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Research article Topical Sections

Stress and serum cortisol levels in major depressive disorder: a cross-sectional study

  • Received: 29 August 2020 Accepted: 10 November 2020 Published: 13 November 2020
  • Major depressive disorder (MDD) is one of the disorders that most causes disability and affects about 265 million people worldwide, according to the World Health Organization (WHO). Chronic stress is one of the most prevalent factors that trigger MDD. Among the most relevant biological mechanisms that mediate stress and MDD are changes in the hypothalamic-pituitary-adrenal (HPA) axis function. Hypercortisolism is one of the relevant mechanisms involved in response to stress and is present in many people with MDD and in animals subjected to stress in the laboratory. This study aimed to investigate the levels of stress and cortisol in individuals diagnosed with MDD from the Basic Health Unit (BHU) in a small city in the western region of Santa Catarina, Brazil. Depression scores were assessed using Beck's inventory. For the investigation of stress, an adaptation with twenty-four questions of the Checklist-90-R manual was performed. The analysis of the cortisol levels in the individuals' serum was by the chemiluminescence method. Depression and stress scores were significantly higher in individuals with MDD than in control subjects (p < 0.001). Cortisol levels were also significantly higher in individuals with MDD (p < 0.05). Besides, depression scores were positively correlated with stress scores in individuals with MDD (Pearson's “r” = 0.70). Conclusion: Individuals with MDD had higher stress levels and cortisol than control subjects. The positive correlation between the levels of stress and depression in MDD individuals suggests that these conditions are related to a dysregulation of the HPA axis function.

    Citation: Amanda G Bertollo, Roberta E Grolli, Marcos E Plissari, Vanessa A Gasparin, João Quevedo, Gislaine Z Réus, Margarete D Bagatini, Zuleide M Ignácio. Stress and serum cortisol levels in major depressive disorder: a cross-sectional study[J]. AIMS Neuroscience, 2020, 7(4): 459-469. doi: 10.3934/Neuroscience.2020028

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  • Major depressive disorder (MDD) is one of the disorders that most causes disability and affects about 265 million people worldwide, according to the World Health Organization (WHO). Chronic stress is one of the most prevalent factors that trigger MDD. Among the most relevant biological mechanisms that mediate stress and MDD are changes in the hypothalamic-pituitary-adrenal (HPA) axis function. Hypercortisolism is one of the relevant mechanisms involved in response to stress and is present in many people with MDD and in animals subjected to stress in the laboratory. This study aimed to investigate the levels of stress and cortisol in individuals diagnosed with MDD from the Basic Health Unit (BHU) in a small city in the western region of Santa Catarina, Brazil. Depression scores were assessed using Beck's inventory. For the investigation of stress, an adaptation with twenty-four questions of the Checklist-90-R manual was performed. The analysis of the cortisol levels in the individuals' serum was by the chemiluminescence method. Depression and stress scores were significantly higher in individuals with MDD than in control subjects (p < 0.001). Cortisol levels were also significantly higher in individuals with MDD (p < 0.05). Besides, depression scores were positively correlated with stress scores in individuals with MDD (Pearson's “r” = 0.70). Conclusion: Individuals with MDD had higher stress levels and cortisol than control subjects. The positive correlation between the levels of stress and depression in MDD individuals suggests that these conditions are related to a dysregulation of the HPA axis function.


    The development of fractional calculus (FC) [1] was traced from a letter dated September 30, 1695, written by L' Hospital to Leibniz regarding the half derivative of the linear function p(x)=x. Leibniz response was "An apparent paradox, from which one-day useful consequences will be drawn". Nowadays, FC is identified as the most effective tool for the modelling of physical phenomena not only in mathematics but also in other branches of sciences, engineering, economics, finance, etc. FC has been identified as one of the fastest-growing research areas in the last few decades. Also, FDEs have played a significant role because of their huge range of uses. Several problems in biology, physics, chemistry, applied science and engineering are modelled by FDEs [2,3,4,5,6,7]. Many research articles were developed to investigate the theory and solutions of FDEs [8,9]. Also, many researchers [10,11,12] developed the existence and uniqueness criteria of the IVP if FDEs of fractional order. The most common and oldest derivatives are Riemann Liouville (RL) and Caputo. In this research article, we are taking the derivative in the Caputo frame as it has many advantages for dealing with the IVP of FDEs:

    CDβx+0y=p(x,y),supplementaryconditiony(x0)=y0,andx(x0,xend], (1.1)

    where CDβx+0 indicates the Caputo fractional order derivative (CFOD) of variable order β such that β(0,1].

    In the survey, we found that most of the non-linear IVP of FDEs don't have any analytic method for finding the solutions [13], therefore numerical technique must be used for such cases. Some of the analytic and numerical methods for solving FDEs consists of Adomian decomposition method (ADM) [14], variational iteration method (VIM) [15], fractional differential transform scheme [16], fractional finite difference scheme [17], fractional Adams scheme [8], homotopy perturbation scheme [18], spectral collocation scheme [19], extrapolation method [20], homotopy analysis scheme [21], and many others. Out of these methods, the ADM and the VIM are the most well-known methods for solving the FDEs for providing instant and visible symbolic terms of numerical solutions. Moreover, the numerical scheme described in the literature [22,23,24] have some drawbacks in the RL derivative sense. In this work, we suggest novel schemes to reduce the drawback.

