Three-dimensional numerical study of flow characteristic and membrane fouling evolution in an enzymatic membrane reactor

In order to enhance the understanding of membrane fouling mechanism, the hydrodynamics of granular flow in a stirred enzymatic membrane reactor was numerically investigated in the present study. A three-dimensional Euler-Euler model, coupled with k-e mixture turbulence model and drag function for interphase momentum exchange, was applied to simulate the two-phase (fluid-solid) turbulent flow. Numerical simulations of single- or two-phase turbulent flow under various stirring speed were implemented. The numerical results coincide very well with some published experimental data. Results for the distributions of velocity, shear stress and turbulent kinetic energy were provided. Our results show that the increase of stirring speed could not only enlarge the circulation loops in the reactor, but it can also increase the shear stress on the membrane surface and accelerate the mixing process of granular materials. The time evolution of volumetric function of granular materials on the membrane surface has qualitatively explained the evolution of membrane fouling.Comment: 10 panges, 8 figure

Pixel-wise Graph Attention Networks for Person Re-identification

Graph convolutional networks (GCN) is widely used to handle irregular data since it updates node features by using the structure information of graph. With the help of iterated GCN, high-order information can be obtained to further enhance the representation of nodes. However, how to apply GCN to structured data (such as pictures) has not been deeply studied. In this paper, we explore the application of graph attention networks (GAT) in image feature extraction. First of all, we propose a novel graph generation algorithm to convert images into graphs through matrix transformation. It is one magnitude faster than the algorithm based on K Nearest Neighbors (KNN). Then, GAT is used on the generated graph to update the node features. Thus, a more robust representation is obtained. These two steps are combined into a module called pixel-wise graph attention module (PGA). Since the graph obtained by our graph generation algorithm can still be transformed into a picture after processing, PGA can be well combined with CNN. Based on these two modules, we consulted the ResNet and design a pixel-wise graph attention network (PGANet). The PGANet is applied to the task of person re-identification in the datasets Market1501, DukeMTMC-reID and Occluded-DukeMTMC (outperforms state-of-the-art by 0.8\%, 1.1\% and 11\% respectively, in mAP scores). Experiment results show that it achieves the state-of-the-art performance. \href{https://github.com/wenyu1009/PGANet}{The code is available here}

恶性黑色素瘤合并嗜肺军团菌感染性肺炎病例报告

Legionella pneumonia is mainly in community-acquired and nosocomial pneumonias caused by the L. pneumophila. The paper reported a case of Legionella pneumonia caused by Legionella pneumophila Sg1 in a man with malignant melanoma. The method for diagnosing Legionella pneumonia by standard culture method，serotyping，PCR-enzymatic digestion analysis and gene sequencing was elaborate. To confirm the diagnosis result of this rapid diagnostic method, sequencing of the bacteria in patient’s sputum partial gene was also carried out. The diagnosis result of this rapid diagnostic method was consistent with the culture method which indicated that it was effective in diagnosing L. pneumophila infection.军团菌肺炎主要是由嗜肺军团菌感染引起的一种社区获得性或医院内感染性肺炎。本文报告了1例临床上极为罕见的恶性黑色素瘤合并嗜肺军团菌血清1型感染引起的军团菌肺炎，并对其实验室诊断作了系统描述，包括病人痰液标本的细菌分离培养、血清学分型、PCR-酶切分型和基因测序鉴定等分子生物学诊断技术，结果表明PCR-酶切分型对于诊断军团菌病是一种快速、准确可靠的试验方法

Energy Efficiency Maximization in IRS-Aided Cell-Free Massive MIMO System

In this paper, we consider an intelligent reflecting surface (IRS)-aided cell-free massive multiple-input multiple-output system, where the beamforming at access points and the phase shifts at IRSs are jointly optimized to maximize energy efficiency (EE). To solve EE maximization problem, we propose an iterative optimization algorithm by using quadratic transform and Lagrangian dual transform to find the optimum beamforming and phase shifts. However, the proposed algorithm suffers from high computational complexity, which hinders its application in some practical scenarios. Responding to this, we further propose a deep learning based approach for joint beamforming and phase shifts design. Specifically, a two-stage deep neural network is trained offline using the unsupervised learning manner, which is then deployed online for the predictions of beamforming and phase shifts. Simulation results show that compared with the iterative optimization algorithm and the genetic algorithm, the unsupervised learning based approach has higher EE performance and lower running time.Comment: 6 pages, 4 figure

Efficient CT Metal Artifact Reduction Based on Fractional-Order Curvature Diffusion

We propose a novel metal artifact reduction method based on a fractional-order curvature driven diffusion model for X-ray computed tomography. Our method treats projection data with metal regions as a damaged image and uses the fractional-order curvature-driven diffusion model to recover the lost information caused by the metal region. The numerical scheme for our method is also analyzed. We use the peak signal-to-noise ratio as a reference measure. The simulation results demonstrate that our method achieves better performance than existing projection interpolation methods, including linear interpolation and total variation

Dynamic GATA4 enhancers shape the chromatin landscape central to heart development and disease.

How stage-specific enhancer dynamics modulate gene expression patterns essential for organ development, homeostasis and disease is not well understood. Here, we addressed this question by mapping chromatin occupancy of GATA4--a master cardiac transcription factor--in heart development and disease. We find that GATA4 binds and participates in establishing active chromatin regions by stimulating H3K27ac deposition, which facilitates GATA4-driven gene expression. GATA4 chromatin occupancy changes markedly between fetal and adult heart, with a limited binding sites overlap. Cardiac stress restored GATA4 occupancy to a subset of fetal sites, but many stress-associated GATA4 binding sites localized to loci not occupied by GATA4 during normal heart development. Collectively, our data show that dynamic, context-specific transcription factors occupancy underlies stage-specific events in development, homeostasis and disease