This research looks into the main DNA markers and the limits of their application in molecular phylogenetic analysis. Melatonin 1B (MTNR1B) receptor genes were analyzed from various biological sources. Based on the coding sequences of this gene, using the class Mammalia as example, phylogenetic reconstructions were made to study the potential of mtnr1b as a DNA marker for phylogenetic relationships investigating. The phylogenetic trees were constructed using NJ, ME and ML methods that establish the evolutionary relationships between different groups of mammals. The resulting topologies were generally in good agreement with topologies established on the basis of morphological and archaeological data as well as with other molecular markers. The present divergences provided a unique opportunity for evolutionary analysis. These results suggest that the coding sequence of the MTNR1B gene can be used as a marker to study the relationships of lower evolutionary levels (order, species) as well as to resolve deeper branches of the phylogenetic tree at the infraclass level.
Citation: Ekaterina Y. Kasap, Оlga K. Parfenova, Roman V. Kurkin, Dmitry V. Grishin. Bioinformatic analysis of the coding region of the melatonin receptor 1b gene as a reliable DNA marker to resolve interspecific mammal phylogenetic relationships[J]. Mathematical Biosciences and Engineering, 2023, 20(3): 5430-5447. doi: 10.3934/mbe.2023251
[1] | K. R. Karthikeyan, G. Murugusundaramoorthy, N. E. Cho . Some inequalities on Bazilevič class of functions involving quasi-subordination. AIMS Mathematics, 2021, 6(7): 7111-7124. doi: 10.3934/math.2021417 |
[2] | Luminiţa-Ioana Cotîrlǎ . New classes of analytic and bi-univalent functions. AIMS Mathematics, 2021, 6(10): 10642-10651. doi: 10.3934/math.2021618 |
[3] | S. O. Olatunji, Hemen Dutta . Coefficient inequalities for pseudo subclasses of analytical functions related to Petal type domains defined by error function. AIMS Mathematics, 2020, 5(3): 2526-2538. doi: 10.3934/math.2020166 |
[4] | Halit Orhan, Nanjundan Magesh, Chinnasamy Abirami . Fekete-Szegö problem for Bi-Bazilevič functions related to Shell-like curves. AIMS Mathematics, 2020, 5(5): 4412-4423. doi: 10.3934/math.2020281 |
[5] | Prathviraj Sharma, Srikandan Sivasubramanian, Nak Eun Cho . Initial coefficient bounds for certain new subclasses of bi-univalent functions with bounded boundary rotation. AIMS Mathematics, 2023, 8(12): 29535-29554. doi: 10.3934/math.20231512 |
[6] | Erhan Deniz, Muhammet Kamali, Semra Korkmaz . A certain subclass of bi-univalent functions associated with Bell numbers and q−Srivastava Attiya operator. AIMS Mathematics, 2020, 5(6): 7259-7271. doi: 10.3934/math.2020464 |
[7] | Hava Arıkan, Halit Orhan, Murat Çağlar . Fekete-Szegö inequality for a subclass of analytic functions defined by Komatu integral operator. AIMS Mathematics, 2020, 5(3): 1745-1756. doi: 10.3934/math.2020118 |
[8] | Sadaf Umar, Muhammad Arif, Mohsan Raza, See Keong Lee . On a subclass related to Bazilevič functions. AIMS Mathematics, 2020, 5(3): 2040-2056. doi: 10.3934/math.2020135 |
[9] | Wenzheng Hu, Jian Deng . Hankel determinants, Fekete-Szegö inequality, and estimates of initial coefficients for certain subclasses of analytic functions. AIMS Mathematics, 2024, 9(3): 6445-6467. doi: 10.3934/math.2024314 |
[10] | Anandan Murugan, Sheza M. El-Deeb, Mariam Redn Almutiri, Jong-Suk-Ro, Prathviraj Sharma, Srikandan Sivasubramanian . Certain new subclasses of bi-univalent function associated with bounded boundary rotation involving sǎlǎgean derivative. AIMS Mathematics, 2024, 9(10): 27577-27592. doi: 10.3934/math.20241339 |
This research looks into the main DNA markers and the limits of their application in molecular phylogenetic analysis. Melatonin 1B (MTNR1B) receptor genes were analyzed from various biological sources. Based on the coding sequences of this gene, using the class Mammalia as example, phylogenetic reconstructions were made to study the potential of mtnr1b as a DNA marker for phylogenetic relationships investigating. The phylogenetic trees were constructed using NJ, ME and ML methods that establish the evolutionary relationships between different groups of mammals. The resulting topologies were generally in good agreement with topologies established on the basis of morphological and archaeological data as well as with other molecular markers. The present divergences provided a unique opportunity for evolutionary analysis. These results suggest that the coding sequence of the MTNR1B gene can be used as a marker to study the relationships of lower evolutionary levels (order, species) as well as to resolve deeper branches of the phylogenetic tree at the infraclass level.
Recently, fractional calculus has attained assimilated bounteous flow and significant importance due to its rife utility in the areas of technology and applied analysis. Fractional derivative operators have given a new rise to mathematical models such as thermodynamics, fluid flow, mathematical biology, and virology, see [1,2,3]. Previously, several researchers have explored different concepts related to fractional derivatives, such as Riemann-Liouville, Caputo, Riesz, Antagana-Baleanu, Caputo-Fabrizio, etc. As a result, this investigation has been directed at various assemblies of arbitrary order differential equations framed by numerous analysts, (see [4,5,6,7,8,9,10]). It has been perceived that the supreme proficient technique for deliberating such an assortment of diverse operators that attracted incredible presentation in research-oriented fields, for example, quantum mechanics, chaos, thermal conductivity, and image processing, is to manage widespread configurations of fractional operators that include many other operators, see the monograph and research papers [11,12,13,14,15,16,17,18,19,20,21,22].
In [23], the author proposed a novel idea of fractional operators, which is called GPF operator, that recaptures the Riemann-Liouville fractional operators into a solitary structure. In [24], the authors analyzed the existence of the FDEs as well as demonstrated the uniqueness of the GPF derivative by utilizing Kransnoselskii's fixed point hypothesis and also dealt with the equivalency of the mixed type Volterra integral equation.
Fractional calculus can be applied to a wide range of engineering and applied science problems. Physical models of true marvels frequently have some vulnerabilities which can be reflected as originating from various sources. Additionally, fuzzy sets, fuzzy real-valued functions, and fuzzy differential equations seem like a suitable mechanism to display the vulnerabilities marked out by elusiveness and dubiousness in numerous scientific or computer graphics of some deterministic certifiable marvels. Here we broaden it to several research areas where the vulnerability lies in information, for example, ecological, clinical, practical, social, and physical sciences [25,26,27].
In 1965, Zadeh [28] proposed fuzziness in set theory to examine these issues. The fuzzy structure has been used in different pure and applied mathematical analyses, such as fixed-point theory, control theory, topology, and is also helpful for fuzzy automata and so forth. In [29], authors also broadened the idea of a fuzzy set and presented fuzzy functions. This concept has been additionally evolved and the bulk of the utilization of this hypothesis has been deliberated in [30,31,32,33,34,35] and the references therein. The concept of HD has been correlated with fuzzy Riemann-Liouville differentiability by employing the Hausdorff measure of non-compactness in [36,37].
Numerous researchers paid attention to illustrating the actual verification of certain fuzzy integral equations by employing the appropriate compactness type assumptions. Different methodologies and strategies, in light of HD or generalized HD (see [38]) have been deliberated in several credentials in the literature (see for instance [39,40,41,42,43,44,45,46,47,48,49]) and we presently sum up quickly a portion of these outcomes. In [50], the authors proved the existence of solutions to fuzzy FDEs considering Hukuhara fractional Riemann-Liouville differentiability as well as the uniqueness of the aforesaid problem. In [51,52], the authors investigated the generalized Hukuhara fractional Riemann-Liouville and Caputo differentiability of fuzzy-valued functions. Bede and Stefanini [39] investigated and discovered novel ideas for fuzzy-valued mappings that correlate with generalized differentiability. In [43], Hoa introduced the subsequent fuzzy FDE with order ϑ∈(0,1):
{(cDϑσ+1Φ)(ζ)=F(ζ,Φ(ζ)),Φ(σ1)=Φ0∈E, | (1.1) |
where a fuzzy function is F:[σ1,σ2]×E→E with a nontrivial fuzzy constant Φ0∈E. The article addressed certain consequences on clarification of the fractional fuzzy differential equations and showed that the aforesaid equations in both cases (differential/integral) are not comparable in general. A suitable assumption was provided so that this correspondence would be effective. Hoa et al. [53] proposed the Caputo-Katugampola FDEs fuzzy set having the initial condition:
{(cDϑ,ρσ+1Φ)(ζ)=F(ζ,Φ(ζ)),Φ(σ1)=Φ0, | (1.2) |
where 0<σ1<ζ≤σ2, cDϑ,ρσ+1 denotes the fuzzy Caputo-Katugampola fractional generalized Hukuhara derivative and a fuzzy function is F:[σ1,σ2]×E→E. An approach of continual estimates depending on generalized Lipschitz conditions was employed to discuss the actual as well as the uniqueness of the solution. Owing to the aforementioned phenomena, in this article, we consider a novel fractional derivative (merely identified as Hilfer GPF-derivative). Consequently, in the framework of the proposed derivative, we establish the basic mathematical tools for the investigation of GPF-FFHD which associates with a fractional order fuzzy derivative. We investigated the actuality and uniqueness consequences of the clarification to a fuzzy fractional IVP by employing GPF generalized HD by considering an approach of continual estimates via generalized Lipschitz condition. Moreover, we derived the FVFIE using a generalized fuzzy GPF derivative is presented. Finally, we demonstrate the problems of actual and uniqueness of the clarification of this group of equations. The Hilfer-GPF differential equation is presented as follows:
{Dϑ,q,βσ+1Φ(ζ)=F(ζ,Φ(ζ)),ζ∈[σ1,T],0≤σ1<TI1−γ,βσ1Φ(σ1)=m∑j=1RjΦ(νj),ϑ≤γ=ϑ+q−ϑq,νj∈(σ1,T], | (1.3) |
where Dϑ,q,βσ+1(.) is the Hilfer GPF-derivative of order ϑ∈(0,1),I1−γ,βσ1(.) is the GPF integral of order 1−γ>0,Rj∈R, and a continuous function F:[σ1,T]×R→R with νj∈[σ1,T] fulfilling σ<ν1<...<νm<T for j=1,...,m. To the furthest extent that we might actually know, nobody has examined the existence and uniqueness of solution (1.3) regarding FVFIEs under generalized fuzzy Hilfer-GPF-HD with fuzzy initial conditions. An illustrative example of fractional-order in the complex domain is proposed and provides the exact solution in terms of the Fox-Wright function.
The following is the paper's summary. Notations, hypotheses, auxiliary functions, and lemmas are presented in Section 2. In Section 3, we establish the main findings of our research concerning the existence and uniqueness of solutions to Problem 1.3 by means of the successive approximation approach. We developed the fuzzy GPF Volterra-Fredholm integrodifferential equation in Section 4. Section 5 consists of concluding remarks.
Throughout this investigation, E represents the space of all fuzzy numbers on R. Assume the space of all Lebsegue measureable functions with complex values F on a finite interval [σ1,σ2] is identified by χrc(σ1,σ2) such that
‖F‖χrc<∞,c∈R,1≤r≤∞. |
Then, the norm
‖F‖χrc=(σ2∫σ1|ζcF(ζ)|rdζζ)1/r∞. |
Definition 2.1. ([53]) A fuzzy number is a fuzzy set Φ:R→[0,1] which fulfills the subsequent assumptions:
(1) Φ is normal, i.e., there exists ζ0∈R such that Φ(ζ0)=1;
(2) Φ is fuzzy convex in R, i.e, for δ∈[0,1],
Φ(δζ1+(1−δ)ζ2)≥min{Φ(ζ1),Φ(ζ2)}foranyζ1,ζ2∈R; |
(3) Φ is upper semicontinuous on R;
(4) [z]0=cl{z1∈R|Φ(z1)>0} is compact.
C([σ1,σ2],E) indicates the set of all continuous functions and set of all absolutely continuous fuzzy functions signifys by AC([σ1,σ2],E) on the interval [σ1,σ2] having values in E.
Let γ∈(0,1), we represent the space of continuous mappings by
Cγ[σ1,σ2]={F:(σ1,σ2]→E:eβ−1β(ζ−σ1)(ζ−σ1)1−γF(ζ)∈C[σ1,σ2]}. |
Assume that a fuzzy set Φ:R↦[0,1] and all fuzzy mappings Φ:[σ1,σ2]→E defined on L([σ1,σ2],E) such that the mappings ζ→ˉD0[Φ(ζ),ˆ0] lies in L1[σ1,σ2].
There is a fuzzy number Φ on R, we write [Φ]ˇq={z1∈R|Φ(z1)≥ˇq} the ˇq-level of Φ, having ˇq∈(0,1].
From assertions (1) to (4); it is observed that the ˇq-level set of Φ∈E, [Φ]ˇq is a nonempty compact interval for any ˇq∈(0,1]. The ˇq-level of a fuzzy number Φ is denoted by [Φ_(ˇq),ˉΦ(ˇq)].
For any δ∈R and Φ1,Φ2∈E, then the sum Φ1+Φ2 and the product δΦ1 are demarcated as: [Φ1+Φ2]ˇq=[Φ1]ˇq+[Φ2]ˇq and [δ.Φ1]ˇq=δ[Φ1]ˇq, for all ˇq∈[0,1], where [Φ1]ˇq+[Φ2]ˇq is the usual sum of two intervals of R and δ[Φ1]ˇq is the scalar multiplication between δ and the real interval.
For any Φ∈E, the diameter of the ˇq-level set of Φ is stated as diam[μ]ˇq=ˉμ(ˇq)−μ_(ˇq).
Now we demonstrate the notion of Hukuhara difference of two fuzzy numbers which is mainly due to [54].
Definition 2.2. ([54]) Suppose Φ1,Φ2∈E. If there exists Φ3∈E such that Φ1=Φ2+Φ3, then Φ3 is known to be the Hukuhara difference of Φ1 and Φ2 and it is indicated by Φ1⊖Φ2. Observe that Φ1⊖Φ2≠Φ1+(−)Φ2.
Definition 2.3. ([54]) We say that ¯D0[Φ1,Φ2] is the distance between two fuzzy numbers if
¯D0[Φ1,Φ2]=supˇq∈[0,1]H([Φ1]ˇq,[Φ2]ˇq),∀Φ1,Φ2∈E, |
where the Hausdroff distance between [Φ1]ˇq and [Φ2]ˇq is defined as
H([Φ1]ˇq,[Φ2]ˇq)=max{|Φ_(ˇq)−ˉΦ(ˇq)|,|ˉΦ(ˇq)−Φ_(ˇq)|}. |
Fuzzy sets in E is also refereed as triangular fuzzy numbers that are identified by an ordered triple Φ=(σ1,σ2,σ3)∈R3 with σ1≤σ2≤σ3 such that [Φ]ˇq=[Φ_(ˇq),ˉΦ(ˇq)] are the endpoints of ˇq-level sets for all ˇq∈[0,1], where Φ_(ˇq)=σ1+(σ2−σ1)ˇq and ˉΦ(ˇq)=σ3−(σ3−σ2)ˇq.
