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
Scientific and technological advancements are rapidly transforming underground engineering,shifting from labor-intensive,time-consuming methods to automated,real-time systems.This timely and comprehensive review cover...Scientific and technological advancements are rapidly transforming underground engineering,shifting from labor-intensive,time-consuming methods to automated,real-time systems.This timely and comprehensive review covers in-situ testing,intelligent monitoring,and geophysical testing methods,highlighting fundamental principles,testing apparatuses,data processing techniques,and engineering applications.The state-of-the-art summary emphasizes not only cutting-edge innovations for complex and harsh environments but also the transformative role of artificial intelligence and machine learning in data interpretations.The integration of big data and advanced algorithms is particularly impactful,enabling the identification,prediction,and mitigation of potential risks in underground projects.Key aspects of the discussion include detection capabilities,method integration,and data convergence of intelligent technologies to drive enhanced safety,operational efficiency,and predictive reliability.The review also examines future trends in intelligent technologies,emphasizing unified platforms that combine multiple methods,real-time data,and predictive analytics.These advancements are shaping the evolution of underground construction and maintenance,aiming for risk-free,high-efficiency underground engineering.展开更多
Purpose–This paper aims to systematically review the evolution of inspection technologies and equipment for heavy-haul railway infrastructure,with a focus on China’s Shuohuang Railway and Daqin Railway.It summarizes...Purpose–This paper aims to systematically review the evolution of inspection technologies and equipment for heavy-haul railway infrastructure,with a focus on China’s Shuohuang Railway and Daqin Railway.It summarizes the technological progression from traditional manual inspections to integrated and intelligent inspection systems,analyzes their practical application outcomes and outlines future research directions to support the safe,efficient and sustainable operation of heavy-haul railways.Design/methodology/approach–The study employs a combination of historical and empirical analysis,primarily drawing on academic literature and operational data from Shuohuang Railway.The development of inspection technologies is categorized into two distinct phases:traditional inspection and integrated inspection.The comprehensive effectiveness of these technologies is evaluated based on actual inspection efficiency,defect detection capability,cost savings and other relevant data.Findings–The adoption of integrated inspection vehicles has significantly improved inspection efficiency and accuracy.In 2014,the world’s first heavy-haul integrated inspection vehicle enabled synchronous multidisciplinary inspections,greatly reducing reliance on manual labor.By 2024,the intelligent heavy-haul integrated inspection vehicle further enhanced detection precision by 30%.Practical applications demonstrate that the annual number of track defects decreased from 25,000 to 3,800,while the track quality index(TQI)remained stable below 6 mm.Additionally,annual maintenance costs were reduced by more than 40 m yuan.Originality/value–This paper provides the first systematic review of the development of inspection technologies for heavy-haul railway infrastructure,highlighting China’s leading achievements in integrated and intelligent inspection.It clarifies the practical value of these technologies in enhancing safety,reducing costs and optimizing maintenance operations.Furthermore,it proposes future directions for development,including system integration,onboard computing capabilities and unmanned operations,offering valuable insights for technological innovation and policymaking in the field.展开更多
The increasing incidence of global warming and frequent heavy precipitation events presents a significant challenge for urban areas in managing extreme precipitation.Strengthening the resilience of communities to clim...The increasing incidence of global warming and frequent heavy precipitation events presents a significant challenge for urban areas in managing extreme precipitation.Strengthening the resilience of communities to climate change is a crucial strategy for fostering sustainable urban development.Green infrastructure offers an ecologically system for rainwater management and ecological restoration,and plays a significant role in adapting to climate risks.This study focuses on climate resilience by examining the implementation of green rainwater infrastructure within the context of climate-adapted green infrastructure in the High Point community of Seattle,USA,and proposes renewal planning strategies,methods,and implementation concepts at the community level.The research indicates that the High Point community has effectively mitigated the issue of waterlogging and enhanced the local microclimate through the implementation of green infrastructure systems,including permeable pavement,rain gardens,bioretention pools,and vegetative buffer zones.It is proposed that the collaborative design of green infrastructure should adhere to principles of systematization,alignment with natural processes,adaptation to the local environment,and engagement of multiple stakeholders,while considering various functions,diverse communities,and differing social contexts.Furthermore,it should be developed in consideration of the unique spatial characteristics,landscape structures,and social needs of each community.展开更多
