The development of shape memory actuators has enabled noteworthy applications in the mechanical engineering, robotics, aerospace, and oil industries and in medicine. These applications have been targeted on miniaturization and taking full advantage of spaces. This article analyses a Ti-Ni shape memory actuator used as part of a flow control system. A Ti-Ni spring actuator is subjected to thermomechanical training and parameters such as transformation temperature, thermal hysteresis and shape memory effect performance were investigated. These parameters were important for understanding the behavior of the actuator related to martensitic phase transformation during the heating and cooling cycles which it undergoes when in service. The multiple regression methodology was used as a computational tool for analysing data in order to simulate and predict the results for stress and cycles where the experimental data was not developed. The results obtained using the training cycles enable actuators to be characterized and the numerical simulation to be validated.

Este artigo apresenta uma modelagem de um problema de decisão no mercado financeiro onde se deseja escolher um portfólio de investimentos de forma a minimizar o risco, utilizando indicadores econômicos para predizer investimentos futuros baseados em dados históricos. O modelo de decisão é baseado na teoria da decisão e análise bayesiana caracterizado por sua estrutura matemática. Em seguida apresenta-se um estudo de caso no mercado financeiro brasileiro considerando dados de Janeiro de 1998 a Junho de 2005.

This study presents one approach to investing in the financial markets using a decision theory point of view, where the main decision is to choose an investment portfolio, based on economic indexes, in order to predict future investments based on historical data, which minimizes the risk involved. The decision model is based on Decision Theory and Bayesian Analysis and the application uses Brazilian financial market data from January 1998 to June 2005 as an input.