Enhancing the resilience of critical infrastructure(CI)systems has become a focal point of national and inter-national policies.However,the formulation of resilience enhancement strategies often requires component-(i....Enhancing the resilience of critical infrastructure(CI)systems has become a focal point of national and inter-national policies.However,the formulation of resilience enhancement strategies often requires component-(i.e.asset-)level prioritization,which entails many complexities.Acknowledging the complex and interdependent nature of infrastructure systems,this paper aims to aid researchers,practitioners and policy-makers by pre-senting a review of the relative literature and current state-of-the-art,and by identifying future research op-portunities to improve the applicability and operationalizability of CI component identification and prioritization methods.Theoretical and practical applications are reviewed for definitions,analysis and modelling approaches regarding the resilience of interdependent infrastructure systems.A detailed review of infrastructure criticality definitions,component criticality assessment and prioritization frameworks,from scientific,policy and other documents,is presented.A discussion on social justice and equity dimensions therein is included,which have the potential to greatly influence decisions and should always be incorporated in infrastructure planning and in-vestment discussions.The findings of this review are discussed in terms of applicability and operationalizability.Key recommendations for future research include:(i)developing quantification frameworks for CI component criticality based on formal definitions and multiple criteria,(ii)incorporating the entire resilience cycle of CI in component prioritization,(iii)accounting for the socio-technical nature of CI systems by integrating social di-mensions and their wider operating environment and(iv)developing comprehensive model validation,cali-bration and uncertainty analysis frameworks.展开更多
Habitat loss driven by land-use change is a major factor shaping the dynamics of urban bird community structures.However,the potential mechanisms by which the spatial configuration and composition of blue-green infras...Habitat loss driven by land-use change is a major factor shaping the dynamics of urban bird community structures.However,the potential mechanisms by which the spatial configuration and composition of blue-green infrastructure,recognized as biodiversity hotspots in urban landscapes,influence urban bird beta diversity remain insufficiently understood.This study was conducted in the built-up area of Yinchuan,an internationally recognized wetland city in Northwest China.From December 2023 to June 2024,we systematically surveyed bird communities during both the breeding and wintering periods across 29 blue-green space mosaics.We quantified taxonomic,functional,and phylogenetic beta diversity,along with their turnover component and nestedness-resultant component,based on both pairwise beta diversity and multiple-site beta diversity.We further assessed the relative importance of landscape variables and spatial geographic distance in shaping beta diversity patterns and used hierarchical modeling of species communities(HMSC)to explore the responses of bird occurrence and functional traits to landscape variables.Our results revealed that species turnover was the dominant driver of taxonomic,functional,and phylogenetic beta diversity.Seasonal differences were observed in the effects of spatial geographic distance and landscape structure on beta diversity and its components,with landscape variables showing higher explanatory power than geographic isolation.In the breeding period,landscape diversity and waterbody area had positive effects on bird occurrence,whereas in the wintering period,most landscape features—except for landscape diversity—exerted neutral or negative influences.Regarding functional traits,we found that reproductive traits,flight ability,and foraging characteristics responded significantly to landscape structure,and that some small-bodied species active in aerial and canopy layers were more adaptable to habitat fragmentation.This study provides novel insights into the assembly processes and driving mechanisms of urban bird communities and offers scientific support for the notion that designing and maintaining blue-green infrastructure can contribute to urban biodiversity conservation.展开更多
In April 2016,a moment magnitude(Mw)7.8 earthquake struck near Muisne(Pedernales),Ecuador,causing 671 fatalities,displacing>30,000 people,and generating approximately USD 3.6 billion in economic losses that severel...In April 2016,a moment magnitude(Mw)7.8 earthquake struck near Muisne(Pedernales),Ecuador,causing 671 fatalities,displacing>30,000 people,and generating approximately USD 3.6 billion in economic losses that severely impacted the coastal province of Manabí.Nine years later,the recovery trajectory of its principal urban centers-Pedernales,Manta,Portoviejo,and Chone-offers a critical perspective to assess adaptive resilience in earthquake-prone coastal cities of Latin America.This study conducts a regional assessment of post-earthquake recovery using the 4Rs resilience framework-robustness,redundancy,resourcefulness,and rapidity-applied across housing,health,education,infrastructure,and economic sectors.Official reports,statistical databases,and field validations collected between 2016 and 2025 provide the basis for documenting both progress and persistent challenges.The findings indicate that robustness improved with the enforcement of the Ecuadorian seismic code NEC-15 and the adoption of advanced technologies such as base isolation and supplemental damping in hospitals and high-rise buildings.Redundancy expanded selectively,being stronger in healthcare yet limited in housing and utilities.Resourcefulness varied across cities:municipal leadership and civic oversight in Manta and Portoviejo facilitated adaptive recovery,whereas Pedernales and Chone remained dependent on central agencies.Rapidity was similarly uneven;lifeline services were restored promptly,but complex projects-including hospitals,sewer systems,and residential complexes-faced delays of five to nine years.Structural assessments of 97 buildings revealed that nearly half remain without reinforcement,with recurrent deficiencies such as soft-story mecha-nisms,brittle masonry infill,and reinforcement corrosion sustaining latent seismic risk.Governance fragmen-tation,equity gaps,and insufficient monitoring thus emerged as critical barriers,underscoring the need for integrated governance,community participation,and AI-enabled monitoring to strengthen long-term disaster recovery in coastal cities.展开更多
The year 2026 marks the 70th anniversary of the establishment of diplomatic relations between China and African countries.It also marks the launch of the China-Africa Year of People-to-People Exchanges.At this pivotal...The year 2026 marks the 70th anniversary of the establishment of diplomatic relations between China and African countries.It also marks the launch of the China-Africa Year of People-to-People Exchanges.At this pivotal juncture linking past achievements with future development,Wang Yi,member of the Political Bureau of the Communist Party of China Central Committee and foreign minister,continued the longstanding diplomatic tradition under which the Chinese foreign minister makes Africa the destination of the first overseas visit of the year,a practice upheld for 36 consecutive years,underscoring the stability,continuity and sincerity of China’s policy towards Africa.展开更多
