diff options
12 files changed, 5465 insertions, 39 deletions
diff --git a/DESCRIPTION b/DESCRIPTION index 57b9fd65..f9cb67be 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -2,7 +2,7 @@ Package: mkin Type: Package Title: Kinetic Evaluation of Chemical Degradation Data Version: 1.2.10 -Date: 2025-05-12 +Date: 2025-05-13 Authors@R: c( person("Johannes", "Ranke", role = c("aut", "cre", "cph"), email = "johannes.ranke@jrwb.de", diff --git a/log/build.log b/log/build.log index c39714cb..b2206986 100644 --- a/log/build.log +++ b/log/build.log @@ -5,7 +5,6 @@ * creating vignettes ... OK * checking for LF line-endings in source and make files and shell scripts * checking for empty or unneeded directories -Removed empty directory ‘mkin/inst/rmarkdown/templates/hierarchical_kinetics_parent’ Removed empty directory ‘mkin/vignettes/web_only’ * building ‘mkin_1.2.10.tar.gz’ diff --git a/log/check.log b/log/check.log index 4ba2d60b..d036724d 100644 --- a/log/check.log +++ b/log/check.log @@ -11,10 +11,7 @@ * checking extension type ... Package * this is package ‘mkin’ version ‘1.2.10’ * package encoding: UTF-8 -* checking CRAN incoming feasibility ... NOTE -Maintainer: ‘Johannes Ranke <johannes.ranke@jrwb.de>’ - -Size of tarball: 5337779 bytes +* checking CRAN incoming feasibility ... OK * checking package namespace information ... OK * checking package dependencies ... OK * checking if this is a source package ... OK @@ -63,20 +60,17 @@ Size of tarball: 5337779 bytes * checking sizes of PDF files under ‘inst/doc’ ... OK * checking installed files from ‘inst/doc’ ... OK * checking files in ‘vignettes’ ... OK -* checking examples ... [12s/12s] OK +* checking examples ... [11s/11s] OK * checking for unstated dependencies in ‘tests’ ... OK * checking tests ... SKIPPED * checking for unstated dependencies in vignettes ... OK * checking package vignettes ... OK -* checking re-building of vignette outputs ... [17s/13s] OK +* checking re-building of vignette outputs ... [13s/11s] OK * checking PDF version of manual ... OK * checking HTML version of manual ... OK * checking for non-standard things in the check directory ... OK * checking for detritus in the temp directory ... OK * DONE -Status: 1 NOTE -See - ‘/home/jranke/git/mkin/mkin.Rcheck/00check.log’ -for details. +Status: OK 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/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_prebuilt.html b/vignettes/web_only/mesotrione_parent_2023_prebuilt.html new file mode 100644 index 00000000..c9cd9cd9 --- /dev/null +++ b/vignettes/web_only/mesotrione_parent_2023_prebuilt.html @@ -0,0 +1,5436 @@ +<!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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BkBAXAcIVHfzgDwr+ogoPDwoBXgoPDwqvrwoPAAMAFwAXBJkEmQAPABsAMQAAADIeAhQOAiIuAjQ+AQQiDgEUHgEyPgE0JgUzMhYVETMyFgcDBiInAyY2OwERNDYB4+rWm1tbm9bq1ptbW5sBv+jFcnLF6MVycv58lgoPiRUKDd8NJg3fDQoViQ8EmVub1urWm1tbm9bq1ps7csXoxXJyxejFDQ8K/u0XEP7tEBABExAXARMKDwAAAAMAFwAXBJkEmQAPABsAMQAAADIeAhQOAiIuAjQ+AQQiDgEUHgEyPgE0JiUTFgYrAREUBisBIiY1ESMiJjcTNjIB4+rWm1tbm9bq1ptbW5sBv+jFcnLF6MVycv7n3w0KFYkPCpYKD4kVCg3fDSYEmVub1urWm1tbm9bq1ps7csXoxXJyxejFAf7tEBf+7QoPDwoBExcQARMQAAAAAAIAAAAABLAEsAAZADkAABMhMhYXExYVERQGBwYjISImJyY1EzQ3Ez4BBSEiBgcDBhY7ATIWHwEeATsBMjY/AT4BOwEyNicDLgHhAu4KEwO6BwgFDBn7tAweAgYBB7kDEwKX/dQKEgJXAgwKlgoTAiYCEwr6ChMCJgITCpYKDAJXAhIEsA4K/XQYGf5XDB4CBggEDRkBqRkYAowKDsgOC/4+Cw4OCpgKDg4KmAoODgsBwgsOAAMAFwAXBJkEmQAPABsAJwAAADIeAhQOAiIuAjQ+AQQiDgEUHgEyPgE0JgUXFhQPAQYmNRE0NgHj6tabW1ub1urWm1tbmwG/6MVycsXoxXJy/ov9ERH9EBgYBJlbm9bq1ptbW5vW6tabO3LF6MVycsXoxV2+DCQMvgwLFQGQFQsAAQAXABcEmQSwACgAAAE3NhYVERQGIyEiJj8BJiMiDgEUHgEyPgE1MxQOAiIuAjQ+AjMyA7OHBwsPCv6WCwQHhW2BdMVycsXoxXKWW5vW6tabW1ub1nXABCSHBwQL/pYKDwsHhUxyxejFcnLFdHXWm1tbm9bq1ptbAAAAAAIAFwABBJkEsAAaADUAAAE3NhYVERQGIyEiJj8BJiMiDgEVIzQ+AjMyEzMUDgIjIicHBiY1ETQ2MyEyFg8BFjMyPgEDs4cHCw8L/pcLBAeGboF0xXKWW5vWdcDrllub1nXAnIYHCw8LAWgKBQiFboJ0xXIEJIcHBAv+lwsPCweGS3LFdHXWm1v9v3XWm1t2hggFCgFoCw8LB4VMcsUAAAAKAGQAAASwBLAADwAfAC8APwBPAF8AbwB/AI8AnwAAEyEyFhURFAYjISImNRE0NgUhIgYVERQWMyEyNjURNCYFMzIWHQEUBisBIiY9ATQ2MyEyFh0BFAYjISImPQE0NgczMhYdARQGKwEiJj0BNDYzITIWHQEUBiMhIiY9ATQ2BzMyFh0BFAYrASImPQE0NjMhMhYdARQGIyEiJj0BNDYHMzIWHQEUBisBIiY9ATQ2MyEyFh0BFAYjISImPQE0Nn0EGgoPDwr75goPDwPA/K4KDw8KA1IKDw/9CDIKDw8KMgoPD9IBwgoPDwr+PgoPD74yCg8PCjIKDw/SAcIKDw8K/j4KDw++MgoPDwoyCg8P0gHCCg8PCv4+Cg8PvjIKDw8KMgoPD9IBwgoPDwr+PgoPDwSwDwr7ggoPDwoEfgoPyA8K/K4KDw8KA1IKD2QPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKDwAAAAACAAAAAARMBLAAGQAjAAABNTQmIyEiBh0BIyIGFREUFjMhMjY1ETQmIyE1NDY7ATIWHQEDhHVT/tRSdmQpOzspA4QpOzsp/ageFMgUHgMgyFN1dlLIOyn9qCk7OykCWCk7lhUdHRWWAAIAZAAABEwETAAJADcAABMzMhYVESMRNDYFMhcWFREUBw4DIyIuAScuAiMiBwYjIicmNRE+ATc2HgMXHgIzMjc2fTIKD2QPA8AEBRADIUNAMRwaPyonKSxHHlVLBwgGBQ4WeDsXKC4TOQQpLUUdZ1AHBEwPCvvNBDMKDzACBhH+WwYGO1AkDQ0ODg8PDzkFAwcPAbY3VwMCAwsGFAEODg5XCAAAAwAAAAAEsASXACEAMQBBAAAAMh4CFREUBisBIiY1ETQuASAOARURFAYrASImNRE0PgEDMzIWFREUBisBIiY1ETQ2ITMyFhURFAYrASImNRE0NgHk6N6jYw8KMgoPjeT++uSNDwoyCg9joyqgCAwMCKAIDAwCYKAIDAwIoAgMDASXY6PedP7UCg8PCgEsf9FyctF//tQKDw8KASx03qP9wAwI/jQIDAwIAcwIDAwI/jQIDAwIAcwIDAAAAAACAAAA0wRHA90AFQA5AAABJTYWFREUBiclJisBIiY1ETQ2OwEyBTc2Mh8BFhQPARcWFA8BBiIvAQcGIi8BJjQ/AScmND8BNjIXAUEBAgkMDAn+/hUZ+goPDwr6GQJYeAcUByIHB3h4BwciBxQHeHgHFAciBwd3dwcHIgcUBwMurAYHCv0SCgcGrA4PCgFeCg+EeAcHIgcUB3h4BxQHIgcHd3cHByIHFAd4eAcUByIICAAAAAACAAAA0wNyA90AFQAvAAABJTYWFREUBiclJisBIiY1ETQ2OwEyJTMWFxYVFAcGDwEiLwEuATc2NTQnJjY/ATYBQQECCQwMCf7+FRn6Cg8PCvoZAdIECgZgWgYLAwkHHQcDBkhOBgMIHQcDLqwGBwr9EgoHBqwODwoBXgoPZAEJgaGafwkBAQYXBxMIZ36EaggUBxYFAAAAAAMAAADEBGID7AAbADEASwAAATMWFxYVFAYHBgcjIi8BLgE3NjU0JicmNj8BNgUlNhYVERQGJyUmKwEiJjURNDY7ATIlMxYXFhUUBwYPASIvAS4BNzY1NCcmNj8BNgPHAwsGh0RABwoDCQcqCAIGbzs3BgIJKgf9ggECCQwMCf7+FRn6Cg8PCvoZAdIECgZgWgYLAwkHHQcDBkhOBgMIHQcD7AEJs9lpy1QJAQYiBhQIlrJarEcJFAYhBb6sBgcK/RIKBwasDg8KAV4KD2QBCYGhmn8JAQEGFwcTCGd+hGoIFQYWBQAAAAANAAAAAASwBLAACQAVABkAHQAhACUALQA7AD8AQwBHAEsATwAAATMVIxUhFSMRIQEjFTMVIREjESM1IQURIREhESERBSM1MwUjNTMBMxEhETM1MwEzFSMVIzUjNTM1IzUhBREhEQcjNTMFIzUzASM1MwUhNSEB9GRk/nBkAfQCvMjI/tTIZAJY+7QBLAGQASz84GRkArxkZP1EyP4MyGQB9MhkyGRkyAEs/UQBLGRkZAOEZGT+DGRkAfT+1AEsA4RkZGQCWP4MZMgBLAEsyGT+1AEs/tQBLMhkZGT+DP4MAfRk/tRkZGRkyGTI/tQBLMhkZGT+1GRkZAAAAAAJAAAAAASwBLAAAwAHAAsADwATABcAGwAfACMAADcjETMTIxEzASMRMxMjETMBIxEzASE1IRcjNTMXIzUzBSM1M2RkZMhkZAGQyMjIZGQBLMjI/OD+1AEsyGRkyGRkASzIyMgD6PwYA+j8GAPo/BgD6PwYA+j7UGRkW1tbW1sAAAIAAAAKBKYEsAANABUAAAkBFhQHAQYiJwETNDYzBCYiBhQWMjYB9AKqCAj+MAgUCP1WAQ8KAUM7Uzs7UzsEsP1WCBQI/jAICAKqAdsKD807O1Q7OwAAAAADAAAACgXSBLAADQAZACEAAAkBFhQHAQYiJwETNDYzIQEWFAcBBiIvAQkBBCYiBhQWMjYB9AKqCAj+MAgUCP1WAQ8KAwYCqggI/jAIFAg4Aaj9RP7TO1M7O1M7BLD9VggUCP4wCAgCqgHbCg/9VggUCP4wCAg4AaoCvM07O1Q7OwAAAAABAGQAAASwBLAAJgAAASEyFREUDwEGJjURNCYjISIPAQYWMyEyFhURFAYjISImNRE0PwE2ASwDOUsSQAgKDwr9RBkSQAgFCgK8Cg8PCvyuCg8SixIEsEv8fBkSQAgFCgO2Cg8SQAgKDwr8SgoPDwoDzxkSixIAAAABAMj//wRMBLAACgAAEyEyFhURCQERNDb6AyAVHf4+/j4dBLAdFfuCAbz+QwR/FR0AAAAAAwAAAAAEsASwABUARQBVAAABISIGBwMGHwEeATMhMjY/ATYnAy4BASMiBg8BDgEjISImLwEuASsBIgYVERQWOwEyNj0BNDYzITIWHQEUFjsBMjY1ETQmASEiBg8BBhYzITI2LwEuAQM2/kQLEAFOBw45BhcKAcIKFwY+DgdTARABVpYKFgROBBYK/doKFgROBBYKlgoPDwqWCg8PCgLuCg8PCpYKDw/+sf4MChMCJgILCgJYCgsCJgITBLAPCv7TGBVsCQwMCWwVGAEtCg/+cA0JnAkNDQmcCQ0PCv12Cg8PCpYKDw8KlgoPDwoCigoP/agOCpgKDg4KmAoOAAAAAAQAAABkBLAETAAdACEAKQAxAAABMzIeAh8BMzIWFREUBiMhIiY1ETQ2OwE+BAEVMzUEIgYUFjI2NCQyFhQGIiY0AfTIOF00JAcGlik7Oyn8GCk7OymWAgknM10ByGT+z76Hh76H/u9WPDxWPARMKTs7FRQ7Kf2oKTs7KQJYKTsIG0U1K/7UZGRGh76Hh74IPFY8PFYAAAAAAgA1AAAEsASvACAAIwAACQEWFx4BHwEVITUyNi8BIQYHBh4CMxUhNTY3PgE/AQEDIQMCqQGBFCgSJQkK/l81LBFS/nk6IgsJKjIe/pM4HAwaBwcBj6wBVKIEr/waMioTFQECQkJXLd6RWSIuHAxCQhgcDCUNDQPu/VoByQAAAAADAGQAAAPwBLAAJwAyADsAAAEeBhUUDgMjITU+ATURNC4EJzUFMh4CFRQOAgclMzI2NTQuAisBETMyNjU0JisBAvEFEzUwOyodN1htbDD+DCk7AQYLFyEaAdc5dWM+Hy0tEP6Pi05pESpTPnbYUFJ9Xp8CgQEHGB0zOlIuQ3VONxpZBzMoAzsYFBwLEAkHRwEpSXNDM1s6KwkxYUopOzQb/K5lUFqBAAABAMgAAANvBLAAGQAAARcOAQcDBhYXFSE1NjcTNjQuBCcmJzUDbQJTQgeECSxK/gy6Dq0DAw8MHxUXDQYEsDkTNSj8uTEoBmFhEFIDQBEaExAJCwYHAwI5AAAAAAL/tQAABRQEsAAlAC8AAAEjNC4FKwERFBYfARUhNTI+AzURIyIOBRUjESEFIxEzByczESM3BRQyCAsZEyYYGcgyGRn+cAQOIhoWyBkYJhMZCwgyA+j7m0tLfX1LS30DhBUgFQ4IAwH8rhYZAQJkZAEFCRUOA1IBAwgOFSAVASzI/OCnpwMgpwACACH/tQSPBLAAJQAvAAABIzQuBSsBERQWHwEVITUyPgM1ESMiDgUVIxEhEwc1IRUnNxUhNQRMMggLGRMmGBnIMhkZ/nAEDiIaFsgZGCYTGQsIMgPoQ6f84KenAyADhBUgFQ4IAwH9dhYZAQJkZAEFCRUOAooBAwgOFSAVASz7gn1LS319S0sABAAAAAAEsARMAA8AHwAvAD8AABMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYyAlgVHR0V/agVHR0VA+gVHR0V/BgVHR0VAyAVHR0V/OAVHR0VBEwVHR0V+7QVHR0ETB0VZBUdHRVkFR3+1B0VZBUdHRVkFR3+1B0VZBUdHRVkFR3+1B0VZBUdHRVkFR0ABAAAAAAEsARMAA8AHwAvAD8AABMhMhYdARQGIyEiJj0BNDYDITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NgMhMhYdARQGIyEiJj0BNDb6ArwVHR0V/UQVHR2zBEwVHR0V+7QVHR3dArwVHR0V/UQVHR2zBEwVHR0V+7QVHR0ETB0VZBUdHRVkFR3+1B0VZBUdHRVkFR3+1B0VZBUdHRVkFR3+1B0VZBUdHRVkFR0ABAAAAAAEsARMAA8AHwAvAD8AAAE1NDYzITIWHQEUBiMhIiYBNTQ2MyEyFh0BFAYjISImEzU0NjMhMhYdARQGIyEiJgE1NDYzITIWHQEUBiMhIiYB9B0VAlgVHR0V/agVHf5wHRUD6BUdHRX8GBUdyB0VAyAVHR0V/OAVHf7UHRUETBUdHRX7tBUdA7ZkFR0dFWQVHR3+6WQVHR0VZBUdHf7pZBUdHRVkFR0d/ulkFR0dFWQVHR0AAAQAAAAABLAETAAPAB8ALwA/AAATITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2MgRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dBEwdFWQVHR0VZBUd/tQdFWQVHR0VZBUd/tQdFWQVHR0VZBUd/tQdFWQVHR0VZBUdAAgAAAAABLAETAAPAB8ALwA/AE8AXwBvAH8AABMzMhYdARQGKwEiJj0BNDYpATIWHQEUBiMhIiY9ATQ2ATMyFh0BFAYrASImPQE0NikBMhYdARQGIyEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2KQEyFh0BFAYjISImPQE0NgEzMhYdARQGKwEiJj0BNDYpATIWHQEUBiMhIiY9ATQ2MmQVHR0VZBUdHQFBAyAVHR0V/OAVHR3+6WQVHR0VZBUdHQFBAyAVHR0V/OAVHR3+6WQVHR0VZBUdHQFBAyAVHR0V/OAVHR3+6WQVHR0VZBUdHQFBAyAVHR0V/OAVHR0ETB0VZBUdHRVkFR0dFWQVHR0VZBUd/tQdFWQVHR0VZBUdHRVkFR0dFWQVHf7UHRVkFR0dFWQVHR0VZBUdHRVkFR3+1B0VZBUdHRVkFR0dFWQVHR0VZBUdAAAG/5wAAASwBEwAAwATACMAKgA6AEoAACEjETsCMhYdARQGKwEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2BQc1IzUzNQUhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2AZBkZJZkFR0dFWQVHR0VAfQVHR0V/gwVHR3++qfIyAHCASwVHR0V/tQVHR0VAlgVHR0V/agVHR0ETB0VZBUdHRVkFR3+1B0VZBUdHRVkFR36fUtkS68dFWQVHR0VZBUd/tQdFWQVHR0VZBUdAAAABgAAAAAFFARMAA8AEwAjACoAOgBKAAATMzIWHQEUBisBIiY9ATQ2ASMRMwEhMhYdARQGIyEiJj0BNDYFMxUjFSc3BSEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYyZBUdHRVkFR0dA2dkZPyuAfQVHR0V/gwVHR0EL8jIp6f75gEsFR0dFf7UFR0dFQJYFR0dFf2oFR0dBEwdFWQVHR0VZBUd+7QETP7UHRVkFR0dFWQVHchkS319rx0VZBUdHRVkFR3+1B0VZBUdHRVkFR0AAAAAAgAAAMgEsAPoAA8AEgAAEyEyFhURFAYjISImNRE0NgkCSwLuHywsH/0SHywsBIT+1AEsA+gsH/12HywsHwKKHyz9RAEsASwAAwAAAAAEsARMAA8AFwAfAAATITIWFREUBiMhIiY1ETQ2FxE3BScBExEEMhYUBiImNCwEWBIaGhL7qBIaGkr3ASpKASXs/NJwTk5wTgRMGhL8DBIaGhID9BIaZP0ftoOcAT7+4AH0dE5vT09vAAAAAAIA2wAFBDYEkQAWAB4AAAEyHgEVFAcOAQ8BLgQnJjU0PgIWIgYUFjI2NAKIdcZzRkWyNjYJIV5YbSk8RHOft7eCgreCBJF4ynVzj23pPz4IIWZomEiEdVijeUjDgriBgbgAAAACABcAFwSZBJkADwAXAAAAMh4CFA4CIi4CND4BAREiDgEUHgEB4+rWm1tbm9bq1ptbW5sBS3TFcnLFBJlbm9bq1ptbW5vW6tab/G8DVnLF6MVyAAACAHUAAwPfBQ8AGgA1AAABHgYVFA4DBy4DNTQ+BQMOAhceBBcWNj8BNiYnLgInJjc2IyYCKhVJT1dOPiUzVnB9P1SbfEokP0xXUEm8FykoAwEbITEcExUWAgYCCQkFEikMGiACCAgFD0iPdXdzdYdFR4BeRiYEBTpjl1lFh3ZzeHaQ/f4hS4I6JUEnIw4IBwwQIgoYBwQQQSlZtgsBAAAAAwAAAAAEywRsAAwAKgAvAAABNz4CHgEXHgEPAiUhMhcHISIGFREUFjMhMjY9ATcRFAYjISImNRE0NgkBBzcBA+hsAgYUFR0OFgoFBmz9BQGQMje7/pApOzspAfQpO8i7o/5wpbm5Azj+lqE3AWMD9XMBAgIEDw4WKgsKc8gNuzsp/gwpOzsptsj+tKW5uaUBkKW5/tf+ljKqAWMAAgAAAAAEkwRMABsANgAAASEGByMiBhURFBYzITI2NTcVFAYjISImNRE0NgUBFhQHAQYmJzUmDgMHPgY3NT4BAV4BaaQ0wyk7OykB9Ck7yLml/nClubkCfwFTCAj+rAcLARo5ZFRYGgouOUlARioTAQsETJI2Oyn+DCk7OymZZ6W5uaUBkKW5G/7TBxUH/s4GBAnLAQINFjAhO2JBNB0UBwHSCgUAAAAAAgAAAAAEnQRMAB0ANQAAASEyFwchIgYVERQWMyEyNj0BNxUUBiMhIiY1ETQ2CQE2Mh8BFhQHAQYiLwEmND8BNjIfARYyAV4BXjxDsv6jKTs7KQH0KTvIuaX+cKW5uQHKAYsHFQdlBwf97QcVB/gHB2UHFQdvCBQETBexOyn+DCk7OylFyNulubmlAZCluf4zAYsHB2UHFQf97AcH+AcVB2UHB28HAAAAAQAKAAoEpgSmADsAAAkBNjIXARYGKwEVMzU0NhcBFhQHAQYmPQEjFTMyFgcBBiInASY2OwE1IxUUBicBJjQ3ATYWHQEzNSMiJgE+AQgIFAgBBAcFCqrICggBCAgI/vgICsiqCgUH/vwIFAj++AgFCq/ICgj++AgIAQgICsivCgUDlgEICAj++AgKyK0KBAf+/AcVB/73BwQKrcgKCP74CAgBCAgKyK0KBAcBCQcVBwEEBwQKrcgKAAEAyAAAA4QETAAZAAATMzIWFREBNhYVERQGJwERFAYrASImNRE0NvpkFR0B0A8VFQ/+MB0VZBUdHQRMHRX+SgHFDggV/BgVCA4Bxf5KFR0dFQPoFR0AAAABAAAAAASwBEwAIwAAEzMyFhURATYWFREBNhYVERQGJwERFAYnAREUBisBIiY1ETQ2MmQVHQHQDxUB0A8VFQ/+MBUP/jAdFWQVHR0ETB0V/koBxQ4IFf5KAcUOCBX8GBUIDgHF/koVCA4Bxf5KFR0dFQPoFR0AAAABAJ0AGQSwBDMAFQAAAREUBicBERQGJwEmNDcBNhYVEQE2FgSwFQ/+MBUP/hQPDwHsDxUB0A8VBBr8GBUIDgHF/koVCA4B4A4qDgHgDggV/koBxQ4IAAAAAQDIABYEMwQ2AAsAABMBFhQHAQYmNRE0NvMDLhIS/NISGRkEMv4OCx4L/g4LDhUD6BUOAAIAyABkA4QD6AAPAB8AABMzMhYVERQGKwEiJjURNDYhMzIWFREUBisBIiY1ETQ2+sgVHR0VyBUdHQGlyBUdHRXIFR0dA+gdFfzgFR0dFQMgFR0dFfzgFR0dFQMgFR0AAAEAyABkBEwD6AAPAAABERQGIyEiJjURNDYzITIWBEwdFfzgFR0dFQMgFR0DtvzgFR0dFQMgFR0dAAAAAAEAAAAZBBMEMwAVAAABETQ2FwEWFAcBBiY1EQEGJjURNDYXAfQVDwHsDw/+FA8V/jAPFRUPAmQBthUIDv4gDioO/iAOCBUBtv47DggVA+gVCA4AAAH//gACBLMETwAjAAABNzIWFRMUBiMHIiY1AwEGJjUDAQYmNQM0NhcBAzQ2FwEDNDYEGGQUHgUdFWQVHQL+MQ4VAv4yDxUFFQ8B0gIVDwHSAh0ETgEdFfwYFR0BHRUBtf46DwkVAbX+OQ4JFAPoFQkP/j4BthQJDv49AbYVHQAAAQEsAAAD6ARMABkAAAEzMhYVERQGKwEiJjURAQYmNRE0NhcBETQ2A1JkFR0dFWQVHf4wDxUVDwHQHQRMHRX8GBUdHRUBtv47DggVA+gVCA7+OwG2FR0AAAIAZADIBLAESAALABsAAAkBFgYjISImNwE2MgEhMhYdARQGIyEiJj0BNDYCrgH1DwkW++4WCQ8B9Q8q/fcD6BUdHRX8GBUdHQQ5/eQPFhYPAhwP/UgdFWQVHR0VZBUdAAEAiP/8A3UESgAFAAAJAgcJAQN1/qABYMX92AIoA4T+n/6fxgIoAiYAAAAAAQE7//wEKARKAAUAAAkBJwkBNwQo/dnGAWH+n8YCI/3ZxgFhAWHGAAIAFwAXBJkEmQAPADMAAAAyHgIUDgIiLgI0PgEFIyIGHQEjIgYdARQWOwEVFBY7ATI2PQEzMjY9ATQmKwE1NCYB4+rWm1tbm9bq1ptbW5sBfWQVHZYVHR0Vlh0VZBUdlhUdHRWWHQSZW5vW6tabW1ub1urWm7odFZYdFWQVHZYVHR0Vlh0VZBUdlhUdAAAAAAIAFwAXBJkEmQAPAB8AAAAyHgIUDgIiLgI0PgEBISIGHQEUFjMhMjY9ATQmAePq1ptbW5vW6tabW1ubAkX+DBUdHRUB9BUdHQSZW5vW6tabW1ub1urWm/5+HRVkFR0dFWQVHQACABcAFwSZBJkADwAzAAAAMh4CFA4CIi4CND4BBCIPAScmIg8BBhQfAQcGFB8BFjI/ARcWMj8BNjQvATc2NC8BAePq1ptbW5vW6tabW1ubAeUZCXh4CRkJjQkJeHgJCY0JGQl4eAkZCY0JCXh4CQmNBJlbm9bq1ptbW5vW6tabrQl4eAkJjQkZCXh4CRkJjQkJeHgJCY0JGQl4eAkZCY0AAgAXABcEmQSZAA8AJAAAADIeAhQOAiIuAjQ+AQEnJiIPAQYUHwEWMjcBNjQvASYiBwHj6tabW1ub1urWm1tbmwEVVAcVCIsHB/IHFQcBdwcHiwcVBwSZW5vW6tabW1ub1urWm/4xVQcHiwgUCPEICAF3BxUIiwcHAAAAAAMAFwAXBJkEmQAPADsASwAAADIeAhQOAiIuAjQ+AQUiDgMVFDsBFjc+ATMyFhUUBgciDgUHBhY7ATI+AzU0LgMTIyIGHQEUFjsBMjY9ATQmAePq1ptbW5vW6tabW1ubAT8dPEIyIRSDHgUGHR8UFw4TARkOGhITDAIBDQ6tBx4oIxgiM0Q8OpYKDw8KlgoPDwSZW5vW6tabW1ub1urWm5ELHi9PMhkFEBQQFRIXFgcIBw4UHCoZCBEQKDhcNi9IKhsJ/eMPCpYKDw8KlgoPAAADABcAFwSZBJkADwAfAD4AAAAyHgIUDgIiLgI0PgEFIyIGHQEUFjsBMjY9ATQmAyMiBh0BFBY7ARUjIgYdARQWMyEyNj0BNCYrARE0JgHj6tabW1ub1urWm1tbmwGWlgoPDwqWCg8PCvoKDw8KS0sKDw8KAV4KDw8KSw8EmVub1urWm1tbm9bq1ptWDwqWCg8PCpYKD/7UDwoyCg/IDwoyCg8PCjIKDwETCg8AAgAAAAAEsASwAC8AXwAAATMyFh0BHgEXMzIWHQEUBisBDgEHFRQGKwEiJj0BLgEnIyImPQE0NjsBPgE3NTQ2ExUUBisBIiY9AQ4BBzMyFh0BFAYrAR4BFzU0NjsBMhYdAT4BNyMiJj0BNDY7AS4BAg2WCg9nlxvCCg8PCsIbl2cPCpYKD2eXG8IKDw8KwhuXZw+5DwqWCg9EZheoCg8PCqgXZkQPCpYKD0RmF6gKDw8KqBdmBLAPCsIbl2cPCpYKD2eXG8IKDw8KwhuXZw8KlgoPZ5cbwgoP/s2oCg8PCqgXZkQPCpYKD0RmF6gKDw8KqBdmRA8KlgoPRGYAAwAXABcEmQSZAA8AGwA/AAAAMh4CFA4CIi4CND4BBCIOARQeATI+ATQmBxcWFA8BFxYUDwEGIi8BBwYiLwEmND8BJyY0PwE2Mh8BNzYyAePq1ptbW5vW6tabW1ubAb/oxXJyxejFcnKaQAcHfHwHB0AHFQd8fAcVB0AHB3x8BwdABxUHfHwHFQSZW5vW6tabW1ub1urWmztyxejFcnLF6MVaQAcVB3x8BxUHQAcHfHwHB0AHFQd8fAcVB0AHB3x8BwAAAAMAFwAXBJkEmQAPABsAMAAAADIeAhQOAiIuAjQ+AQQiDgEUHgEyPgE0JgcXFhQHAQYiLwEmND8BNjIfATc2MgHj6tabW1ub1urWm1tbmwG/6MVycsXoxXJyg2oHB/7ACBQIyggIagcVB0/FBxUEmVub1urWm1tbm9bq1ps7csXoxXJyxejFfWoHFQf+vwcHywcVB2oICE/FBwAAAAMAFwAXBJkEmQAPABgAIQAAADIeAhQOAiIuAjQ+AQUiDgEVFBcBJhcBFjMyPgE1NAHj6tabW1ub1urWm1tbmwFLdMVyQQJLafX9uGhzdMVyBJlbm9bq1ptbW5vW6tabO3LFdHhpAktB0P24PnLFdHMAAAAAAQAXAFMEsAP5ABUAABMBNhYVESEyFh0BFAYjIREUBicBJjQnAgoQFwImFR0dFf3aFxD99hACRgGrDQoV/t0dFcgVHf7dFQoNAasNJgAAAAABAAAAUwSZA/kAFQAACQEWFAcBBiY1ESEiJj0BNDYzIRE0NgJ/AgoQEP32EBf92hUdHRUCJhcD8f5VDSYN/lUNChUBIx0VyBUdASMVCgAAAAEAtwAABF0EmQAVAAAJARYGIyERFAYrASImNREhIiY3ATYyAqoBqw0KFf7dHRXIFR3+3RUKDQGrDSYEif32EBf92hUdHRUCJhcQAgoQAAAAAQC3ABcEXQSwABUAAAEzMhYVESEyFgcBBiInASY2MyERNDYCJsgVHQEjFQoN/lUNJg3+VQ0KFQEjHQSwHRX92hcQ/fYQEAIKEBcCJhUdAAABAAAAtwSZBF0AFwAACQEWFAcBBiY1EQ4DBz4ENxE0NgJ/AgoQEP32EBdesKWBJAUsW4fHfhcEVf5VDSYN/lUNChUBIwIkRHVNabGdcUYHAQYVCgACAAAAAASwBLAAFQArAAABITIWFREUBi8BBwYiLwEmND8BJyY2ASEiJjURNDYfATc2Mh8BFhQPARcWBgNSASwVHRUOXvkIFAhqBwf5Xg4I/iH+1BUdFQ5e+QgUCGoHB/leDggEsB0V/tQVCA5e+QcHaggUCPleDhX7UB0VASwVCA5e+QcHaggUCPleDhUAAAACAEkASQRnBGcAFQArAAABFxYUDwEXFgYjISImNRE0Nh8BNzYyASEyFhURFAYvAQcGIi8BJjQ/AScmNgP2agcH+V4OCBX+1BUdFQ5e+QgU/QwBLBUdFQ5e+QgUCGoHB/leDggEYGoIFAj5Xg4VHRUBLBUIDl75B/3xHRX+1BUIDl75BwdqCBQI+V4OFQAAAAADABcAFwSZBJkADwAfAC8AAAAyHgIUDgIiLgI0PgEFIyIGFxMeATsBMjY3EzYmAyMiBh0BFBY7ATI2PQE0JgHj6tabW1ub1urWm1tbmwGz0BQYBDoEIxQ2FCMEOgQYMZYKDw8KlgoPDwSZW5vW6tabW1ub1urWm7odFP7SFB0dFAEuFB3+DA8KlgoPDwqWCg8AAAAABQAAAAAEsASwAEkAVQBhAGgAbwAAATIWHwEWHwEWFxY3Nj8BNjc2MzIWHwEWHwIeATsBMhYdARQGKwEiBh0BIREjESE1NCYrASImPQE0NjsBMjY1ND8BNjc+BAUHBhY7ATI2LwEuAQUnJgYPAQYWOwEyNhMhIiY1ESkBERQGIyERAQQJFAUFFhbEFQ8dCAsmxBYXERUXMA0NDgQZCAEPCj0KDw8KMgoP/nDI/nAPCjIKDw8KPQsOCRkFDgIGFRYfAp2mBwQK2woKAzMDEP41sQgQAzMDCgrnCwMe/okKDwGQAlgPCv6JBLAEAgIKDXYNCxUJDRZ2DQoHIREQFRh7LAkLDwoyCg8PCq8BLP7UrwoPDwoyCg8GBQQwgBkUAwgWEQ55ogcKDgqVCgSqnQcECo8KDgr8cg8KAXf+iQoPAZAAAAAAAgAAAAwErwSmACsASQAAATYWFQYCDgQuAScmByYOAQ8BBiY1NDc+ATc+AScuAT4BNz4GFyYGBw4BDwEOBAcOARY2Nz4CNz4DNz4BBI0IGgItQmxhi2KORDg9EQQRMxuZGhYqCFUYEyADCQIQOjEnUmFch3vAJQgdHyaiPT44XHRZUhcYDhItIRmKcVtGYWtbKRYEBKYDEwiy/t3IlVgxEQgLCwwBAQIbG5kYEyJAJghKFRE8Hzdff4U/M0o1JSMbL0QJGCYvcSEhHjZST2c1ODwEJygeW0AxJUBff1UyFAABAF0AHgRyBM8ATwAAAQ4BHgQXLgc+ATceAwYHDgQHBicmNzY3PgQuAScWDgMmJy4BJyY+BDcGHgM3PgEuAicmPgMCjScfCic4R0IgBBsKGAoQAwEJEg5gikggBhANPkpTPhZINx8SBgsNJysiCRZOQQoVNU1bYC9QZwICBAUWITsoCAYdJzIYHw8YIiYHDyJJYlkEz0OAZVxEOSQMBzgXOB42IzElKRIqg5Gnl0o3Z0c6IAYWCwYNAwQFIDhHXGF1OWiqb0sdBxUknF0XNTQ8PEUiNWNROBYJDS5AQVUhVZloUSkAAAAAA//cAGoE1ARGABsAPwBRAAAAMh4FFA4FIi4FND4EBSYGFxYVFAYiJjU0NzYmBwYHDgEXHgQyPgM3NiYnJgUHDgEXFhcWNj8BNiYnJicuAQIGpJ17bk85HBw6T257naKde25POhwcOU9uewIPDwYIGbD4sBcIBw5GWg0ECxYyWl+DiINfWjIWCwQMWv3/Iw8JCSU4EC0OIw4DDywtCyIERi1JXGJcSSpJXGJcSS0tSVxiXEkqSVxiXEncDwYTOT58sLB8OzcTBg9FcxAxEiRGXkQxMEVeRSQSMRF1HiQPLxJEMA0EDyIPJQ8sSRIEAAAABP/cAAAE1ASwABQAJwA7AEwAACEjNy4ENTQ+BTMyFzczEzceARUUDgMHNz4BNzYmJyYlBgcOARceBBc3LgE1NDc2JhcHDgEXFhcWNj8CJyYnLgECUJQfW6l2WSwcOU9ue51SPUEglCYvbIknUGqYUi5NdiYLBAw2/VFGWg0ECxIqSExoNSlrjxcIB3wjDwkJJTgQLQ4MFgMsLQsieBRhdHpiGxVJXGJcSS0Pef5StVXWNBpacm5jGq0xiD8SMRFGckVzEDESHjxRQTkNmhKnbjs3EwZwJA8vEkQwDQQPC1YELEkSBAAAAAP/ngAABRIEqwALABgAKAAAJwE2FhcBFgYjISImJSE1NDY7ATIWHQEhAQczMhYPAQ4BKwEiJi8BJjZaAoIUOBQCghUbJfryJRsBCgFZDwqWCg8BWf5DaNAUGAQ6BCMUNhQjBDoEGGQEKh8FIfvgIEdEhEsKDw8KSwLT3x0U/BQdHRT8FB0AAAABAGQAFQSwBLAAKAAAADIWFREBHgEdARQGJyURFh0BFAYvAQcGJj0BNDcRBQYmPQE0NjcBETQCTHxYAWsPFhgR/plkGhPNzRMaZP6ZERgWDwFrBLBYPv6t/rsOMRQpFA0M+f75XRRAFRAJgIAJEBVAFF0BB/kMDRQpFDEOAUUBUz4AAAARAAAAAARMBLAAHQAnACsALwAzADcAOwA/AEMARwBLAE8AUwBXAFsAXwBjAAABMzIWHQEzMhYdASE1NDY7ATU0NjsBMhYdASE1NDYBERQGIyEiJjURFxUzNTMVMzUzFTM1MxUzNTMVMzUFFTM1MxUzNTMVMzUzFTM1MxUzNQUVMzUzFTM1MxUzNTMVMzUzFTM1A1JkFR0yFR37tB0VMh0VZBUdAfQdAQ8dFfwYFR1kZGRkZGRkZGRk/HxkZGRkZGRkZGT8fGRkZGRkZGRkZASwHRUyHRWWlhUdMhUdHRUyMhUd/nD9EhUdHRUC7shkZGRkZGRkZGRkyGRkZGRkZGRkZGTIZGRkZGRkZGRkZAAAAAMAAAAZBXcElwAZACUANwAAARcWFA8BBiY9ASMBISImPQE0NjsBATM1NDYBBycjIiY9ATQ2MyEBFxYUDwEGJj0BIyc3FzM1NDYEb/kPD/kOFZ/9qP7dFR0dFdECWPEV/amNetEVHR0VASMDGvkPD/kOFfG1jXqfFQSN5g4qDuYOCBWW/agdFWQVHQJYlhUI/piNeh0VZBUd/k3mDioO5g4IFZa1jXqWFQgAAAABAAAAAASwBEwAEgAAEyEyFhURFAYjIQERIyImNRE0NmQD6Ck7Oyn9rP7QZCk7OwRMOyn9qCk7/tQBLDspAlgpOwAAAAMAZAAABEwEsAAJABMAPwAAEzMyFh0BITU0NiEzMhYdASE1NDYBERQOBSIuBTURIRUUFRwBHgYyPgYmNTQ9AZbIFR3+1B0C0cgVHf7UHQEPBhgoTGacwJxmTCgYBgEsAwcNFB8nNkI2Jx8TDwUFAQSwHRX6+hUdHRX6+hUd/nD+1ClJalZcPigoPlxWakkpASz6CRIVKyclIRsWEAgJEBccISUnKhURCPoAAAAB//8A1ARMA8IABQAAAQcJAScBBEzG/p/+n8UCJwGbxwFh/p/HAicAAQAAAO4ETQPcAAUAAAkCNwkBBE392v3ZxgFhAWEDFf3ZAifH/p8BYQAAAAAC/1EAZAVfA+gAFAApAAABITIWFREzMhYPAQYiLwEmNjsBESElFxYGKwERIRchIiY1ESMiJj8BNjIBlALqFR2WFQgO5g4qDuYOCBWW/oP+HOYOCBWWAYHX/RIVHZYVCA7mDioD6B0V/dkVDvkPD/kOFQGRuPkOFf5wyB0VAiYVDvkPAAABAAYAAASeBLAAMAAAEzMyFh8BITIWBwMOASMhFyEyFhQGKwEVFAYiJj0BIRUUBiImPQEjIiYvAQMjIiY0NjheERwEJgOAGB4FZAUsIf2HMAIXFR0dFTIdKh3+1B0qHR8SHQYFyTYUHh4EsBYQoiUY/iUVK8gdKh0yFR0dFTIyFR0dFTIUCQoDwR0qHQAAAAACAAAAAASwBEwACwAPAAABFSE1MzQ2MyEyFhUFIREhBLD7UMg7KQEsKTv9RASw+1AD6GRkKTs7Kcj84AACAAAAAAXcBEwADAAQAAATAxEzNDYzITIWFSEVBQEhAcjIyDspASwqOgH0ASz+1PtQASwDIP5wAlgpOzspyGT9RAK8AAEBRQAAA2sErwAbAAABFxYGKwERMzIWDwEGIi8BJjY7AREjIiY/ATYyAnvmDggVlpYVCA7mDioO5g4IFZaWFQgO5g4qBKD5DhX9pxUO+Q8P+Q4VAlkVDvkPAAAAAQABAUQErwNrABsAAAEXFhQPAQYmPQEhFRQGLwEmND8BNhYdASE1NDYDqPkODvkPFf2oFQ/5Dg75DxUCWBUDYOUPKQ/lDwkUl5cUCQ/lDykP5Q8JFZWVFQkAAAAEAAAAAASwBLAACQAZAB0AIQAAAQMuASMhIgYHAwUhIgYdARQWMyEyNj0BNCYFNTMVMzUzFQSRrAUkFP1gFCQFrAQt/BgpOzspA+gpOzv+q2RkZAGQAtwXLSgV/R1kOylkKTs7KWQpO8hkZGRkAAAAA/+cAGQEsARMAAsAIwAxAAAAMhYVERQGIiY1ETQDJSMTFgYjIisBIiYnAj0BNDU0PgE7ASUBFSIuAz0BND4CNwRpKh0dKh1k/V0mLwMRFQUCVBQdBDcCCwzIAqP8GAQOIhoWFR0dCwRMHRX8rhUdHRUDUhX8mcj+7BAIHBUBUQ76AgQQDw36/tT6AQsTKRwyGigUDAEAAAACAEoAAARmBLAALAA1AAABMzIWDwEeARcTFzMyFhQGBw4EIyIuBC8BLgE0NjsBNxM+ATcnJjYDFjMyNw4BIiYCKV4UEgYSU3oPP3YRExwaEggeZGqfTzl0XFU+LwwLEhocExF2Pw96UxIGEyQyNDUxDDdGOASwFRMlE39N/rmtHSkoBwQLHBYSCg4REg4FBAgoKR2tAUdNfhQgExr7vgYGMT09AAEAFAAUBJwEnAAXAAABNwcXBxcHFycHJwcnBzcnNyc3Jxc3FzcDIOBO6rS06k7gLZubLeBO6rS06k7gLZubA7JO4C2bmy3gTuq0tOpO4C2bmy3gTuq0tAADAAAAZASwBLAAIQAtAD0AAAEzMhYdAQchMhYdARQHAw4BKwEiJi8BIyImNRE0PwI+ARcPAREzFzMTNSE3NQEzMhYVERQGKwEiJjURNDYCijIoPBwBSCg8He4QLBf6B0YfHz0tNxSRYA0xG2SWZIjW+v4+Mv12ZBUdHRVkFR0dBLBRLJZ9USxkLR3+qBghMhkZJCcBkCQbxMYcKGTU1f6JZAF3feGv/tQdFf4MFR0dFQH0FR0AAAAAAwAAAAAEsARMACAAMAA8AAABMzIWFxMWHQEUBiMhFh0BFAYrASImLwImNRE0NjsBNgUzMhYVERQGKwEiJjURNDYhByMRHwEzNSchNQMCWPoXLBDuHTwo/rgcPCgyGzENYJEUNy09fP3pZBUdHRVkFR0dAl+IZJZkMjIBwvoETCEY/qgdLWQsUXYHlixRKBzGxBskAZAnJGRkHRX+DBUdHRUB9BUdZP6J1dSv4X0BdwADAAAAZAUOBE8AGwA3AEcAAAElNh8BHgEPASEyFhQGKwEDDgEjISImNRE0NjcXERchEz4BOwEyNiYjISoDLgQnJj8BJwUzMhYVERQGKwEiJjURNDYBZAFrHxZuDQEMVAEuVGxuVGqDBhsP/qoHphwOOmQBJYMGGw/LFRMSFv44AgoCCQMHAwUDAQwRklb9T2QVHR0VZBUdHQNp5hAWcA0mD3lMkE7+rRUoog0CDRElCkj+CVkBUxUoMjIBAgIDBQIZFrdT5B0V/gwVHR0VAfQVHQAAAAP/nABkBLAETwAdADYARgAAAQUeBBURFAYjISImJwMjIiY0NjMhJyY2PwE2BxcWBw4FKgIjIRUzMhYXEyE3ESUFMzIWFREUBisBIiY1ETQ2AdsBbgIIFBANrAf+qg8bBoNqVW1sVAEuVQsBDW4WSpIRDAIDBQMHAwkDCgH+Jd0PHAaCASZq/qoCUGQVHR0VZBUdHQRP5gEFEBEXC/3zDaIoFQFTTpBMeQ8mDXAWrrcWGQIFAwICAWQoFf6tWQH37OQdFf4MFR0dFQH0FR0AAAADAGEAAARMBQ4AGwA3AEcAAAAyFh0BBR4BFREUBiMhIiYvAQMmPwE+AR8BETQXNTQmBhURHAMOBAcGLwEHEyE3ESUuAQMhMhYdARQGIyEiJj0BNDYB3pBOAVMVKKIN/fMRJQoJ5hAWcA0mD3nGMjIBAgIDBQIZFrdT7AH3Wf6tFSiWAfQVHR0V/gwVHR0FDm5UaoMGGw/+qgemHA4OAWsfFm4NAQxUAS5U1ssVExIW/jgCCgIJAwcDBQMBDBGSVv6tZAElgwYb/QsdFWQVHR0VZBUdAAP//QAGA+gFFAAPAC0ASQAAASEyNj0BNCYjISIGHQEUFgEVFAYiJjURBwYmLwEmNxM+BDMhMhYVERQGBwEDFzc2Fx4FHAIVERQWNj0BNDY3JREnAV4B9BUdHRX+DBUdHQEPTpBMeQ8mDXAWEOYBBRARFwsCDQ2iKBX9iexTtxYZAgUDAgIBMjIoFQFTWQRMHRVkFR0dFWQVHfzmalRubFQBLlQMAQ1uFh8BawIIEw8Mpgf+qg8bBgHP/q1WkhEMAQMFAwcDCQIKAv44FhITFcsPGwaDASVkAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEBJSYGHQEhIgYdARQWMyEVFBY3JTY0AeLs1ptbW5vW7NabW1ubAob+7RAX/u0KDw8KARMXEAETEASaW5vW7NabW1ub1uzWm/453w0KFYkPCpYKD4kVCg3fDSYAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgENAQYUFwUWNj0BITI2PQE0JiMhNTQmAeLs1ptbW5vW7NabW1ubASX+7RAQARMQFwETCg8PCv7tFwSaW5vW7NabW1ub1uzWm+jfDSYN3w0KFYkPCpYKD4kVCgAAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEBAyYiBwMGFjsBERQWOwEyNjURMzI2AeLs1ptbW5vW7NabW1ubAkvfDSYN3w0KFYkPCpYKD4kVCgSaW5vW7NabW1ub1uzWm/5AARMQEP7tEBf+7QoPDwoBExcAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEFIyIGFREjIgYXExYyNxM2JisBETQmAeLs1ptbW5vW7NabW1ubAZeWCg+JFQoN3w0mDd8NChWJDwSaW5vW7NabW1ub1uzWm7sPCv7tFxD+7RAQARMQFwETCg8AAAMAGAAYBJgEmAAPAJYApgAAADIeAhQOAiIuAjQ+ASUOAwcGJgcOAQcGFgcOAQcGFgcUFgcyHgEXHgIXHgI3Fg4BFx4CFxQGFBcWNz4CNy4BJy4BJyIOAgcGJyY2NS4BJzYuAQYHBicmNzY3HgIXHgMfAT4CJyY+ATc+AzcmNzIWMjY3LgMnND4CJiceAT8BNi4CJwYHFB4BFS4CJz4BNxYyPgEB5OjVm1xcm9Xo1ZtcXJsBZA8rHDoKDz0PFD8DAxMBAzEFCRwGIgEMFhkHECIvCxU/OR0HFBkDDRQjEwcFaHUeISQDDTAMD0UREi4oLBAzDwQBBikEAQMLGhIXExMLBhAGKBsGBxYVEwYFAgsFAwMNFwQGCQcYFgYQCCARFwkKKiFBCwQCAQMDHzcLDAUdLDgNEiEQEgg/KhADGgMKEgoRBJhcm9Xo1ZtcXJvV6NWbEQwRBwkCAwYFBycPCxcHInIWInYcCUcYChQECA4QBAkuHgQPJioRFRscBAcSCgwCch0kPiAIAQcHEAsBAgsLIxcBMQENCQIPHxkCFBkdHB4QBgEBBwoMGBENBAMMJSAQEhYXDQ4qFBkKEhIDCQsXJxQiBgEOCQwHAQ0DBAUcJAwSCwRnETIoAwEJCwsLJQcKDBEAAAAAAQAAAAIErwSFABYAAAE2FwUXNxYGBw4BJwEGIi8BJjQ3ASY2AvSkjv79kfsGUE08hjv9rA8rD28PDwJYIk8EhVxliuh+WYcrIgsW/awQEG4PKxACV2XJAAYAAABgBLAErAAPABMAIwAnADcAOwAAEyEyFh0BFAYjISImPQE0NgUjFTMFITIWHQEUBiMhIiY9ATQ2BSEVIQUhMhYdARQGIyEiJj0BNDYFIRUhZAPoKTs7KfwYKTs7BBHIyPwYA+gpOzsp/BgpOzsEEf4MAfT8GAPoKTs7KfwYKTs7BBH+1AEsBKw7KWQpOzspZCk7ZGTIOylkKTs7KWQpO2RkyDspZCk7OylkKTtkZAAAAAIAZAAABEwEsAALABEAABMhMhYUBiMhIiY0NgERBxEBIZYDhBUdHRX8fBUdHQI7yP6iA4QEsB0qHR0qHf1E/tTIAfQB9AAAAAMAAABkBLAEsAAXABsAJQAAATMyFh0BITIWFREhNSMVIRE0NjMhNTQ2FxUzNQEVFAYjISImPQEB9MgpOwEsKTv+DMj+DDspASw7KcgB9Dsp/BgpOwSwOylkOyn+cGRkAZApO2QpO2RkZP1EyCk7OynIAAAABAAAAAAEsASwABUAKwBBAFcAABMhMhYPARcWFA8BBiIvAQcGJjURNDYpATIWFREUBi8BBwYiLwEmND8BJyY2ARcWFA8BFxYGIyEiJjURNDYfATc2MgU3NhYVERQGIyEiJj8BJyY0PwE2MhcyASwVCA5exwcHaggUCMdeDhUdAzUBLBUdFQ5exwgUCGoHB8deDgj+L2oHB8deDggV/tQVHRUOXscIFALLXg4VHRX+1BUIDl7HBwdqCBQIBLAVDl7HCBQIagcHx14OCBUBLBUdHRX+1BUIDl7HBwdqCBQIx14OFf0maggUCMdeDhUdFQEsFQgOXscHzl4OCBX+1BUdFQ5exwgUCGoHBwAAAAYAAAAABKgEqAAPABsAIwA7AEMASwAAADIeAhQOAiIuAjQ+AQQiDgEUHgEyPgE0JiQyFhQGIiY0JDIWFAYjIicHFhUUBiImNTQ2PwImNTQEMhYUBiImNCQyFhQGIiY0Advy3Z9fX5/d8t2gXl6gAcbgv29vv+C/b2/+LS0gIC0gAUwtICAWDg83ETNIMykfegEJ/octICAtIAIdLSAgLSAEqF+f3fLdoF5eoN3y3Z9Xb7/gv29vv+C/BiAtISEtICAtIQqRFxwkMzMkIDEFfgEODhekIC0gIC0gIC0gIC0AAf/YAFoEuQS8AFsAACUBNjc2JicmIyIOAwcABw4EFx4BMzI3ATYnLgEjIgcGBwEOASY0NwA3PgEzMhceARcWBgcOBgcGIyImJyY2NwE2NzYzMhceARcWBgcBDgEnLgECIgHVWwgHdl8WGSJBMD8hIP6IDx4eLRMNBQlZN0ozAiQkEAcdEhoYDRr+qw8pHA4BRyIjQS4ODyw9DQ4YIwwod26La1YOOEBGdiIwGkQB/0coW2tQSE5nDxE4Qv4eDyoQEAOtAdZbZWKbEQQUGjIhH/6JDxsdNSg3HT5CMwIkJCcQFBcMGv6uDwEcKQ4BTSIjIQEINykvYyMLKnhuiWZMBxtAOU6+RAH/SBg3ISSGV121Qv4kDwIPDyYAAAACAGQAWASvBEQAGQBEAAABPgIeAhUUDgMHLgQ1ND4CHgEFIg4DIi4DIyIGFRQeAhcWFx4EMj4DNzY3PgQ1NCYCiTB7eHVYNkN5hKg+PqeFeEM4WnZ4eQEjIT8yLSohJyktPyJDbxtBMjMPBw86KzEhDSIzKUAMBAgrKT8dF2oDtURIBS1TdkA5eYB/slVVsn+AeTlAdlMtBUgtJjY1JiY1NiZvTRc4SjQxDwcOPCouGBgwKEALBAkpKkQqMhNPbQACADn/8gR3BL4AFwAuAAAAMh8BFhUUBg8BJi8BNycBFwcvASY0NwEDNxYfARYUBwEGIi8BJjQ/ARYfAQcXAQKru0KNQjgiHR8uEl/3/nvUaRONQkIBGxJpCgmNQkL+5UK6Qo1CQjcdLhJf9wGFBL5CjUJeKmsiHTUuEl/4/nvUahKNQrpCARv+RmkICY1CukL+5UJCjUK7Qjc3LxFf+AGFAAAAAAMAyAAAA+gEsAARABUAHQAAADIeAhURFAYjISImNRE0PgEHESERACIGFBYyNjQCBqqaZDo7Kf2oKTs8Zj4CWP7/Vj09Vj0EsB4uMhX8Ryk7OykDuRUzLar9RAK8/RY9Vj09VgABAAAAAASwBLAAFgAACQEWFAYiLwEBEScBBRMBJyEBJyY0NjIDhgEbDx0qDiT+6dT+zP7oywEz0gEsAQsjDx0qBKH+5g8qHQ8j/vX+1NL+zcsBGAE01AEXJA4qHQAAAAADAScAEQQJBOAAMgBAAEsAAAEVHgQXIy4DJxEXHgQVFAYHFSM1JicuASczHgEXEScuBDU0PgI3NRkBDgMVFB4DFxYXET4ENC4CArwmRVI8LAKfBA0dMydAIjxQNyiym2SWVygZA4sFV0obLkJOMCAyVWg6HSoqFQ4TJhkZCWgWKTEiGBkzNwTgTgUTLD9pQiQuLBsH/s0NBxMtPGQ+i6oMTU8QVyhrVk1iEAFPCA4ZLzlYNkZwSCoGTf4SARIEDh02Jh0rGRQIBgPQ/soCCRYgNEM0JRkAAAABAGQAZgOUBK0ASgAAATIeARUjNC4CIyIGBwYVFB4BFxYXMxUjFgYHBgc+ATM2FjMyNxcOAyMiLgEHDgEPASc+BTc+AScjNTMmJy4CPgE3NgIxVJlemSc8OxolVBQpGxoYBgPxxQgVFS02ImIWIIwiUzUyHzY4HCAXanQmJ1YYFzcEGAcTDBEJMAwk3aYXFQcKAg4tJGEErVCLTig/IhIdFSw5GkowKgkFZDKCHj4yCg8BIh6TExcIASIfBAMaDAuRAxAFDQsRCjePR2QvORQrREFMIVgAAAACABn//wSXBLAADwAfAAABMzIWDwEGIi8BJjY7AREzBRcWBisBESMRIyImPwE2MgGQlhUIDuYOKg7mDggVlsgCF+YOCBWWyJYVCA7mDioBLBYO+g8P+g4WA4QQ+Q4V/HwDhBUO+Q8AAAQAGf//A+gEsAAHABcAGwAlAAABIzUjFSMRIQEzMhYPAQYiLwEmNjsBETMFFTM1EwczFSE1NyM1IQPoZGRkASz9qJYVCA7mDioO5g4IFZbIAZFkY8jI/tTIyAEsArxkZAH0/HwWDvoPD/oOFgOEZMjI/RL6ZJb6ZAAAAAAEABn//wPoBLAADwAZACEAJQAAATMyFg8BBiIvASY2OwERMwUHMxUhNTcjNSERIzUjFSMRIQcVMzUBkJYVCA7mDioO5g4IFZbIAljIyP7UyMgBLGRkZAEsx2QBLBYO+g8P+g4WA4SW+mSW+mT7UGRkAfRkyMgAAAAEABn//wRMBLAADwAVABsAHwAAATMyFg8BBiIvASY2OwERMwEjESM1MxMjNSMRIQcVMzUBkJYVCA7mDioO5g4IFZbIAlhkZMhkZMgBLMdkASwWDvoPD/oOFgOE/gwBkGT7UGQBkGTIyAAAAAAEABn//wRMBLAADwAVABkAHwAAATMyFg8BBiIvASY2OwERMwEjNSMRIQcVMzUDIxEjNTMBkJYVCA7mDioO5g4IFZbIArxkyAEsx2QBZGTIASwWDvoPD/oOFgOE/gxkAZBkyMj7tAGQZAAAAAAFABn//wSwBLAADwATABcAGwAfAAABMzIWDwEGIi8BJjY7AREzBSM1MxMhNSETITUhEyE1IQGQlhUIDuYOKg7mDggVlsgB9MjIZP7UASxk/nABkGT+DAH0ASwWDvoPD/oOFgOEyMj+DMj+DMj+DMgABQAZ//8EsASwAA8AEwAXABsAHwAAATMyFg8BBiIvASY2OwERMwUhNSEDITUhAyE1IQMjNTMBkJYVCA7mDioO5g4IFZbIAyD+DAH0ZP5wAZBk/tQBLGTIyAEsFg76Dw/6DhYDhMjI/gzI/gzI/gzIAAIAAAAABEwETAAPAB8AAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmAV4BkKK8u6P+cKW5uQJn/gwpOzspAfQpOzsETLuj/nClubmlAZClucg7Kf4MKTs7KQH0KTsAAAAAAwAAAAAETARMAA8AHwArAAABITIWFREUBiMhIiY1ETQ2BSEiBhURFBYzITI2NRE0JgUXFhQPAQYmNRE0NgFeAZClubml/nCju7wCZP4MKTs7KQH0KTs7/m/9ERH9EBgYBEy5pf5wpbm5pQGQo7vIOyn+DCk7OykB9Ck7gr4MJAy+DAsVAZAVCwAAAAADAAAAAARMBEwADwAfACsAAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmBSEyFg8BBiIvASY2AV4BkKO7uaX+cKW5uQJn/gwpOzspAfQpOzv+FQGQFQsMvgwkDL4MCwRMvKL+cKW5uaUBkKO7yDsp/gwpOzspAfQpO8gYEP0REf0QGAAAAAMAAAAABEwETAAPAB8AKwAAASEyFhURFAYjISImNRE0NgUhIgYVERQWMyEyNjURNCYFFxYGIyEiJj8BNjIBXgGQpbm5pf5wo7u5Amf+DCk7OykB9Ck7O/77vgwLFf5wFQsMvgwkBEy5pf5wo7u8ogGQpbnIOyn+DCk7OykB9Ck7z/0QGBgQ/REAAAAAAgAAAAAFFARMAB8ANQAAASEyFhURFAYjISImPQE0NjMhMjY1ETQmIyEiJj0BNDYHARYUBwEGJj0BIyImPQE0NjsBNTQ2AiYBkKW5uaX+cBUdHRUBwik7Oyn+PhUdHb8BRBAQ/rwQFvoVHR0V+hYETLml/nCluR0VZBUdOykB9Ck7HRVkFR3p/uQOJg7+5A4KFZYdFcgVHZYVCgAAAQDZAAID1wSeACMAAAEXFgcGAgclMhYHIggBBwYrAScmNz4BPwEhIicmNzYANjc2MwMZCQgDA5gCASwYEQ4B/vf+8wQMDgkJCQUCUCcn/tIXCAoQSwENuwUJEASeCQoRC/5TBwEjEv7K/sUFDwgLFQnlbm4TFRRWAS/TBhAAAAACAAAAAAT+BEwAHwA1AAABITIWHQEUBiMhIgYVERQWMyEyFh0BFAYjISImNRE0NgUBFhQHAQYmPQEjIiY9ATQ2OwE1NDYBXgGQFR0dFf4+KTs7KQHCFR0dFf5wpbm5AvEBRBAQ/rwQFvoVHR0V+hYETB0VZBUdOyn+DCk7HRVkFR25pQGQpbnp/uQOJg7+5A4KFZYdFcgVHZYVCgACAAAAAASwBLAAFQAxAAABITIWFREUBi8BAQYiLwEmNDcBJyY2ASMiBhURFBYzITI2PQE3ERQGIyEiJjURNDYzIQLuAZAVHRUObf7IDykPjQ8PAThtDgj+75wpOzspAfQpO8i7o/5wpbm5pQEsBLAdFf5wFQgObf7IDw+NDykPAThtDhX+1Dsp/gwpOzsplMj+1qW5uaUBkKW5AAADAA4ADgSiBKIADwAbACMAAAAyHgIUDgIiLgI0PgEEIg4BFB4BMj4BNCYEMhYUBiImNAHh7tmdXV2d2e7ZnV1dnQHD5sJxccLmwnFx/nugcnKgcgSiXZ3Z7tmdXV2d2e7ZnUdxwubCcXHC5sJzcqBycqAAAAMAAAAABEwEsAAVAB8AIwAAATMyFhURMzIWBwEGIicBJjY7ARE0NgEhMhYdASE1NDYFFTM1AcLIFR31FAoO/oEOJw3+hQ0JFfod/oUD6BUd+7QdA2dkBLAdFf6iFg/+Vg8PAaoPFgFeFR38fB0V+voVHWQyMgAAAAMAAAAABEwErAAVAB8AIwAACQEWBisBFRQGKwEiJj0BIyImNwE+AQEhMhYdASE1NDYFFTM1AkcBeg4KFfQiFsgUGPoUCw4Bfw4n/fkD6BUd+7QdA2dkBJ7+TQ8g+hQeHRX6IQ8BrxAC/H8dFfr6FR1kMjIAAwAAAAAETARLABQAHgAiAAAJATYyHwEWFAcBBiInASY0PwE2MhcDITIWHQEhNTQ2BRUzNQGMAXEHFQeLBwf98wcVB/7cBweLCBUH1APoFR37tB0DZ2QC0wFxBweLCBUH/fMICAEjCBQIiwcH/dIdFfr6FR1kMjIABAAAAAAETASbAAkAGQAjACcAABM3NjIfAQcnJjQFNzYWFQMOASMFIiY/ASc3ASEyFh0BITU0NgUVMzWHjg4qDk3UTQ4CFtIOFQIBHRX9qxUIDtCa1P49A+gVHfu0HQNnZAP/jg4OTdRMDyqa0g4IFf2pFB4BFQ7Qm9T9Oh0V+voVHWQyMgAAAAQAAAAABEwEsAAPABkAIwAnAAABBR4BFRMUBi8BByc3JyY2EwcGIi8BJjQ/AQEhMhYdASE1NDYFFTM1AV4CVxQeARUO0JvUm9IOCMNMDyoOjg4OTf76A+gVHfu0HQNnZASwAgEdFf2rFQgO0JrUmtIOFf1QTQ4Ojg4qDk3+WB0V+voVHWQyMgACAAT/7ASwBK8ABQAIAAAlCQERIQkBFQEEsP4d/sb+cQSs/TMCq2cBFP5xAacDHPz55gO5AAAAAAIAAABkBEwEsAAVABkAAAERFAYrAREhESMiJjURNDY7AREhETMHIzUzBEwdFZb9RJYVHR0V+gH0ZMhkZAPo/K4VHQGQ/nAdFQPoFB7+1AEsyMgAAAMAAABFBN0EsAAWABoALwAAAQcBJyYiDwEhESMiJjURNDY7AREhETMHIzUzARcWFAcBBiIvASY0PwE2Mh8BATYyBEwC/tVfCRkJlf7IlhUdHRX6AfRkyGRkAbBqBwf+XAgUCMoICGoHFQdPASkHFQPolf7VXwkJk/5wHRUD6BQe/tQBLMjI/c5qBxUH/lsHB8sHFQdqCAhPASkHAAMAAAANBQcEsAAWABoAPgAAAREHJy4BBwEhESMiJjURNDY7AREhETMHIzUzARcWFA8BFxYUDwEGIi8BBwYiLwEmND8BJyY0PwE2Mh8BNzYyBExnhg8lEP72/reWFR0dFfoB9GTIZGQB9kYPD4ODDw9GDykPg4MPKQ9GDw+Dgw8PRg8pD4ODDykD6P7zZ4YPAw7+9v5wHRUD6BQe/tQBLMjI/YxGDykPg4MPKQ9GDw+Dgw8PRg8pD4ODDykPRg8Pg4MPAAADAAAAFQSXBLAAFQAZAC8AAAERISIGHQEhESMiJjURNDY7AREhETMHIzUzEzMyFh0BMzIWDwEGIi8BJjY7ATU0NgRM/qIVHf4MlhUdHRX6AfRkyGRklmQVHZYVCA7mDioO5g4IFZYdA+j+1B0Vlv5wHRUD6BQe/tQBLMjI/agdFfoVDuYODuYOFfoVHQAAAAADAAAAAASXBLAAFQAZAC8AAAERJyYiBwEhESMiJjURNDY7AREhETMHIzUzExcWBisBFRQGKwEiJj0BIyImPwE2MgRMpQ4qDv75/m6WFR0dFfoB9GTIZGTr5g4IFZYdFWQVHZYVCA7mDioD6P5wpQ8P/vf+cB0VA+gUHv7UASzIyP2F5Q8V+hQeHhT6FQ/lDwADAAAAyASwBEwACQATABcAABMhMhYdASE1NDYBERQGIyEiJjURExUhNTIETBUd+1AdBJMdFfu0FR1kAZAETB0VlpYVHf7U/doVHR0VAib+1MjIAAAGAAMAfQStBJcADwAZAB0ALQAxADsAAAEXFhQPAQYmPQEhNSE1NDYBIyImPQE0NjsBFyM1MwE3NhYdASEVIRUUBi8BJjQFIzU7AjIWHQEUBisBA6f4Dg74DhX+cAGQFf0vMhUdHRUyyGRk/oL3DhUBkP5wFQ73DwOBZGRkMxQdHRQzBI3mDioO5g4IFZbIlhUI/oUdFWQVHcjI/cvmDggVlsiWFQgO5g4qecgdFWQVHQAAAAACAGQAAASwBLAAFgBRAAABJTYWFREUBisBIiY1ES4ENRE0NiUyFh8BERQOAg8BERQGKwEiJjURLgQ1ETQ+AzMyFh8BETMRPAE+AjMyFh8BETMRND4DA14BFBklHRXIFR0EDiIaFiX+4RYZAgEVHR0LCh0VyBUdBA4iGhYBBwoTDRQZAgNkBQkVDxcZAQFkAQUJFQQxdBIUH/uuFR0dFQGNAQgbHzUeAWcfRJEZDA3+Phw/MSkLC/5BFR0dFQG/BA8uLkAcAcICBxENCxkMDf6iAV4CBxENCxkMDf6iAV4CBxENCwABAGQAAASwBEwAMwAAARUiDgMVERQWHwEVITUyNjURIREUFjMVITUyPgM1ETQmLwE1IRUiBhURIRE0JiM1BLAEDiIaFjIZGf5wSxn+DBlL/nAEDiIaFjIZGQGQSxkB9BlLBEw4AQUKFA78iBYZAQI4OA0lAYr+diUNODgBBQoUDgN4FhkBAjg4DSX+dgGKJQ04AAAABgAAAAAETARMAAwAHAAgACQAKAA0AAABITIWHQEjBTUnITchBSEyFhURFAYjISImNRE0NhcVITUBBTUlBRUhNQUVFAYjIQchJyE3MwKjAXcVHWn+2cj+cGQBd/4lASwpOzsp/tQpOzspASwCvP5wAZD8GAEsArwdFf6JZP6JZAGQyGkD6B0VlmJiyGTIOyn+DCk7OykB9Ck7ZMjI/veFo4XGyMhm+BUdZGTIAAEAEAAQBJ8EnwAmAAATNzYWHwEWBg8BHgEXNz4BHwEeAQ8BBiIuBicuBTcRohEuDosOBhF3ZvyNdxEzE8ATBxGjAw0uMUxPZWZ4O0p3RjITCwED76IRBhPCFDERdo78ZXYRBA6IDi8RogEECBUgNUNjO0qZfHNVQBAAAAACAAAAAASwBEwAIwBBAAAAMh4EHwEVFAYvAS4BPQEmIAcVFAYPAQYmPQE+BRIyHgIfARUBHgEdARQGIyEiJj0BNDY3ATU0PgIB/LimdWQ/LAkJHRTKFB2N/sKNHRTKFB0DDTE7ZnTKcFImFgEBAW0OFR0V+7QVHRUOAW0CFiYETBUhKCgiCgrIFRgDIgMiFZIYGJIVIgMiAxgVyAQNJyQrIP7kExwcCgoy/tEPMhTUFR0dFdQUMg8BLzIEDSEZAAADAAAAAASwBLAADQAdACcAAAEHIScRMxUzNTMVMzUzASEyFhQGKwEXITcjIiY0NgMhMhYdASE1NDYETMj9qMjIyMjIyPyuArwVHR0VDIn8SokMFR0dswRMFR37UB0CvMjIAfTIyMjI/OAdKh1kZB0qHf7UHRUyMhUdAAAAAwBkAAAEsARMAAkAEwAdAAABIyIGFREhETQmASMiBhURIRE0JgEhETQ2OwEyFhUCvGQpOwEsOwFnZCk7ASw7/Rv+1DspZCk7BEw7KfwYA+gpO/7UOyn9RAK8KTv84AGQKTs7KQAAAAAF/5wAAASwBEwADwATAB8AJQApAAATITIWFREUBiMhIiY1ETQ2FxEhEQUjFTMRITUzNSMRIQURByMRMwcRMxHIArx8sLB8/UR8sLAYA4T+DMjI/tTIyAEsAZBkyMhkZARMsHz+DHywsHwB9HywyP1EArzIZP7UZGQBLGT+1GQB9GT+1AEsAAAABf+cAAAEsARMAA8AEwAfACUAKQAAEyEyFhURFAYjISImNRE0NhcRIREBIzUjFSMRMxUzNTMFEQcjETMHETMRyAK8fLCwfP1EfLCwGAOE/gxkZGRkZGQBkGTIyGRkBEywfP4MfLCwfAH0fLDI/UQCvP2oyMgB9MjIZP7UZAH0ZP7UASwABP+cAAAEsARMAA8AEwAbACMAABMhMhYVERQGIyEiJjURNDYXESERBSMRMxUhESEFIxEzFSERIcgCvHywsHz9RHywsBgDhP4MyMj+1AEsAZDIyP7UASwETLB8/gx8sLB8AfR8sMj9RAK8yP7UZAH0ZP7UZAH0AAAABP+cAAAEsARMAA8AEwAWABkAABMhMhYVERQGIyEiJjURNDYXESERAS0BDQERyAK8fLCwfP1EfLCwGAOE/gz+1AEsAZD+1ARMsHz+DHywsHwB9HywyP1EArz+DJaWlpYBLAAAAAX/nAAABLAETAAPABMAFwAgACkAABMhMhYVERQGIyEiJjURNDYXESERAyERIQcjIgYVFBY7AQERMzI2NTQmI8gCvHywsHz9RHywsBgDhGT9RAK8ZIImOTYpgv4Mgik2OSYETLB8/gx8sLB8AfR8sMj9RAK8/agB9GRWQUFUASz+1FRBQVYAAAAF/5wAAASwBEwADwATAB8AJQApAAATITIWFREUBiMhIiY1ETQ2FxEhEQUjFTMRITUzNSMRIQEjESM1MwMjNTPIArx8sLB8/UR8sLAYA4T+DMjI/tTIyAEsAZBkZMjIZGQETLB8/gx8sLB8AfR8sMj9RAK8yGT+1GRkASz+DAGQZP4MZAAG/5wAAASwBEwADwATABkAHwAjACcAABMhMhYVERQGIyEiJjURNDYXESERBTMRIREzASMRIzUzBRUzNQEjNTPIArx8sLB8/UR8sLAYA4T9RMj+1GQCWGRkyP2oZAEsZGQETLB8/gx8sLB8AfR8sMj9RAK8yP5wAfT+DAGQZMjIyP7UZAAF/5wAAASwBEwADwATABwAIgAmAAATITIWFREUBiMhIiY1ETQ2FxEhEQEHIzU3NSM1IQEjESM1MwMjNTPIArx8sLB8/UR8sLAYA4T+DMdkx8gBLAGQZGTIx2RkBEywfP4MfLCwfAH0fLDI/UQCvP5wyDLIlmT+DAGQZP4MZAAAAAMACQAJBKcEpwAPABsAJQAAADIeAhQOAiIuAjQ+AQQiDgEUHgEyPgE0JgchFSEVISc1NyEB4PDbnl5entvw255eXp4BxeTCcXHC5MJxcWz+1AEs/tRkZAEsBKdentvw255eXp7b8NueTHHC5MJxccLkwtDIZGTIZAAAAAAEAAkACQSnBKcADwAbACcAKwAAADIeAhQOAiIuAjQ+AQQiDgEUHgEyPgE0JgcVBxcVIycjFSMRIQcVMzUB4PDbnl5entvw255eXp4BxeTCcXHC5MJxcWwyZGRklmQBLMjIBKdentvw255eXp7b8NueTHHC5MJxccLkwtBkMmQyZGQBkGRkZAAAAv/y/50EwgRBACAANgAAATIWFzYzMhYUBisBNTQmIyEiBh0BIyImNTQ2NyY1ND4BEzMyFhURMzIWDwEGIi8BJjY7ARE0NgH3brUsLC54qqp4gB0V/tQVHd5QcFZBAmKqepYKD4kVCg3fDSYN3w0KFYkPBEF3YQ6t8a36FR0dFfpzT0VrDhMSZKpi/bMPCv7tFxD0EBD0EBcBEwoPAAAAAAL/8v+cBMMEQQAcADMAAAEyFhc2MzIWFxQGBwEmIgcBIyImNTQ2NyY1ND4BExcWBisBERQGKwEiJjURIyImNzY3NjIB9m62LCsueaoBeFr+hg0lDf6DCU9xVkECYqnm3w0KFYkPCpYKD4kVCg3HGBMZBEF3YQ+teGOkHAFoEBD+k3NPRWsOExNkqWP9kuQQF/7tCg8PCgETFxDMGBMAAAABAGQAAARMBG0AGAAAJTUhATMBMwkBMwEzASEVIyIGHQEhNTQmIwK8AZD+8qr+8qr+1P7Uqv7yqv7yAZAyFR0BkB0VZGQBLAEsAU3+s/7U/tRkHRUyMhUdAAAAAAEAeQAABDcEmwAvAAABMhYXHgEVFAYHFhUUBiMiJxUyFh0BITU0NjM1BiMiJjU0Ny4BNTQ2MzIXNCY1NDYCWF6TGll7OzIJaUo3LRUd/tQdFS03SmkELzlpSgUSAqMEm3FZBoNaPWcfHRpKaR77HRUyMhUd+x5pShIUFVg1SmkCAhAFdKMAAAAGACcAFASJBJwAEQAqAEIASgBiAHsAAAEWEgIHDgEiJicmAhI3PgEyFgUiBw4BBwYWHwEWMzI3Njc2Nz4BLwEmJyYXIgcOAQcGFh8BFjMyNz4BNz4BLwEmJyYWJiIGFBYyNjciBw4BBw4BHwEWFxYzMjc+ATc2Ji8BJhciBwYHBgcOAR8BFhcWMzI3PgE3NiYvASYD8m9PT29T2dzZU29PT29T2dzZ/j0EBHmxIgQNDCQDBBcGG0dGYAsNAwkDCwccBAVQdRgEDA0iBAQWBhJROQwMAwkDCwf5Y4xjY4xjVhYGElE6CwwDCQMLBwgEBVB1GAQNDCIEjRcGG0dGYAsNAwkDCwcIBAR5sSIEDQwkAwPyb/7V/tVvU1dXU28BKwErb1NXVxwBIrF5DBYDCQEWYEZHGwMVDCMNBgSRAhh1UA0WAwkBFTpREgMVCyMMBwT6Y2OMY2MVFTpREQQVCyMMBwQCGHVQDRYDCQEkFmBGRxsDFQwjDQYEASKxeQwWAwkBAAAABQBkAAAD6ASwAAwADwAWABwAIgAAASERIzUhFSERNDYzIQEjNQMzByczNTMDISImNREFFRQGKwECvAEstP6s/oQPCgI/ASzIZKLU1KJktP51Cg8DhA8KwwMg/oTIyALzCg/+1Mj84NTUyP4MDwoBi8jDCg8AAAAABQBkAAAD6ASwAAkADAATABoAIQAAASERCQERNDYzIQEjNRMjFSM1IzcDISImPQEpARUUBisBNQK8ASz+ov3aDwoCPwEsyD6iZKLUqv6dCg8BfAIIDwqbAyD9+AFe/doERwoP/tTI/HzIyNT+ZA8KNzcKD1AAAAAAAwAAAAAEsAP0AAgAGQAfAAABIxUzFyERIzcFMzIeAhUhFSEDETM0PgIBMwMhASEEiqJkZP7UotT9EsgbGiEOASz9qMhkDiEaAnPw8PzgASwB9AMgyGQBLNTUBBErJGT+ogHCJCsRBP5w/nAB9AAAAAMAAAAABEwETAAZADIAOQAAATMyFh0BMzIWHQEUBiMhIiY9ATQ2OwE1NDYFNTIWFREUBiMhIic3ARE0NjMVFBYzITI2AQc1IzUzNQKKZBUdMhUdHRX+1BUdHRUyHQFzKTs7Kf2oARP2/ro7KVg+ASw+WP201MjIBEwdFTIdFWQVHR0VZBUdMhUd+pY7KfzgKTsE9gFGAUQpO5Y+WFj95tSiZKIAAwBkAAAEvARMABkANgA9AAABMzIWHQEzMhYdARQGIyEiJj0BNDY7ATU0NgU1MhYVESMRMxQOAiMhIiY1ETQ2MxUUFjMhMjYBBzUjNTM1AcJkFR0yFR0dFf7UFR0dFTIdAXMpO8jIDiEaG/2oKTs7KVg+ASw+WAGc1MjIBEwdFTIdFWQVHR0VZBUdMhUd+pY7Kf4M/tQkKxEEOykDICk7lj5YWP3m1KJkogAAAAP/ogAABRYE1AALABsAHwAACQEWBiMhIiY3ATYyEyMiBhcTHgE7ATI2NxM2JgMVMzUCkgJ9FyAs+wQsIBcCfRZARNAUGAQ6BCMUNhQjBDoEGODIBK37sCY3NyYEUCf+TB0U/tIUHR0UAS4UHf4MZGQAAAAACQAAAAAETARMAA8AHwAvAD8ATwBfAG8AfwCPAAABMzIWHQEUBisBIiY9ATQ2EzMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2ITMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2ITMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBqfoKDw8K+goPDwr6Cg8PCvoKDw8BmvoKDw8K+goPD/zq+goPDwr6Cg8PAZr6Cg8PCvoKDw8BmvoKDw8K+goPD/zq+goPDwr6Cg8PAZr6Cg8PCvoKDw8BmvoKDw8K+goPDwRMDwqWCg8PCpYKD/7UDwqWCg8PCpYKDw8KlgoPDwqWCg/+1A8KlgoPDwqWCg8PCpYKDw8KlgoPDwqWCg8PCpYKD/7UDwqWCg8PCpYKDw8KlgoPDwqWCg8PCpYKDw8KlgoPAAAAAwAAAAAEsAUUABkAKQAzAAABMxUjFSEyFg8BBgchJi8BJjYzITUjNTM1MwEhMhYUBisBFyE3IyImNDYDITIWHQEhNTQ2ArxkZAFePjEcQiko/PwoKUIcMT4BXmRkyP4+ArwVHR0VDIn8SooNFR0dswRMFR37UB0EsMhkTzeEUzMzU4Q3T2TIZPx8HSodZGQdKh3+1B0VMjIVHQAABAAAAAAEsAUUAAUAGQArADUAAAAyFhUjNAchFhUUByEyFg8BIScmNjMhJjU0AyEyFhQGKwEVBSElNSMiJjQ2AyEyFh0BITU0NgIwUDnCPAE6EgMBSCkHIq/9WrIiCikBSAOvArwVHR0VlgET/EoBE5YVHR2zBEwVHftQHQUUOykpjSUmCBEhFpGRFiERCCb+lR0qHcjIyMgdKh39qB0VMjIVHQAEAAAAAASwBJ0ABwAUACQALgAAADIWFAYiJjQTMzIWFRQXITY1NDYzASEyFhQGKwEXITcjIiY0NgMhMhYdASE1NDYCDZZqapZqty4iKyf+vCcrI/7NArwVHR0VDYr8SokMFR0dswRMFR37UB0EnWqWamqW/us5Okxra0w6Of5yHSodZGQdKh3+1B0VMjIVHQAEAAAAAASwBRQADwAcACwANgAAATIeARUUBiImNTQ3FzcnNhMzMhYVFBchNjU0NjMBITIWFAYrARchNyMiJjQ2AyEyFh0BITU0NgJYL1szb5xvIpBvoyIfLiIrJ/68Jysj/s0CvBUdHRUNivxKiQwVHR2zBEwVHftQHQUUa4s2Tm9vTj5Rj2+jGv4KOTpMa2tMOjn+ch0qHWRkHSod/tQdFTIyFR0AAAADAAAAAASwBRIAEgAiACwAAAEFFSEUHgMXIS4BNTQ+AjcBITIWFAYrARchNyMiJjQ2AyEyFh0BITU0NgJYASz+1CU/P00T/e48PUJtj0r+ogK8FR0dFQ2K/EqJDBUdHbMETBUd+1AdBLChizlmUT9IGVO9VFShdksE/H4dKh1kZB0qHf7UHRUyMhUdAAIAyAAAA+gFFAAPACkAAAAyFh0BHgEdASE1NDY3NTQDITIWFyMVMxUjFTMVIxUzFAYjISImNRE0NgIvUjsuNv5wNi5kAZA2XBqsyMjIyMh1U/5wU3V1BRQ7KU4aXDYyMjZcGk4p/kc2LmRkZGRkU3V1UwGQU3UAAAMAZP//BEwETAAPAC8AMwAAEyEyFhURFAYjISImNRE0NgMhMhYdARQGIyEXFhQGIi8BIQcGIiY0PwEhIiY9ATQ2BQchJ5YDhBUdHRX8fBUdHQQDtgoPDwr+5eANGiUNWP30Vw0mGg3g/t8KDw8BqmQBRGQETB0V/gwVHR0VAfQVHf1EDwoyCg/gDSUbDVhYDRslDeAPCjIKD2RkZAAAAAAEAAAAAASwBEwAGQAjAC0ANwAAEyEyFh0BIzQmKwEiBhUjNCYrASIGFSM1NDYDITIWFREhETQ2ExUUBisBIiY9ASEVFAYrASImPQHIAyBTdWQ7KfopO2Q7KfopO2R1EQPoKTv7UDvxHRVkFR0D6B0VZBUdBEx1U8gpOzspKTs7KchTdf4MOyn+1AEsKTv+DDIVHR0VMjIVHR0VMgADAAEAAASpBKwADQARABsAAAkBFhQPASEBJjQ3ATYyCQMDITIWHQEhNTQ2AeACqh8fg/4f/fsgIAEnH1n+rAFWAS/+q6IDIBUd/HwdBI39VR9ZH4MCBh9ZHwEoH/5u/qoBMAFV/BsdFTIyFR0AAAAAAgCPAAAEIQSwABcALwAAAQMuASMhIgYHAwYWMyEVFBYyNj0BMzI2AyE1NDY7ATU0NjsBETMRMzIWHQEzMhYVBCG9CCcV/nAVJwi9CBMVAnEdKh19FROo/a0dFTIdFTDILxUdMhUdAocB+hMcHBP+BhMclhUdHRWWHP2MMhUdMhUdASz+1B0VMh0VAAAEAAAAAASwBLAADQAQAB8AIgAAASERFAYjIREBNTQ2MyEBIzUBIREUBiMhIiY1ETQ2MyEBIzUDhAEsDwr+if7UDwoBdwEsyP2oASwPCv12Cg8PCgF3ASzIAyD9wQoPAk8BLFQKD/7UyP4M/cEKDw8KA7YKD/7UyAAC/5wAZAUUBEcARgBWAAABMzIeAhcWFxY2NzYnJjc+ARYXFgcOASsBDgEPAQ4BKwEiJj8BBisBIicHDgErASImPwEmLwEuAT0BNDY7ATY3JyY2OwE2BSMiBh0BFBY7ATI2PQE0JgHkw0uOakkMEhEfQwoKGRMKBQ8XDCkCA1Y9Pgc4HCcDIhVkFRgDDDEqwxgpCwMiFWQVGAMaVCyfExwdFXwLLW8QBxXLdAFF+goPDwr6Cg8PBEdBa4pJDgYKISAiJRsQCAYIDCw9P1c3fCbqFB0dFEYOCEAUHR0UnUplNQcmFTIVHVdPXw4TZV8PCjIKDw8KMgoPAAb/nP/mBRQEfgAJACQANAA8AFIAYgAAASU2Fh8BFgYPASUzMhYfASEyFh0BFAYHBQYmJyYjISImPQE0NhcjIgYdARQ7ATI2NTQmJyYEIgYUFjI2NAE3PgEeARceAT8BFxYGDwEGJi8BJjYlBwYfAR4BPwE2Jy4BJy4BAoEBpxMuDiAOAxCL/CtqQ0geZgM3FR0cE/0fFyIJKjr+1D5YWLlQExIqhhALIAsSAYBALS1ALf4PmBIgHhMQHC0aPzANITNQL3wpgigJASlmHyElDR0RPRMFAhQHCxADhPcICxAmDyoNeMgiNtQdFTIVJgeEBBQPQ1g+yD5YrBwVODMQEAtEERzJLUAtLUD+24ITChESEyMgAwWzPUkrRSgJL5cvfRxYGyYrDwkLNRAhFEgJDAQAAAAAAwBkAAAEOQSwAFEAYABvAAABMzIWHQEeARcWDgIPATIeBRUUDgUjFRQGKwEiJj0BIxUUBisBIiY9ASMiJj0BNDY7AREjIiY9ATQ2OwE1NDY7ATIWHQEzNTQ2AxUhMj4CNTc0LgMjARUhMj4CNTc0LgMjAnGWCg9PaAEBIC4uEBEGEjQwOiodFyI2LUAjGg8KlgoPZA8KlgoPrwoPDwpLSwoPDwqvDwqWCg9kD9cBBxwpEwsBAQsTKRz++QFrHCkTCwEBCxMpHASwDwptIW1KLk0tHwYGAw8UKDJOLTtdPCoVCwJLCg8PCktLCg8PCksPCpYKDwJYDwqWCg9LCg8PCktLCg/+1MgVHR0LCgQOIhoW/nDIFR0dCwoEDiIaFgAAAwAEAAIEsASuABcAKQAsAAATITIWFREUBg8BDgEjISImJy4CNRE0NgQiDgQPARchNy4FAyMT1AMMVnokEhIdgVL9xFKCHAgYKHoCIIx9VkcrHQYGnAIwnAIIIClJVSGdwwSuelb+YDO3QkJXd3ZYHFrFMwGgVnqZFyYtLSUMDPPzBQ8sKDEj/sIBBQACAMgAAAOEBRQADwAZAAABMzIWFREUBiMhIiY1ETQ2ARUUBisBIiY9AQHblmesVCn+PilUrAFINhWWFTYFFKxn/gwpVFQpAfRnrPwY4RU2NhXhAAACAMgAAAOEBRQADwAZAAABMxQWMxEUBiMhIiY1ETQ2ARUUBisBIiY9AQHbYLOWVCn+PilUrAFINhWWFTYFFJaz/kIpVFQpAfRnrPwY4RU2NhXhAAACAAAAFAUOBBoAFAAaAAAJASUHFRcVJwc1NzU0Jj4CPwEnCQEFJTUFJQUO/YL+hk5klpZkAQEBBQQvkwKCAVz+ov6iAV4BXgL//uWqPOCWx5SVyJb6BA0GCgYDKEEBG/1ipqaTpaUAAAMAZAH0BLADIAAHAA8AFwAAEjIWFAYiJjQkMhYUBiImNCQyFhQGIiY0vHxYWHxYAeh8WFh8WAHofFhYfFgDIFh8WFh8WFh8WFh8WFh8WFh8AAAAAAMBkAAAArwETAAHAA8AFwAAADIWFAYiJjQSMhYUBiImNBIyFhQGIiY0Aeh8WFh8WFh8WFh8WFh8WFh8WARMWHxYWHz+yFh8WFh8/shYfFhYfAAAAAMAZABkBEwETAAPAB8ALwAAEyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2fQO2Cg8PCvxKCg8PCgO2Cg8PCvxKCg8PCgO2Cg8PCvxKCg8PBEwPCpYKDw8KlgoP/nAPCpYKDw8KlgoP/nAPCpYKDw8KlgoPAAAABAAAAAAEsASwAA8AHwAvADMAAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmBSEyFhURFAYjISImNRE0NhcVITUBXgH0ory7o/4Mpbm5Asv9qCk7OykCWCk7O/2xAfQVHR0V/gwVHR1HAZAEsLuj/gylubmlAfSlucg7Kf2oKTs7KQJYKTtkHRX+1BUdHRUBLBUdZMjIAAAAAAEAZABkBLAETAA7AAATITIWFAYrARUzMhYUBisBFTMyFhQGKwEVMzIWFAYjISImNDY7ATUjIiY0NjsBNSMiJjQ2OwE1IyImNDaWA+gVHR0VMjIVHR0VMjIVHR0VMjIVHR0V/BgVHR0VMjIVHR0VMjIVHR0VMjIVHR0ETB0qHcgdKh3IHSodyB0qHR0qHcgdKh3IHSodyB0qHQAAAAYBLAAFA+gEowAHAA0AEwAZAB8AKgAAAR4BBgcuATYBMhYVIiYlFAYjNDYBMhYVIiYlFAYjNDYDFRQGIiY9ARYzMgKKVz8/V1c/P/75fLB8sAK8sHyw/cB8sHywArywfLCwHSodKAMRBKNDsrJCQrKy/sCwfLB8fLB8sP7UsHywfHywfLD+05AVHR0VjgQAAAH/tQDIBJQDgQBCAAABNzYXAR4BBw4BKwEyFRQOBCsBIhE0NyYiBxYVECsBIi4DNTQzIyImJyY2NwE2HwEeAQ4BLwEHIScHBi4BNgLpRRk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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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(max-width: 768px) { +#TOC { + position: relative; + width: 100%; +} +} + +@media print { +.toc-content { + /* see https://github.com/w3c/csswg-drafts/issues/4434 */ + float: right; +} +} + +.toc-content { + padding-left: 30px; + padding-right: 40px; +} + +div.main-container { + max-width: 1200px; +} + +div.tocify { + width: 20%; + max-width: 260px; + max-height: 85%; +} + +@media (min-width: 768px) and (max-width: 991px) { + div.tocify { + width: 25%; + } +} + +@media (max-width: 767px) { + div.tocify { + width: 100%; + max-width: none; + } +} + +.tocify ul, .tocify li { + line-height: 20px; +} + +.tocify-subheader .tocify-item { + font-size: 0.90em; +} + +.tocify .list-group-item { + border-radius: 0px; +} + + +</style> + + + +</head> + +<body> + + +<div class="container-fluid main-container"> + + +<!