In the present work, poly methyl methacrylate (PMMA) doped with Rhodamine 6G was prepared. The spectral properties (absorption and fluorescence) of the films were studied at different concentrations (1x10^-5, 2x10^-5, 5x10^-5, 7x10^-5, and 1x10^-4mol/l). The investigated samples were made in the form of thin films. This was achieved by dissolving a certain weight of PMMA in a fixed volume of chloroform, composite films was with thickness (25.8μm) at room temperature. The achieved results were pointed out that absorption and fluorescence spectra have taken a wide spectral rang so when increased the concentratio

Phoenix dactylifera l. pinnae (the green leaves of dates palm) were used as natural reinforcing (strengthening) fibers to improve the mechanical properties of polyester as a matrix material, the fibers of the green leaves of dates palm were used in two lengths, 10 and 20mm with five rates of 0, 2.5, 5, 10, and 20% , where the reinforcing with the leaves fibers increases the hardness strength from 76.5 to be about 86.55 , the Impact value raised from about 0.313 to 0.461 , in addition to that the flexural strength from 2.66 to be about 55 , and the thermal conductivity increases from 2.54 𝑤∕𝑚.℃ to 5.41 𝑤∕𝑚.℃. The results of the present search explains that the composite samples reinforced at rate 20% and 10mm fiber length

In this research, A thin film of Rhodamine B dye and TiO2 Nanoparticles doped in PMMA Polymer has been prepared by a casting method. The sample was spectrum absorption by UV-Vis. The nonlinear optical properties were measured by Z- scan technique using Nd:YAG laser with (1064 nm) wavelength. The nonlinear refractive index (n2) and nonlinear absorption coefficient (β) were estimated for the thin film for different energies of the laser, n2 and β were decreased with increasing intensity of incident laser beam. Also, the type of β was two-photon absorption and n2 negative nonlinear reflective.

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an ob

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an object under de