This study focused on the improvement of the quality of gasoline and enhancing its octane number by the reduction of n-paraffins using zeolite 5A. This study was made using batch and continuous mode. The parameters which affected the n-paraffin removal efficiency for each mode were studied. Temperature (30 and 40 ˚C) and mixing time up to 120 min for different amounts of zeolite ranging (10-60 g) were investigated in a batch mode. A maximum removal efficiency of 64% was obtained using 60 g of zeolite at 30 ˚C after a mixing time 120 min. The effect of feed flow rate (0.3-0.8 l/hr) and bed height (10-20 cm) were also studied in a continuous mode. The equilibrium isotherm study was made using different amounts of zeolite (2-20 g) and the

Films of CdSe have been prepared by evaporation technique with thickness 1µm. Doping with Cu was achieved using annealing under argon atmosphere . The Structure properties of these films are investigated by X-ray diffraction analysis. The effect of Cu doping on the orientation , relative intensity, grain size and the lattice constant has been studied. The pure CdSe films have been found consist of amorphous structure with very small peak at (002) plane. The films were polycrystalline for doped CdSe with (1&2wt%) Cu contents and with lattice constant (a=3.741,c=7.096)A°, and it has better crystallinty as the Cu contents increased to (3&5wt%) Cu. The reflections from [(002), (102). (110), (112), and (201)]planes are more prominen

in the present investigation new eight poly esrers as schiff bases wich containing pendant imine group were synthesized by treatment of poly acryloyl chlodire with ethanol amine group were synthesized by treatment of poly acryloyl chloride with ethanol

Acetophenone sulfamethoxazole and 3-Nitrobenzophenone sulfamethoxazole were prepared from the reaction of sulfamethoxazole with two ketones. The prepared ligands were identified by (C.H.N) analysis and UV-VIS, FT-IR spectroscopic techniques. Metal complexes of the two ligands were prepared in an aqueous alcohol with Zn (II), Mn (II) and Cu (II) ions with a molar ratio1:1. The proposed general formula for the resulting complexes was [ML.CL2.H2O]H2O .The complexes were characterized by (C.H.N) technique , spectroscopic methods ,conductivity, atomic absorption ,magnetic susceptibility measurements and melting point. According to the results obtained, the suggested geometry is to be octahedral for all the complexes.

This abstract focuses on the significance of wireless body area networks (WBANs) as a cutting-edge and self-governing technology, which has garnered substantial attention from researchers. The central challenge faced by WBANs revolves around upholding quality of service (QoS) within rapidly evolving sectors like healthcare. The intricate task of managing diverse traffic types with limited resources further compounds this challenge. Particularly in medical WBANs, the prioritization of vital data is crucial to ensure prompt delivery of critical information. Given the stringent requirements of these systems, any data loss or delays are untenable, necessitating the implementation of intelligent algorithms. These algorithms play a pivota

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 internet of medical things (IoMT), which is expected the lead to the biggest technology in worldwide distribution. Using 5th generation (5G) transmission, market possibilities and hazards related to IoMT are improved and detected. This framework describes a strategy for proactively addressing worries and offering a forum to promote development, alter attitudes and maintain people's confidence in the broader healthcare system without compromising security. It is combined with a data offloading system to speed up the transmission of medical data and improved the quality of service (QoS). As a result of this development, we suggested the enriched energy efficient fuzzy (EEEF) data offloading technique to enhance the delivery of dat

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 internet of medical things (IoMT), which is expected the lead to the biggest technology in worldwide distribution. Using 5th generation (5G) transmission, market possibilities and hazards related to IoMT are improved and detected. This framework describes a strategy for proactively addressing worries and offering a forum to promote development, alter attitudes and maintain people's confidence in the broader healthcare system without compromising security. It is combined with a data offloading system to speed up the transmission of medical data and improved the quality of service (QoS). As a result of this development, we suggested the enriched energy efficient fuzzy (EEEF) data offloading technique to enhance the delivery of dat