Search Results (7,825)

Water is increasingly acknowledged as a limited and strategically critical resource, particularly in regions where hydrological imbalances are structurally persistent. Across Europe, countries such as Spain, Turkey, Italy, and Greece face recurrent water scarcity driven by precipitation regimes characterized by low annual rainfall, pronounced temporal variability, and marked spatial heterogeneity. In response to rising water demand associated with tourism, agricultural intensification, and sustained demographic pressures, Spain has implemented a series of national water-management strategies over the past two decades. Notably, the National Hydrological Plan, enacted in July 2005, introduced more than one hundred immediate actions focused on modernizing hydraulic infrastructure and reinforcing the country’s desalination capacity. Furthermore, the Royal Decree issued in December 2007 established a comprehensive regulatory framework to promote and standardize water reuse practices nationwide. Within this context, reverse osmosis has emerged as a central technology for the desalination of seawater and brackish water, as well as for advanced water-reclamation applications. This work presents a consolidated examination of Spain’s water-resource management framework, drawing on historical material and recent advances to outline the current context of desalination and water reuse. It presents operational performance data from several full-scale reverse osmosis facilities, and reviews recent technological developments in the field, including newly engineered membrane modules, innovative system architectures, and the latest generation of large-diameter RO elements. Together, these advancements illustrate the evolving role of membrane-based desalination and water reuse in supporting water security in semi-arid regions. Full article

Hurricanes create compound hazards such as storm surge, flooding, and wind-driven debris that can degrade roadway capacity, fragment network connectivity, and hinder evacuation and shelter operations. From a sustainability perspective, improving evacuation planning is essential for reducing disaster-related losses, protecting vulnerable populations, and strengthening the resilience of coastal communities facing intensifying climate-driven hazards. This paper develops a micro-scale, agent-based evacuation modeling framework to assess evacuation performance under baseline and compound-hazard conditions, with emphasis on municipal decision support. The framework is demonstrated for Westbrook, Connecticut, at the census block-group scale in AnyLogic by integrating household locations, vehicle availability, road-network connectivity, and shelter capacities from publicly available datasets. Evacuation propensity and destination choice are parameterized using survey data, enabling empirically grounded decisions for in-town versus out-of-town evacuation among household-vehicle agents. Compound disruptions are represented through flood-related road closures derived from SLOSH storm-surge outputs and stochastic wind-related disruptions that dynamically constrain accessibility during the simulation. Scenarios are evaluated for Saffir–Simpson Category 1–2 and Category 3–4 hurricanes under baseline and compound conditions. Model outputs quantify normalized evacuation time, congestion and critical intersections, shelter demand and unmet capacity, evacuation failure, and spatial heterogeneity across block groups. Results indicate that compound flooding substantially increases evacuation times and failure rates, with the largest performance degradation concentrated in higher-vulnerability areas. Optimization experiments further compare the effectiveness of behavioral shifts, shelter-capacity expansion, and earlier departure timing in reducing delays and unmet shelter demand. Overall, the proposed framework provides transparent, reproducible, and scalable analytics that town engineers and emergency planners can use to evaluate evacuation readiness under compound hurricane impacts. Full article

Background: Frequent and short-term repeat visits place significant pressure on primary care-centered health systems, particularly in large metropolitan areas. Istanbul, with its high population density and heterogeneous sociodemographic profile, presents a unique context for understanding short-interval healthcare utilization dynamics. Objective: To examine short-term repeat healthcare utilization following an index primary care visit and to explore how district-level population and socioeconomic characteristics shape early post-visit care-seeking dynamics in a large metropolitan setting. Methods: This retrospective cohort study analyzed protocol records from 225 randomly selected FMUs across Istanbul. A total of 11,101 individuals who presented on 7 July 2025 were followed for 21 days, during which 26,743 healthcare contacts (index family medicine unit visits, recurrent FMU visits, and secondary/tertiary care visits) were captured. FMU repeat visits, higher-level utilization, district-level population density, and socioeconomic development level (SEGE-2022) were analyzed using descriptive statistics and district-level comparative analyses. Results: During the 21-day follow-up period, participants generated a total of 26,743 healthcare contacts. Across the full cohort, the median number of recurrent FMU visits was 0 (IQR 0–1), and the median number of secondary/tertiary visits was 0 (IQR 0–1). Among individuals who had repeat contacts, the median number of recurrent FMU visits was 1 (IQR 1–2), and the median number of secondary/tertiary visits was 1 (IQR 1–2). Repeat visits clustered in the first 7 days, whereas higher-level visits increased between days 10–21. Districts with lower SEGE status and high population density (e.g., Esenyurt, Bağcılar, Pendik, Küçükçekmece, Ümraniye) exhibited markedly higher repeat visit intensity. Spatial patterns indicated substantial clustering in western and socioeconomically disadvantaged districts. Multivariable regression analysis showed that visitor patient status was associated with higher secondary/tertiary care utilization (OR = 1.14, 95% CI 1.04–1.24), while higher SEGE scores were modestly associated with increased repeat FMU visits. Conclusions: Short-term repeat visits in Istanbul appear to be influenced not only by clinical needs but also by broader contextual factors such as socioeconomic disadvantage, population density, and health system organization. These findings suggest potential structural pressures within Türkiye’s primary care-centered system and highlight the potential value of district-specific interventions. Full article

