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Research Article | Open Access

Exploring Gould–Hopper Sheffer–based Appell polynomials via operational approach and Riordan arrays

Mohamed Rhaima1Waseem Ahmad Khan2Shahid Ahmad Wani3( )Georgia Irina Oros4( )
Department of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
Department of Electrical Engineering, Prince Mohammad Bin Fahd University, P.O Box 1664, Al Khobar 31952, Saudi Arabia
Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune, India
Department of Mathematics and Computer Science, Faculty of Informatics and Sciences, University of Oradea, 410087 Oradea, Romania
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Abstract

The operational and algebraic framework offers a powerful and systematic approach for investigating the structural properties of hybrid special polynomial families. In this work, we introduce a new class of Gould–Hopper Sheffer-based Appell polynomials (GHSbAP) by combining the Gould–Hopper polynomial structure with the general theory of Sheffer and Appell sequences. The offered construction is implemented by means of exponential generating functions and operational methods based upon the principle of monomiality. Basic properties of the GHSbAP family are constructed including generating functions, series representations, operational identities, quasi-monomial behavior, and differential equations. Further, the computationally efficient characterization of determinant representation is derived through the relation between Sheffer sequences and generalized Riordan arrays. A number of illustrative cases such as Gould–Hopper-Sheffer based Bernoulli and Euler polynomials are demonstrated.

CLC number: 11B83, 11T23, 12E10 33C45, 33E20

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AIMS Mathematics
Pages 14075-14095

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Cite this article:
Rhaima M, Khan WA, Wani SA, et al. Exploring Gould–Hopper Sheffer–based Appell polynomials via operational approach and Riordan arrays. AIMS Mathematics, 2026, 11(5): 14075-14095. https://doi.org/10.3934/math.2026578

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Received: 24 December 2025
Revised: 25 February 2026
Accepted: 26 February 2026
Published: 15 May 2026
©2026 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)