Search Results (305)

Surface deformation triggered by coal extraction activities, together with the consequent development of unstable slopes within rugged mountainous landscapes, constitutes a critical focus for geological risk assessment and mitigation strategies. Conventional SBAS-InSAR processing pipelines suffer from inadequate tropospheric phase mitigation in topographically complex environments, while existing clustering-based recognition approaches fail to incorporate sufficient geophysical constraints. To overcome these deficiencies, the present investigation introduces a refined methodology that synergizes InSAR measurements with an enhanced clustering scheme for the automated screening of potentially unstable slope units. First, a two-stage coupled atmospheric correction framework is constructed within the SBAS-InSAR processing chain, comprising spatially varying stratified atmosphere estimation based on geographically weighted robust regression (GWRR-M) and turbulent atmosphere compensation based on structure-guided deformation-preserving interpolation (SGDPI); both stages require no external meteorological data and effectively protect deformation signals from overcorrection. Second, a spatiotemporally constrained density peak clustering algorithm (STC-DPC) is developed, which constructs a multi-dimensional feature space integrating spatial location, deformation rate, temporal evolution characteristics, and topographic-geological background, and introduces a spatiotemporally constrained distance metric together with an Unstable Slope Index (USI) to achieve automatic identification and quantitative discrimination of unstable slopes. The proposed method was evaluated using 120 ascending-track Sentinel-1A SAR images acquired from 2019 to 2023 over the coal-mining mountainous areas of Mentougou and Fangshan districts in western Beijing, China. The results show that the improved atmospheric correction reduces the phase standard deviation of a representative interferogram from 1.6 rad to 0.6 rad, with an average reduction of 42.3% across all interferograms. A total of 187 unstable slopes were identified by the STC-DPC algorithm, mainly distributed in abandoned mining areas and steep terrain with gradients of 10–35°, with a mean deformation rate of −25.3 mm/a; field investigations at representative sites confirmed significant deformation evidence (e.g., tension cracks and bulging), providing qualitative support for the identification results. Compared with the identification results obtained without atmospheric correction (79 unstable slopes), the improved method improves the detectability of weak deformation signals in areas with strong topographic relief and diverse deformation patterns. This study provides a practical technical pathway for the early screening and monitoring of geological hazards in coal-mining mountainous areas and holds great significance for mine ecological restoration and regional disaster prevention and mitigation. Full article

Air pollution is an important factor affecting urban transportation performance and travel behavior, yet taxi travel resilience under polluted conditions remains underexplored. This study focuses on Lanzhou’s central urban area and integrates taxi trajectory data with AQI data from April 2025 to construct a taxi travel resilience indicator. K-means clustering is applied to identify areas with different resilience levels, followed by Probit models to examine the associations between built environment factors and taxi travel resilience across weekdays/weekends and all-day/peak-hour periods. The results show that: (1) air pollution significantly reduces taxi travel demand on weekdays, while its impact is weaker on weekends; (2) taxi travel resilience exhibits significant spatiotemporal heterogeneity; (3) bus stop density, road density, and population density are consistently and positively associated with taxi travel resilience across different temporal conditions; and (4) robustness tests using an alternative AQI threshold of 200 yield generally consistent results, although the substantially smaller number of polluted days limits the strength of the robustness assessment. This study advances understanding of taxi travel resilience under air pollution and provides insights for improving urban transportation resilience. Full article

The viscosity peak phenomenon at the phase inversion point in crude oil emulsions can be understood through the lens of physical gelation. This study employs coarse-grained molecular dynamics (CG-MD) simulations to investigate the gel-like network structures formed at oil–water interfaces across varying water-to-oil particle-number ratios. We reveal that pure water forms a fully connected hydrogen bond network (500 molecules, 313.15 K, 2.68 H-bonds per molecule) behaving as a flexible physical gel scaffold, while pure oil exhibits a dispersed sol-like structure (35.9 clusters average). At the phase inversion point (50% water cut), the water network fragments into 44 gel-like clusters (193 network bonds) while oil forms 76 small clusters acting as physical crosslinking nodes embedded within the water network voids. This creates an interlocked gel structure with a maximum Interlocking Index (LI_CG = 36.67), directly corresponding to the viscosity peak. At 30% water cut, a W/O morphology with (LI_CG = 22.17) represents a weaker gel state. We demonstrate that gel rigidity rather than network existence determines macroscopic viscosity, with $LI$ serving as an effective crosslinking density metric. Model parameters calibrated via differential evolution optimization against experimental data from three oil wells yield $R^2=0.94$. This work provides a molecular mechanism revealing the flexible-network-to-rigid-gel transition as the origin of emulsion viscosity peaks, offering a gel-science perspective on emulsion rheology control in petroleum engineering. Full article

