In this study the thermal conductivity of the epoxy composites were characterized as function of volume fraction, particle size of fillers and the time of immersion(30,60,90)days in water .Composites plates were prepared by incorporating (bi-directional) (0º-90º) glass fiber and silicon carbide (SiC) particles of (0.1,0.5,1)mm as particle size at (10%,20%,30%,40%) percent volume in epoxy matrix.
The composites shows slightly increase of the thermal conductivity with increasing volume fraction, particle size and increase with increasing the days of immersion in water. The maximum thermal conductivity (0.51W/m.K) was obtained before the immersion in water at 90 days for epoxy reinforcement by bi-directional glass fiber and SiC particl

The present study investigates the characterization of silver nanoparticles (AgNPs) synthesized using Fusarium solani and their impact on tomato seed germination, plant growth, and disease resistance. A visible color change from yellow to dark smoky indicated the formation of AgNPs, while UV-visible spectrophotometry revealed an absorbance peak at 437 nm, confirming their presence. Atomic force microscopy analysis showed that the AgNPs ranged from 0 to 39.27 nm in size, with an average height of 5.772 nm, while scanning electron microscopy highlighted their diverse surface morphology. The application of AgNPs and mycorrhizal fungi significantly improved tomato seed germination rates, plant height, and dry weight compared to untreate

In this study lattice parameters, band structure, and optical characteristics of pure and V-doped ZnO are examined by employing (USP) and (GGA) with the assistance of First-principles calculation (FPC) derived from (DFT). The measurements are performed in the supercell geometry that were optimized. GGA+U, the geometrical structures of all models, are utilized to compute the amount of energy after optimizing all parameters in the models. The volume of the doped system grows as the content of the dopant V is increased. Pure and V-doped ZnO are investigated for band structure and energy bandgaps using the Monkhorst–Pack scheme's k-point sampling techniques in the Brillouin zone (G-A-H-K-G-M-L-H). In the presence of high V content, the ban

Background: The isatin molecule is present in many natural substances, including plants and animals, and is used to prepare compounds with various biological activities. Objectives: To synthesize a new series of isatin derivatives with the expectation that they will have antimicrobial activity. Methods: Thiazole Schiff bases were synthesized from various Mannich bases of isatin to evaluate their antimicrobial properties. Initially, Mannich bases (2a–e) were synthesized by reacting isatin with formaldehyde and different secondary amines. Subsequently, they were treated with 2-aminothiazole to yield the final compounds (3a–e). Spectroscopic characterization was done via FT-IR and 1H-NMR. The antimicrobial screening was conducted o

Activation of farnesoid X receptor (FXR) markedly attenuates development of atherosclerosis in animal models. However, the underlying mechanism is not well elucidated. Here, we show that the FXR agonist, obeticholic acid (OCA), increases fecal cholesterol excretion and macrophage reverse cholesterol transport (RCT) dependent on activation of hepatic FXR. OCA does not increase biliary cholesterol secretion, but inhibits intestinal cholesterol absorption. OCA markedly inhibits hepatic cholesterol 7α‐hydroxylase (Cyp7a1) and sterol 12α‐hydroxylase (Cyp8b1) partly through inducing small heterodimer partner, leading to reduced bile acid pool size and al

Amputation of the upper limb significantly hinders the ability of patients to perform activities of daily living. To address this challenge, this paper introduces a novel approach that combines non-invasive methods, specifically Electroencephalography (EEG) and Electromyography (EMG) signals, with advanced machine learning techniques to recognize upper limb movements. The objective is to improve the control and functionality of prosthetic upper limbs through effective pattern recognition. The proposed methodology involves the fusion of EMG and EEG signals, which are processed using time-frequency domain feature extraction techniques. This enables the classification of seven distinct hand and wrist movements. The experiments conducte