Abstract
Due to today's business environment demands organizations need to create teams to perform work in projects, with quality, within time and budget. Therefore, teams play a very important role in the organizational development, by creating conditions that enable to overcome difficulties and to promote the improvement of the organizational overall performance. Hence the relevance of studying the project teams resilience, identifying the actions that can influence the project development and its final outcomes. The resilience of a team can be defined as the team's ability to deal with problems, overcome obstacles, or resist the pressure of adverse situations, without entering into rupture. This research, focused on team resilience, firstly involved a literature review, followed by brainstorming sessions, resulting in a preliminary list of useful actions to improve project team resilience. Then, a survey was administered in order to identify the most useful actions perceived from the identified list. Completed questionnaires were received from 115 team members of information technologies/information systems projects being developed in an academic setting. By identifying the most useful actions perceived, as those having the highest potential for increasing project team resilience, practitioners and organizations can set their priorities towards improving team resilience. The results showed that the top ten list of useful actions identified is composed by very well-known and recognized actions, such as the promotion of collaboration and solidarity among project team members, and the recognition, appreciation and use of the talents and competencies of each team member. (C) 2015 Published by Elsevier B.V.

Abstract
Bipolar Disorder (BD) is a chronic and disabling disease that usually appears around 20 to 30 years old. Patients who suffer with BD may struggle for years to achieve a correct diagnosis, and only 50% of them generally receive adequate treatment. In this work we apply a machine learning technique called Inductive Logic Programming (ILP) in order to model relapse and no-relapse patients in a first attempt in this area to improve diagnosis and optimize psychiatrists' time spent with patients. We use ILP because it is well suited for our multi-relational dataset and because a human can easily interpret the logical rules produced. Our classifiers can predict relapse cases with 92% Recall and no-relapse cases with 73% Recall. The rules and variable theories generated by ILP reproduce some findings from the scientific literature. The generated multi-relational models can be directly interpreted by clinicians and researchers, and also open space to research biological mechanisms and interventions.

Abstract
Technology plays a key role in daily life of people with special needs, being a mean of integration or even communication with society. By built up experience, we find that support tools play a crucial part in empowerment of persons with special needs and small advances may represent shifts and opportunities. The diversity of solutions and the need for dedicated hardware to each feature represents a barrier to its use, compromising the success of the solutions against, among others, problems of usability and scale. This paper aims to explore the concept of inclusive collaboration to enhance the mutual interaction and assistance. The proposed approach combines and generalizes the usage of human computation in a collaborative environment with assistive technologies creating redundancy and complementarity in the solutions provided, contributing to enhance the quality of life of people with special needs and the elderly. The CanIHelp platform is an embodiment of the concept as a result from an orchestrated model using mechanisms of collective intelligence through social inclusion initiatives. The platform features up for integrating assistive technologies, collaborative tools and multiple multimedia communication channels, accessible through multimodal interfaces for universal access. A discussion of the impacts of fostering collaboration and broadening from the research concepts to the societal impacts is presented. As final remarks a set of future research challenges and guidelines are identified.

Abstract

Abstract
The discrete sampling of a wave-front using a Shack-Hartmann sensor limits the maximum spatial frequency we can measure and impacts sensitivity to frequencies at the high end of the correction band due to aliasing. Here we present Wiener filters for wave-front reconstruction in the spatial-frequency domain, ideally suited for systems with a high number of degrees of freedom. We develop a theoretical anti-aliasing (AA) Wiener filter that optimally takes into account high-order wave-front terms folded in-band during the sensing (i.e., discrete sampling) process. We present Monte-Carlo simulation results for residual wave-fronts and propagated noise and compare to standard reconstruction techniques (in the spatial domain). To cope with finite telescope aperture we've developed and optimised a Gerchberg-Saxton like iterative-algorithm that provides superior performance.

Abstract
Demand response concept has been gaining increasing importance while the success of several recent implementations makes this resource benefits unquestionable. This happens in a power systems operation environment that also considers an intensive use of distributed generation. However, more adequate approaches and models are needed in order to address the small size consumers and producers aggregation, while taking into account these resources goals. The present paper focuses on the demand response programs and distributed generation resources management by a Virtual Power Player that optimally aims to minimize its operation costs taking the consumption shifting constraints into account. The impact of the consumption shifting in the distributed generation resources schedule is also considered. The methodology is applied to three scenarios based on 218 consumers and 4 types of distributed generation, in a time frame of 96 periods.