$ \left(jE^{m}\right) _{M}^{N} $ | $ N=M=20 $ | $ N=M=40 $ | $ N=M=80 $ |
$ m=1 $ | 0.0068, j=6 | 0.0032, j=6 | 0.0016, j=6 |
$ m=2 $ | 0.0071, j=6 | 0.0033, j=6 | 0.0016, j=6 |
$ m=3 $ | 0.0073, j=6 | 0.0034, j=6 | 0.0017, j=6 |
Citation: Khue Vu Nguyen. β-Amyloid precursor protein (APP) and the human diseases[J]. AIMS Neuroscience, 2019, 6(4): 273-281. doi: 10.3934/Neuroscience.2019.4.273
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Mathematical models have been used to understand and control the dynamics of the disease (influenza, covid 19, HIV etc.). After first SIR type mathematical model constructed by Karmic and MC-Kendrick, for different kind of models (SIR, SIS, SEIR, SI etc.) have been proposed and studied by many authors (see, e.g., [1,2,3,4]). One of the important virus that whole world fighting with is the Human Immunodeficiency Virus (HIV) that is a lent virus caused HIV infection [5]. HIV virus can be transmitted in many ways like sexual intercourse, direct contact with contaminated blood products, needle, or during birth or through breastfeeding (mother to child). Since there is no recovery after HIV, SI type models are more appropriate to comprehend the dynamics of the HIV. In [3], J.J.Wang et al. have been studied mother to child transmission of HIV. The system that obtained with the constructed model in [3] extended and the numerical solutions of the system of linear parabolic equations(PEs) is studied in [6]. In the paper [7], Ashyralyev, Hincal and Kaymakamzade investigated the boundedness of solution of the initial boundary value problem for the system of PEs of observing epidemic models with general nonlinear incidence rate. This model constructed in [8] and it is well-known that (see, [9]) such and many other initial boundary value problems for system of PEs can be reduced to the initial-value problem for system of ordinary differential equations
$ {dv1(t)dt+μv1(t)+Av1(t)=−F(t,v1(t),v2(t)),dv2(t)dt+(α+μ)v2(t)+Av2(t)=F(t,v1(t),v2(t))−G(t,v2(t)),dv3(t)dt+μv3(t)+Av3(t)=G(t,v2(t)),0<t<T,vk(0)=ψk,1≤k≤3 $ | (1.1) |
in a Hilbert.space.$ H $.with a self-adjoint.positive.definite.operator $ A\geq\delta I, \delta > 0. $
Existence.and.uniqueness.theorems.of the.bounded.solution of linear and nonlinear systems was.established in the following theorem ([7], [9]).
Theorem 1. Assume.the following.hypotheses hold
1. $ \psi ^{n}, 1 \leq n \leq 3 $ belongs to $ D(A) $ and
$ ‖ψn‖D(A)=M1. $ | (1.2) |
2. The.function.$ F:[0, T]\times H\times H\longrightarrow H $ be.continuous.function, that.is
$ ‖F(t,w(t),z(t))‖H≤M2 $ | (1.3) |
in $ [0, T]\times H\times H $ and Lipschitz condition holds uniformly with respect to $ t $
$ ‖F(t,u,v)−f(t,z,w)‖H≤L1[‖u−z‖H+‖v−w‖H]. $ | (1.4) |
3. The.function.$ G:[0, T]\times H\longrightarrow H $ be.continuous function, that is
$ ‖G(t,v(t))‖H≤M3 $ | (1.5) |
in $ [0, T]\times H $ and Lipschitz condition holds uniformly with respect to $ t $
$ ‖G(t,u)−G(t,z)‖H≤L2‖u−z‖H. $ | (1.6) |
Then, there.exists.a unique bounded solution $ v(t) = \left(v^{1}(t), v^{2}(t), v^{3}(t)\right) ^{\perp } $ of problem (1.1).
In applications, theorems on the bounded solutions of several systems of nonlinear. PEs were given. Moreover the first order of accuracy DS
$ {v1k−v1k−1τ+μv1k+Av1k=−F(tk,v1k,v2k),v2k−v2k−1τ+(α+μ)v2k+Av2k=Ff(tk,v1k,v2k)−G(tk,v2k),v3k−v3k−1τ+μv3k+Av3k=G(tk,v2k),tk=kτ,1⩽k⩽N,Nτ=T,vn0=ψn,1≤n≤3 $ | (1.7) |
for the approximate solution of problem (1.1) was studied. The existence and uniqueness of a bounded solution of DS (1.7) uniformly with.respect.to time.step.$ \tau $ was.established in the following theorem.
Theorem 2. If the assumptions (1.2)-(1.6) and $ \mu +\delta > 2\left(L_{1}+L_{2}\right) $ hold, then there exists a unique solution $ v^{\tau } = \left\{ v_{k}\right\} _{k = 0}^{N} $ of DS (1.7) which is bounded uniformly with respect to $ \tau $.
Bounded solutions of several systems of nonlinear PEs and DSs for the approximate solution of these systems were constituted. Numerical results were provided.
In general, .it is not possible to get exact solution of nonlinear problems. Therefore, we are interested in constructing a uniformly bounded high order of accuracy DSs with respect to time step size for the approximate solutions initial value problem (1.1).
In this work, for the approximate solution.of problem (1.1), the second.order of accuracy.Crank-Nicholson DS is investigated. The existence and uniqueness theorems of bounded solution of Crank-Nicholson DS uniformly with respect to time step $ \tau $ is proved. In practice, theoretical results are presented on four nonlinear systems of parabolic equations to explain how it works on one and multidimensional problems. Numerical results are provided.
In this section, it will be considered the second order of accuracy Crank-Nicholson DS
$ {v1k−v1k−1τ+μv1k+v1k−12+Av1k+v1k−12=−F(tk−τ2,v1k+v1k−12,v2k+v2k−12),v2k−v2k−1τ+(α+μ)v2k+v2k−12+Av2k+v2k−12=F(tk−τ2,v1k+v1k−12,v2k+v2k−12)−G(tk−τ2,v2k+v2k−12),v3k−v3k−1τ+μv2k+v2k−12+Av2k+v2k−12=G(tk−τ2,v2k+v2k−12),tk=kτ,1⩽k⩽N,Nτ=T,vn0=ψn,1≤n≤3 $ | (2.1) |
for the approximate solution.of the initial.value.problem (1.1).
It is interested to studied the existence and uniqueness of a bounded solution of Crank- Nicholson DS (2.1) uniformly with respect to time step $ \tau $ under the assumptions of Theorem 2.
Unfortunately, we are not able to establish the Theorem 3 for the solution of Crank-Nicholson DS (2.1) under the assumption $ \mu +\delta > 2\left(L_{1}+L_{2}\right) $ without restriction to T. Nevertheless, it could be established the such result under assumption $ 1+1/2\tau (\mu +\delta) > 2 (L_{1} + L_{2})T $. It is more strong than $ \mu +\delta > 2\left(L_{1}+L_{2}\right) $ that means it is under assumption with restriction for T. Thus such result we can proved under assumption with restriction for T. It is based on reducing this DS to an equivalent system of nonlinear equations and is used as an operator method to prove the main theorem on the existence and uniqueness of a bounded solution of DS (2.1) uniformly with respect to $ \tau $.
