Search Results (8,101)

Ensuring physical layer security for low-altitude economic networking (LAENet) is critical due to the broadcast nature of wireless channels. In dense urban environments, multi-antenna LAENet systems are often impaired by the keyhole effect, which induces rank deficiency and poses significant security challenges. This paper investigates the secrecy performance of a multiple-input multiple-output multiple-antenna eavesdropper (MIMOME) system in LAENet with keyhole channels. Depending on the availability of channel state information (CSI) at the transmitter, three wiretap scenarios are considered: (i) broadcasting, (ii) passive eavesdropping, and (iii) spoofing. For each scenario, the optimal precoder is designed to maximize the secrecy transmission rate. Based on these designs, we derive closed-form expressions for the secrecy outage probability (SOP) and average secrecy rate (ASR). To provide insights into the effect of keyholes on secrecy diversity order and array gain under this severe rank-deficiency structure, we also obtain asymptotic expressions for SOP and ASR in the high signal-to-noise ratio (SNR) regime using the Mellin transform. Numerical results validate the analytical expressions and illustrate the influence of key parameters on secrecy performance. These findings provide meaningful guidance for the secure design of future LAENet deployments. Full article

In the last decade, as cities increasingly seek sustainable development pathways within the cultural and creative economy, cultural-creative events have gained prominence as strategic instruments for urban transformation. Among them, city design weeks have emerged as complex service systems that connect creative industries, urban governance, and tourism development. This research aims to understand how cultural-creative events (represented by design weeks) facilitate sustainable tourism development from a service design perspective. Adopting a qualitative comparative research design, the study examines 30 design weeks selected through a cross-validated process with the World Design Weeks global network and UNESCO City of Design network. Data from 2020 to 2025 is collected primarily through expert interviews, official reports, and media materials in relation to the United Nations Sustainable Development Goals (SDGs). Grounded in the service design perspective, four Service Design Levels are summarized into 17 assessment dimensions, and experts applied Likert scale to evaluate the relative service intensity of each case. Through cross-case analysis, the findings reveal four distinct models of design weeks, reflecting different configurations of service intensity and strategic orientation. The study contributes theoretically by extending service design theory to cultural-creative tourism research, and practically by providing guidance for the organizers of cultural-creative events seeking to support sustainable city tourism development. Future research may incorporate quantitative impact assessments to further refine these models. Full article

Blue space, as an important natural and social composite feature system in cities, not only provides supporting, regulating, and provisioning services, but also plays a key role in human well-being, recreational experience, and urban sustainable development. The blue space cultural ecosystem service (CES) has gradually attracted the attention of academia in recent years, but there is a lack of systematic integration research in related fields. Therefore, it is necessary to conduct a comprehensive analysis of current studies to clarify how, and to what extent, blue spaces influence CESs. This study adopts a PRISMA-based systematic search combined with qualitative synthesis, aiming to review the research status of CES and its developmental trajectory within blue space studies, and to identify future research trends and critical gaps. A total of 52 studies meeting the inclusion criteria were finally selected through database screening. The research innovatively divides the evolution of blue space CES into three stages (2012–2017/2018–2022/2023–2025), revealing a shift in research focus from single value identification to complex policy support. Secondly, through the mapping of six typical blue space types (such as rivers and wetlands) and 10 CES indicators, combined with a Pearson correlation heatmap, it provides quantitative insights into the coupling mechanisms between indicators, such as the significant synergy between spiritual and educational values. Methodologically, it systematically discriminates between the application boundaries of monetary valuation based on the contingent valuation method and non-monetary valuation represented by social media big data and PPGIS, pointing out that technological progress is driving the evaluation toward high dynamics and refinement. Finally, the study points out current bottlenecks such as uneven geographical distribution and insufficient planning transformation, emphasizing that future research should use artificial intelligence to improve data processing accuracy and transform blue space CESs from “invisible welfare” into “explicit policy assets” to guide sustainable urban renewal and healthy space design. Full article

