Avaliação analítica do uso de agentes móveis na gerência de redes

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Ciência da Computação.A dificuldade em realizar o gerenciamento eficiente de redes de computadores a partit de um modelo centralizado, motivou o desenvolvimetno deste trabalho. Aqui, é apresentado um esquema de gerência descentralizada baseado nos conceitos da mobilidade de código. A gerência de redes de computadores é uma atividade que sofre impactos diretos com o crescimetno do número de recursos e, consequentemente, de informações a serem processadas. A gerência centralizada, solução adotada atualmente, tem se mostrado inflexível e ineficiente, chegando algumas vezes a se inadequada para o sucesso da própria atividade.Este trabalho apresenta os conceitos fundamentais para a compreensão da mobilidade de código, uma vez que existem ambigüidades e contradições nessa área. É apresentado ainda, uma análise de desempenho de agentes móveis no domínio da gerência de redes através de deduções analíticas

Acceso a planes de salud en la región metropolitana de Manaus, AM, Brasil, en 2015 : estudio transversal de base poblacional

analisar a frequência e fatores associados à cobertura por planos privados de saúde na região metropolitana de Manaus, AM, Brasil. Estudo transversal de base populacional realizado em 2015, por meio de entrevista domiciliar; as razões de prevalência (RP) e intervalos de confiança (IC95%) foram calculadas pela regressão de Poisson, com variância robusta ajustada por sexo e faixa etária. foram entrevistados 4.001 indivíduos, dos quais 13% (IC95% 12,0 a 14,1%) tinham plano de saúde; maior cobertura por planos foi observada entre militares (RP=3,18 - IC95% 1,64;6,15), empregados dos setores privado (RP=1,91 - IC95% 1,46;2,52) e público (RP=1,75 - IC95% 1,23;2,49); a cobertura por planos de saúde foi menor entre pessoas mais pobres (RP=0,21 - IC95% 0,13;0,33) e de menor escolaridade (RP=0,66 - IC95% 0,46;0,99). a frequência de planos de saúde foi baixa e associou-se a melhor poder aquisitivo, escolaridade e situação de trabalho291Analizar la frecuencia y los factores asociados a la cobertura por planes de salud en la región metropolitana de Manaus, AM, Brasil. Estudio transversal de base poblacional por entrevista domiciliar. Las razones de prevalencia (RP) e intervalo de confianza (IC95%) fueron calculadas por regresión de Poisson con varianza robusta, ajustadas por sexo y edad. se entrevistaron 4.001 individuos; 13% (IC95%: 12,0 a 14,1%) tenían plan de salud; la mayor cobertura por planes fue observada entre militares (RP=3,18 - IC95% 1,64;6,15), empleados del sector privado (RP=1,91 - IC95% 1,46;2,52) y del sector público (RP=1,75 - IC95% 1,23;2,49); la cobertura fue menor en personas más pobres (RP=0,21 - IC95% 0,13;0,33) y de menor escolaridad (RP=0,66 - IC95% 0,46;0,99). La frecuencia de planes de salud fue baja y se asoció con mejor poder adquisitivo, escolaridad y situación de trabaj

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Série histórica de mortalidade por suicídio em município paulista segundo dados epidemiológicos

Objetivo: Analisar a tendência e o perfil epidemiológico do suicídio, em um município de grande porte do interior do estado de São Paulo, no período de 2000 a 2018. Métodos: Estudo ecológico de série temporal realizado em 2019 que analisou a mortalidade por suicídio na cidade de Campinas, São Paulo, Brasil, no período de 2000 a 2018. A coleta ocorreu por meio de dados do Sistema de Informação sobre Mortalidade, sendo as variáveis: sexo, idade, raça, categoria do óbito, mês e local de ocorrência, com dados analisados pelo número/taxa de óbitos e taxas de óbitos padronizadas por faixa etária e sexo. Estimaramse modelos de regressão linear simples para número de óbitos e ano. Na análise da proporção de óbitos por suicídio e demais variáveis estudadas utilizou-se teste de qui-quadrado com significância de 5%. Resultados: Ocorreram 904 óbitos por suicídio, com aumento significativo ao longo dos anos e taxa geral de 3,20/100.000 hab. (2000) e 5,42/100.000 hab. (2018). Observou-se aumento no sexo masculino 5,30/100.000 hab. (2000) e 8,45/100.000 hab. (2018), faixas etárias de 20 a 40 incompletos com 3,53/100.000 hab. (2000) e 6,84/100.000 hab. (2018) e de 40 a 60 incompletos com 4,69/100.000 hab. (2000) e 7,61/100.000 hab. (2018). A maioria dos óbitos ocorreu com pessoas brancas (673; 74,9%), por enforcamento (503; 55,6%), em domicílio (524;58,0%), em setembro (93; 10,3%) e dezembro (92; 10,2%). Conclusão: Houve crescente aumento das taxas de suicídios em Campinas no período analisado, sendo as vítimas predominantemente homens, adultos, brancos, por enforcamento e realizado em domicílios

