Role of the dynamics of political stability in firm performance: Evidence from Bangladesh

Mohammad Abdullah; Mohammad Ashraful Ferdous Chowdhury; Uttam Karmaker; Md. Habibur Rahman Fuszder; Md. Asif Shahriar; Mohammad Abdullah; Mohammad Ashraful Ferdous Chowdhury; Uttam Karmaker; Md. Habibur Rahman Fuszder; Md. Asif Shahriar

doi:10.3934/QFE.2022022

Quantitative Finance and Economics

2022, Volume 6, Issue 4: 518-536. doi: 10.3934/QFE.2022022

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Research article

Role of the dynamics of political stability in firm performance: Evidence from Bangladesh

1.
Faculty of Business and Management, University Sultan Zainal Abidin, Malaysia
2.
IRC for Finance and Digital Economy, KFUPM Business School, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
3.
Universal College, Bangladesh
4.
Independent University, Bangladesh

Received: 29 August 2022 Revised: 29 September 2022 Accepted: 09 October 2022 Published: 25 October 2022
JEL Codes: G3, G32, G38

This study examines the role of political stability in a firm's financial performance in Bangladesh. By considering 139 listed companies from the Dhaka Stock Exchange over the period of 2011 to 2020, we applied a dynamic generalized method of moments (GMM), dynamic quantile regression and dynamic threshold regression. The empirical evidence of this study shows a significant positive impact of political stability on Bangladeshi firms' financial performances. Using dynamic quantile regression, we found a positive impact of political stability in the firms' upper and lower quantiles. Additionally, we found the threshold effect of political stability on the firms' performance to have a score of 13.680. This study contributes theoretically and empirically by examining the importance of political stability on financial performance. For the investors, policymakers and other stakeholders, this study provides evidence of a threshold of political stability on a firm's financial performance.

Keywords:

Citation: Mohammad Abdullah, Mohammad Ashraful Ferdous Chowdhury, Uttam Karmaker, Md. Habibur Rahman Fuszder, Md. Asif Shahriar. Role of the dynamics of political stability in firm performance: Evidence from Bangladesh[J]. Quantitative Finance and Economics, 2022, 6(4): 518-536. doi: 10.3934/QFE.2022022

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Abstract

1. Introduction

In the past decade, many researchers have studied many fractional partial differential equations (FPDEs) types. Since the beginning of fractional calculus history in 1695, when L'Hospital raised the question: what is the meaning of $\frac{{d}^{n}y}{{dx}^{n}}$ if $n = \frac{1}{2}$ ? That is, what if n is fractional? ^[1] Even for new researchers, the fractional derivatives were complicated-although it appears in many parts of sciences such as physics, engineering, bioengineering, COVID-19 studies, and many other branches of sciences ^{[2,3,4,5,6,7,8,9]}. In addition, many definitions of fractional derivatives have been given ^[10,11]. Fractional order derivatives of a given function involve the entire function history where the following state of a fractional order system is dependent on its current state and all its historical states ^{[4,5,6,7,8,9,10,11]}.

There are several analytical and numerical techniques for handling fractional problems, B-spline functions, Bernoulli polynomials, Adomian decomposition, variational iteration, Homotopy analysis, and many others ^{[12,13,14,15,16,17]}. On top of that, some applicable analytical methods are developed to address nonlinear problems with fractional derivatives. One of these approaches is the Laplace residual power series method (LRPSM) ^{[18,19,20,21,22,23,24,25,26,27,28,29]}, which shows its efficiency and applicability in solving nonlinear problems.

The LRPSM is a very modern technique, and it is a hybrid method of two approaches, the Laplace transform (LT) and the idea of the residual power series method (RPSM) ^{[30,31,32,33]}. In 2020, the authors in the article ^[18] were able to adapt the LT to solve nonlinear neutral fractional pantograph equations using the residual function and the RPSM idea. The LT, usually, is implemented to solve linear equations only, but the LRPSM can overcome this disadvantage and thus adapts it to solve nonlinear equations of different types. The LRPSM presents an approximate analytical solution with a series form using the concept of the Laurent series and the power series ^{[18,19,20,21,22,23,24,25]}. What distinguishes LRPSM from RPSM is the use of the idea of limit at infinity in getting the coefficients of a series solution rather than the concept of a fractional derivative as in RPSM. Many articles used the proposed method to treat several types of differential equations of fractional orders. In 2021, El-Ajou adapted LRPSM to establish solitary solutions of nonlinear dispersive FPDEs ^[19] and to present series solutions for systems of Caputo FPDEs with variable coefficients ^[20]. Newly, the LRPSM is used for solving Fuzzy Quadratic Riccati Differential Equations ^[21], time-fractional nonlinear water wave PDE ^[22], fractional Lane-Emden equations ^[23], Fisher's equation and logistic system model ^[24], and nonlinear fractional reaction-diffusion for bacteria growth model ^[25].

Claude Louis Navier and Gabriel Stokes have created the so-called Navier-Stokes equations (NSEs). A French mechanical engineer Claude was affiliated in continuum mechanics with a physicist specializing and the French government, whose main contribution was the Navier-Stokes equations (1822). This famous equation made his name among the several names incised on the Eiffel Tower. Moreover, Newton's second law for fluid substance, which is central to fluid mechanics has been used in describing many physical phenomena in many applied sciences ^{[34,35,36,37]}. For example, the study of airflow around a wing and water flow in pipes and used as one of the continuity equations needed to build microscopic models in 1985 and also as a special case considered to establish the relationship between external and pressure forces on the fluid to the responses of fluid flow ^[38].

The motivation of this work is to adapt the LRPSM to provide analytical solutions for a multi-dimensional time-fractional Navier-Stokes (M-DT-FNS) system which takes the following form ^[39]:

${D}_{t}^{\alpha }u+\left(u\cdot \nabla \right)u = \upsilon {\nabla }^{2}u-\frac{1}{\rho }\nabla p, \mathrm{ }\mathrm{ }\mathrm{ }\mathrm{ }0 < \alpha \le 1,$

(1.1)

where ${D}_{t}^{\alpha }$ is the Caputo fractional-derivative operator of order $\alpha$ , $p = p\left(\chi, \varsigma, \zeta, t\right)$ is the pressure, $\rho$ is the density, $u$ is a vector field that represents the flow velocity vector, $\upsilon = \frac{\mu }{\rho }$ is the kinematic viscosity ( $\mu$ is the dynamic viscosity), and $\nabla$ & ${\nabla }^{2}$ are the gradient and Laplacian operators, respectively, subject to the initial conditions (ICs) at the initial velocity:

$u = \varphi .$

(1.2)

If the density is constant throughout the fluid domain, then the vector Eq (1.1) is an incompressible NSEs.

The vector Eqs (1.1) and (1.2) can be separated in a system form as follows ^[39,40]:

$\begin{array}{l} {D}_{t}^{\alpha }{u}_{1}+{u}_{1}\frac{\partial {u}_{1}}{\partial \chi }+{u}_{2}\frac{\partial {u}_{1}}{\partial \varsigma }+{u}_{3}\frac{\partial {u}_{1}}{\partial \zeta } = \upsilon \left(\frac{{\partial }^{2}{u}_{1}}{\partial {\chi }^{2}}+\frac{{\partial }^{2}{u}_{1}}{\partial {\varsigma }^{2}}+\frac{{\partial }^{2}{u}_{1}}{\partial {\zeta }^{2}}\right)-\frac{1}{\rho }\frac{\partial p}{\partial \chi }, \\ {D}_{t}^{\alpha }{u}_{2}+{u}_{1}\frac{\partial {u}_{2}}{\partial \chi }+{u}_{2}\frac{\partial {u}_{2}}{\partial \varsigma }+{u}_{3}\frac{\partial {u}_{2}}{\partial \zeta } = \upsilon \left(\frac{{\partial }^{2}{u}_{2}}{\partial {\chi }^{2}}+\frac{{\partial }^{2}{u}_{2}}{\partial {\varsigma }^{2}}+\frac{{\partial }^{2}{u}_{2}}{\partial {\zeta }^{2}}\right)-\frac{1}{\rho }\frac{\partial p}{\partial \varsigma }, \\ {D}_{t}^{\alpha }{u}_{3}+{u}_{1}\frac{\partial {u}_{3}}{\partial \chi }+{u}_{2}\frac{\partial {u}_{3}}{\partial \varsigma }+{u}_{3}\frac{\partial {u}_{3}}{\partial \zeta } = \upsilon \left(\frac{{\partial }^{2}{u}_{3}}{\partial {\chi }^{2}}+\frac{{\partial }^{2}{u}_{3}}{\partial {\varsigma }^{2}}+\frac{{\partial }^{2}{u}_{3}}{\partial {\zeta }^{2}}\right)-\frac{1}{\rho }\frac{\partial p}{\partial \zeta }, \end{array}$

(1.3)

subject to the initial conditions (ICs):

$\begin{array}{l} {u}_{1}\left(\chi , \varsigma , \zeta , 0\right) = f\left(\chi , \varsigma , \zeta \right) = f, \\ {u}_{2}\left(\chi , \varsigma , \zeta , 0\right) = h\left(\chi , \varsigma , \zeta \right) = h, \\ {u}_{3}\left(\chi , \varsigma , \zeta , 0\right) = g\left(\chi , \varsigma , \zeta \right) = g, \end{array}$

(1.4)

where $u = \left\langle{{u}_{1}, {u}_{2}, {u}_{3}}\right\rangle$ and $\varphi = \left\langle{f, h, g}\right\rangle$ such that ${u}_{1}$ , ${u}_{2}$ , ${u}_{3}$ , and $p$ are analytical functions of four variables $\chi, \varsigma, \zeta \ \&\ t$ .

In this equation, the solution represents the fluid velocity and pressure. It is commonly used to describe the motion of fluids in models relevant to weather, ocean currents, water flow in pipes, etc.

The novelty of this study is obvious in the proposed method when dealing with the Navier-Stokes problem, we show the simplicity and the applicability of the method, and we mention also that the method needs no differentiation, linearization, or discretization, the only mathematical step we need after taking the LT and defining the residual functions, is taking the limit at infinity which is much easier compared to other analytical techniques. Moreover, in this research, we obtain a general formula of the solution that neither researcher has, allowing us to compute as many possible terms of the series solution directly.

This study is prepared as follows: After the introduction section, a few fundamental principles and theories are reviewed for constructing an analytic series solution to the M-DT-FNS system using LRPSM. In Section 3, we constructed a Laplace residual power series (LRPS) solution to the goal problem. Three interesting examples are presented to explain the technique's simplicity and accuracy, which are displayed in Section 4. Finally, some conclusions are made about the features of the method used and its applicability in solving other types of problems.

2. Basic concepts

This part presents fundamental definitions and properties of fractional operators and power series.

Definition 2.1. ^[1] The time Caputo fractional-derivative of order $\mathrm{\alpha }$ of the multivariable function $u\left(\chi, \varsigma, \zeta, t\right)$ , is defined by

${D}_{t}^{\alpha }\left[u\left(\chi , \varsigma , \zeta , t\right)\right] = \left\{\begin{array}{c}\frac{1}{\mathrm{\Gamma }(n-\alpha )}{\int }_{0}^{t}\frac{\frac{{\partial }^{n}}{\partial {\xi }^{n}}u\left(\chi , \varsigma , \zeta , \xi \right)}{{\left(t-\xi \right)}^{\alpha +1-n}}d\xi ,\ \ n-1 < \alpha < n, \\ \frac{{\partial }^{n}}{\partial {t}^{n}}u\left(\chi , \varsigma , \zeta , t\right), \ \ \alpha = n.\end{array}\right.$

(2.1)

Lemma 2.1. ^[1] The operator ${D}_{t}^{\alpha }$ , $t\ge 0$ , $n-1 < \alpha \le n$ satisfies the following properties:

1) ${D}_{t}^{\alpha }c = 0$ , $c\in \mathbb{R}.$

2) ${D}_{t}^{\alpha }{t}^{q} = \left\{\begin{array}{c}\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ 0\ \ \ \ \ \ \ \ \ \ , \ \ q < \alpha, q\ {\rm{is}} \ {\rm{integer}}\\ \frac{\mathrm{\Gamma }\left(q+1\right)}{\mathrm{\Gamma }\left(q-\alpha +1\right)}{t}^{q-\alpha },\ \ {\rm{otherwise}}\end{array}, \right.$ for $q > -1.$

Definition 2.2. ^[18] The time-LT for the multivariable function $u(\chi, \varsigma, \zeta, t)$ is defined by:

$U\left(\chi , \varsigma , \zeta , s\right) = \mathcal{L}\left[u\left(\chi , \varsigma , \zeta , t\right)\right] = \int_{0}^{\infty } {e}^{-st}u\left(\chi , \varsigma , \zeta , t\right)dt, s > \delta .$

(2.2)

We denote the inverse LT of the function $U(\chi, \upsilon, \zeta, s)$ and define it as

$u\left(\chi , \varsigma , \zeta , t\right) = {\mathcal{L}}^{-1}\left[U\left(\chi , \varsigma , \zeta , s\right)\right] = \int_{{z-i\mathrm{\infty }}}^{{{z+i\mathrm{\infty }}}}{e}^{st}U\left(\chi , \varsigma , \zeta , s\right)ds;z = Re\left(s\right) > {z}_{0}.$

(2.3)

The most popular properties of the LT are mentioned below.

Lemma 2.2. ^[18,19] Assume that $U\left(\chi, \varsigma, \zeta, s\right) = \mathcal{L}\left[u\left(\chi, \varsigma, \zeta, t\right)\right]$ and $V\left(\chi, \varsigma, \zeta, s\right) = \mathcal{L}\left[v\left(\chi, \varsigma, \zeta, t\right)\right]$ . Then

1) $\underset{s\to \mathrm{\infty }}{\mathrm{lim}}sU\left(\chi, \varsigma, \zeta, s\right) = u\left(\chi, \varsigma, \zeta, 0\right).$

2) $\mathcal{L}\left[a\frac{{t}^{i\alpha }}{\mathrm{\Gamma }\left(i\alpha +1\right)}\right] = \frac{a}{{s}^{i\alpha +1}}, a\in \mathbb{R}, i = 0, \mathrm{ }1, \mathrm{ }\cdots.$

3) $\mathcal{L}\left[{D}_{t}^{m\alpha }u\left(\chi, \varsigma, \zeta, t\right)\right] = {s}^{m\alpha }U\left(\chi, \varsigma, \zeta, s\right)-\sum _{n = 0}^{m-1}{s}^{\left(m-n\right)\alpha -1}{D}_{t}^{n\alpha }u\left(\chi, \varsigma, \zeta, 0\right), \mathrm{ }0 < \alpha \le 1,$

where ${D}_{t}^{m\alpha } = {D}_{t}^{\alpha }{D}_{t}^{\alpha }\dots {D}_{t}^{\alpha }$ ( $m$ -times).

Theorem 2.1. ^[19] The power series expansion of the multivariable function $u(\chi, \varsigma, \zeta, t)$ can be expressed as

$u\left(\chi , \varsigma , \zeta , t\right) = \sum _{i = 0}^{\mathrm{\infty }}\frac{{f}_{i}\left(\chi , \varsigma , \zeta \right){t}^{i\alpha }}{\mathrm{\Gamma }\left(i\alpha +1\right)},$

(2.4)

where ${f}_{i}\left(\chi, \varsigma, \zeta \right) = {D}_{t}^{i\alpha }u(\chi, \varsigma, \zeta, 0)$ , $i = 0, \mathrm{ }1, \mathrm{ }\cdots$ .

