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Optimization of aero-engine pipeline for avoiding vibration based on length adjustment of straight-line segment

Frontiers of Mechanical engineering 2022年第1期17卷 100-116页

作者： Wenhao JI Wei SUN Donghai WANG Zhonghua LIU School of Mechanical Engineering and Automation Northeastern UniversityShenyang 110819China Key Laboratory of Vibration and Control of Aero-Propulsion Systems Department of Education of China Northeastern UniversityShenyang 110819China Shenyang Area 2nd Military Representative Room of Air Force Equipment Department Shenyang 110043China

In the design and troubleshooting of aero-engine pipeline,the vibration reduction of the pipeline system is often achieved by adjusting the hoop layout,provided that the shape of pipeline remains ***,in reality,the pipeline system with the best antivibration performance may be obtained only by adjusting the pipeline *** this paper,a typical spatial pipeline is taken as the research object,the length of straight-line segment is taken as the design variable,and an innovative optimization method of avoiding vibration of aero-engine pipeline is *** relationship between straight-line segment length and parameters that determine the geometric characteristics of the pipeline,such as the position of key reference points,bending angle,and hoop position,are derived in *** on this,the parametric finite element model of the pipeline system is *** the maximum first-order natural frequency of pipeline as the optimization objective and introducing process constraints and vibration avoidance constraints,the optimization model of the pipeline system is *** genetic algorithm and the golden section algorithm are selected to solve the optimization model,and the relevant solution procedure is described in ***,two kinds of pipelines with different total lengths are selected to carry out a case *** on the analysis of the influence of straight-line segment length on the vibration characteristics of the pipeline system,the optimization methods developed in this paper are *** show that the developed optimization method can obtain the optimal single value or interval of the straight-line segment length while avoiding the excitation *** addition,the optimization efficiency of the golden section algorithm is remarkably higher than that of the genetic algorithm for length optimization of a single straight-line segment.

关键词： length adjustment spatial pipeline aero-engine vibration avoidance optimization genetic algorithm golden section algorithm

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On stable and quasi-chaotic regimes in a one-dimensional unimodal mapping obtained by modeling the dynamics of a biological population 12

On stable and quasi-chaotic regimes in a one-dimensional uni...

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12th International Conference on Mathematical Modeling in Physical Sciences, IC-MSQUARE 2023

作者： Polyanovsky, V.O. Kamenev, I.G. Laboratory of system biology and computational genetics Vavilov Institute of General Genetics RAS Gubkin Street 3 Moscow119991 Russia Department of Mathematical Modelling of Economic Systems Federal Research Center 'Computer Science and Control RAS Vavilov Street 44-2 Moscow119333 Russia Department of Fundamental Sciences-11 Bauman Moscow State Technical University 2nd Baumanskaya Street 5-1 Moscow05005 Russia

The paper considers the properties of the difference equation describing the dynamics of the animal population, obtained earlier in the framework of studies of tundra communities. We have considered a special case in which the model is represented by a one-parameter difference equation that defines a one-dimensional unimodal mapping of a segment into itself, similar to the well-known triangular (tent) mapping, supplemented by a region with a constant value. A change in the mapping parameter generates a bifurcation scenario, in which stability zones arise, characterized by orbits of a constant period, interspersed with zones with more complicated, "quasi-chaotic" regimes. Based on the properties of the n-iterated triangular mapping, a necessary and sufficient condition for the localization of cyclic orbits in the considered type of unimodal mappings is formulated, which makes it possible to identify stability regions for any given period n. On the basis of this condition an algorithm for detecting stability zones is proposed. The main subject of the study is the fractal properties of the set, which is the complement of the obtained set of stability regions to the entire domain of mapping definition. The dynamics of the DH (n) value is obtained, the limit of which at n → ∞ is equal to the fractal dimension dH. It is shown that in the studied range of n (2 ≤ n ≤ 22), the DH (n) H T = 0, which means that the complement of the set of stability regions consists of isolated points. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Mapping

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Multi-Agent Economic Modeling Automation Technologies 9

Multi-Agent Economic Modeling Automation Technologies

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9th International Conference on control, Decision and Information Technologies, CoDIT 2023

