834 resultados para Artificial streams


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Distribution of Rhodophyta was investigated in 172 stream segments, which were sampled from May to October in 1992-1993 and 1996-1997 in six natural regions (parts of biomes or geological areas) of São Paulo State, southeastern Brazil. Red algae occurred in 60.5 % of stream segments sampled, a high frequency in comparison with other major surveys in the world (18-65 %). 17 species of freshwater red algae were found, of which the most widespread was Batrachospermum delicatulum occurring in 17 sites of five regions. The proportion of morphological types was as follows: gelatinous filaments (62.5 %), free filaments (19 %), tufts (12.5 %) and crusts (6 %); all but free filaments can be considered as having mechanisms to tolerate stress provoked by current velocity. No significant difference was found between the frequency distributions of variables measured for all streams and for those with red algae. Rhodophyta occurred under the following conditions (means): temperature (19.0 degrees C), current velocity (48 cm s(-1)), specific conductance (74 mu S cm(-1)), turbidity (8 NTU), oxygen (67.3 %) and pH (6.9 +/- 0.7). on the basis of species composition among the regions, the following patterns were evident: 1) the number of red algal species per region ranged from 1 to 10; 2) the highest proportion of sites with red algae (65-73 %) was found in hard water regions and in Atlantic rainforest, whereas the lowest (50 %) was found in tropical rainforest; 3) more than half of the species were exclusive from a single region, whereas the higher proportion of exclusive species was in the subtropical rainforest (50 %). No combination of stream variables was clearly associated with the occurrence of red algae for the regions as a whole. Species composition for streams and rivers of São Paulo State revealed higher similarities with other tropical regions and had few species in common with freshwater red algal floras of other continents.

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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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Aim: To investigate whether composition and structure of stream ichthyofauna vary along the conservation gradient in the Bodoquena Plateau; Methods: Standardized sampling was performed in nine stream stretches (three references, three in the pasture, and three in urban areas), each 80 m long, in April, May and June/08; Results: A quantity of 8,102 individuals representing 36 species was collected. There were no significant differences with respect to absolute species richness among stretches. Urban stretches were more similar to each other than the other two groups, both regarding species composition and abundance. A set of six species was considered an indicator of reference stretches, whereas two were of pasture stretches and four of urban stretches. Poecilia reticulata and Corydoras aeneus, occurring only in urban stretches, were unique in showing the maximum values in the indicator species analysis; Conclusions: Results indicate changes in the ichthyofauna with urbanized surrounding water courses. The presence and abundance of Poecilia reticulata and Corydoras aeneus may indicate the quality loss of these environments.

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This paper presents two approaches of Artificial Immune System for Pattern Recognition (CLONALG and Parallel AIRS2) to classify automatically the well drilling operation stages. The classification is carried out through the analysis of some mud-logging parameters. In order to validate the performance of AIS techniques, the results were compared with others classification methods: neural network, support vector machine and lazy learning.

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Petroleum well drilling monitoring has become an important tool for detecting and preventing problems during the well drilling process. In this paper, we propose to assist the drilling process by analyzing the cutting images at the vibrating shake shaker, in which different concentrations of cuttings can indicate possible problems, such as the collapse of the well borehole walls. In such a way, we present here an innovative computer vision system composed by a real time cutting volume estimator addressed by support vector regression. As far we know, we are the first to propose the petroleum well drilling monitoring by cutting image analysis. We also applied a collection of supervised classifiers for cutting volume classification. (C) 2010 Elsevier Ltd. 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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