Coal Mine Accidents Research Articles

Research on Cause of Coal Face Accident Based on Topic Model Mining

As the first production site of coal, mining working face is the place where coal mine accidents occur frequently. In order to avoid or reduce the occurrence of accidents, it is necessary to eliminate and discover the hidden danger of accidents in mining face in time. However, in the past, the cause of accidents mainly adopts the method of qualitative analysis, and lacks the effective analysis model of the cause of accidents, so that the potential value of a large number of hidden danger data of accidents has not been applied. Therefore, the LDA topic mining algorithm is used to build the accident cause topic mining model, and the Perplexity index is used to determine the best number of topics, and the semantic network graph is used to analyze the internal relationship between the hidden dangers of accidents, which directly reflects the main accident cause and the correlation between the cause factors of coal mining face. Taking 18925 accident hidden danger data of a mine in Shandong Province from August 2013 to September 2019 as an example, the topic of accident causes was extracted by mining the topic model, and the semantic network diagram was drawn. The social network analysis method was applied to analyze the association relationship between the factors of accident hidden danger. The results show that management defects, unsafe behavior of personnel, unsafe state of equipment and unsafe state of environment are the main factors leading to coal face accidents, and their interaction leads to the occurrence of accidents.

Study on the Mechanism and Chain Generation Relationship of Geological Disaster Secondary Coal Mine Accidents

Geological disasters induce coal mine accidents, which usually cause casualties and large economic losses in coal mines. However, the chain relationship and disaster mechanisms of geological disasters secondary coal mine accidents are still unclear, and the research on risk assessment methods of geological disasters induced coal mine accidents is relatively scarce. Based on investigating typical cases of secondary coal mine accidents caused by different types of geological disasters, this paper analyzed and studied the disaster-causing factors and chain generation relationship of geological disaster secondary coal mine accidents and studied the disaster-causing mechanism of secondary coal mine accidents caused by geological disasters. On the basis of analyzing the influencing factors of geological disaster secondary coal mine accidents, the risk index system of geological disaster secondary coal mine accidents was constructed, the risk grade assessment method was studied. The risk assessment model of geological disaster secondary coal mine accidents was constructed, and the weight of each index in the assessment system was determined by using the analytic hierarchy process, and the normalized treatment was carried out. According to the safety risk index of geological disaster secondary coal mine accidents, the grade division was carried out to guide the targeted prevention and control measures.

Method Construction and Evaluation: A More Comprehensive and Reliable Classification of Coal Mine Gas Explosion Causes

Coal is an important fossil fuel energy that occupies a high position in the energy use of China and even the world. However, gas explosions are still the deadliest coal mine accident in China, which has long plagued the safety of energy mining. Only through accident cause analysis can we know the exact cause of the accident so as to make targeted policies, safety trainings, etc. However, the lack of detailed accident cause classification in current coal mine gas explosion accidents affects the comprehensiveness and accuracy of energy mining safety strategies. Therefore, in this study, a classification method for coal mine gas explosion accident causes based on the sixth edition 24Model and the three-element classification of gas explosions was proposed. Then, the consistency and validity of the newly established classification system were evaluated based on the three indicators, i.e., observer consistency, content validity, and criterion validity, and the performance of the classification system was verified. The results showed that the classification method exhibits good consistency and validity, and, compared with other classification methods, it can significantly improve the comprehensiveness of accident cause analysis results so as to obtain a more scientific energy mining safety strategy. In addition, the process used in this study to construct the classification and evaluate the performance of the classification is transferable, and it can provide a reference for the construction and evaluation of accident cause classifications in other fields.

