Listeria spp. is one of the abortion causative agents in animals, especially in ruminants. This work aimed to detect Listeria spp. in milk and aborted fetus cows in Iraq. A total of 50 organ samples from aborted cow fetuses, including (brain, liver, and spleen), and 50 milk samples from the same aborted cows were collected from Baghdad farms, Iraq from (October 2023- March 2024). The bacteria were identified by conventional culture methods, biochemical tests, and the VITEK2 compact system, followed by molecular confirmation. The antimicrobial resistance pattern assay was performed using the disc diffusion method against eight antibiotic agents, and the L.monocytogenes virulence genes involving prfA,actA, and hylA genes were detected using the PCR. The results revealed that only L. monocytogenes was detected at 2/50(4%) from aborted fetuses isolated from the brain and liver, while not in milk samples. The L.monocytogenes showed 100% resistance against erythromycin, ampicillin, cotrimoxazole, chloramphenicol, vancomycin, and tetracycline. At the same time, all the isolates had a high MDR and MAR (Multiple Antibiotic Resistance) index. This study concluded that L.monocytogenes is one of the abortion causative agents in cattle in Iraq, and the high antibiotic resistance of Listeria leads to economic loss and a possible risk to humans.
Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (Mob
... Show MoreOne of the most important features of the Amazon Web Services (AWS) cloud is that the program can be run and accessed from any location. You can access and monitor the result of the program from any location, saving many images and allowing for faster computation. This work proposes a face detection classification model based on AWS cloud aiming to classify the faces into two classes: a non-permission class, and a permission class, by training the real data set collected from our cameras. The proposed Convolutional Neural Network (CNN) cloud-based system was used to share computational resources for Artificial Neural Networks (ANN) to reduce redundant computation. The test system uses Internet of Things (IoT) services through our ca
... Show MorePigeons have accompanied humans since ancient time because they are used as a source of food, pets, hobby, and religious symbols. Pigeons have shown high prevalence rate of infection with gastrointestinal helminths and protozoan. This study was conducted to evaluate the prevalence of parasitic infections in the domestic pigeon (Columba livia domestica) from October, 2017 to April, 2018, purchased from bird market of Zakho City, Kurdistan region. The samples were taken from 50 adult pigeons (28 males and 22 females). The birds were transferred to Parasitology Laboratory, Faculty of Science, Zakho University. In the laboratory, each bird was sacrificed and immediately the feather and skin of under wings, chest and the rest of the
... Show MoreThe subject of the organizational Ambidexterity of the vital Topics through which it seeks organizations to provide mentalities renewable for their members and maintain its survival and continuity according to different organizational methods of access for strategic success. The research aims to demonstrate the impact of organizational Ambidexterity in achieving strategic success in the National bank of Iraq, and the questionnaire was prepared as a tool for collecting data and information through sample survey of (16) managers and heads of departments , The results were analyzed by using the statistical program (SPSS) in calculating mean, standard deviation, percent and test (f), coefficie
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