
Radiomics, providing quantitative data extracted from medical images, has emerged as a critical role in diagnosis and classification of diseases such as glioma. One main challenge is how to uncover key disease-relevant features from the large amount of extracted quantitative features. Many existing methods suffer from low accuracy or overfitting. We propose a new method, Multiple-Filter and Multi-Objective-based method (MFMO), to identify predictive and robust biomarkers for disease diagnosis and classification. This method combines a multi-filter feature extraction with a multi-objective optimization-based feature selection model, which identifies a small set of predictive radiomic biomarkers with less redundancy. Taking magnetic resonance imaging (MRI) images-based glioma grading as a case study, we identify 10 key radiomic biomarkers that can accurately distinguish low-grade glioma (LGG) from high-grade glioma (HGG) on both training and test datasets. Using these 10 signature features, the classification model reaches training Area Under the receiving operating characteristic Curve (AUC) of 0.96 and test AUC of 0.95, which shows superior performance over existing methods and previously identified biomarkers.
Citation: Jingren Niu, Qing Tan, Xiufen Zou, Suoqin Jin. Accurate prediction of glioma grades from radiomics using a multi-filter and multi-objective-based method[J]. Mathematical Biosciences and Engineering, 2023, 20(2): 2890-2907. doi: 10.3934/mbe.2023136
[1] | Jalil Manafian, Onur Alp Ilhan, Sizar Abid Mohammed . Forming localized waves of the nonlinearity of the DNA dynamics arising in oscillator-chain of Peyrard-Bishop model. AIMS Mathematics, 2020, 5(3): 2461-2483. doi: 10.3934/math.2020163 |
[2] | Mahmoud A. E. Abdelrahman, Sherif I. Ammar, Kholod M. Abualnaja, Mustafa Inc . New solutions for the unstable nonlinear Schrödinger equation arising in natural science. AIMS Mathematics, 2020, 5(3): 1893-1912. doi: 10.3934/math.2020126 |
[3] | M. Hafiz Uddin, M. Ali Akbar, Md. Ashrafuzzaman Khan, Md. Abdul Haque . New exact solitary wave solutions to the space-time fractional differential equations with conformable derivative. AIMS Mathematics, 2019, 4(2): 199-214. doi: 10.3934/math.2019.2.199 |
[4] | Haikun Liu, Yongqiang Fu . On the variable exponential fractional Sobolev space Ws(·),p(·). AIMS Mathematics, 2020, 5(6): 6261-6276. doi: 10.3934/math.2020403 |
[5] | Samir Kumar Bhandari, Dhananjay Gopal, Pulak Konar . Probabilistic α-min Ciric type contraction results using a control function. AIMS Mathematics, 2020, 5(2): 1186-1198. doi: 10.3934/math.2020082 |
[6] | Manikandan G, Perumal R . Mate and mutual mate functions in a seminearring. AIMS Mathematics, 2020, 5(5): 4974-4982. doi: 10.3934/math.2020318 |
[7] | Huaji Cheng, Yanxia Hu . Exact solutions of the generalized (2+1)-dimensional BKP equation by the G'/G-expansion method and the first integral method. AIMS Mathematics, 2017, 2(3): 562-579. doi: 10.3934/Math.2017.2.562 |
[8] | Johnny Henderson, Abdelghani Ouahab, Samia Youcefi . Existence results for ϕ-Laplacian impulsive differential equations with periodic conditions. AIMS Mathematics, 2019, 4(6): 1610-1633. doi: 10.3934/math.2019.6.1610 |
[9] | Abdelkrim Salim, Sabri T. M. Thabet, Imed Kedim, Miguel Vivas-Cortez . On the nonlocal hybrid (k,φ)-Hilfer inverse problem with delay and anticipation. AIMS Mathematics, 2024, 9(8): 22859-22882. doi: 10.3934/math.20241112 |
[10] | Murali Ramdoss, Divyakumari Pachaiyappan, Inho Hwang, Choonkil Park . Stability of an n-variable mixed type functional equation in probabilistic modular spaces. AIMS Mathematics, 2020, 5(6): 5903-5915. doi: 10.3934/math.2020378 |
Radiomics, providing quantitative data extracted from medical images, has emerged as a critical role in diagnosis and classification of diseases such as glioma. One main challenge is how to uncover key disease-relevant features from the large amount of extracted quantitative features. Many existing methods suffer from low accuracy or overfitting. We propose a new method, Multiple-Filter and Multi-Objective-based method (MFMO), to identify predictive and robust biomarkers for disease diagnosis and classification. This method combines a multi-filter feature extraction with a multi-objective optimization-based feature selection model, which identifies a small set of predictive radiomic biomarkers with less redundancy. Taking magnetic resonance imaging (MRI) images-based glioma grading as a case study, we identify 10 key radiomic biomarkers that can accurately distinguish low-grade glioma (LGG) from high-grade glioma (HGG) on both training and test datasets. Using these 10 signature features, the classification model reaches training Area Under the receiving operating characteristic Curve (AUC) of 0.96 and test AUC of 0.95, which shows superior performance over existing methods and previously identified biomarkers.
Fractional partial differential equations (FPDEs) are the generalizations of classical partial differential equations with integer orders, which are used to describe several phenomena in many fields of sciences, such as mechanics, signal processing, plasma physics, systems identification, electricity, chemistry, biology, control theory and other areas.
The exact solutions of FPDEs play a crucial role in the study of nonlinear sciences. It is used to describe observed various qualitative and quantitative features of nonlinear phenomenons in many fields of mathematical physics, it can let our better understand some complex physics phenomena. Therefore, it is an important task to seek more exact solutions of different forms for the FPDEs.
In recent decades, with the development of science and technology, especially symbolic computation package such as Maple and Mathematica, many researchers have presented many direct and powerful approaches to establish exact solutions of fractional partial differential equations. For example, the fractional sub-equation method [1,2], the first integral method [3], the extended fractional Riccati expansion method [4], the fractional complex transform [5], the Jacobi elliptic equation method [6], the fractional mapping method [7], the (G′/G)-expansion method [8], the improved fractional (DαG/G) method [9], the extended fractional (DαξG/G)-expansion method [10], the separation variables approach [11], the modified extended tanh method [12], the exp(−Φ(ξ)) method [13,14], the invariant subspace method [15], and other methods [16,17,18,19]. Due to these methods, various exact solutions or numerical solutions of FPDEs have been established successfully.
The two variable (ϕ′/ϕ,1/ϕ)-expansion method is the generalization of (G′/G)-expansion method, the main idea of this method is that the solutions to FPDEs are represented as a polynomial in two variables (ϕ′/ϕ) and (1/ϕ), wherein ϕ=ϕ(ξ) satisfies the second order ODE ϕ″+δϕ=μ, where δ and μ are constants. The objective of this article is to establish further general and some fresh close form solitary wave solution to the time-fractional Kuramoto-Sivashinsky (K-S) equation, (3+1)-dimensional time-fractional KdV-Zakharov-Kuznetsov (KdV-ZK) equation and time-fractional Sharma-Tasso-Olver (FSTO) equation by means of the two variable (ϕ′/ϕ,1/ϕ)-expansion method, the results suggest that the method is significative and further general.
The organization of the paper is as follows. In Section 2, the description of conformable fractional derivative and its properties are given. In Section 3, we describe the algorithm for solving time-fractional partial differential equations by using the two variable (ϕ′/ϕ,1/ϕ)-expansion method with the help of fractional complex transform. In Section 4, we apply the two variable (ϕ′/ϕ,1/ϕ)-expansion to the time-fractional K-S equation, (3+1)-dimensional KdV-ZK equation and FSTO equation. In Section 5, some typical wave figures of the exact solutions are given. Results and discussion part are added in Section 6. The conclusion part is in Section 7.
In fractional calculus, the most famous fractional derivatives are the Riemann-Liouville and the Caputo fractional derivatives, the Riemann-Liouville fractional derivative is defined as follows [20]:
If n is a positive integer and αϵ[n−1,n], the α derivative of a function f is given by
Dαt(f)(t)=1Γ(n−α)dndtn∫taf(x)(t−x)α−n+1dx. | (2.1) |
Also, the Caputo fractional derivative is defined as follows [20]
Dαt(f)(t)=1Γ(n−α)dndtn∫taf(n)(x)(t−x)α−n+1dx. | (2.2) |
Some flaws arise in these definitions of these fractional derivatives, for example, all these derivatives do not satisfy: the known formula of the derivative of the product of two functions, the known formula of the derivative of the quotient of two functions and the chain rule of two functions.
