diff --git a/labs/2022/01-architectures/solucoes/GuilhermeAbacherli/README.md b/labs/2022/01-architectures/solucoes/GuilhermeAbacherli/README.md
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+# Lab01 - Estilos Arquiteturais
+
+# Aluno
+* `Guilherme Pedrozo Abacherli`
+
+## Tarefa 1 - Web Components e Tópicos
+
+> Código da composição de componentes Web da tarefa 1:
+~~~html
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+~~~
+
+> Imagem da composição da tarefa 1 em funcionamento:
+
+
+## Tarefa 2 - Web Components e RSS
+
+> Código da composição de componentes Web da tarefa 2:
+~~~html
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+~~~
+
+> Imagem da composição da tarefa 2 em funcionamento:
+
+
+## Tarefa 3 - Painéis de Mensagens com Timer
+
+> Código da composição de componentes Web da tarefa 3:
+~~~html
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+~~~
+
+> Imagem da composição da tarefa 3 em funcionamento:
+
+
+## Tarefa 4 - Web Components Dataflow
+
+> Imagem (`PNG`) do diagrama de componentes:
+
+
+> Parágrafo de breve discussão sobre a tarefa 4:\
+> Todos os componentes alteram o mesmo tipo de dados, eles têm o mesmo
+> contrato de parâmetros, enviam e recebem informações da mesma natureza,
+> ou seja, "falam a mesma língua", dessa forma são intercambiáveis:
+> pode-se adicionar, remover ou alterar os componentes que manipulam e exibem
+> esses dados, assim é possível obter diferentes resultados que venham a ser desejados.
+>
diff --git a/labs/2022/01-architectures/solucoes/GuilhermeAbacherli/images/tarefa1.png b/labs/2022/01-architectures/solucoes/GuilhermeAbacherli/images/tarefa1.png
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diff --git a/labs/2022/01-architectures/solucoes/Tulio-B/README.md b/labs/2022/01-architectures/solucoes/Tulio-B/README.md
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+# Aluno
+* Tulio Bassaco Bustos
+
+## Tarefa 1 - Web Components e Tópicos
+
+~~~html
+
+
+
+
+
+
+
+
+
+
+~~~
+
+
+
+## Tarefa 2 - Web Components e RSS
+
+~~~html
+
+
+
+
+
+
+
+
+
+
+
+
+
+~~~
+
+## Tarefa 3 - Painéis de Mensagens com Timer
+
+~~~html
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+~~~
+
+## Tarefa 4 - Web Components Dataflow
+> Imagem (`PNG`) do diagrama de componentes (veja exemplo abaixo).
+
+>
+> Escreva aqui o parágrafo de breve discussão.
diff --git a/labs/2022/01-architectures/solucoes/Tulio-B/imagens/Atividade1.png b/labs/2022/01-architectures/solucoes/Tulio-B/imagens/Atividade1.png
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diff --git a/labs/2022/01-architectures/solucoes/dan7sc/README.md b/labs/2022/01-architectures/solucoes/dan7sc/README.md
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+# Modelo para Apresentação do Lab01 - Estilos Arquiteturais
+
+Estrutura de pastas:
+
+~~~
+├── README.md <- arquivo apresentando a tarefa
+│
+└── images <- arquivos de imagens usadas no documento
+~~~
+
+# Aluno
+* `Daniel Salgado Costa`
+
+## Tarefa 1 - Web Components e Tópicos
+
+> Código da composição de componentes Web:
+
+~~~html
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+~~~
+
+> Imagem da composição em funcionamento:
+
+
+
+## Tarefa 2 - Web Components e RSS
+> Código da composição de componentes Web:
+
+~~~html
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+~~~
+
+> Imagem da composição em funcionamento:
+
+
+
+## Tarefa 3 - Painéis de Mensagens com Timer
+> Código da composição de componentes Web:
+
+~~~html
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+~~~
+
+> Imagem da composição em funcionamento:
+
+
+
+## Tarefa 4 - Web Components Dataflow
+> Imagem (`PNG`) do diagrama de componentes.
