Análise de experimento em parcela subsubdividida
##-----------------------------------------------------------------------------
## Definições da sessão.
## Lista de pacotes a serem usados na sessão.
pkg <- c("lattice", "latticeExtra", "gridExtra", "doBy", "multcomp",
"reshape", "plyr", "nlme", "wzRfun")
sapply(pkg, require, character.only=TRUE)
## lattice latticeExtra gridExtra doBy multcomp reshape
## TRUE TRUE TRUE TRUE TRUE TRUE
## plyr nlme wzRfun
## TRUE TRUE TRUE
## source("http://dl.dropboxusercontent.com/u/48140237/apc.R")
## source("http://dl.dropboxusercontent.com/u/48140237/segplotby.R")
## source("http://dl.dropboxusercontent.com/u/48140237/equallevels.R")
sessionInfo()
## R version 3.1.1 (2014-07-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=pt_BR.UTF-8
## [4] LC_COLLATE=en_US.UTF-8 LC_MONETARY=pt_BR.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=pt_BR.UTF-8 LC_NAME=C LC_ADDRESS=C
## [10] LC_TELEPHONE=C LC_MEASUREMENT=pt_BR.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] splines grid stats graphics grDevices utils datasets base
##
## other attached packages:
## [1] wzRfun_0.2 nlme_3.1-117 plyr_1.8.1 reshape_0.8.5
## [5] multcomp_1.3-3 TH.data_1.0-3 mvtnorm_0.9-9997 doBy_4.5-10
## [9] MASS_7.3-34 survival_2.37-7 gridExtra_0.9.1 latticeExtra_0.6-26
## [13] RColorBrewer_1.0-5 lattice_0.20-29 knitr_1.6
##
## loaded via a namespace (and not attached):
## [1] evaluate_0.5.5 formatR_0.10 lme4_1.1-6 Matrix_1.1-4
## [5] methods_3.1.1 minqa_1.2.3 Rcpp_0.11.2 RcppEigen_0.3.2.0.2
## [9] sandwich_2.3-0 stringr_0.6.2 tools_3.1.1 zoo_1.7-10
trellis.device(color=FALSE)
##-----------------------------------------------------------------------------
## Leitura dos dados.
da <- read.table("http://www.leg.ufpr.br/~walmes/data/soja_solo_tcc.txt",
header=TRUE, sep="\t")
str(da)
## 'data.frame': 80 obs. of 16 variables:
## $ sistema: Factor w/ 2 levels "convencional",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ adubpk : int 40 40 40 40 40 40 40 40 60 60 ...
## $ prof : Factor w/ 2 levels "00-20","20-40": 1 1 1 1 2 2 2 2 1 1 ...
## $ bloco : Factor w/ 4 levels "I","II","III",..: 1 2 3 4 1 2 3 4 1 2 ...
## $ Zn : num 0.8 0.9 0.9 1.2 0.7 0.3 0.7 1.1 1.3 1.1 ...
## $ phcacl2: num 5.1 5.3 4.7 5 4.6 4.3 4.4 4.5 5.8 6.5 ...
## $ phh2o : num 5.9 6.3 5.3 5.6 5.2 5 5 5.1 6.6 7.5 ...
## $ P : int 3 7 2 2 2 2 1 2 4 6 ...
## $ K : num 2.2 1.7 2.8 4.4 1.6 1 1.9 3.8 3 1.5 ...
## $ Al : num 0 0 2.5 0.6 3.1 7.4 6.6 8 0 0 ...
## $ Ca : int 45 58 30 37 27 19 23 22 27 81 ...
## $ Mg : int 34 33 16 18 23 12 13 10 36 33 ...
## $ Hal : int 47 38 72 53 69 89 80 76 29 19 ...
## $ sb : num 81.2 92.7 48.8 59.4 51.6 ...
## $ ctc : num 128 131 121 112 121 ...
## $ v : int 63 70 40 52 42 26 32 32 76 85 ...
##-----------------------------------------------------------------------------
## Visualizar.
xyplot(ctc~adubpk|sistema, groups=prof, data=da, type=c("p","smooth"),
auto.key=TRUE)
##-----------------------------------------------------------------------------
## Análise considerando o modelo para experimentos em parcela
## subdividida sendo o sistema a parcela e a adubação a subparcela.
## Efeito categórico para adub para começar.
da$adub <- factor(da$adubpk)
## Níveis das parcelas e subparcelas.
da$parc <- with(da, interaction(bloco, sistema, drop=TRUE))
da$subp <- with(da, interaction(bloco, sistema, adub, drop=TRUE))
str(da)
## 'data.frame': 80 obs. of 19 variables:
## $ sistema: Factor w/ 2 levels "convencional",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ adubpk : int 40 40 40 40 40 40 40 40 60 60 ...
## $ prof : Factor w/ 2 levels "00-20","20-40": 1 1 1 1 2 2 2 2 1 1 ...
## $ bloco : Factor w/ 4 levels "I","II","III",..: 1 2 3 4 1 2 3 4 1 2 ...
## $ Zn : num 0.8 0.9 0.9 1.2 0.7 0.3 0.7 1.1 1.3 1.1 ...
## $ phcacl2: num 5.1 5.3 4.7 5 4.6 4.3 4.4 4.5 5.8 6.5 ...
## $ phh2o : num 5.9 6.3 5.3 5.6 5.2 5 5 5.1 6.6 7.5 ...
## $ P : int 3 7 2 2 2 2 1 2 4 6 ...
## $ K : num 2.2 1.7 2.8 4.4 1.6 1 1.9 3.8 3 1.5 ...
## $ Al : num 0 0 2.5 0.6 3.1 7.4 6.6 8 0 0 ...
## $ Ca : int 45 58 30 37 27 19 23 22 27 81 ...
## $ Mg : int 34 33 16 18 23 12 13 10 36 33 ...
## $ Hal : int 47 38 72 53 69 89 80 76 29 19 ...
## $ sb : num 81.2 92.7 48.8 59.4 51.6 ...
## $ ctc : num 128 131 121 112 121 ...
## $ v : int 63 70 40 52 42 26 32 32 76 85 ...
## $ adub : Factor w/ 5 levels "40","60","90",..: 1 1 1 1 1 1 1 1 2 2 ...
## $ parc : Factor w/ 8 levels "I.convencional",..: 5 6 7 8 5 6 7 8 5 6 ...
## $ subp : Factor w/ 40 levels "I.convencional.40",..: 5 6 7 8 5 6 7 8 13 14 ...
## Adub categórico.
m0 <- lme(ctc~bloco+sistema*adub*prof, random=~1|parc/subp, data=da,
method="ML")
##-----------------------------------------------------------------------------
## Diagnóstico.
r <- residuals(m0, type="pearson")
f <- fitted(m0)
bp <- unlist(ranef(m0)[1])
bs <- unlist(ranef(m0)[2])
grid.arrange(nrow=3,
xyplot(r~f, type=c("p", "smooth")),
xyplot(sqrt(abs(r))~f, type=c("p", "smooth")),
qqmath(r), qqmath(bp), qqmath(bs))
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N0VFp/lgycWz/9yjIwsSSeEDrw86cSKFSs8PT1NTEwKc3JaWlpiYmIBJxT8qBBCaCcxMTE6OrqAE8zMzJ7sAVfq9O/f/8iRI0/tS13ShIWFNWrUSNdRiCKzNCJLRUwqNsYaHo1Mwsrohcf03LxTl1+PM34PxvoMclPPUaRmMv8UMw7TrSodKus6RPHqeUnSienTpwcGBm7YsKEwJ0dGRlatWrUwn3NhYWHPHJoQQsDD68moUaNGjRpVwGk2NjYBAQGlvR+Ch4eHv7//0897gVJTU0+dOtWiRYv89tft2bMnICDgt99+e8GBiWfn7gSw/TqD3fI+FJ+Gb9BLdYetp2D7AHr8w/CtfL6Xpk5kqjh2h/g0OldhbT9dxydeSS9DOjFr1qyff/7Z29u7kLsmrKysvvrqq4LTCT8/Py8vL1tb22KKUQjxqsu+nvTo0eNRDQmNHBwcrKysXlRQr5CJEyfOmjXrp59++uKLL4D169dv2LDBw8OjQoUKqamp//3337x5895+++1u3brpOlJRZB6uVLbmf968Vgnnx5Y8ZakYv5uYFD4sWUU1n5WDGUdG8Pd51vtxLRKgTUUG1uOdutISW+hGqU8npkyZMm/evH379jVu3LiQT9HX158wYULB56xZs8bLy8vAwOCZAxRCCIDs64mnp6enp6euYylmPj4+bdq0eZYL5v79+zt06KBUPscmxtWrVzczM6tatWr2l66urgYGBt9//31oaKihoWGdOnVmz5793nvvPb8AxPNjpMfy3nRZidt8PmlGpyqYGHA5nD9Ocuoen7WgUxVdh1jcDPV4rzHvFfbGR4jnq3SnEz/88MPvv//u4+PToEEDXccihBCvoujo6J49e9arV2/ZsmU1a9Ys6tPj4uK++uqrBQsWbNq0qXfv3sUSUuvWrVUqVZ6DeZaZNWrUaNWqVcXydqIkaOPC4RF8uJ3vfPnOV33Q3pRfujBWOn8I8ZyV4nRi165dU6dO9fb2llxCCCF0xcbG5vDhw4MHD65fv/7o0aO/+uqrcuXKFeaJKSkpy5YtmzZtWkJCwrJly4orlxCvrMaOnBxFYDQn75KSQTVbmjoV1KJBCFFcSms6kZ6ePnbs2G+++aZly5a6jkUIIV5pDRs2PHPmzE8//fTrr7/+8ccf3bt379y5c5s2berWrZtdRv1xd+/ePXz48MGDB9evXx8bG+vp6Tl9+nRp9yaKi6sNrja6DkKIV0xpTScOHToUEBBQv3798+fPP368TJkyzs7OuopKCCFeTUZGRpMmTRo/fvz8+fP//PPPLVu2AGZmZvb29ra2tjY2NomJiVFRUZGRkZGRkYCFhcWAAQO+/PLLKlVeulXtQgjxiimt6cTt27eBN998M8/x999/f8GCBToISJRsMSkERgNUsdFcmLxgaZkcuM3pe6RkUMOOLlWxNy32GIUo9czNzb/44osvvvji1KlTBw8ePH369M2bNyMiIkJCQgwNDa2srJo3b16vXr02bdp06NDB2Ljo/xTF8xedwu8n2erP5XCUCho68lYtPmiCqZQmEULko7SmEyNHjhw5cqSuoxClgF8EX+1nVwCZKgA9BR6uzOxMvbKFfYWNVxmzg7DHGhsa6jHGnR87yapc7alUbLzKigv4RaCCilb0rc2IhhiX1muSyMXd3d3d3V3XUYgiuxZJl5UExVLfgcH1yVJxNJjP9rL0HHsHU95C1/EJIUok+egWL7MDt3l9NQZKRrvTogIKOHGXZedw/4ttnni4Pv0V1lxm0Caq2zG7Cx0qY6TH5XB+O8Evx7kbz9q+z/97KByVissRhCWgp6RBOW1mYF6kpHTe3sD265Q1o4Uz+krO32fMTv46w/YBucrGCyFemOQMXl9NXCq7B9Hl4eVRpWK9H8O20Otfjr8rbQ2EEBpIOiFeWnGpeG7AwYwDw6hsrT74Tl3GN6fLKgZu4vrHT7ntjklhzA7cHDgyArOHE/1tK9K2Il978+MR3qlL7yIXxix+ay7zjTe3HzZm1FPQrw6/dsXB7Jle9n4CJ+6Slom1MS0r5PwEnt2wLey4zg8d+bSFeoYn+5ZlxFa6rebMezLtI4QOLD/PzWg29s/JJQCFgv51CE/k4114XefNGrqLTwhRUkk6IV5aKy4Qloj3kJxcIpuLFUvfpOUSlp7jsxYFvcK2a0Sn8E9HDXfSU9qx6AzLz+s+nZhykKm+1C3LvG7ULENSOrtvsPQcR4I5NDzv915I9+L5dA/r/ch6WLvfwpBPW/BNm6ff6Memsv4Kh4IITaCCJa9Vpk+tXD/Ak3dZ78dnLZjQOudg9i1LWiaDN7PsPO9Lb6ZSTqVSPVnTKTExcebMmVeuXHFxcRk7dmylSpV0EZrI1+4bOJjRS9M1bXhDPt/L7huvUDqRnsWJEEITUCpoUK7I1aJCE1hylmMh6hGZLq4McpPFnOKlJX/a4qXlG0RZM16rpOGhFs44W3Io6CnpxMUwlAo6VNbwkLE+rVy4GFYcgT6DA7eZ6ku/2qzqk3Oj37MGQxvQaQWDN3F4OE/c1D3F3XiaLyYsgTHuvFkTa2OCY1l8lu98OXWPbZ7o5f+Cu24waBNRyVgbY2vCybssO89X+1jfn1YV1Odsu4ZSweeaKjwPrMfX3my7JulEqffxxx8HBATs2bPn0ZGUlJR27dqdOXNGoVCoVKrVq1dfuHDB0dFRh0GKPELiqGaneTmTmQEVrAiJe+ExFatDQSw4zZlQMrKwN+WN6oxpqnmOetl5/udNaELOkfaVWPgG1e0K9UarL/GeF0nplDXD3JD7CWzwY+ZRNr1dhG174pFDQay+xMm7JKVTzZbXqzG4PhaGug5LPEap6wCEKKyMLG5GczOa5IxCnR+ZRDnzfG+mHc15kPSUV0hKx0CZ73i8mQGJ6YWK5Pn57Tg2xizqmTfIZk5804ajdzgdWuTX/GgnEYnsGczcbnSsTGNHetdkxwC+bc/OABaezveJx0Lo/S+WRuwcyIMvCfyEyC/Z2B+g6yr8ItSn3YqhjCnlzDW8gkJBHXtuRRc5ZlHSHDhwoGLFio8f+fnnn8+dO7ds2bLk5OTt27fHxsbOnz9fV+EJjUwNSEzL99GEtFJc3EmlYtxu2v3N9utUtaWxI6mZTDpAvT85fS/vyf/zYcRWnCz5uxen3+PwcKa04/x9mi7ibCGuqFuvMWQzbg5c+pCwzwn8hOivWN+P+FQ6reBu/PP4/l5amSpGedHub1ZcwNQAFysuhDFmJ27zuRyu6+DEYySdEKVAaAKjvLCZietcXOdiO5O+6/B/8JRn2ZkSnpjvo2GJlHlasVdnS1Izc/Yk5HEtkgq63jR8JJiuVbEy0vDQ23XUJxRJUCxb/fmoqYZZnUltaVCOeSfzfe6X+7A04ugIulVVz2DoK+lTi8MjAL7xVp9moCQ9M98XScvEQDZOlH63b9+uXbv2oy/T0tLmzZv39ttvDxs2zMjIqHv37j169Ni2bZsOIxRPalCOS+GaL5tXIrifQINCtTsvib4/xG8neLcRIZ+yYwDr+nHufY6MQKGg66pcsxCHgphxmLfrcGwkQ+vT2JHWLnzbnlOjMNZn4CbSswp6o0wVY3dRxYa9g6n7cCLCUI++tdkxkMgkphx4jt9mIaVnsfUan+6h/3rG72GLP2n5X5N1a8J+Fp/l/cbcGc/REewbTPA41vcjNgWPlUQl6zo+8ZCkE6KkuxxOvT9Zfp7u1fj9deZ3Z5Abu2/QaCF7Awt6YmsX7ifgG6ThoWMhBMfmLL/JzxvVAeZrGo8/HsK5UPUJuqJSEZ1C2Xz2WzuYA0Q+bQYmj5N3UUFPTcujlQreqI7/A2JSNDwaHMuRYD5011BK0tWGIfXZGUBsKkDdskSn5ExWPC4lgzOh1LYvWsyiBMrMzDQyyklzN27cGBER8fHHHz860qBBg5CQEF2EJvI1oiFZKj7bi0qV63hqJp/swkSfQW46iuzZRCbzwxG6V+OvN7B8bPClVQW2eRKbyozDOQf/PIWFIfPfQD/3/VFVW6Z1wP8BPrcKeq+TdwmK5fOWGpbiNHakRw02Xc3Zk6YTp+5RfR69/uX3k+y/yR8n6b2W6vM4cfeZXvZ+Aluvsd6PfTeLbd4+JI7fjtO3NgveyBn+UyjoW5vN73A/gVn/Fc8biWf3MqQTGzdu1NPTi4nJZwxZlGbpWfRbT5aKoyP5ty9j3PmgCYt6cGk05S3w3FjQgqVBbtia8L4X9xNyHY9I4t1tWBkxtMFT3t3NgT61mPUfs/5Tt63IdvQOfdZS1oyxzZ/he3tmCgU2xrnG1R53Lx54+gxMHrEpALYmmh+1MwHUWUEe1yMBmjppfmIzJ9KzuBkN0K8ORnp8uifXjzTb9MPEpTKkftFiFiWQs7PzrVs5t12//PJLgwYNWrTI2atkamoaGxuri9BeNtEp/HuZmUf56Sjbr5P6DMPMDcoxrjmrLtJxBbtvEJlMRBKb/WmxGJ9b/Oyh+/lY7ewNJCWDz1tqWPvasBydquB1Tf3lgyT2BGKkT+cVtF7KhP0EPfZH2qcWwLE7Bb1XwNOuhNEpRBRxlKcY+T+g8wriU1n2JtETiPqKmAms6E1SOh4ruaJplOepQhPw3IjTHHr9S//1eKzEcRZTDhZqxiM1k/03+esMf53hcDAZuWd+dgaQnqV5o127ijR1wuu6NgGL56HUb8VetGjRmDFjsrIKnH0Updbmq/g/YHUf3MvnOl7ZmjV9abqIv87wTRvNz7UzYXUfev2L23w+dKeFMwoFJ0L48xTRKWzoX6g6qkvfJDKJL/bx+0naVcJIj0vhHA+hjCk7Bjy9vUNoAvNP4RtEYhpmhrSvxAdNcHxi20B0CtHJGOgV+dO6XSX2BhKVrCEBWH8FoG3FJ59UkOwtDXficqbpHxcci1KheT4ke7wtv03a2Z/i2edUtGJyO/7nw+urmdGRRuVQKLgZzY9HWHSWvrXpVrVoMYsSqF27ditWrPjoo49cXFx+++23U6dOrVix4vETIiIiLCykKdozUan49QRTDhD/2IYHR3Pmv6F9/aVZnXG25LuDdFudc9DKiAVvlOICCdkLVvOb9qxtz95AMrLYdYOhm4lJwdoYGxMik5j1H7+d4Neu6u/dxhilgmhN07OPFP5KqBMT9pORxbF3qVVGfcTUgMFuNHem8UK+2sf2AUV7wXvxNFtMWAKj3en1sHTHkrNM9eX000p3/H2eb3Lvd69iw5wuOX+92b84NwfNT3dzYPWlokUrnp9SnE74+PhMmzYtICBg8uTJkyZN0nU4olAS0rgeiQpcrLAvxMD5gduY6NO3toaH3MtTw44Dt/NNJ4CuVTk6krG7mOqbc7C5M9u65jt6lIeVET5DWX2JZefYfJWUDKrbMaE1Hzd9eoPYLf4M3UJ8KvUcKGtGeCLf+/LrcVb2zllNtPsG3/ly8q76A8bJgtHufNYSo9z7B/YG8tcZjgQTnkgFKzpWZkxTGjvyeUs2X2XQJtb3z1WM1ecWMw7TsTKNilg4p7ULBkr+Pq/hnj4pnbVXaO6MiabLhqstwNlQXq+m4dGzoegpcqrWft0a4PtDNPkLc0P0FMSmolQwqhG/dStawKJk+vrrr9euXevq6mpjYxMREdG+fftBgwY9fsKJEydq1tR1leVS7qv9/PwfHSszoTUNHcnIYvcNZv1H739Z0VvLhUkKBeObM6IhewO5HK6ukdqpSnG2nXnxDJRAvnse0jNRKrgcTv/1uFhhZkDtsuwZBBAQxfAtfLgdZ0u6V+NeAlmqfNeXZqv68EqocZ/J2VAsjZ7yCs9PVDI7AvigSU4u8Ug1W0Y2Yt4JIpIK9dH8yEc7CU9k96CcEojZ1Tum+jLlIAtOM8Zd8xNnHmXCfhqUY3YX3MuTpeJIMDOP0vtflvdmsBug3keXnqn5Qyc9S/2bFSVBKU4nvL29PTw8vLy8zp07p+tYxNPdieOLvWy8qp7NVEC7SvzcmSblC3pWWAJlzfKtrVTBKu9Cpic1duTICIJj1atxqtpSqYitGJQKBrupr26FdySY/uupU5YVvXMqA14KZ8KemPwAACAASURBVMhm+q3nwFBaVuB/Psw4jLMlX7aisjXxaWy7xv982OKPz1AuhrH0HBfvExRHeCIm+nSrRhlT7sXzzyVWXuS3rox2Z1oH/udD3T8Z0ZCaZUhMY08g669Q3oK/exUtZsDamA+aMO8kLSswtlnO8ZQMhm7hXjzzu2t+oqsNbg7MP62h8OK9eJado23FnCkUhYJv2jCiIeuu4BeBCipa0acWNZ/4kBOlVNWqVY8cOfLjjz/euXNn8ODB06ZNe7wNRVpaWmZm5ptvvqnDCEu4tEzOhpKQhokBjRw13E4dD2HWf3jWZXWfnDU8Q+vTrzbt/mbMTjxctb9ttTKiX236aRrHKY2y51qPBtO/joZHj96hZhn+54OxPr7DmHyAFRcIjsXFimq27B1Mw4WM383rVfnnEqC5dPgjLSrgYsWcY7xTN28hrMvhbPFnkFtBA/bPVUAUGVm0zGfTYKsK/Hqc65FFSCeCY9niz/gWGn4mk9qyxZ95JzSnE/4PmORD5ypsH5Dz+V7djv518FjJRzvpXIVy5uq05+gdDcNbKhX/3SnyRru4VOafZvNVLoQBuDnQuyaj3XPtqBHaKcXpxPTp03UdgiisS+F0WE5iGsMb0K4S+krO32fJWVot5d++BXWCszImMplP97I7gIAozAxo6sRANwa7oVQQmYT105YbZXOxwsWquL6bQvl8L3am7Buca/dCvbLsHUy9P/l8L5PaMeMwvWuysk/OyN9nLVh6jve8aLYYvwiUChzMCE9EX0lyBrei+bUrFSwJicNzIx/tpEYZvmlDPQcmH2Dyw4IhJvq815jvO6i3OhTVzM74P2DcbtZd4c0aWBsTFMvqiwTH8l17zbu0s83pQpeVdFrBH6/T3Fl98MBtxuwgJYOZnfOeX86cT5rlPSheGg0aNPj33381PmRoaOjr66vxIZGWyY9H+OV4Ts0DMwNGu/Nt+1y3p0vPYajH3G559wOYGvBLF9osY71fvgPDr47kDDZdZWcAJvq850ViBoPr5dpjvfwCZ0OZ+hpTffmwCeXM+bwlyy/w1jp2DKCsGaYGjHFn7G4WnuHbg7SvRAvn/N8P9BT81pU+a+m4gkU91JmMSsXWa3ywHQtDvmv/XL/jgmSP5eU3ov9oKqDwCijdoVDwRnW+P0R0ioZVwYvOkqViyZt5xwrNDfn9dRr/xepLfNaC7tWxNVH/2PNk1IvPcT0y3+EtjW7H4LGSgChq2zOwHsCJu3ztzZJz7B2sZctX8UgpTieeRUxMTFRUVAEnhIdLQeNik5bJW2sBTozKGad/uw6ftsBjJYM24f9RvnsGbIxJSOPXY7R2oU1FktI5cIthW1hziV+7cTGMTwvsQ6crd+M5eZcp7TXshLY35f0mfO/LjMM4mrOqT94RrBENmXeS8/cZ7MbMzjRaSDMnDg5j3RXG7MRjJWffx9mSnQOpMY/vDtKxMj2q06M6IXGEJaKnoJZ93rVSRWKiz86B/HqcuSf4aj+AApqU54/udNe0kOmRjpX55y1GbKXFEvVittAE7sVjY8yWd/Lufnnc5XDmneRIMCkZWBjS2ZWPm77o9O+FCQ8Pv3nzZgEn2NraWlvLJ9srKj2L11fjfYvOVRhcX90eZ70fP//HgdscHJYz9HDuPk3Ka6610KoCFoacK3rPmZfMvpsM2kR4IkZ6GCiJTWXEFr47wPYB1C1LeCJ/nmLGYRqUo3t1Jh9Qr9GvbsfCN3h3G7X+YGA96pYlIApg9E4qWrGqz9Pft1dNFrzBuN3Um09VW6yMCI4lIonK1qzth7PutrNnb9sbt5v3vACq2/FOXT5oou7VfeE+QOWiNP/OLsuRb+kOU4BYTenEmXvULav5c7+RI/am6mYgFobM7MQoL9ouY5YHrV3QUxCawNwT/HyUFs6826iwoaZn0WMNoQlsfjtX3/cNfgzbwhv/cP4DWTr1TF7FdCIiIqJixYrJyU+vVxwa+spfj4vDtmsEROVa85PN3pSVvXGbz5+n+KGjhicGx/LXWfQUOFuyob964j49ixmH+fYg7f9GT1mEq8mLkZHF6XvsvYkKbPOZOXFzQAXH7vBeYw1toW7HcCkM4PVqBMVwP4HpHTDWZ0h9TA3ot54/T/FZCywMGejGnGPqjYOAs2WxfVDpK/m8JZ+3JCSOtEysjAs70dG/Dm0qsuoih4IIjaepE+0rMbBeQQWmfjzCpAPoKWjlgp0J9+L55RjzT7GoJ551i+fbKSGyryfjxo0bN25cAaeZmJgEBQXZ27885XIzMzPv3LkTFxdnbW3t7OysVMqHdr5m/4f3LWZ0VO8vyvZOXZae491tTPJhThf1wfhUyuRz56dQYG6Ya3/2K+joHXr8QyVrlr5Jx8oY67P0HJ/sJigWt/lYGhGXigq6VWVZL/VE0KOyQsMaUMueyQeYfzrnYK8aLOulucnPk95rTI8aLDnLkWCikmlXiS6uDHJT37gXXnaVDn1lMQyvhCbw1jqAsEQ862Kox/EQxu9h+QV2DsTMgL/O4OZAxaK8UXbpjpA4za2+7+RfuiM+raD1RdbGxD+sH/huIzKyGL+H9n9jaoCRnnorvIcr/7yVt5hvAdZf4XI4y97MlUsAfWuTlM7QLfx7uchLmsXjXsV0ws7Obvbs2XFxcQWcc+HChTVr1pQtq+mfiCgi3yAM9TQvw61blgblOKSpNQTw01FSM5jbjU920WAB41vQxgU9JQ5mWBsRlsDEtlS3e66x58hSsekq666oq53WsmdAvbwLOldf4htvgh8WFvxkN/9cZm63vAPz2buuM1Wa7/63XVNXUM1e4wTUeLijoG9tqtux+SqftQCoYUeWirvxhV3xpQUt8hNHc75oyReaSvs9ad0VvvbGw5W/e+UUvLoWyTsbGLqZqrYFzWmUOtnXE09Pz/r1CyqFa2lpaWf3ov6snzM/P7/p06d7eXnFx6tbAdvZ2fXv3/+bb75xdi5wycgrKUvFbydoWzFXLpFtREN23WDhGb7voJ6gKG9BYD5T7HGpRCTh9GrXzfpoJ9bG+A7PqeA3oiH96jDrP6YdwsGcj5rSvbp65ZKlEaYGHAvhgybqk5s5sWcQ8WlEJPLHKeYc42ePwuYS2RzNmdhW+/i9bzH5AMdD1J8XjuaMdufLVvnuJCxYpoqea/B/wKS2zDjMjShW9KaqLev9GLqZziuxNCI0gcU9i/ayrSpgqMfKCxr2NiRnsMGPZk6aO6mXt+DCfVQqDaV7UzO5G0/7SjlHPmhC39psusrJuySlU9WW16vlrKctpN03sDRiQD0ND3nW45Nd7Lkh6cQzeRXTCaVS+eGHHxZ8zpo1a9asWaOnJ+15i0FEIrYm+Y7KlLdQb5J+0u4btHFhtDv1HBi7iy/35TzkbElMqnpo5AUIS+SttRy9g60J9R1QwbZrrLpI92r82xdzQ4Dph5noQ92yLOpBJWu6rKJVBfwf0HopuwflajJ9IgQ9BVkqIjXNkAVGo6cgU4WNCSYGAImPjTI2cuTgbfX/J6QBmi/WpUKWii/3UdueLe/kWhdbw47dg6jzBxP24z1Ed/EVt+zrSY8ePTw9PXUdy4uwfv36wYMHp6WlNWnSpFq1ag4ODrGxsSdPnpw/f/66deu2b9/evPnz6tuycePG/v37R0ZGPnXZ2OrVq3/66afr16/b29sPGTJk8uTJhoZPtB97UQKjuZ/AhCdyiWx9arHBj3OhtHYB6FaNL/fhfYuOT+yC/fs8GVl0fYULLl99wPn7zOiYtxp49tYF/wfsCmByu5xbcxN93qzBmkuMb56rIpOFIfGpLD9Pk/K4FmUV0DP65Tif7cHRgi9aUsWGmBQ2XWXSARacxs6UhDSM9GjlwsdN8y2imsemq5y+xx+vM9qd6naM8qL6POqWxcEcW1OuhGGgz9I3i/w3k126Y+4Jmjox7rF/zSkZDN1MUGy+lfo6VWH7dXYHashDVl0kKZ3OrrkOljHlvca89wylikPiqGytORkzUFLFhjsFjTCLp5NJZ/Hc2ZgQk6K5SN/1SK5HolAQlqjh0ZA49cB8GxfOvs/l0azrx7p+nByF/0fqE16ALBU913DyLr90IfQzfIZyYCj3P+fb9uwMYPhWgPP3mXKAN2tw+j3ebUSnKnSuwoUwtnpS0Yqhm0l62CX0RhR/naFTFRqXZ9u1vF17AKVCPRzVwhk3BxSw/2auYJQPh3O8b2FjXFobSwHn7hMUy8dNNVStcTBjSH18b2vuwC1KvuvXrw8aNKhatWqXL18+efLk6tWr58yZs2TJkkuXLu3Zs0epVPbq1avgDWxaW7RokaenZ2GaES1YsGDw4MFdu3bdsWPHhAkT/vjjjwEDilh1v1hFJwP597k3A3KaHnzQBCcLBmzM28x4vR9f7aeNC52rPL9ISzr/B0BBJYzi0/J+fPzQCWtjPFby72X1ZTlLxb6btP+bpHTmvcAC1gdu89keatvzUVNauzCgHl+2YlxzjPS5G09oPI0dcbJk1UUaLWTaoUK95o7rWBoxshHAIDf8xvB1G3XDHxsjjAzoUZ2hWvUPndmJzlUYv4eWS/jpKH+dYaIPtf5ggx/fts+3/8nIhjhZMGIr5+/nOn4oiE/3UMe+oAIt2jE1UA/AaZSYXooH5kqIV3F2QrxgrSqw4DQ7rufd/zTRh2sP5yXKzaJdRX7tmmtkyNQg18B8HXvqPFxPnt0M+/F//+GJ3ImjvAVlTDkbyp1YHMxpUl5zvepCikomJoW9Nzl5Vz2u84iJPlPakaViqi+Hg1l5AX0li3rm7IGe3YVmi+i/nmENmHaIzf68U5cd1xmzExXM7sLJu4zYyv98mNkp15vqK1FBUyd1/fIOlZl/muENqVUGlYpTd9Xru/YEsuM6o92LsHi0pMleFVYr/95SmSpC4p7jUi7x/MycOdPIyGj37t1OTnk7vHh4eGzZsqV169a///775MmTi/FNi9SMKCYm5quvvvryyy9//PFHoEOHDq6url27dt27d6+Hh0cxRlV42VuMQuM1P5rd5/5REU8LQza/Q/fVtFxCh8o0LEdGFoeCOBNKrTKs7adhGcmrozAljPIM5VS0wmcofdfhuZFRXpQ1IzqZ6BTKmbNzYJGX1jySvQlbX0mtMoVap+Rzix5rUMGVCL7xBjA35J26/H2e2vZUt2OjH3O6EJPCwSCWnGXSASpZ59tjJCmd1EyM9QmOpbJ1zsdTZWumd2B6B/WXDRcS9fTNpJoZayrd0bg887rxRvV8n2VuiNcAuq7CfRE9quPupO47sTeQilZs8yz+z7UG5dgZwK0YDRWcgmIJiFS3PBdak3RCPF18GreiMTHA1SZnaLzw3qrNRB8+2ombA1VsAOYc47O9uFhRxYbgWOZ3JyCKhadpuYRdg2j3sJFzg3IcvE1GloYri/ct9QkqFfNP8/tJrj5QP/RodB8w0WeQW5HXvAK7bjD1se5ySohKITUTpYKENPQU6m1k45rzw2E2+nE8hHoOjNvN7htEJWOgpLkzn7Zg2Xn16NGYHYzeQVwqLlbsGEAde2qVYV8gPx3lbCjvN6ayDXGpeF3jj1MoFOgr1d/476/TaiktlzChNVkqbsUwtAFf7GPeCarZ8d1rRfxllCSGBdYlzJ7O0m6hcLHzi+BePEoF9RyK1uPplbVv377evXs/mUtka9myZadOnbZu3Vq86USRmhFt2bIlMTHxs88+e3SkS5cujRo1Wr58ua7SicrWuFix7grjm2tIBv69jIUhDR9rTOlensuj+ekoG/zUc5h17JnZiY+bPdMwyksg+5bxYpjmCYqLYRjqadgYVrcsl0arf5jxqRjrqycHzLVa/rbpKlMOcvlhkUgzA0Y0ZOprBY2P7Ayg179kZGFqQHI6KtBXYqBk8VmM9Ng/hOuRbPCj6aJcnaSHbUGpyLsrYO0VZv/HmVCyVOgrsTTCNP8/ieT0Z1o5rF3pjoblOPc+3/my0Y/N/gDlzBnXnIltNVSCenZDGzDzKON3s+ntXLcxGVl8vBN9JcMaFP+bvlJe7UuOeBqfW0w7xOFg9UCOrQnv1GXqa0VraGCiz6o+dF5Jo4W83wQXK77YR3U7dSOF+W+oqzN93JQ2yxi4kWsfq/cajmzEoE3M+i/vYuKIJCb6UNGKjlXw3MjaK9Qqw6R2bLjC1QfqOc3mznzUlK3+LD7LsRB8h2FrQnQKvx1n01VC4lAoqFeW4Q3VLSweN+UgU31zustNO0RsKpN8+OUYKRnqZUtVbXmvMeOaU8GKm9HcSyA6iSvh9Kihbki3M4DvD9GzBt+2Z8xOrI1pX4n2lXinrnobiVLBqj64OfDjkZzlTAroU4s6ZZnqS481zOlCrTIcGo7nRibsV5/z7UGArlVZ9qaWnSVKiOy5pqN36KRpVcbRYCwMi9xzUAspGSRnEJ9KWiaxqaRkkJxOXCqxqcSkcCKEfTdzhu4U0KMGsz3Uc0ciP/fv369ePf/BSWjWrNncuXOL902L1Izo6NGjderUyVNBq2PHjmvXri3eqApPoeCzFozdzQRvfuyYK6OYfYwdAXzdOm8N6LJmzPJglgeJ6egpilw4qFTIXnS0xV9d8q5BOfrU4rVKBU2/NHKksjXzTjK0Qd7MKiSO1RfpWlXz4hYDJZ51i6Gm3KQDTDuEqw3TO1DZhtgUfG7xxyl2BPDfyLw7OrIlZzDKCwM90rMwNeDjptiYEBLH2ssAqZmcC+V6lPrM7KqpSgW/HWf1JQZuIiJJ3Xg0LpWPd7HiAq42fNqCMqaExrPkHFHJfLqXOU9kyvfiCYiiR/4NhQqvqKU7yluw8A3md1cXnH0eWcQj1WyZ2JZvD9JmGRNaq7fgHw9h+mGOhzCjIzVekvoXOvMyXntEMVl4htE7sDZmtDt17EnOYP9NFpxmZwAHhxWtnFxrF06N4tM9/HRUfeR6JG4OLO6Bx8MdV86WzPbgrXVs9VcPtHjWZdVFvvHmYhgfNaWWPUnp7A3k24OExrNzIIvPsPYKY5sxy4OZR7n6gDldGNuMKQeZdoju1VjXj23X6LOWz/byeUu6ryYolmZO9K1NWiaHghi2hX8usentnGru+28y1Zce1VnTV31wxQXCE0nOICqZila8U5ekdA7c5st9bPUnLZPEdKKSMDXg+seUf1hNJSOLbw8y/TBVbUnPpH8dfnqij5tSwYTWfNKMYyFEJaOvpJmT+hUMlHx/iNp/UN4CIz31LrGaZfBwpYYdHavkuvbdjuFWDJZGuDmU9MrZKhUxqQBxqWSpcC/PnGM0cqS8BZlZxD0sDnjyLv9eoa0L/14mJSPvi8SkkD3/lJROagZAepaGdbEJaaRnqrOF9EwS0lCh3owRm0KWisR00grRs0kB5gZYGvNWLfSV/HWGxn/hOyzXwjyRh5mZWVJSUgEnWFhYJCZq2jL1oty8edPFxSXPQRcXlzt37qSnpxsY6GYl9UdNOXmXn45y4BaD3HC0ICKR9X4cvE27ikxul+8TzV7Sld+xqfRfz95AjPSoZY9KxaKz/HGKXjVZ/UTTnkeUCmZ0xHMjXVayrFfOLurjIQzZTKaKGZpKkxeXA7eZdogeNWjpzHo/zt8nS4WjOR5V8L7NqG1s01SLYcd19Xo2BXjW48eHi2BHNqLhAoD/easn4ae+xsdN1Y9+1JTVl6hXli/2EpnExqv4RQDoKalhR9/aNHMCGNKAJn/xyzHcHfF8bB5DpeKzvSjQcuNEsVAqnm8i8ciUdlgaMeUAPdfkHLQwZLZHCW1gVbq8DOlE69atVSrV088TRXElgjE7aO7MlndyVneMbcauG/Rdx+BNHBpetBesW5a9gwlNoO0yjPTY9LaGGq/dq2Og5FiIOp1QKtj0NuN2s/Qcay7nnOZsyTZPOlZm5Dbcy/NLF4D5p2jhzPjmAN+/xrE7/HqcCa3pWYOBbqy+yN5A0jM5OCxnMVWWinkn+XQPw7ewrp/6YHZ3uX/eyvl4drTg6B3cHHBzYM0lxjVXzwv/fZ6R21CpMDXASJ+kdB4k5aQT+kqmdSAgirknyFLR4YnqK4+YGmiozTKxLUPqs+oip++RkkHPGvSulRP5I0vP8dPRnC0o5oYMqMcPHfPtK1QsktKJSiY6hahkEtJITCMmhfg0EtNISCM2lfhUEtNJSCM2hfg0EtJITicjK99C+I9f3B934DYHbhc5PGtjskctzQzVa6UM9dS/TQXq5XaWRugp1L84Iz3MDLE2xtwQc0PMDLA2xsKI0Hj6rqNdJbw8c921vNuIdn8zcJO0PSpI48aNT58+XcAJiYmJpqa6XDcWHx//ZLFaOzs7lUoVHx9va1vQ9FN8fPxrr70WHR1dwDkJCQlATExMkaJSKljZmxYV+PEIY3erD9qbMqMjn7d8Ff/e+q1j/03+14avWmNhCBCTwveHmHOMwZvZ2D/fJ75TF/8HTD9Etbnqf9EKCIrFzoSN/XO24T0Ps/+jjAl3Yvn6GnXs+agpJvpciWD3DRQKvK5zLVLDWPh/dwB6VCcskV0BZHioV/lm32ob6HEujMwsIFedwOzRllGN+WQX3x+ingOmBpQxpZsr6/xotYRfu/JRUxqV48tWzDzCqO24WKu3ghwPYaovewP5urW6e/dLb3xz3m3E/pvqnn1uDnSqUlAHjBcv+4oxbty4iRMnFnCajY3NgQMHLCxKUDXolyGdEM/DL8cw0GN9v7wrxbtVZXI7JuznxF31sEeROJqTmUVVe839Ioz0sDJWlzfJZqLPwjeY0o59NwmIxNRA3RlNX0lAFMGxjG2GQkFQLHfj+aRZzhM96+F9iwthNHakV01WXOBePHsG5bojVyoY24zgWOYc42wojRxJz+JQEO83ybVYNns7eFMnBrmx6iK+QbxdB2BYA2YewT+S2zH0rsmOADw3smdQrgnfZk6su0Jlm5xJmMJzseKbNgWdMHY3c09Q3Y4fOuJqS0Ia26+z+Cw+t/AdlpPYFFViOndiCUskJI6wBHWz7ZA4opLV/z05XfA4G2MM9TAzxNwQQz1crDDSw9QAPaX6qp396WhhpP6wDE9kxQUeJKGvxEiflAwys6hux8S2OJpjYqBev6GvVN9MAFbGedenGSi1XN9cgOFbMdTjn7fyjoDWtufnzgzfyt7Ap7QJf5UNHz586NChN2/erFJFc4GhoKCg8uVLa1cRPT29OnXqFNwLNTg4ODw8XItq4woFY9wZ3QSf2/T6l+p29KxOu4q8gjurd99g302mdeB/j10JrY2Z7YGBkplHOXqHVpp2R6hUfOfLjMNkqDA3JClDPaVZowzbB1D1OZd8PRSElRGXw/m7F0PcchZlXY/k9dUERrPoLFlZHA8hLRMbE7q4MqqxuovIsAYkpTN4M1/tZ1ZnFAqcLLA2JktFXCoKaFcx162/bxCgru7lasPSnrgvYnI7RjZkRif6r2fsbtydaObEjA6cucf+m7RemvN0UwNmdGRCq+f7AylRLAzpXbP4K0cVl+wrRuXKlZ+cO32ciYlJSetkIOmE0MznFu0rab4lHVCPCfvxuaVNOgHYm+Vb4DUhjehkDXUSy1tomIqNSASoYAWoO1ecvses/2hSnrYV1eVTwxPhYeFFZ0vN9/TjmjPnGDsDaORIdLKG7nJXInC0YPFZ9cKY7NcMjmXSAfwjAdIyqefAkPr0XUedPxlQjwblSMvk4G22+gMMqa/NFvaCbfZn7glGNGTBGzljlsMbsP8mb/zDKC92FFjxUqXibjw3orgVw51YQuK4G09wLCFxGmqzmhviaI69GVVsKGNKWTPsTSljiokBJvpYGWNlhLWx+n+08ENHtl7jSDApGVgY0qkKXVx1X5fmaDBtKmpe5dynFsO3ciRY0gmAtLS09u3b5ynMmpWVlZmZ2b59+/xyBj8/vy5dumh86MWwtLR8slJtZGSkQqF46pifqanp8uXLCz5nzZo1AwYM0Hr4UKGguTOD3NgXyLe+fOuLlRGvVVYXoX5h7Tt1a+0VzAzUc855fNWaOcdYd0VzOvHjUb7z5Y3qzPJQzwPEpPDrcaYfZsQWDg4r/gtyNv8HeF0nPo34NHrXpF/tXNex6nas6EOrJcz5Dz0lDcthY0JQDF/sY/YxdZwmBvSuidd15hzj/H0+aEIVG9q44HUdQKlg8Zs5Lxgcy9wTNHRk7WUczLEyVm/Rzt6JbmvCxrdx/Y3vfdk+AKWCYQ3Yf5NJbdV7Fdwc6FmjlNWWiEpmyTkOB5GcgbkhHSsztEHOSNNLIPuKMXbs2FLXnkjSCaFZRBId89lW5WSB4uFdtRY6VOano/g/oGaZvA+tu0JmgeuCHpddHyMkjk92Mf80wHo/1vsB1CqjLvqWPRB+JxbItwlRBUvMDQmKBbAyRgGRuZd8h8YzshGxKay4ADBhP9MOEZ6IUsH45vxyHCA+ldercXIU//Nh6Tl14mFpRN86rL1MpaLsM3lcSgarLrInkJgUDPVo4cy7jdRLrX47TnkLfn897/qHTlUY25yfjhIQRbWH6zUeJOEXwdUH3IjiRhSBUdyIIvmxSQZzQ1yscLbEvTwuVjhaqPMHR3MczJ97lZjspuka+6brUExKvh+0lkYY6+eaRnuVGRoampiY3L17N8/xGjVqkP9qn/Lly/fo0eO5B5c/V1fXEydO5Dl4584dFxcXXW2cyMPMgPndAYJi8bml/m+LP0Bla7pUpYsrHSqXrKUaxSswipplNG+QsDGmsg03NHUuuRvPVF88XNnyDnoP7+atjfm2PeXM+XAHKy8W/1aBuFTe3ab+AMq22R/nOSx4g/51cg4eDgIw1OP6x7g8/FzwDWLoZvUMwz+X6FaV1X1oUI6fj9J/fa53USrUQzYpGewI4NM9pGbQtzbnQkFFOXP1JG3sw01oVkb0rMGay+qGRbEpAAPqafjwLRU2+DHKi5gUKlpRxhS/CLb4M/0wq/poWDAsXjBtbhNUKpVC5yOH4jmzNlYP/z8powLu7QAAIABJREFUIgnVMxRhGOPOb8fpv54dA3O1YDt6h8/30qAc3Qo34lvdDjsTph8mMomB9ThyB1MDdg1kXyDfHuSHIxjrU78cwD+XUIJxPncIWSpSM9Qraoz0aOTI1mv82CmnOq2ZIftvcuvhMumkdJLSMdKjubP6qm1vqi5cW7csW98hMZ2wBBQKypkzeDPApAN8f4ja9gysR/86hR0YuxhGn7UERlPWjAqWxKayM4Afj7CoJ2/X4b87DG+ovtG/HcPhYO7F42xJu4q8WZOfjjLlIJaGXH2AX4S6TQegVFDBkqq2tKxAVVtcbalqi4uVlrMKLzd7M/XOyCdlr/jKr93YK8jb21vXIRRZixYtlixZEhER8XhxJ29v71atStzKj4pWDG/A8AYAVx+w+wY7A1h2jgWnMVDSsgJvVKd/nZzb05eG4rGq30/KUmleALbRj5QMfuiYk0s88l5jph9mdXGnExlZdP+Ho8F80IRPmtF1FcGxrHmL73zx3AioM4r0LHXdcAO9XL+sdhXxHkrdPwH+uUTPGvSrzdetGdecY3c4dY95JwhNQKkgPYtys7E0VDeUqGTN/iEcCwEIS+S1StR3wEDJroCcxTyVbUjJICYFWxN238DGGNfSWZXuSDADNlLdjt2DclZG+Nzi3W28uYaTo6j9PPfDiKfSJp1o3759q1atZsyYUezRiJKjhbN6UPzJItnZw2Mt8mk4+sixEHxuqdvJtXHhtcrqi7uzJct7M2gTdf5goJt6XZDvbbZew86EtX1zfQZkqTh/n8BogJplqGufM3esr6RFBbZfp2cNVvVhyTne3caMw/z+OumZjN6JkT7xqXyyC6/rVLbl1F1SM/OWWQT+u0N6Vs5q1NHujNz2f/bOM67Jq43DVxL2nrKHgoADJ+6NW6tindU6qrWt+qpdapdt7XLUVlurtVr3qHtvFBciigscLNmy9yZAyPshj0ZoQLRYR3P9+gGfnCQnKZzz3Oe+7/+fL/wEYQ2ZHLGIe1n0ceFsLK1sKJVxPRmpjHNx+McDFJdxNYkVQUxvA6CvKfhptFhFRCbaEtrZUSEn8D6HI/j9KgdGP96a7X4evTYhErF/NIPdhE99O40ph3hzr7Cv2BiQmM87hzgWyaN7ruIb+usWYhH1TejsSGNLPOvRpB5u5iq+gRcTmZz4XORyrAyej2pNVyfWXic2R4VY7ZYQYYCalwipVBoUFNShQwdFzbGPj8+sWbN+/vnnBQsWKAb4+vpeu3btBd/aGlnQyIIP2lNQyqlojt3jWCSzfZntS3MrXnNjkDttbJ9VMc+zJiKTq0lIZbib09aOhmZsvUV+qYpqll13icoiNhvRfKHoZXYnofApIhMtCS1VCa+JRbSx5XpyHU97w03841nWT5Bq7WBPfC4HwvCfRPcNzDrOIHd0Nfj9KgWlGGlX+jgJeSwJ4FikEDhVyBm5ixbW9KyPoTZBiZyKBhEVchb2Ylkg2SV0c8bBiE6ODPVAS8KJKAALPaa0xlibUU1Zf5MRTQRb9OxixCIMtdl9l8MRddbQX1xOZpHQTZdZTGGpoLqRXUxBKQWlFD2Q35DLhQLah4p8NfP3k0pFB92VRMRiXEz5KQCxCGMdABMd+ruy7gav7+R/bdCSCIMV8hsaYqEFX7HhKpruDLVeYu/XF5mnCSfu3LnTu/ffNC/VvFpMa8Ouu0w9wqahlVafiEzm+dHIopK4RBWisnljN0FJlS66mvHXMLxsAUY0xt2ceWcq1QWNa8a33tg9Uml8IoqZx4S+CAXNrFg5UFksm16IjgYHw+m/lTFN6efK6mtsvUVhKaY6ZJdgtUQYGZMFMGQ7x8dWmlVJOZ+cwkibYQ8cMSe2wDeaRRe5nsw7rbmRIngOnI7GxpCEXHJKWNaPwe5030BiPrYGaIopymPGUUJSBUO6rGK6byAxDz1NLk4SFEXLK1h+hY9PMm4fhx5XFfnVWXJKuDyl0qaoUMfyWs1cXzTE+MbwcyAFUuUAAy1MdEjMBzmfdGZe12qFFF9kMoqYf47NwULyR1NMP1e+9aa51b86jVntWHudsXs5MqZS+HclkXlnaGld26o8NS8IX3zxxZIlSxYvXjx79mzAxMRk0aJF06ZNk8lkvXv3Dg8Pnzdv3rBhw56Xh92TYqCFjwc+HgA3UzgcwaEIFvjz/QWs9BnoxiA3+rq+NH52gfeZdoQbKcor1gYMb0xJOQv9lf7NCmYeY/kVgP4NaWxJSgEHwzkYztfd+bKbkLWoroRCXGPG4+nYcBMXU6V46+im7LjD9jvczcBMl9tpmC1CLhc2O7lI2AeBfWFM2EdRGd2dMdPlcqIwvTvp3EwB0NXAQo/EfHo34P329Helz2YOhtPHBSsDlgRwNYmDEQCjmgpJ5iV98I9n4FZmtmN4Yw5H4GrOjKOsuU4zK+ZVrzX8KFIZcTnczyO5gLRCkvNJLSS1gKR8IYQorl6QQ09TUMnjkeoswMmkapSro6Hi9zP7b817RWWk5JMnxUyXGykUlCKTK1XFFYRnMONYrT7aQ3Q1hKoETYmK2T4U/NDXrJWban6p0ot9y+vPVlzxxeRpVpqcnBwjoyd0K1Hz7Cko5XoypTJMdGhpoyLP+0R0d+aTziz0F7rBFL4TvlGsvYGWhK3Dqo3vkwvovoHMIn7oyYTm2BqSWczuu3x+mp6b8J+EZz2AZlYcGE1RGVHZaIpxNav6ghuDmXSA+ib8NoA2tsghIIGfL9FjA7tHMtgdICSVNzwx1eHP6xy/JzyxuAxULUkSESfu4bac+d40NMXWkJBUPvfjejJrB2PxoEpeLGLr6zSzYpE/vo+4y8nkyCGpgE0+tLBmbyjZxcgq+KgjYz1ptQqpjLXXWX1N+Y6Oxhwdq1Qk1BDzQXtyS5h/jrOxdHeu9suXydkbyrDGlWKJ8gpupBCQgKG2EGIFxAN4WNDflbZ2eNniYopIxLCd7A0lIe+ljCWisumyjrRCBrvToz5aEkJS2RJC2zVsG6aM+v4FPCxYM5jJB2i8gsmt8KyHVIZfDNtuYanHzhEv6wHwfxY3Nzd9fX1XV9eHV9577z1DQ8NFixb9+uuvlpaW06ZN++qrr57jDJ+aFta0sOaLrqQWcjiCwxHsuM26G+hr0r8hrzdiYMMXusVCISBhqM23PfCuj5aEW2ksv8xvV2hoxoILFJUxpxM2BgCLLwqxxFstWPegLzlXyoR9fHWW5ta4mCGVqRZjBW6l1X21z+00hjZSLgj9G+JoRFE5YRlCCFFSjliEvhYFpeSX0M8VIDSD0btxMWXPKBpZ8MMFLidy+W1G7Sb6QW2twu9oZjsW9UJTLPh2/3CBbbeELc9cl3HNCEllSzDvtsazHlb6+E9ixlF+vsRPl4TXicpkfHN+6qMiz5NWSFgG4ZnEZBOXS2wOsTkk51fNeNfTF7rpPK0w08VMF3PdBz/oCfGDkTbG2s9kYTx+j/5b2fq68NU9Sk4J224z/QjH3sTNjOJySsqRlivTIxVyoWlEkR7JkyKrEEyHissoKRfMo0A5EojPfcqw00JPiCv+azxNOKGrq1taWo2A/L/L1q1bFy9eHBERYWlpOX78+C+//FJL6xXq8K812SV8dpr1N5A+8OSy0ufTLsxo+4/+sBf0pKEZ357n/Qfy52IRfVz4tb+yx/fvKDzm/CYoS0HMdXm3NT2cabuGD0/gO045WE9TiC6qEJvD1MN42fJ2K4JTCEnFTJf+rgS/R7cNTNxPxAxMdZHKMNHmpz4s6Mn1ZBLysDHAy5Y2a7idBtDViU87IxYRlMSvl0krJDKLMbuVb2SszQafqnW0YhGfdmZWOy7dZ+A2ujiwdgjrbvLNWeQI7RBAcyuCUymTYaXPhx35+CTHxpKQR1YxW0JIzCd6JpK/BV3vt+f7C+wPqymcSCskp4SW1uRKuZRAQAL+8VxJFJY8xX7Q1o4ribiaETK1UvroYDhHIrAx4GAYJeUvmUtuhZzRu8kv5fSESpK+n3eh7xYm7KOtXaV+m2fNhObUNxGa7xVoSRjdlB97qxsnHk9ZWdnOnTv9/PxSUlJkMhV+gWPGjBk/fvyzeGuVZkRTpkyZMmVKlYtjx44dO3YsrwpW+kxuyeSWlJRzMoq9oRyKYPddtCX0bMBQD4Z4vHBKPoVlTNyPnVElu2gvW8Y3Z8I+tt2iuzPLAvklEEdj5BCfCzChBX880slvrM3OETRdyTw/9o5iri/fnWfz0Krvtf02EZlCVWodUlJe6ZRdW8LqwfTfghy8G+AXzRB3RjXh50CuJqEhZvFFxjdnwQUkIk6ME9a0gAQs9WhtQ9RMBm3nRCSL+9DYgo4OlVSwzXX5qQ8/9SG5QHDHE4sIy6DLejqtZU4nBrljqMW0NhSXc/weza34uCM96mNnSFkF4ZmEphOeSXgGoRmEZ1Q6erM2wNmELo44meBsgqMxtoZY6VNP/zkXCNVwJ2Oig54GgI2BYCuk5rnwNPcaTk5OkZGRdT6VJ2XVqlXTpk2bPXv20qVLw8LCPv/887CwsN27dz/+ma8WGUV0WEt0NmM8GeKOiQ7xuay9wfvHCbzPttf/keDmpJa81YK7GURmoqOBl63yFF8lUhl/3WJYYxVl5W7mTGvDQn8S8h5/R7j+JiXlRGfzziEhH5orZaE/3Z35ujvDd7LtFjPbYWMgtFWUVZCQx5VEisrYeZfbaRjpkFfC+ObCYYaFnlBwWSHHyoB2dqQWcicNqQzzapKSepr0cKZcRuN6OBozpRXzzzKhBZ710NekrR2uZhgvpKgMEOqvcqVMaQWw8w6e9VTEEoCJDnaGwo74d+RywjKFY6elgXx6WjggqW/C0EZ0cqCTI6UyvFbTx4UridzLoulK3mqBixn5Ug5HsD8MVzOmteWD44L1+D8hq5jV1/CNJrcEbQ06OjDV6xku2b7RXE1ixYCqhn22huwaQdOV/BLIkn+3FKWrEwGTSS4gKR+xCHfzlzLn8+9TWFjYu3fvS5cuSSQSU1PTvLw8kUhkYqJsQ8nNzdXX139G4YQaHQ0GuzPYnfIKzsayN5T9YRyN5L3DdHHi9UYM9aiqiP28OBxBYj67R1YVZZaIWD6A/WHYGXH5bfaHcSuNXCnxuXzWpWr5E6AlYVxzvjyDjgYz2rEsECNtfugplK9UyNkcwvQjNLbkndZ1/BEcjQW/6ofoayIHGwP8ogEOhHMgHH1NzHTRFBOTwx/XOBTBQDdhNzwXx9FIprUR9uv323E4HBuDmgyLFLkaBR4WBL7N5APMO8O8M8JFHQ3ebEZXJ26lsfsuYRlEZ1P24ODcSBs3cwa64WGBuznuFjQ0e3GPnxqaAwQlqchOAFeT0BQL8rhqnhdP87vTvn37bdu2BQcH1zwsMDDwqaZUK3JycubOnTtnzpyFCxcC3t7eLi4u/fr1O3ny5MtS+VpXzDxGbA57RzHEXXnxrRZ8eppFF+njIuiBPDVxuZSU08CUxpaPP5+IyyG/VIV5s4Juzizw53ba48OJIxEAZrps9KG3C5pisopZc52vz5KSj4UegfeZ2Y5+rmy9xbLLzD9b1S1BS4xYJPSilZQzfCfaGuzxYeh2sos4MBogpYDemxmzh/AZlZbmh4hF2BoSngEI6cuW1kKzHXAlERA+i6YEoOzBCaymWPnz3ymVKWsxpTLupBGSSlgGIakE3hfOikRQXsHMdnR0oJNDJQOQtTcAIQp6vz3HIvn0gayOgRZTWrOgJ34xQLUu1LXkQjwjdpJaiLs5DsZkFLEkgOWXWTGQyS3/0StXh18MInizmYqHPCzwsn0ak+w6wcZA9W+ImupYuHBhYGDgr7/++u6772ppadWvX3/8+PHz589/OGD69Onnz5+v4RXU1AkaYno1oFcDfhvApfvsC2VvKDOPMesYbeyEuOL5ulgEJSIWMUCVoJ+pDh0dCEqklQ0yOW7m3EnnQly1nXuuZgBJ+SzpQ3EZK4PYcJPWNmhJuJNOSgFetuwfXfc3zQPd+PWy4IWq4FAEYhEhUxm3j5P3+KyrYMAaeJ8BW9GSsCmYnGLczZHK2HaLD45jb8SXDxobFEKuCdWcOv0duRyJiLmd8bTiyn1SCkkvpLicLSFsCUEEjsa4W9DXVRk82L1AfsqPp74JXrb8doV3W1fNDEdls/4mfVxe6HK+/wJP81c1cuRIf3//6tTE/x32799fWFj40UcfPbzSt2/fVq1abdy48T8VTiTls+MOU70qxRKASMQPPTkcwdJLTx9ObL/N/HOEPTh0MdTindZ81b0myxiFX3J1i7XiurRGT2WgQs6dNLQk+E9S5uXNdJnbCQ8LfLZjrie0R3/Smc3BfHAcV1NWDyIqm20h3EoDyChCX1PID+wJJSaHHcPxccdMl6xi4Ybe2oCNPrRezR9X+bq76skMcmfNNSIycTJBR4O76cqHfr+Khlg4LFFcr//g2L5pPf66TUFpVavmrGICEkguIKuYN/ZwK5XwTCFQEYtoZIGPB23taGvH8iC2BjO+eVV9kvxSFvnjZEyP+gANTAn7H7E5xORgpE0zK6HwSSHH/tTG2EB4JgO3YqHHxUl0dFBefO8wUw5iqiM4e9QtaYUYaVe7KzgYE3i/7t9UzbNg9+7dPj4+M2bMAIqKimJjYz09PR8d4ODgkJSUVM2z1dQ9YhGdHOjkwJI+3Ehhbyj7QvnkFJ+copEFQzwY4k5bu+fQEZQnRVdVS64Ccz0uJ+KwlJQC5cXXd2CoRXE5WhI6OjCjnRBg5EsBDLSQiFj1GmObseEmt1IpKqOtHa83Yoxn3egaVWFOJzbeZOgOdo+kjS3A/TzM9VgSwPF7TGvDtz2Ekb0bsHcUw3ZwMwVE/BbE0kCKymhpze6RyhvlglKgpkRoZjFBiYSkEprBnTRCM4SnKJ7lbk5HBzwshODB7ZXIqS7rh/dGvDeydohSKPZ0DG8fRCJikVoe6HnzNOFEnz59wsLC6nwqT8TFixebNGnyqF440LNnzx07djyvKT0XLt2nQs4wVf5fYhGD3VngT570aaL2D0+wNBB3cxb2ooEp+VLBp/NIJP6Tqi0QcjBGLKp0z/0od9KAx4ujByRQIsNcV0WN7xB3ujtzPlZYdm0N0dOiXEpcHm8fJE+KhR4SETI5ehoUluGxnJPjOReLniZDGwFoiJGIlMmBVjY0tuR8XLWTmd2RDTfpv5U9I+nrwvbbfNEVK32+OceGm8xsh70RpTKWBWKlr1zj3mzG2huM3Yt3fSIyicshNof4XGW6wDcaY22a1GNSS5pY0qQenvUqHbp8251DYfTbwupBVYVi72WxdxSe9bA1ZMNNprfB2aSSmGlZBRuDcTH9R8nfz09TXsHJccKBnwJ3c46MoeUffHACH4+6v/Mw0aGgtFL25lEyix4vsKvmBSEmJmbSpEmKn6VSKaCrW2nV0NPTy8+vxtdDzTOmpTUtrfm2B+GZ7AtlTyiL/Fnoj40Bg9zx8cC7/r+nKG1lQGEZ6UWqmzrOx5EnxdmE771pZsX22/x0ifxSCsvo54qBFqei2RfG9DYs78+xexhpK5esLo50cfw3PoKNAUfG4rOdtmvwrIeHBYH3yShk0UVGNWFpZfP3QW60sycmm8IykDPWk76u+HhUUk85HQ1UKlXNL+V6MkGJBCURlEjMg+NcQy0aWTKyCY0saFKPRhY4Gf+jCucXlk4O7BvFm3tp/6dgY6eoQbU15PibSskTNc+LF7VQ7nFER0c7OlZdJxwdHRMSEsrKyh7raZqWllZQUFDzgH86xX8FhS9vdV2hiuvZJU8cThyJZGkgo5uy0Ud5YzepJXtCGb2b/x3lr2Gqn2iqQwd7NgXzedeq6tGlMn6/ip2hoJpaA4poJLOY83EqejDq6VOBcF5+NJKcEla9xrfnSMwHyC7GSIfsYqwMiMuloIwu67A3Eu5Tc6RkFGGmp2w+09fE1pDk6u9qnE3YO4rRu2m9mlY2FJXiuRItCelF+HjwY29SCpmwT1CV/TGAsAxC04WUjkK7ELA2oL4JXRyJzCYyk4kt+KbHYyq+7I04OpZRu/HZLtjY5ZQQlY2eJqsHCeqQn3dh+lGmHqnkjZ1fyrQjhGWwfkgNL/8YpDKORjKySaVYQoGeJrPaMf0o15OVcod1RQd7lgVy5BEPpoekFxF4X3Ud1MtFWlpadHR0DQMMDAzq1VMlUPBSIZPJHq7Dpqam2tra2dnZjw7Izs6uEmCo+fdxN+eTznzSmfhc9oWxL1SQpzPUop8rQzwY0PDpHUtrSR8XvjvPpmA+6lD1oZ13SMrHzZygKWhJCLzPskBMdcgvo54eIalEzKBCzoyjrAiiVMaBMKa2qZWsp4KrSUJjWImM9ELupgupkq5OzGhHoyexju5gz62p/HqZzSEcihCUAL/15osuVUfmSbmRzLDGNLPi45N42dK0HjdThKyCWERKAd9fwM0cD0sOhnM2lnNxBKcgkwOY6uBlyxuetLGlpQ1Or5x3YQ0MaEjkTNbd4EIcxeU4GONdn4ktaqqYUPOv8bKGE/n5+fb29lUumpuby+Xy/Px8M7OadOBSU1Pt7e3Lyx9XcwOJiYn/aJbPHkt9gKR81aF5Yj4iHtM8rZLFF7E3Yt2QquvysEZMb8Ovl1nYq9pV7Pue9NzIwK1s8FGW5CYXMO0It9NYP0TFkXZ+KVtCCEhAWo6xjqBPZ67LuH34jqOhGdklgtb11SQOhQPcz+fT05yKBlh7g8R87Iww1yW/VKg3fXh4I5MTlwtgtki4klGo/FmBWITZoqqWN4CBltAU4WlFfA5305EhmCHoaXIxHpOFSvlthUSsor9tkDsNzQhK4vg9KuQUl3ErjYJStCUs6sWc2rnutrUjdDpbQgQ/QSsDJrZgcitlEf9UL0Iz+O0KB8MZ0BBLPVIKOBRBVjFzOzHxH/TMJOVTXK6sA66C4vq9rLoPJwa542jM+8fxsq0UbpXKeGs/UhnvedXxO/6bKNaT999///33369hmIaGxv37962s/l2XjbrGwsLi0fjB2dnZ19f3zTfffHjlypUrzs7Oz2FmalThaMysdsxqR9oDqdmjkey6i6aYrk4M8WCQmwo/xzqhiyOdHJjnh4upcFCiIDqbdw8B7Bsl7ETfnsdYh3NvMXArqQVIZay+xvjmfNQBv1jWXKeRhbKsqGbSCpm4n2P3Kl3UltDShpJy/rjG2hss7/8ETdtyOZuC+eWysugI+PoMuhpVw6S5pygsY3xzOjmwIoh3DysfqqdHBwcu3SezCD1TrH6kQo6GmLZ2zOmEpxVetjXJKv4XMNdldkdmd3ze81DzN17WcOKfYGlpuW7duuLi4hrGBAUF/fnnn9bWjztIf950ckAiYkuI0HP8KFIZu+/iZfvEjsLlFVyM510v1cWso5ryy2XOxzGumnPibk6sHcK7h2i0gg72gu/ExXjKKvjOW8U97okoxu4lswhDbXQ0yC0RhLq1NEjIw+M31T6a358HhOxwTDaAswlJ+cTl1EorWkuCuwW6GqQVEpuDHJrUo6EZBaXIUXZ150uVe4OBFrklFD948aIyQRywiSX9GtLRHkdjnEyq5usjs9hzV4htGlkwosmTNcBFZnElkbvplJRjpE2etJKgtUjE8v70rM+KIDYFU16BppieDZjZjv6q5C9qjyLkq+6blD8ypm7R1WDr6/TdQqs/eL89PZzRkhCcyi+B3EpjfvdqI5yXAsV68vbbb7dpU5NQpa6ubpUyzpeR5s2b37x58+E/hw4dunjx4r59+44ZM6a8vHzjxo3Hjh2rOaxS81yop8+klkxqSUk5Z2M5FMHhCGYeY+YxGlsyoCEDGtLZsbYdCAWlBCWRVYyTcU1uSH8Nx3sjQ3fQzUnwnQhJZV8Y0nJaWNHYEqCkHN8oJrWkqSX+k5iwnzMxvH9cqWMOfOtdK/uwnBJ6bCQqi/nd8bRi5C6aWjK8Cb9eJiyDC29hqM2kA7x3GGsDwePosXx5lu/O096eL7oKVa9fnWVlEB+f5FYay/qiJSE0g0X+7LrL263o7sybe4nJxlRHaRedVsSBcACRCEMtPmiPd326OlXtwVPzAlIh50wsF+JILsDBCO/6dLB/NavOquNlDSeMjIyysrKqXMzMzBSJRIaGj7lfE4vF48aNq3mMoaHhn3/+KZH8W9WjT4uFHhNbsP4mPesz/hHzhPIKph0hNocFPZ/4NXOlyOTV6tgounszi2p6hQnNaWfH6mv4xXA6hnr6jGtOrwY4meAbjURETA4x2URnE5xKaLqwkuZLhUY6DTHlFaQU0NmB+3kk5An30NoSWthwPZm+LqwciJkuG28y/Sjt7fGPR1pObDaAGLQ0KJWpuBsWgRyMtCiXc3A0JjoM3EZKAU3rcTGer7vTsxqf46P38PlLKbGnmIysArGIVa/RWVV5blI+yQXYG/FJ55q+qxr4/gJfnUFTQicHzHRJzOenS/x+lbWDGdlEOUzhjFtUhlSm2mT0KbAxwFBLkK76O5fvA7g/SSVA7ensyKXJvHeYL/yUFx2N2TS02gj2ZUGxnnh7e7/xxuNM0V9+fvzxx7Kysof/nD179s6dO8eOHTtz5szCwsKSkhInJ6dPPvnkOc5QTc3oaNDPlX6urBhAcCpHIoT2uSUBGGnTqwEDGtK7QbW9cOlFfHiCnXeE4yHAQo/32/NJZxVBhYMRN97lxwA23OSrswBG2gxx50ysoBAKpBZSViEsOw5G+I3HYAGe9fDxwEALO0OG7qjqlFwd350nNJ2Db/CaG13WY6KD73gs9BjVhDZrmHqEC29x8A08VzLbl8HuFJSyMZjT0cTnYm1AN2cmtazUQBiWwYILDHFn90il/uGKATQwZY4vG2+y8UFkLRHxcUfmdmbKIbbfxkKPjAebqaE2tgboa3E9mY87sFjdW/zycD2ZN/YI9rIK5p2htQ3bh6soGH5VeVnDCRcXl8uXL1e5mJCQ4Ojo+NjGiVeMpf28DLJKAAAgAElEQVS4k86E/ey8wxAPTHSIy2FjMLfTeL89o5s+8QsaayMRkVao+tHUAgBzPcorOBrJiSiisjDUpoM9o5sKwYbCbd67Pq5mROdwMooNN/nzOsjhkY3ESp/8UnQ0eNeLppakFRGagQjMdYnJYX8YiflcexddDZLyAdKKeHMPOhKW9hUqYfo3RCziXhb5Um4kM6kVa69jZ8SlyewPZ8bRqpOXg60hX3Zj+hFG7yExj8Q81g1hWGPclzP/rOpwIjGPIdsol9PXhbdaklPMlSQuxhOeSZmMkbuInKlMAZVXsDSQP64KhhiAozFzOzHV68kOKv66zRd+9Hdl3RCsH4R2YRm8sYdx+3Axo3Xlc3o9zbrU7tCSMKIJm4J5vz1t7So9lFXM0kCaWNL8mRXjNLMiYDLhmdxKRQ72RrSxfc4mSmqelCo6TmZmZoGBgYsXL75y5YpEImnfvv2HH35oYfFsQlI1dU1zK5pb8VkX0go5EsnhCMEjD3A3p1cDervQw1nZpJeUT5f1xOXwhic+HhhrE5/Luht84ceNZHaMUBFRGGgxvzvzu5NWiFSGjQEaYlqvJvZB2apifSt6EKJmFlNYSg9n5nYCCEhQjqkZhQHFQDdecyOjiIAEPuwglAS7mvFhB748Q1Q2LqZMbcMcX9bf4DM/Ugqw1Edfk7hcjkTy/XlWD1Ie62y4iRx+7V9pmbqSyIILwo5nZ4StAffzSc5n+WV+vkSFHLGIFtZ0d6aZFV62yiO8AVtZfY3vez4TESo1dc7ddLw3IhaxciAjGgs94puDmX+Obhu4MuUl0+R9al7WcKJDhw5r165NT09/tCrg9OnTnTrVriz9FcJQizMT+PY8K4M48sBd0NWMzUOfsm9VQ0x7ew5HsKSPip42xRbiaEzr1YSkoi3BQp98KbvvMtsXW0MKSyu5bCow1qahGXbGaIq5mcy9bIY34otutFjFj73p48Lo3VVtgLQ1iM7G4Wc6O2KkTUwO15Kw0OPIWGW4X9+Ed1qz6ipA/4Yk5gH8PpB7WXzpB7DRh4kHMNFheX/e3AuQlM97hwEu36elNRt98K4PMLYZP18ip0SFdtDoPZTLmdeN0U2YdIDLj5zZl8iEhUNR0y+V4bOd4/doYc333sKysv02049y6T4bfWpbIFQhZ64vLaw58EalHcXDglPjcV/Op6c4+ZgE2z/l2x4cjqD/Vpb1Y1QTtCTI5ZyLY9oRkvPZ+IzfHXA3x/25yuGrqVssLS1//PHH5z0LNf+Ievq81YK3WiCVEZCAbxQHw1kZxIogAC0JDc14pzUno0jM4+Q4YXVV8FYLvj7HN+dYfY2p1TdBPaos4l2fny9xJ50mlljoYmvI6Wg+7wKwKVgYoOB0DFAr187kAtIKBZGP+Fwq5JWepTBNCknFxVQ4MXnvCMba2BqQVMBDzcLict7Yg6muUGZ8M4WGZpUSNblSBv+FjgY33+O1behpkiclOR8NEeUVmOuSXsTiPnzUXsUMRzXl2D2CU+q+OU3Ns+Djk1TIufS2snffxoA5nejiRNf1zPNj3T+QRXmJeFnDCR8fn1mzZv38888LFixQXPH19b127doPP/zwfCf2XNDR4Htv5ncnNINSGSY6uDyJb3F4JgsucDSS9CI0xLSxpZUtFxOYcYyVAysdIx2P4udAmlkx+C+KyjDVIbtEuIkXgaaY+3l0dKB3A5xMcDRmczCbglkxsNLmUSHns9MsuoipLoCDET02oiHmz8EMdsdSj9RC9oXy8Umk0NVJMPI00+XzrsxsV7U54Zd+BCVxLYkT94Q6n49OEJGFgRbIORWNXE5OCa83EmqofuxDQDxnYsktwVKfGyk0s8JCD3dzKuQk5lcNJ3JKCEhAV4MhbnRYi6aYxb3p54q+JhGZvLGHnBIW+vNua0QivjnH8Xss6sXsjspcxBdd+fQUPwbQxpaZ7agNV5NIyGNeNxWnU+a6jG3GiitPqf9be2wNOfEmb+xh/D6mHsbKgJwSsoqx0GPnCLo7P8O3VvPS4efn16VLl3+SGT516pS3t7dYrD6PfTnQltDDmYhMIjLR0aSJJYVlxOdyJ51ZxwEs9TgQTmax0oVTJOKrbuy+y4orNYUTjzKzHSuDGL2bQ2/gbMKE5izwZ08oxtrM86ONrRBOxOSw9BJt7WqlxVRcBg/yGIpkwqOWo4/mQB5Wt2YU4WzCL/1obw8QeJ8lASTkMfkA8R8I46u0N/xxldRCFvZi/jni85DL8bBgSR/GN2f3XaYdAXCo5tBa8XVVVyCg5oUiuYATUXzYQcXvXgd7RjVh5x1WDKybIuQXnJf1I5qYmCxatGjatGkymax3797h4eHz5s0bNmzYf8rDrgoaYjyfXF5y6y3ePohczmtuuJpRUMqJKFZcwcGY1de4lkxfFwpKCc3gRrJQ5RmSCg9KUDwsaGRJIws8LNAQ03kdoemcHIe+JoVlDP6L4Y2r7hxiEQt6cipa6Dn79Qol5Vx9R/nXaKXPe17E5rDoIiII+19N89eS8FZzriUhl1NYBpBfxmedic1l2y02h9DensD75JQI9/dzTiIRY6KDRExUFh9Hsfgie0ZWaxt0NYkKObZGTDyAtoRLbytDtQamfNKZT04Rl8vOu/h4sPwyfV2qCjdJRCzqxalofrrEjLa1KnmKzwWEBsS/08QSmZz7edUOqCtaWHNrKntD8Y0mLgcLPbo48WYztSqfmkpkZ2cPHjzY09Nz/fr1Hh5/k/h9HHl5eXPnzl21atXevXuHDh36LGao5llw7B5TD9OjPtuGYfUgpRCawcCtxOQgEbHiCr9eBrA3wsuWNra0saOPC8sCyS+t1TLiYMT24YzchcdvvN4Idwus9BmxEzk4GrNrJHlS9ofx6Wlkcla9Vqtp2xqiKSY8A6CBKToaBCQwqaXwaHim8L6AXwyATE4nR068qdwd2toxoQXNfic+F/94Ojtib8SJKMor0BBTIefSfVYGoSHmk1PoaaIhoqcLx8YKTx/XjJnHKK+oNmBQXDd9NirKuVLW3eBcLIVl6GsKfSDGaj/pp+VOGhVyIan1d7o5s/UW0dn/CVuMlzWcAN577z1DQ8NFixb9+uuvlpaW06ZN++qrr573pF4ybqUxcT+e9dg3Win8mpTPHF+23sJYm+vJXHtgXCsCd3OmtGLZZeyNuDRZxQt+1oVhOzkdzWB3ghIpKmNEExXDRCKGNeaz0wCBCUxvqyKyzyxGIuZUzOM3HjM9gEV9mHcaqYzkfH7wFx4a3hgHIy7fZ18YZTKQ42bByfF0W08TS85OxDeat/YzcBtetpjqqPCCUBRu5RQRVcLy/lXTPoq8vLaE1ddwMia/lDc8q76C4vOObsrcU8Tm1spaTrtG+3CpDKq3Hq9bNMSMbFKp81uNmiqYmppeuHBh3LhxzZs3nzZt2ty5c2upiVdSUrJ+/frvvvuuoKBg/fr16lji5eLz09gZcWB0pYP5RhZ83InpR9DSIHMuAQlcSSQokYAE9j9ifjt8By1scDenkSXu5jXJMQ1y48oUfrjAkUj+ug2gq0FxOfG5OC9TvumOETibIJNXKx71ED1Nujvz123mdcNSj9cbsfUWM9vRzIoKOauuYq5Le3ticlhzDUBWwe8Dq540GWvzeVfePcSa63R2pJ8rf91mnh+5UvaHkVwAYKrD9z2RyZlxlGbWfHlGqB3o64qNAWmF7A3lf21VzHDPXfQ1H2/Q9BT4xTB6N+lF2BthpU94BgfCWXCB7cMrlaWpqT01b8c6NW7lrxgvcTgBjB07duzYsY8fp6YaFvqjLeHIWPKlrL3B+TguxCkdG3KlDG+MmzlOxjQwo60tRtqUVzDnFCNU+XCD0Lar0DfILAYqKUTdTGHnHe5lwYM/QnM9Moto71D1deJz2X6bltZcTSIy8zHyoB0dEEFaAXM68e15xCJMdHijKYciuJLI5UQs9YW2bF1NEvP5KYDYHL7zBujdgJPjaL4Kvximt1HR76uorcosARj0N8XASwkAHpYEJZJeBFRrTudgDJBeWKtwQnGScT5O9RJ/Pg4jbeEF1ah5EWjZsuW1a9cWL168bNmyFStWDBw4sHfv3l26dGnatKnob/m4xMTECxcunD17dteuXbm5uW+88cb333//d1tSNS8yifncSOHr7io0TG0NAOJzSSmgv6tStDo+l6AkFvkTlERwKicf8XI00aZxPeyNsDfC0RhHY+Fnc120JHjW469hyOQk56MpwUqf68mcjBIUvWNy8I+n23oAUx2GNuKrbtXqTSn41pvO6xi4lV0jWdiLU9F028A3PQhIwD+epf3YE8pHJwRtQAs9mqpK+zsbg5zgFFYGcSYWiYiFFxGL6ObEF11ZdwOJmAamDN2BtoTF/sonfnoKDQkeFpyJZfHFqtnsNdfZF8ZHHeq+POZ6Mq9tw96IPaOUZuH+8Uw+yGvb8J/0cstwPy8Uv2l30+n1N7F+4E4aIv4rm/XLHU6oeWrkcm6lsT8MM11a/UFKAYC+Jm3tGNuM9vaIRQzYyiC3SvqzPM5z4FG/AoWOXmohQFkFM4/xx1UAR2PkCGZzDkZkFrHgAu3taPDg4D8ggfH7kMsZ48nVJCHwqAEnY15vxG9XWDsEAy3q6SGVCa2BCkSgo0FxOW+15M/r/HqZRpa0sxMaPzYGUyFHImK+Kv+jNnboaFBaTkUlVSqAjGI2hyAW0d6ekBQhX1wn+esGpnR04NfLTGxR1TrqQjx7Q5nUUi36oebFQltbe968eR988MHvv/++cuXK/fv3A/r6+paWlmZmZqampoWFhVlZWZmZmZmZmYChoeGYMWPmzJnToIGqfVjNi42iINNDVa9CZ0fEIirkxOdWUlNwNMbKgA9PYK5LaiGaYuqbIpWRmEeOlFup3E5TofRqoImuJroaWOhT3wQLPUx0MNXFVBcjbX64QFI+be0Y3RR9LcIy2BLC/jAOj6FDVZ9bJe3sWD2IqYdxW07P+nR1wjeamccAtCXM9aVUhqsZm1+n9+aqz82Tci+LgARWXQMRwalMP4qhFp0duZtOehEZRdxJRyrjTjL9tiASYaHH4l74eGCiQ3wu88+x7gbpRXR1ZO4p/GJ4sxlW+pyL40A4t9Oob8LUmpxpnpKPTqKrid8E7B858+rsiN8Emv3Ohyc4O7Hu3/SVp6klTsasusp7XlWla3KlbAymrV3Vhs9XFXU48R9CLudOOmdiORvL+TihEUIsop8rnR3p6EBLa+XxvOL2NzG/6otoinE05nqy6rdQXK9vSnQ2FnroaLDjNsMa8fZBNgXznhdfdsPGALmcFquIzuFmCkBoBm7LaWGNqS4JuYRnYqnHvtH4xwPV2m8/yoqBXP+T8Xvp5MiFeEy1aWvHvSyyitEQUy4TvKtXBqEpwVKf0HQaLlc+3dWMe1mqpUj1NZnWhp8vAbT6g5ntaGePXM6lBJZcoqScKa1JK8BSn9a26GmyP0y1OO++UKwNnqBF/rcBdF1Pl/Us7ctrbuhokCdlSwifnsbeqLbOr2rU/MsYGBjMnj179uzZQUFBZ8+evXr1anR0dHp6+v3797W0tIyNjdu3b+/p6dmlSxdvb28dnb/JqKl5SVCcnZeoquKw0KO7M34x+MVU8lctLmfSASEOmdWOr7pjqgNQUMqPAXx3nv6u7BzB/TySC7ifR0Qmf93iXjYFZQDxedxIQVOsNLJ4yOVEpeCejoTcErqsEzQ2THSU/0nEGGihKUZXEwMtFvXmUARXkykqQ1OMuwVmuiDHRIfG9fCyJasYEx0yi5h2hJwSorOJylbaRIhEiKCZFX8MorUNGmJypSwJYMdtVj44zNLRwEiboCnKbImJDiGp6GuRVsCE5vSoz7JATkRV+jgxOTRewbQ2/NCzznIUqYWcj2Nup0qxhAI7Q97zYsEFUguVPTBqaolIxPc9eXMvw3ay6jWlJuy9LCYdIK2Qzf+ZEk51OPHqE54prOznYoWCHCt9ejeghzPTjjKhOb8NUPEsRR5ZZdPCUA+WBXIxgU6Vi5TKKvjmHJoSJu4XlDE0xOy6y/+OsSmYme34pZ8w8pvzhKSxpA9301h3Ew0xxjqEZyIW4WbOgp6864WGmEkHaGMryFzUjJU+Qe/wySk2BSOXk1WitGArr6CZNaa6nIvlh55MbIGNAUFJ+MeTVoiDET0bsCmYHy5U62zwnTeXErh0n/xSwWLpIQMaMr8bDZfj44G+JpNasjKIAcFVUzo/XeJcHD/0fAIn6ZbWnBrPG7sZsQttCXqagptHWzt2DFc6UahR82LSpk2bms2/1bzUuJmjq8G5WCY0V/3omRgW+nM+TvCdiMtlawhxuQD/a8uyfsrBCscJiYivznImloENcTMnPpdPTpFWyDQvhjbCRIeEXNbe4HCE0Px9LJJJB5jZju7O5JQI/+WWkFNCTDbn40kpJPvB9drw6LBj9yo99PtVTHTwsKCvCw3MKC5jbyjxuZTLmdpG8MAGjLX5tofyrGfKIf68jrkul+5TKkMm53wci/yJzmbbMNbfZFMwSR/iZcvQ7ZjqMrkVU1phqkt4BsuvsCyQqCwOjK4bZ+WoLCrk1ZYztbRGDvey1OHE0zDWk4RcPvejwS90chAE4i8lIBGzZpDqIqhXEnU48WqSmM/paMGU+n4eQD192tvTswHe9WlqKaxQa65zMkp1+5rivKSlqtXn0y78dRuf7fzSj1FNhedGZTNuH0FJaIjx8aCTAyIRAQlsC2HFFcQiWlpzKpqkfDYG4xfD640Y4CqUP0nLKS7F2oCoLK4l0deFrCLG7SMpn021juzNdVkziJ/7EpRIdglaEmQVaIhpYY29EWdj6RFLPX2hl6ONLW0ekfS+GI+jsdKKToGiJLe4DAMttg9n0F+CpJUCO0O+6s4gN4buoFTGp10Avu2BfzwT9rM/jJFNlHY2vtH0asBHHWr7WRS0syPsf+wP42ICJeUYatHbhT4N6mZ3UaNGjZqnRk+TEU3YHMLkVlXPlW6lsSmY19xpYcX6m8zxBRBBKxua1ONkFF91U/GCczrxYwA7bjOwIcCs42QUcW6ioM0KtLbBx4MlAcz2ZVsIifloiPneW0XzBtBkJfX0OTNB+Gd2CbklVMgpKKWsgqIypOVoSVQkOkx0Ki2wRWUM+Ys8KTkl3EkTXPxySjDUwkALUx3GVW/u1MOZP69TWMbo3cqL9kbsHsnrjYjO5mQUSQXMOIajCQGTlbfy7e1pb0/Tenx2mi23anqL2qOoUq5u7xA9UqWs5in4pDO9GvDndS7EczMFB2P+15Z3vWqlXPzKoA4nXh0KSjkby8kofKMJywDQ06SbEx92oHcD1c1kk1vx3mGWBAjGog+Jz+W78zS2VC7lj2Kpx6nxDPmLsXv531EamFJQSkQmctDX5OJkpV/yVC9md6L1H5RX8NYB4aKZLgt78XYrWq6ioBQbA0x1icwkPpf2DiTm8f0FFvijJWH1IHo4P9mXYKilun25syMORizyZ0TjqnYNRyI5F1fpG0gvYuph9obycHXVEDOmKRIxN5JpbEl3ZxyNuZTARycoKWeMJ4v8KSnHSJsPOhCcwh9X2fdAxsREh296MLeTCk/Ax6IlUQsrqXk1CQsLMzQ0tLOze/xQNS8kiibmvpuZ34OxnlgbkFbIzjt84Ye+Jr/0o74J3/QgLpesYhyNMddlyHYcjAQL6iroaNDYkqhsgMR8DoYzq52KDejjjuy8w+9X8bLFQk91LAE4mxCZqfynqY5QWPV3kgtIykcswsNCdWXRyXH02UJuCQZaiERYG+BgzJ00THXYP7omH26F+HjBI90gImhpTUcH4fMCp6OJzWHzUBVpgTmdWHONNdfqJpxwNUMs4loSwxqpePR6MiKU/rBqngIv2/+67aA6nHi5kcm5loRvNCejuJRAWYWQB/ikM70b0MkR7RpvYd9uxYEwPjnFrVTGN6eFNWmFHI5gSQBSGeuGVCu618SSkKnsusuJe0RlY2+EmzmHIljvo4wlFDSrRyNLbqUyoy2D3THUppUNmmK+8CMhj1PjmXEUeyP2juLXy5yLJV+KgRYl5fi/Reu6++PUELNiIEO303kdC3vRxwUNMZnFrLnG/HN4WPBZF2FkWiGd1xGXy0cdGdFYmWFYc52GZvh4EJAglMbqadLMivAMNgZjpouJDmmFrL2BuzmnJlAuI7kAByNa2TxNIKFGzUtN06ZNFy5c+Nprqr0AysvL+/btO2DAgN9///1fnpiausLGgDMTeOsAH5/k45PK6162bPBRStg5GVfqf6shuSoCuRzgSiIVcl5zUz1soBtfn6WDPflSKuSqK0hzSzB8nJfC6Rg+P61sutCWMMaTH3pWLSX1suXGu3x/nv1hRGUBWOrxdis+71KTflRxOcsvg5zOznzTAztDsorZG8rSQDqs5eIk7qSjLREkSXqqqoeRiOjuzO67j/kUVaiQcySSMzEUlqGnSVcnBrmhIcZKn65O/HGNaW2qtk+kFrL6Gl2cKskwqlHzpKjDiZeS2BwhC+EXQ1YxgIMR45rTuwE9GzyBjIBExN5RzD3FH1fZekt5vYU1awc/RjZOT5MJzZWFsxP2Y6zNUFUeVl423E4jJqdSEeG+MNrZ0c6O6Gy6OeNuzooHLRx+MfTcRGhGXYYTwCA3tg/n7YMM3IaOBroa5EqpkNPWjt0jlSmLj04Sk8PRscomwgamZBaxO5TwTMHkCLA1ZHxzlgTgYsrukXR3RiyirIJdd5h1nMHbuP4unR+I8eWUEJuDkTb1TdSlSmr+E9y5cycnJ6e6RzU0NIYMGXLkyJE6f9+tW7cuXrw4IiLC0tJy/PjxX375pZZWtbY1GRkZlpZV/aVu3LjRokWLOp/YK4mbORcn4R/PmVgKS9HVpJsT3ZyqXeXczDkaSUaRigRFSTl30hneGCC3BKjWjEJxvZElhSGcj6O7c9UB6UVcTWJCjf8PVwQx4yi2hszriqMJZ2I4H8uGm2wKwdUMNzO8bBnRRChWcTJm9SD+eE0wlLAxePwyvsif2+noaZFTwr4wridRKsPdgq+78+UZ3jnE+Th6uwi6hdXlWAy0KC5HLq/trnEjhfH7uJ2GphgDLQrLWBaImzmbh9LWjiV96LwO742sHVJVKDZfyk//XQdgNXXDqxBO7NmzZ+TIkZmZmSYmtZD0f2nJk+IXg280vlFEZgEYaNHNiT4u9HZ5+hI9HQ1+6ceX3TgcQUoBGmK6OT1Nzi61AFtD1d3Mrzdi/U2CU5RX5HKispjUkl8uI5VVTb8qwhiFPUXdMrwxfVzYH0ZAArlSnE3o40L3Rza/nBJ23ObNZpUESdbfZPIBtB75W7HWRyRioT+mOvhPUm6NmmLGeNLMCq/VfO7HRh8OhrPQn8uJQlmqlT7jmvNlN7WxtJr/OjY2NgkJCXX7mqtWrZo2bdrs2bOXLl0aFhb2+eefh4WF7d69u7rxRUVFwOXLl/X0lLe3rq6u1Y1Xo5LOjsqjk5oZ0ZglAcw/x/L+VR9a6E9BqRBOKPIDsTmqrdxicxDBuGb8FMD0oxwbK0iM1DfBTJfyCqYcpKyCd1tXO4076bx/nM6OHBrDgTDeO0xxOVoS5CCrIDyDqCwORfD1WWa156c+QgJEJKqVLggglwv2dsjxjyc4hfqmGGqz6w6FZdTT50gkmhK+7i64xFbnrRSeiZ1hbWOJiEy8N4KIwe5EZJJagKE2dobE59JnM5en0NqGXSOYuJ+u6wUbu9RC7udhoceeUf/1Qh01/5yXPpxYs2bN9OnTKyoqnvdEngkyOVcS8Y3iZBSXEymvQCKilQ2fd6G3Cx3s66yKxlxXtUBH7THSFvIkf2dgQ/Q0uZ/HjwHMbIe2BJFIqOP84xoDG1aVPlDceddeBOlJ5zm+eVXlpYcEp1JWwaBHkuzxubxzCDnY6FPfjDMxTG7JxQShOyW/lDxp1ZO2pvUY0YQ9d2lgytdnsTbgow64mlFQytFIlgRwLJIzEzHR4XQ0t9KokGNnSD9X1SXFatS8kqSlpenq1s6HpXbk5OTMnTt3zpw5CxcuBLy9vV1cXPr163fy5Mk+fVQfvebm5mpqarZtq8qaWM0zoK0dE1vw2xUkIhVCsQMbCn3YHR3QlrA5BJ+/pbuLy9l1hzZ22BvxdXdmHsPpgTe2WISnFbIKbqfxRdeasus/X0JTzPbhnI5m4n46OFBcTnAKczthqM0XfhhooimhjR3LAsmX8tsAodUhv1QwaXUyrmm5TswnKZ96+txMARFyiM5GJMLJBGuRUDE1uxOtbTDXRSxiZRB/Dq76ImEZnInhrZa1/W4/O01xGboaHAynuRW9GlBYxsV4cqVoSph1jONv8pobUbNYd4NzsRSWYWfEhx2Y1FIwTVKj5p/wEocTfn5+3333XWRk5Jdffjlv3rznPZ26JCKT0zH4RnEmVlCvczbhrRb0dqFn/WpTwM+X9vbsusvlRKVq3kPuZVNchqMxc3xZcIEW1sihVMaVJPq4sG1Y1fEKjVf35yGJoDBRMnmkaW/WMcorGN2Eza+z4w5nYnjXizWDGLGLPaHIKtgUzNfdq75OOzu2hPD1WYZ6sPl19DWJzGLbLcz16OhA4H16biRXKkiwK9DT5LMufNr5WcVRatS8ONy7d2/Hjh2tWrWqw9fcv39/YWHhRx999PBK3759W7VqtXHjxurCibS0NHNzc5UPqXlGrB6EWMQvl1kZRGNLRCLCMigpZ3hjNvoIY4y0mdaGpYEsvsjHHZVLYkEpkw+SkMeyflxMYO4pdDTQ1hB2yQq5kAOf5sU33Wuaw/k4ujhRT59Zx3EzZ2obxu1lSR8+6kB6EV/4McCNv25x8h7A2htsCaGXCzIZp2IorwAQi+juzJI+tFSVP8mVAtxMQUvCzHboanIskjtpxGUDeJgTlkl6ATzY2dfdwMOCDzsoP+nddIbuQFNSVSWlOnKlHIwAOWZ6nH1L2YPOQ50AACAASURBVMFYUs5XZ1l8kZPRpBRgbYCxNh+054P2tXpZNWpqz0scTpw+fbpPnz6HDh26cePG855LHZCUz+kYTkcrpV2NtOnhTG8XejfA7YXf8sY159vzvH2QcxMrBTwFpUw+gI4GZyZyM4Udt4UqJk8rbqbwTquqOkulMr49j6kO/Z9HxYGiFy3ukbv8k9FoStgyDImI2BwAawNEIkY3ZU8oIpHS4OJRFOlpEx02+KCjwccnWRaITI6DERKxYEmup8XawQxoiJaEW2ksCeALPxJyWaW6eVU1pTLOxhKUREk5/2fvvuOauto4gP9CIOy9BAFRVFRUFES0qGgVtY5qHbWuVqtUa6vUt7XD0VbrqHXUVfe2at0Dt+LeAi5EFMUBgshUkA15/wgyw/CKhITf949+zM0hPPfS5OS555zn1DdFZwcWDieFGTRoUEZGRt7DRYsWyfbGLigrKysyMjIwMFAkEm3cuLECf/uFCxecnJyKrIXo2LHjtm3bSvqR58+fW1tzkkel0lDDmo8x0gVbbueO8X7ujAFORSvyzeqE4Bj8dALrb6DXm/2kd9xBTAqmtEOXumiwBMZaODUMDsa4FIHgGEil0JVg2hlsCcLvHUpbQxiXAg9bXIpAxCus/hi7g2GqDR93ADDXgboa9t6FSASxGiZ6YNpZ1DHBofsA0MkBvRwhFuH6c+y4gw/WYEd/OUvG8yb9Hv8c7ewA5KY31yLRbTOiXwPI3wp2cTdEJmHCcSy4jI51kJqJE2FISAMAEdBnG8a6Y3izMm4zPYhHZjbEIuwfCKcC7wAtdczuhHNPcCkClyLkL24kqhBKnE7MmDFD0SGUKDYF2+/kz2P5pCGayKvTmpCG049x8hH8wnA3FgA0xWhti1Gu+LA2WtYscWO1KshcByt74rOdcF6O8a3gYQcRcPUZFlzGwwSs6onaRqhtlP9xlpYFt1UYvBuPEjG6Re5atBvP8d0RXAzH6o+LphmVo7kVzHSw9jqGOUMkwqNEpGTCSh9iEbJysPkWGpjB1gAAHEwAIEeKyGQ5ryPrKrrXh4Emfj6BeZfwqRNmdUQdY7xKh90CvExDagZ238WXzQHkrl/8+iCW+6NPQ3R2KFe0++5h9AE8LxCAhhq+dsNfXmVU9FJVMSlISoeWenmnOFPFCgsLCwoKkv1bV1c3ODg4OLhoYRqJRGJmZta/f38fH59WrSryHmlYWJidXdEp/HZ2duHh4ZmZmRoacip6Pn369MaNG+bm5nZ2du3btx89enS9evUqMCQqSWsbtJZXhTyPphgHB+Ofq1h7HbPPQwpIxGhjh/Gt0KM+Vgci4hUODkI9EwDwsM3f+6KBGVqsxMoATGpb4oubaCMmJbeSbMuamH8JLla5vW1UMrJyIBFjdAssvYZxrbDwKsJfwkwHEnXciMK2frlztKa2h9cmDNqFe2OL1kTafgcARMjf3WhPCP66kFuxSiY0Hq8zoasBbXUcGIS117EyAP/eRA6gJkJDM/R0hJoI++9h5H7svou9n0Gj5O8DsjETJ4tCuUSeXg1wKQJX3qQTsg31rkQgOQMOJvCqI2erbKK3pcTpxLsICwtLSEgopcGjR48Ev/jiq/jlRO4nhYYYiWn4/TQGNcHS7jDQxKt0XHmWu8dcYFTuFnIuVvjJAx/WRhu70upYV3H9G8FkKMYfwfcFigY6msJ3oJz7N1rqODwYg3ZhwnFMOgkbA7zOQPRraKtj0UcYUe4JoxVLLMLEtvjfUYw7gr+7IDoZAF6lITkTXx/A3dj8qVnNLGGlh6hkOX+vsARsvQ0AtQxxPw7zLqFfI/zXN3fIYv+93LolH9bBwVDsu4dejrk/+JcXtt7G2uvlSid2BOOznahrgr+80KkOtNQR9AKLr2DRFUS8ws7+1auE1H9B+PM8br7ZZ7COMca3wtduJVY6VqBHjx4FBASU0sDY2LhOHaXcSfXy5csK/O1JSUk2NkW/opqamkql0qSkJBMTOUX1v/zyyy5dusTExNy6dWv79u1LlixZsWLFsGHDyvxdUql03bp1MTExpbS5efMmAFVd11cJxCKMc8c4d7xKR1wqrPRyFzAAOPMERlroKm8E29UKdU1w9klp6UQbO+wMzq23IatOK7v3f/oxvj4AAFrq8HsEAK/SkZ2D5Ays+RgWuuiwAWsC8cMHAFBDDxt6w3UlVvgXne965jF0NfA6E4N24d8+mHMBU8/AWh/ff4Bbz3H0IdRFeJWOD9bg5Be5yydGusDeCJ03oWtdbOuXfzdt5oeYegZTz+C3U5jZscQzqqkPQP6+GQCSMgDk7tl38hG+3FdoBF4swufOWNyt6EaupBCyTwxfX9+nT5+W0szCwmL48OGVFVS5KEE6kZ6enpqav8jX0NBQ9G5flKKjox0dHbOyssps+eyZvIkspVp6DeMOo40d/vKCe02oiRD+ClPPYG0gzj6BvibuxkAKiEVoVgPftUJ7e7jVxM3nCI7BvTjoStDKpip+ByqnjrVxczSCYnDjOXKkaGKBZjVKHKW1McCZYTgWhgP38SAeuhpwt8HQpkXLfr+Lx4mIS4W6GpzMyzvU4+MO/0gsuYrDoWhnDwCvM2H3NxJS8b/WGNg4t5lIhFY22HMX4S/xMAEOxrnHL0dg6B6oARI1vHiNncHIysHsTvlf7gOjoCZCjhS9HREQif+C8tMJfQk87PIHwUvxKh1jDsLJHBdG5BeJamuHtnb49RT+OIsdwdVo87tRB7AyAPVM8JsnrPURl4rddzH2MA6GYv/A0m7pVTLZ58mkSZMmTZpUSjN1dfVnz55ZWMgb0FQeUqn0HT+oK4GlpaWlpSWALl26TJgwYfLkyd7e3h4eHmWOUbx48WLUqFHl6USiosrxfqZSGWgWHayOS4GFbok9i5UeYlNKe8Gx7th0C1uDACAwCg4muPYMw/dh/Q0AEAHNLHEpAgDar0NyBjTU0LsBJGLYG+HU49x0AoCLFRxNcfZJ0dePTYGVHh4kYG8IbOcjIQ0NzdClLvaF4H4cdDWQLUVDUwTH4NtD2PrmFtW0M7DSx/b+her+iUT4vT1uRuPvy/i5TYmD9rICULeikZBWdMO+pAysCwQAK32cCEP3Laihh5U98VFd6EpwPw7LrmH9DTxOxLGhyjQhQlXJPjG2bt26devWUpqpq6t37969SnUTSpBOLFu2bPz48XkPY2JizMzeaZWuhYXFnj17CqYoxV24cGHhwoVWVqXuvFBMTAp+PgEPW5z4HJpiBEZh0RWce4qwBAAIf4W6Jvj+A7SrhXa1YKgJqRSLruLzPbkTJWVsDLDoIyWe4ygSoYmF/Mldcht3cUCX8s3teSs7gzHlVO7cXAD6Enzlimkdyh78URNhcx/0qI+/L2HDjdyDsrLc/2ud38w/EoceQCRCTAocF8O5Bky0EfEKIbGw0MXBIZh2BodD4WkPMx3UMc7/weQMiNWQkw1PezSrkR9hXpxJGSjTwVDEpmBtLzkFZye3w4oAbLhZXdKJTbewMgDDm2FFz/zM4WcPTD+HX09h+llMba/I8AqSfZ74+Ph4eJS2vlJbW7tKdRLCtG/f3sPDY+bMmZX2Gw0MDOLji5aXjouLE4lE+vrlmv02derUVatWbdmy5bfffiu9paWlZUJCQmZmZiltdu3a5e3tzW2/hXkQj5fp0BSjobmc+2sm2rjyrMQNGV68Lm2DOQCuVpjjhR+OQV2EX/zQvxEO3Mf6G7lfpq31Mb8r3FaiU53cVMFUO7eIop1hocmlAGwNEZUkJ7zUbGiooaUNQmIA4G4sQmLR3AobemOUL9KyMa4lbr/A4iuY3Ql2hkjJxIVwjHOXX0P8y+bYG4LzT9GthDxXJEI9E4TGoesmbOqTv9jycSKG7c3dLqOhGbx9Ya