ψij | name |
i+j | First Zagreb index |
1√ij | Randić index |
2√iji+j | GA index |
√i+j−2ij | ABC index |
1√i+j | Sum–connectivity index |
(ij)3(i+j−2)3 | AZI index |
2i+j | Harmonic index |
|i−j| | Albertson index |
√i2+j2 | Sombor index |
iji+j | ISI index |
In this paper, we investigate a stochastic nutrient-plankton model with impulsive control of the nutrient concentration and zooplankton population. Analytically, we find that the population size is nonnegative for a sufficiently long time. We derive some sufficient conditions for the existence of stable periodic oscillations, which indicate that the plankton populations will behave periodically. The numerical results show that the plankton system experiences a transition from extinction to the coexistence of species due to the emergence of impulsive control. Additionally, we observe that the nutrient pulse has a stronger relationship with phytoplankton growth than the zooplankton pulse. Although the frequency of impulsive control and appropriate environmental fluctuations can promote the coexistence of plankton populations, an excessive intensity of noise can result in the collapse of the entire ecosystem. Our findings may provide some insights into the relationships among nutrients, phytoplankton and zooplankton in a stochastic environment.
Citation: Xin Zhao, Lijun Wang, Pankaj Kumar Tiwari, He Liu, Yi Wang, Jianbing Li, Min Zhao, Chuanjun Dai, Qing Guo. Investigation of a nutrient-plankton model with stochastic fluctuation and impulsive control[J]. Mathematical Biosciences and Engineering, 2023, 20(8): 15496-15523. doi: 10.3934/mbe.2023692
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In this paper, we investigate a stochastic nutrient-plankton model with impulsive control of the nutrient concentration and zooplankton population. Analytically, we find that the population size is nonnegative for a sufficiently long time. We derive some sufficient conditions for the existence of stable periodic oscillations, which indicate that the plankton populations will behave periodically. The numerical results show that the plankton system experiences a transition from extinction to the coexistence of species due to the emergence of impulsive control. Additionally, we observe that the nutrient pulse has a stronger relationship with phytoplankton growth than the zooplankton pulse. Although the frequency of impulsive control and appropriate environmental fluctuations can promote the coexistence of plankton populations, an excessive intensity of noise can result in the collapse of the entire ecosystem. Our findings may provide some insights into the relationships among nutrients, phytoplankton and zooplankton in a stochastic environment.
In mathematics chemistry and biology, a chemical compound can be represented by a molecular graph by converting atoms to vertices and bonds to edges. One of the primary mission of QSAR/QSPR research is to accurately convert molecular graphs into numerical values. Graph theoretic invariants of molecular graphs are called molecular descriptors which can be utilized to simulate the structural information of molecules, in order to make worthwhile physical and chemical properties of these molecules can be acquired by single numerical values. Such kinds of molecular descriptors are also referred to as topological indices.
In the chemical literature, various topological indices relying only on vertex degrees of the molecular graphs can be utilized in QSPR/QSAR investigation on account of them can be obtained directly from the molecular architecture, and can be rapidly calculated for generous molecules (see [1,2]), and we call them VDB (vertex–degree–based) topological indices. To be more precise, for designated nonnegative real numbers {ψij} (1≤i≤j≤n−1), a VDB topological index of a an n-order (molecular) graph G is expressed as
TI(G)=∑1≤i≤j≤n−1mijψij, | (1.1) |
where mij is the amount of edges connecting an i-vertex and a j-vertex of G. A great deal of well–known VDB topological indices can be obtained by different ψij in expression (1.1). We list some VDB topological indices in Table 1.
ψij | name |
i+j | First Zagreb index |
1√ij | Randić index |
2√iji+j | GA index |
√i+j−2ij | ABC index |
1√i+j | Sum–connectivity index |
(ij)3(i+j−2)3 | AZI index |
2i+j | Harmonic index |
|i−j| | Albertson index |
√i2+j2 | Sombor index |
iji+j | ISI index |
The first Zagreb index [3] is the very first VDB topological index, as powerful molecular structure-descriptors [2], Zagreb indices can describe the peculiarities of the degree of branching in molecular carbon-atom skeleton. Thereafter, many VDB topological indices have been put forward to simulate physical, chemical, biological, and other attributes of molecules [4,5,6,7]. In 2021, Gutman [8] introduced a new VDB topological index named as the Sombor index which has a linear correlation with the entropy and the enthalpy of vaporization of octanes [9]. Das et al., give sharp bounds for Sombor index of graphs by means of some useful graph parameters and they reveal the relationships between the Sombor index and Zagreb indices of graphs [10]. Recently, Steiner Gutman index was introduced by Mao and Das [11] which incorporate Steiner distance of a connected graph G. Nordhaus-Gaddum-type results for the Steiner Gutman index of graphs were given in [12]. In 2022, Shang study the Sombor index and degree-related properties of simplicial networks [13]. For more details of VDB topological indices, one can see [3,14,15,16,17,18,19,20,21,22,23,24,25,26] and the books [27,28,29].
Fluoranthene is a eminent conjugated hydrocarbon which abound in coal tar [30]. A fluoranthene–type benzenoid system (f-benzenoid for short) is formed from two benzenoid units joined by a pentagon [31,32]. The ordinary structure modality of a f-benzenoid F is shown in Figure 1, where segments X and Y are two benzenoid systems. Each f-benzenoid possesses exactly one pentagon [32]. More and more attention is paid to f-benzenoids after the flash vacuum pyrolysis experiments of these nonalternant polycyclic aromatic hydrocarbons [33].
In the whole article, the terminology and notation are chiefly derived from [34,35,36,37,38,39,40,41]. A vertex of degree k is called a k-vertex, and an edge linking a k-vertex and a j-vertex is designated as a (k,j)-edge. Let nk be the number of k-vertices and let mkj be the number of (k,j)-edges in the molecular graph G. A benzenoid system without internal vertices is said to be catacondensed. Analogously, a f-benzenoid F containing a unique internal vertex is referred to as catacatacondensed. We use h-hexagon benzenoid system (or h-hexagon f-benzenoid) to represent a benzenoid system (or f-benzenoid) containing h hexagons.
Let Lh represent the h-hexagon linear chain (as shown in Figure 2(a)). An f-benzenoid FLh (h≥3) obtaining from pieces X=L2 and Y=Lh−2 is named as f-linear chain (as shown in Figure 2(b)).
A fissure (resp. bay, cove, fjord and lagoon) of a f-benzenoid F is a path of degree sequences (2,3,2) (resp. (2,3,3,2), (2,3,3,3,2), (2,3,3,3,3,2) and (2,3,3,3,3,3,2)) on the perimeter of F (see Figure 3). Fissures, bays, coves, fjords and lagoons are said to be different kinds of inlets and their number are signified by f, B, C, Fj and L, respectively [32,37]. Inlets determine many electronic and topological properties of f-benzenoids. Then, it can be found that f+2B+3C+4FJ+5L is the number of 3-vertices on the perimeter of F. It is noted that lagoons cannot occur in the theory of benzenoid systems. For convenience, let r=f+B+C+Fj+L to represent the total number of inlets and b=B+2C+3Fj+4L is referred to as the quantity of bay regions, In addition, b is exactly the quantity of (3,3)-edges on the perimeter of F. It is obvious that b≥2 for any f-benzenoid F.
It is noted that any f-benzenoid F contains merely either 2-vertex or 3-vertex. The vertices not on the perimeter are said to be internal, and we use ni to represent their number.
Lemma 1.1. [32] Let F be an n-order, h-hexagon f-benzenoid with m edges and ni internal vertices. Then
(i) n=4h+5−ni;
(ii) m=5h+5−ni.
Lemma 1.2. [32] Let F be an n-order and h-hexagon f-benzenoid with r inlets, Then
(i) m22=n−2h−r;
(ii) m23=2r;
(iii) m33=3h−r.
