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Science China Materials 2025年第6期68卷 1689-1701页

作者： Ma, Ruijie Luo, Zhenghui Zhang, Youdi Zhan, Lingling Jia, Tao Cheng, Pei Yan, Cenqi Fan, Qunping Liu, Shengjian Ye, Long Zhang, Guangye Xu, Xiaopeng Gao, Wei Wu, Yue Wu, Jiaying Li, Yuxiang Liu, Yuhang Liu, Feng Song, Jiali Chen, Haiyang Chen, Weijie Zhang, Xin Liu, Yahui Yuan, Jun Liu, Quan Kan, Zhipeng Yin, Hang Li, Xiaojun Ma, Yunlong Deng, Dan Zhu, Lei Huo, Yong Fan, Baobin Fu, Huiting Liao, Xunfan Hu, Hanlin Li, Chao Yu, Runnan Hu, Huawei Yao, Zhaoyang Cai, Yunhao Qian, Deping Cui, Yong Yao, Huifeng Xu, Bowei Kan, Bin Gao, Ke Duan, Chunhui Hu, Xiaotian Sun, Huiliang School of Chemistry and Chemical Engineering Gannan Normal University Ganzhou341000 China Joint Laboratory for Photonic-Thermal-Electrical Energy Materials and Devices The Hong Kong Polytechnic University 999077 Hong Kong Guangdong Provincial Key Laboratory of New Energy Materials Service Safety Shenzhen Key Laboratory of New Information Display and Storage Materials College of Materials Science and Engineering Shenzhen University Shenzhen518060 China Key Laboratory of Advanced Green Functional Materials College of Chemistry Changchun Normal University Changchun130032 China Key Laboratory of Organosilicon Chemistry and Material Technology Ministry of Education Zhejiang Key Laboratory of Organosilicon Material Technology College of Material Chemistry and Chemical Engineering Hangzhou Normal University Hangzhou311121 China School of Optoelectronic Engineering Guangdong Polytechnic Normal University Guangzhou510665 China College of Polymer Science and Engineering National Key Laboratory of Advanced Polymer Materials Sichuan University Chengdu610065 China State Key Laboratory for Mechanical Behavior of Materials Xi’an Jiaotong University Xi’an710049 China Guangzhou510006 China Tianjin University Tianjin300350 China College of New Materials and New Energies Shenzhen Technology University Shenzhen518118 China College of Chemical Engineering Sichuan University Chengdu610065 China Xiamen Key Laboratory of Optoelectronic Materials and Advanced Manufacturing Institute of Luminescent Materials and Information Displays College of Materials Science and Engineering Huaqiao University Xiamen361021 China Laboratory of Advanced Optoelectronic Materials College of Chemistry Chemical Engineering and Materials Science Soochow University Suzhou215123 China Guangzhou511400 China School of Materials Science and Engineering Xi’an University of Science and Technology Xi’an710054 China Shanghai200240 China Hangzhou International Innovation Institute Beihang U

Organic solar cells (OSCs) have experienced remarkable performance progress up to 20% benchmark power conversion efficiency (PCE) in past years. Considering the © Science China Press 2025.

关键词： Product design

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Energy Dispatch Scheme on Ship Integrated Energy System with Photovoalatic and CHP

Energy Dispatch Scheme on Ship Integrated Energy System with...

引用

Chinese automation Congress (CAC)

作者： Yuxin Zhang Qihe Shan Tieshan Li Fei Teng Navigation College Dalian Maritime University Dalian China College of Automation Engineering University of Electronic Science and Technology Chengdu China Marine Electrical Engineering College Dalian Maritime University Dalian China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

Maritime industry with huge energy consumption and pollution, has become an urgent problem, which promotes us to consider the ship integrated energy system (IES) [1] . A kind of ship IES has been concerned in this paper, which consists of electrical and heating system. An energy dispatch scheme has been proposed as well, which promotes the coupling efficiency of electrical and heating energy for ships. This paper integrates combined heating and power device (CHPD), energy storage system (ESS), renewable generation device like photovoltaic in the energy system (ES) to ensure the eco-friendly sailing. Moreover, a penalty coefficient, relating to the state and outputs of ESS, has been considered in the dispatch scheme which can promote cost function to be more flexible. Through the proposed power dispatch scheme, the heating and electrical power outputs of each generation device during every period of time can be dispatched efficiently. Corresponding objective function with correlation parameters can be solved by dynamic programming (DP). Finally, the simulation illustrates the effectiveness of the proposed scheme.

