The increased connectivity and reliance on digital technologies have exposed smart transportation systems to various cyber threats,making intrusion detection a critical aspect of ensuring their secure operation.Tradit...The increased connectivity and reliance on digital technologies have exposed smart transportation systems to various cyber threats,making intrusion detection a critical aspect of ensuring their secure operation.Traditional intrusion detection systems have limitations in terms of centralized architecture,lack of transparency,and vulnerability to single points of failure.This is where the integration of blockchain technology with signature-based intrusion detection can provide a robust and decentralized solution for securing smart transportation systems.This study tackles the issue of database manipulation attacks in smart transportation networks by proposing a signaturebased intrusion detection system.The introduced signature facilitates accurate detection and systematic classification of attacks,enabling categorization according to their severity levels within the transportation infrastructure.Through comparative analysis,the research demonstrates that the blockchain-based IDS outperforms traditional approaches in terms of security,resilience,and data integrity.展开更多
The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management.Earlier approaches have often...The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management.Earlier approaches have often advanced one dimension—such as Internet of Things(IoT)-based data acquisition,Artificial Intelligence(AI)-driven analytics,or digital twin visualization—without fully integrating these strands into a single operational loop.As a result,many existing solutions encounter bottlenecks in responsiveness,interoperability,and scalability,while also leaving concerns about data privacy unresolved.This research introduces a hybrid AI–IoT–Digital Twin framework that combines continuous sensing,distributed intelligence,and simulation-based decision support.The design incorporates multi-source sensor data,lightweight edge inference through Convolutional Neural Networks(CNN)and Long ShortTerm Memory(LSTM)models,and federated learning enhanced with secure aggregation and differential privacy to maintain confidentiality.A digital twin layer extends these capabilities by simulating city assets such as traffic flows and water networks,generating what-if scenarios,and issuing actionable control signals.Complementary modules,including model compression and synchronization protocols,are embedded to ensure reliability in bandwidth-constrained and heterogeneous urban environments.The framework is validated in two urban domains:traffic management,where it adapts signal cycles based on real-time congestion patterns,and pipeline monitoring,where it anticipates leaks through pressure and vibration data.Experimental results show a 28%reduction in response time,a 35%decrease in maintenance costs,and a marked reduction in false positives relative to conventional baselines.The architecture also demonstrates stability across 50+edge devices under federated training and resilience to uneven node participation.The proposed system provides a scalable and privacy-aware foundation for predictive urban infrastructure management.By closing the loop between sensing,learning,and control,it reduces operator dependence,enhances resource efficiency,and supports transparent governance models for emerging smart cities.展开更多
The contemporary smart cities,smart homes,smart buildings,and smart health care systems are the results of the explosive growth of Internet of Things(IoT)devices and deep learning.Yet the centralized training paradigm...The contemporary smart cities,smart homes,smart buildings,and smart health care systems are the results of the explosive growth of Internet of Things(IoT)devices and deep learning.Yet the centralized training paradigms have fundamental issues in data privacy,regulatory compliance,and ownership silo alongside the scaled limitations of the real-life application.The concept of Federated Deep Learning(FDL)is a privacy-by-design method that will enable the distributed training of machine learning models among distributed clients without sharing raw data and is suitable in heterogeneous urban settings.It is an overview of the privacy-preserving developments in FDL as of 2018-2025 with a narrow scope on its usage in smart cities(traffic prediction,environmental monitoring,energy grids),smart homes/buildings/IoT(non-intrusive load monitoring,HVAC optimization,anomaly detection)and the healthcare application(medical imaging,Electronic Health Records(EHR)analysis,remote monitoring).It gives coherent taxonomy,domain pipelines,comparative analyses of privacy mechanisms(differential privacy,secure aggregation,Homomorphic Encryption(HE),Trusted Execution Environments(TEEs),blockchain enhanced and hybrids),system structures,security/robustness defense,deployment/Machine Learning Operation(MLOps)issues,and the longstanding challenges(non-IID heterogeneity,communication efficiency,fairness,and sustainability).Some of the contributions made are structured comparisons of privacy threats,practical design advice on urban areas,recognition of open problems,and a research roadmap into the future up to 2035.The paper brings out the transformational worth of FDL in building credible,scalable,and sustainable intelligent urban ecosystems and the need to do further interdisciplinary research in standardization,real-world testbeds,and ethical governance.展开更多
