The dielectric constant of most polymers is very low; the addition of TiO2 particles into the polymers provides an attractive and promising way to reach a high dielectric constant. Polymer-based materials with a high dielectric constant show great potential for energy storage applications. Four samples were prepared, one of them was polyurethane (PU) and the other were PU with different weight percent (wt %) of TiO2 (0.1, 0.2, 0.3) powder AFM test was used to distinguish the nanoparticles. The result shows that the most shape of these nanoparticles are spherical and the roughness average is 0.798 nm. The dielectric properties were measured for all samples before and after the exposure to the UV radiation. The result illustrates that the

Sheets of Epoxy (EP) resin with addition of TiO2 of grain size (1.5μm, and 50nm) and weight percentage (1%, 3%, and 5%) were prepared. Discs of 20mm diameter and 3mm thickness were cut for dielectric measurements. Dielectric properties (dielectric constant, dispassion factor and electrical conductivity) over the frequency range 102 -106 Hz were measured.
Comparison was made between the effect of micro and nano particles of TiO2 on the dielectric properties of EP composites with different weight percentage. Epoxy composites with micro sized particles of TiO2 were observed to have the better values of dielectric properties.

In this work, (ZnO)1-x(MnO2)x compounds were synthesized with composition (x=0, 0.1, 0.2, 0.3, 0.4, and 0.5) of manganese oxide content using solid state reaction. Thin films were prepared from these compounds on glass substrates at room temperature using pulsed laser deposition method. The structure of the prepared compounds and thin films were analyzed using x-ray diffraction while the optical properties was measured using UV-visible spectrophotometry. It was found that the synthesized composites declared many peaks in the diffraction pattern which indicate polycrystalline structure with hexagonal wurtzite hexagonal structure of ZnO, and MnO2 and Mn2O3 secondary phases. A narrowing in the optical energy gap was found as Mn content increas

In light of the development in computer science and modern technologies, the impersonation crime rate has increased. Consequently, face recognition technology and biometric systems have been employed for security purposes in a variety of applications including human-computer interaction, surveillance systems, etc. Building an advanced sophisticated model to tackle impersonation-related crimes is essential. This study proposes classification Machine Learning (ML) and Deep Learning (DL) models, utilizing Viola-Jones, Linear Discriminant Analysis (LDA), Mutual Information (MI), and Analysis of Variance (ANOVA) techniques. The two proposed facial classification systems are J48 with LDA feature extraction method as input, and a one-dimen