Development and experimental validation of a mathematical model for friction between fabrics and a volar forearm phantom.

An analytical mathematical model for friction between a fabric strip and the volar forearm has been developed and validated experimentally. The model generalizes the common assumption of a cylindrical arm to any convex prism, and makes predictions for pressure and tension based on Amontons' law. This includes a relationship between the coefficient of static friction (mu) and forces on either end of a fabric strip in contact with part of the surface of the arm and perpendicular to its axis. Coefficients of friction were determined from experiments between arm phantoms of circular and elliptical cross-section (made from Plaster of Paris covered in Neoprene) and a nonwoven fabric. As predicted by the model, all values of mu calculated from experimental results agreed within +/- 8 per cent, and showed very little systematic variation with the deadweight, geometry, or arc of contact used. With an appropriate choice of coordinates the relationship predicted by this model for forces on either end of a fabric strip reduces to the prediction from the common model for circular arms. This helps to explain the surprisingly accurate values of mu obtained by applying the cylindrical model to experimental data on real arms.

Development and validation of a new method for measuring friction between skin and nonwoven materials.

A new method for measuring the coefficient of friction between nonwoven materials and the curved surface of the volar forearm has been developed and validated. The method was used to measure the coefficient of static friction for three different nonwoven materials on the normal (dry) and over-hydrated volar forearms of five female volunteers (ages 18-44). The method proved simple to run and had good repeatability: the coefficient of variation (standard deviation expressed as a percentage of the mean) for triplets of repeat measurements was usually (80 per cent of the time) less than 10 per cent. Measurements involving the geometrically simpler configuration of pulling a weighted fabric sample horizontally across a quasi-planar area of volar forearm skin proved experimentally more difficult and had poorer repeatability. However, correlations between values of coefficient of static friction derived using the two methods were good (R = 0.81 for normal (dry) skin, and 0.91 for over-hydrated skin). Measurements of the coefficient of static friction for the three nonwovens for normal (dry) and for over-hydrated skin varied in the ranges of about 0.3-0.5 and 0.9-1.3, respectively. In agreement with Amontons' law, coefficients of friction were invariant with normal pressure over the entire experimental range (0.1-8.2 kPa).

Application of neural networks to the ranking of perinatal variables influencing birthweight.

In this paper we compare Multi-Layer Perceptrons (a neural network type) with Multivariate Linear Regression in predicting birthweight from nine perinatal variables which are thought to be related. Results show, that seven of the nine variables, i.e., gestational age, mother's body-mass index (BMI), sex of the baby, mother's height, smoking, parity and gravidity, are related to birthweight. We found no significant relationship between birthweight and each of the two variables, i.e., maternal age and social class.

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.