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Ensuring the long-term chemical durability of glasses is critical for nuclear waste immobilization operations. Durable glasses usually undergo qualification for disposal based on their response to standardized tests such as the product consistency test or the vapor hydration test (VHT). The VHT uses elevated temperature and water vapor to accelerate glass alteration and the formation of secondary phases. Understanding the relationship between glass composition and VHT response is of fundamental and practical interest. However, this relationship is complex, non-linear, and sometimes fairly variable, posing challenges in identifying the distinct effect of individual oxides on VHT response. Here, we leverage a dataset comprising 654 Hanford low-activity waste (LAW) glasses across a wide compositional envelope and employ various machine learning techniques to explore this relationship. We find that Gaussian process regression (GPR), a nonparametric regression method, yields the highest predictive accuracy. By utilizing the trained model, we discern the influence of each oxide on the glasses’ VHT response. Moreover, we discuss the trade-off between underfitting and overfitting for extrapolating the material performance in the context of sparse and heterogeneous datasets.

 

The amalgamation of wearable technologies with physiochemical sensing capabilities promises to create powerful interpretive and predictive platforms for real-time health surveillance. However, the construction of such multimodal devices is difficult to be implemented wholly by traditional manufacturing techniques for at-home personalized applications. Here, we present a universal semisolid extrusion–based three-dimensional printing technology to fabricate an epifluidic elastic electronic skin (e³-skin) with high-performance multimodal physiochemical sensing capabilities. We demonstrate that the e³-skin can serve as a sustainable surveillance platform to capture the real-time physiological state of individuals during regular daily activities. We also show that by coupling the information collected from the e³-skin with machine learning, we were able to predict an individual’s degree of behavior impairments (i.e., reaction time and inhibitory control) after alcohol consumption. The e³-skin paves the path for future autonomous manufacturing of customizable wearable systems that will enable widespread utility for regular health monitoring and clinical applications.

 

Biomechanical behavior prior to landing likely contributes to anterior cruciate ligament (ACL) injuries during jump-landing tasks. This study examined prelanding knee kinematics and landing ground reaction forces (GRFs) during single-leg and double-leg landings in males and females. Participants performed landings with the dominant leg or both legs while kinematic and GRF data were collected. Single-leg landings demonstrated less time between prelanding minimal knee flexion and initial ground contact, decreased prelanding and early-landing knee flexion angles and velocities, and increased peak vertical and posterior GRFs compared with double-leg landings. Increased prelanding knee flexion velocities and knee flexion excursion correlated with decreased peak posterior GRFs during both double-leg and single-leg landings. No significant differences were observed between males and females. Prelanding knee kinematics may contribute to the increased risk of ACL injuries in single-leg landings compared with double-leg landings. Future studies are encouraged to incorporate prelanding knee mechanics to understand ACL injury mechanisms and predict future ACL injury risks. Studies of the feasibility of increasing prelanding knee flexion are needed to understand the potential role of prelanding kinematics in decreasing ACL injury risk.