Compact double-side liquid-impingement-cooled integrated power electronic module

This paper presents a compact integrated power electronic module (IPEM) which seeks to overcome the volumetric power density limitations of conventional packaging technologies. A key innovation has been the development of a substrate sandwich structure which permits double side cooling of the embedded dies whilst controlling the mechanical stresses both within the module and at the heat exchanger interface. A 3-phase inverter module has been developed, integrating the sandwich structures with high efficiency impingement coolers, delink capacitance and gate drive units. Full details of the IPEM construction and electrical evaluation are given in the paper. © 2007 IEEE.

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.