Abstract
Emotionally adaptive games are one of the holy grails of modern affective game research. However, current state of the art affective games rely on static game adaptation mechanics that assume a fixed emotional reaction from players every time. Not only this, most commercial titles have no affective adaptation loop whatsoever and their design is based on game design optimizations via typical beta-testing procedures, which falls short of ideal both in the level design and long-term gameplay experience fronts. In this paper, we demonstrate a generalizable approach for building predictive models of players' emotional reactions across different games and game genres. We describe a physiological approach for modelling players' emotional reactions, which relies on features extracted from players' emotional responses to game events, which were collected and extrapolated through their physiological data during actual gameplay sessions. Based on the optimal feature sets found by three feature selection algorithms (best first, sequential feature selection and genetic search), the collected features are used to create computational models of players' emotional reactions on the arousal and valence dimensions of emotion, using several machine learning algorithms. We expect this approach will allow both a more objective and quicker prototyping for digital games, as well as foster a future generation of affective games capable of modelling players' affective profiles over time, thus adapting to their changing preferences and needs.

Abstract
In this work the performance assessment of a digitised radio-over-fibre (DRoF) system is presented considering the transmission of orthogonal frequency division multiplexing (OFDM) signals. Specifically, the impact of the high peak-to-average-ratio (PAPR) in the ADC/DAC quantization noise is addressed by means of simulation results. In order to minimize the impact of the digitisation process, we present a study of the improvement of the system performance when commonly used pre-distortion techniques suitable for PAPR reduction are considered. Additionally, the performance of the selected pre-distortion techniques is assessed experimentally, in terms of the modulation error ratio (MER) in an OFDM-DRoF link transmission, considering the impact of optical attenuation. It is observed that the transmission of OFDM signals in DRoF is severely affected by the inherent high PAPR levels and that, by using pre-distortion techniques with ADC/DAC with 7 bits, it is possible to increase the modulation error ratio (MER) by 15 dB or to increase the optical fibre by 6.5 km (8.5% increase) without the need of additional complexity at the OFDM receiver.

Abstract
A mudança climática não pode ser desmentida. Desde que a humanidade adquiriu o conhecimento da eletricidade que tem transformado todo o seu modo de viver em seu redor. Para a produção de eletricidade recorre-se, em parte, à combustão de materiais que libertam CO2 e que, pela quantidade emitida, potencialmente degrada o ambiente. Noutra perspetiva, em certas zonas geográficas, o acesso à energia elétrica é escasso. É neste contexto que surge a profissão de Software Testing. Neste artigo procura-se quantificar o contributo dos testes de software no desenvolvimento de aplicações que tenham em conta um consumo energético mais reduzido. Para atingir esse objetivo é proposta e aplicada uma metodologia para a medição de consumo e é definida uma fórmula matemática para apuramento da viabilidade económica dos testes. As consequências de um software que consuma menos energia serão de três ordens: a ambiental – na redução da emissão de CO2; a humana – pela possibilidade de mais pessoas utilizarem a capacidade energética instalada; e a financeira – na redução direta do custo do consumo.;Climate change cannot be ignored any longer. Since humanity has developed the knowledge of electricity and has shaped his existence in a way that we are all dependent on it. The generation of electricity is linked to the combustion of products that release CO2 into the atmosphere which has the potential to degrade the environment. On the other hand, some countries have a restricted access to electricity. It is in this context that a new profession emerges – the software testing. This article aims at quantifying the contribution of Software Testing in the energy consumption of software. To achieve this goal it is proposed and applied a simplified methodology for measuring consumption of software and it is defined a mathematical formula to calculate the economic viability of the tests. The consequences of an optimized software in its energy consumption will be: environmental – by reducing CO2 emissions; human – by allowing more people to use the already installed power capacity; and financial – by reduction of economical expenditure.

Abstract
This paper applies optimal power flow (OPF) to evaluate and maximize network benefits of demand-side management (DSM). The benefits are quantified in terms of the ability of demand-responsive loads to relieve upstream network constraints and provide ancillary services, such as operating reserve. The study incorporates detailed information on the load structure and composition, and allows the potential network benefits, which could be obtained through management of different load types, to be quantified and compared. It is demonstrated that the actual network location of demand-manageable load has an important influence on the effectiveness of the applied DSM scheme, since the characteristics of the loads and their interconnecting networks vary from one location to another. Consequently, some network locations are more favorable for implementation of DSM, and OPF can be applied to determine the optimal allocation of demand-side resources. The effectiveness of the presented approach is assessed using a time-sequential OPF applied to typical radial and meshed U.K. distribution networks. The results of the analysis suggest that network operators could not just participate in, but also encourage and add value to the implementation of specific DSM schemes at the optimum network locations in order to maximize the total benefit from DSM. © 2014 IEEE.

Abstract
Traditionally, the Teacher-Student relationship is a close one. The student spends several hours of a day in the presence of the teacher and can talk, express doubts and pose questions. These doubts, or the general feeling towards the object of learning, are not only expressed explicitly but also implicitly. Indeed, the teacher is constantly, even if in an unconscious way, reading the state of the student in search for sings of doubt, frustration, stress or fatigue. This information is then used by the teacher to adapt their methods or to personalize their approach in function of each student. These aspects, intuitively central in education, become less efficient when learning takes place in a Virtual Environment. Indeed, the growth of online courses, in which the student and the teacher often never even meet, make learning more difficult for a number of reasons. In this paper we analyse these reasons and put forward an approach for inferring the student's state that aims to minimize the effects of the absence of the teacher.

Abstract
The energy sector in industrialized countries has been restructured in the last years, with the purpose of decreasing electricity prices through the increase in competition, and facilitating the integration of distributed energy resources. However, the restructuring process increased the complexity in market players' interactions and generated emerging problems and new issues to be addressed. In order to provide players with competitive advantage in the market, decision support tools that facilitate the study and understanding of these markets become extremely useful. In this context arises MASCEM (Multi-Agent Simulator of Competitive Electricity Markets), a multi-agent based simulator that models real electricity markets. To reinforce MASCEM with the capability of recreating the electricity markets reality in the fullest possible extent, it is crucial to make it able to simulate as many market models and player types as possible. This paper presents a new negotiation model implemented in MASCEM based on the negotiation model used in day-ahead market (Elspot) of Nord Pool. This is a key module to study competitive electricity markets, as it presents well defined and distinct characteristics from the already implemented markets, and it is a reference electricity market in Europe (the one with the larger amount of traded power).