Search Results (6,941)

The Lianggaoshan Formation in the Gongshanmiao block of the Sichuan Basin is characterized by narrow channel sand bodies and thin layers, resulting in weak seismic responses, which leads to poor identification of small-scale fault–fracture systems. Initial high-density 3D seismic data exhibit strong shallow surface waves and significant shot-to-shot variations in energy and frequency, necessitating amplitude-preserving noise attenuation and broadband wavelet consistency processing. First, pre-stack multi-domain amplitude-preserving noise attenuation is applied, integrating surface-wave forward modeling, stationary wavelet transform, and matrix singular value decomposition to suppress complex noise. Next, robust deconvolution constrained by a target wavelet improves broadband consistency. Subsequently, anisotropic depth-domain velocity modeling and imaging under rugged topography are conducted using a well-constrained TTI initial velocity model and full-azimuth angle-domain grid tomography. Compared with conventional data, the processed high-density data significantly enhance bandwidth, structural imaging, and thin-layer resolution. Imaging continuity of small faults (6–10 m throw) is markedly improved, and the channel characterization accuracy of the Liang-2 Member increases from 180 m to 60 m. This workflow delivers high-SNR, high-resolution, and high-fidelity results, providing a reliable basis for thin-sandbody prediction and reservoir evaluation. Full article

Phosphorus (P) contamination in agricultural runoff is a major environmental concern due to its contribution to eutrophication and deterioration of water quality. Ortho-phosphate extraction from aqueous model systems and agricultural runoff samples was investigated using cetyltrimethylammonium bromide-magnetic nanoparticles (CTAB-MNPs), sodium dodecylsulfate-magnetic nanoparticles (SDS-MNPs), and Mill scale. It was monitored using UV–visible spectroscopy based on the molybdenum blue method, while the adsorption mechanism of P on MNPs was evaluated through time-dependent studies, FTIR, zeta potential (ζ), X-ray photoelectron spectroscopy (XPS), and FESEM-EDS. CTAB-MNPs promoted P adsorption through favorable electrostatic interactions between positively charged quaternary ammonium groups and negatively charged phosphate ions, while SDS-MNPs enhanced P uptake by improving nanoparticle dispersion, colloidal stability, and accessibility of iron oxide active sites. Time-dependent studies revealed that the adsorption kinetics followed the order SDS-MNPs > Mill Scale > CTAB-MNPs. IR, XPS, FESEM-EDS and ζ analyses confirmed the presence of adsorbed P on the surface of MNPs. The results demonstrated that P removal was also governed by inner-sphere complexation with iron oxide active sites rather than electrostatic interactions alone, highlighting the potential of surfactant-modified MNPs and Mill scale as effective materials for P extraction from agricultural runoff. Full article

Seamless steel pipes require reliable classification of outer-surface anomalies, yet class imbalance, scale variation, and visual overlap between defects and production interference complicate automated inspection. We propose WF-MobileNet for classifying two-dimensional RGB pseudo-colour surface morphology images derived from line-structured-light profiling. The model retains MobileNetV3-Small as its primary descriptor path, applies Selective WTConv to selected middle- and late-stage depthwise operations, and aggregates features at three spatial resolutions through Lite-BiFPN. A gated residual connection adds the multiscale descriptor to the terminal backbone descriptor. Evaluation used 15,463 images covering 12 defect classes and 4 production interference classes, with five training seeds on one fixed partition. WF-MobileNet achieved the highest observed mean macro-F1 and interference F1 among the six evaluated architectures, reaching (82.17 ± 0.36)% and (80.50 ± 0.96)%, respectively (mean ± sample standard deviation). The corresponding gains over MobileNetV3-Small were 4.20 and 6.84 percentage points. With 1.811 million parameters and 65.8 million multiply–accumulate operations (MACs), WF-MobileNet ranked second lowest on both complexity measures. Controlled ablation revealed larger mean macro-F1 gains from the joint configuration than from either component alone. Within the evaluated archive, WF-MobileNet improved class-balanced recognition and defect–interference discrimination while retaining low parameter and MAC counts. Full article

