Lactiplantibacillus plantarum‎, one of lactic acid bacteria (LAB), is found in various foods, ‎‎including dairy products, meat, and vegetables, and most of these bacteria offer beneficial ‎‎effects to humans and animals as potential probiotics with broad-spectrum antimicrobial ‎‎activities. The aim of this study was evaluating the antibacterial efficacy of L. ‎plantarum‎ ‎against ‎some foodborne bacteria isolated from dairy products. This research involved 34 dairy ‎products, ‎including local and imported milk, cheese, and yogurt sold locally in Baghdad ‎province, Iraq, during May ‎‎2022. For the isolation of L. ‎plantarum‎, a special medium called ‎MRS (de Man Rogosa and ‎Sharpe) was applied. Colonie

       The present study was performed to spotlight the potential role of soil bacteria in the Al-Rumaila oil field as a bioindicator of heavy metals pollution. For this purpose, nine soil samples were collected from different sites, with 20cm depth, to assess the pollution status depending on the total and available concentrations of heavy metals.  The result indicates pollution of the studied soils with the following metals: Cd, Cu, Fe, Zn, and Pb. The mean of total concentration for all studied metals was higher than the allowed maximum limit based on the international limit:(3.394, 3.994, 39.993, 8844.979,150.372, and 103.347 µg/g), respectively. While measuring the total Metal concentration is important in determining the de

Nowadays, internet security is a critical concern; the One of the most difficult study issues in network security is "intrusion detection". Fight against external threats. Intrusion detection is a novel method of securing computers and data networks that are already in use. To boost the efficacy of intrusion detection systems, machine learning and deep learning are widely deployed. While work on intrusion detection systems is already underway, based on data mining and machine learning is effective, it requires to detect intrusions by training static batch classifiers regardless considering the time-varying features of a regular data stream. Real-world problems, on the other hand, rarely fit into models that have such constraints. Furthermor

This paper proposed a new method for network self-fault management (NSFM) based on two technologies: intelligent agent to automate fault management tasks, and Windows Management Instrumentations (WMI) to identify the fault faster when resources are independent (different type of devices). The proposed network self-fault management reduced the load of network traffic by reducing the request and response between the server and client, which achieves less downtime for each node in state of fault occurring in the client. The performance of the proposed system is measured by three measures: efficiency, availability, and reliability. A high efficiency average is obtained depending on the faults occurred in the system which reaches to