Coal Mine Safety Accidents, Environmental Regulation and Economic Development—An Empirical Study of PVAR Based on Ten Major Coal Provinces in China

Based on the PVAR model and taking the data of 10 major coal provinces in China from 2011 to 2020 as an example, the dynamic relationship between coal mine accidents, environmental regulation and economic development is analyzed at the provincial level. Research findings include: (1) From the static relationship between coal mine accidents, environmental regulation and economic development in China’s ten major coal provinces, coal mine accidents promote environmental regulation; environmental regulation inhibits coal mine accidents; economic development strongly promotes environmental regulation; environmental regulation has a weak inhibitory effect on economic development; coal mine accidents slightly inhibit economic development; economic development strongly inhibits coal mine accidents. (2) From the dynamic relationship between coal mine accidents, environmental regulation and economic development in China’s ten major coal provinces, there is a strong dynamic response relationship between environmental regulation and coal mine accidents. The impact of environmental regulation on coal mine accidents shows a decreasing volatility trend, and the impact of coal mine accidents on environmental regulation shows a rising volatility trend. There is a short-term positive interaction between economic development and environmental regulation, but the interaction response relationship between them decreases with time. Economic development has a long-term inhibitory effect on coal mine accidents, while the negative impact of coal mine accidents on economic development has gradually decreased to 0.

The Effects of Job Demands and Job Resources on Miners’ Unsafe Behavior—The Mediating and Moderating Role of a Sense of Calling

With the advancement of digital technology, coal mining has gradually become technologically intelligent, but the incidence of coal-mine accidents caused by personal unsafe behavior is still very high. To explore the mechanisms of the significant effects of a sense of calling on miners’ unsafe behavior, based on the job demands–resources (JD–R) model and from the perspective of resource-conservation theory, an empirical test was conducted in two stages with a sample of 660 miners from 6 coal-mining enterprises in China. Job demands and job resources were selected as the independent variables and sense of calling was selected as the mediating and moderating variable. The results showed that job demands had a positive effect on unsafe behavior; a sense of calling weakened the relationship between job demands and unsafe behavior; job resources had a negative effect on unsafe behavior; and a sense of calling partially mediated the relationship between job demands and unsafe behavior. Based on the JD–R model, this study systematically analyzed the occurrence mechanism of unsafe behavior and the effects of a sense of calling on such behavior. It provides practical significance for the management directions of enterprise managers.

Investigating the Obstacle Climbing Ability of a Coal Mine Search-and-Rescue Robot with a Hydraulic Mechanism

Due to the perilous conditions in coal mine accidents, trapped people need to be rescued as soon as possible. The danger and complexity of this environment have brought great difficulties for search and rescue. The emergence of rescue robots has addressed this problem, and the improvement of their search-and-rescue ability is a key concern for rescue robot designers. Based on previous experience of a tracked mobile robot, this paper proposes a new solution via the addition of adjustive rocker arms. For this purpose, a hydraulic unit is installed between the rocker arms and the robot, which enables the rocker arms to be pulled back and forth, enabling its adaptation to more complex environments. In addition, based on kinematics equations, by analyzing the motion rules and characteristics of climbing typical obstacles including stairs, grooves, and lug steps, the present research not only works out the centroid coordinates but also explores the maximal obstacle height. The results show that when the rocker arms are extended by hydraulic pressure, the obstacle-climbing height of the robot increases 16.7 percent. This demonstrates the improvement in obstacle climbing ability and rescue efficiency in complex environments.

Comprehensive Evaluation Model of Coal Mine Safety under the Combination of Game Theory and TOPSIS

