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Combined proteomic and metabolomic studies on the liver of Amur sturgeon Acipenser schrenckii under titanium dioxide nanoparticle exposure

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Journal of Oceanology and Limnology 2024年第3期42卷 1001-1015页

作者： zhou zhou Ting LIU Jie KONG Zhenxing ZHAO Junquan ZHU Key Laboratory of Aquacultural Biotechnology Ministry of Educationand Key Laboratory of Marine Biotechnology of Zhejiang ProvinceCollege of Marine SciencesNingbo UniversityNingbo 315000China Guizhou Fisheries Research Institute Guizhou Academy of Agriculture SciencesGuiyang 550000China

Nanomaterials,particularly titanium dioxide nanoparticles(TiO_(2)-NPs),are extensively utilized across various industries.However,their environmental release has raised concerns regarding their potential ecological and environmental impacts.The reproductive toxicity of TiO_(2)-NPs in fish species has attracted considerable attention,yet conflicting research outcomes have been reported.We investigated the effects of TiO_(2)-NPs exposure on the liver of juvenile Amur sturgeon Acipenser schrenckii using label-free proteomic and untargeted metabolomic analyses.The experiment included a control group and three groups exposed to different concentrations of TiO_(2)-NPs(low,TL;medium,TM;high,TH).Compared to the control group,9,19,and 25 proteins and 35,73,and 158 metabolites were differentially expressed in the TH,TM,and TL TiO_(2)-NP-exposed groups,respectively.The differentially expressed genes(DEGs)were enriched in the Kyoto Encyclopedia for Genes and Genomes(KEGG)pathways related to glycolysis and gluconeogenesis.Moreover,among the 126 correlated proteins,the most enriched pathways were associated with endocytosis and protein processing in the endoplasmic reticulum.Notably,syringic acid was significantly downregulated across all three TiO_(2)-NP-exposed groups.To obtain a comprehensive overview of the TiO_(2)-NP-induced expression changes,a co-regulated network of proteins and metabolites associated with TiO_(2)-NPs exposure was constructed.Exposure to TiO_(2)-NPs led to enrichment and alteration of pathways related to immune responses,including endocytosis,protein processing in the endoplasmic reticulum,and peroxisome proliferator-activated receptor(PPAR)signaling.In conclusion,our findings indicate that exposure to TiO_(2)-NPs might disrupt glucose metabolism and induce immune responses,thus contributing to our understanding of the environmental impacts of nanomaterials and highlighting the need for further research and development of potential mitigation strategies.

关键词： TiO_(2)-nanoparticle exposure proteomics metabolomics glucose metabolism immune response

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Intelligent feedforward gust alleviation based on neural network

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Chinese Journal of Aeronautics 2024年第3期37卷 116-132页

作者： Yitao zhou Zhigang WU Chao YANG School of Aeronautic Science and Engineering Beihang UniversityBeijing 100191China

This paper proposes a neural network-based intelligent feedforward gust alleviation framework,which includes a neural network identification model and a neural network controller.A neural network training dataset is formed by collecting flight data and the gust data encountered during the aircraft flight.A neural network identification model is first trained to accurately predict the aircraft’s output.Then,based on the output of the identification model and the collected flight data,the parameters of the time-delay neural network controller are obtained through a learning process.The simulation results show that the designed intelligent controller has good gust alleviation effects for both continuous turbulence excitation and discrete gust excitation.For example,when the aircraft is 40000 kg and the flight speed is 0.81Ma,the controller achieves a 67.82%reduction in wingtip acceleration and a 35.90%reduction in center of mass acceleration under continuous turbulence excitation.When considering the measurement uncertainties,such as noise existing in the collected data,the trained controller can still achieve an acceptable gust alleviation effect.Finally,considering a flight in which the aircraft mass is constantly changing,the intelligent controller,which continuously learns from new flight data,maintains a good gust alleviation effect throughout the flight.

