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Analysis and Simulations of Open-Source Intelligence Process System Dynamics from User’s Perspective 认领 引用 被引量：1
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作者 Huan Liu Zhenyu Tang +1 位作者 Ning Zhao Wei Qian 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第1期541-558,共18页
In today’s society with advanced Internet,the amount of information increases dramatically with each passing day,which leads to increasingly complex processes of open-source intelligence.Therefore,it is more importan... In today’s society with advanced Internet,the amount of information increases dramatically with each passing day,which leads to increasingly complex processes of open-source intelligence.Therefore,it is more important to rationalize the operation mode and improve the operation efficiency of open-source intelligence under the premise of satisfying users’needs.This paper focuses on the simulation study of the process system of opensource intelligence from the user’s perspective.First,the basic concept and development status of open-source intelligence are introduced in details.Second,six existing intelligence operation process models are summarized and their advantages and disadvantages are compared in focus.Based on users’preference,the open-source intelligence system simulation theory model is constructed from four aspects:intelligence collection,intelligence processing,intelligence analysis,and intelligence delivery.Meanwhile,the dynamics model of the open-source intelligence process system is constructed based on the open-source intelligence system simulation theoretical model,which specifically includes five parts:determination of system boundary,construction of causal loop diagram,construction of stock flow diagram,writing ofmathematical equations,and system sensitivity test.Finally,the system simulation results were analyzed.It was found that improving the system of intelligence agencies,opening up government affairs,improving the professional level of intelligence personnel,strengthening the communication and cooperation among personnel of various intelligence departments,and expressing intelligence products through diverse forms can effectively improve the operational efficiency of the open-source intelligence process system. 展开更多
关键词 Open-source intelligence system dynamics feedback user demand deviation
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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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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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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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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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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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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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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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Artificial intelligence virtual extracellular vesicles(AIVEVs) 认领 引用 被引量：1
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作者 Han Liu Shiyu Li +1 位作者 Jian Wang Jiacan Su 《Bioactive Materials》 SCIE CSCD 2026年第7期34-55,共22页
Recent progress in artificial intelligence(AI)has given rise to AI virtual cells(AIVCs),which are digital twins of predictable or dynamic biological cells.This model can simulate,predict and replicate the behavior of ... Recent progress in artificial intelligence(AI)has given rise to AI virtual cells(AIVCs),which are digital twins of predictable or dynamic biological cells.This model can simulate,predict and replicate the behavior of real cells in digital software.Extracellular vesicles(EVs)are nanoscale phospholipid bilayer structures released by cells and are important for intercellular communication.To fully leverage digital models in EVs research,we propose the interdisciplinary concept of AI virtual EVs(AIVEVs).This review systematically outlines the construction of AIVEVs through both knowledge-driven(white-box)and data-driven(black-box)modeling paradigms,inte-grating multi-omics data to simulate EVs biogenesis,cargo sorting,and intercellular communication.Moreover,we highlight how AIVCs drive models to predict the composition of AIVEVs,analyze cell communication behavior,construct diagnostic atlases of pathological virtual cells,and enhance the ability to trace vesicle ori-gins.Furthermore,we also present a closed-loop workflow from in silico prediction to experimental validation and project the developmental trajectory of AIVEVs toward clinical translation.We firmly believe that AIVEVs can accelerate the development of EVs-based disease diagnosis and treatment,thereby opening a new era of intercellular communication research. 展开更多
关键词 Artificial intelligence Virtual cells Extracellular vesicles Virtual extracellular vesicles Digital model
AI Empowers Supply Chain Intelligence:A Three-Chain Four-Intelligence Framework 认领 引用
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作者 Zuo-Jun Max Shen Shaochong Lin 《Engineering》 SCIE EI CSCD 2026年第6期430-443,共14页
This study introduces a novel conceptual framework to understand the transformative impact of artificial intelligence(AI)on global supply chains.We propose a Three-Chain Four-Intelligence framework that systematically... This study introduces a novel conceptual framework to understand the transformative impact of artificial intelligence(AI)on global supply chains.We propose a Three-Chain Four-Intelligence framework that systematically analyzes how AI reconfigures supply chain architecture and capabilities through enhanced contextual awareness.The Three-Chain perspective examines how AI transforms the logistics chain(physical flow),information chain(data flow),and value chain(value creation)from fragmented opera-tions to synchronized intelligent ecosystems.The Four-Intelligence pathway maps the evolutionary pro-gression from digital connectivity to operational optimization,collaborative ecosystems,and ultimately self-evolving intelligent systems.AI serves as an orchestrating force that processes rich contextual infor-mation spanning product attributes,market dynamics,environmental conditions,and operational reali-ties.We demonstrated the practical application of the framework through a comprehensive case study of JD.com,where AI implementation across all dimensions yielded quantifiable improvements.Our analysis reveals that the most transformative supply chain advancements emerge at the intersection of multiple chains with increasingly sophisticated contextual awareness.The paper concludes by identifying six emerging research frontiers,such as generative AI integration with decision optimization. 展开更多
