Search Results (1,343)

Urban heat islands (UHI) intensify as cities expand, exposing residents to elevated thermal stress and complicating urban climate adaptation planning. Existing satellite-based approaches to detecting surface urban heat islands (SUHI) typically rely on a single class of data and narrow temporal windows, limiting their ability to capture the full range of processes driving surface overheating. This study develops and evaluates a random forest model for SUHI detection in Warsaw, Poland, integrating two classical spectral indices (NDVI, NDBI) derived from Landsat 8/9 Collection 2 imagery with three land-cover probability layers (built-up, tree, water) from the Dynamic World deep-learning product, processed in Google Earth Engine. Both a multi-year summer median composite (2020–2025) and individual annual summer composites were used, the latter enabling a leave-one-year-out temporal validation. Heat island pixels were defined as those whose land surface temperature anomaly exceeded +3 °C relative to the study area mean, a local criterion rather than a city-versus-rural contrast. The model achieved high and stable performance (accuracy = 0.831, AUC = 0.910 on the test set; AUC = 0.907 ± 0.004 in five-fold cross-validation and 0.905 ± 0.018 in leave-one-year-out validation). An ablation analysis showed that combining the probability layers with the spectral indices clearly outperformed the indices alone (AUC = 0.852 vs. 0.905), whereas the additional gain over the Dynamic World layers alone remained within uncertainty. Vegetation-related predictors (NDVI and tree probability) contributed more to classification than built-up indicators. These results indicate that vegetation deficit, rather than built-up presence alone, is the primary driver of surface overheating in Warsaw and that the proposed open-data workflow offers municipalities a low-cost screening tool for identifying priority areas for climate adaptation and, thanks to its reliance solely on open data, can be adapted to other cities, subject to further validation. Full article

Geospatial heterogeneity plays a critical role in shaping housing tenure among China’s floating population. Using three binary indicators—homeownership, social housing, and informal housing—this study measures migrants’ housing affordability, access to public housing resources, and residential stability. Based on nationally representative data from the 2017 China Migrants Dynamic Survey (CMDS), we examine how geospatial characteristics influence migrants’ housing tenure. The results show that both origin and destination characteristics significantly affect housing outcomes. Migrants moving to higher-tier cities are more likely to obtain social housing because these cities provide better public services and greater opportunities to accumulate human and social capital. However, high housing prices in megacities substantially reduce the likelihood of homeownership. A clear birthplace effect is also observed: migrants originating from eastern China and megacities have significantly higher probabilities of obtaining both social and owner-occupied housing, reflecting the advantages associated with more developed places of origin. In addition, marital status, duration of migration, educational attainment, and hukou status consistently influence housing tenure. These findings highlight the importance of geospatial heterogeneity in explaining housing differentiation among China’s floating population and provide evidence for improving housing policies during rapid urbanization. Geospatial factors shape both the housing choices and associated stratification patterns of China’s internal migrants, thereby exerting a discernible influence on the national trajectory toward sustainable development. Accordingly, policymakers must prioritize the pursuit of balanced regional development—with particular emphasis on aligning regional economic performance, infrastructure provision, public service accessibility, and the spatial distribution of population. Such measures would help attenuate the impact of geospatial disparities on migrant housing differentiation and, in turn, advance the country’s broader agenda for sustainable development. Full article

Millimeter-wave (mmWave) vehicular-to-everything (V2X) links are highly vulnerable to sudden blockages in dense urban traffic. Since terrestrial roadside links can degrade rapidly, and alternative ground paths are often limited, maintaining reliable service with only ground networking resources remains challenging. To enhance link reliability by exploiting aerial relay resources in air–ground integrated networks, this paper proposes a vision-assisted unmanned aerial vehicle (UAV) relay triggering framework. The framework uses roadside multi-camera images to predict the future link state of a target vehicle and triggers a UAV decode-and-forward (DF) relay before the direct roadside-unit (RSU)–vehicle link becomes unreliable. To enable target-specific prediction, a template-guided image-matching module is developed to localize the target vehicle in multi-view images. The matched features are fused and temporally modeled to predict future LoS, NLoS, and Absent states, with the predicted NLoS probability further used to determine the UAV activation decision through a probability-based triggering policy. Simulation results on a 3D ray-tracing urban V2X dataset show that the proposed dual-view predictor achieves about 99% validation accuracy, compared with about 87% for the single-view baseline. The proposed relay triggering scheme reduces the outage probability from 15.08% for RSU-only transmission and 4.49% for reactive relaying to 0.76%, and improves the 5th-percentile rate from 11.72 Mbps to 22.49 Mbps over reactive relaying. Full article

