Search Results (8,671)

Traditional meteorological data spatial interpolation methods often rely on linear or static assumptions, which are inadequate for complex terrain and fail to exploit continuous spatiotemporal variation information. This paper proposes a Spatiotemporal Graph Network with Adaptive Temporal Decay (DG) that integrates a learnable graph convolution module and a temporal attenuation mechanism, enabling accurate precipitation estimation for target stations or regions at consecutive time steps. The method is evaluated using daily precipitation data from nine stations in Longnan City, Gansu Province, China, along with ERA5 (0.25°) and GPCP (0.5°) gridded reanalysis products. In the station-to-station interpolation scenario, DG significantly outperforms ordinary Kriging (OK), reducing the average RMSE from 1.4 mm/day to 1.2 mm/day, with a 28.6% improvement at mountainous stations. The DG model also exhibits superior performance in grid-to-station interpolation, achieving an average RMSE of 1.9 mm/day (OK: 2.5 mm/day). On heavy precipitation days (≥20 mm/day), DG reduces the RMSE nearly by half (11.7 mm/day) compared to OK (23.2 mm/day). A temporal-only LSTM baseline and three ablation variants (spatial-only OSI, temporal-only OTI and dgcn-only OD) are also compared, and DG consistently outperforms them, confirming the essential role of spatiotemporal integration. Additional baselines including IDW and Co-Kriging further validate the superiority of DG. The proposed method offers a promising new approach for high-precision spatiotemporal interpolation of meteorological elements in complex terrain. Full article

Background/Objectives: Medical social work is performed in hospital teams, but evidence remains limited on how medical social workers’ perceived communication and cooperation with physicians and nurses are associated with occupational outcomes. Methods: Using the medical social work module of the China Social Work Longitudinal Survey 2019 (CSWLS2019), this cross-sectional study examined job satisfaction, personal accomplishment, self-rated service quality, and emotional exhaustion. We constructed a four-item communication-and-cooperation index and estimated ordinary least squares (OLS) models with HC3 heteroskedasticity-robust standard errors and city fixed effects. Robustness and exploratory supplementary checks assessed sample definition, alternative specifications, single-item ordered logit models, decomposed components, moderation, and a supplementary seemingly unrelated regression (SUR) system. Results: The index was positively associated with job satisfaction (b = 0.260, p = 0.0010), personal accomplishment (b = 0.416, p = 0.0335), and self-rated service quality (b = 0.151, p = 0.0275). Its association with emotional exhaustion was negative but not statistically significant in the main model (b = −0.186, p = 0.1207), although it became significant in the stricter sample. Decomposed and moderation models provided limited evidence for stable component-specific or moderation patterns. Conclusions: The findings should be interpreted as exploratory associational evidence rather than causal effects. Perceived communication and cooperation with physicians and nurses appear more consistently linked to favorable occupational evaluations than to emotional exhaustion among medical social workers in China. Full article

Indian metropolitan cities such as Mumbai grapple with rapid urbanisation, extreme urban density, high built-up areas, loss of green cover, and shrinking open spaces, resulting in increased impermeable surfaces, urban heat island effects, and frequent flooding occurrences. Modern stormwater management has increasingly been characterised by integrated grey-green approaches; however, cities in the Global North benefit from established policies, technical expertise, and financial resources that enable the systematic and large-scale integration of Blue-Green Infrastructure (BGI) through district-wide geospatial assessment frameworks, unlike many cities in the Global South. Despite growing interest in nature-based stormwater solutions, there remains a dearth of geospatial empirical research from India examining the placement, distribution, performance, and functionality of BGI integrated with existing stormwater management systems in cities such as Mumbai. Furthermore, hydrological modelling using tools such as the Storm Water Management Model (SWMM) for the design, planning, and implementation of BGI in Indian cities remains largely unexplored. This study explores the role of BGI strategies in improving urban stormwater management within high-density Indian cities under a 25-year return period extreme rainfall scenario. Using an integrated approach that combines QGIS-based spatial analysis with EPA-SWMM hydrologic-hydraulic modelling, the research examines runoff behaviour, identifies flooding hotspots, and evaluates the effectiveness of Low Impact Development (LID)-based BGI measures such as permeable pavements, infiltration trenches, and green roofs applied at the ward level in Mumbai’s F/North and G/North Wards. Detailed land use classification, spatial mapping, and rainfall simulation corresponding specifically to a 25-year return period rainfall event was used to assess pre- and post-intervention conditions. The findings indicate that the applied BGI measures led to a 12.6% reduction in peak runoff (137.6 m³/s to 120.2 m³/s) and a 5.5% decrease in total runoff volume (783,510 m³ to 740,410 m³). More importantly, the peak flooding flow rate decreased by 45% (94.1 m³/s to 51.7 m³/s), demonstrating that BGI measures can efficiently reduce peak flooding flows by extending runoff hydrographs during extreme rainfall events. These findings are specifically applicable to the simulated 25-year return period extreme rainfall scenario and may vary under different rainfall intensities or return periods. Less extreme events could potentially experience even greater relative reductions or prevent flooding altogether, while also easing downstream hydraulic loads. Overall, strategically placed BGI interventions can significantly reduce surface runoff and peak flow, thereby enhancing stormwater resilience within spatially constrained urban environments. This study provides a replicable, data-driven framework for catchment-scale stormwater planning in dense Indian cities under extreme rainfall conditions, offering practical insights into methods, local contextual considerations, and spatial planning strategies for policymakers and urban planners seeking to retrofit and adapt existing infrastructure under increasing hydrologic stress and climate variability. Full article

