Research article

On Hermite-Hadamard type inequalities for n-polynomial convex stochastic processes

  • Received: 03 January 2021 Accepted: 22 March 2021 Published: 12 April 2021
  • MSC : 34N05, 35A23

  • In this note, our purpose is to introduce the concept of n-polynomial convex stochastic processes and study some of their algebraic properties. We establish new refinements for integral version of Hölder and power mean inequality. Also, we are concerned to extend several Hermite-Hadamard type inequalities for n-polynomial convex stochastic processes by using Hölder, Hölder-İşcan, power mean and improved power mean integral inequalities. Moreover, we give comparison of obtained results.

    Citation: Haoliang Fu, Muhammad Shoaib Saleem, Waqas Nazeer, Mamoona Ghafoor, Peigen Li. On Hermite-Hadamard type inequalities for n-polynomial convex stochastic processes[J]. AIMS Mathematics, 2021, 6(6): 6322-6339. doi: 10.3934/math.2021371

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  • In this note, our purpose is to introduce the concept of n-polynomial convex stochastic processes and study some of their algebraic properties. We establish new refinements for integral version of Hölder and power mean inequality. Also, we are concerned to extend several Hermite-Hadamard type inequalities for n-polynomial convex stochastic processes by using Hölder, Hölder-İşcan, power mean and improved power mean integral inequalities. Moreover, we give comparison of obtained results.



    The relationship between predator and prey is an important research content in ecosystem, and many scholars have studied this interaction by differential equation models [1,2,3,4,5,6]. The direct relationship between predator and prey is the consumption of prey. One of the indirect relationship between predator and prey is the fear of predator. When the prey gets the predator's signal (chemical/vocal), they will increase their vigilance time and reduce their foraging [7], such as mule deer v.s. mountain lions [8], elk v.s. wolves [9]. Consider the fear effect, Panday et al. [10] proposed the following model

    {dudt=R01+Kvu(1uK0)CAuvB+u,dvdt=AuvB+XDv. (1.1)

    All parameters are positive. The biological interpretation of parameters is given in Table 1. They incorporated fear effect by modifying the prey intrinsic growth rate R0 as R01+Kv. By the scaling

    uK0=ˉu,CvK0=ˉv,R0t=ˉt,AK0R0B=a,K0B=b,KK0C=c,DR0=μ, (1.2)
    Table 1.  Biological description of parameters.
    Parameter Definition Parameter Definition
    t Time variable x Spatial variable
    u Prey density v Predator density
    R0 Prey intrinsic growth rate K0 Prey carrying capacity
    A Maximum predation rate B Half-saturation constant of predator
    C1 Conversion efficiency D Death rate of predators
    K Fear parameter τ Averaged memory period of predator
    d1 Diffusion coefficient of prey d2 Diffusion coefficient of predator
    d Memory-based diffusion coefficient

     | Show Table
    DownLoad: CSV

    model (1.1) is changed to (drop the bars)

    {dudt=u(1u)1+cvauv1+bu,dvdt=auv1+buμv. (1.3)

    Panday et al. [10] introduced time delay of perceiving predator signals to model (1.3), and mainly studied the boundedness, persistence, local and global behavior of the delayed model.

    In the real world, in addition to the fear effect of the prey, the clever predator also has spatial-memory and cognition [11], which is often ignored in modeling the predator-prey interaction. For example, blue whales rely on memory for migration, which is presented by B. Abrahms et al. [12] and W. F. Fagan [13]. As another example, animals in polar regions usually determine their spatial movement by judging footprints, which record the history of species distribution and movement, involving time delay [14]. Obviously, highly developed animals can even remember the historical distribution or cluster of species in space. Much progress has been made in implicitly integrating spatial cognition or memory [14,15,16,17,18]. To incorporate the memory effect, Shi et al. proposed a single specie model with spatial memory by introducing a additional delayed diffusion term [14]. They supposed that in addition to the negative gradient of the density distribution function at the present time, there is a directed movement toward the negative gradient of the density distribution function at past time [14]. After this pioneering work, some recent works [19,20,21,22,23] about the population model with memory effect have emerged. In [23], Song et al. obtained a computing method for the normal forms of the Hopf bifurcations in the diffusive predator-prey model with memory effect, which is friendly to use.

