Abstract
This study aims to contribute to the growing literature on teachers' mobility by exploring perceptions and motivations to join these activities, in particular through the collection of evidence on the impact a first and only exchange experience has had on the participants. The research adopts a qualitative methodology in the form of phenomenological interviews with 6 teachers that engaged on only one mobility initiative. The interviewees shared their personal impressions on mobility, including reasons, facilitators, and outcomes of the experience. Bureaucratic, financial and residual professional impact, are among the most cited inhibitors for repeating the initiative. However, most of the participants expect to be become involved again on mobility assignments someday, especially teachers that identified greater impact from this first experience. The analysis provides interesting clues for international offices, Erasmus coordinators and university top managers, who devote considerable effort to the promotion and support of mobility practices.

Abstract
Software systems with a graphical user interface (GUI) front end are typically designed using user interface (UI) Patterns, which describe generic solutions (with multiple possible implementations) for recurrent GUI design problems. However, existing testing techniques do not take advantage of this fact to test GUIs more efficiently. In this paper, we present a new pattern-based GUI testing (PBGT) approach that formalizes the notion of UI Test Patterns, which are generic test strategies to test UI patterns over their different implementations. The PBGT approach is evaluated via 2 case studies. The first study involves 2 fielded Web application subjects; findings show that PBGT is both practical and useful, as testing teams were able to find real bugs in a reasonable time interval. The second study allows deeper analysis by studying software subjects seeded with artificial faults; the findings show that PBGT is more effective than a manual model-based test case generation approach.

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Abstract
This paper analyzes the implementation of new technologies in network industries through the development of a suitable regulatory scheme. The analysis focuses on Smart Grid (SG) technologies which, among others benefits, could save operational costs and reduce the need for further conventional investments in the grid. In spite of the benefits that may result from their implementation, the adoption of SGs by network operators can be hampered by the uncertainties surrounding actual performances. A decision model has been developed to assess the firms' incentives to invest in "smart" technologies under different regulatory schemes. The model also enables testing the impact of uncertainties on the reduction of operational costs, and of conventional investments. Under certain circumstances, it may be justified to support the development and early deployment of emerging innovations that have a high potential to ameliorate the efficiency of the electricity system, but whose adoption faces many uncertainties.

Abstract
With the advent of restructuring electricity sector and smart grids, combined with the increased variability and uncertainty associated with electricity market prices (EMP) signals and players' behavior, together with the growing integration of renewable energy sources, enhancing prediction tools are required for players and different regulators agents to face the non-stationarity and stochastic nature of such time series, which must be capable of supporting decisions in a competitive environment with low prediction error, acceptable computational time and low computational complexity. Hybrid and evolutionary approaches are good candidates to surpass most of the previous concern considering time series prediction. In this sense, this work proposes a hybrid model composed by a novel combination of differential evolutionary particle swarm optimization (DEEPSO) and adaptive neuro-fuzzy inference system (ANFIS) to predict, in the short-term, the wind power and EMP, testing its results with real and published case studies, proving its superior performance within a robust prediction software tool. © 2016 IEEE.