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Synthesis and characterization of Au nanoparticles for nanomedicine application
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Gold nanoparticles AuNPs have proven to be powerful tools in various nanomedicine applications, because of their photo-optical distinctiveness and biocompatibility. Noble metal gold nanoparticles was prepared by pulsed laser ablation method (1064-Nd: YAG with various Laser power from 200 to 800 mJ and 1 Hz frequency) in distil water. The process was characterized using UV-VIS absorption spectroscopy. Morphology and average size of nanoparticles were estimated using AFM and X-ray diffraction (XRD) analysis which show the nature of gold nanoparticles (AuNPs). Antibacterial activity of gold nanoparticles as a function of particles concentration against gram negative bacterium Escherichia coli and gram positive bacterial Staphylococcus aureus was carried out in solid growth media. Gold nanoparticles show high antibacterial activity with zone of inhibition. Antibacterial activities of the synthesized Au nanoparticles were assessed by agar well diffusion method. The stabilized AuNPs exhibited excellent antibacterial sensitivity (12-27 mm) to E. coli and (26-38mm) to Staph aureus.

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Publication Date
Wed Mar 01 2023
Journal Name
International Journal Of Nonlinear Analysis And Applications
The use of ARIMA, ANN and SVR models in time series hybridization with practical application
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Forecasting is one of the important topics in the analysis of time series, as the importance of forecasting in the economic field has emerged in order to achieve economic growth. Therefore, accurate forecasting of time series is one of the most important challenges that we seek to make the best decision, the aim of the research is to suggest employing hybrid models to predict daily crude oil prices. The hybrid model consists of integrating the linear component, which represents Box Jenkins models, and the non-linear component, which represents one of the methods of artificial intelligence, which is the artificial neural network (ANN), support vector regression (SVR) algorithm and it was shown that the proposed hybrid models in the predicti

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Publication Date
Sun Jan 01 2023
Journal Name
International Journal Of Nonlinear Analysis And Applications
The use of ARIMA, LSTM and GRU models in time series hybridization with practical application
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The importance of forecasting has emerged in the economic field in order to achieve economic growth, as forecasting is one of the important topics in the analysis of time series, and accurate forecasting of time series is one of the most important challenges in which we seek to make the best decision. The aim of the research is to suggest the use of hybrid models for forecasting the daily crude oil prices as the hybrid model consists of integrating the linear component, which represents Box Jenkins models and the non-linear component, which represents one of the methods of artificial intelligence, which is long short term memory (LSTM) and the gated recurrent unit (GRU) which represents deep learning models. It was found that the proposed h

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Publication Date
Thu Dec 01 2022
Journal Name
Int. J. Agricult. Stat. Sci.
STRAWBERRY CV. FESTIVAL GROWTH IN RESPONSES TO MAGNETIC WATER AND FOLIAR APPLICATION OF COCONUT EXTRACT
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This study was carried out to study effect of magnetic water ( M0 and M) and different concentrations of coconut extract in Fragaria x ananassa (Duch) C.V Festival. The results showed significant differences in the plants treated with magnetic water ( 0.12 Tesla) and different concentrations of coconut extract C1 (0%), C2 (2.5%), C3 (5%), C4 (7.5%) and C5 (10%) in vegetative parameters as in leaf area and chlorophyll in treatment M0C3 was (53.72 Dcm2, 50.00), respectively, highest leaf number and plant dry weight in MC4 (12.77,14.22 gm), respectively. Results recorded significant differences in fruit parameters such as weight in MC1 (18.97 gm). The maximum fruit number was in MC3 (110), the greatest fruit size was in MC4 (15.78 cm3) and the

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Publication Date
Thu Feb 01 2018
Journal Name
Journal Of Economics And Administrative Sciences
The Comparison between the BEKK and DVECH Models of Multivariate GARCH Models with Practical Application
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The Purpose of this research is a comparison between two types of multivariate GARCH models BEKK and DVECH to forecast using financial time series which are the series of daily Iraqi dinar exchange rate with dollar, the global daily of Oil price with dollar and the global daily of gold price with dollar for the period from 01/01/2014 till 01/01/2016.The estimation, testing and forecasting process has been computed through the program RATS. Three time series have been transferred to the three asset returns to get the Stationarity, some tests were conducted including Ljung- Box, Multivariate Q and Multivariate ARCH to Returns Series and Residuals Series for both models with comparison between the estimation and for

