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Artificial intelligence for geoscience:Progress,challenges,and perspectives

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

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Enhanced Connectivity and Reduced Mind Wandering after Tactile Training in Young Adults

Enhanced Connectivity and Reduced Mind Wandering after Tacti...

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IEEE International Symposium on Biomedical Imaging

作者： Yu Luo Haoyang Chen Jicong Zhang School of Biological Science and Medical Engineering Beihang University China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing Advanced Innovation Center for Biomedical Engineering Beihang University China Beihang University Hefei Innovation Research Institute China

ISBN: (纸本)9781665412469;9781665429474

The intensive practice of specific cognitive activities can lead to improvements of relevant cognitive capability in human beings, which may transfer to gain in untrained activities. Although there are a growing number of studies investigating the behavioral benefits of attention training in mind wandering, few studies have directly examined the neurophysiological basis of the training effects. Here using 128-channel electroencephalography (EEG), we examined whether the tactile training can reduce the mind wandering as measured by the sustained attention to response task (SART), and how the dynamic neurophysiological connectivity changes following training in young adults. The trainees showed significantly less occurrence of mind wandering after the five-day tactile training. Furthermore, the functional connectivity within and between the frontal and parietal regions was enhanced after training. Our findings suggest that the tactile training-induced brain plasticity may provide new therapeutic strategies for attention-related disorders.

关键词： Training Neuroplasticity Electroencephalography Task analysis Biomedical imaging

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Theoretical model of membrane protrusions driven by curved active proteins

arXiv

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arXiv 2023年

作者： Ravid, Yoav Penič, Samo Mimori-Kiyosue, Yuko Suetsugu, Shiro Iglič, Aleš Gov, Nir S. Department of Chemical and Biological Physics Weizmann Institute of Science Rehovot Israel Laboratory of Physics Faculty of Electrical Engineering University of Ljubljana Ljubljana Slovenia Laboratory for Molecular and Cellular Dynamics RIKEN Center for Biosystems Dynamics Research Minatojima-minaminachi Chuo-ku Hyogo Kobe650-0047 Japan Division of Biological Science Graduate School of Science and Technology Nara Institute of Science and Technology 8916-5 Takayama Nara Ikoma630-0192 Japan Data Science Center Nara Institute of Science and Technology Ikoma630-0192 Japan Center for Digital Green-innovation Nara Institute of Science and Technology Ikoma630-0192 Japan

Eukaryotic cells intrinsically change their shape, by changing the composition of their membrane and by restructuring their underlying cytoskeleton. We present here further studies and extensions of a minimal physical model, describing a closed vesicle with mobile curved membrane protein complexes. The cytoskeletal forces describe the protrusive force due to actin polymerization which is recruited to the membrane by the curved protein complexes. We characterize the phase diagrams of this model, as function of the magnitude of the active forces, nearest-neighbor protein interactions and the proteins’ spontaneous curvature. It was previously shown that this model can explain the formation of lamellipodia-like flat protrusions, and here we explore the regimes where the model can also give rise to filopodia-like tubular protrusions. We extend the simulation with curved components of both convex and concave species, where we find the formation of complex ruffled clusters, as well as internalized invaginations that resemble the process of endocytosis and macropinocytosis. We alter the force model representing the cytoskeleton to simulate the effects of bundled instead of branched structure, resulting in shapes which resemble filopodia. © 2023, CC BY.

关键词： Proteins

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Bloch Equation Enables Physics-informed Neural Network in Parametric Magnetic Resonance Imaging

arXiv

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arXiv 2023年

作者： Cai, Qingrui Zhu, Liuhong Zhou, Jianjun Qian, Chen Guo, Di Qu, Xiaobo National Integrated Circuit Industry Education Integration Innovation Platform School of Electronic Science and Engineering National Model Microelectronics College Xiamen University Xiamen361102 China Fudan University Xiamen361015 China Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen361102 China School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China

Magnetic resonance imaging (MRI) is an important non-invasive imaging method in clinical diagnosis. Beyond the common image structures, parametric imaging can provide the intrinsic tissue property thus could be used in quantitative evaluation. The emerging deep learning approach provides fast and accurate parameter estimation but still encounters the lack of network interpretation and enough training data. Even with a large amount of training data, the mismatch between the training and target data may introduce errors. Here, we propose one way that solely relies on the target scanned data and does not need a pre-defined training database. We provide a proof-of-concept that embeds the physical rule of MRI, the Bloch equation, into the loss of physics-informed neural network (PINN). PINN enables learning the Bloch equation, estimating the T2 parameter, and generating a series of physically synthetic data. Experimental results are conducted on phantom and cardiac imaging to demonstrate its potential in quantitative MRI. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Biaxial Signal Decoupling Method for the Longitudinal Magnetic-Field-Modulated Spin-Exchange-Relaxation-Free Comagnetometer in Inertial Rotation Measurement

