The present research investigates hydrodesulfurization (HDS) and hydrodearomatization (HAD) of raw kerosene with 0.364 wt. % sulfur and 16.498 wt. % aromatics supplied from Al-Dura Refinery. Hydrotreating reactions were carried out in one stage reactor over high metal  Ni W/γ-Al2O3 prepared catalyst at different temperatures 275-350oC, pressures 32-62 bar, LHSV 1-4 hr-1, and H2/HC ratios 200-500. The prepared catalyst was pre-sulfided remarkably at low temperature and high pressure to avoid risky of sintering associated with gaining the active sulfide phase at high temperatures.  Results showed an obvious differences between HDS and HAD rates due to difficulty of polyaromatics saturation compared with desulfurization. Sulfur and

This research aims to find how three different types of mouthwashes affect the depth of artificial white spot lesions. Teeth with various depths of white spot lesions were immersed in either splat mouthwash, Biorepair mouthwash, Sensodyne mouthwash, or artificial saliva (control)twice daily for one minute for 4 weeks and 8 weeks at 37°C. After this immersion procedure, lesion depth was measured using a diagnosed pen score. A one-way analysis of variance, Dunnett T3 and Tukey's post hoc α = .05 were used to analyze the testing data. Splat mouthwash enhanced the WSL remineralization and made the lowest ΔF compared with other mouthwashes in shallow and deep enamel after 4 and 8 weeks of treatment. In the repair groups, after 4 weeks

A simple, rapid spectrophotometric method has been established for the determination of chlorpromazine hydrochloride (CPZ) in its pure form and in a tablet formulations. The  suggested  method  is  based  on  the  oxidative coupling  reaction  with4-nitroainlline using KIO₃ in acidic solution to produce a violet colored product with maximum absorption at λ=526 nm.The  analytical data  obtained  throughout  this  study  could  be  summarid  as  follows:  1ml of 1M HCl (pH=2.2), 1 ml  of  4-nitroanilline (1x10^-2M), and 1.5ml  of (1x10^-2)KIO₃ per 25 ml reaction medium. The order of a

Multiple sclerosis (MS) is a chronic, inflammatory demyelinating disease of central nervous system with complex etiopathogenesis that impacts young adults (Lee et al., 2015), and MS impacts younger and middle aged character and leads to a range of disabilities that can alter their daily routines (Yara et al, 2010). Although, the exact cause of MS is still undetermined, the disease is mediated by adaptive immunity through the infiltration of T cells into the central nervous system (Bjelobaba et al, 2017). MS causes the Focal neurological symptomsand biochemical changes in the molecular level and the variation of neural cells such as loss or alteration of sensation, motor function, visible signs such as blurred vision or transient blindness,

Infrared photoconductive detectors working in the far-infrared region and room temperature were fabricated. The detectors were fabricated using three types of carbon nanotubes (CNTs); MWCNTs, COOH-MWCNTs, and short-MWCNTs. The carbon nontubes suspension is deposited by dip coating and drop–casting techniques to prepare thin films of CNTs. These films were deposited on porous silicon (PSi) substrates of n-type Si. The I-V characteristics and the figures of merit of the fabricated detectors were measured at a forward bias voltage of 3 and 5 volts as well as at dark and under illumination by IR radiation from a CO2 laser of 10.6 μm wavelengths and power of 2.2 W. The responsivity and figures of merit of the photoconductive detector

   Due to their recalcitrant characteristics, Azo dyes such as methyl orange (MO) are extremely poisonous substances, making their removal from textile industry wastewater a major problem. By employing various EC-Adsorption combined system configurations and reusing alum sludge as an adsorbent, the current study seeks to investigate the efficiency of these various systems in removing MO dye. To estimate their benefits and limitations, experiments were carried out utilizing nickel foam (NiF) and aluminum plate (Al plate) as anodes, and stainless-steel mesh (SS mesh) as cathode in the presence of alum sludge as an adsorbent in all systems. The EC-Adsorption combined system with NiF as anode and two SS meshes as cathodes with 10 g/L

A new Schiff bases ligand 4- ((2-hyolroxy phenylimino) methyl) -2, 6-dimethoxyphenol  derived from condensation of 2- amino phenol with 4-hydroxy -3, 5-dimethoxy benzaldehyde have been synthesized and characterized by  spectroscopy,  spectra, Mass spectrum and elemental microanalysis (C.H.N). Metal Complexes with ions have been also synthesized and characterized spectroscopic methods  spectroscopy, flame atomic absorption, molar conductivity measurements and magnetic susceptibility. These studies indicate that the moler ratio  for the complexes. The complexes  showed characteristics octahedral geometry with the (O,N) ligand coordinated in bidentate mode while with  showed square planer. The enzyme activity of the ligand and i

After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings

The rapid rise in the use of artificially generated faces has significantly increased the risk of identity theft in biometric authentication systems. Modern facial recognition technologies are now vulnerable to sophisticated attacks using printed images, replayed videos, and highly realistic 3D masks. This creates an urgent need for advanced, reliable, and mobile-compatible fake face detection systems. Research indicates that while deep learning models have demonstrated strong performance in detecting artificially generated faces, deploying these models on consumer mobile devices remains challenging due to limitations in computing power, memory, privacy, and processing speed. This paper highlights several key challenges: (1) optimiz

Analyzing sentiment and emotions in Arabic texts on social networking sites has gained wide interest from researchers. It has been an active research topic in recent years due to its importance in analyzing reviewers' opinions. The Iraqi dialect is one of the Arabic dialects used in social networking sites, characterized by its complexity and, therefore, the difficulty of analyzing sentiment. This work presents a hybrid deep learning model consisting of a Convolution Neural Network (CNN) and the Gated Recurrent Units (GRU) to analyze sentiment and emotions in Iraqi texts. Three Iraqi datasets (Iraqi Arab Emotions Data Set (IAEDS), Annotated Corpus of Mesopotamian-Iraqi Dialect (ACMID), and Iraqi Arabic Dataset (IAD)) col