This work aims to study the exploding copper wire plasma parameters by optical emission spectroscopy. The emission spectra of the copper plasma have been recorded and analyzed The plasma electron temperature (Te), was calculated by Boltzmann plot, and the electron density (ne) calculated by using Stark broadening method for different copper wire diameter (0.18, 0.24 and 0.3 mm) and current
of 75A in distilled water. The hydrogen (Hα line) 656.279 nm was used to calculate the electron density for different wire diameters by Stark broadening. It was found that the electron density ne decrease from 22.4×1016 cm-3 to 17×1016 cm-3 with increasing wire diameter from 0.18 mm to 0.3 mm while the electron temperatures increase from 0.741 to

The purpose of this paper is to identify environmental awareness under the Corona pandemic among students of the Faculty of Physical Education and Sports Sciences at the University of Kufa. and comparison of environmental awareness under the Corona pandemic between students of the Faculty of Physical Education and Sports Sciences at the University of Kufa. The two researchers used the descriptive approach in the style of the survey and comparisons to identify the research community in the students of the College of Physical Education and Sports Sciences at the University of Kufa for the academic year 2020-2021, who numbered (210) students, then a sample of (80) students was chosen randomly, with a percentage of (38.09%) from the research co

Statistical methods of forecasting have applied with the intention of constructing a model to predict the number of the old aged people in retirement homes in Iraq. They were based on the monthly data of old aged people in Baghdad and the governorates except for the Kurdistan region from 2016 to 2019. Using Box-Jenkins methodology, the stationarity of the series was examined. The appropriate model order was determined, the parameters were estimated, the significance was tested, adequacy of the model was checked, and then the best model of prediction was used. The best model for forecasting according to criteria of (Normalized BIC, MAPE, RMSE) is ARIMA (0, 1, 2).

Background: Gray-scale sonography is generally
considered as a first-line diagnostic tool for patient with
suspected acute cholecystitis. It is suggested by gallstones,
Murphy's sign, thickening of the gallbladder wall and bile
sludging, but the specificity of these sonographic findings
are not as high as their sensitivity. Blood flow of the
gallbladder wall is increased in acute inflammation.
Objective: To evaluate the sensitivity and specificity of
power Doppler sonography and compared with conventional
color Doppler and gray-scale sonography in diagnosing
patients with acute cholecystitis.
Type of the study: This was a cross sectional study.
Patients and methods: The study was conducted t

This deals with estimation of Reliability function and one shape parameter (?) of two- parameters Burr – XII , when ?(shape parameter is known) (?=0.5,1,1.5) and also the initial values of (?=1), while different sample shze n= 10, 20, 30, 50) bare used. The results depend on empirical study through simulation experiments are applied to compare the four methods of estimation, as well as computing the reliability function . The results of Mean square error indicates that Jacknif estimator is better than other three estimators , for all sample size and parameter values

Statistical methods of forecasting have applied with the intention of constructing a model to predict the number of the old aged people in retirement homes in Iraq. They were based on the monthly data of old aged people in Baghdad and the governorates except for the Kurdistan region from 2016 to 2019. Using BoxJenkins methodology, the stationarity of the series was examined. The appropriate model order was determined, the parameters were estimated, the significance was tested, adequacy of the model was checked, and then the best model of prediction was used. The best model for forecasting according to criteria of (Normalized BIC, MAPE, RMSE) is ARIMA (0, 1, 2)