Abstract
In this paper, the author combines the geometric theory of analytic function regarding differential superordination and subordination with fuzzy theory for the convolution product of Ruscheweyh derivative and multiplier transformation. Interesting fuzzy inequalities are obtained by the author.
Keywords:
fuzzy set theory; convex function; differential operator; fuzzy best dominant; fuzzy differential subordination; fuzzy best subordinant; fuzzy differential superordination; convolution; multiplier transformation; Ruscheweyh derivative MSC:
30C45; 30A20; 34A40
1. Introduction and Preliminaries
Based on current economic, ecological, and social problems and facts, sustainability implies a continual dynamic evolution that is motivated by human hopes about potential future prospects. The fuzzy set notion, which Lotfi A. Zadeh first proposed in 1965 [1], has multiple applications in science and technology. Fuzzy mathematical models are created in the research by employing the fuzzy set theory to evaluate the sustainable development regarding a socio-scientific environment. Fuzzy set theory connects human expectations for development stated in language concepts to numerical facts, which are reflected in measurements of sustainability indicators, despite the fact that decision-making regarding sustainable development is subjective.
Intuitionistic fuzzy set is applied to introduce a new extension to the multi-criteria decision-making model for sustainable supplier selection based on sustainable supply chain management practices in Ref. [2], taking into account the idea that choosing a suitable supplier is the key element of contemporary businesses from a sustainability perspective. Supply chain sustainability is considered in the fuzzy context for steel industry in Ref. [3] and a model for sustainable energy usage in the textile sector based on intuitionistic fuzzy sets is introduced in Ref. [4]. The study proposed in Ref. [5] using nonlinear integrated fuzzy modeling can help to predict how comfortable an office building will be and how that will affect people’s health for optimized sustainability. Healthcare system is of outermost importance and optimization models are investigated using generalizations of the fuzzy set concept in recent studies proposing an updated multi-criteria integrated decision-making approach involving interval-valued intuitionistic fuzzy sets in Ref. [6] or a flexible optimization model based on bipolar interval-valued neutrosophic sets in Ref. [7]. Another application of the fuzzy theory to integro-differential equations domain is presented in Ref. [8].
The introduction of the notion of a fuzzy set into the studies has led to the development of extensions for many domains of mathematics. Refs. [9,10] exposed different applications in mathematical domains of this notion. In geometric function theory, the introduction of the notion of fuzzy subordination used the notion of fuzzy set in 2011 [11] and the theory of fuzzy differential subordination has developed since 2012 [12], which is when Miller and Mocanu’s classical theory of differential subordination [13] started to be adapted by involving fuzzy theory aspects. The dual notion, namely fuzzy differential superordination, was introduced in 2017 [14]. Since then, numerous researchers have studied different properties of differential operators involving fuzzy differential subordinations and superordinations: Wanas operator [15,16], generalized Noor-Sălăgean operator [17], Ruscheweyh and Sălăgean operators [18] or a linear operator [19]. Univalence criteria were also derived using fuzzy differential subordination theory [20].
It is obvious that applying the fuzzy context to the theories of differential subordination and superordination generates outcomes that are interesting for complex analysis researchers who want to broaden their area of study. For example, Confluent Hypergeometric Function’s fractional integral was used for obtaining classical differential subordinations and superordinations in Ref. [21] and also to develop fuzzy differential subordinations and superordinations in Refs. [22,23]. This demonstrates that both methodologies yield intriguing findings and that studies from a fuzzy perspective are not incompatible with the interesting results attained when applying the traditional theories of differential subordination and superordination to the same subjects.
Considering this idea, in this article, the operator previously introduced in Ref. [24] as the convolution of the generalized Sălăgean operator and the Ruscheweyh derivative is used to apply the dual theories of fuzzy differential subordination and superordination. A novel class of normalized analytic functions in U is introduced via this operator and examined in the fuzzy context created in geometric function theory by embedding the concept of fuzzy set connected with analytic functions. Certain inclusion relations involving the class parameters are proved. Furthermore, interesting fuzzy differential subordinations are developed by using frequently referred to lemmas, the functions from the new class and the previously mentioned operator. When feasible, the fuzzy best dominants are also shown. Additionally, dual findings consisting of new fuzzy differential superordinations emerge, involving the convolution operator, ensuring that the best subordinants are also provided. The significance of the new theoretical findings presented in this study is demonstrated by the numerous examples generated for results obtained regarding the two dual theories, as well as by specific corollaries obtained, implying the appropriate convex functions as the fuzzy best dominants or fuzzy best subordinants within the established theorems.
