999 resultados para Cluster-Analyse
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While Cluster-Tree network topologies look promising for WSN applications with timeliness and energy-efficiency requirements, we are yet to witness its adoption in commercial and academic solutions. One of the arguments that hinder the use of these topologies concerns the lack of flexibility in adapting to changes in the network, such as in traffic flows. This paper presents a solution to enable these networks with the ability to self-adapt their clusters’ duty-cycle and scheduling, to provide increased quality of service to multiple traffic flows. Importantly, our approach enables a network to change its cluster scheduling without requiring long inaccessibility times or the re-association of the nodes. We show how to apply our methodology to the case of IEEE 802.15.4/ZigBee cluster-tree WSNs without significant changes to the protocol. Finally, we analyze and demonstrate the validity of our methodology through a comprehensive simulation and experimental validation using commercially available technology on a Structural Health Monitoring application scenario.
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In this work, cluster analysis is applied to a real dataset of biological features of several Portuguese reservoirs. All the statistical analysis is done using R statistical software. Several metrics and methods were explored, as well as the combination of Euclidean metric and the hierarchical Ward method. Although it did not present the best combination in terms of internal and stability validation, it was still a good solution and presented good results in terms of interpretation of the problem at hand.
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The study of electricity markets operation has been gaining an increasing importance in the last years, as result of the new challenges that the restructuring process produced. Currently, lots of information concerning electricity markets is available, as market operators provide, after a period of confidentiality, data regarding market proposals and transactions. These data can be used as source of knowledge to define realistic scenarios, which are essential for understanding and forecast electricity markets behavior. The development of tools able to extract, transform, store and dynamically update data, is of great importance to go a step further into the comprehension of electricity markets and of the behaviour of the involved entities. In this paper an adaptable tool capable of downloading, parsing and storing data from market operators’ websites is presented, assuring constant updating and reliability of the stored data.
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Local Tourist Systems (LTS) can be analyzed according to an investigation structure that derives from industrial economics on industrial districts, local productive systems or learning regions. LTS concept is a useful analytical tool that can seize the resorts diversity and organization. Resorts can be conceived both as clusters or industrial districts, either with a perfect agreement between productive sphere and local community or a mere industrial juxtaposition without any economic or social connection. On the other hand tourist clusters analysis has cross referred almost exclusively to socio-economic criteria. Environmental issues were almost disregarded. Approaches swing from the “greening” of products and practices to initiatives focused on an integrated approach, linking environment and tourist development. This paper tries to discuss how to favor – inside a tourist destination - the creation of clusters grounded on sustainable tourism. The case studies (the 5 Alentejo Natural Reserves: Estuário do Sado; Lagoas de Santo André e da Sancha; Vale do Guadiana; Sudoeste Alentejano e Costa Vicentina; Serra de S. Mamede) are analyzed under the light of how microstructures groups can allow a territorial sustainable tourist development. The issues of “resources and competences” and “governance” ar
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Tese apresentada para cumprimento dos requisitos necessários à obtenção do grau de Doutor em Geografia e Planeamento Territorial, Especialidade Território e Desenvolvimento
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Biochemistry. 2009 Feb 10;48(5):873-82. doi: 10.1021/bi801773t.
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Biomol NMR Assign (2007) 1:81–83 DOI 10.1007/s12104-007-9022-3
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J Biol Inorg Chem (2006) 11: 307–315 DOI 10.1007/s00775-005-0077-2
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Inorg. Chem., 2003, 42 (4), pp 938–940 DOI: 10.1021/ic0262886
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J Biol Inorg Chem (2004) 9: 145–151 DOI 10.1007/s00775-003-0506-z
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J. Am. Chem. Soc., 2003, 125 (51), pp 15708–15709 DOI: 10.1021/ja038344n