Thus, the LT of Eq (2.4) is a Laurent expansion in the Laplace space of the following form:

$u\left(\chi , \varsigma , \zeta , t\right) = \sum _{i = 0}^{\mathrm{\infty }}\frac{{f}_{i}\left(\chi , \varsigma , \zeta \right){t}^{i\alpha }}{\mathrm{\Gamma }\left(i\alpha +1\right)}.$

(2.5)

Theorem 2.2. ^[19] Assume that

$\left|s\mathcal{L}\left[{{D}_{t}}^{(m+1)\alpha }u\left(\chi , \varsigma , \zeta , t\right)\right]\right|\le M,$

on $0\le s\le q, \mathrm{ }0 < \alpha \le 1$ and $M = M\left(\chi, \varsigma, \zeta \right)$ for some $\chi$ , $\varsigma$ , and $\zeta \in I$ . Then the remainder ${R}_{m}(\chi, \varsigma, \zeta, s)$ of the new fractional Laurent series (2.5) satisfies the following inequality

$\left|{R}_{m}(\chi , \varsigma , \zeta , s)\right|\le \frac{M}{{s}^{\left(m+1\right)\alpha +1}}.$

It is known that the LT cannot be distributed in the case of multiplication. Therefore, the following Lemma is introduced to simplify the calculations at the application of LRPSM, based on the characteristics of the powers of the power series.

Lemma 2.3. Assume that $U\left(\chi, \varsigma, \zeta, s\right) = \mathcal{L}\left[u\left(\chi, \varsigma, \zeta, t\right)\right]\left(s\right)$ and $V\left(\chi, \varsigma, \zeta, s\right) = \mathcal{L}\left[v\left(\chi, \varsigma, \zeta, t\right)\right]\left(s\right)$ . Assume that the functions $U\left(\chi, \varsigma, \zeta, s\right)$ and $V\left(\chi, \varsigma, \zeta, s\right)$ have Laurent expansions as:

$\begin{array}{l} \left(\chi , \upsilon , \zeta , s\right) = \sum _{i = 0}^{\mathrm{\infty }}\frac{{f}_{i}\left(\chi , \upsilon , \zeta \right)}{{s}^{i\alpha +1}}, \\ V\left(\chi , \varsigma , \zeta , s\right) = \sum _{j = 0}^{\mathrm{\infty }}\frac{{g}_{j}\left(\chi , \varsigma , \zeta \right)}{{s}^{j\alpha +1}}. \end{array}$

(2.6)

Then, $\mathcal{L}\left[u\left(\chi, \varsigma, \zeta, t\right)v\left(\chi, \varsigma, \zeta, t\right)\right]\left(s\right)$ can be expanded in a Laurent series form, as follows

$\mathcal{L}\left[u\left(\chi , \varsigma , \zeta , t\right)v\left(\chi , \varsigma , \zeta , t\right)\right]\left(s\right) = \sum _{i = 0}^{\mathrm{\infty }}\sum _{j = 0}^{\mathrm{\infty }}\frac{{f}_{i}\left(\chi , \varsigma , \zeta \right){g}_{j}\left(\chi , \varsigma , \zeta \right)\mathrm{\Gamma }(\left(i+j\right)\alpha +1)}{\mathrm{\Gamma }(i\alpha +1)\mathrm{\Gamma }(j\alpha +1){s}^{(i+j)\alpha +1}}.$

(2.7)

Proof. Directly, we can prove the Lemma as follows:

$\mathcal{L}\left[u\left(\chi , \varsigma , \zeta , t\right)v\left(\chi , \varsigma , \zeta , t\right)\right]\left(s\right) = \mathcal{L}\left[{\mathcal{L}}^{-1}\left[U\left(\chi , \varsigma , \zeta , s\right)\right]{\mathcal{L}}^{-1}\left[V\left(\chi , \varsigma , \zeta , s\right)\right]\right]\left(s\right)$

$= \mathcal{L}\left[{\mathcal{L}}^{-1}\left[\sum _{i = 0}^{\mathrm{\infty }}\frac{{f}_{i}\left(\chi , \varsigma , \zeta \right)}{{s}^{i\alpha +1}}\right]{\mathcal{L}}^{-1}\left[\sum _{j = 0}^{\mathrm{\infty }}\frac{{g}_{i}\left(\chi , \varsigma , \zeta \right)}{{s}^{j\alpha +1}}\right]\right]\left(s\right)$

$= \mathcal{L}\left[\sum _{i = 0}^{\mathrm{\infty }}\frac{{f}_{i}\left(\chi , \varsigma , \zeta \right){t}^{i\alpha }}{\mathrm{\Gamma }\left(i\alpha +1\right)}\sum _{j = 0}^{\mathrm{\infty }}\frac{{g}_{i}\left(\chi , \varsigma , \zeta \right){t}^{j\alpha }}{\mathrm{\Gamma }\left(j\alpha +1\right)}\right]\left(s\right)$

$= \mathcal{L}\left[\sum _{i = 0}^{\mathrm{\infty }}\sum _{j = 0}^{\mathrm{\infty }}\frac{{f}_{i}\left(\chi , \varsigma , \zeta \right){g}_{j}\left(\chi , \varsigma , \zeta \right){t}^{\left(i+j\right)\mathrm{\alpha }}}{\mathrm{\Gamma }\left(i\alpha +1\right)\mathrm{\Gamma }\left(j\alpha +1\right)}\right]\left(s\right)$

$= \sum _{i = 0}^{\mathrm{\infty }}\sum _{j = 0}^{\mathrm{\infty }}\frac{{f}_{i}\left(\chi , \varsigma , \zeta \right){g}_{j}\left(\chi , \varsigma , \zeta \right)\mathrm{\Gamma }(\left(i+j\right)\alpha +1)}{\mathrm{\Gamma }(i\alpha +1)\mathrm{\Gamma }(j\alpha +1){s}^{(i+j)\alpha +1}}.$

It is noteworthy, that we can express the $k$ th-truncated series of the Laurent series (2.7) as follows:

$\mathcal{L}\left[{u}_{k}\left(\chi , \varsigma , \zeta , t\right){v}_{k}\left(\chi , \varsigma , \zeta , t\right)\right]\left(s\right) = \sum _{i = 0}^{k}\sum _{j = 0}^{k}\frac{{f}_{i}\left(\chi , \varsigma , \zeta \right){g}_{j}\left(\chi , \varsigma , \zeta \right)\mathrm{\Gamma }(\left(i+j\right)\alpha +1)}{\mathrm{\Gamma }(i\alpha +1)\mathrm{\Gamma }(j\alpha +1){s}^{(i+j)\alpha +1}},$

(2.8)

which we will use extensively throughout our work on the next pages.

3. The Laplace residual power series method

We employ the LRPSM to establish a series solution for the M-DT-FNS system (1.1) in this part of this article. This technique is mainly based on applying the LT on the target equations, assuming solutions of the generated equations have Laurent expansions, and then using the idea of the limit at infinity with the residual functions to get the unknown coefficients in expansions. Finally, we run the inverse LT to obtain the solution of the given equations in the original space.

To get the LRPS solution of the system (1.1), we first apply the LT to each equation in the system (1.1) and use the third part of Lemma 2.2 with the ICs (1.2). Then, after some simplification, we get the following algebraic system in Laplace:

$\begin{array}{l} {U}_{1}-\frac{f}{s}+\frac{1}{{s}^{\alpha }}\mathcal{L}\left[{u}_{1}\frac{\partial {u}_{1}}{\partial \chi }\right]+\frac{1}{{s}^{\alpha }}\mathcal{L}\left[{u}_{2}\frac{\partial {u}_{1}}{\partial \varsigma }\right]+\frac{1}{{s}^{\alpha }}\mathcal{L}\left[{u}_{3}\frac{\partial {u}_{1}}{\partial \zeta }\right]-\frac{\upsilon }{{s}^{\alpha }}\left(\frac{{\partial }^{2}{U}_{1}}{\partial {\chi }^{2}}+\frac{{\partial }^{2}{U}_{1}}{\partial {\varsigma }^{2}}+\frac{{\partial }^{2}{U}_{1}}{\partial {\zeta }^{2}}\right)+\frac{1}{{\rho s}^{\alpha }}\frac{\partial P}{\partial \chi } = 0, \\ {U}_{2}-\frac{h}{s}+\frac{1}{{s}^{\alpha }}\mathcal{L}\left[{u}_{1}\frac{\partial {u}_{2}}{\partial \chi }\right]+\frac{1}{{s}^{\alpha }}\mathcal{L}\left[{u}_{2}\frac{\partial {u}_{2}}{\partial \varsigma }\right]+\frac{1}{{s}^{\alpha }}\mathcal{L}\left[{u}_{3}\frac{\partial {u}_{2}}{\partial \zeta }\right]-\frac{\upsilon }{{s}^{\alpha }}\left(\frac{{\partial }^{2}{U}_{2}}{\partial {\chi }^{2}}+\frac{{\partial }^{2}{U}_{2}}{\partial {\varsigma }^{2}}+\frac{{\partial }^{2}{U}_{2}}{\partial {\zeta }^{2}}\right)+\frac{1}{{\rho s}^{\alpha }}\frac{\partial P}{\partial \varsigma } = 0, \\ {U}_{3}-\frac{g}{s}+\frac{1}{{s}^{\alpha }}\mathcal{L}\left[{u}_{1}\frac{\partial {u}_{3}}{\partial \chi }\right]+\frac{1}{{s}^{\alpha }}\mathcal{L}\left[{u}_{2}\frac{\partial {u}_{3}}{\partial \varsigma }\right]+\frac{1}{{s}^{\alpha }}\mathcal{L}\left[{u}_{3}\frac{\partial {u}_{3}}{\partial \zeta }\right]-\frac{\upsilon }{{s}^{\alpha }}\left(\frac{{\partial }^{2}{U}_{3}}{\partial {\chi }^{2}}+\frac{{\partial }^{2}{U}_{3}}{\partial {\varsigma }^{2}}+\frac{{\partial }^{2}{U}_{3}}{\partial {\zeta }^{2}}\right)+\frac{1}{{\rho s}^{\alpha }}\frac{\partial P}{\partial \zeta } = 0, \end{array}$

(3.1)

where ${U}_{1} = \mathcal{L}\left[{u}_{1}\right]$ , ${U}_{2} = \mathcal{L}\left[{u}_{2}\right]$ , ${U}_{3} = \mathcal{L}\left[{u}_{3}\right]$ and $P = \mathcal{L}\left[p\right]$ .

Suppose that the solution of the system (3.1), ${U}_{1}$ , ${U}_{2}$ , and ${U}_{3}$ , has the following fractional Laurent expansions as follows:

$\begin{array}{l} {U}_{1} = \sum _{i = 0}^{\mathrm{\infty }}\frac{{f}_{i}\left(\chi , \varsigma , \zeta \right)}{{s}^{i\alpha +1}}, \\ {U}_{2} = \sum _{j = 0}^{\mathrm{\infty }}\frac{{h}_{j}\left(\chi , \varsigma , \zeta \right)}{{s}^{j\alpha +1}}, \\ {U}_{3} = \sum _{r = 0}^{\mathrm{\infty }}\frac{{g}_{r}\left(\chi , \varsigma , \zeta \right)}{{s}^{r\alpha +1}}. \end{array}$

(3.2)

Assume that also $P$ has the following Laurent expansion:

$P = \frac{\varphi }{s}+\sum _{i = 1}^{\mathrm{\infty }}\frac{{\varphi }_{i}}{{s}^{i\alpha +1}}.$

(3.3)

According to Theorem 2.1 and using the ICs (1.2), the expansions in Eq (3.2) can be rewritten as follows:

$\begin{array}{l} {U}_{1} = \frac{f}{s}+\sum _{i = 1}^{\mathrm{\infty }}\frac{{f}_{i}\left(\chi , \varsigma , \zeta \right)}{{s}^{i\alpha +1}}, \\ {U}_{2} = \frac{h}{s}+\sum _{i = 1}^{\mathrm{\infty }}\frac{{h}_{i}\left(\chi , \varsigma , \zeta \right)}{{s}^{i\alpha +1}}, \\ {U}_{3} = \frac{g}{s}+\sum _{r = 1}^{\mathrm{\infty }}\frac{{g}_{r}\left(\chi , \varsigma , \zeta \right)}{{s}^{r\alpha +1}}. \end{array}$

(3.4)

The $k$ th-truncated series of the expansions in Eqs (3.3) and (3.4) are given by:

$\begin{array}{l} {U}_{1, k} = \frac{f}{s}+\sum _{i = 1}^{k}\frac{{f}_{i}}{{s}^{i\alpha +1}}, \\ {U}_{2, k} = \frac{h}{s}+\sum _{j = 1}^{k}\frac{{h}_{j}}{{s}^{j\alpha +1}}, \\ {U}_{3, k} = \frac{g}{s}+\sum _{r = 1}^{k}\frac{{g}_{r}}{{s}^{r\alpha +1}}, \\ {P}_{k} = \frac{\varphi }{s}+\sum _{i = 1}^{k}\frac{{\varphi }_{i}}{{s}^{i\alpha +1}}. \end{array}$

(3.5)

To find the coefficients in the series expansions of Eq (3.4), we establish the Laplace residual functions ( $\mathcal{L}$ RF) of the equations in the system (3.1) as follows:

$\begin{array}{l} \mathcal{L}Re{s}_{1}\left(s\right) = {U}_{1}-\frac{f}{s}+\frac{1}{{s}^{\alpha }}\mathcal{L}\left[{u}_{1}\frac{\partial {u}_{1}}{\partial \chi }\right]+\frac{1}{{s}^{\alpha }}\mathcal{L}\left[{u}_{2}\frac{\partial {u}_{1}}{\partial \varsigma }\right]+\frac{1}{{s}^{\alpha }}\mathcal{L}\left[{u}_{3}\frac{\partial {u}_{1}}{\partial \zeta }\right]-\frac{\upsilon }{{s}^{\alpha }}\left(\frac{{\partial }^{2}{U}_{1}}{\partial \chi }+\frac{{\partial }^{2}{U}_{1}}{\partial {\varsigma }^{2}}+\frac{{\partial }^{2}{U}_{1}}{\partial {\zeta }^{2}}\right)+\frac{1}{{\rho s}^{\alpha }}\frac{\partial P}{\partial \chi }, \\ \mathcal{L}Re{s}_{2}\left(s\right) = {U}_{2}-\frac{h}{s}+\frac{1}{{s}^{\alpha }}\mathcal{L}\left[{u}_{1}\frac{\partial {u}_{2}}{\partial \chi }\right]+\frac{1}{{s}^{\alpha }}\mathcal{L}\left[{u}_{2}\frac{\partial {u}_{2}}{\partial \varsigma }\right]+\frac{1}{{s}^{\alpha }}\mathcal{L}\left[{u}_{3}\frac{\partial {u}_{2}}{\partial \zeta }\right]-\frac{\upsilon }{{s}^{\alpha }}\left(\frac{{\partial }^{2}{U}_{2}}{\partial {\chi }^{2}}+\frac{{\partial }^{2}{U}_{2}}{\partial {\varsigma }^{2}}+\frac{{\partial }^{2}{U}_{2}}{\partial {\zeta }^{2}}\right)+\frac{1}{{\rho s}^{\alpha }}\frac{\partial P}{\partial \varsigma }, \\ \mathcal{L}Re{s}_{3}\left(s\right) = {U}_{3}-\frac{g}{s}+\frac{1}{{s}^{\alpha }}\mathcal{L}\left[{u}_{1}\frac{\partial {u}_{3}}{\partial \chi }\right]+\frac{1}{{s}^{\alpha }}\mathcal{L}\left[{u}_{2}\frac{\partial {u}_{3}}{\partial \varsigma }\right]+\frac{1}{{s}^{\alpha }}\mathcal{L}\left[{u}_{3}\frac{\partial {u}_{3}}{\partial \zeta }\right]-\frac{\upsilon }{{s}^{\alpha }}\left(\frac{{\partial }^{2}{U}_{3}}{\partial {\chi }^{2}}+\frac{{\partial }^{2}{U}_{3}}{\partial {\varsigma }^{2}}+\frac{{\partial }^{2}{U}_{3}}{\partial {\zeta }^{2}}\right)+\frac{1}{{\rho s}^{\alpha }}\frac{\partial P}{\partial \zeta }, \end{array}$

