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权重的多尺度连接网络用于估计宏分散性.

Zhengkun Zhou1, Kai Ji1

  • 1College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China.

Journal of contaminant hydrology
|July 14, 2024
PubMed
概括

一种新的方法,加权多尺度连接 (WMC),增强卷积神经网络 (CNN) 来估计宏分散性. 这种方法通过加强特征传播和减轻梯度问题来提高溶液运输模型的预测准确性.

关键词:
卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.地下水的地下水.液压导电性场的液压导电性场.宏观分散性 宏观分散性

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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