Search Results (27,633)

China’s traditional approach to supplying land for mining operations hinders the sustainable use of land resources, resulting in extensive land degradation and idleness after mining activities conclude. Based on this, the competent national authorities have innovatively launched reforms to the temporary land supply model for open-pit coal mining operations. This study uses the Anjialing open-pit coal mine pilot project in Shanxi Province, China as a case example to construct a comprehensive lifecycle model for temporary mining land use in operational coal mines. It evaluates the land reclamation implementation at this mine and proposes a land management model for future pilot mines establishing new temporary mining sites. Research indicates that: (1) In pilot mining projects currently under construction, the larger the initial mining area, the lower the strip ratio and coal extraction rate, and the longer the overall duration of temporary land use. (2) Based on the overall land use cycle model for temporary mining sites, the land use cycle for the Anjialing open-pit coal mine is approximately 7 to 10 years, making it impossible to complete mining operations and return the land after reclamation within five years. (3) Based on historical image analysis using the GEE platform, by the end of 2020, the coal mine reclamation area barely reached the boundaries of the 2012 temporary land use plan. Consequently, the pilot project for temporary mining land use failed to pass the required acceptance inspection. Overall, the promotion of this new model not only upholds the critical mission of safeguarding national farmland and ensuring food security, but also holds significant implications for future resource extraction and sustainable land utilization. Full article

Traditional orebody modeling methods struggle to efficiently integrate new geological data. Therefore, we propose a novel framework for dynamically updating 3D geological models by directly interpolating geological logging data. The core innovation lies in the innovative interpolation of raw interpreted cross polylines into an implicit scalar field representation without intermediate explicit surface extraction or manual remodeling. To obtain reliable vectorized polylines, we developed image recognition and digitization techniques that are based on the pattern recognition of geological sketches. Moreover, different from existing implicit techniques, we present an improved approach to interpolate complex cross polylines that are dynamically based on the improved principal component analysis. The method allows specifying a priori constraints to adjust the erroneous estimated normal to improve the reliability of the normal estimation results of cross-contour polylines. The a priori information can be combined into the normal estimation algorithm to update the normals of the corresponding adjacent contour polylines in the process of normal estimation at the intersection points and in the process of normal propagation. By leveraging the radial basis functions-based spatial interpolators, the method continuously assimilates incremental geological observations into the interpolation constraints to update the implicit model. Case studies demonstrate a reduction in the modeling cycle time compared to conventional explicit methods while maintaining geologically coherent boundaries. The framework significantly enhances decision agility in resource estimation and mine planning workflows by bridging geological interpretation and dynamic model iteration. Full article

Fine-grained intertidal sediments are typically characterized by low hydraulic conductivity and high nutrient loads, conditions that hinder biogeochemical recovery and exacerbate eutrophication. This study examined the feasibility of calcium-rich steel slag (SS) as a multifunctional capping material for improving both physical and chemical properties of cohesive sediments. Short-term (24 h) column experiments with two slag dosages (25 g and 50 g) revealed that the higher dosage (SS50) increased sediment hydraulic conductivity by 113.2%, likely through Ca²⁺-mediated flocculation and enhanced pore connectivity. Phosphate (PO₄-P) in pore water decreased by up to 64.1%, and effluent dissolved inorganic nitrogen (DIN) declined by 62.8%, indicating combined effects of Ca-driven precipitation, adsorption, and enhanced flushing. However, SS addition also raised pore water pH (to 11.8) and lowered redox potential, leading to transient phosphate release at the effluent boundary under reducing conditions. Cation analysis confirmed Ca²⁺ stability and Na⁺ reduction, suggesting improved sediment structural integrity. The results suggest that steel slag is a promising reactive capping material capable of enhancing permeability and controlling nutrient release in cohesive coastal sediments, yet further investigation into long-term ecological effects and dosage optimization is necessary. Full article

In recent years, hybrid models that integrate Convolutional Neural Networks (CNNs) with Vision Transformers (ViTs) have achieved significant improvements in hyperspectral image classification (HSIC). Nevertheless, their complex architectures often lead to computational redundancy and inefficient feature fusion, particularly struggling to balance global modeling and local detail extraction in high-dimensional spectral data. To solve these issues, this paper proposes a Spectral-Cube Gated Harmony Network (SCGHN) that achieves efficient spectral–spatial joint feature modeling through a dynamic gating mechanism and hierarchical feature decoupling strategy. There are three primary innovative contributions of this paper as follows: Firstly, we design a Spectral Cooperative Parallel Convolution (SCPC) module that combines dynamic gating in the spectral dimension and spatial deformable convolution. This module adopts a dual-path parallel architecture that adaptively enhances key bands and captures local textures, thereby significantly improving feature discriminability at mixed ground object boundaries. Secondly, we propose a Dual-Gated Fusion (DGF) module that achieves cross-scale contextual complementarity through group convolution and lightweight attention, thereby enhancing hierarchical semantic representations with significantly lower computational complexity. Finally, by means of the coordinated design of 3D convolution and lightweight classification decision blocks, we construct an end-to-end lightweight framework that effectively alleviates the structural redundancy issues of traditional hybrid models. Extensive experiments on three standard hyperspectral datasets reveal that our SCGHN requires fewer parameters and exhibits lower computational complexity as compared with some existing HSIC methods. Full article

