
To study the biological control strategy of aphids, in this paper we propose host-parasitoid-predator models for the interactions among aphids, parasitic wasps and aphidophagous Coccinellids incorporating impulsive releases of Coccinellids, and then study the long-term control and limited time optimal control of aphids by adjusting release amount and release timing of Coccinellids. For the long-term control, the existence and stability of the aphid-eradication periodic solution are investigated and threshold conditions about the release amount and release period to ensure the ultimate extinction of the aphid population are obtained. For the limited-time control, three different optimal impulsive control problems are studied. A time rescaling technique and an optimization algorithm based on gradient are applied, and the optimal release amounts and timings of natural enemies are gained. Our simulations indicate that in the limited-time control, the optimal selection of release timing should be given higher priority compared with the release amount.
Citation: Mingzhan Huang, Shouzong Liu, Ying Zhang. Mathematical modeling and analysis of biological control strategy of aphid population[J]. AIMS Mathematics, 2022, 7(4): 6876-6897. doi: 10.3934/math.2022382
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To study the biological control strategy of aphids, in this paper we propose host-parasitoid-predator models for the interactions among aphids, parasitic wasps and aphidophagous Coccinellids incorporating impulsive releases of Coccinellids, and then study the long-term control and limited time optimal control of aphids by adjusting release amount and release timing of Coccinellids. For the long-term control, the existence and stability of the aphid-eradication periodic solution are investigated and threshold conditions about the release amount and release period to ensure the ultimate extinction of the aphid population are obtained. For the limited-time control, three different optimal impulsive control problems are studied. A time rescaling technique and an optimization algorithm based on gradient are applied, and the optimal release amounts and timings of natural enemies are gained. Our simulations indicate that in the limited-time control, the optimal selection of release timing should be given higher priority compared with the release amount.
The field of interval analysis is a subfield of set-valued analysis, which focuses on sets in mathematics and topology. Historically, Archimede's method included calculating the circumference of a circle, which is an example of interval enclosure. By focusing on interval variables instead of point variables, and expressing computation results as intervals, this method eliminates errors that cause misleading conclusions. An initial objective of the interval-valued analysis was to estimate error estimates for numerical solutions to finite state machines. In 1966, Moore [2], published the first book on interval analysis, which is credited with being the first to use intervals in computer mathematics in order to improve calculation results. There are many situations where the interval analysis can be used to solve uncertain problems because it can be expressed in terms of uncertain variables. In spite of this, interval analysis remains one of the best approaches to solving interval uncertain structural systems and has been used for over fifty years in mathematical modeling such as computer graphics [3], decision-making analysis [4], multi-objective optimization, [5], error analysis [40]. In summary, interval analysis research has yielded numerous excellent results, and readers can consult Refs. [7,8,9], for additional information.
Convexity has been recognized for many years as a significant factor in such fields as probability theory, economics, optimal control theory, and fuzzy analysis. On the other hand, generalized convexity of mappings is a powerful tool for solving numerous nonlinear analysis and applied analysis problems, including a wide range of mathematical physics problems. A number of rigorous generalizations of convex functions have recently been investigated, see Refs. [10,11,12,13]. An interesting topic in mathematical analysis is integral inequalities. Convexity plays a significant role in inequality theory. During the last few decades, generalized convexity has played a prominent role in many disciplines and applications of IVFS, see Refs. [14,15,16,17,18,19]. Several recent applications have addressed these inequalities, see Refs. [20,21,22]. First, Breckner describes the idea of continuity for IVFS, see Ref. [23]. Using the generalized Hukuhara derivative, Chalco-Cano et al. [24], and Costa et al. [25], derived some Ostrowski and Opial type inequality for IVFS, respectively. Bai et al. [26], formulated an interval-based Jensen inequality. First, Zhao [27], and co-authors established (H.H) and Jensen inequality using h-convexity for IVFS. In general, the traditional (H.H) inequality has the following definition:
Θ(g)+Θ(h)2≥1h−g∫hgΘ(γ)dγ≥Θ(g+h2). | (1.1) |
Because of the nature of its definition, it is the first geometrical interpretation of convex mappings in elementary mathematics, and has attracted a large amount of attention. Several generalizations of this inequality are presented here, see Refs. [28,29,30,31]. Initially, Awan et al. explored (h1,h2)-convex functions and proved the following inequality [32]. Several authors have developed H.H and Jensen-type inequalities utilizing (h1,h2)-convexity. Ruonan Liu [33] developed H.H inequalities for harmonically (h1,h2)-convex functions. Wengui Yang [34] developed H.H inequalities on the coordinates for (p1,h1)-(p2,h2)-convex functions. Shi et al. [35] developed H.H inequalities for (m,h1,h2)-convex functions via Riemann Liouville fractional integrals. Sahoo et al. [36] established H.H and Jensen-type inequalities for harmonically (h1,h2)-Godunova-Levin functions. Afzal et al. [37] developed these inequalities for a generalized class of Godunova-Levin functions using inclusion relation. An et al. [38] developed H.H type inequalities for interval-valued (h1,h2)-convex functions. Results are now influenced by less accurate inclusion relation and interval LU-order relation. For some recent developments using the inclusion relation for the generalized class of Godunova-Levin functions, see Refs. [39,40,44]. It is clear from comparing the examples presented in this literature that the inequalities obtained using these old partial order relations are not as precise as those obtained by using CR-order relation. As a result, it is critically important that we are able to study inequalities and convexity by using a total order relation. Therefore, we use Bhunia's [41], CR-order, which is total interval order relation. The notions of CR-convexity and CR-order relation were used by several authors in 2022, in an attempt to prove a number of recent developments in these inequalities, see Refs. [42,43]. Afzal et al. using the notion of the h-GL function, proves the following result [45].