Background/Objectives: Socioeconomic indicators and geographic factors influence pediatric cancer outcomes, but evidence in Romania is limited. Methods: This retrospective cohort study included 6247 patients aged 0–19 years diagnosed with cancer and recorded in the Romanian National Pediatric Oncology and Hematology Registry between 1 January 2010, and 31 December 2024. Kaplan–Meier analysis was used to estimate the survival probabilities across four strata of regional and county socioeconomic categorization and two strata of community marginalization status. Unadjusted and multivariable Cox proportional hazards models estimated hazard ratios (HRs) and 95% confidence intervals (CIs), adjusting for sex, age group, primary cancer type (ICCC-3), tumor behavior, and geographical residence. Subgroup analyses assessed the association between rurality and pediatric cancer survival, irrespective of community marginalization status. Results: Survival probabilities were consistently lower among patients residing in more socioeconomically disadvantaged regions, counties, and marginalized communities (log-rank p ≤ 0.0001). In the most deprived strata, 5-year survival for regions, counties, and communities was 69.86% (95% CI: 67.49–72.10), 67.47% (95% CI: 63.32–71.26), and 65.51% (95% CI: 61.33–69.35), respectively. In adjusted models, residence in the least deprived regions (HR = 0.783, 95% CI = 0.744–0.952) and counties (HR = 0.749, 95% CI = 0.620–0.905) was associated with improved survival, a 22% and 25% lower risk of death, respectively, compared with residence in the most deprived categories. Community marginalization was associated with lower survival outcomes in unadjusted analyses, but was not significant after adjustment. Rural residence was associated with a 44% higher risk of death (HR = 1.436, 95% CI = 1.304–1.582), with a 5-year survival of 66.76% (95% CI: 64.91–68.53) among rural patients, compared with 75.93% (95% CI: 74.28–77.48) in urban patients. Conclusions: This is Romania’s first pediatric cancer survival study to evaluate persistent social and geographical disparities. Survival outcomes were consistently lower in more socioeconomically disadvantaged regions and counties, while the findings suggest that rural residence may explain the observed differences in survival at the community level. Policymakers and health systems in Romania should focus on covering pediatric cancer patients with appropriate proximity services across the entire national territory, thus enabling all patients to get timely access to quality care. Full article

This study analyzes educational migration patterns in small towns across Dambovita County, Romania, focusing on spatial accessibility and gender disparities. Officially designated as Rank III urban settlements under national legislation, these small towns face structural regional imbalances. The research is based on a comprehensive structured questionnaire administered to a target sample of 143 graduates from 15 high schools across the surveyed municipalities. Given that the data collection depended on institutional access granted by school administrations, a non-probability purposive sampling approach was employed. The survey incorporated open-ended and closed-ended items capturing students’ institutional choices, secondary education profiles, intentions to pursue matching higher education fields, and underlying motivations. Survey data were integrated with spatial analysis methods using QGIS 3.34 version isochrones modeling and the OpenRouteService (ORS) Matrix to evaluate travel times and distances to competing regional university centers. The results reveal gender-based patterns differences in student mobility: female high school students concentrate on a single metropolitan destination, whereas male high school students are more broadly distributed secondary vocational nodes. Ultimately, this research contributes to understanding how gender intersects with educational migration and highlights the influence of transport infrastructure and spatial friction in students’ decision-making processes. Full article

