Loading [MathJax]/jax/output/SVG/jax.js
Review

Modeling free tumor growth: Discrete, continuum, and hybrid approaches to interpreting cancer development


  • Tumor growth dynamics serve as a critical aspect of understanding cancer progression and treatment response to mitigate one of the most pressing challenges in healthcare. The in silico approach to understanding tumor behavior computationally provides an efficient, cost-effective alternative to wet-lab examinations and are adaptable to different environmental conditions, time scales, and unique patient parameters. As a result, this paper explored modeling of free tumor growth in cancer, surveying contemporary literature on continuum, discrete, and hybrid approaches. Factors like predictive power and high-resolution simulation competed against drawbacks like simulation load and parameter feasibility in these models. Understanding tumor behavior in different scenarios and contexts became the first step in advancing cancer research and revolutionizing clinical outcomes.

    Citation: Dashmi Singh, Dana Paquin. Modeling free tumor growth: Discrete, continuum, and hybrid approaches to interpreting cancer development[J]. Mathematical Biosciences and Engineering, 2024, 21(7): 6659-6693. doi: 10.3934/mbe.2024292

    Related Papers:

    [1] Kaiqing Huang, Yizhi Chen, Miaomiao Ren . Additively orthodox semirings with special transversals. AIMS Mathematics, 2022, 7(3): 4153-4167. doi: 10.3934/math.2022230
    [2] Rukhshanda Anjum, Saad Ullah, Yu-Ming Chu, Mohammad Munir, Nasreen Kausar, Seifedine Kadry . Characterizations of ordered h-regular semirings by ordered h-ideals. AIMS Mathematics, 2020, 5(6): 5768-5790. doi: 10.3934/math.2020370
    [3] Huawei Huang, Xin Jiang, Changwen Peng, Geyang Pan . A new semiring and its cryptographic applications. AIMS Mathematics, 2024, 9(8): 20677-20691. doi: 10.3934/math.20241005
    [4] Waheed Ahmad Khan, Abdul Rehman, Abdelghani Taouti . Soft near-semirings. AIMS Mathematics, 2020, 5(6): 6464-6478. doi: 10.3934/math.2020417
    [5] Pakorn Palakawong na Ayutthaya, Bundit Pibaljommee . On n-ary ring congruences of n-ary semirings. AIMS Mathematics, 2022, 7(10): 18553-18564. doi: 10.3934/math.20221019
    [6] Saba Al-Kaseasbeh, Madeline Al Tahan, Bijan Davvaz, Mariam Hariri . Single valued neutrosophic (m,n)-ideals of ordered semirings. AIMS Mathematics, 2022, 7(1): 1211-1223. doi: 10.3934/math.2022071
    [7] Abdelghani Taouti, Waheed Ahmad Khan . Fuzzy subnear-semirings and fuzzy soft subnear-semirings. AIMS Mathematics, 2021, 6(3): 2268-2286. doi: 10.3934/math.2021137
    [8] B. Amutha, R. Perumal . Public key exchange protocols based on tropical lower circulant and anti circulant matrices. AIMS Mathematics, 2023, 8(7): 17307-17334. doi: 10.3934/math.2023885
    [9] Liaqat Ali, Yaqoub Ahmed Khan, A. A. Mousa, S. Abdel-Khalek, Ghulam Farid . Some differential identities of MA-semirings with involution. AIMS Mathematics, 2021, 6(3): 2304-2314. doi: 10.3934/math.2021139
    [10] Tariq Mahmood, Liaqat Ali, Muhammad Aslam, Ghulam Farid . On commutativity of quotient semirings through generalized derivations. AIMS Mathematics, 2023, 8(11): 25729-25739. doi: 10.3934/math.20231312
  • Tumor growth dynamics serve as a critical aspect of understanding cancer progression and treatment response to mitigate one of the most pressing challenges in healthcare. The in silico approach to understanding tumor behavior computationally provides an efficient, cost-effective alternative to wet-lab examinations and are adaptable to different environmental conditions, time scales, and unique patient parameters. As a result, this paper explored modeling of free tumor growth in cancer, surveying contemporary literature on continuum, discrete, and hybrid approaches. Factors like predictive power and high-resolution simulation competed against drawbacks like simulation load and parameter feasibility in these models. Understanding tumor behavior in different scenarios and contexts became the first step in advancing cancer research and revolutionizing clinical outcomes.



    The concept of semiring was firstly introduced by Dedekind in 1894, it had been studied by various researchers using techniques coming from semigroup theory or ring theory. The algebraic theories of semirings were widely applied in automata theory, optimization theory, parallel computation systems and the mathematical modeling of quantum physics, etc. [7]

    A semiring (S,+,) is an algebra with two binary operations + and such that the additive reduct (S,+) and the multiplicative reduct (S,) are semigroup connected by ring-like distributive laws, that is,

    a(b+c)=ab+acand(b+c)a=ba+ca, a,b,cS.

    In recent several decades, many authors extended the concepts and results of semigroups to semirings which is one of the development power of semiring theory. For instance, many researchers investigate idempotent semirings in which both additive reduct and multiplicative reduct are bands, which play a role in semirings just as the role of bands in semigroups in many aspects [2,4,17,18,19], etc. Semirings whose additive reduct is a band are also studied by many authors [3,11,25], etc. Karvellas introduced additively inverse semirings whose additive reduct is an inverse semigroup [10]. Zeleznikow studied regular semirings in which both additive and multiplicative semigroups are regular [27]. He also introduced the orthodox semirings firstly [28]. Grillet gave the structure theorem of semirings with a completely simple additive semigroup [6].

    Meanwhile, semirings are generalizations of distributive lattices, b-lattices, rings, skew-rings. Sen, Maity, and Shum extended the concept of Clifford semigroup to semiring by defining a class of semiring which is called Clifford semiring and showed that a semiring S is a Clifford semiring if and only if it is a strong distributive lattice of skew-rings [20]. What's more, as a further generalization, they proved that a semiring S is a generalized Clifford semiring if and only if it is a strong b-lattice of skew-rings. Sen and Maity had also extended completely regular semigroups to completely regular semirings by giving a gross structure theorem: A semiring S is completely regular semiring if and only if it is a b-lattice of completely simple semirings [21]. Pastijn and Guo used inspiration for the study of semirings which are disjoint unions of rings in theory developed for completely regular semiring [26]. Maity and Ghosh also extended completely regular semiring to quasi completely regular semirings, and show that S is quasi completely regular semirings if and only if S is an idempotent semiring of quasi skew-rings [13,14]. Since the ideas of transversals are important to study algebraic structures which is useful in the study of semigroups structure [15,16,23,24]. In 2022, Huang et al. introduced some special semiring transversals into semirings and extended the results of completely regular semirings [9].

    On the other hand, in the regular semigroups with inverse transversals, split orthodox semigroups are not only special regular semigroups with inverse transversals but also one of the origins of inverse transversals. In [12], D. B. McAlister and T. S. Blyth introduced a kind of semigroups which are called split orthodox semigroups and used a band, an inverse semigroup, and Munn morphism to give a structure theorem for them. El-Qallali studied the split quasi-adequate semigroups whose idempotents are commutative and extended the result of split orthodox semigroups [5]. Li extended split orthodox semigroups to split P-regular semigroups [22].

    To develop new ways to study semirings, we will study a kind of semirings called split additively orthodox semirings which have the property like split orthodox semigroups. It is also a class of special semirings with transversals. In this paper, after obtaining some properties theorems of such semirings, we obtain a structure theorem for them by idempotent semirings, additively inverse semirings, and Munn semigroup. Consequently, the corresponding results of Clifford semirings and generalized Clifford semirings in [20], and split orthodox semigroups in [12] are also extended and strengthened.

    For the terminology and notions not given in this paper, the reader is referred to [1,8].

    Firstly, we claim that the following theorem will be frequently used without further mention.

    Theorem 2.1. (Miller-Clifford theorem) [8]

    (1) Let e and f be D-equivalent idempotents of a semigroup S. Then each element a of ReLf has a unique inverse a in RfLe, be such that aa=e and aa=f.

    (2) Let a,b be elements of a semigroup S. Then abRaLb if and only if RbLa contains an idempotent.

    In this section, we will list some elementary results of split bands and split orthodox semigroups. The following results are all due to D. B. McAlister and T. S. Blyth. For convenience, throughout this section the letter D will always denote the Green's relation on a band B.

    Definition 2.1. (Definition 1.1 [12]) Let B={Bα:αY} be a band with structure semilattice Y and D-classes the rectangular bands Bα. If :BB/D is the natural morphism then we shall say that B is split if there is a morphism π:B/DB such that π=idB/D. Such a morphism π will be called a splitting morphism.

    Definition 2.2. (Definition 1.2 [12]) Let B={Bα:αY} be a band. Then by a skeleton of B shall mean a subset E={xα:αY} such that xαBα for every αY and xαxβ=xαβ=xβxα for all α,βY.

    Lemma 2.1. (Lemma 1.3 [12]) A band B is split if and only if it has a skeleton. If π:B/DB is splitting morphism then \rm{Im} π is a skeleton of B.

    We recall that the relation

    γ={(x,y)T×T:V(x)=V(y)}

    on an orthodox semigroup T turns out to be the smallest inverse semigroup congruence on T. Moveover, on B the band of idempotents of T, γ is the same to D.

    Definition 2.3. (Definition 1.4 [12]) Let T be an orthodox semigroup and let :TT/γ be natural morphism. Then we shall say that T is split if there is a morphism π:T/γT such that π=idT/γ.