Vehicle–infrastructure cooperative perception (VICP) extends the sensing capability of single-vehicle systems by integrating multi-source information from onboard and roadside sensors, thereby alleviating limitations in sensing range and field-of-view coverage. However, in complex urban environments, the robustness of such systems—particularly in terms of blind-spot coverage and feature representation—is severely affected by both static and dynamic occlusions, as well as distance-induced sparsity in point cloud data. To address these challenges, a 3D object detection framework incorporating point cloud feature enhancement and spatially adaptive fusion is proposed. First, to mitigate feature degradation under sparse and occluded conditions, a Redefined Squeeze-and-Excitation Network (R-SENet) attention module is integrated into the feature encoding stage. This module employs a dual-dimensional squeeze-and-excitation mechanism operating across pillars and intra-pillar points, enabling adaptive recalibration of critical geometric features. In addition, a Feature Pyramid Backbone Network (FPB-Net) is designed to improve target representation across varying distances through multi-scale feature extraction and cross-layer aggregation. Second, to address feature heterogeneity and spatial misalignment between heterogeneous sensing agents, a Spatial Adaptive Feature Fusion (SAFF) module is introduced. By explicitly encoding the origin of features and leveraging spatial attention mechanisms, the SAFF module enables dynamic weighting and complementary fusion between fine-grained vehicle-side features and globally informative roadside semantics. Extensive experiments conducted on the DAIR-V2X benchmark and a custom dataset demonstrate that the proposed approach outperforms several state-of-the-art methods. Specifically, Average Precision (AP) scores of 0.762 and 0.694 are achieved at an IoU threshold of 0.5, while AP scores of 0.617 and 0.563 are obtained at an IoU threshold of 0.7 on the two datasets, respectively. Furthermore, the proposed framework maintains real-time inference performance, highlighting its effectiveness and practical potential for real-world deployment. Full article

Background: Sensorimotor differences have frequently been reported in children with developmental dyslexia, but are often considered secondary or comorbid to phonological deficits. Within an embodied cognition perspective, reading acquisition emerges from dynamic interactions between bodily regulation, multisensory integration, and learning-related neural plasticity. Proprioception contributes to spatial orientation, motor coordination, and perceptual stabilization, while sleep-dependent processes play a critical role in the consolidation and automatization of cognitive and motor skills. Objectives: Building on early clinical observations, including the hypothesis proposed by Martins da Cunha, this review explores whether variations in proprioceptive processing and sensorimotor regulation may influence multisensory stability and the conditions under which reading skills develop in some individuals with dyslexia. Methods: This narrative synthesis integrates clinical observations and experimental paradigms examining proprioceptive function in children with dyslexia, including studies conducted in our laboratory over the past two decades. These investigations address postural regulation under varying attentional demands, laboratory measures of proprioceptive acuity, visuospatial localization tasks, multisensory interactions, and exploratory observations concerning sleep–wake regulation. Results: Across studies, children with dyslexia often show differences in proprioceptive processing associated with variations in postural regulation, visuospatial stability, and multisensory tasks. Laboratory measurements suggest reduced proprioceptive acuity in some individuals, with moderate correlations observed between proprioceptive sensitivity and reading-related measures. Additional observations suggest that nocturnal physiological regulation—including respiratory dynamics and sleep architecture—may interact with daytime sensorimotor stability and attentional functioning. Conclusions: Taken together, these findings support the hypothesis that variations in sensorimotor regulation across the sleep–wake cycle may influence the stability of multisensory processing and attentional conditions relevant for reading acquisition. Within this perspective, proprioception is not proposed as an alternative explanation for dyslexia but as a complementary dimension that may contribute to the heterogeneity of dyslexic profiles. Further longitudinal and controlled studies are required to clarify the relationships between sensorimotor regulation, sleep-dependent plasticity, and learning processes. Full article

