<div><span style="font-size: medium; color: #0000ff;">Por que não estou conseguindo rodar?</span></div>
<div> </div>
<div> #<span style="font-size: 12.8px;"> Modelando a variância e a covariancia dos erros</span></div>
<div><span style="font-size: 12.8px;">> modeloCT_F1.4 <- update(modeloCT_F,weights=varIdent(form=~1|Semana*Tratamento))</span></div>
<div><span style="font-size: 12.8px;">> modeloCT_F1.5 <- update(modeloCT_F1.4,control=controle,correlation=corARMA(q=3,form=~1|Animal))</span></div>
<div><span style="font-size: 12.8px;">Error in lme.formula(fixed = CT ~ GS + Tratamento * Semana + Pbserica +  : </span></div>
<div><span style="font-size: 12.8px;">  objeto 'controle' não encontrado</span></div>
<div><span style="font-size: 12.8px;">> anova(modeloCT_F1.4,modeloCT_F1.5)</span></div>
<div><span style="font-size: 12.8px;">Error in anova.lme(modeloCT_F1.4, modeloCT_F1.5) : </span></div>
<div><span style="font-size: 12.8px;">  objeto 'modeloCT_F1.5' não encontrado</span></div>
<div><span style="font-size: 12.8px;">> </span></div>
<div> </div>
<div> </div>
<div>Prezados</div>
<div> </div>
<div>Estou analisando um dado experimental utilizando a abordagem dos modelos mistos, através da função lme do pacote nlme. O dado no entanto não apresentou distribuição normal e tentativas como remoção de outliers, transformação foram infrutíferas.</div>
<div> </div>
<div>Tentei olhar se outra distribuição se ajusta melhor aos dados e encontrei que a distribuição logistica, se ajusta bem (Veja imagens abaixo), porém não sei como ajustar esse modelo utilizando a abordagem de modelos mistos.</div>
<div> </div>
<div>Visto que meus dados não são dados de proporção, como posso fazer para analisar tal dado?</div>
<div> </div>
<div>Desculpe a quantidade de informação fornecida abaixo, mas este é o mínimo que consegui organizar para ilustrar minha questão</div>
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<pre style="background-color: rgba(0,0,0,0.05); padding: 0.2em 1em;">BiometriaAleit<-structure(list(Animal = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, <br />1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, <br />3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, <br />5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, <br />7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, <br />9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, <br />11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, <br />12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, <br />14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L, <br />15L, 15L, 15L, 15L, 15L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, <br />17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 18L, 18L, 18L, 18L, <br />18L, 18L, 18L, 18L, 18L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, <br />19L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 21L, 21L, 21L, 21L, <br />21L, 21L, 21L, 21L, 21L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, <br />22L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 24L, 24L, 24L, <br />24L, 24L, 24L, 24L, 24L, 24L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, <br />25L, 25L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 26L, 27L, 27L, <br />27L, 27L, 27L, 27L, 27L, 27L, 27L, 28L, 28L, 28L, 28L, 28L, 28L, <br />28L, 28L, 28L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 29L, 30L, <br />30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 31L, 31L, 31L, 31L, 31L, <br />31L, 31L, 31L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 33L, <br />33L, 33L, 33L, 33L, 33L, 33L, 33L, 33L, 34L, 34L, 34L, 34L, 34L, <br />34L, 34L, 34L, 34L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, 35L, <br />36L, 36L, 36L, 36L, 36L, 36L, 36L, 36L, 36L, 37L, 37L, 37L, 37L, <br />37L, 37L, 37L, 37L, 37L, 38L, 38L, 38L, 38L, 38L, 38L, 38L, 38L, <br />38L, 39L, 39L, 39L, 39L, 39L, 39L, 39L, 39L, 39L, 40L, 40L, 40L, <br />40L, 40L, 40L, 40L, 40L, 40L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, <br />41L, 41L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 42L, 43L, 43L, <br />43L, 43L, 43L, 43L, 43L, 43L, 43L, 44L, 44L, 44L, 44L, 44L, 44L, <br />44L, 44L, 44L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L), .Label = c("1", <br />"9", "24", "25", "41", "49", "8016", "8019", "8020", "8024", <br />"8025", "8027", "8028", "8029", "8030", "8031", "8032", "8034", <br />"8035", "8037", "8040", "8042", "8058", "8067", "8068", "8109", <br />"8111", "8112", "8114", "8116", "8124", "8128", "8130", "8137", <br />"8405", "8407", "8415", "8420", "8422", "8424", "8425", "8426", <br />"8429", "8434", "8436"), class = "factor"), Sexo = structure(c(2L, <br />2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, <br />2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, <br />2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, <br />2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, <br />1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, <br />2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, <br />2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, <br />1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, <br />1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, <br />1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, <br />2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, <br />1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, <br />2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, <br />1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, <br />1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, <br />1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, <br />1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, <br />1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, <br />1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, <br />1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, <br />1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, <br />2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, <br />2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, <br />1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, <br />1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("Femea", <br />"Macho"), class = "factor"), GS = structure(c(1L, 1L, 1L, 1L, <br />1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, <br />1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, <br />2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, <br />2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, <br />2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, <br />2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, <br />1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, <br />2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, <br />1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, <br />2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, <br />1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, <br />2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, <br />2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, <br />2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, <br />3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, <br />3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, <br />3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, <br />3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, <br />3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, <br />2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, <br />2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, <br />1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, <br />1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, <br />1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, <br />1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("3/4hz", "5/8hz", <br />"HOL"), class = "factor"), Tratamento = structure(c(2L, 2L, 2L, <br />2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, <br />1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, <br />1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, <br />2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, <br />1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, <br />2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, <br />1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, <br />1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, <br />2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, <br />2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, <br />2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, <br />2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, <br />1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, <br />2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, <br />2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, <br />1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, <br />1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, <br />2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, <br />2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, <br />2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, <br />1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, <br />1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, <br />1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, <br />2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, <br />1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("apex", "controle"<br />), class = "factor"), Pbserica = c(11, 11, 11, 11, 11, 11, 11, <br />11, 11, 9.6, 9.6, 9.6, 9.6, 9.6, 9.6, 9.6, 9.6, 11, 11, 11, 11, <br />11, 11, 11, 11, 11, 11.9, 11.9, 11.9, 11.9, 11.9, 11.9, 11.9, <br />11.9, 10.1, 10.1, 10.1, 10.1, 10.1, 10.1, 10.1, 10.1, 10.1, 10, <br />10, 10, 10, 10, 10, 10, 10, 10, 12, 12, 12, 12, 12, 12, 12, 12, <br />12, 10, 10, 10, 10, 10, 10, 10, 8.9, 8.9, 8.9, 8.9, 8.9, 8.9, <br />8.9, 8.9, 8.9, 11.8, 11.8, 11.8, 11.8, 11.8, 11.8, 11.8, 11.8, <br />8.6, 8.6, 8.6, 8.6, 8.6, 8.6, 8.6, 8.6, 8.6, 9.2, 9.2, 9.2, 9.2, <br />9.2, 9.2, 9.2, 9.2, 9.2, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, 9.9, <br />11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, <br />11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 10.1, 10.1, 10.1, 10.1, <br />10.1, 10.1, 10.1, 10.1, 10.1, 12.4, 12.4, 12.4, 12.4, 12.4, 12.4, <br />12.4, 12.4, 12.4, 11.2, 11.2, 11.2, 11.2, 11.2, 11.2, 11.2, 11.2, <br />11.2, 9.2, 9.2, 9.2, 9.2, 9.2, 9.2, 9.2, 9.2, 10.9, 10.9, 10.9, <br />10.9, 10.9, 10.9, 10.9, 10.9, 10.9, 10.4, 10.4, 10.4, 10.4, 10.4, <br />10.4, 10.4, 10.4, 10.4, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11.1, <br />11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 10.3, 10.3, 10.3, <br />10.3, 10.3, 10.3, 10.3, 10.3, 10.3, 9.7, 9.7, 9.7, 9.7, 9.7, <br />9.7, 9.7, 9.7, 9.7, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, 10.2, <br />10.2, 10.2, 10.4, 10.4, 10.4, 10.4, 10.4, 10.4, 10.4, 10.4, 10.4, <br />9.6, 9.6, 9.6, 9.6, 9.6, 9.6, 9.6, 9.6, 9.6, 9, 9, 9, 9, 9, 9, <br />9, 9, 9, 11.8, 11.8, 11.8, 11.8, 11.8, 11.8, 11.8, 11.8, 9.8, <br />9.8, 9.8, 9.8, 9.8, 9.8, 9.8, 9.8, 9.8, 10, 10, 10, 10, 10, 10, <br />10, 10, 10, 9.4, 9.4, 9.4, 9.4, 9.4, 9.4, 9.4, 9.4, 9.4, 10, <br />10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, <br />11, 8.9, 8.9, 8.9, 8.9, 8.9, 8.9, 8.9, 8.9, 8.9, 11.4, 11.4, <br />11.4, 11.4, 11.4, 11.4, 11.4, 11.4, 11.4, 11.2, 11.2, 11.2, 11.2, <br />11.2, 11.2, 11.2, 11.2, 11.2, 12, 12, 12, 12, 12, 12, 12, 12, <br />12, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11.8, 11.8, 11.8, 11.8, <br />11.8, 11.8, 11.8, 11.8, 11.8, 10.5, 10.5, 10.5, 10.5, 10.5, 10.5, <br />10.5, 10.5, 10.5, 10.8, 10.8, 10.8, 10.8, 10.8, 10.8, 10.8, 10.8, <br />10.8, 10.4, 10.4, 10.4, 10.4, 10.4, 10.4, 10.4, 10.4, 10.4), <br />    Pnasc = c(27.06, 27.06, 27.06, 27.06, 27.06, 27.06, 27.06, <br />    27.06, 27.06, 38.34, 38.34, 38.34, 38.34, 38.34, 38.34, 38.34, <br />    38.34, 39.4, 39.4, 39.4, 39.4, 39.4, 39.4, 39.4, 39.4, 39.4, <br />    32.65, 32.65, 32.65, 32.65, 32.65, 32.65, 32.65, 32.65, 39.7, <br />    39.7, 39.7, 39.7, 39.7, 39.7, 39.7, 39.7, 39.7, 35.9, 35.9, <br />    35.9, 35.9, 35.9, 35.9, 35.9, 35.9, 35.9, 34, 34, 34, 34, <br />    34, 34, 34, 34, 34, 28.58, 28.58, 28.58, 28.58, 28.58, 28.58, <br />    28.58, 31.3, 31.3, 31.3, 31.3, 31.3, 31.3, 31.3, 31.3, 31.3, <br />    33.57, 33.57, 33.57, 33.57, 33.57, 33.57, 33.57, 33.57, 32.314, <br />    32.314, 32.314, 32.314, 32.314, 32.314, 32.314, 32.314, 32.314, <br />    27.3, 27.3, 27.3, 27.3, 27.3, 27.3, 27.3, 27.3, 27.3, 35, <br />    