    Definition 2.4. (Definition 1.5 [12]) Let T be an orthodox semigroup with band of idempotents B. Suppose that E is a D-transversal of B in that E meets every D-class only once. Then we define the span of E by

    Sp(E)={aT:(e,fE)eRaLf}.

    Lemma 2.2. (Lemma 1.6 [12]) Let T be an orthodox semigroup with band of idempotents B. Suppose that E is a D-transversal of B in that E meets every D-class only once. Then Sp (E) meets every γ-class of T exactly once.

    We denote the unique inverse of aSp(E) in Sp(E) by a. Moreover, note that e=aa and f=aa. Then

    Theorem 2.2. (Theorem 1.7 [12]) Let T be an orthodox semigroup with band of idempotents B and suppose that B has a skeleton E. Then the following statements are equivalent:

    (1) There is an inverse subsemigroup S of T that meets every γ-class of T exactly once and has E as semilattice of idempotents;

    (2) aEaE for every a Sp (E);

    (3) Sp (E) is a subsemigroup of T.

    Moreover, if (1) holds, then necessarily S= Sp (E).

    B is a band with a skeleton E. eE. Let θ be a band isomorphism between subbands of B of the form eBe. We say θ is skeleton-preserving if it satisfies that

    fθEfE,fDomθ.

    What is more, as shown in [12], if denote Dom(θ) and Im(θ) by eθBeθ and fθBfθ respectively, then

    eθϕ=(fθeϕ)θ1 and fθϕ=(fθeϕ)ϕ.

    Now, denote by TB the set of skeleton-preserving isomorphisms θ between subbands of B of the form eBe, where eE.

    Lemma 2.3. (Lemma 2.1 [12]) Let B be a band with a skeleton E. Then TB is an inverse semigroup.

    Lemma 2.4. (Lemma 2.2 [12]) Let B be a band with a skeleton E. For θTB, define ˉθ:BB as following:

    bB,bˉθ=(eθbeθ)θ.

    Then ¯θϕ=ˉθˉϕ, θ,ϕTB.

    Given θTB, let θ:eθBeθfθBfθ. It is clear that EDomθ=eθE and ECodθ=fθE. Moreover, θ induces an isomorphism ˆθ:eθEfθE. For every xeθE, we have xˆθ=xθ. The assignment ^:θˆθ is then a morphism from TB to TE (the Munn semigroup of semilattice E).

    Suppose now that S is an inverse semigroup with semilattice of idempotents E, let μ:aμa be a morphism from S to TE.

    If there exists a morphism θ:STB making the following diagram commutative,

    we call it is a triangulation of μ. Denote aθ=θa, then ˆθa=μa, μa:aaEaaE,eaea,eaaE.

    Lemma 2.5. (Lemma 2.3 [12]) Let S be an inverse semigroup with semilattice of idempotents E and let B be a band with skeleton E. For every aS, let the domain and codomain of μa be eaE (so that ea=aa) and faE (so that fa=aa). Let θ be a triangulation of μ. Then given a,bS and e,f,u,vB such that eLea, fRfa, uLeb, vRfb, we have

    e(fu)ˉθaLeab and (fu)ˉθbvRfab.

    Corollary 2.1. (Corollary 2.4 [12]) If

    W=W(B,S,θ)={(e,a,f)B×S×B:eLea,fRfa}

    then the prescription

    (e,a,f)(u,b,v)=(e(fu)ˉθa,ab,(fu)ˉθbv)

    defines a binary operation on W.

    Theorem 2.3. (Throrem 2.5 [12]) W(B,S,θ) is an orthodox semigroup whose band of idempotents is isomorphic to B.

    Theorem 2.4. (Theorem 2.7 [12]) The orthodox semigroup W=W(B,S,θ) is split and W/γS.

    Theorem 2.5. (Theorem 2.8 [12]) Let T be a split orthodox semigroup with band B of idempotents. If π:T/γT is a splitting morphism then the set E=B Imπ of idempotents of Imπ is a skeleton of B and Sp (E)= Imπ. Moreover, if θ: Im πTB is given by aθ=θa, where the domain of θa is aaBaa, the codomain of θa is aaBaa and bθa=aba, then θ is a triangulation of μ: Im πTB and

    TW(B,Imπ,θ).

    Let (S,+,) be a semiring. Then the Green's relation on (S, +) denoted by +R, +L and +H. The set of additive idempotents and the set of multiplicative idempotents of S are denoted by E+(S) and E(S) respectively.

    We also denote the additive inverse of xS by x, (x) by x, and all the additive inverses of x by +V(x) respectively.

    Lemma 3.1. Let (S,+,) be a semiring. x,yS, if x+V(x), then (xy)=xy and (yx)=yx.

    Proof: Clearly.

    In this section, we introduce the concept of split additively orthodox semirings. For convenience, T is denoted the additively orthodox semiring whose additive idempotents forms an idempotent semiring always. The letter +D will always denote the Green's relation on (+E(S),+). Since +D is congruence on (+E(S),+,) and every +D-class is an idempotent semiring for which the additive reduct is a rectangular band. Such idempotent semiring will be called additive rectangular idempotent semiring in this paper.

    T is an additively orthodox semiring, so (T, +) is an orthodox semigroup. Then we have the following corollary.

    Corollary 3.1. The relation

    γ={(x,y)T×T:+V(x)=+V(y)}

    is the smallest additively inverse semiring congruence on T.

    {Proof: }x,y,aT,xγy, then +V(x)=+V(y). (ax)+V(ax), (ax)=ax. Since x+V(x)=+V(y), then

    ax+ay+ax=a(x+y+x)=ax

    and

    ay+ax+ay=a(y+x+y)=ay.

    So +V(ax)+V(ay). Similarly, we can show the other side. Hence, γ is the semiring congruence on T.

    Moreover, γ is an additively inverse semiring congruence on T, since T/γ is an additively inverse semiring.

    Now, let ρ to be a semiring congruence on T such that T/ρ is an additively inverse semiring. For (x,y)γ, let a+V(x)(=+V(y)). Then both xρ and yρ are inverses of aρ in the additively inverse semiring T/ρ. So xρy which implies that γρ.

    Therefore, γ is the smallest additively inverse semiring congruence on T.

    Moveover, on an idempotent semiring I, γ is the same to +D, i.e. I/γ=I/+D which is b-lattice.

    Example 3.1. Let S1={0,a,b} whose additive and multiplicative Cayley tables as following:

    Then (S1,+,) forms an additively inverse semiring.

    Let I1={p,q} and Λ1={u,v} whose additive and multiplicative Cayley tables as following:

    It is easy to verify that (I1,+,) and (Λ1,+,) form idempotent semirings. The additive reduct of (I1,+,) is a left zero band, and its multiplicative reduct is a semilattice. The additive reduct of (Λ1,+,) is a right zero band, and its multiplicative reduct is a semilattice.

    Then the direct product T=I1×S1×Λ1 forms an additively regular semiring, and E+(T)=I1×E+(S1)×Λ1 is an idempotent semiring, hence T is an additively orthodox semiring.

    (i,x,y)T,

    (k,y,l)+V((i,x,j))y+V(x)y=x.

    It implies that

    (i,x,y)γ(k,y,l)y=x,(i,x,y),(k,y,l)T.

    So, T/γ={I1×{x}×Λ1|xS1}S1. Therefore, γ is the smallest additively inverse semiring congruence on T.

    Definition 3.1. Let T be an additively orthodox semiring whose additive idempotents forms an idempotent semiring and :TT/γ be natural morphism. Then we shall say that T is split if there is a morphism π:T/γT such that π=idT/γ.

    Some additively orthodox semirings are neither completely regular semirings nor split additively orthodox semirings, see the following example.

    Example 3.2. Let S1 as shown in Example 1. Then it is also a completely regular semiring. Although the set E+(S1)={0,b} is an ideal of S1, (S1,+,) is not a generalized Clifford semiring [20].

    Let I2={p,q} and Λ2={u,v} whose additive and multiplicative Cayley tables as following:

    We can verify that (I2,+,) and (Λ2,+,) form semirings. The additive reduct of (I2,+,) is a left zero band, and its multiplicative reduct is a group. The additive reduct of (Λ2,+,) is a right zero band, and its multiplicative reduct is a group.

    Then the direct product T=I2×S1×Λ2 forms an additively regular semiring, and E+(T)=I2×E+(S1)×Λ2 forms its subsemiring, hence T is an additively orthodox semiring. But E(T)={p}×E+(S1)×{u}, according to Lemma 2.5 in [21], thus (T,+,) is not a completely regular semiring. Since E+(T) is not an idempotent semiring, T is not a split additively orthodox semiring. Moreover, {(p,x,u)|xS1} is an additively inverse semiring transversal of T [9].

    A split additively orthodox semiring may not be a completely regular semiring, see the following example.

    Example 3.3. Let S2={0,i,a,e,f,x,y} whose additive and multiplicative Cayley tables as following:

    It is easy to verify that (S2,+,) forms an additively inverse semiring, but +Hx={x} is not a skew-ring, so S2 is not a completely regular semiring or a generalized Clifford semiring.

    Let I1 and Λ1 as shown in Example 3.1. Then the direct product T=I1×S2×Λ1 forms a additively regular semiring, and E+(T)=I1×E+(S2)×Λ1 is an idempotent semiring, hence T is an additively orthodox semiring. Additionally, T/γ={I1×{x}×Λ1|xS2}. Let π:T/γ{p}×S2×{u},I1×{x}×Λ1(p,x,u). It is easy to verify that π is a split morphism, so T is a split additively orthodox semiring. Moreover, Imπ is an additively inverse semiring transversal of T. But ImπS2 which means that it is not a generalized Clifford semiring transversal of T. In addition, +H(p,x,u)={(p,x,u)} is not a skew-ring, so T is not a completely regular semiring.