Whale watching is frequently presented as a benign form of wildlife interaction, yet its ethical and ecological acceptability depends on two conditions: vessel practices must minimize disturbance to free-ranging cetaceans, and tours must provide meaningful conservation-oriented education. This study assessed whale-watching operations in the New York City Metropolitan Area using three complementary frameworks: the Whale SENSE “On the Water” evaluation, the World Cetacean Alliance (WCA) Best Practice Guidance, and a Higher-Order Thinking Skills (HOTS) framework for interpretation. Eight trips representing the active full-time commercial sector in the study area were observed between May and November 2022. The results have revealed that certified operators generally performed better than uncertified operators, but the difference was not large enough to demonstrate that certification alone ensured welfare-protective practice. Educational content was often present but shallow, with limited discussion of cetacean threats, conservation measures, and legal protections, while higher-order engagement and multilingual accessibility were notably weak. Vessel behavior showed a similar pattern: certified operators achieved higher average scores, yet close approaches, inconsistent adherence to conservative speed and maneuvering guidance, and occasional unacceptable practices were still recorded. Overall, some operations still expose whales to avoidable disturbance and fail to meet the educational standards that give ecotourism its conservation value. Responsible whale watching should therefore be evaluated not only by whether vessels find whales and satisfy tourists, but also by whether operators demonstrably protect animal welfare and cultivate informed conservation attitudes. As such, this study offers a regionally novel benchmark for future comparative research, management evaluation, and the development of more responsible cetacean ecotourism. Full article

Smart cities increasingly rely on distributed sensing and edge intelligence to support urban planning, mobility management, environmental monitoring, and critical infrastructure operation. However, large-scale urban Internet-of-Things deployments are constrained by heterogeneous device capabilities, limited energy availability, variable communication conditions, and data-governance requirements. Federated learning offers a data-locality-preserving alternative to centralized model training, but conventional federated learning strategies often assume full, random, or fixed client participation, which can lead to unnecessary energy consumption, communication overhead, or client starvation in resource-constrained urban environments. This paper proposes an Energy- and Communication-Aware Federated Learning strategy, termed ECA-FL, for smart city sensing systems. The main novelty of the work lies in the joint use of residual device energy and communication conditions to guide adaptive client participation and local training effort, providing a tunable resource–performance trade-off rather than an accuracy-maximizing strategy alone. The framework is evaluated through a controlled simulation-based study using a synthetic multi-class urban sensing proxy task distributed across 100 federated clients under strongly non-IID conditions. Compared with full-participation FedAvg, ECA-FL reduces cumulative energy consumption by 82.9% and communication overhead by 64.7%, while maintaining a final accuracy of 0.8124 compared with 0.8319 for FedAvg-full. Compared with rigid fixed-participation strategies, ECA-FL avoids severe learning degradation by adapting participation dynamically instead of excluding clients according to a static rule. A sensitivity analysis further shows that the trade-off parameter controls the balance between learning performance and resource conservation, allowing the framework to be adjusted according to different deployment priorities. The results support the hypothesis that adaptive energy- and communication-aware participation can substantially reduce operational cost while preserving acceptable learning performance within the adopted simulation setting. The study provides practical design insights for sustainable, communication-conscious, and data-locality-preserving federated learning in smart city sensing infrastructures. Full article

