Thermal evaluation of rice husk and cement mortar boards warmed by electric resistance

An alternative proposal for floor heating system by means of electric resistance for both chick and piggy installation is presented in this work. Several formulations of rice husk and cement mortar boards were used. An electronic device controlled all board temperature. This system presented a good efficiency design. The conventional cement mortar mixed with rice husk showed a better performance.Neste trabalho, apresenta-se uma proposta alternativa para o sistema de aquecimento de piso por resistência elétrica em granjas de aves e suínos. Após o dimensionamento do sistema, foram confeccionadas placas com argamassa de cimento, areia e casca de arroz, com diferentes traços, a fim de avaliar esse material alternativo de construção, sendo construído um controlador eletrônico de temperatura para dar mais autonomia ao sistema de aquecimento. O sistema mostrou-se eficiente no controle da temperatura da placa. Dentre os traços analisados, aquele com a placa mista de argamassa convencional e casca de arroz demonstrou melhor desempenho.3745Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

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