作者： Zhukova, Aleksandra A. Pilnik, Nikolai P. Kamenev, Ivan G. Iusup-Akhunov, Batyrzhan B. Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Department of Mathematical Modelling in Economic Social and Ecological Systems Vavilov st. 44 building 2 Moscow119333 Russia Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Department of Mathematical Modelling of Economic Systems Vavilov st. 44 building 2 Moscow119333 Russia Hse University Faculty of Economic Sciences Department of Applied Economics Pokrovsky Bulvar 11 Moscow109028 Russia Fsbeihe Bauman Moscow State Technical University Department of Fundamental Sciences-11 2nd Baumanskaya street 5-1 Moscow05005 Russia The Phystech School of Applied Mathematics and Informatics Moscow Institute of Physics and Technology 9 Institutskiy per. Moscow Region Dolgoprudny141701 Russia

ISBN: (纸本)9798350311402

Complex economic models often describe the agent's decisions, i.e. choice of some controlled variables in accordance with criteria set by agents. Unlike purely statistical ones, agent-based models allow to describe processes with different modes, in which the agent's behavior changes quali-tatively when the environment (including the actions of other agents) change. The practical use of mathematical models of complex systems with a number of interacting agents is limited by complexity of their construction and possible errors when dealing with multitude of expressions. computer tools, such as the 'Ecomod' system discussed in the article, can substantially reduce the time spent on model creation and processing, and help to coordinate the work of several researchers on the model. © 2023 IEEE.

关键词： Autonomous agents

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Fault-tolerant control of semi-active suspension in case of oil leakage of magnetorheological damper

Fault-tolerant control of semi-active suspension in case of ...

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European control Conference (ECC)

作者： Hakan Basargan Filip Jeniš András Mihály Péter Gáspár Department of Control for Transportation and Vehicle Systems Budapest University of Technology and Economics Stoczek u. 2 Budapest Hungary Faculty of Mechanical Engineering Institute of Machine and Industrial Designs Brno University of Technology Technicka 2 Brnos Czech Republic Systems and Control Laboratory Institute for Computer Science and Control (SZTAKI) Eötvös Loránd Research Network (ELKH) Kende u. 13-17 Budapest Hungary

The paper presents a reconfigurable fault-tolerant control strategy for a semi-active suspension using magnetorheological (MR) damper. The aim of the control reconfiguration is to handle the adverse behaviour of the MR damper due to oil leakage induced by the wear of the suspension component. The proposed method relies on the data driven model of the MR damper, using an estimation procedure to quantify the healthiness of the damper and to estimate the performance degradation due to the oil leakage. The reconfiguration control strategy is founded on the Linear Parameter Varying (LPV) framework, where a scheduling variable is defined to represent the healthiness level of the MR damper. By the scaling of the control action through the scheduling variable, the performance degradation of the MR damper can be compensated to match the behaviour of the healthy dampers. The proposed method is demonstrated through simulations, comparing the performance of the fault-tolerant LPV control to conventional semi-active control methods

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A Deep Learning Method for Predicting the COVID-19 ICU Patient Outcome Fusing X-Rays, Respiratory Sounds, And ICU Parameters

SSRN

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SSRN 2023年

作者： Wu, Yunan Rocha, Bruno Machado Kaimakamis, Evangelos Cheimariotis, Grigorios-Aris Petmezas, Georgios Chatzis, Evangelos Kilintzis, Vassilis Stefanopoulos, Leandros Pessoa, Diogo Marques, Alda Carvalho, Paulo Paiva, Rui Pedro Kotoulas, Serafeim Bitzani, Militsa Katsaggelos, Aggelos K. Maglaveras, Nicos The Department of Electrical and Computer Engineering Northwestern University EvanstonIL United States The Department of Informatics Engineering Centre for Informatics and Systems of the University of Coimbra The University of Coimbra Coimbra Portugal 1st Intensive Care Unit "G. Papanikolaou" General Hospital of Thessaloniki Greece 2nd Department of Obstetrics and Gynaecology Laboratory of Computing Medical Informatics and Biomedical - Imaging Technologies Medical School Aristotle University of Thessaloniki Greece University of Aveiro Aveiro Portugal

