
Graph labeling is a source of valuable mathematical models for an extensive range of applications in technologies (communication networks, cryptography, astronomy, data security, various coding theory problems). An edge δ− graceful labeling of a graph G with p vertices and q edges, for any positive integer δ, is a bijective f from the set of edge E(G) to the set of positive integers {δ,2δ,3δ,⋯,qδ} such that all the vertex labels f∗[V(G)], given by: f∗(u)=(∑uv∈E(G)f(uv))mod(δk), where k=max(p,q), are pairwise distinct. In this paper, we show the existence of an edge δ− graceful labeling, for any positive integer δ, for the following graphs: the splitting graphs of the cycle, fan, and crown, the shadow graphs of the path, cycle, and fan graph, the middle graphs and the total graphs of the path, cycle, and crown. Finally, we display the existence of an edge δ− graceful labeling, for the twig and snail graphs.
Citation: Mohamed R. Zeen El Deen, Ghada Elmahdy. New classes of graphs with edge δ− graceful labeling[J]. AIMS Mathematics, 2022, 7(3): 3554-3589. doi: 10.3934/math.2022197
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Graph labeling is a source of valuable mathematical models for an extensive range of applications in technologies (communication networks, cryptography, astronomy, data security, various coding theory problems). An edge δ− graceful labeling of a graph G with p vertices and q edges, for any positive integer δ, is a bijective f from the set of edge E(G) to the set of positive integers {δ,2δ,3δ,⋯,qδ} such that all the vertex labels f∗[V(G)], given by: f∗(u)=(∑uv∈E(G)f(uv))mod(δk), where k=max(p,q), are pairwise distinct. In this paper, we show the existence of an edge δ− graceful labeling, for any positive integer δ, for the following graphs: the splitting graphs of the cycle, fan, and crown, the shadow graphs of the path, cycle, and fan graph, the middle graphs and the total graphs of the path, cycle, and crown. Finally, we display the existence of an edge δ− graceful labeling, for the twig and snail graphs.
The financial crisis will have a great impact on the economic order of China and even the whole world. During the past several decades, the international financial crises have continually burst out and spread to many countries or regions quickly. For example, "tequila crisis" of Latin American countries in 1994, "Russian virus" in 1998, and the financial crises of Southeast Asian in 1997, etc. [1]. The financial crises is very harmful and will lead to great disorder of economic development. Thus it is an important task for us to deal with the various financial models to reveal their inherent change law in order to control vicious economic development and serve human beings. At present, there are many valuable works on all kinds of finance models. For example, Yu et al. [2] reported the bifurcation and its control issue for a hyperchaotic finance model, Cao [3] investigated the chaos control of a hyperchaotic finance model, Liao et al. [4] revealed the impact of policy lag on the Hopf bifurcation and chaos for a macroeconomic model. For more related studies, one can see [5-11]
Hopf bifurcation is an important dynamical phenomenon in delayed systems. In particular, Hopf bifurcation and its control in economic systems plays a vital role in maintaining economic stability and virtuous circle of development. Thus it is important for us to explore this topic in economic or financial models.
In 2011, Chen and Ying [1] investigated the following financial crises contagions model:
{du1(t)dt=α−u1(t)u22(t),du2(t)dt=u2(t)(−β+u1(t)u2(t)), | (1.1) |
where u1,u2 denote the stock return rates of country I and country II, respectively, α>0 represents the increasing rate of the average stock returns of country I under the normal situation, and β>0 represents the decreasing rate of the stock returns of country II. In details, one can see [1]. By virtue of the stability theory of ordinary differential equation, Chen and Ying [1] systematically analyzed the stability of different equilibrium points of model (1.1).
Considering that the stock return rate of country I is affected by the stock return rate of country II during the past time and the stock return rate of country II is affected by the stock return rate of country I during the past time, we think that it is more suitable for us to introduce the time delay into model (1.1), then we can establish the following delayed financial crises contagions model:
{du1(t)dt=α−u1(t)u22(t−σ),du2(t)dt=u2(t)(−β+u1(t−σ)u2(t)), | (1.2) |
where u1,u2 denote the stock return rates of country I and country II, respectively, α>0 represents the increasing rate of the average stock returns of country I under the normal situation, and β>0 represents the decreasing rate of the stock returns of country II, σ is a delay.
