987 resultados para Monte do Zambujal
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Um surto de leptospirose foi observado em bovinos leiteiros em Santo Antônio do Monte, Minas Gerais. O rebanho apresentava reações positivas anti-leptospira sorovar Hardjo no teste de microaglutinação (MAT) e havia sido vacinado anteriormente com vacina experimental contendo a sorovariedade Hardjo. O MAT revelou 48,06% dos bovinos positivos para sorovariedade Hardjo genótipo Hardjobovis, 36,82% para sorovariedade Hardjo genótipo Hardjoprajitno. Os animais apresentavam aborto e mastite com presença de sangue no leite. A presente pesquisa teve como objetivos isolar as sorovariedades existentes a partir da urina de vacas sorologicamente positivas, elaborar uma vacina experimental com as sorovariedades isoladas no rebanho, avaliar a eficiência do programa de vacinação por um período de dois anos por meio da sorologia do rebanho. Foi isolada Leptospira spp. a partir da urina de duas vacas com sinais sugestivos da doença. As amostras isoladas foram identificadas pela sorologia com anticorpos monoclonais e seqüenciamento do gene 16S rRNA como pertencentes à espécie Leptospira interrogans, sorogrupo Sejroe, sorovariedade Hardjo e genótipo Hardjoprajitno. O uso da vacina autógena foi eficaz no controle da leptospirose no rebanho no período de dois anos. Os resultados da sorologia revelaram ausência de animais positivos na última prova realizada no rebanho.
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An axisymmetric supersonic flow of rarefied gas past a finite cylinder was calculated applying the direct simulation Monte Carlo method. The drag force, the coefficients of pressure, of skin friction, and of heat transfer, the fields of density, of temperature, and of velocity were calculated as function of the Reynolds number for a fixed Mach number. The variation of the Reynolds number is related to the variation of the Knudsen number, which characterizes the gas rarefaction. The present results show that all quantities in the transition regime (Knudsen number is about the unity) are significantly different from those in the hydrodynamic regime, when the Knudsen number is small.
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This work present the application of a computer package for generating of projection data for neutron computerized tomography, and in second part, discusses an application of neutron tomography, using the projection data obtained by Monte Carlo technique, for the detection and localization of light materials such as those containing hydrogen, concealed by heavy materials such as iron and lead. For tomographic reconstructions of the samples simulated use was made of only six equal projection angles distributed between 0º and 180º, with reconstruction making use of an algorithm (ARIEM), based on the principle of maximum entropy. With the neutron tomography it was possible to detect and locate polyethylene and water hidden by lead and iron (with 1cm-thick). Thus, it is demonstrated that thermal neutrons tomography is a viable test method which can provide important interior information about test components, so, extremely useful in routine industrial applications.
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As florestas alto-montanas são reconhecidas por apresentarem composição florística e estrutura fitossociológica distintas das florestas em cotas altitudinais inferiores. Realizou-se um levantamento fitossociológico em uma floresta alto-montana localizada na Serra da Mantiqueira, distrito de Monte Verde, Camanducaia, Minas Gerais. O principal objetivo foi analisar o efeito do gradiente altitudinal na composição florística e na estrutura fitossociológica da vegetação. Foram instalados sete blocos paralelos com cinco parcelas contíguas de 10 × 10 m, distantes 50 m, entre 1.840 e 1.920 m de altitude. Todos os indivíduos arbóreos com CAP > 15 cm foram amostrados, assim como as "moitas de bambu" que continham no mínimo 10 perfilhos. Foram amostrados 1.191 indivíduos, pertencentes a 64 espécies arbóreas e duas espécies de bambu, distribuídas entre 42 gêneros e 26 famílias, além da classe de indivíduos mortos. A densidade total equivalente foi de 3.403 ind ha-1 e o índice de diversidade de Shannon-Wiener (H') foi de 3,284 nat ind-1. A biomassa morta destacou-se pelo elevado valor de importância (42,06), seguida de Pimenta pseudocaryophyllus (Gomes) Landrum (24,59), Roupala rhombifolia Mart. ex Meisn. (19,98) e Drimys brasiliensis Miers (18,57). Entre os parâmetros estruturais analisados a altura máxima do dossel e o número de indivíduos bifurcados estiveram correlacionados com a altitude. Uma considerável substituição de espécies foi observada, evidenciando um forte gradiente florístico, mesmo sendo o gradiente altitudinal amostrado relativamente curto.
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There is an important pioneer vegetation formation along the Xingu River in the area where the Belo Monte hydroelectric dam is being constructed that is highly adapted to a seasonally fluctuating water levels. The aim of this study was to examine the habitat and flora of the pioneer formations in the Belo Monte area. The area was divided in three sections for study purposes (Reservoir, Low Flow, and Control) that were expected to experience different degrees of impact from the dam project. The calculations of habitat losses were based on satellite imagery classifications, and a total of 111 plots were established in the three areas for vegetation sampling. Habitat losses of the pioneer formations will total 89.7% when the project is fully functional. Forty-five of the 72 recorded species are restricted to single areas. Species richness and diversity were significantly lower in the control area. The completion of the Belo Monte reservoir will result in habitat reductions and will consequently reduce the richness and diversity of pioneer formations. Studies suggest monitoring the populations located in the reduced flow area to determine possible impacts resulting from changes in the regional hydrological cycle caused by the Xingu River dam.
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Julkaisussa: Cosmographia : impressum Ulme opera et expensis justi de Albano de Venetiis per provisorem
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Julkaisussa: Cosmographia : hic finit cosmographia Ptolemei impressa opa dominici de lapis ciuis Bononiensis
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Kartta kuuluu A. E. Nordenskiöldin kokoelmaan
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This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.
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1857/04/30 (N2).
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1858/02/04 (N42).
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1857/07/16 (N13).
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1857/06/25 (N10).
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1857/12/10 (N34).
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1857/11/19 (N31).