An equivalent system of nonlinear equations for the DS (2.1) is
$ {v1k=Bkψ1−∑km=1Bk−mRF(tm−τ2,v1m+v1m−12,v2m+v2m−12)τ,v2k=Bk1ψ2+∑km=1Bk−m1R1[F(tm−τ2,v1m+v1m−12,v2m+v2m−12)−G(tm−τ2v2m+v2m−12)]τ,v3k=Bkψ3+∑km=1Bk−mRG(tm−τ2v2m+v2m−12)τ,1≤k≤N $ | (2.2) |
in $ C_{\tau }\left(H\right) \times C_{\tau }\left(H\right) \times C_{\tau }\left(H\right) $ and the using of successive approximations. Here.and.in.future $ B = (I-\frac{\tau \left(\mu I+A\right) }{2})R, R = (I+\frac{\tau \left(\mu I+A\right) }{2})^{-1}, B_{1} = (I-\frac{\tau \left(\left(\mu +\alpha \right) I+A\right) }{2})R_{1}, R_{1} = (I+\frac{\tau \left(\left(\mu +\alpha \right) I+A\right) }{2})^{-1} $ and $ C_{\tau }\left(H\right) = C\left([0, T]_{\tau }, H\right) $ stands for the Banach space of the mesh functions $ v^{\tau } = \left\{ v_{l}\right\} _{l = 0}^{N} $ defined on $ [0, T]_{\tau } $ with values in $ H, $equipped with the norm
$ ∥vτ∥Cτ(H)=max0≤l≤N‖vl‖H. $ |
For the solution of DS (2.1), the recursive formula is
$ {jv1k−jv1k−1τ+μjv1k+jv1k−12+Ajv1k+jv1k−12=−F(tk−τ2,(j−1)v1k+(j−1)v1k−12,(j−1)v2k+(j−1)v2k−12),jv2k−jv2k−1τ+(α+μ)jv2k+jv2k−12+Ajv2k+jv2k−12=F(tk−τ2,(j−1)v1k+(j−1)v1k−12,(j−1)v2k+(j−1)v2k−12)−G(tk−τ2,(j−1)v2k+(j−1)v2k−12),jv3k−jv3k−1τ+μjv2k+jv2k−12+Ajv2k+jv2k−12=G(tk−τ2,(j−1)v2k+(j−1)v2k−12),tk=kτ,1⩽k⩽N,Nτ=T,jvn0=ψn,1≤n≤3,j=1,2,...,0vnk=Bkψn,n=1,3,0v2k=Bkψ2,0≤k≤N. $ | (2.3) |
From (2.2) and (2.3) it follows
$ {jv1k=Bkψ1−∑km=1Bk−mRF(tk−τ2,(j−1)v1k+(j−1)v1k−12,(j−1)v2k+(j−1)v2k−12)τ,jv2k=Bk1ψ2+∑km=1Bk−m1R1F(tk−τ2,(j−1)v1k+(j−1)v1k−12,(j−1)v2k+(j−1)v2k−12)τ−∑km=1Bk−m1R1G(tk−τ2,(j−1)v2k+(j−1)v2k−12)τ,jv3k=Bkψ3+∑km=1Bk−mRG(tk−τ2,(j−1)v2k+(j−1)v2k−12),(j−1)v2k)τ,1≤k≤N,j=1,2,...,0vmk=Bkψm,m=1,3,0v2k=Bkψ2,0≤k≤N. $ | (2.4) |
Theorem 3. Let the assumptions (1.2)-(1.6) be satisfied and $ 2\left(L_{1}+L_{2}\right) T < 1+\frac{\tau \left(\mu +\delta \right) }{2} $. Then, there exists a unique solution $ v^{\tau } = \left\{ v_{k}\right\} _{k = 0}^{N} $ of DS (2.1) that is bounded in $ C_{\tau }\left(H\right) \times C_{\tau }\left(H\right) \times C_{\tau }\left(H\right) $ of uniformly wrt. $ \tau $.
Proof. Since $ v_{k}^{3} $ does not appear in equations for $ \frac{ v_{k}^{n}-v_{k-1}^{n}}{\tau }, n = 1, 2 $, it is sufficient to analyze the behaviors of solutions $ v_{k}^{^{_{1}}} $ and $ v_{k}^{^{_{2}}} $ of (2.1). According to the method of recursive approximation (2.4), we get
$ vnk=0vnk+∞∑i=0[(i+1)vnk−ivnk],n=1,2, $ | (2.5) |
where
$ 0vnk={Bkψn,n=1,3,Bk1ψ2,n=2. $ | (2.6) |
Applying formula (2.6), estimates
$ ‖B‖H→H≤1,‖B1‖H→H≤1, $ | (2.7) |
we get
$ ‖0vnk‖H≤‖ψn‖H≤M1. $ | (2.8) |
Applying formula (2.4), estimates (2.7) and
$ ‖R‖H→H≤11+τ(μ+δ)2,‖R1‖H→H≤11+τ(μ+δ+α)2, $ | (2.9) |
we get
$ ‖1v1k−0v1k‖H≤k∑m=1‖Bk−mR‖H→H‖f(tm−τ2,0v1m+0v1m−12,0v2m+0v2m−12)‖Hτ $ |
$ ≤M2k∑m=1τ1+τ(μ+δ)2≤M2T1+τ(μ+δ)2, $ |
$ ‖1v2k−0v2k‖H≤k∑m=1‖Bk−m1R1‖H→H[‖F(tm−τ2,0v1m+0v1m−12,0v2m+0v2m−12)‖H $ |
$ +‖G(tm−τ2,0v2m+0v2m−12)‖H]τ $ |
$ ≤(M2+M3)k∑m=1τ1+τ(μ+δ+α)2≤(M2+M3)T1+τ(μ+δ+α)2 $ |
for any $ k = 1, \cdot \cdot \cdot, N. $ With using triangle inequality, is is obtained that
$ ‖1v1k‖H≤M1+(M2+M3)T1+τ(μ+δ)2, $ |
$ ‖1v2k‖H≤M1+(M2+M3)T1+τ(μ+δ)2 $ |
for any.$ k = 1, \cdot \cdot \cdot, N. $ Applying.formula (2.4), and estimates (2.7), (2.9), (1.4), (1.2) and (1.3), we get
$ ‖2v1k−1v1k‖H≤τk∑m=1‖Bk−mR‖H→H $ |
$ ×‖F(tm−τ2,1v1m+1v1m−12,1v2m+1v2m−12)−F(tm−τ2,0v1m+0v1m−12,0v2m+0v2m−12)‖H $ |
$ ≤k∑m=1L1τ1+τ(μ+δ)2[‖1v1m+1v1m−12−0v1m+0v1m−12‖H+‖1v2m+1v2m−12−0v2m+0v2m−12‖H] $ |
$ ≤2L1(M2+M3)T1+τ(μ+δ)2k∑m=1τ1+τ(μ+δ)2≤2(L1+L2)(M2+M3)T2(1+τ(μ+δ)2)2, $ |
$ ‖2v2k−1v2k‖H≤τk∑m=1‖Bk−m1R1‖H→H $ |
$ ×‖F(tm−τ2,1v1m+1v1m−12,1v2m+1v2m−12)−F(tm−τ2,0v1m+0v1m−12,0v2m+0v2m−12)‖H $ |
$ +τk∑m=1‖Bk−m1R1‖H→H‖G(tm−τ2,1v2m+1v2m−12)−G(tm−τ2,0v2m+0v2m−12)‖H $ |
$ ≤L1k∑m=1τ1+τ(μ+δ+α)2 $ |
$ ×[‖1v1m+1v1m−12−0v1m+0v1m−12‖H+‖1v2m+1v2m−12−0v2m+0v2m−12‖H] $ |
$ +L2k∑m=1τ1+τ(μ+δ+α)2‖1v2m+1v2m−12−0v2m+0v2m−12‖H $ |
$ ≤(2L1+L2)(M2+M3)T1+τ(μ+δ)2k∑m=1τ1+τ(μ+δ+α)2≤2(L1+L2)(M2+M3)T2(1+τ(μ+δ)2)2 $ |
for any $ k = 1, \cdot \cdot \cdot, N. $ Then
$ ‖2vnk‖H≤M1+(M2+M3)T1+τ(μ+δ)2+2(L1+L2)(M2+M3)T2(1+τ(μ+δ)2)2,n=1,2 $ |
for any $ k = 1, \cdot \cdot \cdot, N. $ Let
$ ‖jvnk−(j−1)vnk‖H≤2j−1(L1+L2)j−1(M2+M3)Tj(1+τ(μ+δ)2)j,n=1,2. $ |
Applying formula (2.4), estimates (2.7), (1.4), (1.2) and (1.3), we get
$ ‖(j+1)v1k−jv1k‖H≤τk∑m=1‖Bk−mR‖H→H $ |
$ ×‖f(tm−τ2,jv1m+jv1m−12,jv2m+jv2m−12) $ |
$ −f(tm−τ2,(j−1)v1m+(j−1)v1m−12,(j−1)v2m+(j−1)v2m−12)‖H $ |
$ ≤k∑m=1L1τ1+τ(μ+δ)2 $ |
$ ×[‖jv1m+jv1m−12−(j−1)v1m+(j−1)v1m−12‖H $ |
$ +‖jv2m+jv2m−12−(j−1)v2m+(j−1)v2m−12‖H] $ |
$ ≤2L1⋅2j−1(L1+L2)j−1(M2+M3)Tj(1+τ(μ+δ)2)jk∑m=1τ1+τ(μ+δ)2≤(2(L1+L2))j(M2+M3)Tj+1(1+τ(μ+δ)2)j+1, $ |
$ ‖(j+1)v2k−jv2k‖H≤τk∑m=1‖Bk−m1R1‖H→H $ |
$ ×‖F(tm−τ2,jv1m+jv1m−12,jv2m+jv2m−12) $ |
$ −F(tm−τ2,(j−1)v1m+(j−1)v1m−12,(j−1)v2m+(j−1)v2m−12)‖H $ |
$ +τk∑m=1‖Bk−m1R1‖H→H $ |
$ ×‖G(tm−τ2,jv2m+jv2m−12)−F(tm−τ2,(j−1)v2m+(j−1)v2m−12)‖H $ |
$ ≤k∑m=1L1τ1+τ(μ+δ+a)2 $ |
$ ×[‖jv1m+jv1m−12−(j−1)v1m+(j−1)v1m−12‖H $ |
$ +‖jv2m+jv2m−12−(j−1)v2m+(j−1)v2m−12‖H] $ |
$ +k∑m=1L2τ1+τ(μ+δ+a)2‖jv2m+jv2m−12−(j−1)v2m+(j−1)v2m−12‖H $ |
$ ≤(2L1+L2)2j−1(L1+L2)j−1(M2+M3)Tj(1+τ(μ+δ)2)jk∑m=1τ1+τ(μ+δ)2≤(2(L1+L2))j(M2+M3)Tj+1(1+τ(μ+δ)2)j+1 $ |
for any $ k = 1, \cdot \cdot \cdot, N. $ Then
$ ‖(j+1)vnk‖H≤M1+(M2+M3)T1+τ(μ+δ)2 $ |
$ +2(L1+L2)(M2+M3)T2(1+τ(μ+δ)2)2+⋅⋅⋅+(2(L1+L2))j(M2+M3)Tj+1(1+τ(μ+δ)2)j+1,n=1,2 $ |
for any $ k = 1, \cdot \cdot \cdot, N. $ Therefore, for any $ j, j\geq 1 $, we have that
$ ‖(j+1)vnk−jvnk‖H≤(2(L1+L2))j(M2+M3)Tj+1(1+τ(μ+δ)2)j+1,n=1,2, $ |
and
$ ‖(j+1)vnk‖H≤M1+(M2+M3)T1+τ(μ+δ)2 $ |
$ +2(L1+L2)(M2+M3)T2(1+τ(μ+δ)2)2+⋅⋅⋅+(2(L1+L2))j(M2+M3)Tj+1(1+τ(μ+δ)2)j+1,n=1,2 $ |
by mathematical.induction. From that and formula (2.5) it is obtained
$ ‖vnk‖H≤‖0vnk‖H+∞∑i=0‖(i+1)vnk−ivnk‖H $ |
$ ≤M1+(M2+M3)T1+τ(μ+δ)2∞∑i=02i(L1+L2)iTi(1+τ(μ+δ)2)i,n=1,2 $ |
that proves the existence of a bounded solution of DS (2.1) which is bounded.in $ C_{\tau }\left(H\right) \times C_{\tau }\left(H\right) \times C_{\tau }\left(H\right) $ of uniformly wrt. $ \tau $. Theorem 3 is proved.
A study of discretization, over time only, of the initial value problem also permits one to include general DSs in applications, if the differential operator $ A $ is replaced by the difference operator $ A_{h} $ that act in the Hilbert spaces and are uniformly self-adjoint positive definite in $ h $ for $ 0 < h\leq h_{0}. $
In this section it will be given considered some nonlinear partial differential equations(PDEs).
First, we consider the initial-boundary.value problem for one.dimensional system.of nonlinear PDEs.
$ {∂v1(t,x)∂t−(a(x)v1x(t,x))x+(δ+μ)v1(t,x)=−F(t,x;v1(t,x),v2(t,x)),∂v2(t,x)∂t−(a(x)v2x(t,x))x+(δ+μ+α)v2(t,x)=F(t,x;v1(t,x),v2(t,x))−G(t,x;v2(t,x)),∂v3(t,x)∂t−(a(x)v3x(t,x))x+(δ+μ)v3(t,x)=G(t,x;v2(t,x)),0<t<T,0<x<l,vn(0,x)=ψn(x),ψn(0)=ψn(l),φmx(0)=ψnx(l),x∈[0,l],n=1,2,3,vn(t,0)=vn(t,l),vnx(t,0)=vnx(t,l),0≤t≤T,n=1,2,3, $ | (3.1) |
where $ a(x), \psi (x) $ are given sufficiently smooth functions and $ \delta > 0 $ is the sufficiently large number. We will assume that $ a(x)\geq a > 0 $ and $ a(l) = a(0). $
Assume the following.hypotheses hold
1. $ \psi ^{n}, n = 1, 2, 3 $ belongs to $ W_{2}^{2}\left[ 0, l\right] $ and
$ ‖ψn‖W22[0,l]≤M1. $ | (3.2) |
2. The function $ f:[0, T]\times \left[ 0, l\right] \times L_{2}\left[ 0, l \right] \times L_{2}\left[ 0, l\right] \rightarrow L_{2}\left[ 0, l\right] $ be continuous function in $ t $, that is
$ ‖F(t,⋅,u(t,⋅),v(t,⋅))‖L2[0,l]≤M2 $ | (3.3) |
in $ [0, T]\times \left[ 0, l\right] \times L_{2}\left[ 0, l\right] \times L_{2} \left[ 0, l\right] $ and Lipschitz condition holds uniformly with respect to $ t $
$ ‖G(t,⋅,u,v)−G(t,⋅,z,w)‖L2[0,l]≤L1[‖u−z‖L2[0,l]+‖v−w‖L2[0,l]]. $ | (3.4) |
3. The function $ g:[0, T]\times \left[ 0, l\right] \times L_{2}\left[ 0, l \right] \rightarrow L_{2}\left[ 0, l\right] $ be continuous function in $ t $, that is
$ ‖G(t,⋅,u(t,⋅))‖L2[0,l]≤M3 $ | (3.5) |
in $ [0, T]\times \left[ 0, l\right] \times L_{2}\left[ 0, l\right] $ and Lipschitz condition holds uniformly with respect to $ t $
$ ‖G(t,⋅,u)−G(t,⋅,z)‖L2[0,l]≤L2‖u−z‖L2[0,l]. $ | (3.6) |
Here and in future, $ L_{m}, m = 1, 2, M_{m}, \; m = 1, 2, 3 $ are positive constants.
The discretization of problem (3.1) is carried out in two steps. In the first step, let us define the grid space
$ [0,l]h={x:xr=rh,0≤r≤K,Kh=l}. $ |
We introduce the Hilbert spaces $ L_{2h} = L_{2}([0, l]_{h}) $ and $ W_{2h}^{2} = W_{2}^{2}([0, l]_{h}) $\ of the grid functions $ \psi ^{h}(x) = \{\psi ^{r}\}_{0}^{K} $ defined on $ [0, l]_{h}, $ equipped with the norms
$ ‖ψh‖L2h=(∑x∈[0,l]h|ψh(x)|2h)1/2 $ |
and
$ ‖ψh‖W22h=‖ψh‖L2h+(∑x∈[0,l]h|(ψh)x¯x,j|2h)1/2 $ |
respectively. To the differential operator $ A $ generated by problem (3.1), we assign the difference operator $ A_{h}^{x} $ by the formula
$ Axhψh(x)={−(a(x)ψ¯x)x,r+δψr}K−11, $ | (3.7) |
acting in the space of grid functions $ \psi ^{h}(x) = \{\psi ^{r}\}_{0}^{K} $ satisfying the conditions $ \psi ^{0} = \psi ^{K}, \ \psi ^{1}-\psi ^{0} = \psi ^{K}-\psi ^{K-1}. $ With the help of $ A_{h}^{x}, $ we arrive at the initial value problem
$ {dv1h(t,x)dt+μv1h(t,x)+Axhv1h(t,x)=−Fh(t,x;v1h(t,x),v2h(t,x)),dv2h(t,x)dt+(μ+α)v2h(t,x)+Axhv2h(t,x)=Fh(t,x;v1h(t,x),v2h(t,x))−Gh(t,x;v2h(t,x)),dv3h(t,x)dt+μv3h(t,x)+Axhv3h(t,x)=Gh(t,x;v2h(t,x)),0<t<T,x∈[0,l]h,vnh(0,x)=ψn(x),n=1,2,3,x∈[0,l]h $ | (3.8) |
for an infinite system of nonlinear ordinary differential equations (ODEs). In the second step, we replace problem (3.8) by DS (2.1)
$ {v1k−v1k−1τ+μv1k+v1k−12+Axhv1k+v1k−12=−Fh(tk−τ2,x,v1k+v1k−12,v2k+v2k−12),v2k−v2k−1τ+(α+μ)v2k+v2k−12+Axhv2k+v2k−12=Fh(tk−τ2,x,v1k+v1k−12,v2k+v2k−12)−Gh(tk−τ2,x,v2k+v2k−12),v3k−v3k−1τ+μv3k+v3k−12+Axhv3k+v3k−12=Gh(tk−τ2,x,v2k+v2k−12),tk=kτ,1⩽k⩽N,Nτ=T,x∈[0,l]h,vn0=ψn,n=1,2,3. $ | (3.9) |
Theorem 4. Let the assumptions (3.2)-(3.6) be satisfied and $ 2\left(L_{1}+L_{2}\right) T < 1+\frac{\tau \left(\mu +\delta \right) }{2} $. Then, there exists a unique solution $ v^{\tau } = \left\{ v_{k}\right\} _{k = 0}^{N} $ of DS (3.9) which is bounded in $ C_{\tau }\left(L_{2h}\right) \times C_{\tau }\left(L_{2h}\right) \times C_{\tau }\left(L_{2h}\right) $ of uniformly with respect to $ \tau $ and $ h $.
The proof of Theorem 4 is based on the abstract Theorem 3 and symmetry properties of the difference operator $ A_{h}^{x} $ defined by formula (3.7)[11].