    Inspired by the above work, we suppose the predator has spatial-memory diffusion and the prey has fear effect, then modified the model (1.3) as follow

    {u(x,t)t=d1Δu(x,t)+u(1u)1+cvauv1+bu,v(x,t)t=d(v(x,t)u(x,tτ))+d2Δv(x,t)+auv1+buμv,xΩ,t>0u(x,t)ˉν=v(x,t)ˉν=0,xΩ,t>0u(x,θ)=u0(x,θ)0,v(x,θ)=v0(x,θ)0,xˉΩ,θ[τ,0]. (1.4)

    All parameters are positive. The biological description of parameters is given in Table 1. The term d(vu(tτ)) represents the memory-based diffusion effect of the predator. The Neumann boundary conditions is used. The aim of this paper is to study the effect of predator's memory-based diffusion and prey's fear on the model (1.4).

    The rest of this paper is organized as follows. In Section 2, the stability of coexisting equilibrium and the existence of Hopf bifurcation are considered. In Section 3, the property of Hopf bifurcation is studied. In Section 3, some numerical simulations are given to analyze the effect of spatial-memory and fear effect. In Section 4, a short conclusion is given.

    For simplicity, we choose Ω=(0,lπ). Denote N as positive integer set, and N0 as nonnegative integer set. It is easy to obtain (0,0) and (1,0) are two boundary equilibria of model (1.4). Next, we will give the existence of coexisting equilibrium.

    Lemma 2.1. If a>(1+b)μ, model (1.4) has one unique coexisting equilibrium (u,v) where u=μabμ, v=12c(1+1+4c[a(1+b)μ](abμ)2).

    Proof. The coexisting equilibrium of (1.4) is a positive root of the following equations

    {u(1u)1+cvauv1+bu=0,auv1+buμv=0. (2.1)

    From the second equation, we have u=μabμ. Substitute it into the first equation, we have

    cv2+v(a(1+b)μ)(abμ)2=0.

    Then v=12c(1±1+4c(a(1+b)μ)(abμ)2). Obviously, the conclusion holds.

    In this paper, we mainly study the stability of coexisting equilibrium E(u,v). Linearize model (1.4) at E(u,v), we have

    ut(u(x,t)u(x,t))=D1(Δu(t)Δv(t))+D2(Δu(tτ)Δv(tτ))+L(u(x,t)v(x,t)), (2.2)

    where

    D1=(d100d2),D2=(00dv0),L=(a1a2b10),

    and a1=u(abv(1+bu)211+cv), a2=u(1u)(1+2cv)v(1+cv)2<0, b1=av(1+bu)2>0. The characteristic equations are

    λ2+Anλ+Bn+Cneλτ=0,nN0, (2.3)

    where

    An=(d1+d2)n2l2a1,Bn=a2b1a1d2n2l2+d1d2n4l4,Cn=a2dvn2l2.

    When τ=0, the characteristic Eq (2.3) become

    λ2+Anλ+Bn+Cn=0,nN0, (2.4)

    where Bn+Cn=a2b1(a2dv+a1d2)n2l2+d1d2n4l4. Make the following hypothesis

    (H1)a>(1+b)μ,a1<0.

    Theorem 2.1. For model (1.4) with τ=0, E(u,v) is locally asymptotically stable under the hypothesis (H1).

    Proof. If (H1) holds, we can easily obtain that An>0 and Bn+Cn>0. Then the characteristic roots of (2.4) all have negative real parts. Then E(u,v) is locally asymptotically stable.

