scholarly journals Certain subordination results involving a generalized multiplier transformation operator

2013 ◽  
pp. 85-106 ◽  
Author(s):  
Poonam Sharma ◽  
Jugal K. Prajapat ◽  
Ravinder Krishna Raina
Author(s):  
Deepali Khurana ◽  
Raj Kumar ◽  
Sibel Yalcin

We define two new subclasses, $HS(k, \lambda, b, \alpha)$ and \linebreak $\overline{HS}(k, \lambda, b, \alpha)$, of univalent harmonic mappings using multiplier transformation. We obtain a sufficient condition for harmonic univalent functions to be in $HS(k,\lambda,b,\alpha)$ and we prove that this condition is also necessary for the functions in the class $\overline{HS} (k,\lambda,b,\alpha)$. We also obtain extreme points, distortion bounds, convex combination, radius of convexity and Bernandi-Libera-Livingston integral for the functions in the class $\overline{HS}(k,\lambda,b,\alpha)$.


1995 ◽  
Vol 18 (3) ◽  
pp. 463-467
Author(s):  
Nak Eun Cho ◽  
Ji A. Kim

The object of the present paper is to introduce a new class∑n(α)of meromorphic functions defined by a multiplier transformation and to investigate some properties for the class∑n(α)Further we study integrals of functions in∑n(α).


Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 640 ◽  
Author(s):  
Xu Libo ◽  
Li Xingsen ◽  
Pang Chaoyi ◽  
Guo Yan

In this paper, a new approach and framework based on the interval dependent degree for multi-criteria group decision-making (MCGDM) problems with simplified neutrosophic sets (SNSs) is proposed. Firstly, the simplified dependent function and distribution function are defined. Then, they are integrated into the interval dependent function which contains interval computing and distribution information of the intervals. Subsequently, the interval transformation operator is defined to convert simplified neutrosophic numbers (SNNs) into intervals, and then the interval dependent function for SNNs is deduced. Finally, an example is provided to verify the feasibility and effectiveness of the proposed method, together with its comparative analysis. In addition, uncertainty analysis, which can reflect the dynamic change of the final result caused by changes in the decision makers’ preferences, is performed in different distribution function situations. That increases the reliability and accuracy of the result.


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