In 2014, Khalil et al. [21] introduced a novel definition of fractional derivative named the conformable fractional derivative to overcome the flaws found in Riemann-Liouville and the Caupto fractional derivatives.
Definition 1. Suppose f: [0,∞)→R is a function. Then, the conformable fractional derivative of f of order α is defined as
Dαt(f)(t)=lim | (2.3) |
for all t > 0 and \alpha\in(0, 1] . If f is \alpha -differentiable in some (0, a) , a > 0 , and lim_{t\rightarrow 0^+}f^{(\alpha)}(t) exists, then f^{(\alpha)}(0) = lim_{t\rightarrow 0^+}f^{(\alpha)}(t) .
Some properties of the conformable fractional derivative are given below as in [21]
Thereom 1. Suppose \alpha\in(0, 1] , and f = f(t) and g = g(t) are \alpha- differentiable at t > 0 . Then
\begin{equation} \ D^\alpha_t(af+bg) = aD^\alpha_t(f)+bD^\alpha_t(g), \ \ \ \ \ \forall a, b\in R. \end{equation} | (2.4) |
\begin{equation} \ D^\alpha_t(t^\mu) = \mu t^{\mu-\alpha}, \ \ \ \ \ \forall \mu \in R. \end{equation} | (2.5) |
\begin{equation} \ D^\alpha_t(fg) = fD^\alpha_t(g)+gD^\alpha_t(f). \end{equation} | (2.6) |
\begin{equation} \ D^\alpha_t(\frac{f}{g}) = \frac{gD^\alpha_t(f)-fD^\alpha_t(g)}{g^2}. \end{equation} | (2.7) |
If, in addition to f differentiable, then
\begin{equation} \ D^\alpha_t(f)(t) = t^{1-\alpha}\frac{df}{dt}. \end{equation} | (2.8) |
Thereom 2. Suppose functions f, g : [0, \infty) \rightarrow R be \alpha- differentiable, where (0 < \alpha \leq1) . Then the following rule is obtained
\begin{equation} \ D^\alpha_t(f\circ g)(t) = t^{1-\alpha}g'(t)f'(g(t)). \end{equation} | (2.9) |
The above equations play an important role in fractional calculus in the following sections.
In this section we give the description of the two variable (\phi'/\phi, 1/\phi) -expansion method to find exact traveling wave solutions of time-fractional partial differential equation.
Suppose that a time-fractional partial differential equation in the variables x, y, z, t is given by
\begin{equation} \ P(u, u_{x}, u_{y}, u_{z}, u_{xx}, u_{xy}, u_{xz}, u_{xxx}, \cdots, D_{t}^{\alpha}u, D_{tt}^{2\alpha}u, \cdots) = 0, \ \ \ \ 0 \lt \alpha\leq 1, \end{equation} | (3.1) |
where D_{t}^{\alpha}u, D_{tt}^{2\alpha}u are fraction-order derivatives of u with respect to t , P is a polynomial of u = u(x, y, z, t) and its various partial conformable derivatives including the highest order derivatives and nonlinear terms.
We use the conformable wave transformation:
\begin{equation} \ u(x, y, z, t) = u(\xi), \xi = c(x+y+z-\frac{\upsilon t^{\alpha}}{\alpha}), \end{equation} | (3.2) |
where c and \upsilon are constant to be determine later, the FPDE (3.1) is reduced to the following nonlinear ordinary differential equation (ODE) for u(x, y, z, t) = u(\xi) :
\begin{equation} \ P(u, cu', c^2u'', c^3u''', \cdots, -c\upsilon u', \ldots) = 0, \end{equation} | (3.3) |
where u' = u_\xi, u'' = u_{\xi\xi}, \cdots.
We suppose the solution u of (3.3) can be expressed in the following form:
\begin{equation} u = \sum\limits_{i = 0}^{n} a_i (\frac{\phi'}{\phi})^i+\sum\limits_{j = 0}^{n-1} b_i (\frac{\phi'}{\phi})^j\frac{1}{\phi}, \end{equation} | (3.4) |
where a_i, b_j(i = 0, 1, 2, \ldots, n; j = 0, 1, 2, \ldots, n-1) are constants and a_nb_{n-1}\neq 0 . The positive number n can be determined by considering the homogeneous balance between the highest order derivatives and nonlinear terms appearing in (3.3). The function \phi = \phi(\xi) satisfies the second order linear ODE in the form
\begin{equation} \phi''+\delta \phi = \mu, \end{equation} | (3.5) |
where \delta and \mu are constants. Equation (3.5) has three types of general solution with double arbitrary parameters as follows [22]:
\begin{equation} \ \phi(\xi) = \left\{\begin{array}{cc} A_1\cosh(\sqrt{-\delta}\xi)+A_2\sinh(\sqrt{-\delta}\xi)+\frac{\mu}{\delta}, \ \rm{when} \ \ \ \delta \lt 0, \\ A_1\cos(\sqrt{\delta}\xi)+A_2\sin(\sqrt{\delta}\xi)+\frac{\mu}{\delta}, \ \ \ \ \ \ \ \ \ \rm{when} \ \ \ \delta \gt 0, \\ A_1\xi+A_2+\frac{\mu}{2}\xi^2, \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \rm{when} \ \ \ \delta = 0.\ \end{array}\right. \end{equation} | (3.6) |
and
\begin{equation} \ \left(\frac{\phi'}{\phi}\right)^2 = \left\{\begin{array}{cc} (\delta A_1^2-\delta A_2^2-\frac{\mu^2}{\delta})(\frac{1}{\phi})^2-\delta+\frac{2\mu}{\phi}, \ \ \ \rm{when} \ \delta \lt 0, \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \\ (\delta A_1^2+\delta A_2^2-\frac{\mu^2}{\delta})(\frac{1}{\phi})^2-\delta+\frac{2\mu}{\phi}, \ \ \ \rm{when} \ \delta \gt 0, \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \\ (A_1^2-2\mu A_2)(\frac{1}{\phi})^2+\frac{2\mu}{\phi}, \ \ \ \ \ \ \ \ \ \ \ \ \ \ \rm{when} \ \delta = 0. \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \end{array}\right. \end{equation} | (3.7) |
where A_1, A_2 are arbitrary constants.
By substituting (3.4) into (3.3) and using the second order linear ODE (3.5) and (3.7), collecting all terms with the same order of \frac{1}{\phi^i} and \frac{1}{\phi^i}\frac{\phi'}{\phi} together, the left-hand side of (3.3) is converted into another polynomial in \frac{1}{\phi^i} and \frac{1}{\phi^i}\frac{\phi'}{\phi} . Equating each coefficient of this different power terms to zero yields a set of algebraic equations for a_i, b_j(i = 0, 1, 2, \ldots, n; j = 0, 1, 2, \ldots, n-1), \delta, \mu, c and \upsilon .
Assuming constants a_i, b_j(i = 0, 1, 2, \ldots, n; j = 0, 1, 2, \ldots, n-1), \delta, \mu, c and \upsilon can be determined by solving the nonlinear algebraic equations. Then substituting these terms and the general solutions (3.6) of (3.5) into (3.4), we can obtain more exact traveling wave solutions of (3.1).
In this subsection, we investigate more general and new exact traveling wave solutions of time-fractional differential equations by means of the two variable (\phi'/\phi, 1/\phi) -expansion method.
we consider the time-fractional Kuramoto-Sivashinsky (K-S) equation [23]:
\begin{equation} \ D_{t}^{\alpha}u+auu_x+bu_{xx}+ku_{xxxx} = 0, \end{equation} | (4.1) |
where 0 < \alpha\leq 1 and a, b, k are arbitrary constants.
The K-S equation (4.1) was examined as a prototypical example of spatiotemporal chaos in one space dimension. This equation represents the motion of a fluid going down a vertical wall, the variations of the position of a flame front, or a spatially uniform oscillating chemical reaction in a homogeneous medium.
To solve (4.1), we use the wave transformations:
\begin{equation} \ u = u(\xi), \ \ \ \xi = c(x-\frac{\upsilon t^{\alpha}}{\alpha}), \end{equation} | (4.2) |
then (4.1) is reduced into a nonlinear ODE in the form
\begin{equation} \ -c\upsilon u'(\xi)+acu(\xi)u'(\xi)+bc^2u''(\xi)+kc^4u^{(4)}(\xi) = 0. \end{equation} | (4.3) |
By reducing (4.3), we get
\begin{equation} \ -\upsilon u'(\xi)+au(\xi)u'(\xi)+bcu''(\xi)+kc^3u^{(4)}(\xi) = 0. \end{equation} | (4.4) |
By balancing the highest order derivative term u^{(4)} and nonlinear term uu' in (4.4), the value of n can be determined, which is n = 3 in this problem. Therefore, by (3.4), we have the following ansatz:
\begin{equation} u(\xi) = a_0+a_1\left(\frac{\phi'}{\phi}\right)+a_2\left(\frac{\phi'}{\phi}\right)^{2}+a_3\left(\frac{\phi'}{\phi}\right)^{3}+b_0\frac{1}{\phi}+b_1\left(\frac{\phi'}{\phi}\right) \frac{1}{\phi}+b_2\left(\frac{\phi'}{\phi}\right)^{2}\frac{1}{\phi}, \end{equation} | (4.5) |
where a_0, a_1, a_2, a_3, b_0, b_1 and b_2 are constants to be determined later, and function \phi(\xi) satisfies (3.5).
