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.

Abstract
The deployment of Smart Grid technologies opens new opportunities to develop new forecasting and optimization techniques. The growth of solar power penetration in distribution grids imposes the use of solar power forecasts as inputs in advanced grid management functions. This paper proposes a new forecasting algorithm for 6 h ahead based on the vector autoregression framework, which combines distributed time series information collected by the Smart Grid infrastructure. Probabilistic forecasts are generated for the residential solar photovoltaic (PV) and secondary substation levels. The test case consists of 44 micro-generation units and 10 secondary substations from the Smart Grid pilot in Evora, Portugal. The benchmark model is the well-known autoregressive forecasting method (univariate approach). The average improvement in terms of root mean square error (point forecast evaluation) and continuous ranking probability score (probabilistic forecast evaluation) for the first 3 lead-times was between 8% and 12%, and between 1.4% and 5.9%, respectively. (C) 2015 Published by Elsevier Ltd.

Abstract

Abstract
In the last decade, companies tend to become virtualized. This virtualization is due in large part to developments in the use of mobile platforms and the impact on their infrastructure. The concept of BYOD (Bring Your Own Device) adoption and use of cloud computing are having profound implications for how the technologies are being used, as well as the interaction between individuals and these technologies. In this context, we conducted a study based on a questionnaire about the impact of the use of mobile devices and the cloud in organizations, taking into consideration their use in performing professional and personal tasks. With the questionnaire and its subsequent analysis it was found that organizations have a lack of understanding about almost all of the implications and consequences of the use of mobile devices and the cloud.

Abstract
The current operator displays of Distributed Control Systems (DCS) are designed for 2D environments. This limits the full awareness situation of the industrial process, since it is distributed across multiple operator displays and requires the operator to navigate among them. This inspired the idea of creating a single operator display. Through an accurate and systematic literature review and by following the guidelines of design science methodology, it was possible to achieve a set of indicators and artefacts that corresponded to the defined goals. By developing a 2.5D/3D DCS interface application, the authors aimed at creating a way of allowing a full view of the entire manufacturing process, thus increasing the amount and the quality of information that is given to the operator and preventing unnecessary operation navigation between displays.

Abstract
The problem of selecting suppliers/partners is a crucial and important part in the process of decision making for companies that intend to perform competitively in their area of activity. The selection of supplier/partner is a time and resource-consuming task that involves data collection and a careful analysis of the factors that can positively or negatively influence the choice. Nevertheless it is a critical process that affects significantly the operational performance of each company. In this work, trough the literature review, there were identified five broad suppliers selection criteria: Quality, Financial, Synergies, Cost, and Production System. Within these criteria, it was also included five sub-criteria. Thereafter, a survey was elaborated and companies were contacted in order to answer which factors have more relevance in their decisions to choose the suppliers. Interpreted the results and processed the data, it was adopted a model of linear weighting to reflect the importance of each factor. The model has a hierarchical structure and can be applied with the Analytic Hierarchy Process (AHP) method or Simple Multi-Attribute Rating Technique (SMART). The result of the research undertaken by the authors is a reference model that represents a decision making support for the suppliers/partners selection process.