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Institution

Graduate University of Advanced Technology

EducationKerman, Iran
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.


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Journal ArticleDOI
TL;DR: In this paper, the authors proposed two different approaches to predict the critical velocity for sediment deposition in sewer networks: Model Tree (MT) and the Evolutionary Polynomial Regression (EPR), a hybrid data-driven technique that combines genetic algorithms with numerical regression.
Abstract: Prediction of critical velocity for sediment deposition is a significant component in design of sewer pipes. Because of the abrupt changes in velocity and shear stress distributions, traditional equations based on regression analysis can fail in evaluating sediment transport efficiently. Therefore, different artificial intelligence approaches have been applied to investigate sediment transport in sewer pipes. This study proposes two different approaches to predict the critical velocity for sediment deposition in sewer networks: Model Tree (MT) and the Evolutionary Polynomial Regression (EPR), a hybrid data-driven technique that combines genetic algorithms with numerical regression. The hydraulic radius, average size of sediments, volumetric concentration, total friction factor, and non-dimensional sediment size were considered as input parameters to characterize sediment transport in clean sewer pipes. The present study implements data collected from different works in literature. The proposed modeling approaches are compared to some benchmark formulas from literature, and discussed from the accuracy and knowledge discovery points of view, highlighting the advantage of both proposed techniques. Results indicated that both techniques have similar accuracy in predictions, but EPR allows to physical validation of returned formulas, allowing identifying the most influent inputs on the phenomenon at stake.

37 citations

Journal ArticleDOI
TL;DR: The results show that the immobilization of Penaeus vanamei protease on zinc oxide nanoparticles enhanced its appropriateness for a future use in various biotechnological and industrial applications.

37 citations

Journal ArticleDOI
TL;DR: The differential pulse voltammetric method was employed and showed good oxidation response for eugenol in 0.1 mol L(-1) phosphate buffer solution and the peak potential was about +285 mV (vs. Ag/AgCl).

37 citations

Journal ArticleDOI
TL;DR: In this article, the performance of as-prepared nanocatalysts was evaluated for the reactions of methanol, ethanol, ethylene glycol and glycerol electrooxidation in alkaline media by cyclic voltammetry, linear sweep voltammetric and chronoamperometric measurements.

37 citations

Journal ArticleDOI
TL;DR: In this article, a rod-like nanowisker with a simple and green method was synthesized to reduce the survival of Leishmania major promastigotes, at a concentration of 500μg/mL.
Abstract: Greener methods for the synthesis of various nanostructures with well-organized characteristics and biomedical applicability have demonstrated several advantages, including simplicity, low toxicity, cost-effectiveness, and eco-friendliness. Spinel nickel ferrite (NiFe2O4) nanowhiskers with rod-like structures were synthesized using a simple and green method; these nanostructures were evaluated by X-ray diffraction analysis, transmission electron microscopy, scanning electron microscopy, and X-ray energy diffraction spectroscopy. Additionally, the prepared nanowhiskers could significantly reduce the survival of Leishmania major promastigotes, at a concentration of 500 μg/mL; the survival of promastigotes was reduced to ≃ 26%. According to the results obtained from MTT test (in vitro), it can be proposed that further studies should be conducted to evaluate anti-leishmaniasis activity of these types of nanowhiskers in animal models.

37 citations


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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202310
202235
2021300
2020303
2019290
2018259