Beijing defines a new model for digital-friendly cities where technology meets people’s needs and global cooperation drives inclusivity.THE 6,000-square-meter Beijing FUN Digital Complex,a vibrant pedestrian street i...Beijing defines a new model for digital-friendly cities where technology meets people’s needs and global cooperation drives inclusivity.THE 6,000-square-meter Beijing FUN Digital Complex,a vibrant pedestrian street in the historic Qianmen area of central Beijing,was bustling with visitors during the 2025 Beijing Digital Economy Experience Week,held from June 27 to July 5.Some climbed into a full-size C919 flight simulator,while others put on a domestically-made 8K VR headset to test a real-time voice-interaction model,immersing themselves in a dazzling virtual world.展开更多
This paper addresses urban sustainability challenges amid global urbanization, emphasizing the need for innova tive approaches aligned with the Sustainable Development Goals. While traditional tools and linear models ...This paper addresses urban sustainability challenges amid global urbanization, emphasizing the need for innova tive approaches aligned with the Sustainable Development Goals. While traditional tools and linear models offer insights, they fall short in presenting a holistic view of complex urban challenges. System dynamics (SD) models that are often utilized to provide holistic, systematic understanding of a research subject, like the urban system, emerge as valuable tools, but data scarcity and theoretical inadequacy pose challenges. The research reviews relevant papers on recent SD model applications in urban sustainability since 2018, categorizing them based on nine key indicators. Among the reviewed papers, data limitations and model assumptions were identified as ma jor challenges in applying SD models to urban sustainability. This led to exploring the transformative potential of big data analytics, a rare approach in this field as identified by this study, to enhance SD models’ empirical foundation. Integrating big data could provide data-driven calibration, potentially improving predictive accuracy and reducing reliance on simplified assumptions. The paper concludes by advocating for new approaches that reduce assumptions and promote real-time applicable models, contributing to a comprehensive understanding of urban sustainability through the synergy of big data and SD models.展开更多
Rapid urbanization has been happening around the world,leading to many challenges and difficulties in infrastructure,communication network,transportation,environmental and organizational problems.Proper and responsibl...Rapid urbanization has been happening around the world,leading to many challenges and difficulties in infrastructure,communication network,transportation,environmental and organizational problems.Proper and responsible management of urban resources plays a significant role in sustainable development.Smart sustainable cities use ICTs(Information and Communication Technologies)to improve quality of life,efficiency of urban operation and services.The latest advancement in communication,technology,data management,and IoT(Internet of Things)provide a tremendous role for practical implementations and adoption of devices and entities.Smart sustainable cities can be intellectualized as an innovative approach of controlling urban resources and valuable components based on the latest advancement in ICT.Our study focuses on reviewing and discussing the literature that states the vital components of IoT associated with smart sustainable cities in general and specifically with green energy.展开更多
Rurbanization,characterized by the integration of rural and urban attributes,holds significant implications for the development and marketing of small and medium-sized cities.This study investigates the effects of rur...Rurbanization,characterized by the integration of rural and urban attributes,holds significant implications for the development and marketing of small and medium-sized cities.This study investigates the effects of rurbanization on city marketing strategies and urban growth through a comprehensive review of recent literature.Key factors influencing rurbanization are identified,along with their impacts on city marketing practices.The findings indicate that rurbanization enhances city branding,attracts new residents and businesses,and promotes sustainable urban development.However,the phenomenon also presents challenges,including infrastructural strain and socio-cultural integration issues.Furthermore,rurbanization influences the socio-economic dynamics of cities,resulting in both opportunities and inequalities that require careful management.The study concludes with actionable recommendations for leveraging rurbanization to achieve positive city marketing outcomes while addressing associated challenges.This research aims to deepen the understanding of rurbanization and provide practical insights for policymakers,urban planners,and marketers in small and medium-sized cities,enabling them to optimize their growth strategies effectively.展开更多
GB/T 46391-2025,Sustainable cities and communities—General requirements for livable cities,officially came into effect on October 5,which is the first national standard setting up the evaluation framework of livable ...GB/T 46391-2025,Sustainable cities and communities—General requirements for livable cities,officially came into effect on October 5,which is the first national standard setting up the evaluation framework of livable cities.The standard is applicable to livability enhancement in urban planning,construction,management,and evaluation at all levels,and provides a reference for the engagement of communities,enterprises,and social organizations.展开更多
FedCognis is a secure and scalable federated learning framework designed for continuous anomaly detection in Industrial Internet of Things-enabled Cognitive Cities(IIoTCC).It introduces two key innovations:a Quantum S...FedCognis is a secure and scalable federated learning framework designed for continuous anomaly detection in Industrial Internet of Things-enabled Cognitive Cities(IIoTCC).It introduces two key innovations:a Quantum Secure Authentication(QSA)mechanism for adversarial defense and integrity validation,and a Self-Attention Long Short-Term Memory(SALSTM)model for high-accuracy spatiotemporal anomaly detection.Addressing core challenges in traditional Federated Learning(FL)—such as model poisoning,communication overhead,and concept drift—FedCognis integrates dynamic trust-based aggregation and lightweight cryptographic verification to ensure secure,real-time operation across heterogeneous IIoT domains including utilities,public safety,and traffic systems.Evaluated on the WUSTL-IIoTCC-2021 dataset,FedCognis achieves 94.5%accuracy,0.941 AUC for precision-recall,and 0.896 ROC-AUC,while reducing bandwidth consumption by 72%.The framework demonstrates sublinear computational complexity and a resilience score of 96.56%across six security dimensions.These results confirm FedCognis as a robust and adaptive anomaly detection solution suitable for deployment in large-scale cognitive urban infrastructures.展开更多
