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导出匹配可扩图的局部运算(英文)

数学研究 2002年第2期35卷 147-151页

作者：吴龙树王勤原晋江郑州大学计算机科学系郑州大学数学系郑州大学数学系河南郑州450052 河南郑州450052香港城市大学数学系香港

称图 G为导出匹配图可扩的 (简称为 IM-可扩的 ) ,如果图 G的每一个导出匹配都包含在 G的一个完美匹配中 .本文给出了导出匹配可扩图的一些局部运算 .

关键词：完美匹配导出匹配 IM-可扩的

Applications and prospects of cryo-EM in drug discovery

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Military Medical Research 2023年第6期10卷 848-861页

作者： Kong-Fu Zhu Chuang yuan Yong-Ming Du Kai-Lei Sun Xiao-Kang Zhang Horst Vogel Xu-dong Jia yuan-Zhu Gao Qin-Fen Zhang Da-Ping {3. Hua-Wei Zhang department of Biomedical Engineering Southern University of Science and TechnologyShenzhen 518055GuangdongChina department of Biochemistry and Molecular Biology School of Basic Medical SciencesPeking UniversityBeijing 100191China department of Structural Biology St.Jude Children’s Research HospitalMemphisTN 38105USA Center for Protein science and Crystallography School of Life SciencesFaculty of ScienceChinese University of Hong KongHong Kong 999077China Interdisciplinary Center for Brain Information the Brain Cognition and Brain Disease InstituteShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055GuangdongChina Faculty of Life and Health sciences Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055GuangdongChina Shenzhen-Hong Kong Institute of Brain science-Shenzhen Fundamental Research Institutions Shenzhen 518055GuangdongChina Shenzhen Institute of Advanced technology Chinese Academy of SciencesShenzhen 518055GuangdongChina State Key Lab for Biocontrol School of Life SciencesSun Yat-Sen UniversityGuangzhou 510275China Cryo‑EM Facility Center Southern University of Science and TechnologyShenzhen 518055GuangdongChina department of Orthopedics Shenzhen Intelligent Orthopaedics and Biomedical Innovation PlatformGuangdong Provincial Research Center for Artifcial Intelligence and Digital Orthopedic TechnologyShenzhen Second People’s HospitalThe First Affiliated Hospital of Shenzhen UniversityShenzhen 518000GuangdongChina Guangdong Provincial Key Laboratory of Advanced Biomaterials Southern University of Science and TechnologyShenzhen 518055GuangdongChina

Drug discovery is a crucial part of human healthcare and has dramatically benefited human lifespan and life quality in recent centuries, however, it is usually time-and effort-consuming. Structural biology has been demonstrated as a powerful tool to accelerate drug development. Among different techniques, cryo-electron microscopy(cryo-EM) is emerging as the mainstream of structure determination of biomacromolecules in the past decade and has received increasing attention from the pharmaceutical industry. Although cryo-EM still has limitations in resolution, speed and throughput, a growing number of innovative drugs are being developed with the help of cryo-EM. Here, we aim to provide an overview of how cryo-EM techniques are applied to facilitate drug discovery. The development and typical workflow of cryo-EM technique will be briefly introduced, followed by its specific applications in structure-based drug design, fragment-based drug discovery, proteolysis targeting chimeras, antibody drug development and drug repurposing. Besides cryo-EM, drug discovery innovation usually involves other state-of-the-art techniques such as artificial intelligence(AI), which is increasingly active in diverse areas. The combination of cryo-EM and AI provides an opportunity to minimize limitations of cryo-EM such as automation, throughput and interpretation of mediumresolution maps, and tends to be the new direction of future development of cryo-EM. The rapid development of cryo-EM will make it as an indispensable part of modern drug discovery.

