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The study aims to investigate the effect of Al2O3 and Al additions to Nickel-base superalloys as a coating layer on oxidation resistance, and structural behavior of nickel superalloys such as IN 738 LC. Nickel-base superalloys are popular as base materials for hot components in industrial gas turbines such as blades due to their superior mechanical performance and high-temperature oxidation resistance, but the combustion gases' existence generates hot oxidation at high temperatures for long durations of time, resulting in corrosion of turbine blades which lead to massive economic losses. Turbine blades used in Iraqi electrical gas power stations require costly maintenance using traditional processes regularly. These blades are made of nickel superalloys such as IN 738 LC(Inconel 738). Few scientists investigated the impact of Al2O3 or Al additions to Nickel-base superalloys as coating layer by using the slurry coating method on oxidation resistance to enhance the Nickel-base superalloy's oxidation resistance. In this study, IN 738 LC is coated with two different coating percentages, the first being (10 Al+90 Al2O3) and the second being (40 Al+60 Al2O3). Scanning Electron Microscope (SEM) and X-Ray Diffraction (XRD) were performed on all samples before and after oxidation. According to the results, SEM images of the surface revealed that the layer of the surface has a relatively moderated porosity value and that some of the coating layers contain micro-cracks. The best surface roughness of specimens coated with 60 % alumina+40 % aluminum was 5.752 nm. Whereas, the surface roughness of specimens coated with 90 % alumina+10 % aluminum was 6.367 nm.Results reveal that alloys with both Al2O3 and Al additions have reported a positive synergistic effect of the Al2O3and Al additions on oxidation resistance. Moreover,the NiCrAl2O3 thermal coating has good oxidation resistance and the effective temperature of anti-oxidation is raised to 1100 °C in turn reducing the maintenance period of turbine blades

<span lang="EN-US">This paper presents the comparison between optimized unscented Kalman filter (UKF) and optimized extended Kalman filter (EKF) for sensorless direct field orientation control induction motor (DFOCIM) drive. The high performance of UKF and EKF depends on the accurate selection of state and noise covariance matrices. For this goal, multi objective function genetic algorithm is used to find the optimal values of state and noise covariance matrices. The main objectives of genetic algorithm to be minimized are the mean square errors (MSE) between actual and estimation of speed, current, and flux. Simulation results show the optimal state and noise covariance matrices can improve the estimation of speed, current, torque, and flux in sensorless DFOCIM drive. Furthermore, optimized UKF present higher performance of state estimation than optimized EKF under different motor operating conditions.</span>