关键词： Cogeneration Simulation Programming Real-time systems Safety Marine vehicles Resistance heating

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Search for light dark matter from atmosphere in PandaX-4T

arXiv

引用

arXiv 2023年

作者： Ning, Xuyang Abdukerim, Abdusalam Bo, Zihao Chen, Wei Chen, Xun Cheng, Chen Cheng, Zhaokan Cui, Xiangyi Fan, Yingjie Fang, Deqing Fu, Changbo Fu, Mengting Geng, Lisheng Giboni, Karl Gu, Linhui Guo, Xuyuan Han, Chencheng Han, Ke He, Changda He, Jinrong Huang, Di Huang, Yanlin Huang, Junting Huang, Zhou Hou, Ruquan Hou, Yu Ji, Xiangdong Ju, Yonglin Li, Chenxiang Li, Jiafu Li, Mingchuan Li, Shuaijie Li, Tao Lin, Qing Liu, Jianglai Lu, Congcong Lu, Xiaoying Luo, Lingyin Luo, Yunyang Ma, Wenbo Ma, Yugang Mao, Yajun Meng, Yue Qi, Ningchun Qian, Zhicheng Ren, Xiangxiang Shaheed, Nasir Shang, Xiaofeng Shao, Xiyuan Shen, Guofang Si, Lin Sun, Wenliang Tan, Andi Tao, Yi Wang, Anqing Wang, Meng Wang, Qiuhong Wang, Shaobo Wang, Siguang Wang, Wei Wang, Xiuli Wang, Zhou Wei, Yuehuan Wu, Mengmeng Wu, Weihao Xia, Jingkai Xiao, Mengjiao Xiao, Xiang Xie, Pengwei Yan, Binbin Yan, Xiyu Yang, Jijun Yang, Yong Yao, Yukun Yu, Chunxu Yuan, Ying Yuan, Zhe Zeng, Xinning Zhang, Dan Zhang, Minzhen Zhang, Peng Zhang, Shibo Zhang, Shu Zhang, Tao Zhang, Wei Zhang, Yang Zhang, Yingxin Zhang, Yuanyuan Zhao, Li Zheng, Qibin Zhou, Jifang Zhou, Ning Zhou, Xiaopeng Zhou, Yong Zhou, Yubo Su, Liangliang Wu, Lei Shanghai Key Laboratory for Particle Physics and Cosmology Shanghai200240 China Shanghai Jiao Tong University Sichuan Research Institute Chengdu610213 China School of Physics Sun Yat-Sen University Guangzhou510275 China Sino-French Institute of Nuclear Engineering and Technology Sun Yat-Sen University Zhuhai519082 China Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai200240 China Department of Physics Yantai University Yantai264005 China Institute of Modern Physics Fudan University Shanghai200433 China School of Physics Peking University Beijing100871 China School of Physics Beihang University Beijing102206 China International Research Center for Nuclei and Particles in the Cosmos Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang University Beijing100191 China School of Physics and Microelectronics Zhengzhou University Henan Zhengzhou450001 China Yalong River Hydropower Development Company Ltd. 288 Shuanglin Road Chengdu610051 China School of Medical Instrument and Food Engineering University of Shanghai for Science and Technology Shanghai200093 China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Physics University of Maryland College ParkMD20742 United States State Key Laboratory of Particle Detection and Electronics University of Science and Technology of China Hefei230026 China Department of Modern Physics University of Science and Technology of China Hefei230026 China Research Center for Particle Science and Technology Institute of Frontier and Interdisciplinary Science Shandong University Shandong Qingdao266237 China Key Laboratory of Particle Physics and Particle Irradiation Ministry of Education Shandong University Shandong Qingdao266237 China School of Physics Nankai University Tianjin300071 China SJTU Paris Elite Institute of Technology Shanghai Jiao Tong University Shanghai200240 China School of Physics and Astronomy Sun Y

