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Identification method of forest fire risk factors and their coupling relationship driven by attribute dependence

International Journal of Disaster Risk Reduction 2025年 125卷

作者： Zhao, Enhui Wang, Ning Cui, Shibo Zhao, Rui Yu, Yongping School of Economics and Management Dalian University of Technology Liaoning Province Dalian 116024 China Key Laboratory of Social Computing and Cognitive Intelligence Ministry of Education Dalian University of Technology Dalian 116024 China DUT Institute for Advanced Intelligence Dalian University of Technology Dalian 116024 China

Identifying forest fire risk factors and their coupling relationships is crucial for revealing the mechanism of fire occurrence and assessing risks. However, previous studies on forest fire risk factors often ignore the differences and coupling effects between risk factors, resulting in inaccurate risk prediction. Given this, this study introduces the attribute dependence of fuzzy rough set into forest fires and constructs a new method to identify key risk factors and measure their coupling effects. Taking the forest fire risk in the Thompson area of Canada from 1984 to 2023 as the research object, the influence of each risk factor on forest fire is quantified by the attribute dependence function. Then, according to the influence degree of each risk factor, the weighted processing is carried out, and the coupling effect of risk factors is measured by combining the overlapping idea of fuzzy mutual information. The results show that climate factors have a significant impact on forest fires, while topographic factors, vegetation factors, and human factors are relatively weak. The coupling effect of topographic factors, vegetation factors, and human factors is also weak, but the combination of climate factors and topographic factors, vegetation factors, and human factors significantly enhances its influence. © 2025 Elsevier Ltd

关键词： Attribute dependence Forest fires Fuzzy mutual information Risk factor coupling Risk factor identification

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Fine structure in the decay of Hg and Au

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The European Physical Journal A 2025年第3期61卷 1-9页

作者： Špaček, A. Herzáň, A. Sedlák, M. Venhart, M. Ali, F. A. Andreyev, A. N. Auranen, K. Bánovská, S. Balogh, M. Carroll, R. J. Cox, D. M. Cubiss, J. G. Davis, T. Drummond, M. C. Easton, J. L. Grahn, T. Gredley, A. Greenlees, P. T. Henderson, J. Jakobsson, U. Joss, D. T. Julin, R. Juutinen, S. Kantay, G. Kliman, J. Konki, J. Lawrie, E. A. Leino, M. Matoušek, V. Mistry, A. K. McPeake, C. G. O’Donnell, D. Page, R. D. Pakarinen, J. Papadakis, P. Partanen, J. Peura, P. Rahkila, P. Repko, A. Ruotsalainen, P. Sandzelius, M. Sarén, J. Saygi, B. Seweryniak, D. Scholey, C. Sorri, J. Stolze, S. Thornthwaite, A. Timchenko, I. S. Uusitalo, J. Veselský, M. Vielhauer, S. Wearing, F. P. Heavy Ion Laboratory University of Warsaw Warsaw Poland Institute of Physics Slovak Academy of Sciences Bratislava Slovakia Oliver Lodge Laboratory University of Liverpool Liverpool UK Department of Physics College of Science Education University of Sulaimani Sulaimani Iraq School of Physics Engineering and Technology University of York Heslington UK Advanced Science Research Center Japan Atomic Energy Agency (JAEA) Naka-gun Japan Accelerator Laboratory Department of Physics University of Jyväskylä Jyväskylä Finland INFN Laboratori Nazionali di Legnaro Padova Italy Themba Laboratory for Accelerator Based Sciences Somerset West South Africa Department of Physics and Astronomy University of the Western Cape Bellville South Africa Laboratory of Radio-Chemistry Department of Chemistry University of Helsinki Helsinki Finland CERN Geneva 23 Switzerland School of Computing Engineering and Physical Sciences University of the West of Scotland Paisley UK STFC Daresbury Laboratory Daresbury UK Helsinki Institute of Physics Helsinki Finland Department of Physics Faculty of Science Ege University Izmir Turkey Department of Physics Faculty of Science and Arts Sakarya University Sakarya Turkey Physics Division Argonne National Laboratory Lemont USA STUK - Radiation and Nuclear Safety Authority Helsinki Finland Physics Division Argonne National Laboratory Argonne USA Institute of Experimental and Applied Physics Czech Technical University Prague Czech Republic

