How Do COVID-19 Risk, Life-Safety Risk, Job Insecurity, and Work-Family Conflict Affect Miner Performance? Health-Anxiety and Job-Anxiety Perspectives.

The coronavirus pandemic (COVID-19) has created challenging working conditions in coal-production activities. In addition to the massive loss of resources for miners, it has had a devastating impact on these individuals' mental health. Based on the conservation of resources (COR) theory and a resource-loss perspective, this study examined the impact of COVID-19 risk, life-safety risk, perceived job insecurity, and work-family conflict on miners' job performance. Moreover, this study investigated the mediating role of job anxiety (JA) and health anxiety (HA). The study data were collected through online structured questionnaires disseminated to 629 employees working in a coal mine in China. The data analysis and hypothesis generation were conducted using the structural equation modeling (partial least squares) method. The results demonstrated that the perception of COVID-19 risk, life-safety risk, job insecurity, and work-family conflict negatively and significantly impacted miners' job performance. In addition, JA and HA negatively mediated the relationships between the perception of COVID-19 risk, life-safety risk, perceived job insecurity, work-family conflict, and job performance. The findings of this study can give coal-mining companies and their staff useful insights into how to minimize the pandemic's effects on their operations.

Evaluation of human error in workers of an Iranian copper mine during the COVID-19 pandemic using the CREAM.

BACKGROUND: The outbreak of COVID-19 has adversely affected both global economy and public health around the world. These effects have also been observed in many workplaces, including mines. OBJECTIVE: This study aimed to examine the human error of copper miners during the pandemic. METHOD: This descriptive-analytical, cross-sectional study was performed on 192 workers of a copper mine in Iran.. For this, occupation tasks were firstly analyzed using the Hierarchical Task Analysis (HTA), and then the human error in different subunits was assessed using the basic Cognitive Reliability and Error Analysis Method (CREAM). The prevalence of COVID-19 among miners was determined by assessing positive PCR test records. RESULTS: The probability of human error in the operational subunits including mining, crushing, processing, and support subunits was estimated to be 0.0056, 0.056, 0.0315, and 0.0177, respectively. All three operational units were found to be in the scrambling control mode. The support unit was determined to be in the tactical control mode. Approximately 50% of all workers had been infected with COVID-19, with the highest prevalence in support units. CONCLUSION: The results suggest that during the COVID-19 pandemic, copper miners are at higher risk of human error induced by poor working conditions. Therefore, it is recommended to employ some management strategies such as promotion of safety, health monitoring, and adopting supportive measures to control occupational stresses and therefore the probability of human error in the mine's operational units.

Long Work Hours, Overtime, and Worker Health Impairment: A Cross-Sectional Study among Stone, Sand, and Gravel Mine Workers.

BACKGROUND: Research has shown that long work hours and overtime are associated with health impairment, including stress, burnout, and overall health. However, this has not been thoroughly assessed among stone, sand, and gravel mine workers. As such, this study examined whether significant differences in stress, burnout, and overall health existed among workers that worked different hours each week. METHODS: ANOVA analyses were completed for the outcome variables (stress, burnout, and health status). Each analysis included three categorical independent variables: age, sex, and work hours. Age and sex were control variables. BMI was added to the health status analysis as an additional control variable. RESULTS: There were significant differences between work hour groups for all three outcomes. Post hoc analyses determined that workers working >60 h/week had more stress, more burnout, and lower health. Differences were not found between age or sex. There were no differences in health status for different BMI groups, but the interaction of BMI and work hours was significant. CONCLUSIONS: Working more than 60 h per week was problematic. Mine and safety administrators should enact programs to protect and promote worker health, particularly among those working long hours, especially if more than 60 h per week.

Risk Factors Identification of Unsafe Acts in Deep Coal Mine Workers Based on Grounded Theory and HFACS.

The risk factors affecting workers' unsafe acts were comprehensively identified by Human Factors Analysis and Classification System (HFACS) and grounded theory based on interview data and accident reports from deep coal mines. Firstly, we collected accident case and field interview data from deep coal mines issued by authoritative institutions. Then, the data were coded according to grounded theory to obtain relevant concepts and types. The HFACS model was used to classify the concepts and categories. Finally, the relationship between core and secondary categories was sorted out by applying a story plot. The results show that risk factors of unsafe acts of deep coal mine workers include environmental factors, organizational influence, unsafe supervision and unsafe state of miners, and the main manifestations of unsafe acts are errors and violations. Among them, the unsafe state of miners is the intermediate variable, and other factors indirectly affect risky actions of coal miners through unsafe sates. Resource management, organizational processes and failure to correct problems are the top three risk factors that occur more frequently in unsafe acts. The three most common types of unsafe act are unreasonable labor organization, failure to enforce rules, and inadequate technical specifications. By combining grounded theory and the HFACS framework to analyze data, risk factors for deep coal miners can be quickly identified, and more precise and comprehensive conceptual models of risk factors in unsafe acts of deep coal miners can be obtained.