Unmanned Aerial Vehicles(UAVs)have become integral components in smart city infrastructures,supporting applications such as emergency response,surveillance,and data collection.However,the high mobility and dynamic top...Unmanned Aerial Vehicles(UAVs)have become integral components in smart city infrastructures,supporting applications such as emergency response,surveillance,and data collection.However,the high mobility and dynamic topology of Flying Ad Hoc Networks(FANETs)present significant challenges for maintaining reliable,low-latency communication.Conventional geographic routing protocols often struggle in situations where link quality varies and mobility patterns are unpredictable.To overcome these limitations,this paper proposes an improved routing protocol based on reinforcement learning.This new approach integrates Q-learning with mechanisms that are both link-aware and mobility-aware.The proposed method optimizes the selection of relay nodes by using an adaptive reward function that takes into account energy consumption,delay,and link quality.Additionally,a Kalman filter is integrated to predict UAV mobility,improving the stability of communication links under dynamic network conditions.Simulation experiments were conducted using realistic scenarios,varying the number of UAVs to assess scalability.An analysis was conducted on key performance metrics,including the packet delivery ratio,end-to-end delay,and total energy consumption.The results demonstrate that the proposed approach significantly improves the packet delivery ratio by 12%–15%and reduces delay by up to 25.5%when compared to conventional GEO and QGEO protocols.However,this improvement comes at the cost of higher energy consumption due to additional computations and control overhead.Despite this trade-off,the proposed solution ensures reliable and efficient communication,making it well-suited for large-scale UAV networks operating in complex urban environments.展开更多
Industrial Cyber-Physical Systems(ICPSs)play a vital role in modern industries by providing an intellectual foundation for automated operations.With the increasing integration of information-driven processes,ensuring ...Industrial Cyber-Physical Systems(ICPSs)play a vital role in modern industries by providing an intellectual foundation for automated operations.With the increasing integration of information-driven processes,ensuring the security of Industrial Control Production Systems(ICPSs)has become a critical challenge.These systems are highly vulnerable to attacks such as denial-of-service(DoS),eclipse,and Sybil attacks,which can significantly disrupt industrial operations.This work proposes an effective protection strategy using an Artificial Intelligence(AI)-enabled Smart Contract(SC)framework combined with the Heterogeneous Barzilai-Borwein Support Vector(HBBSV)method for industrial-based CPS environments.The approach reduces run time and minimizes the probability of attacks.Initially,secured ICPSs are achieved through a comprehensive exchange of views on production plant strategies for condition monitoring using SC and blockchain(BC)integrated within a BC network.The SC executes the HBBSV strategy to verify the security consensus.The Barzilai-Borwein Support Vectorized algorithm computes abnormal attack occurrence probabilities to ensure that components operate within acceptable production line conditions.When a component remains within these conditions,no security breach occurs.Conversely,if a component does not satisfy the condition boundaries,a security lapse is detected,and those components are isolated.The HBBSV method thus strengthens protection against DoS,eclipse,and Sybil attacks.Experimental results demonstrate that the proposed HBBSV approach significantly improves security by enhancing authentication accuracy while reducing run time and authentication time compared to existing techniques.展开更多
The deep integration of artificial intelligence technology and agricultural industry has pushed smart agriculture into a new stage of"AI+scenario",and put forward a transformation requirement for the talent ...The deep integration of artificial intelligence technology and agricultural industry has pushed smart agriculture into a new stage of"AI+scenario",and put forward a transformation requirement for the talent cultivation of smart agriculture major in universities from"technology application"to"intelligent innovation".In response to the problems of insufficient AI integration,lack of contextualization,and insufficient collaboration between industry and education in the traditional"technology+"practical course system,this paper takes the smart agriculture major at Yulin Normal University as an example to construct a"AI+agriculture"practical course reconstruction framework and propose a four-dimensional transformation path of"goal-content-mode-evaluation".Through the practical exploration of modular curriculum design,scenario based practical design,integration of industry and education,and intelligent evaluation reform,a practical teaching system with local application-oriented university characteristics has been formed,providing a reference example for the cultivation of smart agriculture professionals under the background of new agricultural science.展开更多
