Mathematical modelling of the catalyst layer of a polymer-electrolyte fuel cell

In this paper we derive a mathematical model for the cathode catalyst layer of a polymer electrolyte fuel cell. The model explicitly incorporates the restriction placed on oxygen in reaching the reaction sites, capturing the experimentally observed fall in the current density to a limiting value at low cell voltages. Temperature variations and interfacial transfer of O2 between the dissolved and gas phases are also included. Bounds on the solutions are derived, from which we provide a rigorous proof that the model admits a solution. Of particular interest are the maximum and minimum attainable values. We perform an asymptotic analysis in several limits inherent in the problem by identifying important groupings of parameters. This analysis reveals a number of key relationships between the solutions, including the current density, and the composition of the layer. A comparison of numerically computed and asymptotic solutions shows very good agreement. Implications of the results are discussed and future work is outlined

End-to-end 3D face reconstruction with deep neural networks

Monocular 3D facial shape reconstruction from a single 2D facial image has been an active research area due to its wide applications. Inspired by the success of deep neural networks (DNN), we propose a DNN-based approach for End-to-End 3D FAce Reconstruction (UH-E2FAR) from a single 2D image. Different from recent works that reconstruct and refine the 3D face in an iterative manner using both an RGB image and an initial 3D facial shape rendering, our DNN model is end-to-end, and thus the complicated 3D rendering process can be avoided. Moreover, we integrate in the DNN architecture two components, namely a multi-task loss function and a fusion convolutional neural network (CNN) to improve facial expression reconstruction. With the multi-task loss function, 3D face reconstruction is divided into neutral 3D facial shape reconstruction and expressive 3D facial shape reconstruction. The neutral 3D facial shape is class-specific. Therefore, higher layer features are useful. In comparison, the expressive 3D facial shape favors lower or intermediate layer features. With the fusion-CNN, features from different intermediate layers are fused and transformed for predicting the 3D expressive facial shape. Through extensive experiments, we demonstrate the superiority of our end-to-end framework in improving the accuracy of 3D face reconstruction.Comment: Accepted to CVPR1

Simultaneous Multiple Surface Segmentation Using Deep Learning

The task of automatically segmenting 3-D surfaces representing boundaries of objects is important for quantitative analysis of volumetric images, and plays a vital role in biomedical image analysis. Recently, graph-based methods with a global optimization property have been developed and optimized for various medical imaging applications. Despite their widespread use, these require human experts to design transformations, image features, surface smoothness priors, and re-design for a different tissue, organ or imaging modality. Here, we propose a Deep Learning based approach for segmentation of the surfaces in volumetric medical images, by learning the essential features and transformations from training data, without any human expert intervention. We employ a regional approach to learn the local surface profiles. The proposed approach was evaluated on simultaneous intraretinal layer segmentation of optical coherence tomography (OCT) images of normal retinas and retinas affected by age related macular degeneration (AMD). The proposed approach was validated on 40 retina OCT volumes including 20 normal and 20 AMD subjects. The experiments showed statistically significant improvement in accuracy for our approach compared to state-of-the-art graph based optimal surface segmentation with convex priors (G-OSC). A single Convolution Neural Network (CNN) was used to learn the surfaces for both normal and diseased images. The mean unsigned surface positioning errors obtained by G-OSC method 2.31 voxels (95% CI 2.02-2.60 voxels) was improved to $1.27$ voxels (95% CI 1.14-1.40 voxels) using our new approach. On average, our approach takes 94.34 s, requiring 95.35 MB memory, which is much faster than the 2837.46 s and 6.87 GB memory required by the G-OSC method on the same computer system.Comment: 8 page

Prediction of the capacitance lineshape in two-channel quantum dots

We propose a set-up to realize two-channel Kondo physics using quantum dots. We discuss how the charge fluctuations on a small dot can be accessed by using a system of two single electron transistors arranged in parallel. We derive a microscopic Hamiltonian description of the set-up that allows us to make connection with the two-channel Anderson model (of extended use in the context of heavy-Fermion systems) and in turn make detailed predictions for the differential capacitance of the dot. We find that its lineshape, which we determined precisely, shows a robust behavior that should be experimentally verifiable.Comment: 4 pages, 3 figure

