Search Results (8,909)

The mitigation of traffic noise is essential for the development of sustainable and livable urban environments, a goal that is directly contingent on addressing tire-pavement interaction noise (TPIN) as the dominant acoustic pollutant at medium to high vehicle speeds. This comprehensive review addresses a critical gap in the literature by systematically analyzing the damping properties of pavement systems through a unified, multi-scale framework—from the molecular-scale viscoelasticity of asphalt binders to the composite performance of asphalt mixtures. The analysis begins by synthesizing state-of-the-art testing and characterization methodologies, which establish a clear connection between macroscopic damping performance and the underlying viscoelastic mechanisms coupled with the microscopic morphology of the binders. Subsequently, the review critically assesses the influence of critical factors, such as polymer modifiers including rubber and Styrene-Butadiene-Styrene (SBS), temperature, and loading frequency. This examination elucidates how these variables govern molecular mobility and relaxation processes to ultimately determine damping efficacy. A central and synthesizing conclusion emphasizes the paramount importance of the asphalt binder’s properties, which serve as the primary determinant of the composite mixture’s overall acoustic performance. By delineating this structure-property-performance relationship across different scales, the review consolidates a foundational scientific framework to guide the rational design and informed material selection for next-generation asphalt pavements. The insights presented not only advance the fundamental understanding of damping mechanisms in pavement materials but also provide actionable strategies for creating quieter and more sustainable transportation infrastructures. Full article

Background: Population aging has increased attention on the quality of life and successful aging of older adults. Objective: To examine urban–rural differences in subjective quality of life and self-rated successful aging, explore associations with psychosocial factors, and identify predictors of successful aging, including potential moderating effects of place of residence and chronic illness. Methods: A cross-sectional study was conducted among 403 adults aged ≥ 60 years in Eastern Croatia. Measures included a sociodemographic questionnaire, the Self-assessment of Successful Aging Scale (SSAS), and the Personal Wellbeing Index (PWI). Data were analyzed using nonparametric tests (Mann–Whitney U, Spearman’s correlation), linear regression, and moderation analyses. Significance was set at p < 0.05. Ethical approval was obtained (Class: 602-01/24-12/02; IRB: 2158/97-97-10-24-36). Results: Rural participants reported lower PWI scores (p = 0.005) and self-rated successful aging (p < 0.001) than urban participants. Active community involvement was positively associated with quality of life (Rho = 0.46; p < 0.001), whereas regret about missed opportunities and past actions was negatively associated (Rho = −0.20; p < 0.01). Regression analyses explained 48.3% of the variance in SSAS, with higher PWI scores being strongly associated with higher SSAS scores, and rural residence and chronic illness being associated with lower SSAS scores. Moderation analyses indicated that the association between PWI and SSAS was consistent across different environmental contexts and in the presence of illness. Conclusions: Older adults living in rural areas reported lower quality of life and self-rated successful aging compared with those in urban and suburban areas, with subjective wellbeing emerging as a key predictor. Promoting social engagement and addressing psychosocial barriers may enhance successful aging, particularly in rural populations. Findings suggest that social engagement and psychosocial support are associated with higher level of perceived successful aging, indicating potential areas for future community-based or healthcare interventions. Full article

This study evaluates China’s National Smart Education Platform (NSEP) as a national digital reform aligned with SDG 4 (quality education) and SDG 10 (reduced inequalities), yet evidence remains limited on whether such platforms reduce urban–rural gaps in real-world use and outcomes. A quantitative, stratified, random survey of students, teachers, and administrators used validated scales to measure perceived ease of use (PEOU), perceived usefulness (PU), user satisfaction (US), behavioral intention (BI), engagement level (EL), learning outcomes (LO), and system quality (SQ). The measures demonstrated strong reliability. Hierarchical regression analyses supported an extended technology acceptance model (TAM): SQ, PEOU, and PU significantly predicted US and BI, with PU showing the strongest effect. Interaction effects indicated context-sensitive adoption and the results suggested a persistent rural disadvantage in adoption even after accounting for key predictors. Mediation analyses further showed that US and BI transmitted technology beliefs to LO. Nevertheless, urban–rural gaps remained evident, particularly in PEOU and SQ, and teachers consistently reported a lower PEOU than students and administrators. These findings suggest that NSEP has the potential to support SDG-oriented digital equity, but closing urban–rural gaps requires teacher-centered design, improved usability and system reliability, and targeted infrastructure and capacity-building support in rural contexts. Full article

