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An FSV analysis approach to verify the robustness of the triple-correlation analysis theoretical framework

Among all the gas disasters, gas concentration exceeding the threshold limit value (TLV) has been the leading cause of accidents. However, most systems still focus on exploring the methods and framework for avoiding reaching or exceeding TLV of the gas concentration from viewpoints of impacts on geo...

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Autores principales: Wu, Robert M. X., Zhang, Zhongwu, Zhang, Huan, Wang, Yongwen, Shafiabady, Niusha, Yan, Wanjun, Gou, Jinwen, Gide, Ergun, Zhang, Siqing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10267157/
https://www.ncbi.nlm.nih.gov/pubmed/37316559
http://dx.doi.org/10.1038/s41598-023-35900-3
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author Wu, Robert M. X.
Zhang, Zhongwu
Zhang, Huan
Wang, Yongwen
Shafiabady, Niusha
Yan, Wanjun
Gou, Jinwen
Gide, Ergun
Zhang, Siqing
author_facet Wu, Robert M. X.
Zhang, Zhongwu
Zhang, Huan
Wang, Yongwen
Shafiabady, Niusha
Yan, Wanjun
Gou, Jinwen
Gide, Ergun
Zhang, Siqing
author_sort Wu, Robert M. X.
collection PubMed
description Among all the gas disasters, gas concentration exceeding the threshold limit value (TLV) has been the leading cause of accidents. However, most systems still focus on exploring the methods and framework for avoiding reaching or exceeding TLV of the gas concentration from viewpoints of impacts on geological conditions and coal mining working-face elements. The previous study developed a Trip-Correlation Analysis Theoretical Framework and found strong correlations between gas and gas, gas and temperature, and gas and wind in the gas monitoring system. However, this framework's effectiveness must be examined to determine whether it might be adopted in other coal mine cases. This research aims to explore a proposed verification analysis approach—First-round—Second-round—Verification round (FSV) analysis approach to verify the robustness of the Trip-Correlation Analysis Theoretical Framework for developing a gas warning system. A mixed qualitative and quantitative research methodology is adopted, including a case study and correlational research. The results verify the robustness of the Triple-Correlation Analysis Theoretical Framework. The outcomes imply that this framework is potentially valuable for developing other warning systems. The proposed FSV approach can also be used to explore data patterns insightfully and offer new perspectives to develop warning systems for different industry applications.
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spelling pubmed-102671572023-06-15 An FSV analysis approach to verify the robustness of the triple-correlation analysis theoretical framework Wu, Robert M. X. Zhang, Zhongwu Zhang, Huan Wang, Yongwen Shafiabady, Niusha Yan, Wanjun Gou, Jinwen Gide, Ergun Zhang, Siqing Sci Rep Article Among all the gas disasters, gas concentration exceeding the threshold limit value (TLV) has been the leading cause of accidents. However, most systems still focus on exploring the methods and framework for avoiding reaching or exceeding TLV of the gas concentration from viewpoints of impacts on geological conditions and coal mining working-face elements. The previous study developed a Trip-Correlation Analysis Theoretical Framework and found strong correlations between gas and gas, gas and temperature, and gas and wind in the gas monitoring system. However, this framework's effectiveness must be examined to determine whether it might be adopted in other coal mine cases. This research aims to explore a proposed verification analysis approach—First-round—Second-round—Verification round (FSV) analysis approach to verify the robustness of the Trip-Correlation Analysis Theoretical Framework for developing a gas warning system. A mixed qualitative and quantitative research methodology is adopted, including a case study and correlational research. The results verify the robustness of the Triple-Correlation Analysis Theoretical Framework. The outcomes imply that this framework is potentially valuable for developing other warning systems. The proposed FSV approach can also be used to explore data patterns insightfully and offer new perspectives to develop warning systems for different industry applications. Nature Publishing Group UK 2023-06-14 /pmc/articles/PMC10267157/ /pubmed/37316559 http://dx.doi.org/10.1038/s41598-023-35900-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Wu, Robert M. X.
Zhang, Zhongwu
Zhang, Huan
Wang, Yongwen
Shafiabady, Niusha
Yan, Wanjun
Gou, Jinwen
Gide, Ergun
Zhang, Siqing
An FSV analysis approach to verify the robustness of the triple-correlation analysis theoretical framework
title An FSV analysis approach to verify the robustness of the triple-correlation analysis theoretical framework
title_full An FSV analysis approach to verify the robustness of the triple-correlation analysis theoretical framework
title_fullStr An FSV analysis approach to verify the robustness of the triple-correlation analysis theoretical framework
title_full_unstemmed An FSV analysis approach to verify the robustness of the triple-correlation analysis theoretical framework
title_short An FSV analysis approach to verify the robustness of the triple-correlation analysis theoretical framework
title_sort fsv analysis approach to verify the robustness of the triple-correlation analysis theoretical framework
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10267157/
https://www.ncbi.nlm.nih.gov/pubmed/37316559
http://dx.doi.org/10.1038/s41598-023-35900-3
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