GIS-Based Approach Applied to Study of Seasonal Rainfall Influence over Flood Vulnerability

Flooding occurrence is one of the most common phenomena that impact urban areas, and this intensifies during heavy rainfall periods. Knowing the areas with the greatest vulnerability is of paramount importance as it allows mitigating actions to be implemented in order to minimize the generated impacts. In this context, this study aimed to use Geographic Information System (GIS) tools to identify the areas with greater flooding vulnerability in Espírito Santo state, Brazil. The study was based on the following methodological steps: (1) a Digital Elevation Model (DEM) acquisition and watersheds delimitation; (2) maximum and accumulated rainfall intensity calculations for the three studied periods using meteorological data; (3) a land use and occupation map reclassification regarding flood vulnerability and fuzzy logic application; (4) an application of Euclidean distance and fuzzy logic in hydrography and water mass vector variables; (5) a flood vulnerability model generation. Based on the found results, it was observed that the metropolitan and coastal regions presented as greater flood vulnerability areas during the dry season, as in these regions, almost all of the 9.18% of the state’s area was classified as highly vulnerable, while during rainy season, the most vulnerable areas were concentrated in Caparaó and in the coastal and immigration and metropolitan regions, as in these regions, almost all of the 12.72% of the state’s area was classified as highly vulnerable. In general, by annually distributing the rainfall rates, a greater flood vulnerability was observed in the metropolitan and coastal and immigration regions, as in these areas, almost all of the 7.72% of the state’s area was classified as highly vulnerable. According to the study, Espírito Santo state was mostly classified as a low (29.15%) and medium (28.06%) flood vulnerability area considering the annual period, while its metropolitan region has a very high flood vulnerability risk. Finally, GIS modeling is important to assist in decision making regarding public management and the employed methodology presents worldwide application potential

GIS-Based Approach Applied to Study of Seasonal Rainfall Influence over Flood Vulnerability

Flooding occurrence is one of the most common phenomena that impact urban areas, and this intensifies during heavy rainfall periods. Knowing the areas with the greatest vulnerability is of paramount importance as it allows mitigating actions to be implemented in order to minimize the generated impacts. In this context, this study aimed to use Geographic Information System (GIS) tools to identify the areas with greater flooding vulnerability in Espírito Santo state, Brazil. The study was based on the following methodological steps: (1) a Digital Elevation Model (DEM) acquisition and watersheds delimitation; (2) maximum and accumulated rainfall intensity calculations for the three studied periods using meteorological data; (3) a land use and occupation map reclassification regarding flood vulnerability and fuzzy logic application; (4) an application of Euclidean distance and fuzzy logic in hydrography and water mass vector variables; (5) a flood vulnerability model generation. Based on the found results, it was observed that the metropolitan and coastal regions presented as greater flood vulnerability areas during the dry season, as in these regions, almost all of the 9.18% of the state’s area was classified as highly vulnerable, while during rainy season, the most vulnerable areas were concentrated in Caparaó and in the coastal and immigration and metropolitan regions, as in these regions, almost all of the 12.72% of the state’s area was classified as highly vulnerable. In general, by annually distributing the rainfall rates, a greater flood vulnerability was observed in the metropolitan and coastal and immigration regions, as in these areas, almost all of the 7.72% of the state’s area was classified as highly vulnerable. According to the study, Espírito Santo state was mostly classified as a low (29.15%) and medium (28.06%) flood vulnerability area considering the annual period, while its metropolitan region has a very high flood vulnerability risk. Finally, GIS modeling is important to assist in decision making regarding public management and the employed methodology presents worldwide application potential