By substituting (4.5) into (4.4) and using the second order linear ODE (3.5) and (3.7), collecting all terms with the same order of \frac{1}{\phi^i} and \frac{1}{\phi^i}\frac{\phi'}{\phi} together, the left-hand side of (4.4) is converted into another polynomial in \frac{1}{\phi^i} and \frac{1}{\phi^i}\frac{\phi'}{\phi} . Equating each coefficient of this different power terms to zero yields a set of algebraic equations for a_0, a_1, a_2, a_3, b_0, b_1, b_2, \delta, \mu, k, a, b, c and \upsilon . Solving this system of algebraic equations, with the aid of Maple, we obtain
1. a_1 = \pm\frac{3b_2\delta}{2\sqrt{\Xi_i}}, \ a_2 = -\frac{b_2\mu}{2\Xi_i}, \ a_3 = \pm\frac{b_2}{\sqrt{\Xi_i}}, b_0 = \frac{b_2(\mu^2+\Xi_i\delta)}{\Xi_i}, \ b_1 = \mp\frac{3b_2\mu}{2\sqrt{\Xi_i}}, \ b = \pm\frac{19b_2a\delta}{60c\sqrt{\Xi_i}}, \ \ |
\begin{equation} \ k = \pm\frac{ab_2}{60c^3\sqrt{\Xi_i}}, \ \ \upsilon = \frac{a(b_2\mu\delta+2a_0\Xi_i)}{2\Xi_i}, \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \end{equation} | (4.6) |
2. a_1 = \pm\frac{21b_2\delta}{22\sqrt{\Xi_i}}, \ a_2 = -\frac{b_2\mu}{2\Xi_i}, \ a_3 = \pm\frac{b_2}{\sqrt{\Xi_i}}, b_0 = \frac{b_2(11\mu^2+5\Xi_i\delta)}{11\Xi_i}, \ \ b_1 = \mp\frac{3b_2\mu}{2\sqrt{\Xi_i}}, \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ |
\begin{equation} \ b = \mp\frac{19b_2a\delta}{660c\sqrt{\Xi_i}}, \ \ k = \pm\frac{ab_2}{60c^3\sqrt{\Xi_i}}, \ \ \upsilon = \frac{a(b_2\mu\delta+2a_0\Xi_i)}{2\Xi_i}, \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \end{equation} | (4.7) |
where \Xi_i(i = 1, 2, 3) are given later.
Substituting (4.6) and (4.7) and the general solutions (3.6) of Eq. (3.5) into (4.5), we obtain more exact traveling wave solutions of (4.1):
\begin{equation} u_{1i}(\xi) = a_0\pm\frac{3b_2\delta}{2\sqrt{\Xi_i}}\Phi_i-\frac{b_2\mu}{2\Xi_i}\Phi_i^2\pm\frac{b_2}{\sqrt{\Xi_i}}\Phi_i^3+\Psi_i(\frac{b_2(\mu^2+\Xi_i\delta)}{\Xi_i} \mp\frac{3b_2\mu}{2\sqrt{\Xi_i}}\Phi_i+b_2\Phi_i^2), \end{equation} | (4.8) |
where \xi = c(x-\frac{(b_2\delta\mu+2a_0\Xi_i)a}{2\Xi_i\alpha}t^\alpha), \ \ b = \pm\frac{19b_2a\delta}{60c\sqrt{\Xi_i}}, \ \ k = \pm\frac{ab_2}{60c^3\sqrt{\Xi_i}}.
\begin{equation} u_{2i}(\xi) = a_0\pm\frac{21b_2\delta}{22\sqrt{\Xi_i}}\Phi_i-\frac{b_2\mu}{2\Xi_i}\Phi_i^2\pm\frac{b_2}{\sqrt{\Xi_i}}\Phi_i^3+\Psi_i(\frac{b_2(11\mu^2+5\Xi_i\delta)}{11\Xi_i} \mp\frac{3b_2\mu}{2\sqrt{\Xi_i}}\Phi_i+b_2\Phi_i^2), \end{equation} | (4.9) |
where \xi = c(x-\frac{(b_2\delta\mu+2a_0\Xi_i)a}{2\Xi_i\alpha}t^\alpha), \ \ b = \mp\frac{19b_2a\delta}{660c\sqrt{\Xi_i}}, \ \ k = \pm\frac{ab_2}{60c^3\sqrt{\Xi_i}} .
When i = 1 for \delta < 0 , i = 2 for \delta > 0 , i = 3 for \delta = 0 . And
\begin{equation} \Phi_1 = \frac{A_1\sinh(\sqrt{-\delta}\xi)+A_2\cosh(\sqrt{-\delta}\xi)}{A_1\cosh(\sqrt{-\delta}\xi)+A_2\sinh(\sqrt{-\delta}\xi)+\mu/\delta}\cdot\sqrt{-\delta}, \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \end{equation} | (4.10) |
\begin{equation} \Psi_1 = \frac{1}{A_1\cosh(\sqrt{-\delta}\xi)+A_2\sinh(\sqrt{-\delta}\xi)+\mu/\delta}, \ \ \Xi_1 = A_1^2\delta-A_2^2\delta-\mu^2/\delta. \end{equation} | (4.11) |
\begin{equation} \Phi_2 = \frac{-A_1\sin(\sqrt{\delta}\xi)+A_2\cos(\sqrt{\delta}\xi)}{A_1\cos(\sqrt{\delta}\xi)+A_2\sin(\sqrt{\delta}\xi)+\mu/\delta}\cdot\sqrt{\delta}, \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \end{equation} | (4.12) |
\begin{equation} \Psi_2 = \frac{1}{A_1\cos(\sqrt{\delta}\xi)+A_2\sin(\sqrt{\delta}\xi)+\mu/\delta}, \ \ \Xi_2 = A_1^2\delta+A_2^2\delta-\mu^2/\delta.\ \ \ \ \ \ \ \ \end{equation} | (4.13) |
\begin{equation} \Phi_3 = \frac{A_1+\mu\xi}{A_1\xi+A_2+\mu\xi^2/2}, \ \Psi_3 = \frac{1}{A_1\xi+A_2+\mu\xi^2/2}, \ \ \Xi_3 = A_1^2-2A_2\mu.\ \ \ \end{equation} | (4.14) |
Since A_1 and A_2 are arbitrary constants, one may choose arbitrarily their values. For example, if we choose A_1 = 0 , A_2\neq 0 and \mu = 0 in (4.8), we obtain some traveling wave solutions.
Case 1.1 when \delta < 0 , we have
\begin{equation} u_1(\xi) = a_0+\frac{b_2\delta}{A_2}\left(\frac{3}{2}\coth(\sqrt{-\delta}\xi)-\coth^3(\sqrt{-\delta}\xi)- \rm{csch}^3(\sqrt{-\delta}\xi)\right), (A_2 \gt 0), \ \ \ \end{equation} | (4.15) |
where \xi = c(x-\frac{a_0a}{\alpha}t^\alpha), b = \frac{19b_2a\delta}{60A_2c\sqrt{-\delta}}, \ \ k = \frac{ab_2}{60c^3A_2\sqrt{-\delta}}.
\begin{equation} u_2(\xi) = a_0-\frac{b_2\delta}{A_2}\left(\frac{3}{2}\coth(\sqrt{-\delta}\xi)-\coth^3(\sqrt{-\delta}\xi)+ \rm{csch}^3(\sqrt{-\delta}\xi)\right), (A_2 \lt 0), \ \ \ \end{equation} | (4.16) |
where \xi = c(x-\frac{a_0a}{\alpha}t^\alpha), b = -\frac{19b_2a\delta}{60A_2c\sqrt{-\delta}}, \ \ k = -\frac{ab_2}{60c^3A_2\sqrt{-\delta}}.
