Prediction of radionuclide diffusion enabled by missing data imputation and ensemble machine learning

IF 4.7 1区物理与天体物理 Q1 NUCLEAR SCIENCE & TECHNOLOGY

Nuclear Science and Techniques Pub Date : 2025-07-17 DOI:10.1007/s41365-025-01759-4

Junlei Tian, Jiaxing Feng, Jiacong Shen, Lei Yao, Jingyan Wang, Tao Wu, Yaolin Zhao

引用次数: 3

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通过缺失数据输入和集成机器学习实现放射性核素扩散预测

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Nuclear Science and Techniques 物理-核科学技术

CiteScore

5.10

自引率

39.30%

发文量

141

审稿时长

5 months

期刊介绍： Nuclear Science and Techniques (NST) reports scientific findings, technical advances and important results in the fields of nuclear science and techniques. The aim of this periodical is to stimulate cross-fertilization of knowledge among scientists and engineers working in the fields of nuclear research. Scope covers the following subjects: • Synchrotron radiation applications, beamline technology; • Accelerator, ray technology and applications; • Nuclear chemistry, radiochemistry, radiopharmaceuticals, nuclear medicine; • Nuclear electronics and instrumentation; • Nuclear physics and interdisciplinary research; • Nuclear energy science and engineering.