Tourism development potential and observed development performance do not necessarily evolve synchronously, particularly in old industrial and restructuring regions where attraction supply, market linkage, and visitor experience may be spatially uneven. This study develops a multi-source diagnostic framework for identifying tourism potential–performance mismatch across the 14 prefecture-level cities of Liaoning Province, China. Drawing on Ctrip review texts, rating scores, timestamps, platform-displayed reviewer-origin labels, A-level scenic-spot point data, and annual official city-level tourism statistics, the study constructs three dimension-specific sub-indices—the Scenic Experience Index (ESI), the Market Linkage Index (MLI), and the Attraction Foundation Index (AFI)—and synthesizes them into a Comprehensive Potential Index (CPI). The CPI is then compared with an Observed Performance Index (OPI) constructed from domestic tourist arrivals and domestic tourism revenue for 2016–2022. The results show that attraction foundation contributes most strongly to composite tourism potential, while market linkage and scenic experience condition how this structural basis is associated with observed outcomes. The CPI–OPI comparison identifies three relationship types: matched, potential-leading, and performance-leading cities. Dalian and Shenyang are high-level matched cities, Benxi and Jinzhou are high-potential but under-converted cities, and Anshan and Dandong are performance-leading cities. These findings demonstrate that favorable structural tourism conditions are not automatically transformed into realized market performance. The study contributes a multidimensional, gap-analysis-based diagnostic architecture that can support tourism-related spatial planning and territorial governance in transitional regions. Full article

Introduction: A major health issue in individuals living at high-altitude regions is an increase in the number of red blood cells (RBCs). This condition generates a series of physiological alterations including the nervous system, where damage can occur due to increased blood viscosity. This increased viscosity, in turn, could compromise oxygen uptake, potentially linked to a degree of cognitive impairment. Objective: To determine the association between exposure to chronic hypoxia and sleep quality with the degree of cognitive impairment in a young adult population residing at different altitude levels. Methodology: A cross-sectional study was conducted with 200 apparently healthy subjects (aged 21–26 years) permanently residing in four Peruvian cities: Lima (154 m), Arequipa (2335 m), Puno (3820 m), and La Rinconada (5100 m) (n = 50 per location). Physiological profiles (SpO₂, blood pressure, heart rate, hemoglobin, and hematocrit) were measured. Cognitive impairment and sleep quality were evaluated using the Montreal Cognitive Assessment (MoCA) and the Pittsburgh Sleep Quality Index (PSQI). Sex-stratified hierarchical multiple linear regression models with bootstrapping were utilized for independent correlation analysis. Results: Hemoglobin levels gradually increased with altitude, peaking at 19.47 ± 3.01 g/dL in La Rinconada, while SpO₂ decreased to 81.64%. Moderate-to-severe cognitive impairment was exclusively restricted to the extreme altitude population of La Rinconada, where only 10% of subjects remained unaffected. In the sex-stratified multivariate regression, residency in La Rinconada initially served as a robust negative predictor of MoCA scores among women (β = −5.52, p < 0.001); however, this geographical effect lost statistical significance after adjusting for biological variables in Model 2 (β = −4.72, p = 0.178). In the fully adjusted models, neither individual hemoglobin levels nor SpO₂ fluctuations displayed an independent linear association with cognitive performance in either sex (p > 0.05). Sleep quality was poor across cohorts but showed no significant association with cognitive impairment (p = 0.174). Conclusions: Chronic exposure to severe hypoxia (>5000 m) is associated with a greater presence of cognitive impairment, which is largely accounted for by individual physiological adaptations rather than isolated, linear effects of independent hematological or subjective sleep parameters. Full article

Urban freight externalities are increasingly addressed through regulation policies targeting both vehicle access and loading zones management. While urban vehicle access regulations and loading and unloading zone management are widely applied, existing research has largely regarded them as separate policy domains, overlooking their potential interdependence within urban freight governance. This study develops an exploratory comparative typology of UVARs and loading zone management across selected global cities. A hierarchical clustering method was applied to a harmonized set of indicators to identify distinct urban freight governance typologies. The UVAR clustering analysis was conducted on 39 cities with freight-related UVARs, while the loading zone clustering analysis was conducted on 39 cities with formal loading management zones. The cross-analysis suggests some co-occurrence patterns between UVARs and loading zone typologies. But the chi-square test does not provide statistical evidence of dependence. Therefore, this study can be interpreted as an exploratory mapping of regulatory configurations. The findings provide a comparative basis for future research linking urban freight regulatory typologies with environmental, operational, economic, and social performance indicators. Full article

