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Changes of composition and antibiotic resistance of fecal coliform bacteria in municipal wastewater treatment plant

Journal of Environmental sciences 2024年第12期146卷 241-250页

作者： Luoyao Wen Yunwei Cui Luodong Huang Chunzhong Wei Gangan Wang Junya Zhang Yanbo Jiang Yuansong Wei Peihong Shen State Key Joint Laboratory of Environmental Simulation and Pollution Control Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijing 100085China College of Life Science and Technology Guangxi UniversityNanning 530005China Nanning Engineering&Technology Research Center for Water Safety Guangxi Beitou Environmental Protection&Water Group Co.LTD.Nanning 530025China Department of Isotope Biogeochemistry Helmholtz Centre for Environmental Research–UFZLeipzig 04318Germany University of Chinese Academy of Sciences Beijing 100049China International Graduate School at Shenzhen Tsinghua UniversityShenzhen 518055China

The dynamics of the composition and antibiotic resistance of the fecal coliform bacteria(FCB)in a typical wastewater treatment plant(WWTP)were investigated concerning the seasonal *** showed that WWTP could remove the FCB concentration by 3∼5 logs within the effluent of 10^(4)∼10^(5)CFU/L,but the antibiotic resistant rate of FCB species increased significantly after *** dominant FCB changed from Escherichia coli in the influent(∼73.0%)to Klebsiella pneumoniae in the effluent(∼53.3%)after WWTP,where the Escherichia coli was removed the most,while Klebsiella pneumoniae was the most *** secondary tank removed the most of FCB(by 3∼4 logs)compared to other processes,but increased all the concerned antibiotic resistant *** potential super bugs of FCB community showing resistance to all the target antibiotics were selected in the biological treatment unit of *** FCB showed the highest multiple antibiotic resistance(92.9%)in total which even increased to 100%in the *** has the highest antibiotic resistant rate in FCB,with a multiple antibiotic resistance rate of 98.4%.These indicated that the Klebsiella pneumoniae not just Escherichia coli should be specially emphasized after WWTP concerning the health risk associated with FCB community.

关键词： Fecal coliform bacteria Antibiotic resistance Wastewater treatment plant Antibiotic resistant bacteria Multiple antibiotic resistance

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Skeleton-based Action Recognition with Non-linear Dependency Modeling and Hilbert-Schmidt Independence Criterion

arXiv

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arXiv 2024年

作者： Chen, Haipeng Yang, Yuheng Lyu, Yingda College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China Public Computer Education and Research Center Jilin University China

Human skeleton-based action recognition has long been an indispensable aspect of artificial intelligence. Current state-of-the-art methods tend to consider only the dependencies between connected skeletal joints, limiting their ability to capture non-linear dependencies between physically distant joints. Moreover, most existing approaches distinguish action classes by estimating the probability density of motion representations, yet the high-dimensional nature of human motions invokes inherent difficulties in accomplishing such measurements. In this paper, we seek to tackle these challenges from two directions: (1) We propose a novel dependency refinement approach that explicitly models dependencies between any pair of joints, effectively transcending the limitations imposed by joint distance. (2) We further propose a framework that utilizes the Hilbert-Schmidt Independence Criterion to differentiate action classes without being affected by data dimensionality, and mathematically derive learning objectives guaranteeing precise recognition. Empirically, our approach sets the state-of-the-art performance on NTU RGB+D, NTU RGB+D 120, and Northwestern-UCLA datasets. © 2024, CC BY-SA.

关键词： Probability density function

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Quantum Circuits for the Heat Equation with Physical Boundary Conditions Via Schrödingerisation

SSRN

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SSRN 2024年

作者： Jin, Shi Liu, Nana Yu, Yue School of Mathematical Sciences Institute of Natural Sciences MOE-LSC Shanghai Jiao Tong University Shanghai200240 China University of Michigan Shanghai Jiao Tong University Joint Institute Shanghai200240 China School of Mathematics and Computational Science Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education National Center for Applied Mathematics in Hunan Xiangtan University Hunan Xiangtan411105 China

