1eEFJKZVpwXd0lWtx04skycfSe4llcXqcaHqsKLfMmKo3tCsomps0qVCbX7lDfYFo+tFdPz//CVeq5DJZwtlLBXM3EEYjSZInTpwAAPAo3PgpT162f5raIzaWezu1Z4z0TNG8efOGDx+uVqurZEbiyueqx5wqr5J73OaBDvHx8SRJhoaGwjAMABg4ffknXzxBlF65kztxqSb+5WffQDM1RR3mwY8bNy4zM/Nr9Hz37t3hw4d36dJlx44dlavXjBkzJiEh4TM6NOfB39dom+cLm+UL76g1dW2Lmc/HnAdfG7ihaKZpWQuG3M/5c3gUSypsb2Ux+fCdpUfDLamw6QUAaMFt04AdQIGhXu70i6kaTwrqhCLNHNfwMdvKHaqj49dQnEga+eD0Vn1cSoJBqaGhhpwCAMDIkSPbt20nNciWnVrpesrPLcwPE2CqKDm7A2/jxo2zZ88uLi6OiIionBkPM/0ZLWT3paFNR2MR/2fvLOOjuNo+fGZ31jer2bi7IYEkENzdAgRKsUBxa3FaihZtoVDkwQNtcPcQJEECAeJO3G0l674z835YCCFYgMLzvDDXLx92zhyb2Z3855xzn/uuPtRqvOWxzN/bTmBOC+f8EzFNGH03IiLi77//JhKJ53avC5u7Wmv6AJs7AEGsgd0s54yXRJ7F8Im+b5jIyEhzMGiJRJKdnY0g/87Czf379/v06cNisYKCgrZt2zZ//vyGU3fu3JFIJP9KK98aFgQIARAGAI/4rf/3xnkv3/pPpBWF7N0wM4kBRczzfzo8B3e+bVO/vFW68nqjeKQP7dwzDYqB8RbMSzqYCBEb50FkcpjDDrebcCOoUnPiio+HZ6Ejz1QvBwBAEPTH6I1UAvWK4sY+v22Z3vFUfwazI1d6ppZEIrVr187KygpF0fv372MvXNhiGCaVStuFdl29buf6lVPb+4p/++23CxcuDZt6few8iuneRQDAuHHjjh8/TiKRku/fmnStXmX4EI0HgNbKx37bcmNljWhbJKJUffAdxPn/TGxsbKtWrVJSUgAAt27dcnBwCAgIcHV1TU5O/vTKDx48+PPPP+/du3f9+vUPHz48f/78kSNHPr3ab5k0g2GWqP4PHmcrnzdLXJ9hwN1a4LyLb13gbYjEuWwL82djrV5ytNr8GQLQ6yvaOcr0S7Un/S1J1gxihcLUg0YVIcghYVzDfjkAANnFQZvxrIVFGz7DJqMvxTG3oh6gMhbNfLbtzPYbtmwAAGyi7XTKbuF+trXHpUDBDEdUhwIAioqK5syZU15e3mBwFx8f7+7u7u7uPnr06JTs+qNXZBUVFW3atPHwaj341wLmqLSDW4bk5uaOGjWqsLAw7e7VYFtytripa7zmQHKwha0saxZvNklkH1Ec5/8jycnJo0aNGj9+vJubm8lkmjNnzsSJE+vq6n744YeIiAgM+7A3xddRKpWenp7mz/b29nv27Fm6dKlIJPrUfn+rZBuMs0X1m/ncbjRqZyplHZczS1T/7DVXmDg4L/ncawCfm09fkysxGnUoimGYvkST4fXAnFirMsl1SJOcMkP9nMyxekTfkHJQoZwrrFqUPeVK3RlzCmo0VS/9Xbj9SFFK7LyUMTkT5mQfOGZeLzeDIEiXLl0AAKNHjzanmGTGDNf7umINhmE7duwICAi4evVqk6bf+K2hRvHRP7zP9R2hvPukIacRwUZdEBdJjR9xK3QFpYhao3qUgiFNrx3nc/Pl1+BXrFixfPly8+f79+/TaDSJRIJhmMlkYrFYpaWln1j/+vXrQ0JCRCJRQ0pERETnzp3lcrmDg8Pdux8TH+FbXoPP0Bue6vSNUxK0umy9ofk16EwfEMYC57OCr8F/IXbIlXe1OgAA2YUGjJixSg8AsGYQc9MSXV1dr1271pCTTeK60b1S5I8BABItakSxcAY9QY9pIfpN4aV6oxgAAMFEmzU/kp1sGddzA6qtnkzl+kaMbtGiRUMlBALhyJEjFhYWp06dioqKAgAQ2bD1Ypfi7zNQLTp37tzQ0NBDhw5hjUQdgppOJmQ/3Q8AgGD+9z/Gd1tQLz16VHH9rvkUTADf+dHHXJIk137w9B3Fwxkik1W34mtX/WUS49ugv3LS0tIaTOVv377dtWtXHo8HACASiQKBoK6u7p2l38+CBQuYTKa9vf3UqVPNKbt27WKxWPb29kKh8N1llUqlj4+P+2tMnTr19fAN3wgtyKTgV23m21MpfmTS2/K/TvfjIs0Hmeng/D8HF3jgAsP5L9xH0INZqqdy8+fCwsLS0tJdu3Y1ztyR1yNeegcAsD9NtT9NbUGA7IzpdN60QHa701VHzHkgCpkd1sd6+azv+61L45ZXmSqbtOjq6vrXX38BAGbNmlVQUAAAsJrpSPWgVy7LBwDs3r1bLBavX78+Ojpao9Eolco2bZoGe+UoVgLMAACASJbcHhd4EwqLI4+m7zxkPhvmRfu9O2fs9itnnpZ+6N2AYKL1ijm04JY1S39XP0j80OI4/4/gcDgNi0HR0dE9e/Y0fzaZTJWVlW5ubp9YP5VKvXPnzpMnT6ZMmWJOYTAYV69ejY6OXrt27btjT1tYWMTFxd16jXXr1jXezofzQWhNmKnZ/q1xvgY+9xTB5+bTp+yi1Zo5oudRBWu3llYsyTN/rqurIxAIVCq18USKETXOyxov1NeWyIyR6ap7kps/F27oUFmzsWj93KxxeaqsJpXfEV37vXCFoUaoVirj4+MbnwoPDwcAhIaGGo1GDMNMSlN2UIImQ4lhWHV1taOj419//ZWTk2PO3PRbQ1+Zl0NNipgdnS4OH4Ehz9NzcnIAACS2YMWxuI+7LfqSiqr564VbDyEK1cfVgPNBfPkp+jNnzjg4ONy6dWvr1q0UCqWkpATDMBRFly1b1rJly8/R4ty5c7Ozsz+lhm95iv7TaR1Z+/rKI85/BXyK/gvhRSI1OIBkdGBr0p67n7Oysho9erSLiwuR+NJOHobgdpzOj+rjXNjwmADCxdoTU21HhlIpFO6EHvwBx6sOYeAVMe7G76s0yR/GH9FdipXJZHl5eQ2n9u3b5+TklJCQsGLFCgAAkUn0S2xPa8EEANja2l64cGH9+vVOTk4AgMOHD0e+CoBIAABj2QpEdgcAABEt+sy+2W89pC+IUFeWYwjq7u7et29fo1y0YVL/FbuiPuK2kF0cbDcvJlpyJYfOfERxnP99RowYMW7cuGHDhm3cuHHz5s0uLi4SicTR0fHAgQOnTp36HC1mZmYqlcrPUTMODs7r4AIPnEmwEEG0GAYAYLbnuB4JaDh1/Pjx3NzcJlE3OvN7J8sfAwAu1Zw21Ie70j1nsCwuaEn9rL+jEmjx9XcaZyZAxPEOM6745koSHgU7uaakpGg0GvMpLpd77NgxGIZ///336OjoJr1q27ZtQUEBg8GorGw6w98AkTfEJDz8oiUaxfs4RGQcjZjyeNYSMpl87dq1efPmIQbd+nkTe0xYINN+sM9qiETiTQgT/BShik0Q74pCVZoPrQHnfxkIgjZu3KhQKGpra3/88UcAAIPB2LdvX3FxsY+Pz3+7dzj/AjI92vOEqNtxoflPZcAGnX15WFD/rfix/2b5FgV+RJ3ovk7fcJimN6AA5L+w3BHtrVQnKd5R3JHq8rPHBqGh9oYyN1kT+J9q5UOdzopI+E0qq4W9ztae1CKvCKEnw7cNJzRuIvAm38UAACAASURBVMX09yVvL6/GAeA7deq0du1aFEXHjRtXUlJiTkTkpoJBqYjSxGKxAABHjx5VKN7cH4JFMMXr6MtjiET22NdvY5CIFY/piohE4l9//bVv3z4YhuOitnl3H14g/MjBE6NTEIHJqFqwAW1033C+DggEQsMcFZVKHThwoPmH9zlYvHjxpy/t4zQfDoVwbAgvahDf/EcnQfv6Pj88MYTvyfvWXZV/9XyLAr+Wy/mlXhqn1QEAUvSGH8X1IRRy7osQDoAI5NHid9dAI9IfS+/mUvpqnWr3iiq2SqvLNEUXVYpYKLQGsLJVaU3yj7QdX8SsLfYyeRoIVlZWjU8tW7ZsxIgR9fX1YWFh5sE9kQ1TPegl47MwBAMAGI3G7du36/XvUlZddh+T2DyRDjkHbxiweI4ue8DN9csVRaXTpk27evUqh8MRPrkyuGeX7PziD7hTL4DIJF7EcLtNizGjUXLwNCKVf0QlODgDBgwQCAT/7V58W9gwiI6s538ECNhbPP9syyS+vzDO/3O+tMDn5+f/9NNPQUFBjo6OHA7Hy8urf//+UVFR/5Z3zObgTybts+SvkMoOKlTzxPV/8Lmz2Ba+L3absHrwtJmvjHTz8vJqamqaVPJEGu9G89nh5ENJ4/uQuHvs2vSkW1iTWUvdNwaxOzTJTCFQIxxnX+5cIbK3UCgUmY14+vTpqlWrfH1909PTp02bZs7v8IcXZkSrlhUAAFasWOHk5LR7927T2+NCkt12A9NLx5+w1Tiy+87c9OtlSzdq05/16dPn0aNHHh4eeVlpHfsO2fJE+XGGtEQem0CnE5n06oUblTEPwCc7QsHBwcHB+Xx8UYFPSUkJDAwUiUTz5s2LjIy8cuXK1q1bu3XrtnHjxkmTJn3JnviTSfNZrD8VirFMRiiV0ppMbvVioZ3ZiesW9XLbulqtbt26dbdu3bBX9SyQHVJnlKWbDB3akCt0yFOdvg2FUoOgKhQ1b4hvgi+zpTcz4JL4VF5enkgk0mg0er1eKpU+e/asvr7+9OnTTCbz2LFj27dvBwBAJMjtREtlvFR2WQgAmDBhAoVC2bdv3xtixgMAACDQPGGbGQAAVJkIAAYAIHL6zD0cyQlLF+3YK79409fXNzExcerUqQumTcgSG8dfkYi1H+N8HiISON8Nsl49T/UgsWb5VkPJW+0DcHBw/teA3uRUA+crBsK+4DhsypQptra2v/32W5N0jUbj4eGRlJRkZ2f3oXVeuXIlPDxcp9N9UKkUvWGeuH4222K3Qvkbl9OdRo3RaHvRaeZJq4r5eexBAlZPHgAAwzA3N7fS0tL79+937ty5oQaJQdSnusaXbmNJJD3V6b3IJC6BEKvV9aQQEOHijT576ERGk0a1iGZZ1syO9T2QSiLHks/lcktLSx0cHKhUalBQ0JkzZ0aPHg1B0Pnz54cOHQoAQNUIBEOoESsKS2MO549ZOY6FMJdMWcz24rASqC6H/F+/Ln3OUIgRQHZebz7EDLWap5NTt/FEXduFzZtrTkQw8OdT5ZVC7Z89OUE2HxtrEsNUsY+lJy4zu4dyxw75yEpwGkGn0yUSCY1G+2935H+aj3veccyk1hkCrfHwsv8TfJnn/YuO4FUqla2t7evpdDqdw+F8sf0zZnX/g88dw2SY5+rjtLo9ClX+i2V4siNVceP5KByCoDFjxgAAbt682bgSPlnAJ6DtsMztfK47CR5LZmzjcz1J8EM91sd6vB59wz8gGpHenTjwMfueX2pBvVicm5sbEBDAZrPNZ8PDw3/77TcURceMGWM2xCMwiBCFQGQSYWty3fLiE79HqX30u+/szanJZfV7cwR3is8p2HJ0wyFEtmF0uMCejt1P2YzpylCtDgBAhMDidhZrO7Pn3pRGZqg/8iZCELNnqP32FRQvF5NQon6YjM/Y4+D8j4Or+7fGFxX4sWPHLl++fNeuXQUFBSqVymQySSSSe/fufffddwwGw9vb+8t0Y4tcsYXPDaVSAAD+ZNJuS94OudKfTMp8IfAW3XmKuPqG/AsXLly4cOGECROa1MMnC24pa6+pVRIEXZgmOyVVa1CsHZWcAYdwSfw3Nu0J+Qsg24RRENAbYCIx/UmiLOa+5mmGLrcIALB8+fLp06drtdqwsLDy8vKGUvIbYu5Im/ot1VHTIoMqW3TWhChvv8WPLIFKYLQEABhKFqPaPAAAIFBa9Dn2x8afVbHhJTOWVSc9NwDs5kS5OMISVtbuPXJMqPzImFQEJp0e3BJAkOJaXM3yrSZR/fvL4ODg4OB8Eb6owA8ePPjQoUPHjx/39va2sLAgkUiWlpZhYWE8Hi8mJuaLdeO4lWV7KqXhsBWZfMFG0JJMSnsRe5HeimkSG81O6QEAfD5/y5YtDXGxGhjA4CBk55OKKhoBcnUm3tLpW1BIc1gWV9TaDJ3oZHXkG1sPVnXLhAssKLWtH+WiEKhlUTEUEf95SJOUCQDYuXNnr169ampq+vfvL5fLAQAmiZFAIShuSWitLOp+LJ5yYCY33Ob6wxuxsbFZWVlvu0aCRYipenvDIWw7m97xjyeWt0d9N9Rken6Z1gzirb2rZ04a59G207GH+R96G19WLuDZrl/IGtwTEAiqu09wJ/Y4ODg4/wt80TX4BgwGQ11dnVqtFggEPB6vOXYfKpUqPDz8dUtysVicmZn5DgvzZpJvNP4olkbbPt/DVjI+E7amOGz2hIjv6ttt8dUrtafbcbt05vVypLmYEw8oVOkGPUeyeqbzYnuqU+P8iYmJ2dnZdHfyPeql/rcDqDaulcBkTyR730klWjDsd60GAMhksg4dOuTm5vbp0+fKlStkmJTucN9ymoPwz1LuCBvVU5nVbKcffp9m9MEmT57s5uYWFBT0jh5ihlqIbA0ABADAjKKaxAgujaR5MpQzNpxowUxKSgoLC6usrCTRLf6zY/uUHyZ//B0EAAAgv3xHceEWs3dHdlhvAo36ibV9U+Br8M0BX4PH+Tr4Ms/7FxV4DMPWrVu3Z8+e+vr6/v37HzlypGEF2tvb+9ChQ506dXpH2UePHmm12ibpCQkJ69ate/c28eaAAtC+qvaGrRWPQAAYKBiQokqQcYZZuRzyf5vGn685+kT2gEqg0mELob6mI3uIWNRzWmumAcMG14qWcxhdaE3t7Kqrq8vKyphM5v30A9Vu2lEWc3QanUAgsBErhX8cdD65HSISAQAlJSWhoaF1dXXDhw8/depUxaRc+TWx9U/Oinv1jLZs4f4Kp70+S2+tyMvLmzNnjouLS/v27d92Xfr8CIhII7vveX6MmYyVG+7+9sBS7OqwaLqgQ1uJRDJ9+vRz584BAPiBvY8cOjgo0OlttTUHpF4mPXFVm5rNHtbHom9niIQ702gWuMA3B1zgcb4OvtDz/rmd3TcmMjLS2tr6xIkTN2/eHDBgQJcuXbRarfnU/0J86ClC8R2NFkOx8vnPcjs/LZmWXTgyrXhcBmpEMQwrKysbOnRoQ8CYYnX+kpxpGpP6dPWR63XnZcb6H7MjupzMzhYZMAx7oNX1ra7ToUi+KueNbU1KGzopbejxqoOoTo8aDPF3YjMmL0ZNpoYMGRkZfD4fADD2u7FpbvdKp2en2d9NFcRltXxUOi27YEgKiqILFizw9/e/ePGiXq9/YysYhmGIzlQf3SRNL445OrdFacQcyZEzGIpiGHb06FEulwsAgJnckasj1cZPjRttKK+u27i3YuZKQ0XNJ1b1jfDlg838fwQPNoPzdfBlnvcvOoIfOXJk7969p0+fDgAwGAw9evTo3Lnzxo0bAQCOjo5Hjx7t2rXrh9b5L77R/0ehNKJg+NpqTbrS42Jr8y61spm5BCrB5XDAuYvnwsPDQzt02BUb14ZCPlZ1oFiT78tskavKrNMLfS38anVVVEP7vLJ+/wziAQBmi+t9SYTiqgXzXH5xoXs0bmhy+rCGzxPrB3jf1wlphCwWeej4sQzGy0F/Wlpa9+7dZTLZ5ImTDh4+BEEQqkEIdCLAgL5IQ/GgAwBWr159/Pjx69ev29nZwTDcxG1+Y0x1hyGYQ+SHmQ8xo0iX/VP1df1+Ee/XtVstLCyqq6t/+OGHGzduAADse/9wMnJ3JwfK22prJvqicpKNpfzSHYqbI71dK4DvwX07+Ai+OeAjeJyvg69wm5z51dv8mUwmHzhwYPfu3UlJSV+yD+9gNJ3Ra2WFJl3pcSmQaAHXH6+t/b3UNaoFokRKp2T369OPxWIlPHo058nTKKWaTqATICKFwChFICORV6jKJWCEdjYcsQa5XaoDAKzisk+qdYMdfqHDzMatNFZ3AMDfvOvlomfc3Ao3mB4bG2t84RIfANC6detLly7R6fTIvw8vWrQIAECgEwEApnpjfp9k6Zk6AMDq1asXLVrUuXPn9PT0K1euNC7eBAIzyFR3GKDP/eRDJAGt9VGrsT3rK07d2DZfcf2enY3N9evX9+/fz2azbXWlv96T/5X0qRsXKe5OBAad1sJLfvFW3fr/fGJtODg4ODjN54sKfN++fVetWnXlyhWxWAwA8PX1XbNmzbBhw2JjY7/kREJjLqo1CyRSE4YBANgyk/GU8MoUy+sEAwCAPdBSeklIoBBslrjIrohIEuKMGTNgGF7LoB1VqevIIWKDMBprCZPdh/A6c0iWYqMwmBO6qhM7W2wEAFgRifNYrL1aDp9s07jFyFYXGx9+99Dn5GiRkmrwtbLj8XiNQ9EAALp06XL+/Hkymfznn38uWLDAfJdgPsnzepuqVYV128oAANOmTbtz505wcHCnTp0aR7ZtAoHRguJ3GRDomKEG1RYAAACALJxn7otKGtirXPPw6M3d2zUazdSpU2traxNib1wfLRjmSbtbrj+Vq0E/7cuhtvC23bjIcs4EQ2llza9/ahIz8E3zODg4OJ+bLyrwEyZMmD179vjx4xsc086fP/+nn34KCwurqqr6kj1pYCCdZsCwORKpAcMwPulQpHPIsjJFnAQAQPVmCKY6KFMUWWMz9Ae8Ka60zZs3y2SyXm3b/m3FP29gV9IG6kVr+caMJ7J7YkMNESJSibR2duQfgyzMlYcz6QwCdFSpWlewpECdCwCoRZDTKk0/9+PmDH1sIyV9Z7a2H3h2LpBnpHfo0KFNmzZNeti3b9+zZ89SKJRt27bNnj3b7K2W6sPwutlWcqymdnMJAMDPzw+G4cTExI0bN1ZUVGRkZLzjkjF9uT5nIKpKNh8SaJ700OuWc9ueubd2aN826ofJFBIJhmE6DDmzYUcW8VyeduQFscb4aZIMQUSOBdnZnj2kl/zsjeqFG9XxSeAtnndxcHBwcD6d/842OY1GQ6fTGx9mZmZ6enryeG920PYOPn1Nzohh8yVSIwBEgBUakRFZRr8phfm73X8Y5CCPrX82ISN6s+OS793ojdaPjRg2U1yfqjdOoxtckTQSgdSW3eFk9SFrst1Qm+8AAOfytO3syA4WxHKTaUydeB1TjBhLe1kO+kepPqfWtKaQ8o1GLYa1IpOfGUzBFLKV6li9UTLP5WcC9OYheHR09IgRI7Ra7dixYw8fPkwikQAAiMykSVNYdHt+00Qi0fDhw9ls9sSJE7lcbpcuXd62JI+Z6iECAxAoADMA6HkeVFugyJmrPWdnUNkxIkZbtnnukB8D4NYz0dHNy1CnoB1Lp/Lo/4JVvDYtV37uBiJTsMP6MHuEfnqFXwH4GnxzwNfgcb4OvsI1+AYaq7v5sF27dh+h7v8KJAj6ncfJNRjSDaYlHNaDVhT3fwK8ZhWd/LMgd2KmEYaWjGmq7j9JpHQIumRjeV5PraX27M7vz4LZ4bYT70iuSwwiAIBcj25+rAAAOMHwdJbFXp1NN/5Ac/H2VPIaLqcPjdaBQl3D5fShUwEAY+2nmTDj8epDAMNQtaa4uDgt7ZWYs/37979+/TqLxTp27NjQoUPVajUAgMiBG9QdACAQCOLi4gICAhYvXlxRUXHlyhWZTPbGS4ZgHiBQMFO9NiUAkVwwJxJonpy21wXz2qeBmNLV28v+3I/pDQAACACOoiTqSOSxtbOcWoT8GnXH9MkDb1prX5vf5vNnjdPnlWAGoyruMarSfGqlODg4ODiN+BbjwTfBiGFL6mW+JFIrMum0WvPMYGR34blFBdj/WRW10Z5jScGeKhoyIwDMk0jLrl9fqNM4wPBhAT9KpTquUgMAuCR+T/6Au5IYAEBEC8bPoSxzkfEWDCsiYatc+Ugal60uzjeaTqs06XpjntF4WqXJMBjlKEqEiLOdlxaqn50t2F/103oriFRSUpKent64n926dYuLi7OysoqOju7evXvjCLaIzJTT9rEmTQnD8KZNm7Zv3/7zzz/n5+cLhcJ3XDgE86j+0RDZoVEaAbaZNmTLJdZMORW7oS+/UVdXBwAICQk5deqUvb29ujh1/cTeDh2H/X0v95NvPKD6uvNnfg/BRP2z4so5qxXX7n56nTg4ODg4Zr51gTfPz5sA2GnJ+4vPJQNAIkAPtbqDLeHdKQHlvdi5w7j1J2sb52fnZF+bOH70yJEAADuY+I+VpenFKscQm9HmKXoCBOyYRIkWlelQCIANPM5trbaG6JOjKZSgSLbRUIuaJAiabTTUISY1igEAaET6EvffskDe0ymWss37e7UNKSkpSUpK0mg0p06dunHjRkxMjEgk2rRpk42NTWJiYkhISGpqqrldIge2W+VWOCyt/kQtAGDYsGFxcXFRUVFbt24FABQVFb3NERBEdSdYBAMADEUzjVXbnieS7bz6n7X8YZawYmVoK6/H0xboC8tGjRqVl5e3cuVKOo1W9/jS5F6tvQdPr6571wtEcyEQ+DO/t9+2nOrrrk3LFW7er03Pxa3wcHBwcD6Rb13gr2m0ZAjaxeeSIYgEQdv4XGsCYaNMUWA0Rgr4f1vxLw+2qD5fi+mfz0pTIWhVcJC1tfWTJ0/M8eVsicQJFs83r0MAgiG4VFuEYAgA4G65fvoNKYIBCwLhTz5vmwrqze8VSqGs4XL60mgdqJQ1XE5vGs0Ofr7uTicyFrqtTuUUJ47nyjft790utK6uTiKRGAwGrVbr6+vbokWLjh07rl692s/Pr7KysnPnzhcuPJ9g5wyx8opuU7O5pHJxPgDA19f36dOnrVu3xjBMLpffuXPn3feB5LAcoFpzLHkzRE4vp25PEmKXcv0TNI9311blk0ikNWvW5OXlTZo0CcKQ/Kv7vTzch6+NyhG/dW9e8yFy2WQ3R6q/Jz2ohTTqYtX89cob983h73BwcHBwPoJvXeCHMeh/8rnwiyV2EgT1pNMkKLJPwKdDEI9A2B5gU+FDfXi+uqEIjUZbuHAhAOD27dtvrPOW6PLlulMAgOHeNDoJ2p+mAgC0IJOmWjAvqjW5Bv2vNfFpekOewTxX/0okNwuYtcT9tyR+cfJYLkEoHThwIIfDodFoNjY2mZmZZDI5NTW1Q4cOq1atioiIUKvVI0eO3Lx5s7ks1ZfhEx9Ca/nchp/JZM6cOROCoIqKiszMTARBdDodgiBv7DNEcSA5/gIAhCjuG0oWYCYJAAAQKNZ+v3jNvsboRNi+sv3qLj1rD5204/IjIyPT0tIGDx6sVqnU+QlToqWHPjrsbJNukGBmz1C7LT/zp47W5RRWzlwpOXAKe0ufcXBwcHDewbcu8K8z1YLpTSIpX+zg4hMJXWe72lx6JRDqTz/9dPz48WXLlr2xhlG2EfckMWXaIgiADV3ZhzPU5p3xEyyYjjBRhoFMg0ZsFJEJWLbRwCcSu70alIUFcxa4rXrIy3loX9iQ6OfnR6fTr127FhgY6OTkBMPw4cOHN23aBABYtmzZqFGjlEolAIDIJPLH25qLIIrnAXiCg4Nv3brVrVu32NjYixcv1tbWgrdDZAZBMB/TvgwuB8E8ssumDTvjRy9gQLUXKucsz93zt7+n1+XLl6uqqq4d2RH3vWCEN+2fLPW8W9Jnkn9hNA8AoPp7ChZMtt/+K9nFAQAg2npIdScBH9Dj4ODgNJ//zja5f5Evs20GUZiIrA/YHvZYeu+a8Nwqr60wRIou1t0s0W3ryQEAKFA0vE483YI2mEEhQW91KwsAkBhEW4tXB3E6dK4MuFuQK7C1KSsrM7A0XKJl94497t27N3z4cADApUuXJk6cKJfLvb29z549GxAQ0FBDSUQWwIDjNm+YR0JRdMeOHevXr58+fbq/v7+zs3NwcLB5r907MBROI/KGEnkDX94HeVx13Pq7O6nudg4dovY2zqwxYSdyNIfS1S5ozewOth09rZp/u96LNi1XeeshBBMsZ40zVtWS3T4pHM7/Jvg2ueaAb5PD+Tr4CqPJfQ4+0wOvxzBKo61x0jN1qscyx63eza9hT9kfbJjzvf3UJulFRtNEkXg7n8cyFaIY6sMMeGNxAIAaUW0v+Y2rZrCf2BDpdMAhSFX1RobeGfUwmUwjRowwZysoKAgPD09PT6fT6X/99deUKVPM6agOrVlbVH+mzvFPb85gAQCgpKRkxowZtbW1c+fObdmyZUhIyLsvAVHEo7I7JKdVryZjuroL8sJNbA4vJaU7Q23b8ocx0It3heo6kbOjAwoRPfuM2/DLwuGhH3DHmoOxWij8fT9AMWbXEEaXYFjw39la+TnABb454AKP83XwNe+D/9/ne6G4yPgyxrxFd279qVqTtOn8s16vj4+Pf+PC9kSHWRmK5CT5I/Nhqdy0N1UFANDrc7qbYmeIqnL06r1lf2iRt+7/ZhCZi9zWqGmmVO8UVKkQ6Wp4sCVTwpWpZI3D5np6eiYkJEyaNMnsaDY8PNzsCZhAJdhv8HSLalG1orD+eA0AwNXVNSYmZsGCBb/++isMwwCAqqoquVz+tg4QWZ3M6o5IY/TPRmL6UgAAABDVerh1x8ck66kK4cnMs2cKzu9qsM6z4nOHDR2CGXV5Vw+Ed/IfHDbyjzP3jJ/o6rYRJDsr++2/Ws4db5LKa5b+XrvyL1VsAtbom8LBwcHBMYML/JsJplBuNBJR2JIsmOpgKGs6bvjzzz87d+5sXgtvAp3ImOmy+GjlPrlRCgAQ0IkX8rVb83ecqDoI65JDkIQ1SkYHq3E0Iv31sg1QCNT5nivtBHZJoYkQVhVi5RwQ7Ffkkt6xW4fG2Wg0WmRk5NGjR1kslnmi/uLF5x7vGe3ZfontueEv/eFPnDixtLQ0MDAQw7D4+Pjo6Oh3hKgxQ+T0JPKGYSZz7BmzWhOIliP6LUwec3qak98lbXqXc+HfJ2/4C9Lozpw5k5GRMWnSJBIJvnrx3JJR3fg+7dbs/sdgMLyrjQ+B4unCnzLKYf961qDu2vRcU51YFfdY/TAZ0/9rTeDg4OD8v+dzx6P93Hym+NDpen2/6romiapEOWp6JVD6vXv3IAii0+klJSVvrKdYna9HdObPybLEVXk/GVFDZPnO+5Jb+2Q1bUozSnWyaOGFcs2bi5up1VdPyxi1MHtymboIw7C/K/5zouqQVqt9PQx8aWlpz549zd+seSjf+CyiMpXOyNY+U5kPtVptSEhIx44dk5OTdTpdYWEhgiDvvCsYhmG67EG6/EmvpqEm6c0LmzpcH9uvbPw80Z6/tRU1GIbV1NT88ssvDQ4KebZOs69UptS+PXT9J6BJzqpdt7t88jLUYNQVlKK6z9LKZwWPB98c8HjwOF8HX+Z5x0fwb6YlmQwBkP7qoLNmXbH01Csm6F26dBk7dqxGo4mJiXljPa50TzKBUqUrxwD2RHqXCBHXZu/JVeY+ksZRlOedjGmTxRIq7HCkcvc7OnOq6vAw6+9G203eWrL6kfTuUNLABHFsRnHapUuXRCJR45zOzs63bt3atWsXg8E4c+ZMy5YtT58+3XCWQCfSW1nk902pWlGIqhEqlZqQkDB58uRBgwZNmTLl0aNHFy9eLC8vf/edoficga3GAQAAqkWV5th3EJHTe9jSh333rhMsUBUX/b115Jjrl6NsbGzWr19fXl6+f//+wMBAFhX258OLYuWDzoijsjRKw79p/EFr42+9fJbDvnUQCZafu1Ex5RfR1kPqh8m44T0ODs43C25k91YOKlRlJtNvPE5DivJufcX8PN+k9hDxpf2dSqW6fPlyWFjYO8wldpSst6bYGVB9vVGCalolKe6GWju0YPvdFF0m8GamooJDAr71WyK95qoy/q74zzqfnTBEqtKV7y7d7Ik60+4VlbaGx9gueZTwKCAgICAgAGpkEggAKCoqioiIiI+PBwD07t17165dXl5e5lNGoaFqeYHyvtTzSiDViwEAUKvVu3bt2rJlS9++fXv37m1padmjR4/3Wn9g+gr9s1FE/lCSw7JX08ufPd5E1V7nanoIr7AkfTuHfjfi5VkAHlcZTuZqHlToR/vSl7a3eHcrHweqVGsSM9RP0vU5hRRvV9bA7rRAv8/R0L8IbmTXHHAjO5yvA9yKvll8vgdeiqL9aoQ3ba3YhJfzHAX9U3hjbPgT7D6oKi2i2VT0izczIFX+ZIXnHxuyjzyrcf41xOtE7Z/rfXYdUyMnVeoDAv7lis0deT3asl+JrvZXybpsZTqFQDEfYgAzoHoEQzCAzb/axnFMREJetkAgCAoKatIoiqKHDh36+eefJRIJhUJZtGjRL7/80hDmR5erJjtRCYyXbxVCoXDdunXXrl2Li4uztbUlkUgKhYLFYr3zyjCAmQBEMolPY4Yaks10QHixpx/VmMRnbu7fs+Ncyf7D8y0NXYlsa4q328vbq0PP3bw/97sBdJfW/UaOWzNrjIcN94PuanNAdXpdWi4gEgkMqjTqEi3Qj9kjFLb89xv6dHCBbw64wON8HeAC3yw+6wO/RCJtQSaPf+GJFgCgTlIUf5/hnxraWBqbg8xYv6FwmRvd65kqi0KgqPQUFSpf6rXQj9kSAHBZrf1dLl/J0quUD4bbjmtc0IgZ8vQ6HcAAAGoUMAgAACBUJcbU/dOx3r97jqNg+vcIcNem6QAAIABJREFUghDfMgEgFouXLVt2+PBhFEUdHBzWrFkzceLExplN9caKn/KsZjky2rMbEsvKyk6cOGFnZ+fi4tKlS5f3Xh2qLTDV7iXZzoGorphJCsEvFRRVPjHVHVY/ysw67Uaj070mjGZ0CSGymACAxMTEHj16qFQqAACZSuvRf3BAr5GLxg2yZn2GHz2G6XIKtSnZVD8PVG/QpuXSWvvSWvoQmO8ycvyS4ALfHHCB/8r4/YlyWmsGh/LNLRbjAt8sPusDX4sgChT1etUhTMnELJofw2apa5PMGIatW7fO1dV13Lhx4E1U6yp+L1oxyi5CZqznkLgspG2A5cvx8R2tbmW9bBWP04dGLdbkO9JczJ5wahGkb43QkwQbMazMhHiQYD2GAQCO8kmRFTs1iHqq009WlOfe6548eeLp6fl64N2EhIS5c+cmJycDAAICAjZt2jRw4Av3NRiQRFXX/F5KcaXZ/uLKDOUAAKRS6ZIlS86fPz9u3LjFixczGAyJROLq6vq214jG6LL7QhCF4nf5lZuDKOqLo55dPeOhsNKJbQ5aoJOnLnR1dVWpVOfOnYuKioqLi0NRFABApLPcOg6MiJj8Y3hPBgl6SyOfBKJQaR6laNNydTmFJAdrWms/WiufxrML/xVwgW8OuMB/ZfQ9Jdrdh+vB/QA3Yl8HX+h5/9xWfJ+bL29Vqy/R5PVKej1dqVTCMEwkEm/evPm2svmqnB+zJuSpshtS9qeqnkmM5s+5BkOv6totMnlU5YEtRavMiRVGU6/qWgzDJqUNnZQ2FMOwYqOxf3UdhmEohsYIL83NGh8tvFC7PVJ+JTbvWd6JEycePXqk1WqbNI2i6PHjx93cnstY165d4+LiXp41oOK/q7ICHspjXhreV1RULFiwgM/njxw5cufOnSdPnkxPT3+95tdAUG0RhmGooU5fPB9RZbxyTp0lyV6yMIK/Z0nL8ok/1pw4VVVVhWFYeXn55s2bAwMDzd2DCMSAnXnTouvluvcb9n80qNGkzcyrj7pYvXyrUSiRX74jvxJrqKj5fC2+A9yKvjngVvRfGX1OCgvqjf/tXvwX+DLPOz6Cfw8iBNkiU2zmN121VT+WN57TNvPbb7+tXLmSx+OVl5czGAzwJnJVGQ/r46Y4/Wg+vFGsO5CmOjfc0nwoQ9EFEikMwHym0pfmAgCoNCFj6mo9xbMbahjjdXKLTBFj+9wXrNBQG1W5V64VD46zsS8hMicOy9UqiouLu3fvbmNjA17FYDDs2bNn3bp1Zmc4nTp1+vXXX/v27ft6P7XZKqoPAyJCCoXi0KFDO3bssLe337hxY3l5+bBhw952da+AGUx1kQAiw9aTMZMEM9YTaJ4vTiGI4r6x6NSd67ETtpadWDk9iBlM79Ce6uueX1h4+vRpDMN+XLL8cbWhmxNl/JV6Ggka5E4d6fN5p9N1mfnqRyn6onLbdfNlZ28QOSxqgCfZ0RZAn2UioQn4CL454CP4rwx8BP9ZW8EF/j2gAAypFS7lsDtTKY3Tc9s9sVnmyg17xeM6hmEzZszIy8u7efMmmfwuV/MAAANqIBNe5tEjGIUIAQAQAH6Xye9p9dstuQX1F+KURTL1kyZls/h7nzq8It5PZA9OVx/x0jl2OWG0794HdAokkUhv+/UoFIqdO3du377dLPMhISFLly4dOnRo4xn4wuFpumdqwXRH/gRbmEtCECQ5OTkkJEQul0dGRo4cObKoqMje3t7T0/ONTTQBkd8zFM0gO68n8oe/cgLV6cU30drL6vulRfft6CYqOzTAbtL4xnZwWhN2t1yfWFgdNaefHOaF9Og/ccSg4V3bEAmfUXc1iZna5Cxddj6iVFN93Cm+7rRAP7LThxlXfhC4wDcHXOD/v4Ni4O9MtQF5rjuHMtTDPGl82vM1+AEeNEeLDzNv+n8KLvDN4gs88HFa3Va54qK1AG40klMnKoq/S/fP7ECgf+TPcX3B0pastoOswyEAAQCW35Orjdj6rmzzwvM1jXajVN4NFgmlR4pYi5xFMxsKLvc7NVcsv27bNJqLDtVerTt7X3KzC6dXf9sRDCLTnH737l13d3dHR8cm+VUq1Z49e7Zu3VpXVwcAcHFxmTNnzpQpU9js5zMTmjSl8D8V8msimyWu1j8+j++i1WqXLl16/PjxVq1aDRgwYPbs2TAMi0QiGxsbqHkjXWPZSkSZQPE6ApHtX6aiOq0w5vGVf/wMUmYAeWGksiMncFjYOGbnEIhIAABUVlYGBAQ0ONYlcaw9Q3r269d31uh+7nb/ZmCbJiBSuS63SP+sCGCAPaq/eNthsqsjvV0rildTI4xPBBf45oAL/P93UAzsTVWpjM9153SuprcrlUslAAAIAIz2o+MC/y+CC3yzmCqSdKdRv2e+Mi+tzVJRPOgE6kfafypMsh0lGwRk60mOc8gEis6ErX+keFCp39qD09aGDAAQI+j4qiQVYOggizaaw0ZdKgAAogR0sYk4pWW9LvBmpEbJ5bpTKfLHfQXDWh6sotvbK0P8U/NyYRhu1aqVg4NDExnWarXmGfiCggIAAJPJjIiImDlzpp/f843jpnojIjNR3GgAAGVchjL6PqqoQwTcOAZ6Iu5WcnLy0KFDvb29nZyc3NzcXF1dBQLBe64cMyHKx0RmECBQDaVLIZIVyX4BAC96hSGIMiH5QWTO9bR+Vm4WQx1+3V82wr9vcN/+Jmvezdu3r127Fh0dXV1dbc4OEQj+rdr0XvKfIR1ahtqTKcTPOZ2OYbqcQl1uIYFBp7XyEW7cS3ZzpHi4kD2dKa6OEOU9czbvBhf45oAL/FcGPkX/WVvBBb5ZFBlNE4Xiq7ZWHMIrcl65JJ9gAduteLMB9tmzZ2NjYzds2MDhcN6YwYgZ/qncW6YpmuG8yI7qCAC4U6b79Z58dWd2X1cqAGBL8epsQstEYjsypiRhGgOBQ0YklmSrQCp7He/NdZqp1VdfqD1WoMzuWO7e8qKcGxIib+efU1rs7u7eoNyNQVH0+vXr27dvj42NNf8kOnXq9MMPP4SHhzcst2uepFWvfKxK92J1YTPbo4bcy/y54+r5zGPHjh09enTTpk22trb5+flBQUHu7u4oihII73/1QdVpiPwuyXYugCBj5R8EViiR9XJXHqYrRKQ3IyMP8vNp7ZkBRiWljGiy7NXTf+yIzMzMmJiYmJiY+Ph4nU4XsT7S0GLIME+agwX8uFofbEtuY0Omw5937dxYI9IXlBoKS/UFZYbyapKNgD28D62Vr7FWRHa0/VC9xwW+OeAC/5WBC/xnbQUX+OayViqHIfAL5xXDOpPI8KxzosMfXuZ4rE0YPHjw1atXAwMDY2Ji3jGufVgfe7rmyFDr77pb9ocAJNejEAAsCsGEgiT5vTjJjTHOq6fVZADYahYDuVf5Wz/BsJastrZUh/f2uUpXHi08ny5PbCNy7qkOchr8cv1bJBIxGIwGvzcNZGVm/rXs15O3Y1QGPQCASWcMHzlizJgxvXr1Ev68hTc5nGTnLD1XV3+yVpujEHyfbff7nMbFJ06cGB8fHx4e7uvrS6PR+vbt2zDh/15MtfsxQw3JaRWGqBDRcSKnF0R98eaEahDFI0VhzKVdD/lEuM9S5wqRT1FUncfQwXbdOpWUlDS8tVQokLN5mifVhvun9hpSL3n6B3bt3GHK4C4ebv/yjHoTMAQxllUTWExELJVEnjVW1vCnjCI52emyC8iujmRnOyL7PT77cIFvDrjAf2XgAv9ZW8EFvrnIUHRoreiIFd8VfuW3qElVFg5Pcz/TihHU1OlbRUVF79698/LyZsyYsWfPnndULtTX7Cv/szUreLD1qIbEbseFw7yoNMGBKl2FCTOoqKEPodZ+FEZ/QmmdKn6+28pm9lxiEMWILiZI77Vmh3Tn97PJ1qFafRGLlJ2TY2Nj4+3tbWdn1zBvL79wU5OURZs47PyDu4f+85+ElGTz78PKyqo/x3bq/h0dunQ2Z9aXSoXrfnc8uBFgoGZzCaMti9mZS6AS0tPTz549e+nSJaFQOGjQoKFDh1KpVAqF4uDgYG9vT6FQ3t7T52CIylixDoJ5JIclmKEWkUYTuf0h8nOjQswoROUPqotu3dyR4WgJus52y0izkd1Qe/Tq5dC5A8nBxmz0Pub7sSdPHG+oky2wtfIJahvc7oeBHTq0b/f6m82/C4YgAECmGqHyVryhtMpQVgXBMNnZjuzqwA7rQ2C8oXVc4JsDLvBfGdsSlZNa4o5uPhe4wH8AdQhi+SZXL/IYSfmsHI/LgTR/ZtMidXWrVq2aMGFChw4dXiv3CiiGIsBEgsjVugoBxZoEkWV6NEtk7ORAyVFlPJDctqc6hnC739DT9iuVnajUqRZMgrHkoTR2sNUoNun9vldVJuVDaexdSQzZSAhKtfB7hFq0DxZ5ORaJ60gkUv/+/c3ZKqb8YrtxESx47ion88zlowcOXq4oePbsmTnFwcEhLCxs6NChIZZ2qtPRtusXAABE+yulZ+q0WSqL7jz+BFt2P0sAQElJyeXLl69cuYKi6J49eyoqKtLS0lq2bNmnT5/39rYBzFBtrNxIYLaFrSJQTTYqv0+0HA6RrJ+fNdahioelOTcfHMwKZLF5ege9Ao4jSbv/+pOTU8uH9+8nJCclJCQkJCRIJJKGOlnWTr3/k9zCitxCQAoQkNy58GdduDdjksiMZVXGqjpm9/ZvdJ+HC3xzwAUe5+sAF/hm8eUf+GS9oTWAqxblc8OtLbo8V1bpBWHlojzvO0Fkl0/9wo5XHchQpoy1n9rCoo055XCG+lyedn4Is4czFQJAhWLHVeoolSqQDPsYEzoz7RtyvhcMYLnKjDjJjWeKDF+ZXUAquePsNQiKmjf1qWXy9A07W69e0DDANVbWCrcctN/+a0pKyt8b/7hwK6ZCLjWfYlGovTp2Hjj2u379+tnZ2QEATBKj4qYEM2H88bYAAERh0qQrmSFsiEIAAFRXV3ft2lWtVvfq1atr1646na5t27bt27dv/p3BdIXGmj1Ebj8ipzciu4MqH8HWP0Dk51vXMESFqhIrUm9ei7nTObjW2eQqPON3QpEfviHCw65T5tPcLHFtYlpqYmKinYPj0h3HssTGTKExS2Ss0yAtBKQO5SdTysStW7XqF9ray92l+b36t8AFvjngAo/zdYALfLP48g/8rCpx2OwyF4SgzVS5/hPQoPGaFAXZhYYqEbIz9W1l09PTt2/fPm/evAaXbW8kS5l6rOqAJdlqiPVoT4YvAOB2qW57oopIAHPaMHu7UgEAOgw7rdIcVqo8SfA4C8Yz4V6FsX6u6y8Uwltbb4zSpHgqe/BYel9sEIZwO7WhtOEcegr5eaZmZ4rteFw+39nZ2cPDw/g4TfM002rxFHMpRfS9+ENHr+Wk3akrz6sXmhMhCDKPy7t37965c2cm8/k0hi5XXTYrR/dMwwhlW3Ti8kbbkOwpRUVFcXFxcXFxsbGxZDK5W7dukydPrqysFAgE1tbWVlZWAoEAht+/IIfpik3is0TeQALd31S7F1E+ITutgigujTPIah4mPIgO8qmnlIozz3jyTSwqn8xwt6kL9CE6+fr5+pon81UGLK9KEuxu1fAssFgsC0cfJ0+/li38OwQGjOjbjUFv1l39FHCBbw64wON8HeAC3yy+8AOPGdC8cRlPURM44DP8man4+0zXw/4W3Z5PaBtr9Lkdntr96mb5g/0bi69Zs2b16tUwDC9ZsmT9+vXvaAjBkCey+1fqzrBg9lCbMX7MlhgAd0p1d0r1G7uxwQvHOEYMu6bRHlepZSjajSSfwnEVwHC08EIrVpA91ak5VyTU1zyW3U+WJ8g0Yr86gUe8zrmchgzqJrK0sDURyKdvWi2ZSnCxJxAIDasTGIJCREJJScmNGzdu3LgRGxtrDhgDAIBhODg4uFu3bp06dQoNDeVyuSapURUvUz2QsvrwWb34AABttspYa2AEswqqCx88eBAQENC2bdvDhw9HRUW5urq2a9duzJgxFAqlpqbGwcGhOQ8AZhSjivsQs4vqThaq2QBzC03iX1iDxwKoEiKQIYozACiqLRKXP7RQFOlLS/ffuX80tvbKlDAoUxBjJ5EyebNHTY29l3w3Kz0jNyczM9PsGKABdkhY6MJ93Z2pC4Mt4sp1tkyiGwem/dsm+rjANwdc4HG+DnCBbxZf8oHHDGjx+EyIRKAd8p1QLxnPZA7PMjbReH2Rpui7DJf9/vTAN1hNa7Xa5cuX79ixAwAgkUjea2GOYMgjaVy08MIkxznmobyZahUy8Ix4dhvmlFbP97BFS3QbKuQGAdaaTLI1ZdqYns1wnIFiqNQo4ZPftzEdAACA2FCXLH+cKntcrip0rWK6F1G9TS5ObbsRYGIejOQUFlhbW9vZ2dna2jYJZmMwGOLj42/fvn337t3ExESTyWROhyDI19e3Q4cOoaGhwcHBvr6+5qG5/Ia4bnu5Nk1BdqIxQli2y91IthSVSnX79u2nT58+ffo0KSmJx+O5uLgEBQUtXbrUaDSWlpbyeDwej8flct8aN2/3UaRexh7WEyJJVPcq9AVlgrlGRHaF4ncVIlmZag8CgMDWPwAIBgBgJgmqyUFEWbcf3HqclDG7pR2S61hfRscwiO/FAd+1OnEzR1atrpPKiqoqxo6fEDr4ewMCPLjw8nvyQqmJqyy+urCfEaZZOrg5urp7eXq08vEI9vfw9nB7PdJPM8EFvjngAo/zdYALfLP4kg988ZgMCIZcDgdAMFSDIFNFkvYUytwstGx8lse5VvS2jazoMVA4Is1qthOr5xv+3WdnZ8vl8vea3b3OwfK/uCR+R14PG4qdTI8q9JgTi/ik2pBUY/DgwntSVSeH8+O0uusa7RO9IYRCbk/SPq7a0JPXMczm++a3ojIps1VpWcrUHGU6hKCulQyHLKOj2prs6KtwtxXJ5X369DH/LlUqFY1Ga6y4KpXqwYMH9+7de/jwYVJSUuPvhU6nBwYGtm3btnXr1q1atfLz9kMLjJpkBWeoALYkAwBqNpYYq3S0ACbVn1FOrUnLT0tLS5s6daqDg8OSJUuSkpLs7OwEAsGQIUN69Oih1WoVCoWDg4P5pcFYLaxbu9N+x0qI/Dz0n3DTPnpoILNriPkQkd5AVUkk+0WAQNXnhmGIiup/DUBkRHEfgi0JdD/MKFTXp1dmPbEnSkmC0p/W3g+Qeg1z8+VP1YxbmTqS16p3uxCSg0MdSuX7+oogJDQ0VCaTvX73OFxelx+39RgY1sWR0sqKXKtGrOhE+IWNMKrTE8gk8CYPAbjANwdc4HG+DnCBbxZf8oEvm5FjrNG7nWxFoBEAAEoUXSSROtxSDF1e6XktkObHBADIUdSCQCAAII+RVC7Oo/oy2Os9+B6Md8zn3rp1a968eQMGDJg1a5a7u/s7OlCrr7ovuZUgvccl84PYoUHsDlYU20olcjhDfblQqzFiMwKZk1owmGRIhWJ3tNpbWt0Tvd6DBHej0njGnEd1eyc5zGrJCmr+JdfoK/NVOfnq7Dx5psmo82L4ufNauNG9LFNlRLk+EVFVy6VsNlsgENja2rq4uDQuazAYkpOTExISzIPyoqKixmdhGPbx8QkICPD39/f19fX393cAtrqHKk2mUpupMpTpfJ+2g3kkAIAqXirVyFIk6bllzzIyMnJycvLz8wUCgb29fWRkpI+Pz507d4TFJfZGgm3vzubOQBAkv3wHlSm4E8LecFWoBtVXmSPfGEqXYdo8iu95ACB93liIZEl2+wsAgKpTISIXw3SYrjjxyV3ls7K2bIpJqHl0z8Am0tutVZootv8sqNIBQOvlXVypTE98ViMWV1RXK1WqqSu3+Q/+wZkF+1mSpkTXC9UoXP60cNcPVBKVw+AudPf+Pnw4d2IYgfbKuj4u8M0BF3icrwNc4JvFF52iR7CymbmmWr3bqecaL42RFM3I9j7T2uLFJvi1UrkcRX/nc4kAYHo0Y2ep4q9yy+HWbnOdKR5v3nv9999/R0REAABgGE5JSWnRosW7u4FiaJ46K1mekCx/zILZ/QVh7bld04XGRbGy3q7Ucf50Oybx9DMNmQAN8aSZAJakN9zT6R5o9fWoKZBMCqXS9er4Uvn16U4/OtHcTJgRhkjvbtGMxCAqUOcUa/KLNPnVmjKehm5TR7Isx7jAht+pP4RSgoKCMKMRIxKTkpIYDAaXy+VwOA0G+VKpNCkpKTk5OS0tLSMjIz8/H0GQxvWTyWQ3NzcvLy8vLy9PT08PDw9XV1dHR0fRpnLFbYkuXwPBEK0F0+NyIArQkpKSZw9zeg3oTRXQZsyYcfb0aY1a4+DsZGVl1bZtW19fX6y40j2wZcchAxkMhlKpRBCEyWS+23wP1T7DDDVEdncAgD5/HEDUFN8LAKD6Z6MgqifZZSMAAJHdBhAFgoiovuzuuTuYRN4lzKRRlZ9fzvFj2toxLNR6RGTQ3HarW/VLhPKuKqNEbDmgV0pCweSZESYEAQAMCxtxoOcwTG8QLJjcuPVvROArKystLS2p1I80WsQFHufrABf4ZvGljewQrGxGjqnO4HaqlfK+tHxmjtvpVowg1lO9/qBCNZtt4Uci/SiRUiBoC5+bZzBOF0vWmhg+USLxP9XeN4MgGCLZUcBrw/nU1NRdu3bl5uaePXvWvOUMAFBeXq5QKPz9/d8WxCW+UrctIxUA1KT1RuBiIiuGingZdV6o3s6IEloIyCs6shgkaFeyikMlBNmQOCxCqtGQqDck6vXViNGXRGpJpipU8ULlrd/cllhTbGt0lSQC2ZL8/tgtCIZU6krLtSUVmpJyRX4lWk0ikO0pjuwsGa9YDzMdSBQrlMhWEjA+n2/e+I6iaFVVFYPBYLFYMAxrtdrs7OzMzMzc3NycnJycnJyysjIURZs0RCKRHB0d3dzcnJyc7Pn2DhZ2Hh297O3tHR0dq8fmq5/IAQFQnGm0Fgwd5WytA7/GwbIgOb8oO70qL3v3+VNuAf6DBw+urq5mMpksFsvKysrW1rZv377+/v5Go1Gv17u4uLzXpS6qzsAQBZHVCWBGfeEMiEAmu+8BqE6X2ZXACCR77AWYwVi7H4KtCXQfVFWennBTCzQhLcj1cfLoq9VpzNSNs+GKQ73LykCVQTFozwpL76CKH3522L26scebb0Tgg4ODt2zZ0rVr148rjgs8ztcBLvDN4ss/8BiClU3L0RVoDBU6j/OtzcZ0CACX1JrdCqUHTBrHpJ9Qa/QYlm80ruFyejSaiS0ckmqo0AmmOwhmNI3t9jr29vbV1dXW1tazZ89esWLF6xm0JqxI+tyiLa9efbT4QReP0hpDvgIR25Bd/FjernTPAIvAe2WEu+X6lFpDjQoJsSMfGsADAKTXG+rJaAFqyjIYMgx6FQo8SCSysVSvy5puO9iPynkmu4VgSHd+PwLUrOBO9cb/a+/O46Iq94CBP2eZfWAGGGRTBGRRwqu4pYkomZiaW+6GW0kuabfetNQ+eVvULOu6YN1rmeK1omsvyjXKXOoSEiamYiqGIFvsDMMMs8+cOc/7x/ElrlLpMONMx9/3r3Me8Xd+PM9zzo85cxZ1g6W23vpLfXtlvaGqiWkyk5YgSc9AYXCQKET4TYmvVaaWBiBWbmEIoVjUp0+foUOHIoQcDkdjY6NMJqMoqrKysuz/Ky8vr6ioqKur+60pqlQqw8LCwoLCAmWqHooevfr1kP5U7qcxCkseVFpCFY5AQYhINlTh+/eQ0tLSmuqashM/12rrarV1rfpWm8P+2b8/a25u3rx5M0mSLMsKhUKVSqVSqUJDQydOnBgTE9PY2OhwOLjH/GGMb/8zC9ubEWshROEI2+31uwhSQoesxA695XIKKYkRxWVhRmctnUf5PKg9FCgbHckqTlnNPgH91yFSXL9mq2rVAmHEr3db8K/Asyw7duzYWxrPnz/fp08fpVK5Y8eOAQMG3G1MKPCAH+7N/n7fPQG4+wiK6P1BfMPrFcppPToulacQelwmfUwq+cJk/rtOr2XZFgcbI6RH/+/3rNFHE41ndZZSI9NmL5t4QT5c6ZPiLx+hoAO7eDFJenr63r176+rqsrKyOhf4ffv2mc3m2NjYpKSkhMCb8wMjGb4y4vmYyQghs8NUaS6vMF0v0hZIKdmjUQOVfkUxfYoD6FARCvrF3CtAELwl3yiiiX895m914HfP6hVykvRFFnFsm1/MASNToWvRO/r7I+MRdVu4QFiiPUYyjWvDn+5Fi6+2F4hI0UDfYbdk6y9Q+QtUD/gMRKqbLVbW0mRtaLY1tFibmkeE3TDWtzI1rZRW4KAU0h619LWrvxTReT+LWUmbVEFgKctIxEgSH/vAjBkzWLPFyjoqqqpIklSr1U1NTWq1uq6urrq6uqam5pdffqmvr9dqtVqt9urVq7f13L8RQhRFqQSqgDP+qscDAwICVP4q4RXCzyLvaUqUm6T+Pf31en1oaOjrr7x+aWORFuma7eqW9tZrN0q+s3z31VdfHT9+vKmmaersqSKRSKVSsSxL07RSqVQoFFFRUa+88opAIPjqxDmKogYNEvv7+xukT7IsK9TrhUKh6C9F3FkBglYIIzYjQkiHVthK22WjlWIfFSLFjnYD06QWhLnxLbfegCTJ1NTUv/3tb7Nnzx4xYgTXWFFRMXr06JiYmD9+6yAAoHvu9Sd4h8PxxRdfFBQUNDU16fX6Hj169OnTZ+7cub1793YuoBf+RV9isz/V0jpYKLxgsw0Ti94N8DtpMvei6RgBLez0KdByzag72Wr4TmM4o6MDBNJEX/8nQnwf9rdWmUUREkJw8yfLysr8/PxUqptls6KiouNCvMcffzw7O5tbvtxif+qtj//PEElYWNiwYcNueeS71q75SX++0VrfbG1ostY3WxultEwl7LGo58oQUe+91y5XGZpbDT5N7fIwmf/744K1FnbtaV10GDUgQvAL4/hRa21r4XwvAAAbMUlEQVQj7e0k0cKyDszIkTFarAqiqJ91+SKsnxc0PYCiLmiOqyhqeo9JMoKoNJXJKHkPUUiX/WN0GLR2Tautuc2u0ejrtYZmnU3T7tDpKINRYEWI8KF9JbUmkRFjWk5jMU3JaLEi2C