Hazard risk assessment of tropical cyclones based on joint probability theory

The main hazard-causing factors of tropical cyclones are strong wind,heavy rainfall,and storm surge.Evaluation of the hazard-causing degree of a tropical cyclone requires a joint intensity analysis of these hazard-causing factors.According to the maximum hourly mean wind speed,total rainfall,and maximum tide level at various observation stations in Hong Kong during these tropical cyclones,three hazard-causing indices for tropical cyclones are introduced:the strong-wind index(VI),total-rainfall index(RI),and tide-level index(LI).Through a joint probability analysis of VI,RI,and LI for a tropical cyclone affecting Hong Kong,the joint return period is calculated to evaluate its joint hazard-causing intensity.A limit state function of Hong Kong’s resistance to tropical cyclones is developed and used to evaluate the regional risk of tropical cyclones affecting Hong Kong.The results indicate that the joint return period of VI,RI,and LI can reflect the joint hazard-causing intensity of strong wind,heavy rain,and storm surge caused by tropical cyclones;if the overall design return periods of the regional structures decrease,the regional ability to defend against tropical cyclone disasters is degraded.

Predicting the grades of Astragali radix using mass spectrometrybased metabolomics and machine learning

Astragali radix(AR,the dried root of Astragalus)is a popular herbal remedy in both China and the United States.The commercially available AR is commonly classified into premium graded(PG)and ungraded(UG)ones only according to the appearance.To uncover novel sensitive and specific markers for AR grading,we took the integrated mass spectrometry-based untargeted and targeted metabolomics approaches to characterize chemical features of PG and UG samples in a discovery set(n=16 batches).A series of five differential compounds were screened out by univariate statistical analysis,including arginine,calycosin,ononin,formononetin,and astragalosideⅣ,most of which were observed to be accumulated in PG samples except for astragalosideⅣ.Then,we performed machine learning on the quantification data of five compounds and constructed a logistic regression prediction model.Finally,the external validation in an independent validation set of AR(n=20 batches)verified that the five compounds,as well as the model,had strong capability to distinguish the two grades of AR,with the prediction accuracy>90%.Our findings present a panel of meaningful candidate markers that would significantly catalyze the innovation in AR grading.