7 resultados para path length

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


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This work maps and analyses cross-citations in the areas of Biology, Mathematics, Physics and Medicine in the English version of Wikipedia, which are represented as an undirected complex network where the entries correspond to nodes and the citations among the entries are mapped as edges. We found a high value of clustering coefficient for the areas of Biology and Medicine, and a small value for Mathematics and Physics. The topological organization is also different for each network, including a modular structure for Biology and Medicine, a sparse structure for Mathematics and a dense core for Physics. The networks have degree distributions that can be approximated by a power-law with a cut-off. The assortativity of the isolated networks has also been investigated and the results indicate distinct patterns for each subject. We estimated the betweenness centrality of each node considering the full Wikipedia network, which contains the nodes of the four subjects and the edges between them. In addition, the average shortest path length between the subjects revealed a close relationship between the subjects of Biology and Physics, and also between Medicine and Physics. Our results indicate that the analysis of the full Wikipedia network cannot predict the behavior of the isolated categories since their properties can be very different from those observed in the full network. (C) 2011 Elsevier Ltd. All rights reserved.

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Specific choices about how to represent complex networks can have a substantial impact on the execution time required for the respective construction and analysis of those structures. In this work we report a comparison of the effects of representing complex networks statically by adjacency matrices or dynamically by adjacency lists. Three theoretical models of complex networks are considered: two types of Erdos-Renyi as well as the Barabasi-Albert model. We investigated the effect of the different representations with respect to the construction and measurement of several topological properties (i.e. degree, clustering coefficient, shortest path length, and betweenness centrality). We found that different forms of representation generally have a substantial effect on the execution time, with the sparse representation frequently resulting in remarkably superior performance. (C) 2011 Elsevier B.V. All rights reserved.

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The present work reports on the thermo-optical properties of photorefractive sillenite Bi(12)SiO(20) (BSO) crystals obtained by applying the Thermal Lens Spectrometry technique (TLS). This crystals presents one high photorefractive sensitivity in the region blue-green spectra, since the measurements were carried out at two pump beam wavelengths (514.5 nm and 750 nm) to study of the light-induced effects in this material (thermal and/or photorefractive). We determine thermo-optical parameters like thermal diffusivity (D), thermal conductivity (K) and temperature coefficient of the optical path length change (ds/dT) in sillenite crystals. These aspects, for what we know, not was studied in details up to now using the lens spectrometry technique and are very important against of the promising potentiality of applications these crystals in non linear optics, real time holography and optical processing data.

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In this work, the light-induced lens effect due to thermal and/or photorefractive processes was studied in pyroelectric (undoped and Fe(2+)-doped) lithium niobate crystals (LiNbO(3)) using thermal lens spectrometry with a two-beam (pump-probe) mode-mismatched configuration. The measurements were carried out at two pump beam wavelengths (514.5 and 750 nm) to establish a full understanding of the present effects in this material (thermal and/or photorefractive). We present an easy-to-implement method to determine quantitative values of the pyroelectric coefficient (dPs/dT), its contribution to the thermal effect and other thermo-optical parameters like thermal diffusivity (D), thermal conductivity (K) and temperature coefficient of the optical path length change (ds/dT). These measurements were performed in LiNbO(3) and LiNbO(3): Fe (0.1 ppm Fe(2+)) crystals with c axis along the direction of laser propagation.

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The relationship between the structure and function of biological networks constitutes a fundamental issue in systems biology. Particularly, the structure of protein-protein interaction networks is related to important biological functions. In this work, we investigated how such a resilience is determined by the large scale features of the respective networks. Four species are taken into account, namely yeast Saccharomyces cerevisiae, worm Caenorhabditis elegans, fly Drosophila melanogaster and Homo sapiens. We adopted two entropy-related measurements (degree entropy and dynamic entropy) in order to quantify the overall degree of robustness of these networks. We verified that while they exhibit similar structural variations under random node removal, they differ significantly when subjected to intentional attacks (hub removal). As a matter of fact, more complex species tended to exhibit more robust networks. More specifically, we quantified how six important measurements of the networks topology (namely clustering coefficient, average degree of neighbors, average shortest path length, diameter, assortativity coefficient, and slope of the power law degree distribution) correlated with the two entropy measurements. Our results revealed that the fraction of hubs and the average neighbor degree contribute significantly for the resilience of networks. In addition, the topological analysis of the removed hubs indicated that the presence of alternative paths between the proteins connected to hubs tend to reinforce resilience. The performed analysis helps to understand how resilience is underlain in networks and can be applied to the development of protein network models.

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This paper describes the development of a sequential injection chromatography (SIC) procedure for separation and quantification of the herbicides simazine, atrazine, and propazine exploring the low backpressure of a 2.5 cm long monolithic C(18) column. The separation of the three compounds was achieved in less than 90 s with resolution > 1.5 using a mobile phase composed by ACN/1.25 mmol/L acetate buffer (pH 4.5) at the volumetric ratio of 35:65 and flow rate of 40 mu L/s. Detection was made at 223 nm using a flow cell with 40 mm of optical path length. The LOD was 10 mu g/L for the three triazines and the quantification limits were of 30 mu g/L for simazine and propazine and 40 mu g/L for atrazine. The sampling frequency is 27 samples per hour, consuming 1.1 mL of ACN per analysis. The proposed methodology was applied to spiked water samples and no statistically significant differences were observed in comparison to a conventional HPLC-UV method. The major metabolites of atrazine and other herbicides did not interfere in the analysis, being eluted from the column either together with the unretained peak, or at retention times well-resolved from the studied compounds.

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Coupling a liquid core waveguide cell to a sequential injection chromatograph improved the detection limits for determination of triazine herbicides without compromising peak resolution. Separation of simazine, atrazine, and propazine was achieved in water samples by a 25mm long C18 monolithic column. Detection was made at 238nm using a type II LCW (silica capillary coated with Teflon (R) AF2400) cell with 100cm of optical path length. Detection limits for simazine, atrazine, and propazine were 2.3, 1.9, and 4.5 mu g L-1, respectively. Reduced analysis time and low solvent consumption are other remarkable features of the proposed method.