Urban–suburban PM_2.5 differences are widely used to characterize spatial disparities in air pollution, yet their long-term trends may depend on urban definitions. For China during 2013–2020, this study used nationwide ground PM_2.5 monitoring data and 1 km × 1 km gridded population density data to analyze the sensitivity of urban–suburban PM_2.5 trends to spatial structure-based and population-density-based classification (300, 1500, 2200, 2500 people km⁻²) at national, Eastern and Western China scales. Results showed significant national PM_2.5 decline, with urban reduction rates of −3.1 to −3.3 µg m⁻³ yr⁻¹ in summer and −6.0 to −6.3 µg m⁻³ yr⁻¹ in winter, and faster air quality improvement in winter. Urban–suburban PM_2.5 differences were highly sensitive to classification methods: the spatial structure-based framework showed minimal differences (0.09 µg m⁻³ in summer, 5 µg m⁻³ in winter), while the 300 people km⁻² threshold yielded much larger ones (11 µg m⁻³ in summer, 29 µg m⁻³ in winter) with faster urban declines. Higher population density thresholds narrowed such differences and converged trends with the spatial structure-based results. Pronounced spatial heterogeneity existed: Eastern China had larger PM_2.5 declines with consistent response patterns to national trends, while Western China showed weaker declines, with urban–suburban differences highly sensitive to classification methods and opposite temporal evolution trends. This study confirms that urban definition is a critical methodological factor for interpreting China’s long-term urban–suburban PM_2.5 trends, as different methods cause notable inferential deviations. Future air pollution spatial heterogeneity studies should carefully select and specify urban classification methods to ensure comparable, scientifically rigorous findings. Full article

This study examines the influence of misinformation on vaccination decision-making and the perception of social security in Romania in the context of potential future pandemics. Using a survey-based design, data were collected through an online questionnaire administered to a sample of 1005 respondents. The analysis employed descriptive and inferential statistical methods, including chi-square tests, ANOVA, Kruskal–Wallis tests, principal component analysis (PCA), K-means clustering, random forest models, and Spearman correlations. The results indicate statistically significant associations between belief in misinformation and vaccination attitudes (p < 0.001), with moderate effect sizes. Effect size estimates indicated small-to-moderate associations (e.g., Cramér’s V up to 0.371 for key demographic differences, and Kendall’s W = 0.273 for the increase in willingness across the three severity scenarios). Individuals with higher levels of education, urban residence, and younger age were more likely to report higher willingness to vaccinate, whereas respondents from rural areas and those with lower educational levels showed greater susceptibility to misinformation. In addition, risk perception was significantly associated with vaccination intention, which increased as the severity of hypothetical pandemic scenarios intensified. Predictive modeling identified specific misinformation beliefs—particularly those related to vaccine safety and natural immunity—as key factors associated with vaccination decisions. These findings suggest that misinformation is strongly associated with both individual vaccination behavior and broader perceptions of social security. Full article

Soil erosion remains a critical ecological challenge on China’s Loess Plateau (LP), where fragile geomorphology and intensive human activities jointly amplify land degradation risks. As land-use and land-cover change (LUCC) is a primary determinant of erosion processes, clarifying the nexus between land patterns and erosion intensity is essential for formulating effective conservation strategies. This study integrates the Chinese Soil Loss Equation (CSLE) with the Patch-generating Land Use Simulation (PLUS) model to analyze the spatiotemporal dynamics of soil erosion from 2000 to 2020 and project future patterns for 2060 under five scenarios: Natural Development (ND), Ecological Protection (EP), Economic Development (ED), Cropland Protection (CP), and Planning Guidance (PG). Results indicate a fluctuating decline in LP soil erosion during 2000–2020, marked by a transition toward predominantly slight erosion (~70% of the total area), while high-intensity erosion remained concentrated in central and western cropland and grassland. Scenario projections reveal pronounced divergence in erosion outcomes. The EP scenario, characterized by sustained vegetation expansion, demonstrated the highest efficacy in erosion mitigation. Conversely, the ED scenario exhibited the most severe erosion risk due to urban expansion into ecological areas. The PG scenario effectively reconciled the trade-offs between ecological conservation and socioeconomic demands, maintaining a balanced erosion control performance. In the context of global climate change, the complexity of soil and water conservation governance is expected to intensify. This study suggests that future efforts should focus on scientifically guiding the evolution of land-use patterns through sustainable spatial planning. Furthermore, targeted engineering and biological conservation measures must bae implemented for high-risk land categories to ensure the long-term stability of the regional ecological security barrier. Full article