This paper explores the explicit design of quantum circuits for quantum simulation of partial differential equations (PDEs) with physical boundary conditions. These equations and/or their discretized forms usually do not evolve via unitary dynamics, thus are not suitable for quantum simulation. Boundary conditions (either time-dependent or independent) make the problem more difficult. To tackle this challenge, the Schrödingerisation method can be employed, which converts linear partial and ordinary differential equations with non-unitary dynamics into systems of Schrödinger-type equations, via the so-called warped phase transformation that maps the equation into one higher dimension. Despite advancements in Schrödingerisation techniques, the explicit implementation of quantum circuits for solving general PDEs, especially with physical boundary conditions, remains underdeveloped. We present two methods for handling the inhomogeneous terms arising from time-dependent physical boundary conditions. One approach utilizes Duhamel's principle to express the solution in integral form and employs linear combination of unitaries (LCU) for coherent state preparation. Another method applies an augmentation to transform the inhomogeneous problem into a homogeneous one. We then apply the quantum simulation technique from [CJL23] to transform the resulting non-autonomous system to an autonomous system in one higher dimension. We provide detailed implementations of these two methods and conduct a comprehensive complexity analysis in terms of queries to the time evolution input oracle. © 2024, The Authors. All rights reserved.

关键词： Boundary conditions

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Comprehensive understanding of microplastics in compost: Ecological risks and degradation mechanisms

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science of the Total Environment 2025年 958卷 178104页

作者： Dong, Dazhuang Gao, Wenjing Li, Lele Liu, Yuxin Dai, Yaodan State Key Laboratory of Water Environment Simulation School of Environment Beijing Normal University Beijing100875 China Department of Earth and Space Science University of Science and Technology of China Anhui Hefei230026 China School of Resources and Environmental Engineering Hefei University of Technology Hefei230009 China

The introduction of microplastics (MPs) into soil ecosystems via compost application has emerged as a critical environmental concern. However, the ecological risks and degradation behavior of MPs in compost remain insufficiently understood. This review addresses these gaps by synthesizing recent findings on MPs in composting systems, focusing on their sources, impacts on compost quality, ecological risks, and degradation mechanisms. MP sources vary significantly across compost matrices-domestic waste, sludge, and agricultural waste‑leading to differences in their types and quantities. MPs adversely impact compost quality by disrupting its physical structure and impairing fertility, aeration, and water retention. Furthermore, their persistence after compost application can result in long-term environmental accumulation, posing risks to soil ecosystems and biological health. This review also explores the aging and degradation of MPs during composting, a complex process influenced by physical, chemical, and biological mechanisms. Finally, we propose future research directions, emphasizing the development of standardized methodologies to assess MP behavior in compost and strategies to mitigate associated risks. These insights contribute to advancing sustainable waste management and environmental protection practices. © 2024 Elsevier B.V.

关键词： Compost Degradation mechanisms Ecological risks Influencing factors Microplastics

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WGAN-GP_Glu: A semi-supervised model based on double generator-Wasserstein GAN with gradient penalty algorithm for glutarylation site identification

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Computers in Biology and Medicine 2025年 184卷 109328-109328页

作者： Ning, Qiao Qi, Zedong Information Science and Technology Dalian Maritime University Liaoning Dalian China The School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China Neusoft Education Technology Group Dalian China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China

As an important post-translational modification, glutarylation plays a crucial role in a variety of cellular functions. Recently, diverse computational methods for glutarylation site identification have been proposed. However, the class imbalance problem due to data noise and uncertainty of non-glutarylation sites remains a great challenge. In this article, we propose a novel semi-supervised learning algorithm, called WGAN-GP_Glu, for identifying reliable non-glutarylation lysine sites from those without glutarylation annotation. WGAN-GP_Glu method is a multi-module framework algorithm, which mainly includes a reliable negative sample selection module, a deep feature extraction module, and a glutarylation site prediction module. In reliable negative sample selection module, we design an improved method of Wasserstein GAN with Gradient Penalty (WGAN-GP), named ReliableWGAN-GP, including three parts, two generators G1, G2 and a discriminator D, which can select reliable non-glutarylation samples from a great number of unlabeled samples. Generator G1 is utilized to generate noise data from unlabeled samples. For generator G2, both the positive sample and the noise data are used as inputs to improve the discriminant capability of discriminator D. Then, convolutional neural network and bidirectional long short-term memory network combined with attention mechanism are utilized to extract deep features for glutarylation samples and reliable non-glutarylation samples. Finally, a glutarylation site prediction module based on the three-layer fully connected layer is designed to make class predictions for samples. The sensitivity, specificity, accuracy and Matthew correlation coefficient of WGAN-GP_Glu on the independent test data set reach 90.58 %, 95.82 %, 94.44 % and 0.8645, respectively, which surpassed the existing methods for glutarylation sites prediction. Therefore, WGAN-GP_Glu can serve as a powerful tool in identifying glutarylation sites and the ReliableWGAN-GP algo

