Iron deficiency is considered as a common problem facing the general world population. In the current research, experiments were conducted to evaluate the effects of aqueous extracts of Acacia senegal (Gum Arabic, GA), Spinacia oleracea (spinach), Zea mays (corn), and Capsicum annuum (red chili pepper) on iron and ferritin levels in rats. Vitamin C and fructose levels were first estimated in these plants by using High-Performance Liquid Chromatography (HPLC). The results showed that the GA extract contained the highest level of fructose (853 mg.L^-1), followed by red chili pepper (635 mg.L^-1), corn (521 mg.L^-1), and spinach (271 mg.L^-1

Background Mitral valve (MV) is one of the most complex structures in human heart with a challenging exposure. Traditionally, MV is approached via left atriotomy (LAA) while superior septal approach (SSA) is an alternative.
Objective:  is to highlight the merits and demerits of these two approaches in providing access to the MV in term of the aortic cross clamp time (ACCT), quality of exposure, and potential complications in view of the published literature.

Patients and Method: Over an 18-month period ending at June 30^th, 2019, 56 patients with MV disease ± other cardiac diseases were enrolled in this study. Twenty patients had surgery via LAA (one surgeon) w

The main goal of this paper is to study applications of the fractional calculus techniques for a certain subclass of multivalent analytic functions on Hilbert Space. Also, we obtain the coefficient estimates, extreme points, convex combination and hadamard product.

This research deals with a shrinking method concerned with the principal components similar to that one which used in the multiple regression “Least Absolute Shrinkage and Selection: LASS”. The goal here is to make an uncorrelated linear combinations from only a subset of explanatory variables that may have a multicollinearity problem instead taking the whole number say, (K) of them. This shrinkage will force some coefficients to equal zero, after making some restriction on them by some "tuning parameter" say, (t) which balances the bias and variance amount from side, and doesn't exceed the acceptable percent explained variance of these components. This had been shown by MSE criterion in the regression case and the percent explained

In this study, we review the ARIMA (p, d, q), the EWMA and the DLM (dynamic linear moodelling) procedures in brief in order to accomdate the ac(autocorrelation)  structure of data .We consider the recursive estimation and prediction algorithms based on Bayes and KF (Kalman filtering) techniques for correlated observations.We investigate the effect on the MSE of  these procedures and compare them using generated data.

Estimating an individual's age from a photograph of their face is critical in many applications, including intelligence and defense, border security and human-machine interaction, as well as soft biometric recognition. There has been recent progress in this discipline that focuses on the idea of deep learning. These solutions need the creation and training of deep neural networks for the sole purpose of resolving this issue. In addition, pre-trained deep neural networks are utilized in the research process for the purpose of facial recognition and fine-tuning for accurate outcomes. The purpose of this study was to offer a method for estimating human ages from the frontal view of the face in a manner that is as accurate as possible and takes

In this work (paper), we investigate about the robustness of the modified divergence Information Criterion (MDIC), which proposed by Mantalos, Mattheou and Karagrigoriou (2008), to determine the probability of the Criterion picking up the true lag for Autoregressive process, when the error term of this process is normally and Non normally distributed. We obtained the results for different sample sizes by using simulation.

This Book is intended to be textbook studied for undergraduate course in multivariate analysis. This book is designed to be used in semester system. In order to achieve the goals of the book, it is divided into the following chapters. Chapter One introduces matrix algebra. Chapter Two devotes to Linear Equation System Solution with quadratic forms, Characteristic roots & vectors. Chapter Three discusses Partitioned Matrices and how to get Inverse, Jacobi and Hessian matrices. Chapter Four deals with Multivariate Normal Distribution (MVN). Chapter Five concern with Joint, Marginal and Conditional Normal Distribution, independency and correlations. Many solved examples are intended in this book, in addition to a variety of unsolved relied pro

This Book is intended to be textbook studied for undergraduate course in multivariate analysis. This book is designed to be used in semester system. In order to achieve the goals of the book, it is divided into the following chapters. Chapter One introduces matrix algebra. Chapter Two devotes to Linear Equation System Solution with quadratic forms, Characteristic roots & vectors. Chapter Three discusses Partitioned Matrices and how to get Inverse, Jacobi and Hessian matrices. Chapter Four deals with Multivariate Normal Distribution (MVN). Chapter Five concern with Joint, Marginal and Conditional Normal Distribution, independency and correlations. Many solved examples are intended in this book, in addition to a variety of unsolved relied pro

The analysis of COVID-19 data in Iraq is carried out. Data includes daily cases and deaths since the outbreak of the pandemic in Iraq on February 2020 until the 28th of June 2022. This is done by fitting some distributions to the data in order to find out the most appropriate distribution fit to both daily cases and deaths due to the COVID-19 pandemic. The statistical analysis includes estimation of the parameters, the goodness of fit tests and illustrative probability plots. It was found that the generalized extreme value and the generalized Pareto distributions may provide a good fit for the data for both daily cases and deaths. However, they were rejected by the goodness of fit test statistics due to the high variability of the data.<