Introdução
Estou começando o processo de compra de um carro usado. Como costumo tomar essas decisões fazendo uma pesquisa sobre o assunto, escrevi um código R para raspar dados de websites e construir um banco de dados. Acredito que esse processo pode ser replicado para comprar passagens aéreas, apartamentos, e outros elementos que envolvam a coleta de dados para reduzir a assimetria de informações. Acredito que saber o preço de outros carros com características semelhantes reduz essa assimetria.
A ideia deste texto surge a partir de diversos cursos e palestras sobre o R que fiz em 2018. Um ótimo exemplo disso é o minicurso realizado pela Karla Esquerre e Adelmo Filho sobre Web scraping no III Seminário Internacional de Estatística com R. Também recomendo o minicurso do CURSO-R sobre Web scraping.
Seria a Ciência de Dados um nome bonito para a Estatística?
Existe muita discussão sobre ciência de dados ser apenas um nome bonito para a estatística. Seria a mesma coisa com um outro nome? Recentemente assisti uma palestra da Jenny Bryan sobre o assunto e fiquei convencido que Não. Entre os argumentos colocados, Jenny Bryan coloca uma lista (incompleta) de ações que um cientista de dados consegue fazer e um estatístico não. Tive que concordar com ela. Sendo um estatístico, frequentemente vi a minha inabilidade para:
- Raspar dados da web,
- Solicitar dados por meio de uma API,
- Analisar dados em formato de lista,
- Entender formatos como o JSON ou GeoJSON,
- Utilizar dados no formato XML,
- Ler um simples banco de dados do formato SQL, entre outras coisas são comuns para um cientista de dados.
Como graduado em Estatística, a minha maior preocupação sempre foi o modelo, os seus pressupostos e o procedimento para o teste de hipóteses.
Mas isso não quer dizer que não podemos aprender um pouco de ciência de dados. Esse artigo é justamente para compartilhar o que aprendi sobre raspagem de dados, porque acredito que este é um ótimo exemplo de como alguém pode usar o R de uma forma funcional para a tomada de decisões simples (como comprar um carro usado). Ainda estou no processo de aprendizado. Assim, quero deixar claro que…
Parte 2: Raspagem de dados
A raspagem de dados pode ser realizada com o pacote rvest. O objeto nome_carro receberá um vetor com o nome de todos os carros da primeira página do site. O mesmo acontecerá com os objetos preco_carro,km_local_carro e texto_carro. O objetivo aqui é criar um vetor para cada elemento.
library(rvest)
## Loading required package: xml2
site_carro <- read_html('http://search.vivalocal.com/auto-veiculo-usado/rio-de-janeiro?lb=new&search=1&start_field=1&keywords=&cat_1=40&geosearch_text=Rio+de+Janeiro+-+RJ&searchGeoId=74&sp_common_price%5Bstart%5D=&sp_common_price%5Bend%5D=&sp_vehicules_mileage%5Bstart%5D=&sp_vehicules_mileage%5Bend%5D=&sp_common_year%5Bstart%5D=&sp_common_year%5Bend%5D=&sp_vehicules_energy=')
nome_carro <- site_carro %>%
html_nodes("h4") %>%
html_text()
##########################################
preco_carro <- site_carro %>%
html_nodes(".clad__price") %>%
html_text()
##########################################
km_local_carro <- site_carro %>%
html_nodes(".vip-enabled") %>%
html_text()
##########################################
texto_carro <- site_carro %>%
html_nodes(".clad__summary , .not-tablet-mobile") %>%
html_text()
Vamos dar uma olhada no que conseguimos. Com o comando head
podemos ver as primeiras linhas do vetor.
head(nome_carro)
## [1] "Jac 2- Jac motors- seminovo"
## [2] "Fiat Uno Vivace 1.0 8V (Flex) 4p 2013"
## [3] "Chevrolet Onix 1.0 LT SPE4"
## [4] "Proteção Veicular"
## [5] "Venda"
## [6] "ONIX - 2014 1.4 MPFI LT 8V FLEX 4P MANUAL"
head(preco_carro)
## [1] "R$19.000" "R$14.000" "R$18.000" "R$9.000" "R$500.000" "R$35.900"
head(km_local_carro)
## [1] "50 km Particular Volta Redonda RJ "
## [2] "62000 km Particular Niteroi RJ "
## [3] "80000 km Particular Niteroi RJ "
## [4] "1010101 km Particular Rio de Janeiro RJ "
## [5] "100 km Particular Angra dos Reis RJ "
## [6] "61000 km Particular Petropolis RJ "
head(texto_carro,10)
## [1] "Inserir anúncio"
## [2] "\n Procurar\n "
## [3] "\n Particular (226) "
## [4] "\n Concessionária (143) "
## [5] "\n \n Ordenar: \n Últimos\n \n Preço\n \n "
## [6] "\n \n \n Classificados\n \n Fotos\n "
## [7] " 192945467 Jac 2- Jac motors- seminovo 50 km Particular Volta Redonda RJ R$19.000 Jacmotors- J2 1.4 semi novo completo, sem dividas documento em dia. contato zap( 21)969475739 / 24 992762557 "
## [8] "Jacmotors- J2 1.4 semi novo completo, sem dividas documento em dia. contato zap( 21)969475739 / 24 992762557"
## [9] " 192922832 Fiat Uno Vivace 1.0 8V (Flex) 4p 2013 62000 km Particular Niteroi RJ R$14.000 Fiat Uno Vivace 1.0 8V (Flex) 4p 2013 Ano 2013 62.000 Km Cor Preto Câmbio manual 4 Portas Flex, Final da placa 5 distribuição eletrônica de frenagem, airbag motorista, alarme, freios ABS, airbag passageiro ar-condicionad… "
## [10] "Fiat Uno Vivace 1.0 8V (Flex) 4p 2013 Ano 2013 62.000 Km Cor Preto Câmbio manual 4 Portas Flex, Final da placa 5 distribuição eletrônica de frenagem, airbag motorista, alarme, freios ABS, airbag passageiro ar-condicionad…"
Podemos observar que conseguimos todos os dados que queriamos, mas as informações do objeto texto_carro
tem dois problemas: Primeiro, as seis primeiras linhas do banco de dados não têm informação sobre os carros. Segundo, as linhas parecem replicadas. Isto é, cada registro está sendo contado duas vezes. Podemos ver isso utilizando o comando length
.
length(nome_carro)
## [1] 20
length(preco_carro)
## [1] 20
length(km_local_carro)
## [1] 20
length(texto_carro)
## [1] 46
Corrigindo o objeto “texto_carro”. Encontrei a solução para isso em uma pergunta do stackoverflow.com. Admito que não é uma solução elegante, mas resolveu o problema.
# Resolvendo o problema 1
texto_carro <- texto_carro[-c(1,2,3,4,5,6)]
# Resolvendo o problema 2
to_Delete <- seq(1, length(texto_carro), 2)
texto_carro <- texto_carro[-to_Delete]
length(texto_carro)
## [1] 20
Como os dois problemas corrigidos, podemos montar uma base de dados com o comando data.frame()
. Este comando será utilizado para “juntar” tudo em um único banco de dados.
carro_vivalocal<- data.frame(nome_carro,preco_carro,km_local_carro,texto_carro,stringsAsFactors = FALSE)
Como, até agora, a raspagem está funcionando para a primeira página, podemos tentar desenvolver uma função para ir da página 01 até a página 19. O objetivo é criar uma função que colete todos os dados da página 01, passe para a página 02, colete todos os dados dessa página, passe para a próxima página e assim por diante até a página 19. A função Sys.sleep()
é utilizada para desacelerar a rotina da função for. Pelo que pude entender, esta função é importante porque ao fazer muitos pedidos ao site do viva local podemos sobrecarregar o servidor.
n_paginas <- 19
banco_carro_i <- c()
banco_carro <- c()
for (i in 0:n_paginas) {
url_number <- 19 - i
# Buscando a página
url <- paste0('http://search.vivalocal.com/auto-veiculo-usado/rio-de-janeiro/t+',url_number,'?lb=new&search=1&start_field=1&keywords=&cat_1=40&geosearch_text=Rio+de+Janeiro+-+RJ&searchGeoId=74&sp_common_price%5Bstart%5D=&sp_common_price%5Bend%5D=&sp_vehicules_mileage%5Bstart%5D=&sp_vehicules_mileage%5Bend%5D=&sp_common_year%5Bstart%5D=&sp_common_year%5Bend%5D=&sp_vehicules_energy=')
# lendo a página
page <- read_html(url)
names_i <- page %>% html_nodes("h4") %>% html_text()
names_j <- page %>% html_nodes(".clad__price") %>% html_text()
names_k <- page %>% html_nodes(".vip-enabled") %>% html_text()
names_l <- page %>% html_nodes(".clad__summary , .not-tablet-mobile") %>% html_text()
# Resolvendo o Problema 1
names_l <- names_l[-c(1,2,3,4,5,6)]
# Resolvendo o Problema 2
to_Delete <- seq(1, length(names_l), 2)
names_l <- names_l[-to_Delete]
# Construindo o banco de dados de cada etapa
banco_carro_i<- data.frame(names_i,names_j,names_k,names_l,stringsAsFactors = FALSE)
# Alimentando o banco de dados total
banco_carro<-rbind(banco_carro, banco_carro_i)
# Mostra a página em que o R está
print(i)
# Para suspender a execução do R
Sys.sleep(3)
}
Vamos ver a estrutura do banco de dados. O comando str()
exibe a estrutura interna de um objeto R (é uma ótima alternativa ao summary()
).
str(banco_carro)
## 'data.frame': 165 obs. of 8 variables:
## $ names_i: chr "Logus AP1.8 DocumentoOk ReciboAberto Zap(21)98403-1482" "Mercedes Benz Classe C 180 19971998 Classic" "Minivan Citroen 2000 + Moto XLX350 1990 COM 2018 OK RJ" "Sorento EX2 3.5 4X4 7 lugares automatico." ...
## $ names_j: chr "R$3.800" "R$24.000" "R$18.000" "R$70.000" ...
## $ names_k: chr "127000 km Particular Rio de Janeiro RJ " "187200 km Particular Campos dos Goytacazes RJ " "158000 km Concessionária Rio de Janeiro RJ " "95000 km Concessionária Rio de Janeiro RJ " ...
## $ names_l: chr "vendo logus 94 motor AP 1.8 nunca levo gas ! E meu a 5 anos no meu nome e assim consta ! manutenção em dia todo"| __truncated__ "Veículo em bom estado de conservação. Uso diário. Manual em português de Portugal. Cartão do primeiro dono. Mot"| __truncated__ "VENDO MINIVAN CITROEN EVASION GLX 2.0i 8V ANO E MODELO 2000 COR AZUL MARINHO VENDO MOTOCICLETA HONDA XLX 350 AN"| __truncated__ "Sorento EX2 3.5 G17 top de linha, 4/4, 24 válvulas. 7 lugares. Estudo troca por carro Suv automatico, valor men"| __truncated__ ...
## $ preco : num 3800 24000 18000 70000 16500 7500 15000 30000 9900 39900 ...
## $ Km : chr "127000" "187200" "158000" "95000" ...
## $ Tipo : chr "Particular" "Particular" "Concessionária" "Concessionária" ...
## $ cidade : chr "Rio de Janeiro RJ " "Campos dos Goytacazes RJ " "Rio de Janeiro RJ " "Rio de Janeiro RJ " ...
Parte 3: Transformação de dados
Já estamos com o banco de dados, mas ainda precisamos transformar essa estrutura para uma forma mais analítica. Já vi diversos termos para essa fase. Os mais comuns são limpeza de dados, higienização de dados, e transformação de variáveis.
A primeira etapa é transformar o preço do carro em número. Nesse momento, a variável está no formato texto (caracter).
# Tirando o ponto
banco_carro$preco <- gsub('\\.', '', banco_carro$names_j)
# Tirando o R$
banco_carro$preco <- chartr("R$"," ", banco_carro$preco)
# Tirando o espaço
banco_carro$preco <- gsub('\\s+', '', banco_carro$preco)
# Transformando em número
banco_carro$preco<-as.numeric(banco_carro$preco)
Temos uma variável com três informações: Kilometragem, Tipo, e cidade/local. A segunda etapa é dividir as colunas da variável. Isto é, quero dividir a variável em 03 outras variáveis. A primeira contendo a kilometragem. A segunda com as classes “Particular/Concessionária. A última com o local do carro. (Esse segmento ficou um pouco repetitivo. Preciso de uma programação um pouco mais funcional).
Repare que para dividir a primeira coluna, podemos usar Km como um marcador. Por exemplo,
##################################################################
# Km
##################################################################
mini_banco_carro<-banco_carro$names_k
mini_banco_carro<-data.frame(mini_banco_carro)
colnames(mini_banco_carro)<-'nome'
library(tidyr)
mini_banco_carro<-separate(data = mini_banco_carro, col = nome, into = c("Km", "resto"), sep = "\\ km ")
# Juntando as novas variaveis ao banco
library(dplyr)
banco_carro$chave<-rownames(banco_carro)
mini_banco_carro$chave<-rownames(mini_banco_carro)
banco_carro<-full_join(banco_carro, mini_banco_carro)
remove(mini_banco_carro)
##################################################################
# Tipo
##################################################################
mini_banco_carro<-banco_carro$resto
mini_banco_carro<-data.frame(mini_banco_carro)
colnames(mini_banco_carro)<-'nome'
mini_banco_carro<-separate(data = mini_banco_carro, col = nome, into = c("Tipo", "resto2"), sep = "\\s+")
# Juntando as novas variaveis ao banco
banco_carro$chave<-rownames(banco_carro)
mini_banco_carro$chave<-rownames(mini_banco_carro)
banco_carro<-full_join(banco_carro, mini_banco_carro,by = "chave")
remove(mini_banco_carro)
##################################################################
# Cidade
##################################################################
banco_carro$cidade <- gsub('\\Particular ', '', banco_carro$resto)
banco_carro$cidade <- gsub('\\Concessionária ', '', banco_carro$cidade)
##################################################################
# Retirando os excessos
##################################################################
nomes<-c("names_i", "names_j", "names_k", "names_l","preco","Km","Tipo","cidade")
banco_carro<-banco_carro[,nomes]
Parte 4: Análise de texto