The study of degradation processes is of fundamental and applied interest. To understand the degradation of cellulose nitrate and to develop sensitive diagnostic methods, combined experimental and theoretical investigations are essential. Sensitive, non-destructive, and contactless methods for diagnosing the state of cellulose nitrate include IR absorption and Raman spectroscopy. While significant experimental work exists, the theoretical modeling of degradation processes, including the prediction of potential products, remains underdeveloped. Therefore, in this work, the structures and vibrational properties of molecular clusters representing segments of the cellulose nitrate chain were modeled using density functional theory (DFT). This approach yielded simulated IR and Raman spectra, allowing for the identification of peaks corresponding to the nitrate group. The elimination of this group to form a ketone was shown to alter peak contours across a broad spectral range. The most significant changes in the Raman spectra were observed at 605 and 851 cm⁻¹. Correspondingly, the key changes in the IR absorption spectra occurred at 836, 1034, 1169, 1283, and 1767–1781 cm⁻¹. The frequency trends for these diagnostic peaks across different model structures were analyzed and compared with experimental spectra from the literature. The demonstrated correlation between specific peak-frequency changes and the modeled degradation products constitutes the principal novelty of this work. Full article

Municipal solid waste landfill leachate represents one of the most environmentally challenging liquid effluents in modern waste management; however, its high nitrogen and phosphorus content renders it a potentially valuable nutrient source for microalgal phycoremediation. Here, Chlorella sp. and Scenedesmus sp. were cultivated for 15 days in CHU-10 standard medium and 50% (v/v) municipal landfill leachate from Nauta, Peru, and characterized across 33 biochemical variables, 14 physicochemical parameters, and 32 metal ions and trace elements. A sequential competitive multivariate pipeline comprising principal component analysis (PCA), hierarchical cluster analysis (HCA), permutational multivariate analysis of variance (PERMANOVA), and linear discriminant analysis (LDA) was applied to both the biochemical and bioremediation datasets. Leachate supplementation increased peak biomass density by 26.6–28.3% and elevated total protein by 56.9% in Chlorella sp. and 73.4% in Scenedesmus sp., while reducing total lipids by 37–46% and suppressing polyunsaturated fatty acid production. Both species achieved net biological removal efficiencies (NBRE) exceeding 86% for ammonium and ammonia; toxic elements, including Cd (~96%), Al (~92%), As (~90%), and Pb (~90%), were removed at higher NBRE than macro- and micronutrient categories. LDA achieved 100% leave-one-out cross-validation accuracy for species classification from both physicochemical and 32-element NBRE profiles. These findings indicate two complementary valorization directions, contingent on further biomass safety verification: leachate-grown Scenedesmus sp. shows a favorable combination of protein enrichment and nutrient removal for single-cell protein production integrated with bioremediation, while Chlorella sp. in standard medium shows a more favorable fatty acid profile for nutraceutical applications. Because leachate-grown biomass also accumulates inorganic and trace-element constituents from the medium, its suitability for protein or nutraceutical use requires direct heavy-metal characterization of the harvested biomass, independent of the demonstrated removal efficiency from the liquid phase. Full article

Background/Objectives: Performance measurement and management is a key administrative function; however, its application to child and adolescent healthcare services remains poorly mapped in the international literature. This study attempts a systematic bibliometric mapping of the field. To the best of our knowledge, previous bibliometric studies in pediatric and related healthcare fields have primarily focused on specific clinical or service domains, whereas the intersection of performance measurement and management with child and adolescent healthcare services has not been specifically mapped. The novelty of the present study lies in addressing this intersection through an integrated bibliometric assessment of its thematic, temporal, and geographical structure. Methods: A bibliometric analysis was performed on the Scopus database, with a query that combined a proximity operator (W/10) and Boolean logic, yielding 643 documents (1990–2026). After metadata cleaning and application of an occurrence threshold (≥15), 111 keywords were analyzed with VOSviewer (network, overlay, density, targeted analysis, bibliographic coupling of countries). Results: Research production increased strongly since 2010, peaking in 2025. Four thematic clusters emerged—quality of care, clinical outcomes, administrative framework, emergency/operational care—with the term “quality of health care” as the central hub-bridge. The administrative cluster was linked to older publications relative to the clinical/operational clusters. Country-level bibliographic coupling identified distinct geographical patterns in cited-reference similarity, including a predominantly European cluster, a transcontinental cluster dominated by the United States and Canada, and a separate Australian cluster. The term “performance measurement” itself did not meet the inclusion threshold, reflecting the methodological effect of the selected occurrence threshold rather than the absence of the concept from the literature. Conclusions: The retrieved literature was strongly concentrated in clinical and health-related subject areas, while explicitly administrative and managerial perspectives appeared comparatively limited. This pattern indicates a potential research gap that warrants further investigation rather than confirming the absence of managerial performance frameworks in the broader field. Full article

