Circular thin walled structures have wide range of applications. This type of structure is generally exposed to different types of loads, but one of the most important types is a buckling. In this work, the phenomena of buckling was studied by using finite element analysis. The circular thin walled structure in this study is constructed from; cylindrical thin shell strengthen by longitudinal stringers, subjected to pure bending in one plane. In addition, Taguchi method was used to identify the optimum combination set of parameters for enhancement of the critical buckling load value, as well as to investigate the most effective parameter. The parameters that have been analyzed were; cylinder shell thickness, shape of stiffeners section an

Surface water samples from different locations within Tigris River's boundaries in Baghdad city have been analyzed for drinking purposes. Correlation coefficients among different parameters were determined. An attempt has been made to develop linear regression equations to predict the concentration of water quality constituents having significant correlation coefficients with electrical conductivity (EC). This study aims to find five regression models produced and validated using electrical conductivity as a predictor to predict total hardness (TH), calcium (Ca), chloride (Cl), sulfate (SO₄), and total dissolved solids (TDS). The five models showed good/excellent prediction ability of the parameters mentioned

        In the present study, we have reported investigations on the effect of simultaneous substitution of Tl at the Hg site in the oxygen deficient HgOδ layer of Hg1-xTlxBa2Ca2Cu3O8+ δ cuprate superconductor. Bulk polycrystalline samples were prepared by the two-step solid state reaction process. It was observed that the grown Hg1-xTlxBa2Ca2Cu3O8+ δ corresponds to the 1223 phase. Electrical resistivity, using four probe technique, is used to find the transition temperature Tc. The highest Tc(0ffset) were 108, 102,113, 118, 125 and 121K for Hg1xTlxBa2Ca2Cu3O8+ δ with x = 0.0, 0.05, 0.10, 0.15, 0.20 and 0.25 respectively. The optimum Tc(off) of ~ 125 K and Tc(onset) ~ 136K   was fo

   This study aimed to synthesize a novel amide prodrug of metformin with aspirin by amide bond. The structure was characterized by (FTIR, 1H-NMR, 13C-NMR and CHNO) ,Purification of the prepared compound was using column chromatography.          Using of 40 rabbit having the same weight and devided into 4 groups (4x10) the first group (G1): (the control healthy group) was given drink water and didn’t give any material, second group(G2):( the control infected group) was given hydrogen peroxide concentration  % 0.5 until infecting diabetes  mellitus,  third group(G3): was given hydrogen peroxide concentration as in (G2) and ( 250 mg/kg) of aspirin and (348.8 mg/kg

The problem of Bi-level programming is to reduce or maximize the function of the target by having another target function within the constraints. This problem has received a great deal of attention in the programming community due to the proliferation of applications and the use of evolutionary algorithms in addressing this kind of problem. Two non-linear bi-level programming methods are used in this paper. The goal is to achieve the optimal solution through the simulation method using the Monte Carlo method using different small and large sample sizes. The research reached the Branch Bound algorithm was preferred in solving the problem of non-linear two-level programming this is because the results were better.

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 rise of edge-cloud continuum computing is a result of the growing significance of edge computing, which has become a complementary or substitute option for traditional cloud services. The convergence of networking and computers presents a notable challenge due to their distinct historical development. Task scheduling is a major challenge in the context of edge-cloud continuum computing. The selection of the execution location of tasks, is crucial in meeting the quality-of-service (QoS) requirements of applications. An efficient scheduling strategy for distributing workloads among virtual machines in the edge-cloud continuum data center is mandatory to ensure the fulfilment of QoS requirements for both customer and service provider. E