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
African exporters and the business community have welcomed the elimination of customs duties on exports to China,and companies are already preparing to access this vast market of more than 1.4 billion consumers.China...African exporters and the business community have welcomed the elimination of customs duties on exports to China,and companies are already preparing to access this vast market of more than 1.4 billion consumers.China’s new tariff policy is intended to boost trade flows in favour of African economies.“This zero-tariff policy towards Africa aims to open a new chapter in the annals of Sino-African solidarity,”said Wang Yi,China’s minister of foreign affairs,during a press conference held on the sidelines of the Two Sessions held in March.展开更多
针对Boost变换器存在多种干扰和电子元件具有非整数阶特性的问题,提出了一种分数阶PID(fractional order PID,FOPID)电压外环-分数阶滑模控制器(fractional order sliding mode control,FOSMC)电流内环双闭环控制系统。首先,利用Oustal...针对Boost变换器存在多种干扰和电子元件具有非整数阶特性的问题,提出了一种分数阶PID(fractional order PID,FOPID)电压外环-分数阶滑模控制器(fractional order sliding mode control,FOSMC)电流内环双闭环控制系统。首先,利用Oustaloup算法对电感和电容进行7阶拟合,得到分数阶电路模型;其次,设计了微积分阶次可调的FOPID,并将其作为电压外环的控制器;然后,设计扩张状态观测器(extended state observer,ESO)对系统状态、负载扰动和输入扰动进行估计;最后,基于ESO的估计值,用FOPID作为滑模面构建了FOSMC。结果表明,与其他控制算法相比,FOPID-FOSMC双闭环控制策略结合了电压外环的稳态调节能力和电流内环的快速响应能力,实现了对Boost变换器输出电压和电流的双重优化控制,具有更快的响应速度、更小的超调量、更短的恢复时间和更好的稳定性与鲁棒性。展开更多
为了进一步解决基于电容-二极管(capacitance-diode,CD)升压单元的两相交错并联Boost高增益变换器存在的开关管数量多、输入输出不共地问题,提出了一种基于CD单元的新型3L型两相交错并联Boost变换器拓扑的构建方法,并根据在第3个升压电...为了进一步解决基于电容-二极管(capacitance-diode,CD)升压单元的两相交错并联Boost高增益变换器存在的开关管数量多、输入输出不共地问题,提出了一种基于CD单元的新型3L型两相交错并联Boost变换器拓扑的构建方法,并根据在第3个升压电感前级和后级引入CD单元数量的不同,推演出基于FN-BMCD单元的3L型高增益Boost变换器的演化规律;以F2-B1CD单元的3L型Boost变换器为例,详细分析了5个开关模态的工作原理,揭示了各电感及电容寄生参数对电压增益的影响机理;搭建由数字信号处理(digital signal processing,DSP)芯片和实时仿真机组成的控制在环半实物仿真实验平台,验证了所提新型变换器拓扑理论分析的正确性。展开更多
Despite the widespread use of Decision trees (DT) across various applications, their performance tends to suffer when dealing with imbalanced datasets, where the distribution of certain classes significantly outweighs...Despite the widespread use of Decision trees (DT) across various applications, their performance tends to suffer when dealing with imbalanced datasets, where the distribution of certain classes significantly outweighs others. Cost-sensitive learning is a strategy to solve this problem, and several cost-sensitive DT algorithms have been proposed to date. However, existing algorithms, which are heuristic, tried to greedily select either a better splitting point or feature node, leading to local optima for tree nodes and ignoring the cost of the whole tree. In addition, determination of the costs is difficult and often requires domain expertise. This study proposes a DT for imbalanced data, called Swarm-based Cost-sensitive DT (SCDT), using the cost-sensitive learning strategy and an enhanced swarm-based algorithm. The DT is encoded using a hybrid individual representation. A hybrid artificial bee colony approach is designed to optimize rules, considering specified costs in an F-Measure-based fitness function. Experimental results using datasets compared with state-of-the-art DT algorithms show that the SCDT method achieved the highest performance on most datasets. Moreover, SCDT also excels in other critical performance metrics, such as recall, precision, F1-score, and AUC, with notable results with average values of 83%, 87.3%, 85%, and 80.7%, respectively.展开更多
In a video that has mesmerized audiences worldwide,a humanoid robot displays a magical move of self-defense,executing a flawless 720-degree spinning kick to knock out a baton held in a human hand.This is Chinese compa...In a video that has mesmerized audiences worldwide,a humanoid robot displays a magical move of self-defense,executing a flawless 720-degree spinning kick to knock out a baton held in a human hand.This is Chinese company Unitree Robotics’G1 robot,embodying the innovation that has propelled China forward as the world’s second largest economy.展开更多
Accurate and interpretable fault diagnosis in industrial gear systems is essential for ensuring safety,reliability,and predictive maintenance.This study presents an intelligent diagnostic framework utilizing Gradient ...Accurate and interpretable fault diagnosis in industrial gear systems is essential for ensuring safety,reliability,and predictive maintenance.This study presents an intelligent diagnostic framework utilizing Gradient Boosting(GB)for fault detection in gear systems,applied to the Aalto Gear Fault Dataset,which features a wide range of synthetic and realistic gear failure modes under varied operating conditions.The dataset was preprocessed and analyzed using an ensemble GB classifier,yielding high performance across multiple metrics:accuracy of 96.77%,precision of 95.44%,recall of 97.11%,and an F1-score of 96.22%.To enhance trust in model predictions,the study integrates an explainable AI(XAI)framework using SHAP(SHapley Additive exPlanations)to visualize feature contributions and support diagnostic transparency.A flowchart-based architecture is proposed to guide real-world deployment of interpretable fault detection pipelines.The results demonstrate the feasibility of combining predictive performance with interpretability,offering a robust approach for condition monitoring in safety-critical systems.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African exporters and the business community have welcomed the elimination of customs duties on exports to China,and companies are already preparing to access this vast market of more than 1.4 billion consumers.China’s new tariff policy is intended to boost trade flows in favour of African economies.“This zero-tariff policy towards Africa aims to open a new chapter in the annals of Sino-African solidarity,”said Wang Yi,China’s minister of foreign affairs,during a press conference held on the sidelines of the Two Sessions held in March.