    In the following, we assume (H1) holds. Let iω (ω>0) be a solution of Eq (2.3), then we have

    ω2+Aniω+Bn+Cn(cosωτisinωτ)=0.

    We can obtain cosωτ=ω2BnCn, sinωτ=AnωCn>0 under hypothesis (H1). It leads to

    ω4+(A2n2Bn)ω2+B2nC2n=0. (2.5)

    Let p=ω2, then (2.5) becomes

    p2+(A2n2Bn)p+B2nC2n=0, (2.6)

    and the roots of (2.6) are p±n=12[(A2n2Bn)±(A2n2Bn)24(B2nC2n)]. By direct computation, we have

    {A2n2Bn=a21+2a2b12a1d1n2l2+(d21+d22)n4l4,BnCn=d1d2n4l4+(a2dva1d2)n2l2a2b1,

    and Bn+Cn>0 under hypothesis (H1). Define z±=(a2dva1d2)±(a2dva1d2)24d1d2(a2b1)2d1d2, d=a1d2a2v+2vb1d1d2a2, and M={n|n2l2(z,z+),nN0}. Then we can obtain that

    {BnCn>0,fordd,nN0,BnCn>0,ford>d,nM,BnCn<0,ford>d,nM. (2.7)

    The existence of purely imaginary roots of Eq (2.3) can be divided into the following two cases.

    Case1:a21+2a2b1>0. We can obtain A2n2Bn>a21+2a2b1>0. For d>d and nM, Eq (2.3) has a pair of purely imaginary roots ±iω+n at τj,+n for jN0 and nM. Otherwise, Eq (2.3) does not have characteristic roots with zero real parts.

    Case2:a21+2a2b1<0. This case can be divided into the following two subcases.

    For dd and nM1:={n|A2n2Bn<0,(A2n2Bn)24(B2nC2n)>0,nN0}, Eq (2.3) has two pairs of purely imaginary roots ±iω±n at τj,±n for jN0 and nM1. Otherwise, Eq (2.3) does not have characteristic roots with zero real parts.

    For d>d and nM2:={n|A2n2Bn<0,(A2n2Bn)24(B2nC2n)>0,nN0,nM}, Eq (2.3) has two pairs of purely imaginary roots ±iω±n at τj,±n for jN0 and nM1. For d>d and nM, Eq (2.3) has a pair of purely imaginary roots ±iω+n at τj,+n for jN0 and nM. Otherwise, Eq (2.3) does not have characteristic roots with zero real parts.

    The ω±n and τj,±n are defined as follow

    ω±n=p±n,τj,±n=1ω±narccos((ω±n)2BnCn)+2jπ. (2.8)

    Define

    S={τj,+norτj,n|Eq(2.3)haspurelyimaginaryroots±iω+nor±iωnwhenτ=τj,+norτj,n}.

    We have the following lemma.

    Lemma 2.2. Assume (H1) holds. ThenRe(dλdτ)|τ=τj,+n>0, Re(dλdτ)|τ=τj,n<0 for τj,±nS and jN0.

    Proof. By Eq (2.3), we have

    (dλdτ)1=2λ+AnCnλeλττλ.

    Then

    [Re(dλdτ)1]τ=τj,±n=Re[2λ+AnCnλeλττλ]τ=τj,±n=[1A2nω2+(Bnω)2(2ω2+A2n2Bn)]τ=τj,±n=±[1A2nω2+(Bnω)2(A2n2Bn)24(B2nC2n)]τ=τj,±n.

    Therefore Re(dλdτ)|τ=τj,+n>0, Re(dλdτ)|τ=τj,n<0.

    Denote τ=min{τ0,±n|τ0,±nS}. We have the following theorem.

    Theorem 2.2. Assume (H1) holds, then the following statements are true for model (1.4).

    E(u,v) is locally asymptotically stable for τ>0 when S=.

    E(u,v) is locally asymptotically stable for τ[0,τ) when S.