At present, the frequent occurrence of coal mine accidents in China is a key concern in the field of safety. Although the coal mine accident rate is decreasing due to the progress of technology and the improvement of management, there is still a lack of comprehensive evaluation of the role of various factors in the coal mine. Therefore, the purpose of this study is to establish a game theory combination weight-TOPSIS comprehensive evaluation model based on the analysis of various factors affecting coal mine safety. The model combines the weights obtained from fuzzy analytic hierarchy process (FAHP), entropy weight method (EWM), and back propagation neural network (BP-NN) by using the combination method of game theory and then combines with the TOPSIS method to evaluate 20 indicators in four aspects of a coal mine: personnel, equipment, environment, and management. Taking five mines under Zheng Coal Group as an example, the evaluation results of the game theory combination weight-TOPSIS comprehensive evaluation model are compared with those of the fuzzy analytic hierarchy process and the improved composite weight-TOPSIS method. The results show that the safety grade of the five mine samples under Zhengmei Group is ranked as follows: mine 4 > grade II (relatively safe) > mine 2 > mine 5 > grade III (moderately safe) > mine 3 > mine 1 > grade IV (generally safe). The evaluation result of the game theory combination weight-TOPSIS method is basically consistent with the actual safety situation of the coal mine, which reflects the superiority and practicality of the evaluation model. The model has greater differentiation in the process of determining the coal mine safety grade, and the evaluation results are more intuitive and reasonable, which can provide a more effective evaluation tool for the comprehensive evaluation of coal mine safety. Finally, based on the evaluation results, corresponding policy recommendations to improve the status quo of coal mine safety are proposed, aiming to provide decision-making reference for coal mine safety management personnel.

Risk Assessment of Deep Coal and Gas Outbursts Based on IQPSO-SVM.

Coal and gas outbursts seriously threaten the mining safety of deep coal mines. The evaluation of the risk grade of these events can effectively prevent the occurrence of safety accidents in deep coal mines. Characterized as a high-dimensional, nonlinear, and small-sample problem, a risk evaluation method for deep coal and gas outbursts based on an improved quantum particle swarm optimization support vector machine (IQPSO-SVM) was constructed by leveraging the unique advantages of a support vector machine (SVM) in solving small-sample, high-dimension, and nonlinear problems. Improved quantum particle swarm optimization (IQPSO) is used to optimize the penalty and kernel function parameters of SVM, which can solve the optimal local risk and premature convergence problems of particle swarm optimization (PSO) and quantum particle swarm optimization (QPSO) in the training process. The proposed algorithm can also balance the relationship between the global search and local search in the algorithm design to improve the parallelism, stability, robustness, global optimum, and model generalization ability of data fitting. The experimental results prove that, compared with the test results of the standard SVM, particle swarm optimization support vector machine (PSO-SVM), and quantum particle swarm optimization support vector machine (QPSO-SVM) models, IQPSO-SVM significantly improves the risk assessment accuracy of coal and gas outbursts in deep coal mines. Therefore, this study provides a new idea for the prevention of deep coal and gas outburst accidents based on risk prediction and also provides an essential reference for the scientific evaluation of other high-dimensional and nonlinear problems in other fields. This study can also provide a theoretical basis for preventing coal and gas outburst accidents in deep coal mines and help coal mining enterprises improve their safety management ability.

Evolution dynamic of intelligent construction strategy of coal mine enterprises in China

The intelligent construction of coal mining enterprises is the fundamental strategy to prevent and curb major coal mine accidents, and the core technical support to realize the high-quality development of the coal industry. Considering the intelligent construction of coal mining enterprises enabled by emerging information technologies such as 5G and artificial intelligence, this paper constructs a Moran process stochastic evolutionary game model of intelligent construction of coal mining enterprises. Based on the fixed point probability of the Moran process, the probability of successful invasion of the "intelligent construction" strategy and the "traditional production" strategy in the limited coal mining enterprise group is calculated. By comparing the probability of individual fixed point and the probability of neutral mutation, the dominant condition of the strategy under strong selection and weak selection is obtained. The research shows that external stochastic factors, the number and scale of enterprises, the intensity of capacity replacement and the cost-benefit of intelligent construction are the main factors affecting the intelligent construction behavior of coal mining enterprises. When the intelligent construction of coal mining enterprises is in the cultivation period, the intelligent construction of underground coal mines dominated by stochastic factors can be effectively promoted by increasing the intensity of capacity reduction replacement. For the open-pit coal mine dominated by expected payoffs, reducing the number of mining rights and improving the concentration of open-pit coal mining industry will have a better effect on promoting its intelligent cultivation process. When the intelligent construction of coal mining enterprises is in the mature stage, with the improvement of the cost and benefits of intelligent construction, "intelligent construction" strategy will become a general consensus of coal mining enterprises. In addition, this paper analyzes the relevant parameters through specific examples to verify the effectiveness of the conclusions, which provides a scientific basis for effectively accelerating the intelligent construction of coal mining enterprises and promoting the transformation and upgrading of the coal industry.

