Evaluation of Nuclear Microreactor Cost-competitiveness in Current Electricity Markets Considering Reactor Cost Uncertainties

This paper evaluates the cost competitiveness of microreactors in today's electricity markets, with a focus on uncertainties in reactor costs. A Genetic Algorithm (GA) is used to optimize key technical parameters, such as reactor capacity, fuel enrichment, tail enrichment, refueling interval, and discharge burnup, to minimize the Levelized Cost of Energy (LCOE). Base case results are validated using Simulated Annealing (SA). By incorporating Probability Distribution Functions (PDFs) for fuel cycle costs, the study identifies optimal configurations under uncertainty. Methodologically, it introduces a novel framework combining probabilistic cost modeling with evolutionary optimization. Results show that microreactors can remain cost-competitive, with LCOEs ranging from \$48.21/MWh to \$78.32/MWh when supported by the Production Tax Credit (PTC). High reactor capacity, low fuel enrichment, moderate tail enrichment and refueling intervals, and high discharge burnup enhance cost efficiency. Among all factors, overnight capital cost (OCC) has the most significant impact on LCOE, while O&M and fuel cost uncertainties have lesser effects. The analysis highlights how energy policies like the PTC can reduce LCOE by 22-24%, improving viability despite cost variability. Compared to conventional nuclear, coal, and renewable sources like offshore wind, hydro, and biomass, optimized microreactors show strong economic potential. This research defines a realistic design space and key trade-offs, offering actionable insights for policymakers, reactor designers, and energy planners aiming to accelerate the deployment of affordable, sustainable microreactors.

Towards an AI Observatory for the Nuclear Sector: A tool for anticipatory governance

AI models are rapidly becoming embedded in all aspects of nuclear energy research and work but the safety, security, and safeguards consequences of this embedding are not well understood. In this paper, we call for the creation of an anticipatory system of governance for AI in the nuclear sector as well as the creation of a global AI observatory as a means for operationalizing anticipatory governance. The paper explores the contours of the nuclear AI observatory and an anticipatory system of governance by drawing on work in science and technology studies, public policy, and foresight studies.

On the Impact of Language Nuances on Sentiment Analysis with Large Language Models: Paraphrasing, Sarcasm, and Emojis

Large Language Models (LLMs) have demonstrated impressive performance across various tasks, including sentiment analysis. However, data quality--particularly when sourced from social media--can significantly impact their accuracy. This research explores how textual nuances, including emojis and sarcasm, affect sentiment analysis, with a particular focus on improving data quality through text paraphrasing techniques. To address the lack of labeled sarcasm data, the authors created a human-labeled dataset of 5929 tweets that enabled the assessment of LLM in various sarcasm contexts. The results show that when topic-specific datasets, such as those related to nuclear power, are used to finetune LLMs these models are not able to comprehend accurate sentiment in presence of sarcasm due to less diverse text, requiring external interventions like sarcasm removal to boost model accuracy. Sarcasm removal led to up to 21% improvement in sentiment accuracy, as LLMs trained on nuclear power-related content struggled with sarcastic tweets, achieving only 30% accuracy. In contrast, LLMs trained on general tweet datasets, covering a broader range of topics, showed considerable improvements in predicting sentiment for sarcastic tweets (60% accuracy), indicating that incorporating general text data can enhance sarcasm detection. The study also utilized adversarial text augmentation, showing that creating synthetic text variants by making minor changes significantly increased model robustness and accuracy for sarcastic tweets (approximately 85%). Additionally, text paraphrasing of tweets with fragmented language transformed around 40% of the tweets with low-confidence labels into high-confidence ones, improving LLMs sentiment analysis accuracy by 6%.