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Artificial intelligence for geoscience:Progress,challenges,and perspectives

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The Innovation 2024年第5期5卷 136-160,135页

作者： Tianjie Zhao Sheng Wang Chaojun Ouyang Min Chen Chenying Liu Jin Zhang Long Yu Fei Wang Yong Xie jun Li Fang Wang Sabine Grunwald Bryan MWong Fan Zhang Zhen Qian Yongjun xu Chengqing Yu Wei Han Tao Sun Zezhi Shao Tangwen Qian Zhao Chen Jiangyuan Zeng Huai Zhang Husi Letu Bing Zhang Li Wang Lei Luo Chong Shi Hongjun Su Hongsheng Zhang Shuai Yin ni Huang Wei Zhao Nan Li Chaolei Zheng Yang Zhou Changping Huang Defeng Feng Qingsong xu Yan Wu danfeng Hong Zhenyu Wang Yinyi Lin Tangtang Zhang Prashant Kumar Antonio Plaza Jocelyn Chanussot Jiabao Zhang Jiancheng Shi Lizhe Wang Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China school of computer science China University of GeosciencesWuhan 430078China State Key Laboratory of Mountain Hazards and engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China Data science in Earth Observation Technical University of Munich80333 MunichGermany The National Key Laboratory of Water Disaster Prevention Yangtze Institute for Conservation and DevelopmentHohai UniversityNanjing 210098China Institute of Computing technology Chinese Academy of SciencesBeijing 100190China school of Geographical sciences Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China Soil Water and Ecosystem Sciences DepartmentUniversity of FloridaPO Box 110290GainesvilleFLUSA Materials science engineering Program Cooperating Faculty Member in the Department of Chemistry and Department of Physics Astronomy University of CaliforniaCaliforniaRiversideCA 92521USA Institute of Remote sensing and Geographical Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Key Laboratory of Computational Geodynamics University of Chinese Academy of SciencesBeijing 100049China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China College of Geography and Remote sensing Hohai UniversityNanjing 211100China Department of Geography The University of Hong KongHong Kong 999077SARChina jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Nanjing 210044China school of Environmental science and engineering Nanjing University of Information Science&TechnologyNanjing 210044China Collaborative Inno

This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection *** models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical ***,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world *** contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical *** techniques have shown promise in addressing Earth science-related ***,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into *** integration of physics-based and data-driven methodologies into hybrid models presents an alternative *** models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data *** review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and *** examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in *** paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.

关键词： Earth utilizing landscape

computer simulation of super-magnetoelastic behavior near critical region of magnetic materials based on phase-field method

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稀有金属:英文版 2023年第8期42卷 2477-2488页

作者： Zhao Zhang Cheng-Chao Hu An-Hang Zhou Yu-Xin xu Yuan-Yuan Wu Hai-Hua Huang Hou-Bing Huang jun-Jie ni Wei Li Wei-Feng Rao school of Materials science and engineering Liaocheng UniversityLiaocheng 252059China Advanced Research Institute of Multidisciplinary science Beijing Institute of TechnologyBeijing 100081China school of Mechanical engineering Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China Shandong Institute of Mechanical Design and Research Jinan 250031China

Since the discovery of ferromagnetic morphotropic phase boundary(MPB)in 2010,the connotation and extension of MPB have been becoming more and more *** the last dozen years,much experimental work has been done to design magnetostrictive materials based on the MPB ***,due to the difficulty in direct experimental observations and the complexity of theoretical treatments,the insight into the microstructure property relationships and underlying mechanisms near the ferromagnetic MPB has not been fully ***,we have reviewed our recent computer simulation work about the super-magnetoelastic behavior near the critical region of several typical ***-field modeling and simulation are employed to explore the domain configuration and engineering in single crystals as well as the grain size effect in ***,a general nano-embryonic mechanism for superelasticity is also ***,some future perspectives and challenges are presented to stimulate a deeper consideration of the research paradigm between multiscale modeling and material development.