The $$\upalpha $$ -decay fine structure of $$^{179}$$ Hg and $$^{177}$$ Au was studied by means of decay spectroscopy. Two experiments were performed at the Accelerator laboratory of the University of Jyväskylä (JYFL), Finland, utilizing the recoil separator RITU and a digital data acquisition system. The heavy-ion induced fusion-evaporation reactions $$^{82}_{36}$$ Kr + $$^{100}_{44}$$ Ru and $$^{88}_{38}$$ Kr + $$^{92}_{42}$$ Mo were used to produce the $$^{179}$$ Hg and $$^{177}$$ Au nuclei, respectively. Studying the evaporation residues (ER, recoils)- $$\alpha _1$$ - $$\alpha _2$$ correlations and $$\upalpha $$ - $$\gamma $$ coincidences, a new $$\upalpha $$ decay with E $$_\alpha $$ = 6156(10) keV was observed from $$^{179}$$ Hg. This decay populates the (9/2 $$^-$$ ) excited state at an excitation energy of 131.3(5) keV in $$^{175}$$ Pt. The internal conversion coefficient for the 131.3(5) keV transition de-exciting this state was measured for the first time. Regarding the $$^{177}$$ Au nucleus, a new $$\upalpha $$ decay with E $$_\alpha $$ = 5998(9) keV was observed to populate the 156.1(6) keV excited state in $$^{173}$$ Ir. Two de-excitation paths were observed from this excited state. Moreover, a new 215.7(13) keV transition was observed to depopulate the 424.4(13) keV excited state in $$^{173}$$ Ir. Properties of the $$^{179}$$ Hg and $$^{177}$$ Au $$\upalpha $$ decays were examined in a framework of reduced widths and hindrance factors. For clarity and simplicity, the spin and parity assignments (e.g. $$J^{\pi }$$ ) are presented without brackets throughout the text.

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Curiosity-Driven Science: The in Situ Jungle Biomechanics Lab in the Amazon Rainforest

arXiv

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arXiv 2024年

作者： Stupski, S. David Ferrer, Laura Casas Harrison, Jacob S. Jackson, Justina Mansilla, Carolina Paucarhuanca Lívano, Loribeth Maricielo Bolo Narla, Avaneesh Chai, Chew Clark, Elizabeth Ha, Nami Nina, Jaime Quispe Wold, Ethan Reyes-Quinteros, Johana Gallice, Geoffrey Bhamla, Saad Department of Biology University of Washington SeattleWA United States School of Chemical and Biomolecular Engineering Georgia Institute of Technology AtlantaGA United States Center for Education Integrating Science Mathematics and Computing Georgia Institute of Technology AtlantaGA United States Department of Engineering Pontifical Catholic University of Peru Lima Peru Department of Applied Physics Stanford University StanfordCA United States Department of Bioengineering Stanford University StanfordCA United States Advanced Light Source Lawrence Berkeley National Laboratory BerkeleyCA United States Nature Travel Photography Madre de Dios Tambopata Peru Alliance for a Sustainable Amazon PotomacMD United States Department of Natural History Florida Museum of Natural History University of Florida GainesvilleFL United States

Field work is an essential component not just for organismal biology, but also for the expanding umbrella of disciplines which have turned their attention towards living systems. Observing organisms in naturalistic contexts is a critical component of discovery;however, conducting field research can be a massive barrier for scientists who do not have experience working with organisms in a naturalistic context under challenging field conditions. Here we propose 8 critical steps for organizing and executing interdisciplinary curiosity-driven field research, drawing on the insights from The in Situ Jungle Biomechanics Lab (JBL). The JBL program is a field research course that helps early-career scientists gain experience in organizing and conducting interdisciplinary field research. JBL uses a curiosity-driven approach to field science education by encouraging early-career researchers to explore scientific questions in the Peruvian Amazon with a non-prescriptive approach to research output. We achieve an inclusive research space by bringing scientists from across disciplines together, with local communities to collaborate and spark new questions and ideas. To stoke curiosity, the JBL imparts a naturalist tradition set forth by organismal biologists of the 20th century who have extolled the merits of observing the natural world as a form of scientific exploration. © 2024, CC BY-NC-ND.