(3.6)

and the kth- $\mathcal{L}$ RF for $k = \mathrm{1, 2}, \dots$ , as follows:

$\begin{array}{l} \mathcal{L}Re{s}_{1, k}\left(s\right) = {U}_{1, k}-\frac{f}{s}+\frac{1}{{s}^{\alpha }}\mathcal{L}\left[{u}_{1, k}\frac{\partial {u}_{1, k}}{\partial \chi }\right]+\frac{1}{{s}^{\alpha }}\mathcal{L}\left[{u}_{2, k}\frac{\partial {u}_{1, k}}{\partial \varsigma }\right] \\ \ \ \ \ \ \ \ \ \ \ \ +\frac{1}{{s}^{\alpha }}\mathcal{L}\left[{u}_{3, k}\frac{\partial {u}_{1, k}}{\partial \zeta }\right]-\frac{\upsilon }{{s}^{\alpha }}\left(\frac{{\partial }^{2}{U}_{1, k}}{\partial {\chi }^{2}}+\frac{{\partial }^{2}{U}_{1, k}}{\partial {\varsigma }^{2}}+\frac{{\partial }^{2}{U}_{1, k}}{\partial {\zeta }^{2}}\right)+\frac{1}{{\rho s}^{\alpha }}\frac{\partial {P}_{k}}{\partial \chi }, \\ \mathcal{L}Re{s}_{2, k}\left(s\right) = {U}_{2, k}-\frac{h}{s}+\frac{1}{{s}^{\alpha }}\mathcal{L}\left[{u}_{1, k}\frac{\partial {u}_{2, k}}{\partial \chi }\right]+\frac{1}{{s}^{\alpha }}\mathcal{L}\left[{u}_{2, k}\frac{\partial {u}_{2, k}}{\partial \varsigma }\right]\\ \ \ \ \ \ \ \ \ \ \ \ +\frac{1}{{s}^{\alpha }}\mathcal{L}\left[{u}_{3, k}\frac{\partial {u}_{2, k}}{\partial \zeta }\right]-\frac{\upsilon }{{s}^{\alpha }}\left(\frac{{\partial }^{2}{U}_{2, k}}{\partial {\chi }^{2}}+\frac{{\partial }^{2}{U}_{2, k}}{\partial {\varsigma }^{2}}+\frac{{\partial }^{2}{U}_{2, k}}{\partial {\zeta }^{2}}\right)+\frac{1}{{\rho s}^{\alpha }}\frac{\partial {P}_{k}}{\partial \varsigma }, \\ \mathcal{L}Re{s}_{3, k}\left(s\right) = {U}_{3, k}-\frac{g}{s}+\frac{1}{{s}^{\alpha }}\mathcal{L}\left[{u}_{1, k}\frac{\partial {u}_{3, k}}{\partial \chi }\right]+\frac{1}{{s}^{\alpha }}\mathcal{L}\left[{u}_{2, k}\frac{\partial {u}_{3, k}}{\partial \varsigma }\right] \\ \ \ \ \ \ \ \ \ \ \ \ +\frac{1}{{s}^{\alpha }}\mathcal{L}\left[{u}_{3, k}\frac{\partial {u}_{3, k}}{\partial \zeta }\right]-\frac{\upsilon }{{s}^{\alpha }}\left(\frac{{\partial }^{2}{U}_{3, k}}{\partial {\chi }^{2}}+\frac{{\partial }^{2}{U}_{3, k}}{\partial {\varsigma }^{2}}+\frac{{\partial }^{2}{U}_{3, k}}{\partial {\zeta }^{2}}\right)+\frac{1}{{\rho s}^{\alpha }}\frac{\partial {P}_{k}}{\partial \zeta }. \end{array}$

(3.7)

Using Lemma 2.3 and substituting the expansions in Eq (3.5) into Eq (3.7) gives the Laurent series form to the kth- $\mathcal{L}$ RFs as follows:

$\begin{array}{l} \mathcal{L}Re{s}_{1, \mathrm{k}}\left(s\right) = \sum _{i = 1}^{k}\frac{{f}_{i}}{{s}^{i\alpha +1}}+\sum _{i = 0}^{k}\sum _{j = 0}^{k}\frac{\mathit{\Gamma } \left(\left(i+j\right)\alpha +1\right){f}_{i}\frac{\partial {f}_{j}}{\partial \chi }}{\mathit{\Gamma } \left(i\alpha +1\right)\mathit{\Gamma } \left(j\alpha +1\right){s}^{\left(i+j+1\right)\alpha +1}}\\ +\sum _{i = 0}^{k}\sum _{j = 0}^{k}\frac{\mathit{\Gamma } \left(\left(i+j\right)\alpha +1\right){h}_{i}\frac{\partial {f}_{j}}{\partial \varsigma }}{\mathit{\Gamma } \left(i\alpha +1\right)\mathit{\Gamma } \left(j\alpha +1\right){s}^{\left(i+j+1\right)\alpha +1}} \\ +\sum _{i = 0}^{k}\sum _{j = 0}^{k}\frac{\mathit{\Gamma } \left(\left(i+j\right)\alpha +1\right){g}_{i}\frac{\partial {f}_{j}}{\partial \zeta }}{\mathit{\Gamma } \left(i\alpha +1\right)\mathit{\Gamma } \left(j\alpha +1\right){s}^{\left(i+j+1\right)\alpha +1}}-\frac{\upsilon \frac{{\partial }^{2}f}{\partial {\chi }^{2}}}{{s}^{\alpha +1}}-\sum _{i = 1}^{k}\frac{\upsilon \frac{{\partial }^{2}{f}_{i}}{\partial {\chi }^{2}}}{{s}^{\left(i+1\right)\alpha +1}}\\ -\frac{\upsilon \frac{{\partial }^{2}f}{\partial {\varsigma }^{2}}}{{s}^{\alpha +1}}-\sum _{i = 1}^{k}\frac{\upsilon \frac{{\partial }^{2}{f}_{i}}{\partial {\varsigma }^{2}}}{{s}^{\left(i+1\right)\alpha +1}}-\frac{\upsilon \frac{{\partial }^{2}f}{\partial {\zeta }^{2}}}{{s}^{\alpha +1}}-\sum _{i = 1}^{k}\frac{\upsilon \frac{{\partial }^{2}{f}_{i}}{\partial {\zeta }^{2}}}{{s}^{\left(i+1\right)\alpha +1}}+\frac{\frac{\partial \varphi }{\partial \chi }}{{\rho s}^{\alpha +1}}\\ +\sum _{i = 1}^{k}\frac{\frac{\partial {\varphi }_{i}}{\partial \chi }}{{\rho s}^{\left(i+1\right)\alpha +1}}, \\ \mathcal{L}Re{s}_{2, \mathrm{k}}\left(s\right) = \sum _{i = 1}^{k}\frac{{h}_{i}}{{s}^{i\alpha +1}}+\sum _{i = 0}^{k}\sum _{j = 0}^{k}\frac{\mathit{\Gamma } \left(\left(i+j\right)\alpha +1\right){f}_{i}\frac{\partial {h}_{j}}{\partial \chi }}{\mathit{\Gamma } \left(i\alpha +1\right)\mathit{\Gamma } \left(j\alpha +1\right){s}^{\left(i+j+1\right)\alpha +1}} \\ +\sum _{i = 0}^{k}\sum _{j = 0}^{k}\frac{\mathit{\Gamma } \left(\left(i+j\right)\alpha +1\right){h}_{i}\frac{\partial {h}_{j}}{\partial \varsigma }}{\mathit{\Gamma } \left(i\alpha +1\right)\mathit{\Gamma } \left(j\alpha +1\right){s}^{\left(i+j+1\right)\alpha +1}} \\ +\sum _{i = 0}^{k}\sum _{j = 0}^{k}\frac{\mathit{\Gamma } \left(\left(i+j\right)\alpha +1\right){g}_{i}\frac{\partial {h}_{j}}{\partial \zeta }}{\mathit{\Gamma } \left(i\alpha +1\right)\mathit{\Gamma } \left(j\alpha +1\right){s}^{\left(i+j+1\right)\alpha +1}}-\frac{\upsilon \frac{{\partial }^{2}h}{\partial {\chi }^{2}}}{{s}^{\alpha +1}}-\sum _{i = 1}^{k}\frac{\upsilon \frac{{\partial }^{2}{h}_{i}}{\partial {\chi }^{2}}}{{s}^{\left(i+1\right)\alpha +1}} \\ -\frac{\upsilon \frac{{\partial }^{2}h}{\partial {\varsigma }^{2}}}{{s}^{\alpha +1}}-\sum _{i = 1}^{k}\frac{\upsilon \frac{{\partial }^{2}{h}_{i}}{\partial {\varsigma }^{2}}}{{s}^{\left(i+1\right)\alpha +1}}-\frac{\upsilon \frac{{\partial }^{2}h}{\partial {\zeta }^{2}}}{{s}^{\alpha +1}}-\sum _{i = 1}^{k}\frac{\upsilon \frac{{\partial }^{2}{h}_{i}}{\partial {\zeta }^{2}}}{{s}^{\left(i+1\right)\alpha +1}}+\frac{\frac{\partial }{\partial \varsigma }\varphi }{{\rho s}^{\alpha +1}} \\ +\sum _{i = 1}^{k}\frac{\frac{\partial {\varphi }_{i}}{\partial \varsigma }}{{\rho s}^{\left(i+1\right)\alpha +1}}, \\ \mathcal{L}Re{s}_{3, \mathrm{k}}\left(s\right) = \sum _{i = 1}^{k}\frac{{g}_{i}}{{s}^{i\alpha +1}}+\sum _{i = 0}^{k}\sum _{j = 0}^{k}\frac{\mathit{\Gamma } \left(\left(i+j\right)\alpha +1\right){f}_{i}\frac{\partial {g}_{j}}{\partial \chi }}{\mathit{\Gamma } \left(i\alpha +1\right)\mathit{\Gamma } \left(j\alpha +1\right){s}^{\left(i+j+1\right)\alpha +1}}\\ +\sum _{i = 0}^{k}\sum _{j = 0}^{k}\frac{\mathit{\Gamma } \left(\left(i+j\right)\alpha +1\right){h}_{i}\frac{\partial {g}_{j}}{\partial \varsigma }}{\mathit{\Gamma } \left(i\alpha +1\right)\mathit{\Gamma } \left(j\alpha +1\right){s}^{\left(i+j+1\right)\alpha +1}} \\ +\sum _{i = 0}^{k}\sum _{j = 0}^{k}\frac{\mathit{\Gamma } \left(\left(i+j\right)\alpha +1\right){g}_{i}\frac{\partial {g}_{j}}{\partial \zeta }}{\mathit{\Gamma } \left(i\alpha +1\right)\mathit{\Gamma } \left(j\alpha +1\right){s}^{\left(i+j+1\right)\alpha +1}}-\frac{\upsilon \frac{{\partial }^{2}h}{\partial {\chi }^{2}}}{{s}^{\alpha +1}}-\sum _{i = 1}^{k}\frac{\upsilon \frac{{\partial }^{2}{g}_{i}}{\partial {\chi }^{2}}}{{s}^{\left(i+1\right)\alpha +1}}\\ -\frac{\upsilon \frac{{\partial }^{2}g}{\partial {\varsigma }^{2}}}{{s}^{\alpha +1}}-\sum _{i = 1}^{k}\frac{\upsilon \frac{{\partial }^{2}{g}_{i}}{\partial {\varsigma }^{2}}}{{s}^{\left(i+1\right)\alpha +1}}-\frac{\upsilon \frac{{\partial }^{2}g}{\partial {\zeta }^{2}}}{{s}^{\alpha +1}}-\sum _{i = 1}^{k}\frac{\upsilon \frac{{\partial }^{2}{g}_{i}}{\partial {\zeta }^{2}}}{{s}^{\left(i+1\right)\alpha +1}}+\frac{\frac{\partial \varphi }{\partial \zeta }}{{\rho s}^{\alpha +1}}\\ +\sum _{i = 1}^{k}\frac{\frac{\partial {\varphi }_{i}}{\partial \zeta }}{{\rho s}^{(i+1)\alpha +1}}. \end{array}$

(3.8)

El-Ajou in ^[19] proved the validity of the following formula, which is considered the essential tool of LRPSM:

$\underset{\mathrm{s}\to \mathrm{\infty }}{\mathrm{lim}}{s}^{k\alpha +1}\mathcal{L}Re\mathrm{s}\left(s\right) = \underset{\mathrm{s}\to \mathrm{\infty }}{\mathrm{lim}}{s}^{k\alpha +1}\mathcal{L}Re{s}_{\mathrm{k}}\left(s\right) = 0, k = \mathrm{1, 2}, 3, \dots .$

(3.9)

To find the first coefficients in the expansions in Eq (3.4), consider the first truncated series of the Laurent expansions of Eq (3.5) as follows:

$\begin{array}{l} {U}_{\mathrm{1, 1}} = \frac{f}{s}+\frac{{f}_{1}}{{s}^{\alpha +1}}, \\ {U}_{\mathrm{2, 1}} = \frac{h}{s}+\frac{{h}_{1}}{{s}^{\alpha +1}}, \\ {U}_{\mathrm{3, 1}} = \frac{g}{s}+\frac{{g}_{1}}{{s}^{\alpha +1}}, \\ {P}_{1} = \frac{\varphi }{s}+\frac{{\varphi }_{1}}{{s}^{\alpha +1}}. \end{array}$

(3.10)

Substituting Eq (3.10) into the first- $\mathcal{L}$ Res, multiplying each equation by ${s}^{\alpha +1}$ , and taking the limit as $s\to \mathrm{\infty },$ we have truth in Eq (3.9):

$\begin{array}{l} \underset{\mathrm{s}\to \mathrm{\infty }}{\mathrm{lim}}{s}^{\alpha +1}\mathcal{L}Re{s}_{\mathrm{1, 1}}\left(s\right) = {f}_{1}+f\frac{\partial f}{\partial \chi }+h\frac{\partial f}{\partial \varsigma }+g\frac{\partial f}{\partial \zeta }-\upsilon \frac{{\partial }^{2}f}{\partial {\chi }^{2}}-\upsilon \frac{{\partial }^{2}f}{\partial {\varsigma }^{2}}-\upsilon \frac{{\partial }^{2}f}{\partial {\zeta }^{2}}+\frac{1}{\rho }\frac{\partial \varphi }{\partial \chi } = 0, \\ \underset{s\to \mathrm{\infty }}{\mathrm{lim}}{s}^{\alpha +1}\mathcal{L}Re{s}_{\mathrm{2, 1}}\left(s\right) = {h}_{1}+f\frac{\partial h}{\partial \chi }+h\frac{\partial h}{\partial \varsigma }+g\frac{\partial h}{\partial \zeta }-\upsilon \frac{{\partial }^{2}h}{\partial {\chi }^{2}}-\upsilon \frac{{\partial }^{2}h}{\partial {\varsigma }^{2}}-\upsilon \frac{{\partial }^{2}h}{\partial {\zeta }^{2}}+\frac{1}{\rho }\frac{\partial \varphi }{\partial \varsigma } = 0, \\ \underset{s\to \mathrm{\infty }}{\mathrm{lim}}{s}^{\alpha +1}\mathcal{L}Re{s}_{\mathrm{3, 1}}\left(\mathrm{s}\right) = {g}_{1}+f\frac{\partial g}{\partial \chi }+h\frac{\partial g}{\partial \varsigma }+g\frac{\partial g}{\partial \zeta }-\rho \frac{{\partial }^{2}g}{\partial {\chi }^{2}}-\rho \frac{{\partial }^{2}g}{\partial {\varsigma }^{2}}-\rho \frac{{\partial }^{2}g}{\partial {\zeta }^{2}}+\frac{1}{\rho }\frac{\partial \varphi }{\partial \zeta } = 0. \end{array}$

