Prediction and control of COVID-19 spreading based on a hybrid intelligent model.

The coronavirus (COVID-19) is a highly infectious disease that emerged in the late December 2019 in Wuhan, China. It caused a worldwide outbreak and a major threat to global health. It is important to design prediction and control strategies to restrain its exploding. In this study, a hybrid intelligent model is proposed to simulate the spreading of COVID-19. First, considering the effect of control measures, such as government investment, media publicity, medical treatment, and law enforcement in epidemic spreading. Then, the infection rates are optimized by genetic algorithm (GA) and a modified susceptible-infected-quarantined-recovered (SIQR) epidemic spreading model is proposed. In addition, the long short-term memory (LSTM) is imbedded into the SIQR model to design the hybrid intelligent model to further optimize other parameters of the system model, which can obtain the optimal predictive model and control measures. Simulation results show that the proposed hybrid intelligence algorithm has good predictive ability. This study provide a reliable model to predict cases of infection and death, and reasonable suggestion to control COVID-19

Prediction of Surface Topography at the End of Sliding Running-In Wear Based on Areal Surface Parameters

Running in is a complex process, and it significantly influences the performance and service life of wear components as the initial phase of the entire wear process. Surface topography is an important feature of wear components. Therefore, it is reasonable to investigate the running-in process with the help of surface topography for improvement. Because the surface roughness after running in is independent of the nature of initial roughness, it is difficult to predict the surface topography after running in based on unworn surface topography. Aiming to build a connection of surface topographies before and after the running-in process, a black-box model predicting surface topography after the running-in process was established based on least-squares support vector machine (LS-SVM), and the areal surface evaluation parameters were adopted as model variables. To increase the adaptability of the predictive model, the main factors of the work condition were also taken into consideration. The prediction effect and sensitivity of the model were tested and analyzed. The analysis indicates that the hybrid property of surface topographies before and after running in is closely related. Moreover, the surface topography after running in is influenced more by the initial surface topography than by the work condition

Torsional fretting wear behavior of CuNiAl against 42CrMo4 under flat on flat contact

Torsional fretting exists in many engineering applications and the contact configurations have great effects on the fretting wear behavior. Influence of angular displacement amplitudes and normal loads on torsional fretting were investigated under flat on flat contact. Friction torque versus angular displacement amplitudes and friction torque versus number of cycle curves were used to analyze the fretting kinetics behavior. Evolution of accumulated dissipated energy (ET) and wear volume (VW) with the change of angular displacement amplitudes were analyzed. The wear mechanisms were studied base on examinations under optical microscope (OM), scanning electron microscope (SEM) and Energy Dispersive X-Ray Spectroscopy (EDX). It is found that ET and VW analysis helps to reveal the variation of failure mechanisms with the change of angular displacement amplitudes. Uneven spread of wear scar under mixed slip resulted from the effects of debris on stress-redistribution. Similar to ball on flat contact, the torsional fretting wear mechanism under flat on flat contact was a combination of deformation, cracks, delamination abrasive wear and oxidation wear