Abstract

Abstract
Recently, Serious Games (SG) achieved a recognized position as a learning tool in several contexts. SG provide constructive learning environments in which errors can be made without real life penalties and where students get instant feedback from their actions when facing challenges. These challenges should be in accordance with the intended learning goals and they should adapt and/or be repeated according to the learner's level. This aspect is decisive in the acquisition of knowledge, experience and professional skills through the simulation of different situations and contexts. The effectiveness of competences' training is directly related to the success in their acquisition but, above all, it is related to the ability to apply them to successfully perform a given task. However, an optimal game design methodology for competence training is yet to be created. This article presents a study that identifies the most appropriate game categories to develop specific skills and competences. It considers a taxonomy with eight game categories (Action, Strategy, Playing, Sports, Management Simulation, Adventure, Puzzle and Quiz) that were matched with the Education Competences and Educational Competency Wheel. Analysing 116 serious games allowed identifying which categories were more efficient in the training of a specific competence and therefore should be reused in the same scope.

Abstract
In this work, two all-fiber loop mirrors using a clover microstructured fiber for the simultaneous measurement of temperature and strain are presented. The sensing heads are formed by a short piece of clover microstructured fiber with 35 mm and 89 mm length respectively. The geometry of the fiber allowed observing different interferences created by the microstructured fiber core section. Different sensitivities to temperature and strain were obtained and, using a matrix method, it is possible to discriminate both physical parameters. Resolutions of +/- 2 degrees C and +/- 11 mu epsilon for the first structure and +/- 2.3 degrees C and +/- 18 mu epsilon for the second one, for temperature and strain, respectively, were attained.

Abstract
Electricity power planning is a serious national task that encompasses not only forecasts but more importantly the evolution in short, medium and long term of each element that composes the assumptions, the constraints and/or the parameters of the planning model. Deterministic models can bring simplicity to the electricity power planning but do not consider the uncertainties and sources of risk of the electricity system. On the other hand, stochastic models tend to include the behavior of one or more uncertain parameters that are critical to obtain a robust solution, being however more detailed and lengthy than deteministic models. The aim of this work was to identify the major sources of risk and uncertainties facing electricity system, representing valuable input for the electricit planner task. From this study it can be observed that several different behaviours for each uncertain parameter can be found along a time horizon. Thus, it is concluded that reling on average lowers can represent a reductionist approach and in order to obtain more reliable scenarios for future electricity system, the statistical charcateristics of each parameter should be considered in the electricity power planning.

Abstract
Manufacturing environments require a real-time adaptation and optimization method to dynamically and intelligently maintain the current scheduling plan feasible. This way, the organization keeps clients satisfied and achieves its objectives (costs are minimized and profits maximized). This paper proposes an optimization approach - Selection Constructive based Hyper-heuristic for Dynamic Scheduling - to deal with these dynamic events, with the main goal of maintaining the current scheduling plan feasible and robust as possible. The development of this dynamic adaptation approach is inspired on evolutionary computation and hyper-heuristics. Our empirical results show that a selection constructive hyper-heuristic could be advantageous on solving dynamic adaptation optimization problems. © 2015 AISTI.

Abstract
In highway construction, earthworks refer to the tasks of excavation, transportation, spreading and compaction of geomaterial (e.g. soil, rockfill and soil-rockfill mixture). Whereas relying heavily on machinery and repetitive processes, these tasks are highly susceptible to optimization. In this context Artificial Intelligent techniques, such as Data Mining and modern optimization can be applied for earthworks. A survey of these applications shows that they focus on the optimization of specific objectives and/or construction phases being possible to identify the capabilities and limitations of the analyzed techniques. Thus, according to the pinpointed drawbacks of these techniques, this paper describes a novel intelligent earthwork optimization system, capable of integrating DM, modern optimization and GIS technologies in order to optimize the earthwork processes throughout all phases of design and construction work. This integration system allows significant savings in time, cost and gas emissions contributing for a more sustainable construction.