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[Objective] This study is aimed to understand the spatiotemporal changes in vegetation in the Qinghai Hehuang Valley and clarify the effects of climate change, land development and utilization and human activities on vegetation change. [Methods] The MODIS NDVI dataset from 2000 to 2020 was used to characterize vegetation changes. Based on Theil-Sen Median trend test, partial correlation analysis, geographical detectors and other methods, this paper explored the spatiotemporal changes of NDVI in Qinghai Hehuang Valley and its relationship with temperature, precipitation, slope, soil type, human activities and other influencing factors. [Results] (1) In the past 20 years, the vegetation NDVI in the Hehuang Valley region had shown a fluctuating growth trend, with a significant increase in the area of 2.21×104 km2 (p

Sandwich composites are widely used in aviation, aerospace and marine engineering fields because of their excellent comprehensive properties. However, it is difficult to accurately predict the ultimate performance of sandwich composite structures because of their complex failure modes and uncoordinated damage processes. In this paper, the ultimate load, progressive failure process and failure mode of sandwich composite T-joints under three-point bending and lateral bending loads are predicted and verified by using an improved progressive damage analysis program. Moreover, the bio-inspired optimization of the composite T-joint is carried out, and the corner shape and material combination of the T-joint are designed and optimized based on the contour of the flange bone of the bird. The main factors affecting the bearing capacity of T-joint are further discussed, and a reasonable and feasible bio-inspired optimization scheme is obtained, which provides some reference for the design of marine composite T-joint. [ABSTRACT FROM AUTHOR]

In recent years, there has been growing concern that vegetation changes on the Tibetan Plateau are associated with climate change (temperature and precipitation) and human activities. This study used six types of remote sensing vegetation data, including GIMMS (Global Inventory Modelling and Mapping Studies) NDVI (Normalized Difference Vegetation Index), MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI, MODIS EVI (Enhanced Vegetation Index), SPOT Vegetation (Spot-VGT) NDVI, LAI (Leaf Area Index) and NPP (Net Primary Productivity), and applied the maximum synthesis method, trend analysis, correlation analysis, and multivariate statistical analysis to investigate vegetation change processes since the 1980s. The study showed that the amount of vegetation on the TP had increased significantly since 2000 (p <.01), especially in the northeastern part of the TP. There was no significant change prior to 2000. The different vegetation data sources varied greatly. Four remote sensing indices, MODIS EVI, Spot-VGT NDVI, LAI, and NPP, showed a significant increase in vegetation from 2000, accounting for 16.18%, 44.55%, 30.44% and 8.94% of the total area, respectively (p <.05). Multiple data sources provided a more comprehensive understanding, whereas a single data source had substantial uncertainty. Human activities, such as the implementation of large-scale ecological projects, played a dominant role in increasing vegetation, while climate change played a subsidiary role. The MODIS EVI, Spot-VGT NDVI, LAI, and NPP data showed that the area of increased vegetation caused by human activities accounted for 53.51%, 45.68%, 37.52%, and 31.79% of the total area of the TP, respectively. The relative increase from climate change was 10.28%, 17.49%, 13.15%, and 8.82%, respectively. The current study applied multi-source remotely sensed vegetation data, which effectively reduced the uncertainty caused by individual data sources and provided more rigorous and scientific research conclusions. [ABSTRACT FROM AUTHOR]