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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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AGlobal benchmark of algorithms for segmentinglate gadolinium-enhanced cardiac magnetic resonance imaging

arXiv

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arXiv 2020年

作者： Xiong, Zhaohan Xia, Qing Hu, Zhiqiang Huang, Ning Bian, Cheng Zheng, Yefeng Vesal, Sulaiman Ravikumar, Nishant Maier, Andreas Yang, Xin Heng, Pheng-Ann Ni, Dong Li, Caizi Tong, Qianqian Si, Weixin Puybareau, Elodie Khoudli, Younes Géraud, Thierry Chen, Chen Bai, Wenjia Rueckert, Daniel Xu, Lingchao Zhuang, Xiahai Luo, Xinzhe Jia, Shuman Sermesant, Maxime Liu, Yashu Wang, Kuanquan Borra, Davide Masci, Alessandro Corsi, Cristiana De Vente, Coen Veta, Mitko Karim, Rashed Preetha, Chandrakanth Jayachandran Engelhardt, Sandy Qiao, Menyun Wang, Yuanyuan Tao, Qian Nuñez-Garcia, Marta Camara, Oscar Savioli, Nicolo Lamata, Pablo Zhao, Jichao Auckland Bioengineering Institute University of Auckland Auckland New Zealand State Key Lab of Virtual Reality Technology and Systems Beihang University Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China SenseTime Inc Shenzhen China Tencent Jarvis Laboratory Shenzhen China Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Shenzhen China School of Computer Science Wuhan University Wuhan China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Epita Research and Development Laboratory Paris France Department of Computing Imperial College London London United Kingdom School of Naval Architecture Shanghai Jiao Tong University Ocean & Civil Engineering Shanghai China School of Data Science Fudan University Shanghai China Epione Research Group Universite Cote d'Azur Inria Sophia Antipolis France Harbin Institute of Technology School of Computer Science and Technology Harbin China Department of Electric University of Bologna Electronic and Information Engineering Cesena Italy Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands School of Biomedical Engineering and Imaging Sciences Kings College London London United Kingdom Faculty of Electrical Engineering and Information Technology University of Magdeburg Magdeburg Germany Heidelberg University Hospital Germany Biomedical Engineering Center Fudan University Shanghai China Department of Radiology Leiden University Medical Center Leiden Netherlands Department of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain D

Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI)widely used for visualizing diseased cardiacstructures, is a crucial first step for clinical diagnosis and treatment. However, direct segmentation ofLGE-MRIsischallenging due to *** most clinical studies have relied on manual and labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning *** address this, we organized the "2018 Left Atrium Segmentation Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset, and associated labels of the left atriumsegmented by three medical experts,ultimately attractingthe participation of27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis,offeringan overall picture ofthe major design choices of convolutional neural networks(CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show the top methodachieved a dice score of 93.2%and a mean surface to a surface distance of 0.7mm, significantly outperforming prior state-of-the-art. Particularly, our analysisdemonstrated that double, sequentially usedCNNs, in which a first CNN is used for automatic region-of-interestlocalization and a subsequent CNN is used for refinedregional segmentation, achieved far superiorresults than traditional methods and pipelinescontaining *** findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging *** large-scale benchmarking study makes a significant step towards much-improved segmentation methods for cardiac LGE-MRIs, and will serveas animportant benchmark for evaluating and comparing the future works in the field. Copyright © 2020, Th

关键词： Magnetic resonance imaging

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Donut Modes in Space Wavelength Division Multiplexing: Multimode Optical Fiber Transmission based on Electrical Feedback Equalizer

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Journal of Physics: Conference Series 2021年第1期1755卷

作者： Alaan Ghazi S. A. Aljunid Syed Zulkarnain Syed Idrus R. Endut N. Ali Angela Amphawan Alaa Fareed Aras Al-dawoodi Awab Noori School of Human Development & Techno-communication University Malaysia Perlis Malaysia School of Computer and Communication Engineering University Malaysia Perlis Malaysia. Computer Technical Engineering Al-Qalam University College Iraq Advanced Communication Engineering (ACE) Centre of Excellence Universiti Malaysia Perlis Malaysia. School of Microelectronic Engineering (SCCE) Universiti Malaysia Perlis Perlis Malaysia Lightwave Laboratory School of Engineering and Technology Sunway University 47500 Petaling Jaya Selangor Malaysia. School of Computing University Utara Malaysia Malaysia. Computer Science Department College of Computer Science and Information Technology Kirkuk University Iraq Data Science Research Lab School of Computing Universiti Utara Malaysia Malaysia

