Ocean geoscience is a highly integrated and interdisciplinary field that plays a critical role in understanding the interaction between Earth’s lithosphere,hydrosphere,atmosphere,biosphere,and anthroposphere.Recent y...Ocean geoscience is a highly integrated and interdisciplinary field that plays a critical role in understanding the interaction between Earth’s lithosphere,hydrosphere,atmosphere,biosphere,and anthroposphere.Recent years have seen tremendous progress in global ocean research,driven by rapid advancements in deep-sea manned and unmanned submersibles,ocean drilling,seafloor observatories,big data assimilation,and supercomputing simulations.Representative examples of breakthroughs are highlighted in this work:(1)Probing sub-seafloor processes.A 10,000-meter ocean-bottom seismometer array has achieved high-resolution imaging of the deepest ocean on the Earth-the Challenger Deep of the Mariana Trench,revealing the role of key tectonic and hydrological processes within the subduction zone.The first sub-ice seafloor seismic and magnetotelluric experiments were successfully conducted at the Arctic Gakkel Ridge,providing significant insights into the dynamics of ultraslow seafloor spreading.(2)Exploration of seafloor resources.Near-seafloor investigations employing underwater robotics and multi-sensor systems have been carried out in areas of hydrothermal vents and cold seeps at global locations,including the Southwest Indian Ridge.These efforts have combined geophysical,oceanographic,chemical,and biological observations with extensive seafloor sampling.(3)Interdisciplinary research of complex catastrophic events.High-resolution simulations integrating ocean observations with supercomputing modeling have made it possible to fully model earthquake-induced seafloor deformation,tsunami propagation,and ocean basin-scale transport of the Fukushima Power Plant-derived radionuclides associated with the 2011 Tohoku earthquake.Among the world’s three major oceans,the Indian Ocean is still relatively underexplored.Major scientific challenges include elucidating crust-mantle interaction,air-sea dynamic coupling,large-scale marine hazards,and responses of ecosystems to major environmental changes,all of which require interdisciplinary collaboration.Future efforts should focus on developing intelligent unmanned observation platform systems,big data and digital twins,and AI-driven hazard modeling.Meanwhile,higher educational reforms should emphasize fostering a new generation of students and young scientists with a solid background and strong critical analysis skills to accelerate technological innovation.展开更多
Background:With the rapid development of modern emerging technologies,the ethical dilemmas and social controversies triggered by scientific and technological activities have become increasingly prominent.How to guide ...Background:With the rapid development of modern emerging technologies,the ethical dilemmas and social controversies triggered by scientific and technological activities have become increasingly prominent.How to guide technology for good and prevent and control technological risks has become an important issue of global concern.Research on science and technology ethics is dedicated to integrating ethical theories into governance practices and constructing ethical models that adapt to the development of the times.Methods:This article systematically reviews the six core approaches of scientific and technological ethics thought,including technological autonomy and political philosophy criticism,responsibility ethics and intergenerational obligations,technological intermediation and the integration of life and the world,ethical principles and normative frameworks,participatory governance and ethical practice innovation,as well as domain-specific ethical norms,thereby constructing an ethical analysis framework applicable to medical technology risks.And cross-analysis was conducted by taking medical events such as gene editing and xenotransplantation as examples.Results:Research shows that a single ethical approach has limitations in addressing complex medical ethical challenges,while the six approaches are complementary and synergistic.By criticizing technological autonomy,establishing a responsibility ethics orientation,setting the bottom line of ethical principles,promoting participatory governance,formulating domain norms,and continuously reflecting on the intermediary nature of technology,a multi-level and dynamically adaptive governance system for scientific and technological ethics can be constructed.Conclusion:The key to addressing contemporary medical ethics challenges lies in the comprehensive application of science and technology ethics theories and the integration of ethical considerations throughout the entire process of scientific and technological research and development.In the future,a governance framework that adapts to the development of new technologies should be established to promote cross-cultural and cross-disciplinary ethical dialogue and public participation,ensuring that scientific and technological innovation always serves the dignity of human life and overall well-being.展开更多
I offer suggestions to increase the probability of success of an international research project.Collaborative studies often produce more innovative and transformative scientific results than work done by a single inve...I offer suggestions to increase the probability of success of an international research project.Collaborative studies often produce more innovative and transformative scientific results than work done by a single investigator or an isolated team.My advice is intended for early-career scientists.The product of the collaboration may be high-impact research publications,enhanced geophysical monitoring capabilities in a foreign country,or an advanced training course.Choosing the right international partner is the most important step.Keeping an open mind and being receptive to suggestions to modify the initial concept is critical.Other key steps include having a mutually agreed upon plan with achievable goals and well-defined expected outcomes.International cooperation is a richly rewarding experience that accelerates progress in the Earth Sciences.展开更多
