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

He, Ye; Kuai, Nian-Sheng; Deng, Li-Min; Wang, Zi-Li; Peng, Min-Jun

doi:10.1016/j.heliyon.2024.e28897

Heliyon

2024

DOI: 10.1016/j.heliyon.2024.e28897

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

Ye He,

Nian-Sheng Kuai,

Li-Min Deng

et al.

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2024

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Cited by 2 publications

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Study on the factors that lead to laboratory accidents among university students

Sukri,

Safrin,

Kedri

et al. 2024

BIO Web Conf.

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Accidents always happen in the workplace as well as in the education sector. To reduce the accident rate in the workplace, causal factors must be considered. Hence, this study aims to analyze the level of causal factors that lead to laboratory accidents. This study involved students from higher education and was distributed to 489 students. The socio- demographics results show that 67.3% of respondents are female students compared to 32.7% are male students. Besides that, most of the respondents (35.4%) are from Third Year students. This study focuses on four major causal factors to laboratory accidents: individual factor, environmental factor, equipment factor and organization factor. The findings revealed that environmental factor is the main factor that causes laboratory accidents with an overall mean score of 4.67, followed by the organization factor (overall mean score of 4.66). Meanwhile, the overall mean score for the individual and equipment factors is 4.64. The results of this study can be used by the authorities or stakeholders in organizations, particularly in the education sector, to identify the most effective ways to reduce laboratory accidents in the education sector.

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Study on the factors that lead to laboratory accidents among university students

Sukri,

Safrin,

Kedri

et al. 2024

BIO Web Conf.

View full text Add to dashboard Cite

show abstract

Multi-Target Irregular Behavior Recognition of Chemical Laboratory Personnel Based on Improved DeepSORT Method

Duan,

Li,

Shi

2024

Processes

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The lack of safety awareness and the irregular behavior of chemical laboratory personnel are major contributors to laboratory accidents which pose significant risks to both the safety of laboratory environments and the efficiency of laboratory work. These issues can lead to accidents, equipment damage, and jeopardize personnel health. To address this challenge, this study proposes a method for recognizing irregular behavior in laboratory personnel by utilizing an improved DeepSORT algorithm tailored to the specific characteristics of a chemical laboratory setting. The method first extracts skeletal keypoints from laboratory personnel using the Lightweight OpenPose algorithm to locate individuals. The enhanced DeepSORT algorithm tracks human targets and detects the positions of the relevant objects. Finally, an SKPT-LSTM network was employed to integrate tracking data for behavior recognition. This approach was designed to enhance the detection and prevention of unsafe behaviors in chemical laboratories. The experimental results on a self-constructed dataset demonstrate that the proposed method accurately identifies irregular behaviors, thereby contributing to the reduction in safety risks in laboratory environments.

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

Cited by 2 publications

References 45 publications

Study on the factors that lead to laboratory accidents among university students

Study on the factors that lead to laboratory accidents among university students

Multi-Target Irregular Behavior Recognition of Chemical Laboratory Personnel Based on Improved DeepSORT Method

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