From the perspective of mathematics and chemistry, finding the extremal values of some useful TI for significant classes of graphs is very interesting [14,19,23,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56].
As a matter of convenience, we use Γm to represent the collection of f-benzenoids containing exactly m edges. In [45], we derived extremal values for TI among all f-benzenoids with given order. It is noted that structure of f-benzenoids with given order is different from that of f-benzenoids with given number of edges. And we found that the technique for studying TI among all f-benzenoids with given order can not be used directly to investigate TI for all f-benzenoids with fixed number of edges. For this reason, we concentrate on the research of extremal values for TI among all f-benzenoids with given size.
The main idea of this work is to construct f-benzenoids owning maximal r and minimal h at the same time in Γm depending on the number m is congruent to 0,1,2,3 or 4 modulo 5. By making use of this technique, we obtain the extremum of TI over Γm and characterize their corresponding graphs on the basis of m is congruent to 0,1,2,3 or 4 modulo 5. Afterwards the extremums of some well-known TI over Γm can be got by use of the previous results.
The structure of this paper is as below. We first determine the maximal r in the set Γm in Section 2. By utilizing these results, we find the extremum of several famed TI over Γm in Section 3.
We will find the f-benzenoids with maximal r in Γm in this section. Figure 4 illustrates three f-benzenoids pertaining to Γ42.
At first, we try to obtain the maximum and minimum number of hexagons in any F∈Γm.
The spiral benzenoid system [57] Th is a benzenoid system whose structure is in a "spiral" manner as illustrated in Figure 5. Th has maximal ni in all h-hexagon benzenoid systems.
As a matter of convenience, let SHh (h≥3) represent the collection of f-benzenoids formed by two spiral benzenoids X and Y. Particularly, a f-spiral benzenoid is a f-benzenoid F∗∈SHh in which X=Th−1 and Y=T1 (as shown in Figure 6). It is easy to see that that
ni(F∗)=2h−⌈√12(h−1)−3⌉. |
In [40], we proved that for every F′∈SHh (h≥3), the inequality
ni(F′)≤ni(F∗) | (2.1) |
holds, and the following graph operations were introduced.
Operation 1. For any h-hexagon f-benzenoid F having two segments X and Y, let h1=h(X) and h2=h(Y). By substituting spiral benzenoid systems Th1 and Th2 for X and Y, severally, another f-benzenoid F′∈SHh can be obtained (as shown in Figure 7).
For any h-hexagon f-benzenoid F, when h=3, it is easily checked that
ni(F)=1=2×3−⌈√12(3−1)−3⌉. | (2.2) |
When h≥4, let h1=h(X) and h2=h(Y). Another F′∈SHh (as shown in Figure 7) in which X=Th1 and Y=Th2 can be acquired by applying Operation 1 to F. It is apparently that ni(X)≤ni(Th1), ni(Y)≤ni(Th2), therefore
ni(F)=ni(X)+ni(Y)+1≤ni(Th1)+ni(Th2)+1=ni(F′). | (2.3) |
So, the following Lemma can be deduced by Eqs (2.1) and (2.3).
Lemma 2.1. [41] Let F be an h(h≥3)-hexagon f-benzenoid. Then
ni(F)≤2h−⌈√12(h−1)−3⌉, | (2.4) |
and the equality is established when F is F∗.
For any F∈Γm, h(F) over Γm is variable. Sharp bounds for h(F) in Γm is given below.
Theorem 2.1. For any f-benzenoid F∈Γm,
⌈15(m−4)⌉≤h(F)≤m−1−⌈13(2m+√4m−31)⌉, | (2.5) |
where ⌈x⌉ is the smallest integer larger or equal to x.
Proof. On one hand, from Lemma 1.1 (ii) we know that m=5h(F)+5−ni(F). Combining the fact that ni(F)≥1 for any F∈Γm, we get
h(F)≥⌈15(m−4)⌉. |
On the other hand, by Lemma 2.1 we know that ni(F)≤ni(F∗). Consequently, from m=5h(F)+5−ni(F) we have
m−3h(F)−5≥⌈√12(h(F)−1)−3⌉≥√12(h(F)−1)−3. |
Hence,
(3h(F)+(3−m))2≥4m−31. |
Due to the fact that 3h(F)+(3−m)<0, we deduce
3h(F)+(3−m)≤−√4m−31, |
i.e., h(F)≤m−1−⌈13(2m+√4m−31)⌉.
Remark 1. Theorem 2.1 implies that f-spiral benzenoid F∗ has the maximal number of hexagons over Γm.
For the sake of obtaining the extremum TI among all f-benzenoids in Γm, we need to find the f-benzenoids F∈Γm possessing maximal r.
Recall that convex benzenoid systems (CBS for brevity) are a particular sort of benzenoid systems lack of bay regions [14]. Let HSh be the collection of benzenoid systems containing h hexagons.
Lemma 2.2. [42] Let H∈HSh. Under the below cases, H is definitely not a CBS:
(i) If h≥4 and ni=1;
(ii) If h≥5 and ni=2;
(iii) If h≥6 and ni=3.
Lemma 2.3. [52] Let H∈HSh such that ni(H)=4. Then H is bound to embody a subbenzenoid system given in Figure 8, there does not exist hexagons which are adjacent to fissures.
Lemma 2.4. Let S∈HSh. If h≥7 and ni(S)=4, then S is not a CBS.
Proof. Let S be an h (h≥7)-hexagon benzenoid system, ni(S)=4, then by Lemma 2.3 S must contain one of the benzenoid systems of the form given in Figure 7. The proof is carried out in two cases.
Case 1. If these four internal vertices form a path P4 or a K1,3, then S contains one of benzenoid systems (d)–(f) in Figure 7 as its subbenzenoid systems. It is noted that h≥7, by Lemma 2.2, it must not exist hexagons contiguous to the fissures, so, S has at least one hexagon contiguous to a (2,2)-edge, by means of such hexagons, it is succeeded in converting one of the fissures into a cove, bay or fjord. Hence, b(S)≥1.
Case 2. If these four internal vertices are not adjacent then S has possibility subbenzenoid systems as follows.
1) There exist one type (a) and one type (c) benzenoid systems in S;
2) There exist two type (b) benzenoid systems in S;
3) There exist two type (a) and one type (b) benzenoid systems in S.
4) There exist four type (a) benzenoid systems in S
By Lemma 2.2, neither hexagons may be adjacent to the fissures in any of the cases indicated above. Since h≥7, S has at least one hexagon contiguous to a (2,2)-edge, by means of such hexagons, it is succeeded in making one of the fissures become a cove, bay or fjord. Therefore, b(S)≥1.
The proof is completed.
Lemma 2.5. [45] Let F be an h-hexagon f-benzenoid. Then
1) If ni=1, then r(F)≤r(FLh)=2h−3 (h≥3);
2) If ni=2, then r(F)≤r(Gh)=2h−4 (h≥4);
3) If ni=3, then r(F)≤r(Rh)=2h−5 (h≥5);
4) If ni=4, then r(F)≤r(Zh)=2h−6 (h≥6).
Next we find the f-benzenoids with maximal r in Γm with a fixed ni. Recall that Mh, Nh and Qh (see Figure 9) are benzenoid systems, and Gh (see Figure 10), Rh (see Figure 11), Zh (see Figure 12) are f-benzenoids.
Lemma 2.6. [41] Let F be an h-hexagon f-benzenoid. Then
r(F)≤r(FLh)=2h−3. |
Lemma 2.7. [32] For any h-hexagon f-benzenoid including ni internal vertices and b bay regions, the number of (2,2)-edge and (2,3)-edge are m22=b+5,m23=4h−2ni−2b, respectively.
From Lemmas 1.2 (ii) and 8, we get
r=2h−ni−b | (2.6) |
Furthermore, by Lemma 1.1 (ii) and Eq (2.6), we deduce
r=m−3h−5−b | (2.7) |
Theorem 2.2. Let F be an h-hexagon f-benzenoid. If ni=5, then r(F)≤r(Uh)=2h−7 (h≥7).