The year 2024 marked the 40th anniversary of Advances in Atmospheric Sciences(AAS),as well as the centenary of the Chinese Meteorological Society(CMS).The inaugural issue of AAS was published in 1984,initially being s...The year 2024 marked the 40th anniversary of Advances in Atmospheric Sciences(AAS),as well as the centenary of the Chinese Meteorological Society(CMS).The inaugural issue of AAS was published in 1984,initially being sponsored primarily by Chinese National Committee for the International Association of Meteorological and Atmospheric Sciences(IAMAS)and the Institute of Atmospheric Physics at the Chinese Academy of Sciences.In 2006,Springer became AAS’s international publisher.Then,in 2015,the CMS joined in sponsoring AAS,and in the same year,AAS also became an affiliated journal of the IAMAS.These milestone events helped broaden the reach of AAS,culminating in the journal establishing itself as a truly international journal supporting the advancement of the atmospheric sciences.展开更多
In an academic environment increasingly shaped by metrics and the imperatives of“publish or perish”,it is rare to encounter a leading scientist willing to interweave personal narrative with conceptual reflection.The...In an academic environment increasingly shaped by metrics and the imperatives of“publish or perish”,it is rare to encounter a leading scientist willing to interweave personal narrative with conceptual reflection.The Soul of Geography by Fu(2025)achieves precisely this.The book resists simple categorisation:it is neither a conventional monograph nor a memoir,but rather a hybrid text that integrates autobiography,disciplinary reflection,and scientific arguments.In doing so,Fu articulates not only the trajectory of his own career but also a vision of geography as a discipline of theoretical depth and practical relevance.展开更多
Providing safe and quality food is crucial for every household and is of extreme significance in the growth of any society.It is a complex procedure that deals with all issues focusing on the development of food proce...Providing safe and quality food is crucial for every household and is of extreme significance in the growth of any society.It is a complex procedure that deals with all issues focusing on the development of food processing from seed to harvest,storage,preparation,and consumption.This current paper seeks to demystify the importance of artificial intelligence,machine learning(ML),deep learning(DL),and computer vision(CV)in ensuring food safety and quality.By stressing the importance of these technologies,the audience will feel reassured and confident in their potential.These are very handy for such problems,giving assurance over food safety.CV is incredibly noble in today's generation because it improves food processing quality and positively impacts firms and researchers.Thus,at the present production stage,rich in image processing and computer visioning is incorporated into all facets of food production.In this field,DL and ML are implemented to identify the type of food in addition to quality.Concerning data and result-oriented perceptions,one has found similarities regarding various approaches.As a result,the findings of this study will be helpful for scholars looking for a proper approach to identify the quality of food offered.It helps to indicate which food products have been discussed by other scholars and lets the reader know papers by other scholars inclined to research further.Also,DL is accurately integrated with identifying the quality and safety of foods in the market.This paper describes the current practices and concerns of ML,DL,and probable trends for its future development.展开更多
1.Introduction Artificial intelligence(AI)is rapidly reshaping geoscience,from Earth observation interpretation and hazard forecasting to subsurface characterisation and Earth system modelling(Kochupillai et al.,2022;...1.Introduction Artificial intelligence(AI)is rapidly reshaping geoscience,from Earth observation interpretation and hazard forecasting to subsurface characterisation and Earth system modelling(Kochupillai et al.,2022;Sun et al.,2024).These capabilities emerge at a time when geoscientific evidence is increasingly informing high-stakes decisions about climate adaptation,resource development,and disaster risk reduction(McGovern et al.,2022).展开更多
The rapid advancements in computer vision(CV)technology have transformed the traditional approaches to material microstructure analysis.This review outlines the history of CV and explores the applications of deep-lear...The rapid advancements in computer vision(CV)technology have transformed the traditional approaches to material microstructure analysis.This review outlines the history of CV and explores the applications of deep-learning(DL)-driven CV in four key areas of materials science:microstructure-based performance prediction,microstructure information generation,microstructure defect detection,and crystal structure-based property prediction.The CV has significantly reduced the cost of traditional experimental methods used in material performance prediction.Moreover,recent progress made in generating microstructure images and detecting microstructural defects using CV has led to increased efficiency and reliability in material performance assessments.The DL-driven CV models can accelerate the design of new materials with optimized performance by integrating predictions based on both crystal and microstructural data,thereby allowing for the discovery and innovation of next-generation materials.Finally,the review provides insights into the rapid interdisciplinary developments in the field of materials science and future prospects.展开更多
Periodontitis has emerged as one of the most critical oral diseases, and research on this condition holds great importance for the advancement of stomatology. As the most authoritative national scientific research fun...Periodontitis has emerged as one of the most critical oral diseases, and research on this condition holds great importance for the advancement of stomatology. As the most authoritative national scientific research funding institution in China, the National Natural Science Foundation of China (NSFC) has played a pivotal role in driving the progress of periodontal science by supporting research on periodontitis. This article provides a comprehensive review of the research and development progress related to periodontitis in China from 2014 to 2023, highlighting the significant contributions of the NSFC to this field. We have summarized the detailed funding information from the NSFC, including the number of applicant codes, funded programs and the distribution of funded scholars. These data illustrate the efforts of the NSFC in cultivating young scientists and building research groups to address key challenges in national scientific research. This study offers an overview of the current hot topics, recent breakthroughs and future research prospects related to periodontitis in China.展开更多