关键词： Cryo-electron microscopy(cryo-EM) Drug discovery Structure-based drug design Fragment-based drug discovery Proteolysis targeting chimeras Drug repurposing Artificial intelligence(AI)

A Pipelining Loop Optimization Method for Dataflow Architecture

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Journal of computer science & technology 2018年第1期33卷 116-130页

作者： Xu Tan Xiao-Chun Ye Xiao-Wei Shen yuan-Chao Xu Da wang Lunkai Zhang Wen-Ming Li dong-Rui Fan Zhi-Min Tang State Key Laboratory of computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China School of computer and Control Engineering University of Chinese Academy of Sciences Beijing 1000J9 China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125 China College of Information Engineering Capital Normal University Beijing 100048 China department of computer science The University of Chicago Chicago IL 60637 U.S.A.

With the coming of exascale supercomputing era, power efficiency has become the most important obstacle to build an exascale system. Dataflow architecture has native advantage in achieving high power efficiency for scientific applications. However, the state-of-the-art dataflow architectures fail to exploit high parallelism for loop processing. To address this issue, we propose a pipelining loop optimization method （PLO）, which makes iterations in loops flow in the processing element （PE） array of dataflow accelerator. This method consists of two techniques, architecture-assisted hardware iteration and instruction-assisted software iteration. In hardware iteration execution model, an on-chip loop controller is designed to generate loop indexes, reducing the complexity of computing kernel and laying a good f（mndation for pipelining execution. In software iteration execution model, additional loop instructions are presented to solve the iteration dependency problem. Via these two techniques, the average number of instructions ready to execute per cycle is increased to keep floating-point unit busy. Simulation results show that our proposed method outperforms static and dynamic loop execution model in floating-point efficiency by 2.45x and 1.1x on average, respectively, while the hardware cost of these two techniques is acceptable.

关键词： dataflow model control-flow model loop optimization exascale computing scientific application

CEPC Technical Design Report

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Radiation Detection technology and Methods 2024年第1期8卷 I0003-I0016,1-1091页

