In this work , the ligand [N-(4-Methoxybenzoyl amino)-thioxomethyl] Methionine acid  has been synthesized by the reaction of 4- Methoxybenzoyl isothiocyanate with methionine acid . The metal complexes were prepared through the reaction of metals chlorides of Co(II) , Ni(II), Cu(II), Zn(II) and Cd(II) in ethanol as solvent . The ligand (MbM) and its metal complexes have been characterized by elemental analysis (CHNS), IR, 1H-13CNMR and UV- Vis spectra, magnetic susceptibility measurements, molar conductivity, melting points and atomic absorption. The metal-ligand ratio was determined by mole ratio method. The suggested structures for the Co(II), Ni(II), Cd(II) and Zn(II) complexes are tetrahedral geometry and  the Cu(II) complex

A numerical algorithm for solving linear and non-linear fractional differential equations is proposed based on the Bees algorithm and Chebyshev polynomials. The proposed algorithm was applied to a set of numerical examples. Faster results are obtained compared to the wavelet methods.

The research includes the synthesis and identification of the mixed ligands complexes of M(II) Ions in general composition [M(Lyn)2(phen)] Where L- lysine (C6H14N2O2) commonly abbreviated (LynH) as a primary ligand and 1,10-phenanthroline(C12H8N2) commonly abbreviated as "phen," as a secondary ligand . The ligands and the metal chlorides were brought in to reaction at room temperature in ethanol as solvent. The reaction required the following molar ratio [(1:1:2) (metal): phen:2 Lyn -] with M(II) ions, were M = Mn(II),Cu(II), Ni(II), Co(II), Fe(II) and Cd(II). Our research also includes studying the bio–activity of the some complexes prepared against pathogenic bacteria Escherichia coli(-),Staphylococcus(-) , Pseudomonas (-), Bacillus (-)

New Schiff base ligand 2-((4-amino-5-(3, 4, 5-trimethoxybenzyl) pyrimidin- 2-ylimino) (phenyl)methyl)benzoic acid] = [HL] was synthesized using microwave irradiation trimethoprim and 2-benzoyl benzoic acid. Mixed ligand complexes of Mn((ІІ), Co(ІІ), Ni(ІІ), Cu(ІІ), Zn(ІІ) and Cd(ІІ) are reacted in ethanol with Schiff base ligand [HL] and 8-hydroxyquinoline [HQ] then reacted with metal salts in ethanol as a solvent in (1:1:1) ratio. The ligand [HL] is characterized by FTIR, UV-Vis, melting point, elemental microanalysis (C.H.N), 1H-NMR, 13C-NMR, and mass spectra. The mixed ligand complexes are characterized by infrared spectra, electronic spectra, (C.H.N), melting point, atomic absorption, molar conductance and magnetic moment me

A series of coordination compounds of Zr(IV), Cd(II) and Sn(II) ions with 4-(((3-mercapto-5-phenyl-4H-1,2,4-triazole-4-yl)imino)methyl)-2-methoxyphenol, as a ligand has been successfully prepared in alcoholic medium. The prepared complexes were characterized quantitatively and qualitatively by using: elemental analysis CHNS, FT-IR spectroscopy, UV-visible spectroscopy, 1H and 13CNMR, atomic absorption measurements, magnetic susceptibility, thermal analysis)TG and DTG) and conductivity measurements. This ligand coordinates as a bidentate that to the metal ions through sulphur and nitrogen of (azomethine group) atoms. According to the spectral data, Cd(II)- and Sn(II)-complexes have coordination of 6 with octahedral geometry while the Zr(I

Abstract:

Most of the studies on this subject, small industrial projects, by researchers and scholars in the economic field show the great and increasing importance of doing this kind of projects, the extent of which can be determined by the contribution of these projects to indicators and macroeconomic and sectorial variables. So this research aims to show the extent of the economic contribution of projects in selected international experiences and in the Iraqi economy. As international experiences have provided the opportunity for the progress and growth of small projects in their economies, which led to an increase in the contribution of these projects in the recruitment of economically active manpower, in added

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

The COVID-19 pandemic has necessitated new methods for controlling the spread of the virus, and machine learning (ML) holds promise in this regard. Our study aims to explore the latest ML algorithms utilized for COVID-19 prediction, with a focus on their potential to optimize decision-making and resource allocation during peak periods of the pandemic. Our review stands out from others as it concentrates primarily on ML methods for disease prediction.To conduct this scoping review, we performed a Google Scholar literature search using "COVID-19," "prediction," and "machine learning" as keywords, with a custom range from 2020 to 2022. Of the 99 articles that were screened for eligibility, we selected 20 for the final review.Our system

     is at an all-time high in the modern period, and the majority of the population uses the Internet for all types of communication. It is great to be able to improvise like this. As a result of this trend, hackers have become increasingly focused on attacking the system/network in numerous ways. When a hacker commits a digital crime, it is examined in a reactive manner, which aids in the identification of the perpetrators. However, in the modern period, it is not expected to wait for an attack to occur. The user anticipates being able to predict a cyberattack before it causes damage to the system. This can be accomplished with the assistance of the proactive forensic framework presented in this study. The proposed system combines

The research problem arose from the researchers’ sense of the importance of Digital Intelligence (DI), as it is a basic requirement to help students engage in the digital world and be disciplined in using technology and digital techniques, as students’ ideas are sufficiently susceptible to influence at this stage in light of modern technology. The research aims to determine the level of DI among university students using Artificial Intelligence (AI) techniques. To verify this, the researchers built a measure of DI. The measure in its final form consisted of (24) items distributed among (8) main skills, and the validity and reliability of the tool were confirmed. It was applied to a sample of 139 male and female students who were chosen