Urban unmanned aerial vehicle (UAV) navigation may require path planning that accounts for geometric obstacles and spatially varying communication-related risk. This paper presents a transformer-guided interference-aware 3D path-planning method for urban voxel environments. A 3D convolutional neural network (CNN)–transformer network predicts a dense route probability field from occupancy, electromagnetic risk, start–goal, and auxiliary planning channels. The field is restored to the raw-map resolution and used only as a search prior for A* on the original occupancy and risk maps. Obstacle avoidance, endpoint correctness, 6-connected motion (each move reaches one of six face-adjacent voxels, with no diagonal motion), and final path cost evaluation are enforced by graph search rather than by the neural model. On 320 synthetic urban cases covering four map sizes and four building density settings, Guided A* achieves a 27.7× speedup over A* and an 11.9× speedup over Weighted A*, while reducing expanded nodes by 91.2% relative to A*. The mean path cost and electromagnetic cost increase by 2.7% and 5.7%, respectively. Compared with the rapidly exploring random tree (RRT), the method reduces path cost by 10.1% and electromagnetic exposure by 12.0% at similar runtime. A post-training sensitivity study further identifies an empirical balance between route-prior guidance, electromagnetic risk avoidance, route length, and search effort, while the Manhattan weight exhibits the expected heuristic inflation efficiency–quality trade-off. An extended model trained on a larger mixture of procedural and Sionna RT ray-traced data, including real OpenStreetMap building geometry, is further evaluated without retraining on two real-geometry benchmarks, UrbanRadio3D and an OpenStreetMap–Sionna RT suite, where Guided A* retains a 100% success rate and reduces expanded nodes by 97–99% relative to A* while increasing mean path cost by at most 1.7%. Full article

In signalized urban road networks, periodic signal phase switching is a key factor influencing traffic-flow stability and operational efficiency. With the rapid development of Connected and Automated Vehicle (CAV) technologies, exploiting their enhanced perception, communication, and cooperative control capabilities has become an important research topic. To characterize the acceleration, deceleration, queueing, and discharge disturbances induced by signal phase transitions, this study proposes a Signal-Constrained Optimal Velocity Model (SC-OVM). By introducing a continuous signal decision function, the proposed model dynamically couples traffic signal states with vehicle-following behavior, including preceding-vehicle following and stop-line tracking within a unified optimal-velocity framework. Furthermore, linear stability analysis, boundary critical condition analysis, and disturbance probability modeling are integrated to reveal the instability mechanism caused by abrupt signal phase transitions, with extensions to stochastic prediction errors and adaptive Signal Phase and Timing (SPaT) inputs. Numerical simulations show that SC-OVM-controlled CAVs can smooth vehicle trajectories, reduce average delay, improve end-of-green passing performance, and achieve a balanced performance in efficiency, stability, and safety compared with the Full Velocity Difference Model (FVDM), Virtual Leading Vehicle model (VLV), and Intelligent Driver Model (IDM). The findings provide theoretical support and practical insights for stability modeling and cooperative control of mixed-traffic flow at signalized intersections. Full article