35, 35, 35, 35, 35, 35, 35, 26.8, 26.8, 26.8, 26.8, 26.8, <br />    26.8, 26.8, 26.8, 26.8, 36.6, 36.6, 36.6, 36.6, 36.6, 36.6, <br />    36.6, 36.6, 36.6, 27.2, 27.2, 27.2, 27.2, 27.2, 27.2, 27.2, <br />    27.2, 29.45, 29.45, 29.45, 29.45, 29.45, 29.45, 29.45, 29.45, <br />    29.45, 34.48, 34.48, 34.48, 34.48, 34.48, 34.48, 34.48, 34.48, <br />    34.48, 31.76, 31.76, 31.76, 31.76, 31.76, 31.76, 31.76, 31.76, <br />    31.76, 29.5, 29.5, 29.5, 29.5, 29.5, 29.5, 29.5, 29.5, 25.9, <br />    25.9, 25.9, 25.9, 25.9, 25.9, 25.9, 25.9, 25.9, 33.57, 33.57, <br />    33.57, 33.57, 33.57, 33.57, 33.57, 33.57, 33.57, 35.2, 35.2, <br />    35.2, 35.2, 35.2, 35.2, 35.2, 35.2, 35.2, 31, 31, 31, 31, <br />    31, 31, 31, 31, 31, 29.5, 29.5, 29.5, 29.5, 29.5, 29.5, 29.5, <br />    29.5, 29.5, 26.5, 26.5, 26.5, 26.5, 26.5, 26.5, 26.5, 26.5, <br />    26.5, 29.49, 29.49, 29.49, 29.49, 29.49, 29.49, 29.49, 29.49, <br />    29.49, 30, 30, 30, 30, 30, 30, 30, 30, 30, 33.12, 33.12, <br />    33.12, 33.12, 33.12, 33.12, 33.12, 33.12, 33.12, 35, 35, <br />    35, 35, 35, 35, 35, 35, 35, 35.7, 35.7, 35.7, 35.7, 35.7, <br />    35.7, 35.7, 35.7, 30.7, 30.7, 30.7, 30.7, 30.7, 30.7, 30.7, <br />    30.7, 30.7, 39.3, 39.3, 39.3, 39.3, 39.3, 39.3, 39.3, 39.3, <br />    39.3, 29.3, 29.3, 29.3, 29.3, 29.3, 29.3, 29.3, 29.3, 29.3, <br />    25.72, 25.72, 25.72, 25.72, 25.72, 25.72, 25.72, 25.72, 25.72, <br />    46.3, 46.3, 46.3, 46.3, 46.3, 46.3, 46.3, 46.3, 46.3, 38.9, <br />    38.9, 38.9, 38.9, 38.9, 38.9, 38.9, 38.9, 38.9, 38.2, 38.2, <br />    38.2, 38.2, 38.2, 38.2, 38.2, 38.2, 38.2, 31.1, 31.1, 31.1, <br />    31.1, 31.1, 31.1, 31.1, 31.1, 31.1, 22.3, 22.3, 22.3, 22.3, <br />    22.3, 22.3, 22.3, 22.3, 22.3, 27.6, 27.6, 27.6, 27.6, 27.6, <br />    27.6, 27.6, 27.6, 27.6, 31.7, 31.7, 31.7, 31.7, 31.7, 31.7, <br />    31.7, 31.7, 31.7, 34.7, 34.7, 34.7, 34.7, 34.7, 34.7, 34.7, <br />    34.7, 34.7, 38, 38, 38, 38, 38, 38, 38, 38, 38, 40.7, 40.7, <br />    40.7, 40.7, 40.7, 40.7, 40.7, 40.7, 40.7), Semana = structure(c(1L, <br />    2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 4L, 5L, 6L, 7L, 8L, <br />    9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 5L, 6L, <br />    7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, <br />    4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, <br />    1L, 2L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, <br />    9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, <br />    7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, <br />    4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, <br />    2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 8L, <br />    9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, <br />    6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, <br />    3L, 4L, 5L, 6L, 7L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, <br />    1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, <br />    7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, <br />    4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, <br />    1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, <br />    7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, <br />    4L, 5L, 6L, 7L, 8L, 9L, 1L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, <br />    2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, <br />    8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, <br />    5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, <br />    2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, <br />    8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, <br />    5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, <br />    2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, <br />    8L, 9L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 1L, 2L, 3L, 4L, <br />    5L, 6L, 7L, 8L, 9L), .Label = c("1", "2", "3", "4", "5", <br />    "6", "7", "8", "9"), class = "factor"), CT = c(70, 72, 73, <br />    77, 77, 80, 82, 86, 91, 77, 78, 82, 84, 85, 89, 94, 95, 78, <br />    81, 82, 81, 84, 88, 89, 92, 96, 74, 75, 77, 84, 85, 86, 88, <br />    92, 76, 78, 79, 79, 82, 82, 84, 86, 88, 75, 79, 80, 80, 81, <br />    83, 86, 89, 92, 76, 73, 75, 79, 81, 82, 87, 87, 94, 71, 73, <br />    77, 82, 86, 88, 94, 75, 76, 80, 80, 83, 85, 88, 91, 98, 78, <br />    78, 81, 81, 85, 85, 89, 94, 73, 74, 78, 78, 78, 81, 84, 86, <br />    90, 72, 74, 75, 76, 78, 81, 86, 86, 90, 75, 80, 82, 84, 80, <br />    88, 91, 94, 75, 78, 79, 79, 83, 81, 83, 86, 90, 75, 75, 76, <br />    77, 79, 81, 86, 87, 92, 71, 72, 73, 75, 78, 85, 86, 91, 75, <br />    77, 77, 80, 81, 86, 89, 91, 97, 77, 78, 78, 79, 84, 86, 90, <br />    96, 99, 78, 80, 80, 80, 84, 84, 87, 92, 97, 74, 77, 78, 79, <br />    81, 83, 84, 92, 72, 74, 75, 79, 79, 80, 86, 85, 91, 75, 76, <br />    76, 78, 78, 80, 84, 86, 90, 75, 77, 77, 80, 81, 83, 88, 90, <br />    94, 71, 72, 70, 74, 78, 91, 85, 86, 97, 71, 72, 75, 76, 77, <br />    80, 84, 86, 91, 70.5, 75, 73, 77, 80, 84, 86, 87, 92, 75, <br />    71, 77, 76, 80, 80, 83, 84, 90, 71, 77, 75, 76, 77, 78, 82, <br />    84, 89, 74, 77, 78, 79, 80, 82, 90, 89, 94, 77, 79, 79, 81, <br />    83, 83, 86, 90, 95, 76, 78, 78, 80, 82, 82, 85, 86, 70, 73, <br />    72, 71, 77, 74, 75, 77, 80, 77, 81, 81, 82, 85, 86, 89, 93, <br />    98, 73, 73, 70, 73, 75, 77, 81, 85, 89, 71, 74, 74, 78, 77, <br />    82, 84, 88, 90, 82, 80, 82, 85, 89, 89, 93, 95, 98, 76, 80, <br />    79, 80, 81, 84, 88, 89, 93, 77, 77, 77, 80, 82, 82, 85, 86, <br />    91, 68, 74, 75, 75, 76, 78, 80, 81, 86, 68, 69, 69, 74, 77, <br />    79, 80, 82, 85, 68, 72, 73, 75, 77, 78, 80, 84, 88, 71, 72, <br />    72, 76, 78, 80, 81, 85, 90, 77, 78, 80, 80, 83, 85, 88, 90, <br />    94, 76, 77, 77, 79, 82, 83, 84, 89, 93, 88, 76, 78, 79, 81, <br />    83, 85, 89, 94)), class = "data.frame", row.names = c(1L, <br />2L, 4L, 6L, 8L, 10L, 12L, 14L, 17L, 18L, 20L, 23L, 25L, 27L, <br />29L, 31L, 34L, 35L, 37L, 39L, 41L, 43L, 45L, 47L, 49L, 52L, 53L, <br />55L, 57L, 61L, 63L, 65L, 67L, 70L, 71L, 73L, 75L, 77L, 79L, 81L, <br />83L, 85L, 88L, 89L, 91L, 93L, 95L, 97L, 99L, 101L, 103L, 106L, <br />107L, 109L, 111L, 113L, 115L, 117L, 119L, 121L, 124L, 125L, 127L, <br />133L, 135L, 137L, 139L, 142L, 143L, 145L, 147L, 149L, 151L, 153L, <br />155L, 157L, 160L, 161L, 163L, 165L, 167L, 169L, 171L, 173L, 178L, <br />179L, 181L, 183L, 185L, 187L, 189L, 191L, 193L, 196L, 197L, 199L, <br />201L, 203L, 205L, 207L, 209L, 211L, 214L, 215L, 217L, 219L, 221L, <br />223L, 225L, 227L, 229L, 233L, 235L, 237L, 239L, 241L, 243L, 245L, <br />247L, 250L, 251L, 253L, 255L, 257L, 259L, 261L, 263L, 265L, 268L, <br />269L, 271L, 273L, 275L, 277L, 279L, 283L, 286L, 287L, 289L, 291L, <br />293L, 295L, 297L, 299L, 301L, 304L, 305L, 307L, 309L, 311L, 313L, <br />315L, 317L, 319L, 322L, 323L, 325L, 327L, 329L, 331L, 333L, 335L, <br />337L, 340L, 341L, 343L, 345L, 347L, 349L, 351L, 353L, 358L, 359L, <br />361L, 363L, 365L, 367L, 369L, 371L, 373L, 376L, 377L, 379L, 381L, <br />383L, 385L, 387L, 389L, 391L, 394L, 395L, 397L, 399L, 401L, 403L, <br />405L, 407L, 409L, 412L, 413L, 415L, 417L, 419L, 421L, 423L, 425L, <br />427L, 430L, 431L, 433L, 435L, 437L, 439L, 441L, 443L, 445L, 448L, <br />449L, 451L, 453L, 455L, 457L, 459L, 461L, 463L, 466L, 467L, 469L, <br />471L, 473L, 475L, 477L, 479L, 481L, 484L, 485L, 487L, 489L, 491L, <br />493L, 495L, 497L, 499L, 502L, 503L, 505L, 507L, 509L, 511L, 513L, <br />515L, 517L, 520L, 521L, 523L, 525L, 527L, 529L, 531L, 533L, 535L, <br />538L, 539L, 543L, 545L, 547L, 549L, 551L, 553L, 556L, 557L, 559L, <br />561L, 563L, 565L, 567L, 569L, 571L, 574L, 575L, 577L, 579L, 581L, <br />583L, 585L, 587L, 589L, 591L, 592L, 594L, 596L, 598L, 600L, 602L, <br />604L, 606L, 608L, 609L, 611L, 613L, 615L, 617L, 619L, 621L, 623L, <br />626L, 627L, 629L, 631L, 633L, 635L, 637L, 639L, 641L, 644L, 645L, <br />647L, 649L, 651L, 653L, 655L, 657L, 659L, 661L, 662L, 664L, 666L, <br />668L, 670L, 672L, 674L, 676L, 679L, 680L, 682L, 684L, 686L, 688L, <br />690L, 692L, 694L, 697L, 698L, 700L, 702L, 704L, 706L, 708L, 710L, <br />712L, 715L, 716L, 718L, 720L, 722L, 724L, 726L, 728L, 730L, 733L, <br />734L, 736L, 738L, 740L, 742L, 744L, 746L, 748L, 751L, 752L, 754L, <br />756L, 758L, 760L, 762L, 764L, 766L, 769L, 770L, 772L, 774L, 776L, <br />778L, 780L, 782L, 784L, 787L, 788L, 790L, 792L, 794L, 796L, 798L, <br />800L, 802L, 805L))</pre>
<div><br /><br /><br /></div>
<div>modeloCT_F<- lme(CT~GS+Tratamento*Semana+Pbserica+Pnasc,random=~1|Animal,data=subset(BiometriaAleit,Sexo=="Femea"))</div>
<div> </div>
<div>shapiro.test(resid(modeloCT_F,type="normalized"))</div>
<div>bartlett.test(resid(modeloCT_F,type="normalized")~interaction(Tratamento,Semana),data=subset(BiometriaAleit,Sexo=="Femea"))</div>
<div># Modelando a variância e a covariancia dos erros</div>
<div>modeloCT_F1.4 <- update(modeloCT_F,weights=varIdent(form=~1|Semana*Tratamento))</div>
<div>modeloCT_F1.5 <- update(modeloCT_F1.4,control=controle,correlation=corARMA(q=3,form=~1|Animal))</div>
<div>anova(modeloCT_F1.4,modeloCT_F1.5)</div>
<div> </div>
<div>shapiro.test(resid(modeloCT_F1.5,type="normalized"))</div>
<div>bartlett.test(resid(modeloCT_F1.5,type="normalized")~interaction(Tratamento,Semana),data=subset(BiometriaAleit,Sexo=="Femea"))</div>
<div># Remoção de outliers</div>
<div>modeloCT_F1.6 <- update(modeloCT_F1.5,data=subset(BiometriaAleit[-c(574,571,556,427,425,319,279,788,430,423),],Sexo=="Femea"))</div>
<div>shapiro.test(resid(modeloCT_F1.6,type="normalized"))</div>
<div> </div>
<div>bartlett.test(resid(modeloCT_F1.6,type="normalized")~interaction(Tratamento,Semana),data=subset(BiometriaAleit[-c(574,571,556,427,425,319,279,788,430,423),],Sexo=="Femea"))</div>
<div> </div>
<div># Selecionando a melhor distribuição</div>
<div>install.packages("fitdistrplus")</div>
<div>library(fitdistrplus)</div>
<div>x1<-as.vector(resid(modeloCT_F1.6,type="normalized"))</div>
<div>descdist(x1, discrete = FALSE)</div>
<div> </div>
<div>Graficos sobre a distribuição dos dados.</div>
<div> </div>
<div>library(car)</div>
<div>qqp(resid(modeloCT_F1.6,type="normalized"),"logis")</div>
<div> </div>
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QtSpOnfp6Ux0Y7IiIiL5OZ87A1BStWim7ji8jkeCHH7Bpkw4AFxecOgVra2XXpFQMdkRERF8doRCjRiEtDXfu4KO3dqkMaapbswYAHB2zTp/+2lMdGOyIiIi+QitW4MEDAJgyBQKBsqspkbyprnJl8bZtL5jqoOoPT4jF4hcvXiQkJDg5ORkbGyu7HCIiIhXw5g1++QUAKlfGDz8ou5oSkUgwZkxOqnNxwf79qQJBtrKLKhVUZsROIpGEhIT873//27lzZ1ZWFoBTp05VrVq1YsWKtWvXtrCw8PX1jY+PV3aZREREpd3kyUhKAoDly6Gjo+xqik+a6lavBoAqVaRPS4iVXVRpoRojdkKhsHv37ocOHZJ+dHd337x5c8eOHTMyMurVq1euXLnbt2//+eeft2/fDg0N5dAdERHRp9y4kfNihs6d0a6dsqspPokEfn7vU93p07CxQWKisssqNVRjxG7JkiWHDh3q3r37n3/++dNPP127ds3T01NTU/P8+fNXr149cuTIs2fPfvrpp3/++Wfu3LnKLpaIiKiUEosxejTEYujoYOFCZVdTfNJUt2oVkCfVUV6qMWK3detWd3f3oKAgAD4+PhoaGgsWLJg1a1bjxo2lB2hpaS1YsODcuXNBQUHz5s0restpaWm//fZbdvbnJuZDQ0MBJCQkpKenf8FFFINIJEpOThYKhYrpTg2IRCIAcXFxmpqayq5FZaSnp4tEordv3yq7EFWSkZEBgN+0YhGJRCkpKZ//NUt5SX+hxcfHy+MX2rZtuleuGAEYNSrNxOSdav0sSySYNs1wwwY9AE5OoqCgRC0tsfQSFPwLTfqroHRSjWD39OnTMWPG5H7s3bv3ggULvvnmm7zHCASCBg0a/P7778VqOSkpKSQkRPq36FMiIyMBvHv37vOHyVZWVpZYzDsGikr6vUpPTxeo6MNdyiAUCiUSybt375RdiCqRphN+04pFIpFkZmbyF1rRSSQSAGlpaTJ/Uf3r15qzZ5sDsLXNHjLk7bt3qrTMiUSCWbPMtm7VA1CxovDPP2OMjES5fxcV/HczMzMTgMz/gGRCNYKdlZXV48ePcz86Ozv/8MMPhoaG+Q57+PChubl5sVq2trY+fvz454+ZOHHiwoULLS0tzczMitV4iYWHh1tYWJiYmCimOzWQlZUVERFhZ2enpaWl7FpURkJCwtu3bytWrKjsQlRJdHQ0AFtbW2UXokoiIiIsLCxMTU2VXYjKEAqF4eHhdnZ22trasm15wwYkJwPAunVlqld3kG3jciWRYOzYnFsDK1fG6dNatrb2eQ9ITEx8/fq1wn6hpaam4r8xhdKmNIbNj7Vu3Xr//v2bN2+W/neMkZHRypUr27Rpk3uAWCxeuHDhiRMnOnbsqLwyiYiISi93dxgZ4Ycf0L69skspDmmqW7ECyEl14H9bfYZqBLtZs2bZ29sPHjy4Vq1aH+/ds2ePvb39xIkT7ezsZs+erfjyiIiISj9vbyQnY+VKZddRTFOm5KQ6Z2emusKpRrCztra+fv36qFGjqlev/vHe5OTk2NjYXr16Xb58ufzX/OJfIiL6CqSkpIwbN65ChQra2tr29vZjxoxJ/HC1D6FQuHDhwqpVq+rp6Tk7O8+ZMyffwyuvX78eMWKEk5OTvr5+jRo1Fi9e/KmnW2JjYy0sLBYtWiTH6/msyZMxfz4AVKyIkBCmusKpRrADYGFhsXr16p07d368q0+fPklJSYGBgbzxhYiI1JtQKGzVqtWyZcsqVqzo7+/v7Oy8atWqpk2bpqWl5R7z/fffT5w40dLScvTo0ebm5tOnT/f19c3dGxsb26BBg3Xr1rm6uo4ePVpfX/+nn37q0aPHx31lZGT06NEjLi5OERdWkClTclKdgwNOnwZvCS4K1Xh44vP09fWVXQIREZEi7Nix49q1a8OGDVu7dq10i5+f38qVKzds2ODn5wcgJCRk+/bt/fr127ZtGwCxWOzj47Nr167Bgwe3bt0awMyZM589e7Zhw4bvv/8egEQi6d69+19//RUSEuLl5ZXb0atXr7p3737p0iUlXCQAYMoUSJcvc3DAmTNMdUWlMiN2REREdO3aNQB5R+AGDBgA4NatW9KPq1atAhAQECD9qKGhMX/+fADr16+XbomPj//2228HDx4s/SgQCKTDddevX89tc+3atdWrV798+bKnp6d8r+cTpk59n+o4VlcsDHZEREQqQ7rw1v3793O3REREIM8qPOfPn3dwcHBycpJ+FAoh/XjmzBnplp07d969ezfvqp/SUJh3rZCVK1eWK1cuJCRk9OjR8ryagk2bhv/9DwAqVMDp03B0VHwJKkwdpmKJiIi+En379l28ePHkyZM1NTXr169/69atn376ycbGZtSoUQDi4+MTEhLq1KkDQCxG+/Y4exbHjsHR0fHvv/9OSEjIu6BgSkpKZGRkYGDgkiVL3N3du3fvnrtryZIlLVq00NLS2r9//6cqyc7OPnLkyNWrV1+9elWlSpVWrVrVq1fvyy9w2jRIXw5aoQLOnGGqKzaO2BEREakMFxeXM2fOmJmZDR061NXVdeDAgdra2ufOnbOxsQEgfdBBur79b78hOBgZGXj4EGXLlgWQlJSUt6mpU6fWqVNnwYIF5cuX37JlS971kFu3bv359d6fPHni5ubm6+t79epVkUj0119/NWjQoF+/fllZWV9yddOnM9V9KQY7IiIilZGYmDh9+vTo6Ojx48dv3rx5/PjxcXFxTZs2DQ8PByB9ybiOjs7Ll5g2DQAqVoSvL3R0dPDRG047dOgQFBQUEBCQlJTk5uaWO1dbqIyMjLZt25YvX/7Zs2chISFbt269cuXK1atXT58+LX2Ao2SmT8ecOQBnYL8Mp2KJiIhUxpAhQ06cOHHixIncJ1i7dOnSvHnz/v37X7lyRU9PD0BWVtaoUZAOz61dC0PDnHebGhgY5G2qbdu2ALp3796yZctmzZoNHz78wYMHRXn/6datW5OSkoKCgoyMjHI3urm5bd++3dPTc8qUKQ4OxX5f2YwZH6S6/24RpGLjiB0REZFqSElJ2bdvn7u7e951SZo0aeLl5XX16tXnz59Lb6GLiEg6cAAABgxA69bAf5Ow0gnZjzVp0qRu3boREREvXrwoShknT57s0qVL3lQn1aJFCxsbm1OnThX3umbMwK+/AoC9PVPdl2KwIyIiUg1RUVESieTjV907OjoCePXqlYmJSblylvfvPwVgYYGFC3MOePr0qY2NjbGxcVpa2qZNm/bs2ZOvhXLlygFISEgoShlxcXHW1tYF7ipfvnxxFzT++WemOllisCMiIlINlpaWAoHgn3/+ybf9wYMH+G/FEwMDj+zsSCB6+XKUKwcA//7775MnTxo1agRAQ0Nj5MiRY8eOFYvFuacLhcI7d+5oaWk5FS1VWVpaFji2J5FIoqKirKysin5FP/+MX34B/kt1lSoV/VQqGIMdERGRajA3N2/ZsuW9e/c2bNiQu3H37t1nz5718PCwt7c/exbPnw8AYGs7tU8fCQCJRDJlyhQAw4cPB6Crq9ulS5eXL18uXrxYerpEIpk2bVp0dPR3331nbGxclDLatWu3f//+j0fmjhw5EhcX16pVqyJezsyZTHWyx4cniIiIVMZvv/3m4eExdOjQoKAgV1fX+/fvHzlyxMLCYsOGDenpGDoUEkknTc3O0dHbmjR57OHhcf78+dDQUF9f39x3SCxevPjChQsTJ048efJktWrVLl26dPXq1erVq69YsaKINfj4+Cxbtqxjx447d+7MnRcOCQkZOHDg2LFjpQuvFGrmTMyeDQB2dkx1ssQROyIiIpVRuXLlGzduDBkyJCwsbMmSJTdu3Bg4cOCNGzdcXFz+/huPHgHAvHm7ZsyYER0dvWzZstjY2Llz527cuDG3BTs7u+vXr3///fd3795ds2bN27dvJ06ceOnSJek7LYpCS0vr6NGj2tralStXrlOnTocOHSpVqtS2bdt+/fr9T/rKiMIEBDDVyQtH7IiIiFSJnZ1d7otf83J3R7NmcHDAuHE6mpqzZ0ujU0Gsra3zTuZ+RpcuXSQSSYEtnD179sKFC9euXXv16lXXrl1btGhRqWgBLSAAs2ZJLwSnT8PZuSgnUVEx2BEREamDcuVQ5DWGZcPDw8PDw6NYp8yfn5PqypfHiRNMdbLHqVgiIiJShAULMHkyAJQvj1OnUK2asgtSRwx2REREJHcLFmDSJACwsmKqkyMGOyIiIpKvhQuZ6hSEwY6IiIjkaOFCTJwI/JfqqldXdkFqjcGOiIhIJYnFePgQeV4hURotWsRUp1AMdkRERCppyBBUq4YJE5Rdx6ctXpxTnqUl/v6bqU4RGOyIiIhUz4ED2LwZAFJSlF3KJyxejJ9+AgBLS5w6hW++UXZBXwcGOyIiIhUTF4cRIwCgbFn8/LOyqynIkiVMdcrBYEdERKRifvgBMTEAsGoV7OyUXc1HlizBjz8C/83AMtUpEoMdERGRKjlwAIGBAODtDV9fZVfzkaVLP0h1NWoou6CvDIMdERGRyoiNxfDhAGBujnXrlF3NR5YuxfjxAFCuHFOdcjDYERERqYyRI/H6NQCsWoXy5ZVdzYeWLWOqUz4GOyIiItWwcyeCggCgc2f07q3saj60bBnGjQP+S3Xffqvsgr5WDHZEREQqID4eY8YAQLlypW4Slqmu9GCwIyIiUgG3byMuDgDWrIGlZbFPF4vFW7Zsad++fcWKFV1cXHr27HnixAmZFLZ8+QczsEx1ylVG2QUQERFR4Zo3x7x5sLBAjx7FPjcrK6tbt27nz58fMGDAd999l5WVdeHChQ4dOowbN27BggVfUtXatRg3DhIJTE1x7BhTnfIx2BEREakADQ1MmlTCc2fPnn3z5s2bN29WqlRJumXYsGGDBg1q165d/fr1e5QgKgIA1q3DyJE5qS4kBG5uJSyPZIhTsUREROpMKBSuWbNmzpw5ualOqkWLFsOHD1++fHnJml23DiNGMNWVOgx2RERE6uzx48cJCQlt2rT5eFebNm1u3LhRgjbXr89JdSYmOHGCqa4UYbAjIiJSZ5mZmQD09fU/3qWnp5eVlSUWi4vV4Pr1GD48J9WFhKBuXdnUSTLBYEdERKTOHBwcypQpc/fu3Y93hYWFOTo6amgUIwww1ZVyDHZERESl0ZkzmDAh5z0T+dy5c6d3795OTk7a2trVqlUbMWLEixcvPtWOiYlJu3btfvnlF5FIlHd7YmLi0qVL+/TpU/SSNmz4YAaWqa4UYrAjIiIqdZ4/F3TqhEWLsGJF/l1BQUH169dPS0ubMWPG0aNH/fz8bt++7erqevPmzU+1tmTJktu3b3fo0OHSpUuZmZnJycnHjx9v2rSpkZHRhAkTiljSxo0YPhxicU6qq1evxBdHcsRgR0REVLpIJBg2TDMlBQIBPD0/2PXq1atBgwYFBAQcPHhw0KBBnp6eI0eOvHjxYseOHXv16pWVlVVgg87OzpcuXZJIJI0aNTIwMChbtqy3t7ebm9uZM2eMjIyKUtLGjRg2DGIxypZFcDBTXenFdeyIiIhKl8BA8zNnNAAMG4YWLT7YtW3bNltb20kfrminqam5YsUKGxubkJCQDh06FNims7NzcHBwYmLigwcPpBO4BT5OUaBNm96nuhMnUL9+SS6KFIMjdkRERKXI48eCZcusADg4YOHC/Htv377dvHnzjx93MDExqV279u3btz/fuImJScOGDd3c3IqV6oYOZapTGRyxIyIiKi2yszFwoGZamkBDA1u24ONp0uzsbC0trQLP1dbWzs7Olm09eVNdcDBTnQrgiB0REVFpMWcOrlwRAPDzEzVvXsABVapUKXBJYaFQePfu3SpVqsiwmM2bP0h17u4ybJvkhcGOiIioVLhxA3PmAEClSpmzZokKPKZv375Xr17dv39/vu3z588XCASfusGuBDZvxpAhTHWqh1OxREREypeWhr59IRRCRwfz57/Q1a1Q4GHVq1efOXNm7969J0+e3LVrVzs7u8jIyE2bNm3atCkwMNDY2FgmxWzZkulE2EIAACAASURBVJPqjI1x/DhTnSphsCMiIlK+FSsQHg4AP/6Y+PbtyW3bynz77beurq66urr5jpwxY4aTk9Ps2bNnzZoFQCAQ1K1bNyQkpHmBc7fFt2ULvv8+J9UFB6NBA5m0SgrCYEdERKR8jo7Q0ICNzZ158+rq6GhZWFhER0dbWFgsXbrUx8cn38F9+/bt27dvQkJCVFSUk5OTgYGBrMoIDMwZqzMwwKFDTHWqh8GOiIhI+Xr0EC1f3iUu7nFISLCFhUXVqlUzMzNXr149YMAAsVjs6+v78SmmpqampqYyrCEwEL6+EIlgYICjR9G0qQzbJgVhsCMiIlKoN2/ePHjwwMTEpFq1atra2tKNO3bs+Oefc/fv37e0tAwPDwdgZGQ0efJkTU3NsWPHdu/eXU9PT65V7dqFfv1yUt2RI0x1qopPxRIRESnItWvX6tevb2Vl5enpWatWrbJly44fPz49PR3Avn37evfubWtrm++U0aNHv3v37ty5c3ItbNcu+PoiOzsn1TVrJtfeSI4Y7IiIiBTh0qVLzZo1c3FxuXPnTlpaWnx8/Pbt2/fu3evt7S0SiV68eOHi4vLxWfr6+vb29i9evJBfYbt3v091hw8z1ak2TsUSEREpwvDhw3v37r1p0ybpR1NT0x49etSvX79mzZpbtmwxNDRMTEws8MTExERDQ0M5VbV7N/r2fZ/qZPRkLSkNR+yIiIjkLiwsLCwsLCAgIHdLfDySk1GhQoXBgwcHBgY2btz44MGDYrE434kXL16MjY1t1KiRPKrKTXX6+kx1aoLBjoiISO6ePHliYmJSoULOssO3bsHREc7OSExEzZo1Hz9+PHr06CdPnkyaNClvtnvx4sX333/v4+OTe6IM7dnDVKeGOBVLREQke3Fxcffu3RMIBDVq1DAzM9PR0cnIyBCLxRoaGunp8PVFcjK0tZGRgbS0NF1dXRsbm3379vXs2fPo0aNubm5VqlSJiIj466+/6tat+9tvv8m8vD174OPzPtW1aCHzHkg5OGJHREQkS8+fP+/QoUO5cuW8vLxatWplYWHRqVMnKysroVB49uxZABMm4P59AJg5E+XLIzg4uG7dugA8PT3v37/fo0ePqKioI0eOaGpqrlu37uTJk0ZGRrKtMCjofao7dIipTq1wxI6IiEhmoqOjGzduXLly5dDQ0Dp16gC4cePGpEmTOnfu3KVLlx9++GHKlNA1a8oCaNwYkybhjz/+OHz48OXLl6WnW1tbT58+PTw8vEqVKrlL3MlWUBD69Hmf6lq2lEcnpDQMdkRERDIzbdo0W1vb4ODg3FjWsGHDkJCQxo0bGxgY6OjYDxiQJZFAX1/o5bWjR4/9hw8fXr58eb169RRT3t69H4zVMdWpHwY7IiIi2cjOzt67d+/WrVvzDbbp6OhMmDBh+PARDRvGicUaAHR1x+/bd87V1TU0NFSRqa5PHwiF0NfHwYNMdeqJwY6IiEg23rx5k5qaWqNGjY93ffvtt0lJQ44f1wAwYAC2bFmp4Nr27ctJdXp6OHgQrVopuH9SED48QUREJBvS17mmpaV9vOvGDQkwB4CTE1asUHRh+/ahd2+muq8Cgx0REZFsmJqaVq5c+dixY/m2i0QYP94K0NbSws6dMDZWaFX5Up2np0J7JwVjsCMiIpKZsWPHzps379q1a3k3njt3PTbWCMCsWahfX6H1HDkCHx8IhdDRwZ49THXqj/fYERERyczIkSNv3brVpEmT3r17N2jQQCKRXL58OTAwsHPnuT17jvfxESiymKNH0b07MjOho4O9e9GhgyI7J+VgsCMiIpIZgUCwfv36Tp06/fHHHytWrJC+eWLfvn0dFJ6qjh5Ft25MdV8dBjsiIiIZ8/b29vb2VmIBx47lpDptbQQFMdV9RXiPHRERkVo5dgxdu+akur170bGjsgsiBWKwIyIiUh95U11QEFPdV4fBjoiISE0cP/7BDKxSZ4NJORjsiIiIZMzfH5Ur48YNhXZ6/Di6dkVGBlPdV43BjoiISJZ+/x0rViAyEidPKq7TvKluzx6muq8Xgx0REZHMhIVh/HgAsLPDkCEK6jQ4+INU16mTgvqlUqjw5U7evXsXHBx85syZGzduvH37NiEhQU9Pz97evlatWl5eXu3atdPR0VFAoURERKVcWhp690Z6OjQ0sHUrzM0V0WlwMLp0yUl1u3cz1X3tPhfsXr58uXLlyrVr1yYkJADQ1NQ0MjIyMDCIj49/8eJFaGjomjVrTE1NR48e7e/vb2FhoaiaiYiISiM/P9y/DwAzZ6JlS0X0eOLEB6muc2dFdEqlWcFTsWKxeNmyZVWqVFmxYkWbNm22bt1679699PT0hISEqKio1NTU9PT0a9euLV++vFGjRvPmzXNwcFiwYIFIJFJw9URERKXE7t3YuBEAmjbFtGmK6PHECXTujIwMaGlh1y6mOgI+NWLXqFGj58+fz5kzZ/DgwUZGRh8foKurW7du3bp16/r5+cXExGzevHnBggV79+69cuWKnAsmIiIqdR4/xtChAGBhgR07oKkp9x5DQnLG6rS0sHs3unSRe4+kEgoesWvXrl1kZKS/v3+BqS6f8uXLT5ky5cmTJ15eXrIuj4iIqLQTCuHri+RkCATYuBG2tnLvMSQEnTsjPZ2pjvIreMRu5syZxW3I2Nj4119//eJ6iIiIVMz06bh8GQDGjVPEswt5U92uXUx19IGCg93Dhw8B6OrqVqhQQUODS6IQERF90qFDAFC3Lv73P7n3de4cunZFejo0NbFtG7p2lXuPpFoKDm3VqlWrVq2ao6OjgYFB7dq1w8LCFFwWERGRqlizBv7+OHAA2try7ejcObRvj3fvoKmJP/5A797y7Y5UUcEjdi4uLgB0dXVdXFxq1KhRrlw5xVZFRESkMpo3R/Pmcu/l/Hl06JCT6rZvZ6qjgn1uKpaIiIhKg/Pn0b49UlNzUl2fPsouiEqrgoOddLVhHR0dZ2fnqlWrTpkypWLFigqti4iIiAB8mOq2bWOqo88pONjFxcXlfq2rq5uZmamoeoiIiOi9Cxc+SHU+PsouiEq3goPd27dvAejq6hoaGiq2HiIiIspx4QLatctJdVu3MtVR4Qp+KtbCwsLCwoKpjoiIKJ9ly1CnDkJDcz5KJJJnz56dP3/+1atXsu0oX6rr21e2zZN6KnjErkePHkVvIigoSEbFEBERlWpHjmD8eEgkCApCw4aSdevWBQQExMTESPc6OzsvWrSosyxe2hoaKujUKSfVbdnCVEdFVXCw27t3b4HbNTU1DQ0N09PTs7KyAGhra2tpacmxOiIiolLj+XP07w+JBMbGGDUK06dPX7Zs2ezZs7t162Zvb//48ePNmzf36NFj3bp1gwYN+pKObt3SHz1aKyUlJ9X5+srqCkj9FTwVm5BHWFiYra1tgwYNzp49m5aWlpiYmJ6efvPmTS8vLysrq1u3bim4YiIiIsUTCuHjg/h4AFizBunpYfPnzw8KCvrxxx8dHR3LlCnj4uIyb968JUuW+Pv7x8bGlrij0FDByJEVmeqoZAoOdiZ5BAQEaGlpBQcHN23aVFtbG4CGhkbt2rUPHDhgYGAwZswYxRZMRESkBD/+mHNf3Zgx6NsXgYGB7u7u7dq1y3fY6NGjDQwMDh8+XLJeQkPh7V3m3TsNTU1s3sxUR8VW+HtgT5w40a5dO2Nj43zb9fT02rRpc+HCBfkUVjCJRJKdnf2pvampqYmJiYqsh4iIvgZ79mDlSgCoVw8LFwJAZGSkq6vrx0dqaGh8++23kZGRJeglNBRt2yIlBRoaknXrsvv1+6Ka6etUeLDT1tb+1JM+jx49MjIyknVJBQsPD/f29tbT09PW1q5Vq9aWLVskEkm+Yzp27GhqaqqYeoiI6CsRGYmhQwHA1BS7dkFHBwB0dHQyMjIKPD49PV1HelBxXLqEdu2kqQ6//BLt6yv+oqLpa1V4sGvVqtXBgwf/+uuvfNvXr19/9OhRb29v+RT2gcePHzdo0ODw4cO2trbVq1e/d+/eoEGDunTp8u7dOwX0TkREX62MDHz3HZKSIBBg82Y4OuZsr1u37unTpz+eRIqPj79x44abm1uxerl0CW3bIjkZGhpYv17UqRNnn6iECg92v/76q6mpabdu3Zo2bfrjjz/OmjVr/Pjx9erVGzZsWMWKFWfPnq2AKn/++efExMQtW7Y8fvz43r17//zzj5eX18GDB1u3bp2amqqAAoiI6Os0diykTwlOmoS8y5j07ds3KSlp6tSpeaePhELhiBEjHBwcvLy8it5F3lS3aRP69eNYHZVcwcud5FW5cuXQ0NAJEyYcPXr0/Pnz0o06Ojp9+/ZdtGhR+fLl5VwhAISEhLRv337AgAHSjy4uLsePHx81atTatWu7dOly9OhR6VMdJZCUlPTzzz9/ajhd6sqVKwDi4+M/f5gMiUSixMTEtLQ0xXSnBsRiMYDXr19raBT+3yoklZmZKRKJXr58qexCVIn0byW/acUi/YWWnp6u7EJK6MgRK0CzYcOskSNj8/3Jr1mzZsiQIefOnWvXrp2tre3Tp0/37dsXFxcXGBgofYFTUdy9q9W7t3lysoZAgLlzE7280l6/FgN48+YNf6EVXVZWliJ/oZXmV60WHuwAVKlS5cCBA8nJyf/8809CQoKpqWmNGjUUdncdgISEBBcXl7xbNDQ0fvvtt6ysrM2bNw8YMGDHjh0CgaAELQuFwri4OOmyfJ8izXNisVgkEpWgi5JRcHeqThrsRCLRx3de0qeIxWKJRMIfs2KR/oDxm1YsEolEpX+hLVoUd+6c7vDhKQKBON9FNG7c+Pjx42vXrt2zZ8+rV68qVKjQvHnzYcOGWVhYFPF6w8K0fXzMk5KkqS7BxydVJOIvtJLI/aYppjtpR6UzeRcp2ElpaGgIBAINDY2GDRsmJSVJJJKSZakSsLKyunPnTr6NAoFg3bp1r1+/DgwMtLGxWbx4cQlatrCw+OOPPz5/zMSJExcuXGhhYWFmZlaCLkogPDzczMzMxMREMd2pgaysrJSUFBsbG66YXXQJCQlv3761t7dXdiGqJDo6GoCtra2yC1ElERERZmZmqvtkW69e6NULQP6lIaTs7e09PDxK1vLNm/D1zbl7b80ajBhhCpgCEAqF4eHh1tbWJZ6M+golJia+fv1aYb/QpLeBSdNkaVOksBkTE9OnTx9TU9OGDRtK1+xZuXJl1apVFbbWSfv27U+dOrV06dJ838QyZcrs2bPH3d19yZIlw4YNU9hUKRER0Ze4eRNeXkhIyE11yi6I1EXhwe7169cNGjQIDAysV69e7dq1pRsNDQ0fP37cunXrsLAwOVcIAAEBATY2NuPHj7e2tt6yZUveXfr6+seOHWvYsOH69eulN8MRERGVZtJUFx8PgQCrVzPVkSwVHuwCAgKioqIOHjwYGhraqVMn6caxY8eePHkyOzv7l19+kXOFAGBjY3Pt2rUhQ4aIRKKPRz5NTU1PnTo1btw4jloTEVEpd+vWB6lu5EhlF0TqpfBgd/Dgwe7du3+8Xl3z5s179Ohx6dIl+RSWn42Nzfr162NjY3Ofjc1LV1d3yZIlz58//3i9PSIiolLi1i14euakulWrmOpI9goPdm/fvnVwcChwl7W1ddGf6JYVTU3NT+0qX758ly5dFFkMERGpDaEQ3bvD3h737sml/XypbtQoufRCX7nCg52tre2n7l27ePEinw4jIiL1MGkS9u1DVBSuXpV947dvv5+BZaoj+Sk82PXo0ePcuXP/+9//8i6ok52dPW3atCtXrnTOuw43ERGRatq1C0uXAoCbG3x8ZNz47dvw9ERcHAQCrFzJVEdyVPg6dtOmTTty5MjUqVPXrVsnfTrB19f34sWLz549c3JymjZtmvyLJCIikqPwcAwbBgBmZtizB7q6smw8X6obPVqWjRPlU/iInYmJycWLF4cNG/bmzZuIiAgAf/75Z3R0tI+PT2hoqLm5ufyLJCIikpfUVHTrlvOq1j/+gKOjLBvPm+pWrGCqI7kr0psnTE1N165du3LlygcPHiQmJhoaGlarVk1fX1/exREREcmVRIJBg3D/PgAEBKBdO1k2fufOB6nuhx9k2ThRgQoPdidPnvzmm2+k7zZxdXXNu2vv3r2nT59etWqV3MojIiKSo0WLEBQEAB06QLb3FuVNdcuXM9WRghQ+Fevl5VWrVq2zZ89+vOv8+fOrV6+WQ1VERERyd+oUpkwBgEqVsH07ZPhK97t34emJ2FgIBFi2DGPGyKxlos8r0k/xmzdvPD09V6xYIe9qiIiIFCMpCb17QySCvj727oWpqcxavnsXrVq9T3V+fjJrmahQRQp2fn5+1apV8/f3HzBgQEZGhrxrIiIikreUFCQkQCDAunX48D6jL/LgAdq0QWwsAPzvf0x1pGhFCnaOjo6hoaFdu3bdtm2bh4fHv//+K++yiIiI5MrODhcu4PRp9O0rszYfPkTLloiJAYB58zBpksxaJiqiot5QYGhouHfv3pkzZ968ebNu3bpnzpwBUKZMkR6qJSIiKoXc3dGsmcxae/gQLVrkpLr//Y+pjpSjGMlMIBAEBATUrFmzf//+Xl5eS5Ys4YonRERE+CjVTZ6s7IKUISsLU6fi5UuYmKBsWZiYwNQUZcvmfC39fysrWT6kQh8r9pBbt27dnJ2dO3fu7OfnZ2dnJ4+aiIiIVEjeGdi5c9U51YlECA1FamrBC/79/TcWLy6kherVcfMmdHTyb5dIEBiI2Nj3KTD3Cxk+1/I1KMlcas2aNa9du9azZ0/phCwREdFXKzwcLVvi1SsAmDs3Z/0UNZOcjOBgHDqEo0cRFwcAhw+jQ4f8hzVujM6dcf8+kpKQmIisrAKaioxEamoBwS44+HOv6JWGvNatsXYtBIICDnj0SOP5c12BICcLftz+16PwYHfp0qWKFSvm22hhYRESEjJ37lw+SEFERCrh3j1YWsLSUpZthoejRYucVDdnjrqluufPcegQDh7E2bMfpDQDA9jaFnC8sTH273//MT0diYk5IS8pKecLV1cU+C7SypVhaYk3bwquRHr6hg1YuBBly+bfe/YsWrQwlkiMc7fo6eUf+evYEf36FfG6VVvBwS4mJgaApaWlhoaGNNVJt+QzTPrOZCIiotJtyxYMHgwrK/z7L7S0StjI2bNnDx8+/ODBg7Jly9aqVatx48E9epjnprqpU2VYrzIlJGDZMhw4gDt3PthuZIQ2beDtjQ4dCg5n+ejpQU8P1tZF6rRSJbx8mRP+pP/LzYLSL1JT0aBBAamuQOnpSE9H3uQSFITvvvsqRvIKDnbW1tYAXr16Vb58eevC/kwkEons6yIiIpKRq1cxciQkEmhoFDyRVyiRSDRkyJDt27d7enrWrFkzMTFx9eqTkyf3E4sB4Ndf1SfVAZgxA3nfKlWhAry90akTmjWTbzDS1ISZGczMin1is2a4fDnl9u0kIyO73DHChIT3uTA5GS1bfhWpDp8Kdt27dwegq6ub+zUREZEqiolB9+7IyICWFgIDUbJ1umbOnHnkyJErV664ubkBiIjAkSOQprrx4+OnTSt+GCnFGjfGpk345ht06gRvb9SqpeyCiqBKFVHZsu9cXJRdRylQ8A94kPSVyB99TUREpEKEQvTqhagoAFi2DE2alKSRd+/eLV26dMOGDbmprkULvHwJAA4OG9LTbwJrZFizAoSF4cgRGBrihx8K2NunD/r0UXhNJCMFB7tivTdMOrBHRERU2vj54dw5AOjfH6NGlbCR69evZ2Zmdu3aFR+mul9+gZHRu/Xrz8mqWnlLScHGjVi9GpGROVvc3VGvnlJrIlkrONjp6ekVvQneY0dERKXQ1q34/XcAqFMn54uSSUpKMjQ01NXVffTofaqbPRvTp+PPPy0SExNlU648PXuGlSuxYQOSk99v9PBA1arKq4nko+Bg16ZNGwXXQUREJEOXLmH4cAAoXx4HD6I44xXvZWZmPnjwQCwWJycnX7kS3727WW6qmzEDAB4/flzK1+oPDcXSpfjrL4hEOVuqVMGIEejSBY6OSq2M5KPgYHf8+HEF10FERCQr0dHo3h2ZmdDWRlBQwYuufd7Tp0/9/f2PHj0qEokACARVmjYVSddymzUrJ9WlpqZu2rSp1K78FRWFnj1x+fL7La1aYexYtG/Pl3qpsy/9s42S3pJKRERUasyenbNo8MqVaNy42KdHRka6u7u/e/fuxIkTCQkJ5869MjC4mpVVDsDkyRk//wwAjx49at++vba2tp+fn2yLl5X9+3NSnY4OBg3CnTs4eRIdOzLVqbkiPfZ94sSJwMDAN2/eiEQi6R11EolEKBTGxcWFhYWJpQ98ExERlQ6NG2PbNowZg5KNpvn5+dWqVevYsWOampqRkejTB6mpACAQzFq06NfDh6smJyf/+++/LVq0+Pvvvw0NDWVbvKz07o2bN+HggBEjYGWl7GpIUQoPdvv375c+CvQxfX39jh07yrokIiKiL9K/P/r3L94pGRkZf/zxx8WLF8PDwy9fvjxhwoSsrKw3b/S8vBAdDQCTJkFLKzswsOKwYcNMTExq1ar17bffyqN4WbGwwKZNyi6CFK7wAdnFixeXKVNm9+7dsbGx7u7uffr0iYmJOXfuXOPGjTU0NBYvXqyAKomIiOTn33//dXNzmzJlikQiqV69ukQi2bx5c40aHTw8sp89A4CJEzFvHho0aBATEzNmzJh+/fqVhlSXlYWtW9GpE/bsUXYpVGoUHuzu3r3bqVOnnj17mpubN27c+MKFC1ZWVk2aNDl27FjZsmV//fVXBVRJREQkJyKRqGvXrlZWVo8ePdqyZcvIkSMBHDp099WrnVFRZQBMnIj58wEgMzNTq8QvmpWp7Gxs3owqVTBwIA4dwqxZyi6ISo3Cp2LT09OdnJykX1erVu3FixdxcXHm5uZGRka9evU6fPiwnCskIiKSi4cPH549e/b48eNhYWF79uwxMTEBULVqVV1dl86djdPT9QF07hwxf34V6fF///