Agora precisamos de uma ferramenta para a análise de texto para avaliar o conteúdo de cada anúncio. Vamos utilizar o pacote chamado (tidytext)[https://cran.r-project.org/web/packages/tidytext/index.html]
A primeira etapa para a análise de texto é colocar no formato tidy. O formato tidy é um formato especifico onde:
- Cada coluna é uma variável,
- Cada linha é uma observação, e
- Cada célula é um valor.
Depois de colocar no formato tidy, vamos criar um gráfico com as palavras mais repetidas dos anúncios. Nesse gráfico, podemos ver que temos várias palavras que não são interessantes para a nossa análise. Por exemplo: “vendo”,“completo”, “primeira”,“parcela”,“dias”, etc. Precisamos remover essas palavras para gerar uma visualização de dados.
library(dplyr)
texto1<-banco_carro$names_i
texto1<-tbl_df(texto1)
colnames(texto1)<-'text'
library(tidytext)
tidy_texto1 <- texto1 %>%
mutate(line = row_number()) %>%
unnest_tokens(word, text)
library(forcats)
library(ggplot2)
tidy_texto1 %>%
anti_join(get_stopwords(language = "pt")) %>%
count(word, sort = TRUE) %>%
top_n(50) %>%
ggplot(aes(fct_reorder(word, n), n)) +
geom_col() +
coord_flip() +
scale_y_continuous(expand = c(0,0)) +
labs(x = NULL, y = "Number of occurrences")
![](data:image/png;base64,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)
# tirando as stopwords
pt_stop<-get_stopwords(language = "pt")
palavras_extras<- data.frame(word = c("vendo","completo","fire","8v","4p","primeira","parcela","p","km","dias","90","doc","ano","ok","vistoriado","4x4","troco","top","s","rj","novo","mao","lugare","estado","dono","0"),lexicon=rep("customizado",26),stringsAsFactors = FALSE)
# juntando as minhas palavras com a lista de stopwords
palavra_onibus<-pt_stop %>% bind_rows(palavras_extras)
tidy_texto1 %>%
anti_join(palavra_onibus) %>%
count(word, sort = TRUE) %>%
top_n(50) %>%
ggplot(aes(fct_reorder(word, n), n)) +
geom_col() +
coord_flip() +
scale_y_continuous(expand = c(0,0)) +
labs(x = NULL, y = "Number of occurrences")
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABUAAAAPACAIAAAB7BESOAAAACXBIWXMAAB2HAAAdhwGP5fFlAAAgAElEQVR4nOzdX2gsaZrn918vngJ3H3a22Ko64FBiRYO10Bxo06grkLAYwnhp3LDZjYkWWBc5bLMDleuL3LhYXUwMuAwTe6GbcAp2sqCHHibWJIsqWVdnQ7ONYV6MxhJxRhRbcDzMzkKFcCqMT1U3Z2Y41RenLsoXEfk/U8o8R1WRUn4/N1WKfPON0Anei0fP+z7P17744gsBAAAAAIDV9vfKfgAAAAAAAHAzAngAAAAAAO4AAngAAAAAAO4AAngAAAAAAO4AAngAAAAAAO4AAngAAAAAAO4AAngAAAAAAO4AAngAAAAAAO4AAngAAAAAAO4AAngAAAAAAO4AAngAAAAAAO4AAngAAAAAAO4AAngAAAAAAO6A/6zsB1h1WSc6ik0qSbJrtc04NnKDru985U/yq1/96iu/J4beeOMNXkGJ3njjDbEKVgALoVwshFXAKigXq2AVsArKxSpYBbe7CvJ3uiAC+GslUT02ZT8EAAAAAAAE8NfKepeS5AYt37EkKYnicp7k4OCgnBsDAAAAAL407XZ78cGcgb+ZXdmwyn4GAAAAAMCaIwM/RxJVw2LzfBrXq7HkBl1/1sgs6Zy0T01anJO33YPDIl8/nMeutZre8K8AWadRj9OSztIDAAAAAO4iMvCvJus06mHcj94lpakJ640o6f/s+IErpfFRJxt+5yhOJTcgegcAAAAALIoM/ByO3+36eaZ8JH2ejA9KonqcSrYb9JPuWdI5CuPUhNFuP7vu7NdsE6fxUWen6Vn98N2u7c8J37e3t6cvbm1t3d7vBgAAAAC4e8jAv7ys0zaS3KA53DJvOV6zVbMl0x6k3C3vsGZLaXySSMlJHr4fepyrBwAAAAAsjgz8S8vOT2dn0q2dPTtO09PzzCuidMs7rJ3WYxM2LnVj+H5xcTF9kSr0AAAAALDmCOBfUVHhbtYnvStpkJnPQ/iU7DsAAAAA4KWwhf4lfPqzarUa/VkvvXmoJClLokajHhfDP/33J0l2/RcAAAAAAJhABv4lPHky/P+JBnFTsiSq9/vR5Z5/YsL6pRs0lylC3263f/WrXy37oLhFb7zxBq+gRG+88YYkXkHpWAjlYiGsAlZBuVgFq4BVUC5WwSoocRUQwC/l158O//8fVmwpHdsoPyXrHIVG0sY3H159/FRuEPz2H4cfPJVS0+7sO4tvpecMPAAAAIAVd3x8XPYj3HNsoV9UlnSixnsjufd/uLNna6zcfF8SVavVaqOT5SXn9bb7Wx8/zRu/Oz/+Z24+KC9KDwAAAADAQgjgF5J1GvUwNqkeSNLD/KLlHbiS0rje6AxOtWdJ1AiNJPdg5zxvM/ejb5iRcvWOH7jSdY3gAQAAAACYQgC/MNutBe98d+yS4weuLSmNw3o1Vw9NKtlu4DtXvVTSo0/fN+Ol5539vC380VTqHgAAAACAOTgDvxDLa3Y9SUk08YHjN1u7nZP2qUnzIvO27e4d7HuOpaxzKenBkyfPteG+eXpULcrQ227t4Hdr9rtxGp8k3nQlu+3t7ekH2Nraut3fCAAAAABuV15jbx2U9Zt+7YsvvijlxndYElVDIzfoXl9GPus06nFq12qbp7GZ7Dh3TfF6AngAAAAAd1G73S77Ee45MvBfmnwHfRrHqWw3OPQdS5KyJDoKTZrGR52d2SH8xcXF9EWq0AMAAABYcWvS3+5228gtlcznDPyXzq61mkX0Lsly/GaQ176jCj0AAAAAYGEE8F+ajYotSfbezmSePa9ip8seRewAAAAAAAtary30WTK73tz0wE50NBzn1uYMG0iiamhmn4rfrCiJGu3+ZLLd2sHvSJLS3pV03awAAAAAAPStUQCfRI1wrJhcmpo0NKdT5eTyInUj40wcmt41NeuSaGx8wdrZs+M0/Q9/VDfPJmd7IEl2ZWPhh2+322tynmRl3e5BFywrPxrEKygdC6FcLIRVwCooF6tgFbAKysUqWHNrE8AnUb9B+7CcXOcojNM0rkeVkeC8iMbtWnDoOdZgmAkblVmF47Mkqs8K3yVZlU0pffZsxmzPJW1M762fjyJ2AADgK3N8fFz2IwAAZliTM/BZp20kucFYOTkvLycnc5ZMD+zvmR8MS0/Px8+sZ0knasyL3iXJ+Z3vvCZJerjZv2RtVDYfSpJee+ubbJ8HAAAAACxqTTLweUs3d3dyE7yz68oYXfYyOZak7Px01kBnv2abOD09z7xBDv7yJ3XzifKc/u5ZPTT67KxRDdPRFu+f//YLSdJTE4cmHr/1i9/+/JZ/SQAAAADAPbYmAbzjd7v+AuPmBfr5bvjJqnOD6nbJ2ezpNiq2lOqR6352OSyd9+gb6ZMnS52ABwAAAACsvTUJ4OdJzoxGOr1lvUvNLi6Xh+J5pn7eXwO+sducuF7UsXtiLmtBqzk8Ay/JPZg+T5/b3t6evri1tbXM7wUAAPDy8ipZK2vFH28d8ApKxysoXVmvYK0D+P6B97mx9CuzvGarEh2FJg7rwy30eSW9L+mWAAAAAIB7aY0D+CSqx6lk1/YXjqVfonN7dn52mU5cSy/PrjLHmjPRxcXF9EWq0AMAgK/MynaoooHWKqCNXLlYBavgdlfBUsn8dQ3gk6gRGsl2gxmt4eZa9tx61mnkfyQYbV4XHYXGhPXLqf7zAAAAAADMtSZt5MZknUY1NHm5+C91J3tykqf4W2PN6/yiK118klz3ZQAAAAAARqxdAJ9ERVJ8VgLcqmyq2Cg/IS9Pv1lZJmVelMTrV8gbcvZrtqTLXjb9JQAAAAAAZlmrLfRZEtX7G+dnp95Hi82PfXNuefqvSLvd5qBLuTjuVS6Oe60IFkK5WAirgFUAACjRGgXw/ej9urPnRd+3+CTxRiP8Yi/8dC79WkXz+NPzzBu/YT7dUvl8itgBAHD/HB8fl/0IAIC7ZF220Gedxo3Ru5RH8JJM2IiSfIN7luRfXTp+l5zd/LB7fTCZlBXl85aqfg8AAAAAWHtf++KLL8p+hq9AElVDc83nbtAdZNz7G+0n2W5w6G+cN+pxOvg7QD6v+yPXvG/0QHqeD6wd7Hv5Hvxf/Ivqex/PuOHGdzauPrwau++1yMADAHD/kIFfFgdJVgEHScrFKlgFJbaRW48MfHJ2XfQ+wXL8VlBz7cnrqQnrjZPejG9c/p9/IRXRu6TUxGE9ykvMf/9/dCXpwcPBYNt2g1brv312JcndJQkPAAAAAFjIepyBd/xu119ivOV4vrNT6derDw49x+p3cJ8xPP3kudyg5TtWv897KnOW+I6Tb6M35vm3gu5PhsF61mmnmhe/b29vT1/c2tpa4vkBAMBdsFTWBQP8u5WOV1A6XkHpynoF65GBfxnZ+WkqyQ2axW54y/EPa1N5eUmSXWv1O71bjn/gSsMmcflBeHM20vS9PzX5dwAAAADAotYjA/8S8iB7otKc5R248Yzz8eP15PNedGnvSrLUz8H3M/K6MX6/uLiYvsgZeAAA7h9OsS6L07+rgDPw5WIVrALOwK+eq97MTm8blRk5+Bv6w0/k4Mm/AwAAAACWRwC/hKwThXGqvDNcJyta090sj/qLPfXZ+Z+lkh6+QfwOAAAAAFgcW+ivddnLVBxuVxLV4yWK2Y+wdvbsOE1PzzPP0/kvryTpzeWK0rXbbfbJlIvdYuVit9iKYCGUi4WwClgFAIASkYGfY2qvfNa7lKSHkpT3gbe8ZuAuNJm1s2dL6el5lp2fPpUkvXmLzwoAAAAAuP/IwM9RZM3jk8TzRza7P3g6aPf+ErOdnp9vpi/1OBSxAwDg/jk+Pi77EQAAdwkB/DxFzG3CRuWdzfi9YvN8Hr2nf/6RPGvmGfgs6fzJzzNJMmHVSLbt7h3se07x94C4H7/nn7pB1+csPAAAAADgZmyhn8vy8rbv6SB6v1ESNeph/PjZi+GlNDVxWI+SfBc9AAAAAAAvhwz8NSyv2apER6FJJen1B3r2/C1bn6Sy/5tvzxieRKFJJfuH7ucfmKs8u54lUT00Mu3O/uGeHaep9OiRnjy5Jve+vb09fXFra7midwAAYPUt1fsXA/y7lY5XUDpeQenKegUE8NezHL/Z9SUp6zTqcXH+fbQ9fF7QTlJyZiTZtcMfe9aP/cH392u2idO0d2X5za4nKYmqT77S3wEAAAAAcA8QwM+RdRr1OJ1KlP8mlWRXNmZ8w/G73X7crizLrq7Oe2e9U2OWLFt3cXExfZEidgAA3D90pFsWzRRXAc0Uy8UqWAW3uwqWSuYTwM9hVTalVCaMdlt+3gr+c+VF7Oy9HWvOt7JksOMeAAAAAIBbRAA/j7MfuCY0MmF9rIbdRu3Qmx2/F0n7nG3bm5t7ld2dnd7R8CoAAAAAAC+HAH4uy/FbQeWkfWrSPP5+TXrx1g+DifD907hejd2gu987KuL0jVrrj4aDst5X+MwAAAAAgPuKAP46luP5jpcfbM/z69/4+/PGXvVS5UH+b824/qra7TYHXcrFca9ycdxrRbAQysVCWAWsAgBAiQjgb5fVr0nfl9emly57mZx5R+dvRhE7AMAqOz4+LvsRAAC4//5e2Q9wb2xUbElpfJJkxZUs6TSqoZk9/LKXzf4AAAAAAIAZ1jQDn0TV0MgNuv5GJzqKTSrJdmsH+95kljxLOiPH4JX+PIz0u9PDJMs7cOPpond2Lfj2L8MPnqZxvRpLkv32dx5Kxc/2ZMIeAAAAAICZ1jsD3+s06nG/61tq4rAeJaOfJ1GjHsaD6F2Snl1NDys4/rvu68MfbdsNWt2DXvuDp6Oj0scfPtWDB7f2OwAAAAAA1sKaZuBzJo5l14JDz7H6HdxNGO12fUeSlEShSTUYIUnKkqgeGpl2Z9/xvGbXy3P5hYeVfyA9G6bVs06jblLJdmuH+55ladAp/vn85Pv29vb0xa2trVv/9QEAuC15gb01sVa/7GriFZSOV1A6XkHpynoF652Blxs0i9jccvzDmi3JtDv56fS8/JxdOxzdL285+zVbUtq7unn27Pw0zW/i59F7fp9m4Co/LX+rvwsAAAAA4D5b6wy8Xdt3Rn4sTrGnvSvJkhy/2/X7n2VZdnV13jvrnRqzaFu4fvy+60x84Oy6MmZeYfqLi4vpuahCDwBYZevTWY02cuWimeIqYBWUi1WwCm53FSyVzF/rAH6zMhE+b1RsKR0JrIsN7y83fd4B3q5sTH2SR/D9vxQAAAAAAHCjtQ7gb5B1GvW4H7zbtr25uVfZ3dnpHQ2vLmDqrwQAAAAAACyPAH6erHMU5yfYW/7oRvesJ0kyP0t8Z3Jv/CzjG+XzPwrY7tu3/LQAAAAAgHturQP4yUPoxaH1PGdebICv7U8cU7/6cNH0e74hf3SjfP+PAp9dPtbszfXztNttDrqUi+Ne5eK414pgIZSLhQAAwJpb6wA+PT3PvGErt7lF54ay5PfDJ/n//ubXN01v7ezZcZqasyJX3w/fpd+k0nKb6yliBwBYZcfHx2U/AgAA9996t5FL43qU5E3jsiQqtswX8ftGxVbe7K0/4Ke//7v1fvi+EGtnz5ZkwkbUyT4chO/Sc02VwAcAAAAA4DprnYF3XdeYsG6GV+xayy/C6qKpnMYH6IH7znf/4j3zXJ/dmIGXLK8Z9BqhSU08NolefztoehS3AwAAAAAsbL0z8Lt+K3Dt/P9t2w1aY1G147eCWv9j6bUH9o/e7bb977/xDUn67FfZIrdw/GYrcB8OL7wmaeOf/FOy7wAAAACAZax1Bl6S5fjNrj//U893vMmPt74lPdV3dy1Jcvzu8PuW1+x6U7NcnZmnkl1rNT0rr0L/W9c90vb29vTFra2t638RAABKlBfYWxNr9cuuJl5B6XgFpeMVlK6sV7DeGfivQhKFRrJrh2yZBwAAAAC8vHXPwH/ZXiJ8v7i4mL5IFXoAwCpbn+Z2NFMsF80UVwGroFysglVwu6tgqWQ+GfgvUdZpkH0HAAAAANwKAvhJWSdqVAuNzkKF6uZNdBSns8P33/x5vVqtRskrPSgAAAAAYJ2s6Rb6sdJzo5KoHptZHywtOz9NJSmN69V44rOnH0uS+VniO1SjBwAAAAAsYk0D+Hmy3qUkuUHLd1Zq13u73eagS7k47lUujnutCBZCuVgIAACsOQL4GezKxqtH77NbyuVt5B5+U08/lvuDxdPvFLEDAEhqt9tlPwIAACgNAXxfElXDYvN8sevdDbp+P8TOks5JOzbpcPxnM75u11qHlZOj0KSSbLd26OfH37MkKi7q9Qdf9i8CAAAAALiPCOAXkESNcDR2lyQ9/kmUOP54Dj2L6/XhT5UdK//24C8Dkp4911TwDwAAAADATQjg+xy/2/XzHe52rdUcVI7POnn0bru1w33PsqTkD6vhY+kTEzYqIwMlvZCk13/Y+tMfDy7mjeBlu8Gh71jKfvp79Q+ePv/4mgfZ3t6evri1tXULvyMA4O5bqlssvgy8gtLxCkrHKygdr6B0Zb0C2sjdoCgm7wZN37PysNz5g263G7iS0vhkohWcXRuN3qXkzEiya4dFUTzrxz/ptmr2V/PsAAAAAID7gwz89frx++5kvTln15UxuuxlGqlXb+/tjJW/K+L38avWzp4dp5Nb8gcuLi6mL1LEDgCQowp9uejFUC56MawCVkG5WAWr4HZXwVLJfDLw17vqpZLsysbUJ86uKyntXd08yWZlvKi9Vdm8lYcDAAAAAKwPAvhFTEbgiw4s2soDAAAAAPCqCOAXcdnLXup7c1Ptn0qS+Vky+1MAAAAAACZxBv56GxVbStPelTSRhC+Ot8/YXD9l4qC8st6nyz5Hu93moEu5OO5VLo57rQgWQrmoOQwAwJojgL9eUXDOnCW+M1bHLo/fb9pcn5e6S0/PM2/Ybi47P3267HNQxA4AvjLHx8dlPwIAAMAMbKG/gbWzZ0syYSPqZPlG+izrRI28u3ttf7I6/QRnv2ZLaVxvdIovJ9FRPLcCPQAAAAAAs92lDHwSVUMjN+j6G53oKDapJNutHfqeJSkbXrNrB4eeM15OLumctE/NoHmbbbt7B/sjg/LJ33ZtSdnP69VY+ewH+14z6DVCk5q4buKRKW03aHqWsk6jHqd69Oi620lpXMwpyXUfGfPktv5ZAAAAAADr4C4F8IVepxEOctipieuXah306qHpD0jTODxSqznYtJ5EjdCMZ73T1KSh6QVdfyyF/v/+h0zSi2fD2fNBzdZu56Qd9yfJA3tnzu75Gbcbst3g0Ne/NmbO5wAAAAAAzHL3AngTx7YbtHzHUpZ0jsI4TeN6mAfGo9dOEi8PzpMoNKlk14JhWj5LonpoZNqdfccbicOvnr1QMbmyJDoKTar8/Lvj+U5F1dDIro38bWDgzR90u//qhtsNv/oDV0+M3pz5C25vb09f3NraepV/NADA4la8VtyKP9464BWUjldQOl5B6XgFpSvrFdzBM/B27dDPI2PLyQ+oj1/zDlxp2PqtqBZfG9tUb+Vn05X2riYnbxUTyXL88ZkWstztAAAAAABYzN3LwNt7OyORcWVTSsevTXD8btfv/5Bl2dXVee+sd2pmb3Ifryo/v4ncXMvdbpaLi4vpi1ShB4CvzMq2yqOf4iqgmWK5WAWrgFVQLlbBKrjdVbBUMv/uBfAvo78b/kYLtXW/vdsBAAAAALCguxfA39B5fVpRJj5n2/bm5l5ld2end1T/Utq5fcW3AwAAAACsh7sXwC8p6+Rt192g5TvKu7vFxsRFS7dPfz3nW9HRsAvcX54lmTOv5nzBhFUFXX9j9HaWlPenC/u3M2GV8vMAAAAAgJdw7wP4q14qya7tO1ezurs9ea/R+fZEUfm8JfyIpyasKxgebV/odv0C9L3Ll372Ee12m4Mu5eK4V7k47rUiWAgAAAAluvcBfN9HRXe3otvcMEhP43pUGQnOkyjM68gHh97GeaMep3ogPTdho/Lu3nRNuw9P/t21O+PziP7RIz15omEfuam/EdyEInYA8JU5Pj4u+xEAAABmuINt5JazUbElpfH/ZiS5QdN3lHQag/D5gSSZs6Q//O9+2S4GjrSB++6PXEnp6cdf35QkE0ZJJin7RfQvvHfN81m3O0lGm9jl0TsAAAAAAC/t3gfwVtEW/rkkmbBardbDOJVdC2q2pOcPpZFO75//PxepJHfXGZtka79mS+npb36nZufz1KvVav098/ELvfXo0Yzb5SOq/T8UbBS3oxE8AAAAAODl3PsAXnL8VlBz7f6Ptu0GrW7TcyqbkvTZ2NjP/+ZK0/F70W9eae9zr9kazvXaQ/ed1h//4M3rbifprR8Gv6uzf59Kkgn/eSMqsvMAAAAAACzsa1988UXZz1Ci/Cx6/2R60QJu8OOI+Z8Mp3GDru/M+NasG0+PHdje3p6+uLW1tcjvAwB4de12u+xHAAAAmGENMvDzZZ38wPvBrKD8NuQV7CTbDVrdbrfb7baKBL4JGx3y8AAAAACAha1NFfppSVSP07zl26JfGS9Af6OihVxeOi9nWZ7frKgamvT0PPNm/eXg4uJi+iJV6AHgK7OyrfLop7gKaKZYLlbBKmAVlItVsApudxXk73RB65qBT6JGaCTbDWbuiZ/DrmwscxPLa3a73em98s5uXtX+nBw8AAAAAGBB65iB759Mn3ei/cu3UZluKA8AAAAAwDXWLoBPokZo5kXvVmVzTmCdn2bfrFj5HNXQXFOGblLWiY5OTdo/D187+J38/5bL5wMAAAAA1tlaBfBZEtX7G+dnB995avyyl8kZi+Dz0+x5xJ1ERXP3G2+XZ/ofSk9HrqYmDs1DafDngIW0220OupSL417l4rjXimAhAAAAlGiNAvh+9H7dxnlrZ8+O0zQ+SbzRCD85iVPJ3tspGsYtJs/n66kkuxYceo4lZUnnKIzTp5Lenuo2fw2K2AHAV+b4+LjsRwAAAJhhXYrYZZ3GjdG7lEfwkkzYiJK8xFyW5F/Vw9f/6qi+cPQuSc7vfOc1SdLDzcpG/6694r+v/xdsoAcAAAAALGxNMvB5Cl1K43o1nvH58EC75R0GvXpoUhPWR4P1B3r64WPJdoPD3bMF4/js42cvJElPJybTA+nZR+eZyqmhBwAAAAC4e9YjA5+cLZE4txy/FdRce3DBdmvv1t6U7daCVtN3Fo+588p37juBaw9ms203aAXfVdFUHgAAAACAhaxHBt7xu11/ifGW4/mON/YNr+stO3+/8t23He/7jj/+yexaeZKk7e3t6TtsbW0t9ugAgFeVF01cWSv+eOuAV1A6XkHpeAWl4xWUrqxXsB4ZeAAAAAAA7rj1yMCvpln95iVdXFxMj6UKPQB8ZVa2VR79FFcBzRTLxSpYBayCcrEKVsHtroKlkvlk4MuTt5UHAAAAAGAB65WBz5LOSfvUpKkkybbdvYN9b1ZVuqwTHQ3HubU5wwaSqBqaYSn7m4btv+wvAAAAAABYV2sUwCdRIzTpyIU0NWloTqdawydRdaxNXGri0PSuic6TaGZbOauyKaXDjfLDYXl5+s3K4gXt2+02+2TKxW6xcrFbbEWwEAAAAEq0NgF8EoUmzfu4F53gsqRzFMZpGtejykhwXoTZdi049BxrMMyEjcpkpJ/PEs3tCr9RGRSb1+iwfnn6JXbQcwYewD1zfHxc9iMAAADcMWtyBj7rtI0kNxjp4245XjNwJcmcJdMD+3vmB8PS0/NsfNKkEzXmRu+SrJ09W0rjo98fG/bXJ3Eq2Xs7iyfgAQAAAADrbk0C+HzPurs7uQne2XUl6bJXhObZ+emsgc5+zZ6M4C9/Ug9jk8p2g1b+d4Bp1s63X5eUPkn11tv9YX/xvhHxOwAAAABgOWuyhd7xu11/gXHzAv3J4+y5QXW75EySPjtrVMNU9siheut7v/PWBx98IumTx2H9sSTpufIMP/E7AAAAAGBxaxLAz5OcjWXD5x9OHznOXgTem793U815SbJ+/Mfd7yWdk3Y8qJ/30G39xL8met/e3p6+uLW1deO9AOAOWarlKUbxT1c6XkHpeAWl4xWUjldQurJewVoH8P0D7wfLZcPnpfO/sduced1yPN/x/H55+2/tknsHAAAAACxrjQP4JKrHqWTX9m9OpBcm99B/KS4uLqYvUoUewD1DO7qXQD/FVUAzxXKxClYBq6BcrIJVcLurYKlk/poUsZuSRI3QSPZyh9GX6vwGAAAAAMDtWccMfNZpFLn3WY3dAQAAAABYQesVwGdJ5183/+2T5y/yH09POpV9zxnG8INi81nn5OjUpHndOdutHfyjXippszIn3v9Ukv7yr6WZu/GzTjScTX95lmSOwx8OAAAAAADLWKMAPon+eWiuRi6kqUlDczqah8+Lzf9FWDej40wcmgeav4M++dmT625bDUdn01MT1hUsUsJ+RLvd5qBLuTjuVS6Oe60IFgIAAECJ1iaAT6Iien/4w9ZPfmxJypLOURinaVyPKv1w2trZexinTyXZteDQc6zBsOfS69/emc6bZ0lUD+fH70kUmsFsV1E1NHogPTdho7LU/n2K2AG4Z46Pj8t+BAAAgDtmTYrYZT/9IyNJD2tF9C7Jcrxm4EqSOUsGI/+u2F6v3s2TJp2oUR9Pr0+MKPrUBc2Rjfrf/ZErKT09z5b8JQAAAAAAa2xNMvB/dv5MkvQ0rlfj6Y9N