To obtain the results of the article, we need the notions and results presented below:
contains all holomorphic functions in , the unit disc, and we worked on the particular subclasses
and
with , .
Definition 1.
([11]) The pair is the fuzzy subset of , where and . The set represents the support of the fuzzy set and represents the membership function of , we denote .
Definition 2.
([11]) The function is the fuzzy subordinate to the function denoted , where , when
(1) for a fixed point
(2) ,
Definition 3.
([12] Definition 2.2) Let and is a univalent function in such that . If the analytic function in with the property verifies the fuzzy subordination
then the fuzzy differential subordination has as fuzzy solution, which is the function . A fuzzy dominant of the fuzzy solutions of the fuzzy differential subordination is the univalent function if , , for all verifying (1). The fuzzy best dominant is a fuzzy dominant with the property , , for all fuzzy dominants of (1).
Definition 4.
([14]) Let and an analytic function in .
If and are univalent functions in and the fuzzy differential superordination holds
then p represents a fuzzy solution for the fuzzy differential superordination. A fuzzy subordinant for the fuzzy differential superordination is an analytic function with the property
for all verifying (2). The fuzzy best subordinate is a univalent fuzzy subordination with property , for all fuzzy subordinate of (2).
Definition 5.
([12]) denotes the set of all injective and analytic functions on , with the property for and .
We use the lemmas presented below to show our fuzzy inequalities:
Lemma 1.
([25]) Let be a convex function in and consider the function
with and .
For the holomorphic function
which satisfies the fuzzy differential subordination
the sharp fuzzy differential subordination
is satisfied.
Lemma 2.
([25]) Consider with and a convex function with the property . If satisfies the fuzzy differential subordination
then the fuzzy differential subordinations
is satisfied and
Lemma 3.
([13] [Corollary 2.6g.2, p. 66]) Consider with and a convex function with the property . If the function is univalent in and satisfies the fuzzy differential superordination
then the fuzzy differential superordination
is satisfied and the convex function represents the fuzzy best subordinant.
Lemma 4.
([13] [Corollary 2.6g.2, p. 66]) Taking with and a convex function in , we define the function
- If and the univalent function in satisfies the fuzzy differential superordinationthen the fuzzy differential superordinationis satisfied and represents the fuzzy best subordinant.
We remind the definition of the convolution product between Ruscheweyh derivative and the multiplier transformation:
Definition 6.
([24]) Let . The operator denoted by is defined as the convolution product between the multiplier transformation and the Ruscheweyh derivative , ,
Remark 1.
For , the operator has the following form where Γ is the Gamma function.
We remind also the definition of the multiplier transformation [26]:
For , and , the operator is defined by relation
and has the properties:
The definition of Ruscheweyh derivative [27] follows:
For and , the Ruscheweyh derivative is introduced by relations
For , the operator has the following form , .
Using the operator introduced in Definition 6, a new subclass of functions, the normalized analytic in , is defined in Section 2 of this article and it shows the convexity of this class. Using Lemmas 1 and 2, we obtain fuzzy inequalities regarding differential subordination, implying the operator . In Section 3, we obtain new fuzzy inequalities regarding differential superordinations involving the operator by using Lemmas 3 and 4.
2. Fuzzy Differential Subordination
Using the operator from Definition 6, we introduce the class following the pattern set in Ref. [18] and we study the fuzzy inequalities regarding differential subordinations.
Definition 7.
Consider and . The class contains the functions for which the inequality
is satisfied.
Theorem 1.
Taking a function convex in , we define , where If and , then
implies the sharp inequality
Proof.
The function satisfies the relation and after differentiation operation to apply for it, we get
and applying the operator we have
Applying the differentiation operation to the relation (5), we obtain
In these conditions, the fuzzy inequality (6) can be written as
Using Lemma 1, we get , , written in the following form , where the sharpness is given by the fact that is the fuzzy best dominant. □
Theorem 2.
Consider . For given by Theorem 1, with the following inclusion holds
where
Proof.
Following the same ideas as in the proof of Theorem 1 regarding the convex function , taking account the conditions from Theorem 2, we obtain where the function is defined by (7).
Applying Lemma 2, we have written in the following form and The function is convex, and considering that is symmetric with respect to the real axis, we get
and □
Theorem 3.
