Molecular Dynamics Simulations of the O2- Ion Mobility in Dense Ne Gas at Low Temperature: Influence of the Repulsive Part of the Ion-Neutral Interaction Potential

New Molecular Dynamics simulations have been carried out in order to get an insight on the physical mechanisms that determine the drift mobility of negative Oxygen ions in very dense Neon gas in the supercritical phase close to the critical point. Two ion-neutral interaction potentials have been used that differ by their repulsive part. We have observed that the potential with a harder repulsive part gives much better agreement with the experimental data. The differences with the softer repulsive potential previously used are discussed. We propose that the behavior of the ion mobility as a function of the gas density is related to the number of neutral atoms loosely bound in the first solvation shell around the ion.Comment: submitted to IEEE-TDEI, 6 pages, 9 figure

Molecular Dynamics Simulations of the O2- Ion Mobility in Dense Neon Gas

We report here the results of Molecular Dynamics simulations of the drift mobility of negative oxygen ions in very dense neon gas in the supercritical phase. The simulations relatively well reproduce the trend of the experimental data. The rationalization of the mobility behavior as a function of the gas density is given in terms of the number of atoms correlated in the first solvation shell around the ion.Comment: submitted for ICDL 2017 Conferenc

A thermodynamic model for mO2− mO_2^- mobility in neon gas over broad density and temperature ranges

We report new measurements of the mobility $\mu$ of O$_{2}^{-}$ ions in supercritical neon in the range $45\,\mathrm{K}\le T\le 334\,\mathrm{K}$ for number density $N\ge 0.5\,$nm$^{-3}.$ We rationalize the experimental data of all isotherms with the Stokes-Cunningham formula by computing the ion hydrodynamic radius as a function of $T$ and $N$ with the thermodynamic free volume model developed for the ion mobility in superfluid He. The model parameters are determined by re-analyzing published data for $T=45\,$K for $N$ up to $N\approx 1.65N_{c}$ ($N_{c}\approx 14.4\,$nm$^{-3}$ is the critical number density), which roughly span four orders of magnitude of the Knudsen number $(0.1\le \mathrm{K}_{n}\le 1000)$, covering the transition from the kinetic- to the hydrodynamic transport regime. These parameters provide an excellent description of the dependence of $\mu$ on $N$ for all higher isotherms and yield a strict test of the model validity, thereby bridging the gap between the kinetic- and the hydrodynamic transport regimes

Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network

Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled to the high resolution (HR) space using a single filter, commonly bicubic interpolation, before reconstruction. This means that the super-resolution (SR) operation is performed in HR space. We demonstrate that this is sub-optimal and adds computational complexity. In this paper, we present the first convolutional neural network (CNN) capable of real-time SR of 1080p videos on a single K2 GPU. To achieve this, we propose a novel CNN architecture where the feature maps are extracted in the LR space. In addition, we introduce an efficient sub-pixel convolution layer which learns an array of upscaling filters to upscale the final LR feature maps into the HR output. By doing so, we effectively replace the handcrafted bicubic filter in the SR pipeline with more complex upscaling filters specifically trained for each feature map, whilst also reducing the computational complexity of the overall SR operation. We evaluate the proposed approach using images and videos from publicly available datasets and show that it performs significantly better (+0.15dB on Images and +0.39dB on Videos) and is an order of magnitude faster than previous CNN-based methods

Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network

Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled to the high resolution (HR) space using a single filter, commonly bicubic interpolation, before reconstruction. This means that the super-resolution (SR) operation is performed in HR space. We demonstrate that this is sub-optimal and adds computational complexity. In this paper, we present the first convolutional neural network (CNN) capable of real-time SR of 1080p videos on a single K2 GPU. To achieve this, we propose a novel CNN architecture where the feature maps are extracted in the LR space. In addition, we introduce an efficient sub-pixel convolution layer which learns an array of upscaling filters to upscale the final LR feature maps into the HR output. By doing so, we effectively replace the handcrafted bicubic filter in the SR pipeline with more complex upscaling filters specifically trained for each feature map, whilst also reducing the computational complexity of the overall SR operation. We evaluate the proposed approach using images and videos from publicly available datasets and show that it performs significantly better (+0.15dB on Images and +0.39dB on Videos) and is an order of magnitude faster than previous CNN-based methods

Evaluation of enzyme immunoassays in the diagnosis of camel (Camelus dromedarius) trypanosomiasis:a preliminary investigation

Three enzyme immunoassays were used for the serodiagnosis of Trypanosoma evansi in camels in the Sudan in order to evaluate their ability to discriminate between infected and non-infected animals. Two assays were used for the detection of trypanosomal antibodies, one using specific anti-camel IgG conjugate and another using a non-specific Protein A conjugate. The third assay detected the presence of trypanosomal antigens using anti-T. evansi antibodies in a double antibody sandwich assay. Inspection of the frequency distribution of assay results suggested that the ELISA for circulating trypanosomal antibodies using specific antisera and the ELISA for circulating antigens can distinguish between non-infected camels and infected camels exhibiting patent infections or not. The ELISA using Protein A conjugate to bind non-specifically to camel immunoglobulin did not appear to discriminate between infected and non-infected animals