Proof. Let h1=h(X) and h2=h(Y), X and Y are two segments of F. If ni=5, by the structure of f-benzenoid, equality ni(X)+ni(Y)=4 holds, so, we have the following five cases.
Case 1. ni(X)=1, ni(Y)=3, i.e., there exist one internal vertex and three internal vertices in X and Y, respectively.
Subcase 1.1. If h1=3, then X=M3.
Subcase 1.1.1. If h2=5, i.e., Y=Q5, then F is the f-benzenoid D1, D2 or D3 (see Figure 14). It is clear that r(F)=r(D1)=8≤2h−7, r(F)=r(D2)=7≤2h−7 or r(F)=r(D3)=8≤2h−7.
Subcase 1.1.2. If h2≥6, by Lemma 2.2 and the hypothesis that ni(Y)=3, Y is not a CBS, so b(Y)≥1. Furthermore, b(F)≥3, combining Eq (2.6) we obtain r=2h−ni−b≤2h−8<2h−7.
Subcase 1.2. If h1≥4, according to Lemma 2.2, X is definitely not a CBS, i.e., b(X)≥1.
Subcase 1.2.1. If h2=5, i.e., Y=Q5. It is clear that b(F)≥4, then Eq (2.6) deduces r≤2h−9<2h−7.
Subcase 1.2.2. If h2≥6, Y is definitely not not a CBS according to Lemma 2.2, so, b(Y)≥1. It is clear that b(F)≥5, consequently from Eq (2.6) we obtain r≤2h−10<2h−7.
Case 2. ni(X)=3 and ni(Y)=1.
Subcase 2.1. If h1=5, then X=Q5.
Subcase 2.1.1. If h2=3, i.e., Y=M3, then F is the f-benzenoid D4, D5, D6 (see Figure 14), or D7 (as shown in Figure 15). r(F)=r(D4)=8≤2h−7, r(F)=r(D5)=7≤2h−7, r(F)=r(D6)=8≤2h−7, r(F)=r(D7)=7≤2h−7.
Subcase 2.1.2. If h2≥4, Y is surely not a CBS in light of Lemma 2.2, i.e., b(X)≥1. Hence, we have b(F)≥4, it follows from Eq (2.6) that r≤2h−9<2h−7.
Subcase 2.2. If h1≥6, by Lemma 2.2, X is definitely not a CBS, hence b(X)≥1.
Subcase 2.2.1. If h2=3, i.e., Y=M3. We have b(F)≥4, and Eq (2.6) infers that r≤2h−9<2h−7.
Subcase 2.2.2. f h2≥4, by Lemma 2.2, Y is certainly not a CBS, i.e., b(X)≥1. Hence we have b(F)≥5, by Eq (2.6), r≤2h−10<2h−7.
Case 3. ni(X)=2, ni(Y)=2, i.e., X and Y both have two internal vertices.
Subcase 3.1. If h1=4, we note that ni(X)=2, so X must be the benzenoid system (b) in Figure 9.
Subcase 3.1.1. If h2=4, Y is surely the benzenoid system (b) in Figure 9 according to the hypothesis ni(Y)=2, therefore, F is D8 or D9 (as shown in Figure 15). We get r(F)=r(D8)=8<2h−7 or r(F)=r(D9)=7<2h−7.
Subcase 3.1.2. If h2≥5, by Lemma 2.2 and that ni(Y)=2, Y is not a CBS, so we know that b(X)≥1. Then b(F)≥4, by Eq (2.6) and the fact that ni=5, r≤2h−9<2h−7.
Subcase 3.2. If h2=4, we note that ni(Y)=2, so Y must be the benzenoid system (b) in Figure 8.
Subcase 3.2.1. If h1=4, X must also be the benzenoid system (b) in Figure 9. Hence, F is D8 or D9 (as shown in Figure 15). r(F)=r(D8)=8≤2h−7 or r(F)=r(D9)=7≤2h−7.
Subcase 3.2.2. If h1≥5, by Lemma 2.2 and ni(X)=2, X is definitely not a CBS, i.e., b(X)≥1. Hence, b(F)≥4, by Eq (2.6) and the fact that ni=5, we have r≤2h−9<2h−7.
Subcase 3.3. If h1≥5, h2≥5, it is noted that ni(X)=ni(Y)=2, neither X nor Y are definitely CBS according to Lemma 2.2. So, both b(X) and b(Y) are greater than 1. Hence, b(F)≥5, on the basis of Eq (2.6) we get r≤2h−10<2h−7.
Case 4. ni(X)=4 and ni(Y)=0, i.e., X contains four internal vertices, Y is a catacondensed benzenoid system.
Subcase 4.1. If h1=6, then X is the benzenoid system (d), (e) or (f) in Figure 9.
Subcase 4.1.1. If h2=1, F is the f-benzenoid D10, D11, D12 (see Figure 16), D13 (see Figure 17) or U7 (see Figure 12). r(F)=r(D10)=6≤2h−7, r(F)=r(D11)=6≤2h−7, r(F)=r(D12)=6≤2h−7, r(F)=r(D13)=6≤2h−7 or r(F)=r(U7)=7=2h−7.
Subcase 4.1.2. If h2≥2, we have b(F)≥2, by Eq (2.6), r≤2h−7.
Subcase 4.2. If h1≥7, in the light of Lemma 2.4, X is definitely not a CBS, hence b(Y)≥1. In this situation b(F)≥3, we get the inequality r≤2h−8<2h−7 according to Eq (2.6).
Case 5. ni(X)=0 and ni(Y)=4, i.e., X is a catacondensed benzenoid system, Y has four internal vertices.
Subcase 5.1. If h2=6, then Y is the benzenoid system (d), (e) or (f) in Figure 8.
Subcase 5.1.1. If h1=2, X must be the linear chain L2. In this event, F is D14, D15, D16, D17, D18, D19, D20 or D21 (see Figure 17). By further checking, we gain that r(F)=r(D14)=7≤2h−7, r(F)=r(D15)=8≤2h−7, r(F)=r(D16)=8≤2h−7, r(F)=r(D17)=7≤2h−7, r(F)=r(D18)=7≤2h−7, r(F)=r(D19)=8≤2h−7, r(F)=r(D20)=6≤2h−7 or r(F)=r(D21)=6≤2h−7.
Subcase 5.1.2. If h1≥3, bearing in mind that X is a catacondensed benzenoid system and Y is the benzenoid system (d), (e) or (f) in Figure 8, then F must have f-benzenoid D14, D15, D16, D17, D18, D19, D20 or D21 (see Figure 17) as its subgraph.
Subcase 5.1.2.1. If D14 is a subgraph in F, it is obvious that D14 has two coves. Since X is a catacondensed benzenoid system and h1≥3, F has at least one hexagon contiguous to a (2,2)-edge of X, and such hexagons can convert one fissure into a bay, or convert one cove into a fjord, or convert one fjord into a lagoon. In this instance b(F)≥4. Consequently, r≤2h−9<2h−7 can be got according to Eq (2.6).
Subcase 5.1.2.2. If D15, D16 or D19 is a subpart f-benzenoid in F, it is obvious each one of D15, D16 and D19 has a bay and a cove. Since X is a catacondensed benzenoid system and h1≥3, F contains at least one hexagon adjoining a (2,2)-edge of X, and such hexagons will make one fissure become a bay, or make one cove become a fjord, or make one fjord become a lagoon. Consequently, b(F)≥4, by Eq (2.6) it follows that r≤2h−9<2h−7.
Subcase 5.1.2.3. If D17 is a subpart f-benzenoid in F, it is obvious that D17 has a fjord and a bay. Since X is a catacondensed benzenoid system and h1≥3, F has at least one hexagon adjoining a (2,2)-edge of X, and such hexagons will convert one fissure into a bay, or convert one cove into a fjord, or convert one fjord into a lagoon. Consequently, b(F)≥4, by Eq (2.6) it follows that r≤2h−9<2h−7.