2Pa975Zalkuz+5WMHnCFYGYIxbadeNKoHsE2PVqlV9+xYrU1OAhoaGnl7V2ndQCdKJfv36FdxUyNCw1M+JchCJRCXt1ZpHdttJTe3tUvXdd5GUkT9z/fADbLuDljUxuAk87DB8L+qaYI5XfvuJJ/HnebSsie9bw8UK2VKce4K/LqDvNqzvXWIxUyrTlFOYfhaOpvizE+oYIykdvvcx/xL8HsHv83KVxhrYGAMbIyUTv53G3IvQVseE4zj5CK1tIQKuPMOB+8iRol8j/OWFZddwMRwJqbDSw+fOGOUKE22MbYlPtuHas6L3e7JykJmNtrXQxALqarlzXvM8iM8dti7drWgA6FhbzlMSMdra5W/1qvJmnkMjc6zsWeg6i0SY0g6XIzD/En5uU+IcgEom+zxxd3fv37+/omN57+7cuePl5VV2u4rj4OBw5cqVIgfDw8Pt7OzkLpwoTiwW169fPywsrDyNy+zLdXVZFUGIDTcx7UzuPTgAxlr4tiUmtSu0HqxtLWy+jVOPiy7gBhD0AvfiMKgJsqW4G4P0bBhqom6xmW7/aw0HE4w+gMgkLHzzf00DU9gZ4txTeG2EgSZsDXInCCWk5U5Llo2KFPTitZwOpY0dpp+FtytW+kOshgFOWNYDEjHEIvgcQVo2ahtjWDP4R2LRFZx+jM+d8eI1cqSFtg8vyN4IQNFMpohvW2LcYVx/jgZL4GSB+qZ4EJ+7pZ2FHrTEEKvhcSLW9pJT4vbbllhyFRtuMp2oKnR1dY2NjctuV5VUjW62VDY2NsVnxFZNd2Ogrgb3N8FOalto+mYrGwQUGPe+FonZ5zHACVv65g/aOppiUBN02IBvD6GzQ0VO+1E9kUkIjgGA2sb5c40AHArF9LP4rDE29M7fl+PL5th9FwN2YsxB/NevvL9CRwPTOuDsE1x9hvomuBCOg6EAoCWGVIr6pljVE0Za+Evet6beDTCiOdZcB4C/LsDFCskZOBiKTTcBwMUKOVIEvUCrAv9rB0YhMAqT2pUdWEomxKL8mcRF6ErwuhxDHCogMgkhsZjWQf4Y/dCmOBSKa8/QrlalR1btJSYmGhhU6gLP1q1br1mzJiYmpmBxJz8/v9LHgoqIjo6u2PK19FbGHsaSq2higXmdYWuIxDTsuYs/zuLkIxwZkr979GeN8espePvizLBCy4jjUzF0D3Q1EJcKs79yC8gCcDDGpHa5BTby9HJEL0dMOYU/zyFLCokYQTEIigGA5AxIgXU3oKMBsRqS0nEiDPVMEByDno75ryD7vv5jsf+/vF0x7xICnmG0G5b7Y9sdXH0GbQ3cj8td533qC6ir5Ub+4jWA3FlMsgqzxcmW25VenmSUKzbdhH8kmloiNQs3nkNTHV51cDcWkUk4MAhBL4ASbkKpidChNjbeLO31q6zMHFx4mrsfSD1TeNhW2H5c9FaUIJ1QIpk5EItQ0oiGRIzM7PyHa69DXQ2LPio6AVRHA393gcdabL+Dce7vL1glduM5/ncUpx7nH3GxwtzO6GAPAHMvooYeVn9c9DOlT0OMcsXSa/jLq4yh8IK01eH3OSafxIoApL2ZKZ0txUgXzPYqtENFcbJkY94l/HQi94hYhF4N8CodS64gPgXPk/Nntd2KRt/tMH5Tr7B0NgbIluJhQm5hkyLuxcL2XcfwlINsN1zbEr61FuytqZJpa2sXLBpbCXr37u3j4zN//vxZs2bJjhw/fjwgICBvwlV6evq1a9dat24tFsv/unH06NHQ0NCFCxdWUsRU2ObbWHIVX7nin275Nwi8XbDuBkbsw4TjWNY996ChJrb2RY8taLYco1ugjR1EIlx7hmX+eJ4MOwMsuoKP6uKThjDSQvhLbLiJL/fhwlM5u8X90QFxKVjuj7a18CgB4a+QmQ2RCI0t0NQScSk4+gAAvtwHOyNIxPB+k2wmpuGLvdAUy9l+29YAy7pj2F48fw01EeyNYKiJxDSI1QDg4KDcUQjZR5OxNgCYaKOuCY48wG+ecq7M4QcA4FbqvDmJGEeH4ttD2Hobb8pH4W4MzHSw81N0ccitaa4nbzKV7Hh6FrJylGz5xPY7mHC80A5OtgaY7ZW/ypEqDdOJimRvhPRsPEyQM7QK4E5MoTn016PgYlV05FSmtQ0MNHH9+fuKU6kdCsUn26AnwS9t0K4W1EQIiMI/V9FxA9b3xpCmOP8UI1zkF6kY3BT/XMO5pxjc5C1+o54EC7piagdcDEf4S1jp4wNbmJZjxpRIhLmdEZuKDTfwSQMMaw7PWjDURPRrtFiJTbegrYHA57gTg1vROP4Q+prY91m59sbu4oAfj2O5P+YV257LPxLXIvF9a3k/pnJk6VxcCcugZNu0G1fJbR9VXq1atUJDQyvzNxoZGc2ePXvMmDHZ2dleXl737t2bMmVK37598/awmzx58ty5c//6668JEyYA2LFjx86dOzt37mxra5uenn7x4sXFixcPGDDgo48+qsywKc/ci3A0xZJuRb/RDm+GM4+xJhAzO+avM/6wNi6PxNjDmHEuv2ULa7Swgu99rO2F4flTpDHOHd8cwsoAdKgt58PfWBtSYP9n6LEFEa8wtiX+uYb4FDSvgTZ2SErHxXBEJiEyCV0dcC8OD+IREIWl1/DsFdb2yp2JVMTnzrAxwITjeJaUP3Grgz3md0GzNzvf7Q0BkF8w9ytX/Hgci64UvY14LRJ/X0IXhxKnQuWfiBY298HkdjgciocJ0JPAvSa61csdx5bdXrkXJ79E771YWOkrWS6x3B9fH0RDM2zojda2AHAlArMvYNAuxKfiG07cqlxMJypSz/r4+QTmXMSKYkszDoUi6AX+7JR/JCmjxFnyIhEMNJFUwrhndZaUgeH7UMsQJz7PH2Ho7IDRLdBjC74+iGY1kJlT4rYDsgsu7Ha1oabAnfVW9kBmNrbcxvEwuFpBrIZb0YhNgZ0h9CRYfCV3c5Ixbvi5TXk3TGhqiX6NsOAy7AzxjVt+H3A5Av22w0Q7v/aIarMzQE19+N6Tnz7tvwcNNbi+XT0FqhitWrXasmWLrFhqKSq2tuzo0aP19fVnz569aNEic3PzMWPGFFxUXb9+fV1d3bp1c9/GDg4OGhoaf/zxR1RUlEQicXJymjdv3ldffVWB8VD5vUzHjeeY2FZ+KbYBjbHhJi6FF1qL3NQSZ4bhWRLuxgBAHWMYaqHGXHzuXCiXAKCuhiXdcOYx5lyQk044mgLAqkCceoyZHfFLG/RpiO+O4IdjgBR4M33AUhdHHuLIw9yHzWtgQ285izfyfFgbAV9h/mV8fxR9GmJB10LjqFeeYd4ldKyNRm+m5vm44+gD+BzB1WcY6QI7QySmYW8I5l2CviaWdpf7S+RoaIaG8qrVeNWBWITl/nLSidB4HA97u7tsCvf0Jf53FB/Y4vjQ/Aor9UzQrxF6bsUPx9CtHmrLy/ToPWE6UZEamGFEc6wMgJkOprTLn9p+4D4+3wN7o0LpsrU+Hpaw9UVyBqKTUfPdJh4npuH0Y7x4DbEaWljD2fKdXq2K+C8IL17j3z5FZysZa2F9bzguxtYgqIlKTBhk25EalzpDqcJJxNjcB8ObYdOt3K1b29VCv0b41OmdKgKv7YX4VHx3BPMuokNtaKnjdjQuR8BEG76D5I96qR6RCF+7YfJJLLxSdJKY732sv4EhTcuYkEbvyaeffnr+/PnExMRK/r2DBw8ePHiw3Ke8vb29vb3zHrq4uPz777+VFReVQVba1aqE5YLWJd8Jqqmff2PO9z6ycvCZvIkuGmro0xCzziMxrehnQo/60Jdg7kUAuVse2RhgjheevMTZp9h0Ez964OhDaIoROAqBUciRopG5/DkIxY13x83n2HgTTxLh7QoHYyRl4MB9bLoJSz2s753fUiLGgUH4+QSW+WPz7fzjXRzwT/dCUxuEsTGAtyuW+6OOMX5pkz8Z+FY0+m2HRIzJ5Vi2V3Wsv4HULKz+uGi1Ri11LOuO+oux7jqmdVBQcNWSKqQTbdq0kUqlZberFEu6ISkDM89hhT8+sIWmOm5H414cHE1xYFChaYsf1cX3x3Dqce6M/4I23ERmjvw9esojIxvTz2L+JbwuUMawlQ2Wdc8fZlVSF57CQFP+YrJ6JnCywKVwtLLBgfuY11nOeizZ4LJH0Z1zK0OnOuhUoVuT6UtwfCi2BmHtdey5i7Qs1DPFBA+MbVm9tjj94QOcfozvjuDsEwxrBmt9xKVgZzDWXkcdY8wtNhmMKkfnzp1DQkIUHQUpDVl9pJLuBBVcZlCKOFlOUsIYrywniUstmk7cfoHGFrgUARHg9S8SUwvNxTeQ4FMnhCXgUjis9cs7gJxHJML6XmhqibkXMfpA7kF1NXzSAAu6Fn01LXUs6IqJbXH6MRLTIBGjtW3u4EmFWNAVz5Px+2ks90fH2rn7Tpx9Ah0N7Py0AjKWyuQfCXsj+eMwdU3gYAL/yEqPqXpThXSiSpGIsbUvBjXBuuu4/QI5UljrY1QLfOVadDa/tyv+uoCBO7Hns0KDj7vvYsIxfGCLroJ2Y8iRot92+N5H93r4tiXqm+J1Jg6HYvYFtFmLs8PhosxzPxLScvcQlctCFzGv8asn+u/At4ewrEeh2/8nH+HvS+hRX/7yZWWkJsLgJko2Ql3hNMU4OAhTTmHJVey+m3tQXQ1DnTHHq1wLUYiowr14jX9vwT8SWTkw10X3eviorvxtImSMteBkjr0h+L29nE/4XcEQi+RP+i/IVAcAopMBeaPxstHpgsvesnIw+gDWXIdYlFsQ79ZzALk7jQJwMEZcCjzWoKkl9EstrFQKkQjft8b4VrgZjUcJMNBEy5qllWmy0H1fuwZpirFnAPaGYEUADoYiOQN1jPFtS/i4K1kuASA5A4YlX0MjrXLt4EQViOnEe9GzPnrWL6ONvgT7B6LnVniswYe1c/edOPsE/pFwNMWO/qV98pZiy2343sektpj+Yf7BJhbo1wht1mL4PlwfVeLX8arPXAcxKSVWnwh/CTtD9GuEL5tjVSCuP4e3C+oYIykDvvew6Ras9eUsayFlJxFjdidMaouL4UjKgJY6WtnAnIkEkYKsCMD3R/E6E5a60JXg2SssvYbWNtjSV/7CZRmfVvjKFz+dwF+dCnV/u+9idSAGNSl7DmcrG4hF2HYHnYvdjMuWYmcwnMwLDU18dwRrrmOcO6a0w4ab+OEYLHThYIyrz2Cpi10D4GGLJy/RZi38I9H/3b7iq4nQvAaaV4EJAr0boLfS7pObx1ofN57L/zKQI8XjxNKWtdD7wHRCkVrWRNAY/HUBO4Ph9wgAGpljVkeMcy978+aSrAiAvRF+b1/0eB1j/OqJrw/i/FMlLsPvaY8117E3BP0aFX3KPxKh8RjSFABW90QLa0w/i1FvBpc11DC0KWZ78VumyjLQFD4/kIgqypbbGH0ArW2wuFtuIYSUTKwOxC9+6PovAkbJL7sHYERzHH2AuRdxOQJfNoetARLSsDcE/wWhngn+7lr2rzbXwaAm2HAD3euhT8P841Ipfj6BkNhCt5NCYrHMH587Y2FXAHj2CgCMtHB4CPaF4Iu9uBUND1vYGcDJHBGvSkuEqPJ1qoPNt7EzWM5Smd13EZsCrwqdXUxlYjqhYOY6mOOFOV5IyYRayRuTlV9AJAY3lX/zvktdAAiIUuJ0ol8jTDmJcYfR2AINCkyafJ6ML/ZCX4KvXAHZCt0W+MoVt8o3uExEFSskJOTEiRNltyvs22+/fR/BUKVJzcL4o2hqiROf598U09HAOHfUM0W3zZh3Eb/K21oBgJoI2/pjwWXMPo8v9+Ue1BTj6xaY/mF5ayos/Ch3bfHAJvikAYy08PQl1t3A+acY2hQjC2xRuDcEOVJMfLPVrH8k7A3xMB4tV+E3T1jrY/sdNDDD3Is4+hAaakgooSA1KcTAJphxDt8cgr1Rod1g/SMx+gDsjTC4qeKCq5aYTlQVgocjCsrKQVpWiRMKZR/HSl1/VlsduwbAayNcV+Jz59x9J/wjsfY6UjKx89NC+4iLq8zgMlF1s3Tp0sWLF7/tTzGdUJSgFzj6EA/jYaCJVjboWlfgva1jD/HiNZb3kNOjfVQXH9hiy+0S0wkAYhG+bw0fd1x/jpdpkIjhal3iaIZcxlo4/yV+PoHVgdjypj6SuQ4WdMW4loXmUD1MgK5G/kLn+FTUM8WyHvjKF4N3A0BkEk4/ho4GZnbEcv/c2lNURWiK4TsQXpvgsRbd6uWuq7kcgUOhsNTDgUHQ5tfbysXrrVLU1WCui0cl1GZ8EA/grQtTVDWuVggYhR+OYXUglvsDgJoIHewxt7PS160iUhlXr15t3779nDlz5D57+vTpCRMmbNq0qUED5Z/EreTiUjFsLw7cL3TQQhdrPkaPslYAFnc/DgDcS9i/uWVNLL6CbGkZNbLV1eBm/da/Oo+eBEu6YbYXAqOQngVDLTSvIWfEXk2EghUhzXQQlYyudfFgHPwe4fujeJGChV3hYYfNtxDxCgmpqL8Yza3g7VLBZfpImAZmCByFP85g253c/4HNdTC6BX71rC6l0qsUphOqxqsOdt9FxCs51UJXB0IEdFT+z8HaRtj1KZIycruuWoYs4ENUtYSFhQ0fPrxFixZyn3Vxcfnnn3/Onz8/ZMiQSg6MCkrLQudNuPEc37XCN26oa4LkDBwKxc8n8Mk27Pus0LZx5SEriFRStQ/ZN3hpgb3h3h9dDbQttSZ4fVOkZOL2CzSxAABPe/xxBkEv0NgCnR2QkAb3mqhvijZr8CwJABqaw1wHxx9i+x0MboL1vZVsD2mVZK6DRR9hYVckpgOAkabAGjb07pT43XDy5Mnu3btbWFgYGRm5ublt3Lix6uw+oUA/t0FWDvpsyy2Kl2dFAFYFYHBT1dknUl8CVyu4WjGXIKpyXr58aWxcYu1JNTW1rl27Hjt2rDJDouKWXkNgFFZ/jL+75O7LpifBp0646o1ahvj2ELJy3u4FHYwB4MZz+c/eeI5ahlXlW3j/RtAU47dTuSnQKFfoSjBwF54n45+reJ6Mjx3RcwvSsmGshdpGODMMBwYhcHp9qwAAIABJREFU/H/4rhU238Yvfoo+AXpDJIKxFoy1mEsoUtV4W7+9jRs39uzZ09HRce3atf/995+7u/uwYcOmTZum6LgUr7EFNn6CW9Gotwgj9mP2Bfx6Cm6rMPoAPO2xrLui4yOiakAqlZZ+f8fW1vbZs2eVFg/Jtf4GmtXA8GZFj5vpYIonHiXizJO3e0EvB+hJMPMcsov98c89xalH+KShvB9TBDtDTGyLPSHosw2h8bDWx7peCIlFnYX47ggameNgKF68xusMZOVgW//cxSS6Gvi7C4Y2xcLLCH+l6HMgqjKUdbLTxx9/3KJFi0aNcsuFdu3aNSsra/78+ZMnTxaLi22GXM181hgNzDD1DP4LQkomANQzwfwuGNuyqtwWIiLVZmFhERYWVkqDhIQEXV1OcFakbCmCY/BNS/nPygoA3o5Gx7ep32+oiT86YPxRfLYTS7rB8s1f+MB9fLkPNfTwc5t3C7pCTWmHjGzMvoB991BTHxIxcqRIzQKA4BgEx0AE9KiP2Z2K7vLm0wqbbuHgfYyWP5uPqNpR1nTCyMjIyKjQrJ0PP/xwxYoVL168sLJS5m2fK0izGtgzAJk5SM6AWKSUNVLvxOBwKGJToK6GljXxUT1oMBciUhIeHh779u1bsGCB3JxBKpUeOXKkaVOWclSkrBxkS0ssgCM7np791i/7XSu8SsfUM9h/D65W0JUgNA5PXqKuCfZ9Vhk7/4QlYOY5HLiP6NcQi+Bqja9cMbyZnBUd557iUSKs9BCXipRM6EowyhWDm8LRFE9ewmMtBjXB2o/l/AoncwB48vK9nwuRslDWdKK4hIQEiURSJMeo5jTUYFy+Wt1VSlwqxhzEjjuFym7YG2FlT25MQ6Qcvvnmm+3bt48YMWLTpk0aGkUrfc6YMSMoKGjDhg0KiY1kNMWw1MXdWPnP3okBgFqGQl75V0/0aoBVAfCPREIqGprjhw/wZfOKqYdeuqMP0X870rPxUV00MENKJo49xMj92H0Xuz7Nr36bLcXoA1gdCH0J2taCrgZC43HjOV6mYUBjmOnATAc66ihpvp4sy9Ks7jMhiPKpTjqxcePGgQMHamtrl6fxjRs3YmNL+BAFAAQFBVVQXPR2MrLRbTP8IzG+Nca4wcEYyRk4GIqfT6D7Zhz/HJ5KuwdfeYQl4HEijLXRxIIz01RTUFBQ6fu7mZmZNWtWbDK7smnXrt24ceMWLVoUEhIyduxYd3d3Q0PD6Ojo0NDQtWvXnjhxokePHkOHDlV0mNVd9/rYdBP34vK3X8iz8DIkYuEVUZ0tsaTbO0b31sIS0GcbahnCd1DuonAAOVLMv4Qfj2PsYazqmXtwykmsDsRIF8zrnD96fyEcg3ah5xYEjYGdIRqZ49wT5EjlDGuceQwAjS3e/ykpG6kU9+MR/hJW+mhoVmKNL1I9SpBOpKenp6bmb0dpaGgoKrZ6f8aMGQ8fPty5c2d5XjA6OtrV1TUnp+yKFVwpWPkWX8XVZ1jZE95vti/Vk2CAE9rbw20lvPfj7rdl1CxXRlIpVgRg7kU8TMg9YqCJoU0xo2OJmxKS0omIiAAwc+bMmTNnltJMTU0tMjLS0tKysuJ6XxYsWGBtbT19+vSRI0cWPC6RSMaPHz9r1qzin+RUySa1xfY76LkFuwbk1ksF8Codk07C9z5+bqNkdfNmX0BmNvZ8lp9LAFAT4YcPEBKLtdcxqS3sjRCXir8vo3eD/OxCxsMWhwej2XLMvoB/umFIU3x9EEuv4dvCy0tSMjH5JEy10aVuZZyUssiWYs4FLLmaW1cXgJkOvF3wq6fALRFJuSjBH3nZsmXjx4/PexgTE2NmZlawwdy5c+fMmePn51fOVROWlpZHjhxJTCxhszcAwIULFxYuXFizZgmb8dB7s/Y6XKzyc4k8lrr4rT1G7se5J2hvr4DA3h+pFKMOYFUgGpnjLy/YG+FVOvaFYOk1nHyEM8MrY7YxVQLZ54mPj4+Hh0cpzSwtLVUglwAgEol++uknb2/vgwcP3rp1KzEx0cDAoHHjxt26dVONE1QBdYyx7zP0246my9C8Ru6+E+efIikDXzbH9A8VHd9bOvIAnerIGWkBMMYNa67j2EN85YojD5CWhXHucpo1MkenOtgXgn+6YaQLttzGd0cQ8Qo+rWClh2wpzj/FhGMIeoHNfaEved8npDQyc9B3G3zvw80aP3rASh+xKdhxB7PO4+wTHB36dlubkzJSgnSiX79+BYf+DQ0LzeX87bffFi9efPz4cVdX1/K/ppeXV+kNsrKy3ipIqhBZOQiJhY+8T3kgd5rT7Reqlk7sCMaqQIxxw8Ku+ROcRjTHjmAM3gWfw9jSV6HxUQWR3Yx3d3fv37+/omOpPCYmJpzUVJV9WBu3x2CFP3YG40QY1NXQpS5GuqCLg6Ije0tSKSKT0LuEbdbrmQJAxCsAePoSeLOcujgnCxx5gIxsSMQ4MAgj92P2Bcy+AGMtpGcjJROGmtjaDwOc3sdJKKvFV+B7H390wKS2+Zs/fN0Ci6/C5zCmnsZfZXznIqWnBOmEjY2NjY2N3KdmzZq1ZMmSkydPqsBUYwKQkY0cKbRLuI0hO56ucone35dR2wgLuhZdLNG/Ec4+wdJrmNsZ1voKCo6IVF1NfUzrgGkdFB3HuxGJoK2O5Az5z8qOy9aCy5ZQl1S0Ki0L6mq5n8YGmtjeH/6R8L2P58kQi9CsBj51gpES1jh5f6RSLLiMVjaY3K7oU2Nb4uQjLPPH1A4l1hAj1aDEf97Dhw9PmzbNz8+PuYTK0NGAmQ7ulbBIXna8lmrV7krLwpUI+LSSXwa3XyMsuYpzT3knjJSMVCotvjTi9evXs2fPvnPnjp2dnY+Pj729vSJCI5XlYoVTj5AtlbO+zi8MAJrVAAAnCwA4+wSDm8h5kTOP0dC80BriFtZoYf1eAlYNjxIR/go+reQ/268R9oYgMAoetpUbFlUuZa0dk5mZ6ePjM3HixA8++EDRsVBF6l4Ph0LxIF7OU4uuQFP8dnsqVX0JaZACVnryn5UNSsSlVGZERBVg7NixXbp0KXgkLS3N09Pzjz/+2LNnz4IFC1q2bBkVFaWo8EgljXDBo0T8cabo8ejX+PUUHIzRsQ4AtLdHTX1MPY2X6UVbrr+B2y/kpxlUkrhUgL1Ytaes6cTZs2dDQ0OdnZ1vFCarnULKa3I7qInQYwuCY/IPpmfjuyPYdw8/fACTcpUCVhrGWhABUcnyn41MAgBTLsUmZXPq1KlatQoVdZ4zZ87169fXrVuXmpp64MCBly9fLlu2TFHhkUoa0gTd62HqGQzahUsRSEhDxCusuY4WK/EsCas/zh0E1hRjWQ+EJaDtWuwNwetMAAiNx/fH8JUvXK3kr9Kmksg6ZfZi1ZyyTnZ6/PgxgF69ehU5PmrUqOXLlysgIKogdU2w9zP034Gmy9DeHg4mSM7AiTC8eA1vF0xV8tm9xWmpo4U19oXgLy8585123IEIHCMm5fP48WNvb++8hxkZGYsXLx4wYMCwYcMAdO/evWfPnvv37582bZrCQiSVIxJhe3/84ofl/thaYO8oR1McG1poz6Ke9bF7AEbsxyfbCr3Cx45Y14uFTd9OHSNY62NnML5vLefZHXegowGXctXdJCWmrG+aESNGjBgxQtFR0HvRqQ5ufY15F3EoFIFRUFeDW02McUP3eoqO7P3waYUhu/HdkUKVnQAcuI+VAejTEDYGiguOSJDs7GxNzfw9U3bt2hUTEzN27Ni8I82aNTt9+rQCIiOVpqOBhV3xkweOPUT0a4hFcLFCe3s5+6l97Ijw8dh/D2eeICsHNfTQr1H+5htUfiIRxrnj5xOYfrZQZScAqwOx7x583LkOW/XxL0xVka0BFnTFgq6KjqNSDGqMM4+x9BpOP8awZrA3wss07L+HA/fRwAxLuys6PqK3Z2Nj8+jRo7yHf//9d7NmzVq3zr97qaOj8/LlS0WERqrPWh/DylGiRUsdnzrhUxa6eGf/a40LTzHlFPbfw5Cm+ftOnHoMD1vM6Kjo+Oj9YzpBpGAiEVb0gIsV5l7Ej8dzDxpo4ms3zOSu2KScPD09N27c+O2339rZ2S1cuPDatWsbN24s2CAmJkZfn/WPiVSBhhp2D8Cci/jnKnyO5B4008EvbbgrdnXBPzKR4olEGN0Co1sgLAGPE2GsjSYWRbehIFIiv/zyy7Zt2xwcHIyNjWNiYtq3bz9kyJCCDa5cudKgQQlbjhGRslFXwy9t8LMH7scj/CVq6KGRuZw5ZqSqmE6UZuzYsUU24VaU169fh4WFNWrUSCwWKzoWRYqLi4uNjXV0dFR0IAoWERGRk5NjZ2en6EAULDQ01MjIyNy8hO1tq5hqNbenbt2658+f//PPP8PDw4cOHTp9+vSC21BkZGRkZ2cXr6WheqpIJxIVFZWamlqnTh1FB6IYmZmZISEh9erV09KqpvvPPX36VCQS2dpW07IeqampDx48aNiwobp6Vf/Sq7zdRFW/sori5OTk5uaWk5OTkJCg6FgAIDY2Njo62tTUVFtbteqkvqVnz57FxMRYWFT35XKydIJzRSIiIpKTk6t+D5HHzc3Nyam6zNRu1qzZf//9J/cpiURy5kyx3QFUS5XqRJ49e5aSkmJsbKzoQBQjOTk5OjpaT0/PyEi1tkEtt4iICDU1NT29EvaGUHXx8fHR0dHGxsa6urqKjqVsStpNiKRSqaJjoLKtXr3a29v79u3bjRs3VnQsijRu3LilS5dmZWUpOhAF8/T0TE5ODggIUHQgCmZsbNy7d+9169YpOhCiKu2TTz65ceNGwcXx1crZs2c9PT137drVp08fRceiGG5ublpaWufOnVN0IIqxZcuWwYMHX7lypWXLloqORWVxdjYREREREQnEdIKIiIiIiARiOkFERERERAIxnSAiIiIiIoGYThARERERkUBMJ4iIiIiISCCmE0REREREJBDTCSIiIiIiEojpBBERERERCcR0QjmIRCJFh0BERKR8ZB0ou9Fqi3/6SiD+/fffFR0Dlc3Q0FAkEvXv319DQ0PRsSiSrq6uvb29p6enogNRME1NTVdX1+bNmys6EAVTU1Pz8vJycHBQdCBEVZq2traTk1PLli0VHYhiGBoapqSkDBw4UF9fX9GxKIZEInF3d2/atKmiA1EMQ0PDnJyczz77TCKRKDoWlSWSSqWKjoGIiIiIiJQSJzsREREREZFATCeIiIiIiEggphNERERERCQQ0wkiIiIiIhKI6QQREREREQnEdIKIiIiIiARiOkFERERERAIxnSAiIiIiIoGYThARERERkUBMJ4iIiIiISCCmE1XFrl27xGJxYmKigAabN292dnbW1ta2s7ObPHlyRkbG+4z0/SrpNE+ePNm9e3cLCwsjIyM3N7eNGzdKpdIibVT+OmRnZy9fvrxVq1Z6enra2tpNmjT5888/s7KyivysylyHMt8UAE6ePCmRSL777rsix1XmIhCVRzXvQap5x8H+gp2F4kmpCli5cqWGhgaAhISEt22wbNkykUj0448/+vn5/fPPP0ZGRn379n3/Ib8XJZ3mhg0bdHR0xo8f7+vre/jw4W+++UYkEv3+++8F21SH6/D8+fPOnTsvX77cz8/P19d33Lhxampq48aNK9hGZa5DmW8KqVR669YtU1NTa2trHx+fgsdV5iIQlUc170GqecfB/oKdRVXAdELB/Pz8OnToYGNj88cff8h9M5TeICEhwcDA4Keffso7cuTIEQBHjx6tjOgrTpmneefOnYJHRo0aZWBgkJWVldegOlyH4r799ltjY+O8h6pxHcp5EeLj4+3s7FauXNmlS5eCPYRqXASi8qjmPUg17zjYX7CzqDo42UnB/Pz8OnfuHBIS0r59ewEN9u7d+/r16++//z7vSJcuXVxcXDZs2PB+4n1fSj9NIyOjRo0aFTzy4Ycfvnr16sWLF7KH1eQ6FGdmZmZkZJT3UDWuQzkvgre390cffeTt7V3kuGpcBKLyqOY9SDXvONhfsLOoOtQVHUB1N2PGjHdpcOHCBScnJ3Nz84IHO3bsuG3btgoIrhKVeR2KSEhIkEgkeZ+M1fA6JCYm7tixY/HixatWrco7qBrXoTwXYfXq1bdv375+/Xrxp1TjIhCVRzXvQap5x8H+gp1F1cF0QrmFhYXZ2dkVOWhnZxceHp6ZmSmbTaiSNm7cOHDgQG1tbdnDanUdXrx4UatWrbS0ND09vX///bdXr155T1WT6xATE/Pjjz/u27dPR0en+LPV5CIQvbvq9maphh1HNe8v2FlUGk52Um5JSUkmJiZFDpqamkql0qSkJIWEVAlmzJjx8OHDWbNm5R2pVtfB1NT08uXLe/fu7dGjR//+/ZcuXZr3VDW5Dj/99FO3bt3atm0r99lqchGI3l21erNUz46jmvcX7CwqDUcnSMnMnTt3zpw5fn5+VlZWio5FMcRisbOzs7Ozc69evdauXTtq1KiOHTs6OjoqOq5KcvPmzS1btoSGhio6ECJSGtW246jO/QU7i8rE0QnlZmBgEB8fX+RgXFycSCTS19dXSEjv1W+//TZz5szjx4+7uroWPF7drkOe4cOHm5ubb9q0SfawOlyHWbNmDR061NbWtqQG1eEiEFWIavJmYcchU936C3YWlYnphHJzcHAIDw8vcjA8PNzOzk71pv3NmjVryZIlJ0+edHNzK/JUtboOBYlEImtr68jISNnD6nAddu/evWnTJr0CTpw4sXTpUtk/UD0uAlGFqA5vFnYceapbf8HOojIxnVBurVu3DgoKiomJKXjQz8/Pw8NDUSG9J4cPH542bZqvr2+zZs2KP1t9rkNxT548sbCwkP27OlyH4ODgoKCgGwV88MEHgwcPlv0D1eMiEFUIlX+zsOMoolr1F+wsKhPTCeXWu3dvXV3d+fPn5x05fvx4QEDAF198ocCoKlxmZqaPj8/EiRNlHwHFVZPrUNyGDRtiY2N79+4te1gdrkPdYnR0dAwNDWX/QPW4CEQVQrXfLOw4iqhu/QU7i8rEpdjKzcjIaPbs2WPGjMnOzvby8rp3796UKVP69u3buXNnRYdWkc6ePRsaGurs7Hzjxo2Cx83MzGxsbFBtrsOCBQuuXr3q5eVlY2OTkpJy+vTppUuX+vj4tGrVStagmlyH0vEiEJWTar9ZqnnHwf6iTLwCFUlxG3JTIefOnUPJW8SX3uDff/9t0qSJpqamjY3NxIkT09PT32ek75fc01y9erXc/3tHjRpVsJnKX4fTp0/36dPH2tpaQ0PD0NCwbdu2mzdvLv6zKnMdynxTyHTp0sXHx6fIQZW5CETlUc17kGrecbC/YGehcCKpVFrxOQoREREREVUDXDtBREREREQCMZ0gIiIiIiKBmE4QEREREZFATCeIiIiIiEggphNERERERCQQ0wkiIiIiIhKI6QQREREREQnEdIKIiIiIiARiOkFERERERAIxnSAiIiIiIoGYThARERERkUBMJ4iIiIiISCCmE0REREREJBDTCSIiIiIiEojpBBERERERCcR0goiIiIiIBGI6QUREREREAjGdICIiIiIigZhOEBERERGRQEwniIiIiIhIIHVFB0CkCnJycuLj46VSqZmZmUgkUnQ4RERUpbHXIFXC0QmidxIRETFw4EBDQ0Nzc3MLCwt9ff39+/crOigiIqqi2GuQ6uHoBJFwL1++bNOmTVRU1Ndff92iRQttbe27d+86OjoqOi4iIqqK2GuQSmI6QSTcihUrnjx5snPnzr59+yo6FiIiqurYa5BKEkmlUkXHQKSsunbtevXq1fj4eEUHQkRESoC9Bqkkrp0gEi4yMtLOzk7RURARkXJgr0EqiekEkXAZGRlaWlqKjoKIiJQDew1SSUwniN7JlStXRAWwnyAiolKw1yDVw6XYRO/EwcHh999/z3soFosVFwsREVV17DVI9TCdIHonZmZmQ4YMUXQURESkHNhrkOrhZCciIiIiIhKI6QQREREREQnEdIKIiIiIiARiOkFERERERAIxnSAiIiIiIoGYThARERERkUBMJ4iIiIiISCCmE0REREREJBDTCSIiIiIiEojpBBERERERCSSSSqWKjoGIiIiIiJQSRyeIiIiIiEggphNERERERCQQ0wkiIiIiIhKI6QQREREREQnEdIKIiIiIiARiOkFERERERAIxnSAiIiIiIoGYThARERERkUBMJ4iIiIiISCCmE0REREREJBDTCSIiIiIiEojpBBERERERCcR0goiIiIiIBGI6QUREREREAjGdICIiIiIigZhOEBERERGRQEwniIiIiIhIIKYTREREREQkENMJIiIiIiISiOkEEREREREJxHSCiIiIiIgEYjpBREREREQCMZ0gIiIiIiKBmE4QEREREZFATCeIiIiIiEggphNERERERCQQ0wkiIiIiIhKI6QQREREREQnEdIKIiIiIiARiOkFERERERAIxnSAiIiIiIoGYThARERERkUBMJ4iIiIiISCCmE0REREREJBDTCSIiIiIiEojpBBERERERCcR0goiIiIiIBGI6QUREREREAjGdICIiIiIigZhOEBERERGRQEwniIiIiIhIIKYTREREREQkENMJIiIiIiISiOkEEREREREJxHSCiIiIiIgEYjpBREREREQCMZ0gIiIiIiKBmE4QEREREZFATCeIiIiIiEggphNERERERCQQ0wkiIiIiIhKI6QQREREREQnEdIKIiIiIiARiOkFERERERAIxnSAiIiIiIoGYThARERERkUBMJ4iIiIiISCCmE0REREREJBDTCSIiIiIiEojpBBERERERCcR0goiIiIiIBGI6QUREREREAjGdICIiIiIigZhOEBERERGRQEwniIiIiIhIIKYTREREREQkENMJIiIiIiISiOkEEREREREJxHSCiIiIiIgEYjpBREREREQCMZ0gIiIiIiKBmE4QEREREZFATCeIiIiIiEggphNERERERCQQ0wkiIiIiIhKI6QQREREREQnEdIKIiIiIiARiOkFERERERAIxnSAiIiIiIoGYThARERERkUBMJ4iIiIiISCCmE0REREREJBDTCSIiIiIiEojpBBERERERCcR0QonFxcWNGjUqJiZG0YEIFxsbqxqnEBsbq+hAhIuJiVGNU4iLi1N0IERVxd69e+fPn6/oKOT44Ycfrl27pugoirp///4333yTnp6u6ECKWrdu3bp16xQdRVHp6enffPPN/fv3FR1IUdeuXfvhhx8UHYUc8+fP37dvn6KjeI+YTiixwMDAlStX+vv7KzoQ4WSnEBAQoOhAhAsICFCNUwgMDFR0IML5+/sr+ykQVaxNmzYtXLhQ0VEUlZycPG/evP379ys6kKL8/PyWLl365MkTRQdS1MqVK1euXKnoKIp68uTJ0qVL/fz8FB1IUfv37583b15ycrKiAylq4cKFGzduVHQU7xHTCSUmlUrz/qukeApVAU+BiKo5fnoIwItGeZhOEBERERGRQEwniIiIiIhIIKYTREREREQkENMJIiIiIiISiOkEEREREREJxHSCiIiIiIgEYjpBREQqKDs7e/ny5a1atdLT09PW1m7SpMmff/6ZlZX17q+8efNmZ2dnbW1tOzu7yZMnZ2RkFHw2Kytr4cKF9evXl0gk5ubmgwYNCgsLe/dfSkRUZakrOgAiIqKKFxsbu2fPnuHDh9erVy8lJeX48eOTJk2Kiop6x83dli9fPmbMmAkTJvz9998hISGTJk0KCQnZuXNnXoPRo0f7+vpOnTq1RYsWkZGRM2fObN269c2bN2vUqPHO50REVBUxnSAiIhVkaWl59OjRvIc9evTIycl5x72iExMTf/rppx9//PHPP/8E8OGHHzo4OHTt2vXYsWOdO3cGcOvWrTVr1hw/frxTp06yH/H09LS3t9+0adOECRPe7YSIiKooETc1lOvWrVsDBgwoMoRd1aSmpkZFRdWoUUNHR0fRsQikAqeQkpLy/PlzFTgFKysrbW1tRccikLKcgkQi2bZtW9OmTRUdSDU1derUDRs2FJl6tGLFin/++ef+/fvm5uYDBw6cOnVqKf8XrV+/fuTIkVFRUebm5nkHXV1dGzRosHnzZgBbt24dNGhQenq6RCLJa9CuXbtGjRotX7783U+h/H1TdHR0enq6nZ3du//SCpSTk/P48WNjY2NjY2NFx1LIq1evYmNjbW1tNTQ0FB1LIc+ePQNQs2ZNRQdSSGZmZnh4uJmZmYGBgaJjKSQhISEhIcHe3l5NrWpN5n/69KmmpqalpWWZLZW0m6iOoxPx8fENGjSIiYkps2WbNm2srKwqISRh0tLSbt682bRp0yr+FaoUslNwdnbW0tJSdCwC8RSqgtTU1Fu3blXxU4iKijp//ryzs3PpzSwsLEJCQqraly1ll5iYuGPHjsWLF69atarg8bFjxy5btmzixImdO3cOCwubOHFiUFDQoUOHSnqdCxcuODk5FcwlAHTs2HHbtm2yf8u+9oWFhTVo0EB2JCcn59GjR/369auQE7lz505ISEjHjh1NTExKb/n06dPXr183bNiwQn5vBRKLxXZ2dmZmZooOpJCXL1/ev3/fxcVFLBYrOpZCDA0NAdStW1fRgRSSnZ0tFovr168vC6/qiImJCQ8Pd3FxUXQgReno6Ojq6paZ3sfHx/v5+d25c4fphBLQ09P75ptvUlNTS2kTHBzs6+s7ffp0T0/PSguMiKqm4BhEJkFNhCaWMBc6CnXmzJn27dv37NmzUaNGpTTT1tbW09MT+DuomBcvXtSqVSstLU1PT+/ff//t1atX3lPnzp1bsmTJ6tWrR4wYAaBNmzYNGzZs2bLlkSNHunbtKvfVwsLCin8hsLOzCw8Pz8zM1NDQaNOmjZub28iRI7dt2yZLLebPny8Wi7/88ssKPKkZM2a4u7tX4AsSUYWIT8XNaGTnwFIPjc0HG7Z7AAAgAElEQVQhEr3dj1+5csXPz+/9hPZ+Vcd0QiKR/Pbbb6W32bp1q6+vb1W+00lEleDAfUw4jpDY3IcioKcjFn2EWm9/S072eTJw4MCBAwdWaIxUGlNT08uXLz9+/Pi///7r37//ggULxowZI3tqzZo1jo6OBb/ou7m5ubm5HThwoGvXrunp6QXvOhkaGopEoqSkJBsbm+K/QiqVJiUlmZiYqKmpHTlyZOjQoXXq1Onevbuurm5gYOCJEyfKkyJmZWXNnTs3MTGxlDbBwcEAMjMzy38FiKgSRCVj/BHsCEbOmzUEdYwxqyM+dXqLF5G9tbdu3Xrz5s1SmhkbG3///ffq6lXoO3wVCoWIqEpZdAXfHUFtY0xph8YWSM/G2SfYehuuK3BqGJpYKDo+KgexWOzs7Ozs7NyrV6+1a9eOGjWqY8eOjo6OAPz9/du0aSMqfP+wQYMGDx48ALBs2bLx48fnHY+JiSnn/JwjR44EBgYuXrxYLBb/999/ERERvr6+BV+qJC9fvpw1a9arV6/KbBkfH1+eSIiocjxMQLt1iE/FkKb4sDZ0NHAvFptvY8BOhMTi13JPc5G9tX19fX19fUtpZmBgMHLkSFNT03ePvKIwnSAikuPGc/zvKDrVwd7PoPNmcebQphjfCp7r8dlO3BgNjaq12I/KMHz48MmTJ2/atGn69OkAEhMT169f/++//xZsk5WV1apVKwD9+vVr1qxZ3nHZHHEDA4PiX+Xj4uJEIpG+vj6A4ODgL7744tixYx06dAAwYsQIPz+/jz/+WFdX96uvvio9PFNT05cvX5beRrbUuzwLOomockilGLobSek4MwwtC6yZ/9EDg3fjt9PwtIdnrXK9lOytvWXLFqUbxGY6QUQkx5KrUFfD+t75uYRMI3PM7IivfHEiDB9VreWRVAaRSGRtbR0ZGSl7aGBg0KlTp59//rlIM11dXQA2NjbF5zU5ODhcuXKlyMHw8HA7OztZRaC1a9c2bNhQlkvIdOzYcfTo0YsWLSoznSAiZXTlGS5F4M9OhXIJABIxVvXE8YdYeLm86YTyYjpBRCTH+afwsIO1vpyn+jXCV7648JTphPJ58uRJ3o4QTZo0iYiIyCvBVB6tW7des2ZNTExMweJOfn5+Hh4esn8/e/aseM0lMzOz6OjodwuciKqoC+EA0F9eiQ0jLXg54OyTSo5IAThUT0QkR3wqrEpYPWusBS11xKZUbkD0zjZs2BAbG9u7d2/ZwyFDhpw8efLcuXPlf4XevXvr6urOnz8/78jx48cDAgK++OIL2cNmzZpdv369YPIglUoPHTrk5uZWEWdARFVOXAoA1Cihv7DSQ1wKVH6PN45OEBHJYaKNqGT5TyWkIS0LZsq6b2F1sWDBgqtXr3p5ednY2KSkpJw+fXrp0qU+Pj6ypREAevbs+fnnn/fo0WPs2LEeHh46OjqRkZG3bt2aNWtWSa9pZGQ0e/bsMWPGZGdne3l53bt3b8qUKX379pVtiQ1gzJgxa9eu9fT0/OWXXxo2bPj8+fPly5dfv379rZIWIlIipjoA8DwZdeRtFxSVDFOdt64Yq3SYThARyeFpjw03EPEKNsV2fd1xBwDaqfpcWGXXvHnzc+fOTZ48OSYmRkdHp2nTpuvWrRs0aFDBNuvWrWvbtu3q1asXLVqUlpZma2vbpUsXqVQqKrnzHz16tL6+/uzZsxctWmRubj5mzJiClcf19fWvXbs2a9asX3/9NTIy0sjIqHPn/7N3n2FRXfkDx793ZihDHaoixYKKqNixYkchiS2rMcGaqMmyZF2TuFFj1GyyUWJ216ztr27AWKMmutGgBgsmG0vsKCoiKhaKFJHehin/FzMiIqJRBJHzeeHDnHvm3N+dx5k7vzlt8Llz55o2bfoML1UQhNpjmBex+Tyze1c8lFXMvqsM8qz5oGqaSCcEQRAq8X53vonm9a3sGoOq3A40J1L4KIqODfFvVnvBCY+hb9++j9yHVJKkyZMnG7axe3xjx44dO3bsw47a2NiEhoZW0cUhCMKLpEsj+jfh81/p5sbAcr8bFJQy4QdyS/hrz1qLrcaIdEIQBKESrRxZ8hIhu2i9nMmd8HGmRMuBa3x7DhszNo1C9qJ3XguCIAiPY+2r9AwnYD2j2xDQHAsTLmawOpobOYQOpJvro1uo60Q6IQiCULngLng58nEUn/9qLDGV80ZbQgdWvuKTIAiCUA+52xD9R+YcYN1ZNp03FnZsyP+9wsstajWymiLSCUEQhIfq34Qjk7mVT3wmZnLaNai4DYUgCIIgOFqwcgiLXyImjQI1ze0rmXf3AhPphCAIwiO4WD100VhBEAThWUjKJTyawzfJLqapHS81Z4wPpvLaDqtKZnJ8G9V2ELVB7DshCIIgCIIgPEeWHKPFUv72C9ez0ek5cI23dtD2/4hOre3IhMrU4XTiwIEDr7zyirOzs0ql8vX1Xbdunf6F3yZEEARBeGzP6DaxcePG9u3bK5VKDw+POXPmqNXq8kc1Gs3ixYtbtmxpamrq5OQ0ZsyYhISEpz+pINQf35xhWiRdXbn4LvFTOfkOaX/lu9fIKmbQOpLzajs+4QF1NZ1Yt27d0KFDvby8Vq9evXnz5m7dur355pufffZZbcclCIIgPBee0W1i5cqV48ePDwwM3LVr16xZs5YvX15hL4vg4OAFCxZ88MEHR44cCQ8PT0hI6NGjR2qq+E1VEB6LWstH++nkwp5xtHI0FsokXmvNnnHklLBA7An5/KmrcyeGDRvWpUuX1q1bGx4GBgZqNJpFixbNmTNHLn++B9YJgiAIz96zuE1kZ2fPnDlzxowZX3zxBTBgwABPT8/AwMC9e/caNsaOiYkJDw/ft2+fv7+/4Sl9+/Zt0qTJ+vXrP/zww+q4LEF4wf3vBmkFfBWI+QNfUTu58FJzvr/A8pdrIzLh4epq74RKpSq7SRgMGDAgNzc3PT29tkISBEEQnh+PeZtYtWpVu3btzM3N3d3dZ8yYUVRUVEWb27dvLygomD59ellJQEBAp06d1q5da3h44cIFoE+fPmUVbG1tfXx8rl69+vRXJAj1wZU7AJ1cKj/ayYWMQrKLazIi4dHqajrxoKysLFNTU5VKVduBCIIgCM+jB28TU6dOfffdd0eMGLF///758+d/++23I0eOrKKFw4cPt2nTxsnJqXzhwIEDDx06ZPjb1dUVKD9ZQqfTXbt2rUJiIwjCwxh2CH3YLCd9uTrC86OuDnZ60Lp164KCgpRK5eNUVqvVBQUFVVSo+qggCMKTKSgoyMrKqqKCpaWlqalpjcVTr1S4TRw8eHDZsmVhYWGTJ08G/Pz8vL29u3btGhkZGRgYWGkLCQkJHh4eFQo9PDwSExNLS0tNTEz8/Px8fX2nTJmyZcsWQ2qxaNEiuVw+adKkx4kwPT09Pz+/6gqP044g1F0t7AFOptybOFHe8WQaWmFjVsNB1aj09PSq12+wsrJydnausXgexwuSTsyfP//q1atbt259nMqZmZnNmzfPzs5+ZM20tLSnDk0QBAHufp68/fbbb7/9dhXV7OzsLl++7ODgUFNx1RcP3ibCw8O9vLzKf9H39fX19fXduXNnYGBgSUlJ+YFPtra2kiTl5eW5ublVaNnBwUGv1+fl5dnb28tkssjIyPHjxzdr1uyVV16xtLQ8ffr0/v37rawevXFJWlqam5ubRqN5ZM3k5ORHX7Ag1E29G9PQitBD/MG74rahhxPZe5UQ31qK7NkzvLXfe++99957r4pqCoUiKSmpQYMGNRXXo70I6cQ///nPf/zjH1FRUS4uDxlqdz9bW9uZM2dWnU7ExsZGRETY29tXU4yCINR3hs+ToUOHVj3upUGDBra2tjUVVH1R6W3i5MmTfn5+knTfsIlWrVpduXIFWLFixfvvv19WnpGR4ehY2Y+lD4iMjDx9+vTSpUvlcvnmzZuTkpIiIiLKN/UwTk5Oq1evrnryxokTJ8LCwho2bPg4kQhCXWQi49+BBG0lcAOrhuLtCKDTs+0if9pJQyvm9HlUE3WW4a09ZcoUX9+qcialUllhyGWtq/PpxCeffLJ06dJ9+/Z17tz5MZ+iUChmzZpVdZ1NmzZFRESYmJhUXU0QBOExGT5PgoKCgoKCajuW+uVht4ns7Ow1a9Zs2LChfKFGo+nevTswatSoDh06lJUbcjwbG5s7d+5UaD8zM1OSJGtrayA2NnbixIl79+7t378/MHny5KioqGHDhllaWr7zzjtVxymTycaPH191HWtr67CwMLGAofBie70NBWre3U3r5TS0wtmSq3coKKWVIz+8TgPL2o7vmTG8tQcMGFDnbhN1O50IDQ1dtmzZgQMHyn/oC4IgCIJBFbcJGxsbf3//B39dsrS0BNzc3B4c1+Tp6Xns2LEKhYmJiR4eHoZ0cfXq1d7e3oZcwmDgwIHBwcFLlix5ZDohCEKZSR0Z7El4NMeSKNXRypHA5oz1wVSk0s+lOpxO/PTTT5999llUVJTIJQRBEIQHVX2b8PHxSUpKatWq1eM32KNHj/Dw8IyMjPIjDaKionr16mX4Ozk5+cFRso6OjmImniD8Xm42fNK3toMQHk9dXSi2tLR02rRps2fP7tmzZ23HIgiCIDx3HnmbGDdu3IEDBw4e/B1b7I4YMcLS0nLRokVlJfv27Tt16tTEiRMNDzt06BAdHV0+edDr9bt37656JLQgCEKdVld7J3799dfLly+3b9/+zJkz5csdHR0f7J4WBEEQ6ptH3iaGDh06YcKEIUOGTJ06tVevXhYWFikpKTExMaGhoQ9rU6VSLVy4MCQkRKvVDho06NKlS3Pnzh05cqRhS2wgJCRk9erVffv2/eijj7y9vVNTU1euXBkdHf27khZBEIS6pa6mE9evXweGDx9eofyPf/zjypUrayEgQRCesfhM8tSYK/B2rGoPoxs57E8gIQsbM3q608tdbHhUTz3ObeKbb77p3bt3WFjYkiVLiouL3d3dAwIC9Hp9heWeygsODra2tl64cOGSJUucnJxCQkI++eSTsqPW1tYnTpwIDQ2dN29eSkqKSqUaPHjwuXPnmjZtWv1XKAiC8Hyoq+nE5MmTDRsPCYLwAriRw5JjRCWQp0apoHdjpnaltROATs+qU3xxiJs5xsqOFkzrxoe9MLt/Tl5uCX/axebz6Mptp+ppx5oR+FXceUx48T3ObUKSpCe4m4wdO3bs2LEPO2pjYxMaGlpFF4cgCMILpq6mE4Ig1FFaPWdSySrCVE4nF6xM2XaRN7dTVEoPd1o6kFnEN9GsjmZxIO90Jmgb312gswsf9KCRNZmFbI1l7s9EXmHfBJR3P8NKtLy0kaNJTOrIHzvjaU++ml3xfP4rg9azdzy9RUYhCIIgCM+ASCcEQXhW9HrOpXMpE6C5Pe2cWXmKv/+PtAJjBVM5L7dgVzw+Ddg8ihZ3V8RJyWPidkJ2cSaV7y7wfnf+OfjemKXgLqw4ybu7mPcz/xhkLFx2nCOJ/Gcob3cyltiZE9yFEa3o8h+m/Ejsu8jFqCdBEARBqG4inRAEoXqk5LH5PEeTyC3B056GlnwXy/n0u4f1qMzJLqG7G/MH0tiWnBJ+vMT6s8hkfDvyXi4BNLJmxxu0W8HqaDo2vC+XMPhTF36+xv+d4G/9sDQBWHMGH2emdKwYVUMrZvfm3d38liiGPAmCIDyhCxn8cJHEXCTwacDoNjhZ1HZMwnNDpBOCIFSD5Sf4YA9qLc6WWJly4BqlWkzkfNyHoS2RYNkJ1p8FmNKRyXe/9A9oyvoY9Hrm/cyWUfc1aGHC+Pb87Rf6Nql8LvWo1nwfy4lk+jVBoyM2g790o9IJtAObAcSkiXRCEAThdyvW8JefCDuNHlTmaHTkn2LGPv7enw961HZwwvOhru47IQjC80Cr53o2Xx7mz7vp5MKJt0n7K79NRmlCIxsUcjbG0MqRrq7EptNURVdX/hJ5b1L1zRz0evo14fsLpORVbNzFqqpTN7IGyCwCKNag0xu7KR5kZQpQWPoU1ynUcdu2bZPL5dnZ2dXS2saNG9u3b69UKj08PObMmaNWq8sf1Wg0ixcvbtmypampqZOT05gxYxISEqrlvIJQK8ZsI+w0wV24/h5ZM8mdxfG36ebK9L18dbS2gxOeDyKdEAThSdwp4r1IHL+k6WJm7kcCW3NjN8K6s+SWsP0Nfnid69ks+o0iDadvMbI1a0ZQWEp4tLERExlAZxf0cDix4inMFQB5JZUHYJiA4aAEsDJFZW6cpPGguNsA7rZPcbVCXfb1118HBQXpdLpqaW3lypXjx48PDAzctWvXrFmzli9fPmbMmPIVgoODFyxY8MEHHxw5ciQ8PDwhIaFHjx6pqanVcnZBqGE/xPFDHHP68H+v0NgWQJLwbcSe8fRvwpwDlfwSJNRDIp0QBOF3u5FD+5UsPU7fxrzdGSCwOceS6BbGpvMcTcLFCt9GBHji58Gmc9wpQg8NLPF2pIU9x5KM7TS1w8KEy3cAMgsrnuVyJsCJFPT6ioeALecxV9ClkfHh0Jbsiicxt5KaK05gJse/2dNft1DHHDhwYMCAAZ999tm8efOqpcHs7OyZM2fOmDFj4cKFAwYMCAkJ2bx587Zt2/bu3WuoEBMTEx4evnHjxuDg4C5dugwbNmzPnj1qtXr9+vXVEoAg1LANMThaMLdvxXITGf8cTGEpP8TVRljCc+ZFSCeqtxdbEISq6fWM2UZmIXvGsf0NvBwA/jOUi+/S1plJO0grwF5prNzVlatZ2Jghwa18AAcLcu52OCgVjPRm5yVjeXk3clh+Ak87YtL47NeKMayPYWsskzoaBzIBH/VGqydgPbEZ96oVa3h/D9su8teexn4MoV6JiooaPHhwXFxcv379HlZn1apV7dq1Mzc3d3d3nzFjRlFRURUNbt++vaCgYPr06WUlAQEBnTp1Wrt2reHhhQsXgD59+pRVsLW19fHxuXr16lNeiyDUigvp+DYy9iRX0LEh5goupFdySKhv6vxU7K+//vrdd9+trl5sQRAepNeTmItGh0bPpnP8EMfZVJrZ8ct1vJ2Me8ZJEg2t2Doa72XcLiQtH60euYRMQq/HXIGvKz9cZP4AEnPwdb3XeKg/W2PRaLieTZ4aa1OKNPx4iQ/2UKpj62g++x9/+4VfbzCxPY2sySxiayzbYunmxhf+99rxdmTzKCb+QLsV9GuCpz15JURdI72A0W34tH9Nv2jC82D+/PlVV5g6deqKFStmz549ePDghISE2bNnnz9/fvfu3Q+rf/jw4TZt2jg5OZUvHDhw4JYtWwx/u7q6AgkJCa1atTKU6HS6a9eujRo1CkGog9RaTOWVH5IkzOQUa2o2IOG5VIfTiQMHDnz++eeXL1+eN2/e3LlzazscQXgRXM0iu5hfb3DgGkcSyS7G1pziUorK3TBU5gBKBaGHWHyM4C4Ap2/hao2nHT3cScgiq5jtcYz05lQKHraYyJjRi1HfMWwTyXnMaHKvtUM3UeuwM2fGPmZHYW1KQSlqLZ52fPcaHRry/Wj+cZh/HOHANeNTLEz4oAd/H3BvDzuDV1vROYR//cZPl/nlOjZm9HBnYntea135ik9CPXfw4MFly5aFhYUZdsX28/Pz9vbu2rVrZGRkYGBgpU9JSEjw8Ki4QJiHh0diYmJpaamJiYmfn5+vr++UKVO2bNliSC0WLVokl8snTZr0rC9HEJ6Fpnb3dfmWl5RLTgnN7Go2IOG5VIfTCUMvdkRERHR09KNrC4JQmYM3+Saac+ncLiSjgIK7yx9JEi0d0OnJKkLCuAGcnTl6icxCFDLOh3DxNhN+4N9HsVDw5WFeboFcwt2G2AyaqAjZRYGaX64zrTvASG+C2rLpPAoZuSV8H0tOMTsusSuets788iZHkzhwjTw15gp6ezC8lbF7XS4xy4/pPTl9i7wSzBV0csHiIYs4ediyOJDFlX8VFIT7hIeHe3l5lf+i7+vr6+vru3PnzsDAwJKSkvIDn2xtbSVJysvLc3Nzq9COg4ODXq/Py8uzt7eXyWSRkZHjx49v1qzZK6+8Ymlpefr06f3791tZVblOGQDFxcWTJk3KyHjIdzcADFO6CwsfmGkkCM/GMC/ei2R7HCNaVTz05WEkGOpVG2G9oAxv7QULFqxevbqKas7OzuHh4ebm5jUV16PV4XTikb3YgiBUKiWPpcf58RJxt9HpkcDClAL1valUPdywMWfPFST4Zjg93em0ioJSNr+GbyM6riIhi19u0K8xe8fTbgXAoZu8uplFAWQWYWfOltfo9w1vbsdcQUMrVpzkeDLb47A0wV7J3J+NJ7I1Y0Yv5vbF0oSXW/Byi4fGbCKjm+tDjwrCEzh58qSfn590f9dVq1atrly5AqxYseL9998vK8/IyHB0dHycZiMjI0+fPr106VK5XL558+akpKSIiIjyTT1MSUnJpUuXqp4HmJ+fDxQXFz9OJILw9N7pzLLjTNzON8P5g7exsETLwkMsPc64drRvUKvxvVgMb+309PSqfzIoKCgoKSkR6UTty87OvnPnThUV0tPF3CLhRZNRyNJjbL1I/G20epQm6PS0ciS9gDtFNLTi17do+39odeSpCRtGmysAvyUxpCVFGkzlrDrJwNf4fABjtvHhHk68g505Ib7MjmJ6D746ys54JLBTMnAtuWqsTdHomLUfQKlgeCtCB9JEZRwNZSKjtROKF2E9iN8hPT296l0I7O3tVSpVjcVTn2VnZ69Zs2bDhg3lCzUaTffu3YFRo0Z16NChrNzW1hawsbF58N6RmZkpSZK1tTUQGxs7ceLEvXv39u/fH5g8eXJUVNSwYcMsLS3feeedquOxtbU9depU1XU2bdo0ZswYe3v7qqsJQnVRKtg5hiHfMvI7WtjTviFqLb8lklHIS81ZNaS243uxGN7a//73v4OCgmo7lt+nPqYTGRkZjRs3rnr5DoNbt27VQDyC8KwVlrLsBH//H/lq5DLMFLR05MwtgABPfr0JkF7A+P+i1vLXnvzzCH//FeCllqyOZnQbdHq6urL7Mno9f/DGypRTqVzLpqmKdg0AApvzZgdGfc+l2zSwol0DApvzWmsUMpJyARpZG/eRgHo61tbwefLee++99957VVRTKpU3btyoMNlXeBZsbGz8/f1nzZpVodzS0hJwc3N7cFyTp6fnsWPHKhQmJiZ6eHiYmJgAq1ev9vb2NuQSBgMHDgwODl6yZMkj0wlBeD55OXA2mLDT/Pcip28hQU933uzAcC8xLU0wqo/phIODw7/+9a/c3MoWqL/r7NmzmzZtcnZ2rrGoBKF6FWlYc4aIeM6mGtdZMtDq6OZBT3fO3GJISxYfQ4IZvdDDl4cB/tiZ7y9wPBlgQjt2x3MuHaChJQWlZBbhaMGIVmyIofN/eL87ZgqAfQnsvcql28zuzfwB90VSP5OHBxk+T4KCgtq3b19FNRsbGwcHh5oKql7z8fFJSkoqW4LpcfTo0SM8PDwjI6N8vhcVFdWrVy/D38nJyQ92HTg6OqalpT19wIJQtXw16QW4WFdcpuLpWZjwl278pVs1Nyu8MOpjOiGTyf70pz9VXWfTpk2bNm2Syx+yOpogPN/OpjFyC1ezUJpQVIqpHK3WuHG1qZwjiRxLxkzBjjfwW81vSThYMKq1MZ0oLKWTC/+7ARjvSSYyTGRcywawNAUwlWOmoI0T8+7OgvjyMK7WrB7OWx0qiUcADJ8nQ4cOrXO92C+qcePGDR8+/ODBg717937Mp4wYMWLatGmLFi0KDQ01lOzbt+/UqVMLFiwwPOzQocPu3bvT0tIaNDCOKNfr9bt37/b19a32+AXBQK8nLJpVJ4lORadHLuHTgFl+vN6mtiMT6o36mE4Iwosts4iB6yjR8EpLdsUzry+LjxLYHBszNsTQrgE73qDPN2h1SPCX7vy2lRPJfNgTUxlqHfsT0OmNicSOSwCNrHmpBTvjaWyLUkGxhsgr9HTjwER+uc6QTTS25T9D6epa+VZHgvB8Gjp06IQJE4YMGTJ16tRevXpZWFikpKTExMSUpQoPUqlUCxcuDAkJ0Wq1gwYNunTp0ty5c0eOHDl48GBDhZCQkNWrV/ft2/ejjz7y9vZOTU1duXJldHT0wYMHa+qyhPpFoyNoG1tj8bTjgx44WnArj+9jeWMrRxLFMndCDRHphCDUYRodN3MAzBSkF6CQsTOehYfJKQKJXfEAuy+TU0LHhnzSl/9e5EI6nvb0b8req0TE07URwKFECjVodDha8OVhJIn2znjYsiEGmUQPNy5n8uMl7hSxLZb910jJ46sAdl/m3d3odGz4Ax0b1ubrIDzPdDpdUlJSdna2tbV148aNZbLnKOn85ptvevfuHRYWtmTJkuLiYnd394CAAL1eLz18SHhwcLC1tfXChQuXLFni5OQUEhLyySeflB21trY+ceJEaGjovHnzUlJSVCrV4MGDz50717Rp0xq5IKHeWXCQrbF83JtP+xtX9Aa+8OfPu1lyjI4NeVP0GAvPnkgnBOF5Z9jWTa0lNZ8iDSeS2HKB6FsUVrEXqYSDBZmF9HAj/g7AhXQkic6NOHiDHXFM7creq8z9mU2jAG7lMf6/6PS860voIdRa/JvRx5nfkpBLvLSRmDSa2JFfwqjvAazNeCeCnBIaWRMxRuQSQuWSk5Pnz5//3XffZWZmGkpsbGyGDh06e/bs1q1b12Qkfn5+er3+wXJJkiZPnmzYxu7xjR07duzYsQ87amNjExoaWkUXhyBUF7WWRb8xoCmf3z9jzVzByiEcTiT0kEgnhJog0glBeO5o9aw9w9qznL5FkQatrtwxPZT72dTChMJSABkYahkGLNmY4WRBZiHApT/T6F9sv8TtQv7sy8EbrDnLmuEAMWmM2QrQ24P/XsREzvFkNDpM5WyIuRfMmVsgcT0LwMqUNk542KI0oW9jgnyqf86f8GI4evTokCFDMpqTAOoAACAASURBVDMz27RpM3z4cJVKlZaWdvny5W+//Xbbtm3r168fNWpUbccoCHXbqVvklDChssUdFDLG+DDvZ5LzcLWu8ciEekZ8ERCE50uemlc2cvAmFgoKNZjK0d5fwUxBiQZJQmVGVjGdGnIzl9uFdG7EqRSUpuhLyS1hjA/xmfyWxJFEgnxYe4aZ+/nHIIDDN3lrB+gJbM6eqwC/JQKU6vjpCoBai4mcLi50d8PbidZOpBcYh0L1cLu33qsgPExmZubw4cMNW0QHBASUP3Tx4sWgoKCxY8e2a9euZcuWtRWhILwA0vIBGttWfrSJCiA1X6QTwjP3InwveFgvtiDUOXuuErSVrGKAQg0yCbWW7m7czCE1H7mEQk5RKYBCMk6YPpuGVk9TFTGpeNhyM4dG1hSWsjqanu4cSWTxMX4YzYYY1pxBowN4pSU/XQaJyKvG88rltHPCzwMfZ5qoMJXTyQUr01p6FYS6b/HixRkZGYcPH+7Ro0eFQ97e3j/99JOXl9fChQvDw8NrJTxBeDHYmgPcecg2WoZyW7Oai0eot56jKXGCUM+tPMlLG8gqppsrFgpaOdLEFuBcGil5/MmXUh3dXLEyRSbxl+7kFDPMyzj0KbgLpTpU5rR0wMKE7GLUWgKao9Pz63XkMpqpsDNn3VmAnxPQ6QEsTZnYnp/GkjuLU++wOJApnfBvRp/GIpcQnkpERIS/v/+DuYSBi4vLq6++un///hqOShBeMB0bYipnZ3zlR3fG42xJU7Hzj/DsiXRCEJ4LFzL480/YWyBBG2cKNXzkR56awZ4Yut7GtAVIzcfFCp2eHm4godXjagvQviHArTzcbTCT4+UAcC6Nnu6Uamm5jBs5lGqRwF5JoQZXG/4zlPS/smYEgc0xFTusCNXqypUr3bt3r6JCixYtUlNTayweQXghqcyZ0J71MZVkFF+fZn8CIb73lnsShGdHpBOCUGt0ev53g09+oc8aOv8HrY7MQvSwOhrgi0PcLqS1E51dAM6lAWQU4GwFGHsPUvOxNweMe8xlFHIjBwcL4yof313gaBIyGal5qDXkqtGDUkHoQK5N4+1OWJjU/EUL9UJRUZG1dVXjtQsLCy0tLZ/FqTdu3Ni+fXulUunh4TFnzhy1Wl3zp6iBGATBYKE/nnYM38yUH9lzlVO3iIhn9Pe8E0FPd2b2qu34hPrhSeZOVL0mtyAIlSrScPAGhxM5l0pyPjdzSC8wDjoqI5eh1WFnRlYJibkAV+7QuREHb3L+NiYy1FpcrADOpwNczaKhJcCaaGQSVqZcuUO/Jvx8HRmsHMafd2FnTm4J5mbseIMmdrjb1PB1C/WRUqksLCysosLx48c7d+5c7edduXJlSEjIhx9++NVXX8XFxX388cdxcXFbt26tyVPUQAyCUMZeyeHJfLiXdWcJjzYWKhV80IO/9xcrZwg15En+o/Xr169Xr14LFiyo9mgE4cWQWcSdIq5modcTn4law7aLRKei1lZSWSah0yOTcLEmJRdAoYASBjRl92X2XOGtUfz7KCeSkUnkqYm7jUxi4SGAW3mk5dHImmPJdGpIqZ5zaaw7g0ZHmwYsOkKpjjtFWJiwfwKdXGr0RRDqM1dX1xs3bjzsaHx8/M8//7x+/frqPWl2dvbMmTNnzJjxxRdfAAMGDPD09AwMDNy7d2/ZrtXP+hQ1EIMgVOCgZPVwvhzEkURS83G3pae7mIEt1KgnSScuXLgwaNCgag9FEOo6jY7FR/n6FPF3uK/X4f7NIgAzOVodGj0mEqV67JTkFpNZyN8HMOcApnIUMnbF4+/JniusPQNwMgWtDidLzqfTzI6ELBQyFDKKNaTmI8HpVIBG1qTkId0dHGUq5w/e/L0/ze1r6EUQBKBt27Zbt26NjY2t9GhycrJOp1u8ePGSJUvKlx89evRpTrp9+/aCgoLp06eXlQQEBHTq1Gnt2rXlv8qvWrVq+fLl8fHxTk5OQUFBn376qVKpfLA1lUoVGRlZYQbII0/xmDEIQrVztGCYV20HIdRXT5JOZGdn29iIAROCAKDXcymTrGKWH2drLCXl+x/0yOTodCAhgR7kElo9VqbkqxnqRcQlZDLQklVkXNS1qBQXK5JzcbTgdiEHEkDPj/EAWh0SZBSgkJGQBaDRGRd+LT9iKiUPQCbRyYU/dyXIBxMxQ0qoccOGDTt//nx2dnalRy0tLb28vHJycqr3pIcPH27Tpo2Tk1P5woEDB27ZsqXs4dSpU1esWDF79uzBgwcnJCTMnj37/Pnzu3fvrq5TPE4MgiAIL5gnSSeUSuVzMrFs48aNX375peFHpgkTJsybN8/UVCxvKdQEjY4fL7HkOCeSjftSl+dkQUYhSOh0uNuQmEsTO65l0ciaxFyszchXcyABMKYfcokmKn5LZNkJXm9D2Gljm6W6+5rV3z21QWNbejehrRONVag1KE1wUOJshVZHE5Xo6RZq04QJEyZMmFDDJ01ISPDw8KhQ6OHhkZiYWFpaamJicvDgwWXLloWFhU2ePBnw8/Pz9vbu2rVrZGRkYGBgtZzikRWqbv/IkSPJyclVVDh27NjjxCkIQt117NgxhaKq7+eurq49e/assXgex5OkE40bN758+XK1h/J7ieluQg3T6Ii6xq7L7LnCtSzjd/2yQUwyCZmERodcIqMQhQwvRy6kU6zB0YLrWajMSczFRMatPICCUgClghItKnNS8+nQkOhUNsag0zO6LWuijSOXFDLebE9zB5wscbTAxgxrU1ytaWhVO6+DIDyf8vLy3NzcKhQ6ODjo9fq8vDx7e/vw8HAvL69JkyaVHfX19fX19d25c+djphOPPMUjK1TReFpa2mPuypqUlNStW7fHCVgQhDokKSkJWLx48eLFi6uoJknSrVu3GjRoUFNxPdqTpBPdu3f/9ttvz549W3W1pxwFWzUx