(3.11)

Therefore, the first coefficients of the expansions of Eq (3.4) become known and take the following forms:

$\begin{array}{l} {f}_{1} = \upsilon \left(\frac{\partial f}{\partial {\chi }^{2}}+\frac{\partial f}{\partial {\varsigma }^{2}}+\frac{\partial f}{\partial {\zeta }^{2}}\right)-\frac{1}{\rho }\frac{\partial \varphi }{\partial \chi }-\left(f\frac{\partial f}{\partial \chi }+h\frac{\partial f}{\partial \varsigma }+g\frac{\partial f}{\partial \zeta }\right), \\ {h}_{1} = \upsilon \left(\frac{\partial h}{\partial {\chi }^{2}}+\frac{\partial h}{\partial {\varsigma }^{2}}+\frac{\partial h}{\partial {\zeta }^{2}}\right)-\frac{1}{\rho }\frac{\partial \varphi }{\partial \varsigma }-\left(f\frac{\partial h}{\partial \chi }+h\frac{\partial h}{\partial \varsigma }+g\frac{\partial h}{\partial \zeta }\right), \\ {g}_{1} = \upsilon \left(\frac{\partial g}{\partial {\chi }^{2}}+\frac{\partial g}{\partial {\varsigma }^{2}}+\frac{\partial g}{\partial {\zeta }^{2}}\right)-\frac{1}{\rho }\frac{\partial \varphi }{\partial \zeta }-\left(f\frac{\partial g}{\partial \chi }+h\frac{\partial g}{\partial \varsigma }+g\frac{\partial g}{\partial \zeta }\right). \end{array}$

(3.12)

Now, to obtain the second coefficients of the series expansions in Eq (3.4), we substitute the following second truncated series:

$\begin{array}{l} {U}_{\mathrm{1, 2}} = \frac{f}{s}+\frac{{f}_{1}}{{s}^{\alpha +1}}+\frac{{f}_{2}}{{s}^{2\alpha +1}}, \\ {U}_{\mathrm{2, 2}} = \frac{h}{s}+\frac{{h}_{1}}{{s}^{\alpha +1}}+\frac{{h}_{2}}{{s}^{2\alpha +1}}, \\ {U}_{\mathrm{3, 2}} = \frac{g}{s}+\frac{{g}_{1}}{{s}^{\alpha +1}}+\frac{{g}_{2}}{{s}^{2\alpha +1}}, \\ {P}_{2} = \frac{\varphi }{s}+\frac{{\varphi }_{1}}{{s}^{\alpha +1}}+\frac{{\varphi }_{2}}{{s}^{2\alpha +1}}, \end{array}$

(3.13)

into the 2nd- $\mathcal{L}$ RF, multiply each equation by ${s}^{2\alpha +1}$ , and use Eq (3.9), we obtain the forms of required coefficients as follows:

$\begin{array}{l} {f}_{2} = \upsilon \left(\frac{{\partial }^{2}{f}_{1}}{\partial {\chi }^{2}}+\frac{{\partial }^{2}{f}_{1}}{\partial {\varsigma }^{2}}+\frac{{\partial }^{2}{f}_{1}}{\partial {\zeta }^{2}}\right)-\frac{1}{\rho }\frac{\partial {\varphi }_{1}}{\partial \chi }-\left({f}_{1}\frac{\partial f}{\partial \chi }+f\frac{\partial {f}_{1}}{\partial \chi }+{h}_{1}\frac{\partial f}{\partial \varsigma }+h\frac{\partial {f}_{1}}{\partial \varsigma }+{g}_{1}\frac{\partial f}{\partial \zeta }+g\frac{\partial {f}_{1}}{\partial \zeta }\right), \\ {h}_{2} = \upsilon \left(\frac{{\partial }^{2}{h}_{1}}{\partial {\chi }^{2}}+\frac{{\partial }^{2}{h}_{1}}{\partial {\varsigma }^{2}}+\frac{{\partial }^{2}{h}_{1}}{\partial {\zeta }^{2}}\right)-\frac{1}{\rho }\frac{\partial {\varphi }_{1}}{\partial \varsigma }-\left({f}_{1}\frac{\partial h}{\partial \chi }+f\frac{\partial {h}_{1}}{\partial \chi }+{h}_{1}\frac{\partial h}{\partial \varsigma }+h\frac{\partial {h}_{1}}{\partial \varsigma }+{g}_{1}\frac{\partial h}{\partial \zeta }+g\frac{\partial {h}_{1}}{\partial \zeta }\right), \\ {g}_{2} = \upsilon \left(\frac{{\partial }^{2}{g}_{1}}{\partial {\chi }^{2}}+\frac{{\partial }^{2}{g}_{1}}{\partial {\varsigma }^{2}}+\frac{{\partial }^{2}{g}_{1}}{\partial {\zeta }^{2}}\right)-\frac{1}{\rho }\frac{\partial {\varphi }_{1}}{\partial \zeta }-\left({f}_{1}\frac{\partial g}{\partial \chi }+f\frac{\partial {g}_{1}}{\partial \chi }+{h}_{1}\frac{\partial g}{\partial \varsigma }+h\frac{\partial {g}_{1}}{\partial \varsigma }+{g}_{1}\frac{\partial g}{\partial \zeta }+g\frac{\partial {g}_{1}}{\partial \zeta }\right). \end{array}$

(3.14)

We can repeat the previous steps to find the third and the fourth coefficients of the expansions (3.4) as:

$\begin{array}{l} {f}_{3} = \upsilon \left(\frac{{\partial }^{2}{f}_{2}}{\partial {\chi }^{2}}+\frac{{\partial }^{2}{f}_{2}}{\partial {\varsigma }^{2}}+\frac{{\partial }^{2}{f}_{2}}{\partial {\zeta }^{2}}\right)+\frac{1}{\rho }\frac{\partial {\varphi }_{2}}{\partial \chi }\\ \ \ \ \ \ -\left({f}_{2}\frac{\partial f}{\partial \chi }+f\frac{\partial {f}_{2}}{\partial \chi }+{h}_{2}\frac{\partial f}{\partial \varsigma }+h\frac{\partial {f}_{2}}{\partial \varsigma }+{g}_{2}\frac{\partial f}{\partial \zeta }+g\frac{\partial {f}_{2}}{\partial \zeta }\right) \\ \ \ \ \ \ -\frac{\mathrm{\Gamma }\left(2\alpha +1\right)}{{\mathrm{\Gamma }}^{2}\left(\alpha +1\right)}\left({f}_{1}\frac{\partial {f}_{1}}{\partial \chi }+{h}_{1}\frac{\partial {f}_{1}}{\partial \varsigma }+{g}_{1}\frac{\partial {f}_{1}}{\partial \zeta }\right), \\ {h}_{3} = \upsilon \left(\frac{{\partial }^{2}{h}_{2}}{\partial {\chi }^{2}}+\frac{{\partial }^{2}{h}_{2}}{\partial {\varsigma }^{2}}+\frac{{\partial }^{2}{h}_{2}}{\partial {\zeta }^{2}}\right)-\frac{1}{\rho }\frac{\partial {\varphi }_{2}}{\partial \varsigma } \\ \ \ \ \ \ -\left({f}_{2}\frac{\partial h}{\partial \chi }+f\frac{\partial {h}_{2}}{\partial \chi }+{h}_{2}\frac{\partial h}{\partial \varsigma }+h\frac{\partial {h}_{2}}{\partial \varsigma }+{g}_{2}\frac{\partial h}{\partial \zeta }+g\frac{\partial {h}_{2}}{\partial \zeta }\right)\\ \ \ \ \ \ -\frac{\mathrm{\Gamma }\left(2\alpha +1\right)}{{\mathrm{\Gamma }}^{2}\left(\alpha +1\right)}\left({f}_{1}\frac{\partial {h}_{1}}{\partial \chi }+{h}_{1}\frac{\partial {h}_{1}}{\partial \varsigma }+{g}_{1}\frac{\partial {h}_{1}}{\partial \zeta }\right), \\ {g}_{3} = \upsilon \left(\frac{{\partial }^{2}{g}_{2}}{\partial {\chi }^{2}}+\frac{{\partial }^{2}{g}_{2}}{\partial {\varsigma }^{2}}+\frac{{\partial }^{2}{g}_{2}}{\partial {\zeta }^{2}}\right)-\frac{1}{\rho }\frac{\partial {\varphi }_{2}}{\partial \zeta } \\ \ \ \ \ \ -\left({f}_{2}\frac{\partial g}{\partial \chi }+f\frac{\partial {g}_{2}}{\partial \chi }+{h}_{2}\frac{\partial g}{\partial \varsigma }+h\frac{\partial {g}_{2}}{\partial \varsigma }+{g}_{2}\frac{\partial g}{\partial \zeta }+g\frac{\partial {g}_{2}}{\partial \zeta }\right) \\ \ \ \ \ \ -\frac{\mathrm{\Gamma }\left(2\alpha +1\right)}{{\mathrm{\Gamma }}^{2}(\alpha +1)}\left({f}_{1}\frac{\partial {g}_{1}}{\partial \chi }+{h}_{1}\frac{\partial {g}_{1}}{\partial \varsigma }+{g}_{1}\frac{\partial {g}_{1}}{\partial \zeta }\right). \end{array}$

(3.15)

Also, one can get

$\begin{array}{l} {f}_{4} = \upsilon \left(\frac{{\partial }^{2}{f}_{3}}{\partial {\chi }^{2}}+\frac{{\partial }^{2}{f}_{3}}{\partial {\varsigma }^{2}}+\frac{{\partial }^{2}{f}_{3}}{\partial {\zeta }^{2}}\right)-\frac{1}{\rho }\frac{\partial {\varphi }_{3}}{\partial \chi } \\ \ \ \ \ \ -\left({f}_{3}\frac{\partial f}{\partial \chi }+f\frac{\partial {f}_{3}}{\partial \chi }+{h}_{3}\frac{\partial f}{\partial \varsigma }+h\frac{\partial {f}_{3}}{\partial \varsigma }+{g}_{3}\frac{\partial f}{\partial \zeta }+g\frac{\partial {f}_{3}}{\partial \zeta }\right) \\ \ \ \ \ \ -\frac{\mathrm{\Gamma }\left(3\alpha +1\right)}{\mathrm{\Gamma }\left(2\mathrm{\alpha }+1\right)\mathrm{\Gamma }\left(\alpha +1\right)}\left({f}_{2}\frac{\partial {f}_{1}}{\partial \chi }+{f}_{1}\frac{\partial {f}_{2}}{\partial \chi }+{h}_{2}\frac{\partial {f}_{1}}{\partial \varsigma }+{h}_{1}\frac{\partial {f}_{2}}{\partial \varsigma }+{g}_{2}\frac{\partial {f}_{1}}{\partial \zeta }+{g}_{1}\frac{\partial {f}_{2}}{\partial \zeta }\right), \\ {h}_{4} = \upsilon \left(\frac{{\partial }^{2}{h}_{3}}{\partial {\chi }^{2}}+\frac{{\partial }^{2}{h}_{3}}{\partial {\varsigma }^{2}}+\frac{{\partial }^{2}{h}_{3}}{\partial {\zeta }^{2}}\right)-\frac{1}{\rho }\frac{\partial {\varphi }_{3}}{\partial \varsigma } \\ \ \ \ \ \ -\left({f}_{3}\frac{\partial h}{\partial \chi }+f\frac{\partial {h}_{3}}{\partial \chi }+{h}_{3}\frac{\partial h}{\partial \varsigma }+h\frac{\partial {h}_{3}}{\partial \varsigma }+{g}_{3}\frac{\partial h}{\partial \zeta }+g\frac{\partial {h}_{3}}{\partial \zeta }\right) \\ \ \ \ \ \ -\frac{\mathrm{\Gamma }\left(3\alpha +1\right)}{\mathrm{\Gamma }\left(2\mathrm{\alpha }+1\right)\mathrm{\Gamma }\left(\alpha +1\right)}\left({f}_{2}\frac{\partial {h}_{1}}{\partial \chi }+{f}_{1}\frac{\partial {h}_{2}}{\partial \chi }+{h}_{2}\frac{\partial {h}_{1}}{\partial \varsigma }+{h}_{1}\frac{\partial {h}_{2}}{\partial \varsigma }+{g}_{2}\frac{\partial {h}_{1}}{\partial \zeta }+{g}_{1}\frac{\partial {h}_{2}}{\partial \zeta }\right), \\ {g}_{4} = \upsilon \left(\frac{{\partial }^{2}{g}_{3}}{\partial {\chi }^{2}}+\frac{{\partial }^{2}{g}_{3}}{\partial {\varsigma }^{2}}+\frac{{\partial }^{2}{g}_{3}}{\partial {\zeta }^{2}}\right)-\frac{1}{\rho }\frac{\partial {\varphi }_{3}}{\partial \zeta } \\ \ \ \ \ \ -\left({f}_{3}\frac{\partial g}{\partial \chi }+f\frac{\partial {g}_{3}}{\partial \chi }+{h}_{3}\frac{\partial g}{\partial \varsigma }+h\frac{\partial {g}_{3}}{\partial \varsigma }+{g}_{3}\frac{\partial g}{\partial \zeta }+g\frac{\partial {g}_{3}}{\partial \zeta }\right) \\ \ \ \ \ \ -\frac{\mathrm{\Gamma }\left(3\alpha +1\right)}{\mathrm{\Gamma }\left(2\mathrm{\alpha }+1\right)\mathrm{\Gamma }\left(\alpha +1\right)}\left({f}_{2}\frac{\partial {g}_{1}}{\partial \chi }+{f}_{1}\frac{\partial {g}_{2}}{\partial \chi }+{h}_{2}\frac{\partial {g}_{1}}{\partial \varsigma }+{h}_{1}\frac{\partial {g}_{2}}{\partial \varsigma }+{g}_{2}\frac{\partial {g}_{1}}{\partial \zeta }+{g}_{1}\frac{\partial {g}_{2}}{\partial \zeta }\right) \end{array}$

(3.16)

Now, we can get the form of the unknown $k$ th-coefficients in the expansions of Eq (3.4) as follows:

