Article(id=1281616656191426625, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281616655499366465, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2026.02.0465, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1760112000000, receivedDateStr=2025-10-11, revisedDate=1765728000000, revisedDateStr=2025-12-15, acceptedDate=null, acceptedDateStr=null, onlineDate=1783490822121, onlineDateStr=2026-07-08, pubDate=1772208000000, pubDateStr=2026-02-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783490822121, onlineIssueDateStr=2026-07-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783490822121, creator=13701087609, updateTime=1783490822121, updator=13701087609, issue=Issue{id=1281616655499366465, tenantId=1146029695717560320, journalId=1146031787341344770, year='2026', volume='36', issue='2', pageStart='1', pageEnd='270', issueExtLink='null', onlineDate='null', pubDate='1772208000000', pubDateStr='2026-02-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783490821957, creator='13701087609', updateTime=1783588693138, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282027157069218127, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281616655499366465, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282027157069218128, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281616655499366465, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1, endPage=8, ext={EN=ArticleExt(id=1281616656409530434, articleId=1281616656191426625, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Identification model of miners' risk perception ability under influence of alertness level, columnId=1277328386947150682, journalTitle=China Safety Science Journal, columnName=Safety Science Theories and Methods, runingTitle=null, highlight=null, articleAbstract=

In order to explore the influence of miners' alertness on risk perception ability, miners' alertness tests and risk perception experiments were designed and implemented. During the experiments, fNIRS, behavioral data, and peripheral physiological signals were collected. Methods such as the normality test and one-way analysis of variance (ANOVA) were applied to investigate the differences in risk perception ability among miners with different alertness levels. Thirteen significantly different indicators were selected as feature variables. Thirteen significant differential indicators were selected as feature indicators, and Sine-SSA-BP was introduced to construct a classification and recognition model for miners' risk perception ability. The results show that miners' alertness significantly affects their risk perception ability. With increasing alertness, the correct rate of risk perception improves notably. As the alertness level rises, significant differences appear in the activation index β values of the dorsolateral prefrontal cortex and frontopolar areas. The mean skin conductance (SC_mean) in electrodermal activity (EDA) increases significantly, while the mean inter-beat interval (Mean_IBI), standard deviation of normal to normal R-R intervals(SDNN), and root mean square of successive differences (RMSSD) in heart rate variability (HRV) decrease significantly, and mean heart rate (Mean_HR) increases. The constructed miners' risk perception ability classification and identification model based on the Sine-SSA-BP achieves an accuracy of 92.30%, demonstrating excellent overall performance and robustness.

, authors=Shuicheng TIAN^{1, 2}, Hongyan LI^{1, 2}, Yanbin SHI³, Fangyuan TIAN^{**, 1, 2, 4}, Yajuan WANG^{1, 2}, Mengfei DUAN^{1, 2}, authorsList=Shuicheng TIAN, Hongyan LI, Yanbin SHI, Fangyuan TIAN, Yajuan WANG, Mengfei DUAN, authorCompany=null, correspAuthors=Fangyuan TIAN, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1281616656925429829, articleId=1281616656191426625, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=警觉度影响下矿工风险感知能力识别模型, columnId=1277328387270112092, journalTitle=中国安全科学学报, columnName=安全科学理论与方法, runingTitle=null, highlight=null, articleAbstract=

为探究矿工警觉度对风险感知的影响规律，基于文献调研，设计开展矿工警觉度测试与风险感知试验，采集被试的近红外信号、行为与外周生理数据，运用正态性检验、单因素方差分析（ANOVA）等方法，探究不同警觉度矿工的风险感知差异性；优选13项显著差异指标作为特征指标，引入正弦混沌映射麻雀搜索算法优化反向传播神经网络（Sine-SSA-BP），构建矿工风险感知能力分类识别模型。结果表明：矿工警觉度对风险感知能力存在显著影响，警觉度升高，矿工风险感知正确率显著提升；随着矿工警觉度的升高，矿工的背外侧前额叶区域以及额极区激活指数β值具有显著差异，皮肤电（EDA）中的皮肤电导平均值（SC_mean）显著升高，心率变异性（HRV）中的平均心跳间期（Mean_IBI）、心跳R-R间期标准差（SDNN）、连续心跳间期差值均方根（RMSSD）显著降低，平均心率（Mean_HR）显著升高；构建的矿工风险感知能力分类识别模型综合性能最优，准确率高达92.30%，模型具备较好的鲁棒性。

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田水承（1964—），男，山东淄博人，博士，教授，主要从事安全与应急管理和行为安全方面的研究。E-mail: tiansc@xust.edu.cn。

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田水承（1964—），男，山东淄博人，博士，教授，主要从事安全与应急管理和行为安全方面的研究。E-mail: tiansc@xust.edu.cn。

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