The overall aim of this article is to contribute to the further development of the area of benchmarking in risk management. The article introduces a two-step benchmarking model to assess the efficacy of ISO risk management systems. It furthermore aims at verifying its usefulness in terms of finding hidden risk issues and improvement opportunities. The existence of all key elements of an ISO 31000-based risk management system is examined at the beginning of this study. Then, the quality in terms of efficacy of important aspects of the risk management system is examined in more detail with special benchmarks. The application of the model to six ISO-certified organizations follows and reinforces the novelty of this study, which is to combine risk science knowledge with benchmarking theory in the application of ISO risk management standards in organizations. The results show that the benchmarking model developed in this study provides rigor when assessing and evaluating the efficacy of an ISO risk management system. By applying the model, risk issues and risk factors can be found that had not previously been identified. The findings are of importance for risk management, the benchmarking science, and for the development of ISO risk management standards.

In this study, machine learning models were implemented to predict the classification of coastal waters in the region of Eastern Macedonia and Thrace (EMT) concerning Escherichia coli (E. coli) concentration and weather variables in the framework of the Directive 2006/7/EC. Six sampling stations of EMT, located on beaches of the regional units of Kavala, Xanthi, Rhodopi, Evros, Thasos and Samothraki, were selected. All 1039 samples were collected from May to September within a 14-year follow-up period (2009–2021). The weather parameters were acquired from nearby meteorological stations. The samples were analysed according to the ISO 9308-1 for the detection and the enumeration of E. coli. The vast majority of the samples fall into category 1 (Excellent), which is a mark of the high quality of the coastal waters of EMT. The experimental results disclose, additionally, that two-class classifiers, namely Decision Forest, Decision Jungle and Boosted Decision Tree, achieved high Accuracy scores over 99%. In addition, comparing our performance metrics with those of other researchers, diversity is observed in using algorithms for water quality prediction, with algorithms such as Decision Tree, Artificial Neural Networks and Bayesian Belief Networks demonstrating satisfactory results. Machine learning approaches can provide critical information about the dynamic of E. coli contamination and, concurrently, consider the meteorological parameters for coastal waters classification.

During the last decades, collaborative robots capable of operating out of their cages are widely used in industry to assist humans in mundane and harsh manufacturing tasks. Although such robots are inherently safe by design, they are commonly accompanied by external sensors and other cyber-physical systems, to facilitate close cooperation with humans, which frequently render the collaborative ecosystem unsafe and prone to hazards. We introduce a method that capitalizes on partially observable Markov decision processes (POMDP) to amalgamate nominal actions of the system along with unsafe control actions posed by the System Theoretic Process Analysis (STPA). A decision-making mechanism that constantly prompts the system into a safer state is realized by providing situation awareness about the safety levels of the collaborative ecosystem by associating the system safety awareness with specific groups of selected actions. POMDP compensates the partial observability and uncertainty of the current state of the collaborative environment and creates safety screening policies that tend to make decisions that balance the system from unsafe to safe states in real time during the operational phase. The theoretical framework is assessed on a simulated human–robot collaborative scenario and proved capable of identifying loss and success scenarios.