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A bibliometric analysis of the application of social network analysis in supply chain management

100%

Wang C.

LogForum

2022

tom Vol. 18, no. 1

123--136

Background: This paper presents a bibliometric overview of research published application of social network analysis in supply chain management in recent decades. It may be useful for showing the most important problems in this area. With this aim, Citespace is used to analyse the literature on the application of social network analysis in supply chain management to clarify the development and research trend. Bibliometric analysis is the quantitative study of bibliographic material. It provides a general picture of a research field that can be classified by papers, authors, and journals. The main objective of this study is to investigate the knowledge domain about application social network analysis in the supply chain field and reveal the thematic patterns and topics of high interest to researchers to predict emerging trends in the literature. Methods: To investigate the growth of studies about the applicable social network in supply chain management, 647 articles were reviewed by CiteSpace software. These papers were collected from the Core Collection of Thomson Reuters and published in 16 journals in operations research and management science from 2004 to 2021. Document co-citation analysis, clustering analysis, and citation burst detection were conducted to investigate and examine the thematic patterns, emerging trends, and critical articles of the knowledge domain. Results: Social network approaches are increasingly popular in the supply chain. Four major clusters are discussed in detail, namely multi-objective optimization, sustainable supply chain, supply network, and circular economy. Three research trends of supply chain network design, structural characteristics, and supplier selection and evaluation were identified based on citation bursts analysis. Conclusions: The present study offers a new approach to visualizing relevant data to synthesize scientific research findings of the application of social network analysis in supply chain management. Additionally, directions for future research in this area are presented.

A Classification Method Related to Respiratory Disorder Events Based on Acoustical Analysis of Snoring

51%

Wang C. , Peng J. , Zhang X.

Archives of Acoustics

2020

tom Vol. 45, No. 1

141--151

Acoustical analysis of snoring provides a new approach for the diagnosis of obstructive sleep apnea hypopnea syndrome (OSAHS). A classification method is presented based on respiratory disorder events to predict the apnea-hypopnea index (AHI) of OSAHS patients. The acoustical features of snoring were extracted from a full night’s recording of 6 OSAHS patients, and regular snoring sounds and snoring sounds related to respiratory disorder events were classified using a support vector machine (SVM) method. The mean recognition rate for simple snoring sounds and snoring sounds related to respiratory disorder events is more than 91.14% by using the grid search, a genetic algorithm and particle swarm optimization methods. The predicted AHI from the present study has a high correlation with the AHI from polysomnography and the correlation coefficient is 0.976. These results demonstrate that the proposed method can classify the snoring sounds of OSAHS patients and can be used to provide guidance for diagnosis of OSAHS.