Exploring the evolutionary dynamics of urban, agricultural, and ecological spaces is critical for regional sustainable development and spatial governance. However, traditional spatial simulation methods based on Cellular Automata often struggle to accommodate top-down spatial regulation, non-linear development patterns, and coordinated regional growth. The objective of this scientific research is to address these limitations by proposing a deep learning-based framework for simulating the future distribution of these three spaces. Utilizing the Unet++ model and integrating empirical data sources including multi-period remote sensing land-use mapping and prefecture-level socioeconomic statistical data, the framework predicts regional spatial patterns for the year 2030. Empirical results from the Yangtze River Economic Belt demonstrate that the model achieves high precision in large-scale spatial forecasting (with an average test accuracy of 99.32%) and effectively captures non-linear evolutionary characteristics. Predictions across various growth scenarios reveal that a moderate socioeconomic growth rate facilitates ecological preservation; controlling the expansion of urban space to approximately 20% by 2030 can prevent excessive resource depletion and regional imbalances. Consequently, it is recommended to implement the construction land increment targets outlined in current spatial planning to achieve a balance between economic growth and ecological protection. Full article

This systematic review uses the PRISMA method to comprehensively analyze the current state of research and development of urban mining technologies. Existing technologies, their effectiveness, and areas of application are examined. Research gaps are identified, and the potential of previously unused methods, as well as key developments in the technology sector, are highlighted. Furthermore, connections between technologies and their application areas are explored. A total of 45 publications from the databases Scopus, Web of Science, Google Scholar, ScienceDirect, and SpringerLink, covering the years between 2022 and 2025, are considered. The inclusion and exclusion criteria focus specifically on urban mining-related technologies. Apart from metallurgical processes, only a few established technologies currently exist in urban mining. Three technologies were identified as breakthroughs. Technologies such as membrane processes and composting, originally developed for other areas, are increasingly being transferred to urban mining. Despite these advancements, most research remains at the laboratory stage. Practical implementation and full utilization of waste are currently insufficient. This review represents the first comprehensive technological overview of the future of urban mining. Full article

Rapid urbanization in megacities has exacerbated the spatial mismatch between employment and housing, necessitating effective spatial optimization strategies. However, classical optimization models often rely on the idealized assumption of “proximity maximization,” failing to account for the complex, nonlinear regularities of actual human mobility. To address this disconnect between theoretical modeling and real-world behavior, this study establishes a job–housing balance optimization framework integrated with empirical commuting patterns. Using Shenzhen as a case study, we analyze citywide commuting big data since 2024 to characterize the power law relationship between commuting population size and distance. We propose a novel optimization model that partitions residential areas into “commuting rings” on the basis of observed distance-decay functions rather than simple Euclidean proximity. We applied the proposed method to current and future planning scenarios and successfully generated spatial regulation schemes that decentralize employment functions to peripheral areas while strategically densifying residential zones. By respecting the “heavy-tailed” nature of commuting distributions, this approach offers urban planners a more robust tool for reducing aggregate commuting burdens without violating the behavioral realities of the workforce. Full article

