The placement of buildings and structures on/or adjacent to slopes is possible, but this poses a danger to the structure due to failures that occur in slopes. Therefore, a solution or improvement should be determined for these issues of the collapse of the structure as a result of the failure of the slopes. A laboratory model has been built to test the impact of some variables on the bearing capacity factor. The variables include the magnitude of static axial load applied at the center of footing, the depth of embedment, the spacing between geogrid reinforcement layer and the numbering of the geogrid sheet under the footing, the inclination angle of slope clayey soil (β), the spacing between the footing's edge and the slope's end (b/H). Th

As a result of the developments that occurred in technological and digital technologies, which had a fundamental role in changing the paths of science, including the sciences of communication and the art of design, especially since these developments were the result of the information revolution, which called for the need to find alternatives that contribute to organizing presentations of information and texts in accordance with the manufacture of constructive content to deliver A communication message for the recipient to achieve culture and communication together, as it has become necessary to employ technical and digital developments in organizing that information according to the rules and laws of design and producing newspapers asso

In wireless broadband communications using single-carrier interleave division multiple access (SC-IDMA) systems, efficient multiuser detection (MUD) classes that make use of joint hybrid decision feedback equalization (HDFE)/ frequency decision-feedback equalization (FDFE) and interference cancellation (IC) techniques, are proposed in conjunction with channel coding to deal with several users accessing the multipath fading channels. In FDFE-IDMA, the feedforward (FF) and feedback (FB) filtering operations of FDFE, which use to remove intersymbol interference (ISI), are implemented by Fast Fourier Transforms (FFTs), while in HDFE-IDMA the only FF filter is implemented by FFTs. Further, the parameters involved in the FDFE/

One of the artificial lightweight aggregates with a wide range of applications is Lightweight Expanded Clay Aggregate. Clay is utilized in the production of light aggregates. Using leftover clay from significant infrastructure development projects to manufacture lightweight aggregates has a favorable environmental impact. This research examines the expanded clay aggregate production process and the impact of processing parameters on its physical and mechanical qualities. It also looks at secondary components that can be used to improve the qualities of concrete with expanded clay aggregates. The effect of the quantity of expanded clay aggregate on the fresh, hardened, and durability qualities of concrete is also studied.

In this paper new methods were presented based on technique of differences which is the difference- based modified jackknifed generalized ridge regression estimator(DMJGR) and difference-based generalized  jackknifed ridge regression estimator(DGJR), in estimating the parameters of linear part of the partially linear model. As for the nonlinear part represented by the nonparametric function, it was estimated using Nadaraya Watson smoother. The partially linear model was compared using these proposed methods with other estimators based on differencing technique through the MSE comparison criterion in simulation study.

The method of predicting the electricity load of a home using deep learning techniques is called intelligent home load prediction based on deep convolutional neural networks. This method uses convolutional neural networks to analyze data from various sources such as weather, time of day, and other factors to accurately predict the electricity load of a home. The purpose of this method is to help optimize energy usage and reduce energy costs. The article proposes a deep learning-based approach for nonpermanent residential electrical ener-gy load forecasting that employs temporal convolutional networks (TCN) to model historic load collection with timeseries traits and to study notably dynamic patterns of variants amongst attribute par