The aim of the present study was to characterize the Iraqi Tribulus terrestris for the presence of biologically active phyto-chemicals using methanolic extracts of the plant (aerial parts) by Gas Chromatography –Mass spectrometry (GC/MS), while the mass spectra of the compounds found in the extract was matched with the National Institute of Standards and Technology (NIST) library , in addition to study the antioxidant activity of plant extract , results confirmed the presence of therapeutically potent compounds in the Iraqi Tribulus terrestris extract predominantly alkaloids, flavonoids, saponins, tannins and terpenoids. Antioxidant potential of Iraqi Tribulus terrestris

Infertilityis oneuof the most problemsathatufacingaadvancedunations. In the general, about halfof allacasesaof the infertility are causedby factors thaturelated toathe male partner. Propos educausesvofumalev infertility include evgeneticuand environmental factors. Blood samples from 64 infertileumen allawere living in urban its al-Fallujah city (30 azospermeiauand 34 oligospermeia) and 32 fertile men (asuthe control group) were collected. Heavy metal concentrations inusera of infertile and fertile groupswereumeasured by using Atomic Absorption Spectrophotometer. Y- chromosomemicrodeletions were detected by using PCR techniques. Significantdifferences (P?0.05)uin the concentration ofucopper (0.0267±0.0147 and 0.0278±0.0273, for inf

Infertilityis oneuof the most problemsathatufacingaadvancedunations. In the general, about halfof allacasesaof the infertility are causedby factors thaturelated toathe male partner. Propos educausesvofumalev infertility include evgeneticuand environmental factors. Blood samples from 64 infertileumen allawere living in urban its al-Fallujah city (30 azospermeiauand 34 oligospermeia) and 32 fertile men (asuthe control group) were collected. Heavy metal concentrations inusera of infertile and fertile groupswereumeasured by using Atomic Absorption Spectrophotometer. Y- chromosomemicrodeletions were detected by using PCR techniques. Significantdifferences (P?0.05)uin the concentration ofucopper (0.0267±0.0147 and 0.0278±0.0273, for infertileua

In the present study, multi-walled carbon nanotubes (MWCNTs) with outside diameters of< 8 nm and 20−30 nm were covalently functionalized with β-Alanine using a novel synthesis procedure. The functionalization process was proved successful using Raman spectroscopy, FTIR, and TEM. Utilizing the two-step method with ultrasonication, the MWCNTs treated with β-Alanine (Ala-MWCNTs) with weight concentrations of 0.025%, 0.05%, 0.075%, and 0.1% were dispersed in distilled water to prepare water-based nanofluids. The aqueous colloidal dispersions of pristine MWCNTs were unstable. While for Ala-MWCNTs and after> 50 days from preparation, higher colloidal stability was obtained up to relative concentration of 0.955 and 0.939 for the 0.075-wt% samp

A novel technique Sumudu transform Adomian decomposition method (STADM), is employed to handle some kinds of nonlinear time-fractional equations. We demonstrate that this method finds the solution without discretization or restrictive assumptions. This method is efficient, simple to implement, and produces good results. The fractional derivative is described in the Caputo sense. The solutions are obtained using STADM, and the results show that the suggested technique is valid and applicable and provides a more refined convergent series solution. The MATLAB software carried out all the computations and graphics. Moreover, a graphical representation was made for the solution of some examples. For integer and fractional order problems, solutio

       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