Search Results (140)

Tight oil reservoir quality and development effectiveness are highly dependent on microscopic pore structure characteristics and spatial heterogeneity. In this study, tight sandstones from the Chang 3, Chang 6, Chang 7, and Chang 8 members of the Triassic Yanchang Formation in the Xunyi exploration area, southern Ordos Basin, were selected as research objects. By integrating X-ray diffraction (XRD), cast thin sections, scanning electron microscopy (SEM), high-pressure mercury injection (HPMI) experiments, and multifractal theory, the multi-scale heterogeneity characteristics of pore structures in different layers were quantitatively characterized. The response relationships between multifractal parameters, macroscopic physical properties, and pore size distributions were discussed, and the geological control mechanisms of sedimentation and diagenesis on heterogeneity were revealed. The results indicate that the sedimentary environment plays a fundamental role in controlling reservoir physical properties. The Chang 3 and Chang 8 members, deposited in underwater distributary channels, are dominated by primary and dissolution pores, with physical properties significantly superior to the gravity flow-deposited Chang 6 and Chang 7 members. Multifractal analysis shows that the Chang 3 member has the largest singularity spectrum width (Δα =1.943 ± 0.56) and heterogeneity index (R_d = 1.782 ± 0.99), reflecting its broadest pore size distribution, strongest heterogeneity, and significant intra-layer differences; while the pore structures from Chang 6 to Chang 8 are relatively stable, with the Chang 8 member exhibiting high spatial connectivity. This study demonstrates that the quantitative evaluation method based on multifractal theory can effectively identify microscopic structural differences in tight sandstones, providing a critical supporting basis for reservoir classification characterization and favorable layer selection in the Yanchang Formation of the Ordos Basin. Full article

Multi-view point cloud registration is a fundamental technology for 3D reconstruction and indoor robot navigation and remains a core challenge for robust environmental perception. Its key difficulty lies in achieving globally consistent alignment of multiple partially overlapping point clouds efficiently and reliably. To address the limitations of existing methods, including low registration accuracy under small overlaps, severe error accumulation in long sequences, and the difficulty of balancing computational efficiency with global consistency, this paper proposes a multi-view point cloud registration framework that integrates spanning tree-based global topology constraints with a multi-scale pyramid-based local refinement strategy, specifically validated for indoor environments. First, a Voxel-Guided Normal Consistency Keypoint Extraction (VG-NCKE) method is presented. It leverages voxel grids to guide stable computation of local geometric features and filters candidate keypoints using a neighborhood normal direction consistency metric, effectively improving keypoint repeatability and spatial uniformity on unevenly distributed point clouds. Second, a coarse registration strategy with global constraints is constructed based on the Overlap Confidence-weighted Minimum Spanning Tree (OC-WST). It quantifies inter-frame overlap reliability as edge weights and employs Prim’s algorithm to build the minimum spanning tree as the topological skeleton for global registration. By prioritizing high-overlap frame pairs, the method suppresses error propagation and reduces the complexity of multi-view registration. Additionally, a multi-scale pyramid ICP fine registration algorithm is designed. It adopts a point-to-plane error model instead of the traditional point-to-point distance metric and performs progressive optimization through a three-layer point cloud pyramid from coarse to fine. This expands the convergence basin and gradually improves alignment accuracy, mitigating the sensitivity of single-scale ICP to initial poses. Extensive experiments on the indoor 3DMatch dataset and real indoor LiDAR sequences demonstrate that the proposed method outperforms competing approaches in terms of registration accuracy, computational efficiency, and long-sequence robustness, validating its effectiveness for indoor multi-view point cloud registration tasks. Full article

Flexible base layers can improve deformation compatibility and reduce reflective cracking in asphalt pavements, but conventional graded crushed stone is limited by weak interparticle bonding, poor water stability, and insufficient resistance to permanent deformation. Solution Road Soilfix (SRX) is a water-based polymer stabilizer used to improve the engineering performance of graded crushed stone by enhancing interparticle bonding. This study investigated the effects of SRX dosage, aggregate gradation, degree of compaction, and curing conditions on the load-bearing capacity and pavement performance of SRX-stabilized graded crushed stone. The results showed that SRX stabilization significantly improved the California bearing ratio (CBR), water stability, and permanent deformation resistance of the graded crushed stone mixture, although its permeability decreased due to polymer coating and void filling. At an SRX dosage of 0.50% by dry aggregate mass, the CBR values exceeded 300%, while further dosage increases provided only limited additional improvement. Among the three gradations, the 26.5 mm gradation exhibited the best overall performance due to its balanced coarse aggregate distribution and stable interlocking skeleton. CBR was highly sensitive to the degree of compaction, and a field compaction degree of at least 98% is recommended. Oven curing at 50 °C accelerated moisture evaporation and SRX film formation; the 6-day CBR exceeded 80% of the 30-day reference strength and correlated well with long-term strength. Overall, the recommended parameters are 0.50% SRX dosage, 26.5 mm maximum aggregate size, compaction degree ≥ 98%, and oven curing at 50 °C for 6 days before laboratory CBR evaluation. Full article

