A semantic information-driven stepwise landslide displacement prediction model.

Landslide prediction is critical for the early warning of a landslide occurrence. Existing stepwise landslide displacement prediction methods are mostly data-driven approaches. However, these models are vulnerable to overfitting, and the low-dimensional numerical features with high numerical volatility prevent them from precisely quantifying the rapid increase in daily displacement in the acceleration phase. Therefore, we propose a semantic information-driven stepwise landslide displacement prediction model comprising an identifier in the displacement phase and a predictor in the acceleration phase. First, the raw landslide monitoring data are converted into text-based semantic information and the semantic features are fused. Subsequently, based on the daily displacement and velocity, we propose a sliding window phase division algorithm to divide the stepwise landslide phase into stationary and acceleration phases. Finally, the landslide displacement phase is identified, and the displacement during the acceleration phase is predicted. The experimental results of the model on the Xinpu and Qingshi landslides in Chongqing, China, show that the proposed model exploits the derived semantic information to identify the landslide acceleration phase qualitatively, and predict the daily displacement of the acceleration phase quantitatively. The proposed model provides a valuable reference for the early warning of stepwise landslides.

[Analysis of volatile oil from Ardisia brevicaulis].

OBJECTIVE: To analyze and compare the compounds in the essential oil from the leaves and roots of Ardisia brevicaulis. METHODS: The essential oil were obtained by steam distillation. The chemical components were separated and identified by gas chromatography-mass spectrometry (GC-MS). RESULTS: 38 compounds were identified from the leaves (65.952% of the total essential oil) and 46 compounds were identified from the roots (54.890% of the total essential oil). The main constituents of the leaves essential oil were Palmitic acid (43.329%), Fitone (2.430%), Phytol (3.142%), and so on. The main constituents of the roots essential oil were Calamenene (2.913%), cis-alpha-Bisabolene (5.222%), gamma-Muurolene (14.227%), Caryophyllene (11.592%), and so on. CONCLUSION: The constituents of volatile oil extracted from the leaves and roots of Ardisia brevicaulis were different, so the leaves and roots of Ardisia brevicaulis should be utilized differently in clinical application.