Theorem 1.1. (See [45]) Consider Θ:[g,h]→RI+. Define h:(0,1)→R+ and h(12)≠0. If Θ∈SX(CR-h,[g,h],RI+) and Θ∈ IR[g,h], then
h(12)2Θ(g+h2)⪯CR1h−g∫hgΘ(γ)dγ⪯CR[Θ(g)+Θ(h)]∫10dxh(x). | (1.2) |
Also, by using the notion of the h-GL function Jensen-type inequality was also developed.
Theorem 1.2. (See [45]) Let ui∈R+, ji∈[g,h]. If h is non-negative super multiplicative function and Θ∈SX(CR-h,[g,h],RI+), then this holds :
Θ(1Ukk∑i=1uiji)⪯CRk∑i=1Θ(ji)h(uiUk). | (1.3) |
In addition, it introduces a new concept of interval-valued GL-functions pertaining to a total order relation, the Center-Radius order, which is unique as far as the literature goes. With the example presented in this article, we are able to show how CR-IVFS can be used to analyze various integral inequalities. In contrast to classical interval-valued analysis, CR-order interval-valued analysis differs from it. Using the concept of Centre and Radius, we calculate intervals as follows: MC=M_+¯M2 and MR=¯M−M_2, respectively, where M=[M_,ˉM]. Inspired by the concepts of interval valued analysis and the strong literature that has been discussed above with particular articles, see e.g., Zhang et al. [39], Bhunia and Samanta [41], Shi et al. [42], Liu et al. [43] and Afzal et al. [44,45], we introduced the idea of CR-(h1,h2)-GL function. By using this new concept we developed H.H and Jensen-type inequalities. The study also includes useful examples to back up its findings.
Finally, the article is designed as follows: In Section 2, preliminary is provided. The main problems and applications are provided in Section 3 and 4. Finally, Section 5 provides the conclusion.
As for the notions used in this paper but not defined, see Refs. [42,43,45]. It is a good idea to familiarize yourself with some basic arithmetic related to interval analysis in this section since it will prove very helpful throughout the paper.
[M]=[M_,¯M](x∈R, M_≦x≦¯M;x∈R) |
[N]=[N_,¯N](x∈R, N_≦x≦¯N;x∈R) |
[M]+[N]=[M_,¯M]+[N_,¯N]=[M_+N_,¯M+¯N] |
ηM=η[M_,¯M]={[ηM_,η¯M](η>0){0}(η=0)[η¯M,ηM_](η<0), |
where η∈R.
Let RI and R+I be the set of all closed and all positive compact intervals of R, respectively. Several algebraic properties of interval arithmetic will now be discussed.
Consider M=[M_,ˉM]∈RI, then Mc=¯M+M_2 and Mr=¯M−M_2 are the center and radius of interval M respectively. The CR form of interval M can be defined as:
M=⟨Mc,Mr⟩=⟨¯M+M_2,¯M−M_2⟩. |
Following are the order relations for the center and radius of intervals:
Definition 2.1. The CR-order relation for M=[M_,¯M]=⟨Mc,Mr⟩, N=[N_,¯N]=⟨Nc,Nr⟩∈RI represented as:
M⪯crN⟺{Mc<Nc,ifMc≠Nc;Mr≤Nr,ifMc=Nc. |
Note: For arbitrary two intervals M,N∈RI, we have either M⪯crN or N⪯crM.
Riemann integral operators for IVFS are presented here.
Definition 2.2 (See [45]) Let D:[g,h] be an IVF such that D=[D_,¯D]. Then D is Riemann integrable (IR) on [g,h] if D_ and ¯D are IR on [g,h], that is,
(IR)∫hgD(s)ds=[(R)∫hgD_(s)ds,(R)∫hg¯D(s)ds]. |
The collection of all (IR) IVFS on [g,h] is represented by IR([g,h]).
Shi et al. [42] proved that the based on CR-order relations, the integral preserves order.
Theorem 2.1. Let D,F:[g,h] be IVFS given by D=[D_,¯D] and F=[F_,¯F]. If D(s)⪯CRF(s), ∀ i∈[g,h], then
∫hgD(s)ds⪯CR∫hgF(s)ds. |
We'll now provide an illustration to support the aforementioned Theorem.
Example 2.1. Let D=[s,2s] and F=[s2,s2+2], then for s∈[0,1].
DC=3s2,DR=s2,FC=s2+1 and FR=1. |
From Definition 2.1, we have D(s)⪯CRF(s), s∈[0,1].
