Caracterização espacial utilizando indução orientada aos atributos no SOLAP+

Dissertação para obtenção do Grau de Mestre em Engenharia InformáticaO protótipo SOLAP+, desenvolvido sobre a orientação de João Moura Pires, segue o conceito SOLAP apresentado por Bédard et al. combinando as funcionalidades dos sistemas de apoio à decisão OLAP com as capacidades dos sistemas de informação geográfica (SIG). Com os trabalhos realizados por Rosa Martins (2006), Marlene Vitorino e Rodolfo Caldeira (2008), Ruben Jorge (2009) e Ricardo Silva (2010) foi desenvolvida uma abordagem genérica SOLAP que culminou com a implementação de um protótipo que, presentemente, já apresenta bastantes funcionalidades do ponto de vista da análise de dados e representação dos mesmos através de mapas temáticos e/ou gráficos. Para ajudar nessa tarefa de análise, integramos mecanismos de descoberta de relações entre os dados e apresentamos no mapa essas relações. Esta forma de análise permite ao utilizador retirar conclusões através da extracção de informações que não estão explícitas nos dados presentes na base de dados. Assim, o objectivo desta dissertação consiste em incorporar, respeitando o modelo multidimensional seguido, duas técnicas de indução: generalização de dominância espacial e generalização de dominância não espacial. Com os resultados da aplicação dessas técnicas, é feita uma caracterização semântica dos objectos espaciais no mapa

Portuguese Football Federation consensus statement 2020: nutrition and performance in football

Nutrition is an undeniable part of promoting health and performance among football (soccer) players. Nevertheless, nutritional strategies adopted in elite football can vary significantly depending on culture, habit and practical constraints and might not always be supported by scientific evidence. Therefore, a group of 28 Portuguese experts on sports nutrition, sports science and sports medicine sought to discuss current practices in the elite football landscape and review the existing evidence on nutritional strategies to be applied when supporting football players. Starting from understanding football's physical and physiological demands, five different moments were identified: preparing to play, match-day, recovery after matches, between matches and during injury or rehabilitation periods. When applicable, specificities of nutritional support to young athletes and female players were also addressed. The result is a set of practical recommendations that gathered consensus among involved experts, highlighting carbohydrates periodisation, hydration and conscious use of dietary supplements.info: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 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

Boas práticas ao Serviço do Utente - Centro Hospitalar do Tâmega e Sousa, EPE

O CHTS pretende e ambiciona na literacia em saúde, na vertente do cidadão, que haja mais igualdades em saúde e que, este cidadão perante a necessidade de tomar decisões de forma autónoma (muitas vezes de elevada complexidade), sobre a promoção de saúde, prevenção das doenças ou seu tratamento, esteja informado e com conhecimentos para o fazer. Pretende que o cidadão seja capaz de obter melhor acesso aos cuidados de saúde, usar e usufruir da forma mais adequada e, de forma intencional e consciente, possa obter os maiores benefíciospara a manutenção do seu estado de saúde. A OMS, define Literacia em Saúde como “o grau em que os indivíduos têm a capacidade de obter, processar e entender as informações básicas de saúde para utilizarem os serviços e tomarem decisões adequadas de saúde”, ou seja, a literacia em saúde contempla um conjunto de conhecimentos, atitudes, habilidades e até competências que capacitam a pessoa no acesso, compreensão das informações para que possa avaliar de forma critica a sua relevância no uso responsável desse conhecimento. Foi, neste contexto, que surgiu no CHTS uma nova ótica de leitura e de intervenção das suas equipas multidisciplinares, na consecução de projetos e ações que visam reforçar os níveis de literacia, de forma multidimensional e colaborativa, aproximando-se cada vez mais da centralidade no cidadão, bem como de uma maior eficiência e eficácia dos serviços, qualidade assistencial e satisfação do cidadão e profissional. Deve-se muito à capacidade dos profissionais de saúde, mesmo com diferenças de uns para outros, em identificar as necessidades das pessoas, em estarem disponíveis para promover mudança, a avaliar diariamente o nível de compreensão, capacidades para realizar tarefas prescritas, motivação e nível de mudança comportamental do cidadão, tendo em conta a sua idade e o seu estado de saúde. José Ribeiro Nunes, Enf. Diretor, Prefácioinfo:eu-repo/semantics/publishedVersio