The effect of job satisfaction regulating workload on miners’ unsafe state

Miners’ unsafe behavior is the main cause of accidents in coal mines, and unsafe state have an important influence on unsafe behavior among miners. To minimize accidents from the source of accident chain, we evaluated the impact of workload on miners’ unsafe state. It is important for coal enterprises to monitor miners’ unsafe state and to prevent unsafe accidents. Workload is divided into two dimensions: work time and work demand. Meanwhile, we introduced job satisfaction as a moderating variable. Through empirical research methods, first-line employees from two coal mines in China were enrolled in the questionnaire survey. Regression analysis was used to verify the impact of workload and its various dimensions, job satisfaction, and miners’ unsafe state. We found that workload, work time and work demand have significant positive effects on miners’ unsafe state. Job satisfaction plays a moderating effect in the relationship between workload and miners’ unsafe state. To some extent, a higher job satisfaction was associated with reduced workload, reduced occurrence of miners’ unsafe state and minimal incidences of unsafe accidents. On this basis, measures were proposed to improve miners’ unsafe state in terms of workload and job satisfaction. This study informs the establishment of effective intervention measures to monitor miners’ unsafe state and is also beneficial to the improvement of coal mine safety.

The Influence of Leader Bottom-Line Mentality on Miners' Safety Behavior: A Moderated Parallel Mediation Model Based on the Dual-System Theory.

As a high-risk industry that is always struggling with unsafe factors, coal mine enterprises must prioritize safety in their operation and management, but there are still some short-sighted coal mine managers who choose to leave safety behind in the desperate pursuit of financial benefits, resulting in coal mine accidents from time to time. Unfortunately, this leadership style, known as leader bottom-line mentality, has not yet received sufficient attention in the safety field. Based on dual-system theory, this study aimed to explore the mediating role of emotional exhaustion and safety consciousness between leader bottom-line mentality and miners’ safety behavior, as well as the moderating role of Chinese traditionality. Using a sample of 422 frontline miners in China, the results of the data analysis showed that emotional exhaustion and safety consciousness played parallel mediating roles between leader bottom-line mentality and miners’ safety behavior, and Chinese traditionality moderated the effect of leader bottom-line mentality: the higher the Chinese traditionality, the weaker the mediating effect of emotional exhaustion and the stronger the mediating effect of safety consciousness. Present research explains the mechanisms and boundaries of the influence of leader bottom-line mentality on miners’ safety behavior, contributing to the emerging literature on safety management and bottom-line mentality.

Superposition risk assessment of the working position of gas explosions in chinese coal mines

The prevention of gas explosion accidents is a key factor in enhancing the safety of production of coal mines. The effective management and control of the working position risks is of great significance for the prevention of such accidents. By analyzing data from 201 typical gas explosion accidents, 21 working positions related to the accidents were identified along with the main causing factors. By employing data statistical methods, the structural importance analysis method of fault tree analysis, and the summation method, the risk associated with each working position was evaluated. Then, this work built a risk assessment matrix to categorize risk levels associated with various working positions related to gas explosions in coal mines. Also, based on the kernel density analysis, a superposition risk assessment model was established. In addition, the ArcGIS software was used to simulate and calculate the superimposed risks and draw a risk distribution map. The results show that the number of working positions of Level I is mainly affecting the fully mechanized mining team, the fully mechanized excavation team, and the ventilation team. The risk distribution map intuitively shows that the high-risk working positions involving gas explosion accidents in Chinese coal mines mainly exist in the working face and the tunneling face. It is highlighted that the working position risk and grade distribution have a significant hierarchy after risk superposition.

Comprehensive Evaluation of Deep Coal Miners' Unsafe Behavior Based on HFACS-CM-SEM-SD.

