The impact of interventions on health, safety and environment in the process industry.

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.

Evaluating the effectiveness of machine learning techniques in forecasting the severity of traffic accidents.

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.

Analyzing the impact of psychological capital and work pressure on employee job engagement and safety behavior.

Introduction: Buildings and infrastructure are the primary focus of the construction industry, which also includes related activities such as design, planning, demolition, renovation, maintenance, and repair. Safety performance is crucial to the industry's ability to work effectively in spite of hazardous conditions on the job site during any given project. Improving construction workers' safety performance in Malaysia requires an in-depth examination of the interplay between workers' psychological capital, work pressure, employee engagement, and safety participation.