6 resultados para Nearest Neighbour

em Universidade Federal do Rio Grande do Norte(UFRN)


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The pair contact process - PCP is a nonequilibrium stochastic model which, like the basic contact process - CP, exhibits a phase transition to an absorbing state. While the absorbing state CP corresponds to a unique configuration (empty lattice), the PCP process infinitely many. Numerical and theoretical studies, nevertheless, indicate that the PCP belongs to the same universality class as the CP (direct percolation class), but with anomalies in the critical spreading dynamics. An infinite number of absorbing configurations arise in the PCP because all process (creation and annihilation) require a nearest-neighbor pair of particles. The diffusive pair contact process - PCPD) was proposed by Grassberger in 1982. But the interest in the problem follows its rediscovery by the Langevin description. On the basis of numerical results and renormalization group arguments, Carlon, Henkel and Schollwöck (2001), suggested that certain critical exponents in the PCPD had values similar to those of the party-conserving - PC class. On the other hand, Hinrichsen (2001), reported simulation results inconsistent with the PC class, and proposed that the PCPD belongs to a new universality class. The controversy regarding the universality of the PCPD remains unresolved. In the PCPD, a nearest-neighbor pair of particles is necessary for the process of creation and annihilation, but the particles to diffuse individually. In this work we study the PCPD with diffusion of pair, in which isolated particles cannot move; a nearest-neighbor pair diffuses as a unit. Using quasistationary simulation, we determined with good precision the critical point and critical exponents for three values of the diffusive probability: D=0.5 and D=0.1. For D=0.5: PC=0.89007(3), β/v=0.252(9), z=1.573(1), =1.10(2), m=1.1758(24). For D=0.1: PC=0.9172(1), β/v=0.252(9), z=1.579(11), =1.11(4), m=1.173(4)

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Nowadays, classifying proteins in structural classes, which concerns the inference of patterns in their 3D conformation, is one of the most important open problems in Molecular Biology. The main reason for this is that the function of a protein is intrinsically related to its spatial conformation. However, such conformations are very difficult to be obtained experimentally in laboratory. Thus, this problem has drawn the attention of many researchers in Bioinformatics. Considering the great difference between the number of protein sequences already known and the number of three-dimensional structures determined experimentally, the demand of automated techniques for structural classification of proteins is very high. In this context, computational tools, especially Machine Learning (ML) techniques, have become essential to deal with this problem. In this work, ML techniques are used in the recognition of protein structural classes: Decision Trees, k-Nearest Neighbor, Naive Bayes, Support Vector Machine and Neural Networks. These methods have been chosen because they represent different paradigms of learning and have been widely used in the Bioinfornmatics literature. Aiming to obtain an improvment in the performance of these techniques (individual classifiers), homogeneous (Bagging and Boosting) and heterogeneous (Voting, Stacking and StackingC) multiclassification systems are used. Moreover, since the protein database used in this work presents the problem of imbalanced classes, artificial techniques for class balance (Undersampling Random, Tomek Links, CNN, NCL and OSS) are used to minimize such a problem. In order to evaluate the ML methods, a cross-validation procedure is applied, where the accuracy of the classifiers is measured using the mean of classification error rate, on independent test sets. These means are compared, two by two, by the hypothesis test aiming to evaluate if there is, statistically, a significant difference between them. With respect to the results obtained with the individual classifiers, Support Vector Machine presented the best accuracy. In terms of the multi-classification systems (homogeneous and heterogeneous), they showed, in general, a superior or similar performance when compared to the one achieved by the individual classifiers used - especially Boosting with Decision Tree and the StackingC with Linear Regression as meta classifier. The Voting method, despite of its simplicity, has shown to be adequate for solving the problem presented in this work. The techniques for class balance, on the other hand, have not produced a significant improvement in the global classification error. Nevertheless, the use of such techniques did improve the classification error for the minority class. In this context, the NCL technique has shown to be more appropriated

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Digital Elevation Models (DEM) are numerical representations of a portion of the earth surface. Among several factors which affect the quality of a DEM, it should be emphasized the attention on the input data and the choice of the interpolating algorithm. On the other hand, several numerical models are used nowadays to characterize nearshore hydrodynamics and morphological changes in coastal areas, whose validation is based on field data collection. Independent on the complexity of the physical processes which are modeled, little attention has been given to the intrinsic bathymetric interpolation built within the numerical models of the specific application. Therefore, this study aims to investigate and to quantify the influence of the bathymetry, as obtained by a DEM, on the hydrodynamic circulation model at a coastal stretch, off the coast of the State of Rio Grande do Norte, Northeast Brazil. This coastal region is characterized by strong hydrodynamic and littoral processes, resulting in a very dynamic morphology with shallow coastal bathymetry. Important economic activities, such as oil exploitation and production, fisheries, salt ponds, shrimp farms and tourism, also bring impacts upon the local ecosystems and influence themselves the local hydrodynamics. This fact makes the region one of the most important for the development of the State, but also enhances the possibility of serious environmental accidents. As a hydrodynamic model, SisBaHiA® - Environmental Hydrodynamics System ( Sistema Básico de Hidrodinâmica Ambiental ) was chosen, for it has been successfully employed at several locations along the Brazilian coast. This model was developed at the Coastal and Oceanographical Engineering Group of the Ocean Engineering Program at the Federal University of Rio de Janeiro. Several interpolating methods were tested for the construction of the DEM, namely Natural Neighbor, Kriging, Triangulation with Linear Interpolation, Inverse Distance to a Power, Nearest Neighbor, and Minimum Curvature, all implemented within the software Surfer®. The bathymetry which was used as reference for the DEM was obtained from nautical charts provided by the Brazilian Hydrographic Service of the Brazilian Navy and from a field survey conducted in 2005. Changes in flow velocity and free surface elevation were evaluated under three aspects: a spatial vision along three profiles perpendicular to the coast and one profile longitudinal to the coast as shown; a temporal vision from three central nodes of the grid during 30 days; a hodograph analysis of components of speed in U and V, by different tidal cycles. Small, but negligible, variations in sea surface elevation were identified. However, the differences in flow and direction of velocities were significant, depending on the DEM

