Abstract
The amount of data currently being produced, stored and used in hospital settings is stressing information technology infrastructure, making clinical reports to be stored in secondary memory devices. The aim of this work was to develop a model that predicts the probability of visualization, within a certain period after production, of each clinical report. We collected log data, from January 2013 till May 2011, from an existing virtual patient record, in a tertiary university hospital in Porto, Portugal, with information on report creation and report first-time visualization dates, along with contextual information. The main factors associated with visualization were defined using logistic regression. These factors were then used as explanatory variables for predicting the probability of a piece of information being accessed after production, using Kaplan-Meier analysis and the Weibull probability distribution. Clinical department, type of encounter and report type were found significantly associated with time-to-visualization and probability of visualization.

Abstract
The smart grid concept is a key issue in the future power systems, namely at the distribution level, with deep concerns in the operation and planning of these systems. Several advantages and benefits for both technical and economic operation of the power system and of the electricity markets are recognized. The increasing integration of demand response and distributed generation resources, all of them mostly with small scale distributed characteristics, leads to the need of aggregating entities such as Virtual Power Players. The operation business models become more complex in the context of smart grid operation. Computational intelligence methods can be used to give a suitable solution for the resources scheduling problem considering the time constraints. This paper proposes a methodology for a joint dispatch of demand response and distributed generation to provide energy and reserve by a virtual power player that operates a distribution network. The optimal schedule minimizes the operation costs and it is obtained using a particle swarm optimization approach, which is compared with a deterministic approach used as reference methodology. The proposed method is applied to a 33-bus distribution network with 32 medium voltage consumers and 66 distributed generation units.

Abstract
Distributed generation (DG) represents an important resource to address relevant energy issues, such as reliability and sustainability, in the current and future smart cities. It is expected that distributed generation will gain considerable presence in the following years; however, the selection and sizing of the generation and storage systems is commonly done without an adequate level of detail. This simplified or approximated approach usually results in a suboptimal technology mix with an inadequate type of system and/or scale, which could compromise the economic feasibility of the DG project. To tackle this problem, stakeholders should consider many factors, including geographical characteristics (sun, wind...) energy costs, local regulation, and energetic demand patterns, apart from analysing different technologies. Considering as an example location the city of Madrid, Spain, this paper proposes a linear programming model to evaluate the most common distributed generation technologies, with and without storage systems and under different electricity pricing scenarios. As a result, not only the optimal sizing, but also the optimal operation scheduling of the aforementioned systems are found. Then, an economic feasibility analysis is developed, comparing the different technologies and defining the best option for a given scenario. Furthermore, this study helps to find important milestones, such as battery prices, that could make distributed generation more attractive. © 2013 WIT Press.

Abstract
Today's intensive demand for data such as live broadcast or news feeds requires efficient and robust dissemination systems. Traditionally, designs focus on extremes of the efficiency/robustness spectrum by either using structures, such as trees for efficiency or by using loosely-coupled epidemic protocols for robustness. We present BRISA, a hybrid approach combining the robustness of epidemics with the efficiency of structured approaches. BRISA implicitly emerges embedded dissemination structures from an underlying epidemic substrate. The structures' links are chosen with local knowledge only, but still ensuring connectivity. Failures can be promptly compensated and repaired thanks to the epidemic substrate, and their impact on dissemination delays masked by the use of multiple independent structures. Besides presenting the protocol design, we conduct an extensive evaluation in real environments, analyzing the effectiveness of the structure creation mechanism and its robustness under dynamic conditions. Results confirm BRISA as an efficient and robust approach to data dissemination in large dynamic environments.

Abstract
The paper presents a holistic methodological approach for designing flexible production systems, integrating the management of the production resources, operations planning, internal logistics and quality. The proposed methodology relies on lean principles and encompasses a series of stages and tools that may be used to design and to assess the robustness of the production system for tackling small series and frequent changes in the productive processes due to customized production. This approach has been applied to the redesign of the production system of a Portuguese machinery manufacturing industrial company. Results show that it effectively led to a new flexible production system and a new industrial layout capable of responding to diverse future scenarios of products demand. (C) 2013 The Authors. Published by Elsevier B.V.

Abstract
GROOVE IS A SENSATION OF MOVEMENT OR WANTing to move when we listen to certain types of music; it is central to the appreciation of many styles such as Jazz, Funk, Latin, and many more. To better understand the mechanisms that lead to the sensation of groove, we explore the relationship between groove and systematic microtiming deviations. Manifested as small, intentional deviations in timing, systematic microtiming is widely considered within the music community to be a critical component of music performances that groove. To investigate the effect of microtiming on the perception of groove we synthesized typical rhythm patterns for Jazz, Funk, and Samba with idiomatic microtiming deviation patterns for each style. The magnitude of the deviations was parametrically varied from nil to about double the natural level. In two experiments, untrained listeners and experts listened to all combinations of same and different music and microtiming style and magnitude combinations, and rated liking, groove, naturalness, and speed. Contrary to a common and frequently expressed belief in the literature, systematic microtiming led to decreased groove ratings, as well as liking and naturalness, with the exception of the simple short-long shuffle Jazz pattern. A comparison of the ratings between the two listener groups revealed this effect to be stronger for the expert listener group than for the untrained listeners, suggesting that musical expertise plays an important role in the perception and appreciation of micro timing in rhythmic patterns.