Bridge surface cracks are important early indicators of structural performance degradation. However, affected by complex environmental interferences and irregular morphologies, existing models still fall short in micro-crack recognition, accurate bounding-box localization, and lightweight. To address these challenges, this study proposes a multi-module collaborative lightweight model (MCL-YOLO) based on YOLOv12. Specifically, the existing ADown module from YOLOv9 is incorporated into the YOLOv12 architecture to reduce computational complexity while preserving critical information during feature downsampling. To enhance the representation of slender, curved, and branched crack patterns, a C3k2-RFAConv module is designed by integrating a receptive-field attention mechanism. Furthermore, an iEMA module is embedded before the high-resolution detection branch to strengthen the semantic response to weak-texture cracks. A bridge crack dataset containing 4029 images was constructed to evaluate the proposed model. Experimental results show that MCL-YOLO achieves Precision, Recall, mAP@50, and mAP@50:95 values of 0.891, 0.757, 0.844, and 0.675, with 5.5 GFLOPs, 2.240 M parameters, and a model-file size of 4.689 M. Compared with the YOLOv12n baseline, MCL-YOLO improves the four detection metrics by 2.30%, 4.56%, 4.07%, and 3.21%, while reducing GFLOPs and parameter count (Params) by 12.70% and 12.77%, respectively. Ablation experiments, model version comparisons, attention mechanism comparisons, and qualitative detection results collectively verify the effectiveness of the integrated architectural modifications. Full article

The deep-sea tunnels at the Sanshan Island Gold Mine are subjected to extreme conditions characterized by high stress and complex erosion resulting from high mineralization. Under these conditions, conventional shotcrete is prone to performance degradation and insufficient durability, posing a threat to the long-term safety of the tunnels. At the same time, mine water is difficult to recycle on-site. To address these engineering challenges, this study utilized fly ash (FA), S105-grade ground granulated blast furnace slag (GGBS), polypropylene coarse fiber (PPCF), and hydroxypropyl methylcellulose (HPMC) as modifying components and employed the response surface method (RSM) to optimize the mix design of mine water-blended shotcrete. The study selected compressive strength, direct shear strength, and chloride ion electrical flux at 6 h as response indicators and constructed a quadratic polynomial regression model. Analysis of variance and goodness-of-fit tests indicated that the model possessed good significance and reliability of fit. Based on this model, the optimal mix design was determined: an FA/GGBS blend ratio of 3:7, a cement replacement rate of 20%, a PPCF content of 3.3%, and an HPMC content of 0.18%. Performance testing showed that the optimal mixture achieved a compressive strength of 25.24 MPa, a direct shear strength of 8.08 MPa, and a chloride ion electrical flux of 778 C after 6 h. Compared to the control group, its peak compressive strength decreased by only 9.98%, while its residual strength increased significantly; direct shear strength increased by 18.1%, and electrical flux decreased by 33.8%. This indicates that the material’s mechanical load-bearing capacity, deformation coordination, and corrosion resistance have been enhanced in a synergistic manner. Field industrial trials have verified that this modified concrete possesses excellent ductile yield characteristics, can effectively suppress water seepage in mine tunnels, is capable of withstanding extreme underground operating conditions, and enables the efficient reuse of mine water resources. Full article