Fiber-reinforced high-water-resistance concrete backfill (FHWCB) is a rapid-setting cementitious backfill system used for underground support and backfilling, but its stability is strongly affected by mixture water content and fiber dispersion. This study investigated the fresh-state behavior, mechanical performance, acoustic emission (AE) fractal characteristics, b-value response, and energy evolution of FHWCB. Mixtures with water-to-cement ratios (w/c) of 1.0–1.8 and fiber volume fractions (Vf) of 0–0.5% were prepared and tested using fresh property measurements, unconfined compression, thermogravimetry, AE monitoring, correlation dimension analysis, b-value analysis, and strain energy partitioning. Increasing w/c improved flowability and delayed setting, but weakened the hydration skeleton and reduced early-age compressive strength by approximately 56–61%. Fiber reinforcement showed a non-monotonic effect: Vf = 0.3% increased compressive strength by approximately 16–26%, whereas excessive fiber addition reduced strength because of fiber clustering and weak local zones. AE amplitude sequences exhibited measurable fractal characteristics. A higher correlation dimension indicated distributed microdamage, while decreasing correlation dimension and b-value reflected the transition toward localized macrocrack growth. Energy analysis showed that the peak elastic strain energy density decreased from approximately 0.60 to 0.39 MJ/m³ as w/c increased. The proposed AE fractal–b-value–energy framework provides a quantitative basis for tracking damage progression and optimizing FHWCB for underground engineering. Full article

While e-commerce has transformed consumption patterns, online shopping behavior remains influenced by the physical environment. Using Shanghai as a case study, this research applies machine learning, SHAP analysis, and K-Means clustering to examine the nonlinear impacts and spatial variations in the built environment on e-shopping. The findings reveal that: (1) E-shopping expenditure follows a long-tail distribution and displays a concentric spatial pattern, peaking between the Outer and Suburban Rings while decreasing within the Inner Ring and beyond the Suburban Ring. (2) Built-environment factors exhibit non-linear effects, with local shopping potential and transit distance playing dominant roles. Indicators such as store density and delivery facility coverage show inverted U-shaped threshold effects, indicating a shift from complementarity to substitution between offline and online retail. (3) The urban space can be clustered into three sub-district types—traditional residential, single-function, and mixed-use—each with distinct e-shopping patterns and drivers. This research highlights the spatial mechanisms shaping digital consumption, providing empirical evidence for context-specific retail planning in megacities. Full article

Distribution network partitioning at high distributed-energy-resource penetration is challenged by operating-point-insensitive distance criteria, initialization dependence, and oversized or oscillatory zones. This paper presents a framework that combines a comprehensive electrical distance metric with an improved density peak clustering–label propagation algorithm (DPC-LPA). The metric integrates network impedance and voltage sensitivities to renewable-power variations. DPC determines the initial number and centers of zones, and the modified LPA uses partition-scale, load-balance, and historical-influence factors to regulate zone growth and label switching. Tests on medium- and low-voltage alternating-current/direct-current (AC/DC) networks show improved load balance relative to conventional LPA with only a modest change in modularity. These results support the method for the evaluated networks without implying performance beyond the tested conditions. Full article

