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Wireless Environmental Information Theory:A New Paradigm Toward 6G Online and Proactive Environment Intelligence Communication 认领 引用 被引量：4
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作者 Jianhua Zhang Li Yu +4 位作者 Shaoyi Liu Yichen Cai Yuxiang Zhang Hongbo Xing Tao Jiang 《Engineering》 SCIE EI CSCD 2026年第1期186-200,共15页
Channels are one of the five critical components of a communication system,and their ergodic capacity is based on all realizations of a statistical channel model.This statistical paradigm has successfully guided the d... Channels are one of the five critical components of a communication system,and their ergodic capacity is based on all realizations of a statistical channel model.This statistical paradigm has successfully guided the design of mobile communication systems from first generation(1G)to fifth generation(5G).However,this approach relies on offline channel measurements in specific environments,and thus,the system passively adapts to new environments,resulting in deviation from the optimal performance.As sixth generation(6G)expands into ubiquitous environments and pursues higher capacity,numerous sensing and artificial intelligence(AI)-based methods have emerged to combat random channel fading.However,there remains an urgent need for a proactive and online system design paradigm.From a system perspective,we propose an environment intelligence communication(EIC)based on wireless environmental information theory(WEIT)for 6G.The proposed EIC architecture operates in three steps.First,wireless environmental information(WEI)is acquired using sensing techniques.Then,leveraging WEI and channel data,AI techniques are employed to predict channel fading,thereby mitigating channel uncertainty.Finally,the communication system autonomously determines the optimal air-interface transmission strategy based on real-time channel predictions,enabling intelligent interaction with the physical environment.To make this attractive paradigm shift from theory to practice,we establish WEIT for the first time by answering three key problems:How should WEI be defined?Can it be quantified?Does it hold the same properties as statistical communication information?Subsequently,EIC aided by WEI(EIC-WEI)is validated across multiple air-interface tasks,including channel state information prediction,beam prediction,and radio resource management.Simulation results demonstrate that the proposed EIC-WEI significantly outperforms the statistical paradigm in decreasing overhead and performance optimization.Finally,several open problems and challenges,including regarding its accuracy,complexity,and generalization,are discussed.This work explores a novel and promising way for integrating communication,sensing,and AI capability in 6G. 展开更多
关键词 Sixth generation Intelligent communication Environment intelligence Wireless environmental information theory Environment sensing and reconstruction Channel prediction Digital twin channel ChannelGPT
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AI-driven design of powder-based nanomaterials for smart textiles: from data intelligence to system integration 认领 引用 被引量：3
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作者 Zihui Liang Yun Deng +12 位作者 Zhicheng Shi Xiaohong Liao Huiyi Zong Lizhi Ren Xiangzhe Li Xinyao Zeng Peiying Hu Wei Ke Bing Wu Kai Wang Jin Qian Weilin Xu Fengxiang Chen 《Advanced Powder Materials》 EI CAS CSCD 2026年第1期39-63,共25页
Artificial intelligence(AI)is emerging as a transformative enabler in the development of smart textile systems,particularly those integrating powder-based functional materials.This review highlights recent progress in... Artificial intelligence(AI)is emerging as a transformative enabler in the development of smart textile systems,particularly those integrating powder-based functional materials.This review highlights recent progress in AIguided design of carbon nanomaterials,metallic nanoparticles,and framework-based powders for applications in energy harvesting,intelligent sensing,and robotic actuation.Machine learning techniques,including supervised learning,transfer learning,and Bayesian optimization are discussed for accelerating materials discovery,enhancing integration strategies,and enabling real-time adaptive control.Emphasis is placed on how AI enables multifunctional,wearable platforms that sense,process,and respond to environmental and physiological cues with high accuracy and autonomy.Representative breakthroughs in soft robotics,haptic interfaces,and assistive devices are presented,demonstrating the synergy of AI and responsive textiles.Finally,the review outlines key challenges related to data scarcity,model generalizability,manufacturing scalability,and sustainability,while proposing future directions involving multimodal learning,autonomous experimentation,and ethics-aware design.This work offers a comprehensive outlook on next-generation AI-driven textile systems that seamlessly integrate intelligence,functionality,and wearability. 展开更多
关键词 Smart textiles Artificial intelligence Powder-based functional materials Machine learning AI-driven textile system
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Artificial intelligence-enabled Bioprinting 5.0 认领 引用 被引量：2
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作者 Long Bai Yi Zhang +3 位作者 Sicheng Wang Jinlong Liu Yuanyuan Liu Jiacan Su 《Bio-Design and Manufacturing》 SCIE EI CAS CSCD 2026年第1期32-62,I0002,共31页
