Abstract
This paper describes the design and development of a biologically inspired flying robot prototype (a machine able to fly by beating its wings, as birds do). For its implementation, the flight of biological beings was analysed, as well as the techniques involved in ornithopter's construction. Some parameters adopted by biological beings to maintain a stable flight were studied, and the prototype was designed based on these values. To conclude the project, an ornithopter was built, aiming to perform a stabilized flight and some preliminary experiments were performed to check if its behaviour meets the design expectations.

Abstract
In this paper, a novel hybrid single-objective metaheuristic, the so called C-DEEPSO (Canonical Differential Evolutionary Particle Swarm Optimization), is proposed and tested. C-DEEPSO can be viewed as an evolutionary algorithm with recombination rules borrowed from PSO, or a swarm optimization method with selection and self-adaptiveness properties proper from DE. A case study on the problem of optimal control for reactive sources in energy production by Wind Power Plants (WPP), solved by means of Optimal Power Flow (OPF-like), is used to test the new hybrid algorithm and to evaluate its performance. C-DEEPSO is compared to the baseline algorithm, DEEPSO, and to a reference algorithm, Mean-Variance Mapping Optimization (MVMO). The experiments indicate that the proposed algorithm is efficient and competitive, capable to tackle this large-scale problem. The results also show that the new approach exhibits better results, when compared to MVMO.

Abstract
In the last years, the amount of devices that can be connected to a network grew significantly allowing to, among other tasks, collect data about the environment or the people in it in a non-intrusive way. This generated nowadays well-known topics such as Big Data or the Internet of Things. This also opened the door to the development of novel and interesting applications. In this paper we propose a distributed system for acquiring data about the users of technological devices in a non-intrusive way. We describe how this data can be collected and transformed to produce meaningful interaction features, that reveal the state of the individuals. We analyse the requirements of such a system, namely in terms of storage and speed, and describe three prototypes currently being used in three different domains of application.

Abstract
The hierarchical control scheme for three phase parallel connected voltage source inverters (VSI), forming a low voltage AC MicroGrid (MG)is presented and analyzed in this paper. The proposed control scheme consists of two controllers, a local controller and a centralized controller. The local controller consists of inner control loops that are composed by a voltage and current proportional resonant controllers with selective harmonics compensation, and the power sharing controller that includes the droop control and the virtual output impedance loops in order to share properly the active and reactive power between the connected VSIs. In this system, the centralized controller is designed to restore the magnitude and the frequency deviations of the AC bus voltage produced by the power sharing controller. Simulation results for three parallel connected VSIs forming a low voltage AC MG that operates in islanded mode are provided to show the effectiveness of the proposed control scheme. Active and reactive power are properly shared as well as good harmonics compensation is achieved when both linear and non-linear loads are connected to the load bus.

Abstract
The collection, storage, management, and anticipation of contextual information about the user to support decision-making constitute some of the key operations in most Ambient Intelligent (AmI) systems. When the instructor has a computer-based class it is often difficult to confirm if the students are working in the proposed activities. In order to mitigate problems that might occur in an environment with learning technologies we suggest an AmI system aimed at capturing, measuring, and supervising the students' level of attentiveness in real scenarios and dynamically provide recommendations to the instructor. With this system it is possible to assess both individual and group attention, in real-time, providing a measure of the level of engagement of each student in the proposed activities and allowing the instructor to better steer teaching methodologies.

Abstract
Nowadays, online communities are becoming an important resource for health consumers who want to retrieve and share information about health subjects. These communities have the potential to influence patients' health behaviors and increase their engagement with therapies. However, the interaction dynamics in this type of media remains poorly understood what might hinder the development of strategies that facilitate and encourage participation. Social Network Analysis is a technique that tries to expose the hidden channels of communication and information flow, leading to a better understanding of how members relate to each other on online social network. In this study we do a systematic review of the literature regarding the apply this technique in the study of online health communities. We show that this type of studies is scarce and that, in this domain, Social Network Analysis is mainly applied to identify influential key members, as well as the most active members in terms of posting or answering questions.