Several synchronous applications are based on the graph-structured data; among them, a very important application of this kind is community detection. Since the number and size of the networks modeled by graphs grow larger and larger, some level of parallelism needs to be used, to reduce the computational costs of such massive applications. Social networking sites allow users to manually categorize their friends into social circles (referred to as lists on Facebook and Twitter), while users, based on their interests, place themselves into groups of interest. However, the community detection and is a very effortful procedure, and in addition, these communities need to be updated very often, resulting in more effort. In this paper, we combine parallel processing techniques with a typical data structure like threaded binary trees to detect communities in an efficient manner. Our strategy is implemented over weighted networks with irregular topologies and it is based on a stepwise path detection strategy, where each step finds a link that increases the overall strength of the path being detected. To verify the functionality and parallelism benefits of our scheme, we perform experiments on five real-world data sets:
The detection of community structures is a crucial research area. The problem of community detection has received considerable attention from a large portion of the scientific community and a very large number of papers has already been published in the literature. Even more important is the fact that, this large number of articles is in fact spread across a large number of different disciplines, from computer science, to statistics, and social sciences. These facts necessitate some type of classification and organization of these works. In this work, our basic classification approach divides the community detection schemes into three basic approaches: (a) the bottom-up approaches, (b) the top-down approaches, and (c) the data structurebased approaches. The first category includes the majority of algorithms, so further classification is possible. Such a classification is included in this work. For the other two categories, we make no further categorizations but we simply focus our discussion on the metrics or the data structures being used. Finally, a few possible directions for future research are also suggested.
Recently, a new trend has emerged in the field of parallel and high performance computing, the hybrid implementation using CPU-GPU modules. In such implementations, the computational load is shared between the CPU and GPU, in order to improve the computational efficiency. However, the task of sharing the computational load between the two modules is a rather difficult one, with a number of limitations being imposed. This paper extends our recent work on community detection, which is based on transforming a network of nodes into a set of threaded binary trees. In this work, we share the computational load between the two units: the CPU takes specific samples of the network communities and organizes them in the form of threaded binary trees. The GPU takes over the heavy load of reading this data and transforming it into a path-matrix. Finally, this matrix is sent back to the CPU for analysis, community detection and overlaps, as well as network information upgrades. Our simulation results show significant improvement over our previous strategy and other known community detection strategies found in the literature.
One of the most crucial factors for energy transition and the incorporation of renewable energy sources into the existing energy map is citizen engagement. Local energy communities (LECs), which are cooperative-based coalitions aimed at reducing the carbon footprint of the residential building sector, have received increasing attention in the past decade. This is because residential buildings account for almost half of the total energy consumed worldwide. A resounding 75% of it is used for thermal energy consumption, heating and cooling, cooking and bathing. However, the main focus of the literature worldwide is explicitly on electrical LECs, despite the fact that the significant increase in natural gas and oil prices, creates instability in the heating and cooling prices. The scope of this study is to provide an overview of the research field regarding Thermal LECs, using both a thorough literature review as well as bibliometric analysis (VOSviewer software), in order to validate the findings of the review. The results indicate a collective scarcity of literature in the field of thermal/cooling energy communities, despite their proven value to the energy transition. A significant lack of directives, research background and state initiatives in the context of LECs incorporating thermal/cooling energy production, storage and distribution systems, was also observed. Case studies and the applications of such systems are scarce in the available literature, while published studies need further feasibility assessments.
A navegação consulta e descarregamento dos títulos inseridos nas Bibliotecas Digitais UC Digitalis, UC Pombalina e UC Impactum, pressupõem a aceitação plena e sem reservas dos Termos e Condições de Uso destas Bibliotecas Digitais, disponíveis em https://digitalis.uc.pt/pt-pt/termos.Conforme exposto nos referidos Termos e Condições de Uso, o descarregamento de títulos de acesso restrito requer uma licença válida de autorização devendo o utilizador aceder ao(s) documento(s) a partir de um endereço de IP da instituição detentora da supramencionada licença.Ao utilizador é apenas permitido o descarregamento para uso pessoal, pelo que o emprego do(s) título(s) descarregado(s) para outro fim, designadamente comercial, carece de autorização do respetivo autor ou editor da obra.Na medida em que todas as obras da UC Digitalis se encontram protegidas pelo Código do Direito de Autor e Direitos Conexos e demais legislação aplicável, toda a cópia, parcial ou total, deste documento, nos casos em que é legalmente admitida, deverá conter ou fazer-se acompanhar por este aviso. Reliability Analysis on Crucial Subsystems of a Wind Turbine through FTA Approach Autor(es):Katsavounis, S.; Patsianis, N.; Konstantinidis, E.I.; Botsaris, P.N. Publicado por:Imprensa Abstract -The wind turbine reliability is a crucial factor for the successful operation of a wind power plant, affecting its availability and efficiency. Operation and maintenance costs affect the performance of the whole system and reinforce the necessity of redesign of specific sub-assemblies achieving lower energy production costs.At the first stage, field data make up Weibull sets in order to form the appropriate distribution-curve of the failure rate in each corresponding top event, are presented. These sets are limited to sub-systems having not only adequate data of the corresponding top events, producing more realistic results, but also having great risk priority, according to FMEA approach. These Weibull sets are linked with the corresponding top event of each subsystem and used to quantify the failure rates.The validation of previous studies made on wind turbine reliability FMEA analysis through the FTA method is investigated in this paper, as well as the results from previous studies made on reliability of wind turbines using the FMEA method. Though, the reliability and importance results as derived from a quantitative analysis, seem to be following the same trend like previous studies from different and various approaches. As a result, Electrical and Control systems as far as the Hydraulic System need to be re-designed with better performance and reliability since they are crucial for the operation of each WT separately as well as for the whole wind farm.
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