Missing values in radionuclide diffusion datasets can undermine the predictive accuracy and robustness of the machine learning(ML)models.In this study,regression-based missing data imputation method using a light grad...Missing values in radionuclide diffusion datasets can undermine the predictive accuracy and robustness of the machine learning(ML)models.In this study,regression-based missing data imputation method using a light gradient boosting machine(LGBM)algorithm was employed to impute more than 60%of the missing data,establishing a radionuclide diffusion dataset containing 16 input features and 813 instances.The effective diffusion coefficient(D_(e))was predicted using ten ML models.The predictive accuracy of the ensemble meta-models,namely LGBM-extreme gradient boosting(XGB)and LGBM-categorical boosting(CatB),surpassed that of the other ML models,with R^(2)values of 0.94.The models were applied to predict the D_(e)values of EuEDTA^(−)and HCrO_(4)^(−)in saturated compacted bentonites at compactions ranging from 1200 to 1800 kg/m^(3),which were measured using a through-diffusion method.The generalization ability of the LGBM-XGB model surpassed that of LGB-CatB in predicting the D_(e)of HCrO_(4)^(−).Shapley additive explanations identified total porosity as the most significant influencing factor.Additionally,the partial dependence plot analysis technique yielded clearer results in the univariate correlation analysis.This study provides a regression imputation technique to refine radionuclide diffusion datasets,offering deeper insights into analyzing the diffusion mechanism of radionuclides and supporting the safety assessment of the geological disposal of high-level radioactive waste.展开更多
通过检索Web of Science核心数据库中有关护理人员在核与辐射领域中研究的文献,运用Citespace软件对文献进行可视化分析,分析了护理人员在核与辐射事件中相关研究的现状、热点和趋势。结果显示,共获得262篇文献,发文量成上升趋势,发文...通过检索Web of Science核心数据库中有关护理人员在核与辐射领域中研究的文献,运用Citespace软件对文献进行可视化分析,分析了护理人员在核与辐射事件中相关研究的现状、热点和趋势。结果显示,共获得262篇文献,发文量成上升趋势,发文量最高的国家为日本(66篇),中心性最高的国家为美国(0.27);发文量最高的机构为福岛县立医科大学(26篇),中心性最高的机构为哈佛大学(0.11);关键词分析显示,护理人员在核与辐射事件中的研究热点主要集中在辐射暴露后公众健康、护理人员对核与辐射灾害的预防与应急准备状态、辐射防护方面;灾害预防的应急准备及管理是未来护理人员在核与辐射领域的研究趋势。未来研究应增进国内外学者与研究机构的学术交流与合作,致力于现存问题的解决和辐射护理新技术的发展,进一步推动护理人员在核与辐射领域的发展。展开更多
基金supported by the National Natural Science Foundation of China(No.12475340 and 12375350)Special Branch project of South Taihu Lakethe Scientific Research Fund of Zhejiang Provincial Education Department(No.Y202456326).
文摘Missing values in radionuclide diffusion datasets can undermine the predictive accuracy and robustness of the machine learning(ML)models.In this study,regression-based missing data imputation method using a light gradient boosting machine(LGBM)algorithm was employed to impute more than 60%of the missing data,establishing a radionuclide diffusion dataset containing 16 input features and 813 instances.The effective diffusion coefficient(D_(e))was predicted using ten ML models.The predictive accuracy of the ensemble meta-models,namely LGBM-extreme gradient boosting(XGB)and LGBM-categorical boosting(CatB),surpassed that of the other ML models,with R^(2)values of 0.94.The models were applied to predict the D_(e)values of EuEDTA^(−)and HCrO_(4)^(−)in saturated compacted bentonites at compactions ranging from 1200 to 1800 kg/m^(3),which were measured using a through-diffusion method.The generalization ability of the LGBM-XGB model surpassed that of LGB-CatB in predicting the D_(e)of HCrO_(4)^(−).Shapley additive explanations identified total porosity as the most significant influencing factor.Additionally,the partial dependence plot analysis technique yielded clearer results in the univariate correlation analysis.This study provides a regression imputation technique to refine radionuclide diffusion datasets,offering deeper insights into analyzing the diffusion mechanism of radionuclides and supporting the safety assessment of the geological disposal of high-level radioactive waste.
文摘通过检索Web of Science核心数据库中有关护理人员在核与辐射领域中研究的文献,运用Citespace软件对文献进行可视化分析,分析了护理人员在核与辐射事件中相关研究的现状、热点和趋势。结果显示,共获得262篇文献,发文量成上升趋势,发文量最高的国家为日本(66篇),中心性最高的国家为美国(0.27);发文量最高的机构为福岛县立医科大学(26篇),中心性最高的机构为哈佛大学(0.11);关键词分析显示,护理人员在核与辐射事件中的研究热点主要集中在辐射暴露后公众健康、护理人员对核与辐射灾害的预防与应急准备状态、辐射防护方面;灾害预防的应急准备及管理是未来护理人员在核与辐射领域的研究趋势。未来研究应增进国内外学者与研究机构的学术交流与合作,致力于现存问题的解决和辐射护理新技术的发展,进一步推动护理人员在核与辐射领域的发展。