    A split additively orthodox semiring may be also a completely regular semiring, see the following example.

    Example 3.4. Let T as shown in Example 3.1. So T is an additively orthodox semiring, and T/γ={I1×{x}×Λ1|xS1}S1. Let π:T/γ{p}×S1×{u},I1×{x}×Λ1(p,x,u). It is easy to verify that π is a split morphism, so T is a split additively orthodox semiring. What is more, Imπ={(p,x,u)|xS1} is an additively inverse semiring transversal of T. But ImπS1 which means that it is not a generalized Clifford semiring transversal of T. Meanwhile, T is a completely regular semiring.

    Theorem 3.1. Let T be an additively orthodox semiring with a generalized Clifford semiring transversal S. Then the following statements are equivalent:

    (1) T is a completely regular semiring;

    (2) T is a split additively orthodox semiring.

    Proof: (1)(2): T is an additively orthodox semiring which means that E+(T) forms a band. Moreover, E+(T)E(T), since T is a completely regular semiring. Let π:T/γS,γxx,x+V(x)S. It is easy to verify that π is a split morphism.

    (2)(1): By Theorem 4.1 in [9], T is a b-lattice of additively orthodox semirings with skew-ring transversals. For each additively orthodox semiring with skew-ring transversals Sα, Sα is an additively completely simple semiring by Theorem 3.1 in [9]. Moreover, E+(T)E(T). Hence, Sα is a completely simple semiring. Therefore, T is a b-lattice of completely simple semiring which means that T is a completely regular semiring.

    By Definition 2.1 and Definition 3.1, if there is a split morphism π on the idempotent semiring I, then π is a split morphism on band (I,+).

    Definition 3.2. Let I={Iα:αY} be an idempotent semiring, where Y is a b-lattice. Then by a skeleton of I shall mean a subset E={xα:αY} such that xαIα for every αY, and xα+xβ=xα+β=xβ+xα and xαxβ=xαβ for all α,βY.

    Example 3.5. Let Y={0,e} whose additive and multiplicative Cayley tables as following:

    It is easy to verify that (Y,+,) forms a b-lattice. Let I1 and Λ1 as shown in Example 3.1. Then the direct product I=I1×Y×Λ1 forms an idempotent semiring.

    Additionally, I/γ={I1×{x}×Λ1|xY}Y. Denote I1×{x}×Λ1 by γx, then I=γ0γe.

    Let E={(p,0,u),(p,e,u)}. Then (p,0,u)γ0 and (p,e,u)γe. Moreover,

    (p,0,u)+(p,e,u)=(p,e,u)=(p,e,u)+(p,0,u),

    and

    (p,0,u)(p,e,u)=(p,0e,u)=(p,0,u),

    where (p,0,u)γ0e

    Therefore, E is a skeleton of I.

    By Definition 2.2 and Definition 3.2, we easily get that if E is a skeleton of I then (E, +) is a skeleton of (I, +).

    Lemma 3.2. An idempotent semiring I is split if and only if it has a skeleton. If π:I/+DI is splitting morphism then Imπ is a skeleton of I.

    Proof: By Lemma 2.1, Definition 3.1, and Definition 3.2, it is clear.

    From now on, we explore the structure of split additively orthodox semirings.

    Lemma 4.1. Let T be a split additively orthodox semiring whose additive idempotents forms an idempotent semiring I. Then E=I Imπ is a skeleton of I.

    Proof: Imπ is an additively inverse subsemiring of T, and meets every γ-class of T exactly once. As a result, E=IImπ is the set of all the additive idempotents of Imπ forms b-lattice, and E meets every +D-class of I exactly once, hence it is a skeleton of I.

    The span of E is also the important set:

    Sp(E)={aT:(e,fE)e+Ra+Lf}.

    By Lemma 2.2, the following lemma is obtained directly.

    Lemma 4.2. Let T be a split additively orthodox semiring whose additive idempotents forms an idempotent semiring I. Then Sp (E) meets every γ-class of T exactly once.

    Noting that for every aSp(E), exist e,fE satisfy that e+Ra+Lf, then +Le+Rf contains an additive inverse a of a, and aSp(E). By Lemma 4.2, we get that a is the unique inverse of a in Sp(E), which is denoted by a hereafter. Moreover, e=a+a and f=a+a. Actually, we can get more from the following theorem.

    Theorem 4.1. Let T be an additively orthodox semiring with an idempotent semiring of additive idempotent I and suppose that I has a skeleton E. Then the following statements are equivalent:

    (1) There is an additively inverse subsemiring S of T that meets every γ-class of T exactly once and has E as b-lattice of additive idempotents;

    (2) a+E+aE for every a\rm{Sp}(E);

    (3) Sp (E) is a subsemiring of T.

    Proof: (1)(2): By Theorem 2.2, it is clear.

    (2)(3): By Theorem 2.2, Sp(E) is close under addition. Now we show that it is close under multiplication. Given a,bSp(E), there exist ea,eb,fa,fbE such that ea+Ra+Lfa, and eb+Ra+Lfb. Since +R and +L are multiplicative congruence on S, then eaeb+Rab+Lfafb. Since eaeb,fafbE, it follows that Sp(E) is a subsemiring of T.

    (3)(1): If Sp(E) is a subsemiring, by the remarks following Lemma 9, Sp(E) is an additively orthodox subsemiring which meets every γ-class exactly once and has E as its set of additive idempotents.

    By Lemma 2.2, the following corollary is obtained directly.

    Corollary 4.1. Let T be a split additively orthodox semiring whose additive idempotents forms an idempotent semiring I. Then Sp (E)= Im π.

    For every aImπ, let ea=a+a, fa=a+a. Define θa:ea+I+eafa+I+fa by xθa=a+x+a. According to the proof of Theorem 2.5 (i.e. Theorem 2.8 in [12]), θa is a skeleton-preserving isomorphism from (ea+I+ea,+) to (fa+I+fa,+) satisfies that

    (fI)fθEfE.

    Denote all the skeleton-preserving isomorphisms φ between the subbands such as e+I+e of (I, +) by +TI, where eI. Then define the addition on +TI as composition of maps, i.e. e(φ+ϕ)(eφ)ϕ. By Lemma 2.3, we get that (+TI,+) is an inverse semigroup.

    Lemma 4.3. Let T be a split additively orthodox semiring whose additive idempotents forms an idempotent semiring I. E=I Imπ. TE is the Munn semigroup of (E,+). For μaTE, define θ: Imπ+TI by aθ=θa, where the domain of θa is a+a+I+a+a, and the codomain is a+a+I+a+a. Moreover, bθa=a+b+a, then θ is a triangulation of μ: ImπTE.

    Proof: According to the proof of Theorem 2.3 (i.e. Theorem 2.5 in [12]), the mapping θ:Imπ+TI defined by aθ=θa is a morphism from (Imπ,+) to (+TI,+). By the definition of θa, we obtain that ˆθa=μa, where μaTE. TE is the Munn semigroup of (E,+), and define eμa=a+e+a which is an isomorphism from (a+a+E,+) to (a+a+E,+). As a result, θ is a triangulation of μ:ImπTE.

    Moreover, we can extend each θ+TI to a mapping ˉθ:II by defining

    (bB)bˉθ=(eθ+b+eθ)θ.

    And define the addition between two maps as composition of maps. By Lemma 4, we get that

    (θ,ϕTB)¯θ+ϕ=ˉθ+ˉϕ.

    Theorem 4.2. Let S be an additively inverse semiring with b-lattice of additive idempotents E and I be an idempotent semiring with skeleton E. For every aS, let the domain and codomain of μaTE be ea+E (so that ea=a+a) and fa+E (so that fa=a+a). Let θ be a triangulation of μ. Then given a,bS and e,f,u,vI such that e+Lea, f+Rfa, u+Leb, v+Rfb, we have

    (1) (e+(f+u)ˉθa)+Lea+b and ((f+u)ˉθb+v)+Rfa+b;

    (2) ab+H(ab) and eab=fab=eafb=faeb=eaeb=fafb;

    (3) eu+Leab and fv+Rfab.

    Proof: (1) By Lemma 2.5, it is clear.

    (2) Since ea+La+Rfa and fb+Lb+Reb, then eafb+Lab+Rfaeb. Similarly, we can get that eafb+Rab+Lfaeb. By Lemma 6, ab=(ab)=ab. So eafb+L(ab)+Lfaeb and eafb+R(ab)+Rfaeb, that is eafb+H(ab)+Hfaeb. Similarly, we get that eaeb+Hab+Hfafb. Consequently, ab+H(ab) and eab=fab=eafb=faeb=eaeb=fafb.

    (3) Since e+Lea and u+Leb, then eu+Leaeb=eab. Similarly, fv+Rfab.

    Theorem 4.3. Let T be a split additively orthodox semiring whose additive idempotents forms an idempotent semiring I. For any a,b Imπ and e,f,u,vI such that e+Lea, f+Rfa, u+Leb, v+Rfb, then

    (e+a+f)(u+b+v)=eu+ab+fv.