+xe+831J2flH+UClZnwf/QPp2UfnmfpZ834lPNlucPjGH9fffzo9bXZ+mkpyd8d3y2/t12wTp6fnmUcNPQAAAADAYtYjA5/8akZ4PctVL5XkvhPUXHtw0XZrreC7KtrAj7DdWtBq+nML0hWz7U6edrcqmzNmAwAAAABgvvXIwDt+t+vP+iCvMGfXDr8vScp6l5Lsyrcd7/uON/qNrGNL6WUvUxGuW16z6/U/3fqW9FTf2hqfvD/boPLd4DGy3sRso7a3t6cfdGtra/oiANxdeWFCvAT+6UrHKygdr6B0vILS8QpKV9YrWI8M/Bz9I+oH7DKKYEEAACAASURBVGQHAAAAAKy49cjAz5RE9TiV7Nr+wg3d0t6VdGvB/pzZLi4upsdShR7APUM7updAP8VVQDPFcrEKVgGroFysglVwu6tgqWT+umbgk6gRGsl2g2Wauc1rBP9ybnc2AAAAAMC9to4Z+H4Bebu2VCt2AAAAAADKc78y8ElUrVarUXLtkGui9/nl4fOC8puVa+P9XyedqDG8+yvOBgAAAADA0Fpl4LMkqvc3zvszD75vVGaXh58qKD/Lp38emidyd29ntnHtdpuDLuXiuFe5OO61IlgIAAAAJVqjAL4fvV+3cd7a2bPjNI1PEm80wk9O4lSy93aWS5nf4mwUsQPwco6Pj8t+BAAAANyO+7WFfr6s07gxepfymFuSCRtRkuXfTPKvLh+/3/ZsAAAAAIA1drcz8FkSHYUmlSTbDQ798U+TqBqaPGa/OolTSUrjejWeMdEj1/3M5BNp462H+uRpasK6GRnhvnOgk0bDpGlxwbbdvYN9L98c/5kkPXkiSSasGskNur4jWd5h0KuHZmq2pYrfAwAAAABwhwP4PD7vS01Yl+vOGXpmZn/Q98QMB1x98lR63d54lhbV52y39j2dvheOz5GmJg1NLw/U57s6u5xx9fIskbNw+3kAAAAAAO5sAJ9E+U50Nzj0HatfoG5OnO743W6Rni96yLn9wHvYUy449IYTPUv/q6D7R07/XtVwdIg0KIhn2p19x3P+oNvt/0XBHQnps04jNKlku7XDfc+yNNg0YMJGZc5u/u3t7emLW1tbL/svBWCt5fX/VnlCLItXUDpeQel4BaXjFZSOV1C6sl7BXT0Dn+fU7VoevUuyHL9Vs5eeJzs/TZVvaR9OFLiSzFm/H9zgXt5IMXnL2a/Zmt0lbnpyP4/e8+mbgSspjU+ua3cHAAAAAMCoO5qBL2Lq8UJwRdH3Zebph9i7Y9vZRxL2Uz9mWXZ1dd476532z8wvObkkZ9eVMZruLydJuri4mJ6LKvQAXs7tdn2jjVy56Ke4ClgF5WIVrAJWQblYBavgdlfBUsn8OxrA5zYr4+GvVdmUlgrgr3qpFurIPlIu7xYmzyP4tHclUcoOAAAAALCIuxnAZ73Ll/te0vmTn2dSXivett3Nb0jTfwiQsk50dNqvOG+//Z3fPP7wafGRbdubm3uV3Z2d3lE9LkL6JKqG5tGjWffc/PpVJzqK+8G/7dYO9r2XenoAAAAAwBq7mwH88ql2SUnUGM+hp2nx48Re9vHy9lL6+ENJcoOWP7rjPesNvxDOr3L/f70XvhidzMShOZ0d61+j3W6zT6Zc7BYrF7vFAAAAgLtaxE5SHniPui4xn0R5Pfi33Q1JcoNuK+gXvUs/vBofKMmuBa1ut9vttn5UjPpPvfEZ8x3y+vRn1Tnh+0ZlQ9ILyXaLubrdVuDaUvrkiRbauQ8AAAAAQO5uZuD7h8hPzzNv2IqtKBo3sFGxpTQvE99pG0lucFhp1x/nH1uO1wz+qho+lp78rN+WPcsHPlAahyeVru/I2trMs/1X43fr95Z/8mTeQ1pff02SZH9vmLm3HP+w9n/X4080a+f+fBSxA/Byjo+Py34EAAAA3I67moHPm7ilcb3RybPwWRIdxeO76q3KpiSZMEo+6qWSHqldHx/j/OO3JUlPwkYnyYZ/A3iuYfX4jUo/Uf9n5/17dRqDtPtr33ynFbjDKUe2BXz/X+ad5t5rRJ2s+GrWOfnlJ5L0wN2frE4PAAAAAMA8dzQDL1neYe20HqdpXK/GxTXXdY0Z2c7u7NdsE6cy4XuSpCcmdYNA4eiW928U/03jsB4PL8sNfKd/pwM3Do2kq5F76fXXpc/17PmL//IfFon0r79pS8UD2bVW07Msrxn0GqFJTVw3o7NLb/0P+xSgBwAAAAAs7M4G8JLlNVuV6KidF4u33eDQ14kxk0M0KAFvu8Gh71xFIwOKfvI/fGfvb385KBT/mv1Os/n9kfDa8Vvv/O2/fO/D5/mPtu0eHPqOVVS7u+xlu5Kkb+we1jSsN9//brO12zlpD2eXXkju73nz4vft7e3pi1tbWwv9owDAuKU6i5YyIZbFKygdr6B0vILS8QpKxysoXVmv4GtffPFFKTdeBVmnUY9TuUG3yLbnF/Ls+ayhsz6R+nXrh/PMMFLa3q4Fh54zN/9OAA/gFrXb7bIfAQAAALfjDmfgX1US1eNUsmuLn0VPe1fSS+18z3qXtm1LaZoqjcOjXu3Qn5ODv7i4mL5IETsAL+d2e+/RT7Fc9FNcBayCcrEKVgGroFysglVwu6tgqWT+ugbwSdQIjWS7wcyU+hwv3/jN8ppNT5KUJZ2jMDZxXZVrEvYAAAAAAIy5q1XoX0XWaVRDk8qutZpffQhtOV4zcCWZdie7cTQAAAAAAJLuVwCfJZ2oESXXD0qiRrFzfsZx9rzxXN45ftxVL9WNndt/vcgDSCOd6QAAAAAAWMw92kKfnISxkbs7f0SWRPX+xvnZqfeNii2ll71M40Xmst6lbtxB/+mfh+bJyANM1ch7Be12m4Mu5eK4V7k47gUAAADcowD+Jv3ofU4leUmStbNnx2kanyTeaNCdnMSpZO/tLFXAzqpsSqnMWeI7YxF8Pt1N+fwxFLEDVtnx8XHZjwAAAID77z5tob9O1mncGL1LeQQvyYSNKMlPqGdJ/tWl43fJ2a9NTCZlRfm8parfAwAAAADW3v3oA19sVh8a3baeJZ2Tn8Tmk+smeOC22r7VH5+n6se5QWu3d9KOTXEb260d7Pe7uSd/WA0fTwwvHmCk//uIh28HP/mDZeJ3MvDAKlufDDxnScrFWZJVwCooF6tgFbAKysUqWAUltpG77xn4JGrUwxuid0nPPx3WrbMcv/XOdx4MP7TdWitQux4OondJqYnD+s0V65Kzy1mXnz4+W6DWHQAAAAAAffcjAy+pn+sey70XiXnbrR3ue5YlKUuio9CMVaGf/t7ktcXmmTFRkX63a8FhP1k/rKQ3bzf/9vb29MWtra3b+DcC8KVot9tlPwIAAADuv/ucgc/OT1NJbtD086hbkuX4eRf2ND4pcuDOrivJjKbEkzMjyd11lplnWj6NXRtG75Ks4mT8rGZ1AAAAAADMcY+r0Pfj7t3Js+bOritjNGgWl/88Uix+dvx+4zxTHL/b9QePk2VXV+e9s96pMems0X0XFxfTFzkDD6yy9TmHxrnHcnHucRWwCsrFKlgFrIJysQpWQYln4O9xAH/VSzW7d3seeae9K8ka/jyI4Mfj9yXmmWWw1x4AAAAAgFdwn7fQS1qw2frYLvqJ+H2ZeSZlnUZ9EL3btu26tVrQatXs5acCAAAAAKy3e5yBz83f4N6Xl6h7ID2/7GVyrmbG7wvMM2Piozg/PN/yR7+a9ZaaBgAAAACAex3Ab1RsKZ21wb0oLje2Kf7rtp6np+fZji41Hr8vN8+IYu99bX8i8M+vL6fdbnPQpVwc9yoXx70AAACAexzAWzt7dpymI8XpCnncPdgUb3nNrle0fDs9OVE6kX9fdJ5FFV9bLqdPETtglR0fH5f9CAAAALj/7t0Z+Mte1v9fa2fPlmTCRtTJ8qtZ1okaYd7cbX98k7yz60qpMelUTn25eYYPsFGxlfeZ61/Jkk6jGppb+lUBAAAAAGvkHmXgi63ucb0ay661mp5lec2g1whNauK6iUeHukHT6ye/8zPwdu3dPVtKJdl7O5aypHPSPjVpvtvdfuvhw0+eTs2jhz9q7lxFjaNi3GvSi5EH8A7cODQyYX00Zn/t9UePXnvy4dPrq9cDAAAAADDqHmXgLe+w5k7Wd9+ozBp6VRScH/MwT7TL3ttRp1EP4370Lin95OlTvfXNR8PpHz6UpK9fNOrhcNyLiSkd/51Hr0/e58WzJx8+lcY2CwAAAAAAcL17lIGXLM9vev7IhaIOvF0LDr3iuHmWdI7CODXtzr7jzcp/23tfP6m/l0q2GxwW1eOLL338xA26zXzHfBJVw6dK03RQZH7Q8d0Nun5/0HtPno3fX1kS1UMj2bXDmffX9vb29MWtra2X/EcB8OXLa+ytibX6ZVcTr6B0vILS8QpKxysoHa+gdGW9gnuUgZ+hqAO/tzMsFmc5XrPb7XabU9Hz0/PTVLK//Xe/NJLcoDns/WY5XrNVsyXT7oxnze3aoEWc5fgHrjSSWS+q1NeG0bsky9mv2ZLS3tUt/qYAAAAAgHvuXmXgp/SPxR9FlcN9x7ruwPnnvV/EqeR+7+9/9N6M0nSDYvSn55k3EvuPl6Cf6Djn+N3uYENAlmVXV+e9s96pMde2kbu4uJi+SBV6YJWtT387+imWi36Kq4BVUC5WwSpgFZSLVbAKbncVLJXMv98BfBF0K+3XkbPd2t7ujjfWve2j01TSlXks2bX9b+tIUl6Jbpbx0nNze8APDDbWAwAAAADwCu73FnpZXrMVuPag9lxq4jisV6uNKBluhX/9NUnShhscela+6/6WZJ1GfRC9P3hou26tFrTedacK2wEAAAAAcL37nYGXJMvxm44vZUnn/Oy0aAyXmvCo0srPwX/7kaUPU7sW+I4l5QfT8zZ0r3jnooSe3B+9bd5/rO/+s6bvSEr+MHz2ijMDAAAAANbO/Q/g+yzH8xzP8/sl5SdPsxcmjrG/iqKEXm1/qzfaB16/eYm52u02B13KxXGvcnHcCwAAALjXAXzWadTjdKStmyTJcoqT8TMVx+ZntJlLompobiE3/9ef5v+97GVyFp2JInbAKjs+Pi77EQAAAHD/3dkz8ElUrVYbnSxLoka1Wq1Wq41o2OItSzpRo1GPU0kyofcv/s2HIx/lO9vT+CRRlkThz/Of6o2ok2SyvAO3+LmTZMVM1Wq1GhpJ+uz0pJNkkw9SbYy3l8s6jWo1NA/y2/z1Z8XlD6PfrVbff5r/kMb1apTc/r8MAAAAAOA+uuMZ+N7JUTwo8V7ZyfPZReJ9xIuP33+3+v7olQfSc122G/WRTHxq4tD0gq7vB+5laNI0DutTpeg/KUYFizze5iM9eSLz/mNJMmHV3PQFAAAAAABmu9sBfGqm97QnUT1OJdsNDn3HkpT9Ijr6qUlfFJ/btrt38I964XtGaZrKDd6ttN+N0w337d8yj1OZs8R3HL/Z2u3862b85PnIl/Y9x8qSqB4amfbZ2ws835s/aAXfOWn3/8Zg2+7Boa+TIplv11r+ZLd5Sdvb29MXt7a2Fv9nAfAVW6p75123Vr/sauIVlI5XUDpeQel4BaXjFZSurFdwtwN4ya4djp1IzzptI8kNmoPY2Pq+3/z+fqdRj9NBsJ9ExbdbvmPJ6XqSlKgamv7RdMupvPlcU/XoLWe/Zps4TfWPu90/0HCq/FOvmU+Vddr98Z7vVFQNzeCZ8l3zEwfzAQAAAAC41h0P4O29nbEycNn5aSrZtf3J2LioTTdReX6zMvrtiQL0jt/t+oOJs+zq6rx31js15hYbxc9ycXExfZEidsAqW5/y+LRjKBftGFYBq6BcrIJVwCooF6tgFdzuKlgqmX/HA/jZ0rhenTq8nn8y1h7OrmzcMFOWREfhlx2xAwAAAABwozsewI+n0Put12/JWDE827Y3N/cquzs7vaOJEnkAAAAAAHzp7ngAP9vYufWsEw0q1du1/Y1Ooxqnb9k3TpIV3ebcoOWPtmvPevO/knRO2r3dJofbAQAAAAC37Z4F8BPH2POa9C/XvC1P5tu1fWfRJH9yEsZG7u5L3e4G7Xabgy7l4rhXuTjuBQAAANyzAL6oVWfanX3Hs6SsdylJbtDaPauHJj09V7PZ9ZRE1fDltsEnZ/nfA4pi9aN/M6gMBuW19G4TReyAVXZ8fFz2IwAAAOD++3tlP8Ats7wDV1Ia1xudJCsubuj/OAqNJPfAs6758piNii0pjU8G02RJp1G0cB+9Y2VTkkwY/fVnkvTZX48enZ/rspfdNAQAAAAAgIF7loGX5PiBexmaNI3DelGK/so8lqSHP/KdojLd5Bn4LOmctP8sPylvwsale3DoewduHBqZsD5rD3767/7nqPK/+I6lojO8zPuPJenx+7GkR4/05El/7rO/zOetGknaeP2B9DyvlD/RZR4AAAAAgDnuWwZekhy/2Qpqjx5MXv/W1uzxWadRD2OTXvUvpKkJ641IfiuoufPK3T3/xIT1apRIltd89ztTd8slUaMemqejl66ePV/wFwEAAAAAoO9rX3zxRdnP8CXK8+2jee6pK0lUDY1ku8FhUW0+SzpHYZxKbtDNC8oXDeXsWnDoFTXt+oOmZhp8aWTuke8pS6J6fnFO8n17e3v64tbWnL8+AFgB7Xa77EcAAADA/XcfM/DLyDptI8kNmsNecZbjNVs1WzLtTn5QvahIv7czrEhvOV6z2+12r9sBnxe8s2vD6F2S5ezXbElp72ruFwEAAAAAmHD/zsAvJS8Xb9f2Jzu3F+XsT88zz7P6lebjo6hyuO9Yix5ad/xu1x/cKsuurs57Z71TY64tcXdxcTF9kSr0wCpbn/529FMsF/0UVwGroFysglXAKigXq2AV3O4qyN/pgtY8gM/lBeVmfpJ3lC/CeaX9ina2W9vb3fGcm0P5LImOwusjdgAAAAAAbrTmW+jzvfE3s7xmK3DtQUG71MRxWK9WG1FyXTe4rNOoD6J327Zdt1YLWq3avMJ4AAAAAADMQQZe19STG2U5ftPxpSzpnJ+dnpo0lZSa8Kgy78tZ5yhOJblByx/N1We9W3tyAAAAAMC6WPMAvjjcnm+UX4zleJ7jeX6/Dn3/nPy0ovRdbX9ip/2iaf9R7Xabgy7l4rhXuTjuBQAAAKx5AF8cbjftzr4zHoTnHeDy3HzRRW60QZwkyylOxi8pr00vXfYyLXCIPkcRO2CVHR8fl/0IAAAAuP/W/Ay8LO/AlZTG9UZncJw9S6JGaCS5B56lPMyXZMKRMcqSYof8ZmUsCr/s9YdsVGxJaXwyMnGnUQ3Nl/obAQAAAADupXXJwGdJ56QdD6rBf9p72t807/iBexmaNI3D+ngp+gdvqZdkjmPJ8g5rp/V4xpjXHrr7Tp6ut3/4ww3pKi9p/9o3f9j8X3984MahUb90fX/a7/zov3v2/gfLbdwHAAAAAKy5tcjAf/4krIfxaC+35+bdRqefF3f8ZitwH742+bXnn5g4rEeJVJShrz16MDnG+t7+IAb/278d+eAf/NeW5PitH31z8jvPP3z/g0+l0Vw9AAAAAAA3uOcZeMtrdnc6jXosya4Fh55jSVkS1UOTxieJ1z/TfnVmnr4YGSL1h2lwPt5yvB98N36S94GfUbc+NWbyetY5ev/j55Lt1g73PcvSSF94u3Y4u3j99vb29MWtra1X/scA8GXJa+ytibX6ZVcTr6B0vILS8QpKxysoHa+gdGW9gvufgc/OT/Nebs1+aG45fuBKMmdJMSavK2fXhtG7JMvZr9mS0t7VxJTzYu/J68Nb+3n0nt+8GeTH7k+SGXMAAAAAADDLPc/AD4PoXWf0quN3u/6cH7Msu7o67531To2ZWWLe3tuZmTqfvD771pKcXVfGzCtDf3FxMT03VeiBVbY+/e3op1gu+imuAlZBuVgFq4BVUC5WwSq43VWwVDL/3gfwRTP2ysZNAwd727+CW+cRPFXsAAAAAAALu/cBfG6i1duUotN7zrbtzc29yu7OTu9oePXmyWZfv+nWAAAAAAAsYIXOwCdRtVqtRtcdDB8bknUa1Wp1WEz+OtcWfE/+sJrH6W7Q6na73Waz6fueN2N3+41m/A7UmgcAAAAA3IJ7n4HfqNjSDT3XfyNJeljbn4jZ8z3wX8Kti6J5N+/rH2i32xx0KRfHvcrFcS8AAADgDgfwltfsejeP2tmz4zQ1Z4nvDIvJFVvm3aDrT1aYG8rDbM0rNveStx5OvMzmeorYAavs+Pi47EcAAADA/bdCW+i/JNbOni3JhI1Okm9mz5LOUTxaH/7rkqSn8UnS3+2eJZ1GNTS3duuokxX3zjpRI8yb1u3P/9sBAAAAAADjVjEDnyWdo3acppJst3awP+c0ep5Ft2utpmdJSqJqaOQG3X1FR22Tphp8/7B2Wo/TNA7r8dgMJmwoOPT1jcHP9dGY/bXX9eKZpDSuV09td+9gvzL9qCOl6z/99eTHltcMeo3QpCaum9F7227QnNlKHgAAAACAmVYvgD+L6mYQRqcmDk3v2o3u4y7bjXo6PLne/36zVemctOOpLnGpCetyXUl65Lqf9Ru/P3hNz1/k0Xt/YGrS0Dx6NPrl/E8GQ0/ei950F3xOAAAAAACWsnIBvDFGdi049Byrn982YbS7aAifpqncoOU71jA9bs4S33c83/H8Iua23eCwP6Qe5n8veHPX/1e+r0Fc3n8ISf1xevJZrdUtMudJlO+EnzHVQNZphCaVbLd2uO9ZljT8nRqV1uwk/Pb29vTFra2tBf8BAXz18hp7a2KtftnVxCsoHa+gdLyC0vEKSscrKF1Zr2AFz8C7QbMInC3HP6zZkkx7oWZxkmTX8ug9//qBK410ciuqv9cOR4a0avbYBIMxozv3LWe/ZktKe1eTw66ZKjs/zbvTNf08es+HNQNXUhqfXNcxDwAAAACAUSuXgZ+o7WZ5B24cmuvbwI0aL+0+3smtCLr3dkaHFLXiBz87frfr93/Isuzq6rx31js149vvF5mqH7/vTm4ecHZdGTOvuv3FxcX0r0UVemCVrU9/O/oplot+iquAVVAuVsEqYBWUi1WwCm53FSyVzF+5AH6qt1oegy/aym2B3uqTd7Aqm9L44fix2nQvPVXeRn7WE+UR/OJ/lQAAAAAArL2VC+C/RFnvP0nSX4TVqiTJtt29g31vMj9e9IgfevDN7/z3td/7z/905GrWuxz7ThJVQ+MG3d3pu44H+UXpfPftV/lFAAAAAADrZ40C+OTk319J0vP+hbyy/Gntnc2RUVnRI37E848/fP/dP3FHz7ePp9qLenazje4dmDH5gtrtNvtkysVusXKxWwwAAABYuQB+cq98cZB8amf9spIoNJ9IkjZqrT/yrLzdfBinafzepyPjrj5Mi1FB4DnDYY9n7ai/7GWZTuqD8H08MT9+AH80fP/s8rEW2u4/wBl4YJUdHx+X/QgAAAC4/1auCn16ej5acH5uIbjl/LrTNpIePZJ0VdzBcry8HvwwJy9lZ5eS9MANvEF1+f4waVjQ3tl1JaX/7vfrI8n3X+cP22ft7NmSzFkijWfff5NKt/BHCQAAAADA+li5AF5pXI+SPErOkig/d/6q8bt+3UsluT/4n2q2lMb1RtGWbuONh2PjsvP/9FySnpuTpP93hCzJo/9xG7/9uqTnz/TaN99p5QH+X/7vE/vj+xF82Ih++tMwTiX3hz98XflfDCbK7QMAAAAAcJ2V20Lvuq4xYX0kYrbdwH/VUHdr0Btuo3Zaj9M0rlfjGeOuBn3ex59BD7+ppx+raARvSdLf/wd6/XM9e/7i4/fqoSTp6VPXdY0Z+ZrlNYNeIzSp+SAP7c0HH0iSXn87aHrk3wEAAAAAC1u5AF67fmtX/SZuthsc+ou0j1uY5TVbleiobdJU0sOHevpUD/q76PMz7PYP39n721/GxaF323YPDv2N80b941TDM/qW1+x6ypLBVNKjd/zd/2gmkvWO33zn0+p7TwYXXpNebPyTfzr/TxLb29vTF7e2tl7hlwbw5Vqqe+ddt1a/7GriFZSOV1A6XkHpeAWl4xWUrqxX8LUvvviilBuvgqJhnBt0ixR/0eSt1ppKj8//RCq6yI3MM/1Z/5vXzyOJAB64g9rtdtmPAAAAgPtv9TLwX5nigP0yZ9GHG+gXv0loJLt2uPiG+YuLi+mLVKEHVtn69Lejn2K56Ke4ClgF5WIVrAJWQblYBavgdlfBUsn81Sti99VIokZoJNtd6iz6Mo3fJGWdxtLhOwAAAAAAs6xjBr7YOa9rN7Lfxm2O8rsQvgMAAAAAXtnaBfBJ1AjNvOjdqmxK6ayN8le9VMt1bi862GtWwfvi2uxD8wAAAAAATFurAD5Lonp/4/zsyHmjYktpv9L8yDfz8vTL7aC/Te12m4Mu5eK4V7k47gUAAACsUQDfj96v2zhv7ezZcZrGJ4k3GuEnJ3Eq2Xs7S+yFz/vMTVqgCv1MFLEDqPQOAACANbcuRewG9eRuiJytnT1bkgkbUZLl30zyry4ZvwMAAAAAcJvuRgZ+vJf60FQjdylLOift2KT5T7ZbO9j3HCtPoc8+kC5J9ltKP8nn8Q6DXj00qQnrZmTEg0d7lamvZUl0FPbvpc/m5NezpHPSPjVpPu41SZ+/xL8BAAAAAGCt3Y0MvLPrqujCPqqoE+fu9qP3JGrUw0H0Lik1cVhvRP/mzOhan34ynMdydt9+a2rE8ydxWI+S0bt3GvVw5F56/JOT3tT3sk7+SINxLyRd/fxPkqmRAAAAAADMdzcy8HJ2XRljzhLfGR5Nn4jfs05RX96tHe57lqVBhty8r1qrmyfF82T+eP334loxTxKFjz+R7Fpw6PVL2RXl70y7s+/k8xRN4gbD8ltN/p0gifKGdW5w6OdzZUnnKIzTx2GUzClBv729PX1xa2tr+X814L7JS9mhXLyF0vEKSscrKB2voHS8gtLxCkpX1iu4Gxn4fg7enI0krifj9+LHoOnn0bsky/GbgSspjU+SuRMlZ2ZknvwnuzaM3iVZzn7N1sgmgOHdimGW4x/W7PGHzjptUzzSYC7L8Zqtmi2Zdid7lX8RAAAAAMBauSMZ+Bk5+Dnx++5kVjv/ogat4aYmGo/f5fjdrt//cpZlV1fnvbPeqRndLF/cza7tj97N8g7cODTXD5IGte5PzzNvRkW9i4uL6V+fKvSAaCO3AuinWC76Ka4CVkG5WAWrgFVQLlbBKrjdVbBUMv/OBPCTgfdkwH7VSzW7U3v+xbR3JVkzJpqI3yVNFqebIb/bZmUi/M7byI9fm1c3r0jnU9geIH0sIwAAIABJREFUAAAAALCIu7KFXkV0rMteJs1NuE+F1LOM7aKfjt/HitPZtu26tVrQak3uj79ZHuUDAAAAAHAL7k4GfmzjueZsmB9slB/IC9SNy3Pwl71MztVk/N4vTucGLX90pmy6wPyMu81wY+t5AAAAAABudocC+JEIfkdT8Xu+e33+rvSxzfV5BH96nu3ocmKeYit+bX8iMh9Pp8/aKz9n0O1slG+32xx0KRfHvcpFqVUAAADgLgXwwwj+fHMq/158NtFpTtKn+X/GN9cXEfzJidJZhe+mJUUn+SLpXtwtPkm80XZ04+3m+4807D03GBhVQ7NUbp4idkC73S77EQAAAIAy3aEz8MpDYimN4+m6c8VnMmEj6mR5g7Ys60R//ESSHrgTpeCdXVdKjUknC9/lJ+3T+CTpN3nLkk5jahv+8G5FM7gsiRpTg7yDvIddvdEZma4Y6B6wsx4AAAAAsKg7lYEfJLWlGXlzy2sGvUZoUhPXzUTd9+/ujsbKWdI5+dNBsP03f3WeZIOm7/1ecCasj4bjG7V33vq37334Ij39SJ4lyfIOa6f1OB0vMv/gNT1/8fnwa87uI5knUhqH9bFHevh24N+c9wcAAAAAoHC3MvCDzPecfe+O32wFNXdYL9523UcTY5KoUQ9jczW48OxxHNYbUTKcpBX80H5t/FtXp//xxWuS9Df/Xz+VblU2p3rWPX8h6erPPxpeeVOSXn8wMZ2ePj5LBAAAAADAou5YBl6yvGbXu+5zx/Mdzx/8nERmJJOedRqhSSXbrf3ub5+++0Fq19496P1paFITNiqDI+m9j9IXkuxacOg5VtEW/okk2f/ke1Z/4tBcDcdIkn7x+9X3nkgf/7KTfX90f/yz5y8GZe37PeanT+sXtre3py9ubW0t8I8D3HOUslsFvIXS8QpKxysoHa+gdLyC0vEKSlfWK7hrGfhX0+8eHzT9nY8/SiV7b+c7jt8M8qPqJ8nEqCIytxz/cLwNfNb5vdBIeqs2jN6l5D8+yf8n7V1pjF0bNKWzHP/AlQYd7QEAAAAAWMCdy8C/imzQPT5Lojgd1pHLi9IXJebzUXZtrOxd/2h8/8fKt6Sn0ie9q0yWlWXZ1Ucn7fcmG84PjNfAv76/3MXFxfRFqtADkujkVzr6KZYr/2M/r6BcrIJysQpWAaugXKyCVXC7q2CpZP5aBfBFm3YTVo00FqPnEXweUuejxkNuTbZ+d/YD10zXuptjvNA9AAAAAABLW68t9JKkB5Ik2w0OX6WNm7UxEZQ/eGB/50fvtsa32s/zG0kyP6OOHQAAAABgQWuVgc+9WWu1bw7d8+30cz/OOkfxlTQoTTe8/qe38YwAAAAAAIxbqwB+/tnz5Myov9N9fK/8QLH/fuwnu7Y