    E(u,v) is unstable for τ(τ,τ+ε) for some ε>0 when S.

    Hopf bifurcation occurs at(u,v) when τ=τj,+n (τ=τj,n), jN0, τj,±nS.

    Remark 2.1. In the Theorem 2, if E(u,v) is locally asymptotically stable, the densities of prey and predator will tend to the equilibrium state in the whole region when the initial densities of prey and predator is near E(u,v). When Hopf bifurcation occurs at(u,v), then the densities of prey and predator will produce periodic oscillation. Especially, spatially homogeneous periodic oscillations may occur when τ near the critical value τ=τj,+0 or τ=τj,0, and spatially inhomogeneous periodic oscillations may occur when τ near the critical value τ=τj,+n or τ=τj,n (n>0).

    In this section, we use the algorithm in [23] to compute the normal form of Hopf bifurcation. We denote the critical value of Hopf bifurcation as ˜τ and the purely imaginary roots as ±iωn of Eq (2.3). Let ˉu(x,t)=u(x,τt)u and ˉv(x,t)=v(x,τt)v. Drop the bar, the model (1.4) can be written as

    {ut=τ[d1Δu+(u+u)(1(u+u))1+c(v+v)a(u+u)(v+v)1+b(u+u)],vt=τ[d((v+v)(u(t1)+u))+d2Δv+a(u+u)(v+v)1+b(u+u)μ(v+v)]. (3.1)

    Define the real-valued Sobolev space X={U=(u,v)TW2,2(0,lπ)2,(ux,vx)|x=0,lπ=0}, the inner product

    [U,V]=lπ0UTVdx,forU,VX,

    and C=C([1,0];X). Set τ=˜τ+ε, where ε is small perturbation. Then system (3.1) is rewritten as

    dU(t)dt=d(ε)Δ(Ut)+L(ε)(Ut)+F(Ut,ε), (3.2)

    where for φ=(φ,φ2)TC, d(ε)Δ, L(ε):CX, F:C×R2X. They are defined as

    d(ε)Δ(φ)=d0Δ(φ)+Fd(φ,ε),L(ε)(φ)=(˜τ+ε)Aφ(0),
    F(φ,ε)=(˜τ+ε)(f(ϕ(1)(0)+u,ϕ(2)(0)+v)g(ϕ(1)(0)+u,ϕ(2)(0)+v))L(ε)(φ),

    and

    d0Δ(φ)=˜τD1φxx(0)+˜τD2φxx(1),
    Fd(φ,ε)=d(˜τ+ε)(0ϕ(1)x(1)ϕ(2)x(0)+ϕ(1)xx(1)ϕ(2)(0))+ε(d1ϕ(1)xx(0)dvϕ(1)xx(1)+d2ϕ(2)xx(0)).

    Denote L0(φ)=˜τAφ(0), and rewrite (3.2) as

    dU(t)dt=d0Δ(Ut)+L0(Ut)+˜F(Ut,ε), (3.3)

    where ˜F(φ,ε)=εAφ(0)+F(φ,ε)+Fd(φ,ε). The characteristic equation for the linearized equation dU(t)dt=d0Δ(Ut)+L0(Ut) is ˜Γn(λ)=det(˜Mn((λ))), where

    ˜Mn(λ)=λI2+˜τn2l2D1+˜τeλn2l2D2˜τA. (3.4)

    The eigenvalue problem

    z(x)=νz(x),x(0,lπ);z(0)=z(lπ)=0,

    has eigenvalues n2l2 and normalized eigenfunctions

    zn(x)=cosnxl||cosnxl||2,2={1lπn=0,2lπcosnxln0, (3.5)

    Set β(j)n=zn(x)ej, j=1,2, where e1=(1,0)T and e2=(0,1)T. Define ηn(θ)BV([1,0],R2), such that