From a mathematical point of view, fractional-order dynamical model is more efficient instrument to describe the real financial phenomenon in economics than integer-order ones since fractional-order dynamical model possesses the memory trait and hereditary peculiarity for all kinds of economic variables and inherent development process [12,17-23], Inspired by this idea, we modify the delayed financial crises contagions model (1.2) as the following fractional-order form:
{duμ1(t)dtμ=α−u1(t)u22(t−σ),duμ2(t)dtμ=u2(t)(−β+u1(t−σ)u2(t)), | (1.3) |
where 0<μ<1 is a constant, u1,u2 denote the stock return rates of country I and country II, respectively, α>0 represents the increasing rate of the average stock returns of country I under the normal situation, and β>0 represents the decreasing rate of the stock returns of country II, σ is a delay. The fractional-order financial crises contagions model (1.3) owns greater advantages in describing economic laws than the integer-order ones and Hopf bifurcation property can effectively depict the stock return rates of country I and country II. Motivated by this idea, we think that it is necessary to deal with the Hopf bifurcation and its control issue for model (1.3). In particular, the key object is to discuss the stability and Hopf bifurcation of system (1.3) and analyze the Hopf bifurcation control issue of system (1.3). In addition, we still reveal the effect of delay on Hopf bifurcation of system (1.3).
The key contributions of this work are as follow: (1) A novel fractional-order delayed financial crises contagions model is built. (2) A delay-independent sufficient condition guaranteeing the stability and the creation of Hopf bifurcation for the involved fractional-order delayed financial crises contagions model is obtained. (3) A suitable delayed feedback controller is successfully designed to control the Hopf bifurcation of the involved fractional-order delayed financial crises contagions model.
The work is arranged as follows. The requisite theory about fractional-order differential system is prepared in Section 2. The delay-independent stability and bifurcation criteria remaining the stability and the onset of Hopf bifurcation for fractional-order delayed financial crises contagions model are built in Section 3. The delay-independent stability and bifurcation criteria maintaining the stability and the onset of Hopf bifurcation for fractional-order delayed controlled financial crises contagions model are built in Section 4. The computer simulations substantiating the studied key results are performed in Section 5. The conclusion is drawn in Section 6.
In this section, some necessary important definitions and lemmas about fractional-order dynamical system are given.
Definition 2.1. [12] The fractional integral of order μ of the function g(η) is given by
Iμg(η)=1Γ(μ)∫ηη0(η−s)μ−1g(s)ds, |
where η≥η0,μ>0, and Γ(s)=∫∞0ηs−1e−ηdη denotes Gamma function.
Definition 2.2. [12] Let g(η)∈C([η0,∞),R). Define the Caputo fractional-order derivative of order μ of g(η) as follows:
Dμg(η)=1Γ(l−μ)∫ηη0g(m)(s)(η−s)μ−m+1ds, |
where η≥η0 and m denotes a positive integer which satisfies m−1≤μ<m. Furthermore, when 0<μ<1, then
Dμg(η)=1Γ(1−μ)∫ηη0g′(s)(η−s)μds. |
Definition 2.3. [13] For the given system:
Dμxl(t)=hl(xl(t)),l=1,2,⋯,k, | (2.1) |
where μ∈(0,1],xl(t)=(x1(t),x2(t),⋯,xk(t)),hl(t)=(h1(t),h2(t),⋯,hk(t)). If hl(x∗l)=0, then (x∗1,x∗2,⋯,x∗k) is said to be the equilibrium point of system (2.1).
Lemma 2.1. [14] For the given fractional order system Dμy=Ly,y(0)=y0 where 0<μ<1,y∈Rk,L∈Rk×k. Assume that λh(h=1,2,⋯,k) is the root of the characteristic equation of Dμy=Ly. Then system Dμy=Ly is said to be asymptotically stable ⇔ |arg(λh)|>μπ2(h=1,2,⋯,k). Besides, this system is said to be stable ⇔ |arg(λh)|>μπ2(h=1,2,⋯,k) and every critical eigenvalue that satisfies |arg(λh)|=μπ2(h=1,2,⋯,k) has geometric multiplicity one.
Lemma 2.2. [15] For the given fractional order system Dμw(t)=T1w(t)+T2w(t−σ), where w(t)=ϕ(t),t∈[−σ,0],μ∈(0,1],w∈Rn,T1,T2∈Rn×n,μ∈R+(n×n). The characteristic equation of the system can be expressed as det|sμI−T1−T2e−sσ|=0. Then the zero solution of the system is asymptotically stable if each root of the equation det|sμI−T1−T2e−sσ|=0 owns negative real part.
In this section, we are to analyze the influence of time delay σ on Hopf bifurcation for the fractional-order delayed financial crises contagions model (1.3).