关键词： Gallium nitride

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Troublemaker Learning for Low-Light Image Enhancement

arXiv

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arXiv 2024年

作者： Song, Yinghao Cao, Zhiyuan Xiang, Wanhong Long, Sifan Ge, Hongwei Liang, Yanchun Yang, Bo Wu, Chunguo College of Computer Science and Technology Jilin University Jilin China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Jilin China YGSOFT INC Dalian University of Technology China

Low-light image enhancement (LLIE) restores the color and brightness of underexposed images. Supervised methods suffer from high costs in collecting low/normal-light image pairs. Unsupervised methods invest substantial effort in crafting complex loss functions. We address these two challenges through the proposed TroubleMaker Learning (TML) strategy, which employs normal-light images as inputs for training. TML is simple: we first dim the input and then increase its brightness. TML is based on two core components. First, the troublemaker model (TM) constructs pseudo low-light images from normal images to relieve the cost of pairwise data. Second, the predicting model (PM) enhances the brightness of pseudo low-light images. Additionally, we incorporate an enhancing model (EM) to further improve the visual performance of PM outputs. Moreover, in LLIE tasks, characterizing global element correlations is important because more information on the same object can be captured. CNN cannot achieve this well, and self-attention has high time complexity. Accordingly, we propose Global Dynamic Convolution (GDC) with O(n) time complexity, which essentially imitates the partial calculation process of self-attention to formulate elementwise correlations. Based on the GDC module, we build the UGDC model. Extensive quantitative and qualitative experiments demonstrate that UGDC trained with TML can achieve competitive performance against state-of-the-art approaches on public datasets. The code is available at https://***/Rainbowman0/TML LLIE. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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Lattice Boltzmann simulation reveals supercritical bifurcation in flow mode transitions of power-law fluids in the four-roll mill

arXiv

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arXiv 2025年

作者： Yu, Yuan Jiang, Xiao Gu, Qingqing Lin, Chuandong Zhu, Qingyong Yuan, Hai-Zhuan School of Mathematics and Computational Science Xiangtan University Xiangtan411105 China National Center for Applied Mathematics in Hunan Xiangtan411105 China Hunan Key Laboratory for Computation and Simulation in Science and Engineering Xiangtan University Xiangtan411105 China School of Energy and Power Engineering University of Shanghai for Science and Technology Shanghai200093 China Sino-French Institute of Nuclear Engineering and Technology Sun Yat-sen University Zhuhai519082 China School of Aeronautics and Astronautics Sun Yat-sen University Shenzhen518107 China

The four-roll mill has been traditionally viewed as a device generating simple extensional flow with a central stagnation point. Our systematic investigation using a two-relaxation-time regularized lattice Boltzmann (TRT-RLB) model reveals unexpected richness in the flow physics, identifying two previously unreported supercritical bifurcation modes: a quadrifoliate vortex mode featuring four symmetrical counter-rotating vortices, and a dumbbell-shaped quad-vortex mode where vortices detach from but remain symmetric about the stagnation point. The numerical framework, representing the first successful extension of TRT-RLB method to power-law fluid dynamics, enables comprehensive mapping of flow characteristics across Reynolds numbers (1 ≤ Re ≤ 50), power-law indices (0.7 ≤ n ≤ 1.3), and geometric configurations. The transition from quadrifoliate vortex mode exhibits distinct pathways depending on the power-law index: at relatively small n, the flow undergoes a direct supercritical bifurcation to simple extensional flow, while at relatively large n, it evolves through an intermediate dumbbell-shaped state. Among geometric parameters, the roller radius r emerges as the dominant factor controlling bifurcation points and vortex dimensions, whereas the roller-container gap δ exerts minimal influence on flow regimes. The transitions between flow modes can be precisely characterized through the evolution of vortex dimensions and velocity gradients at the stagnation point, providing quantitative criteria for flow regime identification. These findings enrich our fundamental understanding of bifurcation phenomena in extensional devices and provide quantitative guidelines for achieving desired flow patterns in four-roll mill applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Vortex flow

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Application of Helical Strakes for Suppressing the Flow-Induced Vibration of Two Side-by-Side Long Flexible Cylinders

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China Ocean engineering 2020年第2期34卷 172-184页

