Background: Polycystic ovarian syndrome is one of the common gynaecological diseases encountered nowadays in the gynaecological clinic. Many criteria and diagnostic test had been evolved to be used with different classifications methods.Objectives: The present study aimed to measure the anti-mullerian hormone levels in serum of the women with Polycystic Ovary Syndrome and to test the possibility that if it can be used as a marker for diagnosis of polycystic ovary syndrome patients.Methods: A cross sectional study that had been conductedat Kamal AL-Samaraee Hospital, AL-Suwayrah Hospital andAl-Elwiya Maternity Teaching Hospital during the periodfrom July, 1st, 2013 – Jan. 1st, 2014. Where forty women withPolycystic ovarian syndrome (wit

The research discusses the most important goals and means of IGAD in making peace in Sudan and then regional peace.

: zonal are included in phraseological units, form metaphorical names for a person, give him various emotional and evaluative characteristics. This article examines the topic of zoomorphic metaphors that characterize a person in the Russian and Arabic languages in the aspect of their comparative analysis, since the comparative analysis of the metaphorical meanings of animalisms is an important method for studying cultural linguistics, since zoomorphic metaphors are a reflection of culture in a language.

This study examines the causes of time delays and cost overruns in a selection of thirty post-disaster reconstruction projects in Iraq. Although delay factors have been studied in many countries and contexts, little data exists from countries under the conditions characterizing Iraq during the last 10-15 years. A case study approach was used, with thirty construction projects of different types and sizes selected from the Baghdad region. Project data was gathered from a survey which was used to build statistical relationships between time and cost delay ratios and delay factors in post disaster projects. The most important delay factors identified were contractor failure, redesigning of designs/plans and change orders, security is

This paper proposed a new  method to study functional non-parametric regression data analysis with conditional expectation in the case that the covariates  are functional and the Principal Component Analysis was utilized to de-correlate the multivariate response variables. It  utilized the formula of the Nadaraya Watson estimator (K-Nearest Neighbour (KNN)) for prediction with different types of the semi-metrics, (which are based on Second Derivative and Functional Principal Component Analysis (FPCA))  for measureing the closeness between curves.  Root Mean Square Errors is used for the  implementation of this model which is then compared to the independent response method. R program is used for analysing data. Then, when  the cov

Two- dimensional numerical simulations are carried out to study the elements of observing a Dirac point source and a Dirac binary system. The essential features of this simulation are demonstrated in terms of the point spread function and the modulation transfer function. Two mathematical equations have been extracted to present, firstly the relationship between the radius of optical telescope and the distance between the central frequency and cut-off frequency of the optical telescope, secondly the relationship between the radius of the optical telescope and the average frequency components of the modulation transfer function.

A prepared PMMA/Anthracene film of thickness 70μm was irradiated under reduced pressure ~10-3 to 60Coγ-ray dose of (0.1mrad-10krad) range. The optical properties of the irradiated films were evaluated spectrophotometrically. The absorption spectrum showed induced absorption changes in the 200-400nm range. At 359nm, where there is a decrease in radiation-induced absorption, the optical density as a function of absorbed dose is linear from 10mrad-10Krad.It can therefore, be used as radiation dosimeter for gamma ray in the range 10mrd-10krad

A three-stage learning algorithm for deep multilayer perceptron (DMLP) with effective weight initialisation based on sparse auto-encoder is proposed in this paper, which aims to overcome difficulties in training deep neural networks with limited training data in high-dimensional feature space. At the first stage, unsupervised learning is adopted using sparse auto-encoder to obtain the initial weights of the feature extraction layers of the DMLP. At the second stage, error back-propagation is used to train the DMLP by fixing the weights obtained at the first stage for its feature extraction layers. At the third stage, all the weights of the DMLP obtained at the second stage are refined by error back-propagation. Network structures an