In this paper, simulation study of the frequency shift of photonic bandgaps due to refractive index scaling using liquids filled hollow-core photonic crystal fibers is presented. Different liquids (distilled water, n-hexane, methanol, ethanol and acetone) are used to fill the cladding of 2 types of hollow core photonic crystal fibers (HC19-1060, HC7-1060). These liquids are used to change the effective index scaling and index contrast of the cladding. The effect of increasing temperature of the liquid (20-100 0C for water and 20-70 0C for other liquids ) infiltrated hollow core fiber on the bandgap width and transmission properties has been computed. The maximum photonic bandgap width at 0.0243 has appeared with filling HC7-1060 PCF with methanol at 70 0C at a corresponding refractive index 1.3057. Photonic bandgap structure for both TE and TM modes of a 2D photonic crystal with triangular lattice of air holes embedded in silica background material was studied using Plane Wave Expansion (PWE) method to find the width of photonic bandgap finger in photonic crystal states diagram.
Three-dimensional nonlinear thermal numerical simulations are conducted for the friction stir welding (FSW) of AA 7020-T53. Three welding cases with tool (rotational and travel) speeds of 900rpm-40mm/min, 1400rpm-16mm/min and 1400rpm-40mm/in are analyzed. The objective is to study the variation of transient temperature in a friction stir welded plate of 5mm workpiece thickness. Based on the experimental records of transient temperature at several specific locations during the friction stir welding process for the AA 7020-T53, thermal numerical simulation is developed. The numerical results show that the temperature field in the FSW process is symmetrically distributed with respect to the welding line, increasing travel speed decreasing tran
... Show MoreThe fast evolution of cyberattacks in the Internet of Things (IoT) area, presents new security challenges concerning Zero Day (ZD) attacks, due to the growth of both numbers and the diversity of new cyberattacks. Furthermore, Intrusion Detection System (IDSs) relying on a dataset of historical or signature‐based datasets often perform poorly in ZD detection. A new technique for detecting zero‐day (ZD) attacks in IoT‐based Conventional Spiking Neural Networks (CSNN), termed ZD‐CSNN, is proposed. The model comprises three key levels: (1) Data Pre‐processing, in this level a thorough cleaning process is applied to the CIC IoT Dataset 2023, which contains both malicious and t
This research presents a numerical study to simulate the heat transfer by forced convection as a result of fluid flow inside channel’s with one-sided semicircular sections and fully filled with porous media. The study assumes that the fluid were Laminar , Steady , Incompressible and inlet Temperature was less than Isotherm temperature of a Semicircular sections .Finite difference techniques were used to present the governing equations (Momentum, Energy and Continuity). Elliptical Grid is Generated using Poisson’s equations . The Algebraic equations were solved numerically by using (LSOR (.This research studied the effect of changing the channel shapes on fluid flow and heat transfer in two cases ,the first: cha
... Show MorePlantation of humic acid nanoparticles on the inert sand through simple impregnation to obtain the permeable reactive barrier (PRB) for treating of groundwater contaminated with copper and cadmium ions. The humic acid was extracted from sewage sludge which is byproduct of the wastewater treatment plant; so, this considers an application of sustainable development. Batch tests signified that the coated sand by humic acid (CSHA) had removal efficiencies exceeded 98 % at contact time, sorbent dosage, and initial pH of 1 h, 0.25 g/50 mL and 7, respectively for 10 mg/L initial concentration and 200 rpm agitation speed. Results proved that physicosorption was the predominant mechanism for metals-CSHA interaction because the sorption data followed
... Show MoreA robust and sensitive analytical method is presented for the extraction and determination of six pharmaceuticals in freshwater sediments.
This study proposes a hybrid predictive maintenance framework that integrates the Kolmogorov-Arnold Network (KAN) with Short-Time Fourier Transform (STFT) for intelligent fault diagnosis in industrial rotating machinery. The method is designed to address challenges posed by non-linear and non-stationary vibration signals under varying operational conditions. Experimental validation using the FALEX multispecimen test bench demonstrated a high classification accuracy of 97.5%, outperforming traditional models such as SVM, Random Forest, and XGBoost. The approach maintained robust performance across dynamic load scenarios and noisy environments, with precision and recall exceeding 95%. Key contributions include a hardware-accelerated K
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