Abstract
This paper aims to optimize the behavior of Plug-in Electric Vehicle (PEV) parking lots considering the interaction with renewable energy resources. In the proposed model, the PEV parking lot can participate in the day-ahead energy, spinning reserve and regulation markets. On this basis, behavior of the PEV parking lot in a distribution system is modeled consisting of wind and photovoltaic power producers. Moreover, the model consists of uncertainty characteristics of PEV behavior, wind and photovoltaic power generation. In order to tackle the mentioned uncertainties, a stochastic programming approach is utilized to maximize the profit of the parking lot operator. Numerical cases are studied to show the effectiveness of the proposed model. The results indicate that renewable energy resources have significant impacts on the behavior of PEV parking lot.

Abstract
Collaboration and cooperation are two of the main factors in the development of good quality software. Thus, it is particularly important to examine these two dimensions in the course of a software development process to ensure its quality and enable improvements in the development process. Having as fundamentals these premises the 4C model was proposed, based on the following dimensions: Communication, Coordination, Collaboration and Cooperation. This paper describes the 4C model and its applicability in the analysis of the Hybrid User Centered Development Methodology (HUCDM) which formed the basis for the development of the educational resource Courseware Sere -The Human Being and the Natural Resources. The 4C model allowed the analysis of the process defined in HUCDM to be performed based on the interactions arising in the forums available on Moodle. This paper is based on the interactions that occurred on the development of the software first screen of phase I -Oil, focusing the analysis on the collaboration and cooperation dimensions. The results achieved clearly support the importance of developing educational resources based on collaborative and cooperative work based on multidisciplinary teams.

Abstract
Common breast cancer treatments, as the removal of axillary lymph nodes, cause severe impairments in women's upper-body function. As a result, several daily activities are affected which contributes to a decreased QOL. Thus, the assessment of functional restrictions after treatment is essential to avoid further complications. This paper presents a pioneer work, which aims to develop an upper-body function evaluation method, traduced by the identification of lymphedema. Using the Kinect, features of the upper-limbs motion are extracted and supervised learning algorithms are used to construct a predictive classification model. Very promising results are obtained, with high classification accuracy.

Abstract
The development of new technologies, information systems, decision support systems and clinical parameters prediction algorithms using machine learning and data mining, opens a new outlook in many areas of health. In this context, the concept of Quality of Life (QOL) has relevance in health and the possibility of integrate this measure in developing systems Decision Support Clinic (SADC). Through individual expectation of physical well-being, psychological, mental, emotional and spiritual patient, clinical variables and quality of life assessment, we intend to make a study of data to establish correlations with clinical data and pharmaceutical data, socio-economic factors, among others, for obtaining knowledge in terms of behavioral patterns of chronically ill, reaching a number of reliable data and easily accessible, capable of enhancing the decision-making process on the part of specialist medical teams, seeking to improve treatments and consequently the quality of life related to health chronically ill. This paper studied and compared related studies that develop systems for decision support and prediction in the clinical area, with emphasis on studies in the area of quality of life. © 2015 AISTI.

Abstract
The development of new technologies, information systems, decision support systems and clinical parameters prediction algorithms using machine learning and data mining opens a new perspective in many area of health. In this context, relevance presents the concept of Quality of Life (QOL) in health and the possibility of developing Support Systems Clinical Decision (SADC) that use it. Through individual expectation of physical well-being, psychological, mental, emotional and spiritual patients, discussed variables and measures the quality of research area of life, we intend to make a study of data to establish correlations with laboratory, pharmaceutical data, socio-economic, among others, obtaining knowledge in terms of behavioral patterns of chronic patients, achieving a number of reliable data and easily accessible, capable of enhancing the decision-making process by the specialized medical teams, seeking to improve treatments and consequently the related quality of life with the Health chronically ill. This paper studied and compared related studies that develop systems for decision support and prediction in the clinical area, with emphasis on studies in the area of quality of life.

Abstract
Crete Island is rich in renewable energy resources such as wind and solar. Likewise other European territories, renewable sources already are being explored for power production. Currently a large amount of wind energy on Crete is curtailed during certain daily periods as a result of reduced demand and minimum operating levels on thermal generators. Reducing wind power curtailment magnitude requires additional sources of flexibility in the grid, and electric energy storage is one of them. This paper address wind generation curtailment minimization through the storage of wind energy surplus. NaS (sodium sulfura) battery modelling is used in this study in order to shift wind generation from off-peak to on-peak through a technical-economic analysis, considering the total annualized cost of the storage system and the wind power curtailment based on an annual basis. The obtained results are based on real data, which includes Crete Island demand, renewable and conventional power generation.