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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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Exosuit Prototypic Knee: EPK

Resistive/aerobic exercises are invaluable countermeasures to musculoskeletal/sensorimotor/cognitive deconditioning resulting from spaceflight (i.e., microgravity). In preparation for human exploration to the moon and beyond, exercise countermeasures face new mass/volume constraints, rendering current on-orbit systems infeasible. Further, effective exercise systems are critical for maintaining crew performance/health levels necessary for Lunar extravehicular activities (EVAs) and transit. Exosuits are the pinnacle of wearable and adaptable technology set to provide a lightweight, compact, innovative solution to exploration-constrained exercise. However, reliable wearable kinematic sensors integrated with softgoods have not been demonstrated in (operational) spaceflight applications. To address this gap, the project team developed a prototype exosuit knee-joint, replete with relevant sensors for determination of joint angles, and corresponding laboratory testing bench to validate the exosuit sensing performance. This technology advancement is a pre-cursor to a more expansive multi-purpose exosuit concept that provides metrology, exercise, rehabilitation, and/or augmentation.

Fiber optics↗
DOE OSTI2026

HDSense: An efficient method for ranking observable sensitivity

Identifying which observables most effectively constrain model parameters can be computationally prohibitive when considering full likelihoods of many correlated observables. This is especially important for, e.g., hadronization models, where high precision is required to interpret the results of collider experiments. We introduce the High-Dimensional Sensitivity (HDSense) score, a computationally efficient metric for ranking observable sets using only one-dimensional histograms. Derived by profiling over unknown correlations in the Fisher information framework, the score balances total information content against redundancy between observables. We apply HDSense to rank a set observables in terms of their constraining power with respect to five parameters of the Lund string model of hadronization implemented in Pythia using simulated leptonic collider events at the $Z$ pole. Validation against machine-learning--based full-likelihood approximations demonstrates that HDSense successfully identifies near-optimal observable subsets. The framework naturally handles data from multiple experiments with different acceptances and incorporates detector effects. While demonstrated on hadronization models, the methodology applies broadly to generic parameter estimation problems where correlations are unknown or difficult to model.

Assi, Benoît [Cincinnati U.] (ORCID:00000003092433↗