Tailings backfilling (TB) is widely recognized as an environmentally friendly and engineering safe technique to enhance mining efficiency. However, the heterogeneous particle distribution in TB slurry, also-named the segregation phenomenon, can significantly affect the mechanical strength of the backfill, especially under high goaf conditions. Therefore, elucidating the spatial distribution characteristics of particles during high-goaf filling has become a crucial research focus for improving the mechanical behavior of tailings backfill. A systematic experimental investigation was conducted in this study, incorporating the similarity principle, to analyze the migration behavior of backfill slurry particles and to clarify how the different backfill heights influence the spatial distribution of fine, medium, and coarse particles. The results indicate a clear vertical variation in PSD. Based on statistical analysis of samples collected from different backfill height experiments, coarse particle content increased progressively from the upper to lower layers (median: 16.2%, 23.6%, and 25.0%), while medium-sized particles remained relatively stable (37.0%, 37.3%, 37.0%). Fine particles dominated overall but decreased with layers (45.6%, 38.8%, 38.3%). Coarse particles tended to settle downward due to gravitational forces, whereas fine particles migrated upward. The distribution of medium-sized particles remained largely homogeneous. Fine and coarse particles were subjected to opposing driving forces. Meanwhile, particles maintained an approximately symmetrical distribution in the horizontal direction. Moreover, when the backfill height exceeded 800 mm, a notable intensification of stratification occurred, indicating a strong height-dependent transition in segregation behavior. In contrast, in the horizontal direction, the PSD showed no clear dependence on backfill height. These findings provide new insights into the mechanisms of particle segregation within backfill materials, offering a theoretical foundation and experimental support for optimizing PSD within the backfill body and elucidating the collapse mechanisms of high goafs. Full article

The pronounced heterogeneity of high-Andean soils constitutes a critical constraint to the sustainable productivity of quinoa in southern Peru, where current yields (1.6 t ha⁻¹) remain well below potential (>5 t ha⁻¹). This study aimed to develop a spatially predictive model of a weighted soil quality index (SQIw), the edaphic supply of nitrogen (N), phosphorus (P) and potassium (K), and the agricultural gypsum requirement by integrating edaphoclimatic covariates through regression–kriging. A total of 198 quinoa-cultivated soil samples were analysed; a minimum data set (MDS) was defined using correlation and principal component analyses, and regression–kriging was applied to map SQIw and the variables of interest. The MDS comprised electrical conductivity (EC), organic matter (OM), available P, exchangeable Na, sand, clay, and effective cation exchange capacity (ECEC); exchangeable Na (Wi = 0.160) and available P (Wi = 0.158) received the largest weights in the SQIw. SQIw values ranged from 0.22 to 0.84 and supported a five-class soil quality taxonomy; spatial modelling revealed a dominance of moderate-quality soils across the territory (85.21% of the agricultural area, 13,461.19 ha). The model achieved R² = 0.56, RMSE = 0.05, and MAE = 0.04 for SQIw. Most of the area (12,175.65 ha; 77%) exhibited an intermediate gypsum requirement (9.73–14.33 t ha⁻¹). Nitrogen and phosphorus showed the greatest territorial limitations, whereas potassium was largely non-limiting (84.82–570.17 kg ha⁻¹). These results indicate that sodicity and N–P deficiencies are the primary functional constraints; the generated maps enable prioritisation of gypsum amendments and targeted variable-rate fertilisation strategies to optimise the sustainability of quinoa production in the Altiplano. Full article