The escalating security risks associated with unauthorized unmanned aerial vehicles (UAVs) in advancing smart cities necessitate the development of robust active countermeasures. This work presents a novel approach centered on a UAV-borne flexible net system and provides a rigorous investigation into its complex nonlinear dynamics. This study establishes a lumped-mass, semi-spring–damper dynamic model of the flexible capture net, characterizing its key dynamic properties, including deployment performance, aerodynamic attitude, and the high-impact phenomena of collision and entanglement with the target UAV. To verify the reliability of the proposed method, numerical simulations are combined with field tests for systematic validation. Comparative analysis reveals excellent quantitative agreement, with over 80% conformity in the net’s spatial configuration between simulated and experimental results. This paper illuminates the fundamental principles governing energy dissipation and transient tension dynamics pre- and post-capture. This study provides preliminary evidence for the feasibility of the proposed method and identifies key directions for future investigation. The findings offer guidance for the design and optimization of future systems intended to neutralize low, slow, and small (LSS) aerial threats. Full article

Urban environments increasingly rely on interconnected digital infrastructures like IoT devices, SDN-enabled networks, and cloud platforms to support essential municipal services. Ensuring the resilience of these systems requires advanced, data-driven mechanisms capable of detecting and mitigating cyber disruptions. This study presents Persian Eagle, a hybrid machine learning and deep learning framework designed to enhance the cyber-resilience of urban digital infrastructures by providing high-precision detection of Distributed Denial of Service (DDoS) attacks. DDoS attacks disrupt service availability by flooding targets with massive malicious traffic orchestrated through botnets, and in critical infrastructures, disruptions can be life-threatening. The proposed framework integrates multi-stage data preprocessing, SMOTE-based class balancing, and a four-phase feature-selection pipeline combining filtering, statistical ranking, PCA, and XGBoost. Seven complementary classifiers, including Random Forest, SVM, Gaussian Naive Bayes, XGBoost, MLP, LSTM, and Autoencoder, are bonded through a stacking cooperative with a Gradient Boosting meta-learner. The framework was evaluated on CICDDoS2019 and CICIDS2017 datasets, and achieved near-perfect performance up to 99.9998% accuracy, demonstrating strong generalization across diverse attack scenarios. By offering a scalable, transparent, and data-driven detection mechanism, Persian Eagle maintains urban digital-risk management and supports the continuity and resilience of critical smart-city services. Full article

Public spaces are important components of urban life, supporting social interaction, recreation, and environmental outcomes. Their success, however, depends not only on their physical provision but also on governance structures that guide their daily operation and maintenance routines. This study examines how operational governance and management practices influence user perception of public spaces by comparing two urban parks in Kathmandu: Ratna Park, a major city-level space, and Nandi Keshwor Bagaicha Park, a neighborhood-scale park. Using a mixed-method approach, the research employed a user survey (n = 191), interviews, and field observations. Survey data were used to develop composite indices for maintenance, safety, amenities, and user comfort. Descriptive statistics, Pearson correlations, independent-samples t-tests, and multiple regression models were used to examine the influence of governance on user perception. The findings reveal notable differences between the two parks. Nandi Keshwor Bagaicha Park scored higher on perceived safety (mean = 4.30) and comfort (mean = 4.01), while Ratna Park showed stronger performance in amenities (mean = 3.91). Although correlations between governance indicators and comfort were weak, regression analyses showed that maintenance, safety, and amenities accounted for only a small portion of the comfort variance (r² = 0.03). These findings indicate that operational variables alone do not fully explain user perception and suggest that broader management practices and patterns of use may also influence perceptions of comfort. This study provides exploratory empirical insight into public space governance and highlights the importance of strengthening operational systems and management practices in contemporary cities. Full article

Accurate and automated extraction of solid waste sites from remote-sensing imagery constitutes a pivotal demand for contemporary environmental regulation and risk mitigation. However, in high-resolution remote-sensing imagery, solid waste sites are typically represented as a single semantic image object (SIO), which is composed of multiple physical image parcels (PIPs) exhibiting significant variations in scale, morphology, and spectral properties. This intrinsic heterogeneity substantially increases the complexity and uncertainty of multi-class site identification. To address this challenge, this paper proposes WasteMOE Net (WMN), which is developed based on the core concept of modeling the SIO–PIP relationship. WMN adopts a heterogeneous expert selection mechanism combined with a nested mixture-of-experts architecture. It thus enables adaptive perception of complex PIPs across diverse scenarios and their integrated discrimination at the SIO level. In addition, by incorporating the explicit nonlinear representation capability of the KAN network, WMN effectively improves multi-class recognition accuracy while maintaining computational efficiency. Furthermore, this study constructs a high-resolution solid waste site dataset in accordance with the SIO–PIP-aware annotation principle, encompassing five representative categories: tailings ponds (TP), construction spoil sites (CSS), landfill sites (LS), garbage dump sites (GDS), and excavation sites (ES). Experimental results show that WMN achieves mAP50 values of 74.2% (GDS), 63.5% (CSS), 80.9% (ES), 85.4% (TP), and 83.1% (LS) in detection tasks, and 75.4%, 64.1%, 83.0%, 86.7%, and 84.1% for the corresponding categories in segmentation tasks. It achieves competitive performance compared with state-of-the-art methods in both tasks. Further, in a real-world application over Loudi City, China, WMN completed the processing of a 490.67 km² area within 1.34 h. The recognition accuracies for GDS and ES reached 54.8% and 65.3%, respectively. Finally, the proposed method has been successfully integrated into a GIS-based solid waste pollution risk prevention system, which markedly boosts the overall efficiency of environmental monitoring and on-site inspections. Full article

