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Human activities are critical factors influencing ecosystem sustainability. However, knowledge on regarding the mechanisms underlying the response of vegetation dynamics to human activities remains limited. To detect the driving factors and their individual contribution to the grassland vegetation dynamics in China’s Loess Plateau, a structural equation model (SEM) and a principal component regression model were built. The SEM showed that population change and urbanization, temperature and humidity, and agriculture and economy accounted for 62.5%, 31.2%, and 7.7%, respectively, of the overall impact directly affecting grassland vegetation dynamics. Furthermore, the principal component regression model demonstrated that the effects of the urbanization rate on the grassland above-ground biomass exceeded those of the other factors. The agriculture population had the maximum negative effect on grassland area. The higher the urbanization rate means the higher the number of residents migrates from rural to urban areas. Following this argument, the disturbances of human activities to grassland vegetation were expected to gradually decrease in rural areas, where the vast majority of the Loess Plateau is located. The migration of rural residents to urban areas promoted the increase in biomass and areas of grassland vegetation. Our findings suggest that the effect of urbanization should be considered when assessing vegetation change.

In many areas of the Loess Plateau, groundwater is too deep to extract, making meteoric water (snow and rain) the only viable water resource. Here we traced the rainwater and water vapor sources using the δ2H and δ18O signature of precipitation in the northern mountainous region of Yuzhong on the Loess Plateau. The local meteoric water line in 2016 and 2017 was defined as δ2H = 6.8 (±0.3)∙δ18O + 4.4 (±2.0) and δ2H = 7.1 (±0.2)∙δ18O + 1.5 (±1.6), respectively. The temperature and precipitation amount are considered to be the main factor controlling the δ2H and δ18O variation of precipitation, and consequently, relationships were first explored between δ18O and local surface air temperature and precipitation amount by linear regression analysis. The temperature effect was significant in the wet seasons but was irrelevant in the dry seasons on daily and seasonal scales. The amount effect was significant in the wet seasons on a daily scale but irrelevant in the dry seasons. However, based on the data of the Global Network of Isotopes in Precipitation (GNIP) (1985–1987, 1996–1999) of Lanzhou weather station, the amount effects were absent at seasonal scales and were not useful to discriminate either wetter or drier seasons or even wetter or drier decades. Over the whole year, the resulting air mass trajectories were consistent with the main sources of water vapor were from the Atlantic Ocean via westerlies and from the Arctic region, with 46%, 64%, and 40% of water vapor coming from the westerlies, and 54%, 36%, and 60% water vapor from the north in spring, autumn and winter, respectively. In the summer, however, the southeast monsoon (21%) was also an important water vapor source in the Loess Plateau. Concluding, using the δ2H and δ18O signatures of precipitation water, we disentangled and quantified the seasonal wind directions that are important for the prediction of water resources for local and regional land use.

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Current cloud computing causes serious restrictions to safeguarding users’ data privacy. Since users’ sensitive data is submitted in unencrypted forms to remote machines possessed and operated by untrusted service providers, users’ sensitive data may be leaked by service providers. Program obfuscation shows the unique advantages that it can provide for cloud computing. In this paper, we construct an encrypted threshold signature functionality, which can outsource the threshold signing rights of users to cloud server securely by applying obfuscation, while revealing no more sensitive information. The obfuscator is proven to satisfy the average case virtual black box property and existentially unforgeable under the decisional linear (DLIN) assumption and computational Diffie-Hellman (CDH) assumption in the standard model. Moreover, we implement our scheme using the Java pairing-based cryptography library on a laptop.