Innovative Research on Urban Community Governance Decision-Making Relying on Distributed High-Performance Computing Blockchain Key Algorithms.


Journal

Computational intelligence and neuroscience
ISSN: 1687-5273
Titre abrégé: Comput Intell Neurosci
Pays: United States
ID NLM: 101279357

Informations de publication

Date de publication:
2022
Historique:
received: 18 01 2022
accepted: 16 02 2022
entrez: 25 3 2022
pubmed: 26 3 2022
medline: 29 3 2022
Statut: epublish

Résumé

Since the country began to go global, the country's economy has developed rapidly, cultural exchanges between countries have become more and more frequent, and foreign cultures have begun to gradually spread to the country. At the same time, through the absorption of foreign community governance experience, domestic research on community governance has also begun to be put on the agenda. This paper aims to study the innovative exploration of urban community governance mechanisms through key high-performance computing algorithms. To this end, this paper proposes a combination of the Bayesian algorithm and distributed high-performance computing to analyze and explore the governance and management methods of the community through its efficient and stable computing power, and derive the most suitable community governance mechanism. An experiment was also set up for comparative analysis. The experimental results show that the community governance mechanism derived from the key distributed high-performance computing algorithm improves the community governance capability by 19.4%, effectively improving the community governance and management issues.

Identifiants

pubmed: 35330600
doi: 10.1155/2022/4078014
pmc: PMC8940554
doi:

Types de publication

Journal Article Retracted Publication

Langues

eng

Sous-ensembles de citation

IM

Pagination

4078014

Commentaires et corrections

Type : RetractionIn

Informations de copyright

Copyright © 2022 Jun Wen and Peihong Xie.

Déclaration de conflit d'intérêts

The authors declare no conflicts of interest.

Références

Science. 2016 Jun 24;352(6293):1500-1
pubmed: 27339957
Cell Syst. 2017 Feb 22;4(2):194-206.e9
pubmed: 28089542

Auteurs

Jun Wen (J)

School of Cultural Creative Industries Management, Shanghai Institute of Visual Arts, Shanghai 201620, China.

Peihong Xie (P)

School of Management, Shanghai University of International Business and Economics, Shanghai 201620, China.

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