    Nowadays, the IVP of FDEs used as a weapon to solve the various mathematical models, dynamical models, and many others. This article establishes two fractional numerical algorithms for the IVP of FDEs of order β(0,1]. These schemes are fractional third-order RK3 scheme and fractional SSRK3 scheme, which are based on classical third-order RK3 scheme [25,26,27,28] and classical SSRK3 scheme, respectively. In [29], Muhammad et al. developed a two-stage generalized Rk2 scheme of second order in CFOD sense. In [30], Kumar et al. established two numerical schemes, which are fractional quadratic midpoint scheme and fractional cubic Heun scheme for an IVP of FDEs (1.1). Also, in the year 2016, Tong et al. [31] suggested numerical schemes, which are fractional EM and fractional IEM, to demonstrate the numerical solution of the IVP of FDEs (1.1). These all referred works motivate us to establish more efficient schemes to solve the IVP of FDE in the CFOD sense. Also, our main aim is to show, based on a few application models and a few concrete examples, that FDEs may model the physical problem more efficiently than the ODEs. In recent decades, many research articles have been devoted for numerical iterative scheme [32,33,34,35] of IVP of FDEs. Still, there are few non-linear IVP of FDEs in the Riemann derivative sense where the approximation technique does not work. In our work, we proposed two efficient cubic techniques, which is effectively providing the approximate solution of linear as well as non-linear IVP of FDEs (1.1) of order β where β(0,1]. Both schemes, fractional RK3, and fractional SSRK3 are cubic convergence in which the SSRK3 scheme is more stable and faster than all other cubic schemes for IVP of FDEs. This paper also provides the comparative and convergence analysis of our suggested technique with fractional EM and fractional IEM, which have linear and quadratic convergence, respectively. We organize this work as follows:

    ● In Section 2, we introduce mathematical preliminaries, some basic definitions, and the result of FC.

    ● In Section 3, we proposed our and suggested methodology and their convergence analysis.

    ● In Section 4, we provide numerical solutions of illustrated examples of IVP of FDEs using suggested schemes.

    ● In Section 5, we provide the numerical simulation of two real-world application models, which are the fractional World Population Growth (WPG) model and Nuclear Decay (ND) model using suggested schemes.

    ● In Section 6, we report our conclusion of the proposed scheme with some crucial facts.

    This section is devoted to the preliminary concepts of FC that we need in our study. So, we present some definitions and some properties of FC [36,37,38,39,40,41].

    Definition 2.1. For arbitrary β>0 and a piecewise integrable function κ:[a,b]R, the fractional left and right RL integral of order β are defined by

    RLIβa+κ(s)=1Γ(β)sa(sp)β1κ(p)dp,s>a,andRLIβbκ(s)=1Γ(β)bs(ps)β1κ(p)dp,s<b,

    respectively. Here the notation Γ signify the Gamma function.

    Definition 2.2. For arbitrary β>0 and a piecewise integrable function κ:[a,b]R, the fractional left and right RL derivative of order β are defined by

    RLDβa+κ(s)=1Γ(nβ)dndsnsa(sp)nβ1κ(p)dp,s>a,andRLDβbκ(s)=(1)nΓ(nβ)dndsnbs(ps)nβ1κ(p)dp,s<b,

    respectively, where n1<β<n, nN. If 0<β<1, then the fractional left and right RL derivative are

    RLDβa+κ(s)=1Γ(1β)ddssa(sp)βκ(p)dp,s>a,

    and

    RLDβbκ(s)=1Γ(1β)ddsbs(ps)βκ(p)dp,s<b,

    respectively.

    Definition 2.3. For arbitrary β>0 and a piecewise integrable function κ:[a,b]R, the fractional left and right Caputo derivative of order β are defined by

    CDβa+κ(s)=1Γ(nβ)sa(sp)nβ1κn(p)dp,s>a,andCDβbκ(s)=(1)nΓ(nβ)bs(ps)nβ1κn(p)dp,s<b,

    respectively, where n1<β<n, nN. If we take 0<β<1, then the fractional left and right Caputo derivative are

    CDβa+κ(s)=1Γ(1β)sa(sp)βκ(p)dp,s>a,andCDβbκ(s)=1Γ(1β)bs(ps)βκ(p)dp,s<b,

    respectively.

    The relation between the fractional RL derivative and fractional Caputo derivative is,

    CDβa+κ(s)=RLDβa+κ(s)n1k=0κk(a)(sa)kβΓ(kβ+1),andCDβbκ(s)=RLDβbκ(s)n1k=0κk(b)(bs)kβΓ(kβ+1),

    where n1<β<n,nN.

    Definition 2.4. The one parameter Mittag-Leffler function (Eβ) which was introduced by Mittag-Leffler, is defined as:

    Eβ(s)=n=0snΓ(βn+1),sC,βC,Re(β)>0.

    The two parameter Mittag-Leffler function (Eβ,γ) which was first appeared in a paper by Wiman [42], is defined as:

    Eβ,γ(s)=n=0snΓ(βn+γ),sC,βC,γC,Re(β)>0,Re(γ)>0.

    Lemma 2.1. [36,37] If Re(β)>0, Re(γ)>0 and p(x)Ls[a,b], where 0s, then the equations

    RLIβa+RLIγa+p=RLIβ+γa+p,andRLIβbRLIγbp=RLIβ+γbp, (2.1)

    holds almost everywhere for x[a,b]. If β+γ>1, then the expression (2.1) holds at any point on x[a,b].

    Lemma 2.2. [43] If p(x)Cs[a,b], a<b and sN. Moreover, If β,γ>0 be such that, some mN with ms and β,β+γ[m1,m]. Then,

    CDβa+CDγa+p=CDβ+γa+p.

    Theorem 2.1. (Existence of IVP of FDE) [31] Let p(x,y) be a function that hold the condition p(x0,y(x0))=0 and also the p(x,y) is continuous on the domain R:0xx0k1, |yy0|k2, then FDEs:

    CDβx+0y=p(x,y),supplementaryconditiony(x0)=y0andx(x0,xend], (2.2)

    has at least one solution in the interval 0xx0λ with λ=min{k1,k2M} and max(x,y)RCD1βa+p(x,y)<M.

    Theorem 2.2. (Uniqueness of IVP of FDE) [31] By following the Theorem 2.1, and if px(x,u) holds the condition of Lipschitz in the variable u with Lipschitz constant 0<L,

    |px(x,y1)px(x,y2)|L|y1y2|,

    then the FDEs (2.2) have unique solution.

    Let us assume, an IVP of FDE (1.1) in CFOD frame of variable order β. By following the Lemma 2.2, we apply a suitable analogous operation so that the FDE (1.1) will become the classical ODE, and we will get the CFOD of order (1β). After that, the revised IVP of FDE:

    y=CD1βx+0p(x,y),supplementaryconditiony(x0)=y0,andx(x0,xend]. (3.1)

    In order to obtain the efficient and appropriate approximate solution of the IVP of FDEs (3.1), we proposed two effective and fast numerical scheme for IVP of FDEs which are fractional RK3 [25,26,27,28] and fractional SSRK3 [44,45,46]. These two fractional numerical scheme is more accurate and fast compared to all other linear and quadratic convergence scheme for IVP of FDEs (1.1). Below is our fractional RK3 scheme.

