The economic units always sought to maintain its market position and Trchinh the technology management and modern methods that will support success factors .vdila about it has become a customer and one profitability analysis of the most practical way benefit of economic units as modern management focus their attention on achieving this satisfaction, as the customers make up the axis of the success of every organization and that there are many government units aiming to profit directs attention to customers and the number of these units increased continuously. The administration used the customer profitability analysis in order to obtain information to assist in making and decision-making process. How to use modern tec

In this study, a low-cost biosorbent, dead mushroom biomass (DMB) granules, was used for investigating the optimum conditions of Pb(II), Cu(II), and Ni(II) biosorption from aqueous solutions. Various physicochemical parameters, such as initial metal ion concentration, equilibrium time, pH value, agitation speed, particles diameter, and adsorbent dosage, were studied. Five mathematical models describing the biosorption equilibrium and isotherm constants were tested to find the maximum uptake capacities: Langmuir, Freundlich, Redlich-Peterson, Sips, and Khan models. The best fit to the Pb(II) and Ni(II) biosorption results was obtained by Langmuir model with maximum uptake capacities of 44.67 and 29.17 mg/g for these two ions, respectively, w

Abstract :In this study, amygdaline in Iraqi plant seeds was extracted and isolated from their seeds matrix using reflux procedure and subsequently identified and determined by high performance liquid chromatography (HPLC) on reversed phase column of LC-18 (150mm x 4.6mm, 5?m )with actonitrile :water ( 50 : 50 ) as mobile phase at flow rate of ( 0.5 mL/min ) and detection at wavelength of 215 nm.The experimental results indicated that the linearity of calibration is in the range of 1.0-30.0 mg L-1amygdaline with the correlation coefficient of 0.9949. The limit of detection (LOD) and limit of quantitation (LOQ) for amygdaline were of 0.88 and 2.93 mg L-1 in standard pure sample. The mean recovery percent is 97.34±0.58 at 95% confidence inte

The current study used extracts from the aloe vera (AV) plant and the hibiscus sabdariffa flower to make Ag-ZnO nanoparticles (NPs) and Ag-ZnO nanocomposites (NCs). Ag/ZnO NCs were compared to Ag NPs and ZnO NPs. They exhibited unique properties against bacteria and fungi that aren't present in either of the individual parts. The Ag-ZnO NCs from AV showed the best performance against E. coli, with an inhibition zone of up to 27 mm, compared to the other samples. The maximum absorbance peaks were observed at 431 nm and 410 nm for Ag NPs, at 374 nm and 377 nm for ZnO NPs and at 384 nm and 391 nm for Ag-ZnO NCs using AV leaf extract and hibiscus sabdariffa flower extract, respectively. Using field emission-scanning electron microscopes (FE-

This study was conducted on Lake Hamrin situated in Diyala governorate, focal Iraq, between latitudes 44^º 53ʹ 26.16 '- 45º 07 ʹ 28.03^ʺ and 34º 04ʹ 24.75^ʺ ــ 34º 19ʹ 12.74^ʺ . As in this study, the surface area of Hamrin Lake was calculated from satellite images during the period from October 2019 to September 2020, with an average satellite image for each month, furthermore,by utilizing the Normalized Differences Water Index (NDWI), the largest surface area was 264,617 km² for October and the lowest surface area 140.202 km² for September. The surface temperature of the lake water was also calculated from satellite images of the Landsat 8 satellite, based on ban

In this paper, integrated quantum neural network (QNN), which is a class of feedforward

neural networks (FFNN’s), is performed through emerging quantum computing (QC) with artificial neural network(ANN) classifier. It is used in data classification technique, and here iris flower data is used as a classification signals. For this purpose independent component analysis (ICA) is used as a feature extraction technique after normalization of these signals, the architecture of (QNN’s) has inherently built in fuzzy, hidden units of these networks (QNN’s) to develop quantized representations of sample information provided by the training data set in various graded levels of certainty. Experimental results presented here show that