$\begin{array}{l} {f}_{k} = \upsilon \left(\frac{{\partial }^{2}{f}_{k-1}}{\partial {\chi }^{2}}+\frac{{\partial }^{2}{f}_{k-1}}{\partial {\varsigma }^{2}}+\frac{{\partial }^{2}{f}_{k-1}}{\partial {\zeta }^{2}}\right)-\frac{1}{\rho }\frac{\partial {\varphi }_{k-1}}{\partial \chi } \\ \ \ \ \ \ \ -\sum _{i = 0}^{k-1}\frac{\mathrm{\Gamma }\left(\left(k-1\right)\alpha +1\right)}{\mathrm{\Gamma }\left(i\alpha +1\right)\mathrm{\Gamma }\left(\left(k-i-1\right)\alpha +1\right)}\left({f}_{i}\frac{\partial {f}_{k-i-1}}{\partial \chi }+{h}_{i}\frac{\partial {f}_{k-i-1}}{\partial \varsigma }+{g}_{i}\frac{\partial {f}_{k-i-1}}{\partial \zeta }\right), k = \mathrm{1, 2}, \cdots , \\ {h}_{k} = \upsilon \left(\frac{{\partial }^{2}{h}_{k-1}}{\partial {\chi }^{2}}+\frac{{\partial }^{2}{h}_{k-1}}{\partial {\varsigma }^{2}}+\frac{{\partial }^{2}{h}_{k-1}}{\partial {\zeta }^{2}}\right)-\frac{1}{\rho }\frac{\partial {\varphi }_{k-1}}{\partial \varsigma } \\ \ \ \ \ \ \ -\sum _{i = 0}^{k-1}\frac{\mathrm{\Gamma }\left(\left(k-1\right)\alpha +1\right)}{\mathrm{\Gamma }\left(i\alpha +1\right)\mathrm{\Gamma }\left(\left(k-i-1\right)\alpha +1\right)}\left({f}_{i}\frac{\partial {h}_{k-i-1}}{\partial \chi }+{h}_{i}\frac{\partial {h}_{k-i-1}}{\partial \varsigma }+{g}_{i}\frac{\partial {h}_{\mathrm{k}-i-1}}{\partial \zeta }\right), k = \mathrm{1, 2}, \cdots , \\ {g}_{k} = \upsilon \left(\frac{{\partial }^{2}{g}_{k-1}}{\partial {\chi }^{2}}+\frac{{\partial }^{2}{g}_{k-1}}{\partial {\varsigma }^{2}}+\frac{{\partial }^{2}{g}_{k-1}}{\partial {\zeta }^{2}}\right)-\frac{1}{\rho }\frac{\partial {\varphi }_{k-1}}{\partial \zeta }\\ \ \ \ \ \ \ -\sum _{i = 0}^{k-1}\frac{\mathrm{\Gamma }(\left(k-1\right)\alpha +1)}{\mathrm{\Gamma }\left(i\alpha +1\right)\mathrm{\Gamma }\left(\left(k-i-1\right)\alpha +1\right)}\left({f}_{i}\frac{\partial {g}_{k-i-1}}{\partial \chi }+{h}_{i}\frac{\partial {g}_{k-i-1}}{\partial \varsigma }+{g}_{i}\frac{\partial {g}_{k-i-1}}{\partial \zeta }\right), k = \mathrm{1, 2}, \cdots . \end{array}$

(3.17)

Thus, the solution of the algebraic system (3.1) can be expressed as:

$\begin{array}{l} {U}_{1}\left(\chi , \varsigma , \zeta , s\right) = \frac{f}{s}+\frac{{f}_{1}}{{s}^{\alpha +1}}+\frac{{f}_{2}}{{s}^{2\alpha +1}}+\dots , \\ {U}_{1}\left(\chi , \varsigma , \zeta , s\right) = \frac{h}{s}+\frac{{h}_{1}}{{s}^{\alpha +1}}+\frac{{h}_{2}}{{s}^{2\alpha +1}}+\dots , \\ {U}_{3}\left(\chi , \varsigma , \zeta , s\right) = \frac{g}{s}+\frac{{g}_{1}}{{s}^{\alpha +1}}+\frac{{g}_{2}}{{s}^{2\alpha +1}}+\dots . \end{array}$

(3.18)

Finally, to get the LRPS solution of the M-DT-FNS systems (1.1) and (1.2) in the original space, we apply the inverse LT on the solution in Eq (3.18), to get

$\begin{array}{l} {u}_{1}\left(\chi , \varsigma , \zeta , t\right) = f+\frac{{f}_{1}{t}^{\alpha }}{\mathrm{\Gamma }(\alpha +1)}+\frac{{f}_{2}{t}^{2\alpha }}{\mathrm{\Gamma }\left(2\alpha +1\right)}+\dots , \\ {u}_{2}\left(\chi , \varsigma , \zeta , t\right) = h+\frac{{h}_{1}{t}^{\alpha }}{\mathrm{\Gamma }(\alpha +1)}+\frac{{h}_{2}{t}^{2\alpha }}{\mathrm{\Gamma }\left(2\alpha +1\right)}+\dots , \\ {u}_{3}\left(\chi , \varsigma , \zeta , t\right) = g+\frac{{g}_{1}{t}^{\alpha }}{\mathrm{\Gamma }(\alpha +1)}+\frac{{g}_{2}{t}^{2\alpha }}{\mathrm{\Gamma }\left(2\alpha +1\right)}+\dots . \end{array}$

(3.19)

4. Numerical application

In this section, we present some numerical examples that explain the working mechanism of the LRPSM. Comparisons and graphical illustrations are made to demonstrate the accuracy and efficiency of the technique.

Example 4.1. ^[35,36] Consider the following two-DT-FNSEs:

$\begin{array}{l} {D}_{t}^{\alpha }{u}_{1}+{u}_{1}\frac{\partial {u}_{1}}{\partial \chi }+{u}_{2}\frac{\partial {u}_{1}}{\partial \varsigma } = \upsilon \left(\frac{{\partial }^{2}{u}_{1}}{\partial {\chi }^{2}}+\frac{{\partial }^{2}{u}_{1}}{\partial {\varsigma }^{2}}\right), \\ {D}_{t}^{\alpha }{u}_{2}+{u}_{1}\frac{\partial {u}_{2}}{\partial \chi }+{u}_{2}\frac{\partial {u}_{2}}{\partial \varsigma } = \upsilon \left(\frac{{\partial }^{2}{u}_{2}}{\partial {\chi }^{2}}+\frac{{\partial }^{2}{u}_{2}}{\partial {\varsigma }^{2}}\right), \end{array}$

(4.1)

with the ICs:

$\begin{array}{l} {u}_{1}\left(\chi , \varsigma , 0\right) = -\mathrm{sin}\left(\chi +\varsigma \right), \\ {u}_{2}\left(\chi , \varsigma , 0\right) = \mathrm{sin}\left(\chi +\varsigma \right), \end{array}$

(4.2)

where $\upsilon \in \mathbb{R}$ , and ${u}_{1}$ and ${u}_{2}$ are two functions of three variables $\chi, \varsigma$ , and $t$ .

Note that, when $\alpha = 1$ the exact solution of the systems (4.1) and (4.2) is

$\begin{array}{l} {u}_{1} = -{e}^{-2\upsilon t}\mathrm{sin}\left(\chi +\varsigma \right), \\ {u}_{2} = {e}^{-2\upsilon t}\mathrm{sin}\left(\chi +\varsigma \right). \end{array}$

(4.3)

Based on the algorithm of the solution obtained in Section 3 and the result in Eq (3.19), we can obtain the LRPS solution of the systems (4.1) and (4.2) as follows:

$\begin{array}{l} {u}_{1}\left(\chi , \varsigma , t\right) = f+\frac{{f}_{1}{t}^{\alpha }}{\mathrm{\Gamma }(\alpha +1)}+\frac{{f}_{2}{t}^{2\alpha }}{\mathrm{\Gamma }\left(2\alpha +1\right)}+\dots , \\ {u}_{2}\left(\chi , \varsigma , t\right) = h+\frac{{h}_{1}{t}^{\alpha }}{\mathrm{\Gamma }(\alpha +1)}+\frac{{h}_{2}{t}^{2\alpha }}{\mathrm{\Gamma }\left(2\alpha +1\right)}+\dots , \end{array}$

(4.4)

where the coefficients ${f}_{k}$ , ${h}_{k}$ , $k = \mathrm{1, 2}, \dots$ , are given by the general formula obtained in Eq (3.17) which in the two dimensions will be as follows:

$\begin{array}{l} {f}_{k} = \upsilon \left(\frac{{\partial }^{2}{f}_{k-1}}{\partial {\chi }^{2}}+\frac{{\partial }^{2}{f}_{k-1}}{\partial \varsigma }\right)-\sum _{i = 0}^{k-1}\frac{\mathit{\Gamma } \left(\left(k-1\right)\alpha +1\right)\left({f}_{i}\frac{\partial {f}_{k-i-1}}{\partial \chi }+{h}_{i}\frac{\partial {f}_{k-i-1}}{\partial \varsigma }\right)}{\mathit{\Gamma } \left(i\alpha +1\right)\mathit{\Gamma } \left(\left(k-i-1\right)\alpha +1\right)}, \\ {h}_{k} = \upsilon \left(\frac{{\partial }^{2}{h}_{k-1}}{\partial {\chi }^{2}}+\frac{{\partial }^{2}{h}_{k-1}}{\partial {\varsigma }^{2}}\right)-\sum _{i = 0}^{k-1}\frac{\mathit{\Gamma } \left(\left(k-1\right)\alpha +1\right)\left({f}_{i}\frac{\partial {h}_{k-i-1}}{\partial \chi }+{h}_{i}\frac{\partial {fh}_{k-i-1}}{\partial \varsigma }\right)}{\mathit{\Gamma } \left(i\alpha +1\right)\mathit{\Gamma } \left(\left(k-i-1\right)\alpha +1\right)}. \end{array}$

(4.5)

Thus, we conclude that

${f}_{1} = 2\upsilon \mathrm{sin}\left(\chi +\varsigma \right), \ \ \ \ \ \ \ {h}_{1} = -2\upsilon \mathrm{sin}\left(\chi +\varsigma \right)$

${f}_{2} = -{\left(2\upsilon \right)}^{2}\mathrm{sin}\left(\chi +\varsigma \right), \ \ \ \ \ \ \ {h}_{2} = {\left(2\upsilon \right)}^{2}\mathrm{sin}\left(\chi +\varsigma \right)$

${f}_{3} = {\left(2\upsilon \right)}^{3}\mathrm{sin}\left(\chi +\varsigma \right), \ \ \ \ \ \ \ {h}_{3} = {-\left(2\upsilon \right)}^{3}\mathrm{sin}\left(\chi +\varsigma \right)$

${f}_{4} = -{\left(2\upsilon \right)}^{4}\mathrm{sin}\left(\chi +\varsigma \right), \ \ \ \ \ \ \ {h}_{4} = {\left(2\upsilon \right)}^{4}\mathrm{sin}\left(\chi +\varsigma \right)$

$\ \ \ \ \ \ \ \ \ \ \ ⋮\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ ⋮$

${f}_{k} = {\left(-1\right)}^{k+1}{\left(2\upsilon \right)}^{k}\mathrm{sin}\left(\chi +\varsigma \right), \ \ \ \ \ \ \ {h}_{k} = {\left(-2\upsilon \right)}^{k}\mathrm{sin}\left(\chi +\varsigma \right),\ \ \ \ \ \ \ k = \mathrm{1, 2}, \cdots .$

Hence, we can express the LRPS solution of Eqs (4.1) and (4.2) as:

$\begin{array}{l} {u}_{1}\left(\chi , \varsigma , t\right) = -\mathrm{sin}\left(\chi +\varsigma \right)\sum _{n = 0}^{\mathrm{\infty }}\frac{{\left(-2\upsilon \right)}^{n}{t}^{n\alpha }}{\mathrm{\Gamma }\left(n\alpha +1\right)}, \\ {u}_{2}\left(\chi , \varsigma , t\right) = \mathrm{sin}\left(\chi +\varsigma \right)\sum _{n = 0}^{\mathrm{\infty }}\frac{{\left(-2\upsilon \right)}^{n}{t}^{n\alpha }}{\mathrm{\Gamma }\left(n\alpha +1\right)}. \end{array}$

(4.6)

shows the stability of the results obtained, where the solution is stable with various values of $t$ .

Table 1. Numerical results for Example 1 at

$\alpha = 1, \chi = 0.1, \varsigma = 0.5$ .

$t$	Numerical results of ${u}_{1}\left(\chi, \varsigma, t\right)$
	${u}_{\mathrm{1, 6}}\left(\chi, \varsigma, t\right)$	${u}_{\mathrm{1, 8}}\left(\chi, \varsigma, t\right)$	${u}_{\mathrm{1, 10}}\left(\chi, \varsigma, t\right)$	${u}_{1}\left(\chi, \varsigma, t\right)$	$\left\|{u}_{1}\left(\chi, \varsigma, t\right)-{u}_{1, 10}\left(\chi, \varsigma, t\right)\right\|$
$0.16$	$\begin{array}{c}-0.481157\end{array}$	$\begin{array}{c}-0.481157\end{array}$	$\begin{array}{c}-0.481157\end{array}$	$-0.481157$	$5.551115\times {10}^{-17}$
$0.32$	$\begin{array}{c}-0.410015\end{array}$	$\begin{array}{c}-0.410015\end{array}$	$\begin{array}{c}-0.410015\end{array}$	$-0.410015$	$4.957146\times {10}^{-14}$
$0.48$	$\begin{array}{c}-0.349392\end{array}$	$\begin{array}{c}-0.349391\end{array}$	$\begin{array}{c}-0.349391\end{array}$	$-0.349391$	$4.238276\times {10}^{-12}$
$0.64$	$\begin{array}{c}-0.297736\end{array}$	$\begin{array}{c}-0.297732\end{array}$	$\begin{array}{c}-0.297731\end{array}$	$-0.297732$	$9.907064\times {10}^{-11}$
$0.80$	$\begin{array}{c}-0.253732\end{array}$	$\begin{array}{c}-0.253710\end{array}$	$\begin{array}{c}-0.253710\end{array}$	$-0.253710$	$1.138797\times {10}^{-9}$
$0.96$	$\begin{array}{c}-0.216273\end{array}$	$\begin{array}{c}-0.216199\end{array}$	$\begin{array}{c}-0.216198\end{array}$	$-0.216198$	$8.355848\times {10}^{-9}$
$t$	Numerical results of ${u}_{2}\left(\chi, \varsigma, t\right)$
	${u}_{\mathrm{2, 6}}\left(\chi, \varsigma, t\right)$	${u}_{\mathrm{2, 8}}\left(\chi, \varsigma, t\right)$	${u}_{\mathrm{2, 10}}\left(\chi, \varsigma, t\right)$	${u}_{2}\left(\chi, \varsigma, t\right)$	$\left\|{u}_{2}\left(\chi, \varsigma, t\right)-{u}_{2, 10}\left(\chi, \varsigma, t\right)\right\|$
$0.16$	$\begin{array}{c}0.481157\end{array}$	$\begin{array}{c}0.481157\end{array}$	$\begin{array}{c}0.481157\end{array}$	$0.481157$	$5.551115\times {10}^{-17}$
$0.32$	$\begin{array}{c}0.410015\end{array}$	$\begin{array}{c}0.410015\end{array}$	$\begin{array}{c}0.410015\end{array}$	$0.410015$	$4.957146\times {10}^{-14}$
$0.48$	$\begin{array}{c}0.349392\end{array}$	$\begin{array}{c}0.349391\end{array}$	$\begin{array}{c}0.349391\end{array}$	$0.349391$	$4.238276\times {10}^{-12}$
$0.64$	$\begin{array}{c}0.297736\end{array}$	$\begin{array}{c}0.297732\end{array}$	$\begin{array}{c}0.297731\end{array}$	$0.297732$	$9.907064\times {10}^{-11}$
$0.80$	$\begin{array}{c}0.253732\end{array}$	$\begin{array}{c}0.253710\end{array}$	$\begin{array}{c}0.253710\end{array}$	$0.253710$	$1.138797\times {10}^{-9}$
$0.96$	$\begin{array}{c}0.216273\end{array}$	$\begin{array}{c}0.216199\end{array}$	$\begin{array}{c}0.216198\end{array}$	$0.216198$	$8.355848\times {10}^{-9}$

| Show Table

DownLoad: CSV

This solution is the same as that obtained by the Laplace decomposition method ^[36] and the variational iteration transform method ^[35]. In a special case, taking $\alpha = 1$ gives the exact solution in terms of elementary functions as follows:

$\begin{array}{l} {u}_{1}\left(\chi , \varsigma , t\right) = -{e}^{-2\upsilon t}\mathrm{sin}\left(\chi +\varsigma \right), \\ {u}_{2}\left(\chi , \varsigma , t\right) = {e}^{-2\upsilon t}\mathrm{sin}\left(\chi +\varsigma \right). \end{array}$

(4.7)

The behavior of the velocity field of the two-DT-FNSEs (4.1) and (4.2) is depicted in for various values of $\alpha$ at $t = 0.5$ and $\upsilon = 0.5$ . The 10th-truncated series of Eq (4.6) is plotted in - for $\alpha = 0.6$ , $\alpha = 0.8$ , and $\alpha = 1$ , respectively, whereas, the exact solution at $\alpha = 1$ is plotted in . The graphics indicate consistency in the behavior of the solution at various values of $\alpha$ , as well as the convention of the exact solution with the obtained solution in Figure 1(c, d).

