This paper aims to build a modern vision for Islamic banks to ensure sustainability and growth, as well it aims to highlight the positive Iraqi steps in the Islamic banking sector. In order to build this vision, several scientific research approaches were adopted (quantitative, descriptive analytical, descriptive). As for the research community, it was for all the Iraqi private commercial banks, including Islamic banks. The research samples varied according to a diversity of the methods and the data availability. A questionnaire was constructed and conducted, measuring internal and external honesty. 50 questionnaires were distributed to Iraqi academic specialized in Islamic banking. All distributed forms were subject to a thorough analys

A simple, accurate and sensitive spectrophotometric method for the determinaion of epinephrine is described . The method is based on the coordination of Pr (III) with epinephrine at pH 6. Absorbance of the resulting orange yellow complex is measured at 482 nm . A graph of absorbance versus concentrations shows that beer 's low is obeyed over the concentration range (1-50)mg.ml-1 of epinephrine with molar absorpitivity of ( 2.180x103 L.mol-1.cm-1 ), a sandell sensitivity of (0.084 mg.cm-2 ), a relative error of (-2.83%) , a corrolation coffecient (r= 0.9989) and recovery % ( 97.03 ± 0.75 ) depending on the concentration.This method is applied to analyse EP in several commercially available pharmaceutical preparations

A simple, accurate and sensitive spectrophotometric method for the determination of Procaine penicillin (PP) is described. The method is based on charge-transfer reaction of PP with metol (N-methyl-p-hydroxy aniline) in the presence of ferric sulphate to form a purple-water soluble complex ,which is stable and has a maximum absorption at 510 nm .A graph of absorbance versus concentration shows that Beer’s low is obeyed over the concentration range of 3-80 µg /ml of PP (i.e.,3-80 ppm) with a molar absorbativity of 4.945 ×103 L.mol-1.cm-1 ,Sandell sensitivity of 0.1190 µg cm-2 ,a relative error of (-1.57)-2.79 % and a standard deviation of less than 0.59 depending on the concentration of PP.The optimum conditions for full co

Metal contents in vegetables are interesting because of issues related to food safety and ‎potential health risks. The availability of these metals in the human body ‎may perform many biochemical functions and some of them linked with various diseases at ‎high levels. The current study aimed to evaluate the concentration of various metals in ‎common local consumed vegetables using ICP-MS. The concentrations of metals in vegetables ‎of tarragon, Bay laurel, dill, Syrian mesquite, vine leaves, thymes, arugula, basil, common ‎purslane and parsley of this study were found to be in the range of, 76-778 for Al, 10-333 for B, 4-119 for ‎Ba, ‎2812‎-24645 for Ca, 0.1-0.32 for Co, 201-464 for Fe, 3661-46400 for K, 0.31–‎‎1.

In high-dimensional semiparametric regression, balancing accuracy and interpretability often requires combining dimension reduction with variable selection. This study intro- duces two novel methods for dimension reduction in additive partial linear models: (i) minimum average variance estimation (MAVE) combined with the adaptive least abso- lute shrinkage and selection operator (MAVE-ALASSO) and (ii) MAVE with smoothly clipped absolute deviation (MAVE-SCAD). These methods leverage the flexibility of MAVE for sufficient dimension reduction while incorporating adaptive penalties to en- sure sparse and interpretable models. The performance of both methods is evaluated through simulations using the mean squared error and variable selection cri

       In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from      Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial

Granulation Technique for Gamma Alumina Catalyst Support was employed in inclined disk granulator (IDG), rotary drum granulator (RD) and extrusion – spheronization equipments .Product with wide size range can be produced with only few parameters like rpm of equipment, ratio of binder and angle of inclination. The investigation was conducted for determination the optimum operating conditions in the three above different granulation equipments.
Results reveal that the optimum operating conditions to get maximum granulation occurred at ( speed: 31rpm , Inclination:420 , binder ratio:225,300% ) for the IDG,( speed: 68rpm , Inclination: 12.50 , binder ratio: 300% ) for the RD and ( speed:1200rpm , time of rotation: 1-2min )for the Caleva