Understanding the spatial distribution patterns of pelagic species such as yellowfin tuna (Thunnus albacares) is essential for ecosystem-based fisheries management. However, characterizing CPUE–environment relationships remain challenging because these relationships may be nonlinear and spatially heterogeneous across large oceanic regions. To address these challenges, we developed an interpretable spatial modeling framework, geographically neural network weighted regression integrated with GeoShapley analysis (GNNWR-GeoShapley), which combines the nonlinear learning capability of neural networks with spatially explicit characterization and interpretation of model relationships. Using Pacific longline fishery data and multi-source environmental variables from 2004 to 2023, we constructed quarterly models of CPUE–environment relationships and compared the performance of GNNWR with Generalized additive model (GAM), geographically weighted regression (GWR), graph neural network (GNN) models, and Geographical Random Forest (GRF). The results demonstrated that GNNWR showed the best overall performance across seasons, effectively capturing nonlinear relationships and spatial heterogeneity in yellowfin tuna nominal CPUE. GeoShapley analysis further revealed that sea surface and subsurface (150 m) temperature and salinity were among the most important environmental variables associated with nominal CPUE variations. Nonlinear response patterns indicated that SST values above approximately 25 °C and T150 values above approximately 19 °C were associated with positive model contributions, whereas higher salinity values (>35) exhibited negative contributions. Moreover, spatial effects represented by the geographical location variable (GEO) and their interactions with environmental variables revealed pronounced spatial heterogeneity, with the contribution patterns of environmental factors varying across seasons and regions. This study provides an interpretable spatial modeling framework for characterizing complex species–environment relationships and offers new insights into the spatial variability of Pacific yellowfin tuna nominal CPUE for fisheries oceanography and sustainable resource management. Full article

This paper presents a multi-parameter characterisation of the atmospheric composition at the RADO-Bucharest observatory, a regional WMO-GAW and ACTRIS facility in southeastern Europe, by anchoring recent observations within a multi-annual baseline (2015–2024). This paper utilises data from multi-wavelength active remote sensing, high-resolution near-surface speciation, and modelling to evaluate complex urban and transboundary processes across different aerosol and clouds regimes. The year 2025 was marked by an atmospheric perturbation, which was characterised by a quantifiable departure from the decadal climatology. Using Z-score analysis, this perturbation was identified as a seasonal decoupling: an atypical reversal in the vertical particle-size distribution occurred in the free troposphere between July and October, while a transition toward a high-absorption aerosol regime was recorded near the surface during the winter months. These shifts are quantified by a significant drop in the columnar Single Scattering Albedo and a systemic increase in high-troposphere Lidar ratios exceeding 70 ± 12 sr, indicating the presence of advected combustion products aloft. At the surface level, chemical speciation measurements recorded an overall increase in wintertime particulate mass concentrations alongside elevated levels of More-Oxidized Oxygenated Organic Aerosol (MOOOA) compared to previous years, reflecting an intensified accumulation of aged, processed emissions during the January–February period. Furthermore, this paper documents cloud vertical structure and phase occurrence, highlighting a persistent seasonal stratification. The application of unified inversion frameworks is demonstrated through case studies of smoke and mineral dust, applying the Generalized Retrieval of Atmosphere and Surface Properties (GRASP) algorithm to retrieve vertically distributed aerosol microphysics through lidar–photometer integration. Maintained under rigorous quality assurance protocols, these results demonstrate the value of continuous, multi-instrumental profiling to quantify transboundary perturbations and improve regional energy budget representations. Full article

Discrete element method (DEM) has become an essential tool for analyzing particle-scale behavior and complex interactions in agricultural materials and machinery. This review synthesizes recent advances in DEM-based modeling and applications in agricultural engineering. It systematically examines particle modeling strategies, contact model development, and parameter calibration frameworks for representative materials such as soil, seeds, fertilizers, stalks, and roots. The evolution from simple spherical approximations to multi-sphere, bonded, and 3D scan-based reconstructions is highlighted, along with the shift from classical Hertz–Mindlin models to cohesive, elasto-plastic, and fracture-capable formulations. Calibration methods are traced from empirical assignment to systematic frameworks that integrate design of experiments, response surface methodology, and machine-learning-assisted inverse analysis. These advances in fidelity and accuracy have enabled extensive DEM applications in tillage, seeding, fertilization, and harvesting, with emphasis on equipment optimization, mechanism analysis, and performance prediction. Despite the progress, challenges remain in model standardization, parameter transferability, computational cost, and multiphysics coupling. Future work points to unified modeling frameworks, standardized databases, real-time simulation, and integration with artificial intelligence to support digital agriculture and intelligent machinery. This review aims to serve as a reference for high-fidelity DEM modeling and for advancing the digital transformation of agricultural engineering. Full article

Person-overboard detection from maritime unmanned aerial vehicle (UAV) imagery is challenging because the targets occupy very few pixels, sea-surface clutter is severe, human appearance varies substantially, and real-time processing is required. Existing detectors therefore struggle to meet the demands of practical maritime search and rescue. This paper presents HPSNet, an accuracy- and recall-oriented detector designed for maritime UAV imagery. HPSNet uses YOLOv11 as its baseline and introduces three complementary modifications. A channel transposed attention (CTA) module is embedded in the backbone to improve the discrimination of target features from complex sea-surface interference. A Giraffe feature pyramid network (GFPN) replaces the original feature-fusion network to strengthen multiscale information exchange and preserve cues from extremely small targets. A Dynamic Head (DyHead) adapts the predictions to variations in target scale, location, and appearance. HPSNet is evaluated against 12 representative detectors on the public Person Detection in Water and AFO datasets, and ablation experiments examine the contribution of each component. On Person Detection in Water, HPSNet achieves 78.2% mAP@50, 37.5% mAP@50:95, and 67.2% recall, improving the YOLOv11 baseline by 2.7, 2.2, and 4.2 percentage points, respectively. On AFO, it achieves 88.4% mAP@50 and 59.4% mAP@50:95, with gains of 0.7 and 1.9 percentage points over the baseline. The model contains 4.18 M parameters, requires 9.6 GFLOPs, and processes an image in 13.4 ms on an RTX 4090. These results demonstrate improved detection of small and visually weak maritime targets relative to YOLOv11 while maintaining a moderate model scale, providing a foundation for future deployment and optimization on embedded maritime UAV platforms. Full article

Snow and ice albedo feedback (SIAF) is a key process linking cryospheric changes to global warming. Satellite radiative kernels provide another choice for estimating radiative feedbacks, bypassing the complexity of climate model radiative transfer processes. Based on the established method for estimating SIAF, we use the longest available satellite-derived surface albedo record together with a satellite radiative kernel to quantify the summer SIAF over the Arctic and Antarctic. The summer SIAF was 0.08 ± 0.04 W·W· m⁻² for the Arctic and −0.11 ± 0.11 W·m⁻² for the Antarctic, respectively, averaged over 1979–2024. An overall positive SIAF is observed in the Arctic, while Antarctic SIAF shows pronounced spatial heterogeneity, with negative feedback mainly over the East Antarctic coastal sea-ice regions and positive feedback over the West Antarctic marginal seas. Spatial patterns of SIAF trends closely match the climatological feedback, with strong positive (negative) feedback regions generally showing enhanced positive (negative) trends. This implies that current feedback regimes tend to persist and even intensify over time. Separating terrestrial and sea-ice components further reveals contrasting evolutionary pathways between the two hemispheres. Terrestrial SIAF over both the Greenland and Antarctic Ice Sheets gradually shifted from weak negative toward positive trends, with an enhancement in recent decades. In the Arctic, sea-ice SIAF experienced rapid intensification during 1996–2011, followed by a substantial slowdown after 2012 due to a decline in summer sea-ice loss rate and ice and sheet surface melting. In contrast, Antarctic sea-ice SIAF remained in a persistent negative trend during 1979–2015, associated with sea-ice expansion, but rapidly reversed toward a positive feedback regime after the abrupt sea-ice decline in 2016. Full article

