Objectives: Industrial advances, as a result of globalization, causes many threats to the working life. These threats are generally associated with the level of economic development of countries. While threats from industrialization are decreasing in developed countries, developing countries are still faced with these threats. Therefore, this study aims to examine the relationship between fatal work accidents (FWA), and independent variables which are national income (NI) and employment rate (ER) in a number of selected countries. Methods: In this study the relationship between FWA and independent variables which are NI and ER of 18 developed and developing countries and a region, between 2006 and 2015, was analyzed by applying panel data analysis. Results: According to panel data analysis, whilst a 1% increase in the NI reduces the FWA rate by 1.1%, a 1% increase in the ER results in an increase of approximately 4% in the rate of FWA. Conclusions: As a result, there was a negative relationship between the FWA and NI growth and a positive relationship with the ER

Maximizing the use of explosives is crucial for optimizing blasting operations, significantly influencing productivity and cost-effectiveness in mining activities. This work explores the incorporation of machine learning methods to predict powder factor, a crucial measure for assessing the effectiveness of explosive deployment, using important rock characteristics. The goal is to enhance the accuracy of powder factor prediction by employing machine learning methods, namely decision tree models and artificial neural networks. The analysis finds key rock factors that have a substantial impact on the powder factor, hence enabling more accurate planning and execution of blasting operations. The analysis uses data from 180 blast rounds carried out at a dolomite mine in south-south Nigeria. It incorporates measures such as root mean square error (RSME), mean absolute error (MAE), R-squared (R²), and variance accounted for (VAF) to determine the best models for predicting powder factor. The results indicate that the decision tree model (MD4) outperforms alternative approaches, such as artificial neural networks and Gaussian Process Regression (GPR). In addition, the research presents an efficient artificial neural network equation (MD2) for estimating the values of optimum powder factor, demonstrating outstanding blasting fragmentation. In conclusion, this research provides significant information for improving the accuracy of powder factor prediction, which is especially advantageous for small-scale blasting operations.