We report a search for light dark matter produced through the cascading decay of η mesons, which are created as a result of inelastic collisions between cosmic rays and Earth's atmosphere. We introduces a new and general framework, publicly accessible, designed to address boosted dark matter specifically, with which a full and dedicated simulation including both elastic and quasi-elastic processes of Earth attenuation effect on the dark matter particles arriving at the detector is performed. In the PandaX-4T commissioning data of 0.63 tonne·year exposure, no significant excess over background is observed. The first constraints on the interaction between light dark matter generated in the atmosphere and nucleus through a light scalar mediator are obtained. The lowest excluded cross-section is set at 5.9 × 10−37cm2 for dark matter mass of 0.1 MeV/c2 and mediator mass of 300 MeV/c2. The lowest upper limit of η to dark matter decay branching ratio is 1.6 × 10−7 Copyright © 2023, The Authors. All rights reserved.

关键词： Earth atmosphere

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Is the aspect ratio of cells important in deep learning? A robust comparison of deep learning methods for multi-scale cytopathology cell image classification: From convolutional neural networks to visual transformers

arXiv

引用

arXiv 2021年

作者： Liu, Wanli Li, Chen Rahaman, Md Mamunur Jiang, Tao Sun, Hongzan Wu, Xiangchen Hu, Weiming Chen, Haoyuan Sun, Changhao Yao, Yudong Grzegorzek, Marcin Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang110169 China School of Control Engineering Chengdu University of Information Technology Chengdu610225 China Shengjing Hospital China Medical University Shenyang110001 China Suzhou Ruiguan Technology Company Ltd. Suzhou215000 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110169 China Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07030 United States Institute of Medical Informatics University of Luebeck Luebeck Germany

Cervical cancer is a very common and fatal type of cancer in women. Cytopathology images are often used to screen for this cancer. Given that there is a possibility that many errors can occur during manual screening, a computer-aided diagnosis system based on deep learning has been developed. Deep learning methods require a fixed dimension of input images, but the dimensions of clinical medical images are inconsistent. The aspect ratios of the images suffer while resizing them directly. Clinically, the aspect ratios of cells inside cytopathological images provide important information for doctors to diagnose cancer. Therefore, it is difficult to resize directly. However, many existing studies have resized the images directly and have obtained highly robust classification results. To determine a reasonable interpretation, we have conducted a series of comparative experiments. First, the raw data of the SIPaKMeD dataset are pre-processed to obtain standard and scaled datasets. Then, the datasets are resized to 224 × 224 pixels. Finally, 22 deep learning models are used to classify the standard and scaled datasets. The results of the study indicate that deep learning models are robust to changes in the aspect ratio of cells in cervical cytopathological images. This conclusion is also validated via the Herlev dataset. Copyright © 2021, The Authors. All rights reserved.

关键词： Aspect ratio

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Avian influenza viruses suppress innate immunity by inducingtrans-transcriptional readthrough via SSU72

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Cellular & Molecular Immunology 2022年第6期19卷 702-714页

