2 resultados para 378.018.43

em Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP)


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Laurencia marilzae is recorded for the first time from the western Atlantic Ocean; it was found in Laje de Santos Marine State Park, Sao Paulo, southeastern Brazil. The specimens were collected in the rocky subtidal zone from 7 to 15 m depth. The most distinctive characteristic of this species is the presence of corps en cerise in all cells of the thallus, including cortex, medulla, and trichoblasts. The phylogenetic position of the species was inferred by analysis of the chloroplast-encoded rbcL gene sequences from 43 taxa, using two other rhodomelacean taxa and two members of the Ceramiaceae as outgroups. Within the Laurencia assemblage, L. marilzae from Brazil and from the Canary Islands ( type locality) formed a distinctive lineage sister to all other Laurencia species analyzed. Male plants are described for the first time. This study expands the geographical distribution of L. marilzae to the western Atlantic Ocean.

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This paper presents the formulation of a combinatorial optimization problem with the following characteristics: (i) the search space is the power set of a finite set structured as a Boolean lattice; (ii) the cost function forms a U-shaped curve when applied to any lattice chain. This formulation applies for feature selection in the context of pattern recognition. The known approaches for this problem are branch-and-bound algorithms and heuristics that explore partially the search space. Branch-and-bound algorithms are equivalent to the full search, while heuristics are not. This paper presents a branch-and-bound algorithm that differs from the others known by exploring the lattice structure and the U-shaped chain curves of the search space. The main contribution of this paper is the architecture of this algorithm that is based on the representation and exploration of the search space by new lattice properties proven here. Several experiments, with well known public data, indicate the superiority of the proposed method to the sequential floating forward selection (SFFS), which is a popular heuristic that gives good results in very short computational time. In all experiments, the proposed method got better or equal results in similar or even smaller computational time. (C) 2009 Elsevier Ltd. All rights reserved.