    To establish the numerical solution of the IVP of fractional order differential equation (3.1) in the interval [a,b], we propose the algorithm fractional RK3 scheme [25,26,27,28], which is same as classical RK3 scheme for first order IVP of ODE. For approximating the solution, we consider (xk,yk) as our set points, and we consider these points in such a way that the mesh is equally distributed in the interval [a,b] where we set x0=a and xend=b. This idea will be good by selecting a non-negative integer, say n, and assuming the mesh points. So, the explicit fractional RK3 scheme is given by the Butcher tableau:

    01212112162316

    Here is the algorithm of fractional RK3 scheme of FDE (3.1):

    {xk=x0+khforeachk=0,1,2,,nh=xk+1xk,(stepsize)yk+1=yk+h6(l1+4l2+l3),wherel1=CD1βx+0p(x,yk)|x=xk,l2=CD1βx+0p(x+h2,yk+h2l1)|x=xk,l3=CD1βx+0p(x+h,ykhl1+2hl2)|x=xk.

    This algorithm is a cubic convergence scheme and with the help of Matlab, it is proven to be an efficient and more accurate in the comparison of linear and quadratic convergence scheme.

    Before proceeding the convergence of RK3 scheme, first we are going to state some relevant result and lemmas.

    Lemma 3.1. [31] Let px(x,u) be a function that satisfy the condition of Lipschitz in the unknown variable y, with Lipschitz constant 0<A,

    |px(x,y1)px(x,y2)|A|y1y2|,

    and also hold the conditions of Theorem 2.1, also consider P(x,y)=CD1βx+0p(x,y) holds the condition of Lipschitz in the unknown variable y, with another Lipschitz constant 0<M,

    |P(x,y1)P(x,y2)|M|y1y2|.

    Lemma 3.2. Consider the function P(x,y) satisfy the Lipschitz condition for the unknown variable y and if the conditions of Theorem 2.1 hold, then

    μ(x,y)=16P(x,y)+23P(x+h2,y+h2P(x,y))+16P(x+h,yhP(x,y)+2hP(x+h2,y+h2P(x,y))) (3.2)

    will always fulfill the condition of Lipschitz in the unknown variable y.

    Proof.

    |μ(x,y1)μ(x,y2)|16|P(x,y1)P(x,y2)|+23|P(x+h2,y1+h2P(x,y1))P(x+h2,y2+h2P(x,y2))|+16|P(x+h,y1hP(x,y1)+2hP(x+h2,y1+h2P(x,y1)))P(x+h,y2hP(x,y2)+2hP(x+h2,y2+h2P(x,y2)))|M|y1y2|+5hM26|y1y2|+h2M36|y1y2|[UsingLemma3]=M(1+5hM6+h2M26)|y1y2|=Lμ|y1y2|.

    Therefore, |μ(x,y1)μ(x,y2)|Lμ|y1y2|, where Lμ=M(1+5hM6+h2M26).

    Theorem 3.1. Let us assume the function px(x,u) satisfy the condition of Lipschitz in the unknown variable y, with Lipschitz constant 0<L,

    |px(x,y1)px(x,y2)|L|y1y2|,

    and y(x) be the unique solution of IVP of FDEs (3.1). Also let us assume yk be the approximate solution which is generated by RK3 scheme for non-negative integer n. Then, for every k=0,1,2,n,

    y(xk)yk=O(h3).

    Proof. First we are taking our fractional RK3 iterative scheme which is based on yk=y(xk), then we can write

    ˉyk+1=y(xk)+h6[CD1βx+0p(x,yk)|x=xk+4CD1βx+0p(x+h2,yk+h2CD1βx+0p(x,yk)|x=xk)|x=xk+CD1βx+0p(x+h,ykhCD1βx+0p(x,yk)|x=xk+2hCD1βx+0p(x+h2,yk+h2CD1βx+0p(x,yk)|x=xk)|x=xk)|x=xk].

    Consider, P(x,y)=CD1βx+0p(x,y), then the above expression will become,

    ˉyk+1=y(xk)+h6[P(xk,yk)+4P(xk+h2,yk+h2y(xk))+P(xk+h,ykhy(xk)+2hP(xk+h2,yk+h2y(xk)))]=y(xk)+h6P(xk,yk)+2h3[P(xk,yk)+(h2Px(xk,yk)+h2Py(xk,yk)y(xk))+12!((h2)2Pxx(xk,yk)+h22y(xk)Pxy(xk,yk)+(h2)2(y(xk))2Pyy(xk,yk))+O(h3)]+h6[P(xk,yk)+{hPx(xk,yk)+h(P(xk,yk)+hPx(xk,yk)+hy(xk)Py(xk,yk)+O(h2))Py(xk,yk)}+h22!{Pxx(xk,yk)+2Pxy(xk,yk)(y(xk)+O(h))+Pyy(xk,yk)(y(xk)+O(h))2}+h33!{Pxxx(ξ,η)+3(P(xk,yk)+O(h))Pxxy(ξ,η)+3(P(xk,yk)+O(h))2Pxyy(ξ,η)+(P(xk,yk)+O(h))3Pyyy(ξ,η)}]=y(xk)+hy(xk)+h22![Px(xk,yk)+y(xk)Py(xk,yk)]+h33![Pxx(xk,yk)+2y(xk)Pxy(xk,yk)+Pyy(xk,yk)(y(xk))2+Px(xk,yk)Py(xk,yk)+y(xk)(Py(xk,yk))2]+O(h4)=y(xk)+hy(xk)+h22!y"(xk)+h33!y"(xk)+O(h4). (3.3)

    With the help of Taylor's series, we can write the exact form of the solution,

    y(xk+1)=y(xk)+hy(xk)+h22!y"(xk)+h33!y"(xk)+h44!y""(xk)+ (3.4)

    By Eqs (3.3) and (3.4), we get |y(xk+1)ˉyk+1|=O(h4).

    So,|y(xk+1)ˉyk+1|Ch4.