This study demonstrates the implementation of multi-input-multi-output (MIMO) transmission donut modes in space wavelength division multiplexing (SWDM) over multi-mode optical fiber (MMOF) and simulating the effect of mode coupling of the transmitted signal. However, during the data transmitted over MMF by using SWDM an issue is encountered, which is the inter symbol interference (ISI) due to mode coupling. ISI has been recognized as a major obstacle to high-speed data transmission. The signal processing in SDM of donut modes over MMOF with electrical feedback equalizer is used to improve the performance for increasing the bandwidth. A data rate 80 Gbit/s up for a distance of 1600 meters is achieved. SWDM topology was designed emitted from a spatial vertical cavity surface-emitting laser (VCSEL) through MMOF in conjunction with the configurations of electrical feedback equalizer. The parameters and values used in Opt-Sim simulation topology were listed. The performance measurement of this study based on bit-error rates (BER) and eye diagram before and after electrical feedback equalization were analysed.

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Fostering diversity, equity, and inclusion in interdisciplinary marine science

npj Ocean Sustainability

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npj Ocean Sustainability 2024年第1期3卷 1-8页

作者： Laura Kaikkonen Rebecca J. Shellock Samiya Ahmed Selim Renis Auma Ojwala Beatriz S. Dias Sarah Mahadeo Shenghui Li Charles I. Addey Ignacio Gianelli Katherine M. Maltby Sara Garcia-Morales Juliano Palacios-Abrantes Ayodele Oloko Shan Jiang Marta Albo-Puigserver Virginia A. García Alonso Chelsey A. Baker Colleen B. Bove Stephanie Brodie Lol Iana Dahlet Jewel Das Sebastian C. A. Ferse Aislinn Dunne Ellen Johannesen Julia Jung Eugenia Merayo Garcia Denis B. Karcher Lucia Millan Kasali Oladepo Lawal Kelly Ortega-Cisneros Stephanie Otoabasi-Akpan Durlave Roy Samina Sharmin Rouf Szymon Smoliński Natasa Vaidianu Chris Whidden Mia Strand Finnish Environment Institute Helsinki Finland University of Helsinki Helsinki Finland Centre for Sustainable Development Reform Faculty of Law and Justice University of New South Wales Sydney NSW Australia Center for Sustainable Development University of Liberal Arts Bangladesh Dhaka Bangladesh World Maritime University-Sasakawa Global Ocean Institute Malmö Sweden Cooperative Institute for Climate Ocean and Ecosystem Studies University of Washington Seattle WA USA Guangdong Academy of Coastal Economic Belt Development Guangdong Ocean University Zhanjiang China Department of Oceanography University of Hawai’i at Manoa Honolulu HI USA EqualSea Lab-CRETUS. Department of Applied Economics Universidade de Santiago de Compostela Spain South American Institute for Resilience and Sustainability Studies (SARAS) Maldonado Uruguay Gulf of Maine Research Institute Portland ME USA Marine Environmental and Sciences Centre (MARE-ARNET) University of Lisbon Lisbon Portugal Ecology and Biodiversity Institute (IEB) Santiago Chile Institute for the Oceans and Fisheries The University of British Columbia Vancouver BC Canada State Key Laboratory of Estuarine and Coastal Research East China Normal University Shanghai China Centro Oceanográfico de Baleares Instituto Español de Oceanografía (IEO-CSIC) Ecosystem Oceanography Group Palma Spain Departamento de Biodiversidad y Biología Experimental (DBBE) Facultad de Ciencias Exactas y Naturales Universidad de Buenos Aires AR Argentina National Oceanography Centre Marine Systems Modelling Southampton UK Ursinus College Biology Department Collegeville PA USA Environment Commonwealth Scientific and Industrial Research Organisation Brisbane QLD Australia Leibniz Centre for Tropical Marine Research (ZMT) Bremen Germany Instituto de Estudos Costeiros Universidade Federal do Pará (UFPA) Bragança Aldeia Brazil Institute of Marine Sciences University of Chittagong Chattogram Bangladesh Faculty for Fisheries and Marine Scien

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A Biostablel-DNA Hydrogel with Improved Stability for Biomedical Applications

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Angewandte Chemie 2022年第30期134卷