    01dηn(θ)ϕ(θ)=Ld0(φ(θ))+L0(φ(θ)),φC,

    C=C([1,0],R2), C=C([0,1],R2), and

    <ψ(s),φ(θ)>=ψ(0)φ(0)01θ0ψ(ξθ)dηn(θ)φ(ξ)dξ,ψC,φC. (3.6)

    Let ={i˜ω,i˜ω}, the eigenspace P, and corresponding adjoint space P. Decompose C=PQ, where Q={φC:<ψ,φ>=0,ψP}. Choose Φ(θ)=(ϕ(θ),ˉϕ(θ)), Ψ(θ)=col(ψT(s),ˉψT(s)), where

    ϕ(θ)=ϕei˜ωθ:=(ϕ1(θ)ϕ2(θ)),ψ(s)=ψei˜ωs:=(ψ1(s)ψ2(s)),
    ϕ=(11a2(a1+d1n2l2+i˜ω)),ψ=M(1a2d2n2l2+i˜ω),

    and

    M=(a1l2d1n2d2n2a2dvei˜ωn2˜τ2il2˜ωd2n2+il2˜ω)1.

    Then ϕ(θ) and ψ(s) are the bases of P and P, respectively, and such that <ϕ,ψ>=I2.

    By direct computation, we have

    f20=(f(1)20f(2)20),f11=(f(1)11f(2)11),f02=(f(1)02f(2)02),
    f30=(f(1)30f(2)30),f21=(f(1)21f(2)21),f12=(f(1)12f(2)12),f03=(f(1)03f(2)03),

    where f(1)20=2abv(1+bu)321+cv, f(1)11=a(1+bu)2+c(1+2u)(1+cv)2, f(1)02=2c2(1u)u(1+cv)3, f(1)30=6ab2v(1+bu)4, f(1)21=2ab(1+bu)3+2c(1+cv)2, f(1)12=2c2(12u)(1+cv)3, f(1)03=6c3(1u)u(1+cv)4, f(2)20=2abv(1+bu)3, f(2)11=a(1+bu)2, f(2)02=0, f(2)30=6ab2v(1+bu)4, f(2)21=2ab(1+bu)3, f(2)12=0, f(2)03=0. We can compute the following parameters

    A20=f20ϕ1(0)2+f02ϕ2(0)2+2f11ϕ1(0)ϕ2(0)=¯A02,A11=2f20ϕ1(0)ˉϕ1(0)+2f02ϕ2(0)ˉϕ2(0)+2f11(ϕ1(0)ˉϕ2(0)+ˉϕ1(0)ϕ2(0)),A21=3f30ϕ1(0)2ˉϕ1(0)+3f03ϕ2(0)2ˉϕ2(0)+3f21(ϕ1(0)2ˉϕ2(0)+2ϕ1(0)ˉϕ1(0)ϕ2(0))+3f12(ϕ2(0)2ˉϕ1(0)+2ϕ2(0)ˉϕ2(0)ϕ1(0)), (3.7)
    Ad20=2dτ(0ϕ1(0)(1)ϕ2(0)(0))=¯Ad02,Ad11=2dτ(02Re[ϕ1(1)ˉϕ2(0)]),

    and ˜Aj1j2=Aj1j22n2l2Adj1j2 for j1,j2=0,1,2,j1+j2=2. In addition, h0,20(θ)=1lπ(˜M0(2i˜ω))1A20e2i˜ωθ, h0,11(θ)=1lπ(˜M0(0))1A11, h2n,20(θ)=12lπ(˜M2n(2i˜ω))1˜A20e2i˜ωθ, h2n,11(θ)=1lπ(˜M2n(0))1˜A11.

