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.

Abstract
With the emergence of online Music Streaming Services (MSS) such as Pandora and Spotify, listening to music online became very popular. Despite the availability of these services, users face the problem of finding among millions of music tracks the ones that match their music taste. MSS platforms generate interaction data such as users' defined playlists enriched with relevant metadata. These metadata can be used to predict users' preferences and facilitate personalized music recommendation. In this work, we aim to infer music tastes of users by using personal playlist information. Characterizing users' taste is important to generate trustable recommendations when the amount of usage data is limited. Here, we propose to predict the users' preferred music feature's value (e.g. Genre as a feature has different values like P op, Rock, etc.) by modeling, not only usage information, but also music description features. Music attribute information and usage data are typically dealt with separately. Our method FPMF (Feature Prediction based on Matrix Factorization) treats music feature values as virtual users and retrieves the preferred feature values for real target users. Experimental results indicate that our proposal is able to handle the item cold start problem and can retrieve preferred music feature values with limited usage data. Furthermore, our proposal can be useful in recommendation explanation scenarios.

Abstract
Advances in the last decades in the areas of medical science and patients treatment resulted in a decrease in the mortality rate but also an increase in the number of patients with chronic diseases. This increased life expectancy of patients with chronic diseases often involves great suffering and considerable adverse effects for the patients. Thus, in addition to prolonging life, it is essential to increase the quality of life (QoL) of patients. QoL is now considered an important aspect in clinical practice for patients with chronic illnesses, but the methods to assess, in an automatic or semi-automatic manner, QoL, and their use in clinical decision support are still underexplored and their applications are virtually inexistent. The QVida + solution is based on scientific and technological developments in the areas of quality of life and mobile devices, with the goal of creating a new paradigm for the evaluation and use of QoL in clinical practice. The solution devised is based on an adaptive information system (IS) able to use physical and behavioral data of the patient, gathered by sensors and mobile devices, in conjunction with machine learning techniques, allowing continuous assessment of QoL based on measurement tools that significantly reduce the questionnaires response time without affecting the patients daily life. Continuous assessment of QoL and its rapid use on a clinical decision support system will enable a better quality and more supported decision, patient-centered, by the physician, allowing for an increased quality of life of patients.

Abstract
From the growing trend of the services sector participation in the Brazilian economic scenario, the demand for studies on this subject seek to deepen the understanding about the behaviour of the aspects that influence the occurrence of such evolution. Following this sense, the present study aimed to propose a diagnostic model to measure de performance of innovation and competitiveness in the telecommunications service sector, providing resources to develop strategies, in order to turn the company capable of acting in a position according to direct and indirect main competitors, being development guided by five factors: service production, technology, human resources, finance and marketing. Once constructed the model, the implementation proved to be valid, presenting itself as a practical tool to support managers and consultants in the overall assessment of the company, allowing also to analyse its performance situation regarding to the development of competitive forms of positioning. Copyright © 2016 Inderscience Enterprises Ltd.