The integration of machine learning(ML)into geohazard assessment has successfully instigated a paradigm shift,leading to the production of models that possess a level of predictive accuracy previously considered unatt...The integration of machine learning(ML)into geohazard assessment has successfully instigated a paradigm shift,leading to the production of models that possess a level of predictive accuracy previously considered unattainable.However,the black-box nature of these systems presents a significant barrier,hindering their operational adoption,regulatory approval,and full scientific validation.This paper provides a systematic review and synthesis of the emerging field of explainable artificial intelligence(XAI)as applied to geohazard science(GeoXAI),a domain that aims to resolve the long-standing trade-off between model performance and interpretability.A rigorous synthesis of 87 foundational studies is used to map the intellectual and methodological contours of this rapidly expanding field.The analysis reveals that current research efforts are concentrated predominantly on landslide and flood assessment.Methodologically,tree-based ensembles and deep learning models dominate the literature,with SHapley Additive exPlanations(SHAP)frequently adopted as the principal post-hoc explanation technique.More importantly,the review further documents how the role of XAI has shifted:rather than being used solely as a tool for interpreting models after training,it is increasingly integrated into the modeling cycle itself.Recent applications include its use in feature selection,adaptive sampling strategies,and model evaluation.The evidence also shows that GeoXAI extends beyond producing feature rankings.It reveals nonlinear thresholds and interaction effects that generate deeper mechanistic insights into hazard processes and mechanisms.Nevertheless,several key challenges remain unresolved within the field.These persistent issues are especially pronounced when considering the crucial necessity for interpretation stability,the demanding scholarly task of reliably distinguishing correlation from causation,and the development of appropriate methods for the treatment of complex spatio-temporal dynamics.展开更多
Mortality prediction in respiratory health is challenging,especially when using large-scale clinical datasets composed primarily of categorical variables.Traditional digital twin(DT)frameworks often rely on longi-tudi...Mortality prediction in respiratory health is challenging,especially when using large-scale clinical datasets composed primarily of categorical variables.Traditional digital twin(DT)frameworks often rely on longi-tudinal or sensor-based data,which are not always available in public health contexts.In this article,we propose a novel proto-DT framework for mortality prediction in respiratory health using a large-scale categorical biomedical dataset.This dataset contains 415,711 severe acute respiratory infection cases from the Brazilian Unified Health System,including both COVID-19 and non-COVID-19 patients.Four classification models—extreme gradient boosting(XGBoost),logistic regression,random forest,and a deep neural network(DNN)—are trained using cost-sensitive learning to address class imbalance.The models are evaluated using accuracy,precision,recall,F1-score,and area under the curve(AUC)related to the receiver operating characteristic(ROC).The framework supports simulated interventions by modifying selected inputs and recalculating predicted mortality.Additionally,we incorporate multiple correspondence analysis and K-means clustering to explore model sensitivity.A Python library has been developed to ensure reproducibility.All models achieve AUC-ROC values near or above 0.85.XGBoost yields the highest accuracy(0.84),while the DNN achieves the highest recall(0.81).Scenario-based simulations reveal how key clinical factors,such as intensive care unit admission and oxygen support,affect predicted outcomes.The proposed proto-DT framework demonstrates the feasibility of mortality prediction and intervention simulation using categorical data alone.This framework provides a foundation for data-driven explainable DTs in public health,even in the absence of time-series data.展开更多
The Intelligent Internet of Things(IIoT)involves real-world things that communicate or interact with each other through networking technologies by collecting data from these“things”and using intelligent approaches,s...The Intelligent Internet of Things(IIoT)involves real-world things that communicate or interact with each other through networking technologies by collecting data from these“things”and using intelligent approaches,such as Artificial Intelligence(AI)and machine learning,to make accurate decisions.Data science is the science of dealing with data and its relationships through intelligent approaches.Most state-of-the-art research focuses independently on either data science or IIoT,rather than exploring their integration.Therefore,to address the gap,this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT(IIoT)system by classifying the existing IoT-based data science techniques and presenting a summary of various characteristics.The paper analyzes the data science or big data security and privacy features,including network architecture,data protection,and continuous monitoring of data,which face challenges in various IoT-based systems.Extensive insights into IoT data security,privacy,and challenges are visualized in the context of data science for IoT.In addition,this study reveals the current opportunities to enhance data science and IoT market development.The current gap and challenges faced in the integration of data science and IoT are comprehensively presented,followed by the future outlook and possible solutions.展开更多