Generally, the parametric form of a fuzzy number Φ is a pair [Φ]ˇq=[Φ_(ˇq),ˉΦ(ˇq)] of functions Φ_(ˇq),ˉΦ(ˇq),ˇq∈[0,1], which hold the following assumptions:
(1) μ_(ˇq) is a monotonically increasing left-continuous function;
(2) ˉμ(ˇq) is a monotonically decreasing left-continuous function;
(3) μ_(ˇq)≤ˉμ(ˇq),ˇq∈[0,1].
Now we mention the generalized Hukuhara difference of two fuzzy numbers which is proposed by [38].
Definition 2.4. ([38]) The generalized Hukuhara difference of two fuzzy numbers Φ1,Φ2∈E (gH-difference in short) is stated as follows
Φ1⊖gHΦ2=Φ3⇔Φ1=Φ2+Φ3orΦ2=Φ1+(−1)Φ3. |
A function Φ:[σ1,σ2]→E is said to be d-increasing (d-decreasing) on [σ1,σ2] if for every ˇq∈[0,1]. The function ζ→diam[Φ(ζ)]ˇq is nondecreasing (nonincreasing) on [σ1,σ2]. If Φ is d-increasing or d-decreasing on [σ1,σ2], then we say that Φ is d-monotone on [σ1,σ2].
Definition 2.5. ([39])The generalized Hukuhara derivative of a fuzzy-valued function F:(σ1,σ2)→E at ζ0 is defined as
F′gH(ζ0)=limh→0F(ζ0+h)⊖gHF(ζ0)h, |
if (F)′gH(ζ0)∈E, we say that F is generalized Hukuhara differentiable (gH-differentiable) at ζ0.
Moreover, we say that F is [(i)−gH]-differentiable at ζ0 if
[F′gH(ζ0)]ˇq=[[limh→0F_(ζ0+h)⊖gHF_(ζ0)h]ˇq,[limh→0ˉF(ζ0+h)⊖gHˉF(ζ0)h]ˇq]=[(F_)′(ˇq,ζ0),(ˉF)′(ˇq,ζ0)], | (2.1) |
and that F is [(ii)−gH]-differentiable at ζ0 if
[F′gH(ζ0)]ˇq=[(ˉF)′(ˇq,ζ0),(F_)′(ˇq,ζ0)]. | (2.2) |
Definition 2.6. ([49]) We state that a point ζ0∈(σ1,σ2), is a switching point for the differentiability of F, if in any neighborhood U of ζ0 there exist points ζ1<ζ0<ζ2 such that
Type Ⅰ. at ζ1 (2.1) holds while (2.2) does not hold and at ζ2 (2.2) holds and (2.1) does not hold, or
Type Ⅱ. at ζ1 (2.2) holds while (2.1) does not hold and at ζ2 (2.1) holds and (2.2) does not hold.
Definition 2.7. ([23]) For β∈(0,1] and let the left-sided GPF-integral operator of order ϑ of F is defined as follows
Iϑ,βσ+1F(ζ)=1βϑΓ(ϑ)ζ∫σ1eβ−1β(ζ−ν)(ζ−ν)ϑ−1F(ν)dν,ζ>σ1, | (2.3) |
where β∈(0,1], ϑ∈C, Re(ϑ)>0 and Γ(.) is the Gamma function.
Definition 2.8. ([23]) For β∈(0,1] and let the left-sided GPF-derivative operator of order ϑ of F is defined as follows
Dϑ,βσ+1F(ζ)=Dn,ββn−ϑΓ(n−ϑ)ζ∫σ1eβ−1β(ζ−ν)(ζ−ν)n−ϑ−1F(ν)dν, | (2.4) |
where β∈(0,1], ϑ∈C,Re(ϑ)>0,n=[ϑ]+1 and Dn,β represents the nth-derivative with respect to proportionality index β.
Definition 2.9. ([23]) For β∈(0,1] and let the left-sided GPF-derivative in the sense of Caputo of order ϑ of F is defined as follows
cDϑ,βσ+1F(ζ)=1βn−ϑΓ(n−ϑ)ζ∫σ1eβ−1β(ζ−ν)(ζ−ν)n−ϑ−1(Dn,βF)(ν)dν, | (2.5) |
where β∈(0,1], ϑ∈C,Re(ϑ)>0 and n=[ϑ]+1.
Let Φ∈L([σ1,σ2],E), then the GPF integral of order ϑ of the fuzzy function Φ is stated as:
Φβϑ(ζ)=(Iϑ,βσ+1Φ)(ζ)=1βϑΓ(ϑ)ζ∫σ1eβ−1β(ζ−ν)(ζ−ν)ϑ−1Φ(ν)dν,ζ>σ1. | (2.6) |
Since [Φ(ζ)]ˇq=[Φ_(ˇq,ζ),ˉΦ(ˇq,ζ)] and 0<ϑ<1, we can write the fuzzy GPF-integral of the fuzzy mapping Φ depend on lower and upper mappingss, that is,
[(Iϑ,βσ+1Φ)(ζ)]ˇq=[(Iϑ,βσ+1Φ_)(ˇq,ζ),(Iϑ,βσ+1ˉΦ)(ˇq,ζ)],ζ≥σ1, | (2.7) |
where
(Iϑ,βσ+1Φ_)(ˇq,ζ)=1βϑΓ(ϑ)ζ∫σ1eβ−1β(ζ−ν)(ζ−ν)ϑ−1Φ_(ˇq,ν)dν, | (2.8) |
and
(Iϑ,βσ+1ˉΦ)(ˇq,ζ)=1βϑΓ(ϑ)ζ∫σ1eβ−1β(ζ−ν)(ζ−ν)ϑ−1ˉΦ(ˇq,ν)dν. | (2.9) |
Definition 2.10. For n∈N, order ϑ and type q hold n−1<ϑ≤n with 0≤q≤1. The left-sided fuzzy Hilfer-proportional gH-fractional derivative, with respect to ζ having β∈(0,1] of a function ζ∈Cβ1−γ[σ1,σ2], is stated as
(Dϑ,q,βσ+1Φ)(ζ)=(Iq(1−ϑ),βσ+1Dβ(I(1−q)(1−ϑ),βσ+1Φ))(ζ), |
where DβΦ(ν)=(1−β)Φ(ν)+βΦ′(ν) and if the gH-derivative Φ′(1−ϑ),β(ζ) exists for ζ∈[σ1,σ2], where
Φβ(1−ϑ)(ζ):=(I(1−ϑ),βσ+1Φ)(ζ)=1β1−ϑΓ(1−ϑ)ζ∫σ1eβ−1β(ζ−ν)(ζ−ν)ϑΦ(ν)dν,ζ≥σ1. |
Definition 2.11. Let Φ′∈L([σ1,σ2],E) and the fractional generalized Hukuhara GPF-derivative of fuzzy-valued function Φ is stated as:
(gHDϑ,βσ+1Φ)(ζ)=I1−ϑ,βσ+1(Φ′gH)(ζ)=1β1−ϑΓ(1−ϑ)ϑ∫σ1eβ−1β(ζ−ν)(ζ−ν)ϑΦ′gH(ν)dν,ν∈(σ1,ζ). | (2.10) |
Furthermore, we say that Φ is GPF[(i)−gH]-differentiable at ζ0 if
[(gHDϑ,βσ+1)]ˇq=[[1β1−ϑΓ(1−ϑ)ϑ∫σ1eβ−1β(ζ−ν)(ζ−ν)ϑΦ_′gH(ν)dν]ˇq,[1β1−ϑΓ(1−ϑ)ϑ∫σ1eβ−1β(ζ−ν)(ζ−ν)ϑˉΦ′gH(ν)dν]ˇq]=[(gHD_ϑ,βσ+1)(ˇq,ζ),(gHˉDϑ,βσ+1)(ˇq,ζ)] | (2.11) |
and that Φ is GPF[(i)−gH]-differentiable at ζ0 if
[(gHDϑ,βσ+1)]ˇq=[(gHˉDϑ,βσ+1)(ˇq,ζ),(gHD_ϑ,βσ+1)(ˇq,ζ)]. | (2.12) |
Definition 2.12. We say that a point ζ0∈(σ1,σ2), is a switching point for the differentiability of F, if in any neighborhood U of ζ0 there exist points ζ1<ζ0<ζ2 such that
Type Ⅰ. at ζ1 (2.11) holds while (2.12) does not hold and at ζ2 (2.12) holds and (2.11) does not hold, or
Type Ⅱ. at ζ1 (2.12) holds while (2.11) does not hold and at ζ2 (2.11) holds and (2.12) does not hold.
Proposition 1. ([23]) Let ϑ,ϱ∈C such that Re(ϑ)>0 and Re(ϱ)>0. Then for any β∈(0,1], we have
(Iϑ,βσ+1eβ−1β(s−σ1)ϱ−1)(ζ)=Γ(ϱ)βϑΓ(ϱ+ϑ)eβ−1β(ζ−σ1)(ζ−σ1)ϱ+ϑ−1,(Dϑ,βσ+1eβ−1β(s−σ1)ϱ−1)(ζ)=Γ(ϱ)βϑΓ(ϱ−ϑ)eβ−1β(ζ−σ1)(ζ−σ1)ϱ−ϑ−1,(Iϑ,βσ+1eβ−1β(σ2−s)ϱ−1)(ζ)=Γ(ϱ)βϑΓ(ϱ+ϑ)eβ−1β(σ2−s)(σ2−ζ)ϱ+ϑ−1,(Dϑ,βσ+1eβ−1β(σ2−s)ϱ−1)(ζ)=Γ(ϱ)βϑΓ(ϱ−ϑ)eβ−1β(σ2−s)(σ2−s)ϱ−ϑ−1. |
Lemma 2.13. ([24])For β∈(0,1], ϑ>0, 0≤γ<1. If Φ∈Cγ[σ1,σ2] and I1−ϑσ+1Φ∈C1γ[σ1,σ2], then
(Iϑ,βσ+1Dϑ,βσ+1Φ)(ζ)=Φ(ζ)−eβ−1β(ζ−σ1)(ζ−σ1)ϑ−1βϑ−1Γ(ϑ)(I1−ϑ,βσ+1Φ)(σ1). |
Lemma 2.14. ([24]) Let Φ∈L1(σ1,σ2). If Dq(1−ϑ),βσ+1Φ exists on L1(σ1,σ2), then
Dϑ,q,βσ+1Iϑ,βσ+1Φ=Iq(1−ϑ),βσ+1Dq(1−ϑ),βσ+1Φ. |
Lemma 2.15. Suppose there is a d-monotone fuzzy mapping Φ∈AC([σ1,σ2],E), where [Φ(ζ)]ˇq=[Φ_(ˇq,ζ),ˉΦ(ˇq,ζ)] for 0≤ˇq≤1,σ1≤ζ≤σ2, then for 0<ϑ<1 and β∈(0,1], we have
(i)[(Dϑ,q,βσ+1Φ)(ζ)]ˇq=[Dϑ,q,βσ+1Φ_(ˇq,ζ),Dϑ,q,βσ+1ˉΦ(ˇq,ζ)] for ζ∈[σ1,σ2], if Φ is d-increasing;
(ii)[(Dϑ,q,βσ+1Φ)(ζ)]ˇq=[Dϑ,q,βσ+1ˉΦ(ˇq,ζ),Dϑ,q,βσ+1Φ_(ˇq,ζ)] for ζ∈[σ1,σ2], if Φ is d-decreasing.
Proof. It is to be noted that if Φ is d-increasing, then [Φ′(ζ)]ˇq=[ddζΦ_(ˇq,ζ),ddζˉΦ(ˇq,ζ)]. Taking into account Definition 2.10, we have
[(Dϑ,q,βσ+1Φ)(ζ)]ˇq=[Iq(1−ϑ),βσ+1Dβ(I(1−q)(1−ϑ),βσ+1Φ_)(ˇq,ζ),Iq(1−ϑ),βσ+1Dβ(I(1−q)(1−ϑ),βσ+1ˉΦ)(ˇq,ζ)]=[Dϑ,q,βσ+1Φ_(ˇq,ζ),Dϑ,q,βσ+1ˉΦ(ˇq,ζ)]. |
If Φ is d-decreasing, then [Φ′(ζ)]ˇq=[ddζˉΦ(ˇq,ζ),ddζΦ_(ˇq,ζ)], we have
[(Dϑ,q,βσ+1Φ)(ζ)]ˇq=[Iq(1−ϑ),βσ+1Dβ(I(1−q)(1−ϑ),βσ+1ˉΦ)(ˇq,ζ),Iq(1−ϑ),βσ+1Dβ(I(1−q)(1−ϑ),βσ+1Φ_)(ˇq,ζ)]=[Dϑ,q,βσ+1ˉΦ(ˇq,ζ),Dϑ,q,βσ+1Φ_(ˇq,ζ)]. |
This completes the proof.
Lemma 2.16. For β∈(0,1],ϑ∈(0,1). If Φ∈AC([σ1,σ2],E) is a d-monotone fuzzy function. We take
z1(ζ):=(Iϑ,βσ+1Φ)(ζ)=1βϑΓ(ϑ)ζ∫σ1eβ−1β(ζ−ν)(ζ−ν)ϑ−1Φ(ν)dν, |
and
z(1−ϑ),β1:=(I(1−ϑ),βσ+1Φ)(ζ)=1β1−ϑΓ(1−ϑ)ϑ∫σ1eβ−1β(ζ−ν)(ζ−ν)ϑΦ′gH(ν)dν, |
is d-increasing on (σ1,σ2], then
(Iϑ,βσ+1Dϑ,q,βσ+1Φ)(ζ)=Φ(ζ)⊖m∑j=1RjΦ(ζj)βγΓ(γ)eβ−1β(ζ−σ1)(ζ−σ1)γ−1, |
and
(Dϑ,q,βσ+1Iϑ,βσ+1Φ)(ζ)=Φ(ζ). |
Proof. If z1(ζ) is d-increasing on [σ1,σ2] or z1(ζ) is d-decreasing on [σ1,σ2] and z(1−ϑ),β1(ζ) is d-increasing on (σ1,σ2].
