The method of operational matrices based on different types of polynomials such as Bernstein, shifted Legendre and Bernoulli polynomials will be presented and implemented to solve the nonlinear Blasius equations approximately. The nonlinear differential equation will be converted into a system of nonlinear algebraic equations that can be solved using Mathematica^®12. The efficiency of these methods has been studied by calculating the maximum error remainder ( ), and it was found that their efficiency increases as the polynomial degree (n) increases, since the errors decrease. Moreover, the approximate solutions obtained by the proposed methods are compared with the solution of the 4^th order Runge-Kutta meth

This paper examines the impact of flexural strengthening on the percentage of damaged strands in internally unbonded tendons in partially prestressed concrete beams (0, 14.28%, and 28.57%) and the recovering conditions using CFRP composite longitudinal laminates at the soffit, and end anchorage U-wrap sheets to restore the original flexural capacity and mitigate the delamination of the soffit of longitudinal Carbon Fiber Reinforced Polymer (CFRP) laminates. The composition of the laminates and anchors affected the stress of the CFRP, the failure mode, and thus the behavior of the beam. The experimental results revealed that the usage of CFRP laminates has a considerable impact on strand strain, particularly when anchors are employed

Sorting and grading agricultural crops using manual sorting is a cumbersome and arduous process, in addition to the high costs and increased labor, as well as the low quality of sorting and grading compared to automatic sorting. the importance of deep learning, which includes the artificial neural network in prediction, also shows the importance of automated sorting in terms of efficiency, quality, and accuracy of sorting and grading. artificial neural network in predicting values and choosing what is good and suitable for agricultural crops, especially local lemons.

To date, comprehensive reviews and discussions of the strengths and limitations of Remote Sensing (RS) standalone and combination approaches, and Deep Learning (DL)-based RS datasets in archaeology have been limited. The objective of this paper is, therefore, to review and critically discuss existing studies that have applied these advanced approaches in archaeology, with a specific focus on digital preservation and object detection. RS standalone approaches including range-based and image-based modelling (e.g., laser scanning and SfM photogrammetry) have several disadvantages in terms of spatial resolution, penetrations, textures, colours, and accuracy. These limitations have led some archaeological studies to fuse/integrate multip

The microbend sensor is designed to experience a light loss when force is applied to the sensor. The periodic microbends cause propagating light to couple into higher order modes, the existing higher order modes become unguided modes. Three models of deform cells are fabricated at (3, 5, 8) mm pitchand tested by using MMF and laser source at 850 nm. The maximum output power of (8, 5, 3)mm model is (3, 2.7, 2.55)nW respectively at applied force 5N and the minimum value is (1.9, 1.65, 1.5)nW respectively at 60N.The strain is calculated at different microbend cells ,and the best sensitivity of this sensor for cell 8mm is equal to 0.6nW/N.

This paper presents a cognition path planning with control algorithm design for a nonholonomic wheeled mobile robot based on Particle Swarm Optimization (PSO) algorithm. The aim of this work is to propose the circular roadmap (CRM) method to plan and generate optimal path with free navigation as well as to propose a nonlinear MIMO-PID-MENN controller in order to track the wheeled mobile robot on the reference path. The PSO is used to find an online tune the control parameters of the proposed controller to get the best torques actions for the wheeled mobile robot. The numerical simulation results based on the Matlab package show that the proposed structure has a precise and highly accurate distance of the generated refere

A three-stage learning algorithm for deep multilayer perceptron (DMLP) with effective weight initialisation based on sparse auto-encoder is proposed in this paper, which aims to overcome difficulties in training deep neural networks with limited training data in high-dimensional feature space. At the first stage, unsupervised learning is adopted using sparse auto-encoder to obtain the initial weights of the feature extraction layers of the DMLP. At the second stage, error back-propagation is used to train the DMLP by fixing the weights obtained at the first stage for its feature extraction layers. At the third stage, all the weights of the DMLP obtained at the second stage are refined by error back-propagation. Network structures an

The most important environmental constraints at the present time
is the accumulation of glass waste (transparent glass bottles). A lot of
experiments and research have been made on waste and recycling
glass to get use it as much as possible. This research using recycling
of locally waste colorless glass to turn them into raw materials as
alternative of certain percentages of cement to save the environment
from glass waste and reduce some of the disadvantages of cement
with conserving the mechanical and physical properties of concrete
made. A set of required samples were prepared for mechanical test
with different weight percentage of waste glass (2%, 4%, 5%, 6%,
8%, 10%, 15%, 20% and 25%). American standard