The available experimental data of proton electronic stopping power for Polyethylene, Mylar, Kapton and Polystyrene are compared with Mathematica, SRIM2013, PSTAR and libdEdx programs or databases.  The comparison involves sketching out both experimental and databases data for each polymer to discuss the agreement. Further, we use statistical means via standard deviation resulting from the mean normalized difference to describe the precise agreement among the databases and the experimental data. We found that there is not a specific one database can describe the experimental data for certain material at given proton energy.

Pathological blood clot in blood vessels, which often leads to cardiovascular diseases, are one of the most common causes of death in humans. Therefore, enzymatic therapy to degrade blood clots is vital. To achieve this goal, bromelain was immobilized and used for the biodegradation of blood clots. Bromelain was extracted from the pineapple fruit pulp (Ananas comosus) and purified by ion exchange chromatography after precipitation with ammonium sulphate (0-80 %), resulting in a yield of 70%, purification fold of 1.42, and a specific activity of 1175 U/mg. Bromelain was covalently immobilized on functionalized multi-walled carbon nanotubes (MWCNT), with an enzyme loading of 71.35%. The results of the characterization of free and immobilized

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

The main objective of this paper is to develop and validate flow injection method, a precise, accurate, simple, economic, low cost and specific turbidimetric method for the quantitative determination of mebeverine hydrochloride (MbH) in pharmaceutical preparations.  A homemade NAG Dual & Solo (0-180º) analyser which contains two identical detections units (cell 1 and 2) was applied for turbidity measurements. The developed method was optimized for different chemical and physical parameters such as perception reagent concentrations, aqueous salts solutions, flow rate, the intensity of the sources light, sample volume, mixing coil and purge time. The correlation coefficients (r) of the developed method were 0.9980 and 0.9986 for cell

Titanium dioxide nanotubes were synthesized by anodizing Ti sheets in the ethylene glycol solution and were covered in Pt nanoparticles onto the surface of TiO₂NTs using electrodeposition method from using five derivatives of Mannich base Pt complexes which have been used as precursor of platinum. The mean size, shape, elemental composition of the titanium dioxide nanotubes and platinum deposited on the template were evaluated by different techniques such as field emission scanning electron microscope (FE-SEM), transmission electron microscopy (TEM), X-ray diffraction pattern (XRD), and energy dispersive X-ray (EDX) technique. From all these analyses, the TiO₂NTs prepared and Ptnanoparticles deposited on it were ide

A simple ,accurate and sensitive spectrophotometric method has been developed the determination of Cobalt(II) and Cupper (II) .The method is based on the chelation of Co(II) and Cu(II) ions with 4-(4´-pyrazolon azo) -2-Naphthol(APAN) in aqueous medium . The complexes have a maximum absorption at (513) and (506) nm and ? max 0.531×10 4 and 0.12×10 5 L.mol -1.cm -1 for Co(II) and Cu(II) respectively .The reagent and two complexes have been prepared in ethanolic solution.The stoichiometry of both complexes were found to be 1:2 (metal :legend) .The effects of various cations and anions on Co(II) and Cu(II) determination have been investigated .The stability constants and standard deviations for Co(II) and Cu(II) 0.291 x107 ,0.909X108 L.mol

This article investigates the decline of language loyalty in the age of audiovisual nearness. It is a socio-linguistic review of previous literature related to language disloyalty. It reviews the current theoretical efforts on the impact of audiovisual nearness created by social media and language loyalty. The descriptive design is used. The argument behind this review is that the audiovisual nearness provided by social media negatively affects language loyalty. This article concludes that the current theoretical efforts have paid much attention to the relationship between the audiovisual nearness and language loyalty. Such efforts have highlighted the fact that the social media platforms have provided unprecedented nearness that provoke in