    Taking,

    μ(x,y)=16P(x,y)+23P(x+h2,y+h2P(x,y))+16P(x+h,yhP(x,y)+2hP(x+h2,y+h2P(x,y))).

    From the above Lemmas 3.1 and 3.2, we have

    |ˉyk+1yk+1||y(xk)yk|+h|μ(xk,y(xk))μ(xk,yk)|(hLμ+1)|y(xk)yk|

    Therefore,

    |y(xk+1)yk+1||y(xk+1)ˉyk+1|+|ˉyk+1yk+1|Ch4+(hLμ+1)|y(xk)yk|.

    So, error estimation will be, |ϵk+1|=(hLμ+1)|ϵk|+Ch4.

    Using recursion relation,

    |ϵk|(hLμ+1)k|ϵ0|+Ch3Lμ[(hLμ+1)k1].

    Since, xkx0=khandϵ0=0then,(hLμ+1)kekhLμ=fμ.

    So, we have |ϵk|Ch3Lμ(fμ1), where Lμ=M(1+5hM6+h2M26)[ByLemma3.2]

    Therefore, |y(xk)yk|=O(h3).

    This indicates that our suggested scheme fractional RK3 has a cubic convergence rate.

    Here, we proposed another fractional third-order Runge-Kutta scheme which is more stable and renamed as fractional strong stability preserving third-order Runge Kutta (SSRK3) scheme. So, the explicit fractional SSRK3 scheme is given by the Butcher tableau:

    011121614161623

    Here is the algorithm of fractional SSRK3 scheme of IVP of FDE (3.1):

    {xk=x0+khforeachk=0,1,2,,nh=xk+1xk,(stepsize)yk+1=yk+h6(p1+p2+4p3),wherep1=CD1βx+0p(x,yk)|x=xk,p2=CD1βx+0p(x+h,yk+hp1)|x=xk,p3=CD1βx+0p(x+h2,yk+h4p1+h4p2)|x=xk.

    This algorithm is also a cubic convergence scheme that is more stable and accurate than the fractional RK3 scheme and faster in comparing other linear and quadratic convergence schemes for IVP of FDEs.

    Lemma 3.3. Consider the function P(x,y) satisfy the Lipschitz condition for the unknown variable y and if the conditions of Theorem 2.1 hold, then

    λ(x,y)=16P(x,y)+16P(x+h,y+hP(x,y))+23P(x+h2,y+h4P(x,y)+h4P(x+h,y+hP(x,y))) (3.5)

    will always fulfill the condition of Lipschitz in the unknown variable y.

    Proof.

    |λ(x,y1)λ(x,y2)|16|P(x,y1)P(x,y2)|+16|P(x+h,y1+hP(x,y1))P(x+h,y2+hP(x,y2))|+23|P(x+h2,y1+h4P(x,y1)+h4P(x+h,y1+P(x,y1)))P(x+h2,y2+h4hP(x,y2)+h4P(x+h,y2+hP(x,y2)))|M|y1y2|+hM23|y1y2|+h2M36|y1y2|[UsingLemma3]=M(1+hM3+h2M26)|y1y2|=Lλ|y1y2|,

    Therefore, |λ(x,y1)λ(x,y2)|Lλ|y1y2|, where Lλ=M(1+hM3+h2M26).

    Theorem 3.2. Let us assume the function px(x,u) satisfy the condition of Lipschitz in the unknown variable y, with Lipschitz constant 0<L,

    |px(x,y1)px(x,y2)|L|y1y2|,

    and y(x) be the unique solution of IVP of FDEs (3.1). Also, let us assume yk be the approximate solution which is generated by third order strong stability preserving Runge Kutta scheme for non-negative integer n. Then, for every k=0,1,2,n,

    y(xk)yk=O(h3).

    Proof. First we are taking our fractional SSRK3 iterative scheme which depend on yk=y(xk), then we can write

    ˉyk+1=y(xk)+h6[CD1βx+0p(x,yk)|x=xk+CD1βx+0p(x+h,yk+hCD1βx+0p(x,yk)|x=xk)|x=xk+4CD1βx+0p(x+h2,yk+h4CD1βx+0p(x,yk)|x=xk+h4CD1βx+0p(x+h,yk+hCD1βx+0p(x,yk)|x=xk)|x=xk)|x=xk]

    Consider, P(x,y)=CD1βx+0p(x,y), then the above expression will become,

    ˉyk+1=y(xk)+h6[P(xk,yk)+P(xk+h,yk+hy(xk))+4P(xk+h2,yk+h4y(xk)+h4P(xk+h,yk+hy(xk)))]=y(xk)+h6P(xk,yk)+h6[P(xk,yk)+{hPx(xk,yk)+hPy(xk,yk)y(xk)}+12!{h2Pxx(xk,yk)+2h2y(xk)Pxy(xk,yk)+h2(y(xk))2Pyy(xk,yk)}+O(h3)]+2h3[P(xk,yk)+{h2Px(xk,yk)+h4(2P(xk,yk)+hPx(xk,yk)+hy(xk)Py(xk,yk)+O(h2))Py(xk,yk)}+12!{h24Pxx(xk,yk)+h24Pxy(xk,yk)(2y(xk)+O(h))+h216Pyy(xk,yk)(2y(xk)+O(h))2}+13!{h38Pxxx(ξ,η)+3h316(2y(xk)+O(h))Pxxy(ξ,η)+3h332(2y(xk)+O(h))2Pxyy(ξ,η)+h364(P(xk,yk)+O(h))3Pyyy(ξ,η)}]=y(xk)+hy(xk)+h22![Px(xk,yk)+y(xk)Py(xk,yk)]+h33![Pxx(xk,yk)+2y(xk)Pxy(xk,yk)+Pyy(xk,yk)(y(xk))2+Px(xk,yk)Py(xk,yk)+y(xk)(Py(xk,yk))2]+O(h4)=y(xk)+hy(xk)+h22!y"(xk)+h33!y"(xk)+O(h4). (3.6)

    With the help of Taylor's series, we can write the exact form of the solution,

    y(xk+1)=y(xk)+hy(xk)+h22!y"(xk)+h33!y"(xk)+h44!y""(xk)+ (3.7)

    By Eqs (3.6) and (3.7), we get |y(xk+1)ˉyk+1|=O(h4).