作者： Waleed Abdallah Tiago Carlos Adorno de Freitas Konstantin Afanaciev Shakeel Ahmad Ijaz Ahmed Xiaocong Ai Abid Aleem Wolfgang Altmannshofer Fabio Alves Weiming An Rui An Daniele Paolo Anderle Stefan Antusch Yasuo Arai Andrej Arbuzov Abdesslam Arhrib Mustafa Ashry Sha Bai Yu Bai Yang Bai Vipul Bairathi Csaba Balazs Philip Bambade Yong Ban Tripamo Bandyopadhyay Shou-Shan Bao Desmond P.Barber Ayse Bat Varvara Batozskaya Subash Chandra Behera Alexander Belyaev Michele Bertucci Xiao-Jun Bi yuanjie Bi Tianjian Bian Fabrizio Bianchi Thomas Biekotter Michela Biglietti Shalva Bilanishvili Deng Binglin Denis Bodrov Anton Bogomyagkov Serge Bondarenko Stewart Boogert Maarten Boonekamp Marcello Borri Angelo Bosotti Vincent Boudry Mohammed Boukidi Igor Boyko Ivanka Bozovic Giuseppe Bozzi Jean-Claude Brient Anastasiia Budzinskaya Masroor Bukhari Vladimir Bytev Giacomo Cacciapaglia Hua Cai Wenyong Cai Wujun Cai Yijian Cai Yizhou Cai Yuchen Cai Haiying Cai Huacheng Cai Lorenzo Calibbi Junsong Cang Guofu Cao Jianshe Cao Antoine Chance Xuejun Chang Yue Chang Zhe Chang Xinyuan Chang Wei Chao Auttakit Chatrabhuti Yimin Che Yuzhi Che Bin Chen Danping Chen Fuqing Chen Fusan Chen Gang Chen Guoming Chen Hua-Xing Chen Huirun Chen Jinhui Chen Ji-yuan Chen Kai Chen Mali Chen Mingjun Chen Mingshui Chen Ning Chen Shanhong Chen Shanzhen Chen Shao-Long Chen Shaomin Chen Shiqiang Chen Tianlu Chen Wei Chen Xiang Chen Xiaoyu Chen Xin Chen Xun Chen Xurong Chen Ye Chen Ying Chen Yukai Chen Zelin Chen Zilin Chen Gang Chen Boping Chen Chunhui Chen Hok Chuen Cheng Huajie Cheng Shan Cheng Tongguang Cheng Yunlong Chi Pietro Chimenti Wen Han Chiu Guk Cho Ming-Chung Chu Xiaotong Chu Ziliang Chu Guglielmo Coloretti Andreas Crivellin Hanhua Cui Xiaohao Cui Zhaoyuan Cui Brunella D'Anzi Ling-Yun Dai Xinchen Dai Xuwen Dai Antonio De Maria Nicola De Filippis Christophe De La Taille Francesca De Mori Chiara De Sio Elisa Del Core Shuangxue Deng Wei-Tian Deng Zhi Deng Ziyan Deng Bhupal Dev Tang Dewen Biagio Di Micco Ran Ding Siqin Dingl Yadong Ding Haiyi dong Jianin AGH university of science and technology Faculty of Physics and Applied Computer ScienceKrakow Anhui university HefeiAnhui Applied Physics department Federal Urdu University of ArtsScience and TechnologyIslamabad Banaras Hindu university Institute of ScienceVaranasi Bandirma Onyedi Eylul university Bandirma Beihang university Beijing Beijing Computational science Research Center Beijing Beijing Conveyi LTD Beijing Beijing Glass Research Institute Co. LtdBeijing Beijing Institute of technology Beijing Beijing Normal university Beijing Brown university ProvidenceRI Budker Institute of Nuclear Physics Novosibirsk Cadi Ayyad university Laboratory of Fundamental and Applied PhysicsPolydisciplinary FacultyMarrakech Cairo university Giza Center for Theory and Computation National Tsing Hua UniversityHsinchu Central china Normal university WuhanHubei Central South university ChangshaHunan Centro de Fisica Teorica de Particulas Universidade Tecnica de LisboaLisboa Chengdu KaitengSifang Digital Radio&TV Equipment Co. LtdChengduSichuan china academy of engineering physics MianyangSichuan china building materials academy Beijing china Center of Advanced science and technology Beijing china Institute of Atomic Energy Beijing china Jiliang university HangzhouZhejiang china Nuclear Instrument CO. LTDBeijing china university of Geosciences Beijing china university of Geosciences WuhanHubei china university of Mining and technology XuzhouJiangsu china West Normal university NanchongSichuan Chongqing university Department of PhysicsChongqing Chulalongkorn university Bangkok City university of Hong Kong Department of PhysicsHong Kong Cockcroft Institute Daresbury Cornell university IthacaNY CPPM(AMU/IPhU&CNRS/INP) Marseille Dalian Minzu university DalianLiaoning Dalian university of technology Department of PhysicsDalian Deutsches Elektronen-Synchrotron DESY Hamburg Dzhelepov Laboratory of Nuclear Problems of Joint Institute for Nuclear Research(DLNP JINR) Dubna East china university Of science And Technol

The Circular Electron Positron Collider(CEPC)is a large scientific project initiated and hosted by china,fostered through extensive collaboration with international *** complex comprises four accelerators:a 3. GeV Linac,a 1.1 GeV Damping Ring,a Booster capable of achieving energies up to 180 GeV,and a Collider operating at varying energy modes(Z,W,H,and tt).The Linac and Damping Ring are situated on the surface,while the subterranean Booster and Collider are housed in a 100 km circumference underground tunnel,strategically accommodating future expansion with provisions for a potential Super Proton Proton Collider(SPPC).The CEPC primarily serves as a Higgs *** its baseline design with synchrotron radiation(SR)power of 3. MW per beam,it can achieve a luminosity of 5×10^(3.)cm^(-2)s^(-1)per interaction point(IP),resulting in an integrated luminosity of 13.ab^(-1)for two IPs over a decade,producing 2.6 million Higgs *** the SR power to 50 MW per beam expands the CEPC's capability to generate 4.3.million Higgs bosons,facilitating precise measurements of Higgs coupling at sub-percent levels,exceeding the precision expected from the HL-LHC by an order of *** Technical Design Report(TDR)follows the Preliminary Conceptual Design Report(Pre-CDR,2015)and the Conceptual Design Report(CDR,2018),comprehensively detailing the machine's layout,performance metrics,physical design and analysis,technical systems design,R&D and prototyping efforts,and associated civil engineering ***,it includes a cost estimate and a preliminary construction timeline,establishing a framework for forthcoming engineering design phase and site selection *** is anticipated to begin around 2027-2028,pending government approval,with an estimated duration of 8 *** commencement of experiments and data collection could potentially be initiated in the mid-203.s.