With the rapid advancements in biomedical engineering,bioprinting has emerged as a pivotal solution to address the shortage of organ transplants and advance disease model research.The evolution of bioprinting has prog... With the rapid advancements in biomedical engineering,bioprinting has emerged as a pivotal solution to address the shortage of organ transplants and advance disease model research.The evolution of bioprinting has progressed from the fabrication of simple models(1.0)to the fabrication of permanent implants(2.0),tissue engineering scaffolds(3.0),and complex biostructures utilizing living cells(4.0).Nevertheless,significant challenges remain,particularly in accurately replicating the structure and function of host tissues,selecting appropriate materials,and optimizing printing parameters.The integration of artificial intelligence(AI),especially machine learning,provides promising novel opportunities in bioprinting(5.0).This review systematically summarizes the current applications of AI in bioprinting,discussing both construction strategies and application scenarios.It also explores the potential of AI to improve bioprinting in the preparation of complex functional tissues and in situ tissue repair.Overall,the synergy between AI and bioprinting is poised to drive the development of personalized medicine,facilitate high-throughput preparation of in vitro models,and provide robust tools for regenerative medicine and precision healthcare. 展开更多
关键词 Artificial intelligence Bioprinting Tissue engineering Machine learning
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A comprehensive survey of artificial intelligence applications in UAV-enabled wireless networks 认领 引用 被引量：2
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作者 Li Zhou Hao Yin +3 位作者 Haitao Zhao Jibo Wei Dewen Hu Victor C.M.Leung 《Digital Communications and Networks》 SCIE EI CSCD 2026年第4期561-583,共23页
This comprehensive survey paper examines the applications of Artificial Intelligence(AI)in Unmanned Aerial Vehicle(UAV)-enabled wireless networks.With the increasing demand for efficient and adaptive communication sys... This comprehensive survey paper examines the applications of Artificial Intelligence(AI)in Unmanned Aerial Vehicle(UAV)-enabled wireless networks.With the increasing demand for efficient and adaptive communication systems,the integration of AI with UAV networks promises to revolutionize various aspects of wireless communication.The paper first outlines the background and motivation behind AI integration,highlighting the potential for enhanced network performance,autonomy,and adaptability.It then delves into the key AI applications across different network layers,including data sensing and collection,placement and trajectory optimization,radio resource management,routing and topology control,edge computing and caching,as well as security and privacy enhancement.For each application,the paper discusses relevant AI techniques,main findings,optimization objects,and the potential benefits and challenges.The survey also identifies open issues,such as the practical implementation gap,standardization issues,and real-world application barriers,and proposes future directions to address these challenges and further advance the field.In conclusion,the integration of AI with UAV-enabled Wireless Networks(UWNs)holds tremendous potential for transforming wireless communication,enabling new applications and services with unprecedented capabilities. 展开更多
关键词 Artificial intelligence(AI) Machine learning(ML) Unmanned aerial vehicle(UAV) Wireless network
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Novel Sea Otter Optimization Algorithm for WSN Coverage Intelligence Optimization 认领 引用 被引量：2
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作者 WU Jin GAO Yaqiong +2 位作者 SU Zhengdong CHONG Gege XIONG Hao 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第4期828-842,I0002,共15页
A novel intelligent optimization algorithm inspired by nature,called sea otter optimization algorithm(SOOA),is proposed.The SOOA simulates the natural behaviors of sea otters,such as using tactile senses to search for... A novel intelligent optimization algorithm inspired by nature,called sea otter optimization algorithm(SOOA),is proposed.The SOOA simulates the natural behaviors of sea otters,such as using tactile senses to search for food in seawater,grooming their fur,feeding with the aid of stones,and escaping from danger.In the exploration stage,a wetness factor is introduced to control the behavior of sea otters in foraging and grooming;a danger factor is introduced to control the behavior of sea otters in feeding and avoiding dangers in the exploitation stage,and the behaviors of sea otters in responding to different dangers are mathematically modeled.The proposed algorithm is compared with 9 well-known intelligent optimization algorithms,and evaluated in 13 benchmark functions as well as wireless sensor network coverage optimization problems to verify the effectiveness of the proposed algorithm.The experimental results show that the node coverage after SOOA optimization reaches 91.2%in 2D environment and 90.47%in 3D environment.Compared with other algorithms,SOOA is superior and possesses the ability to solve complex optimization problems. 展开更多
关键词 sea otter optimization algorithm(SOOA) swarm intelligence optimization wireless sensor network coverage optimization
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In-Sensor-Memory Computing for Post-Von Neumann Intelligence:A Perspective 认领 引用 被引量：1
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作者 Hongyu Tang Ninghai Yu +2 位作者 Pengsheng Min Ruiqian Guo Guoqi Zhang 《Nano-Micro Letters》 SCIE EI CAS CSCD 2026年第10期36-67,共32页