Unmanned Aerial Vehicles(UAVs)have become integral components in smart city infrastructures,supporting applications such as emergency response,surveillance,and data collection.However,the high mobility and dynamic top...Unmanned Aerial Vehicles(UAVs)have become integral components in smart city infrastructures,supporting applications such as emergency response,surveillance,and data collection.However,the high mobility and dynamic topology of Flying Ad Hoc Networks(FANETs)present significant challenges for maintaining reliable,low-latency communication.Conventional geographic routing protocols often struggle in situations where link quality varies and mobility patterns are unpredictable.To overcome these limitations,this paper proposes an improved routing protocol based on reinforcement learning.This new approach integrates Q-learning with mechanisms that are both link-aware and mobility-aware.The proposed method optimizes the selection of relay nodes by using an adaptive reward function that takes into account energy consumption,delay,and link quality.Additionally,a Kalman filter is integrated to predict UAV mobility,improving the stability of communication links under dynamic network conditions.Simulation experiments were conducted using realistic scenarios,varying the number of UAVs to assess scalability.An analysis was conducted on key performance metrics,including the packet delivery ratio,end-to-end delay,and total energy consumption.The results demonstrate that the proposed approach significantly improves the packet delivery ratio by 12%–15%and reduces delay by up to 25.5%when compared to conventional GEO and QGEO protocols.However,this improvement comes at the cost of higher energy consumption due to additional computations and control overhead.Despite this trade-off,the proposed solution ensures reliable and efficient communication,making it well-suited for large-scale UAV networks operating in complex urban environments.展开更多
During the 2025 Beijing Digital Economy Experience Week from 27 June to 5 July,interactive projects combining technology and culture-illustrated books created by voice AI,augmented reality tours,and markerless motion ...During the 2025 Beijing Digital Economy Experience Week from 27 June to 5 July,interactive projects combining technology and culture-illustrated books created by voice AI,augmented reality tours,and markerless motion capture-attracted many visitors.“More than a dozen themed areas offered the opportunity to dive into new worlds and discover the latest innovations from more than 50 companies,”Lu Yumin,a Beijing resident who visited the event,told ChinAfrica.展开更多
Urban vitality is one of the key indicators of sustainable urban development and an important factor for shrinking cities to achieve internal optimization.The relationship between the built environment and urban vital...Urban vitality is one of the key indicators of sustainable urban development and an important factor for shrinking cities to achieve internal optimization.The relationship between the built environment and urban vitality has been extensively discussed.However,the moderating effect of housing vacancy on the built environment’s effect on urban vitality in shrinking cities has not been explored in detail.This paper selected Yichun District in Yichun City of Heilongjiang Province,a typical shrinking city in Northeast China,as the study area,focusing on the effect of the built environment on urban vitality in shrinking cities based on residential and commercial electricity consumption data for 2013 and 2018.Moreover,this study also explored the moderating mechanisms of residential and commercial housing vacancies on the built environment’s effect on urban vitality.The results demonstrate that the spatial pattern of urban vitality in the Yichun District is‘high in the center and low in the periphery’.Population density,building age,road density,and catering facilities are recognized as the main built environment factors affecting the vitality of shrinking cities.Residential and commercial housing vacancies have a significant moderating effect on the built environment’s effect on urban vitality.Residential housing vacancies enhance the positive effect of road density and the negative effect of greening rate.In addition,commercial housing vacancies suppress the positive effect of building density and enhance the positive effect of accessibility to urban service facilities.The study indicates that built environment factors exhibit heterogeneous effects on vitality in the context of urban shrinkage,as moderated by housing vacancies.Targeted regulation of built environment factors is of practical significance in realizing the internal development and vitality enhancement of shrinking cities.展开更多
Reducing global carbon dioxide(CO_(2))emissions is essential for meeting climate change mitigation goals,especially in urban areas.In this regard,this study used CO_(2)emissions and energy transition data from 296 Chi...Reducing global carbon dioxide(CO_(2))emissions is essential for meeting climate change mitigation goals,especially in urban areas.In this regard,this study used CO_(2)emissions and energy transition data from 296 China's cities in 2020 and the extended Stochastic Impacts by Regression on Population,Affluence,and Technology(STIRPAT)model to explore the relationship between energy transition and CO_(2)emissions at the city-scale.The findings indicate a spatial distribution of energy transition magnitude that is high in the west and low in the east,which does not align with economic status and total CO_(2)emissions,posing significant challenges for China's energy transition and urban CO_(2)reduction.The STIRPAT model reveals that urban CO_(2)emissions are significantly driven by increases in population size,levels of economic development,and the expansion of transportation infrastructure.Conversely,investments in science and education,the expansion of the tertiary sector,and the disruptive effects of the COVID-19(Coronavirus Disease 2019)pandemic are associated with notable reductions in CO_(2)emissions.Specifically,the analysis estimates that a 1.00%increase in the energy transition degree is associated witha 0.90%decrease in urban CO_(2)emissions.However,regional assessments underscore considerable spatial heterogeneity in the energy transition effect,with CO_(2)reduction benefits being less pronounced in central and western cities.These findings suggest that future clean energy initiatives should be strategically concentrated in eastern China,where the demand and potential for CO_(2)mitigation are greater.This study deepens the understanding of the complex relationship between energy transition and urban CO_(2)emissions,offering valuable insights to inform targeted policy interventions for carbon reduction at the city level.展开更多