13nTp1lFkxIJFgzx78/DPCw3O22NggIECZJVGpUviInaWl5Zs3b6RfOzs7AwgLC5N+tLCwePHihfyKIyIikofs7OzRo0d/8803S5YsuXfvnp6eXs+ePZs1axYTExMXZ6CtffH1a30ATk57nZxyljY+d+7cxo0bR48ercSyjxxBnTro1Ssn1dnbY906PH2KHj2UWBSVLoUHu6ZNm+7fv//mzZsAatasqaGhsee/yfyzZ8+amanVm4+JiEjlbNwIX9+c9U0+RbqAQ2Zm5r59+wICAtzd3f/444+goKDw8PDWrVu3adMmIiIiMzPTy+v7Zs0kycnmAPT11xgazg4LC/vzzz9HjBjh6ek5evToTz1NKG9nzqBxY3TsiNu3AcDSEkuXIiICQ4dCW1spFVEpVfhU7MSJE//666+6detu3Lhx0KBBnTp1WrNmTVRUVHx8/IULF/oX97kjIiIi2dm9G0OHQiKBqysmTMi/Nzs7e9WqVTt27Lh//75EIhGJRAKBoFatWrdu3TI3N+/bt++SJUvs7e0vXbpUsWLF338/6uaWIhYLAIwbJ7KzywgIeBoREfHw4cNvv/1279693t7eSrhCYPdu9OqV87WJCX76Cf7+KK2rrJCSFT5iV6tWrVOnTnl6ekoH55YtW1a1atWDBw9euHChdu3a8+bNk3+RREREBbh5E4MGQSKBqSm6d8+/NyMjo23btnPmzOnQocPKlSsFAkG1atW0tbWzsrLs7Oxev369fPnyMWPGaGtr37lzZ/fuy927m4nFDgB+/BFLlmg2aNDg3bt3oaGhL168OHr0qLJSHYB37wDAwABTpuDJE0ybxlRHn1SkBYobNmx44sQJ6dcODg737t27fv26rq5ujRo1NDU15VkeERFRwWJi0KUL0tJQpgx27cJ/j/m9N3fu3IcPH968edPe3n7o0KG1a9c+d+5cRERE7dq1raysNDQ0hg4dGhUVtWrVqv79p/v4WEvfpurgELRgQbcDBw4NGzZsyJAhtWvXVvyl5TNoEKpXR6VKsLBQdilU6pXkxSKampru7u6urq5MdUREpBSZmejWDdLn9xYtgpdX/gPEYvHatWtnzpxpb28PIDg4eODAgQKBwMXFpV27dtHSRYeBQYMGPX2a/fff00UiBwAaGssSEr43Njb+7rvvBgwYsGrVKkVe1Ge4uzPVUZEUacROKBReu3bt+fPnmZmZH+8dOHCgjIsiIiL6rGHDcOkSAAweDH//Ag549erVmzdvmjdvLv349u1bW1tb6dc+Pj779u0LCQnx8vKSSOyA0y9eaAEYPDgxKGhmp06d+vbt6+bmVq5cOYVcSg6xGIcOwcQEjRopsltSN4UHuwcPHnh7ez9+/PhTBzDYERGRIi1ciG3bAKBRI6xZU/Ax2dnZAMqUyflnzsLC4tV/z81WrFgRwKBBg9atOzZqVDWgEoDhw9MiIrrZ2dlt3LhRW+EPml68CH9/3LgBTU08fixQcO+kTgoPdn5+fi9evPD3969evbrif9aJiIjyOnIEU6YAgIMD/voLOjoFH2ZjY2NsbHz9+nVHR0cArVu33rZtm3Q29tq1azY2Nu7uPTp00Jf+O2hvH7RtW//q1asfPXpUwf/SvXiBiROxaxckEgBwcoKJiSQlRZElkFopPNhdvXp16tSpM2fOVEA1REREn/H8OXx8IBLB0BAHDsDS8pNHamlp9enTZ/bs2e3atTM0NJwyZUrt2rXHjBkzderU+fPnd+vmd+zYJOmRdnZBgwaF1au3u3379hoaJbn1vGTS0rByJX79FampAKCnBz8/TJ/+yahKVBSF/wRraWnZ2dkpoBQiIqLPCw9HcjI0NLBtG1xdCzl4zpw5QqGwUaNGQUFBZcqUWb9+/Z9//lmhQoXYWN3Nm/tL7zBq1uzWixc9Zs2a1bFjR4WlGakvVQAAIABJREFUOokEO3eialVMnozUVAgE6N0b4eGYN4/rmNCXKvyHuEuXLrt27ZJIx4iJiIiUx8sLW7bg+HEU5QUQ5ubmoaGhDRo0GDRokKOjY58+fcRicZMmfcqUOf/unTWA779PPX1a0auZRESgVSv4+OQ80uvmhvPnsXMn7O0VXAipp8KnYufNm9esWbNWrVoNHDjQ3t5eS0sr3wEeHh7yqY2IiOgDAgEGDCjG8WZmZuvWrVu7du2zZ880NTW1tSu0bInERAAYPhy//WYoUPiDCp065bzptXx5zJ2LAQOgwOlfUn+FB7v4+HihUHj69OnTp08XeAAH84iIqDQTCASOjo6vX6NFCzx4AADDhuG336D4VAegenU8eYKRI/HLLzA2VkIBpN4KD3Zjxox59OhRq1atXF1d9fT0FFATERGRbL1+jZYt36e6339XTqoDsG8f0tPBf05JTgoPdqGhoWPGjFmxYoUCqiEiIpI5aaq7fx8Ahg5VZqqTYqoj+Sl8Yl8gENStW1cBpRAREeWVno7jx5GR8UWN5Et1a9cqKNUlJUEoVERHRHkVHuyaNWsWHBysgFKIiIhyZWaiTRu0a4cxY0reiFJSXXo6Jk1CuXKoXx+8C50UrPCp2AULFjRu3HjIkCEDBgxwcHDQ19fPd4AF30tMRESyNnw4zp8HAFPTErbw5g1atcpJdUOGKGgG9uxZDB2KR48AICoKYjE0NeXeKVGuwoOdl5eXUCjcuHHjxo0bCzyAT8USEZFszZ2LrVsBoFEj/PJLSVp48wYtW+KffwBgyBCsXSv3VUWSkjBpEtatyxmlq18fmzYx1ZGiFR7seIMdEREp0t69mDEDABwdsX9/SV6x9eYNGjXKePxYF4C395vVq800NAr/9+5LHD2KESNy1hzW08PMmfjpJ6Y6UoLCf9D379+vgDqIiIgAXL+O/v0hFqNsWRw6hHLlit3CxYuPvLw00tMrATA23nPkiE+1ahU2bdrUrFkz2ZcLxMXBzw87duR8bNUK69bByUkeXREVrvCB6V69ev38888KKIWIiL5yUVHo3BlpaShTBoGB+OabYrdw82ZU8+YiaaobPBgJCT3fvn3dtm3btm3bXrt2TeYFnzmDGjVyUp2pKTZuREgIUx0pU+HB7tChQ5cvX1ZAKURE9DVLTYW3N16+BIBly9C2bfFODwkJqV+/g5tbYnZ2VQA2NseHDbumoQEzM7PVq1d369btxx9/lHnN06YhJgYAvL3xzz8YPFjJK+QRFR7srKysUlNTFVAKERF9tSQS9O2L27cBYMwYjB5djHNjYmLGjh3btm2/yMi1QA0ArVtHNWmyrUmTxocPH5Ye4+/vf+HChbi4ONmWPXo0mjTB1q04eBDW1rJtm6gkCr/HbvPmzd27dx84cOCgQYOcnZ0NDAzyHWBiYiKf2oiI6Gvx4AEOHgSAdu2wdGlRz3r27NmIESOCg4OBcgLBqYQEOwAtWjw7dqyihsaOGTMqDR48ODIy0tjY2NnZWSKRREdHm5uby7BsHx/4+MiwPaIvVfiI3cCBAyUSydatW5s3b25nZ2f6EQVUSURE6q1KFQwciF69EBhY1IdJX7586eHhIRQKBw2aqKNzQSKpAUAg2HL7dt3o6BcAZsyYIRaLDx06BCA+Ph6AsbGxHK+BqBQofMSuRo0aCqiDiIi+ZmXKYPPm4p0yffp0GxubbduO1ajxOjPTHsDAgYiODrxzR3Pq1Knbt2/X1tauU6fO/fv/Z+++A5o4+ziAfy8JSdgbWUoRrXugaHGDiCgiDkBf99ZubR1VsXW2dWtta6t1W9GqdeBCRQTEgQu3iIAiiMjeZF3u/eOU0hYELYT1+/yV8dzl92hNv7l7xgMAhw4datSokZ2dXVUUT0jNUX6wKx6gQAghhNQQarX6zz///OGHXYMGibOzGwIYNw5btyI8fG6fPn0OHDiwbds2LS0ttVrNMExISMiSJUvWrFnDvNPUhrt3MW4cEhJw4QJatqzsnhBSqd5iHe6nT58ePXo0ICDg9OnTifwijIQQQkh1yMjIyM0Vfvuty40bAKCvf2LLFk4ggIuLy6JFi+RyeefOnT/77LOIiIijR4+6u7t//PHH06ZNe9tPUauxahU6dUJUFDIzER1d+R0hpHJVKNjFxcX17t3b3t5+8ODBo0aN6tevX6NGjVxdXWNjY6u6PkIIIeQfoqOjR478BDgbG2sIQCo9KpP5bdjwas7FgAEDAPTp0+fUqVMA3NzcLl++vHLlyrf9lMRE9OmDOXMgl0MkwqJFGDKkUrtBSBUo/1bs8+fPu3fvnpKS4uzs3L17d2tr66ysrLCwsNDQ0F69ekVFRVlYWGigUEIIIXWJTAap9O0OSU1NXbp0aWBg4LNnuQwTDHQE0K9fTr9+SQsWCGfNmhUVFeXj43P27FkrK6sHDx4kJyefOHHCzc3tHco7eBBTpyIrCwDefx+7d6Nz53c4DSGaVn6wW7x4cUpKyubNm6dMmVLy9T179owZM+b7779fV/GJ6YQQQgiwcCGWLsWCBViypKKHPH78uFevXhYWFoCRvv6ZvLxmAID9jRtfnD79Bze3Xk5OTvfu3Tty5Eh+fr6enp62tva1a9davf3mFYWFmDcPGza8ejpmDDZuhJ7e256GkOpRfrDjf+78I9UBGDVq1P79+48cOULBjhBCSMVt3vwqz92+/RZHjR07tn379gsWrO7WrQBoBmDYMOTl/b5x47G4uEf9+vXr2LFjQkKCXC6fPHny5s2b322exNWrGDUK/DgjU1Ns3oyhQ9/hNIRUm/KDXWpqqp+fX6lvNWvWLCgoqLJLIoQQUmedOfNqVwkLC6xfX9GjoqKiIiMjo6Ke+PoaAMYAhg3Dnj0QiQI7duz45MmTXbt2paen5+bmBgYG9nvbzcheS0yEqysKCwGgTx/s2AEbm3c7EyHVpvzJE5aWlrf4TV7+5caNGzTAjhBCSAXdu4dhw6BSQVsbR4/C3r6iB965c8fWtvWUKXaxscYA/Pz4VAcAQ4cONTc3v3nz5rx582xsbN451fE4DmIxVq3C6dOU6kitVH6w8/b2DgsL27BhA8dxxS9yHPfjjz+GhIQMHDiwKssjhBBSR7x4gQEDkJMDgQC7dsHZ+S2OTU2Vp6TsvHaNf3Zg5swbotc3nEQikUqlAhAWFtauXbv/UmHDhoiJQVwcZs2C4C1WAyOkBin/Vuw333xz9OjR6dOnb926tWvXrpaWlikpKZcuXbpz5461tfXXX3+tgSoJIYTUavn5GDgQz54BwPLl8PUt/xC5XB4eHn7u3Lljx8IfPvyB4xwB6Oufzs8f5e4uvXLlSsuWLQFcu3atRYsWQUFBBw8eDA4O/o912tr+xxMQUs3KD3YNGjS4ePHip59+euLEiTt37vAvMgwzYMCAn3/+2crKqoorJIQQUruxLMaOBb+S8IcfYvbsctrv3bt3+fLl9+7dU6vVAoGxWh0EdAIAHPT3T3B1jXBxcWnfvv3SpUulUunhw4cHDBgwcOBAf39/FxeXKu4KITVd+cEOgJ2d3bFjxzIyMu7cuZObm6uvr9+2bVszM7OqLo4QQkgdMG+eDr85Zf/++PHHchr7+/uvWbPG09Pz4cOHK1b8OneuI+AEwMnp6d274+fPL/rmm28uXbrUo0ePZcuW5efnS6VSmUx29OhRT0/Pipd05Qr09fH2a6EQUtO9xSACU1NTV1fXQYMG9e7dm1IdIYSQinj8WLJliwRA+/b44w+I3ng94cqVK8uXLw8MDIyLi/vkk/nbt/+PZZ0A2Nldz8sbeO3a5UaNGi1evNjR0TE/P1+lUv3666+FhYVnzpypeKqTyfDxx+jSBR074uXL/9w9QmqYCl2xu3nz5pYtW+Li4hQKRckpFLzQ0NDKr4sQQkidYGWlbN2aFQqFx49DX7+cxtu3b+/Xr98HH3xw506CWj37wQNdAD4+2LDBzs4uOicnZ/fu3T179rxz505ERMSKFSvedvvX2FgMG4aoKAAwNYW29jt2ipAaq0ILFHt7e6vVag1UQwghpI7R01OHh+caGxtXpPHDhw/d3d1fvCgAgu7d0wUgEBzZsaO/np5548aNHzx40L17d47jGjRokJ+fX8FzFjtwAJMnIzcXALp1w759MDB4+/4QUrOVfyt2yZIlpqamJ0+ezMrKUpZGA1USQgipDxiGkcnE48ZZAs4AvL1VUun4wMA/AXAcJxAIYmNjtbW1TUxM9u/fX/GpEnI5Pv0Uw4YhNxcMgzlzEBpKE2BJ3VT+Fbvbt29/++23/fv310A1hBBC6rPmzTv+8otnVpYAQIMGFw8c6Prtt9M///xzAwODuLi4li1bfvPNN56enl988UVMTMyhQ4cqcs6nTzFsGPg18ExNsXMnBgyo0k4QUp3KD3Y6OjqGhoYaKIUQQkh9VlCA69eXZGXpAejVKz8qasiUKf2///77lJQUb29vbW3t8ePHJyQkmJqasiwbGBjYsGHDcs95/DjGjUNmJgB06YJ9+9CoUVX3g5DqVH6wc3NzO3To0OTJkzVQDSGEkPqpoAADBuDmTT0ADHOqefOgwYPnb9iwwdbWViwWMwxTWFiYmprq5eXl5uY2cuRIIyOjcs/54AGGDIFKBYbB9OlYuRJaWlXfE0KqVfnB7vvvv+/WrdvEiRMnT55sb2+v9a9/FrT0CSGEEN7XX2PjRvzyC4YNe4uj+FQXFgYA/fvjww8Fa9feDgjYnpeXZ25u/v777w8dOrRXr16Ojo6Ct9nqS0cHEgl0dbF1K3x83rInhNRO5Qe77t27FxQUbN++ffv27aU2+PcCKIQQQuqh9euxbBkABAe/RbArKICX16tU16ePcsKEU/fv3x85cuSKFStat26tq6v7zvW89x6ePoWWFmg8Eak/yg92zm+1UTMhhJB66c8/MXMmANjYoOK7iBcWwssL/HKo2tqh5871Dw1V2draamlpxcfHOzs7BwQENPoPw+LolhKpb8oPdkeOHNFAHYQQQmqviAiMHg21GgYGOHECFZjVAPw91enpRRQVeY0fP8LMzGzPnj16enpXrlyZPXu2m5tbVFSUnp5eVZZPSN1R+mCF6Ojo6Ojop0+f0rrEhBBC3iw6GoMGQSaDlhYOHkS7dhU6ik91588DgJHRFZb19vX1WrNmzcqVK+/fv6+jo/PNN98cP35coVD8/PPP5Z4tK4v2ByMEKCvYtWjRokWLFvb29rq6uo6Ojnfv3tVwWYQQQmqFlBT074/MTDAMfvsN7u4VOqpkqhMIzrRtu1Amyw4PD3dwcAgMDDQyMtqyZcupU6cSExPHjBlz4sSJN5/t6FHY28PeHnFx/7k/hNRypd+KbdasGQCpVNqsWbPWrVubm5trtipCCCG1QH4+BgzA06cAsGQJxo2r0FGFhRg48FWq09G5MGNGZNu2Ex89uv3s2bNly5b5+fldunSpY8eONjY2165ds7e337dvX1mnUquxcCG+/RYcB6EQMlll9IqQ2qz0YBcdHa3hOgghhNQuKhWGDcPNmwAwZQoWLKjQUXyqCwkBAJHoXGGh1/r1gpYtW2ZmZqrV6kWLFt2/f3/x4sWBgYE6OjoymSw9Pb2sPWGzsjBqFE6dAgBDQ+zahVatKqdrhNRepd+KNTMzMzMzs7Gx6dWr17Rp057yP8cIIYSQ1/7441Wo8vLCxo0VOqSwEF5eHJ/qgDNOTsvat2/u5OTUvHlzpVL50UcfARg9evS5c+dycnISEhLs7e0PHDjg6ur671PdvYtOnV4V0KoVrl6Ft3fl9IuQWq30K3YZGRnFj6VSqVwu11Q9hBBCaoe2bWFmhvbtsW8fROUvsYDCQnh4yCMiJADE4lCFYpCOTtebNx+oVKqIiIicnJxt27a5u7s7ODgUFhZ+++23ZmZmBw4cePLkyYwZM/5xqv37MXEiCgoAwNcX27eDZs0Swiv932JaWhoAqVRKM8wJIYSUqk0bpKVVtHFREQYO5PhUxzBnL1+2dXKSr1y50sLCok2bNi4uLtu2bYuMjBw5cqSDg4NAIFi9erWJiUlgYODx48etra2Lz8Oy8PfHypWvBtUtW4avvgLDVEX/CKmVSg92tEsYIYSQyiKTMcOGISSEAeDgEK9Wz2jV6qaTk9PWrVs3btx45cqVVq1aTZ48WSaTCQSC2NhYkUjUr1+/AQMGjBw50sDAoOSpRo3CH38AgIkJAgLg4VEtHSKk5qrA1XNCCCHkXclkzIQJ4kuXAIBhguPivIEiPT29li1bbt682c7O7ssvv/zggw/69OmTnJwcGBiYn59/7dq1VmXMg+AH1bVpg8OH4eCgwW4QUktQsCOEEFJV5HKMHat9+7YOAOCspeWHlpbNo6KiOnbs2LBhw4cPHy5dunTNmjUAEhMTU1JSxGLxiRMnykp1AAIDcfUqPvqIBtURUrrSZ8USQggh/5FcjgEDZLdv2wJgmAt+fgGtWjWOjIy0tbW9f/++mZnZhx9+aGpqum7dOoZhLCwsWJYNCgoqdQ5ssV69MHs2pTpCylQ7rtitXr264o1nzZpVdZUQQkg9lJcHLy9ERyMsDM2bV+gQuRw+Pjh3TgqgQYNHHTv++Mknn7i7u8fGxu7Zs8fd3X3Tpk3btm379ddfo6Ki0tLScnJy5s6d26NHj6rtCSF1Xe0IdsuXLy+5AsubUbAjhJBKpFDAxwfh4QBw716Fgp1CAV9f8DuB2djEN2z46Qcf9OjVq5enp6eXl1dAQEBkZGSnTp2mTp2qVCrXrFljZGT0448/jh49ump7Qkg9UDuCXXR0tK+vb1hYmLu7+8cff1yJZ05LS5s+fbpKpXpDmzt37gBIT08v4BdNqnosy2ZmZubl5Wnm4+oAtVoNIDk5WSCg0QUVpVAoVCpVYmJidRdSmxQVFQGoV39oHIfp003PntUB0LdvkZNTerm9VyqZqVNNz53TBqCtfX3UqFMREYUZGRmJiYkrVqyYP39+ly5dbGxsBAKBtrZ2Tk5O586d9+7dq6WlVfIP9vp1yf79upMm5TVrpqzK/tVE/Bfaixcv6Aut4pRKJcuyGvu3ya/vy9TIhXbKD3YzZsxo06bNpEmTAPz222/3799fv3591Rf2N2ZmZkFBQb179w4ODp4/f76Li0tlnVkikdjb27Ms+4Y2z549AyASicRicWV97psxDKPJj6sD+L9BsVhM34MVp1arFQoF/Wf2Vvhv83r1h7Z4sf6RIzoAOndWbt6cJ5WW03elkvnwQ8Nz5yQAGjdOzs8f/uuv6Wq1+vr16/Hx8S4uLhs2bJg5c+alS5fmzp3r4+OzY8eOxYsX6+rqljzJ7t3a8+cbKJXIzxdu2ZJTdb2rmfhgp6WlJRQKq7uWWoPjOIZhNPZvk/87qqG48gAYNGgQ/3jAgAEVOaSKJCcnGxkZNWvWTKlUavJzZ8+eDSAjI0NjnxgdHZ2VlaWxj6sD5HL53bt3FQpFdRdSm2RmZj569Ki6q6hlkpKSkpKSqrsKzVm1igM4gGvRgqvIV6Bczg0c+OoQqfSagYG1QCAQCASWlpYAzM3NjYyMunbtmp2dvWLFCjMzM09PTycnJ7VaXXwGpZL79NPiM3AnTlRh72oshUJx9+5duVxe3YXUJllZWdHR0Rr7OP6W2q+//qqxT6y42nR5w8rKavny5SKR6Pz589VdCyGE1HF79mDOHACwsUFQEExMymmvUMDPD8eOAYBYfLVXr+VyecaSJUtatGhhbm4+derUtLQ0IyOjp0+fOjk5zZs3T0dH5+bNm7///nvx/aysLPTvj59+AgArK5w/D0/PKuwgIXVSbQp2AKZNm3bv3j13d/fqLoQQQuqykBBMmgSOg6EhTpxAo0bltOdTXWAgALz33nNLy4lNmlg6OTn5+/tv3bq1UaNGmzdv1tPTe/r0aXJycmxsrLm5+eDBg2/fvt2sWTP+DI8e4YMPEBwMAB074upVODtXaRcJqZtqWbAjhBBS1W7cwKBBkMshlSIwEO3aldO+ZKrr1g3vvz/dx6fv9evX+dE7xsbGu3fvjo2N3bZtm4GBwbx584yMjDZu3PjDDz9YWFjwZzh7Fs7OePwYAIYNQ3g4bG2rsoeE1F0U7AghhPzN118jPx9CIfbsQc+e5TRWKDBs2F+p7tQp5OUlm5ubFxQU6OvrFzdzcHDw8/Nr1KiRtbW1vr5+yUUGNm6Epyeys8EwWLgQ+/ZBR6dK+kVIfVA7ljshhBCiMX5+iI7GN99g6NByWvKp7uhRAOjaFadOQV8fVlZWT58+tbe3f/jwYcnG/PI6BgYGL168sLe35188ehSffAIAOjrYvh3DhlVBfwipTyjYEUII+ZsJEzBhQvnNlEoMH/5XqgsKgr4+njx5YmJisnPnztatWwcFBdna2rZr187c3BzA3r17FQrFrVu3LC0tnV8PoBMKwTCwtsaRI3ByqsJOEVJPULAjhBDy1pRKDBuGI0eA16lOR4f98svZGzZs0NLSksvlN2/e5DhuwYIFHMcNHz68f//+H330UadOnX788cdDhw6JRK/+7+PlhUeP0KABDAyqszuE1Bnlj7GzsbExMzPjH5uZmdnY2FRxSYQQQmq0kqmuS5dXd2BnzZq1e/fu1q1bN2nSJCIiwsPDg18wnOO4ffv2jRs3rrCwMCkpKTAwcODAgSXP1rQppTpCKk35V+ySkpKKH+/YsaMKayGEEFLj8Xdgi1NdUBAMDPD06dMNGzZ06NDh5s2bPXv2PHHixM8//5yfn3/69Gl/f//u3buHhoYGBgb279+fdlMgpErRrFhCCKnXnj+HTFbRxiyLsWNx+DDwOtWlpMTs3Lmzc+fOarU6Pj7exsamQ4cOwcHBrVu3vn///uzZs3v37m1ra9uqVauYmBhKdYRUNRpjRwgh9dfGjfjsMzg5ITKy/MYsi9GjsW8fAHTowPn57WzbdlFCQkJxg6ysrOzs7L17927btu3Bgwfjxo1r3bq1jY3N8+fPbWwc9uzpcv48tm/H69E9hJDKR1fsCCGkntq3D599BrUa6enlN2ZZjBnzKtW1b89KJF6zZ0/OzMyUSCQCgcDIyEggELRv355hmCZNmgwaNMjZ2blPnz6rV69OT0/X1raLiFhy82aX48cRElLV3SKkXqNgRwgh9dGZMxg3Dmo1DAxw8GA5jflUt3cvAHToAI7rc/36WZZl8/PzGYbhOO7YsWNqtfrBgwcikejq1atDhw6dM2fOoEGDIiIizp9PCQ1dXlDQFoCLC7y8qr5vhNRjFOwIIaTeuXoVPj5QKCCV4sgRODq+qTE/ro5PdY6OnKGh7+3boe+9917v3r11dXW7desG4IsvvhgyZAgAAwMDtVptbW196dIluVz+7FmTgoKz2dnGAMaPx+nTtKsEIVWLgh0hhNQv0dEYMOCvTcNcXd/UmE91AQEA0KED/ve/rVevBhkZGenr67///vv5+fmTJk3iOC49Pb1BgwaNGjVKT09Xq9Xr16/nOG7GjNsq1XG12gDgFi5UbN8OsVgzXSSk/nqLyRP5+fn37t3Lzs7u169fTk6OgYEBwzBVVxkhhJBKl5gIDw+kp4Nh8Msv5WwaJper+vZ9ER7eEIBAcPvOnb43b6byb928efPmzZsMw1y/fr1Zs2b29vYBAQH+/v5bt25NS0szMDBMSJjMcf4AtLTYX3+VTZyoW/WdI4RU7IpdSkrKiBEjjI2Nu3Tp0r9/fwA//vhj8+bNIyIiqrg8QgghlSY9HR4eePYMAJYtw5Qpb2qckJBkbX2WT3VAFNBHpUoFwDAMwzBaWloMw+jq6q5fv97FxSU4ODg3N/fy5cs2NjZZWQXALsAfgIkJt2NH8pAhiirvGyEEQEWC3cuXL52dnfft29epUyfH1wMx9PT04uLi+vbte/fu3SqukBBCSCWQyzFgAB4+BIAZMzB//psaKxRshw53MjP7AxAI7jg7f21jo83vA9awYUOO4+zs7AQCgUwm4zhu69ata9euBXDkyJHw8HCG+S4hoQcABwf26lXGyamo6jtHCHml/GC3aNEifhOYS5cueXt78y/OmDEjODhYpVItXbq0iiskhBBSCe7cwdWrADBmDNaufVNLloW7+/PMTE8A+vpxDg7THj6M6Nq1a4sWLRo3bpyYmNi6deu4uDgA/FonLMvOnDkTAMMwYrGYYQoA9OyJq1eFDg5V3zFCSAnlj7ELDAz08fH5x9Z+AFxcXHx9fS9cuFA1hRFCCKlMHTti7lwIBFi0CG8YIM2ymDAB4eGNAAC3mjSZbWgoffw4RyAQtG3btm3btgsWLGjatGlcXJxKpSooKADAMIxSqRSLxU5OTp6enj4+vgyDJk1A20wQonnlB7u0tDQ7O7tS37KyskpLS6vskgghhFQ+gQDff19OGz7V7d4NAELhXZbtc/t2Fj9PLjQ0tE2bNtOnT1+5cmVQUJBMJmvfvr2xsfH58+elUqlEInn8+LEZ7SlBSHUrP9jZ2NhElrHXzMWLF21sbCq7JEIIIdWAZTFx4qtUB9xmWTcgQyjUUiqVQqFQS0srODg4ISHB0tKSYZiHDx9GRUUBEAqFLMuuWbOGUh0hNUH5Y+x8fX3Dw8O///57juOKX1SpVP7+/pGRkYMGDarK8gghhGiCWo2JE7FrF//strGxn0iUY2lpqaWlBUBLSysrK0skErm5uT18+FAkErVr187Y2NjCwqJt27bW1tYjRoyo1vIJIa+Uf8XO39//xIkT8+fP37