The rapid growth of artificial intelligence,ubiquitous sensing,and edge computing is exposing fundamental limitations of conventional von Neumann architectures,in which the physical separation of sensing,memory,and co... The rapid growth of artificial intelligence,ubiquitous sensing,and edge computing is exposing fundamental limitations of conventional von Neumann architectures,in which the physical separation of sensing,memory,and computation leads to excessive data movement,high energy consumption,and latency.As transistor scaling slows in the post-Moore era,architectural innovation has become essential to sustain progress in intelligent systems.In-sensor-memory computing(ISMC)addresses these challenges by co-locating perception,storage,and computation within unified device and system architectures,enabling in situ signal processing,mixed-signal computation,and event-driven intelligence at the data source.Recent advances in memristive and ferroelectric devices,low-dimensional and multifunctional materials,three-dimensional heterogeneous integration,and neuromorphic architectures have significantly expanded the functional scope of ISMC platforms.In parallel,the co-evolution of algorithms—including spiking neural networks,reservoir computing,and neuromorphic compilers—has facilitated the translation of device-level advantages into system-level performance.This perspective surveys the technological foundations,architectural trends,and emerging applications of ISMC,examines global industry-academia-research(IAR)collaboration,and outlines key challenges related to variability,reliability,scalability,and benchmarking.Collectively,ISMC is positioned as a post-von Neumann hardware paradigm for energy-efficient,distributed intelligence. 展开更多
关键词 In-sensor-memory computing(ISMC) Post-von Neumann intelligence Neuromorphic hardware Industry-academia-research(IAR)
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A Chinese Expert Consensus on the Artificial Intelligence Proficiency of Medical Students:Competencies and the Multi-Modal Assessment 认领 引用 被引量：1
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作者 Mengchun Gong Jiao Li +8 位作者 Yonghui Ma Bo Jin Wei Chen Yan Hou Li Hong Tianwen Lai Bohan Zhang Ge Wu Zhirong Zeng 《Health Care Science》 CSCD 2026年第1期49-57,共9页
Background:Artificial intelligence(AI)is transforming healthcare,demanding reevaluation of medical education.China's“New Medical Education”initiative urgently requires a standardized AI literacy framework for me... Background:Artificial intelligence(AI)is transforming healthcare,demanding reevaluation of medical education.China's“New Medical Education”initiative urgently requires a standardized AI literacy framework for medical students to address fragmented standards,rapid technological evolution,and insufficient localized ethical norms.Objective:To establish a Chinese expert consensus defining core AI competencies and a multi-modal assessment framework for medical students.Methods:A multidisciplinary(including medical education,clinical medicine,medical AI,public health,and medical ethics)expert group(n=32)developed an initial competency list based on the“Knowledge-Skills-Attitude”Medical Competency Model.Two Delphi rounds(100%response rate;consensus threshold:mean≥4.0,CV≤0.25)refined the framework.Core competencies were prioritized via Analytic Hierarchy Process(AHP).The final consensus document was established after multiple expert group meetings.Results:The consensus defines AI literacy for medical students as a comprehensive attribute for integrating AI into profes-sional knowledge,clinical practice,research,and health management.It comprises a 21-item Competencies of AI Proficiency(CAIP)list across knowledge(eight indicators),skills(seven indicators),and attitude(six indicators)dimensions.Key com-petencies prioritized include understanding AI's role in multidisciplinary knowledge integration(CAIP3),identifying AI output biases(CAIP4),understanding health data governance(CAIP2),maintaining physician-led AI-assisted diagnosis(CAIP16),and identifying AI diagnostic biases(CAIP12).A multi-modal assessment framework is recommended,including paper-based/computerized tests for knowledge,situational judgment tests(SJTs)for attitudes,and objective structured clinical examinations(OSCEs)with a specific“AI Clinical Decision Conflict Scoring Scale”for skills.A multi-stage dynamic assessment system(“Pre-enrollment-Pre-clinical-Post-clinical”)is proposed for longitudinal tracking.Educational integration pathways emphasize embedding AI literacy modularly from early undergraduate years,constructing an integrated curriculum covering fundamental principles,advanced large model applications(e.g.,prompt engineering,agent development),and ethical considerations,supported by a"digital twin hospital platform."Conclusion:This consensus provides authoritative,China-specific guidance for defining and assessing medical students'AI literacy,adhering to national policies and regulations.It offers a core action framework for optimizing AI integration into medical education,fostering future healthcare professionals proficient in both AI technology and medical humanism,with a commitment to dynamic updating to adapt to evolving AI advancements. 展开更多
关键词 AI proficiency artificial intelligence(AI) assessment competency framework medical education
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Artificial intelligence in petroleum and natural gas prospect evaluation and prediction 认领 引用
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作者 Qiao-Chu Wang Dong-Xia Chen +5 位作者 Sha Li Yu-He Chen Xiang Chen Mao-Sen Wang Zai-Quan Yang Fu-Wei Wang 《Petroleum Science》 SCIE EI CAS CSCD 2026年第6期3110-3135,共26页
