## 5 Research Questions  
The literature review of recent years from bibliographic reviews between 2014 and 2023 demonstrated that the use of research questions is minimal. The only ones who outline key questions in their introduction, which they attempt to answer throughout their work, are [PERSON] [9] and [PERSON] [10]. [PERSON] and colleagues [9] attempt to approach the evaluation of occupational health and safety risk assessments (OHSRAs) proposed in the literature. Additionally, they seek to identify the risk criteria considered in addressing OHSRAs, as well as weighting methods for determining risk criteria and the theories and uncertainty methods used to handle risk assessment information. On the other hand, [PERSON] and colleagues [10] attempt to answer questions related to machine learning methods: which sectors adopt them, how machine learning is applied alongside risk assessment, and which phase of risk assessment can be supported using machine learning. Furthermore, they explore trends in publications related to the subject under review. Below, the research questions outlined in the introduction are answered and analyzed. The results provide a very good estimation and approach to the current trends regarding the focused points of the questions in the global risk assessment in the workplace based on the four techniques. 1. Which of the four techniques has been used more by researchers, why, and how are the articles distributed? The answer to the first sub-question, i.e., which technique is most analyzed by authors, it is fuzzy logic. This is clearly deduced from the statistical analysis of the literature database as the number of articles using fuzzy logic in risk assessment in workplaces is double compared to the next most common technique, which is neural networks. Specifically, in the present study, 160 out of 289 articles were found to be using the fuzzy logic technique to address problems related to workplace safety and risk more broadly. Neural networks follow next with a total of 79 articles that employ this technique. Next are genetic algorithms and machine learning techniques with 20 and 22 articles, respectively, out of the total 289. It becomes clear that fuzzy logic is overwhelmingly the most sought-after technique, far outpacing the others. To address the second part of the research question--why researchers favor fuzzy logic over other techniques--an analysis of the key characteristics of each technique's application to the specific topic is required. Fuzzy logic has been widely used in the assessment of occupational risks due to its ability to handle uncertainty and ambiguity in decision-making. Fuzzy logic enables researchers to model and represent vague or subjective information, making it suitable for certain aspects of risk assessment where precise measurements may be challenging, difficult, or overly expensive [95; 96; 97]. Fuzzy logic enables the representation of imprecise or subjective information, which is common in occupational health and safety [98]. Furthermore, fuzzy logic is particularly suitable for modeling complex systems where there may be a lack of precise data [99; 100; 101]. Artificial Neural Networks (ANNs) are computational models inspired by the structure and function of the human brain. They excel at learning patterns from data, making them powerful tools for performing tasks such as classification, regression, and pattern recognition. Additionally, by leveraging their ability to learn complex patterns from data, ANNs can be taught, using historical data, to recognize and predict patterns related to occupational risks, making them valuable for risk assessment and prediction tasks [18]. The ability of neural networks to generalize using training data in order to make predictions about new, \"unseen\", or future data contributes to their popularity [102]. Genetic algorithms have been applied to optimize parameters and decision-making processes related to occupational risk assessment.