7,077 research outputs found

    Towards a Unified Description of Isoscalar Giant Monopole Resonances in a Self-Consistent Quasiparticle-Vibration Coupling Approach

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    "Why is the EoS for tin so soft?" is a longstanding question, which prevents us from determining the nuclear incompressibility K∞K_\infty accurately. To solve this puzzle, a fully self-consistent quasiparticle random phase approximation (QRPA) plus quasiparticle-vibration coupling (QPVC) approach based on Skyrme-Hartree-Fock-Bogoliubov is developed. We show that the many-body correlations introduced by QPVC, which shift the ISGMR energy in Sn isotopes by about 0.4 MeV more than the energy in 208^{208}Pb, play a crucial role in providing a unified description of the ISGMR in Sn and Pb isotopes. The best description of the experimental strength functions is given by SV-K226 and KDE0, which are characterized by incompressibility values K∞=K_\infty= 226 MeV and 229 MeV, respectively, at mean field level

    Numerical Simulation of Solid-liquid Flow in Hydrocyclone

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    Hydrocyclone is widely used as the centrifugal separation equipment to separate, classify and concentrate the product. In this paper, the multiphase flow models of mixture and Euler-Euler are used to simulate the internal three-dimensional flow field of hydrocyclone. It is found that compared to the experiment, the mixture model is shown to have the best performance among the models of mixture, Euler-Euler and discrete phase for the separation simulation when the diameter of solid particle is less than 30 Îźm. Otherwise, the discrete phase model holds the best performance. Furthermore, the field of static pressure, axial and tangential velocity, and volume fraction in the hydrocyclone is obtained by the mixture model. The outcome is very helpful to explain the separation procedure and optimize the hydrocyclone design

    Insights into the role of silicon and graphite in the electrochemical performance of silicon/graphite blended electrodes with a multi-material porous electrode model

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    Silicon/graphite blended electrodes are promising candidates to replace graphite in lithium ion batteries, benefiting from the high capacity of silicon and the good structural stability of carbon. Models have proven essential to understand and optimise batteries with new materials. However, most previous models treat silicon/graphite blends as a single “lumped” material, offering limited understanding of the behaviors of the individual materials and thus limited design capability. Here, we present a multi-material model for silicon/graphite electrodes with detailed descriptions of the contributions of the individual active materials. The model shows that silicon introduces voltage hysteresis to silicon/graphite electrodes and consequently a “plateau shift” during delithiation of the electrodes. There will also be competition between the silicon and graphite lithiation reactions depending on silicon/graphite ratio. A dimensionless competing factor is derived to quantify the competition between the two active materials. This is demonstrated to be a useful indicator for active operating regions for each material and we demonstrate how it can be used to design cycling protocols for mitigating electrode degradation. The multi-material electrode model can be readily implemented into full-cell models and coupled with other physics to guide further development of lithium ion batteries with silicon-based electrodes

    Attentive Dual Embedding for Understanding Medical Concept in Electronic Health Record

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    Electronic health records contain a wealth of information on a patient’s healthcare over many visits, such as diagnoses, treatments, drugs administered, and so on. The untapped potential of these data in healthcare analytics is vast. However, given that much of medical information is a cause and effect science, new embedding methods are required to ensure the learning representations reflect the comprehensive interplays between medical concepts and their relationships over time. Unlike one-hot encoding, a distributed representation should preserve these complex interactions as high-quality inputs for machine learning-based healthcare analytics tasks. Therefore, we propose a novel attentive dual embedding method called MC2Vec. MC2Vec captures the proximity relationships between medical concepts through a two-step optimization framework that recursively refines the embedding for superior output. The framework comprises a Skip-gram model to generate the initial embedding and an attentive CBOW model to fine-tune the embedding with temporal information gleaned from sequences of patient visits. Experiments with two public datasets demonstrate that MC2Vec’s produces embeddings of higher quality than five state-of-the-art methods

    On Determining Dead Layer and Detector Thicknesses for a Position-Sensitive Silicon Detector

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    In this work, two particular properties of the position-sensitive, thick silicon detectors (known as the "E" detectors) in the High Resolution Array (HiRA) are investigated: the thickness of the dead layer on the front of the detector, and the overall thickness of the detector itself. The dead layer thickness for each E detector in HiRA is extracted using a measurement of alpha particles emitted from a 212^{212}Pb pin source placed close to the detector surface. This procedure also allows for energy calibrations of the E detectors, which are otherwise inaccessible for alpha source calibration as each one is sandwiched between two other detectors. The E detector thickness is obtained from a combination of elastically scattered protons and an energy-loss calculation method. Results from these analyses agree with values provided by the manufacturer.Comment: Accepted for publication in Nuclear Instruments and Methods in Physics Researc
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