Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are...Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are commonly used for stroke screening,accurate administration is dependent on specialized training.In this study,we proposed a novel multimodal deep learning approach,based on the FAST,for assessing suspected stroke patients exhibiting symptoms such as limb weakness,facial paresis,and speech disorders in acute settings.We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements,facial expressions,and speech tests based on the FAST.We compared the constructed deep learning model,which was designed to process multi-modal datasets,with six prior models that achieved good action classification performance,including the I3D,SlowFast,X3D,TPN,TimeSformer,and MViT.We found that the findings of our deep learning model had a higher clinical value compared with the other approaches.Moreover,the multi-modal model outperformed its single-module variants,highlighting the benefit of utilizing multiple types of patient data,such as action videos and speech audio.These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke,thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.展开更多
The brain,with its trillions of neural connections,different cellular types,and molecular complexities,presents a formidable challenge for researchers aiming to comprehend the multifaceted nature of neural health.As t...The brain,with its trillions of neural connections,different cellular types,and molecular complexities,presents a formidable challenge for researchers aiming to comprehend the multifaceted nature of neural health.As traditional methods have provided valuable insights,emerging technologies offer unprecedented opportunities to delve deeper into the underpinnings of brain function.In the everevolving landscape of neuroscience,the quest to unravel the mysteries of the human brain is bound to take a leap forward thanks to new technological improvements and bold interpretative frameworks.展开更多
Gas sensor is an indispensable part of modern society withwide applications in environmental monitoring,healthcare,food industry,public safety,etc.With the development of sensor technology,wireless communication,smart...Gas sensor is an indispensable part of modern society withwide applications in environmental monitoring,healthcare,food industry,public safety,etc.With the development of sensor technology,wireless communication,smart monitoring terminal,cloud storage/computing technology,and artificial intelligence,smart gas sensors represent the future of gassensing due to their merits of real-time multifunctional monitoring,earlywarning function,and intelligent and automated feature.Various electronicand optoelectronic gas sensors have been developed for high-performancesmart gas analysis.With the development of smart terminals and the maturityof integrated technology,flexible and wearable gas sensors play an increasingrole in gas analysis.This review highlights recent advances of smart gassensors in diverse applications.The structural components and fundamentalprinciples of electronic and optoelectronic gas sensors are described,andflexible and wearable gas sensor devices are highlighted.Moreover,sensorarray with artificial intelligence algorithms and smart gas sensors in“Internet of Things”paradigm are introduced.Finally,the challengesand perspectives of smart gas sensors are discussed regarding the future need of gas sensors for smart city and healthy living.展开更多
Artificial intelligence(AI)has taken breathtaking leaps forward in recent years,evolving into a strategic technology for pioneering the future.The growing demand for computing power—especially in demanding inference ...Artificial intelligence(AI)has taken breathtaking leaps forward in recent years,evolving into a strategic technology for pioneering the future.The growing demand for computing power—especially in demanding inference tasks,exemplified by generative AI models such as ChatGPT—poses challenges for conventional electronic computing systems.Advances in photonics technology have ignited interest in investigating photonic computing as a promising AI computing modality.Through the profound fusion of AI and photonics technologies,intelligent photonics is developing as an emerging interdisciplinary field with significant potential to revolutionize practical applications.Deep learning,as a subset of AI,presents efficient avenues for optimizing photonic design,developing intelligent optical systems,and performing optical data processing and analysis.Employing AI in photonics can empower applications such as smartphone cameras,biomedical microscopy,and virtual and augmented reality displays.Conversely,leveraging photonics-based devices and systems for the physical implementation of neural networks enables high speed and low energy consumption.Applying photonics technology in AI computing is expected to have a transformative impact on diverse fields,including optical communications,automatic driving,and astronomical observation.Here,recent advances in intelligent photonics are presented from the perspective of the synergy between deep learning and metaphotonics,holography,and quantum photonics.This review also spotlights relevant applications and offers insights into challenges and prospects.展开更多
With the continuous advancement and maturation of technologies such as big data,artificial intelligence,virtual reality,robotics,human-machine collaboration,and augmented reality,many enterprises are finding new avenu...With the continuous advancement and maturation of technologies such as big data,artificial intelligence,virtual reality,robotics,human-machine collaboration,and augmented reality,many enterprises are finding new avenues for digital transformation and intelligent upgrading.Industry 5.0,a further extension and development of Industry 4.0,has become an important development trend in industry with more emphasis on human-centered sustainability and flexibility.Accordingly,both the industrial metaverse and digital twins have attracted much attention in this new era.However,the relationship between them is not clear enough.In this paper,a comparison between digital twins and the metaverse in industry is made firstly.Then,we propose the concept and framework of Digital Twin Systems Engineering(DTSE)to demonstrate how digital twins support the industrial metaverse in the era of Industry 5.0 by integrating systems engineering principles.Furthermore,we discuss the key technologies and challenges of DTSE,in particular how artificial intelligence enhances the application of DTSE.Finally,a specific application scenario in the aviation field is presented to illustrate the application prospects of DTSE.展开更多
Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather an...Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather and climate phenomena.In the past few decades,dynamical numerical models have been the primary tools for predictability studies,achieving significant progress.Nowadays,with the advances in artificial intelligence(AI)techniques and accumulations of vast meteorological data,modeling weather and climate events using modern data-driven approaches is becoming trendy,where FourCastNet,Pangu-Weather,and GraphCast are successful pioneers.In this perspective article,we suggest AI models should not be limited to forecasting but be expanded to predictability studies,leveraging AI's advantages of high efficiency and self-contained optimization modules.To this end,we first remark that AI models should possess high simulation capability with fine spatiotemporal resolution for two kinds of predictability studies.AI models with high simulation capabilities comparable to numerical models can be considered to provide solutions to partial differential equations in a data-driven way.Then,we highlight several specific predictability issues with well-determined nonlinear optimization formulizations,which can be well-studied using AI models,holding significant scientific value.In addition,we advocate for the incorporation of AI models into the synergistic cycle of the cognition–observation–model paradigm.Comprehensive predictability studies have the potential to transform“big data”to“big and better data”and shift the focus from“AI for forecasts”to“AI for science”,ultimately advancing the development of the atmospheric and oceanic sciences.展开更多
Artificial intelligence(AI)is significantly advancing precision medicine,particularly in the fields of immunogenomics,radiomics,and pathomics.In immunogenomics,AI can process vast amounts of genomic and multi-omic dat...Artificial intelligence(AI)is significantly advancing precision medicine,particularly in the fields of immunogenomics,radiomics,and pathomics.In immunogenomics,AI can process vast amounts of genomic and multi-omic data to identify biomarkers associated with immunotherapy responses and disease prognosis,thus providing strong support for personalized treatments.In radiomics,AI can analyze high-dimensional features from computed tomography(CT),magnetic resonance imaging(MRI),and positron emission tomography/computed tomography(PET/CT)images to discover imaging biomarkers associated with tumor heterogeneity,treatment response,and disease progression,thereby enabling non-invasive,real-time assessments for personalized therapy.Pathomics leverages AI for deep analysis of digital pathology images,and can uncover subtle changes in tissue microenvironments,cellular characteristics,and morphological features,and offer unique insights into immunotherapy response prediction and biomarker discovery.These AI-driven technologies not only enhance the speed,accuracy,and robustness of biomarker discovery but also significantly improve the precision,personalization,and effectiveness of clinical treatments,and are driving a shift from empirical to precision medicine.Despite challenges such as data quality,model interpretability,integration of multi-modal data,and privacy protection,the ongoing advancements in AI,coupled with interdisciplinary collaboration,are poised to further enhance AI’s roles in biomarker discovery and immunotherapy response prediction.These improvements are expected to lead to more accurate,personalized treatment strategies and ultimately better patient outcomes,marking a significant step forward in the evolution of precision medicine.展开更多
Cancer poses a serious threat to human health worldwide and is a leading cause of death1.The analysis of radiological imaging is crucial in early detection,accurate diagnosis,effective treatment planning,and ongoing m...Cancer poses a serious threat to human health worldwide and is a leading cause of death1.The analysis of radiological imaging is crucial in early detection,accurate diagnosis,effective treatment planning,and ongoing monitoring of patients with cancer.However,several challenges impede the effectiveness of cancer imaging analysis in clinical practice.One difficulty is that healthcare professionals’immense clinical workloads can result in time constraints and increase pressure,thereby hindering their ability to maintain high accuracy and thoroughness in image analysis.Additionally,subjective variability among radiologists can lead to inconsistent interpretations and diagnoses.Because this variability is often influenced by personal biases,standardized assessments are often difficult to achieve.Moreover,the inherent complexity of cancer imaging necessitates extensive clinical experience;this aspect can also be a limiting factor,particularly if expertise or resources are limited.The application of artificial intelligence(AI)can alleviate these problems by enhancing the accuracy,objectivity,and efficiency of cancer imaging analysis while assisting physicians.Therefore,the advancement of AI research is crucial for achieving progress in radiology.展开更多
Since the release of ChatGPT in late 2022,Generative Artificial Intelligence(GAI)has gained widespread attention because of its impressive capabilities in language comprehension,reasoning,and generation.GAI has been s...Since the release of ChatGPT in late 2022,Generative Artificial Intelligence(GAI)has gained widespread attention because of its impressive capabilities in language comprehension,reasoning,and generation.GAI has been successfully applied across various aspects(e.g.,creative writing,code generation,translation,and information retrieval).In cartography and GIS,researchers have employed GAI to handle some specific tasks,such as map generation,geographic question answering,and spatiotemporal data analysis,yielding a series of remarkable results.Although GAI-based techniques are developing rapidly,literature reviews of their applications in cartography and GIS remain relatively limited.This paper reviews recent GAI-related research in cartography and GIS,focusing on three aspects:①map generation,②geographical analysis,and③evaluation of GAI’s spatial cognition abilities.In addition,the paper analyzes current challenges and proposes future research directions.展开更多
