Abstract
Electricity markets are complex environments comprising several negotiation mechanisms. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a simulator developed to allow deep studies of the interactions between the players that take part in the electricity market negotiations. ALBidS (Adaptive Learning Strategic Bidding System) is a multiagent system created to provide decision support to market negotiating players. Fully integrated with MASCEM it considers several different methodologies based on very distinct approaches. The Six Thinking Hats is a powerful technique used to look at decisions from different perspectives. This paper aims to complement ALBidS strategies usage by MASCEM players, providing, through the Six Thinking hats group decision technique, a means to combine them and take advantages from their different perspectives. The combination of the different proposals resulting from ALBidS' strategies is performed through the application of a Genetic Algorithm, resulting in an evolutionary learning approach.

Abstract
The constant changes occurring in today's teaching and learning processes provide a continuous improvement in the interaction of information. At the same time, globalization and the need for streamlining time facilitate the development of new methods of imparting knowledge. With the new information technologies, new forms of learning occur adding greater dynamism to the information. This form of learning, which optimizes time and space, is known as distance learning and it is being amply used in many different areas. Distance education presents the advantage of facilitating the teaching and learning processes without face-to-face interaction as knowledge is transmitted, partly or as a whole using technology. In fact, technological innovation has assumed a vital role in education. Looking to achieve more efficiency in carrying out the various activities, the job market constantly requires an investment in new skills and knowledge so that the professionals can operate the equipment and working tools, successfully and with quality. This paper presents a new system of learning a curricular unit of Circuit Theory using desktop virtual reality (VR). The software provides the possibility to understand the relationship between the physical concepts of an electrical circuit, direct or alternating current, through computer simulations and animations. This work was developed to demonstrate how a desktop VR prototype, "Virtual Electric Manual"-VEMA, can be applied to an engineering unit and used to enhance security and resourcefulness in using electrical equipment. Several interactive scenes were developed to illustrate the idea using a measurements and instrumentation laboratory as virtual environment. A range of interactive learning environments are presented: the menu with the first interactive experiment is the simulator of a DC circuit. It allows the student to analyze the setting up of a parallel, or series-parallel resistive circuit series powered by direct current; the second menu, features the simulator of an alternating current circuit and demonstrates how the student can perform the analysis of a circuit with resistors, inductors and capacitors; the third interactive experiment is the simulator of a three-phase alternating current circuit; the fourth menu refers to a study on the transitional phenomena. Finally, the fifth menu is an interactive experiment related to the phenomena of resonance. VEMA offers students the opportunity to understand the processes, helping them to better discern the procedures and trains them to interact with the equipment. The VEMA prototype addresses these issues and highlights the potential benefits of using VR for this purpose. Each experiment will provide technical and scientific knowledge in order to give the student/user adequate information and training in the subject.

Abstract
Traditional classification algorithms can be limited in their performance when a specific user is targeted. User preferences, e.g. in recommendation systems, constitute a challenge for learning algorithms. Additionally, in recent years user's interaction through crowdsourcing has drawn significant interest, although its use in learning settings is still underused. In this work we focus on an active strategy that uses crowd-based non-expert information to appropriately tackle the problem of capturing the drift between user preferences in a recommendation system. The proposed method combines two main ideas: to apply active strategies for adaptation to each user; to implement crowdsourcing to avoid excessive user feedback. A similitude technique is put forward to optimize the choice of the more appropriate similitude-wise crowd, under the guidance of basic user feedback. The proposed active learning framework allows non-experts classification performed by crowds to be used to define the user profile, mitigating the labeling effort normally requested to the user. The framework is designed to be generic and suitable to be applied, to different' scenarios, whilst customizable for each specific user. A case study on humor classification scenario is used to demonstrate experimentally that the approach can improve baseline active results.

Abstract
In the design of Wireless Sensor Networks (WSN), lifetime estimation is an important issue. This benchmark is closely related to the energy consumption of nodes. However, this assessment is not a trivial task. In such context, we propose an innovative energy measurement system for WSN. The designed system was employed for energy consumption measurements on a MicaZ node. With these measurements, a set of polynomial functions could be designed to emulate a realistic energy discharging curve for WSN nodes. These functions were then implemented in the OMNeT++ simulator to emulate the behavior of batteries discharging, potentially benefiting performance assessment of WSN. ©2013 International Information Institute.

Abstract
Wireless sensor networks can be deployed for a large set of monitoring functions, providing information as humidity, pressure, temperature, luminosity, among many others. When monitored data is transmitted over wireless links, packets can be corrupted requiring some error recovery strategy. Hop-by-hop retransmission can provide an acceptable level of reliability, but can potentially increase the energy consumption of the network. In fact, wireless communications are error prone, and interferences may be concentrated in specific parts of the network. We propose a semi-reliable retransmission mechanism where only packets carrying critical information will be always retransmitted if corrupted. The remaining corrupted packets will not be retransmitted, saving energy of the network. We designed an energy consumption model to evaluate the proposed approach.

Abstract
The capacity of important hydroxytyrosol metabolites (homovanillyl alcohol, hydroxytyrosol acetate, homovanillyl alcohol acetate, hydroxytyrosol 3' and 4'-O-glucuronides, and homovanillyl alcohol 4'-O-glucuronide) to protect red blood cells (RBCs) from oxidative injury induced by the radical initiator 2,2'-azo-bis(2-amidinopropane) dihydrochloride (AAPH) or by the natural radical initiator H2O2 was evaluated. In the presence of AAPH, all compounds showed to protect RBCs from hemolysis in a dose-dependent manner, exccept for the homovanillyl alcohol glucuronide, with the order of activity being at 20 mu M hydroxytyrosol > hydroxytyrosol glucuronides = hydroxytyrosol acetate = homovanillyl alcohol = homovanillyl acetate > homovanillyl alcohol glucuronide. At 10 mu M, hydroxytyrosol, hydroxytyrosol acetate, and hydroxytyrosol glucuronides still protected hemoglobine from oxidation and from morphological RBC changes. In the presence of H2O2, hydroxytyrosol showed to significantly protect RBCs from oxidative hemolysis in a dose-dependent manner, but the hydroxytyrosol glucuronides showed only a limited protection that was independent of the concentration used.