Evidence map›Paper›PMID 36554469›Full record

ArticleInternational journal of environmental research and public health2022

Variance Analysis in China's Coal Mine Accident Studies Based on Data Mining.

Tianmo Zhou, Yunqiang Zhu, Kai Sun, Jialin Chen, Shu Wang, Huazhong Zhu, Xiaoshuang Wang

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Tianmo ZhouInstitute of Geographic Sciences and Natural Resources Research, Beijing 100101, China.
Yunqiang ZhuInstitute of Geographic Sciences and Natural Resources Research, Beijing 100101, China.
Kai SunInstitute of Geographic Sciences and Natural Resources Research, Beijing 100101, China.ORCID 0000-0002-0343-6032
Jialin ChenInformation Institute of the Ministry of Emergency Management of PRC (IIEM), China Coal Information Institute (CCII), Beijing 100029, China.
Shu WangInstitute of Geographic Sciences and Natural Resources Research, Beijing 100101, China.ORCID 0000-0002-5355-2015
Huazhong ZhuInstitute of Geographic Sciences and Natural Resources Research, Beijing 100101, China.
Xiaoshuang WangBeijing Municipal Ecology Environment Bureau Integrating Business Center, Beijing 100048, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The risk of coal mine accidents rises significantly with mining depth, making it urgent for accident prevention to be supported by both scientific analysis and advanced technologies. Hence, a comprehensive grasp of the research progress and differences in hotspots of coal mine accidents in China serves as a guide to find the shortcomings of studies in the field, promote the effectiveness of coal mine disaster management, and enhance the prevention and control ability of coal mine accidents. This paper analyzes Chinese and foreign literature based on data mining algorithms (LSI + Apriori), and the findings indicate that: (1) 99% of the available achievements are published in Chinese or English-language journals, with the research history conforming to the stage of Chinese coal industry development, which is characterized by "statistical description, risk evaluation, mechanism research, and intelligent reasoning". (2) Chinese authors are the primary contributors that lead and contribute to the continued development of coal mine accident research in China globally. Over 81% of the authors and over 60% of the new authors annually are from China. (3) The emphasis of the Chinese and English studies is different. Specifically, the Chinese studies focus on the analysis of accident patterns and causes at the macroscale, while the English studies concentrate on the occupational injuries of miners at the small-scale and the mechanism of typical coal mine disasters (gas and coal spontaneous combustion). (4) The research process in Chinese is generally later than that in English due to the joint influence of the target audience, industrial policy, and scientific research evaluation system. After 2018, the Chinese studies focus significantly on AI technology in deep mining regarding accident rules, regional variation analysis, risk monitoring and early warning, as well as knowledge intelligence services, while the hotspots of English studies remain unchanged. Furthermore, both Chinese and English studies around 2019 focus on "public opinion", with Chinese ones focusing on serving the government to guide the correct direction of public opinion while English studies focus on critical research of news authenticity and China's safety strategy.

Indexed as

Accidents, OccupationalCoal MiningAnalysis of VarianceChinaCoalCoalAprioriChinaCiteSpacecoal mine accidentsdata miningLSIvariance analysis

Identifiers

PMID36554469
PMCPMC9778805

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.