Obtenção e caracterização de um compósito polimérico de matriz poliéster e reforço/carga de tecido plano de algodão

He was obtained and studied the feasibility of using TPA (Tissue Cotton Plan) screen type, for bagging, with a weight of 207.9 g / m2 in a composite of orthophthalic crystal polyester resin matrix. The process for obtaining the composite was tested against the maximum number of layers that could be used without compromising the processability and manufacturing of CPs in compression mold. Five configurations / formulations were selected and tested at 1, 4, 8, 10 and 12 layers of cotton tissue - TPA. TPA was not subjected to chemical treatment, only by passing a mechanical washing process. The composite in its various configurations / formulations was characterized to determine its physical properties. The properties of the composite were higher viability resistance to bending, approaching the matrix and impact resistance, superiority in relation to the polyester resin. Another property that has shown good result compared to other composite has water absorption. Analyzing all the properties set the settings / formulations with higher viability were TA8 and TA10, by combining good processability and higher mechanical strength, with lower loss compared to polyester resin matrix. The composite showed lower mechanical behavior of the resin matrix for all the formulations studied except the impact resistance. The SEM showed a good adhesion between the layers of TPA and polyester resin matrix, without the presence of micro voids in the matrix confirming the efficient manufacturing process of the samples for characterization. The composite proposed proved to be viable for the fabrication of structures with low requests from mechanical stresses, and as demonstrated for the manufacture of solar and wind prototypes, and packaging, shelving, decorative items, crafts and shelves, with good visual appearance.

Previsão do índice bovespa por meio de redes neurais artificiais: uma análise comparada aos métodos tradicionais de séries de tempo

Forecast is the basis for making strategic, tactical and operational business decisions. In financial economics, several techniques have been used to predict the behavior of assets over the past decades.Thus, there are several methods to assist in the task of time series forecasting, however, conventional modeling techniques such as statistical models and those based on theoretical mathematical models have produced unsatisfactory predictions, increasing the number of studies in more advanced methods of prediction. Among these, the Artificial Neural Networks (ANN) are a relatively new and promising method for predicting business that shows a technique that has caused much interest in the financial environment and has been used successfully in a wide variety of financial modeling systems applications, in many cases proving its superiority over the statistical models ARIMA-GARCH. In this context, this study aimed to examine whether the ANNs are a more appropriate method for predicting the behavior of Indices in Capital Markets than the traditional methods of time series analysis. For this purpose we developed an quantitative study, from financial economic indices, and developed two models of RNA-type feedfoward supervised learning, whose structures consisted of 20 data in the input layer, 90 neurons in one hidden layer and one given as the output layer (Ibovespa). These models used backpropagation, an input activation function based on the tangent sigmoid and a linear output function. Since the aim of analyzing the adherence of the Method of Artificial Neural Networks to carry out predictions of the Ibovespa, we chose to perform this analysis by comparing results between this and Time Series Predictive Model GARCH, developing a GARCH model (1.1).Once applied both methods (ANN and GARCH) we conducted the results' analysis by comparing the results of the forecast with the historical data and by studying the forecast errors by the MSE, RMSE, MAE, Standard Deviation, the Theil's U and forecasting encompassing tests. It was found that the models developed by means of ANNs had lower MSE, RMSE and MAE than the GARCH (1,1) model and Theil U test indicated that the three models have smaller errors than those of a naïve forecast. Although the ANN based on returns have lower precision indicator values than those of ANN based on prices, the forecast encompassing test rejected the hypothesis that this model is better than that, indicating that the ANN models have a similar level of accuracy . It was concluded that for the data series studied the ANN models show a more appropriate Ibovespa forecasting than the traditional models of time series, represented by the GARCH model