Electroencephalography (EEG) is a non-invasive biosensing platform with a spatial-frequency content that is of significant relevance for a multitude of aspects in the neurosciences, ranging from optimal spatial sampling of the EEG to the design of spatial filters and source reconstruction. In the past, simplified spherical head models had to be used for this analysis. We propose a method for spatial frequency analysis in EEG for realistically shaped volume conductors, and we exemplify our method with a five-compartment Boundary Element Method (BEM) model of the head. We employ the recently developed technique for spatial harmonic analysis (Sphara), which allows for spatial Fourier analysis on arbitrarily shaped surfaces in space. We first validate and compare Sphara with the established method for spatial Fourier analysis on spherical surfaces, discrete spherical harmonics, using a spherical volume conductor. We provide uncertainty limits for Sphara. We derive relationships between the signal-to-noise ratio (SNR) and the required spatial sampling of the EEG. Our results demonstrate that conventional 10–20 sampling might misestimate EEG power by up to 50%, and even 64 electrodes might misestimate EEG power by up to 15%. Our results also provide insights into the targeting problem of transcranial electric stimulation. Full article

Accurate shade matching is essential in restorative and prosthetic dentistry yet remains difficult due to subjectivity in visual assessments. We develop and evaluate a deep learning approach for the simultaneous segmentation of natural teeth and shade guides in intraoral photographs using four fine-tuned variants of Segment Anything Model 2 (SAM2: tiny, small, base plus, and large) and a UNet baseline trained under the same protocol. The spatial performance was assessed using the Dice Similarity Coefficient (DSC), the Intersection over the Union (IoU), and the 95th-percentile Hausdorff distance normalized by the ground-truth equivalent diameter (HD95). The color consistency within masks was quantified by the coefficient of variation (CV) of the CIELAB components (L*, a*, b*). The perceptual color difference was measured using CIEDE2000 (ΔE00). On a held-out test set, all SAM2 variants achieved a high overlap accuracy; SAM2-large performed best (DSC: 0.987 ± 0.006; IoU: 0.975 ± 0.012; HD95: 1.25 ± 1.80%), followed by SAM2-small (0.987 ± 0.008; 0.974 ± 0.014; 2.96 ± 11.03%), SAM2-base plus (0.985 ± 0.011; 0.971 ± 0.021; 1.71 ± 3.28%), and SAM2-tiny (0.979 ± 0.015; 0.959 ± 0.028; 6.16 ± 11.17%). UNet reached a DSC = 0.972 ± 0.020, an IoU = 0.947 ± 0.035, and an HD95 = 6.54 ± 16.35%. The CV distributions for all of the prediction models closely matched the ground truth (e.g., GT L*: 0.164 ± 0.040; UNet: 0.144 ± 0.028; SAM2-small: 0.164 ± 0.038; SAM2-base plus: 0.162 ± 0.039). The full-mask ΔE00 was low across models, with the summary statistics reported as the median (mean ± SD): UNet: 0.325 (0.487 ± 0.364); SAM2-tiny: 0.162 (0.410 ± 0.665); SAM2-small: 0.078 (0.126 ± 0.166); SAM2-base plus: 0.072 (0.198 ± 0.417); SAM2-large: 0.065 (0.167 ± 0.257). These ΔE00 values lie well below the ≈1 just noticeable difference threshold on average, indicating close chromatic agreement between the predictions and annotations. Within a single dataset and training protocol, fine-tuned SAM2, especially its larger variants, provides robust spatial accuracy, boundary reliability, and color fidelity suitable for clinical shade-matching workflows, while UNet offers a competitive convolutional baseline. These results indicate technical feasibility rather than clinical validation; broader baselines and external, multi-center evaluations are needed to determine its suitability for routine shade-matching workflows. Full article

