Objective: The evaluation of serum osteocalcin (OSN) for Iraqi infertile patients to see the effect of osteocalcin insufficiency, which may lead to a decreased level of testosterone production in males that may cause infertility. Methods: Forty two newly diagnosed infertile males age range (24–47) years and thirty two apparently healthy males as controls age range (25–58) years. Serum levels of testosterone (TEST), stimulating follicle hormone (FSH) and luteinizing hormone (LH), prolactin (PROL), osteocalcin OSN, and fasting blood sugar (FBS) were performed in both patients and controls. Estimation of serum OSN by Immulite1000 auto-analyzer, TEST, FSH, LH, PROL, and FBS by Immulite2000 auto-analyzer. Results: Infertile patients

Integrating Renewable Energy (RE) into Distribution Power Networks (DPNs) is a choice for efficient and sustainable electricity. Controlling the power factor of these sources is one of the techniques employed to manage the power loss of the grid. Capacitor banks have been employed to control phantom power, improving voltage and reducing power losses for several decades. The voltage sag and the significant power losses in the Iraqi DPN make it good evidence to be a case study proving the efficiency enhancement by adjusting the RE power factor. Therefore, this paper studies a part of the Iraqi network in a windy and sunny region, the Badra-Zurbatya-11 kV feeder, in the Wasit governorate. A substation of hybrid RE sources is connected to this

The purpose of this research is to investigate the impact of corrosive environment (corrosive ferric chloride of 1, 2, 5, 6% wt. at room temperature), immersion period of (48, 72, 96, 120, 144 hours), and surface roughness on pitting corrosion characteristics and use the data to build an artificial neural network and test its ability to predict the depth and intensity of pitting corrosion in a variety of conditions. Pit density and depth were calculated using a pitting corrosion test on carbon steel (C-4130). Pitting corrosion experimental tests were used to develop artificial neural network (ANN) models for predicting pitting corrosion characteristics. It was found that artificial neural network models were shown to be

This study included 46 patients with liver hydatid cyst diagnosed clinically and surgically. Control group consist of 22 healthy volunteers. The patients were divided according to the size of the cysts into more and less than 5 cm diameter size, 33 and 13, respectively. Also they were divided into primary and secondary hydatid cyst infection, 30 and 16, respectively. Significant increase of GOT, GPT and ALP levels were recorded due to hydatid cyst infection and had direct effect on the liver function, beside an increase in total bilirubin in patients serum compared with the control, also the same occurred in the secondary infection compared with primary infection, patients with> 5 cm showed significant increase in the above levels compared

The purpose of this research is to find the estimator of the average proportion of defectives based on attribute samples. That have been curtailed either with rejection of a lot finding the kth defective or with acceptance on finding the kth non defective.

The MLE (Maximum likelihood estimator) is derived. And also the ASN in Single Curtailed Sampling has been derived and we obtain a simplified Formula All the Notations needed are explained.

The estimation of the initial oil in place is a crucial topic in the period of exploration, appraisal, and development of the reservoir. In the current work, two conventional methods were used to determine the Initial Oil in Place. These two methods are a volumetric method and a reservoir simulation method. Moreover, each method requires a type of data whereet al the volumetric method depends on geological, core, well log and petrophysical properties data while the reservoir simulation method also needs capillary pressure versus water saturation, fluid production and static pressure data for all active wells at the Mishrif reservoir. The petrophysical properties for the studied reservoir is calculated using neural network technique