Nanocrystalline copper sulphide (Cu2-xS) powders were synthesized by chemical precipitation from their aqueous solutions composed of different molar ratio of copper sulfate dehydrate (CuSO4.5H2O) and thiorea (NH2)2CS as source of Cu+2, S-2 ions respectively, and sodium ethylene diamine tetra acetic acid dehydrate (EDTA) as a complex agent. The compositions, morphological and structural properties of the nanopowders were characterized by energy dispersive spectroscopy (EDS), scanning electron microscope (SEM), and X-ray diffraction (XRD), respectively. The compositional results showed that the copper content was high and the Sulfur content was low for both CuS and Cu2S nanopowders. SEM images shows that all products consist of aggregate o

The physical, mechanical, electrical and thermal properties containing (Viscosity, curing, adhesion force, Tensile strength, Lap shear strength, Resistively, Electrical conductivity and flammability) of adhesive material that prepared from Nitrocellulose reinforced with graphite particles and aluminum streat. A comparison is made between the properties of adhesive material with varying percentage of graphite powder (0%, 25%, 30%, 35%, 40%) to find out the effect of reinforcement on the adhesive material. The ability of property an electrical was studied through the measurement of conductivity a function of temperature varying. The results of comparison have clearly shown that the increasing of conten

Abstract Background: The daily usage of maxillofacial prostheses causes them to mechanically deteriorate with time. This study was aimed to evaluate the reinforcement of VST50F maxillofacial silicone by using yttrium oxide (Y2O3) nanoparticles (NPs) to resist aging and mechanical deterioration. Materials and Method: Y2O3 NPs (30–45nm) were loaded into VST50F maxillofacial silicone in two weight percentages (1 and 1.5 wt%), which were predetermined in a pilot study as the best rates for improving tear strength with minimum increase in hardness values. A total of 120 specimens were prepared and divided into the control and experimental groups (with 1 and 1.5 wt% Y2O3 addition). Each group included 40 specimens, 10 specimens for each paramet

Estimation of the unknown parameters in 2-D sinusoidal signal model can be considered as important and difficult problem. Due to the difficulty to find estimate of all the parameters of this type of models at the same time, we propose sequential non-liner least squares method and sequential robust  M method after their development through the use of sequential  approach in the estimate suggested by Prasad et al to estimate unknown frequencies and amplitudes for the 2-D sinusoidal compounds but depending on Downhill Simplex Algorithm in solving non-linear equations for the purpose of obtaining non-linear parameters estimation which represents frequencies and then use of least squares formula to estimate

Pure and doped barium titanate with Mg2+ ion at two molar ratios x= (5%, 10%) mol. has been synthesized by solid state reaction technique. The powders sintered at two temperatures (1000 °C and 1400 °C). An XRD technique was used in order to study the crystal structure of pure and doped barium titanate, which confirmed the formation of the tetragonal phase of BaTiO3, and then calculate the lattice parameters of pure and doped barium titanate, the addition of magnesium ion Mg2+ can lead to decreases lattice parameters.

Pure and doped barium titanate with Mg2+ ion at two molar ratios x= (5%, 10%) mol. has been synthesized by solid state reaction technique. The powders sintered at two temperatures (1000 °C and 1400 °C). An XRD technique was used in order to study the crystal structure of pure and doped barium titanate, which confirmed the formation of the tetragonal phase of BaTiO3, and then calculate the lattice parameters of pure and doped barium titanate, the addition of magnesium ion Mg2+ can lead to decreases lattice parameters.

     In the last few years, the literature conferred a great interest in studying the feasibility of using memristive devices for computing. Memristive devices are important in structure, dynamics, as well as functionalities of artificial neural networks (ANNs) because of their resemblance to biological learning in synapses and neurons regarding switching characteristics of their resistance. Memristive architecture consists of a number of metastable switches (MSSs). Although the literature covered a variety of memristive applications for general purpose computations, the effect of low or high conductance of each MSS was unclear. This paper focuses on finding a potential criterion to calculate the conductance of each MMS rather t

The most significant function in oil exploration is determining the reservoir facies, which are based mostly on the primary features of rocks. Porosity, water saturation, and shale volume as well as sonic log and Bulk density are the types of input data utilized in Interactive Petrophysics software to compute rock facies. These data are used to create 15 clusters and four groups of rock facies. Furthermore, the accurate matching between core and well-log data is established by the neural network technique. In the current study, to evaluate the applicability of the cluster analysis approach, the result of rock facies from 29 wells derived from cluster analysis were utilized to redistribute the petrophysical properties for six units of Mishri