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Using mixed kernel support vector machine to improve the predictive accuracy of genome selection 认领 引用 被引量：2
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作者 Jinbu Wang Wencheng Zong +6 位作者 Liangyu Shi Mianyan Li Jia Li Deming Ren Fuping Zhao Lixian Wang Ligang Wang 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2026年第2期775-787,共13页
The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects acc... The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects accurately.Machine learning models have demonstrated remarkable potential in addressing these challenges.In this study,we introduced the concept of mixed kernel functions to explore the performance of support vector machine regression(SVR) in GS.Six single kernel functions(SVR_L,SVR_C,SVR_G,SVR_P,SVR_S,SVR_L) and four mixed kernel functions(SVR_GS,SVR_GP,SVR_LS,SVR_LP) were used to predict genome breeding values.The prediction accuracy,mean squared error(MSE) and mean absolute error(MAE) were used as evaluation indicators to compare with two traditional parametric models(GBLUP,BayesB) and two popular machine learning models(RF,KcRR).The results indicate that in most cases,the performance of the mixed kernel function model significantly outperforms that of GBLUP,BayesB and single kernel function.For instance,for T1 in the pig dataset,the predictive accuracy of SVR_GS is improved by 10% compared to GBLUP,and by approximately 4.4 and 18.6% compared to SVR_G and SVR_S respectively.For E1 in the wheat dataset,SVR_GS achieves 13.3% higher prediction accuracy than GBLUP.Among single kernel functions,the Laplacian and Gaussian kernel functions yield similar results,with the Gaussian kernel function performing better.The mixed kernel function notably reduces the MSE and MAE when compared to all single kernel functions.Furthermore,regarding runtime,SVR_GS and SVR_GP mixed kernel functions run approximately three times faster than GBLUP in the pig dataset,with only a slight increase in runtime compared to the single kernel function model.In summary,the mixed kernel function model of SVR demonstrates speed and accuracy competitiveness,and the model such as SVR_GS has important application potential for GS. 展开更多
关键词 genome selection machine learning support vector machine kernel function mixed kernel function
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数字赋能广东乡村产业振兴的时空效应和驱动因素 认领 引用 被引量：2
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作者 肖莉 黄丽僮 +1 位作者 邓乐 李福夺 《经济地理》 CSSCI CSCD 北大核心 2026年第3期184-194,共11页
文章在深入分析数字技术赋能乡村产业振兴的作用机理的基础上,从信息化、智能化、市场化和绿色化4个维度构建了数字赋能乡村产业振兴发展水平评价指标体系;继而利用2011—2021年广东省及其20个城市的面板数据,通过熵值法、核密度估计、... 文章在深入分析数字技术赋能乡村产业振兴的作用机理的基础上,从信息化、智能化、市场化和绿色化4个维度构建了数字赋能乡村产业振兴发展水平评价指标体系;继而利用2011—2021年广东省及其20个城市的面板数据,通过熵值法、核密度估计、莫兰指数和地理探测器模型,对数字赋能乡村产业振兴的发展水平以及时空格局和驱动因素进行了探讨。研究发现:(1)广东省数字赋能乡村产业振兴发展水平总体呈上升趋势,地域分布表现为珠三角>粤西>粤东>粤北,而提升速度呈现粤东>珠三角>粤北>粤西,各维度发展水平均有所提升。(2)广东省及四大区域内部城市间的发展水平绝对差异扩大,其中珠三角和粤北地区出现不同程度的极化特征。(3)广东省数字赋能乡村产业振兴发展水平的空间正向集聚趋势不断增强,但大部分城市间关联较弱,形成孤立发展态势。(4)影响这种时空格局变化的主要驱动因素包括人均GDP、城镇化率、互联网普及率、城市创新指数等,且这些因素间交互作用明显大于单个因素作用,特别是互联网普及率和城市创新指数的交互作用最为显著。 展开更多
关键词 数字技术 乡村产业 Kernel核密度 地理探测器 区域差异 广东省
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基于熵权-TOPSIS法的农业新质生产力评价与障碍分析 认领 引用 被引量：2
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作者 黄和平 许梦园 甘仙女 《中国生态农业学报（中英文）》 CAS CSCD 北大核心 2026年第6期1375-1389,共15页
农业生产力是社会生产力中最传统、最基础,也是最薄弱的部分,因而农业也是发展新质生产力任务最繁重、前景最广阔的领域。为探究农业新质生产力的发展水平,本文从高素质劳动者、高科技含量劳动资料、广范围劳动对象入手构建农业新质生... 农业生产力是社会生产力中最传统、最基础,也是最薄弱的部分,因而农业也是发展新质生产力任务最繁重、前景最广阔的领域。为探究农业新质生产力的发展水平,本文从高素质劳动者、高科技含量劳动资料、广范围劳动对象入手构建农业新质生产力评价指标体系,运用2013—2022年省际面板数据对农业新质生产力展开测度及时空特征分析。研究发现:1)全国农业新质生产力发展在总体上呈现出增长趋势,年均增长率为3.26%,但整体水平偏低,为0.12~0.18;农业新质生产力发展水平表现为东部>中部>东北部>西部。2)2013—2022年农业新质生产力各地区内部不平衡趋势逐渐扩大,尤其是西部和东北地区多极分化明显。3)影响农业新质生产力发展的主要因素依次为产业融合、机械化程度、数字化和农业劳动者教育水平。基于此得出以下政策启示:加强数据监测与评估机制,制定地区发展差异化政策,支持农业农村产业集群发展,推动农业科技创新与人才培养协同发展。 展开更多
关键词 农业新质生产力 熵权-TOPSIS法 Kernel密度估计 障碍因子识别
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数字赋能产业嬗变:陕西数实融合动态演进分析 认领 引用 被引量：1
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作者 王敏 惠梓萌 赵楷文 《湖南财政经济学院学报》 2026年第3期98-109,共12页
基于2012—2023年陕西地级市的面板数据,构建数实融合发展指标体系并测算其发展水平,进而探究陕西省数实融合发展的分布动态特征和关键因素。研究发现:陕西省数实融合水平不断提高,地区层面有明显的阶梯分布特征,空间上呈现显著的正向... 基于2012—2023年陕西地级市的面板数据,构建数实融合发展指标体系并测算其发展水平,进而探究陕西省数实融合发展的分布动态特征和关键因素。研究发现:陕西省数实融合水平不断提高,地区层面有明显的阶梯分布特征,空间上呈现显著的正向相关性和空间差异性,电子商务销售额与专利发明数量为大多数地级市的关键障碍因子,且各地级市存在特有的障碍瓶颈。因此,陕西省各市需采取加强数字基础设施建设等相应措施促进数字经济与实体经济深度融合。 展开更多
关键词 数实融合 时空演化特征 莫兰指数 Kernel核密度 障碍因子识别
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Regulation of maize kernel development via divergent activation ofα-zein genes by transcription factors O11,O2,and PBF1 认领 引用
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作者 Runmiao Tian Zeyuan Yang +7 位作者 Ruihua Yang Sihao Wang Qingwen Shen Guifeng Wang Hongqiu Wang Qingqian Zhou Jihua Tang Zhiyuan Fu 《Journal of Genetics and Genomics》 SCIE CAS CSCD 2026年第1期154-162,共9页