Artificial intelligence has shown great performance and considerable potential in petroleum and natu ral gas prospect evaluation and prediction(PPEP).Multiple machine learning(ML)and deep learning(DL)methods are appli... Artificial intelligence has shown great performance and considerable potential in petroleum and natu ral gas prospect evaluation and prediction(PPEP).Multiple machine learning(ML)and deep learning(DL)methods are applied in petroleum geology,development geology and geophysical exploration.The PPEP work in petroleum geology field which aims to provide a probability distribution for petroleum prospects with small data.The PPEP work in development field utilize seismic data as the fundamental data to predict petroleum reservoir features and favourable geological facies with the constrain of well log data.While PPEP work in geophysical exploration focus on seismic or well log facies recognition and prediction using multiple intelligent seismic denoising and well log augmentation methods.The popular AI algorithms are almost all used in all kinds of PPEP work without a unique choice and standard procedure.The decisive factors for AI application in PPEP are data quality and model interpretability.The current data size and precision used in PPEP work are hard to fulfill the requirement for AI model training,which need further augmentation and multimodal fusion.The interpretability is significant for evaluating the reliability and rationality of these data-driven models.At present,the dual-driven model driven by both of data and physical constrain may be a possible way to further decrease the require ment of data quality and increase the model interpretability.In the future,an artificial generative model which can determine petroleum prospects in multiple dimensions with a large amount of data may be the final goal for the utilization of AI in the petroleum industry. 展开更多
关键词 Artificial intelligence Machine learning Deep learning Petroleum prospect Geological facies Petroleum exploration and exploitation
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Embodied Interactive Intelligence Towards Autonomous Driving 认领 引用
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作者 Nan Ma Jia Pan +7 位作者 Yongjin Liu Yajue Yang Yiheng Han Jiacheng Guo Zhixuan Wu Zecheng Yang Zhiwei Yang Deyi Li 《Engineering》 SCIE EI CSCD 2026年第4期337-351,共15页
Autonomous driving depends on successful interactions among humans,vehicles,and roads.However,people often lack an understanding of autonomous vehicle(AV)behaviours and decisions.Moreover,AVs have difficulty aligning ... Autonomous driving depends on successful interactions among humans,vehicles,and roads.However,people often lack an understanding of autonomous vehicle(AV)behaviours and decisions.Moreover,AVs have difficulty aligning with human intentions in their interactions.To overcome the obstacles associated with the absence of interactive intelligence,especially in complex and uncertain environments,we introduce the concept of embodied interactive intelligence towards autonomous driving(EIIAD),which establishes representation and learning methods aligned with the physical world,enhancing human-machine integration.Building on this concept,we propose an end-to-end unified constrained vehicle environment interaction(UniCVE)model,which involves the construction of an end-to-end perception-cognition-behaviour closed-loop feedback paradigm and continuous learning through accumulated split driving scenarios.This model realizes interaction cognition through networks designed for pedestrians and vehicles,and it unifies the cognition as a value network of AVs to generate socially compatible behaviours.The UniCVE model is implemented on Dongfeng autonomous buses,which have successfully travelled 22 thousand kilometres and completed 45 thousand navigation tasks in Xiong’an New Area,China,demonstrating its general applicability in various driving scenarios.In addition,we highlight the high-level interactive intelligence of the UniCVE model in selected simulated complex interaction scenarios,demonstrating that it makes AVs more intelligent,more reliable,and more attuned to human relationships.Furthermore,the UniCVE model’s capacity for self-learning and self-growth allows it to infinitely approximate true intelligence,even with limited experience. 展开更多
关键词 Embodied interactive intelligence Autonomous driving Cognition behaviour Continuous learning Hypergraph learning
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Integration of functional magnetic resonance imaging and artificial intelligence in Alzheimer's disease 认领 引用
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作者 Liqin Yang Yuxin Li +2 位作者 Kuangyu Shi Axel Rominger Ruiqing Ni 《Neural Regeneration Research》 SCIE CAS CSCD 2026年第9期4241-4242,共2页
Alzheimer's disease(AD),the most common cause of dementia,is a progressive neurodegenerative disease characterized by progressive cognitive decline and memory loss.Mild cognitive impairment(MCI)is the prodromal st... Alzheimer's disease(AD),the most common cause of dementia,is a progressive neurodegenerative disease characterized by progressive cognitive decline and memory loss.Mild cognitive impairment(MCI)is the prodromal stage of AD,with a conversion rate of 10%-15%per year and 50%conversion rate longitudinally.The pathological features of AD include the aberrant accumulation of amyloid-βplaques,neurofibrillary tangles formed by hyperphosphorylated tau and synaptic dysfunction(Nussbaumer et al.,2025),all of which have cascading effects on the brain activity of AD patients. 展开更多
关键词 integration artificial intelligence Alzheimers disease functional magnetic resonance imaging synaptic dysfunction nussbaumer hyperphosphorylated tau cognitive decline alzheimers disease ad
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From spatial maps to therapeutic targets:Next challenge for artificial intelligence in cancer spatial omics 认领 引用
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作者 Hui Xu Yuyuan Zhang +2 位作者 Yuqing Ren Peng Luo Haidong Zhu 《Chinese Journal of Cancer Research》 SCIE CAS CSCD 2026年第2期200-203,共4页