Deep-time Earth research plays a pivotal role in deciphering the rates,patterns,and mechanisms of Earth's evolutionary processes throughout geological history,providing essential scientific foundations for climate...Deep-time Earth research plays a pivotal role in deciphering the rates,patterns,and mechanisms of Earth's evolutionary processes throughout geological history,providing essential scientific foundations for climate prediction,natural resource exploration,and sustainable planetary stewardship.To advance Deep-time Earth research in the era of big data and artificial intelligence,the International Union of Geological Sciences initiated the“Deeptime Digital Earth International Big Science Program”(DDE)in 2019.At the core of this ambitious program lies the development of geoscience knowledge graphs,serving as a transformative knowledge infrastructure that enables the integration,sharing,mining,and analysis of heterogeneous geoscience big data.The DDE knowledge graph initiative has made significant strides in three critical dimensions:(1)establishing a unified knowledge structure across geoscience disciplines that ensures consistent representation of geological entities and their interrelationships through standardized ontologies and semantic frameworks;(2)developing a robust and scalable software infrastructure capable of supporting both expert-driven and machine-assisted knowledge engineering for large-scale graph construction and management;(3)implementing a comprehensive three-tiered architecture encompassing basic,discipline-specific,and application-oriented knowledge graphs,spanning approximately 20 geoscience disciplines.Through its open knowledge framework and international collaborative network,this initiative has fostered multinational research collaborations,establishing a robust foundation for next-generation geoscience research while propelling the discipline toward FAIR(Findable,Accessible,Interoperable,Reusable)data practices in deep-time Earth systems research.展开更多
Musculoskeletal injuries are among the most common causes of disability worldwide,with early detection and appropriate intervention critical to minimizing long-term complications.Infrared thermography(IRT)has emerged ...Musculoskeletal injuries are among the most common causes of disability worldwide,with early detection and appropriate intervention critical to minimizing long-term complications.Infrared thermography(IRT)has emerged as a noninvasive,real-time imaging modality that captures superficial temperature changes reflecting underlying physiological processes such as inflammation and vascular alterations.This review explores the fundamental principles of medical thermography,differentiates between passive and active approaches,and outlines key technological advancements including artificial intelligence integration.The clinical utility of IRT is discussed in various contexts–ranging from acute soft tissue injuries and overuse syndromes to chronic pain and rehabilitation monitoring.Comparative insights with conventional imaging techniques such as ultrasound and magnetic resonance imaging are also presented.While IRT offers functional imaging capabilities with advantages in portability,safety,and speed,its limitations–such as lack of deep-tissue penetration and protocol standardization–remain significant barriers to broader adoption.Future directions include the integration of IRT with other imaging modalities and digital health platforms to enhance musculoskeletal assessment and injury prevention strategies.展开更多
The convergence of materials science and biotechnology has catalyzed the development of innovative platforms,including nanotechnology,smart sensors,and supramolecular materials,significantly advancing the progress in ...The convergence of materials science and biotechnology has catalyzed the development of innovative platforms,including nanotechnology,smart sensors,and supramolecular materials,significantly advancing the progress in the field of life sciences[1−7].Among them,supramolecular materials have garnered increasing attention in life sciences owing to their distinctive self-assembly capabilities and intelligent responsiveness[8−12].展开更多
Background:The advent of the self-media age,digital humanities,and artificial intelligence(AI)technologies is gradually reshaping the narrative frameworks of the history of science and technology in general and the hi...Background:The advent of the self-media age,digital humanities,and artificial intelligence(AI)technologies is gradually reshaping the narrative frameworks of the history of science and technology in general and the history of medicine in particular,as it transforms the specific shape of contemporary medical science and health communication practice with the help of interactive,scenario-based communication ecosystems.Methods:This paper focuses on the interactive relationship between the history of science and science communication,employing historical tracing and case study comparison as research methods to explore the pathways and innovative models for reintegrating the history of science and technology including the history of medicine into contemporary scientific discourse.Results:The study finds that in the Chinese context,three key pathways facilitate the engagement of the history of science and technology including the history of medicine in science communication:administrative intervention,value reconstruction,and personalized adaptation.Specifically,administrative intervention promotes the integration of the history of science education into talent development through policy design;value reconstruction,centered on the scientific spirit,enhances societal cultural recognition of technological progress;and personalized adaptation leverages big data and social media technologies to enable precise and tailored knowledge dissemination.Conclusion:The rise of the“web-based knowledge brokering model”in the era of social media has introduced professional knowledge brokers,ensuring the accuracy and accessibility of science communication.These innovations not only serve as decision-making simulation tools for medical science and health communication,linking historical insights with contemporary practice,but also provide theoretical foundations and practical paradigms for realizing the value of the history of science and technology in the digital era.展开更多
The 2024 MRE HP Special Volume selects papers on new theoretical and experimental developments in the use of static largevolume presses(LVPs)1–3 and dynamic compression4,5 for studies under extreme high-pressure and ...The 2024 MRE HP Special Volume selects papers on new theoretical and experimental developments in the use of static largevolume presses(LVPs)1–3 and dynamic compression4,5 for studies under extreme high-pressure and high-temperature(HPHT)conditions.It also continues the previous year’s6 contemporary focus on superhydrides7–11 with extremely high superconducting temperatures Tc and addresses some controversial issues.12–14 In addition,it explores unconventional pressure-induced chemistry,particularly novel chemical stoichiometry and its impact on geochemistry and cosmochemistry in the deep interiors of Earth and other planets.18–21.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.92258303)the National Key Research and Development Program of China(Grant Nos.2024YFF0506704 and 2023YFF0803404).