Electrochromic smart windows(ESWs)can significantly reduce building energy consumption,but the high cost hinders large-scale production.The in situ growth of tungsten oxide(WO_(3))films is only by a simple immersion p...Electrochromic smart windows(ESWs)can significantly reduce building energy consumption,but the high cost hinders large-scale production.The in situ growth of tungsten oxide(WO_(3))films is only by a simple immersion process,the silver nanowires(AgNWs)undergo oxidation to Ag^(+)ions through electron loss,and the liberated electrons provide driving force for the deposition of WO_(4)^(2-).Enabled the fabrication of large-area WO_(3)films and ESWs were fabricated under minimal laboratory conditions,demonstrating the economic feasibility,efficient and reliable nature of industrial production.Structural characterization and density functional theory calculations were combined to confirm that AgNWs effectively regulate oxygen vacancies of WO_(3)films and promote the in situ growth process.The optimized WO_(3)exhibits a maximum transmittance modulation of 90.8%and excellent cycling stability of 20,000 cycles.The largescale WO_(3)-based ESWs can save building energy up to 140.0 MJ m^(-2)compared to traditional windows in tropical regions,as verified by simulations more than40 global cities.This research provides a new approach for improving the performance and industrial production of ESW,providing the full understanding and development direction to short the distance of the ESW commercial production.展开更多
Smart cities,as a typical application in the field of the Internet of Things,can combine cloud computing to realize the intelligent control of objects and process massive data.While cloud computing brings convenience ...Smart cities,as a typical application in the field of the Internet of Things,can combine cloud computing to realize the intelligent control of objects and process massive data.While cloud computing brings convenience to smart city services,a serious problem is ensuring that confidential data cannot be leaked to malicious adversaries.Considering the security and privacy of data,data owners transmit sensitive data in its encrypted form to cloud server,which seriously hinders the improvements of potential utilization and efficient sharing.Public key searchable encryption ensures that users can securely retrieve the encrypted data without decryption.However,most existing schemes cannot resist keyword guessing attacks or the size of trapdoors linearly increases with the number of data owners.In this work,by utilizing certificateless encryption and proxy re-encryption,we design an authenticated searchable encryption scheme with constant trapdoors.The designed scheme preserves the privacy of index ciphertexts and keyword trapdoors,and can resist keyword guessing attacks.In addition,data users can generate and upload trapdoors with lower computation and communication overheads.We show that the proposed scheme is suitable for smart city implementations and applications by experimentally evaluating its performance.展开更多
Metabolic dysfunction-associated steatotic liver disease(MASLD)is now considered to be among the most prevalent chronic liver diseases worldwide.Its comprehensive management encompasses multiple stages,including risk ...Metabolic dysfunction-associated steatotic liver disease(MASLD)is now considered to be among the most prevalent chronic liver diseases worldwide.Its comprehensive management encompasses multiple stages,including risk assessment,early detection,stratified intervention,and long-term follow-up.Among these,improving diagnostic accuracy and optimizing individualized therapeutic strategies remain key challenges in both research and clinical practice.In recent years,artificial intelligence and smart devices have developed rapidly and have gradually been applied in the medical field,offering novel tools and pathways for MASLD risk stratification,non-invasive diagnosis,therapeutic evaluation,and patient self-management.This review summarizes the current applications of artificial intelligence and smart devices in MASLD care,highlights their benefits and limitations,and discusses future directions to support precision diagnosis and treatment strategies.展开更多
As blockchain technology rapidly evolves,smart contracts have seen widespread adoption in financial transactions and beyond.However,the growing prevalence of malicious Ponzi scheme contracts presents serious security ...As blockchain technology rapidly evolves,smart contracts have seen widespread adoption in financial transactions and beyond.However,the growing prevalence of malicious Ponzi scheme contracts presents serious security threats to blockchain ecosystems.Although numerous detection techniques have been proposed,existing methods suffer from significant limitations,such as class imbalance and insufficient modeling of transaction-related semantic features.To address these challenges,this paper proposes an oversampling-based detection framework for Ponzi smart contracts.We enhance the Adaptive Synthetic Sampling(ADASYN)algorithm by incorporating sample proximity to decision boundaries and ensuring realistic sample distributions.This enhancement facilitates the generation of high-quality minority class samples and effectively mitigates class imbalance.In addition,we design a Contract Transaction Graph(CTG)construction algorithm to preserve key transactional semantics through feature extraction from contract code.A graph neural network(GNN)is then applied for classification.This study employs a publicly available dataset from the XBlock platform,consisting of 318 verified Ponzi contracts and 6498 benign contracts.Sourced from real Ethereum deployments,the dataset reflects diverse application scenarios and captures the varied characteristics of Ponzi schemes.Experimental results demonstrate that our approach achieves an accuracy of 96%,a recall of 92%,and an F1-score of 94%in detecting Ponzi contracts,outperforming state-of-the-art methods.展开更多
