Non-orthogonal Multiple Access (NOMA) is a multiple-access technique allowing multiusers to share the same communication resources, increasing spectral efficiency and throughput. NOMA has been shown to provide significant performance gains over orthogonal multiple access (OMA) regarding spectral efficiency and throughput. In this paper, two scenarios of NOMA are analyzed and simulated, involving two users and multiple users (four users) to evaluate NOMA's performance. The simulated results indicate that the achievable sum rate for the two users’ scenarios is 16.7 (bps/Hz), while for the multi-users scenario is 20.69 (bps/Hz) at transmitted power of 25 dBm. The BER for two users’ scenarios is 0.004202 and 0.001564 for

This paper proposes a hybrid artificial intelligence (AI) model that combines an Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) to predict the Quality of Service (QoS) in 5G networks. The model utilizes radio channel indicators such as Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), Received Signal Strength Indicator (RSSI), and Channel Quality Indicator (CQI) to forecast throughput and latency levels. These indicators are critical factors affecting network performance; however, the nonlinear relationship among them makes traditional analytical models inadequate for accurate QoS prediction. The importance of the current study lies in estimating QoS in 5G networks by combining

The permeability is the most important parameter that indicates how efficient the reservoir fluids flow through the rock pores to the wellbore. Well-log evaluation and core measurements techniques are typically used to estimate it. In this paper, the permeability has been predicted by using classical and Flow zone indicator methods. A comparison between the two methods shows the superiority of the FZI method correlations, these correlations can be used to estimate permeability in un-cored wells with a good approximation.

The current study was conducted for studying the impact of cold plasma on the expression level of three genes that participate in the biosynthesis of the phenylpropanoid pathway in Ocimum basilicum. These studied genes were cinnamate 4-hydroxylase (c4h), 4-coumarate CoA ligase (4cl), and eugenol O-methyl transferase (eomt). Also, the cold plasma impact was studied on the essential oil components and their relation with the gene expression level. The results demonstrated that cold plasma seeds germination of the treated groups 2 (initially for 3 minutes and 3 minutes after 7 days) ,and group 3(initially for 5 minutes and 3 minutes after 7 days)  were faster than the control group. Also, the height average of the mature plants of

In this study, the two researchers try to identify the degree of psychological flow among third-stage students in the College of Physical Education and Sports Sciences / University of Baghdad, by constructing a psychometric flow meter for third-stage students in the College of Physical Education and Sports Sciences / University of Baghdad, and the research sample reached 123 female students They represent 100% of the research community, and after conducting the scientific foundations for building the scale, the two researchers reached the final version of the psychometric flow scale with 21 items with four axes.

Precision is one of the main elements that control the quality of a geodetic network, which defines as the measure of the network efficiency in propagation of random errors. This research aims to solve ZOD and FOD problems for a geodetic network using Rosenbrock Method to optimize the geodetic networks by using MATLAB programming language, to find the optimal design of geodetic network with high precision. ZOD problem was applied to a case study network consists of 19 points and 58 designed distances with a priori deviation equal to 5mm, to determine the best points in the network to consider as control points. The results showed that P55 and P73 having the minimum ellipse of error and considered as control points. FOD problem was applie

This paper includes an experimental study of hydrogen mass flow rate and inlet hydrogen pressure effect on the fuel cell performance. Depending on the experimental results, a model of fuel cell based on artificial neural networks is proposed. A back propagation learning rule with the log-sigmoid activation function is adopted to construct neural networks model. Experimental data resulting from 36 fuel cell tests are used as a learning data. The hydrogen mass flow rate, applied load and inlet hydrogen pressure are inputs to fuel cell model, while the current and voltage are outputs. Proposed model could successfully predict the fuel cell performance in good agreement with actual data. This work is extended to developed fuel cell feedback

Background: This study aimed to evaluate the effect of zirconia different surface treatments (primer, sandblast with 50μmAl2O3, Er,Cr:YSGG laser) on shear bond strength between zirconia surface and resin cement. Material and methods: Sixty presintered Y-TZP zirconia cylinder specimens (IPS e.max ZirCAD, Ivoclar vivadent) will be fabricated and sintered in high temperature furnace of (1500 C for 8 hours) according to manufacturer’s instructions to the selected size and shape of (5mm. in diameter and 6mm in height). All specimens were ground flat using 600.800.1000.1200, aluminum oxide abrasive paper to obtain a standardized surface roughness. Surface roughness values were then recorded in µm using surface roughness tester (profi