Reliable intensity–duration–frequency (IDF) curves are essential for the design of stormwater drainage systems and flood mitigation infrastructure; however, conventional IDF relationships assume stationarity and may underestimate future rainfall extremes under climate change. This study developed climate-informed IDF curves for Makkah, Saudi Arabia, using hourly observed rainfall records (1985–2025) and projections from five CMIP6 Global Climate Models (EC-Earth3-CC, CNRM-CM6-1, GFDL-ESM4, MPI-ESM1-2-LR, and UKESM1-0-LL) under the SSP245 and SSP585 scenarios. Spatial downscaling was first carried out using bilinear interpolation, after which the resulting data were corrected for systematic bias using the Delta Change method. Daily precipitation projections were subsequently disaggregated to an hourly timescale using an enhanced KNN-MOF approach. Annual maximum precipitation series were then derived for durations of 1, 2, 3, 6, 12, and 24 h and fitted to a range of candidate probability distributions. The goodness of fit was evaluated using the log-likelihood, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). Across the five CMIP6 models, two emission scenarios, and six rainfall durations, the Log-Pearson Type III distribution consistently yielded the most satisfactory fit. Historical analysis estimated 100-year rainfall depths ranging from 7.84 mm (1 h) to 38.29 mm (24 h), while future projections indicated substantially higher design rainfall intensities under several climate models. For example, under the SSP585 scenario, the 100-year 1 h rainfall intensity reached 29.73 mm h⁻¹ for EC-Earth3-CC, whereas MPI-ESM1-2-LR projected a 102% increase in the 6 h 100-year intensity relative to SSP245. Sherman equations were successfully fitted to develop continuous IDF relationships, while bootstrap resampling and Bayesian inference quantified projection uncertainty. The multi-model ensemble indicated increasing uncertainty with return period, particularly for the 100-year event, highlighting the importance of incorporating uncertainty into engineering design. The proposed framework provides robust climate-informed IDF curves for supporting resilient urban drainage design, flood-risk assessment, and water resources planning in arid environments. Full article

Long-term influent flow projection governs the sizing of wastewater treatment plants (WWTPs), yet conventional practice estimates future flows using per capita generation rates and coefficients assumed to remain constant over time, neglecting the fact that, in urbanizing catchments, the fraction of generated wastewater that reaches the treatment plant increases as the sewer network expands and densifies. This study develops and validates an explicit-structure model that separates demographic wastewater generation from hydraulic conveyance, disaggregates the service area into fully contributing and partially contributing sectors, and introduces a time-dependent transport coefficient, $k\left(t\right)$. Applied to the La Chira WWTP (Lima, Peru; approximately 2.6 million inhabitants), the model was evaluated through leave-one-year-out cross-validation against both a static transport model and an aggregated formulation. The proposed formulation consistently outperformed the alternatives in out-of-sample prediction (Nash–Sutcliffe efficiency of 0.965 and mean absolute percentage error of 1.88%, compared with 0.825 and 0.799 for the benchmark models), providing falsifiable evidence of the value of spatial disaggregation and time-varying transport representation. The transport coefficient increases from 0.491 in 2017 to 0.813 in 2040 under the linear reference specification, with a logistic alternative—statistically indistinguishable in calibration—bounding the projection from below; a formal Shapley decomposition attributes 44% of the projected flow increase to this coefficient. The observed flow rate in 2025 (7.341 m³ s⁻¹) provides an external validation point, predicted with a relative error of 0.7%. Mean influent flow is projected to reach 10.12 m³ s⁻¹ by 2040 (95% CI: 8.55–11.69), representing a 61% increase above the design average flow and approaching the design peak capacity (11.3 m³ s⁻¹), with an exceedance probability of the annual mean of approximately 5%. These results indicate a progressive approach to hydraulic saturation within the planning horizon. The proposed framework is robust to alternative per capita generation assumptions, mechanistically grounded, interpretable, and transferable to sanitation systems characterized by evolving coverage and network connectivity. Full article

Mobile crowd sensing (MCS) is an emerging sensing paradigm that leverages mobile devices for large-scale data collection, where an MCS platform recruits workers to perform time-sensitive sensing tasks. In practice, worker travel times are inherently uncertain due to dynamic urban environments—traffic congestion, road closures, and unplanned detours cause actual delays to deviate from historical patterns. However, existing worker recruitment methods typically optimize expected-case performance based on static historical distributions, providing no robustness guarantee under distribution shift. To address these challenges, this paper proposes DRO-IGR, a worker recruitment framework based on Distributionally Robust Optimization (DRO) and an Iterative Greedy Recruitment algorithm. The framework introduces a DRO Probability Estimation Engine that treats each worker’s true delay distribution as lying within a Wasserstein ambiguity set centered on its empirical history, with a radius that adapts to data sparsity, behavioral variability, and task importance. Strong duality reduces the resulting worst-case optimization to an efficient one-dimensional convex search. Driven by these robust probability estimates, a marginal-gain scoring rule iteratively selects worker–task pairs that maximize importance-weighted robust utility per unit cost. Extensive experiments on three synthetic spatial distributions and the real-world T-Drive Beijing taxi dataset show that, compared to existing approaches, DRO-IGR completes more tasks, achieves a higher total importance sum, and attains greater utility within the same budget constraint. Full article