For a function convex such that we consider the function , . For , which satisfies the fuzzy inequality
then the sharp inequality holds
Proof.
Denoting we obtain , . The fuzzy inequality , using the notation made above, can be written in the following form , . Applying Lemma 1, we obtain , , written as , The sharpness is given by the fact that is the fuzzy best dominant. □
Theorem 4.
When is a convex function such that and satisfies the fuzzy inequality
we get the fuzzy inequality as a differential subordination
and the fuzzy best dominant is the convex function .
Proof.
Take and after differentiation operator applying for it, yields and the fuzzy inequality (11) takes the form Applying Lemma 2, we obtain written by considering the notation made above , , and is a convex function that verifies the differential equation associated to the fuzzy differential subordination (11) , therefore it is the fuzzy best dominant. □
Corollary 1.
For the convex function in , when satisfies the fuzzy inequality
then
where the function is the convex fuzzy best dominant.
Proof.
From Theorem 4 considering , the fuzzy inequality (12) takes the following form , and applying Lemma 2, we deduce , written as and , is the fuzzy best dominant. □
Example 1.
Let the convex function in having the property and , . For , , we get and and We have
Using Theorem 4 we get imply
Theorem 5.
Taking a function convex with the property consider the function , . If satisfies the fuzzy inequality
then the sharp inequality holds
Proof.
Denote and differentiating this relation, we get written as The fuzzy differential subordination (13) takes the following form using the notation above , and by applying Lemma 1, we get , , written as , The sharpness is given by the fact that is the fuzzy best dominant. □
Theorem 6.
Taking a function convex with the property consider the function , , with . If meets the fuzzy inequality
then the sharp inequality holds
Proof.
Consider with , and differentiating the relation we deduce for that
Therefore
The fuzzy differential subordination from the hypothesis takes the form
- , . By applying Lemma 1, we get , , written as, and this result is sharp because the function g is the fuzzy best dominant. □
Theorem 7.
For a function convex such that and for , which meets the fuzzy differential subordination
then
and the fuzzy best dominant is the convex function
Proof.
Taking and using the properties of the operator and the calculus made in the proof of Theorem 6, we deduce
Corollary 2.
Taking the convex function in , and which satisfies the fuzzy inequality
then
and the fuzzy best dominant represents the convex function
Proof.
Taking and by Theorem 7, we can write the fuzzy inequality (16) as
Using Lemma 2, we have , written as and
- is the fuzzy best dominant. □
Example 2.
Let and , , as in the Example 1. For , , , we have Then We obtain also
- , where We have
Using Theorem 7 we deduce generates
3. Fuzzy Differential Superordination
In this section we deduce interesting properties of the studied differential operator by using the fuzzy differential superordinations.
Theorem 8.
Considering a function convex in such that , for suppose that is univalent in , , where , and
then
and the fuzzy best subordinant represents the convex function
Proof.
The function satisfies the relation and applying on it the operation of differentiating, we get
and applying the operator we get
By applying the differentiation operation to relation (18) again, we obtain
In this condition, the fuzzy inequality involving differential superordination (17) becomes
Denoting
the fuzzy inequality (19) takes the following form
From Lemma 3, we deduce
written as
and the fuzzy best subordinant represents the convex function □
Corollary 3.
Considering with , for assume that is univalent in , and
then
and the fuzzy best subordinant represents the convex function
Proof.
From Theorem 8, denoting , the fuzzy inequality (21) becomes
From Lemma 3, we obtain , written as
and
is the convex fuzzy best subordinant. □
Example 3.
Let and , , as in Example 1. For , , we have and univalent functions in
For we get and , and , so
We deduce
Applying Theorem 8, we get
induce
Theorem 9.
For a convex function in consider where Re For assume that is univalent in , and
then
and the fuzzy best subordinant is
Proof.
Taking and following the ideas from the proof of Theorem 8, the fuzzy inequality (22) takes the form
and from Lemma 4, we deduce
written as
and the function represents the fuzzy best subordinant. □
Theorem 10.
For a function convex such that and assume that is univalent and . The fuzzy inequality
implies the fuzzy inequality
and the convex function represents the fuzzy best subordinant.
Proof.
Set and applying differentiation operation, we get , .
Then the fuzzy differential superordination (23) becomes
Applying Lemma 3, we get
written as
and the fuzzy best subordinant represents the convex function □
Corollary 4.