    Proof: Firstly, on the one hand, since f+L(e+a+f)+Re and v+L(u+b+v)+Ru, then fv+L(e+a+f)(u+b+v)+Reu. On the other hand, since e+Lea, f+Rfa, u+Leb and v+Rfb, then eu+Leab and fv+Rfab, so fv+L(eu+ab+fv)+Reu. Hence, (e+a+f)(u+b+v)+H(eu+ab+fv).

    Secondly, since e+Lea+La and f+Rfa+Ra, then

    e+a+f+a+e+a+f=e+a+a+a+f=e+a+f

    and

    a+e+a+f+a=a+a+a=a,

    so a+V(e+a+f), i.e. a is the unique additive inverse of e+a+f in Imπ. Similarly, (u+b+v)=b and (eu+ab+fv)=(ab). Thus,

    ((e+a+f)(u+b+v))=(e+a+f)(u+b+v)=(e+a+f)b=(e+a+f)b=ab=(ab).

    Consequently, (e+a+f)(u+b+v)=eu+ab+fv as required.

    Theorem 4.4. Let T be a split additively orthodox semiring whose additive idempotents forms an idempotent semiring I. For any a,b,c Imπ and e,f,u,v,g,hI such that e+Lea, f+Rfa, u+Leb, v+Rfb, g+Lec, h+Rfc, then satisfies the following four equations:

    ge+g(f+u)ˉθa=ge+(hf+gu)ˉθca,
    h(f+u)ˉθb+hv=(hf+gu)ˉθcb+hv,
    eg+(f+u)ˉθag=eg+(fh+ug)ˉθaoc,

    and

    (f+u)ˉθbh+vh=(fh+ug)ˉθbc+vh.

    Proof: For any a,b,cImπ and e,f,u,v,g,hI such that e+Lea, f+Rfa, u+Leb, v+Rfb, g+Lec, h+Rfc. On the one hand, by Theorem 4.2, we get that

    (e+(f+u)ˉθa)+Lea+b and ((f+u)ˉθb+v)+Rfa+b,

    then

    g(e+(f+u)ˉθa)+Lecea+b and h((f+u)ˉθb+v)+Rfcfa+b,

    that is

    (ge+g(f+u)ˉθa)+Lec(a+b) and (h(f+u)ˉθb+hv)+Rfc(a+b),

    where

    ec(a+b)=c(a+b)+(c(a+b))=c(a+b)+c(a+b)

    and

    fc(a+b)=(c(a+b))+c(a+b)=c(a+b)+c(a+b).

    So,

    ge+g(f+u)ˉθa=ge+g(f+u)ˉθa+ec(a+b)=ge+g(f+u)ˉθa+c(a+b)+c(a+b)=ge+g(f+u)ˉθa+c(a+b)+fc(a+b)+c(a+b)=ge+g(f+u)ˉθa+c(a+b)+h(f+u)ˉθb+hv+fc(a+b)+c(a+b)=ge+g(f+u)ˉθa+c(a+b)+h(f+u)ˉθb+hv+c(a+b)=(g+c+h)(e+(f+u)ˉθa+a+b+(f+u)ˉθb+v)+c(a+b)=(g+c+h)(e+(fa+f+u+fa)θa+a+b+(eb+f+u+eb)θb+v)+c(a+b)=(g+c+h)(e+(a+fa+f+u+fa+a)+a+b+(b+eb+f+u+eb+b)+v)+c(a+b)=(g+c+h)(e+(a+f+u+a)+a+b+(b+f+u+b)+v)+c(a+b)=(g+c+h)(e+a+f+u+(a+a)+(b+b)+f+u+b+v)+c(a+b)=(g+c+h)(e+a+f+u+(b+b)+(a+a)+f+u+b+v)+c(a+b)=(g+c+h)(e+a+f+u+f+u+b+v)+c(a+b)=(g+c+h)(e+a+f+u+b+v)+c(a+b).

    On the other hand, since +L and +R are multiplicative congruence on T, then

    ge+Lecea=eca,
    hf+Rfcfa=fca,
    gu+Leceb=ecb,
    hv+Rfcfb=fcb.

    By Theorem 4.2, we get that

    (ge+(hf+gu)ˉθ(ca))+Leca+cb and ((hf+gu)ˉθcb+hv)+Rfca+cb,

    that is

    (ge+(hf+gu)ˉθca)+Leca+cb and ((hf+gu)ˉθcb+hv)+Rfca+cb,

    where

    eca+cb=ca+cb+(ca+cb)=ca+cb+(c(a+b))=c(a+b)+c(a+b)

    and

    fca+cb=(ca+cb)+c(a+b)=(c(a+b))+c(a+b)=c(a+b)+c(a+b).

    So,

    ge+(hf+gu)ˉθ(ca)=ge+(hf+gu)ˉθ(ca)+eca+cb=ge+(hf+gu)ˉθ(ca)+ca+cb+(ca+cb)=ge+(hf+gu)ˉθ(ca)+ca+cb+fca+cb+(ca+cb)=ge+(hf+gu)ˉθ(ca)+ca+cb+(hf+gu)ˉθcb+hv+fca+cb+(ca+cb)=ge+(fca+hf+gu+fca)θ(ca)+ca+cb+(ecb+hf+gu+ecb)θcb+hv+(ca+cb)=ge+(ca+hf+gu+(ca))+ca+cb+(cb+hf+gu+cb)+hv+(ca+cb)=ge+ca+hf+gu+((ca)+ca)+(cb+(cb))+hf+gu+cb+hv+(ca+cb)=ge+ca+hf+gu+(cb+(cb))+((ca)+ca)+hf+gu+cb+hv+(ca+cb)=ge+ca+hf+gu+ecb+fca+hf+gu+cb+hv+(ca+cb)=ge+ca+hf+gu+hf+gu+cb+hv+(ca+cb)=ge+ca+hf+gu+cb+hv+(ca+cb)=(g+c+h)(e+a+f)+(g+c+h)(u+b+v)+(ca+cb)=(g+c+h)[(e+a+f)+(u+b+v)]+(ca+cb)=(g+c+h)(e+a+f+u+b+v)+(ca+cb).

    Therefore,

    ge+g(f+u)ˉθa=ge+(hf+gu)ˉθca

    as required. Similarly, we can show the following three equations:

    h(f+u)ˉθb+hv=(hf+gu)ˉθcb+hv,
    eg+(f+u)ˉθag=eg+(fh+ug)ˉθaoc,

    and

    (f+u)ˉθbh+vh=(fh+ug)ˉθbc+vh.

    Theorem 4.5. Let S be an additively inverse semiring with b-lattice of additive idempotents E and I be an idempotent semiring with skeleton E. For every aS, let the domain and codomain of μaTE be ea+E (so that ea=a+a) and fa+E (so that fa=a+a). Let θ be a triangulation of μ. Then given a,bS and e,f,u,vI such that e+Lea, f+Rfa, u+Leb, v+Rfb, g+Lec, h+Rfc, and satisfies the following four equations:

    ge+g(f+u)ˉθa=ge+(hf+gu)ˉθca,
    h(f+u)ˉθb+hv=(hf+gu)ˉθcb+hv,
    eg+(f+u)ˉθag=eg+(fh+ug)ˉθaoc,

    and

    (f+u)ˉθbh+vh=(fh+ug)ˉθbc+vh.

    Define two binary operations

    (e,a,f)+(u,b,v)=(e+(f+u)ˉθa,a+b,(f+u)ˉθb+v)

    and

    (e,a,f)(u,b,v)=(eu,ab,fv)

    on

    W=W(I,S,θ)={(e,a,f)I×S×I:e+Lea,f+Rfa}.

    Then W is a split additively orthodox semiring whose additive idempotents form an idempotent semiring which is isomorphic to I, and W/γS.

    Conversely, every split additively orthodox semiring is of the form W(I,S,θ).

    Proof: By Theorem 2.3, (W,+) is an orthodox semigroup. And the associativity of multiplication is clear. We only need to prove the distributivity of the semiring W. Give (e,a,f),(u,b,v),(g,c,h)W, by the four equations,

    (g,c,h)[(e,a,f)+(u,b,v)]=(g,c,h)(e+(f+u)ˉθa,a+b,(f+u)ˉθb+v)=(g(e+(f+u)ˉθa),c(a+b),h((f+u)ˉθb+v))=(ge+g(f+u)ˉθa,ca+cb,h(f+u)ˉθb+hv)=(ge+(hf+gu)ˉθca,ca+cb,(hf+gu)ˉθcb+hv)=(ge,ca,hf)+(gu,cb,hv).

    Thus the distributivity on left is hold. And the distributivity on right can be proved similarly. Hence W is additively orthodox semiring as required.

    By the proof of Theorem 2.3 (i.e. Theorem 2.5 in [12]), we get that +E(W)={(e,a,f)W:a+E(S)}, moreover, the map ϕ defined from +E(W) to I by (e,a,f)ϕ=e+f is bijective and preserves addition. So we need to show it also preserves multiplication well. For any (e,a,f),(u,b,v)+E(W), since a,b+E(S) then a=a and b=b, we find that ea=a+a=a+a=a=a+a=fa and eb=b+b=b+b=b=b+b=fb, so e+La+Rf and u+Lb+Rv. On the one hand, by Theorem 2.1, we get that e+R(e+f)+Lf and u+R(u+v)+Lv, so eu+R(e+f)(u+v)+Lfv since +R and +L are both multiplicative congruence on I. On the other hand, we get that eu+Lab+Rfv, then eu+R(eu+fv)+Lfv by Theorem 2.1. Therefore, (e+f)(u+v)+H(eu+fv) which means that (e+f)(u+v)=eu+fv. Hence,

    [(e,a,f)(u,b,v)]ϕ=(eu,ab,fv)ϕ=eu+fv=(e+f)(u+v)=(e,a,f)ϕ(u,b,v)ϕ.