关键词： Biotic

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Automated identification and quantification of myocardial inflammatory infiltration in digital histological images to diagnose myocarditis

arXiv

引用

arXiv 2023年

作者： Liu, Yanyun Hua, Xiumeng Zhu, Shouping Wang, Congrui Chen, Xiao Shi, Yu Song, Jiangping Zhou, Weihua School of Life Science and Technology Xidian University Engineering Research Center of Molecular and Neuro Imaging Ministry of Education Shaanxi Xi'An China Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment School of Life Science and Technology Xidian University Shaanxi Xi'An China Innovation Center for Advanced Medical Imaging and Intelligent Medicine Guangzhou Institute of Technology Xidian University Guangdong Guangzhou China Beijing Key Laboratory of Preclinical Research and Evaluation for Cardiovascular Implant Materials Animal Experimental Centre Fuwai Hospital National Centre for Cardiovascular Disease Chinese Academy of Medical Sciences Peking Union Medical College Beijing China State Key Laboratory of Cardiovascular Disease Fuwai Hospital National Center for Cardiovascular Diseases Chinese Academy of Medical Sciences Peking Union Medical College 167A Beilishi Road Xi Cheng District Beijing China Department of Cardiovascular Surgery Fuwai Hospital National Center for Cardiovascular Diseases National Clinical Research Center of Cardiovascular Diseases State Key Laboratory of Cardiovascular Disease Chinese Academy of Medical Sciences Peking Union Medical College Beijing China The Cardiomyopathy Research Group Fuwai Hospital Beijing China Department of Applied Computing Michigan Technological University HoughtonMI United States Center for Biocomputing and Digital Health Institute of Computing and Cybersystems Health Research Institute Michigan Technological University HoughtonMI United States

Background Given the dimensionality of WSIs and the increase in the number of potential cases, the manual histological diagnosis of myocarditis based on Dallas Criteria is time-consuming, experience-dependent. Objectives To overcome the limitations of manual diagnosis methods, this study aims to develop a new computational pathology approach that automates the identification and quantification of myocardial inflammatory infiltration in digital hematoxylin and eosin (HE)-stained images to assist pathologists in diagnosing myocarditis. Methods 898 HE-stained whole slide images (WSIs) of myocardium from 154 heart transplant patients diagnosed with myocarditis or dilated cardiomyopathy (DCM) were included in this study. An automated deep-learning (DL)-based computational pathology approach was developed to identify nuclei and detect myocardial inflammatory infiltration, enabling the quantification of the lymphocyte nuclear density (LND) on myocardial WSIs. A cutoff value based on the quantification of LND was proposed to determine if the myocardial inflammatory infiltration was present. The performance of our approach was evaluated with a five-fold cross-validation experiment, tested with an internal test set from the myocarditis group, and confirmed by an external test from a double-blind trial group. Results An LND of 1.02/mm2 could distinguish WSIs with myocarditis from those without. The accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) in the five-fold cross-validation experiment were 0.899 ± 0.035, 0.971 ± 0.017, 0.728 ± 0.073 and 0.849 ± 0.044, respectively. For the internal test set, the accuracy, sensitivity, specificity, and AUC were 0.887, 0.971, 0.737, and 0.854, respectively. The accuracy, sensitivity, specificity, and AUC for the external test set reached 0.853, 0.846, 0.858, and 0.852, respectively. Conclusion Our new approach provides accurate and reliable quantification of the LND of myocardial WSIs bas

关键词： Pathology

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Constraints, effectiveness and solutions in using Google Classroom as a Learning Management System during Covid-19 pandemic: a systematic literature review

Constraints, effectiveness and solutions in using Google Cla...