Recent advances in spatial omics have transformed cancer research by allowing tumors to be studied not as simple aggregates of malignant cells,but as spatially organized ecosystems.Within these ecosystems,tumor cells,... Recent advances in spatial omics have transformed cancer research by allowing tumors to be studied not as simple aggregates of malignant cells,but as spatially organized ecosystems.Within these ecosystems,tumor cells,immune populations,fibroblasts,vascular elements,and extracellular matrix components are arranged in structured local contexts that shape invasion,immune evasion,therapy resistance,and clinical outcome(1-4).Artificial intelligence(AI)has become the central analytical engine of this transformation.By integrating machine learning,computer vision,graph-based modeling,and multimodal analysis,AI has enabled the identification of cellular neighborhoods,the inference of local communication networks,and the linkage of tissue architecture to prognosis and therapeutic response(5-8).These advances have greatly expanded our ability to study cancer in situ,but they have also exposed a conceptual bottleneck.The field is increasingly adept at describing where biology happens,but remains far less capable of determining which spatially organized processes actively drive disease and therefore represent tractable therapeutic targets. 展开更多
关键词 cellular neighborhoods local communication networks tumor ecosystems analytical engine artificial intelligence cancer research spatial omics extracellular matrix components
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Artificial intelligence-assisted biliary stent length selection for common bile duct strictures in endoscopic retrograde cholangiopancreatography:Model development and validation 认领 引用
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作者 Wen-Lin Zhang Xue-Jun Shao +5 位作者 Xuan-Yuan Dong Hong-Ting Shao Guang-Chao Li Zhen Li Ning Zhong Rui Ji 《Hepatobiliary & Pancreatic Diseases International》 SCIE CAS CSCD 2026年第1期76-82,共7页
Background:Biliary stent placement during endoscopic retrograde cholangiopancreatography(ERCP)is important for drainage in common bile duct(CBD)strictures,while the stent length is associated with many stent-related c... Background:Biliary stent placement during endoscopic retrograde cholangiopancreatography(ERCP)is important for drainage in common bile duct(CBD)strictures,while the stent length is associated with many stent-related complications.We aimed to develop an artificial intelligence(AI)model for stent length selection during ERCP.Methods:Images of the patients who underwent ERCP and were diagnosed with CBD strictures were collected.Training involved identifying and delineating the duodenoscope,CBD and guidewire,calculating the pixel distance of the target guidewire and determining the required biliary stent length based on the diameter of the duodenoscope.The performance of the model,accuracy for length calculation and the assistance for endoscopists were validated using the testing set.Results:A total of 794 images from 431 patients were included and data augmentation was conducted.The mean intersection over union(mIoU)for duodenoscope,CBD and guidewire were 90.46%,84.79%and 84.64%,respectively.The accuracy in identifying the strictures was 97.58%(121/124).The accuracy for stent length calculation achieved 85.95%(104/121)with an error margin of±1 cm.The mean absolute error(MAE)and mean relative error(MRE)of the AI model was 0.81 cm and 0.13,respectively.The AI model could reduce approximately 202 mGycm2of the radiation exposure for each patient.It significantly improved both MAE and MRE for less experienced endoscopists(P=0.01 and P=0.02,respectively).Conclusions:The AI model could accurately identify duodenoscope,CBD and guidewire,enabling accurate strictures identification and stent length selection. 展开更多
关键词 Endoscopic retrograde cholangiopancreatography Artificial intelligence Common bile duct stricture Stent placement
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Advancements in nanophotonics and smart nanomaterials integrated with artificial intelligence-driven gene editing:A paradigm shift in cancer diagnosis and therapeutic 认领 引用
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作者 Bakr Ahmed Taha Ali J.Addie +5 位作者 Luai Farhan Zghair Kolie Saba Talib Wahhab Sinan Adnan Abdulateef Adawiya J.Haider Khalid Ibnaouf Norhana Arsad 《Chinese Chemical Letters》 SCIE CAS CSCD 2026年第4期26-37,共12页
Traditional cancer therapies are limited by side effects and damage to healthy tissues,while modern targeted treatments face challenges such as drug resistance and restricted applicability across cancer types.Early di... Traditional cancer therapies are limited by side effects and damage to healthy tissues,while modern targeted treatments face challenges such as drug resistance and restricted applicability across cancer types.Early diagnosis also remains difficult,as many methods lack the sensitivity and specificity needed to reliably detect small,early-stage tumors.This review explores hybrid nanomaterial-based delivery systems,such as lipid-gold nanoparticle composites combined with polymeric nanocarriers,to improve the precision and efficacy of gene therapy.Advances in nanotechnology are highlighted for their ability to augment gene-editing tools including RNA interference and clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9(CRISPR/Cas9),supported by techniques like optical tweezers,plasmonics,fluorescence imaging,and metamaterials.Nanophotonics in particular offers ultra-sensitive molecular imaging and real-time biomarker detection,underscoring its value for early cancer diagnosis.Artificial intelligence further strengthens these approaches by optimizing nanocarrier design,predicting therapeutic outcomes,and guiding personalized treatment strategies.Machine learning and deep learning platforms enable efficient analysis of complex genomic and clinical datasets,improving predictive accuracy and therapeutic customization.The review also outlines molecular mechanisms of gene therapy,from editing to expression,and addresses barriers to clinical translation,such as data integration,model validation,and regulatory considerations.Combining nanotechnology,artificial intelligence(AI),and geneediting advances holds promise for more effective,targeted,and minimally invasive cancer treatments.These integrated strategies support earlier detection,enhance therapeutic precision,and provide a framework for translating experimental breakthroughs into clinical applications that better align with the goals of personalized medicine. 展开更多