    Thus ϕ is an isomorphism as require.

    By the proof of Theorem 2.3 (i.e. Theorem 2.5 in [12]), we get that

    (e,a,f)γ(u,b,v)a=ba=b,

    and the mapping π:W/γW given by γ(e,a,f)π=(ea,a,fa) preserves addition and satisfies that π=idW/γ. Now we will show that it also preserves multiplication:

    γ(e,a,f)πγ(u,b,v)π=(ea,a,fa)(eb,b,fb)=(eaeb,ab,fafb)=(eab,ab,fab)=γ(e,a,f)(u,b,v)π.

    Finally, it is clear that

    W/γImπS,

    the second isomorphism being that given by (ea,a,fa)a.

    Conversely, let T be a split additively orthodox semiring with the idempotent semiring I of additive idempotents, and mapping π:T/γT be a splitting morphism. By Lemma 4.1 and Corollary 4.1, the set E=IImπ of additive idempotents of Imπ is a skeleton of I and Sp(E) = Imπ.

    Moreover, define θ: ImπTI by aθ=θa, where the domain of θa is a+a+I+a+a, the codomain of θa is a+a+I+a+a and bθa=a+b+a. By Lemma 4.3, θ is a triangulation of μ:ImπTE. By Theorem 4.4, W(I,Imπ,θ) satisfies the four equations. We can therefore construct the split additively orthodox semiring W=W(I,Imπ,θ).

    Define the map ψ:WT by

    (e,a,f)ψ=e+a+f.

    By the proof shown in [12]. ψ is bijection and preserves addition.

    Since for any a,bImπ and e,f,u,vI such that e+Lea, f+Rfa, u+Leb, v+Rfb, then

    (e+a+f)(u+b+v)=eu+ab+fv.

    So it also preserves multiplication clearly.

    Therefore, ψ is a semiring isomorphism.

    As shown in [20], a generalized Clifford semiring S is not only an additively orthodox semiring, but also a strong b-lattice T of skew-rings Rα(αT), i.e. S=<T,Rα,ϕα,β>. Hence, S/γ=T. Let π:T/γS,αxRα. It is easy to verify that π is a split morphism, so S is a split additively orthodox semiring.

    Remark 1. From Theorem 4.5, we can see that the class of split additively orthodox semiringsis actually not only a general extension of the class of Clifford semirings and generalized Clifford semirings studied in [20], but also a general extension of the class of split orthodox semigroups in [12].

    In this paper, we introduce and explore split additively orthodox semirings. Some property theorems are obtained, and a structure theorem is established by using idempotent semirings, additively inverse semirings, and Munn semigroup. It not only extends and strengthens the corresponding results of Clifford semirings and split orthodox semigroups but also develops a new way to study semirings.

    This work was supported in part by the Dongguan Science and Technology of Social Development Program (2022), in part by the NNSF of China (11801239, 12171022).

    All authors declare no conflicts of interest in this paper.