Lamellar structure Ni-P catalysts were prepared on copper by the electrochemical deposition method for the hydrolysis of NaBH₄ solution. Voltage, time and temperature are key variables in the electroplating process, affecting the corrosion performance of the catalyst. The results show that as the deposition voltage (4–7 V) increases, the corrosion resistance of Ni-P at first is enhanced and then decreases, peaking at 5 V due to a more complete structure. Electroplating time and temperature affect the deposition of the nickel-phosphorus catalyst and then the corrosion resistance of the catalyst. Prolonged time and elevated temperature cause holes and cracks, degrading corrosion resistance. Therefore, a mild electroplating environment is preferred. The optimal electroplating temperature and time are 30 °C and 3 min, respectively. The polarization curve test shows that the Ni-P catalyst is greatly influenced by seawater temperature and chloride ion concentration in the actual service process, that the chloride ion is the dominant factor, and that the corrosion rate increases exponentially. Moreover, Ni-P/Cu catalysts mainly undergo localized corrosion and dissolution. Combined with Scanning Electron Microscope (SEM) and Energy Dispersive Spectrometer (EDS) analyses, the corrosion mechanism in seawater was systematically discussed. Full article

Litter decomposition regulates the quantity and quality of plant-derived carbon (C) and nitrogen (N) inputs to soil and is closely associated with microbial community structure. However, how elevated ozone (O₃) and nitrogen (N) addition interactively affect residual litter inputs and their associations with soil microbial communities remains poorly understood, especially in agroforestry systems. Here, we conducted a 12-month in situ litter decomposition experiment using two poplar clones (107 and 546) under ambient or elevated O₃ with or without N addition (60 kg N ha⁻¹ yr⁻¹) at an O₃-FACE platform in northern China. Litter mass and chemical traits were measured during decomposition, and endpoint soil microbial community structure was characterized using phospholipid fatty acid (PLFA) profiling. Treatment effects and litter–microbe associations were evaluated using linear mixed-effects models, correlation analysis, and redundancy analysis (RDA). Endpoint litter mass remaining was significantly affected by O₃, clone identity, and their interactions with N addition, while endpoint litter chemical traits showed trait-specific responses. PLFA-derived microbial community indices also showed treatment- and clone-dependent responses, particularly in bacterial groups, AM fungi, and the fungal-to-bacterial ratio. Endpoint litter mass remaining showed the strongest statistical association with PLFA-derived microbial community structure, whereas individual nutrient concentrations showed weaker independent effects. These findings suggest that O₃- and N-induced changes in residual litter quantity and quality are associated with shifts in PLFA-derived microbial community structure. Because PLFA characterizes microbial community structure rather than process rates, these findings should be interpreted as evidence of structural microbial reorganization associated with altered residual litter inputs, rather than direct evidence of changes in C or N cycling rates. Full article

Glaze flaking is widespread in Hongzhou kiln celadon dating from the Eastern Han to the Tang Dynasty, yet its underlying mechanism cannot be attributed to a single factor. In this study, 11 Hongzhou kiln celadon specimens from the Eastern Han, Southern Dynasties, and Sui–Tang periods were examined using microscopic observation, SEM–EDS, Raman spectroscopy, crack-width measurements, glaze-area analysis, water-absorption tests, and burial environment analysis to investigate the characteristics and causes of glaze flaking. The results show that crazing-crack width is significantly and positively correlated with the extent of glaze flaking. The body–glaze interlayer generally exhibited heterogeneous features, including anorthite crystallization, unmelted quartz grains, bubbles, and locally phase-separated droplets. Anorthite crystals and adjacent regions were frequently associated with crystal-shaped corrosion pits, irregular voids, and localized structural loosening; degraded areas showed depletion of Ca and Si and relative enrichment of Al and Fe. The burial soils were generally neutral to slightly alkaline and showed no evident salt accumulation, suggesting that high salinity was not the primary direct cause of glaze flaking in these samples. These findings suggest that glaze flaking in Hongzhou kiln celadon results from the interaction between firing-induced heterogeneity at the body–glaze interface and prolonged post-burial corrosion. Crazing and interconnected cracks acted as pathways for moisture and soluble ions to penetrate the body–glaze interlayer, triggering selective corrosion of Ca-rich crystalline phases and adjacent glassy phases and ultimately causing interfacial destabilization and glaze loss. Full article