关键词： initiated exceeding precise

Establishment and validation of a computer-assisted colonic polyp localization system based on deep learning

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World Journal of Gastroenterology 2021年第31期27卷 5232-5246页

作者： Sheng-Bing Zhao Wei Yang Shu-Ling wang Peng Pan Run-dong wang Xin Chang Zhong-Qian Sun Xing-Hui Fu Hong Shang Jian-Rong Wu Li-Zhu Chen Jia Chang Pu Song Ying-Lei Miao Shui-Xiang He Lin Miao Hui-Qing Jiang Wen wang Xia Yang yuan-Hang dong Han Lin Yan Chen Jie Gao Qian-Qian Meng Zhen-dong Jin Zhao-Shen Li Yu Bai Changhai Hospital Second Military Medical University/Naval Medical UniversityShanghai 200433China Tencent AI Lab National Open Innovation Platform for Next Generation Artificial Intelligence on Medical ImagingShenzhen 518063Guangdong ProvinceChina department of Gastroenterology Changhai HospitalSecond Military Medical University/Naval Medical UniversityShanghai 200433China Tencent Healthcare(Shenzhen)Co.LTD. Shenzhen 518063Guangdong ProvinceChina department of Gastroenterology The First Affiliated Hospital of Kunming Medical UniversityKunming 650000Yunnan ProvinceChina department of Gastroenterology The First Affiliated Hospital of Xi'an Jiaotong UniversityXi'an 710061Shaanxi ProvinceChina Institute of Digestive Endoscopy and Medical Center for Digestive Disease The Second Affiliated Hospital of Nanjing Medical UniversityNanjing 210011Jiangsu ProvinceChina department of Gastroenterology The Second Hospital of Hebei Medical UniversityHebei Key Laboratory of GastroenterologyHebei Institute of GastroenterologyShijiazhuang 050000Hebei ProvinceChina department of Gastroenterology 900th Hospital of Joint Logistics Support ForceFuzhou 350025Fujian ProvinceChina department of Gastroenterology No.905 Hospital of The Chinese People's Liberation ArmyShanghai 200050China

BACKGROUND Artificial intelligence in colonoscopy is an emerging field,and its application may help colonoscopists improve inspection quality and reduce the rate of missed polyps and *** deep learning-based computer-assisted detection(CADe)techniques were established from small single-center datasets,and unrepresentative learning materials might confine their application and generalization in wide *** CADes have been reported to identify polyps in colonoscopic images and videos in real time,their diagnostic performance deserves to be further validated in clinical *** To train and test a CADe based on multicenter high-quality images of polyps and preliminarily validate it in clinical *** With high-quality screening and labeling from 55 qualified colonoscopists,a dataset consisting of over 71000 images from 20 centers was used to train and test a deep learning-based *** addition,the real-time diagnostic performance of CADe was tested frame by frame in 47 unaltered full-ranged videos that contained 86 histologically confirmed ***,we conducted a selfcontrolled observational study to validate the diagnostic performance of CADe in real-world colonoscopy with the main outcome measure of polyps per colonoscopy in Changhai *** The CADe was able to identify polyps in the test dataset with 95.0%sensitivity and 99.1%*** colonoscopy videos,all 86 polyps were detected with 92.2%sensitivity and 93.6%specificity in frame-by-frame *** the prospective validation,the sensitivity of CAD in identifying polyps was 98.4%(185/188).Folds,reflections of light and fecal fluid were the main causes of false positives in both the test dataset and clinical *** can detect more polyps(0.90 vs 0.82,P3. vs 0.3.,P=0.045)with the aid of CADe,particularly polyps<5 mm and flat polyps(0.65 vs 0.57,P<0.001;0.74 vs 0.67,P=0.001,respectively).However,high effica