Assessing the health status of critically ill patients with COVID-19 and predicting their outcome are highly challenging problems and one of the reasons for poor management of ICU resources worldwide. A better pathophysiological understanding of patients’ state evolution in the ICU can enhance effective medical interventions. Therefore, there is a need to monitor and analyze the pulmonary function of a ICU patient with COVID-19 and its impact on cardiovascular and other systems. To achieve this, chest X-rays (CXRs), respiratory sounds and all the routinely monitored parameters, scores and metrics in the COVID-19 ICU were recorded from 171 ICU patients with COVID-19 from June 2020 until December 2021. Features were extracted from respiratory sounds, deep learning analysis was conducted on CXRs, and logistic regression analysis was performed on routine ICU clinical variables. Deep learning pipelines were established to classify patients’ outcomes (survival or death) at two time points (ICU mortality or 90-day mortality) using three input configurations: (a) CXRs, (b) a fusion of CXRs and respiratory sounds features, or (c) a fusion of CXRs, respiratory sounds features, and principal features of the ICU clinical measurements. The performance of the latter approach was promising, achieving, for ICU mortality, an accuracy of 0.761 and an AUC of 0.759, and for 90-day mortality, an accuracy of 0.743 and an AUC of 0.752, while the performance of approaches (a) and (b) was worse. Therefore, using multi-source data and longitudinal COVID-19 ICU data offers a better prediction of the outcome in the ICU, thereby optimizing medical decisions and interventions. Furthermore, we show that adding the adventitious respiratory sounds features significantly increased AUC and accuracy for mortality prediction of ICU patients with COVID-19. © 2023, The Authors. All rights reserved.

关键词： COVID-19

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A digital microfluidic single-cell manipulation systemoptimized by extending-depth-of-field device

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Journal of Innovative Optical Health Sciences 2023年第3期16卷 43-55页

作者： Qiushu Chen Qi Meng Yuzhe Liu Xiangan Long Yawei Kong Longfang Yao Liwen Chen Chuanyong Wu Kaiqin Chu Lan Mi Jiong Ma Department of Optical Science and Engineering Shanghai Engineering Research Center of Ultra-precision Optical Manufacturing Key Laboratory of Micro and Nano Photonic Structures(Ministry of Education)School of Information Science and Technology Fudan University Shanghai 200433P.R.China Department of Precision Machinery and Precision Instrumentation University of Science and Technology of China Hefei 230027 AnhuiP.R.China Institute of Biomedical Engineering and Technology Academy for Engineering and Technology Fudan University Shanghai 200433P.R.China Institute of Photonic Chips Centre for Articial-Intelligence Nanophotonics School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai 200093 P.R.China Ruidge Biotech Co.Ltd. No.888Huanhu West 2nd Road Lin-Gang Special AreaChina(Shanghai)Pilot Free Trade Zone Shanghai 200131P.R.China Shanghai Hengxin BioTechnology Ltd.1688 North Guo Quan RdBldg A8Rm 801 Shanghai 200438P.R.China Centre for Articial-Intelligence Nanophotonics School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai 200093 P.R.China Shanghai Engineering Research Center of Industrial Microorganisms The Multiscale Research Institute of Complex Systems(MRICS)School of Life SciencesFudan UniversityShanghai 200433P.R.China

Microfuidic systems have been widely utilized in high-throughput biology analysis,but thedificulties in iquid manipulation and cell cultivation limit its *** work has developed a new digital microfluidic(DMF)system for on-demand droplet *** adopting anextending-depth-of-field(EDoF)phase modulator to the optical system,the entire depth of themicrofluidic channel can be covered in one image without any refocusing process,ensuring that 95%of the particles in the droplet are captured within three shots together with shaking *** this system,suspension droplets are generated and droplets containing only oneyeast cll can be recognized,then each single cell is cultured in the array of the *** growth in cell numbers and the green fluorescence protein(GFP)production via fluorescence imaging,the single cell with the highest production can be *** results haveproved the heterogeneity of yeast cells,and showed that the combined system can be applied forrapid single-cell sorting,cultivation,and analysis.

