Abstract

Abstract
In this study, a two-stage stochastic programming joint day-ahead energy and reserve scheduling model to address uncertainty in wind power generation is developed. Apart from the generation side, the demand side is also eligible as a reserve resource and is modeled through responsive load aggregations, as well as large industrial loads that directly participate in the scheduling procedure. The main contribution of this paper is the inclusion of a risk metric, namely the conditional value-at-risk, which renders a conceptually different resource scheduling framework. The proposed model is employed in order to analyze the behavior of energy and reserve scheduling by both generation and demand for a risk-averse independent system operator. To reach practical conclusions, the proposed methodology is tested on the real non-interconnected insular power system of Crete, Greece, which is characterized by a significant penetration of wind power generation.

Abstract
This research pretends to understand the profile of the young Portuguese army in levels of Aggressiveness, Perfectionism and Self-concept, according to comparative analyze by gender and military rank. The sample consisted of 183 soldiers, aged between 18 and 35 years, which are 18 female and 165 male. The study variables were evaluated by applying three instruments, respectively, Multidimensional Scale of Perfectionism, Aggression Questionnaire and Self-Concept Clinical Inventory. The main results show that elevated levels of general Perfectionism and general Self-concept, as moderate levels of general Aggressiveness, characterize the overall profile of the young military. The comparative gender analysis showed that there are significant differences in the results regarding levels of aggression only. On the other hand the comparative analysis of military ranks indicates the existence of significant differences in levels of self-concept and aggression. Finally, the correlation analysis shows that aggressiveness correlates negatively with self-concept and positively with perfectionism.

Abstract
This paper develops a decision support tool that can help fishery authorities to forecast bivalve landings for the dredge fleet accounting for several contextual conditions. These include weather conditions, phytotoxins episodes, stock-biomass indicators per species and tourism levels. Vessel characteristics and fishing effort are also taken into account for the estimation of landings. The relationship between these factors and monthly quantities landed per vessel is explored using multiple linear regression models and data mining techniques (random forests, support vector machines and neural networks). The models are specified for different regions in the Portugal mainland (Northwest, Southwest and South) using six years of data 2010-2015). Results showed that the impact of the contextual factors varies between regions and also depends on the vessels target species. The data mining techniques, namely the random forests, proved to be a robust decision support tool in this context, outperforming the predictive performance of the most popular technique used in this context, i.e. linear regression.

Abstract
This paper describes a set of models and algorithms used under a Tourism Recommendation System based in Users and Points-of-Interest (POI) profiles. This work aims to propose a recommendation system that considers user's functionality levels regarding physical and psychological issues. This proposal considers also in a different way to classify (POI) including their accessibility levels mapped with similar physical and psychological issues.

Abstract
This paper proposes an hybrid approach for short term solar intensity forecasting, which combines different forecasting methodologies with a clustering algorithm, which plays the role of data filter, in order to support the selection of the best data for training. A set of methodologies based on Artificial Neural Networks (ANN) and Support Vector Machines (SVM), used for short term solar irradiance forecast, is implemented and compared in order to facilitate the selection of the most appropriate methods and respective parameters according to the available information and needs. Data from the Brazilian city of Florianopolis, in the state of Santa Catarina, has been used to illustrate the methods applicability and conclusions. The dataset comprises the years of 1990 to 1999 and includes four solar irradiance components as well as other meteorological variables, such as temperature, wind speed and humidity. Conclusions about the irradiance components, parameters and the proposed clustering mechanism are presented. The results are studied and analysed considering both efficiency and effectiveness of the results. The experimental findings show that the hybrid model, combining a SVM approach with a clustering mechanism, to filter the data used for training, achieved promising results, outperforming the approaches without clustering.