1 Researchers have discovered the remains of ancient cities located above the ancient Silk Road in the rocky mountains of southeastern Uzbekistan.The groundbreaking discovery,made possible by new drone‑based lidar(激...1 Researchers have discovered the remains of ancient cities located above the ancient Silk Road in the rocky mountains of southeastern Uzbekistan.The groundbreaking discovery,made possible by new drone‑based lidar(激光雷达)technology,challenges long‑held assumptions that urban life was rarely seen in the remote mountains of Central Asia.展开更多
The rapid digitalization of urban infrastructure has made smart cities increasingly vulnerable to sophisticated cyber threats.In the evolving landscape of cybersecurity,the efficacy of Intrusion Detection Systems(IDS)...The rapid digitalization of urban infrastructure has made smart cities increasingly vulnerable to sophisticated cyber threats.In the evolving landscape of cybersecurity,the efficacy of Intrusion Detection Systems(IDS)is increasingly measured by technical performance,operational usability,and adaptability.This study introduces and rigorously evaluates a Human-Computer Interaction(HCI)-Integrated IDS with the utilization of Convolutional Neural Network(CNN),CNN-Long Short Term Memory(LSTM),and Random Forest(RF)against both a Baseline Machine Learning(ML)and a Traditional IDS model,through an extensive experimental framework encompassing many performance metrics,including detection latency,accuracy,alert prioritization,classification errors,system throughput,usability,ROC-AUC,precision-recall,confusion matrix analysis,and statistical accuracy measures.Our findings consistently demonstrate the superiority of the HCI-Integrated approach utilizing three major datasets(CICIDS 2017,KDD Cup 1999,and UNSW-NB15).Experimental results indicate that the HCI-Integrated model outperforms its counterparts,achieving an AUC-ROC of 0.99,a precision of 0.93,and a recall of 0.96,while maintaining the lowest false positive rate(0.03)and the fastest detection time(~1.5 s).These findings validate the efficacy of incorporating HCI to enhance anomaly detection capabilities,improve responsiveness,and reduce alert fatigue in critical smart city applications.It achieves markedly lower detection times,higher accuracy across all threat categories,reduced false positive and false negative rates,and enhanced system throughput under concurrent load conditions.The HCIIntegrated IDS excels in alert contextualization and prioritization,offering more actionable insights while minimizing analyst fatigue.Usability feedback underscores increased analyst confidence and operational clarity,reinforcing the importance of user-centered design.These results collectively position the HCI-Integrated IDS as a highly effective,scalable,and human-aligned solution for modern threat detection environments.展开更多
The rise of 6G networks and the exponential growth of smart city infrastructures demand intelligent,real-time traffic forecasting solutions that preserve data privacy.This paper introduces NeuroCivitas,a federated dee...The rise of 6G networks and the exponential growth of smart city infrastructures demand intelligent,real-time traffic forecasting solutions that preserve data privacy.This paper introduces NeuroCivitas,a federated deep learning framework that integrates Convolutional Neural Networks(CNNs)for spatial pattern recognition and Long Short-Term Memory(LSTM)networks for temporal sequence modeling.Designed to meet the adaptive intelligence requirements of cognitive cities,NeuroCivitas leverages Federated Averaging(FedAvg)to ensure privacypreserving training while significantly reducing communication overhead—by 98.7%compared to centralized models.The model is evaluated using the Kaggle Traffic Prediction Dataset comprising 48,120 hourly records from four urban junctions.It achieves an RMSE of 2.76,MAE of 2.11,and an R^(2) score of 0.91,outperforming baseline models such as ARIMA,Linear Regression,Random Forest,and non-federated CNN-LSTM in both accuracy and scalability.Junctionwise and time-based performance analyses further validate its robustness,particularly during off-peak hours,while highlighting challenges in peak traffic forecasting.Ablation studies confirm the importance of both CNN and LSTM layers and temporal feature engineering in achieving optimal performance.Moreover,NeuroCivitas demonstrates stable convergence within 25 communication rounds and shows strong adaptability to non-IID data distributions.The framework is built with real-world deployment in mind,offering support for edge environments through lightweight architecture and the potential for enhancement with differential privacy and adversarial defense mechanisms.SHAPbased explainability is integrated to improve interpretability for stakeholders.In sum,NeuroCivitas delivers an accurate,scalable,and privacy-preserving traffic forecasting solution,tailored for 6G cognitive cities.Future extensions will incorporate fairness-aware optimization,real-time anomaly adaptation,multi-city validation,and advanced federated GNN comparisons to further enhance deployment readiness and societal impact.展开更多
Egypt's ready made garment exports increased by 22 per cent year-on-year during the first four months this year,reaching$1.028 billion,according to the Apparel Export Council of Egypt(AECE).In terms of regional ma...Egypt's ready made garment exports increased by 22 per cent year-on-year during the first four months this year,reaching$1.028 billion,according to the Apparel Export Council of Egypt(AECE).In terms of regional market performance,the United States remains the largest export destination for Egyptian garments,with exports amounting to$384 million in the first four months,followed by the European market,with exports amounting to$259 million,and the Arab countries,which showed a moderate growth trend,with an increase of 6%yearon-year,and exports amounting to$190 million.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
Promoting environmental sustainability in South Africa’s cities through public participation is vital for foster-ing inclusive governance and equitable decision-making.Currently,63%of South Africa’s population—and ...Promoting environmental sustainability in South Africa’s cities through public participation is vital for foster-ing inclusive governance and equitable decision-making.Currently,63%of South Africa’s population—and 64%of its youth—live in urban areas,with this figure expected to rise to nearly 80%by 2050.Rapid urbanisation brings significant environmental challenges,including air and noise pollution,greenhouse gas(GHG)emissions,and inadequate waste management.Globally,cities contribute over 70%of GHG emissions and consume two-thirds of the world’s energy.South African cities face similar issues:worsening air quality in regions like the Highveld,water scarcity,urban flooding,waste management problems,and biodiversity loss due to urban sprawl.This article explores how South Africa’s consti-tutional and legislative frameworks support public participation in promoting urban environmental sustainability.Using doctrinal research,it examines key legal instruments—including the Constitution and environmental laws—that establish participatory rights and promote transparency,accountability,and inclusivity.The article draws on court decisions and case studies to highlight ongoing barriers to meaningful participation,particularly for marginalised communities.These include administrative inefficiencies,political interference,and unequal access to information and resources.The article concludes by proposing strategies such as capacity-building initiatives,the integration of traditional knowledge systems,and enhanced institutional coordination to strengthen public participation and improve urban environmental outcomes,addressing both global environmental pressures and South Africa’s unique urban sustainability challenges.展开更多