Structural Health Monitoring(SHM)plays a critical role in ensuring the safety,integrity,longevity and economic efficiency of civil infrastructures.The field has undergone a profound transformation over the last few de...Structural Health Monitoring(SHM)plays a critical role in ensuring the safety,integrity,longevity and economic efficiency of civil infrastructures.The field has undergone a profound transformation over the last few decades,evolving from traditional methods—often reliant on visual inspections—to data-driven intelligent systems.This review paper analyzes this historical trajectory,beginning with the approaches that relied on modal parameters as primary damage indicators.The advent of advanced sensor technologies and increased computational power brings a significant change,making Machine Learning(ML)a viable and powerful tool for damage assessment.More recently,Deep Learning(DL)has emerged as a paradigm shift,allowing for more automated processing of large data sets(such as the structural vibration signals and other types of sensors)with excellent performance and accuracy,often surpassing previous methods.This paper systematically reviews these technological milestones—from traditional vibration-based methods to the current state-of-the-art in deep learning.Finally,it critically examines emerging trends—such as Digital Twins and Transformer-based architectures—and discusses future research directions that will shape the next generation of SHM systems for civil engineering.展开更多
The integration of large-scale foundation models(e.g.,GPT series and AlphaFold)into oncology is fundamentally transforming both research methodologies and clinical practices,driven by unprecedented advancements in com...The integration of large-scale foundation models(e.g.,GPT series and AlphaFold)into oncology is fundamentally transforming both research methodologies and clinical practices,driven by unprecedented advancements in computational power.This review synthesizes recent progress in the application of large language models to core oncological tasks,including medical imaging analysis,genomic interpretation,and personalized treatment planning.Underpinned by advanced computational infrastructures,such as graphics processing unit/tensor processing unit clusters,heterogeneous computing,and cloud platforms,these models enable superior representation learning and generalization across multimodal data sources.This review examines how these infrastructures overcome key bottlenecks in intelligent oncology through scalable optimization strategies,including mixed-precision training,memory optimization,and heterogeneous computing.Alongside these technical advancements,the review explores pressing challenges,such as data heterogeneity,limited model interpretability,regulatory uncertainties,and the environmental impact of artificial intelligence(AI)systems.Special emphasis is placed on emerging solutions,encompassing green AI and edge computing,which offer promising approaches for low-resource deployment scenarios.Additionally,the review highlights the critical role of interdisciplinary collaboration among oncology,computer science,ethics,and policy to ensure that AI systems are not only powerful but also transparent,safe,and clinically relevant.Finally,the review outlines potential avenues for future research aimed at developing robust,scalable,and human-centered frameworks for intelligent oncology.展开更多
Highways are a vital component of global transportation infrastructure,but their environmental impact,particularly in terms of carbon emissions,poses significant challenges to achieving global sustainability and clima...Highways are a vital component of global transportation infrastructure,but their environmental impact,particularly in terms of carbon emissions,poses significant challenges to achieving global sustainability and climate goals.This literature review discusses the new area of low-carbon highways,including next-generation construction technologies and near-zero carbon operation strategies.It discusses the purpose of innovative materials,such as low-carbon cements,recycled asphalt,and geopolymers,and intelligent infrastructure solutions,including intelligent traffic control and electric vehicle charging systems.The article also explores the significance of Lifecycle Assessment(LCA)and carbon accounting in the measurement and minimization of the carbon footprint of highways.Even though a lot of progress has been made,there are still several technical,economic,and regulatory obstacles,among which are the prohibitive initial cost of low-carbon technologies and the non-standardization of policies.The future outlook indicates that to alleviate these challenges,a coordinated effort among the research,policy,industry,and communities is necessary.Low-carbon highways can help to have a robust,sustainable transport system that meets global climate goals by incorporating sustainable processes in highway design,construction,and operation.展开更多
As an Indian journalist who has lived and worked in China for more than a decade,I have witnessed firsthand the astounding pace of China’s urban transformation-high-speed rail networks linking megacities,digital paym...As an Indian journalist who has lived and worked in China for more than a decade,I have witnessed firsthand the astounding pace of China’s urban transformation-high-speed rail networks linking megacities,digital payment systems reshaping daily life,smart manufacturing emerging across industrial zones,and modern infrastructure spreading nationwide.Yet,my recent eight-day media tour to Zhejiang Province o!ered a new and deeply thought-provoking perspective.展开更多
Against the backdrop of accelerated development of new forms of trade,the question of whether rapid expansion of cross-border e-commerce(CBEC)can help to reduce carbon emissions among Chinese enterprises is of great s...Against the backdrop of accelerated development of new forms of trade,the question of whether rapid expansion of cross-border e-commerce(CBEC)can help to reduce carbon emissions among Chinese enterprises is of great significance for seizing new opportunities in foreign trade,and advancing firms’green and low-carbon transformation.This study treats the creation of CBEC pilot zones as a quasi-natural experiment,employing panel data from Chinese A-share listed companies matched with city-level information from 2006 to 2021.We construct a multi-period difference-in-differences model to identify the impact of CBEC pilot zone policy on corporate carbon emissions.Our findings indicate the construction of these pilot zones significantly reduces firms’carbon emissions intensity,and the results are robust across multiple tests.We show the pilot zone initiative contributes to emission reductions by enhancing the adoption of digital infrastructure,promoting green technological innovation,and increasing environmental awareness among enterprises.Quantile regressions reveal pilot zones exert a more pronounced carbon-reduction effect on firms characterized by high carbon emissions intensity and advanced levels of digital transformation.Moreover,the policy effect is especially significant in heavily polluting industries,and regions with weaker governmental environmental regulations or lower public environmental concerns.This study makes an innovative contribution to the literature by empirically verifying the environmental governance effect of establishing CBEC pilot zones,and offers practical guidance for governments in formulating cross-border e-commerce policies and for enterprises pursuing low carbon development.展开更多