x5s1gsBjB69OiLFy8+ffq0cePG/v7+VV8kIYSQKvSPVMcw7lKpSCIxY1lWoVDo6ekVFhbKZDIdHZ2kpCQtLa179+6xLKujY5Kf75+bKwkL66StrV29XSCE8Mq/YmdkZHTx4sWpU6empqbGxMQA2LNnz/Pnz0eOHHnp0iVTU9OqL5IQQsjbuXYNO3ZApSq/JZ/qdu7kn93R0urPcWnu7u49e/ZMT09XqVRFRUWW+yzLAAAgAElEQVRqtRpAYWEhAKVSqVKpGMZUqTypVH4uk01j2Q5V2RVCyFuo0ALFxsbGmzZtysrKunXrVmho6PXr17Ozs/fs2dOgQYOqro8QQsjbCg5Gjx6YMOHVBq9vwHH4+OO/Up1E4qmtXQBg7969qampe/fuFQqFn3zyiUAgaNiwYcOGDQFERkZeu5b2/vtpCkVnAK6u6NixartDCKm4t9hSrKCgICcnJzc3V0y7/RFCSE0VGYkhQyCXQyJBq1Zvaslx+OgjbNrEP3soEg0wM0NamoxhmM6dO2dnZ2/atGnMmDFxcXHa2tpNmzbNzMzU1dXV0uo8YABevACA//0PO3bQDrCE1CAVumIXExPj7e1tbm7eq1cvb2/vrl27mpmZTZgwITU1tarrI4QQUnH37sHTE/n5EAqxZw86lH2PlOPw0Ufc61QXDfRWqZJevHihUCgGDx588eLFzp07X7x40draOjIysqCg4Pr16w8fPuzff12vXq9S3ezZCAiARKKRjhFCKqb8K3YxMTFdu3bNy8vz9PRs06aNtrZ2WlpaeHj4jh07IiIiLl++TFPcCSGkJoiPh4cHMjPBMNi8GT4+ZbbkOEyerNi2jb/UFm1oODg396WOjm5hYaG2tvapU6c6deq0efNmc3PzY8eOZWVlicXivLw8N7cdP/3UT6mEUIj16/Hpp5rqGCGkwio0K1ZPT+/SpUvvv/9+yde3bds2efLkxYsX//jjj1VWHiGEkApJToa7O5KTAWDVKkycWGZLjsMnn4BPdQzzqEGDEZaW2rdvIygoyNfXNz09nWEYfo26/Pz8e/fucRwnlUq9vS//+WcHjoNUit9/f1NqJIRUo/JvxZ4/f37lypX/SHUAJk6cOHTo0MOHD1dNYYQQQioqMxMeHoiPBwB/f8ycWWbLoiJZly43f/mFfxbNca5paXf59DZ+/Pg7d+5IpVKBQKBSqQAUFRUB8PT0XLAg6+DBDhwHExOcPUupjpCaq/xgV1hYWNb2Era2tllZWZVdEiGEkLeQnw9PT9y7BwAff4xly8psmZOT+957xyMj+ZF3j4De+vr5HMepVKqGDRvGx8d369atZ8+e3bp149clBtC6detDhw7FxWkBsLNDRAS6d6/yHhFC3ln5t2I7d+68devWbt26/eP1wsLCU6dOubi4VEldhBBCKmbKFPD7Po4ahbKGxrAsu2HDj3Pm6KhUUwEAjwBX4IW9fVu5XB4bG5ucnNyhQ4ebN2/GxsYWH+Xg4HDp0iWJRLJmDXr2hIcHLCyqvj+EkP+g/GC3Zs0aFxeXvLy8uXPntmnTRiwWq9XqGzduzJkzJyMjY//+/enp6cWNaSIFIYRo2MuXADBwILZvh6C02zCxsbF9+rgnJMwGpgJgmBiOcxWJ0lQqxMfHKxSKJk2avHjx4saNG0KhUFdXt6CggGVZJyensLAwHR0dAAYGGDNGo50ihLyb8oOdh4eHUqk8ePDgwYMHGYYxMTHJy8tTKBT8u+3bty/ZuOR+soQQQjTgwAFERqJvX4j+9Y2ekpKyZs2adevWs+x64GMAQIyennePHo4nT540NTXld4BNSUnJy8sDIBKJcnNzAXh7ex89elTDHSGE/HflB7vuNJ6CEEJqMFNTeHq+elxQULBt27bff//9wYMHBQUFHMcBDPAj8AkAIAZwVamyzp9/5u7ufvbsWZFIxLKsnp5eQUGBSqWSy+UMw3z++efr16+vvg4RQt5d+cHuyJEjGqiDEELIf/To0SMPD4/nz5+zLPv6/gkDbChOdQzTG3ghk0EoFHIcJ5FIFAoFwzAvXrwovt+ycOExZ+cB1dUFQsh/VP6s2FGjRp0+fZplWQ1UQwgh5N3I5fKBAwdmZ2fr6urq6elJpVJLSytt7c3ApwAYJlYgcAOSRSKRUCgUCATnzp2zsrJq2bJlw4YNGYYxNDSUSCSurgFLlw7o1w/h4dXdH0LIOyk/2O3du7dfv362trYzZ868deuWBmoihBDytvbv35+SklJUVJSXl5eXlyeTyVNS5hUVTQYAPGaY3mp1UqNGjQCwLKtQKDiOe/r0aXR09LNnz9RqdXZ2dtOmO0NDR7As9PRQxiJXhJCarvxg9+TJk2+//dbExGTt2rWOjo5t27ZdtWrV8+fPNVAcIYSQkv74A23bYt++Ut66cOGCsbExf48VYID1wGcADAxeikTuwHOpVJqammppaSkoMXWWv2nbokUrb++Ee/eGcxzMzHDuHBwcNNYnQkhlKj/Y2dnZzZ8///79+zdv3pw5c2ZGRsacOXMaNWrk7u6+a9eu/Px8DVRJCCHk0CGMHo27d7FjRynvXr9+/dmzZ1Kp1MrKWiL5BfgcAPC4S5f5YnGaWCyWyWQKheL58+daWlrF2U4qlR47drpNm7uBgY3wegnizp011SVCSGUrP9gVc3R0XL16dWJiYlhY2MyZMx89ejRu3LgGDRqMHTs2nIZjEEJIVQoKwogRUKmgq4tFi/75bnR09O3btxs0aKCnp5eRMUsunwZALH5mYuJz5sx2fX19tVrNMAzHcWq1WiaTqdVqoVA4evToqKjHP/zQ948/AKBNG1y8iGbNNN01Qkgleotgx4uLi4uIiLhw4QJ/N9bOzu7AgQO9evVyc3NLTU2tggoJIaS+u3ABPj5QKCCR4PBhODv/s8H27dtbtmyZnp6elPSpXD4DgFiczHF9CgpiALx8+VKpVALgOI5hGIZhmjdvnpWVtXbt7jFjbIODAaB7d4SF0dA6Qmq9iga75OTkdevWderU6f333/f393/27NmXX3559+7dBw8epKSkzJs3LyQkZNy4cVVaKyGE1EPXr8PLC4WFEImwbx/c3Utpc//+/X79+r3//p8cNwcAkCAQuFlbK5RKJb+OCfeaWCz+/PPPb9++nZ+v3707rl8HgIEDceYMjI012CtCSNUofx27LVu2BAQEhIWFqdVqbW3tESNGjBs3rk+fPkKhkG9gaGj43XffHT16lG7IEkJI5bp3D/36ITcXAgF27sTgwaU34zjuwgXPhw9dAQAJDOMqkz1JSPirgVgsdnZ2Xrx4cc+ePfkBdr/9hpgYABg/Hr/9VsquFYSQ2qj8f8pTpkxhGKZHjx7jxo3z8/PT19cvtZmJiUnTpk0ruzxCCKm/Hj+GuzsyMsAw+OUXjBxZZsvMzFmRka4A7Oywf7/2kyffX7hwIS8vz9TU9KefftqzZ4+fn98/DvH1xblzcHPD11+DYaq0H4QQzSk/2C1ZsmTs2LF2dnZvbnbhwoVKKokQQggSE9GnD1JSAGDVKkydWmbL+fNx5YorAG3tVGvrL8aPj2rRokWfPn0mTZo0ffp0W1vbQYMG/fuoli0RFlZVxRNCqkv5Y+z8/PwMDQ1Lfevhw4cnTpyo7JIIIYTghx/w7BkALFqEmTPLbObvj++/5x8+Kypyjok5bWVlxXHc3LlzLSwsdu7cuWvXLrFYrJGSCSHVr/xg16JFi99//73Utz777LMRI0ZUdkmEEEIwdCjatMHSpVi4sMw2/v747jv+4TPARSp9kZmZGRIScvjwYblcDqBLly7du3fXTMGEkJqg9FuxoaGhsbGxxU8vX74slUr/0SY7OzsyMrJ4CgUhhJBK1LUr7tx5U4MFC/5KdUKhW+/eTRwd/Tw9PaOiombNmsVx3NSpU1etWvX48WMaAE1I/VF6sJNKpdOmTVOr1fzTgICAgICAUlvOmDGjqkojhBBShgUL8O23/MNngKuhYbZMZnXhwoXVq1dPnjx58eLFq1at2rx5s5WV1ZUrV7dubRoYiN9+Q7du1Vs1IaTKlR7snJ2dT5w4kZ6eDmDMmDFjx451//vSSQzDaGlp2dnZffDBB5ookxBCyGtff12c6hIBV4Z5wnFGz549S0xMHDt27MGDB6dNm5abmysSifT1TX76qfPVqwBw7BgFO0LqvjJnxfbr149/EBQUNHr0aPdS18QkhBCiWV9/jWXL+IeJDNNbVzfV1LSRtrZ2cnKyo6PjgQMHhgwZ8tNPPxkYGAgEusnJPyYlNQXQsiXo/goh9UH5y52UNXOCEEJIpWBZHD2Kdu3g4FBOy2+++Vuq47jY/HwoFAq1Wn3gwIHPP/+8c+fOQUFB+fn5AoGRWr2X43oC6NwZJ0/C1LSq+0EIqX5vvVcsIYSQSsSyGD0aPj6l7xVW0sKFWLoUAITCZIbpLRYndu3atUGDBmZmZjo6On5+fl5eXleuXElPTxcKLVn2LJ/q3Nxw7hylOkLqCwp2hBBSbTgOU6Zg3z4AsLd/U8uFC7FkCQAYGxeo1b0kkiRra+sePXrk5+dnZWV5eHiYmZlt2rSpqKgIsFWpQoCOAIYMwYkT0NPTQFcIITUCBTtCCKkeHIdPP8X27QDQrh0OHCiz5aJFr1KdrS0aNRonFD5Vq9UpKSlr167t0qWLUCg8fPhwRkaGWq0GmgIXgOYAxo3jDhyARKKZ3hBCagQKdoQQUj1mzsTGjQDQogXOnIGJSenNFi3C4sUAYGuL5csj79w5pFKp1K8FBwcXFhZ26tTJ2dkZMALCgPcATJ+O7dsZWmmUkPqm/MkThBBCKt2CBVi3DgCaNMG5c7CwKL3ZihWvUp2lJbZuTRwypDfHcY6OjsnJyWq1umPHjvHx8TExMVeuXDE3NweMgQYAFi16034VhJA6jIIdIYRo2rJlrxaie+89nDsHK6vSm61ciblzAcDSEiEhWLVqoVqtZhgmKioKgEAgiIiIkMvlFhYW6enpqampAEaM2DRp0kdubprqCSGkhqFgRwghGrV6Nb7+GgBsbRESgkaNSm+2ciW++goAGjRASAhevAjZsWMHx3EikYhlWWNj46ysrPz8fACvR9fB2dk5IOAjTfWDEFITvfsYu5ycHGdnZ2dnZ3d392+++aawsLASyyKEkDrp9GnMmQMAlpY4d67MmbCrVv0t1T18eKhPnz4cxzEM4+joaGFhkZmZ2ahRI4lEYmFh0bBhQ/6oufz1PUJIPfbuV+yUSmVkZCSAoKCggICAUaNGHT58uPIKI4SQOqigABwHc3MEB+P990tvs2rVq/CnrZ0rl/dv0+ZK8c7dHMfdvn2bYRg3N7fw8HCWZfk7sLw2bdpUeQcIITXbuwc7IyOjy5cvA3B2dvbw8Hj69GmlFUUIIXXU0KG4ehUNG8LSsvQGq1e/SnUMkyqXu8lk9zmOK9lAoVAYGRldunTJy2vmyZNblMpMABKJpEmTJo0bN67yDhBCarZ3D3YikcjZ2bn46XvvvVcJ5RBCSF3XqVOZb61ejdmzAYBhUjnOleMe8K8zDKOjo+Po6Hjx4kWO4/Ly8nR1fzh8+BOGGaml5aRQKORy+Q8//KCR8gkhNdrbjbHLy8u7d+9efHy8UqmsooIIIaR+WrOmONWlcVxvgSBaLBZLJBKRSMRxXGFhYVFRkZGREcMIWHZ9bu4nADhOqFAoAOzZs8fV1bV66yeE1ARlBrvHjx+PHDly0aJF/NO4uDgvLy9jY+M2bdo4ODiYm5t//PHHmZmZGiqTEELqtDVrMGsW8CrVuQL31Wo1fylOS0uLnx5x48aNpk2ba2vvBT4FwDBPGGagQCDw8vL63//+V731E0JqiNJvxT548KBLly65ubmffPIJgNjYWGdn54yMDAcHh44dO3IcFxUV9csvv5w+ffr1qpiEEELe0dq1r1IdkMpxvYH7AoGA4ziBQABAqVS+ePFCKBRynNa1azM5zgeAUBjDcW5qdZKJiemmTZuqs3pCSE1S+hU7f3//wsLCwMDAn376CcBXX32VkZGxdOnSmJiYP/74Y//+/Y8ePVq/fn18fPw333yj2YIJIaR2OHsWJiZwdy+n2dq1mDmTf5jKMH1EokdGRkZCoZAPdhzH8VuHSaVGavVBPtUBUSzbXa1Oaty4cUxMjLW1ddX2hBBSe5Qe7C5fvuzn5zdw4ED+6dmzZx0dHRcsWMD/fAQgEAimT5/u6ekZGBiooUoJIaT2OH8egwcjKwuXL0OlKrPZunXFqS4NcAPuCQQCXV1dhmEYhlEqlfxWE1paxoWFBzjOEwBwGegNpPXo0SMuLs6krC1mCSH1UunBLi8vr+RPQIZhmjZt+u9mLVq0oGF2hBDyDxcuYOBAFBZCJMKuXRCVsfzAunX48ksAYJh0PtUBUCgUz58/5yeoMQwDgGX15fJjarULACAE6Atk9+7dOyQkRCO9IYTUJqUHu9atWx89erSoqIh/6urqGhkZKZPJSrZhWfbMmTNNmjSp8hoJIaT2uHQJAwagoABCIX7/HUOHlt5s/fpXqU4ozOS43sBdAAzDmJiYaGtrAxAKhfwDYD3QBQBwHBhgairZtGnTuXPnRGUFRkJIPVZ6sJs+fXpsbGy/fv0eP34MYPny5ZmZmaNHj05PT+cbJCUl+fn53b17d8qUKZorlhBCararV9G/P/LyIBRi504MH156s/Xr8cUXACAQZLCsS3GqU6vVWVlZMplMIBDwN2EBAC8AAH/Mnn0lK+tFenr61KlTNdEZQkgtVPoPvpEjRz548OC7775r3rx5hw4dHB0dXVxc/vzzz8DAQAcHB47jYmJiOI7z9fXlp80SQgi5fh0eHsjNhUCALVswalTpzX744a9rdSzryqc6AAzDSCQSlUrFMIxUKs3LyysoKAAAzGOYn48f3+TpuUwj/SCE1GJlXslftmyZt7f3zz//fOrUqevXr/MvKpXK6OhooVD4wQcffPjhh2PHjn39g5IQQuq1mzfRty+ysyEQYPNmjB9ferNNm/DFF+A4aGnlK5V9gbsMw4jFYrlcznEcP7ROpVIplUqJRCKXywGIxeKTJ3e4ublpsDeEkNrqTUM0Onfu3LlzZwCpqanJyckFBQUCgcDQ0NDBwUEikWiqQkIIqeni49G3L7KywDDYuBGTJpXebPNmfPQROA4Mk61U9gFuAOA4zsTEJDc3l99egmEYkUikUqn4VNeiRYvw8HAzMzNNdocQUntVaOythYWFhYVFVZdCCCG1VGgoMjLAMPjpJ0ybVnqbzZvx4YfgOABZHOfOpzreixcvRCKRvr5+YWEhP7qOf93ExOT+/ft0Y4QQUnE0qYoQQv6r//0P8fFo3x6+vqU3+O03TJvGAQyQDfQFbpiamhYWFvKrDXAcx7JsYWGhk9OI6Gg9lv1DJssDsHHjRkp1hJC38u7BLjs728XFBcCtW7cqrRxCCKmFdHSwrOyJDb/9xl/G41OdO3AdQG5urkqlEgqFAoFAoVBwHCeXd7x+/RfAENABfli2bNnwsmbVEkJIGd492KlUqtu3b1diKYQQUvds3sz9O9UBUCqVr9cfZqVSqUzWBQgE9AA1w8TMnDnL39+/OusmhNRO7x7sjI2No6KiKrEUQgipY7ZswYcfgk91AkE/holiWQDgB9Lxd2A5jmPZPsBhQBtggUlfftly5cqV1Vw6IaR2evdgJxQK27dvX4mlEEJIXbJlC6ZO5Tju1bg6gSCKZdUABAKBQCAQCoV8tpNIfGSy3YAUUAJjNm7s9dFHH1V37YSQ2qr0nScIIYSUKiFB5OWFFSvKabZqVWbJVAdcU6lUHMfxec7Q0FAgELAsCwyVyfYAUkABDBs5UkipjhDyX9CsWEIIqainT0V+fmYvXuDcOXz1VeltIiIihg8/k5y8CGCAHMCDYa5ra+sUFhYCYFmWYZjMzEyO4xhmpFq9ExABMobxbds2adu2SE12hxBS99AVO0IIqZC4OAwbZvbihRDAggWlt1m3bl3PnjuSkxcBAiAH6Atc5VcebtasGd+G4ziO44CxHLcLEAGFDOM9erTJlStXaO13Qsh/VPoVu9WrV1f8FLNmzaqkYgghpIaKi4OrK5KThQC+/hqlzli9fv36zJn3OW4zn+rEYi+J5KGlZdP4+HiWZWNjY1u1avX48WOVSqVWGwNbACGQ//nnZ9auPSUUCjXdJUJIXVR6sFu+fHlGRkYFT0HBjhBSt8XHw9UViYkAMGNG3pIl+qU2mz79dnGqEwj6KxSXWVaYm5srFApZlmVZNiYmRiKRSCSSvLw84B5g89VXV5YvH6rRzhBC6rTSg110dLSvr29YWJi7u/vHH3+s4ZoIIaTmKJnqpk/PmzUrFygl2C1YEHfp0oTXd2A91OpIANra2qmpqQ0bNkxKSlKr1UqlUqlU8u0bNOgfELCvd29vDXaFEFL3lR7szMzMgoKCevfuHRwcPH/+fH6HCUIIqW/4VPfsGQD4++Ojj3L/3SY3N7dTp40xMXMAAZAL9AMiBQIBgPz8fDMzs8TERI7jSh4yYMCA48ePa6QHhJD6pcxZsVKp9M8//2zZsuWHH3547949kYjmzxJC6pfExL9S3YIFWLoUz5+/eis5OfnixYvJycknT548e9aG47bwqY5h+nHcFYZh1Go1v1hdeno6fyuWYRg+3llZWR05cqT6ukUIqcveNCvWyspq+fLlIpHo/PnzGiuIEEJqiO3b/5bqADx79szT01MoFNrY2AwbNmzGjBlnzphw3G+AAChgGG+OuwxAIBDw2U6tVvMPAPCpzs7O7sGDB/RTmRBSRcpZ7mTatGn37t1zd3fXTDWEEFJzjBiBvn2xatWrVDdjxoyuXbveuXOHD2oAgP8BvwNCoADwBML5xMZvFIbXK5tw3FDAQ0tL67vvvouPjzcyMqquHhFC6jz61UgIIaVr2hSnT796PGHChB07dvCPGYYRCAQs6wvsfp3qBjDMBQASiURLS6uoqKjEaVYAcxiGe/iQdXCgr1xCSNUq/Vvm+PHjpqamXbp00XA1FZSVlZWTk1NUVCQWi01NTennLyGkSu3cuZNPdQzDMAwDgGV9gd8BEZ/qgDB+dkRhYaGhoaGBgYFcLs/OzgFWATMBvPceY2lJqY4QUuVKvxU7cODAefPmlXzl1q1boaGhmqiobEFBQX5+fubm5iYmJvb29i1btmzSpImxsbGZmZmPj09ISEj1lkcIqZMSEhImTJjAP+aDnVpdMtV5AWFCoZC/jMdxXHZ2dnZ2dm5uHrCeT3W2tsrz56GrW63dIITUDxX9BTljxoywsLB/zNjXGKVSOXLkyIMHDwJo3Lhxu3btDA0NJRKJXC7Pycl58uTJoUOHDh06NHbs2C1btmhpaVVLkYSQuufx48ctWrQo/upTq9UMM/zvqS6Uf10oFKpUKgAMwygUSo7bAHwCwMZGdvGitFGj6usDIaQ+qR23Br7//vuDBw8OHz58xYoVdnZ2/26QkJAwd+7cXbt2tW7devbs2ZqvkBBSq8XEYNUqjBwJV9e/Xly5cuXcuXP//oN2GMf9/nqD14FAGMDw8yOKUx3HAfgZ+BBA48ZsWJjU1laznSGE1GO1I9jt2LGjc+fOAQEB/Jqf/2ZnZxcQEJCQkLB161YKdoSQt/LgAdzckJKCGzdw8+arFwcPHnz06FH+sVAolEgkCsUglWoXn+oAL4YJ4zj84z4GxzHAr8AUAM2aISREaG2t2c4QQuq32hHskpOTfXx8ykp1PIZhevTosWHDhrc987Bhw2Qy2RvaJCUlAXjx4kVWVtZbnfydqVSq1NTUim/XS/j/uSYkJPAD20lFsCyrVCrj4uKqu5Bq9uiReOxY64wMIQAvr/S4uJy4uLghQ4YUFhbyDfiF6AoLBwB/pTrg/F9rnvxFwDC/cdxEAA4Oih07kouK2Hr/BwylUpmWlpaZmVndhdQa/Bfas2fP6Aut4liWValUGvtCUygUAN4cS6pL7Qh2dnZ2kZGRb27DcVxERISVldVbndnY2NjHx4f/GyrLqVOnXr58qaurq6upwc9yuVwqlWpra2vm4+oAlmVlMpmenp5QKKzuWmqNoqIilmUNDAyqu5DqdP++aMwYw8xMAYCFCwsmTiz09R1x7dq1km04jgN8gYCSqe4f5xEKhRYWFq6uPwYE+ABo1oz98888CwuaLgEACoWCvtDeCn2hvYOioiKVSqWxLzT+epC6tJ931a52BLvx48fPnz9/7Nixixcvtre3/3eDpKSkefPmXbp06euvv36rM2tra3/xxRdvbpORkREWFmZgYGBiYvJWJ39nmZmZBgYGtIxLxSkUivT0dFNTU5o6U3FZWVkymczc3Ly6C6k2UVHw9QV/IWnNGpiZ/WlvP6G0b2pfYO/rcXXeHPdXqvPw8AgKCip+evcuTpxA48YIChJaWJhqoAu1QlZWlr6+vrGxcXUXUmsolcr09HQTExOxWFzdtdQa2dnZRUVFGvtCy8/P18wHvYMyg118fPzcuXNLPgVQ8pViy5cvr4rKSpo5c+b9+/d37969e/duOzu7Jk2aGBkZSSQShUKRnZ399OnT2NhYAIMGDfL396/qYgghdcCNG+jbF5mZYBisWwcjo53jx0/gOI5ftcTAwKCoqEihUHCcD8cVX6sbyDChQqGInyfRoEGDkydPljxnmzZITwftFkYIqUZlfgMlJiauWLHiHy/++xVoJNiJxeLff/99zJgxmzZtunz58rlz50q+a2Fh4ePjM27cuIEDB1Z1JYSQOiAyEv36ITsbDIOffsLIkdkWFlOKNwFjWTYrK0tLS8va+rOkpFXFqQ4I4TiGT3V6enoxMTH/HmFDqY4QUr1K/xLavXu3huuoCA8PDw8PDwA5OTk5OTkFBQVSqdTY2JhuWRJCKu7qVfTti9xcMAx++QUeHk+trVsqlcqSbQQCgVLpnZS0CtACChlmEMeF4PWodn19/SdPntTz4YmEkJqp9GA3evTotz2RQqE4dOhQ7969LSws/nNV5TA0NDQ0NKzqTyGE1ElLliA3FwIBNm/G4MEZtrYt+HHQAoGAH2AnEolUKm9gL6AFFAkEgznur7sE5ubmsbGxlOoIITVTpc3Uzc3NHTFixIMHDyrrhIQQUhUmToSjI3bvxsCBqfb29nyq49c0YRhGIpGw7CBgH5/qhMIhHBdcvFidk5NTZGSkgYGBWo2jR/DxByIAACAASURBVEFLmRBCapqauAQLIYRUnUGD2I4dp0yYIGnQoEFeXh7/YnF0UygGcBx/rU4O+LHsaf4thmGWLVt2+PBhsVjMshg3DoMHw8WlujpBCCGlo4G+hJB6JCkpqUOHDmlpafxThmGKcxvHcYAnxwW8TnU+wAm+Wffu3c+dOycWi58/f86nuj17AKC0xZcIIaQ6UbAjhNQXSqWyS5cuaWlp/IL+DMPo6uoKBILc3Fx9ff38/J5q9UFAAsgZxpfjTgAQiUQ3btxo27YtfwaWxRdfGB86BACOjjhypPo6QwghpaFbsYSQeuHGjRsNGjTgdwjkOI7jOLVanZ+fz8+vz83t9jrVKRjGj79WxzBMcHBwyVT35ZfGhw7pAGjfHsHB0NSa5YQQUlEU7AghdVZgIKZNw/PnWLt2badOnUpu9ywUCvll/VUqlULhBhx+nep8BYKT/P3Z1atX9+rVi2/PspgwAX/+SamOEFKj0a1YQkjdtH07pkwBy+LevfBLl2byt191dXVlMhn7mlAo5Lh+LPvqWh3gy3HHWBZCoTA8PLxr1678qdRqTJoEfn3PVq2UwcFaprRhGCGkRqJgRwipg379FZ98ArUaOjrs5csz+Re1tbUlEolUKi0sLJTJZAKBQCgcoFD8UZzqGOY4x0EgEMTFxdnZ2fFH8alu504AaNlSuXdvuqmpVXX1ixBC3oxuxRJC6poNG/Dxx1CrYWiILl0WMsxN/nWBQJCVlZWRkSGTyYRCIcu6KxR/AFI+1QHH+L1iT58+XZzqAPz6K3bsAIC2bbFvX7qJibo6+kQIIRVCwY4QUqesWIHp08FxMDVFcDB35cp6fj8JoVBoZmbm7u6ur6/PcZxK1Qc4/DrV+QHHAGhra4eEhPTp06fkCfk9Jtq148fVUaojhNRoFOwIIXXHokWYOxcALCwQEoKkpKNFRUUikcjExARAUlLSlClT+vbtyzD9/p7qAk1NTRctWpSRkeHyr0WHR49GXByuXIG5ucb7Qwghb6n0YNe+ffvJkye/1YkMDQ2PHTvWpk2byqiKEELe2ldfYfFiALC2Rmgo2rbF8ePHLSwsJBIJy7IqlYrjuFGjRjVqNEUsPlmc6hjmWLdu3dLS0hYuXKitrV3qmRs3hlSq0b4QQsi7KX3yxO3bt42MjN7qRFpaWl5eXpVREiGEvLWZM7F2LQA0bIhz59C0KQDExMRkZWUpFAqBQODo6Hjr1i2FwmXdul4AAyiAYUBgz569jh49ys+ZJYSQ2o5uxRJCar3c3FeprnFjhIe/SnUKheLWrVvW1tY+Pj5isfj27dvNm3/OMIGvr9UN09UNvnDhQmhoqKGhYfXWTwghlYWCHSGk1jMwwFdfYdAghIfjvfdevXjy5EmFQpGampqVlSWVShnG4+HD5RwnAZQM8z/g6OXLl7t37/6PUz19CoVCw+UTQkilKXMdO6VSmZ6eXpFTmJmZVV49hBDyLpYv/+crYWFhWlpaMpksPj5+5MjtmzcPYFkRoJw8+cz+/edMTe3/PSZ43jwsXw5PT5w4oaGyCSGkcpUZ7C5dumResTlg/N47hBBSo+zdu7eoqEggEDx50uTnn/sCIoZRWVt/sWXLz5aWlh988ME/2i9Y8Codlth4jBBCapkyg52urm6TJk00WQohhFQWLy+vly9fAjAzG5GWtkWtlgAsMNbI6I6OTtO8vLzmzZuXbL9oEb79FgDs7BAQUC0lE0JIJSgz2Dk5OYWGhmqwEkIIqQRZWVk9evS4f/8+AKDny5e/AhKA9fLad+bMn48fo2PHjrGxscOGDSs+5LvvXq2TYmuLkJC/RukRQkitQ5MnCCG1SXo6hg/H55+j1DEgZ86csbOz41Mdw/RkmFOALsOoGWbsqVPjevfuLRaLr1y5Ym1t3aJFC/6QVavg7w8A1tYICUHjxprrCyGEVLoyr9gRQkhN8+IF3N3BX4ybORPFG7pevHhx0qRJjx49KtG2B8edAHQAlmHGqdUBLIugoCAAWlpavXr14hutX485cwDA0hIhIa/WSSGEkNqLrtgRQmqHhAT07Pkq1U2c+CrVxcTEfPDBB927d/9HqgNOAnoAK5VONTQ82bp16+IliFUq1fDhwwH8/DO+/BIAzM0RHIxmzTTbH0IIqQKlX7H76quvGtMNCUJIjRETgz59kJgIAB99hJ9+wsuXL0eMGHH+/Pl/te1enOqAsUrlH0olcnJyDAwMcnJyAJiZmXl7ewcG4rPPwHEwM0NwMFq10nSPCCGkKpQe7Ja/XhIqOTkZgLW1dcl3Fy9ePHToUNoWlhCiGXfvom9fpKQAwKxZWLkScrmsXbt2qampxW1EIpGWllZRUUfgFJ/qRKJJenon8/MZlUrFMAyf6hiGWb16NYBbt8BxMDbGmTNo27aaOkYIIZWtzFuxKpXq008/tbe3D/j71P/79+8vWrSoXbt2EyZMKCwsrPoKCSH12tWrcHF5leoWL8a33yp++GG9gYHBy5cvixfR5G+zymRODBPEpzqGGa9S7czNzdXR0TE2NhaLxXzLQYMGjRkzBsDs2di8GZcvw9GxevpFCCFVofQrdmq1esiQIcePHzc0NDQyMir5lomJyezZs7dv375jx46UlJSTJ0/S5tmEkCoSGgpvb+TlgWGwdi2srfebmU3Oy8sDwDBMydXRWdaZ404AugALjOe43wGo1erc3NziNr6+vvv37+e/srS1MWWKxvtDCCFVrPQrdlu3bj1+/HiXLl3i4+MnT55c8i0rK6uVK1dGRUW1a9cuKCho9+7dGqmTEFLvXLwIT0/k5UEoxM8/K48f7zN8+PB/pzqGYTiuK8edBPQBViyeCvzOv/V/9u48rqb0jwP459zbbdG+aCd7yS5LCJHQULYGKco6tixjxiCGwchgkMaMZeyixpYsWdKGyKhIyZatRasWrXc7vz9uP9PQuJXbpXzfr3l51bnPec73mddoPj3nnOfhcrlvf/Ncu3btsWPH6BdRQkjDVnWwO3DggKKiYkBAgI6OTpUNTE1NDxw4wOFw9u7dW5flEUK+XOfPo7QUPB4OHWJ37epx5coVAAzDcLlcAFwul8PhMAzDMDZAsCTVMcxkPr/ihxLDMCKRiGVZhmG2b9/uJVmtjhBCGrSqb8XGx8dbWVk1adLkA2d26tSpXbt2d+7cqZvCCCFfugULIBJBKDw3Y8b4oqIiyUGWZUUikZ6eXkFBAcuyQG+h8Oz/U90Ulv3nHoJkSk9dXf3w4cNOTk6fZgyEECJfVc/YCYVCbW1tqSc3adKkvLxc1iURQggAaGiUR0cP2LzZsaioiGEYRUXFt+9A5OXliUQiobDH21QHTGbZg2/PVVRU7Nu375UrVwoLC/X0nKyssHbtJxoGIYTIUdXBrkmTJs+ePZN68vPnz01MTGRdEiHkS8ey7LZt23R0dMLDwyUTbyzLCgQCLperq6sLQCQScTg2wIX/p7opQMVcnaqqanh4eHl5eWRk5MCBA//+Gw4OiI3Fjh2fckSEECIfVQe7fv36JSUl3b59+wNn3r59+/79+7SaHSFEhtLT052cnLhc7vz5898uqPT2jYfS0tLc3FxlZWUer59QeAZQB8QczjTgoKTZmjVr8vPz3+4YducOhgxBYSE4HGzY8EkGRAghclV1sJs5cybDMB4eHtnZ2VU2yM7OdnNzk7Ssw+oIIV8MgQCRkZEtWrQ4c+ZM5TdeJS/ASv5UUFAAwOdbCQRnAA1ADEwRi/cDUFNTS0xMXL58uaQNgMREDB6MvDwwDHbuxIQJn2hghBAiR1UHOysrqx9++CExMbF9+/a//PJLQkKCQCAAUFpaGhcX9/PPP1tYWDx8+NDd3X3w4MHyLZgQ0tCUl2PsWKiowM7uT7FY/HaNEg6H8/ZPyUGRSKSo2F8sPv821QEHGIZxd3fPz89v27bt2z4lW5BlZ4Nh4OODf6/aRAghDVbVb8UCWLt2LY/HW7du3ZIlS5YsWQJAUVGRz+dLPlVUVFy8ePHPP/9Mi0IRQj5GURFGjsSVK5Lv2nE4IrFYLFnTRCQSARCJREZGRpmZmWpqam/etOHzTwEaAAvMBg707t07MDCwcePGlft89gyDBlVsVrFhAzw95T4qQgj5RP4z2HG53NWrV48fP/7gwYOXLl1KTU0tKCgwMjJq0aLF0KFDJ0yY0KJFC3kWSghpeF6/xldfITpa8l0Eh+P99q6rWCxu3759Xl5efn7+69evuVzumzdtgMuANsACs4CdO3bs+Oabb97pMzUVgwYhJQUAfvoJ330n5zERQsin9J/BTsLS0nL9+vXr16+XTzWEkC9HejoGD0ZiIgD07JkdG+skFL6RPF0nWdbE1dV15cqVDMPw+Xyga6VUN5vD2b1nzz4PD493+szJwaBBePoUAH74AT/+KN8hEULIpyYl2FX2/Pnzu3fvFhcX6+rqWlpafnj5YkII+YAnTzB4MCSrKrm6wt090cGhWPKqBJfL5XK5YrHY19f36NGjs2fPzsw0AS79P9XNad/+mr9/fLt27d7v9vBhPHwIAPPmgX4hJYR8gaoV7JKTk6dPnx4WFlb5oK2t7e7du1u1alU3hRFCGqy7dzF0aMUzcHPmYNs2pKQ0F4vFCgoKXC5XRUWloKCAx+Olp6e7uLioq/cDAgAdgGWYuWvXmi5bdu+/enZyQlAQevfGmjXyGw4hhHw+pAe7tLQ0GxubjIwMa2trGxsbY2PjvLy8iIiI8PDw/v37x8XF6evry6FQQkjDcO0aHB2Rnw8Ay5dXJLDVq1erqqrq6uqWl5fn5uYqKSlxOBw+n8/nt8vNrUh1ZmYbDx0a37dv3w903qIFQkPlMgxCCPksSQ92P/30U0ZGxq5du6ZPn175uJ+f38SJE729vbds2VJn5RFCGpTsbDg4oKgIDIMtWzB/PgDk5uYeOHBAQ0PjxYsXCgoKLMuWlpYCALoAIYAOw+C335jZsxd/0toJIaQeqHodu8rOnTtnZ2f3TqoD4Orq6ujoGBgYWDeFEUIaIIYBw4DHw4EDFakOgI+Pj0gk8vT0TE9P9/Ly6tSpk6KiIpdrxTAhkrm6337D7NmftG5CCKknpAe7rKys9u3bV/mRubl5enq6rEsihDRYenp4+BCPHmHixIojQqFw+/btSkpKP/30k4aGxqpVq2JiYqKjyzU0brGsDsDq6KykVEcIIdUkPdgZGhreuXOnyo9iYmLoATtCSI0YGaFZs3++vXnzZkFBQXl5uYmJiZqamoaGhpXV1H79+Hl5HIDV1V3VqdO1KvthWRw7hlu35FM1IYTUD9KDnZOTU0RExLZt297u3giAZVlfX9/Q0FBHR8e6LI8Q0tCwLHvz5s0VK1Y4ODhYW1u7uLiIRCIOh1NeXu7p6enpuef+/W1v3igyDJycQvLzfx47dmyV/cybh7FjYWuL4mI5j4AQQj5f0l+e+PHHH0+fPj1//vw9e/b07t3b0NAwIyMjKioqPj7e2Nh4xYoVcqiSENIwPHz48Ouvv05ISKj8iyIAsVicm5vr6xsJrARUGQbAvIiIgyoqKlOnTn2/n2XL8NtvANCqFZSV5VI6IYTUB9KDnYGBwfXr1+fOnXvu3Ln4+HjJQYZhhg0btn37diMjozqukBBSLxUVYd8+2NqiQ4eKI1lZWQMGDCgqKlJTU5NsGiYUCiU7UHM4HA6ni1B4AdAFWJadzzC/8fnKHh4ePB7vnZ69veHtDQAtWuDCBXC5ch0XIYR8zqq1QLGZmdmZM2dyc3Pj4+MLCwvV1dU7duyop6dX18URQuqprCx89RViYtC6NR49qjj4yy+/SDaWKCsrEwgEkkk7AwMDAwODhASuSHRRkuo4nIUKCjtbtrR4+PDh999//07Pvr5YtgwATE0REgJjY7mOixBCPnNVB7uysrL3D6qqqvbq1ev9Nsp0I4QQUsmzZxgyBI8fA0CfPv8cP3v2rLq6ura2tlgs5vP5paWlxcXF6urqP/10atQoDUmqU1FZqqx8sKSEefTokbm5efPmzSv3vH9/xSIp+vq4fBn//pAQQsh/BDsVFZXqd/HOszKEkC9ZfDwcHCBZB2nKFOzc+c9H6enpOjo6qqqqkq3DeDyejo7O/fsKU6c2AzgAq6y8hGF+Ewq5GhoaYrG4T+VUCJw4gWnTwLLQ0sLFi7CwkOu4CCGkXqg62A0ZMkTOdRBCGoDISIwYUbFd2A8/wNsbDPPPp1paWpK9wliWFQqFZWVlYrE5h3P49WsOAB7vR23tQ6qqxjwer7i4OCUlxcPD4+25wcGYMAEiEdTUcP48OneW88gIIaR+qDrYXbhwQc51EELqu1OnMGECysrAMNi8GQsWvNvA1tb2xo0beXl5JSUlZWVlAkFLkWg/oAeAw1kmFm949UrE5XI5HI5AILC3t387Y5eSAmdn8PlQVkZgICo9EkIIIeRfqvXyhERRUVFCQkJ+fv7QoUMLCgo0NDSYyr+ME0K+YDt3Ys4ciETg8bBvH1xd323w7NkzIyOjZ8+eSR7e4HAsxeIrgAEABYUVLLtBLBYDEIlELMvq6elV3q6wtBTl5eDx4O8POzv5DYoQQuod6QsUA8jIyHBxcdHW1u7Vq5eDgwMAX19fCwuLa9eqXhGeEPJF2bABM2dW3Cc9c+bdVMey7NKlS1u1arVx40axWMyyLMuai0QhLGsAAFgmFK6V5DnJO7NNmjSJjIxs1KjR2x7atEFcHO7exYgRch0XIYTUO9Jn7DIzM62trV+8eNGrV6+ysrK4uDgAampqycnJgwcPjo6O7vB2lSpCyBdp0yYAaNwY586he/d/fSQQCMaOHRsUFCSZkGvdurW1tcfJk3OLizUAMIwXy3orKiryeDxVVdUOHTqMHz/e1dX1/fe36McMIYRUh/QZu1WrVqWmpgYFBUVFRTk5OUkOLliwICQkRCgUrlmzpo4rJIR87jZuxLhxuH793VTn5+enra0dGBgoFosZhmnUqNHjxxw/v6mSVKeh8YuZ2ZGuXbu2atWqqKgoMzMzJCRk2rRpNXornxBCSGXSg11QUNCYMWPe3xPW1tbW2dn5xo0bdVMYIaTecHeHvz9at/7XweXLl7u5uZWVlXG5XAAsy4rFrRUVr4vFBgA0NTdMmpSqqqqakZFx//79fMmbtIQQQj6O9GCXnZ1tZmZW5UdGRkbZ2dmyLokQUu8FBwd7e3urqakpKCgYGhoCAMxVVaP5fF0AHM6PXO4vubm55eXlr1+/BlBUVPRJ6yWEkAZC+jN2JiYm0dHRVX50/fp1ExMTWZdECKn3tm7dampqyuPxysvLc3JygDYME5abqwRASWmNpuZOM7OWDx48UFJSUlNTYxhGX19fcmJZGaZMwYsXOHECFYGQEEJItUmfsXN2do6MjPT29q68w4RQKPTy8oqOjh5Bb6kR8iUpLYVIJKXNvXv3rly58vLly+Tk5NTU1LKypkAYyxoB+OGHovLyH7W0tMRi8ePHj8vKyng83vDhwxUVFQEIhRg3DkePIioKUVFyGA0hhDQ00oOdl5dX27Ztly1b1qJFi6NHjwJwc3Nr3br1unXrWrRo4eXlVfdFEkI+C/7+0NVFt24Qi/+zzf79+zt37iwSiRiGUVZWVlXtwuNdA4wBMMyPMTGjAGRkZCQlJfH5/OfPn5eWlnp7ewMQizF5MoKCAGDgQAwfLp8xEUJIgyI92GlpaV2/fn3GjBlZWVmPHj0C4Ofnl5aWNmHChKioKF1d3bovkhDy6f36KyZMQGkpnj6FQFB1m99//33y5MkAuFyuiYkJy7YqLj4rEDQGAPzIsmuuXLkCoLCwsKSkhM/nt27d+tq1ay1btgQwbx4OHwaAHj0QGAhFRfkMixBCGpRq7Tyhra29c+dOX1/fpKSk/Px8NTW1tm3bVl4+lBDSgInFWLQIW7cCgKYmAgOhpFRFs99//33OnDkMw4jF4o4dOz54IOLzL0jm6tTUNpibn8/NbfbixQsADMN069Zt586dXbp0kZy7YgW2bweAdu1w/jzU1eU0NEIIaWBqsKWYoqJip06d6q4UQshnqKwMEyfi+HEAMDXF+fNVrxV8+/btefPmqaurGxgYPHnyZMeOK/37iwF9AAyzqqjop5iYipbKysobNmyYO3fu2z0Jt27F2rUA0Lw5Ll0C3QYghJBaqzrYOTs7V7+L45If+YSQBuf1a4wYAcnege3bIzgYpqZVt/T29jY0NGzZsuWrV6+A1qNHawsEXAAMs4plf2IYhmVZHo/H5XILCwt5PN7bEw8cwLffAoCRES5fhrFx3Y+KEEIarqqD3YkTJ6o8zuVy1dTUSktL+Xw+AMlGQHVYHSHk03n2DF99hQcPAMDWFqdOQUvrPxtHRkY2a9bMwMDg+vVMDicsI4MLYNUqjBs3vm3bn1q3bl1SUqKurt6iRYvKPzROn8a0aWBZaGvjwgW0bFnngyKEkIat6pcn8iq5d++eiYmJtbV1RERESUlJfn5+aWlpbGysvb29gYGBZOtYQkgDExuL3r0rUp2LCy5c+FCqA1BQUNCiRYvnz8HhXBGLjQBMnvxi5UpIljd/+fJlenr6o0ePlixZUvmsBQsgFEJVFWfPomPHuhsNIYR8KaqesdOq9CN82rRpPB7v4sWLGhoakiMcDqdLly6nT5/u2rWrp6fnhQsX5FEpIUSO3N2RkQEAP/wAb2/8/3G4/6Svrx8X9/rx492ACQCG2bB//5L793tIfm6UlZUpKCjs2LHDxsam8lkuLjh2DH/8gd6962gchBDyZZH+8sSlS5fc3Nzeprq3VFRUhgwZ8ueff9ZNYYSQT6l/fzx7hk2bMHOm9MYPHjzIylIRCv8EzACYmQVoavolJDDR0dGSNyT69eu3Z8+eVq1avXPiunVYt64OqieEkC+V9GCnqKj46tWrKj96/PixOi1LQEhD9Ntv+O23arXMyMjo1m2MQHBZkup0dfe8fDm98kY1mzdvXrhwYR3VSQghpDLpCxTb2dkFBQWdOnXqneO7d+8+f/68o6Nj3RRGCKkHXr582aqVXXHxWaAZAGBjbu40fX19ZWVlhmEaNWrUoUMHSnWEECI30mfs1q5de+XKldGjR/ft27d79+4aGhoFBQVXr169fft2s2bNVq9eLYcqCSGfoYsXLw4fPlsoDAGaA+DxfGbPTsvMHH/s2DF3d/fDhw/Pnj37yJEjn7pMQgj5gkgPdq1bt46Kivr+++/Pnz9/9epVyUElJSVXV9dNmzYZGhrWcYWEkM/OrVu3Jk+efP9+ERAuSXVKSr4uLndOnAixtLScPn369evX+/bt++DBAy6XKzmFZZGYCAsLKNRgWXRCCCE1I/1WLIA2bdqcPn06Nzc3Kirq3LlzUVFR2dnZhw8fplRHSH0XFYWOHTF9enXbi8ViNze3nj17Vk51wK9WVv4BAQGTJk2Kjo5u2rTpvXv3VFRUnjx5ItmuRiTCuHHo0AFz5tTROAghhADVDHYSr1+/zsrKys/PLywszM/Pr7uaCCHyERysbmeHe/dw6BBEIunt8/Ly2rdv7+fnxzBmQNj/78Bus7I6euPGDXd3982bN48bN+7y5ctcLjchIeHRo0fffPMNy2L2bBw7BgBlZXU8JEII+bJV66ZIcnLy9OnTw8LCKh+0tbXdvXv3++sXEELqhS1blNeu1WZZ8HjYvRv/v2X6n1iWtbGxefjwIYdjJhaHAi0AWFlFxMTMz89vqa2tvW/fPmVl5dTU1Pj4eJFI9Pz5cxcXl+HDhy9fjl27AKBzZ2zbVvcDI4SQL5j0YJeWlmZjY5ORkWFtbW1jY2NsbJyXlxcREREeHt6/f/+4uDh9fX05FEoIkRWBADNnYu9eFQBaWjh+HHZ2Uk558uRJv379Xr16BTRhmIpUp6CwLTl55YgRI4KCgiZOnHjw4EEulxscHCw5xcbG5siRIz4++PlnAGjZEsHB0NSs05ERQsiXTnqw++mnnzIyMnbt2jX934/h+Pn5TZw40dvbe8uWLXVWHiFExvLy4OyM0FAAMDUVXLzIs7SUcsqtW7dsbW3Ly8v19a2ysgJYtgUAYMu4cTFHjhRkZ2ebmZkdOnQIAMMwXC5XKBRaWVldvnz5yBF8+y0AGBri0iXQQ7mEEFLXpD9jd+7cOTs7u+nvPVzt6urq6OgYGBhYN4URQmQvORm9e1ekum7dhAEBL6WmOj6f7+Li0qhRo86dHYuKzgAtAaip7e7S5dDRo0ddXFyioqKKiooMDAy4XG5JSYlQKOzfv390dHR4uLKHB8RiaGriwgW0aFH3wyOEkC+e9GCXlZXVvn37Kj8yNzdPT0+XdUmEkDpx/TqsrfHgAQB8/TWCgor09KS/MXHp0qVXr14VFKg/ebK7pMQIQJMmJ9TVV7Zt21ZRUTEoKAgAy7K5ubksy7IsO23atPDw8L//5jo7QyCAsjKCgtCpUx2PjRBCCIDqBDtDQ8M7d+5U+VFMTAw9YEdIvSASwdEROTkAsHQpAgKgrMxKOwkA4uPjW7ToJxReLixsDKBp05OvX7t37Njx8uXLSkpKSkpKAHJzcwUCgaqq6qlTp3bv3v30KYYPR3ExuFz4+6Nfv7ocGCGEkEqkBzsnJ6eIiIht27ZV3vyRZVlfX9/Q0FDaUoyQeoHLhYUFVFSwbx/WrQPDVPfEkJAHiYm/Aa0A6OsfycycUFpaGhMTk52dXVpampeXB4DH43l5eWVlZY0cORLAhQvIzQXDYOdOjBhRZ0MihBDyHukvT/z444+nT5+eP3/+nj17evfubWhomJGRERUVFR8fb2xsvGLFCjlUSQj5eNeugc+HsnINTvHzuxIWtlSS6hQUti9dKnzyZNoff/yRm5vbpEmTV69eicViExOTBw8eqKmpvT3L1RWPHqFbN7i5yXwQhBBCPkR6sDMwMLh+/frcuXPPnTsXHx8vOcgwzLBhw7Zv325kZFTHFRJCZIPDqVmqi4/PmjTJFDAHiTouugAAIABJREFU0LjxSWD1okU5JiYmhw4d8vf3v3TpkkgkMjIyiomJqZzqAGhqYutW2dZOCCGkWqq1QLGZmdmZM2dyc3Pj4+MLCwvV1dU7duyop6dX18URQj6VjAzW2rpELDYHMHToy5CQ8WPHfi0SiU6ePOnq6ipp07lz55CQEF1d3U9aKSGEkH/UYDtuXV3dAQMG1F0phJDPRFLS6969y0pLmwFQUNh79+4KG5s+8fHxDx8+FAgEAIyNjdPT0w8fPkypjhBCPivVCnYCgeDvv/9+8eJFeXn5+596eHjIuChCyEfYsAG//oplyzB/fm1O//33E56e7cRiCwAczp4mTdalpmZzOExWVtY333zj6enp6empo6Nz7NgxExMTGZdOCCHk40gPdklJSY6OjsnJyf/VgIIdIZ+J8nJMn45DhwDg/PkaBzuhUDhx4nf+/jMACwCKigeEwm+ys1WuXLni4eFhb28veYPKwMDg6tWrAwYM0NLSAnD1KgoKMHy47IdDCCGkpqQHu3nz5qWkpMyfP9/S0lJRUVEONRFCaiEzE6NHIyoKAJo3h49PzU7n8/n9+n0dHe0NWAJQUjpkZPRTQECUjY3N8OHDt23bNmXKlBkzZmzatCk1NTU/P1+y60xAACZMgFiMGzdgbS37QRFCCKkR6cHu1q1by5YtW7lypRyq+S+bNm2qfuPvvvuu7ioh5PN05w5GjMDLlwDQty9OnEDjxjU4vbi42MZm9J07myWpDvhz5MjQgIBny5Yti4yM7Nu3r4eHB4fD8fPze/PmDYC9e/d26tTpyhVMmgSxGBoaMDWtg1ERQgipIenBjsfjmX7qn9nr16/Pzc2tZmMKduRLc/w4PDxQXAwAU6fi999R07n1xYs33bvnI7kD27Hj3/Hx3/j6ZggE5adOnfLz83N1dc3JyYmNjTUzM7t586aLi8vkyZPj4jB6NPh8KCkhMJCCHSGEfBakB7uRI0cGBARMmTKFqf5a9bL24MEDZ2fniIgIe3v72bNnf6oyCPncsCx++gmrV4NlweVi0yYsWFDjTtLSBDt3jhWJLAA0bXq5e/djDx4oxMfH+/v79+3bd/v27SoqKgoKCm/evMnIyFBXV9+3b9+zZ/jqKxQWgsPBwYOg1+UJIeQzIT3YrV+/vn///nZ2dh4eHk2aNOHxeO80sLGxqZva/qGnp3fhwoWBAweGhIQsW7bM1ta2rq9IyOePz4eLC06eBAAtLfj7Y8iQGneSlYWBA1mRqC0AVdWAefPSf/75uL29/Zo1a0JCQtavXz9o0KCePXs+evRIJBLp6upOmDDhzRslBwdkZADA1q0YO1a2wyKEEFJ70oPd69evBQJBWFhYWFhYlQ0q7yFbd5SVlU+cOGFpaTlz5syEhAQFhRqswEdIg3TiREWqMzdHUBDatKlxD69fc4cMKXr+XLJvxN7i4unffSfmcrlXr17lcDgODg5jxowRi8W3bt0qLS01MTFRV1efP3/Z8OF4+BAAliyBp6csR0QIIeQjSY9Hnp6ejx8/trOz69Spk4qKihxq+i9GRkbr16/39fUNCwuzt7eXSZ8vX760t7cXCoUfaCN5vC8tLS0nJ0cmF5VKKBRmZGRkZWXJ53INgOS3i6dPn37CBwbkz9hYoX174yZNBKtXZwLiR49qdnpODjNqlEZWVkWqGzQoIDu7Q1JSEsuyYrG4sLAwPDw8JCQEQHFxMYfDsbKyWrx4mYsLJzoaAEaNKvTwyKjpRes7yc+KYsnzjKR6BAJBZmZmdnb2py6k3pD8QHv27NkX9QPtI4nFYpFI9EheP5L4fD4ADocjn8vVCCN1vk1dXX3y5Mnbtm2TT0FyJhAIAgICSkpKPtDmxIkTly5devHixTsbYtadzMxMdXX1Ro0ayedyDYBQKMzKyjIwMOByuZ+6lvohJ4dxcFB8+lQVAMPsNzVdxbKiy5cvr1y58q+//lJUVFRXVy8tLS0tLVVXV9+6devgwYNVVFS8vBrt2KEMwM5O4Of35r3nMhq+/Px8AJIF/Eg10Q+0mhKJRJmZmfr6+nRvqvpKSkrevHljYGAgn8uVlpaampru2LHjm2++kc8Vq0/6fzQMw3Tr1k0OpXwSPB7Pzc3tw22ePHly6dIlNTU1HR0d+VSVnZ2tpqZG//OoPj6fn5WVpaWl9f4zoOR92dkYOVL49KkCABUV/3bt/jAz6xYYGNi5c+eNGzc6Ozu7urpqaWnl5ua2atUqLi7u7a80cXEA0K0bAgN5ampy+uvwWSktLQUgtx8FDUNOTo6qqqq2tvanLqTekMxxamlp0dqx1cfhcIqLi+X2d7OoqEg+F6oF6bOI/fv3v3jxohxKqR0+n19WVvapqyCk3sjOhp0dkpIUADRrFq6js3jBgnnHjx9PTk7W19dfsGDB2LFjBQLBq1evuFzu1q1bK09UHziAX3/FpUuQ1+Q1IYSQmpEe7DZs2BAcHDxt2rSrV6++fPky5z1yqPIDunbt+mmf/CNEDp48QXy8DPrJy4ODA+7dAwA1teMikUdBQV5oaGhxcbGZmdm9e/c0NTW3bNnSrVs3Ly8vDofzzl8uc3N8+y1o5oUQQj5b0oOdvb29QCDYs2dPv379zMzMGr9HDlUS8iXbuRPt2qFLF9y581H95OXB3h4xMZLv/LW1vy8pKVJXV/f392/Xrt2dO3c0NTUHDRoUHx+flJTE4XBEIlG7du1kMABCCCHyIv0Zuwb8gB0hn7nycsydiz//BABlZaiq1r6rd1Id4JaSIgLQt2/f6OjoNm3afPXVV0lJSXp6eiEhIdra2idPnhwxYoTcnkQmhBAiE9KDnWSrb0KInKWlwdkZN28CgLExjh9H69a17Co/HwMHCu/ckfx9D1BSmsblKm3YsCEoKOjSpUtaWloRERFKSkrOzs63b98uLCw0NTXNy8s7ffq0rMZCCCFEPj7HJVgIIZGRsLKqSHW9e+P2bfTqVcuu8vPRt2+ZJNWpqARpac3duNG7tLR03rx5HTt23L59e0FBgVgsfvPmTUhISH5+vp6e3pgxY65evR0QYHTjhuyGRAghpO5VHexcXFxevHhRo44eP3489lNsLbR+/fpDhw7J/7qE1B0fH9jZITMTAGbPRlgYjIxq2VV+Prp0yUlIUAYA/CUUji8oyL137968