关键词： Super-magnetoelastic behavior Phase-field simulation Morphotropic phase boundary(MPB)

Status of aquatic organisms resources and their environments in Yangtze River system(2017-2021)⋆

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Aquaculture and Fisheries 2024年第5期9卷 833-850页

作者： Haile Yang Li Shen Yongfeng He Huiwu Tian Lei Gao Jinming Wu Zhigang Mei nian Wei Lin Wang Tingbing Zhu Feifei Hu Jinling Gong Hongchun Du Xinbin Duan Huatang Deng Daoqun Wang Fengyue Zhu Yunfeng Li Fan Wu Huijun Ru Yan Zhang junyi Li junlin Yang Yuntao Zhou Dongdong Fang Yinping Wang danqing Lin Yanping Yang Peijie Li Silei Liu Jian Yang Ping Zhuang Sikai Wang Tao Zhang Gang Yang Wenbo Yang Lilai Yuan Kun Cao Shuo xu Huiyuan Liu Zhiqiang Liang Chongrui Wang Hong Li Xiping Yuan Xin Yang Yilong Fu Yanping Zhang Haixin Zhang Zhiying Tao Sheng Wang Xiaoping Gao Binsong Jin Kemao Li Guojie Wang Shenglong Jian Yingqin Li Chenjiang xue Chunyun Lei Shaowei xue Yi Sun Bin Zhu Ke Shao Xingkun Hu Meihua Xiong jun Du Bin He Tao Yan Yingying Huang Yuanchao Zou Biwen Xie Yongming Wang Bin Li Fei Liu Yaoyao Zhang Fei Fan Zhijian Wang Jing Huang Haoran Gu Hailong Ge Yan dan Yan Li Shuqiao Wang Chuang Zhang Lu Zhou xue Wang Sheng Zeng Yan Xiang xugang He Jianhui Qin Chengxing Xia Jie Hou Yifu Shi Lifang Gao Zhiqiang Zhu Hongbao Shen Yun Du xuejun Duan Jiawu Xiong Deguo Yang Shaoping Liu Zhaohui ni Hui Zhang Kai Liu Feng Zhao Yingren Li Jianwei Wang Qiwei Wei Yangtze River Fisheries Research Institute Chinese Academy of Fishery SciencesWuhan430223China Institute of Hydrobiology Chinese Academy of SciencesWuhan430072China Fishery Resource and Environment Research Center Chinese Academy of Fishery SciencesBeijing100141China Key Laboratory of Freshwater Fisheries and Germplasm Resources Utilization Ministry of Agriculture and Rural AffairsFreshwater Fisheries Research CenterChinese Academy of Fishery SciencesWuxi214081China East china Sea Fisheries Research Institute Chinese Academy of Fishery SciencesShanghai200090China Institute of Fisheries engineering Chinese Academy of Fishery SciencesBeijing100141China Hunan Fisheries science Institute Changsha410153China Jiangxi Fisheries Research Institute Nanchang330039China Jiujiang Academy of Fishery sciences Jiujiang332000China school of Life sciences Nanchang UniversityNanchang330031China Qinghai Provincial Fishery technology Extension Center Xining810012China Yunnan Academy of Fishery sciences Kunming650111China Institute of Hydroecology Ministry of Water Resources and Chinese Academy of SciencesWuhan430079China Fisheries Institute Sichuan Academy of Agricultural SciencesChengdu611731China school of Life sciences Neijiang Normal UniversityNeijiang641100China school of Life sciences Southwest UniversityChongqing400715China Chongqing Fishery sciences Research Institute Chongqing401120China Guizhou Fisheries Research Institute Guiyang550025China College of Fisheries Huazhong Agricultural UniversityWuhan430072China Hubei Fisheries science Research Institute Wuhan430070China Shaanxi Fisheries Research Institute Xi’an710086China Innovation Academy for Precision Measurement science and technology Chinese Academy of SciencesWuhan430077China Nanjing Institute of Geography and Limnology Chinese Academy of SciencesNanjing210008China Central South Inventory and Planning Institute of State Forestry and Grassland Administration Changsha410014China

