Hydrochloric acid (HCl) is a substance that is frequently utilized in industrial operations for important tasks such as chemical cleaning and pickling metallic surfaces.Therefore, the corrosion inhibition ability of three newly synthesized quinazoline derivatives namely, 3-allyl-2-(propylthio) quinazolin-4(3H)-one) (APQ), (3-allyl-2-(allylthio) quinazolin-4(3H)-one) (AAQ), (3-allyl- 2-( Prop -2-yn -1-ylthio) Quinazolin - 4 (3H) - one) (AYQ) were theoretically determined and these compounds were characterized using Fourier Transform Infra-Red (FTIR) and 1H and 13C Nuclear Magnetic Resonance (NMR) spectroscopic. A series of quantum chemical properties of these derivatives: EHOMO, ELUMO, energy gap (ΔE),dipole moment (μ), hardness (η), soft

Based on a finite element analysis using Matlab coding, eigenvalue problem has been formulated and solved for the buckling analysis of non-prismatic columns. Different numbers of elements per column length have been used to assess the rate of convergence for the model. Then the proposed model has been used to determine the critical buckling load factor () for the idealized supported columns based on the comparison of their buckling loads with the corresponding hinge supported columns . Finally in this study the critical buckling factor () under end force (P) increases by about 3.71% with the tapered ratio increment of 10% for different end supported columns and the relationship between normalized critical load and slenderness ratio was g

In this paper, a self-tuning adaptive neural controller strategy for unknown nonlinear system is presented. The system considered is described by an unknown NARMA-L2 model and a feedforward neural network is used to learn the model with two stages. The first stage is learned off-line with two configuration serial-parallel model & parallel model to ensure that model output is equal to actual output of the system & to find the jacobain of the system. Which appears to be of critical importance parameter as it is used for the feedback controller and the second stage is learned on-line to modify the weights of the model in order to control the variable parameters that will occur to the system. A back propagation neural network is appl