Enhancing the supply efficiency of ecosystem services plays a central role in improving both natural ecosystems and human well-being. Taking the Loess Plateau of Northern Shaanxi as an example, this study utilizes InVEST to assess the ecosystem services of water yield and habitat quality. The optimal solutions for the combination of these two services are calculated on the basis of the Pareto principle. The production possibility frontier curves for the two services are fitted, and the services’ supply efficiency is measured. Furthermore, this study employs ordinary least squares and geographically weighted regression to analyze the dominant factors affecting supply efficiency. The results comprise the following findings: (1) There are eighteen solutions representing the optimal combinations between the two services. (2) The supply efficiency of the two services increases from northwest to southeast in spatial distribution. (3) The dominant factors vary among different zones of supply efficiency. Population, hydrology, and gross domestic product (GDP) are the dominant factors in the general-efficiency, sub-low-efficiency, and low-efficiency supply zones, respectively. Hydrology, NDVI, and GDP are the dominant factors in the sub-high-efficiency supply zone, while GDP, terrain, and population are the dominant factors in the high-efficiency supply zone. In conclusion, this paper proposes recommendations for reducing trade-offs and enhancing supply efficiency between ecosystem services. These include dynamic supervising for the high-efficiency supply zone, moderate greening in the sub-high-efficiency supply zone, stabilizing the population in the general-efficiency supply zone, and reducing development intensity in low- and sub-low-efficiency zones. The study reveals the potential and approaches for improving the supply of ecosystem services and offers guidance for formulating ecological protection plans.

The increasing scale and complexity of engineering projects have made understanding the dynamic mechanical behavior of rocks crucial for scenarios such as blasting and impact. To investigate the role of mineral-scale heterogeneity in dynamic compression failure, this study develops a grain-based discontinuous deformation analysis (GB-DDA) model using Voronoi tessellation and calibrates it against experimental data for Barre granite. By employing a refined loading protocol, the model incorporates grain-scale heterogeneity into the DDA framework, thus enabling realistic simulation of multi-mineral rocks under dynamic loading while mitigating stress nonuniformity and premature boundary failures in high-strain-rate uniaxial compression tests. By applying varying loading rates to models with different average grain sizes, grain roundness, and mineral contents, we reveal that increasing strain rate intensifies fragmentation, promotes shear and intragranular (TG) cracking, and enhances both dynamic uniaxial compressive strength (DUCS) and the dynamic strength increase factor (DIF). Furthermore, at constant strain rates, larger grain size, higher grain roundness, and greater biotite content facilitate shear crack initiation, while larger grain size, higher roundness, and greater quartz content promote intragranular cracking and further increase DUCS. Larger grain size and lower roundness elevate DIF, whereas mineral composition has a limited effect on DIF. Two-way analysis of variance (ANOVA) further revealed that, within the tested ranges, mesostructure-strain rate interactions vary by parameter type, with synergistic effects primarily governing shear-dominated failure and strength rather than intragranular cracking.

High-Resolution Spatiotemporal Monitoring of Seasonal Land-Use and Land-Cover (LULC) Changes (HRST-MSLC) holds broad application prospects. However, existing unmanned aerial vehicle (UAV) change-detection benchmarks are predominantly based on nadir-view, bi-temporal imagery, providing limited lateral structural information and seasonal observations, while data-acquisition costs and multi-temporal registration constrain dataset construction. To address this, this paper introduces the Unmanned Aerial Vehicle Oblique Multi-View Panoramic Change Detection Dataset (UAV-OMVCD) for fine-scale human-induced LULC change monitoring. It contains 864 panoramic observations acquired in four study areas across Nanjing, Hefei, and Tangshan using DJI Matrice 4D/4TD and Matrice 4E platforms. Each panorama is exported at 1448 × 7200 pixels, with nominal nadir-equivalent source-frame ground sampling distances (GSDs) of approximately 3.7–8.5 cm, yielding 2,181 paired 512 × 512 samples. UAV-OMVCD documents seasonal changes in impervious surfaces, engineering machinery, buildings, greenhouses, and piled soil, including structural additions/removals and morphological evolution. Its oblique views capture lateral facades and occluded areas while reducing seasonal pseudo-change interference. Five benchmark models achieved overall accuracies of 0.9429–0.9492, macro-averaged F1-scores of 0.8861–0.9007, and macro-averaged Intersection over Union (IoU) values of 0.8049–0.8266. UAV-OMVCD provides a standardized benchmark for fine-scale human-induced land-cover change detection using oblique drone panoramic imagery.