Accurately characterizing the spatiotemporal variations in precipitation in China’s drylands is important for solving water scarcity in the region, guaranteeing security in the ecological environment, and conducting precise drought disaster management. To reduce the uncertainty in the existing precipitation products, we developed a two-stage machine-learning framework combining extreme gradient boosting (XGBoost) and random forest (RF) residual corrections. Based on the ground-based observation data from 1030 meteorological stations and numerous high-precision precipitation products (GPM IMERG Final V6, MSWEP V2, CMFD 2.0, TerraClimate), a monthly fused precipitation dataset (XGB-RF) for China’s drylands was produced during the 2001–2020 period at the 0.1° resolution. The validation results showed that the XGB-RF had a monthly Kling–Gupta Efficiency (KGE) of 0.941, and it improved 20.6–62.2% relatively with that of input individual products. For the dataset as a whole, we found very consistent, reliable performance in all seasons and topography, in particular in winter time and data-scarce western areas where individual products have large biases. More importantly, the XGB-RF was employed for drought monitoring based on the 1-month Standardized Precipitation Index that calculated the median KGE of 0.888, which made good drought trend tracking and drought features possible. Notably, the KGE for the mean drought intensity was 0.757, which was higher than that of independent original products. This study provides a high-resolution precipitation forcing dataset and demonstrates the effectiveness of two-stage machine learning strategies in enhancing hydroclimatic monitoring and drought risk assessment in arid and semi-arid regions. Full article

The i_p-i_q harmonic detection method, which is based on instantaneous reactive power theory, involves cumbersome and complex computations. In addition, the adoption of a low-pass filter (LPF) degrades the dynamic response performance of harmonic detection. To achieve accurate and fast detection of grid harmonic currents for efficient power grid compensation, this paper proposes a total current harmonic detection method using a dual second-order generalized integrator (DSOGI). This method eliminates the calculation steps of the active and reactive components of load current that are required in the conventional i_p-i_q method. More importantly, it replaces the LPF in the traditional detection scheme with a positive-sequence fundamental component extraction structure based on the DSOGI. Simulations and experimental tests are conducted on the proposed method under balanced grid conditions; the total harmonic distortion (THD) is approximately 2%, and the system stabilizes within 0.04 s. The detection speed and accuracy of the proposed method are superior to those of the traditional i_p-i_q harmonic detection method, the sinusoidal amplitude integrator (SAI)-based method, and the complex coefficient filter (CCF)-based method. Full article

The accuracy of Digital Elevation Models (DEMs) plays a crucial role in determining their reliability for geoscientific and engineering applications. Next-generation distributed interferometric synthetic aperture radar (SAR) constellations, such as the PIESAT-1 wheel constellation with its “one primary, three secondary” setup, provide a novel method for efficiently acquiring high-precision DEMs. However, a comprehensive and systematic performance evaluation of DEMs derived from such an innovative constellation is lacking, particularly in the context of comparative studies under complex terrain conditions. This study uses PIESAT-1 SAR imagery to generate a 10 m resolution DEM through multi-baseline interferometric processing. The ICESat-2 ATL08 dataset serves as the reference baseline, and mainstream products, including ZY-3, GLO-30, TanDEM-X DEM, and AW3D30, are incorporated for a multidimensional vertical accuracy evaluation, considering land cover, slope, aspect, and topographic profiles. The results indicate that, in three representative mountainous regions, the PIESAT-1 DEM achieves optimal overall accuracy (RMSE = 3.25 m). Furthermore, in regions with significant radar geometric distortions, such as south-facing slopes, vegetation-covered areas, and regions with noticeable anthropogenic topographic changes, the PIESAT-1 DEM demonstrates superior stability and information capture capabilities relative to conventional single- or dual-baseline SAR systems. This study validates the technological potential of the PIESAT-1 wheel constellation in enhancing DEM accuracy and terrain adaptability, and provides insights for the scientific selection of high-resolution topographic data and the design of future spaceborne interferometric missions. Full article

