964 resultados para Bayesian Population Modelling


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Ecological niche modelling combines species occurrence points with environmental raster layers in order to obtain models for describing the probabilistic distribution of species. The process to generate an ecological niche model is complex. It requires dealing with a large amount of data, use of different software packages for data conversion, for model generation and for different types of processing and analyses, among other functionalities. A software platform that integrates all requirements under a single and seamless interface would be very helpful for users. Furthermore, since biodiversity modelling is constantly evolving, new requirements are constantly being added in terms of functions, algorithms and data formats. This evolution must be accompanied by any software intended to be used in this area. In this scenario, a Service-Oriented Architecture (SOA) is an appropriate choice for designing such systems. According to SOA best practices and methodologies, the design of a reference business process must be performed prior to the architecture definition. The purpose is to understand the complexities of the process (business process in this context refers to the ecological niche modelling problem) and to design an architecture able to offer a comprehensive solution, called a reference architecture, that can be further detailed when implementing specific systems. This paper presents a reference business process for ecological niche modelling, as part of a major work focused on the definition of a reference architecture based on SOA concepts that will be used to evolve the openModeller software package for species modelling. The basic steps that are performed while developing a model are described, highlighting important aspects, based on the knowledge of modelling experts. In order to illustrate the steps defined for the process, an experiment was developed, modelling the distribution of Ouratea spectabilis (Mart.) Engl. (Ochnaceae) using openModeller. As a consequence of the knowledge gained with this work, many desirable improvements on the modelling software packages have been identified and are presented. Also, a discussion on the potential for large-scale experimentation in ecological niche modelling is provided, highlighting opportunities for research. The results obtained are very important for those involved in the development of modelling tools and systems, for requirement analysis and to provide insight on new features and trends for this category of systems. They can also be very helpful for beginners in modelling research, who can use the process and the experiment example as a guide to this complex activity. (c) 2008 Elsevier B.V. All rights reserved.

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The greenhouse effect and resulting increase in the Earth`s temperature may accelerate the mean sea-level rise. The natural response of bays and estuaries to this rise, such as this case study of Santos Bay (Brazil), will include change in shoreline position, land flooding and wetlands impacts. The main impacts of this scenario were studied in a physical model built in the Coastal and Harbour Division of Hydraulic Laboratory, University of Sao Paulo, and the main conclusions are presented in this paper. The model reproduces near 1,000 km(2) of the study area, including Santos, Sao Vicente, Praia Grande, Cubatao, Guaruja and Bertioga cities.

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A study on the use of artificial intelligence (AI) techniques for the modelling and subsequent control of an electric resistance spot welding process (ERSW) is presented. The ERSW process is characterized by the coupling of thermal, electrical, mechanical, and metallurgical phenomena. For this reason, early attempts to model it using computational methods established as the methods of finite differences, finite element, and finite volumes, ask for simplifications that lead the model obtained far from reality or very costly in terms of computational costs, to be used in a real-time control system. In this sense, the authors have developed an ERSW controller that uses fuzzy logic to adjust the energy transferred to the weld nugget. The proposed control strategies differ in the speed with which it reaches convergence. Moreover, their application for a quality control of spot weld through artificial neural networks (ANN) is discussed.

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High-angle grain boundary migration is predicted during geometric dynamic recrystallization (GDRX) by two types of mathematical models. Both models consider the driving pressure due to curvature and a sinusoidal driving pressure owing to subgrain walls connected to the grain boundary. One model is based on the finite difference solution of a kinetic equation, and the other, on a numerical technique in which the boundary is subdivided into linear segments. The models show that an initially flat boundary becomes serrated, with the peak and valley migrating into both adjacent grains, as observed during GDRX. When the sinusoidal driving pressure amplitude is smaller than 2 pi, the boundary stops migrating, reaching an equilibrium shape. Otherwise, when the amplitude is larger than 2 pi, equilibrium is never reached and the boundary migrates indefinitely, which would cause the protrusions of two serrated parallel boundaries to impinge on each other, creating smaller equiaxed grains.

