Abstract
In this paper it is applied a Rough Set approach that takes into account an incomplete information system to study the steady-state security of an electric power system. The Rough Set Theory has been conceived as a tool to conceptualize, organize and analyze various types of data, in particular, to deal with inexact, uncertain or vague knowledge. The knowledge acquisition process is a complex task, since the experts have difficulty to explain how to solve a specified problem. So, an incomplete set of relevant information may arise. The study presents a systematic approach to transform examples in a reduced set of rules. These rules can be used successfully to avoid security problems and provides a deeper insight into the influence of parameters on the steady-state system performance. ©2009 IEEE.

Abstract
This paper presents an innovative approach to support the definition of strategies for the design of alternative configurations of hospital supply chains. This approach was developed around a hybrid Tabu Search / Variable Neighbourhood Search metaheuristic, that uses several neighbourhood structures. The flexibility of the procedure allows its application to supply chains with different topologies and atypical cost characteristics. A preliminary computational experience shows the approach potential in solving large scale supply chain configuration problems. The future incorporation of this approach in a broader Decision Support System (DSS) will provide a tool that can significantly contribute to an increase of healthcare supply chains efficiency and encourage the establishment of collaborative partnerships between their members.

Abstract
Semi-infinite programming (SIP) problems arise in several engineering areas such as, for example, robotic trajectory planning, production planning, digital filter design and air pollution control. In spite of being an active research area with many seminal works it lacks available software that could be used by the research community. The only exceptions are the fseminf MATLAB function, available in the Optimization Toolbox, and the NSIPS solver, but neither of them provide an implementation of a method belonging to the well known reduction type class. This paper proposes an implementation of a reduction type algorithm base on a penalty technique and provides a compare between several well known penalty functions. The provided numerical results with a significant number of SIP test problems are reported as performance profiles.

Abstract
Many people with severe disabilities find it difficult or even impossible to use traditional powered wheelchairs independently by manually controlling these electrical devices. Intelligent wheelchairs are a very good solution to assist severely handicapped people who are unable to operate classical electrical wheelchair by themselves in their daily activities. This paper describes a development platform for intelligent wheelchairs called IntellWheels. The intelligent system developed may be added to commercial powered wheelchairs with minimal modifications in a very straightforward manner. The paper describes the concept and design of the platform, including the hardware and software, multimodal input interface and the intelligent wheelchair prototype developed to validate the approach. Preliminary results concerning automatic movement of the IntellWheels prototype are also described showing the autonomous movement capabilities of the prototype. © 2009 Springer-Verlag Berlin Heidelberg.

Abstract
Sensors distributed all around electrical-power distribution networks produce streams of data at high-speed. From a data mining perspective, this sensor network problem is characterized by a large number of variables ( sensors), producing a continuous flow of data, in a dynamic non-stationary environment. Companies make decisions to buy or sell energy based on load profiles and forecast. In this work we analyze the most relevant data mining problems and issues: continuously learning clusters and predictive models, model adaptation in large domains, and change detection and adaptation. The goal is to continuously maintain a clustering model, defining profiles, and a predictive model able to incorporate new information at the speed data arrives, detecting changes and adapting the decision models to the most recent information. We present experimental results in a large real-world scenario, illustrating the advantages of the continuous learning and its competitiveness against Wavelets based prediction. We also propose a light electrical load visualization system which enhances the ability to inspect forecast results in mobile devices.

Abstract
In this paper, we analyze important aspects related to voltage stability indices in electrical power systems, which allows operators to determine the weakest bus of a system as well as its critical line settings of each bus. Conventional techniques are analyzed and a comparison of the performance of several indices is presented. The feasibility of the reported methods is shown by numerical studies in IEEE 30 busbar test system.