In this work, a joint quadrature for numerical solution of the double integral is presented. This method is based on combining two rules of the same precision level to form a higher level of precision. Numerical results of the present method with a lower level of precision are presented and compared with those performed by the existing high-precision Gauss-Legendre five-point rule in two variables, which has the same functional evaluation. The efficiency of the proposed method is justified with numerical examples. From an application point of view, the determination of the center of gravity is a special consideration for the present scheme. Convergence analysis is demonstrated to validate the current method.

In this work was prepared three different types of modified screen printed carbon electrode (SPCEs) with drops casted method, the used carbone nanomaterials were the MWCNT, functionalized –MWCNT (f-MWCNT) and After several experiments were made to find an appropriate ratio to make good GOT/f-MWCNT nanocomposite, and found the suspension mixture (1:1) from GOT/f-MWCNT (f-MWCNT-GOT). The electrical and physical properties were performed with cyclic voltammeter technique, and studied the maximum current response, the effective surface area, effect of the pH value and the determination of active surface area for MWCNT-SPCE , f-MWCNT-SPCE and f-MWCNT-GOT/SPCE as (0.04 cm2), (0.119 cm2) and (0.115 cm2) respectively, the surface coverage concent

The work include synthesis of nanocomposites (X / S / Ag) based on blend from Xanthan gum / sodium alginate polymers (X / S) with different loading of synthesized silver nanoparticales (0.01, 0.03 and 0.05 wt%) were added to the blend. The silver nanoparticles were prepared by reduction method and were characterized and analyzed using X-ray diffraction (XRD) and Atomic force microscope (AFM). XRD study showed the presence nanoparticle of silver with crystalline nature and face-centered cubic (FCC) structure and an average size of nanoparticles ranging from 32 to 37 nm. The surface study was performed using AFM which showed a fairly uniform shape to the nanocomposites and a spherical nature for the silver nanoparticles. The nanocomposite exh

Well dispersed Cu2FeSnSe4 (CFTSe) nanofilms were synthesized by hot-injection method. The structural and morphological measurements were characterized using XRD (X-ray diffraction), Raman spectroscopy, SEM (scanning electron microscopy), and TEM (transmission electron microscopy). Chemical composition and optical properties of as-synthesized CFTSe nanoparticles were characterized using EDS (energy dispersive spectroscopy) and UV-Vis spectrophotometry. The average particle size of the nanoparticles was about 7-10 nm. The UV-Vis absorption spectra showed that the synthesized CFTS nanofilms have a band gap (Eg) of about 1.16 eV. Photo-electrochemical characteristics of CFTSe nanoparticles were studied and indicated their potential application

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The MTX was converted to MTX nanoparticles by the modified method based on changing the pH gradually with exposure to ultrasound and shaking , changing the pH with exposure to ultrasound plays an significant role in the formation of nanoparticles, and this is shown in some previous studies. As the change in pH affects the nature of bonding between molecules, as well as the strength of bonding that depends on the change of electrical charges The exposure to ultrasound waves will greatly affect the breakdown of large particles into small particles that reach the level of nanoparticles. The MTX NPs formation was characterized by UV-Vis spectra analysis , Atomic force microscopy (AFM) analysis, Scanning electron microscope (SEM) and Fou

Recommendation systems are now being used to address the problem of excess information in several sectors such as entertainment, social networking, and e-commerce. Although conventional methods to recommendation systems have achieved significant success in providing item suggestions, they still face many challenges, including the cold start problem and data sparsity. Numerous recommendation models have been created in order to address these difficulties. Nevertheless, including user or item-specific information has the potential to enhance the performance of recommendations. The ConvFM model is a novel convolutional neural network architecture that combines the capabilities of deep learning for feature extraction with the effectiveness o