The effect of flow arrangement on the pressure drop of plate heat exchangers

For the optimal design of plate heat exchangers (PHEs), an accurate thermal-hydraulic model that takes into account the effect of the flow arrangement on the heat load and pressure drop is necessary. In the present study, the effect of the flow arrangement on the pressure drop of a PHE is investigated. Thirty two different arrangements were experimentally tested using a laboratory scale PHE with flat plates. The experimental data was used for (a) determination of an empirical correlation for the effect of the number of passes and number of flow channels per pass on the pressure drop; (b) validation of a friction factor model through parameter estimation; and (c) comparison with the simulation results obtained with a CFD (computational fluid dynamics) model of the PHE. All three approaches resulted in a good agreement between experimental and predicted values of pressure drop. Moreover, the CFD model is used for evaluating the flow maldistribution in a PHE with two channels Per Pass. (c) 2008 Elsevier Ltd. All rights reserved.

Spatio-temporal modeling of agricultural yield data with an application to pricing crop insurance contracts

This article presents a statistical model of agricultural yield data based on a set of hierarchical Bayesian models that allows joint modeling of temporal and spatial autocorrelation. This method captures a comprehensive range of the various uncertainties involved in predicting crop insurance premium rates as opposed to the more traditional ad hoc, two-stage methods that are typically based on independent estimation and prediction. A panel data set of county-average yield data was analyzed for 290 counties in the State of Parana (Brazil) for the period of 1990 through 2002. Posterior predictive criteria are used to evaluate different model specifications. This article provides substantial improvements in the statistical and actuarial methods often applied to the calculation of insurance premium rates. These improvements are especially relevant to situations where data are limited.

In vitro antimicrobial activity of Caesalpinia ferrea Martius fruits against oral pathogens

Aim: In the Amazon region of Brazil, the fruits of Caesalpinia ferrea Martius (Brazilian ironwood) are widely used as an antimicrobial and healing medicine in many situations including oral infections. This study aimed to evaluate the antimicrobial activity of Caesalpinia ferrea Martius fruit extract against oral pathogens. Materials and methods: Polyphenols estimation and spectral analysis ((1)H NMR) of the methanol extract were carried out. The microorganisms Candida albicans, Streptococcus mutans, Streptococcus salivarius, Streptococcus oralis and Lactobacillus casei were tested using the microdilution method for planktonic cells (MIC) and a multispecies biofilm model. Chlorhexidine was used as positive control. Results: Polyphenols in the extract were estimated at 7.3% and (1)H NMR analysis revealed hydroxy phenols and methoxilated compounds. MIC values for Candida albicans, Streptococcus mutans, Streptococcus salivarius, Streptococcus oralis and Lactobacillus casei were 25.0, 40.0, 66.0, 100.0, 66.0 mu g/mL, respectively. For the biofilm assay, chlorhexidine and plant extract showed no growth at 10(-4) and 10(-5) microbial dilution, respectively. At 10-4 and 10-5 the growth values (mean +/- SD) of the negative controls (DMSO and saline solution) for Streptococcus mutans, Streptococcus sp. and Candida albicans were 8.1 +/- 0.7, 7.0 +/- 0.6 and 5.9 +/- 0.9 x 10(6) CFU, respectively. Conclusion: Caesalpinia ferrea fruit extract can inhibit in vitro growth of oral pathogens in planktonic and biofilm models supporting its use for oral infections. (C) 2009 Elsevier Ireland Ltd. All rights reserved.

Transformed generalized linear models

The estimation of data transformation is very useful to yield response variables satisfying closely a normal linear model, Generalized linear models enable the fitting of models to a wide range of data types. These models are based on exponential dispersion models. We propose a new class of transformed generalized linear models to extend the Box and Cox models and the generalized linear models. We use the generalized linear model framework to fit these models and discuss maximum likelihood estimation and inference. We give a simple formula to estimate the parameter that index the transformation of the response variable for a subclass of models. We also give a simple formula to estimate the rth moment of the original dependent variable. We explore the possibility of using these models to time series data to extend the generalized autoregressive moving average models discussed by Benjamin er al. [Generalized autoregressive moving average models. J. Amer. Statist. Assoc. 98, 214-223]. The usefulness of these models is illustrated in a Simulation study and in applications to three real data sets. (C) 2009 Elsevier B.V. All rights reserved.

Multiscale Curvature Analysis of Asphaltic Aggregate Particles

A new method for characterization and analysis of asphaltic mixtures aggregate particles is reported. By relying on multiscale representation of the particles, curvature estimation, and discriminant analysis for optimal separation of the categories of mixtures, a particularly effective and comprehensive methodology is obtained. The potential of the methodology is illustrated with respect to three important types of particles used in asphaltic mixtures, namely basalt, gabbro, and gravel. The obtained results show that gravel particles are markedly distinct from the other two types of particles, with the gabbro category resulting with intermediate geometrical properties. The importance of each considered measurement in the discrimination between the three categories of particles was also quantified in terms of the adopted discriminant analysis.

