UMA IMPLEMENTAÇÃO DO MEC SIMÉTRICO DE GALERKIN PARA PROBLEMAS DE ELASTICIDADE 2D

O Método dos Elementos de Contorno (MEC) pode ser derivado por diferentes metodologias, resultando em implementações computacionais distintas, que se baseiam em reduzir as equações integrais de contorno contínuas em sistemas de equações lineares. A formulação clássica do MEC é conhecida como Método da Colocação, onde se procura satisfazer as equações integrais de contorno de forma forte, diretamente em nós específicos do contorno do modelo, usualmente, os próprios nós de discretização do problema. Em contraste, no Método de Galerkin procura-se satisfazer as equações integrais de contorno de forma fraca. A estratégia utilizada pelo método consiste em aplicar a Técnica de  resíduos Ponderados de Galerkin às equações integrais de contorno, distribuindo-se o erro cometido pela aproximação da melhor forma possível. Pode-se ainda, fazendo uso das equações hipersingulares de contorno, reduzir as equações integrais de contorno a um sistema simétrico de equações lineares. Denomina-se essa estratégia de Método Simétrico de Galerkin. Apresenta-se no trabalho os principais passos para a implementação numérica do MEC Simétrico de Galerkin para problemas de elasticidade linear bidimensional. São apresentadas as estratégias para obtenção do sistema de equações simétrico, construção de uma matriz de rigidez simétrica global e as técnicas utilizadas no cálculo das integrais singulares decorrentes deste método

Charcot-Marie-Tooth disease and rehabilitation: a perfect match

Reabilitação e doença de Charcot-Marie-Tooth: um casamento perfeit

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

UMA IMPLEMENTAÇÃO DO MEC SIMÉTRICO DE GALERKIN PARA PROBLEMAS DE ELASTICIDADE 2D

O Método dos Elementos de Contorno (MEC) pode ser derivado por diferentes metodologias, resultando em implementações computacionais distintas, que se baseiam em reduzir as equações integrais de contorno contínuas em sistemas de equações lineares. A formulação clássica do MEC é conhecida como Método da Colocação, onde se procura satisfazer as equações integrais de contorno de forma forte, diretamente em nós específicos do contorno do modelo, usualmente, os próprios nós de discretização do problema. Em contraste, no Método de Galerkin procura-se satisfazer as equações integrais de contorno de forma fraca. A estratégia utilizada pelo método consiste em aplicar a Técnica de  resíduos Ponderados de Galerkin às equações integrais de contorno, distribuindo-se o erro cometido pela aproximação da melhor forma possível. Pode-se ainda, fazendo uso das equações hipersingulares de contorno, reduzir as equações integrais de contorno a um sistema simétrico de equações lineares. Denomina-se essa estratégia de Método Simétrico de Galerkin. Apresenta-se no trabalho os principais passos para a implementação numérica do MEC Simétrico de Galerkin para problemas de elasticidade linear bidimensional. São apresentadas as estratégias para obtenção do sistema de equações simétrico, construção de uma matriz de rigidez simétrica global e as técnicas utilizadas no cálculo das integrais singulares decorrentes deste método

Strategies and performance of the CMS silicon tracker alignment during LHC Run 2

The strategies for and the performance of the CMS silicon tracking system alignment during the 2015–2018 data-taking period of the LHC are described. The alignment procedures during and after data taking are explained. Alignment scenarios are also derived for use in the simulation of the detector response. Systematic effects, related to intrinsic symmetries of the alignment task or to external constraints, are discussed and illustrated for different scenarios

Characterisation of microbial attack on archaeological bone

As part of an EU funded project to investigate the factors influencing bone preservation in the archaeological record, more than 250 bones from 41 archaeological sites in five countries spanning four climatic regions were studied for diagenetic alteration. Sites were selected to cover a range of environmental conditions and archaeological contexts. Microscopic and physical (mercury intrusion porosimetry) analyses of these bones revealed that the majority (68%) had suffered microbial attack. Furthermore, significant differences were found between animal and human bone in both the state of preservation and the type of microbial attack present. These differences in preservation might result from differences in early taphonomy of the bones. © 2003 Elsevier Science Ltd. All rights reserved

Strategies and performance of the CMS silicon tracker alignment during LHC Run 2

The strategies for and the performance of the CMS silicon tracking system alignment during the 2015–2018 data-taking period of the LHC are described. The alignment procedures during and after data taking are explained. Alignment scenarios are also derived for use in the simulation of the detector response. Systematic effects, related to intrinsic symmetries of the alignment task or to external constraints, are discussed and illustrated for different scenarios