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Publication Date
Wed Feb 01 2023
Journal Name
International Journal Of Advanced And Applied Sciences
Application of the updated DeLone and McLean IS success method to investigate e-CRM effectiveness
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Information and communication technology has a significant influence on employee procedures. Businesses are investing in e-CRM technologies, yet it is difficult to assess the performance of their e-CRM platforms. The DeLone and McLean Information Systems Success framework can be modified to the current e-CRM assessment difficulties. The new framework's different aspects provide a concise framework for organizing the e-CRM key metrics identified in this study. The purpose of this study is to apply and verify that the Updated DeLone and McLean IS Model can be employed to explain e-CRM adoption among employees, along with the extended Updated DeLone and McLean Model with its five output factors, namely system quality, service quality,

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Publication Date
Sat May 27 2017
Journal Name
Journal Of Pharmaceutical Sciences And Research
Synthesis, Preliminary Antimicrobial Evaluation and Molecular Docking of new Schiff bases of Ceftizoxime
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Schiff bases of Ceftizoxime sodium were synthesized in an attempt to improve the antimicrobial spectrum of Ceftizoxime. Aminothiazole ring of Ceftizoxime is linked directly through an imino group to different aromatic aldehydes reacted by nucleophilic addition using trimethylamine (TEA), as a catalyst and refluxed in methanol. The antimicrobial activity was evaluated for such Schiff bases using disc diffusion method. Molecular docking was conducted on certain penicillin-binding proteins (PBPs) and carboxypeptidases using 1- click docking software. Schiff bases of Ceftizoxime were prepared with reasonable yields and their chemical structures were confirmed by spectral analysis (FTIR, 1H-NMR) and elemental microanalysis (CHNS). The antibacter

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Publication Date
Fri Apr 01 2022
Journal Name
Separation And Purification Technology
Application of central composite design approach for optimisation of zinc removal from aqueous solution using a Flow-by fixed bed bioelectrochemical reactor
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Publication Date
Fri Apr 01 2022
Journal Name
Separation And Purification Technology
Application of central composite design approach for optimisation of zinc removal from aqueous solution using a Flow-by fixed bed bioelectrochemical reactor
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Publication Date
Sat Oct 01 2022
Journal Name
Colloids And Surfaces A: Physicochemical And Engineering Aspects
Application of natural deep eutectic solvents in bulk liquid membrane system for removal of free glycerol from crude fatty acid methyl ester
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Fatty Acid Methyl Ester (FAME) produced from biomass offers several advantages such as renewability and sustainability. The typical production process of FAME is accompanied by various impurities such as alcohol, soap, glycerol, and the spent catalyst. Therefore, the most challenging part of the FAME production is the purification process. In this work, a novel application of bulk liquid membrane (BLM) developed from conventional solvent extraction methods was investigated for the removal of glycerol from FAME. The extraction and stripping processes are combined into a single system, allowing for simultaneous solvent recovery whereby low-cost quaternary ammonium salt-glycerol-based deep eutectic solvent (DES) is used as the membrane phase.

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Publication Date
Thu Nov 03 2022
Journal Name
Sensors
A Novel Application of Deep Learning (Convolutional Neural Network) for Traumatic Spinal Cord Injury Classification Using Automatically Learned Features of EMG Signal
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In this study, a traumatic spinal cord injury (TSCI) classification system is proposed using a convolutional neural network (CNN) technique with automatically learned features from electromyography (EMG) signals for a non-human primate (NHP) model. A comparison between the proposed classification system and a classical classification method (k-nearest neighbors, kNN) is also presented. Developing such an NHP model with a suitable assessment tool (i.e., classifier) is a crucial step in detecting the effect of TSCI using EMG, which is expected to be essential in the evaluation of the efficacy of new TSCI treatments. Intramuscular EMG data were collected from an agonist/antagonist tail muscle pair for the pre- and post-spinal cord lesi

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