Digital infrastructure possesses dual attributes as both an international public good and a strategic communication tool for major countries. In recent years, the US has been active in the field of global digital infr...Digital infrastructure possesses dual attributes as both an international public good and a strategic communication tool for major countries. In recent years, the US has been active in the field of global digital infrastructure, showing a trend of deep coupling and mutual embedding with strategic communication. The US has built a strategic communication system for digital infrastructure. This system is designed to set the international agenda, collect information and intelligence, and deter its competitors. The system presents a three-way coherent infrastructure of a basic layer, application layer,and value layer. The mode of operation is characterized by commercial force collaboration, alliance system linkage, and global multi-domain network layout. However, to maintain its unipolar digital hegemony,the United States has over-instrumentalized its digital infrastructure and exploited and amplified the asymmetry of digital science and technology for a long period of time, which not only highlights its unilateral stance and exclusionary nature but also results in a global digital divide and trust deficit, which will pose constraints on its sustainability in the long term.展开更多
The accelerated global adoption of electric vehicles(EVs)is driving significant expansion and increasing complexity within the EV charging infrastructure,consequently presenting novel and pressing cybersecurity challe...The accelerated global adoption of electric vehicles(EVs)is driving significant expansion and increasing complexity within the EV charging infrastructure,consequently presenting novel and pressing cybersecurity challenges.While considerable effort has focused on preventative cybersecurity measures,a critical deficiency persists in structured methodologies for digital forensic analysis following security incidents,a gap exacerbated by system heterogeneity,distributed digital evidence,and inconsistent logging practices which hinder effective incident reconstruction and attribution.This paper addresses this critical need by proposing a novel,data-driven forensic framework tailored to the EV charging infrastructure,focusing on the systematic identification,classification,and correlation of diverse digital evidence across its physical,network,and application layers.Our methodology integrates open-source intelligence(OSINT)with advanced system modeling based on a three-layer cyber-physical system architecture to comprehensively map potential evidentiary sources.Key contributions include a comprehensive taxonomy of cybersecurity threats pertinent to EV charging ecosystems,detailed mappings between these threats and the resultant digital evidence to guide targeted investigations,the formulation of adaptable forensic investigation workflows for various incident scenarios,and a critical analysis of significant gaps in digital evidence availability within current EV charging systems,highlighting limitations in forensic readiness.The practical application and utility of this method are demonstrated through illustrative case studies involving both empirically-derived and virtual incident scenarios.The proposed datadriven approach is designed to significantly enhance digital forensic capabilities,support more effective incident response,strengthen compliance with emerging cybersecurity regulations,and ultimately contribute to bolstering the overall security,resilience,and trustworthiness of this increasingly vital critical infrastructure.展开更多
Spatial spillover effects,either positive or negative,of transport infrastructure,highways/expressways,etc.,on regional economic growth are proposed.Using the panel data for 11 cities of Zhejiang province from 1994 to...Spatial spillover effects,either positive or negative,of transport infrastructure,highways/expressways,etc.,on regional economic growth are proposed.Using the panel data for 11 cities of Zhejiang province from 1994 to 2003,a spatial production function is applied to examine the spatial spillovers which can be generated as a positive output spillover from the transport infrastructure between neighboring cities.Some spatial weighted matrices are adopted to define different neighboring cities to measure how easily factors or economic activities can migrate between regions.The estimation results show that the output elasticity of the highway infrastructure in 11 cities are all insignificant at a 5% significance level;hence,highway infrastructure in a region cannot explain the same region's economic growth.On the other hand,the highway infrastructure of other contiguous regions has positive spillover effects on a same region's economic growth.展开更多
本研究利用2017—2023年陕西VLF/LF闪电定位系统地闪数据,基于GIS(Geographic Information System)核密度场模型,建立了基于强度权重的地闪核密度表面;应用热点探测模型对地闪密度热点进行提取和等级分类;分析了地闪核密度及其热点的分...本研究利用2017—2023年陕西VLF/LF闪电定位系统地闪数据,基于GIS(Geographic Information System)核密度场模型,建立了基于强度权重的地闪核密度表面;应用热点探测模型对地闪密度热点进行提取和等级分类;分析了地闪核密度及其热点的分布与地形地貌、植被覆盖等环境因素的关系。研究结果显示:①利用GIS核密度场模型和热点探测模型可以准确识别闪电活动密集区域,并深入揭示地闪空间分布特征。②陕西地闪活动存在明显的时空差异,地闪在夏季发生次数多强度大,春秋季次之,冬季最少;空间分布总体表现为南北高—中间低的空间格局。③核密度在海拔高度1 km以下与海拔高度正相关,在海拔高度1 km以上与海拔高度负相关;地形地貌、植被及两者交叉项均对核密度产生显著性差异影响。地形地貌影响的差异超过植被。④中海拔黄土梁峁和中海拔中起伏山地的草地和林区是大型及中大型热点的主要分布区。展开更多
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.