Nursing education is undergoing a paradigm shift from skill training to clinical thinking cultivation.The integration of artificial intelligence technology offers technical possibilities for this transformation,but it...Nursing education is undergoing a paradigm shift from skill training to clinical thinking cultivation.The integration of artificial intelligence technology offers technical possibilities for this transformation,but it also brings about a deep tension between the cultivation of humanistic qualities and a standardized training.Based on the analysis of the practical forms of nursing smart education,this paper examines the cognitive gap between the deterministic feedback of virtual simulation systems and the complexity of real clinical scenarios,reveals the potential narrowing effect of data-driven ability profiling on the all-round development of nursing students,and then proposes the design logic of intelligent teaching resources centered on real clinical problems,a hierarchical teaching model with clear human-machine division of labor,and a dynamic assessment mechanism for technology application led by professional nursing teachers,in an attempt to find a balance between technological empowerment and humanistic commitment in smart nursing education.展开更多
The smart meter communication system has substantial application value for the construction and upgrading of the entire power system.The deployment of the transmitter(Tx)of the smart meter system in the residential sc...The smart meter communication system has substantial application value for the construction and upgrading of the entire power system.The deployment of the transmitter(Tx)of the smart meter system in the residential scenarios is vexed by the need for more theoretical support.This paper mainly studies the communication channel between the Tx at semibasement and receiver(Rx)at outdoor.The design of an effective communication system relies on an accurate understanding of channel characteristics.Channel measurements and ray-tracing channel modeling are conducted to obtain channel data.The influence of different positions at same semi-basement is studied.Typical channel characteristics are analyzed,such as power delay profile(PDP),power angular profile(PAP),root-mean-square(RMS)delay spread(DS),channel capacity,received power,and path loss.The influence of different semi-basement placements and different floor heights is also compared.Besides,the channel measurements and simulation data fit well,which can illustrate the validity and reliability of the acquired channel data.This paper can provide theoretical support for the design and optimization of smart meter communication systems in semi-basement scenarios.展开更多
The decarbonization of transportation and environmental quality enhancement have become more and more reliant on eco-innovation,which incorporates both technological change and systemic coordination and governance.The...The decarbonization of transportation and environmental quality enhancement have become more and more reliant on eco-innovation,which incorporates both technological change and systemic coordination and governance.The review is a summary of the evidence that can be translated into environmental sustainability outcomes on how smart vehicle technologies,including electrified powertrains and vehicle-grid interfaces,connected and cooperative systems(Vehicleto-Everything,V2X),automation and advanced automation,and Artificial Intelligence(AI)-enabled optimization can be transformed.Using a structured analytical framework linking technology capability to eco-innovation mechanisms and sustainability impacts,we reconcile findings across operational,well-to-wheel,and life-cycle boundaries.The literature indicates that electrification delivers strong local air-quality benefits and,in most contexts,substantial climate gains,but net outcomes depend on grid carbon intensity,charging time profiles,battery production,and end-of-life pathways,making managed charging and circularity pivotal complements.Connectivity and cooperative control improve energy efficiency primarily through coordination effects such as traffic smoothing,eco-routing,and platooning,yet benefits are non-linear and sensitive to penetration rates and infrastructure interoperability.Automation offers efficiency and safety co-benefits but exhibits the widest uncertainty because induced demand,empty travel,and mode substitution can offset per-vehicle improvements.AI-driven fleet optimization can reduce empty miles and extend component life,although computational and hardware overhead and rapid obsolescence can introduce trade-offs.We identify persistent gaps in comparability,non-exhaust emissions assessment,causal evaluation at scale,and equity-aware impact metrics,and propose a research and policy agenda emphasizing integrated Life Cycle Assessment(LCA)system modeling,standardized reporting,interoperable data governance,and demand management to secure durable environmental gains.展开更多
基金supported by the National Research Foundation(NRF),Republic of Korea,under project BK21 FOUR(4299990213939).
文摘The increased connectivity and reliance on digital technologies have exposed smart transportation systems to various cyber threats,making intrusion detection a critical aspect of ensuring their secure operation.Traditional intrusion detection systems have limitations in terms of centralized architecture,lack of transparency,and vulnerability to single points of failure.This is where the integration of blockchain technology with signature-based intrusion detection can provide a robust and decentralized solution for securing smart transportation systems.This study tackles the issue of database manipulation attacks in smart transportation networks by proposing a signaturebased intrusion detection system.The introduced signature facilitates accurate detection and systematic classification of attacks,enabling categorization according to their severity levels within the transportation infrastructure.Through comparative analysis,the research demonstrates that the blockchain-based IDS outperforms traditional approaches in terms of security,resilience,and data integrity.
