Application of the Kalman Filter in Functional Magnetic Resonance Image Data

The Kalman-Bucy filter was applied on the preprocessing of the functional magnetic resonance image-fMRI. Numerical simulations of hemodynamic response added Gaussian noise were performed to evaluate the performance of the filter. After the proceeding was applied in auditory real data. The Kohonen’s self-organized map was employed as tools to compare the performance of the Kalman’s filter with another type of pre-processing. The results of the application of Kalman-Bucy filter for simulated data and real auditory data showed that it can be used as a tool in the temporal filtering step in fMRI data

Optimization of soybean outflow routes from Mato Grosso, Brazil

The purpose of this work is to apply a transshipment model, based on the theory of linear programming in a problem of optimization of the flow cost of soybeans from the State of Mato Grosso. The model consisted of analyzing the cost of transportation through the current transportation infrastructure, proposing two new options, being waterway and railway, as well as maintaining the port capacity of Arco Norte. 2018 production and projections for 2025 and 2030 were also considered. The results showed that the greatest reductions in transportation costs in 2018 occurred in the flow of production through Arco Norte. In addition, the new intermodal routes have significantly changed the transportation matrix, contributing to Brazilian competitiveness in the foreign market and assisting in the development of the North and Northeast regions

Optimization of filament antennas using the Gauss-Newton method

The project of the Yagi-Uda antenna was optimized using the Gauss-Newton method. The optimization consisted of specifying value interval for directivity, front-to-back ratio and beamwidth and, starting from a pre-defined initial model, the best values for the length and spacing of the elements were determined. For the direct modeling, the method of moments on the integral Pocklington equation was used, which consisted of obtaining the values of directivity, front-to-back ratio and beamwidth from the length and spacing between known elements. The procedure was applied to the synthesis of Yagi-Uda antennas with five and six elements and the results were found to be as good as those obtained in the literature using other optimization methods

A comparison of the Normal and Laplace distributions in the models of fuzzy probability distribution for portfolio selection

The propose of this work is applied the fuzzy Laplace distribution on a possibilistic mean-variance model presented by Li et al which appliehe fuzzy normal distribution. The theorem necessary to introduce the Laplace distribution in the model was demonstrated. It was made an analysis of the behavior of the fuzzy normal and fuzzy Laplace distributions on the portfolio selection with VaR constraint and risk-free investment considering real data. The results showns that were not difference in assets selection and in return rate, however, There was a change in the risk rate, which was higher in the Laplace distribution than in the normal distribution

Use of Physical Education Classes as a Didactic Laboratory for Teaching Mathematics: An Example with a Quadratic Function

The research objective of this study was to evaluate the use of Physical Education classes as didactic laboratory for lessons in Mathematics, presenting an alternative way to conduct classes, mainly of quadratic functions, illustrating basic concepts such as graphs plotting and determination coefficients, analyze if such use achieves some of the goals of using a Didactic Laboratory in addition to research ways to interdisciplinary with Physics. Discusses an action in which students work in groups to solve problems proposed based on empirical data obtained through play activities and measures of athletics values practiced by the students allowing may have the opportunity to produce arguments and more meaningful answers, which would improve the overall learning. The athletics and recreational activities are then used as problematic objects both empirically and qualitatively. As a result, it was observed that some of the objectives of a Didactic Laboratory are achieved when using the Physical Education classes and it appears that this feature is much more available in public schools than they are equipped with a science laboratory

Optimization method applied to decision-making on intermodal alternative for soybean outflow in the State of Pará-Brazil

In Brazil, the sector of agribusiness suffers with the lack of infrastructure for the production outflow and as transportation costs increase, they affect its competitiveness in the world scenario. In this work, an optimization model was developed to support decision-making to soybeans transport for export, in regions in the state of Para (Brazil) near to Tocantins-Araguaia waterway. The study applied a Linear Programming model, adopting the transport problem, in search of minimizing the costs of alternative routes, restricted to the respective supply and demand limitations. The model aimed to minimize the transport costs from the production centers to the exporting port, through the current infrastructure and suggesting the road and waterway intermodal alternative by means of the interior navigation, considering diverse points for transshipment. The results showed a cost reduction for production outflow in the base year of the study when the intermodal transport was used. Furthermore, the new routes only by waterway created a new transport network configuration, decreasing the road distances for the municipalities production outflow, supporting the increasing of competitiveness of the state, as well as providing wealth generation in the region

A COMPARATIVE STUDY OF BOX JENKINS MODELS AND ARTIFICIAL NEURAL NETWORKS IN FORECASTING PLUVIOMETRIC FLOWS AND PRECIPITATIONS OF ARAGUAIA-TOCANTINS BASIN/BRAZIL

Estudar a variabilidade dos parâmetros hidroclimáticos locais em baciashidrográficas é importante para melhorar o gerenciamento dos recursos hídricos.Para tal, foram utilizados o modelo estatístico baseado na metodologia Box-Jenkins, adotado por muitas empresas na análise de séries temporais, inclusivetodo o setor elétrico brasileiro, e a tecnologia de redes neurais, que se apresentacomo poderosa ferramenta para previsões. Na comparação entre as duas técnicas,foram utilizadas observações de médias mensais de duas estações meteorológicasda Bacia Araguaia-Tocantins, Brasil, uma de vazões mensais (m3/s) e outra deprecipitações pluviométricas mensais (mm), da Agência Nacional de Águas (ANA),com registros contínuos nos períodos de 1969 a 2017 e 1974 a 2017. As previsõesforam testadas para 12 e 24 meses. Uma comparação entre os dois métodos,usando o teste de hipótese a partir de intervalos de confiança de 95%, mostrouque não houve diferenças estatisticamente significativas nas previsões individuaistanto de precipitações pluviométricas como de vazões. Entretanto, o uso do rootmean square error (RMSE) mostrou que o método de Box-Jenkins apresentamelhores resultados. A maior dificuldade nesse método é na construção domodelo, sobretudo em séries com alta variabilidade. O método de redes neurais,em geral, consome mais tempo computacional em relação ao Box-Jenkins.Studying the variability of local hydro-climatic parameters in river basins is important for the better management of water resources. In order to do so, we used two methods: Box-Jenkins methodology, adopted by many companies in the time series analysis, including the entire Brazilian electric sector, and the Neural Networks technology, which presents itself as a powerful tool for forecasting. Observations of monthly averages of two meteorological stations of the Araguaia-Tocantins basin (Brazil) were made for comparation purposes, one of monthly flows (m3/s) and one of monthly rainfall (mm), of the National Waters Agency (Agência Nacional de Águas — ANA) with continuous records from 1969 to 2017 and 1974 to 2017. The forecasts were tested for twelve and twenty- four months. A comparison between the two methods using a hypothesis test from 95% confidence intervals, showed that there were no statistically significant differences between them in individual rainfall and flow forecasts. However, if the RMSE method is used, the Box-Jenkins method presented better results in the forecasts. The main difficulty in the Box-Jenkins method is the construction of the model, especially in high variability series. The method of Neural Networks, in general, consumes more computational time compared to the Box-Jenkins model