Since,
∫10[s,2s]ds=[12,1] |
and
∫10[s2,s2+2]ds=[13,73]. |
Also, from above Theorem 2.1, we have
∫10D(s)ds⪯CR∫10F(s)ds. |
Definition 2.3. (See [42]) Define h1,h2:[0,1]→R+. We say that Θ:[g,h]→R+ is called (h1,h2)-convex function, or that Θ∈SX((h1,h2),[g,h],R+), if ∀ g1,h1∈[g,h] and γ∈[0,1], we have
Θ(γg1+(1−γ)h1)≤ h1(γ)h2(1−γ)Θ(g1)+h1(1−γ)h2(γ)Θ(h1). | (2.1) |
If in (2.1) "≤" replaced with "≥" it is called (h1,h2)-concave function or Θ∈SV((h1,h2),[g,h],R+).
Definition 2.4. (See [42]) Define h1,h2:(0,1)→R+. We say that Θ:[g,h]→R+ is called (h1,h2)-GL convex function, or that Θ∈SGX((h1,h2),[g,h],R+), if ∀ g1,h1∈[g,h] and γ∈[0,1], we have
Θ(γg1+(1−γ)h1)≤Θ(g1)h1(γ)h2(1−γ)+Θ(h1)h1(1−γ)h2(γ). | (2.2) |
If in (2.2) "≤" replaced with "≥" it is called (h1,h2)-GL concave function or Θ∈SGV((h1,h2),[g,h],R+).
Now let's introduce the concept for CR-order form of convexity.
Definition 2.5. (See [42]) Define h1,h2:[0,1]→R+. We say that Θ:[g,h]→R+ is called CR−(h1,h2)-convex function, or that Θ∈SX(CR-(h1,h2),[g,h],R+), if ∀ g1,h1∈[g,h] and γ∈[0,1], we have
Θ(γg1+(1−γ)h1)⪯CR h1(γ)h2(1−γ)Θ(g1)+h1(1−γ)h2(γ)Θ(h1). | (2.3) |
If in (2.3) "⪯CR" replaced with "⪰CR" it is called CR-(h1,h2)-concave function or Θ∈SV(CR-(h1,h2),[g,h],R+).
Definition 2.6. (See [42]) Define h1,h2:(0,1)→R+. We say that Θ:[g,h]→R+ is called CR-(h1,h2)-GL convex function, or that Θ∈SGX(CR-(h1,h2),[g,h],R+), if ∀ g1,h1∈[g,h] and γ∈[0,1], we have
Θ(γg1+(1−γ)h1)⪯CRΘ(g1)h1(γ)h2(1−γ)+Θ(h1)h1(1−γ)h2(γ). | (2.4) |
If in (2.4) "⪯CR" replaced with "⪰CR" it is called CR-(h1,h2)-GL concave function or Θ∈SGV(CR-(h1,h2),[g,h],R+).
Remark 2.1. ● If h1=h2=1, Definition 2.6 becomes a CR-P-function [45].
● If h1(γ)=1h1(γ), h2=1 Definition 2.6 becomes a CR-h-convex function [45].
● If h1(γ)=h1(γ), h2=1 Definition 2.6 becomes a CR-h-GL function [45].
● If h1(γ)=1γs, h2=1 Definition 2.6 becomes a CR-s-convex function [45].
● If h(γ)=γs, Definition 2.6 becomes a CR-s-GL function [45].
Proposition 3.1. Consider Θ:[g,h]→RI given by [Θ_,¯Θ]=(ΘC,ΘR). If ΘC and ΘR are (h1,h2)-GL over [g,h], then Θ is called CR-(h1,h2)-GL function over [g,h].
Proof. Since ΘC and ΘR are (h1,h2)-GL over [g,h], then for each γ∈(0,1) and for all g1,h1∈[g,h], we have
ΘC(γg1+(1−γ)h1)⪯CRΘC(g1)h1(γ)h2(1−γ)+ΘC(h1)h1(1−γ)h2(γ), |
and
ΘR(γg1+(1−γ)h1)⪯CRΘR(g1)h1(γ)h2(1−γ)+ΘR(h1)h1(1−γ)h2(γ). |
Now, if
ΘC(γg1+(1−γ)h1)≠ΘC(g1)h1(γ)h2(1−γ)+ΘC(h1)h1(1−γ)h2(γ), |
then for each γ∈(0,1) and for all g1,h1∈[g,h],
ΘC(γg1+(1−γ)h1)<ΘC(g1)h1(γ)h2(1−γ)+ΘC(h1)h1(1−γ)h2(γ). |
Accordingly,
ΘC(γg1+(1−γ)h1)⪯CRΘC(g1)h1(γ)h2(1−γ)+ΘC(h1)h1(1−γ)h2(γ). |
Otherwise, for each γ∈(0,1) and for all g1,h1∈[g,h],
ΘR(νg1+(1−γ)h1)≤ΘR(g1)h1(γ)h2(1−γ)+ΘR(h1)h1(1−γ)h2(γ) |
⇒Θ(γg1+(1−γ)h1)⪯CRΘ(g1)h1(γ)h2(1−γ)+Θ(h1)h1(1−γ)h2(γ). |
Taking all of the above into account, and Definition 2.6 this can be written as
Θ(γg1+(1−γ)h1)⪯CRΘ(g1)h1(γ)h2(1−γ)+Θ(h1)h1(1−γ)h2(γ) |
for each γ∈(0,1) and for all g1,h1∈[g,h].
