The current study performs an explicit nonlinear finite element simulation to predict temperature distribution and consequent stresses during the friction stir welding (FSW) of AA 7075-T651 alloy. The ABAQUS® finite element software was used to model and analyze the process steps that involve plunging, dwelling, and traverse stages. Techniques such as Arbitrary Lagrangian–Eulerian (ALE) formulation, adaptive meshing, and computational feature of mass scaling were utilized to simulate sequence events during the friction stir welding process. The contact between the welding tool and workpiece was modelled through applying Coulomb’s friction model with a nonlinear friction coefficient value. Also, the model considered the effect of nonlin

Background: Rheumatoid arthritis (RA) is a chronic and systemic autoimmune disease that is characterized by severe synovial inflammation, cartilage erosion, bone loss, and generalized vasculopathy. Although the immunologic mechanism of RA is still unclear, it is now thought to be a primarily Th17-driven disease. Along with other factors, IL-23 stimulates the expansion of Th17 cells from naive CD4+ T cells.

Objective: The objective of this study is to assess the circulating levels of interleukin (IL)-23 in rheumatoid arthritis (RA) and determine the correlation between plasma/serum IL-23 levels and disease activity. So, we performed a systematic review with meta-analysis comparing

BP algorithm is the most widely used supervised training algorithms for multi-layered feedforward neural net works. However, BP takes long time to converge and quite sensitive to the initial weights of a network. In this paper, a modified cuckoo search algorithm is used to get the optimal set of initial weights that will be used by BP algorithm. And changing the value of BP learning rate to improve the error convergence. The performance of the proposed hybrid algorithm is compared with the stan dard BP using simple data sets. The simulation result show that the proposed algorithm has improved the BP training in terms of quick convergence of the solution depending on the slope of the error graph.

Abstract

  Bivariate time series modeling and forecasting have become a promising field of applied studies in recent times. For this purpose, the Linear Autoregressive Moving Average with exogenous variable ARMAX model is the most widely used technique over the past few years in modeling and forecasting this type of data. The most important assumptions of this model are linearity and homogenous for random error variance of the appropriate model. In practice, these two assumptions are often violated, so the Generalized Autoregressive Conditional Heteroscedasticity (ARCH) and (GARCH) with exogenous varia

To create a highly efficient photovoltaic-thermal (PV-T) system and maximise the energy and exergy efficiency, this study aims to propose an innovative configuration of a PV-T system comprising wavy tubes with twisted-tape inserts. Following the validation of a numerical model, a parametric study has been conducted to assess the geometrical effects of twisted tape and wavy tubes, as well as the coolant fluid type and velocity, on the overall performance of a PV-T system, located in Shiraz, Iran. It is found that employing twisted tape improves the energy and exergy efficiency by approx. 6.3%. The best configuration yields 12.4% and 16.8% increase in energy and exergy efficiency compared to conventional PV systems. This is achieved at 15% vo

Within the framework of big data, energy issues are highly significant. Despite the significance of energy, theoretical studies focusing primarily on the issue of energy within big data analytics in relation to computational intelligent algorithms are scarce. The purpose of this study is to explore the theoretical aspects of energy issues in big data analytics in relation to computational intelligent algorithms since this is critical in exploring the emperica aspects of big data. In this chapter, we present a theoretical study of energy issues related to applications of computational intelligent algorithms in big data analytics. This work highlights that big data analytics using computational intelligent algorithms generates a very high amo

KA Sharquie, AA Al-Nuaimy, Annals of Saudi Medicine, 2002 - Cited by 48

In this work  Polyynes was synthesized by pulse laser ablation of graphite target in ethanol solution. UV-Visible Spectrophotometer, Fourier Transform Infrared Spectroscopy (FTIR) and Transmission electron microscopy (TEM) were used to study the optical absorption, chemical bonding, particle size and the morphology.  UV absorption peaks coincide with the electronic transitions corresponding to linear hydrogen – capped polyyne (C_n+1H₂), the absorption peaks intensity increased when the polyynes were produced at different laser energies and the formation rats of polyynes increased with the increasing of laser pulse number. The FTIR absorption peak at 2368.4 cm^-1, 1640.0 cm^-1 and 1276.