Second, we consider the initial-boundary value problem for one dimensional system of nonlinear PDs with involution
$ {∂v1(t,x)∂t−(a(x)v1x(t,x))x−β(a(−x)vx(t,−x))x+(δ+μ)v1(t,x)=−F(t,x;v1(t,x),v2(t,x)),∂v2(t,x)∂t−(a(x)v2x(t,x))x−β(a(−x)vx(t,−x))x+(δ+μ+α)v2(t,x)=F(t,x;v1(t,x),v2(t,x))−G(t,x;v2(t,x)),∂v3(t,x)∂t−(a(x)v3x(t,x))x−β(a(−x)vx(t,−x))x+(δ+μ)v3(t,x)=G(t,x;v2(t,x)),0<t<T,−l<x<l,vn(0,x)=ψn(x),ψn(−l)=ψn(l)=0,x∈[−l,l],n=1,2,3,vn(t,−l)=vn(t,l)=0,0≤t≤T,n=1,2,3, $ | (3.10) |
where $ a(x), \psi (x) $ are given sufficiently smooth functions and $ \delta > 0 $ is the sufficiently large number. We will assume that $ a\geq a\left(x\right) = a\left(-x\right) \geq \delta > 0, \; \delta -a\left\vert \beta \right\vert \geq 0 $.
Assume the following hypotheses:
1. $ \psi ^{n}, n = 1, 2, 3 $ belongs to $ W_{2}^{2}\left[ -l, l\right] $ and
$ ‖ψn‖W22[−l,l]≤M1. $ | (3.11) |
2. The function $ F:[0, T]\times \left[ -l, l\right] \times L_{2}\left[ -l, l \right] \times L_{2}\left[ -l, l\right] \rightarrow L_{2}\left[ -l, l\right] $ be continuous function in $ t $, that is
$ ‖F(t,⋅,u(t,⋅),v(t,⋅))‖L2[−l,l]≤M2 $ | (3.12) |
in $ [0, T]\times \left[ -l, l\right] \times L_{2}\left[ -l, l\right] \times L_{2}\left[ -l, l\right] $ and Lipschitz condition holds uniformly with respect to $ t $
$ ‖F(t,⋅,u,v)−F(t,⋅,z,w)‖L2[−l,l]≤L1[‖u−z‖L2[−l,l]+‖v−w‖L2[−l,l]]. $ | (3.13) |
3. The function $ G:[0, T]\times \left[ -l, l\right] \times L_{2}\left[ -l, l \right] \rightarrow L_{2}\left[ -l, l\right] $ be continuous function in $ t $, that is
$ ‖G(t,⋅,u(t,⋅))‖L2[0,l]≤M3 $ | (3.14) |
in $ [0, T]\times \left[ -l, l\right] \times L_{2}\left[ -l, l\right] $ and Lipschitz condition holds uniformly with respect to $ t $
$ ‖G(t,⋅,u)−G(t,⋅,z)‖L2[−l,l]≤L2‖u−z‖L2[−l,l]. $ | (3.15) |
The discretization of problem (3.10) is carried out in two steps. In the first step, let us define the grid space
$ [−l,l]h={x:xr=rh,−K≤r≤K,Kh=l}. $ |
We introduce the Hilbert spaces $ L_{2h} = L_{2}([-l, l]_{h}) $ and $ W_{2h}^{2} = W_{2}^{2}([-l, l]_{h}) $\ of the grid functions $ \psi ^{h}(x) = \{\psi ^{r}\}_{-K}^{K} $ defined on $ [-l, l]_{h}, $ equipped with the norms
$ ‖ψh‖L2h=(∑x∈[−l,l]h|ψh(x)|2h)1/2 $ |
and
$ ‖ψh‖W22h=‖ψh‖L2h+(∑x∈[−l,l]h|(ψh)x¯x,j|2h)1/2 $ |
respectively. To the differential operator $ A $ generated by problem (3.10), we assign the difference operator $ A_{h}^{x} $ by the formula
$ Axhψh(x)={−(a(x)ψ¯x(x))x,r−β(a(−x)ψ¯x(−x))x,r+δψr}K−1−K+1, $ | (3.16) |
acting in the space of grid functions $ \psi ^{h}(x) = \{\psi ^{r}\}_{-K}^{K} $ satisfying the conditions $ \psi ^{-K} = \psi ^{K} = 0. $ With the help of $ A_{h}^{x}, $ we arrive at the initial value problem
$ {dv1h(t,x)dt+μv1h(t,x)+Axhv1h(t,x)=−Fh(t,x;v1h(t,x),v2h(t,x)),dv2h(t,x)dt+(μ+α)v2h(t,x)+Axhv2h(t,x)=Fh(t,x;v1h(t,x),v2h(t,x))−Gh(t,x;v2h(t,x)),dv3h(t,x)dt+μv3h(t,x)+Axhv3h(t,x)=Gh(t,x;v2h(t,x)),0<t<T,x∈[−l,l]h,vnh(0,x)=ψn(x),n=1,2,3,x∈[−l,l]h $ | (3.17) |
for an infinite system of nonlinear ODEs. In the second step, we replace problem (3.17) by DS (2.1)
$ {v1k−v1k−1τ+μv1k+v1k−12+Axhv1k+v1k−12=−Fh(tk−τ2,x,v1k+v1k−12,v2k+v2k−12),v2k−v2k−1τ+(α+μ)v2k+v2k−12+Axhv2k+v2k−12=Fh(tk−τ2,x,v1k+v1k−12,v2k+v2k−12)−Gh(tk−τ2,x,v2k+v2k−12),v3k−v3k−1τ+μv3k+v3k−12+Axhv3k+v3k−12=Gh(tk−τ2,x,v2k+v2k−12),tk=kτ,1⩽k⩽N,Nτ=T,x∈[−l,l]h,vn0=ψn,n=1,2,3. $ | (3.18) |
Theorem 5. Let the assumptions (3.11)-(3.15) be satisfied and $ 2\left(L_{1}+L_{2}\right) T < 1+\frac{\tau \left(\mu +\delta \right) }{2} $. Then, there exists a unique solution $ v^{\tau } = \left\{ v_{k}\right\} _{k = 0}^{N} $ of DS (3.18) which is bounded in $ C_{\tau }\left(L_{2h}\right) \times C_{\tau }\left(L_{2h}\right) \times C_{\tau }\left(L_{2h}\right) $ of uniformly with respect to $ \tau $ and $ h $.
The proof of Theorem 5 is based on the abstract Theorem 3 and symmetry properties of the difference operator $ A_{h}^{x} $ defined by formula (3.16)[12].
Third, let $ \Omega \subset R^{n} $ be a bounded open domain with smooth boundary $ S $, $ \overline{\Omega } = \Omega \cup S $. In $ \left[ 0, T\right] \times \Omega $ we consider the initial-boundary value problem for multidimensional system of nonlinear PDEs
$ {∂v1(t,x)∂t−n∑r=1(ar(x)v1xr)xr+(δ+μ)v1(t,x)=−F(t,x;v1(t,x),v2(t,x)),∂v2(t,x)∂t−n∑r=1(ar(x)v2xr)xr+(δ+μ+α)v2(t,x)=F(t,x;v1(t,x),v2(t,x))−G(t,x;v2(t,x)),∂v3(t,x)∂t−n∑r=1(ar(x)v3xr)xr+(δ+μ)v3(t,x)=G(t,x;v2(t,x)),0<t<T,x=(x1,...,xn)∈Ω,vm(0,x)=ψm(x),x∈¯Ω,m=1,2,3,vm(t,x)=0,0≤t≤T,x∈S,m=1,2,3, $ | (3.19) |
where $ a_{r}(x) $ and $ \psi ^{m}(x) $ are given sufficiently smooth functions and $ \delta > 0 $ is the sufficiently large number and $ a_{r}(x) > 0. $
Assume the following hypotheses:
1. $ \psi ^{m}, m = 1, 2, 3 $ belongs to $ L_{2}(\overline{\Omega }) $ and
$ ‖ψm‖W22(¯Ω)≤M1. $ | (3.20) |
2. The function $ f:[0, T]\times \left[ 0, l\right] \times L_{2}(\overline{ \Omega })\times L_{2}(\overline{\Omega })\rightarrow L_{2}(\overline{\Omega }) $ be continuous function in $ t $, that is
$ ‖F(t,⋅,u(t,⋅),v(t,⋅))‖L2(¯Ω)≤M2 $ | (3.21) |
in $ [0, T]\times \left[ 0, l\right] \times L_{2}(\overline{\Omega })\times L_{2}(\overline{\Omega }) $ and Lipschitz condition holds uniformly with respect to $ t $