The unsafe behavior of miners seriously affects the safety of deep mining. A comprehensive evaluation of miners’ unsafe behavior in deep coal mines can prevent coal mine accidents. This study combines HFACS-CM, SEM, and SD models to evaluate miners’ unsafe behaviors in deep coal mining. First, the HFACS-CM model identifies the risk factors affecting miners’ unsafe behavior in deep coal mines. Second, SEM was used to analyze the interaction between risk factors and miners’ unsafe behavior. Finally, the SD model was used to simulate the sensitivity of each risk factor to miners’ unsafe behavior to explore the best prevention and control strategies for unsafe behavior. The results showed that (1) environmental factors, organizational influence, unsafe supervision, and unsafe state of miners are the four main risk factors affecting the unsafe behavior of miners in deep coal mines. Among them, the unsafe state of miners is the most critical risk factor. (2) Environmental factors, organizational influence, unsafe supervision, and the unsafe state of miners have both direct and indirect impacts on unsafe behaviors, and their immediate effects are far more significant than their indirect influence. (3) Environmental factors, organizational influence, and unsafe supervision positively impact miners’ unsafe behavior through the mediating effect of miners’ unsafe states. (4) Mental state, physiological state, business abilities, resource management, and organizational climate were the top five risk factors affecting miners’ unsafe behaviors. Taking measures to improve the adverse environmental factors, strengthening the organization’s supervision and management, and improving the unsafe state of miners can effectively reduce the risk of miners’ unsafe behavior in deep coal mines. This study provides a new idea and method for preventing and controlling the unsafe behavior of miners in deep coal mines.

A Mobile Intelligent Mine Platform with a Hybrid Fuzzy NN and ATT-CNN Prewarning Model

Given the existence of coal production risk, effective prewarning is important to the reliability and safety of coal mine. So, the development of a risk prewarning system has become an important safety management tool. To improve the prediction ability and the supervision level of safety production, and handle different multidimensional (temporal and spatial) information for risk prewarning, we built a new platform based on the Internet, cloud platform, mobile communication, GIS, and artificial intelligence technology, i.e., a mobile intelligent mine platform. The terminal of the platform provides real-time queries and procedures of coal mine production and risk prewarning and provides data support and technical means for daily supervision, remote networking analysis, law enforcement inspection, and emergency rescue. The prewarning model of safety risk is an essential means to realize prewarning. The complexity of production of coal mine leads to the dynamic characteristics, fuzziness, and randomness of coal mine accidents. The complex nonlinear relationship between index and risk level leads to low accuracy of the traditional back propagation (BP) neural network prewarning method. A novel model based on a compensation fuzzy neural network (NN) and an attention mechanism-convolutional neural network (ATT-CNN) are a critical part of the new design. First, to full use of the convolutional network to get a larger receptive field, one-dimensional time series is transformed into two-dimensional matrix as the input of the CNN network by mapping. The neural network is utilized to extract the advanced features of the input signal. The results are finally output through a fully connected classifier. The model fuses multisource data at the feature level, employs the temporal and spatial relationships of monitoring data, and dynamically evaluates the risk. The experiment shows that the proposed model achieves impressive performance in both quantitative and qualitative evaluations and has improved the model generalization ability. The combination of data integration, remote examinations, and approval from existing information systems enables this platform to provide dynamic reminders of approval information, various risk prewarning, and management process automation through a mobile network.

A new family of decreasing density and hazard quantile functions for modeling time-to-event data

In this paper, a convenient and parsimonious family of the quantile functions is proposed for modeling time-to-event data. Detailed mathematical arguments are presented for deriving the explicit expressions of the quantile density, quantile hazard and quantile mean remaining life functions. Their functional properties are also explored. Some important members of the family are provided. A special case, the so-called additive power logarithmic quantile family, is discussed with detailed properties, parameter estimation and application to time-to-event data. Measures of skewness and kurtosis for the proposed quantile function are derived. Important statistical measures useful for reliability analysis are also provided with their explicit expressions. Stochastic ordering, tail weight and order statistics are also discussed. L-moments are explicitly derived for the proposed quantile function. Estimation is approached using the likelihood function and percentiles. A real data set consists of times between occurrence of the coal-mining accidents is analyzed for illustration purposes.