Utilizing the Definitions 2.6, 2.10 and Lemma 2.13 with the initial condition (I1−γ,βσ+1Φ)(σ1)=0, we have
(Iϑ,βσ+1Dϑ,q,βσ+1Φ)(ζ)=(Iϑ,βσ+1Iq(1−ϑ),βσ+1DβI(1−q)(1−ϑ),βσ+1Φ)(ζ)=(Iγ,βσ+1DβI1−γ,βσ+1Φ)(ζ)=(Iγ,βσ+1Dγ,βσ+1Φ)(ζ)=Φ(ζ)⊖I1−γ,βσ+1Φβγ−1Γ(γ)eβ−1β(ζ−σ1)(ζ−σ1)γ−1. | (2.13) |
Now considering Proposition 1, Lemma 2.13 and Lemma 2.14, we obtain
(Dϑ,q,βσ+1Iϑ,βσ+1Φ)(ζ)=(Iq(1−ϑ),βσ+1Dq(1−ϑ),βσ+1Φ)(ζ)=Φ(ζ)⊖(I1−q(1−ϑ),βσ+1Φ)(σ1)eβ−1β(ζ−σ1)βq(1−ϑ)Γ(q(1−ϑ))(ζ−σ1)q(1−ϑ)−1=Φ(ζ). |
On contrast, since Φ∈AC([σ1,σ2],E), there exists a constant K such that K=supζ∈[σ1,σ2]¯D0[Φ(ζ),ˆ0].
Then
¯D0[Iϑ,βσ+1Φ(ζ),ˆ0]≤K1βϑΓ(ϑ)ζ∫σ1eβ−1β(ζ−ν)(ζ−ν)ϑ−1dν≤K1βϑΓ(ϑ)ζ∫σ1|eβ−1β(ζ−ν)|(ζ−ν)ϑ−1dν=KβϑΓ(ϑ+1)(ζ−σ1)ϑ, |
where we have used the fact |eβ−1βζ|<1 and Iϑ,βσ+1Φ(ζ)=0 and ζ=σ1.
This completes the proof.
Lemma 2.17. Let there be a continuous mapping Φ:[σ1,σ2]→R+ on [σ1,σ2] and hold Dϑ,q,βσ+1Φ(ζ)≤F(ξ,Φ(ξ)),ξ≥σ1, where F∈C([σ1,σ1]×R+,R+). Assume that m(ζ)=m(ζ,σ1,ξ0) is the maximal solution of the IVP
Dϑ,q,βσ+1ξ(ζ)=F(ζ,ξ),(I1−γ,βσ+1ξ)(σ1)=ξ0≥0, | (2.14) |
on [σ1,σ2]. Then, if Φ(σ1)≤ξ0, we have Φ(ζ)≤m(ζ),ζ∈[σ1,σ2].
Proof. The proof is simple and can be derived as parallel to Theorem 2.2 in [53].
Lemma 2.18. Assume the IVP described as:
Dϑ,q,βσ+1Φ(ζ)=F(ζ,Φ(ζ)),(I1−γ,βσ+1Φ)(σ1)=Φ0=0,ζ∈[σ1,σ2]. | (2.15) |
Let α>0 be a given constant and B(Φ0,α)={Φ∈R:|Φ−Φ0|≤α}. Assume that the real-valued functions F:[σ1,σ2]×[0,α]→R+ satisfies the following assumptions:
(i) F∈C([σ1,σ2]×[0,α],R+),F(ζ,0)≡0,0≤F(ζ,Φ)≤MF for all (ζ,Φ)∈[σ1,σ2]×[0,α];
(ii) F(ζ,Φ) is nondecreasing in Φ for every ζ∈[σ1,σ2]. Then the problem (2.15) has at least one solution defined on [σ1,σ2] and Φ(ζ)∈B(Φ0,α).
Proof. The proof is simple and can be derived as parallel to Theorem 2.3 in [53].
In this investigation, we find the existence and uniqueness of solution to problem 1.3 by utilizing the successive approximation technique by considering the generalized Lipschitz condition of the right-hand side.
Lemma 3.1. For γ=ϑ+q(1−ϑ),ϑ∈(0,1),q∈[0,1] with β∈(0,1], and let there is a fuzzy function F:(σ1,σ2]×E→E such that ζ→F(ζ,Φ) belongs to Cβγ([σ1,σ2],E) for any Φ∈E. Then a d-monotone fuzzy function Φ∈C([σ1,σ2],E) is a solution of IVP (1.3) if and only if Φ satisfies the integral equation
Φ(ζ)⊖gHm∑j=1RjΦ(ζj)βγΓ(γ)eβ−1β(ζ−σ1)(ζ−σ1)γ−1=1βϑΓ(ϑ)ζ∫σ1eβ−1β(ζ−ν)(ζ−ν)ϑ−1F(ν,Φ(ν))dν,ζ∈[σ1,σ2],j=1,2,...,m. | (3.1) |
and the fuzzy function ζ→I1−γσ+1F(ζ,Φ) is d-increasing on (σ1,σ2].
Proof. Let Φ∈C([σ1,σ2],E) be a d-monotone solution of (1.3), and considering z1(ζ):=Φ(ζ)⊖gH(I1−γ,βσ+1Φ)(σ1),ζ∈(σ1,σ2]. Since Φ is d-monotone on [σ1,σ2], it follows that ζ→z1(ζ) is d-increasing on [σ1,σ2] (see [43]).
From (1.3) and Lemma 2.16, we have
(Iϑ,βσ+1Dϑ,q,βσ+1Φ)(ζ)=Φ(ζ)⊖m∑j=1RjΦ(ζj)βγΓ(γ)eβ−1β(ζ−σ1)(ζ−σ1)γ−1,∀ζ∈[σ1,σ2]. | (3.2) |
Since F(ζ,Φ)∈Cγ([σ1,σ2],E) for any Φ∈E, and from (1.3), observes that
(Iϑ,βσ+1Dϑ,q,βσ+1Φ)(ζ)=Iϑ,βσ+1F(ζ,Φ(ζ))=1βϑΓ(ϑ)ζ∫σ1eβ−1β(ζ−ν)(ζ−ν)ϑ−1F(ν,Φ(ν))dν,∀ζ∈[σ1,σ2]. | (3.3) |
Additionally, since z1(ζ) is d-increasing on (σ1,σ2]. Also, we observe that ζ→Fϑ,β(ζ,Φ) is also d-increasing on (σ1,σ2].
Reluctantly, merging (3.2) and (3.3), we get the immediate consequence.
Further, suppose Φ∈C([σ1,σ2],E) be a d-monotone fuzzy function fulfills (3.1) and such that ζ→Fϑ,β(ζ,Φ) is d-increasing on (σ1,σ2]. By the continuity of the fuzzy mapping F, the fuzzy mapping ζ→Fϑ,β(ζ,Φ) is continuous on (σ1,σ2] with Fϑ,β(σ1,Φ(σ1))=limζ→σ+1Fϑ,β(ζ,Φ)=0. Then
Φ(ζ)=m∑j=1RjΦ(ζj)βγΓ(γ)eβ−1β(ζ−σ1)(ζ−σ1)γ−1+(Iϑ,βσ+1F(ζ,ζ))(ζ),I1−γ,βσ+1Φ(ζ)=m∑j=1RjΦ(ζj)+(I1−q(1−ϑ)σ+1F(ζ,Φ(ζ)))(ζ), |
and
I1−γ,βσ+1Φ(0)=m∑j=1RjΦ(ζj). |
Moreover, since ζ→Fϑ,β(ζ,Φ) is d-increasing on (σ1,σ2]. Applying, the operator Dϑ,q,βσ+1 on (3.1), yields
Dϑ,q,βσ+1(Φ(ζ)⊖gHm∑j=1RjΦ(ζj)βγΓ(γ)eβ−1β(ζ−σ1)(ζ−σ1)γ−1)=Dϑ,q,βσ+1(1βϑΓ(ϑ)ζ∫σ1eβ−1β(ζ−ν)(ζ−ν)ϑ−1F(ν,Φ(ν))dν)=F(ζ,Φ(ζ)). |
This completes the proof.
In our next result, we use the following assumption. For a given constant ℏ>0, and let B(Φ0,ℏ)={Φ∈E:¯D0[Φ,Φ0]≤ℏ}.
Theorem 3.2. Let F∈C([σ1,σ2]×B(Φ0,ℏ),E) and suppose that the subsequent assumptions hold:
(i) there exists a positive constant MF such that ¯D0[F(ζ,z1),ˆ0]≤MF, for all (ζ,z1)∈[σ1,σ2]×B(Φ0,ℏ);
(ii) for every ζ∈[σ1,σ2] and every z1,ω∈B(Φ0,ℏ),
¯D0[F(ζ,z1),F(ζ,ω)]≤g(ζ,¯D0[z1,ω]), | (3.4) |
where g(ζ,.)∈C([σ1,σ2]×[0,β],R+) satisfies the assumption in Lemma 2.18 given that problem (2.15) has only the solution ϕ(ζ)≡0 on [σ1,σ2]. Then the subsequent successive approximations given by Φ0(ζ)=Φ0 and for n=1,2,...,
Φn(ζ)⊖gHm∑j=1RjΦ(ζj)βγΓ(γ)eβ−1β(ζ−σ1)(ζ−σ1)γ−1=1βϑΓ(ϑ)ζ∫σ1eβ−1β(ζ−ν)(ζ−ν)ϑ−1F(ν,Φn−1(ν))dν, |
converges consistently to a fixed point of problem (1.3) on certain interval [σ1,T] for some T∈(σ1,σ2] given that the mapping ζ→Iϑ,βσ+1F(ζ,Φn(ζ)) is d-increasing on [σ1,T].
Proof. Take σ1<ζ∗ such that ζ∗≤[βϑℏ.Γ(1+ϑ)M+σ1]1ϑ, where M=max{Mg,MF} and put T:=min{ζ∗,σ2}. Let S be a set of continuous fuzzy functions Φ such that ω(σ1)=Φ0 and ω(ζ)∈B(Φ0,ℏ) for all ζ∈[σ1,T]. Further, we suppose the sequence of continuous fuzzy function {Φn}∞n=0 given by Φ0(ζ)=Φ0,∀ζ∈[σ1,T] and for n=1,2,..,
Φn(ζ)⊖gHm∑j=1RjΦn−1(ζj)βγΓ(γ)eβ−1β(ζ−σ1)(ζ−σ1)γ−1=1βϑΓ(ϑ)ζ∫σ1eβ−1β(ζ−ν)(ζ−ν)ϑ−1F(ν,Φn−1(ν))dν. | (3.5) |
Firstly, we show that Φn(ζ)∈C([σ1,T],B(Φ0,ℏ)). For n≥1 and for any ζ1,ζ2∈[σ1,T] with ζ1<ζ2, we have
¯D0(Φn(ζ1)⊖gHm∑j=1RjΦn−1(ζj)βγΓ(γ)eβ−1β(ζ−σ1)(ζ−σ1)γ−1,Φn(ζ2)⊖gHm∑j=1RjΦn−1(ζj)βγΓ(γ)eβ−1β(ζ−σ1)(ζ−σ1)γ−1)≤1βϑΓ(ϑ)ζ1∫σ1[eβ−1β(ζ1−ν)(ζ1−ν)ϑ−1−eβ−1β(ζ2−ν)(ζ2−ν)ϑ−1]¯D0[F(ν,Φn−1(ν)),ˆ0]dν+1βϑΓ(ϑ)ζ2∫ζ1eβ−1β(ζ2−ν)(ζ2−ν)ϑ−1¯D0[F(ν,Φn−1(ν)),ˆ0]dν. |
Using the fact that |eβ−1βζ|<1, then, on the right-hand side from the last inequality, the subsequent integral becomes 1βϑΓ(1+ϑ)(ζ2−ζ1)ϑ. Therefore, with the similar assumption as we did above, the first integral reduces to 1βϑΓ(1+ϑ)[(ζ1−σ1)ϑ−(ζ2−σ1)ϑ+(ζ2−ζ1)ϑ]. Thus, we conclude
¯D0[Φn((ζ1),Φn(ζ2))]≤MFβϑΓ(1+ϑ)[(ζ1−σ1)ϑ−(ζ2−σ1)ϑ+2(ζ2−ζ1)ϑ]≤2MFβϑΓ(1+ϑ)(ζ2−ζ1)ϑ. |
In the limiting case as ζ1→ζ2, then the last expression of the above inequality tends to 0, which shows Φn is a continuous function on [σ1,T] for all n≥1.
Moreover, it follows that Φn∈B(Φ0,ℏ) for all n≥0,ζ∈[σ1,T] if and only if Φn(ζ)⊖gHm∑j=1RjΦ(ζj)βγΓ(γ)eβ−1β(ζ−σ1)(ζ−σ1)γ−1∈B(0,ℏ) for all ζ∈[σ1,T] and for all n≥0.
Also, if we assume that Φn−1(ζ)∈S for all ζ∈[σ1,T],n≥2, then
¯D0[Φn(ζ)⊖gHm∑j=1RjΦn−1(ζj)βγΓ(γ)eβ−1β(ζ−σ1)(ζ−σ1)γ−1,ˆ0]≤1βϑΓ(ϑ)ζ∫σ1eβ−1β(ζ−ν)(ζ−ν)ϑ−1¯D0[F(ν,Φn−1(ν)),ˆ0]dν=MF(ζ−σ1)ϑβϑΓ(1+ϑ)≤ℏ. |
It follows that Φn(ζ)∈S,∀∈[σ1,T].
Henceforth, by mathematical induction, we have Φn(ζ)∈S,∀ζ∈[σ1,T] and ∀n≥1.
Further, we show that the sequence Φn(ζ) converges uniformly to a continuous function Φ∈C([σ1,T],B(Φ0,ℏ)). By assertion (ii) and mathematical induction, we have for ζ∈[σ1,T]
¯D0[Φn+1(ζ)⊖gHm∑j=1RjΦn(ζj)βγΓ(γ)eβ−1β(ζ−σ1)(ζ−σ1)γ−1,Φn(ζ)⊖gHm∑j=1RjΦn−1(ζj)βγΓ(γ)eβ−1β(ζ−σ1)(ζ−σ1)γ−1]≤ϕn(ζ),n=0,1,2,..., | (3.6) |
where ϕn(ζ) is defined as follows:
ϕn(ζ)=1βϑΓ(ϑ)ζ∫σ1eβ−1β(ζ−ν)(ζ−ν)ϑ−1g(ν,ϕn−1(ν))dν, | (3.7) |
where we have used the fact that |eβ−1βζ|<1 and ϕ0(ζ)=M(ζ−σ1)ϑβϑΓ(ϑ+1). Thus, we have, for ζ∈[σ1,T] and for n=0,1,2,...,
¯D0[Dϑ,qσ+1Φn+1(ζ),Dϑ,qσ+1Φn(ζ)]≤¯D0[F(ζ,Φn(ζ)),F(ζ,Φn−1(ζ))]≤g(ζ,¯D0[Φn(ζ),Φn−1(ζ)])≤g(ζ,ϕn−1(ζ)). |
Let n≤m and ζ∈[σ1,T], then one obtains
Dϑ,qσ+1¯D0[Φn(ζ),Φm(ζ)]≤¯D0[Dϑ,qσ+1Φn(ζ),Dϑ,qσ+1Φm(ζ)]≤¯D0[Dϑ,qσ+1Φn(ζ),Dϑ,qσ+1Φn+1(ζ)]+¯D0[Dϑ,qσ+1Φn+1(ζ),Dϑ,qσ+1Φm+1(ζ)]+¯D0[Dϑ,qσ+1Φm+1(ζ),Dϑ,qσ+1Φm(ζ)]≤2g(ζ,ϕn−1(ζ))+g(ζ,¯D0[Φn(ζ),Φm(ζ)]). |
From (ii), we observe that the solution ϕ(ζ)=0 is a unique solution of problem (2.15) and g(.,ϕn−1):[σ1,T]→[0,Mg] uniformly converges to 0, for every ϵ>0, there exists a natural number n0 such that
Dϑ,qσ+1¯D0[Φn(ζ),Φm(ζ)]≤g(ζ,¯D0[Φn(ζ),Φm(ζ)])+ϵ,forn0≤n≤m. |
Using the fact that ¯D0[Φn(σ1),Φm(σ1)]=0<ϵ and by using Lemma 2.17, we have for ζ∈[σ1,T]
¯D0[Φn(ζ),Φm(ζ)]≤δϵ(ζ),n0≤n≤m, | (3.8) |
where δϵ(ζ) is the maximal solution to the following IVP:
(Dϑ,qσ+1δϵ)(ζ)=g(ζ,δϵ(ζ))+ϵ,(I1−γσ+1δϵ)=ϵ. |
Taking into account Lemma 2.17, we deduce that [ϕϵ(.,ω)] converges uniformly to the maximal solution ϕ(ζ)≡0 of (2.15) on [σ1,T] as ϵ→0.
