New types of hydrodesulfurization (HDS) catalyst Re-Ni-Mo/ γ-Al₂O₃ was prepared and tested separately with two prepared conventional HDS catalysts (Ni-Mo/ γ-Al₂O₃ and Co-Mo//γ-Al₂O₃) by using a pilot plant hydrotreatment unit. Activities of three prepared hydrodesulfurization catalysts were examined in hydrodesulfurization (HDS) of atmospheric gas oil at different temperatures 275 to 350 °C and LHSV 1 to 4 h^-1, the reactions conducted under constant pressure 40 bar and H₂/HC ratio 500 ml/ml .Moreover, the hydrogenation of aromatic (HAD) in gas oil has been studied. HDS was much improved by adding promoter Re to the Ni-Mo/Al₂O₃

New types of hydrodesulfurization (HDS) catalyst Re-Ni-Mo/ γ-Al2O3 was prepared and tested separately with two prepared conventional HDS catalysts (Ni-Mo/ γ-Al2O3 and Co-Mo//γ-Al2O3) by using a pilot plant hydrotreatment unit. Activities of three prepared hydrodesulfurization catalysts were examined in hydrodesulfurization (HDS) of atmospheric gas oil at different temperatures 275 to 350 °C and LHSV 1 to 4 h-1, the reactions conducted under constant pressure 40 bar and H2/HC ratio 500 ml/ml .Moreover, the hydrogenation of aromatic (HAD) in gas oil has been studied. HDS was much improved by adding promoter Re to the Ni-Mo/Al2O3 catalyst. The results showed that Re-Ni-Mo/ γ-Al2O3 have more activity in desulfurization than Ni-Mo//γ-Al2O3

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

Background: As a multifactorial disorder, temporomandibular joint (TMD) is difficult to diagnose, and multiple factors affect the joint and cause the temporomandibular disorder. Standardization of clinical diagnosis of TMD should be used to reach a definite clinical diagnosis; the condylar bone may degenerate in accordance with these disorders. Aims: Evaluate the correlation between the clinical diagnosis and degenerative condylar change (flattening, sclerosis, erosion, and osteophyte). Materials and Methods: A prospective study with a study group of 97 TMD patients (total of 194 joints) aged 20 to 50. Patients were sent to cone beam computed tomography (CBCT) to assess the degenerative condylar change. Results: No association was found bet

This research is addressing the effect of different ferrocene concentration (0.00, 2.15x10-3, 4.30x10-3, 8.60x10-3, and 12.9x10-3) on the bulk free radical polymerization of methyl methacrylate monomer in benzene using benzoyl peroxide as initiator. The polymerization was conducted at 60º C under free oxygen atmosphere. The resulting polymers were characterized by FTIR. The results were compared with the presence and absence of ferrocene at 10% conversion. The %conversion was 3.04% with no ferrocene present in the polymerization medium and its increase to 9.06 with a first lowest ferrocene concentration added, i.e. 2.15 x10-3mol/l. This was positively reflected on the poly(methyl methacrylate) molecular weight measured by viscosity techniq

The electrospun nanofibers membranes (ENMs) have gained great attention due to their superior performance. However, the low mechanical strength of ENMs, such as the rigidity and low strength, limits their applications in many aspects which need adequate strength, such as water filtration. This work investigates the impact of electrospinning parameters on the properties of ENMs fabricated from polyacrylonitrile (PAN) solved in N, N-Dimethylformamide (DMF). The studied electrospinning parameters were polymer concentration, solution flow rate, collector rotating speed, and the distance between the needle and collector. The fabricated ENMs were characterized using scanning electron microscopy (SEM) to understand the surface morphology and es

Security concerns in the transfer of medical images have drawn a lot of attention to the topic of medical picture encryption as of late. Furthermore, recent events have brought attention to the fact that medical photographs are constantly being produced and circulated online, necessitating safeguards against their inappropriate use. To improve the design of the AES algorithm standard for medical picture encryption, this research presents several new criteria. It was created so that needs for higher levels of safety and higher levels of performance could be met. First, the pixels in the image are diffused to randomly mix them up and disperse them all over the screen. Rather than using rounds, the suggested technique utilizes a cascad

Experimental measurements of viscosity and thermal conductivity of single layer of graphene . based DI-water nanofluid are performed as a function of  concentrations (0.1-1wt%) and temperatures between (5 to 35ºC). The result reveals that the thermal conductivity of GNPs nanofluids was increased with increasing the nanoparticle weight fraction concentration and temperature, while the maximum enhancement was about 22% for concentration of 1 wt.% at
35ºC. These experimental results were compared with some theoretical models and a good agreement between Nan’s model and the experimental results was observed. The viscosity of the graphene nanofluid displays Newtonian and Non-Newtonian behaviors with respect to nanoparticles concen