Zero-day attacks present a critical cybersecurity challenge for Internet of things(IoT)infrastructures,where the inability of signature-based intrusion detection systems(IDSs)to recognize novel threat behaviors compro...Zero-day attacks present a critical cybersecurity challenge for Internet of things(IoT)infrastructures,where the inability of signature-based intrusion detection systems(IDSs)to recognize novel threat behaviors compromises both system reliability and operational continuity.Existing hybrid IDS solutions often struggle to balance accurate classification of known attacks with reliable anomaly detection,particularly under the computational constraints of IoT environments.To address this gap,we introduce ZeroDefense,an adaptive fusion-based IDS designed for simultaneous detection of known intrusions and emerging zero-day threats.The framework employs a four-layer architecture consisting of i)feature standardization and class balancing,ii)anomaly detection using isolation forest,autoencoder,and local outlier factor,iii)fine-grained attack classification via random forest,extreme gradient boosting(XGBoost),light gradient boosting machine(LightGBM),and attentive interpretable tabular learning(TabNet),and iv)a confidence-aware fusion engine that adaptively selects the most reliable decision path.Suspicious or previously unseen traffic is isolated early through fused anomaly scoring,while benign and known-malicious flows are processed through supervised classification for precise attack labeling.With an anomaly cascaded decision pipeline,a dynamic confidence-driven fusion mechanism,and a deploymentconscious design,ZeroDefense enables real-time inference on IoT edge gateways.Evaluation on the CICIoT2023 benchmark demonstrates 99.94% overall accuracy and 95.64%macro-average F1-score for known attacks,while 5.76% of traffic is successfully flagged as potential zero-day activity,with inference latency maintained below 100 ms/flow.These results indicate that ZeroDefense offers a scalable,resilient,and practically deployable defense capability for modern IoT infrastructures.展开更多
Purpose-This paper provides a comprehensive analysis of the Brazilian freight railway system,examining the efficacy of the current concession renewal model in light of persistent structural problems such as market con...Purpose-This paper provides a comprehensive analysis of the Brazilian freight railway system,examining the efficacy of the current concession renewal model in light of persistent structural problems such as market concentration,cargo dependence on export commodities and underutilization of the network.Situating Brazil within the broader international debate on railway reforms,the paper evaluates whether the ongoing early renewal of concessions can deliver a more diversified and competitive freight system.Design/methodology/approach-The study adopts a sequential mixed-methods research design that integrates longitudinal quantitative analysis with qualitative institutional and policy evaluation.The quantitative component examines time-series indicators published by ANTT,DNIT and INFRA S.A.from 1999 to 2023 to identify structural patterns in traffic growth,investment,safety and market concentration.The qualitative component employs a process-tracing logic to reconstruct the evolution of concession renewals and the implementation of Railway Law 14.273/2021,drawing on concepts from regulatory economics,institutional theory and industrial organization.These empirical streams are synthesized through an analytical framework that connects three dimensions-regulatory design,market structure and system performance-allowing for a systematic assessment of how Brazil’s institutional configuration shapes incentives,competitive dynamics and network utilization.Findings-The analysis confirms that the early renewal of concessions has successfully secured substantial private investment for capacity expansion on existing trunk lines.However,it has perpetuated the vertically integrated model,reinforcing the market power of incumbent operators and failing to significantly promote intramodal competition or cargo diversification.The system remains dominated by iron ore and agricultural commodities,with general cargo representing a minuscule share.The new authorization regime and short-line railway policies present a viable pathway for market opening but face significant operational and institutional barriers to implementation.Originality/value-This research offers a timely and critical assessment of a pivotal moment in Brazilian railway policy.It moves beyond a simplistic evaluation of volume growth to a structural analysis of market failures and the interplay between concession renewal and regulatory innovation.The findings provide actionable insights for policymakers in Brazil and other emerging economies seeking to balance private investment with public interest goals in railway infrastructure,highlighting the necessity of complementary,pro-competitive measures alongside financial investment.展开更多
In a first for the African continent,Senegal will host the Dakar 2026 Youth Olympic Games(YOG)from 31 October to 13 November.The Dakar 2026 YOG carry a strong symbolic ambition,embodied by their motto“Africa welcomes...In a first for the African continent,Senegal will host the Dakar 2026 Youth Olympic Games(YOG)from 31 October to 13 November.The Dakar 2026 YOG carry a strong symbolic ambition,embodied by their motto“Africa welcomes,Dakar celebrates.”Host Senegal sees the event as a catalyst for its influence,the modernisation of its infrastructure,and the mobilisation of its youth.展开更多
Hainan is transforming into a world-class hub,with expanded free trade policies,robust infrastructure and global business opportunities The Hainan Free Trade Port(FTP)officially launched island-wide special customs op...Hainan is transforming into a world-class hub,with expanded free trade policies,robust infrastructure and global business opportunities The Hainan Free Trade Port(FTP)officially launched island-wide special customs operations on 18 December 2025.One month in,reporters from China International Communications Group(CICG)conducted an exclusive interview with Feng Fei,secretary of the Hainan Provincial Committee of the Communist Party of China(CPC)and chairman of the Standing Committee of the Hainan Provincial People’s Congress.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
As the frontier of multidimensional transportation systems,urban air mobility(UAM)is receiving increasing attention from international organizations,governments,and stakeholders in industry and academia owing to its h...As the frontier of multidimensional transportation systems,urban air mobility(UAM)is receiving increasing attention from international organizations,governments,and stakeholders in industry and academia owing to its high efficiency,low carbon footprint,and operational flexibility.Vertical take-off and landing(VTOL)infrastructure is the core facility that enables UAM and is therefore essential for its safe,efficient,and large-scale commercial implementation.However,the key technologies for establishing low-altitude VTOL infrastructure are still nascent,and government,industry,and academia have yet to harmonize the corresponding construction,management,and operation standards.To address this gap,we herein systematically review the related progress and trends,comprehensively surveying the key technologies of establishing VTOL infrastructure serving unmanned aerial vehicles(UAVs)and electric VTOL aircraft from three complementary perspectives of ground-side,airspace-side,and communication,navigation,surveillance,and information services.In the light of future UAM operations characterized by diverse vehicle types and dense air traffic,we propose a conceptual design for a public multioperator VTOL site to provide constructive insights into the sustainable growth of the low-altitude economy.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
