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Exercise Recommendation Model Based on Cognitive Level and Educational Big Data Mining
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Spatial-Temporal Evolution Characteristics and Influencing Factors of Vegetation NDVI in Hebei Province
Published 2024-01-01“…In order to explore the vegetation growth status and influencing factors,Hebei Province is taken as the research area.Based on satellite remote sensing data and meteorological data,the Mann-Kendall trend test,Sen slope,and partial correlation analysis are used to explore the temporal and spatial distribution characteristics of vegetation in Hebei Province,and the influencing factors of vegetation NDVI change in Hebei Province are analyzed by combining the correlation among NDVI,climatic factors,and human activities.The results show that ① the NDVI value of vegetation in Hebei Province from 2001 to 2020 shows an overall growth trend,with an average annual growth rate of 2.84×10<sup>-3</sup>.Specifically,it shows a rapid growth trend from 2001 to 2005 and a slow growth trend from 2005 to 2020, with fluctuations.②The NDVI value of vegetation in the study area increases gradually from south to north,and the vegetation improvement area (81.71%) is greater than that in the degraded area (13.79%),and the improvement area is mainly concentrated in Chengde and Zhangjiakou City.③Human activities (GDP) are one of the main influencing factors affecting the growth of NDVI,followed by climatic factors (precipitation and temperature).The change in GDP has a significant impact on vegetation growth,and the improvement of vegetation in Hebei Province is mainly related to forest land and grassland.Among the climatic factors,precipitation is the main influencing factor.…”
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Wind-Induced Response of Inclined and Yawed Ice-Accreted Stay Cable Models
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Toward Intelligent Intrusion Prediction for Wireless Sensor Networks Using Three-Layer Brain-Like Learning
Published 2012-10-01Get full text
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The Optimization of a Virtual Dual Production-Inventory System under Dynamic Supply Disruption Risk
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An Improved Strong Tracking Kalman Filter Algorithm for the Initial Alignment of the Shearer
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Forecasting the number of rural migrant workers in Chongqing using the optimized grey N_Verhulst model
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Robust sparse time‐frequency analysis for data missing scenarios
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Modeling Simultaneous Multiple Fracturing Using the Combined Finite-Discrete Element Method
Published 2018-01-01Get full text
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