Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne

Abstract: Urinary Tract Infections (UTIs) are the most common bacterial infection in humans and a major cause of morbidity and they are the most common cause of hospital visits worldwide. Proper knowledge in identifying factors associated with urinary tract infection may allow the intervention to easily control of the disease in a timely manner. Therefore, the purpose of the study is determining the prevalence of UTI, diagnosis of causative bacterial agents and identifying the factors associated to the urinary tract infection among patients attending Medical City Hospital in Baghdad, Iraq. A total of 237, morning mid-stream urine samples were collected aseptically and the samples were diagnosed according to the standard methods. I

In this study, manganese dioxide (MnO₂) nanoparticles (NPs) were synthesized via the hydrothermal method and utilized for the adsorption of Janus green dye (JG) from aqueous solutions. The effects of MnO₂ NPs on kinetics and diffusion were also analyzed. The synthesized NPs were characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD), energy-dispersive X-ray analysis (EDX), and Fourier-transform infrared spectroscopy (FT-IR), with XRD confirming the nanoparticle size of 6.23 nm. The adsorption kinetics were investigated using three models: pseudo-first-order (PFO), pseudo-second-order (PSO), and the intraparticle diffusion model. The PSO model provided the best fit (R² = 0.999), indicating that the adsorpti

A simple, environmental friendly and selective sample preparation technique employing porous membrane protected micro-solid phase extraction (μ-SPE) loaded with molecularly imprinted polymer (MIP) for the determination of ochratoxin A (OTA) is described. After the extraction, the analyte was desorbed using ultrasonication and was analyzed using high performance liquid chromatography. Under the optimized conditions, the detection limits of OTA for coffee, grape juice and urine were 0.06 ng g−1, 0.02 and 0.02 ng mL−1, respectively while the quantification limits were 0.19 ng g−1, 0.06 and 0.08 ng mL−1, respectively. The recoveries of OTA from coffee spiked at 1, 25 and 50 ng g−1, grape juice and urine samples at 1, 25 and 50 ng mL

Introduction: Ostrich farming has emerged as a new livestock industry in Iraq, but scientists lack sufficient information on health concerns, including intestinal parasites that cause significant production losses and financial instability over extended periods. Methods: Researchers collected 150 fecal samples from ostriches that dwelled in central and southern Iraq for microscopic examination of intestinal parasite occurrence. Results: The six parasite species included Entamoeba sp., which made up 26.66% of the population, and Cryptosporidium sp. at 11.33%, Ascaridia galli at 10%, Giardia sp. at 4.6%, Raillietina sp. at 2%, and Trichostrongyl. Molecular analysis was performed on a subset of positive samples because Entamoeba sp. is