关键词： Gust alleviation Intelligent control Feedforward control Neural networks Time-varying aircraft

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Frictional contact analysis of a rigid solid with periodic surface sliding on the thermoelectric material

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Applied Mathematics and Mechanics(English Edition) 2024年第1期45卷 179-196页

作者： Yali ZHANG Yueting zhou Shenghu DING School of Mathematics and Statistics Ningxia UniversityYinchuan 750021China School of Aerospace Engineering and Applied Mechanics Tongji UniversityShanghai 200092China

Understanding and characterizing rough contact and wavy surfaces are essential for developing effective strategies to mitigate wear,optimize lubrication,and enhance the overall performance and durability of mechanical systems.The sliding friction contact problem between a thermoelectric(TE)half-plane and a rigid solid with a periodic wavy surface is the focus of this investigation.To simplify the problem,we utilize mixed boundary conditions,leading to a set of singular integral equations(SIEs)with the Hilbert kernels.The analytical solutions for the energy flux and electric current density are obtained by the variable transform method in the context of the electric and temperature field.The contact problem for the elastic field is transformed into the second-kind SIE and solved by the Jacobi polynomials.Notably,the smoothness of the wavy contact surface ensures that there are no singularities in the surface contact stress,and ensures that it remains free at the contact edge.Based on the plane strain theory of elasticity,the analysis primarily examines the correlation between the applied load and the effective contact area.The distribution of the normal stress on the surface with or without TE loads is discussed in detail for various friction coefficients.Furthermore,the obtained results indicate that the in-plane stress decreases behind the trailing edge,while it increases ahead of the trailing edge when subjected to TE loads.

关键词： wavy surface periodic contact thermoelectric(TE)material Hilbert integral kernel

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Investigation on UV-curing Reprocessable Thermosets Bearing Hindered Urea Bonds and Their Composites with Modified Zinc Oxide Nanoparticles

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Chinese Journal of Polymer Science 2024年第6期42卷 751-765页

作者： Jun-Hao zhou Li-Ming Tang Key Laboratory of Advanced Materials of Ministry of Education of China Department of Chemical EngineeringTsinghua UniversityBeijing100084China

In this study,a series of hindered urea bond(HUB)containing polyurethane-urea methacrylate prepolymers and a none HUB containing polyurethane methacrylate prepolymer were prepared using isobornyl methacrylate as the reactive diluent via one-pot procedure.The prepolymers were characterized fully by various techniques.Then,their thermosets were fabricated via UV curing in presence of a photo initiator,and their mechanical property and thermal behavior were investigated and compared.Different from the none HUB containing thermoset,the HUB containing thermosets(defined as PUT)could be recycled and reprocessed by hot press under relatively mild conditions with high recovery ratio of mechanical property.Furthermore,zinc oxide(ZnO)nanoparticles were modified with 3-(trimethoxysilyl)propyl methacrylate and the modified ZnO(defined as ZnO-TPM)was dispersed and polymerized into PUT matrix to prepare their nanocomposites.The influence of ZnO-TPM on the mechanical performance of the composites was evaluated,which indicated that the Young’s modulus and tensile strength increased gradually to the maximum values at ZnO-TPM content of 1 wt%and then decreased.The composites also displayed good reprocessability with improved recovery ratio compared to the pure PUT sample.In addition,the composite materials exhibited strong UV absorption capacity,implying their potential application in the circumstance where UV-shielding was required.

关键词： Polyurethane-urea methacrylate Hindered urea bond ZnO nanoparticles Reprocessing UV curing

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Audio Mixing Inversion via Embodied Self-supervised Learning

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Machine Intelligence Research 2024年第1期21卷 55-62页

作者： Haotian zhou Feng Yu Xihong Wu Department of AI Music and Music Information Technology Central Conservatory of MusicBeijing 100031China Laboratory of Music Artificial Intelligence Laboratory of Philosophy and Social SciencesMinistry of EducationBeijing 100031China School of Intelligence Science and Technology Peking UniversityBeijing 100871China

Audio mixing is a crucial part of music production.For analyzing or recreating audio mixing,it is of great importance to conduct research on estimating mixing parameters used to create mixdowns from music recordings,i.e.,audio mixing inversion.However,approaches of audio mixing inversion are rarely explored.A method of estimating mixing parameters from raw tracks and a stereo mixdown via embodied self-supervised learning is presented.In this work,several commonly used audio effects including gain,pan,equalization,reverb,and compression,are taken into consideration.This method is able to learn an inference neural network that takes a stereo mixdown and the raw audio sources as input and estimate mixing parameters used to create the mixdown by iteratively sampling and training.During the sampling step,the inference network predicts a set of mixing parameters,which is sampled and fed to an audio-processing framework to generate audio data for the training step.During the training step,the same network used in the sampling step is optimized with the sampled data generated from the sampling step.This method is able to explicitly model the mixing process in an interpretable way instead of using a black-box neural network model.A set of objective measures are used for evaluation.The experimental results show that this method has better performance than current state-of-the-art methods.