作者： Yan Zhao Fengming Huang Zhen Zou Yuhai Bi Yang Yang Cong Zhang Qiang Liu Daozhen Shang Yiwu Yan Xiangwu Ju Song Mei Peng Xie Xiao Li Mingyao Tian Shuguang Tan Huijun Lu Zongsheng Han Kangtai Liu Yuqing Zhang Junbo Liang Zhu Liang Qingchao Zhang Jiahui Chang William JLiu Cong Feng Tanshi Li Michael Q.Zhang Xiaoyue Wang George FGao Yingxia Liu Ningyi Jin Chengyu Jiang State Key Laboratory of Medical Molecular Biology Institute of Basic Medical SciencesChinese Academy of Medical SciencesDepartment of BiochemistryPeking Union Medical CollegeBeijing 100005China Shenzhen Key Laboratory of Pathogen and Immunity State Key Discipline of Infectious DiseaseSecond Hospital Affiliated with Southern University of Science and TechnologyShenzhen Third People’s HospitalShenzhenGuangdong 518112China CAS Key Laboratory of Pathogenic Microbiology and Immunology Institute of MicrobiologyCenter for Influenza Research and Early-warning(CASCIRE)CAS-TWAS Center of Excellence for Emerging Infectious Diseases(CEEID)Chinese Academy of SciencesBeijing 100101China School of Life Science University of Science and Technology of ChinaHefeiAnhui 230027China Department of Automation Tsinghua UniversityBeijing 100084China Genetic Engineering Laboratory Institute of Military VeterinaryAcademy of Military Medical SciencesChangchunJilin 130062China State Key Laboratory of Biotherapy/Collaborative Innovation Center for Biotherapy West China HospitalSichuan UniversityChengduSichuan 610000China State Key Laboratory of Medical Molecular Biology Department of Biochemistry and Center for BioinformaticsInstitute of Basic Medical SciencesChinese Academy of Medical SciencesSchool of Basic MedicinePeking Union Medical CollegeBeijing 100005China National Institute for Viral Disease Control and Prevention Chinese Centers for Disease Control and PreventionBeijing 102206China Department of Emergency General Hospital of the PLAHaidian DistrictBeijing 100853China Department of Biological Sciences and Center for Systems Biology The University of Texas at DallasRichardsonTXUSA

Innate immunity plays critical antiviral roles. The highly virulent avian influenza viruses (AIVs) H5N1, H7N9, and H5N6 can betterescape host innate immune responses than the less virulent seasonal H1N1 virus. Here, we report a mechanism by whichtranscriptional readthrough (TRT)-mediated suppression of innate immunity occurs post AIV infection. By using cell lines, mouselungs, and patient PBMCs, we showed that genes on the complementary strand (“trans” genes) influenced by TRT were involved inthe disruption of host antiviral responses during AIV infection. The trans-TRT enhanced viral lethality, and TRT abolishmentincreased cell viability and STAT1/2 expression. The viral NS1 protein directly bound to SSU72, and degradation of SSU72 inducedTRT. SSU72 overexpression reduced TRT and alleviated mouse lung injury. Our results suggest that AIVs infection induce TRT byreducing SSU72 expression, thereby impairing host immune responses, a molecular mechanism acting through the NS1-SSU72-trans-TRT-STAT1/2 axis. Thus, restoration of SSU72 expression might be a potential strategy for preventing AIV pandemics.

关键词： AIV infection TRT SSU72 NS1 Immune escape

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Model Identification of 2-DOF Lower Limb Exoskeleton with Neighborhood Field Optimization Algorithm ⁎

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IFAC-PapersOnLine 2020年第2期53卷 8704-8709页

作者： Zhenlei Chen Huiyu Xiong Xinran Wang Qing Guo Yan Shi Yao Yan Gan Liu Dan Jiang School of Aeronautics and Astronautics University of Electronic Science and Technology of China and Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu 611731 China Glasgow College University of Electronic Science and Technology of China Chengdu 611731 China School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu 611731 China

For the lower limb exoskeleton, the system control performance and stability of human-robot coordinated movement is often degraded by some model parametric uncertainties. To address this problem, the model parameter identification method based on Neighborhood Field Optimization (NFO) algorithm is proposed to obtain the accurate model parameters of 2-DOF exoskeleton, which guides the model-based controller design. For the 2-DOF lower limb exoskeleton experimental platform, the model is constructed by Lagrange equation. Meanwhile, the excitation trajectory with the setting mechanical constraints is designed by NFO to guarantee the identification accuracy. Meanwhile, the Huber fitness function is adopted to suppress the influence of the disturbance points in sampling dataset with respect to the identification accuracy. Finally, the NFO algorithm with the Huber fitness function is verified by 2-DOF lower limb exoskeleton experimental platform.