This completes the proof.
The next step is to establish the H.H inequality for the CR-(h1,h2)-GL function.
Theorem 3.1. Define h1,h2:(0,1)→R+ and h1(12)h2(12)≠0. Let Θ:[g,h]→RI+, if Θ∈SGX(CR-(h1,h2),[t,u],RI+) and Θ∈ IR[t,u], we have
[H(12,12)]2Θ(g+h2)⪯CR1h−g∫hgΘ(γ)dγ⪯CR[Θ(g)+Θ(h)]∫10dxH(x,1−x). |
Proof. Since Θ∈SGX(CR-(h1.h2),[g,h],RI+), we have
[H(12,12)]Θ(g+h2)⪯CRΘ(xg+(1−x)h)+Θ((1−x)g+xh). |
Integration over (0, 1), we have
[H(12,12)]Θ(g+h2)⪯CR[∫10Θ(xg+(1−x)h)dx+∫10Θ((1−x)g+xh)dx]=[∫10Θ_(xg+(1−x)h)dx+∫10Θ_((1−x)g+xh)dx,∫10¯Θ(xg+(1−x)h)dx+∫10¯Θ((1−x)g+xh)dx]=[2h−g∫hgΘ_(γ)dγ,2h−g∫hg¯Θ(γ)dγ]=2h−g∫hgΘ(γ)dγ. | (3.1) |
By Definition 2.6, we have
Θ(xg+(1−x)h)⪯CRΘ(g)h1(x)h2(1−x)+Θ(h)h1(1−x)h2(x). |
Integration over (0, 1), we have
∫10Θ(xg+(1−x)h)dx⪯CRΘ(g)∫10dxh1(x)h2(1−x)+Θ(h)∫10dxh1(1−x)h2(x). |
Accordingly,
1h−g∫hgΘ(γ)dγ⪯CR[Θ(g)+Θ(h)]∫10dxH(x,1−x). | (3.2) |
Now combining (3.1) and (3.2), we get required result
[H(12,12)]2Θ(t+u2)⪯CR1h−g∫hgΘ(γ)dγ⪯CR[Θ(g)+Θ(h)]∫10dxH(x,1−x). |
Remark 3.1. ● If h1(x)=h2(x)=1, Theorem 3.1 becomes result for CR- P-function:
12Θ(g+h2)⪯CR1h−g∫hgΘ(γ)dγ⪯CR [Θ(g)+Θ(h)]. |
● If h1(x)=h(x), h2(x)=1 Theorem 3.1 becomes result for CR-h-GL-function:
h(12)2Θ(g+h2)⪯CR1h−g∫hgΘ(γ)dγ⪯CR ∫10dxh(x). |
● If h1(x)=1h(x), h2(x)=1 Theorem 3.1 becomes result for CR-h-convex function:
12h(12)Θ(g+h2)⪯CR1h−g∫hgΘ(γ)dγ⪯CR ∫10h(x)dx. |
● If h1(x)=1h1(x), h2(x)=1h2(x) Theorem 3.1 becomes result for CR-(h1,h2)-convex function:
12[H(12,12)]Θ(g+h2)⪯CR1h−g∫hgΘ(γ)dγ⪯CR ∫10dxH(x,1−x). |
Example 3.1. Consider [t,u]=[0,1], h1(x)=1x, h2(x)=1, ∀ x∈ (0,1). Θ:[g,h]→RI+ is defined as
Θ(γ)=[−γ2,2γ2+1]. |
where
[H(12,12)]2Θ(g+h2)=Θ(12)=[−14,32], |
1h−g∫hgΘ(γ)dγ=[∫10(−γ2)dγ,∫10(2γ2+1)dγ]=[−13,53], |
[Θ(g)+Θ(h)]∫10dxH(x,1−x)=[−12,2]. |
As a result,
[−14,32]⪯CR[−13,53]⪯CR[−12,2]. |
This proves the above theorem.