India’s water supply and demand from 2025-2050: business-as-usual scenario and issues

Water demandEstimationIrrigation waterRiver basinsWater supplySimulation modelsPopulation growthFood productionFood consumptionCrop yieldGroundwater irrigation

Risk of hypertension with bevacizumab, an antibody against vascular endothelial growth factor A: a systematic review and meta-analysis

Bevacizumab, a humanized antibody against VEGF, is effective in the treatment of patients with many cancers. However, as with many therapeutic agents, significant side effects are associated with bevacizumab, Hypertension is one of the predominant toxicity. We performed a systematic review and meta-analysis of published clinical trials of bevacizumab to quantify the risk of hypertension. 15 studies following PRISMA guidelines and matching inclusion and exclusion criteria were collected in which a group of patients were either treated with Bevacizumab and a concurrent chemotherapy and another group treated with Placebo and the same chemotherapy. Relative risk (RR) was calculated. P<0.05 was considered statistically significant. RevMan 5.3 software was used for the analysis. A total of 13,070 patients were included. Bevacizumab was associated with a significant increased risk of overall hypertension (RR=3.509; 95% C.I:2.451 to 5.023). 11 trials are included for determining the risk of Grade 3 hypertension including 8799 patients with a significant increased risk (RR=3.909; 95%C.I:1.983 to 7.707). 7 trials are included for determining the risk of hypertension at low dose (2.5 mg/kg/cycle) including 3691 patients associated with a significant increased (RR=2.640; 95%C.I: 1.408 to 4.950). 10 trials are included for determining the risk of hypertension at high dose (5 mg/kg/cycle) including 9379 patients associated with a significant (RR=4.036; 95%C.I: 2.948 to 5.525). Our meta-analysis has demonstrated that bevacizumab may be associated with a significantly increased risk of hypertension in patient with a variety of metastatic solid tumors irrespective of dosing

Probabilistic analysis of bladed turbine disks and the effect of mistuning

Probabilistic assessment of the maximum blade response on a mistuned rotor disk is performed using the computer code NESSUS. The uncertainties in natural frequency, excitation frequency, amplitude of excitation and damping are included to obtain the cumulative distribution function (CDF) of blade responses. Advanced mean value first order analysis is used to compute CDF. The sensitivities of different random variables are identified. Effect of the number of blades on a rotor on mistuning is evaluated. It is shown that the uncertainties associated with the forcing function parameters have significant effect on the response distribution of the bladed rotor

Large-amplitude chirped coherent phonons in tellurium mediated by ultrafast photoexcited carrier diffusion

We report femtosecond time-resolved reflectivity measurements of coherent phonons in tellurium performed over a wide range of temperatures (3K to 296K) and pump laser intensities. A totally symmetric A$_{1}$ coherent phonon at 3.6 THz responsible for the oscillations in the reflectivity data is observed to be strongly positively chirped (i.e, phonon time period decreases at longer pump-probe delay times) with increasing photoexcited carrier density, more so at lower temperatures. We show for the first time that the temperature dependence of the coherent phonon frequency is anomalous (i.e, increasing with increasing temperature) at high photoexcited carrier density due to electron-phonon interaction. At the highest photoexcited carrier density of $\sim$ 1.4 $\times$ 10$^{21}$cm$^{-3}$ and the sample temperature of 3K, the lattice displacement of the coherent phonon mode is estimated to be as high as $\sim$ 0.24 \AA. Numerical simulations based on coupled effects of optical absorption and carrier diffusion reveal that the diffusion of carriers dominates the non-oscillatory electronic part of the time-resolved reflectivity. Finally, using the pump-probe experiments at low carrier density of 6 $\times$ 10$^{18}$ cm$^{-3}$, we separate the phonon anharmonicity to obtain the electron-phonon coupling contribution to the phonon frequency and linewidth.Comment: 22 pages, 6 figures, submitted to PR