Accurate spatiotemporal forecasting underpins high-stakes decision making in smart urban systems, from traffic control and energy scheduling to environment monitoring. Yet two persistent gaps limit current models: (i) spatial modules are often biased toward low-pass smoothing and struggle to reconcile slow global trends with sharp local dynamics; and (ii) the graph structure required for forecasting is frequently latent, while learned graphs can be unstable when built from temporally derived node features alone. We propose SpeQNet, a query-enhanced spectral graph filtering framework that jointly strengthens node representations and graph construction while enabling frequency-selective spatial reasoning. SpeQNet injects global spatial context into temporal embeddings via lightweight learnable spatiotemporal queries, learns a task-oriented adaptive adjacency matrix, and refines node features with an enhanced ChebNetII-based spectral filtering block equipped with channel-wise recalibration and nonlinear refinement. Across twelve real-world benchmarks spanning traffic, electricity, solar power, and weather, SpeQNet achieves state-of-the-art performance and delivers consistent gains on large-scale graphs. Beyond accuracy, SpeQNet is interpretable and robust: the learned spectral operators exhibit a consistent band-stop-like frequency shaping behavior, and performance remains stable across a wide range of Chebyshev polynomial orders. These results suggest that query-enhanced spatiotemporal representation learning and adaptive spectral filtering form a complementary and effective foundation for effective spatiotemporal forecasting. Full article

Urban noise and soundscape assessment is critical for sustainable, human-centered city planning. A comprehensive overview of key standards is essential to ensure consistent measurements, enable cross-study comparisons, and support practical applications. This review examines standards from the American National Standards Institute/Acoustical Society of America (ANSI/ASA), ASTM International, and the International Organization for Standardization (ISO), highlighting their principles, methodologies, and roles in evaluating urban acoustic environments. It discusses how these standards facilitate accurate noise quantification, capture human perceptual responses, and guide soundscape design and management across occupational, community, and experimental settings. Standardized questionnaires, rating scales, and perceptual frameworks are also reviewed. Finally, this paper identifies gaps in current guidance, including limited approaches to continuous monitoring, cultural adaptation, multisensory interactions, and integration with urban planning. Full article

Urban heat islands are among the most intense and unequal climate impacts in Mediterranean cities, with direct effects on health, thermal comfort, and habitability. This reality calls for the incorporation of binding and verifiable climate criteria into spatial planning and urban planning law. This article examines the extent to which the Spanish legal framework—at national, regional, and municipal levels—incorporates measurable standards to mitigate urban heat islands and how it might evolve towards operational climate-responsive urbanism. A legal–analytical and comparative methodology is applied, based on multilevel normative content analysis and a comparison of four autonomous communities, four Spanish cities, and four international reference cases with consolidated metrics. The results show that, despite progress in recognising adaptation, territorial asymmetries persist, enforceable parameters remain scarce, and there is a prevailing reliance on strategic or voluntary instruments. In response to these gaps, the study proposes a coherent set of urban climate standards (urban vegetation, functional soil permeability, roof albedo/cool roofs, green roofs and façades, plot-scale performance indices, urban ventilation, and thermal diagnostics) and a multilevel integration model aimed at guiding legislative reforms and strengthening cities’ adaptive capacity and thermal equity. Full article

Campus community gardens are expected to leverage disciplinary resources and spatial conditions to deliver ecological, educational, and social benefits beyond those of general community gardens. In China, these gardens are primarily established under the guidance of educational authorities, leading to issues such as significant homogenization and a lack of diversity, which hinders the full realization of their potential. This study investigates the potential factors influencing the development of campus gardens. Focusing on university campuses in Beijing, it employs stratified sampling and a questionnaire survey (n = 1008), utilizing methods including exploratory factor analysis (EFA), multiple linear regression, and analysis of variance (ANOVA) to systematically identify the factors affecting their differentiated development. The results indicate that: (1) the willingness to participate is collectively driven by four dimensions: “planting expectation,” “funding and site selection,” “personal motivation,” and “organizational support,” with “planting expectation” being the most significant factor. (2) Students’ academic disciplines influence their perceptions of the need for organizational support and spatial resources for gardens. (3) Campus location and size moderate the demand for gardens, with students in the urban expansion belt (between the 4th and 5th Ring Roads) and those from smaller campuses showing a stronger “pro-nature compensation” tendency. Based on campus spatial scale, urban location, and the academic backgrounds of participants, the study proposes integrated “space-organization” development strategies. This research provides targeted planning strategies for campus community gardens in China, aiming to leverage institutional disciplinary strengths, respond to participant needs, and maximize the gardens’ benefits. Full article