Therefore, in view of (3.8), we can obtain n0∈N is large enough such that, for n0<n,m,
supζ∈[σ1,T]¯D0[Φn(ζ)⊖gHm∑j=1RjΦn−1(ζj)βγΓ(γ)eβ−1β(ζ−σ1)(ζ−σ1)γ−1,Φm(ζ)⊖gHm∑j=1RjΦn−1(ζj)βγΓ(γ)eβ−1β(ζ−σ1)(ζ−σ1)γ−1]≤ϵ. | (3.9) |
Since (E,¯D0) is a complete metric space and (3.9) holds, thus {Φn(ζ)} converges uniformly to Φ∈C([σ1,σ2],B(Φ0,ℏ)). Hence
Φ(ζ)⊖gHm∑j=1RjΦ(ζj)βγΓ(γ)eβ−1β(ζ−σ1)(ζ−σ1)γ−1=limn→∞(Φn(ζ)⊖gHm∑j=1RjΦn−1(ζj)βγΓ(γ)eβ−1β(ζ−σ1)(ζ−σ1)γ−1)=1βϑΓ(ϑ)ζ∫σ1eβ−1β(ζ−ν)(ζ−ν)ϑ−1F(ν,Φn−1(ν))dν. | (3.10) |
Because of Lemma 3.1, the function Φ(ζ) is the solution to (1.3) on [σ1,T].
In order to find the unique solution, assume that Ψ:[σ1,T]→E is another solution of problem (1.3) on [σ1,T]. We denote κ(ζ)=¯D0[Φ(ζ),Ψ(ζ)]. Then κ(σ1)=0 and for every ζ∈[σ1,T], we have
Dϑ,q,βσ+1κ(ζ)≤¯D0[F(ζ,Φ(ζ)),F(ζ,Ψ(ζ))]≤g(ζ,κ(ζ)). | (3.11) |
Further, using the comaprison Lemma 2.17, we get κ(ζ)≤m(ζ), where m is a maximal solution of the IVP Dϑ,q,βσ+1m(ζ)≤g(ζ,m(ζ)),(I1−γσ+1m)(σ1)=0. By asseration (ii), we have m(ζ)=0 and hence Φ(ζ)=Ψ(ζ),∀∈[σ1,T].
This completes the proof.
Corollary 1. For β∈(0,1] and let C([σ1,σ2],E). Assume that there exist positive constants L,MF such that, for every z1,ω∈E,
¯D0[F(ζ,z1),F(ζ,ω)]≤L¯D0[z1,ω],¯D0[F(ζ,z1),ˆ0]≤MF. |
Then the subsequent successive approximations given by Φ0(ζ)=Φ0 and for n=1,2,..
Φn(ζ)⊖gHΦ0=1βϑΓ(ϑ)ζ∫σ1eβ−1β(ζ−ν)(ζ−ν)ϑ−1F(ν,Φn−1(ν))dν, |
converges consistently to a fixed point of problem (1.3) on [σ1,T] for certain T∈(σ1,σ2] given that the mapping ζ→Iϑ,βσ+1F(ζ,Φn(ζ)) is d-increasing on [σ1,T].
Example 3.3. For β∈(0,1],γ=ϑ+q(1−ϑ),ϑ∈(0,1),q∈[0,1] and δ∈R. Assume that the linear fuzzy GPF-FDE under Hilfer-GPF-derivative and moreover, the subsequent assumptions hold:
{(Dϑ,qσ+1Φ)(ζ)=δΦ(ζ)+η(ζ),ζ∈(σ1,σ2],(I1−γ,βσ+1Φ)(σ1)=Φ0=m∑j=1RjΦ(ζj),γ=ϑ+q(1−ϑ). | (3.12) |
Applying Lemma 3.1, we have
Φ(ζ)⊖gHm∑j=1RjΦ(ζj)βγΓ(γ)eβ−1β(ζ−σ1)(ζ−σ1)γ−1=δ1βϑΓ(ϑ)ζ∫σ1eβ−1β(ζ−ν)(ζ−ν)ϑ−1Φ(ν)dν+1βϑΓ(ϑ)ζ∫σ1eβ−1β(ζ−ν)(ζ−ν)ϑ−1η(ν)dν,ζ∈[σ1,σ2]=δ(Iϑ,βσ+1Φ)(ζ)+(Iϑ,βσ+1η)(ζ), |
where η∈C((σ1,σ2],E) and furthermore, assuming the diameter on the right part of the aforementioned equation is increasing. Observing F(ζ,Φ):=δΦ+η fulfill the suppositions of Corollary 1.
In order to find the analytical view of (3.12), we utilized the technique of successive approximation. Putting Φ0(ζ)=Φ0 and
Φn(ζ)⊖gHm∑j=1RjΦ(ζj)βγΓ(γ)eβ−1β(ζ−σ1)(ζ−σ1)γ−1=δ(Iϑ,βσ+1Φn−1)(ζ)+(Iϑ,βσ+1η)(ζ),n=1,2,... |
Letting n=1,δ>0, assuming there is a d-increasing mapping Φ, then we have
Φ1(ζ)⊖gHm∑j=1RjΦ(ζj)βγΓ(γ)eβ−1β(ζ−σ1)(ζ−σ1)γ−1=δm∑j=1RjΦ(ζj)(ζ−σ1)ϑβϑΓ(ϑ+1)+(Iϑ,βσ+1η)(ζ). |
In contrast, if we consider δ<0 and Φ is d-decreasing, then we have
(−1)(m∑j=1RjΦ(ζj)βγΓ(γ)eβ−1β(ζ−σ1)(ζ−σ1)γ−1⊖gHΦ1(ζ))=δm∑j=1RjΦ(ζj)(ζ−σ1)ϑβϑΓ(ϑ+1)+(Iϑ,βσ+1η)(ζ). |
For n=2, we have
Φ2(ζ)⊖gHm∑j=1RjΦ(ζj)βγΓ(γ)eβ−1β(ζ−σ1)(ζ−σ1)γ−1=m∑j=1RjΦ(ζj)[δ(ζ−σ1)ϑβϑΓ(ϑ+1)+δ2(ζ−σ1)2ϑβ2ϑΓ(2ϑ+1)]+(Iϑ,βσ+1η)(ζ)+(I2ϑ,βσ+1η)(ζ), |
if δ>0 and there is d-increasing mapping Φ, we have
(−1)(m∑j=1RjΦ(ζj)βγΓ(γ)eβ−1β(ζ−σ1)(ζ−σ1)γ−1⊖gHΦ2(ζ))=m∑j=1RjΦ(ζj)[δ(ζ−σ1)ϑβϑΓ(ϑ+1)+δ2(ζ−σ1)2ϑβ2ϑΓ(2ϑ+1)]+(Iϑ,βσ+1η)(ζ)+(I2ϑ,βσ+1η)(ζ), |
and there is δ<0,andd-increasing mapping Φ. So, continuing inductively and in the limiting case, when n→∞, we attain the solution
Φ(ζ)⊖gHm∑j=1RjΦ(ζj)βγΓ(γ)eβ−1β(ζ−σ1)(ζ−σ1)γ−1=m∑j=1RjΦ(ζj)∞∑l=1δl(ζ−σ1)lϑβlϑΓ(lϑ+1)+ζ∫σ1∞∑l=1δl−1(ζ−σ1)lϑ−1βlϑ−1Γ(lϑ)η(ν)dν=m∑j=1RjΦ(ζj)∞∑l=1δl(ζ−σ1)lϑβlϑΓ(lϑ+1)+ζ∫σ1∞∑l=0δl(ζ−σ1)lϑ+(ϑ−1)βlϑ+(ϑ−1)Γ(lϑ+ϑ)η(ν)dν=m∑j=1RjΦ(ζj)∞∑l=1δl(ζ−σ1)lϑβlϑΓ(lϑ+1)+1βϑ−1ζ∫σ1(ζ−σ1)ϑ−1∞∑l=0δl(ζ−σ1)lϑβlϑΓ(lϑ+ϑ)η(ν)dν, |
for every δ>0 and Φ is d-increasing, or δ<0 and Φ is d-decreasing, accordingly. Therefore, by means of Mittag-Leffler function Eϑ,q(Φ)=∞∑l=1ΦκΓ(lϑ+q),ϑ,q>0, the solution of problem (3.12) is expressed by
Φ(ζ)⊖gHm∑j=1RjΦ(ζj)βγΓ(γ)eβ−1β(ζ−σ1)(ζ−σ1)γ−1=m∑j=1RjΦ(ζj)Eϑ,1(δ(ζ−σ1)ϑ)+1βϑ−1ζ∫σ1(ζ−σ1)ϑ−1Eϑ,ϑ(δ(ζ−σ1)ϑ)η(ν)dν, |
for every of δ>0 and Φ is d-increasing. Alternately, if δ<0 and Φ is d-decreasing, then we get the solution of problem (3.12)
Φ(ζ)⊖gHm∑j=1RjΦ(ζj)βγΓ(γ)eβ−1β(ζ−σ1)(ζ−σ1)γ−1=m∑j=1RjΦ(ζj)Eϑ,1(δ(ζ−σ1)ϑ)⊖(−1)1βϑ−1ζ∫σ1(ζ−σ1)ϑ−1Eϑ,ϑ(δ(ζ−σ1)ϑ)η(ν)dν. |
Consider IVP
{(gHDϑ,βσ+1Φ)(ζ)=F(ζ,Φ(ζ),H1Φ(ζ),H2Φ(ζ)),ζ∈[ζ0,T]Φ(ζ0)=Φ0∈E, | (4.1) |
where β∈(0,1] and ϑ∈(0,1) is a real number and the operation gHDϑσ+1 denote the GPF derivative of order ϑ, F:[ζ0,T]×E×E×E→E is continuous in ζ which fulfills certain supposition that will be determined later, and
H1Φ(ζ)=ζ∫ζ0H1(ζ,s)Φ(s)ds,H2Φ(ζ)=T∫ζ0H2(ζ,s)Φ(s)ds, | (4.2) |
with H1,H2:[ζ0,T]×[ζ0,T]→R such that
H∗1=supζ∈[ζ0,T]ζ∫ζ0|H1(ζ,s)|ds,H∗2=supζ∈[ζ0,T]T∫ζ0|H2(ζ,s)|ds. |
Now, we investigate the existence and uniqueness of the solution of problem (4.1). To establish the main consequences, we require the following necessary results.
Theorem 4.1. Let F:[σ1,σ2]→E be a fuzzy-valued function on [σ1,σ2]. Then
(i) F is [(i)−gH]-differentiable at c∈[σ1,σ2] iff F is GPF[(i)−gH]-differentiable at c.
(ii) F is [(ii)−gH]-differentiable at c∈[σ1,σ2] iff F is GPF[(ii)−gH]-differentiable at c.
Proof. In view of Definition 2.18 and Definition 2.11, the proof is straightforward.
Lemma 4.2. ([44]) Let there be a fuzzy valued mapping F:[ζ0,T]→E such that F′gH∈E∩χrc(σ1,σ2), then
Iϑ,βζ0(gHDϑ,βσ+1F)(ζ)=F(ζ)⊖gHF(ζ0). | (4.3) |
Lemma 4.3. The IVP (4.1) is analogous to subsequent equation
Φ(ζ)=Φ0+1βϑΓ(q)ζ∫ζ0eβ−1β(ζ−ν)(ζ−ν)ϑ−1F(ν,Φ(ν),H1Φ(ν),H2Φ(ν))dν, | (4.4) |
if Φ(ζ) be GPF[(i)−gH]-differentiable,
Φ(ζ)=Φ0⊖−1βϑΓ(q)ζ∫ζ0eβ−1β(ζ−ν)(ζ−ν)ϑ−1F(ν,Φ(ν),H1Φ(ν),H2Φ(ν))dν, | (4.5) |
if Φ(ζ) be GPF[(ii)−gH]-differentiable, and
Φ(ζ)={Φ0+1βϑΓ(q)ζ∫ζ0eβ−1β(ζ−ν)(ζ−ν)ϑ−1F(ν,Φ(ν),H1Φ(ν),H2Φ(ν))dν,ζ∈[σ1,σ3],Φ0⊖−1βϑΓ(q)ζ∫ζ0eβ−1β(ζ−ν)(ζ−ν)ϑ−1F(ν,Φ(ν),H1Φ(ν),H2Φ(ν))dν,ζ∈[σ3,σ2], | (4.6) |
if there exists a point σ3∈(σ1,σ2) such that Φ(ζ) is GPF[(i)−gH]-differentiable on [σ1,σ3] and GPF[(ii)−gH]-differentiable on [σ3,σ2] and F(σ3,Φ(σ3,Φ(σ3),H1Φ(σ3))∈R.
