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

"Why is the EoS for tin so soft?" is a longstanding question, which prevents us from determining the nuclear incompressibility $K_\infty$ 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 $^{208}$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 $K_\infty=$ 226 MeV and 229 MeV, respectively, at mean field level

Numerical Simulation of Solid-liquid Flow in Hydrocyclone

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 μm. 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

Effect of high pressure die casting on the castability, defects and mechanical properties of aluminium alloys in extra-large thin-wall castings

The manufacturing of extra-large thin-wall castings using high pressure die casting is one of the most significant challenges for structural applications requiring excellent ductility. The present study aims to understand the effect of process parameters on the castability, defect formation and mechanical properties of aluminium alloys in extra-large thin-wall castings with a maximum flow length of 1230 mm in the 2.8 mm thick channel. Numerical simulation and experimental verification were carried out to tailor the process parameters in high pressure die casting. It is found that the process parameters can significantly affect the castability and mechanical properties of as-cast components. For a complete casting, the yield strength is slightly increased but the elongation is significantly decreased at the locations further away from runners. A new concept of effective flow length (EFL) is proposed and used to assess the castability in extra-large thin-wall high pressure die castings. Under the optimum casting condition, the EFL can reach 525 mm, at which the ratio of EFL to wall thickness is 187 and the yield strength and elongation are greater than 120 MPa and 10%, respectively. Although the extra-large thin-wall castings can be geometrically filled under several conditions, the heterogeneity of mechanical properties is the most significant concern, in which the variation of elongation is overwhelmingly important for the structural applications requiring excellent ductility under as-cast conditions. Therefore, the criteria of casting quality should consider both geometrical soundness and the homogeneity of mechanical properties in the casting body.The financial support from Innovate UK under No. 113151 is gratefully acknowledged

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

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

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

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 $^{212}$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