/EeSPj7o97Xabgy7l4rhXuSi1CgAAAKxVAG/t7Nlxmk43cMvj9+LcezEqPkk835kccoP+qBvy98ujiB3QbrfLfgQAAACgTOt1Bt7a2bMlmbARdbK8iVuWdaJGaDRS0244qlOMSYohkvQ0iRrVav5jGv9J8mGnUa1WG50Pk06jOhj1dx91oka1Py77818m9IwDAAAAALyCtcrAS5bXDHqN0KQmrpt45APbDZqDg/GWd1g7rcdpGter8WCAK2PSv/n5u+Gzke89Dt99LElp/G4oya4Fe6dhnKYfvDe6lf7Fxx+E9Y/coOmPJf4BAAAAAFjUmgXwkhy/2drtnLRjU4TYtls72PfGN7xbXrNViY7CfIztBoe+TqpGevZMkl0LDj3Hyn4RvftT8/SFJOn1DfefB/7/z979hEaSpnme/+XA5qFKzHRQkakFk9OyZEcFtQE1JKo0JFo01rDkkjDeefAUg6Cs2KIX0msPPn5oHdoHJhba6qA++HpAtQdkU03ZgE+jdIpsb6jZuvRLoxkJixLJFMTUzlZDmhaXLSiqi5hulLUQfYg9mJn/l+QeoZB5yL+fS6bMX3tf8zDew+Pv+z6PY+knB0kXK65X2y6d7hd9o+V3Vs6+iIxfKTQbV+bPW19fH7+4trb2Ut8auBU4CT8PeAu54xXkjleQO15B7ngFueMV5C6vV/DG8+fPcxn4tRPW0/3wbq3TX0iP25VyEEm2l4bm6YWhRgO3p9eTv8YaSSKABy7AGXgAAAAsuMVbgb/S5BA8S0+3kp2UT1ilHTfw+/nt4qODSJK7ORqYO5uujJkmu93x8fH4RZLYAZIoBJA7yjHkK/mxn1eQL2ZBvpgF84BZkC9mwTy43lkw02L+YiWxm4pVWJUk49d7iefisJ4F6f+8MBJ+rxTsgb/S8nKFlbFunU1XUtQ9ve7nBQAAAAAsAlbgxznbNdf4RsYvD5WOW5LOp+thdTTKBwAAAADg5bACP4HlVJs1z7V7S+u27Xq1j78lJRXer3R5ozhs/+XPXvohAQAAAAALhRX4ySynVHVK1cFLcduWorGWyab5zErBlqKoeyqNLMInh+jtwkq4Xw4ez/o8rVaLgy754rhXvki1CgAAABDAT83a2LKDKAr2w9JAdrssud1wI3MYVp3hLPRJu9WCpe4LDE4SO4As9AAAAFhwbKGfnrWxZUsyfqWd7JCPw3rFNxc1qrfjtFncTtvZwynsAQAAAACYVt514OOwvd86MFGyDd223Z3d6niZtThs77cCM0Mj2a63s10abRWH9b1WNpps214d7qpfn32lXd9LOhvsKSv7PubNdz78wz/8btpTWK/4ZkKrt209Gb48uRL8BKzAAxwkmQecJckXpYPmAbMgX8yCecAsyBezYB4sahm5uF0p+0EvnpaiyPjlSj2c2Gq4UXs4TVxYH26kyASjXcXtStkfGE1RFBm/XBwZT1K3XSn3OotM4JfTNlap0ay59mh76dkXn/WHc6qNZs27tzTa6Kurk24FAAAAAOBqOa7AJ6vdst1atggeh+09P4iGFqazNW/bq+2WHEuKw3rZN5PbuN7udsmylCy1+yaSbK/ZKFn9Rr1+BsbrtcmeSf3xeh31xxse7nS/6Bvdu2c/fjw43EBfA9f61y9ee19fXx+/uLa29jL/1sAtwBl4AAAALLjcVuDjdstIcmuN/hZ2yyk1mp4tmVa2wB4fHURJsyzqtpxqzZVkDrOF836bahK9J60aNVdSFOwn7ZJs8fbWRn/HvOWUGp1OpzMYXif641lOddezJZkf3f9esVgs1n8yNFyaXP7d/21kuB7b2x3rHgAAAACAGeWVhT4JuickdUvTuB8cxaWS1YvNN4eaOdVOpzrS1WgbSc6mK2N00o3lWFmBt2CvXtjddqzLYuqRx7JKO27gm9NnvyWdyjz8sbLh4rCeJKfb2rBkDQ3X62trY8bw/fj4ePwiZ+ABcdxrDnDuMV+ce5wHzIJ8MQvmAbMgX8yCeZDjGfh8y8hFQbkYTP4kqaSeLpsXVi7p5OI2SQSfdpX+MqDI+OVkZ7vrbW1ujOW5U1LrbUgS/H/5P3zsPn5odC5Jxi9m+eezNfah4QAAAAAAuE55baFPou4pjQXUL9QmTUDXSyMXmSDwy8VipR7Gl93X90+/WW3W3OX+Bdt2vVpzfAf+TM8FAAAAAMDV8l2BH0nvdpGRXekv3EaWU204VSkO20eHB2n1usj4e4WpnkOS5Wx+Q+Zs6Ml7Wek2JUnGL2ra6nAAAAAAAEwnrwA+PZF+1XbzaZpd3CZNMDe+ud5ySiWnVKpmeeizI/ep0R8D0lP2yWL6VcO9GlTAzh3HvfI109EgAAAA4FbKK4BPT6SbVnvbGV77Ttaz0xXurNlhWHX6S9ppHbe0EtvkNuoF1EncPXxP7zGc9GT8kJF4fjhL3oXD/fIVxu8ksQMoIwcAAIBFl1sZOau0k5RdK1favTPocViv+EaSu5PGz9bGli3J+L1WcdjeC4bSzvfb1Ntx2ihupz1lGeXHOxrsa+SkehSU673h6lcPJ0k/+9RIWn5nyu9/0p3y4D0AAAAAAJLeeP78eW6Dh/WKb8aT2dlurVEdW28fkS2lx2F7vxVM6GWwp0sbyXZrO/J9Y3vN9z8vP3w8ocmdd1aefnGaNfd2vt5tPRzvLusoebjsaHxzs7vfSs/bS9LKnaXTp+fpHdMlAWAFHuAgyXzgLEm+KB00D5gF+WIWzANmQb6YBfNgUcvIOdVGc7M9EN3atru1sz1S2c0qNZqFgQDctt2d3aozsC9+Etv10p4u+JlgaLywLknd/R/3ove37ZUn0amk5aWls/Ne9C4pMoFvbPfj2tb/PfijwDsfNf+Pb6cdDfjFJ2XzZOhKL3oHAAAAAGBqua7Av6QsfLe92m7JsaQ4rJd9o8GD7r02rre7XbIsJZvifRMNLX8nq+WSlqTzgfunur2Xhb7qDP1/r9veA0r9h7xw8X19fX384tra2rX+2wGvH87AAwAAYMHldgb+5WW55WqNLDi2nGrNlWQOw7E21ST8Tlo1asn5+/1wuEvb/cYFQ0x1+6g0C763O7inwHK2PVtS1D298EYAAAAAAEbkWwf+ZQznhs841U6nekUbSc6mK2NG68XZW1//ajSQSn7G28cMPUwcx6enR93D7oG56DB+4vj4ePwiZ+ABcdxrDnDuMV+ce5wHzIJ8MQvmAbMgX8yCebCoZ+Bfymk30sQi71O1SULwK+rQv+TtkgZ23AMAAAAA8BJe3wA+MVL/rS85vb7UbxOH7f1Wd3Mwv/14Z1+bZYirDSXZs217dXWrsLmx0d27KPUeAAAAAACTve4B/FW72HttTvf9wMjdvLzp8A786YeYKM4K1tea1cEO4u4LdAYAAAAAWGyvbwC/UrCl6MJd7Fap0SklK+CT0sWl+eUu3YB/yRBT3Z7uwPe2R8L/5PpsqICdO4575Wumo0EAAADArfT6BvDWxpYdRJE5DKtOf1d8umk9reM20GZ46T0JwK/aHT95iKlvv0B682wr+ySxAygjBwAAgAX3+gbw0lfuLCk6N37RSLbt7uxu6yjdtL7pJJG8/eGHtqIoaSMp+Z/lZZ1JWpLxK4XmbmF/LykC/7O/bMdOWps9Dtv7rSDJPmf80i/e/W75/gfvSnHc3t8LkvJw247isL3/yc+yrm373lf7z5cu4Afl4n+tdf4X1fdaJuLoOwAAAADghbyuAfxQejhJUWT8cloBzq1Vneyg+T99v1H7f7/jP3o6ePPZmWS7H62aT426+3tBL0n8WxtJ9B7WK8OZ45+dff7wfvFh/4Lt1holjTyEoujxwF9WaccNfCPpkV98NPYVpslhDwAAAABA4vUM4LP0cLZX+06h+zetXgi+dK/2J98f2Zfu/JsfNcP2DxrB4/Pkgu16O9sl57RupMgY2V5zp1v2jb61aUmK20n0brve7nbJsqT4J/X7PzRnz4Zut8J6MYgk2/3oLfPpI7m1zrba/9YPnki/+KXkSHKqH98zD3tBfbpNYD+tK/eLyXvo19fXx7/x2tray/+zAa87TsLPA95C7ngFueMV5I5XkDteQe54BbnL6xW8lgF8fHSQZHdvlBzJedcpVaWwXvTN+VuTM9o5pd//VvDYZGfjJUlpZjvb2y1ZlnrZ5/ud91paH1Q/+WCzXvRN/3rcbples+q305alP2uqUg6i43b87ZIl6WtvpYM0G6Xsyao7rvGN7Pc3WH8HAAAAAEzpdQzgsxB7cyix3KQKcFOwt4bD6MmdS3I2XRmTpZ5Lmtne9mizNPHdwVFcKvU7Hk53d3kC/ePj4/GLJLEDJFEIIHeUY8hX8mM/ryBfzIJ8MQvmAbMgX8yCeXC9s2CmxfzXMYBPy7NdXsPt+jtPIvihsDsKysVgYjfD0fkrelYAAAAAwOL4J3k/wAt7sRpuU3ZzZecvUssdAAAAAIAX9jquwCdmKqOe+dVhvdIr5rYkSV9ObPe3/+GPK61Habssa914s97R9rBe9M3gAXtpMFP+5JV685ej9eUBAAAAALjA6xjAX36E/FKPjen/cS5Jjz7p137vd376+UDZt8gEvunWajLq7YYfeoaw7g90+0q0Wi0OuuSL4175ItUqAAAA8DoG8GmeOHM4tICdrnePLoNnfvkLSdLbbu1/ryaL6T/5o+LDx9KToFwv9O+xvvKGJGnJrf1J1bGkOGzv+UFk/Iak3ub67BlaP/z64WcPB8P3sF70TbI2X2p0Cv2/sh8J4vb3ysGppPd+f/rld5LYAa1WK+9HAAAAAPL0Wp6Btza2bEnGr7TDWFISZQeTs8cnn7f/5kySlv/n7d5W+LTCmyRzGPYb/vQLSdK5ae2341iynNLux+8oWbDvp523SjvvSYqGovc4rFd8I8ndKV2wOSBu7wVpAbuvzvSlAQAAAAAL7XVcgZes0q53UA6iKPDLg2fL3drE1XfptPtEknQWlIvB0IL4snTWP0+fFpG7d89+/DgyQdkMnVxfcncH1tFbjzTM+GUjSfaFj6E4+Z3hzh09fTrD9wUAAAAALLzXcgVeklVqNGuea2d/27Zba07ePC8lReKbA80vkiSXd3//+8O9y76XrMGfDrW2Xe/jjz3XHnwKr9ZsXPgY4X4QSXrvX/zWVQ8CAAAAAMCQN54/f573M+QoHD6inpyjHzqxnrr4k343Fx2/H+7jymbr6+vjF9fW1qb4OsBtxhl4AAAALLjXdQX+WsTt1uUH1q9VsvzeP0UPAAAAAMD0Xs8z8NcirJdnjahfqHRdNtyhkWRvbVx1//Hx8fhFstADkqjklzvqKeYrqafIK8gXsyBfzIJ5wCzIF7NgHlzvLJipXvKirsCn6eJttzZxT/wF0iLwL+BmF/sBAAAAALfOIq7Ap2fRddGJ9lcxZJLd/oIidwAAAAAAXGWxAvg4bP+g8RePz58lfx7stwvbJacfw1uFVSmKuqdxe3/vwESRJMl2vZ2vdyNJq4UL4v1fSdIvfilNCNDjsL3/yY8jSTJ+8cR2t3aGBgUAAAAA4GoLFMCH9e/5ZrAOXBSZyDcHg+vwKwVbin6WFnTP2pnAN0u6eAd9+JePLx604pvo8kGn0Wq1OOiSL4575Wumo0EAAADArbQwAXxYT6P35Q+bn3zXkhSH7T0/iKKgXC9khd2sja3lIDqTZHu13ZJj9ZqdS3e+OSEBXRzWy/5F8XtY900krbyzfPrFme01GyVNGHQaJLEDKCMHAACABbcgSeziH/6pkaRlL43eJVlOqVFzJckchr2W/5Bur1f36k7Ddr1S9s2Fn6eJ62rf+e0zpfvvJw8KAAAAAMAVFmQF/q+PnkqSzoJyMRj/2LR+UnU+kBQf/TxpqMgM76Nfkp7+/ChWtvE9y4Qn263t3v2z8qdnY72edpPEdSvdlgb33zubrozRSTcWR+EBAAAAANNZjBX48O/Gw+uJ0pj745rn2r2Ltus1a99SWgZ+gO16tWajelEU7lQ7nU6n6iSdAgAAAADwEhZjBd6pdjrVSR+E9aJvZHu7H0iS4u6JJLvwTaf0gVMavCNu21I0sGhulRqdUvbp2jekM31jbbrRw0Mjyd6acKJektbX18cvrq1d0DmwSEhlNw94C7njFeSOV5A7XkHueAW54xXkLq9XsBgr8BfIDqnv3FA1+PwGBQAAAAC87hZjBX6isF4OIsn2tqdOBh91T6WXirunGPT4+Hj8IlnoAUlU8ssd9RTzlfzYzyvIF7MgX8yCecAsyBezYB5c7yyYaTF/UVfgw3rFN5Lt1mapx35RIfhXOigAAAAAAIu5Ap8lkLe95s0F0rkMCgAAAAC4NRYrgI/D9g8af/H4PK31frDfLmyXBpLIW4VVKYq6p3F7f+/AREn2eNv1dr7ejZSWch/sr13vN9MvDsPYmZCSPg7/6A//JBs0GhsUAAAAAICrvfH8+fO8n+GGhPXv+eZ07PLQkniyTr4knY82W5LOh1fPkwz2I9xapzp0uj3+4R+UP3ty6aBT4qBLvjjulS+Oe80JJkK+mAjzgFmQL2bBPGAW5ItZMA9yPAO/MCvwYT2N3pc/bH7yXUtSHLb3/CCKgnK9kEXd1sbWchCdSbK92m7JsXrNzqU73xwo/BbWfdNrdlov+kZL0rnxK4XBHwTqf/jZE0lvvvtx4/4H1sCge+2NWUJ4ktgBrVYr70cAAAAA8rQgSeziH/6pkaRlL43eJVlOqVFzJckchr2W/5Bur1f30v7SWnC1xsBm+G995EqKDo7i3qh/ZM4lvfNx+/4H1vCgA80AAAAAALjSgqzA//XRU0nSWVAuBuMfm9ZPqs4HkuKjnycNFRm/PLhBfkl6+vOjWCVLUnx0EElyN4drwa1te7YJooOjuFSypHD/s6SzLx4Wiw9Hx4yCH4Sl709dwQ4AAAAAsNgWYwU+/Luz6RqediNJ7sc1z7V7F23Xa9a+pbQM/ECzzdHw2yqs9puFh2Mn5Id9ZbpnAgAAAABgUVbgnWqnU530QZKIzvZ2P5Akxd0TSXbhm07pA6c0eEfctqXopBvLsXrNekXhe/3H3X4zp9rpbPeKxw2fqJ+Q7a5nfX19/OLa2toLfXPgVpkpwwdeEd5C7ngFueMV5I5XkDteQe54BbnL6xUsRgB/gewk+84rK8xulRrNQn3PN4Ff7m/dt93a7gXROwAAAAAAEy1wAB/W08Xx7alj6ah7Kl0d7A82i48OT6LRz08OT2PHuqCj4+Pj8YtkoQdExZQ5QOmgfFE6aB4wC/LFLJgHzIJ8MQvmAWXkblxYr/hGst3aLMXcBnbNT9UsznbQu7XdapKuPg7re74xfvnkBUrBAwAAAAAW1iIG8L2w+kVD6J//x0jSb65sF+4HkWR/+PHqz1vlop9ctF3vo/eiTx9FwX5YYiM9AAAAAGA6CxfAh/WKby6K3q3CqhRN2iif5J1fLVhSWH/4hSSdXd4sS4l357999nAwHX1kgkh37ujp0zQl3lRarRb7ZPLFbrF8kakFAAAAWKgAPg7r5Wzj/OS175XCQLL5wTvTvPP/XZK2PnFxs8GN9k+fajwL/dOnMz46Z+CBVquV9yMAAAAAeVqMOvCSpCx695oXRO+SrI0tW4qC/XD41v0gku688df3/YHF9Aua2VsbSVRvFd5Krr9Xa5TSSN9ySo3aveTyWwWOwAMAAAAAprQoAXzcrmTR+6XH3pMIXsav1MM4uTNMbpWefnEq2601a64kLV3QrBe/S85vL0uSHrWyVlIc1n/0WJL0zrscgAcAAAAATGtBttAna+NSFJSLwYTP3VonXZW3Sru1btk3kfHLg0fXl2R/y9vZLjmWwkNJ+sZH7qNPx5oNZ7X/vfdXPg1OpdFWkrTyO9+8jm8GAAAAAFgMixHAh4ej4fMlLKfarBX2W4FJ67fbrrdbHVu3/+ra1c2sjd+zgyCS3ryjZ+mx9ztv6ukzyf3ORTsB1tfXxy+ura1N/w2A24pUdvOAt5A7XkHueAW54xXkjleQO15B7vJ6BW88f/48l4FfY0kiu/6i/eWSwu/R4KWBsvATEMADE5HEDgAAAAtuMVbgcxQfHZ5EI9eik8PT2LEuiOCPj4/HL5KFHpBEJb/cUU8xX8mP/byCfDEL8sUsmAfMgnwxC+bB9c6CmRbzCeBfpbhdKQfR0JJ7siBv/PLJVfn0AAAAAADoW5Qs9LlIy8p5zUZ/w7zlVBs1VxOK0AEAAAAAcDEC+Fcn7p5oqKxcxtn2bEkn3Xj8JgAAAAAAJmEL/euh1Wpx0CVfHPfKF6lWAQAAAAL4V8cqrEpRdHAUl4YPu6dV6VcL0x+BJ4kdbsyDBw/yfgQAAAAAE7CF/hVyNpPD7uVKPcx2y8dhveIbSba3PU0VOgAAAAAAJFbgJ0oKvdvDaeLTjPJurbMpSfqFXyzKrXW2Vd9rmSiSpDelZ4P3OdWmd1IOosj4ZTM0hO3tkoIeAAAAADA9AviXctKqlKN+mfdnkvSr7kADq7C6ouh09D57lu3zAAAAAAAQwL8Ap9rpVMN60T9TFEVya82qY2UV3iOd61RKwvO4XfHNqWS73u52ybLUa2X8SuGCOvDr6+vjF9fW1l7pdwJ65jld3Dw/2+LgLeSOV5A7XkHueAW54xXkjleQu7xeAWfgX47tNbMa75ZT3XGlgfJw8dFBJMmtNapJ9C7qwAMAAAAAXgwr8C9neCv8SsGWoqibLMFn8fvmaLI6Z9OVMTrpxnLG1+CPj4/HxyELPW7MfFbLS37jnM9nWyjUU8wXE2EeMAvyxSyYB8yCfDEL5sH1zoKZFvNZgX8pdmHl4g9Pu9EFTdL09N2xs/EAAAAAAExGAP+qka4OAAAAAHAN2EI/LKkgt3yNPV6wUR4AAAAAgFkQwL86wyfiB4WHRlfsvx/RarU46JIvjnsBAAAAyBcB/CRfXZbOhiPvNCPdLKyNLTuIInMYVp2hPHZJ/D7T5nqS2OHGPHjwIO9HAAAAADABZ+An+cpbkmT8ehhLUhy2K+Vg1vg9ieAlGb9Sb8dJabk4btcrvpFke9uj2ekBAAAAALjInK3Ax2F9r2WiNFa2bXt1Z7c6doI8Dtv7rYNeM9m2u7WzXeq3S06yu7XOtvr92a431Kg3oG/Sz2u71eTqW7/j2Y+DSMYvm7Th0ts6fyLJ+JUTdydrODie7TW/0/2VJBm/eOJ6u9WSVWrUuhXfRCYom2DgBtutNUocjAcAAAAATG2eAvh4dJ07iqLILxu31qn2F6vDeiWNuAfamcg33aFmkk5alXLUbxmZYKRREuf3P/fLcl1J0tdKjabqe0F/oPMnA6P55ZN798aev7v/Z+Y8+6OwQXwOAAAAALg+8xPAx+29IJJsr7abLZPHYXvPDyLTam876Xp1WPfNSCvFYb3sGw02kyRFUSS31qw6Vn+lfeBAelhPtrK7td2sSdk3vYDeKlUbpWoa4/ca9R7q8WN35AeDyBjZXnNwZT1uJz822K63u12yLPUexPiVQnPyIvz6+vr4xbW1tdn/SYEXcffu3bwf4ULz/GyLg7eQO15B7ngFueMV5I5XkDteQe7yegXzcwZedkRpAAAgAElEQVT+tBtJsrc2+pvcLafU6HQ6nX6gm6Zv93YHt8JbzrZnS4q6p8Nd2l4z239vOdUdV0qqug13NdCk6dlDHcTtlpHk1hr9ffyWU2o0PVsyrXY8Ot7uUEieJr5za41qEr0nozRqrqQo2A+n/bcBAAAAACy8+VmBT4uuBXv1wu62Y03egO5UO53e8fM4jk9Pj7qH3QNjJmaYG07zPlzVLY3ft4Z2uqd543sDHB1EE7PNpe0OjuLSQMQ+0lkvft8cvd3ZdGXMRRXij4+Px78KWehxY+azWl7yG+d8PttCoZ5ivpgI84BZkC9mwTxgFuSLWTAPrncWzLSYPz8BfBY8R1nmONv1tjY3RrPODeWdu9wUddZHK7lZhVVppOsoKBcDTTKpxPuAdE/BhKdIIvgrbgcAAAAAoG9+ttDLKjWaNdfubWKPTBD45WKxkhZzk5QkuutF77Ztu67n1ZqjW9+vFndPrm6UROBTm1zXffxqHLb/8mezdAwAAAAAwPyswEvJAXGnKsVh++jwIC0UFxl/L034lia666Wmy8TdmUeasNR+gZHEdLMa2ygf7vvB4xfuDgAAAACwmOYrgM9YTqnklErVLOV7dto83ZTubY/sq59xqbxvNLoeXpgfPjU/s6tun2KLf0+r1eKgS7447gUAAAAgX3MTwKdF4Edqs1nOcFq5yZKEdBMWuy+RHkMfzkOXpp3rjZ4MPlafLqsgf8XafHZ7v3LdsMlb7icjiR1uzIMHD/J+BAAAAAATzE0An0W7/o7Rudxap7rSrvuBSSvD/X+S+pnq98ONqmPFYXv/R/+nOX2S9fGbfxjq8jf/sVwMxqLsk0+KRV9urbPt2SaIgnJF93e6PxpKi3fySbHo216zUdpxA99EQbmi2ncK3b9ppdv6JUn33p8Uvcdhe78VZL29KT0zfkVJHfi4/b1y0Kt1Z/yiRmrJAwAAAAAw2dwE8LJKu95BOYjOJcn4RTP4YfRpub7WqTpJQK0sUf2Is8/+bf1//LNpI+JswCi474989Jve/znVmnvimygK/PujHTx+WKl/rTE0XFivDCfIf5Y8vgnKZnImewAAAAAApjE/AXyShr5Qv++bs+zKiu3+3s7vFg5/5JvI+PXNTtWpNmuF/gr30vJ7lfv/xrEU/nHRfyQ9SVpNP+D97s59c54M5tZq1V//oPjwsc6e9Bs51Ubz7r8uf/pFdsG23a2d3y10f+QH2UMlH3x5mETvtpust2ug5N2bWSwv+9470eMvRs8KDFhfXx+/uLa2NuWXAl7STIUob9g8P9vi4C3kjleQO15B7ngFueMV5I5XkLu8XsE8BfCSLGfzGzJn0lCA6yx7J+UgO4vulLa7ByYaPi/v/JtOJzlHb1rt7Ua106kqrBf9Lwb6LjU6JcXtSrm/RB5/cXI+PNj3O99MTuP3xUfHX4w8kSTn3abS4ZxStdOpxu1K+ZHk1gYW5S2n2qip6Jtn/dvDevHxwGMBAAAAADCFOQvgM7a3Pbg8baVn0ZOE7kmquZEmUu8c/XBiustM7Ckd7PJG48OlCfDczdFmSbq86TPsHR8fj18kiR1uzHwm209+45zPZ1solGPIFxNhHjAL8sUsmAfMgnwxC+bB9c6CmRbz5zSAH0vQnqSvGwyCo6BcnHysfPq6b0n5uQsGG+n0quHSEncTSsOlCe9ftBodAAAAAADSP8n7AV7AC1d9v4HhZikNBwAAAADA1OZ0BX4KVxRhn8FUm9snDJelnD85bIcrpeTaf/oPn1edd6/jqQAAAAAAGDCnAfxoTJ0eME/Wt9Nq8NewJX3SXnmNrrlPHi5u9wrGRSbwsyJxz57941h34aHR5M3102u1Whx0yRfHvQAAAADka04D+JFEdMMJ4tLkcWlS+sHbwnrRN73F8kmRd9pTKu0p2A9LAwnmk4B7tNHIcKfdSFqSzrXifvjPTz7LasU9PgzlDOWxS7t7uc31JLHDjXnw4EHejwAAAABggnk9Ax8F5XoYS0pqqQdDCd6t0o6btKm000ZSHNYrvpHk7qRhtlVYlSTjpz3F4WiBOFkbW7Yk41fa8XAvg40mDbey6do6l+R+p/rdaqPT6XSa/yrtq96O097idtrdWBL7k24sAAAAAACm9cbz58/zfoYhySK667rGDMXRo2fQswPoI+yhMuzxWMTu1mryfdOryp4MN+zNZfvts+jU9pq72t8LTCTp7aU7T86fjg23tPT2+fmTbGzbXf3q35rHp6Otlu17X4kep89hv/fubx59fjb5W12IFXjcmPlcgadiypzgLEm+mAjzgFmQL2bBPGAW5ItZMA9yLCM3ryvwm9VmzbWT/7dttzYW5zrVRrPmubadXbBt16s1B6J3SVap0fSybmS7tWZ1tEq7JOmefWfgr2dn0amk3/zHcjnIfiN4cv5Ub7/7Xn+45aUlSf3oXVIUGfP49L2P+iNK9r133tZZL3qXFD36/ExLS1f/EwAAAAAA0DevK/DpAvkNDSfJdmu7VcdSHLb3/HTVfuxa77HS22yvtlvKku3FYb2cXOz92pDtALBdb3e7ZFlJqz3fRJcsvq+vr49fXFtbu/4vD0zSarXyfgQAAAAAE8zrCvzNiNt/luyft73dahKIW87GarI8fufD/rXkFHzv4HqaV97rR++SLGfbsyVF3WwPfZZ7r9aoJtG7JMupNmrJkfr98BV/PQAAAADA7TGnWehviFV4SzqTpK9kl+Jw35xL0p3fff/Cw+lOtdOp9u6I49PTo+5h98AMH8ofzp0/ePumK2Muqj9/fHw8PiJn4HFj5vNIFce95gTnHvPFRJgHzIJ8MQvmAbMgX8yCeZDjGfjFDuDl/M49PX4sRQ/LxYfDH/3WP7381t5e+Asl5eQn1X9PIvjrqGQPAAAAAFgQi72FXvraW5K0vDyYC8+9d+VtcbtS7kXvtm27rufVmk3PHm/6cvXfAQAAAACQNIcr8EO701OvPLHdN/6g8Um/67BuRivLDYvbaWH6WrM6uAc+7k5ofMFGeQAAAAAAZjF3AfzrIN0b722PBObJ9Z6Vgi1FkzbKp0nwJmyuv0ir1eKgS7447gUAAAAgXwTw1ycJy/tr7tbGlh1EkTkMq44