关键词： computer-assisted detection Artificial intelligence Deep learning Colonoscopy Clinical validation Colorectal polyp

Artificial intelligence system of faster region-based convolutional neural network surpassing senior radiologists in evaluation of metastatic lymph nodes of rectal cancer

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Chinese Medical Journal 2019年第4期132卷 379-387页

作者： Lei Ding Guang-Wei Liu Bao-Chun Zhao Yun-Peng Zhou Shuai Li Zheng-dong Zhang Yu-Ting Guo Ai-Qin Li Yun Lu Hong-Wei Yao Wei-Tang yuan Gui-Ying wang Dian-Liang Zhang Lei wang Shandong Key Laboratory of Digital Medicine & computer Assisted Surgery Qingdao UniversityQingdaoShandong 266003China department of General Surgery Qingdao Municipal HospitalQingdaoShandong 266011China department of General Surgery The Sixth Affiliated Hospital of Sun Yat-Sen UniversityGuangzhouGuangdong 510655China. department of Medical Administration Affiliated Hospital of Qingdao UniversityQingdao UniversityQingdaoShandong 266003China department of Outpatient Administration Affiliated Hospital of Qingdao UniversityQingdao UniversityQingdaoShandong 266003China department of Follow-up Affiliated Hospital of Qingdao UniversityQingdao UniversityQingdaoShandong 266003China department of Gastroenterology Surgery Affiliated Hospital of Qingdao UniversityQingdao UniversityQingdaoShandong 266003China State Key Laboratory of Virtual Reality technology &Systems Beihang UniversityBeijing 100191China department of General Surgery Beijing Friendship HospitalCapital Medical University & National Clinical Research Center for Digestive DiseasesBeijing 100050China department of General Surgery First Affiliated Hospital of Zhengzhou UniversityZhenzhouHenan 450052China department of General Surgery Fourth Hospital of Hebei Medical UniversityShijiazhuangHebei 050011China

Background: An artificial intelligence system of Faster Region-based Convolutional Neural Network (Faster R-CNN) is newly developed for the diagnosis of metastatic lymph node (LN) in rectal cancer patients. The primary objective of this study was to comprehensively verify its accuracy in clinical use. Methods: Four hundred fourteen patients with rectal cancer discharged between January 2013.and March 2015 were collected from 6 clinical centers, and the magnetic resonance imaging data for pelvic metastatic LNs of each patient was identified by Faster R-CNN. Faster R-CNN based diagnoses were compared with radiologist based diagnoses and pathologist based diagnoses for methodological verification, using correlation analyses and consistency check. For clinical verification, the patients were retrospectively followed up by telephone for 3. months, with post-operative recurrence of rectal cancer as a clinical outcome;recurrence-free survivals of the patients were compared among different diagnostic groups, by methods of Kaplan-Meier and Cox hazards regression model. Results: Significant correlations were observed between any 2 factors among the numbers of metastatic LNs separately diagnosed by radiologists, Faster R-CNN and pathologists, as evidenced by rradiologist-Faster R-CNN of 0.912, rPathologist-radiologist of 0.13., and rPathologist-Faster R-CNN of 0.448 respectively. The value of kappa coefficient in N staging between Faster R-CNN and pathologists was 0.573. and this value between radiologists and pathologists was 0.473. The 3.groups of Faster R-CNN, radiologists and pathologists showed no significant differences in the recurrence-free survival time for stage N0 and N1 patients, but significant differences were found for stage N2 patients. Conclusion: Faster R-CNN surpasses radiologists in the evaluation of pelvic metastatic LNs of rectal cancer, but is not on par with pathologists.

