Muthanna Journal of Pure Science (MJPS)

In this paper, we present a new complex double integral transform namely “Complex Double Sadik Transform”, to solve general linear partial differential equations. Several functions are used (applied) to show the usefulness of this new double transformation.

The optical properties of a cadmium selenide (CdSe) film doped with aluminum (Al) at a rate of (3%) have been studied. It was deposited on glass substrates at 250 K with a thickness of 0.15 µm and a deposition rate of 2± 0.1 m.sec-1 using a thermal evaporation technique to create the compound. The optical characteristics of the produced film were investigated after being subjected to irradiation by 50 mW, 1064 nm IR laser for 15 minutes at a distance of 0.5 m. Through monitoring the absorbance and transmission spectra of prepared thin film across the wavelength range (190-1100 nm), the optical energy gap value for the permitted direct electronic transitions was determined, and the absorption and reflectivity coefficients were computed and investigated. The optical experiments included computing the optical constants, and result explained that the optical energy gap decreased from (1.76 eV) to (1.64 eV) following irradiation (extinction coefficient, refractive index, and absorption coefficient).

The presence of COVID-19 infection and its threat to human life has led to the urgent need to contribute to combating it, so global initiatives have emerged to regulate and manage the virus, with the aim of eliminating it once and for all. Since the use of image processing has succeeded in solving many complicated problems in the medical field, it can now be employed to aid in the fight against COVID-19 and control it by extracting the relevant features collected from medical imaging datasets and categorizing using machine learning and deep learning approaches in order to diagnose and predict the COVID-19 disease. This study provides an overview of current findings on machine learning methods that have been applied to COVID-19 and aspects related to the types of algorithms, the medical dataset used, and the accuracy of researchers’ results. This paper, review the latest proposed models for COVID-19 classification and diagnosis applied to CT scans and chest X-ray images to provide further insight into disease reduction. COVID-19 diagnosis is critical for identifying an infected person and preventing the virus from spreading. Because diffusion is rapid, an automated rapid diagnostic mechanism is required to deal with a huge population so image processing methods are the most suitable technology for this. These models’ accuracy ranged from 80.6% to 100%, demonstrating that image- processing methods can be used to assess and diagnose COVID-19 clinically.

This work included the preparation of a new ligand of heterogeneous azo ligands derived from thiazole 2- [2‾-(4,5-Dimethyl thaizolyl azo]-5-Amino Anilin (DMeTAAA) and some of its metallic complexes were diagnosed by means of the infrared spectrum, mass spectrum, nuclear magnetic resonance proton spectra, ultraviolet-visible spectrum, thermogravimetric analysis, and in addition to the careful analysis of the elements. Eight metal complexes were prepared by reaction the prepared ligand with Chromium (III), Iron (II), Cobalt (III), Nickel (II), Copper (II), Zinc (II), mercury (II) and silver (I). The effect of the solvent on ligand has been studied and the results have indicated that all the obtained complexes possess high stability. The percentage of metallic ions in the prepared complexes was obtained by the flame atomic absorption spectrometer. A careful analysis of the elements showed a great compatibility between the theoretically calculated and practically obtained ratios. The results of magnetic sensitivity showed that Cr(III),Ni(II) and Cu(II)-complexes possessed magnetic para qualities while the other complexes possessed magnetic dia qualities, as well as the molar conductivity of the prepared complexes was studied and the results showed that all complexes possess the ionic Based on the results obtained, the synthetic formulas of chelated complexes were proposed revealing that the investigated thiazolele azo ligand behaves as a triple ligand.

In recent years, there has been a growing interest in the synthesis and characterization of nanomaterials for various applications. Carbon nanotubes (CNTs) and quantum dots (QDs) are among the promising nanomaterials due to their unique properties. This study aims to synthesize and characterize multi-walled CNTs and QDs using the hydrothermal method. MWCNTs were synthesized at a temperature of 200°C using Ferrocene and Sulfur as precursors, while QDs were prepared at a temperature of 160°C using Polyethylene glycol (PEG). To fabricate the nanocomposite films, MWCNTs and QDs were mixed at different weight ratios (3:1, 2:2, and 1:3) through ultrasonic dispersion. The resulting mixture was then used to form films on glass surfaces. Characterization of the composites was carried out using a series of analyses including Fourier-transform infrared spectroscopy (FT-IR), field emission scanning electron microscopy (FESEM), X-ray diffraction (XRD), and electrical conductivity measurements. From the FESEM measurements, spherical particles of various sizes were observed, with a wide size distribution of less than 10nm. XRD analysis revealed d-spacing values of 0.34nm, 0.39nm, and 0.46nm for all the mentioned weight ratios. Additionally, the diameter of the particles ranged from 32.48nm to 31.96nm, with a length of approximately 1µm. The electrical conductivity of the films was also measured, and the obtained results indicated activation energies of 0.155eV, 0.14eV, and 0.25eV for the respective weight ratios. The resulting nanocomposite films exhibited unique properties as characterized by various techniques. These findings contribute to the understanding and potential applications of nanomaterials in diverse