Search Results (208)

Repeated freeze–thaw (F–T) action, together with dynamic disturbances, can progressively weaken rock masses in cold regions. However, how freezing temperature affects the relationship between microstructural evolution and dynamic strength decay remains insufficiently quantified. This study investigated yellow sandstone subjected to F–T cycles at freezing temperatures of 0, −3, −5, and −20 °C. CT-based 3D reconstruction and Split Hopkinson pressure bar (SHPB) tests were combined with grey relational analysis (GRA) to characterize pore-structure evolution, dynamic strength decay, and their relationship. The results indicated that lower freezing temperatures promoted increases in pore connectivity and structural complexity. After 60 F–T cycles at −20 °C, connected porosity increased from 11.15% to 18.67%, while the ratio of connected porosity to total porosity increased from 51.1% to 85.7%. At an impact pressure of 0.3 MPa, the dynamic strength after 60 cycles decreased by 9.51%, 20.9%, 38.1%, and 61.5% at 0, −3, −5, and −20 °C, respectively. Among the examined microstructural parameters, average throat length had the highest overall grey relational grade (0.821), suggesting that throat development is closely associated with dynamic strength decay. Lower freezing temperatures enhanced pore-ice expansion and unfrozen-water migration, promoting pore enlargement, throat extension, and crack connection. These results quantitatively link pore-network evolution to dynamic strength decay under different freezing temperatures, providing a microstructural basis for assessing the dynamic deterioration of sandstone in cold regions. Full article

Traditional target-oriented task allocation and path planning methods often struggle to balance real-time responsiveness to dynamic task alterations with multi-UAV cooperative operations in complex urban environments under meteorological disturbances. To address these challenges, this paper proposes a dynamic path planning method for low-altitude Unmanned Aerial Vehicles (UAVs) tailored for urban inspection missions. Integrating an improved Discrete Particle Swarm Optimization (DPSO) algorithm with a decoupled Soft Actor–Critic (SAC) and B-spline smoothing framework, the proposed approach optimizes upper-level task allocation and lower-level trajectory planning within a 3D joint meteorological-obstacle feasible region. For task scheduling, an improved DPSO algorithm embedded with a spatial topology guidance mechanism dynamically coordinates task flows governed by Poisson processes. effectively addressing the spatial blindness and fragmented route assignments typical of conventional discrete optimization. Concurrently, local trajectory replanning executes receding-horizon spatial exploration via SAC deep reinforcement learning, followed by B-spline refinement to strictly enforce UAV kinematic limits, systematically bridging continuous-space exploration with low-level flight compliance to overcome the kinematic infeasibility common in pure learning-based models. Validated through extensive Monte Carlo comparative simulations ($N=50$) and further verified by a high-fidelity AirSim dynamic physics engine, the results demonstrate that: (1) The improved DPSO constrains the average response latency for high-priority emergency tasks to within 40 s even under 50 concurrent dynamic tasks. (2) The lower-level replanning achieves an average execution time of $3.60\pm 0.18 \mathrm{s}$ and a path success rate of $95.8\pm 1.2\%$, in numerical tests, while maintaining a 96.2% kinematic feasibility rate under realistic rigid-body inertia and aerodynamic drag. While the current 3.60 s latency presents a potential bottleneck for millisecond-level dynamic emergency reactions, the developed framework offers a highly effective and safe closed-loop dynamic scheduling solution that lays a rigorous computational foundation for low-altitude urban inspections. Full article