Marine infrastructure is increasingly vulnerable to harsh environmental conditions that accelerate the degradation of traditional materials such as Portland cement concrete and carbon steel.This review systematically ...Marine infrastructure is increasingly vulnerable to harsh environmental conditions that accelerate the degradation of traditional materials such as Portland cement concrete and carbon steel.This review systematically investigates recent advancements in sustainable alternatives,including geopolymer concrete,engineered innovacementitious composites(ECC),bio-concrete,fiber-reinforced polymers(FRPs),and bamboo,stainless steel,and steel-CFRP hybrid bars.Each material is evaluated based on marine durability,mechanical performance,environmental impact,and cost feasibility using life cycle assessment,durability modelling,and a multi-criteria decisionsupport framework.The results reveal that geopolymer concrete and FRP reinforcement’s exhibit superior corrosion resistance and environmental benefits,while ECC and steel-CFRP composites offer structural resilience with moderate environmental trade-offs.However,challenges remain in long-term performance validation,standardization,and market integration.The review concludes that a combined approach involving innovative materials,computational tools,and sustainability assessment is essential for advancing marine infrastructure.Outlook recommendations include focused field studies,development of regulatory guidelines,and interdisciplinary collaboration to drive the practical adoption of eco-efficient materials in coastal and offshore construction.展开更多
The national grid and other life-sustaining critical infrastructures face an unprecedented threat from prolonged blackouts,which could last over a year and pose a severe risk to national security.Whether caused by phy...The national grid and other life-sustaining critical infrastructures face an unprecedented threat from prolonged blackouts,which could last over a year and pose a severe risk to national security.Whether caused by physical attacks,EMP(electromagnetic pulse)events,or cyberattacks,such disruptions could cripple essential services like water supply,healthcare,communication,and transportation.Research indicates that an attack on just nine key substations could result in a coast-to-coast blackout lasting up to 18 months,leading to economic collapse,civil unrest,and a breakdown of public order.This paper explores the key vulnerabilities of the grid,the potential impacts of prolonged blackouts,and the role of AI(artificial intelligence)and ML(machine learning)in mitigating these threats.AI-driven cybersecurity measures,predictive maintenance,automated threat response,and EMP resilience strategies are discussed as essential solutions to bolster grid security.Policy recommendations emphasize the need for hardened infrastructure,enhanced cybersecurity,redundant power systems,and AI-based grid management to ensure national resilience.Without proactive measures,the nation remains exposed to a catastrophic power grid failure that could have dire consequences for society and the economy.展开更多
Unmanned aerial vehicles(UAVs)technology is rapidly advancing,offering innovative solutions for various industries,including the critical task of oil and gas pipeline surveillance.However,the limited flight time of co...Unmanned aerial vehicles(UAVs)technology is rapidly advancing,offering innovative solutions for various industries,including the critical task of oil and gas pipeline surveillance.However,the limited flight time of conventional UAVs presents a significant challenge to comprehensive and continuous monitoring,which is crucial for maintaining the integrity of pipeline infrastructure.This review paper evaluates methods for extending UAV flight endurance,focusing on their potential application in pipeline inspection.Through an extensive literature review,this study identifies the latest advancements in UAV technology,evaluates their effectiveness,and highlights the existing gaps in achieving prolonged flight operations.Advanced techniques,including artificial intelligence(AI),machine learning(ML),and deep learning(DL),are reviewed for their roles in pipeline monitoring.Notably,DL algorithms like You Only Look Once(YOLO)are explored for autonomous flight in UAV-based inspections,real-time defect detection,such as cracks,corrosion,and leaks,enhancing reliability and accuracy.A vital aspect of this research is the proposed deployment of a hybrid drone design combining lighter-than-air(LTA)and heavier-than-air(HTA)principles,achieving a balance of endurance and maneuverability.LTA vehicles utilize buoyancy to reduce energy consumption,thereby extending flight durations.The paper details the methodology for designing LTA vehicles,presenting an analysis of design parameters that align with the requirements for effective pipeline surveillance.The ongoing work is currently at Technology Readiness Level(TRL)4,where key components have been validated in laboratory conditions,with fabrication and flight testing planned for the next phase.Initial design analysis indicates that LTA configurations could offer significant advantages in flight endurance compared to traditional UAV designs.These findings lay the groundwork for future fabrication and testing phases,which will be critical in validating and assessing the proposed approach’s real-world applicability.By outlining the technical complexities and proposing specialized techniques tailored for pipeline monitoring,this paper provides a foundational framework for advancing UAV capabilities in the oil and gas sector.Researchers and industry practitioners can use this roadmap to further develop UAV-enabled surveillance solutions,aiming to improve the reliability,efficiency,and safety of pipeline monitoring.展开更多
Road infrastructure is facing significant digitalization challenges within the context of new infrastructure construction in China and worldwide.Among the advanced digital technologies,digital twin(DT)has gained promi...Road infrastructure is facing significant digitalization challenges within the context of new infrastructure construction in China and worldwide.Among the advanced digital technologies,digital twin(DT)has gained prominence across various engineering sectors,including the manufacturing and construction industries.Specifically,road engineering has demonstrated a growing interest in DT and has achieved promising results in DT-related applications over the past several years.This paper systematically introduces the development of DT and examines its current state in road engineering by reviewing research articles on DT-enabling technologies,such as model creation,condition sensing,data processing,and interaction,as well as its applications throughout the lifecycle of road infrastructure.The findings indicate that research has primarily focused on data perception and virtual model creation,while realtime data processing and interaction between physical and virtual models remain underexplored.DT in road engineering has been predominantly applied during the operation and maintenance phases,with limited attention given to the construction and demolition phases.Future efforts should focus on establishing uniform standards,developing innovative perception and data interaction techniques,optimizing development costs,and expanding the scope of lifecycle applications to facilitate the digital transformation of road engineering.This review provides a comprehensive overview of state-of-the-art advancements in this field and paves the way for leveraging DT in road infrastructure lifecycle management.展开更多
基金supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No.101037424.