    So,|y(xk+1)ˉyk+1|Ch4.

    Taking,

    λ(x,y)=16P(x,y)+16P(x+h,y+hP(x,y))+23P(x+h2,y+h4P(x,y)+h4P(x+h,y+hP(x,y))).

    From the above Lemmas 3.1 and 3.2, we have

    |ˉyk+1yk+1||y(xk)yk|+h|λ(xk,y(xk))λ(xk,yk)|(hLλ+1)|y(xk)yk|

    Therefore,

    |y(xk+1)yk+1||y(xk+1)ˉyk+1|+|ˉyk+1yk+1|Ch4+(hLλ+1)|y(xk)yk|.

    So, error estimation will be, |ϵk+1|=(hLλ+1)|ϵk|+Ch4.

    Using recursion relation,

    |ϵk|(hLλ+1)k|ϵ0|+Ch3Lλ[(hLλ+1)k1].

    Since, xkx0=khandϵ0=0then,(hLλ+1)kekhLλ=fλ.

    So, we have |ϵk|Ch3Lλ(fλ1), where Lλ=M(1+hM3+h2M26).[ByLemma3.3]

    Therefore, |y(xk)yk|=O(h3).

    This indicates that our suggested scheme fractional SSRK3 has a cubic convergence rate.

    In this section, we present two examples of IVP of FDEs. We provide the numerical solution using our suggested scheme by comparing it with the existing fractional EM and fractional IEM using Matlab. In this illustrated example, the first example is linear IVP of FDE and the second example is non-linear IVP of FDE.

    Example 4.1. [47] Consider the following linear IVP of FDE:

    CDβ0+y=y,0.1<x1,withsuplementarycondition,y(0.1)=Eβ((0.1)β). (4.1)

    The analytic solution of FDE (4.1) is,

    y(x)=Eβ(xβ).

    For β=12 and with the help of Matlab, by following our proposed scheme with step size h=110, the analytic and approximate solution of the FDE (4.1) is graphically shown in Figure 1 and tabulated in the Tables 1 and 2. In addition to this, the absolute error graph is shown in the Figure 2. Also, the order of convergence is tabulated in Table 3 and graphically shown in Figure 3.

    Figure 1.  Analytical & numerical solution of Example 4.1.
    Table 1.  Numerical solution of Example 4.1 when β=12, with step length h=110.
    EM IEM RK3
    x yexact yEM |yexactyEM| yIEM |yexactyIEM| yRK3 |yexactyRK3|
    1/10 0.72358 0.72358 0.00000 0.72358 0.00000 0.72358 0.00000
    2/10 0.64379 0.61752 0.02626 0.63966 0.00413 0.64372 0.00007
    3/10 0.59202 0.55575 0.03627 0.58687 0.00515 0.59194 0.00008
    4/10 0.55361 0.51194 0.04166 0.54805 0.00556 0.55353 0.00008
    5/10 0.52316 0.47810 0.04506 0.51739 0.00577 0.52308 0.00008
    6/10 0.49802 0.45062 0.04740 0.49213 0.00589 0.49794 0.00008
    7/10 0.47670 0.42759 0.04911 0.47073 0.00597 0.47662 0.00008
    8/10 0.45825 0.40783 0.05042 0.45223 0.00602 0.45817 0.00008
    9/10 0.44202 0.39057 0.05145 0.43596 0.00606 0.44194 0.00008
    1 0.42758 0.37530 0.05228 0.42150 0.00608 0.42750 0.00008

     | Show Table
    DownLoad: CSV
    Table 2.  Numerical solution of Example 4.1 when β=12, with step length h=110.
    EM IEM SSRK3
    x yexact yEM |yexactyEM| yIEM |yexactyIEM| ySSRK3 |yexactySSRK3|
    1/10 0.72358 0.72358 0.00000 0.72358 0.00000 0.72358 0.00000
    2/10 0.64379 0.61752 0.02626 0.63966 0.00413 0.64372 0.00007
    3/10 0.59202 0.55575 0.03627 0.58687 0.00515 0.59194 0.00008
    4/10 0.55361 0.51194 0.04166 0.54805 0.00556 0.55353 0.00008
    5/10 0.52316 0.47810 0.04506 0.51739 0.00577 0.52308 0.00008
    6/10 0.49802 0.45062 0.04740 0.49213 0.00589 0.49794 0.00008
    7/10 0.47670 0.42759 0.04911 0.47073 0.00597 0.47662 0.00008
    8/10 0.45825 0.40783 0.05042 0.45223 0.00602 0.45817 0.00008
    9/10 0.44202 0.39057 0.05145 0.43596 0.00606 0.44194 0.00008
    1 0.42758 0.37530 0.05228 0.42150 0.00608 0.42750 0.00008

     | Show Table
    DownLoad: CSV
    Figure 2.  Absolute error visualization of Example 4.1.
    Table 3.  Order of convergence table of Example 4.1.
    EM IEM RK3 SSRK3
    n Error Order Error Oredr Error Order Error Order
    10 0.046549 --- 0.004972 --- 0.000056 --- 0.000056 ---
    20 0.022073 1.0764 0.001285 1.9523 0.000004 2.7265 0.000004 2.7265
    40 0.010719 1.0422 0.000324 1.9859 0.000000 2.9098 0.000000 2.9098
    80 0.005278 1.0219 0.000081 1.9963 0.000000 2.9750 0.000000 2.9750
    160 0.002619 1.0111 0.000020 1.9991 0.000000 2.9935 0.000000 2.9935
    320 0.001304 1.0056 0.000005 1.9998 0.000000 2.9984 0.000000 2.9984
    640 0.000651 1.0028 0.000001 1.9999 0.000000 2.9993 0.000000 2.9993
    1280 0.000325 1.0014 0.000000 2.0000 0.000000 3.0077 0.000000 3.0077
    2560 0.000162 1.0007 0.000000 2.0000 0.000000 2.9533 0.000000 2.9533
    5120 0.000081 1.0004 0.000000 2.0000 0.000000 3.0559 0.000000 3.0559

     | Show Table
    DownLoad: CSV
    Figure 3.  Order of convergence plot for Example 4.1.