Forest fires are among the most critical disturbance agents reshaping Mediterranean ecosystems under accelerating climate change. This study employs a multi-scale remote sensing approach to examine the relationship between post-fire vegetation dynamics and climate variability in high-fire-risk areas of southern İzmir, Türkiye. Burned areas were delineated using the Burned Area Index (BAI) and differenced Normalized Burn Ratio (dNBR) applied to Landsat imagery (1990–2024) and Sentinel-2 imagery (2017–2024). Post-fire vegetation recovery was quantified through the Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Vegetation Condition Index (VCI), Leaf Area Index (LAI), and Land Surface Temperature (LST) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) products. Climate variables, including soil moisture, precipitation, and maximum, minimum, and mean air temperature, were derived from the TerraClimate dataset. Long-term spatiotemporal trends were assessed using the non-parametric Mann–Kendall (MK) test and Sen’s slope estimator for the 2000–2023 period. Results indicate a statistically significant increase in mean temperature (p < 0.05) and a concurrent decline in soil moisture over the past three decades, consistent with progressive atmospheric aridification. Vegetation indices exhibited marked seasonal asymmetry: significant declines in NDVI, SAVI, and LAI were recorded during summer months, whereas partial recovery was confined to the winter–spring wet season. A pronounced warm-dry shift was identified in the post-2015 period, characterized by positive Land Surface Temperature anomalies and compressed vegetation recovery windows. These findings highlight that increasing thermal stress and diminishing soil moisture collectively constrain post-fire ecosystem resilience in the Mediterranean climatic zone (MCZ). The integrated remote sensing framework developed here provides a robust and transferable basis for fire ecosystem monitoring and the formulation of climate adaptation strategies in fire-prone dryland regions. Full article

Gully topography significantly disturbs the in-situ stress field of underlying coal and rock masses, but the quantitative relationship between gully morphology and stress anomalies remains unclear. Taking the Kuangou Coal Mine as an example, this study combines numerical simulation and field validation to investigate stress distribution characteristics in a gully region. The results show that mountain height is positively correlated with vertical stress, while gully depth is negatively correlated. In contrast, gully width and angle mainly affect the horizontal stress distribution, and their influence decays rapidly with increasing burial depth. Based on these findings, a stress disturbance coefficient λ is proposed, and a rock burst hazard classification criterion using the D/H ratio (burial depth D to mountain height H) is established: D < 1.6H for high hazard, 1.6H ≤ D < 2.4H for medium, 2.4H ≤ D < 3.6H for low, and D ≥ 3.6H for no hazard. Field validation using 16 large-energy mine tremor events confirms the reliability of the proposed criterion. The method provides a quantitative basis for differentiated rock burst prevention in gully regions. Full article

Under complex environmental disturbances and visual measurement noise, globally coherent components, damage-induced local anomalies, and random disturbances are coupled in full-field vertical displacement responses. This coupling limits conventional low-rank–sparse decomposition because of its lack of mechanics-based constraints and can obscure weak damage-induced anomalies. To address this issue, this study proposes a physics-regularized low-rank–sparse damage identification method incorporating a physics prior derived from curvature-strain-energy perturbation. The method first extracts deflection curvature from the full-field displacement responses of the healthy and damaged states. A normalized physical evidence field is then constructed from the curvature-energy difference through Gaussian spatial regularization and mapped into spatially varying sparsity weights to modulate anomaly separation. Subsequently, the Physics-Regularized Differential Damage Index (PRDDI) is constructed from the difference in physics-regularized sparse anomaly intensity between the two states for damage localization and severity-sensitive characterization. The proposed method is primarily intended for beam-like structures satisfying the small-deformation bending assumption. For more complex structures, such as continuous beams, frames, plates, and shells, the corresponding mechanics-based physical evidence and spatial neighborhood relationships can be extended according to their load-transfer mechanisms and spatial geometries. Experimental and numerical results show that the peak-to-background ratio of the physics-regularized sparse anomaly field reaches approximately 2.77 times that of conventional robust principal component analysis (RPCA), while the background level is reduced by approximately 60%, and spurious peaks in non-damaged regions are markedly suppressed. For local stiffness reductions of 5–30%, the PRDDI localization error remains within 0–1 spatial measurement points. Both the peak value and local integrated area within the damaged region increase consistently with the degree of stiffness reduction, with coefficients of determination $R^2$ exceeding 0.99 and Spearman rank correlation coefficients of 1.00. For representative dual-damage cases, the proposed method maintains good dual-peak resolution. Under 10 dB noise, the complete dual-damage detection rate is approximately 87%, while the missed-detection rate for weak damage is approximately 10%. The physics prior derived from curvature-strain-energy perturbation improves consistency with structural mechanics, spatial separability, and the identification reliability of local damage anomaly extraction under complex measurement conditions. By exploiting spatially continuous, vision-based full-field displacement measurements, the proposed method can identify local damage regions in bridges and characterize variations in damage severity, providing a basis for subsequent detailed inspection and condition assessment. Full article

