Development of calibration models for quality control in the production of ethylene/propylene copolymers by FTIR spectroscopy,multivariate statistical tools,and artificial neural networks

Principal component regression (PCR), partial least squares (PLS), StepWise ordinary least squares regression (OLS), and back‐propagation artificial neural network (BP‐ANN) are applied here for the determination of the propylene concentration of a set of 83 production samples of ethylene–propylene copolymers from their infrared spectra. The set of available samples was split into (a) a training set, for models calculation; (b) a test set, for selecting the correct number of latent variables in PCR and PLS and the end point of the training phase of BP‐ANN; (c) a production set, for evaluating the predictive ability of the models. The predictive ability of the models is thus evaluated by genuine predictions. The model obtained by StepWise OLS turned out to be the best one, both in fitting and prediction. The study of the breakdown number of samples to be included in the training set showed that at least 52 experiments are necessary to build a reliable and predictive calibration model. It can be concluded that FTIR spectroscopy and OLS can be properly employed for monitoring the synthesis or the final product of ethylene–propylene copolymers, by predicting the concentration of propylene directly along the process line. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

Assessment of the final metrological characteristics of a MOEMS-based NDIR spectrometer through system modeling and data processing

A model of a miniaturized non-dispersive infrared (NDIR) gas analysis system, aiming to predict the final system specifications, is presented. It comprises the different elements of the NDIR detector, including a surface micromachined Fabry-Perot tunable filter. These models have been used to estimate the response of the NDIR system to different gas mixtures. Multivariate regression methods like partial least squares allow recovering the true sample composition from the IR absorption spectra measured with the NDIR system, despite the limited selectivity of the filter. Combining model and data processing permits to predict the effect on the final system specification of design parameters. Here, we compare the effect of the technology used for the filter on the system errors.     

Aerobic degradation by white‐rot fungi of trichloroethylene (TCE) and mixtures of TCE and perchloroethylene (PCE)

BACKGROUND: Trichloroethylene (TCE) and perchloroethylene (PCE) are considered among the most important groundwater pollutants around the world. These compounds are usually found together in polluted environments but little is known about the ability of microorganisms to simultaneously degrade TCE and PCE. RESULTS: Data showed that several species of white‐rot fungi, including Trametes versicolor, Ganoderma lucidum, and Irpex lacteus, degrade substantial levels of TCE in pure culture. T. versicolor was chosen for further study since it degraded higher levels of TCE than the other organisms. Initial glucose concentration and reoxygenation of samples increased the amount of TCE dechlorination, but no significant difference in percentage TCE degradation was observed. T. versicolor was able to degrade 34.1 and 47.7% of PCE and TCE added as mixtures (containing 5 and 10 mg L⁻¹, respectively). CONCLUSIONS: The degradation ability of TCE was extended to other species of white‐rot fungi. Percentage degradation as well as chloride release from mixtures of TCE and PCE showed that T. versicolor degrades mixtures of TCE and PCE almost as well as its ability to degrade individually added TCE or PCE. The results suggest the potential promise of T. versicolor for bioremediation of TCE and PCE in the environment. Copyright © 2008 Society of Chemical Industry     

Kinetic modelling of textural changes in ready-to-eat breakfast cereals during soaking in semi-skimmed milk

Summary Ready‐to‐eat breakfast cereals immersed in milk undergo undesirable changes in texture because of sudden moisture uptake. The textural changes are ascribable to a plasticizing effect of water, which modifies the mechanical strength of products by softening the starch/protein matrix. In this work, some textural parameters of different cereal flakes were derived from the force–displacement curves monitored during 300 s of immersion in milk. Hardness loss, deformability increment and changes in the force–displacement curve profile were calculated and plotted against soaking time. The application of a sugar coating process to a model cereal flake increased the initial product hardness and improved the preservation of the textural parameters during immersion. The Peleg model closely fitted the experimental data, with regression coefficients from 0.967 to 0.999.     

Shear mode coupling and tilted grain growth of A1N thin films in BAW resonators.

