A correlational research on developing an innovative integrated gas warning system: a case study in ZhongXing, China

Wu, Mingxuan; Yan, Wanjun; Zhang, Zhongwu; Gou, Jinwen; Fan, Jianfeng; Bao, Liu; Shi, Yangyan; Shen, Bo; Zhao, Hongwen; Ma, Yanyun; Soar, Jeffrey; Sun, Xiangyu; Gide, Ergun; Sun, Zhigang; Wang, Peilin; Cui, Xinxin; Wang, Ya

doi:10.1080/19475705.2021.2002953

Cited by 8 publications

(14 citation statements)

References 47 publications

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“…Ten gas sensors were installed in Case Study mine 1 [ 7 ] ( Fig 2 ). Table 2 shows the codes of the sensors used.…”

Section: Case Studiesmentioning

confidence: 99%

“…State Administration of China Coal Safety prevention regulations for Coal and Gas Outburst was updated on 1 October 2019 [ 6 ]. The majority of the current research mainly focused on exploring the methods and frameworks for avoiding reaching or exceeding the threshold limit value of the gas concentration from viewpoints of impacts on geological conditions and coal mining working-face elements [ 7 ]. Many techniques and methods were used to avoid reaching or exceeding the threshold limit value of the gas concentration.…”

Section: Introductionmentioning

confidence: 99%

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A comparative analysis of the principal component analysis and entropy weight methods to establish the indexing measurement

Zhang

Yan

et al. 2022

PLoS ONE

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Background As the world’s largest coal producer, China was accounted for about 46% of global coal production. Among present coal mining risks, methane gas (called gas in this paper) explosion or ignition in an underground mine remains ever-present. Although many techniques have been used, gas accidents associated with the complex elements of underground gassy mines need more robust monitoring or warning systems to identify risks. This paper aimed to determine which single method between the PCA and Entropy methods better establishes a responsive weighted indexing measurement to improve coal mining safety. Methods Qualitative and quantitative mixed research methodologies were adopted for this research, including analysis of two case studies, correlation analysis, and comparative analysis. The literature reviewed the most-used multi-criteria decision making (MCDM) methods, including subjective methods and objective methods. The advantages and disadvantages of each MCDM method were briefly discussed. One more round literature review was conducted to search publications between 2017 and 2019 in CNKI. Followed two case studies, correlation analysis and comparative analysis were then conducted. Research ethics was approved by the Shanxi Coking Coal Group Research Committee. Results The literature searched a total of 25,831publications and found that the PCA method was the predominant method adopted, and the Entropy method was the second most widely adopted method. Two weighting methods were compared using two case studies. For the comparative analysis of Case Study 1, the PCA method appeared to be more responsive than the Entropy. For Case Study 2, the Entropy method is more responsive than the PCA. As a result, both methods were adopted for different cases in the case study mine and finally deployed for user acceptance testing on 5 November 2020. Conclusions The findings and suggestions were provided as further scopes for further research. This research indicated that no single method could be adopted as the better option for establishing indexing measurement in all cases. The practical implication suggests that comparative analysis should always be conducted on each case and determine the appropriate weighting method to the relevant case. This research recommended that the PCA method was a dimension reduction technique that could be handy for identifying the critical variables or factors and effectively used in hazard, risk, and emergency assessment. The PCA method might also be well-applied for developing predicting and forecasting systems as it was sensitive to outliers. The Entropy method might be suitable for all the cases requiring the MCDM. There is also a need to conduct further research to probe the causal reasons why the PCA and Entropy methods were applied to each case and not the other way round. This research found that the Entropy method provides higher accuracy than the PCA method. This research also found that the Entropy method demonstrated to assess the weights of the higher dimension dataset was higher sensitivity than the lower dimensions. Finally, the comprehensive analysis indicates a need to explore a more responsive method for establishing a weighted indexing measurement for warning applications in hazard, risk, and emergency assessments.

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“…Ten gas sensors were installed in Case Study mine 1 [ 7 ] ( Fig 2 ). Table 2 shows the codes of the sensors used.…”

Section: Case Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A comparative analysis of the principal component analysis and entropy weight methods to establish the indexing measurement

Zhang

Yan

et al. 2022

PLoS ONE

Self Cite

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“…Correlational research studies could also provide invaluable information about what future research might be required to investigate the variables correlated with the outcomes or attributes previously studied (Curtis et al, 2016). Recent research identified that the correlational research method might be a more effective practical approach than a machine learning method to developing a warning system (Wu et al, 2021).…”