Case 1.2 when \delta > 0 , we have
\begin{equation} u_3(\xi) = a_0+\frac{b_2\delta}{A_2}\left(\frac{3}{2}\cot(\sqrt{\delta}\xi)+\cot^3(\sqrt{\delta}\xi)+\csc^3(\sqrt{\delta}\xi)\right), (A_2 \gt 0), \ \ \ \end{equation} | (4.17) |
where \xi = c(x-\frac{a_0a}{\alpha}t^\alpha), b = \frac{19b_2a\delta}{60A_2c\sqrt{\delta}}, \ \ k = \frac{ab_2}{60c^3A_2\sqrt{\delta}}.
\begin{equation} u_4(\xi) = a_0-\frac{b_2\delta}{A_2}\left(\frac{3}{2}\cot(\sqrt{\delta}\xi)+\cot^3(\sqrt{\delta}\xi)-\csc^3(\sqrt{\delta}\xi)\right), (A_2 \lt 0), \ \ \ \end{equation} | (4.18) |
where \xi = c(x-\frac{a_0a}{\alpha}t^\alpha), b = -\frac{19b_2a\delta}{60A_2c\sqrt{\delta}}, \ \ k = -\frac{ab_2}{60c^3A_2\sqrt{\delta}}.
Again if A_2 = 0 , A_1\neq 0 and \mu = 0 in (4.8), we obtain some traveling wave solutions.
Case 2.1 when \delta < 0 , we have
\begin{equation} u_5(\xi) = a_0+\frac{b_2\delta \sqrt{-\delta}}{\sqrt{A_1^2\delta}}\left(\frac{3}{2}\tanh(\sqrt{-\delta}\xi)-\tanh^3(\sqrt{-\delta}\xi)\right)+\frac{b_2\delta}{A_1} \rm{sech}^3(\sqrt{-\delta}\xi), \end{equation} | (4.19) |
where \xi = c(x-\frac{a_0a}{\alpha}t^\alpha), b = \frac{19b_2a\delta}{60c\sqrt{A_1^2\delta}}, \ \ k = \frac{ab_2}{60c^3\sqrt{A_1^2\delta}}.
Case 2.2 when \delta > 0 , we have
\begin{equation} u_6(\xi) = a_0-\frac{b_2\delta}{A_1}\left(\frac{3}{2}\tan(\sqrt{\delta}\xi)+\tan^3(\sqrt{\delta}\xi))-\sec^3(\sqrt{\delta}\xi)\right), (A_1 \gt 0), \end{equation} | (4.20) |
where \xi = c(x-\frac{a_0a}{\alpha}t^\alpha), b = \frac{19b_2a\delta}{60c A_1\sqrt{\delta}}, \ \ k = \frac{ab_2}{60c^3 A_1\sqrt{\delta}}.
\begin{equation} u_7(\xi) = a_0+\frac{b_2\delta}{A_1}\left(\frac{3}{2}\tan(\sqrt{\delta}\xi)+\tan^3(\sqrt{\delta}\xi))+\sec^3(\sqrt{\delta}\xi)\right), (A_1 \lt 0), \end{equation} | (4.21) |
where \xi = c(x-\frac{a_0a}{\alpha}t^\alpha), b = -\frac{19b_2a\delta}{60c A_1\sqrt{\delta}}, \ \ k = -\frac{ab_2}{60c^3 A_1\sqrt{\delta}}.
If we choose A_1 = 0 , A_2\neq 0 and \mu = 0 in (4.9), we obtain some traveling wave solutions, for example
Case 3.1 when \delta < 0 , we have
\begin{equation} u_8(\xi) = a_0+\frac{b_2\delta}{A_2}\left(\frac{21}{22}\coth(\sqrt{-\delta}\xi)-\coth^3(\sqrt{-\delta}\xi)+ \left(\frac{5}{11}-\coth^2(\sqrt{-\delta}\xi)\right) \rm{csch}(\sqrt{-\delta}\xi)\right), (A_2 \gt 0), \ \ \ \end{equation} | (4.22) |
where \xi = c(x-\frac{a_0a}{\alpha}t^\alpha), b = -\frac{19b_2a\delta}{660A_2c\sqrt{-\delta}}, \ \ k = \frac{ab_2}{60c^3A_2\sqrt{-\delta}}.
\begin{equation} u_9(\xi) = a_0-\frac{b_2\delta}{A_2}\left(\frac{21}{22}\coth(\sqrt{-\delta}\xi)-\coth^3(\sqrt{-\delta}\xi)- \left(\frac{5}{11}-\coth^2(\sqrt{-\delta}\xi)\right) \rm{csch}(\sqrt{-\delta}\xi)\right), (A_2 \lt 0), \ \ \ \end{equation} | (4.23) |
where \xi = c(x-\frac{a_0a}{\alpha}t^\alpha), b = \frac{19b_2a\delta}{660A_2c\sqrt{-\delta}}, \ \ k = -\frac{ab_2}{60c^3A_2\sqrt{-\delta}}.
Case 3.2 when \delta > 0 , we have
\begin{equation} u_{10}(\xi) = a_0+\frac{b_2\delta}{A_2}\left(\frac{21}{22}\cot(\sqrt{\delta}\xi)+\cot^3(\sqrt{\delta}\xi)+ \left(\frac{5}{11}+\cot^2(\sqrt{\delta}\xi)\right)\csc(\sqrt{\delta}\xi)\right), (A_2 \gt 0), \ \ \ \end{equation} | (4.24) |
where \xi = c(x-\frac{a_0a}{\alpha}t^\alpha), b = -\frac{19b_2a\delta}{660A_2c\sqrt{\delta}}, \ \ k = \frac{ab_2}{60c^3A_2\sqrt{\delta}}.
\begin{equation} u_{11}(\xi) = a_0-\frac{b_2\delta}{A_2}\left(\frac{21}{22}\cot(\sqrt{\delta}\xi)+\cot^3(\sqrt{\delta}\xi)- \left(\frac{5}{11}+\cot^2(\sqrt{\delta}\xi)\right)\csc(\sqrt{\delta}\xi)\right), (A_2 \lt 0), \ \ \ \end{equation} | (4.25) |
where \xi = c(x-\frac{a_0a}{\alpha}t^\alpha), b = \frac{19b_2a\delta}{660A_2c\sqrt{\delta}}, \ \ k = -\frac{ab_2}{60c^3A_2\sqrt{\delta}}.
Again if A_2 = 0 , A_1\neq 0 and \mu = 0 in (4.9), we obtain some traveling wave solutions.
Case 4.1 when \delta < 0 , we have
\begin{equation} u_{12}(\xi) = a_0+\frac{b_2\delta\sqrt{-\delta}}{\sqrt{A_1^2\delta}}\left(\frac{21}{22}\tanh(\sqrt{-\delta}\xi)-\tanh^3(\sqrt{-\delta}\xi)\right)+\frac{b_2\delta}{A_1} \left(\frac{5}{11}-\tanh^2(\sqrt{-\delta}\xi)\right) \rm{sech}(\sqrt{-\delta}\xi), \end{equation} | (4.26) |
where \xi = c(x-\frac{a_0a}{\alpha}t^\alpha), b = -\frac{19b_2a\delta}{660c\sqrt{A_1^2\delta}}, \ \ k = \frac{ab_2}{60c^3\sqrt{A_1^2\delta}}.
Case 4.2 when \delta > 0 , we have
\begin{equation} u_{13}(\xi) = a_0-\frac{b_2\delta}{A_1}\left(\frac{21}{22}\tan(\sqrt{\delta}\xi)+\tan^3(\sqrt{\delta}\xi)- \left(\frac{5}{11}+\tan^2(\sqrt{\delta}\xi)\right)\sec(\sqrt{\delta}\xi)\right), (A_1 \gt 0), \ \ \ \end{equation} | (4.27) |
where \xi = c(x-\frac{a_0a}{\alpha}t^\alpha), b = -\frac{19b_2a\delta}{660A_1c\sqrt{\delta}}, \ \ k = \frac{ab_2}{60c^3A_1\sqrt{\delta}}.
\begin{equation} u_{14}(\xi) = a_0+\frac{b_2\delta}{A_1}\left(\frac{21}{22}\tan(\sqrt{\delta}\xi)+\tan^3(\sqrt{\delta}\xi)+ \left(\frac{5}{11}+\tan^2(\sqrt{\delta}\xi)\right)\sec(\sqrt{\delta}\xi)\right), (A_1 \lt 0), \ \ \ \end{equation} | (4.28) |
where \xi = c(x-\frac{a_0a}{\alpha}t^\alpha), b = \frac{19b_2a\delta}{660A_1c\sqrt{\delta}}, \ \ k = -\frac{ab_2}{60c^3A_1\sqrt{\delta}}.