    S2(ϕ(θ),hn,q1q2(θ))=2ϕ1h(1)n,q1q2f20+2ϕ2h(2)n,q1q2f02+2(ϕ1h(2)n,q1q2+ϕ2h(1)n,q1q2)f11,
    S2(ˉϕ(θ),hn,q1q2(θ))=2ˉϕ1h(1)n,q1q2f20+2ˉϕ2h(2)n,q1q2f02+2(ˉϕ1h(2)n,q1q2+ˉϕ2h(1)n,q1q2)f11,
    Sd,12(ϕ(θ),h0,11(θ))=2d˜τ(0ϕ1(1)h(2)0,11(0)),Sd,12(ˉϕ(θ),h0,11(θ))=2d˜τ(0ˉϕ1(1)h(2)0,20(0)),
    Sd,12(ϕ(θ),h2n,11(θ))=2d˜τ(0ϕ1(1)h(2)2n,11(0)),Sd,12(ˉϕ(θ),h2n,20(θ))=2d˜τ(0ˉϕ1(1)h(2)2n,20(0)),
    Sd,22(ϕ(θ),h2n,11(θ))=2d˜τ(0ϕ1(1)h(2)2n,11(0))2d˜τ(0ϕ2(0)h(1)2n,11(1)),
    Sd,22(ˉϕ(θ),h2n,20(θ))=2d˜τ(0ˉϕ1(1)h(2)2n,20(0))2d˜τ(0ˉϕ2(0)h(1)2n,20(1)),
    Sd,32(ϕ(θ),h2n,11(θ))=2d˜τ(0ϕ2(0)h(1)2n,11(1)),Sd,32(ˉϕ(θ),h2n,20(θ))=2d˜τ(0ˉϕ1(0)h(2)2n,20(1)).

    Then we have

    B21=32lπψTA21,B22=1lπψT(S2(ϕ(θ),h0,11(θ))+S2(ˉϕ(θ),h0,20(θ)))+12lπψT(S2(ϕ(θ),h2n,11(θ))+S2(ˉϕ(θ),h2n,20(θ))),B23=1lπn2l2ψT(Sd,12(ϕ(θ),h0,11(θ))+Sd,12(ˉϕ(θ),h0,20(θ)))+12lπψTj=1,2,3b(j)2n(Sd,j2(ϕ(θ),h2n,11(θ))+Sd,j2(ˉϕ(θ),h2n,20(θ))),

    where b(1)2n=n2l2, b(2)2n=2n2l2, b(3)2n=4n2l2. The normal form of the Hopf bifurcation is

    ˙z=Bz+12(B1z1εˉB1z2ε)+13!(B2z21z2εˉB2z1z22ε)+O(|z|ε2+|z4|), (3.8)

    where

    B1=2i˜ωψTϕ,B2=B21+32(B22+B23).

    By the coordinate transformation z1=ω1iω2, z2=ω1+iω2, and ω1=ρcosξ, ω2=ρsinξ, the normal form (3.8) can be rewritten as

    ˙ρ=K1ερ+K2ρ3+O(ρε2+|(ρ,ε)|4), (3.9)

    where K1=12Re(B1), K2=13!Re(B2).

    By the work [23], we have the following theorem.

    Theorem 3.1. If K1K2<0(>0), the Hopf bifurcation is supercritical (subcritical), and the bifurcating periodic orbits is stable(unstable) for K2<0(>0).

    Remark 3.1. In the Theorem 3.1, when Hopf bifurcation is supercritical (subcritical), then the bifurcating periodic solutions exist for τ>˜τ (τ<˜τ), where ˜τ is some critical value τ=τj,±n. When the periodic solution is stable, the densities of prey and predator will produce periodic oscillation, and finally continue to oscillate.

