Researchers have increased interest in recent years in determining the optimum sample size to obtain sufficient accuracy and estimation and to obtain high-precision parameters in order to evaluate a large number of tests in the field of diagnosis at the same time. In this research, two methods were used to determine the optimum sample size to estimate the parameters of high-dimensional data. These methods are the Bennett inequality method and the regression method. The nonlinear logistic regression model is estimated by the size of each sampling method in high-dimensional data using artificial intelligence, which is the method of artificial neural network (ANN) as it gives a high-precision estimate commensurate with the dat

Unconfined compressive strength (UCS) of rock is the most critical geomechanical property widely used as input parameters for designing fractures, analyzing wellbore stability, drilling programming and carrying out various petroleum engineering projects. The USC regulates rock deformation by measuring its strength and load-bearing capacity. The determination of UCS in the laboratory is a time-consuming and costly process. The current study aims to develop empirical equations to predict UCS using regression analysis by JMP software for the Khasib Formation in the Buzurgan oil fields, in southeastern Iraq using well-log data. The proposed equation accuracy was tested using the coefficient of determination (R²), the average absolute

The Purpose of this study is mainly to improve the competitive position of products economic units using technique target cost and method reverse engineering and through the application of technique and style on one of the public sector companies (general company for vegetable oils) which are important in the detection of prices accepted in the market for items similar products and processing the problem of high cost which attract managerial and technical leadership to the weakness that need to be improved through the introduction of new innovative solutions which make appropriate change to satisfy the needs of consumers in a cheaper way to affect the decisions of private customer to buy , especially of purchase private economic units to

In this research the Empirical Bayes method is used to Estimate the affiliation parameter in the clinical trials and then we compare this with the Moment Estimates for this parameter using Monte Carlo stimulation , we assumed that the distribution of the observation is binomial distribution while the distribution with the unknown random parameters is beta distribution ,finally we conclude that the Empirical bayes method for the random affiliation parameter is efficient using Mean Squares Error (MSE) and for different Sample size .

The UV−VIS absorption spectroscopy technique was used to study the formation of a new complex of charge transfer (CT) between bioactive organic molecules as (Nystatin) containing both a π-electrons from a conjugated system and lone-pair of electrons (amine) with Tetrachloro-1,4 benzoquinone (TCBQ) as a π-acceptor in which the transferred electron goes into its vacant anti-bonding molecular orbitals. The Tyrian purple-colored complex formed was quantitatively measured at 544 nm. This complex shows obeying Beer's law within the concentration range of (10-90) μg.ml-1The stoichiometry of the formed complex between the (Nys.) and (TCBQ) was found 1:2 as evaluated by continuous variation (Job's method) and mole ratio method The value of mola

Background: Posterior superior alveolar artery (PSAA) is branch of the maxillary artery. It usually supplies the lateral wall of the sinus and overlying membrane. Evaluation and awareness of the anatomy of maxillary sinus before surgery is crucial to avoid surgical complications. The aim of this study is to examine the prevalence, location of the (PSAA) in relation to the floor of the maxillary sinus and alveolar crest using computerized tomography (CT) scans. Materials and Methods: This study included 180 Iraqi subjects (99 males and 81 females) with age more than 16 years old. CT scans for (right and left) Maxillary sinuses were done for each patient. The information obtained was assessed in a coronal multi planar reconstructions images (