关键词： Audio mixing inversion intelligent audio mixing self-supervised learning audio signal processing deep learning

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TendiffPure:a convolutional tensor-train denoising diffusion model for purification

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Frontiers of Information Technology & Electronic Engineering 2024年第1期25卷 160-169页

作者： Mingyuan BAI Derun zhou Qibin ZHAO RIKEN AIP Tokyo 1030027Japan School of Environment and Society Tokyo Institute of TechnologyTokyo 1528550Japan

Diffusion models are effective purification methods,where the noises or adversarial attacks are removed using generative approaches before pre-existing classifiers conducting classification tasks.However,the efficiency of diffusion models is still a concern,and existing solutions are based on knowledge distillation which can jeopardize the generation quality because of the small number of generation steps.Hence,we propose TendiffPure as a tensorized and compressed diffusion model for purification.Unlike the knowledge distillation methods,we directly compress U-Nets as backbones of diffusion models using tensor-train decomposition,which reduces the number of parameters and captures more spatial information in multi-dimensional data such as images.The space complexity is reduced from O(N^(2))to O(NR^(2))with R≤4 as the tensor-train rank and N as the number of channels.Experimental results show that TendiffPure can more efficiently obtain high-quality purification results and outperforms the baseline purification methods on CIFAR-10,Fashion-MNIST,and MNIST datasets for two noises and one adversarial attack.

关键词： Diffusion models Tensor decomposition Image denoising

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Cybersecurity Landscape on Remote State Estimation:A Comprehensive Review

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 851-865页

作者： Jing zhou Jun Shang Tongwen Chen the Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 1H9Canada the Department of Control Science and Engineering and Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 200092China

Cyber-physical systems(CPSs)have emerged as an essential area of research in the last decade,providing a new paradigm for the integration of computational and physical units in modern control systems.Remote state estimation(RSE)is an indispensable functional module of CPSs.Recently,it has been demonstrated that malicious agents can manipulate data packets transmitted through unreliable channels of RSE,leading to severe estimation performance degradation.This paper aims to present an overview of recent advances in cyber-attacks and defensive countermeasures,with a specific focus on integrity attacks against RSE.Firstly,two representative frameworks for the synthesis of optimal deception attacks with various performance metrics and stealthiness constraints are discussed,which provide a deeper insight into the vulnerabilities of RSE.Secondly,a detailed review of typical attack detection and resilient estimation algorithms is included,illustrating the latest defensive measures safeguarding RSE from adversaries.Thirdly,some prevalent attacks impairing the confidentiality and data availability of RSE are examined from both attackers'and defenders'perspectives.Finally,several challenges and open problems are presented to inspire further exploration and future research in this field.

关键词： Cyber-attacks Kalman filtering remote state estimation unreliable transmission channels

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Physiological and psychological responses to tended plant communities with varying color characteristics

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Journal of Forestry Research 2024年第1期35卷 183-201页

作者： Siyuan Zheng Yanzhen zhou Haiyan Qu School of Architecture and Urban Planning Shenyang Jianzhu UniversityShenyang 110168People’s Republic of China

