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
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. © Springer International Publishing Switzerland 2016.

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. © 2016 ACM.

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.

Abstract
In this paper we consider the problem of motion planning for perception of a target position. A robot has to move to a position from where it can sense the target, while minimizing both motion and perception costs. The problem of finding paths for robots executing perception tasks can be solved optimally using informed search. In perception path planning, the solution for the perception task considering a straight line without obstacles is used as heuristic. In this work, we propose a heuristic that can improve the search efficiency. In order to improve the node expansion using a more informed search, we use the robot Approximate Visibility Map (A-VM), which is used as a representation of the observability capability of a robot in a given environment. We show how the critical points used in A-VM provide information on the geometry of the environment, which can be used to improve the heuristic, increasing the search efficiency. The critical points allow a better estimation of the minimum motion and perception cost for targets in non-traversable regions that can only be sensed from further away. Finally, we show the contributed heuristic dominates the common heuristic (based on the euclidian distance), and present the results of the performance increase in terms of node expansion.