Figure 1. The 3D surface plot of the 10th approximate solutions of

${u}_{1}$ and

${u}_{2}$ at different values of

$\alpha$ and

$t = 0.5$ &

$\upsilon = 0.5$ for the problem in Example 4.1. (a)

$\alpha = 0.6$ , (b)

$\alpha = 0.8$ , (c)

$\alpha = 1$ , (d)

$\alpha = 1$ (Exact solutions).

DownLoad: Full-Size Img PowerPoint

shows the action of the 10th approximate analytical solution of the initial value problems (IVP) (4.1) and (4.2) along the line $\varsigma = \chi$ and in the region $D = \{\left(\chi, t\right):-3\le \chi \le 3, \mathrm{ }0\le t < 1\}$ for distinct values of $\alpha$ , and at $\upsilon = 0.5$ . The 10th approximate solution is plotted in - for $\alpha = 0.6$ , $\alpha = 0.8$ , and $\alpha = 1$ , respectively, whereas, the exact solution at $\alpha = 1$ is plotted in (d). Also, the graphics indicate consistency in the action of the solution at distinct values of $\alpha$ , the accord nation of the exact solution with the approximate solution in Figure 2(c, d) as well as the region of convergence of the series solution.

Figure 2. The graph of the 3D surface of the 10th approximate solutions of

${u}_{1}$ and

${u}_{2}$ along the line

$\varsigma = \chi$ and at various values of

$\alpha$ and

$\upsilon = 0.5$ for the problem in Example 4.1. (a)

$\alpha = 0.6$ , (b)

$\alpha = 0.8$ , (c)

$\alpha = 1$ , (d)

$\alpha = 1$ (Exact).

DownLoad: Full-Size Img PowerPoint

Example 4.2. ^[35,36] Consider the following two-DT-FNSEs:

(4.8)

with the ICs:

$\begin{array}{l} {u}_{1}\left(\chi , \varsigma , 0\right) = -{e}^{\chi +\varsigma }, \\{u}_{2}\left(\chi , \varsigma , 0\right) = {e}^{\chi +\varsigma }, \end{array}$

(4.9)

where $\upsilon \in \mathbb{R}$ and ${u}_{1}$ and ${u}_{2}$ are two functions of three variables $\chi, \varsigma,$ and $t$ .

The exact solution to problems (4.8) and (4.9) can be obtained, when putting $\alpha = 1$ , to be ${u}_{1} = -{e}^{-2\upsilon t+\chi +\varsigma }$ and ${u}_{2} = {e}^{-2\upsilon t+\chi +\varsigma }$ . Applying the same procedure in Example 4.1, one can obtain the following recurrence relations:

$\begin{array}{l} {f}_{k} = \upsilon \left(\frac{{\partial }^{2}{f}_{k-1}}{\partial {\chi }^{2}}+\frac{{\partial }^{2}{f}_{k-1}}{\partial {\varsigma }^{2}}\right)-\sum _{i = 0}^{k-1}\frac{\mathit{\Gamma } \left((k-1)\alpha +1\right)\left({f}_{i}\frac{\partial {f}_{k-i-1}}{\partial \chi }+{h}_{i}\frac{\partial {f}_{k-i-1}}{\partial \varsigma }\right)}{\mathit{\Gamma } \left(i\alpha +1\right)\mathit{\Gamma } \left(\left(k-i-1\right)\alpha +1\right)}, \\{h}_{k} = \upsilon \left(\frac{{\partial }^{2}{h}_{k-1}}{\partial {\chi }^{2}}+\frac{{\partial }^{2}{h}_{k-1}}{\partial {\varsigma }^{2}}\right)-\sum _{i = 0}^{k-1}\frac{\mathit{\Gamma } \left(\left(k-1\right)\alpha +1\right)\left({f}_{i}\frac{\partial {h}_{k-i-1}}{\partial \chi }+{h}_{i}\frac{\partial {fh}_{k-i-1}}{\partial \varsigma }\right)}{\mathit{\Gamma } \left(i\alpha +1\right)\mathit{\Gamma } \left(\left(k-i-1\right)\alpha +1\right)}. \end{array}$

(4.10)

Thus, we conclude that

${f}_{1} = -2\upsilon {e}^{\chi +\varsigma },\ \ \ \ \ \ {h}_{1} = 2\upsilon {e}^{\chi +\varsigma }$

${f}_{2} = -{\left(2\upsilon \right)}^{2}{e}^{\chi +\varsigma }, \ \ \ \ \ \ {h}_{2} = {\left(2\upsilon \right)}^{2}{e}^{\chi +\varsigma }$

${f}_{3} = -{\left(2\upsilon \right)}^{3}{e}^{\chi +\varsigma }, \ \ \ \ \ \ {h}_{3} = {\left(2\upsilon \right)}^{3}{e}^{\chi +\varsigma },$

${f}_{4} = -{\left(2\upsilon \right)}^{4}{e}^{\chi +\varsigma },\ \ \ \ \ \ {h}_{3} = {\left(2\upsilon \right)}^{3}{e}^{\chi +\varsigma }$

$\ \ \ \ \ \ \ \ \ \ \ \ ⋮\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ ⋮$

${f}_{k} = -{\left(2\upsilon \right)}^{k}{e}^{\chi +\varsigma }, \ \ \ \ \ \ {h}_{k} = {\left(2\upsilon \right)}^{k}{e}^{\chi +\varsigma }, \ \ \ \ \ \ k = \mathrm{1, 2}, \cdots .$

Thus, the LRPS solution of the systems (4.8) and (4.9) can be expressed as follows:

$\begin{array}{l} {u}_{1}\left(\chi , \varsigma , t\right) = -{e}^{\chi +\varsigma }\sum _{i = 0}^{\mathrm{\infty }}\frac{{\left(-2\upsilon \right)}^{i}{t}^{i\alpha }}{\mathrm{\Gamma }\left(i\alpha +1\right)}, \\ {u}_{2}\left(\chi , \varsigma , t\right) = {e}^{\chi +\varsigma }\sum _{j = 0}^{\mathrm{\infty }}\frac{{\left(-2\upsilon \right)}^{j}{t}^{j\alpha }}{\mathrm{\Gamma }\left(j\alpha +1\right)}. \end{array}$

(4.11)

For $\alpha = 1$ , the solution in Eq (4.11) has the form:

${u}_{1}\left(\chi , \varsigma , t\right) = -{e}^{-2\upsilon t+\chi +\varsigma },$

${u}_{2}\left(\chi , \varsigma , t\right) = {e}^{-2\upsilon t+\chi +\varsigma }.$

shows the velocity field behavior of the two-DT-FNSEs (4.8) and (4.9) for distinct values of $\alpha$ at $t = 0.5$ and $\upsilon = 0.5$ . The 10th LRPS approximate analytical solution of the IVP (4.8) and (4.8) plotted in - for $\alpha = 0.6$ , $\alpha = 0.8,$ and $\alpha = 1$ respectively, while the exact solution at $\alpha = 1$ is plotted in (d). The graphics indicate the consistency in the solution behavior at various values of $\alpha$ , as well as the exact solution agreement with the proposed analytical solution in Figure 3(c, d).

Figure 3. The 3D surface plot of the 10th approximate solutions of

${u}_{1}$ and

${u}_{2}$ at distinct values of

$\alpha$ and

$t = 0.5$ &

$\upsilon = 0.5$ for the problem in Example 4.2. (a)

$\alpha = 0.6$ , (b)

$\alpha = 0.8$ , (c)

$\alpha = 1$ , (d)

$\alpha = 1$ (Exact solutions).

DownLoad: Full-Size Img PowerPoint

illustrates the behavior of the 10th approximate solution of the IVP (4.8) and (4.9) along the line $\varsigma = \chi$ and in the region $D = \{\left(\chi, t\right):-1\le \chi \le \mathrm{1, 0}\le t < 1\}$ for different values of $\alpha$ , and at $\upsilon = 0.5$ . The 10th approximate solution is plotted in - for $\alpha = 0.6$ , $\alpha = 0.8$ , and $\alpha = 1$ , respectively, whereas, the exact solution at $\alpha = 1$ is plotted in (d). Also, the graphics indicate consistency in the action of the solution at distinct values of $\alpha$ , the coordination between the exact solution and the approximate analytical solution as illustrated in Figure 4(c, d) as well as the determination of the region of convergence for the series solution, is clear.

Figure 4.

${u}_{1}$ and

${u}_{2}$ along the line

$\varsigma = \chi$ and at various of

$\alpha$ and

$\upsilon = 0.5$ for the problem in Example 4.2. (a)

$\alpha = 0.6$ , (b)

$\alpha = 0.8$ , (c)

$\alpha = 1$ , (d)

$\alpha = 1$ (Exact).

DownLoad: Full-Size Img PowerPoint

Example 4.3. ^[36,41] Consider the following three-DT-FNSEs:

$\begin{array}{l} {D}_{t}^{\alpha }{u}_{1}+{u}_{1}\frac{\partial {u}_{1}}{\partial \chi }+{u}_{2}\frac{\partial {u}_{1}}{\partial \varsigma }+{u}_{3}\frac{\partial {u}_{1}}{\partial \zeta } = \upsilon \left(\frac{{\partial }^{2}{u}_{1}}{\partial {\chi }^{2}}+\frac{{\partial }^{2}{u}_{1}}{\partial {\varsigma }^{2}}+\frac{{\partial }^{2}{u}_{1}}{\partial {\zeta }^{2}}\right), \\ {D}_{t}^{\alpha }{u}_{2}+{u}_{1}\frac{\partial {u}_{2}}{\partial \chi }+{u}_{2}\frac{\partial {u}_{2}}{\partial \varsigma }+{u}_{3}\frac{\partial {u}_{2}}{\partial \zeta } = \upsilon \left(\frac{{\partial }^{2}{u}_{2}}{\partial {\chi }^{2}}+\frac{{\partial }^{2}{u}_{2}}{\partial {\varsigma }^{2}}+\frac{{\partial }^{2}{u}_{2}}{\partial {\zeta }^{2}}\right), \\ {D}_{t}^{\alpha }{u}_{3}+{u}_{1}\frac{\partial {u}_{3}}{\partial \chi }+{u}_{2}\frac{\partial {u}_{3}}{\partial \varsigma }+{u}_{3}\frac{\partial {u}_{3}}{\partial \zeta } = \upsilon \left(\frac{{\partial }^{2}{u}_{3}}{\partial {\chi }^{2}}+\frac{{\partial }^{2}{u}_{3}}{\partial {\varsigma }^{2}}+\frac{{\partial }^{2}{u}_{3}}{\partial {\zeta }^{2}}\right), \end{array}$

(4.12)

where $v\in \mathbb{R}$ , and with the ICs:

$\begin{array}{l} {u}_{1}\left(\chi , \varsigma , \zeta , 0\right) = -0.5\chi +\varsigma +\zeta , \\ {u}_{2}\left(\chi , \varsigma , \zeta , 0\right) = \chi -0.5\varsigma +\zeta , \\ {u}_{3}\left(\chi , \varsigma , \zeta , 0\right) = \chi +\varsigma -0.5\zeta , \end{array}$

(4.13)

and the exact solution, when $\alpha = 1$ , is

${u}_{1} = \frac{-0.5\chi +\varsigma +\zeta -2.25\chi t}{1-2.25{t}^{2}}, {u}_{2} = \frac{\chi -0.5\varsigma +\zeta -2.25\varsigma t}{1-2.25{t}^{2}}, {u}_{3} = \frac{\chi +\varsigma -0.5\zeta -2.25\zeta t}{1-2.25{t}^{2}}.$

The general formula (3.12) for the systems (4.12) and (4.13) will be as follows:

${f}_{k} = \upsilon \left(\frac{{\partial }^{2}{f}_{k-1}}{\partial {\chi }^{2}}+\frac{{\partial }^{2}{f}_{k-1}}{\partial {\varsigma }^{2}}+\frac{{\partial }^{2}{f}_{k-1}}{\partial {\zeta }^{2}}\right)$

$-\sum\limits_{i = 0}^{k-1}\frac{\varGamma \left((k-1)\alpha +1\right)\left({f}_{i}\frac{\partial {f}_{k-i-1}}{\partial \chi }+{h}_{i}\frac{\partial {f}_{k-i-1}}{\partial \varsigma }+{g}_{i}\frac{\partial {f}_{k-i-1}}{\partial \zeta }\right)}{\varGamma \left(i\alpha +1\right)\varGamma \left(\left(k-i-1\right)\alpha +1\right)},$

${h}_{k} = \upsilon \left(\frac{{\partial }^{2}{h}_{k-1}}{\partial {\chi }^{2}}+\frac{{\partial }^{2}{h}_{k-1}}{\partial {\varsigma }^{2}}+\frac{{\partial }^{2}{h}_{k-1}}{\partial {\zeta }^{2}}\right)$

$-\sum\limits_{i = 0}^{k-1}\frac{\varGamma \left(\left(k-1\right)\alpha +1\right)\left({f}_{i}\frac{\partial {h}_{k-i-1}}{\partial \chi }+{h}_{i}\frac{\partial {h}_{k-i-1}}{\partial \varsigma }+{g}_{i}\frac{\partial {h}_{k-i-1}}{\partial \zeta }\right)}{\varGamma \left(i\alpha +1\right)\varGamma \left(\left(k-i-1\right)\alpha +1\right)},$

${g}_{k} = \upsilon \left(\frac{{\partial }^{2}{g}_{k-1}}{\partial {\chi }^{2}}+\frac{{\partial }^{2}{g}_{k-1}}{\partial {\varsigma }^{2}}+\frac{{\partial }^{2}{g}_{k-1}}{\partial {\zeta }^{2}}\right)$

$-\sum\limits_{i = 0}^{k-1}\frac{\varGamma \left(\left(k-1\right)\alpha +1\right)\left({f}_{i}\frac{\partial {g}_{k-i-1}}{\partial \chi }+{h}_{i}\frac{\partial {g}_{k-i-1}}{\partial \varsigma }+{g}_{i}\frac{\partial {g}_{k-i-1}}{\partial \zeta }\right)}{\varGamma \left(i\alpha +1\right)\varGamma \left(\left(k-i-1\right)\alpha +1\right)}.$

Thus, we conclude that

${f}_{1} = -2.25\chi , \ \ \ \ \ \ \ {h}_{1} = -2.25\varsigma ,\ \ \ \ \ \ \ {g}_{1} = -2.25\zeta ,$

${f}_{2} = -2.25\chi +4.5\varsigma +4.5\zeta ,\ \ \ \ \ \ \ {h}_{2} = 4.5\chi -2.25\varsigma +4.5\zeta ,\ \ \ \ \ \ \ {g}_{2} = 4.5\chi +4.5\varsigma -2.25\zeta ,$