$ ‖F(t,⋅,u,v)−F(t,⋅,z,w)‖L2(¯Ω)≤L1[‖u−z‖L2(¯Ω)+‖v−w‖L2(¯Ω)]. $ | (3.22) |
3. The function $ G:[0, T]\times \left[ 0, l\right] \times L_{2}(\overline{ \Omega })\rightarrow L_{2}(\overline{\Omega }) $ be continuous function in $ t $, that is
$ ‖G(t,⋅,u(t,⋅))‖L2(¯Ω)≤M3 $ | (3.23) |
in $ [0, T]\times \left[ 0, l\right] \times L_{2}(\overline{\Omega }) $ and Lipschitz condition holds uniformly with respect to $ t $
$ ‖G(t,⋅,u)−G(t,⋅,z)‖L2(¯Ω)≤L2‖u−z‖L2(¯Ω). $ | (3.24) |
The discretization of problem (3.19) is also carried out in two steps. In the first step, let us define the grid sets
$ ¯Ωh={x=xr=(h1r1,...,hmrm),r=(r1,...,rm), $ |
$ 0≤rj≤Nj,hjNj=1,j=1,...,m}, $ |
$ Ωh=¯Ωh∩Ω,Sh=¯Ωh∩S. $ |
We introduce the Banach spaces $ L_{2h} = L_{2}(\overline{\Omega }_{h}) $ and $ W_{2h}^{2} = W_{2}^{2}(\overline{\Omega }_{h}) $\ of the grid functions $ \varphi ^{h}(x) = \left\{ \psi (h_{1}r_{1}, ..., h_{m}r_{m})\right\} $ defined on $ \overline{\Omega }_{h}, $ equipped with the norms
$ ‖ψh‖L2h=(∑x∈¯Ωh|ψh(x)|2h1⋅⋅⋅hm)1/2 $ |
and
$ ‖ψh‖W2h=‖ψh‖L2h+(∑x∈¯Ωhm∑r=1|(ψh)xr¯xr,jr|2h1⋅⋅⋅hm)1/2 $ |
respectively. To the differential operator $ A $ generated by problem (3.19), we assign the difference operator $ A_{h}^{x} $ by the formula
$ Axhvhx=−m∑r=1(ar(x)vh¯xr)xr,jr $ | (3.25) |
acting in the space of grid functions $ u^{h}(x) $, satisfying the conditions $ v^{h}(x) = 0 $ for all $ x\in S_{h}. $ It is known that $ A_{h}^{x} $ is a self-adjoint positive definite operator in $ L_{2}(\overline{\Omega }_{h}). $ With the help of $ A_{h}^{x}, $ we arrive at the initial value problem
$ {dv1h(t,x)dt+μv1h(t,x)+Axhv1h(t,x)=−Fh(t,x;v1h(t,x),v2h(t,x)),dv2h(t,x)dt+(μ+α)v2h(t,x)+Axhv2h(t,x)=Fh(t,x;v1h(t,x),v2h(t,x))−Gh(t,x;v2h(t,x)),dv3h(t,x)dt+μv3h(t,x)+Axhv3h(t,x)=Gh(t,x;v2h(t,x)),0<t<T,x∈¯Ωh,vmh(0,x)=ψm(x),m=1,2,3,x∈¯Ωh $ | (3.26) |
for an infinite system of nonlinear ODEs. In the second step, we replace problem (3.26) by DS (2.1)
$ {v1k−v1k−1τ+μv1k+v1k−12+Axhv1k+v1k−12=−Fh(tk−τ2,x,v1k+v1k−12,v2k+v2k−12),v2k−v2k−1τ+(α+μ)v2k+v2k−12+Axhv2k+v2k−12=Fh(tk−τ2,x,v1k+v1k−12,v2k+v2k−12)−Gh(tk−τ2,x,v2k+v2k−12),v3k−v3k−1τ+μV3k+v3k−12+Axhv3k+v3k−12=Gh(tk−τ2,x,v2k+v2k−12),tk=kτ,1⩽k⩽N,Nτ=T,x∈¯Ωh,vm0=ψm,m=1,2,3. $ | (3.27) |
Theorem 6. Let the assumptions (3.20)-(3.24) be satisfied and $ 2\left(L_{1}+L_{2}\right) T < 1+\frac{\tau \left(\mu +\delta \right) }{2} $. Then, there exists a unique solution $ v^{\tau } = \left\{ v_{k}\right\} _{k = 0}^{N} $ of DS (3.27) which is bounded in $ C_{\tau }\left(L_{2h}\right) \times C_{\tau }\left(L_{2h}\right) \times C_{\tau }\left(L_{2h}\right) $ of uniformly with respect to $ \tau $ and $ h $.
The proof of Theorem 6 is based on the abstract Theorem 4 and symmetry properties of the difference operator $ A_{h}^{x} $ defined by formula (3.25) and the following theorem on coercivity inequality for the solution of the elliptic problem in $ L_{2h}. $
Theorem 7. For the solutions of the elliptic difference problem
$ {Axhvh(x)=gh(x), x∈Ωh,vh(x)=0, x∈Sh $ |
the following coercivity inequality
$ m∑r=1‖vhxr¯xr,jr‖L2h≤M‖gh‖L2h. $ |
holds (see [13]).
Fourth, in $ \left[ 0, T\right] \times \Omega $ we consider the initial-boundary value problem for multidimensional system of nonlinear PDEs
$ {∂v1(t,x)∂t−n∑r=1(ar(x)v1xr)xr+(δ+μ)v1(t,x)=−F(t,x;v1(t,x),v2(t,x)),∂v2(t,x)∂t−n∑r=1(ar(x)v2xr)xr+(δ+μ+α)v2(t,x)=f(t,x;v1(t,x),v2(t,x))−G(t,x;v2(t,x)),∂v3(t,x)∂t−n∑r=1(ar(x)v3xr)xr+(δ+μ)v3(t,x)=G(t,x;v2(t,x)),0<t<T,x=(x1,...,xn)∈Ω,vm(0,x)=ψm(x),x∈¯Ω,m=1,2,3,∂v∂→p(t,x)=0,0≤t≤T,x∈S,m=1,2,3, $ | (3.28) |
where $ a_{r}(x) $ and $ \psi ^{m}(x) $ are given sufficiently smooth functions and $ \delta > 0 $ is the sufficiently large number and $ a_{r}(x) > 0. $ Here, $ \overrightarrow{p} $ is the normal vector to $ \Omega. $
The discretization of problem (3.28) is also carried out in two steps. In the first step, to the differential operator $ A $ generated by problem (3.28), we assign the difference operator $ A_{h}^{x} $ by the formula
$ \begin{equation} A_{h}^{x}v_{x}^{h} = -\sum\limits_{r=1}^{m}\left( a_{r}(x)v_{ \overline{x}_{r}}^{h}\right) _{x_{r},j_{r}}+\delta v^{h}(x) \end{equation} $ | (3.29) |
acting in the space of grid functions $ v^{h}(x) $, satisfying the conditions $ D^{h}v^{h}(x) = 0 $ for all $ x\in S_{h}. $ Here $ D^{h} $ is the approximation of operator $ \dfrac{\partial }{\partial \overrightarrow{p}} $. It is known that $ A_{h}^{x} $ is a self-adjoint positive definite operator in $ L_{2}(\overline{ \Omega }_{h}). $ With the help of $ A_{h}^{x}, $ we arrive at the initial value problem (3.26) for an infinite system of nonlinear ODEs. In the second step, we replace problem (3.26) by DS (2.1), we get DS (3.27).
Theorem 8. Let the assumptions (3.20)-(3.24) be satisfied and $ 2\left(L_{1}+L_{2}\right) T < 1+\frac{\tau \left(\mu +\delta \right) }{2} $. Then, there exists a unique solution $ v^{\tau } = \left\{ v_{k}\right\} _{k = 0}^{N} $ of DS (3.27) which is bounded in $ C_{\tau }\left(L_{2h}\right) \times C_{\tau }\left(L_{2h}\right) \times C_{\tau }\left(L_{2h}\right) $ of uniformly with respect to $ \tau $ and $ h $.