关键词： Single-cell analysis digital microfluidic(DMF) extending-depth-of-field system

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A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers

arXiv

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arXiv 2022年

作者： Placed, Julio A. Strader, Jared Carrillo, Henry Atanasov, Nikolay Indelman, Vadim Carlone, Luca Castellanos, José A. Universidad de Zaragoza C/María de Luna 1 Zaragoza50018 Spain The Laboratory for Information & Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States Genius Sports One Plaza Avenida El Poblado #5A - 113 North Tower 2nd & 10th Flr Medellín AN Colombia The Department of Electrical and Computer Engineering UC San Diego 9500 Gilman Drive La Jolla CA92093-0411 United States The Department of Aerospace Engineering Technion - Israel Institute of Technology Haifa32000 Israel

Active Simultaneous Localization and Mapping (SLAM) is the problem of planning and controlling the motion of a robot to build the most accurate and complete model of the surrounding environment. Since the first foundational work in active perception appeared, more than three decades ago, this field has received increasing attention across different scientific communities. This has brought about many different approaches and formulations, and makes a review of the current trends necessary and extremely valuable for both new and experienced researchers. In this work, we survey the state-of-the-art in active SLAM and take an in-depth look at the open challenges that still require attention to meet the needs of modern applications. After providing a historical perspective, we present a unified problem formulation and review the well-established modular solution scheme, which decouples the problem into three stages that identify, select, and execute potential navigation actions. We then analyze alternative approaches, including belief-space planning and deep reinforcement learning techniques, and review related work on multi-robot coordination. The manuscript concludes with a discussion of new research directions, addressing reproducible research, active spatial perception, and practical applications, among other topics. © 2022, CC BY.

关键词： Reinforcement learning

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CARDIOCARE: An integrated platform for the management of elderly multimorbid patients with breast cancer therapy induced cardiac toxicity

CARDIOCARE: An integrated platform for the management of eld...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： G. Karanasiou L. Koumakis S. Sfakianakis G. Manikis G. Kalliatakis A. Antoniades L. Lakkas D. Mauri C. Cipolla K. Mazzocco A. Papakonstantinou G. Filippatos A. Constantinidou B. Šeruga C. Conti A. Bucur E. Pacella K. Marias M. Tsiknakis D.I. Fotiadis Unit of Medical Technology and Intelligent Information Systems University of Ioannina Ioannina Greece Foundation for Research and Technology - Hellas Institute of Computer Science Heraklion Crete Greece Computational Bio-Medicine Laboratory Institute of Computer Science Foundation for Research and Technology – Hellas Heraklion Greece Stremble Ventures Ltd Limassol Cyprus 2nd Department of Cardiology School of Medicine University of Ioannina Department of Medical Oncology University of Ioannina Greece Cardiology Division European Institute of Oncology Milan Italy Department for Breast Cancer Endocrine Tumors and Sarcoma Theme Cancer Karolinska University Hospital and the Department of Oncology-Pathology Karolinska Institute Stockholm Sweden Department of Cardiology National and Kapodistrian University of Athens School of Medicine Attikon University Hospital Athens Greece Bank of Cyprus Oncology Centre Bank of Cyprus Oncology Centre Nicosia Cyprus Department of Medical Oncology Institute of Oncology Ljubljana Ljubljana Slovenia Istituto di Management Sanitario S.r.l Milano Italy Philips Research Europe Eindhoven Netherlands European Society of Cardiology Brussels Belgium Department of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion Crete Greece Medical Technology and Intelligent Information Systems University of Ioannina Ioannina Greece Biomedical Research Institute Foundation for Research and Technology – Hellas University Campus of Ioannina Greece

Breast cancer (BC) remains the most diagnosed cancer in women, accounting for 12% of new annual cancer cases in Europe and worldwide. Advances in surgery, radiotherapy and systemic treatment have resulted in improved clinical outcomes and increased survival rates in recent years. However, BC therapy-related cardiotoxicity, may severely impact short- and long-term quality of life and survival. This study presents the CARDIOCARE platform and its main components, which by integrating patient-specific data from different categories, data from patient-oriented eHealth applications and wearable devices, and by employing advanced data mining and machine learning approaches, provides the healthcare professionals with a valuable tool for effectively managing BC patients and preventing or alleviating treatment induced *** Relevance— Through the adoption of CARDIOCARE platform healthcare professionals are able to stratify patients for their risk for cardiotoxicity and timely apply adequate interventions to prevent its onset.