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Physical Review Applied 2022年第2期17卷 024004-024004页

作者： Yixiang Liang Liwei Jiang Jiali Liu Wenfeng Fan Weijia Zhang Shimiao Fan Wei Quan Jiancheng Fang School of Instrumentation and Optoelectronic Engineering Beihang University Beijing 100191 China Research Institute for Frontier Science Beihang University Beijing 100191 China Beijing Academy of Quantum Information Sciences Beijing 100191 China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing 100191 China

We propose a practical biaxial signal decoupling method for the longitudinal magnetic-field-modulated spin-exchange-relaxation-free comagnetometer used in the inertial rotation measurement. The method is implemented based on a model, which had been verified by a rotation response experiment. According to our model, by adjusting the magnetic field in the pumping direction to the decoupling point, the modulated comagnetometer can independently output the rotation information of two orthogonal directions. The rotational scale factor also reaches the maximum value, and the rotation sensitivity and long-term stability can be further improved. Our method not only retains the advantages of the modulated comagnetometer that it can output the dual-axis signal simultaneously with only one probe beam, but also realizes biaxial signal decoupling, and achieves better practical performance.

关键词： Exchange interaction Magnetism Metrology Optical pumping Atomic gases Devices Precision measurements Pump-probe spectroscopy

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Solvothermal synthesis of sugarcane bagasse-bentonite-magnetite nanocomposite for efficient Cr(VI) removal from electroplating wastewater: Kinetics and isotherm investigations

引用

Case Studies in Chemical and Environmental engineering 2024年 10卷

作者： Rahmawati, Istiqomah Pratama, Agus Wedi Amalia, Rima Wahab, Imroatul Aulia Qolbi, Nurus Shafirah Putri, Brainy Aprillia Fachri, Boy Arief Palupi, Bekti Fitriana, Meta Rizki Reza, Muhammad Mahardika, Melbi Department of Chemical Engineering Faculty of Engineering University of Jember Jember 68121 Indonesia Department of Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya 60111 Indonesia Department of Chemistry Faculty of Mathematics and Natural Sciences Universitas Jember Jember 68121 Indonesia Research Center for Biomass and Bioproducts National Research and Innovation Agency of Indonesia (BRIN) Bogor 16911 Indonesia

Hexavalent chromium (Cr(VI)) is a toxic heavy metal released from electroplating industrial processes and poses a significant threat to human health and the environment due to its high solubility and carcinogenicity. Effective removal of Cr(VI) from wastewater is crucial to mitigate its adverse effects. This study explores the potential of a novel sugarcane bagasse-bentonite magnetite nanocomposite, synthesized via the solvothermal method, for efficient Cr(VI) adsorption. The resulting sugarcane bagasse-bentonite magnetite nanocomposite was characterized and evaluated for its Cr(VI) removal efficiency from electroplating wastewater. FTIR analysis confirmed the presence of characteristic functional groups in the nanocomposite, while PSA revealed an average particle size of 262 nm. SEM imaging further validated the porous structure of the material. Adsorption kinetics followed the Avrami model, reaching equilibrium at 270 minutes with a capacity of 11.40 mg/g. The Langmuir isotherm model best described the adsorption process, with a maximum capacity of 7.15 mg/g. Removal efficiency decreased with increasing initial Cr(VI) concentration, highlighting the impact of available adsorption sites. This study demonstrates the potential of sugarcane bagasse-bentonite magnetite nanocomposite as an effective and sustainable adsorbent for Cr(VI) removal from wastewater. © 2024 The Authors

关键词： Adsorption Bentonite Heavy metal Magnetite Nanocomposite Sugarcane bagasse

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Record High Temperatures in the Ocean in 2024

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Advances in Atmospheric sciences 2025年第6期 1092-1109页