Theorem 3.2. Define h1,h2:(0,1)→R+ and h1(12)h2(12)≠0. Let Θ:[g,h]→RI+, if Θ∈SGX(CR-(h1,h2),[t,u],RI+) and Θ∈ IR[g,h], we have
[H(12,12)]24Θ(g+h2)⪯CR△1⪯CR1h−g∫hgΘ(γ)dγ⪯CR△2 |
⪯CR{[Θ(g)+Θ(h)][12+1H(12,12)]}∫10dxH(x,1−x), |
where
△1=H(12,12)4[Θ(3g+h4)+Θ(3h+g4)], |
△2=[Θ(g+h2)+Θ(g)+Θ(h)2]∫10dxH(x,1−x). |
Proof. Take [g,g+h2], we have
Θ(g+g+h22)=Θ(3g+h2)⪯CRΘ(xg+(1−x)g+h2)H(12,12)+Θ((1−x)g+xg+h2)H(12,12). |
Integration over (0, 1), we have
Θ(3g+h2)⪯CR1H(12,12)[∫10Θ(xg+(1−x)g+h2)dx+∫10Θ(xg+h2+(1−x)h)dx] |
=1H(12,12)[2h−g∫g+h2gη(γ)dγ+2h−g∫g+h2gΘ(γ)dγ] |
=4H(12,12)[1h−g∫g+h2gΘ(γ)dγ]. |
Accordingly,
H(12,12)4Θ(3g+h2)⪯CR1h−g∫g+h2gΘ(γ)dγ. | (3.3) |
Similarly for interval [g+h2,h], we have
H(12,12)4Θ(3h+g2)⪯CR1h−g∫g+h2gΘ(γ)dγ. | (3.4) |
Adding inequalities (3.3) and (3.4), we get
△1=H(12,12)4[Θ(3g+h4)+Θ(3h+g4)]⪯CR[1h−g∫hgΘ(γ)dγ]. |
Now
[H(12,12)]24Θ(g+h2) |
=[H(12,12)]24Θ(12(3g+h4)+12(3h+g4)) |
⪯CR[H(12,12)]24[Θ(3g+h4)h(12)+Θ(3h+g4)h(12)] |
=H(12,12)4[Θ(3g+h4)+Θ(3h+g4)] |
=△1 |
⪯CRH(12,12)4{1H(12,12)[Θ(g)+Θ(g+h2)]+1H(12,12)[Θ(h)+Θ(g+h2)]} |
=12[Θ(g)+Θ(h)2+Θ(g+h2)] |
⪯CR[Θ(g)+Θ(h)2+Θ(g+h2)]∫10dxH(x,1−x) |
=△2 |
⪯CR[Θ(g)+Θ(h)2+Θ(g)H(12,12)+Θ(h)H(12,12)]∫10dxH(x,1−x) |
⪯CR[Θ(g)+Θ(h)2+1H(12,12)[Θ(g)+Θ(h)]]∫10dxH(x,1−x) |
⪯CR{[Θ(g)+Θ(h)][12+1H(12,12)]}∫10dxH(x,1−x). |
Example 3.2. Thanks to Example 3.1, we have
[H(12,12)]24Θ(g+h2)=Θ(12)=[−14,32], |
△1=12[Θ(14)+Θ(34)]=[−516,138], |
△2=[Θ(0)+Θ(1)2+Θ(12)]∫10dxH(x,1−x), |
△2=12([−14,32]+[−12,2]), |
△2=[−38,74], |
{[Θ(g)+Θ(h)][12+1H(12,12)]}∫10dxH(x,1−x)=[−12,2]. |
Thus we obtain
[−14,32]⪯CR[−516,138]⪯CR[−13,53]⪯CR[−38,74]⪯cr[−12,2]. |
This proves the above theorem.
Theorem 3.3. Let Θ,θ:[g,h]→RI+,h1,h2:(0,1)→R+ such that h1,h2≠0. If Θ∈SGX(CR-h1,[g,h],RI+), θ∈SGX(CR-h2,[g,h],RI+) and Θ,θ∈ IR[g,h] then, we have
1h−g∫hgΘ(γ)θ(γ)dγ⪯CRM(g,h)∫10dxH2(x,1−x)+N(g,h)∫10dxH(x,x)H(1−x,1−x), |
where
M(g,h)=Θ(g)θ(g)+Θ(h)θ(h),N(g,h)=Θ(g)θ(h)+Θ(h)θ(g). |
Proof. Conider Θ∈SGX(CR-h1,[g,h],RI+), θ∈SGX(CR-h2,[g,h],RI+) then, we have
Θ(gx+(1−x)h)⪯CRΘ(g)h1(x)h2(1−x)+Θ(h)h1(1−x)h2(x), |
θ(gx+(1−x)h)⪯CRθ(g)h1(x)h2(1−x)+θ(h)h1(1−x)h2(x). |
Then,
Θ(gx+(1−x)h)θ(tx+(1−x)u) |
⪯CRΘ(g)θ(g)H2(x,1−x)+Θ(g)θ(h)+Θ(g)θ(g)H2(1−x,x)+Θ(h)θ(h)H(x,x)H(1−x,1−x). |
Integration over (0, 1), we have
∫10Θ(gx+(1−x)h)θ(gx+(1−x)h)dx |
=[∫10Θ_(gx+(1−x)h)θ_(gx+(1−x)h)dx,∫10¯Θ(gx+(1−x)h)¯θ(gx+(1−x)h)dx] |
=[1h−g∫hgΘ_(γ)θ_(γ)dγ,1h−g∫hg¯Θ(γ)¯θ(γdγ]=1h−g∫hgΘ(γ)θ(γ)dγ |
⪯CR∫10[Θ(g)θ(g)+Θ(h)θ(h)]H2(x,1−x)dx+∫10[Θ(g)θ(h)+Θ(h)θ(g)]H(x,x)H(1−x,1−x)dx. |
It follows that
1h−g∫hgΘ(γ)θ(γ)dγ⪯CRM(g,h)∫10dxH2(x,1−x)+N(g,h)∫10dxH(x,x)H(1−x,1−x). |
Theorem is proved.