    In this section, we give some numerical simulations to analyze the effect of spatial memory in predator and fear in prey on the model (1.4). Fix the following parameters

    a=0.5,b=1,μ=0.2,d1=0.1,d2=0.2,l=2. (4.1)

    If we choose c=1, then model (1.4) has a unique coexisting equilibrium (u,v)(0.6667,0.0.6667), and a21+2a2b10.0352>0, d0.6206. To study the effect of memory-based diffusion coefficient d on the model (1.4), we give the bifurcation diagram of model (1.4) with parameter d as in Figure 1. By the Theorem 2.2, we know that (u,v) is locally stable for τ0 when d<d. But when d>d, the inhomogeneous Hopf bifurcation curves exist. This means that increasing parameter d is not conducive to the stability of the equilibrium (u,v), and the densities of prey and predator will produce spatially inhomogeneous periodic oscillation.

    Figure 1.  Stability region and Hopf bifurcation curves in τd plane. The dotted region is the stability region of (u,v) and τ=τ0,+i, i=2,3,4, are Hopf bifurcations curves.

    Choose d=0.7, we have M={2,3} and τ=τ0,+217.4593<τ0,+319.1380. When τ[0,τ), (u,v) is locally stable (Figure 2). By direct calculation, we can obtain K10.0166, and K20.0699. Then, the Hopf bifurcation is supercritical and the bifurcating periodic solution is stable (Figure 3). At this time, the bifurcating periodic solution is spatially inhomogeneous and with mode-2.

    Figure 2.  The numerical simulations of model (1.4) with d=0.7, τ=9 and initial values u0(x)=u+0.01cosx, v0(x)=v+0.01cosx. The coexisting equilibrium (u,v) is asymptotically stable.
    Figure 3.  The numerical simulations of model (1.4) with d=0.7, τ=18 and initial values u0(x)=u+0.01cosx, v0(x)=v+0.01cosx. The coexisting equilibrium (u,v) is unstable and there exists a spatially inhomogeneous periodic solution with mode-2 spatial pattern.

    Choose d=0.8, we have M={2,3} and τ=τ0,+38.4754<τ0,+29.9645. When τ[0,τ), (u,v) is locally stable (Figure 4). By direct calculation, we can obtain K10.1236, K20.3994. Then, the Hopf bifurcation is supercritical and the bifurcating periodic solution is stable (Figure 5). At this time, the bifurcating periodic solution is spatially inhomogeneous with mode-3. When τ=τ0,+3<τ0,+2<τ=10, there is an unstably spatially inhomogeneous periodic solution with mode-2 which transitions to the stably spatially inhomogeneous periodic solution with mode-3 (Figure 6).

    Figure 4.  The numerical simulations of model (1.4) with d=0.8, τ=5 and initial values u0(x)=u+0.01cosx, v0(x)=v+0.01cosx. The coexisting equilibrium (u,v) is asymptotically stable.
    Figure 5.  The numerical simulations of model (1.4) with d=0.8, τ=9 and initial values u0(x)=u+0.01cosx, v0(x)=v+0.01cosx. The coexisting equilibrium (u,v) is unstable and there exists a spatially inhomogeneous periodic solution with mode-3 spatial pattern.
    Figure 6.  The numerical simulations of model (1.4) with d=0.8, τ=10 and initial values u0(x)=u+0.01cosx, v0(x)=v+0.01cosx. The coexisting equilibrium (u,v) is unstable and pattern transitions from a spatially inhomogeneous periodic solution with mode-2 to a spatially inhomogeneous periodic solution with mode-3.

    Next, we will study the effect of fear effect c on the model (1.4). Fix the parameters as (4.1), then model (1.4) has a unique coexisting equilibrium (u,v). And a21+2a2b1>0 when 0<c<2.6194. We give the figure of d with parameter c as in Figure 7. Set parameter d=0.7 and d=0.8, we give the bifurcation diagrams of model (1.4) with parameter c as in Figure 8.

    Figure 7.  Figure of d with parameter c.
    Figure 8.  Stability region and Hopf bifurcation curves in τc plane. The dotted region is the stability region of (u,v) and τ=τ0,+i, i=2,3, are Hopf bifurcations curves.

