Addressed this research the impact of intelligence emotional dimensions of the main(self awareness, and self-management, and social awareness, and relationship management) in the performance excellence the university(performance optimization, and strategic development) this is by middling the styles decision making which are (rational and intuitive, and dependent, and spontaneous, and avoidant), and Go search of an intellectual dilemma raise fundamental questions revolve around the search was to answer those questions through a theoretical framework for search variables first and test models of the relationship and second through the impact six hypotheses President.The objective of the research to make sure the contr

It is often needed to have circuits that can display the decimal representation of a binary number and specifically in this paper on a 7-segment display. In this paper a circuit that can display the decimal equivalent of an n-bit binary number is designed and it’s behavior is described using Verilog Hardware Descriptive Language (HDL).
This HDL program is then used to configure an FPGA to implement the designed circuit.

This work illustrates an enhanced visible light photocatalytic degradation of methyl orange dye (M.O.) by employing BiOI / BiOCl composites prepared under room temperature and without any organic precursors. Various experimental parameters have been studied, namely; composition of the composite, irradiation time and cell material. Composition D which implied 75% BiOI and 25% BiOCl has shown the highest bleaching of M.O. dye. This confirms the optimum photo-sensitization phenomenon for this composition in comparison to others. In the optimum photo-sensitized composite the electron of the conduction band reveals better reducing power and the hole of the valence band exhibits more oxidative power than those of pure BiOI electron and hole. Acco

Precise forecasting of pore pressures is crucial for efficiently planning and drilling oil and gas wells. It reduces expenses and saves time while preventing drilling complications. Since direct measurement of pore pressure in wellbores is costly and time-intensive, the ability to estimate it using empirical or machine learning models is beneficial. The present study aims to predict pore pressure using artificial neural network. The building and testing of artificial neural network are based on the data from five oil fields and several formations. The artificial neural network model is built using a measured dataset consisting of 77 data points of Pore pressure obtained from the modular formation dynamics tester. The input variables

Generally, statistical methods are used in various fields of science, especially in the research field, in which Statistical analysis is carried out by adopting several techniques, according to the nature of the study and its objectives. One of these techniques is building statistical models, which is done through regression models. This technique is considered one of the most important statistical methods for studying the relationship between a dependent variable, also called (the response variable) and the other variables, called covariate variables. This research describes the estimation of the partial linear regression model, as well as the estimation of the “missing at random” values (MAR). Regarding the

       In this paper, a fixed point theorem of nonexpansive mapping is established to study the existence and sufficient conditions for the controllability of nonlinear fractional control systems in reflexive Banach spaces. The result so obtained have been modified and developed in arbitrary space having Opial’s condition by using fixed point theorem deals with nonexpansive mapping defined on a set has normal structure. An application is provided to show the effectiveness of the obtained result.

This research aims to analyze and simulate biochemical real test data for uncovering the relationships among the tests, and how each of them impacts others. The data were acquired from Iraqi private biochemical laboratory. However, these data have many dimensions with a high rate of null values, and big patient numbers. Then, several experiments have been applied on these data beginning with unsupervised techniques such as hierarchical clustering, and k-means, but the results were not clear. Then the preprocessing step performed, to make the dataset analyzable by supervised techniques such as Linear Discriminant Analysis (LDA), Classification And Regression Tree (CART), Logistic Regression (LR), K-Nearest Neighbor (K-NN), Naïve Bays (NB

Visual analytics becomes an important approach for discovering patterns in big data. As visualization struggles from high dimensionality of data, issues like concept hierarchy on each dimension add more difficulty and make visualization a prohibitive task. Data cube offers multi-perspective aggregated views of large data sets and has important applications in business and many other areas. It has high dimensionality, concept hierarchy, vast number of cells, and comes with special exploration operations such as roll-up, drill-down, slicing and dicing. All these issues make data cubes very difficult to visually explore. Most existing approaches visualize a data cube in 2D space and require preprocessing steps. In this paper, we propose a visu