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We study the spreading of contagious diseases in a population of constant size using susceptible-infective-recovered (SIR) models described in terms of ordinary differential equations (ODEs) and probabilistic cellular automata (PCA). In the PCA model, each individual (represented by a cell in the lattice) is mainly locally connected to others. We investigate how the topological properties of the random network representing contacts among individuals influence the transient behavior and the permanent regime of the epidemiological system described by ODE and PCA. Our main conclusions are: (1) the basic reproduction number (commonly called R(0)) related to a disease propagation in a population cannot be uniquely determined from some features of transient behavior of the infective group; (2) R(0) cannot be associated to a unique combination of clustering coefficient and average shortest path length characterizing the contact network. We discuss how these results can embarrass the specification of control strategies for combating disease propagations. (C) 2009 Elsevier B.V. All rights reserved.

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The thermodynamic assessment of an Al(2)O(3)-MnO pseudo-binary system has been carried out with the use of an ionic model. The use of the electro-neutrality principles in addition to the constitutive relations, between site fractions of the species on each sub-lattice, the thermodynamics descriptions of each solid phase has been determined to make possible the solubility description. Based on the thermodynamics descriptions of each phase in addition to thermo-chemical data obtained from the literature, the Gibbs energy functions were optimized for each phase of the Al(2)O(3)-MnO system with the support of PARROT(R) module from ThemoCalc(R) package. A thermodynamic database was obtained, in agreement with the thermo-chemical data extracted from the literature, to describe the Al(2)O(3)-MnO system including the solubility description of solid phases. (C) 2009 Elsevier Ltd. All rights reserved.

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We studied the structure of a population of Hydromedusa maximiliani associated with a stream in Parque Estadual da Serra do Mar, Nucleo Itutinga-Piloes, southeastern Brazil, between October 2004 and October 2005. Twenty-five individuals were captured, and a population size of 43.72 +/- 23.7 individuals was estimated. This value is similar to that of the population of Parque Estadual Carlos Botelho, another Atlantic forest reserve of southeastern Brazil. Males were recaptured more frequently than females, suggesting higher activity and/or greater movement of males.

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This survey evaluated the presence of AFM(1) in human urine samples from a specific Brazilian population, as well as corn, peanut, and milk consumption measured by two types of food inquiry. Urine samples from donors who live in the city of Piracicaba, State of Sao Paulo, Brazil were analyzed to detect the presence of aflatoxin M(1) (AFM(1)). an aflatoxin B(1) metabolite, which may be used as aflatoxin B(1) exposure biomarker. The AFM(1) analysis was performed using immunoaffinity clean-up and detection by high-performance-liquid chromatography with fluorescence detector. A total of 69 samples were analyzed and 45 of them (65%) presented contaminations >= 1.8 pg ml(-1), which was the limit of quantification (LOQ). Seventy eight percent (n = 54) of the samples presented detectable concentrations of AFM(1) (>0.6 pg ml(-1)). The AFM(1) concentration among samples above LOQ ranged from 1.8 to 39.9 pg ml(-1). There were differences in food consumption profile among donors, although no association was found between food consumption and AFM(1) concentration in urine. The high frequency of positive samples suggests exposure of the populations studied to aflatoxins. (C) 2009 Elsevier Ltd. All rights reserved.

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Joint generalized linear models and double generalized linear models (DGLMs) were designed to model outcomes for which the variability can be explained using factors and/or covariates. When such factors operate, the usual normal regression models, which inherently exhibit constant variance, will under-represent variation in the data and hence may lead to erroneous inferences. For count and proportion data, such noise factors can generate a so-called overdispersion effect, and the use of binomial and Poisson models underestimates the variability and, consequently, incorrectly indicate significant effects. In this manuscript, we propose a DGLM from a Bayesian perspective, focusing on the case of proportion data, where the overdispersion can be modeled using a random effect that depends on some noise factors. The posterior joint density function was sampled using Monte Carlo Markov Chain algorithms, allowing inferences over the model parameters. An application to a data set on apple tissue culture is presented, for which it is shown that the Bayesian approach is quite feasible, even when limited prior information is available, thereby generating valuable insight for the researcher about its experimental results.