Understanding spatial heterogeneity in groundwater responses to multiple factors is critical for water resource management in coastal cities.Daily groundwater depth(GWD)data from 43 wells(2018-2022)were collected in t...Understanding spatial heterogeneity in groundwater responses to multiple factors is critical for water resource management in coastal cities.Daily groundwater depth(GWD)data from 43 wells(2018-2022)were collected in three coastal cities in Jiangsu Province,China.Seasonal and Trend decomposition using Loess(STL)together with wavelet analysis and empirical mode decomposition were applied to identify tide-influenced wells while remaining wells were grouped by hierarchical clustering analysis(HCA).Machine learning models were developed to predict GWD,then their response to natural conditions and human activities was assessed by the Shapley Additive exPlanations(SHAP)method.Results showed that eXtreme Gradient Boosting(XGB)was superior to other models in terms of prediction performance and computational efficiency(R^(2)>0.95).GWD in Yancheng and southern Lianyungang were greater than those in Nantong,exhibiting larger fluctuations.Groundwater within 5 km of the coastline was affected by tides,with more pronounced effects in agricultural areas compared to urban areas.Shallow groundwater(3-7 m depth)responded immediately(0-1 day)to rainfall,primarily influenced by farmland and topography(slope and distance from rivers).Rainfall recharge to groundwater peaked at 50%farmland coverage,but this effect was suppressed by high temperatures(>30℃)which intensified as distance from rivers increased,especially in forest and grassland.Deep groundwater(>10 m)showed delayed responses to rainfall(1-4 days)and temperature(10-15 days),with GDP as the primary influence,followed by agricultural irrigation and population density.Farmland helped to maintain stable GWD in low population density regions,while excessive farmland coverage(>90%)led to overexploitation.In the early stages of GDP development,increased industrial and agricultural water demand led to GWD decline,but as GDP levels significantly improved,groundwater consumption pressure gradually eased.This methodological framework is applicable not only to coastal cities in China but also could be extended to coastal regions worldwide.展开更多
The exponential growth of the Internet of Things(IoT)has revolutionized various domains such as healthcare,smart cities,and agriculture,generating vast volumes of data that require secure processing and storage in clo...The exponential growth of the Internet of Things(IoT)has revolutionized various domains such as healthcare,smart cities,and agriculture,generating vast volumes of data that require secure processing and storage in cloud environments.However,reliance on cloud infrastructure raises critical security challenges,particularly regarding data integrity.While existing cryptographic methods provide robust integrity verification,they impose significant computational and energy overheads on resource-constrained IoT devices,limiting their applicability in large-scale,real-time scenarios.To address these challenges,we propose the Cognitive-Based Integrity Verification Model(C-BIVM),which leverages Belief-Desire-Intention(BDI)cognitive intelligence and algebraic signatures to enable lightweight,efficient,and scalable data integrity verification.The model incorporates batch auditing,reducing resource consumption in large-scale IoT environments by approximately 35%,while achieving an accuracy of over 99.2%in detecting data corruption.C-BIVM dynamically adapts integrity checks based on real-time conditions,optimizing resource utilization by minimizing redundant operations by more than 30%.Furthermore,blind verification techniques safeguard sensitive IoT data,ensuring privacy compliance by preventing unauthorized access during integrity checks.Extensive experimental evaluations demonstrate that C-BIVM reduces computation time for integrity checks by up to 40%compared to traditional bilinear pairing-based methods,making it particularly suitable for IoT-driven applications in smart cities,healthcare,and beyond.These results underscore the effectiveness of C-BIVM in delivering a secure,scalable,and resource-efficient solution tailored to the evolving needs of IoT ecosystems.展开更多
The smart city is not only a crucial means of promoting sustainable development,but also a strategic approach to advancing scientific urban development and governance on a global scale.Based on a textual co-occurrence...The smart city is not only a crucial means of promoting sustainable development,but also a strategic approach to advancing scientific urban development and governance on a global scale.Based on a textual co-occurrence analysis of policy documents,this study examines the components,development pathways,and models of smart cities in China.It identifies three distinct phases in the development process:the information project-driven phase,the holistic development phase,and the collaborative development phase.Overall,the development of smart cities in China-characterized by a strong focus on information technology projects-was primarily initiated by both central and local governments.Reflecting on the informatization process and the trajectory of smart city development in China since the mid-1990s,this paper argues that smart city initiatives must evolve beyond a purely technology-driven framework toward a more human-centered approach.Against the backdrop of ongoing urbanization and urban transformation in China,this study proposes future development strategies that integrate top-down government planning with bottom-up public engagement.It advocates for a comprehensive framework that integrates technology,human needs,and spatial planning,emphasizing the spatiotemporal coordination between technological implementation and stakeholder demands.This research offers valuable insights and strategic guidance for the future development of smart cities worldwide.展开更多
文摘Beijing defines a new model for digital-friendly cities where technology meets people’s needs and global cooperation drives inclusivity.THE 6,000-square-meter Beijing FUN Digital Complex,a vibrant pedestrian street in the historic Qianmen area of central Beijing,was bustling with visitors during the 2025 Beijing Digital Economy Experience Week,held from June 27 to July 5.Some climbed into a full-size C919 flight simulator,while others put on a domestically-made 8K VR headset to test a real-time voice-interaction model,immersing themselves in a dazzling virtual world.