This study investigated the long-term relationship between land-use change and land-cover-based ecological quality patterns in Ordos City, a typical coal resource-based city in northern China. To explicitly capture land-use transitions driven by coal exploitation, mining areas were classified as an independent land-cover type. An improved U-Net semantic segmentation model integrating multispectral information and land surface temperature was subsequently employed to generate multi-temporal land-cover maps. In the internal semantic segmentation validation, the proposed model achieved a mean Intersection over Union (mIoU) of 69.51%, a mean accuracy (mAcc) of 81.65%, and a pixel-level overall accuracy (aAcc) of 83.16%. An independent point-based accuracy assessment of the final land-cover map yielded an overall accuracy (OA) of 66.00% and a Kappa coefficient of 0.6033, indicating acceptable classification reliability in complex mining areas. Based on the classification results, three indicators, namely the land-use transition matrix, ecological environmental quality index (EQI), and ecological contribution index, were adopted to analyze spatiotemporal land-use dynamics and associated land-cover-based ecological quality patterns in Ordos City over the past 25 years. The results indicate that: (1) Marked land-use changes occurred in the land-use pattern of the study area during 2000–2025, with grassland and unused land consistently remaining the dominant land-use types. The area classified as Mining increased from 105.83 km² to 1184.17 km² in 2020, exhibiting distinct characteristics of phased expansion and subsequent adjustment. Built-up land continued to expand, whereas the water area decreased by 47.55%. (2) The land-cover-based EQI exhibited a pattern of decline, recovery, and stabilization, decreasing from 0.529 in 2000 to 0.489 in 2015 before recovering to 0.522 in 2025. This pattern was associated with changes in land-cover composition over the study period, including mining expansion and restoration-related transitions. The findings provide a scientific basis for ecological restoration planning and high-quality transformation in Ordos and other coal resource-based cities with similar arid and semi-arid environmental conditions. Full article

Karst aquifers supply drinking water for about a quarter of the global population. Despite their often shallow occurrence, delineating these aquifers remains challenging due to the fine-scale nature of conduits and pore spaces in the shallow karst zone, compounded by highly complex spatial distributions. This study integrates self-potential probability tomography (SPPT) with electrical resistivity tomography (ERT) and borehole data to identify and characterize preferential flow paths within a heterogeneous karst aquifer in Guangxi, China. Seasonal surface and borehole self-potential datasets acquired in March, August, and December effectively captured the spatiotemporal hydrological dynamics. Results indicate that as the groundwater level drops from 13.7 m to 25.5 m, low-potential anomaly zones and the maximum charge occurrence probability (COP) shift toward the center of the depression. Concurrently, self-potential intensity increases with depth, particularly within areas of well-developed karst fracturing. By synthesizing SPPT, ERT, and borehole constraints, a 3D preferential flow channel model was constructed, enabling the in situ identification of groundwater flow pathways in shallow karst aquifers. This work provides a reliable technical framework for the precise mapping and sustainable management of groundwater resources in karst regions. Full article

The timberline region of the Western Himalaya is a crucial ecological transition area highly sensitive to climate changes, which significantly influence vegetation patterns, soil formation, and carbon dynamics. This study aimed to investigate the spatial and altitudinal changes in soil organic carbon (SOC) across different topographic orientations and to evaluate machine-learning models for spatial SOC prediction in the timberline ecotone (2100–3300 m) of the Kedarnath Wildlife Sanctuary. Through systematic random sampling across 100 m elevation bands, composite soil samples were collected from three aspects (North-East, South-West, and North-West) at two depths (0–15 cm and 15–30 cm) and analyzed alongside topographic, spectral, and climatic covariates. Results indicated that SOC trends varied significantly by aspect; the North-East aspect exhibited a considerable increase in SOC with elevation, while the North-West and South-West sides responded differently. Furthermore, Digital Soil Mapping using the Random Forest (RF) model outperformed Support Vector Machine and XGBoost, explaining 62% of surface and 74% of subsurface SOC variability. In conclusion, aspect-induced microclimatic gradients play a major role in controlling soil characteristics near the Himalayan timberline, and machine-learning models like RF can successfully capture these complex spatial patterns. Consequently, it is recommended that the established baseline SOC maps be utilized as reference points for future climate-change monitoring, carbon accounting, and directing conservation strategies in high-altitude forests. Full article