3U2oYYdu8sZWkvPuKzTMopbA3oLMQuOMCK0eEzkaLTey8LAlMYd2DbhdiJkCoOyrgmH4k4kcW3NuF1JYiqsNaQUUaSgoZU00QGo+chnRwbR1QhCEp3Ty5Ek/P78KKxO2atXqypUrtRVSeQ0aNDh+/PjDRogZ/PzzzwsWLHgwYxEE4QVgeGvPnj27f//+VVRzdnZ+rnIJniydGD169KFDh6r+yHvWxHQ3oQbo9Ww8x7ITnEi+b36CBBam2JuTlIuJnFItOcUApXr00ETF9Wy8HIjPpKMtN3OM8xnuFAFo9AByCR04KrldSJ8m7LzE1Syu3KGTC7/e4JWWHEjAzoKUXP7WV+QSQt2m1WoTExNzc3NVKpWbm5tM9qxm89jY2Ny5c6dCYWZmpiRJhk0wsrOz16xZs2HDhvIVNBpN2Xzr5s2bX79+vSxsPz+/smoXLlzw8vJ65CkeWaFqXbp0qbpCRkbGIxsRBKFOa9u2rb+/f21H8fs8SToxePDguLi4ag/ldxHT3YRnTa3l1S3svoypDJ0euYRCjlqDHixMKFDj7UhirnEmg0JCA+2cOX2LRtZcz8ZUgR6u391dDijVApjKUOvo7MLxFMzkAIdvAmQWotNz8CalOn65RrGGlFwmdWRun9q5fEF4erGxsfPnz4+IiMjLM/brOTg4jB49evbs2c/i93VPT88HpxYkJiZ6eHgYJi3Y2Nj4+/vPmjWrQp2yDfV2795dNjOwZ8+eK1eubNeuneFh06ZNH+cUj6wgCILw4qmrG5w85XS39PT0/Pz8qis8bYhCHffa9+y+jEKGWgfQvgGnUxnbjo0xFGsBTqYAtHXmXBo+DTiZQnoB3F2hNf42YNyKzkxBSSlOlmQU0tiOy5nGTayT85BBVhHcXZ2pqBSgQEe7Bszry0jvmrxioSakp6cnJCRUUcHKysrZ2bnG4nl2vv/++/Hjx6vV6i5durRo0aJBgwY5OTnHjx9fsWLFd999t3PnzgprsD69Hj16hIeHZ2RklP+lKSoqqlcv487APj4+SUlJrVq1elgLLVu2LPtbJpM1a9asbdu2v+sUj6wg1Gc5JRxJJDEHF2t6uOFoUdsBCUI1qavpxFNOd3Nzc9NoNI88S9UrbAh1WnoBiblo9cglsotJyWN/AmdSScklqxjt3aFNGp1xXVfDrg6G5ZIUMrQ67JXcKaKxLefSSMxFD0m5AHlqwLhjnakctRaNDoXcOD/7ciaSZOyvAMqWbjKR0dCa1k4MacGbHbASS5S9cAyfJ++99957771XRTWFQpGUlPS8jYv9veLj48eNG9eyZcstW7a0bt26/KG9e/eOGzduxIgRsbGxVX9W/14jRoyYNm3aokWLQkNDDSX79u07depU2aar48aNGz58+MGDB3v37v2MTvHICkL9lKdm+h7Wnb23mLhCxkhvlr6Mk0gqhLqvrqYTT8PJyWn16tVFRUVV1Dlx4kRYWFjDhg1rLCrhWUsr4L8X2X6R06nkFhv7HO7zwGZzBgVqgDd82HyONWcBvJ04ewtrU+4UEX0LIDUfoKkd17KwMKGwFIWMUp0xqdDqAOO6T4BejyRhrsDenOb29GvKKG/avgi/RwtVMXyeTJkyxdfXt4pqSqWywjDOumjhwoVmZmaRkZGurq4VDg0ePHj79u1+fn7Lli2bN29eNZ5UpVItXLgwJCREq9UOGjTo0qVLc+fOHTlyZNmEuqFDh06YMGHIkCFTp07t1auXhYVFSkpKTExM2Vf/pz/FIysI9VC+moFrOZnCa20Y3w4Xa24XsjWWNWc4fYtDk3C2rO0QBeHp1NV04mmmu8lksvHjx1ddx9raOiwsTC6XP1WUQm3LKCQxhxs5/PMIRxLvJQzlswbpbrFeMm4zB6jMyS6mlSNxt7E1J7uYHXEAxaXYK7mehR5u5MDdAUtN7biaRVIOSkXlW0YY2JjRy4PX2zChHVJlqYvwAjN8ngwYMCAoKKi2Y3nm9u3b9+qrrz6YSxj07NnT399/x44d1ZtOAMHBwdbW1gsXLlyyZImTk1NISMgnn3xSvsI333zTu3fvsLCwJUuWFBcXu7u7BwQE6PV66YE35P79+ysdFvXIUzyyglDf/P1XTqSw/lXGtbtXGODJ6214aSPT97L+1doLThCqQ11NJ8R0N6G8q1kcvklSLufSUWtJySU5n6xC8u/fYE6S0ENLB+IzAeQSchlqLZ52XM3Cy5FLt41DmAw7SV+6DZBdjIWCwlIaWZNRiFwyznZwseZWnnGt2IQsY3dEWRYhlxHYnD92xl6JkwVmCjxsRAoh1Bepqanl5yE8qFu3bkuWLHkWpx47duzYsWMfdlSSpMmTJxu2sataFYssVX2Kx6kg1B8aHeGnGdTsvlzCwL8Z49qxMYalL6Eyr43gBKGa1NV0Qkx3EwzSCvhjBD9e4t46rg+MWZIkJNDpkcnQ6biWha0Z1mYk5dLfg/3XQEKSuJENd5dzNcxtkCTQY2WKhy0XMshTo9NxuxAHCzILSc1DAp0OJPTlNqu2NWdie74cZFy4SRDqIUtLy8LCwioqWFtbFxQU1Fg8glBbrmWTWcSQhyTXQ1qy5gxnUunXpEajEoTq9azW/37WRowYYWlpuWjRorISw3S3iRMn1mJUQs04l870vXT5D6qFuPyTHXHGXMLNBkClBFCZ38sp5BI6PSNaodXR0QWNDknC2hRJ4uJt7JVcz0ZlTrEGuWHkE0h6nC2RwNmS/FLy1LR1Jq8EQA+3C41/6AEJEzn9GrM4kCOTOR/CnRksDhS5hFCvde7c+eTJk1VUKCgosLAQU1CFF59hVyJ7ZeVHDeU5JTUXjyA8C3W1d0JMd6u3vjjEnAPoy81sNuQNDkqSclEqUJljaUJyHjIJZ0vS8gnyYf1ZTOUA59PQQ3YxMglHJdklqMy4U4RaA4bVWiUAcxN6uBERT1YxJhI3c3C1wdqMolLQG6dbWJoS6Mmr3rzW2ti4IAgGb7311sSJExMSEpo1a1ZphRs3bjRq1KiGoxKEmtfACu7OtXuQYW+ihlY1F48gPAt1NZ1ATHerl8Kj+SiKwOacSgHILOLtTpzP4LdE8tUAWriezd/68s/fKNHQsQGR+ShkAMeSAdRaFHK0urt9F3pjV0NBqeER3J2H7WyFQoaVqXH4U3LuvTCcLJjXj5AuYi6EIKBWq/v166fT3bf4gE6n02q1/fr1e1jOEBsbGxAQUCMBCkJtcrehhT0bY5jlh/yBW8a6s6jM6eRSG5EJQvWpq4OdDMaOHRsTE1NcXJyYmDh//nxTU7FW/4tMrWX6HpwsuJVHRiFd3dDpGepFah79Ghs3cJjqC5BdYpwY7a4CyCsGSM7FzhwkvByQJOyU3C7E0ZISLVZmyCVkEpamAC+3BNh8HrWWzi4ArRwBJPB25NgUUv7Ku74ilxAEAFNTU6VSmX2/3NxcLy8vCwuL7Ido1KjR0KFDayzIbdu2yeXy7Ozsamlt48aN7du3VyqVHh4ec+bMKdtI20Cj0SxevLhly5ampqZOTk5jxoypeuNC4YU3y4+Lt/ljBMXl9rvS6PhgD79cZ5afcfEPQai76nDvhFCvpObz0gZySlDIyC9FLrErHuC3JEq02Clp48SZVP7Qmn/9xvFkbMzILqadM0BaPnZKsovo05gdl/BQcSGDUh16KCiBu//qMa7xuuUc5grjTIl9CQBxt5FgfHuWvoSNWW1cvyA8x6Kiomo7hKp8/fXX7777boX+kye2cuXKkJCQDz/88KuvvoqLi/v444/j4uK2bt1aViE4ODgiIuLTTz/t0qVLSkrKggULevTocfbsWbGRUb31VgfOprHkGD9d4bXWxn0nfrjI1SyC2vJhz9qOTxCemkgnhDqgsBT/9cZlW/dNZOouGlgyph2Td7DgV1o4EJtBRxfOpHIjG4VE3G087biZg7stwMlbuFiTXcyBa0gSP10GuJGNBLeLkCT0d5eFMvyh1aMt9xuSQka/JnwVIHaaE4Q65sCBA59//vnly5fnzZs3d+7cp28wOzt75syZM2bM+OKLL4ABAwZ4enoGBgbu3bvXMHMvJiYmPDx83759/v7+hqf07du3SZMm69ev//DDD58+AKEukiQWBxLgycLDLD+BRodMomNDvh7K5I6io1t4EYh0Qnh+nU9nzRmirhOfQeHd7/eB67A2RS5jrA/vR6LXk5xLQalx7OmKk5jIuVOEDiR4dxcSmMhIyKKBFWn5d5vWG1d3hXu5hIG5CR42uFjTwBJ3W9o3ILA5TmIFGkGog6KiogYPHhwREREdHf2wOqtWrVq+fHl8fLyTk1NQUNCnn36qVD5kFR7Yvn17QUHB9OnTy0oCAgI6deq0du1aQzpx4cIFoE+fPmUVbG1tfXx8rl69Wj2XJNRZL7fg5RYUabiVh7MlVmJ0tvACEemE8Dy6kM7M/ey+DCCT0Orv9SG0cuRCBrdTibrGR358FAXQwIrvLmAq5+ANAEsTsouwNiU5D1MZhRrksnK5BDhY4tuIrq40VmGhoKgUcxPjfLgGljV/uYIgPBPz58+vusLUqVNXrFgxe/bswYMHJyQkzJ49+/z587t3735Y/cOHD7dp06b8fkfAwIEDt2zZYvjbsBF4QkJC2Y7aOp3u2rVro0aNeqorEZ4xvZ7tl1h7hvPp6MHdhpGtmdIJZXV/S1IqaGZXzW0KQq0T6YTwvMgsYkMMUdc4lkR6ue2tdDpkMt7pTP8mjNnG+QyWvkTILkZuJnE6hxPZGU+hmlId6JFk6HUUlCJBnhpAff946Z7uLAqkm1igUhDqvYMHDy5btiwsLMywSbafn5+3t3fXrl0jIyMDAwMrfUpCQoKHh0eFQg8Pj8TExNLSUhMTEz8/P19f3ylTpmzZssWQWixatEgul0+aNOlZX47wxApKGfUdkVdwsqCnOyZyzqbyl59YdpyIIFo61HZ8gvDcE+mEUMv0eo6nsPIkm89RrEV2dyM5wM2GpFy6eXA0kT1XWPoSof7M2MfcA/RpzK836P4107qzM964SiwSej1yGWZyLE1RKWlmR0t7OrrgbIG5CW2dReeDILyASkpKioqKyh7a2tpKjzEgPTw83MvLq/wXfV9fX19f3507dwYGBlbaZl5enpubW4V2HBwc9Hp9Xl6evb29TCaLjIwcP358s2bNXnnlFUtLy9OnT+/fv9/K6tE7C+Tk5Pj5+SUnJ1dRx7CK1J07dx7ZmvD4/hjBnit81p8Zve7tQPrfi0zawdBNnA3GXHxXEmqE4a399ttvv/vuu1VUc3V1PXTokK2tbU3F9WjiLSLUtKRc1FpszTFXsOMSf/uFy5nGyQyAXk9gc366wvvdWXocGewMotVSrmWz6hQf9uTr0yTc4dfrSBJXs/jLT3A3AWlkw6JBvN62Fi9OEIRasGLFivfff7/sYUZGhqOj4yOfdfLkST8/vwqJR6tWra5cufLEbQKRkZGnT59eunSpXC7fvHlzUlJSRERE+aYexszMrFu3brm5uVXUuXnz5rFjx8zNzR8nEuFxRKfy7Tk+7MXcPveV/8EbMwVDvmXVKaZ1q6XghHrG8NZu27btg72g5dnY2JiZPV+rTL4I6cS2bdtGjx6dmZmpUqlqOxbhoTQ6Fh9j8VES794ry/ohJLAx598v89YPeNjy0xWAT/px6CYnUvjHYd7qxD8O89VR3vVlYFMSc1j3KtP3UqLFVEZmER/1prcH/ZsYd6wTBKFeGTVqVIcOHcoePuaPdtnZ2WvWrNmwYUP5Qo1G071794e1aWNj82DPQGZmpiRJ1tbWQGxs7MSJE/fu3du/f39g8uTJUVFRw4YNs7S0fOedd6qOx9zcPCwsrOo6mzZtGjNmjIWFWB2i2uyIQw9/qSxheKUFze3ZESfSCaGGGN7a06ZNCwoKqu1Yfp86n05U74LiwrNwPZvdV/j3b1y+QwsHhrZk12XjNnPo6dCQM6nklvD+boDvR/P2j5xN49BNmqg4m8a354gO5v+Oc/UO38ei0yGTGOPDJz8DpObzhT8ze9XuJQqCUJvc3NweHIP0SDY2Nv7+/rNmzapQbmlp+bA2PT09jx07VqEwMTHRw8PDxMQEWL16tbe3tyGXMBg4cGBwcPCSJUsemU4IteJGDipzXK0rP9raiXNpNRuQINRBdTidqPYFxYXqotez+wq7L3Mrn5hUrmVRlu1dzuTKHdo5M7UbU36klweHbwJ8P5rx/wWwNWdadybt4F+/MaQF38eSmItSwVudWHaM0d9jKsdMQadVXMkC+GtPZog9gARB+P18fHySkpLKlmB6HD169AgPD8/IyCi/uFNUVFSvXsafNJKTk+3t7Ss8y9HRMS1NfCd9TpnJKdE89GiJRkycEIRHq8NDQwwLisfFxfXr16+2YxEAolNZfIw/7aLZEoZ8S/hpfrrM1bu5RIeGXH+PPo3R67mWzaGbSBARhK05QMeGjG0H8N9YRnpjIuNoIq+3NX6OZxZSUoqJjEkdUWuxNuXKHRQSu8fyj0FiDyBBEJ7EuHHjDhw4cPDgwcd/yogRIywtLRctWlRWsm/fvlOnTk2cONHwsEOHDtHR0eWTB71ev3v3bl9f3+oKW6hebZwp0nC8sgnwxRqOJ9NGbGAqCI9Sh5PuRy4oLjwL2cXcKUIhw92Gc+n8EEdKHnlqfkvkevZ9NV1tSMhi+cuodbwfydlUjiaRVURnFxKy2BWPuQI7c/o1ZscljiUzqzdhp9kQwyw/OrlwLJmx22iiIu423cPJKaaRNRvO0sgKczkpar4eykvNa+lVEASh7hs6dOiECROGDBkyderUXr16WVhYpKSkxMTEhIaGPuwpKpVq4cKFISEhWq120KBBly5dmjt37siRIw172AEhISGrV6/u27fvRx995O3tnZqaunLlyujo6N+VtAg1aaQ3M/cxcz97x2Ny/0+sf/+VrGLG+tRSZIJQd9ThdOJpJCQkZGVlVVHh2rVrNRZMXRF1jU9/4XAiOj2AuYJiDYCtGbkl6MFUTg83/neD5S+TkseCQ5jKebUVu68ANHdg2k9IEn0bM7I1s6MA0gro5cGOS3xxiJPvYCLjQgZfHEIGrtYcTaJEi5mC1Dx0UJADkJKPlwMRQQxpWVuvhCA8uWvXrp06daqKCnZ2ds2aNauxeOq5b775pnfv3mFhYUuWLCkuLnZ3dw8ICNDr9VWsMxscHGxtbb1w4cIlS5Y4OTmFhIR88sknZUetra1PnDgRGho6b968lJQUlUo1ePDgc+fONW3atEYuSPjdGlkT6s97kQSs5wt/fBshSVzP5svDrDzJq60Y8TtGwwlCPVUH0oknW1C8CmlpaV5eXhrNwwdL3lX1+t8vGI2O2AxKddiZG/fsLNFy6Cbn0wHibrPqJA2smN4DTzsWHSU+E8DbiaYqDlxj5RDCTvO/GzS3J8SXIg3zD1KqY/YBXmkBML4d837G3YaMQgKbG9OJ1dEUlQLEpDFiExodHjZ8FIUErRzJKUGrR6dDr6dTI/yb0doJH2c6NEQmBjgJdY3h8+Tjjz/++OOPq6imUCiSk5OdncUAi+rk5+en1+sfLJckafLkyYZt7B7f2LFjx44d+7CjNjY2oaGhVXRxCM+bad0wkfHXvXQLw8oUhYzsYiR4pzOLX6rt4AShLqgD6cQTL/79MM7Ozj/88EP5FOVBhw8fXrx4sYuLy9OcqK4o0vDpL6w8SU6JsaSlA30asyOOjMK7lfSYKfi8P5M7sfEc8ZmEDsTdlok/EJfBn3yZ2J5RrbFewM0cbhcaV4D1cea7C3zkhwTJeQCuNhxJpFQL4OXA337BzQYvByZ3YtY+9CCXYSpHreXibQBTGQObMbcvPX73qi2C8HwxfJ5MmzatbNpupZRKpcglBKGGhfgyqjXfXeB8Onpwt+FVb9o4PfqJgiBQJ9KJJ1tQvAqSJA0ZMqTqOoa+C5msDk9Vf0w5Jfiv41QKQ1oyzAtbc25k848jhJ3G2ZINf6CrK8ERHEvBzYYpEZRo2R6Hmw0f9kIusT2OrbG0dQKMV4ZIjwAAIABJREFUo07VWrbH8VZHzOTYmFFYSomWEa1YewaghzvHk5kWCTCjFzP3k5BFc3tu5mBhilrLjWyAji683oaxPjhZ3tujVBDqNMPnSbdu3V577bXajkUQhIqcLflz19oOQhDqpjqQTjzZguLCwxRpOH2LolKszejkwl9+4vQtNvyBMXdnm13I4KMo3GxIykWpoIU9x5L5gzdhwwjYwPS9qMzp5Y5cAujjwdZYLmcBmMrxsOVmDrEZyCUCmrM/AaCwlGUv034lxRou3ebNDqyORoKFh7ldiIsVaQUsOw5gYcJrrZnXl5YOtfLaCIIgCIIgCL9PHUgnhOqSr+aTX1h5ksJSY4mtObklvNn+Xi4BrD2DTk/UBAau41+/EdicglLcbTGVs+Ql2q0gvwTTuz0GPg0BLmcaH77qzb/v7ns9ry874wFMZDhb4mlPdjGRV4wzufUYZ1/cykeCPk0IHUBXV7GttSAIgiAIQl0ivrvVF1nF9Aznq994qTmbR7FvPOtepbEtej0XMtCU21U8OpVWjrR0YKQ3R5OQSZjJuVME4ONMI2tMFcRmGCs7WwBE3zK2MLoNQORldl2mtRP2SiTw+4amizmWRFdXGlkDdGzI7nGcfIftb7B7LKl/5X8T6ekucglBEKrTgQMHXnnlFWdnZ5VK5evru27dukonZP9eGzdubN++vVKp9PDwmDNnjlqtLn9Uo9EsXry4ZcuWpqamTk5OY8aMSUhIePqTCoIgPLfE17f6YnYUFzLY8Ae2jub1Nvg3Y3w7grsAHE/m30fv1cwrwcYMwNUGnZ47RXR3I/IKai2ArRkOFpxNY89VgIh4gOQ8xv2XO0WEn0YmYadkyLfYL+R2IU3sKNGQlAtwIpkmKjaP4tQ7vORJZxeGe/FSc5wta/a1EAShHli3bt3QoUO9vLxWr169efPmbt26vfnmm5999tlTNrty5crx48cHBgbu2rVr1qxZy5cvHzNmTPkKwcHBCxYs+OCDD44cORIeHp6QkNCjR4/U1NSnPK8gCMJzSwx2egFdzeLwTYo1WJvRrwkuVuSpWXOGoLb3DWoC7MwB2jXg30eZ3sO4vXQja44kotOTXgBgpyTEl9e3Mn0v/xxEYi4jWqHWELSVv/Xj81/p5c6Apsw/yA9xqLXGpWNTCyjR4GaDswUvN+ePXfARa9UIglCDhg0b1qVLl9atWxseBgYGajSaRYsWzZkzRy5/whUesrOzZ86cOWPGjC+++AIYMGCAp6dnYGDg3r17DTvZxcTEhIeH79u3z9/f3/CUvn37NmnSZP369R9++GF1XJYgCMJz50VIJx62oHg9lJjL1N38eImyl0Mh480ODPeiWFPJXjw93ZHA1pyYNK7n0FQF8FILfojjx0tEXKKTC0oFo9uw9yrLjrMvgXw1bZwJaM5fdjMtErmEtRlnUrE2JacEpYKLGeSV8EYb/uQrVncVBKHWqFQqlUpVvmTAgAGrVq1KT08vvwj4qlWrli9fHh8f7+TkFBQU9OmnnyqVyoe1uX379oKCgunTp5eVBAQEdOrUae3atYZ04sKFC0CfPn3KKtja2vr4+Fy9erUaL00QBOG58iKkE4LBjRx6hpNZyIe9GNUaByUpeaw9S/hpfr4GVDKmyMOWka354SJAVpExnRjfjvm/ErSNYg3rXjXW/HooMhlhpwA+2g8gQWsnLEyIz0QmMbAZkzoad6wTBEF4DmVlZZmampbPMf6fvTsPiKL8/wD+XhaWY7kPUcQVQQVFxQtPzPvKs9RMzSzvLLPyZ5pZdhmaZSmat6Z5pvU18cADU9EUxQvBC0GQQ+5D7mN3f38sISIgDLi7rO/XX+4zwzOfGZmZfTPHM3v27LVr1y5cuHDAgAERERELFy4MCQk5cuRIRT2cP3/e3d3dzu6p8Qj69u27d+9e1b8bNmwIICIiws2t+O83CoXiwYMHo0ePrv310V35cuy6Cb/7SM2FgR66OGJKezQ003RZRFQBxgndMfcYUnNx8m14yYpbnK3gJUOfJhj/JwDEZZbzU2texakHSM3FkgCMbw0LQ0Smw8IIURkAsOkqghOgVOLfaFyIgZMllvSFgR709dDRAY3M1bVuREQ1tn379nHjxpVcfAgICFi9evWmTZtUo2J7eXm1aNGiU6dOfn5+gwYNKreHiIgImUxWplEmk0VHRxcWFhoYGHh5eXl6ek6dOnXv3r2qaLFixQqxWDx58uQXuWY65XYyXtuDuymwNUFjCzzOx9H7WHoOvw7BO22f/+NEpH6MEzoiMRsH7mCW55MsUWJcK2y5Bv8I/B6MN1uVnWprggZmKJTj0D38dbu40V6Kpf2RkYc/QvBjFAC42uDrXvi4K8wkL3hNiIiqKT8/Pzc3t+SjhYWFSPUoWClLliwJDw/fv39/ScvmzZtdXV1Lf9H39PT09PQ8dOjQoEGDyu0zMzPz2XGQbGxslEplZmamtbW1np6en5/fxIkTnZ2dhwwZIpVKr169evLkSVNT0+euhUKh2LhxY3p6eiXz3LhxA4BcLn9ub3VUQjb6bkOBHPvG4PUW0BMBwJ1kTPfF5L8hlWBMS02XSPTCqHZtX1/fhw8fVjKblZXV1KlTtWqoZcYJHXEtHnIlBjYtf+qQZjgZgSNhWBmIOZ2ftCuU+PQEQhOx+lVM8sClWKTlwckSbesXj1L3fR/kFgGAMX9TiEhbrV279uOPPy75mJSUZGtrW3qGH3/8cfny5f7+/qWfmggKCvLy8ioTPNzc3O7fv1+VPivi5+d39epVHx8fsVi8Z8+emJgYX1/f0l1VJCkp6YMPPigqKnrunDr8nqhvzyAxG+cmo0up1OZmC7+30HkT5hzFSDcYaNGXKKLapNq1d+/evXv37kpm09fXHzFihL29vbrqej5+SdQRWQUAYGFY/lQLIwDo6oiP/HDwLsa0hI0J4jKxIxhBcZjkgfc6Qk+EPk3K+VkGCSLScqNHj27b9sl9MBYWFqWnLl682MfH58SJEx06dCjdnp6e/ttvv+3YsaN0Y1FRUZcuXSrq09zcPDU1tczSU1JSRCKRmZkZgFu3bk2aNOn48eO9e/cGMGXKFH9//+HDh0ul0unTp1e+Fvb29mlpaYWFhZXM8+eff06bNk11G5VO2ncLQ5s/lSVUTAzwmRcm/IXzD9HLSQOFEamBatfeuHHjqFGjKpnNwMCgKhc81YlfFXVEA1MAeJBezs1OAO6nAsCfY7HlGn65iFMPitsbmWPdUExvj2fuCyAiqjMcHR2fvQdJxdvbe/Xq1adOnSqdDVTMzc379eu3YMGCMu1SqbSiPl1cXAIDA8s0RkdHy2QyAwMDAFu2bGnRooUqS6j07dt35syZq1atem6cAPDcrwiq2nRVeh4Ss9GuQflTVe33UhgnSMdJpVIrKytNV1E9jBM6wrMhLI2w8Qreal02G+QWYWcw2tZHA1N83gOfeeFOMnKLYCqBq42GyiUievGOHj36zTff+Pv7P5slALRu3TomJqbkFUxV0bVr182bNyclJZV+uZO/v3/37t1V/46NjbW2ti7zU7a2tgkJCdUv/6WjesV5RX/dUrUr+Fp4Iu1Th+9APHXq1JAhQ+rVq2dpaenp6bl9+/aXefQJAz0s8ELAQ8w9DnmpzZBTiIl/ISoDi/57DbqeCC3t0KEBswQR6bLCwsI5c+YsXLiwW7du5c7w1ltvnTp1KiAgoOp9jhw5UiqVrlixoqTlxIkTV65cmTRpkupj27Ztr127Vjo8KJXKI0eOeHp6ClqJl4uVEepJcfVR+VNV7c145iLSPnX16sT27dvfe++9GTNmvPfee/r6+ocOHXrnnXcePHiwePFiTZemMfO64U4yfr6IQ/cw0q346Yg/QpGQhW97Y1QLTddHRKRGZ8+eDQsL8/DwuH79eul2W1tb1V1Mw4YNe/vtt4cOHTp79uzu3bubmJjExcUFBwd7e3tX1KelpeWyZctmzZoll8v79+9/9+7dL774YtSoUaox7ADMmjVry5YtPXv2/Oyzz1q0aBEfH79u3bpr165VK7S8zMa0xLogXIwp+/hEXhGW/4sGpnilsYYqI6KK1dU4MXz48I4dO7ZsWfzGuEGDBhUVFa1YsWLRokVisViztWmKnghbR6CfM36+gJ8uQKGEvh68ZNj+Gvo7a7o4IiL1ioyMBDBixIgy7TNmzFi3bp3q31u3bu3Ro8emTZtWrVqVl5fXqFGjgQMHKpXKZ98zW2LmzJlmZmbLli1btWqVnZ3drFmzSv8Zy8zM7PLly97e3l9++WVcXJylpeWAAQNu3rzZpEl5b7qgZ3zRE/tvYegurB2KUf+9KPZ2MmYewvV47B7F1zoRaaO6GicsLS1Lj2wKoE+fPuvXr09MTCz9HkCdUajAkTBcjkWRArYmGNwM7nblzzmhNSa0RnYhCuQw1odRXf0fJiKqkSlTpqjGp6uESCSqymxlTJgwYcKECRVNNTc39/b2ruQSB1XCXoqjb2HMH3hj35Nh7MJSYaSP1a+WM3QSEWkD3fmymZaWJpFIymQM3XA8HNN9i4epVvn0BN5wx5ohsDEu/0ekBpAaqKc6IiKiWtOuPkLfx66b8LuP1FzUk2JCG0xpB0dzTVdGRBXQnTixffv2cePGGRtX8P36adevX09OTq5khpCQkBrWk1OI4ATky+Fqg/o1eDvw6UgM3YWG5tgzGkOawVSC8DT8ehkrLyIyHWffheQlvbeLqE4KCQk5efJkJTPY2tqW+xoiohqKeYz7qZBK0MYehtp94jAU4922eJf7AVEdUQfiRH5+fm5ubslHCwuLZ+9qXbJkSXh4+P79+6vSYUJCQocOHRQKxXPnjI2NrVapKpHpmHcCB++iQF7c4umAr3rh1WbV7kquxHRf1DfFhSlPMomLFX4aADdbTPfF6kv4pKuAGolI3WJiYgB8//3333//fSWz6enpxcXFadVwp1TX7Q3Fd2cRklj80VgfY9yxrF+N/tRFRFSiDsSJtWvXfvzxxyUfk5KSbG1tS8/w448/Ll++3N/fv4pPTdjb2/v5+aWnp1cyz/nz51euXClg5NHgBPTehqwCjG+NPk1gKMbNRGy+iqG7sGowPuhUvd4CohCWik3DyznoT2uPdUHYco1xgqhuUB1P5syZUzJGQbns7e2ZJagWfXUaX5+BkyW+6Q03W+QU4th97AyGfwTOvgvnOjZYFhFpozoQJ0aPHl360r+FhUXpqYsXL/bx8Tlx4kSHDh2q3mf//v0rn6GoqKhaRarIlZjwF/RECJqO1vWKG99wx9yuGLkHH/mhTxO0rOAR6nLdTARQ4QigPRtjZSCKFNDnmy6ItJ7qsmrnzp3HjBmj6VroZXHuIb4+g9Et8ftrT97MMckD0zpg+G68+zfOvKPJ8ohIN9SB76GOjo69SjEwePKIsbe39+rVq0+dOqUlIwSdjEBIIrz7PskSKpZG2DUK+npYFVi9DvOLAFT4diZjAyiUT26pIiKiEnK5fN26dV26dDE1NTU2Nm7duvXSpUuF/amojJ07d3p4eBgbG8tkskWLFhUUFJSeWlRUtHLlyubNm0skEjs7u/Hjx0dERNR8ocL8chGWRtg0vOx5pLcTPu+Bs1G4UsGYcUREVVcH4kRFjh49+s033/j6+mrPY4tnIgFgdMtyJjmYoVsjnI2qXocyCwC4l1L+1LvJsDOBCd/gRET0jOTk5P/973/vvvvuwYMH9+3b16dPn88//3zu3Lk17HbdunUTJ04cNGjQ4cOHFyxYsGbNmvHjx5eeYebMmd9///0nn3zy77//bt68OSIiomvXrvHx8TVcrjBnotDfGRaG5UxSnaqqe1YiInpWHbjZqVyFhYVz5sxZuHBht27dNF3LEym5MNaHpVH5UxuYPXkSror6OcNQjFWB6O1UdlJYKo6E4Q336ldJRPQSsLe3P3bsWMnHoUOHKhSK33//feXKlYL7TE9Pnz9//qeffrp06VIAffr0cXFxGTRo0PHjx1UDYwcHB2/evPnEiRP9+vVT/UjPnj2dnJx+//33efPm1WyFqk2pRGouGpiVP9XBDACSc9RZERHpprp6deLs2bNhYWEeHh7Xn6Z6d4qm2BgjtwjpeeVPfZQJW5PqdWhtjP/rhgN3MMcP+aVuarqVhGG7oCfColeEV0tE9FKxtbV9dmyi9evXt2nTxsjIqFGjRp9++mnpFwk+68CBA9nZ2aUvcQwcOLB9+/bbtm1TfQwNDQXwyitPDs0WFhatW7cODw+vtdWoMpEI1sZ4lFn+1LhMABUOXkREVHV19epEZGQkgBEjRpRpnzFjxrp16zRQEADASwYA+29havuyk2Iz8W80JnpUu8+veyM+C6sCsScE/ZxhKsH9VJyJhLkh/h6Hpta1UDYRkW5LT0/ft2+fj4/Pxo0bS7fPnj177dq1CxcuHDBgQERExMKFC0NCQo4cOVJRP+fPn3d3d7eze+qVGn379t27d6/q36r3d0VERLi5ualaFArFgwcPRo8eXcurVDVeMpyIQEZ+Ofc77btVPAMRUQ3V1TgxZcqUKVOmaLqKsvq7oIUtPvNHZ8ennsbOKsA7B1CkwPvVf2JcLMKm4RjphrVBOBGOIgVsTfBBJ/xfNw4RSkT0HImJiY0bN87LyzM1Nd2xY0fpP0IFBASsXr1606ZNqrOJl5dXixYtOnXq5OfnN2jQoHJ7i4iIkMnKfgGXyWTR0dGFhYUGBgZeXl6enp5Tp07du3evKlqsWLFCLBZPnjy5KtXm5ubm5VVwgRsAkJ2dXZV+SszpjAN3MPXgU292AnAhBt8HoKsjPB2q1R8RvXDZ2dlpaWmVzGBkZFTFUZvVpq7GCe1koIc9o9F7GzpuKDvuRHwWVg5G2/oCex7aHEOb12qtRES6opLRTm1sbC5evBgZGblnz54xY8b88ssvs2bNUk3avHmzq6tr6S/6np6enp6ehw4dGjRoULl9ZmZmOjo6llm6jY2NUqnMzMy0trbW09Pz8/ObOHGis7PzkCFDpFLp1atXT548aWr6/BHjUlJSnJ2dHz9+/Nw5ExISnjuPSi8nLO6Jr88gKA6T28HNFtkFOBaO/bdgL8Xvr+OZUWGJSGNUu/a0adOmTZtWyWzm5uYRERE2Njbqquv5GCdqWRt7XJmOeSew6yZ+u17c2NEBm4YLGRWbiIieq5LRTsVisYeHh4eHx4gRI7Zs2TJjxoy+ffu6uroCCAoK8vLyEj39hdrNze3+/fuV91k5Pz+/q1ev+vj4iMXiPXv2xMTE+Pr6lu6qIhYWFt9++23lD2/cuHFj9+7d1tbVuM/1q15oYYfvzuLLf4pbjPUxrhV+6M9RsYm0i2rXHjdunIdHZTfHGxsblxmETeMYJ2qfkyX2jUFOIYITkC9Hcxs04CGbiOiFqXy00xLvvvvuokWLfv/99++++w5Aenr6b7/9tmPHjtLzFBUVdenSpaI+zc3NU1NTy3SbkpIiEonMzMwA3Lp1a9KkScePH+/duzeAKVOm+Pv7Dx8+XCqVTp8+vfK10NfX//DDDyufZ/fu3bt37y49/lJVjHXHWHfEPMb9VEglaGMPQ3G1OiAidVDt2sOGDRs3bpyma6kexonKzJ49W9vyX2kFBQV3795t3ry5oWF5LxWvC3RgFfLz8+/du6cDq+Dq6iqRSDRdi0B1YhUyMjI0XYLOcnR0fPYepGeJRCIHB4e4uDjVR3Nz8379+i1YsKDMbFKptKI+XVxcAgPLDkcaHR0tk8lU3wO2bNnSokULVZZQ6du378yZM1etWvXcOFF1VTk3xcfH5+TkODs719ZCa8utW7ccHByefcWWZmVlZUVGRrZs2VJPT7veeBkdHQ2gUaNGmi7kKQqF4tatW05OTlW5i0+d0tLSHj161LJleeN/aVRERISJiUn9+s+55b3uniYYJ8rn7u7u6empUCgqfxpGsx4/fpyQkGBmZqbNmadyOrMK5ubm5uZ19dH4jIwMroJ6eHp6urtzsBhNioqKKhkRonXr1jExMSWvYKqKrl27bt68OSkpqfTLnfz9/bt37676d2xs7LN3Itna2lb9aYfKVf3cFBMTk5OTY2VlVSvLrS0KhSIuLk6pVCqVSk3X8pSkpKT4+HgbGxsjowqGjtIQ1evvte1be15eXnx8vGrcd03X8pS4uLhHjx7Z29trWyyMiYkxMTGpyp8d6+ppQkl1lmqEpsOHD2u6EOH8/PwAHDlyRNOFCHf06FEAR48e1XQhwqlei+nn56fpQoQ7fPgwgGPHjmm6ENJqv/32G4ALFy6oPh48eFAkEp09e7bqPaSlpZmbmy9YsKCk5fjx46V/95YuXWpubh4fH18yg0Kh8PLyGjx4cG2sQTW8/vrrMplMzQt9rszMTACLFi3SdCFlrVmzBsDdu3c1XUhZXbp06dKli6arKOvu3bsA1qxZo+lCylq0aBGAzMxMTRdSlkwme/311zVdxQvEqxNERKSDfvnll0uXLvXv39/R0TEnJ+f06dO//vrrnDlzVI9GABg2bNjbb789dOjQ2bNnd+/e3cTEJC4uLjg42Nvbu6I+LS0tly1bNmvWLLlc3r9//7t3737xxRejRo1SDYkNYNasWVu2bOnZs+dnn33WokWL+Pj4devWXbt2LSAgQB3rTESkCYwTRESkg9q1axcQELBo0aKkpCQTE5M2bdps3bp1/PjxpefZunVrjx49Nm3atGrVqry8vEaNGg0cOFCpVIoqfn/qzJkzzczMli1btmrVKjs7u1mzZi1evLhkqpmZ2eXLl729vb/88su4uDhLS8sBAwbcvHmzSZMmL3BViYg0inGCiIh0UM+ePXv27Fn5PCKRSMCgqBMmTJgwYUJFU83Nzb29vSu5xEFEpGO061EVIiIiIiKqQxgniIiIiIhIIMYJIiIiIiISiHGCiIiIiIgEYpwgIiIiIiKBGCeIiIiIiEggxok6TPVm9Erej679uAragKtARC85Hj0E4EajEuKvvvpK0zWQQJaWljk5OePGjZNKpZquRSALC4vc3Nw6vQqWlpaqVTAxMdF0LQKV/C/U3VXQgf8FotplbGzs7u7euXNnTRfyFIlEUlBQMHLkyIYNG2q6lqdYWFjo6+uPHj1aX1+7xuOSSCSenp4eHh6aLuQppqamWVlZb7zxho2NjaZreYqZmZmtre3gwYM1XUhZ+vr6PXv2dHV11XQhL4pIqVRqugYiIiIiIqqTeLMTEREREREJxDhBREREREQCMU4QEREREZFAjBNERERERCQQ4wQREREREQnEOEFERERERAIxThARERERkUCME0REREREJBDjBBERERERCcQ4QUREREREAjFO1G2nTp0aMmRIvXr1LC0tPT09t2/frlQqNV2UQH/++adYLE5PT9d0IdWwc+dODw8PY2NjmUy2aNGigoICTVckUF3c+Cq6tAsQ1VAVd+Tk5GTRM65fv67ZqqDGI2q1FqSGzVXdFVfPhtK2rVSGFv5eVb0q9W+uF05Jdda2bdtMTEw+/vhjX1/fo0ePvv/++yKR6KuvvtJ0XUJs2LDBwMAAQFpamqZrqaq1a9eKRKJPP/3U399/zZo1lpaWo0aN0nRRQtTFja+iS7sAUQ1VfUeOiooCEBgYeLOU3NxczValtiNqdRf0ojdXdetRz4bStq1Uhhb+XlWrKjVvLjVgnKjD0tLSQkNDS7fMmDHD3Ny8qKhIUyUJ4O/v37t3b0dHx2+//bYOfaNNS0szNzefP39+SYufnx+AY8eOabCq6qqjG7+EbuwCRDVU3R05ODjYwMBAq6pS2xFVwIJe6Oaqbj3q2VDatpVK087fK+3cB9WJNzvVYZaWli1btizd0qdPn8ePHycmJmqqJAH8/f0HDBhw586dXr16abqWajhw4EB2dvbcuXNLWgYOHNi+fftt27ZpsKrqqqMbv4Ru7AJENVTdHTkxMdHGxuYFF1W9qtR2RBWwoBe6uapbj3o2lLZtpdK08/dKO/dBddLXdAFUm9LS0iQSiaWlpaYLqYYlS5ZougQhzp8/7+7ubmdnV7qxb9++e/fu1VRJAtTRjV+JurgLENVQdXfk+Ph4BweHF1RMiWpVpbYjqoAFvdDNVd161LOhtG0rlaadv1fauQ+qE69O6JTt27ePGzfO2NhY04XovoiICJlMVqZRJpNFR0cXFhZqpCQCdwGiKnj48OH169ft7Ow6dOgwd+7csLAwTVekviOqgAW90M1V3XrUs6G0bSsJprVnau3cXDXBOFE35Ofnp5eiLO/dNUuWLAkPD/f29lZ/eVVRlVWoQzIzM62trcs02tjYKJXKzMxMjZREWr4LENVQbR1FJ0+efPny5R07drz55ptnz55t1arVb7/9ptmqXsQRtdzCBCyodjdXGdWtRz2nHm3bSoJp7ZlaOzdXTTBO1A1r1661KiUlJaXMDD/++OPy5csPHz7coEEDjVT4XM9dBaKa0P5dgKiGausoam9v3759+4EDB86bN+/y5cvz5s2bNm2a4L+Pau2xXTs3l67iVqoW3dtcfHaibhg9enTbtm1LPlpYWJSeunjxYh8fnxMnTnTo0EHtpVVV5atQ55ibm6emppZpTElJEYlEZmZmGinpZVYndgGiGnpBR9Gvv/5648aNu3btWrx4saaqehFH1HILq/mCari5yqhuPeo59WjbVhKsrpyptWRz1QTjRN3g6Ojo6OhY7iRvb+/Vq1efOnWq9HFTC1WyCnWRi4tLYGBgmcbo6GiZTKZ67TSpTV3ZBYhq6AUdRcVicfPmzSMiIoT9eK1U9SKOqOUWVvMF1XBz1bAe9Zx6tG0rCVZXztRasrlqgjc71W1Hjx795ptvfH19+UVKzbp27RoSEpKUlFS60d/fv3v37poq6eXEXYCo5hISEjT7PjS1HVFrZUG1uLmqW496NpS2bSXB6tCZWhs2V00wTtRhhYWFc+bMWbhwYbdu3TRdy0tn5MiRUql0xYpImHoBAAAgAElEQVQVJS0nTpy4cuXKpEmTNFjVy4a7ANFz5efnnzt3Ti6XVzTDsWPHwsLCBg0apMGq1HZEfe6C1Ly5qluPejaUtm2lqtPU71W1qnqWpjZXbdLM6HlUG06ePAng77//vva06OhoTZcmREBAAOrUwMxr164ViUTz5s07fvy4j4+PpaXlqFGjNF2UQHVu46vo2C5AVEPl7sj/93//B+CHH35Qffzjjz/eeOONTZs2HTt27ODBgwsWLJBKpWPHjtVsVUo1HlErX5D6N1e16nnu/BqpSv2/VErt+72qelUa2VwvGuNEHbZp06ZyI+KMGTM0XZoQdfEb7Y4dO1q3bm1oaOjo6Lhw4cL8/HxNVyRQXdz4Sp3bBYhqqNwdecOGDVKp9K+//lJ9vHLlyoQJExo3biyRSExNTTt37rxu3TqFQqHZqlTUdkStZEEa2VxVr+e582ukKvX/Uim18veqilVpZHO9aCJlHX/9PxERERERaQqfnSAiIiIiIoEYJ4iIiIiISCDGCSIiIiIiEohxgoiIiIiIBGKcICIiIiIigRgniIiIiIhIIMYJIiIiIiISiHGCiIiIiIgEYpwgIiIiIiKBGCeIiIiIiEggxgkiIiIiIhKIcYKIiIiIiARinCAiIiIiIoEYJ4iIiIiISCDGCSIiIiIiEohxgoiIiIiIBGKcICIiIiIigRgniIiIiIhIIMYJIiIiIiISiHGCiIiIiIgEYpwgIiIiIiKBGCeIiIiIiEggxgkiIiIiIhKIcYKIiIiIiARinCAiIiIiIoEYJ4iIiIiISCDGCSIiIiIiEohxgnTcuXPnJkyYIJPJDA0NjYyM6tWr16VLlw8//PDOnTulZ9u1a9c777zToUMHGxsbiURibGzctGnTOXPmJCQklNvtwYMHBw8ebGNjY2Rk1KxZs08++SQ+Pv7Z2Y4fPz516tTWrVubm5sbGBjY2dn17NlTqVSqlvj222+3bdvWyspKIpGYmJi0adNm+fLlcrn8zp07H374YYsWLUxNTc3MzNq3b+/j46NQKF7E9iEiIsF4iiECIFL92hHpHqVS+fHHH69cudLW1nbEiBHOzs4ikSglJSUoKOjMmTM+Pj4ffPBBycxubm5xcXEjRoxo3LixmZlZbm7u5cuXjx496ujoePHiRQcHh5I5FQrFtGnTtmzZ4u7uPnz4cAsLi9u3b+/bt8/IyMjX17dbt26la3Bzc4uNjR07dmzz5s0NDQ0zMjJyc3O9vb1Llvjaa681btzY1NQ0IyPj6NGj165da9euXXBwsIODw8CBA2UyWW5u7sGDB0NDQ2fPnr1q1Sq1bT0iIqoETzFETyiJdNTSpUsBjB49OjMzs3T75cuXAfj4+JRudHV17dy5c5ke9u/fD2Dy5MmlG7/77jsAc+fOlcvlJY337993cnKytbWNi4t7brcVTZLL5SNHjgTw5ZdfFhUVlbQXFhZ6eXmJxeKoqKjnrzYREb14PMUQleDNTqSbUlNTv/vuu9atW+/cudPU1FRYJ6NGjeratev+/ftLrgKnpaV5e3t37tx5+fLlenpPdh8XF5dt27YlJyerTjDC6OnpzZ8/H4CNjY1YLC5p19fX/+ijj+Ry+enTpwV3TkREtYWnGKLSGCdINx0+fDgrK+vDDz+USCQ16cfLy+vx48cPHz4s6TY7O/u9994TiURl5nzllVfc3d337dtXk8W1adMGwO3bt8ttj4uLq0nnRERUK3iKISqNcYJ0U3BwMIAuXbrUsB/VLa2pqamqjzdu3ADQsWPHcmf29PR89OhRRY/WVYWJiYlEIklPTy/TbmNjAyA5OVlwz0REVFt4iiEqjXGCdJPqcGlhYVHDfoyMjAAUFBRUpVtVe0ZGRk2WaGBgUFhYWKZR9QewvLy8mvRMRES1gqcYotIYJ0g3mZubA6jJX3HKVfnRXNVe8xMMERFpM55iiEpjnCDd5O7uDiAsLKx2u/Xw8AAQFBRU7tSgoKD69evb29vX7kKJiEir8BRDVBrjBOmmV199VSKRbN26tXa7HTJkiImJybp165TPDNgSEBAQEhIyevTo2l0iERFpG55iiEpjnCDdVL9+/ffff//EiRNLliwpfVxWKBQ1eReetbX1/PnzL168OG/evNJjiIaHh7/99tvW1tYLFiyoSdlERKT9eIohKk1f0wUQvSje3t5hYWGLFi3at2/fwIEDLSwsoqOj/fz8IiMja9Lt559//uDBg59++unIkSPDhg2zsLC4c+fO/v37jYyMDh482LBhw1oqn4iItBdPMUQlGCdIZxkaGh48eHD79u1btmzZsGFDTk6Og4PDgAEDevToMXHiRMHdisXirVu3jhw5cu3atZs2bcrKymrUqNGMGTPmz59fv379WqyfiIi0Fk8xRCVEz96fR6TbgoKCPD09fXx8PvjgA03XQkREOoWnGHoJ8dkJIiIiIiISiHGCiIiIiIgEYpwgIiIiIiKBGCeIiIiIiEggPopNREREREQC8eoEEREREREJxDhBREREREQCMU4QEREREZFAjBNERERERCQQ4wQREREREQnEOEFERERERAIxThARERERkUCME0REREREJBDjBBERERERCcQ4QUREREREAjFOEBERERGRQIwTREREREQkEOMEEREREREJxDhBREREREQCMU4QEREREZFAjBNERERERCQQ4wQREREREQnEOEFERERERAIxThARERERkUCME0REREREJBDjBBERERERCcQ4QUREREREAjFOEBERERGRQIwTREREREQkEOMEEREREREJxDhBREREREQCMU4QEREREZFAjBNERERERCQQ4wQREREREQnEOEFERERERAIxThARERERkUCME0REREREJBDjBBERERERCcQ4QUREREREAjFOEBERERGRQIwTREREREQkEOMEEREREREJxDhBREREREQCMU4QEREREZFAjBNERERERCQQ4wQREREREQnEOEFERERERAIxThARERERkUCME0REREREJBDjBBERERERCcQ4QUREREREAjFOEBERERGRQIwTREREREQkEOMEEREREREJxDhBREREREQCMU4QEREREZFAjBNERERERCQQ4wQREREREQnEOEFERERERAIxThARERERkUCME0REREREJBDjBBERERERCcQ4QUREREREAjFOEBERERGRQIwTREREREQkEOMEEREREREJxDhBREREREQCMU4QEREREZFAjBNERERERCQQ4wQREREREQnEOEFERERERAIxThARERERkUCME0REREREJBDjBBERERERCcQ4QUREREREAjFOEBERERGRQIwTREREREQkEOMEEREREREJxDhBREREREQCMU4QEREREZFAjBNERERERCQQ4wQREREREQnEOEFERERERAIxThARERERkUCME3VJbGzszJkzHz9+rOlCao1SqZw7d25QUJCmC6lNW7du3bp1q6arqE1BQUFz585VKpWaLqTWPH78eObMmbGxsZouhIiIqM5jnKhLLly4sH79+ps3b2q6kFqTmZm5YsUKX19fTRdSmzZs2LBhwwZNV1GbfH19V6xYkZmZqelCas3NmzfXr19/4cIFTRdCRERU5zFO1CW69OdhIo3jDkVERFRzjBNERERERCQQ4wQREREREQnEOEFERERERAIxThARERERkUCME0REREREJBDjBBERERERCcQ4QUREREREAjFOEBERERGRQIwTREREREQkkIjjwgoTHBw8duzYgoICdS40Ozs7ISHBwcHByMhInct9cRQKRWRkpJWVlZWVlaZrqTWxsbEAGjZsqOlCak1aWlpaWpqTk5Oeno78ASIvLy8uLs7e3l4qlapzuRKJZO/evW3atFHnQomIiF4ofU0XUFeFhobeuXOnb9++1tbWaltodnZ2aGho27ZtJRKJ2hb6ounp6TVu3NjW1lbThdQaCwsLAE2bNtV0IbUmOTk5KiqqQ4cOmi6k1hQUFFy/ft3d3V2dcSI1NdXf3z80NJRxgoiIdAnjRI0sWbKkc+fOmq6CiF6sfDmuPkJ2AUwM0MEBhmIhnQQGBvr7+9d2aURERBrGOEFEVKFCBZacxc8X8Ti/uMVMgg8748uekAgKFURERDqGcYKIqHz5cgzfjePh6NMEr7nB3hSJ2Th4F0sCcCEGh8bDmEdQIiJ66fFkSERUPu8AHA/HioH4uMuTxvc9sTYIsw7j2zP4vq/miiMiItIOOvKeFiKi2iVXYs1l9HJ6KkuovNcRA13w62UUyDVRGRERkTZhnCAiKsedZCTnYEzL8qeObomMfIQkqrcmIiIi7cM4QURUjpQcAGhgVv5UBzMASMlVXz1ERETaiXGCiKgcNiYA8Ciz/KlxmQBgY6y+eoiIiLQT4wQRUTncbFFPir2h5U/ddwvWxmhtr96aiIiItI8ux4nk5GTRM65fv67puoio9qXnIS0PcmWtdSgWYW5XnI3C4tNQPt3tD+dxPBxzOsNAl4+gREREVaLLL4rNyckBEBgYaGJiUtLYtGlTzVVERLUsqwBLz2HbDcQ8BgBLI4x0w+KecLKshc4/6YozUfjmDE6EY3zr4nEn9oTg3EP0d8YCr1pYBBERUV2ny3EiIyPDwMCgU6dOmi6EiF6IuEz0247byejTBNM7wEAP1+Ox+yYO3IHvOHjJatq/vh7+fhM+l7D8PGYfLW6sb4of+uPjLtDnpQkiIiLdjhOJiYk2NjaaroKIXgilEmP24UE6Do3HkGZP2u+lYNAOjNiDOx/AzqTin68afT183AUfdcbdFMQ8hoMZ3GyhJ6ppt0RERDpDl/+8Fh8f7+DgoOkqiOiF8H+Af6Pxbe+nsgSA5jbYPRqpuVhzqdaWJRLBzRb9nNHSjlmCiIjoKbocJx4+fHj9+nU7O7sOHTrMnTs3LCxM0xURUa05GQE9Eaa2L2dS54ZoY4+TEWqviYiI6OWjyzc7TZ48eeDAgUlJScHBwX/88cfq1avXr1//zjvvPPcHFQrFmjVrYmNjK5nn1q1bAORyeW1VS/QyyCxAkQJSA0jE5UwtkOOv2/gjFA8zoCdC2/qY0h6dG5bfVVwmrIxgaVT+1CaWuJFQa2XXCtXhYvfu3Tdu3KhktoYNG77//vt6err8tx4iItIlIqWy9l6sqN0WLVq0bNmyW7duNWvWrPI5k5KSZDJZXl7ec/v83//+N3LkyFoqkEhnFSmw/gp+vYxbSQBgpI++TfBFz6eiwoN0jNiNm4kwk6C5DXKLcCcZSiU+6oKfBkD0zC1Gsw5jyzVkfw5xeXcf9d6GlBwEv/fCVqn6Dhw48Nprrz13NiMjo4cPH9rZ2amhJCIioprT5asTZXz99dcbN27ctWvX4sWLK5/Tzs4uOTm5oKCgknn+/PPPadOmNWjQoFZrJNJBuUUYtANno9DGHp/3gJkhHqThj1B034xfBuGDTgCQXYj+25GUgx2v4w334vEc4rOw6BR+vggTA3zXp2y3HR2wNginHqC/c9lJaXkIjMGbrV78ulWH6nCxcePGUaNGVTKbRCKRSqXqKoqIiKimXqI4IRaLmzdvHhFRpfuppVJp5Wd0nu+JypArERCFizHILICLFQa4wNEcAOYdx9koLO+P/+v2ZObv++L1vZjjB8+G6NwQqwIRnoa/38Rw1yfz1DfFxmFIzsHyfzGzY3FvJUa3xGf+mH0E56fAxvipMj44grwizOj4QldXIKlUamVlpekqiIiIas3LdXtuQkKCpWVtjG5FRE/7JxJNV6H3Nnzmj+8DMOUgnH7Bu38jIh3rr+Btj6eyBABrY/w5FpZG+PYMAPzvNlrVeypLqIhE+KwHCuQ4/MybFMwNsXEYwlLRfj3WBeFOMsLT8Odt9NiCXTfxafcKH7ogIiKiWvQSXZ04duxYWFjYypUrNV0Ika7xf4BXd8JeivVD8WozSCUIS8HaIGy7jkuxKFJgcrtyfsrGGCNcsTsECiXC0zC0efmdt6sPERCeWs6k4a7wewsfHsV7h5802ppg9auYpZWXJoiIiHSPzsaJffv27d+/f8CAAY0aNcrPz//33399fHzGjh07ePBgTZdGpFMKFZh2EA1McXn6k2HjOjVEp4bo0KB4MGmnCi4KNrFCXhHS8yACFBW8FULV/Oyj2Cr9nRE6C4GxCEmEUolGFujZGCYGNVkhIiIiqgadjRMuLi4GBgbffvvto0ePJBKJu7v7Tz/9NH36dE3XRaQLojKQnAN9PbS0w9koPEjHpuHlDEE9yxNLAhCfhfQ8NLYop5+0XOiJYGYIV1tcquDNzJdioQRcKx7gXk+Ero7o6ih0ZYiIiKgGdDZOtG/ffseOHZqugkjXHLiDRacQmlT80VSC9g0AoG+TcmbWE+GVxvgjFEfD4GFfdqpCiePhaFsfBnqY0BrvH8HvwZjY5ql5ihT4+jRMJeU8VkFERETa4OV6FJuIauK7s3htL7ILMa8b1g/FTwPQQ4aAhwBQUMGIjjILAFh6DjcTy05aEoDQJEzvAADTOqCjA6b8je8D8Di/eIbQJAzZBf8HWNIHts9c+iAiIiJtoLNXJ4iodvk/wBf/4PUW2Pk6jP47cnzSFR8fwy8XMdUXZ98p56fuJhcnge6b8Wl3DHOFmQQRaVgXhD9vY7grprYHAAM9+I7DW3/h81NYfBoyC+QW4lEWDMVY1g8fdlbTOhIREVF1MU4QUZX8+C9sjLF1xJMsoTK/O1YFIiAK91PR1PqpSeFpOB6OiR5Y4IUpf+OLf/DFP8WTjPXxmRe+7v1kTOv6pjj5Nk49wN93EZYCI310aoiJHmho9uLXjYiIiIRinCCiKjkbhTEtYW5Ytr2+Kca0xN5QzD6KA2/CUFzcfjMRo/+AgRif94CTJU6/g9AkBMagSAFrY/Ru8tTYcyX6NEGf8h7DICIiIu3EOEFE5csswF+3cTEGCiVspcgpLDsudYkf+mNvKPzuw+kX9G0CqQT3UnA2CiYG2DfmyVti3e3gbqe28omIiEgdGCeIqBxbr2POUWQW/PdZCYiwLxQLe5QzqkN6HgDM7IjIdBwOQ2Y+XKzxQSd82BkuVuqsmoiIiNSNcYKIimXkIy0XDcyw/Qam+6J7I3zREz0bw0gfIYkYvBP3UvH6XhydUHZQuQN3AOB9T7Sqp5HCiYiISGMYJ4hedkUKrAzEb9cRkggAEjEUCrSwhf+kJw9CtKqHX4dg+G4cC8fvN/F2qdEhAh7ih/Po78wsQURE9DJinCB6qeUUYsgunI5Eq3pY2APmhvC7j9ORuJOMny9ggdeTOYc1x4wOWH8Fs48gpwBNrZFVgMNh2HYd9aTYNFxz60BERESawzhB9FL75BjORGLlIMzuVHwLU2I2zkahnzM+80e7Bhjo8mTmtUNwKRbBiXjvcHGLvh7Gt8YP/WEv1UDxREREpHGME0Qvr+QcbLqKCW2eGicuvwj6etg/Bs18sPTcU3FCJIKbLWIzcewtRKTB3BCeDWHxzKtjiYiI6OXBOEH0MipS4F4KjodDrsTENk9NklmgQI5HWXitBTZdRaECBnpPpoYmwckSbeujbX01l0xERETaSO/5sxCRDnmQjrf+gtUyuP+Kj48BwA/ncSPhyQwj3KAnwqpAOFmiSIHknCeT/olEcAJGuKq7ZiIiItJavDpBpOPS8vBHKK7HQ6GEUom9IcgtwqiW6CFD0CNsvYZ/o9FlE/4ai8FNAcDVBm+1wZrL6OII4Mm9TIfDMOl/kFlgducKl0VEREQvG8YJIp0SmY4f/4XffaTmwkAMR3PcSUZOIUwMIBEXjzf3ajNsHAZTCe6nYus1TG6HExF4cz/uzS5+onrdUKTn4eBdiEV4cz8M9XEzAXdT0MQSB8fBTKLZVSQiIiItwpudiHTH/ltw/xXrr8DZCmPc4WyFq4+QU4ghzZGxACsHAcBINxy7j+G7IVeiqTUGuGDbDSx6BY/zseFKcT/G+mjfAAA86uNGAs5GwU6Knwbg5iwOLkFERERP4dUJIm2UW4QT4YjPgr4eWtsjOAFno5BbCFMJWtVDRh4uxiItF06WcLLCvWQExiKnEJn5sDLGgTfR3xkZ+XBeiXb10bEhNl7BD+cRngapAf4Yg18v4yM/bL2Gqe2x5lV4bcF0X1ga4e+7eLUZItOx8QqOhWO4K/43Fnqi51dLRERELy3GCSLtUqTAd2fxy0Vk5D/Vbm4IWxPEZSKvCAAczWEvxeEw5BRATw+9nfAgHTkFyCrAiN3YOhIKJVJzsWc0+jvjUSa+D8ArjWFvCgM9zO4En0DsvImp7dHUGhen4r3D8LuPK3HouAEATCX4tDu+7c0sQURERM/BOEGkPkUKXIjB7SQAcLaCrQnORyOrAEZiFCgQEIXQRCTkILsArephtRdEwJS/YWyAQgXEIkxph89PoXU9xGchMx/T2+PKI/RojIh0XH2EvCKMa43v+uCNfXjrL7zVGgC6NQKAGR1w6B7y5EjJgUIJPRG6OOJ4eHFVTpY4OgFuq6Evxked4WSJro0gNdDQNiIiIqI6hXGCSE3+votPjiEircIZJGK42iI7HZZGCEmEdwBMJZBKcG0GcovQbTMWn4aHPQKnISINHTdgSQA6N8SpSbiTjPbrUaiAqy1kFjg0Hi3X4J8oiACJGADcbAGgnhQZ+Tj1AP2cYahffJVD5V4K7qVgvhemtn/BW4GIiIh0i44/ir1z504PDw9jY2OZTLZo0aKCggJNV0QvhfA07AnB1us49xAXY+F9DsN2Y+Qe5Bdh4zBcmgoXK+jrFb9GqYUtRMBAF+jr4V4yHM2Q8H/YPQpR6bgUi1mekFnA1QZvuKNIgZFuMBSjhS0GNUW+HFPaQ18PrephcHMAyCkEAFsTjG+N6AwogVtJT9q9ZLAxxuyjSMhGaCKcLIurzSrAlIMw0sc0ZgkiIiKqJl2+OrFu3bpZs2bNmzfv559/vnPnzueff37nzp39+/drui7SQUolQpNwPxVxWdhyFVcelT9bbCZ+uQj3eniQDt9x6OeMHltxKRbTO2D9UJx/CK+tMNSHRIw3W+FWMr49U/xqVwANzQCgUFH8URVFjP+7JamTAw7egd99LOkDAM1toFBCrIfl57HjdZyMAICujtj+Gl7bi+Y+eJyPca1wPBxBcVgbhNjH+G0knK1e0OYhIiIinaWzcSI9PX3+/Pmffvrp0qVLAfTp08fFxWXQoEHHjx8fMGCApqujOiMlFxeikVsEUwn0xfAPR0gi9ERoYoWMPISlIr8I2YVIyEJa3pOfamqNz3pgyVk8zEA9KeIyAeDUJESmY8FJ7AvFQBe82gwA2jfApVjo6wFAewcAeJCOtDxYGaGbIwBciinu01QCAFn/XWAz0geAnP8+KpUAcPURdgTjrTbFNzJNaoMt12FigAN30boe2teHSIQf+mPeCQDYHYLdIQDQqh7WDy2uh4iIiKhadDZOHDhwIDs7e+7cuSUtAwcObN++/bZt2xgnqBK5RTgbhah05CtwMhxHw55cEAAgAmQWeJwP33sAYGcCEwNEZQCAqQTG+pArMaQ59odi1iHky3HgTQxphuar8SANcgXebYsGZhi8A2H/PUGRmgsDPYQkAoCxPowNkFuIkET0kKGZDYDizoHiZ6Mz/8sPqlxR8iRGYCxsTeBkiXf/Rkgigh7Bxhjf90NoEjZehQhwtcHY/QhNwq0kNDLHxuEolEOhhHs9uPCiBBEREQmls3Hi/Pnz7u7udnZ2pRv79u27d+9eTZVE2u+365h/EonZT1rMDfFtDxy8iwsxcLXGnRRIJYjKQFdHyCywNxQARrfEex0x4U/EZ2NpP8zvjo86w3MDzCQY1hx6IrjZ4mE6fr2Mfs6wMgKA8FSEpaKZNQzFAJD53zthOzogIKr4aoaLFaQGSMstvlhxLQEAzkTicT6yCvDnbVgaYV0QJrdDSi6OhuH9TviqF6YdxLLzxb3V/xEAHMzgZouHGYjLRH1TLOuH9zw5sjURERHVDiFxQqlUikRlX0efnZ29bNmy0NBQmUw2Z84cJyenWqiuBiIiImQyWZlGmUwWHR1dWFhoYMC3YNJTUnOx9ByW/wtPB/gMxoG72BuCt9vgTBQ+PwW5At/0xhevYO0VzDoEOxP4T4KxPi7HIiIdszvhlcYY0wo+gbgQAwANzKAAMgtwNgq9nNDIHBDhYiwAOJoXLzE4Ac2s4V4PhQqYGxY3dmuEgCgsO4feTjAxgFyJQgUG/I5Xm2F9EPo0wT8P4LkRhXJk5WPba5jui44bUKSEvRm+eAXZBfCoD9+7MDPEW21gZojODTGoafHNVERERES1TkicmD17dlhY2LFjx0pa8vLyevbseeXKFZFIpFQqd+7ceePGjQYNGtRendWWmZnp6OhYptHGxkapVGZmZlpbW1f+s/37909KSqpknqysLADp6ek1L5XU4J9IHLuPtDxIxOjgAIUC5x4iqwCG+sguRGgC7qUCgFgEOxNYGeN/tzG6JbaORHoemqxERh6mtAOAdvYAkCcvfgFrdhEA+N3HK41hoAcAJ8NRqEBuYfFyzz1ELyf0c8aGK0jNAYCGZmhqjfupSM8v/gggMRtyJUSAfwQsDXEzEc18ILNAXhG8ZPg3GkFxkBpAqURDM9xLAQBzQ3x+CgVy5MsBILcQdsuLFzrABZuGo9F/uYW0hOpw8dFHHy1atKiS2erVq3f8+HEzMzN11UVERFQjQuLEP//8071799Ity5cvv3bt2tatW8eNG3fy5MnXX3997dq133zzTS0VqW5isdjV1fXZixulPXz4MDExUSwWq60qqq68IuQWIS4LM31x7iHEIpgbIrsQBZcAwMQA9aSIzoBcCQMxmtvgfirGtsKhe/ALh0KJ11sAgKUR6psiPQ+H7mF6B4SnAUBmPsJT0dwGmfmQSoq/36tetZRdiEeZaGAGAz0UKoqfz37NDaYS5BTgTBR6NsYwV/x8AVuuoIUN5lGFmKcAACAASURBVJ2AuSFuJ2PQDhjpIygOG4fB1gRz/IofqDj3EC5WcLZGTiEe58PFGhM8YGGIG/FIy0M/Z/RrAqkEgbHIL4K5IQa4oKODJjY3PY/qcNGkSZPKjy3GxsY8sBARUR0iJE5ERkZOmzat5GNBQYGPj8/YsWPfeecdAEOGDBk2bNjBgwc1GyfMzc1TU1PLNKakpIhEouf+2c/ExGTbtm2Vz7N79+7x48fzL4haokgBv/sIjEWhHNbGsDTC3lAERBU/Ra0HvNkKa15FSi46boCpAST6SM1FU2s8zMD7nXD4Hu6noqUddr6OxGz03oZbSUjNLe7cVAKxCJfjML0D9P67y0+VE+xMkJgDJQCgd5PiSWI9GOujayOcjYKJAQDEPEa+HFIJ+mxDf2fcTYG+Hv6NQfctMNLHm61wKwn+EVACBmL833Fk5ENPhPc74eMusDKCtfHztwDfy6T9VIeLOXPmjBs3TtO1EBER1RohcUIulxsaGpZ8/PPPP5OSkmbPnl3S0rZt29OnT9e8uJpwcXEJDAws0xgdHS2TyfjghA64FIvDYbifCmN9WBjhr9uIfPq+M0Mx3vbAvRQEPERDc+wJQX4R5EoolLg0AxaGaLseJyMwuxNWDcZCL8h+KX4Aup4UKwZi0A5svYb3OgJAU2tcjUNaLgC0sC3uv54UADo1xL5bqG8GAJ4OsJciIRtX4tDQFa3q4WwU9t/CMFfMPgIoceJt7AzGpqvILQIAC0NYGiE5B79dh7E+ejqhpR2KFADQ2AKjWsLVRh1bkoiIiKgmhMQJR0fHBw8elHz8+eef27Zt27Vr15IWExOTjIyM8n5Ufbp27bp58+akpKTSL3fy9/cvc5sWaT+FEvtvYd8tPEgDABdrRKQi6BEAWBkhtwh5RRAB77TDyoE4E4Xhu9HMGg/ScTUOsVkY1BSHx2OhP5adh54IszyL34vaywk7g4uHenAwg6MZojIQ/RiNzNHbCRI9XIsvLmCEG/aEICEbAFrXg5E+iuTFb0bSFwPA5VhkFiAkESm5sDTCyD1obY/bSbAxQVgKOm+EHtCrCX76FycjkFuEGR3xfR9YGj251kFERERURwl54UvPnj23b9/+8OFDACtXrrx8+fInn3xSeoakpCSN3wU0cuRIqVS6YsWKkpYTJ05cuXJl0qRJGqyKqis1F323Y+x+/PMAFkYwkWB/KIIewc0WD+Yg5VM0tYa1MdrWx2/XsCcE35yBkyVuvIetI3AtAYnZ6NYIeiIs7YeujlAo0bZ+cc+WRkCpUeFeaQwAB+8CgESMro1QpMD8k1AqkZkHAOejsfg03j+CvCJABK+tGPUHdgejWyNciYPsZ/T+DVIDfNgZLtYITkChAik5MNRHEyvYm+Hao+K3PJ1+B+uGwNqYWYKIiIh0gZCrE5999tnevXtdXFysrKySkpJ69er11ltvlZ4hMDDQzc2tlioUyNLSctmyZbNmzZLL5f3797979+4XX3wxatQojmGn5UISsfoSTkQgMh1WRlACGXlYNRgzO8JAD+uvICAKrzbD0TAsOoUPOiEkET6DMbU9em/DpyfwOB8LvGCsj7faYFUgLscVv4IJwHBXXIh5Mu6bhSEAPP5vKOuhzfF7ML49i95OaGmHptYIeIgfzuPP24jJgKM5jPTxzRkAsDaGhRHuJuNuMiT6eJQFAzHS8wAgX45vzsBIH1PbY153GOujvmnxG5+IiIiIdJKQONG0adNz584tXbo0Ojp64sSJ3333XelhKAoKCuRy+YgRI2qvSIFmzpxpZma2bNmyVatW2dnZzZo1a/HixZouiiqz8yam/A25En2aoJ8zQhLxbzQAXH1U/Lf832+gqTUOjcPHx7AyEI0tAaCXE4z08XUvDNwBoLgRQD8XXI7DtUfFHzs4AEBwQvFHQ30AiH5c/PF+KgBk5aPdegxtjlMP0MgCxvq4kwwAMY8BwMkSTayQXwQ9EQY