The sustainability of resources and ecological integrity are significantly influenced by land use and land cover change (LULCC) dynamics, particularly in ecotonal semi-arid regions where biome transitions are highly sensitive to anthropogenic disturbance and climatic variability. This study aims to assess historical LULCC dynamics and spatial reconfiguration across nine classes (grassland, shrubland, wetlands, forestland, waterbodies, farmed land, built-up land, bare land, and mines/quarries) in the C5 Secondary Drainage Region of South Africa over the three periods 1990–2014, 2014–2022, and 1990–2022. Using the South African National Land Cover datasets and the TerrSet liberaGIS v20.03 Land Change Modeller, this research applied post-classification comparison, transition matrices, asymmetric gain–loss metrics, and patch-based landscape analysis to quantify the magnitude, direction, source–sink dynamics, and spatial reconfiguration of LULCC. Results showed that between 1990 and 2014, Shrubland expanded markedly (+49.1%), primarily at the expense of Grassland, Wetlands, and Bare land, indicating bush encroachment and hydrological stress. From 2014 to 2022, the trend reversed as Grassland increased substantially (+261.2%) while Shrubland declined sharply (−99.3%). Forestland also regenerated extensively (+186%) along riparian corridors, and Waterbodies expanded more than fivefold (+384.6 km²). Over the long period between 1990 and 2022, Built-up land (+30.6%), Cultivated land (+16%), Forestland (+140%), Grassland (+94.4%), and Waterbodies (+25.6%) increased, while Bare land (−58.1%), Mines and Quarries (−56.1%), Shrubland (−98.9%), and Wetlands (−82.5%) decreased. Asymmetric analysis revealed strongly directional transitions, with early Grassland-to-Shrubland conversion likely driven by grazing pressure, fire suppression, and climate variability, followed by a later Shrubland-to-Grassland reversal consistent with fire, herbivory, and ecotonal climate sensitivity. LULC dynamics in the C5 catchment show class-specific spatial reconfiguration, declining landscape diversity (SHDI 1.3 → 0.9; SIDI 0.7 → 0.43), and patch metrics indicating urban and cultivated fragmentation, shrubland loss, and grassland consolidation. Based on these quantified trajectories, we recommend targeted catchment-scale land management, shrubland restoration, and monitoring of anthropogenic hotspots to support ecosystem services, hydrological stability, and sustainable land use in ecotonal regions. Full article

Water-quality degradation from nutrient pollution remains a major challenge for resource managers. Developing effective strategies requires tools to characterize nutrient sources and transport. This study used the RSPARROW framework to develop and assess new, smaller-scale models for Total Nitrogen (TN) and Total Phosphorus (TP) transport across Mississippi (MS). These state-level models were built using 15 years (2005–2020) of observation data and considered variables including multiple nutrient sources, land characteristics, and attenuation processes. The MS models demonstrated comparable accuracy to larger regional SPARROW models, validating the use of smaller-scale models for local management. Results showed agricultural sources are the major contributors to TN, dominated by fertilizer in northern MS and livestock manure in the south. Urban land cover also significantly influenced TN and was the second most significant source of TP, following geologic material (background P). Fertilizer and manure were also important TP sources. This study provides valuable, spatially explicit data on nutrient distribution in MS streams, supporting the state’s nutrient reduction planning. It concludes by highlighting the need for future model improvements via updated source data and mean annual flow estimates. Full article