作者： Bo Yang Bini Zhou Cuifeng Li Xiaowei Li Ziwei Shi Yuxin Li Chenyou Zhu Xin Li Yi Hua Yufan Pan Jian He Tianyang Cao Prof. Yawei Sun Prof. Wanli Liu Min Ge Prof. Yuhe R. Yang Prof. Yuanchen Dong Prof. Dongsheng Liu Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology Department of Chemistry Tsinghua University 100084 Beijing China School of Life Sciences Institute for Immunology Ministry of Education Key Laboratory of Protein Sciences Beijing Advanced Innovation Center for Structural Biology Collaborative Innovation Centre for Diagnosis and Treatment of Infectious Diseases Beijing Key Lab for Immunological Research on Chronic Diseases Tsinghua University 100084 Beijing China Tsinghua-Peking Center for Life Sciences 100084 Beijing China Zenji Research Laboratories (Nanjing) Ltd. Building 9 Phase 2 Zifeng Research and Development Center Qiaolin Tablet Economic Development Zone Pukou District Nanjing 211806 Jiangsu China CAS Key Laboratory of Colloid Interface and Chemical Thermodynamics Institute of Chemistry Chinese Academy of Sciences University of Chinese Academy of Sciences 100190 Beijing China College of Chemical Engineering China University of Petroleum (East China) 258000 Qingdao China CAS Key Laboratory of Nanosystem and Hierarchical Fabrication CAS Center for Excellence in Nanoscience National Center for Nanoscience and Technology 100190 Beijing China

DNA hydrogels have attracted increasing attention owing to their excellent permeability and high mechanical strength, together with thixotropy, versatile programmability and good biocompatibility. However, the moderate biostability and immune stimulation of DNA have arisen as big concerns for future potential clinical applications. Herein, we report the self-assembly of a novel l -DNA hydrogel, which inherited the extraordinary physical properties of a d -DNA hydrogel. With the mirror-isomer deoxyribose, this hydrogel exhibited improved biostability, withstanding fetal bovine serum (FBS) for at least 1 month without evident decay of its mechanical properties. The low inflammatory response of the l -DNA hydrogel has been verified both in vitro and in vivo. Hence, this l -DNA hydrogel with outstanding biostability and biocompatibility can be anticipated to serve as an ideal 3D cell-culture matrix and implanted bio-scaffold for long-term biomedical applications.

关键词： 3D Cell Culture Biocompatibility Biostability DNA Hydrogels l-DNA

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Integrating machine learning for planetary science: perspectives for the next decade: A white paper to the NRC planetary science and astrobiology decadal survey 2023-2032

arXiv

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arXiv 2020年

作者： Azari, Abigail R. Biersteker, John B. Dewey, Ryan M. Doran, Gary Forsberg, Emily J. Harris, Camilla D.K. Kerner, Hannah R. Skinner, Katherine A. Smith, Andy W. Amini, Rashied Cambioni, Saverio Poian, Victoria Da Garton, Tadhg M. Himes, Michael D. Millholland, Sarah Ruhunusiri, Suranga Annex, Andrew M. Aye, K. Michael Argall, Matthew R. Cameron, Marissa E. Cao, Xin Crary, Frank Crichton, Daniel J. Cross, Matthew D’Amore, Mario de Wit, Julien Francis, Raymond Helbert, Jörn Hood, Don R. Jackman, Catriona M. Lagain, Anthony Larson, Raven E. Mai, Chuhong Mandrake, Lukas Needham, Andrew Pankratius, Victor Parr, James Prockter, Louise M. Pérez, Fernando Radebaugh, Jani Theiling, Bethany P. Tiscareno, Matthew Vanderburg, Andrew Vandegriff, Jon Hook, Josh Vander Varatharajan, Indhu Wagstaff, Kiri Waldmann, Ingo Space Sciences Lab University of California Berkeley United States Massachusetts Institute of Technology United States University of Michigan United States Jet Propulsion Laboratory California Institute of Technology United States University of Idaho United States University of Maryland at College Park United States Georgia Institute of Technology United Kingdom Lunar and Planetary Laboratory University of Arizona United States NASA Goddard Space Flight Center University of Southampton United Kingdom University of Central Florida United States Princeton University United States University of Iowa United States United States Laboratory for Atmospheric and Space Physics University of Colorado United States Space Science Center University of New Hampshire United States Institute for Earth and Space Exploration Western University United Kingdom Texas A&M University United States Dublin Institute for Advanced Studies Space Science and Technology Centre Curtin University Australia Arizona State University United States Jacobs NASA Johnson Space Center United States Frontier Development Laboratory Universities Space Research Association Lunar and Planetary Institute University of California Berkeley Lawrence Berkeley National Laboratory United States Brigham Young University United States SETI Institute University of Wisconsin Madison United States JHU Applied Physics Laboratory Centre for Space Exochemistry Data UCL

