Graduate University of Advanced Technology

About: Graduate University of Advanced Technology is a education organization based out in Kerman, Iran. It is known for research contribution in the topics: Carbon paste electrode & Electrochemical gas sensor. The organization has 890 authors who have published 2169 publications receiving 31027 citations.

Eggshell/Fe3O4 nanocomposite: novel magnetic nanoparticles coated on porous ceramic eggshell waste as an efficient catalyst in the synthesis of 1,8-dioxo-octahydroxanthene

Abstract: An eggshell/Fe3O4 nanocomposite was synthesized simply via an economic and novel method, using recycled eggshell biowaste as a starting material and an aqueous solution of FeSO4 as a coating agent without any additional alkali or a protective atmosphere. Furthermore, the catalytic activity of the magnetic nanocomposite was investigated in the synthesis of 1,8-dioxo-octahydroxanthenes under solvent-free conditions. The reaction proceeds to completion in a short reaction time with an excellent yield. The suggested strategy for synthesizing 1,8-dioxo-octahydroxanthenes is of great interest because a novel, green, and low-cost magnetic nanocomposite is used as a heterogeneous catalyst, on account of its convenient preparation and high reusability.

Generalized performance analysis of mixed RF/FSO systems

Abstract: We investigate the performance of dual-hop fixed-gain amplify-and-forward relaying systems over Nakagami-m and double generalized Gamma fading channels with pointing error for mixed radio frequency/free space optical (RF/FSO) wireless systems. Our analysis generalizes several previous results, since it considers general fading channels and both detection methods in FSO systems. In particular, exact expressions for the end-to-end outage probability and bit error probability are derived in dosed-form, while useful expressions are presented for the asymptotically high signal to noise ratio (SNR) regime. The numerical results provide important physical insights into the implications of model parameters on the system performance; for instance, it is demonstrated that in all cases under consideration, the lowest outage and bit error rates would not guarantee only by decreasing the pointing error and using the heterodyne detection and not increasing the RF link parameters.

Evaluation of conjugate depths of hydraulic jump in circular pipes using evolutionary computing

Abstract: Hydraulic jump phenomenon occurring in the circular pipes is a complex issue which has been paid studious attentions by hydraulic engineers. Several experimental and numerical studies were performed so as to characterize hydraulic jump in circular pipes. There are few comprehensive equations to predict the conjugate depth in circular pipes. In this investigation, three numerical models on the basis of evolutionary computing as gene-expression programming (GEP), model tree (MT), and evolutionary polynomial regression (EPR) have been utilized to evaluate the conjugate depths of the hydraulic jump in the circular pipes. Two non-dimensional parameters were yielded from conceptions of specific force to determine a functional relationship between the input and output variables. The performances of proposed approaches were compared with those obtained using conventional methods. The performance of MT indicated an accurate prediction of conjugate depths (R = 0.995 and RMSE = 0.023) in comparison with those other artificial intelligence (AI) models and empirical equations. The uncertainties prediction of the improved models were quantified and compared with those of existing models. The results of the proposed models demonstrated that linear equations provided by MT had more convenient application than empirical equations extracted from experimental investigations.