Conventional hydrogen peroxide (HP) tooth whitening treatments frequently induce structural deterioration of enamel, necessitating the development of biocompatible and highly efficient alternatives. This in vitro study investigated the application of a novel functional biomaterial, the Type I aggregation-induced emission (AIE) photosensitizer CPTQ, for peroxide-free photodynamic tooth whitening. Under white-light irradiation (25 mW/cm²), CPTQ (12.5 μM) demonstrated rapid degradation of representative chromogenic molecules (crystal violet, malachite green, and rhodamine B). Extracted human teeth stained with both model pigments and complex beverage mixtures (coffee, tea, and fruit juices) were allocated into four treatment groups: negative control (NC), CPTQ, 7.5% HP, and 30% HP (n = 6 per group). Colorimetric parameters (ΔE₀₀, Δa*, Δb*, and ΔL*) and enamel-related endpoints—including surface morphology, roughness, mineral composition (Ca/P), and microhardness—were evaluated over 6 h and analyzed using one-way ANOVA. For pigment-stained teeth, the photodynamic whitening efficiency of CPTQ was comparable to that of 30% HP (ΔE₀₀, p > 0.05) during the 6 h treatment period. At 1.5 h, the ΔE₀₀ value in the CPTQ group (23.35 ± 2.32) was significantly greater than those in the NC group (15.70 ± 1.78, p < 0.05) and the 7.5% HP group (15.02 ± 1.63, p < 0.05). For beverage-stained teeth, the whitening efficiency (ΔE₀₀) of CPTQ was significantly greater than that of the control group at 1.5 h (6.22 ± 2.09 vs. 1.82 ± 0.56, p < 0.01) and was significantly greater than that of 7.5% HP at 4.5 h (10.94 ± 2.82 vs. 7.56 ± 1.78, p < 0.05). Crucially, unlike the change observed after 30% HP treatments, CPTQ treatment resulted in no statistically significant differences from NC in preserved enamel surface integrity, including morphology assessed by SEM, surface roughness (R_a and S_a, both p > 0.05), mineral composition (Ca and P, both p > 0.05), and microhardness (ΔH_V, p > 0.05). Mechanistic investigations using reactive oxygen species (ROS) scavengers (TBA and DABCO) suggested that hydroxyl radicals generated via the Type I photodynamic pathway are the primary drivers of pigment degradation, reducing ΔE₀₀ from 12.56 to 1.09 upon •OH inhibition. CPTQ achieved measurable in vitro whitening through a predominantly Type I ROS-mediated mechanism while causing limited changes in the evaluated enamel surface endpoints. Full article

Reaction control systems (RCSs) provide rapid and effective attitude-control capabilities for high-speed vehicles operating in high-altitude, low-density environments, where conventional aerodynamic control surfaces become inefficient. However, interactions between lateral jets and high-speed crossflows generate complex shock, separation, and vortex structures, which may significantly alter RCS control effectiveness. In this study, the nonlinear coupled constitutive relations (NCCR) model is employed to investigate dual lateral jet interactions over a cone–cylinder vehicle at an altitude of 80 km and a freestream Mach number of 10. The effects of the jet pressure ratio on the normal force coefficient, force amplification factor, moment amplification factor, wall pressure distribution, and flowfield structure are systematically analyzed. The results show that the absolute value of the normal force coefficient increases with increasing jet pressure ratios, whereas both the force and moment amplification factors decrease. At high-pressure ratios, the direct jet thrust becomes dominant and the relative contribution of jet-induced surface aerodynamic forces decreases. A second small-scale high-pressure region is observed between the two jet orifices, which is attributed to the expanded region of influence of the horseshoe vortex. In addition, the enhanced wrapping effect at high-pressure ratios adversely affects the aerodynamic contribution to RCS control efficiency. As the jet pressure ratio decreases from 750–1500 to 25–50, the relative deviations between Navier–Stokes- and NCCR-predicted force and moment amplification factors decrease from 0.019% to 0.262% and from 0.015% to 0.228%, indicating more pronounced rarefaction and non-equilibrium effects at low jet pressure ratios. Full article