Section: No Correlational Research Methods Reportedmentioning

confidence: 99%

Comparison Reviews on E-commerce Research Methods: Suggestions for Post-COVID-19

Zhang

Tian

2022

GES

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This research aimed to understand the differences in research methods adopted between Chinese and international e-commerce research and provided suggestions and recommendations for further research agenda in e-commerce post-COVID-19. This research studied 4,425 Chinese publications between 2015 and 2019 compared to international reviews in e-commerce research. Twelve findings were discussed: 1) Most of China's e-commerce publications (80.24%) did not state any research method. 2) Mixed methods have been well-adopted in China's e-commence research (35.05%), including quantitative-mixed, qualitative-mixed, and quantitative-qualitative mixed methods. They were highly adopted compared to other studies. 3) Survey methods (27.97%) were predominantly single research - purely quantitative method in Chinese research, but they were still behind international research. 4) Case studies (14.05%) were adopted as the second single method in Chinese research. This finding was significantly different from international views. 5) There were different views on whether laboratory experiments should be considered a research method. 6) Document and text analysis methods were reported by Chinese scholars (6.48%), but less and even not be reported internationally. 7) The focus group method was less adopted internationally. 8) The content analysis method (0.84%) was less adopted in Chinese research and was not reported by international scholars. 9) The observation method (0.6%) was less adopted in Chinese research but more adopted in Ain et al. (2019) (2%). Other international studies reported no studies. 10) Interviews (0.36%) were least adopted in Chinese research but highly reported by international scholars. 11) Field study, secondary, and Delphi methods were only adopted in international research, and 12) The correlational research method was not reported in e-commerce research. The main contributions of this study could be stated: Being valuable for researchers interested in Chinese e-commerce research; Adding research value for comparing reviews in e-commerce research methods.

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“…The correlation coefficient was used to evaluate and measure the correlation between two pairs of input and output variables. The recent research found that there was no standard classification of the correlation coefficient scales and suggested six scales classifying the degree and magnitude of correlation as great (between ± 0.9 and ± 1), very good (between ± 0.75 and ± 0.89), good (between ± 0.5 and ± 0.74), fair (between ± 0.3 and ± 0.49), poor (between ±0.0 and ± 0.29), and no correlation (zero) [ 43 ]. This research follows a correlation value of ±0.3 or above to indicate the existence of a correlation between two variables.…”

Section: Systems Designmentioning

confidence: 99%

“…S5 followed activated decision rules to answer the question of DI6 (Integrating an early warning system into the gas monitoring system). It proposed integrating the warning sub-system into the current gas monitoring system with an early warning decision-making rule [ 43 ]. The warning system might use the weighted indexing measurement in risk assessments [ 44 ].…”

Section: Systems Designmentioning

confidence: 99%

Using multi-focus group method as an effective tool for eliciting business system requirements: Verified by a case study

Wang²,

Shafiabady³

et al. 2023

PLoS ONE

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This research aims to explore the multi-focus group method as an effective tool for systematically eliciting business requirements for business information system (BIS) projects. During the COVID-19 crisis, many businesses plan to transform their businesses into digital businesses. Business managers face a critical challenge: they do not know much about detailed system requirements and what they want for digital transformation requirements. Among many approaches used for understanding business requirements, the focus group method has been used to help elicit BIS needs over the past 30 years. However, most focus group studies about research practices mainly focus on a particular disciplinary field, such as social, biomedical, and health research. Limited research reported using the multi-focus group method to elicit business system requirements. There is a need to fill this research gap. A case study is conducted to verify that the multi-focus group method might effectively explore detailed system requirements to cover the Case Study business’s needs from transforming the existing systems into a visual warning system. The research outcomes verify that the multi-focus group method might effectively explore the detailed system requirements to cover the business’s needs. This research identifies that the multi-focus group method is especially suitable for investigating less well-studied, no previous evidence, or unstudied research topics. As a result, an innovative visual warning system was successfully deployed based on the multi-focus studies for user acceptance testing in the Case Study mine in Feb 2022. The main contribution is that this research verifies the multi-focus group method might be an effective tool for systematically eliciting business requirements. Another contribution is to develop a flowchart for adding to Systems Analysis & Design course in information system education, which may guide BIS students step by step on using the multi-focus group method to explore business system requirements in practice.

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A correlational research on developing an innovative integrated gas warning system: a case study in ZhongXing, China

Cited by 8 publications

References 47 publications

A comparative analysis of the principal component analysis and entropy weight methods to establish the indexing measurement

A comparative analysis of the principal component analysis and entropy weight methods to establish the indexing measurement

Comparison Reviews on E-commerce Research Methods: Suggestions for Post-COVID-19

Using multi-focus group method as an effective tool for eliciting business system requirements: Verified by a case study

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