Transforming urban spatial structures to promote green and low-carbon development is an effective strategy.Although prior studies have examined the impact of urban polycentricity on carbon emissions and economic devel...Transforming urban spatial structures to promote green and low-carbon development is an effective strategy.Although prior studies have examined the impact of urban polycentricity on carbon emissions and economic development,research on its role in the synergistic relationship between these factors regarding carbon emission efficiency is limited.Furthermore,existing literature often overlooks nonlinear effects and interactions with other urban variables.This paper analyzed data from 295 Chinese cities in 2020,calculating urban population polycentricity,population dispersion indices,and carbon emission efficiency.Utilizing local spatial autocorrelation tools,we reveal interactions among urban population polycentricity,dispersion,carbon emissions,and carbon emission efficiency.We then employ a gradient boosting decision tree model(GBDT)to explore nonlinear and synergistic effects of polycentric urbanization.Key findings include:1)polycentric urbanization in Chinese cities exhibits significant spatial differentiation characteristics.The Polycentricity index is relatively high in economically developed eastern coastal regions with an overall low level,carbon emissions are concentrated in industrialized north-central cities and some Yangtze River Delta hubs,and carbon emission efficiency is the highest in the Yangtze River Delta while relatively low in Northeast China;there are significant spatially heterogeneous interaction characteristics among population polycentricity,population dispersion,carbon emissions,and carbon emission efficiency.2)Urban population polycentricity contributes 9.42%to total carbon emissions and 6.24%to carbon emission efficiency.3)The polycentricity index has a nonlinear impact on carbon emissions and carbon emission efficiency:no significant effect when below 0.50 or above 0.55,increased carbon emissions in 0.50-0.53,and reduced carbon emissions with improved efficiency in 0.53-0.55.4)The polycentricity index has an interaction effect with other variables;specifically,when the polycentricity index is between 0.53 and 0.55,its interaction with urban gross domestic product(GDP),urban population,urban built-up area,green coverage rate in built-up areas,urban technological expenditure,and the proportion of the output value of the secondary industry will reduce carbon emissions and improve carbon emission efficiency.These findings enhance the understanding of urban spatial structures and carbon emissions,providing valuable insights for policymakers in developing green and low-carbon strategies.展开更多
目的本研究旨在构建一个基于临床和影像学特征的极端梯度提升(extreme gradient boosting,XGBoost)模型,以鉴别乳腺非肿块病变的良恶性。材料与方法收集2018年1月至2024年7月2个机构,2种乳腺X线设备检查的有病理结果的首诊乳腺非肿块病...目的本研究旨在构建一个基于临床和影像学特征的极端梯度提升(extreme gradient boosting,XGBoost)模型,以鉴别乳腺非肿块病变的良恶性。材料与方法收集2018年1月至2024年7月2个机构,2种乳腺X线设备检查的有病理结果的首诊乳腺非肿块病变480个。患者被分为建模组[n=310,数字乳腺X线摄影(digital mammography,DM)检查]、内部验证组(n=108,DM检查),和外部验证组[n=62,数字乳腺体层合成摄影(digital breast tomosynthesis,DBT)检查]。记录患者术前乳腺X线(DM或DBT),MRI以及临床特征。采用XGBoost算法和多因素逻辑回归分析,分别构建XGBoost模型和逻辑回归(logistic regression,LR)模型。使用受试者工作特征(receiver operating characteristic,ROC)曲线评估模型的诊断效能。结果在建模组中,患者以7∶3随机分为训练集(n=217)和测试集(n=93)。训练集、测试集、训练集的内部验证组及训练集的外部验证组中,恶性非肿块病灶分别为159(73%)、58(62%)、73(68%)和43(69%)。XGBoost模型的诊断效能明显优于LR模型,在独立的训练集、测试集、训练集的内部验证组及训练集的外部验证组中均表现出良好的诊断效能,曲线下面积(area under the curve,AUC)在0.884~0.913之间。XGBoost模型在四个队列中也表现出良好的校准能力和临床净获益。结论XGBoost模型能够准确鉴别乳腺非肿块病变的良恶性,具有推广应用的潜力。展开更多
