The process industry is recognized for its intricate nature and potential risks, necessitating the implementation of effective measures to ensure the well-being of individuals, promote safety, and protect the operational environment. This study aims to assess the impact of interventions on health, safety, and environmental (HSE) outcomes within the process industry. Various intervention strategies, such as engineering controls, administrative controls, personal protective equipment (PPE), and behavioral interventions, have been adopted to address challenges associated with fire, explosions, product leaks, occupational health hazards, and mechanical failures. These interventions are designed to mitigate risks resulting in injuries, illnesses, environmental incidents, and property damage. Research indicates that interventions have positively affected HSE outcomes in the short term, including improved worker safety, reduced hazards, and enhanced safety behavior. Furthermore, long-term evaluations have demonstrated sustained improvements and risk reduction. Combining multiple intervention types, such as engineering controls, administrative controls, PPE, and behavioral interventions, has proven to be the most effective approach, synergistically enhancing HSE outcomes. The findings emphasize the importance of thoughtfully considering and implementing interventions in the process industry to safeguard individuals' well-being, protect the operational environment, facilities and enhance overall safety performance.

Traffic accidents pose a significant public safety concern, leading to numerous injuries and fatalities worldwide. Predicting the severity of these accidents is crucial for developing effective road safety measures and reducing casualties. This paper proposes an analytic framework that utilizes machine learning models, including Naive Bayes, Random Forest, Logistic Regression, and Artificial Neural Networks, to predict the severity of traffic accidents based on contributing factors. This study analyzed ten years of UK traffic accident data (2005–2014, N = 2,047,256) to develop and compare different ML models. Results show that the proposed Random Forest and Logistic Regression models achieved an 87% overall prediction accuracy, outperforming Naive Bayes (80%) and Artificial Neural Networks (80%). By employing Random Forest-based feature importance analysis, the study identified Engine Capacity, Age of the vehicle, make of vehicle, Age of the driver, vehicle manoeuvre, daytime, and 1st road class as the most sensitive variables influencing traffic accident severity prediction. Additionally, the suggested RF model outperformed most existing models, attaining a remarkable overall accuracy and superior predictive performance across various injury severity classes. The findings have significant implications for developing efficient road safety measures and enhancing the current traffic safety system. The proposed framework and models can be adapted to various datasets to achieve accurate and effective predictions of traffic accident severity, serving as a valuable reference for implementing traffic accident management and control measures. Future research could extend the proposed framework to datasets containing Casualty Accident information to further improve the accuracy of injury severity prediction.

Digitalization, driven by the widespread adoption of information and communication technology (ICT), reshapes occupational safety (OSH). This study examines emerging OSH risks linked to digitalization, assessing its benefits and challenges. Through a comprehensive literature review, key technologies influencing OSH are identified, their effects are categorized, and mitigation strategies are proposed. While ICT enhances workplace safety through improved monitoring and decision-making, it also introduces risks such as stress and information overload. The findings emphasize the need for further research on long-term impacts and effective risk management. This paper contributes to the field by highlighting ICT’s positive and negative implications on OSH and underscoring the importance of responsible technology adoption. The insights presented are valuable for policymakers, researchers, and industry practitioners committed to fostering a safe and healthy work environment.

Weak signals in risk analysis digitalization are of great importance for preventing major accidents in risk analysis in the process industry, especially for process operations and production. However, some of the negative impacts are incorrect operational risk identification, significant inventory carrying costs, disruption of risk frequency, and risk consequence analysis, all of which will signal inaccurate information about unforeseen and current dangers in process facilities and operational environments. While the positive impacts are viewed as an early warning system that provides information on operational risk system status, the identification of potential risk weaknesses in process facilities, indicators of a transition or an emerging problem that may become significant in the future, highlighting future assumptions, challenge our views of the future and expand the selection of a processing facility. Lastly, weak signal identification in the digitalization of risk analysis can provide relevant information in supporting, assessing and analyzing the risks associated with the operation, in order to design a technical system and estimate the industry’s level of accident risk, as well as the possible control of a system. The present research will provide valuable information to the process industry on how to protect their operational facilities and increase process safety by providing information on weak safety risk monitoring systems in operations, strengthening the processes of the operational area.