关键词： AI (Artificial Intelligence) Magnetic resonance imaging Pathology Lymph nodes Rectal cancer

Was Wuhan the early epicenter of the COVID-19 pandemic?——a critique

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National science Review 2023年第4期10卷 19-21页

作者： Yanan Cao Lingling Chen Hua Chen Yupeng Cun Xiaofeng Dai Hongli Du Feng Gao Fengbiao Guo Yalong Guo Pei Hao Shunmin He Shunping He XiongLei He Zheng Hu Boon-Peng Hoh Xin Jin Qian Jiang Qinghua Jiang Asifullah Khan Hong-Zhi Kong Jinchen Li Shuai Cheng Li Ying Li Qiang Lin Jianquan Liu Qi Liu Jian Lu Xuemei Lu Shujin Luo Qinghua Nie Zilong Qiu Tieliu Shi Xiaofeng Song Jianzhong Su Sheng-ce Tao Chaolong wang Chuan-Chao wang Guo-dong wang Jiguang wang Qi Wu Shaoyuan Wu Shuhua Xu Yu Xue Wenjun Yang Zhaohui Yang Kai Ye yuan-Nong Ye Li Yu Fangqing Zhao Yiqiang Zhao Weiwei Zhai Dandan Zhang Liye Zhang Houfeng Zheng Qi Zhou Tianqi Zhu Ya-ping Zhang Ruijin Hospital Shanghai Jiao Tong University College of Life science and technology Guangxi University Beijing Institute of Genomics Chinese Academy of Sciences Children's Hospital of Chongqing Medical university Wuxi School of Medicine Jiangnan University School of Biology and Biological Engineering South China University of Technology department of Physics School of ScienceTianjin University School of Pharmaceutical sciences Wuhan University Institute of Botany Chinese Academy of Sciences Institut Pasteur of Shanghai Chinese Academy of Sciences Institute of Biophysics Chinese Academy of Sciences Institute of Hydrobiology Chinese Academy of Sciences School of Life sciences Sun Yat-sen University Institute of Synthetic Biology Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Faculty of Medicine and Health sciences University College Sedaya International School of Medicine South China University of Technology department of Medical Genetics Capital Institute of Pediatrics School of Life science and technology Harbin Institute of Technology department of Biochemistry Abdul Wali Khan University Xiangya Hospital Central South University department of computer science City University of Hong Kong College of Life science and technology Foshan University South china Sea Institute of Oceanology Chinese Academy of Sciences College of Ecology Lanzhou University School of Life sciences and technology Tongji University School of Life sciences Peking University Kunming Institute of Zoology Chinese Academy of Sciences College of Animal science South China Agricultural University Institute of Neuroscience Chinese Academy of Sciences School of Life sciences East China Normal University Nanjing university of Aeronautics and Astronautics Wenzhou Institute University of Chinese Academy of Sciences Institute of Systems Biomedicine Shanghai Jiao Tong University Tongji Medical College Huazhong University of Science and Technology School of Life sciences Xiamen University Division of Life science and department of

Recently, Worobey et al.(2022) published a report entitled ‘The Huanan Seafood Wholesale Market in Wuhan was the early epicenter of the COVID-19 pandemic’ that succinctly summarizes their study [1]. A pre-print version of this study had earlier elicited a series of highprofile media coverages [2,3.. All these reports deliver a social-political message that the Huanan market is the epicenter of COVID-19.

关键词： SARS-COV-2 ORIGIN

Reciprocating Magnetic Fields in the Pulsar Wind Observed from the Black Widow Pulsar J1720-053.

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Research in Astronomy and Astrophysics 2023年第10期23卷 255-266页

