In this research the Empirical Bayes method is used to Estimate the affiliation parameter in the clinical trials and then we compare this with the Moment Estimates for this parameter using Monte Carlo stimulation , we assumed that the distribution of the observation is binomial distribution while the distribution with the unknown random parameters is beta distribution ,finally we conclude that the Empirical bayes method for the random affiliation parameter is efficient using Mean Squares Error (MSE) and for different Sample size .

This work was conducted to study the extraction of eucalyptus oil from natural plants (Eucalyptus camadulensis leaves) by organic solvents. the effects of the main operating parameters were studied; type of solvent (n-hexane and ethanol), time to reach equilibrium, the temperature (45°C to 65°C) for n-hexane and (45°C to 75°C) for ethanol, solvent to solid ratio (5:1 to 8:1 (v/w)), agitation speed (0 to 900 rpm) and the particle size (0.5 to 2.5 cm) of fresh leaves to find the best processing conditions for the achieving maximum oil yield. The concentration of eucalyptus oil in solvent was measured by using UV-spectrophotometer. The results (for n-hexane) showed that the agitation speed of 900 rpm, temperature 65°C with solvent to soli

New, easy, simple, and fast spectral method for estimation of sulfamethoxazole (SMZ) in pure and pharmaceutical forms. The proposed method is based on the azotization of the drug compound by sodium nitrite in an acidic medium and then coupling with 2,3dimethyl phenol reagent (DMP) in a basic medium to yield an orange-coloured dye which shows λmax at 402 nm. Different affection of the optimization reaction has been completed, following the classical univariate sequence. The concentration of sulfamethoxazole about (1-15) μg. mL-1 with molar absorptivity of (14943.461) L.mol1 .cm-1 that obeyed Beer’s law. The detection and quantification limits were (0.852, 2.583) μg. mL-1 respectively, while the value of Sandell’s sensitivity (

A simple, and rapid spectrophotometric method for the estimation of paracetamol has been developed. The methods is based on diazotisation of 2,4-dichloroaniline followed by a coupling reaction with paracetamol in sodium hydroxide medium. All variables affecting the reaction conditions were carefully studied. Beer's law is obeyed in the concentration range of 4-350 ?gml?1 at 490 nm .The method is successfully employed for the determination of paracetamol in pharmaceutical preparations. No interferes observed in the proposed method. Analytical parameters such as accuracy and precision have been established for the method and evaluated statistically to assess the application of the method.

Abstract:

 The research concerned the study of the railway transport sector in selected countries that sought to raise the efficiency of the railway network and develop it, after realizing the importance of this vital sector, which is a link between it and the rest of the other economic sectors.

The research sought to explain the methods, methods and procedures adopted by these countries for the development of the railway sector, and to benefit from these experiments to improve the efficiency of the railway transport sector in Iraq.

The railway transport sector in Iraq suffers from the erosion of railway lines and mobile units such as locomotives, pas

Background: In the traditional protocol, the patient should wait after extraction up to six months to place the dental implant in healed bone, this waiting time accompanied by varying degrees of alveolar bone changes. In order to overcome these problems, immediate implant placement in the fresh extraction socket was introduced. The Aim of this study was to evaluate the outcome of the immediate implant placement utilizing Resonance Frequency Analysis (RFA) to quantify implant stability and osseointegration. Materials and Methods: A total of (23) patients participated in the study, receiving (44) implants placed in the sockets of teeth indicated for extraction. Clinical and radiographic preoperative assessment was accomplished for each patie

Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (Mob

BN RASHİD, 2023