sos model
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2020 ◽  
Vol 86 (3) ◽  
pp. 315-324
Author(s):  
Muzaffar M. Rahmatullaev ◽  
B.U. Abraev


2020 ◽  
Vol 24 (6) ◽  
pp. 711-733
Author(s):  
Rune Bjerke ◽  
Erlend Kirkesaether

This article proposes a sponsorship activation framework that shows the way sponsors realize internal and external brand building objectives and how important sponsorship characteristics and capabilities can be used as tools in sponsors' brand building. The framework is a result of a conceptual and exploratory approach, a merger of theories from marketing and organization, and findings based on qualitative data. As well as reviewing relevant literature, we interviewed two marketing managers from institutions representing sports sponsorship objects and eight marketing managers with sponsorship responsibility working for eight different sponsors. Additionally, applying a case study methodology, we analyzed documents describing sponsorship strategies of three sports sponsors and interviewed their marketing managers. In the article we present a sponsorship activation framework (Sports Event and Athlete-Based Brand Building) (SEA-BB) and the Sports Event and Athlete Sponsorship Object Star (SEA-SOS) model. The framework serves as a specific guideline for sponsorship objects, such as sports events and sports athletes and suggests the important characteristics and capabilities they should develop to attract sponsors. For sponsors, the Sponsorship Object Star recommends what object characteristics and capabilities are important to facilitate sponsors' internal and external brand building. The proposed frameworks serve as effective guidelines for both sports sponsors and sports sponsorship objects like events, sports organizations, teams, and athletes.





Author(s):  
Chaojie Wang

Clinical decisions require timely availability of holistic patient health information including clinical, demographic, behavioral, and socioeconomic risk factors. Health information exchanges (HIEs) help bridge the technical and organizational divides between disparate Electronic health records (EHR) systems and facilitate the sharing of health information between providers and between patients and providers through collaborative governance, secure protocols, and interoperable standards. HIEs come in many varieties and can be highly complex, both technically and organizationally. This conceptual paper adopts a system of systems (SoS) framework from the systems engineering discipline to analyze and break down the complexity of HIEs. The mnemonic nature of the five characteristics of the Boardman and Sauser SoS Model (A for Autonomy, B for Belonging, C for Connectivity, D for Diversity, and E for Emergence) makes it easier to understand the intricacy of HIEs and helps remove the barriers to effective use of HIEs for care coordination, patient safety, and patient-centered care quality.



2020 ◽  
Vol 2020 (2) ◽  
pp. 145-156
Author(s):  
M.M. Rahmatullaev ◽  
M.R. Abdusalomova ◽  
M.A. Rasulova


2019 ◽  
Vol 199 (1) ◽  
pp. 586-592 ◽  
Author(s):  
M. A. Rasulova
Keyword(s):  


2019 ◽  
Vol 24 (0) ◽  
Author(s):  
Florian Henning ◽  
Christof Külske ◽  
Arnaud Le Ny ◽  
Utkir A. Rozikov
Keyword(s):  


Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Bong Gu Kang ◽  
Seon Han Choi ◽  
Se Jung Kwon ◽  
Jun Hee Lee ◽  
Tag Gon Kim

Simulation of a system-of-systems (SoS) model, which consists of a combat model and a network model, has been used to analyze the performance of network-centric warfare in detail. However, finding the combat model parameters satisfying the required combat power using simulation can take a long time for two reasons: (1) the prolonged execution time per simulation run and (2) the enormous number of simulation runs. This paper proposes a simulation-based optimization method for the SoS-based simulation model to overcome these problems. The method consists of two processes: (1) the transformation of the SoS-based model into an integrated model using the neural network to reduce the execution time and (2) the optimization of the integrated model using the genetic algorithm with ranking and selection to decrease the number of simulation runs. The experimental result reveals that the proposed method significantly reduced the time for finding the optimal combat parameters with an acceptable level of accuracy.



Author(s):  
Derek L. Milne ◽  
Robert Reiser

AbstractIn this Introduction to the Special Issue of the Cognitive Behaviour Therapist on clinical supervision we start by highlighting the unmet and overdue need for coherent organizational systems to support, guide and develop clinical supervisors. We identify a seven-step, cyclical model that describes how such a system might work, with particular reference to CBT supervision. These steps start with conceptualization (e.g. definition of CBT supervision) and complete the problem-solving cycle with evaluation (e.g. corrective feedback). We provide an overview of typical research and development activity for each part of this model to illustrate how a sound supervision infrastructure might best be developed. The SOS model provides a systematic approach to indicate the organizational conditions under which CBT supervision might flourish.



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