The flavor problem and discrete symmetries

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Carbon Nuclear Magnetic Resonance Spectroscopic Profiles coupled to Partial Least-Squares Multivariate Regression for Prediction of Several Physicochemical Parameters of Brazilian Commercial Gasoline

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Logistic regression to estimate the welfare of broiler breeders in relation to environmental and behavioral variables

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

Bayesian binary regression model: an application to in-hospital death after AMI prediction

Um modelo bayesiano de regressão binária é desenvolvido para predizer óbito hospitalar em pacientes acometidos por infarto agudo do miocárdio. Métodos de Monte Carlo via Cadeias de Markov (MCMC) são usados para fazer inferência e validação. Uma estratégia para construção de modelos, baseada no uso do fator de Bayes, é proposta e aspectos de validação são extensivamente discutidos neste artigo, incluindo a distribuição a posteriori para o índice de concordância e análise de resíduos. A determinação de fatores de risco, baseados em variáveis disponíveis na chegada do paciente ao hospital, é muito importante para a tomada de decisão sobre o curso do tratamento. O modelo identificado se revela fortemente confiável e acurado, com uma taxa de classificação correta de 88% e um índice de concordância de 83%.

Correlation models of diffuse solar-radiation applied to the city of São Paulo, Brazil

Measurements of global and diffuse solar-radiation, at the Earth's surface, carried out from May 1994 to June 1999 in São Paulo City, Brazil, were used to develop correlation models to estimate hourly, daily and monthly values of diffuse solar-radiation on horizontal surfaces. The polynomials derived by linear regression fitting were able to model satisfactorily the daily and monthly values of diffuse radiation. The comparison with models derived for other places demonstrates some differences related mainly to altitude effects. (C) 2002 Elsevier B.V. Ltd. All rights reserved.

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.

A partial least squares and principal component regression study of quinone compounds with trypanocidal activity

A quantitative structure-activity relationship (QSAR) study of 19 quinone compounds with trypanocidal activity was performed by Partial Least Squares (PLS) and Principal Component Regression (PCR) methods with the use of leave-one-out crossvalidation procedure to build the regression models. The trypanocidal activity of the compounds is related to their first cathodic potential (Ep(c1)). The regression PLS and PCR models built in this study were also used to predict the Ep(c1) of six new quinone compounds. The PLS model was built with three principal components that described 96.50% of the total variance and present Q(2) = 0.83 and R-2 = 0.90. The results obtained with the PCR model were similar to those obtained with the PLS model. The PCR model was also built with three principal components that described 96.67% of the total variance with Q(2) = 0.83 and R-2 = 0.90. The most important descriptors for our PLS and PCR models were HOMO-1 (energy of the molecular orbital below HOMO), Q4 (atomic charge at position 4), MAXDN (maximal electrotopological negative difference), and HYF (hydrophilicity index).

Thermodynamic models for water sorption by grape skin and pulp

The enthalpy-entropy compensation theory was applied to water sorption for grapes of Italy variety. The moisture sorption isotherms were analyzed using the static gravimetric method at 35, 40, 50, 60, 70 and 75 degrees C. For isotherms construction, the skin and pulp of the grape were used separately and it was possible to observe significant differences. The GAB equation was fitted to the experimental data, using direct nonlinear regression analysis; the agreement between experimental and calculated values was satisfactory. The net isosteric heat or enthalpy of water sorption, determined from the equilibrium sorption data, showed a different behavior when compared with other works, as it was obtained for skin and pulp separately. Plots of Delta h vs Delta S for skin and pulp provided the isokinetic temperatures T-Bs = 423.2 +/- 27.6 K and T-Bp = 424.5 +/- 25.3 K, respectively, indicating an enthalpy-controlled desorption process over the whole range of moisture content considered.

The Geometric Birnbaum-Saunders regression model with cure rate

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Bayesian outlier analysis in binary regression

We propose alternative approaches to analyze residuals in binary regression models based on random effect components. Our preferred model does not depend upon any tuning parameter, being completely automatic. Although the focus is mainly on accommodation of outliers, the proposed methodology is also able to detect them. Our approach consists of evaluating the posterior distribution of random effects included in the linear predictor. The evaluation of the posterior distributions of interest involves cumbersome integration, which is easily dealt with through stochastic simulation methods. We also discuss different specifications of prior distributions for the random effects. The potential of these strategies is compared in a real data set. The main finding is that the inclusion of extra variability accommodates the outliers, improving the adjustment of the model substantially, besides correctly indicating the possible outliers.

Additive genetic relationship of longevity with fertility and production traits in Nellore cattle based on bivariate models

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

Observed supersymmetry of hadrons and the tachyonless string models

The experimental mesonic density of states ρmeson(m)≃ρbaryon(m) from 0.9 to 1.3 GeV. In this region the ρmeson fits the ρ(m) deduced for it from discrete bag model states. Beyond 1.3 GeV one can expect exotic mesons. If ρmeson is replaced by the baryon density (as suggested by string model studies [D. Kutasov and N. Seiberg, Nucl. Phys. B 358 (1991) 600; P.G.O. Freund and J.L. Rosner, Phys. Rev. Lett. 68 (1992) 765]), agreement with theory is obtained up to 1.7 GeV. Beyond 1.7 GeV exotic baryons may be expected.

H2 and/or H∞-norm model reduction of uncertain discrete-time systems

This paper addresses the problem of model reduction for uncertain discrete-time systems with convex bounded (polytope type) uncertainty. A reduced order precisely known model is obtained in such a way that the H2 and/or the H∞ guaranteed norm of the error between the original (uncertain) system and the reduced one is minimized. The optimization problems are formulated in terms of coupled (non-convex) LMIs - Linear Matrix Inequalities, being solved through iterative algorithms. Examples illustrate the results.

Fractional polynomial response surface models

Second-order polynomial models have been used extensively to approximate the relationship between a response variable and several continuous factors. However, sometimes polynomial models do not adequately describe the important features of the response surface. This article describes the use of fractional polynomial models. It is shown how the models can be fitted, an appropriate model selected, and inference conducted. Polynomial and fractional polynomial models are fitted to two published datasets, illustrating that sometimes the fractional polynomial can give as good a fit to the data and much more plausible behavior between the design points than the polynomial model. © 2005 American Statistical Association and the International Biometric Society.

Linear quadratic Gaussian control of discrete-time Markov jump linear systems with horizon defined by stopping times

The linear quadratic Gaussian control of discrete-time Markov jump linear systems is addressed in this paper, first for state feedback, and also for dynamic output feedback using state estimation. in the model studied, the problem horizon is defined by a stopping time τ which represents either, the occurrence of a fix number N of failures or repairs (T N), or the occurrence of a crucial failure event (τ δ), after which the system paralyzed. From the constructive method used here a separation principle holds, and the solutions are given in terms of a Kalman filter and a state feedback sequence of controls. The control gains are obtained by recursions from a set of algebraic Riccati equations for the former case or by a coupled set of algebraic Riccati equation for the latter case. Copyright © 2005 IFAC.