Background and Objectives: Out-of-hospital cardiac arrest (OHCA) is one of the most clinically significant conditions and can lead to rapid death if cardiopulmonary resuscitation (CPR) or defibrillator use is not performed. Despite this, in approximately half of cases of witnessed OHCA, bystanders do not initiate resuscitation maneuvers. The factors underlying this phenomenon are varied but not yet fully understood. The aim of our analysis is to identify predictors that may indicate a higher likelihood of not performing resuscitation maneuvers. Materials and Methods: Data from cardiac arrests managed by the emergency medical system of the Lombardy region between 1 July 2024 and 30 June 2025 were analyzed. All missions involving cardiac arrests assisted only by lay bystanders were included in the analysis. Results: A total of 12,066 events were analyzed, of which only 4233 were assisted by laypeople. According to the logistic regression model, cardiac arrests occurring in urban settings (OR 0.60; 95% CI: 0.51–0.71), non-medical events (OR 0.44; 95% CI: 0.37–0.51), female patients (OR 0.80; 95% CI: 0.70–0.91), patients over 80 years old (OR 0.40; 95% CI: 0.35–0.45), and events occurring at home (OR 0.33; 95% CI: 0.28–0.39) were associated with a lower likelihood of performing chest compressions. Conversely, when the emergency service arrived within 15 min (OR 1.31; 95% CI: 1.14–1.51), the probability of receiving chest compressions increased. Conclusions: The presence of such a significant gap related to age and sex is noteworthy, prompting greater attention to the educational material presented during BLS-D courses. Moreover, these factors should be considered by the dispatch center when providing pre-arrival instructions to laypeople for initiating chest compressions. Specific communication training should be developed, and communication in these critical phases should be the subject of further study. Full article

Heavy metal contamination in urban topsoil is one of the most serious environmental threats to children’s health, particularly through ingestion, dermal contact, and inhalation exposure routes. The objectives of this study were: (1) to assess the probabilistic exceedance-based priority of eight heavy metals (As, Cd, Co, Cr, Cu, Ni, Pb, and Zn) with respect to regulatory threshold exceedance in Debrecen, Hungary; (2) to map the spatial distribution of exceedance probabilities using sequential indicator simulation (SISIM) with 100 equiprobable realizations per element (1000 for Cr) on a 50 m grid; and (3) to develop a toxicologically weighted composite exceedance index based on the Hungarian regulatory action thresholds and classify the results into priority categories. For Cd, the exceedance probability exceeded p > 0.50 in approximately 98% of the study area, and for Cr, in approximately 82% of the study area (regenerated at $N = 1000$; the Cr threshold lies near the sample median, so the p > 0.50 area is ensemble-size sensitive and was under-converged at $N = 100$). Approximately 86% of the study area fell into the Very Low Priority class, approximately 14% into the Low Priority class, and less than 0.1% of the area exceeded the Moderate Priority threshold. Monte Carlo perturbation of the child exposure relevance factors confirmed strong spatial rank stability of H(x) (median Spearman $\rho = 0.989$), indicating that the priority pattern is robust even though areas close to the Very Low Priority/Low Priority boundary may change class. This paper contributes single-threshold exceedance-probability maps at regulatory limits and a toxicity-weighted exceedance-priority index $H\left(x\right)$—a methodological and interpretive advance over our previous concentration mapping, using the same measurements with no new sampling. By constructing the composite index is toxicity-weighted: arsenic and cadmium carry ≈88% of the child weight, so $H\left(x\right)$ chiefly resolves As- and Cd-driven priority, with the remaining metals refining local class boundaries. Receptor prioritization is a screening output to guide confirmatory sampling, not a definitive risk classification. Full article

