An experimental model is used to simulate the loss of soil lateral confinement due to excavation nearby an individual axially loaded pile. The effects of various parameters, such as the horizontal distance of excavation, depth of excavation and pile slenderness ratios are investigated. The experimental analysis results showed the effect of excavation is more remarkable as the horizontal distance of excavation becomes closer to the pile than half pile length. The effect of excavation diminishes gradually as the horizontal distance increases beyond that distance for all the investigated pile slenderness ratios and depths of excavation. The pile head deflection, settlement and bending moments along pile increase with decreasing horizontal d

In this article, a numerical method integrated with statistical data simulation technique is introduced to solve a nonlinear system of ordinary differential equations with multiple random variable coefficients. The utilization of Monte Carlo simulation with central divided difference formula of finite difference (FD) method is repeated n times to simulate values of the variable coefficients as random sampling instead being limited as real values with respect to time. The mean of the n final solutions via this integrated technique, named in short as mean Monte Carlo finite difference (MMCFD) method, represents the final solution of the system. This method is proposed for the first time to calculate the numerical solution obtained fo

The numerical analysis was conducted to studying the influence of length to diameter ratio (L/D) on the behavior of the soil treated with sand columns treated with 8% sodium silicate for both floating and end bearing type by using finite element method (Plaxis 3D Foundation ) for isolated foundation of real dimensions. The analysis’s study indicate that in the floating type the best improvement ratio was achieved at (L/D=8) when using columns with a diameter of (0.5, 0.7), but when using columns with a diameter of 0.3 m, it was noticed that the bearing improvement ratio increases with increasing (L/d). While the results of the analysis for end bearing type show that the higher improvement ratio was achieved at (L/D=4) when using columns w

    The main goal of this paper is to introduce the higher derivatives multivalent harmonic function class, which is defined by the general linear operator. As a result, geometric properties such as coefficient estimation, convex combination, extreme point, distortion theorem and convolution property are obtained. Finally, we show that this class is invariant under the Bernandi-Libera-Livingston integral for harmonic functions.

Copper oxide (CuO) nanoparticles were synthesized through the thermal decomposition of a copper(II) Schiff-base complex. The complex was formed by reacting cupric acetate with a Schiff base in a 2:1 metal-to-ligand ratio. The Schiff base itself was synthesized via the condensation of benzidine and 2-hydroxybenzaldehyde in the presence of glacial acetic acid. This newly synthesized symmetric Schiff base served as the ligand for the Cu(II) metal ion complex. The ligand and its complex were characterized using several spectroscopic methods, including FTIR, UV-vis, 1H-NMR, 13C-NMR, CHNS, and AAS, along with TGA, molar conductivity and magnetic susceptibility measurements. The CuO nanoparticles were produced by thermally decomposing the

This research has come out with that, function-based responsibility accounting system has harmful side – effects preventing it of achieving its controlling objective, that is, goal congruence, which are due to its un integrated measures, its focus on measuring measurable behaviors while neglecting behaviors that are hardly measured, and its dependence on standard operating procedures.

In addition, the system hypotheses and measures are designed to fit previous business environment, not the current environment.

The research has also concluded that the suggestive model, that is, activity-based responsibility accounting is designed to get ride of harmful side – effects of functi

Metasurface polarizers are essential optical components in modern integrated optics and play a vital role in many optical applications including Quantum Key Distribution systems in quantum cryptography. However, inverse design of metasurface polarizers with high efficiency depends on the proper prediction of structural dimensions based on required optical response. Deep learning neural networks can efficiently help in the inverse design process, minimizing both time and simulation resources requirements, while better results can be achieved compared to traditional optimization methods. Hereby, utilizing the COMSOL Multiphysics Surrogate model and deep neural networks to design a metasurface grating structure with high extinction rat