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As video games, particularly, social games are growing in popularity and number of users, there has been an increasing interest in its potential as innovative teaching tools. Gamification is a new concept intending to use elements from video games in non-game applications. Education is an area with high potential for application of this concept since it seeks to promote people's motivation and engagement. The research in progress will try to find how to apply social gamification in education, testing and validating the results of that application. To fulfil these objectives, this paper presents the guidelines and main features of a social gamification framework to be applied in an existent K-6 social learning environment.

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This paper proposes a wind speed forecasting model that contributes to the development and implementation of adequate methodologies for Energy Resource Management in a distribution power network, with intensive use of wind based power generation. The proposed forecasting methodology aims to support the operation in the scope of the intraday resources scheduling model, namely with a time horizon of 10 minutes. A case study using a real database from the meteorological station installed in the GECAD renewable energy lab was used. A new wind speed forecasting model has been implemented and it estimated accuracy was evaluated and compared with a previous developed forecasting model. Using as input attributes the information of the wind speed concerning the previous 3 hours enables to obtain results with high accuracy for the wind shortterm forecasting.

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In this work we aim at increasing the utility of a preelection poll, by improving the quality of the vote share estimates, both at macro and micro level. Three different methodologies are applied with that purpose: (1) polls aggregation, using existing auxiliary polling; (2) application of multilevel regression methods, using the multilevel structure of the data; and (3) methods of small area estimation, making use of auxiliary information through the application of the Empirical Best Linear Unbiased Prediction (EBLUP). These methods are applied to real data collected from a survey with the aim of estimating the vote share in the Portuguese legislative elections. When auxiliary information is required, we concluded that polls aggregations and EBLUP have to be applied with caution, since this information is extremely important for a good application of these models to the data set and to obtain good reliable forecasts. On the other hand, if auxiliary information is not available or if it is not of good quality, then multilevel regression can and should be seen as a safe alternative to obtain more precise estimates, either at the micro or macro level. Besides, this is the method which further improves the precision of the estimates. In the presence of good auxiliary information, EBLUP proved to be the method with greater proximity with real values. © Springer-Verlag Berlin Heidelberg 2013.

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The coordinated control of vehicle actuators is gaining more importance as new platforms are becoming available, with chassis endowed with many different actuators that may help controlling the vehicle motion. Further, in-wheel motors (IWMs) allow using a single system to apply both positive and negative torques at the wheels, which can be actuated independently one from the other. In electric vehicles (EVs), moreover, such a freedom in the actuation mechanisms opens the way to the combined optimization of performance and energy consumption issues. In this paper, the problem of torque allocation for maximizing the vehicle performance in EVs is addressed. The proposed strategy is compared against a benchmark, a-causal optimal solution showing that only a negligible loss of performance is experienced.