文摘Enhancing the resilience of critical infrastructure(CI)systems has become a focal point of national and inter-national policies.However,the formulation of resilience enhancement strategies often requires component-(i.e.asset-)level prioritization,which entails many complexities.Acknowledging the complex and interdependent nature of infrastructure systems,this paper aims to aid researchers,practitioners and policy-makers by pre-senting a review of the relative literature and current state-of-the-art,and by identifying future research op-portunities to improve the applicability and operationalizability of CI component identification and prioritization methods.Theoretical and practical applications are reviewed for definitions,analysis and modelling approaches regarding the resilience of interdependent infrastructure systems.A detailed review of infrastructure criticality definitions,component criticality assessment and prioritization frameworks,from scientific,policy and other documents,is presented.A discussion on social justice and equity dimensions therein is included,which have the potential to greatly influence decisions and should always be incorporated in infrastructure planning and in-vestment discussions.The findings of this review are discussed in terms of applicability and operationalizability.Key recommendations for future research include:(i)developing quantification frameworks for CI component criticality based on formal definitions and multiple criteria,(ii)incorporating the entire resilience cycle of CI in component prioritization,(iii)accounting for the socio-technical nature of CI systems by integrating social di-mensions and their wider operating environment and(iv)developing comprehensive model validation,cali-bration and uncertainty analysis frameworks.
基金supported by the National Natural Science Foundation of China(32401409)the Central Government’s Special Fund for National Key Protected Wildlife Conservation Projects in Yinchuan City(HXCG-ZC2023148,XZ-2024-16)。
文摘Habitat loss driven by land-use change is a major factor shaping the dynamics of urban bird community structures.However,the potential mechanisms by which the spatial configuration and composition of blue-green infrastructure,recognized as biodiversity hotspots in urban landscapes,influence urban bird beta diversity remain insufficiently understood.This study was conducted in the built-up area of Yinchuan,an internationally recognized wetland city in Northwest China.From December 2023 to June 2024,we systematically surveyed bird communities during both the breeding and wintering periods across 29 blue-green space mosaics.We quantified taxonomic,functional,and phylogenetic beta diversity,along with their turnover component and nestedness-resultant component,based on both pairwise beta diversity and multiple-site beta diversity.We further assessed the relative importance of landscape variables and spatial geographic distance in shaping beta diversity patterns and used hierarchical modeling of species communities(HMSC)to explore the responses of bird occurrence and functional traits to landscape variables.Our results revealed that species turnover was the dominant driver of taxonomic,functional,and phylogenetic beta diversity.Seasonal differences were observed in the effects of spatial geographic distance and landscape structure on beta diversity and its components,with landscape variables showing higher explanatory power than geographic isolation.In the breeding period,landscape diversity and waterbody area had positive effects on bird occurrence,whereas in the wintering period,most landscape features—except for landscape diversity—exerted neutral or negative influences.Regarding functional traits,we found that reproductive traits,flight ability,and foraging characteristics responded significantly to landscape structure,and that some small-bodied species active in aerial and canopy layers were more adaptable to habitat fragmentation.This study provides novel insights into the assembly processes and driving mechanisms of urban bird communities and offers scientific support for the notion that designing and maintaining blue-green infrastructure can contribute to urban biodiversity conservation.
基金funded by the Chilean National Agency for Research and Development(ANID)through the Beca de Doctorado Nacional 21220089.
文摘In April 2016,a moment magnitude(Mw)7.8 earthquake struck near Muisne(Pedernales),Ecuador,causing 671 fatalities,displacing>30,000 people,and generating approximately USD 3.6 billion in economic losses that severely impacted the coastal province of Manabí.Nine years later,the recovery trajectory of its principal urban centers-Pedernales,Manta,Portoviejo,and Chone-offers a critical perspective to assess adaptive resilience in earthquake-prone coastal cities of Latin America.This study conducts a regional assessment of post-earthquake recovery using the 4Rs resilience framework-robustness,redundancy,resourcefulness,and rapidity-applied across housing,health,education,infrastructure,and economic sectors.Official reports,statistical databases,and field validations collected between 2016 and 2025 provide the basis for documenting both progress and persistent challenges.The findings indicate that robustness improved with the enforcement of the Ecuadorian seismic code NEC-15 and the adoption of advanced technologies such as base isolation and supplemental damping in hospitals and high-rise buildings.Redundancy expanded selectively,being stronger in healthcare yet limited in housing and utilities.Resourcefulness varied across cities:municipal leadership and civic oversight in Manta and Portoviejo facilitated adaptive recovery,whereas Pedernales and Chone remained dependent on central agencies.Rapidity was similarly uneven;lifeline services were restored promptly,but complex projects-including hospitals,sewer systems,and residential complexes-faced delays of five to nine years.Structural assessments of 97 buildings revealed that nearly half remain without reinforcement,with recurrent deficiencies such as soft-story mecha-nisms,brittle masonry infill,and reinforcement corrosion sustaining latent seismic risk.Governance fragmen-tation,equity gaps,and insufficient monitoring thus emerged as critical barriers,underscoring the need for integrated governance,community participation,and AI-enabled monitoring to strengthen long-term disaster recovery in coastal cities.