The Yangtze River is the mother river of *** promote the aquatic ecosystem protection of the great river,the Project of Yangtze Fisheries Resources and Environment Investigation(2017-2021)supported by the Ministry of Agriculture and Rural Affairs,*** carried out by 24 institutes and universities that located in the Yangtze River basin surveys the status of(1)fish species composition and spatial distribution,(2)current fish abundance,(3)endangered fishes,(4)Yangtze finless porpoise,(5)aquatic eco-environments,(6)water-level fluctuation areas,(7)capture fisheries and recreational fisheries of the Yangtze River mainstream and 10 of its main tributaries,including Yalong River,Heng River,Min River(including Dadu River),Chishui River,Tuo River,Jialing River,Wu River,Han River,Dongting Lake and Poyang *** results showed that there were 443 fish species(belonging to 163 genus,37 families,and 18 orders)before 2017,but only 323 fish species(including 15 new recorded exotic species)were recorded in the project of *** them,Cyprinus carpio,Carassius auratus,Hypophthalmichthys molitrix,Pelteobagrus fulvidraco,Coilia brachygnathus,Silurus asotus,Saurogobio dabryi,Ctenopharyngodon idellus,Pelteobagrus nitidus,Hemiculter leucisculus,Siniperca chuatsi,Coreius heterodon,Culter alburnus,Parabramis pekinensis,and Aristichthys nobilis were the dominant species across the whole Yangtze River *** is estimated that there were 886 million individuals weighing 124.8 million kg,merely equivalent to 27.3%of the resources in 1950s,30.9%of the resources in 1960s,or 58.7%of the resources in *** the new list of protected fishes that recorded in the Yangtze River system,only 15 of 29 were collected in this project of *** gladius has been affirmed to be Extinct by International Union for Conservation of Nature(IUCN).The wild individuals of Tenualosa reevesii and Luciobrama macrocephalus have disappeared for many years and maybe have been extinct

关键词： Fishes Yangtze finless porpoise Resources Aquatic ecosystem Fisheries Background investigation Yangtze river system

Handwashing quality assessment via deep learning:a modelling study for monitoring compliance and standards in hospitals and communities

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Intelligent Medicine 2022年第3期2卷 152-160页

作者： Ting Wang jun Xia Tianyi Wu Huanqi ni Erping Long Ji-Peng Olivia Li Lanqin Zhao Ruoxi Chen Ruixin Wang Yanwu xu Kai Huang Haotian Lin State Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityGuangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangzhouGuangdong 510275China school of computer science and engineering Sun Yat-sen UniversityGuangzhouGuangdong 510275China Key Laboratory of Adaptive Optics Institute of Optics and ElectronicsChinese Academy of SciencesChengduSichuan 610209China Moorfields Eye Hospital NHS Foundation Trust LondonUnited Kingdom Cixi Institute of Biomedical engineering Ningbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingboZhejiang 315300China Center for Precision Medicine Sun Yat-sen UniversityGuangzhouGuangdong 510275China