Considering the function convex in , for suppose that is univalent and . If the fuzzy inequality holds
then the fuzzy inequality holds
and the convex function , represents the fuzzy best subordinant.
Proof.
From Theorem 10 denoting , the fuzzy differential superordination (24) becomes
Using Lemma 3, we get , written as
with
and is the convex fuzzy best subordinant. □
Example 4.
Let and , . For , , as in Example 3, we obtain and univalent in
We get
From Theorem 10, we get
imply
Theorem 11.
Let a function g convex in and take the function Let and assume that is univalent, and the fuzzy inequality involving superordination
holds, then
and the function represents the fuzzy best subordinant.
Proof.
Denoting , applying the differentiation operation on it, we get and the fuzzy inequality (25) is
By Lemma 4, we derive
written as
and the best subordinate is . □
Theorem 12.
Considering a function convex such that for suppose that is univalent and . When the fuzzy inequality holds
then
and the convex function represents the fuzzy best subordinant.
Proof.
Denote applying on it the differentiation operation, we derive and In this condition, the fuzzy inequality (26) takes the following form
and applying Lemma 3, we get , , written as
and the fuzzy best subordinant becomes the convex function □
Corollary 5.
Considering the function convex in , , for assume that is univalent and When the fuzzy inequality
holds, then
and the convex function represents the fuzzy best subordinant.
Proof.
Applying Theorem 12 for , the fuzzy inequality (27) has the form
and from Lemma 3, we derive , i.e.,
and the function
becomes the convex fuzzy best subordinant. □
Theorem 13.
Considering a function convex in , define For , assume that is univalent, and satisfies the fuzzy inequality involving superordination
then
and the fuzzy best subordinant represents the function
Proof.
Denote and differentiating this relation, we derive With this notation, the fuzzy inequality (28) becomes
From Lemma 4, we obtain , written as
where is the fuzzy best subordinant. □
Theorem 14.
For a function convex such that and for assume that is univalent and . When the fuzzy inequality
for holds, then
and the convex function is the fuzzy best subordinant.
Proof.
Set , with , we obtain after differentiating this relation that
With the notation above, the fuzzy differential superordination (29) becomes
and from Lemma 3, we deduce which implies
and the convex function is the fuzzy best subordinant. □
Corollary 6.
Considering the function convex in , for assume that is univalent and When the fuzzy inequality involving superordination
for holds, then
and the convex function is the fuzzy best subordinant.
Proof.
From Theorem 14 for , the fuzzy superordination (30) is
and from Lemma 3, we get equivalent with
and the convex function
represents the fuzzy best subordinant. □
Example 5.
Let the convex function in , and , . For , , as in Example 2 we get and .
Assume that function
- is univalent in , where
We deduce
Using Theorem 14, we get
imply
Theorem 15.
Setting the function convex in consider . Assume that is univalent for and for which the fuzzy superordination
holds for then
where represents the fuzzy best subordinant.
Proof.
Denoting , differentiating it and making some calculus, we obtain With this notation the fuzzy differential superordination (31) takes the following form
From Lemma 4, we deduce equivalent with
and represents the fuzzy best subordinant. □
4. Conclusions
The primary goal of the study described in this paper is to present new results concerning fuzzy aspects introduced in the geometric theory of analytic functions in the hope that it will be useful in future research on sustainability, similar to how numerous other applications of the fuzzy set concept have prompted the creation of sustainability models in a variety of economic, environmental, and social activities.
The operator resulted from the convolution product of the Ruscheweyh derivative and multiplier transformation from Definition 6. In Definition 7 of Section 2 we introduced a new subclass of functions in . Fuzzy inequalities involving subordinations are studied in the theorems of Section 2 using the convexity property and involving the operator and functions from the newly introduced class. Moreover, examples are provided to establish how the findings might be applied. In Section 3, fuzzy inequalities involving superordinations regarding the operator are established and the best subordinants are given. The relevance of the results is also illustrated using examples.
As future research, the operator studied in this paper could be adapted to quantum calculus and obtain differential subordinations and superordinations for it by involving q-fractional calculus, as seen in Ref. [28]. In addition, coefficient studies can be done regarding the new class introduced in Definition 7 such as estimations for Hankel determinants of different orders and Toeplitz determinants or the Fekete–Szegö problem. Hopefully, the new fuzzy results presented here will find applications in future studies concerning real life contexts.
Funding
The publication of this research was partially supported by University of Oradea.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Not applicable.
Conflicts of Interest
The author declares no conflict of interest.
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