Electromagnetic properties of Carbon-Graphene Xerogel, Graphite and Ni-Zn Ferrite composites in polystyrene matrix in the X-Band (8.2 – 12.4 GHz)

The electromagnetic properties of Carbon-Graphene Xerogel (CGX), Flaky graphite (GR) and Nickel-Zinc ferrite (FeNiZn) composites in polystyrene (PS) matrix were studied in the X-Band range (8.2 – 12.4 GHz). In this work the Expanded Polystyrene (EPS) waste material was processed into polystyrene through the recycling of EPS. The polystyrene obtained was utilized as dielectric matrix, mainly because PS is a wellknown organic polymer that presents low dielectric loss and light weight, which contribute to applications in composites for the aerospace field. In order to produce the final composite specimens, the CGX additive was previously synthesized through a sustainable method that employed the use of waste from the paper and pulp industry (black liquor). Afterwards, the morphological and structural analysis were made through Scanning Electron Microscope (SEM) and Raman Spectrometer, respectively. On the other hand, the magnetic ferrite material, FeNiZn, was obtained for the composite production through calcination, whereas the GR utilized was commercially obtained. It was observed that the increase of CGX and GR influenced on the increase of the Complex Permittivity, and that 10 wt% CGX + 50wt% FeNiZn composite sample demonstrated an absorption peak at 10.5 GHz. The results are relevant concerning the recycling of EPS waste through its use as dielectric matrix, thus developing greener and low-weight composite materials to be used in microwave applications.Keywords: Carbon-Graphene. Composite. Recycling. Polystyrene. Microwave.

Produção da Cunhã Forrageira sob diferentes níveis de adubação fosfatada: Cunhã Forrage production under different levels of phosphate fertilization

A Cunhã (Clitoria ternatea L.) é uma leguminosa que apresenta elevado teor de proteína e que consegue produção satisfatória, mesmo em condições de semiaridez, em períodos em que a produção animal é baixa, devido à escassez de alimento no semiárido, tanto em quantidade quanto em qualidade nutricional. Diante desse contexto, se propõe com esse trabalho avaliar o efeito de doses de P2O5 sobre a produtividade de matéria natural da Cunhã forrageira. O experimento foi conduzido no Instituto Federal Baiano, campus Serrinha, Bahia. O estudo foi baseado na aplicação de diferentes níveis de adubação fosfatada: (i) 0 Kg ha-1 de P2O5; (ii) 150 Kg ha-1 de P2O5; (iii) 300 Kg ha-1 de P2O5; e (iv) 450 Kg ha-1 de P2O5. Com aplicação de 355 Kg ha-1 de P2O5, a Cunhã apresenta maior resposta em produtividade de matéria natural (49,5 Mg ha-1); e a maior eficiência agronômica ocorre com a aplicação de até 263,5 Kg ha-1 de P2O5

Enfisema pulmunar: aspectos fisiopatológicos e sua associação com o tabagismo

O tabagismo é um tema de preocupação global para a saúde, pois a inalação recorrente das substâncias tóxicas liberadas pelo cigarro é uma das principais causas de doenças pulmonares obstrutivas crônicas (DPOC). Nesse grupo de enfermidades, encontra-se o enfisema, patologia respiratória em que há redução da elasticidade do tecido pulmonar associada a destruição dos alvéolos, resultando em uma gradual perda da capacidade respiratória, sem que haja prognóstico de reversão ou melhora do quadro. Este artigo pretende fornecer uma revisão de literatura sobre a influência das toxinas do tabaco no desenvolvimento do enfisema pulmonar, bem como sua patogênese e epidemiologia. Portanto, foi realizada uma busca por artigos científicos nas bases de dados: US National Library of Medicine (PubMed), Scientific Electronic Library Online (SciELO), Medical Literature Analysis and Retrieval System Online (MEDLINE) e Literatura Latino-Americana em Ciências da Saúde (LILACS)

Uma série histórica do HTLV na Bahia durante o período entre 2015 a 2019

Objetivo:  Descrever a frequência do HTLV na Bahia e no Sudoeste Baiano no período entre 2015 a 2019. Métodos: pesquisa trata-se de um estudo epidemiológico, retrospectivo, observacional de abordagem quantitativa do tipo série histórica. Os dados foram coletados do banco de dados do Departamento de Informática do Sistema Único de Saúde (DATASUS), por meio da consulta às bases de dados do Sistema de Informações de Agravos de Notificação (SINAN) e do Departamento de Doenças e Condições Crônicas e Infecções Sexualmente Transmissíveis (DCCI). Resultados e discussão: A região leste, foi a mais acometida nos anos analisados, sendo que, no ano de 2015 foram notificados 153 casos, em 2016 foram notificados 170 casos, em 2017 foram notificados 477 casos, em 2018 cerca de 390 casos e no ano de 2019 foram registradas 52 notificações de casos de HTLV. Conclusão: Os dados deste estudo sugerem que, durante os anos de 2015 a 2019 a região leste foi a mais acometida pelo HTLV na Bahia, seguido da região sul e sudeste. A região que apresentou o maior número de casos confirmados na Bahia foi a região leste, seguido da região centro-leste e da região sul. Além disso, todas as regiões de saúde apresentaram casos inconclusivos no diagnóstico, exceto a região oeste. A região leste foi a que apresentou o maior número de casos inconclusivos no período analisado

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