Background: This study examined how clinical nurses’ communication competence, grit, and leader–member exchange (LMX) contribute to nursing performance, aiming to identify key predictors to support workforce development and organizational policy planning. Methods: This study was conducted as a descriptive correlation study targeting 190 clinical nurses working at a general hospital in S City. Data were collected through a self-report questionnaire and analyzed using descriptive statistics, t-test, ANOVA, Pearson correlation coefficient, and hierarchical regression analysis using the IBM SPSS/WIN 25.0 program. Results: On a 5-point scale, the mean score of nursing performance was 4.05. Communication skills (β = 0.52, p < 0.001), grit (β = 0.23, p = 0.002), and clinical experience (β = 0.18, p = 0.022) significantly affected nursing work performance, with communication skills having the greatest effect. This model explained 47.3% of the nursing performance. Conclusions: To improve the nursing performance of nurses, strengthening communication skills and grit, supporting professional development, and recognizing clinical experience are crucial. These findings suggest that integrating such programs into ward nurse training may contribute to improved nursing performance and organizational effectiveness. To develop these competencies and evaluate their effectiveness, follow-up research is continuously needed. Full article

Background/Objectives: This study sought to investigate the relationship between workplace violence exposure and job burnout among Korean paramedics and the moderating roles of different sources of social support, including family, coworker, and organizational support, on this relationship. Methods: Participants were 175 paramedics working in B city, South Korea. Data were collected through an online survey conducted from 15 July to 30 July 2025. Workplace violence exposure, family support, coworker support, organizational support, and job burnout were assessed using validated self-report questionnaires. Descriptive statistics, correlation analyses, and three-step hierarchical regression analyses were performed using the SPSS program. Results: Workplace violence exposure was positively associated with job burnout among paramedics. Among the three sources of social support, organizational support was associated with lower levels of job burnout. Family support moderated the association between workplace violence exposure and job burnout, whereas the moderating effects of coworker support and organizational support were not statistically significant. Conclusions: The findings suggest that organizational support and family-based support strategies may be important resources for addressing job burnout among paramedics exposed to workplace violence. These findings may contribute to a better understanding of support mechanisms associated with job burnout among paramedics and inform future intervention development and organizational support strategies. Full article

Smart Mobility plays a key role in Smart Cities, given its ability to support the rollout of intelligent transport systems, allowing for more sustainable urban transportation and greater interoperability across diverse mobility modes. Furthermore, Smart Mobility is essential to maximize the quality of life for the community while advancing principles of sustainability, economic development, technological innovation, and collaborative governance. Real-Time Traffic Management (RTTM) emerges as a vital technology for optimizing traffic management in Smart Mobility. Using the PRISMA framework, the proposed systematic literature review examines 165 peer-reviewed publications related to RTTM research work published between 2019 and 2025. This review identified eleven application domains, with Urban Traffic Management Systems (36.97%) and Artificial Intelligence (AI) and Predictive Analytics (12.73%) representing the most prominent areas. A retrospective analysis of the literature on control architecture used in closed-loop feedback systems indicates that most studies (89%) have adopted a more dynamic control model, while 7.8% adopted a Digital Twin (DT)-based approach. However, several implementation barriers persist, including limited integration of online optimization and learning loops into RTTM systems, gaps in performance comparisons between simulation and reality, scalability issues due to heterogeneous environments, inconsistent data quality caused by various sensor types, and difficulties integrating sensors into a control system. In addition, this paper proposes a taxonomy of RTTM applications and control architectures, while outlining key practical barriers to implementation and charting future research directions for advancing Smart Mobility through robust RTTM. Full article