Exposure to plants has been reported to promote health and reduce stress,and plant color has direct impacts on physical and mental health.We used images of common types of tended plant communities in Shenyang,China,with combinations of yellow,green,and red foliage,as experimental stimuli.A total of 27 images were used as visual stimuli.We used electroencephalography to measureαwave activity(8-13 Hz)in 40 subjects while they viewed visual stimuli.These data were combined with subjective questionnaire data to analyze the relaxing effect of images of tended plant communities with different color types and proportions on people.The results revealed that,although there were slight differences between the electroencephalography and psychological findings,women were significantly more relaxed than men after viewing the images.Physiological and psychological responses varied with the types and proportions of colors in the tended plant communities:those of foliage with combinations of two or three colors induced stronger responses than images with a single color.Specifically,(1)for one-color plant communities,green or yellow plant communities induced a stronger relaxation effect than red plant communities;(2)for two-color plant communities,the optimal color proportion was 55%+45%,and the green+yellow and green+red color combinations induced a stronger relaxation effect;(3)for three-color plant communities,the relaxation effect was strongest when the color proportion was 55%green+25%yellow+20%red.These data would provide a plant color matching in future plant landscape design,which may be helpful for creating healthy and relaxing environments.

关键词： Plant community Color type Color ratio Electroencephalography Subjective questionnaire

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Binary Hybrid Artificial Hummingbird with Flower Pollination Algorithm for Feature Selection in Parkinson’s Disease Diagnosis

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Journal of Bionic Engineering 2024年第2期21卷 1003-1021页

作者： Liuyan Feng Yongquan zhou Qifang Luo College of Artificial Intelligence Guangxi University for NationalitiesNanning530006China Xiangsihu College of Guangxi University for Nationalities Nanning532100GuangxiChina Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis Nanning530006China

Parkinson’s disease is a neurodegenerative disorder that inflicts irreversible damage on humans.Some experimental data regarding Parkinson’s patients are redundant and irrelevant,posing significant challenges for disease detection.Therefore,there is a need to devise an effective method for the selective extraction of disease-specific information,ensuring both accuracy and the utilization of fewer features.In this paper,a Binary Hybrid Artificial Hummingbird and Flower Pollination Algorithm(FPA),called BFAHA,is proposed to solve the problem of Parkinson’s disease diagnosis based on speech signals.First,combining FPA with Artificial Hummingbird Algorithm(AHA)can take advantage of the strong global exploration ability possessed by FPA to improve the disadvantages of AHA,such as premature convergence and easy falling into local optimum.Second,the Hemming distance is used to determine the difference between the other individuals in the population and the optimal individual after each iteration,if the difference is too significant,the cross-mutation strategy in the genetic algorithm(GA)is used to induce the population individuals to keep approaching the optimal individual in the random search process to speed up finding the optimal solution.Finally,an S-shaped function converts the improved algorithm into a binary version to suit the characteristics of the feature selection(FS)tasks.In this paper,10 high-dimensional datasets from UCI and the ASU are used to test the performance of BFAHA and apply it to Parkinson’s disease diagnosis.Compared with other state-of-the-art algorithms,BFAHA shows excellent competitiveness in both the test datasets and the classification problem,indicating that the algorithm proposed in this study has apparent advantages in the field of feature selection.

关键词： Artificial Hummingbird Algorithm Flower pollination algorithm Feature selection Parkinson’s disease Meta-heuristic

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Construction Activity Analysis of Workers Based on Human Posture Estimation Information

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Engineering 2024年第2期33卷 225-236页

作者： Xuhong zhou Shuai Li Jiepeng Liu zhou Wu Yohchia Frank Chen School of Civil Engineering Chongqing UniversityChongqing 400044China School of Automation Chongqing UniversityChongqing 400044China Department of Civil Engineering The Pennsylvania State UniversityMiddletownPA 17057USA

Identifying workers’construction activities or behaviors can enable managers to better monitor labor efficiency and construction progress.However,current activity analysis methods for construction workers rely solely on manual observations and recordings,which consumes considerable time and has high labor costs.Researchers have focused on monitoring on-site construction activities of workers.However,when multiple workers are working together,current research cannot accu rately and automatically identify the construction activity.This research proposes a deep learning framework for the automated analysis of the construction activities of multiple workers.In this framework,multiple deep neural network models are designed and used to complete worker key point extraction,worker tracking,and worker construction activity analysis.The designed framework was tested at an actual construction site,and activity recognition for multiple workers was performed,indicating the feasibility of the framework for the automated monitoring of work efficiency.

关键词： Pose estimation Activity analysis Object tracking Construction workers Automatic systems

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