In this paper, series of new complexes of Manganese(II), Cobalt(II), Nickel (II) Cupper(II) Zinc(II), Cadmium(II) and Mercury (II) are prepared from the new ligand [2-(3-benzoylthioureido)-3-(-4- hydroxyphenyl) propanoic acid (BHP) derived from tyrosine and benzoylisothiocyanate .Chemical structures are obtained from their 1 H, 13CNMR spectra (for BHP), elemental microanalyses, molar conductance, FTIR, UV–Vis, magnetic susceptibility in addition to TGA/DTG and DSC analysis, the suggested geometry for all complexes was tetrahedral. The biological activity of BHP and its complexes has been extensively studied against two bacterial species Staphylococcus aurous (G+) and Escherichia coli (G-) by agar-well diffusion technique, where Mn(II), Co

New schiff bases series (VIII) a-e and 1,3-thiazolidin-4-one derivatives (IX) a-e containing the 1,2,4-triazole and 1,3,4-thiazazole rings were synthesized and screening their biological activities. These compounds were identified via Fourier transform infrared (FT-IR) spectra, some via Proton nuclear magnetic resonance (1H-NMR) and mass spectra. The biological results indicated that all of these compounds did not reveal antibacterial effectiveness against (Escherichia coli and Klebsiella species) (G-). Some of these compounds showed moderate antibacterial activity against (Staphylococcus aureus, and Staphylococcus epidermidis) (G+), and all compounds exhibited moderate activity against Candida albicans.

Corncob is an agricultural biomass waste that was widely investigated as an adsorbent of contaminants after transforming it into activated carbon. In this research carbonization and chemical activation processes were achieved to synthesize corncob-activated carbon (CAC). Many pretreatment steps including crushing, grinding, and drying to obtain corncob powder were performed before the carbonization step. The carbonization of corncob powder has occurred in the absence of air at a temperature of 500 °C. The chemical activation was accomplished by using HCl as an acidic activation agent. Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), X-ray diffraction (XRD), and Brunauer–Emmett–Teller (BET) facilitate

A theoretical and protection study was conducted of the corrosion behavior of carbon steel surface with different concentrations of the derivative (Quinolin-2-one), namly (1-Amino-4,7-dimethyl-6-nitro-1H-quinolin-2-one (ADNQ2O)). Theoretically, Density Functional Theory (DFT) of B3LYP/ 6-311++G (2d, 2p) level was used to calculate the optimized geometry, physical properties and chemical inhibition parameters, with the local reactivity to predict both the reactive centers and to locate the possible sites of nucleophilic and electrophilic attacks, in vacuum, and in two solvents (DMSO and H2O), all at the equilibrium geometry. Experimentally, the inhibition efficiencies (%IE) in the saline solution (of 3.5%) NaCl were studied using potentiomet

This study aimed to conduct a comparative analysis of seven calliphorid species of third- instar larvae that were collected from central Iraq. The purpose was to morphologically characterize these species and develop an identification key to differentiate them from closely related species. Scanning Electron Microscope (SEM) graphical images and high-resolution traditional optical microscopes were used to analyze the morphological characteristics of the species Calliphora vomitoria (Linnaeus, 1758), C. vicina Robineau-Desvoidy, 1830, Chrysomya albiceps (Widemann, 1819), Ch. rufifacies (Macquart, 1844), Ch. megacephala (Fabricius, 1794), Lucilia cuprina (Wiedemann, 1830), and L. sericata (Meigen, 1826). An identification key was const

This paper aims to decide the best parameter estimation methods for the parameters of the Gumbel type-I distribution under the type-II censorship scheme. For this purpose, classical and Bayesian parameter estimation procedures are considered. The maximum likelihood estimators are used for the classical parameter estimation procedure. The asymptotic distributions of these estimators are also derived. It is not possible to obtain explicit solutions of Bayesian estimators. Therefore, Markov Chain Monte Carlo, and Lindley techniques are taken into account to estimate the unknown parameters. In Bayesian analysis, it is very important to determine an appropriate combination of a prior distribution and a loss function. Therefore, two different

1-Objective:- Polyphenols are biochemical compounds with antioxidant activity against differences diseases related to Lipid peroxidation such as diabetes mellitus. Polyphenols distributed widely in medical plants, the aim of the study is to extract and analyze some polyphenolic compounds from grape seeds and examine their effects on (STZ) induced diabetic mice. 2-Methods:- In the present study , a group of polyphenols has been extracted from Iraq

This article aims to estimate the partially linear model by using two methods, which are the Wavelet and Kernel Smoothers. Simulation experiments are used to study the small sample behavior depending on different functions, sample sizes, and variances. Results explained that the wavelet smoother is the best depending on the mean average squares error criterion for all cases that used.

Abstract

   The method binery logistic regression and linear discrimint function of the most important statistical methods used in the classification and prediction when the data of the kind of binery (0,1) you can not use the normal regression therefore resort to binary logistic regression and linear discriminant function in the case of two group in the case of a Multicollinearity problem between the data (the data containing high correlation) It became not possible to use binary logistic regression and linear discriminant function, to solve this problem, we resort to Partial least square regression.

In this, search th

Diabetic retinopathy is an eye disease in diabetic patients due to damage to the small blood vessels in the retina due to high and low blood sugar levels. Accurate detection and classification of Diabetic Retinopathy is an important task in computer-aided diagnosis, especially when planning for diabetic retinopathy surgery. Therefore, this study aims to design an automated model based on deep learning, which helps ophthalmologists detect and classify diabetic retinopathy severity through fundus images. In this work, a deep convolutional neural network (CNN) with transfer learning and fine tunes has been proposed by using pre-trained networks known as Residual Network-50 (ResNet-50). The overall framework of the proposed