Let (u1∗,u2∗) be the equilibrium point of model (1.3), then
{α−u1∗u22∗=0,u2∗(−β+u1∗u2∗)=0 | (3.1) |
It follows from (3.1) that system (1.3) has the unique positive equilibrium point U(u1∗,u2∗) where u1∗=β2α,u2∗=αβ.
let
{˜u1(t)=u1(t)−u1∗,˜u2(t)=u2(t)−u2∗, | (3.2) |
then system (1.3) is expressed as the following form:
{d˜uμ1(t)dtμ=α−(˜u1(t)+u1∗)(˜u2(t−σ)+u2∗)2,d˜uμ2(t)dtμ=(˜u2(t)+u2∗)[−β+(˜u1(t−σ)+u1∗)(˜u2(t)+u2∗)]. | (3.3) |
The linear system of (3.3) near (0,0) owns the expression:
{d˜uμ1(t)dtμ=−u22∗˜u1(t)−2u1∗u2∗˜u2(t−σ),d˜uμ2(t)dtμ=u22∗˜u1(t−σ)+(2u1∗u2∗−β)˜u2(t). | (3.4) |
Let ui denote ˜ui(i=1,2), then system (3.4) becomes
{duμ1(t)dtμ=−u22∗u1(t)−2u1∗u2∗u2(t−σ),duμ2(t)dtμ=u22∗u1(t−σ)+(2u1∗u2∗−β)u2(t). | (3.5) |
The characteristic equation of Eq (3.5) is given by
det[sμ+u22∗2u1∗u2∗e−sσ−u22∗e−sσsμ−(2u1∗u2∗−β)]=0, | (3.6) |
which leads to
s2μ+a1sμ+a2+b1e−2sσ=0, | (3.7) |
where
{a1=u22∗−2u1∗u2∗+β,a2=u2∗(β−2u1∗u2∗),b1=2u1∗u32∗. | (3.8) |
Assume that
(K1)a1>0,a2+b1>0 |
holds.
Lemma 3.1. For system (1.3), the positive equilibrium point U(u1∗,u2∗) is locally asymptotically stable provided that (K1) holds true.
Proof. If σ=0, then (3.7) becomes
λ2+a1λ+a2+b1=0. | (3.9) |
It follows from (K1) that every root λh of (3.7) satisfies |arg(λh)|>μπ2(h=1,2). By Lemma 3.1, one knows that the positive equilibrium point U(u1∗,u2∗) is locally asymptotically stable. The proof completes.
Assume that s=iγ=γ(cosπ2+isinπ2) is a root of Eq. (3.7), then
{b1cos2γσ=−γ2μcosγπ−a1γμcosγπ2−a2,b1sin2γσ=−γ2μsinγπ−a1γμsinγπ2. | (3.10) |
According to (3.10), we have
{cos2γσ=1b1[−γ2μcosγπ−a1γμcosγπ2−a2],sin2γσ=1b1[−γ2μsinγπ−a1γμsinγπ2]. | (3.11) |
and
b21=[γ2μcosγπ+a1γμcosγπ2+a2]2+[γ2μsinγπ+a1γμsinγπ2]2, | (3.12) |
which leads to
γ4μ+ϵ1γ3μ+ϵ2γ2μ+ϵ3γμ+ϵ4=0 | (3.13) |
where
{ϵ1=2a1(cosγπcosγπ2+sinγπsinγπ2),ϵ2=2a2cosγπ,ϵ3=2a1a2cosγπ2,ϵ4=a22−b21. | (3.14) |
Denote
Ψ(γ)=γ4μ+ϵ1γ3μ+ϵ2γ2μ+ϵ3γμ+ϵ4. | (3.15) |
Suppose that
(K2)a22<b21. |
By (K2), one derives ϵ4<0. Notice that dΨ(γ)dγ>0, for each γ>0, then Eq (3.13) has at least one positive real root. So, Eq (3.7) has at least a pair of purely roots.
Suppose that Eq (3.15) has four real roots (say γh>0(h=1,2,3,4). By (3.11), we have
σlh=12γh[arccos(−γ2μcosγπ−a1γμcosγπ2−a2b1)+2lπ], | (3.16) |
where l=0,1,2,⋯,h=1,2,3,4. Let
γ0=minh=1,2,3,4{γ0h},γ0=γ|σ=σ0. | (3.17) |
Assume that
(K3)Q1S1+Q2S2>0, |
where
{Q1=2μγ2μ−10cos(2μ−1)π2+μa1γμ−10cos(μ−1)π2,Q2=2μγ2μ−10sin(2μ−1)π2+μa1γμ−10sin(μ−1)π2,S1=2bγ0sin2γ0σ0,S2=2bγ0cos2γ0σ0. | (3.18) |
Lemma 3.2. Suppose that s(σ)=ρ1(σ)+iρ2(σ) is the root of (3.7) near σ=σ0 such that ρ1(σ0)=0,ρ2(σ0)=γ0, then Re[dsdσ]σ=σ0,γ=γ0>0.