Calving front position is a key indicator of glacier and ice-sheet dynamics and an important variable for assessing mass loss and sea-level rise. Rapid growth in satellite data availability and image analysis techniques has driven the development of numerous automated calving front detection algorithms; however, the methodological landscape remains fragmented. This scoping review aims to map the existing literature on automated calving front detection, characterize the types of algorithms and data sources used, and identify trends, gaps, and challenges in current approaches. A systematic search of major bibliographic databases and complementary sources was conducted to identify studies describing automated or semi-automated calving front detection from satellite imagery or derived datasets. Eligible studies included peer-reviewed articles and relevant grey literature using optical, synthetic aperture radar (SAR), or multi-sensor data. Data were charted using a predefined framework that captures the algorithmic approach, input data characteristics, spatial and temporal coverage, validation strategies, and reported performance metrics. The review identifies a wide range of methods, from early threshold- and edge-based techniques to recent machine learning and deep learning approaches, with a strong shift toward convolutional neural networks over the past few years. Despite methodological progress, validation practices and evaluation metrics remain heterogeneous, and standardized benchmark datasets are scarce. This scoping review provides a structured overview of the field and highlights priorities for future methodological development and benchmarking. Full article

Background: Endovascular aneurysm repair (EVAR) requires lifelong imaging surveillance because endoleaks, aneurysm sac expansion, and severe adverse events occur in up to one-third of the patients. Conventional follow-up based on sac diameter and visual assessment may fail to detect early microstructural changes that precede clinical deterioration. Methods: This narrative review summarizes the current evidence on texture-based radiomics and artificial intelligence (AI) applied to computed tomography (CT) and CT angiography (CTA) for post-EVAR outcome prediction and surveillance. Original studies evaluating radiomic features and AI-based models for endoleak detection, aneurysm sac behavior, and EVAR-related adverse events were included and qualitatively synthesized. Results: Ten studies were included. Radiomic features describing texture heterogeneity, gray-level nonuniformity, entropy, and spatial complexity were extracted from the aneurysm sac, intraluminal thrombus, and perivascular adipose tissue. Machine learning and deep learning models achieved good to excellent performance, with reported AUC values ranging from 0.78 to 0.95 for predicting endoleaks, sac expansion, and severe adverse events. Texture-based radiomics consistently outperformed morphology-only assessments and showed complementary value to deep learning, including applications on non-contrast CT. Conclusions: CT texture radiomics combined with AI represents an emerging research approach with potential relevance for post-EVAR surveillance, although current evidence remains limited. By capturing tissue heterogeneity beyond conventional morphology, radiomics may enable the earlier detection of complications and support risk-adapted follow-up. However, the heterogeneity of methods limited external validation, and reproducibility issues remain major barriers to clinical translation. Full article

Background: Multiple myeloma (MM) is characterised by clonal expansion of plasma cells (PCs) in the bone marrow (BM). Disease assessment and monitoring typically rely on invasive, single-site procedures, such as BM biopsies (BMBs), which may inadequately capture intra- and extra-medullary spatial heterogeneity. Circulating plasma cells (CPCs), enriched from peripheral blood (PB), may represent a minimally invasive alternative or adjunct for molecular profiling. Objectives: This study aimed to evaluate the feasibility of using CPCs, enriched from PB, for mRNA analysis in plasma cell dyscrasia, including MM. A secondary objective was to assess whether mRNA expression levels of the endoplasmic reticulum (ER) stress sensors X-box-binding protein 1 (uXBP1) and activating transcription factor 6 (ATF6), and the chaperone-mediated autophagy marker Lysosomal-Associated Membrane Protein 2 (LAMP2A) by droplet digital PCR (ddPCR), were associated with resistance to the second-generation proteasome inhibitor (PI) carfilzomib (Cfz). Methods: Multiple myeloma (MM) cell lines (H929 and U266) and their carfilzomib-adapted derivatives were used to establish and validate droplet digital PCR (ddPCR) assays targeting ER stress (uXBP1, ATF6) and autophagy-related (LAMP2A) transcripts. Solid tumour cell lines, including serum-starved HeLa cells, served as biological controls to support assay specificity and sensitivity. Total RNA was extracted and reverse-transcribed to complementary DNA prior to analysis. Transcript levels were normalised to those of β-actin or GAPDH, as appropriate. ddPCR was performed using the BioRad QX200 system, with results reported as the normalised transcript copy number per microlitre of reaction. Matched bone marrow aspirate (BMA) and peripheral blood (PB) samples were collected at a single clinical time point from adults undergoing investigation for plasma cell dyscrasia between January 2021 and December 2023. Samples were obtained as part of standard clinical care and/or during treatment with Bortezomib (Btz) or Cfz. Mononuclear cells were isolated by density gradient centrifugation, and CD138⁺ plasma cells were enriched by fluorescence-activated cell sorting. Enrichment purity was assessed qualitatively by immunofluorescence microscopy using CD138 and CD117 markers. Samples yielding fewer than 1000 CD138⁺ plasma cells were excluded, resulting in 10 evaluable matched patient pairs. Results: Carfilzomib-adapted MM cell lines demonstrated reduced levels of uXBP1, ATF6, and LAMP2A mRNA compared to treatment-naïve cells. In matched BM and PB samples, uXBP1 mRNA levels were consistently lower in circulating PCs than in BM-derived PCs, whereas ATF6 mRNA levels were concordant between compartments. LAMP2A mRNA levels exhibited marked inter-patient heterogeneity. Conclusions: This study demonstrates the feasibility of using CPCs as a minimally invasive source for mRNA-based biomarker assessment and highlights ddPCR as a sensitive platform for quantifying ER stress and chaperone-mediated autophagy related transcripts in CPCs. Cfz adaptation was associated with reduced levels of uXBP1 and LAMP2A mRNA in MM cell lines. Future prospective studies evaluating the clinical utility of ER stress and chaperone-mediated autophagy associated transcripts in CPCs as predictors of resistance to PI are warranted. Full article