Accurate traffic flow forecasting is a fundamental component of Intelligent Transportation Systems and proactive urban mobility management. However, the inherent complexity of urban traffic flow, characterized by non-stationary dynamics and multi-scale temporal dependencies, poses significant modeling challenges. Existing spatio-temporal models often struggle to reconcile the discrepancy between static physical road constraints and highly dynamic, state-dependent spatial correlations, while their reliance on fixed temporal receptive fields limits the capacity to disentangle overlapping periodicities and stochastic fluctuations. To bridge these gaps, this study proposes a novel Multi-scale Graph-Decoupling Spatial–temporal Network (MS-GSTN). MS-GSTN leverages a Hierarchical Moving Average decomposition module to recursively partition raw traffic flow signals into constituent patterns across diverse temporal resolutions, ranging from systemic daily trends to high-frequency transients. Subsequently, a Tri-graph Spatio-temporal Fusion module synergistically models scale-specific dependencies by integrating an adaptive temporal graph, a static spatial graph, and a data-driven dynamic spatial graph within a unified architecture. Extensive experiments on four large-scale real-world benchmark datasets demonstrate that MS-GSTN consistently achieves superior forecasting accuracy compared to representative state-of-the-art models. Quantitatively, the proposed framework yields an overall reduction in Mean Absolute Error of up to 6.2% and maintains enhanced stability across multiple forecasting horizons. Visualization analysis further confirms that MS-GSTN effectively identifies scale-dependent spatial couplings, revealing that long-term traffic flow trends propagate through global network connectivity while short-term variations are governed by localized interactions. Full article

GNN approaches to property valuation typically rely on spatial proximity, assuming that nearby properties exhibit similar price patterns. In practice, this assumption often fails as neighborhood and administrative boundaries create sharp price discontinuities, a form of spatial heterophily. This study proposes a Boundary-Aware Dual-Path Graph Neural Network (BD-GNN), a heterophily-oriented GNN specifically designed for continuous regression tasks. The model uses a dual and adaptive message passing design, separating inter- and intra-boundary pathways and combining them through a learnable gating parameter $\alpha$. This allows it to capture boundary effects while preserving spatial continuity. Experiments conducted on three structurally contrasting housing datasets, namely Bangkok, King County (USA), and Singapore, demonstrate consistent performance improvements over strong baselines. The proposed BD-GNN reduces MAPE by 7.9%, 4.4%, and 4.5% and increases $R^2$ by 3.2%, 0.7%, and 5.0% for the respective datasets. Beyond predictive performance, $\alpha$ provides a clear picture of how spatial and administrative factors interact across urban scales. GNN Explainer provides local interpretability by showing which neighbors and features shape each prediction. BD-GNN bridges predictive accuracy and structural insight, offering a practical, interpretable framework for applications such as property valuation, taxation, mortgage risk assessment, and urban planning. Full article

Semantic segmentation of high-resolution remote sensing imagery is a critical technology for the intelligent interpretation of sensor data, supporting automated environmental monitoring and urban sensing systems. However, processing data from dense urban scenarios remains challenging due to sensor signal occlusions (e.g., shadows) and the complexity of parsing multi-scale targets from optical sensors. Existing approaches often exhibit a trade-off between the accuracy of global semantic modeling and the precision of complex boundary recognition. While the Segment Anything Model (SAM) offers powerful zero-shot structural priors, its direct application to remote sensing is hindered by domain gaps and the lack of inherent semantic categorization. To address these limitations, we propose a dual-branch cooperative network, PriorSAM-DBNet. The main branch employs a Densely Connected Swin (DC-Swin) Transformer to capture cross-scale global features via a hierarchical shifted window attention mechanism. The auxiliary branch leverages SAM’s zero-shot capability to exploit structural universality, generating object-boundary masks as robust signal priors while bypassing semantic domain shifts. Crucially, we introduce a parameter-efficient Scaled Subsampling Projection (SSP) module that employs a weight-sharing mechanism to align cross-modal features, freezing the massive SAM backbone to ensure computational viability for practical sensor applications. Furthermore, a novel Attentive Cross-Modal Fusion (ACMF) module is designed to dynamically resolve semantic ambiguities by calibrating the global context with local structural priors. Extensive experiments on the ISPRS Vaihingen, Potsdam, and LoveDA-Urban datasets demonstrate that PriorSAM-DBNet outperforms state-of-the-art approaches. By fine-tuning only 0.91 million parameters in the auxiliary branch, our method achieves mIoU scores of 82.50%, 85.59%, and 53.36%, respectively. The proposed framework offers a scalable, high-precision solution for remote sensing semantic segmentation, particularly effective for disaster emergency response where rapid feature recognition from sensor streams is paramount. Full article