Example 3.3. Consider [g,h]=[1,2], h1(x)=1x, h2(x)=1 ∀ x∈ (0,1). Θ,θ:[g,h]→RI+ be defined as
Θ(γ)=[−γ2,2γ2+1],θ(γ)=[−γ,γ]. |
Then,
1h−g∫hgΘ(γ)θ(γ)dγ=[154,9], |
M(g,h)∫101H2(x,1−x)dx=M(1,2)∫10x2dx=[−7,7], |
N(g,h)∫101H(x,x)H(1−x,1−x)dx=N(1,2)∫10x(1−x)dx=[−156,156]. |
It follows that
[154,9]⪯CR[−7,7]+[−156,156]=[−192,192]. |
It follows that the theorem above is true.
Theorem 3.4. Let Θ,θ:[g,h]→RI+,h1,h2:(0,1)→R+ such that h1,h2≠0. If Θ∈SGX(CR-h1,[g,h],RI+), θ∈SGX(CR-h2,[g,h],RI+) and Θ,θ∈ IR[g,h] then, we have
[H(12,12)]22Θ(g+h2)θ(g+h2)⪯CR1h−g∫hgΘ(γ)θ(γ)dγ+M(g,h)∫10dxH(x,x)H(1−x,1−x)+N(g,h)∫10dxH2(x,1−x). |
Proof. Since Θ∈SGX(CR-h1,[g,h],RI+), θ∈SGX(CR-h2,[g,h],RI+), we have
Θ(g+h2)⪯CRΘ(gx+(1−x)h)H(12,12)+Θ(g(1−x)+xh)H(12,12), |
θ(g+h2)⪯CRθ(gx+(1−x)h)H(12,12)+θ(g(1−x)+xh)H(12,12). |
Then,
Θ(g+h2)θ(g+h2)⪯CR1[H(12,12)]2[Θ(gx+(1−x)h)θ(gx+(1−x)h)+Θ(g(1−x)+xh)θ(g(1−x)+xh)]+1[H(12,12)]2[Θ(gx+(1−x)h)θ(g(1−x)+xh)+Θ(g(1−x)+xh)θ(gx+(1−x)h)]+⪯CR1[H(12,12)]2[Θ(gx+(1−x)h)θ(gx+(1−x)h)+Θ(g(1−x)+(xh)θ(g(1−x)+xh)]+1[H(12,12)]2[(Θ(g)H(x,1−x)+θ(h)H(1−x,x))(θ(h)H(1−x,x)+θ(h)H(x,1−x))]+[(Θ(g)H(1−x,x)+Θ(h)H(x,1−x))(θ(g)H(x,1−x)+θ(h)H(1−x,x))]⪯CR1[H(12,12)]2[Θ(gx+(1−x)h)θ(gx+(1−x)h)+Θ(g(1−x)+xh)θ(g(1−x)+xh)]+1[H(12,12)]2[(2H(x,x)H(1−x,1−x))M(g,h)+(1H2(x,1−x)+1H2(1−x,x))N(g,h)]. |
Integration over (0,1), we have
∫10Θ(g+h2)θ(g+h2)dx=[∫10Θ_(g+h2)θ_(g+h2)dx,∫10¯Θ(g+h2)¯θ(g+h2)dx]=Θ(g+h2)θ(g+h2)dx⪯CR2[H(12,12)]2[1h−g∫hgΘ(γ)θ(γ)dγ]+2[H(12,12)]2[M(g,h)∫101H(x,x)H(1−x,1−x)dx+N(g,h)∫101H2(x,1−x)dx]. |
Multiply both sides by [H(12,12)]22 above equation, we get the required result
[H(12,12)]22Θ(g+h2)θ(g+h2)⪯CR1h−g∫hgΘ(γ)θ(γ)dγ+M(g,h)∫10dxH(x,x)H(1−x,1−x)+N(g,h)∫10dxH2(x,1−x). |
As a result, the proof is complete.
Example 3.4. Consider [g,h]=[1,2], h1(x)=1x, h2(x)=14, ∀ x∈ (0,1). Θ,θ:[g,h]→RI+ be defined as
Θ(γ)=[−γ2,2γ2+1],θ(γ)=[−γ,γ]. |
Then,
[H(12,12)]22Θ(g+h2)θ(g+h2)=18Θ(32)θ(32)=[−3332,3332], |
1h−g∫hgΘ(γ)θ(γ)dγ=[154,9], |
M(g,h)∫10dxH(x,x)H(1−x,1−x)=(16)M(1,2)∫10x(1−x)dx=[−56,56], |
N(g,h)∫10dxH2(x,1−x)=(16)N(1,2)∫10x2dx=[−80,80]. |
It follows that
[−3332,3332]⪯CR[154,9]+[−56,56]+[−80,80]=[−5294,145]. |
This proves the above theorem. Next, we will develop the Jensen-type inequality for CR-(h1,h2)-GL functions.