The security of the seed industry is crucial for ensuring national food security.Currently,developed countries in Europe and America,along with international seed industry giants,have entered the Breeding 4.0 era.This...The security of the seed industry is crucial for ensuring national food security.Currently,developed countries in Europe and America,along with international seed industry giants,have entered the Breeding 4.0 era.This era integrates biotechnology,artificial intelligence(AI),and big data information technology.In contrast,China is still in a transition period between stages 2.0 and 3.0,which primarily relies on conventional selection and molecular breeding.In the context of increasingly complex international situations,accurately identifying core issues in China's seed industry innovation and seizing the frontier of international seed technology are strategically important.These efforts are essential for ensuring food security and revitalizing the seed industry.This paper systematically analyzes the characteristics of crop breeding data from artificial selection to intelligent design breeding.It explores the applications and development trends of AI and big data in modern crop breeding from several key perspectives.These include highthroughput phenotype acquisition and analysis,multiomics big data database and management system construction,AI-based multiomics integrated analysis,and the development of intelligent breeding software tools based on biological big data and AI technology.Based on an in-depth analysis of the current status and challenges of China's seed industry technology development,we propose strategic goals and key tasks for China's new generation of AI and big data-driven intelligent design breeding.These suggestions aim to accelerate the development of an intelligent-driven crop breeding engineering system that features large-scale gene mining,efficient gene manipulation,engineered variety design,and systematized biobreeding.This study provides a theoretical basis and practical guidance for the development of China's seed industry technology.展开更多
In recent years,Volunteered Geographic Information(VGI)has emerged as a crucial source of mapping data,contributed by users through crowdsourcing platforms such as OpenStreetMap.This paper presents a novel approach th...In recent years,Volunteered Geographic Information(VGI)has emerged as a crucial source of mapping data,contributed by users through crowdsourcing platforms such as OpenStreetMap.This paper presents a novel approach that Integrates Large Language Models(LLMs)into a fully automated mapping workflow,utilizing VGI data.The process leverages Prompt Engineering,which involves designing and optimizing input instructions to ensure the LLM produces desired mapping outputs.By constructing precise and detailed prompts,LLM agents are able to accurately interpret mapping requirements,and autonomously extract,analyze,and process VGI geospatial data.They dynamically interact with mapping tools to automate the entire mapping process—from data acquisition to map generation.This approach significantly streamlines the creation of high-quality mapping outputs,reducing the time and resources typically required for such tasks.Moreover,the system lowers the barrier for non-expert users,enabling them to generate accurate maps without extensive technical expertise.Through various case studies,we demonstrate the LLM application across different mapping scenarios,highlighting its potential to enhance the efficiency,accuracy,and accessibility of map production.The results suggest that LLM-powered mapping systems can not only optimize VGI data processing but also expand the usability of ubiquitous mapping across diverse fields,including urban planning and infrastructure development.展开更多
In the wave of digital and intelligent applications,artificial intelligence(AI)is transforming the development trajectories of industries across the globe.Traditional Chinese medicine(TCM),as a cultural treasure of th...In the wave of digital and intelligent applications,artificial intelligence(AI)is transforming the development trajectories of industries across the globe.Traditional Chinese medicine(TCM),as a cultural treasure of the Chinese nation,carries thousands of years of wisdom and practical experience.However,in the context of the rapid advancements in modern medicine and technology,TCM faces dual challenges:preserving its heritage while innovating.DeepSeek,a major achievement in the field of AI,offers a new opportunity for the development of TCM with its powerful technological capabilities.Exploring the integration of DeepSeek with TCM not only helps modernize the practice but also promises unique contributions to global health.展开更多
Composed of natural materials but constructed using artificial structures through ingenious design,metamaterials possess properties beyond nature.Unlike traditional materials studies,metamaterials research requires gr...Composed of natural materials but constructed using artificial structures through ingenious design,metamaterials possess properties beyond nature.Unlike traditional materials studies,metamaterials research requires great human creativity in order to realize the desired properties and thereby the required functionalities through design.Such properties and functionalities are not necessarily available in nature,and their design can break through the existing materials ideology.This paper reviews progress in metamaterials research over the past 20 years in terms of the materials innovations that have achieved the designation of “meta.” In particular,we discuss future trends in metamaterials in the fields of both fundamental science and engineering.展开更多
The advent of artificial intelligence(AI)in recent years has brought about transformative changes across various sectors,including healthcare.In nursing practice,education,and research,AI has the potential to revoluti...The advent of artificial intelligence(AI)in recent years has brought about transformative changes across various sectors,including healthcare.In nursing practice,education,and research,AI has the potential to revolutionize traditional methodologies,enhance learning experiences,and improve patient outcomes.Integrating AI tools and techniques can provide clinicians with smarter clinical solutions and nursing students with more robust and interactive learning environments,while also advancing research capabilities in the field.Despite the promising prospects,the incorporation of AI into nursing practice,education,and research presents several challenges.Firstly,there is a concern about the potential displacement of human roles in nursing due to automation,which may affect the human-centric nature of nursing care.Secondly,there are issues related to the lag in AI competency among nurses.Many current nursing curricula do not include comprehensive AI training,leading to a lack of preparedness in utilizing these technologies effectively.Lastly,the ethical implications of AI in healthcare,such as data privacy,patient consent,and the potential for biased algorithms,need to be meticulously addressed.To harness the full potential of AI in nursing practice,education,and research,several strategic actions including reinvesting in humanistic practice,revising core competencies and curriculum,and developing new ethical guidelines.展开更多
Objectives Diabetes remains a major global health challenge in China.Artificial intelligence(AI)has demonstrated considerable potential in improving diabetes management.This study aimed to assess healthcare providers...Objectives Diabetes remains a major global health challenge in China.Artificial intelligence(AI)has demonstrated considerable potential in improving diabetes management.This study aimed to assess healthcare providers’perceptions regarding AI in diabetes care across China.Methods A cross-sectional survey was conducted using snowball sampling from November 12 to November 24,2024.We selected 514 physicians and nurses by a snowball sampling method from healthcare providers across 30 cities or provinces in China.The self-developed questionnaire comprised five sections with 19 questions assessing medical workers’demographic characteristics,AI-related experience and interest,awareness,attitudes,and concerns regarding AI in diabetes care.Statistical analysis was performed using t-test,analysis of variance(ANOVA),and linear regression.Results Among them,20.0%and 48.1%of respondents had participated in AI-related research and training,while 85.4%expressed moderate to high interest in AI training for diabetes care.Most respondents reported partial awareness of AI in diabetes care,and only 12.6%exhibited a comprehensive or substantial understanding.Attitudes toward AI in diabetes care were generally positive,with a mean score of 24.50±3.38.Nurses demonstrated significantly higher scores than physicians(P<0.05).Greater awareness,prior AI training experience,and higher interest in AI training in diabetes care were strongly associated with more positive attitudes(P<0.05).Key concerns regarding AI included trust issues from AI-clinician inconsistencies(77.2%),increased workload and clinical workflow disruptions(63.4%),and incomplete legal and regulatory frameworks(60.3%).Only 34.2%of respondents expressed concerns about job displacement,indicating general confidence in their professional roles.Conclusions While Chinese healthcare providers show moderate awareness of AI in diabetes care,their attitudes are generally positive,and they are considerably interested in future training.Tailored,role-specific AI training is essential for equitable and effective integration into clinical practice.Additionally,transparent,reliable,ethical AI models must be prioritized to alleviate practitioners’concerns.展开更多
Colorectal cancer(CRC)ranks among the top causes of cancer-related fatalities globally.Recent progress in genomics,proteomics,and bioinformatics has greatly improved our comprehension of the molecular underpinnings of...Colorectal cancer(CRC)ranks among the top causes of cancer-related fatalities globally.Recent progress in genomics,proteomics,and bioinformatics has greatly improved our comprehension of the molecular underpinnings of CRC,paving the way for targeted therapies and immunotherapies.Nonetheless,obstacles such as tumor heterogeneity and drug resistance persist,hindering advancements in treatment efficacy.In this context,the integration of artificial intelligence(AI)and organoid technology presents promising new avenues.AI can analyze genetic and clinical data to forecast disease risk,prognosis,and treatment responses,thereby expediting drug development and tailoring treatment plans.Organoids replicate the genetic traits and biological behaviors of tumors,acting as platforms for drug testing and the formulation of personalized treatment approaches.Despite notable strides in CRC research and treatment-from genetic insights to therapeutic innovations-numerous challenges endure,including the intricate tumor microen-vironment,tumor heterogeneity,adverse effects of immunotherapies,issues related to AI data quality and privacy,and the need for standardization in organoid culture.Future initiatives should concentrate on clarifying the pathogenesis of CRC,refining AI algorithms and organoid models,and creating more effective therapeutic strategies to alleviate the global impact of CRC.展开更多
The concept of Supply Chain 4.0 represents a transformative phase in supply chain management through advanced digital technologies like IoT, AI, blockchain, and cyber-physical systems. While these innovations deliver ...The concept of Supply Chain 4.0 represents a transformative phase in supply chain management through advanced digital technologies like IoT, AI, blockchain, and cyber-physical systems. While these innovations deliver operational improvements, the heightened interconnectivity introduces significant cybersecurity challenges, particularly within military logistics, where mission-critical operations and life-safety concerns are paramount. This paper examines these unique cybersecurity requirements, focusing on advanced persistent threats, supply chain poisoning, and data breaches that could compromise sensitive operations. The study proposes a hybrid cybersecurity framework tailored to military logistics, integrating resilience, redundancy, and cross-jurisdictional security measures. Real-world applicability is validated through simulations, offering strategies for securing supply chains while balancing security, efficiency, and flexibility.展开更多
Memristors have a synapse-like two-terminal structure and electrical properties,which are widely used in the construc-tion of artificial synapses.However,compared to inorganic materials,organic materials are rarely us...Memristors have a synapse-like two-terminal structure and electrical properties,which are widely used in the construc-tion of artificial synapses.However,compared to inorganic materials,organic materials are rarely used for artificial spiking synapses due to their relatively poor memrisitve performance.Here,for the first time,we present an organic memristor based on an electropolymerized dopamine-based memristive layer.This polydopamine-based memristor demonstrates the improve-ments in key performance,including a low threshold voltage of 0.3 V,a thin thickness of 16 nm,and a high parasitic capaci-tance of about 1μF·mm^(-2).By leveraging these properties in combination with its stable threshold switching behavior,we con-struct a capacitor-free and low-power artificial spiking neuron capable of outputting the oscillation voltage,whose spiking fre-quency increases with the increase of current stimulation analogous to a biological neuron.The experimental results indicate that our artificial spiking neuron holds potential for applications in neuromorphic computing and systems.展开更多
基金supported by the Ministry of Science and Technology of China,No.2020AAA0109605(to XL)Meizhou Major Scientific and Technological Innovation PlatformsProjects of Guangdong Provincial Science & Technology Plan Projects,No.2019A0102005(to HW).
