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  1. The rare gas solids exhibit systematic differences in crystal structure, phase transition conditions, bond strength, and other physical properties. The physical properties of heavy rare gas solids krypton and xenon are modified by the martensitic phase transition from face-centered cubic to hexagonal close packed structure over a broad pressure range. Crystal structure, strength, and plastic deformation of krypton and xenon have been investigated at 300 K using compression in the diamond-anvil cell with synchrotron angle-dispersive x-ray diffraction and complementary ruby fluorescence spectroscopy for Xe. Stacking faults indicative of the fcc–hcp phase transition are observed at pressures at and above 1.23 ± 0.05 and 1.9 ± 0.6 GPa in Kr and Xe, respectively. The transition remains incomplete in both solids to pressures greater than 100 GPa. Strength determined from stress measurements in Pt and ruby standards at pressures up to 111 GPa and complemented by observations of strain and texture measurements obtained by x-ray diffraction in the radial geometry to 100 GPa indicates similar or higher strength than Ar at all conditions, with significant stiffening at 15–20 GPa. Radial diffraction data reveal the persistence of broad highly textured fcc diffraction lines to 101 GPa in Xe, suggesting that the axial measurements may underestimate the metastable persistence of the fcc phase due to biased sampling of hcp crystallites resulting from preferred crystallite orientation. Kr and Xe are compared with He, Ne, and Ar for a systematic understanding of physical properties and phase equilibria of rare gas solids.

     
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  7. Context.An accurate28P(p,γ)29S reaction rate is crucial to defining the nucleosynthesis products of explosive hydrogen burning in ONe novae. Using the recently released nuclear mass of29S, together with a shell model and a direct capture calculation, we reanalyzed the28P(p,γ)29S thermonuclear reaction rate and its astrophysical implication.

    Aims.We focus on improving the astrophysical rate for28P(p,γ)29S based on the newest nuclear mass data. Our goal is to explore the impact of the new rate and associated uncertainties on the nova nucleosynthesis.

    Methods.We evaluated this reaction rate via the sum of the isolated resonance contribution instead of the previously used Hauser-Feshbach statistical model. The corresponding rate uncertainty at different energies was derived using a Monte Carlo method. Nova nucleosynthesis is computed with the 1D hydrodynamic code SHIVA.

    Results.The contribution from the capture on the first excited state at 105.64 keV in28P is taken into account for the first time. We find that the capture rate on the first excited state in28P is up to more than 12 times larger than the ground-state capture rate in the temperature region of 2.5 × 107K to 4 × 108K, resulting in the total28P(p,γ)29S reaction rate being enhanced by a factor of up to 1.4 at ~1 × 109K. In addition, the rate uncertainty has been quantified for the first time. It is found that the new rate is smaller than the previous statistical model rates, but it still agrees with them within uncertainties for nova temperatures. The statistical model appears to be roughly valid for the rate estimation of this reaction in the nova nucleosynthesis scenario. Using the 1D hydrodynamic code SHIVA, we performed the nucleosynthesis calculations in a nova explosion to investigate the impact of the new rates of28P(p,γ)29S. Our calculations show that the nova abundance pattern is only marginally affected if we use our new rates with respect to the same simulations but statistical model rates. Finally, the isotopes whose abundance is most influenced by the present28P(p,γ)29S uncertainty are28Si,33,34S,35,37Cl, and36Ar, with relative abundance changes at the level of only 3% to 4%.

     
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    Free, publicly-accessible full text available July 1, 2025
  8. Navigating safely and independently presents considerable challenges for people who are blind or have low vision (BLV), as it re- quires a comprehensive understanding of their neighborhood environments. Our user study reveals that understanding sidewalk materials and objects on the sidewalks plays a crucial role in navigation tasks. This paper presents a pioneering study in the field of navigational aids for BLV individuals. We investigate the feasibility of using auditory data, specifically the sounds produced by cane tips against various sidewalk materials, to achieve material identification. Our approach utilizes ma- chine learning and deep learning techniques to classify sidewalk materials solely based on audio cues, marking a significant step towards empowering BLV individuals with greater autonomy in their navigation. This study contributes in two major ways: Firstly, a lightweight and practical method is developed for volunteers or BLV individuals to autonomously collect auditory data of sidewalk materials using a microphone-equipped white cane. This innovative approach transforms routine cane usage into an effective data-collection tool. Secondly, a deep learning-based classifier algorithm is designed that leverages a dual architecture to enhance audio feature extraction. This includes a pre-trained Convolutional Neural Network (CNN) for regional feature extraction from two-dimensional Mel-spectrograms and a booster module for global feature enrichment. Experimental results indicate that the optimal model achieves an accuracy of 80.96% using audio data only, which can effectively recognize sidewalk materials. 
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    Free, publicly-accessible full text available March 27, 2025
  9. Navigating safely and independently presents considerable challenges for people who are blind or have low vision (BLV), as it re- quires a comprehensive understanding of their neighborhood environments. Our user study reveals that understanding sidewalk materials and objects on the sidewalks plays a crucial role in navigation tasks. This paper presents a pioneering study in the field of navigational aids for BLV individuals. We investigate the feasibility of using auditory data, specifically the sounds produced by cane tips against various sidewalk materials, to achieve material identification. Our approach utilizes ma- chine learning and deep learning techniques to classify sidewalk materials solely based on audio cues, marking a significant step towards empowering BLV individuals with greater autonomy in their navigation. This study contributes in two major ways: Firstly, a lightweight and practical method is developed for volunteers or BLV individuals to autonomously collect auditory data of sidewalk materials using a microphone-equipped white cane. This innovative approach transforms routine cane usage into an effective data-collection tool. Secondly, a deep learning-based classifier algorithm is designed that leverages a dual architecture to enhance audio feature extraction. This includes a pre-trained Convolutional Neural Network (CNN) for regional feature extraction from two-dimensional Mel-spectrograms and a booster module for global feature enrichment. 
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    Free, publicly-accessible full text available March 27, 2025
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