This research presents a method of using MATLAB in analyzing a nonhomogeneous soil (Gibson-type) by
estimating the displacements and stresses under the strip footing during applied incremental loading
sequences. This paper presents a two-dimensional finite element method. In this method, the soil is divided into a number of triangle elements. A model soil (Gibson-type) with linearly increasing modulus of elasticity with depth is presented. The influences of modulus of elasticity, incremental loading, width of footing, and depth of footing are considered in this paper. The results are compared with authors' conclusions of previous studies.

In this article, we design an optimal neural network based on new LM training algorithm. The traditional algorithm of LM required high memory, storage and computational overhead because of it required the updated of Hessian approximations in each iteration. The suggested design implemented to converts the original problem into a minimization problem using feed forward type to solve non-linear 3D - PDEs. Also, optimal design is obtained by computing the parameters of learning with highly precise. Examples are provided to portray the efficiency and applicability of this technique. Comparisons with other designs are also conducted to demonstrate the accuracy of the proposed design.

This research depends on the relationship between the reflected spectrum, the nature of each target, area and the percentage of its presence with other targets in the unity of the target area. The changes occur in Land cover have been detected for different years using satellite images based on the Modified Spectral Angle Mapper (MSAM) processing, where Landsat satellite images are utilized using two software programming (MATLAB 7.11 and ERDAS imagine 2014). The proposed supervised classification method (MSAM) using a MATLAB program with supervised classification method (Maximum likelihood Classifier) by ERDAS imagine have been used to get farthest precise results and detect environmental changes for periods. Despite using two classificatio

Because of the quick growth of electrical instruments used in noxious gas detection, the importance of gas sensors has increased. X-ray diffraction (XRD) can be used to examine the crystal phase structure of sensing materials, which affects the properties of gas sensing. This contributes to the study of the effect of electrochemical synthesis of titanium dioxide (TiO₂) materials with various crystal phase shapes, such as rutile TiO₂ (R-TiO₂NTs) and anatase TiO₂ (A-TiO₂NTs). In this work, we have studied the effect of voltage on preparing TiO₂ nanotube arrays via the anodization technique for gas sensor applications. The results acquired from XRD, energy dispersion spectro

New two experiments of the three factors, in this study were constructed to investigate the effects, of the fixed variations to the box plot on subjects' judgments of the box lengths. These two experiments were constructed as an extension to the group B experiments, the ratio experiments the experiments with two variables carried out previously by Hussin, M.M. (1989, 2006, 2007). The first experiment box notch experiment, and the second experiment outlier values experiment. Subjects were asked to judge what percentage the shorter represented of the longer length in pairs of box lengths and give an estimate of percentage, one being a standard plot and the other being of a different box lengths and

The implementation of technology in the provision of public services and communication to citizens, which is commonly referred to as e-government, has brought multitude of benefits, including enhanced efficiency, accessibility, and transparency. Nevertheless, this approach also presents particular security concerns, such as cyber threats, data breaches, and access control. One technology that can aid in mitigating the effects of security vulnerabilities within e-government is permissioned blockchain. This work examines the performance of the hyperledger fabric private blockchain under high transaction loads by analyzing two scenarios that involve six organizations as case studies. Several parameters, such as transaction send ra

Abstract:

There is a close relationship between rigidity and distort structure of production and productivity and inflation rates. The effects of this relationship are distorted the contribution rate of the productive sectors and the disproportionate of exchange rate in foreign trade.

raising the general level of prices is one of the way that have been used by previous governments (inflationary financing or deficit financing) in order to speed up the process of capital formation, depending on the availability of economic resources idle.

The fabricating inflation for development does not represent a true understanding of the nature of the

The UN plans to achieve several development objectives by 2030. These objectives address global warming, a major issue. This method aims to improve sustainable accounting performance (AP). In this circumstance, AI is being applied in various fields, notably in economic, social, and environmental (ESE) domains. This research investigates how sustainable development (SD) influences AI methodologies and AP improvement. The research examined a sample of Iraqi banks listed on the Iraq Stock Exchange from 2014 to 2022. AI was measured by ATM and POS prevalence. A three-dimensional approach examined economic, social, and environmental (ESE) sustainability. Meanwhile, the performance of sustainable accounting was measured through the return on asse

Correct grading of apple slices can help ensure quality and improve the marketability of the final product, which can impact the overall development of the apple slice industry post-harvest. The study intends to employ the convolutional neural network (CNN) architectures of ResNet-18 and DenseNet-201 and classical machine learning (ML) classifiers such as Wide Neural Networks (WNN), Naïve Bayes (NB), and two kernels of support vector machines (SVM) to classify apple slices into different hardness classes based on their RGB values. Our research data showed that the DenseNet-201 features classified by the SVM-Cubic kernel had the highest accuracy and lowest standard deviation (SD) among all the methods we tested, at 89.51 %  1.66 %. This