Urban green-space carbon sinks can contribute to reducing urban carbon emissions and supporting carbon-neutrality goals. This study examined the associations between urban green-space landscape patterns and above-ground carbon storage (AGCS) in Shenyang. The spatial distribution of estimated AGCS was mapped using ArcGIS and remotely sensed imagery. Pearson correlation analysis, geographically weighted regression, and an exploratory data-binning approach were used to evaluate associations between landscape pattern metrics (LPMs) and AGCS and to identify their thresholds. At the study-area scale, AGCS exhibited strong positive correlations with class area (CA) and largest patch index, weak negative correlations with patch density and landscape division index, and a weak positive correlation with Connectivity Index (CONNECT). These relationships varied among the three ring zones. Spatially, CA was positively associated with AGCS throughout the study area, whereas positive local coefficients for CONNECT occurred in 51.8% of the study area. The low- and high-carbon-storage thresholds were 7.26 and 10.79 for CA and 8.33 and 12.08 for CONNECT, respectively. Higher CA and CONNECT values above their low-carbon-storage thresholds were associated with a lower probability of a low-carbon-storage zone. These findings provide context-specific evidence that may inform urban green-space planning in Shenyang and offer a methodological reference for comparable case studies. Full article

The receiver autonomous integrity monitoring (RAIM) and Global Navigation Satellite System (GNSS) fault detection literature uses a group of statistical detectors that are often introduced with different names, coordinate systems, and derivation styles. This makes it difficult for new researchers to determine whether two methods use different information or only express the same inconsistency through different statistics. This paper provides a detector-centered tutorial review of statistical snapshot fault detection with a unified whitened linearized model. The chi-squared detector, parity-space detector, Baarda w-test, range comparison detector, jackknife detector, solution separation detector, and generalized likelihood-ratio test are derived with consistent notation. For each detector, the statistic construction, null and alternative distributions, threshold rule, and minimum detectable bias (MDB) are presented. A relationship map was developed to distinguish exact equivalence, projection relations and linear transformations among these detectors. An illustrative validation was then conducted with a real satellite geometry collected in an urban environment and synthetic Gaussian faults. The results verify the relationship checks, detection-probability behavior, and MDB calculations in a reproducible setting. Full article

Against the backdrop of China’s advancing strategy for ecological protection and high-quality development in the Yellow River Basin, improving urban land green-use efficiency is critical to reconciling ecological conservation, resource efficiency, and coordinated regional development. Using data for 64 cities across seven urban agglomerations, this study applies a Super-SBM model to estimate urban land green-use efficiency from 2012 to 2024 and combines Dagum Gini decomposition, kernel density estimation, and conventional and spatial Markov chains to examine its spatial disparities and dynamic evolution. Four principal findings emerge. First, efficiency increased overall but exhibited a clear spatial gradient, with higher levels in the upper and middle reaches and lower levels downstream. Second, overall disparities initially widened, subsequently narrowed, and rebounded slightly toward the end of the study period. Between-agglomeration disparities were the largest component on average and during most of the study period; however, their contribution declined, and transvariation density became the largest component in 2023–2024, indicating greater overlap among the efficiency distributions of urban agglomerations. Third, both within- and between-agglomeration disparities exhibited marked heterogeneity, with distinct trajectories across and within urban agglomerations. Fourth, the conventional Markov-chain analysis revealed strong state persistence, a pronounced tendency for high-efficiency states to persist, and transitions occurring predominantly between adjacent classes. The spatial Markov results further showed that local transition probabilities varied across neighborhood efficiency conditions. Overall, efficiency disparities did not exhibit sustained unidirectional convergence; instead, they were characterized by phased adjustment, increasing cross-agglomeration overlap, and neighborhood-conditioned state transitions. By establishing a sequential framework of “efficiency measurement–disparity decomposition–distributional evolution–state transition,” this study provides empirical evidence for understanding the spatial disparities and dynamic evolution of urban land green-use efficiency in the Yellow River Basin. Full article