In this paper, a statistical method is employed to develop a driving cycle for Basrah city and to find out the factor score a nd the Euclidean distance analysis by the Statistical Package for the Social Sciences (SPSS). A simple electronic system is built to construct the driving cycle, the system considered a microcontroller and a GPS sensor connected to a PC through a simple C++ code. The development of the proposed driving cycle represents the first model driving cycle in the city of Basra. The advisor software package is used to investigate the economic performance of the internal combustion engine based on HC, CO, and NOx exhaust emissions. It was found that the obtained driving cycle is significantly different than the other driving cycles in terms of exhaust emissions and fuel consumption and within the expected range of emissions. The developed driving cycle model obtained is a representative delicate estimation of the exhaust emissions and fuel consumption, and will be utilized for future work to obtain a good performance of the hybrid electric vehicles.

This research involved a study of the heat treatment conditions effect on the mechanical properties of martensitic stainless steel type AISI 410. Heat treatment process was hardening of the metal by quenching at different temperature 900°C, 950°C, 1000°C, 1050°C and 1100°C, followed by double tempering at 200°C, 250°C, 300°C, 350°C, 400°C, 450°C, 500°C, 550°C, 600°C, 650°C and 700°C, were evaluated and study of some mechanical properties such as hardness, impact energy and properties of tensile test such as yield and tensile strength is carried out. Multiple outputs Artificial Neural Network model was built with a Matlab package to predict the quenching and tempering temperatures. Also, linear and nonlinear regression analyses (using Data fit package) were used to estimate the mathematical relationship between quenching and tempering temperatures with hardness, impact energy, yield, and tensile strength. A comparison between experimental, regression analysis and ANN model show that the multiple outputs ANN model is more accurate and closer to the experimental results than the regression analysis results.