In this paper, the relationship between urban growth, land use, site availability, and impacts on the general appearance of the city generated by functional characteristics of port cities were analyzed. Various data were used to identify patterns of land use change and city growth and expansion. The research aims to analyze the impact of the port on the growth of the city, study the urban growth of the city of Umm Qasr and predicting the growth trends, and to identify the most influential factors in this growth. The study dealt with the concepts of urban growth and the factors that characterize the growth of port cities and the current situation of the city. The practical part included the mechanisms used by the researcher to analyze the

Land Use / Land Cover (LULC) classification is considered one of the basic tasks that decision makers and map makers rely on to evaluate the infrastructure, using different types of satellite data, despite the large spectral difference or overlap in the spectra in the same land cover in addition to the problem of aberration and the degree of inclination of the images that may be negatively affect rating performance. The main objective of this study is to develop a working method for classifying the land cover using high-resolution satellite images using object based method. Maximum likelihood pixel based supervised as well as object approaches were examined on QuickBird satellite image in Karbala, Iraq. This study illustrated that

Density Functional Theory (DFT) with B3LYP hybrid exchange-correlation functional and 3-21G basis set and semi-empirical methods (PM3) were used to calculate the energies (total energy, binding energy (Eb), molecular orbital energy (EHOMO-ELUMO), heat of formation (?Hf)) and vibrational spectra for some Tellurium (IV) compounds containing cycloctadienyl group which can use as ligands with some transition metals or essential metals of periodic table at optimized geometrical structures.

This paper presents a comparative study of two learning algorithms for the nonlinear PID neural trajectory tracking controller for mobile robot in order to follow a pre-defined path. As simple and fast tuning technique, genetic and particle swarm optimization algorithms are used to tune the nonlinear PID neural controller's parameters to find the best velocities control actions of the right wheel and left wheel for the real mobile robot. Polywog wavelet activation function is used in the structure of the nonlinear PID neural controller. Simulation results (Matlab) and experimental work (LabVIEW) show that the proposed nonlinear PID controller with PSO
learning algorithm is more effective and robust than genetic learning algorithm; thi

            This study tests the effect of a large number of independent variables that control the growth of the total productivity, which amounted to 112 variables, gathered from what is mentioned in the specialized theoretical and applied literature. The data for these variables were taken from global reports of sound international organizations and reliable databases covering the period 1991-2016. The data of the dependent variable, the growth of the total factor productivity, were taken from the database of the world development indicators. The study covered 61 countries for which data were available. The study included three regression models to explain