引用

Latin American Conference on Learning Objects and technology (LACLO)

作者： Danielli A. Lima Seiji Isotani Instituto Federal do Triângulo Mineiro (IFTM) Campus Patrocínio Laboratório de Inteligência Computacional e Robótica (LICRo) Patrocínio Brasil Universidade de São Paulo (USP) São Carlos Brasil Instituto de Ciências Matemáticas e de Computação (ICMC) Applied Computing in Education and Advanced Social Technology Laboratory (CAEd) Universidade de São Paulo (USP) São Carlos Brasil

ISBN: (纸本)9781665423595

The purpose of this paper is to determine the impact of using Google Classroom as an educational tool during pandemic of Covid-19. This research is a meta-analysis study based on the search for several articles from newspapers and conferences that fit within the search string and the research protocol. From the selection and extraction of articles that fit within the research protocol, the accepted articles were classified considering the criteria of effectiveness, limitations and challenges and the solutions adopted by the educational stakeholders during the pandemic. Among the results found, we can highlight that the Google Classroom tool has good usability, the mobile application facilitates communication and interaction between hyperlinks. Among the limitations of the use of the tool during the pandemic, the internet quota or connection quality, or the impossibility of downloading large files, stand out. The solutions presented were to reduce the size of files and the good preparation of students and teachers in using this tool.

关键词： COVID-19 Training Protocols Systematics Pandemics Mobile applications Stakeholders

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DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

arXiv

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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

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Monitoring the daily variation of Sun-Earth magnetic fields using galactic cosmic rays

arXiv

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arXiv 2024年

作者： Cao, Zhen Aharonian, F. Axikegu Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Bian, W. Bukevich, A.V. Cao, Q. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, A.M. Chen, E.S. Chen, H.X. Chen, Liang Chen, Lin Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, M.Y. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, J.H. Fang, K. Feng, C.F. Feng, H. Feng, L. Feng, S.H. Feng, X.T. Feng, Y. Feng, Y.L. Gabici, S. Gao, B. Gao, C.D. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Geng, L.S. Giacinti, G. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. Hasan, M. He, H.H. He, H.N. He, J.Y. He, Y. Hor, Y.K. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, Q. Hu, S.C. Huang, D.H. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Ji, X.L. Jia, H.Y. Jia, K. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kang, M.M. Karpikov, I. Kuleshov, D. Kurinov, K. Li, B.B. Li, C.M. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, Jian Li, Jie Li, K. Li, S.D. Li, W.L. Li, W.L. Li, X.R. Li, Xin Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, D.B. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Luo, Q. Luo, Y. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Min, Z. Mitthumsiri, W. Mu, H.J. Nan, Y.C. Neronov, A. Ou, L.J. Pattarakijwanich, P. Pei, Z.Y. Qi, J.C. Qi, M.Y. Qiao, B.Q. Qin, J.J. Raza, A. Ruffolo, D. S'aiz, A. Saeed, M. Semikoz, D. Shao, L. Shchegolev, O. Sheng, X.D. Shu, F.W. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, D.X. University of Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu China Dublin Institute for Advanced Studies 31 Fitzwilliam Place 2 Dublin Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Tsung-Dao Lee Institute School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Institute for Nuclear Research Russian Academy of Sciences Moscow117312 Russia Hebei Normal University Hebei Shijiazhuang050024 China University of Science and Technology of China Anhui Hefei230026 China State Key Laboratory of Particle Detection and Electronics China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Research Center for Astronomical Computing Zhejiang Laboratory Zhejiang Hangzhou311121 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Key Laboratory of Cosmic Rays Tibet University Ministry of Education Tibet Lhasa850000 China Key Laboratory of Radio Astronomy and Technology National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Sun Yat-sen University Guangdong Zhuhai51900 China Sun Yat-sen University Guangdong Guangzhou510275 China Institute of Frontier and Interdisciplinary Science Shandong University Shandong Qingdao266237 China APC Universit'e Paris Cit'e CNRS IN2P3 CEA IRFU Observatoire de Paris Paris11

