Abstract
This paper presents the application of collaborative reinforcement learning models to enable the distributed learning of energy contracts negotiation strategies. The learning model combines the learning process on the best negotiation strategies to apply against each opponent, in each context, from multiple learning sources. The diverse learning sources are the learning processes of several agents, which learn the same problem under different perspectives. By combining the different independent learning processes, it is possible to gather the diverse knowledge and reach a final decision on the most suitable negotiation strategy to be applied. The reinforcement learning process is based on the application of the Q-Learning algorithm; and the continuous combination of the different learning results applies and compares several collaborative learning algorithms, namely BEST-Q, Average (AVE)-Q; Particle Swarm Optimization (PSO)-Q, and Weighted Strategy Sharing (WSS)-Q. Results show that the collaborative learning process enables players’ to correctly identify the negotiation strategy to apply in each moment, context and against each opponent.

Abstract
Air exposures occurring in indoor swimming pools are an important public health issue due to their popularity and regular use by the general population, including vulnerable groups such as children and elderly people. More comprehensive information on indoor air quality (IAQ) in swimming pools is thus needed in order to understand health risks, establish appropriate protective limits and provide evidence-based opportunities for improvement of IAQ in these facilities. In this context, twenty public indoor swimming pools located in the Northern Region of Portugal were examined in two sampling campaigns: January-March and May-July 2018. For each campaign, a comprehensive set of environmental parameters was monitored during the entire period of the facilities' operating hours of a weekday, both indoors and outdoors. In addition, four air (1-h samplings) and water samples were collected. Findings show that comfort conditions, ultrafine particles number concentrations and exposure to substances in the indoor air (concentration and composition) is likely to vary greatly from one public indoor swimming pool to another. Trihalomethanes (THM) and dichloroacetonitrile were the predominant disinfection by-products identified in the indoor air but other potentially hazardous volatile organic compounds, such as limonene, 1,2,4-trimethylbenzene, 2,2,4,4,6,8,8-heptamethylnonane, 2- and 3-methylbutanenitrile, acetophenone, benzonitrile, and isobutyronitrile were found to have relevant putative emission sources in the environment of the swimming pools analyzed. Furthermore, indicators of poor ventilation conditions (namely carbon dioxide, relative humidity and existence of signs of condensation in windows) and some water-related parameters (THM levels, conductivity and salinity) were found to be determining factors of the measured airborne THM concentrations that appeared to significantly potentiate the exposure. In summary, this work provides evidence for the need to establish adequate standards for the comprehensive evaluation of IAQ in public swimming pools, in order to guide further development of evidence-based prevention/remediation strategies for promoting healthy environments in swimming pools.

Abstract
Motivation: Network alignment (NA) finds conserved regions between two networks. NA methods optimize node conservation (NC) and edge conservation. Dynamic graphlet degree vectors are a state-of-the-art dynamic NC measure, used within the fastest and most accurate NA method for temporal networks: DynaWAVE. Here, we use graphlet-orbit transitions (GoTs), a different graphlet-based measure of temporal node similarity, as a new dynamic NC measure within DynaWAVE, resulting in GoT-WAVE. Results: On synthetic networks, GoT-WAVE improves DynaWAVE's accuracy by 30% and speed by 64%. On real networks, when optimizing only dynamic NC, the methods are complementary. Furthermore, only GoT-WAVE supports directed edges. Hence, GoT-WAVE is a promising new temporal NA algorithm, which efficiently optimizes dynamic NC. We provide a user-friendly user interface and source code for GoT-WAVE.

Abstract
This paper presents an innovative and motivating methodology to learn vision systems using a humanoid robot, NAO robot. Vision systems are an area of growing development and interest of engineering students. This approach to learning was applied in students of Master of Electrical Engineering. The goal is to introduce students the main approaches of visual object recognition and human face recognition using computer vision techniques to be embedded in a social robot and therefore he is able to iteract with human beings. NAO robot as an educational platform easy to learn how to program, and it has a high sensory ability and two cameras that can capture the images for processing.

Abstract
A computational analysis of physiological systems has been used to support the understanding of how these systems work, and in the case of foetal heart rate, many different approaches have been developed in the last decades. Our objective was to apply a new method of classification, which is based on spectral analysis, in foetal heart rate (FHR) traces to predict foetal acidosis diagnosed with umbilical arterial blood pH <= 7.05. Fast Fourier transform was applied to a real database for the classification approach. To evaluate the models, sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve were used. Sensitivity equal to 1, specificity equal to 0.85 and an area under the ROC curve of 0.94 were found. In addition, when the definition of metabolic acidosis of umbilical arterial blood pH <= 7.05 and base excess <= -10 mmol/L was used, the proposed methodology obtained sensitivity = 1, specificity = 0.97 and area under the ROC curve = 0.98. The proposed methodology relies exclusively on the spectral frequency decomposition of the FHR signal. After further successful validation in more datasets, this approach can be incorporated easily in clinical practice due to its simple implementation. Likewise, the incorporation of this novel technique in an intrapartum monitoring station should be straightforward, thus enabling the assistance of labour professionals in the anticipated detection of acidaemia.

Abstract
The role of transportation in the overall emissions in light of the increasing environmental awareness has led to a rapid transition to the use of electric vehicles (EVs), especially in the last decade. The EVs have seminal advantages in terms of different point of views; however, they may pose vital challenges for the electric power system operation due to their stochastic characteristics as an electrical load. Several industrial and academic research studies have already been and are still conducted in this respect. Specifically, the development of combined technical and business-oriented operational models is extremely significant for sustainable penetration of EVs. In this study, an interoperability platform is proposed for EV charging service taking dual sides of the mentioned service as system operator and EV owner into account, being proposed as a new perspective in this area, also compared to industrial software platforms for EV charging service by service providers rather than power system operators. The developed software platforms are demonstrated and case study based analyses are conducted to present the applicability of the proposed concept. © 2019 IEEE.