Modeling and simulation credibility assessments of whole-body finite element computational models for use in NASA extravehicular activity applications.

Computational finite element (FE) models are used in suited astronaut injury risk assessments; however, these models' verification, validation, and credibility (VV&C) procedures for simulating injuries in altered gravity environments are limited. Our study conducts VV&C assessments of THUMS and Elemance whole-body FE models for predicting suited astronaut injury biomechanics using eight credibility factors, as per NASA-STD-7009A. Credibility factor ordinal scores are assigned by reviewing existing documentation describing VV&C practices, and credibility sufficiency thresholds are assigned based on input from subject matter experts.

Predicting Space Radiation Single Ion Exposure in Rodents: A Machine Learning Approach.

This study presents a data-driven machine learning approach to predict individual Galactic Cosmic Radiation (GCR) ion exposure for He, O, Si, Ti, or Fe up to 150 mGy, based on Attentional Set-shifting (ATSET) experimental tests. The ATSET assay consists of a series of cognitive performance tasks on irradiated male Wistar rats. The GCR ion doses represent the expected cumulative radiation astronauts may receive during a Mars mission on an individual ion basis.

Machine Learning Models to Predict Cognitive Impairment of Rodents Subjected to Space Radiation.

This research uses machine-learned computational analyses to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is from a rodent model exposed to ≤15 cGy of individual galactic cosmic radiation (GCR) ions: He, O, Si, Ti, or Fe, expected for a Lunar or Mars mission. This work investigates rats at a subject-based level and uses performance scores taken before irradiation to predict impairment in attentional set-shifting (ATSET) data post-irradiation.