A novel mathematical model is developed to calculate the temperature distribution on the surface and bulk of a steel plate under the laser hardening process. The model starts with the basic heat equation then it is developed into a volumetric form and is connected to the various solid existing phases. The proposed model is based on three influencing parameters of the laser hardening process which are the velocity of the laser spot and irradiation time. The results are compared with the available experimental data reported in the literature. The volumetric model provides an assessment of temperature distribution in both the vertical and horizontal axis. Laser irradiation at sufficiently high fluence can be used to create a solid-state phase change on the surface. Primary calculations show that the temperature profile has a Gaussian distribution in horizontal x and y-axis and presents an exponentially decreasing in the horizontal and vertical depth directions.

Prediction of crop yield is essential for food security policymaking, planning, and trade. The objective of the current study is to propose novel crop yield prediction models based on hybrid machine learning methods. In this study, the performance of artificial neural networks-imperialist competitive algorithm (ANN-ICA) and artificial neural networks-gray wolf optimizer (ANN-GWO) models for the crop yield prediction is evaluated. According to the results, ANNGWO, with R of 0.48, RMSE of 3.19, and MEA of 26.65, proved a better performance in the crop yield prediction compared to the ANN-ICA model. The results can be used by either practitioners, researchers or policymakers for food security.