Navegando por Assunto "Python"

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Acesso aberto (Open Access)
Introdução à neurociência computacional com a linguagem python
(Universidade Federal do Pará, 2023-12-29) NASCIMENTO, Weverson Vieira do; PEREIRA JÚNIOR, Antonio; http://lattes.cnpq.br/3239362677711162
This work presents a proposal for an introductory course on computational neuroscience, using the Python programming language. The brain is a complex organ, and there is significant interest in understanding the biological mechanisms underlying its functioning. Computational neuroscience is one of the fields of study that seeks to contribute to this understanding. The introductory course is aimed at undergraduate students interested in acquiring basic knowledge in Computational Neuroscience. The course initially provides a theoretical foundation in both neurophysiology and mathematics, as well as algorithmic concepts, to enable students from diverse backgrounds to benefit from its content with minimal prerequisites. The course then introduces models of neurons, ranging from simple to more elaborate ones, and explores how these neurons connect with each other, including some well-known neural connection circuits and how learning is implemented in these neuron networks. It also includes content on artificial intelligence, such as neural and neuromorphic networks, the latter using the models mentioned initially. The course utilizes interactive Python code, which is free and open-source, for simulating the presented content.
Acesso aberto (Open Access)
Protótipo para análise técnico-econômica de bombas funcionando como turbinas em redes de distribuição de água
(Universidade Federal do Pará, 2025-02-27) VIANA, Ingrid Luna Baia; SOUZA, Davi Edson Sales e; http://lattes.cnpq.br/6130270007673176; HTTPS://ORCID.ORG/0000-0001-9632-5667; ISHIHARA, Júnior Hiroyuki; http://lattes.cnpq.br/3498874642887006; https://orcid.org/0000-0002-0081-7913
The growing need for sustainable solutions in water distribution networks requires technologies that optimize energy efficiency and reduce operating costs. However, choosing the best alternative for implementing Pumps Operating as Turbines (BFTs) is challenging due to the multiple technical and economic criteria involved. Therefore, this study proposes a decision support model, employing multicriteria techniques and computer programming to assist in selecting the most viable alternative within different scenarios. The analysis focuses on the technical-economic feasibility of BFTs in Water Distribution Networks (RDAs), using the TOPSIS method, where data will be evaluated and classified by similarity to ideal conditions. Implemented in Python, the system ensures calculation accuracy, with a graphical interface called FliessEnergy, hosted by Vercel, with a responsive and scalable layout, providing an optimized user experience. Practical application with real RDA data demonstrated its effectiveness in prioritizing the most advantageous alternatives. The results indicated that Scenario 1 presented the best configuration for implementing BFTs, while Scenario 5 was the least favorable alternative. This classification of alternatives helps managers make decisions, taking into account both the proximity to the best conditions and the distance from the worst. To assess the reliability and consistency of the codes, PyTOPS, free software that allows several simulations with changes in the weights of the criteria, was used. A total of 500 simulations were performed, resulting in reduced variability between the scenarios, which reinforces the robustness of the model and its ability to generate consistent classifications even with small changes in the parameters. The model proved consistent and reliable, offering a useful instrument for evaluating the implementation of BFTs in RDAs. It is expected that, with future improvements and integration of databases, this solution can contribute significantly to strategic decisions in the sanitation sector in Brazil.