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CIRCA: comprehensible online system in support of chest X-rays-based COVID-19 diagnosis

arXiv

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arXiv 2022年

作者： Prazuch, Wojciech Suwalska, Aleksandra Socha, Marek Tobiasz, Joanna Foszner, Pawel Jaroszewicz, Jerzy Gruszczynska, Katarzyna Sliwinska, Magdalena Walecki, Jerzy Popiela, Tadeusz Przybylski, Grzegorz Cieszanowski, Andrzej Nowak, Mateusz Pawlowska, Malgorzata Flisiak, Robert Simon, Krzysztof Zapolska, Gabriela Gizycka, Barbara Szurowska, Edyta Marczyk, Michal Polanska, Joanna Department of Data Science and Engineering Silesian University of Technology Gliwice Poland Department of Computer Graphics Vision and Digital Systems Silesian University of Technology Gliwice Poland Department of Infectious Diseases and Hepatology Medical University of Silesia Katowice Poland Department of Diagnostic Imaging Voivodship Specialist Hospital Wroclaw Poland Department of Diagnostic Radiology Central Clinical Hospital of The Ministry of Internal Affairs and Administration Warsaw Poland Department of Radiology Jagiellonian University Medical College Krakow Poland Department of Lung Diseases Cancer and Tuberculosis Kujawsko-Pomorskie Pulmonology Center Bydgoszcz Poland Department of Imaging Diagnostics National Institute of Oncology Warsaw Poland Department of Radiology Silesian Hospital Cieszyn Poland Department of Infectious Diseases and Hepatology Collegium Medicum in Bydgoszcz Nicolaus Copernicus University Torun Poland Department of Infectious Diseases and Hepatology Medical University of Bialystok Bialystok Poland Department of Infectious Diseases and Hepatology Wroclaw Medical University Wroclaw Poland Department of Radiology Czerniakowski Hospital Warsaw Poland PDepartment of Imaging Diagnostics MEGREZ Hospital Tychy Poland 2nd Department of Radiology Medical University of Gdansk Poland Yale Cancer Center Yale School of Medicine New HavenCT United States

Background: Due to the large accumulation of patients requiring hospitalization, the COVID-19 pandemic disease caused a high overload of health systems, even in developed countries. Deep learning techniques based on medical imaging data can help in the faster detection of COVID-19 cases and monitoring of disease progression. However, regardless of the numerous proposed solutions using lung X-rays, none of them is a product that can be used in the clinic. Methods: Five different datasets (clinical POLCOVID and AIforCOVID databases, a public collection of images called COVIDx set, pneumonia patients from NIH, and artificially generated data) were used to construct a representative dataset of 23 799 CXRs for model training, 1 050 images as a hold-out test set and 44 247 CXRs as independent test set (BIMCV database). A U-Net-based model was developed to identify a clinically relevant region of the CXR that contains the lungs and each class (normal, pneumonia, and COVID-19) was divided into 3 subtypes using a 2D Gaussian mixture model. A decision tree was used to aggregate predictions from the InceptionV3 convolutional network based on processed CXRs and a dense 7-layer neural network on radiomic features determined independently for the upper, middle, and lower lung fragments. Results: The lung segmentation model gave the Sorensen-Dice coefficient of 94.86% in the validation dataset, and 93.36% in the testing dataset. In 5-fold cross-validation, the accuracy for all classes ranged from 91% to 93%, keeping slightly higher specificity than sensitivity. Also, NPV was higher than PPV for each class to guarantee a low rate of false negative cases. In the hold-out test set, the balanced accuracy ranged between 68% and 100%. The highest performance was obtained for the subtypes N1, P1, and C1, while images assigned to subtypes N3, P3, or C3 have been wrongly predicted among themselves. A similar performance was obtained on the independent dataset for normal and COVID-19 class

