China suffers the worst coal mine disasters in the world. Lots of miners lose their lives or suffer occupational injury. Fortunately, China is developing vigorously intelligent coal mining, which is the combination of traditional coal mining and the latest technology. Mining expects to relieve or solve coal mine safety, health and intensive labor issues and ensure energy security by applying intelligent coal mining. This paper fully reviews the promotion of intelligent coal mining to coal mine safety. Firstly, a brief history of intelligent coal mining is introduced. Then the safety motivation of the intelligent coal mine is discussed in four perspectives, including current the coal mine safety tendency, the positive impact of mechanized coal mining on safety, coal mine safety conception of “Mechanization Replacement and Automation Reduction”, and government initiatives. The intelligent prevention and control scheme of major disasters matching intelligent coal mining are also reviewed in the present paper, including intelligent gas extraction, intelligent coal and gas outburst/rock-burst prevention, and the real-time monitoring of water diversion fissure zone. Finally, the positive impacts of intelligent coal mining on safety are evaluated. Compared with traditional longwall face, the number of miners of coal cutting shift is reduced from 20~30 to 5~7, and the working environment is greatly improved. The statistics have shown that the employees in large coal mines, the mortality rates per 10⁶ tons of coal output, and the number of deaths decreased by 33%, 72.2%, and 66.9% during the period of rapid development of intelligent mining technology (2016–2021). In the future, more and more key technologies and management skills should be introduced, aiming at workless mining and the intrinsic safety of the coal mine. This paper provides a way for safety researchers around the world to understand the tendency of coal mine safety in China.

Aim: Pilot study to examine the impact of shift work on cognitive function in Chinese coal mine workers. Background: Shift work is commonly used in modern industries such as the coal industry, and there is growing concern over the impact that shift work has on miners’ work performance and personal well-being. Method: A total of 54 miners working three shifts (17 in morning shift, 18 in afternoon, and 19 in night shift) participated in this exploratory study. A resting-state fNIRS functional connectivity method was conducted to assess the cognitive ability before and after the work shift. Results: Results showed significant differences in cognitive ability between before and after the work shifts among the three-shift workers. The brain functional connectivity was reduced ranking as the night, afternoon, and morning shifts. Decreased brain functional connectivity at the end of the working shift was found compared with before in the morning and afternoon shifts. Opposite results were obtained during the night shift. The resting-state functional brain networks in the prefrontal cortex of all groups exhibited small-world properties. Significant differences in betweenness centrality and nodal local efficiency were found in the prefrontal cortex in the morning and night shifts. Conclusions: The current findings provide new insights regarding the effect of shift work on the cognitive ability of Chinese coal mine workers from the view of brain science.

Background: The goal of this study was to assess the effectiveness of machine learning models and create an interpretable machine learning model that adequately explained 3-year all-cause mortality in patients with chronic heart failure. Methods: The data in this paper were selected from patients with chronic heart failure who were hospitalized at the First Affiliated Hospital of Kunming Medical University, from 2017 to 2019 with cardiac function class III-IV. The dataset was explored using six different machine learning models, including logistic regression, naive Bayes, random forest classifier, extreme gradient boost, K-nearest neighbor, and decision tree. Finally, interpretable methods based on machine learning, such as SHAP value, permutation importance, and partial dependence plots, were used to estimate the 3-year all-cause mortality risk and produce individual interpretations of the model's conclusions. Result: In this paper, random forest was identified as the optimal aools lgorithm for this dataset. We also incorporated relevant machine learning interpretable tand techniques to improve disease prognosis, including permutation importance, PDP plots and SHAP values for analysis. From this study, we can see that the number of hospitalizations, age, glomerular filtration rate, BNP, NYHA cardiac function classification, lymphocyte absolute value, serum albumin, hemoglobin, total cholesterol, pulmonary artery systolic pressure and so on were important for providing an optimal risk assessment and were important predictive factors of chronic heart failure. Conclusion: The machine learning-based cardiovascular risk models could be used to accurately assess and stratify the 3-year risk of all-cause mortality among CHF patients. Machine learning in combination with permutation importance, PDP plots, and the SHAP value could offer a clear explanation of individual risk prediction and give doctors an intuitive knowledge of the functions of important model components.