This research  studyies  wear  rate  of  composite  materials  by using Epoxy Resin and Polyurethane Rubber as a matrix  of weigt percentage (90:10) (Ep/Pu) and reinforced by PVC fibers and Aluminum fibers two dimension knitted mat with fractional volume(15 %), in different conditions like: lab conditions and after submerge the samples in water for different periods of time. . four kinds of materials were prepared: (Ep+pu), (Ep+Pu+PVC), (Ep+Pu+Al.F), (Ep+Pu+PVC+Al. F) .And the results have shown that the best wear resistance are for the hybrid composite material    (Ep + Pu+ PVC + Al. F) and wear rate of  all samples increased when it was submerged in water

The rheological behavior among factors that are present in Stokes law can be used to control the stability of the colloidal dispersion system. The felodipine lipid polymer hybrid nanocarriers  (LPHNs) is an interesting colloidal dispersion system that is used for rheological characteristic analysis. The LPHNs compose of polymeric components and lipids. This research aims to prepare oral felodipine LPHNs to investigate the effect of independent variables on the rheological behavior of the nanosystem. The microwave-based technique was used to prepare felodipine LPHNs (H1-H9) successfully. All the formulations enter the characterization process for particle size and PDI to ascertain the colloidal properties of the prepared nanosystem t

            The research is dealing with the absorption and fluorescence spectra for the hybrid of  an Epoxy Resin doped with organic dye Rhodamine (R6G) of different concentrations (5*10^-6, 5*10^-5, 1*10^-5, 1*10^-4, 5*10^-4) Mol/ℓ at room temperature. The Quantum efficiency Q_fm, the rate of fluorescence emission K_fm (s^-1), the non-radiative lifetime τ_fm (s), fluorescence lifetime τ_f and the Stokes shift were calculated. Also the energy gap (Eg) for each dye concentration was evaluated. The results showed that the maximum quantum effi

The paper reports the influence of annealing temperature under vacuum for one hour on the some structural and electrical properties of p-type CdTe thin films were grown at room temperature under high vacuum by using thermal evaporation technique with a mean thickness about 600nm. X-ray diffraction analysis confirms the formation of CdTe cubic phase at all annealing temperature. From investigated the electrical properties of CdTe thin films, the electrical conductivity, the majority carrier concentration, and the Hall mobility were found increase with increasing annealing temperatures.

A comparative investigation of gas sensing properties of SnO2 doped with WO3 based on thin film and bulk forms was achieved. Thin films were deposited by thermal evaporation technique on glass substrates. Bulk sensors in the shape of pellets were prepared by pressing SnO2:WO3 powder. The polycrystalline nature of the obtained films with tetragonal structure was confirmed by X-ray diffraction. The calculated crystalline size was 52.43 nm. Thickness of the prepared films was found 134 nm. The optical characteristics of the thin films were studied by using UV-VIS Spectrophotometer in the wavelength range 200 nm to 1100 nm, the energy band gap, extinction coefficient and refractive index of the thin film were 2.5 eV , 0.024 and 2.51, respective

In the present study a new synthesis method has been introduced for the decoration of platinum(Pt) on the functionalized graphene nanoplatelet (GNP) and also highlighted the preparation method of nanofluids. GNP–Pt uniform nanocomposite was produced from a simple chemical reaction procedure, which included acid treatment for functionalization of GNP. The surface characterization was performed by various techniques such as XRD, FESEMand TEM. The effective thermal conductivity, density, viscosity, specific heat capacity and stability of functionalized GNP–Pt water based nanofluids were investigated in different instruments. The GNP–Pt hybrid nanofluids were prepared by dispersing the nanocomposite in base fluid without adding any surfac

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class