/0gT4P4B9KHQ663GCW0BLGRhjsVoyxw+Goq6traGioqalpaWnRaDRqtVqj0dz5kMlEMh+RXEZL5TJ5YFyQr6+vVCS1fW0QmGmEsEOIBaFC8Sgfo9FoMVpwFZM+aLGP0veZ/zzfYlBbGatOpxMKhRKxRCKR9ArvtXrV6hnTZ6xdt7aurk4gEAwYMMBHKKorKJKEh0Y9ED950IOmz49JhvRXzprQOQf+fYLn/PjjjwsXLpwxY8bGjRsFAgGcogcA8fIUfUtLS1JSksViGTt2bEhIiFgs1mq1JSUl+fn577zzzooVK5yI6W07/GWbfYW6dbOfcrREbMX4WbVGSpI+JPGT1V7NMBE03VcoiKDpXjTVTyi4+d4ajCzlJtOFdkGQkFIIKhdetjdYQzZE+aYGtOytFQSJhOFiQQ+hIEhIBwjpYOGBgwfOnDlTXl4+b968pUtvPmPu36fOzh1387GvTz755EcffcQta7XaZcuW2e12lUq1bNmywYMHI4Qwwjp726mCk3JGoZArULTtovGsjmnTMVqtXUMgQk77kKziYd/V43v1zlcXfnL9Z4NV0m4W68zC+ED/eYmBDRbhx9eloSoiogfRyhBX2+3tpNVGYoYQMCQSYZMQWUOoQD+arDFdFCF7it9IKUFcavvOh6LH+o+WkmRJ+w++pOAh5TAKEc3mazJK2lcahRBqtjaYWTOD7UZGrze26o2tRmQySxxGxmBiDO3qejM2myibhbI7KFaExFJKJqVlqKqJYmm7UE4imkA0RYgDFCpf2ld/+mqrwVyjM5nbzVaj3WpnkYNgrIyuoUWra9e26/UGg8Fg7P5eQNO0BIkJTEgJiQAJFL4Ko8JMkiTLsEyDjSAJO2XHBBYK6B0zZo97Yq589DD0v+f8+VrgEUJGo/G55567ePHiwYMHFy5ceCcFHmNcWFhoNptvaT9z5symTZusVqvbkgXgXuDhKfqtW7cOGDDg008/veVi5mvXro0YMSItLc3H5/deqdne3n7LddcIoY5nqHmJMxbrFn+/ZLEIISQiiAyV/xttumU+PmF+lBXjMjvzs91ezTCnzPbLNvtLSt82lp3S2CyTk4oxtILEMpKRfxcvt+MEoXA8FgoT5CU1RvobvbDZTjbbKA3jWN2zX/SjA0/2EfjQQaKIqwVq++46JCIiJZJFQ1c2GCp0Vu1DMaNsNZb2U61alr3mX3no0CEut5YK3bvr3pOThIQgrlivzpowl2tf+NSyN7ZnCK+bKISqa8pTFowxGA00TftuDh7+7HNijfBhP2Gjvu7vf91lbDNeF5GaFcOjU3svEzwTagwtqPrggzXbGSMmSCLhiQFhw8MlrEwm9Y3XT395/SqabBRImJi0STmxmEQChvKxme2frl0gMNhsFOEz7wnfYUFCmtRjud1oad6+jDUaSQL5zZ4dMeRBu0PWbpGSVj/tex+x7XoTZoJnTuk1ME3gkJuYWjHRWpPxuUGjY4mGvrOT4wdO05pMemsDYbdf/+Bru77dQVsemJkYkBRMO+wUqRebbSUfFtlsNkTjuCdiwyP7IYJlSLvD6jj/wWWLxsoyOPLhcHmA2K5nLWbGprNWflNr09uxgw2IVgkJZDMyNoa1GezaX7QOu8PBsGKZyG6yORyszWRjGEaPDAihdqRHCP2iqUO/cQZhf2PZ9DEP3oOp6D1kMtmHH3545MiR1NRUvV5/J//FaDRu2rSp46FJHXQ63W896QgAcIt7WuBv3Lgxffr0229V6tevX3R0dEVFxe9cdKPX6+Pj400m0y3tDMPcfum1Bz3t+z8vlBMSxBv+Sm5ZRBAJQkGC8H/uSfMjya+Ce7SyrI5ldSw2saweY5OAFdIULRHIngw9o9ObMDZhjBAyY2zDGCE06Hh8OiFpD6C3tbaFTZTRVlZoZGN7PxNrw0Izq0/0cxgYTXF7nsly+ZGwae9/1evgZZNdP9Qx/NzbNxQk2UdA+z8eFJcyQ3yhhmFtBrL/1pM1qW83IoQMtkaZTWJABoZhvs6vL+tdPf9Vsx/q/eVEWW1JPetwIITEb8pSPpqDsboVqYtSw6u/beB+lwjhiHXxz1Zuv65hHXVXC2qrbv5hITKLhq0NH/GFjkK20rCmtz67+bVC3E+aqfPHO2gHI7ZfQM2HPvyQaw+q0gc9/8DAoryGBM0vpZX/958fcO1yfR35mnnYvjjSJstNuFj8/l6unW03iNeHsCTBUIqm700XPjrKtWvM8tgtEyVGFlNsa+nliwfOc+1qS0T0tmUCG0YItVwtubr/MNdutyUOWfkG07eF8TXpzl+p3byBa6ceGNB3y3J5s4x0EI1lhbXpm7j2wNTB/bcswixCCGkLrp99+l2uPTh1YJ/0sQRyMAaL7lrdtbe/4Nr9E3v3nDYoOfXpu5xTPDF9+vShQ4ceP348Li7uD39YLpcfO3bs9vaSkpJZs2a5ITsAeOienqLfsWPHvn37Pv3004SEhI5Gi8WSmZn56quv1tbW3slbRm7B7fBdXWkFXEyj0Wi1WpZlo6KiOt9aVlJSUl9fT5LkiBEjOp9xKiwsrKuroyjqkUce8fX99TF/3377bWtrK0EQDz/8cMeDXTHGX331Ffel9eTJkzuuOcAYHz16tKmpiSTJKVOm9OjRo6Ode+U8QRBTpkwJCgrqaM/JyWlpaSEIYvLkycHBwX/YfujQIZ1O58L2xx57LCQk5Pfbb8fjU/QdUlJStm7d+uCDzp/AgP0d8AMPv4NnWfb555/fu3evUqnkvoPX6XQ3btyIjIw8ePDgoEF3+s7TzmCHB/wABf5OwP4O+IGH38GTJLlz585XX331woUL9fX1RqMxMDAwOjraidthAQAAAPA7PHAfvJ+f3+2PvwAA8N4nn3wSEBDg6SwAuF/Ag24AAPdIx2OYAQD3wB88hRsAAAAAf0ZQ4AEAAAAeggIPAAAA8NCf/jt4iqIqKiqGDBly+z+p1eqGhgYn7q2/EwzDkCT5h28adY7dbqdp+rfeEttNNptNKOzi3TbdhzFmGEYguKO3y98th8OBMf6Tjmbfvn3/sM9tNpubEuATT+3vTmBZ1uFwuGl3cIJbd0/nuO9Y5ByGYfr3739vtnVv9vc//etiMcaXLl26/RG2CKHDhw8XFBSkp6e7Y7uZmZm9evVy0+0AGzduXLJkSWRkpMsjY4wXL1584MABl0dGCJWVlWVlZW3cuNEdwU+cONHU1LRgwQJ3BP/nP//Zv3//kSNHuiP4iy++uHv37j98nZJEIomPj3dHAnziqf3dCcXFxd98880LL7zg6URuqqyszMzMfO211zydyE1qtXrz5s3bt2/3dCI32e325cuXFxYW3pvN3aP9HfPXvn37lixZ4qbgK1aseP/9990UfPDgwT/++KM7IjMMQ1GUOyJjjAsLC0eMGOGm4Dt37nz22WfdFDwtLe3gwYNuCh4bG1taWuqm4KCDW/d3J+Tm5k6aNMnTWfyqqKho6NChns7iV1VVVb179/Z0Fr8ymUwSicTTWbgYnBIEAAAAeAgKPAAAAMBDUOABAAAAHoICDwAAAPAQFHgAAACAh/hc4GmapijKTcEpinLfHbc0TbspOEEQIpHIHZGRmzscRhP8PrfOECd4YT5eNQ+9LR+SJL3qIQEu8ae/D/53WK1Wg8HgprdXaTQaqVQqFovdEbyhoSE4ONhND7qpr6930zs/MMaNjY0hISHuCG42my0Wi5+fnzuCq9VqX19fNz1zw30dDjpz6/7uBIfDoVarg4KCPJ3ITW7dPZ3jbbuGt+XTfXwu8AAAAMB9i8+n6AEAAID7FhR4AAAAgIegwAMAAAA8BAUeAAAA4CEo8AAAAAAPQYEHAAAAeAgKPAAAAMBDUOABAAAAHqJeffVVT+fgFgaDITs7u6CgIDAwUKlUuiTmmTNnDh8+XFJSEhERIZFIuMaLFy8eOnSopaUlJibGtc+ewxgfP348NzeXoqiwsDCn47Asm5eXd+TIEYfDER4ezjW6qn+uXbuWlZX1008/9ezZUy6Xc421tbVZWVmlpaURERHOPRlXq9UKhcKO/uwyW3cMcfdpNJqOufFbut8/4BZeMhnKysqOHDlSWFioVCo7nqnnbcPtwXzceixyglqtzsrKOn36tEwm63jmoJfMJdfAfNTe3p6QkDBy5Mg5c+YolcrCwsLux1y3bl1AQMBTTz2VnJwcGBhYVVWFMf7oo4/8/f0XLVoUExMzc+bMbm7CbrfPmTPn4sWL3OqiRYv69OmzaNEif3//Xbt2OReTYZj58+fHxcXNmzdPJpNlZGRg1/VPVlaWTCabNm1acnKyUqm8cuUKxvjChQv+/v6zZs0aNWpUbGxsa2vr3YZ1OBwRERGNjY3capfZumOIu+/GjRt9+/a9vT03N3f16tXccvf7B9zCSybDf/7zH5lMNnv27Dlz5ojF4pycHOwdw+0l08+txyInlJaW+vv7JyUlPf744zKZLDMz07P5uAM/C3xGRsawYcMYhsEYb9iwYeLEid0MWFlZSZLkhQsXuNVJkyYtX77cZrOFhIR88cUXGOPm5maFQlFUVNSdrbzyyisIoby8PIzx5cuX5XJ5Q0MDxvjEiRMBAQFms9mJmDk5OZGRkTqdDmN84MABlUrFsqyr+ic+Pn7z5s3c8uTJk5cuXYoxnjFjxgsvvIAxdjgcSUlJb7/99p0HtFgsX3755cyZMxFCHQW+y2xdPsTd1NzcnJWVlZiYeHuBb2xsDAwMHDNmDLfanf4BXfKSyRAREbF9+3Zuedu2bdxM8Phwe8/0c+uxyAkrV64cN24ct/zuu+9GR0djr5lLrsLP7+BzcnLmzJnDvcpp/vz5X3/9tdVq7U7A0tLSuLi4xMREbnX48OHXr18/e/as2WyeMGECQigwMHDcuHE5OTlOb+L7778/fPiwr69vx6+QkpISHByMEHrkkUdIkjx9+rQTYbOyshYsWMCFfeKJJwoKCliWdVX/CASCjlN8UqmUJEmM8dGjR+fPn48QIkly7ty5d9Unra2t+/fvt9lsnRu7zNblQ9xNxcXF2dnZXb58aMmSJZGRkdxyN/sHdMkbJoPJZFKr1dOnT+dWhw8fXl5ezrKsx4fbe6afW49FTrj98IW8Yy65ED8LfH19fVRUFLccFRXFsmxzc3N3Ao4fP76kpIRbttvtubm5gwYNqq+v7927d8cbIaOiohobG52L397evmTJkszMzI4XmnX+FQiCiIyMdC54aWlpv379srOzt2zZcvLkybi4OIqiXNU/77zzzo4dO9LT02fOnHnp0qUXX3yxpaXFbrd3Dn5XaYeGhn7++ef/+te/Ojd2ma3Lh7ibxo0b9/nnn7/00ku3tGdkZAiFwgULFnCr3ewf0CVvmAxSqVSv1/fu3ZtbPXLkyMCBA9VqtWeH26umn1uPRU54/vnnKysrp0+fvmzZss2bN2/fvh15x1xyIX4WeLVa7ePjwy1LJBKKotRqtUsiNzQ0TJ48mSTJDRs2dN4KQsjHx8fprTzzzDNpaWlDhgzpaHFV8Obm5o0bNx44cECtVi9duvSZZ55Brusfo9FIEIRYLJZKpQzDWCwWLk5H8O70SYcus3XfELvQ1atX33333Q8//LDjakF39A/wqslgsVheeumlTz/9dO/evZ4dbm+bfm49FjnBYrEwDCOTybjP8SaTybP5uAPt6QTcws/Pz2AwcMtms9nhcLjkYsiDBw+uXr162rRphw8flkqlnbeCENLr9c5t5bPPPrt+/fr+/fs7N7oqOEEQKSkpe/bsQQjNnDlz5MiRL774okv6x2w2z507t6CgYPDgwQihjIyMpUuXHj58GCFkMBgUCkV30u6sy2zdNMQuZLVa58+fn5GRERgY2NHIvc/etf0DvGcynDt3Li0tLSAg4Pvvv4+KimpoaEAeGm4vnH7uOxY55+mnn05PT3/hhRcQQunp6YMHDx4/frz3zCWX4Ocn+LCwsKqqKm65pqaGoqiQkJBuxnzttdfWrFmTk5OTmZkplUq5rdTU1LAs27Ghnj17OhH55ZdfDgsLW79+/dq1a41G4z/+8Y+srKzOv0J3ggcHBz/00EPc8ogRI+RyeVVVlUv65/LlywghrrojhJKTk8+fPx8QECAQCDoHdy7tzrrM1h1D7FqHDh1qamrKz89fu3btkSNHbty4sXbtWpIkXd4/wEsmw6lTp8aMGbNq1SquuiOEAgMDPTXcXjj93Hcscs6PP/6YlJTELffv318mkxUXF3vJXHIVfhZ47kM2xhghlJ2dPX78+C6vfrpz5eXlmzZtys/PHzNmTEfjQw89RNN0Xl4eQqi9vf3UqVPTpk1zIviqVasGDBggl8vlcjlBEBKJRCwWT5s27dtvv21ra0MIFRYWWq3W5ORkJ4JPnDjxxIkT3HJxcbHVao2Pj3dJ/0RHRzMMc/bsWW41Ly8vLi5OKBQ+9thj2dnZXGN2drZzfdJZl9m6fIhdLiEhYcWKFdywikQiiqLkcrk7+gd4w2RgWXbp0qU7d+5cvXp1xylxmqY9NdxeOP3cdyxyTlxcHHf0RghdunRJr9fHxsZ6w1xyJU9dvu9WOp1u4MCBU6ZMWblypUqlOnfuXDcD7ty5U6FQpHXyzjvvYIwzMzNDQ0PXrFkzaNCg+fPndz9zlUrF3SaHMV6yZMnAgQPXrFkTGhq6Z88e5wK2tLTEx8c/8sgjy5cvDwgI4NJ2Vf9s27ZNqVSmp6fPnj3b19f31KlTGOPi4uIePXqsWLFi6tSpCQkJGo3mbsNqtVrU6Ta5LrN1+RC7RE5OTpf3we/evbvjPqXu9w+4hTdMhkuXLiGEZs6c2XGIeOqpp7B3DLeXTD+3HouckJeX5+vrO2vWrKefftrf33/Lli2ezccdCIyxp//GcIv29vYvv/xSq9VOmDAhIiKim9G++eabgoKCzi2xsbHz5s1DCJ0/f/706dMxMTETJkzgbrTojm3bts2ePZu7Fpdl2ePHj5eWlo4cOXLo0KFOx9TpdMeOHWtsbExOTh40aBDX6Kr+KS4uzs/Pl0gkjz76aK9evbjG6urqr7/+Wi6XT5o0yYlvsKxW69atW9esWSOTyX4nW9cOsUuUlpaeOnWKu3qos3PnzpWUlCxatIhb7Wb/gNt5fDLcuHHj448/7twiFArXr1+PvGC4vWf6ufVY5IS6urpjx44ZjcZRo0Z5Qz4ux9sCDwAAANzP+PkdPAAAAHCfgwIPAAAA8BAUeAAAAICHoMADAAAAPAQFHgAAAOAhKPAAAAAAD0GBBwAAAHgICjwAAADAQ1DgAQAAAB6CAg8AAADwEBR4AAAAgIegwAMAAAA8BAUeAAAA4CEo8AAAAAAPQYEHAAAAeAgKPAAAAMBDUOABAAAAHoICDwAAAPAQFHgAAACAh6DAAwAAADxEezoB4DFXrlxZvXr17e0vv/zy+fPnw8LC0tLS7n1WAIC79cMPP6xfv55bJggiPDw8PT195MiRdxvHbrenpqbm5ubu3r3bVUcALuZ///vf7ocCdwsK/P0rODh4+fLlCCGLxbJ48eKtW7dGREQghGJiYsrLy2UymYfzAwDcmdbW1qKion379iGEbDbbyZMnk5OTz549O2TIkLuKw7JsXl4ewzAKheJ3jgD5+fmFhYXr1q2785h3lQZwFQJj7OkcgIcZjUa5XF5cXDxgwABP5wIAuGtffvllWlpaW1sbt4oxjoqKmjp16o4dO+4qjtVqFYvFWq1WoVD8zo8dOHDgs88+O3bs2J3HhELjEfAJHnThrbfe4k7Q7dq1S6FQFBUVVVZWDhs2bP369du3b//+++/j4uJef/11qVSKEDp79uzevXsNBkNqaurChQspivJ0+gDc1wiCiIuLU6vVCKHt27eHh4er1eorV65kZGR0ubdmZmbm5uaqVCrulB7qdARACB09evTQoUMIoTlz5kyePDk/P3/r1q3Nzc1paWkff/wx+o0jwO0xwb0HF9mBLvz888/V1dUIobKyspUrVyqVyrlz57733ntRUVEY47lz537yyScZGRkIoby8vAkTJvTt23fq1Kl79uz561//6uncAbjflZeXf/vtt3/5y18QQtevX3/77bcrKysXLlzY5d66efPmdevWpaSk9O/ff+bMmVyEjiPA/v37ly1bNnr06MTExMWLF+fk5CQmJi5atCg+Pv71119Hv3EE6DIm8AAM7nsGgwEhVFxc3NGyePHiTZs2YYxXrVo1bdo0rjE9PT0pKYlbfu6555YuXYoxHjNmzIYNG7jG5uZmkiTLysruafYA3Pdyc3NJkkxISEhISIiLixMIBKNGjTKZTBjj5cuXjx8/nvux2/fW0tJSHx+fY8eOcY3cJ3WtVttxBIiIiMjOzub+9cCBA7t27cIYZ2ZmPvroo3cV8x50ArgdnKIHf6Bv377cgkKhGDhwILesVCq5Pwt++umntra2wsJCrp2iqOrq6ujoaI+kCsB9SyqV7t69m1sODAzs168fQRDcKvdRHnW1txYWFur1+uTkZK5l9OjRnWMajcaqqqrhw4dzqwsXLrx9u3cbE9xLUOBBt8hksnXr1iUmJna09OzZ04P5AHB/omn6t0qpSCTiFm7fW5VKJUJIo9Fw19OYzebO/1EsFguFQr1ez63W1tZWV1ffcvfd3cYE9xJ8Bw+6JTU1NScnp0+fPnFxcVeuXElJSaFp+KsRAG90+96qVCqTkpLeeOMN7ozum2++2fnnKYoaO3bszp07EUIOh2PlypWZmZncPzEM41xMcC9BgQfd8uabbzY0NISGhsbFxS1btuyDDz7o+LgAAPAqXe6te/bsOXXqVK9evcLDwwmC8Pf37/xfdu/effr06cjIyJCQkMrKSu7CutjY2IKCAu4aeydignsG7oMHiGXZ06dPDxkypOPRFqWlpRKJJDw8vLy8nKKoyMhIhFBFRQXLstz369XV1VarNTY2lvv5qqqq1tbWfv36cSflAAAAeBwUeAAAAICH4BQ9AAAAwENQ4AEAAAAeggIPAAAA8BAUeAAAAICHoMADAAAAPAQFHgAAAOAhKPAAAAAAD0GBBwAAAHgICjwAAADAQ1DgAQAAAB6CAg8AAADwEBR4AAAAgIegwAMAAAA8BAUeAAAA4CEo8AAAAAAPQYEHAAAAeAgKPAAAAMBDUOABAAAAHoICDwAAAPDQ/wO56NWObNo4NwAAAABJRU5ErkJggg==" style="display: block; margin: auto;" /></p> +<p>Endpoints for a model with covariates are by default calculated for +the median of the covariate values. This quantile can be adapted, or a +specific covariate value can be given as shown below.</p> +<pre class="r"><code>endpoints(sfo_pH)</code></pre> +<pre><code>$covariates + pH +50% 5.75 + +$distimes + DT50 DT90 +meso 18.52069 61.52441</code></pre> +<pre class="r"><code>endpoints(sfo_pH, covariate_quantile = 0.9)</code></pre> +<pre><code>$covariates + pH +90% 7.13 + +$distimes + DT50 DT90 +meso 8.237019 27.36278</code></pre> +<pre class="r"><code>endpoints(sfo_pH, covariates = c(pH = 7.0))</code></pre> +<pre><code>$covariates + pH +User 7 + +$distimes + DT50 DT90 +meso 8.89035 29.5331</code></pre> +</div> +<div id="fomc" class="section level2"> +<h2>FOMC</h2> +<pre class="r"><code>fomc_pH <- saem(f_sep_const["FOMC", ], no_random_effect = "meso_0", covariates = pH, + covariate_models = list(log_alpha ~ pH), center_covariates = "median")</code></pre> +<pre class="r"><code>summary(fomc_pH)$confint_trans |> kable(digits = 2)</code></pre> +<table> +<thead> +<tr class="header"> +<th align="left"></th> +<th align="right">est.</th> +<th align="right">lower</th> +<th align="right">upper</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="left">meso_0</td> +<td align="right">92.84</td> +<td align="right">90.75</td> +<td align="right">94.93</td> +</tr> +<tr class="even"> +<td align="left">log_alpha</td> +<td align="right">1.11</td> +<td align="right">0.48</td> +<td align="right">1.75</td> +</tr> +<tr class="odd"> +<td align="left">beta_pH(log_alpha)</td> +<td align="right">0.58</td> +<td align="right">0.37</td> +<td align="right">0.79</td> +</tr> +<tr class="even"> +<td align="left">log_beta</td> +<td align="right">4.21</td> +<td align="right">3.44</td> +<td align="right">4.99</td> +</tr> +<tr class="odd"> +<td align="left">a.1</td> +<td align="right">5.03</td> +<td align="right">4.32</td> +<td align="right">5.73</td> +</tr> +<tr class="even"> +<td align="left">SD.log_alpha</td> +<td align="right">0.00</td> +<td align="right">-23.77</td> +<td align="right">23.78</td> +</tr> +<tr class="odd"> +<td align="left">SD.log_beta</td> +<td align="right">0.37</td> +<td align="right">0.01</td> +<td align="right">0.74</td> +</tr> +</tbody> +</table> +<p>As in the case of SFO, the confidence interval of the slope parameter +(here <code>beta_pH(log_alpha)</code>) quantifying the influence of soil +pH does not include zero, and the model comparison clearly indicates +that the model with covariate influence is preferable. However, the +random effect for <code>alpha</code> is not well-defined any more after +inclusion of the covariate effect (the confidence interval of +<code>SD.log_alpha</code> includes zero).</p> +<pre class="r"><code>illparms(fomc_pH)</code></pre> +<pre><code>[1] "sd(log_alpha)"</code></pre> +<p>Therefore, the model is updated without this random effect, and no +ill-defined parameters remain.</p> +<pre class="r"><code>fomc_pH_2 <- update(fomc_pH, no_random_effect = c("meso_0", "log_alpha")) +illparms(fomc_pH_2)</code></pre> +<pre class="r"><code>anova(f_saem_2[["FOMC", "const"]], fomc_pH, fomc_pH_2, test = TRUE)</code></pre> +<pre><code>Data: 116 observations of 1 variable(s) grouped in 18 datasets + + npar AIC BIC Lik Chisq Df Pr(>Chisq) +f_saem_2[["FOMC", "const"]] 5 783.25 787.71 -386.63 +fomc_pH_2 6 766.50 771.84 -377.25 18.753 1 1.488e-05 *** +fomc_pH 7 770.07 776.30 -378.04 0.000 1 1 +--- +Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre> +<p>Model comparison indicates that including pH dependence significantly +improves the fit, and that the reduced model with covariate influence +results in the most preferable FOMC fit.