Proof. By means of the integral operator (2.6) on both sides of (4.1), yields
\begin{eqnarray} \mathcal{I}_{\zeta_{0}}^{\vartheta,\beta}\big(\,_{\mathfrak{g}H}\mathcal{D}_{\sigma_{1}^{+}}^{\vartheta,\beta}\Phi(\zeta)\big) = \mathcal{I}_{\zeta_{0}}^{\vartheta,\beta}\big(\mathcal{F}(\zeta,\Phi(\zeta),\mathcal{H}_{1}\Phi(\zeta),\mathcal{H}_{2}\Phi(\zeta)\big). \end{eqnarray} | (4.7) |
Utilizing Lemma 4.2 and Definition 2.6, we gat
\begin{eqnarray} \Phi(\zeta)\ominus_{\mathfrak{g}H}\Phi_{0} = \frac{1}{\beta^{\vartheta}\Gamma(\mathfrak{q})}\int\limits_{\zeta_{0}}^{\zeta}e^{\frac{\beta-1}{\beta}(\zeta-\nu)}(\zeta-\nu)^{\vartheta-1}\mathcal{F}\big(\nu,\Phi(\nu),\mathcal{H}_{1}\Phi(\nu),\mathcal{H}_{2}\Phi(\nu)\big)d\nu. \end{eqnarray} | (4.8) |
In view of Defnition 2.17 and Theorem 4.1, if \Phi(\zeta) be \, ^{GPF}[(i)-\mathfrak{g}H] -differentiable,
\begin{eqnarray} \Phi(\zeta) = \Phi_{0}+\frac{1}{\beta^{\vartheta}\Gamma(\mathfrak{q})}\int\limits_{\zeta_{0}}^{\zeta}e^{\frac{\beta-1}{\beta}(\zeta-\nu)}(\zeta-\nu)^{\vartheta-1}\mathcal{F}\big(\nu,\Phi(\nu),\mathcal{H}_{1}\Phi(\nu),\mathcal{H}_{2}\Phi(\nu)\big)d\nu \end{eqnarray} | (4.9) |
and if \Phi(\zeta) be \, ^{GPF}[(ii)-\mathfrak{g}H] -differentiable
\begin{eqnarray} \Phi(\zeta) = \Phi_{0}\ominus\frac{-1}{\beta^{\vartheta}\Gamma(\mathfrak{q})}\int\limits_{\zeta_{0}}^{\zeta}e^{\frac{\beta-1}{\beta}(\zeta-\nu)}(\zeta-\nu)^{\vartheta-1}\mathcal{F}\big(\nu,\Phi(\nu),\mathcal{H}_{1}\Phi(\nu),\mathcal{H}_{2}\Phi(\nu)\big)d\nu. \end{eqnarray} | (4.10) |
In addition, when we have a switchpoint \sigma_{3}\in(\sigma_{1}, \sigma_{2}) of type (I) the \, ^{GPF}[\mathfrak{g}H] -differentiability changes from type (I) to type (II) at \zeta = \sigma_{3}. Then by (4.9) and (4.10) and Definition 2.12, The proof is easy to comprehend.
Also, we proceed with the following assumptions:
({\mathbb{A}_{1}}). \mathcal{F}:[\zeta_{0}, \mathcal{T}]\times\mathfrak{E}\times\mathfrak{E}\times\mathfrak{E}\rightarrow \mathfrak{E} is continuous and there exist positive real functions \mathcal{L}_{1}, \mathcal{L}_{2}, \mathcal{L}_{3} such that
\begin{eqnarray*} &&\bar{\mathcal{D}_{0}}\Big(\mathcal{F}(\zeta,\Phi(\zeta),\mathcal{H}_{1}\Phi(\zeta),\mathcal{H}_{2}\Phi(\zeta)),\mathcal{F}(\zeta,\Psi(\zeta),\mathcal{H}_{1}\Psi(\zeta),\mathcal{H}_{2}\Psi(\zeta))\Big)\nonumber\\&&\leq\mathcal{L}_{1}(\zeta)\bar{\mathcal{D}_{0}}(\Phi,\Psi)+\mathcal{L}_{2}(\zeta)\bar{\mathcal{D}_{0}}(\mathcal{H}_{1}\Phi,\mathcal{H}_{1}\Psi)+\mathcal{L}_{3}(\zeta)\bar{\mathcal{D}_{0}}(\mathcal{H}_{2}\Phi,\mathcal{H}_{2}\Psi). \end{eqnarray*} |
({\mathbb{A}_{2}}). There exist a number \epsilon such that \delta\leq\epsilon < 1, \, \zeta\in[\zeta_{0}, \mathcal{T}]
\delta = \mathcal{I}_{\zeta_{0}}^{\vartheta,\beta}\mathcal{P}(1+\mathcal{H}_{1}^{*}+\mathcal{H}_{2}^{*}) |
and
\mathcal{I}_{\zeta_{0}}^{\vartheta,\beta}\mathcal{P} = \sup\limits_{\zeta\in[0,\mathcal{T}]}\big\{\mathcal{I}_{\zeta_{0}}^{\vartheta,\beta}\mathcal{L}_{1},\mathcal{I}_{\zeta_{0}}^{\vartheta,\beta}\mathcal{L}_{2},\mathcal{I}_{\zeta_{0}}^{\vartheta,\beta}\mathcal{L}_{3}\big\}. |
Theorem 4.4. Let \mathcal{F}:[\zeta_{0}, \mathcal{T}]\times\mathfrak{E}\times\mathfrak{E}\times\mathfrak{E}\rightarrow \mathfrak{E} be a bounded continuous functions and holds (\mathbb{A}_{1}). Then the IVP (4.1) has a unique solution which is \, ^{GPF}[(i)-\mathfrak{g}H] -differentiable on [\zeta_{0}, \mathcal{T}], given that \delta < 1, where \delta is given in (\mathbb{A}_{2}).
Proof. Assuming \Phi(\zeta) is \, ^{GPF}[(i)-\mathfrak{g}H] -differentiability and \Phi_{0}\in\mathfrak{E} be fixed. Propose a mapping \mathfrak{F}:\mathcal{C}([\zeta_{0}, \mathcal{T}], \mathfrak{E})\rightarrow \mathcal{C}([\zeta_{0}, \mathcal{T}], \mathfrak{E}) by
\begin{eqnarray} \big(\mathfrak{F}\Phi\big)(\zeta) = \Phi_{0}+\frac{1}{\beta^{\vartheta}\Gamma(\mathfrak{q})}\int\limits_{\zeta_{0}}^{\zeta}e^{\frac{\beta-1}{\beta}(\zeta-\nu)}(\zeta-\nu)^{\vartheta-1}\mathcal{F}\big(\nu,\Phi(\nu),\mathcal{H}_{1}\Phi(\nu),\mathcal{H}_{2}\Phi(\nu)\big)d\nu,\quad for\,all\,\zeta\in[\zeta_{0},\mathcal{T}]. \end{eqnarray} | (4.11) |
Next we prove that \mathfrak{F} is contraction. For \Phi, \Psi\in\mathcal{C}([\zeta_{0}, \mathcal{T}], \mathfrak{E}) by considering of (\mathbb{A}_{1}) and by distance properties (2.3), one has
\begin{eqnarray} &&\bar{\mathcal{D}_{0}}\big(\mathfrak{F}\Phi(\zeta),\mathfrak{F}\Psi(\zeta)\big)\\&&\leq\frac{1}{\beta^{\vartheta}\Gamma(\mathfrak{q})}\int\limits_{\zeta_{0}}^{\zeta}\big\vert e^{\frac{\beta-1}{\beta}(\zeta-\nu)}\big\vert\big\vert(\zeta-\nu)^{\vartheta-1}\big\vert \bar{\mathcal{D}_{0}}\big(\mathcal{F}(\zeta,\Phi(\zeta),\mathcal{H}_{1}\Phi(\zeta),\mathcal{H}_{2}\Phi(\zeta)),\mathcal{F}(\zeta,\Psi(\zeta),\mathcal{H}_{1}\Psi(\zeta),\mathcal{H}_{2}\Psi(\zeta))\Big)d\nu\\&&\leq\frac{1}{\beta^{\vartheta}\Gamma(\mathfrak{q})}\int\limits_{\zeta_{0}}^{\zeta}\big\vert e^{\frac{\beta-1}{\beta}(\zeta-\nu)}\big\vert\big\vert(\zeta-\nu)^{\vartheta-1}\big\vert\big[\mathcal{L}_{1}\bar{\mathcal{D}_{0}}(\Phi,\Psi)+\mathcal{L}_{2}\bar{\mathcal{D}_{0}}(\mathcal{H}_{1}\Phi,\mathcal{H}_{1}\Psi)+\mathcal{L}_{3}\bar{\mathcal{D}_{0}}(\mathcal{H}_{2}\Phi,\mathcal{H}_{2}\Psi) \big]d\nu\\&&\leq\frac{1}{\beta^{\vartheta}\Gamma(\mathfrak{q})}\int\limits_{\zeta_{0}}^{\zeta}\big\vert e^{\frac{\beta-1}{\beta}(\zeta-\nu)}\big\vert\big\vert(\zeta-\nu)^{\vartheta-1}\big\vert\mathcal{L}_{1}\bar{\mathcal{D}_{0}}(\Phi,\Psi)d\nu+\frac{1}{\beta^{\vartheta}\Gamma(\mathfrak{q})}\int\limits_{\zeta_{0}}^{\zeta}\big\vert e^{\frac{\beta-1}{\beta}(\zeta-\nu)}\big\vert\big\vert(\zeta-\nu)^{\vartheta-1}\big\vert\mathcal{L}_{2}\bar{\mathcal{D}_{0}}(\mathcal{H}_{1}\Phi,\mathcal{H}_{1}\Psi)d\nu\\&&\quad+\frac{1}{\beta^{\vartheta}\Gamma(\mathfrak{q})}\int\limits_{\zeta_{0}}^{\zeta}\big\vert e^{\frac{\beta-1}{\beta}(\zeta-\nu)}\big\vert\big\vert(\zeta-\nu)^{\vartheta-1}\big\vert\mathcal{L}_{3}\bar{\mathcal{D}_{0}}(\mathcal{H}_{2}\Phi,\mathcal{H}_{2}\Psi)d\nu. \end{eqnarray} | (4.12) |
Now, we find that
\begin{eqnarray} &&\frac{1}{\beta^{\vartheta}\Gamma(\mathfrak{q})}\int\limits_{\zeta_{0}}^{\zeta}\big\vert e^{\frac{\beta-1}{\beta}(\zeta-\nu)}\big\vert\big\vert(\zeta-\nu)^{\vartheta-1}\big\vert\mathcal{L}_{2}\bar{\mathcal{D}_{0}}(\mathcal{H}_{1}\Phi,\mathcal{H}_{1}\Psi)d\nu\\&&\leq\frac{1}{\beta^{\vartheta}\Gamma(\mathfrak{q})}\int\limits_{\zeta_{0}}^{\zeta}\Big(\big\vert e^{\frac{\beta-1}{\beta}(\zeta-\nu)}\big\vert\big\vert(\zeta-\nu)^{\vartheta-1}\big\vert\mathcal{L}_{2}\bar{\mathcal{D}_{0}}(\Phi,\Psi)\int\limits_{\zeta_{0}}^{\nu}\vert\mathcal{H}_{1}(\nu,x)\vert dx \Big)d\nu\\&&\leq\mathcal{I}_{\zeta_{0}}^{\vartheta,\beta}\mathcal{L}_{2}\mathcal{H}_{1}^{*}.\bar{\mathcal{D}_{0}}(\Phi,\Psi). \end{eqnarray} | (4.13) |
Analogously,
\begin{eqnarray} &&\frac{1}{\beta^{\vartheta}\Gamma(\mathfrak{q})}\int\limits_{\zeta_{0}}^{\zeta}\big\vert e^{\frac{\beta-1}{\beta}(\zeta-\nu)}\big\vert\big\vert(\zeta-\nu)^{\vartheta-1}\big\vert\mathcal{L}_{3}\bar{\mathcal{D}_{0}}(\mathcal{H}_{2}\Phi,\mathcal{H}_{2}\Psi)d\nu\leq\mathcal{I}_{\zeta_{0}}^{\vartheta,\beta}\mathcal{L}_{3}\mathcal{H}_{1}^{*}.\bar{\mathcal{D}_{0}}(\Phi,\Psi),\\ &&\frac{1}{\beta^{\vartheta}\Gamma(\mathfrak{q})}\int\limits_{\zeta_{0}}^{\zeta}\big\vert e^{\frac{\beta-1}{\beta}(\zeta-\nu)}\big\vert\big\vert(\zeta-\nu)^{\vartheta-1}\big\vert\mathcal{L}_{1}\bar{\mathcal{D}_{0}}(\Phi,\Psi)d\nu = \mathcal{I}_{\zeta_{0}}^{\vartheta,\beta}\mathcal{L}_{1}\bar{\mathcal{D}_{0}}(\Phi,\Psi). \end{eqnarray} | (4.14) |
Then we have
\begin{eqnarray} \bar{\mathcal{D}_{0}}\big(\mathfrak{F}\Phi,\mathfrak{F}\Psi\big)&&\leq\mathcal{I}_{\zeta_{0}}^{\vartheta,\beta}\mathcal{L}_{1}\bar{\mathcal{D}_{0}}(\Phi,\Psi)+\mathcal{I}_{\zeta_{0}}^{\vartheta,\beta}\mathcal{L}_{2}\mathcal{H}_{1}^{*}.\bar{\mathcal{D}_{0}}(\Phi,\Psi)+\mathcal{I}_{\zeta_{0}}^{\vartheta,\beta}\mathcal{L}_{3}\mathcal{H}_{2}^{*}.\bar{\mathcal{D}_{0}}(\Phi,\Psi)\\&&\leq\mathcal{I}_{\zeta_{0}}^{\vartheta,\beta}\mathcal{P}(1+\mathcal{H}_{1}^{*}+\mathcal{H}_{2}^{*})\bar{\mathcal{D}_{0}}(\Phi,\Psi)\\&& < \bar{\mathcal{D}_{0}}(\Phi,\Psi). \end{eqnarray} | (4.15) |
Consequently, \mathfrak{F} is a contraction mapping on \mathcal{C}([\zeta_{0}, \mathcal{T}], \mathfrak{E}) having a fixed point \mathfrak{F}\Phi(\zeta) = \Phi(\zeta). Henceforth, the IVP (4.1) has unique solution.
Theorem 4.5. For \beta\in(0, 1] and let \mathcal{F}:[\zeta_{0}, \mathcal{T}]\times\mathfrak{E}\times\mathfrak{E}\times\mathfrak{E}\rightarrow \mathfrak{E} be a bounded continuous functions and satisfies (\mathbb{A}_{1}). Let the sequence \Phi_{n}:[\zeta_{0}, \mathcal{T}]\rightarrow \mathfrak{E} is given by
\begin{eqnarray} \Phi_{n+1}(\zeta)&& = \Phi_{0}\ominus\frac{-1}{\beta^{\vartheta}\Gamma(\vartheta)}\int\limits_{\zeta_{0}}^{\zeta}(\zeta-\nu)^{\vartheta-1}\mathcal{F}\big(\nu,\Phi_{n}(\nu),\mathcal{H}_{1}\Phi_{n}(\nu),\mathcal{H}_{2}\Phi_{n}(\nu)\big)d\nu,\quad\\ \Phi_{0}(\zeta)&& = \Phi_{0}, \end{eqnarray} | (4.16) |
is described for any n\in\mathbb{N}. Then the sequence \{\Phi_{n}\} converges to fixed point of problem (4.1) which is \, ^{GPF}[(ii)-\mathfrak{g}H] -differentiable on [\zeta_{0}, \mathcal{T}], given that \delta < 1, where \delta is defined in (\mathbb{A}_{2}).