The proof of Theorem 8 is based on the abstract Theorem 3 and symmetry properties of the difference operator $ A_{h}^{x} $ defined by formula (3.29) and the following theorem on coercivity inequality for the solution of the elliptic problem in $ L_{2h}. $
Theorem 9. For the solutions of the elliptic difference problem
$ \begin{equation*} \left\{ \begin{array}{c} A_{h}^{x}v^{h}(x) = g^{h}(x),\ x\in \Omega _{h}, \\ D^{h}v^{h}(x) = 0,\ x\in S_{h} \end{array} \right. \end{equation*} $ |
the following coercivity inequality holds (see [13]):
$ \begin{equation*} \sum\limits_{r=1}^{m}\left\Vert v_{x_{r}\overline{x} _{r},j_{r}}^{h}\right\Vert _{L_{2h}}\leq M\left\Vert g^{h}\right\Vert _{L_{2h}}. \end{equation*} $ |
In present section, we consider the initial-boundary value problem
$ \begin{equation} \left\{ \begin{array}{l} \frac{\partial v^{^{_{1}}}(t,x)}{\partial t}-\lambda +\mu v^{^{_{1}}}(t,x)-\beta \frac{\partial ^{2}v^{^{_{1}}}(t,x)}{\partial x^{2}} \\ \\ = -\lambda +\left( -1+\mu +\beta \right) e^{-t}\sin x-\sin \left( v^{^{_{1}}}(t,x)v^{^{_{2}}}(t,x)-e^{-2t}\sin ^{2}x\right) ,\quad \\ \\ \frac{\partial v^{^{_{2}}}(t,x)}{\partial t}+(\mu +\alpha )v^{^{_{2}}}(t,x)-d \frac{\partial ^{2}v^{^{_{2}}}(t,x)}{\partial x^{2}} = \left( -1+\mu +\alpha +d\right) e^{-t}\sin x \\ \\ +\sin \left( v^{^{_{1}}}(t,x)v^{^{_{2}}}(t,x)-e^{-2t}\sin ^{2}x\right) -\cos \left( v^{^{_{2}}}(t,x)-e^{-t}\sin x\right) ,\quad \\ \\ \frac{\partial v^{^{_{3}}}(t,x)}{\partial t}+\mu v^{^{_{1}}}(t,x)-\gamma \frac{\partial ^{2}v^{^{_{1}}}(t,x)}{\partial x^{2}} = \left( -1+\mu +\gamma \right) e^{-t}\sin x \\ \\ +\cos \left( v^{^{_{2}}}(t,x)-e^{-t}\sin x\right) ,0 < t < 1 ,0 < x < \pi , \\ \\ v^{m}(0,x) = \sin \left( x\right) ,0\leq x\leq \pi ,m = 1,2,3, \\ \\ v^{m}(t,0) = u^{m}(t,\pi ) = 0,0\leq t\leq 1,m = 1,2,3 \end{array} \right. \end{equation} $ | (4.1) |
for the system of nonlinear PDEs. The spatial factor, $ x, $ can be spatially discrete or spatially continuous. In either case, the spatial factor is used to describe the mobility of the population. This mobility can be due to travel and migration, and it could be between cities, towns or even countries, depending on the studied case. The exact solution of problem (4.1) is $ v^{m}\left(t, x\right) = e^{-t}\sin x, m = 1, 2, 3. $
Numerical solutions of system (4.1) will be given for first and second order of DS. Firstly, we consider the first order of accuracy iterative DS
$ \begin{equation} \left\{ \begin{array}{l} \frac{jv_{n}^{1,k}-jv_{n}^{1,k-1}}{\tau }+\mu jv_{n}^{^{_{1,k}}}-\beta \frac{ _{j}v_{n+1}^{1,k}-2\left( jv_{n}^{1,k}\right) +jv_{n-1}^{1,k}}{h^{2}} \\ \\ = \left( -1+\mu +\beta \right) e^{-t_{k}}\sin x_{n}-\sin \left( \left( j-1\right) v_{n}^{^{_{1,k}}}\left( j-1\right) v_{n}^{^{_{2,k}}}-e^{-2t_{k}}\sin ^{2}x_{n}\right) , \\ \\ \frac{jv_{n}^{2,k}-jv_{n}^{2,k-1}}{\tau }+\left( \alpha +\mu \right) jv_{n}^{^{_{2,k}}}-d\frac{_{j}v_{n+1}^{2,k}-2\left( jv_{n}^{2,k}\right) +jv_{n-1}^{2,k}}{h^{2}} = \left( -1+\mu +\alpha +d\right) e^{-t_{k}}\sin x_{n} \\ \\ +\sin \left( \left( j-1\right) v_{n}^{^{_{1,k}}}\left( j-1\right) v_{n}^{^{_{2,k}}}-e^{-2t_{k}}\sin ^{2}x_{n}\right) -\cos \left( \left( j-1\right) v_{n}^{^{_{2,k}}}-e^{-t_{k}}\sin x_{n}\right) , \\ \\ \frac{jv_{n}^{3,k}-jv_{n}^{3,k-1}}{\tau }+\mu jv_{n}^{^{_{3,k}}}-\gamma \frac{_{j}v_{n+1}^{3,k}-2\left( jv_{n}^{3,k}\right) +jv_{n-1}^{3,k}}{h^{2}} = \left( -1+\mu +\gamma \right) e^{-t_{k}}\sin x_{n} \\ \\ +\cos \left( \left( j-1\right) v_{n}^{^{_{2,k}}}-e^{-t_{k}}\sin x_{n}\right) , \\ \\ t_{k} = k\tau ,1\leq k\leq N,N\tau = 1,x_{n} = nh,1\leq n\leq M-1,Mh = \pi , \\ \\ jv_{n}^{m,0} = \psi ^{m}(x_{n}),jv_{0}^{m,k} = ju_{M}^{m,k} = 0,0\leq k\leq N,m = 1,2,3,j = 1,2,\cdot \cdot \cdot , \\ \\ 0v_{n}^{m,k},0\leq k\leq N,0\leq n\leq M,m = 1,2,3\text{ is given} \end{array} \right. \end{equation} $ | (4.2) |
and secondly, the second order of accuracy iterative Crank-Nicholson DS
$ \begin{equation} \left\{ \begin{array}{l} \frac{jv_{n}^{1,k}-jv_{n}^{1,k-1}}{\tau }+\mu \frac{ jv_{n}^{^{_{1,k}}}+jv_{n}^{^{_{1,k-1}}}}{2}-\beta \frac{_{j}v_{n+1}^{1,k}-2 \left( jv_{n}^{1,k}\right) +jv_{n-1}^{1,k}}{2h^{2}}-\beta \frac{ _{j}v_{n+1}^{1,k-1}-2\left( jv_{n}^{1,k-1}\right) +jv_{n-1}^{1,k-1}}{2h^{2}} \\ \\ = \left( -1+\mu +\beta \right) e^{-\left( t_{k}-\frac{\tau }{2}\right) }\sin x_{n} \\ \\ -\sin \left( \frac{\left( j-1\right) v_{n}^{^{_{1,k}}}+\left( j-1\right) v_{n}^{^{_{1,k-1}}}}{2}\frac{\left( j-1\right) v_{n}^{^{_{2,k}}}+\left( j-1\right) v_{n}^{^{_{2,k-1}}}}{2}-e^{-2\left( t_{k}-\frac{\tau }{2}\right) }\sin ^{2}x_{n}\right) , \\ \\ \frac{jv_{n}^{2,k}-jv_{n}^{2,k-1}}{\tau }+\left( \alpha +\mu \right) \frac{ jv_{n}^{^{_{2,k}}}+jv_{n}^{^{_{2,k-1}}}}{2}-d\frac{_{j}v_{n+1}^{2,k}-2\left( jv_{n}^{2,k}\right) +jv_{n-1}^{2,k}}{2h^{2}}-d\frac{_{j}v_{n+1}^{2,k-1}-2 \left( jv_{n}^{2,k-1}\right) +jv_{n-1}^{2,k-1}}{2h^{2}} \\ \\ = \left( -1+\mu +\alpha +d\right) e^{-\left( t_{k}-\frac{\tau }{2}\right) }\sin x_{n} \\ \\ +\sin \left( \frac{\left( j-1\right) v_{n}^{^{_{1,k}}}+\left( j-1\right) v_{n}^{^{_{1,k-1}}}}{2}\frac{\left( j-1\right) v_{n}^{^{_{2,k}}}+\left( j-1\right) v_{n}^{^{_{2,k-1}}}}{2}-e^{-2\left( t_{k}-\frac{\tau }{2}\right) }\sin ^{2}x_{n}\right) \\ \\ -\cos \left( \frac{\left( j-1\right) v_{n}^{^{_{2,k}}}+\left( j-1\right) v_{n}^{^{_{2,k-1}}}}{2}-e^{-\left( t_{k}-\frac{\tau }{2}\right) }\sin x_{n}\right) , \\ \\ \frac{jv_{n}^{3,k}-jv_{n}^{3,k-1}}{\tau }+\mu \frac{ jv_{n}^{^{_{3,k}}}+jv_{n}^{^{_{3,k-1}}}}{2}-\gamma \frac{_{j}v_{n+1}^{3,k}-2 \left( jv_{n}^{3,k}\right) +jv_{n-1}^{3,k}}{2h^{2}}-\gamma \frac{ _{j}v_{n+1}^{3,k-1}-2\left( jv_{n}^{3,k-1}\right) +jv_{n-1}^{3,k-1}}{2h^{2}} \\ \\ = \left( -1+\mu +\gamma \right) e^{-\left( t_{k}-\frac{\tau }{2}\right) }\sin x_{n}+\cos \left( \frac{\left( j-1\right) v_{n}^{^{_{2,k}}}+\left( j-1\right) v_{n}^{^{_{2,k-1}}}}{2}-e^{-\left( t_{k}-\frac{\tau }{2}\right) }\sin x_{n}\right) , \\ \\ t_{k} = k\tau ,1\leq k\leq N,N\tau = 1,x_{n} = nh,1\leq n\leq M-1,Mh = \pi , \\ \\ jv_{n}^{m,0} = \psi ^{m}(x_{n}),jv_{0}^{m,k} = jv_{M}^{m,k} = 0,0\leq k\leq N,m = 1,2,3,j = 1,2,\cdot \cdot \cdot , \\ \\ 0v_{n}^{m,k},0\leq k\leq N,0\leq n\leq M,m = 1,2,3\text{ is given} \end{array} \right. \end{equation} $ | (4.3) |
for the.approximate solution of the.initial-boundary.value.problem (4.1) for the system.of nonlinear. PEs. Here and in future $ j $ denotes the iteration.index and an.initial guess.$ _{0}u_{n}^{k}, k\geq 1, 0\leq n\leq M $ is to be made. For solving DS (4.3), the numerical.steps are.given.below. For $ 0\leq k < N, 0\leq n\leq M $ the algorithm.is as follows.[10] :
1. $ j = 1 $.