基金sponsored by the U.S.Department of Housing and Urban Development(Grant No.NJLTS0027-22)The opinions expressed in this study are the authors alone,and do not represent the U.S.Depart-ment of HUD’s opinions.
文摘This paper addresses urban sustainability challenges amid global urbanization, emphasizing the need for innova tive approaches aligned with the Sustainable Development Goals. While traditional tools and linear models offer insights, they fall short in presenting a holistic view of complex urban challenges. System dynamics (SD) models that are often utilized to provide holistic, systematic understanding of a research subject, like the urban system, emerge as valuable tools, but data scarcity and theoretical inadequacy pose challenges. The research reviews relevant papers on recent SD model applications in urban sustainability since 2018, categorizing them based on nine key indicators. Among the reviewed papers, data limitations and model assumptions were identified as ma jor challenges in applying SD models to urban sustainability. This led to exploring the transformative potential of big data analytics, a rare approach in this field as identified by this study, to enhance SD models’ empirical foundation. Integrating big data could provide data-driven calibration, potentially improving predictive accuracy and reducing reliance on simplified assumptions. The paper concludes by advocating for new approaches that reduce assumptions and promote real-time applicable models, contributing to a comprehensive understanding of urban sustainability through the synergy of big data and SD models.
文摘Rapid urbanization has been happening around the world,leading to many challenges and difficulties in infrastructure,communication network,transportation,environmental and organizational problems.Proper and responsible management of urban resources plays a significant role in sustainable development.Smart sustainable cities use ICTs(Information and Communication Technologies)to improve quality of life,efficiency of urban operation and services.The latest advancement in communication,technology,data management,and IoT(Internet of Things)provide a tremendous role for practical implementations and adoption of devices and entities.Smart sustainable cities can be intellectualized as an innovative approach of controlling urban resources and valuable components based on the latest advancement in ICT.Our study focuses on reviewing and discussing the literature that states the vital components of IoT associated with smart sustainable cities in general and specifically with green energy.
文摘Rurbanization,characterized by the integration of rural and urban attributes,holds significant implications for the development and marketing of small and medium-sized cities.This study investigates the effects of rurbanization on city marketing strategies and urban growth through a comprehensive review of recent literature.Key factors influencing rurbanization are identified,along with their impacts on city marketing practices.The findings indicate that rurbanization enhances city branding,attracts new residents and businesses,and promotes sustainable urban development.However,the phenomenon also presents challenges,including infrastructural strain and socio-cultural integration issues.Furthermore,rurbanization influences the socio-economic dynamics of cities,resulting in both opportunities and inequalities that require careful management.The study concludes with actionable recommendations for leveraging rurbanization to achieve positive city marketing outcomes while addressing associated challenges.This research aims to deepen the understanding of rurbanization and provide practical insights for policymakers,urban planners,and marketers in small and medium-sized cities,enabling them to optimize their growth strategies effectively.
文摘GB/T 46391-2025,Sustainable cities and communities—General requirements for livable cities,officially came into effect on October 5,which is the first national standard setting up the evaluation framework of livable cities.The standard is applicable to livability enhancement in urban planning,construction,management,and evaluation at all levels,and provides a reference for the engagement of communities,enterprises,and social organizations.
基金the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2025).
文摘FedCognis is a secure and scalable federated learning framework designed for continuous anomaly detection in Industrial Internet of Things-enabled Cognitive Cities(IIoTCC).It introduces two key innovations:a Quantum Secure Authentication(QSA)mechanism for adversarial defense and integrity validation,and a Self-Attention Long Short-Term Memory(SALSTM)model for high-accuracy spatiotemporal anomaly detection.Addressing core challenges in traditional Federated Learning(FL)—such as model poisoning,communication overhead,and concept drift—FedCognis integrates dynamic trust-based aggregation and lightweight cryptographic verification to ensure secure,real-time operation across heterogeneous IIoT domains including utilities,public safety,and traffic systems.Evaluated on the WUSTL-IIoTCC-2021 dataset,FedCognis achieves 94.5%accuracy,0.941 AUC for precision-recall,and 0.896 ROC-AUC,while reducing bandwidth consumption by 72%.The framework demonstrates sublinear computational complexity and a resilience score of 96.56%across six security dimensions.These results confirm FedCognis as a robust and adaptive anomaly detection solution suitable for deployment in large-scale cognitive urban infrastructures.