文摘Ocean geoscience is a highly integrated and interdisciplinary field that plays a critical role in understanding the interaction between Earth’s lithosphere,hydrosphere,atmosphere,biosphere,and anthroposphere.Recent years have seen tremendous progress in global ocean research,driven by rapid advancements in deep-sea manned and unmanned submersibles,ocean drilling,seafloor observatories,big data assimilation,and supercomputing simulations.Representative examples of breakthroughs are highlighted in this work:(1)Probing sub-seafloor processes.A 10,000-meter ocean-bottom seismometer array has achieved high-resolution imaging of the deepest ocean on the Earth-the Challenger Deep of the Mariana Trench,revealing the role of key tectonic and hydrological processes within the subduction zone.The first sub-ice seafloor seismic and magnetotelluric experiments were successfully conducted at the Arctic Gakkel Ridge,providing significant insights into the dynamics of ultraslow seafloor spreading.(2)Exploration of seafloor resources.Near-seafloor investigations employing underwater robotics and multi-sensor systems have been carried out in areas of hydrothermal vents and cold seeps at global locations,including the Southwest Indian Ridge.These efforts have combined geophysical,oceanographic,chemical,and biological observations with extensive seafloor sampling.(3)Interdisciplinary research of complex catastrophic events.High-resolution simulations integrating ocean observations with supercomputing modeling have made it possible to fully model earthquake-induced seafloor deformation,tsunami propagation,and ocean basin-scale transport of the Fukushima Power Plant-derived radionuclides associated with the 2011 Tohoku earthquake.Among the world’s three major oceans,the Indian Ocean is still relatively underexplored.Major scientific challenges include elucidating crust-mantle interaction,air-sea dynamic coupling,large-scale marine hazards,and responses of ecosystems to major environmental changes,all of which require interdisciplinary collaboration.Future efforts should focus on developing intelligent unmanned observation platform systems,big data and digital twins,and AI-driven hazard modeling.Meanwhile,higher educational reforms should emphasize fostering a new generation of students and young scientists with a solid background and strong critical analysis skills to accelerate technological innovation.
基金supported by the National Key Research and Development Program(Grant No.2024YFA0917200)the Projects of the Chinese Center for Disease Control and Prevention(Grant No.BB2110240093)World Medical History under the Education Innovation Plan of the University of Science and Technology of China(Grant No.2024YCHX07).
文摘Background:With the rapid development of modern emerging technologies,the ethical dilemmas and social controversies triggered by scientific and technological activities have become increasingly prominent.How to guide technology for good and prevent and control technological risks has become an important issue of global concern.Research on science and technology ethics is dedicated to integrating ethical theories into governance practices and constructing ethical models that adapt to the development of the times.Methods:This article systematically reviews the six core approaches of scientific and technological ethics thought,including technological autonomy and political philosophy criticism,responsibility ethics and intergenerational obligations,technological intermediation and the integration of life and the world,ethical principles and normative frameworks,participatory governance and ethical practice innovation,as well as domain-specific ethical norms,thereby constructing an ethical analysis framework applicable to medical technology risks.And cross-analysis was conducted by taking medical events such as gene editing and xenotransplantation as examples.Results:Research shows that a single ethical approach has limitations in addressing complex medical ethical challenges,while the six approaches are complementary and synergistic.By criticizing technological autonomy,establishing a responsibility ethics orientation,setting the bottom line of ethical principles,promoting participatory governance,formulating domain norms,and continuously reflecting on the intermediary nature of technology,a multi-level and dynamically adaptive governance system for scientific and technological ethics can be constructed.Conclusion:The key to addressing contemporary medical ethics challenges lies in the comprehensive application of science and technology ethics theories and the integration of ethical considerations throughout the entire process of scientific and technological research and development.In the future,a governance framework that adapts to the development of new technologies should be established to promote cross-cultural and cross-disciplinary ethical dialogue and public participation,ensuring that scientific and technological innovation always serves the dignity of human life and overall well-being.
文摘I offer suggestions to increase the probability of success of an international research project.Collaborative studies often produce more innovative and transformative scientific results than work done by a single investigator or an isolated team.My advice is intended for early-career scientists.The product of the collaboration may be high-impact research publications,enhanced geophysical monitoring capabilities in a foreign country,or an advanced training course.Choosing the right international partner is the most important step.Keeping an open mind and being receptive to suggestions to modify the initial concept is critical.Other key steps include having a mutually agreed upon plan with achievable goals and well-defined expected outcomes.International cooperation is a richly rewarding experience that accelerates progress in the Earth Sciences.