2. $ _{j-1}v_{n}^{k} $ is.known.
3. $ _{j}v_{n}^{k} $ is.calculated.
4. If the.max absolute.error between $ _{j-1}v_{n}^{k} $ and $ _{j}v_{n}^{k} $ is greater.than the given.tolerance value $ \varepsilon = 10^{-8} $, take $ j = j+1 $ and go.to.step 2. Otherwise, terminate.the iteration.process and take $ _{j}v_{n}^{k} $ as the result of the given problem. The errors are computed by
$ \left( jE^{m}\right) _{M}^{N} = \max\limits_{1\leq k\leq N, 1\leq n\leq M-1}\left\vert v^{m}(t_{k},x_{n})-\left( jv^{m}\right) _{n}^{k}\right\vert, m = 1,2,3 $ | (4.4) |
of the numerical solutions, where $ v^{m}(t_{k}, x_{n}) \;, m = 1, 2, 3 $ represents the exact solutions and $ \left(jv^{m}\right) _{n}^{k} \;, m = 1, 2, 3 $ represents.the numerical.solutions at $ (t_{k}, x_{n}) $ and the results of the first and second order of DS are given.in Table 1 and Table 2 respectively.
$ \left(jE^{m}\right) _{M}^{N} $ | $ N=M=20 $ | $ N=M=40 $ | $ N=M=80 $ |
$ m=1 $ | 0.0068, j=6 | 0.0032, j=6 | 0.0016, j=6 |
$ m=2 $ | 0.0071, j=6 | 0.0033, j=6 | 0.0016, j=6 |
$ m=3 $ | 0.0073, j=6 | 0.0034, j=6 | 0.0017, j=6 |
$ \left(jE^{m}\right) _{M}^{N} $ | $ N=M=20 $ | $ N=M=40 $ | $ N=M=80 $ |
$ m=1 $ | 5.5516e-5, j=7 | 1.3882e-5, j=7 | 3.4708e-6, j=7 |
$ m=2 $ | 8.7420e-5, j=7 | 2.1857e-5, j=7 | 5.4645e-6, j=7 |
$ m=3 $ | 1.1120e-4, j=7 | 2.7803e-5, j=7 | , 6.9510e-6j=7 |
According to Table 1 and Table 2, if $ N $ and $ M $ are doubled, the value of errors in the first order of accuracy DS decrease by a factor of $ 1/2, $ the errors in the second order of accuracy DS $ (4.3) $ decrease approximately by a factor of $ 1/4 $. The errors presented in the tables indicate the stability of the DS and the accuracy of the results. Thus, the second order of accuracy DS increases faster than the first order of accuracy DS.
In the present paper, the.initial boundary value problem for the nonlinear system of PEs observing epidemic models with general nonlinear incidence rate is investigated. The main theorem on the existence and uniqueness of a bounded solution of Crank-Nicholson DS uniformly with respect to time step $ \tau $ is established. Applications of the theoretical results are presented for the four systems of one and multidimensional problems with different boundary conditions. Numerical results are given.
The publication has been prepared with the support of the "RUDN University Program 5-100" and published under target program BR05236656 of the Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan.
The author declares that there are no conflicts of interest.
[1] |
Nguyen KV (2018) Alzheimer's disease. AIMS Neuroscience 5: 74–80. doi: 10.3934/Neuroscience.2018.1.74
![]() |
[2] |
Zheng H, Koo EH (2006) The amyloid precursor protein: beyond amyloid. Mol Neurodegener 1: 5. doi: 10.1186/1750-1326-1-5
![]() |
[3] | Nguyen KV (2015) The human β-amyloid precursor protein: biomolecular and epigenetic aspects. BioMol Concepts 6: 11–32. |
[4] |
Di Luca M, Colciaghi F, Pastorino L, et al. (2000) Platelets as a peripheral district where to study pathogenetic mechanisms of Alzheimer disease: The case of amyloid precursor protein. Eur J Pharmacol 405: 277–283. doi: 10.1016/S0014-2999(00)00559-8
![]() |
[5] |
Ray B, Long JM, Sokol DK, et al. (2011) Increased secreted amyloid precursor protein-α(sAPPα) in severe autism: proposal of a specific, anabolic pathway and putative biomarker. PLoS One 6: e20405. doi: 10.1371/journal.pone.0020405
![]() |
[6] |
Sokol DK, Maloney B, Long JM, et al. (2011) Autism, Alzheimer's disease, and fragile X, APP, FMRP, and mGluR5 are molecular links. Neurology 76: 1344–1352. doi: 10.1212/WNL.0b013e3182166dc7
![]() |
[7] | Lahiri DK, Sokol DK, Erickson C, et al. (2013) Autism as early neurodevelopmental disorders: evidence for an sAPPα-mediated anabolic pathway. Front Cell Neurosci 7: 1–13. |
[8] |
Hagerman RJ, Berry-Kravis E, Kaufmann WE, et al. (2009) Advance in the treatment of fragile X syndrome. Pediatrics 123: 378–390. doi: 10.1542/peds.2008-0317
![]() |
[9] |
Bryson JB, Hobbs C, Parsons MJ, et al. (2012) Amyloid precursor protein (APP) contributes to pathology in the SOD1G93A mouse model of amyotrophic lateral sclerosis. Hum Mol Genet 21: 3871–3882. doi: 10.1093/hmg/dds215
![]() |
[10] |
Gehrmann J, Banati RB, Cuzner ML, et al. (1995) Amyloid precursor protein (APP) expression in multiple sclerosis lesions. Glia 15: 141–151. doi: 10.1002/glia.440150206
![]() |
[11] | Grant JL, Ghosn EE, Axtell RC, et al. (2012) Reversal of paralysis and reduced inflammation from peripheral administration of β-amyloid in TH1and TH17 versions of experimental autoimmune encephalomyelitis. Sci Transl Med 4: 145ra 105. |
[12] | Hohlfeld R, Wekerle H (2012) β-Amyloid: enemy or remedy. Sci Transl Med 4: 145fs24. |
[13] | Chandra A (2015) Role of amyloid from a multiple sclerosis. Perspective: a literature review. Neuroimmunomodulation 22: 343–346. |
[14] | Matias-Guiu JA, Oreja-Guevara C, Cabrera-Martin MN, et al. (2016) Amyloid proteins and their role in multiple sclerosis. Considerations in the use of amyloid-PET imaging. Front Neurol 7: 53. |
[15] |
Imamura A, Yamanouchi H, Kurokawa T, et al. (1992) Elevated fibrinopeptide A (FPA) in patients with Lesch-Nyhan syndrome. Brain Dev 14: 424–425. doi: 10.1016/S0387-7604(12)80355-X
![]() |
[16] | Irbaz bin R, Muhammmad H, Huthayfa A (2014) Recurrent thrombosis in a patient with Lesch-Nyhan syndrome. Am J Med 127: e12. |
[17] |
Canobbio I, Visconte C, Momi S, et al. (2017) Platelet amyloid precursor protein is a modulator of venous thromboembolism in mice. Blood 130: 527–536. doi: 10.1182/blood-2017-01-764910
![]() |
[18] |
Nguyen KV (2014) Epigenetic regulation in amyloid precursor protein and the Lesch-Nyhan syndrome. Biochem Biophys Res Commun 446: 1091–1095. doi: 10.1016/j.bbrc.2014.03.062
![]() |
[19] |
Nguyen KV (2015) Epigenetic regulation in amyloid precursor protein with genomic rearrangements and the Lesch-Nyhan syndrome. Nucleosides Nucleotides Nucleic Acids 34: 674–690. doi: 10.1080/15257770.2015.1071844
![]() |
[20] |
Nguyen KV, Nyhan WL (2017) Quantification of various APP-mRNA isoforms and epistasis in Lesch-Nyhan disease. Neurosci Lett 643: 52–58. doi: 10.1016/j.neulet.2017.02.016
![]() |
[21] |
Hardy JA, Higgin GA (1992) Alzheimer's disease: the amyloid cascade hypothesis. Science 256: 184–185. doi: 10.1126/science.1566067
![]() |
[22] | Haass C, Selkoe DJ (2007) Soluble protein oligomers in neurodegeneration: lessonsrom the Alzheimer's amyloid beta-peptide. Nat Rev Mol Cell Biol 8: 102–112. |
[23] |
Bettens K, Sleegers K, Van Broeckhoven C (2010) Current status on Alzheimer's disease molecular genetics: from past, to present, to future. Hum Mol Genet 19: R4–R11. doi: 10.1093/hmg/ddq142
![]() |