Improved likelihood inference for the shape parameter in Weibull regression

We obtain adjustments to the profile likelihood function in Weibull regression models with and without censoring. Specifically, we consider two different modified profile likelihoods: (i) the one proposed by Cox and Reid [Cox, D.R. and Reid, N., 1987, Parameter orthogonality and approximate conditional inference. Journal of the Royal Statistical Society B, 49, 1-39.], and (ii) an approximation to the one proposed by Barndorff-Nielsen [Barndorff-Nielsen, O.E., 1983, On a formula for the distribution of the maximum likelihood estimator. Biometrika, 70, 343-365.], the approximation having been obtained using the results by Fraser and Reid [Fraser, D.A.S. and Reid, N., 1995, Ancillaries and third-order significance. Utilitas Mathematica, 47, 33-53.] and by Fraser et al. [Fraser, D.A.S., Reid, N. and Wu, J., 1999, A simple formula for tail probabilities for frequentist and Bayesian inference. Biometrika, 86, 655-661.]. We focus on point estimation and likelihood ratio tests on the shape parameter in the class of Weibull regression models. We derive some distributional properties of the different maximum likelihood estimators and likelihood ratio tests. The numerical evidence presented in the paper favors the approximation to Barndorff-Nielsen`s adjustment.

Bayesian Analysis for Nonlinear Regression Model Under Skewed Errors, with Application in Growth Curves

We have considered a Bayesian approach for the nonlinear regression model by replacing the normal distribution on the error term by some skewed distributions, which account for both skewness and heavy tails or skewness alone. The type of data considered in this paper concerns repeated measurements taken in time on a set of individuals. Such multiple observations on the same individual generally produce serially correlated outcomes. Thus, additionally, our model does allow for a correlation between observations made from the same individual. We have illustrated the procedure using a data set to study the growth curves of a clinic measurement of a group of pregnant women from an obstetrics clinic in Santiago, Chile. Parameter estimation and prediction were carried out using appropriate posterior simulation schemes based in Markov Chain Monte Carlo methods. Besides the deviance information criterion (DIC) and the conditional predictive ordinate (CPO), we suggest the use of proper scoring rules based on the posterior predictive distribution for comparing models. For our data set, all these criteria chose the skew-t model as the best model for the errors. These DIC and CPO criteria are also validated, for the model proposed here, through a simulation study. As a conclusion of this study, the DIC criterion is not trustful for this kind of complex model.

Generalized Birnbaum-Saunders distributions applied to air pollutant concentration

The generalized Birnbaum-Saunders (GBS) distribution is a new class of positively skewed models with lighter and heavier tails than the traditional Birnbaum-Saunders (BS) distribution, which is largely applied to study lifetimes. However, the theoretical argument and the interesting properties of the GBS model have made its application possible beyond the lifetime analysis. The aim of this paper is to present the GBS distribution as a useful model for describing pollution data and deriving its positive and negative moments. Based on these moments, we develop estimation and goodness-of-fit methods. Also, some properties of the proposed estimators useful for developing asymptotic inference are presented. Finally, an application with real data from Environmental Sciences is given to illustrate the methodology developed. This example shows that the empirical fit of the GBS distribution to the data is very good. Thus, the GBS model is appropriate for describing air pollutant concentration data, which produces better results than the lognormal model when the administrative target is determined for abating air pollution. Copyright (c) 2007 John Wiley & Sons, Ltd.

Load Ratio Estimation Through Striation Height and Spacing Analysis of an Aerospace Al Alloy 7475-T7351

It is well known that striation spacing may be related to the crack growth rate, da/dN, through Paris equation, as well as the maximum and minimum loads under service loading conditions. These loads define the load ratio, R, and are considered impossible to be evaluated from the inter-spacing striations analysis. In this way, this study discusses the methodology proposed by Furukawa to evaluate the maximum and minimum loads based on the experimental fact that the relative height of a striation, H, and the striation spacing, s, are strongly influenced by the load ratio, R. Fatigue tests in C(T) specimens were conducted on SAE 7475-T7351 Al alloy plates at room temperature and the results showed a straightforward correlation between the parameters H, s, and R. Measurements of striation height, H, were performed using scanning electron microscopy and field emission gun (FEG) after sectioning the specimen at a large inclined angle to amplify the height of the striations. The results showed that for increasing R the values of H/s tend to increase. Striation height, striation spacing, and load ratio correlations were obtained, which allows one to estimate service loadings from fatigue fracture surface survey.

Spin filtering and disorder-induced magnetoresistance in carbon nanotubes: Ab initio calculations

Nitrogen-doped carbon nanotubes can provide reactive sites on the porphyrin-like defects. It is well known that many porphyrins have transition-metal atoms, and we have explored transition-metal atoms bonded to those porphyrin-like defects inN-doped carbon nanotubes. The electronic structure and transport are analyzed by means of a combination of density functional theory and recursive Green's function methods. The results determined the heme B-like defect (an iron atom bonded to four nitrogens) is the most stable and has a higher polarization current for a single defect. With randomly positioned heme B defects in nanotubes a few hundred nanometers long, the polarization reaches near 100%, meaning they are effective spin filters. A disorder-induced magnetoresistance effect is also observed in those long nanotubes, and values as high as 20 000% are calculated with nonmagnectic eletrodes.