Safety warning of coal mining face based on big data association rule mining

The hidden danger of accidents seriously threatens the safety of the working face of coal mining. By digging the internal association rules of hidden danger, the construction of safety warning rule base can improve the ability of coal mining enterprises to deal with hidden danger as soon as possible. Coal mining work face hazards are analyzed with three types of hazard theory, and the types, attributes and dimensions of hidden accidents in coal mining face are studied, combined with the production practice of coal mining working face, and referring to the thought and method of “5W1H”. Then, the structure of safety early warning knowledge base is studied and the key of safety early warning knowledge base is put forward. On this basis, the paper designs the mining model of mining face data to mine the association rules of accident hidden danger data. The empirical analysis shows that the rule base of safety warning can be used to deal with hidden danger of coal mining working face, and realize the early warning of hidden danger of accident, providing a method for dealing with hidden danger of coal mine accident and managing dangerous source.

Иерархическая структура аэрологических рисков в угольных шахтах

Introduction. As the actual data show, the safety of coalmines is the most vulnerable in the event of explosions of methane and coal dust, often accompanied by a complex manifestation and implementation of several hazardous factors, such as gas, dust, exogenous fires, leading to catastrophic consequences. To assess, predict, manage and reduce the consequences of the complex implementation of dangerous aerological factors in the occurrence of accidents in coalmines, a methodology has been developed for assessing and managing aerological risks in coalmines, based on the hierarchical structure of aerological risk. Objective. Assessment, forecasting, management and reduction of rank III aerological risks for extraction areas and development workings of highly gas-rich coalmines. Methodology. To assess, predict, control and reduce the consequences of the complex implementation of hazardous production factors in the occurrence of accidents associated with explosions of gas and dust in coal mines, a methodology has been developed for assessing and managing aerological risks in coal mines, based on a systematic approach in accordance with the hierarchical structure of aerological safety. The aerological safety of mines can be quantified in terms of aerological risks: the lower the risks, the higher the safety. All aerological risks were divided into three ranks according to their importance in the production activities of the mine, corresponding to the hierarchical structure of the aerological safety of the mine. Aerological risks of the I rank are understood as aerological risks covering the entire mine, aerological risks of the II rank cover layers, wings, deposits; aerological risks of III rank cover separate mining or development sites. Comparison of mines by aerological risks should be made according to aerological risks of the same rank. When performing research, an analysis of literary domestic and foreign sources was used; generally accepted methods, including methods of mathematical statistics and probability theory; a method of expert assessments to obtain the values of the hazard and vulnerability coefficients of ventilation methods for excavation areas and development workings. Results and discussion. A methodology has been developed for constructing a hierarchical structure of aerological risks in coal mines. A block diagram of aerological risks of III rank has been compiled, including the main hazards in the excavation areas and development workings and the main types of vulnerability of the ventilation schemes of the excavation areas and the methods of ventilation of dead-end workings. Calculations have shown that when developing the most dangerous mine seams, the lowest values of aerological risk of III rank for excavation areas are characterized by combined ventilation schemes in the absence of a diagonal connection in the scheme; the presence of an unstable diagonal connection in the ventilation scheme of the excavation area increases the aerological risk by 2.7 times. For development workings carried out along seams prone to rock bumps, spontaneous combustion, sudden outbursts of coal and gas, with a specific dust emission of more than 1200 g/t, only ventilation methods with a high degree of gas mixing activity in the bottomhole zone can be applicable. Conclusions. The developed hierarchical structure of aerological risks makes it possible to rank risks by levels and compare risks of the same rank. The application of the developed risk assessment methodology makes it possible to predict and reduce aerological risks in the design and operation of coal mines.