Theorem 4.1. Let ui∈R+, ji∈[g,h]. If h1,h2 is super multiplicative non-negative functions and if Θ∈SGX(CR-(h1,h2),[g,h],RI+). Then the inequality become as :
Θ(1Ukk∑i=1uiji)⪯CRk∑i=1[Θ(ji)H(uiUk,Uk−1Ek)], | (4.1) |
where Uk=∑ki=1ui
Proof. When k=2, then (4.1) holds. Suppose that (4.1) is also valid for k−1, then
Θ(1Ukk∑i=1uiji)=Θ(ukUkvk+k−1∑i=1uiUkji) |
⪯CRΘ(jk)h1(ukUk)h2(Uk−1Uk)+Θ(k−1∑i=1uiUkji)h1(Uk−1Uk)h2(ukUk) |
⪯CRΘ(jk)h1(ukUk)h2(Uk−1Uk)+k−1∑i=1[Θ(ji)H(uiUk,Uk−2Uk−1)]1h1(Uk−1Uk)h2(ukUk) |
⪯CRΘ(jk)h1(ukUk)h2(Uk−1Uk)+k−1∑i=1[Θ(ji)H(uiUk,Uk−2Uk−1)] |
⪯CRk∑i=1[Θ(ji)H(uiUk,Uk−1Uk)]. |
It follows from mathematical induction that the conclusion is correct.
Remark 4.1. ● If h1(x)=h2(x)=1, Theorem 4.1 becomes result for CR- P-function:
Θ(1Ukk∑i=1uiji)⪯CRk∑i=1Θ(ji). |
● If h1(x)=1h1(x), h2(x)=1h2(x) Theorem 4.1 becomes result for CR-(h1,h2)-convex function:
Θ(1Ukk∑i=1uiji)⪯CRk∑i=1H(uiUk,Uk−1Uk)Θ(ji). |
● If h1(x)=1x, h2(x)=1 Theorem 4.1 becomes result for CR-convex function:
Θ(1Ukk∑i=1uiji)⪯CRk∑i=1uiUkΘ(ji). |
● If h1(x)=1h(x), h2(x)=1 Theorem 4.1 becomes result for CR-h-convex function:
Θ(1Ukk∑i=1uiji)⪯CRk∑i=1h(uiUk)Θ(ji). |
● If h1(x)=h(x), h2(x)=1 Theorem 4.1 becomes result for CR-h-GL-function:
Θ(1Ukk∑i=1uiji)⪯CRk∑i=1[Θ(ji)h(uiUk)]. |
● If h1(x)=1(x)s, h2(x)=1 Theorem 4.1 becomes result for CR-s-convex function:
η(1Ukk∑i=1uiji)⪯CRk∑i=1(uiUk)sΘ(ji). |
A useful alternative for incorporating uncertainty into prediction processes is IVFS. The present study introduces the (h1,h2)-GL concept for IVFS using the CR-order relation. As a result of utilizing this new concept, we observe that the inequality terms derived from this class of convexity and pertaining to Cr-order relations give much more precise results than other partial order relations. These findings are generalized from the very recent results described in [37,42,43,45]. There are many new findings in this study that extend those already known. In addition, we provide some numerical examples to demonstrate the validity of our main conclusions. Future research could include determining equivalent inequalities for different types of convexity utilizing various fractional integral operators, including Katugampola, Riemann-Liouville and generalized K-fractional operators. The fact that these are the most active areas of study for integral inequalities will encourage many mathematicians to examine how different types of interval-valued analysis can be applied. We anticipate that other researchers working in a number of scientific fields will find this idea useful.
The authors declare that there is no conflict of interest in publishing this paper.
