Problematização contextualizada para o ensino de química: dissolução do isopor com acetona

Anais do II Seminário Seminário Estadual PIBID do Paraná: tecendo saberes / organizado por Dulcyene Maria Ribeiro e Catarina Costa Fernandes — Foz do Iguaçu: Unioeste; Unila, 2014Procurando aumentar o interesse dos alunos nas aulas de Química, os bolsistas do projeto PIBID do curso de Licenciatura em Química da Universidade Tecnológica Federal do Paraná – Campus Apucarana, apresenta uma proposta de aula problematizada sobre os conteúdos polaridade e solubilidade. O trabalho consiste em uma aula experimental problematizada que possa proporcionar aos alunos uma maneira diferente de entender os conteúdos químicos por meio desses experimento

Forms of nitrogen fertilizer application in Panicum maximum

Nitrogen is one of the most important nutrients for increasing the yield and quality of forages. This study aimed to evaluate the effect of the different forms of application, spread on the total area using urea fertilizer and foliar using liquid urea, aiming at evaluating the yield and qualitative traits of Panicum maximum cv. Mombaça, at different times of the year. The experimental design was completely randomized blocks in subdivided plots, with three blocks, four treatments, and three collections. The treatments were applied in March 2015 and consisted of the following treatments: 1-control; 2-urea; 3-liquid urea; 4-urea + liquid urea; 5-urea + micronutrients; 6-urea + N liquid; 7-urea + N liquid + micronutrients; 8-control. Samples were collected in May, October, and December 2015. Crude protein (CP) dry matter (DM), mineral matter (MM), and acid detergent fiber (ADF) were evaluated. Results revealed that nitrogen was determinant in improving the yield and forage quality. Treatments with urea spread on total area resulted in increased dry matter production. For the crude protein, the source and the application form are not decisive. Collection time with higher rainfall positively affected the dry matter production, crude protein, and ADF, while urea spread on total area showed the best cost-benefit due to the good results of yield and quality

PROJETO DE EXTENSÃO EDUCAÇÃO EM SAÚDE AMBIENTAL E REDES SOCIAIS

O Projeto de Extensão Educação em Saúde Ambiental (ESA) tem a proposta de trabalhar questões relacionadas à Saúde Ambiental como estratégia importante para uma formação universitária completa. Este trabalho teve como objetivo apresentar dados uma das estratégias utilizada no período da Pandemia de Covid-19 para dar continuidade as atividades de extensão. Trata-se de um de estudo descritivo com abordagem quantitativa compreendendo levantamento de dados das postagens nas redes sociais do ESA, do período de agosto de 2020 até agosto de 2022. Foram contabilizados 333 seguidores no Instagram e 344 no Facebook e 144/126 postagens respectivamente. Foi possível observar que dentre os temas postados, as datas comemorativas foram responsáveis pelo maior número de postagens tanto no Instagram como no Facebook seguida por Dengue e Aedes aegypti e Resíduos Sólidos, Coleta Seletiva, Descarte de Medicamentos. Foi possível concluir que as redes sociais são ótimos canais para divulgação de informações e interação com os seguidores observou-se aceitação principalmente por meio das curtidas e alcance das postagens, no entanto o baixo número de comentários pode demonstrar a necessidade de rever o formato e elaborar conteúdos que possibilitem maior interação com os usuários

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