关键词 Gene therapy Nanocarriers Artificial intelligence Personalized medicine CRISPR/Cas9
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Artificial intelligence application in multiscale biomechanics 认领 引用
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作者 Erqian Xu Jin Zhou +4 位作者 Yan Zhang Qing Luo Guanbin Song Shouqin Lü Mian Long 《Theoretical & Applied Mechanics Letters》 EI CAS CSCD 2026年第1期108-117,共10页
The rapid development and widespread application of artificial intelligence(AI)technology have significantly improved understanding across various fields,including biomechanics.To deepen the understanding of AI ap-pli... The rapid development and widespread application of artificial intelligence(AI)technology have significantly improved understanding across various fields,including biomechanics.To deepen the understanding of AI ap-plications in this field and explore future developments,this review focuses on the progress of AI in multiscale biomechanics.We first outline the progress history,fundamental principles,and typical models of AI.Next,we introduce the main applications of the two typical AI paradigms-data-driven and knowledge-driven-in the context of multiscale biomechanical studies.The first paradigm focuses primarily on predicting protein structure,interactions,and conformational dynamics at the molecular level,as well as on subcellular structure recognition,cell mechanics prediction and cell trajectory tracking at the cellular level.The second paradigm concentrates on biological fluid and solid mechanics at the tissue level.Finally,the existing issues and challenges faced by current AI technologies in biomechanics are discussed,and potential future issues are proposed from the perspective of informative integration. 展开更多
关键词 Artificial intelligence Data-driven paradigms Knowledge-driven paradigms Biomechanics PINNs
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PRIME:an interpretable artificial intelligence model based on liquid biopsy improves prediction of progression risk in non-small cell lung cancer 认领 引用
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作者 Yu Wang Yong-Bo Xiang +7 位作者 Xiao-Wei Chen Tao Zhang Jian-Yang Wang Wen-Yang Liu Lei Deng Lu-Hua Wang Shu-Geng Gao Nan Bi 《Military Medical Research》 SCIE CAS CSCD 2026年第5期747-765,共19页
Background:Despite the predictive impact of circulating tumor DNA(ctDNA)minimal residual disease(MRD),accurate prediction of failure risk after curative-intent treatments for early-stage or localized non-small cell lu... Background:Despite the predictive impact of circulating tumor DNA(ctDNA)minimal residual disease(MRD),accurate prediction of failure risk after curative-intent treatments for early-stage or localized non-small cell lung cancer(NSCLC)patients to guide personalized therapy remains challenging.This study aimed to develop and validate an interpretable artificial intelligence-assisted model using global data resources.Methods:Liquid biopsy data,blood-based genomic alterations,clinicopathological features,and survival outcomes of stage I-III NSCLC patients who underwent surgery or definitive chemoradiotherapy were collected from six cohorts.PRIME(Progression Risk prediction by Interpretable Machine learning on ctDNA-MRD,Mutations,and clinical-therapeutic features)was trained by 6 machine learning algorithms across four cohorts and validated in two independent cohorts.Model performance was evaluated by the area under the curve(AUC)and interpreted by SHapley Additive exPlanations(SHAP).Whole-exome sequencing(WES)or whole-genome sequencing(WGS)of tumor tissue from 430 stageⅡ-ⅢNSCLC patients and RNA-sequencing(RNA-seq)data from 1149 subjects,sourced from The Cancer Genome Atlas,were used to validate the prognostic effect of mutations identified in peripheral blood and investigate the underlying mechanisms.Results:A global dataset encompassing 781 blood samples from 493 patients was analyzed.Clinical stage,pretreatment ctDNA,post-treatment MRD,blood-based Kelch-like ECH-associated protein 1(KEAP1),serinehreonine kinase 11(STK11),and cyclin-dependent kinase inhibitor 2A(CDKN2A)mutations,and treatment modality were significantly associated with the risk of disease progression and were thereby included in the model training.WES/WGS and RNA-seq confirmed the poor prognostic effect of KEAP1,STK11,and CDKN2A mutations,which were characterized by the suppressive tumor microenvironment and attenuated humoral immunity.The neural network(NN)model exhibited optimal prediction of treatment failure risk in the training(AUC=0.85,95%CI 0.81-0.89)and validation sets(AUC=0.82,95%CI 0.74-0.89).SHAP analysis indicated that MRD(+0.306),treatment modality(+0.128),and pre-treatment ctDNA(+0.043)ranked in the top 3 contributions.NN-PRIME outperformed single liquid biopsy biomarkers and clinical-therapeutic signatures,and demonstrated consistent robustness across different clinical scenarios.High-risk patients identified by NN-PRIME had poorer prognoses but derived significant benefits from adjuvant therapy after surgery.Conclusions:As an interpretable model integrating readily-accessible and crucial clinical-genomic predictors,PRIME achieves enhanced performance,allowing for early outcome prediction,refined risk stratification,and personalized clinical decision-making. 展开更多
关键词 Non-small cell lung cancer Artificial intelligence(AI) Liquid biopsy Machine learning(ML) Circulating tumor DNA Minimal residual disease
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Artificial Intelligence-Driven Subsurface Hydraulic Fracturing Engineering:Connotation and Practices 认领 引用
