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Yuanhui Chen

Publications and source records attributed to Yuanhui Chen.

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Optimal convergence rate to the nonrelativistic limit of Chandrasekhar variational model for Neutron stars

In this paper, we consider the nonrelativistic limit of Chandrasekhar variational model for neutron stars. We show that the minimizer $ρ_{c}$ of Chandrasekhar energy $E_c(N)$ converges strongly to the minimizer $ρ_{\infty}$ of limit energy $E_{\infty}(N)$ in $L^1\cap L^{\frac{5}{3}}(\mathbb{R}^3)$ as the speed of light $c\rightarrow\infty$, this is a limit between two free boundary problems. Moreover, we develop a novel approach to obtain the convergence rates, we show that the above nonrelativistic limit has the optimal convergence rate $\frac{1}{c^2}$. For the radius $R_c$ of the compact support of $ρ_c(x)$ and the radius $R_\infty$ of the compact support of $ρ_\infty(x)$, we also get the optimal convergence rate $\frac{1}{c^2}$, this means that $R_\infty-R_c=O(\frac{1}{c^2})$ as $c\rightarrow\infty$. Moreover, we also obtain the optimal uniform bounds of $R_c$ and $L^\infty$-norm of $ρ_c$ with respect to $N$ as $c\rightarrow \infty$.

math.AP

A Transformer-based Prediction Method for Depth of Anesthesia During Target-controlled Infusion of Propofol and Remifentanil

Accurately predicting anesthetic effects is essential for target-controlled infusion systems. The traditional (PK-PD) models for Bispectral index (BIS) prediction require manual selection of model parameters, which can be challenging in clinical settings. Recently proposed deep learning methods can only capture general trends and may not predict abrupt changes in BIS. To address these issues, we propose a transformer-based method for predicting the depth of anesthesia (DOA) using drug infusions of propofol and remifentanil. Our method employs long short-term memory (LSTM) and gate residual network (GRN) networks to improve the efficiency of feature fusion and applies an attention mechanism to discover the interactions between the drugs. We also use label distribution smoothing and reweighting losses to address data imbalance. Experimental results show that our proposed method outperforms traditional PK-PD models and previous deep learning methods, effectively predicting anesthetic depth under sudden and deep anesthesia conditions.

cs.LG