Subcase 5.1.2.4. If D18 is a subpart f-benzenoid in F, it is obvious that D18 has a fjord and two bays. Since X is a catacondensed benzenoid system and h1≥3, there exists has at least one hexagon adjoining a (2,2)-edge of X in F, and these hexagons will convert one of the fissures into a bay, or convert one cove into a fjord, or convert one fjord into a lagoon. Consequently, b(F)≥4, in light of Eq (2.6), r≤2h−9<2h−7.
Subcase 5.1.2.5. If D20 or D21 is a subpart f-benzenoid in F, it is obvious that both D20 and D21 have a bay and two fjords. Since X is a catacondensed benzenoid system and h1≥3, F contains at least one hexagon adjoining a (2,2)-edge of X, and such hexagons will make one fissure become a bay, or make one cove become a fjord, or make one fjord become a lagoon. Consequently, b(F)≥4, according to Eq (2.6), r≤2h−9<2h−7.
Subcase 5.2. If h2≥7, by Lemma 2.4 and the fact that ni(Y)=4, Y is certainly not a CBS, i.e., b(Y)≥1.
Subcase 5.2.1. If h1=2, i.e., X=L2. From the structure of f-benzenoid, F is formed from X and Y joined by a pentagon, it is easily seen that there are at least one bay or one cove arisen in the process of construction of F. It is clear that b(F)≥2, by Eq (2.6) we have r≤2h−7.
Subcase 5.2.2. If h1≥3, we know that F is formed by joining from X and Y through a pentagon, in this construction process of F, it is easily seen that there are at least one bay or one cove arisen. Then b(F)≥2, by Eq (2.6), r≤2h−7.
The proof is completed.
We recall that FLh is the f-linear chain with h hexagons [40]. Extremal f-benzenoids with maximal r in Γm were determined in the following theorem.
Theorem 2.3. Let F∈Γm. Then
1) If m≡0(mod5), then r(F)≤2m−355=r(Um5);
2) If m≡1(mod5), then r(F)≤2m−325=r(Zm−15);
3) If m≡2(mod5), then r(F)≤2m−295=r(Rm−25);
4) If m≡3(mod5), then r(F)≤2m−265=r(Gm−35);
5) If m≡4(mod5), then r(F)≤2m−235=r(FLm−45).
Proof. We know by Eq (2.5) that
⌈15(m−4)⌉≤h(F)≤m−1−⌈13(2m+√4m−31)⌉. |
1) If m≡0(mod5), then ⌈15(m−4)⌉=m5. If h=m5, then by Lemma 1.1 (ii)
m=5h(F)+5−ni(F)=m+5−ni(F), |
it means that ni(F)=5. Furthermore, Theorem 2.2 infers that r(F)≤r(Um5) and we are done. So assume now that h(F)≥m5+1, then by equality (2.7) and the fact that b(F)≥2
r(F)=m−5−3h(F)−b(F)≤m−5−3(m5+1)−b(F) |
≤2m5−10=2m−505≤2m−355=r(Um5). |
2) If m≡1(mod5), then ⌈15(m−4)⌉=m−15. If h(F)=m−15, then by Lemma 1.1 (ii)
m=5h(F)+5−ni(F)=m+4−ni(F), |
thus ni(F)=4. Then r(F)≤r(Zm−15) by part 4 of Lemma 2.5. Otherwise h(F)≥m−15+1, then by equality (2.7) and the obvious fact that b(F)≥2
r(F)=m−5−3h(F)−b(F)≤m−5−3(m−15+1)−b(F) |
≤2m+35−10=2m−475≤2m−325=r(Zm−15). |
3) If m≡2(mod5), then ⌈15(m−4)⌉=m−25. If h(F)=m−25, then by Lemma 1.1 (ii)
m=5h(F)+5−ni(F)=m+3−ni(F), |
and so ni(F)=3. Then r(F)≤r(Rm−25) by part 3 of Lemma 2.5. So assume now that h(F)≥m−25+1, then by Eq (2.7) and the fact that b(F)≥2
r(F)=m−5−3h(F)−b(F)≤m−5−3(m−25+1)−b(F) |
≤2m+65−10=2m−445≤2m−295=r(Rm−25). |
4) If m≡3(mod5), then ⌈15(m−4)⌉=m−35. If h(F)=m−35, then by Lemma 1.1 (ii)
m=5h(F)+5−ni(F)=m+2−ni(F), |
thus ni(F)=2. By Lemma 2.5, r(F)≤r(Gm−35) and we are done. If h(F)≥m−35+1, then by equality (2.7) and the fact that b(F)≥2
r(F)=m−5−3h(F)−b(F)≤m−5−3(m−35+1)−b(F) |
≤2m+95−10=2m−415≤2m−265=r(Gm−35). |
5) If m≡4(mod5), then ⌈15(m−4)⌉=m−45. Since h≥m−45 and b(F)≥2, then by Eq (2.7), we have
r(F)=m−5−3h(F)−b(F)≤m−5−3m−125−b(F) |
≤2m+125−7=2m−235=r(FLm−45). |
In this part, we attempt to find the extremal values of TI over Γm.
It is noted that a f-benzenoid F contains only 2-vertex and 3-vertex. Hence, equation (1.1) reduces to
TI(F)=m22ψ22+m23ψ23+m33ψ33, | (3.1) |
In the light of Lemmas 1.1 and 1.2,
TI(F)=ψ22m+3(ψ33−ψ22)h+(2ψ23−ψ22−ψ33)r, | (3.2) |
If U,V∈Γm then clearly
TI(U)−TI(V)=3(ψ33−ψ22)(h(U)−h(V)) +(2ψ23−ψ22−ψ33)(r(U)−r(V)). | (3.3) |
For convenience, we set s=ψ33−ψ22, q=2ψ23−ψ22−ψ33.
Theorem 3.1. For any F∈Γm, we have the following results.
a. If s≤0 and q≥0,
TI(F)≤{TI(Um5),if m≡0(mod 5)TI(Zm−15),if m≡1(mod 5)TI(Rm−25),if m≡2(mod 5)TI(Gm−35),if m≡3(mod 5)TI(FLm−45),if m≡4(mod 5) |
b. If s≥0 and q≤0,
TI(F)≥{TI(Um5),if m≡0(mod 5)TI(Zm−15),if m≡1(mod 5)TI(Rm−25),if m≡2(mod 5)TI(Gm−35),if m≡3(mod 5)TI(FLm−45),if m≡4(mod 5) |
Proof. Let F∈Γm. By Eq (2.5)
h(F)≥⌈15(m−4)⌉={h(Um5),if m≡0(mod 5)h(Zm−15),if m≡1(mod 5)h(Rm−25),if m≡2(mod 5)h(Gm−35),if m≡3(mod 5)h(FLm−45),if m≡4(mod 5) |
i.e., f-benzenoids Um5, Zm−15, Rm−25, Gm−35 and FLm−45 have minimal h over the set Γm. Meanwhile, by Theorem 2.3, we have
r(F)≤{r(Um5),if m≡0(mod 5)r(Zm−15),if m≡1(mod 5)r(Rm−25),if m≡2(mod 5)r(Gm−35),if m≡3(mod 5)r(FLm−45),if m≡4(mod 5) |
i.e., these five f-benzenoids have maximal number of inlets over Γm. Hence, for any f-benzenoids F∈Γm and V∈{Um5,Zm−15,Rm−25,Gm−35,FLm−45}, h(F)−h(V)≥0 and r(F)−r(V)≤0 hold simultaneously, from Eq (2.7), we have
TI(F)−TI(V)=3s(h(F)−h(V))+q(r(F)−r(V)). |
If s≤0 and q≥0, then TI(F)−TI(V)≤0, i.e., V reaches the maximum value of TI over Γm. If s≥0 and q≤0, then TI(F)−TI(V)≥0, i.e., V reaches the minimum value of TI over Γm. Furthermore, which V∈{Um5,Zm−15,Rm−25,Gm−35,FLm−45} is the extremal graph depending on m is congruent to 0,1,2,3 or 4 modulo 5.
