检索结果-内蒙古大学图书馆

arXiv 2019年

作者： Yuan, Ye Li, Junlin Li, Liang Jiang, Frank Tang, Xiuchuan Zhang, Fumin Liu, Sheng Goncalves, Jorge Voss, Henning U. Li, Xiuting Kurths, Jürgen Ding, Han School of Artificial Intelligence and Automation State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan430074 China School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan430074 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta30332 United States School of Power and Mechanical Engineering Wuhan University Wuhan430074 China Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom Luxembourg Centre for Systems Biomedicine University of Luxembourg BelvauxL-4367 Luxembourg Citigroup Biomedical Imaging Center Weill Cornell Medical College New York10021 United States Research Domain IV - Transdisciplinary Concepts & Methods Potsdam Institute for Climate Impact Research Potsdam14473 Germany School of Mechanical Science and Engineering State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan430074 China

The study presents a general framework for discovering underlying Partial Differential Equations (PDEs) using measured spatiotemporal data. The method, called Sparse Spatiotemporal System Discovery (S3d), decides which physical terms are necessary and which can be removed (because they are physically negligible in the sense that they do not affect the dynamics too much) from a pool of candidate functions. The method is built on the recent development of Sparse Bayesian Learning;which enforces the sparsity in the to-be-identified PDEs, and therefore can balance the model complexity and fitting error with theoretical guarantees. Without leveraging prior knowledge or assumptions in the discovery process, we use an automated approach to discover ten types of PDEs, including the famous Navier-Stokes and sine-Gordon equations, from simulation data alone. Moreover, we demonstrate our data-driven discovery process with the Complex Ginzburg-Landau Equation (CGLE) using data measured from a traveling-wave convection experiment. Our machine discovery approach presents solutions that has the potential to inspire, support and assist physicists for the establishment of physical laws from measured spatiotemporal data, especially in notorious fields that are often too complex to allow a straightforward establishment of physical law, such as biophysics, fluid dynamics, neuroscience or nonlinear optics. Copyright © 2019, The Authors. All rights reserved.

关键词： Nonlinear optics

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Author Correction: π-HuB: the proteomic navigator of the human body

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Nature 2025年第8046期637卷 E22页

作者： Fuchu He Ruedi Aebersold Mark S Baker Xiuwu Bian Xiaochen Bo Daniel W Chan Cheng Chang Luonan Chen Xiangmei Chen Yu-Ju Chen Heping Cheng Ben C Collins Fernando Corrales Jürgen Cox Weinan E Jennifer E Van Eyk Jia Fan Pouya Faridi Daniel Figeys George Fu Gao Wen Gao Zu-Hua Gao Keisuke Goda Wilson Wen Bin Goh Dongfeng Gu Changjiang Guo Tiannan Guo Yuezhong He Albert J R Heck Henning Hermjakob Tony Hunter Narayanan Gopalakrishna Iyer Ying Jiang Connie R Jimenez Lokesh Joshi Neil L Kelleher Ming Li Yang Li Qingsong Lin Cui Hua Liu Fan Liu Guang-Hui Liu Yansheng Liu Zhihua Liu Teck Yew Low Ben Lu Matthias Mann Anming Meng Robert L Moritz Edouard Nice Guang Ning Gilbert S Omenn Christopher M Overall Giuseppe Palmisano Yaojin Peng Charles Pineau Terence Chuen Wai Poon Anthony W Purcell Jie Qiao Roger R Reddel Phillip J Robinson Paola Roncada Chris Sander Jiahao Sha Erwei Song Sanjeeva Srivastava Aihua Sun Siu Kwan Sze Chao Tang Liujun Tang Ruijun Tian Juan Antonio Vizcaíno Chanjuan Wang Chen Wang Xiaowen Wang Xinxing Wang Yan Wang Tobias Weiss Mathias Wilhelm Robert Winkler Bernd Wollscheid Limsoon Wong Linhai Xie Wei Xie Tao Xu Tianhao Xu Liying Yan Jing Yang Xiao Yang John Yates Tao Yun Qiwei Zhai Bing Zhang Hui Zhang Lihua Zhang Lingqiang Zhang Pingwen Zhang Yukui Zhang Yu Zi Zheng Qing Zhong Yunping Zhu State Key Laboratory of Medical Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China. hefc@***. International Academy of Phronesis Medicine (Guangdong) Guangdong China. hefc@***. Department of Biology Institute of Molecular Systems Biology ETH Zurich Zurich Switzerland. aebersold@imsb.biol.ethz.ch. Macquarie Medical School Macquarie University Sydney New South Wales Australia. Institute of Pathology and Southwest Cancer Center Southwest Hospital Third Military Medical University (Army Medical University) and Key Laboratory of Tumor Immunopathology Ministry of Education of China Chongqing China. Institute of Health Service and Transfusion Medicine Beijing China. Department of Pathology and The Sidney Kimmel Comprehensive Cancer Center Johns Hopkins University Baltimore MD USA. State Key Laboratory of Medical Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China. Key Laboratory of Systems Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Shanghai China. Department of Nephrology First Medical Center of Chinese PLA General Hospital Nephrology Institute of the Chinese People's Liberation Army State Key Laboratory of Kidney Diseases National Clinical Research Center for Kidney Diseases Beijing Key Laboratory of Kidney Disease Research Beijing China. Institute of Chemistry Academia Sinica Taipei China. National Biomedical Imaging Center State Key Laboratory of Membrane Biology Institute of Molecular Medicine Peking-Tsinghua Center for Life Sciences College of Future Technology Peking University Beijing China. School of Biological Sciences Queen's University of Belfast Belfast UK. Functional Proteomics Laboratory Centro Nacional de Biotecnología-CSIC Madrid Spain. Computational Systems Biochemistry Research Group Ma