文摘Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are commonly used for stroke screening,accurate administration is dependent on specialized training.In this study,we proposed a novel multimodal deep learning approach,based on the FAST,for assessing suspected stroke patients exhibiting symptoms such as limb weakness,facial paresis,and speech disorders in acute settings.We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements,facial expressions,and speech tests based on the FAST.We compared the constructed deep learning model,which was designed to process multi-modal datasets,with six prior models that achieved good action classification performance,including the I3D,SlowFast,X3D,TPN,TimeSformer,and MViT.We found that the findings of our deep learning model had a higher clinical value compared with the other approaches.Moreover,the multi-modal model outperformed its single-module variants,highlighting the benefit of utilizing multiple types of patient data,such as action videos and speech audio.These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke,thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.
文摘The brain,with its trillions of neural connections,different cellular types,and molecular complexities,presents a formidable challenge for researchers aiming to comprehend the multifaceted nature of neural health.As traditional methods have provided valuable insights,emerging technologies offer unprecedented opportunities to delve deeper into the underpinnings of brain function.In the everevolving landscape of neuroscience,the quest to unravel the mysteries of the human brain is bound to take a leap forward thanks to new technological improvements and bold interpretative frameworks.
基金supported by the National Natural Science Foundation of China(No.22376159)the Fundamental Research Funds for the Central Universities.
文摘Gas sensor is an indispensable part of modern society withwide applications in environmental monitoring,healthcare,food industry,public safety,etc.With the development of sensor technology,wireless communication,smart monitoring terminal,cloud storage/computing technology,and artificial intelligence,smart gas sensors represent the future of gassensing due to their merits of real-time multifunctional monitoring,earlywarning function,and intelligent and automated feature.Various electronicand optoelectronic gas sensors have been developed for high-performancesmart gas analysis.With the development of smart terminals and the maturityof integrated technology,flexible and wearable gas sensors play an increasingrole in gas analysis.This review highlights recent advances of smart gassensors in diverse applications.The structural components and fundamentalprinciples of electronic and optoelectronic gas sensors are described,andflexible and wearable gas sensor devices are highlighted.Moreover,sensorarray with artificial intelligence algorithms and smart gas sensors in“Internet of Things”paradigm are introduced.Finally,the challengesand perspectives of smart gas sensors are discussed regarding the future need of gas sensors for smart city and healthy living.
基金supported by the National Natural Science Foundation of China(62035003 and 62235009).
文摘Artificial intelligence(AI)has taken breathtaking leaps forward in recent years,evolving into a strategic technology for pioneering the future.The growing demand for computing power—especially in demanding inference tasks,exemplified by generative AI models such as ChatGPT—poses challenges for conventional electronic computing systems.Advances in photonics technology have ignited interest in investigating photonic computing as a promising AI computing modality.Through the profound fusion of AI and photonics technologies,intelligent photonics is developing as an emerging interdisciplinary field with significant potential to revolutionize practical applications.Deep learning,as a subset of AI,presents efficient avenues for optimizing photonic design,developing intelligent optical systems,and performing optical data processing and analysis.Employing AI in photonics can empower applications such as smartphone cameras,biomedical microscopy,and virtual and augmented reality displays.Conversely,leveraging photonics-based devices and systems for the physical implementation of neural networks enables high speed and low energy consumption.Applying photonics technology in AI computing is expected to have a transformative impact on diverse fields,including optical communications,automatic driving,and astronomical observation.Here,recent advances in intelligent photonics are presented from the perspective of the synergy between deep learning and metaphotonics,holography,and quantum photonics.This review also spotlights relevant applications and offers insights into challenges and prospects.
基金Supported by Beijing Municipal Natural Science Foundation of China(Grant No.24JL002)China Postdoctoral Science Foundation(Grant No.2024M754054)+2 种基金National Natural Science Foundation of China(Grant No.52120105008)Beijing Municipal Outstanding Young Scientis Program of Chinathe New Cornerstone Science Foundation through the XPLORER PRIZE。
文摘With the continuous advancement and maturation of technologies such as big data,artificial intelligence,virtual reality,robotics,human-machine collaboration,and augmented reality,many enterprises are finding new avenues for digital transformation and intelligent upgrading.Industry 5.0,a further extension and development of Industry 4.0,has become an important development trend in industry with more emphasis on human-centered sustainability and flexibility.Accordingly,both the industrial metaverse and digital twins have attracted much attention in this new era.However,the relationship between them is not clear enough.In this paper,a comparison between digital twins and the metaverse in industry is made firstly.Then,we propose the concept and framework of Digital Twin Systems Engineering(DTSE)to demonstrate how digital twins support the industrial metaverse in the era of Industry 5.0 by integrating systems engineering principles.Furthermore,we discuss the key technologies and challenges of DTSE,in particular how artificial intelligence enhances the application of DTSE.Finally,a specific application scenario in the aviation field is presented to illustrate the application prospects of DTSE.