</p> +<pre class="r"><code>summary(fomc_pH_2)$confint_trans |> kable(digits = 2)</code></pre> +<table> +<thead> +<tr class="header"> +<th align="left"></th> +<th align="right">est.</th> +<th align="right">lower</th> +<th align="right">upper</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="left">meso_0</td> +<td align="right">93.20</td> +<td align="right">91.10</td> +<td align="right">95.29</td> +</tr> +<tr class="even"> +<td align="left">log_alpha</td> +<td align="right">0.81</td> +<td align="right">0.33</td> +<td align="right">1.30</td> +</tr> +<tr class="odd"> +<td align="left">beta_pH(log_alpha)</td> +<td align="right">0.55</td> +<td align="right">0.35</td> +<td align="right">0.75</td> +</tr> +<tr class="even"> +<td align="left">log_beta</td> +<td align="right">3.85</td> +<td align="right">3.22</td> +<td align="right">4.47</td> +</tr> +<tr class="odd"> +<td align="left">a.1</td> +<td align="right">5.00</td> +<td align="right">4.30</td> +<td align="right">5.70</td> +</tr> +<tr class="even"> +<td align="left">SD.log_beta</td> +<td align="right">0.38</td> +<td align="right">0.25</td> +<td align="right">0.52</td> +</tr> +</tbody> +</table> +<pre class="r"><code>plot(fomc_pH_2)</code></pre> +<p><img src="data:image/png;base64,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" style="display: block; margin: auto;" /></p> +<pre class="r"><code>endpoints(fomc_pH_2)</code></pre> +<pre><code>$covariates + pH +50% 5.75 + +$distimes + DT50 DT90 DT50back +meso 16.86448 83.26704 25.06588</code></pre> +<pre class="r"><code>endpoints(fomc_pH_2, covariates = c(pH = 7))</code></pre> +<pre><code>$covariates + pH +User 7 + +$distimes + DT50 DT90 DT50back +meso 7.835768 31.46451 9.47176</code></pre> +</div> +<div id="dfop" class="section level2"> +<h2>DFOP</h2> +<p>In the DFOP fits without covariate effects, random effects for two +degradation parameters (<code>k2</code> and <code>g</code>) were +identifiable.</p> +<pre class="r"><code>summary(f_saem_2[["DFOP", "const"]])$confint_trans |> kable(digits = 2)</code></pre> +<table> +<thead> +<tr class="header"> +<th align="left"></th> +<th align="right">est.</th> +<th align="right">lower</th> +<th align="right">upper</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="left">meso_0</td> +<td align="right">93.61</td> +<td align="right">91.58</td> +<td align="right">95.63</td> +</tr> +<tr class="even"> +<td align="left">log_k1</td> +<td align="right">-1.53</td> +<td align="right">-2.27</td> +<td align="right">-0.79</td> +</tr> +<tr class="odd"> +<td align="left">log_k2</td> +<td align="right">-3.42</td> +<td align="right">-3.73</td> +<td align="right">-3.11</td> +</tr> +<tr class="even"> +<td align="left">g_qlogis</td> +<td align="right">-1.67</td> +<td align="right">-2.57</td> +<td align="right">-0.77</td> +</tr> +<tr class="odd"> +<td align="left">a.1</td> +<td align="right">4.74</td> +<td align="right">4.02</td> +<td align="right">5.45</td> +</tr> +<tr class="even"> +<td align="left">SD.log_k2</td> +<td align="right">0.60</td> +<td align="right">0.38</td> +<td align="right">0.81</td> +</tr> +<tr class="odd"> +<td align="left">SD.g_qlogis</td> +<td align="right">0.94</td> +<td align="right">0.33</td> +<td align="right">1.54</td> +</tr> +</tbody> +</table> +<p>A fit with pH dependent degradation parameters was obtained by +excluding the same random effects as in the refined DFOP fit without +covariate influence, and including covariate models for the two +identifiable parameters <code>k2</code> and <code>g</code>.</p> +<pre class="r"><code>dfop_pH <- saem(f_sep_const["DFOP", ], no_random_effect = c("meso_0", "log_k1"), + covariates = pH, + covariate_models = list(log_k2 ~ pH, g_qlogis ~ pH), + center_covariates = "median" +)</code></pre> +<p>The corresponding parameters for the influence of soil pH are +<code>beta_pH(log_k2)</code> for the influence of soil pH on +<code>k2</code>, and <code>beta_pH(g_qlogis)</code> for its influence on +<code>g</code>.</p> +<pre class="r"><code>summary(dfop_pH)$confint_trans |> kable(digits = 2)</code></pre> +<table> +<thead> +<tr class="header"> +<th align="left"></th> +<th align="right">est.</th> +<th align="right">lower</th> +<th align="right">upper</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="left">meso_0</td> +<td align="right">92.84</td> +<td align="right">90.85</td> +<td align="right">94.84</td> +</tr> +<tr class="even"> +<td align="left">log_k1</td> +<td align="right">-2.82</td> +<td align="right">-3.09</td> +<td align="right">-2.54</td> +</tr> +<tr class="odd"> +<td align="left">log_k2</td> +<td align="right">-3.96</td> +<td align="right">-4.47</td> +<td align="right">-3.44</td> +</tr> +<tr class="even"> +<td align="left">beta_pH(log_k2)</td> +<td align="right">1.31</td> +<td align="right">0.69</td> +<td align="right">1.92</td> +</tr> +<tr class="odd"> +<td align="left">g_qlogis</td> +<td align="right">-0.12</td> +<td align="right">-0.57</td> +<td align="right">0.33</td> +</tr> +<tr class="even"> +<td align="left">beta_pH(g_qlogis)</td> +<td align="right">-0.57</td> +<td align="right">-1.04</td> +<td align="right">-0.09</td> +</tr> +<tr class="odd"> +<td align="left">a.1</td> +<td align="right">4.96</td> +<td align="right">4.26</td> +<td align="right">5.65</td> +</tr> +<tr class="even"> +<td align="left">SD.log_k2</td> +<td align="right">0.76</td> +<td align="right">0.47</td> +<td align="right">1.05</td> +</tr> +<tr class="odd"> +<td align="left">SD.g_qlogis</td> +<td align="right">0.01</td> +<td align="right">-9.96</td> +<td align="right">9.97</td> +</tr> +</tbody> +</table> +<pre class="r"><code>illparms(dfop_pH)</code></pre> +<pre><code>[1] "sd(g_qlogis)"</code></pre> +<p>Confidence intervals for neither of them include zero, indicating a +significant difference from zero. However, the random effect for +<code>g</code> is now ill-defined. The fit is updated without this +ill-defined random effect.</p> +<pre class="r"><code>dfop_pH_2 <- update(dfop_pH, + no_random_effect = c("meso_0", "log_k1", "g_qlogis")) +illparms(dfop_pH_2)</code></pre> +<pre><code>[1] "beta_pH(g_qlogis)"</code></pre> +<p>Now, the slope parameter for the pH effect on <code>g</code> is +ill-defined. Therefore, another attempt is made without the +corresponding covariate model.</p> +<pre class="r"><code>dfop_pH_3 <- saem(f_sep_const["DFOP", ], no_random_effect = c("meso_0", "log_k1"), + covariates = pH, + covariate_models = list(log_k2 ~ pH), + center_covariates = "median") +illparms(dfop_pH_3)</code></pre> +<pre><code>[1] "sd(g_qlogis)"</code></pre> +<p>As the random effect for <code>g</code> is again ill-defined, the fit +is repeated without it.</p> +<pre class="r"><code>dfop_pH_4 <- update(dfop_pH_3, no_random_effect = c("meso_0", "log_k1", "g_qlogis")) +illparms(dfop_pH_4)</code></pre> +<p>While no ill-defined parameters remain, model comparison suggests +that the previous model <code>dfop_pH_2</code> with two pH dependent +parameters is preferable, based on information criteria as well as based +on the likelihood ratio test.</p> +<pre class="r"><code>anova(f_saem_2[["DFOP", "const"]], dfop_pH, dfop_pH_2, dfop_pH_3, dfop_pH_4)</code></pre> +<pre><code>Data: 116 observations of 1 variable(s) grouped in 18 datasets + + npar AIC BIC Lik +f_saem_2[["DFOP", "const"]] 7 782.94 789.18 -384.47 +dfop_pH_4 7 767.35 773.58 -376.68 +dfop_pH_2 8 765.06 772.18 -374.53 +dfop_pH_3 8 769.00 776.12 -376.50 +dfop_pH 9 769.10 777.11 -375.55</code></pre> +<pre class="r"><code>anova(dfop_pH_2, dfop_pH_4, test = TRUE)</code></pre> +<pre><code>Data: 116 observations of 1 variable(s) grouped in 18 datasets + + npar AIC BIC Lik Chisq Df Pr(>Chisq) +dfop_pH_4 7 767.35 773.58 -376.68 +dfop_pH_2 8 765.06 772.18 -374.53 4.2909 1 0.03832 * +--- +Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre> +<p>When focussing on parameter identifiability using the test if the +confidence interval includes zero, <code>dfop_pH_4</code> would still be +the preferred model. However, it should be kept in mind that parameter +confidence intervals are constructed using a simple linearisation of the +likelihood. As the confidence interval of the random effect for +<code>g</code> only marginally includes zero, it is suggested that this +is acceptable, and that <code>dfop_pH_2</code> can be considered the +most preferable model.</p> +<pre class="r"><code>plot(dfop_pH_2)</code></pre> +<p><img src="data:image/png;base64,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" style="display: block; margin: auto;" /></p> +<pre class="r"><code>endpoints(dfop_pH_2)</code></pre> +<pre><code>$covariates + pH +50% 5.75 + +$distimes + DT50 DT90 DT50back DT50_k1 DT50_k2 +meso 18.46687 74.91602 22.55197 4.568623 24.64483</code></pre> +<pre class="r"><code>endpoints(dfop_pH_2, covariates = c(pH = 7))</code></pre> +<pre><code>$covariates + pH +User 7 + +$distimes + DT50 DT90 DT50back DT50_k1 DT50_k2 +meso 8.370528 28.37824 8.542701 4.568623 8.785409</code></pre> +</div> +<div id="sforb" class="section level2"> +<h2>SFORB</h2> +<pre class="r"><code>sforb_pH <- saem(f_sep_const["SFORB", ], no_random_effect = c("meso_free_0", "log_k_meso_free_bound"), + covariates = pH, + covariate_models = list(log_k_meso_free ~ pH, log_k_meso_bound_free ~ pH), + center_covariates = "median")</code></pre> +<pre class="r"><code>summary(sforb_pH)$confint_trans |> kable(digits = 2)</code></pre> +<table> +<thead> +<tr class="header"> +<th align="left"></th> +<th align="right">est.</th> +<th align="right">lower</th> +<th align="right">upper</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="left">meso_free_0</td> +<td align="right">93.42</td> +<td align="right">91.32</td> +<td align="right">95.52</td> +</tr> +<tr class="even"> +<td align="left">log_k_meso_free</td> +<td align="right">-2.94</td> +<td align="right">-3.23</td> +<td align="right">-2.65</td> +</tr> +<tr class="odd"> +<td align="left">beta_pH(log_k_meso_free)</td> +<td align="right">0.42</td> +<td align="right">0.18</td> +<td align="right">0.67</td> +</tr> +<tr class="even"> +<td align="left">log_k_meso_free_bound</td> +<td align="right">-3.49</td> +<td align="right">-4.92</td> +<td align="right">-2.05</td> +</tr> +<tr class="odd"> +<td align="left">log_k_meso_bound_free</td> +<td align="right">-2.91</td> +<td align="right">-4.30</td> +<td align="right">-1.52</td> +</tr> +<tr class="even"> +<td align="left">beta_pH(log_k_meso_bound_free)</td> +<td align="right">1.23</td> +<td align="right">-0.21</td> +<td align="right">2.67</td> +</tr> +<tr class="odd"> +<td align="left">a.1</td> +<td align="right">4.90</td> +<td align="right">4.18</td> +<td align="right">5.63</td> +</tr> +<tr class="even"> +<td align="left">SD.log_k_meso_free</td> +<td align="right">0.35</td> +<td align="right">0.23</td> +<td align="right">0.47</td> +</tr> +<tr class="odd"> +<td align="left">SD.log_k_meso_bound_free</td> +<td align="right">0.13</td> +<td align="right">-1.95</td> +<td align="right">2.20</td> +</tr> +</tbody> +</table> +<p>The confidence interval of +<code>beta_pH(log_k_meso_bound_free)</code> includes zero, indicating +that the influence of soil pH on <code>k_meso_bound_free</code> cannot +reliably be quantified. Also, the confidence interval for the random +effect on this parameter (<code>SD.log_k_meso_bound_free</code>) +includes zero.</p> +<p>Using the <code>illparms</code> function, these ill-defined +parameters can be found more conveniently.</p> +<pre class="r"><code>illparms(sforb_pH)</code></pre> +<pre><code>[1] "sd(log_k_meso_bound_free)" "beta_pH(log_k_meso_bound_free)"</code></pre> +<p>To remove the ill-defined parameters, a second variant of the SFORB +model with pH influence is fitted. No ill-defined parameters remain.</p> +<pre class="r"><code>sforb_pH_2 <- update(sforb_pH, + no_random_effect = c("meso_free_0", "log_k_meso_free_bound", "log_k_meso_bound_free"), + covariate_models = list(log_k_meso_free ~ pH)) +illparms(sforb_pH_2)</code></pre> +<p>The model comparison of the SFORB fits includes the refined model +without covariate effect, and both versions of the SFORB fit with +covariate effect.</p> +<pre class="r"><code>anova(f_saem_2[["SFORB", "const"]], sforb_pH, sforb_pH_2, test = TRUE)</code></pre> +<pre><code>Data: 116 observations of 1 variable(s) grouped in 18 datasets + + npar AIC BIC Lik Chisq Df Pr(>Chisq) +f_saem_2[["SFORB", "const"]] 7 783.40 789.63 -384.70 +sforb_pH_2 7 770.94 777.17 -378.47 12.4616 0 +sforb_pH 9 768.81 776.83 -375.41 6.1258 2 0.04675 * +--- +Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre> +<p>The first model including pH influence is preferable based on +information criteria and the likelihood ratio test. However, as it is +not fully identifiable, the second model is selected.