Modelagem pedomorfogeológica no detalhamento do mapa de solos no Parque Ecológico dos Pequizeiros, DF

Monografia (graduação)—Universidade de Brasília, Faculdade de Agronomia e Medicina Veterinária, 2012.O trabalho visa gerar um modelo de distribuição pedológica para o detalhamento do mapa de solos do Parque Ecológico dos Pequizeiros – DF, estabelecendo relações entre solos, geologia, geomorfologia (pedomorfogeologia), por meio de geotecnologias

RENDIMENTO ACADÊMICO E OS ESTILOS DE APRENDIZAGEM: UM ESTUDO NA DISCIPLINA ANÁLISE DE CUSTOS

O objetivo foi verificar se o estilo de aprendizagem impacta no rendimento acadêmico nas avaliações formativas e somativas dos alunos que cursaram a disciplina de Análise de Custos, no curso de Ciências Contábeis. Oportunamente, comparou-se a associação do rendimento na disciplina citada e o rendimento do aluno no semestre, e também no coeficiente geral do curso. Para a análise do rendimento acadêmico utilizou-se a Teoria da Avaliação. Para a coleta de dados, utilizou-se o instrumento proposto por Felder e Soloman (1991), que possibilitou mensurar o estilo de aprendizagem. Para a análise dos dados utilizou-se estatística descritiva, e o teste de Wilcoxon. A amostra compreende 111 alunos matriculados no segundo semestre do ano de 2015 e primeiro semestre de 2016, representando 88% da população. Os resultados apontam a predominância dos estilos ativo, sensorial, visual e sequencial. Quanto ao rendimento acadêmico, verificaram-se maiores médias para as avaliações individuais (somativas). Na comparação do rendimento e os estilos de aprendizagem dos alunos, identificou-se um equilíbrio nas dimensões, ou seja, as médias são próximas tanto para avaliações individuais quanto em grupo e, também, nas médias do Coeficiente de Rendimento Acadêmico (CRA) semestral e geral. O teste não paramétrico Wilcoxon indicou não haver diferença na maioria dos resultados, com exceção entre a avaliação somativa e estilo sequencial/global

Abordagem sobre o projeto de amplificadores de biopotenciais / Approach on biopotential amps project

As medições dos biopotenciais nos seres humanos são muito sensíveis e susceptíveis às interferências eletromagnéticas. Os biopotenciais são medidos como sinais de tensão gerados por nervos e músculos. Esses sinais envolvem níveis de tensão muito baixos, tipicamente na faixa de 1 ?V(microvolt) a 100 mV (milivolt). Consequentemente, eles são susceptíveis à interferência eletromagnética, presente inevitavelmente no ambiente de medição. Os amplificadores utilizados para medir esses sinais são projetados para satisfazer certos requisitos específicos. Eles devem fornecer uma amplificação seletiva do sinal fisiológico, apresentar expressiva rejeição aos sinais de ruído e interferências sobrepostas e garantir a proteção quanto a surtos de tensão e corrente, tanto para o paciente quanto para o próprio equipamento de medição

Abordagem sobre o projeto de amplificadores de biopotenciais/ Approach on biopotential amps project

 As medições dos biopotenciais nos seres humanos são muito sensíveis e susceptíveis às interferências eletromagnéticas. Os biopotenciais são medidos como sinais de tensão gerados por nervos e músculos. Esses sinais envolvem níveis de tensão muito baixos, tipicamente na faixa de 1 ?V(microvolt) a 100 mV(milivolt). Consequentemente, eles são susceptíveis à interferência eletromagnética, presente inevitavelmente no ambiente de medição. Os amplificadores utilizados para medir esses sinais são projetados para satisfazer certos requisitos específicos. Eles devem fornecer uma amplificação seletiva do sinal fisiológico, apresentar expressiva rejeição aos sinais de ruído e interferências sobrepostas e garantir a proteção quanto a surtos de tensão e corrente, tanto para o paciente quanto para o próprio equipamento de medição

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

Catálogo Taxonômico da Fauna do Brasil: setting the baseline knowledge on the animal diversity in Brazil

The limited temporal completeness and taxonomic accuracy of species lists, made available in a traditional manner in scientific publications, has always represented a problem. These lists are invariably limited to a few taxonomic groups and do not represent up-to-date knowledge of all species and classifications. In this context, the Brazilian megadiverse fauna is no exception, and the Catálogo Taxonômico da Fauna do Brasil (CTFB) (http://fauna.jbrj.gov.br/), made public in 2015, represents a database on biodiversity anchored on a list of valid and expertly recognized scientific names of animals in Brazil. The CTFB is updated in near real time by a team of more than 800 specialists. By January 1, 2024, the CTFB compiled 133,691 nominal species, with 125,138 that were considered valid. Most of the valid species were arthropods (82.3%, with more than 102,000 species) and chordates (7.69%, with over 11,000 species). These taxa were followed by a cluster composed of Mollusca (3,567 species), Platyhelminthes (2,292 species), Annelida (1,833 species), and Nematoda (1,447 species). All remaining groups had less than 1,000 species reported in Brazil, with Cnidaria (831 species), Porifera (628 species), Rotifera (606 species), and Bryozoa (520 species) representing those with more than 500 species. Analysis of the CTFB database can facilitate and direct efforts towards the discovery of new species in Brazil, but it is also fundamental in providing the best available list of valid nominal species to users, including those in science, health, conservation efforts, and any initiative involving animals. The importance of the CTFB is evidenced by the elevated number of citations in the scientific literature in diverse areas of biology, law, anthropology, education, forensic science, and veterinary science, among others