关键词： COVID-19

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POLCOVID: a multicenter multiclass chest X-ray database (Poland, 2020-2021)

arXiv

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arXiv 2022年

作者： Suwalska, Aleksandra Tobiasz, Joanna Prazuch, Wojciech Socha, Marek Foszner, Pawel Piotrowski, Damian Gruszczynska, Katarzyna Sliwinska, Magdalena Walecki, Jerzy Popiela, Tadeusz Przybylski, Grzegorz Nowak, Mateusz Fiedor, Piotr Pawlowska, Malgorzata Flisiak, Robert Simon, Krzysztof Zapolska, Gabriela Gizycka, Barbara Szurowska, Edyta Marczyk, Michal Cieszanowski, Andrzej Polanska, Joanna Oronowicz-Jaskowiak, Agnieszka Golebiewski, Bogumil Sliwinska, Magdalena Rataj, Mateusz Chmielarz, Przemyslaw Tur, Adrianna Drabik, Grzegorz Kozub, Justyna Kozanecka, Anna Hildebrandt, Sebastian Krutul-Walenciej, Katarzyna Baron, Jan Jaroszewicz, Jerzy Walecki, Jerzy Wasilewski, Piotr Mazur, Samuel Klaude, Krzysztof Rataj, Katarzyna Golębiewski, Bogumil Pawlowska, Malgorzata Rabiko, Piotr Rajewski, Pawel Gizycka, Barbara Blewaska, Piotr Sznajder, Katarzyna Plesniak, Robert Department of Data Science and Engineering Silesian University of Technology Gliwice Poland Department of Computer Graphics Vision and Digital Systems Silesian University of Technology Gliwice Poland Department of Infectious Diseases and Hepatology Medical University of Silesia Katowice Poland Department of Radiology and Nuclear Medicine Medical University of Silesia Katowice Poland Department of Diagnostic Imaging Voivodship Specialist Hospital Wroclaw Poland Department of Diagnostic Radiology Central Clinical Hospital The Ministry of Internal Affairs and Administration Warsaw Poland Department of Radiology Jagiellonian University Medical College Krakow Poland Department of Lung Diseases Cancer and Tuberculosis Kujawsko-Pomorskie Pulmonology Center Bydgoszcz Poland Department of Radiology Silesian Hospital Cieszyn Poland Department of General and Transplantation Surgery Medical University of Warsaw Warsaw Poland Department of Infectious Diseases and Hepatology Collegium Medicum in Bydgoszcz Nicolaus Copernicus University Torun Poland Department of Infectious Diseases and Hepatology Medical University of Bialystok Bialystok Poland Department of Infectious Diseases and Hepatology Wroclaw Medical University Wroclaw Poland Department of Radiology Czerniakowski Hospital Warsaw Poland Department of Imaging Diagnostics MEGREZ Hospital Tychy Poland 2nd Department of Radiology Medical University of Gdansk Poland Yale Cancer Center Yale School of Medicine New HavenCT United States Department of Radiology I The Maria Sklodowska-Curie National Research Institute of Oncology Warsaw Poland The Maria Sklodowska-Curie National Research Institute of Oncology Warsaw Poland Voivodship Specialist Hospital Wroclaw Poland Prognostic Specialist Clinic Knurow Poland Collegium Medicum of the Jagiellonian University Krakow Poland Medical University of Silesia Katowice Poland Central Clinical Hospital of the Ministry of Interior in Warsaw Warsaw Poland Collegium Medicum

The outbreak of the SARS-CoV-2 pandemic has put healthcare systems worldwide to their limits, resulting in increased waiting time for diagnosis and required medical assistance. With chest radiographs (CXR) being one of the most common COVID-19 diagnosis methods, many artificial intelligence tools for image-based COVID-19 detection have been developed, often trained on a small number of images from COVID-19-positive patients. Thus, the need for high-quality and well-annotated CXR image databases increased. This paper introduces POLCOVID dataset, containing chest X-ray (CXR) images of patients with COVID-19 or other-type pneumonia, and healthy individuals gathered from 15 Polish hospitals. The original radiographs are accompanied by the preprocessed images limited to the lung area and the corresponding lung masks obtained with the segmentation model. Moreover, the manually created lung masks are provided for a part of POLCOVID dataset and the other four publicly available CXR image collections. POLCOVID dataset can help in pneumonia or COVID-19 diagnosis, while the set of matched images and lung masks may serve for the development of lung segmentation solutions. © 2022, CC BY.

关键词： COVID-19

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