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
On the verge of Cyber-Physical Systems (CPS), companies will need, sooner or later, to adapt their systems in order to follow the emergent visions of Industrie 4.0 and Industrial Internet demanding the digitalization of their processes, preventing the losing of their competiveness levels. The engineering of such innovative manufacturing control systems assumes a crucial challenge without which becomes hard to convince researchers and, primarily, practitioners of the proposed architecture potentials. This paper describes the engineering aspects of deploying an ADACOR (ADAptive holonic COntrol aRchitecture for distributed manufacturing systems) based CPS for a real small-scale production system. Since the solution is using agent technology, a special attention is devoted to the interface from the agent control layer to the physical resources using the industrially adopted OPC-UA (OPC Unified Architecture). At the end, some lessons learned in engineering this CPS are drawn. © 2016 IEEE.

Abstract
High levels of dependability are required to promote the adherence by public and medical communities to wearable medical devices. The study presented herein addresses fault detection and diagnosis in these systems. The main objective resides on correctly classifying the captured physiological signals, in order to distinguish whether the actual cause of a detected anomaly is a wearer health condition or a system functional flaw. Data fusion techniques, namely fuzzy logic, artificial neural networks, decision trees and naive Bayes classifiers are employed to process the captured data to increase the trust levels with which diagnostics are made. Concerning the wearer condition, additional information is provided after classifying the set of signals into normal or abnormal (e.g., arrhythmia, tachycardia and bradycardia). As for the monitoring system, once an abnormal situation is detected in its operation or in the sensors, a set of tests is run to check if actually the wearer shows a degradation of his health condition or if the system is reporting erroneous values. Selected features from the vital signals and from quantities that characterize the system performance serve as inputs to the data fusion algorithms for Patient and System Status diagnosis purposes. The algorithms performance was evaluated based on their sensitivity, specificity and accuracy. Based on these criteria the naive Bayes classifier presented the best performance.

Abstract
This paper extends the symmetric/constrained fuzzy power flow models by including the potential correlations between nodal injections. Therefore, the extension of the model allows the specification of fuzzy generation and load values and of potential correlations between nodal injections. The enhanced version of the symmetric/constrained fuzzy power flow model is applied to the 30-bus IEEE test system. The results prove the importance of the inclusion of data correlations in the analysis of transmission system adequacy. Copyright (c) 2015 John Wiley & Sons, Ltd.

Abstract

Abstract
Recently, roundabouts in a series have been installed along corridors to enhance road safety. However, the benefits of this traffic-calming technique on traffic performance and pollutant emissions compared with other forms of intersections, such as traffic lights and stop-controlled solutions, are not properly known. This study used a microscopic approach to evaluate the effects of a corridor with four roundabouts on traffic performance and emissions, in comparison with traffic lights and stop-controlled solutions. Average travel time and number of vehicle stops were used as measures of traffic performance; carbon dioxide, monoxide carbon, nitrogen oxides, hydrocarbons, and particulate matter were used to quantify emissions. The traffic and emissions performance of each solution was evaluated on three levels: (a) arterial, (b) intersection, and (c) morning peak versus evening peak periods. It was found that, regardless of the time period, traffic lights in corridors at the arterial level produced higher total emissions (> 6%), while stop-controlled intersections produced lower emissions (approximate to 12%) compared with roundabouts, mainly because of unbalanced traffic flows between main and minor roads. The results for traffic performance showed advantages in implementing roundabouts when the main concern was the number of vehicle stops. At the intersection level, an emissions improvement (between 2% and 14%) was observed at traffic lights on four-leg intersections.