    [1] R. L. Siegel, A. N. Giaquinto, A. Jemal, Cancer statistics, CA Cancer J. Clin., 74 (2024), 12–49. https://doi.org/10.3322/caac.21820 doi: 10.3322/caac.21820
    [2] K. A. Schafer, The cell cycle: A review, Vet. Pathol., 35 (1998), 461–478. https://doi.org/10.1177/030098589803500601 doi: 10.1177/030098589803500601
    [3] B. Alberts, A. Johnson, J. Lewis, M. Raff, K. Roberts, P. Walter, Molecular Biology of the Cell, 4th edition, Garland Science, New York, 2002.
    [4] Z. Wang, Cell cycle progression and synchronization: An overview, Methods Mol. Biol., 2579 (2002), 3–23. https://doi.org/10.1007/978-1-0716-2736-5_1 doi: 10.1007/978-1-0716-2736-5_1
    [5] E. A. Kolokotroni, D. D. Dionysiou, N. K. Uzunogulu, G. S. Stamatakos, Studying the growth kinetics of untreated clinical tumors by using an advanced discrete simulation model, Math. Model., 54 (2011), 1989–2006. https://doi.org/10.1016/j.mcm.2011.05.007 doi: 10.1016/j.mcm.2011.05.007
    [6] M. Gyllenberg, G. F. Webb, A nonlinear structured population model of tumor growth with quiescence, J. Math. Biol., 28 (1990), 671–694. https://doi.org/10.1007/BF00160231 doi: 10.1007/BF00160231
    [7] Z. Wang, J. D. Butner, R. Kerketta, V. Cristini, T. S. Deisboeck, Simulating cancer growth with multiscale agent-based modeling, Semin. Cancer Biol., 30 (2015), 70–78. https://doi.org/10.1016/j.semcancer.2014.04.001 doi: 10.1016/j.semcancer.2014.04.001
    [8] T. S. Deisboeck, Z. Wang, P. Macklin, V. Cristini, Multiscale cancer modeling, Annu. Rev. Biomed. Eng., 13 (2011), 127–155. https://doi.org/10.1146/annurev-bioeng-071910-124729 doi: 10.1146/annurev-bioeng-071910-124729
    [9] J. West, M. Robertson-Tessi, A. R. A. Anderson, Agent-based methods facilitate integrative science in cancer, Trends Cell Biol., 33 (2023), 300–311. https://doi.org/10.1016/j.tcb.2022.10.006 doi: 10.1016/j.tcb.2022.10.006
    [10] Z. Wang, T. S. Deisboeck, Computational modeling of brain tumors: discrete, continuum or hybrid?, Sci. Model Simul., 15 (2008), 381. https://doi.org/10.1007/s10820-008-9094-0 doi: 10.1007/s10820-008-9094-0
    [11] T. Trisilowati, D. G. Mallet, In silico experimental modeling of cancer treatment, ISRN Oncol., 2012 (2012), 1–8. https://doi.org/10.5402/2012/828701 doi: 10.5402/2012/828701
    [12] K. Bhuvaneshwar, A. Belouali, V. Singh, R. M. Johnson, L. Song, A. Alaoui, et al., G-DOC Plus–an integrative bioinformatics platform for precision medicine, BMC Bioinf., 17 (2016), 193. https://doi.org/10.1186/s12859-016-1010-0 doi: 10.1186/s12859-016-1010-0
    [13] L. B. Edelman, J. A. Eddy, N. D. Price, In silico models of cancer, WIREs Mech. Dis., 2 (2010), 438–459. https://doi.org/10.1002/wsbm.75 doi: 10.1002/wsbm.75
    [14] B. Colom, M. P. Alcolea, G. Piedrafita, M. W. J. Hall, A. Wabik, S. C. Dentro, Spatial competition shapes the dynamic mutational landscape of normal esophageal epithelium, Nat. Genet., 52 (2020), 604–614. https://doi.org/10.1038/s41588-020-0624-3 doi: 10.1038/s41588-020-0624-3
    [15] H. B. Frieboes, An integrated computational/experimental model of tumor invasion, Cancer Res., 66 (2006), 1597–1604. https://doi.org/10.1158/0008-5472.CAN-05-3166 doi: 10.1158/0008-5472.CAN-05-3166
    [16] H. P. Greenspan, Models for the growth of a solid tumor by diffusion, Stud. Appl. Math., 51 (1972), 317–340. https://doi.org/10.1002/sapm1972514317 doi: 10.1002/sapm1972514317
    [17] A. R. A. Anderson, A. M. Weaver, P. T. Cummings, V. Quaranta, Tumor morphology and phenotypic evolution driven by selective pressure from the microenvironment, Cell J., 127 (2006), 905–915. https://doi.org/10.1016/j.cell.2006.09.042 doi: 10.1016/j.cell.2006.09.042
    [18] H. Byrne, D. Drasdo, Individual-based and continuum models of growing cell populations: a comparison, J. Math. Biol., 58 (2009), 657–687. https://doi.org/10.1007/s00285-008-0212-0 doi: 10.1007/s00285-008-0212-0
    [19] C. Drapaca, S. Sivaloganathan, Mathematical Modelling and Biomechanics of the Brain, Springer, New York, 2019.
    [20] P. Castorina, D. Carcò, C. Guiot, T. S. Deisboeck, Tumor growth instability and its implications for chemotherapy, Cancer Res., 69 (2009), 8507–8515. https://doi.org/10.1158/0008-5472.CAN-09-0653 doi: 10.1158/0008-5472.CAN-09-0653
    [21] J. T. Oden, E. A. B. F. Lima, R. C. Almeida, Y. Feng, M. N. Rylander, D. Fuentes, et al., Toward predictive multiscale modeling of vascular tumor growth: computational and experimental oncology for tumor prediction, Arch. Comput. Methods Eng., 23 (2016), 735–779. https://doi.org/10.1007/s11831-015-9156-x doi: 10.1007/s11831-015-9156-x
    [22] A. M. Jarrett, E. A. B. F. Lima, D. A. Hormuth, M. T. McKenna, X. Fent, D. A. Ekrut, et al., Mathematical models of tumor cell proliferation: A review of the literature, Expert Rev. Anticancer Ther., 18 (2018), 1271–1286. https://doi.org/10.1080/14737140.2018.1527689 doi: 10.1080/14737140.2018.1527689
    [23] H. Murphy, J. Jaafari, H. M. Dobrovolny, Differences in predictions of ODE models of tumor growth: a cautionary example, BMC Cancer, 16 (2016), 1471–2407. https://doi.org/10.1186/s12885-016-2164-x doi: 10.1186/s12885-016-2164-x
    [24] B. Heesterman, J. Bokhorst, L. De Point, B. Verbist, J. Bayley, A. Van Der Mey, et al., Mathematical models for tumor growth and the reduction of overtreatment, J. Neurol. Surg. B., 80 (2019), 72–78. https://doi.org/10.1055/s-0038-1667148 doi: 10.1055/s-0038-1667148
    [25] P. Gerlee, A. R. A. Anderson, An evolutionary hybrid cellular automaton model of solid tumour growth, J. Theor. Biol., 246 (2007), 583–603. https://doi.org/10.1016/j.jtbi.2007.01.027 doi: 10.1016/j.jtbi.2007.01.027
    [26] N. M. Dimitriou, E. Demirag, K. Strati, G. D. Mitsis, A calibration and uncertainty quantification analysis of classical, fractional and multiscale logistic models of tumour growth, Comput. Methods Programs Biomed., 243 (2024), 107920. https://doi.org/10.1016/j.cmpb.2023.107920 doi: 10.1016/j.cmpb.2023.107920
    [27] H. J. Huber, H. B. Mistry, Explaining in-vitro to in-vivo efficacy correlations in oncology pre-clinical development via a semi-mechanistic mathematical model, J. Pharmacokinet. Pharmacodyn., 51 (2024), 169–185. https://doi.org/10.1007/s10928-023-09891-7 doi: 10.1007/s10928-023-09891-7
    [28] D. Tatro, The Mathematics of Cancer: Fitting the Gompertz Equation to Tumor Growth, Ph.D thesis, Bard College, 2018.
    [29] P. Gerlee, The model muddle: In search of tumor growth laws, Cancer Res., 73 (2013), 2407–2411. https://doi.org/10.1158/0008-5472.CAN-12-4355 doi: 10.1158/0008-5472.CAN-12-4355
    [30] A. Talkington, R. Durrett, Estimating tumor growth laws in vivo, Bull. Math. Biol., 77 (2015), 1934–1954. https://doi.org/10.1007/s11538-015-0110-8 doi: 10.1007/s11538-015-0110-8
    [31] C. Vaghi, A. Rodallec, R. Fanciullino, J. Ciccolini, J. P. Mochel, M. Mastri, et al., Population modeling of tumor growth curves and the reduced Gompertz model improve prediction of the age of experimental tumors, PLoS Comput. Biol., 16 (2020), e1007178. https://doi.org/10.1371/journal.pcbi.1007178 doi: 10.1371/journal.pcbi.1007178
    [32] S. Benzekry, C. Lamont, A. Beheshti, A. Tracz, J. M. L. Ebos, L. Hlatky, et al., Classical mathematical models for description and prediction of experimental tumor growth, PLoS Comput. Biol., 10 (2014), e1003800. https://doi.org/10.1371/journal.pcbi.1003800 doi: 10.1371/journal.pcbi.1003800
    [33] S. Vieira, R. Hoffman, Comparison of the logistic and the Gompertz growth functions considering additive and multiplicative error terms, J. R. Stat., 26 (1977), 143–148. https://doi.org/10.2307/2347021 doi: 10.2307/2347021
    [34] N. M. Dimitriou, S. Flores-Torres, J. M. Kinsella, G. D. Mitsis, Quantifying the morphology and mechanisms of cancer progression in 3D in-vitro environments: Integrating experiments and multiscale models, IEEE Trans. Biomed. Eng., 70 (2023), 1318–1329. https://doi.org/10.1109/TBME.2022.3216231 doi: 10.1109/TBME.2022.3216231
    [35] N. C. Atuegwu, L. R. Arlinghaus, X. Li, A. B. Chakravarthy, V. G. Abramson, M. E. Sanders, et al., Parameterizing the logistic model of tumor growth by DW-MRI and DCE-MRI data to predict treatment response and changes in breast cancer cellularity during neoadjuvant chemotherapy, Transl. Oncol., 6 (2013), 256–264. https://doi.org/10.1593/tlo.13130 doi: 10.1593/tlo.13130
    [36] A. K. Laird, Dynamics of tumor growth, Br. J. Cancer, 18 (1964), 490–502. https://doi.org/10.1038/bjc.1964.55 doi: 10.1038/bjc.1964.55
    [37] B. Gompertz, XXIV. On the nature of the function expressive of the law of human mortality, and on a new mode of determining the value of life contingencies. In a letter to Francis Baily, Esq. F. R. S. & c, Phil. Trans. R. Soc., 115 (1825), 513–583. https://doi.org/10.1098/rstl.1825.0026 doi: 10.1098/rstl.1825.0026
    [38] C. L. Frenzen, J. D. Murray, A cell kinetics justification for Gompertz' Equation, SIAP, 46 (1986), 614–629. https://doi.org/10.1137/0146042 doi: 10.1137/0146042
    [39] R. Chignola, A. Schenetti, G. Andrighetto, E. Chiesa, R. Foroni, S. Sartoris, et al., Forecasting the growth of multicell tumour spheroids: implications for the dynamic growth of solid tumours, Cell Prolif., 33 (2000), 219–229. https://doi.org/10.1046/j.1365-2184.2000.00174.x doi: 10.1046/j.1365-2184.2000.00174.x
