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Because of the complex nonlinear relationship between working conditions, the prediction of tribological properties has become a difficult problem in the field of tribology. In this study, we employed three distinct machine learning (ML) models, namely random forest regression (RFR), gradient boosting regression (GBR), and extreme gradient boosting (XGBoost), to predict the tribological properties of polytetrafluoroethylene (PTFE) composites under high-speed and high-temperature conditions. Firstly, PTFE composites were successfully prepared, and tribological properties under different temperature, speed, and load conditions were studied in order to explore wear mechanisms. Then, the investigation focused on establishing correlations between the friction and wear of PTFE composites by testing these parameters through the prediction of the friction coefficient and wear rate. Importantly, the correlation results illustrated that the friction coefficient and wear rate gradually decreased with the increase in speed, which was also proven by the correlation coefficient. In addition, the GBR model could effectively predict the tribological properties of the PTFE composites. Furthermore, an analysis of relative importance revealed that both load and speed exerted a greater influence on the prediction of the friction coefficient and wear rate. [ABSTRACT FROM AUTHOR]

The principal strain rate, derived from GPS velocities from the Tibetan Plateau and the India Plate, shows that the Himalayan tectonic belt exhibits compression deformation in a NE–NS–NE direction from the west to the east. The GPS velocity profiles reflect that the distribution of strain accumulation is uneven: there is a 17.1 mm/yr. compressive deformation distributed over 400 km along 85° E longitude, a 20.9–22.2 mm/yr. compressive deformation dispersed across 400–500 km along 79° E longitude, and a 15.3–16.9 mm/yr. compressive deformation spread across 500–600 km along 91° E longitude. The M W 7.8 Gorkha earthquake occurred at the edge of an intense compression deformation zone of about 6.0 × 10 − 8 /yr. in a north–south direction, and an about 90% compressive strain is absorbed in the 300 km region near the Main Frontal Thrust (MFT). Coseismic displacements, larger than three standard deviations (3SD), are mainly concentrated within 360 km from the epicenter. The nodal plane of the main shock divides the areas where tensile strain release in an exponential attenuation pattern is dominant to the north, and where compressive strain with upward movement is dominant to the south. The results inversed by the SDM program reveal that the maximal slip of the Gorkha earthquake is 5.33 m, and its moment magnitude is M W 7.85, and the largest stress drop is 4.21 Mpa. Slips larger than 0.5 m are mainly distributed in a region approximately 135 km × 105 km, which show that the rupture of the Gorkha earthquake does not reach the surface. Finally, we estimate that the recurrence period of this earthquake is about 166 ± 20 years according to the strain accumulation before the earthquake and the coseismic release feature. [ABSTRACT FROM AUTHOR]