Example 1. Values of s and q for several famous TI are listed in Table 2:
ij | 1√ij | 2√iji+j | 1√i+j | (ij)3(i+j−2)3 | √i+j−2ij | |
q | -1 | -0.0168 | -0.0404 | -0.0138 | -3.390 | 0.040 |
s | 5 | -0.1667 | 0 | -0.091 | 3.390 | -0.040 |
Therefore, the minimum extreme value of TI for the second Zagreb index, GA index and the AZI index can be determined in the light of Theorems 2.3 and 3.1, and we can obtain the maximum extreme value of TI for the ABC index.
If f-benzenoid F∈Γm, then from the Eqs (2.3) and (2.6) and Lemma 1.1(ii) we have
TI(F)=(2ψ23−ψ33)m+6(ψ33−ψ23)h−(2ψ23−ψ22−ψ33)b −5(2ψ23−ψ22−ψ33). | (3.4) |
Consequently, for f-benzenoids U,V∈Γm
TI(U)−TI(V)=6(ψ33−ψ23)(h(U)−h(V)) +(−2ψ23+ψ22+ψ33)(b(U)−b(V)). | (3.5) |
Set u=6(ψ33−ψ23) and keep in mind that q=2ψ23−ψ22−ψ33. Then
TI(U)−TI(V)=u(h(U)−h(V))−q(b(U)−b(V)). | (3.6) |
It is noted that Eq (3.6) can be decided only by h, b and the signs of u and q. For any F∈Γm, We know that
h(F)≤m−1−⌈13(2m+√4m−31)⌉, |
and the equality can be achieved precisely when F is the f-spiral benzenoid F∗ [41].
In [41], we proved that ni(F∗)=2h−⌈√12(h−1)−3⌉. But, b(F∗)≠2 may occur. It is noticeable if X in F∗ is a CBS, F∗ is a f-benzenoid satisfying that b(F∗)=2 or 3. For the sake of simplicity, Let N be the set of positive integers.
The CBS, W=H(l1,l2,l3,l4,l5,l6) (as shown in Figure 18), can be completely determined by the positive integers l1,l2,l3,l4 [14].
The following lemma gave requirements that there exists CBS with maximal ni [53].
Lemma 3.1. [53] Let h∈N. The conditions below are isovalent:
(a) There is a CBS W containing h hexagons and 2h+1−⌈√12h−3 ⌉ number of internal vertices.
(b) There exist l1,l2,l3,l4∈N satisfying the following equation
h=l1l3+l1l4+l2l3+l2l4−l2−l3−12l1(l1+1)−12l4(l4+1)+1⌈√12h−3 ⌉=l1+2l2+2l3+l4−3} | (3.7) |
If for h∈N, Eq (3.7) has a solution l1,l2,l3,l4∈N, then there is a CBS W meeting the conditions that ni(W)=ni(Th).
Now, we concentrate on the research for TI of f-benzenoids. For a h−1∈N, supposing that the system below
h−1=l1l3+l1l4+l2l3+l2l4−l2−l3−12l1(l1+1)−12l4(l4+1)+1⌈√12(h−1)−3 ⌉=l1+2l2+2l3+l4−3∃ li∈{l1,l2,l3,l4,l5,l6}, li=2} | (3.8) |
has a solution {l1,l2,l3,l4}, then a CBS Wh−1 containing ni(Wh−1)=2(h−1)+1−⌈√12(h−1)−3⌉ number of internal vertices exists. Note that li=2 in system (3.8), i.e., there exists one fissure on the side of li of Wh−1, let u,w,v in Figure 1 represent the three vertices of this fissure. Now, we obtain an f-spiral benzenoid F∗1 in which X=Wh−1 and Y=L1. It is obvious that
ni(F∗1)=2h−⌈√12(h−1)−3⌉ | (3.9) |
and b(F∗1)=2. (as shown in Figure 19)
Theorem 3.2. Let h−1∈N such that the Eq (3.8) has a solution, and m=3h+5+⌈√12(h−1)−3⌉. Then for any F∈Γm
1) TI(F∗1)≥TI(F), when u≥0 and q≥0;
2) TI(F∗1)≤TI(F), when u≤0 and q≤0.
Proof. From Lemma 1.1 (ii) and Eq (3.9), we have
m(F∗1)=5h+5−(2h−⌈√12(h−1)−3⌉)=3h+5+⌈√12(h−1)−3⌉ |
and so
h=m−1−⌈13(2m+√4m−31)⌉. |
It is obvious that b(F∗1)=2 and b(F)≥2 for any F∈Γm. Hence by Eq (3.6), we have
TI(F)−TI(F∗1)=u(h(F)−h(F∗1))−q(b(F)−b(F∗1)) |
=u[h(F)−(m−1−⌈13(2m+√4m−31)⌉)]−q[b(F)−2]. |
And by Eq (2.5)
h(F)≤m−1−⌈13(2m+√4m−31)⌉. |
If u≥0 and q≥0 then TI(F)−TI(F∗1)≤0, i.e., F∗1 achieves maximal TI in Γm. Similarly, if u≤0 and q≤0 then TI(F)−TI(F∗1)≥0, i.e., F∗1 obtains minimal TI in Γm.
Example 2. The values of u and q for some famous TI are listed in the following Table 3:
ij | 1√ij | 2√iji+j | 1√i+j | (ij)3(i+j−2)3 | √i+j−2ij | |
q | -1 | -0.0168 | -0.0404 | -0.0138 | -3.390 | 0.040 |
u | 18 | -0.449 | 0.121 | -0.233 | 20.344 | -0.242 |
Hence, by Theorem 3.1 we can deduce the minimal values of the Randć index and the the sum–connectivity index in f-spiral benzenoid F∗1 for those h such that Eq (3.8) holds.
Example 3. Take consideration of the generalized Randć index
Rα(G)=∑1≤i≤j≤n−1mij(ij)α, |
where α∈R. Note that
q=2(6α)−4α−9α=−4α((32)α−1)2≤0 |
for all α∈R. Moreover, s=9α−4α≥0 if and only if α≥0 if and only if u=6(9α−6α)≥0. Hence, by Theorem 3.1, the minimal value of Rα(G) is obtained for all α≥0, and for any α≤0, the minimal value of Rα(G) can be attained by the f-spiral benzenoid F∗1 for those h such that Eq (3.8) holds.
This work investigates extremum TI over the collection of f-benzenoids having same number of edges. In practical terms, there are many other types of very useful topological indices for instance graph energy [58,59,60,61,62], Wiener index [63], Randić energy [64], Wiener polarity index [65], incidence energy [66], Harary index [67], entropy measures [68,69] and HOMO-LUMO index [70]. So, determining these topological indices for f-benzenoids is going to be extraordinary fascinating.
It is noted that the current framework is for studying topological indices of deterministic networks. But random networks would be a very promising direction. In [71,72], the distance Estrada index of random graphs was discussed, and the author went deeply into (Laplacian) Estrada index for random interdependent graphs. So, studying VDB topological indices of random and random interdependent graphs is another interesting problem.
This work was supported by Ningbo Natural Science Foundation (No. 2021J234). The authors are very grateful to anonymous referees and editor for their constructive suggestions and insightful comments, which have considerably improved the presentation of this paper.
The authors declare there is no conflict of interest.
