Migração de Software em Engenharia Química e suas vantagens

Numa sociedade cada vez mais tecnológica em que cada dia novas tecnologias são desenvolvidas, muitas empresas deparam-se com um problema de como atualizar os seus sistemas informáticos para englobar estas novas alterações sem perder as funcionalidades que tinham implementado ou, o elevado custo que teriam para voltar a projetar e instalar um sistema de raiz. Como resposta a este problema surgiu a possibilidade de migração de tecnologia que tem como garantia manter as funcionalidades iniciais e alargar o sistema para estar de acordo com a nova tecnologia, seja ela Java, Python, C#, entre outras. É importante salientar que as indústrias ligadas à engenharia química estão cada vez mais recetivas a novas tecnologias, e esforçam-se por encontrar formas de modernizar os seus softwares mediante o aperfeiçoamento ou delineando novos meios para atingir os objetivos. O objetivo desta dissertação foi exemplificar um processo de migração de software a partir de um ficheiro de Excel de uma reação de titulação para um software elaborado em Java e abordar as vantagens do mesmo em relação ao original. Conclui-se que uma das grandes vantagens de novas tecnologias são a acessibilidade, dado que permite acesso em qualquer dispositivo com uma ligação à internet e permitir que vários utilizadores em simultâneo consultem, alterem e insiram os dados sem ocorrerem conflitos entre os vários utilizadores

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

Measurement of the charge ratio of atmospheric muons with the CMS detector

We present a measurement of the ratio of positive to negative muon fluxes from cosmic ray interactions in the atmosphere, using data collected by the CMS detector both at ground level and in the underground experimental cavern at the CERN LHC. Muons were detected in the momentum range from 5 GeV/ c to 1 TeV/ c . The surface flux ratio is measured to be 1.2766±0.0032(stat.)±0.0032(syst.) , independent of the muon momentum, below 100 GeV/ c . This is the most precise measurement to date. At higher momenta the data are consistent with an increase of the charge ratio, in agreement with cosmic ray shower models and compatible with previous measurements by deep-underground experiments.We present a measurement of the ratio of positive to negative muon fluxes from cosmic ray interactions in the atmosphere, using data collected by the CMS detector both at ground level and in the underground experimental cavern at the CERN LHC. Muons were detected in the momentum range from 5 GeV/c to 1 TeV/c. The surface flux ratio is measured to be 1.2766 \pm 0.0032(stat.) \pm 0.0032 (syst.), independent of the muon momentum, below 100 GeV/c. This is the most precise measurement to date. At higher momenta the data are consistent with an increase of the charge ratio, in agreement with cosmic ray shower models and compatible with previous measurements by deep-underground experiments