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Magnesium alloys are emerging as promising alternatives to traditional orthopedic implant materials thanks to their biodegradability, biocompatibility, and impressive mechanical characteristics. However, their rapid in-vivo degradation presents challenges, notably in upholding mechanical integrity over time. This study investigates the impact of high-temperature thermal processing on the mechanical and degradation attributes of a lean Mg-Zn-Ca-Mn alloy, ZX10. Utilizing rapid, cost-efficient characterization methods like X-ray diffraction and optical, we swiftly examine microstructural changes post-thermal treatment. Employing Pearson correlation coefficient analysis, we unveil the relationship between microstructural properties and critical targets (properties): hardness and corrosion resistance. Additionally, leveraging the least absolute shrinkage and selection operator (LASSO), we pinpoint the dominant microstructural factors among closely correlated variables. Our findings underscore the significant role of grain size refinement in strengthening and the predominance of the ternary Ca2Mg6Zn3 phase in corrosion behavior. This suggests that achieving an optimal blend of strength and corrosion resistance is attainable through fine grains and reduced concentration of ternary phases. This thorough investigation furnishes valuable insights into the intricate interplay of processing, structure, and properties in magnesium alloys, thereby advancing the development of superior biodegradable implant materials. 

Abstract Humulus lupulus L., commonly known as hops, is a perennial crop grown worldwide and is well known for its pharmacological, commercial, and most importantly brewing applications. For hundreds of years, hops have undergone intense artificial selection with over 250 cultivated varieties being developed worldwide, all displaying differences in key characteristics such as bitter acid concentrations, flavor and aroma profiles, changes in photoperiod, growth, and pathogen/pest resistances. Previous studies have individually explored differences between cultivars, aiming to identify markers that can quickly and cost-effectively differentiate between cultivars. However, little is known about their evolutionary history and the variability in their associated rhizospheric microbial communities. Coupling phenotypic, genomic, and soil metagenomic data, our study aims to explore the global population structure and domestication history of 98 hops cultivars. Additionally, we assessed differences in growth rates, rates of viral infection, usage of dissolvable nitrogen, and soil microbial community compositions between US and non-US based cultivars. Contrary to previous studies, our study revealed that worldwide hop cultivars cluster into four primary subpopulations; Central European, English, and American ancestry as previously reported, and one new group, the Nobles, revealing further substructure amongst Central European cultivars. Modeling the evolutionary history of domesticated hops reveals an early divergence of the common ancestors of modern US cultivars around 2800 ybp, and more recent divergences with gene flow across English, Central European, and Noble cultivars, reconciled with key events in human history and migrations. Furthermore, cultivars of US origin were shown to overall outperform non-US cultivars in both growth rates and usage of dissolvable nitrogen and display novel microbial composition. 

In this work, we present two embedded soft optical waveguide sensors designed for real-time onboard configuration sensing in soft actuators for robotic locomotion. Extending the contributions of our collaborators who employed external camera systems to monitor the gaits of twisted-beam structures, we strategically integrate our OptiGap sensor system into these structures to monitor their dynamic behavior. The system is validated through machine learning models that correlate sensor data with camera-based motion tracking, achieving high accuracy in predicting forward or reverse gaits and validating its capability for real-time sensing. Our second sensor, consisting of a square cross-section fiber pre-twisted to 360 degrees, is designed to detect the chirality of reconfigurable twisted beams. Experimental results confirm the sensor’s effectiveness in capturing variations in light transmittance corresponding to twist angle, serving as a reliable chirality sensor. The successful integration of these sensors not only improves the adaptability of soft robotic systems but also opens avenues for advanced control algorithms.