Measurement while drilling technology (MWD) has emerged as a pivotal approach for geological exploration. However, the accuracy of existing geological recognition models remains limited, primarily due to data fluctuations that result in high overlap rates and reduced reliability of drilling parameters. This study takes torque data as an example and analyzes the frequency distribution laws of torque responses across rock with varying strengths. A quantitative model of the frequency distribution characteristic interval is established, and a rock information prediction approach based on frequency distribution characteristics is proposed. The results indicate that torque frequency distributions for homogeneous rock exhibit a unimodal pattern, whereas those for composite rocks display multimodal characteristics. The boundaries of the frequency distribution characteristic intervals are mathematically defined as CIS = T_p|_{(dF/dT) = 0} ± σ and CIM = x_li ± 0.5∆x_i. The strength prediction model constructed using torque within the characteristic interval achieves an average accuracy of 85.3%. Furthermore, the frequency of torque within the characteristic interval enables the estimation of rock stratum thickness. This research contributes to enhancing the accuracy of rock information identification. Full article

Urban heat island conditions increase heat exposure and constrain safe outdoor activities. Urban forests can mitigate thermal loads; however, stand morphology can produce divergent microclimates. We aimed to quantify how stand type (open vs. closed), season (spring, summer, fall), and activity intensity (MET 1.0–6.0) jointly modulate thermal comfort and to identify season-specific optimal MET levels in an urban forest in Daejeon, Republic of Korea. We combined site-specific 3D canopy modeling with hourly Predicted Mean Vote (PMV) simulations driven by AMOS tower data (2023–2024). Comfort was defined as |PMV| ≤ 0.5. Analyses included seasonal means, Cliff’s delta, and generalized estimating equation logistic models to estimate the SITE × SEASON × MET interactions and predict comfort probabilities. Across most seasons and MET levels, C1 was more comfortable than C2. However, at MET 1.0 in summer, the pattern was reversed, which may reflect the canopy shading and associated decreases in mean radiant temperature (MRT) of C2. Comfort peaked at MET 2.0–3.0 and declined sharply at ≥4.5 MET. The three-way SITE × SEASON × MET interaction was significant (p < 0.001). The season-specific optimal MET values under our boundary conditions were 3.0 (spring), 2.0–2.5 (summer), and 3.0 (fall). These simulation-based PMV-centered findings represent model-informed tendencies. Nevertheless, they support actionable guidance: prioritize high-closure stands for low-intensity summer use, leverage open stands for low-to-moderate activities in spring and fall, and avoid high-intensity programs during warm periods. These results inform the programming and design of urban-forest healing and recreation by matching stand type and activity intensity to season to maximize comfortable hours. Full article

This review aims to evaluate the carbon mitigation potential of waste tire (WT) pyrolysis through a life cycle assessment (LCA) perspective, with a focus on clarifying methodological differences across studies. The scope of the review covers the three main pyrolysis products—tire pyrolysis oil (TPO), recovered carbon black (rCB), and tire pyrolysis gas (TPG)—and their roles in two interconnected mitigation pathways: (i) material substitution, where TPO can displace fossil diesel (≈2.7–3.2 kg CO₂e/kg) and rCB can replace virgin carbon black (≈1.8–2.2 kg CO₂e/kg), and (ii) energy self-sufficiency, where TPG (≈30–40 MJ/m³) offsets external fuels. Unlike earlier reviews that emphasized technical feasibility, this study synthesizes comparative LCA evidence to explain why reported mitigation factors vary widely, highlighting the influence of system boundaries, substitution ratios, and product quality. Harmonizing these conventions provides a consistent basis for cross-study comparison and positions WT pyrolysis as a strategic contributor to circular economy and decarbonization agendas. Full article

In this study, we discuss the water masses and their transport in the Chain fracture zone (CFZ), which is a poorly studied part of the Equatorial Atlantic. Our study is based on measurements carried out during the 63rd cruise of R/V “Akademik Ioffe” in 2022. We identified water masses in the CFZ, determined their physical and chemical properties, localized their boundaries and components of the North Atlantic Deep Water (NADW), and calculated the transport of water masses. A four-layer structure of the NADW was identified with two components of middle NADW, which are defined by minimal and maximal oxygen concentrations. The upper boundary of the Antarctic Bottom Water (AABW) corresponds approximately to the isotherm θ = 1.5 °C. The assessed proportion of AABW in the bottom layer at the western entrance to the CFZ is 50%, and not higher than 33% at the eastern exit from the CFZ. For the first time, instrumental observations were carried out at the exit of the CFZ and in its western part. They showed that the AABW flux has an intensity of about 0.02–0.5 Sv depending on the upper boundary of AABW and moves through a passage in the northern wall (at 13° W), and not through the main sill. Full article

