Experimental Investigation on Heat Transfer Enhancement with Passive Inserts in Flat Tubes in due Consideration of an Efficiency Assessment

This paper presents results of an experimental investigation on pressure drop and heat transfer for a wide range of Reynolds and Prandtl numbers ranging from 8 < Pr < 60 and 40 < Re < 3500, for flat tubes without and with passive inserts. For three different kinds of passive insert designs, the impact on heat and momentum transfer due to coaction of the total set of passive inserts with different shape and amount was investigated. Experimental results were analyzed regarding two main aspects: Heat transfer mechanisms and pressure drop induced by friction and form drag forces due to the presence of different shapes. After heat and momentum transfer mechanisms for each passive insert design were analyzed, heat transfer and pressure drop enhancement were compared to each other, leading to an efficiency discussion. Different concepts for efficiency evaluation, which are cited in literature, were applied to the presented experimental data. Pros and cons of the different concepts are discussed. Finally, we propose an equation for evaluation of total performance, which fully respects the energetic and exergetic aspects of heat transfer and pressure drop enhancement

KIRMES: kernel-based identification of regulatory modules in euchromatic sequences

Motivation: Understanding transcriptional regulation is one of the main challenges in computational biology. An important problem is the identification of transcription factor (TF) binding sites in promoter regions of potential TF target genes. It is typically approached by position weight matrix-based motif identification algorithms using Gibbs sampling, or heuristics to extend seed oligos. Such algorithms succeed in identifying single, relatively well-conserved binding sites, but tend to fail when it comes to the identification of combinations of several degenerate binding sites, as those often found in cis-regulatory modules

Experimental Investigation on Heat Transfer Enhancement with Passive Inserts in Flat Tubes in due Consideration of an Efficiency Assessment

This paper presents results of an experimental investigation on pressure drop and heat transfer for a wide range of Reynolds and Prandtl numbers ranging from 8 &lt; Pr &lt; 60 and 40 &lt; Re &lt; 3500, for flat tubes without and with passive inserts. For three different kinds of passive insert designs, the impact on heat and momentum transfer due to coaction of the total set of passive inserts with different shape and amount was investigated. Experimental results were analyzed regarding two main aspects: Heat transfer mechanisms and pressure drop induced by friction and form drag forces due to the presence of different shapes. After heat and momentum transfer mechanisms for each passive insert design were analyzed, heat transfer and pressure drop enhancement were compared to each other, leading to an efficiency discussion. Different concepts for efficiency evaluation, which are cited in literature, were applied to the presented experimental data. Pros and cons of the different concepts are discussed. Finally, we propose an equation for evaluation of total performance, which fully respects the energetic and exergetic aspects of heat transfer and pressure drop enhancement

Discriminative Densities from Maximum Contrast Estimation

Meinicke P, Twellmann T, Ritter H. Discriminative Densities from Maximum Contrast Estimation. In: Becker S, Thrun S, Obermayer K, eds. Advances in Neural Information Processing Systems 15. Proceedings of the 2002 conference. Cambridge, Mass.: MIT-Press; 2003: 1009-1018.We propose a framework for classifier design based on discriminative densities for representation of the differences of the class-conditional distributions in a way that is optimal for classification. The densities are selected from a parametrized set by constrained maximization of some objective function which measures the average (bounded) difference, i.e. the contrast between discriminative densities. We show that maximiza- tion of the contrast is equivalent to minimization of an approximation of the Bayes risk. Therefore using suitable classes of probability density functions, the resulting maximum contrast classifiers(MCCs) can approximate the Bayes rule for the general multiclass case. In particular for a certain parametrization of the density functions we obtain MCCs which have the same functional form as the well-known Support Vector Machines (SVMs). We show that MCC-training in general requires some nonlinear optimization but under certain conditions the problem is concave and can be tackled by a single linear program. We indicate the close relation between SVM- and MCC-training and in particular we show that Linear Programming Machines can be viewed as an approxi- mate realization of MCCs. In the experiments on benchmark data sets, the MCC shows a competitive classification performance

Improving Transfer Rates in Brain Computer Interfacing: a Case Study

Meinicke P, Kaper M, Hoppe F, Heumann M, Ritter H. Improving Transfer Rates in Brain Computer Interfacing: a Case Study. In: Becker S, Thrun S, Obermayer K, eds. Advances in Neural Information Processing Systems 15. Cambridge, MA: MIT Press; 2003: 1131