Consider the (3+1)-dimensional time-fractional KdV-Zakharov-Kuznetsov (KdV-ZK) equation [24,25]:
\begin{equation} \ D_{t}^{\alpha}u+auu_x+u_{xxx}+b(u_{xyy}+u_{xzz}) = 0, \end{equation} | (4.29) |
where 0 < \alpha\leq 1 and a, b are arbitrary constants.
It is well known that the Korteweg-de Vries (KdV) equation arises as an model for one-dimensional long wavelength surface waves propagating in weakly nonlinear dispersive media, as well as the evolution of weakly nonlinear ion acoustic waves in plasmas. The ZK equation is one of two well-studied canonical two-dimensional extensions of the Korteweg-de Vries equation. In recent, S. Shoo et al. [25] found some new exact traveling wave solutions of Eq. (4.29) by the simplest equation method.
To solve (4.29), we use the wave transformations:
\begin{equation} \ u = u(\xi), \ \ \ \xi = c(x+y+z-\frac{\upsilon t^{\alpha}}{\alpha}), \end{equation} | (4.30) |
then (4.29) is reduced into a nonlinear ODE in the form
\begin{equation} \ -c\upsilon u'(\xi)+acu(\xi)u'(\xi)+c^3(1+2b)u'''(\xi) = 0. \end{equation} | (4.31) |
By reducing (4.31), we get
\begin{equation} \ -\upsilon u'(\xi)+au(\xi)u'(\xi)+c^2(1+2b)u'''(\xi) = 0. \end{equation} | (4.32) |
Further by integrating (4.32) with respect to \xi , we get
\begin{equation} \ -\upsilon u(\xi)+\frac{a}{2}u^2(\xi)+c^2(1+2b)u''(\xi) = 0. \end{equation} | (4.33) |
By balancing the highest order derivative term u'' and nonlinear term u^2 in (4.33), the value of n can be determined, which is n = 2 in this problem. Therefore, by equation (3.4), we have the following ansatz:
\begin{equation} u(\xi) = a_0+a_1\left(\frac{\phi'}{\phi}\right)+a_2\left(\frac{\phi'}{\phi}\right)^{2}+b_0\frac{1}{\phi}+b_1\left(\frac{\phi'}{\phi}\right) \frac{1}{\phi}, \end{equation} | (4.34) |
where a_0, a_1, a_2, b_0 and b_1 are constants to be determined later, and function \phi(\xi) satisfies (3.5).
By substituting (4.34) into (4.33) and using the second order linear ODE (3.5) and (3.7), collecting all terms with the same order of \frac{1}{\phi^i} and \frac{1}{\phi^i}\frac{\phi'}{\phi} together, the left-hand side of (4.33) is converted into another polynomial in \frac{1}{\phi^i} and \frac{1}{\phi^i}\frac{\phi'}{\phi} . Equating each coefficient of this different power terms to zero yields a set of algebraic equations for a_0, a_1, a_2, b_0, b_1, \delta, \mu, a, b, c and \upsilon . Solving this system of algebraic equations, with the aid of Maple, we obtain
\begin{equation} \ 1. a_0 = \pm\frac{b_1\delta}{\sqrt{\Xi_i}}, \ \ a_1 = 0, \ \ a_2 = \pm\frac{b_1}{\sqrt{\Xi_i}}, \ \ b_0 = \mp\frac{b_1\mu}{\sqrt{\Xi_i}}, \ \ b = -\frac{6c^2\sqrt{\Xi_i}\pm ab_1}{12c^2\sqrt{\Xi_i}}, \ \ \upsilon = \pm\frac{\delta ab_1}{6\sqrt{\Xi_i}}.\ \ \end{equation} | (4.35) |
\begin{equation} \ 2. a_0 = \pm\frac{2b_1\delta}{3\sqrt{\Xi_i}}, \ \ a_1 = 0, \ \ a_2 = \pm\frac{b_1}{\sqrt{\Xi_i}}, \ \ b_0 = \mp\frac{b_1\mu}{\sqrt{\Xi_i}}, \ \ b = -\frac{6c^2\sqrt{\Xi_i}\pm ab_1}{12c^2\sqrt{\Xi_i}}, \ \ \upsilon = \mp\frac{\delta ab_1}{6\sqrt{\Xi_i}}.\ \ \end{equation} | (4.36) |
Substituting (4.35) and (4.36) and the general solutions (3.6) of Eq. (3.5) into (4.34), we obtain more exact solutions of (4.29):
\begin{equation} u_{1i}(\xi) = \pm\frac{b_1\delta}{\sqrt{\Xi_i}}\pm\frac{b_1}{\sqrt{\Xi_i}}\Phi_i^2+b_1\Psi_i(\mp\frac{\mu}{\sqrt{\Xi_i}}+\Phi_i), \end{equation} | (4.37) |
where \xi = c(x+y+z\mp\frac{\delta ab_1}{6\sqrt{\Xi_i}\alpha}t^\alpha), \ \ b = -\frac{6c^2\sqrt{\Xi_i}\pm ab_1}{12c^2\sqrt{\Xi_i}} .
\begin{equation} u_{2i}(\xi) = \pm\frac{2b_1\delta}{3\sqrt{\Xi_i}}\pm\frac{b_1}{\sqrt{\Xi_i}}\Phi_i^2+b_1\Psi_i(\mp\frac{\mu}{\sqrt{\Xi_i}}+\Phi_i), \end{equation} | (4.38) |
where \xi = c(x+y+z\pm\frac{\delta ab_1}{6\sqrt{\Xi_i}\alpha}t^\alpha), \ \ b = -\frac{6c^2\sqrt{\Xi_i}\pm ab_1}{12c^2\sqrt{\Xi_i}} , in which i = 1 for \delta < 0 , i = 2 for \delta > 0 , i = 3 for \delta = 0 , and \Phi_i, \Psi_i, \Xi_i, \ (i = 1, 2, 3) see (4.10)-(4.14).
Here A_1 and A_2 are arbitrary constants. Therefore, one can freely select their values. If we choose A_1 = 0 , A_2\neq 0 , \mu = 0 and \delta > 0 in (4.37), we obtain some traveling wave solutions.
\begin{equation} u_1(\xi) = \frac{b_1\sqrt{\delta}}{A_2}\left(\csc^2(\sqrt{\delta}\xi)+\cot(\sqrt{\delta}\xi)\csc(\sqrt{\delta}\xi)\right), (A_2 \gt 0), \ \end{equation} | (4.39) |
where \xi = c(x+y+z-\frac{\delta ab_1}{6A_2\alpha\sqrt{\delta}}t^\alpha), b = -\frac{6c^2A_2\sqrt{\delta}+ ab_1}{12c^2A_2\sqrt{\delta}}.
\begin{equation} u_2(\xi) = -\frac{b_1\sqrt{\delta}}{A_2}\left(\csc^2(\sqrt{\delta}\xi)-\cot(\sqrt{\delta}\xi)\csc(\sqrt{\delta}\xi)\right), (A_2 \lt 0), \ \end{equation} | (4.40) |
where \xi = c(x+y+z+\frac{\delta ab_1}{6A_2\alpha\sqrt{\delta}}t^\alpha), b = \frac{-6c^2A_2\sqrt{\delta}+ ab_1}{12c^2A_2\sqrt{\delta}}.
If we choose A_1 = 0 , A_2\neq 0 , \mu = 0 and \delta < 0 in (4.38), we obtain some other traveling wave solutions.
\begin{equation} u_3(\xi) = -\frac{b_1\sqrt{-\delta}}{A_2}\left(\frac{2}{3}-\coth^2(\sqrt{-\delta}\xi)-\coth(\sqrt{-\delta}\xi) \rm{csch}(\sqrt{-\delta}\xi)\right), (A_2 \gt 0), \ \end{equation} | (4.41) |
where \xi = c(x+y+z+\frac{\delta ab_1}{6A_2\alpha\sqrt{-\delta}}t^\alpha), b = -\frac{6c^2A_2\sqrt{-\delta}+ ab_1}{12c^2A_2\sqrt{-\delta}}.
\begin{equation} u_4(\xi) = \frac{b_1\sqrt{-\delta}}{A_2}\left(\frac{2}{3}-\coth^2(\sqrt{-\delta}\xi)+\coth(\sqrt{-\delta}\xi) \rm{csch}(\sqrt{-\delta}\xi)\right), (A_2 \lt 0), \ \end{equation} | (4.42) |
where \xi = c(x+y+z-\frac{\delta ab_1}{6A_2\alpha\sqrt{-\delta}}t^\alpha), b = \frac{-6c^2A_2\sqrt{-\delta}+ ab_1}{12c^2A_2\sqrt{-\delta}}.