${f}_{3} = -\chi \left(\frac{5.0625\varGamma \left(2\alpha +1\right)}{{\varGamma \left(\alpha +1\right)}^{2}}+20.25\right),$

${h}_{3} = -\varsigma \left(\frac{5.0625\varGamma \left(2\alpha +1\right)}{{\varGamma \left(\alpha +1\right)}^{2}}+20.25\right),$

${g}_{3} = -\zeta \left(\frac{5.0625\varGamma \left(2\alpha +1\right)}{{\varGamma \left(\alpha +1\right)}^{2}}+20.25\right),$

${f}_{4} = \frac{\varGamma \left(2\alpha +1\right)\left(-5.0625\chi +10.125\varsigma +10.125\zeta \right)}{{\varGamma \left(\alpha +1\right)}^{2}}+\frac{\varGamma \left(3\alpha +1\right)\left(-10.125\chi +20.25\varsigma +20.25\zeta \right)}{\varGamma \left(\alpha +1\right)\varGamma \left(2\alpha +1\right)}$

$-20.25\chi +40.5\varsigma +40.5\zeta ,$

${h}_{4} = \frac{\varGamma \left(2\alpha +1\right)\left(10.125\chi -5.0625\varsigma +10.125\zeta \right)}{{\varGamma \left(\alpha +1\right)}^{2}}+\frac{\varGamma \left(3\alpha +1\right)\left(20.25\chi -10.125\varsigma +20.25\zeta \right)}{\varGamma \left(\alpha +1\right)\varGamma \left(2\alpha +1\right)}$

$+40.5\chi -20.25\varsigma +40.5\zeta ,$

${g}_{4} = \frac{\mathrm{\Gamma }\left(2\alpha +1\right)\left(10.125\chi +10.125\varsigma -5.0625\zeta \right)}{{\mathrm{\Gamma }\left(\alpha +1\right)}^{2}}+\frac{\mathrm{\Gamma }\left(3\alpha +1\right)\left(20.25\chi +20.25\varsigma -10.125\zeta \right)}{\mathrm{\Gamma }\left(\alpha +1\right)\mathrm{\Gamma }\left(2\alpha +1\right)}$

$+40.5\chi +40.5\varsigma -20.25\zeta , \text{}$

${f}_{5} = \chi \left(\begin{array}{l}-182.25-\frac{22.78125\mathrm{\Gamma }\left(4\alpha +1\right)\mathrm{\Gamma }\left(2\alpha +1\right)}{{\mathrm{\Gamma }\left(\alpha +1\right)}^{3}\mathrm{\Gamma }\left(3\alpha +1\right)}-\frac{45.5625\mathrm{ }\mathrm{\Gamma }\left(2\alpha +1\right)}{{\mathrm{\Gamma }\left(\alpha +1\right)}^{2}}\\ +\frac{-\frac{91.125\mathrm{ }\mathrm{\Gamma }(3\alpha +1)}{\mathrm{\Gamma }(2\alpha +1)}-\frac{91.125\mathrm{ }\mathrm{\Gamma }(4\alpha +1)}{\mathrm{\Gamma }(3\alpha +1)}}{\mathrm{\Gamma }(\alpha +1)}-\frac{45.5625\mathrm{ }\mathrm{\Gamma }(4\alpha +1)}{{\mathrm{\Gamma }(2\alpha +1)}^{2}}\end{array}\right),$

${h}_{5} = \varsigma \left(\begin{array}{l}-182.25-\frac{22.78125\mathrm{\Gamma }\left(4\alpha +1\right)\mathrm{\Gamma }\left(2\alpha +1\right)}{{\mathrm{\Gamma }\left(\alpha +1\right)}^{3}\mathrm{\Gamma }\left(3\alpha +1\right)}-\frac{45.5625\mathrm{ }\mathrm{\Gamma }\left(2\alpha +1\right)}{{\mathrm{\Gamma }\left(\alpha +1\right)}^{2}}\\ +\frac{-\frac{91.125\mathrm{ }\mathrm{\Gamma }(3\alpha +1)}{\mathrm{\Gamma }(2\alpha +1)}-\frac{91.125\mathrm{ }\mathrm{\Gamma }(4\alpha +1)}{\mathrm{\Gamma }(3\alpha +1)}}{\mathrm{\Gamma }(\alpha +1)}-\frac{45.5625\mathrm{ }\mathrm{\Gamma }(4\alpha +1)}{{\mathrm{\Gamma }(2\alpha +1)}^{2}}\end{array}\right),$

${g}_{5} = \zeta \left(\begin{array}{l}-182.25-\frac{22.78125\mathrm{\Gamma }\left(4\alpha +1\right)\mathrm{\Gamma }\left(2\alpha +1\right)}{{\mathrm{\Gamma }\left(\alpha +1\right)}^{3}\mathrm{\Gamma }\left(3\alpha +1\right)}-\frac{45.5625\mathrm{ }\mathrm{\Gamma }\left(2\alpha +1\right)}{{\mathrm{\Gamma }\left(\alpha +1\right)}^{2}}\\ +\frac{-\frac{91.125\mathrm{ }\mathrm{\Gamma }(3\alpha +1)}{\mathrm{\Gamma }(2\alpha +1)}-\frac{91.125\mathrm{ }\mathrm{\Gamma }(4\alpha +1)}{\mathrm{\Gamma }(3\alpha +1)}}{\mathrm{\Gamma }(\alpha +1)}-\frac{45.5625\mathrm{ }\mathrm{\Gamma }(4\alpha +1)}{{\mathrm{\Gamma }(2\alpha +1)}^{2}}\end{array}\right).$

So, the LRPS solution of the systems (4.12) and (4.13) has the following series form:

$\begin{array}{l} {u}_{1}\left(\chi , \varsigma , \zeta , t\right) = -0.5\chi +\varsigma +\zeta -\frac{2.25}{\mathrm{\Gamma }\left(1+\alpha \right)}\chi {t}^{\alpha }+\frac{2\left(2.25\right)}{\mathrm{\Gamma }\left(1+2\alpha \right)}\left(-0.5\chi +\varsigma +\zeta \right){t}^{2\alpha } \\ \ \ \ \ \ \ \ \ \ \ \ \ -\frac{{\left(2.25\right)}^{2}}{\mathrm{\Gamma }\left(1+3\alpha \right)}\left(4+\frac{\mathrm{\Gamma }\left(1+2\alpha \right)}{{\mathrm{\Gamma }}^{2}\left(1+\alpha \right)}\right)\chi {t}^{3\alpha }+\frac{{\left(2.25\right)}^{2}}{\mathrm{\Gamma }\left(1+4\alpha \right)}\left(8+\frac{2\mathrm{\Gamma }\left(1+2\alpha \right)}{{\mathrm{\Gamma }}^{2}\left(1+\alpha \right)}\right. \\ \ \ \ \ \ \ \ \ \ \ \ \ \left.+\frac{4\mathrm{\Gamma }\left(1+3\alpha \right)}{\mathrm{\Gamma }\left(1+\alpha \right)\mathrm{\Gamma }\left(1+2\alpha \right)}\right)\left(-0.5\chi +\varsigma +\zeta \right){t}^{4\alpha }+\cdots \mathrm{ }, \\ {u}_{2}\left(\chi , \varsigma , \zeta , t\right) = \chi -0.5\varsigma +\zeta -\frac{2.25}{\mathrm{\Gamma }\left(1+\alpha \right)}\varsigma {t}^{\alpha }+\frac{2\left(2.25\right)}{\mathrm{\Gamma }\left(1+2\alpha \right)}\left(\chi -0.5\varsigma +\zeta \right){t}^{2\alpha }\\ \ \ \ \ \ \ \ \ \ \ \ \ -\frac{{\left(2.25\right)}^{2}}{\mathrm{\Gamma }\left(1+3\alpha \right)}\left(4+\frac{\mathrm{\Gamma }\left(1+2\alpha \right)}{{\mathrm{\Gamma }}^{2}\left(1+\alpha \right)}\right)\varsigma {t}^{3\alpha }+\frac{{\left(2.25\right)}^{2}}{\mathrm{\Gamma }\left(1+4\alpha \right)}\left(8+\frac{2\mathrm{\Gamma }\left(1+2\alpha \right)}{{\mathrm{\Gamma }}^{2}\left(1+\alpha \right)}\right. \\ \ \ \ \ \ \ \ \ \ \ \ \ \left.+\frac{4\mathrm{\Gamma }\left(1+3\alpha \right)}{\mathrm{\Gamma }\left(1+\alpha \right)\mathrm{\Gamma }\left(1+2\alpha \right)}\right)\left(\chi -0.5\varsigma +\zeta \right){t}^{4\alpha }+\cdots , \\ {u}_{3}\left(\chi , \varsigma , \zeta , t\right) = \chi +\varsigma -0.5\zeta -\frac{2.25}{\mathrm{\Gamma }\left(1+\alpha \right)}\zeta {t}^{\alpha }+\frac{2\left(2.25\right)}{\mathrm{\Gamma }\left(1+2\alpha \right)}\left(\chi +\varsigma -0.5\zeta \right){t}^{2\alpha }\\ \ \ \ \ \ \ \ \ \ \ \ \ -\frac{{\left(2.25\right)}^{2}}{\mathrm{\Gamma }\left(1+3\alpha \right)}\left(4+\frac{\mathrm{\Gamma }\left(1+2\alpha \right)}{{\mathrm{\Gamma }}^{2}\left(1+\alpha \right)}\right)\zeta {t}^{3\alpha }+\frac{{\left(2.25\right)}^{2}}{\mathrm{\Gamma }\left(1+4\alpha \right)}\left(8+\frac{2\mathrm{\Gamma }\left(1+2\alpha \right)}{{\mathrm{\Gamma }}^{2}\left(1+\alpha \right)}\right.\\ \ \ \ \ \ \ \ \ \ \ \ \ \left.+\frac{4\mathrm{\Gamma }\left(1+3\alpha \right)}{\mathrm{\Gamma }\left(1+\alpha \right)\mathrm{\Gamma }\left(1+2\alpha \right)}\right)\left(\chi +\varsigma -0.5\zeta \right){t}^{4\alpha }+\cdots . \end{array}$

(4.14)

The sum of the series in Eq (4.14) at $\alpha = 1$ has the following rational forms:

$\begin{array}{l} {u}_{1}\left(\chi , \varsigma , \zeta , t\right) = \frac{-0.5\chi +\varsigma +\zeta -2.25\chi t}{1-2.25{t}^{2}}, \\ {u}_{2}\left(\chi , \varsigma , \zeta , t\right) = \frac{\chi -0.5\varsigma +\zeta -2.25\varsigma t}{1-2.25{t}^{2}}, \\ {u}_{3}\left(\chi , \varsigma , \zeta , t\right) = \frac{\chi +\varsigma -0.5\zeta -2.25\zeta t}{1-2.25{t}^{2}}. \end{array}$

(4.15)

The behavior of the velocity field of the Three-DT-FNSEs (4.12) and (4.13) is depicted in Figure 5 for various values of at and. The th-truncated series of Eq (4.10) is plotted in Figure 5(a-c) for, and, respectively, whereas, the exact solution at is plotted in (d). The graphics indicate consistency in the behavior of the solution at various values of, as well as the agreement of the exact solution with the approximate solution in Figure 5(c, d).

Figure 5. The 3D surfaces plot of the 10th approximate solutions of

${u}_{1}, {u}_{2}$ , and

${u}_{3}$ at various values of

$\alpha$ and

$t = 0.5$ &

$\zeta = 3$ for the problem in Example 4.3. (a)

$\alpha = 0.6$ , (b)

$\alpha = 0.8$ , (c)

$\alpha = 1$ , (d)

$\alpha = 1$ (Exact solutions).

DownLoad: Full-Size Img PowerPoint

5. Mathematical reviews

This article presents the LRPSM in a new scheme. We proposed the method and used it to solve the M-DT-FNS system. In the following, we state the advantages of using the presented method and the disadvantages of treating the M-DT-FNS.

5.1. Advantages of the method

1) The method is simple to apply to solve linear and non-linear FPDE compared to other techniques, other power series methods are based on finding derivatives and the calculations are usually complex, but LRPSM mainly depends on computing the limit at infinity which is much easier.

2) The proposed method is applicable in finding approximate solutions for physical applications, and in finding many terms of the analytical series solutions.

3) The method is accurate and gives approximate solutions close to the exact ones.

5.2. Disadvantages of the method

LRPSM needs first to find the LT of the target equations and finally to run the inverse LT to obtain the solution in the original space. So, if we have nonhomogeneous equations, the source functions need to be piecewise continuous and of exponential order, and after the computations, the inverse LT must exist.

6. Conclusions

In this article, we have introduced the LRPSM in a new scheme and simplified the technique to present series solutions for the M-DT-FNS system in the sense of the Caputo derivative. It is worth noting here that we obtained a general formula for an analytic solution of M-DT-FN, which other researchers have not previously obtained by other methods. We tested three examples by solving them in the proposed technique and then analyzing the results. In the future, we will use LRPSM to solve more problems and make new modifications to address the flaws of the presented technique.

Acknowledgments

The authors express their gratitude to the dear referees, who wish to remain anonymous, and the editor for their helpful suggestions, which improved the final version of this paper.

Conflict of interest

The authors declare no conflicts of interest.

