BACKGROUND: Polycystic ovary syndrome(PCOS) is one of the most common endocrine disorder affecting women in reproductive age. No single etiologic factor fully accounts for the spectrum of abnormalities in the polycystic ovary syndrome. Different changes in hormonal, metabolism and the inflammatory markers as squealy of PCOS with adverse effect on the women life. OBJECTIVE: To study the relationship between polycystic ovary syndrome and levels of C-reactive protein, human interleukin and hormonal and metabolic alteration in women with PCOS PATIENTS AND METHODS: Thirty women with Polycystic Ovary syndrome (PCOS) and other thirty women without PCOS were included. Venous blood samples were taken in early follicular phase of menstrual cycle [day

In this paper, variable gain nonlinear PD and PI fuzzy logic controllers are designed and the effect of the variable gain characteristic of these controllers is analyzed to show its contribution in enhancing the performance of the closed loop system over a conventional linear PID controller. Simulation results and time domain performance characteristics show how these fuzzy controllers outperform the conventional PID controller when used to control a nonlinear plant and a plant that has time delay.

Electronic remote identification (ER-ID) is a new radio frequency (RF) technology that is initiated by the Federal Aviation Authorities (FAA). For security reasons, traffic control, and so on, ER-ID has been applied for drones by the FAA to enable them to transmit their unique identification and location so that unauthorized drones can be identified. The current limitation of the existing ER-ID algorithms is that the application is limited to the Wi-Fi and Bluetooth wireless controllers, which results in a maximum range of 10–20 m for Bluetooth and 50–100 m for Wi-Fi. In this study, a mathematical computing technique based on finite state automaton (FSA) is introduced to expand the range of the ER-ID RF system and reduce the ene

Social risks posed a great challenge to the development path in Iraq, which resulted in widening the development gaps, whether these gaps were between rural and embargoed areas, or between Iraqi governorates, and the gender gap. Besides, the nature of the reciprocal relationship between the social risks and the development process requires the adoption of development trends that are sensitive to the risks that take upon themselves the prompt and correct response to these risks, away from randomness and confusion that Iraq suffered from for decades. However, currently, the situation has differed a great deal. This is because the size and types of such gaps have widened and become more complicated than before; a matter which has led to hav

Background: Bacteriocin is a peptidic toxin has many advantages to bacteria in their ecological niche and has strong antibacterial activity. Objective: The aim of this study was to evaluation of bacteriocin using Streptococcus sanguinis isolated from human dental caries.

Subjects and Methods: Thirty five streptococcus isolates were diagnosed and tested for their production of bacteriocin, and then the optimal conditions for production of bacteriocin were determined.  After that, the purification of bacteriocin was made partially by ammonium sulfate at 95% saturation levels, followed by and gel filtration chromatography

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

Various theories have been proposed since in last century to predict the first sighting of a new crescent moon. None of them uses the concept of machine and deep learning to process, interpret and simulate patterns hidden in databases. Many of these theories use interpolation and extrapolation techniques to identify sighting regions through such data. In this study, a pattern recognizer artificial neural network was trained to distinguish between visibility regions. Essential parameters of crescent moon sighting were collected from moon sight datasets and used to build an intelligent system of pattern recognition to predict the crescent sight conditions. The proposed ANN learned the datasets with an accuracy of more than 72% in comp