Uropathogenic Escherichia coli is the main cause of urinary tract infections, the ability of this bacteria to cause urinary tract infections is related to a variety of virulence factors that enhance colonization and evade the immune response, one of these virulence factors is cytotoxic necrotizing factor 1 toxin which converts the glutamine residue to glutamic acid to activated GTPase Rho family. The study was meant to find out the prevalence rate of the cnf1 gene in Uropathogenic Escherichia coli isolated from Iraqi patients. Conventional laboratory methods were used for primary bacterial identification and molecular methods were used to confirm bacterial identity and gene detection. Escherichia coli was identified in 89/165 (53.93%) of th

The logistic regression model is an important statistical model showing the relationship between the binary variable and the explanatory variables.                                                        The large number of explanations that are usually used to illustrate the response led to the emergence of the problem of linear multiplicity between the explanatory variables that make estimating the parameters of the model not accurate.    

The estimation of the initial oil in place is a crucial topic in the period of exploration, appraisal, and development of the reservoir. In the current work, two conventional methods were used to determine the Initial Oil in Place. These two methods are a volumetric method and a reservoir simulation method. Moreover, each method requires a type of data whereet al the volumetric method depends on geological, core, well log and petrophysical properties data while the reservoir simulation method also needs capillary pressure versus water saturation, fluid production and static pressure data for all active wells at the Mishrif reservoir. The petrophysical properties for the studied reservoir is calculated using neural network technique

Today, the five Caspian riparian states on the shores of the Caspian Sea (Kazakhstan, Turkmenistan, Azerbaijan, Russia, and Iran) have become a front for ambitions and international and regional competition, especially in light of the features and characteristics that natural geography has endowed them with and their enjoyment of a group of economic and mineral wealth that are not optimally exploited so far which made it a strategic attraction area for international trends and interventions, especially Western ones. It is a battleground for major international companies aiming to monopolize promising industrial investments in order to impose control and influence on the region’s resources and economic wealth and thus impose their forei

In this paper, we derived an estimator of reliability function for Laplace distribution with two parameters using Bayes method with square error loss function, Jeffery’s formula and conditional probability random variable of observation. The main objective of this study is to find the efficiency of the derived Bayesian estimator compared to the maximum likelihood of this function and moment method using simulation technique by Monte Carlo method under different Laplace distribution parameters and sample sizes. The consequences have shown that Bayes estimator has been more efficient than the maximum likelihood estimator and moment estimator in all samples sizes

A non-parametric kernel method with Bootstrap technology was used to estimate the confidence intervals of the system failure function of the log-normal distribution trace data. These are the times of failure of the machines of the spinning department of the weaving company in Wasit Governorate. Estimating the failure function in a parametric way represented by the method of the maximum likelihood estimator (MLE). The comparison between the parametric and non-parametric methods was done by using the average of Squares Error (MES) criterion. It has been noted the efficiency of the nonparametric methods based on Bootstrap compared to the parametric method. It was also noted that the curve estimation is more realistic and appropriate for the re