4w9gAyTnIL4JFEwxwQaeGSM4pHrTOWGfHhyQiIiJ6isBvPW3btt2zZ0+5kyQSyZkzZ2pQUm2aMGHChAkTNF0FVSirAKcjEZcJPRH0xZh2EB0dsPN1OFsBwBv7YGGEMS2x6SoaW+CrXridjCHNIBJhgRd8LuFyHAAY6QNAt0bFfWb8d8HBRB8ADtxFeFrx8xIA/onEoyzYmeDQXYhF+CMUH3WBnQlWX4KHPf4eh2/OYFcw8uRIz4ODGeZ3x7vtYKAHU0nx49f/3969x1VVJmocfzbI5s5GQEBEnPGGSd4lBK08XfA2KqZpVqb1mcYsp5vT2DSmaXrMY3ZK7GjpWHnKTLRjmKmZjpe8pU5mSph4i0RRkZuIctn7/AGjiKS1wL1h8/v+x7te13o2uHQ/rLX2ex3NLDX77QEAAKjt+CUq7OHMBX24T7tOqMSqIC/1a60+LTRnlyZtLL9NqIyrSZN7lncJSUey1T5Y7/5BP+fpv7ZqXJyK/71+XKiPwnx1oViSfsxSywC5ucpkk4+HVqfpr90l6WCWvM2STX0+1OLBWn1ILiYVl+o/3leERbsy9Pfb9fYudZoni4fOXNDLPbU9XYfP6WKpBkRqXj815nOAAQAArqu6daK0tDQ9PT0vL8/f3z88PNzFhfvEUdn8f+n5tTpfpGBv+Zh1Ik9zdyvUR6fO685mei5W7YJ1vkhd3pG5gQYu0eqH1PN3kmQyyWqTyaQnumpNmjYf1+8bli/uJslqU7CXvNw0e6f6tNS+TNlMurOZPv9R8/+l+BZakap+rTS6i4YmKXq+TFKoj/zKHnvIUrC3dp5QqLdSs1R4XpKe/FySLO56pacm3CFXHpUGAAC4EeN1IiUlZdq0aStXrszPzy8bCQwMHDp06EsvvRQeHl5D8VAn5V2Sl5sauEjSJwc0eqVua6LEvooOk6TCEo1fp8Rv5GdW8nD5uUvSyfMqsWl8rD78TiNXKHWsPBsoMlDJB3WxpHwFhp/zNCBSUzdr4zG1CNDJfEUFq0uYJmzQmFU6c0ENXPRGvHIvavRK+XuoqFSPdZKfux5qp//ZLZPk5irZNOxWWdx18KyyCxXkrZfaakCkjucov0jNGyo2vPzuKQAAANyQwfdNSUlJI0aMKCoq6tq1a6tWrUJCQnJzc7/55pu5c+cuXbr0888/79atW80GRe2395RmbdfatPJ39l3D9MCtmr5FUcFaP1Le/1480LOBMgvk2UB5RZq1XZN7SipfPdpFeu0eDV+uNWka1EYPtddH3+vVzRrUpnzO87Fa+K2GJqljqEzSA7eqbZCO5+idPZIUGajXt8vNVSZT+arVvT8sP+gfWmtO3+s92xDT5OZ8UwAAAJyakTrx448/Pvzww61bt/7kk0/atm1bcdOXX3758MMPJyQkpKSkBAQE1FBI1AGLv9ejn8kk9WutVgEqKNaaND27RpJm9brSJcrsOqG7fq/cS/pkf3mdsLirmUXrj+qF7pL0zQkNaqM+LZXQRtO3aPNxSWofooYeei9Bg5do3RH5uOml9cq5qG9OSFKEn7IvKumAAjw1pqtGdVTaOeVdknsDxTVVK/4yAgAA3ARG6sSMGTPc3d3XrFnTpEnl3+jGx8evWLGiR48ec+bMmThxYk0kRB3wXaZGrlCXxkoaqqZ+5YNWmwYv1YpUJafqoXZXzc+7pABPRQbpzR0qsZbfFjWqoyZv0qofZXZV3qXymf87SA99Wr66XMISlVqVnicXk3pEqNSm/aflYtIfWuvxzopvUTlV17Cb+JIBAAAgY3Vi3bp1gwYNurZLlImLi7vnnns+++wz6kT98drXMrvq02EKq/BRSC4mxTXVilQlpWhmriIq3GgU4qNjOWrsK5tNpbbyv4UvdFfyQY34P5VYZZOyLyr3opIPasfP8nbT0CidLpCLSY920oPtyp+mAAAAgGMZqROnTp1q3br1dSbExMTMnj3baCTUPesOq0/Lq7pEmZYBkmST1h/Vox2vjMe30JxvZDKpqUXu/1612ttNXz2iO97XgUzN3aW5u8rH2wRp1YNcagAAAKiNjNQJb2/vCxcuXGeCr69vQUGB0UioYy6VKqtQLap6OOGe5vI1K7+ofFXpy8bF6p092nJcozpeNf7PY0rLUo8IjeigYzlq6KnuTRUbLhOf2QoAAFArGakTXbp02b1793UmFBQUeHl5GY2EOsbsInfXK2tRV+Rr1rPd9OpmLUvR450V6lM+vi9TZlddLNbH+1VUqnYhulii9Ue05Se1CdKyYQq50frTAAAAqA2M1IlHH3105MiRR44cad68eZUTjh8/HhbGvSlOK/uijufI4qHf+0uSyaTOjfXVEZXaqlj6LcRHkvafVsR/q0uYfMxKO6djOWpm0YL++r9UJR/UR99LUjOLJvfUX+LKPzQWAAAAtd8N6kRRUVHPnj2tVmvFQavVWlpa2rNnz1/qDCkpKb169aqxjLj5bDYtOaB//Et7T8lqU2NfJbTRc90UdPVFpuU/6L+2aneGrDZJCvPVyA6acIdGd9WoFZqyqfxTXy9LO6dXN6ljqN5P0IJ/adcJZRcqMlDPx+qxTvJ205C2KrHq5Hl5NFAjLmgBAADUNTeoE2az2dPT88SJE5XGIyMjJeXk5FT5p8LCwvr3718j+WAHF4o1cIm+OqKmfurbSp5uOnBa07do/h6teEBxTcunTdigaVvUxFcvxKl5Q+UXaeVBTf9aa9L05SPq20pTNunbk3rqNrUK0PkirU7TjK9VbNXCgeoQosQ+VR+9gcuVz5YFAABA3XLjm53Wr19vhxxwoLFf6KsjmnaX/tq9fAkISTtPaMhSJSxRylMK8tLaw5q2RQlt9OF9V9akGxer9/fqj8l6bo0+HaZXN+nNHVr545U9dwjRPwaqU6i9XxEAAADsw8izE3AmqWf1/l6N6aqXbr9qPKaJkoer67uatV3T79br2xTqc1WXKDOqo3ZlaN5uzbhHU+/SuDhtPKbD5+Trrm7h6hBiz5cCAAAAe3O58RQ4tVWHZJOe6VbFpk6himmiz39UqU2bjmlgZOUuUWb4rbLatPGYJDX00KA2+kucRnehSwAAADg/6kR993OeXEzl681dKzJI6bnKuahiq8J/4QmHsvHTLDQCAABQ/1An6jsvN1ltulRS9dYLxfI2y89dLiZlFVY9p6xINPS8WQkBAABQa1En6rv2IZK06XgVm0qs2pau9iFyc1HXMCUfVLG1imkrUiUpNvxmpgQAAECtRJ2o7/q2UiMv/XWd8osqb5q6WT/n6ZEOkvRktI5k629fyWa7as62dL25Q3f/Xq0D7RQYAAAAtYcz14mzZ8+arrF3715H56pdfM2a+wf9cEZx/9DKH1VYIkn7T+uPyZq8SQMj9UCUJD3SXg+106ztilmgd/foqyP69Ac9+pnufF9BXlowwLEvAgAAAI7hzB8Ue+HCBUk7d+708rqy3nLLli0dl6iWGnyLPh2mx1dqwMdXBl1NGnubXo+XySRJJpMWDVL7EL2xXaM/L5/j5qL7btFbvRXq44DYAAAAcDhnrhO5ublubm633Xabo4PUAQMidU9zff6j9p6S1abGvurfWs0bXjXHxaS/dte4OO3L1NFsWTwUHSY/dwclBgAAQC3gzHXi9OnTgYHc0f9reblpaJSGRt1gmqtJnUJZ6BoAAACScz87cerUqbCwMEenAAAAAJyWM1+d+Omnn/bu3duoUaOIiIiePXs+8cQTrVq1+pV/Ni0tLTc39zoTjh49WhMZAdQvR48e3bNnz3UmWCwWHvECANQhJlulD/6sgy5dulRYeGWJNYvFYjKZJGVmZp44ceLMmTP79u1bunTpvn373nnnnVGjRt1wh5mZmWFhYVZrVYssXG358uX33XdfNbIDqC+WL18+ZMiQG05zcXHJyMgICQmxQyQAAKrPGerEm2+++dxzz13+8syZM0FBQddOmzBhwowZM1JSUn7NNYq1a9fm5eVdZ8LWrVvfeuutHTt2xMTEGMgMoL7ZsWNHbGzsM88807179+tM8/Pz69Wrl91SAQBQTc5ws9OQIUM6dux4+UuLxVLltMmTJ8+fP3/x4sWTJk264T5v+N95SUnJbwoJoJ4ru2oaExNz//33OzoLAAA1xhnqRHh4eHh4+A2nubq6tm7d+siRI3aIBAAAANQHzvzJTtfKzMz09/d3dAoAAADASdSjOrF27dpDhw717t3b0UEAAAAAJ+EMNztVKSkpadmyZfHx8U2bNr106dK2bdsSExOHDRvWp08fR0cDAAAAnITT1okWLVq4ubm9+uqrJ0+eNJvNUVFRs2bN+tOf/uToXAAAAIDzcNo60blz5w8//NDRKQAAAABnVo+enQAAAABQs6gTAAAAAAyiTgAAAAAwyGmfnajPbDZ9kablKUrPk0m6NVgjO6pDiKNjAQAAwOlQJ5xNZoGGJWnTcXm7KTJIxaXacFRv7tCYaM3uI1eTo/MBAADAiVAnnEqpTQlLtCdDM+/V2Nvk0UCSMgv04lf6n13yctPMex0dEQAAAE6EZyecyv9+px0/683e+ktceZeQFOKt9wZqWJTe3KFD5xyaDwAAAM6FOuFUluxXE1+N6VrFplfvUolVy1PsngkAAADOizrhVI5kq2OoTFU9INEqQD5mHc62eyYAAAA4L+qEU7Gp6i5RxiTZbHZMAwAAAGdHnXAqrQP17cmqO8Ohc8ovUmSQ3TMBAADAeVEnnMrQKJ3I19zdVWyasEGuJg1qY/dMAAAAcF7UCafycHvFNdWza/T6Nl0sKR/MLNCjn2npAT0fq5YBDs0HAAAA58K6E07F1aSVwzVsmV5Yp1c2KjJIRaX64YysNv0lTq/d4+h8AAAAcC7UCcew2pR6Vhn5CvdTZOD1np/+rQI89eXD+iJNy1OUnieTdG9zjeyoDiE1dggAAACgDHXC3opKNXWz3tmj0wXlI419NCZaf+uhBjV065nJpH6t1K9VzewNAAAA+CXUCbsqLFHfj7TxmG6P0MQ7FeytzPNa/L0m/lPb0vXZAzK7OjoiAAAA8KtRJ+zqP7do4zEl9tHY264MPhWtqVs08Z96fZteut1x4QAAAIDfiE92sp+iUr39jXq3vKpLSDKZ9PIdurOZZu9UKcvMAQAAoO6gTtjP96eVfVH3t61665C2yizQwbP2zQQAAABUA3XCfrIuSFJj36q3hvlKUlah/fIAAAAA1USdsJ9AL0k6mV/11ox8SQr0tF8eAAAAoJqoE/bTLlh+7kpKqXprUooaeSkyyL6ZAAAAgGqgTtiP2VVjumpNmuZ8U3nT69u0+biejJZrza1nBwAAANxsfFCsXU3qqZ0n9OfVWnpAw25VsLdOndfH32v7z+rVQn/jU2IBAABQp1AnqlBSUvLKK6+cOXPmOnPS0tIkFRcX/6Y9ezbQ2ofLV8Ue+0X5YKiPpvxHTa6KDaAWKvvnYsGCBRs3brzOtODg4EmTJjVowD/OAIC6wWSzsdJBZVlZWS1btszJybnhzM8++2zAgAEGDmG1KfWsMvIV7qfIQJm4xwlwdsnJyQMHDrzhtJCQkAMHDgQGBtohEgAA1UedMOjjjz9+8MEHo6OjLRaL3Q5aWFiYlpbWpk0bNzc3ux30ZktJSQkLC/P393d0kBqTnp4uqWnTpo4OUmNycnIyMjLatv2FNVPqoOLi4tTU1JYtW3p62u/D1HJzc3ft2rV48eLhw4fb7aAAANxsXE83KCoqKjo62mq1Zmdn2+2g2dnZmZmZ/v7+Pj4+djvoTVVaWpqRkWGz2Zyp1v7888+SnOZnJCkjI+PkyZPBwcGurq6OzlIzzp8/n5mZ6e3t3bBhQ3seNzo6Oioqyp5HBADgZuPqRF2SlJQ0dOjQr7/+unv37o7OUjPy8vIsFsvEiRMnT57s6Cw1JjY2VtL27dsdHaTGTJo0acqUKbm5uX5+fo7OUjO2bt3ao0ePpUuX3n///Y7OAgBA3cbDvwAAAAAMok4AAAAAMIg6AQAAAMAg6gQAAAAAg6gTAAAAAAyiTgAAAAAwiDoBAAAAwCDqBAAAAACDqBMAAAAADKJO1CUmk8nREQDnwQkFAED1ub7yyiuOzoBfy2KxFBcXDx8+3N3d3dFZaobZbL506VJCQkJYWJijs9QYs9kcHR3doUMHRwepMb6+voGBgX379nWa99/+/v4FBQUPPPCAn5+fo7MAAFC3mWw2m6MzAAAAAKiTuNkJAAAAgEHUCQAAAAAGUScAAAAAGESdAAAAAGAQdQIAAACAQdQJAAAAAAZRJwAAAAAYRJ0AAAAAYBB1AgAAAIBB1AkAAAAABlEn6pgNGzb069cvODjY398/Ojp60aJFNpvN0aFqxvLly11dXXNychwdpFo++uijDh06eHp6RkRETJgwoaioyNGJaoZz/HTk1GcQAAAOQZ2oSxYtWtS/f//IyMiFCxcuWbIkJiZm1KhRU6ZMcXSuGjB//vzhw4dbrVZHB6mWefPmjRgxonfv3qtWrXrxxRfffvvtBx980NGhaoBz/HTk1GcQAACOYuI3c3VITk5ORkZG27ZtL4888cQTH3/88blz51xdXR0YrDo2bNgwderUQ4cOjR49+uWXX87Ozvb393d0KCNycnKaNWs2ZsyY1157rWxk7dq1vXv3Xrt2bXx8vGOzGeY0P50yTnkGAQDgWFydqEv8/f0rvhOSdNddd+Xl5Z0+fdpRkapv/fr18fHxqampPXv2dHSWalmxYkVBQcG4ceMuj/Tq1atz584ffPCBA1NVk9P8dMo45RkEAIBjNXB0AFRLdna22Wyu078wnjZtmqMj1IytW7dGRUU1atSo4uDdd9/9ySefOCpS9TnNT+eXOMEZBACAY3F1om5btGjR8OHDPT09HR0EOnLkSERERKXBiIiI9PT04uJih0TCDXEGAQBQTVydqKUuXbpUWFh4+UuLxWIymSrNmTZt2uHDh5ctW2bfaAb9mldUp+Xn54eHh1caDAwMtNls+fn5AQEBDkmF66hbZxAAALUTVydqqblz5zasICsrq9KE119/febMmatWrWrcuLFDEv5WN3xFgD3VuTMIAIDaiasTtdSQIUM6dux4+UuLxVJx66RJkxITE9etW9elSxe7RzPo+q/ICfj5+Z07d67SYFZWlslk8vX1dUgk/JK6eAYBAFA7USdqqfDw8GvvnCkzffr0OXPmbNiwoeK789rvOq/IObRo0WLnzp2VBtPT0yMiItzc3BwSCVWqo2cQAAC1Ezc71TGrV6+eMmXKypUreSdU28TGxu7fv//MmTMVB9evX9+9e3dHRcK1OIMAAKhZ1Im6pLi4+JlnnnnppZfi4uIcnQWVJSQkeHt7v/HGG5dH1q1bt2fPnpEjRzowFSriDAIAoMZxs1Ndsnnz5kOHDnXo0GHv3r0Vx4OCgpz7PqI6wd/ff8aMGU8++WRpaem999578ODBl19+efDgwXV3SWznwxkEAECNo07UJceOHZM0cODASuOjR4+eN2+eAwLhak888YSvr++MGTNmz57dqFGjJ598ctKkSY4OhSs4gwAAqHEmm83m6AwAAAAA6iSenQAAAABgEHUCAAAAgEHUCQAAAAAGUScAAAAAGESdAAAAAGAQdQIAAACAQdQJAAAAAAZRJwAAAAAYRJ0AAAAAYBB1AgAAAIBB1AkAAAAABlEnAAAAABhEnQAAAABgEHUCAAAAgEHUCQAAAAAGUScAAAAAGESdAAAAAGAQdQIAAACAQdQJAAAAAAZRJwAAAAAYRJ0AAAAAYBB1AgAAAIBB1AkAAAAABlEnAAAAABhEnQAAAABgEHUCAAAAgEHUCQAAAAAGUSdQ73z99dcPPfRQRESEu7u7h4dHcHBwt27dnn766dTU1IrTFi9ePGrUqC5dugQGBprNZk9Pz5YtWz7zzDOZmZlV7jY5OblPnz6BgYEeHh6tWrV6/vnnT506de20L7/88o9//GO7du38/Pzc3NwaNWp055132my2siM+8sgjHTt2bNiwodls9vLyat++/cyZM0tLS1NTU59++ulbbrnFx8fH19e3c+fOiYmJVqv1Znx/AAAAfj1T2fsYoD6w2WzPPffcW2+9FRQUNHDgwObNm5tMpqysrN27d2/atCkxMXHs2LGXJ7dp0yYjI2PgwIHNmjXz9fUtLCzctWvX6tWrw8PDd+zYERYWdnmm1Wp9/PHHFy5cGBUVNWDAAIvF8sMPPyQlJXl4eKxcuTIuLq5ihjZt2pw4cWLYsGGtW7d2d3fPzc0tLCycPn365SMOGjSoWbNmPj4+ubm5q1ev/vbbbzt16rRv376wsLBevXpFREQUFhYmJycfOHDgz3/+8+zZs+323QMAAKiCDag3XnvtNUlDhgzJz8+vOL5r1y5JiYmJFQcjIyNjYmIq7WHZsmWSHnvssYqDU6dOlTRu3LjS0tLLg2lpab/73e+CgoIyMjJuuNtf2lRaWpqQkCBp4sSJJSUll8eLi4t79Ojh6up6/PjxG79sAACAm4abnVBfnDt3burUqe3atfvoo498fHyM7WTw4MGxsbHLli27fKNRdnb29OnTY2JiZs6c6eJy5YRq0aLFBx98cPbs2bIOY4yLi8v48eMlBQYGurq6Xh5v0KDBs88+W1paunHjRsM7BwAAqD7qBOqLVatWnT9//umnnzabzdXZT48ePfLy8n766afLuy0oKBgzZozJZKo084477oiKikpKSqrO4dq3by/phx9+qHI8IyOjOjsHAACoJuoE6ot9+/ZJ6tatWzX3U/bUxLlz58q+/O677yR17dq1ysnR0dEnT578pae3fw0vLy+z2ZyTk1NpPDAwUNLZs2cN7xkAAKD6qBOoL8rekVsslmrux8PDQ1JRUdGv2W3ZeG5ubnWO6ObmVlxcXGmw7BrLxYsXq7NnAACAaqJOoL7w8/OTVJ0LBVW6fmEoG69+hwEAAKidqBOoL6KioiQdOnSoZnfboUMHSbt3765yDWz/nwAAAfVJREFU6+7du0NDQ0NCQmr2oAAAALUEdQL1Rd++fc1m83vvvVezu+3Xr5+Xl9e8efNs1yzhsmXLlv379w8ZMqRmjwgAAFB7UCdQX4SGhj711FPr1q2bNm1axbf+Vqu1Oh+3GhAQMH78+B07drzwwgsVl6k+fPjwI488EhAQ8OKLL1YnNgAAQG3WwNEBAPuZPn36oUOHJkyYkJSU1KtXL4vFkp6evmbNmmPHjlVnt3//+9+PHj06a9asL774on///haLJTU1ddmyZR4eHsnJyU2aNKmh+AAAALUOdQL1iLu7e3Jy8qJFixYuXPjuu+9euHAhLCwsPj7+9ttvHzFihOHdurq6vvfeewkJCXPnzl2wYMH58+ebNm06evTo8ePHh4aG1mB+AACA2sZ07Q3fQH2ze/fu6OjoxMTEsWPHOjoLAABAXcKzEwAAAAAMok4AAAAAMIg6AQAAAMAg6gQAAAAAg3gUGwAAAIBBXJ0AAAAAYBB1AgAAAIBB1AkAAAAABlEnAAAAABhEnQAAAABgEHUCAAAAgEHUCQAAAAAGUScAAAAAGESdAAAAAGAQdQIAAACAQdQJAAAAAAZRJwAAAAAYRJ0AAAAAYBB1AgAAAIBB1AkAAAAABlEnAAAAABhEnQAAAABgEHUCAAAAgEHUCQAAAAAG/T9gbCYvLCpugAAAAABJRU5ErkJggg==)
##-----------------------------------------------------------------------------
## Quadro para o teste dos efeitos fixos (Wald).
anova(m0)
## numDF denDF F-value p-value
## (Intercept) 1 30 12774 <.0001
## bloco 3 3 2 0.2364
## sistema 1 3 3 0.1747
## adub 4 24 1 0.3130
## prof 1 30 16 0.0004
## sistema:adub 4 24 1 0.4574
## sistema:prof 1 30 5 0.0303
## adub:prof 4 30 1 0.5711
## sistema:adub:prof 4 30 0 0.8630
## Abandono das interações não significativas.
m1 <- update(m0, .~bloco+sistema*prof+adubpk)
## m2 <- update(m0, .~bloco+sistema*prof)
anova(m1, m0)
## Model df AIC BIC logLik Test L.Ratio p-value
## m1 1 11 572.1 598.3 -275.1
## m0 2 26 589.9 651.8 -268.9 1 vs 2 12.24 0.6605
anova(m1)
## numDF denDF F-value p-value
## (Intercept) 1 38 13649 <.0001
## bloco 3 3 3 0.2211
## sistema 1 3 3 0.1645
## prof 1 38 18 0.0002
## adubpk 1 31 4 0.0585
## sistema:prof 1 38 6 0.0222
## Sem efeito da adubação. O que existe é uma diferença entre camadas
## para a que depende do sistema.
##-----------------------------------------------------------------------------
## Obter as médias de ctc em cada prof para cada sistema. Pode-se ficar
## a adubação em qualquer nível, bem como bloco.
X <- LSmatrix(lm(formula(m1), data=da), effect=c("sistema","prof"),
at=list(adubpk=120))
head(X)
## (Intercept) blocoII blocoIII blocoIV sistemadireto prof20-40 adubpk
## [1,] 1 0.25 0.25 0.25 0 0 120
## [2,] 1 0.25 0.25 0.25 1 0 120
## [3,] 1 0.25 0.25 0.25 0 1 120
## [4,] 1 0.25 0.25 0.25 1 1 120
## sistemadireto:prof20-40
## [1,] 0
## [2,] 0
## [3,] 0
## [4,] 1
grid <- attr(X, "grid")
rownames(X) <- apply(grid, 1, paste, collapse="|")
L <- by(X, grid$sistema, apc, lev=c(20,40))
L <- lapply(L, as.matrix); L
## $convencional
## (Intercept) blocoII blocoIII blocoIV sistemadireto prof20-40 adubpk
## 20-40 0 0 0 0 0 -1 0
## sistemadireto:prof20-40
## 20-40 0
##
## $direto
## (Intercept) blocoII blocoIII blocoIV sistemadireto prof20-40 adubpk
## 20-40 0 0 0 0 0 -1 0
## sistemadireto:prof20-40
## 20-40 -1
str(L)
## List of 2
## $ convencional: num [1, 1:8] 0 0 0 0 0 -1 0 0
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr "20-40"
## .. ..$ : chr [1:8] "(Intercept)" "blocoII" "blocoIII" "blocoIV" ...
## $ direto : num [1, 1:8] 0 0 0 0 0 -1 0 -1
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr "20-40"
## .. ..$ : chr [1:8] "(Intercept)" "blocoII" "blocoIII" "blocoIV" ...
Xc <- do.call(rbind, L)
rownames(Xc) <- paste0(names(L), "/", rownames(Xc))
Xc
## (Intercept) blocoII blocoIII blocoIV sistemadireto prof20-40 adubpk
## convencional/20-40 0 0 0 0 0 -1 0
## direto/20-40 0 0 0 0 0 -1 0
## sistemadireto:prof20-40
## convencional/20-40 0
## direto/20-40 -1
## Teste para os contrastes.
summary(glht(m1, linfct=Xc))
##
## Simultaneous Tests for General Linear Hypotheses
##
## Fit: lme.formula(fixed = ctc ~ bloco + sistema + prof + adubpk + sistema:prof,
## data = da, random = ~1 | parc/subp, method = "ML")
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## convencional/20-40 == 0 2.69 1.99 1.35 0.32
## direto/20-40 == 0 9.78 1.99 4.90 1.9e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)
## Médias com IC.
ci <- confint(glht(m1, linfct=X), calpha=univariate_calpha())
grid <- cbind(grid, ci$confint)
grid <- equallevels(grid, da)
grid
## sistema prof adubpk Estimate lwr upr
## convencional|00-20|120 convencional 00-20 120 124.7 121.1 128.4
## direto|00-20|120 direto 00-20 120 132.2 128.5 135.9
## convencional|20-40|120 convencional 20-40 120 122.1 118.4 125.7
## direto|20-40|120 direto 20-40 120 122.4 118.7 126.1
##-----------------------------------------------------------------------------
## Representação.
## names(trellis.par.get())
l <- levels(grid$prof)
key <- list(corner=c(x=0.9, y=0.05),
type="o", divide=1,
title="Camada do solo (cm)", cex.title=1.1,
lines=list(pch=1:length(l),
col=trellis.par.get("plot.symbol")$col),
text=list(l))
segplot(sistema~lwr+upr, centers=Estimate, groups=grid$prof, f=0.05,
data=grid, draw=FALSE, panel=panel.segplotBy, key=key,
scales=list(y=list(rot=90)),
pch=as.integer(grid$prof),
xlab="CTC do solo",
ylab="Sistema de cultivo")
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)
##-----------------------------------------------------------------------------
## Os erros padrões de comparações de prof dentro de sistema e de
## sistema dentro de prof são diferentes pois são fatores com níveis em
## estratos diferentes no modelos misto de parcela subsubdividida.
## Desdobrar prof dentro (fixando) de sistema.
L <- by(X, grid$sistema, apc, lev=c(20,40))
L <- lapply(L, as.matrix)
Xc <- do.call(rbind, L)
rownames(Xc) <- paste0(names(L), "/", rownames(Xc))
summary(glht(m1, linfct=Xc))
##
## Simultaneous Tests for General Linear Hypotheses
##
## Fit: lme.formula(fixed = ctc ~ bloco + sistema + prof + adubpk + sistema:prof,
## data = da, random = ~1 | parc/subp, method = "ML")
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## convencional/20-40 == 0 2.69 1.99 1.35 0.32
## direto/20-40 == 0 9.78 1.99 4.90 1.9e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)
## Desdobrar sistema dentro (fixando) de prof.
L <- by(X, grid$prof, apc, lev=levels(grid$sistema))
L <- lapply(L, as.matrix)
Xc <- do.call(rbind, L)
rownames(Xc) <- paste0(names(L), "/", rownames(Xc))
summary(glht(m1, linfct=Xc))
##
## Simultaneous Tests for General Linear Hypotheses
##
## Fit: lme.formula(fixed = ctc ~ bloco + sistema + prof + adubpk + sistema:prof,
## data = da, random = ~1 | parc/subp, method = "ML")
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## 00-20/convencional-direto == 0 -7.42 2.46 -3.02 0.005 **
## 20-40/convencional-direto == 0 -0.34 2.46 -0.14 0.987
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)
summary(glht(m1, linfct=X))
##
## Simultaneous Tests for General Linear Hypotheses
##
## Fit: lme.formula(fixed = ctc ~ bloco + sistema + prof + adubpk + sistema:prof,
## data = da, random = ~1 | parc/subp, method = "ML")
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## convencional|00-20|120 == 0 124.75 1.88 66.5 <2e-16 ***
## direto|00-20|120 == 0 132.17 1.88 70.4 <2e-16 ***
## convencional|20-40|120 == 0 122.06 1.88 65.0 <2e-16 ***
## direto|20-40|120 == 0 122.40 1.88 65.2 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)