efNUVFR+/fVXKysrNze3UaNGTZgwQVFRcdSoUZmZmRs2bJ46VXPhQowYIasxEUIIkYeqgx3DMObm5nPmzHnw4IHULm7cuDFlyhRLS8tPskb28OHDpS5ER0h9UVyMCROwYAGEQigrY98+bN+OWi9l9fffj01N7z9/rgeAxzulojJt3brVqqqqISEh9+/fnzlzJsMwffv2DQ0NPXny5NGjR01NTf38/ADMmoXz5wGg7jeCJoQQIktVB7sjR46cOHHi7Nmzbdu27dat2/LlywMDAx89evT69euysrLMzMzExMS//vpr3rx5FhYWvXv3Dg0NDQgICAgIkHP1hDQwDg44ehQAzMxw7Ro8PGrf1dmz16ytC4uLLQEoKgbOmBEhFvO9vLwEAsGcOXOuX7/eoUOH2bNnm5mZWVlZNWnSRENDY8WKFSoqKmvWVLyuYWWFgwdlMSpCCCHy8p8vTwwbNuzx48dHjhzZvHnzzz///F/NOnfuvG/fPjc3N9r5hJCPxLJ4+BAA7O1x5Aj09GrfVXp6yejRamJxZwDduz9PT18wb96l7dt95syZs2fPnhUrVnTt2vXMmTMWFhaqqqr5+flv3rxp06aNm5vbwYNYuRIAmjfH2bO0EDEhhNQzH0pjioqKHh4eHh4eiYmJ4eHhsbGxWVlZBQUFOjo6RkZGHTp0GDZsmJmZmdxqJaRhYxhERuL+fTg5odZ73oaEhOzc6R8YOFso7AoAOH73rrtAUJqSkmJnZ3fv3j19ff3OnTsHBwezLBsaGlpSUlJeXm5jY3Pq1KmwMIVp08Cy0NVFcDAMDWU3NkIIIXJRrWm2du3a0TqlhMiBuTnMzWt/+nfffefjs5/HCxMKOwDQ0gopKJjQp49NTEzMkCFD/P39Fy9enJ+fn5mZOWTIkNu3b2dmZkrO2rhx4927cHaGQAAVFZw+/VFlEEII+VTo/ikh9dXTp0+vXLmSkJCQnp5eVlZ27969ly8LGOZSaWkHAHp64ZGRZpaWgtjYWHd39x07dri7ux88eHD58uVJSUm3bt0CYGho6OfnN2DAgJQUDBuGwkJwODh0CH36fOqxEUIIqZWqg52zs3P1uzh+/LiMiiGESCEQCF6/fn3v3j1vb+/w8HBtbe3i4mKRSCQQCBhGi2EuicXdAWhoXM7NHR4Ts2fUqFHR0dF//fVXeHh43759x48fLxQKAXA4HC8vrwkTJlhYWIhEcHREWhoAbNqEMWM+7RAJIYTUXtXB7sSJE1Ue53K5ampqpaWlfD4fgKKiIo/Hq8PqCGmI8vMxbRrCw3H8OGxtq3vW/v37fXx8EhISJMmMYRhTU9PU1FRVVVVLS0uxWP3OnV9YtjsALjdw+vTo7ds5Hh4et27dmj59emxs7JYtWxo3bqylpfXo0SNNTc3Tp0936NAhOzsbQHExkpIAYP58LFxYN2MmhBAiF1Uvd5JXyb1790xMTKytrSMiIkpKSvLz80tLS2NjY+3t7Q0MDOLi4uRcMSH12u3b6NoVJ04gNxdRUdU9a9asWXPmzLG2tlZQUHByclJQUGjRokVeXl6bNm0cHR1jY58UFR0HegJQV788bNjh33/3+eOPP8RisYeHh5eXF4Ds7Ozc3NxHjx4NHz784cOH/fr1e9u5hgYuXsSBA9i8uQ4GTAghRI6qDnZalaxatYrH4128eLFfv36KiooAOBxOly5dTp8+raqq6unpKd+CCanHfH1hY4NnzwBg1CjMn1+ts86ePbt3794rV66kp6fb29u7urpqaGgkJCRwOByhUNi/vyOHc/nJEz0AjRtfbdzYMzc3QywWm5iYTJo06cWLF5ItYa5evSoSiX755ZegoCB9ff13LmFri0mTwKEtBgkhpJ6T/vLEpUuX3NzcNDQ03jmuoqIyZMiQPyUrmRJCPqigANOmQfI8Ko+HX37BggWo5l4te/bsmTBhgpWV1cWLF0+ePPn06VNjY2NlZWVNTc3k5Iz16/uIxU0BtGx5980bF1VVnejoaFVV1by8PEtLy6SkJGVlZSUlJTU1teTk5EWLFtXlKAkhhHxi0n9DV1RUfPXqVZUfPX78WF1dXdYlEdLQxMTAyqoi1ZmZITISCxdWN9UBSExM7N279+vXr8vLy5s3b66np5eRkcGyrIlJG4YJfv68KQDgnL8/h88vTklJmT59ekFBwf79+/39/ZOTk+Pi4rKzsyMjIw8fPsyhSTlCCGnQpP+Ut7OzCwoKOnXq1DvHd+/eff78eUdHx7opjJCGgGXh44M+fZCcDACOjoiNhbV1zTopLy+/du3ajh07GIZ59OiRra1tfn7+qVOXMjP3smxfAIqKIcCYpk0NlJWVzczMduzYAeDChQtxcXG5ubmamprdunW7c+dOhw4dZD9CQgghnxPpt2LXrl175cqV0aNH9+3bt3v37hoaGgUFBVevXr19+3azZs1Wr14thyoJqadmz8aOHQDA48HbG99+W4OJOgCvXr2aOHFiSkpKYGBgx44duVzumDFj1q1bN3PmovHj1QWCpgC0tW9YWW29coXfvXt3BQWFadOmffvtt4MHD75+/bqtra2/v7+SklLlPvPy4OcHJyfQbDshhDQ80oNd69ato6Kivv/++/Pnz1+9elVyUElJydXVddOmTYa06xAh/y0kBACaN4e/P3r0qNYp5eXlSUlJLMtmZGRMnTqVw+FMmTLl4MGDGzduzM/PHz58+IoV65s0uSsQNAHA5V4SCl1DQnIYhklNTRWLxZL3mSIjI7/99lsvL693FiQqKsKgQYiNRVAQAgJkPlxCCCGfWLV2nmjTps3p06cLCwsTExPz8vK0tbXbt29PT9cRItWxYwgLw+TJ0NKS3jgzM3PhwoXHjh2TrFQHgGEYa2vrc+fOicXi/v37L1myZM6cxb6+Q5KTmwDgcC7q6EzLzX3ds2fPTp06GRsbGxsbGxgYtGrVqnXr1u+vMSkSwcUFsbEA0KWLbAdKCCHks1B1sMvIyACgr6/P4XAkX0s0b968efPmAIqLi4uLiyUHadKOkP/SuTM6d65Wy6ysrN69e+vq6vr7+8+fP9/S0jInJyc1NTUnJyc+Pj44OHjq1Kl79/qnpe1i2f4AlJRCHRz29ew5Z+zYsS1atKjOJRYswNmzAODggJ9/xps3tR8XIYSQz1PVwc7IyAjAq1evDA0NJV9/AMuysq+LkC/M8uXLNTQ0wsPDN27cqKKicubMmT59+ixYsODIkSOrV6/29fXNzHzj5dWJZW0AqKpe/eWXZ3Pm+Fe//61b8dtvANC5MwICoEDbRBNCSENU9U/3MWPGAFBWVn77NSHkA0QilJZCTa2WpwuFwoCAgN27dzdq1Cg4OHjixIlKSkra2tqFhYVLliyZP3/++vXbzpz5RiBQADBkCHvr1jh9fZ/q93/6NCQL2Jma4uxZem2CEEIarKqD3XHJilvvfU0IeV9cHNzc8OQJLl9GpZ26aiArK6uwsLBz584AMjMzmzZtCmDAgAH79u0bP37869dlQ4cKr13jAejSJXPOnPiQkKy+fftWs/Pbt+HqCrEYamo4cwYmJrWpkBBCSL1Qm9VKS0pKYmNjs7KyZF4NIfWLWIxNm2Btjfv3wefj5cta9iPZrK+8vByAjo5OZmYmgFmzZr1582bx4lXAGUmqY5hLM2YEz5zpMWvWrGo+25qSAicnFBeDy4W/f3Uf+COEEFJPVSvYnT171sHBgc/nA4iOjjYzM7OysjI2Nl60aBE9YEe+WGlpGDwY338PPh88HtauhatrbfphWfb27duampqTJk1avHhx06ZNjxw5IhaLtbW1T526GBb2LTAQgI7ObQ5n9Ny50/r37//rr79Wp+fycgwfDsnGMT4+GDasNuURQgipR6Q/QX3u3DknJycAaWlpzZs3nzZtWk5OjpOTU0pKyubNm9u2bTtt2rS6r5OQz8uxY5g1C7m5ANCqFfz8qrtMXWUsy967d2/u3Lm3bt0yMzN78OCBhoZGTExMeXm5i4vL77/vX7CgpVCoBqBx47j8/IF9+3ZfvXp19W/CvnyJ+HgAWLAAc+bUuDxCCCH1jvQZu82bN2tpacXHxzdv3jw+Pj4hIWH48OGnT5++detW+/btd+3aJYcqCfl85Odj4kSMHVuR6iZPRlxcjVNdTk7O1KlTNTU1O3XqdPXq1UaNGo0ePfqrr76KjY2dMGGCqanpiRPn9fVv3rqlBkBD42ZOjo2n5/TQ0NDqpzoArVvjjz+waRM2bapZeYQQQuop6TN2MTExLi4u7du3B3D+/HkAzs7OABQUFOzt7SnYkS9KWBjc3ZGSAgB6eti5E6NH1+D0oKCgffv23b17NyUlpVGjRsOHDw8ICDh37lx6evqqVavMzMx8fHwCAgJKSyESnQIGADA2Tpw9O8rJ6WbtdnqdObMWJxFCCKmvpAc7Pp+voaEh+frixYsABg0aJPlWIBBIHvom5EuQloavvkJZGQAMG4Y//0T1F+dmWXbWrFn79+93c3MzNDQUi8WDBw/ev3+/lpaWvb09l8t1cHCwsrLKysoKDLzo5ITMTACwt8fp0+1UVNrV1ZAIIYQ0LNJvxTZv3jw6OhpARkbGtWvXOnXqZGJiAqCsrOzcuXPVXPKekAZAVRVaWlBTw86dOHOmBqkOwN69e/38/CIiInx8fOLi4n7//fddu3a5ubm9efNm8+bNAIyMjBYuXLh//9Gvv67YYdbQ8N7p01BRqZvBEEIIaYikB7vx48dHRETY2dnZ2NgIhcIpU6YAOHfuXK9evZ49ezZjxoy6L5KQz4KWFpKTkZ6OGTPAMDU718fH59tvv+3Zs+fz58/Lysq6d+8OoH379np6er6+vpK3yzt37vnokfe5cwCgo5MwZsxhSnWEEEJqRHqwW7Zs2bx58yIjI58/fz59+vQ5c+YAuHbt2t27dxcuXDh16tS6L5KQz0WjRrXZtqG8vDwhIWHw4MEAFBQUAAiFQgAODg6ZmZkpKSmZmZnl5VixwpJlvwLQvXtpcXH/ESMG1egqIhGuX0dpaY3LI4QQ0mBID3ZcLtfHx6ewsPDNmze7du3icrkApk+fnpqaunnzZsm3hJAPKC8vZ1m2UaNGAJo1a6ahoREREQGgbdu248aNA3DpUrizM6Kj9QB06VL0+nWvgQN72tvbV/8SYjHGjYONDaZMqZsxEEIIqQ+qu/OEioqKSCS6efPmhQsXAOjq6hoZGdVlYYR8MnfuYPx4/PabzDrU0NBo3LhxfHw8ACUlpcmTJ3t5eUn2bhk7dqyCQiMPD/WzZwGAx7t5546xlZV5QEBAjS6xdClOnAAAJSWZlU0IIaTeqVawy8jIcHFx0dbW7tWrl4ODAwBfX18LC4tr167VcXmEyFVZGby80KMHAgKweLHMuhWJRA4ODuvWrSsuLgawdu1afX39Ll26rFu3bsmSHzU1Q1h2GABV1dgtWx7eu3cjICBAvSZ3fPftw4YNAGBpCR8fmZVNCCGk3pG+3ElmZqa1tfWLFy969epVVlYWFxcHQE1NLTk5efDgwdHR0bVbXouQz821a5g2DQ8fAgCPh9WrZdBnamrqd999FxgYKNkHVktLa9y4cT4+PsHBwT/88IO3969FRfuBXgBat86Miemirt61ppeIiKhYrK5xY5w9C01NGZRNCCGknpI+Y7dq1arU1NSgoKCoqCjJ3mIAFixYEBISIhQK16xZU8cVElLnSkqwZAn6969IdZ064cYNfPfdx3b7/PnzHj16pKamnjhxIjU19caNG127dvXz89PT09PV1d29+4Ca2gXAEYCNDWJjDdTVa/iqLfDkCcaMAZ8PJSWcOoXmzT+2ZkIIIfWa9Bm7oKCgMWPGODo6vnPc1tbW2dn56tWrdVMYIXISHIyZM/HyJQCoqGDxYnh5gcf7qD5v3rx5+/btbdu2aWho7N27t02bNgBMTEyio6MfPHhgbW3t7j79yRPv8+cVANjYIDgYamo1vkphIUaORG4uGAa7d6NPn4+qmRBCSAMgPdhlZ2ebmZlV+ZGRkVF2drasSyJETrKzsXAh/PwqvrW1xe7daNWqNl1du3YtKirq5cuXurq6wcHBsbGxbdq0efLkiYGBQdu2befNm7dp0ybJK+QWFhaenot++61/fr4CgD59cP58bVKdUAhnZyQmAsCyZZg4sTZlE0IIaWCkBzvJNEOVH12/fl2yCwUh9dGYMZDMOGtqYsMGTJ9e42WHAeTn57u4uFy+fLlr167NmjXbv39/cXGxnZ3dsmXL7Ozsnj17FhkZOWHCBAUFhY0bNwLg83H58oz8fAMAffogOLg2C+MBWLAAly8DgLMz6IEIQgghEtKfsXN2do6MjPT29pYsji8hFAq9vLyio6NHjBhRl+URUocMDABg1CgkJtZmMwmJsWPHpqSk3L9//9atWyNHjlRQULh69WpqauqqVasACIXCwYMHHzx4cOvWrampqXw+xo5FdPTHprrsbGzfDgBWVjhwoJaVE0IIaXikz9h5eXmdO3du2bJlu3btUlRUBODm5nb9+vXnz5+3aNHCy8ur7oskpE74+yM7u2Zbvr719OnTu3fv3r59Ozw8PDExsXXr1gCCg4OdnJxsbGwCAwM7dOigqKgYFhbm6Ojo4OBgYGAQHHzl3Dn306cBQFMzMTi4Xe1SHQBdXXh44OVLHDyIRo1q2QkhhJCGR/qMnZaW1vXr12fMmJGVlfXo0SMAfn5+aWlpEyZMiIqK0tXVrfsiCakTXG5tUl1KSsqQIUNatmw5ZcqUbdu2CQSCYcOGSXaSyMzMbNq0KQALC4sePXq0b99+8eLFOTk5DMM0adJi69ZeklTH4dz08XlY61QHgMPBvn24cgX0KAQhhJDKpM/YAdDW1t65c6evr29SUlJ+fr6amlrbtm0b0UQBqSfKy6GgAJnsfpeXl2dra2tiYhIfH9+hQ4cpU6YUFxfr6ekNHTo0NDRUV1c3IyND0tLExERTUzMuLq5z584zZ3rGxS0tL28DgMOJnjz5r0mTfpVBNYQQQsi/SZ+xGzdu3I8//ghAUVGxU6dO/fv3t7KyolRH6oXiYixfDh0dWFlBLJZBhxs2bODxeBcuXJCsy62vr5+bm7t9+/bx48fPnz/fzs4uKCiosLAQQFpamomJybVr12bMmPPLL13Lyx0AaGre37s3/c8/NzP0WBwhhJA6ID3YnTlz5ubNm3IohRAZYlkcOgRzc/z8M0pKkJwMPl8G3Z48eXLWrFlvf7EZPHjw1atXk5OTFy1a9Pfff9va2mprazs7O9+4cSM6Onrw4ME8nvKNG7OKiuwB9OqFly8t3d1HyaAOQgghpCrSg52BgUFRUZEcSiFEVqKj0asXJk1CWhoA9OyJ0FAoK8ug55SUFMlqwxIDBw60tbV1cnKSzMBlZmYGBwe/ePHCxsZGR0dn9+69hoYhFy5oAejSRXzuHDQ0ZFADIYQQ8l+kB7t9+/Y9fPjQw8MjIiIiLS0t/z1yqJKQakpPxzffoHdvSNZeNDbGzp2IikL37rLpX01NraCgoPKRv/76q1mzZp06dQKwatWqr7/++smTJ927d3dzcw8Pn5qTMwiAlRWuXOFoa9fyouHhaNYM8+d/dPWEEEIaOunBzsPDg2XZAwcO2Nrampqaar9HDlUSIpVAwPj6ciwssGsXxGIoKmLePCQlYcYMcKT/Z16d/gWFhYV9+vQJDAysfFxTU/PcuXPff/99o0aN2rRpI3lb/Pr1mxkZG58/7wWga1dcvoxa/0V5/BhjxuDFCxw//vGDIIQQ0sBJfyu2ffv2cqiDkI/BsnBxafnyLSfuAAAgAElEQVTwYcWLr87O2LgRzZrJpvMDBw5s27YtISGBz+c3btw4JyenZ8+eCxcufNsgNjZ2165dy5YtkyzrKBJh0iQcPQp8dKorKICTE16/BsPgV3qPlhBCiDTSg93Zs2flUAchH0MgQFqaIoDOnbF1K/r3l1nPM2fOPHTo0Lfffrtx40Ztbe27d+/++OOPixYtOnLkyKBBg1RVVWNjY8+ePevq6rpkyRL8P9UdOQL8P9Xp6NTy0iIRxo/HgwcAsGwZxo+X1ZgIIYQ0WNVax46Qz5yiIg4dSgaaOzoqfOR6daWlpQkJCU+ePGnatGlKSsq+ffsiIyN79uwp+bRLly4uLi59+/bNycmJi4srKSmxtLQ8e/bs4MGDAYhEcHevSHVdunxUqgOwZAkuXACAkSOxevVHDYoQQsgXgoIdaSBatSo3N2c/MtVt3779xx9/zMvLMzQ0zMrKAtChQwcrK6vKbZSUlP7444/u3btHREQ0adLk7XFJqvPzA4AuXRAS8lGp7uBBbNoEAB074tAh2TwmSAghpMGj/12Q+uTxYxw5grKyOul8/fr133///U8//VRYWJienl5cXKyrq/vkyZNZs2a907Jr167KysqJiYlvj4hE8PCQWaqLicHMmQCgq4uTJ6GmVvuuCCGEfFEo2JH64cULTJkCS0u4umLDBtn3n5aWtmrVqr17986dO1dNTQ2AkpKSqqrqwoUL9+3b984a3ZJV61iWlXwrSXWHDwNA584fewc2LQ2OjigtBY+HkyfRsmXtuyKEEPKloWBHPncZGfD0RJs22LcPQiG4XFhayv4qZ86cMTQ0HP/vNxQsLS0zMzP79et34sSJysfj4uJKS0stLS3xXqoLCYGu7kdVMm0aXr0CAF9f9Ov3UV0RQgj50tAzduTzlZWFzZvh64uSEgBgGIwciTVr0K6dDDoXiUQ3btxITExkWbZdu3bPnz+3sLB4p83UqVNdXFycnJxevHjx9qBAIPjuu+/s7e3NzMxEIkyeLMtUB0AkAgBPT3zzzcd2RQgh5EtDwY58ju7dg48P/Pz+eZxu8GCsXSuzDSSio6MnTpz47Nmzli1bMgzz5MkTTU1NIyOjd5qNGjXK1dX14MGDHTp0uHbtmpaWVnx8/JYtW9LT069duyZJdYcOAbJLdQBOnUJiosxGSggh5ItCt2LJZ2fpUnTqhD17KlKdjQ0iInDxosyyTlJSkr29vY2NTUZGxoMHD5KSkjIzM3v27JmQkBAWFvZOY19f30aNGmVlZQ0YMKBDhw7z58/v2LFjTExM06bNp0ypSHWdOsks1QFQVUWPHmAY2fRGCCHki0IzduSzExIClgWXixEjsHAhbGxk3L+Xl5eNjc2ePXuY/6cnHR2doKAgPT29sWPHJicna2hoSI7z+fyZM2eqq6s/ePCAx+MVFhY2btwYgFiMyZNx8CAg61RHCCGEfAwKduSzc+gQLlyAkxNatJB95wKB4Pz58ydOnGD+PSfG5XK3bdvm7u5ubm7+9ddft2zZMjU19fTp02/evDl79qzkPdm3qW7KlIpU17EjQkKgpyf7OgkhhJBaoGBHPjsWFnjvNQaZyc3NLS8vb1nVIiLW1tYsy86ePfvu3bsRERFNmzZ1d3efOXOmbqXpOJbFrFk4cAAAOnbElSuU6gghhHxGKNgReROJcPo0tm3Dixc4fRodO8r16pqamgzD5Obmvv9Rbm4uwzALFy5U+48VgSWpbtcuAGjbFhcvyiDVhYSgoABjxnxsP4QQQgjo5QkiT2/ewMcHbdpgzBhEROD5c9y4UbdXDA0NHTlyZPPmzTU1Na2trdeuXcuybI8ePf7666/3Gx87dqxr164fSHWzZ2PnTgCwsEBoKAwNP7a8kycxeDCcnfHv9Y8JIYSQWqIZO1Ln+HxERCAwEIcPo7Cw4qCODmbOhLt7HV7X29t7xYoVrq6uq1at0tbWjo+P37VrV0BAwNKlS93d3Xv06OHq6vq2sb+/v6+v77Fjx6rsSpLqduwAAAsLhIXJINUlJsLdHSwLLS00bfqxvRFCCCGgYEfq1KlTOHoUFy/+k+cAtGmDBQswaRJUVevw0teuXVu+fPnJkydHjBghOeLk5OTp6TlgwIBTp079+uuvkydP3rp1a8+ePRmGiY6OjouL27Bhw9vGlbEs5syRcarLy8PIkSgqAoeDw4dhbPyxHRJCCCGgYEfqTmgoRo/+51seD3Z2mDMHX30FTt0/ArB9+/bRo0e/E9Q0NTW3bdvWv3//bdu2DR069OjRowkJCSzLfvXVV4cOHTI3N3+/H0mq++MPQHZ3YMViuLnhyRMAWLMGw4Z9bIeEEEKIBAU7UlfMzKCtDYaBgwOcnDBkCDQ15Xf1uLi4hQsXvn+8T58+SkpKd+/eHTp06MqVKz/cCcti7tyKVGdujtBQvLc5RW0sW4bz5wFg5EgsXSqDDgkhhBAJCnbyVlqK/HxoaqJRo09dykd78wYXL+LpU8ycif+v6fuPli2RmQkOB1zuJ6iNz+crKiq+f5xhGB6Px+fzpfYgSXW//w4A5uYIC5NNqjt+HBs2AED79jh0iHaYIIQQIksU7OQqKwvt2iEnBwAUFaGpCS2tij8l/2hqols3dOnyqQv9oJcvceYMgoIQEYHycgAoKcGqVVW05PHkW1klbdq0iYuLmzx58jvHk5OTCwsLW7du/eHT6yjVJSZi8mSwLLS1ERiI/3gBlxBCCKklCnZyVVCA168rvubzkZ2N7Owqmp0/r2hg8O7BwkI4OeHFC2hq/pMI3/miSxe0alUnlbMsYmIQFIQzZ3Dnzr8+0tJC//51ctGP4ebmNmvWLE9Pz8oZjmVZLy8vKyurtm3bfuBcloWn5z+pTlZ3YAsKMGoUiorA5eLIEVS1RjIhhBDyUSjYyVXr1oiKwt27yMtDQQHy89/9s6AA5uYwNBS+f25iIiIipPSvooJnz/B+KOTzsWQJ0tL+NTX4TjQ0Nq76nmlBAZYvx8mTSE//1/EmTTB8OEaMgK0tlJRq8C9BPiZMmBAQENC3b19vb+8BAwbo6OjEx8dv2rQpNDQ04oP/HiWpbvt2AGjTBqGhMntl9fhxPH4MAGvWYOhQ2fRJCCGEVEbBTt569kTPnlLaPHwofv+gtTU2b0Z8fEX+y8+v+KegACJRRZtGjVDVc2UID8eWLVIuam6OO3egrPzu8X378NtvFV8zDLp2hZMTHB0/9/vFHA7n5MmTP//886JFi/Ly8iRHBgwYcOPGjXbt2v3XWSyLefP+SXVhYbJciGTIEAwciC5dsGSJzPokhBBCKqNgV28wDKp6yxMAiooqEp6xMbS1q2jQuzfGjMH9+xXNSkqqaPPsGYqKqgh2Q4fi8GEYGsLREcOHw8Tk44YhRzweb9WqVStXrnz+/Hl+fr65uXmjD76xIkl1khQr81QHwNQUV67IskNCCCHkHRTsGgI1NaipwdT0Qw2OH//nWz7/X7eAJfeFO3WqeudTCwvcvi37muWGYZjmzZtLbcaymD+/ItW1bi37VEcIIYTIAQW7L5GiIho3RuPGn7qOz4Yk1fn6ApTqCCGE1Gd1vwMAIZ83lsWCBf9KdfXodjMhhBBSGQU78kWTpLpt2wCgVStKdYQQQuo3Cnbki7Z0aUWqa9YMly/LMtVFRaFfPxw6JLMOCSGEEKnoGTvy5VqyBL/8AgBmZggLQ7NmMuv55UuMGIGcHJSUYOJEmXVLCCGEfBjN2JEv1NKl/6S68HBZprrycnz9dcXGcYsWyaxbQgghRCoKduRLtGwZ1q8H6iDVAZg3D7duVXzh4iLLngkhhJAPo1uxpH6LiYm5fPny/fv3eTzewIEDnZ2dlaRtcLZsGby9gTq4Awvg8GHs2gUA1tbYuFGWPRNCCCFS0Ywdqa+EQuH06dO7d+9+8uRJhmEeP348d+7cDh06JCYmfuAsL6+KVNe0KcLCUI2li2vg7l188w0AGBjg+PGqt3cjhBBC6g7N2JH6aunSpUFBQVFRUdbW1nw+/9GjR0ZGRjNmzBgyZEhiYqKmpub7p3h5Yd06AGjaFOHhMk51eXkYPRolJVBQQEAALZtCCCHkf+3deUBU5f7H8e+wyiKICyIiKNpVy7guGe5bZSgmcCNNBcNcr3nNpHszLTGLcl8r993rVXFJxQRpMXf8mabXXcFdvIqChsoyzPz+GMMRUMGYOTPH9+svz3POnOd7ptM5H87yjAK4YgerdP369enTp8+bN69Zs2YFjW5ubitWrHBwcJg9e3bRj3zyiQlTnU4nERGSmioiMm6ctG1blisHAKCErCzY6fV6rVb7qLlZWVmZmZnmrAcmkp+fHxcXN3jw4KCgoH79+s2dO/fu3bvGC2zfvt3FxaVLly6FPujo6BgeHv7jjz8Wav/kE4mNFTHNHVgRmTRJvv9eROStt2T48DJeOQAAJWQ1we7kyZNvvPGGk5OTg4NDw4YNFy9erNfrCy3TpUsXDw8PRcpDGbpx40br1q3ffffd69evN27cODc3d/To0QEBASdOnDBextPT08ammB3Yy8vrxo0bxi2ffvpQqvP3L/uaf/hBRKRePVmwQDSasl8/AAAlYR3P2KWkpDRr1iwzM9Pf39/JyenIkSN9+vRZv379ihUrXFxclK4OZaxnz57Z2dmGZ+YMLVlZWREREV26dDly5Ei5cuVExMvLKy0tLS8vz97evtDHL1y4ULVq1YLJTz+VL74QEalRw1SpTkSmT5d//1sGDpTy5U2yfgAASsI6rtiNHj06MzNz8eLFKSkpR44cOXr06GuvvbZx48aOHTtmZWUpXR3K0r59+3744YeVK1cWpDoRcXV1XbZs2a1bt1asWGFoadu2bX5+