Polycrystalline A1N thin films were deposited by RF reactive magnetron sputtering on Pt(111)/Ti electrode films. The substrates were tilted by an angle ranging from 40 degrees to 70 degrees with respect to the target normal. A low deposition temperature and a high sputter gas pressure were found ideal for tilted growth. The resulting grain tilt angle amounts to about half the substrate tilt angle. For coupling evaluation, 5 GHz solidly mounted resonator structures have been realized. The tilted grain A1N films exhibited a permittivity in the 9.5-10.5 range and loss tangent of 0.3%. Two shear modes as well as the longitudinal mode could be clearly identified. The coupling coefficient k2(eff) of the fundamental thickness shear mode (TS0) was found to be about 0.5%, which is compatible with a c-axis tilt of about 6 degrees.     

Ultrasonographically assessed maximum carotid artery wall thickness in Mexico City residents and Mexican Americans living in San Antonio, Texas. Association with diabetes and cardiovascular risk factors

Measurements of carotid artery wall thickness are often used as a surrogate for atherosclerosis. However, few studies have performed these measurements in populations of Mexican origin. Since Mexicans in Mexico City consume high-carbohydrate diets and have carbohydrate-induced dyslipidemia (high triglyceride and low HDL cholesterol levels) compared with Mexican Americans living in San Antonio, Tex, we questioned whether they also had more atherosclerosis than San Antonio Mexican Americans. Mean maximum intimal-medial thickness (IMT) of the common (CCA) and internal (ICA) carotid arteries were measured in 867 subjects aged 35 to 64 years (40% men) in two Mexican-origin populations, one from San Antonio (n = 202) and the other from Mexico City (n = 665). IMT's in the two cities were compared, and their associations with cardiovascular risk factors were analyzed. Older age, male sex, high levels of total cholesterol, low levels of HDL cholesterol, and high systolic blood pressure were positively associated with both CCA IMT and ICA IMT. Cigarette smoking was significantly associated with ICA IMT. CCA and ICA IMTs in diabetic subjects were thicker than in nondiabetic subjects in both men and women (all P < = .05). CCA IMT was thicker in the San Antonio than the Mexico City subjects after adjustment for cardiovascular risk factors (0.81 versus 0.76 mm in men and 0.77 versus 0.71 mm in women; P < .001 for city difference). San Antonio men also had thicker ICA IMT than their counterparts in Mexico City (0.88 versus 0.83 mm), but the reverse was true for women (0.73 versus 0.77 mm; interaction between sex and city, P < .05). Our results indicate that men had higher carotid IMTs than women. CCA IMT was thicker in San Antonio Mexican Americans than in Mexico City residents. The differences in ICA IMTs between San Antonio and Mexico City were inconsistent. Thus, since Mexico City residents consume high-carbohydrate diets, the data do not support an atherogenic effect of such diets. The interaction between sex and city on ICA IMT deserves further study.     

Parameter identification for nonlinear systems: Guaranteed confidence regions through LSCR

In this paper we consider the problem of constructing confidence regions for the parameters of nonlinear dynamical systems. The proposed method uses higher order statistics and extends the LSCR (leave-out sign-dominant correlation regions) algorithm for linear systems introduced in Campi and Weyer [2005, Guaranteed non-asymptotic confidence regions in system identification. Automatica 41(10), 1751-1764. Extended version available at 〈http://www.ing.unibs.it/∼campi〉]. The confidence regions contain the true parameter value with a guaranteed probability for any finite number of data points. Moreover, the confidence regions shrink around the true parameter value as the number of data points increases. The usefulness of the proposed approach is illustrated on some simple examples.     

Active state estimation for nonlinear systems: a neural approximation approach.

In this paper, we consider the problem of actively providing an estimate of the state of a stochastic dynamic system over a (possibly long) finite time horizon. The active estimation problem (AEP) is formulated as a stochastic optimal control one, in which the minimization of a suitable uncertainty measure is carried out. Toward this end, the use of the Renyi entropy as an information measure is proposed and motivated. A neural control scheme, based on the application of the extended Ritz method (ERIM) and on the use of a Gaussian sum filter (GSF), is then presented. Simulation results show the effectiveness of the proposed approach.