This study aimed at the investigation of abnormal liver and renal functions by biochemical manifestations of underlying metabolic abnormalities in relation to hyperglycemia in non-insulin-dependent diabetic patients. The study comprised 118 diabetic patients (56 males, 62 females) and 60 age-matched healthy non-diabetic controls (30 males, 30 females). All subjects were tested for serum levels of liver enzymatic indicators, which include aspartate transaminase (AST), alanine transaminase (ALT), and alkaline phosphatase (ALP), as well as non enzymatic parameters, including total bilirubin and total proteins.Also, serum levels of renal function markers, including microalbumin, creatinine, urea, and uric acid were measured.

The find

The paper is concerned with posterior analysis of five exponentiated (Weibull, Exponential, Inverted Weibull, Pareto, Gumbel) distrebutions. The expressions for Bayes estimators of the shape parameters have been derived under four different prior distributions assuming four different loss functions. The posterior predictive distributions have been obtained, and the comparison between estimators made by using the mean squared errors through generated different sample sizes by using simulation technique. In general, the performance of estimators under Chi-square prior using squared error loss function is the best.

In this paper, two parameters for the Exponential distribution were estimated using the
Bayesian estimation method under three different loss functions: the Squared error loss function,
the Precautionary loss function, and the Entropy loss function. The Exponential distribution prior
and Gamma distribution have been assumed as the priors of the scale γ and location δ parameters
respectively. In Bayesian estimation, Maximum likelihood estimators have been used as the initial
estimators, and the Tierney-Kadane approximation has been used effectively. Based on the MonteCarlo
simulation method, those estimators were compared depending on the mean squared errors (MSEs).The results showed that the Bayesian esti

In this paper we use non-polynomial spline functions to develop numerical methods to approximate the solution of 2nd kind Volterra integral equations. Numerical examples are presented to illustrate the applications of these method, and to compare the computed results with other known methods.

     The objective of the study is to demonstrate the predictive ability is better between the logistic regression model and Linear Discriminant function using the original data first and then the Home vehicles to reduce the dimensions of the variables for data and socio-economic survey of the family to the province of Baghdad in 2012 and included a sample of 615 observation with 13 variable, 12 of them is an explanatory variable and the depended variable is number of workers and the unemployed.

     Was conducted to compare the two methods above and it became clear by comparing the  logistic regression model best of a Linear Discriminant  function written

A newly derivative of oxazolidin-5- one namely [2-(2-biphenyl-4-yl-imidazo [1,2-a] pyridine-3-yl)-3-(4-nitro-phenyl)-oxazolidin-5-one (BIPNO5)] was examined as an corrosion inhibitor for carbon steel surface. Quantum mechanical method of Density Functional Theory (DFT) with (B3LYP (6-311++G (2d, 2p)) level of theory was used to calculate the minimize structure, physical properties and inhibition chemical parameters, in vacuum and two solvents (DMSO and H₂O), all at equilibrium geometry. The results indicated that the new derivative could adsorb on the surface of carbon steel through the heteroatom, showing that the new inhibitor has good corrosion inhibition performance.

Linear discriminant analysis and logistic regression are the most widely used in multivariate statistical methods for analysis of data with categorical outcome variables .Both of them are appropriate for the development of linear  classification models .linear discriminant analysis has been that the data of explanatory variables must be distributed multivariate normal distribution. While logistic regression no assumptions on the distribution of the explanatory data. Hence ,It is assumed that logistic regression is the more flexible and more robust method in case of violations of these assumptions.

In this paper we have been focus for the comparison between three forms for classification data belongs

            In this paper the definition of fuzzy anti-normed linear spaces and its basic properties are used to prove some properties of a finite dimensional fuzzy anti-normed linear space.