Cálculo de funções de Wannier eletrônicas para aplicações em ciência dos materiais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Posição e densidade dos zeros de Yang-Lee do modelo de Blume-Emery-Griffiths unidimensional sobre anéis conexos e desconexos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Modelo estatístico de rede de resistores para o estudo de processos de condução em nanocompósitos poliméricos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Funções de Wannier para cristais fotônicos unidimensionais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Preparação e caracterização elétrica do compósito de poliuretano/negro de fumo

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Utilização de redes neurais artificiais no ajuste de controladores suplementares e dispositivo FACTS STATCOM para a melhoria da estabilidade a pequenas perturbações do sistema elétrico de potência

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Obtenção e caracterização de nanocompósitos de borracha natural reforçada com nanofibras de celulose recobertas com polianilina

Pós-graduação em Ciência dos Materiais - FEIS

Aplicação de redes neurais no controle de teores de cobre e ouro do depósito de Chapada (GO)

Pós-graduação em Geologia Regional - IGCE

Projeto otimizado de redes neurais artificiais para predição da rugosidade em processos de usinagem com a utilização da metodologia de projeto de experimentos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Estatística espacial e redes neurais aplicadas no estudo de epidemias de huanglongbing e mancha preta na cultura dos citros

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Estudo da influência da flexibilidade dos discos nas rotações críticas de rotores

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Chitosan and alginate biopolymer membranes for remediation of contaminated water with herbicides

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

The use of artificial neural networks in analysing the nutritional ecology of Chrysomya megacephala (F.) (Diptera: Calliphoridae), compared with a statistical model

Artificial neural networks (ANNs) have been widely applied to the resolution of complex biological problems. An important feature of neural models is that their implementation is not precluded by the theoretical distribution shape of the data used. Frequently, the performance of ANNs over linear or non-linear regression-based statistical methods is deemed to be significantly superior if suitable sample sizes are provided, especially in multidimensional and non-linear processes. The current work was aimed at utilising three well-known neural network methods in order to evaluate whether these models would be able to provide more accurate outcomes in relation to a conventional regression method in pupal weight predictions of Chrysomya megacephala, a species of blowfly (Diptera: Calliphoridae), using larval density (i.e. the initial number of larvae), amount of available food and pupal size as input data. It was possible to notice that the neural networks yielded more accurate performances in comparison with the statistical model (multiple regression). Assessing the three types of networks utilised (Multi-layer Perceptron, Radial Basis Function and Generalised Regression Neural Network), no considerable differences between these models were detected. The superiority of these neural models over a classical statistical method represents an important fact, because more accurate models may clarify several intricate aspects concerning the nutritional ecology of blowflies.

Pole placement method using the system matrix transfer function and sparsity

This paper presents a pole placement method using both the augmented Jacobian and the corresponding system transfer function matrices. From the manipulation of these matrices a straightforward approach results to get the coefficients of a non-linear system, whose solution gives the parameters of the stabilizers that can provide a pre-specified minimum damping to the system. (C) 2001 Elsevier B.V. Ltd. All rights reserved.

Multi-spectroscopic monitoring of cisplatin-derived species delivery from ureasil polyether hybrid matrix

Driven by the challenges involved in the development of new advanced materials with unusual drug delivery profiles capable of improving the therapeutic and toxicological properties of existing cancer chemotherapy, the one-pot sol-gel synthesis of flexible, transparent and insoluble urea-cross-linked polyether-siloxane hybrids has been recently developed. In this one-pot synthesis, the strong interaction between the antitumor cisplatin (CisPt) molecules and the ureasil-poly(propylene oxide) (PPO) hybrid matrix gives rise to the incorporation and release of an unknown CisPt-derived species, hindering the quantitative determination of the drug release pattern from the conventional UV-Vis absorption technique. In this article, we report the use of an original synchrotron radiation calibration method based on the combination of XAS and UV-Vis for the quantitative determination of the amount of Pt-based molecules released in water. Thanks to the combination of UV-Vis, XAS and Raman techniques, we demonstrated that both the CisPt molecules and the CisPt-derived species are loaded into an ureasil-PPO/ureasil-poly(ethylene oxide) (PEO) hybrid blend matrix. The experimentally determined molar extinction coefficient of the CisPt-derived species loaded into ureasil-PPO hybrid matrix enabled the simultaneous time-resolved monitoring of each Pt species released from this hybrid blend matrix.