Cybersecurity in radio frequency (RF) technologies has become a critical concern, driven by the expansion of connected systems in urban and industrial environments. Although research on wireless networks and the Internet of Things (IoT) has advanced, comprehensive studies that provide a global and integrated view of cybersecurity development in this field remain limited. This work presents a scientometric and systematic review of international publications from 2009 to 2025, integrating the PRISMA protocol with semantic screening supported by a Large Language Model to enhance classification accuracy and reproducibility. The analysis identified two interdependent axes: one focusing on signal integrity and authentication in GNSS systems and cellular networks; the other addressing the resilience of IoT networks, both strongly associated with spoofing and jamming, as well as replay, relay, eavesdropping, and man-in-the-middle (MitM) attacks. The results highlight the relevance of RF cybersecurity in securing communication infrastructures and expose gaps in widely adopted technologies such as RFID, NFC, BLE, ZigBee, LoRa, Wi-Fi, and unlicensed ISM bands, as well as in emerging areas like terahertz and 6G. These gaps directly affect the reliability and availability of IoT and wireless communication systems, increasing security risks in large-scale deployments such as smart cities and cyber–physical infrastructures. Full article

This study provides a quantitative assessment of the combined effects of progressive urbanization and changes in precipitation patterns (PPs) on the urban water cycle. The primary objective was to evaluate historical (1940–2024) and projected (to 2060) changes in total annual surface runoff (TSR) and retention capacity (RC) in the highly urbanized catchment of the Dłubnia River in Cracow, Poland. Simulations were performed using the EPA SWMM hydrodynamic model, supported by digitized historical land-use maps and long-term meteorological records. The results demonstrate that the dominant driver of the observed 6.4-fold increase in TSR and 6.8-fold loss of retention capacity (LRC) over the study period was the progressive increase in impervious surfaces. Although inter-annual variability in the amount and structure of annual precipitation (AP) strongly correlates with annual TSR (r = 0.97), its contribution to the long-term upward trend in TSR is marginal (r = 0.19). Land use and land cover change (LULC) exhibits an extremely strong correlation with the long-term TSR trend (r = 0.998). The study also highlights the high effectiveness of nature-based solutions (NbSs), particularly bioretention cells (BCs)/rain gardens, in mitigating the adverse hydrological effects of excessive surface sealing. Implementation of BCs covering just 3–4% of the total drained roof and road area is sufficient to fully offset the projected combined negative impacts of further urbanization and climate change (CC) in scope Representative Concentration Pathways (RCP4.5 and RCP8.5) projections on catchment retention capacity by 2060. These findings position strategically targeted, relatively small-scale bioretention as one of the most effective and feasible urban adaptation measures in mature, densely developed cities. Full article

With the continuous expansion of high-density urban forms, residents’ opportunities for daily contact with natural environments have been increasingly reduced, making the equity of urban green space allocation a critical challenge for sustainable urban development. Existing studies have largely focused on green space quantity or accessibility at single time points, lacking systematic investigations into the spatiotemporal evolution of green space exposure (GSE) and its equity from the perspective of residents’ actual environmental experiences. GSE refers to the integrated level of residents’ contact with urban green spaces during daily activities across multiple dimensions, including visual exposure, physical accessibility, and spatial distribution, emphasizing the relationship between green space provision and lived environmental experience. Based on this framework, this study takes the central urban area of Hangzhou as the study area and integrates multi-temporal remote sensing imagery with large-scale street view data. A deep learning–based approach is developed to identify green space exposure, combined with spatial statistical methods and equity measurement models to systematically analyze the spatiotemporal patterns and evolution of GSE and its equity from 2013 to 2023. The results show that (1) GSE in Hangzhou increased significantly over the study period, with accessibility exhibiting the most pronounced improvement. However, these improvements were mainly concentrated in peripheral areas, while changes in the urban core remained relatively limited, revealing clear spatial heterogeneity. (2) Although overall GSE equity showed a gradual improvement, pronounced mismatches between low exposure and high demand persisted in densely populated areas, particularly in older urban districts and parts of newly developed residential areas. (3) The spatial patterns and evolutionary trajectories of equity varied significantly across different GSE dimensions. Composite inequity characterized by “low visibility–low accessibility” formed stable clusters within the urban core. This study further explores the mechanisms underlying green space exposure inequity from the perspectives of urban renewal patterns, land-use intensity, and population concentration. By constructing a multi-dimensional and temporally explicit analytical framework for assessing GSE equity, this research provides empirical evidence and decision-making references for refined green space management and inclusive, sustainable urban planning in high-density cities. Full article