1. Introduction
Let
represent the class of holomorphic and univalent functions on
such that
and let
denote the class of holomorphic functions on
. The class of holomorphic functions in the open unit disk of the complex plane
is denoted in this study by a note
, with
standing as the unit disk’s boundary. For
, we define
and
We denote by
which is the set of convex functions on
.
Let
and
be analytic in
Then
is subordinate to
written as
if there exists a Schwarz function
which is analytic in
with
and
for all
such that
Furthermore, if the function
is univalent in
then we have the following equivalence (see [
1,
2]):
In order to introduce the notion of fuzzy differential subordination, we use the following definitions and propositions:
Definition 1 ([
3])
. Assume that is a Fuzzy subset and is an application. A pair of where anda fuzzy subset. The fuzzy set is called a function . Let
be denoted by
and
Proposition 1 ([
4])
. (i) If , then we have where and if , then we have where and Definition 2 ([
4])
. Let be a fixed point and let the functions The function Υ is said to be fuzzy subordinate to g, and we write or which satisfies the following conditions:- (i)
- (ii)
Proposition 2 ([
4])
. Assume that is a fixed point and the functions If then- (i)
- (ii)
where and are defined by (2) and (3), respectively. Definition 3 ([
5])
. Assume that and , . If p satisfies the requirements of the second-order fuzzy differential subordination and is analytic in Λ, with ,If q is a fuzzy dominant of the fuzzy differential subordination solutions, then p is said to be a fuzzy solution of the fuzzy differential subordination and satisfiesfor each and every p satisfying (4). Definition 4. A fuzzy dominant that satisfiesthen The fuzzy best dominant of (4) is referred to for all fuzzy dominants. Assume the function
is given by
The Hadamard (or convolution) product of
and
is defined as
A variable
x is said to have the
Pascal distribution if it takes the values
with the probabilities
,
,
,
, …, respectively, where
q and
r are called the parameters, and thus we have the probability formula
Now we present a power series whose coefficients are Pascal distribution probabilities, i.e.,
We easily determine from the ratio test that the radius of convergence of the above power series is at least ; hence, .
El-Deeb and Bulboacă [
6] introduced the linear operator
defined by
where
is given by (
1), and the symbol “*” stands for the
Hadamard (or convolution) product.
Remark 1. (i) For the operator reduces to , introduced and studied by El-Deeb et al. [7]; (ii) for and the operator reduces to , introduced and studied by El-Deeb et al. [7]. Using the operator , we create a class of analytical functions and derive several fuzzy differential subordinations for this class.
Definition 5. If the function belongs to the class for all and satisfies the inequality 2. Preliminary
The following lemmas are needed to show our results.
Lemma 1 ([
2])
. Assume that and . If , then . Lemma 2 (Theorem 2.6 in [
8])
. If is a convex function such that , with If such that , is an analytic function in Λ andthenwhereThe function q is convex, and it is the fuzzy best dominant.
Lemma 3 (Theorem 2.7 in [
8])
. Let be a convex function in Λ and where and ifandThis result is sharp.
We define the fuzzy differential subordination general theory and its applications (see [
9,
10,
11,
12,
13]). The method of fuzzy differential subordination is applied in the next section to obtain a set of fuzzy differential subordinations related to the operator
.
3. Main Results
Assume that , and are mentioned throughout this paper.
Theorem 1. Let k belong to in Λ, and If andthenimplies Proof. Since
by differentiating, we obtain
and
and also, by differentiating (
7), we obtain
The fuzzy differential subordination (
6) technique is used
Putting (
10) in (
9), we have
Using Lemma (3), we obtain
and therefore,
where
k is the fuzzy best dominant. □
Putting and in Theorem 1, we obtain the following example since the operator reduces to .
Example 1. Let k be an element of in Λ and If and is given by (5), thenimplies Theorem 2. Assume that and is given by (5), thenwhere Proof. A function
h belongs to
, and we obtain from the hypothesis of Theorem 2 using the same technique as that in the proof of Theorem 1 that
where
is defined in (
10). By using Lemma 2, we obtain
which implies
where
where
is symmetric with respect to the real axis, so we have
and
□
Theorem 3. Assume that k belongs to in Λ, that and that When and the fuzzy differential subordination is satisfied,holds, then Proof. Let
and we obtain that
so
implies
Using the Lemma 3, we obtain
and we obtain
□
Theorem 4. Consider , which satisfies when . If the fuzzy differential subordinationthenwherethe function k is convex, and it is the fuzzy best dominant. Proof. Let
where
From Lemma 1, we have
belongs to the class
which satisfies the fuzzy differential subordination (
17). Since
it is the fuzzy best dominant. We have
then (
17) becomes
By using Lemma 3, we obtain
then
□
Putting in Theorem 4. As a result, we have the following corollary:
Corollary 1. Let be a convex function in Λ, with If and verifies the fuzzy differential subordinationthenthenwherethe function k is convex and it is the fuzzy best dominant. Putting and in Corollary 1, we obtain the following example.
Example 2. Let be a convex function in Λ, with If and verifies the fuzzy differential subordinationthenwhere 4. Conclusions
All of the above results provide information about fuzzy differential subordinations for the operator ; we also provide certain properties for the class of univalent analytic functions. Using these classes and operators, we can create some simple applications.