Prevalence of accident occurrence among scientific laboratory workers of the public university in Lebanon and the impact of safety measures

Safety climate in Pakistani universities' laboratories: an assessment of individual factors

Objectives. Current research aims to identify factors that affect the occupational safety climate in university laboratories despite their perception as low-risk areas compared to industrial environments. Methods. A safety climate survey was conducted in science laboratories across various engineering universities in Pakistan. The survey questionnaire was administered to 406 personnel, and a quantitative method for analysis was selected to examine the socio-demographic variables. A 5-point Likert scale (1 =  strongly disagree to 5 =  strongly agree) was used to perceive responses from participants. Additionally, a scale reliability test was conducted, and multivariate analysis of variance was performed to determine the relationship between selected dependent and independent variables. Results. The study found an overall safety climate score of 3.16 ± 0.55, indicating a moderate to high perception of safety on a scale of 1-5. Parameters such as role in the laboratory, departments/disciplines, accident experience and safety training significantly affected the safety climate score, while gender, age group, duration in university and accident witnessing did not. Conclusion. Upper management involvement, safety communication and direct supervision are crucial for improving the safety climate of university laboratories. The study recommends the consideration of the identified significant safety climate dimensions in laboratory safety policy-making at academic institutes.

An investigation into accidents in laboratories in universities in China caused by human error: A study based on improved CREAM and SPAR-H

Although considerable research has been devoted to improving safety in university laboratories, accidents, in that environment, have still occurred frequently at the cost of serious injury or even death of laboratory personnel. Currently, few Human Reliability Analyses (HRA) have been conducted with respect to a university laboratory. The aim of the research was to conduct a reliability study relating to human behaviour in a university laboratory to explore quantitatively the causes and influencing factors relating to the frequency of laboratory accidents. Improved Cognitive Reliability and Error Analysis Method (CREAM) and improved Standardized Plant Analysis Risk HRA (SPAR-H) were employed to assess Human Error Probability (HEP) of 23 subjects. The HEP calculated through improved CREAM proved more accurate than results obtained through improved SPAR-H. Unexpectedly, the results demonstrated that under similar environmental conditions, the HEP of subjects did not decrease with an increase in educational background, including additional experimental time and experience. Moreover, environmental conditions exerted greater impact on personnel reliability than Human Inherent Factors (HIFs) in laboratories. It is anticipated that the study would provide valuable insights, in respect of research methods, and to serve as a practical basis for lowering the accident rate in university laboratories.

Evaluating the academic scientific laboratories' safety by applying failure mode and effect analysis (FMEA) at the public university in Lebanon

Workers at scientific academic laboratories are at risk of potential exposure to different types of hazards. The study's purpose was to assess the potential failure modes (FMs) of hazards facing them through the application of the Failure Mode and Effect Analysis (FMEA) method to propose corrective actions preventive actions (CAPA) to mitigate them and to improve the safety outcomes in these workplaces (WP) at the Lebanese public University (PbU). The potential FMs leading to accident occurrence in biological and chemical labs were identified and prioritized, their causes and effects were determined by applying two surveys, and the risk priority number (RPN) for each failure was calculated. A total of 24 FMs were identified. The most alarming FM having the highest RPN scores (80) was found in the workplace 'category requiring an emergency for corrective actions (CA), it is related to the unavailability of a hazard pictogram plot and the lack of labeling of chemicals and waste containers according to their categories. The FMs having RPN scores (75-60) requiring an urgent CA were assigned to other hazards of the WP, chemical, biological, and failure of the educational system. The need to program for the remaining FMs (RPN scores 20-48) is related to the safety, biological, physical, and radioactive categories 'hazards. It is recommended to apply continuously FMEA and implement the CA proposed for each detected FM in the scientific laboratories of the PbU in order to support the decision-makers to improve laboratory safety.

Characteristics and statistical analysis of university accidents in China from 2017 to 2021

University accidents in China are frequent, and to find out the relationship pattern of factors influencing accidents, 248 university accidents occurring within 2017-2021 were studied using difference analysis (Independent-samples T-test, Mann-Whitney U test), logistic regression analysis, and diagnostic analysis of receiver operating characteristic curves. The results show: The variability in time, space, and qualifications was statistically significant (p < 0.05), and when the number of university safety policies ≥77 would significantly reduce the frequency of university accidents, with an influence strength value of 0.884 and a diagnostic accuracy of 79.8 %. In addition, the perpetrators, the time and the location of the accidents were usually undergraduate students, first semester of university, and economically developed and educationally rich provinces, respectively, with influence strength value and diagnostic accuracy of greater than 1 and 70%, respectively. Finally, specific suggestions are offered for the future prevention and reduction of accidents at the University based on the findings of the studies.

Safety accident analysis of power transmission and substation projects based on association rule mining

To achieve the sustainable development goals (SDGs) of power transmission and substation project construction, statistical analysis was used to provide an outline of safety accidents, the 4M1E method was applied for sorting out and analyzing the risk factors, and the Apriori algorithm was carried out for exploring the intrinsic interaction among risk factors based on association rule mining. The results showed that the safety accidents that happen in the construction of power transmission and substation projects were not much frequent, but deadly; the process of foundation construction and high fall was the most accident-prone process and injury type respectively. In addition, human behaviors were the foremost factors leading to accidents, and there was a strong correlation among the risk factors of low project management level, lack of safety awareness, and poor risk identification ability. For improving the security situation, measures should be taken for controlling human factors, performing flexible management, and strengthening safety training. In further research, more detailed and diversified accident reports and case data should be analyzed, and more consideration should be put on the weighted risk factor analysis to obtain more comprehensive and objective safety accident analysis results for power transmission and substation projects. This study highlights the risks in power transmission and substation project construction and introduces a novel method for better analyzing the intrinsic interaction among risk factors, which provides theoretical support for related departments to conduct sustainable safety management.

Application of Multidimensional Structural Equations in the Emergency Management of Coal Mine Accidents

The use of coal as a source of energy is crucial for the growth of the national economy, but mining poses numerous risks and a potential for significant disasters. Coal mine safety is the prerequisite and guarantee for coal industry to achieve new industrialization and sustainable development. Therefore, it is crucial to predict a safety accident in the coal mine in advance. In order to facilitate the early warning of coal mine safety accidents, this study seeks to present a prediction model based on emergency management of safety accidents, which is a fusion model of principal component analysis (PCA) and long short-term memory neural network. According to the results, the correlation coefficients of risk identification and monitoring (a11), safety inspection and warning (a12), emergency planning and training (a13), material and technical support (a15), and macroenvironmental management (a21) were 0.718, 0.653, 0.628, 0.444, and 0.553, respectively, after the PCA dimensionality reduction process, demonstrating that the previous principal component analysis had a better effect. The absolute relative errors of each evaluation index of safety accident emergency management did not exceed the limit of 5%, including a15 and a21, whose values were 4.5% and −3.8%, while the relative errors of the remaining indicators were kept at a relatively low level. In conclusion, it is clear that the algorithm model suggested in this research improved the warning capabilities of safety accident emergency risk.