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Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2024年第10期21卷 1959页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

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Modeling and Robust Integral Sliding Mode Control for a Quadrotor Unmanned Aerial Vehicle

Modeling and Robust Integral Sliding Mode Control for a Quad...

引用

International Renewable and Sustainable Energy Conference

作者： Moussa Labbadi Mohamed Cherkaoui Yassine El houm M'hammed Guisser Engineering For Smart and Sustainable Systems Research Center Mohammed V University Rabat Morocco Laboratory of Signal Analysis and Information Processing Faculty of Science and Technology Settat Morocco

ISBN: (纸本)9781728111834;9781728111827

In the last years, many research studies have been presented on quadrotors due to their different applications in daily life. In this paper, a complete mathematical model of generalized flight is developed based on the Newton-Euler method. This one takes into account external disturbances (aerodynamic forces and moments) acting on the vehicle. A nonlinear control strategy based on the sliding mode control technique has been designed. This controller is employed to control the drone attitude and position as well as to compensate for external disturbances acting on the quadrotor. The stability of the system in closed-loop is ensured by Lyapunov's theorem and analysis. A control method integral sliding mode controller (ISMC) has been developed to improve the controller performance of the SMC. The problem of chattering in the suggested controllers was treated without degradation of the system performance (precision, rapidity, robustness). Finally, to validate the choice of controllers proposed, several simulations were developed with the presence of aerodynamic disturbances.

关键词： Quadrotor unmanned aerial vehicle (UAV) Sliding mode controller Robust control Newton-Euler Chattering reduction

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R3-Net: A deep network for multi-oriented vehicle detection in aerial images and videos

arXiv

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arXiv 2018年

作者： Li, Qingpeng Mou, Lichao Xu, Qizhi Zhang, Yun Zhu, Xiao Xiang State Key Laboratory of Virtual Reality Technology and Systems Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beihang University Beijing100191 China Remote Sensing Technology Institute German Aerospace Center Wessling82234 Germany Signal Processing in Earth Observation Technical University of Munich Munich80333 Germany Canada Research Chair Laboratory in Advanced Geomatics Image Processing Department of Geodesy and Geomatics Engineering University of New Brunswick FrederictonNBE3B 5A3 Canada

Vehicle detection is a significant and challenging task in aerial remote sensing applications. Most existing methods detect vehicles with regular rectangle boxes and fail to offer the orientation of vehicles. However, the orientation information is crucial for several practical applications, such as the trajectory and motion estimation of vehicles. In this paper, we propose a novel deep network, called rotatable region-based residual network (R3-Net), to detect multi-oriented vehicles in aerial images and videos. More specially, R3-Net is utilized to generate rotatable rectangular target boxes in a half coordinate system. First, we use a rotatable region proposal network (R-RPN) to generate rotatable region of interests (R-RoIs) from feature maps produced by a deep convolutional neural network. Here, a proposed batch averaging rotatable anchor (BAR anchor) strategy is applied to initialize the shape of vehicle candidates. Next, we propose a rotatable detection network (R-DN) for the final classification and regression of the R-RoIs. In RDN, a novel rotatable position sensitive pooling (R-PS pooling) is designed to keep the position and orientation information simultaneously while downsampling the feature maps of RRoIs. In our model, R-RPN and R-DN can be trained jointly. We test our network on two open vehicle detection image datasets, namely DLR 3K Munich Dataset and VEDAI Dataset, demonstrating the high precision and robustness of our method. In addition, further experiments on aerial videos show the good generalization capability of the proposed method and its potential for vehicle tracking in aerial videos. The demo video is available at https://***/xCYD-tYudN0. Copyright © 2018, The Authors. All rights reserved.