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The tomato red spider mite, Tetranychus evansi (Acari: Tetranychidae) was recently introduced in Africa and Europe, where there is an increasing interest in using natural enemies to control this pest on solanaceous crops. Two promising candidates for the control of T. evansi were identified in South America, the fungal pathogen, Neozygites floridana and the predatory mite Phytoseiulus longipes. In this study, population dynamics of T. evansi and its natural enemies together with the influence of environmental conditions on these organisms were evaluated during four crop cycles in the field and in a protected environment on nightshade and tomato plants with and without application of chemical pesticides. N. floridana was the only natural enemy found associated with T. evansi in the four crop cycles under protected environment but only in the last crop cycle in the field. In the treatments where the fungus appeared, reduction of mite populations was drastic. N. floridana appeared in tomato plants even when the population density of T. evansi was relatively low (less than 10 mites/3.14 cm(2) of leaf area) and even at this low population density, the fungus maintained infection rates greater than 50%. The application of pesticides directly affected the fungus by delaying epizootic initiation and contributing to lower infection rates than unsprayed treatments. Rainfalls did not have an apparent impact on mite populations. These results indicate that the pathogenic fungus, N. floridana can play a significant role in the population dynamics of T. evansi, especially under protected environment, and has the potential to control this pest in classical biological control programs. (C) 2009 Elsevier Inc. All rights reserved.

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This paper applies Hierarchical Bayesian Models to price farm-level yield insurance contracts. This methodology considers the temporal effect, the spatial dependence and spatio-temporal models. One of the major advantages of this framework is that an estimate of the premium rate is obtained directly from the posterior distribution. These methods were applied to a farm-level data set of soybean in the State of the Parana (Brazil), for the period between 1994 and 2003. The model selection was based on a posterior predictive criterion. This study improves considerably the estimation of the fair premium rates considering the small number of observations.

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Over the years, crop insurance programs became the focus of agricultural policy in the USA, Spain, Mexico, and more recently in Brazil. Given the increasing interest in insurance, accurate calculation of the premium rate is of great importance. We address the crop-yield distribution issue and its implications in pricing an insurance contract considering the dynamic structure of the data and incorporating the spatial correlation in the Hierarchical Bayesian framework. Results show that empirical (insurers) rates are higher in low risk areas and lower in high risk areas. Such methodological improvement is primarily important in situations of limited data.

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This article considers alternative methods to calculate the fair premium rate of crop insurance contracts based on county yields. The premium rate was calculated using parametric and nonparametric approaches to estimate the conditional agricultural yield density. These methods were applied to a data set of county yield provided by the Statistical and Geography Brazilian Institute (IBGE), for the period of 1990 through 2002, for soybean, corn and wheat, in the State of Paran. In this article, we propose methodological alternatives to pricing crop insurance contracts resulting in more accurate premium rates in a situation of limited data.

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Host status of citrus rootstocks for the population K 5 of Pratylenchus jaehni Pratylenchus jaehni has been reported as the main nematode in Brazilian citrus orchards due to the economic losses it causes. Considering the reports of differences and similarities in populations from that species, the host reaction of citrus rootstocks to K 5 population of P. jaehni was evaluated and compared with topotypes of the species. Two experiments were carried out with initial population (Pi) of 180 and 200 specimens per plant. In each experiment, the evaluation was carried out in two periods: at 120 and 245 days after inoculation (DAI) in experiment 1 and at 60 and 240 DAI in experiment 2. The variables obtained were the reproduction factor [RF = final population (Pf)/Pi)] and nematodes per gram of roots (N/g). The nematode density was reduced (RF = 0.00 to 0.02) in trifoliata `Limeira`, sour orange, citrange `Carrizo`, `Cleopatra` tangerine, `Sunki` tangerine, `Volkameriano` lemon and citrumelo `Swingle`; but the nematode density increased (RF = 1.89 to 22.28) in Rangpur lime in both periods of experiment 1 and in the second period of experiment 2. Therefore, the host reaction of citrus rootstocks to K 5 was very similar to that reported to topotypes of P. jaehni in the literature.

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Evaluation of damage caused on Coffea arabica by a population of Pratylenchus coffeae considered non-pathogenic on coffee Two greenhouse experiments were carried out in order to evaluate the damage caused on Arabica coffee (Coffea arabica) by an M(2) population of Pratylenchus coffeae, apparently non-pathogenic to coffee. Experiment 1, with `Catua Vermelho` coffee at stage of two leaf pairs and with the initial nematode densities (Pi) of 0; 333; 1,000; 3.000; and 9,000 per plant, demonstrated that M(2) can damage young coffee plants, although it is unable to reproduce on coffee roots. Experiment 2, with the same coffee cultivar and nematode densities, but with plants at stage of six leaf pairs, showed that the M2 population was unable to cause damage. Therefore, it was established that M(2) is a population of P. coffeae without reproduction on Arabica coffee, which causes damage only in the first generation on young coffee below stages of six leaf pairs.