[1] |
D. M. Anderson, Prevention, control and mitigation of harmful algal blooms: Multiple approaches to HAB management, Harmful Algae Manage. Mitigation, 2004 (2004), 123–130. https://doi.org/10.1007/s10311-022-01457-2 doi: 10.1007/s10311-022-01457-2
![]() |
[2] |
B. Balaji-Prasath, Y. Wang, Y. P. Su, D. P. Hamilton, H. Lin, L. Zheng, et al., Methods to control harmful algal blooms: A review, Environ. Chem. Lett., 20 (2022), 3133–3152. https://doi.org/10.1007/s10311-022-01457-2 doi: 10.1007/s10311-022-01457-2
![]() |
[3] |
J. J. Gallardo-Rodríguez, A. Astuya-Villalón, A. Llanos-Rivera, V. Avello-Fontalba, V. Ulloa-Jofré, A critical review on control methods for harmful algal blooms, Rev. Aquacult., 11 (2019), 661–684. https://doi.org/10.1111/raq.12251 doi: 10.1111/raq.12251
![]() |
[4] |
D. M. Anderson, Approaches to monitoring, control and management of harmful algal blooms (HABs), Ocean Coastal Manage., 52 (2009), 342–347. https://doi.org/10.1016/j.ocecoaman.2009.04.006 doi: 10.1016/j.ocecoaman.2009.04.006
![]() |
[5] |
A. Burson, M. Stomp, E. Greenwell, J. Grosse, J. Huisman, Competition for nutrients and light: testing advances in resource competition with a natural phytoplankton community, Ecology, 99 (2018), 1108–1118. https://doi.org/10.1002/ecy.2187 doi: 10.1002/ecy.2187
![]() |
[6] |
D. W. Schindler, R. E. Hecky, D. L. Findlay, M. P. Stainton, B. R. Parker, M. J. Paterson, et al., Eutrophication of lakes cannot be controlled by reducing nitrogen input: Results of a 37-year whole-ecosystem experiment, Proc. Natl. Acad. Sci., 105 (2008), 11254–11258. https://doi.org/10.1073/pnas.0805108105 doi: 10.1073/pnas.0805108105
![]() |
[7] |
M. J. Vanni, Effects of nutrients and zooplankton size on the structure of a phytoplankton community, Ecology, 68 (1987), 624–635. https://doi.org/10.2307/1938467 doi: 10.2307/1938467
![]() |
[8] |
X. H. Ji, S. L. Yuan, T. H. Zhang, H. P. Zhu, Stochastic modeling of algal bloom dynamics with delayed nutrient recycling, Math. Biosci. Eng., 16 (2019), 1–24. https://doi.org/10.3934/mbe.2019001 doi: 10.3934/mbe.2019001
![]() |
[9] |
G. D. Liu, X. Z. Meng, S. Y. Liu, Dynamics for a tritrophic impulsive periodic plankton–fish system with diffusion in lakes, Math. Methods Appl. Sci., 44 (2021), 3260–3279. https://doi.org/10.1002/mma.6938 doi: 10.1002/mma.6938
![]() |
[10] |
P. M. Glibert, V. Kelly, J. Alexander, L. A. Codispoti, W. C. Boicourt, T. M. Trice, et al., In situ nutrient monitoring: A tool for capturing nutrient variability and the antecedent conditions that support algal blooms, Harmful Algae, 8 (2008), 175–181. https://doi.org/10.1016/j.hal.2008.08.013 doi: 10.1016/j.hal.2008.08.013
![]() |
[11] |
H. W. Han, R. S. Xiao, G. D. Gao, B. S. Yin, S. K. Liang, X. Q. lv, Influence of a heavy rainfall event on nutrients and phytoplankton dynamics in a well-mixed semi-enclosed bay, J. Hydrol., 617 (2023), 128932. https://doi.org/10.1016/j.jhydrol.2022.128932 doi: 10.1016/j.jhydrol.2022.128932
![]() |
[12] |
X. D. Li, X. Y. Yang, T. W. Huang, Persistence of delayed cooperative models: Impulsive control method, Appl. Math. Comput., 342 (2019), 130–146. https://doi.org/10.1016/j.amc.2018.09.003 doi: 10.1016/j.amc.2018.09.003
![]() |
[13] |
M. K. Alijani, H. Wang, J. J. Elser, Modeling the bacterial contribution to planktonic community respiration in the regulation of solar energy and nutrient availability, Ecol. Complexity, 23 (2015), 25–33. https://doi.org/10.1016/j.ecocom.2015.05.002 doi: 10.1016/j.ecocom.2015.05.002
![]() |
[14] |
C. J. Dai, M. Zhao, H. G. Yu, Dynamics induced by delay in a nutrient–phytoplankton model with diffusion, Ecol. Complexity, 26 (2016), 29–36. https://doi.org/10.1016/j.ecocom.2016.03.001 doi: 10.1016/j.ecocom.2016.03.001
![]() |
[15] |
P. Feketa, V. Klinshov, L. Lücken, A survey on the modeling of hybrid behaviors: How to account for impulsive jumps properly, Commun. Nonlinear Sci. Numer. Simul., 103 (2021), 105955. https://doi.org/10.1016/j.cnsns.2021.105955 doi: 10.1016/j.cnsns.2021.105955
![]() |
[16] | A. M. Samoilenko, N. A. Perestyuk, Impulsive Differential Equations, World Scientific, 1995. https://doi.org/10.1142/2892 |
[17] | T. Yang, Impulsive Control Theory, Springer Science & Business Media, 2001. |
[18] | V. Lakshmikantham, P. S. Simeonov, Theory of Impulsive Differential Equations, World Scientific, 1989. |
[19] |
H. Liu, C. J. Dai, H. G. Yu, Q. Guo, J. B. Li, A. M. Hao, et al., Dynamics of a stochastic non-autonomous phytoplankton–zooplankton system involving toxin-producing phytoplankton and impulsive perturbations, Math. Comput. Simul., 203 (2023), 368–386. https://doi.org/10.1016/j.matcom.2022.06.012 doi: 10.1016/j.matcom.2022.06.012
![]() |
[20] |
X. W. Yu, S. L. Yuan, T. H. Zhang, Survival and ergodicity of a stochastic phytoplankton–zooplankton model with toxin-producing phytoplankton in an impulsive polluted environment, Appl. Math. Comput., 347 (2019), 249–264. https://doi.org/10.1016/j.amc.2018.11.005 doi: 10.1016/j.amc.2018.11.005
![]() |
[21] |
D. Z. Li, Y. Liu, H. D. Cheng, Dynamic complexity of a phytoplankton-fish model with the impulsive feedback control by means of Poincaré map, Complexity, 2020 (2020), 8974763. https://doi.org/10.1155/2020/8974763 doi: 10.1155/2020/8974763
![]() |
[22] |
J. Yang, M. Zhao, A mathematical model for the dynamics of a fish algae consumption model with impulsive control strategy, J. Appl. Math., 2012 (2012), 452789. https://doi.org/10.1155/2012/452789 doi: 10.1155/2012/452789
![]() |
[23] |
W. Li, T. H. Zhang, Y. F. Wang, H. D. Cheng, Dynamic analysis of a plankton–herbivore state-dependent impulsive model with action threshold depending on the density and its changing rate, Nonlinear Dyn., 107 (2022), 2951–2963. https://doi.org/10.1007/s11071-021-07022-w doi: 10.1007/s11071-021-07022-w
![]() |
[24] |
S. Spatharis, G. Tsirtsis, D. B. Danielidis, T. Do Chi, D. Mouillot, Effects of pulsed nutrient inputs on phytoplankton assemblage structure and blooms in an enclosed coastal area, Estuarine Coastal Shelf Sci., 73 (2007), 807–815. https://doi.org/10.1016/j.ecss.2007.03.016 doi: 10.1016/j.ecss.2007.03.016
![]() |
[25] |