关键词： Remote sensing

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Common Limitations of Image processing Metrics: A Picture Story

arXiv

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arXiv 2021年

作者： Reinke, Annika Tizabi, Minu D. Sudre, Carole H. Eisenmann, Matthias Rädsch, Tim Baumgartner, Michael Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Bankhead, Peter Benis, Arriel Blaschko, Matthew Buettner, Florian Cardoso, M. Jorge Chen, Jianxu Cheplygina, Veronika Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Engelhardt, Sandy Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hamprecht, Fred Hashimoto, Daniel A. Heckmann-Nötzel, Doreen Hirsch, Peter Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kavur, A. Emre Kenngott, Hannes Kleesiek, Jens Kleppe, Andreas Koehler, Sven Kofler, Florian Kopp-Schneider, Annette Kooi, Thijs Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moher, David Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Noyan, M. Alican Petersen, Jens Polat, Gorkem Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Rieke, Nicola Riegler, Michael A. Rivaz, Hassan Saez-Rodriguez, Julio Sánchez, Clara I. Schroeter, Julien Saha, Anindo Selver, M. Alper Sharan, Lalith Shetty, Shravya Smeden, Maarten V.A.N. Stieltjes, Bram Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben V.A.N. Varoquaux, Gaël Wiesenfarth, Manuel Yaniv, Ziv R. Jäger, Paul Maier-Hein, Lena Division of Intelligent Medical Systems and HI Helmholtz Imaging Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Intelligent Medical Systems Heidelberg Germany NCT Heidelberg DKFZ University Medical Center Heidelberg Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Division of Medical Image Computing Heidelberg Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Institute of Genetics and Cancer University of Edinburgh Edinburgh United Kingdom Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Kasteelpark Arenberg 10 - box 2441 Leuven3001 Belgium Frankfurt/Mainz DKFZ UCT Frankfurt-Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany Frankfurt Cancer Insititute Germany Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for St

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. These are typically related to (1) the disregard of inherent metric properties, such as the behaviour in the presence of class imbalance or small target structures, (2) the disregard of inherent data set properties, such as the non-independence of the test cases, and (3) the disregard of the actual biomedical domain interest that the metrics should reflect. This living dynamically document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. In this context, it focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide. © 2021, CC BY.

关键词： Medical imaging

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Optimal tracking cooperative control for multi-agent systems with periodic sampling via robust model predictive control approach 36

Optimal tracking cooperative control for multi-agent systems...

引用

第36届中国控制会议

作者： Bohui Wang Weisheng Chen Jingcheng Wang Bin Zhang Zhengqiang Zhang Hai Lin Bin Ma the School of Aerospace Science and Technology Xidian University the Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of China the Autonomous Systems and Intelligent Control international Joint Research Center Xian Technological University the Department of Electrical Engineering University of South Carolina the with the School of Engineering Qufu Normal University

ISBN: (纸本)9781538629185

This paper addresses the optimized tracking cooperative control problem for multi-agent systems with periodic sampling and directed communication topology via robust model predictive control *** proposed optimized tracking cooperative control strategy relaxes the assumptions in existing works that the control gain and the local input must be continuous and the states information exchange has no recourse *** the conditions of the optimized consensus and the communication cost being satisfied,the tracking cooperative control law with bounded parameters is developed based on the periodic *** shows that if the sampling condition is satisfied,the multi-agent systems will reach the optimized *** results are provided to verify the proposed approach.