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The fauna of Brazilian reef fishes comprises approximately 320 species distributed along the coast of the mainland and islands ocean. Little is known about the levels of connectivity between their populations, but has been given the interest in the relations between the offshore and the islands of the Brazil, in a biogeographical perspective. The oceanic islands Brazilian hosting a considerable number of endemic species, which are locally abundant, and divide a substantial portion of its reef fish fauna with the Western Atlantic. Among the richest families of reef fish in species are Pomacentridae. This study analyzed through analysis of sequences of the mitochondrial DNA control region (D-loop), the standards-breeding population of C. Multilineata in different areas of the NE coast of Brazil, involving both oceanic islands (Fernando de Noronha Archipelago and of St. Peter and St. Paul) and continental shelf (RN and BA). To this aim, partial sequences were used in the region HVR1 of mtDNA (312pb). The population structure and parameters for the estimates of genetic variability, molecular variance (AMOVA), estimation of the index for fixing (FST) and number of migrants were determined. The phylogenetic relationships between the populations were estimated using neighbor-joining (NJ) method. A group of Bayesian analysis was used to verify population structure, according to haplotype frequency of each individual. The genetic variability of populations was extremely high. The populations sampled show moderate genetic structure, with a higher degree of genetic divergence being observed for the sample of the Archipelago of St. Peter and St. Paul. At smaller geographical scale, the sample of Rio Grande do Norte and the Archipelago of Fernando de Noronha do not have genetic differentiation. Three moderately differentiated population groups were identified: a population group (I), formed by the Rio Grande do Norte (I') and the archipelago of Fernando de Noronha (I''), and two other different groups formed by the island population of the archipelago of Saint Peter and St. Paul (II) and Bahia (III). The genetic patterns found suggest that the species has suffered a relatively recent radiation favoring the absence of shared haplotypes. C. multilineata seems to constitute a relatively homogenous population along the West Atlantic coast, with evidence of a moderate population genetic structure in relation to the Archipelago of St. Peter and St. Paul. These data supports the importance of the dispersal larvae by marine current and the interpopulation similarity this species.

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The purpose of this study was to investigate the social-environmental implications of the first large scale wind farm recently built in Brazil (2006), Parque Eólico de Rio do Fogo (PERF), to the nearby communities. The research was base on the adjustment of the DIS/BCN tool to analyze social impact and it was linked to the multi-method approach. Applying the autophotography strategy, cameras were given to five children from the district of Zumbi, the nearest location to PERF, and they were asked to individually photograph the six places they liked the most and the six places they liked the least in their community. Then, these children were interviewed individually and collectively about the photographs. Adult locals in Zumbi, residents of Zumbi/Rio do Fogo settlement, members of the State and Municipal government and representatives of the PERF were also interviewed with the aid of some of the pictures taken by the children and others that might trigger something to say, as a strategy called sample function. The five children presented positive image towards PERF; all of them chose to photograph it as one of places they liked. Adult population of Zumbi presented positive visual evaluation towards PERF. A small number of the interviewees were aware of the environmental and social benefits of wind energy production. Residents did not participate of the decision making process regarding PERF. They approved the project, especially because of the jobs provided during construction. Nowadays, PERF is something apart from their lives because it no longer provides jobs or any other interaction between the facility and the locals. Residents relate to the land, not with the facility. However, there is no evidence of rejection towards PERF, it is simply seen as something neutral to their lives. The low levels of education, traditional lack of social commitment and citizenship, and the experience accumulated by PERF´s planners and builders in other countries, may be contributing points to the fact that Zumbi residents did not oppose to PERF. It is clear that the country needs a legislation which seriously considers the psycho-social dimension involved in the implementation of wind farms

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The objective of the researches in artificial intelligence is to qualify the computer to execute functions that are performed by humans using knowledge and reasoning. This work was developed in the area of machine learning, that it s the study branch of artificial intelligence, being related to the project and development of algorithms and techniques capable to allow the computational learning. The objective of this work is analyzing a feature selection method for ensemble systems. The proposed method is inserted into the filter approach of feature selection method, it s using the variance and Spearman correlation to rank the feature and using the reward and punishment strategies to measure the feature importance for the identification of the classes. For each ensemble, several different configuration were used, which varied from hybrid (homogeneous) to non-hybrid (heterogeneous) structures of ensemble. They were submitted to five combining methods (voting, sum, sum weight, multiLayer Perceptron and naïve Bayes) which were applied in six distinct database (real and artificial). The classifiers applied during the experiments were k- nearest neighbor, multiLayer Perceptron, naïve Bayes and decision tree. Finally, the performance of ensemble was analyzed comparatively, using none feature selection method, using a filter approach (original) feature selection method and the proposed method. To do this comparison, a statistical test was applied, which demonstrate that there was a significant improvement in the precision of the ensembles