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作者 Bin Yuan Mingze Zhao +3 位作者 Wei Zhang Siwei Meng Aoran Jin Birol Dindoruk 《Engineering》 SCIE EI CSCD 2026年第3期144-156,共13页
Motivated by the global energy transition and subsurface energy resource(oil,gas,coal-bed-methane,geothermal,etc.)development,subsurface hydraulic fracturing technology is undergoing a paradigm shift from traditional ... Motivated by the global energy transition and subsurface energy resource(oil,gas,coal-bed-methane,geothermal,etc.)development,subsurface hydraulic fracturing technology is undergoing a paradigm shift from traditional experience-driven approaches to data-or intelligence-driven techniques.This work systematically elaborates on the connotation,recent practices,and future trends of artificial intelligence(AI)-driven subsurface hydraulic fracturing technology.This work proposes a three-step technical evolution framework centered on data-driven→dynamic optimization→autonomous decision-making.Recent key practices in the framework are also introduced,including smart characterization and optimization of hydraulic fracturing,smart forecast of production operation after fracturing,and real-time regulation of entire fracturing-to-production lifecycle.The smart characterization of three-dimensional fracture propagation is achieved by constructing the Dy-Fracture-Net model.A dual-model collaborative architecture is developed to enable real-time warning and smart optimization during the fracturing process.Furthermore,the innovative Dy-Production-Net network is designed to predict the dynamics of postfracturing reservoir parameters and production.Through integrating with intelligent optimization algorithms,a real-time regulation system encompassing the entire fracturing-to-production workflow is formed.To address the bottlenecks such as the lack of downhole monitoring data and insufficient model interpretability,future efforts are recommended as follows:miniaturization of multimodal perception agents,self-interpretability of mechanism-data fusion modeling,and autonomous closed-loop control.The findings of this work provide theoretical support and practical pathways for realizing the future AI-driven subsurface fracturing technology,holding significant strategic importance for advancing the digital transformation of the oil and gas industry. 展开更多
关键词 Hydraulic fracturing Artificial intelligence Data-driven optimization Autonomous decision-making Digital transformation
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Evaluatingg ChatGPT's Adherence to Medical Ethics:A Prerequisite for Artificial Intelligence in Medicine 认领 引用
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作者 Ying Zhang Yuyang Liu +3 位作者 Tingyu Lv Junhui Wang Hui Liu Xiaoying Li 《Health Care Science》 CSCD 2026年第2期98-108,共11页
Background:As artificial intelligence continues to play an expanding role in healthcare,ensuring its compliance with medical ethics is essential.However,the ethical performance of artificial intelligence in medical co... Background:As artificial intelligence continues to play an expanding role in healthcare,ensuring its compliance with medical ethics is essential.However,the ethical performance of artificial intelligence in medical contexts remains insufficiently studied.This study aimed to evaluate the ability of ChatGPT to address questions related to medical ethics and to compare its performance with that of human experts.Methods:A Medical Ethics Evaluation dataset was developed,consisting of 465 single-choice questions derived from a range of medical ethics standards.These questions were used to assess two artificial intelligence models,GPT-3.5 and GPT-4.Model responses were compared with those provided by two medical ethics experts.Each test was conducted independently twice to ensure consistency.Accuracy was calculated for each model and expert,and chi-square tests were used to compare differences in performance.Results:GPT-3.5 achieved an overall accuracy of 38.92%,while GPT-4 achieved 27.10%.In comparison,two medical ethics experts achieved substantially higher accuracies of 86.23%and 78.32%,respectively.Both experts performed significantly better than GPT-3.5 and GPT-4.These findings indicate a substantial gap between artificial intelligence models and human experts in understanding and applying medical ethics principles.The relatively low performance of the models,compared with their reported strengths in diagnostic tasks,may reflect the complexity and nuance of ethical reasoning in medicine.Nevertheless,the large language models showed some ability to align with core medical ethics principles,particularly in ethical dilemma scenarios,and were also able to generate responses that addressed psychological needs.Conclusions:Artificial intelligence models currently show limited accuracy in medical ethics decision-making compared with human experts.Although these models demonstrate some alignment with fundamental ethical principles,the performance is not yet sufficient for reliable use in ethically sensitive medical contexts.Further optimization is needed to improve their ability to meet the ethical demands of medical practice. 展开更多
关键词 artificial intelligence ChatGPT ethical dilemmas medical ethics
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Using geospatial artificial intelligence to advance the Sustainable Development Goals 认领 引用
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作者 Chenzhe Fan Chunhui Wang +2 位作者 Jing Song Huilin Yu Gang Li 《Geography and Sustainability》 CSCD 2026年第3期255-259,共5页