基金in part supported by the National Natural Science Foundation of China(Grant Nos.42288101,42405147 and 42475054)in part by the China National Postdoctoral Program for Innovative Talents(Grant No.BX20230071)。
文摘Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather and climate phenomena.In the past few decades,dynamical numerical models have been the primary tools for predictability studies,achieving significant progress.Nowadays,with the advances in artificial intelligence(AI)techniques and accumulations of vast meteorological data,modeling weather and climate events using modern data-driven approaches is becoming trendy,where FourCastNet,Pangu-Weather,and GraphCast are successful pioneers.In this perspective article,we suggest AI models should not be limited to forecasting but be expanded to predictability studies,leveraging AI's advantages of high efficiency and self-contained optimization modules.To this end,we first remark that AI models should possess high simulation capability with fine spatiotemporal resolution for two kinds of predictability studies.AI models with high simulation capabilities comparable to numerical models can be considered to provide solutions to partial differential equations in a data-driven way.Then,we highlight several specific predictability issues with well-determined nonlinear optimization formulizations,which can be well-studied using AI models,holding significant scientific value.In addition,we advocate for the incorporation of AI models into the synergistic cycle of the cognition–observation–model paradigm.Comprehensive predictability studies have the potential to transform“big data”to“big and better data”and shift the focus from“AI for forecasts”to“AI for science”,ultimately advancing the development of the atmospheric and oceanic sciences.
基金supported by grants from the National Natural Science Foundation of China(Grant No.82272008)The Science&Technology Development Fund of Tianjin Education Commission for Higher Education(Grant No.2021KJ194)Tianjin Key Medical Discipline(Specialty)Construction Project(Grant No.TJYXZDXK-009A).
文摘Artificial intelligence(AI)is significantly advancing precision medicine,particularly in the fields of immunogenomics,radiomics,and pathomics.In immunogenomics,AI can process vast amounts of genomic and multi-omic data to identify biomarkers associated with immunotherapy responses and disease prognosis,thus providing strong support for personalized treatments.In radiomics,AI can analyze high-dimensional features from computed tomography(CT),magnetic resonance imaging(MRI),and positron emission tomography/computed tomography(PET/CT)images to discover imaging biomarkers associated with tumor heterogeneity,treatment response,and disease progression,thereby enabling non-invasive,real-time assessments for personalized therapy.Pathomics leverages AI for deep analysis of digital pathology images,and can uncover subtle changes in tissue microenvironments,cellular characteristics,and morphological features,and offer unique insights into immunotherapy response prediction and biomarker discovery.These AI-driven technologies not only enhance the speed,accuracy,and robustness of biomarker discovery but also significantly improve the precision,personalization,and effectiveness of clinical treatments,and are driving a shift from empirical to precision medicine.Despite challenges such as data quality,model interpretability,integration of multi-modal data,and privacy protection,the ongoing advancements in AI,coupled with interdisciplinary collaboration,are poised to further enhance AI’s roles in biomarker discovery and immunotherapy response prediction.These improvements are expected to lead to more accurate,personalized treatment strategies and ultimately better patient outcomes,marking a significant step forward in the evolution of precision medicine.
基金funded by grants from the National Natural Science Foundation of China(Grant Nos.82171932 and 82302180)the Ministry of Science and Technology of China(Grant No.2024ZD0520002)+3 种基金the Chinese National Key Research and Development Project(Grant Nos.2021YFC2500402 and 2021YFC2500400)the National Health Commission Capacity Building and Continuing Education Center(Grant No.YXFSC2022JJSJ011)the Tianjin Key Medical Discipline(Specialty)Construction Project(Grant No.TJYXZDXK-010A)the Scientific Developing Foundation of Tianjin Education Commission(Grant No.2024KJ182).
文摘Cancer poses a serious threat to human health worldwide and is a leading cause of death1.The analysis of radiological imaging is crucial in early detection,accurate diagnosis,effective treatment planning,and ongoing monitoring of patients with cancer.However,several challenges impede the effectiveness of cancer imaging analysis in clinical practice.One difficulty is that healthcare professionals’immense clinical workloads can result in time constraints and increase pressure,thereby hindering their ability to maintain high accuracy and thoroughness in image analysis.Additionally,subjective variability among radiologists can lead to inconsistent interpretations and diagnoses.Because this variability is often influenced by personal biases,standardized assessments are often difficult to achieve.Moreover,the inherent complexity of cancer imaging necessitates extensive clinical experience;this aspect can also be a limiting factor,particularly if expertise or resources are limited.The application of artificial intelligence(AI)can alleviate these problems by enhancing the accuracy,objectivity,and efficiency of cancer imaging analysis while assisting physicians.Therefore,the advancement of AI research is crucial for achieving progress in radiology.
基金National Natural Science Foundation of China(Nos.4210144242394063).
文摘Since the release of ChatGPT in late 2022,Generative Artificial Intelligence(GAI)has gained widespread attention because of its impressive capabilities in language comprehension,reasoning,and generation.GAI has been successfully applied across various aspects(e.g.,creative writing,code generation,translation,and information retrieval).In cartography and GIS,researchers have employed GAI to handle some specific tasks,such as map generation,geographic question answering,and spatiotemporal data analysis,yielding a series of remarkable results.Although GAI-based techniques are developing rapidly,literature reviews of their applications in cartography and GIS remain relatively limited.This paper reviews recent GAI-related research in cartography and GIS,focusing on three aspects:①map generation,②geographical analysis,and③evaluation of GAI’s spatial cognition abilities.In addition,the paper analyzes current challenges and proposes future research directions.