Background Hand hygiene can be a simple,inexpensive,and effective method for preventing the spread of infectious ***,a reliable and consistent method for monitoring adherence to the guidelines within and outside healthcare settings is *** aim of this study was to provide an approach for monitoring handwashing compliance and quality in hospitals and *** We proposed a deep learning algorithm comprising three-dimensional convolutional neural networks(3D CNNs)and used 230 standard handwashing videos recorded by healthcare professionals in the hospital or at home for training and internal *** assessment scheme with a probability smoothing method was also proposed to optimize the neural network’s output to identify the handwashing steps,measure the exact duration,and grade the standard level of recognized ***-two videos by healthcare professionals in another hospital and 28 videos recorded by civilians in the community were used for external *** Using a deep learning algorithm and an assessment scheme,combined with a probability smoothing method,each handwashing step was recognized(ACC ranged from 90.64%to 98.87%in the hospital and from 87.39%to 96.71%in the community).An assessment scheme measured each step’s exact duration,and the intraclass correlation coefficients were 0.98(95%CI:0.97-0.98)and 0.91(95%CI:0.88-0.93)for the total video duration in the hospital and community,***,the system assessed the quality of handwashing,similar to the expert panel(kappa=0.79 in the hospital;kappa=0.65 in the community).Conclusions This work developed an algorithm to directly assess handwashing compliance and quality from videos,which is promising for application in healthcare settings and communities to reduce pathogen transmis-sion.

关键词： Handwashing Infectious control Deep learning Action recognition Standard level grading

会仙岩溶湿地丰平枯水期地表水污染及灌溉适用性评价

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环境科学 2021年第5期42卷 2240-2250页

作者：朱丹尼邹胜章李军樊连杰赵一谢浩朱天龙潘民强徐利中国地质科学院岩溶地质研究所自然资源部/广西岩溶动力学重点实验室桂林541004 河北建筑工程学院河北省水质工程与水资源综合利用重点实验室张家口075000 桂林理工大学环境科学与工程学院桂林541006 河北省张家口生态环境监测中心张家口075000

以我国典型的亚热带低海拔岩溶湿地为研究区,对该区丰水期、平水期和枯水期共采集的27组地表水样品进行12种常规离子和10种金属元素检测,在分析地表水主要离子化学特征基础上,分别运用内梅罗污染指数法以及钠吸附比法(SAR)、钠含量法(SC... 详细信息

以我国典型的亚热带低海拔岩溶湿地为研究区,对该区丰水期、平水期和枯水期共采集的27组地表水样品进行12种常规离子和10种金属元素检测,在分析地表水主要离子化学特征基础上,分别运用内梅罗污染指数法以及钠吸附比法(SAR)、钠含量法(SC)、渗透指数法(PI)和残余碳酸钠法(RSC)这4种评价体系对不同时期地表水进行污染评价和灌溉适用性评价.结果表明,研究区内地表水主要为弱碱性水,Ca^(2+)和HCO_(3)^(-)为优势离子.常规指标中仅NH_(4)^(+)超过生活饮用水卫生标准限值,超标率为25.93%,金属元素中超标组分为Al(11.11%)、Mn(44.44%)和Hg(37.04%).常规指标浓度受时空尺度影响程度整体低于金属元素,两者均具有丰水期>平水期>枯水期的时期分布特征.据污染评价结果,区内地表水总体水质尚可,内梅罗综合污染指数介于0.75~2.69,主要污染指标为NH_(4)^(+)、Mn、Al和Hg,部分样点存在季节性污染,污染级别为轻度~中度,丰水期污染程度最高,污染点多分布于湿地核心区.综合农田灌溉水质标准和地表水环境质量标准限值以及SAR、SC、PI和RSC灌溉适用性评价结果,会仙岩溶湿地地表水总体适合灌溉,枯水期灌溉适用性优于丰水期和平水期,平水期PH1(Hg浓度1.91μg·L^(-1))和丰水期FH8(NH_(4)^(+)浓度13.70 mg·L^(-1))地表水已不适用于农业灌溉.