Proof. It follows from (3.7) that
(2μs2μ−1+μa1sμ−1)dsdσ−2b1e−2sσ(dsdσσ+s)=0. | (3.19) |
It follows from (3.19) that
(dsdσ)−1=2μs2μ−1+μa1sμ−12sb1e−2sσ−σs, | (3.20) |
which leads to
Re[(dsdσ)−1]=Re[2μs2μ−1+μa1sμ−12sb1e−2sσ]. | (3.21) |
Thus
Re[(dsdσ)−1]σ=σ0,γ=γ0=Q1S1+Q2S2S21+S22. | (3.22) |
Applying (K3), we have
Re[(dsdσ)−1]σ=σ0,γ=γ0>0, | (3.23) |
which ends the proof.
By means of the analysis above, the following assertion holds.
Theorem 3.1. If (K1)–(K3) are satisfied, then the positive equilibrium point (u1∗,u2∗) of system (1.3) is locally asymptotically stable if 0≤σ<σ0 and system (1.3) generates Hopf bifurcation around the positive equilibrium point (u1∗,u2∗) when σ passes through the delay value σ0.
In this section, we are to analyze the influence of time delay σ on Hopf bifurcation for the fractional-order delayed controlled financial crises contagions model. we design a time delay feedback controller [16] which takes the form:
ξ(t)=θ[u1(t−σ)−u1(t)], | (4.1) |
where θ is feedback gain coefficient.
{duμ1(t)dtμ=α−u1(t)u22(t−σ)+θ[u1(t−σ)−u1(t)],duμ2(t)dtμ=u2(t)(−β+u1(t−σ)u2(t)), | (4.2) |
Clearly, system (4.2) has the unique positive equilibrium point U(u1∗,u2∗) where u1∗=β2α,u2∗=αβ.
let
{ˉu1(t)=u1(t)−u1∗,ˉu2(t)=u2(t)−u2∗, | (4.3) |
then system (4.2) is expressed as the following form:
{dˉuμ1(t)dtμ=α−(ˉu1(t)+u1∗)(ˉu2(t−σ)+u2∗)2+θ[ˉu1(t−σ)−ˉu1(t)],dˉuμ2(t)dtμ=(ˉu2(t)+u2∗)[−β+(ˉu1(t−σ)+u1∗)(ˉu2(t)+u2∗)]. | (4.4) |
The linear system of (4.4) near (0,0) owns the expression:
{dˉuμ1(t)dtμ=−(u22∗+θ)ˉu1(t)+θˉu1(t−σ)−2u1∗u2∗ˉu2(t−σ),dˉuμ2(t)dtμ=u22∗ˉu1(t−σ)+(2u1∗u2∗−β)ˉu2(t). | (4.5) |
Let ui denote ˉui(i=1,2), then system (4.5) becomes
{duμ1(t)dtμ=−(u22∗+θ)u1(t)+θu1(t−σ)−2u1∗u2∗u2(t−σ),duμ2(t)dtμ=u22∗u1(t−σ)+(2u1∗u2∗−β)u2(t). | (4.6) |
The characteristic equation of Eq (4.6) is given by
det[sμ+(u22∗+θ)−θe−sσ2u1∗u2∗e−sσ−u22∗e−sσsμ−(2u1∗u2∗−β)]=0, | (4.7) |
which leads to
s2μ+c1sμ+c2−(sμ+d1)e−sσ+d2e−2sσ=0, | (4.8) |
where
{c1=u22∗−2u1∗u2∗+β+θ,c2=(u22∗−θ)(β−2u1∗u2∗),d1=β−2u1∗u2∗,d2=2u1∗u32∗. | (4.9) |
Assume that
(K4)c1>1,c2−d1+d2>0 |
holds.
Lemma 4.1. For system (4.2), the positive equilibrium point U(u1∗,u2∗) is locally asymptotically stable provided that (K4) holds true.
Proof. If σ=0, then (4.8) becomes
λ2+(c1−1)λ+c2−d1+d2=0. | (4.10) |
It follows from (K4) that every root λj of (4.8) satisfies |arg(λj)|>μπ2(j=1,2). By Lemma 3.1, one knows that the positive equilibrium point U(u1∗,u2∗) is locally asymptotically stable. The proof completes.
