Abstract
Transmission congestion management plays a key role in deregulated energy markets. To correctly model and solve this problem, power system voltage and transient stability limits should be considered to avoid obtaining a vulnerable power system with low stability margins. Congestion management is modeled as a multi-objective optimization problem in this paper. The proposed scheme includes the cost of congestion management, voltage stability margin and transient stability margin as its multiple competing objectives. Moreover, a new effective Multi-objective Mathematical Programming (MMP) solution approach based on normalized normal constraint (NNC) method is presented to solve the multi-objective optimization problem of the congestion management, which can generate a well-distributed and efficient Pareto frontier. The proposed congestion management model and MMP solution approach are implemented on the New-England's test system and the obtained results are compared with the results of several other congestion management methods. These comparisons verify the superiority of the proposed approach.

Abstract
Wireless sensor networks are being widely used to support time-critical applications, even knowing that previous versions of the IEEE 802.15.4 standard have a limited capacity to ensure a timely communication service. Its main limitation is the reduced number of available GTS slots. More recently, the IEEE 802.15.4e LLDN mode has been released to address this issue. It provides static TDMA-based communication, with a larger number of available slots to support time-critical communication. However, it does not provide any guidelines on how to allocate slots, in order to guarantee the message real-time requirements. In this paper, we propose a methodology to allocate LLDN slots based on the traffic load imposed by the supported message streams. The proposed allocation scheme is able to handle message streams with periodicities that are not multiple of the beacon interval, and also to support static and dynamic prioritizing of message streams.

Abstract
The recent interest in the smart grid vision and the technological advancement in the communication and control infrastructure enable several smart applications at different levels of the power grid structure, while specific importance is given to the demand side. As a result, changes in load patterns due to demand response (DR) activities at end-user premises, such as smart households, constitute a vital point to take into account both in system planning and operation phases. In this study, the impact of price-based DR strategies on smart household load pattern variations is assessed. The household load datasets are acquired using model of a smart household performing optimal appliance scheduling considering an hourly varying price tariff scheme. Then, an approach based on artificial neural networks (ANN) and wavelet transform (WT) is employed for the forecasting of the response of residential loads to different price signals. From the literature perspective, the contribution of this study is the consideration of the DR effect on load pattern forecasting, being a useful tool for market participants such as aggregators in pool-based market structures, or for load serving entities to investigate potential change requirements in existing DR strategies, and effectively plan new ones.

Abstract
Multimedia content has the potential for significant impact on users’ emotions, their sense of presence and engagement experiencing the service, application or information being provided, in immersive environments. The evolution of technology, user expectations and results from research activities have led to an enormous increase in the amount of content delivered in different formats, via a number of heterogeneous communication networks, to a range of devices, many of them portable and offering tremendous opportunities for immersion, user participation and personalization. New paradigms for media production, distribution and consumption have been emerging, introducing different sensory modalities and audio-visual surround effects, for an increased sense of presence, and also enabling participation and social interaction in the media chain, thus increasing the sense of belonging and contributing to the success of the services being provided

Abstract
Selecting the best desirable Enterprise Resources Planning (ERP) system has been a critical problem for organizations for a long time, as the failure on the selection process may have a highly negative impact in terms of costs and market share of a company. It is one of the most important decision making issues covering both qualitative and quantitative factors for organization. Multiple-criteria decision-making has been proved to be a useful approach to analyze these conflicting qualitative and quantitative factors. Literature offers proposals and approaches to handle this kind of problem; Analytic Hierarchy Process (AHP) has been applied successfully in most cases of software packages selection problems. This paper proposes an AHP model for the selection of an ERP system. The model's set of criteria was extracted from the literature review and validated by Portuguese organizations. This model can be applied in the ERP system selection using a software application that is under development. This software application eases the application of the AHP process to the selection of ERP packages and will provide input from real-world cases that will allow updating and refining the model.

Abstract
The movement of the human body offers neurologists important clues for the diagnosis and follow-up of many neurological diseases. The typical diagnosis approach is accomplished through simple observation of movements of interest (MOI) associated with a specific neurological disease. This approach is highly subjective because it is mainly based on qualitative evaluation of MOIs. Quantitative movement techniques are then obvious diagnosis-aid systems to approach these cases. Nevertheless, the use of motion quantification techniques in these pathologies is still relatively rare. In this paper, we intend to review this area and provide a clear picture of the current state of the art, both in the methods used and their applications to the main movement-related neurological diseases. We approach some historic aspects and the current state of the motion capture techniques and present the results of a survey to the literature that includes 82 papers, since 2006, covering the usage of these techniques in neurological diseases. Furthermore, we discuss the pros and cons of using quantitative approaches in these clinical scenarios. Finally, we present some conclusions and discuss the trends we foresee for the future. © 2008-2011 IEEE.