文摘The year 2024 marked the 40th anniversary of Advances in Atmospheric Sciences(AAS),as well as the centenary of the Chinese Meteorological Society(CMS).The inaugural issue of AAS was published in 1984,initially being sponsored primarily by Chinese National Committee for the International Association of Meteorological and Atmospheric Sciences(IAMAS)and the Institute of Atmospheric Physics at the Chinese Academy of Sciences.In 2006,Springer became AAS’s international publisher.Then,in 2015,the CMS joined in sponsoring AAS,and in the same year,AAS also became an affiliated journal of the IAMAS.These milestone events helped broaden the reach of AAS,culminating in the journal establishing itself as a truly international journal supporting the advancement of the atmospheric sciences.
文摘In an academic environment increasingly shaped by metrics and the imperatives of“publish or perish”,it is rare to encounter a leading scientist willing to interweave personal narrative with conceptual reflection.The Soul of Geography by Fu(2025)achieves precisely this.The book resists simple categorisation:it is neither a conventional monograph nor a memoir,but rather a hybrid text that integrates autobiography,disciplinary reflection,and scientific arguments.In doing so,Fu articulates not only the trajectory of his own career but also a vision of geography as a discipline of theoretical depth and practical relevance.
文摘Providing safe and quality food is crucial for every household and is of extreme significance in the growth of any society.It is a complex procedure that deals with all issues focusing on the development of food processing from seed to harvest,storage,preparation,and consumption.This current paper seeks to demystify the importance of artificial intelligence,machine learning(ML),deep learning(DL),and computer vision(CV)in ensuring food safety and quality.By stressing the importance of these technologies,the audience will feel reassured and confident in their potential.These are very handy for such problems,giving assurance over food safety.CV is incredibly noble in today's generation because it improves food processing quality and positively impacts firms and researchers.Thus,at the present production stage,rich in image processing and computer visioning is incorporated into all facets of food production.In this field,DL and ML are implemented to identify the type of food in addition to quality.Concerning data and result-oriented perceptions,one has found similarities regarding various approaches.As a result,the findings of this study will be helpful for scholars looking for a proper approach to identify the quality of food offered.It helps to indicate which food products have been discussed by other scholars and lets the reader know papers by other scholars inclined to research further.Also,DL is accurately integrated with identifying the quality and safety of foods in the market.This paper describes the current practices and concerns of ML,DL,and probable trends for its future development.
基金supported by the Natural Science Foundation of Jiangsu Province,China(Grant No.BK20240937)the Natural Science Foundation of Shandong Province(Grant No.ZR2021QE187)+2 种基金the Shandong Higher Education“Young Entrepreneurship Talents Introduction and Cultivation Program”Project(Grant No.ZXQT20221228001)the Natural Science Foundation of China(Grant No.42502273)the Science and Technology Innovation Program of Hunan Province(Grant No.2022RC4028).
文摘1.Introduction Artificial intelligence(AI)is rapidly reshaping geoscience,from Earth observation interpretation and hazard forecasting to subsurface characterisation and Earth system modelling(Kochupillai et al.,2022;Sun et al.,2024).These capabilities emerge at a time when geoscientific evidence is increasingly informing high-stakes decisions about climate adaptation,resource development,and disaster risk reduction(McGovern et al.,2022).
基金financially supported by the National Science Fund for Distinguished Young Scholars,China(No.52025041)the National Natural Science Foundation of China(Nos.52450003,U2341267,and 52174294)+1 种基金the National Postdoctoral Program for Innovative Talents,China(No.BX20240437)the Fundamental Research Funds for the Central Universities,China(Nos.FRF-IDRY-23-037 and FRF-TP-20-02C2)。
文摘The rapid advancements in computer vision(CV)technology have transformed the traditional approaches to material microstructure analysis.This review outlines the history of CV and explores the applications of deep-learning(DL)-driven CV in four key areas of materials science:microstructure-based performance prediction,microstructure information generation,microstructure defect detection,and crystal structure-based property prediction.The CV has significantly reduced the cost of traditional experimental methods used in material performance prediction.Moreover,recent progress made in generating microstructure images and detecting microstructural defects using CV has led to increased efficiency and reliability in material performance assessments.The DL-driven CV models can accelerate the design of new materials with optimized performance by integrating predictions based on both crystal and microstructural data,thereby allowing for the discovery and innovation of next-generation materials.Finally,the review provides insights into the rapid interdisciplinary developments in the field of materials science and future prospects.
文摘Periodontitis has emerged as one of the most critical oral diseases, and research on this condition holds great importance for the advancement of stomatology. As the most authoritative national scientific research funding institution in China, the National Natural Science Foundation of China (NSFC) has played a pivotal role in driving the progress of periodontal science by supporting research on periodontitis. This article provides a comprehensive review of the research and development progress related to periodontitis in China from 2014 to 2023, highlighting the significant contributions of the NSFC to this field. We have summarized the detailed funding information from the NSFC, including the number of applicant codes, funded programs and the distribution of funded scholars. These data illustrate the efforts of the NSFC in cultivating young scientists and building research groups to address key challenges in national scientific research. This study offers an overview of the current hot topics, recent breakthroughs and future research prospects related to periodontitis in China.
