Ni/Ni3Al interface-dominated nanoindentation deformation and pop-in events.

Nickel-based single crystal alloys have excellent mechanical properties due to its unique two-phase structure and interface. Therefore, molecular dynamics methods were used to simulate nanoindentation and microstructural evolution. We found the indenter reaction force and hardness of the Ni3Al phase is the largest. The pop-in event in Ni3Al phase is more obvious than that in the Ni phase and Ni/Ni3Al phase. Because lots of dislocations in the Ni3Al phase break through the barrier of the interface and cut into the Ni phase, while dislocations in the Ni phase only slip inside the Ni phase. Moreover, we found that the position of the starting point of the adhesion force recovery is mainly related to the elastic recovery of the material. The stronger the elastic recovery of the phase, the smaller the depth value corresponding to the starting point of the recovery. We further studied the variation of potential energy with indentation depth and found that the change of wave trough of the load-displacement (P-h) curve is related to stacking fault energy. This study has important theoretical guiding significance for the in-depth understanding and engineering application of the mechanical properties of nickel-based single crystal alloys.

Deep learning-based forecasting model for COVID-19 outbreak in Saudi Arabia.

COVID-19 outbreak has become a global pandemic that affected more than 200 countries. Predicting the epidemiological behavior of this outbreak has a vital role to prevent its spreading. In this study, long short-term memory (LSTM) network as a robust deep learning model is proposed to forecast the number of total confirmed cases, total recovered cases, and total deaths in Saudi Arabia. The model was trained using the official reported data. The optimal values of the model's parameters that maximize the forecasting accuracy were determined. The forecasting accuracy of the model was assessed using seven statistical assessment criteria, namely, root mean square error (RMSE), coefficient of determination (R2), mean absolute error (MAE), efficiency coefficient (EC), overall index (OI), coefficient of variation (COV), and coefficient of residual mass (CRM). A reasonable forecasting accuracy was obtained. The forecasting accuracy of the suggested model is compared with two other models. The first is a statistical based model called autoregressive integrated moving average (ARIMA). The second is an artificial intelligence based model called nonlinear autoregressive artificial neural networks (NARANN). Finally, the proposed LSTM model was applied to forecast the total number of confirmed cases as well as deaths in six different countries; Brazil, India, Saudi Arabia, South Africa, Spain, and USA. These countries have different epidemic trends as they apply different polices and have different age structure, weather, and culture. The social distancing and protection measures applied in different countries are assumed to be maintained during the forecasting period. The obtained results may help policymakers to control the disease and to put strategic plans to organize Hajj and the closure periods of the schools and universities.