关键词： Index Terms-multi-agent systems tracking cooperative control periodic sampling optimized consensus robust model predictive control

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Exploring nutritional supplement use for countering respiratory tract infections through an X (formerly Twitter)-based survey

Current Research in Biotechnology

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Current research in Biotechnology 2025年 9卷

作者： Singla, Rajeev K. Mondal, Himel Singla, Shailja De, Ronita Behzad, Sahar Găman, Mihnea-Alexandru Sai Chandragiri, Siva Cenanovic, Merisa Patra, Jayanta Kumar Depew, Jennifer R. Boyina, Hemanth Kumar Maigoro, Abdulkadir Yusif Lee, Soojin Atrooz, Omar M. Das, Gitishree Schultz, Fabien Abdallah, Emad Mohamed Chopra, Hitesh Ahmad, Jamil Gautam, Rupesh K. Patnaik, Sourav S. Goh, Bey Hing Babiaka, Smith B. Vats, Sharad Okoh, Michael P. Ahmed, Atallah F. Dubey, Ankit Kumar Lordan, Ronan Subramani, Parasuraman Aiya Singh, Amit Kumar Alvarez-Suarez, José M. Chellappan, Dinesh Kumar Paswan, Shravan Kumar Semwal, Prabhakar Khan, Johra Sheshe, Sadeeq Sethiya, Neeraj Kumar Karpiński, Tomasz M. Riaz, Muhammad Emam-Djomeh, Zahra Gupta, Girish Kumar Madaan, Reecha Kumar, Suresh Choudhary, Neeraj Parisi, Salvatore Willschke, Harald Pirgozliev, Vasil Rayan, Rehab A. Ritschl, Valentin Mondal, Shaikat Zengin, Gokhan Verma, Pritt Kapoor, Bhupinder Gulati, Monica Balla, Gareeballah Osman Adam Le, Dan Khoa Pittalà, Valeria El-Demerdash, Amr Khalid, Garba M. Simal-Gandara, Jesus Alzahrani, Qushmua E. Russo, Gian Luigi Kharat, Kiran R. Bishayee, Anupam Wang, Dongdong Orhan, Ilkay Erdogan Ullah, Hammad Heinrich, Michael Baral, Bikash Tzvetkov, Nikolay T. Yeung, Andy Wai Kan Dias-Ferreira, João M.L. Olea, Scarlett Perez Mohanta, Yugal Kishore Kureshi, Azazahemad A. Supuran, Claudiu T. Rani, Neeraj Gundamaraju, Rohit Mulholland, Eoghan Joseph Lonardo, Sara Di Dinkova-Kostova, Albena T. González-Burgos, Elena Hritcu, Lucian Badhe, Pravin Singh, Abhilasha Al-Rimawi, Fuad Sureda, Antoni Abiri, Rambod Braidy, Nady Kapral, Lorenz Abdullahi, A.N. Medina, Christhian Delfino Villanueva Sheridan, Helen Lucarini, Massimo Durazzo, Alessandra Giampieri, Francesca Barreca, Davide Maria, Witkowska Anna Andrade, José Carlos Fimognari, Carmela Akram, Faizan Tikhonov, Aleksei Battino, Maurizio Oladipupo, Akolade R. Emerald, Mila Efferth, Thomas Amrani, Said Echeverría, Javier Maria Louka, Anna Tripathi, Surya Kant Fiebich, Bernd L. Es-Safi, Nour Eddine Khan Department of Critical Care Medicine and Institutes for Systems Genetics Frontiers Science Center for Disease-related Molecular Network West China Hospital Sichuan University Chengdu China Department of Physiology All India Institute of Medical Sciences Jharkhand Deoghar India iGlobal Research and Publishing Foundation New Delhi India Evidence-based Phytotherapy and Complementary Medicine Research Center Alborz University of Medical Sciences Karaj Iran Department of Hematology Center for Clinical and Basic Research (CCBR Clinic) Bucharest Romania Department of Cellular and Molecular Pathology Stefan S. Nicolau Institute of Virology Romanian Academy Bucharest Romania Department of Pathology University of Oklahoma Health Sciences Center Oklahoma City OK United States Sarajevo Bosnia and Herzegovina Research Institute of Integrative Life Sciences Dongguk University-Seoul Goyang-Si South Korea United States University Institute of Pharma Sciences Chandigarh University Punjab Mohali 140413 India Department of Microbiology and Molecular Biology College of Bioscience and Biotechnology Chungnam National University Daejeon 34134 South Korea Department of Biological Sciences Mutah University Jordan Ethnopharmacology & Zoopharmacognosy Research Group Department of Agriculture and Food Sciences Neubrandenburg University of Applied Sciences Germany Department of Biology College of Science Qassim University Buraydah 51452 Saudi Arabia Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai 602105 India Department of Human Nutrition The University of Agriculture Peshawar Khyber Pakhtunkhwa 25120 Pakistan Department of Pharmacology Indore Institute of Pharmacy IIST Campus Rau (M.P.) Indore 453331 India The University of Texas Southwestern Medical Center Dallas TX United States Sunway Biofunctional Molecules Discovery Centre (SBMDC) School of Medical and Life Sciences Sunway University S