Coastal lagoons provide vital ecological functions, supporting diverse flora and fauna while being highly sensitive to environmental changes. In the southern Baltic Sea, the Puck Lagoon is a hydrologically distinct subregion of the Gulf of Gdańsk characterized by variable exchange of water with the outer bay and substantial freshwater inflows. Its benthic communities are particularly sensitive to salinity, yet the processes shaping this parameter remain insufficiently understood. In situ measurements in summer 2020 revealed relatively high salinity in the lagoon (up to 7.7 PSU) compared to the adjacent outer bay (7.2–7.4 PSU), with localized reductions near the Kuźnica Passage and the Reda River mouth. As a first step toward explaining the hydrodynamic processes responsible for these anomalies, we applied a high-resolution, two-dimensional model focused on three fundamental physical drivers: river inflows, open-boundary exchange, and wind forcing. These processes represent the primary controls on salinity in shallow lagoons and provide a basis for evaluating additional mechanisms. The model reproduced observed patterns with a mean absolute error of 0.15 PSU, confirming that this selective framework captures the key features of salinity variability and establishes a baseline for future three-dimensional modeling that will incorporate further processes such as vertical mixing, precipitation, and evaporation. Full article

Small object detection in UAV imagery remains challenging due to complex aerial perspectives and the presence of dense, small targets with blurred boundaries. To address these challenges, we propose ACD-DETR, an adaptive end-to-end Transformer detector tailored for UAV-based small object detection. The framework introduces three core modules: the Multi-Scale Edge-Enhanced Feature Fusion Module (MSEFM) to preserve fine-grained details; the Omni-Grained Boundary Calibrator (OG-BC) for boundary-aware semantic fusion; and the Dynamic Position Bias Attention-based Intra-scale Feature Interaction (DPB-AIFI) to enhance spatial reasoning. Furthermore, we introduce ACD-DETR-SBA+, a fusion-enhanced variant that removes OG-BC and DPB-AIFI while deploying densely connected Semantic–Boundary Aggregation (SBA) modules to intensify boundary–semantic fusion. This design sacrifices computational efficiency in exchange for higher detection precision, making it suitable for resource-rich deployment scenarios. On the VisDrone2019 dataset, ACD-DETR achieves 50.9% mAP@0.5, outperforming the RT-DETR-R18 baseline by 3.6 percentage points, while reducing parameters by 18.5%. ACD-DETR-SBA+ further improves accuracy to 52.0% mAP@0.5, demonstrating the benefit of SBA-based fusion. Extensive experiments on the VisDrone2019 and DOTA datasets demonstrate that ACD-DETR achieves a state-of-the-art trade-off between accuracy and efficiency, while ACD-DETR-SBA+ achieves further performance improvements at higher computational cost. Ablation studies and visual analyses validate the effectiveness of the proposed modules and design strategies. Full article

This study investigates the thermal stability of fine-grained structures achieved through different severe plastic deformation (SPD) and heat treatment paths. Bulk fine-grained Al-0.1Sc-0.1Zr (wt%) alloy was produced via equal channel angular pressing (ECAP) using routes Bc or C, with aging before or after the ECAP. Electron back-scattered diffraction (EBSD) and transmission electron microscopy (TEM) analyses demonstrate excellent thermal stability of all four specimens. They maintain mean grain sizes below 5 μm after a 10 h thermal test at 450 °C, attributed to the presence of nano Al₃(Sc,Zr) precipitates within the microstructures. Route Bc in the ECAP method forms more stable high-angle grain boundaries (HAGBs) than route C. Whether aging occurs before or after the ECAP, similar microstructural changes are observed after thermal testing, allowing fine-tuning of the microstructure depending on the application or subsequent processes. Full article

Droplet-based microlens fabrication using Ultra Violet (UV) curable polymers demands the precise measurement of three-dimensional geometries, especially for non-axisymmetric shapes influenced by electric field deformation. In this work, we present a polar coordinate-based contour fitting method combined with Hermite interpolation to reconstruct 3D droplet geometries from two orthogonal shadowgraphy images. The image segmentation process integrates superpixel clustering with active contours to extract the droplet boundary, which is then approximated using a spline-based polar fitting approach. The two resulting contours are merged using a polar Hermite interpolation algorithm, enabling the reconstruction of freeform droplet shapes. We validate the method against both synthetic Computer-Aided Design (CAD) data and precision-machined reference objects, achieving volume deviations below 1% for axisymmetric shapes and approximately 3.5% for non-axisymmetric cases. The influence of focus, calibration, and alignment errors is quantitatively assessed through Monte Carlo simulations and empirical tests. Finally, the method is applied to real electrically deformed droplets, with volume deviations remaining within the experimental uncertainty range. This demonstrates the method’s robustness and suitability for metrology tasks involving complex droplet geometries. Full article