基金partially supported by the Construction of Collaborative Innovation Center of Beijing Academy of Agricultural and Forestry Sciences(KJCX20240406)the Beijing Natural Science Foundation(JQ24037)+1 种基金the National Natural Science Foundation of China(32330075)the Earmarked Fund for China Agriculture Research System(CARS-02 and CARS-54)。
文摘The security of the seed industry is crucial for ensuring national food security.Currently,developed countries in Europe and America,along with international seed industry giants,have entered the Breeding 4.0 era.This era integrates biotechnology,artificial intelligence(AI),and big data information technology.In contrast,China is still in a transition period between stages 2.0 and 3.0,which primarily relies on conventional selection and molecular breeding.In the context of increasingly complex international situations,accurately identifying core issues in China's seed industry innovation and seizing the frontier of international seed technology are strategically important.These efforts are essential for ensuring food security and revitalizing the seed industry.This paper systematically analyzes the characteristics of crop breeding data from artificial selection to intelligent design breeding.It explores the applications and development trends of AI and big data in modern crop breeding from several key perspectives.These include highthroughput phenotype acquisition and analysis,multiomics big data database and management system construction,AI-based multiomics integrated analysis,and the development of intelligent breeding software tools based on biological big data and AI technology.Based on an in-depth analysis of the current status and challenges of China's seed industry technology development,we propose strategic goals and key tasks for China's new generation of AI and big data-driven intelligent design breeding.These suggestions aim to accelerate the development of an intelligent-driven crop breeding engineering system that features large-scale gene mining,efficient gene manipulation,engineered variety design,and systematized biobreeding.This study provides a theoretical basis and practical guidance for the development of China's seed industry technology.
基金National Natural Science Foundation of china(No.42371446)Natural Science Foundatiorof Hubei Province(No.2024AFD412)Fundamental Research Funds for National Universities,China University of Geosciences(Wuhan)(No.2024XLA17).
文摘In recent years,Volunteered Geographic Information(VGI)has emerged as a crucial source of mapping data,contributed by users through crowdsourcing platforms such as OpenStreetMap.This paper presents a novel approach that Integrates Large Language Models(LLMs)into a fully automated mapping workflow,utilizing VGI data.The process leverages Prompt Engineering,which involves designing and optimizing input instructions to ensure the LLM produces desired mapping outputs.By constructing precise and detailed prompts,LLM agents are able to accurately interpret mapping requirements,and autonomously extract,analyze,and process VGI geospatial data.They dynamically interact with mapping tools to automate the entire mapping process—from data acquisition to map generation.This approach significantly streamlines the creation of high-quality mapping outputs,reducing the time and resources typically required for such tasks.Moreover,the system lowers the barrier for non-expert users,enabling them to generate accurate maps without extensive technical expertise.Through various case studies,we demonstrate the LLM application across different mapping scenarios,highlighting its potential to enhance the efficiency,accuracy,and accessibility of map production.The results suggest that LLM-powered mapping systems can not only optimize VGI data processing but also expand the usability of ubiquitous mapping across diverse fields,including urban planning and infrastructure development.
文摘In the wave of digital and intelligent applications,artificial intelligence(AI)is transforming the development trajectories of industries across the globe.Traditional Chinese medicine(TCM),as a cultural treasure of the Chinese nation,carries thousands of years of wisdom and practical experience.However,in the context of the rapid advancements in modern medicine and technology,TCM faces dual challenges:preserving its heritage while innovating.DeepSeek,a major achievement in the field of AI,offers a new opportunity for the development of TCM with its powerful technological capabilities.Exploring the integration of DeepSeek with TCM not only helps modernize the practice but also promises unique contributions to global health.
基金supported by the National Key Research and Development Program of China (2022YFB3806000)the Key Program of National Natural Science Foundation of China (52332006)。
文摘Composed of natural materials but constructed using artificial structures through ingenious design,metamaterials possess properties beyond nature.Unlike traditional materials studies,metamaterials research requires great human creativity in order to realize the desired properties and thereby the required functionalities through design.Such properties and functionalities are not necessarily available in nature,and their design can break through the existing materials ideology.This paper reviews progress in metamaterials research over the past 20 years in terms of the materials innovations that have achieved the designation of “meta.” In particular,we discuss future trends in metamaterials in the fields of both fundamental science and engineering.
文摘The advent of artificial intelligence(AI)in recent years has brought about transformative changes across various sectors,including healthcare.In nursing practice,education,and research,AI has the potential to revolutionize traditional methodologies,enhance learning experiences,and improve patient outcomes.Integrating AI tools and techniques can provide clinicians with smarter clinical solutions and nursing students with more robust and interactive learning environments,while also advancing research capabilities in the field.Despite the promising prospects,the incorporation of AI into nursing practice,education,and research presents several challenges.Firstly,there is a concern about the potential displacement of human roles in nursing due to automation,which may affect the human-centric nature of nursing care.Secondly,there are issues related to the lag in AI competency among nurses.Many current nursing curricula do not include comprehensive AI training,leading to a lack of preparedness in utilizing these technologies effectively.Lastly,the ethical implications of AI in healthcare,such as data privacy,patient consent,and the potential for biased algorithms,need to be meticulously addressed.To harness the full potential of AI in nursing practice,education,and research,several strategic actions including reinvesting in humanistic practice,revising core competencies and curriculum,and developing new ethical guidelines.