    When d=0.7 and τ=20, increasing parameter c can destroy the stability of the coexisting equilibrium (u,v), and induce spatially inhomogeneous periodic solution (Figure 9). This means that increasing parameter c is not conducive to the stability of the coexisting equilibrium (u,v).

    Figure 9.  The numerical simulations of model (1.4) with d=0.7, τ=20 and initial values u0(x)=u+0.01cosx, v0(x)=v+0.01cosx. The coexisting equilibrium (u,v) is asymptotically stable for c=0.3 ((a), (b)). The coexisting equilibrium (u,v) is unstable and there exists a spatially inhomogeneous periodic solution with mode-2 for c=1.5 ((c), (d)).

    When d=0.8 and τ=9, increasing parameter c can destroy the stability of the coexisting equilibrium (u,v), and induce spatially inhomogeneous periodic solution initially (Figure 10). But when c is larger enough, increasing parameter c can rule out the spatially inhomogeneous periodic oscillation and stabilize the coexisting equilibrium (u,v) (Figure 10). This means that increasing parameter c is not conducive to the stability of the equilibrium (u,v), initially. But when c is large, increasing parameter c is conducive to the stability of the coexisting equilibrium (u,v).

    Figure 10.  The numerical simulations of model (1.4) with d=0.8, τ=9 and initial values u0(x)=u+0.01cosx, v0(x)=v+0.01cosx. The coexisting equilibrium (u,v) is asymptotically stable for c=0.3 ((a), (b)) and c=1.5 ((e), (f)). The coexisting equilibrium (u,v) is unstable and there exists a spatially inhomogeneous periodic solution with mode-3 for c=0.8 ((c), (d)).

    In this paper, we incorporate the memory effect in predator and fear effect in prey into a predator-prey model. By using time delay in the memory of predator as bifurcating parameter, we analyze the local stability of coexisting equilibrium, the existence of Hopf bifurcation, and the property of Hopf bifurcation by the method in [23]. Through the numerical simulations, we analyzed the effect of memory effect in predator and fear in prey on the model.

    The spatial memory effect plays an important role in the dynamics of the predator-prey model. Through the numerical simulations, we observed that the memory-based diffusion coefficient d has destabilizing effect on the predator-prey model when it is larger than some critical value. In addition. when d crosses the critical value, time delay τ in the memory of predator can affect the stability of the equilibrium (u,v). In the numerical simulations, we observe that the first Hopf bifurcation curve is inhomogeneous bifurcation curve, and homogeneous Hopf bifurcation curve does not exist. This is different from the predator-prey model without the spatial memory effect. When τ crosses the critical value τ, the densities of prey and predator will produce spatially inhomogeneous periodic oscillation. When τ crosses the second critical value, the spatially inhomogeneous periodic oscillations with different modes exist, but the densities of prey and predator will converge to the spatially inhomogeneous periodic solution corresponding to the first bifurcation curve. This shows that the spatially memory effect in predator can destroy the stability of the coexisting equilibrium, and induce spatially inhomogeneous periodic oscillations.

    In addition, the fear effect parameter c in prey can also affect the stability of the coexisting equilibrium (u,v). A small fear effect parameter c means a large birth rate 11+cv, then the large birth rate can support fluctuations. Increasing parameter c can destroy the stability of the coexisting equilibrium (u,v), and induce spatially inhomogeneous periodic solution. Hence, we observed the destabilizing effect on the the coexisting equilibrium (u,v). A large fear effect parameter c means a low birth rate, then the low birth rate can not support fluctuations. Increasing parameter c can rule out the spatially inhomogeneous periodic oscillation and stabilize the coexisting equilibrium (u,v). Hence, we observed the stabilizing effect on the the coexisting equilibrium (u,v).

    This research is supported by the Fundamental Research Funds for the Central Universities (Grant No. 2572022BC01) and Postdoctoral Program of Heilongjiang Province (No. LBH-Q21060).

    The authors have no relevant financial or non-financial interests to disclose.



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