This paper proposes improving the structure of the neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Two learning algorithms are used to adjust the parameters weight of the hybrid neural structure with its serial-parallel configuration; the first one is supervised learning algorithm based Back Propagation Algorithm (BPA) and the second one is an intelligent algorithm n

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Objective(s): This study aims to evaluate the hardness of two commercially available cold cured acrylic resin material
(Vertex and PAN) when polymerized at different temperature in comparison to those polymerized by conventional
methods in air at 23C ± 5C.
Methodology: Eighty specimens, forty from cold cured acrylic (Vertex Type) and forty from cold cured acrylic (PAN
type) were prepared, flasking and packing procedure were done according to manufacturer direction and divided
according to processing as follow: 20 specimens (10 from Vertex type and 10 from PAN type) were processed in air for
two hours at 23C ± 5C under press (bench curing) as a control, and 60 specimens (30 from Vertex type and 30 from
PAN type) wer

The buildup factor was measured after irradiating Iraq carbon black powder using each of and sources respectively, using mixing ratios 40% & 50% for thickness range . The results showed that the buildup factor depends on energy and has limited dependence on the mixing ratio. The QIFT program succeeded accenting for the experimental results even for expected values more than 4 m.f.p outside the thickness range.

In the current study, a novel approach for separating ethanol-water mixture by microbubble distillation technology was investigated. Traditional distillation processes require large amounts of energy to raise the liquid to its boiling point to effect removal of volatile components. The concept of microbubble distillation by comparison is to heat the gas phase rather than the liquid phase to achieve separation. The removal of ethanol from the thermally sensitive fermentation broths was taken as a case of study. Consequently the results were then compared with those which could be obtained under equilibrium conditions expected in an “ideal” distillation unit. Microbubble distillation has achieved vapour compositions higher than th

In the present work, nanocomposite of poly (vinyl alcohol) (PVA) incorporated with functionalized graphene oxide (FGO) were fabricated using casting method. PVA was dispersed by varying content of FGO (0.3, 0.5, 0.8, 1 wt %). The PVA- FGO nanocomposite was characterized by FT‐IR, FE-SEM and XRD. Frequency dependence of real permittivity (ε’), imaginary (ε’’) and a.c conductivity of PVA/FGO and PVA/GO nanocomposite were studied in the frequency range 100 Hz- 1 MHz. The experimental results showed that the values of real (ε’) and imaginary permittivity (ε’’) increased dramatically by increasing the FGO content in PVA matrix. PVA/ FGO (1 wt %) nanocomposite revealed higher electrical conductivity of 6.4×10-4 Sm-1 compared to

Objective: The approximate life span of a silicone maxillofacial prosthesis is as short as1.5–2 years of clinical service, then a new prosthesis should be fabricated. The most common reasonfor re-making the prosthesis is silicone mechanical properties degradation. The aim of this studywas to assess some mechanical properties of VST-30 silicone for maxillofacial prostheses after addi-tion of intrinsic pigments.Methods: Two types of intrinsic pigments (rayon flocking and burnt sienna); each of them wasincorporated into silicone. One hundred and twenty samples were prepared and split into 4 groupsaccording to the conducted tests (tear strength, hardness, surface roughness, and tensile strengthand elongation percentage) with 30 samples for ea

Abstract—In this study, we present the experimental results of ultra-wideband (UWB) imaging oriented for detecting small malignant breast tumors at an early stage. The technique is based on radar sensing, whereby tissues are differentiated based on the dielectric contrast between the disease and its surrounding healthy tissues. The image reconstruction algorithm referred to herein as the enhanced version of delay and sum (EDAS) algorithm is used to identify the malignant tissue in a cluttered environment and noisy data. The methods and procedures are tested using MRI-derived breast phantoms, and the results are compared with images obtained from classical DAS variant. Incorporating a new filtering technique and multiplication procedure, t

Vehicular Ad Hoc Networks (VANETs) are integral to Intelligent Transportation Systems (ITS), enabling real-time communication between vehicles and infrastructure to enhance traffic flow, road safety, and passenger experience. However, the open and dynamic nature of VANETs presents significant privacy and security challenges, including data eavesdropping, message manipulation, and unauthorized access. This study addresses these concerns by leveraging advancements in Fog Computing (FC), which offers lowlatency, distributed data processing near-end devices to enhance the resilience and security of VANET communications. The paper comprehensively analyzes the security frameworks for fog-enabled VANETs, introducing a novel taxonomy that c