基金The researchers would like to thank the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2025)。
文摘The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management.Earlier approaches have often advanced one dimension—such as Internet of Things(IoT)-based data acquisition,Artificial Intelligence(AI)-driven analytics,or digital twin visualization—without fully integrating these strands into a single operational loop.As a result,many existing solutions encounter bottlenecks in responsiveness,interoperability,and scalability,while also leaving concerns about data privacy unresolved.This research introduces a hybrid AI–IoT–Digital Twin framework that combines continuous sensing,distributed intelligence,and simulation-based decision support.The design incorporates multi-source sensor data,lightweight edge inference through Convolutional Neural Networks(CNN)and Long ShortTerm Memory(LSTM)models,and federated learning enhanced with secure aggregation and differential privacy to maintain confidentiality.A digital twin layer extends these capabilities by simulating city assets such as traffic flows and water networks,generating what-if scenarios,and issuing actionable control signals.Complementary modules,including model compression and synchronization protocols,are embedded to ensure reliability in bandwidth-constrained and heterogeneous urban environments.The framework is validated in two urban domains:traffic management,where it adapts signal cycles based on real-time congestion patterns,and pipeline monitoring,where it anticipates leaks through pressure and vibration data.Experimental results show a 28%reduction in response time,a 35%decrease in maintenance costs,and a marked reduction in false positives relative to conventional baselines.The architecture also demonstrates stability across 50+edge devices under federated training and resilience to uneven node participation.The proposed system provides a scalable and privacy-aware foundation for predictive urban infrastructure management.By closing the loop between sensing,learning,and control,it reduces operator dependence,enhances resource efficiency,and supports transparent governance models for emerging smart cities.
文摘The contemporary smart cities,smart homes,smart buildings,and smart health care systems are the results of the explosive growth of Internet of Things(IoT)devices and deep learning.Yet the centralized training paradigms have fundamental issues in data privacy,regulatory compliance,and ownership silo alongside the scaled limitations of the real-life application.The concept of Federated Deep Learning(FDL)is a privacy-by-design method that will enable the distributed training of machine learning models among distributed clients without sharing raw data and is suitable in heterogeneous urban settings.It is an overview of the privacy-preserving developments in FDL as of 2018-2025 with a narrow scope on its usage in smart cities(traffic prediction,environmental monitoring,energy grids),smart homes/buildings/IoT(non-intrusive load monitoring,HVAC optimization,anomaly detection)and the healthcare application(medical imaging,Electronic Health Records(EHR)analysis,remote monitoring).It gives coherent taxonomy,domain pipelines,comparative analyses of privacy mechanisms(differential privacy,secure aggregation,Homomorphic Encryption(HE),Trusted Execution Environments(TEEs),blockchain enhanced and hybrids),system structures,security/robustness defense,deployment/Machine Learning Operation(MLOps)issues,and the longstanding challenges(non-IID heterogeneity,communication efficiency,fairness,and sustainability).Some of the contributions made are structured comparisons of privacy threats,practical design advice on urban areas,recognition of open problems,and a research roadmap into the future up to 2035.The paper brings out the transformational worth of FDL in building credible,scalable,and sustainable intelligent urban ecosystems and the need to do further interdisciplinary research in standardization,real-world testbeds,and ethical governance.
基金funded by Hung Yen University of Technology and Education under grand number UTEHY.L.2025.62.
文摘Unmanned Aerial Vehicles(UAVs)have become integral components in smart city infrastructures,supporting applications such as emergency response,surveillance,and data collection.However,the high mobility and dynamic topology of Flying Ad Hoc Networks(FANETs)present significant challenges for maintaining reliable,low-latency communication.Conventional geographic routing protocols often struggle in situations where link quality varies and mobility patterns are unpredictable.To overcome these limitations,this paper proposes an improved routing protocol based on reinforcement learning.This new approach integrates Q-learning with mechanisms that are both link-aware and mobility-aware.The proposed method optimizes the selection of relay nodes by using an adaptive reward function that takes into account energy consumption,delay,and link quality.Additionally,a Kalman filter is integrated to predict UAV mobility,improving the stability of communication links under dynamic network conditions.Simulation experiments were conducted using realistic scenarios,varying the number of UAVs to assess scalability.An analysis was conducted on key performance metrics,including the packet delivery ratio,end-to-end delay,and total energy consumption.The results demonstrate that the proposed approach significantly improves the packet delivery ratio by 12%–15%and reduces delay by up to 25.5%when compared to conventional GEO and QGEO protocols.However,this improvement comes at the cost of higher energy consumption due to additional computations and control overhead.Despite this trade-off,the proposed solution ensures reliable and efficient communication,making it well-suited for large-scale UAV networks operating in complex urban environments.
