Polycystic ovary syndrome (PCOS) is one of the most common endocrine disorder. To determine the metabolic disorders in women with PCOS, (25) women with PCOS ages (15 - 47) years have been investigated and compared with (20) healthy individuals. All the studied groups were carried out to measure fasting blood sugar, (anti-GAD Ab, anti ?-islet cell Ab by IFAT) and measured insulin level by ELISA. There was significant elevation in the concentration of fasting blood sugar than in control groups (p ? 0.05) and there was negative results for anti-GAD Ab and anti ?-islet cell Ab by IFAT test for serum of women with PCOS, while there was significant differences in the insulin level for women with PCOS compared with control groups (p ? 0.05), these indicated that women with PCOS are at high risk for type -2 Diabetes mellitus and reflecting insulin resistance.
This work was conducted to study the recovery of catalyst and desirable components from tar formed in phenol production unit and more particularly relates to such a method whereby better recovery of copper salts, phenol, benzoic acid and benzoate salts from tar by aqueous acid solution was accomplished.
The effect of solvent type, solvent concentration (5, 10, 15, 20, 25 and 30 wt%), agitation speed (100, 200, 300 and 400 rpm), agitation time (5, 10, 15, 20 and 25 min), temperature (90, 100, 110, 120, 130 and 140 oC) , phase ratio (1/1, 2/1, 3/1, 4/1 and 5/1) and number of extraction (1, 2, 3, 4, and 5) were examined in order to increase the catalyst and desirable components extraction.
Four types of solvent were used; hydrochloric
Air pollution is one of the important problems facing Iraq. Air pollution is the result of uncontrolled emissions from factories, car exhaust electric generators, and oil refineries and often reaches unacceptable limits by international standards. These pollutants can greatly affect human health and regular population activities. For this reason, there is an urgent need for effective devices to monitor the molecular concentration of air pollutants in cities and urban areas. In this research, an optical system has been built consisting of aHelium-Neonlaser,5mWand at 632.8 nm, a glass cell with a defined size, and a power meter(Gentec-E-model: uno) where a scattering of the laser beam occurs due to air pollution. Two pollutants were examin
... Show MoreAbstract :H.pylori is an important cause of gastric duodenal disease, including gastric ulcers, Mucosa-associated lymphoid tissue (MALT), and gastric carcinoma. biosensors are becoming the most extensively studied discipline because the easy, rapid, low-cost, highly sensitive, and highly selective biosensors contribute to advances in next-generation medicines such as individualized medicine and ultrasensitive point-of-care detection of markers for diseases. Five of ten patients diagnosed with H.pylori ranging in age from 15–85 participated in this research. who [gastritis, duodenitis, duodenal ulcer (DU), and peptic ulcer (PU)] Suspected H.pylori colonies w
... Show MoreThe goal of this work is to check the presence of PNS (photon number splitting) attack in quantum cryptography system based on BB84 protocol, and to get a maximum secure key length as possible. This was achieved by randomly interleaving decoy states with mean photon numbers of 5.38, 1.588 and 0.48 between the signal states with mean photon numbers of 2.69, 0.794 and 0.24. The average length for a secure key obtained from our system discarding the cases with Eavesdropping was equal to 125 with 20 % decoy states and 82 with 50% decoy states for mean photon number of 0.794 for signal states and 1.588 for decoy states.
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
... Show MoreThis study included the isolation and identification of Aspergillus flavus isolates associated with imported American rice grains and local corn grains which collected from local markets, using UV light with 365 nm wave length and different media (PDA, YEA, COA, and CDA ). One hundred and seven fungal isolates were identified in rice and 147 isolates in corn.4 genera and 7 species were associated with grains, the genera were Aspergillus ,Fusarium ,Neurospora ,Penicillium . Aspergillus was dominant with occurrence of 0.47% and frequency of 11.75% in rice grains whereas in corn grains the genus Neurospora was dominant with occurrence of 1.09% and frequency 27.25% ,results revealed that 20 isolates out of 50 A. flavus isolates were able
... Show MoreIn this work Laser wireless video communication system using intensity modualtion direct
detection IM/DD over a 1 km range between transmitter and receiver is experimentally investigated and
demonstrated. Beam expander and beam collimeter were implemented to collimete laser beam at the
transmitter and focus this beam at the receiver respectively. The results show that IM/DD communication
sysatem using laser diode is quite attractive for transmitting video signal. In this work signal to noise
ratio (S/N) higher than 20 dB is achieved in this work.
The meniscus has a crucial function in human anatomy, and Magnetic Resonance Imaging (M.R.I.) plays an essential role in meniscus assessment. It is difficult to identify cartilage lesions using typical image processing approaches because the M.R.I. data is so diverse. An M.R.I. data sequence comprises numerous images, and the attributes area we are searching for may differ from each image in the series. Therefore, feature extraction gets more complicated, hence specifically, traditional image processing becomes very complex. In traditional image processing, a human tells a computer what should be there, but a deep learning (D.L.) algorithm extracts the features of what is already there automatically. The surface changes become valuable when
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