Objective. Infection with Coxsackie virus. This virus that damages pancreatic cells, has long been linked to the onset of insulin-dependent diabetic mellitus (IDDM). Pro-inflammatory cytokines can be produced as a result of this illness. Tumor necrosis factor-a is one of these pro-inflammatory cytokines. Materials and Methods. Blood sample were collected from 180 Iraqi participants. Ninety of them is type 1 diabetic patients and other 90 is healthy control .both groups were tested for the incidence of Coxsackie virus B IgG. So the patients groups is divided to two groups according to sero positivity of CVB-IgG .all 180 patients tested to measure of level of TNF-α. Results. The Results showed increasing in levels of TNF-α in CBV positive Type 1 Diabetes mellitus was (34.85 ± 11.00 pg/ml). The level of this interleukin in Type 1 Diabetes mellitus negative to that virus was (26.16 ± 7.79 pg/ml). While the results of this interleukin in control group was (13.82 ± 3.93 pg/ml) with p-value 0. Conclusion. The concentration of TNF-α, according to results, has been shown to be associated with type 1 diabetes mellitus patients infected with CVB-IgG and diabetic patients without CVB.
Convolutional Neural Networks (CNN) have high performance in the fields of object recognition and classification. The strength of CNNs comes from the fact that they are able to extract information from raw-pixel content and learn features automatically. Feature extraction and classification algorithms can be either hand-crafted or Deep Learning (DL) based. DL detection approaches can be either two stages (region proposal approaches) detector or a single stage (non-region proposal approach) detector. Region proposal-based techniques include R-CNN, Fast RCNN, and Faster RCNN. Non-region proposal-based techniques include Single Shot Detector (SSD) and You Only Look Once (YOLO). We are going to compare the speed and accuracy of Faster RCNN,
... Show MoreBackground: Legionella pneumophila (L. pneumophila) is gram-negative bacterium, which causes Legionnaires’ disease as well as Pontiac fever. Objective: To determine the frequency of Legionella pneumophila in pneumonic patients, to determine the clinical utility of diagnosing Legionella pneumonia by urinary antigen testing (LPUAT) in terms of sensitivity and specificity, to compares the results obtained from patients by urinary antigen test with q Real Time PCR (RT PCR) using serum samples and to determine the frequency of serogroup 1 and other serogroups of L. pneumophila. Methods: A total of 100 pneumonic patients (community acquired pneumonia) were enrolled in this study during a period between October 2016 to April 2017; 92 sam
... Show MoreWith 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
... Show MoreThis 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%.
Self-Assertion is the individual ability to express any emotion well, except the anxiety. The decrease of the individuals asserting behavior makes them face many difficulties that prevent their social adjustment. Moreover it reflexes many negative behavioral and physical cases. The individual, who fails to express his or her negative feelings in required situations, feels with dissatisfaction, loneliness, depression, anxiety, social anxiety, conflict, and psychological disorder.
Accordingly, the importance of this study is represented in studying the self-assertion and studying the university students who reflect the strength of society.
The following are the two aims of the study:
1. Construct an asserting behavior scale.
2.