基金funded by Hung Yen University of Technology and Education under grand number UTEHY.L.2025.62.
文摘Unmanned Aerial Vehicles(UAVs)have become integral components in smart city infrastructures,supporting applications such as emergency response,surveillance,and data collection.However,the high mobility and dynamic topology of Flying Ad Hoc Networks(FANETs)present significant challenges for maintaining reliable,low-latency communication.Conventional geographic routing protocols often struggle in situations where link quality varies and mobility patterns are unpredictable.To overcome these limitations,this paper proposes an improved routing protocol based on reinforcement learning.This new approach integrates Q-learning with mechanisms that are both link-aware and mobility-aware.The proposed method optimizes the selection of relay nodes by using an adaptive reward function that takes into account energy consumption,delay,and link quality.Additionally,a Kalman filter is integrated to predict UAV mobility,improving the stability of communication links under dynamic network conditions.Simulation experiments were conducted using realistic scenarios,varying the number of UAVs to assess scalability.An analysis was conducted on key performance metrics,including the packet delivery ratio,end-to-end delay,and total energy consumption.The results demonstrate that the proposed approach significantly improves the packet delivery ratio by 12%–15%and reduces delay by up to 25.5%when compared to conventional GEO and QGEO protocols.However,this improvement comes at the cost of higher energy consumption due to additional computations and control overhead.Despite this trade-off,the proposed solution ensures reliable and efficient communication,making it well-suited for large-scale UAV networks operating in complex urban environments.
文摘During the 2025 Beijing Digital Economy Experience Week from 27 June to 5 July,interactive projects combining technology and culture-illustrated books created by voice AI,augmented reality tours,and markerless motion capture-attracted many visitors.“More than a dozen themed areas offered the opportunity to dive into new worlds and discover the latest innovations from more than 50 companies,”Lu Yumin,a Beijing resident who visited the event,told ChinAfrica.
基金Under the auspices of the National Natural Science Foundation of China(No.42171191,41771172,42201211,42401249)Science and Technology Development Plan Project of Jilin Province,China(No.20220508025RC)Young Scientist Group Project of Northeast Institute of Geography and Agroecology,Chinese Academy of Sciences(No.2022QNXZ02)。
文摘Urban vitality is one of the key indicators of sustainable urban development and an important factor for shrinking cities to achieve internal optimization.The relationship between the built environment and urban vitality has been extensively discussed.However,the moderating effect of housing vacancy on the built environment’s effect on urban vitality in shrinking cities has not been explored in detail.This paper selected Yichun District in Yichun City of Heilongjiang Province,a typical shrinking city in Northeast China,as the study area,focusing on the effect of the built environment on urban vitality in shrinking cities based on residential and commercial electricity consumption data for 2013 and 2018.Moreover,this study also explored the moderating mechanisms of residential and commercial housing vacancies on the built environment’s effect on urban vitality.The results demonstrate that the spatial pattern of urban vitality in the Yichun District is‘high in the center and low in the periphery’.Population density,building age,road density,and catering facilities are recognized as the main built environment factors affecting the vitality of shrinking cities.Residential and commercial housing vacancies have a significant moderating effect on the built environment’s effect on urban vitality.Residential housing vacancies enhance the positive effect of road density and the negative effect of greening rate.In addition,commercial housing vacancies suppress the positive effect of building density and enhance the positive effect of accessibility to urban service facilities.The study indicates that built environment factors exhibit heterogeneous effects on vitality in the context of urban shrinkage,as moderated by housing vacancies.Targeted regulation of built environment factors is of practical significance in realizing the internal development and vitality enhancement of shrinking cities.
基金Under the auspices of National Natural Science Foundation of China(No.42471191)Fujian Provincial Department of Science and Technology(No.2023R1039)Ministry of Education of Humanities and Social Science Research Project(No.21YJCZH006)。
文摘Reducing global carbon dioxide(CO_(2))emissions is essential for meeting climate change mitigation goals,especially in urban areas.In this regard,this study used CO_(2)emissions and energy transition data from 296 China's cities in 2020 and the extended Stochastic Impacts by Regression on Population,Affluence,and Technology(STIRPAT)model to explore the relationship between energy transition and CO_(2)emissions at the city-scale.The findings indicate a spatial distribution of energy transition magnitude that is high in the west and low in the east,which does not align with economic status and total CO_(2)emissions,posing significant challenges for China's energy transition and urban CO_(2)reduction.The STIRPAT model reveals that urban CO_(2)emissions are significantly driven by increases in population size,levels of economic development,and the expansion of transportation infrastructure.Conversely,investments in science and education,the expansion of the tertiary sector,and the disruptive effects of the COVID-19(Coronavirus Disease 2019)pandemic are associated with notable reductions in CO_(2)emissions.Specifically,the analysis estimates that a 1.00%increase in the energy transition degree is associated witha 0.90%decrease in urban CO_(2)emissions.However,regional assessments underscore considerable spatial heterogeneity in the energy transition effect,with CO_(2)reduction benefits being less pronounced in central and western cities.These findings suggest that future clean energy initiatives should be strategically concentrated in eastern China,where the demand and potential for CO_(2)mitigation are greater.This study deepens the understanding of the complex relationship between energy transition and urban CO_(2)emissions,offering valuable insights to inform targeted policy interventions for carbon reduction at the city level.
