Abstract
This paper proposes a novel Energy Aggregator model responsible for managing the flexibility of low voltage customers in order to reduce electricity costs. The flexibility of the customers is represented by the availability of their controllable loads to reduce/ increase power consumption. The flexible loads are managed according to the customers' preferences and the technical limitations of the flexible loads. The Energy Aggregator model developed includes an algorithm designed to manage customers' flexibility in quasi-real-time, with the objective of minimizing the deviations from the energy bought by the aggregator in the market. A scenario with 30 households located in a semi-urban area is used to illustrate the application of the algorithm and validate the proposed approach.

Abstract
Mental models are students' representations of the real world based on their knowledge and understanding. In a meaningful learning it is essential to analyse students' mental models and promote a reconstruction of scientific knowledge, by allowing them to become consistent with the scientific models. Having this in mind, we aimed to analyse undergraduate students' mental models concerning the seismic effects on soils and buildings, before they learn these contents at the university. To collect the data we applied a questionnaire with several items. The questionnaire was answered by a convenient sample of 31 freshmen from an undergraduate degree in Geology, in a northern Portuguese university. The results show that, in the majority of the items, students recognized the seismic effects on soils and buildings. However, some of their mental models were inconsistent with the scientific model, highlighting the importance of diagnosing students' mental models and their restructuring to promote a meaningful learning and scientific literacy.

Abstract
Over the past years, inverse perspective mapping has been successfully applied to several problems in the field of Intelligent Transportation Systems. In brief, the method consists of mapping images to a new coordinate system where perspective effects are removed. The removal of perspective associated effects facilitates road and obstacle detection and also assists in free space estimation. There is, however, a significant limitation in the inverse perspective mapping: the presence of obstacles on the road disrupts the effectiveness of the mapping. The current paper proposes a robust solution based on the use of multimodal sensor fusion. Data from a laser range finder is fused with images from the cameras, so that the mapping is not computed in the regions where obstacles are present. As shown in the results, this considerably improves the effectiveness of the algorithm and reduces computation time when compared with the classical inverse perspective mapping. Furthermore, the proposed approach is also able to cope with several cameras with different lenses or image resolutions, as well as dynamic viewpoints. © 2014 Elsevier B.V.

Abstract
"The Fifth Empire" is a single-player action adventure game in 2D. Centering around an adaptation of the myth the Fifth Empire (Nebuchadnezzar's dream and its interpretation by prophet Daniel) [1] is an epic allegory of the human condition, with a reinterpretation of European and Portuguese history as backdrop. This is a project that is still in process, which main goal is the development of an attractive videogame, with a strong and enriched educational purpose, since the game takes place in a real and historical setting. Moreover, through entertainment, the game should promote curiosity about the Portuguese history and motivate a more detailed study by the target audience.

Abstract
The combustion of coal wastes resulting from mining is of particular environmental concern and therefore the importance of the proper management involving real-time assessment of their status and identification of probable evolution scenarios is recognized. Continuous monitoring of combustion temperature and emission levels of certain gases opens the possibility to plan corrective actions to minimize their negative impact in the surroundings. Optical fiber technology is well-suited to this purpose and in this work it is described the main attributes of a fiber optic sensing system projected to gather data on distributed temperature and gas emission in these harsh environments.

Abstract
Three-dimensional object detection and recognition is increasingly in manipulation and navigation applications in autonomous service robots. It involves clustering points of the point cloud from an unstructured scene into objects candidates and estimating features to recognize the objects under different circumstances such as occlusions and clutter. This paper presents an efficient approach capable of learning and recognizing object categories in an interactive and open-ended manner. In this paper, "open-ended" implies that the set of object categories to be learned is not known in advance. The training instances are extracted from actual experiences of a robot, and thus become gradually available, rather than being available at the beginning of the learning process. This paper focuses on two state-of-the-art questions: (1) How to automatically detect, conceptualize and recognize objects in 3D unstructured scenes in an open-ended manner? (2) How to acquire and utilize high-level knowledge obtained from the user (e. g. category label) to improve the system performance? This approach starts with a pre-processing phase to remove unnecessary information and prepare a suitable point cloud. Clustering is then applied to detect object candidates. Subsequently, all object candidates are described based on a 3D shape descriptor called spin-image. Finally, a nearest-neighbor classification rule is used to assign category labels to the detected objects. To examine the performance of the proposed approach, a leave-one-out cross validation algorithm is utilized to compute precision and recall. The experimental results show the fulfilling performance of this approach on different types of objects.