基金supported by the Jiangsu Provincial Department of Science and Technology Social Development Project(No.BE2020787)。
文摘Objectives Diabetes remains a major global health challenge in China.Artificial intelligence(AI)has demonstrated considerable potential in improving diabetes management.This study aimed to assess healthcare providers’perceptions regarding AI in diabetes care across China.Methods A cross-sectional survey was conducted using snowball sampling from November 12 to November 24,2024.We selected 514 physicians and nurses by a snowball sampling method from healthcare providers across 30 cities or provinces in China.The self-developed questionnaire comprised five sections with 19 questions assessing medical workers’demographic characteristics,AI-related experience and interest,awareness,attitudes,and concerns regarding AI in diabetes care.Statistical analysis was performed using t-test,analysis of variance(ANOVA),and linear regression.Results Among them,20.0%and 48.1%of respondents had participated in AI-related research and training,while 85.4%expressed moderate to high interest in AI training for diabetes care.Most respondents reported partial awareness of AI in diabetes care,and only 12.6%exhibited a comprehensive or substantial understanding.Attitudes toward AI in diabetes care were generally positive,with a mean score of 24.50±3.38.Nurses demonstrated significantly higher scores than physicians(P<0.05).Greater awareness,prior AI training experience,and higher interest in AI training in diabetes care were strongly associated with more positive attitudes(P<0.05).Key concerns regarding AI included trust issues from AI-clinician inconsistencies(77.2%),increased workload and clinical workflow disruptions(63.4%),and incomplete legal and regulatory frameworks(60.3%).Only 34.2%of respondents expressed concerns about job displacement,indicating general confidence in their professional roles.Conclusions While Chinese healthcare providers show moderate awareness of AI in diabetes care,their attitudes are generally positive,and they are considerably interested in future training.Tailored,role-specific AI training is essential for equitable and effective integration into clinical practice.Additionally,transparent,reliable,ethical AI models must be prioritized to alleviate practitioners’concerns.
基金Supported by the National Human Genetic Resources Sharing Service Platform,No.PT-2024-0303Qingdao Medical and Health Research Guidance Project,No.2023-WJZD202.
文摘Colorectal cancer(CRC)ranks among the top causes of cancer-related fatalities globally.Recent progress in genomics,proteomics,and bioinformatics has greatly improved our comprehension of the molecular underpinnings of CRC,paving the way for targeted therapies and immunotherapies.Nonetheless,obstacles such as tumor heterogeneity and drug resistance persist,hindering advancements in treatment efficacy.In this context,the integration of artificial intelligence(AI)and organoid technology presents promising new avenues.AI can analyze genetic and clinical data to forecast disease risk,prognosis,and treatment responses,thereby expediting drug development and tailoring treatment plans.Organoids replicate the genetic traits and biological behaviors of tumors,acting as platforms for drug testing and the formulation of personalized treatment approaches.Despite notable strides in CRC research and treatment-from genetic insights to therapeutic innovations-numerous challenges endure,including the intricate tumor microen-vironment,tumor heterogeneity,adverse effects of immunotherapies,issues related to AI data quality and privacy,and the need for standardization in organoid culture.Future initiatives should concentrate on clarifying the pathogenesis of CRC,refining AI algorithms and organoid models,and creating more effective therapeutic strategies to alleviate the global impact of CRC.
文摘The concept of Supply Chain 4.0 represents a transformative phase in supply chain management through advanced digital technologies like IoT, AI, blockchain, and cyber-physical systems. While these innovations deliver operational improvements, the heightened interconnectivity introduces significant cybersecurity challenges, particularly within military logistics, where mission-critical operations and life-safety concerns are paramount. This paper examines these unique cybersecurity requirements, focusing on advanced persistent threats, supply chain poisoning, and data breaches that could compromise sensitive operations. The study proposes a hybrid cybersecurity framework tailored to military logistics, integrating resilience, redundancy, and cross-jurisdictional security measures. Real-world applicability is validated through simulations, offering strategies for securing supply chains while balancing security, efficiency, and flexibility.
基金support from the Beijing Natural Science Foundation-Xiaomi Innovation Joint Fund(No.L233009)National Natural Science Foundation of China(NSFC Nos.62422409,62174152,and 62374159)from the Youth Innovation Promotion Association of Chinese Academy of Sciences(No.2020115).
文摘Memristors have a synapse-like two-terminal structure and electrical properties,which are widely used in the construc-tion of artificial synapses.However,compared to inorganic materials,organic materials are rarely used for artificial spiking synapses due to their relatively poor memrisitve performance.Here,for the first time,we present an organic memristor based on an electropolymerized dopamine-based memristive layer.This polydopamine-based memristor demonstrates the improve-ments in key performance,including a low threshold voltage of 0.3 V,a thin thickness of 16 nm,and a high parasitic capaci-tance of about 1μF·mm^(-2).By leveraging these properties in combination with its stable threshold switching behavior,we con-struct a capacitor-free and low-power artificial spiking neuron capable of outputting the oscillation voltage,whose spiking fre-quency increases with the increase of current stimulation analogous to a biological neuron.The experimental results indicate that our artificial spiking neuron holds potential for applications in neuromorphic computing and systems.
