Grassland degradation is influenced by climate change and human activities, and has become a major obstacle for the development of arid and semi-arid areas, posing a series of environmental and socio-economic problems. An in-depth understanding of the inner relations among grassland vegetation dynamics, climate change, and human activities is therefore greatly significant for understanding the variation in regional environmental conditions and predicting future developmental trends. Based on MODIS (moderate resolution imaging spectroradiometer) NDVI (normalized difference vegetation index) data from 2000 to 2020, our objective is to investigate the spatiotemporal changes of NDVI in the Xilin Gol grassland, Inner Mongolia Autonomous Region, China. Combined with 12 natural factors and human activity factors in the same period, the dominant driving factors and their interactions were identified by using the geographic detector model, and multiple scenarios were also simulated to forecast the possible paths of future NDVI changes in this area. The results showed that: (1) in the past 21 a, vegetation cover in the Xilin Gol grassland exhibited an overall increasing trend, and the vegetation restoration (84.53%) area surpassed vegetation degradation area (7.43%); (2) precipitation, wind velocity, and livestock number were the dominant factors affecting NDVI (the explanatory power of these factors exceeded 0.4). The interaction between average annual wind velocity and average annual precipitation, and between average annual precipitation and livestock number greatly affected NDVI changes (the explanatory power of these factors exceeded 0.7). Moreover, the impact of climate change on NDVI was more significant than human activities; and (3) scenario analysis indicated that NDVI in the Xinlin Gol grassland increased under the scenarios of reduced wind velocity, increased precipitation, and ecological protection. In contrast, vegetation coverage restoration in this area was significantly reduced under the scenarios of unfavorable climate conditions and excessive human activities. This study provides a scientific basis for future vegetation restoration and management, ecological environmental construction, and sustainable natural resource utilization in this area.
With the rapid development of the modern city, technologies of smart cities are indispensable for solving urban problems. Medical services are one of the key areas related to the lives of urban residents. In particular, how to effectively manage the human resources of a hospital is a complex and challenging problem to improve treatment capabilities. Due to the grievous shortage of medical personnel, hospitals have to make quality schedules to improve the efficiency of the hospital and the utilization rate of human resources. Although there have been a large number of researches on hospital staff scheduling, few people also consider future patient population forecasts, doctor scheduling and hospital structure. These factors are very important in the hospital staff scheduling problem. Concerning this, this paper establishes an optimization system combining a two-layer mixed-integer linear programming and an extended prophet model for the hospital personnel scheduling. The model considers factors such as weather, disease types, number of patients, room resources, doctor resources, working hours, etc., and can quickly obtain a timetable with complex constraints. Finally, the convergence and the practicability of the model has been verified with real data from a hospital in China.
At present, the relationship between the diversity distribution patterns of different taxonomic levels of plants and climatic factors is still unclear. This paper explored the diversity pattern of vascular plant families, genera, and species in China at the municipal scale. It also studied the effects of accumulated temperature ≥ 10°C, annual precipitation, and hydrothermal base which reflect the effect of hydrothermal resources on the plant diversity pattern. The results showed that: There were extremely significant correlations among the diversities of plant families, genera, and species, and the interpretation degree of diversity between adjacent the taxonomic levels was more than 90%. The diversity pattern of plant families was mainly affected by dominant climatic state indicators such as the maximum value of accumulated temperature, annual precipitation, and hydrothermal base, and the gradient range of the hydrothermal base, which showed a clear latitudinal gradient law. The diversity pattern of plant species was found to be mainly dependent on the climatic heterogeneity indicators, being closely related to the heterogeneity indicators and sum indicators of the hydrothermal base. It was also affected by the range of precipitation gradient range. Plant genus and its diversity pattern are not only significantly affected by heterogeneity and sum indicators but also closely related to climate state indicators. In comparison with the humidity index in vegetation ecological studies, the related indicators of the hydrothermal base proposed in this paper excelled at revealing the relationship between climate and diversity patterns of plant families, genera, and species, and could effectively solve the species-area relationship issue in arid and low-temperature areas. The results of this paper have presented important theoretical and practical values for comprehensively understanding the correlation between climate and diversity of plant families, genera, and species, clarifying the impact of climate difference and climate change on plant diversity.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.