Testing the robustness of the job demands-resources model.

[Correction Notice: An erratum for this article was reported in Vol 14(2) of International Journal of Stress Management (see record 2007-07240-008). Several corrections of errata in a paper by the authors published in the 2006 issue of the International Journal of Stress Management are presented: (1) The sentence beginning on page 385, line 16 should read "To avoid identification problems, the error variance of Organizational commitment and Emotional load were constrained using the formula (1 - α) × sigma2." (2) The sentence on page 385, line 18, should be deleted. (3) Degrees of freedom for M3 in Table 2 were incorrectly reported. The correct degrees of freedom were 54. The corrected table is provided.] According to the job demands-resources (JD-R) model, job demands and resources evoke two relatively independent processes: health impairment and employee motivation. The robustness of the JD-R model was tested in two different occupational samples, the first of 654 Spanish employees and the second of 477 Dutch employees. Structural equation modeling analyses provided partial evidence for the two processes. Multigroup analyses showed that the structural paths of the model were invariant across countries, although the strength of the relationships differed. We conclude that the basic structure of the JD-R model is maintained, even when applied in different national and occupational contexts, when using different ways of gathering data (computerized versus paper and pencil), and when using slightly different measures to assess the key variables of the model. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A regression model describing the effect of pH,NaCl and temperature on<Emphasis Type="Italic"> D</Emphasis> values of<Emphasis Type="Italic"> Bacillus stearothermophilus</Emphasis> spores

A new microbiological predictive model was developed, relating pH, NaCl concentration and temperature (T) with the decimal reduction coefficient D as dependent variable, using data previously reported by [1]. The model was obtained by multiple linear regression using only three predictor variables (T ², pH² and NaCl), easily computed from the basic variables pH, NaCl concentration and temperature (T). The fitted model is robust, stable and satisfies all the basic hypotheses of the regression models. It also provides simplicity when compared with previously published models based on multiple linear regression tools.     

Hybrid ceramic-polymer material for wood coating with high wearing resistance

The synthesis and characterization of a hybrid ceramic-polymer coating with high wearing resistance and designed to cover wood surfaces, are reported in this work. This hybrid material provides a high wearing resistance (400% higher than a conventional commercial product typically used to protect these surfaces); additionally it has a high UV resistance and high transparency and gloss. The wood surfaces were previously treated either with a primer coating or with a special coupling agent, before the hybrid coating was applied; in both cases, good adhesion with the substrate was obtained. Electronic Publication     

Nanohybrid scratch resistant coatings for teeth and bone viscoelasticity manifested in tribology

We cover teeth surfaces with nanohybrid coatings containing an inorganic ceramic and an organic copolymer constituents. We report the first ever values of scratch penetration depth and scratch recovery for bare and coated teeth. We find that uncoated teeth undergo viscoelastic recovery (healing) after microscratching – the first manifestation of bone viscoelasticity in tribology. The coatings fill "valleys" in teeth surfaces. In each case a large improvement in the scratch resistance as compared to uncoated teeth is seen. The extent of the improvement depends on the inorganic/organic component ratios in the nanohybrids. Electronic Publication     

Estimating soybean ground cover from satellite images using neural-networks models

The ground cover is a necessary parameter for agronomic and environmental applications. In Argentina, soybean (Glycine max (L.) Merill) is the most important crop; therefore it is necessary to determine its amount and configuration. In this work, neural-network (NN) models were developed to calculate soybean percentage ground cover (fractional vegetation cover, fCover) and to compare the accuracy of the estimate from Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat satellites data. The NN design included spectral values of the red and near-infrared (NIR) bands as input variables and one neuron output, which expressed the estimated coverage. Data of fCover were acquired throughout the growing season in the central plains of Córdoba (Argentina); they were used for training and validating the networks. The results show that the NNs are an appropriate methodology for estimating the temporal evolution of soybean coverage fraction from MODIS and Landsat images, with coefficients of determination (R ²) equal to 0.90 and 0.91, respectively.     

Multi-stage modeling using fuzzy multi-criteria feature selection to improve survival prediction of ICU septic shock patients

In many binary medical classification problems, the cost of misclassifying one category is higher than the other, and in these applications it is desirable to employ a classifier with selective sensitivity or specificity. This work explores the utility of a fuzzy multi–criteria function for performance evaluation during knowledge–based medical classification and prediction. The method presented here uses fuzzy optimization to combine the sensitivity, specificity, and accuracy of classification as goals in a single objective function. This approach is used to assign flexible goals, which can be used to maximize the outcome in terms of each one of the goals. The proposed approach significantly increases the sensitivity and the specificity while maintaining or increasing accuracy. The versatility of the method is further exploited in a multi-model approach, using individual structures of multi-objective optimization of sensitivity and specificity separately, and then combining their outcomes through a decision-making module. Among various medical benefits derived from applying this technique, the divergent feature sets selected by high sensitivity and specificity models lend insight into factors more integrally connected to what causes risk of death for patients.     

Is the ubiquitous presence of barium carbonate responsible for the poor aqueous processing ability of barium titanate?

The ubiquitous presence of barium carbonate (BaCO₃ – BC) as an impurity in barium titanate (BaTiO₃ – BT) has been pointed out as the main reason for the well-known difficulties found by many investigators when attempting to process BT powders in aqueous media. Different and controversial arguments have been put forward to justify the observed aqueous processing difficulties of BT, but a satisfactory explanation is still to be found. With this aim, a systematic study was here undertaken to shed further light on the solid/liquid interactions occurring at the surface of BC and BT particles and their impact on the dispersion ability of both powders, separately and mixed in certain proportions. Long term colloidal stability and high solid loadings (60 vol.%) were obtained for BC, while colloidal instability and a lower maximum content of solids (50 vol.%) could be achieved for BT. This responds to the question risen in the title.