Abstract

Abstract
While affective computing and the entertainment industry still maintain a substantial gap between themselves, biosignals are subject of digital acquisition through low budget technologic solutions at neglectable invasive levels preventing users from focusing their awareness in the equipment. The integration of electroencephalography, galvanic skin response and oximeter in a multichannel framework constitutes an effort in the path to identify emotional states via biosignals expression. In order to induce and detect specific emotions, gender specific sessions were defined based on the International Affective Picture System and performed in a controlled environment, Results granted by distinct analysis techniques showed that high frequency EEG waves are strongly related to emotions and are a solid ground to perform accurate emotion classification. They have also given strong indications that females are more sensitive to emotion induction. On the other hand, one might conclude that the attained success levels concerning relating emotions to biosignals are extremely encouraging not only to the continuation of this research topic but also to the application of these results in domains such as multimedia entertainment, advertising and medical treatments.

Abstract
Short-term risk management is highly dependent on long-term contractual decisions previously established; risk aversion factor of the agent and short-term price forecast accuracy. Trying to give answers to that problem, this paper provides a different approach for short-term risk management on electricity markets. Based on long-term contractual decisions and making use of a price range forecast method developed by the authors, the short-term risk management tool presented here has as main concern to find the optimal spot market strategies that a producer should have for a specific day in function of his risk aversion factor, with the objective to maximize the profits and simultaneously to practice the hedge against price market volatility. Due to the complexity of the optimization problem, the authors make use of Particle Swarm Optimization (PSO) to find the optimal solution. Results from realistic data, namely from OMEL electricity market, are presented and discussed in detail.

Abstract
Asthma patients incur a great cost in terms of loss of quality of life. The purpose of this study is to evaluate the relative contribution and relationship of several patient- and disease-related factors, measured by several variables, to the quality of life in adults with asthma. Two hundred and ten asthmatic outpatients over 18 years old, registered in a Family Health Unit, were randomly selected to complete the Asthma Quality of Life (AQLQ) and Short Form Generic questionnaires (SF-36), respectively. Single and multiple linear regression models were developed to explain the variability of the summary scores of AQLQ and Physical and Mental Health SF-36. As potential predictors, the following independent variables were used: gender, age, number of comorbidities, asthma severity following the Global Initiative for Asthma (GINA) criteria, asthma control (measured by ACQ questionnaire), %FEV1 (forced expiratory volume in the first second) and, for the first time, Graffar Score to assess socioeconomical features. The Graffar Score is an index that divides the population in 5 socioeconomic layers. We report the best Adjusted R Square of these models published in the literature, ranging from 0.40 to 0.76. Women showed poorer quality of life than men. The best predictor of AQLQ was ACQ, followed by Asthma Severity, Gender and %FEV1. The best predictors of Physical and Mental Health SF-36 were, by decreasing importance, ACQ, number of comorbidities, Gender and Graffar Score. We note that the variable Dumber of comorbidities was included in both SF-36 models, but not in AQLQ model. Asthma Severity and %FEV1 did not enter into SF-36 models. We conclude that besides clinical and functional measures, the evaluation process of the overall health status must incorporate quality-of-life measures.

Abstract

Abstract
Recently the area of bioimaging has benefited from new types of image enhancing agents such as quantum dots, carbon nanotubes and other nanoparticles. Cellular or even molecular level resolution has been achieved with different techniques during these last years (i.a. Fluorescence microscopy, PET/CT scan, AFM). Optical Coherence Tomography (OCT) as an imaging technique should also profit from newly developed probes. In this work we explored the tunable properties of different types of nanoparticles as contrast enhancers in OCT applications. We mainly studied the development and characteristics of metallic nanoparticles with tunable properties: gold nanoshells made of a silica core coated with a gold shell. Nanoshell and nanoparticles processing techniques are discussed, as well as their optimization for designing particles with specific absorption and scattering characteristics, and its use in OCT imaging.