By (4.8), we have
(s2μ+c1sμ+c2)esσ−(sμ+d1)+d2e−sσ=0. | (4.11) |
Let s=iϱ=ϱ(cosπ2+isinπ2) be the root of Eq. (4.11), then
{m1cosϱσ−m2sinϱσ=m3,n1cosϱσ+n2sinϱσ=n3, | (4.12) |
where
{m1=ϱ2μcosμπ+c1ϱμcosμπ2+c2+d2,m2=ϱ2μsinμπ+c1ϱμsinμπ2+d2,m3=ϱμcosμπ2+d1,n1=ϱ2μsinμπ+c1ϱμsinμπ2,n2=ϱ2μcosμπ+c1ϱμcosμπ2+c2−d2,n3=ϱμsinμπ2. | (4.13) |
It follows from (4.12) that
{cosϱσ=m3n2+n3m2m1n2+n1m2,sinϱσ=m1n3−n1m3m1n2+n1m2, | (4.14) |
which leads to
(m1n2+n1m2)2=(m3n2+n3m2)2+(m1n3−n1m3)2. | (4.15) |
For the convenience of calculation, we deal with this formula (4.13) properly. Let
{ν1=cosμπ,ν2=c1cosμπ2,ν3=c2+d2,ν4=sinμπ,ν5=c1sinμπ2,ν6=d2,ν7=cosμπ2,ν8=d1,ν9=sinμπ,ν10=c1sinμπ2,ν11=cosμπ,ν12=c1cosμπ2,ν13=c2−d2,ν14=sinμπ2. | (4.16) |
then (4.13) becomes
{m1=ν1ϱ2μ+ν2ϱμ+ν3,m2=ν4ϱ2μ+ν5ϱμ+ν6,m3=ν7ϱμ+ν8,n1=ν9ϱ2μ+ν10ϱμ,n2=ν11ϱ2μ+ν12ϱμ+ν13,n3=ν14ϱμ. | (4.17) |
Notice that
{(m1n2+n1m2)2=ς1ϱ8μ+ς2ϱ7μ+ς3ϱ6μ+ς4ϱ5μ+ς5ϱ4μ+ς6ϱ3μ+ς7ϱ2μ+ς8ϱμ+ς9,(m3n2+n3m2)2=ς10ϱ6μ+ς11ϱ5μ+ς12ϱ4μ+ς13ϱ3μ+ς14ϱ2μ+ς15ϱμ+ς16,m1n3−n1m3)2=ς17ϱ6μ+ς18ϱ5μ+ς19ϱ4μ+ς20ϱ3μ+ς21ϱ2μ, | (4.18) |
where
{ς1=(ν1ν11+ν4ν9)2,ς2=2(ν1ν11+ν4ν9)(ν1ν12+ν2ν11+ν5ν9+ν4ν10),ς3=(ν1ν12+ν2ν11+ν5ν9+ν4ν10)2+2(ν1ν11+ν4ν9)(ν1ν13+ν2ν12+ν3ν11+ν6ν9+ν5ν10),ς4=2(ν1ν11+ν4ν9)(ν2ν13+ν3ν12+ν6ν10)+2(ν1ν12+ν2ν11+ν5ν9+ν4ν10)×(ν1ν13+ν2ν12+ν3ν11+ν6ν9+ν5ν10),ς5=(ν1ν13+ν2ν12+ν3ν11+ν6ν9+ν5ν10)2+2(ν1ν12+ν2ν11+ν5ν9+ν4ν10)×(ν2ν13+ν3ν12+ν6ν10)+2ν3ν13(ν1ν11+ν4ν9),ς6=2(ν1ν13+ν2ν12+ν3ν11+ν6ν9+ν5ν10)×(ν2ν13+ν3ν12+ν6ν10)+2ν3ν13(ν1ν12+ν2ν11+ν5ν9+ν4ν10),ς7=2ν3ν13(ν1ν13+ν2ν12+ν3ν11+ν6ν9+ν5ν10)+(ν2ν13+ν3ν12+ν6ν10)2,ς8=2ν3ν13(ν2ν13+ν3ν12+ν6ν10),ς9=(ν3ν13)2,ς10=(ν9ν11+ν4ν14)2,ς11=2(ν9ν11+ν4ν14)(ν9ν12+ν8ν11+ν5ν14),ς12=(ν9ν11+ν4ν14)2+2(ν9ν11+ν4ν14)×(ν9ν13+ν8ν12+ν6ν14),ς13=ν8ν13(ν9ν11+ν4ν14)+2(ν9ν13+ν8ν12+ν6ν14)×(ν9ν12+ν8ν11+ν5ν14),ς14=(ν9ν13+ν8ν12+ν6ν14)2+ν8ν13(ν9ν12+ν8ν11+ν5ν14),ς15=ν8ν13(ν9ν12+ν8ν11+ν5ν14),ς16=(ν8ν13)2,ς17=(ν1ν14−ν7ν9)2,ς18=2(ν1ν14−ν7ν9)(ν2ν14−ν8ν9−ν7ν10),ς19=2(ν1ν14−ν7ν9)(ν3ν14−ν8ν10)+(ν2ν14−ν8ν9−ν7ν10)2,ς20=2(ν3ν14−ν8ν10)(ν2ν14−ν8ν9−ν7ν10),ς21=(ν3ν14−ν8ν10)2. | (4.19) |