关键词： Lower limb exoskeleton Model identification Excitation trajectory Neighborhood Field Optimization Huber fitness function

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Prediction of Fish School based on Neural Network and Gray Prediction

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Journal of Physics: Conference Series 2021年第4期1802卷

作者： MuZhen Cai Bo Dong Yuhui Xiao College of Software Harbin Engineering University Harbin Heilongjiang 150000 School of Information Science and Engineering Dalian PolyTechnic University Dalian Liaoning 116034 The College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu Sichuan 610059

Rising ocean temperatures affect the distribution of fish. The livelihoods of many small Scottish fishing companies have also been affected. We designed a fish migration prediction model, a system that predicts fish migration and helps small fishing companies make decisions. First, we creatively proposed the neural network algorithm modified to establish habitat evaluation model based on previous environmental data. Then, the gray prediction algorithm is used to predict the environmental data of each water area in the next 50 years, and the best habitat location of each year is evaluated by the evaluation model based on the environmental data. The migration route of the fish can be obtained by summarizing the best habitats each year in chronological order. We use the knowledge of graph theory to establish a 'fishing judgment model' to determine whether small fisheries companies can catch fish in their fishing range. Then we use a triple nested loop to establish a continuous time calculation model, and use this model to discuss the continuous fishing time of a fishing company located in a certain place from a certain year in the future

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Broad Learning System-Based Adaptive Optimal Course-Keeping Control of Marine Surface Vessel

Broad Learning System-Based Adaptive Optimal Course-Keeping ...

引用

IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Yuzhuo Ma Tieshan Li C. L. Philip Chen Liang'en Yuan Navigation College Dalian Maritime University Dalian China School of Automation Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Science and Technology University of Macau Macau China

ISBN: (数字)9781728151694

ISBN: (纸本)9781728151700

In this paper, a novel optimal control method by broad learning system (BLS)-based adaptive dynamic programming is proposed. This method is used to course-keeping control of ship under the conditions of unknown system dynamics, energy saving and reduced equipment waste. First, it is necessary to identify the unknown nonlinear dynamics in the ship's course keeping system, so a model network is established by BLS. Then, a BLS based optimal control scheme is proposed, the data used for the BLSs training is composed of current data and recorded data. The connection weights of the approximator is obtained by real system without need of iteration. Therefore, compared with the traditional adaptive dynamic programming (ADP) algorithm that requires multiple iterations, BLS-based ADP prove the effectiveness and high performance of the proposed optimal control law for course-keeping in ship autonomous driving.

关键词： Adaptation models Adaptive systems Heuristic algorithms Optimal control Approximation algorithms Dynamic programming Marine vehicles

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Review of Radon and Its Progeny Measurement technology in Environmental Gamma Measurement

引用

Journal of Physics: Conference Series 2021年第1期1739卷

作者： Chenhao Zeng Jinge Zhou Wanchang Lai Xiaojie Feng Jiehao Chen Yongping Yu College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu 610059 Sichuan China Dept. of Military Installation Army Logistical University of PLA Chongqing 401311 China

The contribution of radon and its progeny to environmental gamma dose should not be ignored. In recent years, a large number of scholars all over the world have studied the migration behavior of radon and its progeny in the environment, and analyzed their impact on environmental gamma dose. Especially in uranium (thorium) bearing mining areas and other special areas, the influence of radon and its progeny is more prominent, which poses an important challenge to radiation monitoring. This paper summarizes the analysis and processing technology of radon and its progeny in environmental gamma monitoring, and puts forward the research trend and development direction of radon and its progeny analysis technology.

