Radiological complexity of nuclear facilities: an information complexity approach to workplace monitoring.

Nuclear energy is crucial for achieving net-zero carbon emissions. A big challenge in the nuclear sector is ensuring the safety of radiation workers and the environment, while being cost-effective. Workplace monitoring is key to protecting workers from risks of ionising radiation. Traditional monitoring involves radiological surveillance via installed radiation monitors, continuously recording measurements like radiation fields and airborne particulate radioactivity concentrations, especially where sudden radiation changes could significantly impact workers. However, this approach struggles to detect incremental changes over a long period of time in the radiological measurements of the facility. To address this limitation, we propose abstracting a nuclear facility as a complex system. We then quantify the information complexity of the facility's radiological measurements using an entropic metric. Our findings indicate that the inferences and interpretations from our abstraction have a firm basis for interpretation and can enhance current workplace monitoring systems. We suggest the implementation of a radiological complexity-based alarm system to complement existing radiation level-based systems. The abstraction synthesized here is independent of the type of nuclear facility, and hence is a general approach to workplace monitoring at a nuclear facility.

Exploring the role of large language models in radiation emergency response.

In recent times, the field of artificial intelligence (AI) has been transformed by the introduction of large language models (LLMs). These models, popularized by OpenAI's GPT-3, have demonstrated the emergent capabilities of AI in comprehending and producing text resembling human language, which has helped them transform several industries. But its role has yet to be explored in the nuclear industry, specifically in managing radiation emergencies. The present work explores LLMs' contextual awareness, natural language interaction, and their capacity to comprehend diverse queries in a radiation emergency response setting. In this study we identify different user types and their specific LLM use-cases in radiation emergencies. Their possible interactions with ChatGPT, a popular LLM, has also been simulated and preliminary results are presented. Drawing on the insights gained from this exercise and to address concerns of reliability and misinformation, this study advocates for expert guided and domain-specific LLMs trained on radiation safety protocols and historical data. This study aims to guide radiation emergency management practitioners and decision-makers in effectively incorporating LLMs into their decision support framework.