[1] | R. Singh, G. Singh, Aphids and their biocontrol, New York: Academic Press, 2016, 63–108. https://doi.org/10.1016/B978-0-12-803265-7.00003-8 |
[2] |
B. Zheng, J. Yu, J. Li, Modeling and analysis of the implementation of the Wolbachia incompatible and sterile insect technique for mosquito population suppression, SIAM J. Appl. Math., 81 (2021), 718–740. https://doi.org/10.1137/20M1368367 doi: 10.1137/20M1368367
![]() |
[3] |
B. Zheng, J. Yu, Existence and uniqueness of periodic orbits in a discrete model on Wolbachia infection frequency, Adv. Nonlinear Anal., 11 (2022), 212–224. https://doi.org/10.1515/anona-2020-0194 doi: 10.1515/anona-2020-0194
![]() |
[4] | B. Zheng, J. Li, J. Yu, One discrete dynamical model on Wolbachia infection frequency in mosquito populations, Sci. China Math., 2021. https://doi.org/10.1007/s11425-021-1891-7 |
[5] |
E. W. Evans, Multitrophic interactions among plants, aphids, alternate prey and shared natural enemies-a review, Eur. J. Entomol., 105 (2008), 369–380. https://doi.org/10.14411/eje.2008.047 doi: 10.14411/eje.2008.047
![]() |
[6] | G. A. Polis, C. A. Myers, R. D. Holt, The ecology and evolution of intraguild predation: Potential competitors that eat each other, Annu. Rev. Ecol. Sys., 20 (1989), 297–330. |
[7] |
J. A. Rosenheim, H. K. Kaya, L. E. Ehler, J. J. Marois, B. A. Jaffee, Intraguild predation among biological-control agents: Theory and evidence, Biol. Control, 5 (1995), 303–335. https://doi.org/10.1006/bcon.1995.1038 doi: 10.1006/bcon.1995.1038
![]() |
[8] |
J. Brodeur, J. A. Rosenheim, Intraguild interactions in aphid parasitoids, Entomol. Exp. Appl., 97 (2000), 93–108. https://doi.org/10.1046/j.1570-7458.2000.00720.x doi: 10.1046/j.1570-7458.2000.00720.x
![]() |
[9] |
R. G. Colfer, J. A. Rosenheim, Predation on immature parasitoids and its impact on aphid suppression, Oecologia, 126 (2001), 292–304. https://doi.org/10.1007/s004420000510 doi: 10.1007/s004420000510
![]() |
[10] | W. E. Snyder, A. R. Ives, Generalist predators disrupt biological control by a specialist parasitoid, Ecology, 82 (2001), 705–716. |
[11] |
E. Bilu, M. Coll, The importance of intraguild interactions to the combined effect of a parasitoid and a predator on aphid population suppression, Biocontrol, 52 (2007), 753–763. https://doi.org/10.1007/s10526-007-9071-7 doi: 10.1007/s10526-007-9071-7
![]() |
[12] |
L. M. Gontijo, E. H. Beers, W. E. Snyder, Complementary suppression of aphids by predators and parasitoids, Biol. Control, 90 (2015), 83–91. https://doi.org/10.1016/j.biocontrol.2015.06.002 doi: 10.1016/j.biocontrol.2015.06.002
![]() |
[13] |
T. Nakazawa, N. Yamamura, Community structure and stability analysis for intraguild interactions among host, parasitoid, and predator, Popul. Ecol., 48 (2006), 139–149. https://doi.org/10.1007/s10144-005-0249-5 doi: 10.1007/s10144-005-0249-5
![]() |
[14] |
X. Y. Liang, Y. Z. Pei, M. X. Zhu, Y. F. Lv, Multiple kinds of optimal impulse control strategies on plant-pest-predator model with eco-epidemiology, Appl. Math. Comput., 287-288 (2016), 1–11. https://doi.org/10.1016/j.amc.2016.04.034 doi: 10.1016/j.amc.2016.04.034
![]() |
[15] |
Y. Z. Pei, M. M. Chen, X. Y. Liang, C. G. Li, X. Z. Meng, Optimizing pulse timings and amounts of biological interventions for a pest regulation model, Nonlinear Anal.-Hybrid Syst., 27 (2018), 353–365. https://doi.org/10.1016/j.nahs.2017.10.003 doi: 10.1016/j.nahs.2017.10.003
![]() |
[16] | S. Z. Liu, L. Yu, M. Z. Huang, X. Shi, Studies on the optimization selection of injection timings and injection doses based on the integrated control effect of glucose, J. Xinyang Normal Univ. (Nat. Sci. Edit.), 33 (2020), 345–350. |
[17] |
M. Z. Huang, S. Z. Liu, X. Y. Song, Study of the sterile insect release technique for a two-sex mosquito population model, Math. Biosci. Eng., 18 (2021), 1314–1339. https://doi.org/10.3934/mbe.2021069 doi: 10.3934/mbe.2021069
![]() |
[18] |
M. Z. Huang, L. You, S. Z. Liu, X. Y. Song, Impulsive release strategies of sterile mosquitos for optimal control of wild population, J. Biol. Dyn., 15 (2021), 151–176. https://doi.org/10.1080/17513758.2021.1887380 doi: 10.1080/17513758.2021.1887380
![]() |
[19] | D. Bainov, P. Simeonov, Impulsive differential equations: Periodic solutions and applications, 1 Ed., New York: Routledge, 1993. https://doi.org/10.1201/9780203751206 |
[20] | A. Zhao, W. Yan, J. Yan, Existence of positive periodic solution for an impulsive delay differential equation, Singapore: World Scientific, 2003. https://doi.org/10.1142/9789812704283_0029 |
[21] |
X. Y. Song, Z. Y. Xiang, The prey-dependent consumption two-prey one-predator models with stage structure for the predator and impulsive effects, J. Theor. Biol., 242 (2006), 683–698. https://doi.org/10.1016/j.jtbi.2006.05.002 doi: 10.1016/j.jtbi.2006.05.002
![]() |
[22] |
M. Z. Huang, J. X. Li, X. Y. Song, H. J. Guo, Modeling impulsive injections of insulin analogues: Towards artificial pancreas, SIAM J. Appl. Math., 72 (2012), 1524–1548. https://doi.org/10.1137/110860306 doi: 10.1137/110860306
![]() |
[23] |
Y. Liu, K. L. Teo, L. S. Jennings, S. Wang, On a class of optimal control problems with state jumps, J. Optim. Theory Appl., 98 (1998), 65–82. https://doi.org/10.1023/A:1022684730236 doi: 10.1023/A:1022684730236
![]() |
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