作者： Chen-Chen Miao Victoria Blackmon Wei-Wei Zhu dong-Zi Li Ming-Yu Ge Xiao-Peng You Maura McLaughlin Di Li Na wang Pei wang Jia-Rui Niu M.Cruces Jian-Ping {3. Jun-Tao Bai D.J.Champion Yu-Tong Chen Ming-Min Chi P.C.C.Freire Yi Feng Zhen-Ye Gan M.Kramer Fei-Fei Kou Yu-Xi Li Xue-Li Miao Ling-Qi Meng Chen-Hui Niu Sheng-Nan Sun Zhong-Yi Sun H.M.Tedila Shuang-Qiang wang Qing-dong Wu Jing-Bo wang Zhi-Gang Wen Shen wang Ya-Biao wang Cheng-Jie wang Meng-Yao Xue You-Ling Yue Mao {3. Ju-Mei Yao Wen-Ming Yan Ru-Shuang Zhao Lei Zhang De Zhao National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China School of Astronomy and Space science University of Chinese Academy of SciencesBeijing 100049China West Virginia university 1500 University AveMorgantownWV 26506USA Institute for Frontiers in Astronomy and Astrophysics Beijing Normal UniversityBeijing 102206China Cahill Center for Astronomy and Astrophysics California Institute of Technology1216 E California BoulevardPasadenaCA 91125USA Key Laboratory of Particle Astrophysics Institute of High Energy PhysicsChinese Academy of SciencesBeijing 100049China School of Physical science and technology Southwest UniversityChongqing 400715China Research Center for Intelligent Computing Platforms Zhejiang LaboratoryHangzhou 311100China Xinjiang Astronomical Observatory Chinese Academy of SciencesUrumqi 830011China Max-Planck Institut für Radioastronomie Auf dem Hügel 69D-53121 BonnGermany School of computer science Fudan UniversityShanghai 200433China Tencent Youtu Lab Shanghai 200233China College of Physical science and technology Central China Normal UniversityWuhan 430079China Institute of Optoelectronic technology Lishui UniversityLishui 323000China Guizhou Normal university Guiyang 550001China

We report the radio observations of the eclipsing black widow pulsar J1720-053., a 3.26 ms pulsar in orbit with a low mass companion of mass 0.029 to 0.03. M⊙. We obtain the phase-connected timing ephemeris and polarization profile of this millisecond pulsar(MSP) using the Five-hundred-meter Aperture Spherical radio Telescope(FAST), the Green Bank Telescope(GBT), and the Parkes Telescope. For the first time from such a system, an oscillatory polarization angle change was observed from a particular eclipse egress with partial depolarization, indicating 10-milliGauss-level reciprocating magnetic fields oscillating in a length scale of 5 ×10^(3.km(assuming an orbital inclination angle of 90°) outside the companion's magnetosphere. The dispersion measure variation observed during the ingresses and egresses shows the rapid raising of the electron density in the shock boundary between the companion's magnetosphere and the surrounding pulsar wind. We suggest that the observed oscillatory magnetic fields originate from the pulsar wind outside the companion's magnetosphere.

关键词： (stars:)pulsars:general (stars:)pulsars:individual(J1720-053.) (stars:)binaries:eclipsing

An FPGA‑based trigger system for CSHINE

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Nuclear science and Techniques 2022年第12期33卷 138-146页

作者： dong Guo Yu‑Hao Qin Sheng Xiao Zhi Qin Yi‑Jie wang Fen‑Hai Guan Xin‑Yue Diao Bo‑yuan Zhang Yao‑Peng Zhang Da‑Wei Si Shi‑Wei Xu Xiang‑Lun Wei He‑Run Yang Peng Ma Tian‑Li Qiu Hai‑Chuan Zou Li‑Min Duan Zhi‑Gang Xiao department of Physics Tsinghua UniversityBeijing 100084China Institute of Modern Physics Chinese Academy of ScienceLanzhou 730000China School of Nuclear science and technology Lanzhou UniversityLanzhou 730000China

A trigger system of the general function was designed using the commercial module of CAEN V2495 for heavy-ion nuclear reaction experiments at Fermi *** system was applied and verified on the compact spectrometer for heavy IoN experiment(CSHINE).Based on the field-programmable logic gate array technology of the command register access and remote computer control operation,trigger functions can be flexibly configured according to experimental physical *** the trigger system on CSHINE,we conducted a beam experiment at 25 MeV/u^(86)Kr+^(124)Sn on the Radioactive Ion Beam Line 1 in Lanzhou,*** online results demonstrated that the trigger system worked normally and *** system can be extended to other experiments as well.

关键词： CSHINE Trigger system FPGA Heavy ion experiment