zoeEsm+tJYocb8+DBg7wfAQAAAMAEi34G/oWsFGwldeDi9EoctitpQfkB1saWLcn4lXo7TprGcbte8ZMidNs3UukeAAAAAHArvH4r8MmBeNtrNkr9Bey4XSkH0fAx+YE08bZb262uHFXKQTRy469/JUnGL6bBt227W+tfjo1nu+9JUhSUi4Fs13vf1UMj45eHY/a37yw9eXre3zRvlRq1bsU3kQnKJhhouOLWBp8CAAAAAIAr3NYV+KE08YqMX67sj6WYC+uVh4+HL0WRCT59NN7fr/6fgT8KGx9UmzXPHTvC/uTpuST9ohv3Lq0UJj3eqTkMr/wOAAAAAAD0vH4r8NPI0sTbXm235FjpYvzIDvew7puo3ya5MayXfaPRBX5Fj89GrzkF+VK6up/cH4ftPT+IdNZbgk8fZHCMtJFptbediYvw6+vr4xfX1tZe9B8DmM3du3fzfoQLzfOzLQ7eQu54BbnjFeSOV5A7XkHueAW5y+sV3MoV+PjoIKny1kijZsup7o7WaE8zwXv96F2S5Wx7tqSoezrSp+3tDoXbcbtlkjH6leQsp9RoerZkWu1kDT7NV7+10R/DckqNTqfTYQs9AAAAAGAGt3EFPonfR5LEWaUdNxhMMzdUcD6O49PTo+5h98CYoVJwGXtrYyjenjiG1Es9f3AUl0pWVkku2KsXdrcda6qQ/fj4ePwiWehxY+azWl7yG+d8PttCoZ5ivpgI84BZkC9mwTxgFuSLWTAPrncWzLSYfxsD+GTZe6xIWxJMD10ayHP3QpKkdhM/SXbRp+G8oizfne16W5sbJYfVdwAAAADATG7lFvrpDOW5s23bdT2v1myObrVPjP4ckPxIcDWr1GjWXLvXZ2SCwC8Xi5V6GF92HwAAAAAAQ27XCrzxK4VmI8n7ftKNdclCd5bnzq01q4PN4rFc9ZcYzXWX1rgbqmYny6k2nKoUh+2jw4MDE0WSIuPvFZqcgwcAAAAATOl2BfCJCXvlpdFF8zS9nLftvNDSenq4vZdufgqWUyo5pVI1y0OfnZOfSqvV4qBLvjjuBQAAACBfr18APyl0TtPOp9KD58F+WBpYCE+yzl8la3XF+n02xuRicMYvnnjNRsmK25VyEI2syMty0pPxMyCJHW7MgwcP8n4EAAAAABO8fmfgrcKqJBk/PUUeh0mUPNhkY8uWZPxKWs0tDusVfzh+XynYkqJgv3cWPQ7blaI/TZQvJVntJUVBudLud/HLXyT/4+6UrOEH6R95j8N09/5Ymj0AAAAAAC7y+q3Ay9n2bBNEyhK7S3JrNfm92Dtu76cL8oNZ4m3XVVojLg7b+6005h/oRZJsr7Z14AdRFJTrhc626nufmKRZRd7O9mD6eKdac//WN6dR4JeHU9Evf5QtuFulXe+vy8GENrZbq46WoAMAAAAA4CKv3wp8ktndc7PE7rZbaw6Ewt39SjkYrQxnu7VmYzM5F9/980rZH2shLdturdlplJxkhV/621al7JvoSfp5ZAK/XA8H7ggPzelwH0tLy5L0jbV+m/2DkTap1U3CdwAAAADA9N54/vx53s9wPZIE8JJke7XdkmP1q7wnZ9CTBkvSuWS73u52ybI0UAu+n1K+31WWo36kq4FGtlvbzZqUe3eljbI26QNJ6rcby2CfWV9fH7+4trY2fhF4FVqtVt6PAAAAAGCC13EF/lK219xVq1wsFuunTnXHlZKMdGl6ujfPJbm1RjWJ3pVUeaslx9n3w9Gusgpz1lBXUtad7e0ONBmtId9rM7jz3nK2PVtS1J28NA8AAAAAwASv4Rn4y60WLEurUiTj1zfvpynrfx7WHxpJbz7TM7lju9edTVfGjKaeH04yN5z9Po3NtzYGm1gj2eWdaqdTzf6I4/j09Kh72D0w4/v3Bx0fH49fJAs9bsx8Vsu7e/eu5vXZFgr1FPPFRJgHzIJ8MQvmAbMgX8yCeXC9syB5p1O6bQG8XViRrO2aa3wj499PrpqHviQtv/Ps7IukyYgkgh+uTTep3YjRPPJWYXW0An1v8z0AAAAAAC/htgXwCcupNmuF/daBSdfDl233/Z3twmHZ/+Ilq7cZv2gk6c1pGseDFe5s215d3Spsbmx090bq3gEAAAAAcIXbGcBLspxS1SltJxG0+weNqiOFh5JGN8pPrfurwb/elJ5dcUOc1nvPMuH1rndnH7zVarFPJl/sFgMAAACQr1sbwE8yfIx9UHqk/bJN82d//0ySlj9qfvJtK80vP/pbQNw9GbjjtBtJsr3tkZ8Lkuuz4Qw8bsyDBw/yfgQAAAAAE9y6LPSXsTa2bEnmMBz5IInfp9pc/5WvWVJyaF6KDo7igc/io4MpQvN0sH5CewAAAAAArrRQAXwvgvcr9XachM9x3K5X/KTc2/ZodvpUWC8Wi/c/fyZJ0cNisVish0kxuCgoV374k3a9UiwWi8XsZPuXyW0rBVtSFJSLxUo7lqQ4bFeyEvOKgj8f/SEBAAAAAIALLNQWeskqNWrdim8iE5RNMPCB7dYapZkOxlulXe+gHETRZw9Hl90ffVIPnapjlXbcIAnXo6BczIazP3T/22fmqXTvf7rgBwMAAAAAAEYtWAAvyak2mpvt/VaQ1XazXW9nu3RZWjun2ulUP79fuv/5M9kfdxofJJetja2VIDpV1k1tt6o/L/qPpCfGrxSajVKSDP9HgUkardjud3a3u3vlJJb/cnI2vfX19fFHWFtbe9EvDMxmpkKUN2yen21x8BZyxyvIHa8gd7yC3PEKcscryF1er+CN58+f5zLwaycpCWd7zd5KfVokzq11qkNL6UmCu8HrAy1VL/pGg92MIoBHvlqtVt6PAAAAAGCCxVuBvzZpyjp3c3QjvLPpygylqE+32xu/cqJIsr3dS7brHx8fj18kCz1uzHxWy0t+45zPZ1so1FPMFxNhHjAL8sUsmAfMgnwxC+bB9c6CmRbzFyuJ3bVKq8RNKD2XpqjvnvYvWaVdz5aiK8N3AAAAAAAmIYB/SdOUnpMkWYXVGe8AAAAAAKDnVgXwcVbPrVgsVtpx3K4UexXcXtrZ41hS/Phs+PK01dzjdistH2f8OuXjAAAAAAAzukVn4MN6OTA3ON5KwZaiqHsqjSyph4dGw5vr4/ZeEElurVZo+YFptbed2bbRt1otDrrki+NeAAAAAPJ1ewL4uHsiSW6tWe3VZmt0StfW//I9S59H1r3l7IK1sWUHUWQOw6oznIX+0EiDW+V74XvVcSQ38E2w196Yqe48SexwYx48eJD3IwAAAACY4FZtoZdkF1Zu7IS5tbFlSzJ+pd6Ok430cdyuV3wjyfa2s6g+3A+i/gWnWnOlKNi7pq39AAAAAICFcCtW4NPC65IUBeVikNZgH6/cLklx2N5vBSZK/rJdb2e75Fijvdlec7ewv+ebKGm0W32ndwa+ZPXGdD9y//FTE5mgbIKBLmy31hszrPtGejN7Mtmu9/6HtvksmnkVHgAAAACwwG7bCvwVwnql7Peid0mRCfxyZUJWuW4WvUtSYeOCOPvk+OR09No7HzUb1d7q+x8lPy08Gxzx4Wf/+M6yFAX7ZLMDAAAAAEznVqzAO9VOpzp5vX1Q3K74JkoW1LdLliUpDut7vomMXykM3xcZo5G+Rs/AS1IURb1T92lf+uLvemnt4nbrsTRpxC/OLnzS9fX18Ytra2sz/ZMAL+zu3bt5P8KF5vnZFgdvIXe8gtzxCnLHK8gdryB3vILc5fUKFmgFPj46iCS5tUY1iaUlWU61UXM1aTXc9nan2N9ue72ceZZT3XGlgcpyM44IAAAAAMCFbsUK/FSyaHrTGfnA2XRljE66sQbOwttbF22bH9JPNS+NVpabccTM8fHx+DhkoceNmc9qeclvnPP5bAuFeor5YiLMA2ZBvpgF84BZkC9mwTy43lkw02L+4qzAn3YjDRdnzzibrqSoO3aa/WqTunu1IwIAAAAAFtPiBPCJ4RXz62h4Yx0BAAAAABbZ4myhT/znf/+9in+aZJe3bXdrtIZczy8+qRT9tJ3r7WyXLllqT4vKubVmYfyjk24ca3+vZaKscp3t7rz/Mt8BAAAAALCAFieAX9GSdP70i9On2ZUoMpFvDryPV40Gt7r/SpLOnvRqzUUm8E33Y+/CrpNS7+MjJifif1ounw1ejiLjP5Su2H8/otVqcdAlXxz3AgAAAJCvhQngw31zLkm693Hn+x9IUhy29/wgioKHkdTf6h7+5WNJWnJrf5JWh2vv+UFkHv7VnUn9noX18sTwXbI2tlaC6PRMku3WdpNs9Wl3kvTWDJvrSWKHG/PgwYO8HwEAAADABAtyBj5ut4ykJUmPH1bq7TiWLKe0+/E7aQPb23bSho8lSd/a7FWHKyV1355+Od7vr/7q/gXRuyRZX3kz+Z/l1WyxPVa3ew3fBwAAAACwaBZkBT5JCO9Wamr5JjJB2QSDHy+5ac33tPLbKGfbs00w9MmvTyTp/Klst7a7eThpFT5u//QLSUvS2eiItltrVEerywEAAAAAcKEFCeCdaqdTTf6vudnebwUmjcaXl3V2prey5HNJoD/OKqxKEz66495vVN9VeDjppuxXg+Zmd2DEJCXe5Mx5AAAAAABc4PYE8Fap0SldcUWSLKdUdUpJNK+wXvTPZG9tJPF03D2RZHvNRmkkwE4S0tnebvbBB43OB5c/Q9pZYcVyVjYPD06iJA19dNLtnsaOdWEEv76+Pn5xbW3tovbA9bp7927ej3CheX62xcFbyB2vIHe8gtzxCnLHK8gdryB3eb2CBTkDP1l6Mt7dGQ3Xr9VRvVL2e0XkkqT25Uo7foVDAgAAAABunduzAj+zsF4Oon76umlE3VNp2mA/2UEfBUE0kIRecVjf800UBXvtjbF1fknS8fHx+EWy0OPGzGe1vOQ3zvl8toVCPcV8MRHmAbMgX8yCecAsyBezYB5c7yyYaTF/UVfgw3rFN0kyuRmW32ep3N6/yWs2qr0z75ZTTZLaR8F+OHtnAAAAAIAFtVgr8HHY3m8dZNvZ37zz3tbm5Ig8btf3eu1s19v53aub6ReHYez0s9Ot/LM70lMpCsrFYNLd5t//pOqMH6QHAAAAAGDcAgXwYb3im8FM8s+ePgr8RwcDKeuSZPPRT8vls4F2kQl8syxJq4WB1fqwXhyuHXdm/LJqnenLw33566kfvtVqsU8mX+wWAwAAAJCvhdlCH9b70bvt1jqdTrPm2ZKioFzv7WVfKdiSziTZXq3Z6fSbnUlaGdhBH9Z902tWcyVpSZLxe+nprPf/pS3J9pJ+epqenQ72/u+92u8MAAAAALg9FmQFPs03Lw0UibOcUqPWLfpG5jCsOo4kWe/ceVPRMy1/1CilC+mWU2p89NPip2d68+13rOH+3FrS7FSS9K2PXPOpiQ6O4lIyQLKeH+yHpf6qfNzeC9Kd+b+3Mf3pe5LY4cY8ePAg70cAAAAAMMGCrMCf/tc0aB4p8e5supJ00k1XzeMvnjyTpLNPK/UwuRaH7cqnZ5L07MkXWbOjg0iSuzm8W35t27Ol6OAobeZse7Yk41fq7aS3uO2n4Xu/+DwAAAAAAFdbkBV4fTX5zwUJ5aKDn6tkSTrtnkpals4i45cHT7gvS2enWRW5pELcaPzeW3PvFZuzSo1at+KbyAS+GRx3xuz3AAAAAICFtxgBfHhoLm/w1tckSXH3RJL9/v0d/U0ryM7M2663u629chCddGM5VtZsQk25lYItZc0kSU610dwczH2vpXu1P/m+c3H0vr6+Pn5xbW3t8m8AXJeZClHesHl+tsXBW8gdryB3vILc8QpyxyvIHa8gd3m9gsUI4J1qp1Mdvxy3K+UgkjuWOX7ZKVWdUnWo6VT9x90Jn1pJb0nWetu7NHoHAAAAAGCixQjgJwrr5SCSbG976rpv/c3xszdLdgFMcfL9+Ph4/CJJ7HBj5rNaXvIb53w+20KhnmK+mAjzgFmQL2bBPGAW5ItZMA+udxbMtJi/IEnsxoT1im9mPos+adf8dM2ytPU7nHwHAAAAALyIRVyBT3fOj6Wkf5VDTk5bDwAAAADAlBYrgI/D9g8af/H4/Fny58F+u7BdGjiR3ssiH7f393pp52zX2/l6N5K0WrBmaNYftv2DH0eSZPyisV1vZ2hQAAAAAACutkABfFj/nm9OBy5EkYl8czC4Dp9kkf/ZcAW5yAS+WdLA1vgpm6XDFv2xZt2xvHlXaLVaHHTJF8e9AAAAAORrYQL4sJ5G78sfNj/5riUpDtt7fhBFQbleyMJpa2NrOYjOJNlebbfkWL1m59Kdb2YJ6KZslg4r6U3pme01GyUlzYxfKcy0f58kdrgxDx48yPsRAAAAAEywIEns4h/+qZGkZS+N3iVZTqlRcyXJHIa9lv+Qbq/XpIJwmrFZmrru3r1nSjfW9waNDo7imb8GAAAAAGBhLcgK/F8fPZUknQXlYjD+sWn9pOp8ICk++nnSUJEZ3iC/JD39+VGskjVLs4NI0ntvfflocGO9s+3ZJogOjuISOekBAAAAANNZjBX48O/Opmt42o0kuR/XPNfuXbRdr1n7ltL67rM3+21Fw6NYhdXBZgAAAAAAXGkxVuCdaqdTnfRBkmHO9nY/kCTF3RNJduGbTukDpzR4R9y2peikG8uxZm32e9XOt4dHT5Lgpc1Gra+vjz/o2traDN8XeAl3797N+xEuNM/Ptjh4C7njFeSOV5A7XkHueAW54xXkLq9XsBgr8BdIj6i7O+xkBwAAAADMucVYgZ8orJeDSLK97akLukXdU+nqYP/lmh0fH4+3JQs9bsx8VstLfuOcz2dbKNRTzBcTYR4wC/LFLJgHzIJ8MQvmwfXOgpkW8xd1BT6sV3wj2W5tlmJuwxXeb6gZAAAAAACLuQIftyvp2vtMpdgBAAAAAMjPwq3Ah/VLovcL08PH+z+NJEVBuVistPUVSYp+vD9WyT3JO79asC7tbbgZAAAAAABXW6gV+Disl7ON89WJB98np4cP62UzVIfuv7/zpqJnz/7+bOQMe5Z3fuWy3saaTaPVanHQJV8c9wIAAACQrwUK4LPo/bKN89bGlh1EUbAflvoRftz9L5Kk5Q+bn3w3uXPjX9nlIPr8b0K9O/BDQLgfRJK9tWFd0tt4s2mQxO6WefDgQd6PAAAAAOA1syhb6ON25croXUpibknGr9TDZIN8HP70r55I0p2N963LmiUjDAXmUzYDAAAAAOAqbzx//jzvZ3gVwnrRNy9wn22731z/8rNPH13UwK11qk6SBm9JOh/9ePltnT2RZLvebrVkSXH4x2V/rLd7H3e+/8Esz8UK/C3DCvysqJgyJzhLki8mwjxgFuSLWTAPmAX5YhbMA8rIzY0oMp99+ui9jzzXvrLtWx9+PNjszeV3VpLoXZIKyQJ7uN+a9FvA44f18DoeFwAAAACwKG7rCvy4tHhcfxN9ukhve7XdUpZkLp50UD65c/DS6JXeev/IFv2phxi0vr4+fnFtbe0a/g0wN1qtVt6PAAAAAOA1sygr8GG9HESSW+sFzeGhkWR7/dBakuVse7Ym1367mu3tDoXkr2AIAAAAAMCCWows9GE9XfMeSAbvVDudavZHHMenp0fdw+6BMdGLjjKWl+7Fhjg+Ph6/yBn4W4ZjS7PiuNec4NxjvpgI84BZkC9mwTxgFuSLWTAPcjwDvwABfC8B/e7YjvU4rO/5Lx6xTzX6qx8CAAAAALAAbn0AP753PpMeik/Ytr26ulXY3Njo7vWvzma1MPYDweAQS8v2t76xVdjceOfwj+6bpy80BAAAAABgUd3uAL5f/X1g73z20V4QSXJrzerACXXF3WscPR3io/fMpw2fofMAACAASURBVI/0rT9oVB1J4R/7RO8AAAAAgBnd5gD+4sV3SafdSJLtbTvWhOvXojfEWneoJP1vXqCvVqvFQZd8cdwLAAAAQL5ubwCfJK6TWxtbfL/0rsMk1j7pxnImFXl7eb/8lWYfgiR2t8yDBw/yfgQAAAAAr5lbWkbukr3zqZWCLSkK9sM4uydsV9Jy7jPpFYEfeYD0ahTs//LL9Orn9e8Ui5+eJX9EQblYD2ceDgAAAACwkG7nCny4n6SOi4JyMRj/2K11qk5pxw18I+OXB6Nv26ttHfhBFHVPpemWx51q7Z7xH0snv84upaff3Y9c86mR+fSRJBm/OPuvAwAAAAAASLqtAfxUnGqzVthvBWmNN9t2d3arjqWwK0UzbXB3fsfW40jRj9vxByUrC99tb/vbpe21sSG0n67NX7A/YH19ffzi2trai31LzKeZij2ih3+3ecBbyB2vIHe8gtzxCnLHK8gdryB3eb2CN54/f57LwLdMWjDOrXWqqhd9I9tr9nLnJZvs3VonyUI/9Nc4AvhF0Gq18n4EAAAAAK+ZBV6Bv1ZWadc7KAfGr5wokmxvd0Lm++kcHx+PXySJ3S1DQvtZJb9x8u+WO8ox5IuJMA+YBfliFswDZkG+mAXz4HpnwUyL+bc0iV0OrNKuZ0vRS4bvAAAAAABMQgB/fazCavI/qwXCdwAAAADA9SKAv0JYLxaLU5V7i9utNMu88SkPBwAAAAC4XpyBvyZZ5bhardDyA9NqbzvXuY2+1Wpx0CVfHPcCAAAAkC8C+GvRC9+rjiO5gW+CvfZG4/pCeJLY3TIPHjzI+xEAAAAAvGbYQn8dwv208LsjSU615kpRsNeOL77lpHvJhwAAAAAAjFjoAD45314ZjrPjdmXiqfc4bFcqxWKxWCxW6u0wHuzGN5LtfadwVE+a+EaSosAf7jqsF4vFyn/5ZyuSoqBcLBZL//qHxPEAAAAAgCksdAA/g8N62Q+iKPkjMoFfziL8NHz/8JsH9/3AZE0Sp8H9fzfW1d///cAfv/UvSFgPAAAAAJgCZ+CnYoyR7dV2S46lOKzv+SYyfn2zU3XkVDudalgv+kay3dpu1bEkKQ7be34QnX1aD79drXY6VUlhXVJkjGyvefH5+PX19fGLa2trr+zLIQd3797N+xFeS/y7zQPeQu54BbnjFeSOV5A7XkHueAW5y+sVsAI/JbfWKCWhueVUdz1bkmmlO+TTAnJurZFG75Isp9RoevZAqx7b273OBPUAAAAAgEXACvxUsvx0Kau04wa+ibqnkqX46CAaayJJ1saWHUTRwVFcGojY7a2NS8P34+Pj8Ytkob9lqEg3q+Q3Tv7dckc9xXwxEeYBsyBfzIJ5wCzIF7NgHlzvLJhpMZ8AfiqrhZGYe6VgS9FJN1a25h4F5WIw8eY0zgcAAAAA4MWxhf7lnXajqxv1jf0YAAAAAADAlViBn0Hcru8FJonW79wZ+fDSxHQAAAAAALwcAvipnHRjab8cmN6VL58qW0xPttO/2o3yrVaLgy754rgXAAAAgHwtdAA/KfJOMtKNig6OPt86kSS31qw6alfKQSR305F6uepMq73tDK/BJ9XlrmNtniR2t8yDBw/yfgQAAAAAr5mFPgNvFVYlyfj1MJakOEwC8wmi4P7Bl5LsworCetIojd+TpPSSoqBcaYdZzbg4rFd8I8ndYWc9AAAAAOBl3fgKfBzW91omSqNk27ZXd3b71dOzRWu31qmu9E6c2663s11yrNGe2vutg15Xsm1366Jm2cl123YHh3O2PdsEkYxfNsN36cuxJ4+eSIqCcjnpya1Vnf4A6e1R4JeHUtHb9+4Zv2hGFuHjdAW/1qmOlp4DAAAAAGCSm12Bj9uVst8PuaUoioxfLtbD0ZbddqWcRd2KTOCXh9uE9UrZDwa7UjShWdxOmg208cuVdrZKLqvUaHrvjeajk6RHn4w/VN+9jxtJ6J09x3gT23a9WrPx/e/XXCkK9vqD/rq9F0RS7xcAAAAAAACu9Mbz589vaqx03dn2arvZOnkctvf8IBo4Jp6swEvqtYvD+p5vIg0sWKeNBntSHNbLycXeYnc6YL9d1mZw6Tvry61lS/PZQw2vkCedDS2l9/p3vd3tkmUlT5E87PhjpFeG/5pgfX19/OLa2tqL/rNjHrVarbwfAQAAAMBr5iZX4JN66fbWRn+Xu+WUGp1OpzMey7q1RhqbW05117MlmVa6ih0eGkm2tzu4X95ytj1bUtQ9Ta+k+ej6PclyqjVXkjnMVtfjdsskbfob6y2n1Gh69sCAk/X7rybRezJCo5YciN/PxrBKu56dXQn3k58UdjkYDwAAAACYwU2egU+Tvgd79cLutmNdFsDa3vbg9nKrtOMGvsnyxTvVTqeafRbH8enpUfewe2CGd7Jn8fXm0Eb1oXvTNiOjSb3M8gdHcemiSHty/5KcTVfG6KQbK/vhoLTrHZQD41dOdGX4fnx8PH6RLPS3DBXpZnX37l3x7zYHqKeYLybCPGAW5ItZMA+YBfliFsyD650FyTud0k0G8GlQrCjLGWe73tbmxljaOWUF1gckwf9ATNzbq36hdMG/sHLlg0VBuRhM/uSS4u4X959E8EP3piF8xOo7AAAAAOCF3GgSO6vUaNZc287+jkwQ+OVisVIPL9uqPi5Nhpf8Ydu263perdn07PGmYz8FDEmC8Jdxef8D0pp1M9wBAAAAAEDPTZeRs5xqw6lKcdg+OjxIi8BFxt8rXJTTbVycZHGXW2sOFqBT3J3Q+D+NF3EbsySdv2hNt4FNAeoVwfvInfDQWak549c3KR8HAAAAAJjNjdeBT1lOqeSUStUs5fvIafPhsLh33jxZvE73rnvbI3vvR5bTk133zy59jKTN+Qt9hfRI/4RN9r/6v4yGN9fHWeW4WqHlB6bV3nZm20bfarU46JIvjnsBAAAAyNcNBvBp/bSRlW7LSU/GDxmJ5y/MFzcoyU3fD/6zI/dubXD5ffgprEmDJ73Vi765pNhb715zGFad4ef61WNpcKt83C/87khu4Jtgr70x9Y4DiSR2t86DBw/yfgQAAAAAr5kbPANvbWzZkoxfafePvMdhuh9+5GR4FJSzg/FxWE+3zKfx+0rBVlKVLe73UsnKx182YG+03k8BVmkn3e7+s8OB7uoV30hydy4Lsvv919txcu+vv5SkMw1lts8qxyUXkkp2UbB3aYk6AAAAAACGvPH8+fObGy1d/h5ju7Ud+b6xveb7n5cfPp5w69KdlfOnWYX3t5f0ZMK+97ffXXnyedbGdr3d6juflO5/PraL/t49+/HjKB14t/rL+8VPzyaMuPT2+fmTrDf7vTu/efT52ciSfPzv/tfKp2djAyy7tU+ybQbJUv6bd/TsadqT+/43Tx5+Fl26vD+KFfhbhhX4WVExZU5wliRfTIR5wCzIF7NgHjAL8sUsmAc5lpG70Sz0SRp6byAPvWzb9WrNRhbtdvd/3Ive37bTM+RLb0rqR+9SGr0vDXTy0XuSvvrsHwcGK2xYWr5nSVq60295756URu+SIuOX63/3DUlaXu491fLSkqR+9C4pih59Phrkh/VieUL0Lr33B71DAuEfJxsD0uhdUhSZh5/94zvLyR6C8ZsBAAAAAJjgxpPYWU6p6pSqkz+MjBlNCZ8sYMv2aru9gvFxWC/7RucDS9hh3UjR47ORZe1kl/pb/7LZSq6F9aIfpQvvjpX1ZCTpG3/Q+MS5YkTZWxtZ32HdNxrrSpL01d4XCg8fabCRsqR9X5xdmPV+fX19/OLa2tol/6h47cz0Mxt6+HebB7yF3PEKcscryB2vIHe8gtzxCnKX1yu42RX4q9nuN4b+TjLT2V4/lpZkOdueLSnqng7fLdvbvWxTeq+3rPyc5VRHq8dPOeI0XaWl49xao1/uznJKjaZnS6bFIXgAAAAAwNTyKiN3AXvr61+NBpPROdVOp7dcH8fx6elR97B7YMykzPGDC+STpEH3cKPRRPTTjThNV0nu/IFsdiPthjPt9xwfH48/OmfgbxmOLc2K415zgnOP+WIizANmQb6YBfOAWZAvZsE8yPEM/JwF8BPFYX3Pnxyxv4iRfPeyCqvScO/TjjhFV4qCcjGYePekEvIAAAAAAEw0ZwH8auFrI1eGEtfbtr26ulXY3Njo7k1KZz8aUI901T2Z5hmmGXGqrk671/ajAwAAAABg0c1ZAD+yg11xVre91qwOHElX3H2Brietj4+ZbsSpukrMUizuYq1Wi30y+WK3GAAAAIB8zV0APyxZxLa9bceacP3FnHRjDYfmJy864uVdrRRsKbqejfKcgb9lqAMPAAAAYFbzloV+OkkKuSSAnoGz6UqKDo4G70pSzc064jRdWRtbtiammw/rxWKxWCENPQAAAABgWnMewK8U/n/27i80kiw/9PyvL0w/2IXtYjRdi0PJVfSDbGYLfGk0FUggLrEsDAw4Z1iixSJwGBsbOuc+JAF788Hhh35wzIP8kDcFdja08TBhCBZ14m2nYWC44IPRIhE1orkXanttX+gjk4oHVd+h7Ev1LNQ8aB8iIjPyn5SpkipSqe/npUqhEyeOMjgPvzzn/H6miOjwIM5j3STu1KuBuuym6dJicDqs5bFzEjf3hs/Sz/rEGboSw9m1JWsUJ1nc/v3mn3w/7U2HtXqTIB4AAAAAMIsF30JvOLt2GChRQa0YQZuuv30YhPNvTzechntYC3UxNbxt20qpQYsZn3h1VyJieb59Giitw6CWNTpTTwsNKpfWvQMAAAAAILPgAbyI5bX9ykEUZkXdTNPebXiWIXFPRI8dQr+a4bTaleZepLQWEdP