A modified chemical method was used to prepare titanium dioxide nanoparticles (TiO2 NPs), which were diagnosed by several techniques: X-ray diffraction, Fourier transform infrared, field emission scaning electron microscopy, energy disperse X-ray, and UV-visible spectroscopy, which proved the success of the preparation process at the nanoscale level. Where the titanium oxide particles have an average particle size equal to 6.8 nm, titanium dioxide particles were used in the process of adsorption of Congo red dye from its aqueous solutions using a batch system. The titanium oxide particles gave an adsorption efficiency of Congo red dye up to more than 79 %. The experimental data of the adsorption process were analyzed with kinetic models and

A series of heterogeneous basic catalysts of CaO, MgO and CaMgO₂ at different calcination temperature were synthesized via solution combustion method. Different characterization techniques have been carried out to investigate the structure of the produced catalysts i.e. X-ray diffraction (XRD), particle size analyzer, morphology by atomic force microscope (AFM) and reflection using UV-VIS diffuse reflectance spectra. The particles size analyzer revealed that the mixed oxide catalysts calcined at different calcination temperature possess smaller nano size particles compared to pure CaO. Moreover, the energy band gap was calculated based on the results of diffuse reflectance spectra. The energy band gap was redu

The objective of this research was to estimate the dose distribution delivered by radioactive gold nanoparticles (198 AuNPs or 199 AuNPs) to the tumor inside the human prostate as well as to normal tissues surrounding the tumor using the Monte-Carlo N-Particle code (MCNP-6.1. 1 code). Background Radioactive gold nanoparticles are emerging as promising agents for cancer therapy and are being investigated to treat prostate cancer in animals. In order to use them as a new therapeutic modality to treat human prostate cancer, accurate radiation dosimetry simulations are required to estimate the energy deposition in the tumor and surrounding tissue and to establish the course of therapy for the patient. Materials and methods A simple geometrical

Scleral acrylic resin is widely used to synthesize ocular prosthesis. However, the properties of this material change over time, thus requiring the prosthesis to be refabricated. Many studies were conducted to improve these properties by reinforcing this material with nanoparticles. This study aims to evaluate the effect of silver nanoparticle powder on the mechanical properties (transverse flexural strength, impact strength, shear bond strength, surface microhardness, and surface roughness) of scleral acrylic resin used for ocular prostheses. Two concentrations were selected from the pilot study and evaluated for their effects on scleral acrylic resin properties. According to the pilot study, 0.01 and 0.02wt% AgNPs powder improved

One of the most popular and legally recognized behavioral biometrics is the individual's signature, which is used for verification and identification in many different industries, including business, law, and finance. The purpose of the signature verification method is to distinguish genuine from forged signatures, a task complicated by cultural and personal variances. Analysis, comparison, and evaluation of handwriting features are performed in forensic handwriting analysis to establish whether or not the writing was produced by a known writer. In contrast to other languages, Arabic makes use of diacritics, ligatures, and overlaps that are unique to it. Due to the absence of dynamic information in the writing of Arabic signatures,

Document source identification in printer forensics involves determining the origin of a printed document based on characteristics such as the printer model, serial number, defects, or unique printing artifacts. This process is crucial in forensic investigations, particularly in cases involving counterfeit documents or unauthorized printing. However, consistent pattern identification across various printer types remains challenging, especially when efforts are made to alter printer-generated artifacts. Machine learning models are often used in these tasks, but selecting discriminative features while minimizing noise is essential. Traditional KNN classifiers require a careful selection of distance metrics to capture relevant printing

Knowledge of the distribution of the rock mechanical properties along the depth of the wells is an important task for many applications related to reservoir geomechanics. Such these applications are wellbore stability analysis, hydraulic fracturing, reservoir compaction and subsidence, sand production, and fault reactivation. A major challenge with determining the rock mechanical properties is that they are not directly measured at the wellbore. They can be only sampled at well location using rock testing. Furthermore, the core analysis provides discrete data measurements for specific depth as well as it is often available only for a few wells in a field of interest. This study presents a methodology to generate synthetic-geomechani