In this study, mean free path and positron elastic-inelastic scattering are modeled for the elements hydrogen (H), carbon (C), nitrogen (N), oxygen (O), phosphorus (P), sulfur (S), chlorine (Cl), potassium (K) and iodine (I). Despite the enormous amounts of data required, the Monte Carlo (MC) method was applied, allowing for a very accurate simulation of positron interaction collisions in live cells. Here, the MC simulation of the interaction of positrons was reported with breast, liver, and thyroid at normal incidence angles, with energies ranging from 45 eV to 0.2 MeV. The model provides a straightforward analytic formula for the random sampling of positron scattering. ICRU44 was used to compile the elemental composition data. In this

This research a study model of linear regression problem of autocorrelation of random error is spread when a normal distribution as used in linear regression analysis for relationship between variables and through this relationship can predict the value of a variable with the values of other variables, and was comparing methods (method of least squares, method of the average un-weighted, Thiel method and Laplace method) using the mean square error (MSE) boxes and simulation and the study included fore sizes of samples (15, 30, 60, 100). The results showed that the least-squares method is best, applying the fore methods of buckwheat production data and the cultivated area of the provinces of Iraq for years (2010), (2011), (2012),

The purpose of this paper is to find the best multiplier approximation of unbounded functions in    –space by using some discrete linear positive operators. Also we will estimate the degree of the best multiplier approximation in term of modulus of continuity and the averaged modulus.

 The experiment was carried out in the field of botanical garden belonging to the Department of Biology Sciences, College of Education for Pure Science -Ibn AL-Haitham ,Baghdad University. for the growing season. 2014 -2013 to study the effect of the electromagnetic field which included five different intensities (0,5,10,15,20) MT and three periods of time, namely, (1,2,3) an hour and their interaction on  some of the morphological characteristics of the safflower plant . designed experiment by Randomized Complete Block Design (RCBD) and three replicates per treatment, compared to the average using  less significant difference at the level of probability (0.05) , the results showed the following:- 1-Exposing seeds to diffe

In this paper new methods were presented based on technique of differences which is the difference- based modified jackknifed generalized ridge regression estimator(DMJGR) and difference-based generalized  jackknifed ridge regression estimator(DGJR), in estimating the parameters of linear part of the partially linear model. As for the nonlinear part represented by the nonparametric function, it was estimated using Nadaraya Watson smoother. The partially linear model was compared using these proposed methods with other estimators based on differencing technique through the MSE comparison criterion in simulation study.

  An experiment was carried out by using pots in kalar horticulture station/ Sulaimani province on soil which is talken form on once region field in the seasoning growth(20062007). The objective was to study interaction of different levels from urea fertilizer (zero, 0.20, 0.40, 0.80 gm / 4 kg soil in pot). These levels were equal to (zero, 25, 50, 100, kg uera / D) and super phosphate levels (zero, 0.24, 0.48, gm / 4 kg soil in pot).These levels were equal to(zero, 30, 60, kg / D) in morphological and physiological characteristies (ex. dry weight, leaf area, absolute growth rate, protein percentage, and chlorophyll content) of Ipa (95) wheat variety. This experiment was carried out by completely Randomized Design (C. R. D.). Re

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.