文摘针对Boost变换器存在多种干扰和电子元件具有非整数阶特性的问题,提出了一种分数阶PID(fractional order PID,FOPID)电压外环-分数阶滑模控制器(fractional order sliding mode control,FOSMC)电流内环双闭环控制系统。首先,利用Oustaloup算法对电感和电容进行7阶拟合,得到分数阶电路模型;其次,设计了微积分阶次可调的FOPID,并将其作为电压外环的控制器;然后,设计扩张状态观测器(extended state observer,ESO)对系统状态、负载扰动和输入扰动进行估计;最后,基于ESO的估计值,用FOPID作为滑模面构建了FOSMC。结果表明,与其他控制算法相比,FOPID-FOSMC双闭环控制策略结合了电压外环的稳态调节能力和电流内环的快速响应能力,实现了对Boost变换器输出电压和电流的双重优化控制,具有更快的响应速度、更小的超调量、更短的恢复时间和更好的稳定性与鲁棒性。
文摘为了进一步解决基于电容-二极管(capacitance-diode,CD)升压单元的两相交错并联Boost高增益变换器存在的开关管数量多、输入输出不共地问题,提出了一种基于CD单元的新型3L型两相交错并联Boost变换器拓扑的构建方法,并根据在第3个升压电感前级和后级引入CD单元数量的不同,推演出基于FN-BMCD单元的3L型高增益Boost变换器的演化规律;以F2-B1CD单元的3L型Boost变换器为例,详细分析了5个开关模态的工作原理,揭示了各电感及电容寄生参数对电压增益的影响机理;搭建由数字信号处理(digital signal processing,DSP)芯片和实时仿真机组成的控制在环半实物仿真实验平台,验证了所提新型变换器拓扑理论分析的正确性。
文摘Despite the widespread use of Decision trees (DT) across various applications, their performance tends to suffer when dealing with imbalanced datasets, where the distribution of certain classes significantly outweighs others. Cost-sensitive learning is a strategy to solve this problem, and several cost-sensitive DT algorithms have been proposed to date. However, existing algorithms, which are heuristic, tried to greedily select either a better splitting point or feature node, leading to local optima for tree nodes and ignoring the cost of the whole tree. In addition, determination of the costs is difficult and often requires domain expertise. This study proposes a DT for imbalanced data, called Swarm-based Cost-sensitive DT (SCDT), using the cost-sensitive learning strategy and an enhanced swarm-based algorithm. The DT is encoded using a hybrid individual representation. A hybrid artificial bee colony approach is designed to optimize rules, considering specified costs in an F-Measure-based fitness function. Experimental results using datasets compared with state-of-the-art DT algorithms show that the SCDT method achieved the highest performance on most datasets. Moreover, SCDT also excels in other critical performance metrics, such as recall, precision, F1-score, and AUC, with notable results with average values of 83%, 87.3%, 85%, and 80.7%, respectively.
文摘In a video that has mesmerized audiences worldwide,a humanoid robot displays a magical move of self-defense,executing a flawless 720-degree spinning kick to knock out a baton held in a human hand.This is Chinese company Unitree Robotics’G1 robot,embodying the innovation that has propelled China forward as the world’s second largest economy.
文摘Accurate and interpretable fault diagnosis in industrial gear systems is essential for ensuring safety,reliability,and predictive maintenance.This study presents an intelligent diagnostic framework utilizing Gradient Boosting(GB)for fault detection in gear systems,applied to the Aalto Gear Fault Dataset,which features a wide range of synthetic and realistic gear failure modes under varied operating conditions.The dataset was preprocessed and analyzed using an ensemble GB classifier,yielding high performance across multiple metrics:accuracy of 96.77%,precision of 95.44%,recall of 97.11%,and an F1-score of 96.22%.To enhance trust in model predictions,the study integrates an explainable AI(XAI)framework using SHAP(SHapley Additive exPlanations)to visualize feature contributions and support diagnostic transparency.A flowchart-based architecture is proposed to guide real-world deployment of interpretable fault detection pipelines.The results demonstrate the feasibility of combining predictive performance with interpretability,offering a robust approach for condition monitoring in safety-critical systems.
