In order to save the time and cost of friction and wear experiments, the coating composition (different contents of Al, Ti, and Cu elements), ratio of hardness and elastic modulus (H³/E²), vacuum heat treatment (VHT) temperature, and wear form were used as input variables, and the wear rates of high-entropy alloy (HEA) coatings were used as output variables. The dataset was entirely obtained by experiment. Four machine learning algorithms (classification and regression tree (CART), random forest (RF), gradient boosting decision tree (GBDT), and adaptive boosting (AdaBoost)) were used to predict the wear resistance of HEA coatings based on a small amount of data. The results show that except for the GBDT model, the other three models had good performance. Because of the small amount of data, the CART model demonstrated the best prediction performance and can provide guidance for predicting the wear resistance of AlCoCrFeNi-X (Ti, Cu) HEA coatings for drilling equipment. Furthermore, the contribution of different factors to the wear rate of AlCoCrFeNi-X (Ti, Cu) HEA coatings was obtained. Al content had the greatest influence on wear rate, followed by H³/E², wear form, and VHT temperature.

Relief supply warehouses provide necessities of life for victims in disaster relief. The science behind site selection for large relief supply warehouses determines whether victims in earthquakes with Ms ≥ 7 can receive timely and effective relief. This study proposed a method of site selection for relief supply warehouses based on the prediction of earthquake disasters and the quantification of site selection criteria. This method considered four factors, i.e., the prediction of earthquake-prone areas, the identification of building distribution, the mode of post-earthquake transport of relief supplies, and the construction standards of relief supply warehouses. Taking western Yunnan Province for a case study, this study first identified seven disaster-affected areas, then determined the helicopter's most extensive safe flight range in post-earthquake transport of relief supplies. Next, it quantified and classified the site selection criteria of relief supply warehouses in China and selected the top ten candidate sites meeting the necessary conditions. Finally, it obtained the ideal site using reference conditions.