If we choose A_1 = 0 , A_2\neq 0 , \mu = 0 and \delta > 0 in (4.38), we obtain some traveling wave solutions.
\begin{equation} u_5(\xi) = \frac{b_1\sqrt{\delta}}{A_2}\left(\frac{2}{3}+\cot^2(\sqrt{\delta}\xi)+\cot(\sqrt{\delta}\xi)\csc(\sqrt{\delta}\xi)\right), (A_2 \gt 0), \ \end{equation} | (4.43) |
where \xi = c(x+y+z+\frac{\delta ab_1}{6A_2\alpha\sqrt{\delta}}t^\alpha), b = -\frac{6c^2A_2\sqrt{\delta}+ ab_1}{12c^2A_2\sqrt{\delta}}.
\begin{equation} u_6(\xi) = -\frac{b_1\sqrt{\delta}}{A_2}\left(\frac{2}{3}+\cot^2(\sqrt{\delta}\xi)-\cot(\sqrt{\delta}\xi)\csc(\sqrt{\delta}\xi)\right), (A_2 \gt 0), \ \end{equation} | (4.44) |
where \xi = c(x+y+z-\frac{\delta ab_1}{6A_2\alpha\sqrt{\delta}}t^\alpha), b = \frac{-6c^2A_2\sqrt{\delta}+ ab_1}{12c^2A_2\sqrt{\delta}}.
Similarly, we can write down the other families of exact solutions of Eq. (4.29) which are omitted for convenience.
Consider the time-fractional Sharma-Tasso-Olver (FSTO) equation [26,27]:
\begin{equation} \ D_{t}^{\alpha}u+3au_x^2+3au^2u_{x}+3auu_{xx}+au_{xxx} = 0, \end{equation} | (4.45) |
where a is an arbitrary constant and 0 < \alpha\leq 1 . The function u(x, t) is assumed to be a causal function of time. i.e. vanishing for t < 0 . The general response expression contains a parameter describing the order of the fractional derivative that can be varied to obtain various responses. In the case of \alpha = 1 , Eq. (4.45) reduces to the classical nonlinear STO equation. L. Song [26] found a rational approximation solution of Eq. (4.45) by the variational iteration method, the Adomian decomposition method and the homotopy perturbation method.
To solve (4.45), we use the wave transformations:
\begin{equation} \ u = u(\xi), \ \ \ \xi = c(x-\frac{\upsilon t^{\alpha}}{\alpha}), \end{equation} | (4.46) |
then (4.45) is reduced into a nonlinear ODE in the form
\begin{equation} \ -c\upsilon u'(\xi)+3ac^2u'^2(\xi)+3acu^2(\xi)u'(\xi)+3ac^2u(\xi)u''(\xi)+ac^3u'''(\xi) = 0. \end{equation} | (4.47) |
By reducing (4.47), we get
\begin{equation} \ -\upsilon u'(\xi)+3acu'^2(\xi)+3au^2(\xi)u'(\xi)+3acu(\xi)u''(\xi)+ac^2u'''(\xi) = 0. \end{equation} | (4.48) |
Further by integrating (4.48) with respect to \xi , we get
\begin{equation} \ -\upsilon u(\xi)+3acu(\xi)u'(\xi)+au^3(\xi)+ac^2u''(\xi) = 0. \end{equation} | (4.49) |
By balancing the highest order derivative term u'' and nonlinear term u^3 in (4.49), the value of n can be determined, which is n = 1 in this problem. Therefore, by Eq. (3.4), we have the following ansatz:
\begin{equation} u(\xi) = a_0+a_1\left(\frac{\phi'}{\phi}\right)+b_0\frac{1}{\phi}, \end{equation} | (4.50) |
where a_0, a_1 and b_0 are constants to be determined later, and function \phi(\xi) satisfies (3.5).
By substituting (4.50) into (4.49) and using the second order linear ODE (3.5) and (3.7), collecting all terms with the same order of \frac{1}{\phi^i} and \frac{1}{\phi^i}\frac{\phi'}{\phi} together, the left-hand side of (4.49) is converted into another polynomial in \frac{1}{\phi^i} and \frac{1}{\phi^i}\frac{\phi'}{\phi} . Equating each coefficient of this different power terms to zero yields a set of algebraic equations for a_0, a_1, b_0, \delta, \mu, a, c and \upsilon . Solving this system of algebraic equations, with the aid of Maple, we obtain
\begin{equation} \ 1. a_0 = 0, \ \ a_1 = c, \ \ b_0 = \pm\sqrt{\Xi_i}c, \ \ \upsilon = -ac^2\delta.\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \end{equation} | (4.51) |
\begin{equation} \ 2. a_0 = 0, \ \ a_1 = \pm\frac{b_0}{\sqrt{\Xi_i}}, \ \ c = \pm\frac{2b_0}{\sqrt{\Xi_i}}, \ \ \upsilon = -\frac{b_0^2a\delta}{\Xi_i}.\ \ \ \ \ \ \ \ \ \ \ \ \end{equation} | (4.52) |
\begin{equation} \ 3. a_0 = \pm\sqrt{-\frac{\delta}{\Xi_i}}b_0, \ \ a_1 = \frac{b_0}{\sqrt{\Xi_i}}, \ \ c = \frac{2b_0}{\sqrt{\Xi_i}}, \ \ \upsilon = -\frac{4b_0^2a\delta}{\Xi_i}.\ \ \ \end{equation} | (4.53) |
\begin{equation} \ 4. a_0 = \pm\sqrt{-\frac{\delta}{\Xi_i}}b_0, \ \ a_1 = -\frac{b_0}{\sqrt{\Xi_i}}, \ \ c = -\frac{2b_0}{\sqrt{\Xi_i}}, \ \ \upsilon = -\frac{4b_0^2a\delta}{\Xi_i}. \end{equation} | (4.54) |
Substituting (4.51)-(4.54) and the general solutions (3.6) of Eq. (3.5) into (4.50), we obtain more exact solutions of (4.45):
\begin{equation} u_{1i}(\xi) = c\Phi_i\pm\sqrt{\Xi_i}c\Psi_i, \end{equation} | (4.55) |
where \xi = c(x+\frac{ac^2\delta t^\alpha}{\alpha}).
\begin{equation} u_{2i}(\xi) = \pm\frac{b_0}{\sqrt{\Xi_i}}\Phi_i+b_0\Psi_i, \end{equation} | (4.56) |
where \xi = \pm\frac{2b_0}{\sqrt{\Xi_i}}(x+\frac{b_0^2a\delta t^\alpha}{\Xi_i\alpha}).
\begin{equation} u_{3i}(\xi) = \pm\sqrt{-\frac{\delta}{\Xi_i}}b_0+\frac{b_0}{\sqrt{\Xi_i}}\Phi_i+b_0\Psi_i, \end{equation} | (4.57) |
where \xi = \frac{2b_0}{\sqrt{\Xi_i}}(x+\frac{4b_0^2a\delta t^\alpha}{\Xi_i\alpha}).
\begin{equation} u_{4i}(\xi) = \pm\sqrt{-\frac{\delta}{\Xi_i}}b_0-\frac{b_0}{\sqrt{\Xi_i}}\Phi_i+b_0\Psi_i, \end{equation} | (4.58) |
where \xi = -\frac{2b_0}{\sqrt{\Xi_i}}(x+\frac{4b_0^2a\delta t^\alpha}{\Xi_i\alpha}), in which i = 1 for \delta < 0 , i = 2 for \delta > 0 , i = 3 for \delta = 0 , and \Phi_i, \Psi_i, \Xi_i, \ (i = 1, 2, 3) see (4.10)-(4.14).
Here A_1 and A_2 are arbitrary constants. Therefore, one can freely select their values. If we choose A_1 = 0 , A_2\neq 0 , \mu = 0 and \delta < 0 in (4.55), we the traveling wave solution:
\begin{equation} u_1(\xi) = c\sqrt{-\delta}\left(\coth(\sqrt{-\delta}\xi)\pm \rm{csch}(\sqrt{-\delta}\xi)\right).\ \end{equation} | (4.59) |
If we choose A_1 = 0 , A_2\neq 0 , \mu = 0 and \delta > 0 in (4.55), we obtain the traveling wave solution:
\begin{equation} u_2(\xi) = c\sqrt{\delta}\left(\cot(\sqrt{\delta}\xi)\pm\csc(\sqrt{\delta}\xi)\right).\ \end{equation} | (4.60) |
If we choose A_2 = 0 , A_1\neq 0 , \mu = 0 and \delta > 0 in (4.55), we obtain the traveling wave solution:
\begin{equation} u_3(\xi) = -c\sqrt{\delta}\left(\tan(\sqrt{\delta}\xi)\pm\sec(\sqrt{\delta}\xi)\right).\ \end{equation} | (4.61) |
where \xi = c(x+\frac{ac^2\delta}{\alpha}t^\alpha).