文摘1 Researchers have discovered the remains of ancient cities located above the ancient Silk Road in the rocky mountains of southeastern Uzbekistan.The groundbreaking discovery,made possible by new drone‑based lidar(激光雷达)technology,challenges long‑held assumptions that urban life was rarely seen in the remote mountains of Central Asia.
基金funded and supported by the Ongoing Research Funding program(ORF-2025-314),King Saud University,Riyadh,Saudi Arabia.
文摘The rapid digitalization of urban infrastructure has made smart cities increasingly vulnerable to sophisticated cyber threats.In the evolving landscape of cybersecurity,the efficacy of Intrusion Detection Systems(IDS)is increasingly measured by technical performance,operational usability,and adaptability.This study introduces and rigorously evaluates a Human-Computer Interaction(HCI)-Integrated IDS with the utilization of Convolutional Neural Network(CNN),CNN-Long Short Term Memory(LSTM),and Random Forest(RF)against both a Baseline Machine Learning(ML)and a Traditional IDS model,through an extensive experimental framework encompassing many performance metrics,including detection latency,accuracy,alert prioritization,classification errors,system throughput,usability,ROC-AUC,precision-recall,confusion matrix analysis,and statistical accuracy measures.Our findings consistently demonstrate the superiority of the HCI-Integrated approach utilizing three major datasets(CICIDS 2017,KDD Cup 1999,and UNSW-NB15).Experimental results indicate that the HCI-Integrated model outperforms its counterparts,achieving an AUC-ROC of 0.99,a precision of 0.93,and a recall of 0.96,while maintaining the lowest false positive rate(0.03)and the fastest detection time(~1.5 s).These findings validate the efficacy of incorporating HCI to enhance anomaly detection capabilities,improve responsiveness,and reduce alert fatigue in critical smart city applications.It achieves markedly lower detection times,higher accuracy across all threat categories,reduced false positive and false negative rates,and enhanced system throughput under concurrent load conditions.The HCIIntegrated IDS excels in alert contextualization and prioritization,offering more actionable insights while minimizing analyst fatigue.Usability feedback underscores increased analyst confidence and operational clarity,reinforcing the importance of user-centered design.These results collectively position the HCI-Integrated IDS as a highly effective,scalable,and human-aligned solution for modern threat detection environments.
文摘The rise of 6G networks and the exponential growth of smart city infrastructures demand intelligent,real-time traffic forecasting solutions that preserve data privacy.This paper introduces NeuroCivitas,a federated deep learning framework that integrates Convolutional Neural Networks(CNNs)for spatial pattern recognition and Long Short-Term Memory(LSTM)networks for temporal sequence modeling.Designed to meet the adaptive intelligence requirements of cognitive cities,NeuroCivitas leverages Federated Averaging(FedAvg)to ensure privacypreserving training while significantly reducing communication overhead—by 98.7%compared to centralized models.The model is evaluated using the Kaggle Traffic Prediction Dataset comprising 48,120 hourly records from four urban junctions.It achieves an RMSE of 2.76,MAE of 2.11,and an R^(2) score of 0.91,outperforming baseline models such as ARIMA,Linear Regression,Random Forest,and non-federated CNN-LSTM in both accuracy and scalability.Junctionwise and time-based performance analyses further validate its robustness,particularly during off-peak hours,while highlighting challenges in peak traffic forecasting.Ablation studies confirm the importance of both CNN and LSTM layers and temporal feature engineering in achieving optimal performance.Moreover,NeuroCivitas demonstrates stable convergence within 25 communication rounds and shows strong adaptability to non-IID data distributions.The framework is built with real-world deployment in mind,offering support for edge environments through lightweight architecture and the potential for enhancement with differential privacy and adversarial defense mechanisms.SHAPbased explainability is integrated to improve interpretability for stakeholders.In sum,NeuroCivitas delivers an accurate,scalable,and privacy-preserving traffic forecasting solution,tailored for 6G cognitive cities.Future extensions will incorporate fairness-aware optimization,real-time anomaly adaptation,multi-city validation,and advanced federated GNN comparisons to further enhance deployment readiness and societal impact.
文摘Egypt's ready made garment exports increased by 22 per cent year-on-year during the first four months this year,reaching$1.028 billion,according to the Apparel Export Council of Egypt(AECE).In terms of regional market performance,the United States remains the largest export destination for Egyptian garments,with exports amounting to$384 million in the first four months,followed by the European market,with exports amounting to$259 million,and the Arab countries,which showed a moderate growth trend,with an increase of 6%yearon-year,and exports amounting to$190 million.
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 Research Foundation(NRF)of South Africa grant number[115581].
文摘Promoting environmental sustainability in South Africa’s cities through public participation is vital for foster-ing inclusive governance and equitable decision-making.Currently,63%of South Africa’s population—and 64%of its youth—live in urban areas,with this figure expected to rise to nearly 80%by 2050.Rapid urbanisation brings significant environmental challenges,including air and noise pollution,greenhouse gas(GHG)emissions,and inadequate waste management.Globally,cities contribute over 70%of GHG emissions and consume two-thirds of the world’s energy.South African cities face similar issues:worsening air quality in regions like the Highveld,water scarcity,urban flooding,waste management problems,and biodiversity loss due to urban sprawl.This article explores how South Africa’s consti-tutional and legislative frameworks support public participation in promoting urban environmental sustainability.Using doctrinal research,it examines key legal instruments—including the Constitution and environmental laws—that establish participatory rights and promote transparency,accountability,and inclusivity.The article draws on court decisions and case studies to highlight ongoing barriers to meaningful participation,particularly for marginalised communities.These include administrative inefficiencies,political interference,and unequal access to information and resources.The article concludes by proposing strategies such as capacity-building initiatives,the integration of traditional knowledge systems,and enhanced institutional coordination to strengthen public participation and improve urban environmental outcomes,addressing both global environmental pressures and South Africa’s unique urban sustainability challenges.