    Example 4.2. Consider the non-linear IVP of FDE:

    CDβ1+y=(35π32)y67,1<x2,withsuplementarycondition,y(1)=1. (4.2)

    The analytic solution of FDE (4.2) is,

    y(x)=x3.5.

    For β=12 and with the help of Matlab, by following our proposed scheme with step size h=110, the analytic and approximate solution of the FDE (4.2) is graphically shown in Figure 4 and tabulated in the Tables 4 and 5. In addition to this, the absolute error graph is shown in the Figure 5. Also, the order of convergence is tabulated in Table 6 and graphically shown in Figure 6.

    Figure 4.  Analytical & numerical solution of Example 4.2.
    Table 4.  Numerical solution of Example 4.2 when β=12, with step length h=110.
    EM IEM RK3
    x yexact yEM |yexactyEM| yIEM |yexactyIEM| yRK3 |yexactyRK3|
    1 1.00000 1.00000 0.00000 1.00000 0.00000 1.00000 0.00000
    11/10 1.39596 1.35000 0.04596 1.39184 0.00413 1.39567 0.00029
    12/10 1.89293 1.78367 0.10925 1.88345 0.00948 1.89229 0.00064
    13/10 2.50497 2.31282 0.19214 2.48883 0.01614 2.50391 0.00106
    14/10 3.24674 2.94987 0.29688 3.22256 0.02418 3.24521 0.00153
    15/10 4.13351 3.70782 0.42569 4.09983 0.03369 4.13144 0.00208
    16/10 5.18108 4.60027 0.58081 5.13635 0.04473 5.17839 0.00269
    17/10 6.40577 5.64135 0.76442 6.34839 0.05738 6.40240 0.00336
    18/10 7.82445 6.84574 0.97870 7.75275 0.07169 7.82033 0.00411
    19/10 9.45448 8.22866 1.22581 9.36673 0.08775 9.44954 0.00494
    2 11.31371 9.80582 1.50789 11.20811 0.10559 11.30787 0.00583

     | Show Table
    DownLoad: CSV
    Table 5.  Numerical solution of Example 4.2 when β=12, with step length h=110.
    EM IEM SSRK3
    x yexact yEM |yexactyEM| yIEM |yexactyIEM| ySSRK3 |yexactySSRK3|
    1 1.00000 1.00000 0.00000 1.00000 0.00000 1.00000 0.00000
    11/10 1.39596 1.35000 0.04596 1.39184 0.00413 1.39575 0.00021
    12/10 1.89293 1.78367 0.10925 1.88345 0.00948 1.89247 0.00046
    13/10 2.50497 2.31282 0.19214 2.48883 0.01614 2.50420 0.00076
    14/10 3.24674 2.94987 0.29688 3.22256 0.02418 3.24564 0.00110
    15/10 4.13351 3.70782 0.42569 4.09983 0.03369 4.13202 0.00149
    16/10 5.18108 4.60027 0.58081 5.13635 0.04473 5.17914 0.00193
    17/10 6.40577 5.64135 0.76442 6.34839 0.05738 6.40335 0.00242
    18/10 7.82445 6.84574 0.97870 7.75275 0.07169 7.82149 0.00296
    19/10 9.45448 8.22866 1.22581 9.36673 0.08775 9.45093 0.00355
    2 11.31371 9.80582 1.50789 11.20811 0.10559 11.30952 0.00419

     | Show Table
    DownLoad: CSV
    Figure 5.  Absolute error visualization of Example 4.2.
    Table 6.  Order of convergence table of Example 4.2.
    EM IEM RK3 SSRK3
    n Error Order Error Order Error Order Error Order
    10 1.507887 --- 0.105594 --- 0.005834 --- 0.004193 ---
    20 0.803028 0.9090 0.028540 1.8875 0.000787 2.8901 0.000563 2.8971
    40 0.414814 0.9530 0.007426 1.9424 0.000102 2.9470 0.000073 2.9488
    80 0.210871 0.9761 0.001894 1.9708 0.000013 2.9741 0.000009 2.9745
    160 0.106319 0.9880 0.000478 1.9853 0.000002 2.9872 0.000001 2.9873
    320 0.053383 0.9940 0.000120 1.9926 0.000000 2.9937 0.000000 2.9936
    640 0.026748 0.9970 0.000030 1.9963 0.000000 2.9968 0.000000 2.9968
    1280 0.013388 0.9985 0.000008 1.9982 0.000000 2.9984 0.000000 2.9984
    2560 0.006697 0.9992 0.000002 1.9991 0.000000 2.9992 0.000000 2.9992

     | Show Table
    DownLoad: CSV
    Figure 6.  Order plot for Example 4.2.

    Example 5.1. (Fractional WPG model) [48] Consider the following linear IVP of FDE of WPG model,

    CDβt+0n(t)=Pn(t),t>t0,withsuplementarycondition,n(t0)=n0. (5.1)

    Here, P=BM is the population production rate where B is the birth rate and M is the mortality rate. Also, n(t) is the individuals population at time t and CDβ is the β order rate of change of population. This model (5.1) is known as fractional WPG model of order β. The analytic solution of (5.1) is,

    n(t)=n0Eβ(Pt),t0.

    Particularly, if we take β=1, then that model will be classical WPG such as:

    dn(t)dt=Pn(t),t>t0,with,n(t0)=n0. (5.2)

    The analytic solution of (5.2) is,

    n(t)=n0ePt,t0.

    The population at the initial time t0 is denoted by the symbol n0. It is found from the survey that the fractional population model (5.1) precisely fitted with world statistical population data with proper order β. In this article, we have taken the world census population data from the year 1920 to 2018 which is collected from the world population data sites SITE-1 or SITE-2 (taken from https://www.worldbank.org/) and also one population data from United Nations [49]. Here, this statistical population data is fitted for fractional order β=1.3932987548432 [48] and production rate P0.0034399. As per statistical world population data, we found that the initial population in the year 1920 is n0=1860 million. Also, we have taken the classical population model for which the production rate is P0.013501.

