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Rational surface charge engineering of haloalkane dehalogenase for boosting the enzymatic performance in organic solvent solutions

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Chinese Journal of Chemical engineering 2024年第1期65卷 276-285页

作者： Yin Wu Yan Sun Department of Biochemical Engineering School of Chemical Engineering and Technology and Key Laboratory of Systems Bioengineering and Frontiers Science Center for Synthetic Biology(Ministry of Education)Tianjin UniversityTianjin 300350China

Biocatalysis in organic solvents(OSs)has numerous important applications,but native enzymes in OSs often exhibit limited catalytic ***,we proposed a computation-aided surface charge engineering strategy to improve the catalytic performance of haloalkane dehalogenase DhaA in OSs based on the energetic analysis of substrate binding to the DhaA *** variants with enhanced OS resistance were obtained by replacing negative charged residues on the surface with positive charged residue(Arg).Particularly,a four-substitution variant E16R/E93R/E121R/E257R exhibited the best catalytic performance(five-fold improvement in OS resistance and seven-fold half-life increase in 40%(vol)dimethylsulfoxide).As a result,the overall catalytic performance of the variant could be at least 26 times higher than the wild-type *** spectroscopy and molecular dynamics simulation studies revealed that the residue substitution mainly enhanced OS resistance from four aspects:(a)improved the overall structural stability,(b)increased the hydrophobicity of the local microenvironment around the catalytic triad,(c)enriched the hydrophobic substrate around the enzyme molecule,and(d)lowered the contact frequency between OS molecules and the catalytic *** findings validate that computationaided surface charge engineering is an effective and ingenious rational strategy for tailoring enzyme performance in OSs.

关键词： Surface charge engineering Organic solvent resistance Molecular dynamics simulation Haloalkane dehalogenase

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Compact TE-pass polarizer based on lithium-niobate-on-insulator assisted by indium tin oxide and silicon nitride

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Chinese Physics B 2023年第6期32卷 284-289页

作者：刘家敏张德龙 Department of Opto-electronics and Information Engineering School of Precision Instruments and Opto-electronics EngineeringKey Laboratory of Optoelectronic Information Technology(Ministry of Education)and Key Laboratory of Micro-Opto-Electro-Mechanical Systems(MOEMS)Technology(Ministry of Education)Tianjin UniversityTianjin 300072China

An indium tin oxide(ITO) and silicon nitride(Si_(3)N_(4)) assisted compact TE-pass waveguide polarizer based on lithiumniobate-on-insulator is proposed and numerically *** properly designing the ITO and Si_(3)N_(4) assisted structure and utilizing the epsilon-near-zero effect of ITO,the TM mode is strongly confined in the ITO layer with extremely high loss,while the TE mode is hardly affected and passes through the waveguide with low *** simulation results show that the polarizer has an extinction ratio of 22.5 dB and an insertion loss of 0.8 dB at the wavelength of 1.55 μm,and has an operating bandwidth of about 125 nm(from 1540 nm to 1665 nm) for an extinction ratio of>20 dB and an insertion loss of<0.95 ***,the proposed device exhibits large fabrication *** notably,the device is compact,with a length of only 7.5 μm,and is appropriate for on-chip applications.

关键词： polarizer lithium-niobate-on-insulator integrated optics devices optical waveguide

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Fractal autoencoder with redundancy regularization for unsupervised feature selection

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science China(information sciences) 2025年第2期68卷 89-102页

作者： Meiting SUN Fangyu LI Honggui HAN School of Information Science and Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital Community Ministry of Education Beijing Institute of Artificial Intelligence

Feature selection is a crucial step in data preprocessing because feature selection reduces the dimensionality of data by eliminating irrelevant and redundant features. Since manual labeling is expensive, unsupervised feature selection has received increasing attention in recent years. However, existing unsupervised feature selection methods tend to prioritize selecting highly correlated features over exploring feature diversity. Thus, a regularized fractal autoencoder(RFAE) method is proposed to select informative features in an unsupervised way. Specifically, the fractal autoencoder network extends autoencoders to construct a correspondence neural network and a selection neural network. The correspondence neural network exploits interfeature correlations and the selection neural network selects the informative features. A redundancy regularization strategy consists of a redundancy elimination regularization term based on the dependency between features and a sparse regularization term based on the group lasso. The redundancy regularization strategy eliminates feature subset redundancy and enhances network generalization ability. Extensive experimental results on six publicly available datasets show that the proposed RFAE outperforms the compared methods regarding clustering accuracy and classification accuracy. Moreover, the proposed RFAE achieves acceptable computation efficiency.