Background: Respiratory tract infections are a common health issue, driving interest in preventive strategies like nutritional supplements, while evidence on their usage and effectiveness remains limited. In this context, social media platforms, particularly X (formerly Twitter), provide a unique opportunity to gather large-scale public health-related data. Objectives: In this study, we aimed to survey participants’ uses and opinions on nutritional supplements in prevention or treatment of respiratory tract infections, by using X. Methods: A survey was conducted between 1st and 15th December 2022. A single open-ended question “Which are the best dietary supplements to counteract respiratory infections?“ was asked. One week after the start of the survey, a poll was posted to get more relevant information and boost the survey's reach. Total endorsements were calculated for each tweet posted as the total sum of replies, retweets, and likes. Results: The open-ended question received a total of 118 retweets, 39 quotes, and 371 likes, while the poll received 56 retweets, 13 quotes, and 67 likes. A total of 495 replies, 2,251 retweets, 5,118 likes, and 148 quotes were received for the question and its related tweets. Vitamin D (1,607 endorsements), zinc (1,347 endorsements), vitamin C (803 endorsements), magnesium (694 endorsements), and honey (661 endorsements) were the nutritional supplements that received most endorsements. Conclusion: Various foods, drinks, and natural ingredients have been suggested as potentially helpful for counteracting respiratory infections. Approximately half of respondents indicated using such supplements for themselves. The result of this study supports the idea that the X platform can be used as an effective survey tool to study global health-related behaviours and trends. © 2025 The Authors

关键词： Bacteria COVID-19 Nutritional supplements Phytomedicine Respiratory tract infections X (formerly Twitter)

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Construction of One-Gray Weight Codes and Two-Gray Weight Codes over Z4+uZ4

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Journal of systems science & Complexity 2016年第5期29卷 1472-1484页

作者： SHI Minjia WANG Dandan GAO Jian WU Bo Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei 230039 China National Mobile Communications Research Laboratory Southeast University Nanjing 210096China School of Mathematical Sciences Anhui University Hefei 230601 China. School of Science Shandong University of Technology Zibo 255091 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Bcijing 100093 China

This paper firstly gives some necessary conditions on one-Gray weight linear codes. And then we use these results to construct several classes of one-Gray weight linear codes over Z_4 +uZ_4(u^2=u) with type 16^(k_1)8^(k_2)8^(k_3)4^(k_4)4^(k_5)4^(k_6)2^(k_7)2^(k_8) based on a distance-preserving Gray map from(Z4 + u Z4)n to Z2n4. Secondly, the authors use the similar approach to do works on two-Gray(projective) weight linear codes. Finally, some examples are given to illustrate the construction methods.

关键词： Generator matrix Gray map linear code one-Gray weight code two-Gray weight code

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Continuous use of ERP-based BCIs with different visual angles in ALS patients

arXiv

引用

arXiv 2017年

作者： Jin, Jing Allison, Brendan Z. Zhang, Yu Chen, Yan Zhou, Sijie Dong, Yi Wang, Xingyu Chchocki, Andrzej Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China Department of Neurology Huashan hospital Fudan University Shanghai China Department of Cognitive Science University of California at San Diego San DiegoCA United States Laboratory for Advanced Brain Signal Processing Brain Science Institute RIKEN Wako-shi Japan Systems Research Institute PAS Warsaw Poland Torun Poland

Objective: Amyotrophic lateral sclerosis (ALS) is a rare disease, but is also one of the most common motor neuron diseases, and people of all races and ethnic backgrounds are affected. There is currently no cure. Brain computer interfaces (BCIs) can establish a communication channel directly between the brain and an external device by recognizing brain activities that reflect user intent. Therefore, this technology could help ALS patients in promoting functional independence through BCI-based speller systems and motor assistive devices. Methods: In this paper, two kinds of ERP-based speller systems were tested on 18 ALS patients to: (1) assess performance when they spelled 42 characters online continuously, without a break;and (2) to compare performance between a matrix-based speller paradigm (MS-P, mean visual angle 6◦) and a new speller paradigm that used a larger visual angle called the large visual angle speller paradigm (LS-P, mean visual angle 8◦). Results: Although results showed that there were no significant differences between the two paradigms in accuracy trend over continuous use (p>0.05), the fatigue during the LS-P condition was significantly lower than that of MS-P (p Copyright © 2017, The Authors. All rights reserved.

关键词： Brain computer interface

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