基金supported by Ministry of Education of Singapore,under Academic Research Fund Tier 1(Grant Number RG143/23).
文摘Scientific and technological advancements are rapidly transforming underground engineering,shifting from labor-intensive,time-consuming methods to automated,real-time systems.This timely and comprehensive review covers in-situ testing,intelligent monitoring,and geophysical testing methods,highlighting fundamental principles,testing apparatuses,data processing techniques,and engineering applications.The state-of-the-art summary emphasizes not only cutting-edge innovations for complex and harsh environments but also the transformative role of artificial intelligence and machine learning in data interpretations.The integration of big data and advanced algorithms is particularly impactful,enabling the identification,prediction,and mitigation of potential risks in underground projects.Key aspects of the discussion include detection capabilities,method integration,and data convergence of intelligent technologies to drive enhanced safety,operational efficiency,and predictive reliability.The review also examines future trends in intelligent technologies,emphasizing unified platforms that combine multiple methods,real-time data,and predictive analytics.These advancements are shaping the evolution of underground construction and maintenance,aiming for risk-free,high-efficiency underground engineering.
基金supported by 2020 Science and Technology Innovation Project of Shuo-Huang Railway Development Company(SHTL-20-12).
文摘Purpose–This paper aims to systematically review the evolution of inspection technologies and equipment for heavy-haul railway infrastructure,with a focus on China’s Shuohuang Railway and Daqin Railway.It summarizes the technological progression from traditional manual inspections to integrated and intelligent inspection systems,analyzes their practical application outcomes and outlines future research directions to support the safe,efficient and sustainable operation of heavy-haul railways.Design/methodology/approach–The study employs a combination of historical and empirical analysis,primarily drawing on academic literature and operational data from Shuohuang Railway.The development of inspection technologies is categorized into two distinct phases:traditional inspection and integrated inspection.The comprehensive effectiveness of these technologies is evaluated based on actual inspection efficiency,defect detection capability,cost savings and other relevant data.Findings–The adoption of integrated inspection vehicles has significantly improved inspection efficiency and accuracy.In 2014,the world’s first heavy-haul integrated inspection vehicle enabled synchronous multidisciplinary inspections,greatly reducing reliance on manual labor.By 2024,the intelligent heavy-haul integrated inspection vehicle further enhanced detection precision by 30%.Practical applications demonstrate that the annual number of track defects decreased from 25,000 to 3,800,while the track quality index(TQI)remained stable below 6 mm.Additionally,annual maintenance costs were reduced by more than 40 m yuan.Originality/value–This paper provides the first systematic review of the development of inspection technologies for heavy-haul railway infrastructure,highlighting China’s leading achievements in integrated and intelligent inspection.It clarifies the practical value of these technologies in enhancing safety,reducing costs and optimizing maintenance operations.Furthermore,it proposes future directions for development,including system integration,onboard computing capabilities and unmanned operations,offering valuable insights for technological innovation and policymaking in the field.
文摘The increasing incidence of global warming and frequent heavy precipitation events presents a significant challenge for urban areas in managing extreme precipitation.Strengthening the resilience of communities to climate change is a crucial strategy for fostering sustainable urban development.Green infrastructure offers an ecologically system for rainwater management and ecological restoration,and plays a significant role in adapting to climate risks.This study focuses on climate resilience by examining the implementation of green rainwater infrastructure within the context of climate-adapted green infrastructure in the High Point community of Seattle,USA,and proposes renewal planning strategies,methods,and implementation concepts at the community level.The research indicates that the High Point community has effectively mitigated the issue of waterlogging and enhanced the local microclimate through the implementation of green infrastructure systems,including permeable pavement,rain gardens,bioretention pools,and vegetative buffer zones.It is proposed that the collaborative design of green infrastructure should adhere to principles of systematization,alignment with natural processes,adaptation to the local environment,and engagement of multiple stakeholders,while considering various functions,diverse communities,and differing social contexts.Furthermore,it should be developed in consideration of the unique spatial characteristics,landscape structures,and social needs of each community.