关键词：会仙岩溶湿地地表水常规指标金属元素污染灌溉适用性

腐殖酸-重金属对ANAMMOX菌脱氮性能的影响及其动力学分析

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环境科学 2022年第6期43卷 3221-3229页

作者：李芸崔楠熊星星黄志远李泽兵王东亮李朝明许丹李军东华理工大学核资源与环境国家重点实验室南昌330013 东华理工大学水资源与环境工程学院南昌330013 北京工业大学建筑工程学院北京100124

通过批次实验研究了两种典型重金属离子[Cu(Ⅱ)和ni(Ⅱ)]与腐殖酸对厌氧氨氧化(ANAMMOX)脱氮效果(SAA)的影响并进行了动力学模型分析,同时探讨了腐殖酸-重金属对ANAMMOX脱氮的影响.结果表明,在投加ρ[Cu(Ⅱ)]和ρ[ni(Ⅱ)]均为3 mg·L^(... 详细信息

通过批次实验研究了两种典型重金属离子[Cu(Ⅱ)和ni(Ⅱ)]与腐殖酸对厌氧氨氧化(ANAMMOX)脱氮效果(SAA)的影响并进行了动力学模型分析,同时探讨了腐殖酸-重金属对ANAMMOX脱氮的影响.结果表明,在投加ρ[Cu(Ⅱ)]和ρ[ni(Ⅱ)]均为3 mg·L^(-1)时,对ANAMMOX有促进作用,SAA分别提高了8.64%和7.78%;当ρ[Cu(Ⅱ)]和ρ[ni(Ⅱ)]分别为20 mg·L^(-1)和5 mg·L^(-1)时则对ANAMMOX有抑制作用,抑制效果随重金属离子浓度增加而越发显著,指数拟合表明Cu(Ⅱ)和ni(Ⅱ)对ANAMMOX的IC50分别为29.67 mg·L^(-1)和28.75 mg·L^(-1).在投加ρ(腐殖酸)为1 mg·L^(-1)时SAA提高了7.37%,40 mg·L^(-1)时抑制率即达到36.80%,线性拟合表明腐殖酸对ANAMMOX菌的IC50为58.36 mg·L^(-1).修正的Michaelis-Menten模型能较好地描述重金属和腐殖酸对ANAMMOX的抑制动力学行为,拟合获得的Cu(Ⅱ)、ni(Ⅱ)和腐殖酸对ANAMMOX菌活性完全抑制时的浓度(I)分别为49.59、74.46和84.27 mg·L^(-1).适量的腐殖酸有利于改善重金属对ANAMMOX菌活性的抑制,过量的腐殖酸又会重新对ANAMMOX菌形成抑制.

关键词：重金属厌氧氨氧化(ANAMMOX) 腐殖酸动力学协同抑制

虚实系统互驱的混合增强智能开放创新平台的架构与方案

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智能科学与技术学报 2019年第4期1卷 379-391页

作者：张俊李灵犀林懿伦张天韵张科许沛东阮克羽沈聃武汉大学电气与自动化学院湖北武汉430072 印第安纳大学-普渡大学印第安纳波利斯分校电子与计算机工程系美国印第安纳波利斯IN 46202 中国科学院自动化研究所北京100190 普渡大学西拉法叶分校电气和计算机工程学院美国印第安纳波利斯IN 46202

首先提出了混合增强智能开放创新平台建设的总体目标以及面临的问题,提出了一种虚实系统互驱的平台架构方案。然后阐述了平台的基本形态,并详述了平台各层的模块机理和作用,以及涉及的关键技术,如人机交互技术、数据处理技术和数字虚拟...

首先提出了混合增强智能开放创新平台建设的总体目标以及面临的问题,提出了一种虚实系统互驱的平台架构方案。然后阐述了平台的基本形态,并详述了平台各层的模块机理和作用,以及涉及的关键技术,如人机交互技术、数据处理技术和数字虚拟工业技术。最后设计了开放创新的保障和激励机制,并探讨了区块链技术在混合增强智能开放创新平台数据安全方面的应用。

关键词：混合增强智能开放创新平台虚实系统人机交互数字虚拟工业技术

Small P values may not yield robust findings:an example using REST-meta-PD

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science Bulletin 2021年第21期66卷 2148-2152,M0003页