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Influence of annealing temperature of seed layer on the structural and optical properties of ZnO nanorods synthesized by SILAR and CBD techniques

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Materials and Processing Report 2025年第1期40卷

作者： Ahmed Fattah Abdulrahman Sozan Hussien Mostafa Shayma Mohammed Ahmed Amad Nori Abdulqodus Awaz Adil Kareem Sabah M. Ahmed Shujahadeen B. Aziz Munirah Abdullah Almessiere Samir Mustafa Hamad Dilshad Shaikhah a Department of Energy Engineering Technical College of Engineering Duhok Polytechnique University Kurdistan Region Iraqb Department of Medical Laboratory Technology Technical Institute of Amedi Duhok Polytechnique University Kurdistan Region Iraq c Department of General Science College of Basic Education University of Zakho Kurdistan Region Iraq d Department of Physics College of Science University of Duhok Kurdistan Region Iraq a Department of Energy Engineering Technical College of Engineering Duhok Polytechnique University Kurdistan Region Iraq e Department of Physics Faculty of Science University of Zakho Kurdistan Region Iraq f Hameed Majid Advanced Polymeric Materials Research Lab. Research and Development Center University of Sulaimani Sulaymaniyah Kurdistan Region Iraqg Department of Physics College of Science Charmo University Chamchamal Sulaymaniyah Iraq h Department of Physics College of Science Imam Abdulrahman Bin Faisal University Dammam Saudi Arabiai Department of Biophysics Institute for Research and Medical Consultation (IRMC) Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia j Scientific Research Centre Soran University Soran Kurdistan-Region Iraq j Scientific Research Centre Soran University Soran Kurdistan-Region Iraqk Institute of Functional Surfaces School of Mechanical Engineering University of Leeds Leeds UK

ABSTRACTIn this study, zinc oxide nanorods were synthesized using chemical bath deposition with a ZnO seed layer on glass substrates via successive ionic layer adsorption and reaction. The effect of annealing temperature on the seed layer was investigated using various characterization techniques. Results show that annealing temperature significantly influenced the morphology and quality of ZnO NRs. As the temperature increased from 0°C to 500°C, the nanorod length grew from 591 to 1008 nm. Optimal conditions were found at 450°C, with a growth rate of 4.2 nm/min and an aspect ratio of 11. The crystalline size ranged from 10.56 to 52.93 nm, confirmed by XRD analysis, showing the hexagonal wurtzite structure. The bandgap energy varied from 3.221 eV to 3.2656 eV, reflecting the impact of annealing on optical properties. Optical transmittance was highest at 0°C, decreasing from 57% to 13% as the annealing temperature increased.

关键词： Post-annealing seed-layer alignment crystal quality ZnO NRs

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Results of dark matter search using the Full Pandax-II exposure

arXiv

引用

arXiv 2020年

作者： Wang, Qiuhong Abdukerim, Abdusalam Chen, Wei Chen, Xun Chen, Yunhua Cheng, Chen Cui, Xiangyi Fan, Yingjie Fang, Deqing Fu, Changbo Fu, Mengting Geng, Lisheng Giboni, Karl Gu, Linhui Guo, Xuyuan Han, Ke He, Changda He, Shengming Huang, Di Huang, Yan Huang, Yanlin Huang, Zhou Ji, Xiangdong Ju, Yonglin Li, Shuaijie Liu, Huaxuan Liu, Jianglai Ma, Wenbo Ma, Yugang Mao, Yajun Meng, Yue Ni, Kaixiang Ning, Jinhua Ning, Xuyang Ren, Xiangxiang Shang, Changsong Si, Lin Shen, Guofang Tan, Andi Wang, Anqing Wang, Hongwei Wang, Meng Wang, Siguang Wang, Wei Wang, Xiuli Wang, Zhou Wu, Mengmeng Wu, Shiyong Wu, Weihao Xia, Jingkai Xiao, Mengjiao Xie, Pengwei Yan, Binbin Yang, Jijun Yang, Yong Yu, Chunxu Yuan, Jumin Yuan, Ying Yue, Jianfeng Zeng, Xinning Zhang, Dan Zhang, Tao Zhao, Li Zheng, Qibin Zhou, Jifang Zhou, Ning Zhou, Xiaopeng INPAC and School of Physics and Astronomy Shanghai Jiao Tong University MOE Key Lab for Particle Physics Astrophysics and Cosmology Shanghai Key Laboratory for Particle Physics and Cosmology Shanghai200240 China Shanghai Institute of Applied Physics Chinese Academy of Sciences Shanghai201800 China University of Chinese Academy of Sciences Beijing100049 China Shanghai Jiao Tong University Sichuan Research Institute Chengdu610213 China Yalong River Hydropower Development Company Ltd. 288 Shuanglin Road Chengdu610051 China School of Physics Sun Yat-Sen University Guangzhou510275 China Tsung-Dao Lee Institute Shanghai200240 China School of Physics Nankai University Tianjin300071 China Institute of Modern Physics Fudan University Shanghai200433 China School of Physics Peking University Beijing100871 China School of Physics Beihang University Beijing100191 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 Medical Instrument and Food Engineering University of Shanghai for Science and Technology Shanghai200093 China Department of Physics University of Maryland College ParkMD20742 United States School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China Shandong University Jinan250100 China Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai201210 China Center of High Energy Physics Peking University Beijing100871 China