Background/Objectives: Vaccine hesitancy remains a significant public health challenge worldwide, yet nationally representative data from Central Asia are scarce. Evidence on the multidimensional structure of vaccination attitudes and their social patterning in Kazakhstan is limited. The study aimed to assess the distribution of anti-vaccination attitudes among adults in Kazakhstan and to examine their associations with socio-demographic, behavioural, clinical, and territorial characteristics. Methods: We conducted a cross-sectional, nationally representative survey of adults aged 18–69 years across all 17 regions of Kazakhstan between May and October 2025 (n = 6712). A multistage, stratified cluster sampling design was applied, and analyses incorporated sampling weights and design-based corrections. Vaccination attitudes were measured using the 12-item Vaccination Attitudes Examination (VAX) scale, comprising four subscales: mistrust of vaccine benefit, worries about unforeseen future effects, concerns about commercial profiteering, and preference for natural immunity. Internal consistency and confirmatory factor analysis were performed. Design-adjusted linear regression models were used to identify factors independently associated with each subscale and the overall VAX score. Results: The weighted mean overall VAX score was 3.70 (95% CI 3.67–3.73) on a 1–6 scale. The highest scores were observed for worries about unforeseen future effects (4.12; 95% CI 4.10–4.14), followed by preference for natural immunity (3.93; 95% CI 3.87–3.98), concerns about commercial profiteering (3.49; 95% CI 3.45–3.52), and mistrust of vaccine benefit (3.27; 95% CI 3.23–3.31). Internal consistency was high for the overall scale (Cronbach’s α = 0.861), and the four-factor structure demonstrated acceptable fit (CFI = 0.965; TLI = 0.952; RMSEA = 0.071). In multivariable design-adjusted models, age showed a generally consistent gradient, with lower scores in younger groups and the clearest differences observed among the youngest respondents. Married/cohabiting respondents had lower adjusted scores than single respondents across all subscales and for the overall VAX score. Men had lower adjusted worries scores than women, but sex was not independently associated with the overall VAX score. Diabetes was associated with higher adjusted mistrust, concerns about commercial profiteering, and overall VAX score, but not with worries or preference for natural immunity. Territorial differences were domain-specific: urban residence was associated with lower mistrust and higher worries, while macro-region was significant at the factor level only for worries. Conclusions: Anti-vaccination attitudes in Kazakhstan exhibit a multidimensional structure and clear socio-demographic patterning. Concerns about long-term safety were the most prominent attitudinal domain, whereas mistrust of vaccine benefit was comparatively less pronounced. Territorial differences were domain-specific rather than uniform, supporting the need for targeted communication strategies tailored to specific attitudinal domains and population subgroups. Full article

Timely fault detection, classification, and localization are fundamental to enabling fast service restoration in modern distribution networks, and are especially vital for maintaining the reliability and resilience of smart city electricity infrastructures. A new AI-based method for classifying and localizing fault types is presented in this paper, which enhances situational awareness in smart distribution grids that supply dense urban loads and critical smart city services. The proposed approach targets various fault conditions, which include three-phase-to-ground, three-phase, two-phase-to-ground, two-phase, and single-phase-to-ground faults. The proposed method utilizes a wavelet-based signal processing technique to analyze the feeder’s current data captured by waveform measurement units (WMUs) and extracts features for fault analysis. As a result of these features, a multi-stage machine learning architecture incorporating deep learning components is developed to accurately determine the occurrence, type, and location of faults. To evaluate the performance of the proposed approach, simulations were conducted on a 16-bus distribution network. Results show a high level of accuracy in fault detection, classification, and localization. This indicates that the method can be a valuable tool for enhancing the resilience and intelligence of future power grids, as well as supporting self-healing and fast service restoration in smart city services. Full article

Food safety is a major global public health concern and a key contributor to the burden of foodborne diseases. This scoping review examined the knowledge, attitudes, and practices (KAP) related to food safety among urban consumers in low- and lower-middle-income countries (LLMICs). A systematic search was conducted across seven electronic databases: Medline (PubMed), Web of Science (Social Science Citation Index), Embase (Ovid), Global Health (Ovid), PsycINFO (Ovid), Econlit (EBSCOhost), and Scopus to identify studies published in English between 2000 and 2025. Data extraction and quality appraisal were conducted independently by two reviewers, and findings were synthesized in a narrative analysis. Twenty-six studies from 14 LLMICs met the inclusion criteria. Of the 25 studies assessing knowledge and awareness, the majority reported that consumers had some understanding of food safety, although 10 (40%) highlighted limited awareness. Fifteen studies examined practices, with several noting appropriate behaviours; however, nine (56.2%) reported poor practices. Seven studies assessed attitudes, with most reflecting positive perceptions, while one (16.7%) identified negative views. Only four studies examined the full KAP triad. Across studies, factors such as age, education, gender, marital status, training, employment status, income, field of study, and residential status were found to influence food safety KAP. Overall, the evidence suggests that while consumers in urban LLMIC settings generally demonstrate some knowledge and positive attitudes towards food safety, there remain significant gaps in practices that could compromise public health. Future research should prioritise underrepresented regions, employ more rigorous study designs, and incorporate longitudinal and qualitative approaches to gain deeper insights and inform targeted interventions. Full article