By (4.15) and (4.18), we get
υ1ϱ8μ+υ2ϱ7μ+υ3ϱ6μ+υ4ϱ5μ+υ5ϱ4μ+υ6ϱ3μ+υ7ϱ2μ+υ8ϱμ+υ9=0, | (4.20) |
where
{υ1=ς1,υ2=ς2,υ3=ς3−ς10−ς17,υ4=ς4−ς11−ς18,υ5=ς5−ς12−ς19,υ6=ς6−ς13−ς20,υ7=ς7−ς14−ς21,υ8=ς8−ς15,υ9=ς9−ς16. | (4.21) |
Denote
Φ(ϱ)=υ1ϱ8μ+υ2ϱ7μ+υ3ϱ6μ+υ4ϱ5μ+υ5ϱ4μ+υ6ϱ3μ+υ7ϱ2μ+υ8ϱμ+υ9. | (4.22) |
Suppose that
(K5)ς9<ς16. |
By (K5), one derives υ9<0. Notice that dΦ(ϱ)dϱ>0, for each ϱ>0, then Eq (4.20) has at least one positive real root. So, Eq (4.11) has at least a pair of purely roots.
Suppose that Eq (4.11) owns eight real roots (say ϱj>0(j=1,2,⋯,8). By (4.14), we have
σij=1ϱj[arccos(m3n2+n3m2m1n2+n1m2)+2iπ], | (4.23) |
where i=0,1,2,⋯,j=1,2,⋯,8. Let
σ0=minj=1,2,⋯,8{σ0j},ϱ0=ϱ|σ=σ0. | (4.24) |
Assume that
(K6)R1V1+R2V2>0, |
where
{R1=[2μϱ2μ−10cos(2μ−1)π2+μc1ϱμ−10cos(μ−1)π2]cosϱ0σ0−[2μϱ2μ−10sin(2μ−1)π2+μc1ϱμ−10sin(μ−1)π2]sinϱ0σ0−μϱμ−10cos(μ−1)π2,R2=[2μϱ2μ−10cos(2μ−1)π2+μc1ϱμ−10cos(μ−1)π2]sinϱ0σ0+[2μϱ2μ−10sin(2μ−1)π2+μc1ϱμ−10sin(μ−1)π2]cosϱ0σ0−μϱμ−10sin(μ−1)π2,V1=(ϱ2μ0cosμπ+c1ϱμ0cosμπ2+c2)ϱ0sinϱ0σ0+d2ϱ0sinϱ0σ0+(ϱ2μ0sinμπ+c1ϱμ0sinμπ2+c2)ϱ0cosϱ0σ0,V2=−(ϱ2μ0cosμπ+c1ϱμ0cosμπ2+c2)ϱ0cosϱ0σ0+d2ϱ0cosϱ0σ0+(ϱ2μ0sinμπ+c1ϱμ0sinμπ2+c2)ϱ0sinϱ0σ0. | (4.25) |
Lemma 4.2. Suppose that s(σ)=η1(σ)+iη2(σ) is the root of (4.11) near σ=σ0 such that η1(σ0)=0,η2(σ0)=ϱ0, then Re[dsdσ]σ=σ0,ϱ=ϱ0>0.
Proof. It follows from (4.11) that
(2μs2μ−1+μc1sμ−1)dsdσesσ+esσ(dsdσσ+s)(s2μ+c1sμ+c2)−μsμ−1dsdσ−d2e−sσ(dsdσσ+s)=0. | (4.26) |
It follows from (4.26) that
(dsdσ)−1=RV−σs, | (4.27) |
where
{R=(2μs2μ−1+μc1sμ−1)esσ−μsμ−1,V=e−sσs(s2μ+c1sμ+c2)+d2se−sσ. | (4.28) |
Then
Re[(dsdσ)−1]=Re[(RV)−1]. | (4.29) |
Thus
Re[(dsdσ)−1]σ=σ0,ϱ=ϱ0=R1V1+R2V2V21+V22. | (4.30) |
Applying (K6), we have
Re[(dsdσ)−1]σ=σ0,ϱ=ϱ0>0, | (4.31) |
which ends the proof.
By means of the analysis above, the following assertion holds.
