The linear non-polynomial spline is used here to solve the fractional partial differential equation (FPDE). The fractional derivatives are described in the Caputo sense. The tensor products are given for extending the one-dimensional linear non-polynomial spline to a two-dimensional spline  to solve the heat equation. In this paper, the convergence theorem of the method used to the exact solution is proved and the numerical examples show the validity of the method. All computations are implemented by Mathcad15.

This study involves the design of 24 mixtures of fiber reinforced magnetic reactive powder concrete containing nano Silica. Tap water has been used in mixing 12 of these mixtures, while the other 12 have been mixed using magnetic water. Nano Silica (NS) with ratios (1, 1.5, 2, 2.5 and 3) % were used. The results showed that the mixture containing 2.5%NS gives the highest compressive strength at age 7 days. Many different other tests were carried out, the results showed that the fiber reinforced magnetic reactive powder concrete containing 2.5% NS (FRMRPCCNS)  has the higher bulk density, dynamic modulus of elasticity, ultrasonic pulse velocity  electrical resistivity and lesser absorption than fiber reinforced

A non-polynomial spline (NPS) is an approximation method that relies on the triangular and polynomial parts, so the method has infinite derivatives of the triangular part of the NPS to compensate for the loss of smoothness inherited by the polynomial. In this paper, we propose polynomial-free linear and quadratic spline types to solve fuzzy Volterra integral equations (FVIE) of the 2nd kind with the weakly singular kernel (FVIEWSK) and Abel's type kernel. The linear type algorithm gives four parameters to form a linear spline. In comparison, the quadratic type algorithm gives five parameters to create a quadratic spline, which is more of a credit for the exact solution. These algorithms process kernel singularities with a simple techniqu

Due to the large-scale development in satellite and network communication technologies, there is a significant demand for preserving the secure storage and transmission of the data over the internet and shared network environments. New challenges appeared that are related to the protection of critical and sensitive data
from illegal usage and unauthorized access. In this paper, we address the issues described above and develop new techniques to eliminate the associated problems. To achieve this, we propose a design of a new sensor node for tracking the location of cars and collecting all information and all visited locations by cars, followed by
encryption in a sensor node and saving in the database. A microcontroller of Arduino es

Ziegler and Nichols proposed the well-known Ziegler-Nichols method to tune the coefficients of PID controller. This tuning method is simple and gives fixed values for the coefficients which make PID controller have weak adaptabilities for the model parameters variation and changing in operating conditions. In order to achieve adaptive controller, the Neural Network (NN) self-tuning PID control is proposed in this paper which combines conventional PID controller and Neural Network learning capabilities. The proportional, integral and derivative (K_P, K_I, K_D) gains are self tuned on-line by the NN output which is obtained due to the error value on the desired output of the system under control. The conventio

In this paper, Bayes estimators of Poisson distribution have been derived by using two loss functions: the squared error loss function and the proposed exponential loss function in this study, based on different priors classified as the two different informative prior distributions represented by erlang and inverse levy prior distributions and non-informative prior for the shape parameter of Poisson distribution. The maximum likelihood estimator (MLE) of the Poisson distribution has also been derived. A simulation study has been fulfilled to compare the accuracy of the Bayes estimates with the corresponding maximum likelihood estimate (MLE) of the Poisson distribution based on the root mean squared error (RMSE) for different cases of the

     In this research, an unknown space-dependent force function in the wave equation is studied. This is a natural continuation of [1] and chapter 2 of [2] and [3], where the finite difference method (FDM)/boundary element method (BEM), with the separation of variables method, were considered. Additional data are given by the one end displacement measurement. Moreover, it is a continuation of [3], with exchanging the boundary condition, where  are extra data, by the initial condition. This is an ill-posed inverse force problem for linear hyperbolic equation. Therefore, in order to stabilize the solution, a zeroth-order Tikhonov regularization method is provided. To assess the accuracy, the minimum error between

This project aims to fabricate nanostructures (AgNPS) using the  electrical exploding wire (EEW) technique using Rhodamine 6G dye as the probe molecule, investigate the effect of AgNPS on the absorption spectra and surface-enhanced Raman scattering (SERS) activities, and advance  using porous silicon as an active substrate for surface-enhanced Raman scattering (SERS). X-Ray diffraction (XRD) was used to investigate the structural properties of the nanostructures (AgNPs). Field emission scanning electron microscopy (FE-SEM) was used to investigate surface morphology. A double beam UV-Vis Spectrophotometer was used to analyze the mixed R6G laser dye(of concentration 1x  M)  absorption spectra with the nanostructures AgNPS (of concentra

The evolution in the field of Artificial Intelligent (AI) with its training algorithms make AI very important in different aspect of the life. The prediction problem of behavior of dynamical control system is one of the most important issue that the AI can be employed to solve it. In this paper, a Convolutional Multi-Spike Neural Network (CMSNN) is proposed as smart system to predict the response of nonlinear dynamical systems. The proposed structure mixed the advantages of Convolutional Neural Network (CNN) with Multi -Spike Neural Network (MSNN) to generate the smart structure. The CMSNN has the capability of training weights based on a proposed training algorithm. The simulation results demonstrated that the proposed

A low-cost reverse flow plasma system powered by argon gas pumping was built using homemade materials in this paper. The length of the resulting arc change was directly proportional to the flow rate, while using the thermal camera to examine the thermal intensity distribution and demonstrating that it is concentrated in the centre, away from the walls at various flow rates, the resulting arc's spectra were also measured. The results show that as the gas flow rate increased, so did the ambient temperature. The results show that the medium containing the arc has a maximum temperature of 34.1 ˚C at a flow rate of 14 L/min and a minimum temperature of 22.6 ˚C at a flow rate of 6 L/min.