Abstract
The design of injection mold tools is a complex process for which market pressures demand ever-shorter development time and higher quality level. Thus, it is considered imperative to adopt new methods and tools to support the design process, in order to achieve a more effective mold design solution. Based on this assumption, a multidisciplinary framework was developed, centered on Design for Six Sigma methodology and reinforced with a set of highly valued techniques, namely: the European Customer Satisfaction, the Axiomatic Design and the Multidisciplinary Design Optimization. As a result, a platform was built to support the design of any mold tool without undercuts, which tackles the design of an injection mold as a fully integrated multidisciplinary optimization problem, oriented by customer preferences and their impositions. A set of specific analysis sub-modules is integrated through an overseeing optimization code system, responsible for the numerical simulation of the injection process. This platform was validated through the comparison with an existing mold, whose results attained highlight the great potential of the proposed framework to achieve mold design improvement. In particular, the value of mold solutions generated led to a global improvement on mold performance by 5 %.

Abstract
This paper presents an approach for integrated simulation of pacemaker models and heart models, each developed with the appropriate formalism. Heart models are developed in MathWorks, a powerful tool for the simulation of complex systems, whereas pacemakers are developed in PVS, a theorem-proving environment enabling both simulation and formal verification of safety requirements. The two tools communicate over a Web-based interface, which makes it possible to integrate the simulation of the MathWorks model of the heart and the PVS model of the pacemaker. In this paper, we illustrate the architecture developed for integrated simulation of the pacemaker-heart system and present an example application for realistic models. Copyright

Abstract
The development of the Smart Grid (SG) concept is the pathway for assuring flexible, reliable and efficient distribution networks while integrating high shares of Distributed Energy Resources (DER): renewable energy based generation, distributed storage and controllable loads such as Electric Vehicles (EV). Within the SG paradigm, the Microgrid (MG) can be regarded as a highly flexible and controllable Low Voltage (LV) cell, which is able to decentralize the distribution management and control system while providing additional controllability and observability. A network of controllers interconnected by a communication system ensures the management and control of the LV microgrid, enabling both interconnected and autonomous operation modes. This new distribution operation philosophy is in line with the SG paradigm, since it improves the security and reliability of the system, being able to tackle the technical challenges resulting from the large scale integration of DER and provide the adequate framework to fully integrate SG new players such as the EV. By exploiting the MG operational flexibility and controllability, this chapter aims to provide an extended overview on MG self-healing capabilities, namely on its ability of operating autonomously from the main grid and perform local service restoration. The MG hierarchical management and control structure is revisited and adapted in order to exploit the flexibility of SG new players, like the EV and flexible loads and integrate smart metering infrastructures. The implementation of the MG architecture and communication infrastructure in a laboratorial facility is also presented and used to validate the MG self-healing capabilities. © 2014, Springer Science+Business Media Singapore.

Abstract
Health-related quality of life (HR-QoL) is a subjective concept, reflecting the overall mental and physical state of the patient, and their own sense of well-being. Estimating current and future QoL has become a major outcome in the evaluation of critically ill patients. The aim of this study is to enhance the inference process of 6 weeks and 6 months prognosis of QoL after intensive care unit (ICU) stay, using the EQ-5D questionnaire. The main outcomes of the study were the EQ-5D five main dimensions: mobility, self-care, usual activities, pain and anxiety/depression. For each outcome, three Bayesian classifiers were built and validated with 10-fold cross-validation. Sixty and 473 patients (6 weeks and 6 months, respectively) were included. Overall, 6 months QoL is higher than 6 weeks, with the probability of absence of problems ranging from 31% (6 weeks mobility) to 72% (6 months self-care). Bayesian models achieved prognosis accuracies of 56% (6 months, anxiety/depression) up to 80% (6 weeks, mobility). The prognosis inference process for an individual patient was enhanced with the visual analysis of the models, showing that women, elderly, or people with longer ICU stay have higher risk of QoL problems at 6 weeks. Likewise, for the 6 months prognosis, a higher APACHE II severity score also leads to a higher risk of problems, except for anxiety/depression where the youngest and active have increased risk. Bayesian networks are competitive with less descriptive strategies, improve the inference process by incorporating domain knowledge and present a more interpretable model. The relationships among different factors extracted by the Bayesian models are in accordance with those collected by previous state-of-the-art literature, hence showing their usability as inference model.

Abstract
Recently the RoboCup@Work league emerged in the world's largest robotics competition, intended for competitors wishing to compete in the field of mobile robotics for manipulation tasks in industrial environments. This competition consists of several tasks with one reflected in this work (Basic Navigation Test). This project involves the simulation in Virtual Robot Experimentation Platform (V-REP) of the behavior of a KUKA youBot. The goal is to verify that the robots can navigate in their environment, in a standalone mode, in a robust and secure way. To achieve the proposed objectives, it was necessary to create a program in Lua and test it in simulation. This involved the study of robot kinematics and mechanics, Simultaneous Localization And Mapping (SLAM) and perception from sensors. In this work is introduced an algorithm developed for a KUKA youBot platform to perform the SLAM while reaching for the goal position, which works according to the requirements of this competition BNT. This algorithm also minimizes the errors in the built map and in the path travelled by the robot.

Abstract