+
+
+
+>
+> O CSVReader é o componente responsável por criar a tabela a partir dos dados contidos no arquivo CSV. Os dados em forma de tabela serão a mensagem do tipo Data que será enviada ao componente Selection. O componente Selection será reponsável por filtrar as linhas da tabela a partir da seleção do valor de uma determinada coluna da tabela. A mensagem enviada será uma mensagem também do tipo Data mas com os dados filtrados por uma determinada coluna. O componente Projection é reponsável por projetar uma tabela com os valores de determinada coluna no eixo X e de outra coluna no eixo Y. A mensagem enviada contém uma tabela do tipo Data com uma coluna contendo os valores da coordenada X e outra coluna contendo os valores da coordenada Y. Por fim, o componente ScatterPlot recebe a mensagem do Projection para desenhar o gráfico de dispersão usando as coordenadas X e Y das colunas da tabela vinda como mensagem do componente Projection.
diff --git a/labs/2022/01-architectures/solucoes/dan7sc/images/lab1_diagramas_de_referencia_01.png b/labs/2022/01-architectures/solucoes/dan7sc/images/lab1_diagramas_de_referencia_01.png
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diff --git a/labs/2022/01-architectures/solucoes/igorsantosp/lab01/README.md b/labs/2022/01-architectures/solucoes/igorsantosp/lab01/README.md
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+# Aluno
+* João Igor dos Santos Pereira
+
+## Tarefa 1 - Web Components e Tópicos
+
+> Escreva aqui o código da sua composição de componentes Web, como mostra o exemplo a seguir:
+
+~~~html
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+~~~
+
+
+
+
+## Tarefa 2 - Web Components e RSS
+~~~html
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+~~~
+
+
+
+## Tarefa 3 - Painéis de Mensagens com Timer
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+## Tarefa 4 - Web Components Dataflow
+
+
+> Entendo que o primeiro componente represente o dataser ou entrada de dados, a mensagem que ele enviará corresponde a própria informação que ele representa, nesse caso todos os dados de zumbi, escolhi representar a mensagem como uma unica linha da tabela e pensar o dataser como algo dinamico assim como os componentes e mensagens, essa mensagem entre o primeiro e segundo componente teria todos os dados de um zumbi, considerando que a mensagem estou tomando como apenas uma linha, chegaria no segundo componente e seria filtrada, passando ou não para a próxima etapa de acordo com o atributo selecionado neste componente, na mesma lógica seguiriamos para a projeção e plot, sendo sempre uma linha de tabela, passando ou não pelo bloco de acordo com suas configuração e mudando sua quantidade de atributos também.