作者： XU Wan-hai YANG Meng AI Hua-nan HE Ming LI Mu-han State Key Laboratory of Hydraulic Engineering Simulation and Safety Tianjin UniversityTianjin 300072China School of Hydro Science&Engineering Taiyuan University of TechnologyTaiyuan 030024China

Helical strakes have been widely applied for suppressing the vibration of flexible cylinders undergoing vortexshedding in offshore ***,most research works have concerned on the application of helical strakes for the isolated flexible cylinder subjected to vortex-induced vibration(VIV).The effectiveness of helical strakes attached to side-by-side flexible cylinders in vibration reduction is still *** this paper,the response characteristics of two side-by-side flexible cylinders with and without helical strakes were experimentally investigated in a towing *** configuration of the helical strakes used in the experiment had a pitch of 17.5D and a height of 0.25D(where D is the cylinder diameter),which is usually considered the most effective for VIV suppression of isolated marine risers and *** center-to-center distance of the two cylinders was *** uniform flow with a velocity ranging from 0.05 m/s to 1.0 m/s was generated by towing the cylinder models along the *** results,including the displacement amplitude,the dominant frequency,the dominant mode,and the mean drag force coefficient,were summarized and *** the case where only one cylinder in the two-cylinder system had helical strakes,the experimental results indicated that helical strakes can remarkably reduce the flow-induced vibration(FIV)of the staked *** the case of two straked cylinders in a side-by-side arrangement,it was found that the performance of helical strakes in suppressing the FIV is as good as that for the isolated cylinder.

关键词： side-by-side FIV reduction helical strakes flexible cylinder center-to-center distance

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An Ecological-Network Input–Output Clustering Model for Analyzing CO2 Emission System 7th

An Ecological-Network Input–Output Clustering Model for Ana...

引用

Proceedings of 2022 7th International Conference on Environmental engineering and Sustainable Development (CEESD 2022)

作者： Wang, P.P. Huang, G.H. Li, Y.P. State Key Joint Laboratory of Environmental Simulation and Pollution Control China-Canada Center for Energy Environment and Ecology Research UR-BNU School of Environment Beijing Normal University Beijing100875 China Faculty of Engineering and Applied Science University of Regina ReginaSKS4S 0A2 Canada

ISBN: (纸本)9783031281921

Excessive carbon dioxide (CO2) emission has been challenges to regional sustainable development. In this study, an ecological-network input–output clustering (ENIOC) model is developed to analyze CO2 emission system. ENIOC can recognize the complex characteristics and relationships within CO2 emission system from network and statistical perspectives. ENIOC is applied to Jiangsu. Some major findings are as follows: (i) electricity-heat supply and metal smelting industries are dominant CO2 emission contributors (i.e., 16.4% and 12.3% of the total CO2 emission, respectively);also, they provide plenty of carbon-intensive products for supporting construction and heavy industries;(ii) the CO2 emission system is relatively unstable;optimize the structure of key pathways (e.g., mining → metal smelting → metal products) could mitigate CO2 emission across the supply chain;(iii) CO2 mitigation policy should focus on the sectors in cluster 2 (e.g., chemical, metal product and equipment industries) to reduce their dependency on carbon-intensive products from other sectors;CO2 mitigation policy also should focus on metal smelting and electricity-heat supply industries (i.e., the sectors in cluster 3) to improve their efficiencies of energy use. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Carbon dioxide

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Deep Feature-Based Text Clustering and Its Explanation

Deep Feature-Based Text Clustering and Its Explanation

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International Conference on Data engineering

作者： Renchu Guan Hao Zhang Yanchun Liang Fausto Giunchiglia Lan Huang Xiaoyue Feng Key Laboratory for Symbol Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Changchun China DISI University of Trento Trento Italy

Text clustering is a critical step in text data analysis and has been extensively studied by the text mining community. Most existing text clustering algorithms are based on the bag-of-words model, which faces the high-dimensional and sparsity problems and ignores text structural and sequence information. Deep learning-based models such as convolutional neural networks and recurrent neural networks regard texts as sequences but lack supervised signals and explainable results. In this paper, we propose a deep feature-based text clustering (DFTC) framework that incorporates pretrained text encoders into text clustering tasks. This model, which is based on sequence representations, breaks the dependency on supervision. The experimental results show that our model outperforms classic text clustering algorithms on almost all the considered datasets. In addition, the explanation of the clustering results is significant for understanding the principles of the deep learning approach. Our proposed clustering framework includes an explanation module that can help users understand the meaning and quality of the clustering results. Our code is available at https://***/KEAML-JLU/DeepTextClustering.

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