Traffic flow prediction is a key component of Intelligent Transportation Systems (ITS), crucial for alleviating urban congestion, optimizing traffic management, and improving the overall efficiency of road networks. With the rapid growth in vehicle numbers and the increasing complexity of urban traffic patterns, accurate short-term traffic flow prediction has become increasingly important. This paper comprehensively reviews the latest advancements in traffic flow prediction methods, focusing on graph neural network (GNN)-based approaches and hybrid deep learning frameworks. First, we introduce the fundamental theoretical foundations, including graph neural networks, deep learning algorithms, heuristic optimization methods, and attention mechanisms. Subsequently, we summarize GNN-based prediction methods into four paradigms: (1) federated learning and privacy-preserving methods, enabling cross-regional collaboration while protecting sensitive data; (2) dynamically adaptive graph structure methods, capturing time-varying spatial dependencies; (3) multi-graph fusion and attention mechanism methods, enhancing feature representations from multiple perspectives; and (4) cross-domain technology integration methods, fusing novel architectures and interdisciplinary technologies. Furthermore, we investigate hybrid methods combining signal decomposition, heuristic optimization, and attention mechanisms with LSTM networks to address challenges related to non-stationarity and model optimization. For each category, we analyzed representative works and summarized their core innovations, strengths, and limitations using a systematic comparative table. Finally, we discussed current challenges, including computational complexity, model interpretability, and generalization ability, and outlined future research directions such as lightweight model design, uncertainty quantification, multimodal data fusion, and integration with traffic control systems. This review provides researchers and practitioners with a systematic understanding of the latest advances in traffic flow prediction and offers guidance for methodological selection and future research. Full article

The integration of electric mobility and energy systems has emerged as a key research domain in the transition toward sustainable energy and decarbonized transport, yet the literature is lacking systematic quantitative overviews of its scientific development. This study addresses this gap by conducting a bibliometric analysis of research activities across five domains central to electric vehicle–energy system integration: central energy management systems; renewable energy, hydrogen production, and large-scale storage; industrial applications; smart energy communities, virtual power plants, and vehicle-to-X; and urban high-power charging parks with local storage. Using publication data from Web of Science and Scopus, performance analysis and science mapping techniques were applied to examine publication dynamics, thematic structures, and intellectual linkages. Results indicate strong growth and consolidation around smart grids and decentralized flexibility solutions, particularly within energy management, renewable integration, and community-based energy systems, while industrial applications and high-power charging infrastructures remain comparatively underrepresented. The findings suggest a maturing interdisciplinary field characterized by expanding connections between mobility and energy research, alongside emerging opportunities related to industrial integration, charging infrastructure, and vehicle-to-grid deployment. The study provides a structured, multi-domain perspective on the convergence of electric mobility and energy systems, enabling a differentiated understanding of research dynamics. The study provides a structured, multi-domain perspective on the convergence of electric mobility and energy systems. The findings highlight priority areas for future research, particularly industrial integration and scalable charging infrastructure, and offer insights for policymakers and industry stakeholders. Full article

The growing reliance on virgin resources in construction, alongside accelerated urban development and the significant volumes of waste generated at the end-of-life phase of buildings, has intensified environmental impacts across the built environment. These challenges highlight the urgent need to transition towards a circular economy (CE) in the construction sector. At the same time, the sector’s ongoing digital transformation presents opportunities to enhance stakeholder collaboration and improve construction and demolition waste management (CDWM) practices. This paper aims to develop a conceptual framework for a Digital Information Management System (DIMS) to support CE implementation in construction through improved CDWM. Following the Design Science Research methodology, this paper addresses the first two stages: problem identification and solution proposition. A questionnaire survey with industry experts was conducted to validate the problem areas identified in the literature and assess the applicability of the proposed conceptual framework. The findings confirm critical gaps in CDWM, including limited stakeholder collaboration, fragmented processes, and the absence of lifecycle-spanning information systems, and validate the proposed conceptual framework solution, particularly the integration of BIM and IoT to support material and product flow tracking throughout the project lifecycle, supported by clearly defined stakeholder roles and engagements. However, respondents expressed reservations regarding Blockchain due to concerns about energy consumption and long-term data storage. Overall, the validated conceptual framework for DIMS provides a robust foundation for future studies, to focus on co-creating and developing a detailed conceptual model for DIMS for future real-world implementation. Full article