Abstract
This work presents a screening and evaluation platform for depression and suicidality that has been tested in the scope of primary healthcare. The main objective is to improve the efficiency and effectiveness of screening processes. A web-based, decision support platform was provided for qualified healthcare professionals. The platform provides several assessment tools for patient evaluation and monitoring of their treatment, along with follow up appointment management. A preliminary evaluation process was carried out to understand the health professional's satisfaction. This revealed there was general satisfaction with its integrated functions and all the provided methods of assessment. In conclusion, the project sustains the goal of improving the treatment outcomes for clinical depression by refining the screening methods and consequently increase the screening effectiveness and efficiency.

Abstract
Software Product Lines (SPLs) enable the development of families of software systems by taking advantage of the commonalities and variabilities of the members of the family. Despite its many advantages, it is an unexplored area in the electronic government domain, an area with evident families of services, and with high demands to develop faster and better services to citizens and businesses while reducing costs. This paper discusses the need of formal methods to model SPLs for such domain. It presents a case study of a family of public licensing services modeled in UPPAAL and based on Featured Timed Automata, an extension of Timed Automata to model real-time SPLs. It analyzes the suitability of FTA to model distributed families of services, while provides hints on a possible enrichment of FTA to better support modularization and compositionality of services.

Abstract
This work proposes a new approach to detect the market power in power systems consisting of renewable energy sources. A model powered by game theory and agent-based systems is proposed to analyze the behaviors of each market player from the regulatory body's viewpoint. The proposed method considers both the tacit collusive behavior of Generation Companies, and the possibility of explicit collusion. In addition, three new market power indexes are introduced to quantify the market power level as well as to precise its causes. In order to evaluate the efficiency of the methodology, various numerical studies are carried out, and the effect of several market regulations is investigated on the market behaviors.

Abstract

Abstract
Increasing demand for containerization compels container terminals to improve their performance. Uncoordinated scheduling of operations is one of the main factors accounting for poor performance at automated container terminals (ACTs). To increase land utilization efficiency and lower operational times, a new storage system called the split-platform automated storage/retrieval system (SP-AS/RS) has been introduced for temporary storage of containers. This paper describes a multi-objective mixed-integer programming (MIP) model that is based on a combination of multiple interacting sub-tasks. It is aimed at optimizing the integrated scheduling of handling and storage operations in ACTs. The MIP model objective function is to minimize delays in the loading/unloading tasks of the cranes and the travel time of vehicles and platforms in the SP-AS/RS. At the same time, a simulated annealing algorithm (SAA) that provides near-optimal solutions for the problem in a reasonable computation time is appraised. The results of this study show that the objective function of the MIP model is, on average, 58 % lower than that of the non-integrated scheduling method. On the other hand, the best objective function values obtained by the SAA indicate only a 3.7 % disadvantage in comparison with optimal values determined by the MIP model, demonstrating that the SAA is able to provide near-optimal solutions for the integrated scheduling of handling and storage operations.

Abstract
In this paper we analyze the information propagated through three social networks. Previous research has shown that most of the messages posted on Twitter are truthful, but the service is also used to spread misinformation and false rumors. In this paper we focus on the search for automatic methods for assessing the relevance of a given set of posts. We first retrieved from social networks, posts related to trending topics. Then, we categorize them as being news or as being conversational messages, and assessed their credibility. From the gained insights we used features to automatically assess whether a post is news or chat, and to level its credibility. Based on these two experiments we built an automatic classifier. The results from assessing our classifier, which categorizes posts as being relevant or not, lead to a high balanced accuracy, with the potential to be further enhanced.