K. L. Cottingham, S. Glaholt, A. C. Brown, Zooplankton community structure affects how phytoplankton respond to nutrient pulses, Ecology, 85 (2004), 158–171. https://doi.org/10.1890/02-0570 doi: 10.1890/02-0570
![]() |
[26] |
J. dos Santos Severiano, V. L. dos Santos Almeida-Melo, E. M. de Melo-Magalhães, M. do Carmo Bittencourt-Oliveira, A. do Nascimento Moura, Effects of zooplankton and nutrients on phytoplankton: An experimental analysis in a eutrophic tropical reservoir, Mar. Freshwater Res., 68 (2016), 1061–1069. https://doi.org/10.1071/MF15393 doi: 10.1071/MF15393
![]() |
[27] |
Z. Zhao, C. G. Luo, L. Y. Pang, Y. Chen, Nonlinear modelling of the interaction between phytoplankton and zooplankton with the impulsive feedback control, Chaos Solitons Fractals, 87 (2016), 255–261. https://doi.org/10.1016/j.chaos.2016.04.011 doi: 10.1016/j.chaos.2016.04.011
![]() |
[28] |
H. J. Guo, L. S. Chen, X. Y. Song, Qualitative analysis of impulsive state feedback control to an algae-fish system with bistable property, Appl. Math. Comput., 271 (2015), 905–922. https://doi.org/10.1016/j.amc.2015.09.046 doi: 10.1016/j.amc.2015.09.046
![]() |
[29] |
C. R. Tian, S. G. Ruan, Pattern formation and synchronism in an allelopathic plankton model with delay in a network, SIAM J. Appl. Dyn. Syst., 18 (2019), 531–557. https://doi.org/10.1137/18M1204966 doi: 10.1137/18M1204966
![]() |
[30] |
N. K. Thakur, A. Ojha, D. Jana, R. K. Upadhyay, Modeling the plankton–fish dynamics with top predator interference and multiple gestation delays, Nonlinear Dyn., 100 (2020), 4003–4029. https://doi.org/10.1007/s11071-020-05688-2 doi: 10.1007/s11071-020-05688-2
![]() |
[31] |
Q. Guo, C. J. Dai, H. G. Yu, H. Liu, X. X. Sun, J. B. Li, et al., Stability and bifurcation analysis of a nutrient-phytoplankton model with time delay, Math. Methods Appl. Sci., 43 (2020), 3018–3039. https://doi.org/10.1002/mma.6098 doi: 10.1002/mma.6098
![]() |
[32] |
S. Q. Zhang, T. H. Zhang, S. L. Yuan, Dynamics of a stochastic predator-prey model with habitat complexity and prey aggregation, Ecol. Complexity, 45 (2021), 100889. https://doi.org/10.1016/j.ecocom.2020.100889 doi: 10.1016/j.ecocom.2020.100889
![]() |
[33] |
X. R. Mao, M. Glenn, R. Eric, Environmental Brownian noise suppresses explosion in population dynamics, Stochastic Process Their Appl., 97 (2002), 95–110. https://doi.org/10.1016/s0304-4149(01)00126-0 doi: 10.1016/s0304-4149(01)00126-0
![]() |
[34] |
F. Q. Deng, Q. Luo, X. R. Mao, S. L. Pang, Noise suppresses or expresses exponential growth, Syst. Control Lett., 57 (2008), 262–270. https://doi.org/10.1016/j.sysconle.2007.09.002 doi: 10.1016/j.sysconle.2007.09.002
![]() |
[35] |
Q. Guo, Y. Wang, C. J. Dai, L. J. Wang, H. Liu, J. B. Li, et al., Dynamics of a stochastic nutrient–plankton model with regime switching, Ecol. Modell., 477 (2023), 110249. https://doi.org/10.1016/j.ecolmodel.2022.110249 doi: 10.1016/j.ecolmodel.2022.110249
![]() |
[36] |
J. A. Freund, S. Mieruch, B. Scholze, K. Wiltshire, U. Feudel, Bloom dynamics in a seasonally forced phytoplankton–zooplankton model: Trigger mechanisms and timing effects, Ecol. Complexity, 3 (2006), 129–139. https://doi.org/10.1016/j.ecocom.2005.11.001 doi: 10.1016/j.ecocom.2005.11.001
![]() |
[37] |
J. P. DeLong, C. E. Cressler, Stochasticity directs adaptive evolution toward nonequilibrium evolutionary attractors, Ecology, 104 (2022), e3873. https://doi.org/10.1002/ecy.3873 doi: 10.1002/ecy.3873
![]() |
[38] |
H. Liu, C. J. Dai, H. G. Yu, Q, Guo, J. B. Li, A. M. Hao, et al., Dynamics induced by environmental stochasticity in a phytoplankton-zooplankton system with toxic phytoplankton, Math. Biosci. Eng., 18 (2021), 4101–4126. https://doi.org/10.3934/mbe.2021206 doi: 10.3934/mbe.2021206
![]() |
[39] |
X. W. Yu, S. L. Yuan, T. H. Zhang, Asymptotic properties of stochastic nutrient-plankton food chain models with nutrient recycling, Nonlinear Anal. Hybrid Syst., 34 (2019), 209–225. https://doi.org/10.1016/j.nahs.2019.06.005 doi: 10.1016/j.nahs.2019.06.005
![]() |
[40] |
X. M. Feng, J. X. Sun, L. Wang, F. Q. Zhang, S. L. Sun, Periodic solutions for a stochastic chemostat model with impulsive perturbation on the nutrient, J. Biol. Syst., 29 (2021), 849–870. https://doi.org/10.1142/S0218339021500200 doi: 10.1142/S0218339021500200
![]() |
[41] |
Q. Guo, C. J. Dai, L. J. Wang, H. Liu, Y. Wang, J. B. Li, et al., Stochastic periodic solution of a nutrient–plankton model with seasonal fluctuation, J. Biol. Syst., 30 (2022), 695–720. https://doi.org/10.1142/S0218339022500255 doi: 10.1142/S0218339022500255
![]() |
[42] |
C. J. Dai, H. G. Yu, Q. Guo, H. Liu, Q. Wang, Z. L. Ma, et al., Dynamics induced by delay in a nutrient-phytoplankton model with multiple delays, Complexity, 2019 (2019), 3879626. https://doi.org/10.1155/2019/3879626 doi: 10.1155/2019/3879626
![]() |
[43] |
D. Song, M. Fan, S. H. Yan, M. Liu, Dynamics of a nutrient-phytoplankton model with random phytoplankton mortality, J. Theor. Biol., 488 (2020), 110119. https://doi.org/10.1016/j.jtbi.2019.110119 doi: 10.1016/j.jtbi.2019.110119
![]() |
[44] |
H. Wang, M. Liu, Stationary distribution of a stochastic hybrid phytoplankton–zooplankton model with toxin-producing phytoplankton, Appl. Math. Lett., 101 (2020), 106077. https://doi.org/10.1016/j.aml.2019.106077 doi: 10.1016/j.aml.2019.106077
![]() |
[45] |
A. Mandal, P. K. Tiwari, S. Pal, A nonautonomous model for the effects of refuge and additional food on the dynamics of phytoplankton-zooplankton system, Ecol. Complexity, 46 (2021), 100927. https://doi.org/10.1016/j.ecocom.2021.100927 doi: 10.1016/j.ecocom.2021.100927
![]() |
[46] |
J. Chattopadhayay, R. R. Sarkar, S. Mandal, Toxin-producing plankton may act as a biological control for planktonic blooms—field study and mathematical modelling, J. Theor. Biol., 215 (2002), 333–344. https://doi.org/10.1006/jtbi.2001.2510 doi: 10.1006/jtbi.2001.2510
![]() |
[47] |
C. S. Holling, The components of predation as revealed by a study of small-mammal predation of the European pine sawfly, Can. Entomol., 91 (1959), 293–320. https://doi.org/10.4039/Ent91293-5 doi: 10.4039/Ent91293-5
![]() |
[48] |