diff --git a/labs/2022/01-architectures/solucoes/igorsantosp/lab01/images/Atividade1.png b/labs/2022/01-architectures/solucoes/igorsantosp/lab01/images/Atividade1.png
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diff --git a/labs/2022/01-architectures/solucoes/igorsantosp/lab01/images/web-composition.png b/labs/2022/01-architectures/solucoes/igorsantosp/lab01/images/web-composition.png
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diff --git a/labs/2022/02-design/solucoes/GuilhermeAbacherli/README.md b/labs/2022/02-design/solucoes/GuilhermeAbacherli/README.md
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+# Lab02 - Aprendizagem de Máquina no Brechó Online
+
+Estrutura de pastas:
+
+~~~
+├── README.md <- arquivo apresentando a tarefa
+│
+└── images <- arquivos de imagens usadas no documento
+~~~
+
+# Aluno
+* `Guilherme Pedrozo Abacherli`
+
+## Tarefa 1 - Dados para Treinamento e Recomendação
+
+> Coloque a lista de campos como itens e subitens, conforme exemplo a seguir:
+
+### Treinamento
+- Cliente
+ - dataNascimento: (faixa etária, infantil, jovens, adultos, idosos, etc
+ - genero: produtos preferidos por homens/mulheres
+ - localizacao: historico de compra de habitantes da regiao, se for fria recomenda casacos, se for quente recomenda roupas de praia, etc
+
+- Produto
+ - avaliacao: se for muito mal avaliado não recomenda
+ - preco: se o cliente está disposto a pagar mais ou menos
+ - estado: se o cliente aceita produtos usados ou prefere novos
+ - garantia: se o produto tem garantia ou não
+
+- Vendedor
+ - localizacao: se o vendedor está próximo do cliente
+ - avaliacao: se o vendedor tem um bom histórico de atendimento
+ - velocidadeEntrega: se prepara e entrega de rapidamente
+
+### Recomendação
+- Cliente
+ - dataNascimento: (faixa etária, infantil, jovens, adultos, idosos, etc
+ - genero: produtos preferidos por homens/mulheres
+ - localizacao: historico de compra de habitantes da regiao, se for fria recomenda casacos, se for quente recomenda roupas de praia, etc
+
+- Produto
+ - avaliacao: se for muito mal avaliado não recomenda
+ - preco: se o cliente está disposto a pagar mais ou menos
+ - estado: se o cliente aceita produtos usados ou prefere novos
+ - garantia: se o produto tem garantia ou não
+
+- Vendedor
+ - localizacao: se o vendedor está próximo do cliente
+ - avaliacao: se o vendedor tem um bom histórico de atendimento
+ - velocidadeEntrega: se prepara e entrega de rapidamente
+
+## Tarefa 2 - Breve descrição de Composições Dinâmica e Estática
+
+### Composição Dinâmica
+> Os componentes de entrada, alimentação da aprendizagem, podem ser dinâmicos,
+> pois a aprendizagem pode evoluir durante o tempo de vida do sistema, e estes sendo
+> dinâmicos permitem que a aprendizagem permaneça atualizada, conforme novos campos surgem por exemplo.
+> A coleta de dados pode ser dinâmica, pois os dados são fornecidos de forma assíncrona.
+### Composição Estática
+> Os componentes de saída seriam estáticos conforme as visualizações que o administrador
+> do sistema deseja manter para as análises posteriores. A conexão das pipes pode ser mais estática, pois os dados são tratados de forma síncrona.
+
+## Tarefa 3 - Composição para Treinamento e Recomendação
+
+> Imagem PNG do diagrama:
+
diff --git a/labs/2022/02-design/solucoes/GuilhermeAbacherli/images/lab2_tarefa3.png b/labs/2022/02-design/solucoes/GuilhermeAbacherli/images/lab2_tarefa3.png
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diff --git a/labs/2022/02-design/solucoes/dan7sc/README.md b/labs/2022/02-design/solucoes/dan7sc/README.md
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+++ b/labs/2022/02-design/solucoes/dan7sc/README.md
@@ -0,0 +1,68 @@
+# Modelo para Apresentação do Lab01 - Estilos Arquiteturais
+
+Estrutura de pastas:
+
+~~~
+├── README.md <- arquivo apresentando a tarefa
+│
+└── images <- arquivos de imagens usadas no documento
+~~~
+
+# Aluno
+* `Daniel Salgado Costa`
+
+## Tarefa 1 - Dados para Treinamento e Recomendação
+
+### Treinamento
+* Cliente
+ * gênero
+ * idade
+ * localização
+ * produtos favoritos
+ * vendedores favoritos
+* Vendedor
+ * nicho
+ * localização
+ * número de vendas
+ * quantidade de favoritos
+ * quantidade de vendas
+* Produto
+ * preço
+ * tamanho
+ * cor
+ * categoria
+ * quantidade de favoritos
+ * quantidade de vendas
+
+### Recomendação
+* Cliente
+ * gênero
+ * idade
+ * localização
+ * produtos favoritos
+ * vendedores favoritos
+* Vendedor
+ * nicho
+ * localização
+ * número de vendas
+ * quantidade de favoritos
+ * quantidade de vendas
+* Produto
+ * preço
+ * tamanho
+ * cor
+ * categoria
+ * quantidade de favoritos
+ * quantidade de vendas
+
+
+## Tarefa 2 - Breve descrição de Composições Dinâmica e Estática
+
+### Composição Dinâmica
+> Os sistemas de treinamento e recomendação são dinâmicos pois recebem como dados as entidades que apesar de serem únicas, sejam elas Cliente, Vendedor ou Produto, para cada tipo de entidade podemos ter instâncias dessas entidades que se diferenciam uma da outra em relação as suas propriedades. Os sistemas de treinamento e recomendação portanto precisam lidar com essas diferenças.
