Three-dimensional simulation of a new cooling strategy for proton exchange membrane fuel cell stack using a non-isothermal multiphase model

In this study, a new cooling strategy for a proton exchange membrane (PEM) fuel cell stack is investigated using a three-dimensional (3D) multiphase non-isothermal model. The new cooling strategy follows that of the Honda's Clarity design and further extends to a cooling unit every five cells in stacks. The stack consists of 5 fuel cells sharing the inlet and outlet manifolds for reactant gas flows. Each cell has 7-path serpentine flow fields with a counter-flow configuration arranged for hydrogen and air streams. The coolant flow fields are set at the two sides of the stack and are simplified as the convective heat transfer thermal boundary conditions. This study also compares two thermal boundary conditions, namely limited and infinite coolant flow rates, and their impacts on the distributions of oxygen, liquid water, current density and membrane hydration. The difference of local temperature between these two cooling conditions is as much as 6.9 K in the 5-cell stack, while it is only 1.7 K in a single cell. In addition, the increased vapor concentration at high temperature (and hence water saturation pressure) dilutes the oxygen content in the air flow, reducing local oxygen concentration. The higher temperature in the stack also causes low membrane hydration, and consequently poor cell performance and non-uniform current density distribution, as disclosed by the simulation. The work indicates the new cooling strategy can be optimized by increasing the heat transfer coefficient between the stack and coolant to mitigate local overheating and cell performance reduction

Biodegradable Polylactic Acid (PLA) Microstructures for Scaffold Applications

In this research, we present a simple and cost effective soft lithographic process to fabricate PLA scaffolds for tissue engineering. In which, the negative photoresist JSR THB-120N was spun on a glass subtract followed by conventional UV lithographic processes to fabricate the master to cast the PDMS elastomeric mold. A thin poly(vinyl alcohol) (PVA) layer was used as a mode release such that the PLA scaffold can be easily peeled off. The PLA precursor solution was then cast onto the PDMS mold to form the PLA microstructures. After evaporating the solvent, the PLA microstructures can be easily peeled off from the PDMS mold. Experimental results show that the desired microvessels scaffold can be successfully transferred to the biodegradable polymer PLA.Comment: Submitted on behalf of EDA Publishing Association (http://irevues.inist.fr/EDA-Publishing

Robust filtering for gene expression time series data with variance constraints

This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2007 Taylor & Francis Ltd.In this paper, an uncertain discrete-time stochastic system is employed to represent a model for gene regulatory networks from time series data. A robust variance-constrained filtering problem is investigated for a gene expression model with stochastic disturbances and norm-bounded parameter uncertainties, where the stochastic perturbation is in the form of a scalar Gaussian white noise with constant variance and the parameter uncertainties enter both the system matrix and the output matrix. The purpose of the addressed robust filtering problem is to design a linear filter such that, for the admissible bounded uncertainties, the filtering error system is Schur stable and the individual error variance is less than a prespecified upper bound. By using the linear matrix inequality (LMI) technique, sufficient conditions are first derived for ensuring the desired filtering performance for the gene expression model. Then the filter gain is characterized in terms of the solution to a set of LMIs, which can easily be solved by using available software packages. A simulation example is exploited for a gene expression model in order to demonstrate the effectiveness of the proposed design procedures.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grants GR/S27658/01 and EP/C524586/1, the Biotechnology and Biological Sciences Research Council (BBSRC) of the UK under Grants BB/C506264/1 and 100/EGM17735, the Nuffield Foundation of the UK under Grant NAL/00630/G, and the Alexander von Humboldt Foundation of Germany

Nonlinear response and scaling law in the vortex state of d-wave superconductors

We study the field dependence of the quasi-particle density of states, the thermodynamics and the transport properties in the vortex state of d-wave superconductors when a magnetic field is applied perpendicular to the conducting plane, specially for the low field and the low temperature compared to the upper critical field and transition temperature, respectively, $H/H_{c2} \ll 1$ and $T/T_c \ll 1$. Both the superfluid density and the spin susceptibility exhibit the characteristic $\sqrt{H}$-field dependence, while the nuclear spin lattice relaxation rate T$_1^{-1}$ and the thermal conductivity are linear in field $H$. With increasing temperature, these quantities exhibit the scaling behavior in $T/\sqrt{H}$. The present theory applies to 2D $f$-wave superconductor as well; a possible candidate of the superconductivity in Sr$_2$RuO$_4$.Comment: 11 pages, 4 figure

Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation

Automatic brain tumor segmentation plays an important role for diagnosis, surgical planning and treatment assessment of brain tumors. Deep convolutional neural networks (CNNs) have been widely used for this task. Due to the relatively small data set for training, data augmentation at training time has been commonly used for better performance of CNNs. Recent works also demonstrated the usefulness of using augmentation at test time, in addition to training time, for achieving more robust predictions. We investigate how test-time augmentation can improve CNNs' performance for brain tumor segmentation. We used different underpinning network structures and augmented the image by 3D rotation, flipping, scaling and adding random noise at both training and test time. Experiments with BraTS 2018 training and validation set show that test-time augmentation helps to improve the brain tumor segmentation accuracy and obtain uncertainty estimation of the segmentation results.Comment: 12 pages, 3 figures, MICCAI BrainLes 201

An unexpectedly low-redshift excess of Swift gamma-ray burst rate

Gamma-ray bursts (GRBs) are the most violent explosions in the Universe and can be used to explore the properties of high-redshift universe. It is believed that the long GRBs are associated with the deaths of massive stars. So it is possible to use GRBs to investigate the star formation rate (SFR). In this paper, we use Lynden-Bell's $c^-$ method to study the luminosity function and rate of \emph{Swift} long GRBs without any assumptions. We find that the luminosity of GRBs evolves with redshift as $L(z)\propto g(z)=(1+z)^k$ with $k=2.43_{-0.38}^{+0.41}$. After correcting the redshift evolution through $L_0(z)=L(z)/g(z)$, the luminosity function can be expressed as $\psi(L_0)\propto L_0^{-0.14\pm0.02}$ for dim GRBs and $\psi(L_0)\propto L_0^{-0.70\pm0.03}$ for bright GRBs, with the break point $L_{0}^{b}=1.43\times10^{51}~{\rm erg~s^{-1}}$. We also find that the formation rate of GRBs is almost constant at $z<1.0$ for the first time, which is remarkably different from the SFR. At $z>1.0$, the formation rate of GRB is consistent with the SFR. Our results are dramatically different from previous studies. Some possible reasons for this low-redshift excess are discussed. We also test the robustness of our results with Monte Carlo simulations. The distributions of mock data (i.e., luminosity-redshift distribution, luminosity function, cumulative distribution and $\log N-\log S$ distribution) are in good agreement with the observations. Besides, we also find that there are remarkable difference between the mock data and the observations if long GRB are unbiased tracers of SFR at $z<1.0$.Comment: 33 pages, 10 figures, 1 table, accepted by ApJ