Substance use disorders are a widely recognized problem among hepatitis C-infected patients; moreover, substance abuse by intravenous injection is a common mode of transmission of the hepatitis C virus worldwide. The frequency of substance use disorders and their relation to hepatitis C infection are still unknown in Iraq. This cross-sectional study, conducted among a sample of hepatitis C- infected patients attending the Gastrointestinal Tract Center in Baghdad Medical City, aimed to examine the prevalence of substance use disorders, the sociodemographic characteristics of the abusers, and the relation between intravenous

Little is known about hesitancy to receive the COVID‐19 vaccines. The objectives of this study were (1) to assess the perceptions of healthcare workers (HCWs) and the general population regarding the COVID‐19 vaccines, (2) to evaluate factors influencing the acceptance of vaccination using the health belief model (HBM), and (3) to qualitatively explore the suggested intervention strategies to promote the vaccination.

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