关键词： unsupervised feature selection fractal autoencoder correspondence neural network selection neural network redundancy regularization strategy

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Abnormal event detection in surveillance videos through LSTM auto-encoding and local minima assistance

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Discover Internet of Things 2025年第1期5卷 1-25页

作者： Sengonul, Erkan Samet, Refik Abu Al-Haija, Qasem Alqahtani, Ali Alsemmeari, Rayan A. Alghamdi, Bandar Alturki, Badraddin Alsulami, Abdulaziz A. Department of Computer Engineering Ankara University Ankara06100 Turkey Department of Cybersecurity Faculty of Computer & Information Technology Jordan University of Science and Technology PO Box 3030 Irbid22110 Jordan Department of Networks & Communications Engineering College of Computer Science & Information Systems Najran University Najran61441 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

Abnormal event detection in video surveillance is critical for security, traffic management, and industrial monitoring applications. This paper introduces an innovative methodology for anomaly detection in video data, encompassing three primary stages: preprocessing, feature learning, and anomaly detection. We employ background subtraction and noise reduction during preprocessing to refine the data. The feature-learning stage involves training an LSTM autoencoder to capture the essential features of normal video sequences. For anomaly detection, we map video data to a lower-dimensional space (latent code) and compare it against the distribution of codes from normal sequences. We determine regularity scores and identify local minima points exceeding a specified threshold while scrutinizing shadows between adjacent maxima to confirm and pinpoint anomalies. When tested on the CUHK Avenue and UMN datasets, our methodology demonstrated performance with AUCs of 93.8% and 94.1%, respectively, outperforming several baseline models. Our results show the high precision of our method that can detect anomalies, highlighting its potential advantages that it can achieve for enhancing systems of surveillance. © The Author(s) 2025.

关键词： Noise abatement

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FDCPNet:feature discrimination and context propagation network for 3D shape representation

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虚拟现实与智能硬件(中英文) 2025年第1期7卷 83-94页

作者： Weimin SHI Yuan XIONG Qianwen WANG Han JIANG Zhong ZHOU State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Background Three-dimensional(3D)shape representation using mesh data is essential in various applications,such as virtual reality and simulation *** methods for extracting features from mesh edges or faces struggle with complex 3D models because edge-based approaches miss global contexts and face-based methods overlook variations in adjacent areas,which affects the overall *** address these issues,we propose the Feature Discrimination and Context Propagation Network(FDCPNet),which is a novel approach that synergistically integrates local and global features in mesh *** FDCPNet is composed of two modules:(1)the Feature Discrimination Module,which employs an attention mechanism to enhance the identification of key local features,and(2)the Context Propagation Module,which enriches key local features by integrating global contextual information,thereby facilitating a more detailed and comprehensive representation of crucial areas within the mesh *** Experiments on popular datasets validated the effectiveness of FDCPNet,showing an improvement in the classification accuracy over the baseline ***,even with reduced mesh face numbers and limited training data,FDCPNet achieved promising results,demonstrating its robustness in scenarios of variable complexity.

关键词： 3D shape representation Mesh model MeshNet Feature discrimination Context propagation

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Simulation and Experiment of Windage Power Loss of A Shrouded Spiral Bevel Gears under Oil Injection Lubrication

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Chinese Journal of Mechanical engineering 2025年第2期38卷 413-432页

作者： Bo Li Kai Rong Xuyang Zhang Sanmin Wang U-Xuan Tan Xuanyuan Su Laifa Tao School of Mechanical Engineering Northwestern Polytechnical University Department of Engineering Product Development Singapore University of Technology and Design Hangzhou International Innovation Institute Beihang University National Key Laboratory of Science and Technology on Reliability and Environmental Engineering School of Reliability and Systems Engineering Beihang University