Theorem 4.1. If (K4)-(K6) are satisfied, then the positive equilibrium point (u1∗,u2∗) of system (4.2) is locally asymptotically stable if 0≤σ<σ0 and system (4.2) generates Hopf bifurcation around the positive equilibrium point (u1∗,u2∗) when σ passes through the delay value σ0.
Example 5.1 Consider the following fractional-order financial crises contagions model:
{du0.671(t)dt0.67=2−u1(t)u22(t−σ),du0.672(t)dt0.67=u2(t)(−1.45+u1(t−σ)u2(t)). | (5.1) |
Apparently, system (5.1) owns the unique positive equilibrium point (1.0513,1.3793). Making use of Matlab software, we get γ0=0.5091 and σ0=0.0332. The conditions (K1)–(K3) of Theorem 3.1 are fulfilled. In order to check the stability of the positive equilibrium point (1.0513,1.3793) and the appearance of Hopf bifurcation of system (5.1), we choose two unequal delay values. Let σ=0.025<σ0=0.0332, we obtain the computer simulation results that are presented in Figure 1. According to Figure 1, one can clearly see that the positive equilibrium point (1.0513,1.3793) keeps locally asymptotically stable situation. Figure 1 contains 4 subfigures. Subfigure 1 of Figure 1 shows that the state variable u1→1.0513 when the time increases. Subfigure 2 of Figure 1 implies that the state variable u2→1.3793 when the time increases. Subfigure 3 of Figure 1 manifests the numerical relation of u1 and u2. Subfigure 4 of Figure 1 display the numerical relation of t-u1-u2. Let σ=0.045>σ0=0.0332, we get the computer simulation results which are presented in Figure 2. According to Figure 2, we can clearly see that a Hopf bifurcation arises around the positive equilibrium point (1.0513,1.3793). Figure 2 contains 4 subfigures. Subfigure 1 of Figure 2 implies that the state variable u1 will keep a periodic oscillatory level around the value 1.0513 when the time increases. Subfigures 2 of Figure 2 implies that the state variable u2 will keep a periodic oscillatory state near the value 1.3793 when the time increases. Subfigures 3 of Figure 2 manifests the numerical relation of u1 and u2. Subfigures 4 of Figure 2 displays the numerical relation of t-u1-u2. The correlation for μ, γ0 and σ0 is listed in Table 1. Also, the bifurcation plots are presented to show that the bifurcation value is approximately equal to 0.0332 (see Figures 3 and 4).
μ | γ0 | σ0 |
0.18 | 0.9713 | 0.2109 |
0.23 | 0.8107 | 0.2655 |
0.36 | 0.7861 | 0.2872 |
0.48 | 0.6723 | 0.0301 |
0.67 | 0.5091 | 0.0332 |
0.76 | 0.4728 | 0.0457 |
0.81 | 0.4011 | 0.0504 |
0.89 | 0.3856 | 0.0609 |
0.94 | 0.2781 | 0.00821 |
Example 5.2 Consider the following fractional-order controlled financial crises contagions model with delays:
{du0.671(t)dt0.67=2−u1(t)u22(t−σ)+θ[u1(t−σ)−u1(t)],du0.672(t)dt0.67=u2(t)(−1.45+u1(t−σ)u2(t)), | (5.2) |
Apparently, system (5.2) owns the unique positive equilibrium point (1.0513,1.3793). Let θ=3. Making use of Matlab software, one gets ϱ0=0.9012 and σ0=0.0583. The conditions (K4)-(K6) in Theorem 4.1 are satisfied. In order to check the stability of the positive equilibrium point (1.0513,1.3793) and the onset of Hopf bifurcation of system (5.2), we choose two unequal delay values. Let σ=0.045<σ0=0.0583, we get the computer simulation results that are presented in Figure 5. According to Figure 1, one can distinctly see that the equilibrium point (1.0513,1.3793) keeps locally asymptotically stable situation. Figure 5 includes 4 subfigures. Subfigure 1 of Figure 1 shows that the state variable u1→1.0513 when the time increases. Subfigure 2 of Figure 5 implies that the state variable u2→1.3793 when the time increases. Subfigure 3 of Figure 5 manifests the numerical relation of u1 and u2. Subfigure 4 of Figure 5 display the numerical relation of t-u1-u2. Let σ=0.075>σ0=0.0583, we obtain the computer simulation results which are presented in Figure 6. According to Figure 6, we can clearly see that a Hopf bifurcation arises around the positive equilibrium point (1.0513,1.3793). Figure 2 contains 4 subfigures. Subfigure 1 of Figure 6 implies that the state variable u1 will keep a periodic oscillatory level around the value 1.0513 when the time increases. Subfigures 2 of Figure 6 implies that the state variable u2 will keep a periodic oscillatory state near the value 1.3793 when the time increases. Subfigures 3 of Figure 6 manifests the numerical relation of u1 and u2. Subfigures 4 of Figure 6 displays the numerical relation of t-u1-u2. The correlation for μ, ϱ0 and σ0 is listed in Table 2. Also, the bifurcation plots are presented to show that the bifurcation value is approximately equal to 0.0583 (see Figures 7 and 8).