We report the dark matter search results using the full 132 ton·day exposure of the PandaX-II experiment, including all data from March 2016 to August 2018. No significant excess of events were identified above the expected background. Upper limits are set on the spin-independent dark matter-nucleon interactions. The lowest 90% confidence level exclusion on the spin-independent cross section is 2.0 × 10−46 cm2 at a WIMP mass of 15 GeV/c2. Copyright © 2020, The Authors. All rights reserved.

关键词： Germanium alloys

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Parametric decoupling control strategy for a class of nonlinear uncertain systems via observer-based output feedback 23

Parametric decoupling control strategy for a class of nonlin...

引用

23rd IEEE International Conference on Automation and Computing, ICAC 2017

作者： Zhang, Qichun Yin, Xin School of Engineering and Sustainable Development De Montfort University LeicesterLE1 9BH United Kingdom Sino-British Joint Advanced Control System Technology Lab University of Manchester ManchesteM13 9PL United Kingdom

ISBN: (纸本)9780701702618

In this paper, the system decoupling problem has been investigated and a novel decoupling control strategy is presented for Lipschitz nonlinear uncertain multivariable systems. This control strategy consists of an explicit parametric state feedback controller and a linear state observer, where the free parameters of the controller can be adjusted to attenuate the coupling effects. In addition, the optimal parameters can be obtained using H infinity norm based performance criterion. The convergence of the observer, the robust stabilization of the controller and closed-loop system are analyzed while the sufficient conditions are determined. Following the design procedure of the presented control strategy, an illustrative numerical example is given to demonstrate the effectiveness and correctness of the presented control strategy. © 2017 Chinese Automation and Computing Society in the UK - CACSUK.

关键词： Controllers

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Author Correction: Activation of subnanometric Pt on Cu-modified CeO2 via redox-coupled atomic layer deposition for CO oxidation

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Nature communications 2020年第1期11卷 5879页

作者： Xiao Liu Shuangfeng Jia Ming Yang Yuanting Tang Yanwei Wen Shengqi Chu Jianbo Wang Bin Shan Rong Chen State Key Laboratory of Digital Manufacturing Equipment and Technology School of Mechanical Science and Engineering Huazhong University of Science and Technology 430074 Wuhan Hubei People's Republic of China. State Key Laboratory of Materials Processing and Die and Mould Technology School of Materials Science and Engineering Huazhong University of Science and Technology 430074 Wuhan Hubei People's Republic of China. School of Physics and Technology Center for Electron Microscopy MOE Key Laboratory of Artificial Micro- and Nano-structures and Institute for Advanced Studies Wuhan University 430072 Wuhan Hubei People's Republic of China. General Motors Global Research and Development Chemical Sciences and Materials Systems Lab 3500 Mound Road Warren MI 48090 USA. Department of Chemical and Biomolecular Engineering Clemson University Clemson SC 29634 USA. Institute of High Energy Physics Chinese Academy of Sciences 100049 Beijing People's Republic of China. State Key Laboratory of Materials Processing and Die and Mould Technology School of Materials Science and Engineering Huazhong University of Science and Technology 430074 Wuhan Hubei People's Republic of China. bshan@***. State Key Laboratory of Digital Manufacturing Equipment and Technology School of Mechanical Science and Engineering Huazhong University of Science and Technology 430074 Wuhan Hubei People's Republic of China. rongchen@***.

A Correction to this paper has been published: https://***/10.1038/s41467-020-19663-3 .

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