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Identification model of miners' risk perception ability under influence of alertness level
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Shuicheng TIAN1, 2, Hongyan LI1, 2, Yanbin SHI3, Fangyuan TIAN**, 1, 2, 4, Yajuan WANG1, 2, Mengfei DUAN1, 2
China Safety Science Journal | 2026, 36(2) : 1 - 8
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China Safety Science Journal | 2026, 36(2): 1-8
Safety Science Theories and Methods
Identification model of miners' risk perception ability under influence of alertness level
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Shuicheng TIAN1, 2, Hongyan LI1, 2, Yanbin SHI3, Fangyuan TIAN**, 1, 2, 4, Yajuan WANG1, 2, Mengfei DUAN1, 2
Affiliations
  • 1College of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an Shaanxi 710054, China
  • 2Institute of Safety and Emergency Management, Xi'an University of Science and Technology, Xi'an Shaanxi 710054, China
  • 3The Second Company of Luoyang Molybdenum Group Co., Ltd., Luanchuan Henan 471500, China
  • 4College of Management, Xi'an University of Science and Technology, Xi'an Shaanxi 710054, China
Published: 2026-02-28 doi: 10.16265/j.cnki.issn1003-3033.2026.02.0465
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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.

alertness  /  miners  /  risk perception ability  /  identification model  /  functional near-infrared spectroscopy (fNIRS)  /  sine chaotic mapping sparrow search algorithm-back propagation neural network (Sine-SSA-BP)
Shuicheng TIAN, Hongyan LI, Yanbin SHI, Fangyuan TIAN, Yajuan WANG, Mengfei DUAN. Identification model of miners' risk perception ability under influence of alertness level[J]. China Safety Science Journal, 2026 , 36 (2) : 1 -8 . DOI: 10.16265/j.cnki.issn1003-3033.2026.02.0465
Year 2026 volume 36 Issue 2
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2026.02.0465
  • Receive Date:2025-10-11
  • Online Date:2026-07-08
  • Published:2026-02-28
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  • Received:2025-10-11
  • Revised:2025-12-15
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Affiliations
    1College of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an Shaanxi 710054, China
    2Institute of Safety and Emergency Management, Xi'an University of Science and Technology, Xi'an Shaanxi 710054, China
    3The Second Company of Luoyang Molybdenum Group Co., Ltd., Luanchuan Henan 471500, China
    4College of Management, Xi'an University of Science and Technology, Xi'an Shaanxi 710054, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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