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  1. Free, publicly-accessible full text available June 1, 2025
  2. Abstract

    The active-particle number density is a key parameter for plasma material processing, space propulsion, and plasma-assisted combustion. The traditional actinometry method focuses on measuring the density of the atoms in the ground state, but there is a lack of an effective optical emission spectroscopy method to measure intra-shell excited-state densities. The latter atoms have chemical selectivity and higher energy, and they can easily change the material morphology as well as the ionization and combustion paths. In this work, we present a novel state-resolved actinometry (SRA) method, supported by a krypton line-ratio method for the electron temperature and density, to measure the number densities of nitrogen atoms in the ground and intra-shell excited states. The SRA method is based on a collisional-radiative model, considering the kinetics of atomic nitrogen and krypton including their excited states. The densities measured by our method are compared with those obtained from a dissociative model in a miniature electron cyclotron resonance (ECR) plasma source. Furthermore, the saturation effect, in which the electron density remains constant due to the microwave propagation in an ECR plasma once the power reaches a certain value, is used to verify the electron density measured by the line-ratio method. An ionization balance model is also presented to examine the measured electron temperature. All the values obtained with the different methods are in good agreement with each other, and hence a set of verified rate coefficient data used in our method can be provided. A novel concept, the ‘excited-state system’, is presented to quickly build an optical diagnostic method based on the analysis of quantum number propensity and selection rules.

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    Free, publicly-accessible full text available May 1, 2025
  3. Free, publicly-accessible full text available December 1, 2024
  4. Free, publicly-accessible full text available December 1, 2024
  5. Abstract

    There are continuous efforts to elucidate the structure and biological functions of short hydrogen bonds (SHBs), whose donor and acceptor heteroatoms reside more than 0.3 Å closer than the sum of their van der Waals radii. In this work, we evaluate 1070 atomic-resolution protein structures and characterize the common chemical features of SHBs formed between the side chains of amino acids and small molecule ligands. We then develop a machine learning assisted prediction of protein-ligand SHBs (MAPSHB-Ligand) model and reveal that the types of amino acids and ligand functional groups as well as the sequence of neighboring residues are essential factors that determine the class of protein-ligand hydrogen bonds. The MAPSHB-Ligand model and its implementation on our web server enable the effective identification of protein-ligand SHBs in proteins, which will facilitate the design of biomolecules and ligands that exploit these close contacts for enhanced functions.

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  6. Free, publicly-accessible full text available August 4, 2024
  7. Key Points Group 1 alkenones are reliable indicators of cold‐season temperatures Pre‐industrial cold‐season warmth between 1750 and 1850 CE in northeastern China Relatively warm cold season may be related to positive cold‐season Arctic Oscillation conditions 
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  8. Falls in the elderly are associated with significant morbidity and mortality. While numerous fall detection devices incorporating AI and machine learning algorithms have been developed, no known smartwatch-based system has been used successfully in real-time to detect falls for elderly persons. We have developed and deployed a SmartFall system on a commodity-based smartwatch which has been trialled by nine elderly participants. The system, while being usable and welcomed by the participants in our trials, has two serious limitations. The first limitation is the inability to collect a large amount of personalized data for training. When the fall detection model, which is trained with insufficient data, is used in the real world, it generates a large amount of false positives. The second limitation is the model drift problem. This means an accurate model trained using data collected with a specific device performs sub-par when used in another device. Therefore, building one model for each type of device/watch is not a scalable approach for developing smartwatch-based fall detection system. To tackle those issues, we first collected three datasets including accelerometer data for fall detection problem from different devices: the Microsoft watch (MSBAND), the Huawei watch, and the meta-sensor device. After that, a transfer learning strategy was applied to first explore the use of transfer learning to overcome the small dataset training problem for fall detection. We also demonstrated the use of transfer learning to generalize the model across the heterogeneous devices. Our preliminary experiments demonstrate the effectiveness of transfer learning for improving fall detection, achieving an F1 score higher by over 10% on average, an AUC higher by over 0.15 on average, and a smaller false positive prediction rate than the non-transfer learning approach across various datasets collected using different devices with different hardware specifications. 
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