基金Under the auspices of National Natural Science Foundation of China(No.42571300)。
文摘Transforming urban spatial structures to promote green and low-carbon development is an effective strategy.Although prior studies have examined the impact of urban polycentricity on carbon emissions and economic development,research on its role in the synergistic relationship between these factors regarding carbon emission efficiency is limited.Furthermore,existing literature often overlooks nonlinear effects and interactions with other urban variables.This paper analyzed data from 295 Chinese cities in 2020,calculating urban population polycentricity,population dispersion indices,and carbon emission efficiency.Utilizing local spatial autocorrelation tools,we reveal interactions among urban population polycentricity,dispersion,carbon emissions,and carbon emission efficiency.We then employ a gradient boosting decision tree model(GBDT)to explore nonlinear and synergistic effects of polycentric urbanization.Key findings include:1)polycentric urbanization in Chinese cities exhibits significant spatial differentiation characteristics.The Polycentricity index is relatively high in economically developed eastern coastal regions with an overall low level,carbon emissions are concentrated in industrialized north-central cities and some Yangtze River Delta hubs,and carbon emission efficiency is the highest in the Yangtze River Delta while relatively low in Northeast China;there are significant spatially heterogeneous interaction characteristics among population polycentricity,population dispersion,carbon emissions,and carbon emission efficiency.2)Urban population polycentricity contributes 9.42%to total carbon emissions and 6.24%to carbon emission efficiency.3)The polycentricity index has a nonlinear impact on carbon emissions and carbon emission efficiency:no significant effect when below 0.50 or above 0.55,increased carbon emissions in 0.50-0.53,and reduced carbon emissions with improved efficiency in 0.53-0.55.4)The polycentricity index has an interaction effect with other variables;specifically,when the polycentricity index is between 0.53 and 0.55,its interaction with urban gross domestic product(GDP),urban population,urban built-up area,green coverage rate in built-up areas,urban technological expenditure,and the proportion of the output value of the secondary industry will reduce carbon emissions and improve carbon emission efficiency.These findings enhance the understanding of urban spatial structures and carbon emissions,providing valuable insights for policymakers in developing green and low-carbon strategies.
文摘目的本研究旨在构建一个基于临床和影像学特征的极端梯度提升(extreme gradient boosting,XGBoost)模型,以鉴别乳腺非肿块病变的良恶性。材料与方法收集2018年1月至2024年7月2个机构,2种乳腺X线设备检查的有病理结果的首诊乳腺非肿块病变480个。患者被分为建模组[n=310,数字乳腺X线摄影(digital mammography,DM)检查]、内部验证组(n=108,DM检查),和外部验证组[n=62,数字乳腺体层合成摄影(digital breast tomosynthesis,DBT)检查]。记录患者术前乳腺X线(DM或DBT),MRI以及临床特征。采用XGBoost算法和多因素逻辑回归分析,分别构建XGBoost模型和逻辑回归(logistic regression,LR)模型。使用受试者工作特征(receiver operating characteristic,ROC)曲线评估模型的诊断效能。结果在建模组中,患者以7∶3随机分为训练集(n=217)和测试集(n=93)。训练集、测试集、训练集的内部验证组及训练集的外部验证组中,恶性非肿块病灶分别为159(73%)、58(62%)、73(68%)和43(69%)。XGBoost模型的诊断效能明显优于LR模型,在独立的训练集、测试集、训练集的内部验证组及训练集的外部验证组中均表现出良好的诊断效能,曲线下面积(area under the curve,AUC)在0.884~0.913之间。XGBoost模型在四个队列中也表现出良好的校准能力和临床净获益。结论XGBoost模型能够准确鉴别乳腺非肿块病变的良恶性,具有推广应用的潜力。