Mining the Associations between V(D)J Gene Segments and COVID-19 Disease Characteristics

The emerging COVID-19 variants lead to a new wave of infections, spreading more rapidly with more severe illnesses. The adaptive immune system plays an essential role in the control and clearance of viral infection and influences clinical outcomes. However, the understanding of the adaptive immune responses to COVID-19 is not sufficient, which impedes the development progress of treatments and vaccines. To address this issue, we proposed a machine-learning-based method (termed as VDJ-Seg-Miner) to mine the underlying associations between the V(D)J gene segments of the T cell receptor in personalized immune repertoires and COVID-19 disease characteristics for immune system analysis. Our VDJ-Seg-Miner can interpretively reveal multiple associations between the V(D)J gene segments and COVID-19 disease characteristics and assign confidence scores to indicate its confidence in each revealed association. Furthermore, experimental results based on the real-world dataset suggested that the identified associations were highly consistent with those reported in previous work. © 2021 IEEE.

Mojiang mine, RaTG13, miners' disease and related samples remain essential clues in the origin SARS-CoV-2

We had published an article about the link between the Mojiang mineshaft, RaTG13, and the miners' disease (2012), Yunnan, China which has gained a lot of attention in the question on the origin of the COVID-19. The miners' pneumonia resembled COVID-19 in many aspects on retrospective analysis. Recently, Frutos et al. reported that the miners did not have SARS-CoV-2 infection, and hence they tried to debunk the laboratory origin of the coronavirus. In the present article, we indicate the flaws in the interpretation and analysis of Frutos et al. We also discuss how Wuhan Institute of Virology (WIV), China, shared the information about the mine, miners and RaTG13 in an incomplete and delayed manner. None of the samples have been shared with the international community so far. Many of the journalists' attempts to visit the mineshaft were in vain, as they were not allowed to reach the site. We also discuss why all the information related to the Mojiang mine samples, details of experiments done on these and sequences of all the SARS-like viruses would be crucial for the investigation on the origins of SARS-COV-2. [ FROM AUTHOR] Copyright of Current Science (00113891) is the property of Indian Academy of Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

An Education Process Mining Framework: Unveiling Meaningful Information for Understanding Students’ Learning Behavior and Improving Teaching Quality

This paper focuses on the study of automated process discovery using the Inductive visual Miner (IvM) and Directly Follows visual Miner (DFvM) algorithms to produce a valid process model for educational process mining in order to understand and predict the learning behavior of students. These models were evaluated on the publicly available xAPI (Experience API or Experience Application Programming Interface) dataset, which is an education dataset intended for tracking students’ classroom activities, participation in online communities, and performance. Experimental results with several performance measures show the effectiveness of the developed process models in helping experts to better understand students’ learning behavioral patterns.

Tuberculosis and Silicosis Burden in Artisanal and Small-Scale Gold Miners in a Large Occupational Health Outreach Programme in Zimbabwe.

Artisanal and small-scale miners (ASMs) labour under archaic working conditions and are exposed to high levels of silica dust. Exposure to silica dust has been associated with an increased risk of tuberculosis and silicosis. ASMs are highly mobile and operate in remote areas with near absent access to health services. The main purpose of this study was to evaluate the prevalence of tuberculosis, silicosis and silico-tuberculosis among ASMs in Zimbabwe. A cross-sectional study was conducted from 1 October to 31 January 2021 on a convenient sample of 514 self-selected ASMs. We report the results from among those ASMs who attended an outreach medical facility and an occupational health clinic. Data were collected from clinical records using a precoded data proforma. Data variables included demographic (age, sex), clinical details (HIV status, GeneXpert results, outcomes of chest radiographs, history of tuberculosis) and perceived exposure to mine dust. Of the 464 miners screened for silicosis, 52 (11.2%) were diagnosed with silicosis, while 17 (4.0%) of 422 ASMs were diagnosed with tuberculosis (TB). Of the 373 ASMs tested for HIV, 90 (23.5%) were sero-positive. An HIV infection was associated with a diagnosis of silicosis. There is need for a comprehensive occupational health service package, including TB and silicosis surveillance, for ASMs in Zimbabwe. These are preliminary and limited findings, needing confirmation by more comprehensive studies.