作者： Xi-Ze Jia Na Zhao Hao-Ming Dong Jia-Wei Sun Marek Barton Roxana Burciu nicolas Carrière Antonio Cerasa Bo-Yu Chen jun Chen Stephen Coombes Luc Defebvre Christine Delmaire Kathy Dujardin Fabrizio Esposito Guo-Guang Fan Federica Di Nardo Yi-xuan Feng Brett W.Fling Saurabh Garg Moran Gilat Martin Gorges Shu-Leong Ho Fay BHorak Xiao Hu Xiao-Fei Hu Biao Huang Pei-Yu Huang Ze-Juan Jia Christina Jones Jan Kassubek Lenka Krajcovicova Ajay Kurani Jing Li Qing Li Ai-Ping Liu Bo Liu Hu Liu Wei-Guo Liu Renaud Lopes Yu-Ting Lou Wei Luo Tara Madhyastha ni-ni Mao Grainne McAlonan Martin J.McKeown Shirley Pang Andrea Quattrone Irena Rektorova Alessia Sarica Hui-Fang Shang James M.Shine Priyank Shukla Tomas Slavicek Xiao-Peng Song Gioacchino Tedeschi Alessandro Tessitore David Vaillancourt Jian Wang Jue Wang Z.Jane Wang Lu-Qing Wei Xia Wu Xiao-jun xu Lei Yan Jing Yang Wan-Qun Yang Nai-Lin Yao De-Long Zhang Jiu-Quan Zhang Min-Ming Zhang Yan-Ling Zhang Cai-Hong Zhou Chao-Gan Yan Xi-nian Zuo Mark Hallett Tao Wu Yu-Feng Zang Center for Cognition and Brain Disorders the Affiliated HospitalHangzhou Normal UniversityHangzhou 310015China Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments Hangzhou 311121China National Basic science Data Center Beijing 100190China State Key Laboratory of Cognitive Neuroscience and Learning&McGovern Institute for Brain Research Beijing Normal UniversityBeijing 100875China school of Information and Electronics technology Jiamusi UniversityJiamusi 154007China Neuroscience Program Central European Institute of TechnologyCEITECMasaryk UniversityBrno 62500Czech Republic Department of Applied Physiology and Kinesiology University of FloridaGainesville FL 32611USA Univ.Lille InsermCHU LilleU1172-LilNCog-Lille Neuroscience&CognitionLille F-59000France The Institute for Biomedical Research and Innovation National Research CouncilMangone CS 87050Italy Department of Radiology The First Affiliated Hospital of China Medical UniversityShenyang 110001China Department of Radiology Guangdong Provincial Hospital of Chinese MedicineGuangzhou 510120China Department of Medicine Surgery and DentistryScuola Medica SalernitanaUniversity of SalernoFiscianoSA 132-84084Italy Department of Advanced Medical and Surgery sciences University of Campania“Luigi Vanvitelli”Caserta 81100Italy Eye Center of the 2nd Affiliated Hospital Medical College of Zhejiang UniversityHangzhou 310020China Zhejiang Provincial Key Lab of Ophthalmology Hangzhou 310020China Department of Health and Exercise science Colorado State UniversityFort Collins CO 80523USA Pacific Parkinson’s Research Centre University of British ColumbiaVancouver BC V6E 2M6Canada Department of Medicine(Neurology)university of British Columbia Vancouver BC V6T 1B7Canada Brain and Mind Center The University of SydneySydney NSW 2006Australia Department of Neurology Ulm UniversityUlm 89081Germany Division of Neurology Department of MedicineQueen Mary HospitalUniversity of Hong KongHong Kong 999077China Department of

Thousands of resting state functional magnetic resonance imaging(RS-f MRI)articles have been published on brain *** precise localization of abnormal brain activity,a voxel-level comparison is *** of the large number of voxels in the brain,multiple comparison correction(MCC)must be performed to reduce false positive rates,and a smaller P value(usually including either liberal or stringent MCC)is widely recommended[1].

关键词：帕金森病 precise correction