Proof. We now prove that the sequence \{\Phi_{n}\} , given in (4.16), is a Cauchy sequence in \mathcal{C}([\zeta_{0}, \mathcal{T}], \mathfrak{E}). To do just that, we'll require
\begin{eqnarray} \bar{\mathcal{D}_{0}}(\Phi_{1},\Phi_{0})&& = \bar{\mathcal{D}_{0}}\bigg(\Phi_{0}\ominus\frac{-1}{\beta^{\vartheta}\Gamma(\vartheta)}\int\limits_{\zeta_{0}}^{\zeta}e^{\frac{\beta-1}{\beta}(\zeta-\nu)}(\zeta-\nu)^{\vartheta-1}\mathcal{F}\big(\nu,\Phi_{0}(\nu),\mathcal{H}_{1}\Phi_{0}(\nu),\mathcal{H}_{2}\Phi_{0}(\nu)\big)d\nu,\Phi_{0}\bigg)\\&&\leq\frac{1}{\beta^{\vartheta}\Gamma(\vartheta)}\int\limits_{\zeta_{0}}^{\zeta}\big\vert e^{\frac{\beta-1}{\beta}(\zeta-\nu)}\big\vert\big\vert(\zeta-\nu)^{\vartheta-1}\big\vert\bar{\mathcal{D}_{0}}\Big(\mathcal{F}\big(\nu,\Phi_{0}(\nu),\mathcal{H}_{1}\Phi_{0}(\nu),\mathcal{H}_{2}\Phi_{0}(\nu)\big), \hat{0}\Big)d\nu\\&&\leq\mathcal{I}_{\zeta_{0}}^{\vartheta,\beta}\mathcal{M}, \end{eqnarray} | (4.17) |
where \mathcal{M} = \sup_{\zeta\in[\zeta_{0}, \mathcal{T}]}\bar{\mathcal{D}_{0}}\big(\mathcal{F}(\zeta, \Phi, \mathcal{H}_{1}\Phi, \mathcal{H}_{2}\Phi), \hat{0}\big).
Since \mathcal{F} is Lipschitz continuous, In view of Definition (2.3), we show that
\begin{eqnarray} &&\bar{\mathcal{D}_{0}}(\Phi_{n+1},\Phi_{n})\\&&\leq\frac{1}{\beta^{\vartheta}\Gamma(\vartheta)}\int\limits_{\zeta_{0}}^{\zeta}\big\vert e^{\frac{\beta-1}{\beta}(\zeta-\nu)}\big\vert\big\vert(\zeta-\nu)^{\vartheta-1}\big\vert \bar{\mathcal{D}_{0}}\big(\mathcal{F}\big(\nu,\Phi_{n}(\nu),\mathcal{H}_{1}\Phi_{n}(\nu),\mathcal{H}_{2}\Phi_{n}(\nu)\big),\mathcal{F}\big(\nu,\Phi_{n-1}(\nu),\mathcal{H}_{1}\Phi_{n-1}(\nu),\mathcal{H}_{2}\Phi_{n-1}(\nu)\big)\Big)d\nu\\&&\leq\frac{1}{\beta^{\vartheta}\Gamma(\vartheta)}\int\limits_{\zeta_{0}}^{\zeta}\big\vert e^{\frac{\beta-1}{\beta}(\zeta-\nu)}\big\vert\big\vert(\zeta-\nu)^{\vartheta-1}\big\vert\mathcal{L}_{1}.\bar{\mathcal{D}_{0}}\big(\Phi_{n},\Phi_{n-1}\big)d\nu\\&&\quad+\frac{1}{\beta^{\vartheta}\Gamma(\vartheta)}\int\limits_{\zeta_{0}}^{\zeta}\big\vert e^{\frac{\beta-1}{\beta}(\zeta-\nu)}\big\vert\big\vert(\zeta-\nu)^{\vartheta-1}\big\vert\mathcal{L}_{2}.\bar{\mathcal{D}_{0}}\big(\mathcal{H}_{1}\Phi_{n},\mathcal{H}_{1}\Phi_{n-1}\big)d\nu\\&&\quad+\frac{1}{\beta^{\vartheta}\Gamma(\vartheta)}\int\limits_{\zeta_{0}}^{\zeta}\big\vert e^{\frac{\beta-1}{\beta}(\zeta-\nu)}\big\vert\big\vert(\zeta-\nu)^{\vartheta-1}\big\vert\mathcal{L}_{3}.\bar{\mathcal{D}_{0}}\big(\mathcal{H}_{2}\Phi_{n},\mathcal{H}_{2}\Phi_{n-1}\big)d\nu\\&&\leq\mathcal{I}_{\zeta_{0}}^{\vartheta,\beta}\mathcal{P}(1+\mathcal{H}_{1}^{*}+\mathcal{H}_{2}^{*})\bar{\mathcal{D}_{0}}(\Phi_{n},\Phi_{n-1})\leq\delta \bar{\mathcal{D}_{0}}(\Phi_{n},\Phi_{n-1})\leq\delta^{n}\bar{\mathcal{D}_{0}}(\Phi_{1},\Phi_{0})\leq\delta^{n}\mathcal{I}_{\zeta_{0}}^{\vartheta,\beta}\mathcal{M}. \end{eqnarray} | (4.18) |
Since \delta < 1 promises that the sequence \{\Phi_{n}\} is a Cauchy sequence in \mathcal{C}([\zeta_{0}, \mathcal{T}], \mathfrak{E}). Consequently, there exist \Phi\in\mathcal{C}([\zeta_{0}, \mathcal{T}], \mathfrak{E}) such that \{\Phi_{n}\} converges to \Phi. Thus, we need to illustrate that \Phi is a solution of the problem (4.1).
\begin{eqnarray} &&\bar{\mathcal{D}}_{0}\bigg(\Phi(\zeta)+\frac{-1}{\beta^{\vartheta}\Gamma(\mathfrak{q})}\int\limits_{\zeta_{0}}^{\zeta}e^{\frac{\beta-1}{\beta}(\zeta-\nu)}(\zeta-\nu)^{\vartheta-1}\mathcal{F}\big(\nu,\Phi(\nu),\mathcal{H}_{1}\Phi(\nu),\mathcal{H}_{2}\Phi(\nu)\big)d\nu, \Phi_{0}\bigg)\\&& = \bar{\mathcal{D}}_{0}\bigg(\Phi(\zeta)+\frac{-1}{\beta^{\vartheta}\Gamma(\mathfrak{q})}\int\limits_{\zeta_{0}}^{\zeta}e^{\frac{\beta-1}{\beta}(\zeta-\nu)}(\zeta-\nu)^{\vartheta-1}\mathcal{F}\big(\nu,\Phi(\nu),\mathcal{H}_{1}\Phi(\nu),\mathcal{H}_{2}\Phi(\nu)\big)d\nu, \Phi_{n+1}\\&&\quad+\frac{-1}{\beta^{\vartheta}\Gamma(\mathfrak{q})}\int\limits_{\zeta_{0}}^{\zeta}e^{\frac{\beta-1}{\beta}(\zeta-\nu)}(\zeta-\nu)^{\vartheta-1}\mathcal{F}\big(\nu,\Phi_{n}(\nu),\mathcal{H}_{1}\Phi_{n}(\nu),\mathcal{H}_{2}\Phi_{n}(\nu)\big)d\nu\bigg)\\&&\leq \bar{\mathcal{D}_{0}}\big(\Phi(\zeta),\Phi_{n+1}\big)+\frac{1}{\beta^{\vartheta}\Gamma(\vartheta)}\int\limits_{\zeta_{0}}^{\zeta}\big\vert e^{\frac{\beta-1}{\beta}(\zeta-\nu)}\big\vert\big\vert(\zeta-\nu)^{\vartheta-1}\big\vert\mathcal{L}_{1}.\bar{\mathcal{D}_{0}}\big(\Phi(\nu),\Phi_{n}\big)d\nu\\&&\quad+\frac{1}{\beta^{\vartheta}\Gamma(\vartheta)}\int\limits_{\zeta_{0}}^{\zeta}\big\vert e^{\frac{\beta-1}{\beta}(\zeta-\nu)}\big\vert\big\vert(\zeta-\nu)^{\vartheta-1}\big\vert\mathcal{L}_{2}.\bar{\mathcal{D}_{0}}\big(\mathcal{H}_{1}\Phi(\nu),\mathcal{H}_{1}\Phi_{n}\big)d\nu\\&&\quad+\frac{1}{\beta^{\vartheta}\Gamma(\vartheta)}\int\limits_{\zeta_{0}}^{\zeta}\big\vert e^{\frac{\beta-1}{\beta}(\zeta-\nu)}\big\vert\big\vert(\zeta-\nu)^{\vartheta-1}\big\vert\mathcal{L}_{3}.\bar{\mathcal{D}_{0}}\big(\mathcal{H}_{2}\Phi(\nu),\mathcal{H}_{2}\Phi_{n}\big)d\nu\\&&\leq \bar{\mathcal{D}_{0}}\big(\Phi(\zeta),\Phi_{n+1}\big)+\mathcal{I}_{\zeta_{0}}^{\vartheta,\beta}\mathcal{P}(1+\mathcal{H}_{1}^{*}+\mathcal{H}_{2}^{*})\bar{\mathcal{D}_{0}}(\Phi(\zeta),\Phi_{n}). \end{eqnarray} | (4.19) |
In the limiting case, when n\rightarrow \infty. Thus we have
\begin{eqnarray} \Phi(\zeta)+\frac{-1}{\beta^{\vartheta}\Gamma(\mathfrak{q})}\int\limits_{\zeta_{0}}^{\zeta}e^{\frac{\beta-1}{\beta}(\zeta-\nu)}(\zeta-\nu)^{\vartheta-1}\mathcal{F}\big(\nu,\Phi(\nu),\mathcal{H}_{1}\Phi(\nu),\mathcal{H}_{2}\Phi(\nu)\big)d\nu = \Phi_{0}. \end{eqnarray} | (4.20) |
By Lemma 4.3, we prove that \Phi is a solution of the problem (4.1). In order to prove the uniqness of \Phi(\zeta), let \Psi(\zeta) be another solution of problem (4.1) on [\zeta_{0}, \mathcal{T}]. Utilizing Lemma 4.3, gets
\begin{eqnarray*} \bar{\mathcal{D}_{0}}(\Phi,\Psi)\leq\frac{1}{\beta^{\vartheta}\Gamma(\mathfrak{q})}\int\limits_{\zeta_{0}}^{\zeta}\big\vert e^{\frac{\beta-1}{\beta}(\zeta-\nu)}\big\vert\big\vert(\zeta-\nu)^{\vartheta-1}\big\vert \bar{\mathcal{D}}_{0}\bigg(\mathcal{F}\big(\nu,\Phi(\nu),\mathcal{H}_{1}\Phi(\nu),\mathcal{H}_{2}\Phi(\nu),\mathcal{F}\big(\nu,\Psi(\nu),\mathcal{H}_{1}\Psi(\nu),\mathcal{H}_{2}\Psi(\nu)\big)\bigg)d\nu. \end{eqnarray*} |
Analogously, by employing the distance properties \bar{\mathcal{D}}_{0} and Lipschitiz continuity of \mathcal{F}, consequently, we deduce that (1-\delta)\bar{\mathcal{D}_{0}}(\Phi, \Psi)\leq0, since \delta < 1, we have \Phi(\zeta) = \Psi(\zeta) for all \zeta\in[\zeta_{0}, \mathcal{T}]. Hence, the proof is completed.
Example 4.6. Suppose the Cauchy problem by means of differential operator (2.4)
\begin{eqnarray} \mathcal{D}_{z}^{\vartheta,\beta}\Phi(z) = \mathcal{F}(z,\Phi(z)), \end{eqnarray} | (4.21) |
where \mathcal{F}(z, \Phi(z)) is analytic in \Phi and \Phi(z) is analytic in the unit disk. Therefore, \mathcal{F} can be written as
\begin{eqnarray*} \mathcal{F}(z,\Phi) = \varphi \Phi(z). \end{eqnarray*} |
Consider \mathcal{Z} = z^{\vartheta}. Then the solution can be formulated as follows:
\begin{eqnarray} \Phi(\mathcal{Z}) = \sum\limits_{j = 0}^{\infty}\Phi_{j}\mathcal{Z}^{j}, \end{eqnarray} | (4.22) |
where \Phi_{j} are constants. Putting (4.22) in (4.21), yields
\begin{eqnarray*} \frac{\partial}{\partial z}\sum\limits_{j = 0}^{\infty}\Upsilon_{\vartheta,\beta,j}\Phi_{j}\mathcal{Z}^{j}-\varphi\sum\limits_{j = 0}^{\infty}\Phi_{j}\mathcal{Z}^{j} = 0. \end{eqnarray*} |
Since
\begin{eqnarray*} \Upsilon_{\vartheta,\beta,j} = \frac{\beta^{\vartheta}\Gamma\big(\frac{j\vartheta}{\beta}+1\big)}{j\Gamma\big(\frac{j\vartheta}{\beta}+1-\vartheta\big)}, \end{eqnarray*} |
then the simple computations gives the expression
\begin{eqnarray*} \frac{\beta^{\vartheta}\Gamma\big(\frac{j\vartheta}{\beta}+1\big)}{\Gamma\big(\frac{j\vartheta}{\beta}+1-\vartheta\big)}\Phi_{j}-\varphi\Phi_{j-1} = 0. \end{eqnarray*} |
Consequently, we get
\begin{eqnarray*} \Phi_{j} = \Big(\frac{\varphi}{\beta^{\vartheta}}\Big)^{j}\frac{\Gamma\big(\frac{(j-1) {\vartheta}}{\beta}+1-\vartheta\big)\Gamma\big(\frac{j\vartheta}{\beta}+1-\vartheta\big)}{\Gamma\big(\frac{(j-1)\vartheta}{\beta}+1\big)\Gamma\big(\frac{j\vartheta}{\beta}+1\big)}. \end{eqnarray*} |
Therefore, we have the subsequent solution
\begin{eqnarray*} \Phi(\mathcal{Z}) = \sum\limits_{j = 0}^{\infty}\Big(\frac{\varphi}{\beta^{\vartheta}}\Big)^{j}\frac{\Gamma\big(\frac{(j-1) {\vartheta}}{\beta}+1-\vartheta\big)\Gamma\big(\frac{j\vartheta}{\beta}+1-\vartheta\big)}{\Gamma\big(\frac{(j-1)\vartheta}{\beta}+1\big)\Gamma\big(\frac{j\vartheta}{\beta}+1\big)}\mathcal{Z}^{j}, \end{eqnarray*} |
or equivalently
\begin{eqnarray*} \Phi(\mathcal{Z}) = \sum\limits_{j = 0}^{\infty}\Big(\frac{\varphi}{\beta^{\vartheta}}\Big)^{j}\frac{\Gamma(j+1)\Gamma\big(\frac{(j-1) {\vartheta}}{\beta}+1-\vartheta\big)\Gamma\big(\frac{j\vartheta}{\beta}+1-\vartheta\big)}{\Gamma\big(\frac{(j-1)\vartheta}{\beta}+1\big)\Gamma\big(\frac{j\vartheta}{\beta}+1\big)}\frac{\mathcal{Z}^{j}}{j!}, \end{eqnarray*} |
where \varphi is assumed to be arbitrary constant, we take
\varphi: = \beta^{\vartheta}. |
Therefore, for appropriate \vartheta, we have
\begin{eqnarray*} \Phi(\mathcal{Z})&& = \sum\limits_{j = 0}^{\infty}\Big(\frac{\varphi}{\beta^{\vartheta}}\Big)^{j}\frac{\Gamma(j+1)\Gamma\big(\frac{(j-1) {\vartheta}}{\beta}+1-\vartheta\big)\Gamma\big(\frac{j\vartheta}{\beta}+1-\vartheta\big)}{\Gamma\big(\frac{(j-1)\vartheta}{\beta}+1\big)\Gamma\big(\frac{j\vartheta}{\beta}+1\big)}\frac{\mathcal{Z}^{j}}{j!}\nonumber\\&& = \,_{3}\Psi_{2}\begin{bmatrix} (1,1),\Big(1-\vartheta-\frac{\vartheta}{\beta},\frac{\vartheta}{\beta}\Big),\Big(1-\vartheta,\frac{\vartheta}{\beta}\Big);\\\qquad\qquad\qquad\quad\quad\quad\quad\quad\qquad\qquad\qquad\qquad\mathcal{Z}\\\Big(1-\frac{\vartheta}{\beta},\frac{\vartheta}{\beta},\Big),\Big(1,\frac{\vartheta}{\beta}\Big); \end{bmatrix}\nonumber\\&& = \,_{3}\Psi_{2}\begin{bmatrix} (1,1),\Big(1-\vartheta-\frac{\vartheta}{\beta},\frac{\vartheta}{\beta}\Big),\Big(1-\vartheta,\frac{\vartheta}{\beta}\Big);\\\qquad\qquad\qquad\quad\quad\quad\quad\quad\qquad\qquad\qquad\qquad z^{\vartheta\beta}\\\Big(1-\frac{\vartheta}{\beta},\frac{\vartheta}{\beta},\Big),\Big(1,\frac{\vartheta}{\beta}\Big); \end{bmatrix}, \end{eqnarray*} |
where \vert z\vert < 1.