The interplanetary magnetic field (IMF) between the Sun and Earth is an extension of the solar magnetic field carried by the solar wind into interplanetary space. Monitoring variations in the IMF upstream of the Earth would provide very important information for the prediction of space weather effects, such as effects of solar storms and the solar wind, on human activity. In this study, the IMF between the Sun and Earth was measured daily for the first time using a cosmic-ray observatory. Cosmic rays mainly consist of charged particles that are deflected as they pass through a magnetic field. Therefore, the cosmic-ray Sun shadow, caused by high-energy charged cosmic rays blocked by the Sun and deflected by the magnetic field, can be used to explore the transverse IMF between the Sun and Earth. By employing the powerful kilometer-square array at the Large High Altitude Air Shower Observatory, the cosmic-ray Sun shadows were observed daily with high significance for the first time. The displacement of the Sun shadow measured in 2021 correlates well with the transverse IMF component measured in situ by spacecraft near the Earth, with a time lag of 3:31 ± 0:12 days. The displacement of the Sun shadow was also simulated using Parker’s classic IMF model, yielding a time lag of 2:06 ± 0:04 days. This deviation may provide valuable insights into the magnetic field structure, which can improve space weather research. Copyright © 2024, The Authors. All rights reserved.

关键词： Cosmology

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The effects of different travel modes and travel destinations on COVID-19 transmission in global cities

引用

Science Bulletin 2022年第6期67卷 588-592,M0003页

作者： Rui Zhu Luc Anselin Michael Batty Mei-Po Kwan Min Chen Wei Luo Tao Cheng Che Kang Lim Paolo Santi Cheng Cheng Qiushi Gu Man Sing Wong Kai Zhang Guonian Lü Carlo Ratti Department of Land Surveying and Geo-Informatics the Hong Kong Polytechnic UniversityHong KongChina Key Laboratory of Virtual Geographic Environment(Ministry of Education) Nanjing Normal UniversityNanjing 210023China Senseable City Laboratory Future Urban Mobility IRGSingapore-MIT Alliance for Research and TechnologySingapore 138602Singapore Center for Spatial Data Science Division of the Social SciencesUniversity of ChicagoChicago IL 60637USA School of Geography Science Nanjing Normal UniversityNanjing 210023China Centre for Advanced Spatial Analysis Faculty of the Built EnvironmentUniversity College LondonLondon WC1E 6BTUK Department of Geography and Resource Management the Chinese University of Hong KongHong KongChina Institute of Space and Earth Information Science the Chinese University of Hong KongHong KongChina Department of Human Geography and Spatial Planning Utrecht UniversityUtrecht 3584 CSNetherlands State Key Laboratory Cultivation Base of Geographical Environment Evolution Nanjing 210023China Department of Geography National University of SingaporeSingapore 117570Singapore SpaceTimeLab Department of CivilEnvironmental and Geomatic EngineeringUniversity College LondonLondon WC1E 6BTUK Department of Clinical Translational Research Singapore General HospitalSingapore 169856Singapore Division of Clinical Immunology and Transfusion Medicine Department of Laboratory MedicineKarolinska InstitutetStockholm 17177Sweden Senseable City Laboratory Department of Urban Studies and PlanningMassachusetts Institute of TechnologyCambridgeMA 02139USA Istituto di Informatica e Telematica del CNR Pisa 56124Italy Department of System Science Institute of High Performance Computing Agency for ScienceTechnology and ResearchSingapore 138632Singapore Department of Tourism Studies School of HumanitiesSoutheast UniversityNanjing 210096China

The international community has made significant efforts to flatten the COVID-19 curve,including predicting transmission[1,2],executing unprecedented global lockdowns and social distancing[3,4],promoting the wearing of facemasks and social distancing measures[5],and isolating confirmed cases and contacts[6].Because of the adverse consequences of these lockdown measures[7],many cities have reopened so they can rebuild their ***,as mobility has gradually returned towards normal,imported cases from unknown sources have disrupted the recovery situation,and cities are continually at high risk of new waves of infection[8,9]since airborne transmission is the dominant transmission route[10].

