phenomenon in the Arab women's contemporary literature, but it is rare in Iraqi women's
literature and falls short to the level of the phenomenon until now. Perhaps this is one of the
main reasons that caused the reluctance of researchers to study it, and this is probably the first
academic research in this field.
This research followed the discourse of Autobiography of women in Iraq, depending on the
two factors of charter and reference of the elements of Philippe Lejeune’s definition of the
genre of Autobiography, without focusing on narrative method, so it handled notes,
memories, diaries and biography of the other and counted all under the genre of the
Biography.
It came in three sections, the first follo

Porous silicon (PS) layers are prepared by anodization for
different etching current densities. The samples are then
characterized the nanocrystalline porous silicon layer by X-Ray
Diffraction (XRD), Atomic Force Microscopy (AFM), Fourier
Transform Infrared (FTIR). PS layers were formed on n-type Si
wafer. Anodized electrically with a 20, 30, 40, 50 and 60 mA/cm2
current density for fixed 10 min etching times. XRD confirms the
formation of porous silicon, the crystal size is reduced toward
nanometric scale of the face centered cubic structure, and peak
becomes a broader with increasing the current density. The AFM
investigation shows the sponge like structure of PS at the lower
current density porous begi

In this research, we studied the multiple linear regression models for two variables in the presence of the autocorrelation problem for the error term observations and when the error is distributed with general logistic distribution. The auto regression model is involved in the studying and analyzing of the relationship between the variables, and through this relationship, the forecasting is completed with the variables as values. A simulation technique is used for comparison methods depending on the mean square error criteria in where the estimation methods that were used are (Generalized Least Squares, M Robust, and Laplace), and for different sizes of samples (20, 40, 60, 80, 100, 120). The M robust method is demonstrated the best metho

In this research, we studied the multiple linear regression models for two variables in the presence of the autocorrelation problem for the error term observations and when the error is distributed with general logistic distribution. The auto regression model is involved in the studying and analyzing of the relationship between the variables, and through this relationship, the forecasting is completed with the variables as values. A simulation technique is used for comparison methods depending

In the present study, composites were prepared by Hand lay-up molding and investigated. The composites constituents were epoxy resin as the matrix, 6% volume fractions of Glass Fibers (G.F) as reinforcement and 3%, 6% of industrial powder (Calcium Carbonate CaCO₃, Potassium Carbonate K₂CO₃ and Sodium Carbonate Na₂CO₃) as filler. Density, water absorption, hardness test, flexural strength, shear stress measurements and tests were conducted to reveal their values for each type of composite material. The results showed that the non – reinforced epoxy have lower properties than composites material. Measured density results had show an incremental increase with volume fraction increase

The slurry infiltrated fiber concrete (SIFCON) is nowadays considered a special type of high fiber content concrete; it is high strength and high performance material. This paper investigates the effect of spread steel fiber into the slurry mortar on some properties of SIFCON. According to fiber distribution, two sets were used in this investigation. The first set consisted of randomly distributing fibers inside the slurry. The second set was by placing the fibers in an orderly manner inside the slurry. Crimped steel fibers with an aspect ratio of (60) were used. Two different volume fractions percentage of (7% and 9%) by volume of mold were used in both sets for this study. Also, a w/c ratio of (0.35) and superplasticiz

Spatial data observed on a group of areal units is common in scientific applications. The usual hierarchical approach for modeling this kind of dataset is to introduce a spatial random effect with an autoregressive prior. However, the usual Markov chain Monte Carlo scheme for this hierarchical framework requires the spatial effects to be sampled from their full conditional posteriors one-by-one resulting in poor mixing. More importantly, it makes the model computationally inefficient for datasets with large number of units. In this article, we propose a Bayesian approach that uses the spectral structure of the adjacency to construct a low-rank expansion for modeling spatial dependence. We propose a pair of computationally efficient estimati