Mesoporous silica (MPS) nanoparticle was prepared as carriers for drug delivery systems by sol–gel method from sodium silicate as inexpensive precursor of silica and Cocamidopropyl betaine (CABP) as template. The silica particles were characterized by SEM, TEM, AFM, XRD, and N2adsorption–desorption isotherms. The results show that the MPS particle in the nanorange (40-80 nm ) with average diameter equal to 62.15 nm has  rods particle morphology, specific surface area is 1096.122 m²/g, pore volume 0.900 cm³/g, with average pore diameter 2.902 nm, which can serve as efficient carriers for drugs. The adsorption kinetic of Ciprofloxacin (CIP) drug was studied and the data were analyzed and found to match well with

The compounds 3-[4̄-(4˭-methoxybenzoyloxy) benzylideneamino]-2-thioxo-imidazolidine-4-one(3)aand 4-(1-(5-oxo- 2-thioxoimidazolidin-1-ylimino)ethyl)phenyl acetate(3)b were prepared from the reaction of aromatic aldehyde or ketone(1)a,bwith thiosemicarbazide to give aryl thiosemicarbazones(2)a,b ,followed by cyclization with ethylchloroacetate in the presence of fused sodium acetate. Treatment the compounds(3)a,bwith 4- hydroxybenzenediazoniumchloride yielded the correspondings4-((4-((4-hydroxyphenyl)diazenyl)-5-oxo-2- thioxoimidazolidin-1-ylimino)methyl)phenyl 4-methoxybenzoate(4)aand4-(1-(4-((4-hydroxyphenyl)diazenyl)-5-oxo-2- thioxoimidazolidin-1-ylimino)ethyl)phenyl acetate(4)b.The new 2-thioxo-imidazolidin-4-one with esters (5-7)a,b sy

The growing demand for sustainable and high-performance asphalt binders has prompted the exploration of waste-derived modifiers. This study investigates the performance enhancement of Natural Asphalt (NA) using Sugarcane Molasses (SM) and Waste Engine Oil (WEO). The modified blends were prepared by partially replacing 50 % NA with varying proportions of SM and WEO ranging from 10 % to 40 % of the total weight of NA. Comprehensive testing was conducted, including penetration, softening point, ductility, viscosity, Bending Beam Rheometer (BBR), Multiple Stress Creep Recovery (MSCR), Energy Dispersive X-ray Spectroscopy (EDX), Fourier Transform Infrared (FTIR) spectroscopy, and Scanning Electron Microscopy (SEM). The results demonstrated that

   The aim of this research is to identify the Andalusia  poet  Abu Izac Al-Zauali , whose  most of his poetry and biography had been lost, as well as many Andalusia  poets. So this research is hoped to be the first step to shed the light on the poet’s life and poetry  and to document his poetry as other  writers do. It is also hoped that this research would pave the way , God willing , to the steps of finding more references about this poet .

Key Words: (Al-Zauali, Cordoba, Al-Ruaini choices, Andalusia Poetry , Almuahdi Era).

The Skyrme–Hartree–Fock (SHF) method with the Skyrme
parameters; SKxtb, SGII, SKO, SKxs15, SKxs20 and SKxs25 have
been used to investigate the ground state properties of some 2s-1d
shell nuclei with Z=N (namely; 20Ne, 24Mg, 28Si and 32S) such as, the
charge, proton and matter densities, the corresponding root mean
square (rms) radii, neutron skin thickness, elastic electron scattering
form factors and the binding energy per nucleon. The calculated
results have been discussed and compared with the available
experimental data.

Eco-friendly concrete is produced using the waste of many industries. It reduces the fears concerning energy utilization, raw materials, and mass-produced cost of common concrete. Several stress-strain models documented in the literature can be utilized to estimate the ultimate strength of concrete components reinforced with fibers. Unfortunately, there is a lack of data on how non-metallic fibers, such as polypropylene (PP), affect the properties of concrete, especially eco-friendly concrete. This study presents a novel approach to modeling the stress-strain behavior of eco-friendly polypropylene fiber-reinforced concrete (PFRC) using meta-heuristic particle swarm optimization (PSO) employing 26 PFRC various mixtures. The cement was partia

Deep learning has recently received a lot of attention as a feasible solution to a variety of artificial intelligence difficulties. Convolutional neural networks (CNNs) outperform other deep learning architectures in the application of object identification and recognition when compared to other machine learning methods. Speech recognition, pattern analysis, and image identification, all benefit from deep neural networks. When performing image operations on noisy images, such as fog removal or low light enhancement, image processing methods such as filtering or image enhancement are required. The study shows the effect of using Multi-scale deep learning Context Aggregation Network CAN on Bilateral Filtering Approximation (BFA) for d