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In order to explore the internal relation of mechanical and electrical accidents in coal mine from the perspective of human factors, a method of mechanical and electrical accidents analysis is proposed, which integrates human factors analysis and classification system and complex network. Firstly, an improved human factors analysis and classification system is used to identify the cause of the accident and the cause chain, and the cause network is constructed. Secondly, the key causes are identified after the standardization of each index, and the disaster rate and vulnerability are introduced to identify the key cause chain. Finally, the evolution path of unsafe behavior is constructed. The results show that the network has the characteristics of small world, and the chain effect between the accident causes is obvious. The key cause of the accident is insufficient organizational safety supervision and mechanical and electrical management disorder, the key cause chain is poor individual safety awareness caused by insufficient safety education, and organizational factors are the source of the evolution path of unsafe behavior of miners.

Copyright of Journal of Engineering Management / Gongcheng Guanli Xuebao is the property of Journal of Engineering Management Editorial Office 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 abstract 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 abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Engineering Management / Gongcheng Guanli Xuebao is the property of Journal of Engineering Management Editorial Office 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 abstract 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 abstract. (Copyright applies to all Abstracts.)

Non-coal-mining accidents occur frequently in China, and individual unsafe behaviors are the direct cause. The cognitive diversity of practitioners in the non-coal-mining industry leads to various behaviors in work and hinders communication between groups. The aim of this study is to analyze the differences in risk perception (accidents and occupational diseases) between non-coal-mining practitioners (experts, miners, and managers) and to explore the contributing factors. The questionnaire survey method was used to collect the data on risk perception and influencing factors from 402 respondents working in non-coal mines and universities in China. Project analysis and exploratory factor analysis were used for preprocessing. A t-test and linear regression analysis were used to test the significance of the differences and assess the function of the factors, respectively. Regarding risk perception, two risks both have significant differences between the three groups. With the perceptions of accidents and occupational diseases ranked from high to low, the order of the practitioners is as follows: managers (3.88), experts (3.71), miners (3.55) and experts (4.14), miners (3.90), and managers (3.88). Regarding the influencing factors, risk attitude, risk communication, educational level, enterprise trust, and occupational satisfaction have great effects on the three groups. More precisely, three groups have different important predictors. Risk attitude has the greatest impact on miners (0.290) and experts (0.369), but sensibility preference has the greatest impact on managers (0.518). In summary, cognitive discrepancies are common among non-coal-mining practitioners, but the degree of deviation varies with the type and dimension of the risk. There are six factors that have a significant impact on all practitioners, but the effect is limited by specific risks and groups. [ABSTRACT FROM AUTHOR]

Social scientists have long been interested in the motives of hackers, particularly financially motivated attackers. This article analyzes web defacements, a less studied and more public form of cyberattack, in which the content of a web page is deliberately substituted with unwanted text and graphics chosen by the perpetrator. These attacks use a variety of strategies and are performed for a variety of motives, including political and ideological goals. The proliferation of such attacks has resulted in vast amounts of data that open new opportunities for qualitative and quantitative analysis. This article explores the usefulness of machine learning techniques to better understand attacker strategies and motivations. To detect overall attack patterns, this analysis utilized a sample of 40,000 images posted on defaced websites analyzed through deep machine learning methods. The approach demonstrates the potential of machine learning approaches for the study of cyberattacks, but it also reveals the considerable challenges that need to be overcome. [ABSTRACT FROM AUTHOR]

In order to find out the human factors leading to coal mine general accidents, the paper counts the coal mine accidents occurred in 2019. Based on the investigation reports, HFACS is used to carry out the statistics and analysis of 43 coal mine general accidents. Then the relationships between different human factors, which are involved in the HFACS-PEFE framework, are identified by conducting chi-square test and odds ratio analysis. Four routes of coal mine general accidents are obtained. Among them, the route comprised of organizational process loopholes → invalid correction → individual mental state → habitual violation is the easiest way to lead to general accidents in coal mines. Finally, according to the analysis results, the measures to prevent coal mine general accidents are proposed.

Non-coal-mining accidents occur frequently in China, and individual unsafe behaviors are the direct cause. The cognitive diversity of practitioners in the non-coal-mining industry leads to various behaviors in work and hinders communication between groups. The aim of this study is to analyze the differences in risk perception (accidents and occupational diseases) between non-coal-mining practitioners (experts, miners, and managers) and to explore the contributing factors. The questionnaire survey method was used to collect the data on risk perception and influencing factors from 402 respondents working in non-coal mines and universities in China. Project analysis and exploratory factor analysis were used for preprocessing. A t-test and linear regression analysis were used to test the significance of the differences and assess the function of the factors, respectively. Regarding risk perception, two risks both have significant differences between the three groups. With the perceptions of accidents and occupational diseases ranked from high to low, the order of the practitioners is as follows: managers (3.88), experts (3.71), miners (3.55) and experts (4.14), miners (3.90), and managers (3.88). Regarding the influencing factors, risk attitude, risk communication, educational level, enterprise trust, and occupational satisfaction have great effects on the three groups. More precisely, three groups have different important predictors. Risk attitude has the greatest impact on miners (0.290) and experts (0.369), but sensibility preference has the greatest impact on managers (0.518). In summary, cognitive discrepancies are common among non-coal-mining practitioners, but the degree of deviation varies with the type and dimension of the risk. There are six factors that have a significant impact on all practitioners, but the effect is limited by specific risks and groups.

Disruptive innovations may serve both good and ill causes. This means that disruption can stimulate positive outcomes or tip people and organizations into chaos. The Langston Center for Innovation in Quality and Safety plays a moderating role by ensuring that innovations consider the upstream and downstream consequences potentiated by change initiatives characterized by a rapid implementation cycle. Drawing on change management and implementation science approaches, human factor variables are intentionally considered to generate acceptance of substantive disruptions. [ABSTRACT FROM AUTHOR]

l A new accident causation model based on safety information cognition is developed. l The root cause of accidents includes false safety information and signal noise. l A versatile, complete, and explicit method of human factor analysis is proposed. Accident causation models can provide a framework to explain how an accident occurs from various perspectives. In the past, human error has become the main cause of accidents, which is inextricably linked with safety information cognition (SIC). In this study, an accident causation model—SIC-based Accident-causing Model (SICAM) is developed and then a new human reliability analysis methodology—SIC-based human factor analysis (SICHFA) is provided. Three steps are followed to construct the model. First, the role of SIC in the safety information flow (SIF) has been confirmed. Secondly, the mechanism of accident causation based on SIC were addressed, the effect of negative signal noise mainly involved. Thirdly, the influence objects of signal noise on the SIC process (SICP) were classified at information home, cognitive environment, and cognitive object. To demonstrate the viability of SICAM & SICHFA, the electrical fire, which occurred frequently and resulted in serious adverse social impact in recent years, was selected as the case in this study. Results showed that the proposed accident causation model and SICHFA can provide a new approach for preventing & predicting human accidents. [ABSTRACT FROM AUTHOR]