During high-speed rotation, the surface of aeronautic spiral bevel gears will generate significant pressure and viscous forces, which will cause a certain amount of windage power loss and reduce the efficiency of the transmission system. Based on the computational fluid dynamics, this paper analyzes the windage power loss of a single spiral bevel gear and a spiral bevel gear pair under oil injection lubrication. In addition, the shroud is used to suppress gear windage loss, and the clearance size and opening angle of the designed shroud are optimized. Finally, by comparing and analyzing the experimental results, the following conclusions were obtained:(1) For a single gear, the speed is the most important factor affecting windage loss, followed by the hand of spiral, and rotation direction;(2) For gear pairs, under oil injection lubrication, the input speed has the greatest impact on windage power loss, followed by the influence of oil injection port speed, temperature and oil injection port pressure;(3) Installing a shroud is an effective method to reduce windage power loss;(4) In the pure air phase, the smaller the clearance between the shroud and the gear surface, and the smaller the radial direction between the shroud and the shaft, the better the effect of reducing windage;(5) In the two-phase flow of oil and gas, it is necessary to design oil drainage holes on the shroud to ensure the smooth discharge of lubricating oil and improve the drag reduction effect.

关键词： Computational fluid dynamics (CFD) Oil injection lubrication Shroud Spiral bevel gear Windage power loss

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Health monitoring by optical fiber sensing technology for rechargeable batteries

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escience 2024年第1期4卷 79-95页

作者： Yi Zhang Yanpeng Li Zezhou Guo Jianbo Li Xiaoyu Ge Qizhen Sun Zhijun Yan Zhen Li Yunhui Huang State Key Laboratory of Material Processing and Die&Mould Technology School of Materials Science and EngineeringHuazhong University of Science and TechnologyWuhanHubei430074China School of Optical and Electronic Information National Engineering Laboratory for Next Generation Internet Access SystemWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhanHubei430074China Henan Key Laboratory of Laser and Opto-electric Information Technology School of Electrical and Information EngineeringZhengzhou UniversityZhengzhouHenan45001China

With the proposal of a“smart battery,”real-time sensing by rechargeable batteries has become progressively more important in both fundamental research and practical ***,many traditional sensing technologies suffer from low sensitivity,large size,and electromagnetic interference problems,rendering them unusable in the harsh and complicated electrochemical environments of *** optical sensor is an alternative approach to realize multiple-parameter,multiple-point measurements ***,it has garnered significant *** analyzing these measured parameters,the state of interest can be decoded to monitor a battery's *** review summarizes current progress in optical sensing techniques for batteries with respect to various sensing parameters,discussing the current limitations of optical fiber sensors as well as directions for their future development.

关键词： Optical fiber sensing Rechargeable battery Real-time sensing Health monitoring

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Extinction Chains Reveal Intermediate Phases Between the Safety and Collapse in Mutualistic Ecosystems

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engineering 2024年第12期43卷 89-98页

作者： Guangwei Wang Xueming Liu Ying Xiao Ye Yuan Linqiang Pan Xiaohong Guan Jianxi Gao Hai-Tao Zhang The MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Digital Manufacturing Equipment and TechnologySchool of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Guangdong HUST Industrial Technology Research Institute Huazhong University of Science and TechnologyDongguan 523808China Guangdong Provincial Engineering Technology Center of Autonomous Unmanned Vessels Dongguan 523808China MOE Key Laboratory of Intelligent Networks and Network Security&State Key Laboratory of Manufacturing Systems Xi’an Jiaotong UniversityXi’an 710049China Tsinghua National Laboratory of Information Science and Technology Department of AutomationTsinghua UniversityBeijing 100084China Department of Computer Science Rensselaer Polytechnic InstituteTroyNY 12180USA