References

[1]	Abdullah M (2015) An Empirical Analysis of Liquidity, Profitability and Solvency of Bangladeshi Banks. J Bus Fin Aff 5: 1–12. https://doi.org/10.4172/2167-0234.1000157 doi: 10.4172/2167-0234.1000157
[2]	Abdullah M (2021) The implication of machine learning for financial solvency prediction: an empirical analysis on public listed companies of Bangladesh. J Asian Bus Econ Stud 28: 303–320. https://doi.org/10.1108/JABES-11-2020-0128 doi: 10.1108/JABES-11-2020-0128
[3]	Abdullah M, Wali Ullah GM, Chowdhury MAF (2022) The Asymmetric Effect of COVID-19 Government Interventions on Global Stock Markets: New evidence from QARDL and Threshold Regression Approaches. Invest Anal J 51. https://doi.org/10.1080/10293523.2022.2112665 doi: 10.1080/10293523.2022.2112665
[4]	Abreo C, Bustillo R, Rodriguez C (2021) The role of institutional quality in the international trade of a Latin American country: evidence from Colombian export performance. J Econ Struct 10: 1–21. https://doi.org/10.1186/s40008-021-00253-5 doi: 10.1186/s40008-021-00253-5
[5]	Acharya A, Roemer JE, Somanathan R (2015) Caste, corruption and political competition in India. Res Econ 69: 336–352. https://doi.org/DOI:10.1016/j.rie.2015.02.001
[6]	Ahmed SU, Ahmed SP, Abdullah M, et al. (2022) Do socio-political factors affect investment performance? Cogent Econ Financ 10: 2113496. https://doi.org/10.1080/23322039.2022.2113496 doi: 10.1080/23322039.2022.2113496
[7]	Ahmed SU, Mahtab N, Islam MN, et al. (2017) Impact of Working Capital Management on Profitability: A Study on Textile Companies of Bangladesh. J Bus Financ Aff 06. https://doi.org/10.4172/2167-0234.1000292 doi: 10.4172/2167-0234.1000292
[8]	Altayligil YB, Çetrez M (2020) Macroeconomic, institutional and financial determinants of current account balances: a panel data assessment. J Econ Struct 9: 1–23. https://doi.org/10.1186/s40008-020-00225-1 doi: 10.1186/s40008-020-00225-1
[9]	Altman EI, Hotchkiss E (2010) Corporate financial distress and bankruptcy: Predict and avoid bankruptcy, analyze and invest in distressed debt. John Wiley & Sons.
[10]	Anyanwu JC, Yaméogo ND (2015) Regional Comparison of Foreign Direct Investment to Africa: Empirical Analysis. Afr Dev Rev 27: 345–363. https://doi.org/10.1111/1467-8268.12152 doi: 10.1111/1467-8268.12152
[11]	Arellano M, Bond S (1991) Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations. Rev Econ Stud 58: 277–297. https://doi.org/10.2307/2297968 doi: 10.2307/2297968
[12]	Athari SA (2021) Domestic political risk, global economic policy uncertainty, and banks' profitability: evidence from Ukrainian banks. Post-Communist Econ 33: 458–483. https://doi.org/10.1080/14631377.2020.1745563 doi: 10.1080/14631377.2020.1745563
[13]	Avina J (2013) The evolution of corporate social responsibility (CSR) in the Arab Spring. Middle East J 67: 77–92. https://doi.org/10.2307/23361694 doi: 10.2307/23361694
[14]	Ayyagari M, Demirgüç-Kunt A, Maksimovic V (2008) How important are financing constraints? The role of finance in the business environment. World Bank Econ Rev 22: 483–516. https://doi.org/10.1093/wber/lhn018 doi: 10.1093/wber/lhn018
[15]	Baker M (2016) QREGPD: Stata module to perform quantile regression for panel data.
[16]	Batzilis D (2020) The political determinants of government spending allocation and growth. Res Econ 74: 213–220. https://doi.org/DOI:10.1016/j.rie.2020.07.001
[17]	Borojo DG, Yushi J (2020) The impacts of institutional quality and business environment on Chinese foreign direct investment flow to African countries. Econ Res-Ekon Istraz 33: 26–45. https://doi.org/10.1080/1331677X.2019.1696691 doi: 10.1080/1331677X.2019.1696691
[18]	Bucciol A (2018) False claims in politics: Evidence from the US. Res Econ 72: 196–210. https://doi.org/DOI:10.1016/j.rie.2018.04.002
[19]	Chowdhury MAF (2017) The nexus between institutional quality and foreign direct investments (FDI) in South Asia: dynamic heterogeneous panel approach. In: Dorożyński, T., Kuna-Marszałek, A., Outward Foreign Direct Investment (FDI) in Emerging Market Economies, IGI Global, 293–310. https://doi.org/10.4018/978-1-5225-2345-1.ch015
[20]	Çolak G, Durnev A, Qian Y (2017) Political uncertainty and IPO activity: Evidence from U.S. gubernatorial elections. J Financ Quant Anal 52: 2523–2564. https://doi.org/10.1017/S0022109017000862 doi: 10.1017/S0022109017000862
[21]	Constantine C (2017) Economic structures, institutions and economic performance. J Econ Struct 6. https://doi.org/10.1186/s40008-017-0063-1 doi: 10.1186/s40008-017-0063-1
[22]	Das NC, Chowdhury MAF, Islam MN (2021a) Nonlinear threshold effects of institutional quality on capital flight: insights from Bangladesh. Int J Asian Bus Inf Manage 12: 43–59. http://doi.org/10.4018/IJABIM.20210101.oa3
[23]	Das NC, Chowdhury MAF, Islam MN (2021b) The heterogeneous impact of leverage on firm performance: empirical evidence from Bangladesh. S Asian J Bus Stud 11: 235–252. https://doi.org/10.1108/SAJBS-04-2020-0100 doi: 10.1108/SAJBS-04-2020-0100
[24]	Diamonte RL, Liew JM, Stevens RL (1996) Political risk in emerging and developed markets. Financ Anal J 52: 71–76. https://doi.org/10.2469/faj.v52.n3.1998 doi: 10.2469/faj.v52.n3.1998
[25]	Dietrich A, Wanzenried G (2011) Determinants of bank profitability before and during the crisis: Evidence from Switzerland. J Int Financ Mark I 21: 307–327. https://doi.org/10.1016/j.intfin.2010.11.002 doi: 10.1016/j.intfin.2010.11.002
[26]	Dupasquier C, Osakwe PN (2006) Foreign direct investment in Africa: Performance, challenges, and responsibilities. J Asian Econ 17: 241–260. https://doi.org/10.1016/j.asieco.2005.07.002 doi: 10.1016/j.asieco.2005.07.002
[27]	Fairfield PM, Yohn TL (2001) Using asset turnover and profit margin to forecast changes in profitability. Rev Account Stud 6: 371–385. https://doi.org/10.1023/A:1012430513430 doi: 10.1023/A:1012430513430
[28]	Gaganis C, Pasiouras F, Voulgari F (2019) Culture, business environment and SMEs' profitability: Evidence from European Countries. Econ Model 78: 275–292. https://doi.org/10.1016/j.econmod.2018.09.023 doi: 10.1016/j.econmod.2018.09.023
[29]	Gan H, Park MS, Suh SH (2020) Non-financial performance measures, CEO compensation, and firms' future value. J Bus Res 110: 213–227. https://doi.org/10.1016/j.jbusres.2020.01.002 doi: 10.1016/j.jbusres.2020.01.002
[30]	Goldsmith AA (1987) Does Political Stability Hinder Economic Development? Mancur Olson's Theory and the Third World. Compa Polit 19: 471. https://doi.org/10.2307/421818 doi: 10.2307/421818
[31]	Gruszczynski M (2006) Corporate governance and financial performance of companies in Poland. Int Adv Econ Res 12: 251–259. https://doi.org/10.1007/s11294-006-9007-5 doi: 10.1007/s11294-006-9007-5
[32]	Hansen BE (1999) Threshold effects in non-dynamic panels: Estimation, testing, and inference. J Econom 93: 345–368. https://doi.org/10.1016/S0304-4076(99)00025-1 doi: 10.1016/S0304-4076(99)00025-1
[33]	Hansen LP (1982) Large Sample Properties of Generalized Method of Moments Estimators. Econometrica 50: 1029–1054. https://doi.org/10.2307/1912775 doi: 10.2307/1912775
[34]	Hayakawa K, Hayakawa, Kazuhiko (2007) Small sample bias properties of the system GMM estimator in dynamic panel data models. Econ Lett 95: 32–38. https://EconPapers.repec.org/RePEc:eee:ecolet:v:95:y:2007:i:1:p:32-38
[35]	Hillman AJ, Hitt MA (1999) Corporate Political Strategy Formulation: A Model of Approach, Participation, and Strategy Decisions. Acad Manage Rev 24: 825–842. https://doi.org/10.2307/259357 doi: 10.2307/259357
[36]	Hoque ME, Akhter T, Yakob NA (2018) Revisiting endogeneity among foreign direct investment, economic growth and stock market development: Moderating role of political instability. Cogent Econ Financ 6:1–21. https://doi.org/10.1080/23322039.2018.1492311 doi: 10.1080/23322039.2018.1492311
[37]	Hosny A (2017) Political Stability, Firm Characteristics and Performance: Evidence from 6,083 Private Firms in the Middle East. Rev Middle East Econ Financ 13. https://doi.org/10.1515/rmeef-2017-0005 doi: 10.1515/rmeef-2017-0005
[38]	Hosny A (2020) What is the Cost of Political Instability in Tunisia? Evidence from 592 Private Firms. Afr J Econ Rev 8: 1–19.
[39]	Jens CE (2017) Political uncertainty and investment: Causal evidence from U.S. gubernatorial elections. J Financ Econ 124: 563–579. https://doi.org/10.1016/j.jfineco.2016.01.034 doi: 10.1016/j.jfineco.2016.01.034
[40]	Julio B, Yook Y (2012) Political uncertainty and corporate investment cycles. J Financ 67: 45–83. https://doi.org/10.1111/j.1540-6261.2011.01707.x doi: 10.1111/j.1540-6261.2011.01707.x
[41]	Killins RN (2020) Firm-specific, industry-specific and macroeconomic factors of life insurers' profitability: Evidence from Canada. North Am J Econ Financ 51: 101068. https://doi.org/10.1016/j.najef.2019.101068 doi: 10.1016/j.najef.2019.101068
[42]	Kim H (2010) Political Stability and Foreign Direct Investment. Int J Econ Financ 2: 59. https://doi.org/10.5539/ijef.v2n3p59 doi: 10.5539/ijef.v2n3p59
[43]	Kirikkaleli D (2020) Does political risk matter for economic and financial risks in Venezuela? J Econ Struct 9. https://doi.org/10.1186/s40008-020-0188-5 doi: 10.1186/s40008-020-0188-5
[44]	Koenker R (2004) Quantile regression for longitudinal data. J Multivar Anal 91: 74–89. https://doi.org/DOI:10.1016/j.jmva.2004.05.006
[45]	Kurecic P, Kokotovic F (2017) The relevance of political stability on FDI: A VAR analysis and ARDL models for selected small, developed, and instability threatened economies. Economies 5: 22. https://doi.org/10.3390/economies5030022 doi: 10.3390/economies5030022
[46]	Lucas RE (1990) Why Doesn't Capital Flow from Rich to Poor Countries? Am Econ Rev 80: 92–96. http://www.jstor.org/stable/2006549
[47]	Manolova TS, Eunni RV, Gyoshev BS (2008) Institutional environments for entrepreneurship: Evidence from emerging economies in Eastern Europe. Entrep: Theory Pract 32: 203–218. https://doi.org/10.1111/j.1540-6520.2007.00222.x doi: 10.1111/j.1540-6520.2007.00222.x
[48]	Marquis C, Raynard M (2015) Institutional Strategies in Emerging Markets. Acad Manag Ann 9: 291–335. https://doi.org/10.1080/19416520.2015.1014661 doi: 10.1080/19416520.2015.1014661
[49]	Nariswari TN, Nugraha NM (2020) Profit Growth: Impact of Net Profit Margin, Gross Profit Margin and Total Assests Turnover. Int J Financ Bank Stud 9: 87–96. https://doi.org/10.20525/ijfbs.v9i4.937 doi: 10.20525/ijfbs.v9i4.937
[50]	Nguyen TNL, Nguyen VC (2020) The determinants of profitability in listed enterprises: A study from vietnamese stock exchange. J Asian Financ Econ Bus 7: 47–58. https://doi.org/10.13106/jafeb.2020.vol7.no1.47 doi: 10.13106/jafeb.2020.vol7.no1.47
[51]	OuÃ E, OuÃ I, Lompo E (2020) Political Instability and Firm Performance: A Microeconomic Evidence from Ivory Coast. Am J Econ Bus Admin 12: 49–55. https://doi.org/10.3844/ajebasp.2020.49.55 doi: 10.3844/ajebasp.2020.49.55
[52]	Petracco C, Schweiger H (2012) The Impact of Armed Conflict on Firms' Performance and Perceptions. European Bank for Reconstruction and Development Working Paper, 152. http://dx.doi.org/10.2139/ssrn.2189409
[53]	Rahman R, Rashid MM (2018) Political instability and economic growth in Bangladesh. Innov Issues Approaches Soc Sci 11: 91–105. http://dx.doi.org/10.12959/issn.1855-0541.ⅡASS-2018-no2-art5 doi: 10.12959/issn.1855-0541.ⅡASS-2018-no2-art5
[54]	Rashid A, Nasimi AN, Nasimi RN (2021) The uncertainty–investment relationship: scrutinizing the role of firm size. Int J Emerg Mark. https://doi.org/10.1108/IJOEM-09-2019-0698 doi: 10.1108/IJOEM-09-2019-0698
[55]	Roe MJ, Siegel JI (2011) Political instability: Effects on financial development, roots in the severity of economic inequality. J Comp Econ 39: 279–309. https://doi.org/10.1016/j.jce.2011.02.001 doi: 10.1016/j.jce.2011.02.001
[56]	Roxas B, Chadee D, Erwee R (2012) Effects of rule of law on firm performance in South Africa. Eur Bus Rev 24: 478–492. https://doi.org/10.1108/09555341211254544 doi: 10.1108/09555341211254544
[57]	Seo MH, Shin Y (2016) Dynamic panels with threshold effect and endogeneity. J Econom 195: 169–186. https://doi.org/DOI:10.1016/j.jeconom.2016.03.005
[58]	Shaddady A (2021) Business environment, political risk, governance, Shariah compliance and efficiency in insurance companies in the MENA region. Geneva Pap Risk Insur Issues Pract 47: 1–44. https://doi.org/10.1057/s41288-021-00232-8 doi: 10.1057/s41288-021-00232-8
[59]	Taani K, Hamed Banykhaled H (2011) THE EFFECT OF FINANCIAL RATIOS, FIRM SIZE AND CASH FLOWS FROM OPERATING ACTIVITIES ON EARNINGS PER SHARE: (AN APPLIED STUDY: ON JORDANIAN INDUSTRIAL SECTOR). Int J Soc Sci Humanity Stud 3: 197–205.
[60]	Tiwari AK (2013) Taxation, Economic Growth and Political Stability. Transit Stud Rev 20: 49–61. https://doi.org/10.1007/s11300-013-0264-8 doi: 10.1007/s11300-013-0264-8
[61]	Uddin A, Chowdhury MAF, Islam MN (2017a) Do Socio-Economic Factors Matter for the Financial Development of a Muslim Country? A Study in Bangladesh Banking Sector. Int J Bus Soc 18: 59–78.
[62]	Uddin A, Chowdhury MAF, Sajib SD, et al. (2020) Revisiting the impact of institutional quality on post-GFC bank risk-taking: Evidence from emerging countries. Emerg Mark Rev 42: 100659. https://doi.org/10.1016/j.ememar.2019.100659 doi: 10.1016/j.ememar.2019.100659
[63]	Uddin Ahmed M, Habibullah Pulok M (2013) The role of political stability on economic performance: The case of Bangladesh. J Econ Coop Dev 34: 61–99.
[64]	Uddin MA, Ali MH, Masih M (2017b) Political stability and growth: An application of dynamic GMM and quantile regression. Econ Model 64: 610–625. https://doi.org/10.1016/j.econmod.2017.04.028 doi: 10.1016/j.econmod.2017.04.028
[65]	WGI (2020) The Worldwide Governance Indicators (WGI) project. Available from: http://info.worldbank.org/governance/wgi/
[66]	Wolin SS (1981) Max weber: Legitimation, Method, and the Politics of Theory. Polit Theory 9: 401–424. https://doi.org/10.1177/009059178100900308 doi: 10.1177/009059178100900308
[67]	Yahya AT, Akhtar A, Tabash MI (2017) The impact of political instability, macroeconomic and bank-specific factors on the profitability of Islamic banks: An empirical evidence. Invest Manage Financ Innov 14: 30–39. https://doi.org/10.21511/imfi.14(4).2017.04 doi: 10.21511/imfi.14(4).2017.04
[68]	Yoffie DB (1988) How an industry builds political advantage. Harvard Bus Rev 66: 82–89.
[69]	Zhao M (2012) CSR-Based Political Legitimacy Strategy: Managing the State by Doing Good in China and Russia. J Bus Ethics 111: 439–460. https://doi.org/10.1007/s10551-012-1209-6 doi: 10.1007/s10551-012-1209-6

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Quantitative Finance and Economics

Role of the dynamics of political stability in firm performance: Evidence from Bangladesh

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Abstract

1. Introduction

2. Basic concepts

3. The Laplace residual power series method

4. Numerical application

5. Mathematical reviews

5.1. Advantages of the method

5.2. Disadvantages of the method

6. Conclusions

Acknowledgments

Conflict of interest

References

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Quantitative Finance and Economics

Role of the dynamics of political stability in firm performance: Evidence from Bangladesh

Related Papers:

Abstract

1. Introduction

2. Basic concepts

3. The Laplace residual power series method

4. Numerical application

5. Mathematical reviews

5.1. Advantages of the method

5.2. Disadvantages of the method

6. Conclusions

Acknowledgments

Conflict of interest

References

Reader Comments

通讯作者: 陈斌, bchen63@163.com

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