作者： Lijing CHENG John ABRAHAM Kevin E.TRENBERTH James REAGAN Huai-Min ZHANG Andrea STORTO Karina VON SCHUCKMANN Yuying PAN Yujing ZHU Michael E.MANN Jiang ZHU Fan WANG Fujiang YU Ricardo LOCARNINI John FASULLO Boyin HUANG Garrett GRAHAM Xungang YIN Viktor GOURETSKI Fei ZHENG Yuanlong LI Bin ZHANG Liying WAN Xingrong CHEN Dakui WANG Licheng FENG Xiangzhou SONG Yulong LIU Franco RESEGHETTI Simona SIMONCELLI Gengxin CHEN Rongwang ZHANG Alexey MISHONOV Zhetao TAN Wangxu WEI Huifeng YUAN Guancheng LI Qiuping REN Lijuan CAO Yayang LU Juan DU Kewei LYU Albertus SULAIMAN Michael MAYER Huizan WANG Zhanhong MA Senliang BAO Henqian YAN Zenghong LIU Chunxue YANG Xu LIU Zeke HAUSFATHER Tanguy SZEKELY Flora GUES National Key Laboratory of Earth System Numerical Modeling and Application Institute of Atmospheric PhysicsChinese Academy of Sciences University of St. Thomas School of Engineering NSF National Center for Atmospheric Research University of Auckland National Oceanic and Atmospheric Administration National Centers for Environmental Information National Research Council (CNR) Institute of Marine Sciences (ISMAR) Mercator Ocean International Department of Earth and Environmental Science University of Pennsylvania Institute of Oceanology Chinese Academy of Sciences National Marine Environmental Forecasting Center Ministry of Natural Resources of China North Carolina Institute for Climate Studies (NCICS) North Carolina State University Oceanographic Data Center Chinese Academy of Sciences College of Oceanography Hohai University National Marine Data and Information Service Istituto Nazionale di Geofisica eVulcanologia Sede di Bologna South China Sea Institute of Oceanology Chinese Academy of Sciences ESSIC/CISESS-MD University of Maryland Computer Network Information Center Chinese Academy of Sciences Eco-Environmental Monitoring and Research Center Pearl River Valley and South China Sea Ecology and Environment Administration Ministry of Ecology and Environment National Meteorological Information Center China Meteorological Administration International Research Center of Big Data for Sustainable Development Goals Xiamen University Research Center for Climate and Atmosphere National Research and Innovation Agency (BRIN) Research Department European Centre for Medium-Range Weather Forecasts Department of Meteorology and Geophysics University of Vienna College of Meteorology and Oceanography National University of Defense Technology State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of OceanographyMinistry of Natural Resources Stripe Inc. South San Francisco Ocean Scope CELAD

Heating in the ocean has continued in 2024 in response to increased greenhouse gas concentrations in the atmosphere,despite the transition from an El Ni?o to neutral conditions. In 2024, both global sea surface temperature(SST) and upper2000 m ocean heat content(OHC) reached unprecedented highs in the historical record. The 0–2000 m OHC in 2024exceeded that of 2023 by 16 ± 8 ZJ(1 Zetta Joules = 1021 Joules, with a 95% confidence interval)(IAP/CAS data), which is confirmed by two other data products: 18 ± 7 ZJ(CIGAR-RT reanalysis data) and 40 ± 31 ZJ(Copernicus Marine data,updated to November 2024). The Indian Ocean, tropical Atlantic, Mediterranean Sea, North Atlantic, North Pacific, and Southern Ocean also experienced record-high OHC values in 2024. The global SST continued its record-high values from2023 into the first half of 2024, and declined slightly in the second half of 2024, resulting in an annual mean of 0.61°C ±0.02°C(IAP/CAS data) above the 1981–2010 baseline, slightly higher than the 2023 annual-mean value(by 0.07°C ±0.02°C for IAP/CAS, 0.05°C ± 0.02°C for NOAA/NCEI, and 0.06°C ± 0.11°C for Copernicus Marine). The record-high values of 2024 SST and OHC continue to indicate unabated trends of global heating.

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Hierarchical Voronoi Structure Inspired by Cat Paw Pads Substantially Enhances Landing Impact Energy Dissipation

SSRN

引用

SSRN 2023年

作者： Lu, Da Pei, Baoqing Li, Shengbin Xu, Yangyang Hu, Mengyuan Zhang, Shijia Zhang, Le Huang, Xin Wang, Yangwei Wu, Shuqin Wu, Xueqing Beijing key laboratory for design and evaluation technology of advanced implantable & interventional medical devices Beijing Advanced Innovation Center for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing100083 China National Key Laboratory of Science and Technology on Materials under Shock and Impact Beijing Institute of Technology Beijing100081 China School of Big Data and Information Shanxi College of Technology Shanxi036000 China

When a human lands from a high drop, there is a high risk of serious injury to the lower limbs. On the other hand, cats can withstand jumps and falls from heights without being fatally wounded, largely due to their impact-resistant paw pads. The present study aimed to investigate the biomechanism of impact resistance in paw pads, propose an optimal hierarchical Voronoi structure, and apply the structure to bionic shoes to reduce the impact force of landing. The microstructure of cat paw pads was observed via tissue section staining, and a simulation model was reconstructed to verify and optimize the structural cushioning capacity. The bionic structures were changed and simulated to achieve an optimal composed structure. A bionic sole was 3D-printed, and its performance was evaluated via compression test and a jumping-landing experiment. The results show that the optimal configuration of the three layers should have a uniform distribution with 0.3-0.5 mm wall thickness, a 1:1-2 thickness ratio of epidermis and dermis, and 100-150 compartments. A bionic sole with an optimized structure can reduce the peak impact force and delay the peak arrival time. This study provides theoretical and technical support for effective protection against human lower limb landing injuries. © 2023, The Authors. All rights reserved.