Three-dimensional spatial effects in deep excavations critically govern the mechanical response of retaining structures and adjacent soils, yet their quantitative characterization remains a challenge. This study systematically investigates the spatial behavior of row-pile-supported foundation pits through an integrated approach combining model tests, theoretical analysis, and numerical simulations. A novel formulation for the spatial effect influence coefficient K is derived from limit equilibrium principles and subsequently validated via ABAQUS-based finite element simulations. Model test results reveal pronounced spatial heterogeneity in earth pressure and bending moment distributions along the pit perimeter: lateral earth pressure at corner regions exceeds that at mid-side locations at equivalent depths, whereas bending moments in mid-side piles are substantially larger than those at corners. Displacement field measurements further demonstrate that corner zones, constrained bidirectionally, undergo minimal deformation, while maximum displacement occurs at the midpoints of the long sides. These observations collectively confirm the existence of a marked corner effect and a subdued side-midpoint effect under three-dimensional confinement. Complementary numerical analyses indicate that the coefficient K decreases monotonically with increasing half-angle corners and distance from the corner, thereby quantitatively capturing the decay of spatial constraint intensity. Together, these findings establish a theoretical framework for assessing excavation-induced spatial effects and provide actionable guidance for the rational design of deep foundation pit support systems. Full article

Accurately assessing dynamic water resource carrying capacity (WRCC) is essential and challenging, particularly in regions like the Gansu sections of the Yellow River Basin (GSYRB), a core water source protection zone in the arid northwest of China, due to its pressing challenge of balancing water resources for socioeconomic needs and ecological security. This study proposes a novel integrated computational assessment framework named SD-VIKOR to address the complexities arising from nonlinear interactions within the “water resources–socioeconomic–ecological environment” (W–S–E) system. The core of this framework is the tight coupling of a system dynamics (SD) simulation model with a VIKOR multi-criteria evaluation module, where indicator weights are objectively–subjectively determined via an Analytic Hierarchy Process (AHP)–entropy weight method. This integrated SD-VIKOR engine enables dynamic, scenario-based WRCC trajectory simulation. To move beyond simulation and enable mechanistic insight, the framework further incorporates a diagnostic suite: a Geodetector module quantifies dominant drivers and their interactions; an obstacle degree model pinpoints key limiting factors; and a coupling coordination degree model evaluates subsystem synergies. Together, they form a closed-loop “dynamic simulation → multi-criteria assessment → driving mechanism analysis and constraint diagnosis → subsystem coordination analysis” workflow. Applied to the GSYRB from 2012 to 2030 under five development scenarios, the framework demonstrated high efficacy. It successfully captured path-dependent WRCC evolution, revealing that the ecological-priority scenario (B2), which shifts system drivers from economic-scale expansion to resource-efficiency and environmental governance, yielded optimal WRCC and the highest system coordination. In contrast, business-as-usual and single-minded economic expansion scenarios underperformed. Six key obstacle factors were quantitatively identified, linking WRCC constraints to natural endowments, economic patterns, and domestic demand. The results reveal pronounced spatial–temporal heterogeneity in WRCC across the GSYRB, with socioeconomic development, water resource use efficiency, and ecological conditions acting as the primary joint drivers of WRCC evolution. Critically, several key indicators are identified as persistent constraints on regional water sustainability. In contrast to conventional static evaluations, the integrated framework captures the complex dynamics and multi-subsystem interactions governing WRCC, offering a more robust diagnostic of resource–environment systems. These insights provide a transferable analytical basis for designing sustainable water management strategies in arid river basins. Full article