+### Composição Estática
+> As entidades Cliente, Vendedor e Produto tem propriedades estáticas, ou seja, que não podem ser modificadas, com isso cada entidade é única e não é confundida com outra entidade.
+
+## Tarefa 3 - Composição para Treinamento e Recomendação
+
+
diff --git a/labs/2022/02-design/solucoes/dan7sc/images/recomendation-composition.png b/labs/2022/02-design/solucoes/dan7sc/images/recomendation-composition.png
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diff --git a/labs/2022/02-design/solucoes/igorsantosp/README.md b/labs/2022/02-design/solucoes/igorsantosp/README.md
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@@ -0,0 +1,55 @@
+# Aluno
+* João Igor dos Santos Pereira
+
+## Tarefa 1 - Dados para Treinamento e Recomendação
+
+### Treinamento
+
+* Comprador
+ * Id
+ * Nome
+ * Historico de Compras
+ * Gênero
+ * Histórico de Produtos Abertos
+ * Histórico de Pesquisa
+ * Cep
+
+* Vendedor
+ * id
+ * Produtos vendidos
+ * Avaliação dos usuários
+ * Tempo médio de despacho de item
+ * Cep
+
+* Produto
+ * id
+ * Nome
+ * Categoria
+ * Vendedor
+ * Avaliação
+ * Taxa de Devolução
+ * Taxa de entrega dentro do prazo
+ * Quantidade de vendas
+ * Estado
+
+
+### Recomendação
+* Pagina de Produtos recomendados
+ * Produtos
+ * Quantidade Total de páginas
+ * Pagina Atual
+ * Produtos por página
+ * Vendedores
+ * Cliente
+
+
+## Tarefa 2 - Breve descrição de Composições Dinâmica e Estática
+
+### Composição Dinâmica
+> Como composição dinamica penso em todas as entradas do treinamento, com a utilização de um módulo que trate e agrege essas entradas é possível faze-las tão dinamicas quanto possível
+### Composição Estática
+> Como composição estática, penso nos módulos de agregação/tratamento, de treinamento e o de recomendação.
+
+## Tarefa 3 - Composição para Treinamento e Recomendação
+
+
diff --git a/labs/2022/02-design/solucoes/igorsantosp/images/recomendation-composition.png b/labs/2022/02-design/solucoes/igorsantosp/images/recomendation-composition.png
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diff --git a/project/2022/solucoes/equipe06/README.md b/project/2022/solucoes/equipe06/README.md
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+# Projeto
+
+# Equipe
+
+Grupo 06
+* Daniel Salgado Costa
+* Flavia Machado Vilar
+* Gerson Macedo
+* Mayara Ferreira Fernandes
+* Tulio Bassaco Bustos
+
+# Nível 1
+
+## Diagrama Geral do Nível 1
+
+
+
+* O componente _Customer_ inicia a comunicação ao pedir informações sobre um determinado produto, emitindo no barramento uma mensagem de tópico **/product/{id}/get-info**.