High-precision 3D building data are pivotal for smart city development, urban planning, and disaster management. However, large-scale building extraction from airborne LiDAR point clouds remains challenging due to semantic ambiguity, uneven point density, and complex architectural structures. To address these limitations, we propose a novel framework integrating geometric topology perception with cross-dimensional attention mechanisms within a Sparse Voxel Convolutional Neural Network (SPVCNN). The key contributions include: (1) an enhanced LaserMix++ multi-scale hybrid augmentation strategy featuring cross-scene block replacement, ground normal–constrained rotation, and non-uniform scaling; (2) a dual-branch SPVCNN architecture embedding a collaborative module of Geometric Self-Attention (GSA) and Cross-Space Residual Attention (CSRA) to preserve topological consistency and enable cross-dimensional feature interaction; and (3) a Boundary Enhancement Module (BEM) specifically designed to resolve boundary ambiguity and overlapping predictions. Evaluated on a 177 km² dataset covering Washington, D.C., our method significantly outperforms the baseline SPVCNN, improving accuracy by 12.04 percentage points (0.8212 to 0.9416) and Intersection over Union (IoU) by 9.96 percentage points (0.866 to 0.9656). Furthermore, it surpasses mainstream networks such as Cylinder3D and MinkResNet by over 50% in absolute accuracy gain. These results demonstrate the effectiveness of synergistically combining geometric perception with adaptive attention for robust building extraction from large-scale LiDAR data. Full article

Cytomegalovirus (CMV) pneumonia presents diagnostic challenges in AIDS patients, as plasma monitoring often fails to reflect pulmonary viral burden. This retrospective study evaluated the prognostic value of bronchoalveolar lavage fluid (BALF) CMV DNA loads in 189 AIDS patients with pulmonary infections and CD4⁺ T cell counts < 200 cells/μL. CMV DNA in BALF and plasma was quantified to analyze associations with immune status and 90-day all-cause mortality. CMV detection was significantly more frequent in BALF (49.7%) than plasma (26.6%), indicating viral compartmentalization. An optimal BALF cutoff of 10,000 copies/mL was established for mortality prediction. Patients exceeding this threshold exhibited significantly lower CD4⁺ counts, increased mechanical ventilation requirements (34.4% vs. 11.5%), and prolonged hospital stays. Crucially, a BALF CMV load > 10,000 copies/mL was identified as an independent predictor of 90-day mortality (adjusted odds ratio = 3.78; 95% CI: 1.12–12.71). In conclusion, pulmonary CMV replication is prevalent and often compartmentalized in AIDS patients. A BALF CMV DNA load exceeding 10,000 copies/mL serves as a biomarker of profound immunosuppression and independently predicts poor clinical outcomes, highlighting the necessity of quantitative BALF monitoring for risk stratification. Full article

This study examined the effects of varying enzymatic pretreatment durations (0–80 min) of wheat gluten on flavour characteristics of high-moisture plant-based extrudates (HMPEs). Through a comprehensive analysis involving sensory evaluation, electronic tongue, free amino acid (FAA) profiling, electronic nose, and headspace solid-phase microextraction-gas chromatography-mass spectrometer (HS-SPME-GC-MS) analysis of volatile odour compounds, it was found that HMPEs with moderate enzymatic pretreatment (40 min) achieved the highest overall sensory score. Electronic tongue and FAA results confirmed a significant enhancement in umami and sweetness, while electronic nose effectively discriminated differences in odour profiles. Extending pretreatment durations gradually reduced beany off-flavours substances (hexanal reduced by up to 174.7 μg/kg) and encouraged the formation of meaty aroma compounds (furans and pyrazines). However, excessive pretreatment (>40 min) reduced acceptance due to burnt odour caused by the excessive accumulation of pyrazines, particularly 2,3-diethyl-5-methylpyrazine. Six key volatile odour compounds were identified by integrating the analysis of variable importance projection (VIP ≥ 1) and relative odour activity value (ROAV ≥ 1), offering a foundation for targeted flavour regulation in HMPEs. Full article