文摘The integration of machine learning(ML)into geohazard assessment has successfully instigated a paradigm shift,leading to the production of models that possess a level of predictive accuracy previously considered unattainable.However,the black-box nature of these systems presents a significant barrier,hindering their operational adoption,regulatory approval,and full scientific validation.This paper provides a systematic review and synthesis of the emerging field of explainable artificial intelligence(XAI)as applied to geohazard science(GeoXAI),a domain that aims to resolve the long-standing trade-off between model performance and interpretability.A rigorous synthesis of 87 foundational studies is used to map the intellectual and methodological contours of this rapidly expanding field.The analysis reveals that current research efforts are concentrated predominantly on landslide and flood assessment.Methodologically,tree-based ensembles and deep learning models dominate the literature,with SHapley Additive exPlanations(SHAP)frequently adopted as the principal post-hoc explanation technique.More importantly,the review further documents how the role of XAI has shifted:rather than being used solely as a tool for interpreting models after training,it is increasingly integrated into the modeling cycle itself.Recent applications include its use in feature selection,adaptive sampling strategies,and model evaluation.The evidence also shows that GeoXAI extends beyond producing feature rankings.It reveals nonlinear thresholds and interaction effects that generate deeper mechanistic insights into hazard processes and mechanisms.Nevertheless,several key challenges remain unresolved within the field.These persistent issues are especially pronounced when considering the crucial necessity for interpretation stability,the demanding scholarly task of reliably distinguishing correlation from causation,and the development of appropriate methods for the treatment of complex spatio-temporal dynamics.
文摘Mortality prediction in respiratory health is challenging,especially when using large-scale clinical datasets composed primarily of categorical variables.Traditional digital twin(DT)frameworks often rely on longi-tudinal or sensor-based data,which are not always available in public health contexts.In this article,we propose a novel proto-DT framework for mortality prediction in respiratory health using a large-scale categorical biomedical dataset.This dataset contains 415,711 severe acute respiratory infection cases from the Brazilian Unified Health System,including both COVID-19 and non-COVID-19 patients.Four classification models—extreme gradient boosting(XGBoost),logistic regression,random forest,and a deep neural network(DNN)—are trained using cost-sensitive learning to address class imbalance.The models are evaluated using accuracy,precision,recall,F1-score,and area under the curve(AUC)related to the receiver operating characteristic(ROC).The framework supports simulated interventions by modifying selected inputs and recalculating predicted mortality.Additionally,we incorporate multiple correspondence analysis and K-means clustering to explore model sensitivity.A Python library has been developed to ensure reproducibility.All models achieve AUC-ROC values near or above 0.85.XGBoost yields the highest accuracy(0.84),while the DNN achieves the highest recall(0.81).Scenario-based simulations reveal how key clinical factors,such as intensive care unit admission and oxygen support,affect predicted outcomes.The proposed proto-DT framework demonstrates the feasibility of mortality prediction and intervention simulation using categorical data alone.This framework provides a foundation for data-driven explainable DTs in public health,even in the absence of time-series data.
基金supported in part by the National Natural Science Foundation of China under Grant 62371181in part by the Changzhou Science and Technology International Cooperation Program under Grant CZ20230029+1 种基金supported by a National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(2021R1A2B5B02087169)supported under the framework of international cooperation program managed by the National Research Foundation of Korea(2022K2A9A1A01098051)。
文摘The Intelligent Internet of Things(IIoT)involves real-world things that communicate or interact with each other through networking technologies by collecting data from these“things”and using intelligent approaches,such as Artificial Intelligence(AI)and machine learning,to make accurate decisions.Data science is the science of dealing with data and its relationships through intelligent approaches.Most state-of-the-art research focuses independently on either data science or IIoT,rather than exploring their integration.Therefore,to address the gap,this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT(IIoT)system by classifying the existing IoT-based data science techniques and presenting a summary of various characteristics.The paper analyzes the data science or big data security and privacy features,including network architecture,data protection,and continuous monitoring of data,which face challenges in various IoT-based systems.Extensive insights into IoT data security,privacy,and challenges are visualized in the context of data science for IoT.In addition,this study reveals the current opportunities to enhance data science and IoT market development.The current gap and challenges faced in the integration of data science and IoT are comprehensively presented,followed by the future outlook and possible solutions.