+* Algum componente _Store_ observa essa requisição, e se ela possui o produto solicitado, emite as informações deste no barramento utilizando o tópico **/product/{id}/send-info**, sendo esta recebida pelo _Customer_.
+* Juntamente a esta informação, o componente _Store_ pode também solicitar o cálculo de frete para o endereço do _Customer_, emitindo no barramento com o tópico **/product/{id}/get-shipping**.
+* Algum componente _Partner_ observa essa requisição, emitindo de volta as informações de frete e prazo de entrega no barramento, pelo tópico **/product/{id}/send-shipping**.
+* Ao concluir a compra, o _Customer_ emite no barramento o pedido de compra através do tópico **/purchase/{storeId}**, que é observado pelo _Store_.
+* Com a compra concluída, o componente _Store_ emite no barramento através do tópico **/ship-products/{partnerId}**, concretizando o envio do produto ao _Customer_.
+* Conforme necessário, o componente _Store_ pode emitir no barramento as informações de rastreio, utilizando o tópico **/shipped**.
+
+## Componentes
+
+### Customer
+
+Indica um consumidor do brechó. Possui produtos associados a ele. Se comunica com o barramento a partir dos eventos de solicitação de informações de um produto, solicitação do custo e prazo de frete de um produto, concretização de uma compra e envio de produtos ao parceiro logístico.
+
+### Store
+
+Indica um vendedor do brechó. Possui um carrinho de compras para guardar os produtos que possui interesse. Se comunica com o barramento a partir de eventos de solicitação de informações de um produto e concretização de uma compra
+
+### Partner
+
+Indica um parceiro logístico do brechó, responsável por calcular o frete e prazo de entrega de um produto, receber os eventos de solicitação de envio de produtos a um comprador e alimentação de informações de rastreamento.
+
+### Cart
+
+Indica um carrinho de compras associado a um consumidor.
+
+### Product
+
+Indica um produto associado a um vendedor.
+
+## Interfaces
+
+
+
+## Mensagens
+
+
+
+# Nível 2
+
+## Diagrama Geral do Nível 2
+
+
+
+* O Componente _ViewProduto_ exibe ao comprador a interface de detalhamento do produto e o formulário de compra.
+* As informações do Produto são obtidas a partir do _ControllerProduto_, que por sua vez encaminha a requisição ao _RepositoryProduto_, componente responsável por obter os dados do DB.
+* O componente _ControllerProduto_ também é responsável por obter informações dos parceiros logísticos existentes, através do barramento, pelo tópico **/product/{id}/get-shipping**.
+* Ao preencher as informações requeridas (frete e forma de pagamento), o comprador pode solicitar a compra do produto, que também é gerenciado pelo _ControllerProduto_.
+* Ao concluir a compra, o _ControllerProduto_ emite no barramento uma mensagem no tópico **/ship-products/{partnerId}**.
+* Assume-se que o cadastro de endereço e forma de pagamento já foram realizadas e validadas pelo comprador anteriormente, em seu cadastro.
+
+# Nível 3
+
+
+
+
+
+* Ao acessar a view de um produto, suas informações são carregadas diretamente pelo componente _Obter Produto_, que emite no barramento tal solicitação e obtém a resposta através das interfaces _SendProductInfoRequest_ e _GetProductInfoResponse_.
+* Ao preencher as opções de quantidade, forma de pagamento, e parcelas, o componente _Validador_ é responsável, através de troca de mensagens, verificar se as informações são válidas/aceitas através das interfaces _SendProductParametersRequest_ e _GetProductParametersResponse_.
+* Ao clicar no botão adicionar ao carrinho, o produto é adicionado utilizando uma interface requerida, sem troca de mensagens pelo barramento.
+* Finalmente, ao clicar em "Comprar Agora", o componente _Efetuar Compra_ é responsável por fazer as verificações finais e emitir a ação no barramento.
\ No newline at end of file
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@@ -0,0 +1 @@
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