/qpVqwp9/Pbt26tXr+7UqZNhcvRoc6Q6EalfX774QmrUMNX6AQAoCesIdklJSZ07d37nnXcMk3Xr1k1ISBg4cODu3btDQ0Nzc3OVLQ9laPv27QEBAX/5y18KtZcvX/7111/fvn27YdLd3f2TTz4ZPHjw+vXrC5a5dOlS165d3d3d+/XrJyKjR8vnn4v8kepq1zbTJgAAoBTruBWbkZFRt25d4xYbG5tZs2bl5uYuWrTonXfeWbFihYYnm1Th1q1blSpVKnZW5cqVz58/XzA5YsSI7Ozs7t27V61atV69epcvXz59+nTTpk0TExOdnJxiYkh1AIBnjnUEu6pVqx46dKhQo0ajmTt37v/+97+VK1d6e3tPnjxZkdpQtqpXr55qGDWkiJSUlJpGPxOh0Wg+++yzfv36/fzzz8eOHWvcuHFQUFC7du00Gk1MjIwdKyLi40OqAwA8Q6zjVmznzp1/+umnqVOn6nQ643Y7O7u4uLjAwMApU6YMGDAgOztbqQpRVjp37nzp0qVNmzYVaj9x4sTWrVu7du1aqL1GjRq9e/ceO3ZsZGRkq1atNBrNmDGkOgDAM0pTdNAQC3TlypWmTZteuXLF09Nz/PjxUVFRxnMzMjKCg4P37NljmCzVFqWmpr7wwgslSYQHDhwo+gImTGHGjBn/+c9/YmJiOnbsaBjQZP/+/aNGjapbt+6MGTMe/9lvv/WcNctTRCpX1i5ceLZWrRxTVDhuXLUNGyp89dWldu1+N8X6AQCWLC8vr3HjxnPnzu3fv7/StRRmHbdivb29/+///i8mJmb9+vWFLtqJiIeHx08//TRy5MhvvvmmtC9S1KpVKzEx8fGfio+Pnz59evXq1Z2dnUtd+lO5ePGih4eHq6urebqzNJMnT65Spcqnn3762Wef1apV6/Lly5mZmRERERMnTnzUfwKtVnvp0qU1a2rPmuUkIlWr6jdvzq1bt1qxC/9Jixfb//vfjiJy5ky1qKjiHwe0CoYBvX18fJQuxJqkp6eLSOXKlZUuxJpcunSpQoUKz+wB7SkYDmg+Pj52dtZxjrYEWVlZGRkZNcw1NoFhzPyigcQSWM1O4+3tPW/evHnz5uXn5xedW65cuSlTpvzrX//au3dvqVar0WjatGnz+GVSUlJExNnZ2WwHJhsbm3Llyj3Lx8HY2Nhhw4bt27fvzJkzPj4+TZs29fX1fczyubm5CxdWnjrVkOrk55819eubJIUfPiz/+pcYevnoIwdXVwdT9GIeeXl5NjY2z/Ju9hRu3bolInxppWJjY+Po6MiXVnJ5eXki4uzs7OBgxUcYM9NqtRzQDKwm2BWwtbU1nszNzdXpdAWD1oaGhipUF8pYlSpVgoODS7jwlCm2U6d6iUjVqvLTT1K/vklK+v136dZN7t0TOztZvVqqmeSCIAAAT886Xp54jMaNGzs5OSldBZQ0caJ8/LGt/JHqnn/eVB316SMnT4qIfPGFPOk6LwAACrD6YIdn3KRJ9++NVqqk3bpVa7pUN22arF0rItKly/0eAQCwNAQ7WLFJk+Sf/xQR8fTUL1hwtn59U73ivW+ffPSRiEjNmrJkiTAYNgDAMlnfM3aAweTJBalOEhO1dnYmGdlERDIypHt3yc0VBwdZvVoqVjRRPwAA/FlcsYNVmjxZPvxQRMTT0/BcnQmHY3zvPTl3TkRkwgRp2tR0/QAA8GdZ/RW7cePGZWZmKl0FniwtLS07O7tmzZp//ld9p0x5KNW98IKUcvjC0jH8ml1oqAwdasJeAAD486z+il2XLl0iIiKUrgKPlJ2dPXLkyCpVqnh7e/v7+7u5ufXp0+fatWtPvcIpUyQ6WkTE01N+/FFeeKHMSn2UNWvk229lxQoerQMAWDqrv2IHS5aTk9OxY8dz586NHz++VatWjo6Ov/76a2xsbGBg4O7du6uVfiC4qVMfSnUNGpR9zUXVr2+qgfEAAChbBDuY0PTp00+fPv3rr796e3sbWvz8/Dp37tymTZvo6OgVK1aUam1Tp8rw4SIiVaqYL9UBAGBFrP5WLCzZkiVLhg0bVpDqDMqVKzd27Nh169ZlZWWVfFXTppHqAAB4AoIdTEWn0506dappce+RNm3aNCcn5+zZsyVc1bRp8sEHIn+kuhdfLMMyAQBQD4IdTEWj0dja2mq12qKzDI2Ffvb3UUh1AACUEMEOpqLRaAICAn755Zeis3755Zfy5cv7+/s/cSXTpz90B9YMqW7IEHFzk/h4k3cEAECZI9jBhAYOHDhz5szDhw8bN167dm3kyJFRUVHlypV7/MfnzJEPPhC9Xjw8ZMsWc6S6BQvkm2/k999l1y6T9wUAQJnjrViYUJ8+fbZt29ayZcshQ4a0aNHCyclp//79M2fOrFGjRmxs7OM/O3eu/P3v91NdUpI0aWLyao8duz8EcdWq8v77Ju8OAIAyR7CDCdnY2CxdunTJkiXz5s379ttvc3Jynn/++SFDhgwfPtzR0fExH5w7VwYNEr1eKlSQrVvNkeqys6VnT7l7V2xsZPly8fIyeY8AAJQ5gh1MS6PRREVFRUVFiUh+fn5JXpiYN+9BqktKkpdeMnmRIjJs2P2fDhs1Sl591Rw9AgBQ5njGDuZTwlQ3cKC5U93atTJnjohIq1YyerQ5egQAwBQIdrAg8+c/dAfWPKnu4kUZMEBExMNDli8XO65iAwCsFsEOlmL+fBk4UHS6+6muuIGNy55WK2+/LTdvikYjCxeKn585OgUAwEQIdrAICxYokOpE5LPPZPduEZEhQyQ01EydAgBgIgQ7KG/BAhkwQHQ6cXeXxETzpToR2bFDRKRRI5k40XydAgBgIjxPBIUtXPgg1W3dKi+/bNbev/1W4uJk4EB57OgrAABYB4IdlLRwofTvr1iqE5Hnn5eYGHN3CgCAiXArFooxTnWJiQqkOgAAVIZgB2UsWvRQqgsMVLogAACsH8EOCli0SPr1I9UBAFDGCHYwt8WL76c6NzdJSCDVAQBQZgh2MKvFi6Vv3/upLjFRmjUza+87dkjLlrJsmVk7BQDAbHgrFuazcuX9a3UuLrJpk7lT3dWrEh4u166JViuRkWbtGgAA8+CKHcxk5UqJiJD8fHFxke+/lzZtzNq7Xi9RUXLtmojI+++btWsAAMyGYAdzWLVKIiPvp7rNm82d6kRk6lRJTBQR6d1bevY0d+8AAJgHwQ4mt2qVRESIVns/1bVta+4CjhyRUaNERPz9ZeZMc/cOAIDZEOxgWqtXP0h18fEKpLp796RbN8nOFnt7WbFC3NzMXQAAAGZDsIMJrV4tvXqJVivOzhIfL+3aKVDDRx/J8eMiIjExDK0CAFA5gh1MJS5O+VSXlCRffy0i0rKljBihQAEAAJgTwQ4mERcnPXs+SHXt2ytQw82bEhUler24ucmyZWJrq0ANAACYE8EOZW/NGuVTnYisWydXroiITJ8utWopUwMAAObEAMUoRkZGxr59+06cOOHt7f3SSy/VKk0sWrNGevS4n+o2bVIs1YlIp07SsaM0bSpRUYrVAACAORHsUNjkyZNjYmL0en2dOnWuXLly48aNHj16zJo1y60EL5QWSnUdOpih3keqXv3+2HUAADwjuBWLh0yYMGH06NEzZ868ffv2oUOHrl+/vnv37l9//TUsLEyn0z3+s2vXPrgDq3iqAwDgGUSwwwPp6eljxoyZPXt2nz59bP9416BZs2aJiYnJycnr1q17zGfXrpUePSQvT5ydZeNGUh0AAArgVqz56HS6gwcPHjlyREQaNGjQqFEjGxvLCtZJSUkuLi49i/zklp+fX0hIyMaNG8PDw4v94Lp191Odk5Ns3CivvGL6WgEAQBEEOzM5cODAO++8c+TIkZo1a4rIuXPnGjRosGTJksaNGytd2gOXL1+uWbOmbXHjgtSpU2fHjh3FfmrdOnn7bVIdAADKs6wrRmp18uTJDh06BAQEpKWlnT179uzZs2lpaQEBAR06dDh16pTS1T3g7u5+8+bNYmfduHHD3d29aHuhVPfqqyYu8bF+/lmzcqWSBQAAoCyCnTmMHDkyMDBw+fLlXl5ehhYvL69ly5YFBgaOHDlS2dqMtW7dOjU19cCBA4Xac3JyNm3a1KZNm0LtmzdLz56SlyeOjhIXp3Cq27fPpVMnux495JdflCwDAAAFEexMLi8v7/vvvx86dKhGozFut7Gx+cc//rF58+a8vDylaiukXr164eHhERER58+fL2jMzs7u27dvXl5e3759jRf+/nt5803JyRFHR1m7VoKDzV6ukcxMGTXKR6cTV1fx91eyEgAAFMQzdiaXnp6enZ393HPPFZ313HPPZWdnp6enV6tWzfyFFWvBggVhYWHPP/98UFBQvXr10tLStm7dqtFoNm/ebDyO3fffy9/+ZimpTkSGDrW7etVGRKZMkRo1FC4GAAClcMXO5MqXLy8ixT67dvPmTY1GU5KBf83Gzc0tKSlp+fLl1apV279/v06nGzFixNGjRxs1alSwzJYt91Odg4OsWaN8qlu5UlatshGR4GBd//4KFwMAgIK4Ymdyrq6ujRs3XrduXbNmzQrNWrduXaNGjVxcXBQp7FFsbGzCwsLCwsKKnbtli4SF3U91a9dKly5mrq6wq1dlyBARkYoVtXPm6PlbBQDwLOMsaA4ff/zx9OnT169fb9y4fv36GTNmWNTLE09knOrWrFE+1YnI4MFy44aIyKhRVzw9la4GAABFccXOHMLDw8+cOfPWW2+1bNkyMDBQRJKTk3ft2vXFF1+8+eabSldXUgkJD92BfeMNpQsSWbJEDGn57bd1HTveFrGURxUBAFAEV+zMZMSIEQcOHGjevPnRo0ePHj3avHnzAwcOjBgxQum6SiohQcLCJDtbHBwkLs4iUt2VK/LBByIi1arJlClapcsBAEB5XLEzn4CAgICAAKWreBqJiQ+luq5dlS5IREQ+/lgyMkREZs+WSpXu35AFAOBZxhU7PEFiooSGWlyqE5HcXBGRqCgLKgkAAGUR7PA4W7c+SHWrV1tWhFq0SHbtkvnzla4DAACLwa1YPNLWrRIS8iDVhYQoXdDDypWTFi2ULgIAAEvCFTsUryDV2dvLqlUWl+oAAEBRBDsUIynp/h1Ye3tZvVpCQ5UuCAAAlADBDoUlJUlIiNy7R6oDAMDKEOzwEONUt2oVqQ4AAGtCsMMD27dLWJjcuye2trJ0qTzi12KVcfasNGwo3buLXq90KQAAWCreisV927dL585y547Y2sry5fL220oXZESnk6goOXRIjh6VO3fE1VXpggAAsEhcsYOIyI4dEhx8P9UtW2ZZqU5Epk+X7dtFRAYNItUBAPBIBDvIjh3SubNkZd1PdT16KF3Qw06elFGjRERq1ZKvvlK6GgAALBjB7llnnOqWLrW4VKfTSb9+cu+e2NjIokVcrgMA4HEIds+0nTsfSnU9eypdUBGTJsnOnSIi778vbdsqXQ0AAJaNYPfs2rlTOnW6n+qWLLHEVHfihIwZIyLyl79IbKzCxQAAYPkIds8o41S3eLH06qV0QUXodPLuu/fHXlm0SJyclC4IAACLR7B7Fu3a9eAO7OLFEhGhdEHFmTVL9uwRERk2TFq0ULoaAACsgh5PsnTpUqX/K5WtliK3RfQiWhGLzHT3nRTRi6SIOCtdCQAAhS1dulTphFIMjZ6B/EsgLi4uJyfHbN2NGTOmUaNGAQEBZb7mvDz7H39sl5vrICJ16qQ+//zxMu+irOzd2/TaNc+GDQ/7+l584sKZmZnLly+PjIx0d3c3Q23q8Ntvv/32229RUVFKF2JNEhISRCQoKEjpQqzJ4sWLGzZs2LBhQ6ULsRq3bt1atmxZREREhQoVlK7Fahw+fPjgwYNjDM9lm4Wjo+Nbb71ltu5KjmBniXx9fWNjYyMjI5UuxGqkpqbWrl37/Pnzvr6+StdiNRYtWvT555+npqYqXYg16dOnj4gsWrRI6UKsib+//6effmr46lASFy5c8PPzS0lJ8ff3V7oWq7Fs2bJRo0ZduHBB6UKUxzN2AAAAKkGwAwAAUAmCHQAAgEoQ7AAAAFSCYAcAAKASBDsAAACVINgBAACoBMEOAABAJSVL20EAABIKSURBVAh2AAAAKmGndAEohoODg4ODg9JVWBPD18WXVirsZk+Bb+wpsKeVFge0p8BuVoCfFLNEFy5c8Pb2trMjdpdCamoqP79TKnl5eVevXq1Ro4bShViTjIwMEfHw8FC6EGty8eJFLy8ve3t7pQuxJhzQSkur1V65coVflRSCHQAAgGrwjB0AAIBKEOwAAABUgmAHAACgEgQ7AAAAlSDYAQAAqATBDgAAQCUIdgAAACpBsAMAAFAJgh0AAIBKEOwAAABUgmAHAACgEgQ7AAAAlSDYAQAAqATBDgAAQCUIdpZrxYoVgYGBLi4u1apVCwsLO3bsmNIVWY0PP/xQo9Hs3btX6UIsXVpa2sCBA2vUqOHg4ODl5RUZGXn27Fmli7JQeXl5EydOrFevnpOTU506dWJjY7VardJFWTT2rj+J41jJcbp8iB4WadSoUSJSp06d6OjoHj162NnZubm5nTp1Sum6rEBycrKtra2I7NmzR+laLNrly5erV68uIh06dBg2bFinTp1EpGLFiuxmxYqMjBSR1q1bR0dHv/zyyyLSvXt3pYuyXOxdfxLHsZLjdFkIwc4S7du3T6PRtGnT5u7du4aWjRs3ikiPHj2ULczy5eTkNGjQwPBHCwfEx+vXr5+ITJkypaBl8eLFIvLGG28oWJVl2rp1q4hERkYaJvPz87t37y4iiYmJyhZmsdi7/gyOYyXH6bIogp0l6t27t42NTaE/OAYPHhwTE6NQRVZj9OjRdnZ2HTt25ID4RJUqVfL09MzPzzdu9Pf3d3R01Ol0SlVlmbp27SoiKSkpBS3nzp0TkfDwcAWrsmTsXX8Gx7GS43RZlJ3J7/Wi9LZs2dK4cePnnnvOuPGbb75Rqh5rcfjw4a+++urDDz/U/3GJBY+Sn58/fvx4GxsbG5uHHrR1cnLKzc3Nz8+3s+Pg8MCOHTv8/Pz8/f0LWgyT27ZtU64oy8Xe9WdwHCsVTpdF8fKExbl27dr169cbNWp04sSJ0NDQChUquLu7h4eHp6amKl2aRcvPz3/33Xdr1qwZExOjdC1WwNbWtm/fvn369DFuPHny5IkTJwICAjjvGrt582ZGRkadOnUKtdeqVSs9PT0jI0ORqiwZe9dT4zhWKpwui0WwszhXrlwRkatXrwYGBh48eLBjx47+/v5r165t1qwZ75Q9xqRJkw4cODBv3rxy5copXYtV0ul0//jHP/Lz899//32la7EsN27cEJEKFSoUand3dxeRW7duKVCTtWHvKiGOY6XC6bJYBDuLc+fOHRHZtGnTK6+8cvLkydWrVx88eHDMmDHXr18fOnSo0tVZqFOnTo0ZM2bAgAFt27ZVuharpNfrBw8enJSUFBwcHBUVpXQ5liUvL09EHB0dC7UbWrKzsxWoyaqwd5UQx7HS4nRZLIKdxTE8lWJvbz9nzpyCP9o++eST2rVrb9my5ffff1e0Okuk1+v79u1bqVKlCRMmKF2LVcrPz+/Xr9+cOXOaN2++cuVKjUajdEWWxcnJSURyc3MLtefk5IiIi4uLAjVZD/auEuI49hQ4XRaLZx2UFB8fP2LECOOWAwcOGO7v1K5du0qVKgXttra2TZo0SUlJOX/+fMFr8M+gYr+xuXPn7ty5c8OGDW5ubkoVZsmK/dIcHBwM/7579263bt02b97coUOHDRs2uLq6KlGjRfPw8JDibrkaWgz/w6JY7F0l980333AcKy1Ol8Ui2CkpMzPz6NGjxi06na527dq2trZ6vb7QwoYWZ2dn89VneYr9xtasWSMiISEhhRZu3ry5iBw/frxevXpmq9ACFfulGf6RkZHRqVOn5OTkt99+e8mSJQVpD8YqVKjg6elZ9JGds2fPent7cxp+FPauUuE49hQ4XRZLU/QbgeKaN2+enJx88eJFw9DtIqLT6erVq2d4A8je3l7Z8ixNTExMoeBy5MiRkydPtm/fvmLFijNmzPD29laqNkuWnZ39yiuv7N69e/jw4ZMmTeIe2WO8+eab69atu3TpUsH/khcuXPDz8wsPD4+Li1O2NsvE3lVaHMeeDqfLYig0fh4exzBE+9/+9jetVmtomTFjhoj8/e9/V7Ywa/HRRx8JA3s+yQcffCAi/fv3V7oQK7BhwwYR6d27t2FwXZ1O17NnTxFJSkpSujQLxd7153EcKwlOl0VxK9YS9e7de9OmTWvXrm3UqNHrr79+4sSJ+Ph4f3//L774QunSoBJXr141jOF5+PDhdu3aFZobHx/P41DGunbtGhISsnTp0pSUlFatWu3YsWP37t0RERGvvvqq0qVZIvYumA2ny6IIdpZIo9GsWrXq66+/njdv3syZMytVqjRw4MDPP/+8YsWKSpcGldi7d6/hNc/k5OSic7VardkrsnSrVq2KjY1dtmzZtGnT/Pz8vvzyy+joaKWLslDsXTAbTpdF8YwdAACASjCOHQAAgEoQ7AAAAFSCYAcAAKASBDsAAACVINgBAACoBMEOAABAJQh2AAAAKkGwAwAAUAmCHQAAgEoQ7AAAAFSCYAcAAKASBDsAAACVINgBAACoBMEOAABAJQh2AAAAKkGwAwAAUAmCHQAAgEoQ7AAAAFSCYAcAAKASBDsAAACVINgBAACoBMEOAABAJQh2AAAAKkGwAwAAUAmCHQAAgEoQ7AAAAFSCYAcAAKASBDsAAACVINgBAACoBMEOAABAJQh2AAAAKkGwAwAAUAmCHQBlBAUFaTSa7OzsMllbeHi4RqO5evVqmaxNEcnJydu2bSuYNN6isv2uAKgYwQ4AlJeQkNCiRYszZ84oXQgA60awA6AG8+fPT0tL8/T0VLqQp5Senq7T6YxbrH2LACjCTukCAKAMVKhQQekSypj6tgiAGXDFDoBFuHbt2pAhQ3x9fR0cHHx8fAYOHHjlyhXjBVJTU3v16uXl5eXq6hoUFHT06NEGDRo0a9bMMNf4iTSdThcbG/vXv/7VxcXFw8PjlVde2bJly+N7T0lJ6dGjR5UqVcqXLx8aGnr+/HkfH5/Q0FDD3KKPuGVmZmo0moIFROTQoUM9e/b08fFxcHBwd3dv06bNhg0bCuaGhoZ6eXmlpKR069atUqVKzs7OLVq0SEhIMMyNiIiIjIwUkf79+2s0mhMnTshjnxq8devWP//5T39/f0dHRx8fn/fee+/69esFc59i8wGoBlfsACjvwoULLVq0uHz5cps2bcLCwo4cOTJ37txNmzbt3LnT399fRFJTU1u0aHHt2rXg4GBfX9+EhIRWrVrZ2dm5uroWXVt0dPS0adPatm07ZMiQ27dvr1q1Kjg4OD4+vnPnzsX2fvr06ebNm2dmZkZGRtapU2fVqlVt27a9e/duyetPTk5u3769nZ1daGho1apVU1NTN27cGBYWlpiY+NprrxmWycrKatGiRZMmTcaPH5+RkTF+/PguXbrs37+/YcOG77zzjq2t7dKlS3v27Nm8eXMvL6/H9JWZmdmyZctjx461bt06PDz83Llzc+bM2bJly549e6pWrfoUmw9AVfQAoITXX39dRO7du6fX67t06SIiM2fOLJg7a9YsEenQoYNh0nBtbPny5YbJ33//vXXr1iISGBhoaHnzzTdFJC0t7e7du3Z2du3bty9Y1X//+1+NRhMUFPSoSgy9r1+/3jCZnZ3dqlUrEQkJCSlaqkFGRobxAq+99ppGo9m/f3/BAqtXrxaRPn36GCZDQkKMJ/V6fXx8vIgMHjzYMLls2TIRmTdvXsECBVtUqIABAwaIyNixYwuWNFwa7NGjh16vf4rNB6Am3IoFoLAbN25s3ry5adOmQ4YMKWgcNGhQYGDgTz/9dO7cuYyMjE2bNgUGBvbq1csw19XVddKkSY9aoV6vv3DhQsFNzAYNGpw5c8b4xqixzMzMhISEwMDAgvuqjo6OEyZMKNUmvPfee/Pnz2/SpElBS4cOHUTk2rVrxov169ev4N8tW7YUkdK+Bpubm7t8+XI/P79Ro0YVNHbt2rV58+ZxcXFZWVlSys0HoDLcigWgsMOHD+v1+rZt2xZqb926dXJy8qFDh1xdXfPz8wsepzNo2rSpg4ND0bU5OTlFRUUtWLDA19e3ZcuWQUFBwcHBDRo0eFTvJ06c0Gq1L730knHjyy+/7OjoWPJNMFyQu3nz5n//+9+UlJRjx47t3LlTRPLz840Xq1WrVsG/DTeRc3NzS96LiBw/fvzu3buOjo5jx441bs/OztZqtUePHg0MDCzV5gNQGYIdAIXdvn1bRNzc3Aq1e3t7i8idO3cMby0UevJMo9E86lm02bNnBwQELFiwYNu2bdu2bRsxYsRf//rXuXPnvvzyy0UXvnXrloi4uLgYN9ra2rq7u5d8Ey5cuBAdHb1u3TrDkCU+Pj6GVKrX640Xs7e3L/TBQgs8UWZmpoicOnXqs88+KzrXcIO4VJsPQGW4FQtAYeXLlxeRQu/Ayh8xpVKlSobMZ0hgxgyJsCg7O7uhQ4ceOnTo0qVLixYtCg4OPnToUHBwcLHvQ3h4eIhIWlqacWN+fv7NmzcLJjUajTx8+e3OnTvGCwcFBa1Zs2bgwIE///xzenr6xYsXp06d+sQNfwqG63y9evUq9tmaoKAgKeXmA1AZgh0AhQUEBGg0mt27dxe6fLV9+3YRqV+/fqNGjTQazd69e43nnjx50nD5qpDU1NSRI0du3rxZRKpXrx4VFRUfH9+1a9f09HTDMCKFvPjii46Ojnv37jUeH/i3337TarUFk4Z7vsY58tixYwX/3r9///Hjx7t27frtt9+2a9euUqVKInLq1CkpzQU5Q3Z8ovr169vb2+/atcu4PBGZPn16bGzsrVu3Srv5AFSGYAdAYZUrV+7cufPhw4e//vrrgsbFixf/8ssvbdq08fX19fLy6tSp07Zt29asWWOYm52dHR0dXezaypUrN27cuE8//TQnJ8fQotVqL168aGtr6+PjU3R5JyenXr16nT59eubMmYaWnJycjz/+2HiZevXqicjKlSsNk1lZWcZ3Qg1P4xlf4cvKyjKsIS8vr4RfguEubdGrkoU4Ozu/9dZb586di4mJKWjcvn17dHT00qVL3dzcSrv5AFSGZ+wAKO/rr78+ePDg0KFDv/vuu4YNGx49ejQxMdHLy2v+/PmGBaZNm7Znz55u3bp16dLFz88vKSnpxo0bImJra1toVd7e3sOGDZs6deoLL7wQHBxsZ2e3devWI0eODB8+/FE/zzVhwoSdO3cOGzYsISGhXr16W7duLXRnNioqasqUKR9++OEPP/xQpUqVxMREb2/vKlWqGOa++OKLjRo12rlzZ1BQULt27dLT01evXn3z5k1nZ2dDkSXh6+srImPHjk1ISJgxY0b9+vUfteSUKVP27Nnz5Zdf/vjjjy1btvzf//4XFxfn4OCwYMECjUbzFJsPQFXMNa4KADyk0OBwV65cGTRoUPXq1R0cHPz8/IYMGWIYwq3A8ePHQ0JC3N3dnZ2dO3fubLgZ+uqrrxrmGo/6ptVqZ8+e3aRJkwoVKjg7O7/00kvz5s3T6XSPKSY9PX3QoEGenp5OTk6dOnXat2+fGA1Tp9frDSnKycmpcuXKAwYMyMjIqF69esECly9f7t27t7e3t5OT03PPPRcZGXnq1KmuXbtqNJqLFy/q/xjH7vr16wUrNFzMa9u2bUHL+++/7+7u7urqumnTJv2jx7HT6/XXr18fNmxYzZo1HRwcqlevHh4ebniz2OApNh+Aamj0pXwnCwDM7/Tp0zVr1jR+qzQtLc3b27tPnz4LFy4s8+4yMzM9PDxCQkK+++67Ml85AJgOz9gBsALNmjWrW7eu8a+1Tpw4UUTat2+vXFEAYHF4xg6AFejXr9+ECRNefPHF4OBge3v7PXv27Nq1q3379gW/RQEAEIIdAKswbty4unXrzp07d+nSpTk5Of7+/l9++WV0dLSNDbcdAOABnrEDAABQCf7YBQAAUAmCHQAAgEoQ7AAAAFSCYAcAAKASBDsAAACVINgBAACoBMEOAABAJQh2AAAAKkGwAwAAUAmCHQAAgEoQ7AAAAFSCYAcAAKASBDsAAACVINgBAACoBMEOAABAJQh2AAAAKkGwAwAAUAmCHQAAgEoQ7AAAAFSCYAcAAKASBDsAAACVINgBAACoBMEOAABAJQh2AAAAKkGwAwAAUAmCHQAAgEr8P0JTW/rfvij6AAAAAElFTkSuQmCC" alt="" /></div>
<div> </div>
<div>#install.packages("fitdistrplus")</div>
<div>library(fitdistrplus)</div>
<div>x1<-as.vector(resid(modeloCT_F1.6,type="normalized"))</div>
<div>descdist(x1, discrete = FALSE)</div>
<div> </div>
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" alt="" /></div>
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