Against the backdrop of global climate change, wildfires have emerged as key disturbance factors accelerating permafrost degradation. However, how wildfires affect ground-surface deformation, including spatial patterns and potential driving mechanisms, remains unclear. Therefore, in this study, the permafrost region in the northern Da Xing’an Mountains affected by the catastrophic Great Black Dragon Fire (1987) is taken as a case study. On the basis of Sentinel-1 SAR imagery acquired from 2016 to 2021, the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technique was employed to derive surface deformation rates. These rates were combined with historical fire severity (dNBR) and topographic factors. Random forest and spatial autocorrelation analyses were used to evaluate the long-term association between wildfire disturbance and surface deformation and its potential controls. The results indicate that (1) thirty-five years after the wildfire, vegetation in the permafrost region had not fully recovered to prefire levels; (2) surface deformation from 2016 to 2021 was dominated by subsidence overall. When unburned patches within the same region were used as controls for climate-driven background subsidence, the proportion of areas experiencing severe subsidence (annual rate ≤ −50 mm yr⁻¹) reached 12.86% in high-severity fire zones, compared with 10.21% in unburned areas, suggesting that high-severity fires may amplify regional background subsidence; and (3) the random forest model had low explanatory power (R² = 0.03) and was therefore used for exploratory comparison of the selected predictors rather than for accurate prediction of surface deformation. Among the selected variables, dNBR had the greatest relative importance, followed by terrain ruggedness and slope, whereas the remaining spatial variability may reflect unmeasured hydrological and subsurface controls. This study provides a quantitative basis for understanding the wildfire-induced “abrupt degradation” of permafrost, defined here as disturbance-driven acceleration of thaw and subsidence beyond gradual climate-driven degradation, and contributes to understanding carbon–climate feedback mechanisms in permafrost regions. Full article

Background/Objectives: Vitamin D deficiency is commonly observed in patients with breast cancer and may affect disease course and treatment outcomes. The determinants of vitamin D status in this population remain incompletely understood, particularly with respect to lifestyle, psychosocial, and tumour-related factors. The aim of this study was to identify environmental and clinical factors associated with serum 25-hydroxyvitamin D [25(OH)D] concentrations in women with breast cancer, with particular emphasis on dietary patterns, psychological distress, sleep quality, and tumour molecular subtype. Methods: A cross-sectional observational study was conducted among 101 women with histopathologically confirmed invasive breast cancer, recruited at the St. John of Dukla Oncology Centre of the Lublin Region (COZL) in Lublin, Poland, between 2018 and 2019. Serum 25(OH)D concentrations were measured by electrochemiluminescence immunoassay (ECLIA; Roche Diagnostics) within 2–8 weeks after surgical treatment. Clinical and lifestyle data were collected using standardised questionnaires and medical records. Dietary patterns were assessed with the KomPAN questionnaire (pro-healthy diet index pHDI-10 and non-healthy diet index nHDI-14). Psychological distress was measured with the Distress Thermometer. Multiple linear regression with backward stepwise elimination was applied to identify factors independently associated with of 25(OH)D concentration. Results: Insufficient vitamin D status (25(OH)D < 30 ng/mL) was found in 56.4% of participants. The multivariable regression model was statistically significant (F(6,92) = 15.298; p < 0.001), explaining 49.9% of the variance in 25(OH)D concentrations. Factors independently associated with lower 25(OH)D included higher BMI (b = −0.715; p = 0.005), sleep disturbances (b = −6.257; p = 0.020), higher psychological distress score (b = −2.263; p < 0.001), and luminal B versus luminal A subtype (b = −5.909; p = 0.025). A higher pro healthy diet index (pHDI-10) was independently associated with higher 25(OH)D (b = 0.245; p = 0.040). Conclusions: Vitamin D status in patients with breast cancer is shaped by complex interactions among modifiable lifestyle factors and tumour characteristics. Targeted interventions addressing diet quality, psychological well-being, sleep health, and weight management may improve vitamin D status in this population. The association between molecular subtype and 25(OH)D concentrations warrants further prospective investigation. These findings should be interpreted with caution, as vitamin D supplementation and direct measures of sun exposure—both established determinants of 25(OH)D—could not be included as covariates and may account, at least in part, for the associations reported. Full article

