Dwell time effect in peeling PSA laminates

Air, trapped interfacially between the adhesive and the substrate, can have a detrimental effect on the peel strength of bonds formed by a PSA and relatively impermeable adherends. If the adhesive wets the substrate surface so that the contact angle is small then the forces of the surface tension within the adhesive can lead to the gradual expulsion of these pockets of air and thereby to the enhancement of the peel strength-the dwell-time effect. Using a high-performance PSA transfer tape it has been found that this strengthening effect may operate over many thousands of hours. With increasing hydrophobicity of the surfaces, this effect can be suppressed and a poor peel strength remains essentially constant with time. The observed rates at which the peel strength increases are quantitatively consistent with diffusion of entrapped air out of the interface. © 2012 Copyright Taylor and Francis Group, LLC.

On the time delay in continuous in-cylinder sampling from IC engines

When gas sample is continuously drawn from the cylinder of an internal combustion engine, the sample that appears at the end of the sampling system corresponds to the in-cylinder content sometime ago because of the finite transit time which is a function of the cylinder pressure history. This variable delay causes a dispersion of the sample signal and makes the interpretation of the signal difficult An unsteady flow analysis of a typical sampling system was carried out for selected engine loads and speeds. For typical engine operation, a window in which the delay is approximately constant may be found. This window gets smaller with increase in engine speed, with decrease in load, and with the increase in exit pressure of the sampling system.

A robust Bayesian two-sample test for detecting intervals of differential gene expression in microarray time series.

Understanding the regulatory mechanisms that are responsible for an organism's response to environmental change is an important issue in molecular biology. A first and important step towards this goal is to detect genes whose expression levels are affected by altered external conditions. A range of methods to test for differential gene expression, both in static as well as in time-course experiments, have been proposed. While these tests answer the question whether a gene is differentially expressed, they do not explicitly address the question when a gene is differentially expressed, although this information may provide insights into the course and causal structure of regulatory programs. In this article, we propose a two-sample test for identifying intervals of differential gene expression in microarray time series. Our approach is based on Gaussian process regression, can deal with arbitrary numbers of replicates, and is robust with respect to outliers. We apply our algorithm to study the response of Arabidopsis thaliana genes to an infection by a fungal pathogen using a microarray time series dataset covering 30,336 gene probes at 24 observed time points. In classification experiments, our test compares favorably with existing methods and provides additional insights into time-dependent differential expression.