关键词：多变量时间序列疫情传播出行方式谷歌公司交通出行病毒传播出行行为大城市

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DPA-2: a large atomic model as a multi-task learner

arXiv

引用

arXiv 2023年

作者： Zhang, Duo Liu, Xinzijian Zhang, Xiangyu Zhang, Chengqian Cai, Chun Bi, Hangrui Du, Yiming Qin, Xuejian Peng, Anyang Huang, Jiameng Li, Bowen Shan, Yifan Zeng, Jinzhe Zhang, Yuzhi Liu, Siyuan Li, Yifan Chang, Junhan Wang, Xinyan Zhou, Shuo Liu, Jianchuan Luo, Xiaoshan Wang, Zhenyu Jiang, Wanrun Wu, Jing Yang, Yudi Yang, Jiyuan Yang, Manyi Gong, Fu-Qiang Zhang, Linshuang Shi, Mengchao Dai, Fu-Zhi York, Darrin M. Liu, Shi Zhu, Tong Zhong, Zhicheng Lv, Jian Cheng, Jun Jia, Weile Chen, Mohan Ke, Guolin Weinan, E. Zhang, Linfeng Wang, Han AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China CAS Key Laboratory of Magnetic Materials and Devices Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of Sciences Ningbo315201 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development School of Chemistry and Molecular Engineering East China Normal University Shanghai200062 China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States Department of Chemistry Princeton University PrincetonNJ08540 United States College of Chemistry and Molecular Engineering Peking University Beijing100871 China Yuanpei College Peking University Beijing100871 China School of Electrical Engineering and Electronic Information Xihua University Chengdu610039 China State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun130012 China Key Laboratory of Material Simulation Methods & Software of Ministry of Education College of Physics Jilin University Changchun130012 China International Center of Future Science Jilin University Changchun130012 China Key Laboratory for Quantum Materials of Zhejiang Province Department of Physics School of Science Westlake University Zhejiang Hangzhou310030 China Atomistic Simulations Italia

The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demonstrated the capability to conduct large-scale, long-duration simulations with the accuracy of ab initio electronic structure methods. However, the model generation process remains a bottleneck for large-scale applications. We propose a shift towards a model-centric ecosystem, wherein a large atomic model (LAM), pre-trained across multiple disciplines, can be efficiently fine-tuned and distilled for various downstream tasks, thereby establishing a new framework for molecular modeling. In this study, we introduce the DPA-2 architecture as a prototype for LAMs. Pre-trained on a diverse array of chemical and materials systems using a multi-task approach, DPA-2 demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning methodologies. Our approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi-task learning

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A Segmentation Method of Lung Parenchyma From Chest CT Images Based on Dual U-Net

A Segmentation Method of Lung Parenchyma From Chest CT Image...

引用

IEEE International Conference on Bioinformatics and Biomedicine

作者： Wenjun Tan Jinzhu Yang Yao Liu Xiaoxia Yin Huangying Liu Yanchun Zhang Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Cyberspace Institute of Advanced Technology Guangzhou University Centre for Applied Informatics College of Engineering and Science Victoria University

ISBN: (纸本)9781728118680

Segmentation of lung parenchyma as a pretreatment step for the detection of some lung lesions requires high accuracy to provide more reliable information for lesion detection. In this paper, we proposed a segmentation method of lung parenchyma from chest CT Images by Dual U-Net algorithm based on the characteristics of different lung region. Firstly, the original lung region is extracted from the full chest CT images of patient by an automatic threshold selection method. Then, the images of the different regions of lung is obtained by calculating the area. Finally, the Dual U-Net is trained according the different lung regions. The first U-Net algorithm operates these middle chest images and other images are operated by the second U-Net algorithm to reduce the interference of other images. The experimental results shown that the segmenting results of lung parenchyma by this method are more accurate than the results by single U-Net and U-Net++ algorithm.

关键词： Lung parenchyma segmentation U-Net chest CT images Lung Tissue Chest imaging chest ct images Lung Dual lung lesion

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