Urban living streets are core venues for promoting public health; however, existing studies often lack a multidimensional quantitative evaluation system that integrates physical, psychological, and social health dimensions. To address this gap, this study constructs a space quality evaluation model comprising 15 indicators across three health dimensions, integrating multi-source data (including Street View Imagery, POI data, and field measurements). Taking six typical living streets in the central urban area of Changsha as a case study, we applied the Analytic Hierarchy Process to determine indicator weights and evaluate space quality. The results reveal significant spatial heterogeneity: (1) The comprehensive quality scores vary markedly, with Cai’e South Road ranking highest (66.62) and Zengjiawan Lane lowest (28.37); (2) key factor analysis indicates that seven indicators—including Street Width, Motorization Level, and POI Functional Diversity—are significantly associated with space quality, among which Sidewalk Width and Relative Sidewalk Width are identified as critical determinants; (3) addressing identified deficits in slow-traffic spaces and service amenities, this study proposes health-oriented micro-renewal strategies. This study provides a transferable analytical framework and practical decision support for the assessment and improvement of urban living street space quality. Full article

Freight-dominant port collection and distribution road networks exhibit strong spatial congestion, early spillback, and heterogeneous vehicle dynamics that challenge conventional traffic signal control strategies. Although Max-Pressure (MP) signal control provides strong decentralized stability properties, its classical queue-based formulation lacks sensitivity to incipient spatial congestion and performs poorly when heavy-duty vehicles (HDVs) dominate traffic composition. This paper proposes a gain-modulated Max-Pressure (Gain-MP) control framework, in which conventional pressure computation is augmented by an occupancy-dependent feedback gain that dynamically adjusts phase priorities according to real-time spatial congestion states and current right-of-way conditions. Without altering the decentralized structure of MP, the proposed method introduces a nonlinear feedback mechanism that enhances system responsiveness to congestion formation while suppressing excessive phase switching. The approach is evaluated using microscopic simulation on a signalized grid network representing port access corridors under time-varying demand and high HDV penetration. Results demonstrate that the dynamic Gain-MP controller performs better than classical queue-based MP, PCU-weighted MP, and fixed-time control. Moreover, constant-demand experiments indicate that the dynamic Gain-MP controller maintains bounded vehicle accumulation over a wider empirical demand range than the benchmark MP-based methods under the tested settings. Full article

Accurately assessing the accessibility of fire services is critical for enhancing urban safety and the resilience of the built environment. However, existing studies often lack a systematic analysis of spatiotemporal dynamics across an entire municipality. To address this gap, this study develops a citywide dynamic assessment framework for Shanghai, integrating GIS with real-time traffic data across 240 consecutive intervals to assess the service accessibility of 195 fire stations in relation to 7973 key units of fire safety. The principal findings are threefold. First, the results reveal significant urban–suburban heterogeneity in emergency response times. Notably, the proximity advantage of fire stations in central urban areas is offset by traffic congestion, and the marginal benefit of traffic speed improvement exhibits a sharp decline once the average speed exceeds a critical threshold of 13.7–21.0 km/h. Second, the accessibility ratio demonstrates a clear temporal pattern, being highest on holidays and lowest during weekday peak hours, and follows a nonlinear spatial decline from the urban centre to the periphery. This pattern is influenced more critically by the matching of supply and demand than by fire station density alone. Third, the analysis identifies dynamic vulnerability hotspots, which display a ‘bimodal (M-shaped)’ pattern on weekdays and a ‘unimodal (A-shaped)’ pattern on weekends and holidays. This spatiotemporal mismatch shows that central urban areas, despite higher station density, can suffer from both high fire risk and low accessibility, revealing structural patterns consistent with the ‘Inverse Care Law’ in emergency service provision. This study concludes that merely improving traffic conditions is insufficient; optimising the spatial matching of resources is paramount for effective urban disaster prevention. By developing a refined dynamic assessment framework, this study advances current knowledge by focusing on demand locations consistent with actual fire regulatory priorities and examining spatiotemporal patterns across both urban and suburban areas, thereby providing quantitative, evidence-based support for the strategic planning of fire stations and the enhancement of infrastructure resilience. Full article