文摘Industrial Cyber-Physical Systems(ICPSs)play a vital role in modern industries by providing an intellectual foundation for automated operations.With the increasing integration of information-driven processes,ensuring the security of Industrial Control Production Systems(ICPSs)has become a critical challenge.These systems are highly vulnerable to attacks such as denial-of-service(DoS),eclipse,and Sybil attacks,which can significantly disrupt industrial operations.This work proposes an effective protection strategy using an Artificial Intelligence(AI)-enabled Smart Contract(SC)framework combined with the Heterogeneous Barzilai-Borwein Support Vector(HBBSV)method for industrial-based CPS environments.The approach reduces run time and minimizes the probability of attacks.Initially,secured ICPSs are achieved through a comprehensive exchange of views on production plant strategies for condition monitoring using SC and blockchain(BC)integrated within a BC network.The SC executes the HBBSV strategy to verify the security consensus.The Barzilai-Borwein Support Vectorized algorithm computes abnormal attack occurrence probabilities to ensure that components operate within acceptable production line conditions.When a component remains within these conditions,no security breach occurs.Conversely,if a component does not satisfy the condition boundaries,a security lapse is detected,and those components are isolated.The HBBSV method thus strengthens protection against DoS,eclipse,and Sybil attacks.Experimental results demonstrate that the proposed HBBSV approach significantly improves security by enhancing authentication accuracy while reducing run time and authentication time compared to existing techniques.
基金Supported by the Autonomous Region-level Research and Practice Projects for New Engineering,New Medicine,New Agriculture,and New Humanities of Guangxi Department of Education(XNK202409)the Undergraduate Teaching Reform Project of Guangxi Higher Education(2024JGB332+1 种基金2024JGA304)the Guangxi Degree and Graduate Education Reform Project(JGY2025382).
文摘The deep integration of artificial intelligence technology and agricultural industry has pushed smart agriculture into a new stage of"AI+scenario",and put forward a transformation requirement for the talent cultivation of smart agriculture major in universities from"technology application"to"intelligent innovation".In response to the problems of insufficient AI integration,lack of contextualization,and insufficient collaboration between industry and education in the traditional"technology+"practical course system,this paper takes the smart agriculture major at Yulin Normal University as an example to construct a"AI+agriculture"practical course reconstruction framework and propose a four-dimensional transformation path of"goal-content-mode-evaluation".Through the practical exploration of modular curriculum design,scenario based practical design,integration of industry and education,and intelligent evaluation reform,a practical teaching system with local application-oriented university characteristics has been formed,providing a reference example for the cultivation of smart agriculture professionals under the background of new agricultural science.
基金the National Natural Science Foundation of China(grant No.52163022,62305076)Sichuan Science and Technology Program(2024ZYD0196)+1 种基金China Postdoctoral Science Foundation(2023M740505)Sichuan Postdoctoral Science Special Foundation(No.TB2023010)。
文摘Electrochromic smart windows(ESWs)can significantly reduce building energy consumption,but the high cost hinders large-scale production.The in situ growth of tungsten oxide(WO_(3))films is only by a simple immersion process,the silver nanowires(AgNWs)undergo oxidation to Ag^(+)ions through electron loss,and the liberated electrons provide driving force for the deposition of WO_(4)^(2-).Enabled the fabrication of large-area WO_(3)films and ESWs were fabricated under minimal laboratory conditions,demonstrating the economic feasibility,efficient and reliable nature of industrial production.Structural characterization and density functional theory calculations were combined to confirm that AgNWs effectively regulate oxygen vacancies of WO_(3)films and promote the in situ growth process.The optimized WO_(3)exhibits a maximum transmittance modulation of 90.8%and excellent cycling stability of 20,000 cycles.The largescale WO_(3)-based ESWs can save building energy up to 140.0 MJ m^(-2)compared to traditional windows in tropical regions,as verified by simulations more than40 global cities.This research provides a new approach for improving the performance and industrial production of ESW,providing the full understanding and development direction to short the distance of the ESW commercial production.