文摘The year 2026 marks the 70th anniversary of the establishment of diplomatic relations between China and African countries.It also marks the launch of the China-Africa Year of People-to-People Exchanges.At this pivotal juncture linking past achievements with future development,Wang Yi,member of the Political Bureau of the Communist Party of China Central Committee and foreign minister,continued the longstanding diplomatic tradition under which the Chinese foreign minister makes Africa the destination of the first overseas visit of the year,a practice upheld for 36 consecutive years,underscoring the stability,continuity and sincerity of China’s policy towards Africa.
基金The authors would like to thank CNPq(Conselho Nacional de Desenvolvimento Científico e Tecnológico)—grants 407256/2022-9,303550/2025-2,402533/2023-2 and 303982/2022-5FAPEMIG(Fundação de AmparoàPesquisa do Estado de Minas Gerais)—grants APQ-00032-24 and APD-01113-25 for their financial support.
文摘Structural Health Monitoring(SHM)plays a critical role in ensuring the safety,integrity,longevity and economic efficiency of civil infrastructures.The field has undergone a profound transformation over the last few decades,evolving from traditional methods—often reliant on visual inspections—to data-driven intelligent systems.This review paper analyzes this historical trajectory,beginning with the approaches that relied on modal parameters as primary damage indicators.The advent of advanced sensor technologies and increased computational power brings a significant change,making Machine Learning(ML)a viable and powerful tool for damage assessment.More recently,Deep Learning(DL)has emerged as a paradigm shift,allowing for more automated processing of large data sets(such as the structural vibration signals and other types of sensors)with excellent performance and accuracy,often surpassing previous methods.This paper systematically reviews these technological milestones—from traditional vibration-based methods to the current state-of-the-art in deep learning.Finally,it critically examines emerging trends—such as Digital Twins and Transformer-based architectures—and discusses future research directions that will shape the next generation of SHM systems for civil engineering.
文摘The integration of large-scale foundation models(e.g.,GPT series and AlphaFold)into oncology is fundamentally transforming both research methodologies and clinical practices,driven by unprecedented advancements in computational power.This review synthesizes recent progress in the application of large language models to core oncological tasks,including medical imaging analysis,genomic interpretation,and personalized treatment planning.Underpinned by advanced computational infrastructures,such as graphics processing unit/tensor processing unit clusters,heterogeneous computing,and cloud platforms,these models enable superior representation learning and generalization across multimodal data sources.This review examines how these infrastructures overcome key bottlenecks in intelligent oncology through scalable optimization strategies,including mixed-precision training,memory optimization,and heterogeneous computing.Alongside these technical advancements,the review explores pressing challenges,such as data heterogeneity,limited model interpretability,regulatory uncertainties,and the environmental impact of artificial intelligence(AI)systems.Special emphasis is placed on emerging solutions,encompassing green AI and edge computing,which offer promising approaches for low-resource deployment scenarios.Additionally,the review highlights the critical role of interdisciplinary collaboration among oncology,computer science,ethics,and policy to ensure that AI systems are not only powerful but also transparent,safe,and clinically relevant.Finally,the review outlines potential avenues for future research aimed at developing robust,scalable,and human-centered frameworks for intelligent oncology.
文摘Highways are a vital component of global transportation infrastructure,but their environmental impact,particularly in terms of carbon emissions,poses significant challenges to achieving global sustainability and climate goals.This literature review discusses the new area of low-carbon highways,including next-generation construction technologies and near-zero carbon operation strategies.It discusses the purpose of innovative materials,such as low-carbon cements,recycled asphalt,and geopolymers,and intelligent infrastructure solutions,including intelligent traffic control and electric vehicle charging systems.The article also explores the significance of Lifecycle Assessment(LCA)and carbon accounting in the measurement and minimization of the carbon footprint of highways.Even though a lot of progress has been made,there are still several technical,economic,and regulatory obstacles,among which are the prohibitive initial cost of low-carbon technologies and the non-standardization of policies.The future outlook indicates that to alleviate these challenges,a coordinated effort among the research,policy,industry,and communities is necessary.Low-carbon highways can help to have a robust,sustainable transport system that meets global climate goals by incorporating sustainable processes in highway design,construction,and operation.
文摘As an Indian journalist who has lived and worked in China for more than a decade,I have witnessed firsthand the astounding pace of China’s urban transformation-high-speed rail networks linking megacities,digital payment systems reshaping daily life,smart manufacturing emerging across industrial zones,and modern infrastructure spreading nationwide.Yet,my recent eight-day media tour to Zhejiang Province o!ered a new and deeply thought-provoking perspective.
基金support provided by the National Natural Science Foundation of China[Grant No.72204202]2025 Annual Research Program of the China Society for Commercial Statistics[Grant No.2025STY10]General Project of Philosophy Society in Jiangsu Province Universities[Grant Nos.2023SJYB0923 and 2025SJYB0708].
文摘Against the backdrop of accelerated development of new forms of trade,the question of whether rapid expansion of cross-border e-commerce(CBEC)can help to reduce carbon emissions among Chinese enterprises is of great significance for seizing new opportunities in foreign trade,and advancing firms’green and low-carbon transformation.This study treats the creation of CBEC pilot zones as a quasi-natural experiment,employing panel data from Chinese A-share listed companies matched with city-level information from 2006 to 2021.We construct a multi-period difference-in-differences model to identify the impact of CBEC pilot zone policy on corporate carbon emissions.Our findings indicate the construction of these pilot zones significantly reduces firms’carbon emissions intensity,and the results are robust across multiple tests.We show the pilot zone initiative contributes to emission reductions by enhancing the adoption of digital infrastructure,promoting green technological innovation,and increasing environmental awareness among enterprises.Quantile regressions reveal pilot zones exert a more pronounced carbon-reduction effect on firms characterized by high carbon emissions intensity and advanced levels of digital transformation.Moreover,the policy effect is especially significant in heavily polluting industries,and regions with weaker governmental environmental regulations or lower public environmental concerns.This study makes an innovative contribution to the literature by empirically verifying the environmental governance effect of establishing CBEC pilot zones,and offers practical guidance for governments in formulating cross-border e-commerce policies and for enterprises pursuing low carbon development.