As dynamic random-access memory (DRAM) continues to scale down and achieve higher integration density, the cell layout has transitioned to 6F², resulting in narrower spacing between adjacent word lines (WLs). Consequently, cell-to-cell disturbance has become more severe. In particular, the row-hammer effect (RHE) has emerged as a critical reliability issue that must be mitigated to ensure stable operation in next-generation DRAM devices. In this study, a novel DRAM cell structure is proposed, in which a low-k dielectric material is embedded beneath the storage node (SN) to mitigate the electric field. This structural modification effectively suppresses the RHE compared to the conventional partial-isolation type buried channel array transistor (Pi-BCAT). The feasibility and performance of the proposed structure were verified through 2D Sentaurus technology computer-aided design (TCAD) simulations. The device embedding the low-k dielectric beneath the SN exhibits a mitigation of approximately 20.45% in D0 failure and about 12.12% in D1 failure. This improvement is attributed to the reduced electric field in the region underneath the SN, which suppresses stored charge leakage. These results confirm that the proposed structure not only enhances DRAM reliability in advanced process nodes but also provides an effective design guideline for highly integrated and low-power memory devices. Full article

3D reconstruction of construction scenes is an important enabling technology for digital and intelligent construction project management. Recurring foreground occluders and dynamic disturbances in tower-crane imagery can destabilize image registration and introduce spurious depth responses. This paper proposes an occluder-mask-constrained 3D reconstruction framework driven by multi-view geometric anomalies. Adjacent-view geometric outliers are spatially aggregated to generate foreground prompt points, which are converted into occluder masks using Segment Anything Model 2 (SAM2). The masks are propagated as unified pixel-validity constraints through sparse feature filtering, Adaptive Patch Deformation Multi-View Stereo (APD-MVS) matching-cost evaluation, support-region selection, and depth-map fusion. Experiments on three real construction-site datasets show increased sparse-registration completeness in the tested sequences and fewer visually identifiable occluder-induced artifacts in dense point clouds. A representative 308-image sequence was further evaluated against no-mask reconstruction, You Only Look Once version 8 (YOLOv8) bounding-box removal, manually prompted Segment Anything Model 2.1 (SAM2.1), a Segment Anything Model 3 (SAM3) text-prompt baseline, and Visibility-Aware Multi-View Stereo Network (Vis-MVSNet). The evaluation combines sparse-reconstruction metrics, pixel-level mask-quality metrics from a manually annotated validation subset, module-wise runtime accounting, controlled ablations, and aligned dense-point-cloud visualization. These results show improved sparse-stage registration completeness and visible artifact suppression. Because high-precision 3D reference point clouds are unavailable, the dense results are interpreted as visual evidence of artifact suppression rather than as proof of improved absolute dense-reconstruction accuracy. Full article

During geomagnetic storms, ionospheric disturbances can undergo substantial spatiotemporal restructuring and affect high-precision GNSS applications. This study investigates the multiscale ionospheric response over Brazil during the intense geomagnetic storm of 12 November 2025 and examines the associated changes in precise point positioning (PPP) convergence. Multi-source observations, including GNSS TEC/dSTEC, ROTI, JPL Global Ionospheric Maps, ionosonde parameters, and three-dimensional ionospheric tomography, were jointly analyzed. The results show that the storm produced pronounced and nonuniform global TEC anomalies, with the Brazilian sector embedded in a disturbed background. Over Brazil, clear traveling ionospheric disturbance (TID) propagation and ROTI enhancement were observed during the main response phase. The TID developed after approximately 02:05 UT and reached its maximum intensity during 02:25–03:00 UT. Ionosonde observations indicated decreased foF2 and increased h′F2, suggesting electron density depletion and an apparent uplift of the F-region reflection height. The GNSS dSTEC-constrained tomographic reconstruction suggested that the relative perturbation structures were more evident at 150–400 km, especially near 250–350 km. PPP analysis further revealed longer convergence times on the storm day, particularly in the vertical component. These results indicate that the Brazilian ionosphere experienced a multiscale response from global anomalies to regional propagation and vertical restructuring, which was associated with delayed PPP convergence performance. Full article