Estimation of the thickness and the optical parameters of several stacked thin films using optimization

The reverse engineering problem addressed in the present research consists of estimating the thicknesses and the optical constants of two thin films deposited on a transparent substrate using only transmittance data through the whole stack. No functional dispersion relation assumptions are made on the complex refractive index. Instead, minimal physical constraints are employed, as in previous works of some of the authors where only one film was considered in the retrieval algorithm. To our knowledge this is the first report on the retrieval of the optical constants and the thickness of multiple film structures using only transmittance data that does not make use of dispersion relations. The same methodology may be used if the available data correspond to normal reflectance. The software used in this work is freely available through the PUMA Project web page (http://www.ime.usp.br/similar to egbirgin/puma/). (C) 2008 Optical Society of America

Estimation of hexenuronic acids and kappa number in kraft pulps of Eucalyptus globulus by Fourier transform near infrared spectroscopy and multivariate analysis

Fourier transform near infrared (FT-NIR) spectroscopy was evaluated as an analytical too[ for monitoring residual Lignin, kappa number and hexenuronic acids (HexA) content in kraft pulps of Eucalyptus globulus. Sets of pulp samples were prepared under different cooking conditions to obtain a wide range of compound concentrations that were characterised by conventional wet chemistry analytical methods. The sample group was also analysed using FT-NIR spectroscopy in order to establish prediction models for the pulp characteristics. Several models were applied to correlate chemical composition in samples with the NIR spectral data by means of PCR or PLS algorithms. Calibration curves were built by using all the spectral data or selected regions. Best calibration models for the quantification of lignin, kappa and HexA were proposed presenting R-2 values of 0.99. Calibration models were used to predict pulp titers of 20 external samples in a validation set. The lignin concentration and kappa number in the range of 1.4-18% and 8-62, respectively, were predicted fairly accurately (standard error of prediction, SEP 1.1% for lignin and 2.9 for kappa). The HexA concentration (range of 5-71 mmol kg(-1) pulp) was more difficult to predict and the SEP was 7.0 mmol kg(-1) pulp in a model of HexA quantified by an ultraviolet (UV) technique and 6.1 mmol kg(-1) pulp in a model of HexA quantified by anion-exchange chromatography (AEC). Even in wet chemical procedures used for HexA determination, there is no good agreement between methods as demonstrated by the UV and AEC methods described in the present work. NIR spectroscopy did provide a rapid estimate of HexA content in kraft pulps prepared in routine cooking experiments.

H(infinity) filtering for rectangular discrete-time descriptor systems

This paper deals with the H(infinity) recursive estimation problem for general rectangular time-variant descriptor systems in discrete time. Riccati-equation based recursions for filtered and predicted estimates are developed based on a data fitting approach and game theory. In this approach, the nature determines a state sequence seeking to maximize the estimation cost, whereas the estimator tries to find an estimate that brings the estimation cost to a minimum. A solution exists for a specified gamma-level if the resulting cost is positive. In order to present some computational alternatives to the H(infinity) filters developed, they are rewritten in information form along with the respective array algorithms. (C) 2009 Elsevier Ltd. All rights reserved.

Noise reduction in biomedical speech signal processing based on time and frequency Kalman filtering combined with spectral subtraction

The goal of this paper is to study and propose a new technique for noise reduction used during the reconstruction of speech signals, particularly for biomedical applications. The proposed method is based on Kalman filtering in the time domain combined with spectral subtraction. Comparison with discrete Kalman filter in the frequency domain shows better performance of the proposed technique. The performance is evaluated by using the segmental signal-to-noise ratio and the Itakura-Saito`s distance. Results have shown that Kalman`s filter in time combined with spectral subtraction is more robust and efficient, improving the Itakura-Saito`s distance by up to four times. (C) 2007 Elsevier Ltd. All rights reserved.

Innovation concept for measurement gross error detection and identification in power system state estimation

In this study, the innovation approach is used to estimate the measurement total error associated with power system state estimation. This is required because the power system equations are very much correlated with each other and as a consequence part of the measurements errors is masked. For that purpose an index, innovation index (II), which provides the quantity of new information a measurement contains is proposed. A critical measurement is the limit case of a measurement with low II, it has a zero II index and its error is totally masked. In other words, that measurement does not bring any innovation for the gross error test. Using the II of a measurement, the masked gross error by the state estimation is recovered; then the total gross error of that measurement is composed. Instead of the classical normalised measurement residual amplitude, the corresponding normalised composed measurement residual amplitude is used in the gross error detection and identification test, but with m degrees of freedom. The gross error processing turns out to be very simple to implement, requiring only few adaptations to the existing state estimation software. The IEEE-14 bus system is used to validate the proposed gross error detection and identification test.