2G54cjATdMz7x6q5ExPJa7a3OQXSoCrXm3/le+y9+f9qwNzY2xi+ur6/P81di0c1V7BF9fG6LgLdQOl5B6XgFpeMVlI5XUDpeQenKegVvXVxclPLg+yhuVgN1ZV56Avj7IIqisocAAAAA4I5Z+BX45WNuX75t/uTkZPwiWeiXDBXp5pV+x8nnVjrqKZaLibAImAXlYhYsAmZBuZgFi+BmZ8Fci/kLnsQOAAAAAACIEMCXYK1C3joAAAAAwLwI4GeUxJ1mvRmXPQwAAAAAwD3FGfjZxAdBqMTeKuv5URRx0KVcHPcCAAAAUC4C+LuBJHZLZn9/v+whAAAAALhj2EIPAAAAAMAdsAwr8Gl5ddvvequd5l6otIiYtru741ij6eKSuHMQpS0mN4qb1UCZbrtROdgL0p5M0Vl7UUFVidh+17MK/R2qfgPTtLfHnpvEzb1o0OanHzfjVW9saAAAAAAAXGKJVuB7nXqtH5trFQa1kZxzcbNeC/ot8kaTMtP18uhdRGTNNic/MO9v0KHosecmnXotGGrz8rkKalXy4QEAAAAA5vHWxcVF2WN4XekKvIiI6foNxzIkiZtpBD5YLE869VqoRUzbbew4hiEyaGW67ZZjDPdVuDa4Xlh7z1vmj0wfEjdr6cXs5uypxUZJ3NkLQj36gIGNjY3xi+vr66/9OWGBRFFU9hAAAAAA3DFLtAIvtt/KomTD8hquKSIq6iQiIpIcH+q0iZdG72mrlm+LiA4PRtbDTbcxMbgeiI9U1q6wGd6wdlxTRHTvLL1w1tMiYm5vDhoZltPqdrvdydE7AAAAAAATLcMZ+JTp7liFHw1n1w4DpXtnIkY/ft+yRu6ytmxRSk57iRQCcXN786ro2vK6XS//IUmSs7Pj3lHvUBV26IvIasUU0Trca1YaO5YxU8h+cnIyfpEs9EuGinTzWllZET63BUA9xXIxERYBs6BczIJFwCwoF7NgEdzsLEjf6YyWJ4Bfq4yEx2nwnIbm2Up4ZXXstjSCz+L8OfX34E9lbG6bodaiVVBLd+bb7vbW5nh2PQAAAAAALrVMW+ivNBbjv07DLDtd+oNpmrbtun677Q5nvDOcVtu3zf5FrcIwqFWr9WaczD5wAAAAAMC9tzwr8DMY2Sg/0ZciIupvYs8a3W0/JOnshemh+vZQRbikN9bUsLyW5Ykkcef46DCrOadVsFeZksUOAAAAAIAxyxPAj0bn2bH3dDE9O4s+YaN8loxuwub6S2V78t2dkS8E0usTGZbjWI7j5Xno9eFx4swawUdRxEGXcnHcCwAAAEC5lieAH4mHh9PWZWfR1dHoynoavxf2zH9DRMT+7qXL79Nl3eXfJmRV5IrV50TEsLKT8XMgid2S2d/fL3sIAAAAAO6YJToDr8NafrA8iZvZDvc87byxuW2KiArqzU6SNUo6zXqQFoPbmS1gP+3lB9dXK6ak9efyK0ncqef16HODp3YGR96TONt+P/ORfAAAAAAAlmcFXkQkz/WeevDeB4PA3HBafq8eKK3CmgoL97z9yN4tFI2bdAY+iTv/+b+8LfJKh7VqKG+/+0HrP6VV6kaeKI/efef8i+ci+q//rLP5A8cwnIZ7WAu1DoNa8aEipu1711zmBwAAAADcQ0u0Ai/yxDbfLvz48rOPas148LPl7T55Z+ymV+cqGGo2IunUa0H49MWrwR1ffFSrN8Vr+65dyDn/4PET8/yL51mbt99NvxQ4G89qJyLvPNklfAcAAAAAzOGti4uLssfwuuJmYeu66foNxzIGNdoHB9CzdnkLERFJ4mYtvZilhE8b9W/K77H9RpZtPktBV+x4MIJ+N1nv6Rl4MW23seMYhhRqx480HdjY2Bi/uL6+/tqfExZIFEVlDwEAAADAHbNUK/Aitt/KYnPD8hquKSIq6qTHz7N88+4gehcRw9pxTRHRvbMJ3SWdSKW9DmrFGZbTartmoeM+020MheR5Ij2/5aXRezqulm9Len7+tf9eAAAAAMB9sVRn4EeS0RnpOfW8eJzldbte/rskSc7OjntHvUOlpqaDTwPwCSnusqT2I3XgzO1NY/z2QSK9PmvLFqWmlaU/OTkZHwpZ6JcMFenmtbKyInxuC4B6iuViIiwCZkG5mAWLgFlQLmbBIrjZWZC+0xktVQA/ltY9Lf9eCJT7G9jnkGaum/ibCXXlC7Ja8RNKzKcR/BW3AwAAAAAwsFQB/BXyE+kiImKa5tradmVrc7O3N7g6LI3AZza5LNxaxZCk09wLs+8NTLfdkI+ViMhX8/QOAAAAALjXliGAt7yuL6Ml2MckWfV12297xY3rycQ08QVTs83N5rT342YQXjG4K0VRxD6ZcrFbDAAAAEC5liGA7xs9VJ4dQk9XxrMN7e7OyLHzS5bZ0x34197pnt3+D/8oIsNfHMT/s8hzMX9zfHM9AAAAAAATLVUAP5JVbmoSuaI0N/148C/Sz1Wnos6ONbwGn9aNu2JtPrv96elXImJWVvsts2dO3nI/GUnslsz+/n7ZQwAAAABwxyxXGTkd1ppxWtstiZvZlvksfl+tmJIWb8uLvyVxp97fea/DWjMWSY4+FxFRQbXe7CSGs5tWfKtVq9VqtVqvd+IkiZv1QInI418Oa9VqMxb52Wl2U7Xe7PT7F+OX3hIR0c8HffzJjzvZ3fLwV1mABwAAAADMaqlW4G3bViqoFc6bm27by9bfs6JyMtxATNffPgzSLHa9Tj1Q59kvtAprp9LefSzqWd5Y6zCopRnpTdv/rgTP0rsGSfC0CgPV87vpU7/+b0W+GBri049CEZG3RV792q/c0J8NAAAAALgH3rq4uCh7DDcg3dJu+90dyevEmaa92/CGd8Uncecg6qeDzxukN6fX7Pe/oT55Kk/ed7/8JAvLTfuDb1f+8SeHSmdh+oPHbv0/OJYxuO8dU55rsf321lH6dDsP4UUk+cs/qH36vD8G03a//auHH32qp23A39jYGL+4vr5+/U8HiyeKorKHAAAAAOCOWaoVeBExLK/V9ab/1vEsZ/TXlufbKlAiptvwKgdKRH55ffM3zVDr9Np3DPnOdxwv/57gG+9tDn0vYPt/0Y/WrYZ7WguHj83/yi/LcC77pHN4Q38uAAAAAOC+WLYA/nWY25uGnGU/GJU1EZ1eu/wud6eYJC/bqX/t1PUiJycn4xdJYrdkqEg3r5WVFeFzWwDUUywXE2ERMAvKxSxYBMyCcjELFsHNzoL0nc5ouZLYvb6kdyoiZmX2/HJjueTTbHmnvWRicwAAAAAAroMV+IG3flKrphnq5inwBgAAAADAG7BwK/BJp16tVuud/vp1Enea9WZ84w+Km9VqWgQu91WagN60/f6RdknCWnEws3RbC7WI/OKSNufPEhFJnp1f0gYAAAAAgKKFX4GPD4JQib11eSvL605PXTerb/rdj62rm4047SVSzGn3VfrPr9/sIn4URRx0KRfHvQAAAACUa+ECeMNpdZ2yBzFgTKn1NqAPjxNn0ORnaYhnvnfJVwGPHhvymTYeP5p9HCSxWzL7+/tlDwEAAADAHbNwW+jvHh3WmnG6yT6Jm//XFyIiD9Z+q9QxAQAAAACWzS2uwKdF022/6612mnuh0iJi2m7DcwwRSQbXTHe34Vj9Gun1WqhNt91ypFNPz5OLqKCqRGy/m55OT+LmXqR0+jsxTXNtt+EV9rGnjzaHV8/Tnged9K/HnY9/mj6kLu7ujlPcEJ+EterhyCp88rd5sjsRkUePn5yroKaG/vZvVIp/ocjwmfjBGfjLF/cBAAAAAMjd/gp8r1Ov9eNYrcJavZPEzWrhmg6DvTnyxCWdei3oR+8iorVWQa16vUR3R81aED5/2R9eULukn5+dioi8elG8dv7sqTx+bKY/mObjxyIi0jso/NUiImd/+xPqygEAAAAAru3Wz8CrMDRtv+1ZhiRxZy8ItQ5rgZi23xi6dhA73sixccNpdZ3BOn7626SzF2oR0/X7q/ZZHyrq7FjzLmkrpfp9JXFzL1BaBc2trmdZXre7M9gDICJJ5ydaCo9Oh/ZA5OWzr7bb3ZYhIhI3q89E9Hi3Lz49iH8//SMuPwO/sbExfnF9fX2+PwyLbWVlpewh3El8bouAt1A6XkHpeAWl4xWUjldQOl5B6cp6Bbe/Am+6+e52w9rcNseuObu2SJrMfRZnPS0i5vbmYKO7YTmtbrfbvSLb3BS233LysXgN1xQRFU3cEDDh0SLyLX/So023Xeh2vj8RAAAAAIAxt74Cb25vDmJbo7ImooevzWm1YopoHe41K40dy3jdM+Smu1Nc9zecXTsMlO6diYx1Pc+j14aqyGV3Tux1zMnJyfhFstAvGSrSzSv9jpPPrXTUUywXE2ERMAvKxSxYBMyCcjELFsHNzoK5FvMXrozcVYzNbTPUWnSeOM603e2tzaHMc3NYGy3Xnsbao8XdJz/6gUi/7vsws7J6reEAAAAAADDZrW+hH4uQX5fhtNq+bZr5z1qFYVCrVut5LbfbM/rolyIiT4M38WgAAAAAwD13J+vAG5bXanW73bbvuv14Wqu5ctmX8WgV1LNmPxcRHf7wWnnzAQAAAAD30Z3bQl9kWI5jOY7Xz2V/eJw482WyG90rnxwfaplh38ANPHouURRx0KVcHPcCAAAAUK67FsAnaWG3QVk5Ecny24eD0vCTksZlofmIkcA7a2RvWeNNZ3v0ZWz/eonyhSR2S2d/f7/sIQAAAAC4Y+7IFvp+BTYjrUSngnpncO48idPi8P2Fc6OyJiKiguxwehIPFXQv0mEtP8GexM200cT4fcZHAwAAAABwGxZ+BT6v3VarhmlxdafhHtZCrcOgFg61NG2/vzRu7bimCrXk+eJFxPZ9CQIlw2zbVoVGI90MM6Y+Wh48ztPO/+xURESHteqh6e42hrLjq6BeaV97ER4AAAAAcJ8t/Aq84TRc2xy+0mr7biEZvJim7frt1tDOdqfVHtxn2n57SlS+5bX9oXatKQ37j7Yfjf/i5bOwVu8kcbP6UX+dX+vwTaTVAwAAAADcC29dXFyUPYY7Jm5Wg6wCvd/wLCPPYycTr+VH5tMT9KabrsCnfYwcp89sbGyMP3R9ff1W/yi8YVEUlT0EAAAAAHfMwq/ALyzTbXjp/njDSk/HD19zdm2Rwul9AAAAAABew8KfgV9U5vbm4Cy7UVkT0cPXru3k5GT8IlnolwwV6ea1srIifG4LgHqK5WIiLAJmQbmYBYuAWVAuZsEiuNlZkL7TGbECDwAAAADAHUAAf02FsnH9Q/GpJO406824jEEBAAAAAJYWAfxNiw+CUE0sOQ8AAAAAwLVxBv5uiKKIgy7l4rgXAAAAgHIRwN8NJLFbMvv7+2UPAQAAAMAdQwB/g37xLKiGZyIiooJqdiy+98Nq9WneQoe1athv/9OjRKwbyFsPAAAAAFh+nIEHAAAAAOAOYAX+Bn3tsd/98KxZDZTYfnfrqBookcrvdbt/LJJ06rVQm2675UinXgu1iHxza9Ly+8bGxvjF9fX12x493qS5ij2ij89tEfAWSscrKB2voHS8gtLxCkrHKyhdWa+AAH5ultfteuOXbb/rWSJyNrmd4bS6johI3KyGetAcAAAAAIBZEMC/WXEzUCKm254evZ+cnIxfJIndkiGh/bzS7zj53EpHOYZyMREWAbOgXMyCRcAsKBezYBHc7CyYazGfM/BvUNKpp+F7wyF1HQAAAABgLgTwb0zcrKV751uE7wAAAACAed2vAD5uVqvVar2TXNmmGV/dWxJ3mvV6tRooEZGfftzsxJM7TjrN7/9u1kxOj6Y1AwAAAABgKs7AX1PcrAdKFy68fK7CQB26H6yNNsxD95RWYaB686awi6KIgy7l4rgXAAAAgHIRwF9L3AyUFjFtv+FZaeG4x7b5TGkdfvTz0YbZf22/61lJ3NkLQq2CeqU9z1Z6ktgtmf39/bKHAAAAAOCOuV9b6G9I0omUiNh+y7P6Mfg3tlq+LSJyLiJy2ksGDWWQd96wnLSZPjxmIz0AAAAAYGb3dQU+6TT3wnQLvGm7DW/CYngSd/aiUOu0ye6O0w/Wz3paROyt0U3w1pYtSsnbIq90WKuG8vBh/qv05yId1qoh1eABAAAAISI5LQAAIABJREFUALO5lwF876AeDs6vaxXWTmV0Q/tRs6ZUoUnx3Lrldbve9O7fee/J1z57qkXkFy9ueugAAAAAgHvqrYuLi7LH8OYMEsqZrt9wLEOSuLkXKJ2dUJ+1zaXdm272ZUDSqddC3f+xYPpvRGRjY2O86/X19Wv9xVhQURSVPQQAAAAAd8y9PANvuu1WtiHesLxdWyQ/tF5g+4U2DdcUERVdVoAuPxm/S5l3AAAAAMCNu5db6NcqxRB7tWKKaN07ExlcNt2d4lq74ezaYaBGGhXFzVqox+671JTeTk5OxtuShX7JUJFuXisrK8LntgCop1guJsIiYBaUi1mwCJgF5WIWLIKbnQXpO53RfVyBNyurV7YZjvElC/PH1+lTcbMeKBHT9ucpDTfLOAAAAAAAEJF7ugJ/o9Lz7CJTTrQDAAAAAHAT7uMK/A2KmxOj97hZrVarB//fmmQb5UekdejGVvkBAAAAAJiGFfjJTnuJWIX4Ojk+HI25k7hZyzfOT05O//WKKaJHuxJJeqcy5w76KIo46FIujnsBAAAAKBcB/GT68DhxBmvqWfxub/UD9Tx6v2zj/Nc3t81Q6/AgdooRfnwQahFze3OOBXiS2C2Z/f39socAAAAA4I5hC/0UOqw14zRjXZJlmB/E70mnfmX0LiJibG6bIqKC+qCv9NY543cAAAAAwH3HCvxktm0rFdTU4Ipp+/kyerqELqLDWjWcdPP7dvY/w/n24/CjZ6KH+xLzHR3Wqj2/O3nvPQAAAAAAI1iBn2LLa/u2mf1g2n57cM49/s9q2l3jvvMD/8nQBdP+3vdM/Vxk8IUAAAAAAABXeevi4qLsMdwxg7pxfsOxDEni5l6gtMhgT33crAZK7GyBPbsh++XwTxNsbGyMX1xfX7/VPwpvWBRFZQ8BAAAAwB3DCvy88nx2fstJc8sbltdwzUvuMJyGa4ro8CDuZ7BzG9SMBwAAAADMgTPwc0rjd9PdKW5/N5xdOwymb6w3nIZ7WAtVUD+VK8P3k5OT8YtkoV8yVKSb18rKivC5LQDqKZaLibAImAXlYhYsAmZBuZgFi+BmZ0H6TmfECvycznqj9eBFRGS1ctka/GAVntV3AAAAAMC1EMC/KUZlLf3PWPAPAAAAAMCVCOAvETer1Wq1GYuISNKpV6vV+tFXIiKnvWTOvpJOlG2xV0HWIwAAAAAAM+MM/Jx+9ddNET12Od1ZP1XS2Qu1iO37lSgIVdTZsebbRh9FEQddysVxLwAAAADlIoCfleG0uo6IJJ3/+qnW4UHsFMq4x0eXlYbvh++eZYnYYaDCvc7mlCpyk5HEbsns7++XPQQAAAAAdwxb6OdlbG6bIqKCeifdRp/EzfolGegHlePSxPWW59siOtzrzLsLHwAAAABwjy3UCnwSN/cCpUVETNtveKvH9VqoTbddXKxO4s5BdKh0vmXdNO3t3Z2sJnuxp6jfxjTNtd2GZ42teCdx5yAKVd7Mdsc6KrTt9AfTcA9rodZhrRqOtPm/fxJv/v5wD/GfBErkbek3N80n7z2Sz/Tcq/AAAAAAgHtscVbgk069FuSxtGgV1OoHvdFGcbNeC8JB9C4iWqswqBXzwmU96UITrYJadSR3XN7XoJkKg1p9hgxzhtNqv/9owi9effHp8FDkr/4weCoi8qo44KefncsDER0ekM0OAAAAADCbRVmBz46Ji+n6DccyssX4kY3pcTNQetAmvTFu1gIlg7xwWU/FRknc2QtCXcwdl3TqaV+229hxDEP66/8qqFfaV66MG+vfFDkXERHbb3vWWbMaKHkg8lLUUex53a6XDvhcJg/45cjGgoGNjY3xi+vr6zN8irgzVlZWyh7CncTntgh4C6XjFZSOV1A6XkHpeAWl4xWUrqxXsCAr8MnxoRYR229lga5heQ3XHG6UZooz3UZxm7th7bimiOjeWXohzQdvbm8OGhmW0+p2u91BwDx4npdG7+kjW74tV6yM/4+f1KvVarX5T+mPptv2LCMf2reeiBRqzM02YAAAAAAArrYYK/BpPJ2necsYzq4dFrPDWdm6dnpLkpydHfeOeodKDVVwW62YIlqHe81KY8cyJq6k5/H7ljXyC2vLFqXktJfIlLPwv/I/rYloUZ98LiIiaxVDkrgZKBExt//9b4p6qnXvTMSYecCjTk5Oxi+ShX7JUJFuXul3nHxupaOeYrmYCIuAWVAuZsEiYBaUi1mwCG52Fsy1mL8YAXy6ar5WGYmZ01h86FIhz90Uxua2GWotWgU1JSJi2u721uZQcrpslb6yOnZ3GsH3Q/AJfmvHt1Wgsv3zKqhmXzGYbsN5dHw42vzqAQMAAAAAcLXFCOBnlCaCz34wTXNtbbuytbnZ2xtclTTFXKWQhF6rUKswTBPbF8P4sS8MZmNYXtuv/FkrfPYyH4m9vfsbvaBWDeXtawwYAAAAAIArLVIAf8nGdZFBnjvbbw/F4clYrnoxLK9leSJJ3Dk+OsxqzmkV7A2lp7vieZcwLOe73wqfKckz0SWdeigiT2zj6WC1fY4BXymKIvbJlIvdYgAAAADKtRgB/IS98iL5Tvfhn0x3ZyToHm41xLAcx3IcL89Drw+PE8cx8nPyEzbKZ3nnJmyuv0x8EGox3fbvyd7T1xrwVJyBXzL7+/tlDwEAAADAHbMYWeiNzW1zPPt7GkxfJW+V5X5POmmW+OE88oa1uV3IaZ8+T9TRaLb5rLN5N9db3lCO+7kGDAAAAADADBZjBT7PPDeowZ7mfhtqk6eXP4g3sx3p2br6tJ6GC8GHxTx5g1aS14FPks7BXpjWfduxRJK4+bESEVFBXfyG97VniYgkz86lEKj/oveTZv2/5qftRR4Wz8DnA/7hX/3DlydPNafeAQAAAADXtyABvBhOwz2shVqHtWqYXTNtWwol1/Kycnl2+byV628fBmF/Q/ygp6AWDj3DtH3P6nfW8nv1QGkV1lQ43KjlGEPZ50SroHb65N0Jwz5Tnw5Vc3/xSkTk838SsQYDfvrJ0wm3ymWp7gEAAAAAGLYoAXw/eXxWcs20/YYnB9WhRXjLa/uVgyjMgnrTtHcbnmVI3BPRg5x0htNqVzoH0WF/Zdw07e3dHWf4LLrltdpbnUF3Ytpu2ijPPveOKc+12H5762gvUF8Mj/fL9J9V1/f7HX/W3P1QvZTzv+8kv+MYItbO995Rnz7PbzFNe7exI8fZtoH/NjmJ3sbGxvins76+PtOniDtirmKP6ONzWwS8hdLxCkrHKygdr6B0vILS8QpKV9YreOvi4qKUB88iblYDJbbf7S+cvxHZ8vvQc/Ml+TztfDa2/o9T7p3S6lIE8PdBFEVlDwEAAADAHbMYK/CTQubrZoR//cEcH+r8IHxfvh2+z/K6Xa9/S5KcnR33jnqHhS3/IoNj8HvNSmPHMmYK4k9OTsYvkoV+yVCRbl7pd5x8bqWjnmK5mAiLgFlQLmbBImAWlItZsAhudhbMtZi/GAG8UVkT0aKC5la7n6CuGSgRMbc33/A58bTK21gi+gml7tJMe5clp8uS5YnOD+6btru9telcr/w8AAAAAOD+WowAXqwd31bjCerEdBuz7z1/s4by3Jmmuba2Xdna3Ozt1Yp58bOT/VF+HF+rUKswTM/4E8YDAAAAAGa1IAG8GFmCun7iuYl5527EbCfrTydnmMvlee5svz2Iw5O488O//XK0qWF5LcsTSeLO8dFh9gdqFexV5jkZDwAAAAC43xYlgBcRw3I8y/GubnjLJuyVF8l31g//ZLo7hSA/PgjCiQXjUoblOJbjeHn5en14nDizRvBRFHHQpVwc9wIAAABQrgUK4BdFdm49PIidwhp9mlFvVtn6/eTkfIaVnYyfA0nslsz+/n7ZQwAAAABwx/ybsgewgIzNbVNEVFDvJCIiksTNejAcv69WTBHR4UGcZFeS+Oinl/TUbydJnG2/H8uTBwAAAADANG9qBT6JO8MH3HeLOdwmlkufWI497hxEoZrcy2zPmml4T+xVrc50WKuGww1/0fthvf50sHo+mnUvo3/yd+L8jojhNNzDWqh1GNSGezJt/81WtwcAAAAA3GlvZAU+6dRrQagGYa/WKqjVm3H+s+X5togO9zqDdep0mboY5ubdDPcyuGW2Z800vKfqbPXxYzP72bTff5L990w9nbz3/e3hH7/678mkVrl3nuwSvgMAAAAA5vAGVuDjZi3UUiyclmVxU0FzK19dt3ZcU4U63OtsthwjD99Nd6cQvqcbz03XbziWIZLEzVqghs+qz/SsGYf37Jntd1tW1ihfZ3/st3+QPT2tAT/YITAhvf3Uzp8GzXhKGvyNjY3xi+vr6zN+3LgTVlZWyh7CncTntgh4C6XjFZSOV1A6XkHpeAWl4xWUrqxXcOsr8EknUiJi+63BNnbDclpt1xRRUX/93HAarpmeKheJD9LwvVAEPjk+TMu2tfLScka6bi/qKJ7zWZcPLzmOQi3yYNItD7/3HwZP37VF0nx1r/m3AwAAAABwldtegU/j7uJKeibL9V4opZadF1dB/VRGw/d+/L411I/ldbveSJuZnnXZLUZlTUTLSxHJb0n+6XMREXnw775duD+tN6d7ZyITT9hfYzwiIicnJ+N9kYV+yVCRbl7pd5x8bqWjnmK5mAiLgFlQLmbBImAWlItZsAhudhbMtZj/ZpLYTcgGl/+mGP72U76Nhu/9uuuV1Zt61oy3DP3m4f+6M3/e+GuMBwAAAACAUbe9hT4NvGdmVNbS/0wusXZ54bU5nzXHLeajRyIiv/Yrt9E5AAAAAABXezMr8CMF4qbKDo2LyOSkc6e9RK4oCDfzs+a6JW5Wg/O5+ryk8yTuHES9rRZ56AEAAAAAM7vtAP6qc+JF/cpxfiUKQhV1dqx+5DtLP/M86/q3zG5K5/FBECqxt+bqK4oiDrqUi+NeAAAAAMp12wF8lq9tOBoXkbzo2mB9ulD43RKxw0DlNeWK/RzFnjVYuE469Vqo88ptsz/rGsO70b9dRFRQPZ2jc5LYLZn9/f2yhwAAAADgjrn1MnKGs2uLiA5r9U6c101L4mY9UCJi7+YRbF45Lk3ZnlaI0+HeoMzc5rYpIirod5PEWWX4fmr6WZ91jeHNrlBWbnLnWUb7a3UOAAAAALiv3rq4uLj1h8TNeqDGE7qZtp+fA5+w3p2trheuZVdGZMvv2dHyQ6UnZo7LnpXEzb2PlX6eXTTNtd2GJwcTh5ffadq7ja2j2tj44r/80z/99ItXef/u7r+X6MN8fKbb3u3VAmXaT36hnp5N67w/+KuwAr9kWIGfFxVTFgRnScrFRFgEzIJyMQsWAbOgXMyCRVBiGblbX4EXEbG8Vtt3bdPML5im7frtfha3uBmoscpxhtNwTREdHsT9K2k3hV78dh69d+q1IJwUvQ+elXTqtaAfvYuI1loFterR1sjwhmitgtrHp8MX42a9FvSjdxHRKgw+PPyGbY918stfm/axAAAAAAAwszeyAn/r0gV8MW2/4aVZ6pO4sxekZ+oLIX6oxXT9hpNlss8bTVjl7zdL4mYtUENr5f02ttvYcQxDRJK4uRcoPZRzPhvUWB769Pr0tfeNjY3xi+vr6zfxQWFRRFFU9hAAAAAA3DFvZAX+lmXF52y/5fVrzBmW02q7poiKsmP0aVV2c3tzUIfOsJxWt9vtFvbFHx/qtKs8yDfS4/iijuKxNl4avaetWn563r2/YSAzsrEAAAAAAIDreDN14G9VGlDn6e8Ksizwh8eJ4xh5Ubdwr1lp7FjGxJg6j823hrqyvG7Xu6KNiFhbtig1Wqve3N6cM3w/OTkZv8gZ+CXDsaV5cdxrQXDusVxMhEXALCgXs2ARMAvKxSxYBCWegV+CAD6lw1o1nPybtBB7Fs6LVkEt3dluu9tbm04h2M5X6Surlzxoeps0gr+lovIAAAAAgPttCbbQpxH1VeJmLdTy+PEgVZ1WYRjUqtV6s1/iLbVWuTr+nqXNpQ1VUK0244m/AgAAAABgzNKswI9kipviG99t/eAHIsmPP/yjjz57kV7TKtirFG8e2QQ/0SxtblIUReyTKRe7xQAAAACUawlW4FcrpmQb5WdlfOfDH3W73W637bumiOjD42TWrqa3iY+UXLUBHwAAAACAa1mCFfjscLuKOjvW8Bp8WrAtXZv/2T+LiKi/iT2rkH3OsLKT8cNdHQ21ysrGZYXfJreRPH6ffXP9XEhit2T29/fLHgIAAACAO2YJVuDFcHbTCm61eqd/nP2zv/pDJy3Drg8P4kS+nmb2exbUO3EiSaderVbrf/njzl6oJY+6k/j4H/7lbUkPqFe/X2/GiSRx1iRLO5/EB3/3L9JvkzVKOs16oGSQDD+JO3/z036z+tg5ewAAAAAA5vLWxcVF2WO4CXGzHqhLktnZtq2Umvw70/ZbnpWts0+73+96llzeJutIpjSb9NvZTu4LK/BLhxX4eVExZUGQDKJcTIRFwCwoF7NgETALysUsWAQllpFblgBeRJK4cxAdqv5++Afvvv9//KffeU8kiZu1dDFenrzv/vLJoM3DVfu3f3fHsYxst72Ytt/wLIk7B1GYfx/w4LH/pz+wjH7kbbp+w7GMZKjRwyfu938vLUmXd/X4sX72TGy/663Gnb0g1PnXAJJ0/ug/hs9eyuQAfmNjY/yPW19fv+nPC2WKoqjsIQAAAAC4Y5YogM8VT77n1/JV7yyCzn7utxk+5T4w3GxSx2Pm6WqskYgQwN8PBPAAAAAA5rUESexGZLngtzeLQbYxlKtuVHJ8qAfH18dvOzxOHMdI889rHe41K40dy5gYxM/Y1WVOTk7GL7KFfsmw62le7BZbEGybLBcTYREwC8rFLFgEzIJyMQsWQYlb6JcvgE+N5oI3Kmsil5yRFxEd1qrh5N/0zkSM/rcAWgU1JSJi2u721qYzXg/+yq4AAAAAAJjPMmShH5L0Tue+56x3eWifM5xW27dNM/9ZqzAMatVqIcf8rF0BAAAAADCXpVuBLyy1z3RmfWCmhobltSxPJIk7x0eHWTo8rYK9SvHmmZ8JAAAAAMBsli6Az5z2EqkUfr50YT473D7P7nbDchzLcTxJ0gzzI+fkb3yjfBRFHHQpF8e9AAAAAJRr+QJ4a8sWpfThcVJI+5bmlpsmO9yuos6ONbxwXlzEn5xf3rCG8uPN1tX8fxVJ7JYMdeABAAAAzGvpzsCLWDuuKaLD2l/8c3rhPG7uhZceTTecXVtEdFird/rH2ZO4WQ+UiNi7jiFpbC4iKii0kSTupH3nWfNm6goAAAAAgDnd7RX4JO4cRKFKg3PTtHcbnmWI4TTcw1qoz78QEdHhh4HIowdy/lJEBXVxG54z1k9P3nkgz1+KDoPaUP540/Y9K6/b/oFrfhTqsTby8NE7KqgqEdN0dxueb58GakIzERVUT017t7F1s58DAAAAAGDp3eEV+KRTrwV59C4iWqugVu8kkqeLf/Qg/cWDByLnL/NWKqzVO+eFfuJmvRaE6vlLGfHgseu3W8X98j8fG8TD1cfvirw4f56PIQz2Oqteq+27hXT1RVqroPbx5/P9rQAAAACA++6ti4uLssdwLdmJdDFdv+FYhkgSN2uBksIZ9XTZXGTQKImbe4HSExr1uxEZdDU4sN7vyrT9hmcZee46mXhtvPeshcikRiM2NjbGL66vr9/QB4eFEEVR2UMAAAAAcMfc1RX4LCud7bfysNuwPN8WEXUUDzc13XbeyLC8XVskTVIvIiLxkRIR0x1E7yJipMfoRffORrvK43DDSk/ED19zhntPOpFKB+n1ezcsp9V2TREVdRIBAAAAAGA2d/QMfB6/bw2tYVtet+uNtc3Ty6VG6rwN3ZIkydnZce+od6jUpKR35vZmIcpPC84PXZs0SNPdGV1oz1LVZ5XnRp2cnIz3RRb6JUNFunmtrKwIn9sCoJ5iuZgIi4BZUC5mwSJgFpSLWbAIbnYWpO90Rnc0gD/raRExK6tXN726UX9j/a3QYa06mssu+82NV4sHAAAAACytOxrAp4bX1q8nP0svIiKmaa6tbVe2Njd7e7XxynNzPi/9lgEAAAAAgBtwpwP4014i1mvF8ElWxd32216xp6R3rd7io/Hs8oNMeAAAAAAAXNsdDeBHTrJfW7YV390Z+R7gWovn8UGgivXpbmqQIiJRFHHQpVwc9wIAAABQrjsawGdp4NRR7FmDFHHZdvipBdpmluamf90l/nyQUWfHGl6DT6vLzbM2TxK7JbO/v1/2EAAAAADcMXe1jJyRVnFTQb0TZzXb4nw7/Nbs0ftqxRQRHR7EeUm3JO7U8/Lxrz3ItKycDmv9UYokcbMeKBGxd9lZDwAAAACY1R1dgRcxnIZ7WAu1DoPacJJ3FdVFGt74ynkSdw6iv0vTzaugfmrvNjxn1w4DJSqoFWP2R+++c/7FcxH913/W2fxBP87+/ON6NegnvHvnUaHrYio80WGtWel6lojl+fZpoMZHadr+a+4SAAAAAADcK3d1BV5EDKfV9l3bHPuF1iqo1TvJ0MWkU68FodJnw62a4o108uDxE/P8i+fpD6/eftcQEfmnz0VEzp8XTsbr5+ciIj+/1thX1+bYJgAAAAAAgLx1cXFR9hheT774bbp+w7EMkSRu1gIlQ0fh00PnYtp+vjSfxJ29INRDzbJWY5nj87vzJ6T3p08pNE2bFR/bH5vtNnYcw5BC0fmpJ+A3NjbGL66vr7/uB4VFEkVR2UMAAAAAcMfc4RX4VHJ8mNaBa+WxtWF5vi0i6ijO23QilbYZbKw3LKfVdk0RFY2s1YvpNoYi6zSlnekOoncRMawd1xQR3TuTaQZj89LoPR1dy09Pxh/EU+8EAAAAAGDYnT0Dn8lj5OEd6ZbX7XojbUx3Z3TXepYm/vA4cQoRu7m9ObwwPtRbkiRnZ8e9o96hUleUmps8NhGxtmxRalqO+5OTk/G+yEK/ZKhIN6+VlRXhc1sA1FMsFxNhETALysUsWATMgnIxCxbBzc6C9J3O6K4H8Fkh98rqlS11WKuGk38zQ6X2/s73GxlbGsHfTIV4AAAAAMC9sAhb6JO406w3X2c/+Vrlsjg4DaQvoYJqIedd1llhVEmnXutH76umaduu67fb7nj+vLnHBgAAAADATBZgBT4+CEIl9tZrdDFlM/qQiUnjsixztt9yDJHiafbBqJKsvrz5jujnpuvnvSS9GxsbAAAAAABXWIAA/rWsVkwRfflm9Olt4oNQi+m2Ly3Jnu2Edxsj8f+VC/uXPDdNizfDxv9cFEUcdCkXx70AAAAAlOuuB/BZHjp1FHvWIArvL6x3PWvQJursWBMKt40msZtRGoRftsI+eWyDW+fZXE8SuyWzv79f9hAAAAAA3DGvFcAncecgOlQ6X4k2TXt7d6dQbC2tjD6yeb0QXa9mddJFRAXVkdrtSdw5iML86Llpu0Nd98uuf+CaonV6e1pwvXKc7nl/kF40TXf327Z8pHRYq0tayj2JOz/8+K+fPn8pIqLDWv3Q3t7dqaQd/6xTrw6N6oHISx3u/UGon5tuu+VIXkNeROTLv82T473zQGQooM8j+OB3P3/44vxF2v7ho4cvzmViVnwAAAAAAKa5fgAfN+ujadm1VjpQvUIQfnO9axUG6tD2W6N9/1x+MdSqpvIfXuajCoOfuB/Y5kdK6zCoTUpFn4788ePJg1l7LM+eiX4uxVz2pvvBw//zo89evXyRN+t/HVANsy8tDKf1/k+cT8770buIvDh/IfL2o/fHD+QDAAAAADDVdQP4uBkoLWK66Yq2iIgkcbMWKJm2VX0Cw2l1nWwlvbj23kmjd9N2GzuOYUi/jJsK6pXhXHQqDE3b/3Cr9/eD1XoRWbV930uX2veCUOvwH3e77a3ihoGHj5789h/8Xjr4bOTPTkVE5Ovjo0rizg//PHyahuGmae82vNXjeu2ViMhD+8Mfee/1OylKOvVPzl9lq+5ZDP/o0cPz8xfnn9Q7/8vkGH5jY2P84vr6+iwfKO6KuYo9oo/PbRHwFkrHKygdr6B0vILS8QpKxysoXVmv4Jpl5LIsbG6juKndsHZcU7K66q8hOT7UImL7LS+N3kXEsLyWb4uIDg+GC86ZbsOz3rMcr9Xt5pXdTNf30oEZlrNri4ic9hLDcrxW+pPptn/08R/3B5+P/KXtdyduHzAs5/d+20xv7LZanmX0B9n9kfdePkTflvRat+UYhT/kRx//qJv7+OMfTf5DAAAAAACY7por8JbX7Xr5D0mSnJ0d9456h0pdkZh9FnnYuzUaSFtbtig1kjXO3N4sfIVQWRPRw9duZ+STBznU+Zx/SN/Jycn480hit2RIaD+v9DtOPrfSUY6hXEyERcAsKBezYBEwC8rFLFgENzsL5lrMf40kdtmu9ut3MEVWtm1CjbU08L28ZtwsbmDk0wc5S5ub+kMAAAAAAPfGdQP4ZJA/XsQ0zbW17crW5mZvb3D19cxaY22eWmwicrMjn+Xhcw8QAAAAAIBx1wvgk05aqM32215xE3jSu5lRichP//xPYuuP+5vPk7hzEPVW1l632zlHfvo39aYUE9/rsFaXdsvJfj21BnyhixnaAAAAAABwhesF8NnmcHdnJDJNr7+m1cojkfOXL35euBYfBKGSx4+v3rd+hTlHrp/pte9OHqQpoi/fBD+9TZYDcI4/JIoiDrqUi+NeAAAAAMr1Gmfgx6VxaWHReVIIm6V2m87Y/OaD8PylfPlPIsPp3758JnI7e9LHRj5NWt9dRDa3zVBrdRR71mCQ2fb8rPicMbHN4Gnz/CEksVsy+/v7ZQ8BAAAAwB1zvTJyqxVT0kJoSXYliTv16kgV9CwrvIgKmmnDJC4eQB9y2kv6d/3GOyIi55/Um50kvfqzr0REziVdPL/WmOcb+ZWMzW1TRFRQ72RdJXG+PT9POz9o0/9DkqTTrAfq9f8QAAAAAMD98tbFxcUCnZxqAAAgAElEQVR17oubE4Je0/W3D4PBErSMpIwTERH7g/dPP/qkcGn14YOzFy+zHr73Pfn008tX6NPO8wE8ed/+8hOlRcS03cZWrxYoMb/3wfa//uRQ6UJHD+0P04LtE0cuIg/flhev8pEnne/XwmI1e9tvV6L0L0kX4ad1I+Y7op8PPoG4WZ+Q8N60/dakevNTsQK/ZFiBnxcVUxYEZ0nKxURYBMyCcjELFgGzoFzMgkVQYhm5663Ai1he23dtM//RNG2/3W05VrrkPlhON5xWe9DOtN9/Mhy9i0g/er+Wzz/ph8eVvPp78ulH4VD0LiIv1IfNeMLIBw1eiYj8l/8nG/nXrniutWWLiDy2hz+ED115LsXa76uVef4aAAAAAAAmu+4K/PVky9am6zec/Kh5Ejdr6cXsdHnebLCMP/2KDN03xyPynQH9dnmbwjPGHprelPcyPqbRQ/CD0dh+I0t6n8SdvSDUMnJjwcbGxvjF9fX1eT5oLLooisoeAgAAAIA75ror8NeSJV93B6G1iBjWjmuKiO6dTb1xOtNtOMVUcDM+IsukZ/utvJ1heb4tIuoonvHR6Rr8UPu82638/ECk0qcMStYZltNqu6aIijqJAAAAAAAwmxvNQn8Vy+t2vfyHJEnOzo57R71DNX5GfFbm9uZwJvfZHjESaE+6dwbWli1KFZLMj8bvx4d6YrK6LD394XHiOOOJ6E9OTsafxRn4JcOxpXlx3GtBcO6xXEyERcAsKBezYBEwC8rFLFgEJZ6Bf6MBvIhIEjf3JmR1e7OPyKrBv05BeZGxCH7y1wI6rFXDibdfXkUeAAAAAICCNxvAD+WkN01zbW27srW52dubUlvuSuOl1Od4xOsXlE/r3GfF48fi9/RrAgAAAAAAbsCbDOCTvE663/YKJ9Ql6YmIyJd/U60GYvtzbWOf8gh5/EH3B98Ze4QKqkrE9n0RSXPlW68Vwxf3wksav4sK6pViWr2RJHsAAAAAAFzHmwzgs43r7s5I1HyDK9X9rr7x9eHr/6BFRB7Y/v92GoTBj959JHJ+A1vYBxH8phxqEXkg0i+Kl67P38xG+SiKOOhSLo57AQAAACjXGz8DPy5NHC/y5c9v7RF/9RdPRUTk1yqrzo57WAu/OH9b5FUh/5zIeA24WfQj+OM1LSJ2vXBv9jsVdXas4TX4tLrcPGvzJLFbMvv7+2UPAQAAAMAd8ybLyK1WTBHR4UGc109L4k49K+c+7rQ3WmZt/MrkR4jIlz8rPuKT8/SHr4mI4bS63W77fzdFRAX1TjaYJM43+A/noLvqocbmtimiw1CN32s4u7aI6LDWf4xIEjfrgRIRe5ed9QAAAACAWV1nBT6JOwfRodKDVHH29u5OofB6usBs+11vtdPcC5UWEdN2G96uHQZKVFArxuym628fBqE+/yrv/2f/+rbIqzR9+0P7wx9572W70bOE7qtu+88rhfEUVs6d3cdh8Ezk2UfV6kf9Fo/ee/zq2bMXWZfpgH/XNT8MtQ6DWjFH/OPHp1G1GoiImE/eeyRynt5juu2Wkzb58m/ztPKmae82vHSdXUTE/o1evRrowsq65fn2fwvU2ehjZNX2Z1/mBwAAAABg7hX4uFmvBeEgehcRrVUY1JrxaNNep14L83JuWoW1em/ng8cjjVbdhmNV1kRE0gD+qFn76LNX/d+/UB82YzGchmubs4zP+u6T4o+m+fjxqpx/9uzFoMt0wB/2dtv+UKcP3lmVZ8/6f5l++tm5PHpU6Oz8WSIiL18U+glq9eN3t9NO7K3fGhtOfHR6NmmYEy8CAAAAADDNnCvwcTNQWsR0/UZ/xT2Jm7VAydhRbxWGZpZwPok7e0GodVj7SEzbbxSunYUHseN53a6XrtsrpfrdZwXdVdDc6nqO13KyNl8TEcvrTklX/8si0j/Int4wZcDHOy2v5Xh5q5fPz/oZ8vNa8uff9Lsfp0vlSWfvs1dS6CvtR4d/v9vtdrMm0bU/rr6NjY3xi+vr67O8H9wVKysrZQ/hTuJzWwS8hdLxCkrHKygdr6B0vILS8QpKV9YrmG8FPs03Z7qNwn55Mawd1xQR3RtZVjbdRlYuzrA2s1Xq4rX0hPjYIXPbbzl5E6/hmiKios5Vp99vZsD9+naG5Y2MLi/z3h+dGJbn2yKijsY2H1zn6QAAAAAATDffCvzQsneSJGdnx72j3qFSk+rAmdubhbC1siaih69NZLo7xbPhhrNrh4G6bi22uQYsa5XiI4arwOXx+3Cauun7AOZ/eu7k5GT8IlnolwwV6eaVfsfJ51Y66imWi4mwCJgF5WIWLAJmQbmYBYvgZmfBXIv58yexyzeX35LhIFryOPq0l4h1vaTtMw/YrKxO/2VWxP6yJq/3dAAAAAAALjFnAJ8lfE+Zprm2tl3Z2tzs7Q2u9o2F4mWYZ8AzmPNvuuGnAwAAAADur7kC+CQvle73z4qn13s3PKpr+enHWf03kbwO/PiAk7i5F4VZ+Kw+ros0vMtX9r86qlcDbdpPRNIT8f3mw+X0TNPe/vavFu8cf3rcrAZB75fcG/hrAQAAAAD3y1wBfLaN3N0ZCXnT6zdjdK98dvb8sqXv896XIiIvnxeuPfuo3vx6a2towFn+94LnWgW1U9u/7Hz55+n+91/99eKJeJG4WR/eGa+10h/J24Urox9X3Eyf/rNrfFxRFHHQpVwc9wIAAABQrvnPwI9Lk62Px97Xog+PE2dQXm0kd9xwYrm0xV+21EsRkQe2/6eeZaQl4R6IVkFdCjXhk85eoETEtP1vS/CREpFH762ef3amVXQ0VDt+2Mv0n1/5d9vmp1qro9izrH59uCf2L56qM7H97o509oJQvxKRn0/opfDlwZf/7zU+LpLYLZn9/f2yhwAAAADgjpmrjNxqxRQRHR7EeVG3JO7Uq8Or2q9Lh7Vm1n8SN9PD4v3c70ZlTUREBVmTJG7+x09fpL/71lYhHP7W+7aIVv/9UT7g+CDUIvLk/e3TNHoXkV967LddU0Srf540kv/xL8WfHqWV8FRQ7/z4LyOV9vWlOsuGZ1hOy0+/B/gqO1GQf1x/1vyTemHp/9mza34yAAAAAIB7bK4A3sgqt6ugVk3VglCL6fo3V9nctu1+/7V8ydzr127LiqjnTWqBejm5n/UtW0S++MbjrHUWQD/9ZHjAhtPqdrv+N8c7SH7y9y9EREzbzP/6tCi9Dj/6VPf7EhkMz3rvXRGRV/96nt2QflzP1NPinvkH9g1+XAAAAACAe2KuAF7E8tq+m0e0IqZp++1uy7HShfHTXjL1zplteW2//wTT9tstb7gwfKs9GIH5xF4VkQcT+knXv79678NrDThu1rKV/W8MP3u8t6HhDbO8D+2Hg3Pxjx6IiHxr6yY/LgAAAADA/fDWxcVF2WN4HWmhNtNttxxj+m8K/5e4uRf1M8fb7u6OM34QPT1HP3bvVQfWi7dN/73tXxbzi2xsbIxfXF9fv+LhuFOiKCp7CAAAAADumDlX4BdOeihehwfxyC+yM+/Djpv1WpBH7yKiVRjU6p2RhfA0W7zpNq6M2IcknUiJiL07320AAAAAAMzgJrLQl8racU0VahXUxW8Uir1nZ97ThPVpQTcdhlpMe6SZ1uFeZ3OwZn7N8D3Lt2e6O5etrs/i5ORk/CJZ6JcMFenmtbKyInxuC4B6iuViIiwCZkG5mAWLgFlQLmbBIrjZWZC+0xnd9RX4Qm65Ymo9pW3XNUXErKwW2ppuu+X1d8wbltfybSku4Ced+nXC97iZ3mb7V++zBwAAAADgGu78CrykueUqnYMoVFpExDTt3YZnSace5i3S8vFibm+OhtfZAn5akz3p7KWr6POE7+kZeZlw9D3/zQgVVLOScledhgcAAAAAILcMAbyIGJbjWY5XvBT3tIisVQox9dAPQ9KSbseHWkREh7VqONogvTYacsfNeqAmRu83LIoi9smUi91iAAAAAMq1JAH8uPhISX8HvbG5bYY6W2cfbpcejx/eaT+bJG7W8o3zExfSDafVdYbG1KwGSp7Y5lOlxXTbcyy/cwZ+yezv75c9BAAAAAB3zJ0P4CdXecsywvf3zBuVNRGtw4PYGY6aszg/bTcacF/yAMmj9/mX3j9Xc2/TBwAAAADce3c+iV1eR26vGWfV4JK4fyp9ECRbO64pIiqoNzuDhmnuuUln4y/Xz3V3nY3zLwnfAQAAAABzu/Mr8GJ5vn0aKK2CmipeHwmSDafl9+qB0ioMVPGE+zVSx+c15icdlhe5JDndl9nvKRUPAAAAAJjT3Q/gRSyv1d5q7kVKpynfszT0YzGy5bXaW52D6PCqhldIt93PLzk6FRF59P7lZ983NjbGL66vr1/rmVhQcxV7RB+f2yLgLZSOV1A6XkHpeAWl4xWUjldQurJewVsXFxelPPj+SVPYXb3tngD+PoiiqOwhAAAAALhjlmEF/m4YSpd3mZOTk/GLZKFfMlSkm1f6HSefW+mop1guJsIiYBaUi1mwCJgF5WIWLIKbnQVzLebf+SR2d0SWFp/T7wAAAACA6yGAfyOS40MtIvbW7JXfAQAAAAAoYAv9bUuPvqdUUO3nv5uaqh4AAAAAgHEE8Lcs6Z3eRDdRFHHQpVwc9wIAAABQLgL4W3bW0yLyQOTl1fnnL0ESuyWzv79f9hAAAAAA3DGcgb9dae75By9FZK1C/joAAAAAwHURwN+qdAP9w6+JiFlZLXkwAAAAAIA7jC30t+qsp0Xe+bUXz19IEtaqoYiYpr3b8CxW4wEAAAAA83jr4uKi7DEsr6RTr4V60m8uyUG/sbExfnF9ff0mB4ayRVFU9hAAAAAA3DFsob9NaQY7EdP2291ut9vtttuubYqICuqdpNzBAQAAAADuErbQ36KshJztt/qr7YbheK2KVAOlD48TZ1JW+pOTk/GLZKFfMlSkm9fKyorwuS0A6imWi4mwCJgF5WIWLAJmQbmYBYvgZmdB+k5nxAr8LTKcVrfbHd8rb23ZIqIPj1mDBwAAAADMiAC+DKsVU0R076zsgQAAAAAA7gq20L9ZaVq71fdWhcJyAAAAAIA5EMDfniwHfSHhfNLZC7WIvHpxJiJrldlryUVRxEGXcnHcCwAAAEC5COBvj1FZE9GijmLPsmQQvov8ixYRe2tKHblJSGK3ZPb398seAgAAAIA7hjPwt8jacbOScc04GYTvIq9ETHdnjvgdAAAAAHDfEcDfJsNpuKaIaBXUav3wXUTMDxqTKsgBAAAAADAFAfztMpxWt+0/ftC/8PBtETG3f4vwHQAAAAAwD87A376zo2cvRUy33XKMLLHdZTY2NsYvrq+v39LoUIqVlZWyh3An8bktAt5C6XgFpeMVlI5XUDpeQel4BaUr6xWwAn/b4magREyXPfMAAAAAgNfACvztukb4fnJyMn6RLPRLhop080q/4+RzKx31FMvFRFgEzIJyMQsWAbOgXMyCRXCzs2CuxXxW4G9R0qmz+g4AAAAAuBH3awU+iTsHUaiyM+im7e7uONalsXXcrAbK9rtesebblQfZbb/rWVnduGL4HjevOgAPAAAAAMBE9yiAHwu7tQoDdWj7LW9aRfZ0A/w1H3d8qEVEdFirhiO/y66NfjEAAAAAAMA09yaAz9fDbb/hpYvuSdzcC5RWQXNrUhydxM3alPDdcFpdZ9Ij6rVQi+m2PUskudHhR1HEQZdycdwLAAAAQLnuSwAfH2Tb2b3+lnnD8hruaS3U6ij2rKEt8kM77Wc0umO+H+VP6C0tKTdP7ySxWzL7+/tlDwEAAADAHXNfAnjL63a9satGZU1kJE7v77Q3bb+xdTRtFX7EhAPvr9EbAAAAAAAj7ksAP1nSOxURs7I6cr2f3i4+mq2jdIFf7N1J6+pz9wYAAAAAwKj7G8BnR+DHou4pB9wv7aoTKRGx/fGj9NfobWNjY/zi+vr6fL1gsc1V7BF9fG6LgLdQOl5B6XgFpeMVlI5XUDpeQenKegX3MYCPm9V8J7vp+g3ndRPB5+frd2briNzzAAAAAID53cMAPumdmqYporUWHQZ7PbfhzZdRblh8pETE3N58jT6KTk5Oxi+SxG7JkNB+Xul3nHxupaMcQ7mYCIuAWVAuZsEiYBaUi1mwCG52Fsy1mH8PA3jDabXSXe1J3NkLQhXWpHLtRfF8+/zE0+8AAAAAANyQf1P2AMpkWE7Lt0VERZ1rlm1Pjg+1iNhbbIoHAAAAANyme7gCP2y1Yo5VkptREncOPv5rLSKiguqpaW+nyeYnNIsOlc4f8vlRnFjjzQAAAAAAuMQ9CeCzcuw3mD8ubtYDVQz8tVY6UIduu1XcTD/WTORcBbXTkWZXiqKIgy7l4rgXAAAAgHLdkwDeqKyJaFFHsWcNRfBZAfe1ynwr4nEzUFpk9d1HZ1+cm2675Ujc2QtCrcNac3CgPmtm2n7Ds4ws/f0DkZfDzWZAErsls7+/X/YQAAAAANwx9+UMvLXjmiKignozzo+7J3GzHiiZowJcfmNe9/13/+25ZNF//zy9qKN4pFnLK+6Y/9b7w80AAAAAALjaPVmBFzGclt+rBkqroKaKvzBtf77d7CJnvTRx3WovEhGzsppdt7ZsUUpOe4lYxqDZ6JcD68PNAAAAAAC42r0J4EXE8rrtreZelOeTM6elnZuho64nInEzmKUZAAAAAACv7a2Li4uyx7A00jPu5lUJ6q5otrGxMX5xfX39pkaJRRBFUdlDAAAAAHDH3Jcz8G9AfuR99/IN+TM2AwAAAACg6D5tob9VcbMW6qvz4c3Q7OTkZPwiWeiXDBXp5rWysiJ8bguAeorlYiIsAmZBuZgFi4BZUC5mwSK42VmQvtMZsQJ/E7J09lflw5uxGQAAAAAAY1iBf11Jp54tql969H3GZgAAAAAATHTzAXwSdw6iwxkyvSed5t6gne1elRA+blYDZftdb3zzeZoWbtxo6xsfW9ysB0qLPHpXdHgQOxPGJiLJX/1h/ZPzVyIiosO9em+34VFADgAAAAAwjxsO4POAtk9rpQN1OLbsPBpyaxUGqjcxOs9vmBiii4hI0jstYWxJ3KylO+LfX1OfTBtc8uM/qn30rHhFaxXUTm2/NfVvnSCKIg66lIvjXgCA/7+9Owht5M4TPf5reBt4bF/C66wP1YKu0PHAEAgEzxZWMEsdHkN6eCY8FB/6oIGB547mougQH572kMPWxXuolmFGSQYCWwcxOOLR6LFu9pJi8LZNZUwg0BMe6zDVINfB2Tz6MfjtQs/Be6gquSSVpCq37JJU388lsfRX1b/q77+rf/r//78/AADZmmoA75iG7Yqoej0cYfac9rZhua5VMQuRADiIxtVyfaukKb1itlEtxE0wD0PlUU66rgwPt19t3cLo/b0P7jz6ZPRXC+ZHnzwVkVde/6Dx8J4SOavdam9oyefSk8Ruwezs7GRdBQAAAABzZopJ7ML90eqNi/nhilZq1HUREfvAGS4YzkvvFXP3D73+gzptszo2ehdxDmwRUQu3r61uXrtq2CJLb/+1jInexTM/ss9E5PUP2n70HhyuWVZFXGvXGflJAAAAAAD6TXEEPhgHLw6OgmtFXWxbnnU90RQR8Q734wpqG2XVttz9Q68UDkyHid9E1etbxYMRcbw/gV5dWx0znD3dujm7lisicvr1V9FStrHuVzCYC7C3bZ+JiMgfP1lf/2R05QAAAAAAmGiKAbxW63RqCcqNCqaVwh0R1+2eiERC8V4GOedg3PHUtcJJ29y2/EXuqqr354mbat38Ef+JnH92JxcCAAAAACCRa9hGLpjhHo6QBwPmMRPebxdUEbc3Gi6ilBqd0qTD+8dzLcO4eM11baMyImP9y9dt+NsAP+3dwPluv63KU1ctNxslccztVtKE+ysrK8MvLi8vj78SzJdbt25lXYW5xH2bBbRC5miCzNEEmaMJMkcTZI4myFxWTTDFNfDxwkXl969q83N/0FxE1evNTqfT6XSazbKuiohtVNveuI9eed1EROTQrFaMMHoXP6l9ZULNAAAAAADod8Uj8I5ZsVwRtbyReMu0wTn0EwRbyEV3ZVOUUq1RkHXD7ltRf911879ZcC3L7c99b24btuta2+3VmIT7InJ0dDT8IlnoFww70qXlf8fJfcsc+ylmi44wC+gF2aIXzAJ6QbboBbNgur0g1WD+VY7AO2bV3ye9Hh+pxhubTX6YUmp0Op3hufJaMTar/bXWLfhQudmX+74WJLUnCz0AAAAAILmrGoEPE8ir5biN3a+Dv2q9f8zcc9q7rf1wOvsrr/71WjE+Ivfa5vZ+dNH630wuJt8eOJ52sbbdr4Coa6+fmNXtvhXwflL7yGp/AAAAAADGu5IA3jGrhj0qeo9PNi8Szjm/U5hmTBsZMw9r1fPi+VeW8dV+pJJB3f6pUjmNlHNty7CXZLBufua6iFPbqMhQ4rx/sz6OpNfzD/bl60uSerEAAAAAACDPph7Ae45ZCSenx+eAH0o2H35yZAr4cafzR/pjEs4PfB3gmBfRu183z2lvG5brWhWzEH7ar9upiKjl+lZJU0SCYqcicjtSN8c07F6xE3PdsOWmyJltVAvBFwLK6tptyz05lejRgjXwfzxNeamtVouFLtliuRcAAACAbE05gA+j93ET55XVNdVyXWvXKUWDbmfXciM7uiXkj5mLfeDUtGgEHyaYD3Z0D34UidRN0UqNenfdsCOfVl5/9RVxX8jS+41ScDhFKzXe/6f1L07llb96XRk4fN0vdiIiIj95X7e/iCTOU/79zyIiclOP3A1FqzXe/3b9i1O5eSfFpZLEbsHs7OxkXQUAAAAAc2aaSey8dnVi9C7iR/AiYhtV0/FTzHmO/9HU8buItlEeOJh4jlkdSDB/8n+CFegDdfNT3cmzbvjRP37/QkTk9Iu+un1xKiLy4vs/hsUO9125+HogtLxRViOJ87z//BciInJmb5vtaOX8w539/5N0lwoAAAAAyLEpjsD7Q+girlVZt2Lev5jmrpS26t2KYbu2UbH7Slwi4Z1S2irvVyx38GCi17cujvaX/n9G1M3d/0ZKioicdE9EZEnkdPBwSyKnJ+GidX96/mD8Pry+/y9E5OarcubalmFHz/vqTXl+9q8ksQMAAAAAJDW9AN45sCcXCilarVkv7LascF26qpe3apdMV6+UGp1Vx9xu9TK9q/r9rVokNp5Yt9f+i4j0luH/9OP78rv+um3IdqWXN370av2Y9f2v/bePq/K7i9z3qqrf/3mh9bF1NqIuKysrwy8uLy+PvwLMl1SbPaKH+zYLaIXM0QSZowkyRxNkjibIHE2QuayaYHoBvFbrdGopyitaqaaVkn9i/PEVrdbQRr494rMjM+AtDdXNayeqm9eN1skfj7d+J51aI3o0x1x3BQAAAACAFK5qH/g54JgDC+UnS7jxW1hM8/d7t42q1MMZAX4W+nFHOzo6Gj4kSewWDAnt0/K/4+S+ZY7tGLJFR5gF9IJs0QtmAb0gW/SCWTDdXpBqMD+vAbxjVsPN7lJM3E+48Vuv2Ij1+Xq5/Myy3JQ75gEAAAAA8myaWejnhdeurhu2K2q5OWKr+ulRSo1mvayrwY+qqtebndrq1Z4UAAAAALB48jUC7zntXzV++/Tshf/j/m67sFGKJLvrZZH32rvbF2nn9PL9H3VdEblTUFIUCw+qrRYP9p+5riviurax/UxfeW24GAAAAAAAY+QogHfMXxp2dOt117Vdw96Pbg3vZ5H/ff8Ocq5tGfZNiUyNT1hMRMRzzIrRlwPfdf2QP9UM+larxUKXbLHcCwAAAEC2chPAO2YQvS+91/zNLxQR8Zz2tmG5rlUxC70d6lfXliz3VETUcn2rpCm9Ymcir761GgT6CYt57V9WrBMREVUPs9h5zuf/03j0XOSVV19PMQBPErsFs7Ozk3UVAAAAAMyZnKyB9z7/tS0islQOoncRUbRSo66LiNgHTq/kn4Lp9dKVcRIVU/79zyIicvPN+xthDvrD1qPnIiLy4uvfOaM/CgAAAABAn5yMwH956EfNp1Zl3Rp+227t1bR7IuIdfuMXlIHE8XJT5Pk3h56UlMTFvPbRqYiInD01KuvR86nRafsAAAAAAEyWjxF454fTZAVPuq6I6B9EEseLqHq5Wf+JBBu3Jy/WK6WrvVJ+GnqidwAAAABASvkYgddqnU4t7g3HXDdsUctb90RExOs+ExG18JZWuqeVop/w2qqI+6zriaYkLBYp9ZbIdssO0tC3dgvSl/t+wMrKyvCLy8vLKa8ZM+3WrVtZV2Eucd9mAa2QOZogczRB5miCzNEEmaMJMpdVE+RjBH4Er92yRUS/f6UD4odmtWKEe82Jn62+Um17V3hKAAAAAMDCyccIfCzHrFiuiFre0JJ+xO2eiEwO9v1i/gx617LcSBJ68Rxz27Bd19pur8bPpD86Ohp+kSz0C4Yd6dLyv+PkvmWO/RSzRUeYBfSCbNELZgG9IFv0glkw3V6QajA/ryPwjlk1bBFVr6dZj55w6/b+Ymq52aj1ZswrWs3Pfe9au2ShBwAAAAAklccA3mtX1w3bDSLrKzvN7YIqIqKurQ5+Q6BtlFURedZlGj0AAAAAIKF8TaH3nPavGr99ehZs4r6/2y70pZNTCndEXLd74rV3t/fDdeuqXr7/o64rIncKSopivr/8OnbnOkk8IR8AAAAAgFwF8I75S8M+ibzgurZr2PvRPdlvF1QR9/f9e7u7tmXYNyUyNT5RMWV1TbVc1xs5zJ5wQr6IiLRaLRa6ZIvlXgAAAACylZsA3jGD6H3pveZvfqGIiOe0tw3Lda2KWegEU+mV1bUlyz0VEbVc3yppSq/Ymcirb4WT4ZMV8wfqnz8XEb3eiUzWDzavG55bPxpJ7BbMzs5O1lUAAAAAMGdysgbe+/zXtojIUjmI3kVE0Up+OjmxDy7Syf0pmF4v3bEHTFRM2ygviYjIK8lRtX8AABZ/SURBVM/+4AQD8V6QPi9d/A4AAAAAyLucjMB/efhcREROrdgF6XZrr6bdExHv8Bu/oLh2/wT5myLPvzn0pKQkLybK6k//yrK+lxfuI6PyKFIsZfZ7AAAAAEDu5WME3vnhNFlBf+92/YN6WVd7L6p6uVn/iQRZ51IUEznpfi9yW9fffPVm+NIrryy9//FVZr8HAAAAACyifIzAa7VOpxb3RrAavbx1T0REvO4zEVELb2mle1op+gmvrYq4z7qeaErSYuHxTuxo7rwXL06/+Hj9h75F8X1WVlaGX1xeXk56sZgHt27dyroKc4n7NgtohczRBJmjCTJHE2SOJsgcTZC5rJogHyPwI3jtli0i+v2rms3uj9SLqHq92el0Op1Os+kP2ttGtc0u8AAAAACAxPIxAh/LMSuWK6KWNxJPZ0+4c3tYzB+AF71+MWNeUUq1RkHWDdvdP/RKcd8cHB0dDb9IFvoFw450afnfcXLfMsd+itmiI8wCekG26AWzgF6QLXrBLJhuL0g1mJ/XEfggF3zKZHIJd24PiymlRqfTGZ4rrxV1EXH3DxmDBwAAAAAklMcReK9dDcbemxmlgr9dUEXchOP5AAAAAADkLYD3nPavGr99ehZs4r6/2y5slLSLGFop3PEDa6+9u71vu+EK9vL9H3VdEblTUFIUG+aY64at1zsb/geSjecDAAAAAJCrAN4xf2lE08GL69quYe9Hx+H9ofHf9+/t7tqWYd+USMSdrFgw0q+HCecd0wjKn4yP82O0Wi0WumSL5V4AAAAAspWbAN4xg+h96b3mb36hiIjntLcNy3WtilkI16krq2tLlnsqImq5vlXSlF6xM5FX31oNIu5kxfyBerEPnJp22zErYfj+f4Pk98UUe8GTxG7B7OzsZF0FAAAAAHMmJ0nsvM9/bYuILJWD6F1EFK3UqOsiIvaB0yv5p2B6vXTHHjBRMW2j7G8Zd/9+L3oX+f0naZPfAwAAAACQkxH4Lw+fi4jIqVVZt4bftlt7Ne2eiHiH3/gFxbX7J8jfFHn+zaEnJSV5MVFKP3/7tx9//eLsLFLkTET0+lY26fMAAAAAAHMqHyPwzg+nyQr6i9P1D+plXe29qOrlZv0nEuzvnqKYiCy9qcjtN9WlSLq6pfebnZpG+A4AAAAASCMfI/BardOpxb3hmOuGLWp5656IiHjdZyKiFt7SSve0UvQTXlsVcZ91PdGUpMVE/K3gS/1n+/Hy+OB9ZWVl+MXl5eUE14m5cevWrayrMJe4b7OAVsgcTZA5miBzNEHmaILM0QSZy6oJ8jECP4IXZJO7z3R2AAAAAMCMy8cIfCzHrKTNJud2T0QmB/sJi41wdHQ0/CJZ6BcMO9Kl5X/HyX3LHPspZouOMAvoBdmiF8wCekG26AWzYLq9INVgfl5H4B2zatgiql5vpBh+720EP5ViAAAAAAAklscReK9dDcbemymi9z+LiPe/I0ns/1/3NOE4u9c2t/dt1/V/+vbA8TSS2AEAAAAAUsldAO+YVcMeFb0rhTsibswMeOdX/+tERF48j7z23P64Whg4iJ+e/k4h8pqfuS7i1DYqUu/UUm0D32q1mCeTLWaLAQAAAMhWrgJ4zzEr4cT5+Pj5dmEgi7yIiDim8fRMRG7qH7dqb4uI9/n/qDw6FXGtilmIhOJhevrb0Y/aIqKW61sl7cRcN2y5KXJmG0Ox/3isgV8wOzs7WVcBAAAAwJzJ0Rr4MHovN0dE7yKirK6pIq6161y8FuSqF1H/+8bbQbGf/jTc/90+uCjq7FquiLq2qvR/VK83ShdfCPzkfV1E3P1DbyqXBQAAAADIhbwE8F67GkbvY8e9/QhebKNqOn6A7X35z/7i9UhgHhYTEfnDHzwREc/xz9AXvx/uuyKiF/u/L1jeKKtE8AAAAACAVHIyhd4fGxdxrUgWugi9tyZdKW3VuxXDdm2jYveV6Iv8ldJW/euK8VTk+0eV9UfxxfwV8YPx+5il9gAAAAAAjJCPAN45sCcXCilarVkv7LYsO0gbr+rlrdpwwjt5TUTkFZEXI4oNr4gPxS+1D6ysrAxXanl5OfkVYPal2uwRPdy3WUArZI4myBxNkDmaIHM0QeZogsxl1QT5COC1WqdTS1Fe0Uo1rTT2E+Hy9o9SpJPvVcPrpqgMAAAAAAB5CeCnzjGDneQ3Um0G12/EHPqjo6PhsmShXzDsSJeW/x0n9y1z7KeYLTrCLKAXZIteMAvoBdmiF8yC6faCVIP5eUliN02OWQ03o0uxEdywuMn1AAAAAADEYgQ+Ha9dDcbeU23jDgAAAADAyyGAT8Ix1w1br9elZdjD0bvnmNst2w1T3qn6/a2apoxLNu+np79T4BsAAAAAAEBCBPCTOaZhi8i3hnHqT5yPpK3zHLNi9KW4d13bqDzT642aNirZ/Oj09CO1Wi0WumSL5V4AAAAAskUAP14kQD8dnjjvtbcNW0RUve4PuovntLcNy3XtVntDK62uqZbrWrtOKZqq3t+UXl1bTTEATxK7BbOzs5N1FQAAAADMGZLYjeQ5bbMaGV6/qQ8ue/cjcdHrjVo4xK5opUazrIq41q4jyuqaKiK2UTUdLzxo1Y/5U8XvAAAAAIC8YwQ+XpisTlT9/dfsL74SkTO7sm7HFH2zqPV9Ri03G51S8GbhNRFXxLWNSvSz+gdr+5V1S/R6im3kAQAAAAA5xgj8aKperjcbxR++Gl/stXFvav9VFxFZWooetdnZ+Ld9V0T0ItE7AAAAACARRuDjKaXeKHqtU5d1wx4cLffH29Vys6b1paFX78iJJ0o4P14r6mLbpz+ud34TSX3XHhO/r6ysDL+4vLw8jcvCrLh161bWVZhL3LdZQCtkjibIHE2QOZogczRB5miCzGXVBIzAv6xDs1oxepvIibi2ZVSqbS/8WSvqImIfOBcf8Q4ZfwcAAAAApMMI/GX5e7m7luVGktCL55jbhu261nZ7NUh554/B2wdOTfMD9gnx+9HR0fCLZKFfMOxIl5b/HSf3LXPsp5gtOsIsoBdki14wC+gF2aIXzILp9oJUg/mMwL8stdy8SEIvilZr1HUJstD7BsbgGX8HAAAAAKRHAH9ZtwuqSOx2cNpGWRWRZ12vr2jwM/E7AAAAAOASCOBf0p3CqO3c3e5J8H/+dvDu/qFH/A4AAAAAuBzWwF+WsrqmWq77rOuJNhDD+8vj1cLtgbL7h96qXC5+b7VaLHTJFsu9AAAAAGSLAP7SlMIdEde1dp1SrS8cdw5sGZhb34vgD+9cIn73N5aLTW6H68R2HRmiF8wOOkKG6Agzgl6QIXrBjKAXZIheMCPYRm7+BGvdbaNqtp1gvbvnmFVjKH7vzaK3LFuYPw8AAAAASI8R+JeglBr1btWwXdsybCvyhqrXgz3kLsr6Y/AixO8AAAAAgEtgBP7laLVGs17WVTV8QVX1erNRGw7R/TF4IX4HAAAAAFwGI/AJaLVOpzbyXUUr1bTS6Pd75UqNTmma1QIAAAAA5Agj8AAAAAAAzAECeAAAAAAA5gABPAAAAAAAc+DG+fl51nUAAAAAAAATMAIPAAAAAMAcIIAHAAAAAGAOEMADAAAAADAHCOABAAAAAJgDBPAAAAAAAMwBAngAAAAAAOYAATwAAAAAAHPgP2VdAYzhtc3tfdt1RURE1cv3N0qaknGdgGvkmOuGHfO6Xu/UtGuvDXDtHHPdsEf+vvOMQB6M6QU8I7DIPKe927r4G6/qayP+yPMsyB1G4GeWY65XrLA3iohrW0bFdLKsEnC9vO6zrKsAZMgxY4OT8E2eEciBcb2AZwQWl2NWK0bf33jXtoxKte0NFeRZkD+MwM+o4ImllutbJU0R8Zz2tmG5tlEtNBslvlZDLpx0XWEoBfnkOWZlbPjOMwILb0Iv4BmBheWYhu2KqHp9q+YPpgd/5F2rYhYiv/E8C/KJEfiZ5LVbtojo9UY4B0bRSo26LiLu/uHgd2/AYnIObBFRC7ezrghwrTynbVbHxi08I7DoJvcCnhFYWBd/4mu9qfC9P/JiHzjDBXkW5AsB/CzyDvddEdGL/V8paxtllR6J3PAnR6prq3yDjBzx2v60SVH1etP/19pwGZ4RWGhJegHPCCyuYG5JcXBmiVbURUSedYM/8jwLcosp9LNoVMdVCndEXLd7IsLjCgvvpOuKqGuFk7a5bdmuiIiq6ve3aqRmwYLr5SByDmLf5xmBxTepF/CMwOLSap1OLUE5ngW5RQA/g4IvlWMmhd0uqCLus64nPJ6w6Px+4FqGcfGa69pGZXRObmD+KaVGpzS+CM8ILLgEvYBnBHIoWDUSzjrhWZBfTKEHMJP8L5ZFVL3e7HQ6nU6n2SzrqojYxnAWVgBAnvCMQM6EC97vk5sOBPBzyO2eZF0F4KoF2wNFU7goSqlGbhZgEp4RyAGeEcgXx6xYroha3kg8vYRnweIigJ9DZFxFDiilRqfTGZ4H6adw4V9nwEg8I5ADPCOQI45ZNWwRVa+n2RqOZ8HiYg08gLnir+wiNwsAYBjPCCwWr10Nxt7Z2B0hRuBnkFK4I/ETX/wVX3cK9F/kHd8rI794RgCT8IzAQnDMMdE7z4L8IoCfRbcLqkS2eewZnW4SWDBeu7q+vr5uOkPv8FhC7vGMQO7xjMDC8xxz3bBdUfV6J37snWdBbhHAzyJldU0Vca3d/geTs2u5kd0jgAXmf68s9sHAv87CJKxDu54C+cEzArnHMwILzjErhu0PvY/cFZFnQW4RwM8kv0eKbVRNx/9azXPaVaNv90dgoWkb5YFOIN7FVLLkSViBBcQzArnHMwILzPP/oE9c9s6zIK9unJ+fZ10HxPGCr9766fXhjKvAggoTtwzQ683erkHAQnPMdcOO/8PPMwI5MboX8IzAgvJ/6UeLdgeeBbnECPysUrRas17W1d4Lql5u0huRJ0qp0WnWdbXXC1RVrzc7/MsM4BkB8IzAgnIOxkXvA3gW5BIj8AAAAAAAzAFG4AEAAAAAmAME8AAAAAAAzAECeAAAAAAA5gABPAAAAAAAc4AAHgAAAACAOUAADwAAAADAHCCABwAAAABgDhDAAwAAAAAwBwjgAQAAAACYAwTwAAAAAADMAQJ4AAAAAADmAAE8AAAAAABzgAAeAAAAAIA5QAAPAFg0jx/c8L3T+C5RwQePr6lmEwT1mVTtufDd48aDd270PFiEawIAIGsE8ACAhXXw4c+JGzPx+MEb9z787CDyyvLdzCoDAMDCIIAHACwwQvgsfNf4u89ERGTz4fG579N3M64TAACLgAAeALDQCOGv3/G3/tj75ntVxt0BAJgiAngAwIIjhM9I8cdvZF0FAAAWCwE8AGBRbe7tbYoIITwAAFgMBPAAgMX17qfpQ/hxqeC/a7wzlLa+77X+3OvvvPPgce8o3z1uPHgn8t6DxuNxNfrucaT0O+NLDxx6ZPGgqg8e99fznWRp7xOcJTjBPX8F/MGHbyTbDSDdVcSXT3WLRhS+ZNOPuJ/J7nbSq47WLc2Vv8RdmlB8sPDEBgMATMM5AACLJQjaZXMv+pMUexnV4gv2vzhc+vz8/PhhcegDvdce9s7Ub3Pv/Hxvsxj31sBJwlNvPow/UlydwtPHFe+7rouym5sDhx8sN/rC404SqdSIYrH38jLHn1h+6JJTFb5k04+4n5Pvdpq269Xt4YhfpZgrn8pdii1/POJ3feTRAQBTQgAPAFg0g3H5yBB+qgF8GLyEHzu+CNqLRfEj0ePwrd5n+k7TFxXFH6o/OOqdu7j5sFf6/PiifH8wFa1qeOLjvcgn4118rLi517uGkZUafwuncPxI+V7dI5fcf9Y0hV+m6Yfv54S7nbLtor8bCX6VLnuXElXmonT0dydSFUJ4ALgyBPAAgEUzHJf3op/+yGLKAfzQRyJB11BIE3fui/LDZw9PE3mnd+ZxA6rF2AHyNBFWWKuYOzKiBukC+LTHH1V+aoUv0fTjh7THDY4nbbuL343h4fCY4qkuPGVl4m7FwFmJ4AHgqrAGHgCw+N79KIg5PrsXXcM8XZt/O7hp2rvv9aKojwb3QX/jx36Nnv5LzKrh4UPJ3erfboqIHHz498EVfPePuwciIpt7cZus363+w8NiX/no8d9LvC17uKd78eE/DO8JF55EPvu7y2YJTH38x4+CPeYH71F4h6K3NFXhyxt/P2PevXzbxfwq3V1+U0REDr497r2W5sIvW5mYW/fup/4/L+MOBACYBgJ4AEAO9CLBqwvhx22a9ubycGwaRF1x4uPB8OuAIG7qhV2jgsfwDMNxVpr93cI93WMuIXqSaPCYStrjf/cvT0Uk/rKD8PFJL2ZNVfjyxt/PmHdfou3i7lP4ZVDkBCkuPHVlem3y4Rt+3joS1wHA9SGABwDkwpWH8CMi0EuYEF8HsWwY+X5278YIYTL4odg6RVXDSHBkncbOJLiC4weXnew7iFSFL2/8/Yx59/Jtl/BS0lx4+sq8+2m4Nv7g4LMP773xxkUOemJ5ALhiBPAAgJy4hlH46RgVDw4PtF6fkTHquJkEM3R8TNW7nz453nu4WYz8QvZieTaTA4CrRAAPAMiNOQnhRw1mh0Ol/SbnC5vGguSRA+zhEPqMH382XU/bXU1l7r5b/fTJk/Pz8+PjvYebkVj+4LMP770xw90LAOYbATwAIEdeMoSPD6GvWTAv+iXnric0cY37hDXsV3X8+PLfNd65cePGjRsDTZuq8AhTbfrrabuEF/6ylbl7993qp0Es39tJ7vJZDQEAYxHAAwByJRrCB2t7Y8XFPtc0Gnyw+49xwU+QWLy48bO7IpHQN770RZz2zkuFUmF499mj2Dg3zHZ+6WXmaY8fpvKLKz+Uji1V4Z6rb/orb7s0F562MmO++Lj7bvVJsJPcpbMaAgDGIoAHAOTMRQgfa/SA5OO///B6BuDjtg97/OBeX/x+sTfewYc/jwvzwtrGbEmXRm/bsbgx1XAPuJc4Serj92LTofK9K74IyVMVvsamv/K2S3PhKStz92cbY7YOnJiVEADwciatdwIAYM4EY4DjFvUe94Xw/QUv3is+3DsOXtt7uFmM/0BYPO5s42oSvFd8eDxUfPTJo6X7q7rZKx6dydz/gXFVHSN6lr3jsFLHe5uxJ4m/tKs4fv89GnGLUhWeYtNPvNsp227cPY0/0yUvPEVl+hqsv3jKXzEAQFIE8ACARZMggO8P4QcLHseP0Bcf7sVEStMP4IsPH24On12kGHeYEXWNjfYuGcCPPUtxczikTBnApz7+6PKpblFc4ak1fZK7nabt0gfwU7pL8WcdVzpNwwMAUmIKPQAgl8ZNpL9bHdgkq1jc3Ds+f1K9rlnBP/v0/Hhv8PzHT+JSkt+tPvEHiaNbehX9cdQnLzV5Pv4sfSfZOz5+8uk0TpL2+MPl/UuecIsmF77Wpr/qtrvkXUpSmRGl/RH56f3eAQAG3Tg/P8+6DgAAAAAAYAJG4AEAAAAAmAME8AAAAAAAzAECeAAAAAAA5gABPAAAAAAAc4AAHgAAAACAOUAADwAAAADAHCCABwAAAABgDhDAAwAAAAAwBwjgAQAAAACYAwTwAAAAAADMAQJ4AAAAAADmAAE8AAAAAABzgAAeAAAAAIA5QAAPAAAAAMAcIIAHAAAAAGAOEMADAAAAADAHCOABAAAAAJgDBPAAAAAAAMwBAngAAAAAAObAfwAT/iQtE4AuzgAAAABJRU5ErkJggg==)
Flex é uma palavra interessante para a decisão de comprar um carro. Podemos ter como um dos objetivos comparar os anúncios de carros Flex. Para fazer uma análise do preço do carro Flex, precisamos incluir uma variável binária (zero = carro não-Flex e um = carro Flex). Desse modo, temos:
# diversas maneiras de escrever a palavra
Flex<-c("flex","Flex","FLEX")
banco_carro$Flex <- as.integer(grepl(paste(Flex,collapse="|"), banco_carro$names_i))
Vamos olhar como ficou resultado com o pacote DT. O DT pode ser utilizado para apresentar o data.frame que conseguimos. Apresentar o banco de dados é cada vez mais importante na pesquisa reprodutível. Como consequencia da Reprodutibilidade científica, os critérios do SciELO buscam visam ampliar a transparência na pesquisa. Acredito que, em breve, todas as revistas devem pedir o banco de dados (e o código) junto com o artigo para publicação. Para quem quiser saber mais, recomendo o livro Reproducible Research with R and R Studio.
library(DT)
datatable(banco_carro, options = list(pageLength = 5))
A partir deste momento será possível muitas análises. Por exemplo, utilizar o pacote lexiconPT do Sillas Gonzaga para fazer uma análise no texto. Podemos também fazer uma comparação do Chevrolet Corsa com o Fiat Palio. Esse é um bom momento para construir alguns objetivos de pesquisa. Depois disso, podemos passar para uma análise estatística tradicional. Vou ficar por aqui.
Referências:
- Allaire,JJ; Xie, Yihui; McPherson,Jonathan; Luraschi, Javier;Ushey, Kevin;Atkins, Aron;Wickham, Hadley;Cheng, Joe;Chang, Winston rmarkdown: Dynamic Documents for R, 2018 Disponível aqui
- CURSO R: Minicurso de Webscraping. Disponível aqui
- Esquerre,Karla e Filho,Adelmo Minicurso de Webscraping com pacote Rvest no III SER. Disponível aqui
- Gandrud, C. Reproducible Research with R and RStudio. CRC Press, 2014.
- Gonzaga, S. lexiconPT: Lexicons for Portuguese Text Analysis R package version 0.1.0 https://CRAN.R-project.org/package=lexiconPT
- Knoepfle, D. How to buy a used car with R Disponível aqui
- Seminário de Estatística com o R – SER. Disponível aqui. Acesso em 07 de novembro de 2018.
- Wickham, H. rvest: Easily Harvest (Scrape) Web Pages. R package version 0.3.2. https://CRAN.R-project.org/package=rvest