Abstract
Urban traffic emissions have been increasing in recent years. To reverse that trend, restrictive traffic measures can be implemented to complement national policies. We have proposed a methodology to assess the impact of three restrictive traffic measures in an urban arterial by using a microsimulation model of traffic and emissions integrated platform. The analysis is extended to some alternative roads and to the overall network area. Traffic restriction measures provided average reductions of 45%, 47%, 35%, and 47% for CO2, CO, NOx, and HC, respectively, due to traffic being diverted to other roads. Nevertheless, increases of 91%, 99%, 55%, and 121% in CO2, CO, NOx, and HC, respectively, can be expected on alternative roads.

Abstract
Recent advances in mobile device technology have triggered research on using their aggregate computational and/or storage resources to form edge-clouds. Whilst traditionally viewed as simple clients, smart-phones and tablets today have hardware resources that allow more sophisticated software to be installed, and can be used as thick clients or even thin servers. Simultaneously, new standards and protocols, such as Wi-Fi Direct and Wi-Fi TDLS (Tunneled Direct Link Setup), have been established that allow mobile devices to talk directly with each other, as opposed to over the Internet or across Wi-Fi access points. This can, potentially, lead to ubiquitous, low-latency, device-to-device (D2D) communication. In this paper, we study whether D2D protocols can support mobile-edge clouds by benchmarking different protocols and configurations for a specific application. The results show that decentralized device-to-device techniques can be used to efficiently disseminate multimedia contents while diminishing contention in the wireless infrastructure, allowing for up to 65% traffic reduction at the access points.

Abstract
This paper presents an approach to design the transformer and the link inductor for the high-frequency link matrix converter. The proposed method aims to systematize the design process of the HF-link using analytic and software tools. The models for the characterization of the core and winding losses have been reviewed. Considerations about the practical implementation and construction of the magnetic devices are also provided. The software receives the inputs from the mathematical analysis and runs the optimization to find the best design. A 10 kW / 20 kHz transformer plus a link inductor are designed using this strategy achieving a combined efficiency of 99.32%.

Abstract
This study presents an integrated approach for reliability planning and risk estimation in active distribution systems. By incorporating the use of accurate reliability equivalents for different medium voltage/low voltage networks and load subsectors, a probabilistic methodology is proposed to capture both power quality and reliability aspects in power system planning, which potentially avoids the underestimation of system's performance at bulk supply points. A 'time to restore supply' concept, based on security of supply legislation, is introduced to quantify the effect of different network functionalities such as the use of backup supply or automatic/manual reconfiguration schemes. The range of annual reliability indices reported by 14 network operators in the UK is also used for the validation of reliability results, which allows estimating the risk of interruption times above the regulator-imposed limits. Accordingly, conventional reliability assessment procedures are extended in this study by analysing a meshed urban distribution network through the application of a time-sequential Monte Carlo simulation. The proposed methodology also acknowledges the use of time-varying fault probabilities and empirical load profiles for a more realistic estimation of customer interruptions. A decision-making approach is shown by assessing the impact of several network actions on the accuracy of reliability performance results.

Abstract
A great number of masonry arch bridges dates back to past centuries, being preserved by society due to their historical and still economic importance. Thereby, adequate preservation measures are required. Regarding masonry arch bridge's structural condition, it is relevant to consider its age, and consequently deterioration, and the fact that these bridges are submitted to loads higher than those for which they were conceived, being imperative to assess their structural performance. Regarding safety assessment requirements, there are different reliability levels, whose objectives are to analyse the ultimate load carrying capacity and the serviceability performance. This paper presents and discusses a framework that allows to determine the ultimate load-carrying capacity (Ultimate Limit State) of masonry arch bridges, using limit analysis and probabilistic approaches. Geometric and material data and load characterization, as well as inherent uncertainties will be also introduced. In order to determine the ultimate load-carrying capacity, the plastic theory will be employed, namely the limit analysis theorem, which is based on kinematic mechanisms. Since one of the main drawbacks of a probabilistic analysis is the required high computational resources, a sensitivity analysis is incorporated in order to reduce the analysis time. The presented framework is validated with an application to a set of existing Portuguese railway masonry arch bridges. (C) 2016 Published by Elsevier Ltd.

Abstract
Today online social network services are challenging stateof- the-art social media mining algorithms and techniques due to its realtime nature, scale and amount of unstructured data generated. The continuous interactions between online social network participants generate streams of unbounded text content and evolutionary network structures within the social streams that make classical text mining and network analysis techniques obsolete and not suitable to deal with such new challenges. Performing event detection on online social networks is no exception, state-of-the-art algorithms rely on text mining techniques applied to pre-known datasets that are being processed with no restrictions on the computational complexity and required execution time per document analysis. Moreover, network analysis algorithms used to extract knowledge from users relations and interactions were not designed to handle evolutionary networks of such order of magnitude in terms of the number of nodes and edges. This specific problem of event detection becomes even more serious due to the real-time nature of online social networks. New or unforeseen events need to be identified and tracked on a real-time basis providing accurate results as quick as possible. It makes no sense to have an algorithm that provides detected event results a few hours after being announced by traditional newswire.