    With the help of Matlab by following our proposed scheme with step size h=1, the statistical world population data from the year 1920 to 2018 is graphically shown in Figure 7. The analytic and approximate solution of the FDE (5.1) graphically shown in Figure 8. In addition to this, the analytic and approximate solution is tabulated in Tables 7 and 8. The absolute error graph is represented in the Figure 9. Thus, from the figure and table, we found that our suggested scheme fractional RK3 and fractional SSRK3 are much more effective and accurate to the statistical population data than EM and IEM.

    Figure 7.  Population date from 1920 to 2018.
    Figure 8.  Analytical & numerical solution of Example 5.1.
    Table 7.  Numerical solution of Example (5.1) when β=1, β=1.3932987548432, with h=1 year.
    Analytical EM IEM RK3
    Year(t) nLinear nFrac nEM Error nIEM Error nRK3 Error
    1920 1.8600×103 1.8600×103 1.8600×103 0.0000×100 1.8600×103 0.0000×100 1.8600×103 0.0000×100
    1930 2.1289×103 1.9909×103 1.9799×103 1.1060×101 1.9892×103 1.7315×100 1.9906×103 3.1405×101
    1940 2.4366×103 2.2169×103 2.2020×103 1.4921×101 2.2152×103 1.7413×100 2.2166×103 3.1405×101
    1950 2.7888×103 2.5173×103 2.4987×103 1.8625×101 2.5156×103 1.7390×100 2.5170×103 3.1405×101
    1960 3.1919×103 2.8943×103 2.8717×103 2.2615×101 2.8926×103 1.7320×100 2.8940×103 3.1405×101
    1970 3.6533×103 3.3561×103 3.3290×103 2.7117×101 3.3544×103 1.7219×100 3.3558×103 3.1405×101
    1980 4.1814×103 3.9147×103 3.8824×103 3.2308×101 3.9130×103 1.7090×100 3.9144×103 3.1405×101
    1990 4.7858×103 4.5858×103 4.5474×103 3.8363×101 4.5841×103 1.6930×100 4.5855×103 3.1405×101
    2000 5.4775×103 5.3885×103 5.3431×103 4.5471×101 5.3869×103 1.6737×100 5.3882×103 3.1405×101
    2010 6.2693×103 6.3462×103 6.2923×103 5.3847×101 6.3445×103 1.6505×100 6.3459×103 3.1405×101
    2020 7.1755×103 7.4865×103 7.4228×103 6.3739×101 7.4849×103 1.6229×100 7.4862×103 3.1405×101

     | Show Table
    DownLoad: CSV
    Table 8.  Numerical solution of Example 5.1 when β=1, β=1.3932987548432, with h=1 year.
    Analytical EM IEM SSRK3
    Year(t) nLinear nFrac nEM Error nIEM Error nSSRK3 Error
    1920 1.8600×103 1.8600×103 1.8600×103 0.0000×100 1.8600×103 0.0000×100 1.8600×103 0.0000×100
    1930 2.1289×103 1.9909×103 1.9799×103 1.1060×101 1.9892×103 1.7315×100 1.9906×103 3.1405×101
    1940 2.4366×103 2.2169×103 2.2020×103 1.4921×101 2.2152×103 1.7413×100 2.2166×103 3.1405×101
    1950 2.7888×103 2.5173×103 2.4987×103 1.8625×101 2.5156×103 1.7390×100 2.5170×103 3.1405×101
    1960 3.1919×103 2.8943×103 2.8717×103 2.2615×101 2.8926×103 1.7320×100 2.8940×103 3.1405×101
    1970 3.6533×103 3.3561×103 3.3290×103 2.7117×101 3.3544×103 1.7219×100 3.3558×103 3.1405×101
    1980 4.1814×103 3.9147×103 3.8824×103 3.2308×101 3.9130×103 1.7090×100 3.9144×103 3.1405×101
    1990 4.7858×103 4.5858×103 4.5474×103 3.8363×101 4.5841×103 1.6930×100 4.5855×103 3.1405×101
    2000 5.4775×103 5.3885×103 5.3431×103 4.5471×101 5.3869×103 1.6737×100 5.3882×103 3.1405×101
    2010 6.2693×103 6.3462×103 6.2923×103 5.3847×101 6.3445×103 1.6505×100 6.3459×103 3.1405×101
    2020 7.1755×103 7.4865×103 7.4228×103 6.3739×101 7.4849×103 1.6229×100 7.4862×103 3.1405×101

     | Show Table
    DownLoad: CSV
    Figure 9.  Absolute error plot for Example 5.1.

    As per "The Census Bureau's International Data Base", the world population reached around 7.5 billion till June 2018. So, we conclude that our fractional population model for β=1.3932987548432 is more accurate and near the statistical population data by following our fractional RK3 scheme and fractional SSRK3 scheme. Also, both our schemes give more accurate and faster results than EM and IEM algorithms.

    Example 5.2. (Fractional ND model) [50] Consider the following time fractional radioactive decay equation:

    CDβt+0N(t)=λN(t),t>t0,supplementarycondition,N(t0)=N0. (5.3)

    Here CDβt+0 denotes the CFOD of arbitrary order β, β(0,1] and N(t) represents the number of radioactive particle at any time t. The quantity N0 is the initial number of particles at t=0 and λ is decay constant, where λ=1τ, and τ is mean life time. The analytic solution for fractional order 0<β<1 of FDE (5.3) is

    N(t)=N0Eβ(λtβ),t0.

    Particularly, for β=1, the fractional order nuclear decay equation given by FDE (5.3) reduces to the classical one,

    dN(t)dt=λN(t),t>t0,withN(t0)=N0. (5.4)

    The analytical solution of (5.4) is,

    N(t)=N0eλt.

    In this example, we have taken the experimental data of radioactivity of isotope of Aluminum (28Al), which represents a decay model, since particles are emitted over the time and we collect the data from the survey of research article [51] for both the classical nuclear model and fractional nuclear model. It is also found that our fractional model is best fit with our experimental data [51] for fractional order β=0.8252 and decay constant λ=0.0314. Here, we have also taken the classical model for comparison purpose where the values of β=1 and decay constant λ=0.0121. For the radioactivity of Aluminum, the initial number of radioactive particle is N0=1200 at time t=0 second.