The present investigation deal with an IVP for \mathcal{GPF} fuzzy FDEs and we employ a new scheme of successive approximations under generalized Lipschitz condition to obtain the existence and uniqueness consequences of the solution to the specified problem. Furthermore, another method to discover exact solutions of \mathcal{GPF} fuzzy FDEs by utilizing the solutions of integer order differential equations is considered. Additionally, the existence consequences for \mathcal{FVFIDE}s under \mathcal{GPF} - \mathcal{HD} with fuzzy initial conditions are proposed. Also, the uniqueness of the so-called integrodifferential equations is verified. Meanwhile, we derived the equivalent integral forms of the original fuzzy \mathcal{FVFIDE}s whichis utilized to examine the convergence of these arrangements of conditions. Two examples enlightened the efficacy and preciseness of the fractional-order \mathcal{HD} and the other one presents the exact solution by means of the Fox-Wright function. For forthcoming mechanisms, we will relate the numerical strategies for the estimated solution of nonlinear fuzzy FDEs.
The authors would like to express their sincere thanks to the support of Taif University Researchers Supporting Project Number (TURSP-2020/217), Taif University, Taif, Saudi Arabia.
The authors declare that they have no competing interests.
[1] |
V. V. Grechko, Molecular DNA markers in phylogeny and systematics, Russ. J. Genet., 38 (2002), 851–868. https://doi.org/10.1023/A:1016890509443 doi: 10.1023/A:1016890509443
![]() |
[2] |
A. Patwardhan, S. Ray, A. Roy, Molecular markers in phylogenetic studies—A review, J. Phylogen. Evolution Biol., 2 (2014), 1–9. https://doi.org/10.4172/2329-9002.1000131 doi: 10.4172/2329-9002.1000131
![]() |
[3] |
L. Wei, Selection On synonymous Mutations Revealed by 1135 Genomes of Arabidopsis thaliana, Evol. Bioinform. Online, 16 (2020) 1176934320916794. https://doi.org/10.1177/1176934320916794 doi: 10.1177/1176934320916794
![]() |
[4] |
W. M. Brown, M. George, A. C. Wilson, Rapid evolution of animal mitochondrial DNA, PNAS, 76 (1979), 1967–1971. https://doi.org/10.1073/pnas.76.4.1967 doi: 10.1073/pnas.76.4.1967
![]() |
[5] | A. A. Bannikova, Molecular markers and modern phylogenetics of mammals, Zh. Obshch. Biol., 65 (2004), 278–305. (in Russian) |
[6] | A. C. Wilson, R. L. Cann, S. M. Carr, M. George, U. B. Gyllensten, K. M. Helm-Bychowski, et al., Mitochondrial DNA and two perspectives on evolutionary genetics, Biol. J. Linnean Soc., 26 (1985), 375–400. doi.org/10.1111/j.1095-8312.1985.tb02048.x |
[7] |
G. Pesole, E. Sbisa, G. Preparata, C. Saccone, The evolution of the mitochondrial D-loop region and the origin of modern man, Mol. Biol. Evol., 9 (1992), 587–598. https://doi.org/10.1093/oxfordjournals.molbev.a040747 doi: 10.1093/oxfordjournals.molbev.a040747
![]() |
[8] | W. M. Brown, E. H. Prager, A. Wang, A. C. Wilson, Mitochondrial DNA sequences of primates: Tempo and mode of evolution, J. Mol. Evol., 18 (1982), 225–239. |
[9] |
H. Cernohorska, S. Kubickova, O. Kopecna, A. I. Kulemzina, P. L. Perelman, F. F. Elder, et al., Molecular cytogenetic insights to the phylogenetic affinities of the giraffe (Giraffa camelopardalis) and pronghorn (Antilocapra americana), Chromosome Res., 2 (2013), 447–460. https://doi.org/10.1007/s10577-013-9361-0 doi: 10.1007/s10577-013-9361-0
![]() |
[10] |
D. Huchon, O. Madsen, M. Sibbald, K. Ament, M. J. Stanhope, F. Catzeflis, et al., Rodent phylogeny and a timescale for the evolution of glires: Evidence from an extensive taxon sampling using three nuclear genes, Mol. Biol. Evol., 19 (2002), 1053–1065. https://doi.org/10.1093/oxfordjournals.molbev.a004164 doi: 10.1093/oxfordjournals.molbev.a004164
![]() |
[11] | M. Weksler, Phylogeny of Neotropical oryzomyine rodents (Muridae: Sigmodontinae) based on the nuclear IRBP exon, Mol. Phylogenet. Evol., 29 (2003), 331–349. doi.org/10.1016/S1055-7903(03)00132-5 |
[12] |
E. Zietkiewicz, A. Rafalski, D. Labuda, Genome fingerprinting by simple sequence repeat (SSR)-anchored polymerase chain reaction amplification, Genomics, 20 (1994), 176–183. https://doi.org/10.1006/geno.1994.1151 doi: 10.1006/geno.1994.1151
![]() |
[13] |
H. Nybom, Comparison of different nuclear DNA markers for estimating intraspecific genetic diversity in plants, Mol Ecol., 13 (2004), 1143–1155. https://doi.org/10.1111/j.1365-294X.2004.02141.x doi: 10.1111/j.1365-294X.2004.02141.x
![]() |
[14] |
S. R. Pandi-Perumal, V. Srinivasan, G. J. Maestroni, D. P. Cardinali, B. Poeggeler, R. Hardeland, Melatonin: Nature's most versatile biological signal? FEBS, 273 (2006), 2813–2838. https://doi.org/10.1111/j.1742-4658.2006.05322.x doi: 10.1111/j.1742-4658.2006.05322.x
![]() |
[15] |
A. F. Alamdari, S. Rahnemayan, H. Rajabi, N. Vahed, H. R. K. Kashani, A. Rezabakhsh, et al., Melatonin as a promising modulator of aging related neurodegenerative disorders: Role of microRNAs, Pharmacol. Res., 173 (2021), 105839. https://doi.org/10.1016/j.phrs.2021.105839 doi: 10.1016/j.phrs.2021.105839
![]() |
[16] | S. Arias-Santiago, J. Aneiros-Fernández, B. Arias-Santiago, M. S. Girón-Prieto, M. Caba-Molina, A. López-Valverde, et al., MTNR1A receptor expression in normal and pathological human salivary glands, Anticancer Res., 32 (2012), 4765–4771. |
[17] |
Y. Y. Li, H. Wang, Y. Y. Zhang, Melatonin receptor 1B gene rs10830963 C/G polymorphism associated with type 2 diabetes mellitus: An updated meta-analysis of 13,752 participants, Heliyon, 8 (2022), e11786. https://doi.org/10.1016/j.heliyon.2022.e11786 doi: 10.1016/j.heliyon.2022.e11786
![]() |
[18] |
N. R. Sundaresan, M. D. Marcus Leo, J. Subramani, D. Anish, M. Sudhagar, K. A. Ahmed, et al., Expression analysis of melatonin receptor subtypes in the ovary of domestic chicken, Vet. Res. Commun., 33 (2009), 49–56. https://doi.org/10.1007/s11259-008-9071-9 doi: 10.1007/s11259-008-9071-9
![]() |
[19] |
C. Jones, G. Helfer, R. Brandstätter, Melatonin receptor expression in the zebra finch brain and peripheral tissues, Chronobiol. Int., 29 (2012), 189–202. https://doi.org/10.3109/07420528.2011.642912 doi: 10.3109/07420528.2011.642912
![]() |
[20] |
D. Y. Li, D. G. Smith, R. Hardeland, M. Y. Yang, H. L. Xu, L. Zhang, et al., Melatonin receptor genes in vertebrates, Int. J. Mol. Sci., 14 (2013), 11208–11223. https://doi.org/10.3390/ijms140611208 doi: 10.3390/ijms140611208
![]() |
[21] |
C. von Gall, J. H. Stehle, D. R. Weaver, Mammalian melatonin receptors: molecular biology and signal transduction, Cell Tissue Res., 309 (2002), 151–162. https://doi.org/10.1007/s00441-002-0581-4 doi: 10.1007/s00441-002-0581-4
![]() |
[22] |
M. Migaud, A. Daveau, B. Malpaux, MTNR1A melatonin receptors in the ovine premammillary hypothalamus: day-night variation in the expression of the transcripts, Biol. Reprod., 72 (2005), 393–398. https://doi.org/10.1095/biolreprod.104.030064 doi: 10.1095/biolreprod.104.030064
![]() |
[23] |
J. Drew, P. Barrett, J. Mercer, K. Moar, E. Canet, P. Delagrange, et al., Localization of the melatonin-related receptor in the rodent brain and peripheral tissues, J. Neuroendocrinol., 13 (2001), 453–458. https://doi.org/10.1046/j.1365-2826.2001.00651.x doi: 10.1046/j.1365-2826.2001.00651.x
![]() |
[24] |
L. Pinato, D. Ramos, A. Hataka, P. S. Rossignoli, M. D. J. Granado, M. C. Mazzetto, et al., Day/night expression of MT1 and MT2 receptors in hypothalamic nuclei of the primate Sapajus paella, J. Chem. Neuroanat., 81 (2017), 10–17. https://doi.org/10.1016/j.jchemneu.2017.01.005 doi: 10.1016/j.jchemneu.2017.01.005
![]() |
[25] |
H. Ma, J. Kang, W. Fan, H. He, F. Huang, ROR: Nuclear receptor for melatonin or not? Molecules, 26 (2021), 2693. https://doi.org/10.3390/molecules26092693 doi: 10.3390/molecules26092693
![]() |
[26] |
S. D. Huo, R. J. Long, Melatonin receptor (MTNR1A and MTNR2B) expression during the breeding season in the yak (Bos grunniens), Czech J. Anim. Sci., 59 (2014), 140–145. https://doi.org/10.17221/7294-CJAS doi: 10.17221/7294-CJAS
![]() |
[27] |
J. Baker, K. Kimpinski, Role of melatonin in blood pressure regulation: An adjunct anti-hypertensive agent, CEPP, 45 (2018), 755–766. https://doi.org/10.1111/1440-1681.12942 doi: 10.1111/1440-1681.12942
![]() |
[28] |
M. A. Quera-Salva, U. Kilic-Huck, M. F. Vecchierini, Members of the MEL consensus group of the SFRMS. Melatonin (MEL) and its use in circadian rhythm sleep-wake disorders: Recommendations of the French Medical and Research Sleep Society (SFRMS), Rev. Neurol. (Paris)., 177 (2021), 235–244. https://doi.org/10.1016/j.neurol.2020.07.021 doi: 10.1016/j.neurol.2020.07.021
![]() |
[29] |
L. A. Ostrin, Ocular and systemic melatonin and the influence of light exposure, Clin. Exp. Optom., 102 (2019), 99–108. https://doi.org/10.1111/cxo.12824 doi: 10.1111/cxo.12824
![]() |
[30] |
Y. Tao, B. Hu, Z. Ma, H. Li, E. Du, G. Wang, et al., Intravitreous delivery of melatonin affects the retinal neuron survival and visual signal transmission: in vivo and ex vivo study, Drug Deliv., 27 (2020), 1386–1396. https://doi.org/10.1080/10717544.2020.1818882 doi: 10.1080/10717544.2020.1818882
![]() |
[31] |
S. Gurunathan, M. Qasim, M. H. Kang, J. H. Kim, Role and therapeutic potential of melatonin in various type of cancers, Onco. Targets Ther., 14 (2021), 2019–2052. https://doi.org/10.2147/OTT.S298512 doi: 10.2147/OTT.S298512
![]() |
[32] |
Y. J. Guh, T. K. Tamai, T. Yoshimura, The underlying mechanisms of vertebrate seasonal reproduction, Proc. Jpn. Acad. Ser. B Phys. Biol. Sci., 95 (2019), 343–357. https://doi.org/10.2183/pjab.95.025 doi: 10.2183/pjab.95.025
![]() |
[33] |
M. V. Danilova, E. N. Usoltseva, Significance of the pineal gland hormone melatonin in maintaining the health of women of reproductive age (a review), Obstetr. Gynecol. Reproduct., 13 (2019), 337–344. https://doi.org/10.17749/2313-7347.2019.13.4.337-344 doi: 10.17749/2313-7347.2019.13.4.337-344
![]() |
[34] |
C. C. Maganhin, L. F. Fuchs, R. S. Simões, R. M. Oliveira-Filho, M. de Jesus Simões, E. C. Baracat, et al., Effects of melatonin on ovarian follicles, Eur. J. Obstet.Gynecol. Reprod. Biol., 166 (2013), 178–184. https://doi.org/10.1016/j.ejogrb.2012.10.006 doi: 10.1016/j.ejogrb.2012.10.006
![]() |
[35] |
J. S. Heo, S. Pyo, J. Y. Lim, D. W. Yoon, B. Y. Kim, J. H. Kim, et al., Biological effects of melatonin on human adipose‑derived mesenchymal stem cells, Int. J. Mol. Med., 44 (2019), 2234–2244. https://doi.org/10.3892/ijmm.2019.4356 doi: 10.3892/ijmm.2019.4356
![]() |
[36] |
A. Sotthibundhu, P. Phansuwan-Pujito, P. Govitrapong, Melatonin increases proliferation of cultured neural stem cells obtained from adult mouse subventricular zone, J. Pineal. Res., 49 (2010), 291–300. https://doi.org/10.1111/j.1600-079X.2010.00794.x doi: 10.1111/j.1600-079X.2010.00794.x
![]() |
[37] |