基金Strategic Priority Research Program of the Chinese Academy of Sciences,No.XDB0740000National Key Research and Development Program of China,No.2022YFB3904200,No.2022YFF0711601+1 种基金Key Project of Innovation LREIS,No.PI009National Natural Science Foundation of China,No.42471503。
文摘Deep-time Earth research plays a pivotal role in deciphering the rates,patterns,and mechanisms of Earth's evolutionary processes throughout geological history,providing essential scientific foundations for climate prediction,natural resource exploration,and sustainable planetary stewardship.To advance Deep-time Earth research in the era of big data and artificial intelligence,the International Union of Geological Sciences initiated the“Deeptime Digital Earth International Big Science Program”(DDE)in 2019.At the core of this ambitious program lies the development of geoscience knowledge graphs,serving as a transformative knowledge infrastructure that enables the integration,sharing,mining,and analysis of heterogeneous geoscience big data.The DDE knowledge graph initiative has made significant strides in three critical dimensions:(1)establishing a unified knowledge structure across geoscience disciplines that ensures consistent representation of geological entities and their interrelationships through standardized ontologies and semantic frameworks;(2)developing a robust and scalable software infrastructure capable of supporting both expert-driven and machine-assisted knowledge engineering for large-scale graph construction and management;(3)implementing a comprehensive three-tiered architecture encompassing basic,discipline-specific,and application-oriented knowledge graphs,spanning approximately 20 geoscience disciplines.Through its open knowledge framework and international collaborative network,this initiative has fostered multinational research collaborations,establishing a robust foundation for next-generation geoscience research while propelling the discipline toward FAIR(Findable,Accessible,Interoperable,Reusable)data practices in deep-time Earth systems research.
文摘Musculoskeletal injuries are among the most common causes of disability worldwide,with early detection and appropriate intervention critical to minimizing long-term complications.Infrared thermography(IRT)has emerged as a noninvasive,real-time imaging modality that captures superficial temperature changes reflecting underlying physiological processes such as inflammation and vascular alterations.This review explores the fundamental principles of medical thermography,differentiates between passive and active approaches,and outlines key technological advancements including artificial intelligence integration.The clinical utility of IRT is discussed in various contexts–ranging from acute soft tissue injuries and overuse syndromes to chronic pain and rehabilitation monitoring.Comparative insights with conventional imaging techniques such as ultrasound and magnetic resonance imaging are also presented.While IRT offers functional imaging capabilities with advantages in portability,safety,and speed,its limitations–such as lack of deep-tissue penetration and protocol standardization–remain significant barriers to broader adoption.Future directions include the integration of IRT with other imaging modalities and digital health platforms to enhance musculoskeletal assessment and injury prevention strategies.
基金supported by the National Natural Science Foundation of China(22101043)the Fundamental Research Funds for the Central Universities(N2205013,N232410019,N2405013)+3 种基金Natural Science Foundation of Liaoning Province(2023-MSBA-068)the Opening Fund of State Key Laboratory of Heavy Oil Processing(SKLHOP202203006)the Key Laboratory of Functional Molecular Solids,Ministry of Education(FMS2023005)Northeastern University。
文摘The convergence of materials science and biotechnology has catalyzed the development of innovative platforms,including nanotechnology,smart sensors,and supramolecular materials,significantly advancing the progress in the field of life sciences[1−7].Among them,supramolecular materials have garnered increasing attention in life sciences owing to their distinctive self-assembly capabilities and intelligent responsiveness[8−12].
基金The National Key R&D project granted by the Ministry of Science and Technology(2024YFA0917200)Digital Museum Construction Project of Chinese Centre for Disease Control and Prevention(BB2110240080)Science Communication Project of Chinese Academy of Sciences(CX2090000008).
文摘Background:The advent of the self-media age,digital humanities,and artificial intelligence(AI)technologies is gradually reshaping the narrative frameworks of the history of science and technology in general and the history of medicine in particular,as it transforms the specific shape of contemporary medical science and health communication practice with the help of interactive,scenario-based communication ecosystems.Methods:This paper focuses on the interactive relationship between the history of science and science communication,employing historical tracing and case study comparison as research methods to explore the pathways and innovative models for reintegrating the history of science and technology including the history of medicine into contemporary scientific discourse.Results:The study finds that in the Chinese context,three key pathways facilitate the engagement of the history of science and technology including the history of medicine in science communication:administrative intervention,value reconstruction,and personalized adaptation.Specifically,administrative intervention promotes the integration of the history of science education into talent development through policy design;value reconstruction,centered on the scientific spirit,enhances societal cultural recognition of technological progress;and personalized adaptation leverages big data and social media technologies to enable precise and tailored knowledge dissemination.Conclusion:The rise of the“web-based knowledge brokering model”in the era of social media has introduced professional knowledge brokers,ensuring the accuracy and accessibility of science communication.These innovations not only serve as decision-making simulation tools for medical science and health communication,linking historical insights with contemporary practice,but also provide theoretical foundations and practical paradigms for realizing the value of the history of science and technology in the digital era.
基金financial support from the Shanghai Key Laboratory of MFree,China(Grant No.22dz2260800)the Shanghai Science and Technology Committee,China(Grant No.22JC1410300).
文摘The 2024 MRE HP Special Volume selects papers on new theoretical and experimental developments in the use of static largevolume presses(LVPs)1–3 and dynamic compression4,5 for studies under extreme high-pressure and high-temperature(HPHT)conditions.It also continues the previous year’s6 contemporary focus on superhydrides7–11 with extremely high superconducting temperatures Tc and addresses some controversial issues.12–14 In addition,it explores unconventional pressure-induced chemistry,particularly novel chemical stoichiometry and its impact on geochemistry and cosmochemistry in the deep interiors of Earth and other planets.18–21.