[24] | Hampel H, Frank R, Broich K, et al. (2010) Biomarkers for Alzheimer's disease: academic, industry and regulatory perspectives. Nat Rev 9: 560–574. |
[25] |
Jiang T, Yu JT, Zhu XC, et al. (2013) TREM2 in Alzheimer's disease. Mol Neurobiol 48: 180– 185. doi: 10.1007/s12035-013-8424-8
![]() |
[26] |
Ulrich JD, UllandTK, Colonna M, et al. (2017) Elucidating the role of TREM2 in Alzheimer's disease. Neuron 94: 237–248. doi: 10.1016/j.neuron.2017.02.042
![]() |
[27] |
Klafki HW (2006) Therapeutic approaches to Alzheimer's disease. Brain 129: 2840–2855. doi: 10.1093/brain/awl280
![]() |
[28] |
Hardy J, Selkoe DJ (2002) The amyloid hypothesis of Alzheimer's disease: progress and problems on the road to therapeutics. Science 297: 353–356. doi: 10.1126/science.1072994
![]() |
[29] |
Chetelat G (2013) Aβ-independent processes-rethinking preclinical AD. Nat Rev Neurol 9: 123–124. doi: 10.1038/nrneurol.2013.21
![]() |
[30] |
Wang SC, Oelze B, Schumacher A (2008) Age-specific epigenetic drift in late-onset Alzheimer's disease. PLoS One 3: e2698. doi: 10.1371/journal.pone.0002698
![]() |
[31] |
Combarros O, Cortina-Borja M, Smith AD, et al. (2009) Epistasis in sporadic Alzheimer's disease. Neurobiol Aging 30: 1333–1349. doi: 10.1016/j.neurobiolaging.2007.11.027
![]() |
[32] | Czeczor JK, McGee SL (2017) Emerging roles for the amyloid precursor protein and derived peptides in the regulation of cellular and systemic metabolism. J Neuroendocrinol 29: 1–8. |
[33] |
Aulston B, Schapansky J, HuangYW, et al. (2018) Secreted amyloid precursor protein alpha activates neuronal insulin receptor and prevents diabetes-induced encephalopathy. Exp Neurol 303: 29–37. doi: 10.1016/j.expneurol.2018.01.013
![]() |
[34] |
Moreno-Gonzalez I, Edwards III G, Salvadores N, et al. (2017) Molecular interaction between type 2 diabetes and Alzheimer's disease through cross-seeding of protein misfolding. Mol Psychiatry 22: 1327–1334. doi: 10.1038/mp.2016.230
![]() |
[35] |
Saitoh T, Sundsmo M, Roch JM, et al. (1989) Secreted form of amyloid beta protein precursor is involved in the growth regulation of fibroblast. Cell 58: 615–622. doi: 10.1016/0092-8674(89)90096-2
![]() |
[36] |
Thinakaran G, Koo EH (2008) Amyloid precursor protein trafficking, processing, and function. J Biol Chem 283: 29615–29619. doi: 10.1074/jbc.R800019200
![]() |
[37] |
Zheng H, Koo EH (2011) Biology and pathology of the amyloid precursor protein. Mol Neurodegener 6: 27. doi: 10.1186/1750-1326-6-27
![]() |
[38] |
Roe CM, Fitzpatrick AL, Xiong C, et al. (2010) Cancer linked to Alzheimer disease but not vascular dementia. Neurology 74: 106–112. doi: 10.1212/WNL.0b013e3181c91873
![]() |
[39] | Hansel DE, Rahman A, Wehner S, et al. (2003) Increase expression and processing of the Alzheimer amyloid precursor protein in pancreatic cancer may influence cellular proliferation. Cancer Res 63: 7032–7037. |
[40] |
Takayama KI, Tsutsumi S, Suzuki T, et al. (2009) Amyloid precursor protein is a primary androgen target gene that promotes prostate cancer growth. Cancer Res 69: 137–142. doi: 10.1158/0008-5472.CAN-08-3633
![]() |
[41] |
Venkataramani V, Rossner C, Iffland L, et al. (2010) Histone deacetylase inhibitor valproic acid inhibits cancer cell proliferation via dow-regulation of the Alzheimer amyloid precursor protein. J Biol Chem 285: 10678–10689. doi: 10.1074/jbc.M109.057836
![]() |
[42] |
Venkataramani V, Thiele K, Behnes CL, et al. (2012) Amyloid precursor protein is a biomarker for transformed human pluripotent stem cells. Am J Pathol 180: 1636–1652. doi: 10.1016/j.ajpath.2011.12.015
![]() |
[43] | Takagi K, Ito S, Miyazaki T, et al. (2013) Amyloid precursor protein in human breast cancer: an androgen-induced gene associated with cell proliferation. Cancer Res 104: 1532–1538. |
[44] |
MiyazakiT, Ikeda K, Horie-Inoue K, et al. (2014) Amyloid precursor protein regulates migration and metalloproteinase gene expression in prostate cancer cells. Biochem Biophys Res Commun 452: 828–833. doi: 10.1016/j.bbrc.2014.09.010
![]() |
[45] |
Lim S, Yoo BK, Kim HS, et al. (2014) Amyloid-β precursor protein promotes cell proliferation and motility of advanced breast cancer. BMC Cancer 14: 928. doi: 10.1186/1471-2407-14-928
![]() |
[46] | Pandey P, Sliker B, Peters HL, et al. (2016) Amyloid precursor protein and amyloid-precursor-like protein 2 in cancer. Oncotarget 7: 19430–19444. |
[47] |
Cordell HJ (2002) Epistasis: what it means, what it doesn't mean, and statistical method to detect it in humans. Hum Mol Genet 11: 2463–2468. doi: 10.1093/hmg/11.20.2463
![]() |
[48] |
Moore JH (2003) The ubiquitous nature of epistasis in determining susceptibility to common human diseases. Hum Hered 56: 73–82. doi: 10.1159/000073735
![]() |
[49] |
Riordan JD, Nadeau JH (2017) From peas to disease: modifier genes, network resilience, and the genetics of health. Am J Hum Genet 101: 177–191. doi: 10.1016/j.ajhg.2017.06.004
![]() |
[50] |
Pan Q, Shai O, Lee LJ, et al. (2008) Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nat Genet 40: 1413–1415. doi: 10.1038/ng.259
![]() |
[51] |
Faustino NA, Cooper TA (2003) Pre-mRNA splicing and human disease. Genes Dev 17: 419–437. doi: 10.1101/gad.1048803
![]() |
[52] |
Nguyen KV (2019) Potential epigenomic co-management in rare diseases and epigenetic therapy. Nucleosides Nucleotides Nucleic Acids 38: 752–780. doi: 10.1080/15257770.2019.1594893
![]() |
[53] | Saonere JA (2011) Antisense therapy, a magic bullet for the treatment of various diseases: present and future prospects. J Med Genet Genom 3: 77–83. |
1. | Nezihal Gokbulut, Evren Hincal, Hasan Besim, Bilgen Kaymakamzade, Reducing the Range of Cancer Risk on BI-RADS 4 Subcategories via Mathematical Modelling, 2022, 133, 1526-1506, 93, 10.32604/cmes.2022.019782 |
$ \left(jE^{m}\right) _{M}^{N} $ | $ N=M=20 $ | $ N=M=40 $ | $ N=M=80 $ |
$ m=1 $ | 0.0068, j=6 | 0.0032, j=6 | 0.0016, j=6 |
$ m=2 $ | 0.0071, j=6 | 0.0033, j=6 | 0.0016, j=6 |
$ m=3 $ | 0.0073, j=6 | 0.0034, j=6 | 0.0017, j=6 |
$ \left(jE^{m}\right) _{M}^{N} $ | $ N=M=20 $ | $ N=M=40 $ | $ N=M=80 $ |
$ m=1 $ | 5.5516e-5, j=7 | 1.3882e-5, j=7 | 3.4708e-6, j=7 |
$ m=2 $ | 8.7420e-5, j=7 | 2.1857e-5, j=7 | 5.4645e-6, j=7 |
$ m=3 $ | 1.1120e-4, j=7 | 2.7803e-5, j=7 | , 6.9510e-6j=7 |
$ \left(jE^{m}\right) _{M}^{N} $ | $ N=M=20 $ | $ N=M=40 $ | $ N=M=80 $ |
$ m=1 $ | 0.0068, j=6 | 0.0032, j=6 | 0.0016, j=6 |
$ m=2 $ | 0.0071, j=6 | 0.0033, j=6 | 0.0016, j=6 |
$ m=3 $ | 0.0073, j=6 | 0.0034, j=6 | 0.0017, j=6 |
$ \left(jE^{m}\right) _{M}^{N} $ | $ N=M=20 $ | $ N=M=40 $ | $ N=M=80 $ |
$ m=1 $ | 5.5516e-5, j=7 | 1.3882e-5, j=7 | 3.4708e-6, j=7 |
$ m=2 $ | 8.7420e-5, j=7 | 2.1857e-5, j=7 | 5.4645e-6, j=7 |
$ m=3 $ | 1.1120e-4, j=7 | 2.7803e-5, j=7 | , 6.9510e-6j=7 |