Abstract
This study addresses the product-launch planning problem in the chemical-pharmaceutical industry under technical and market uncertainties, and considering resource limitations associated to the need of processing in the same plant products under development and products in commercialization. A novel approach is developed by combining a mixed integer linear programming (MILP) model and a Monte Carlo simulation (MCS) procedure, to deal with the integrated process design and production planning decisions during the New Product Development (NPD) phase. The Monte Carlo simulation framework was designed as a two-step sampling procedure based on Bernoulli and Normal distributions. Results show the unquestionable influence of the uncertainty parameters on the decision variables and objective function, thus highlighting the inherent risks associated to the deterministic models. Process designs and scale-ups that maximize expected profit were determined, providing a valuable knowledge frame to support the long-term decision-making process, and enabling earlier and better decisions during NPD.

Abstract
The information generated by a network monitoring system is overwhelming. Monitoring is imperative but very difficult to accomplish due to several reasons. More so for the case of non tech-savvy home users. Security Information Event Management applications generate alarms that correlate multiple occurrences on the network. These events are classified accordingly to their risk. An application that allows the sonification of events generated by a Security Information Event Management can facilitate the security monitoring of a home network by a less tech-savvy user by allowing him to just listen to the result of the sonification of such events.

Abstract
With the overwhelming volume of online multimedia content and increasing ubiquity of Internet-enabled mobile devices, pervasive use of the Web for content sharing and consumption has become our everyday routines. Consequently, people seeking online access to content of interest are becoming more and more frustrated. Thus, deciding which content to consume among the deluge of available alternatives becomes increasingly difficult. Context-aware personalization, having the capability to predict user's contextual preferences, has been proposed as an effective solution. However, some existing personalized systems, especially those based on collaborative filtering, rely on rating information explicitly obtained from users in consumption contexts. Therefore, these systems suffer from the so-called cold-start problem that occurs as a result of personalization systems' lack of adequate knowledge of either a new user's preferences or of a new item rating information. This happens because these new items and users have not received or provided adequate rating information respectively. In this paper, we present an analysis and design of a context-aware personalized system capable of minimizing new user cold-start problem in a mobile multimedia consumption scenario. The article emphasizes the importance of similarity between contexts of consumption based on the traditional k-nearest neighbor algorithm using Pearson Correlation model. Experimental validation, with respect to quality of personalized recommendations and user satisfaction in both contextual and non-contextual scenarios, shows that the proposed system can mitigate the effect of user-based cold-start problem.

Abstract

Abstract
This paper investigates the utilization of two different six-leg configurations of single-phase to three-phase converters. One of the topologies is transformerless and the other is transformer based. The studied converters allow feeding the load voltage with sinusoidal voltages with constant amplitude and frequency, and to operate with sinusoidal grid current with high power factor. The system model and pulse-width modulation techniques for one of the topologies are given. Control strategies for both topologies are provided. The studied topologies are compared with the conventional in terms of dc-link specification, voltages harmonic distortions, semiconductor losses, and other characteristics. Simulation and experimental results are provided to illustrate the operation of the systems.

Abstract
Plenty of literature exists about how to model liberalized electricity generation markets for the medium and long terms, contributing to the analyze and understanding of those markets, helping companies to plan cost-efficient shortterm market strategies and/or long-term generation capacity investments, and supporting regulators and policymakers in policy decisions and market designs. However, those models do not explicitly consider the impact on investment decisions, mix of technologies and wholesale market prices; of policy decisions but as an external passive input to the model. This paper reviews existing approaches to model policy decisions in such a context, and provides a theoretical modeling framework that explicitly considers the interaction of policymakers' decisions with the generation investment and operation, and customers' response in a liberalized power system. Such kind of model, based on bi-level optimization, contributes to the longterm assessment of some policy decisions in the electricity sector. © 2017 IEEE.