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Deep underground laboratory measurement of 13C(α,n)16O in the Gamow windows of the s- and i-processes

arXiv

引用

arXiv 2022年

作者： Gao, B. Jiao, T.Y. Li, Y.T. Chen, H. Lin, W.P. An, Z. Ru, L.H. Zhang, Z.C. Tang, X.D. Wang, X.Y. Zhang, N.T. Fang, X. Xie, D.H. Fan, Y.H. Ma, L. Zhang, X. Bai, F. Wang, P. Fan, Y.X. Liu, G. Huang, H.X. Wu, Q. Zhu, Y.B. Chai, J.L. Li, J.Q. Sun, L.T. Wang, S. Cai, J.W. Li, Y.Z. Zhang, H. Li, Z.H. Li, Y.J. Li, E.T. Chen, C. Shen, Y.P. Lian, G. Guo, B. Li, X.Y. Zhang, L.Y. He, J.J. Sheng, Y.D. Chen, Y.J. Wang, L.H. Zhang, L. Cao, F.Q. Nan, W. Nan, W.K. Li, G.X. Song, N. Cui, B.Q. Chen, L.H. Ma, R.G. Zhang, Z.C. Yan, S.Q. Liao, J.H. Wang, Y.B. Zeng, S. Nan, D. Fan, Q.W. Qi, N.C. Sun, W.L. Guo, X.Y. Zhang, P. Chen, Y.H. Zhou, Y. Zhou, J.F. He, J.R. Shang, C.S. Li, M.C. Kubono, S. Liu, W.P. deBoer, R.J. Wiescher, M. Pignatari, M. Joint department for nuclear physics Lanzhou University Institute of Modern Physics Chinese Academy of Sciences Lanzhou730000 China CAS Key Laboratory of High Precision Nuclear Spectroscopy Institute of Modern Physics Chinese Academy of Sciences Lanzhou73000 China School of Nuclear Science and Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Radiation Physics and Technology The Ministry of Education Institute of Nuclear Science and Technology Sichuan University Chengdu610064 China Sino-French Institute of Nuclear Engineering and Technology Sun Yat-sen University Guangdong Zhuhai519082 China School of Nuclear Science and Technology Lanzhou University Lanzhou730000 China China Institute of Atomic Energy Beijing102413 China Institute of Modern Physics Chinese Academy of Sciences Lanzhou730000 China Shandong Provincial Key Laboratory of Optical Astronomy and Solar-Terrestrial Environment Institute of Space Sciences Shandong University Weihai264209 China Key Laboratory of Beam Technology and Material Modification of Ministry of Education College of Nuclear Science and Technology Beijing Normal University Beijing100875 China College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen518060 China Yalong River Hydropower Development Company Chengdu610051 China RIKEN Nishina Center Saitama Wako351-0198 Japan Center for Nuclear Study University of Tokyo Saitama Wako351-0198 Japan Department of Physics the Joint Institute for Nuclear Astrophysics University of Notre Dame Notre DameIN46556 United States Wolfson Fellow of Royal Society School of Physics and Astronomy University of Edinburgh King’s Buildings EdinburghEH9 3FD United Kingdom Konkoly Thege Miklós út 15-17 BudapestH-1121 Hungary CSFK MTA Centre of Excellence Konkoly Thege Miklós út 15-17 BudapestH-1121 Hungary E. A. Milne Centre for Astrophysics Department of Physics and Mathematics University of Hull HU6 7RX United

The 13C(α,n)16O reaction is the main neutron source for the slow-neutron-capture (s-) process in Asymptotic Giant Branch stars and for the intermediate (i-) process. Direct measurements at astrophysical energies in above-ground laboratories are hindered by the extremely small cross sections and vast cosmic-ray induced background. We performed the first consistent direct measurement in the range of Ec.m. =0.24 MeV to 1.9 MeV using the accelerators at the China Jinping Underground Laboratory (CJPL) and Sichuan university. Our measurement covers almost the entire i-process Gamow window in which the large uncertainty of the previous experiments has been reduced from 60% down to 15%, eliminates the large systematic uncertainty in the extrapolation arising from the inconsistency of existing data sets, and provides a more reliable reaction rate for the studies of the s- and i-processes along with the first direct determination of the alpha strength for the near-threshold state. © 2022, CC BY.

关键词： Neutron sources

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