基金supported by the Shandong Provincial Key Research and Development Program(No.2021CXGC010107)the National Natural Science Foundation of China(Nos.U21A20466,62325209)+3 种基金the New 20 Project of Higher Education of Jinan(No.202228017)the Special Project on Science and Technology Program of Hubei Province(No.2021BAA025)the Fundamental Research Funds for the Central Universities(Nos.2042023kf0203,20420241013)the Researchers Supporting Project Number(RSP2024R509),King Saud University,Riyadh,Saudi Arabia。
文摘Smart cities,as a typical application in the field of the Internet of Things,can combine cloud computing to realize the intelligent control of objects and process massive data.While cloud computing brings convenience to smart city services,a serious problem is ensuring that confidential data cannot be leaked to malicious adversaries.Considering the security and privacy of data,data owners transmit sensitive data in its encrypted form to cloud server,which seriously hinders the improvements of potential utilization and efficient sharing.Public key searchable encryption ensures that users can securely retrieve the encrypted data without decryption.However,most existing schemes cannot resist keyword guessing attacks or the size of trapdoors linearly increases with the number of data owners.In this work,by utilizing certificateless encryption and proxy re-encryption,we design an authenticated searchable encryption scheme with constant trapdoors.The designed scheme preserves the privacy of index ciphertexts and keyword trapdoors,and can resist keyword guessing attacks.In addition,data users can generate and upload trapdoors with lower computation and communication overheads.We show that the proposed scheme is suitable for smart city implementations and applications by experimentally evaluating its performance.
基金supported by the National Key Research and Development Program(2024YFA1307101).
文摘Metabolic dysfunction-associated steatotic liver disease(MASLD)is now considered to be among the most prevalent chronic liver diseases worldwide.Its comprehensive management encompasses multiple stages,including risk assessment,early detection,stratified intervention,and long-term follow-up.Among these,improving diagnostic accuracy and optimizing individualized therapeutic strategies remain key challenges in both research and clinical practice.In recent years,artificial intelligence and smart devices have developed rapidly and have gradually been applied in the medical field,offering novel tools and pathways for MASLD risk stratification,non-invasive diagnosis,therapeutic evaluation,and patient self-management.This review summarizes the current applications of artificial intelligence and smart devices in MASLD care,highlights their benefits and limitations,and discusses future directions to support precision diagnosis and treatment strategies.
基金supported by the Key Project of Joint Fund of the National Natural Science Foundation of China“Research on Key Technologies and Demonstration Applications for Trusted and Secure Data Circulation and Trading”(U24A20241)the National Natural Science Foundation of China“Research on Trusted Theories and Key Technologies of Data Security Trading Based on Blockchain”(62202118)+4 种基金the Major Scientific and Technological Special Project of Guizhou Province([2024]014)Scientific and Technological Research Projects from the Guizhou Education Department(Qian jiao ji[2023]003)the Hundred-Level Innovative Talent Project of the Guizhou Provincial Science and Technology Department(Qiankehe Platform Talent-GCC[2023]018)the Major Project of Guizhou Province“Research and Application of Key Technologies for Trusted Large Models Oriented to Public Big Data”(Qiankehe Major Project[2024]003)the Guizhou Province Computational Power Network Security Protection Science and Technology Innovation Talent Team(Qiankehe Talent CXTD[2025]029).
文摘As blockchain technology rapidly evolves,smart contracts have seen widespread adoption in financial transactions and beyond.However,the growing prevalence of malicious Ponzi scheme contracts presents serious security threats to blockchain ecosystems.Although numerous detection techniques have been proposed,existing methods suffer from significant limitations,such as class imbalance and insufficient modeling of transaction-related semantic features.To address these challenges,this paper proposes an oversampling-based detection framework for Ponzi smart contracts.We enhance the Adaptive Synthetic Sampling(ADASYN)algorithm by incorporating sample proximity to decision boundaries and ensuring realistic sample distributions.This enhancement facilitates the generation of high-quality minority class samples and effectively mitigates class imbalance.In addition,we design a Contract Transaction Graph(CTG)construction algorithm to preserve key transactional semantics through feature extraction from contract code.A graph neural network(GNN)is then applied for classification.This study employs a publicly available dataset from the XBlock platform,consisting of 318 verified Ponzi contracts and 6498 benign contracts.Sourced from real Ethereum deployments,the dataset reflects diverse application scenarios and captures the varied characteristics of Ponzi schemes.Experimental results demonstrate that our approach achieves an accuracy of 96%,a recall of 92%,and an F1-score of 94%in detecting Ponzi contracts,outperforming state-of-the-art methods.