Not all particle-size fractions in widely graded granular materials contribute equally to compaction densification. For non-ideal particle-size distributions (PSDs) with local deviations or fine-end disturbances, the full-range fractal index may be influenced by particle-size fractions that contribute weakly to densification and, therefore, may not consistently represent the maximum dry density response. To address this problem, this study proposes a response-constrained truncation framework to identify a more effective PSD fitting range for fractal characterization. First, 20 concave and S-shaped PSDs from previous experiments were re-analyzed to compare full-range and truncated indices. Then, 21 progressively truncated specimens derived from three standard fractal PSDs were tested by relative density experiments. A unit-mass densification contribution coefficient, η_j, was defined from adjacent maximum dry density differences and particle-fraction mass contents. The η_j-d responses exhibited unimodal patterns, and the transition diameter d_c shifted with PSD coarseness. For the two material sources, replacing the full-range index with the truncated index increased the R² values between the fractal index and maximum dry density from 0.195 to 0.886 and from 0.191 to 0.856, respectively. A continuous percentile search showed that the optimal characteristic scale was concentrated near q ≈ 30, with a robust common optimum of q = 30.53. Sensitivity analysis for β = 0.85–0.95 indicated that 0.225d₃₀ falls within the transition region from highly effective filling to reduced densification efficiency. Accordingly, d_L = 0.225d₃₀ is proposed as a preliminary engineering estimate of the lower fitting limit for non-ideal PSDs. The framework is intended for widely graded materials whose full-range fractal parameters are inconsistent with compaction response. Full article

The severe deformation and failure of reused roadways due to repeated mining disturbances pose considerable challenges to roadway maintenance. In this study, field measurements were taken at the 13092 reused roadway of Zhaozhuang Coal Mine to determine the deformation characteristics of its surrounding rock. Based on the equation for the plastic zone boundary of a circular roadway under a non-uniform stress field, the distribution characteristics of the plastic zone of the reused roadway under different stress conditions were analyzed, and their associated risk levels were assessed. Furthermore, the distribution characteristics of the plastic zone at different locations under primary and secondary mining, the non-uniform evolution of the mining-induced stress field, and the deformation behavior of the surrounding rock under repeated mining disturbances were investigated using FLAC3D 7.0 numerical simulations. The following conclusions were reached: Repeated mining is the primary cause of severe deformation and instability of the surrounding rock in the reused roadway, and there are marked spatial differences in severe deformation between different locations. Under a non-uniform stress field, the distribution of the plastic zone in the surrounding rock varies markedly with the ratio of the maximum principal stress to the minimum principal stress (λ). Specifically, as the ratio λ grows, the shape of the plastic zone evolves from circular to elliptical and ultimately to a butterfly shape. Once the plastic zone becomes butterfly-shaped, further increases in λ cause rapid expansion of the plastic zone. Under repeated mining disturbances, the plastic zone of the surrounding rock can be regarded as a superposition of plastic zones induced by multiple mining activities. The stress distribution of the surrounding rock is markedly different at different locations. The ratio λ, which is the dominant factor responsible for the distinct deformation and failure modes observed in different regions, also varies spatially. Based on these findings, a grouting reinforcement control technique was proposed. The grouting timing, grouting pressure, and grouting radius were determined to formulate a practical grouting control scheme for field application. Field tests demonstrate that the proposed grouting control method effectively covers the deformation range of the surrounding rock and achieves satisfactory control performance. The results of this study are expected to provide a valuable reference for grouting reinforcement control in similar mining scenarios. Full article