1.Introduction Sustainable development is widely regarded as a crucial way for human civilization to survive.To operationalize sustainability across social,economic,and environmental dimensions,the United Nations adop... 1.Introduction Sustainable development is widely regarded as a crucial way for human civilization to survive.To operationalize sustainability across social,economic,and environmental dimensions,the United Nations adopted the 17 Sustainable Development Goals(SDGs)and 169 targets as part of the 2030 Agenda for Sustainable Development in 2015(United Nations,2015).Until 2025,with the joint efforts of the world,SDGs have made significant progress in social resource allocation,disease prevention and control,and energy transformation,but the current rate of change remains insufficient to achieve all SDGs in 2030(United Nations Department of Economic and Social Affairs,2025).How to accelerate the high-quality implementation of SDGs,with only four years left until 2030,is one of the core issues in current sustainable development research. 展开更多
关键词 sustainable development geospatial artificial intelligence disease prevention control sustainable development goals energy transformation social resource allocation sustainable development goals sdgs high quality implementation
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Artificial intelligence assisted 3D in the robotic urooncology?A systematic review and narrative synthesis of current applications,challenges and future directions 认领 引用
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作者 Bara Barakat Bilal Al-Absi +3 位作者 Boris Hadaschik Christian Rehme Samer Schakaki Joerg Bauer 《The Canadian Journal of Urology》 SCIE 2026年第1期105-116,共12页
Background:Artificial intelligence(AI)-assisted threedimensional(3D)surgical platforms,integrated with augmented reality,have the potential to improve intraoperative anatomical recognition and provide surgeons with an... Background:Artificial intelligence(AI)-assisted threedimensional(3D)surgical platforms,integrated with augmented reality,have the potential to improve intraoperative anatomical recognition and provide surgeons with an immersive,dynamic operating environment during urooncological procedures.This review aims to examine the current applications of AI in robotic uro-oncology,with a particular focus on its role in facilitating intraoperative navigation during complex surgeries.Methods:A systematic literature search was performed across PubMed,the National Library of Medicine,MEDLINE,the Cochrane Central Register of Controlled Trials(CENTRAL),ClinicalTrials.gov,and Google Scholar to identify relevant studies published up to July 2025.The search strategy incorporated a predefined set of keywords,including AI,machine learning,radical prostatectomy(RP),robotic-assisted radical prostatectomy(RARP),robotassisted partial nephrectomy(RAPN),and robot-assisted radical cystectomy(RARC).Only clinical trials,full-text peer-reviewed publications,and original research articles were included.Studies were eligible for inclusion if they evaluated or described applications of AI in RARP,RAPN,or RARC.Results:Technological advancements have substantially transformed the field of uro-oncologic surgery.In particular,AI and AI-assisted intraoperative navigation in RARP demonstrate considerable potential to objectively assess surgical performance and predict clinical outcomes.In RAPN,the adoption of preoperative,interactive 3D virtualmodels for surgical planning has influenced surgical decisions,thus,enhanced precision in resection planning correlates with superior nephron-sparing outcomes and optimized selective clamping.AI applications in RARC,techniques such as augmented reality(AR)can overlay critical information on the surgical field,by facilitating navigation through complex anatomical planes and enhancing identification of critical structures.Conclusion:AI appears to enhance robotic uro-oncologic procedures by increasing operative precision and supporting individualised surgical treatment strategies. 展开更多
关键词 artificial intelligence robot-assisted surgery machine learning deep learning automatic three-dimensional surgical navigation intuitive surgical systematic review
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The role of artificial intelligence in precision exercise nutrition:a shift from data to diets 认领 引用
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作者 Mohammad Nasb Ying Zhang Ning Chen 《Food Science and Human Wellness》 SCIE CAS CSCD 2026年第3期963-975,共13页
The integration of artificial intelligence(AI)in precision exercise nutrition is reshaping how athletes optimize their dietary intake for performance,recovery,and overall well-being.This article discusses the intersec... The integration of artificial intelligence(AI)in precision exercise nutrition is reshaping how athletes optimize their dietary intake for performance,recovery,and overall well-being.This article discusses the intersection of AI technologies in formulating precision nutrition strategies tailored to distinct physiological and metabolic requirements of athletes.AI-based mechanisms,such as real-time diet monitoring,continuous glucose monitoring,and nutrient optimization systems,offer unique insights into the impact of AI on advancing precision nutrition applications through the involvement in analyzing complex datasets,merging genetic,metabolic and environmental factors,thereby contributing precise dietary recommendations that adjust to evolving necessities of an athlete.However,the obstacles presented by AI in this domain,including ethical considerations,data confidentiality,and the necessity for uniformity across diverse populations are also confronted.By using AI,athletes can attain greater precision in their nutrition plans,ultimately enhancing exercise performance and promoting fatigue or injury recovery in ways that traditional methods cannot rival.This article further culminates with an address on future trends,emphasizing the role of AI in boosting precision nutrition engagement for athletes,even common exercise enthusiasts. 展开更多
关键词 Artificial intelligence Precision exercise nutrition Exercise performance Dietary recommendation Multi-omics technology Wearable device
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