基金a phased achievement of a major project of the National Social Science Fund of China,titled “Research on the Security Impact of the Situation in the Bay of Bengal Region on China’s East Data West Computing Project”(Project No.:22ZDA181)。
文摘Digital infrastructure possesses dual attributes as both an international public good and a strategic communication tool for major countries. In recent years, the US has been active in the field of global digital infrastructure, showing a trend of deep coupling and mutual embedding with strategic communication. The US has built a strategic communication system for digital infrastructure. This system is designed to set the international agenda, collect information and intelligence, and deter its competitors. The system presents a three-way coherent infrastructure of a basic layer, application layer,and value layer. The mode of operation is characterized by commercial force collaboration, alliance system linkage, and global multi-domain network layout. However, to maintain its unipolar digital hegemony,the United States has over-instrumentalized its digital infrastructure and exploited and amplified the asymmetry of digital science and technology for a long period of time, which not only highlights its unilateral stance and exclusionary nature but also results in a global digital divide and trust deficit, which will pose constraints on its sustainability in the long term.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2023-00242528,50%)supported by a grant from the Korea Electric Power Corporation(R24XO01-4,50%)for basic research and development projects starting in 2024.
文摘The accelerated global adoption of electric vehicles(EVs)is driving significant expansion and increasing complexity within the EV charging infrastructure,consequently presenting novel and pressing cybersecurity challenges.While considerable effort has focused on preventative cybersecurity measures,a critical deficiency persists in structured methodologies for digital forensic analysis following security incidents,a gap exacerbated by system heterogeneity,distributed digital evidence,and inconsistent logging practices which hinder effective incident reconstruction and attribution.This paper addresses this critical need by proposing a novel,data-driven forensic framework tailored to the EV charging infrastructure,focusing on the systematic identification,classification,and correlation of diverse digital evidence across its physical,network,and application layers.Our methodology integrates open-source intelligence(OSINT)with advanced system modeling based on a three-layer cyber-physical system architecture to comprehensively map potential evidentiary sources.Key contributions include a comprehensive taxonomy of cybersecurity threats pertinent to EV charging ecosystems,detailed mappings between these threats and the resultant digital evidence to guide targeted investigations,the formulation of adaptable forensic investigation workflows for various incident scenarios,and a critical analysis of significant gaps in digital evidence availability within current EV charging systems,highlighting limitations in forensic readiness.The practical application and utility of this method are demonstrated through illustrative case studies involving both empirically-derived and virtual incident scenarios.The proposed datadriven approach is designed to significantly enhance digital forensic capabilities,support more effective incident response,strengthen compliance with emerging cybersecurity regulations,and ultimately contribute to bolstering the overall security,resilience,and trustworthiness of this increasingly vital critical infrastructure.
基金The National Key Technology R&D Program of China during the 11 th Five-Year Plan Period(No.2006BAH02A06)Program for New Century Excellent Talents in China(No.NCET-05-0529)
文摘Spatial spillover effects,either positive or negative,of transport infrastructure,highways/expressways,etc.,on regional economic growth are proposed.Using the panel data for 11 cities of Zhejiang province from 1994 to 2003,a spatial production function is applied to examine the spatial spillovers which can be generated as a positive output spillover from the transport infrastructure between neighboring cities.Some spatial weighted matrices are adopted to define different neighboring cities to measure how easily factors or economic activities can migrate between regions.The estimation results show that the output elasticity of the highway infrastructure in 11 cities are all insignificant at a 5% significance level;hence,highway infrastructure in a region cannot explain the same region's economic growth.On the other hand,the highway infrastructure of other contiguous regions has positive spillover effects on a same region's economic growth.
文摘本研究利用2017—2023年陕西VLF/LF闪电定位系统地闪数据,基于GIS(Geographic Information System)核密度场模型,建立了基于强度权重的地闪核密度表面;应用热点探测模型对地闪密度热点进行提取和等级分类;分析了地闪核密度及其热点的分布与地形地貌、植被覆盖等环境因素的关系。研究结果显示:①利用GIS核密度场模型和热点探测模型可以准确识别闪电活动密集区域,并深入揭示地闪空间分布特征。②陕西地闪活动存在明显的时空差异,地闪在夏季发生次数多强度大,春秋季次之,冬季最少;空间分布总体表现为南北高—中间低的空间格局。③核密度在海拔高度1 km以下与海拔高度正相关,在海拔高度1 km以上与海拔高度负相关;地形地貌、植被及两者交叉项均对核密度产生显著性差异影响。地形地貌影响的差异超过植被。④中海拔黄土梁峁和中海拔中起伏山地的草地和林区是大型及中大型热点的主要分布区。