Desorption and diffusion characteristics of methane in the non-ventilation working face

Based on the rapid development of intelligent unmanned mining technology in coal mines, a new concept of non-ventilation working face is put forward. In the absence of ventilation, it is more possible to extract high purity methane and eliminate coal mine accidents. There are few studies on the desorption and diffusion characteristics of methane under constant positive pressure (higher than atmospheric pressure) in the non-ventilation working face. In this study, a set of self-designed gas positive pressure desorption equipment was used to carry out desorption experiments under various positive environmental pressures. The diffusion coefficient of methane in coal was calculated by using the unipore diffusion model. The results indicate that the extended Langmuir EXT1 model can well describe the variation of methane desorption capacity with time under different positive pressures. Under the same temperature and equilibrium pressure, the limit desorption capacity and desorption rate of methane in coal decrease with the increase of external positive pressure. With the increase of temperature and equilibrium pressure, the negative effect of positive pressure is weakened. At the same temperature and equilibrium pressure, the diffusion coefficient of methane in coal decreases with the increase of positive pressure. The higher equilibrium pressure increases the methane concentration in coal matrix and weakens the influence of positive pressure on the concentration difference, which slows down the decreasing rate of diffusion coefficient. On the contrary, the increase in temperature reduces the methane concentration in coal matrix and accelerates the decreasing rate of diffusion coefficient. These results provide theoretical guidance for the realization of the non-ventilation working face.

The Impacts of Workplace Environment on Coal Miners' Emotion and Cognition Depicted in a Mouse Model.

Most coal mine accidents are caused by the unsafe behavior of employees. Previous studies have shown that there is a significant connection among the working environment, the psychological state of employees, and unsafe behaviors. However, the internal biological mechanism has not been revealed. To explore the physiological and psychological alterations of coal mine workers and the underlying mechanisms that cause unsafe behaviors, the current study established a novel coal mine environment biological simulation (CEBS) model in mice. This model recreated the underground workplace environment facts in coal mines such as temperature, humidity, and noise, and mice were employed to receive these conditioning stresses according to the 8-h work. Animal behavior tests were performed to evaluate the evolution of the mental state including anxiety and depression, as well as the abilities of learning and memory during the 4-week environmental simulation. CEBS mice showed the adaptation process of anxiety from occurrence to stability in the process of environmental simulation, and also suffered from severe depression compared to the control mice. In addition, impaired spatial memory was also implicated in mice after 4-week CEBS. The behavior results of CEBS mice were consistent with the previous psychological investigation of coal workers. In summary, a novel mouse model was established in this study to depict the occurrence of negative emotions and impaired cognition in coal miners by simulating the underground workplace environment, which provided a basis for further exploring the biological mechanism of miners’ unsafe behavior.

Analysis of Factors Influencing Miners' Unsafe Behaviors in Intelligent Mines using a Novel Hybrid MCDM Model.

Coal mine accidents seriously affect people’s safety and social development, and intelligent mines have improved the production safety environment. However, safety management and miners’ work in intelligent mines face new changes and higher requirements, and the safety situation remains challenging. Therefore, exploring the key influencing factors of miners’ unsafe behaviors in intelligent mines is important. Our work focuses on (1) investigating the relationship and hierarchy of 20 factors, (2) using fuzzy theory to improve the decision-making trial and evaluation laboratory (DEMATEL) method and introducing the maximum mean de-entropy (MMDE) method to determine the unique threshold scientifically, and (3) developing a novel multi-criteria decision-making (MCDM) model to provide theoretical basis and methods for managers. The main conclusions are as follows: (1) the influence degree of government regulation, leadership attention, safety input level, safety system standardization, and dynamic supervision intensity exert the most significant influence on the others; (2) the causality of government regulation, which is the deep factor, is the highest, and self-efficacy displays the smallest causality, and it is the most sensitive compared to various other factors; (3) knowledge accumulation ability, man–machine compatibility, emergency management capability, and organizational safety culture has the highest centrality among the individual factors, device factors, management factors, and environmental factors, respectively. Thus, corresponding management measures are proposed to improve coal mine safety and miners’ occupational health.