The rise of antibiotic-resistant bacteria necessitates the exploration of novel antimicrobial agents. Yttrium oxide nanoparticles (Y₂O₃) have shown potential due to their unique physicochemical properties and antibacterial activities against various pathogens. This study investigates the cytotoxic and antibacterial effects of Y₂O₃ nanoparticles against Serratia fonticuli and Citrobacter koseri, bacteria isolated from cholangitis patients. Bacterial strains were isolated from bile specimens and confirmed using standard microbiological techniques. The methods of X-ray diffraction (XRD), (SEM), and Frequency transform-infrared spectroscopic (FT-IR) were used to characterize YO₃ particles. Using a microdilution technique, the minimum

Healthcare professionals routinely use audio signals, generated by the human body, to help diagnose disease or assess its progression. With new technologies, it is now possible to collect human-generated sounds, such as coughing. Audio-based machine learning technologies can be adopted for automatic analysis of collected data. Valuable and rich information can be obtained from the cough signal and extracting effective characteristics from a finite duration time interval that changes as a function of time. This article presents a proposed approach to the detection and diagnosis of COVID-19 through the processing of cough collected from patients suffering from the most common symptoms of this pandemic. The proposed method is based on adopt

Background The appropriate disposal of medication is a well-recognized issue that has convened growing recognition in several contexts. Insufficient awareness relating to appropriate methods for the disposal of unneeded medicine may result in notable consequences. The current research was conducted among the public in Iraq with the aim of examining their knowledge, attitude, and practices regarding the proper disposal of unused and expired medicines. Methods The present study used an observational cross-sectional design that was community-based. The data were obtained from using an online questionnaire. The study sample included people of diverse genders, regardless of their race or occupational status. The study mandated that all pa

This study aimed to explore the manufacture of high-fat pellets for obesity induction diets in male Wistar rats and determined its effect on lipid profiles and body mass index. It was an experimental laboratory method with a post-test randomized control group. Formulation of high-fat pellets (HFD) and physico-chemical characteristics of pellets were conducted in September 2019. This study used about 28 male Wistar white rats, two months old, and 150-200 g body weight. Rats were acclimatized for seven days, then divided into four groups: 7 rats were given a standard feed of Confeed PARS CP594 (P0), and three groups (P1, P2, P3) were given high-fat feed (HFD FII) 30 g/head/day. The result showed that the mean fat content of Formula II pell

The research included preparation of new Schiff base (L) by two steps: preparation of precursor [bis(2-formyl-6-methoxyphenyl) succinate] (P) by reacting (3-methoxy salicyl aldehyde) with (succinoyl dichloride) as first step then react the prepared precursor (P) with (ethanethioamide) to have the new Schiff base [bis(2-((ethane thioyl imino) methyl)-6-methoxy phenyl) succinate] (L) as second step. Characterized compounds based on Mass spectra, 1 H, 13CNMR (for ligand (L)), FT-IR and UV spectrum, melting point, molar conduct, %C, %H, and %N, the percentage of the metal in complexes %M, magnetic susceptibility, while study corrosion inhibition (mild steel) in acid solution by weight loss. These measurements proved that by (Oxygen, Nitrogen, a

The experiment was conducted in Al- Mahaweel Research Station in Babel Governorate, Ministry of Agriculture during autumn season 2016-2017 to determine the role of irrigation management processes and micronutrient fertilization in growth and productivity of two varieties of wheat IPA 99 and Al-Rasheed 22 in clay loam soil classified as Typic Torriflovent. The experiment included four irrigation treatments and six fertilization treatments. The experiment was designed under randomized complete block design (RCBD) with three replications. Wheat grain IPA 99 and Al-Rasheed 22 varieties were planted in 23/11/2016 and harvested in 13/5/2017. The amount and periods of irrigation depended on sensors reading of volumetric water content was measured

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