113 resultados para Ecological label
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Environmental aspects have been acknowledged as an important issue in decision making at any field during the last two decades. There are several available methodologies able to assess the environmental burden, among which the Ecological Footprint has been widely used due to its easy-to-understand final indicator. However, its theoretical base has been target of some criticisms about the inadequate representation of the sustainability concept by its final indicator. In a parallel way, efforts have been made to use the theoretical strength of the Emergy Accounting to obtain an index similar to that supplied by the Ecological Footprint. Focusing on these aspects, this work assesses the support area (SA) index for Brazilian sugarcane and American corn crop through four different approaches: Embodied Energy Analysis (SA(EE)), Ecological Footprint (SA(EF)), Renewable Empower Density (SA(R)), and Emergy Net Primary Productivity (SA(NPP)). Results indicate that the load on environment varies accordingly to the methodology considered for its calculation, in which emergy approach showed the higher values. Focusing on crops comparison, the load by producing both crops are similar with an average of 0.04 ha obtained by SA(EE), 1.86 ha by SA(EF), 4.24 ha by SA(R), and 4.32 ha by SA(NPP). Discussion indicates that support area calculated using Emergy Accounting is more eligible to represent the load on the environment due to its global scale view. Nevertheless, each methodology has its contribution depending of the study objectives, but it is important to consider the real meaning and the scope of each one. (C) 2012 Elsevier Ltd. All rights reserved.
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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In general, pattern recognition techniques require a high computational burden for learning the discriminating functions that are responsible to separate samples from distinct classes. As such, there are several studies that make effort to employ machine learning algorithms in the context of big data classification problems. The research on this area ranges from Graphics Processing Units-based implementations to mathematical optimizations, being the main drawback of the former approaches to be dependent on the graphic video card. Here, we propose an architecture-independent optimization approach for the optimum-path forest (OPF) classifier, that is designed using a theoretical formulation that relates the minimum spanning tree with the minimum spanning forest generated by the OPF over the training dataset. The experiments have shown that the approach proposed can be faster than the traditional one in five public datasets, being also as accurate as the original OPF. (C) 2014 Elsevier B. V. All rights reserved.
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)