| 1 | Machine Learning-Based Evaluation of Susceptibility to Geological Hazards in the Hengduan Mountains Region, China显示文摘The Hengduan Mountains Region(HMR) is one of the areas that experience the most frequent geological hazards in China. However, few reports are available that address the geological hazard susceptibility of the region.This study developed six machine learning models to assess the geological hazard susceptibility. The results show that areas with medium and high susceptibility to geological hazards as a whole account for almost 21% of the total area, while both are 18% when it comes to the single hazard of landslide and rockfall respectively. Medium and high geological hazard susceptibility is found in three parts of the HMR with different characteristics:(1)the central and southern parts, where the population of the region concentrates;(2) the northern part, where higher geological hazard susceptibility is found along the mountain ranges;and(3) the junction of Tibet, Yunnan, and Sichuan in the eastern part, which is prone to larger-scale geological hazards. Of all the potential influencing factors,topographic features and climatic variables act as the major driving factors behind geological hazards and elevation,slope, and precipitation are crucial indicators for geological hazard susceptibility assessment. This study developed the geological hazard susceptibility maps of the HMR and provided information for the multi-hazard risk assessment and management of the region. | Jiaqi Zhao Qiang Zhang Danzhou Wang Wenhuan Wu Ruyue Yuan | 2022 | International Journal of Disaster Risk Science2022，13，2： | 1 |
| 2 | Morphological and Chemical Variation of Prunella vulgaris Populations from Different Locations in China显示文摘Objective To investigate the variation of chemical characteristics with environmental factors and establish a relationship between morphological characters and chemical composition of Prunella vulgaris collected in different areas of China. Methods Twelve phenotypic traits and three chemical compositions were assessed in 28 populations of P. vulgaris collected from different locations in China. Results The variability ranges observed at phenotypic and chemical levels were polymorphic. According to the morphological traits, 28 populations of P. vulgaris could be grouped into six clusters, and two morpho-types could be clearly distinguished. Perceptible differences could be discerned in the plant height, leaf length, corolla length, calyx length, fruiting spikes length, and maturity period. Based on three kinds of components including ursolic acid, total flavonoids, and total polysaccharides, all populations could be identified as four types. Cluster IV showing high content of ursolic acid, total flavonoids, and total polysaccharides could be utilized to develop superior derivatives. Conclusion The variation of chemical characteristics is influenced by the genetic and environmental factors, such as soil, climate, longitude, and altitude. It provides a solid basis for efficiently evaluating qualities and establishing good agricultural practices for P. vulgaris. | LIAO Li1, 2, LIU Li1, GUO Qiao-sheng1, WANG Zhi-yong2, CHEN Yu-hang1 1. Institute of Chinese Medicinal Materials, Nanjing Agricultural University, Nanjing 210059, China 2. Agronomy College, Hainan University, Danzhou 571737, China | 2010 | Chinese Herbal Medicines2010，2，4： | 0 |
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