A. M. Siepielski, A. Nemirov, M. Cattivera, A. Nickerson, Experimental evidence for an eco-evolutionary coupling between local adaptation and intraspecific competition, Am. Nat., 187 (2016), 447–456. https://doi.org/10.1086/685295 doi: 10.1086/685295
![]() |
[49] | R. M. May, Stability and Complexity in Model Ecosystems, Princeton university press, 2019. |
[50] |
Y. Zhang, S. H. Chen, S. J. Gao, X. Wei, Stochastic periodic solution for a perturbed non-autonomous predator–prey model with generalized nonlinear harvesting and impulses, Phys. A, 486 (2017), 347–366. http://dx.doi.org/10.1016/j.physa.2017.05.058 doi: 10.1016/j.physa.2017.05.058
![]() |
[51] |
W. J. Zuo, D. Q. Jiang, Periodic solutions for a stochastic non-autonomous Holling–Tanner predator–prey system with impulses, Nonlinear Anal. Hybrid Syst., 22 (2016), 191–201. http://dx.doi.org/10.1016/j.nahs.2016.03.004 doi: 10.1016/j.nahs.2016.03.004
![]() |
[52] |
S. W. Zhang, D. J. Tan, Dynamics of a stochastic predator–prey system in a polluted environment with pulse toxicant input and impulsive perturbations, Appl. Math. Modell., 39 (2015), 6319–6331. https://doi.org/10.1016/j.apm.2014.12.020 doi: 10.1016/j.apm.2014.12.020
![]() |
[53] |
N. Dalal, D. Greenhalgh, X. R. Mao, A stochastic model for internal HIV dynamics, J. Math. Anal. Appl., 341 (2008), 1084–1101. https://doi.org/10.1016/j.hal.2020.101845 doi: 10.1016/j.hal.2020.101845
![]() |
[54] |
Y. Zhang, S. H. Chen, S. J. Gao, X. Wei, Stochastic periodic solution for a perturbed non-autonomous predator–prey model with generalized nonlinear harvesting and impulses, Phys. A, 486 (2017), 347–366. https://doi.org/10.1016/j.physa.2017.05.058 doi: 10.1016/j.physa.2017.05.058
![]() |
[55] |
J. X. Zhao, Y. F. Shao, Stochastic periodic solution and permanence of a holling–leslie predator-prey system with impulsive effects, J. Math., 2021 (2021), 6694479. https://doi.org/10.1155/2021/6694479 doi: 10.1155/2021/6694479
![]() |
[56] | R. Khasminskii, Stochastic stability of differential equations, 2nd edition, Springer Science and Business Media, 2011. https://doi.org/10.1007/978-3-642-23280-0 |
[57] |
J. J. Elser, M. Kyle, L. Steger, K. R. Nydick, J. S. Baron, Nutrient availability and phytoplankton nutrient limitation across a gradient of atmospheric nitrogen deposition, Ecology, 90 (2009), 3062–3073. https://doi.org/10.1890/08-1742.1 doi: 10.1890/08-1742.1
![]() |
[58] | G. Borics, I. Grigorszky, S. Szabó, J. Padisák, Phytoplankton associations in a small hypertrophic fishpond in East Hungary during a change from bottom-up to top-down control, in Developments in Hydrobiology (eds. C. S. Reynolds, M. Dokulil and J. Padisák), 150 (2000), 79–90. https://doi.org/10.1007/978-94-017-3488-2_7 |
[59] |
Y. Kang, F. Koch, C. J. Gobler, The interactive roles of nutrient loading and zooplankton grazing in facilitating the expansion of harmful algal blooms caused by the pelagophyte, Aureoumbra lagunensis, to the Indian River Lagoon, FL, USA, Harmful Algae, 49 (2015), 162–173. https://doi.org/10.1016/j.hal.2015.09.005 doi: 10.1016/j.hal.2015.09.005
![]() |
[60] |
X. D. Wang, B. Q. Qin, G. Gao, H. W. Paerl, Nutrient enrichment and selective predation by zooplankton promote Microcystis (Cyanobacteria) bloom formation, J. Plankton Res., 32 (2010), 457–470. https://doi.org/10.1093/plankt/fbp143 doi: 10.1093/plankt/fbp143
![]() |
[61] |
A. P. Belfiore, R. P. Buley, E. G. Fernandez-Figueroa, M. F. Gladfelter, A. E. Wilson, Zooplankton as an alternative method for controlling phytoplankton in catfish pond aquaculture, Aquacult. Rep., 21 (2021), 100897. https://doi.org/10.1016/j.aqrep.2021.100897 doi: 10.1016/j.aqrep.2021.100897
![]() |
[62] |
D. M. Anderson, A. D. Cembella, G. M. Hallegraeff, Progress in understanding harmful algal blooms: paradigm shifts and new technologies for research, monitoring, and management, Ann. Rev. Mar. Sci., 4 (2012), 143–176. https://doi.org/10.1146/annurev-marine-120308-081121 doi: 10.1146/annurev-marine-120308-081121
![]() |
[63] |
V. Ittekkot, U. Brockmann, W. Michaelis, E. T. Degens, Dissolved free and combined carbohydrates during a phytoplankton bloom in the northern North Sea, Mar. Ecol. Progress Ser., 4 (1981), 299–305. https://doi.org/10.1016/j.amc.2015.09.046 doi: 10.1016/j.amc.2015.09.046
![]() |
[64] |
M. Rehim, Z. Z. Zhang, A. Muhammadhaji, Mathematical analysis of a nutrient–plankton system with delay, SpringerPlus, 5 (2016), 1055. https://doi.org/10.1186/s40064-016-2435-7 doi: 10.1186/s40064-016-2435-7
![]() |
[65] |
N. K. Thakur, A. Ojha, P. K. Tiwari, R. K. Upadhyay, An investigation of delay induced stability transition in nutrient-plankton systems, Chaos Solitons Fractals, 142 (2021), 110474. https://doi.org/10.1016/j.chaos.2020.110474 doi: 10.1016/j.chaos.2020.110474
![]() |
[66] |
S. R. J. Jang, J. Baglama, J. Rick, Nutrient-phytoplankton-zooplankton models with a toxin, Math. Comput. Modell., 43 (2006), 105–118. https://doi.org/10.1016/j.mcm.2005.09.030 doi: 10.1016/j.mcm.2005.09.030
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ψij | name |
i+j | First Zagreb index |
1√ij | Randić index |
2√iji+j | GA index |
√i+j−2ij | ABC index |
1√i+j | Sum–connectivity index |
(ij)3(i+j−2)3 | AZI index |
2i+j | Harmonic index |
|i−j| | Albertson index |
√i2+j2 | Sombor index |
iji+j | ISI index |
ij | 1√ij | 2√iji+j | 1√i+j | (ij)3(i+j−2)3 | √i+j−2ij | |
q | -1 | -0.0168 | -0.0404 | -0.0138 | -3.390 | 0.040 |
s | 5 | -0.1667 | 0 | -0.091 | 3.390 | -0.040 |
ij | 1√ij | 2√iji+j | 1√i+j | (ij)3(i+j−2)3 | √i+j−2ij | |
q | -1 | -0.0168 | -0.0404 | -0.0138 | -3.390 | 0.040 |
u | 18 | -0.449 | 0.121 | -0.233 | 20.344 | -0.242 |
ψij | name |
i+j | First Zagreb index |
1√ij | Randić index |
2√iji+j | GA index |
√i+j−2ij | ABC index |
1√i+j | Sum–connectivity index |
(ij)3(i+j−2)3 | AZI index |
2i+j | Harmonic index |
|i−j| | Albertson index |
√i2+j2 | Sombor index |
iji+j | ISI index |
ij | 1√ij | 2√iji+j | 1√i+j | (ij)3(i+j−2)3 | √i+j−2ij | |
q | -1 | -0.0168 | -0.0404 | -0.0138 | -3.390 | 0.040 |
s | 5 | -0.1667 | 0 | -0.091 | 3.390 | -0.040 |
ij | 1√ij | 2√iji+j | 1√i+j | (ij)3(i+j−2)3 | √i+j−2ij | |
q | -1 | -0.0168 | -0.0404 | -0.0138 | -3.390 | 0.040 |
u | 18 | -0.449 | 0.121 | -0.233 | 20.344 | -0.242 |