In this study, the sonochemical degradation of phenol in water was investigated using two types of ultrasonic wave generators; 20 kHz ultrasonic processor and 40 kHz ultrasonic cleaner bath. Mineralization rates were determined as a function of phenol concentration, contact time, pH, power density, and type of ultrasonic generator. Results revealed that sonochemical degradation of the phenol conversion was enhanced at increased applied power densities and acidic conditions. At 10 mg/L initial concentration of phenol, pH 7, and applied power density of 3000 W/L, the maximum removal efficiency of phenol was 93% using ultrasonic processor at 2h contact time. Whereby, it was 87% using and ultrasonic cleaner bath at 16h contact time and 150 W

Energy Loss Function (ELF) of 2 5 Ta O derived from optical limit
and extended to the total part of momentum and their energy
excitation region ELF plays an important function in calculating
energy loss of electron in materials. The parameter Inelastic Mean
Free Path (IMFP) is most important in quantitative surface sensitive
electron spectroscopies, defined as the average distance that an
electron with a given energy travels between successive inelastic
collisions. The stopping cross section and single differential crosssection
SDCS are also calculated and gives good agreement with
previous work.

Commercial fish catch in the Iraqi marine waters from December 2018 to December 2019 was investigated. The study is based on three stations: the first station is located at the Shatt Al-Arab estuary, the second represents the area between the Shatt Al-Arab Estuary and open marine waters, and the third is associated with the Iraqi territorial marine waters. The total weight of the catch was 1881 kg, represented by 500, 654, and 727 kg in the first, second and third stations respectively. The third station was the highest with a majority of the family Sciaenidae by 464 kg, while the lowest was the family Scombridae by 0.5 kg. The total number of species included 37 species, belonging to 27 genera, 19 families, and 6 orders, the largest ord

Colloidal silver nanoparticles were prepared by single step green synthesis using aqueous extracts of the leaves of thyme as a function of different molar concentration of AgNO₃ (1,2,3,4 mM(. The Field Emission Scanning Electron Microscopy (FESEM), UV-Visible and X-ray diffraction (XRD) were used to characterize the resultant AgNPs. The surface Plasmon resonance was observed at wavelength of 444 nm. The four intensive peaks of XRD pattern indicate the crystalline nature and the face centered cubic structure of the AgNPs. The average crystallite size of the AgNPs ranged from 18 to 22 nm. The FESEM image illustrated the well dispersion of the AgNPs and the spherical shape of the nanoparticles with a particle size distribution be

Background: This research describes the methodology used for the preparation of selenium nanoparticles from Pseudomonas aeruginosa and their administration to lambs for lipid profile checking, administration of selenium nanoparticles as a medication in lambs results in hypolipidemia. Aim: The study aimed to investigate the potential of selenium nanoparticles in improving lipid profiles in lambs. Methods: Healthy lambs (n=10) of similar age and weight were selected for the study. The animals were housed in individual pens with free access to water and a standard diet. The lambs were randomly divided into two groups: the control group (n=5) and the treatment group (n=5). The control group received a standard diet, while the treatme

The Internet of Things (IoT) is an expanding domain that can revolutionize different industries. Nevertheless, security is among the multiple challenges that it encounters. A major threat in the IoT environment is spoofing attacks, a type of cyber threat in which malicious actors masquerade as legitimate entities. This research aims to develop an effective technique for detecting spoofing attacks for IoT security by utilizing feature-importance methods. The suggested methodology involves three stages: preprocessing, selection of important features, and classification. The feature importance determines the most significant characteristics that play a role in detecting spoofing attacks. This is achieved via two techniques: decision tr