The blueberry variety ‘Liberty’ shows excessive vegetative growth and difficulty in flower bud differentiation under container cultivation. Paclobutrazol (PBZ), a widely used plant growth regulator, effectively modulates the balance between vegetative and reproductive growth in plants; however, its application in container-cultivated blueberries remains understudied. This study systematically investigated the effects of different PBZ concentrations (0–200 mg·L⁻¹) on the growth and development, physiological characteristics, and fruit quality of container-cultivated ‘Liberty’ blueberries and further clarified the optimal application time. Results showed that low-concentration treatments (≤75 mg·L⁻¹) significantly enhanced root development, increased new shoot diameter, and elevated the root-to-shoot ratio. Concurrently, it induced the coordinated thickening of palisade and spongy tissues in leaves and significantly increased the maximum photochemical efficiency (Fv/Fm) and chlorophyll content. The contents of endogenous hormones indole-3-acetic acid (IAA) and gibberellin (GA₃) in new shoots were significantly reduced, while the cytokinin-to-gibberellin ratio (CTK/GA₃) in flower buds was markedly elevated. These changes effectively promoted flower bud differentiation, increased bud number, and advanced the flowering time by approximately 2 days. Fruit quality was significantly improved. Under high concentration treatments, the content of malondialdehyde (MDA) continuously accumulated, and the activities of antioxidant enzymes (CAT, POD, SOD) significantly decreased. Furthermore, the efficacy of paclobutrazol weakened with the delay of application time. Comprehensive analysis indicated that the 50 mg·L⁻¹ concentration effectively suppressed excessive vegetative growth and maximally improved fruit quality, with its application at the mid-stage of secondary shoot growth exhibiting favorable coordination of plant growth and development. This provides a theoretical basis for the application of paclobutrazol in blueberry production. Full article

Skin lesion segmentation has achieved notable progress in recent years; however, accurate delineation remains challenging due to complex backgrounds, ambiguous boundaries, and low lesion-to-skin contrast. To address these issues, we propose the lesion-aware triple-path feature fusion network (LTPNet), an end-to-end framework that progressively processes features through extraction, refinement, and aggregation stages. In the extraction stage, we incorporate a general foreground–background attention to suppress background interference and accelerate model convergence. In the refinement stage, we introduce an attentive spatial modulator (ASM) to jointly exploit local structural cues and global semantic context for precise spatial modulation. We further develop a lesion-aware lite-gate attention (LALGA) module that performs local spatial feature modulation and global channel recalibration tailored to lesion characteristics. In the aggregation stage, we propose a triple-path feature fusion (TPFF) module that explicitly models feature relationships across scales via three complementary pathways: a common path (CP) for semantic consistency, a saliency path (SP) for highlighting co-activated regions, and a difference path (DP) for accentuating structural discrepancies. Extensive experiments on in-domain and cross-domain datasets show that LTPNet achieves superior segmentation accuracy with reasonable inference efficiency and model complexity, demonstrating its potential for efficient and reliable clinical decision support. Full article

To explore the potential of phosphogypsum for resource utilization in asphalt pavements, this study evaluated its feasibility as a warm-mix asphalt (WMA) additive and investigated its influence on the rheological properties of asphalt binder. Phosphogypsum warm-mix asphalt was prepared by incorporating varying dosages of phosphogypsum warm-mix additive (PGWA) into both base asphalt and styrene–butadiene–styrene (SBS)-modified asphalt. The high-, medium-, and low-temperature performance of phosphogypsum warm-mix asphalt was evaluated using rheological tests. The results revealed that the complex modulus of PGWA-added base asphalt was higher than that of the base asphalt, with only minor changes in phase angle. The incorporation of the SBS modifier significantly enhanced the stiffness and elasticity of the asphalt binder. Compared with the control asphalt, PGWA-added asphalt exhibited lower creep strain and accumulated strain, higher creep recovery rates, and smaller non-recoverable compliance under the same stress level, indicating an improved resistance to high-temperature permanent deformation. PGWA increased the cumulative damage capacity and extended the fatigue life of the asphalt binder. Although the PGWA slightly reduced the low-temperature performance, the SBS modifier effectively compensated for this drawback. The Burgers model accurately captured the low-temperature rheological behavior of PGWA-added asphalt. Overall, PGWA-added asphalt demonstrated excellent rheological performance and high application potential, offering a promising pathway for the resource utilization of phosphogypsum and the development of sustainable, eco-friendly pavement materials. Full article