Background The outbreak of the 2019 novel coronavirus disease (COVID-19) not only caused physical abnormalities, but also caused psychological distress, especially for undergraduate students who are facing the pressure of academic study and work. We aimed to explore the prevalence rate of probable anxiety and probable insomnia and to find the risk factors among a longitudinal study of undergraduate students using the approach of machine learning. Methods The baseline data (T1) were collected from freshmen who underwent psychological evaluation at two months after entering the university. At T2 stage (February 10th to 13th, 2020), we used a convenience cluster sampling to assess psychological state (probable anxiety was assessed by general anxiety disorder-7 and probable insomnia was assessed by insomnia severity index-7) based on a web survey. We integrated information attained at T1 stage to predict probable anxiety and probable insomnia at T2 stage using a machine learning algorithm (XGBoost). Results Finally, we included 2009 students (response rate: 80.36%). The prevalence rate of probable anxiety and probable insomnia was 12.49% and 16.87%, respectively. The XGBoost algorithm predicted 1954 out of 2009 students (translated into 97.3% accuracy) and 1932 out of 2009 students (translated into 96.2% accuracy) who suffered anxiety and insomnia symptoms, respectively. The most relevant variables in predicting probable anxiety included romantic relationship, suicidal ideation, sleep symptoms, and a history of anxiety symptoms. The most relevant variables in predicting probable insomnia included aggression, psychotic experiences, suicidal ideation, and romantic relationship. Conclusion Risks for probable anxiety and probable insomnia among undergraduate students can be identified at an individual level by baseline data. Thus, timely psychological intervention for anxiety and insomnia symptoms among undergraduate students is needed considering the above factors.

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The application of the acoustic emission (AE) and damage theory to the evaluation of coal rock stability requires a clear comprehension of the influence of joints. The mechanical and AE response characteristics and damage evolution characteristics of samples with different joint angles were analysed through uniaxial compression tests. The results show that the angle between the loading principal stress and joint surface considerably influences the mechanical characteristics of samples and the damage degree characterised by the AE. However, the existing models of AE damage characterisation do not fully reflect the damage characteristics of samples with different loading principal stresses and joint surface angles. In this paper, the improved damage model characterised by the AE count accumulation is proposed. The model has clear physical and mechanical bases and can effectively characterise the damage catastrophe characteristics of the samples with different joint angles. It also has an important application value in coal and rock dynamic disaster monitoring and early warning, damage detection and other projects.

According to the new stress distribution pattern and the strong strata behaviors as the characteristics of the coal pillars in the close-distance multiseam coal pillar mining, the common characteristics of different types of overlying coal pillars were summarized and analyzed. Moreover, a theoretical model for the mechanism of strong strata behaviors in the close-distance multiseam coal pillar mining was established, which was validated by the monitoring data of seismic computed tomography CT, microseism, and electromagnetic radiation (EMR). Furthermore, the results of the study indicated that the main factors affecting the strong strata behaviors were the static stress concentration caused by the overlying coal pillars and the dynamic disturbance caused by the fracturing and slipping of the overlying coal pillars and roof under the influence of mining. In the case of Xinzhouyao coal mine, the transmitted stress and lateral support pressure of the overlying coal pillars accounted for 78.3% and 16% of the vertical concentrated stress, respectively, and the areas closer to the overlying coal pillars were more susceptible to dynamic load disturbances. The monitoring results of seismic computed tomography CT and EMR demonstrated the static load stress concentration area was distributed near the overlying coal pillar, and the stress concentration degree was greater in the area of superimposed lateral support pressure and advanced support pressure. Moreover, microseismic spatial positioning revealed that the high-energy microseismic events were mainly concentrated near the overlying large coal pillars and roof. The on-site multiparameter detection results were highly consistent with the characteristics of actual strata behaviors and the conclusions of the theoretical model. This method could provide a reference for the quantitative calculation of stress distribution under similar conditions and the identification of the danger zone of strata behaviors.