文摘Zero-day attacks present a critical cybersecurity challenge for Internet of things(IoT)infrastructures,where the inability of signature-based intrusion detection systems(IDSs)to recognize novel threat behaviors compromises both system reliability and operational continuity.Existing hybrid IDS solutions often struggle to balance accurate classification of known attacks with reliable anomaly detection,particularly under the computational constraints of IoT environments.To address this gap,we introduce ZeroDefense,an adaptive fusion-based IDS designed for simultaneous detection of known intrusions and emerging zero-day threats.The framework employs a four-layer architecture consisting of i)feature standardization and class balancing,ii)anomaly detection using isolation forest,autoencoder,and local outlier factor,iii)fine-grained attack classification via random forest,extreme gradient boosting(XGBoost),light gradient boosting machine(LightGBM),and attentive interpretable tabular learning(TabNet),and iv)a confidence-aware fusion engine that adaptively selects the most reliable decision path.Suspicious or previously unseen traffic is isolated early through fused anomaly scoring,while benign and known-malicious flows are processed through supervised classification for precise attack labeling.With an anomaly cascaded decision pipeline,a dynamic confidence-driven fusion mechanism,and a deploymentconscious design,ZeroDefense enables real-time inference on IoT edge gateways.Evaluation on the CICIoT2023 benchmark demonstrates 99.94% overall accuracy and 95.64%macro-average F1-score for known attacks,while 5.76% of traffic is successfully flagged as potential zero-day activity,with inference latency maintained below 100 ms/flow.These results indicate that ZeroDefense offers a scalable,resilient,and practically deployable defense capability for modern IoT infrastructures.
文摘Purpose-This paper provides a comprehensive analysis of the Brazilian freight railway system,examining the efficacy of the current concession renewal model in light of persistent structural problems such as market concentration,cargo dependence on export commodities and underutilization of the network.Situating Brazil within the broader international debate on railway reforms,the paper evaluates whether the ongoing early renewal of concessions can deliver a more diversified and competitive freight system.Design/methodology/approach-The study adopts a sequential mixed-methods research design that integrates longitudinal quantitative analysis with qualitative institutional and policy evaluation.The quantitative component examines time-series indicators published by ANTT,DNIT and INFRA S.A.from 1999 to 2023 to identify structural patterns in traffic growth,investment,safety and market concentration.The qualitative component employs a process-tracing logic to reconstruct the evolution of concession renewals and the implementation of Railway Law 14.273/2021,drawing on concepts from regulatory economics,institutional theory and industrial organization.These empirical streams are synthesized through an analytical framework that connects three dimensions-regulatory design,market structure and system performance-allowing for a systematic assessment of how Brazil’s institutional configuration shapes incentives,competitive dynamics and network utilization.Findings-The analysis confirms that the early renewal of concessions has successfully secured substantial private investment for capacity expansion on existing trunk lines.However,it has perpetuated the vertically integrated model,reinforcing the market power of incumbent operators and failing to significantly promote intramodal competition or cargo diversification.The system remains dominated by iron ore and agricultural commodities,with general cargo representing a minuscule share.The new authorization regime and short-line railway policies present a viable pathway for market opening but face significant operational and institutional barriers to implementation.Originality/value-This research offers a timely and critical assessment of a pivotal moment in Brazilian railway policy.It moves beyond a simplistic evaluation of volume growth to a structural analysis of market failures and the interplay between concession renewal and regulatory innovation.The findings provide actionable insights for policymakers in Brazil and other emerging economies seeking to balance private investment with public interest goals in railway infrastructure,highlighting the necessity of complementary,pro-competitive measures alongside financial investment.
文摘In a first for the African continent,Senegal will host the Dakar 2026 Youth Olympic Games(YOG)from 31 October to 13 November.The Dakar 2026 YOG carry a strong symbolic ambition,embodied by their motto“Africa welcomes,Dakar celebrates.”Host Senegal sees the event as a catalyst for its influence,the modernisation of its infrastructure,and the mobilisation of its youth.
文摘Hainan is transforming into a world-class hub,with expanded free trade policies,robust infrastructure and global business opportunities The Hainan Free Trade Port(FTP)officially launched island-wide special customs operations on 18 December 2025.One month in,reporters from China International Communications Group(CICG)conducted an exclusive interview with Feng Fei,secretary of the Hainan Provincial Committee of the Communist Party of China(CPC)and chairman of the Standing Committee of the Hainan Provincial People’s Congress.
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 the National Natural Science Foundation of China(No.U2333214).
文摘As the frontier of multidimensional transportation systems,urban air mobility(UAM)is receiving increasing attention from international organizations,governments,and stakeholders in industry and academia owing to its high efficiency,low carbon footprint,and operational flexibility.Vertical take-off and landing(VTOL)infrastructure is the core facility that enables UAM and is therefore essential for its safe,efficient,and large-scale commercial implementation.However,the key technologies for establishing low-altitude VTOL infrastructure are still nascent,and government,industry,and academia have yet to harmonize the corresponding construction,management,and operation standards.To address this gap,we herein systematically review the related progress and trends,comprehensively surveying the key technologies of establishing VTOL infrastructure serving unmanned aerial vehicles(UAVs)and electric VTOL aircraft from three complementary perspectives of ground-side,airspace-side,and communication,navigation,surveillance,and information services.In the light of future UAM operations characterized by diverse vehicle types and dense air traffic,we propose a conceptual design for a public multioperator VTOL site to provide constructive insights into the sustainable growth of the low-altitude economy.
