Optimal Wind Bidding Strategies in Day-Ahead Markets

This paper presents a computer application for wind energy bidding in a day-ahead electricity market to better accommodate the variability of the energy source. The computer application is based in a stochastic linear mathematical programming problem. The goal is to obtain the optimal bidding strategy in order to maximize the revenue. Electricity prices and financial penalties for shortfall or surplus energy deliver are modeled. Finally, conclusions are drawn from an illustrative case study, using data from the day-ahead electricity market of the Iberian Peninsula.

Logic Programming and Artificial Neural Networks in Pharmacological Screening of Schinus Essential Oils

Some plants of genus Schinus have been used in the folk medicine as topical antiseptic, digestive, purgative, diuretic, analgesic or antidepressant, and also for respiratory and urinary infections. Chemical composition of essential oils of S. molle and S. terebinthifolius had been evaluated and presented high variability according with the part of the plant studied and with the geographic and climatic regions. The pharmacological properties, namely antimicrobial, anti-tumoural and anti-inflammatory activities are conditioned by chemical composition of essential oils. Taking into account the difficulty to infer the pharmacological properties of Schinus essential oils without hard experimental approach, this work will focus on the development of a decision support system, in terms of its knowledge representation and reasoning procedures, under a formal framework based on Logic Programming, complemented with an approach to computing centered on Artificial Neural Networks and the respective Degree-of-Confidence that one has on such an occurrence.