Generation IV (Gen-IV) nuclear power plants are envisioned to replace the current reactor fleet, bringing improvements in performance, safety, reliability, and sustainability. However, large cost investments currently inhibit the deployment of these advanced reactor concepts. Digital twins bridge real-world systems with digital tools to reduce costs, enhance decision-making, and boost operational efficiency. In this work, a digital twin framework is designed to operate the Gen-IV Fluoride-salt-cooled High-temperature Reactor, utilizing data-enhanced methods to optimize operational and maintenance policies while adhering to system constraints. The closed-loop framework integrates surrogate modeling, reinforcement learning, and Bayesian inference to streamline end-to-end communication for online regulation and self-adjustment. Reinforcement learning is used to consider component health and degradation to drive the target power generations, with constraints enforced through a Reference Governor control algorithm that ensures compliance with pump flow rate and temperature limits. These input driving modules benefit from detailed online simulations that are assimilated to measurement data with Bayesian filtering. The digital twin is demonstrated in three case studies: a one-year long-term operational period showcasing maintenance planning capabilities, short-term accuracy refinement with high-frequency measurements, and system shock capturing that demonstrates real-time recalibration capabilities when change in boundary conditions. These demonstrations validate robustness for health-aware and constraint-informed nuclear plant operation, with general applicability to other advanced reactor concepts and complex engineering systems.

HOUSTON, Texas – The Johnson Space Center welcomed 12 new astronauts – 10 Americans and two from the United Arab Emirates – after the class completed a two-year training program through NASA. These astronauts will be assigned missions to the International Space Station and future commercial space stations, and will also focus on missions to the moon in preparation for Mars. Luke Delaney, a retired United States Marine Corps major from DeBary, Florida, said graduating from the program was a dream – for some, a dream that was decades in the making. Ten American astronauts and two United Arab Emirates astronauts recently graduated after completing a two-year training through NASA. When putting on his spacesuit, Delaney said he felt like he made it.

SS&C Technologies Holdings, Inc. (Nasdaq: SSNC) today announced the results of its study detailing innovative technology adoption by investment operations and accounting users in the insurance sector. SS&C's "2019 Insurance Asset Management Technology Outlook" revealed that three quarters of insurance asset managers surveyed are actively deploying or considering deploying innovative technologies such as Robotic Process Automation (RPA), Machine Learning (ML) and Artificial Intelligence (AI). Within this group, 38 percent are using third-party disruptive applications in conjunction with existing investment systems, while another 37 percent are considering using these technologies in the future for recoded or new systems. "Insurance firms are relying more on bank data without their own independent calculation, reconciliation and valuation. Key internal control functions, particularly, independently reconciling positions and cash, are enhanced by artificial intelligence, machine learning and robotic process automation," said Christy Bremner, Senior Vice President and General Manager, SS&C Institutional and Investment Management.

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