Abstract
Our main aim was to compare eating behaviour between Portuguese undergraduate nutrition students and students attending other courses. Several eating behaviour dimensions were compared between 154 nutrition students and 263 students from other areas. Emotional and external eating were assessed by the Dutch Eating Behavior Questionnaire, dietary restraint was measured using the flexible and rigid control of eating behaviour subscales, binge eating was measured using the Binge Eating Scale, and eating self-efficacy using the General Eating Self-Efficacy Scale. Higher levels of flexible and rigid control were found in nutrition students from both sexes when compared to students from other courses. Female nutrition students also presented higher binge eating levels than their colleagues from other courses. To our knowledge no other work has previously assessed all eating behaviour dimensions considered in the current study among nutrition students. Besides the results by themselves, the data obtained from this study provide several clues to further studies to be developed regarding the still rarely approached issue of eating behaviour among nutrition students.

Abstract
Knowledge discovery on biomedical data can be based on on-line, data-stream analyses, or using retrospective, timestamped, off-line datasets. In both cases, changes in the processes that generate data or in their quality features through time may hinder either the knowledge discovery process or the generalization of past knowledge. These problems can be seen as a lack of data temporal stability. This work establishes the temporal stability as a data quality dimension and proposes new methods for its assessment based on a probabilistic framework. Concretely, methods are proposed for (1) monitoring changes, and (2) characterizing changes, trends and detecting temporal subgroups. First, a probabilistic change detection algorithm is proposed based on the Statistical Process Control of the posterior Beta distribution of the Jensen-Shannon distance, with a memoryless forgetting mechanism. This algorithm (PDF-SPC) classifies the degree of current change in three states: In-Control, Warning, and Out-of-Control. Second, a novel method is proposed to visualize and characterize the temporal changes of data based on the projection of a non-parametric information-geometric statistical manifold of time windows. This projection facilitates the exploration of temporal trends using the proposed IGT-plot and, by means of unsupervised learning methods, discovering conceptually-related temporal subgroups. Methods are evaluated using real and simulated data based on the National Hospital Discharge Survey (NHDS) dataset.

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.

Abstract
The constant increment in the penetration of renewable energy sources requires the improvement of the flexibility of the power system. Due to its high generation cost, it is expected that insular power systems experience an important increment in the power generation from renewable energy sources and, under this perspective, an energy storage system (ESS) could be the key factor to meet this goal. For these reasons, new approaches are necessary to optimize the scheduling of insular power systems, considering the specific characteristics of available ESSs. In this paper, a new approach that enables to fully incorporate the characteristics of a determined battery energy storage system (BESS) is proposed. The effectiveness of the proposed approach is illustrated by using a vanadium redox battery (VRB) in the analysis of an insular power system with renewable energy sources.

Abstract
In recent years, the significant advancements in miniaturised computing and pervasive communication networks have paved the way for ubiquitous computing environments. In such environments users interact with systems through novel and implicit methods. In this context, affective computing provides a dimension of interaction, raising a number of opportunities to address not only utilitarian but also hedonic needs. At the same time, a number of challenges arise beyond the technical aspects, that are related to the individual and other societal implications. A review of the main opportunities and challenges is presented, supporting the identification of the main requirements for the design and development of systems in interactive pervasive environments, focusing on an affective loop of interaction. A framework is proposed, identifying main modules and functionality alongside a methodology to instantiate in specific domains of application.

Abstract
Data stream mining is the process of extracting knowledge structures from continuous, rapid data records. Many decision tasks can be formulated as stream mining problems and therefore many new algorithms for data streams are being proposed. Decision rules are one of the most interpretable and flexible models for predictive data mining. Nevertheless, few algorithms have been proposed in the literature to learn rule models for time-changing and high-speed flows of data. In this paper we present the very fast decision rules (VFDR) algorithm and discuss interesting extensions to the base version. All the proposed versions are one-pass and any-time algorithms. They work on-line and learn ordered or unordered rule sets. Algorithms designed to work with data streams should be able to detect changes and quickly adapt the decision model. In order to manage these situations we also present the adaptive extension (AVFDR) to detect changes in the process generating data and adapt the decision model. Detecting local drifts takes advantage of the modularity of the rule sets. In AVFDR, each individual rule monitors the evolution of performance metrics to detect concept drift. AVFDR prunes rules whenever a drift is signaled. This explicit change detection mechanism provides useful information about the dynamics of the process generating data, faster adaptation to changes and generates more compact rule sets. The experimental evaluation demonstrates that algorithms achieve competitive results in comparison to alternative methods and the adaptive methods are able to learn fast and compact rule sets from evolving streams.