Forest disturbances significantly affect the terrestrial carbon cycle, yet high-resolution detection, driver attribution, and carbon loss quantification remain challenging in cloudy and complex terrains. Here, we investigated the Northeast China and Southwest Hengduan Mountains forest regions from 2021 to 2024. We developed a Bayesian Model Averaging (BMA) framework integrating multi-source remote sensing (Sentinel-1/2, Landsat 8/9) and multi-algorithm ensembles (LandTrendr, CCDC, 1D-CNN) to extract 10 m disturbance features. Automated driver attribution and carbon loss quantification were achieved utilizing the Fire Information for Resource Management System (FIRMS), Dynamic World, and GEDI L4B LiDAR data. Validation yielded overall spatial accuracies of 91.15% in the Northeast and 89.62% in the Hengduan Mountains, with corresponding ensemble F1-Scores of 0.92 in both regions. Results indicated the disturbed area in the Northeast (1084.58 ha) significantly exceeded the Hengduan region (133.48 ha). Natural degradation dominated both regions (Northeast: 72.25%; Hengduan: 88.43%), though the Northeast experienced more wildfires and anthropogenic activities. Topographically, Northeast disturbances clustered on low-lying, gentle landscapes, whereas Hengduan events occurred on steep, high-altitude terrains. Due to denser per-pixel carbon storage, the Hengduan area exhibited higher carbon emission costs per unit area. Ultimately, this framework provides a quantitative technical foundation supporting high-resolution forest conservation and spatial evaluations for carbon neutrality commitments. Full article

This paper presents a control design framework that systematically translates nonlinear equilibrium operability analysis into frequency-domain robust synthesis for continuous microbial electrolysis cells (MEC). Since MEC operation is threatened by washout and highly variable influent conditions, analytical local conditions for the existence and local stability of normal operating conditions (NOC) and washout equilibria are first established. Departing from these nonlinear properties, the model is linearized within the locally validated NOC region, and a parametric sensitivity screening is used to identify dominant uncertainty sources ($\alpha$, ${\mu }_{max}$, $K_d$). These are embedded into an unstructured multiplicative uncertainty weight, enabling the synthesis of nominal and robust $H_{\infty }$ controllers that explicitly account for actuator effort, disturbance rejection, and measurement noise. Controller order reduction via balanced truncation is performed while preserving closed-loop local robustness properties. As a benchmark, an internal model control proportional–integral (IMC-PI) controller is derived, and its single tuning parameter is selected by solving a univariate multi-objective optimization that balances integral absolute error and maximum control effort, yielding a Pareto-optimal compromise. Numerical simulations under simultaneous inlet disturbances, parametric variations, measurement noise, and actuator saturation show that the reduced-order robust $H_{\infty }$ controller outperforms the optimized IMC-PI in the tracking–effort trade-off, while the nominal $H_{\infty }$ controller satisfies an a posteriori robust stability test for the linearized dynamics. The proposed framework provides a systematic path from nonlinear operability analysis to implementable robust control, demonstrating that high-order $H_{\infty }$ designs can be reduced to low-order transfer functions suitable for standard industrial control hardware while preserving local stability properties against realistic process perturbations. Full article

Landslide detection has largely relied on supervised learning or DEM-based representations, which can limit rapid deployment and generalization across heterogeneous terrain. In this work, we present a zero-shot, fully unsupervised framework that identifies landslide-like geomorphic instability candidates from raw UAV-mounted LiDAR, removing the need for labeled data, pre-event baselines, or rasterized terrain abstractions. Our approach is motivated by the observation that landslides manifest as localized geometric inconsistencies in the terrain surface. We capture this through a multi-scale formulation that combines point-level and cluster-level indicators of instability. At the point level, a PCA-based residual depth metric reduces slope-induced bias and highlights surface discontinuities, while local concavity captures terrain depletion patterns. At the cluster level, geomorphometric descriptors such as curvature concentration, surface roughness, elevation discontinuity, and slope variation are extracted using density-aware 3D clustering and integrated through adaptive feature fusion. The resulting probabilistic instability field enables spatially coherent delineation of landslide scars, including rupture boundaries, displaced material, and emerging failure regions. In addition, the detected patches provide useful priors for post-event susceptibility analysis without requiring temporal observations. Experiments across diverse geomorphic settings show that the proposed method improves detection of subtle terrain disturbances compared to DEM-based pipelines and supervised learning approaches, while remaining robust to noise and terrain variability. Overall, this work demonstrates that geometry-driven, unsupervised inference on raw 3D data can serve as a practical and scalable alternative for near real-time landslide detection using UAV-based systems. Full article