Ecosystems are undergoing unprecedented persistent deterioration due to unsustainable anthropogenic human activities,such as overfishing and deforestation,and the effects of such damage on ecological stability are *** recent advances in experimental and theoretical studies on regime shifts and tipping points,theoretical tools for understanding the extinction chain,which is the sequence of species extinctions resulting from overexploitation,are still lacking,especially for large-scale nonlinear networked *** this study,we developed a mathematical tool to predict regime shifts and extinction chains in ecosystems under multiple exploitation situations and verified it in 26 real-world mutualistic networks of various sizes and *** discovered five phases during the exploitation process:safe,partial extinction,bistable,tristable,and collapse,which enabled the optimal design of restoration strategies for degraded or collapsed *** validated our approach using a 20-year dataset from an eelgrass restoration ***,we also found a specific region in the diagram spanning exploitation rates and competition intensities,where exploiting more species helps increase *** computational tool provides insights into harvesting,fishing,exploitation,or deforestation plans while conserving or restoring the biodiversity of mutualistic ecosystems.

关键词： Complex system Network science Overexploitation Regime shift Metastability

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Enhancing Offensive Language Detection with Data Augmentation and Knowledge Distillation

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Research 2024年第2期6卷 29-40页

作者： Jiawen Deng Zhuang Chen Hao Sun Zhexin Zhang Jincenzi Wu Satoshi Nakagawa Fuji Ren Minlie Huang The CoAl group DCSTInstitute for Artificial InteligenceState Key Lab of Intelligent Technology and SystemsBeijing National Research Center for Information Science and TechnologyTsinghua UniversityBeijing 100084China Graduate School of Information Science&Technology The University of Tokyo.Tokyo 1138654Japan School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengduSichuan611731China

Offensive language detection has received important attention and plays a crucial role in promoting healthy communication on social platforms,as well as promoting the safe deployment of large language *** data is the basis for developing detectors;however,the available offense-related dataset in Chinese is severely limited in terms of data scale and coverage when compared to English *** significantly affects the accuracy of Chinese offensive language detectors in practical applications,especially when dealing with hard cases or out-of-domain *** alleviate the limitations posed by available datasets,we introduce AugCOLD(Augmented Chinese Offensive Language Dataset),a large-scale unsupervised dataset containing 1 million samples gathered by data crawling and model ***,we employ a multiteacher distillation framework to enhance detection performance with unsupervised *** is,we build multiple teachers with publicly accessible datasets and use them to assign soft labels to *** soft labels serve as a bridge for knowledge to be distilled from both AugCOLD and multiteacher to the student network,i.e.,the final offensive *** conduct experiments on multiple public test sets and our well-designed hard tests,demonstrating that our proposal can effectively improve the generalization and robustness of the offensive language detector.

关键词： COLD dealing limitations

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Enhanced Growth Optimizer and Its Application to Multispectral Image Fusion

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Computers, Materials & Continua 2024年第11期81卷 3033-3062页

作者： Jeng-Shyang Pan Wenda Li Shu-Chuan Chu Xiao Sui Junzo Watada College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Information Management Chaoyang University of TechnologyTaichung413310Taiwan Graduate School of Information Production and SystemsWaseda UniversityKitakyushu808-0135Japan

The growth optimizer(GO)is an innovative and robust metaheuristic optimization algorithm designed to simulate the learning and reflective processes experienced by individuals as they mature within the social ***,the original GO algorithm is constrained by two significant limitations:slow convergence and high mem-ory *** restricts its application to large-scale and complex *** address these problems,this paper proposes an innovative enhanced growth optimizer(eGO).In contrast to conventional population-based optimization algorithms,the eGO algorithm utilizes a probabilistic model,designated as the virtual population,which is capable of accurately replicating the behavior of actual populations while simultaneously reducing memory ***,this paper introduces the Lévy flight mechanism,which enhances the diversity and flexibility of the search process,thus further improving the algorithm’s global search capability and convergence *** verify the effectiveness of the eGO algorithm,a series of experiments were conducted using the CEC2014 and CEC2017 test *** results demonstrate that the eGO algorithm outperforms the original GO algorithm and other compact algorithms regarding memory usage and convergence speed,thus exhibiting powerful optimization ***,the eGO algorithm was applied to image *** a comparative analysis with the existing PSO and GO algorithms and other compact algorithms,the eGO algorithm demonstrates superior performance in image fusion.

关键词： Growth optimizer probabilistic model Lévy flight image fusion

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