To determine the relationship between Helicobacter pylori infection and reproduction disorder (recurrent spontaneous abortion), twenty women patients who undergo spontaneous abortion during first trimester of pregnancy (20-38) years and have been investigated from 2015/12/1 -2016/3/1 and compared to fifteen healthy individuals. All subjects were carried out to measure anti-H. pylori IgA and anti- H. pylori IgG antibodies by enzyme linked immunosorbent assay (ELISA). There was significant elevation (p≤ 0.05) in concentration of anti- H. pylori IgG Abs (6.30± 0.99) compared to control group (4.48± 0.61) and IgA Abs (5.42 ± 0.90 U /ml) as compared to control group (3.92 ± 0.41 U/ml). The percentage of H. pylori IgG and IgA was 20% and 25%, respectively. There was high significant (P ≤ 0.01) difference between control and test groups. While, there was no significant differences (P > 0.05) in the concentration of IgA and IgG of H. pylori according to the age. These results indicate that infection with H. pylori play an important role in reproduction disorder of spontaneous abortion.
For several applications, it is very important to have an edge detection technique matching human visual contour perception and less sensitive to noise. The edge detection algorithm describes in this paper based on the results obtained by Maximum a posteriori (MAP) and Maximum Entropy (ME) deblurring algorithms. The technique makes a trade-off between sharpening and smoothing the noisy image. One of the advantages of the described algorithm is less sensitive to noise than that given by Marr and Geuen techniques that considered to be the best edge detection algorithms in terms of matching human visual contour perception.
The seizure epilepsy is risky because it happens randomly and leads to death in some cases. The standard epileptic seizures monitoring system involves video/EEG (electro-encephalography), which bothers the patient, as EEG electrodes are attached to the patient’s head.
Seriously, helping or alerting the patient before the seizure is one of the issue that attracts the researchers and designers attention. So that there are spectrums of portable seizure detection systems available in markets which are based on non-EEG signal.
The aim of this article is to provide a literature survey for the latest articles that cover many issues in the field of designing portable real-time seizure detection that includes the use of multiple
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Pavement crack and pothole identification are important tasks in transportation maintenance and road safety. This study offers a novel technique for automatic asphalt pavement crack and pothole detection which is based on image processing. Different types of cracks (transverse, longitudinal, alligator-type, and potholes) can be identified with such techniques. The goal of this research is to evaluate road surface damage by extracting cracks and potholes, categorizing them from images and videos, and comparing the manual and the automated methods. The proposed method was tested on 50 images. The results obtained from image processing showed that the proposed method can detect cracks and potholes and identify their severity levels wit
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HM Al-Dabbas, RA Azeez, AE Ali, Iraqi Journal of Science, 2023
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With the development of communication technologies for mobile devices and electronic communications, and went to the world of e-government, e-commerce and e-banking. It became necessary to control these activities from exposure to intrusion or misuse and to provide protection to them, so it's important to design powerful and efficient systems-do-this-purpose. It this paper it has been used several varieties of algorithm selection passive immune algorithm selection passive with real values, algorithm selection with passive detectors with a radius fixed, algorithm selection with passive detectors, variable- sized intrusion detection network type misuse where the algorithm generates a set of detectors to distinguish the self-samples. Practica
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This research describes a new model inspired by Mobilenetv2 that was trained on a very diverse dataset. The goal is to enable fire detection in open areas to replace physical sensor-based fire detectors and reduce false alarms of fires, to achieve the lowest losses in open areas via deep learning. A diverse fire dataset was created that combines images and videos from several sources. In addition, another self-made data set was taken from the farms of the holy shrine of Al-Hussainiya in the city of Karbala. After that, the model was trained with the collected dataset. The test accuracy of the fire dataset that was trained with the new model reached 98.87%.