Algumas refle xões so bre a enge nharia civil e o am bie nte no co nte xto do século XX I

Diante do contexto de mudanças climáticas e demanda pelo aprimoramento na gestão de materiais, o objetivo desse trabalho é fazer uma análise da interação entre a construção civil e o meio ambiente. Constataram-se que por meio de modificações na indústria da construção civil é possível conseguir ganhos ambientais consideráveis em relação à redução da emissão de gases causadores do efeito estufa, contaminação da atmosfera, solo e fontes de água, e melhor uso de matérias primas. A sociedade de maneira geral tem se tornado mais exigente em relação a esses aspectos o que se reflete em atitudes da iniciativa privada como a busca por construções mais sustentáveis e por certificações das mesmas

Performance and amylase activity in Nile tilapia submitted to different temperatures

O objetivo deste trabalho foi avaliar o desempenho e a atividade de amilase em quimo de tilápias-do-nilo macho, linhagem tailandesa, submetidas a quatro diferentes temperaturas. O experimento foi conduzido em delineamento inteiramente casualizado, com quatro tratamentos (20, 24, 28 e 32oC), seis repetições e dez peixes por unidade experimental. A dieta utilizada foi igual para todos os tratamentos. Aos 55 dias do experimento, o consumo de ração aparente, ganho de peso, conversão alimentar aparente, atividade de amilase e atividade específica da amilase foram avaliados. O consumo de ração aparente e o ganho de peso aumentaram linearmente com o aumento da temperatura. Na conversão alimentar aparente, foi observado efeito quadrático em função da temperatura com melhora na conversão de 1,79 a 1,00 com o aumento da temperatura até 29,15oC. Observou-se efeito linear na atividade da amilase e na atividade específica da amilase em função da temperatura, com maior atividade de amilase e menor atividade específica de amilase a 32oC. A temperatura da água influencia o desempenho e a atividade da amilase em tilápias-do-nilo.The objective of this work was to evaluate the performance and amylase activity in chime of Nile tilapia male, Thai line, submitted to four different temperatures. The experimental design was completely randomized with four treatments (20, 24, 28 and 32oC), six replicates and ten fishes per experimental unit. The diet was the same for all treatments. At 55 days of experiment, apparent feed intake, weight gain, apparent feed conversion, amylase activity and specific amylase activity were evaluated. The apparent feed intake and weight gain increased linearly with temperature increase. For apparent feed conversion, quadratic effect was observed as a function of temperature, showing a conversion improvement of 1.79 to 1.00 with the increase of the temperature until 29,15oC. Linear effect in amylase activity and specific amylase activity was observed as a result of temperature, comprising high amylase activity and low specific amylase activity at 32oC. Water temperature influences the performance and amylase activity in Nile tilapia

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

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications 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, 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