</p> +<pre class="r"><code>summary(sforb_pH_2)$confint_trans |> kable(digits = 2)</code></pre> +<table> +<thead> +<tr class="header"> +<th align="left"></th> +<th align="right">est.</th> +<th align="right">lower</th> +<th align="right">upper</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="left">meso_free_0</td> +<td align="right">93.32</td> +<td align="right">91.17</td> +<td align="right">95.47</td> +</tr> +<tr class="even"> +<td align="left">log_k_meso_free</td> +<td align="right">-3.02</td> +<td align="right">-3.27</td> +<td align="right">-2.78</td> +</tr> +<tr class="odd"> +<td align="left">beta_pH(log_k_meso_free)</td> +<td align="right">0.54</td> +<td align="right">0.33</td> +<td align="right">0.75</td> +</tr> +<tr class="even"> +<td align="left">log_k_meso_free_bound</td> +<td align="right">-3.80</td> +<td align="right">-5.18</td> +<td align="right">-2.42</td> +</tr> +<tr class="odd"> +<td align="left">log_k_meso_bound_free</td> +<td align="right">-2.95</td> +<td align="right">-4.24</td> +<td align="right">-1.65</td> +</tr> +<tr class="even"> +<td align="left">a.1</td> +<td align="right">5.08</td> +<td align="right">4.37</td> +<td align="right">5.79</td> +</tr> +<tr class="odd"> +<td align="left">SD.log_k_meso_free</td> +<td align="right">0.33</td> +<td align="right">0.22</td> +<td align="right">0.45</td> +</tr> +</tbody> +</table> +<pre class="r"><code>plot(sforb_pH_2)</code></pre> +<p><img src="data:image/png;base64,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" style="display: block; margin: auto;" /></p> +<pre class="r"><code>endpoints(sforb_pH_2)</code></pre> +<pre><code>$covariates + pH +50% 5.75 + +$ff +meso_free + 1 + +$SFORB + meso_b1 meso_b2 meso_g +0.09735824 0.02631699 0.31602120 + +$distimes + DT50 DT90 DT50back DT50_meso_b1 DT50_meso_b2 +meso 16.86549 73.15824 22.02282 7.119554 26.33839</code></pre> +<pre class="r"><code>endpoints(sforb_pH_2, covariates = c(pH = 7))</code></pre> +<pre><code>$covariates + pH +User 7 + +$ff +meso_free + 1 + +$SFORB + meso_b1 meso_b2 meso_g +0.13315233 0.03795988 0.61186191 + +$distimes + DT50 DT90 DT50back DT50_meso_b1 DT50_meso_b2 +meso 7.932495 36.93311 11.11797 5.205671 18.26</code></pre> +</div> +<div id="hs" class="section level2"> +<h2>HS</h2> +<pre class="r"><code>hs_pH <- saem(f_sep_const["HS", ], no_random_effect = c("meso_0"), + covariates = pH, + covariate_models = list(log_k1 ~ pH, log_k2 ~ pH, log_tb ~ pH), + center_covariates = "median")</code></pre> +<pre class="r"><code>summary(hs_pH)$confint_trans |> kable(digits = 2)</code></pre> +<table> +<thead> +<tr class="header"> +<th align="left"></th> +<th align="right">est.</th> +<th align="right">lower</th> +<th align="right">upper</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="left">meso_0</td> +<td align="right">93.33</td> +<td align="right">91.47</td> +<td align="right">95.19</td> +</tr> +<tr class="even"> +<td align="left">log_k1</td> +<td align="right">-3.08</td> +<td align="right">-3.28</td> +<td align="right">-2.89</td> +</tr> +<tr class="odd"> +<td align="left">beta_pH(log_k1)</td> +<td align="right">0.47</td> +<td align="right">0.23</td> +<td align="right">0.72</td> +</tr> +<tr class="even"> +<td align="left">log_k2</td> +<td align="right">-3.68</td> +<td align="right">-3.91</td> +<td align="right">-3.46</td> +</tr> +<tr class="odd"> +<td align="left">beta_pH(log_k2)</td> +<td align="right">0.54</td> +<td align="right">0.21</td> +<td align="right">0.87</td> +</tr> +<tr class="even"> +<td align="left">log_tb</td> +<td align="right">2.68</td> +<td align="right">2.38</td> +<td align="right">2.97</td> +</tr> +<tr class="odd"> +<td align="left">beta_pH(log_tb)</td> +<td align="right">-0.10</td> +<td align="right">-0.43</td> +<td align="right">0.23</td> +</tr> +<tr class="even"> +<td align="left">a.1</td> +<td align="right">4.49</td> +<td align="right">3.78</td> +<td align="right">5.21</td> +</tr> +<tr class="odd"> +<td align="left">SD.log_k1</td> +<td align="right">0.37</td> +<td align="right">0.24</td> +<td align="right">0.51</td> +</tr> +<tr class="even"> +<td align="left">SD.log_k2</td> +<td align="right">0.29</td> +<td align="right">0.10</td> +<td align="right">0.48</td> +</tr> +<tr class="odd"> +<td align="left">SD.log_tb</td> +<td align="right">0.25</td> +<td align="right">-0.07</td> +<td align="right">0.57</td> +</tr> +</tbody> +</table> +<pre class="r"><code>illparms(hs_pH)</code></pre> +<pre><code>[1] "sd(log_tb)" "beta_pH(log_tb)"</code></pre> +<p>According to the output of the <code>illparms</code> function, the +random effect on the break time <code>tb</code> cannot reliably be +quantified, neither can the influence of soil pH on <code>tb</code>. The +fit is repeated without the corresponding covariate model, and no +ill-defined parameters remain.</p> +<pre class="r"><code>hs_pH_2 <- update(hs_pH, covariate_models = list(log_k1 ~ pH, log_k2 ~ pH)) +illparms(hs_pH_2)</code></pre> +<p>Model comparison confirms that this model is preferable to the fit +without covariate influence, and also to the first version with +covariate influence.</p> +<pre class="r"><code>anova(f_saem_2[["HS", "const"]], hs_pH, hs_pH_2, test = TRUE)</code></pre> +<pre><code>Data: 116 observations of 1 variable(s) grouped in 18 datasets + + npar AIC BIC Lik Chisq Df Pr(>Chisq) +f_saem_2[["HS", "const"]] 8 780.08 787.20 -382.04 +hs_pH_2 10 766.47 775.37 -373.23 17.606 2 0.0001503 *** +hs_pH 11 769.80 779.59 -373.90 0.000 1 1.0000000 +--- +Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre> +<pre class="r"><code>summary(hs_pH_2)$confint_trans |> kable(digits = 2)</code></pre> +<table> +<thead> +<tr class="header"> +<th align="left"></th> +<th align="right">est.</th> +<th align="right">lower</th> +<th align="right">upper</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="left">meso_0</td> +<td align="right">93.33</td> +<td align="right">91.50</td> +<td align="right">95.15</td> +</tr> +<tr class="even"> +<td align="left">log_k1</td> +<td align="right">-3.05</td> +<td align="right">-3.25</td> +<td align="right">-2.86</td> +</tr> +<tr class="odd"> +<td align="left">beta_pH(log_k1)</td> +<td align="right">0.46</td> +<td align="right">0.22</td> +<td align="right">0.69</td> +</tr> +<tr class="even"> +<td align="left">log_k2</td> +<td align="right">-3.74</td> +<td align="right">-3.94</td> +<td align="right">-3.54</td> +</tr> +<tr class="odd"> +<td align="left">beta_pH(log_k2)</td> +<td align="right">0.50</td> +<td align="right">0.21</td> +<td align="right">0.79</td> +</tr> +<tr class="even"> +<td align="left">log_tb</td> +<td align="right">2.70</td> +<td align="right">2.33</td> +<td align="right">3.08</td> +</tr> +<tr class="odd"> +<td align="left">a.1</td> +<td align="right">4.45</td> +<td align="right">3.74</td> +<td align="right">5.16</td> +</tr> +<tr class="even"> +<td align="left">SD.log_k1</td> +<td align="right">0.36</td> +<td align="right">0.22</td> +<td align="right">0.49</td> +</tr> +<tr class="odd"> +<td align="left">SD.log_k2</td> +<td align="right">0.23</td> +<td align="right">0.02</td> +<td align="right">0.43</td> +</tr> +<tr class="even"> +<td align="left">SD.log_tb</td> +<td align="right">0.55</td> +<td align="right">0.25</td> +<td align="right">0.85</td> +</tr> +</tbody> +</table> +<pre class="r"><code>plot(hs_pH_2)</code></pre> +<p><img src="data:image/png;base64,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" style="display: block; margin: auto;" /></p> +<pre class="r"><code>endpoints(hs_pH_2)</code></pre> +<pre><code>$covariates + pH +50% 5.75 + +$distimes + DT50 DT90 DT50back DT50_k1 DT50_k2 +meso 14.68725 82.45287 24.82079 14.68725 29.29299</code></pre> +<pre class="r"><code>endpoints(hs_pH_2, covariates = c(pH = 7))</code></pre> +<pre><code>$covariates + pH +User 7 + +$distimes + DT50 DT90 DT50back DT50_k1 DT50_k2 +meso 8.298536 38.85371 11.69613 8.298536 15.71561</code></pre> +</div> +<div id="comparison-across-parent-models" class="section level2"> +<h2>Comparison across parent models</h2> +<p>After model reduction for all models with pH influence, they are +compared with each other.</p> +<pre class="r"><code>anova(sfo_pH, fomc_pH_2, dfop_pH_2, dfop_pH_4, sforb_pH_2, hs_pH_2)</code></pre> +<pre><code>Data: 116 observations of 1 variable(s) grouped in 18 datasets + + npar AIC BIC Lik +sfo_pH 5 783.09 787.54 -386.54 +fomc_pH_2 6 766.50 771.84 -377.25 +dfop_pH_4 7 767.35 773.58 -376.68 +sforb_pH_2 7 770.94 777.17 -378.47 +dfop_pH_2 8 765.06 772.18 -374.53 +hs_pH_2 10 766.47 775.37 -373.23</code></pre> +<p>The DFOP model with pH influence on <code>k2</code> and +<code>g</code> and a random effect only on <code>k2</code> is finally +selected as the best fit.</p> +<p>The endpoints resulting from this model are listed below. Please +refer to the Appendix for a detailed listing.</p> +<pre class="r"><code>endpoints(dfop_pH_2)</code></pre> +<pre><code>$covariates + pH +50% 5.75 + +$distimes + DT50 DT90 DT50back DT50_k1 DT50_k2 +meso 18.46687 74.91602 22.55197 4.568623 24.64483</code></pre> +<pre class="r"><code>endpoints(dfop_pH_2, covariates = c(pH = 7))</code></pre> +<pre><code>$covariates + pH +User 7 + +$distimes + DT50 DT90 DT50back DT50_k1 DT50_k2 +meso 8.370528 28.37824 8.542701 4.568623 8.785409</code></pre> +</div> +</div> +<div id="conclusions" class="section level1"> +<h1>Conclusions</h1> +<p>These evaluations demonstrate that covariate effects can be included +for all types of parent degradation models. These models can then be +further refined to make them fully identifiable.</p> +</div> +<div id="appendix" class="section level1"> +<h1>Appendix</h1> +<div id="hierarchical-fit-listings" class="section level2"> +<h2>Hierarchical fit listings</h2> +<div id="fits-without-covariate-effects" class="section level3"> +<h3>Fits without covariate effects</h3> +<caption> +Hierarchical SFO fit with constant variance +</caption> +<pre><code> +saemix version used for fitting: 3.4 +mkin version used for pre-fitting: 1.2.11 +R version used for fitting: 4.5.1 +Date of fit: Fri Sep 12 22:11:42 2025 +Date of summary: Fri Sep 12 22:12:34 2025 + +Equations: +d_meso/dt = - k_meso * meso + +Data: +116 observations of 1 variable(s) grouped in 18 datasets + +Model predictions using solution type analytical + +Fitted in 0.592 s +Using 300, 100 iterations and 3 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + meso_0 log_k_meso + 90.832 -3.192 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + meso_0 log_k_meso +meso_0 6.752 0.0000 +log_k_meso 0.000 0.9155 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 800 804.5 -395 + +Optimised parameters: + est. lower upper +meso_0 92.0705 89.9917 94.1493 +log_k_meso -3.1641 -3.4286 -2.8996 +a.1 5.4628 4.6421 6.2835 +SD.meso_0 0.0611 -98.3545 98.4767 +SD.log_k_meso 0.5616 0.3734 0.7499 + +Correlation: + meso_0 +log_k_meso 0.1132 + +Random effects: + est. lower upper +SD.meso_0 0.0611 -98.3545 98.4767 +SD.log_k_meso 0.5616 0.3734 0.7499 + +Variance model: + est. lower upper +a.1 5.463 4.642 6.284 + +Backtransformed parameters: + est. lower upper +meso_0 92.07053 89.99172 94.14933 +k_meso 0.04225 0.03243 0.05505 + +Estimated disappearance times: + DT50 DT90 +meso 16.41 54.5 + +</pre> +<p></code></p> +<caption> +Hierarchical FOMC fit with constant variance +</caption> +<pre><code> +saemix version used for fitting: 3.4 +mkin version used for pre-fitting: 1.2.11 +R version used for fitting: 4.5.1 +Date of fit: Fri Sep 12 22:11:43 2025 +Date of summary: Fri Sep 12 22:12:34 2025 + +Equations: +d_meso/dt = - (alpha/beta) * 1/((time/beta) + 1) * meso + +Data: +116 observations of 1 variable(s) grouped in 18 datasets + +Model predictions using solution type analytical + +Fitted in 0.872 s +Using 300, 100 iterations and 3 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + meso_0 log_alpha log_beta + 93.0520 0.6008 3.4176 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + meso_0 log_alpha log_beta +meso_0 6.287 0.00 0.000 +log_alpha 0.000 1.53 0.000 +log_beta 0.000 0.00 1.724 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 787.4 793.6 -386.7 + +Optimised parameters: + est. lower upper +meso_0 93.5648 91.42864 95.7009 +log_alpha 0.7645 0.28068 1.2484 +log_beta 3.6597 3.05999 4.2594 +a.1 5.0708 4.29823 5.8435 +SD.meso_0 0.1691 -34.01517 34.3535 +SD.log_alpha 0.3764 0.05834 0.6945 +SD.log_beta 0.3903 -0.06074 0.8414 + +Correlation: + meso_0 log_lph +log_alpha -0.2839 +log_beta -0.3443 0.8855 + +Random effects: + est. lower upper +SD.meso_0 0.1691 -34.01517 34.3535 +SD.log_alpha 0.3764 0.05834 0.6945 +SD.log_beta 0.3903 -0.06074 0.8414 + +Variance model: + est. lower upper +a.1 5.071 4.298 5.843 + +Backtransformed parameters: + est. lower upper +meso_0 93.565 91.429 95.701 +alpha 2.148 1.324 3.485 +beta 38.850 21.327 70.770 + +Estimated disappearance times: + DT50 DT90 DT50back +meso 14.8 74.64 22.47 + +</pre> +<p></code></p> +<caption> +Hierarchical DFOP fit with constant variance +</caption> +<pre><code> +saemix