Abstract
Nowadays, every public transportation company uses Automatic Vehicle Location (AVL) systems to track the services provided by each vehicle. Such information can be used to improve operational planning. This paper describes an AVL-based evaluation framework to test whether the actual Schedule Plan fits, in terms of days covered by each schedule, the network's operational conditions. Firstly, clustering is employed to group days with similar profiles in terms of travel times (this is done for each different route). Secondly, consensus clustering is used to obtain a unique set of clusters for all routes. Finally, a set of rules about the groups content is drawn based on appropriate decision variables. Each group will correspond to a different schedule and the rules identify the days covered by each schedule. This methodology is simultaneously an evaluator of the schedules that are offered by the company (regarding its coverage) and an advisor on possible changes to such offer. It was tested by using data collected for one year in a company running in Porto, Portugal. The results are sound. The main contribution of this paper is that it proposes a way to combine Machine Learning techniques to add a novel dimension to the Schedule Plan evaluation methods: the day coverage. Such approach meets no parallel in the current literature.

Abstract

Abstract
Education, like many institutions in contemporary society, faces significant challenges to the completion of its mission. This is especially true of higher education, which is often expected to be and seen as a facilitator of social and cultural advancement. Gamification is a relatively new concept intending to use elements from video games in non-game applications. Education is therefore an area with high potential for application of this concept since it seeks to promote people's motivation and engagement. The research in progress aims at creating a model for applying gamification in a course of Human Resources Management for Engineers, where testing and validating the results of that application can be possible. This paper presents the state of the art of gamification in higher education, as well as some guidelines and main features of a gamification framework to be applied in the course of a Masters in Engineering.

Abstract
Despite the technological advances, healthcare systems still face several issues. One of the most important is the lack of communication between systems or the communication process speed. If the information about a patient is not promptly shared in time between services, it may jeopardise the practicioner-pacient relationship. In a worse scenario, the system can even become a handicap and turn the Healthcare processes down into a state of total uselessness. A lot has been done in Portugal to enable the interconnection of external healthcare applications with the ones used in the National Health Service. In this article we present an architecture to facilitate this interconnection. Based on a REST architecture and HL7 communication standards, it connects two current solutions, one for melanoma and one for blood donors, with a central healthcare data repository of the National Healthcare Service. © 2015 AISTI.

Abstract
Many combinatorial optimization problems (COPs) encountered in real-world logistics, transportation, production, healthcare, financial, telecommunication, and computing applications are NP-hard in nature. These real-life COPs are frequently characterized by their large-scale sizes and the need for obtaining high-quality solutions in short computing times, thus requiring the use of metaheuristic algorithms. Meta-heuristics benefit from different random-search and parallelization paradigms, but they frequently assume that the problem inputs, the underlying objective function, and the set of optimization constraints are deterministic. However, uncertainty is all around us, which often makes deterministic models oversimplified versions of real-life systems. After completing an extensive review of related work, this paper describes a general methodology that allows for extending metaheuristics through simulation to solve stochastic COPs. 'Simheuristics' allow modelers for dealing with real-life uncertainty in a natural way by integrating simulation (in any of its variants) into a metaheuristic-driven framework. These optimization-driven algorithms rely on the fact that efficient metaheuristics already exist for the deterministic version of the corresponding COP. Simheuristics also facilitate the introduction of risk and/or reliability analysis criteria during the assessment of alternative high-quality solutions to stochastic COPs. Several examples of applications in different fields illustrate the potential of the proposed methodology. (c) 2015 The Authors. Published by Elsevier Ltd.

Abstract
This paper intends to give some insights on the performance comparison of two main conversion system technologies from a set of wind farms from two major promoters in the Portuguese wind energy sector. Conversion system technologies under analysis are based on the generator type, synchronous and asynchronous, which are the basis of the dominant technological trends in actual market. The performance assessment is accomplished using Data Envelopment Analysis (DEA) methodology, by computing the Malmquist index for group's comparison. From the obtained results, it is possible to conclude that farms with conversion systems based on synchronous generators have a better performance than the ones using conversion systems based on asynchronous generators. These conclusions may support the decision makers in repowering and overpowering processes.