基金supported by the Natural Science Foundation of Jiangsu province,China(BK20240937)the Belt and Road Special Foundation of the National Key Laboratory of Water Disaster Prevention(2022491411,2021491811)the Basal Research Fund of Central Public Welfare Scientific Institution of Nanjing Hydraulic Research Institute(Y223006).
文摘Understanding spatial heterogeneity in groundwater responses to multiple factors is critical for water resource management in coastal cities.Daily groundwater depth(GWD)data from 43 wells(2018-2022)were collected in three coastal cities in Jiangsu Province,China.Seasonal and Trend decomposition using Loess(STL)together with wavelet analysis and empirical mode decomposition were applied to identify tide-influenced wells while remaining wells were grouped by hierarchical clustering analysis(HCA).Machine learning models were developed to predict GWD,then their response to natural conditions and human activities was assessed by the Shapley Additive exPlanations(SHAP)method.Results showed that eXtreme Gradient Boosting(XGB)was superior to other models in terms of prediction performance and computational efficiency(R^(2)>0.95).GWD in Yancheng and southern Lianyungang were greater than those in Nantong,exhibiting larger fluctuations.Groundwater within 5 km of the coastline was affected by tides,with more pronounced effects in agricultural areas compared to urban areas.Shallow groundwater(3-7 m depth)responded immediately(0-1 day)to rainfall,primarily influenced by farmland and topography(slope and distance from rivers).Rainfall recharge to groundwater peaked at 50%farmland coverage,but this effect was suppressed by high temperatures(>30℃)which intensified as distance from rivers increased,especially in forest and grassland.Deep groundwater(>10 m)showed delayed responses to rainfall(1-4 days)and temperature(10-15 days),with GDP as the primary influence,followed by agricultural irrigation and population density.Farmland helped to maintain stable GWD in low population density regions,while excessive farmland coverage(>90%)led to overexploitation.In the early stages of GDP development,increased industrial and agricultural water demand led to GWD decline,but as GDP levels significantly improved,groundwater consumption pressure gradually eased.This methodological framework is applicable not only to coastal cities in China but also could be extended to coastal regions worldwide.
基金supported by King Saud University,Riyadh,Saudi Arabia,through Researchers Supporting Project number RSP2025R498.
文摘The exponential growth of the Internet of Things(IoT)has revolutionized various domains such as healthcare,smart cities,and agriculture,generating vast volumes of data that require secure processing and storage in cloud environments.However,reliance on cloud infrastructure raises critical security challenges,particularly regarding data integrity.While existing cryptographic methods provide robust integrity verification,they impose significant computational and energy overheads on resource-constrained IoT devices,limiting their applicability in large-scale,real-time scenarios.To address these challenges,we propose the Cognitive-Based Integrity Verification Model(C-BIVM),which leverages Belief-Desire-Intention(BDI)cognitive intelligence and algebraic signatures to enable lightweight,efficient,and scalable data integrity verification.The model incorporates batch auditing,reducing resource consumption in large-scale IoT environments by approximately 35%,while achieving an accuracy of over 99.2%in detecting data corruption.C-BIVM dynamically adapts integrity checks based on real-time conditions,optimizing resource utilization by minimizing redundant operations by more than 30%.Furthermore,blind verification techniques safeguard sensitive IoT data,ensuring privacy compliance by preventing unauthorized access during integrity checks.Extensive experimental evaluations demonstrate that C-BIVM reduces computation time for integrity checks by up to 40%compared to traditional bilinear pairing-based methods,making it particularly suitable for IoT-driven applications in smart cities,healthcare,and beyond.These results underscore the effectiveness of C-BIVM in delivering a secure,scalable,and resource-efficient solution tailored to the evolving needs of IoT ecosystems.
基金Under the auspices of National Natural Science Foundation of China(No.42330510,42471245,52478061,52478060)。
文摘The smart city is not only a crucial means of promoting sustainable development,but also a strategic approach to advancing scientific urban development and governance on a global scale.Based on a textual co-occurrence analysis of policy documents,this study examines the components,development pathways,and models of smart cities in China.It identifies three distinct phases in the development process:the information project-driven phase,the holistic development phase,and the collaborative development phase.Overall,the development of smart cities in China-characterized by a strong focus on information technology projects-was primarily initiated by both central and local governments.Reflecting on the informatization process and the trajectory of smart city development in China since the mid-1990s,this paper argues that smart city initiatives must evolve beyond a purely technology-driven framework toward a more human-centered approach.Against the backdrop of ongoing urbanization and urban transformation in China,this study proposes future development strategies that integrate top-down government planning with bottom-up public engagement.It advocates for a comprehensive framework that integrates technology,human needs,and spatial planning,emphasizing the spatiotemporal coordination between technological implementation and stakeholder demands.This research offers valuable insights and strategic guidance for the future development of smart cities worldwide.