version used for fitting: 3.4 +mkin version used for pre-fitting: 1.2.11 +R version used for fitting: 4.5.1 +Date of fit: Fri Sep 12 22:11:44 2025 +Date of summary: Fri Sep 12 22:12:34 2025 + +Equations: +d_meso/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * + time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) + * meso + +Data: +116 observations of 1 variable(s) grouped in 18 datasets + +Model predictions using solution type analytical + +Fitted in 1.374 s +Using 300, 100 iterations and 3 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + meso_0 log_k1 log_k2 g_qlogis +93.14689 -2.05241 -3.53079 -0.09522 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + meso_0 log_k1 log_k2 g_qlogis +meso_0 6.418 0.000 0.000 0.00 +log_k1 0.000 1.018 0.000 0.00 +log_k2 0.000 0.000 1.694 0.00 +g_qlogis 0.000 0.000 0.000 2.37 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 787.6 795.6 -384.8 + +Optimised parameters: + est. lower upper +meso_0 93.6684 91.63599 95.7008 +log_k1 -1.7354 -2.61433 -0.8565 +log_k2 -3.4015 -3.73323 -3.0697 +g_qlogis -1.6341 -2.66133 -0.6069 +a.1 4.7803 4.01269 5.5479 +SD.meso_0 0.1661 -30.97086 31.3031 +SD.log_k1 0.1127 -2.59680 2.8223 +SD.log_k2 0.6394 0.41499 0.8638 +SD.g_qlogis 0.8166 0.09785 1.5353 + +Correlation: + meso_0 log_k1 log_k2 +log_k1 0.1757 +log_k2 0.0199 0.2990 +g_qlogis 0.0813 -0.7431 -0.3826 + +Random effects: + est. lower upper +SD.meso_0 0.1661 -30.97086 31.3031 +SD.log_k1 0.1127 -2.59680 2.8223 +SD.log_k2 0.6394 0.41499 0.8638 +SD.g_qlogis 0.8166 0.09785 1.5353 + +Variance model: + est. lower upper +a.1 4.78 4.013 5.548 + +Backtransformed parameters: + est. lower upper +meso_0 93.66841 91.63599 95.70082 +k1 0.17633 0.07322 0.42466 +k2 0.03332 0.02392 0.04643 +g 0.16327 0.06529 0.35277 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +meso 16.04 63.75 19.19 3.931 20.8 + +</pre> +<p></code></p> +<caption> +Hierarchical SFORB fit with constant variance +</caption> +<pre><code> +saemix version used for fitting: 3.4 +mkin version used for pre-fitting: 1.2.11 +R version used for fitting: 4.5.1 +Date of fit: Fri Sep 12 22:11:44 2025 +Date of summary: Fri Sep 12 22:12:34 2025 + +Equations: +d_meso_free/dt = - k_meso_free * meso_free - k_meso_free_bound * + meso_free + k_meso_bound_free * meso_bound +d_meso_bound/dt = + k_meso_free_bound * meso_free - k_meso_bound_free * + meso_bound + +Data: +116 observations of 1 variable(s) grouped in 18 datasets + +Model predictions using solution type analytical + +Fitted in 1.385 s +Using 300, 100 iterations and 3 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + meso_free_0 log_k_meso_free log_k_meso_free_bound + 93.147 -2.305 -4.230 +log_k_meso_bound_free + -3.761 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + meso_free_0 log_k_meso_free log_k_meso_free_bound +meso_free_0 6.418 0.0000 0.000 +log_k_meso_free 0.000 0.9276 0.000 +log_k_meso_free_bound 0.000 0.0000 2.272 +log_k_meso_bound_free 0.000 0.0000 0.000 + log_k_meso_bound_free +meso_free_0 0.000 +log_k_meso_free 0.000 +log_k_meso_free_bound 0.000 +log_k_meso_bound_free 1.447 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 787.4 795.4 -384.7 + +Optimised parameters: + est. lower upper +meso_free_0 93.6285 91.6262 95.631 +log_k_meso_free -2.8314 -3.1375 -2.525 +log_k_meso_free_bound -3.2213 -4.4695 -1.973 +log_k_meso_bound_free -2.4246 -3.5668 -1.282 +a.1 4.7372 3.9542 5.520 +SD.meso_free_0 0.1634 -32.7769 33.104 +SD.log_k_meso_free 0.4885 0.3080 0.669 +SD.log_k_meso_free_bound 0.2876 -1.7955 2.371 +SD.log_k_meso_bound_free 0.9942 0.2181 1.770 + +Correlation: + ms_fr_0 lg_k_m_ lg_k_ms_f_ +log_k_meso_free 0.2332 +log_k_meso_free_bound 0.1100 0.5964 +log_k_meso_bound_free -0.0413 0.3697 0.8025 + +Random effects: + est. lower upper +SD.meso_free_0 0.1634 -32.7769 33.104 +SD.log_k_meso_free 0.4885 0.3080 0.669 +SD.log_k_meso_free_bound 0.2876 -1.7955 2.371 +SD.log_k_meso_bound_free 0.9942 0.2181 1.770 + +Variance model: + est. lower upper +a.1 4.737 3.954 5.52 + +Backtransformed parameters: + est. lower upper +meso_free_0 93.62849 91.62622 95.63075 +k_meso_free 0.05893 0.04339 0.08004 +k_meso_free_bound 0.03990 0.01145 0.13903 +k_meso_bound_free 0.08851 0.02825 0.27736 + +Estimated Eigenvalues of SFORB model(s): +meso_b1 meso_b2 meso_g +0.15333 0.03402 0.20881 + +Resulting formation fractions: + ff +meso_free 1 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_meso_b1 DT50_meso_b2 +meso 14.79 60.81 18.3 4.521 20.37 + +</pre> +<p></code></p> +<caption> +Hierarchical HS fit with constant variance +</caption> +<pre><code> +saemix version used for fitting: 3.4 +mkin version used for pre-fitting: 1.2.11 +R version used for fitting: 4.5.1 +Date of fit: Fri Sep 12 22:11:45 2025 +Date of summary: Fri Sep 12 22:12:34 2025 + +Equations: +d_meso/dt = - ifelse(time <= tb, k1, k2) * meso + +Data: +116 observations of 1 variable(s) grouped in 18 datasets + +Model predictions using solution type analytical + +Fitted in 2.069 s +Using 300, 100 iterations and 3 chains + +Variance model: Constant variance + +Starting values for degradation parameters: +meso_0 log_k1 log_k2 log_tb +92.920 -2.409 -3.295 2.471 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + meso_0 log_k1 log_k2 log_tb +meso_0 6.477 0.0000 0.0000 0.00 +log_k1 0.000 0.8675 0.0000 0.00 +log_k2 0.000 0.0000 0.4035 0.00 +log_tb 0.000 0.0000 0.0000 1.16 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 781.9 789.9 -382 + +Optimised parameters: + est. lower upper +meso_0 93.34242 91.4730 95.2118 +log_k1 -2.77312 -3.0826 -2.4637 +log_k2 -3.61854 -3.8430 -3.3941 +log_tb 2.00266 1.3357 2.6696 +a.1 4.47693 3.7059 5.2479 +SD.meso_0 0.07963 -63.1661 63.3253 +SD.log_k1 0.47817 0.2467 0.7097 +SD.log_k2 0.39216 0.2137 0.5706 +SD.log_tb 0.94683 0.4208 1.4728 + +Correlation: + meso_0 log_k1 log_k2 +log_k1 0.1627 +log_k2 0.0063 -0.0301 +log_tb 0.0083 -0.3931 -0.1225 + +Random effects: + est. lower upper +SD.meso_0 0.07963 -63.1661 63.3253 +SD.log_k1 0.47817 0.2467 0.7097 +SD.log_k2 0.39216 0.2137 0.5706 +SD.log_tb 0.94683 0.4208 1.4728 + +Variance model: + est. lower upper +a.1 4.477 3.706 5.248 + +Backtransformed parameters: + est. lower upper +meso_0 93.34242 91.47303 95.21181 +k1 0.06247 0.04584 0.08512 +k2 0.02682 0.02143 0.03357 +tb 7.40872 3.80282 14.43376 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +meso 16 76 22.88 11.1 25.84 + +</pre> +<p></code></p> +</div> +<div id="fits-with-covariate-effects" class="section level3"> +<h3>Fits with covariate effects</h3> +<caption> +Hierarchichal SFO fit with pH influence +</caption> +<pre><code> +saemix version used for fitting: 3.4 +mkin version used for pre-fitting: 1.2.11 +R version used for fitting: 4.5.1 +Date of fit: Fri Sep 12 22:11:59 2025 +Date of summary: Fri Sep 12 22:12:34 2025 + +Equations: +d_meso/dt = - k_meso * meso + +Data: +116 observations of 1 variable(s) grouped in 18 datasets + +Model predictions using solution type analytical + +Fitted in 1.373 s +Using 300, 100 iterations and 3 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + meso_0 log_k_meso + 90.832 -3.192 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + meso_0 log_k_meso +meso_0 6.752 0.0000 +log_k_meso 0.000 0.9155 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 783.1 787.5 -386.5 + +Optimised parameters: + est. lower upper +meso_0 91.3481 89.2688 93.4275 +log_k_meso -3.2854 -3.4590 -3.1118 +beta_pH(log_k_meso) 0.5871 0.3684 0.8059 +a.1 5.4750 4.7085 6.2415 +SD.log_k_meso 0.3471 0.2258 0.4684 + +Correlation: + meso_0 lg_k_ms +log_k_meso 0.1797 +beta_pH(log_k_meso) -0.0183 -0.2379 + +Random effects: + est. lower upper +SD.log_k_meso 0.3471 0.2258 0.4684 + +Variance model: + est. lower upper +a.1 5.475 4.709 6.242 + +Backtransformed parameters: + est. lower upper +meso_0 91.34814 89.26880 93.42748 +k_meso 0.03743 0.03146 0.04452 + +Covariates used for endpoints below: + pH +50% 5.75 + +Estimated disappearance times: + DT50 DT90 +meso 18.52 61.52 + +</pre> +<p></code></p> +<caption> +Hierarchichal FOMC fit with pH influence +</caption> +<pre><code> +saemix version used for fitting: 3.4 +mkin version used for pre-fitting: 1.2.11 +R version used for fitting: 4.5.1 +Date of fit: Fri Sep 12 22:12:00 2025 +Date of summary: Fri Sep 12 22:12:34 2025 + +Equations: +d_meso/dt = - (alpha/beta) * 1/((time/beta) + 1) * meso + +Data: +116 observations of 1 variable(s) grouped in 18 datasets + +Model predictions using solution type analytical + +Fitted in 1.067 s +Using 300, 100 iterations and 3 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + meso_0 log_alpha log_beta + 93.0520 0.6008 3.4176 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + meso_0 log_alpha log_beta +meso_0 6.287 0.00 0.000 +log_alpha 0.000 1.53 0.000 +log_beta 0.000 0.00 1.724 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 770.1 776.3 -378 + +Optimised parameters: + est. lower upper +meso_0 92.840646 90.750461 94.9308 +log_alpha 1.114051 0.475668 1.7524 +beta_pH(log_alpha) 0.577505 0.369805 0.7852 +log_beta 4.214099 3.438851 4.9893 +a.1 5.027768 4.322028 5.7335 +SD.log_alpha 0.004034 -23.766993 23.7751 +SD.log_beta 0.374640 0.009252 0.7400 + +Correlation: + meso_0 log_lph bt_H(_) +log_alpha -0.3220 +beta_pH(log_alpha) -0.0789 0.1148 +log_beta -0.3544 0.9709 0.1628 + +Random effects: + est. lower upper +SD.log_alpha 0.004034 -23.766993 23.78 +SD.log_beta 0.374640 0.009252 0.74 + +Variance model: + est. lower upper +a.1 5.028 4.322 5.734 + +Backtransformed parameters: + est. lower upper +meso_0 92.841 90.750 94.931 +alpha 3.047 1.609 5.769 +beta 67.633 31.151 146.840 + +Covariates used for endpoints below: + pH +50% 5.75 + +Estimated disappearance times: + DT50 DT90 DT50back +meso 17.28 76.37 22.99 + +</pre> +<p></code></p> +<caption> +Refined hierarchichal FOMC fit with pH influence +</caption> +<pre><code> +saemix version used for fitting: 3.4 +mkin version used for pre-fitting: 1.2.11 +R version used for fitting: 4.5.1 +Date of fit: Fri Sep 12 22:12:04 2025 +Date of summary: Fri Sep 12 22:12:34 2025 + +Equations: +d_meso/dt = - (alpha/beta) * 1/((time/beta) + 1) * meso + +Data: +116 observations of 1 variable(s) grouped in 18 datasets + +Model predictions using solution type analytical + +Fitted in 3.058 s +Using 300, 100 iterations and 3 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + meso_0 log_alpha log_beta + 93.0520 0.6008 3.4176 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + meso_0 log_alpha log_beta +meso_0 6.287 0.00 0.000 +log_alpha 0.000 1.53 0.000 +log_beta 0.000 0.00 1.724 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 766.5 771.8 -377.3 + +Optimised parameters: + est. lower upper +meso_0 93.1950 91.1047 95.2853 +log_alpha 0.8125 0.3292 1.2957 +beta_pH(log_alpha) 0.5497 0.3490 0.7504 +log_beta 3.8464 3.2179 4.4750 +a.1 4.9972 4.2976 5.6968 +SD.log_beta 0.3829 0.2499 0.5159 + +Correlation: + meso_0 log_lph bt_H(_) +log_alpha -0.3094 +beta_pH(log_alpha) -0.0779 0.0841 +log_beta -0.3493 0.9533 0.1434 + +Random effects: + est. lower upper +SD.log_beta 0.3829 0.2499 0.5159 + +Variance model: + est. lower upper +a.1 4.997 4.298 5.697 + +Backtransformed parameters: + est. lower upper +meso_0 93.195 91.10 95.285 +alpha 2.253 1.39 3.654 +beta 46.826 24.98 87.793 + +Covariates used for endpoints below: + pH +50% 5.75 + +Estimated disappearance times: + DT50 DT90 DT50back +meso 16.86 83.27 25.07 + +</pre> +<p></code></p> +<caption> +Hierarchichal DFOP fit with pH influence +</caption> +<pre><code> +saemix version used for fitting: 3.4 +mkin version used for pre-fitting: 1.2.11 +R version used for fitting: 4.5.1 +Date of fit: Fri Sep 12 22:12:07 2025 +Date of summary: Fri Sep 12 22:12:34 2025 + +Equations: +d_meso/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * + time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) + * meso + +Data: +116 observations of 1 variable(s) grouped in 18 datasets + +Model predictions using solution type analytical + +Fitted in 2.658 s +Using 300, 100 iterations and 3 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + meso_0 log_k1 log_k2 g_qlogis +93.14689 -2.05241 -3.53079 -0.09522 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + meso_0 log_k1 log_k2 g_qlogis +meso_0 6.418 0.000 0.000 0.00 +log_k1 0.000 1.018 0.000 0.00 +log_k2 0.000 0.000 1.694 0.00 +g_qlogis 0.000 0.000 0.000 2.37 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 769.1 777.1 -375.5 + +Optimised parameters: + est. lower upper +meso_0 92.843344 90.8464 94.84028 +log_k1 -2.815685 -3.0888 -2.54261 +log_k2 -3.956384 -4.4741 -3.43868 +beta_pH(log_k2) 1.308417 0.6948 1.92203 +g_qlogis -0.121394 -0.5691 0.32627 +beta_pH(g_qlogis) -0.565988 -1.0394 -0.09262 +a.1 4.955518 4.2597 5.65135 +SD.log_k2 0.758963 0.4685 1.04943 +SD.g_qlogis 0.005215 -9.9561 9.96656 + +Correlation: + meso_0 log_k1 log_k2 b_H(_2) g_qlogs +log_k1 0.2706 +log_k2 -0.0457 -0.0667 +beta_pH(log_k2) 0.0554 -0.1291 -0.5566 +g_qlogis 0.1004 -0.4462 -0.4397 0.5042 +beta_pH(g_qlogis) 0.1267 0.4226 0.0123 -0.0438 0.2029 + +Random effects: + est. lower upper +SD.log_k2 0.758963 0.4685 1.049 +SD.g_qlogis 0.005215 -9.9561 9.967 + +Variance model: + est. lower upper +a.1 4.956 4.26 5.651 + +Backtransformed parameters: + est. lower upper +meso_0 92.84334 90.84641 94.84028 +k1 0.05986 0.04556 0.07866 +k2 0.01913 0.01140 0.03211 +g 0.46969 0.36145 0.58085 + +Covariates used for endpoints below: + pH +50% 5.75 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +meso 20.23 88.45 26.62 11.58 36.23 + +</pre> +<p></code></p> +<caption> +Refined hierarchical DFOP fit with pH influence +</caption> +<pre><code> +saemix version used for fitting: 3.4 +mkin version used for pre-fitting: 1.2.11 +R version used for fitting: 4.5.1 +Date of fit: Fri Sep 12 22:12:12 2025 +Date of summary: Fri Sep 12 22:12:34 2025 + +Equations: +d_meso/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * + time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) + * meso + +Data: +116 observations of 1 variable(s) grouped in 18 datasets + +Model predictions using solution type analytical + +Fitted in 3.732 s +Using 300, 100 iterations and 3 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + meso_0 log_k1 log_k2 g_qlogis +93.14689 -2.05241 -3.53079 -0.09522 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + meso_0 log_k1 log_k2 g_qlogis +meso_0 6.418 0.000 0.000 0.00 +log_k1 0.000 1.018 0.000 0.00 +log_k2 0.000 0.000 1.694 0.00 +g_qlogis 0.000 0.000 0.000 2.37 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 765.1 772.2 -374.5 + +Optimised parameters: + est. lower upper +meso_0 93.1612 91.0766 95.24580 +log_k1 -1.8857 -2.8975 -0.87395 +log_k2 -3.5711 -3.8859 -3.25622 +beta_pH(log_k2) 0.8252 0.4952 1.15513 +g_qlogis -1.5326 -2.6994 -0.36574 +beta_pH(g_qlogis) -0.8365 -1.7163 0.04333 +a.1 4.9218 4.2328 5.61070 +SD.log_k2 0.4011 0.2604 0.54184 + +Correlation: + meso_0 log_k1 log_k2 b_H(_2) g_qlogs +log_k1 0.1734 +log_k2 0.0553 0.6421 +beta_pH(log_k2) -0.0382 -0.5121 -0.5860 +g_qlogis 0.0616 -0.8501 -0.7251 0.4941 +beta_pH(g_qlogis) 0.1408 -0.3240 -0.2716 -0.0041 0.6264 + +Random effects: + est. lower upper +SD.log_k2 0.4011 0.2604 0.5418 + +Variance model: + est. lower upper +a.1 4.922 4.233 5.611 + +Backtransformed parameters: + est. lower upper +meso_0 93.16118 91.07655 95.24580 +k1 0.15172 0.05516 0.41730 +k2 0.02813 0.02053 0.03853 +g 0.17762 0.06301 0.40957 + +Covariates used for endpoints below: + pH +50% 5.75 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +meso 18.47 74.92 22.55 4.569 24.64 + +</pre> +<p></code></p> +<caption> +Further refined hierarchical DFOP fit with pH influence +</caption> +<pre><code> +saemix version used for fitting: 3.4 +mkin version used for pre-fitting: 1.2.11 +R version used for fitting: 4.5.1 +Date of fit: Fri Sep 12 22:12:20 2025 +Date of summary: Fri Sep 12 22:12:35 2025 + +Equations: +d_meso/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * + time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) + * meso + +Data: +116 observations of 1 variable(s) grouped in 18 datasets + +Model predictions using solution type analytical + +Fitted in 3.237 s +Using 300, 100 iterations and 3 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + meso_0 log_k1 log_k2 g_qlogis +93.14689 -2.05241 -3.53079 -0.09522 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + meso_0 log_k1 log_k2 g_qlogis +meso_0 6.418 0.000 0.000 0.00 +log_k1 0.000 1.018 0.000 0.00 +log_k2 0.000 0.000 1.694 0.00 +g_qlogis 0.000 0.000 0.000 2.37 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 767.4 773.6 -376.7 + +Optimised parameters: + est. lower upper +meso_0 93.3011 91.1905 95.4118 +log_k1 -2.1487 -2.7607 -1.5367 +log_k2 -3.6066 -3.9305 -3.2828 +beta_pH(log_k2) 0.7821 0.4126 1.1517 +g_qlogis -1.0373 -1.9337 -0.1409 +a.1 5.0095 4.3082 5.7108 +SD.log_k2 0.4622 0.3009 0.6235 + +Correlation: + meso_0 log_k1 log_k2 b_H(_2) +log_k1 0.2179 +log_k2 0.0271 0.5067 +beta_pH(log_k2) -0.0326 -0.5546 -0.5485 +g_qlogis 0.0237 -0.8479 -0.6866 0.6123 + +Random effects: + est. lower upper +SD.log_k2 0.4622 0.3009 0.6235 + +Variance model: + est. lower upper +a.1 5.009 4.308 5.711 + +Backtransformed parameters: + est. lower upper +meso_0 93.30113 91.19050 95.41175 +k1 0.11664 0.06325 0.21508 +k2 0.02714 0.01963 0.03752 +g 0.26168 0.12635 0.46483 + +Covariates used for endpoints below: + pH +50% 5.75 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +meso 17.09 73.67 22.18 5.943 25.54 + +</pre> +<p></code></p> +<caption> +Hierarchichal SFORB fit with pH influence +</caption> +<pre><code> +saemix version used for fitting: 3.4 +mkin version used for pre-fitting: 1.2.11 +R version used for fitting: 4.5.1 +Date of fit: Fri Sep 12 22:12:23 2025 +Date of summary: Fri Sep 12 22:12:35 2025 + +Equations: +d_meso_free/dt = - k_meso_free * meso_free - k_meso_free_bound * + meso_free + k_meso_bound_free * meso_bound +d_meso_bound/dt = + k_meso_free_bound * meso_free - k_meso_bound_free * + meso_bound + +Data: +116 observations of 1 variable(s) grouped in 18 datasets + +Model predictions using solution type analytical + +Fitted in 2.741 s +Using 300, 100 iterations and 3 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + meso_free_0 log_k_meso_free log_k_meso_free_bound + 93.147 -2.305 -4.230 +log_k_meso_bound_free + -3.761 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + meso_free_0 log_k_meso_free log_k_meso_free_bound +meso_free_0 6.418 0.0000 0.000 +log_k_meso_free 0.000 0.9276 0.000 +log_k_meso_free_bound 0.000 0.0000 2.272 +log_k_meso_bound_free 0.000 0.0000 0.000 + log_k_meso_bound_free +meso_free_0 0.000 +log_k_meso_free 0.000 +log_k_meso_free_bound 0.000 +log_k_meso_bound_free 1.447 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 768.8 776.8 -375.4 + +Optimised parameters: + est. lower upper +meso_free_0 93.4204 91.3213 95.5195 +log_k_meso_free -2.9408 -3.2344 -2.6471 +beta_pH(log_k_meso_free) 0.4232 0.1769 0.6695 +log_k_meso_free_bound -3.4889 -4.9243 -2.0535 +log_k_meso_bound_free -2.9130 -4.3047 -1.5212 +beta_pH(log_k_meso_bound_free) 1.2290 -0.2107 2.6687 +a.1 4.9031 4.1795 5.6268 +SD.log_k_meso_free 0.3454 0.2252 0.4656 +SD.log_k_meso_bound_free 0.1277 -1.9459 2.2012 + +Correlation: + ms_fr_0 lg_k_m_ b_H(___) lg_k_ms_f_ lg_k_ms_b_ +log_k_meso_free 0.3460 +beta_pH(log_k_meso_free) -0.0930 -0.4206 +log_k_meso_free_bound 0.2439 0.7749 -0.3492 +log_k_meso_bound_free 0.1350 0.6400 -0.2912 0.9346 +beta_pH(log_k_meso_bound_free) -0.2216 -0.4778 -0.0111 -0.6566 -0.6498 + +Random effects: + est. lower upper +SD.log_k_meso_free 0.3454 0.2252 0.4656 +SD.log_k_meso_bound_free 0.1277 -1.9459 2.2012 + +Variance model: + est. lower upper +a.1 4.903 4.18 5.627 + +Backtransformed parameters: + est. lower upper +meso_free_0 93.42040 91.321340 95.51946 +k_meso_free 0.05282 0.039384 0.07085 +k_meso_free_bound 0.03054 0.007268 0.12829 +k_meso_bound_free 0.05431 0.013505 0.21845 + +Covariates used for endpoints below: + pH +50% 5.75 + +Estimated Eigenvalues of SFORB model(s): +meso_b1 meso_b2 meso_g + 0.1121 0.0256 0.3148 + +Resulting formation fractions: + ff +meso_free 1 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_meso_b1 DT50_meso_b2 +meso 16.42 75.2 22.64 6.185 27.08 + +</pre> +<p></code> </p> +<caption> +Refined hierarchichal SFORB fit with pH influence +</caption> +<pre><code> +saemix version used for fitting: 3.4 +mkin version used for pre-fitting: 1.2.11 +R version used for fitting: 4.5.1 +Date of fit: Fri Sep 12 22:12:27 2025 +Date of summary: Fri Sep 12 22:12:35 2025 + +Equations: +d_meso_free/dt = - k_meso_free * meso_free - k_meso_free_bound * + meso_free + k_meso_bound_free * meso_bound +d_meso_bound/dt = + k_meso_free_bound * meso_free - k_meso_bound_free * + meso_bound + +Data: +116 observations of 1 variable(s) grouped in 18 datasets + +Model predictions using solution type analytical + +Fitted in 3.799 s +Using 300, 100 iterations and 3 chains + +Variance model: Constant variance + +Starting values for degradation parameters: + meso_free_0 log_k_meso_free log_k_meso_free_bound + 93.147 -2.305 -4.230 +log_k_meso_bound_free + -3.761 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + meso_free_0 log_k_meso_free log_k_meso_free_bound +meso_free_0 6.418 0.0000 0.000 +log_k_meso_free 0.000 0.9276 0.000 +log_k_meso_free_bound 0.000 0.0000 2.272 +log_k_meso_bound_free 0.000 0.0000 0.000 + log_k_meso_bound_free +meso_free_0 0.000 +log_k_meso_free 0.000 +log_k_meso_free_bound 0.000 +log_k_meso_bound_free 1.447 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 770.9 777.2 -378.5 + +Optimised parameters: + est. lower upper +meso_free_0 93.3196 91.1650 95.4743 +log_k_meso_free -3.0207 -3.2655 -2.7759 +beta_pH(log_k_meso_free) 0.5435 0.3329 0.7542 +log_k_meso_free_bound -3.8001 -5.1809 -2.4193 +log_k_meso_bound_free -2.9462 -4.2411 -1.6513 +a.1 5.0825 4.3709 5.7940 +SD.log_k_meso_free 0.3338 0.2175 0.4502 + +Correlation: + ms_fr_0 lg_k_m_ b_H(___ lg_k_ms_f_ +log_k_meso_free 0.3556 +beta_pH(log_k_meso_free) -0.0422 -0.2041 +log_k_meso_free_bound 0.2513 0.6866 -0.0400 +log_k_meso_bound_free 0.1292 0.5341 -0.0129 0.9214 + +Random effects: + est. lower upper +SD.log_k_meso_free 0.3338 0.2175 0.4502 + +Variance model: + est. lower upper +a.1 5.082 4.371 5.794 + +Backtransformed parameters: + est. lower upper +meso_free_0 93.31965 91.165044 95.47425 +k_meso_free 0.04877 0.038177 0.06230 +k_meso_free_bound 0.02237 0.005623 0.08898 +k_meso_bound_free 0.05254 0.014392 0.19179 + +Covariates used for endpoints below: + pH +50% 5.75 + +Estimated Eigenvalues of SFORB model(s): +meso_b1 meso_b2 meso_g +0.09736 0.02632 0.31602 + +Resulting formation fractions: + ff +meso_free 1 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_meso_b1 DT50_meso_b2 +meso 16.87 73.16 22.02 7.12 26.34 + +</pre> +<p></code> </p> +<caption> +Hierarchichal HS fit with pH influence +</caption> +<pre><code> +saemix version used for fitting: 3.4 +mkin version used for pre-fitting: 1.2.11 +R version used for fitting: 4.5.1 +Date of fit: Fri Sep 12 22:12:31 2025 +Date of summary: Fri Sep 12 22:12:35 2025 + +Equations: +d_meso/dt = - ifelse(time <= tb, k1, k2) * meso + +Data: +116 observations of 1 variable(s) grouped in 18 datasets + +Model predictions using solution type analytical + +Fitted in 2.809 s +Using 300, 100 iterations and 3 chains + +Variance model: Constant variance + +Starting values for degradation parameters: +meso_0 log_k1 log_k2 log_tb +92.920 -2.409 -3.295 2.471 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + meso_0 log_k1 log_k2 log_tb +meso_0 6.477 0.0000 0.0000 0.00 +log_k1 0.000 0.8675 0.0000 0.00 +log_k2 0.000 0.0000 0.4035 0.00 +log_tb 0.000 0.0000 0.0000 1.16 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 769.8 779.6 -373.9 + +Optimised parameters: + est. lower upper +meso_0 93.32599 91.4658 95.1862 +log_k1 -3.08497 -3.2830 -2.8870 +beta_pH(log_k1) 0.47472 0.2334 0.7160 +log_k2 -3.68267 -3.9077 -3.4577 +beta_pH(log_k2) 0.54151 0.2124 0.8706 +log_tb 2.67815 2.3846 2.9717 +beta_pH(log_tb) -0.09889 -0.4258 0.2280 +a.1 4.49487 3.7766 5.2132 +SD.log_k1 0.37191 0.2370 0.5068 +SD.log_k2 0.29210 0.0994 0.4848 +SD.log_tb 0.25353 -0.0664 0.5735 + +Correlation: + meso_0 log_k1 b_H(_1) log_k2 b_H(_2) log_tb +log_k1 0.2301 +beta_pH(log_k1) -0.0452 -0.2842 +log_k2 -0.0029 -0.0322 -0.0008 +beta_pH(log_k2) -0.0071 0.0000 -0.0391 -0.2655 +log_tb -0.1003 -0.2052 0.1180 -0.4197 0.1794 +beta_pH(log_tb) 0.0097 0.1125 -0.1265 0.1894 -0.3653 -0.3449 + +Random effects: + est. lower upper +SD.log_k1 0.3719 0.2370 0.5068 +SD.log_k2 0.2921 0.0994 0.4848 +SD.log_tb 0.2535 -0.0664 0.5735 + +Variance model: + est. lower upper +a.1 4.495 3.777 5.213 + +Backtransformed parameters: + est. lower upper +meso_0 93.32599 91.46575 95.18624 +k1 0.04573 0.03752 0.05574 +k2 0.02516 0.02009 0.03150 +tb 14.55810 10.85502 19.52445 + +Covariates used for endpoints below: + pH +50% 5.75 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +meso 15.65 79.63 23.97 15.16 27.55 + +</pre> +<p></code> </p> +<caption> +Refined hierarchichal HS fit with pH influence +</caption> +<pre><code> +saemix version used for fitting: 3.4 +mkin version used for pre-fitting: 1.2.11 +R version used for fitting: 4.5.1 +Date of fit: Fri Sep 12 22:12:34 2025 +Date of summary: Fri Sep 12 22:12:35 2025 + +Equations: +d_meso/dt = - ifelse(time <= tb, k1, k2) * meso + +Data: +116 observations of 1 variable(s) grouped in 18 datasets + +Model predictions using solution type analytical + +Fitted in 2.817 s +Using 300, 100 iterations and 3 chains + +Variance model: Constant variance + +Starting values for degradation parameters: +meso_0 log_k1 log_k2 log_tb +92.920 -2.409 -3.295 2.471 + +Fixed degradation parameter values: +None + +Starting values for random effects (square root of