    [40] L. Von Bertalanffy, Quantitative laws in metabolism and growth, Q. Rev. Biol., 32 (1957), 217–231. https://doi.org/10.1086/401873 doi: 10.1086/401873
    [41] K. Renner-Martin, N. Brunner, M. Kühleitner, W. G. Nowak, K. Scheicher, On the exponent in the Von Bertalanffy growth model, PeerJ, 6 (2018), e4205. https://doi.org/10.7717/peerj.4205 doi: 10.7717/peerj.4205
    [42] H. H. Diebner, T. Zerjatke, M. Griehl, I. Roeder, Metabolism is the tie: The Bertalanffy-type cancer growth model as common denominator of various modelling approaches, Biosystems, 167 (2018), 1–23. https://doi.org/10.1016/j.biosystems.2018.03.004 doi: 10.1016/j.biosystems.2018.03.004
    [43] K. C. L. Wong, R. M. Summers, E. Kebebew, J. Yao, Tumor growth prediction with reaction-diffusion and hyperelastic biomechanical model by physiological data fusions, MedIA, 25 (2015), 72–85. https://doi.org/10.1016/j.media.2015.04.002 doi: 10.1016/j.media.2015.04.002
    [44] R. A. Gatenby, E. T. Gawlinski, A reaction-diffusion model of cancer invasion, Cancer Res., 56 (1996), 5745–5753.
    [45] V. Cristini, J. Lowengrub, Q. Nie, Nonlinear simulation of tumor growth, J. Math. Biol., 46 (2003), 191–224. https://doi.org/10.1007/s00285-002-0174-6 doi: 10.1007/s00285-002-0174-6
    [46] C. Hogea, C. Davatzikos, G. Biros, An image-driven parameter estimation problem for a reaction–diffusion glioma growth model with mass effects, J. Math. Biol., 56 (2008), 793–825. https://doi.org/10.1007/s00285-007-0139-x doi: 10.1007/s00285-007-0139-x
    [47] O. Clatz, M. Sermesant, P. Y. Bondiau, H. Delingette, S. K. Warfield, G. Malandain, et al., Realistic simulation of the 3-D growth of brain tumors in MR images coupling diffusion with biomechanical deformation, IEEE Trans. Med. Imaging, 24 (2005), 1334–1346. https://doi.org/10.1109/TMI.2005.857217 doi: 10.1109/TMI.2005.857217
    [48] X. Chen, R. M. Summers, J. Yao, Kidney tumor growth prediction by coupling reaction-diffusion and biomechanical model, IEEE Trans. Biomed. Eng., 60 (2013), 169–173. https://doi.org/10.1109/TBME.2012.2222027 doi: 10.1109/TBME.2012.2222027
    [49] E. Konukoglu, O. Clatz, P. Bondiau, H. Delingette, N. Ayache, Extrapolating glioma invasion margin in brain magnetic resonance images: Suggesting new irradiation margins, MedIA, 14 (2010), 111–125. https://doi.org/10.1016/j.media.2009.11.005 doi: 10.1016/j.media.2009.11.005
    [50] Y. Liu, S. M. Sadowski, A. B. Weisbrod, E. Kebebew, R. M. Summers, J. Yao, Patient specific tumor growth prediction using multimodal images, MedIA, 18 (2014), 555–566. https://doi.org/10.1016/j.media.2014.02.005 doi: 10.1016/j.media.2014.02.005
    [51] B. H. Menze, K. Van Leemput, A. Honkela, E. Konukoglu, M. Weber, N. Ayache, et al., A generative approach for image-based modeling of tumor growth, in Information Processing in Medical Imaging (eds. G. Székely, H. K. Hahn), Springer, (2011), 735–747.
    [52] C. Martens, A. Rovai, D. Bonatto, T. Metens, O. Debeir, C. Decaestecker, et al., Deep learning for reaction-diffusion glioma growth modeling: Towards a fully personalized model?, Cancers, 14 (2022), 2530. https://doi.org/10.3390/cancers14102530 doi: 10.3390/cancers14102530
    [53] S. Jbabdi, E. Mandonnet, H. Duffau, L. Capelle, K. R. Swanson, M. Pélégrini-Issac, et al., Simulation of anisotropic growth of low‐grade gliomas using diffusion tensor imaging, Magn. Reson. Med., 54 (2005), 616–624. https://doi.org/10.1002/mrm.20625 doi: 10.1002/mrm.20625
    [54] E. Konukoglu, O. Clatz, B. H. Menze, B. Stieltjes, M. Weber, E. Mandonnet, et al., Image guided personalization of reaction-diffusion type tumor growth models using modified anisotropic Eikonal equations, IEEE Trans. Med. Imaging, 29 (2010), 77–95. https://doi.org/10.1109/TMI.2009.2026413 doi: 10.1109/TMI.2009.2026413
    [55] S. Subramanian, K. Scheufele, M. Mehl, G. Biros, Where did the tumor start? An inverse solver with sparse localization for tumor growth models, Inverse Probl., 36 (2020), 045006. https://doi.org/10.1088/1361-6420/ab649c doi: 10.1088/1361-6420/ab649c
    [56] K. Scheufele, S. Subramanian, G. Biros, Fully automatic calibration of tumor-growth models using a single mpMRI scan, IEEE Trans. Med. Imaging, 40 (2021), 193–204. https://doi.org/10.1109/TMI.2020.3024264 doi: 10.1109/TMI.2020.3024264
    [57] B. Tunc, D. Hormuth, G. Biros, T. E. Yankeelov, Modeling of glioma growth with mass effect by longitudinal magnetic resonance imaging, IEEE Trans. Biomed. Eng., 68 (2021), 3713–3724. https://doi.org/10.1109/TBME.2021.3085523 doi: 10.1109/TBME.2021.3085523
    [58] V. Cristini, J. Lowengrub, Multiscale Modeling of Cancer: An Integrated Experimental and Mathematical Modeling Approach, Cambridge University Press, 2010.
    [59] J. Retzlaff, X. Lai, C. Berking, J. Vera, Integration of transcriptomics data into agent-based models of solid tumor metastasis, Comput. Struct. Biotechnol. J., 21 (2023), 1930–1941. https://doi.org/10.1016/j.csbj.2023.02.014 doi: 10.1016/j.csbj.2023.02.014
    [60] G. De Vries, T. Hillen, M. Lewis, J. Müler, B. Schönfisch, A Course in Mathematical Biology: Quantitative Modeling with Mathematical and Computational Methods, Society for Industrial and Applied Mathematics, Philadelphia, 2006. https://doi.org/10.1137/1.9780898718256
    [61] D. Kamel, Dynamics in a discrete-time three dimensional cancer system, Int. J. Appl. Math., 49 (2019), 625–631.
    [62] J. Poleszczuk, H. Enderling, A high-performance cellular automaton model of tumor growth with dynamically growing domains, Appl. Math., 5 (2014), 144–152. https://doi.org/10.4236/am.2014.51017 doi: 10.4236/am.2014.51017
    [63] A. Adamatzky, Game of Life Cellular Automata, Springer, London, 2010. https://doi.org/10.1007/978-1-84996-217-9
    [64] V. García-Morales, J. A. Manzanares, J. Cervera, Modeling tumour growth with a modulated game of life cellular automaton under global coupling in Cancer, Complexity, Computation (eds. I. Balaz, A. Adamatzky), Springer International Publishing, (2022), 117–131. https://doi.org/10.1007/978-3-031-04379-6_5
    [65] G. Migliaccio, R. Ferraro, Z. Wang, V. Cristini, P. Dogra, S. Caserta, Exploring cell migration mechanisms in cancer: From wound healing assays to cellular automata models, Cancers, 15 (2023), 5284. https://doi.org/10.3390/cancers15215284 doi: 10.3390/cancers15215284
    [66] C. A. Valentim, J. A. Rabi, S. A. David, Cellular-automaton model for tumor growth dynamics: Virtualization of different scenarios, Comput. Biol. Med., 153 (2023), 106481. https://doi.org/10.1016/j.compbiomed.2022.106481 doi: 10.1016/j.compbiomed.2022.106481
    [67] F. Pourhasanzade, S. H. Sabzpoushan, A cellular automata model of chemotherapy effects on tumour growth: targeting cancer and immune cells, MCMDS, 25 (2019), 63–89. https://doi.org/10.1080/13873954.2019.1571515 doi: 10.1080/13873954.2019.1571515
    [68] J. Santos, A. Monteagudo, Analysis of behaviour transitions in tumour growth using a cellular automaton simulation, IET Syst. Biol., 9 (2015), 75–87. https://doi.org/10.1049/iet-syb.2014.0015 doi: 10.1049/iet-syb.2014.0015
    [69] C. Tanade, S. Putney, A. Randles, Developing a scalable cellular automaton model of 3D tumor growth, in Computational Science – ICCS 2022 (eds. D. Groen, C. De Mulatier, M. Paszynski, V. Krzhizhanovskaya, J. J. Dongarra, P. M. A. Sloot), Springer International Publishing, (2022), 3–16. https://ldoi.org/10.1007/978-3-031-08751-6_1
    [70] C. M. Macal, M. J. North, Agent-based modeling and simulation: ABMS examples, 2008 Winter Simulation Conference, IEEE, (2008), 101–112. https://doi.org/10.1109/WSC.2008.4736060
    [71] P. Van Liedekerke, A. Buttenschön, D. Drasdo, Off-Lattice agent-based models for cell and tumor growth, in Numerical Methods and Advanced Simulation in Biomechanics and Biological Processes, Elsevier, (2018), 245–267. https://doi.org/10.1016/B978-0-12-811718-7.00014-9
    [72] P. Macklin, H. B. Frieboes, J. L. Sparks, A. Ghaffarizadeh, S. H. Friedman, E. F. Juarez, et al., Progress towards computational 3-D multicellular systems biology, in Systems Biology of Tumor Microenvironment (ed. K. A. Rejniak), Springer International Publishing, (2016), 225–246. http://doi.org/10.1007/978-3-319-42023-3_12
    [73] E. Kim, V. Rebecca, I. V. Fedorenko, J. L. Messina, R. Mathew, S. S. Maria-Engler, et al., Senescent fibroblasts in melanoma initiation and progression: An integrated theoretical, experimental, and clinical approach, Cancer Res., 73 (2013), 6874–6885. https://doi.org/10.1158/0008-5472.CAN-13-1720 doi: 10.1158/0008-5472.CAN-13-1720
    [74] V. Estrella, T. Chen, M. Lloyd, J. Wojtkowiak, H. H. Cornnell, A. Ibrahim-Hashim, et al., Acidity generated by the tumor microenvironment drives local invasion, Cancer Res., 73 (2013), 1524–1535. https://doi.org/10.1158/0008-5472.CAN-12-2796 doi: 10.1158/0008-5472.CAN-12-2796
    [75] A. El-Kenawi, C. Gatenbee, M. Robertson-Tessi, R. Bravo, J. Dhillon, Y. Balagurunathan, et al., Acidity promotes tumour progression by altering macrophage phenotype in prostate cancer, Br. J. Cancer, 121 (2019), 556–566. https://doi.org/10.1038/s41416-019-0542-2 doi: 10.1038/s41416-019-0542-2
    [76] I. Bozic, T. Antal, H. Ohtsuki, H. Carter, D. Kim, S. Chen, et al., Accumulation of driver and passenger mutations during tumor progression, Proc. Natl. Acad. Sci. USA, 107 (2010), 18545–18550. https://doi.org/10.1073/pnas.1010978107 doi: 10.1073/pnas.1010978107