Similarly, we can write down the other families of exact solutions of Eq. (4.45) which are omitted for convenience.
In this section, some typical wave figures are given as follows (Figure 1-3):
The basic idea of the two variable (\phi'/\phi, 1/\phi) -expansion method is to research the new exact traveling wave solutions of the mentioned Eqs. (4.1), (4.29) and Eq. (4.45). The Eqs. (4.1), (4.29) and (4.29) have been studied using various techniques, among them, Authors obtained some new solutions, but the researches considered the Jumaries modified Riemann-Liouville derivative sense for their solution techniques. Nonetheless, the existing analytical solutions reported in [23,24,27] are not correct because the utilized definitions of fractional derivative have some shortcomings that could not be overlooked [21]. Chen et al. [25] found some new solutions of Eq. (4.29) expressed by tanh, coth, tan and cot form. In our case, first time we considered the conformable fractional derivative sense and two variable (\phi'/\phi, 1/\phi) -expansion method for the Eqs. (4.1), (4.29) and (4.45), we found some new solutions expressed by tanh, coth, sech, csch, tan, cot, sec and csc form. To our knowledge, the solutions obtained have not been reported in former literature. So, all the solutions are new in this article.
In this study, the two variable (\phi'/\phi, 1/\phi) -expansion method with the help of conformable wave transformation has been applied to find out exact traveling wave solutions of time-fractional differential equations. We have obtained some new and further general solitary wave solutions to three nonlinear time fractional differential equation, namely, time-fractional K-S equation, (3+1)-dimensional KdV-ZK equation and FSTO equation in terms of hyperbolic, trigonometric and rational function solution involving parameters. These solutions have important physical implications, for example, these solutions forces are convenient to characterize the hydromagnetic waves in cold plasma, acoustic waves in inharmonic crystals and acoustic-gravity waves incompressible fluids. The obtained results show that the two variable (\phi'/\phi, 1/\phi) -expansion method is direct, consistent, reliable, very much attractive and an effective powerful mathematical tool for obtaining the exact solutions of other time fractional differential equations, and it can be generalized to nonlinear space-time fractional differential equations and space fractional differential equations. Finally, our results in this article have been checked using the Maple by putting them back into the original equation.
The authors would like to express their deepest appreciation to the reviewers for their valuable suggestions and comments to improve the article.
This work was supported by the Middle-Aged Academic Backbone of Honghe University (No. 2014GG0105; No.2015GG0207), Scientific Research Foundation of Yunnan Education Department (No. 2018JS479) Yunnan Applied Basic Research Project(No.2018FH001013; 2018FH001014) and the Natural Science Foundation of Education Committee of Yunnan Province (No. 2019J0558).
The authors declare no conflict of interest.
[1] |
P. Y. Wen, J. T. Huse, 2016 World Health Organization classification of central nervous system tumors, Continuum (Lifelong Learn. Neurol.), 23 (2017), 1531–1547. https://doi.org/10.1212/con.0000000000000536 doi: 10.1212/con.0000000000000536
![]() |
[2] |
S. C. Thust, S. Heiland, A. Falini, H. R. Jager, A. D. Waldman, P. C. Sundgren, et al., Glioma imaging in Europe: A survey of 220 centres and recommendations for best clinical practice, Eur. Radiol., 28 (2018), 3306–3317. https://doi.org/10.1007/s00330-018-5314-5 doi: 10.1007/s00330-018-5314-5
![]() |
[3] |
C. Gui, J. C. Lau, S. E. Kosteniuk, D. H. Lee, J. F. Megyesi, Radiology reporting of low-grade glioma growth underestimates tumor expansion, Acta Neurochir., 161 (2019), 569–576. https://doi.org/10.1007/s00701-018-03783-3 doi: 10.1007/s00701-018-03783-3
![]() |
[4] |
A. Bink, J. Benner, J. Reinhardt, A. De Vere-Tyndall, B. Stieltjes, N. Hainc, et al., Structured reporting in neuroradiology: Intracranial tumors, Front. Neurol., 9 (2018), 32. https://doi.org/10.3389/fneur.2018.00032 doi: 10.3389/fneur.2018.00032
![]() |
[5] |
L. B. Nabors, J. Portnow, M. Ahluwalia, J. Baehring, H. Brem, S. Brem, et al., Central nervous system cancers version 3.2020, NCCN clinical practice guidelines in oncology, JNCCN, 18 (2020), 1537–1570. https://doi.org/10.6004/jnccn.2020.0052 doi: 10.6004/jnccn.2020.0052
![]() |
[6] |
Z. Y. Lin, R. W. Yang, K. S. Li, G. Z. Yi, Z. Y. Li, J. L. Guo, et al., Establishment of age group classification for risk stratification in glioma patients, BMC Neurol., 20 (2020), 310. https://doi.org/10.1186/s12883-020-01888-w doi: 10.1186/s12883-020-01888-w
![]() |
[7] |
P. Grossmann, O. Stringfield, N. El-Hachem, M. M. Bui, E. R. Velazquez, C. Parmar, et al., Defining the biological basis of radiomic phenotypes in lung cancer, ELife, 6 (2017), e23421. https://doi.org/10.7554/elife.23421 doi: 10.7554/elife.23421
![]() |
[8] |
H. H. Cho, C. K. Kim, H. Park, Overview of radiomics in prostate imaging and future directions, Br. J. Radiol., 95 (2022), 1131. https://doi.org/10.1259/bjr.20210539 doi: 10.1259/bjr.20210539
![]() |
[9] |
J. E. van Timmeren, D. Cester, S. Tanadini-Lang, H. Alkadhi, B. Baessler, Radiomics in medical imaging-"how-to" guide and critical reflection, Insights Imaging, 11 (2020), 91. https://doi.org/10.1186/s13244-020-00887-2 doi: 10.1186/s13244-020-00887-2
![]() |
[10] |
P. Lambin, R. T. H. Leijenaar, T. M. Deist, J. Peerlings, E. E. C. de Jong, J. van Timmeren, et al., Radiomics: the bridge between medical imaging and personalized medicine, Nat. Rev. Clin. Oncol., 14 (2017), 749–762. https://doi.org/10.1038/nrclinonc.2017.141 doi: 10.1038/nrclinonc.2017.141
![]() |
[11] |
T. Asklund, R. Birgander, P. Brynolfsson, A. Garpebring, J. Hauksson, R. Henriksson, et al., ADC texture—An imaging biomarker for high grade glioma, Neuro-Oncol., 16 (2014), ii78. https://doi.org/10.1093/neuonc/nou174.299 doi: 10.1093/neuonc/nou174.299
![]() |
[12] | H. H. Cho, H. Park, Classification of low-grade and high-grade glioma using multi-modal image radiomics features, in 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), (2017), 3081–3084. https://doi.org/10.1109/embc.2017.8037508 |
[13] |
H. Aerts, E. R. Velazquez, R. T. H. Leijenaar, C. Parmar, P. Grossmann, S. Carvalho, et al., Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach, Nat. Commun., 5 (2014), 4006. https://doi.org/10.1038/ncomms5006 doi: 10.1038/ncomms5006
![]() |
[14] |
Q. J. Chen, L. H. Wang, L. Wang, Z. Y. Deng, J. Zhang, Y. M. Zhu, Glioma grade prediction using wavelet scattering-based radiomics, IEEE Access, 8 (2020), 106564–106575. https://doi.org/10.1109/ACCESS.2020.3000895 doi: 10.1109/ACCESS.2020.3000895
![]() |
[15] |
Y. W. Park, Y. S. Choi, S. S. Ahn, J. H. Chang, S. H. Kim, S. K. Lee, Radiomics MRI phenotyping with machine learning to predict the grade of lower-grade gliomas: A study focused on nonenhancing tumors, Korean J. Radiol., 20 (2019), 1381–1389. https://doi.org/10.3348/kjr.2018.0814 doi: 10.3348/kjr.2018.0814
![]() |