基金Funding Project for Ideological and Political Model Courses of“Epidemic Fighting”Courses in Henan Province(Project No.:531,2020)University-level Curriculum Ideological and Political Demonstration Course Support Project of Zhengzhou Sias University(Project No.:34,2024)+2 种基金University-level Key Discipline Support Project of Zhengzhou Sias University(Project No.:1,2022)2025 Key Scientific Research Projects of Henan Universities(Project No.:25B360003)Henan Province Private Brand Professional Support Project(Project No.:527,2019)。
文摘Nursing education is undergoing a paradigm shift from skill training to clinical thinking cultivation.The integration of artificial intelligence technology offers technical possibilities for this transformation,but it also brings about a deep tension between the cultivation of humanistic qualities and a standardized training.Based on the analysis of the practical forms of nursing smart education,this paper examines the cognitive gap between the deterministic feedback of virtual simulation systems and the complexity of real clinical scenarios,reveals the potential narrowing effect of data-driven ability profiling on the all-round development of nursing students,and then proposes the design logic of intelligent teaching resources centered on real clinical problems,a hierarchical teaching model with clear human-machine division of labor,and a dynamic assessment mechanism for technology application led by professional nursing teachers,in an attempt to find a balance between technological empowerment and humanistic commitment in smart nursing education.
基金supported by the Natural Science Foundation of Shandong Province under Grant ZR2024MF062the open research fund of National Mobile Communications Research Laboratory,Southeast University under Grants 2025D03+1 种基金the Future Plan Program for Young Scholars of Shandong University,and the Innovation and Technology Support Program for Young Scholars of Colleges and Universities in Shandong Province under Grant 2022KJ009The B6G R&D Group in Shandong University is greatly thanked for channel measurements.
文摘The smart meter communication system has substantial application value for the construction and upgrading of the entire power system.The deployment of the transmitter(Tx)of the smart meter system in the residential scenarios is vexed by the need for more theoretical support.This paper mainly studies the communication channel between the Tx at semibasement and receiver(Rx)at outdoor.The design of an effective communication system relies on an accurate understanding of channel characteristics.Channel measurements and ray-tracing channel modeling are conducted to obtain channel data.The influence of different positions at same semi-basement is studied.Typical channel characteristics are analyzed,such as power delay profile(PDP),power angular profile(PAP),root-mean-square(RMS)delay spread(DS),channel capacity,received power,and path loss.The influence of different semi-basement placements and different floor heights is also compared.Besides,the channel measurements and simulation data fit well,which can illustrate the validity and reliability of the acquired channel data.This paper can provide theoretical support for the design and optimization of smart meter communication systems in semi-basement scenarios.
文摘The decarbonization of transportation and environmental quality enhancement have become more and more reliant on eco-innovation,which incorporates both technological change and systemic coordination and governance.The review is a summary of the evidence that can be translated into environmental sustainability outcomes on how smart vehicle technologies,including electrified powertrains and vehicle-grid interfaces,connected and cooperative systems(Vehicleto-Everything,V2X),automation and advanced automation,and Artificial Intelligence(AI)-enabled optimization can be transformed.Using a structured analytical framework linking technology capability to eco-innovation mechanisms and sustainability impacts,we reconcile findings across operational,well-to-wheel,and life-cycle boundaries.The literature indicates that electrification delivers strong local air-quality benefits and,in most contexts,substantial climate gains,but net outcomes depend on grid carbon intensity,charging time profiles,battery production,and end-of-life pathways,making managed charging and circularity pivotal complements.Connectivity and cooperative control improve energy efficiency primarily through coordination effects such as traffic smoothing,eco-routing,and platooning,yet benefits are non-linear and sensitive to penetration rates and infrastructure interoperability.Automation offers efficiency and safety co-benefits but exhibits the widest uncertainty because induced demand,empty travel,and mode substitution can offset per-vehicle improvements.AI-driven fleet optimization can reduce empty miles and extend component life,although computational and hardware overhead and rapid obsolescence can introduce trade-offs.We identify persistent gaps in comparability,non-exhaust emissions assessment,causal evaluation at scale,and equity-aware impact metrics,and propose a research and policy agenda emphasizing integrated Life Cycle Assessment(LCA)system modeling,standardized reporting,interoperable data governance,and demand management to secure durable environmental gains.