μ | ϱ0 | σ0 |
0.25 | 1.5209 | 0.0278 |
0.38 | 1.4155 | 0.0357 |
0.43 | 1.2376 | 0.0433 |
0.55 | 0.9904 | 0.0502 |
0.67 | 0.9012 | 0.0583 |
0.73 | 0.8155 | 0.0624 |
0.82 | 0.7466 | 0.0743 |
0.90 | 0.6123 | 0.0829 |
0.96 | 0.5842 | 0.0925 |
Remark 5.1 From the computer numerical simulation results of Example 5.1 and Example 5.2, we know that the stability region of system (5.1) is [0,σ0=0.0332) and the Hopf bifurcation value is 0.0332, the stability region of system (5.2) is [0,σ0=0.0583) and the Hopf bifurcation value is 0.0583. Thus the stability region of system (5.1) is enlarged and the time of the onset Hopf bifurcation is postponed by designing a suitable delayed feedback controller.
Fractional-order differential system has displayed underlying application prospect in the economic sphere. Based on the previous publications, we propose a new fractional-order delayed financial crises contagions model. By virtue of the stability theory and bifurcation knowledge of fractional-order differential equation, we derive a novel delay-independent stability and bifurcation condition to remain the stability and generate Hopf bifurcation for the involved fractional-order delayed financial crises contagions model. Through designing an appropriate delayed feedback controller, we can successfully control the stability region and the time of onset of Hopf bifurcation for the involved fractional-order delayed financial crises model. The investigated fruits are helpful for us to grasp the inherent law of economic operation and then serve mankind effectively. Also, the research approach can be applied to control the dynamical peculiarity of numerous other dynamical models in lots of disciplines.
This work is supported by National Natural Science Foundation of China (No.61673008, No.62062018), Project of High-level Innovative Talents of Guizhou Province ([2016]5651), Key Project of Hunan Education Department (17A181), University Science and Technology Top Talents Project of Guizhou Province (KY[2018]047), Foundation of Science and Technology of Guizhou Province ([2019]1051), Guizhou University of Finance and Economics(2018XZD01). The authors would like to thank the referees and the editor for helpful suggestions incorporated into this paper.
The authors declare that they have no conflict of interest.
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μ | γ0 | σ0 |
0.18 | 0.9713 | 0.2109 |
0.23 | 0.8107 | 0.2655 |
0.36 | 0.7861 | 0.2872 |
0.48 | 0.6723 | 0.0301 |
0.67 | 0.5091 | 0.0332 |
0.76 | 0.4728 | 0.0457 |
0.81 | 0.4011 | 0.0504 |
0.89 | 0.3856 | 0.0609 |
0.94 | 0.2781 | 0.00821 |
μ | ϱ0 | σ0 |
0.25 | 1.5209 | 0.0278 |
0.38 | 1.4155 | 0.0357 |
0.43 | 1.2376 | 0.0433 |
0.55 | 0.9904 | 0.0502 |
0.67 | 0.9012 | 0.0583 |
0.73 | 0.8155 | 0.0624 |
0.82 | 0.7466 | 0.0743 |
0.90 | 0.6123 | 0.0829 |
0.96 | 0.5842 | 0.0925 |
μ | γ0 | σ0 |
0.18 | 0.9713 | 0.2109 |
0.23 | 0.8107 | 0.2655 |
0.36 | 0.7861 | 0.2872 |
0.48 | 0.6723 | 0.0301 |
0.67 | 0.5091 | 0.0332 |
0.76 | 0.4728 | 0.0457 |
0.81 | 0.4011 | 0.0504 |
0.89 | 0.3856 | 0.0609 |
0.94 | 0.2781 | 0.00821 |
μ | ϱ0 | σ0 |
0.25 | 1.5209 | 0.0278 |
0.38 | 1.4155 | 0.0357 |
0.43 | 1.2376 | 0.0433 |
0.55 | 0.9904 | 0.0502 |
0.67 | 0.9012 | 0.0583 |
0.73 | 0.8155 | 0.0624 |
0.82 | 0.7466 | 0.0743 |
0.90 | 0.6123 | 0.0829 |
0.96 | 0.5842 | 0.0925 |