G. N. Georgiev, E. Marinova, R. Konakchieva, P. Todorov, Melatonin selectively influences the transcription of pluripotency and differentiation markers in human non-cancer cells, Biotechnol. Biotechnol. Equipment, 33 (2019), 286–293. https://doi.org/10.1080/13102818.2019.1571440 doi: 10.1080/13102818.2019.1571440
![]() |
[38] |
J. Fu, S. D. Zhao, H. J. Liu, Q. H. Yuan, S. M. Liu, Y. M. Zhang, et al., Melatonin promotes proliferation and differentiation of neural stem cells subjected to hypoxia in vitro, J. Pineal. Res., 51 (2011), 104–112. https://doi.org/10.1111/j.1600-079X.2011.00867.x doi: 10.1111/j.1600-079X.2011.00867.x
![]() |
[39] |
C. Tchio, S. K. Musani, A. Quarshie, G. Tosini, Association between MTNR1B polymorphisms and obesity in African American: Findings from the Jackson Heart Study, BMC Med. Genom., 14 (2021), 136. https://doi.org/10.1186/s12920-021-00983-2 doi: 10.1186/s12920-021-00983-2
![]() |
[40] | R. M. Slominski, R. J. Reiter, N. Schlabritz–Loutsevitch, R. S. Ostrom, A. T. Slominski, Melatonin membrane receptors in peripheral tissues: Distribution and functions, Mol. Cell Endocrinol., 351 (2012), 152–166. |
[41] |
Q. Xia, Z. X. Chen, Y. C. Wang, Y. S. Ma, F. Zhang, W. Che, et al., Association between the melatonin receptor 1B gene polymorphism on the risk of type 2 diabetes, impaired glucose regulation: A meta-analysis, PLoS One, 7 (2012), e50107. https://doi.org/10.1371/journal.pone.0050107 doi: 10.1371/journal.pone.0050107
![]() |
[42] |
D. Buonfiglio, C. Tchio, I. Furigo, J. Jr. Donato, K. Baba, J. Cipolla-Neto, et al., Removing melatonin receptor type 1 signaling leads to selective leptin resistance in the arcuate nucleus, J. Pineal. Res., 67 (2019), e12580. https://doi.org/10.1111/jpi.12580 doi: 10.1111/jpi.12580
![]() |
[43] |
A. Karamitri, R. Jockers, Melatonin in type 2 diabetes mellitus and obesity, Nat. Rev.Endocrinol., 1 (2019), 105–125. https://doi.org/10.1038/s41574-018-0130-1 doi: 10.1038/s41574-018-0130-1
![]() |
[44] |
H. S. Dashti, C. Vetter, J. M. Lane, M. C. Smith, A. R. Wood, M. N. Weedon, et al., Assessment of MTNR1B Type 2 diabetes genetic risk modification by shift work and morningness-eveningness preference in the UK Biobank, Diabetes, 69 (2020), 259–266. https://doi.org/10.2337/db19-0606 doi: 10.2337/db19-0606
![]() |
[45] |
P. Chaste, N. Clement, O. Mercati, J. L. Guillaume, R. Delorme, H. G. Botros, et al., Identification of pathway-biased and deleterious melatonin receptor mutants in autism spectrum disorders and in the general population, PLoS One, 5 (2010), e11495. https://doi.org/10.1371/journal.pone.0011495 doi: 10.1371/journal.pone.0011495
![]() |
[46] |
D. M. Irwin, T. D. Kocher, A. C. Wilson, Evolution of the cytochrome b gene of mammals, J. Mol. Evol., 32 (1991), 128–144. https://doi.org/10.1007/BF02515385 doi: 10.1007/BF02515385
![]() |
[47] |
S. S. Tobe, A. C. Kitchener, A. M. Linacre, Reconstructing mammalian phylogenies: A detailed comparison of the cytochrome b and cytochrome oxidase subunit I mitochondrial genes, PLoS One, 5 (2010), e14156. https://doi.org/10.1371/journal.pone.0014156 doi: 10.1371/journal.pone.0014156
![]() |
[48] |
J. D. Thompson, D. G. Higgins, T. J. Gibson, CLUSTAL W: Improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice, Nucleic Acids Res., 22 (1994), 4673–4680. https://doi.org/10.1093/nar/22.22.4673 doi: 10.1093/nar/22.22.4673
![]() |
[49] |
M. A. Larkin, G. Blackshields, N. P. Brown, R. Chenna, P. A. McGettigan, H. McWilliam, et al., Clustal W and Clustal X version 2.0., Bioinformatics, 23 (2007), 2947–2948. https://doi.org/10.1093/bioinformatics/btm404 doi: 10.1093/bioinformatics/btm404
![]() |
[50] |
K. Tamura, G. Stecher, D. Peterson, A. Filipski, S. Kumar, MEGA6: Molecular Evolutionary Genetics Analysis Version 6.0., Mol. Biol. Evol., 30 (2013), 2725–2729. https://doi.org/10.1093/molbev/mst197 doi: 10.1093/molbev/mst197
![]() |
[51] |
F. Tajima, N. Takezaki, Estimation of evolutionary distance for reconstructing molecular phylogenetic trees, Mol. Biol. Evol., 11 (1994), 278–286. https://doi.org/10.1093/oxfordjournals.molbev.a040109 doi: 10.1093/oxfordjournals.molbev.a040109
![]() |
[52] |
M. Hasegawa, H. Kishino, T. Yano, Dating of the human-ape splitting by a molecular clock of mitochondrial DNA, J. Mol. Evol., 22 (1985), 160–174. https://doi.org/10.1007/BF02101694 doi: 10.1007/BF02101694
![]() |
[53] | D. Posada, K. A. Crandall, Selecting the best-fit model of nucleotide substitution, Syst. Biol., 50 (2001), 580–601. |
[54] |
J. Felsenstein, Confidence limits on phylogenies: An approach using the bootstrap, Evolution, 39 (1985), 783–791. https://doi.org/10.1111/j.1558-5646.1985.tb00420.x doi: 10.1111/j.1558-5646.1985.tb00420.x
![]() |
[55] |
W. J. Murphy, P. A. Pevzner, S. J. O'Brien, Mammalian phylogenomics comes of age, Trends Genet., 20 (2004), 631–639. https://doi.org/10.1016/j.tig.2004.09.005 doi: 10.1016/j.tig.2004.09.005
![]() |
[56] |
M. Buckley, Ancient collagen reveals evolutionary history of the endemic South American «ungulates», Proc. R. Soc. B Biol. Sci., 282 (2015), 20142671. https://doi.org/10.1098/rspb.2014.2671 doi: 10.1098/rspb.2014.2671
![]() |
[57] |
A. Sánchez-Gracia, J. Rozas, Divergent evolution and molecular adaptation in the Drosophila odorant-binding protein family: inferences from sequence variation at the OS-E and OS-F genes, BMC Evol. Biol., 8 (2008), 323. https://doi.org/10.1186/1471-2148-8-323 doi: 10.1186/1471-2148-8-323
![]() |
[58] |
C. A. Driscoll, D. W. Macdonald, S. J. O'Brien, From wild animals to domestic pets, an evolutionary view of domestication, PNAS, 106 (2009), 9971–9978. https://doi.org/10.1073/pnas.0901586106 doi: 10.1073/pnas.0901586106
![]() |
[59] |
C. J. Hoskin, M. Higgie, K. R. McDonald, C. Moritz, Reinforcement drives rapid allopatric speciation, Nature, 437 (2005), 1353–1356. https://doi.org/10.1038/nature04004 doi: 10.1038/nature04004
![]() |
[60] |
B. M. Fitzpatrick, J. A. Fordyce, S. Gavrilets, What, if anything, is sympatric speciation? J. Evol. Biol., 21 (2008), 1452–1459. https://doi.org/10.1111/j.1420-9101.2008.01611.x doi: 10.1111/j.1420-9101.2008.01611.x
![]() |
[61] |
W. F. Bottke, D. Vokrouhlický, D. Nesvorný, An asteroid breakup 160 Myr ago as the probable source of the K/T impactor, Nature, 449 (2007), 48—53. https://doi.org/10.1038/nature06070 doi: 10.1038/nature06070
![]() |
[62] | G. Keller, T. Adatte, S. Gardin, A. Bartolini, S. Bajpai, Main Deccan volcanism phase ends near the K–T boundary: Evidence from the Krishna–Godavari Basin, SE India, Earth Planet. Sci. Lett., 268 (2008), 293–311. doi.org/10.1016/j.epsl.2008.01.015 |
[63] | R. Nielsen, Statistical tests of selective neutrality in the age of genomics, Heredity, 86 (2001), 641–647. doi.org/10.1046/j.1365-2540.2001.00895.x |
[64] | F. Tajima, Statistical method for testing the neutral mutation hypothesis by DNA polymorphism, Genetics, 123 (1989), 585–595. |
[65] | K. Tamura, D. Peterson, N. Peterson, G. Stecher, M. Nei, S. Kumar, MEGA5: Molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods, Mol. Biol. Evol., 28 (2011), 2731–2739. doi.org/10.1093/molbev/msr121 |
[66] | M. Nei, S. Kumar, Molecular Evolution and Phylogenetics, NY.: Oxford University Press, (2000), 333. |
[67] | R. S. Holmes, L. A. Cox, Comparative structures and evolution of vertebrate lipase H (LIPH) genes and proteins: A relative of the phospholipase A1 gene families, 3 Biotech, 2 (2012), 263–275. doi.org/10.1007/s13205-012-0087-z |
[68] |
C. Lavialle, G. Cornelis, A. Dupressoir, C. Esnault, O. Heidmann, C. Vernochet, et al., Paleovirology of 'syncytins', retroviral env genes exapted for a role in placentation, Philos. Trans. R. Soc. Lond. B. Biol. Sci., 368 (2013), 20120507. https://doi.org/10.1098/rstb.2012.0507 doi: 10.1098/rstb.2012.0507
![]() |
[69] |
A. Cooper, R. Fortey, Evolutionary explosions and the phylogenetic fuse, Trends Ecol. Evol., 13 (1998), 151–156. https://doi.org/10.1016/s0169-5347(97)01277-9 doi: 10.1016/s0169-5347(97)01277-9
![]() |
[70] |
M. Spaulding, M. A. O'Leary, J. Gatesy, Relationships of Cetacea (Artiodactyla) among mammals: Increased taxon sampling alters interpretations of key fossils and character evolution, PLoS One, 4 (2009), e7062. https://doi.org/10.1371/journal.pone.0007062 doi: 10.1371/journal.pone.0007062
![]() |
[71] |
Z. Luo, In search of the whales' sisters, Nature, 404 (2000), 235–237. https://doi.org/10.1038/35005194 doi: 10.1038/35005194
![]() |
[72] |
R. M. D. Beck, C. Baillie, Improvements in the fossil record may largely resolve current conflicts between morphological and molecular estimates of mammal phylogeny, Proc. R. Soc. B: Biol. Sci., 285 (2018), 20181632. https://doi.org/10.1098/rspb.2018.1632 doi: 10.1098/rspb.2018.1632
![]() |
1. | Maysaa Al-Qurashi, Saima Rashid, Fahd Jarad, Madeeha Tahir, Abdullah M. Alsharif, New computations for the two-mode version of the fractional Zakharov-Kuznetsov model in plasma fluid by means of the Shehu decomposition method, 2022, 7, 2473-6988, 2044, 10.3934/math.2022117 | |
2. | Saima Rashid, Rehana Ashraf, Zakia Hammouch, New generalized fuzzy transform computations for solving fractional partial differential equations arising in oceanography, 2023, 8, 24680133, 55, 10.1016/j.joes.2021.11.004 | |
3. | Saima Rashid, Rehana Ashraf, Ahmet Ocak Akdemir, Manar A. Alqudah, Thabet Abdeljawad, Mohamed S. Mohamed, Analytic Fuzzy Formulation of a Time-Fractional Fornberg–Whitham Model with Power and Mittag–Leffler Kernels, 2021, 5, 2504-3110, 113, 10.3390/fractalfract5030113 | |
4. | Saima Rashid, Rehana Ashraf, Fatimah S. Bayones, A Novel Treatment of Fuzzy Fractional Swift–Hohenberg Equation for a Hybrid Transform within the Fractional Derivative Operator, 2021, 5, 2504-3110, 209, 10.3390/fractalfract5040209 | |
5. | Saima Rashid, Sobia Sultana, Bushra Kanwal, Fahd Jarad, Aasma Khalid, Fuzzy fractional estimates of Swift-Hohenberg model obtained using the Atangana-Baleanu fractional derivative operator, 2022, 7, 2473-6988, 16067, 10.3934/math.2022880 | |
6. | Shuang-Shuang Zhou, Saima Rashid, Asia Rauf, Khadija Tul Kubra, Abdullah M. Alsharif, Initial boundary value problems for a multi-term time fractional diffusion equation with generalized fractional derivatives in time, 2021, 6, 2473-6988, 12114, 10.3934/math.2021703 | |
7. | Manar A. Alqudah, Rehana Ashraf, Saima Rashid, Jagdev Singh, Zakia Hammouch, Thabet Abdeljawad, Novel Numerical Investigations of Fuzzy Cauchy Reaction–Diffusion Models via Generalized Fuzzy Fractional Derivative Operators, 2021, 5, 2504-3110, 151, 10.3390/fractalfract5040151 | |
8. | Ravichandran VIVEK, Kangarajan K., Dvivek VİVEK, Elsayed ELSAYED, Dynamics and Stability of \Xi-Hilfer Fractional Fuzzy Differential Equations with Impulses, 2023, 6, 2651-4001, 115, 10.33434/cams.1257750 | |
9. | Saima Rashid, Fahd Jarad, Hind Alamri, New insights for the fuzzy fractional partial differential equations pertaining to Katugampola generalized Hukuhara differentiability in the frame of Caputo operator and fixed point technique, 2024, 15, 20904479, 102782, 10.1016/j.asej.2024.102782 |