Abstract
The electricity market restructuring, along with the increasing necessity for an adequate integration of renewable energy sources, is resulting in an rising complexity in power systems operation. Various power system simulators have been introduced in recent years with the purpose of helping operators, regulators, and involved players to understand and deal with this complex environment. This paper focuses on the development of an upper ontology which integrates the essential concepts necessary to interpret all the available information. The restructuring of MASCEM (Multi-Agent System for Competitive Electricity Markets), and this system's integration with MASGriP (Multi-Agent Smart Grid Platform), and ALBidS (Adaptive Learning Strategic Bidding System) provide the means for the exemplification of the usefulness of this ontology. A practical example is presented, showing how common simulation scenarios for different simulators, directed to very distinct environments, can be created departing from the proposed ontology.

Abstract

Abstract
In this paper we analyze and discuss two methods that are based on the traditional k-means for document clustering and that feature integration of social tags in the process. The first one allows the integration of tags directly into a Vector Space Model, and the second one proposes the integration of tags in order to select the initial seeds. We created a predictive model for the impact of the tags' integration in both models, and compared the two methods using the traditional k-means++ and the novel k-C algorithm. To compare the results, we propose a new internal measure, allowing the computation of the cluster compactness. The experimental results indicate that the careful selection of seeds on the k-C algorithm present better results to those obtained with the k-means++, with and without integration of tags.

Abstract
Publically available text-based documents (e.g. news, meeting transcripts) are a very important source of knowledge, especially for organizations. These documents mention domain entities such as persons, places, professional positions, decisions and actions. Querying these documents (instead of browsing, searching and finding) is a very relevant task for any person in general, and particularly for professionals dealing with intensive knowledge tasks. Querying text-based documents' data, however, is not supported by common technology. For that, such documents' content has to be explicitly and formally captured as facts into a knowledge base. Making use of automatic NLP processes for capturing such facts is a common approach, but their relatively low precision and recall give rise to data quality problems. Furthermore, facts existing in the documents are often insufficient to answer complex queries, thus the need to enrich the captured facts with facts from third-party repositories (e.g. public LOD). This paper describes the adopted process to clean, populate and enrich a knowledge base repository that is further exploited to answer complex queries. This process is triggered by a previous NLP parsing process and conducted by the (rich) ontology describing such repository.

Abstract
Network coding has the potential to enhance energy efficiency of multicast sessions by providing optimal communication subgraphs for the transmission of the data. However, the coding requirement at intermediate nodes may introduce additional complexity and energy consumption in order to code the data packets. Previous work has shown that in lossless wireline networks, the performance of tree-packing mechanisms is comparable to network coding, albeit with added complexity at the time of computing the trees. This means that most nodes in the network need not code. Thus, mechanisms that identify intermediate nodes that do require coding is instrumental for the efficient operation of coded networks and can have a significant impact in overall energy consumption. We present a distributed, low complexity algorithm that allows every node to identify if it should code and, if so, through what output link should the coded packets be sent. Our algorithm uses as input the optimal subgraph determined by Lun et al's optimization formulation [13]. Numerical results are provided using common Internet Service Provider (ISP) network topologies and also random network deployments. Our results show that the number of coding nodes in the expectation is very low (typically below 1) and that the number of sessions that require coding is limited, e.g., less than 15% for sessions of 4 receivers for the ISP networks and below 0.1% for networks with random node deployments in a square of 1 × 1 km2 with of up to 30 nodes and up to 20 receivers. © 2013 IEEE.

Abstract
Spatial solitons are robust localized nonlinear waves that are able to propagate without significant changes to their structure. Most of the proposal of the application of solitons uses them to transmit and process information in optical fibers and optical circuits. In the later the solitons can be guided through different paths by presetting some soliton characteristics (such as the phase), and even using some solitons to control the path of other pulses. In this paper, we use these properties of optical spatial solitons in a cubic nonlinear media to have lightons: phonon-like oscillations of a chain of solitonic light pulses. Conceptually, this work aims to explore the dual nature of solitons as a particle-like wave, by considering the displacement wave of solitons in a 1-dimensional chain.