Emission inventories for high-altitude Andean cities rely on sea-level certification cycles that misrepresent real-world combustion conditions. This study derives altitude-resolved emission factors (EFs) for light-duty gasoline vehicles across 0–4000 m a.s.l. in Ecuador using the purpose-built Andean Ecuador Driving Cycle (aedc), validated against naturalistic data via the Percentile Speed Vector metric (PSV_m10; IGS $=1.89$ vs. IGS $=2.30$ for the WLTC). Ten vehicles (Euro III–V) were instrumented with OBD-II and portable analysers recording $\mathrm{CO}$, $\mathrm{NO}$, $\mathrm{HC}$, and ${\mathrm{CO}}_2$ at 1 Hz over a four-year campaign (2021–2025; ≈2000 h). K-Means clustering on PSV_m10 identified five operating regimes (silhouette ≈0.384). Under dynamically equivalent aedc conditions, $\mathrm{NO}$, $\mathrm{CO}$, and $\mathrm{HC}$ all peaked in the 1000–2000 m band ($\mathrm{NO}$: $0.188\mathrm{g}{\mathrm{km}}^{-1}$, $6.7\times$ the sea-level value; $\mathrm{CO}$: $4.47\mathrm{g}{\mathrm{km}}^{-1}$, $+50\%$; $\mathrm{HC}$: $0.047\mathrm{g}{\mathrm{km}}^{-1}$, $+292\%$), fell in the 2000–3000 m band, and partially rebounded above 3000 m ($\mathrm{NO}$: $0.186\mathrm{g}{\mathrm{km}}^{-1}$); ${\mathrm{CO}}_2$ instead declined monotonically with altitude (182 to $119\mathrm{g}{\mathrm{km}}^{-1}$, $-35\%$), tracking a near-stable-to-slightly-declining fuel consumption (8.56 to $8.11\mathrm{L}/100\mathrm{km}$) consistent with reduced aerodynamic drag at altitude partially offsetting the density penalty. These results show that altitude affects pollutants through distinct, non-monotonic mechanisms rather than a uniform trend, so that single-coefficient altitude corrections introduce systematic bias in Andean emission inventories. Full article

High-precision estimation of forest aboveground biomass (AGB) is crucial for global carbon cycle monitoring and sustainable forest management. However, existing machine learning-based approaches often suffer from high-dimensional feature redundancy, uneven spatial distribution of training samples, and inefficient hyperparameter optimization, which collectively limit estimation accuracy and computational efficiency. To address these issues, this study proposes a synergistic feature-sample optimization framework (DFS) for high-precision forest AGB estimation. First, with the involvement of forestry experts, we constructed the Hunan and Hubei datasets covering typical subtropical forest types through multi-source remote sensing and ground plot sampling. Second, we propose the Dual-Criteria Adaptive Feature Selection (DCAFS) method, integrating ReliefF and mutual information criteria to adaptively select key features highly correlated with AGB, eliminating spectral redundancy while preserving biomass-sensitive information. Next, we introduce a Bidirectional Active Learning Sample Optimization mechanism, called BALSO, and in its forward step, plots with high uncertainty and representativeness are given priority, so samples with high AGB variability can be captured effectively; in the backward step, spatially redundant samples and feature-redundant samples are removed through density peak clustering, and by doing this, sample selection and spatial distribution are optimized at the same time, so plot balance gets improved. Finally, the framework brings in a parameter tuning structure based on Dream Optimization Algorithm, namely DOA, and through staged exploration together with local fine-tuning, DOA makes model hyperparameters and AGB data distribution characteristics align in an adaptive manner, which helps improve convergence efficiency and estimation stability. Input variables comprise Landsat 8 OLI spectral bands, GLCM texture features, vegetation indices, and Sentinel-1/2 data. On the Hunan dataset, the framework achieved an $R^2$ of 0.83 and an RMSE of 25.6 Mg·ha⁻¹; on the Hubei dataset, it achieved an $R^2$ of 0.86 and an RMSE of 26.8 Mg·ha⁻¹. The framework was further validated on an independent public dataset from Inner Mongolia. These results demonstrate that the DFS framework provides an effective and feasible approach for regional-scale forest AGB estimation and carbon monitoring. Full article

This study presents a comprehensive multidimensional analysis of pedestrian accidents in Amman, Jordan, from 2014 to 2023. By integrating spatial, temporal, and statistical techniques, the research identifies critical risk patterns to inform evidence-based safety interventions. Characterizing a decade-long database of 14,821 cases, the study utilizes radar graphs, Kernel Density Estimation (KDE), and DBSCAN cluster analysis to delineate high-risk zones and temporal peaks. Temporal findings indicate that Thursdays recorded the highest accident frequency (2382 cases), with peak occurrences between 17:00 and 23:00. Spatial clustering identified five significant high-risk zones, with Central Amman emerging as the primary critical area. The study’s novelty lies in being the first in the Jordanian context to bridge accident frequency with severity mechanisms by integrating advanced spatial clustering and KDE with a robust Ordered Logit Model. Severity analysis reveals that while 59.34% of incidents resulted in minimal injuries, fatalities accounted for 5.02%. The model demonstrates that injury outcomes are systematically associated with traffic dynamics and behavior rather than environmental factors. Speed-related driver error was identified as the strongest predictor of severe outcomes (OR = 81.3). Significant dependencies were confirmed between vehicle category and road type (χ² = 2182.20, p < 0.001), lighting and road surface (χ² = 76.21, p < 0.001), and vehicle type and lighting (χ² = 148.52, p < 0.001). The study proposes a multi-layered framework combining site-specific nodal improvements with corridor-level strategies to enhance urban safety in Amman City. Full article

