Stochastic simulation of time-series models combined with geostatistics to predict water-table scenarios in a Guarani Aquifer System outcrop area, Brazil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Transfer function-noise modeling and spatial interpolation to evaluate the risk of extreme (shallow) water-table levels in the Brazilian Cerrados

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Padrões ecológicos da comunidade de ácaros em euforbiáceas de um fragmento de mata estacional semidecidual, no Estado de São Paulo

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Modelagem de séries de monitoramento de nível freático em área de afloramento do sistema aquífero guarani (sag) sob influência de diferentes usos agrícolas da terra

Pós-graduação em Agronomia (Irrigação e Drenagem) - FCA

Modelagem dos níveis freáticos do sistema aquífero Bauru (SAB) em diferentes usos da terra no município de Assis - SP

Pós-graduação em Agronomia (Irrigação e Drenagem) - FCA

Comparison between the complete Bayesian method and empirical Bayesian method for ARCH models using Brazilian financial time series

In this work we compared the estimates of the parameters of ARCH models using a complete Bayesian method and an empirical Bayesian method in which we adopted a non-informative prior distribution and informative prior distribution, respectively. We also considered a reparameterization of those models in order to map the space of the parameters into real space. This procedure permits choosing prior normal distributions for the transformed parameters. The posterior summaries were obtained using Monte Carlo Markov chain methods (MCMC). The methodology was evaluated by considering the Telebras series from the Brazilian financial market. The results show that the two methods are able to adjust ARCH models with different numbers of parameters. The empirical Bayesian method provided a more parsimonious model to the data and better adjustment than the complete Bayesian method.

Structural Health Monitoring in Smart Structures Through Time Series Analysis

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

In Vivo Models for Measuring Placental Glutatione-S-transferase (GST-P 7-7) Levels: A Suitable Biomarker for Understanding Cancer Pathogenesis

The Glutatione-S-transferases (GSTs) comprise a family of enzymes closely associated with the cell detoxification of xenobiotics. GSTs exist as homo- or heterodimers and have been grouped into at least seven distinct classes. The main function of GSTs is to catalyze the conjugation of reduced glutathione (GSH) to an electrophilic site of a broad range of potentially toxic and carcinogenic compounds, thereby making such compounds less dangerous and enabling their ready-excretion. Placental GST, known as GST-P 7-7, is the main isoform found in normal placental tissue and comprises 67% of the total GST concentration in this tissue. During development, GST-P 7-7 decreases in concentration and is absent in adult tissues. Interestingly, GST-P 7-7 expression has been detected in adult tissues after exposure to carcinogenic agents in several experimental test systems, being considered a reliable biomarker of exposure and susceptibility in early phases of carcinogenesis. In this article, we review a series of studies involving GST-P 7-7 expression as a suitable tool for understanding cancer pathogenesis, especially cancer risk.

Comments about some species abundance patterns: classic, neutral, and niche partitioning models

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Weather-based prediction of anthracnose severity using artificial neural network models

Data were collected and analysed from seven field sites in Australia, Brazil and Colombia on weather conditions and the severity of anthracnose disease of the tropical pasture legume Stylosanthes scabra caused by Colletotrichum gloeosporioides. Disease severity and weather data were analysed using artificial neural network (ANN) models developed using data from some or all field sites in Australia and/or South America to predict severity at other sites. Three series of models were developed using different weather summaries. of these, ANN models with weather for the day of disease assessment and the previous 24 h period had the highest prediction success, and models trained on data from all sites within one continent correctly predicted disease severity in the other continent on more than 75% of days; the overall prediction error was 21.9% for the Australian and 22.1% for the South American model. of the six cross-continent ANN models trained on pooled data for five sites from two continents to predict severity for the remaining sixth site, the model developed without data from Planaltina in Brazil was the most accurate, with >85% prediction success, and the model without Carimagua in Colombia was the least accurate, with only 54% success. In common with multiple regression models, moisture-related variables such as rain, leaf surface wetness and variables that influence moisture availability such as radiation and wind on the day of disease severity assessment or the day before assessment were the most important weather variables in all ANN models. A set of weights from the ANN models was used to calculate the overall risk of anthracnose for the various sites. Sites with high and low anthracnose risk are present in both continents, and weather conditions at centres of diversity in Brazil and Colombia do not appear to be more conducive than conditions in Australia to serious anthracnose development.

Bayesian L-optimal exact design of experiments for biological kinetic models

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Probability distribution models for daily rainfall data for an interior station of Brazil

Two stochastic models have been fitted to daily rainfall data for an interior station of Brazil. Of these two models, the results show a better fit to describe the data, by truncated negative probability model in comparison with Markov chain probability model. Kolmogorov-Smirnov test is applied for significance for these models. © 1983 Springer-Verlag.

Relations between two-dimensional models from dimensional reduction

In this work we explore the consequences of dimensional reduction of the 3D Maxwell-Chern-Simons and some related models. A connection between topological mass generation in 3D and mass generation according to the Schwinger mechanism in 2D is obtained. In addition, a series of relationships is established by resorting to dimensional reduction and duality interpolating transformations. Non-Abelian generalizations are also pointed out.

Damage detection in a benchmark structure using AR-ARX models and statistical pattern recognition

Structural health monitoring (SHM) is related to the ability of monitoring the state and deciding the level of damage or deterioration within aerospace, civil and mechanical systems. In this sense, this paper deals with the application of a two-step auto-regressive and auto-regressive with exogenous inputs (AR-ARX) model for linear prediction of damage diagnosis in structural systems. This damage detection algorithm is based on the. monitoring of residual error as damage-sensitive indexes, obtained through vibration response measurements. In complex structures there are. many positions under observation and a large amount of data to be handed, making difficult the visualization of the signals. This paper also investigates data compression by using principal component analysis. In order to establish a threshold value, a fuzzy c-means clustering is taken to quantify the damage-sensitive index in an unsupervised learning mode. Tests are made in a benchmark problem, as proposed by IASC-ASCE with different damage patterns. The diagnosis that was obtained showed high correlation with the actual integrity state of the structure. Copyright © 2007 by ABCM.

Corona effect in frequency dependent transmission line models

The inclusion of the corona effect in a frequency dependent transmission line model is proposed in this paper. The transmission line is represented through a cascade of π circuits and the frequency dependence of the longitudinal parameters is synthesized with series and parallel resistors and inductors. The corona effect will be represented using the Gary and Skilling-Umoto models. The currents and voltages along the line are calculated by using state-space technique. To demonstrate the accuracy and validity of the proposed frequency dependent line model, time domain simulations of a 10 km length single-phase line response under energization procedure will be presented. ©2008 IEEE.