-- setup 3col/9col grid for toc_float and main content --> +<div class="row"> +<div class="col-xs-12 col-sm-4 col-md-3"> +<div id="TOC" class="tocify"> +</div> +</div> + +<div class="toc-content col-xs-12 col-sm-8 col-md-9"> + + + + +<div id="header"> + +<div class="btn-group pull-right float-right"> +<button type="button" class="btn 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 13 May 2025 (rebuilt 2025-05-13)</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 1.2.10, 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 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.</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))</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">-6.66</td> +<td align="right">-7.97</td> +<td align="right">-5.35</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,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" 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 + +$distimes + DT50 DT90 +meso 8.89035 29.5331</code></pre> +</div> +<div id="fomc" class="section level2"> +<h2>FOMC</h2> +<pre class="r"><code>fomc_pH <- saem(f_sep_const["FOMC", ], no_random_effect = "meso_0", covariates = pH, + covariate_models = list(log_alpha ~ pH))</code></pre> +<pre class="r"><code>summary(fomc_pH)$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">-2.21</td> +<td align="right">-3.49</td> +<td align="right">-0.92</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 6 767.49 772.83 -377.75 17.762 1 2.503e-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)$confint_trans |> kable(digits = 2)</code></pre> +<table> +<thead> +<tr class="header"> +<th align="left"></th> +<th align="right">est.</th> +<th align="right">lower</th> +<th align="right">upper</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="left">meso_0</td> +<td align="right">93.05</td> +<td align="right">90.98</td> +<td align="right">95.13</td> +</tr> +<tr class="even"> +<td align="left">log_alpha</td> +<td align="right">-2.91</td> +<td align="right">-4.18</td> +<td align="right">-1.63</td> +</tr> +<tr class="odd"> +<td align="left">beta_pH(log_alpha)</td> +<td align="right">0.66</td> +<td align="right">0.44</td> +<td align="right">0.87</td> +</tr> +<tr class="even"> +<td align="left">log_beta</td> +<td align="right">3.95</td> +<td align="right">3.29</td> +<td align="right">4.62</td> +</tr> +<tr class="odd"> +<td align="left">a.1</td> +<td align="right">4.98</td> +<td align="right">4.28</td> +<td align="right">5.68</td> +</tr> +<tr class="even"> +<td align="left">SD.log_beta</td> +<td align="right">0.40</td> +<td align="right">0.26</td> +<td align="right">0.54</td> +</tr> +</tbody> +</table> +<pre class="r"><code>plot(fomc_pH_2)</code></pre> +<p><img src="data:image/png;base64,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" style="display: block; margin: auto;" /></p> +<pre class="r"><code>endpoints(fomc_pH_2)</code></pre> +<pre><code>$covariates + pH +50% 5.75 + +$distimes + DT50 DT90 DT50back +meso 17.30248 82.91343 24.95943</code></pre> +<pre class="r"><code>endpoints(fomc_pH_2, covariates = c(pH = 7))</code></pre> +<pre><code>$covariates + pH +User 7 + +$distimes + DT50 DT90 DT50back +meso 6.986239 27.02927 8.136621</code></pre> +</div> +<div id="dfop" class="section level2"> +<h2>DFOP</h2> +<p>In the DFOP fits without covariate effects, random effects for two +degradation parameters (<code>k2</code> and <code>g</code>) were +identifiable.</p> +<pre class="r"><code>summary(f_saem_2[["DFOP", "const"]])$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))</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">-11.48</td> +<td align="right">-15.32</td> +<td align="right">-7.64</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">3.13</td> +<td align="right">0.47</td> +<td align="right">5.80</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)) +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 8 765.14 772.26 -374.57 +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 8 765.14 772.26 -374.57 4.2153 1 0.04006 * +--- +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 + +$distimes + DT50 DT90 DT50back DT50_k1 DT50_k2 +meso 18.36876 73.51841 22.13125 4.191901 23.98672</code></pre> +<pre class="r"><code>endpoints(dfop_pH_2, covariates = c(pH = 7))</code></pre> +<pre><code>$covariates + pH +User 7 + +$distimes + DT50 DT90 DT50back DT50_k1 DT50_k2 +meso 8.346428 28.34437 8.532507 4.191901 8.753618</code></pre> +</div> +<div id="sforb" class="section level2"> +<h2>SFORB</h2> +<pre class="r"><code>sforb_pH <- saem(f_sep_const["SFORB", ], no_random_effect = c("meso_free_0", "log_k_meso_free_bound"), + covariates = pH, + covariate_models = list(log_k_meso_free ~ pH, log_k_meso_bound_free ~ pH))</code></pre> +<pre class="r"><code>summary(sforb_pH)$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">-5.37</td> +<td align="right">-6.94</td> +<td align="right">-3.81</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">-9.98</td> +<td align="right">-19.22</td> +<td align="right">-0.74</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">91.16</td> +<td align="right">95.48</td> +</tr> +<tr class="even"> +<td align="left">log_k_meso_free</td> +<td align="right">-6.15</td> +<td align="right">-7.43</td> +<td align="right">-4.86</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">-5.20</td> +<td align="right">-2.40</td> +</tr> +<tr class="odd"> +<td align="left">log_k_meso_bound_free</td> +<td align="right">-2.95</td> +<td align="right">-4.26</td> +<td align="right">-1.64</td> +</tr> +<tr class="even"> +<td align="left">a.1</td> +<td align="right">5.08</td> +<td align="right">4.38</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,iVBORw0KGgoAAAANSUhEUgAAAqAAAAHgCAIAAAD17khjAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nOzdd1xTV/sA8JNFQhaEvQQREJw4UMFtceBAcVarpVb7OupstRWrFeu2Wl+3WEUromhftThwVXHgQhSRioAIsnfCSkL2/f1x2/xSCBggELg83w8fP/eenHvOuYfIk3tz731IGIYhAAAAABAL2dADAAAAAID+QYAHAAAACAgCPAAAAEBAEOABAAAAAoIADwAAABAQBHgAAACAgCDAAwAAAAQEAR4AAAAgIAjwAAAAAAFBgAcAAAAICAI8AAAAQEAQ4AEAAAACggAPAAAAEBAEeAAAAICAIMADAAAABAQBHgAAACAgCPAAAAAAAUGABwAAAAgIAjwAAABAQBDgDSYpKcnf39/Ozo7H4/n6+j59+lS/7WdlZdFoNH21Nm/ePJKGy5cvI4SKi4unTJnC4/H69u37+PHjurZVqVR3797V10gaZ9SoUaRa3NzcdNw8Ly+PRCIplcpmHSTBhIaG1pjwDh06IIQyMjL8/Px4PJ6jo+OWLVvwylrfYKAR6pr26OjoPn36sFgsDw+Ps2fP4pW1/i4AYUCAN4z4+Pj+/fv36tXr5s2bDx486NOnz+jRo1NTU/XYhbm5+aFDh+qp0L9///j4eB1bS09P37dvX8o/fH19EUJffvmlkZHRq1evAgMDx44dW1VVVV1dPXv2bDMzMx8fn5SUFHzbU6dOPXnyRPeR5yqUjyRSzZJ3cnm8VKZ7C7X9/vvvhYWFhYWFixcv9vPzw5f1/qGqrVNhypcVT49k7XpW/lCONWnCEULTpk1L0TBnzpxp06bJZLJhw4Y5OTn99ddfp06d2rt374kTJ1AdbzDQCFqnXSAQTJ06NTAwMC0tbdmyZYGBgW/fvq3rdwGIAwOG4OPjs2HDBs2SCRMmLF++XF/tq1QqqVRaf52OHTs+e/ZMxwbt7OwSExM1SzIzMykUSmFhIb7au3fvo0ePbt68+T//+Y9YLA4PDx8yZAiGYUKhsF+/fiKRSPfBZ8nlQ/IKr4jE+GqSVDYor+BBtUT3FuqxevXqgICAhm6Vm5uLEFIoFHoZQ+skVFT9mLJi1/sf//N66i8ZG9e8XSiQleqr8bdv37q6ugqFwocPH3I4HJlMhpevX7/e398f0/YGaz+a7w+yetqjoqI6d+6sLu/UqdOJEyfq+l0AwoAjeAPIzs5++vTpsmXLNAtPnTr1/fff48svXrwYPHgwh8Pp1q1beHg4Qsjf3z8oKAh/VSqVmpqaPnz48NatW15eXiwWy97efuPGjQihoqIiHo93+/ZtZ2fnc+fOqU/R1645fPjwnJyccePGXbx4ESGUnp7u5+dnamo6aNAgvEdNYrE4Pz9/7dq1XC7Xzc3t6NGjCKGkpCRnZ2dra2u8zsCBA9+8eYNhGJlMJpPJNBoNP6G9c+fOZcuWMZlM3efHkUo9ZWW+p7zyiqg6RS5fWMrfyDMdyqA3YIp1Vnuq09PTORyOusLYsWNDQ0Mbt8nOnTsdHR1ZLNawYcPS0tLwV+ufagO6WHC6O6fXapdNVBLta6fvR1iMDc/7VV+NL126dNu2bSwWy8bGZu/evep3pkAgIJPJWt9g7QSJRKpntYnU0z506ND79+/jhQUFBXw+v1u3blp/F3rsHRgc1dADMLwPHz706dOnvLy86U2RyeRDhw4tWrSo/mpJSUnm5uYWFhaahWZmZviCQCAYPXr0unXrLl26FBsb+9lnnzk6Os6aNWvDhg07duxACF2/ft3CwqJv376WlpY7duz49NNPExMTJ0yYEBAQYGtrKxaLz507FxsbK5FI8AbFYvHkyZNr1Lx//z7+IWDAgAFSqXTUqFGzZ88+ffp0UlLSZ599Zm5uPnbsWPXY0tPTyWTymDFjfv3110ePHs2dO7dDhw4FBQXm5ubqOhYWFsnJyTt27Jg/f76trW3nzp1PnjyZm5v74MGDn376qfYkFCiVZ4Wi2uX96fQhDDqDRBpiTN9YVq5EaJgx/bVM9lomU79aY9sRDEYfulH9c66V1qm2t7fXyyb37t3bunXrzZs3HR0dV6xY8d1330VGRn50qptbdOkNgbykRiGNZORvPf1lxTMbuu3WtO9lKumu9B8RhrKrM38v+I1OYvhbTyeTKJrbMinscVZTdOz03r17RUVF06ZNQwi5ubmpL324d+9eeHh4RESE1jfYuHHj9LTTBpaYmKj+zltTQECAt7d37XL8ozz+ao1tP//8827duunYr+a0s9lsNptdUVExcuTIlJSUoKCg/v37I4Rq/y4auHOgVYMAjygUCoPB0EtTdDqdTv/4gaZSqaznc3pkZKSLi8uqVasQQv7+/l9++WVYWNi+ffv+85//JCQk9OrV69y5c3PnzsW//HZ3d1epVDY2Nkwmk8/n29raymSytWvXWltbZ2Vl4Q3SaLTaNTV7vHnzplQq3bBhA41GGz58+OLFi48ePaoZdXr06CGTySgUCkJoxowZDx48OH36tK+vb429UCgUTCZT829EYGDgli1btO4sBSETkpbDBQaJhL9qTCIhEqJgiIqR1DXVr2puS2/sQY/WqV67dq1eNpFKpSqVqrS0tG/fvufPn5fL5UiHqW5uxhRjpopdo5BGoiFEopAoDDJ+ooXEpLBUGEIIY1E4RiQjhEg1tmVSGnBKZtOmTWvWrNF8G1RXVwcHBx8+fPjUqVN4IK/9BiNMgDcyMuLxeLXL6/pbgVfGX62xbYMum6097SwWa8eOHXfv3j1w4ICvry/+8aL27wIQBgR45OjoWFBQ0JI9du3atbS0tKSkxNLSUl146dKlsLCwyMjI3NxcV1dXdbmbm9vVq1dZLJa/v/+FCxdcXV2vX7++e/duKpV67dq1KVOmUCgUNzc3zXNrTk5Omt3VUxOXkZHB5/MdHR3VJV5eXjXq4H98cR4eHhcvXrS2thYIBOpCgUBga2urucmLFy8kEsmQIUOuX7++fPnyqqqqJUuWbNiwAX/VikL5ilsz0vx/ayrVdXH1z2Y8Fxp1XjF/uDFjIstY/Wr92+pO61TXqIP9+ytS3Tfx8/PbtWvX1q1bZ82a5eXltWHDBl9fX12muln58IbX9ZKXiY8SU85xWLjkr8+WdAy6VXKZTWWPt5qqy7b1SEtLi4uLu3btmrokPT194sSJtra2L1++dHd3xwtrv8Ea0Vfr5OHh4eHhoXv9NWvWNHpbtRrTjp+hNDU19fX19fX1TU1NPXPmjLe3t9bfBSAM+MbFAJydnXv27Pnf//5Xs/D48ePGxsYIIXt7+4yMDHV5RkaGg4MDQmjWrFkXLly4fPmyj49Phw4dbty4sXPnzuvXrycmJl68eFF9hh/9+28lQqiemjhbW1svL6+Cf8TGxu7bt0+zwtmzZ2fPnq1eTU9Pd3Fx8fT0xMMVXhgbG+vp6am5VVBQ0Pbt26VS6fz58y9fvpyUlHT16tWHDx9+dH4+KBRflfCDeaYjjRnOVOpxS/PdFZV3qyUf3bCh6ppqpVKpjuuFhYWN2yQjI2PEiBGxsbHFxcUTJ04MDAxUKpUfnWoDmmr7eY4kc0va93JMvidj04vyJ7Pt/9P0ZsPDw/39/VksFr4ql8vHjh3r5+d3+/ZtdUTR+gZretdtAlbrIju9NFtj2vfv3//FF1+oX7W3t5dIJFp/F4BQDHiBX3v25MkTGo32/fffv379Oj4+fsmSJXQ6Hb9IraSkxMTEZN++fQKB4MaNGxwO5/79+xiG4dfW4fewYhgWHh5uY2OTmZkpEon27duH3zqMhxb8Yu/MzEwqlVpXTQzDOnbseOvWLQzDysrKrKysfvnll5KSkpiYGGtr64iICM3RJiUlUSiU7du35+XlXb58mcvlxsXFYRg2ZsyYBQsWVFVVhYeHm5qalpeXqze5cOHC6tWrMQwTi8VWVlYpKSllZWU+Pj5379796OQUKBSxkn9dM/9BLn/9sZsCdKR5Fb3Wqebz+SQS6cSJE9XV1ceOHUMIHT9+XH0Vve6bHD9+3NnZOTExsbS0dPv27VZWViqV6qNTbVgqTJVUmXAie39CxXOlSj+3DPTr1y8kJES9eunSJTMzs5SUlPf/yM/Pr+sNBhqtxrS/evWKyWRevHhRIBDcvn3bxMTk2rVrWn8XBhwz0DsI8Abz/PlzX19fCwsLc3NzX19fzTvWnj175uPjw2azu3TpEh4eri6fP3++qalpdXU1hmEymWz27NkcDqdTp067du1au3Ytj8d78+ZN7QCvtWZJScmaNWvYbPbvv/+OYdirV6+GDh3KZrOdnJz27NlTe7TR0dEDBgxgs9menp5XrlzBC/l8/sSJE01NTb28vDTHL5VK+/fvX1ZWhq9evny5U6dOVlZWwcHB+p7FBqtxm5zWqd6zZ4+lpSWZTO7bt+/EiRM1A7zum8jl8q+//trS0pLJZPr4+Dx58gSv+dGpJpKKigoKhZKQkKAuwW/i0DRhwgSsjjcYaJza045h2JUrV/r3748/6CY0NBSr+3cBCIOE6emMEAAAAABaD/gOHgAAACAgCPAAAAAAAUGABwAAAAgIAjwAAABAQBDgAQD/UiCErLgAEAEEeIMhQD742vSb+r00NC/3u3dIpZ8bPfr166ceP4fDGTlyZHJyMvrYRD1+/Fj3tPF67LeFiRVYhVRVIVW9KZENO1v8MEeCr4rkjZ983fPB11UTNMJHJzM7O/vDhw+aJcnJyVqflg/aOgjwhkGAfPB6Sf1ej5JjuYV7MsWJVVlLkvUV4zdt2oRngn/06JFEIpk+fTrSYaJ0VM98Nmu/euF3vmTw6eIRZ0umRwoQRlpwo3zE2ZIh4cUDThUpVY1sU/d88Fpr6nX/WrvMaUszpy3VS1P1T6ZIJPL19Q0LC1OXKJXKTZs2xcTE6KV30LoY+kb8dooA+eD1kvq9LiUncv/q8kiSIVaKFO/Gx2cuTMKUqia26eXltXfvXvXq9evXEUIffXTXo0ePXF1ddWm/rvlsXL8tbG9c5Zp75bmVir4nC0vFyqHhRS8KpL/EVv7woPzjG+vgo/nga9fUS79txYepSz5MXaL3ZmtP5vz58+l0+saNG/HVrVu32tjYIIQWLVqk996BwcERPEIIicXisjo0qH5FRYUu3REjHzymj9TvWpWezCvclekW1YfubExmUlx+7ynLkejxOF6TXC7XPFX+7Nkzb29vLpfr4+MTFxenWVOhUEyfPn3ChAlSqbT2dNWYz4b2q3X+tQ6mmdLJz+vJvpsl2fmsak43prkxeUEv9v4Xwohk8aLeekjqgz6WD15rTb302yaoj931dRCvVmMyL126lJycrJm9MDAw8M6dO3PmzNFvv6CVgGxy6N27d7169aqurtb66urVq3ft2qV7/V9++eXbb7+tv0di5INftWqVjqnftZJ+qC76bxZCiOtrZtyTgy8jhKTpYtHLStOxFtWJVQghvNziC7uiw7nJ3s9Z3iaajXB9zUwnWenYI0JIKBTi2XGKioq2b9/u5ubWoUOH7Oxs/NXi4uIxY8bs2bNn3LhxoaGh48ePz8/Px19SKBSzZ8+urq7G43ft6dKcz4b2q3X++/btW3swSqWyienkQ14Jc6qUDAopyIcT+lqUU/X/19OZM8k3M6ofB1qFvBJmVSqeF8gGO9Cj3ldr1kEI4dvSyA1I0fvRfPBaaxKG9H2W8O4ThBBrUF8Sg44v10Xy5p3WOqxBfRndOzeo3xqTmZeX980330RHR6sPFRBCDg4ODg4OFhYW6j8XgEggwCP8qeBFRUW1X6JQKDVSr9Zfn0aj2dvbf7RHYuSDHzdunI6p37WicCjMXhyEkJEDQ72MECKzKKKXVTQHumY5hUtVlMpYfbnqajgjB4aO3eHWr1+/fv16fE68vLwuXryoOeCIiIh+/frNnz8fIfTDDz9YWFjgp2Tw6B4VFVVYWEin0y9fvtzQnO7196t1/keMGFF7MI8ePWpiOnlXHtWUTqZRSBQSCV9Wv5QqUJBJSCxHrjwqg0JGSFItx2rUQQjh2+reI9ItH3xdNQmAwmUbdXJECFFMuSQjGr6sxj8aoblauHG/w+GfatTBt21ov5qTiWHYF198sX79+vaTpg8gCPAIITs7O/yq5maqXxsx8sFrvlp/6netqBZGFvP+/8OQ5jJ3lHnml2+4oy3wOvIiWdq4eIu5djarO9bToC727t27YsWKul7NyspSH1ySyeRFixbhy5mZmQMHDrS1td2/f/8PP/zQiJzu9fertUGtg2l6OvmRHRlalwuEyl2xVXO6sY4lCLcMNdn/Qjjehf40T27FpGhWawQd88FrrUkMVCtzzqhB6lXNZa3n5HO/Du544WATO60xmQcOHCCRSLNmzRKJRAqFQiaTicXipn+bBlo5CPAGoM4Hv23bNnXh8ePHTUxMEEL29vZRUVHqcs188GvWrOnSpQueD/769es7d+6Mi4vDw7nmrVxa88FrrYnDk5Q/evQIX83OzlYoFJoVzp49GxUVdebMGXy1drruoKCgo0eP4qnf79y5Y21tPXbs2OHDhw8dOrQR88P9xKzjye4f5vzlHN6D0ZmVNi7ebJZN06P7R9nb26vv8cMwbOPGjYGBgQghBweH06dP3717d9KkSTNnzvzodDWU1gb/+OOP2oPRe9dqR14JfTvSJ3c2nnNVMLIj47e/RAdH8axY0kPxwqN+vKa0rDUfvL+//65du2p81qxRsz1oeiCvS43JjIuLu3PnDofz9wmwyMjIsLCwnJycZuodtBJwkZ0BkEikkJCQ3bt3r1mzJjEx8dWrV0uXLo2OjsbP4k6aNCk1NXX//v1lZWU3b94MDQ394osvEEJjx44tKirasmXLl19+iRAqKyvDA7lYLN6/f396erpIJNLaXT018VPQfn5+aWlpe/bsKS0tffToUf/+/Z8/f67ZQq9evc6fP79jx478/PwrV66cPHly8eLF6lcvXrzYu3dvFxcXlUqlUqmoVCqVSqXRaE2JPdxPzDqe6P7h8zfvRr4w/9y2BaI7Qmj69OkPHz48c+ZMaWnpL7/8sm/fPnNzc4QQg8Egk8mjRo0KCAj4+uuv65kuHa+yrEFrg1oH89HfVKOl8BXPC2RL/izDSNh/bgjkKuyHh+XX06szKxSKxt4mh7tx48bw4cPVq9euXePz+QsWLPjw4UN6enp6enpBQYHWmqApakzm6dOn1VdWT5s2bePGjRDd2wUDXb0PiJAPHmvm1O+V9/glJ/Oa3g6uxu1qauqJwjAsOjq6T58+TCazd+/e9+/fx/59m1xhYaGpqem5c+e0TpfmfDa0X60N1h5MXTX1iC9W9j1ZmF4m10truueD15rCHDRO/ZOJB3jNkpUrV8JtcoQE+eABAP+iUCEqnNoDoO2DAA8AAAAQEHxQBwAAAAgIAjwAAABAQBDgAQAAAAKCAA8AAAAQEAR4g2lb+eDj4uKGDBnC5XL79++vzixpkCTxjaN7bnI1SJLdRA2a84SEhGHDhrHZbA8PD/UjlUAjNCgffD3vf0AEhr5Pr516+fIlk8lcv37969evX79+vXr1ajabnZKSoscuqqqqjh49Wk+Ffv36vXz5Upem8Pu/9+/fX1xcHBERweFwcnNzMQwbOnSoZpL4qqoqsVj82Wef8Xg8b2/v5ORkfPMTJ05s2rSp6XvUFOXl5TWSZK9cuVIqlTo4OCxYsCAnJyc6Otrc3Dw0NBSvr1AoZs6c2a5uDt5ZVrGyVLCyVOBfULyiVLCyVLBBUKZsQoO6z7lcLre3t1+8ePH79+9PnTpFoVB0fGeC2rROu/pVoVDo6uqK3wdfz/sfEAMEeMNoW/ngjx8/3q9fP/XqzJkzf/rpJ6x5ksQrBDdET3mip6a1f2TZW3VspH4fzU3ePpNk98stuCQU3RCJPbPzIoWiGyJxz5z8apVKL43XP+cZGRkIoezsbLywR48ehw8f1ku/bcWXCZO+TJik92brzwdf1/sfEAacokcIoeLi4oyMjIyMDJVK1ZRyHbXFfPCVlZXqVRaLlZqa2kxJ4ikmw0l0B0a360zvMvWPsVcGiWZFMZ/cgFmu20dzk7fbJNkjjRl+TGMqiTSSaezHNNbjX4f659zJycnFxeXw4cPl5eXXr19///79oEGD6m+QSOa9DqixoC/154Ov6/0PCAOSzaC3b996enriD05fsmTJwYMHG1euuzaXD3706NGrVq06c+ZMQEBATExMRETE4MGDm5gkXlWdJs/djpC25yzRrKWpn5FNhiH0d9pQleg1onDkeT9rm04S1fIziulIHSdfl9zkBE6SvaeiMkeh1PqSSIX5FxWTEEmMYeMLi0kISTFsjaBc6598czJ5Pc9Ex04/OudkMjkiImLAgAH4O3zr1q09e/Zs+M61Ummi5DuldabIiyt/rLk673VAP1PtH27IiDzK0r8TU9es8B/NB1/X+x8QBgR4ZGlp2a9fv6KiIgqFopl8s6Hlumtz+eDd3d0jIiK+++67uXPnDhw4cMaMGWKxuIlJ4kk0K6rZxDrmAJPl7CBRzSncIQghlSxfVR5Nc9pKorC11iYbu2ot10r33OSENITB4Cu1B/joaokvnUEjkc4KRb4MBg2hM0LRSAadpu3Xx27Iod5H57ywsDAgIODYsWOffvrp69evZ86c6enpOX78+IbuXetkaWTtZTKwrle9TAYeydqlXl3s9F09TZkbWdbzag065oNvV+//9gYCPLK0tHzy5EnTy3XXFvPBT506derUqSqVikwmL1y4kMvloqYliSdRTSjmdZ6QpDM6SVM+pTkGkyhcRfJUmuMGqpUeTpjrnpucqPrRjep6aUNZxUpTLodMviASf2vKZZFI50XiUUxjRt0fRnWhy5xfu3atU6dO8+fPRwgNGjRo4cKFv/32G2ECvCnNrK6Dclw/00H4yfkTnpH66lTHfPDt7f3f3kCAN4A2lw8+IyNjy5YtISEhRkZGCKE///xz3759zZoknszypJiOVOT9l8zug8nyqFZffHQTXeiem7wdIiG0VlBOI5GkCK0VlFMQkusjUYUucy6TyTQvZ8EwTC6XN73rNkSPoR2nSz74jIwMeP8TnIEv8muvnjx5QqPRvv/++9evX8fHxy9ZsoROp7958wbDsJKSEhMTk3379gkEghs3bnA4HDxVKH5tnYeHx9mzZzEMCw8Pt7GxyczMFIlE+/btw29DLywsRLXSxWqtiWFYx44db926hWFYWVmZlZXVL7/8UlJSEhMTY21tHRERoTlaiURibW39/fffZ2dnf/fdd87OzjKZLCkpiUKhbN++PS8v7/Lly1wuNy4uTr3JhQsXVq9ejWGYWCy2srJKSUkpKyvz8fG5e/eujlOkkhWJ41zEL7spyu/rY8oxDMP69esXEhKiXr106ZKZmVlKSsr7f+Tn56tfbW85NJ9JJDdE4hsicUhFJb7woLq66c3qMueZmZlsNvvgwYOlpaXR0dFWVlb4mxw0Wo1p16ROF1v/+x8QAAR4g2lb+eAfP37co0cPExOT8ePHq29nau4k8fL8g5J3cxu0ST10z02Oa28BvjnoPucPHz709vZmsVhubm6HDh0y3JCJQMd88PW//wEBQLpYAAAAgIDgexcAAACAgCDAAwAAAAQEAR4AAAAgIAjwAAAAAAFBgAcAAAAICAK8YQwZMkSdrZlMJjs6OgYHB+Mv1Z/Hnc/nk0gkZR1PG9W7tWvXfvPNNy3TV7NqUG5ySJKtFw2a87i4uCFDhnC53P79+8fExBh04AAQBDzJ7uNU4uqqGw9l2fkUUw7bd6CRo51eml23bh2eUE4ul9+7dy8wMNDDw2PWrFnm5uaHDh3SSxdtVHp6ekJCApv9/0+el0gkNjY2AwYMaHSb06ZNGzx4sHp1y5YtFhYWMpls2LBh48aNO378eFpa2vTp0+3s7ObMmVO7cN68eU3apbYgb10ab7pN+ZViaXq1kSODN92GfzKvwx531Ngn1eo+5+PHjx89evSmTZsuXbp09+7d8ePHJycn29vb62fHAGivIMB/hKpKlL/mZ2NPD2b/nopiftGmg+aLP2P27d70ljkcjrW1Nb78+eef//zzz48ePZo1axabzV6wYEHT22+7ZDJZdXW1ubl5586dEUJFRUVv3rxRP2WzcUxMTPAnASOEkpOTnz17lpCQEBsbW1FRcfDgQRqN5uDgsHjx4sjISDc3t9qF7SHAk+jk1GFx5l/Ymk60FMVXpg59zptu3ejojhoy5xiGubm54Z93Z86cefny5dDQUHXaAgBA48Ap+o8ov3SL5dPbfOEs1qC+JpNHWwUtFBw73xwd2dra2tnZoX+fon/27Jm3tzeXy/Xx8YmLi1NXvnbtWpcuXTgczowZM6RSKZ/PNzU1/eGHH0xMTOzs7I4cOXL06NGOHTvyeDw8/ybSlmOez+fzeLyQkBAbGxs2m403hTfeo0cPHo83bdq0srKy5tjZ+lGpVEdHx6qqqry8PJlMlpKS0q1bNwaDoa/2689N3m6TZEveiU0mWFbeFTDcWcJH5dxR5rJMvWXLrX/OxWJxZWWlujKLxUpNTdVX1wC0W3AEjxBC5b9fl6ak/71CoVgsmUMx5eLlwuinNBuLok0H8HK6qxOmUArCIuWZObXrN4hIJMLTtsrl8ocPHyYlJe3fv1+zQnFx8ZgxY/bs2TNu3LjQ0NDx48fn5+fjL4WFhcXExBQUFAwcOPDChQt+fn4VFRU5OTnJycnnz5//+uuvZ86cmZiYeObMmaVLly5btkwqldbOMd+tW7fKyspbt269efMmLy9vxIgR586d8/b2njp16oEDByZMmPDHH38sW7ZsxYoVjZ/ZupWVlT1//ly92rFjRzyZVVlZ2Zs3b2QyGZfLTUlJSU5OHj9+fGlpaUVFxa1bt2rXb6iP5iYndpLsnG9TJWlifJlsTO50ugeJTsbLK26Vsjy5JAo5eWCsxWd2DnvdE50e5nyTInlfXbt+g3x0zvH8yGfOnAkICIiJiRQ8yS0AACAASURBVImIiNA8tw8AaCRDPyu3VZDlFIhfp+A/1YmpKoVSXZ6/bk/Z+Sh1uUqhyJqzSvohR2t93dX++6V+Trv6GfJ79+719fXFC5VK5ZEjR0pLS0tLSxFCaWlpePmUKVP27t2LF+bk5GAYhh9zv379GvsnJVdubm5oaKiXl5e692XLls2fP79GUzNmzNi9e/f69esDAgLUNUeMGLFy5cqG7p0u5HJ5noaqqip1+fPnz2/duhUfHx8eHn7hwoXnz5+npKTExMRord9Qw4cPDwsL0ywRi8Xfffcdi8W6cOFC/YUEIEqorIzm4z9Vj8o0y994Pi7am/Wm15O3A2P/6vpI9Lwi0S2mrvoNosucX7hwwdnZmUqlDh06dO7cuTNmzGj0PgIAcHAEjxBCNAcbmoON1nLumCEVV+5w/IZSuGykUglOX2b09DDq6ND0Tnfs2LFmzRqEkFwux/O1jxkzxsfHR10hKytLfaxDJpMXLVqEEMIP+tUZ3/H8rTg8uzx+8hO/QIlK/fv3qzXHPL6M56JVN5WXl6d5ZNyjR4+m76lWVCoV/0qidrmpqSmfz09JSfHz8+NwOLdu3RIIBBwOR2v9BtExHzyBk2QzPbVfysD05JhNtynY+cHsM9sOuzsX7ct+N+Gl2RSbuurrTsc5nzp16tSpU1UqFZlMXrhwIZfb4FNiAIAa2sWXi03BGuLF7Nczb8XmgvV7chdvkOcUmC+aqd8uaDTa5MmTu3btiueCU7O3t8/KysKXMQwLDg5OT//7e4SGfitsb2+fkZGhXlXnmEcIkUj/uozKwcHh3bt36lX1AFqSUCgsLCzkcDgvXry4d+8elUotKioqLy9vestac5P7+fndvn1bHWm0FrYHVffL6E6Msv8Vvhv5omhvFsOVVfW4HDU5F5Uuc56RkTFv3jyZTIa/sf/888+RI0c2tWMA2j04gv840xnjuOOHy3MLKTwTqpV5M/VibW1d44q26dOnBwcHnzlzZsyYMb/99tu+ffu++eabxt0BP2nSpFWrVu3fv//zzz+PjY0NDQ1VH8HXMHv27J49e548eXLChAlRUVG3bt3Czxy0JHd3dxMTE82r6hQKhTpCNMWNGzfmz5+vXr127Rqfz1+wYMGHDx/wEiaT+ezZs9qFtra2Te+9lXM84MHwYClKZJJ0Md3RmGZHl6SImnIVPU6XObe3t79+/fqPP/64dOnSAwcOIIT8/Pya2jEA7R4cweuEzGLS3Ts1X3RHCHl4eJw9e1azxNHR8erVq3v27HFycjp79uzly5dNTU0b17iFhcWtW7fOnTvn6Oj47bffHjlyZNiwYVpruru7X7hwYc+ePa6urpGRkevWrWtcj03BZDI7depkp8HR0dHcvKmTX1lZGR8f7+3trS5JTEwUCAQeHh6u/1iwYIHWwiZ23SYwPFgIIaqlEdvblGZHV5c0hY5zTqfTL126dOPGjR49erx9+/bBgwf1POsJAKAjyAcPAAAAEBAcwQMAAAAEBAEeAAAAICAI8AAAAAABQYAHAAAACAgCvK6SqhLkmMzQowAAAAB0AgFeJ3mS7P9+2HyzOFJfDbbpfPAtli79woULq1ev1ktTDcpNHh0d3adPHxaLVfveRaC7uuZc6/QWFxdPmTKFx+P17dv38ePHBh04AAQBD7rRSUR+6GjLiX+WXhts5suj6edu+DaaD15rPm/9ZlONjY29fPny1q1by8vLKyoqEEJhYWEqlWru3LmNblP33OQBAQFTp04NDg6eMWPGH3/8ERgY2KtXr65duzZ9v1o52ftFmLwIIUxVnUpmuCISFZEZ9M7hiERpXINa51wgEGid3i+//JLD4bx69ery5ctjx47Ny8trYoJgAAAkm/m4l+VP16UsVaoUlwrOHM36RS9tDh48eMeOHZol3bt3//rrrz+6IZ4hRqFQ6GUYHxUUFFQj2czDhw85HI5MJsNX169f7+/vr99OKysrBw4cuGjRol9//fWrr74KDQ3FH6Crr/bfvn3r6uoqFAq17ktUVFTnzp3VlTt16nTixAl9dd2aieOcFPxr0sx1omdmkveLFeX3RLHWmFKsl8bVc651ejMzMykUSmFhIV7Yu3fvo0eP6qVfANozOEX/EQpM8b+CsFl288kkyniraWmi5Heit83RUVvJB98C6dI5HM7t27eTk5NPnz6dmpq6fv3627dvq/PuNF39ucmHDh16//59vKSgoIDP53fr1k1fXbdyZM4ApSDKqNNBVdltMqu3Hr/CU8+51ulNSkpydna2trbGywcOHFgjLwMAoDEM/QmjVTif/9uWd9+Xyfi1l1e9nb8n/Sd1+d2SaxtTv62rvu4GDx78448/4ulfCwoKzp8/b2dnl5ycjGmkiy0qKuJyucePH8/Pz9+8ebOlpaVcLseP4KdMmVJSUpKYmMhms8PDw/HCOXPm5OXl7dmzByE0c+bMioqKw4cPk8lkoVCIx/Ldu3cXFRVduXKFzWY/ePCgtLSUTCYHBASUlJQkJCTweLzffvstJSXFyMjo6NGjeXl5Bw8eJJFI9aSLjY6O5nK5UVFRjZjz+Ph4Ly+vvXv31lj+4YcfmExmhw4devfujWfDs7Gx6du3b5cuXaysrGrXb6jo6Ohu3bqpVKr696W8vNzLy4vNZm/ZsqURvbRa0rSF1W/GYsrq2suiZxayvAOS5KnStIXiF26ynG2iWFvpu6+01m+Q2nNeY3qPHz8+YMAA9av4Cfym7ywA7RwEeAzDML6sJFP8XoWpaixnidOX/PVZgSRXXa7ElNvT1t4sjtRaX3dtPR9809OlSySSFy9e4GdlNZdzc3NPnz59+/btDRs2sFgsDoczduzY58+fP3ny5MqVK7XrN5SO+eDlcvmdO3fWrl1rbW399OnTxu1jK6SqzlCKkrQui587iF/2VFY+VVVnKAS3xXEuolgbleit1voNUnvOa0zvsWPHvL291a8GBwdPmTKlMbsHANAAF9khhJAZzcKMZlF7+W7pdTOaRXxFrLpmUtVrMyPLm8WRw83HkBCpRv0Gabv54PWSLp1Op/ft27f2sr29/Zw5c06cOHHs2LE1a9ZkZGR8+PDh119/PXr0qPq7AM36DaJLbnI8L62pqamvr6+vr29qauqZM2c006W0aSSGM6mOZYSUJGNnMscbIURhOFN4YxSlv5MYHUlk49r1dVdjzrVO7+jRowUCgXoTgUDQHtL3AdDc4Dv4+nRidu7J7StWCjV/zGjmQ8x8VZhKX720rXzwLZAuPSYmJjg4+MGDB7a2tlQq9erVq2/fvv3vf//b9JZ1yU2+f//+L774Qr2Jvb29RCJpetetH6aUqaqeiJ/b4z+K0osIUzS92RpzrnV6PT09MzIy8A+vCKHY2FhPT8+mdw1Ae2foUwjtVO2r6EeMGLFz505M4xR9VlYWi8UKDw8vKSnZtWuXiYlJWVlZjavoZ86cqT5FL5FIMAwTCoUIodLSUrwCQig3N7ekpMTExGTfvn0CgeDGjRscDuf+/fuaW2EYNmfOnN27d+PfwZ84caK4uPjkyZMMBqPGKfpLly6ZmZmlpKS8/0d+fr5+J6e6urqkpATDsOPHj3/11VcYhonFYvUeNUW/fv1CQkLUq1r35dWrV0wm8+LFiwKB4Pbt2yYmJteuXWt6162fsvy+vOT8v34EN5rebI05r2t6x4wZs2DBgqqqqvDwcFNT0/Ly8qZ3DUA7BwHeMGoH+MWLF3t6emIaAR7DMPyRIEwms3fv3vfv38dq3SanY4DHMOzZs2c+Pj5sNrtLly7h4eHqpmoEeAzDrly50r17dy6XO2nSpM2bN9cI8Bs3bqzxGXHChAnNNEs5OTkvX77UV2sVFRUUCiUhIUFdUte+XLlypX///viTWEJDQ/U1gHao9pxjdUwvn8+fOHGiqampl5fXs2fPDDFYAIgG8sEDAAAABATfwQMAAAAEBAEeAAAAICAI8AAAAAABQYAHAAAACAgCvK7kKrgascWpVCqZzNCDAACANgkCvK4+jeTnVOotC3vv3r21pspGdaeKHzVqFKkW9aPukpKS/P397ezseDyer6/v06dP9TVUg1GpCn7cm7dog6pK1PTGGpQPXi07O/vDhw9N7719alA+eFxycnKNEgBAo0GA15VYgUkU+jyI37ZtW6GG+Ph49Uvr1q3DC7Ozs7du3bpp06aIiIjff/8dL1y8eLGfnx++jAfy+Pj4/v379+rV6+bNmw8ePOjTp8/o0aNTU1P1ONqWplKV7A9TVUsoptzCnw40PcZPmzYtRcOcOXOmTZuG54N3cnL666+/Tp06tXfv3hMnTqg3EYlEvr6+YWFhTey63dI653g++MDAwLS0tGXLlgUGBr59+3d6RqVSuWnTppiYGMMOGwDCgGfRGwyXy1Xnx6yBw+GoX/r8889//vnnR48ezZo1Cy9hsVgMBkNz26VLl65evfqnn37CV3ft2pWSknL48OF9+/Y15x40G5Wq9OBplVDE8RsiT88hm3IKf9pvHbyMwmE3ukkTExMTExN8OTk5+dmzZwkJCbGxsRUVFQcPHqTRaA4ODosXL46MjJw3bx5ebcWKFTk5OXrYnbbj5MmTNUq+/PLLRremdc4fPHhgZWW1cuVKhNCSJUv27NkTGxvbtWvXbdu2HThwoLCwEM+5AABoOgjwbYA6VbxW2dnZT58+vXLlimbhqVOnqqurm39ozUClKj14WlkptFqzQPgwDiHEm+WPECr66UATY7xa/fng8eVLly4lJyePHTu26d0B9LF88AihwMDASZMm7dixw5CjBIBYIMAjhFBwTMWTPNlpfzMbFiU4puJBjhQhhCFUIlKZM8kUEiqTqIRyLPCagE5FZRKVRIF4DBKDSqKRSeqt1C3o2OnSpUuXLl2qXg0ICPjjjz/wZZFIhCfekMvlDx8+TEpK2r9/f13tJCUlmZubW1j8K6OdmZlZQyehhUnfZ5XsPcn5ZKDJlNH4MsIQQkhZXonJ5VRLs9wlPynLK0lkcvWbd5hcoSyryP3qB4qFGUKI4/v/W+Et6N7vvXv3ioqKpk2bhhByc3NTX8Rw79698PDwiIgIhFBeXt4333wTHR0dFBSk9x03rLt371ZVVfn7+1MolLt37+LJhRFCVCrV39+/dv0LFy5oVsC3UregY6eac85ms9lsdkVFxciRI1NSUoKCgvr3748QcnBwcHBwsLCwaCepfQBoARDgEULom/6cr2QYHpu/6c/5yvPvw8TSaqWFMQUhVClTfX27bMtg046mlEqZSiTDrJgUChkhEvr/rf5pQUfbtm1Tnw1GCNHpdPXy5s2bN2/erF4NDg728PCoqx2lUlkjI1ybQO/UwebHpWQuW72Ml8sLS0uPhLO8e3FGDqq680RRWMILnCxNy+Qf+503Z6JxTw+EkOZW+LLuNm3atGbNGs0Zq66uDg4OPnz48KlTp8aNG4dh2BdffLF+/XoXFxe97W2rMWjQIKVSicfmQYMGyeVyvJxMJmsN2GPGjKlRQbMFHdWecxaLtWPHjrt37x44cMDX15cw2XgBaFUgwCOEkCmdbErXstyBq/4rRjGmkmzY5A5cCkJa/rRpbqWjer6D/2iqeE1du3YtLS0tKSnB88HjLl26FBYWFhkZ2bAxtSQymWptUXuZam1hu2VV4cZ9ZDaLamelEooVggrByYtW333F6N65zhZ0o0s++AMHDpBIpFmzZolEIoVCIZPJxGIxk8lswq62IgwGQ3NZc1UrDodTTwu60CUfPAR4AJoDXEXfqtWVKl6Ts7Nzz549a6RLP378uLGxcfMPsFlQLXg2G1dU3X1S/SpJWV5Vsvu45Tdf1ozujaJLPvi4uLg7d+5wOBw2mx0ZGblt27ZmSnvfTuiSD95AQwOA4OAIvgH0eyJcKBTiX7Sr8Xg89UVemqytrdXflWobFSkkJGTYsGFKpXL27NlKpTI0NDQ6Ovrly5f6HG7LwmN8/vc7sWqJ9Y9LGV1d9dLsjRs35s+fr169du0an89fsGCB+mZ3JpN5+vTp06dP46vTp0/v3r07/hyC9qAp18zXpcacT5w4cefOnZcuXRoxYsSLFy/CwsLOnDmj904BAAiO4HW3bahJRxN9fh4KCgqy+Le6bsqq/TyQGnx8fB4/fvzy5UtfX99Ro0alpKQ8ePAAvzi57aJa8GyCl1l+O09f0b2ysjI+Pl7zbHBiYqJAIPDw8HD9x4IFC/TSF8DVnvNevXqdO3du586dHTp0WL58+Z49e8aPH2/AEQJAYJAPHgAAACAgOIIHAAAACAgCPAAAAEBAEOABAAAAAoIADwAAABAQBHgAAACAgCDAAwAAAAQEAR4AAAAgIAjwAAAAAAFBgAcAAAAICAI8AAAAQEAQ4AEAAAACggAPAAAAEBAEeAAAAICAIMADAAAABAQBHgAAACAgCPAAAAAAAUGABwAAAAgIAjwAAABAQBDgAQAAAAKCAA8AAAAQEAR4AAAAgIAgwAMAAAAEBAEeAAAAICAI8AAAAAABQYAHAAAACAgCPAAAAEBAEOABAAAAAoIADwAAABAQBHgAAACAgCDAAwAAAAQEAR4AAAAgIAjwAAAAAAFBgAcAAAAICAI8AKCFiMXiDh06GHoUALQXVEMPAABATB8+fAgLC9Mskclkubm5P/30E0IoODjYQOMCoL1o8wFeqVReuXJFoVAYeiAANAmbzR47dqyhR6FPFAolPDw8Jydn7NixVCoVIYT/P33z5o0umyclJUkkkhqFKpXq6dOntra2eh8tAC2pZf6/kzAMa+4+mlVycrK3t/eYMWMMPRAAmuTSpUtCoZDBYBh6IPokEolWrFiRnJwcFhbm4uIiEonYbLYuf3OEQuHIkSNrf3CvqKhIT0+fNm1a84wXgBbSMv/f23yAf/v27fTp05OSkgw9EACahMlk8vl8Y2NjQw9E/y5evPjtt9+uX79+1qxZHA6nKX9zrl69On369NpH9gC0LS3z/x0usgMANK+pU6c+efLk/PnzkydPNvRYAGhH2vx38ACA1s/e3v7PP/88cOCAjY2NoccCQHvR0gG+qqoqIiLizZs3RUVFCoXC1tbW09Pz008/5XK5eu9LJVIqRUqalZHeWwYANBSJRFq+fLmhRwFAO9Kip+gfP35sb28fEhKiUqm6dOnSo0cPMpl8/PhxJyen58+f67cvZZUibeKrFO9YSapIvy0DAAAArV+LHsEvX758y5YttT/Fnz59eunSpXqM8coqxftJCcbd2FaLO6RNeOV6uZdxV7a+GgcAAABavxY9gk9PT580aVLt8oCAgPfv3+urF3V0d9zvwZtm7bDT7f2khOq3Qn21DwAAALR+LRrghw8fHhQUVFRUpFnI5/NXrVo1dOhQvXShGd0RCSGEeFMgxgMAAGh3WjTA//rrr5WVlfb29m5ubt7e3j4+Pu7u7jY2NtnZ2SdOnNBLFyWHc5Rl8g573REJFYmUf5XIEUK8Kdbc0ea5a9L00gUAAADQ+rXod/BWVlZRUVFZWVlJSUkFBQUYhllbW3t6ejo6Ouqti2WOVQ/LspcmOx3qEpMjjSuU7xxuwg/Lr7zNd7vWW1+9AAAAAK2cAW6Tu3XrluZtcoWFhXq8TY7MpLj8zzN9+uusJcmkZU4IIX5Yfv7mDLdrvRnuLL10AQAAALR+BLxNjsykuPzuKcuUWGzN6HSzpGDbh843+0B0BwAA0K60mdvk5HL54cOHaz+DOj8/v6SkRL1aJlHlVSkRQiikq2r2X11jyrCLvdK4NFQiN2GQO3Ao+tkTAAAAoHVr0QBfz21yK1asqH9bpVJZUlJSO7tUXl5eWVmZevX2B8nZt2J8ueorR0yo4GbLULYMIdTbmrZxsEmTdgAAAABoI1o0m1xAQICxsfHevXutra3VhXw+f+3atcXFxZGRkY1os67sUtKUjHNP8l8p2Ft7YCzvXohEatLQAWhmBM4mp0eQTQ4QAwGzybXAbXK4yit3S/afIjMYZJZx5bV7xbtDURvPigsAAAA0CNFuk0MIycoqCm88cNm2yriIRiuU285ambthL//FG/N+PfTYCwAAANCaGSBdrJOTk5OTU/O1/zy3cOOywHAuOzvneZZEKirqFjx9bLeKym+br0sAAACglSFgPngvKnXyX6mfmbJcSGetuCXzM5dwylVT/vhT5mhv1NHB0KMDAAAAWkKLBvizZ8/GxMTU9eqRI0f00gu9U4dJ+35L6ip5yVjcgU0qx/K3hd3n+Q0r/vmY3Z4fyAy6XnoBAAAAWrMWvciud+/eZWVlISEhQqGQUYu+eiHRjeQrx5GqzvGqlOmI5fX+8rtAO5Mpo2i2ltLUDH31AgAAALRmLXoE36VLl/Dw8Pv37wcFBXXr1q35Orpg9GeRyWrnKuVUmfJMt+/LKnYPVlRRTLnK8qrm6xQAAABoPVr0CB4hRKVSly1bZmLSjA+ciS1/epM8zJpm+cPVu//p3HEel/eas/R0/mVJcjq9U4fm6xcAAABoPQxwkd26deuar3EFJg8tju5iMnOfU8dSY+OiHUenfeIjMibfJguGDrRz6GDbfF0DAAAArQfRrqJPqnqtqI5TVMf9pxChCXjZTYQhnhIlfRLQjN8KAAAAAK0J0QK8J9frhGdjHnkLAAAAEAnRAjxCSCV8IctcgzAVpqxCKgmJZokQoloFUq2/NPTQAAAAgBZCxABf/Z5EtaB1CPL2GZKZK81L34uEsSpRgqHHBQAAALQcogV4DKH0StmTp37oaULcGxHC0PwVYSNGjBjYK7eTCqORIaccAACAdqGlb5NrbnczJX88rcCXyVQyQijpr78QQkUpol3P4CZ4AAAA7QXRAvyQDnSxNQ0hFNPnMp1LQwi9L0tDCJVbUSe7Q6ZtAAAA7QXRAjydQuomkcf0uYwQcpvkiBASF0li+ly2KxN2MacZenQAAABACyFagEcIdVKkDImfhBCyH2iNEFJIlF1uDbRmZBp4WAAAAEALImCATyDPQAht2Oxg4szGSw5s2vp7xg8GHRQAAADQoggY4G9Vs+fNm4cQ2n7YhUajIIQ4VKO7HBdRzIvy81HCu09VEqmhxwgAAAA0L6IF+HKpSvbO9MPUJQihCpmURqUghKoUMufnyvvPMhGZXP06OX/lFkVRqaFHCgAAADQjogX4ly+zss3+3ql4QaG4WoYQIiHSdyXH33bwMJ0+1vLbeSaTR/OP/27QYQIAAADNi2gBnvLbkcj7v+DLHKoRvjDKtiNCaNj/DuCr7OEDpCkZSKUyxAABAACAlkC0J9kN//Xn3/8IfmSTVPlBWS2qIsWQMBV2o6Kk5xTh0skheB1MqURkMiLBU+0AAAAQFtGO4BFCo8evkJFUM66bDCP1GLp5KkKoTChwf2GFSWR4hcord417eUCABwAAQGBEO4JHCCU8SSx+LIleZmyVWeziYhFDJUskkooyef7q7cZ9u8uz8zGl0nr9EkMPEwAAAGhGRAvwKuELH+t9PlPo7xXCqwoyT0FmOZlVpZdeY5oNlOfQO3dkD/FidHNDZAKeugAAtF38apUxlcSkwZlFoDdEi3MkI3tl1XOK+WSnsrFfiN0c8ruz3OwRQtKMKOSqotlZM3q4Q3QHALQ2+15UXX1fbehRAEIhWqgjGdnS7FcqK+4pCh2NTYbfEhoLP/ARQhf/jJt44J7o0QtDDxAAALRQYUiFGXoQgFiIFuARQjTbZSrhKyOHImlaZta+c8LUXISQSoUl5H7IT3xr6NEBAAAALYGAAR6RGUaOG8nsE8ryclP0dwY5CoWCISwm6bVSUGHY0QEAAAAtgGgX2eEo5gHyouMWy8x7P6I/TUNkKkmpVCKEHkiq5jx+yfX/xNADBAAAtPBm2Z1MiWbJ+od/H4Gs7MdZ1pdtiEEB4iBmgEcIGTltk6ZM/2l7B/mLrRdCdpellyGE7mWkCmPiIMADAFqDo3489fL6hxXdLGizujINOB5iw+QKEo2wIU8rwu4tmdWTwhurlD5WdLfkuFmWpZcxjEglxfmZuTkW2flGjnaGHiAAAICWIE3NEJy8KMspIJHJrIF9eIEBZFa7+CBFxO/g/2HUcVusURiPL2e5OSKEqEZGCKEXNmwM0sUCAED7oCgqLd513GTKGKfTux1CNiMSqXR/mKEH1UKIHOARmam0d6AiCrWzB0JIrkAIoQdZaZhShTC4HwUA0IpQSIhC6L/HhiJ8GMf+xJvZvycik8ksY/MFn8qy8hQlAkOPqyUQ/A1FpisunT1VkS1GCMmlchIJ3bwXIzjxP0lKhqGHBgAA/++b/pxJbsaGHgUBKYr5NFur/18nk6k2loqiUsONqOW09HfwVVVVERERb968KSoqUigUtra2np6en376KZfLbY7uitJT3l9/yvxQQqVTFFLlqi+Y56jrmIO6iR7GMbq4NEePAADQCKZ0gh9uGQrN3kb6LpM9whtfVUmk8qx8moOtYUfVMlr0LfX48WN7e/uQkBCVStWlS5cePXqQyeTjx487OTk9f/68OXrMwN4ghJQVQpNOHIRQtWpEv65eyb1NmF7dm6M7AAAArQrH16c64W3FxVvy/CJpakbxzl+ZA/tQTDmGHldLaNEj+OXLl2/ZsmX58uU1yk+fPr106VK9x/gCaW4+PY1hxCSz6OauLH5y+eNXOVfIv50dwPJkzJemZ9NdHPXbIwAAgFaFzGHZbP6m/HxU8c5fycYM1mAvztihhh5UC2nRAJ+enj5p0qTa5QEBAStWrNB7d/dKb5DIxaMifim0MMMuH0QoncFklA2Lv/ziwOTOb3l3yq3WLNR7pwAAAFoVqgXPYskcQ4/CAFr0FP3w4cODgoKKioo0C/l8/qpVq4YO1f9Hqhl2Xx7uenBnSJ+u1QMY7q4IobLyco4tJqZYmwzwl7xNV5bBY2sBAAAQU4sG+F9//bWystLe3t7Nzc3b29vHx8fd3d3GxiY7O/vEiRN6745KqjHewQAAIABJREFUorIobBWLZq0UMrr2RQhlZGSkieb5SN8/KELMAZ7Ch3F67xQAAABoDVr0FL2VlVVUVFRWVlZSUlJBQQGGYdbW1p6eno6OH/8uXCgU+vn5SSSSGuUVFRUKhULrJonF8h8fVYyhKHJY0RKzLsaWnOqSqm8j3vW3+X3Z107vD/xYdey8yaSRetgxAADQgCGkUGE0MsnQA2lLxK+rlJUKzhDex6sC3bT0bXLx8fGxsbGDBg0aN25cVFTUb7/9JpfLP/3001mzZtW/IZvNPnToUO1Y/vDhw6CgIK2buJtTtw4xOXrizYugUOaO7eadubklVYzUBy8TU0rSYp4WfNGdRJKkpDM84H45APQPw7CTJ09GRkaSyeSFCxeOHTsWLy8oKFiwYMHVq1cNO7xmdStD8jBHum2YiaEH0maI4irTZ7xGCDkd6mIyzsLQwyGIFj1FHx4e3r9//yNHjnh7e2/btm3RokWenp59+/ZduXLlkSNHPro5XrkGV1dXEkn7x2Q6hdTdkpZY+CDtWXLRnRfmHhyEUOr790P6miKEfrt4nTt+uDQ5Xb/7CADAbd26dc2aNZ6envb29jNnzrx48SJeLhaLr127ZtixNTeJApMq4XGZuhK/qsr49LXTwS6ukb2ylyWXXy0x9IgIokWP4Ddv3nzixInAwMCoqKgJEyY8fvx44MCBCKFBgwZ9/fXXixcvbo5O6aY8hJCypNjcxR6hFJlc3mPQcnRu/fN7Nzghe1TiaoRhqI6PCACARvv111//97//DR8+HCE0derUiRMndu/e3d3dXZdtlUplRESEWCyuUZ6YmKhSqfQ+VGBA4ldV6VMTHA90MRlvgRByudQrfUoCQsjU39LQQ2vzWjTA5+XlDRo0CCGE/+vp6YmX9+zZMy8vr5k6pfbvgu4iY7aRqZsHQneRCnF5ziZsSsa7lHdpaeb3X9HsrLjjhzdT7wC0W1VVVW5ubvjyJ598snjx4iVLlty+fVuXbaVS6dOnT2t/JZeVlYVBIgkCEb+sfD/ttdORLiZ+FgghaUpG1dUbnD7CrPli6Rqu9SofQw+wbWvRAN+nT589e/YEBQUdOnSIRCKdO3du/vz5CKFz58717NmzmTrlfj0pZLBJpIeVTdkrlglTVCH+8/LvI/oZRd6rDjlzadvMafwjZ7njhsFBPGhvlEplPa9SKJQmtt+vX7/NmzcfOHCARqMhhDZt2tS3b9/vvvtuwYIFH92WyWQeOnSodvnVq1fv37/fxIHhzghFU1lMxj//8aUY9j+ReA6b1egGT78RhyYK8WWRDJOqsOFni/HVnpa0/aPg2jEtKu8LaFZG7EGmCCFpWmbxL6Fmc6eYfdVJ9lmS4Fwm01PFGTnI0GNsy7AW9OrVKzs7O4SQi4vL8+fP7ezsBg8ePHDgQDabHRMT07g2r1y5QqfT66mw9sqH21/cGP/+f1PebHcf2Akh5OzsfGxLN4SQs6c3hmGCsD9UckXjegdAX4yNjcVicUv22Nx/Gd6/f+/g4MDhcEJDQ/GStLQ0Nzc3MzOzRrf/0f/vulvHL5tbXFqtUmEYJlGp5heXfl8qaEqDYrkqu0KB/xxPEC66KVCvCqqVehkzAamw7FWpKZ/EKSrlRT8fq7r/TCVTpc98/X7KK2l6fs7C9YYeX3Npmf/vLXoE36tXr6ysrJycHCcnJzKZ/PLly2vXrslksrNnzzo5OTVTp13Mjc0yOEbVNAVloGXPS6lPMry8vAZPWWcU3Dfrr+clJSUWs/yrX75hDvBspgEA0Drl5ubW9VJpqR5ybbm4uKSnp8fGxtrY2OAlrq6ub968iYqKSkxMbHr7TbTJzHSdoPyrEv5BC7MgQTmXTN5m3qSDbGMqqQP379Me5sZkJu3/V0GdSKjDrs453717H5DA9Cgxne74IfAvTKbqdLYniU5WCcWYVEaiGxl6lG1VS98mR6VSnZ2d8WUbG5uvvvqquXv8rKt54eBKN5LFK4qI6W6EEMrNzRVXywd7mEcnFZ+9dG353NmlIWftXJ2o5qbNPRgAWg97e3v1ckJCAp/Px5dFItHcuXMFAj0kzDYyMhoyZEiNksmTJ0+ePLnpjTcRGaGtZqZrBeV+BcUDGPRd5rw2HY1VokRMIUAIYfISEs0SIUSimpJZvQw9Lh3gMX5VasX1ntVp7yk8bqeIniQjsqJEQGLQW1V0x+Tyyqj7ksQURKEw+3ZnjxpMotR3J1rVfYGxJ4fKo7XYCGto6QDf8iq55P0VJ+6se4t2H+rjYYsQehX3KvTY0cVzUdxmywyKPYluxB7Sr+p2DG+Wv6EHC4ABBAcH79ixw9jYmEql8ni89PT0tWvXGnpQLUGBYeUqFZtMLlUqpRjG1N+FOC1/SY80eRKZ2R1hMmVVHJndi0QxUVbGMPvlITK9Obqb9zoAIXTCM1I/zZFQh1/clYIyUVyW4y92JBpJnldUeuQMd9xw/bSvFxhWtPUIhcPiBoxGCkVl1D1JSoblyrl1VeeH5ed+/47uwnS91ttQMZ74GYiPVgpPhZyIv3xFWFJgzjMzMWVLFJInMXGjPHptXzIiUehQeiicObCP8O4TTC439GABMIBjx45FRUU9fPhw+PDhaWlpe/fu5fGIf0WYDMOW88tYJNINWytHKnVBCV+sv+vzhznSF/dm66s1nWAqI/czJLozmdWLRDGld/kDkWgINcsdB3h011zQAxLq+Ju3c6g7PzQia+bKok0HmP16mkwepbf2m0wcn4QpFJbfzjPu6W7cp5vV2sWyjGxZRrbWyvyw/PxNGe4P+nFHm6eNf6UQGCa4ED/AG5NIXCoXIUQpKrGId7RxtUIIpaW8UwoWzZueqTJnfeDaCI7/buTcQfTopaEHC4ABlJeX9+zZs3v37mlpaQihr776KiQkxNCDanbL+WVsEmmXOY+G0FYzUwcqdVmpHr6VwJnSya68lj4/ikkylRXR9C6XVNJsVdXTZuqlRlDXZ4xHiOndy37veqeI/zoc3WIyaWSrurlJnpnH6OamHhKJSqF3cZFlarnBG4/ublG9Ge4su2AXkzHmaRMME+OJH+C5YpULciKTyTyalFXCMu9mhhAiIVTk5SpEnT0yHsd4DCBzWHR358pr9ww9WAAMoEuXLufOnSOTyXK5PDs7u7i4uKSE+I8Sm8Fi7vzne3cyQtvMTD9rwj1yrYE8fx/VZhGJakqzWynP3dVMvdQ4La+3s/SayK0xMFHMTJSlZZolytIyilnNK7f4Yfn5WzM63+rDcP/77WQX7ML9xOy9/ytFWUvH+NY4j3qkrFK4z0wNsgrav3Uhr4tj2TwPXjc2QsiIQj/52ymW/N7w9NfX3lYZOTuQaTREIcsLiP93DYAatm3bFhQUlJqaOm/ePC8vLx8fn0mTJhl6UM3uE2OG5lV1ZIR8jRkGG40eqFSVD6k2CxBCVMtZKmk2Qs31yD91UG+W6N5aGffuVv1XqjguESGEMKzqzmNFEZ/RpWYqk+JDOeazbekuTM1Cq+WOsmyJ8GEZallEvshOVa16H5Cg6skqJ1e7dB4gqi67K6qy68tBCInk4jd/vTVavaX7qD102rL896Wd/Fztfl6jElcbetQAtLQxY8aUlpbSaLRvv/22a9euZWVlM2bMMPSgQAOp/o+9846L4vgC+Gy7Xrnj4OhIERBEQewK9h57N1ZskVh+NmyJsZtorKjBGjGixooVY1fsiFiR3stRrvfb3d8fZ5Do2SJFzX0//HEztzPz9o7bNzPvzXt6iNHAVLLTXILpnrg+q+ZG+0+pdjMIjy2aFV6+Lbb8twMkjmNiW1HkxDed/D2OBKZ1e4DaUkSTnc01plJDes8k2++ceb1FtSzzV63g1SZDrg4ZbqfKKRPpbe0opUxGsDP1pohvK5GWPrp7H6Ic9G6WPOHQhCePnRrMHQMAKIr8RTRnAuZkX9eyW7FSe+zcufO1mr1795qjTFr5UoCozqQ21aRNfVWD2QPoa37C1z5Ub3eHX+fjMgWEIDDbskGH4kTzOhuU1u0BAEA02dlUakjrkcTrKxLPc69dYQH4uhU8KqR4nwt62i3R2JjC0clZUBFJEdimiZw5Yom0FOXD6fN/tm1yf8jiWyaE0kX9C5/HZYU2k8ddFH43vK5lt2Kl9jh8+LD5BUmSxcXFycnJ33zzjVXBf1nQGll9hGsJhMd59wUUJ5rX6aC0Hg8IhanizxL+ADtxZB1od/DV2+CpHgyHU43t75aF/2+ObtejVImadgJht6EBABrDwSm9gumN5fVtWYRBu27PYQAAGtpk+6HYupbaipVa5ezfnDt37uHDh9HR0YWFhXUtlBUrXzAUF5rXmaDyfUU2A+tMu4OvXsEDAOy9WYZOutuyF4+2JsppSHG7HF4jHgAgJzc/LSUd9dgzbkggAGDr+l9KS0sNCLw9JVGb+CQ1NXXKlCl1LbsVK3XAiBEjkpKS6loKK1a+bCjOtAaPW9rPrTPtDv4LCh4AQPUSAQCkQApwoPaz5zaiQRCUYkr9ZdXPNyXSoRHTMBiuyEtrExpWVlZGgeDiDXv6dukWEhJS14JbsVLbkCQZGxsrEAjqWhArVqx8Kv8JBb+2oS2PwUU9ENKgLcJpdB5VLLLV4jqdWvfX7XNc5F6AA5cgCK5ncI8ePQrVytPZL6KGjhs9enRdC27FSm3ArgKTyQwPD/+PhKr9opHpia1JqrqW4nVOabRFVdIQkwAcUKmVRE0d2LPybr5mJ7tKZL68xEtPztsc2q6WYowgAZnl7uVYWCJxtXHNSytI98izdTWAfPAo8Q7bKFMqlTGFqdHPHVqHNIVo1Li4uP9C2E4r/2UePnxYtcjn880ZXa18buhMZMxTDUGQAIDrefpbhYZCJe7ERgAA9iyktxe9rgUEaoIYKSn73VbogCIkAKtk8od6Yx8m4/0tv0buFBoaCDEWpc7i8b1VwV+7du3333/Py8tzcnIaPXp027Zta1Os6sWtwKT6rkB/VCPAXhQBniOH7tbRPeFGEmlP5j0t2N5bOGvhvBv9FqsLU890Gd754kEJboDqu0V3bGloFcjjWVPMWfk6mTFjxjveXbduXa1JYuUDIQDY9kDVyolqy4CTJMYgO8q1PEMPT9rtAr2Q/lko+MEsJgSgkaVle2yFB9Xq+3rDLlsB7WMizpKA3JMX1cd+KB/74u1EG+4rpzVhN3Oos4R4lrfoY2JievTowWQye/XqxeVye/bsuX///lqWrBqBOSheYWSjHDFFokdsCUBgDSEAQGFhYeKVJB9KmwZhwzvXFwEAjuW+SOs9cdSoUXPOHWEmPg/09YM+p2DIVqxUI7S/0Wq1GzZsSE5OJggiKytr27ZtQqGwrqWzYgEGCo1pyKSigEWBenvRY3sLIECGOlPLtcSU4NrNbfN2BrEYE9jsfiWSK1r9LlsB9yPjzl6vuHBHdv3Por01JN5/Cssr+JUrV8bGxvbs2dNc7Ny58+zZs4cNG1aLglUnKB8j5aZm3LYpuss5sNu3jhP3Ezs5HE5FgdRR7EhPFAjYuwmmmoogNAQFACxevPhHkrxwP3VETgHVuy59IK1YqTlWrlxpftGvX79NmzZVHhvZs2dPXFxc3cll5V2MDmCG7pcQJDjWT4DCYFIQ68frcg8+Gij6oISk2QMiAABuhzfXnIQkABkmIwuCVSShIEjux+h3La45Xhz7v3o/bs9dl6Z+7sX0rTEx/xNY/uzz8vKaNWtWWWzWrFlOTk5tiVT9MDD4HpI8uNXwvId3S0k8WfGwQJvlV88DAOBucsnKyoKZ/tEbuib3Gv9d/WAAQPaAiJ9++qnztp9hBp004e/r3oqVL5tLly7169evsti9e/dLly7VoTxW3gGLAtW3QW3osBsXBQD08WJkyU0d3T4u6btZzdcEZrv7fb3hmL3tdxz26NKyvI95hMaVHAzkNPFm+vWzH76/YAdZM+lua5RMmelJqdH8pzaSWf8s1rIwlhV8QEBATExMZXHfvn0NGzasLZGqHxYM34WSnz55lnH1CR+Czsnu00iaq6s9AECg5HUJ64IKB2pWCqlI1dwTwNnZWX7iguKU9Uln5SvH2dn51KlTlcVTp065uLjUoTxW3oHKQL6oMEm1eJ4CBwAcT9O48dAL2foPaVtzer2STXLlQ71xj62AC8MDmYyxbNbY0jLFh3nRS/RFCdLLfeyHAgCa80MpMOVmxZeX4XPVLeWCa3LzX5bMtOOR2vz6h+vyu4WGWhbG8hb9hg0bunTpEhsb6+npmZmZmZqaGh8fX8uSVSMtadR7HCEoBw5aVxGN0pnzfUbOVsIXASdADjVfrBHl5eW91iR7QITb4c3c3h2LF2/gdAt9M6OAFStfDStXruzfv/+ZM2fq16//4sWLM2fOHDlypK6FsmKZPY/V7Vyojmxka5JqSRvutgeqVWG8uVdkyRLjB+7SmzE/4qpdvBY06lg2iwW/dF0azmK6oyjlwzyZ9hfu7CHqz0X5AAAIQMMcw9dnLQviNqcjX5ITfnS3V6euhsWVf45OdiEhIenp6ZMnT/b19Z0wYUJaWlrTpk1rWbJqZACT0aZ3KwRBgt2amZSggrTxEDbgNLFBUfSpKuVY7FF8xuo3W2UPiMCc7AXjh4B/ruytWPnK6NmzZ3Jysr+/v0Qiadiw4ePHjyv9b6x8VmiM5O5HagMOVEbyeJp2aFw5AUHX8vUCOhyV+J4z8bWwfAcAhFApldrdTEsa9UO86J8pk4v1BR2E3StrXOke/uzGpyXWuea/563H5GxsbMaOHQsAwHEc/kg3yM8NBUE0+b5zwff5Kjv5pCcKPUQMZlBIgca/vs/Dp0/uFideoCgPbV+HaTc8PpFaUNHvXJshW7q8nIUxmgWWRx/gD+31ttxBVqx8Bfj4+CxdurSupbDyHmAITApimc/BB4mwW4WGYX4MLgXq4k6zY75rHWJRu9fQIv7fcaR4nx7XrUiLrFqpJbTlhtLuon4MxPr4/TdYVvCpqalz5syJjY1NSEgYNWoUjuPHjx9v3rx5LQtXXZzWaOlrM1s15t/vflNEccjHveRGqV5kat+l88OnTxj21FsXrj3fdKTxvNUNB+9nRN7PYquKg8fZC/+O9QHBsqPxNqP6vXMQK1a+MDAMi4qKmjBhAoZZ2No1Go21L5KVd0NDofGBL1XdYF/GH880U4I+6HTc56PI38Yk11lq3MImBAKQL1e7c6hwHUa5AW9T8OHh4SKRCMOwJUuWzJw502AwzJo168aNG7UsXHVBhyAVDzGVGoUUkQ1SmII07Gk38J4soWXrYPAreHr5mkKpTKfD4h9+Fc0OWSGJ3Bcp3XMzJenEyyTZvEHdCqYv43Rti9pZDwdb+XpIT083x5xPT0+va1msfDQ8GvyB2v2LwJZiZwvs6lqKamZblzqOgmpZwd+/fz8tLQ3H8cTExLNnz2q12mXLltWyZNUIG4aftGAxYIYtxZ4F39fDJB21cWHUs/dmwxCUVFLQzatVQX4Bs1NbQ8aY5kHe+87feRR/MEfyq6uICwBAuGxOt1DZwdPCqaPq+lasWKk2XF1dX3sB/jbJWeM7WbHyFWDZuC4QCHJycuLj4wMCAphMZmFhIZP5pW6SAABYMJTjT4+69Nuo9uO0qlx7BNlc8IcT7JB57bivk6vBZLQvcvrj/o1jKuWhjJUM74ne3t6EXr1w06tQSpxvOmgfpxoyX3e2t2LlKyA1NbVPnz5arfbChQsuLi5isfj27dt1LZQVK5+Kxkj+ckdZ11LUJZYV/JQpU7p27Tps2LDRo0enpKT06dNn4MCBtSxZNcKCYG6i6szeUw8Tk9OS0+vTsOd6BefI0zIbTQiVAwAw6nUpKSmVRsd27doBAI7v247/HZYAplF5/btIY0/W0R1YsVKDhIeHoyhaaZKbPn36rFmz6looK1Y+FZmeiEvT1rUUdYnlLfrIyMhmzZrp9fquXbtmZmYuWLDgy82dukOhCqFSKjiwAPABAKYKMkWp6sodEDxKEf8isnHjxnvSH12nZbmIXHJzcz08PAAAwcHBHA5Hkf14cvTF6IkdzP2wOrWC6LS6vBMrVmqGr8wkZ8VKnWMql8kPnzNk5sIsJiusGbN1MKgLs9dbz79ptdpt27b16tULAMBkMpEv9iw4AcBcqayYCwciDRAEgcVirV6arkPthJ4mBhbIosEwnKl4PHny5Hr16pmboCjapk0bAEB87G+V/UAIwmwVXLblDxK35ja28lXxlZnkrFipW3C5snj+GsSGazNhCLt7qDzugvzo+TqRxPIKPiYmZu7cuZMnT96yZQuKotOnTy8pKZk2bVotC1ctTOCwIAjE8I19Td2zM6dx0ZwQxCGO0B4uinFneOZ2oDY4bf9YUpifn+/n51fZKiws7MzZs0W34oqLi+3t7c2VEIrgZRXK89c53ULr6G6sWKl+zCY5giDWrFnzFZjkvg4I5W3SUGzhDQhF+F0B9NYQJv9xStT48dSX2/IKA6EykL8lvTx9hyLQaH8mUvNRXZRnrzJaBvEGdjMXqR6uBd//xPmmPWTpPGqNYvlet2zZEhsbu2jRIgCAi4vLn3/+uX79+toVrDoZz2YNt+PoAeAD2nqHVl2ENkUElKy458n1Y3Wo12XwAADA8+fPN2zYUBmzViAQtPbwNBr0E5f9VrUr/uj+mtsP6+AerFipMSIjI0+cOHH48OFJkyZRKJQFCxZs2LChroX6r2MsWGPIW2KqOFH1z1j8mz5jEmnpvPhXzNGjR+Vy+QdeTJBAYSDlBlJuIJUGQICXr+UGUmckayfZiyG3kObrWVlEeGzUTmAskNTK4P/A8jTw+fPnVZezvr6+5eXl1TKeUqmMjY198uRJSUmJyWQSi8WBgYGDBw/mcDjV0r9FdCR5Rav3EiClRbpMkSRLlq7C3SbSp+U/vX6V9SAspPkaAC4+LWzZpl1ycvKMGTNOP7uRXvAouAE7R2LzBDiVqPHKKFEUFwf7n6YZ84owZ3HNCWzFSi2j1Wqjo6OjoqI2bNjwRZvkvhowl8X65/0p7ushlFtZqU8diTn8D0J5722ujL/KDG0O0z4uy9xnRWpqqru7O4Zhq1evdnJyatq0aUpKipeX17v/OcUsZHYztvl1oQq/nKOb83ex1kCFfJOkrLJI4ripXIYK6+BMvOUVfLNmzTZu3FjpVb53797g4OBPHywhIcHR0XHbtm0EQfj6+gYEBMAwvGPHDldX17t37356/xbRkeR3ZRXpJqP4O5cujuxZJamXpSeAikzdecqpiJqDFTndeohgmD43Ofi2y8Ok28/vbzmURvvWbi9tyPj4Hn3G9Ozy1z8zNZE4XvzTJkN2QQ0JbMVKLRMTExMeHh4cHHz//n2zSc66gq9zYIY/wu9uLPilsgZXXCfUjzDxd+9tqzh9pXzHYcmKreSXHI5w7dq1AwcONBheZmA7ceJEWFhYdS01axRmaDN53EV9ahYAgNQbKqIP0hp4waw6SJljeQUfFRXVqVOnXbt2SaXS1q1bp6WlnT9fDT4CU6dOXbZs2dSpU1+rj4mJiYiIqCEdP6tcagvDrihqase4Nn/Gw6cPuNsbGk3gTnhjDgdl5/JuNHGkXwpSvbjDlMvL9dptNrfcJZ0oTnMTE5pO7L8nMgTRc+gkAJUekBCC8Ab3qNhxyH7p9DpxjLRipXoxm+RCQ0O3bNliNsmNHDnyC/W5+ZrAnBfokpsTohEw3QeQuDF7PsV1ydjHQ3YFHn9HK8Wpy4rTl1GRAKLTJauiRZETat/0Wy1s3rx50KBBffv2JUny5s2bq1atOnnypEgkqmu53g/Vw0Uwfkjp+j2kVk8YDMyWQcLJw+tEEssreE9Pz5SUlKioqJUrV0ZERKSmpgYGBn76YBkZGb17936zvk+fPjUXLHM8h7VCwGfDsC4q79pf13IfJGtzSjCVKQ/wsjRpnpwGj21gmk87AECiKqnf1bYAAKnPkoQeXmNiSy6wh81fPG7tXeWuR+qqfbI7tiRNJtW1mtp1sGKlNqk5k5yVTwHChJjjDGP2fACASfI7hPLH5+0CAIxN7vO2Jmbtbv/TNAhFbEZ8A3NYklXRX+I6/tatW1evXp04caJcLn/8+PEPP/ywaNEiuVx+4cKFioqKD+yER4U7u9fB2eb5FbId3m5OW35y3LjIZe8v2olDuygU+Sa89iV5q0OhyWQKCQkZNGhQmzZtVCpVQUE17EiHhYVFRkaWlJRUrSwvL585c2bbtm0/vX+LBFIoCAAsCDIKMAe2PQAALgR2ujIV5NDPfoQHs76YlfGLMwwAuMK/UNnqzB+re64d922Qa+nNzEaP/trxUKUzVfHPgCDBhMHSfXGESlNDYluxUmvUkEnOyqeD2k8gDfmmssPG/NWTja8e12OT+xz+m4MHDx44cAAAIDt4RhF30fb7kYAkSROOS+X8Qd2MgLw+Yhqh0799kM+R9evXr169eu3atSqVSqfTAQCOHj26evXqX3755enTpx/YCQODFrV6q3fX2szFZyRHq0fcfxLJ41zT6dbIFDCbWUSCcaVlY9ksJ7QO/Fosb9GvWrVqwYIFBPGPA9/kJ3sgRkdHjxkzxtHR0d3dXSAQQBBUUVGRmZnZoUOH/fv3f2Ln74YDw9KO3I5lHRMLH7q6ubXJuXy64XAEQjyY3qfZB1y49nyEJy2QybNVXLeX+RvUt3owQ2wXLxDBl+7792h3NFU7zO+VEYVSz4XZvJF03wnBpKE1KrkVKzVNDZnkrFQDEIa5Lte/GIqKRgJFZtV3WrduzWQyy8rKbt68iSAIaTTJj5yDmYzSqH0AAFOZNGv1NhaXazIYnE2w6vJtdSPvkydPTpgwoY7u5OM4ePAgAODMmTNjx4718/OztbWl0WhHjx6lUqvHbfCB/E6ZQXKu9HhLfhgPs3l/g4+BA8O7bAVjS8sXV8hv6nVj2KyhrLoJLGF5Bb969erNmzcbjUa/LMOBAAAgAElEQVSyCp8+mEgkOn36dEZGxoYNG8aPHz9u3Lg1a9ZkZGScO3fOxub9H3FhYWHmG7y2H/A2uDBc6suYNWWmSqUKCGzsFsgoMhhXX47Q7z+vQpQCD6ydHxcAkJ/wsreVCrY21Q1mt3BsHW4zMbs05n+Llq56LcINb1gvk1QOaunkhRUrNYJery8oKKgJk5yVagHhdcKcFmDOC6qa3ufSVl+/fr2srOz27dstW7aEYRjCUPHPcwCK2Izq5xS1GLMX/izLWlqWChB4Z3GqKtCrXbt2CoWiDm/kY4mLixs7duzp06dZLNby5csxDBs8eHC1ZDE2kaY/i34f4TghzKbz4aK972/w8XBgeAWfd0SttIWMdaXdwdtW8BwOZ/jw4Sha/bEUbty4UVhYOGjQIIIgtmzZEhUVxWAwRowY0a/fe7KtK5XK9u3bv/ntqtXqD/nKOTAEEuX5W0u9TjW2pdoXOkgZBKrn11P62NRjeUFdmF2T4aOPATM+Gwz3mH7Ai/As53ZOIPUDEE7rvZvmnDpYSgBoR/iQiR1emSphOs1u3qTsARGuBzZAdbH9YsXKp0OhUEaNGnX37t0+fd5q2bVSt2COM8wvdgUeH5vcp/PzIfWH1FcoFFevXm3Tpk2l3xnFzcluweSS5VvNq46Ny1e+WLbxeFHmvoLUY506jRgx4stKMfDixYszZ84EBQUBADAMO3To0Pz582Uyma2t7Sf2HF963JHm0oDdyIvpu+BFRJr6uRfTtzpEfkWhCR9fWuyqO5VDtlgj48zi1eA58HdgeQU/f/78SZMmPXr0qLwKnz7Yxo0b27dvn5mZCQBYsGDB8uXL/fz8GjVqNGnSpOjo6He3ZbPZKSkpGW+wfft27AN8RJtQqS52dFOpAQDQkh8WxOmgk8PB3pODWw324jVM15e086+PQPDN5+plT1JStE+LDtsjHBRXXDfkLOozMDzM0YUk8JU/r7HYufz4Xx//YVix8lkAQVB0dPS8efPS09MNBgP+N3Ut138d0kgqzlt46kZ5xwIASkpK0tPT/fz8Hjx4oFK9intDcXOymz+5ZOsfsqLirJVRD+jg53tXcnNzGQxGXl7exIkTp02b9uaKiFBrVFfuKOIuapNT3iZP9oCI7AER1XRzH8Ts2bPN2t3V1ZXP51MolDVr1ny6dleYZPGlcQPFIwEAFJja135YbMEOElTnRmyhCR9VWuaku7hUFNROf/iipnyNrG72Tiyv0XEcj42NjY2NrVr56bv0q1atOnjwYN++fQEAO3bsuHjxYsOGDQEAbdu2nThxYo0ah5pQKUZndkqpAQDAQXmNbHiDcHma0QgA3Yvpe6rsalsKhJMEbgSOI14A8AKAs7mjR3MF/QBpsud3XzH1ZIs5UPHV2OLiFZWRawEA5v942YFT7M6tEQ6r5uS3YqXmGDx4sEaj2bVrV9XKarHKfQXc1OnXyhU7bQU8GAYA4ADMLZd6YugkTg2GTyGNZNaox4rz5Xb/cxXPr/fauwRBXLp0KSwsTCwWc7ncq1evVvWXorg7MaZ+C1ZGZ7qJFCI6cZ6AYZggiMaNG8MwTKPRXtua1WfkSlb9RvPzRAQ81fV7ijM80ZwJUC0EdP1gDh06VI29/Vm0N9Smsx3VwVxswQ+7Wn7+lvRqS35YdQ2xQ6lqA2fTQWZj7mgOypPnrruPLsw3MWvfz86ygl+6dOm6detGjx79IYvjDwdBEF9fXwAASZJGo9HNzc1c7+PjI5HUbBg/I0nmcCGT1Ni5U+fyivIFp/4XzOt/TK2e83zCj96/5prKNXalrzXJ43k6wzRUNBIA0Khfh45RqvPZme2+W/b0yGb4jdPvMP0Ljhhl5T9OSspb121WWtKo9/S0MZLyXSIBB4bnlUtlBDGGXYOzebN2J3SE38MWGf2SCR3huORV3FMIgvR6PYZhly5dMtcQBPHabMwuKICIWcuQS6e2bz98+PCTJ042EPhevHjx91W7gYIk1ARMg41FeoozDQBQtmmvYMIQRkgAAACQpGR1tPLsVU7PdlU7rFy7Zw+IcDu82aLYxkKJ7OApQ1Y+zGSw2jdnd2gJ4HfNEnbv3v1azZgxYz7g4/kksjRpjxT3f/Baq64S7ren3cA9eVHB3OZUuHrO1M3kIAtSts7y+AkA4MGs34Dh2oG45IQOqZbOPwrLCl6tVg8ePJjHe39AxI9i8ODB48aNi46ObtCgwbBhw3755ZelS5fiOL5y5cqaOyZnJsVoWiaXL+Bij5KTS0olJ58fCXTo9JhJdMX4MIAFJqzJrIzXmiQ8NDbvRMAwbMz9CRNP8nc9+1c2SD+7J/be3OFNnUGVf3oAQM7QGTaj+7/2q7Bi5YvAzs6urkX4rJnGZQMAxkjK3VBERZJRQhtqjUW4qtTuHrENISrsdbpxWo8kAECljqfRaEOHDoX/qTvf3G7Rk3hoaOioUaMGDRp0/fjVmSkTCvQF91tcFtW3M+ToMAcasynHZaMPLlMSKs1L7Q4AgCBWx5aqCzfNjzKlgZxwtiK2t+C9YpvKpMU/buD27cQb3BOXKaT7TpjKpPyhvT7586hmHiruESSxOHXGm2+lq1MasBtVyyjHivc35bVyormai4McRv+YOqO1TQcB5VPtCx+L5RnWlClT5syZk5+fr6/Cpw+2evXq4ODg5s2bOzk53bx5c9myZfb29iKR6N69e9u3b//0/t8BG4bkBEFxpDqJnAAAaDnNpC2WEcS0eqvoCMNdotu12+m1JkmJN8yn/wnNY0L7fPwC3xZ+7iadevvSxRaHqNhzxJhvKfuTFStWvnAiuGwCgBs6/XIbXq1pdwAAakvxOt1Yca684IdXocBoNBrln7x5eIxCoWzbtm3evHkAACksp9ZjOAAxNptT/0oIswVXn6nhdhUCAAAEXj8HRILKAJ1aE5ktN71merdoiVeeu8pq35zTPQxzENH8PO3mTVKeufoZBtjpaz9sk/8+i3/Vpd0LdXn3ZQnf2L1ar/MxQQdB9xpy1383llfwW7dulcvl+/btq1r56TY5BEE2bty4atWqW7dulZSUyGQyGxsbb29vsydFjcKFYTlB2E506vm0V5mqvJ5LPRt2OV0nSjUaG2LA8QknuZ0bAI+rNrl5+0l+fr6zszPFczuAMO/mDj+2m9v1Wda9+H3Xj4923nfw9TFIULZln3jZ/969MWXFipUvCxyAeeVSEQKH0VmTSit2iQT8f/sbh6pMDt58omqfquRny3wSmpq1uxnUlmL3P9e8GS/Eke4w4/1GXFz2lzFvBQCguQDoHv/E0+JTujP1R4rdNu7PixydduEBzEScV3vLTkq43YUIl43wOOqEB8xWQQAAEieU8dcYTQJwEoehj7hHY34Jq2PLyiLMZqIigbFQQnF1/PBOvg5OlBzASfzXzMVVK42ksVCX18tukAPNuTaFsazgMzJe36+uRhgMRocOHWquf4twYFhJEPQWvPlt5y5evfhwUYzeKDEU+ySoCy7Lfu+l9D4JHj6N+CGptGjEwe0BYqcLEfPD9m0yt4VQPiDJioNZDVCPtg6ZVwtzJpx6dGvaaF6bJv8YgySLl2yWx13k9ulUy3dnxYqVGsKs3SsIwrwzjwAwVlL+73Q89M+lPwRBr+l4RiO2y0bf9G8eep1qTPN5eXhacbEif26qx+HAD9HuAACY4U/q0ikeURDVBQBAAWDSBDzzshxzu0Hzn6ROULjt8YcwyFT2cnkt/H6kZNU29Y37qJCvffyC4ixmd2mzNusnT4ZPC86g2Lilbw7xpiXerM4ri6TRaCqrQEXv39v/+hjiMFZukr1ZDwFgT63t6Y5lBS8QfG1fDAIAE4YlR4opRuCwqJ491SHF+JhUQIUkyxGm8thCphKC+jTsKRrKPLHvSVF+EUqOGTPm1KlTwcHBFAyTHlyK2pwTzVvyv5Ty7g2bPOvS6/dzN8MhwGxdRcdDkPC74UWRv9Ab+/0H561WvjhmzLBgiaxk3bp1tSbJ58x9vd4AQKXd3WyPP6nWjmTXSPQSwbdiAEBazySzjldcrMge+6Te/oasVh/qEQVRxKjDNFPZUar3HnMN3Y/EpecLFq+E6RzPs+5ZQx/RA9nMpi+z0FLcHB3WL9QmPcNlCmHr4DyRy4QT2SX6zjjAnaiSZ10WhIhf7v/TUWhdRx4DtWChYHVoUbI0iuLqQG/ogytUFbv+ZIQ0hOl1EAe+zuFjAj72uSjQ6g9l89nCg2GDmArdkgMA7KmOl8vPbWnO36BWrHSbl5e+0Kdz45T0h7yL2R18A+KS7h47eX7ytxvJbylnzpxp/jjfVKDhDcZVF1SN3WxCxE552pTvnZt3if7NQarg9GpfOQRqa2MzdoD2wVOrgrfy+UOjvXz+SqXS6OjosLCwgICAnJyc+Pj4hQsX1q1s/x6SJDRamFltqTmbUanN/mnhNuv4mkPwrRiQZFrvJPFs98JlmR6HApnNuJWr/w8xlWLiCG1yc1x+GeG2AwAQ2mzUPl9zr57DYhtTsZ4RzJGfLxeOe+V1BNOozBaNza/tjEa+3eEBgh7F+vxc9bVsaduJjV5OZVAYolvS7gAAiquj7bRRFb8fkxRth1CE1a75ez3sasFn3sp/S8FrW3FEfIrJZBJh4lJ9cRCTkiYzaUw6nUHl79jpKhLvbqw3LPB/cUOGnElL6rs4y39IKy6XCygSQKKm0h9hJoYw7ACe5qzzbgrbHXJuMv2NYH/MVsGEVqe+lVT5g7Fi5fNk5cqV5hf9+vXbtGnTlClTzMU9e/bExcXVnVz/EhInZAdPK89eBTAMYQhvcA92p9Z1LdQ/IEny3Tb4SgQjHQAA+fPTPI80qqrdgaWNfQvANIrrT8bs+UjD6wBCtY+WM4M6u2xsXHGwWHVLzmzOFUW4qO/IQU+h+XL1rSTVhZu4Qklxc7rZnQgWE8Pd/AyE56zH82C4VSunDzoDTAuo77AmkjSaIOw/pFY+c/5D30QvJt2WT4UyDI0aNWIymUev/nkv30A1gYi0RY0GaIakonvYmanlTj1GjqBTqImSPE1v08mVx3Y+2Xvy5EnpH3Gae49EczqYCtyKDMn605dG2WdODBkzKVRsIQIhSUr3HIEZdHqgT+3fphUrH8ulS5eioqIqi927d58+fXodyvPvkB8+a8jIcdz0A8LjGPOLJWt2wCzm5zbP/nBXZcFIB5sh9hDFgqX/Q3Q8YtPbVLzDJImB6B4wLdF122YA01gtX+3zs0P5/ziJ7sQAgNFJyD4viZnvuhQAQIEpPe0GXcI1BEl8uMOdZe1OAkO+juJMM5UajCUGqjsdZiCGAh3F6b+4h1+bvP9rw3H864hpNYLFtNOBrDGPMzMz7969y8MFXCpslGu1qGuRK9DvPGmjY5QKNLI9R1rbuxAkecs+0SPJ6datW/fv3+cM6UFv5Kc8Pxexvego6samICIGNDaIe6vAwulBmEEXRoys2H249u/RipV/gbOz86lTpyqLp06dcnFxqUN5/h3KCzcFk4cjPA4AAHOy5w/tKT9yTnkhwZCZV9ei/UssavcPB3NbacxfZcyaQ3FdCj4shEt8o5wW6gDa2Zf5WBtxQiAIul5x4d2t3guuNL1oe+9Fu3vPgm9nhz994pPwov39/Dlpn9itlfdi+R8oNTW1T58+Wq32woULLi4uYrH49u3btSxZtVOM42VMCCKAi5MLAOD0s+MV8EUn5jUfwWAIw8qXdvdSOuhpxSU6zdB5MwEAZ1PPg3R8cP/BW7ZsSU1N5Q//BoBhumeEMj7LdpAE0cu8Hx3PvfpnidpC1G5agLfDL3ONBSXWXHNWPn/MSeT69u0bGRnZt2/fyZMnr1ixoq6F+jhIE06oNKjg5QpVn55TFn3IVFxqSM+VrN1ZtvWPavklvjAa18oVa+WKBVLZvArZWrlinVzxzFDbp7137dq1e/fuNyPBvQbM8EdsekAUe8Sm5wf2nCS/00wYVlyeWWooKTWUEHDpuCDNseI/tLjmQ5obC0rU1+9pk5+/dgIe4aD0QJb2icp5vY/vrWbcbkLtEyW3V21HffkPYlnBh4eHoyiKYdiSJUtmzpw5ffr0LysNkUXi1NpYlRoTUwd06e/v74/awKdK/+zO9c4wQd3t+h+SHmrQqIfcWS9lUbv16Y1h2JVrV0VXvCZOmRgfH//s2TMAAH94OMLszB/Sk9N5qsQ98fvZs6aOHzVszVGLw0EYVvH7UfmJi2++Vcs5G6xYeTc9e/ZMTk729/eXSCQNGzZ8/Phxz54fqhI+EyAUQW35+vQcAAAgybINe1gtG9MC/QSThjquX2gsKFFdu/vpo9zQ6ZP1Bi4El5qIIhPOheCnBuMFre7Te343VfdQX0sZ8G4obquo9fd/+PVMhPWbadeuNslrMn5Yk/HDuqwfiihRVJiWoXlRec3QoUOVSuWbbSt2/VmyZJPmwTP50fMF05cbcgtfyW8g1Lfl3meD8me/SO/7UJehcYnylZ+s2fDkVsDbbPD3799PS0vDcTwxMfHs2bNarXbZsmW1LFm1w4GhAiOBOdLm9pu5bNPyw0UxOo22KNvviVhXZMj3uhf8iPKs0LeoiB9ScvGir6/vo0ePTpw5MWHCBAcHh0ePHrVs2VIsFtvODjdkfo+wf4F9SI1JDwC4vX3BkcHd+vtYiE0tmDi0aO7PNJ96VJ9X6SKs2t3KZ0hmZubjx49xHF+4cOHdu3e9vb3rWqKPhje0V+m63fxv+8B0KqHSqG89sFsYAQCAMJTTpY026RkrtNnH9mlWbB6M+pU1DSmUcA6LUKjUJBHOYRkVpLH6NulIY6khZx4gcUBoSUMhRPMAACDslqj9eJIk37tktwBEARDlwy9fgs0v3bBDOH00zcfjtbfWrVs3bNgwOzu7a9euKZVKKpW6evXqyMhIc74S9fX7+tRsx40/QFSKuVi2fo/Dr/PNbXG5CaIjjGAuqxVPdrLU9bcGVBeascTw0bdj5SOxvIIXCATm0zIBAQFMJrOwsJDJrLOU9dUFH4ZlOIE5UA2FegNhuC29giEUEYrCOGnHagYhkIkwOdFcGvX0b9myZatWrQAABw8eJPXExHETb9y4kZqaau6HNJaXFp5W1t+wfFtXsbOrpiBt2vxle68+S8jXJ+Tr1VV+7qiAJ/xueOn63YRS/ZowVjVv5fMhJiYmPDw8ODj4/v37KIpOnz59w4YNdS3UR8Ns0Vgwcajqyp2KXYdJk8luUQTF7e/TqjBM4sQ7W1vARBq356zbnrPORL7ccC7F8Xt6w1q54oZOf0evXytXJOj0kupLrUvqcwlVEmrTG+A6Qv0EYTWBGX6mcst7hDVB6dqd/FH93tTuAACNRtO+ffuSkhIAgNFoHDBgwMOHDysT02kfPmd3CzVrdwAAs00TQqM1lUnNRdSWAlGgrJFPDAV677PBBfNSSzbnMoPqJkX6fwrLK/gpU6Z07dqVIIg1a9akpKT06dNn4MCBtSxZtcNDYClBUBypxgL9udJjHkyfR4pEL6GJqoBlAkcURrFGOCigXjact3vhPmPGjB07dly9evVp1MM26U3mPZpXmSWW6rVz7z3N/ucmesVIbsfgot2ziy/u2tR+ZH258nah4eemRL+gVwdM6UENGC2DSjfH2EVOBBBk1etWPkO2bNkSGxsbGhq6ZcsWFxeXP//8c+TIkdOmTfv0npVKZWxs7JMnT0pKSkwmk1gsDgwMHDx4MIdTIw93eqAPPdAHkGT+5B9ezaoJQnXpFuMNd3oNrj4tOXxXeoOBsNoIOrQXdIOhf4SKO18a50R3JQE4X3qyu6gfAACBICoEuBBMhSCYhLgQTIMgpFpD00MoH2Y1IbJnY47TcdUDzH48LrtUjf2bMRIkBkNVT6KTOEFqdXgnReGsVab8Yu7Abq81WbBgAYqioaGhOI5PnDiRyWQeOHDg1el8gwGi/CP1KETBSKPp794B3YclPy0R/+hhkhlZbfmyoxKXKN9qvy8rr2F5BR8ZGXnixInDhw9PmjSJQqEsWLDgS5zRvwYfhmUEwe8rYofxb5RculN2XU/o9koiykt06wrOa0yGi/hJE2p4oX9CShVOYofGjRvjOH5WeVF3Qv7nH386OLzMHwxg+nB/LgtSzPHXmzzbOouccEVpRtymzNPRNE1Jr4bi18cd/g2p1cmPnv+QnA1WrNQ+z58/9/Pzqyz6+vqWl5d/ercJCQmOjo7btm0jCMLX1zcgIACG4R07dri6ut69Ww0W8bcCQcKpI0vX7y6L2ifdH1c452eIRmV3aFn1EiNpWJ2xQE/oXOjuvpyGDxX3dudHVb1AYZLFl54YKB41xGHsudLjcqMUAGADw+Yt+hAqJZBKCeewgqkUQXXnnjAWrEHtxmKOswjFDUKXXb2dAwBIAAadKFubrril01/X6uLU2ls6/W2jMUemKP5pE8RiKP5KUJ67Vnm9VquNiIiYOHFiZmYmh8ORSCRPnjyxsbExV5rjmlN9PdVX71Z6MupfZJJ6A2b/8pw9rjJhYornySB9urZsRwHVne4Z11if9kGOe1Y+hbeeg9dqtdHR0VFRURs2bGAymQhS25nqqx0eDEsJguJKl98sjxkbV69evTGxg3xY/iWJdBqzFSDj+0NjVKW6Gz6n5Gxj+u17rVq1unv37oG4A/1ad3YqsmUwGMePH+/VqxeCIHZI7gHvfotlf3nbplyQFQEAZJd2Fsw/G9XLG0Nf/6AgBLGdOU6fmvWmSO9IrmzFSq3RrFmzjRs3/vDDD+bi3r17g4ODP73bqVOnLlu2bOrUqa/Vx8TERERE1KiOp/l5Oaydr7n7iFCq+SP70hvWf+2Ce7IEASYa4ThxT16UA9Wpn93wBS+mFOsL7akv5/EHC3fbU53WZS0xEkZbit3Bot0TXP4HADBv0SfrDQZArpUrkvSGpm9kcvskSAMuPU0LvAtgBiqegpcdqHznE0O//XhdfjJdBwAwEqSOQ6TAxl0v1AADJI0MoFEMhJGblr15RG/58b8EYwaU/RZLEiSneygAgEKhhISE6PV6k8l0584dGIYNBoO3tzeXy8UwzMbGBgDA7tJGe/9R0aJ1jKAGpgq55laScOqoyqx0CBt13eYHAKgacJcdyv+U27HyIVhW8DExMXPnzp08efKWLVvMNrmSkpJq2bKrQ/gwLCNIXYamcHlGaWlpaWnpfOR/pYaSox3atykoAhCQPVV16tBJrik08MuT8rICAwNtbGwePHhQNkll3GU4pDmhUqmkUqlQKIRo9S7doyYzSrRn9rnaw7iUk6mUlh1ZOv+Jz5K820uWruza9R8bXAiXLVkdXVc3bsXKu4mKiurUqdOuXbukUmnr1q3T0tLOnz//6d1mZGT07t37zfo+ffq892Gi0+mWLVtmMpleq09PTzeZTJGRkeaip6dneHg4AECtVq9YsQL/2xxurmd3aqVWq5etWIHvx1+7/qrkr2xZSkTeAAOduEFeOgB2k3T8njyhPbP7ihUr9Fy1tFMOpYBlQxF6+vggEHpTeiX9cL7WIDK5e9wEgOng0Ng/gAvBrRD4WfRvkeVlHyLPe+vtOUVDmqcnZvvlJx0JDw/H7MM1BeuKK9DomA+633fXp187DrMC25Ycu2rfrzFf+UznOQPjSGm6LVJ1w/1rXdh2B8JCDqQ97QYAwudw5o7Pnrv6UVzcEx5GQQ2eHu6jRgwePHSMq4tjZmaGj7f76tUrvx0+uEFAMJ/PBwBodNpN+mJnhUF4Ns+AwKbGnt8G+vzrz+G/UA9qBcsKvuZscnUIBkET2CwMpZBFuKura2ZmpqfG383DlQJDfABXMJkMvfavv/6SczQSXkGDYkcKhvXv33/79u1xWWemDZrk4ODw888/z54929xbmeNAUxlo19P7XjbiRXXMNeGa59fRST+vYh8PDrJ7c3S3w5uV564pLySIV8x6zVhlxUrd4unpmZKScvbs2aysLLFY3K3b6/bXf0dYWFhkZOT69evt7F79IsrLy+fNm9e2bdt3t0UQxNbWVqd7/QQai8UCAJiVCqiSFgtFUaFQaDAYPqQelyndr0pJPsv9sTgtsNC5AGHZtUpu9LgBsyGKogKhIC0kCzFgrk8boCEmNsod7DAmXZ1S0bLI9arIKTsTAOBNQfvyOQAAvV6/BUUMnyZPZb0tq4xJ1TZxT2mKrtQmrQcAAELLZaCfeL/m+iCGNAWjF4maQyjFn2ss4cBbH6vYQsCk6nxpwqeO9lQYbXk306TSStbuAhgKULRVuZ7FpnTsvZcE2Ld95wEjGbPCtv4tzW+zsmNO6Q+f2NWqTYfK/gVCoYxjMKdR83a0BwAo469TQkP+xefwpdcH2F9z5mUDAAScY/qnfwKYAZOom6N/hZL22vU1jeWQhzweLy0tzdbWViwWFxUVlZWV1atXT6FQ1I5MH8XJkycHDhz45oPgrZCgfH/Rjvy9R+JOj/jlMJXOyFPipxlaTA51Z0r1uO6B4pqzQ0L/xM5dvx114/attm3bOjs7Z2dnAwC8vLz27NnTuHFjOpPVZX/+bNHs48XtSs5eTyuTYAHhumeX2H0jvnW/E9Fp7NsGlx0+y+7Yyhxsy4qVqjAYjPLycjqdXvtDu7u7R0REzJw5s7KGTqdrtdpP7FYikYwZMyY+Pt7d3V0gEEAQVFFRkZmZ2aFDh/3795u3dj+Wj/69W6Isap9aTF3vc2ak46RkxX1xDqgozEprDBZ6roYh5J4sYVvuLzBAMIhiIk0AkCiEmUgjDohJLjNDeK0+Zej3QKiM+b8A4h/7FhCrESqsHh/nY6naH6/LV7fj4XbkMpncKANaiORwIa9yqdRgJIT84wQpWbvLZsQ3qEggO3wOL5fb/RBhKl0EIMrd/B7NmjXDMMzR0fHevbs20nEJaU3b9/4JeouPoXTfCfnxvzjdQm3GDgDV6odYhxBanfzYed2jFwBBGMH+nF7tIMzCak3/vA/MbQ+hPEPWDELfHM8AACAASURBVJjdAnOcZcxZQHFfA7NbVF5TO793yyv4GrLJ1TkvjEYHBEHYyOzvZ/UbP/N4mjYdrLNBx6IKxMghX8gKyvUST1YbpeGRoJ83hKGtW7f28PDIyMi4dOlSx44dIyIi1q5dO3HiRJ1HmAminLyoW9Rj+a9j7z67n9EaSwqBgpcbmT6CALOHqkUBeAO6ae4k4woVu1NNPiasWPkYcnNz9+7dm5ycHB0dXZli7tMRiUSnT5/Oycl5+vRpUVERSZJ2dnaBgYF1HgdX/zzDof93EeyGMQW/FepzAQeqV0qJcF1v9qJ3orl2FPRKkt9pK+ycpn6uwzUBnOB70gQXRj0nmmvNSgazMJefaq57Pg024IBHhSGE9KVg6jSQiRr9m2E7Gvqeu3o3Vl2IBNSHKBjmaK84d41QauyWTINpVIy2UJfcvGXQSBjDAADTp0/ngsuA0HfosxiAt2j32JPah88gBNanZ5dHHxRMGPwV6HgSx0uWbsacxTbhgwCOy+Mulv66SzRngsVbg5mBJsnvmNN8U8lOCLMFCLf2BQZv86KPiorat2+fm5ub2Sa3evXq9evX17JkNcEOheqaTl+yIVf7SOVlg85uxnbiGAYGlEb6ckge2c03rbtf+pawxj48m9MlRwAAEASNHDkSALBnzx7FX+UDnHpfv349NzdXJyt14aClTTasKlocl0GY2A65HM94N+/G9KJyqt+g4+XvOHNL8XCR/XlGm/Tsw8XOHhBhdbm3UnPAMHzz5k2lUhkaGlpYWPj+Bh+Dq6tr9+7dx40bFx4e3qtXrzrX7sB8gstg9GDWX+z962qf39Y5bx1+ysWA2Dw1GJ8ajDnA1k84VIu5lxJ0H6a/O8OLjXA0hGqIwxgxzen9vX/GbH2gGuRL3/xACQCQGPEsxMgWwrka0y2dPrexHyrkF/+4gTThssPnjLlFooXfwTQqAABC+ZjjDGPOPHMns2dGwCWrKW4r36Y+pLEntYlP7H78HgBINH+yITu/PPrgVxC0W5v4FKJQhJOHUz1dqfXr2f5vnLG47GXwxDcg9XmE4iYm/g4Vf2csWFvLolZieQX/pk2Oy62bCUj1IkQQCY77iqmGIr05cI8zzS1flyM0+ZowcEuRtNRzKQBgmNOEZamzH9677IXYdis1/gRBhw8cXOjamzjnPGbMmDt37gz39NzbswMANsYCmX3yZRruOSag9NCOyScSQwaNvXQ6S7/7sTo80HJoIFTIF80cJ/l5u/1P0zAn+1q8eytW3gqTyTx69OiiRYuaNm169GjtRVapE+hBforjfwkjvgUwzEdtKk4cpQc1GF1aDgFAhaB0o8kVRSD26EtGvIniJ4qpwovlN919EROxEK3SImpclay4TwKyESfkw1vVNNfz9DI9sagVt+vB0vIKUqYgBP4wioIcLRGtVEEAhLk4sjq0kv5xAmHQ7ZdMq4xaAwBA7cJNkr249BzC72osWIuwW8Hs5vHx8QaDgUJ5dZlSqWwh0YLUbOHUUaRGBwAglGrBxCFlm/eVbYoRfv/tF72ON+YV0arEJIUQmObtbswtonq5vXlxzGkjAHPBowMAMAFoAwAYVRczW8sKfufOneYXHA5HrVYfPnwYADBu3Ljak6tmsEXgUtwc60YnlUpNJpMT3TVV/awwV0+ycXfb/k+VDxtzm7nTPdko91LWdeM1ssGI/p1e3I3/6/zh5EN9lOPDm45u/V1Y69atlUolm83GHKbMKPaHmYGkWjVscPsCWaniaPyqzh1v5FvIMlcJtX49/si+Jau2iVfMQjjv+f1Xrt2tZ+qs1CgQBC1btszf379r1656/bv+gb90eIN7SH7ZUTB9OcXDxZhbCNNponmTcIVyq62NG4p2KZJsEtq4oGjHopIlXpvFKEn5sFRsZhLlt2Lyf6MjdJIk/yzcM9xxwptm+zeDzr52BI7QETANBgCYSg2oAAMwVFnzrznyQpurwJv9XmLAyRknZSQgNRQSAkCjB9P6cRqJMAAA6NMR4Di7a9uq2h0AACAUc11pyJpBpXmYSnbRAq4BADQajUqlatKkCZfLJUkyKSkJKS43XnkM87mla3cB85728q0AANJoVF2/y+rUkuZbS97jNQEi5GmTnletMRaXMlpZMF7j+MsgS9eDTrR5YOEgSa1hWcGbNToAgCTJ4uLi5OTkb7755itQ8CIEeW4wYg40Y6G+Q9sexcXFCWlXL2rPuPDKyDIIcwl5odiDQmgrm/Ytbdrdtb2a5ekf0qzR+IkT4v86vz/jyUC7Z+iftitXrgwLC3sZuxeiYK7LjJkRECOA5Xdg3piW0v0Jt/IytsYdvdJn4YbRIRjb8jqeFdrUVCSR/Bxt/+NUyxmUAQDWYDhWaoUjR45Uvh4yZIinp2flFP+rBMIwu/mTDZl5xoIStFtbqpcbgCCTXJlmNKkJ0kCSaUaTkiCNJInCKAX+iBAgUmN5TP5vMz0WP5TfNZGmprzWvz5bUs/f2wYR4goTyscAAITu/UFzUzvcp9ajKy5XQBhM6ghuJ4HyjiwgpfVbTN7vhzSaFuqTv1fnwjQaJaTR0OecgT6skQEM87tc6qupA7d/F4s9INxQmO6tf9YDFU+BqE4AAAqFEhQUlJyc3K5duydPnjAYDLWrA31KgOGPk7YzxlA9XXMGT3PcuMhUXFayZJPw+1FftHYHADCC/GWxp1RX77DaNiUJUnH6Mq5Q0XzrvXmlXJsHALgedAJU0fHZ2sJ67FoW+S0K/uzZs1WL27dvj47+Go5x2yKwBCcoDlRNkkKr1ZaVlekKjMVEAUHZQZRPjlcWDodp2dr0oqJ8pVGmoisUTFbhxt3NiivsObznKSnP+j6rnxUSPjocwl79zlDhIEJ+FbUbAyFczGVRivevG2Mo1xISnuUq6skXzZzdpepcmNTnAYADAEhcw+3TAJeVGLJTqV5+FmS1hHURb6Um+Oabb6oWmzRp0qRJk7oSptag1HOm1HOuLMoIYpNcQYGgCoLYIFdQIEhGENqPtBwnym4KKaJMdWq2Jh0HhEArHNh7QGrzB/RrVJgCQ3SY2YRLGgjQ4z398AfYFS7LtJ/jJp7rXn6gOG/yM8EoR9JkettiQGVSstC3ag/ShBf/sB4V8u2bNybUGsWe/bvah7kFhaIfuSNAcV2uz5yGiadU1tja2rZu3To+Pt7W1rZLly6nT59GAryFk4dLVm0TRU4CABgLSkqWbeGP6M1qG2JuYl60fInPMZjNFM2bVL79YMWOP0mCoPl62EVOtOhF//D8nOs9T1QWzTpeLm0DRLUoLgDgbV4SrzFixIikpKSaFqUWsIWRUhzHHKnGAn39+vUBAHnZ+QKKbWNuAxFuKCS5XIo9HxMyEJY9zQkmIRyWlGg1wpF9RvTuAwDY/zDB91YzCIP++uuv7du3JyQkmLuleESRhIZQ3kJtB/s2FDpIFQIaQ5J+b/OzJwmr9hF/Z5Mk1I+0ySG6Z311jzvrHofpnvdlNF1jKlysOHXZorQWl+/WNb2V6gLDMPPEHbNEXUtX2wgReKPQ5rCdrT2CbBbaHLaztUUQ+kfajKVGqZbQZGvTZaYKmbEiC0sv9y+nXEDcf/dvmNeW100oP1tmM/T9njeyE8UOCzhlu/Jyp6cUzk8Tz2NUHErPHTkrb0yk7NCZ1xLnqEzKeSmTb0otP0YAAKortxEu23bmOGarIHbn1vZLplPizsDaj44UC9E8aH6nQBWDBUmSz16kXkYalcmUleGN6cH+gvGDJau2AZIsWRZlM7LPa9r9y4Xi6ihe9j/nHStc9qy2WzgFtRO+ec3xVO1Bn/Zv1sdoKKWaj8549Im8dXO4EpIkY2Nja+1gfo0iQpBSAmc0YtvNcJ0CpiAIEhwcXGbK8WY2iAgQxyAqJ26PQnlcCLelHdWhJOk2pMmx9w7GxKLwceN/3ff7qdxUuVLBs7HhcrlLly7dunVr1c4NmdNogXfZ+ojVTY+FOnlOPn9McmJ55PxTf6z4zXn+RJhOA6QBZgTQGpzTPWoJEAbmOBOmeejTVsmO3IAwlN2lzWvSfomTXCtfEOnp6ebfdXp6el3L8lmQ/s8tesPbV+8HDx4kSbIylxpBEHq9ftCgQS1sQu/Krw8Sj75QdspEmrrwez9JvM7ZJU4b81DeQMPMplX0V8k33wcj3yWG/Gi87hmOtyhk1IfLdxl4fVBj5iVS38b1j3WmCln5ln2yQ6f5Q3tVXn+8JNaV4XGkaF8wtwXVkruAISOPHuxfWUT4XIqroyG7gNbA6yM/oVdEPVA9kjvGXSwqwp2fqVlqW+Gl01kI4vD0GTG3LWA0awQAkPyy0+bbPszWFnaDvujNyNcdFP7J83JjvWtyZ0Vv8xY9AMC8RX85U7PAp7YjoFhW8Gz2q90eHMd1Ot2vv/5aWyLVICwYGs9mwwzEUKjrPq579+7dAQCjwHcAgAY+IEtmStQbWEb5Y2WSHdWhQSb/ZIgUjs8qPHWNxWaGBgZdfpi4c9OW6TPnNQD169evL5FI/uksMxk82N0xIZU/3Llvke7Uc8/TeelZsfN2TV4zcfEGu4UR5sWAqfwYQHlUl2X69HHUepsgOm638LuKvcfeVPBWrNQo7z77um7dulqT5HMgmErZplACAPQkuVmhRABwRBDOWwJakCSJ43hISIhIJNJqtVeuXAEAEAThRHdtwQ/7KW2mkCIiSCL5+b0erO5efRvc2lnEv8Ykf2K0DAkqXJiRBSRvE0Ob9Ex15S6jeXcC9tI8KRYvgEt+reB06kDzoQAYQoV84dTRBdOX8of0NHukF+ry7ssSltXffLBo96mSw/3FI97sE2YzCYWqag0uV8JvcQ/6QC7n6DRa1BmSZZuEAQz50zJWOyaUqUVz83FzWF1Gs0Yue1fDDEZlky99+f6BhAeyji/Vc4M6yk514PWcOmjavLML63ec+6zPHFJAr+a8RO/FsoJ/+PBh1SKfz/93Yac+QyZwWACAoqWZ/D4iVPhyImYiTSiE8jTwfcyw3n6oubIecNEiKcTMb5xpTgCAqccaXO7X77d9v383fEbmkEcLti0YP2V8ZGQk/FouKVIvP0YxaWnLGoUmlhcXPr29+8LJjhwqPe4Ct7cjAMBU+CvmuhJmh8A0L1x2GQCAigSiWeHKCwkUJzHVx4LLhhUrNUFlTBupVBodHR0WFhYQEJCTkxMfH79w4cK6la32WWbDe/9FfwPDcIsWLW7evNm8efPExER/f/9Hjx6Z3+pnPzyAHXS1/Lw6Se63NoiiwfJnv8DSYdoKG83yssywFDVPXb9+fa1W6+Xl9WZUAE3iE073UCzdtmB+mt0MN243kfzIHtlJhjDc1nwBwmMDEhAaLcxkAABiC3d+YzeYhbIHikcufDG1tU17u7/z5VTCaBYoWR3NaNEIc7ADACjPXYNgmPJpZ3SnBLGXJxgbu/FlRfC6FpzIO1B9kWNWLjm96auPsap2f5MvehH/DmwZsNqZIrlaPiWhNOvxHE6mHIvN4RXqW7Wqg6Pm/1DwM2bMeMelX8eM/qHBUA9FMUeasVBvVvAEScx+Nn6t384/EjSaFgQFsTF7zbJDm/kmXrlOOSm4jLQbN6plscaJL0hLT7+Sdq1+cydxHu5iRyQkJLRp84+VN7vTNVQcxQjG9NN/XdO2x7fxh0pOrtUtXssY8g3QJpKmCggTItxQAADmFKlPHQH9n72zjo/i+AL42z3X3OWihLgLCRQaCO4ugRQrLVa0oUF/uBQrUKBYseLuGghuwZ1AIO56Ocu57u7vjwtpSEOBKIX7fvaP2bmRt3u393Zm3rzH8DRXpDjYFq3aZj93AtXNqZbviYWvk2XLlpkT/fr127BhQ2RkifHU7t27z549W3dy1Tb61CxjTgFJwKcHeiOkjx1j2djYNG/e/MaNG35+fgEBAaUKHgC8Wf6s36mKqxLnP3xTejwT/ZWvG4dz+DqrHgLFEemulS73nd/+1+fkkwB22dl8+3bWl9DqEQZN/UjhtjOw+Jw4c1wCJhfY/qTXpWuBAEDAmC9EaBSzdn8mfyAzStoKugAAl8zrYtv7WMHeCW4zy4lK83ThD+ldOH8tWcDHVBoSm2k79SeoWpTbdq60dU8pe1LxdZ149RypM1oZh52TWjPQrh4V+179SobvZjTtGJ3H5Sa1s9q70tk/0TD8+/RcT2pTh2rzEfnxvKPgv4Y3+n1KdQcGvdUyb6oHU6FQJCYmhoaG0kisaeIC+29pGYB3Kyji44UsMjvIyb5zcc+9okPBlI4pkfOsQwImTJk8c97cdevWnVxxrGDRntlDSQNnnw0LCytdigMAml9zyeZogF4cnm2vcOWQ/ND9Lx9OvRjdtPEPW1rihCGPYvcDrrxjLoyQ2ISxZLKOHuQjGDNYuHSTw69RFKcKwtVYsFBDXL9+fePGv6Ohd+/efdKkSXUoT61BmDDRqu3GgiKaj7sxT0jo9PZzfyZ93FBeo9E8fvzY398/PT3d1fUdF7b5C9IUVyVe0Y0QEsJowNbGF6MxOtVxvdrJcGdGvT6XC6Oa6X+nMXqwmENcnH8RS+X437ZXNF939b3n7nvGAoLwv3Mw5hQUzj1OKEgOUX1MQrGxoEi65ySvf3cAMBGmYwV7f3AaY/awCwBdbMPnJUW9Vr4I5DQsJy27TSirWUNDbiHKZFAcbKrF4Uxje+oxmeZbRyoABNpQcIJobE99z5rG12VR5LlVlNCQ7ZKFrf8mEaGRYkN4/okK3RMZNLOtZUneUfBfwxu9DQkVYRhKQ/XJ6imbpuzYsSM2NtbJxWe7DlbYsY6pNY/1hq4UjQgrOq5iTm7eBxJi2FIQN/L2/WX4aKl08fJlly9fzlidzXWmNbZ2q1ePfufOnbZt25a2T6m/yn6etHDRBoTMUZxVeTIZbb/5tlufLo90+JUMTWsCN2T/BgA4QQAAiiAI06+0LjM0GNfqhIv/dFg0iWz3JVg1WvhP4OzsfO7cudGjR5tPz5079zk4lK0F5KcuIxRyvT/mmAfu8lOXJVsP280a98GKBEHcuHEjKCgoICDAzc3t+vXr+FslXardydYUALAdlqNPUT/AaUZfwsnDMRcQj1aMjIwMo2fFBm7sDs3V954VLtrA/DYEV6iUV+9ajx1EcbApPnax+MRFMt+KN7AHK6wRAFwWnWWQmCwyJ1ObVlq9Ga/1kfxdv/r8Uar1S0FoVJpntX2tGAG3svV2LNK1TF1HN/rtHD2Xit7O1RswgkpCcnVZf2Ysm++zmkl670o/glQc6uy/TpbctCrCeWcD5fB02xPnFx+Ztkxrwp9evxsjMta+IVvFa/Bf8Bu9HYkkwnDVg2Jxjk4HVAB48uSJp68PYjB1ZTIeFRoeUfVT7JvJcPxWQRECSFNea0WwQiskN5UWW1tbDx8+fOPGjWvXrt3060B9EmXw4MFFRe/Yy5BtqLgyy26yVeGiPGZYyDcNLgx6MdildUdlEF+obpGcfzkzM1Ph1Sn98j5VfuqqVauuX79Ojo0tjZ7JbhOKqzTCJRsdlkz5oJM7CxaqhWXLlkVERMTExPj6+iYlJcXExJR1ffMFo41LsP4xvHRantuzXc7JywSGIaQPOLfR6/VkMvnNmzdv3rwBABRFdTodgiDqJwrh2qyAp83M2h0ANPefOfw2DT9/VWNUZWbnvPy26Q0mEzxcACAeYElOPgLwDY1W0iyuM4HJYcEv6jtP9SmZKIvh8GuU2aG13Ywx5WSQGIqAIPbmbCqXT0VpCpOcAOKJ/J4Lw92b6f9PZQ8ApVHgKqdlz6ZorRno6BDWuieqDm70jc9UU5tyTiZpTyRpBwcwD+XtAAQ5Izw8uF4F7tFKuzYnvjA1v/m5GqVgS5K1VEQFAANXzZrafbTR3iGxAJmsxJw5n+A3qepUrOC/4Dd6WxIp2WCkuTEKLkt0wT4AkJaW1pbZzahHJ0tk1+Q6kw1MlEiNBGH2cdGc33aVbEFHZjtFdq61NW/ixImbN2/ev3//wundWErVxNbb5LdOiFapbQZs5/cxqB+KDTmDKQ4euGZY/W13SGybbnlaY5NHVA/nVyJjvMh4MN6u6PZNcqjcht7Kjsefcvjp8WOXTgY5Y42+IXFK1Dm3R1tAEJNQbFHwFmoBvV7P4XDi4uIOHDiQl5cXHBy8YsUKHx+fuparVjCaoMz6WsmyNI7DhxR8//79TaZ3g7oiCJ1OhyZ0h+luaQNeel/4hmJPBYIwqTSSzTc91jEYYYVU29y7/sHNrtwfNGbgNLmyB4vZhcn4RSx1Jpd0tzd3s9hQNNPrN1arJqxWH/A19GP99840nCw8ECu5jAPOJnFICDnSbabDu5Z3ZWO8Vm4kvemZypqO3szR56uw7sdEBSr8fr5BjxGbn6u86sUpTMWzvZbPT4pqbd3Jif4B3VFOAEJvMArFZAHPbGfwn6OvDwMtEGqVqhWX9phzTv7UcMqOaz0D6/Fpn4cV/Rf8Rm+HokU4TnVn0PP0IZPCeeL4oUOHutLdESjoSKdkGUxJYPKmkCWaV0A4A4AjvT6HbOUeEWLNDgIAb2/v3r17nz59euNfp2Z1LTBKB1Pr2f+aOWbmzWGkDr+RqTzCgCMUW0ajOEBpAECpNxmTdVm6alSG37wCprMDl6prPkCd/Ubi4EMYSC+vP2R2jELu/ilc9KfDwokos8Q+hdu9DSaVF63YavPL0NJMCxZqAiqVOmzYsEePHi1evLiuZalBnugNTcpsX8YA4vQGtwAv1Y0HpRPX6jtPqa5OFfomK8e/hPF2nOMBACndnpXoeJN94Tq6y2p/wdAemLQYf/yKTKVSqdTyu28A0jRJiap4Lpn3qPhOU17lN80+ld9/IX+81G/jn5nLwu2/L9Tnbc1avcCnmgOaTWzCVhkIAEABDr7RfOfLDLahBNtQKCT8ROHeH53GcslW3e0iDuXvmObxCQFwi4/GKM7fIAv4JomM2aSBYMygf990/hnStB5Vxpd3+77bxIiSNe6MiEj3nzoUFhayqbUda6fiF4qePXvGxcUFBQUVFRUFBwe/evWqZ8+e1dKfUqn866+/oqKiBg4cGBERMWHChG3btikUimpp/GOwJZFEGEbzYNDydSyeYPv27aGhoVSUigLhj+R35zARAkwE9OU6olCyqNbSuv0d6bXSFv73v/8BwJYdpzVIEWCnSOyu/C6P9thMvZ4+krCW0TyYAAAoDXAdprgDCJnmta194JNGF7YJxWrW3c0aE1FflyG7tDErK7ejtaBFwXOGQZlh0BT+uh5X/e1bisTnku0EwiUbCaPRhMP3ZyWZ8ndGDBYsVAsIgvz111+zZs1KTU01GAzYW+paruoEA1gkK94gV5aezpLIdipVvAHd9YnpwiUbi49fFK3bIztwRjBucNW7c5zjwetrl9r7ufKmtPhaA1bDBBLjjeZhnCLmJkujWdytlX9O/iWNdpJE5p+Tf1Wr46AIAcTBvO39HYf+4DTmWP4ePa6rdO93Zdd9WP5vlHFKkzxRFc8kscTGIpGhsOrXVZaeXoxBAcxBAcxFra1mhXF/a2tlPqXxLjjT3cxWfh1tesiNspeKJx/Zpurafe2z107r5tX7Y7bzX0sJnJDuPVVdAuMarT4x3ZCdD3htuJPLiIj8cKGa572e7Pz8/Kr9jf7u3bvdunXz8vJq3ry5v78/AEil0u3bt0+fPv3SpUuhoaHV290/EaoxiRorMOEJOgKjoMoCfbyIDgBaIDCgLlORML4OMxKHVJoUGo+OGsy1wvhtTxcekirFpqQ8uyYhzZs3b9Wq1e3bt/863HiUhxMQxKw+Xs1+ieni91ih6svvgJHYJAAgMKUhZQQtIAZleId1W9nY98/ky6mPeM10WwZntR2H5cTZtx/1gEQegV4hpZLwzmF0obpw0QaH+b+gbCYAAIKs8ejoIHkQFy3KMlIyik2Dz0q8+RQAICGwoRO/9l8GLXypDBw4UKPR7Ny5s2zml7QySgLYbWczokiiJ4jJPO5sqUyC4xttrFEEcVwxXfPguTGngB7gJRg9oLomzOrN8wCA1PAXrJB4193DxorHLr/cg+Jk/1tIwOQlm+pvXVKu/F3pdRTQpvzWCCDeLP8LRafCHSr5qiHVS1QmpRJTKE2KRHlcrjgBJ0wyk9SWWiPBqRGA0tDYcpPssjh6rtcK8ymKkAbX+2lv3pYATggZ+XtehCCIsmsEpb809d0nvMG9SDwuACA0qmBU/9yffxWMGlB1m3/llTvFh86R69nhKg1CItlOHlHLobrrSt+/o+ApFMrGjRvHjBlToSdqo9FYxc6ioqKWLFkSFRVVLn/fvn0TJkx49OhRFdv/ICeTtRfTdWx7ZI5QPkxASXqhnPN2VNzVm9nJiw4AdhLSVULbhk77gU1TmIq5ZB6LxA7kNLxdeNnwQBHh742ymD9PmXH79u0/j+dG3buSPy6VzKeMauux/2X2pvBRhAEHIAEAQrGl+RxEyHwAINsOwuU3OzhHn3zYqDDuNvr6QeDcy/Z6aSEG3qItdOTbBtrHFL+exsKiglkr7RdNIvOtAIBAkLjApqPIOdLjFzKHj96cYAj3pqcXY9eydEyKRbtbqDYSExPL5YjF4jqRpOawRtFddoLhRZJbhUV2JNJGG2s6ggAAQkJZFUX8rDr15nnYjKgn2fBkrHgsAMzsfH5nyGnVzYdUt/rlSupx3cnCAxPcZiKAAMCAesMXJE9uad3BhlqZ4CRh1m3S1EnjnacuuTc27KKKZSVIbGnk73hMjPOucPWhGt/kjuXvaSvoYkdzLM0J4IQ40JyuiWO62L4TNbXCTrFiJYn/tzNXlMkADCMMxirO0uuT0uUnLjku/595a5Ly2r2i1TucVs+qoieAf8Fv+7l/ZmojF0Ot7xV85wpTU1O///57c+KfVL2ztLS0Pn0qCI4bHh5eO96wxzdin4mwudXS/kyEjWK2Z9N21qf6Wg82Xt3WCl8dzG+AiroyNoHe/QAAIABJREFUGcudea0YdCqC8E3pGzNLXkVbWXd8Cc8ROi3z8i0AuM0I8wpsKJQJ91076Pqnv/alcv6+hY9fPn7s8PLBm4el3aGcbxGKDeB6AKB6/HGT1sixnpeDSxBuMsq2/JRP5aE8h7TkRpgRNSRbae4/J/RGU5HUmJlnrv7zN+ynhYYEew+VwCH8/J4xfuRHBcZHBYYJjTnv22xqwUIlsLe3LygoiH/Lw4cPy7lv+jKwQlEPCinfhHtTKPTq2Aj+Qaj16XO+v1d6OjIuXLbvNG9w+eXOaOGxAE6IO7Nk4xyfIuhg0/14wd7KddrBpnuxSfr7w4lykipxpOu+7hkjfaeCEZMd+lvrEGWoXC//RI/rnsjvnxMeGxkXXvZ4qXhyV/reQDhlobo7a1/8HXBd9yaVZMOv+hq85vFLTrc2pRuPOR2aA4Ax/73egqvOr2zVCxuGhsNwP7HxpYCmZ1AXaHOKpv9rBIKa4R0F7+rqymazzYlS6tev7+LiUs6TQ+Vo27btzJkzhUJh2UyJRDJ16tTSfWK1wAOdXobjqJGweqm8evXq4MGDp0yZAgBrMxaLDcJCNYYXQaxO78Xy92CW2BIHchoqTMU8V3pSagqmUGKAfD9hJgAsX76c2YATnN6axWNnZmbOmTNHJBJlZGSU7U77sgWujsvX0O7pOs/xWiCYvMvdwzO3MN2wd5IPHU8f0gVhUAQ/j+D0aGvMLbSfG8loVBI9th6b1NmDvv6p2mXCIIqrU5+zu28nF0u0eDePOvCIZOELZsGCBU2bNo2IiBg4cOC4cePCw8PHjx9f10JVMxjAbKlMiRPnHG3v6/SrimvP7qcsDosmlRvBiwzCq+Jof3aDN8q40sOV4RmneJKsflOJLsgIZabn0sAXNBuOM48mmOn1WyPrMP7Qvpp7z6rpIiqGhtK3NDiyM+T0P49Fvv8W9aAU3sDuirPXZIeitc/fKM5dF63ZZT0iouqC4UpNybrnW0hsJqZSV73l9zHBq5FVtlA6oOOF3sP5YY3P6sQTaQ5BDuVnbmqBiucokpOTw8PDtVrt1atXXVxcHB0dHzx4UPXO/vrrL4VC4eTk5O3t3axZs7CwMF9fXwcHh+zs7HLrfzXKUbXmgU5Pz9XaXxPb2dkBwOPHjwHAg+mTok6gosid54Z7Oj2CkAfWG2GugivUDV+x04quSayYmZOWGHMLW3Tu3bBhw9zc3LIts1isoqKix48fl13OoLouwrXJm5+pBExKiv0CJ77OdtBSlMZMfX7xQcyeS/KWxSw7fWqmaOV220kj6A3e2Z7kxSOrTLivgCIY1Z/p77H00T4vss4yfLdQvWzbtu38+fOxsbFt27ZNSUlZu3Ytn8+va6GqmVkSmRjDN9pYO5JIO20FsTrdJoWyhvqS4/h3QlEhhgHAzpDTpfk7Q07/00llkb7Ai+V/T3YzRnSy9LgqPufJ8hXpK2kZhyKkkDjGVIdZA5yGmzfIaV+bcJUGPn68ThhxdRyufoGrnuDK+7j6Ba5+QWCqD1esAmQ7Qb3fZxBGkyLmpjFXaD83khHiX/Vmqd6umkd/OxI2iaSGnAKqa015BBev2+uQK8bDQ/9KWBk9XX7U5niBj8qpQ0vJgnX65Mwa6vR9VLwDsnXr1nZ2docPH27fvn14eLjBYDh37tydO3eqpcusrKzXr18XFBQQBGFvbx8SEvIxm+xNJtPmzZs1mvIxjBMSEg4ePGgwGD5egGXFcicSmX1eZbM2q1lsQy6XSxCEXq+/Io4u0OUNrT8uvdg0TS+by7NqRKO8VDwN4TYpWrlN6Uxf3+DKdNpcW471mAPpET50zJTet2/fco0XFhampKRQqdRyNoNXM3VpMhMAIco7sTunE/vegZcHZiIoEjBuxwFVKh8x2k79iR70jnbHCOh6ROTMJTe0o0Q1Yd/N1T/dcsZblBm0amote0uwUAswmUyJRPIv+69qtOvMzEwbG5tGjRrFxcVpNJrg4ODPM4ZsdHR0//79dbpPMzLHADbLlaO47NKZeQmGn1JrRnDYIg3mwKr+p2mvUr1Xpdpja+NEJl3SaI+kDJ4ZcMznIzbgvY958+b5+fkNGTLkI8sXrdzGaODL6doaAPLmphZtyGI1FPvcGviR1U2iw8asuQjNmTDkA65F6J6ESUzid6e6rajkBdQW/4xhQ2CYcMF6lMVghjXClWpFzE2r8E41F73zVf+fZXTj4TFipwckZRDwn+IFTRDnN9Q+d62Lwxo0mVbivaB2nveKreifPHmSkpKCYdjTp08vXLig1WqXLClv9llpzDP/5rRGo/H19c3JyflgLZPJJBQKyzmXAACVSvWpy0i2KEmEYz9+Z6f1YVCp1IMHD2q1WgDwZvrHSq4AgAeP3FZOv6XThVBJ27PXLvL8Q/cyabrHDJMoub8kXigKA6bz7VwAwuafjdvb29Pp9HLu7QCgDW1bO1dXknVvIrgf7+yvXr2KYwXdl607X3Bw6pXwqR1H9HUOKu9a5GyKlk9Hf23J7XtSPLwB88+nqkHDeqXlK5IvJES2dbT4srVQXfj7+x8+fDgqKspoNGZnZ+M4LhKJ6lqo6oQEMMGKUzZHQEJHcdlPCw1/PFYe6FX9j9JQDgsAhonEP7HZm5Wq7ZXV7gcPHtTpdCNHjiwuLpbJZACwcOHCDh06tGzZ8t8r8n8IL5z3h0ksVcTaKW9r+F3vabM65M5Mqb/842LAE0aSdXeK8zxdXDMg86muS3FtIq55XYlLqH3K6XiERLJfGKW6/kAXn4wy6baTR9B83Guud13U98mpe/2LnM5eSmzowblxL60XvX98m+dFzIY+35UfENY0FSt4gUCQlZX19OnTBg0asFistLQ0FqtKwYPNZGRk7N37juWIwWDIzc1duHAhACxYsOBf6tLp9ApfMqKjo8+dq8Bk8V+wJaEpeiNCRuQXxTRvZr9+/cz5Lgx3qVGsxlQmE/PZK4PEC5tsxR3tMoliQBAy+USE7UtFnzPCI/O8+40+ntsp8d6AuQOQn99pObH9YyCgWbNm5S4TAEjctoasmSTr3giJO759H31ivyAB58mdRleePn9Mk57MFBxvhDmy3xlJ7H6lFmnwX67IAKDVgSIDBmqTWmdEPZKF+VcOO8wZ909zXAsWKsFvv/3Wt2/fLl26jBw5skmTJiQSqUJj2C8PjKjBTdFDOaxEg3GpXL7FRlDpsXuzZs3atWtXOkM5ZcqU2NjYiRMnfrAixdG23sqZWWPvq5/JHacZeBETECo3pffzT9DxAKaCP0k2/VFWiDF3BUnwH/hJvC9mHUIicTq14HRqUQsyfNuq5QnBPqUs32cOp5hLuM/3TdE+N1GA831jG24FY8IapWIFHxkZ2bVrVxzHV61alZiYGB4e3r9//6p3RiKR9u/fn5OT061bN3MENvOIPD4+vuqNfzx2JFIRhgOA4qqU086aHVYSPApFSF5Mv2TV6xBu09dpJpo7ZJtMwdwmAFDMoOkS0xr4NT6Qt+21OK6B9DXZ1u758+dPnz5t3Lhkj83LF3GKEeKC/Vlz5swZO3bsvn37ACAwMLCkcVYDesD5kvtg1YZsExH//OxwK/f2QxoNnTclRsQafl56LFzALePLcH1HntJAAEC+CptwWfZLE047FxoAkDq1tkkVKKJv2PzyY+3cMQtfNl26dBGLxRQKZcqUKQEBATKZbMCAAXUt1H+eSxrtHb1+BJv9q6zYPFdfiUY8PDxiY2Pbt2/v7OyclJQkFouvXr3K431UvDvherE+19r/UaNSx/jeZxt9go7H9aaiA/Tg2wjFzpS/htClfbjKZ0PdBpsvfq1R5JlmK5ocbPKq2XNrqmPQQeolWjELuB+uW71UrOBnzpzZtGlTvV7ftWvX9PT0OXPmDB8+vOqdubi4vHjxYuLEiQkJCXv37vX09FSr1Ww2+9ixY1Vv/OOxI5FEGAYAdG+mPllTquABwJcdlKiOb2TVtGV9mkSPXdXqRnLYl0VnA8d0Fa3azu3WtlX94Ov5OxBoZiIb0tPTtVrtpk2bfv75ZyaTGf/mNaknQV1M/iHph7179545c6ZRo0alCt4Moc/F5NfJdkOpnn91oDidVccMSQ/iJmWPatcsV2macKV4R3c+5a0RnatVybcTZEvZ31vQ2J76d6xqQUNm04aK8zfJAh6zWfnQkBYsfAyTJ09+30dPnjxZs2ZNbQrzhXFJo11arNhua+1DodiSSMNE4kro+GnTpt28eRMAWCxWbGwsiqJBQUGdO3cGgOHDh0+Y8G8R1g25OuGarNKgdmZIVuT6v3mndH9mF+lMdf7AZhxc85pk8x1CdQQAcr3Jprw1qNVnvXny8wk5PyTkp5MBe2gO4aQ78bizc0E7JLC4kUc9z9qX5L07/bVa7ZYtW3r16gUALBaL9KHoCx8Ji8Xavn37lClT2rdvv23btjrxlmVLQkUYDgB0X5YuWQ0AsbGx69atIwgijN8mhNMEANq70nR5cEmjA4Bik+yJY5bDwkmYWtPwCS2ZX5RvW0+nLWQyGB4eHnZ2dt27d9doNHv27GF8y7HZ4QYIbNmyZc2aNb6+vuX7JrGMeSvNS1kUl3k9e7gJhmvEe0/qEtPmtbBiUZCp1+R4Rbck1LGMdn8LPchbsvO48mJsdd8hC18F9Ldotdp169bFxcXhOJ6RkbFlyxYbm9qeS6w1zqdpPbcUmI/BZySPCgylp+0PVY/lgRzHl7/V7gAwlMMaymb/Viz/1HZ++eWXrVu3bt26tXHjxhwOh81md+nSxZzzwSlVan26++6gzBGvtfF/m77rktUZI+JdtwZ8ULsTmBLXJqE0F5P4qEl8FAAIQw5R3f5u34cxe6H2eaMKj7islw/yKjCp/qd2r0N938qn7TDnn7cXbcx3NcWExBFA/BIwk0x+r9/YmqPiLvft2zdjxozx48dv2rSJTCZPmjRJKBR+zMLPRxIREdGsWbNhw4YdPXq0utr8eLgoOpvHxbU4EKBLVAPAvHnzYmNjw8LCQkND+RQBALRxpu18pRZ5mPJNWB/7gVpMQ6FYWw/taw3QPB/RC7JoUlQnkTVs1IjL5bZt2/bSpUvXrl3r06dP06ZNAcDDw2PWrFk//fTT6dOnpVKpu3uJTQdC5tODriFkawAAQKne2/TxnVfkoqEH2CMWz1/bkRd1pThJavQXfNSKHdXVyXHJZOGSjSaxlD+kT9UdOlr4qli2bJk50a9fvw0bNkRGlnjT3L1799mzZ+tOrpqlhyejh2eJ6fKjAsOaR8pDfarZyM4KRW/Ue2c73FAOy2x290mY7ZFnzZoVFxc3YMAAZ2fnPXv2uLi4lH5T/w4v3A4AUsNfeJ1uyAhi65LVKT2fOy3ysh70YS+tCEIBhGrIWVomD0XeH9y9ekG5LUF0iBYQjaAUAMhRInfzUUKbhhVf3P7GXm2UTfm2xNnXNw5UH+v3Ks46nKhvyP22IfdbiUHEo1iTKgrXW0sQFdGsWbObN28SBOHg4EAQRGxsrJubW4UlqwKO4+vWrfvhhx+q0sjZs2dpNNqn1sI0WEqvZ8+sr7+of4sgiJEjRwLA+vXrCYJIVMZnalLNxeZIZLsUynJ1CxXZv7z6ISX+xalDh/Pz81+9ejXiRZ8uXboAQEhIiFAoJAhC+0aFmbDWrVuvXr360KFDEomkXCMm2VUC0xIEgesyMi55HtgxBzcYcJ3+0KFDJ67cOfBa/QnXolIXzFsrXLkN1xs+9T5Y+HxgMBgajaZOurayssrPzy89FQqFVlZWdSLJB6nc8/4+HubrB50WV1drNcHGjRsbN24slUonTJiwYcOGtLQ0FxeXq1evfnwL0hOFLz1ipccKX3relh4rrDlRqxdd4kBD/npz+kqG1u+vghmn9g8/9eabnQXN9woHnRHPvlXccGfh/vhP+Kv8rKid573iKfqEhISAgIDSU39/f4lEUu3vFgiCREVFmY3RahNCjz8dEofxyIGPm+LFxqIN2c2bNweAG6lXpr4ZtS5jybbstSqTEgBaUejmWfoUdcKunD8xabFw2Rb9+LWuifDi9jYqmZKRkfEHNhcABLNRc1Dty5cvA0DujGTp7oIdO3YUFhaGhobeunWr3AY/THrWkDUPABCam0vzI31DjilvRb+csmjw4MHf9+q4+vCVvfEf62gJZTHt50UiJFS86UC13icLXwvOzs5lt6KcO3fuY1xTWKgFBg0adOvWrVK/Qx4eHs+ePQsLC/v4Fvj97Ouv9Mkc+6b+cm/+d+Xd7Hy2UF2XmfLWEcYiAOjgRndnKzvavFCD3eLWvA2deAUqbLAfk06CCF9LNO1/o2JHN126dAkNDZ0/f76Li0tBQcEff/wRHR1948ZH+ROuZT7V8QWhx9OHvIqjYIytft24TPUDeebo11bjHGaY/tesY6gd305qEsfJnzjS6k92W97+kITaEU442PJAvyRl+oS97owQP6vvuuUastcm/9rkXrMrzS+WtvzmYPrD31+5u7snJibiacbkbs/874dSHGkAcOfOHQRBWrQos0kD1+HaJJQVYj7DJKeT7v5v8wK7Yn/X/YcOcbhWHZeePjmhLWBqQ85iszf7cqAML7JjmZk6gsBkcmNeEcna6p/esix8/tSho5tz585FRER0797d19c3KSkpJibmxIkT1RUhunqpnKOb91Fz++CrnRcvXvB4PDc3t8pVx7U4yqip2Co1hDFrHoEpqR5rCUx5+sb0zaLZCEq/MMAWReCHaInSQIR7M4YHMgpmrUKoFMfFk/5ba5S187xX/JVv3Lhx//79bm5uMpmsZcuWK1asWLv2o5wJf+bgWjz1uziURUrb4JmP4ACgS9M4zvV4feOJR5N6EwKnW5F5AoodnyIgI5RU7dO+3syWVFqMRssgMX9lz8XVGt7AHggJdWa41eO46etL+zz8e2+o3wA3V1fXjIyM1atX0/1ZtmPqZ/+SCAAxMTGvX7+mUt8NmYDSUVYIgalwbSIAkATh3k1+6TRGPmPmxKGDBisV8juLBmy9EncxE8dkMQAmlB1S9sA18bjmXT/VCEKy5uFaXeH8tdpn/w1/FBY+E3r27BkXFxcUFFRUVBQcHPzq1avPU7tXO43sqX+0/6gtZ3VOw4YNK63dAeA/p90BgFx/BlZ8CVc/N+au6ObOyFdTOrnRzEvv/X2ZCWLjd9508dpduFaLFcvFf+7/BEe8Xw0Vmyd4eXklJiZeuHAhIyPD0dGxW7duVlZWtSxZTaB+JFc/KA54GlaPjqUZTQBgEhmMQkPW6JxCWn5U/I9G3EAgAAQhAeE92Y05zZs90uuXyRQ/cdgPJLcuRyQvAsIc0rGXff+dxasisuz7Xut4qsNVANjd+OytPbfatWu3ePHiQYMGuf3PLanDE/UTRWBg4IgRIyr0xkMYcvVvetODriE0Z0q9qK7dCnD1wl9DeuTExd9IeLX4pz52k04s6bChvXoe1X0tvLXUIPSZxuwlFJ8KljaYocEkHkd95wnjm8B/fmrBQoXodLqHDx/yeDzzBuvo6GgAmDp1al3LVeOgCNjXgJ9aC5UGk5zC9VkAgGsSUIYPym5iSBxCgOGJ4J4VDe7nl8T4uJKpq88hndp+rYtOz2oaglKp2oQ00drdNlHDkH9uN/qKeedeEARx8ODByMjIpUuXZmRkhIeHN23aVKlUbt26tWHDL2GzNacNv94Cz+RuT13yTLkmEwAwAtmqe7J6m+1dyO7L/bd0t4toLeg0ynUSDWU2sWoOAKRitBjH3+jVp0kXhWz1iZxd5qZ8WIFsJaoINqqtWHMTRppDSrRp02bQoEFarXby5MkIBfGL/ZbVhOvq6rp58+bBgwfn5+dfvXoVL+M6C2X40bx3w1vbVKrbbyjD8wa2P8q1QaiPX15Olnh9/7k3uNeLW5tE+0trGbLmkR0jEWrFprA0H3frkf0VMbfEf+4jysS8sWDhfQwdOnTy5MmPHj16UYa6FsrC14ghZwmhyyJ0GZj4GK64h9I9CZOQbDdyUxzpf005Sj1+L0+fKjM9KjBMEj/eQfIQTBsNKAnIJPuZYzGFSrx+D4HVmG/C/yDvjODnz5+/ZMmSoKAgR0fHTZs2jR49euHChQEBAfb29mYztC8AuwkuAGDq+0q1xx1sAVdh+ixd67XdHzOfDBzznaOLfYcxbSTGIi2u9mEFAsCaR4rgEMoacXxTbohtvPoyFtOhKITB5KnvPm0vsj3b5/lw9ti42PuuChWJywaA1atXx8TEnDlz5tSpU6WhaPr163flypXJkydHRkbu2bOnnEgjRjQndKkItT6gdKr72iH9R5ygFuxx7fcjnHqSnIBuGDBv8kmSYXXnNuEI2QpT3MbVr2he2/79Mjkdm4s3ZRTOW2s7bRTZ5kuLDGaheomJibl3715wcHBdC/KfYWRceNkwcRaqEUq9Xww5S8k232GKezTfgybJ8Vf44OdCYytnzE9AnnlDzqEiPtLchCK13Jl6cF1M+8xnCIro4pMRAtc+fy1cutFh/i91fRGfC+8o+F27du3bt++HH34AgCNHjgwaNOjUqVPh4eF1JFtNYTfBxYQRQ4elCScSRb9nsBpxWZ7c8ch0pz3OCAnw7ooGNt/4sAIL9LnWVJuenoyLqYrnntaLHYY4ONsIHy84k7Wv63NXmp9ns6gF9wqXFdlLeA72xmKFWcE7Ojr+9ttvkZGREyZMaNeuHY/HUz9RGPN0a9asadasWXp6OgCYd+WZMcfJNQp3E8ZCmtd2QEg0r7/69YgAg36jw6jx0QeePXs2wyZrbtY8StyR9t/8ZMycRXVdDOgH/FQgVIrtxGHy6OsFs1bZRg2lN/iHyx0LFt7CYrGcnGoqeuaXx8i4cLDo+BoD12XgituMhs/1qaNMov0ACIuKjAxhqYyEE5d8J9cg0ePfOnGUUnoET+kWHESjyhAqhdWyiT4tR5+ey+34gTA8Xxdl98whCJKTk2NO5+fnA4DB8Llvra70vtgFi+Ofu8ZqXiqTOj6Rni5KaPWogVsgANy4eYMgCC2mwXCMIIhiHea/LbNbduJljZYgiGxNxs+vBhfo8syNJKve/O/NGCNuLNsyhmHmWE+jRo0iCEKXpolzjdW8Ur5+/drW1rbirwDXm8SnSlvATXLty1Zjf2waERFx68pVHMfjC4RXrg1WZczTve71SZepfZWUPWp28anLlbhFFmqTOtwHv3nz5r59+6akpGi1Wt1b6kSSD1K9++ArwYgXfcoedSjJF4nm+Te6hAHmHfCY6oXmaaDmWSCuyyktcCNLdzpZQxCE9nVK9siZ2vhk6YGzxScual4kZA+foY1PrjPR34Kb5Lguo4JDn1e2WB3sgycIgvI26pHZ6ptShQDGnzmdp3n5JzVnNGDTvJm5kxLZzazCFE0AADNhAEBHGSiCAkCRKUnAS22otz2u0gAAncTACexowW5zI94sf0eaU6zkctmWURTdtm0bnU7fsWPHxYsXaR4M59U+6UNe+br4/v777xVLg1BJgnAAwNUvAQAhcWl+J1dNRiK/t/O69Fh982Ggg10b/+aDYtucw/74pMukB/k4Lv+fITOP0Ffg39GCBQCYNWvWmTNnvL29GQxGqf/auhbKwlcJYcBVTyh2IwEAZYWgrAaAveMRpK0LrY83AwDoAV62U38S/bHTJJYaC0Titbttp/1ED/zYQHk1hyEtSveyle5N33ePXtpngbgmoZaF+XoNDpvTaTQayVhkUFyRkB2o9X/3WXPvz3PuhxoJSzz8bM9el6x6cyDvr4mhpIHWpDiDPt2gYJJYY1ynZGvSXytLrJC+cxwaXXRMj+tu7j+ieF6yP83Pz2/JkiUEQfz0009SqZQfYc8Os8qdkfyBmD0Epk8ZYfb8jFBsWEFnwzyfW/8It+/fMxqNFIexf7Sjr43jXsn4tE3AZAHPdtJwk0ia98tCQ3r2J94nC18+qampRUVF4jIkJibWtVCfKWWn5S1T9NUOYSoGlKZLGaZL/E6X+B1hyCcwBRAVGwvTA7xsooZpH8Zp7j2znTH6c9DuAEBxmgQoix4cy2j0vPQgO4wh8bugTP9aFqb8NrkNGzZwOBwAUKvVALBixYrSj2bMmFGbktU0iUajqFAv6BvP72mjuFMMAExfdodLvVN6PgMA6+8d69HrX5dckBhEtwzbbymAw+gblSV31183V39cfDeQ0xAAnBlugeyQmKKTtjz+4yvX2wf5IhQyAEyePDk6OvrWrVtRUVH79+93Xu0r3pMPADt37iy7Bv8OCInuf4YwSUvOKLa0gLP6N7223tbckBUsHhPp7d3iaG8DhmFPCw2NHagVN/IeKPUdeN/3Fi7dbL/gF6pLvcrdNAtfJAKB4MWLF6XeKtVq9fDhw6VSad1K9dmyM+S0ZQG+hiBb9yK0qWBSlJwjNJTbAqFWsLJphhHiZz1qAFDIdL86iNVWISirIYnX3pS/huI835xDGEWm/PW0gJjaF+YdT3atWv1bNMDbt2/XvDyfTKU9W13QaGmtnvv1sXec6R5XPzY4q7XZF4QuSZ3U7onn0WBtqOG3lJmrA3agCPow33BNprturb3kaE8COFN42J3pZQ4VDwAyo2RB0qTZ7iuuH77Ykud4z79NVw86j45mZGSEhIQolcqjR4+Wjf6Ulpbm5eUFACKRqMKwXYRJRuhzUVYDACAMhcqXPYqRvuguE7dzq/C1SxMSEgKn7m/6bZNFra1In+i7CVdrECpFfuISt1cHlGXx8vgZUYee7BYsWLB8+XIGg0Emk/l8flpa2qxZs5YuXfrhmrVO9Xqys2ChJiCMRbq45vSgSwjdEwAM6RMRkhXFdVHZMnXgye72v1KjctQ+rmTyqw4ceYwYU2KMBmxjfslfhuxkEcWRRvdl2VLt+RTrZPUbAKjHIV19qbdBSbFaHQB4sfzuyW6WNsWnCNrZdDsrORIS2OBpTvrReGWWAgMAd3d386L7+PHjc3KLso7IAAAgAElEQVRyzIWNRQYPF49Lly45OjqqVCqDwWCeLCkLYRTrEyNwbRIAIFQHTvA5W9IZ5nBy6sFTAj1WWFj4cFHfZ3eujr8o05o+zXkTymIiFAquM+T/b7k+Mb1Sd87Cl8a2bdvOnz8fGxvbtm3blJSUtWvXljo/t2DBwqeCUOzIjpGG7F8BAFe/wmQXyfWn1YkkX+8avCuFvDvKhtfTNrnzU4+DwTRPJgBk/57cfGHbZZ5/km2pANCE1+Jx8R0AcOaQLgyw+Z7D2q9SA0Agp+E413e+sG7M7kkFj8Wnd3HlWlNuni4xzZw/duzYHj16SCSSQYMGGY1GAChal5U9MbFz587Tp0/v0aNHTk7O+fPny41IUIY3zf80QnU0nyJUR3rgeQYlZjfp6XSPRgNatlMpldcWDhbeOfrDWYlM98mOHayH9xOMHSRas1O2/wyBYZW4exa+JIqLi4ODg4OCglJSUgBg1KhRW7ZsqWuhLFj4D0OpF4lrEjD5dUPWLIrzbITErRMxvl4Fz0IQKxSFWa78vnZJbR5njn0j/CNLdCA3Hy88fOpIgUhmxIlQXoun8gc4gQEAjYR0oNJSjaZUY0lcuDzd3wZrio2He+R+c3U4qd38ySQHe/nJy4aMXABAEGTfvn2urq737t2bO3cuADjO9tA8VYi25U6aNKlFixaTJ08ODQ1F0fJfBMoMQEhcTHpO89BW84CvfepLaBOW/5IYGLln12raggULjEZj9LLxigt/DDojKVB9spJmhPg7/j7DmFNQOOcPY6Go0rfRwheAv7//4cOHURQ1Go3Z2dlFRUUikeUnYcFCFUCoVJcFhpRRgCnJtj/UlRRfr4IHAHcKOd1ocpzrwe1oLTtcKDlQEHylZctWLU0m0+St0ScStTZUe1uq/WtVnLl8/5OSziT6bqXKfLor588XiscAgMmVl6WUIr/xWgN39etoKUF53KT9vmtZh99ojiRoDFTukSNHKBTKypUrz5w5g7JIHoeDC5dnqO7INm7cqNFo1q9fT6VS5XI59o/BNIGpAYDRJINk1RYh8ynua1BWQxP+5vjxY/O/H44gyNXty0jR83PlldkCR7Li2M0cy2r9rSL6c4wTaKHW+O2332bOnJmUlDRy5MgmTZqEhYX16dPnw9UsWLDwfkjWvUj8rlS330vDiNQ+X7WC9yST040mAHBe50dxpHnHfEOxo0ZFRbm6unIdXIw4AEBfh++pCM1cvrM7XZNGXNPqRBgGAEOcRtNRBgBgkuI3ArdXEhNJMSTZdNKIFKO6jEy57JXY+FJkFKrxpk2bLl68mCCI4T8Mj+1zSbw1F6Ggqd/FFfwvbY3LUnIqAgCJiYm3bt0q66keAFBWMMrwILTJuD6L5rPPVLCB6vYbhYRMG8rs5eW3beh4Op1+4cCWFRN/7HhIdDZF+8m3AEG43dsKRg9QXLhVtGwLJpVX8ZZa+C/SpUsXsVjs4eExZcqU3bt3r1q1aseOHXUt1OeCSWzQxCnrWgoL/0monptQTtM6FODrVvAUcobJBAAICQmMb062oQBAnz59MjMz6/k1MpcJ4IT4sksis30fyLqYpO9Cp+9TqQHAnentxw4CAIqT/bjXFxaFoCtaBnRz6OLpeqwx4AGsghnM7KWtrYLtKAAwffr0Pn36FKuKJ1yaUnQy1/N4sP/dppqHCuycZunO5QDQoEEDk8l0584donzQQ5IxdxmJE0oQRpTqhMlvI3TX7/u3DYo4+/0fk9evXy8QCK5du7ahA+ePR8rzaZ+u4wEAgNO5FdXLNX/6Ckt8mq+QEydOMJlMCoWComj37t2HDBly+rRlDxgAgElkSO72LKXrM8VlSe33vlGhfF7GP5WOIOZLiyWWYCoWPpqvWsH3YjJHcNjmtGhzTuHvmRUWy9FmpqgTAMCOibZ3ozFzScdUGiWOAwBO4Dty1uNUErd3B+HSTdqn8Z1VTVFyZoHieX2Bzb3rNzBFyXw+giDbt2+vb+/0Sv9mA7Izf0Fa0boshImymvHUd2QEQfj5+REEoVarHz58+E73uBbXZ5Fthxmz5uaavjcV7QUAqvtKst2P4uc9Fv4693TvYU+Pnfa3o5/9zqZlfdqpZK3S8MlxkRESyuvfzenPBZhcJdl6CCtWfLiOhf8+z549e/jw4ZgxYx6W4fLly+911fA1YRIbUno85/e19475JmvcG3mMuJYFaESlThBLn+kNAKAjiEixVEsQPEs4VAsfTcXx4L8S2CjCRktWR+gB7DuzU3s4MM2nOhOOIOiax0oA4HAygrzObGy4FgAmNuEkiI0yBn2/Sj2ey0ERVGmSv1A8avxdV4qDrSLmFq7WKAPDHzfbF3aCeOHuGLdkXfCUsWQHGwCwsbHZPmZLr6X9diXv91K79YSOAXHNM4fFF6zK5PWxO3jwYERExPHjx8t5CSWMIorbCpQbls/e271bt7jjToRJAgBkh3EOZOub22eR0CDy0csKA3C7twGAZKlp41Pxlq58L/4nf7konYZQyCQrTv60Zfwfw9lt6nJyyUItMG7cOJFIJJfLBw0aVDY/MjKyrkT6TDBrd164neNsdwDwPNEwLeKFy5/+Vt0rcFxRQzSn01YK+BPE0lUC3l6VxgpFlwv4lvD1Fj6BmnZ2X9NUMfjE2mKF2IQRBIGpTM/tbkiLjcU6rFiHTTj2etb+W+a0TGeamTA+Xf13GIMMo7F5boEKxwmC0GN6DDeVfmTIE05ZePHPFyu3Z69TyIrzDpzOm/IbbiopoLglXeQ3GwBoKPV4+72p/Z6nfvciPvhe3oJUgiAuX75sb2//6NEjgiBUKhVBEJjqhfo+r9xRfNeuc+fO5gZN8luaJz7ChxsfDfi5YPMBc+aZZM23uwtj0rSVvi36jJy8acsKF/9pFIor3YiFT6IOg820adOmTvqtBLUTbMYo0r8JfZC/NL1spvqZ4qV7bPF5UU33Xo5bGm2D7PxhRSLTh8ta+M9QB8FmvkISjMY4gwEAUBaJEcCivFZZ0VArGnp64Yjfh3UQ52ZY0VAejdTKuuNNySVzFZyAPQ80Lei0fUoVAFBRKoqQCnS55k81j17OddeOajAhRfUmFUms930fIKHGrDzzp+ymVn20XQfzIvS4ITL+fxlpmapbUq/TDeWXxML12Z06ddq9e3evXr2ePXt248aN169fo6wQZjPZTekBj3DaxfwdIT/anEyYeyAG2bx5s7lBErc1PfACh7r7GPtZfHJiyab2hCuu1+Ysupi66qES++TZegAAqlt9x+XT6UE+BTNWKq/dq8odtvD5c/PmTXMiMzPTHNH4K0d2sghTmuwnupTNZDbi8HrbCddl1aYkBoI4qNY0pFGTDaanen1tdm3hC+BrV/A+FHLyW7Mydgu+6rbMnLZ28sAw07Fjx8ynra07PpM/UGMqAEARSJIam6ho+5RqBY4DgIkwrUibk6/LAQCsWE7iW9FQ+miXyXtzt8iMEtSal3nnkTmYm/yimOZGX9hjbhjtW2GRcHzGVKwJSXFJ7HW8YfHpIlyLd+3addOmTT179qxXrx6XyyUIQiaThYWFrV+/fvTo0WKxePaCdWERF/h8vkp0H3ANACB0D3rQpcVTbVuNTQXQ5tx7vO+v7cf37chc0uX67btzblXSMB4hoVbhnRyXTSOxmcZ8oT61Vv/XLNQOa9euDQwssSFdvHixu7u7p6dnmzZtxOLaXm/+rLAdXd+qi01qn+dYGQ8TwvXZiptS991BtSaGgSCiJDImguyyE6wS8CeJZY8sOt7CJ1HTUwQ1TRWn7KLVmsliqTldHCNK6fXMnB687BAAdOnSpbTklsxVV0TR5rTKgBswfKG0+HeZ3JzzUvE0W5tBEITyxoPCpZtKZCs8sjx5Ztq42Yd27Iz/c2fZfuVyeYMGDQCgffv2er2+nFT79u378ccfCYKIjY1t1arVP/2G8vn8NdNtNa86/10HN+jTp6iehYYHhtwLH926RQsAoFKpC5evupqhvZ1TpfDeuoS07NFzxFsO4QZDVdqx8C/U/hT9xo0b3d3d9+/fTxBESkoKlUrdsWNHYWFhjx49IiMja1OSj6f24sHjRPakxKQOj01KE0EQhWuz4kPuGfKr9Bx9KrMksmliaenM/F2trnlugciE1aYMFmqI2nnev3YFn2Qw9CgQmtMmhTFnepI5na8wLFy6/Pbt23+XVL2enRCJE3hpTqbWFJZbkG00bctaEy08Zs7Ejab8Gb8Xrd2tefZaeffJ4otDj19YJMzPP7Bte/7ZK2W7zsrKcnR0BIDBgwdj2N8PrSHv7z8RHMejo6Pv379f4WsZpoordzlG4Z7Myx53Ti83Go2TJ09GEAQA2nTuEbopcd1jZaXvEkEQmFojOxaDKdXa+GQCxz9cwcInUvsKvlWrVleulPwmFy1aFBoaak4/fvzYzc2tNiX5eGpPwRMEgeFZkW+SOj3JX5r+umFta3eCIApM5ZV5gclkefa+DCxr8LWBO5mcZ8L0BAEAJA7ZdpyzSWIEAEcOZf7sGS1btiwt6cMKsKHaSQ0lU5eZctOwk5KBLOYyaU6C6tXlojNSo3h1+q8qUDssnEh1cVTE3NTefzGcO/y2c5rKShkSHHI3K1WTkFraoIuLy/nz57lc7qFDhyZNmmTOxDVYYqtH8vNiAMBxvGHDhnZ2dmFhYeXEfvToEQCgrGAAMOavw2Ql9gFku6HOzU9943oEz53rrNJv6THImse/dfl87tKOGXH3dSZCoq3kJlqUyeB91w2h02QHowtmr7bM2H8BvHjxIjg42Jy+evVqv379zGkHBwehUFj19gmC2LlzZ+/evcPDwy9cuFCaX1BQ0KtXr6q3X+OgiMt6P7ovS3Zc6H3hG4ojrZb7dyCV3xLnQPqE+JGRl2VGvFI2OBa+FL52BU9BEA8KOentMrz0UGHRxuz3FZ7sMV/wNjKxmxXZ15pCyUYf6Iyh9uM62PQ4lr/HneH1uPguQqNa9e1sP+dn26k/1Q9rN6z++M1ZK52/8arv5amUyso22KhRo1OnTtFotA0bNsybNw8AUCbJ61TD7KgEeYwYRdGlS5f26tUrICCgnCTmIHVmSNxWxvx1ACVPMsoKpgddI3Rp9QIfGFnU850HNQ0Kzs/L/XNc9w17jnQ9KjqRVElnOACAkEmOSyZzurQq+v0v8cb9lu3y/2l4PF5cXBwAyOXy+/fvd+jQwZyfkZHh6VkN0bWXLl06Y8aMkJAQJyenQYMGnThxwpyv0WjOnTtX9fZrAxRx2eDn/yCU4lDb2r3q3MnV6011LYSFuqWmpwhqmqpP2S2UFp9Qqc1p5V1ZQqtH/1JYpBfKDBJzOk1mDNmV/WPKwcFCkQ7TT3szKkkVX2Gt0wWHlqbMMOLGCj89c+YMmUwGgBUrVphzVI/lcW6x8itigiAuXrxoZ2f3Md8abhARWOmcD2bIWa55GqB9eTpp1MzI7n3MrwspUmPPY6KfL0ll2iqt5GEarXTfqezhM4pPXLLM2FcLtT9FHxkZ2b59+7t37w4dOtTf39+8TqRSqdq3bx8VFVX19p2dnW/cuGFOX7t2jcViJSYmEgSRmppa6X+eWp2i/48TvKNAqbc8m58plin6WmI6j9uTyTCnWaFW/H5/a1O9Xj9kyJCyoTMTVC/35G4yp+txjTbWT5wKWmAExGhN3zkOPZi3gwDiqfy+2d6+lN4OA3lk68P5Jf69NXmF73zau/fu3btRFJ05c+b69esBgNWE63U8xFRkAIAuXbqMHDmSy+X+/PPPO99S4YVg4qO6+I6AmwfoKKX+DKrXVsI4+5zN47TMzJycnNmzZ3vxycf7ChzZpB7HxDezK2+RizLo/B/CHVf8D1epCYNR9yoZynvYtfC5s2TJEjab3aZNmwcPHmzatAlF0YsXL9avX1+hUCxbtqzq7SuVSm9vb3O6ffv248ePj4yMLBdtwYIFCzUHQvzH/5ejo6P79+9fLp56VdAlqgkjwWjABoC8vDxnZ2cWi5Wfn8/hcADARBhnJIyb4DbTnel9NH93sV7fmjsS4cJPIkm0g93W9HktrNsV6HK1uGZY/Z/faRbXLk2Z0dGmZ1NW65MHDrbi2DoPeGcZcuvWrePHjweAlStXTp06texHu3btysnJWbt2bdeuXTt16gQAI0aMqFB4rPgqyaoNIJTSHMIkMaRNyMzK9mm9X3bwKdmGz+3dAQAe5htm3iwe24g9yJ9ZxTuGa3XCRX8CQQjGDKJ6OFexta8WJpMpkUgYDEYt92swGKhUqjmdlZWVnp7eunVrEqkaHKZ17tzZw8Njw4YNFAoFALRabePGjbt16zZmzBizY+ZKtFntz/uXhMpArHyoML29ryeSNL29mBQSAAACMKEx24Fl8YP3uVA7z/vXOIKPEIpidX8PXp/qDd/kFqjf/t1oXirzl5b4+nBycmrTpo1KpTpy5Ig5h4xQutv1ixYeKzIUXlAk2NhEZDJM9zT6AAplkljmbjv6eOHhbnZ9O9v0LtcpHWVEus08Wbg/G0v/NrTpHWmh6MrtsgXGjh37119/IQgybdq0xYsXl+brEtQA4OzsPHv27ISEhNIB0D9jywIAidcREAomv2HImg2EAQAQsoDme9Az+Efd6y43NXcObd6am5MDAE3rUfe0wfgFj7a9UN+qwlAeAFAG3fG3qdxeHTC5Up+aZcjMq0prFmqZUu0OAK6uru3atasW7Q4AmzdvPn/+vEAgMM85MRiMs2fPRkdHN2/e/IN1VSpV9+7dO/2D+fPnm0yWheWKoZORIFtKA5uSA0UgUFCSDrKlsClf47/9V87X6It+EZ83VixZzOe1Y9Cf6Q0TxdL6ZNJLvSGMTgMAqy42OROTMKWJxCEDwNSpU+/cuVP2L6+1oNOFolMXi04l0Lq+Eb2mEEopGkw3ivV0/lMdEQDcJPXrJlbNVSYlDjiXbFVa0YFW7yfnqK1Zq2f7LveTB9568bKzFZcbGlJaYNSoUUwmc9iwYfPnz1er1cuXLwcAsm3JcFwgEERFRQGAXC7XaDS3b98WCASNGzc274UrC8r+FhMfwzVvUFZDAABAyA7jUE6L7pRxRw0knUaoTzaZhOKfVyw8f/58m669Db2WdAl2XdCSW/l7iiCsFt8AgObxS8nWw4xgX96AHmYP/Ba+Wjw9PdPS0h4+fOjg4GDO8fLyio+PP3/+/MuXL/+9LovFmjlzpsFgKJd///79N2/e1Ii4/33IKPT3+3tCbtkDRYQfg039eLt7C18atT1Fr1QqDx06FB8fLxQKTSaTo6NjSEjIwIEDudxKapfKTdm9NhjHiiXD2ezdStVKAf+eXs9AkJ+5HPOnaREvrAc78r+zN59qtdpyEym3pVfvy26+4Uweb8VuSqP1yxP5Sah9fOljRNKNAn5LBh0AnsjvnS08Ot9nFRn5+y0qNzc3puBkCjV+IDq6KCWPptGxvd1tbW09PDxKyxw4cGDYsGEYhs2ePXvJkiUIguzatats7xcvXkxMTDx69GhGRgaKom3bti07CHsHXIMp7pN4JdbRQBiNeatNwt1KfEbqysfxuGb6lVNyhYJvbT1h/u8//jg0WfZ/9s4zMKpi7eNz2vaeTbLpnZBOC72HjgKK0gUEFAHFglQVURRRFK4VBBEQUKrSi7TQkpBACOltUzfZzSbb+6nvh40xhMDNexG8cvf3hTBnzpw5e3b2f+aZZ56HHBPBfcjfA9rpMh+/aDl1mdcrSfzcKFTeNkqPh3b5u0z0j5OUlJQLFy48TAseE33HSfpRc32Gr0fg/zt5Ak30169fDwgI2LJlC03TMTExCQkJMAz/8MMPISEh7o3dj404FvamSLTRbJ4u4PfhsLuyWNmt8i5LxvsYjmhb/nvvM+gnHWomTU2kNdPlOmN3uBAmMAQ2UDQHAnusNnedHuK+KfIxDLjLpUitVrMKRN5O/yPUbq8QBRbsX1xcXFtb27rO9OnT9+7di6LounXr5s2bd69BctSoUYsWLRo8eDAMwyKR6OTJk677BLBkKDtR8x6pbg5cDyAMC1zB7rRLgH6dFpkbEOL/+6gZI3v1Mej1a9+YN3vCiE2n7rxwXFdleigTKMxhS54fHfD1aljEr1+63nLmysO05uFJIi3Nk9fAg4fHx2OdwXfv3n3WrFluO3Nrdu/e/fXXX/9nGv+fvdFnu/DFTfpFYuG3ZstaqaQ7m5WibsgI8HMb4kk9URCXllDeH76/T0qh5c7LRkE4R+wFIzecrk4szGFnKhCSC0NrpOKh3D9TvhIMjkHNM+yCggKdTlevrlcnltEI7Z8dFRQYDBFUj6hodqCidfvnzp2bOHGixWIZNnTYZ8zqoKnh+sMNZIOLEysQDZZaM0yqFw2TJ09evHjxjBkzfH193Rvt2oEhAYMDmAdoJ4D/6BVtx2s+pPRHIc57Vw6obnOZjRs3arVaNps96sUltd1fmZEkWdBVwEEf9t2fMllohxOvqHWVVorHpSAyyUM2+ATzvzCD53K5Dsd/HoYBeGbw/x++y7bO7yr4f0TG8fAYeQJn8Eqlcvz48feWT5gwwb019vHgVvcNXtKpAv73cq/3DMZbLjwQQQv+WPBDZRi/p8hyzdj6rCtXrrSef8QKkyLZkkUi4SYvaQSGvioSvs0SOY3MHB5/jcHU2MoDblXxIqW9pOW/HA5n+LDhPjnhDVpNQ5dKkVik1Wov7DvoVN4VG2748OEXLlzw9vY+f/H87NyFd5bdkL/gF34gCQtg1ywpYYdx+vfvn5aWtn///pUrV+I43tjYePPmzXY87yAUwDwAgDM/hahd11wI81ih69lRPwHia7Xgt717dmZt2Tlr6Egcx49uWcfaOb3SSI3a35hW97CZLRCxEFN4c+I7AQiqe2sdoW58yAY9/KNpyYLo4TGwsJtH3f/XeawCP3jw4BUrVrSJgqnT6ZYsWTJw4MDH1o3PTebPvaRul7o4FvatXPaVydKTw8p0/mmlD/0xXjjwrsXjsWPHDhs2rE3nfzOW/Wpp0lP0DadLJ6Z4POhivmuqgLdMb2xR2hcDX20JcNsCxEDxVb0N2qozNd9LdWbEz/vc0eP2krsydSYnJ1+9ejUsLCxXl/8SsiR9zRV7ltl4qEH+gn/D17X1HyqDA4OvXbsGQdDMmTMlEondbm9svK+CsmNPwsIerUtgYTIn8fK0KeNv7CR946vXjJ349SefduvWTeHt9dVwyfrBEpOLKTeQSuPDOi0jIoFs1rNBWz/G/LzVyz9r2ryXqP8LIqF6+Mcxe/bsv7sLHjz8D/FYBX7r1q1mszkgICAqKqp37959+vSJjo5WKBQ1NTX3C97yKPjZR96b82fgySQW6zeF93Au18z8uV6OemEV03NJPdFSMmzYMIfD8cUXX7SUpHA5lSS901jOgaECgjjrcA4Qs7VNtFc9AgHwrcnirhYrTEqW9GMAQzEUAMDpdJ47d65PdEz3zMrIip5NIntGeG7MlTyWXHbhxEm8pr51V6Ojoy+fSI1hdarUVc1QzT/w0p6gf3WWzw1gR/DsN83lT93GrOiuXbt+/PFHDMP69++vUChomq6vv6sRNxAqQSQjAABE/Zd45VJAuwAAAGJhgSvYsScBccoQvu2rH75Zs2z5D6Mnk1pd7wDW6HBOrZmadlT3/lXTwwe1hlgYAMB3zWJWcIBmzVfaT7a4yqoesk0PHjx48HA//oZAN9XV1QUFBWq1mmEYX1/fpKSk4ODgf3uW1Wp9/vnn7/U4a2pqysvL++u3xjKgdFS2ZLzcZ2Fz37Kzs5OTk5OTkzMyMlpqFVvzv65aF82Pf0YxLYgbCgCoMpGlerJHCOs5TeO7rRbj75hvntAeTNE/U1xYHBISAueVUQYzHayoRpylkRmBlcT4G2GGicNi42IxueyuntBMesDZj4K/PJV5BoWRpT6vvfHeW7Z0Y+jO+Po1SmeRLeJQ80a7KVOm8Pn8zz///MqVK0KhsF+/fmx2OwG0GcpM1n+F+i2AUK/W5ZThDF61FOb1c9wZqTt6VTx2sNfEURCbZXLR+wrt0+L4V2pdw0PZrL/C6se4cMuFNPOxC6ifj+zFiaxg/4dv85/O/8Ia/MPjWYP38GTweMb7YxV4hmF27Nhx5MgRGIbnz58/evRod7larX755ZePHz/+4HPT0tLu9dBJT0//6KOP7udG/v/CSjMMYIQwDBhQ82axfo8a9WHF5fWF/pC0tLQ0hULRsqXtV/WeG8arGIQ1Eg18WDjKZ8II7z/j2+ThxIJG3XYfr2gMAwAwgDmq2RdLdNWpDAKBwLD/JKdTGLdrrN1ul8jFR83bkGL168/vRNF2NrxVvpBnONm4d/jxTQe/ZBjmKdaIrdu3+s0IaVPNZrO9/vrrly9f3rFjB5vNrqio6N+/f0BAwP3ul3HVEqr1WNA7EKtZXxnKQtSuo3SH31qnGEyE9vMPUXz4Binix8TECITCkMmrLaED3+opfDqSC/8Va3sMRdkzcjA/H7xWzZCkYGBPCPtfDMzgxiPwHcEj8B6eDB7TeH/Uwe5bs3btWrlc/u677y5cuFAkEh06dMhd/l+SfOIXi/UDvZGhmZo3i4uHZhFNeNkzt5VT7tBEOwkbKmylywpftpO2A/U7Py5b9l31hjcKZmtdGvfRg0W2RWf1Z+yOlHpN0905nQkaZxjmxZzxL+aMbym0aeo/OTD5S+VaF+VkGObinv01+465D1FWMjf8atX8gpyA1M9E7/NgLgAgRhxdUlLSulnttlpCjzMMc+rUqYCAgMWLF1dXVx87dowkyfveME3gqs8J7d42xZSt0Fk43pDZy1Hwa0lBYXp6elznzu5vS69Bw1I+v/DUwcbU6r8yN7azvFrz8Xc181ZRdsdf2Ow/i8efbOafiCfZjIcngycw2czWrVsPHjy4du3ab7/99rfffps1a1ZJScm/P+1xkcxmX3U4a5eU2O9YIo90Qb0w6bO+tixz1VMpBPEAACAASURBVIv5zB/xnSkA3Dvm0wypAlR0Unuo2JrvZOBCcw4CITcMzXu+x3fiitjwAJQ9gcd7pUnnbGUm2VH7zZw7E9x/u/+gTBbz5v1TjvnzUOFnyvfMpCkoKf6qRVuy+SeGIGE+El/YN2RLbJJq0BL1e5m5WZ06dSoylfTo0WPv3r0tzRIqV3GfTEuqfvTo0dnZ2dXV1RMmTAgPD0cQxG63azR3pbdpBkKxgCWo9zQAAFG3iTJdchfDvBh2zBFhxHsM/nFt9sKZk5870OupDyfPkkqlNy6fv7h0mGXXgnd/uz31qM7s+msSh7Ajgn1XLfD/YiXMYasWrG78107P8rwHDx48PCSPVeD/y7NLRaDoM2vqdTnmyKNd3XFqJRN8GCdNNOJV8woYigEA4AyzXG9YfvSYXeOAIYQN86soqJHGeksGmQgDC242sGMwtG6QmIdBi8TCYBRdrjO0ONWnGy63vuicOxPqXv2ANtuFA3rODV6cIOz2cdkyfpRo0PDht9hU1qbvaYsNYjc/JpiHxMXF3bx5c/r06RaLZcaMGTNnzrTZbAAA/zURwd/GVM0vVC0rlYvkR44cefXVV1977TUAAEEQ165dq66+axteG2B+Iqne3JJUHgCAyMZyk9IHDZt851d+xCr7io3LVq9evfDFuRw2+/KJQxlvDzD9/Ja2vva9K6Ysddt4ov8ZiEgAIMj/i5XsiODGf+1Ur9hgvZTB4MS/P9ODBw8ePNzDYxX45OTktWvXEkTzT/aHH35YX1+/dOnSvzF7xBGb/S2dgWQYAACpw5OPGPfMkZ2CmxULESBes/wwX7bxeCNe4wQAcCHo3SbtZxPGr572VYNNc5pJRFkRT0m6DJaP5MGCnpIBbdqfcVw33sl1AbBKZ3C/yPyYdKR1hY/39YfYLNTfRzbrWQhA4xVTxvlO/rT8XauX5amJz+r8ZS61tk2bQqFwz54927Zt43K5u3fv7tWrlzuytyhFFpPRiyEYvNoBAJgzZ87FixcBAPX19cHBwW5PRp1O1+7ngEhS2J0PAADR9nxCtZ6hLAAAAGGo4hVOl5sw15+sf7qu5IBUq08dN+fFwSMgCDp94Kenx4zqrmAtu2T84Jr5YZ5Ca2AeV/T00MBv3pdMHmu/cad2/rv2W/l/VeMePHjw8D/Eo14DaE15eXlgYKBQKNy+fbu7pKysLCoqSiaT/cc9ecg1OZymFzXq5jfqXDSN0/Tyk1WXglP3nKhtqWD4vemW4HzGT3+WuFyuyM6dAQDxb0+bkztjZfHC94oXv54/M8t4nWGYEmt+6/Yz6lzJOzW5OnxWQ9MqnaGeJPdbbPstNvca/Iqq8z+XV1Vom9r0qsCc83r+zOv6iw/ufF5eXmxsLACAzWZ/+umnFEW1W+3q1at+fn6LFy82m82nTp06ceKETqe7X5s0oXdVvEnqjrYtxxtcFW/ZsyIcRevy1m06/9SsFwYP//TTTxmGISmm0U5dqHJOO9aUWu1sx2HhISAa9aTRbNh/Uv3eJsvlTOY+9/gE4FmD7wieNXgPTwZP4Bq8O7vUyZMnBwxonum6s0v98MMPa9aseZw9aQGDoE1eUhSA13SG13V6Y0/h7m+DghYqt59QAQBsOZailwuqBwgV5X966bNYrM3ffQfBcM2Nup4BHw+WjRwiH/Vh9Fc9xH0ZwOxSbb6qP99SuZc/6/3+4vknDUsxUTVJLtMZ9lptBQTu8NvW5LvFxg07IuAfxrA2vYoVJi2NWHui4eCeuq0kQwIAcs5eUO48yBB3mTri4+OzsrIWLlyI4/jy5csHDx6sVCpbjlrTjAWJaebzuv79++fn51ssltjYWIPBEBERcfbs2czMzBZTSmsgVMoK24jIxgEA8Op3Kd2vzeWYDyvsC07CBRhT8bt9eUygXPjss8uWLWMoCtisci48KJg9JYb32Q3LuENNpyucD71tvhlULkXEQvHEkaKxQxxZuZTRbE29gVeq/prWPXjw4OEJ5lG/QTxq/pI3ehtFDahT963TWChqk9H0zcnaK0Gpv3xRmhZyefXPSpPKcSfoMmVtdkfHaXpho27KpdTbKtWI+oZdZmvrptTOutfzZ5VYC1oXHi21v3XBYKaoQXWaSZpGhmF+NFs+NZgYhvnBbHm1PttJteM97qDs31Su/6B0SZNLW1tRsXf7j2nrv8bV2ntrnjlzxt/fHwDA5/M3btzY4jlvvqwv6Jpe/lyOq9bBMExWVlavXr0GDhyYlZWVnp7uDkXwAChzhkv5+r3ltKPMVTbffjMSr/1kyaRJRZNetVzOdB/auGnTwDHPDPzgtyE/a/cV2h7c/n+G6fjF2gWr65asMx2/SBrNj+ISfwueGXxH8MzgPTwZPJ7x7hH4Ziv9y9qmBY26+Y26BpLMdrpUqU1XAi4t3F1mo2mGYdwCyTAMyTCvNOpea9QRNM0wTB1BDqvX7LXcpfEF5pw38mernap7r7XBYOpWq56nbXq9UT9H27TfYlvcpJ9ck7a1emO7faMZ+rjmwFsFc4ot+RaL5eien4998bXuSua9NXU63QsvvOB+aevWrdvNmzfd5ZSdqvtQmRd9jaEZhmFIkvzuu+98fHyOHz/urnDt2rX6+voHf0SU9Y4jfwRlybqrbw6lS7m45HjQphUjiSYlTdOa1PTBffq6+9A5oeu0Ndv0VufWHKvW9lfb1WnakV/a+PVP1TPfbli/xVFY/he3/3fgEfiO4BF4D08Gj2e8/w2R7P5aHjLwBcEwb+oMJABfeUkhANx/f+klXW80FxGEjWYGcdhvS0QAgOJBWaE/xIFI7o8W68tCAQo1h3pRU9Q5u3OmkN+62TTDpSOafSsj10mxP6PFUTQYcLNBEAjsDAAQ4wUjXdhYCUH4w+inXvyWjHNtsNvtOy9uKwjIDLfHxFq624xmiKJYIuHTTz99b+VTp04tWrSoqqoKQZBXX3117dq1QqEQAABoBrSKTWM2m1EU5fF4N27ckMvlOp2uZ8+eD/ycGMpwFiACRNQf0E4AswFobo3B6wn1N1Tjvhrb4A8W3H4jvvdvetWPOelanQ4A4OfvH/PUvMb4qW8PCZoZz3/gJf4TaKfLnpEDYRjqJbGm3uAP6MGJjQTQPzLDhifQTUfwBLrx8GTwBGaT+y/kpN3BgqBvvKQsCHKvx7MBmNeov+hw/ujttcvH66rT9bnRDACQTvCpf7+cA0ELRUK0lYRcOXRoz8RnVKq7VoX7SoekyMdsUK42kYaWQgQG/eVsfyf6u7+PEIKdDLNCIh7O5fqjiFvdP1O+e8N4tU0PKYoSGmQDasc6pdbM4PMhXUOcgLHb7QAAymJtU3nMmDH5+flvv/02BEFffvllbGzsvn37GOZPddfv01RMy2M3ojweDwDw3XffDR8+vKKigmGY2tray5cvm83t+sNDiHQUIuoPAMCrVzlzBzKkqfkAy58Vso6TlBkeEf2v762K1zQrl065+OxLa1OeTkxMVNfXX9z6Yfnybmc3vLYjQ/Xcb01HSh049Ze9U8IctmBwL36/bliwHxbgq9/1qy39Nl5R6yp/0J5ADx48ePhf4H9d4CfweRu9pC2CjUFQTw4bAgwAoI6kZDD8o7fXFafzosPpvSAIEaPgHm26cuWKO3d7m1nFSO/xA2TDsoxprQvjvTFICE7YHMEQ4mSYpzTaTOef7nszAuYb7sk7BwDgcrkh3mFJlf0TeT222zZBCTiGYWazef++/fnf7Ggj83w+f8OGDVlZWT179lSpVFOnTh0wYMCtW7fcR6XP+vB7ikpSbqlWlpE6YteuXTt37ty4cWP37t1v374tlUpPnjx5/fp1q7Xtq0MLrLAvWOH/ghABYChKdwTQdgAAhMmxwOU+/fL8o8ezFF9q+p29bq/atWPb6f2HRnRNdrlcP/+0A79zakE3wZEyR/892vUZ5mrTX7k30r25zv+z5fy+3YiGpqavf1ItWmPYewyvqPkLr+LBgwcP/yD+10309+JkGASAbRarhqQ+lEncJSgA7peAph/qZFMVMB9pqa/T6Xr16qVUKtevX798+fIHN16IE/ttNoIGx6sdkV4IxoGVBLFCIn5ewGtdLct4PYQX4cNSAAAsFsvZs2efe+651NTU6urqqF7hZ8GvTqPr7W7vU43k1XPnBVpTzx7JXkN6tzFN0zS9Y8eOd955p6GhAYbh2bNnf/zxxwqFAgBANuLqTyotqYbY7N7uyufPn1+2bBmLxVq+fHlISIhSqZw4cSIMP/D9j3a5yl+GYBYrctvdBxjKeI7UbKVtebBw+s4385pEfP8Rg55/dqJAJAQQVG0ifymy/1riiPfGto+RPSJ7Ol5VZ0u7ZU+/zVA0v3936dSn/8tN9x4TfUfwmOg9PBk8gbHoHwWPyOlGT1G9VOqGewK5Vy0srH69qE1hUVHRM888k5nZju8bwzB1jpqVRQsbXHe5rJfpiSE/az9JN521Ofqq1IesdzmcX9dfWlr4Es3QDMOYzeaDBw8aDIZ9+/Zt/e27g0cPaBsbPr/w0eL8F46of3HizoxzF/Zu/cGcdafdq5tMpqVLl7JYLAAAn89ftWqVwWBoU4cmaRqnKYr65ZdfunbtWlxc7C632+0ZGRkmk+lBnxTDMAxDGs65lK/RztrWhbSjzFW5XHkqeM+/BpLGCw17fyueuaTpwEnSYGIYxkXS73z2zfYdOwf+WPHBNVO5gfi3V/nPcFWqzKcvMxRVv/yzpq37nSUVj+hCD4nHya4jeJzsPDwZeJzsOsSje6P/zGh2Mcx7UnHrQspCFvXJDPq8k3iUvONNXdNfOKTevSh0eRQ/pqXQ6KJf+90o5UBvDhG+0WTozEI/kEo4f8wySYZEIdRJO2wW24WzFxmGEXbmnMeOhlTHhOKdSJIc+HT/n1SbraRldtDCQFYogiD3uTgAAJSXly9btuzIkSMMw8hkshUrVrz66qstL4+G37Sq5aW+r4fIX/SHeQgAgGGYGTNmTJo0KSQkBMOwuLi4B98gQ1nIhh2IqB8s6M7g9RAmB3/4DDKUlWo6SGp3NTXpPvqEN84/OR7wOHGd0Befkft4MwzD4/HjBo/r+dS0pdNGXq7Fx0ZwvbiPZOWIUDfab+TQDqdo1EDDvhPcxBhulxiY/98yY/bM4DuCZwbv4cngCUwX+yh4dAPeTNNj1Nq9vvIQ9K4cptZ0Y+UL+Z2vJGP+7WRbvx8FlpxtNZumB7zMR4XX9BfEqKS/LEXBCr6txXsoWA6GeV9vLCHIz7wk0a3i3mQZr/+m/CUqt7tEIlExVUGcUC2u4dj5MA3PmDEDAJBmuHSw/qdu4l7PKKYLUGFNubL4yOmkuHifEQMhpK1M3rhxY9WqVe74tQEBAcuWLXvppZfc3zBHrkX9aZUtw+jzeojvq0EAhn799dePP/4Yx/ElS5ZMnTq1rKyspqYmPj4+KCgIeqCtG69aTumPcZKyIETQupy23SYbdlH6oxCn79lf5WuPXp303KSje39OL8x3fwkDg0MCBjxn7jyub/f48VHc4aEcDvpIjOoMSVkvpdtv5rsKy1mRwbzu8dzuCZif96O4VsfxCHxH8Ai8hycDj8B3iEc64L83W0sJ4gsvaZvyho3VxpONnU53g1htRbSqqmrixInz5s1bsGBBm0M1jop15St5CE+MyoSYuNZe+azf9AGyYQCAzbetCAR8ouANRvOLQsGLIgHS6iyXhrxpS3NSjpHe45tw7cH6XXOCXwsLbE5Lb6dsRzS/ZBqvjvOd3F+ckpeWWVReFtho7dK/r6R/j3tv6vfff1+1apXb7U6hULz99tuvvPIKn88HADgKrbqf1P6rw93zeADAuXPnvvjii9zc3IULF44dO7aurs7pdMbFxcXExNzbcgsM0QRhcsAQeMUbiNeziCTlz2OUjdQfxRv2XE/Pi4qfICrtcjGt8KBRlXnjRtUf6XDCY5O6v72zDPgNDmav6iPy5j0qV1DGhTtySxy38h23C8STxrCjQimdkRMXBbHaxhZ8DHgEviN4BN7Dk4Fnm9zfzywhfxiXc2+575shmIKt/qTy3kMajeb27dsLFy788MMP2xwyEgZvlg8fEeGMM1nc952o9YfVu62kGQDwbCeu0kiNYHEPKbyvu1zTG5qq/kjAE8wNFweI0sCFQiy7SVCfGNolKrjTDTi1pVkewp8WMG9pxNrb5swPlEugLqxnpk6GY8JPF+Y4i5XgHkaMGJGVlXX06NEePXpoNJq33347LCzs448/1ul03FhB4Poot7oT9a66VWUDQvudOXPm3LlzVVVVKSkp/v7+gwYNslqt7vfC+znbQ5jc/Q8iGUbbCwAAgLbT9kIAAED4qPc0XvypYTOvBgT5cxO/RhNPOs2F6Xu/PzByyrQuvaVCUUXhnXkB2gtTvHv7sxAIrEw1rUg1Xa11tXuthwFis3jJCV6vTA38/iNhSl/abDUdOVc7d6WruAKvqsOr68A//PXXgwcP/8t4ZvD/IbSTpgwEZSY50W3jt+zcufOll14iSdJkMolEopbyzVWfNxGaAE5IrvmmghPoxw7It9x+2nfSQNnwljpKI5mlxvEA5nuLZb5IOF3IRwD4qnJdFD8mRpBgpSzxwq42yvpO8aKlEWsDOMFtLp1nyT6s3oNAyETFjGhefMvCPGUwIXc7E7g5ffr02rVr09PTAQB8Pn/27NlvvvlmREQEAIB20A2bqpp+qOMmCb3nB4pHeBnNJqFQiCDIqlWrkpKSxowZc/r06aCgoD59+vz7j8te6CqZhvrOwfwX332EoS2ZZNNBSncEYJ1WL7ElIb5hfEm399/kJjUbCbR2+mS542J+zZo+3F+1Xl18sAFB7EdkvQcAMAQBoajlQpr56Hna4eImRnMSojkJ0ai8rSHnr8Uzg+8Inhm8hycDj4m+QzyGAb/PaqujqCVi0b2Hivrc8HrB32dhUJvyq1evqtXqSZMmtS7crdqiJ3RdRMlphkveLAUH5mSbM6YHvNRd3LelToONeuO8kWLAqwMFOymrgaJncRsz6r/6qPPXKNRsN95bt63KUc6C2UvD2xoJAAAMYG4a045ofpFgsmcU0yL5nVU1tVdPn4lwMPEjh/JiIu89JTU1dcOGDadPn2YYBkGQCRMmvPbaa4MGDQIA0E7acLhBt6POd2mYeGRzVL4TJ05s2rSpsLBw5syZM2bMSEhI0Gg01dXVERERcvkDfQ8ZEkAoqdlKGU6xwr6AOBGtDhGU6RKl+83WcIbNi29kBg97/tutT8+I8/GXv/oCJOB5e3vrdLrg6Hhx19HOTsNT+nQfFsYdGsKWch6hFYrU6hx5Jc7cEmd+KSzk+29YgVfUoL5yRNLOl+Eh8Qh8R/AIvIcnA4/Ad4jHMOAtNP1sQ+MKiTjlHnM9XussHXErdHucoK/k37ajtJdsqf78vagNh9V7Ivmd2Qh3W/WmGEHCnKDXJJispRoDwN4C+5c3LbMT+GGdkdW6OqErJ9r1O8bY/6jAEAxO0MS6zt8p2P7tXotm6DTDpWMNB+Qsn7E+E33s/rcupuot5lAL0e3Zpzhhbd9IAAAFBQUbN27cu3evy+UCACQmJi5YsGD69OnNwW7/oGl7HSeWL+gjKS0t3bFjx65duyIiImbNmhUfH69SqVAUjYyMjImJeZBXP+2iTBdhQQ8I8yYbtgNEhMqfa4l9C2gXZbpE6Y4U3TlpdwbHez1VxI3ese8cXqw8nJVusjUvCngpAvy6D7dHprw0ccTiPt61Zipaht73ig8Pw5A6IyqXNn27x56VywryU3z4huN2IRYSgHr9+0ffETwC3xE8Au/hycAj8B3i8Qz4XBxf2KQ/4OPtj7bVLVJHQCzIes0oHt3+5NXhcLz00kuJiYlLliw5rz9xWvsbG2ZzEK6ZNM0Nel1pL77UdHq8Yupgr5EQ+NPsrLZSJ5TOl5L4Bsr1sdF8zUGO4WP9WKgYgQAAchjyRVA7ZfuobNmMwJe7i9s3ktMMlWG8ekp7mA1zxvo8F0pGFF3LSOwcI4yOaLc+AECj0WzevHnr1q0ajQYAIBQKp06dOmfOnF69erkr6H6qb/iqBtDA6wU/2VQFJEdOnTq1ffv2IUOGvPHGGyqVqrq6OjExUSgU6vV6oVCI3ZMMtzWU8Tyl+xUL/QxCBJT5CsxLgNA/LOEMTpmuUoaThpoTu0+RfYNSQonO506mn6orv95UX9fU6K7F5fHWfLHltGgkQYPV/UQDg9g0A3jYo4xpwzCUyQoLeU1f7nLml0IcNicmgh0TwYkOxwIV/3E4HY/AdwSPwP/jKNYRmWr8UaSi+EfjEfgO8dgG/HaL9ZzdudPHi3PPLzhto4oHZ4kGywI+iYLuWRuurq6OjIwkSbJPnz5Hjx6FxeCG8aoEk3YV9eIiPABAnbNmt2oLyZAzAxcEc8PanH6kxrEKGIIRRAdoG834owgHgiAAjit8AAAqZzVOu8J5ncykiYtw281YwwDmtunGSe1hB2VLkY/tL0thwxyCIK789EsQmx8+aui9q8s4jv/6669btmy5fPmyuyQuLm7u3LkzZszw9vYGANhumHR71LSNCv3xri3yX3311fr166dOnTplyhSSJGtqaiZNmvTgbXV/XrT8ZdpVy4k7DQBgiEYIa9m3RtOWW5TxNGX4/dad2uOHfaeMHEMnD9389Y83LlwqUKs2bNjw1ltvVRhJPgal1eHvXzXFe2P9A9n9A9lx3hjyiOPXESqNs1jpKqpwlVSwO4dLp4+zpt5gdwpjR4X+v7zxPQLfETwC/4/jlNJ5usLx9fBH68Lyj8Mj8B3isQ14BoBlOoOLYTbJZfdanykTWTk7H9BM2K4ERNLWVnz+/PkXX3xRpVL99NNPLUld726cuaa/cFi95/Wwd8J4Ua0P1RDUVHXjNp7X53XPAwAIvx+0FOVgwCV/39bdOKU9fK7pxLuRn3mx7rufu8xWdL7pRJElt59s6BCv0U13VIUFBbTNEcZgsSkD+bFR955SXFy8ffv23bt3NzQ0AAAwDBs9evSMGTOefvppDufPBQtbpqnxhzrpBB9Riqysunzv3r0HDhxwuVzPP//8pEmTunTpcvDgQV9f35CQkKCgoAfP6QEADKF15g5EpKNZ4ZvaHnLVUsbfKeM52py254xEeU48e+6UzjNfyTzxu2+d3rd/Mjs63AUh/9r92/bN3zBBXaCIfoMH9P04xVfMhtiPWuoBAABQFqvpt3Ou0kpul1hetzjLuevsTqHsyBAswBc8MPSvR+A7gkfg/3F4BL5dPALfIR7ngCcYZlGT3gdBPpK1s+zKUEzdu+Wmk41huxJ4XYVtjhqNxitXrowaNcodNbZdSIZAIBQCUIbhShdxMgfmAgBUJDW1QRPVtKilWrBi1xHC7osg80SC8Twu64/5sQ5vlGJeEAT93nisq7iXO5T9veiJpotNp67qzwdzwwd5jfA1+hWn31RQcML0ife9cYI4efLk9u3bz5w5Q5IkAEAkEk2YMGHy5MnDhg1jsVi0jdL9rDYc1joLrOIxcr93wlnBnNzc3AMHDhw4cABBkOzsbLf1XqPRJCQkJCUl3fdTboZmKBuECGlLpqtkChb8Aepz94sR7aIs6bTxImU6z+CadVvEvDKvWb0HkfUmp69sk7p4y8+73RUxFrtnr15kaG+Lf8+UgX0/HaHAKUbMfhwbRGmb3Xo5Cy+vcpXXUAYTKyyIHRUifnZku+HzPALfETwC/4/DI/Dt4hH4DvGYB7yLYdKdrsHtbY53Y/hNq1pSEvxNjHjMg/zJbTbb4cOHx4wZc6/bOQOYnbXf5lpujfF5dpBsxPfq47eb9rSpkyX+bjVXch52lBLkLCH/OT5f0Crd+7GG/RpX/cvBbz6gAyRD3DJlXNb9rnaq+soG95em+HECAQB3LqTaCsqiEuLkfXtA7LbvIg0NDfv27fv5558zMzPdJRKJZNy4cc8999yIESPYbDahcRmPNQqHyDhRPAAAXuNEZFiTrcnX1xfH8cTExD59+owYMeKpp54ymUw5OTndu3f38/N7QD8BAAyhBQCCMG+yaT9ZtwkL+9yduLZ1BcqUSptSKdNlBsc+/Zh3R2efv/Kt07tOXbh2taSpgaIod00UxcaMGSOYu63MQHdXsJL9WN0UrAQ5dk/Qv78e2u7AlTV4pUowpDcsbGc90iPwHcEj8P8I7miJjLrmwBXFOrLUQIyLbP5i+/CRZzp5vuQege8Yf9eAP2yzD+VypO0ZXQm1C0DAfE7P7yHixLTvWvL1118vXrxYLBavWrVq6dKl965S1zqqjjXsL7LmkYhvmfjtkMY/4+Itjd7/coNhvlU0I45X4CR+tFmvO11P87gzhPw2UXXLbcU7ar+Z7P9ioqj7/W5E46q/qj+XYbgixqR9pIOi6fiq63m1Bh3PZAvlCBImTWjXS7y8vHz//v2HDh3KyclxlwiFwpEjR44bN27MmDFeXs0b6ureK2/aXsfvKRaPlotHyTWQ9tixYydOnEhLS+vZs+eAAQPGjh3bo0eP8vLy+vr6wMBAPz+/B37jadpWALH8IExO1G2gjOdY4d/C3LtWFmhHGW2+Rlmu06ZrVgv7y0+5Khb07AsvnNp26urt7BJdg1goqq2sNLOEWWr8lgbPUhO1ZnJIIPx5itfNBqqTDH1EkfD/LR6B7wgegf9HkKMlfq9sfkZKA1lpIoeFNk+KQkTI5Bje/U/9X8Ej8B3i7xrwW8yWQzb7Bpm06z3TXDf6n9Wqd8q95wX4vRN+79HGxsYXX3zx5MmTAIAbN2707Nmz3UY+V75fgdvSePO6OnZBriIAAMSOH6iYvd8hOuXnAwDYV2j/+pb1k5HiHDZ+wGaLxrDn+LwULqfFbl9pL2PB7ABOcKmtwE7ZkkTJrX31W2AAU2TJTTdezjFlhvM6JYv7yRu9dPlVvQYPcrvguTSNLC+JI7fEfPQCqW1C/Xwkz47gJESXl5cfOnToj68H2gAAIABJREFU0KFDLSnnEQTp16/f2LFjR40alZiYSFkpywWd6azOkWfpfKWn++I2m+3ChQunTp06e/bs7Nmzly9fXlJSUlVVZbVaBQLB0KFD2+zNawfaRVmzYH4XCBHgFW/QjmJ2p58gzOeum3KWU+YM2pJOWW5o6wzbt8hiJKHdfCPZDfjZLoHDnxkfQABELDLw+IkxnWwWkyAsiQ7s2r1H8sZZAzPt3rFyNFaOcR9ZRJ02eAS+I3gE/h+Hx0TfLh6B7xB/44BPdTjfMxinCvjzRcJ2N32TOsKWYeLE8K3XDLLJCuiedd/Lly/funXr9ddfv9+u8S8q1pCAfQ4bZ6FsGMJ1ACFKNcpZPl054hY/gHIDqeAjAha0t8hWz6cKuEQpTozlcZ/l82JaeXEXWnNPaQ9P9n8xiBNqo6w8hN+u0uO0K8eclWW8Xmi9E8nr3F3Sp4uoJ5viHPr5F57B6t1oDo2N9hnUl6xv0O845LXoBW5itPvEmpqa48ePHzt2LDU1Fcdxd6G/v/+oUaNGjhw5ZMgQt/s9AIB20Pmx13kJAuFQmXCIjJcgADB09OjRWbNmhYWF9e7de/To0YMGDcrOznY4HMOHD/+3TnmAdtL2fJjfBUCoq+gZhtRx4n4HMIchDRAqce+wZwgtbcmkLTcoa5aqKmfjXiS5R+I4/gB9julLYLmadj03N7d1k3yptyAskfFPjB01o2dcRLwcGxzCDmr/Of81eAS+I3gE/h+HR+DbxSPwHeLvHfBailqlNxpp+n2pJOE+e6JwlbP29WJHgc13SYh8pv+9Mt9CbW3t2LFjO3fuvHDhwsGDBwMAMgyXL+nOLI/46Juq9QO9RkhQ6cflyxXsgEFeI3pJBgrRu+Kpna5wbr9jqzWTg6M5nBBwHTiFEDyWxx3N4wbcvX3/66pP6hzVH3f+FoHuUSyGMR+/aD5zxWHRVyazSwewi7EqP3ZgorA79+di2CtSQ7qC9PYBSxfZb+aZj19UfPB6mwbMZvPvv/9+5syZM2fO1NXVuQshCEpISEhJSRk6dOjAgQP5EM962WC+qLdcNvC6i0K3xgIASJLMyspKTU1NTU3NyMiIiIhITk5eu3atj4/PsWPHAAC+f/CgUcGQjKsK4kQCAJwFIxhCx+1yCwBAW29CnHAIlQEAAIPTtjzamk3bshtqMn45VpXcPTpA3+XUaVVxlbrQrC8wNen/iKgzYtzE2Z/8lN9ExHphUTJ0zVVTtBc2JYbXTYFZXIzsr7PnewS+I3gE/h+HR+DbxSPwHeJvH/AMAMdtji9M5lE8zkpJO/He3dhvmdWfVDryrb5vhXjN8GtJ19aaoqKibt26ue9l8+bNr7zyCgOYrdUb65y1JIPLWYpqR/mcoNdYMPu6/mKOOStaEN9POiRR1AOF/lx6rzKRx8qctRby0yGSLCd+yGRLJ10BKDqGxxnO5bYE6jGTRhEqoRnqw7Kl3cS9xvlOdpebfvvdfjNfvmAa6ufjKlY2fbNbvGBKTRh+x5x1U3kWlUvjRd3jBV1ihIksK5Wz7iu1QuLLEwZHR8q7JSIiQZs7ys3NPXPmzPnz569du+ZwONyFCIIkJiYOHDhw4MCB/fv39/Fptqtb043K5+4Ieov5vSWsnrwiUJqWmT59+nR/f/+VK1dWVFRERUUFBwcHBASMHTuWpmmlUunt7S2RPDCQHO0EMAcA2lUyDTAku/MhQLtI7Q5Y0BMWdGt+gpSZtuXQtjsO/a20tPROIsJbGvfJce03P9yZGd1lQt+Bfotm6bSN3VA+FuhXzpWfvH7rs4XPiWVygziKHxQT1Tmue5f43rER0XJWjNd/7q/nEfiO8LePdw//X0r0ZLYGnxrrWXe/C4/Ad4j/kgFvY5gCHO/JZtsZxkEzXvf5mbffMmu+qLLnWuPz+5IGApW2nfSr1eqtW7eePn16/fr17kk8AKDQmntVdz6AE9RPNlSKNfuvOWnHLWP6dcNFlbPmhYD5yZJ+916uzkJNP6YbHMYZ0Y192u645HB6I8hQLmcolxPLag72pnWpG3B1grCbgdDtq/+x867aAQvWoN7NoXNt6bdtVzJ9ls8HANS98RH56qhicV2+5Xa5rTiQUQTnM/6xKUyZs9FsBTjZR+IbOnV8uzfucrnS09MvXLhw6dKlrKysFhs+ACAyMrJPnz69e/fu27dvZ99OziybNc1ou2GUzw30muEHACB1RGVRxeXiq+np6RkZGbW1td26devevbuPj8/gwYN79erlcrlycnKkUqlcLpdIJPADt5sD2kXUbQAAxoJWMYQWr3gTkY1Fvae1HGcoC2MvoG15Nv1tjChk7OX7LqL7jpLfpDwjsPOdats1o2Z+6jEXgbduFWVz+QFRPbslTXptdZbdK0KKTo/lSTkwzQABq0Or+B6B7wj/JePdg4eHxCPwHeJvGfCMi659u1T6vK9wYFu7U4bT9abOkMLlzBDyO99n8Zhx0TROF3ZJ53TmK94OFQ6RtVuthZkzZx47dmzcuHFz585154BpjZ5oQiBEjEqv6S+U2gqTRD1iBUnuGHlucIphIdD5Kuc7V0zxURjLD1SySCdg+nPY/TjsPhy2BIYBADRDZTZdMW7eN2r197WOqnRDan9ZincTpv38h4B/vQsAsJy9armQ5v3aTCzIz6pU3jr8vXqIj1LaqHJUh/GiQtGIGEF8J2k8G+akHz9tKq/0Fop8Q4J8E2IxxV2xdxwOx40bN65cuXL16tWMjIzWOWd5PF7Xrl179OiRnJzco0ePqKgoGIYNBxvqVpfTVorXVcjrJmJNF92pyc3KyiotLf3+++8xDJs9ezYMw0FBQd7e3kKhMD4+vnv37iRJ6vV6mUyGovePUc9QlPE8gDmIeBDtKHPmDcb85mNBqxnKQlsyYEEPCJUChqKdFYyjkLYV0I7i7JvZt66YRw0NaUClH6wr8jKw1SxKabWoVM2LEZ9/sC5+yqsVNmh4KOeayvVFpoWLm9CScxGhIUmxnZI7h/QP5LT7+ucR+I7gEXgPTwYege8Qj3/AMy66Ynoe7aIdedawn+Lv1XgjTR+w2n+x2nwR5Fk+bzSPI2xvTsngtPFkE6AZVhCnJQxcuyv0ixYt+u677wAALBbLbrffzyPPSpozjdfumG+W2YrCeFGJou4Jwm7+nD/zyqgs1OUaV2qNM1ONvz9c5JIyqXbXbQIPQ9FeHFYym92dzdK/8p7ve6+6FLwr+nNB3LCwbCKn4mLdSEWKfIyc5Ws+fdl87AKlNyHeUsnEUYIhvQEALtpZaisssRaU2QprHJUKdkAoK1LQwMZUwGWAXAw1LqGHqHcXAABhsSIIAvP+/E5TFJWXl5eWlpaRkZGenl5eXt76jgQCQVJSUteuXbt06RIfHBvqDGKKSOnzvuwwLgCgcZvKVW7nJgiz8dysuls5eTm3b99Wq9UxMTFJSUm9e/dWKBRBQUFJSUlGo1GpVEqlUolEIhaL75sIh3YwDAEhIsapxKtWwOKBmN9rtO0Oqd6M+ExDRAMBoAGNAwDRjlLGUUpYC1XVeYHSetqp/Ox7TJ8rTwxXpPhEkxo8jeOcsu0r2GDXXLz+/u+ndvy0zX0FlM1d8d6ate8su/fiHoHvCB6B9/Bk4BH4DvGYBzyD0xUv5EEYHLYz3pZlqpiWF7YjTji4nSk4BcA1h/NXmz3dhQ/hsD/xkt7PcEzbKN0vauNhrT3XIhwiE4+Wi0d6ofK7dt+Vl5cfOXJEIBC88sor7hIcx9944w0Mw0aMGDFs2DA2m91SGaddhdbcXPOtfEs2zdAfd/6GDXNwGmfBzW0SNINAEAyBL29atuda146TqFjkNZurlCLC7c64/LLe8ZE9vL24RUr9jsOspZNuSZRxwqQQbsRV/flyW/HT8uflXN9274VkyCp7ebm9uMJWWukoc1HOUG5kOL9TMDc8hBt+Zf/vTqtN5CQlbI6vj0/42GHw3SGDDAZDVlZWVlbWzZs3s7Oza2pqWh+FYTgiIiIxMTE+Pj42NjacHaool5GFLkeB1VXpiMns6SrI1F7Lyi/UKFEXq3fEa2+/aTKZkpKSgoODFQqFv7+/VCoVi8WjR4+Ojo42mUxarTYoKKh1zN12njhlofQnYW4kLOhBGc+5Sl9ghaxDfefQjmLalIpIn4LYgYChGFcN7VQyznLaUZabk1NQqJyY4gSOiAMbBYeKlYpk35L8ppIylc5pXzTv5W+2fX/vhTwC3xE8Au/hycAj8B3icQ741uruTipjTTc+QOPdGGn6jgsfxOXgDPOhwZTMZg3gcmTtzenJRtx0Rmc602S5pI863pXGaQABfncx1F5uNJVKFRoa6o7RNmvWrPf+9cOaq+bdT7fthoU0u53t15S+aaOsUfyYSF7nKEFsADsYhmAAgJNkOCgEAPjgmvlQmV0RgIgENhds08j5UheexON08faKZ2GdMYwDQQZCd8eclSRKlmJeB9W7lLbSl4Lf8GJ54zSOQqi7wdaYSEOlvazSXl7tqKh2KCmaDGAFS21ijg4T6Fhjhk3ieHnTNJ154AgXJ2V+fl7hwdzgAAhrNqrr9frs7OycnJzbt2/n5eUVFxcTBNG6fRRFIyIiYmJiOnXq5F+nD+MIus2cxBxmGY/pSAPECuFzE4S8dd4FZQVFRUUFuQWlytLi4mKz2Txv3rx333331q1bJSUlycnJKIpqNBqxWCwSiUQiUUBAwH0jCjMUAAyAUNpRTDbsQGRjEdFAsvEXQrWOFfoZIh1NO4ppWy4iGQ4hfNpZzbgqGWcF7axinNVnjhWpldaXN56F+e1k8/MIfEfwCLyHJwOPwHeIxzngK6bmQigUuiO+dco4y1VD5Qv5kYeTeN1FDzjXzUm743e7I92FB6NIHw67N5vdjc3i3ifZmm5XfeM2lavSEZvdx5FrQYQor6uwtQ3/9u3bR44cuXz58ty5c7uMmvLeFdORifKSkpK5c+cGBwf36dNn9uzZrSPGaFx15bbiMltRua3YSOpDuBHdxL2Hyce2VCBoJk9L3NYS5Xpy9QBRHU2tzjPhfAaWMGUEGYKicSwsGsNiWFg0hrIBXu1QhvM6oRC2vfarbFPGJ52/E6GSQssdNsyJ4Effe0cm0lDjqKx1VKmcVSpHtRZXe7F8AtjBsJbBtBBiYjMunk+TffSil2A+j2GYusxskUAoCAmEBTwAAEEQRUVFeXl5+fn5RUVFBQUFlZWVLWFoWxCLxREREYEEHBEWFxbRJW5sYlh4WFBQUMWoHHuelR3BI4IoLJoT80FXtUa9bNmykpISpVJJEERQUJBCofD29p42bdq4ceMKCwsLCwsHDRrk7e1tNBotFotQKOTz+e3ty6cZVy2EeQOYR5uvk9pdqM9sWNSX1HxPaL5nhX+JiAbQ1pu0vQDij0S4Pu1mnfEIfEfwCLyHJ4PHM97v73/k4R4QMUqoXQzBtBZ42kwBCEB/7Ic20bTwbjduE02L4OZI8WN53LE8LgVAjgtPd7o2my2FODFfJJwvEjgZxkDTfq2Wh71m+XvN8qcdNMyF9flWw+EGZ5mdGy/g9xDxuon4PcVdu3bt2rWru3JeY/PUVqlUXr9+/fr167/88ovVal25cqW7HMfxssyKkJCo/sEpAAAbZa2yl5MMCQAwEYYPypYo2P6BnNAgbmhKVNgL7EAWDEUi6BJ/YbmBnOzLIxhm6InG62I8W4bjfMaA0WIYjsQUUYQjAiN6+L7yjP/LIoQDAKhyKAstOa+FrWLDnD1136MQNslvNgzBDsouQiUJwm4Jwub9aSRDalx1aqeqnlNb71tb71RqXRoRJi7SrPdl+8tg7zq1krGyOBlcoZ32J0DvN+YnJiYmJiaaq1R8iQiRiJxOZ0lJSWlpacHFywWZt2pQuqyszGAwZGdnZwMA8m4CsAtsAgAAFEWDgoKCOgUFSgICGX//Gr/ac42BgYGbN2+u7pfHKGinglCLGzWyJl2kyR052KAzzJgxQyqVBgcHe3l5cblciUTC5/M7d+48adIkuVxeU1NjNBqjo6PZbDaNBgIGIADAon4sUfOOBtR3HiIZBrECAADOkmK8+Ad7VirRKBUMSJbOega+f0YDDx48eHh4PDP4/wcMxVQvKCI1rvD9STAXBgCYzupqFhSGH0ji92ievn9oMJlo+jMvqVuo83BiQZPuZx958H18uV0MQwLAh6AbLtfbOgPNgDgWFsvCYlmszhgaiKKt3xVoG2XPsdiyTPZbZtJARh3vqpyay/JnCwdKa/tJ11w0HJ7sAwAoLi6+fv16QUHB4sWLQ0ND3eeuW7funXfeAQB4e3ufPXu25c3AjZU01zgqa51VtY5KlbNa46qToLIU+djh3k+7aGeNo9KX7YcCcZmeLNETZXoSADCrJ7/Qga8rsIikEEcCKggSZaAQDI1ioWEYGoSiwShC4dUmvLaPdDAEoHXlK3R44+exP0AAOqX9VYrJ+kgHt/2EAaPDtRpXfYOrvsGl1uIarUutwxu5gCsDUn9JqBfLh08JS9OKEJwrMcECEg7CuF0WzHaVVzdt2yeYN4krEZtoUllZceeHn6udlnqUqaysrKioqK+vp2m63UcgFov9ffwVYl9fro9C7BsxslNAQICPt49ler2MEjt9iEaxXqcwWXq4ampqVCrV8G4pc+fMPZ99cc68Od7e3qGhoSEhIRRFEQQhkUgSExN9fX379u0rEAgaGhocDkdQUBBVXa/5ZIvPmy9yYiNph1P/4yHGhXu/Nad1Nzwz+I7gmcF7eDLwmOg7xON2sqOY6lcKyQY8fH+S5YqhjboDAAiGeV1nYEPQ517SEpyY36T7QCoZ2uG5WgNFFeJEAU4UEkQpQegp+nMv6VAuR0mQtSQZhqEBCIL+YdK/rnL9vFcTqHSgOHVpmPfyt4sYNtykYOt82dYg9kvPeHMThZhP81ryrVu3VqxYkZubazQaL1682K9f8yxzw4YNq1atCgwMTEhI2LFjhztPDM3QWlyDQqic5VPnrNml+k7jqqcY0pfl581WeLMU3izfOGEXOcunWEe4KJDkgxld9Jzz+kqSxDlAJIUkMogtgGpIEqFBEIqGsFF/BPFHkQAEUaBIuekKRTaOV0xhALOkcK4X5v1O1KcMYI43HPBh+fWWDgQAOCg7B+FCAGIAo8ebdIS2Cdc24g1NuFaHN+qJJr2rkQ3YYkbsIw6QojLiWrGBJYEZrsDOEtrQUKUxedViRCbBXa6aKxkYC9MTrkarVaXT1tTWVlVVqVSqurq6mpoau93+gCci5AsUUoVcIldE+fn5+cmlcuagQ2jmS6wiIAAgDhO/7NPQ0NBQr1GmljU6miaOfGbUmDE3KjLfWPmmWCwWCASRkZESvZWUCmk+t1evXtOnTxfweLVzVwZ+uwbm/7mb0SPwHcEj8B6eDDwm+v9GIAQK2RJb/XJh6chbeK0z8tcubVK/YxD0pZf0dZ3h5UZdKUH8v9QdAOCLIL5cZMgfp1hphgMBAEAdRf1stVWRpJaifREkGEUCUdRXCE+foxABmAYgykB+8q/Yz6O5sgobU+FAKh0NX9awgjn+70eUjc7G/NlBY3zOHjtrOtmIKDBOII8hGLfvHo/HgyCoqqqqqqpKqVS6BR6G4M3rvl+/fr2/v39CQsKePXtEIpGNsmpd6jprbYGyoEGo4aMCOcvHhd3ZqflWZpJLMa/nusglmIwLSXFcGsyNjJEJmhz0yusmLzk0qBO70EZsKrWgfMCwgQuLZWCwRa31RmCBbAPNgnZZrFIEroMURsLViaZFEL2m9E0JJlsZ+QlBE4fUPwVxQ8f4TGQAk2fO9mb5upPbmkiDAdcZSb0e1xl6se3Ft4yEQRNI2QTUxb7MQc0SsU7KoXkOxITYMYRkwRA7DIZmLZsuQiW43lly/CIHxXCKMjntLhQ2sxCVSqXRaOrr67VabV1dXUNDg8VmtdjKy1TlIP+ep2UFQAPABSAQCLxkXiJCIIJFJ38+eXX3Zam3bP78+TAMU3ayYV+ty4aTAtzOtYsVfLdXBCqXkY0GFt8T3suDBw+PCo/A/7+BEChka6z6wwrJBJ826u4Gg6BXhIIXGnVxLGzQw62ztmR5H8hhD+SwAQAEw6goqpakVCSpIqlAKRKMol+YzD9xbUx3sEHsUgSzvFK4CgTxRuAhXA4GQaEHE8lKJyJAaDtl+l2HVzpwlZNowFEpivmxn5v41IuVs3I2ZgExlMCLBTTjqnaiMgxFUZIkq6qqampq1Gq1SCTiI4IwXtSquav37dsHAAgKCsrOzk6S91jTaaOeaLqede3CuVSIz0gjRPLuYgl/FAADKlxpsoDdQkxS1iCWoaJXfSUEKXDhPNgaHyyWxijgbJP9y3zc6oNigXS6Fc/SRzNs5gt7A4kAoeADXwyZpdVBOBBg4+sgWm+1CSAq01giRSom+z7DYlwflSzuLIh7NXSFg7Lvtm/p3HN0imgkiTtvEbe9Wb4yltxEGDSuOkKOWyiziTRaSbOKMBertlgos5W0kMkEi2KxSIxFsMQM1y80OhoJCjf5qkq82DjMITAehfmQbK+evc06i7qqvuhKmsluM9ttRovF5LAbrBadTqfT6axWa+twPQAAoAYg9+5nqQMAgN/WHy9ASj5YtoJsaMICfIAHDx48PDIet4neYrH88ssv+fn5DQ0NJEn6+fklJSVNnjxZJPr3Lujt8l9osivEiflNutUS8W92BwuCvvhjPf6RcqsRfy/L9H6KqIGimyiqgaL0FL1YLApCkY8Npl+sNhmCSGFYAsNSBJbBsBRA0x0YR0vWc4FRgrB/qEcbSWGcQDZIWjkpl2zAaef/tXfvYU3c6R7Af5MJ4ZZAwkVElIiI2FLqtqIrdT1a3T4FW1ufWqs9tbasW1q7Lepua7XyqLW065499eCDoq6tS7229bJ4it3H+6V4pVrtcvEKAgIBQwQSyG0yv/NHelgK2BqYSeLw/fzF5PLOy2/m975kyMw4eA3Rq+7Ig30envWo+qnwhg3VbLB8p37v2t3ramtqAwMCi/aeidD2l6lYNkg+ceLEo0ePEkIYhqmoqNBqtYQQSuib8+bu2LzDL9BfO3zQwk3zrazZ5DD+JmRSS3Hbxvz1ZfZLQXGB8eOGBLKqQFbJOwLsVh9lw6OMLLaf1sEG63QWv6N1bL+gwCHhyjqz7HidQ6bwkSkY4kNYBbEwNIBhCEc0LKtkW/0Iw9qUASzRW69rZOx/hD3gS6wF9VuiFaEzI2dwfFte5V8TlcNeGjCb8q1f1G7SBgwZGZR8x2442/StklWqfUJMXEuNpZp3OKw2c6vNaHIYHSxnYzmzo83Cm30dCrlDzjrkLMeqGN+IgbEKma+lyVxz7Zb9js3ebLM22QLbiG/UwNamNkOdoe5ahaXN1tpqNreZWxubWqmjxdz2TtrvMzQx/kmJ6umpHbcgDtHfCy+c7wA9IMH/wZ88eTI1NXXo0KGPPfZYWFgYIcRgMJw9e/bq1av79++/2z3Rf563TXjnt+o+0qjH+/tZKc3QGwJlsr+K3+P/ddvuPE2u22ftlBp4vonn7zj4OzzfxPMmnr6gDAiWyT4zmo6ZLSaemijfwlMjz/szTJBMtk0dEt7Mb6tqqrpt5QYoFBr50C8afZs4x69UjgcDH1lWTZvtbc2cw8TJjDwxcjf+s+0Yf0KfX64J0Cx7L1PzRHjdh+WyIPmi40u/KNlFCAlQBJT+7w8Dx2hbjhgYhqR++MyZC2cJIQzD/Kvwe2VEoMXHYlGY33w549ShM4SQsH6hWceWMIHE4jBbePM/sw9f3FuiUPkkDh+5dn3+udYjZ41fBMoDrh5puHysmmUV/YdrH3h5Su2dR9q4MJv8dnN1kb3kqr+ClQ+ObUkYFeTvG+ArN/Py2/UG85VSKiN+0dqAQVGhcpbwfJPZpuLN4UYrw3FVCiY4wD9c0d+PcVTaTsf4+z2keFRvJcXmr4cEqMcEja5tM5Q0HQ9VqIYrYwgxlZkuBDP+Ghpsthsbrbd8FD5KvyCOWG+b6/k2m4NwdhnnYBycjOMZ6m+Tv3l8lDZpgnL8aPLT0yPR4O+Ft813gJ6R4P/gMzIysrKyMjIyOj2+ZcuWt95669y5cz//9paWlq4nPXc+NOpppy3Wj0M0zsPpvgyTExby4Z1mHefodMNWwcVp5O+NuetREB+GiWDZCJYlXU7hnqNSzlH95C5wrZRanPfLCSATw0Nv2DkLpUaeWt7yN1HaTHlKyZN7HlbLZHlG02mL1U5IK095B28iv9b83tLaQvmEcIXat+BJZWmDOUq7fOGdPzmam9XEf5WeNpXVLfpHky9Pnnkik0k8HHmwXiMPqU9vaeSNrIVnrfxvHhqnG9TQcqs5zBDqt2W89YnIUa9fpz7M3qrDTaYWQsjJy8fLR5ex/zFyyn+r7H62PWfnXb9RSggpJWfSrI8/Qk0WI60KqZ2384OqKxWEEIZh3i9Y5qv1DbxiHlvzeHrWu1cqvyOEsAr57IOvyUNVDovMwjqOvrXn+MkbhBBflWr84UXNAcpGxt+HIQVv5m3cd4oQohwYPXP/kTze2GxptMtH3Vz2SWPBQcIQv5jouC+2Whm1ipVb1FztX1c2F3xJCaOIiRm4KU/OMj4M5Xmiy8ltPfhPhqGD3pq0aMKvhd36AABdufUTvFqtvnTpkvPgbUdGo1Gr1RoMhp95r9FofOCBB7p+4ZnjOIvF0vHuZOBV2ijlKCWENPOUEMIQMlDOEkLqHY4KO+fc+eyEtPE8IUTGMBP8fBUM853VVmGyUZYxslReZ2OslOcdd6qqRrS0RYdFXH8gssyPD/6uleHpdXlDSfG3ysutIZrI98ak+jY7iqzWGs5xyn7hYtE/VTfMYeqopPnzTYP95uxr9TU6Pjn/5f6SfZTQEFXkpPf/qzk+4NENDTJKLpp2/OPkrrYmW4hywJ9SV8uJg1h5SsjaupzvT37NWSwxQdrMj/cYiTakAAAPIElEQVQV/cp3xJdlrNW28ej/FF07SQgJClB/tmH9hF+Nv7iwuk5tWHF58fXvvyeE+PsFvP/2R35+gYH+9vgfhr95YemVqycJIVOfm/GP3V90HSh8gr8X+AQP0iDBQ/RTp0719/fPzs6OiPj3lcwbGxsXL17c0NCQn5/fg5ilpaXTp08vKSkRLk2Ae9Lc3MzzfGBgYMfr2vI8X1VVxfN8aGhocHBw++N2u724uJgQ8tBDD3V3LTw0+HuCBg/SIMFD9H/729/S0tKioqJiYmJCQ0MZhjEYDOXl5ZMmTdq+fbs7MwHovY79u51MJmu/uFBHPj4+nS4uBAAgKrc2+H79+u3bt6+ysrKkpKSuro5SGhER4bzZlzvTAAAAkDwPnAev1Wq7/hseACSvra0tPj6+urra04kA9Am40A0AiKKiomLz5s0dH7HZbLdu3frggw8IIcuWLfNQXgB9BRo8AIiCZdmtW7dWV1enpqbK5XJCCMdxhBDnlw0BQGxo8AAgiujo6IsXL86bN6+srGzz5s2xsbGtra1KpXLnzp2eTg2gT5D98ksAAHokMDDw008//eMf/zhx4sSNGzfe7/euBLi/3Pef4FmWLS8vT0pK6vqUXq+vq6uT3+VG7L3EcZxMJpPJRPkLyW63y+Vy5qeXMhWKzWbreN62gCilHMd1e5J37zkcDkrpfbo1hw8f/otjbrPZRErA46ZNmzZmzJhXXnnlq6++use3mEymYcOGdT3fneM4m83m/vnuKp7neZ73kmREnZiu8qpkCCE8zyckJLh/ve6Z7/f9/eAppZcuXep6CVtCyJ49ewoLC1977TUx1puXlzdo0KBJkyaJEXzp0qVpaWkxMTGCR6aUvvrqq59//rngkQkh165d27Fjx9KlS8UIfuDAgfr6+pdfflmM4OvXr09MTBw7dqwYwRcuXLhmzZpfPHPE39//wQcfFCMBL0EpzcnJKSoq2rJly728vttLU1NKS0tLu708iKjz3VUXLlw4fvz4ggULPJ0IIYRcuXJl586dmZmZnk6EEEJqampycnJWrlzp6UQIIaSpqWnp0qUHDx50/6rdNN+pdG3atCktLU2k4HPnzs3NzRUp+MiRI7/77jsxInMcx7KsGJEppadOnUpOThYp+OrVqzMyMkQKPmvWrC1btogUfNiwYVeuXBEp+P1l4sSJ4gUXdb67Kj8//9lnn/V0Fj86ceLEuHHjPJ3Fj4qLixMSEjydxY9qa2sjIyM9nYWIpHlIEAC80KlTpzydAkAfggYPAAAgQWjwAOAm69at83QKAH0IGjwAuMmrr77q6RQA+hA0eAAAAAmScoOXy+Usy4oUnGVZ8U5ylcvlIgVnGMbX11eMyETkAcfWhJ8n6h7iKiRzN141HbwqGTHc9+fB/wyr1WoymUJDQ8UIbjAYAgIC/Pz8xAheV1fXv39/kS50U1tbO2DAADEiU0p1Ol1kZKQYwc1ms8Vi0Wg0YgTX6/VBQUEiXf9HvAGHjkSd767iOM5gMPTr18/TiRAi8sTsAa+aEV6VjOCk3OABAAD6LCkfogcAAOiz0OABAAAkCA0eAABAgtDgAQAAJAgNHgAAQILQ4AEAACQIDR4AAECC0OABAAAkSLINvqGh4bnnntNoNCNHjjx58mTvA5aXl6ekpGg0mujo6KysLJHWUlVVVVFRIWBwh8Px7rvvRkdHR0VF5eTkCJv2xYsXx48fr1Qqhw8fvm3bNgGDl5WVbd++vX2x25iCD37vdUq7neCbFTrx+Ki6pz64yht2PFFLkKvEK1leikrU5MmTZ8yYUVFRkZ2drVKpWlpaehPNarUOHDgwPT29urr6yJEjoaGhn332meBrMZlMQ4cOXb58uYC/wttvv52cnFxaWrp7924fH59vv/1WqMh2uz0qKmru3LnXr1///PPPWZY9f/68IME5jps5c+Ybb7zR/ki3MYUd/N7rmraTGJsVOvHsqLqnPrjKS3Y88UqQq8QrWV5Lmg3+5s2bLMvqdDrn4iOPPLJhw4beBDxx4oRKpbLZbM7FzMzMKVOmCL6WOXPm+Pr6OiekIMFbW1uVSmVxcbFz8c9//vOXX34pVNrl5eWEkKqqKudiYmJibm5u74N/9NFH/fv3J4S0d8puYwo++L3UNe12gm9W6MTjo+qe+uAqb9jxRC1BrhKpZHkzaR6iLykpiYmJiYiIcC4+9thjxcXFvQnYv3//7OxsHx8f56LBYJDJZMKuZc+ePWVlZampqc5FQYKfPn06ODg4ISGBUkoIWbRo0QsvvCBU2lqtNjY2Njc3t6mp6Ztvvrl+/frYsWN7H3z27NmHDh2aNWtW+yPdxhR8E/dS17SdxNis0InHR9UN9cFVXrLjiVqCXCVSyfJm0mzwdXV1HW8qFRYWVl9f35uAcXFxv/vd75w/Hz16dOvWrenp6QKupaamZsGCBZs3b26/d6Egwevr68PCwv7whz+EhISEh4cvXryY53mh0pbJZDt27PjLX/6i0WieeuqpzMzMhx9+uPfBBw4cmJCQEBYW1v5ItzEF38S91DVtItpmhU48Pqpi1wdXec+OJ2oJcpVIJcubSbPBU0o73WuV47jehzWbzQsXLpwyZcqmTZsmT54s1Foopa+88kpmZmZsbGzHB3sfvKmp6dKlSxqN5ubNm4cOHcrLy8vNzRUqbZ1ON3Xq1I0bNxqNxsLCwnXr1u3bt0+Mke82pkibWEDibVboxEtGVaT64Cqv2vFELUGuclvJ8h7SbPAREREGg6F90WAw9P5eyDdu3EhKSrpw4cL58+enTZsm4FpycnIYhnnxxRdbW1s5jrPZbG1tbYIEDwkJiYiIyMrKCg4OHjFixIsvvlhQUCBU2gUFBUOGDJkzZ45SqRw7duzrr7+el5cnxsh3G1OMFQlLvM0KnXjDqIpXH1zlVTueqCXIVW4rWd5Dmg1+xIgR5eXljY2NzsWzZ8+OGDGiNwHtdntqampKSsqBAwfi4+OFXUtRUdGhQ4dUKpVSqczPz//444/j4+MFCT5s2DC73e5wOJyLarU6ICBAqLRtNhvP8+2LlFK73S74yJO7jLMYKxKWeJsVOvH4qIpaH1zlVTueqCXIVW4rWV7Evd/pc58nn3wyPT3daDRu3bpVrVY3NTX1JtqePXtCQkIuX758/f/V1tYKvhZK6fPPP99+WosgwZOTkzMyMnQ63bFjx8LDw3ft2iVU5Js3byqVyjVr1uj1+iNHjvTr12/79u1CBZ8/f37Hr6N3G1Pwwe+9Tmm3E3yzQieeHVW31QdXecOOJ14JcpWoJcs7SbbBNzY2PvPMM2q1Oikp6cyZM72Mtnz58k5/GD399NOCr4X+dEIKElyn0z399NPBwcFxcXHr168XMDKl9MSJE2PGjAkMDIyLi1u7dq2AwTt1ym5jCj74vXcvDd4L05YAz46q2+qDq7xhxxO1BLlKvJLlnRhKqXsOFQAAAIDbSPN/8AAAAH0cGjwAAIAEocEDAABIEBo8AACABKHBAwAASBAaPAAAgAShwQMAAEgQGjwAAIAEocEDAABIEBo8AACABKHBAwAASBAaPAAAgAShwQMAAEgQGjwAAIAEocEDAABIEBo8AACABKHBAwAASBAaPAAAgAShwQMAAEgQGjwAAIAEocEDAABIEBp83/XEE08wXcTFxVVWVvr4+Hg6OwC4V6NGjWqfwiqV6re//W1ZWVkP4ixevHjBggXCVgBnTKGigUvQ4Puur776SqfT6XS6uXPnpqSkOH8+ffp0aGjo2rVrPZ0dALhgxYoVzilcWFhosVimT5/e41C/WAFGjx594cKFHscHt5F7OgHwGI1G4/whMDDQz88vIiKi/an09HQPJQUAPREUFOScwhEREUuWLJk8eXJdXV1kZKTzWUqp3W5XKBT3EkqpVP58Bbh9+7bdbu99ziA2fIKHztoP0DU2NqrV6vfffz84OHjAgAHr1q3bsGHD4MGDNRrNypUrnS++ceNGSkqKWq0eO3bs1q1bPZo4APyb3W6vr6/XaDQHDhyIiYk5ffp0t7O1oKAgMTFRo9E8//zzd+7cIR0qACHkzJkzY8aMCQoKSk5OLioqIoRMmDChurp68uTJu3fvJnepAF1jgmdQ6PPeeeedqVOnti/evHlTLpdTSvV6PSFk1qxZNTU1q1atIoTMnDmzubk5NzdXJpOZTCaLxRITE5OZmdnQ0HD06NHIyMhvvvnGc78HQB+VlJSUlZWl1+v1en1JScm4cePi4uJ4ntfpdAqFIi0tTafTdTtbL1++rFAoNmzYUFNTs2bNGoZh5s+f314B6uvrg4KCPv3009ra2g8//DA8PNxut1NKBw8efObMGUrpvcf08AD1VWjw8AsNvrq6mlLq/DP80qVLlFLn0blbt27l5+cPGDDAZrM537hixYpnn33WE78BQJ+WlJTU/pnNx8cnOTn5hx9+oJTqdDpCyNWrVyml3c7WzMzMjnP/8ccf79jgs7OzJ02a5HzK4XCsW7dOr9fTDg3+3mO6YRCgK/wPHn5BeHg4IcR5yC4qKooQIpf/uNuUl5c3NjZGR0e3v7hjoQEAt8nOzp43b163T2m1WnKX2VpTUxMfH9/+SGJiYsc3VlZWxsXFOX+WyWRvvPFGp8g9iAnuhAYPPRcZGZmUlFRYWOhcrKqq4jjOsykBQCcsy5K7zNa8vLzi4uL2V1ZWVsbExLQvRkVFHT582PkzpXT58uWzZ8+OjY1tf0EPYoI74Ut20HMpKSnXrl1btWqVXq8vLCwcPXr0uXPnPJ0UAHSj29n60ksv7du37+9///vt27fz8vL279/f8S3Tp08/ceLEtm3b9Hr9J598snr16tDQUOdTzc3NPYsJ7oQGDz2nVqv379+/d+/emJiYWbNmvffeezNnzvR0UgDQjW5na3x8/K5du1atWjV06ND8/PwlS5Z0fEt0dPTXX3+9atUqrVa7ffv2vXv3qtVqQsiMGTOmTZu2c+fOHsQEd2IopZ7OAQAAAASGT/AAAAAShAYPAAAgQWjwAAAAEoQGDwAAIEFo8AAAABKEBg8AACBBaPAAAAAShAYPAAAgQWjwAAAAEoQGDwAAIEFo8AAAABKEBg8AACBBaPAAAAAShAYPAAAgQWjwAAAAEoQGDwAAIEFo8AAAABKEBg8AACBBaPAAAAAShAYPAAAgQf8HPCi5zkb2E9QAAAAASUVORK5CYII=" 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 + +$distimes + DT50 DT90 DT50back DT50_meso_b1 DT50_meso_b2 +meso 7.932495 36.93311 11.11797 5.205671 18.26</code></pre> +</div> +<div id="hs" class="section level2"> +<h2>HS</h2> +<pre class="r"><code>hs_pH <- saem(f_sep_const["HS", ], no_random_effect = c("meso_0"), + covariates = pH, + covariate_models = list(log_k1 ~ pH, log_k2 ~ pH, log_tb ~ pH))</code></pre> +<pre class="r"><code>summary(hs_pH)$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">-5.81</td> +<td align="right">-7.27</td> +<td align="right">-4.36</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">-6.80</td> +<td align="right">-8.76</td> +<td align="right">-4.83</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">3.25</td> +<td align="right">1.25</td> +<td align="right">5.25</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)$confint_trans |> kable(digits = 2)</code></pre> +<table> +<thead> +<tr class="header"> +<th align="left"></th> +<th align="right">est.</th> +<th align="right">lower</th> +<th align="right">upper</th> +</tr> +</thead> +<tbody> +<tr class="odd"> +<td align="left">meso_0</td> +<td align="right">93.33</td> +<td align="right">91.50</td> +<td align="right">95.15</td> +</tr> +<tr class="even"> +<td align="left">log_k1</td> +<td align="right">-5.68</td> +<td align="right">-7.09</td> +<td align="right">-4.27</td> +</tr> +<tr class="odd"> +<td align="left">beta_pH(log_k1)</td> +<td align="right">0.46</td> +<td align="right">0.22</td> +<td align="right">0.69</td> +</tr> +<tr class="even"> +<td align="left">log_k2</td> +<td align="right">-6.61</td> +<td align="right">-8.34</td> +<td align="right">-4.88</td> +</tr> +<tr class="odd"> +<td align="left">beta_pH(log_k2)</td> +<td align="right">0.50</td> +<td align="right">0.21</td> +<td align="right">0.79</td> +</tr> +<tr class="even"> +<td align="left">log_tb</td> +<td align="right">2.70</td> +<td align="right">2.33</td> +<td align="right">3.08</td> +</tr> +<tr class="odd"> +<td align="left">a.1</td> +<td align="right">4.45</td> +<td align="right">3.74</td> +<td align="right">5.16</td> +</tr> +<tr class="even"> +<td align="left">SD.log_k1</td> +<td align="right">0.36</td> +<td align="right">0.22</td> +<td align="right">0.49</td> +</tr> +<tr class="odd"> +<td align="left">SD.log_k2</td> +<td align="right">0.23</td> +<td align="right">0.02</td> +<td align="right">0.43</td> +</tr> +<tr class="even"> +<td align="left">SD.log_tb</td> +<td align="right">0.55</td> +<td align="right">0.25</td> +<td align="right">0.85</td> +</tr> +</tbody> +</table> +<pre class="r"><code>plot(hs_pH_2)</code></pre> +<p><img src="data:image/png;base64,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" style="display: block; margin: auto;" /></p> +<pre class="r"><code>endpoints(hs_pH_2)</code></pre> +<pre><code>$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 + +$distimes + DT50 DT90 DT50back DT50_k1 DT50_k2 +meso 8.298536 38.85371 11.69613 8.298536 15.71561</code></pre> +</div> +<div id="comparison-across-parent-models" class="section level2"> +<h2>Comparison across parent models</h2> +<p>After model reduction for all models with pH influence, they are +compared with each other.</p> +<pre class="r"><code>anova(sfo_pH, fomc_pH_2, dfop_pH_2, dfop_pH_4, sforb_pH_2, hs_pH_2)</code></pre> +<pre><code>Data: 116 observations of 1 variable(s) grouped in 18 datasets + + npar AIC BIC Lik +sfo_pH 5 783.09 787.54 -386.54 +fomc_pH_2 6 767.49 772.83 -377.75 +dfop_pH_4 7 767.35 773.58 -376.68 +sforb_pH_2 7 770.94 777.17 -378.47 +dfop_pH_2 8 765.14 772.26 -374.57 +hs_pH_2 10 766.47 775.37 -373.23</code></pre> +<p>The DFOP model with pH influence on <code>k2</code> and +<code>g</code> and a random effect only on <code>k2</code> is finally +selected as the best fit.</p> +<p>The endpoints resulting from this model are listed below. Please +refer to the Appendix for a detailed listing.</p> +<pre class="r"><code>endpoints(dfop_pH_2)</code></pre> +<pre><code>$covariates + pH +50% 5.75 + +$distimes + DT50 DT90 DT50back DT50_k1 DT50_k2 +meso 18.36876 73.51841 22.13125 4.191901 23.98672</code></pre> +<pre class="r"><code>endpoints(dfop_pH_2, covariates = c(pH = 7))</code></pre> +<pre><code>$covariates + pH +User 7 + +$distimes + DT50 DT90 DT50back DT50_k1 DT50_k2 +meso 8.346428 28.34437 8.532507 4.191901 8.753618</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: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.5.0 +Date of fit: Tue May 13 06:54:35 2025 +Date of summary: Tue May 13 07:05:04 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 0.573 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: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.5.0 +Date of fit: Tue May 13 06:54:36 2025 +Date of summary: Tue May 13 07:05:04 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 0.832 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: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.5.0 +Date of fit: Tue May 13 06:54:36 2025 +Date of summary: Tue May 13 07:05:04 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 1.483 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: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.5.0 +Date of fit: Tue May 13 06:54:37 2025 +Date of summary: Tue May 13 07:05:04 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 1.563 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: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.5.0 +Date of fit: Tue May 13 06:54:37 2025 +Date of summary: Tue May 13 07:05:04 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 1.457 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: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.5.0 +Date of fit: Tue May 13 06:54:52 2025 +Date of summary: Tue May 13 07:05:04 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 1.802 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 -6.6614 -7.9715 -5.3514 +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 0.0414 +beta_pH(log_k_meso) -0.0183 -0.9917 + +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 91.348139 8.927e+01 93.427476 +k_meso 0.001279 3.452e-04 0.004741 + +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: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.5.0 +Date of fit: Tue May 13 06:54:54 2025 +Date of summary: Tue May 13 07:05:04 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 1.512 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 -2.206602 -3.494546 -0.9187 +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 -0.0865 +beta_pH(log_alpha) -0.0789 -0.8704 +log_beta -0.3544 0.3302 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 92.8406 90.75046 94.9308 +alpha 0.1101 0.03036 0.3991 +beta 67.6332 31.15113 146.8404 + +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: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.5.0 +Date of fit: Tue May 13 06:54:57 2025 +Date of summary: Tue May 13 07:05:04 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 2.52 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 + 767.5 772.8 -377.7 + +Optimised parameters: + est. lower upper +meso_0 93.0536 90.9771 95.1300 +log_alpha -2.9054 -4.1803 -1.6304 +beta_pH(log_alpha) 0.6590 0.4437 0.8744 +log_beta 3.9549 3.2860 4.6239 +a.1 4.9784 4.2815 5.6754 +SD.log_beta 0.4019 0.2632 0.5406 + +Correlation: + meso_0 log_lph bt_H(_) +log_alpha -0.0397 +beta_pH(log_alpha) -0.0899 -0.9146 +log_beta -0.3473 0.2038 0.1919 + +Random effects: + est. lower upper +SD.log_beta 0.4019 0.2632 0.5406 + +Variance model: + est. lower upper +a.1 4.978 4.281 5.675 + +Backtransformed parameters: + est. lower upper +meso_0 93.05359 90.97713 95.1300 +alpha 0.05473 0.01529 0.1958 +beta 52.19251 26.73597 101.8874 + +Covariates used for endpoints below: + pH +50% 5.75 + +Estimated disappearance times: + DT50 DT90 DT50back +meso 17.3 82.91 24.96 + +</pre> +<p></code></p> +<caption> +Hierarchichal DFOP fit with pH influence +</caption> +<pre><code> +saemix version used for fitting: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.5.0 +Date of fit: Tue May 13 06:55:00 2025 +Date of summary: Tue May 13 07:05:04 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 2.218 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 -11.479779 -15.3203 -7.63923 +beta_pH(log_k2) 1.308417 0.6948 1.92203 +g_qlogis 3.133036 0.4657 5.80035 +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 -0.0571 0.1096 +beta_pH(log_k2) 0.0554 -0.1291 -0.9937 +g_qlogis -0.1125 -0.5062 -0.1305 0.1294 +beta_pH(g_qlogis) 0.1267 0.4226 0.0419 -0.0438 -0.9864 + +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 9.284e+01 9.085e+01 9.484e+01 +k1 5.986e-02 4.556e-02 7.866e-02 +k2 1.034e-05 2.221e-07 4.812e-04 +g 9.582e-01 6.144e-01 9.970e-01 + +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: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.5.0 +Date of fit: Tue May 13 06:55:05 2025 +Date of summary: Tue May 13 07:05:04 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 4.711 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 + 765.1 772.3 -374.6 + +Optimised parameters: + est. lower upper +meso_0 93.3333 91.2427 95.42394 +log_k1 -1.7997 -2.9124 -0.68698 +log_k2 -8.1810 -10.1819 -6.18008 +beta_pH(log_k2) 0.8064 0.4903 1.12257 +g_qlogis 3.3513 -1.1792 7.88182 +beta_pH(g_qlogis) -0.8672 -1.7661 0.03177 +a.1 4.9158 4.2277 5.60390 +SD.log_k2 0.3946 0.2565 0.53281 + +Correlation: + meso_0 log_k1 log_k2 b_H(_2) g_qlogs +log_k1 0.1730 +log_k2 0.0442 0.5370 +beta_pH(log_k2) -0.0392 -0.4880 -0.9923 +g_qlogis -0.1536 0.1431 -0.1129 0.1432 +beta_pH(g_qlogis) 0.1504 -0.3151 -0.0196 -0.0212 -0.9798 + +Random effects: + est. lower upper +SD.log_k2 0.3946 0.2565 0.5328 + +Variance model: + est. lower upper +a.1 4.916 4.228 5.604 + +Backtransformed parameters: + est. lower upper +meso_0 9.333e+01 9.124e+01 95.42394 +k1 1.654e-01 5.435e-02 0.50309 +k2 2.799e-04 3.785e-05 0.00207 +g 9.661e-01 2.352e-01 0.99962 + +Covariates used for endpoints below: + pH +50% 5.75 + +Estimated disappearance times: + DT50 DT90 DT50back DT50_k1 DT50_k2 +meso 18.37 73.52 22.13 4.192 23.99 + +</pre> +<p></code></p> +<caption> +Further refined hierarchical DFOP fit with pH influence +</caption> +<pre><code> +saemix version used for fitting: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.5.0 +Date of fit: Tue May 13 06:55:12 2025 +Date of summary: Tue May 13 07:05:04 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 3.202 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 -8.1039 -10.4225 -5.7853 +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 0.0337 0.5791 +beta_pH(log_k2) -0.0326 -0.5546 -0.9932 +g_qlogis 0.0237 -0.8479 -0.6571 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 9.330e+01 9.119e+01 95.411751 +k1 1.166e-01 6.325e-02 0.215084 +k2 3.024e-04 2.975e-05 0.003072 +g 2.617e-01 1.263e-01 0.464832 + +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: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.5.0 +Date of fit: Tue May 13 06:55:16 2025 +Date of summary: Tue May 13 07:05:04 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 3.153 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 -5.3742 -6.9366 -3.8117 +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 -9.9797 -19.2232 -0.7362 +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 0.1493 +beta_pH(log_k_meso_free) -0.0930 -0.9854 +log_k_meso_free_bound 0.2439 0.4621 -0.3492 +log_k_meso_bound_free 0.2188 0.1292 -0.0339 0.7287 +beta_pH(log_k_meso_bound_free) -0.2216 -0.0797 -0.0111 -0.6566 -0.9934 + +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 9.342e+01 9.132e+01 95.51946 +k_meso_free 4.635e-03 9.716e-04 0.02211 +k_meso_free_bound 3.054e-02 7.268e-03 0.12829 +k_meso_bound_free 4.633e-05 4.482e-09 0.47894 + +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: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.5.0 +Date of fit: Tue May 13 06:55:19 2025 +Date of summary: Tue May 13 07:05:04 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 2.832 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 91.1633 95.4760 +log_k_meso_free -6.1460 -7.4306 -4.8614 +beta_pH(log_k_meso_free) 0.5435 0.3329 0.7542 +log_k_meso_free_bound -3.8001 -5.2027 -2.3975 +log_k_meso_bound_free -2.9462 -4.2565 -1.6359 +a.1 5.0825 4.3793 5.7856 +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 0.1086 +beta_pH(log_k_meso_free) -0.0426 -0.9821 +log_k_meso_free_bound 0.2513 0.1717 -0.0409 +log_k_meso_bound_free 0.1297 0.1171 -0.0139 0.9224 + +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 4.379 5.786 + +Backtransformed parameters: + est. lower upper +meso_free_0 93.319649 9.116e+01 95.47601 +k_meso_free 0.002142 5.928e-04 0.00774 +k_meso_free_bound 0.022369 5.502e-03 0.09095 +k_meso_bound_free 0.052539 1.417e-02 0.19478 + +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: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.5.0 +Date of fit: Tue May 13 06:55:21 2025 +Date of summary: Tue May 13 07:05:04 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 1.397 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 -5.81463 -7.2710 -4.3583 +beta_pH(log_k1) 0.47472 0.2334 0.7160 +log_k2 -6.79633 -8.7605 -4.8322 +beta_pH(log_k2) 0.54151 0.2124 0.8706 +log_tb 3.24674 1.2470 5.2465 +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 0.0744 +beta_pH(log_k1) -0.0452 -0.9915 +log_k2 0.0066 -0.0363 0.0376 +beta_pH(log_k2) -0.0071 0.0372 -0.0391 -0.9939 +log_tb -0.0238 -0.1483 0.1362 -0.3836 0.3696 +beta_pH(log_tb) 0.0097 0.1359 -0.1265 0.3736 -0.3653 -0.9905 + +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 93.325994 9.147e+01 9.519e+01 +k1 0.002984 6.954e-04 1.280e-02 +k2 0.001118 1.568e-04 7.969e-03 +tb 25.706437 3.480e+00 1.899e+02 + +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: 3.3 +mkin version used for pre-fitting: 1.2.10 +R version used for fitting: 4.5.0 +Date of fit: Tue May 13 06:55:23 2025 +Date of summary: Tue May 13 07:05:05 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 1.404 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 -5.6796 -7.08789 -4.2714 +beta_pH(log_k1) 0.4567 0.22400 0.6894 +log_k2 -6.6083 -8.33839 -4.8781 +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 0.0740 +beta_pH(log_k1) -0.0453 -0.9912 +log_k2 0.0115 -0.0650 0.0661 +beta_pH(log_k2) -0.0116 0.0649 -0.0667 -0.9936 +log_tb -0.0658 -0.1135 0.0913 -0.1500 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 93.325134 9.150e+01 95.152036 +k1 0.003415 8.352e-04 0.013962 +k2 0.001349 2.392e-04 0.007611 +tb 14.939247 1.028e+01 21.707445 + +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 4.5.0 (2025-04-11) +Platform: x86_64-pc-linux-gnu +Running under: Debian GNU/Linux 12 (bookworm) + +Matrix products: default +BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0 +LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0 LAPACK version 3.11.0 + +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_3.3 npde_3.5 knitr_1.49 mkin_1.2.10 +[5] rmarkdown_2.29 nvimcom_0.9-167 + +loaded via a namespace (and not attached): + [1] gtable_0.3.6 jsonlite_1.9.0 dplyr_1.1.4 compiler_4.5.0 + [5] tidyselect_1.2.1 colorout_1.3-2 gridExtra_2.3 jquerylib_0.1.4 + [9] scales_1.3.0 yaml_2.3.10 fastmap_1.2.0 lattice_0.22-6 +[13] ggplot2_3.5.1 R6_2.6.1 generics_0.1.3 lmtest_0.9-40 +[17] MASS_7.3-65 tibble_3.2.1 munsell_0.5.1 bslib_0.9.0 +[21] pillar_1.10.1 rlang_1.1.5 cachem_1.1.0 xfun_0.51 +[25] sass_0.4.9 cli_3.6.4 magrittr_2.0.3 digest_0.6.37 +[29] grid_4.5.0 mclust_6.1.1 lifecycle_1.0.4 nlme_3.1-168 +[33] vctrs_0.6.5 evaluate_1.0.3 glue_1.8.0 zoo_1.8-13 +[37] colorspace_2.1-1 tools_4.5.0 pkgconfig_2.0.3 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: 64927780 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_prebuilt.rmd index f11effc7..153c22ab 100644 --- a/vignettes/prebuilt/2023_mesotrione_parent.rmd +++ b/vignettes/web_only/mesotrione_parent_2023_prebuilt.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 13 May 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 @@ -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 @@ -511,7 +514,7 @@ 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 +523,58 @@ 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 +597,4 @@ 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_prebuilt_files/figure-html/unnamed-chunk-14-1.png b/vignettes/web_only/mesotrione_parent_2023_prebuilt_files/figure-html/unnamed-chunk-14-1.png Binary files differnew file mode 100644 index 00000000..67fa91dc --- /dev/null +++ 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