关键词： Bionics

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Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Bag-Level Classifier is a Good Instance-Level Teacher

arXiv

引用

arXiv 2023年

作者： Wang, Hongyi Luo, Luyang Wang, Fang Tong, Ruofeng Chen, Yen-Wei Hu, Hongjie Lin, Lanfen Chen, Hao The College of Computer Science and Technology Zhejiang University Hangzhou310063 China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Department of Radiology Sir Run Run Shaw Hospital Hangzhou310016 China College of Computer Science and Technology Zhejiang University Hangzhou310063 China Research Center for Healthcare Data Science Zhejiang Lab Hangzhou311121 China College of Information Science and Engineering Ritsumeikan University Kusatsu5250058 Japan Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Shenzhen China

Multiple Instance Learning (MIL) has demonstrated promise in Whole Slide Image (WSI) classification. However, a major challenge persists due to the high computational cost associated with processing these gigapixel images. Existing methods generally adopt a two-stage approach, comprising a non-learnable feature embedding stage and a classifier training stage. Though it can greatly reduce the memory consumption by using a fixed feature embedder pre-trained on other domains, such scheme also results in a disparity between the two stages, leading to suboptimal classification accuracy. To address this issue, we propose that a bag-level classifier can be a good instance-level teacher. Based on this idea, we design Iteratively Coupled Multiple Instance Learning (ICMIL) to couple the embedder and the bag classifier at a low cost. ICMIL initially fix the patch embedder to train the bag classifier, followed by fixing the bag classifier to fine-tune the patch embedder. The refined embedder can then generate better representations in return, leading to a more accurate classifier for the next iteration. To realize more flexible and more effective embedder fine-tuning, we also introduce a teacher-student framework to efficiently distill the category knowledge in the bag classifier to help the instance-level embedder fine-tuning. Thorough experiments were conducted on four distinct datasets to validate the effectiveness of ICMIL. The experimental results consistently demonstrate that our method significantly improves the performance of existing MIL backbones, achieving state-of-the-art results. The code is available at: https://***/Dootmaan/ICMIL/tree/confidence based Copyright © 2023, The Authors. All rights reserved.

关键词： Image classification

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Interlayer antisynchronization in degree-biased duplex networks

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Physical Review E 2023年第3期107卷 034313-034313页

作者： Sayantan Nag Chowdhury Sarbendu Rakshit Chittaranjan Hens Dibakar Ghosh Department of Environmental Science and Policy University of California Davis California 95616 USA Technology Innovation Hub (TIH) IDEAS (Institute of Data Engineering Analytics and Science Foundation) Indian Statistical Institute 203 B. T. Road Kolkata 700108 India Department of Mechanical Engineering University of California Riverside California 92521 USA Center for Computational Natural Sciences and Bioinformatics International Institute of Information Technology Gachibowli Hyderabad 500032 India Physics and Applied Mathematics Unit Indian Statistical Institute 203 B. T. Road Kolkata 700108 India

With synchronization being one of nature's most ubiquitous collective behaviors, the field of network synchronization has experienced tremendous growth, leading to significant theoretical developments. However, most previous studies consider uniform connection weights and undirected networks with positive coupling. In the present article, we incorporate the asymmetry in a two-layer multiplex network by assigning the ratio of the adjacent nodes' degrees as the weights to the intralayer edges. Despite the presence of degree-biased weighting mechanism and attractive-repulsive coupling strengths, we are able to find the necessary conditions for intralayer synchronization and interlayer antisynchronization and test whether these two macroscopic states can withstand demultiplexing in a network. During the occurrence of these two states, we analytically calculate the oscillator's amplitude. In addition to deriving the local stability conditions for interlayer antisynchronization via the master stability function approach, we also construct a suitable Lyapunov function to determine a sufficient condition for global stability. We provide numerical evidence to show the necessity of negative interlayer coupling strength for the occurrence of antisynchronization, and such repulsive interlayer coupling coefficients cannot destroy intralayer synchronization.

关键词： Coupled oscillators Dynamics of networks Synchronization

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