Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D

Transportability refers to the ease with which people, goods, or services may be transferred. When transportability is high, distance becomes less of a limitation for activities. Transportation networks are frequently represented by a set of locations and a set of links that indicate the connections between those places which is usually called network topology. Hence, each transmission network has a unique topology that distinguishes its structure. The most essential components of such a framework are the network architecture and the connection level. This research aims to demonstrate the efficiency of the road network in the Al-Karrada area which is located in the Baghdad city. The analysis based on a quantitative evaluation using graph th

In this work, a new development of predictive voltage-tracking control algorithm for Proton Exchange Membrane Fuel Cell (PEMFCs) model, using a neural network technique based on-line auto-tuning intelligent algorithm was proposed. The aim of proposed robust feedback nonlinear neural predictive voltage controller is to find precisely and quickly the optimal hydrogen partial pressure action to control the stack terminal voltage of the (PEMFC) model for N-step ahead prediction. The Chaotic Particle Swarm Optimization (CPSO) implemented as a stable and robust on-line auto-tune algorithm to find the optimal weights for the proposed predictive neural network controller to improve system performance in terms of fast-tracking de

<p>Vehicular ad-hoc networks (VANET) suffer from dynamic network environment and topological instability that caused by high mobility feature and varying vehicles density. Emerging 5G mobile technologies offer new opportunities to design improved VANET architecture for future intelligent transportation system. However, current software defined networking (SDN) based handover schemes face poor handover performance in VANET environment with notable issues in connection establishment and ongoing communication sessions. These poor connectivity and inflexibility challenges appear at high vehicles speed and high data rate services. Therefore, this paper proposes a flexible handover solution for VANET networks by integrating SDN and

The dynamic thermomechanical properties, sealing ability, and voids formation of an experimental obturation hydroxyapatite-reinforced polyethylene (HA/PE) composite/carrier system were investigated and compared with those of a commercial system [GuttaCore (GC)]. The HA/PE system was specifically designed using a melt-extrusion process. The viscoelastic properties of HA/PE were determined using a dynamic thermomechanical analyser. Human single-rooted teeth were endodontically instrumented and obturated using HA/PE or GC systems, and then sealing ability was assessed using a fluid filtration system. In addition, micro-computed tomography (μCT) was used to quantify apparent voids within the root-canal space. The data were statistically analys

This numerical study explores dynamic melting as an enhancement strategy to improve heat transfer in thermal energy storage (TES) systems utilizing phase change materials (PCM) with openings. Optimizing such systems is crucial for advancing renewable energy storage and integration. A 3D model simulates RT35 PCM flowing through a shell-and-tube heat exchanger annulus. The effects of varying PCM inlet slot diameter (2.5–7.5 mm), inlet pressure (1–40 Pa), and inlet/outlet port positioning on melting fraction and temperature distributions are computationally evaluated. Results show that increasing slot diameter from 2.5 mm to 7.5 mm reduces melting time by 13.6 % (from 550 to 475 min). Raising inlet pressure from 10 Pa to 40 Pa cuts melting

Optical losses represent one of the primary obstacles to increasing the efficiency of silicon solar cells. The recommended solution to minimize optical losses is the use of plasmonic metal nanoparticles; however, they act as recombination centers within the solar cell construction, leading to a decrease in performance. The goal of this article is to introduce cobalt/graphene nanoparticles into the solar cell to minimize the optical losses. An ultra-thin film silicon PIN solar cell of dimensions (400 ×400 ×900) nm3 with ring metal contact shape was designed and numerically investigated using COMSOL Multiphysics software version 6.2 by the finite element method (FEM). Core/shell cobalt-graphene (Co/Gr) nanoparticles are periodically int

In the present investigation, bed porosity and solid holdup in viscous three-phase inverse fluidized bed (TPIFB) are determined for aqueous solutions of carboxy methyl cellulose (CMC) system using polyethylene and polypropylene as  a particles with low-density and diameter (5 mm) in a (9.2 cm) inner diameter with height (200 cm) of vertical perspex column. The effectiveness of gas velocity U_g , liquid velocity U_L, liquid viscosity μ_L, and particle density ρ_s on bed porosity B_P and solid holdups ε_g were determined. The bed porosity increases with "increasing gas velocity", "liquid velocity", and "liquid viscosity". Solid holdup decreases with increasing gas, liquid