Resumo O objetivo desse trabalho é fazer a análise climática, utilizando modelos de redes neurais artificiais, como auxílio no processo de tomada de decisão no plantio de alguns tipos de produtos agrícolas. Um banco de dados com os principais elementos do clima foi construído a partir do site do INMET (Instituto Nacional de Meteorologia) e desses elementos foram identificados, em um nível de significância de 0,05, os que mais influenciavam no valor da temperatura média. Modelos de Redes Neurais Artificiais foram criados e avaliados por meio do MAD (Mean Absolute Deviation), MSE (Mean Squared Error), RMSE (Root-Mean Squared Error) e MAPE (Mean Absolute Percentage Error), e finalmente, associou-se o valor da previsão com o melhor cultivo agrícola a ser aplicado. Foram elaboradas 12 redes neurais, oito são referentes à previsão da temperatura e as outras quatro à previsão da precipitação. As redes que demostraram melhor desempenho são as que consideram todos os elementos do clima. É possível concluir que as redes neurais demostraram um desempenho adequado na previsão de séries temporais caóticas, e consequentemente, tiveram o seu resultado associado com o melhor cultivo a ser aplicado para cada previsão. Um cronograma é apresentado no final, sugerindo a melhor época para plantar cada uma das culturas estudadas. Observa-se que para os próximos cinco anos, o cultivo mais adequado à faixa prevista é o da cenoura.

Abstract The aim of this study is to conduct climate forecasting with models of artificial neural networks as a tool in the decision-making process for the planting of some types of agricultural products. A database with the main climate elements was built from the National Institute of Meteorology (INMET), and those elements that influenced the average temperature value the most were found at a significance level of 0.05. Models of Artificial Neural Networks were developed and tested using Mean Absolute Deviation (MAD), Mean Squared Error (MSE), Root-Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE), before being linked to the best agricultural cultivation forecast value. Twelve neural networks were elaborated, eight of them are related to the temperature forecast and the other four are related to the precipitation forecast. The networks that showed the best performance are those that consider all the elements of climate. It is possible to conclude that the artificial neural networks showed an adequate performance in predicting chaotic time series, and that their results were therefore linked to the optimum cultivation to use for each forecast. A schedule is supplied at the end, indicating the ideal time to plant each of the crops evaluated. Carrot is found to be the best suited crop for the forecasted range over the next five years.

Abstract This study aims at presenting a new analysis method for adhesive (interfacial) and cohesive (bulk) failures in aluminum (Al) and high-density polyethylene (HDPE) sandwich composites. The samples were submitted to tensile strength tests, according to ASTM C297 so as to obtain the pull-off stresses. The delaminated aluminum surfaces were analyzed with SEM/EDS (Scanning Electronic Microscopy with Energy Dispersive Scanning). The images were calculated using a genetic algorithm (GA), where the areas with cohesive and adhesive failures were identified by the presence or absence of organic compounds. The proposed method also uses the data from the pull-off tests to determine the stress values of adhesion and cohesion separately. Applying the new method, the mean stress of cohesion was 4.17 MPa, and for adhesion it was 0.57 MPa. Thus, it was possible to distinguish and calculate the failure stresses applied to metal/polymer composites.

ABSTRACT In this paper we analyzed the probabilities of transitions of state between Ibovespa and Dow Jones indexes using High-order Multivariate Markov Chain. While the stock market may be profitable, the existence of risks can lead to large losses. A mathematical model capable of considering different sources can aid in decision making. This model can work with stochastic data, causing different databases to be transformed into transitional matrices between states. For this, a set of a daily variation data were used between January 2008 and March 2018. Through this application, it was possible to show an interaction between the indexes and that the highest frequency of events was of the variation of -0.49 to 0.5% in Dow Jones to -0.49 to 0.5% in Ibovespa, with 428 cases, and the probability of this situation occurring again, of Dow Jones at time t to Ibovespa at time t+2, is 27.21%. Empirical results suggest that this application can help investors make decisions based on transition probabilities.

The focus of this paper is to establish a characterisation method for seven polyamide (PA) grades to determine the major material to manufacture an automotive worm gear. The composite properties were measured according to the worm gear loadings: tensile strength, Young's modulus, abrasion and impact resistance. They were also correlated to the PA moisture absorption and its glass fibre (GF) reinforcement. The data from mechanical tests were applied in the finite element analysis (FEA) using the von Mises stress criterion. Before the rig tests of the PA worm gears, the injection process was evaluated, through the capillary rheometry. A higher difficulty to process PA 6/6 30% GF was found, due to its lower apparent viscosity. In the end, the influence of moisture absorption was as decisive to the gear's material selection as the GF to the pinion. Thus, the PAs with the best performance were: PA 6 with 30% GF (gear) and with PA 60% GF (pinion).