关键词 Artificial intelligence Supply chain intelligence Machine learning Intelligent decision-making Generative AI
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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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Artificial Intelligence Tools for Carbon Nanotube Research:Opportunities From Synthesis to Applications 认领 引用
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作者 Yanlong Zhao Fei Wang +4 位作者 Qixuan Cai Aike Xi Kangkang Wang Khaixien Leu Rufan Zhang 《Carbon and Hydrogen》 SCIE CAS CSCD 2026年第3期304-319,共16页
Carbon nanotube(CNT)research is strongly constrained by the coupled relationships among synthesis conditions,multiscale structure,and functional performance,which make rational optimization difficult using trial-and-e... Carbon nanotube(CNT)research is strongly constrained by the coupled relationships among synthesis conditions,multiscale structure,and functional performance,which make rational optimization difficult using trial-and-error alone.Artificial intelligence(Al)is emerging as a useful set of tools for navigating this complexity,particularly under data-scarce experimental conditions.This review summarizes recent progress in AI-assisted CNT research across three connected stages:synthesis optimization,automated characterization,and application-oriented structure-property modeling.Current evidence shows that supervised learning and Bayesian optimization can accelerate the exploration of CNT growth windows,whereas computer vision and spectral learning can convert microscopy and spectroscopy outputs into quantitative descriptors for downstream modeling.We further discuss emerging language model-based literature mining workflows while emphasizing that their CNTspecific validation remains less mature than that of synthesis and characterization models.Finally,we outline the major barriers to model transferability,including small heterogeneous datasets,inconsistent reporting,and uncertainty in characterizationderived labels and highlight how standardized descriptors and closed-loop workflows could make AI more actionable in CNT research. 展开更多
关键词 application artificial intelligence carbon nanotube characterization synthesis
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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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From Molecular Pathology to Cognitive Phenotypes:The Emerging Role of Artificial Intelligence in Neuroimaging 认领 引用
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作者 Xiaohui Zhang Yan Zhong +4 位作者 Rui Zhou Chentao Jin Jing Wang Mei Tian Hong Zhang 《iRADIOLOGY》 CSCD 2026年第3期185-187,共3页
Molecular imaging has transformed contemporary neuroscience by enabling in vivo assessment of disease-relevant biology,including abnormal protein aggregation,altered metabolism,neuroinflammation,and neurotransmitter d... Molecular imaging has transformed contemporary neuroscience by enabling in vivo assessment of disease-relevant biology,including abnormal protein aggregation,altered metabolism,neuroinflammation,and neurotransmitter dysfunction[1].However,the ability to visualize molecular pathology does not by itself explain how such pathology disrupts brain organization and,ultimately,causes clinical cognitive symptoms.This translational gap remains a central challenge in neurology,psychiatry,and cognitive neuroscience[2]. 展开更多
关键词 artificial intelligence cognitive phenotype molecular imaging neurodegenerative disorder
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Real-time monitoring of tunnel structures using digital twin and artificial intelligence:A short overview 认领 引用
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作者 Mohammad Afrazi Danial Jahed Armaghani +2 位作者 Hossein Afrazi Hadi Fattahi Pijush Samui 《Deep Underground Science and Engineering》 EI CAS CSCD 2026年第2期315-330,共16页
Tunnels are essential components of contemporary infrastructure,yet guaranteeing their safety,longevity,and efficiency remains a persistent challenge.Recent breakthroughs in artificial intelligence(AI)and digital twin... Tunnels are essential components of contemporary infrastructure,yet guaranteeing their safety,longevity,and efficiency remains a persistent challenge.Recent breakthroughs in artificial intelligence(AI)and digital twin(DT)technology provide innovative solutions for the real-time monitoring of tunnel systems,suggesting proactive maintenance tactics and improved safety protocols.This review paper offers a comprehensive examination of the application of AI and DT methodologies in tunnel surveillance.We explore the core concepts of AI and DT and their applicability to structural monitoring,encompassing machine learning,computer vision,and sensor integration.Through the utilization of these AI-powered technologies,engineers are equipped with unparalleled insights into the state and behavior of tunnels,facilitating the early identification of irregularities and the optimization of maintenance timelines.We discuss the array of AI techniques utilized for the immediate monitoring of tunnel systems,emphasizing their foundations,benefits,and practical uses.Numerous studies have showcased the effectiveness and adaptability of AI-based monitoring systems in various tunnel settings.Moreover,we address the hurdles and constraints inherent in AI and DT methodologies and suggest strategies for overcoming them,such as data augmentation,interpretable AI,edge computing,and continuous monitoring.Ultimately,the incorporation of AI and DT technologies into tunnel surveillance signifies a paradigm shift,offering substantial advantages over conventional techniques.By adopting AI-driven monitoring systems,tunnel operators can augment safety,prolong the lifespan of infrastructure,and decrease operational expenses,molding the future of subterranean infrastructure management. 展开更多
关键词 artificial intelligence digital twin machine learning monitoring real-time tunnelling
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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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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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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-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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