Avaliação ambiental do rio mongaguá, sp, utilizando macroinvertebrados bentônicos

Estudar a qualidade ambiental de um corpo d’água leva ao conhecimento da qualidade ambiental do seu entorno. Uma das comunidades biológicas muito estudada, neste sentido, é o grupo dos macroinvertebrados bentônicos, seres com dimensões entre milímetros e centímetros que habitam o sedimento do fundo dos corpos d’água. Segundo a literatura, muitos invertebrados macroscópicos são usados para diagnosticar a saúde de rios e lagos. O presente trabalho buscou compreender a qualidade das águas do Rio Mongaguá, SP, através da análise de parâmetros físicos, químicos e biológicos, utilizando a contagem e identificação de macroinvertebrados bentônicos em dois pontos do Rio Mongaguá, e um terceiro ponto em um afluente localizado na divisa entre Praia Grande e Mongaguá, os pontos foram denominados 1, 2 e 3. Os resultados das análises químicas e físicas demonstraram o pH e turbidez dentro dos valores referências para águas doces de classe 2 da resolução CONAMA 357/05. No ponto 1 foram encontrados organismos Chironomidae, que possuem resistência para sobreviver em águas com baixos níveis de oxigênio. Este ponto apresentava grande quantidade de lixo como garrafas plásticas e restos de alimentos. Não foi possível determinar a qualidade ambiental do ponto 2, pois o único organismo encontrado não foi identificado. Os organismos encontrados no ponto 3, dos grupos Plecoptera, Trichoptera, Ephemeroptera e Lepdoptera, habitam águas limpas com alto índice de oxigênio, podendo-se inferir que, neste ponto, a qualidade da água era boa. &nbsp

A hybrid deep learning forecasting model using GPU disaggregated function evaluations applied for household electricity demand forecasting.

As the new generation of smart sensors is evolving towards high sampling acquisitions systems, the amount of information to be handled by learning algorithms has been increasing. The Graphics Processing Unit (GPU) architectures provide a greener alternative with low energy consumption for mining big-data, harnessing the power of thousands of processing cores in a single chip, opening a widely range of possible applications. Here, we design a novel evolutionary computing GPU parallel function evaluation mechanism, in which different parts of time series are evaluated by different processing threads. By applying a metaheuristics fuzzy model in a low-frequency data for household electricity demand forecasting, results suggested that the proposed GPU learning strategy is scalable as the number of training rounds increases

Didactic Sequence for Teaching Exponential Function

This paper presents a methodological proposal for the teaching of exponential function, resulting from the application of a didactic sequence involving exponential function, where evidence of learning and the consolidation and application of mathematical concepts in problem solving were identified and analyzed. The Didactic Engineering of Michèle Artigue (1988) was used as a research methodology. As theoretical contributions that guided and enabled the development of the research, we chose the use of Mathematical Investigation in the classroom; Didactic Sequence in the conception of Zabala (1999); the Articulated Units of Conceptual Reconstruction proposed by Cabral (2017) and assumptions of Vygotsky\u27s theory. A didactic sequence composed of five UARC\u27s was elaborated to work the exponential function, with a view to minimizing the difficulties naturally imposed by the content to be explained. Microgenetic analysis of verbal interactions between teacher and students was used to analyze the results of the application. The results show that the students participating in the experiment showed evidence of learning, recorded during the process, and began to have a good understanding of the concepts and properties related to the topic, in addition to a good performance in carrying out the activities, facts that corroborate the potential of the didactic sequence proposed herein

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