[16] | G. Cui, J. J. Jeong, B. Press, Y. Lei, H. K. G. Shu, T. X. Liu, et al., Machine-learning-based classification of lower-grade gliomas and high-grade gliomas using radiomic features in multi-parametric MRI, preprint, arXiv: 1911.10145. |
[17] |
K. L. C. Hsieh, C. Y. Chen, C. M. Lo, Quantitative glioma grading using transformed gray-scale invariant textures of MRI, Comput. Biol. Med., 83 (2017), 102–108. https://doi.org/10.1016/j.compbiomed.2017.02.012 doi: 10.1016/j.compbiomed.2017.02.012
![]() |
[18] | T. H. Xiao, W. Q. Hua, C. Li, S. S. Wang, Glioma grading prediction by exploring radiomics and deep learning features, in Proceedings of the Third International Symposium on Image Computing and Digital Medicine, (2019), 208–213. https://doi.org/10.1145/3364836.3364877 |
[19] |
M. Gao, S. Y. Huang, X. Q. Pan, X. Liao, R. Yang, J. Liu, Machine learning-based radiomics predicting tumor grades and expression of multiple pathologic biomarkers in gliomas, Front. Oncol., 10 (2020), 1676. https://doi.org/10.3389/fonc.2020.01676 doi: 10.3389/fonc.2020.01676
![]() |
[20] |
H. H. Cho, S. H. Lee, J. Kim, H. Park, Classification of the glioma grading using radiomics analysis, PeerJ, 6 (2018), e5982. https://doi.org/10.7717/peerj.5982 doi: 10.7717/peerj.5982
![]() |
[21] |
J. J. M. van Griethuysen, A. Fedorov, C. Parmar, A. Hosny, N. Aucoin, V. Narayan, et al., Computational radiomics system to decode the radiographic phenotype, Cancer Res., 77 (2017), e104–e107. https://doi.org/10.1158/0008-5472.can-17-0339 doi: 10.1158/0008-5472.can-17-0339
![]() |
[22] |
A. Lancichinetti, S. Fortunato, J. Kertész, Detecting the overlapping and hierarchical community structure in complex networks, New J. Phys., 11 (2009), 033015. https://dx.doi.org/10.1088/1367-2630/11/3/033015 doi: 10.1088/1367-2630/11/3/033015
![]() |
[23] |
K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput., 6 (2002), 182–197. https://dx.doi.org/10.1109/4235.996017 doi: 10.1109/4235.996017
![]() |
[24] |
D. N. Reshef, Y. A. Reshef, H. K. Finucane, S. R. Grossman, G. McVean, P. J. Turnbaugh, et al., Detecting novel associations in large data sets, Science, 334 (2011), 6062. https://doi.org/10.1126/science.1205438 doi: 10.1126/science.1205438
![]() |
[25] | S. M. Lundberg, S. I. Lee, A unified approach to interpreting model predictions, in Advances in Neural Information Processing Systems 30 (NIPS 2017), (2017), 4768–4777. https://dl.acm.org/doi/10.5555/3295222.3295230 |
[26] | F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, et al., Scikit-learn: Machine learning in Python, J. Mach. Learn. Res., 12 (2011), 2825–2830. https://dl.acm.org/doi/10.5555/1953048.2078195 |
[27] |
Q. H. Kha, V. H. Le, T. N. K. Hung, N. Q. K. Le, Development and validation of an efficient MRI radiomics signature for improving the predictive performance of 1p/19q Co-deletion in lower-grade gliomas, Cancers, 13 (2021), 5398. https://doi.org/10.3390/cancers13215398 doi: 10.3390/cancers13215398
![]() |
[28] |
S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J. S. Kirby, et al., Data descriptor: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features, Sci. Data, 4 (2017), 170117. https://doi.org/10.1038/sdata.2017.117 doi: 10.1038/sdata.2017.117
![]() |
[29] |
K. Clark, B. Vendt, K. Smith, J. Freymann, J. Kirby, P. Koppel, et al., The cancer imaging archive (TCIA): Maintaining and operating a public information repository, J. Digital Imaging, 26 (2013), 1045–1057. https://doi.org/10.1007/s10278-013-9622-7 doi: 10.1007/s10278-013-9622-7
![]() |
[30] |
B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, et al., The multimodal brain tumor image segmentation benchmark (BRATS), IEEE Trans. Med. Imaging, 34 (2015), 1993–2024. https://doi.org/10.1109/tmi.2014.2377694 doi: 10.1109/tmi.2014.2377694
![]() |
[31] |
M. Kistler, S. Bonaretti, M. Pfahrer, R. Niklaus, P. Buchler, The virtual skeleton database: An open access repository for biomedical research and collaboration, J. Med. Int. Res., 15 (2013), e245. https://doi.org/10.2196/jmir.2930 doi: 10.2196/jmir.2930
![]() |
[32] |
M. Ghaffari, A. Sowmya, R. Oliver, Automated brain tumor segmentation using multimodal brain scans: A survey based on models submitted to the BraTS 2012–2018 Challenges, IEEE Rev. Biomed. Eng., 13 (2020), 156–168. https://doi.org/10.1109/rbme.2019.2946868 doi: 10.1109/rbme.2019.2946868
![]() |
[33] |
S. Pati, R. Verma, H. Akbari, M. Bilello, V. B. Hill, C. Sako, et al., Reproducibility analysis of multi-institutional paired expert annotations and radiomic features of the Ivy Glioblastoma Atlas Project (Ivy GAP) dataset, Med. Phys., 47 (2020), 6039–6052. https://doi.org/10.1002/mp.14556 doi: 10.1002/mp.14556
![]() |
[34] |
L. H. T. Lam, D. T. Do, D. T. N. Diep, D. L. N. Nguyet, Q. D. Truong, T. T. Tri, et al., Molecular subtype classification of low-grade gliomas using magnetic resonance imaging-based radiomics and machine learning, NMR Biomed., 35 (2022), e4792. https://doi.org/10.1002/nbm.4792 doi: 10.1002/nbm.4792
![]() |
[35] |
A. Zwanenburg, M. Vallieres, M. A. Abdalah, H. Aerts, V. Andrearczyk, A. Apte, et al., The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping, Radiology, 295 (2020), 328–338. https://doi.org/10.1148/radiol.2020191145 doi: 10.1148/radiol.2020191145
![]() |
[36] | T. M. Hamdani, J. M. Won, A. M. Alimi, F. Karray, Multi-objective feature selection with NSGA II, in International Conference on Adaptive and Natural Computing Algorithms, (2007), 240–247. https://doi.org/10.1007/978-3-540-71618-1_27 |
[37] |
N. Q. K. Le, T. N. K. Hung, D. T. Do, L. H. T. Lam, L. H. Dang, T. T. Huynh, Radiomics-based machine learning model for efficiently classifying transcriptome subtypes in glioblastoma patients from MRI, Comput. Biol. Med., 132 (2021), 104320. https://doi.org/10.1016/j.compbiomed.2021.104320 doi: 10.1016/j.compbiomed.2021.104320
![]() |
![]() |
![]() |
1. | Aniqa Aniqa, Jamshad Ahmad, Soliton solution of fractional Sharma-Tasso-Olever equation via an efficient (G′/G)-expansion method, 2022, 13, 20904479, 101528, 10.1016/j.asej.2021.06.014 | |
2. | Hongkua Lin, Explicit Exact Solutions of Space-time Fractional Drinfel’d-Sokolov-Wilson Equations, 2021, 2068, 1742-6588, 012005, 10.1088/1742-6596/2068/1/012005 | |
3. | Sevil Çulha Ünal, Ayşegül Daşcıoğlu, Exact solutions of time fractional Korteweg–de Vries–Zakharov–Kuznetsov equation, 2021, 44, 0170-4214, 9557, 10.1002/mma.7379 | |
4. | Shumaila Naz, Qazi Mahmood Ul-Hassan, Jamshad ahmad, Aniqa Zulfiqar, Dynamics of new optical solutions for nonlinear equations via a novel analytical technique, 2022, 54, 0306-8919, 10.1007/s11082-022-03793-7 | |
5. | Mst Munny Khatun, M Ali Akbar, Zarina K Fayazova, Onur Alp İlhan, Various wave solutions to the nonlinear fractional Korteweg-de Vries Zakharov-Kuznetsov equation by a new approach to the two-variable expansion scheme, 2024, 99, 0031-8949, 035218, 10.1088/1402-4896/ad23ba | |
6. | Ramima Akter, Shuvo Sarker, Anish Adhikary, M. Ali Akbar, Pinakee Dey, M.S. Osman, Dynamics of geometric shape solutions for space-time fractional modified equal width equation with beta derivative, 2024, 11, 26668181, 100841, 10.1016/j.padiff.2024.100841 | |
7. | Yating Hao, Ben Gao, Exact solutions to the (3 + 1)-dimensional time-fractional KdV–Zakharov–Kuznetsov equation and modified KdV equation with variable coefficients, 2024, 98, 0973-7111, 10.1007/s12043-023-02693-z |