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.
文摘Marine infrastructure is increasingly vulnerable to harsh environmental conditions that accelerate the degradation of traditional materials such as Portland cement concrete and carbon steel.This review systematically investigates recent advancements in sustainable alternatives,including geopolymer concrete,engineered innovacementitious composites(ECC),bio-concrete,fiber-reinforced polymers(FRPs),and bamboo,stainless steel,and steel-CFRP hybrid bars.Each material is evaluated based on marine durability,mechanical performance,environmental impact,and cost feasibility using life cycle assessment,durability modelling,and a multi-criteria decisionsupport framework.The results reveal that geopolymer concrete and FRP reinforcement’s exhibit superior corrosion resistance and environmental benefits,while ECC and steel-CFRP composites offer structural resilience with moderate environmental trade-offs.However,challenges remain in long-term performance validation,standardization,and market integration.The review concludes that a combined approach involving innovative materials,computational tools,and sustainability assessment is essential for advancing marine infrastructure.Outlook recommendations include focused field studies,development of regulatory guidelines,and interdisciplinary collaboration to drive the practical adoption of eco-efficient materials in coastal and offshore construction.
文摘The national grid and other life-sustaining critical infrastructures face an unprecedented threat from prolonged blackouts,which could last over a year and pose a severe risk to national security.Whether caused by physical attacks,EMP(electromagnetic pulse)events,or cyberattacks,such disruptions could cripple essential services like water supply,healthcare,communication,and transportation.Research indicates that an attack on just nine key substations could result in a coast-to-coast blackout lasting up to 18 months,leading to economic collapse,civil unrest,and a breakdown of public order.This paper explores the key vulnerabilities of the grid,the potential impacts of prolonged blackouts,and the role of AI(artificial intelligence)and ML(machine learning)in mitigating these threats.AI-driven cybersecurity measures,predictive maintenance,automated threat response,and EMP resilience strategies are discussed as essential solutions to bolster grid security.Policy recommendations emphasize the need for hardened infrastructure,enhanced cybersecurity,redundant power systems,and AI-based grid management to ensure national resilience.Without proactive measures,the nation remains exposed to a catastrophic power grid failure that could have dire consequences for society and the economy.
基金supported by the Yayasan Universiti Teknologi PETRONAS(YUTP)under Cost Center 015LC0-485.
文摘Unmanned aerial vehicles(UAVs)technology is rapidly advancing,offering innovative solutions for various industries,including the critical task of oil and gas pipeline surveillance.However,the limited flight time of conventional UAVs presents a significant challenge to comprehensive and continuous monitoring,which is crucial for maintaining the integrity of pipeline infrastructure.This review paper evaluates methods for extending UAV flight endurance,focusing on their potential application in pipeline inspection.Through an extensive literature review,this study identifies the latest advancements in UAV technology,evaluates their effectiveness,and highlights the existing gaps in achieving prolonged flight operations.Advanced techniques,including artificial intelligence(AI),machine learning(ML),and deep learning(DL),are reviewed for their roles in pipeline monitoring.Notably,DL algorithms like You Only Look Once(YOLO)are explored for autonomous flight in UAV-based inspections,real-time defect detection,such as cracks,corrosion,and leaks,enhancing reliability and accuracy.A vital aspect of this research is the proposed deployment of a hybrid drone design combining lighter-than-air(LTA)and heavier-than-air(HTA)principles,achieving a balance of endurance and maneuverability.LTA vehicles utilize buoyancy to reduce energy consumption,thereby extending flight durations.The paper details the methodology for designing LTA vehicles,presenting an analysis of design parameters that align with the requirements for effective pipeline surveillance.The ongoing work is currently at Technology Readiness Level(TRL)4,where key components have been validated in laboratory conditions,with fabrication and flight testing planned for the next phase.Initial design analysis indicates that LTA configurations could offer significant advantages in flight endurance compared to traditional UAV designs.These findings lay the groundwork for future fabrication and testing phases,which will be critical in validating and assessing the proposed approach’s real-world applicability.By outlining the technical complexities and proposing specialized techniques tailored for pipeline monitoring,this paper provides a foundational framework for advancing UAV capabilities in the oil and gas sector.Researchers and industry practitioners can use this roadmap to further develop UAV-enabled surveillance solutions,aiming to improve the reliability,efficiency,and safety of pipeline monitoring.
基金supported by the National Key Research and Development Program of China(2022YFB2602103 and 2023YFA1008900)。
文摘Road infrastructure is facing significant digitalization challenges within the context of new infrastructure construction in China and worldwide.Among the advanced digital technologies,digital twin(DT)has gained prominence across various engineering sectors,including the manufacturing and construction industries.Specifically,road engineering has demonstrated a growing interest in DT and has achieved promising results in DT-related applications over the past several years.This paper systematically introduces the development of DT and examines its current state in road engineering by reviewing research articles on DT-enabling technologies,such as model creation,condition sensing,data processing,and interaction,as well as its applications throughout the lifecycle of road infrastructure.The findings indicate that research has primarily focused on data perception and virtual model creation,while realtime data processing and interaction between physical and virtual models remain underexplored.DT in road engineering has been predominantly applied during the operation and maintenance phases,with limited attention given to the construction and demolition phases.Future efforts should focus on establishing uniform standards,developing innovative perception and data interaction techniques,optimizing development costs,and expanding the scope of lifecycle applications to facilitate the digital transformation of road engineering.This review provides a comprehensive overview of state-of-the-art advancements in this field and paves the way for leveraging DT in road infrastructure lifecycle management.