    [77] R. C. Kennedy, G. E. Ropella, C. A. Hunt, A cell-centered, agent-based framework that enables flexible environment granularities, Theor. Biol. Med. Model., 13 (2016), 4. https://doi.org/10.1186/s12976-016-0030-9 doi: 10.1186/s12976-016-0030-9
    [78] S. Jamous, A. Comba, P. R. Lowenstein, S. Motsch, Self-organization in brain tumors: How cell morphology and cell density influence glioma pattern formation, PLoS Comput. Biol., 16 (2020), e1007611. https://doi.org/10.1371/journal.pcbi.1007611 doi: 10.1371/journal.pcbi.1007611
    [79] P. Macklin, M. E. Edgerton, A. M. Thompson, V. Cristini, Patient-calibrated agent-based modelling of ductal carcinoma in situ (DCIS): From microscopic measurements to macroscopic predictions of clinical progression, J. Theor. Biol., 301 (2012), 122–140. https://doi.org/10.1016/j.jtbi.2012.02.002 doi: 10.1016/j.jtbi.2012.02.002
    [80] J. D. Butner, V. Cristini, Z. Wang, Development of a three dimensional, multiscale agent-based model of ductal carcinoma in situ, in 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), (2017), 86–89. https://doi.org/10.1109/EMBC.2017.8036769
    [81] J. D. Butner, D. Fuentes, B. Ozpolat, G. A. Calin, X. Zhou, J. Lowengrub, et al., A multiscale agent-based model of ductal carcinoma in situ, IEEE Trans. Biomed. Eng., 67 (2020), 1450-1461. https://doi.org/10.1109/TBME.2019.2938485 doi: 10.1109/TBME.2019.2938485
    [82] A. Ghaffarizadeh, R. Heiland, S. H. Friedman, S. M. Mumenthaler, P. Macklin, PhysiCell: An open source physics-based cell simulator for 3-D multicellular systems, PLoS Comput. Biol., 14 (2018), e1005991. https://doi.org/10.1371/journal.pcbi.1005991 doi: 10.1371/journal.pcbi.1005991
    [83] G. Letort, A. Montagud, G. Stoll, R. Heiland, E. Barillot, P. Macklin, et al., PhysiBoSS: a multi-scale agent-based modelling framework integrating physical dimension and cell signalling, Bioinformatics, 35 (2019), 1188–1196. https://doi.org/10.1093/bioinformatics/bty766 doi: 10.1093/bioinformatics/bty766
    [84] J. Ozik, N. Collier, J. M. Wozniak, C. Macal, C. Cockrell, S. H. Friedman, et al., High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow, BMC Bioinf., 19 (2018), 483. https://doi.org/10.1186/s12859-018-2510-x doi: 10.1186/s12859-018-2510-x
    [85] M. Robertson-Tessi, R. J. Gillies, R. A. Gatenby, A. R. A. Anderson, Impact of metabolic heterogeneity on tumr growth, invasion, and treatment outcomes, Cancer Res., 75 (2015), 1567–1579. https://doi.org/10.1158/0008-5472.CAN-14-1428 doi: 10.1158/0008-5472.CAN-14-1428
    [86] A. R. A. Anderson, A hybrid mathematical model of solid tumour invasion: the importance of cell adhesion, Math. Med. Biol., 22 (2005), 163–186. https://doi.org/10.1093/imammb/dqi005 doi: 10.1093/imammb/dqi005
    [87] D. Toker, F. T. Sommer, M. D'Esposito, A simple method for detecting chaos in nature, Commun. Biol., 3 (2020), 11. https://doi.org/10.1038/s42003-019-0715-9 doi: 10.1038/s42003-019-0715-9
    [88] F. R. Marotto, Snap-back repellers imply chaos in Rn, J. Math. Anal. Appl., 63 (1978), 199–223. https://doi.org/10.1016/0022-247X(78)90115-4 doi: 10.1016/0022-247X(78)90115-4
    [89] T. Saeed, K. Djeddi, J. L. G. Guirao, H. H. Alsulami, M. S. Alhodaly, A discrete dynamics approach to a tumor system, Mathematics, 10 (2022), 1774. https://doi.org/10.1016/0022-247X(78)90115-4 doi: 10.1016/0022-247X(78)90115-4
    [90] E. R. Paquet, M. T. Hallett, Absolute assignment of breast cancer intrinsic molecular subtype, JNCI, 107 (2015). https://doi.org/10.1093/jnci/dju357 doi: 10.1093/jnci/dju357
    [91] C. Letellier, F. Denis, L. A. Aguirre, What can be learned from a chaotic cancer model?, J. Theor. Biol., 322 (2013), 7–16. https://doi.org/10.1016/j.jtbi.2013.01.003 doi: 10.1016/j.jtbi.2013.01.003
    [92] N. Debbouche, A. Ouannas, G. Grassi, A. A. Al-Hussein, F. R. Tahir, K. M. Saad, et al., Chaos in cancer tumor growth model with commensurate and incommensurate fractional-order derivatives, Comput. Math. Methods. Med., 2022 (2022), 1–13. https://doi.org/10.1155/2022/5227503 doi: 10.1155/2022/5227503
    [93] A. Cucuianu, Chaos in cancer?, Nat. Med., 4 (1998), 1342–1342. https://doi.org/10.1038/3904 doi: 10.1038/3904
    [94] K. A. Rejniak, A. R. A. Anderson, Hybrid models of tumor growth, WIREs Mech. Dis., 3 (2011), 115–125. https://doi.org/10.1002/wsbm.102 doi: 10.1002/wsbm.102
    [95] M. Branicky, Studies in Hybrid Systems: Modeling, Analysis, Control, Ph.D thesis, Massachusetts Institute of Technology, 1995.
    [96] T. A. Henzinger, The theory of hybrid automata, in Verification of Digital and Hybrid Systems (eds. M. K. Inan, R. P. Kurshan), Springer, Berlin, (2000), 265–292. http://doi.org/10.1007/978-3-642-59615-5_13
    [97] R. Alur, C. Belta, F. Ivančić, V. Kumar, M. Mintz, G. J. Pappas, et al., Hybrid modeling and simulation of biomolecular networks in Hybrid Systems: Computation and Control (eds. G. Goos, J. Hartmanis, J. Van Leeuwen, M. D. Di Benedetto, A. Sangiovanni-Vincentelli, R. Alur, et al.), Springer, Berlin, (2001), 19–32. http://doi.org/10.1007/3-540-45351-2_6
    [98] G. Lorenzo, S. R. Ahmed, D. A. Hormuth, B. Vaughn, J. Kalpathy-Cramer, L. Solorio, et al., Patient-specific, mechanistic models of tumor growth incorporating artificial intelligence and big data, preprint, arXiv: 2308.14925.
    [99] Z. Frankenstein, D. Basanta, O. E. Franco, Y. Gao, R. A. Javier, D. W. Strand, et al., Stromal reactivity differentially drives tumour cell evolution and prostate cancer progression, Nat. Ecol. Evol., 4 (2020), 870–884. https://doi.org/10.1038/s41559-020-1157-y doi: 10.1038/s41559-020-1157-y
    [100] A. G. López, J. M. Seoane, M. A. F. Sanjuán, Modelling cancer dynamics using cellular automata, in Advanced Mathematical Methods in Biosciences and Applications (eds. F. Berezovskaya, B. Toni), Springer International Publishing, Cham, (2019), 159–205. http://doi.org/10.1007/978-3-030-15715-9_8
    [101] L. Messina, R. Ferraro, M. J. Peláez, Z. Wang, V. Cristini, P. Dogra, et al., Hybrid cellular automata modeling reveals the effects of glucose gradients on tumor spheroid growth, Cancers, 15 (2023), 5660. https://doi.org/10.3390/cancers15235660 doi: 10.3390/cancers15235660
    [102] S. Suveges, I. Chamseddine, K. A. Rejniak, R. Eftimie, D. Trucu, Collective cell migration in a fibrous environment: A hybrid multiscale modelling approach, Front. Appl. Math. Stat., 7 (2021), 680029. https://doi.org/10.3389/fams.2021.680029 doi: 10.3389/fams.2021.680029
    [103] J. A. Gallaher, S. C. Massey, A. Hawkins-Daarud, S. S. Noticewala, R. C. Rockne, S. K. Johnston, et al., From cells to tissue: How cell scale heterogeneity impacts glioblastoma growth and treatment response, PLoS Comput. Biol., 16 (2020), e1007672. https://doi.org/10.1371/journal.pcbi.1007672 doi: 10.1371/journal.pcbi.1007672
    [104] A. Stéphanou, A. C. Lesart, K. Deverchère, A. Juhem, A. Popov, F. Estève, How tumour-induced vascular changes alter angiogenesis: Insights from a computational model, J. Theor. Biol., 419 (2017), 211–226. https://doi.org/10.1016/j.jtbi.2017.02.018 doi: 10.1016/j.jtbi.2017.02.018
    [105] Y. Chen, H. Wang, J. Zhang, K. Chen, Y. Li, Simulation of avascular tumor growth by agent-based game model involving phenotype-phenotype interactions, Sci. Rep., 5 (2015), 17992. https://doi.org/10.1038/srep17992 doi: 10.1038/srep17992
    [106] J. Kremheller, A. Vuong, B. A. Schrefler, W. A. Wall, An approach for vascular tumor growth based on a hybrid embedded/homogenized treatment of the vasculature within a multiphase porous medium model, Numer. Methods Biomed. Eng., 35 (2019), e3253. https://doi.org/10.1002/cnm.3253 doi: 10.1002/cnm.3253
    [107] C. M. Phillips, E. A. B. F. Lima, R. T. Woodall, A. Brock, T. E. Yankeelov, A hybrid model of tumor growth and angiogenesis: In silico experiments, PLoS One, 15 (2020), e0231137. https://doi.org/10.1371/journal.pone.0231137 doi: 10.1371/journal.pone.0231137
    [108] T. Duswald, E. A. B. F. Lima, J. T. Oden, B. Wohlmuth, Bridging scales: A hybrid model to simulate vascular tumor growth and treatment response, Comput. Methods Appl. Mech. Eng., 418 (2024), 116566. https://doi.org/10.1016/j.cma.2023.116566 doi: 10.1016/j.cma.2023.116566
    [109] I. M. Chamseddine, K. A. Rejniak, Hybrid modeling frameworks of tumor development and treatment, WIREs Mech. Dis., 12 (2020), e1461. https://doi.org/10.1002/wsbm.1461 doi: 10.1002/wsbm.1461
    [110] Q. Chen, Q. Ye, W. Zhang, H. Li, X. Zheng, TGM-Nets: A deep learning framework for enhanced forecasting of tumor growth by integrating imaging and modeling, Eng. Appl. Artif. Intell., 126 (2023), 106867. https://doi.org/10.1016/j.engappai.2023.106867 doi: 10.1016/j.engappai.2023.106867
    [111] H. N. Matin, S. Setayeshi, A computational tumor growth model experience based on molecular dynamics point of view using deep cellular automata, J. Med. Artif. Intell., 148 (2024), 102752. https://doi.org/10.1016/j.artmed.2023.102752 doi: 10.1016/j.artmed.2023.102752
    [112] A. Amanzholova, A. Coşkun, Enhancing cancer stage prediction through hybrid deep neural networks: a comparative study, Front. Big Data, 7 (2024), 1359703. https://doi.org/10.3389/fdata.2024.1359703 doi: 10.3389/fdata.2024.1359703
  • Reader Comments
  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1529) PDF downloads(112) Cited by(0)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog