The aesthetic expression and its orders are important for steel structures forming. Steel structures are a compilation of structural elements, where its shapes have standard dimensions and pre-fabricated. As the steel construction systems not only aim to achieve the functional requirements for users, but must also have the symbolic aesthetics which provides visually and cognitive expression for viewers. In this sense the research interested in expressional aesthetics in these systems and highlights the importance of attention as structural items. Therefore the visual items which related with steel structures contain some of the most powerful forms of modern architecture, steel structures with a glass cladding, agility an

Sorting and grading agricultural crops using manual sorting is a cumbersome and arduous process, in addition to the high costs and increased labor, as well as the low quality of sorting and grading compared to automatic sorting. the importance of deep learning, which includes the artificial neural network in prediction, also shows the importance of automated sorting in terms of efficiency, quality, and accuracy of sorting and grading. artificial neural network in predicting values and choosing what is good and suitable for agricultural crops, especially local lemons.

Theoretical spectroscopic study of  Beryllium Oxide has been carried out, Boltzmann distribution of P, Q and R branches in the range of (0<J<13) at temperature 4200K for (0-0) band for electronic transitions B¹Σ⁺-A¹Π and B¹Σ-X¹Σ. The Boltzmann distribution of these branches has a maximum values at equal J approximately while the values of relative population are different. For the B¹Σ⁺- X¹Σ⁺ transition the branch's lines extend towards lower wavenumber. This is because (B_v'-B_v") value is negative, i.e  B_v'< B_v" For B¹Σ⁺-A¹Π

Optimum perforation location selection is an important study to improve well production and hence in the reservoir development process, especially for unconventional high-pressure formations such as the formations under study. Reservoir geomechanics is one of the key factors to find optimal perforation location. This study aims to detect optimum perforation location by investigating the changes in geomechanical properties and wellbore stress for high-pressure formations and studying the difference in different stress type behaviors between normal and abnormal formations. The calculations are achieved by building one-dimensional mechanical earth model using the data of four deep abnormal wells located in Southern Iraqi oil fields. The magni

Total dissolved solids are at the top of the parameters list of water quality that requires investigations for planning and management, especially for irrigation and drinking purposes. If the quality of water is sufficiently predictable, then appropriate management is possible. In the current study, Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models were used as indicators of water quality and for the prediction of Total Dissolved Solids (TDS) along the Tigris River, in Baghdad city. To build these models five water parameters were selected from the intakes of four water treatment plants on the Tigris River, for the period between 2013 and 2017. The selected water parameters were Total Dissolved Solids (TDS

(Use of models of game theory in determining the policies to maximize profits for the Pepsi Cola and Coca-Cola in the province of Baghdad)

Due to the importance of the theory of games especially theories of oligopoly in the study of the reality of competition among companies or governments and others the researcher linked theories of oligopoly to Econometrics to include all the policies used by companies after these theories were based on price and quantity only the researcher applied these theories to data taken from Pepsi Cola and Coca-Cola In Baghdad Steps of the solution where stated for the models proposed and solutions where found to be balance points is for the two companies according to the princi

Imitation learning is an effective method for training an autonomous agent to accomplish a task by imitating expert behaviors in their demonstrations. However, traditional imitation learning methods require a large number of expert demonstrations in order to learn a complex behavior. Such a disadvantage has limited the potential of imitation learning in complex tasks where the expert demonstrations are not sufficient. In order to address the problem, we propose a Generative Adversarial Network-based model which is designed to learn optimal policies using only a single demonstration. The proposed model is evaluated on two simulated tasks in comparison with other methods. The results show that our proposed model is capable of completing co