initial entries in omega): + meso_0 log_k1 log_k2 log_tb +meso_0 6.477 0.0000 0.0000 0.00 +log_k1 0.000 0.8675 0.0000 0.00 +log_k2 0.000 0.0000 0.4035 0.00 +log_tb 0.000 0.0000 0.0000 1.16 + +Starting values for error model parameters: +a.1 + 1 + +Results: + +Likelihood computed by importance sampling + AIC BIC logLik + 766.5 775.4 -373.2 + +Optimised parameters: + est. lower upper +meso_0 93.3251 91.49823 95.1520 +log_k1 -3.0535 -3.24879 -2.8582 +beta_pH(log_k1) 0.4567 0.22400 0.6894 +log_k2 -3.7439 -3.94307 -3.5447 +beta_pH(log_k2) 0.4982 0.20644 0.7899 +log_tb 2.7040 2.33033 3.0777 +a.1 4.4452 3.73537 5.1551 +SD.log_k1 0.3570 0.22104 0.4930 +SD.log_k2 0.2252 0.01864 0.4318 +SD.log_tb 0.5488 0.24560 0.8521 + +Correlation: + meso_0 log_k1 b_H(_1) log_k2 b_H(_2) +log_k1 0.2233 +beta_pH(log_k1) -0.0453 -0.2955 +log_k2 0.0028 -0.0420 0.0126 +beta_pH(log_k2) -0.0116 0.0112 -0.0667 -0.2097 +log_tb -0.0658 -0.1928 0.0913 -0.2843 0.1210 + +Random effects: + est. lower upper +SD.log_k1 0.3570 0.22104 0.4930 +SD.log_k2 0.2252 0.01864 0.4318 +SD.log_tb 0.5488 0.24560 0.8521 + +Variance model: + est. lower upper +a.1 4.445 3.735 5.155 + +Backtransformed parameters: + est. lower upper +meso_0 93.32513 91.49823 95.15204 +k1 0.04719 0.03882 0.05737 +k2 0.02366 0.01939 0.02888 +tb 14.93925 10.28132 21.70744 + +Covariates used for endpoints below: + pH +50% 5.75 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +meso 14.69 82.45 24.82 14.69 29.29 + +</pre> +<p></code></p> +</div> +</div> +<div id="session-info" class="section level2"> +<h2>Session info</h2> +<pre><code>R version 4.5.1 (2025-06-13) +Platform: x86_64-pc-linux-gnu +Running under: Debian GNU/Linux 13 (trixie) + +Matrix products: default +BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.12.1 +LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.1; LAPACK version 3.12.0 + +locale: + [1] LC_CTYPE=de_DE.UTF-8 LC_NUMERIC=C + [3] LC_TIME=de_DE.UTF-8 LC_COLLATE=de_DE.UTF-8 + [5] LC_MONETARY=de_DE.UTF-8 LC_MESSAGES=de_DE.UTF-8 + [7] LC_PAPER=de_DE.UTF-8 LC_NAME=C + [9] LC_ADDRESS=C LC_TELEPHONE=C +[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C + +time zone: Europe/Berlin +tzcode source: system (glibc) + +attached base packages: +[1] parallel stats graphics grDevices utils datasets methods +[8] base + +other attached packages: +[1] saemix_3.4 npde_3.5 knitr_1.50 mkin_1.2.11 +[5] rmarkdown_2.29 nvimcom_0.9-167 + +loaded via a namespace (and not attached): + [1] gtable_0.3.6 jsonlite_2.0.0 dplyr_1.1.4 compiler_4.5.1 + [5] tidyselect_1.2.1 colorout_1.3-2 dichromat_2.0-0.1 gridExtra_2.3 + [9] jquerylib_0.1.4 scales_1.4.0 readxl_1.4.5 yaml_2.3.10 +[13] fastmap_1.2.0 lattice_0.22-7 ggplot2_3.5.2 R6_2.6.1 +[17] generics_0.1.4 lmtest_0.9-40 MASS_7.3-65 tibble_3.3.0 +[21] bslib_0.9.0 pillar_1.10.2 RColorBrewer_1.1-3 rlang_1.1.6 +[25] cachem_1.1.0 xfun_0.52 sass_0.4.10 cli_3.6.5 +[29] magrittr_2.0.3 digest_0.6.37 grid_4.5.1 mclust_6.1.1 +[33] lifecycle_1.0.4 nlme_3.1-168 vctrs_0.6.5 evaluate_1.0.3 +[37] glue_1.8.0 cellranger_1.1.0 farver_2.1.2 codetools_0.2-20 +[41] zoo_1.8-14 colorspace_2.1-1 tools_4.5.1 pkgconfig_2.0.3 +[45] htmltools_0.5.8.1 </code></pre> +</div> +<div id="hardware-info" class="section level2"> +<h2>Hardware info</h2> +<pre><code>CPU model: AMD Ryzen 9 7950X 16-Core Processor</code></pre> +<pre><code>MemTotal: 64933716 kB</code></pre> +</div> +</div> + + + +</div> +</div> + +</div> + +<script> + +// add bootstrap table styles to pandoc tables +function bootstrapStylePandocTables() { + $('tr.odd').parent('tbody').parent('table').addClass('table table-condensed'); +} +$(document).ready(function () { + bootstrapStylePandocTables(); +}); + + +</script> + +<!-- tabsets --> + +<script> +$(document).ready(function () { + window.buildTabsets("TOC"); +}); + +$(document).ready(function () { + $('.tabset-dropdown > .nav-tabs > li').click(function () { + $(this).parent().toggleClass('nav-tabs-open'); + }); +}); +</script> + +<!-- code folding --> +<script> +$(document).ready(function () { + window.initializeCodeFolding("show" === "show"); +}); +</script> + +<script> +$(document).ready(function () { + + // temporarily add toc-ignore selector to headers for the consistency with Pandoc + $('.unlisted.unnumbered').addClass('toc-ignore') + + // move toc-ignore selectors from section div to header + $('div.section.toc-ignore') + .removeClass('toc-ignore') + .children('h1,h2,h3,h4,h5').addClass('toc-ignore'); + + // establish options + var options = { + selectors: "h1,h2,h3", + theme: "bootstrap3", + context: '.toc-content', + hashGenerator: function (text) { + return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_'); + }, + ignoreSelector: ".toc-ignore", + scrollTo: 0 + }; + options.showAndHide = true; + options.smoothScroll = true; + + // tocify + var toc = $("#TOC").tocify(options).data("toc-tocify"); +}); +</script> + +<!-- dynamically load mathjax for compatibility with self-contained --> +<script> + (function () { + var script = document.createElement("script"); + script.type = "text/javascript"; + script.src = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"; + document.getElementsByTagName("head")[0].appendChild(script); + })(); +</script> + +</body> +</html> diff --git a/vignettes/prebuilt/2023_mesotrione_parent.rmd b/vignettes/web_only/mesotrione_parent_2023.rmd index f11effc7..eb30b455 100644 --- a/vignettes/prebuilt/2023_mesotrione_parent.rmd +++ b/vignettes/web_only/mesotrione_parent_2023.rmd @@ -1,13 +1,16 @@ --- title: "Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione" -author: Johannes Ranke -date: Last change on 4 August 2023, last compiled on `r format(Sys.time(), "%e - %B %Y")` +author: "Johannes Ranke" +date: Last change 12 September 2025 (rebuilt `r Sys.Date()`) output: - pdf_document: - extra_dependencies: ["float", "listing"] -toc: yes -geometry: margin=2cm + html_document: + toc: true + toc_float: true + code_folding: show + fig_retina: null +vignette: > + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} --- ```{r setup, echo = FALSE, cache = FALSE} @@ -125,7 +128,7 @@ is larger, with convergence problems appearing for a number of non-SFO fits. \clearpage -# Hierarchical model fits without covariate effect +# Hierarchical models without covariate The following code fits hierarchical kinetic models for the ten combinations of the five different degradation models with the two different error models in @@ -152,15 +155,15 @@ illparms(f_saem_1) |> kable() ``` For obtaining fits with only well-defined random effects, we update -the set of fits, excluding random effects that were ill-defined -according to the `illparms` function. +the set of fits, excluding random effects for degradation parameters that were +ill-defined according to the `illparms` function. ```{r f-saem-2, dependson = "f-saem-1"} f_saem_2 <- update(f_saem_1, no_random_effect = illparms(f_saem_1)) status(f_saem_2) |> kable() ``` -The updated fits terminate without errors. +The updated fits terminate without errors, and the only ill-defined ```{r dependson = "f-saem-2"} illparms(f_saem_2) |> kable() @@ -170,7 +173,7 @@ No ill-defined errors remain in the fits with constant variance. \clearpage -# Hierarchical model fits with covariate effect +# Hierarchical models with covariate In the following sections, hierarchical fits including a model for the influence of pH on selected degradation parameters are shown for all parent @@ -184,7 +187,7 @@ has shown that only for such parameters a significant pH effect could be found. ```{r sfo-pH, dependson = "f-sep-const"} sfo_pH <- saem(f_sep_const["SFO", ], no_random_effect = "meso_0", covariates = pH, - covariate_models = list(log_k_meso ~ pH)) + covariate_models = list(log_k_meso ~ pH), center_covariates = "median") ``` ```{r dependson = "sfo-pH"} @@ -227,7 +230,7 @@ endpoints(sfo_pH, covariates = c(pH = 7.0)) ```{r fomc-pH, dependson = "f-sep-const"} fomc_pH <- saem(f_sep_const["FOMC", ], no_random_effect = "meso_0", covariates = pH, - covariate_models = list(log_alpha ~ pH)) + covariate_models = list(log_alpha ~ pH), center_covariates = "median") ``` ```{r dependson = "fomc-pH"} @@ -295,7 +298,9 @@ and including covariate models for the two identifiable parameters `k2` and `g`. ```{r dfop-pH, dependson = "f-sep-const"} dfop_pH <- saem(f_sep_const["DFOP", ], no_random_effect = c("meso_0", "log_k1"), covariates = pH, - covariate_models = list(log_k2 ~ pH, g_qlogis ~ pH)) + covariate_models = list(log_k2 ~ pH, g_qlogis ~ pH), + center_covariates = "median" +) ``` The corresponding parameters for @@ -323,7 +328,8 @@ Therefore, another attempt is made without the corresponding covariate model. ```{r dfop-pH-3, dependson = "f-sep-const"} dfop_pH_3 <- saem(f_sep_const["DFOP", ], no_random_effect = c("meso_0", "log_k1"), covariates = pH, - covariate_models = list(log_k2 ~ pH)) + covariate_models = list(log_k2 ~ pH), + center_covariates = "median") illparms(dfop_pH_3) ``` As the random effect for `g` is again ill-defined, the fit is repeated without it. @@ -368,7 +374,8 @@ endpoints(dfop_pH_2, covariates = c(pH = 7)) ```{r sforb-pH, dependson = "f-sep-const"} sforb_pH <- saem(f_sep_const["SFORB", ], no_random_effect = c("meso_free_0", "log_k_meso_free_bound"), covariates = pH, - covariate_models = list(log_k_meso_free ~ pH, log_k_meso_bound_free ~ pH)) + covariate_models = list(log_k_meso_free ~ pH, log_k_meso_bound_free ~ pH), + center_covariates = "median") ``` ```{r dependson = "sforb-pH"} @@ -428,7 +435,8 @@ endpoints(sforb_pH_2, covariates = c(pH = 7)) ```{r hs-pH, dependson = "f-sep-const"} hs_pH <- saem(f_sep_const["HS", ], no_random_effect = c("meso_0"), covariates = pH, - covariate_models = list(log_k1 ~ pH, log_k2 ~ pH, log_tb ~ pH)) + covariate_models = list(log_k1 ~ pH, log_k2 ~ pH, log_tb ~ pH), + center_covariates = "median") ``` ```{r dependson = "hs-pH"} @@ -511,7 +519,7 @@ for (deg_mod in deg_mods) { fit <- f_saem_1[[deg_mod, err_mod]] if (!inherits(fit$so, "try-error")) { caption <- paste("Hierarchical", deg_mod, "fit with", errmods[err_mod]) - tex_listing(fit, caption) + summary_listing(fit, caption) } } } @@ -520,58 +528,58 @@ for (deg_mod in deg_mods) { ### Fits with covariate effects ```{r listing-sfo, cache = FALSE, results = "asis", echo = FALSE} -tex_listing(sfo_pH, "Hierarchichal SFO fit with pH influence") +summary_listing(sfo_pH, "Hierarchichal SFO fit with pH influence") ``` \clearpage ```{r, cache = FALSE, results = "asis", echo = FALSE} -tex_listing(fomc_pH, "Hierarchichal FOMC fit with pH influence") +summary_listing(fomc_pH, "Hierarchichal FOMC fit with pH influence") ``` \clearpage ```{r, cache = FALSE, results = "asis", echo = FALSE} -tex_listing(fomc_pH_2, "Refined hierarchichal FOMC fit with pH influence") +summary_listing(fomc_pH_2, "Refined hierarchichal FOMC fit with pH influence") ``` \clearpage ```{r, cache = FALSE, results = "asis", echo = FALSE} -tex_listing(dfop_pH, "Hierarchichal DFOP fit with pH influence") +summary_listing(dfop_pH, "Hierarchichal DFOP fit with pH influence") ``` \clearpage ```{r, cache = FALSE, results = "asis", echo = FALSE} -tex_listing(dfop_pH_2, "Refined hierarchical DFOP fit with pH influence") +summary_listing(dfop_pH_2, "Refined hierarchical DFOP fit with pH influence") ``` \clearpage ```{r, cache = FALSE, results = "asis", echo = FALSE} -tex_listing(dfop_pH_4, "Further refined hierarchical DFOP fit with pH influence") +summary_listing(dfop_pH_4, "Further refined hierarchical DFOP fit with pH influence") ``` \clearpage ```{r, cache = FALSE, results = "asis", echo = FALSE} -tex_listing(sforb_pH, "Hierarchichal SFORB fit with pH influence") +summary_listing(sforb_pH, "Hierarchichal SFORB fit with pH influence") ``` \clearpage ```{r, cache = FALSE, results = "asis", echo = FALSE} -tex_listing(sforb_pH_2, "Refined hierarchichal SFORB fit with pH influence") +summary_listing(sforb_pH_2, "Refined hierarchichal SFORB fit with pH influence") ``` \clearpage ```{r, cache = FALSE, results = "asis", echo = FALSE} -tex_listing(hs_pH, "Hierarchichal HS fit with pH influence") +summary_listing(hs_pH, "Hierarchichal HS fit with pH influence") ``` \clearpage ```{r, cache = FALSE, results = "asis", echo = FALSE} -tex_listing(hs_pH_2, "Refined hierarchichal HS fit with pH influence") +summary_listing(hs_pH_2, "Refined hierarchichal HS fit with pH influence") ``` \clearpage @@ -594,3 +602,4 @@ if(!inherits(try(meminfo <- readLines("/proc/meminfo")), "try-error")) { cat(gsub("model name\t: ", "System memory: ", meminfo[grep("MemTotal", meminfo)[1]])) } ``` + diff --git a/vignettes/web_only/mesotrione_parent_2023_files/figure-html/unnamed-chunk-14-1.png b/vignettes/web_only/mesotrione_parent_2023_files/figure-html/unnamed-chunk-14-1.png Binary files differnew file mode 100644 index 00000000..d5533e87 --- /dev/null +++ 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