This study evaluates the flexural behavior of ultra-thin (50 mm) one‑way reinforced‑concrete (RC) slabs retrofitted with near‑surface mounted (NSM) carbon‑fiber‑reinforced polymer (CFRP) rods under quasi‑static loading. T300‑grade CFRP rods (≈4 mm diameter) were bonded in pre‑cut 7 mm × 7 mm grooves using a two‑part epoxy. As a proof-of-concept experimental baseline, three simply‑supported specimens (1000 mm × 500 mm × 50 mm) were tested in a six‑point bending configuration (four applied loads + two reactions): two conventional controls and one strengthened slab. A load‑control rate of ~15 kN/min was applied; the controls were cycled twice and the strengthened slab four times. Relative to the average of

 In this paper, an intelligent tracking control system of both single- and double-axis Piezoelectric Micropositioner stage is designed using Genetic Algorithms (GAs) method for the optimal Proportional-Integral-Derivative (PID) controller tuning parameters. The (GA)-based PID control design approach is a methodology to tune a (PID) controller in an optimal control sense with respect to specified objective function. By using the (GA)-based PID control approach, the high-performance trajectory tracking responses of the Piezoelectric Micropositioner stage can be obtained. The (GA) code was built and the simulation results were obtained using MATLAB environment. The Piezoelectric Micropositioner simulation model with th

Face Recognition Systems (FRS) are increasingly targeted by morphing attacks, where facial features of multiple individuals are blended into a synthetic image to deceive biometric verification. This paper proposes an enhanced Siamese Neural Network (SNN)-based system for robust morph detection. The methodology involves four stages. First, a dataset of real and morphed images is generated using StyleGAN, producing high-quality facial images. Second, facial regions are extracted using Faster Region-based Convolutional Neural Networks (R-CNN) to isolate relevant features and eliminate background noise. Third, a Local Binary Pattern-Convolutional Neural Network (LBP-CNN) is used to build a baseline FRS and assess its susceptibility to d

In the present work, a density functional theory (DFT) calculation to simulate reduced graphene oxide (rGO) hybrid with zinc oxide (ZnO) nanoparticle's sensitivity to NO2 gas is performed. In comparison with the experiment, DFT calculations give acceptable results to available bond lengths, lattice parameters, X-ray photoelectron spectroscopy (XPS), energy gaps, Gibbs free energy, enthalpy, entropy, etc. to ZnO, rGO, and ZnO/rGO hybrid. ZnO and rGO show n-type and p-type semiconductor behavior, respectively. The formed p-n heterojunction between rGO and ZnO is of the staggering gap type. Results show that rGO increases the sensitivity of ZnO to NO2 gas as they form a hybrid. ZnO/rGO hybrid has a higher number of vacancies that can b

Zinc Oxide nanoparticles were prepared using pulsed laser ablation process from a pure zinc metal placed inside a liquid environment. The latter is composed of acetyltrimethylammonium bromide (CTAB) of 10−3 molarity and distilled water. A Ti:Sapphire laser of 800 nm wavelength, 1 kHz pulse repetition rate, 130 fs pulse duration is used at three values of pulse energies of 0.05 mJ, 1.11 mJ and 1.15 mJ. The evaluation of the optical properties for the obtained suspension was applied through ultraviolet–visible absorption spectroscopy test (UV/VIS). The result showed peak wavelengths at 210 nm, 211 nm and 213 nm for the three used pulse energies 0.05 mJ, 1.11 mJ and 1.15 mJ respectively. This indicates a blue shift,