    With the help of Matlab by following our proposed scheme with step size h=10 second, the analytic and approximate solution of the fractional model (5.3) as well as classical model (5.4) is graphically shown in Figure 10. In addition to this, the analytic and approximate solution is tabulated in the Tables 9 and 10. The absolute error graph is represented in the Figure 11. Thus from the figure and table, we found that our suggested scheme fractional RK3 and fractional SSRK3 are much more efficiency and accurate to the statistical population data compare to EM and IEM.

    Figure 10.  Analytical & numerical solution of Example 5.2.
    Table 9.  Numerical solution of Example 5.2 when β=10sec, β=0.8252, with step size h=10 second.
    Analytical EM IEM RK3
    Time(sec) NLinear NFrac NEM Error NIEM Error NRK3 Error
    0.00 1200.000000 1200.000000 1200.000000 0.000000 1200.000000 0.000000 1200.000000 0.000000
    50.00 655.289312 551.999494 560.334119 8.334625 552.321059 0.321565 552.160287 0.160792
    100.00 357.836735 326.316340 336.069553 9.753213 326.689759 0.373419 326.503032 0.186692
    150.00 195.405490 225.162844 234.125085 8.962241 225.505641 0.342797 225.334224 0.171379
    200.00 106.705941 164.801057 172.792163 7.991106 165.105400 0.304344 164.953198 0.152142
    250.00 58.269386 127.698306 134.676180 6.977874 127.963163 0.264857 127.830699 0.132393
    300.00 31.819421 103.296152 109.401183 6.105030 103.527234 0.231082 103.411656 0.115504
    350.00 17.375772 86.385264 91.765128 5.379864 86.588438 0.203174 86.486814 0.101550
    400.00 9.488465 74.133931 78.920527 4.786596 74.314379 0.180448 74.224118 0.090188
    450.00 5.181408 64.921999 69.222799 4.300800 65.083905 0.161906 65.002917 0.080918
    500.00 2.829434 57.777720 61.677770 3.900050 57.924374 0.146654 57.851014 0.073294

     | Show Table
    DownLoad: CSV
    Table 10.  Numerical solution of Example 5.2 when β=1, β=0.8252, with step size h=10 second.
    Analytical EM IEM SSRK3
    Time(sec) NLinear NFrac NEM Error NIEM Error NSSRK3 Error
    0.00 1200.000000 1200.000000 1200.000000 0.000000 1200.000000 0.000000 1200.000000 0.000000
    50.00 655.289312 551.999494 560.334119 8.334625 552.321059 0.321565 552.015574 0.016080
    100.00 357.836735 326.316340 336.069553 9.753213 326.689759 0.373419 326.335008 0.018668
    150.00 195.405490 225.162844 234.125085 8.962241 225.505641 0.342797 225.179981 0.017136
    200.00 106.705941 164.801057 172.792163 7.991106 165.105400 0.304344 164.816268 0.015211
    250.00 58.269386 127.698306 134.676180 6.977874 127.963163 0.264857 127.711542 0.013236
    300.00 31.819421 103.296152 109.401183 6.105030 103.527234 0.231082 103.307699 0.011547
    350.00 17.375772 86.385264 91.765128 5.379864 86.588438 0.203174 86.395416 0.010152
    400.00 9.488465 74.133931 78.920527 4.786596 74.314379 0.180448 74.142946 0.009015
    450.00 5.181408 64.921999 69.222799 4.300800 65.083905 0.161906 64.930088 0.008089
    500.00 2.829434 57.777720 61.677770 3.900050 57.924374 0.146654 57.785046 0.007326

     | Show Table
    DownLoad: CSV
    Figure 11.  Absolute error plot for Example 5.2.

    In this research article, a remarkable study has been done for finding the numerical approximation of the IVP of FDEs (1.1) of fractional order β where β(0,1]. Here, we proposed two cubic convergence schemes: Fractional RK3 scheme and fractional SSRK3 scheme, and these schemes are based on classical RK3 scheme and classical SSRK3 scheme, respectively. We follow the analogous properties of the Caputo derivative to reduce the IVP of FDEs into an IVP of ODE of integer order. From the numerical point of view, both our suggested schemes are efficient and more accurate compared to other cubic convergence schemes as well as all linear and quadratic convergence schemes of IVP of FDEs (1.1). Also, we demonstrate the comparative numerical study of our proposed method with the existing fractional EM and fractional IEM of IVP of FDEs. In addition to this, we provide the numerical solution of two physical application examples: The fractional WPG model and fractional ND model, using our suggested work to compare with the existing scheme EM and IEM. It is proven that our suggested schemes are faster and more accurate compared with EM and IEM. The significant concluding remark of our scheme are as follows:

    ● The fractional RK3 scheme is a cubic convergence scheme, faster than the other linear and quadratic convergence schemes for the IVP of FDEs.

    ● The fractional SSRK3 scheme also has a cubic convergence scheme that is more stable and faster compared to RK3 and as well as all other cubic convergence schemes for the IVP of FDEs.

    ● Computationally, both schemes are effective, and we get the satisfactory numerical approximation with the help of Matlab by enlargement of step length.

    The first and third author extended their appreciation to Distinguished Scientist Fellowship Program (DSFP) at King Saud University, Saudi Arabia.

    All authors declare no conflicts of interest.


    Abbreviation BHU: Basic health unit; HPA: Hypothalamic-Pituitary-Adrenal axis; MDD: Major depressive disorder; WHO: World Health Organization;
    Acknowledgments



    The Translational Psychiatry Program (USA) is funded by the Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston (UTHealth). Laboratory of Translational Psychiatry (Brazil) is one of the members of the Center of Excellence in Applied Neurosciences of Santa Catarina (NENASC). Its research is supported by grants from CNPq (JQ, GZR, ZMI, and MDB), FAPESC (JQ, GZR, ZMI, and MDB), Instituto Cérebro e Mente (JQ and GZR), UFFS (MDB and ZMI), and UNESC (JQ and GZR). JQ is a 1A CNPq Research Fellow. ZR assisted in writing and revised the manuscript. All authors read and approved the manuscript.

    Author contributions



    VAG and ZMI applied the questionnaires; MGB made the blood collections. AGB, MEP and MGB prepared the samples and tabulated the data. GZR did the statistical analysis. ZMI and AGB wrote the manuscript. JQ and G.

    Conflict of interest



    The authors declare no conflict of interest.

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