Objectives: The aim of this study was to compare insertion torque (IT) and primary stability, assessed by resonance frequency analysis (RFA), between two implant systems with a shared macrodesign but different surface morphology and titanium grade in a standardized ex vivo porcine bone model. Methods: Sixty implants (OmniTaper EV and Xive; n = 30 per group) were inserted into standardized porcine rib bone blocks using identical osteotomy protocols. Peak insertion torque and primary stability, assessed by resonance frequency analysis (RFA), were recorded immediately after placement. Between-group differences were evaluated using appropriate statistical methods. Results: Bone density did not differ significantly between groups. OmniTaper EV implants demonstrated significantly higher insertion torque (28.2 ± 6.5 Ncm vs. 24.2 ± 6.2 Ncm; p = 0.020) and higher ISQ values (74.1 ± 6.0 vs. 69.8 ± 6.9; p = 0.013) than Xive implants. Mixed-effects analyses confirmed both findings after adjustment for clustering. No significant correlation was found between insertion torque and ISQ. Conclusions: Despite sharing a common implant design concept and standardized osteotomy preparation, the investigated implant systems demonstrated significant differences in insertion torque and primary stability. The findings indicate that implant-specific characteristics, including surface morphology and titanium grade, may influence implant–bone interface mechanics during insertion. However, their individual contributions cannot be distinguished within the present study design and should therefore be interpreted as the combined influence of implant-specific characteristics. Full article

An innovative strategy for collagen self-assembly with accelerated intra and extrafibrillar mineralisation is introduced to generate bone scaffolds with biomimetic properties. This method, termed Rapid Fibrillogenic Mineralisation (RFM), leverages coprecipitation with 10× Simulated Body Fluid (10× SBF) during fibril formation to maximise nucleation, particularly within intrafibrillar zones at molecular termini. Densification is achieved within minutes via plastic compression driven by capillary action, producing bone-like scaffold density without compromising the collagen matrix. Transmission electron microscopy confirms intrafibrillar hydroxyapatite crystals within 15 min, while X-ray diffraction demonstrates distinct HA peaks across groups. Scanning electron microscopy verified extrafibrillar mineralisation after 4 h, with saturation by 6 h, yielding ‘nanoflower’ crystal clusters. Infrared spectra showed increased carbonate content over time, indicating lattice substitutions characteristic of natural bone. Enhanced mineralisation translated into significant mechanical gains as Dynamic Mechanical Analysis revealed compressive moduli approaching cancellous bone (up to 283 ± 31 MPa). In addition, a decrease in the piezoelectric coefficient occurs with increased mineralisation process, highlighting the effects of mineral inclusions on collagen fibre composition and anisotropy. Biologically, mineralised scaffolds supported cellular growth compared to collagen controls. RFM thus enables rapid, reproducible fabrication of biomimetic bone scaffolds that closely emulate native mineralisation patterns and mechanical behaviour. Beyond offering a practical route for scaffold production in tissue engineering, the process also provides new insights into bone physiology and in vitro modelling. By reshaping collagen into a synthetic echo of nature’s bone, RFM establishes a rapid approach for designing functional biomaterials with translational potential. Full article

Forest aboveground live biomass carbon storage (hereafter referred to as “forest carbon storage”) is an important indicator of forest vegetation carbon sequestration, and its spatial patterns and associated factors are highly heterogeneous. Identifying these variations can improve the understanding of carbon accumulation in complex mountain forests and support fine-scale carbon assessment in similar ecosystems. The results showed the following. (1) The total forest carbon storage in the study area was 3.75 × 10⁶ t C, with a carbon density of 44.83 t C·hm⁻². Pinus massoniana and hard broad-leaved tree species were the main contributors, and carbon storage peaked at the mature forest stage. (2) Carbon storage exhibited significant spatial clustering (Moran’s I = 0.312), with high-value areas concentrated within the national nature reserve and low-value areas distributed in regions with frequent human activities. (3) The GWR model outperformed the ordinary least squares model, with R² increasing to 0.88 and residual spatial autocorrelation reduced by 42.22%. (4) The positive effect of stand volume increased from northeast to southwest, the effect of stand age differed between eastern and western areas, and shrub layer height, soil depth, and slope exhibited region-specific positive and negative effects, with significant interactions among factors. Full article