α.-Zeins,the major maize endosperm storage proteins,are transcriptionally regulated by Opaque2(O2)and prolamin-box-binding factor 1(PBF1),with Opaque11(O11)functioning upstream of them.However,whether O11 directly bi... α.-Zeins,the major maize endosperm storage proteins,are transcriptionally regulated by Opaque2(O2)and prolamin-box-binding factor 1(PBF1),with Opaque11(O11)functioning upstream of them.However,whether O11 directly binds toα-zein genes and its regulatory interactions with O2 and PBF1 remain unclear.Using the small-kernel mutant sw1,which exhibits decreased 19-kDa and increased 22-kDaα-zein,we positionally clone O11 and find it directly binds to G-box/E-box motifs.O11 activates 19-kDaα-zein transcription,stronger than PBF1 but weaker than O2.Notably,PBF1 competitively binds to an overlapping E-box/P-box motif,and represses O11-mediated transactivation.Although O11 does not physically interact with O2,it participates in the O2-centered hierarchical network to enhanceα-zein expression.sw1 o2 and sw1 pbf1 double mutants exhibit smaller,more opaque kernels with further reduced 19-kDa and 22-kDaα-zeins compared to the single mutants,suggesting distinct regulatory effects of these transcription factors on 19-kDa and 22-kDaα-zein genes.Promoter motif analysis suggests that O11,PBF1,and O2 directly regulate 19-kDaα-zein genes,while O11 indirectly controls 22-kDaα-zein genes via O2 and PBF1 modulation.These findings identify the unique and coordinated roles of O11,O2,and PBF1 in regulatingα.-zein genes and kernel development. 展开更多
关键词 Maize α-Zein Kernel development Endosperm 011 O2 PBF1
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Support Vector Clustering Uncovered:Insights,Challenges,and Future Outlook 认领 引用
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作者 M.Tanveer Mohammad Tabish +2 位作者 Anuradha Kumari Ashwani Kumar Malik Weiping Ding 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第4期749-775,共27页
Support vector clustering(SVC)has emerged as a powerful unsupervised learning technique,derived from support vector machines(SVMs),offering a robust solution to a wide range of complex clustering challenges.Its unique... Support vector clustering(SVC)has emerged as a powerful unsupervised learning technique,derived from support vector machines(SVMs),offering a robust solution to a wide range of complex clustering challenges.Its unique ability to handle noise,outliers,and clusters of diverse,irregular shapes sets it apart from traditional clustering methods.SVC's distinct advantage lies in its capacity to autonomously determine the optimal number of clusters without prior topological knowledge of the data.SVC maps data to a higher-dimensional space,encloses it in a minimal sphere,and identifies clusters when mapped back,supporting complex shapes and ensuring optimality through kernel functions.This review paper provides a comprehensive analysis of the SVC algorithms,exploring their variants such as robust,sparse,and fuzzy-based models and adaptations for large-scale data.Moreover,we analyze the potential of twin support vector clustering(TWSVC),with an emphasis on the use of various loss functions.Finally,the paper explores emerging trends and outlines promising future research directions for both SVC and twin SVC.These include advancements in feature engineering,extension to semi-supervised and weakly supervised learning,and the integration of multi-view and multi-modal data.Our work aims to deepen the understanding of SVC,fostering advancements that address the evolving needs of clustering in real-world scenarios. 展开更多
关键词 Clustering kernel methods support vector machine(SVM) twin support vector clustering (TWSVC) unsupervised learning
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Computational Framework for Fractional Order Neurological Disorder Model under Interpreting Transmission Patterns 认领 引用
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作者 Kottakkaran Sooppy Nisar Muhammad Farman +2 位作者 Ali Hasan Mohammed Altaf Ahmed Mohammad Tabish 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第6期762-788,共27页
A global health concern,neurodegenerative disorders like Parkinson's and Alzheimer's impact both mental and physical functioning.The complex interplay among immunological response,protein accumulation,and brai... A global health concern,neurodegenerative disorders like Parkinson's and Alzheimer's impact both mental and physical functioning.The complex interplay among immunological response,protein accumulation,and brain health necessitates sophisticated mathematical modeling.This study introduces a fractional-order mathematical model using the Mittag-Leffler derivative to describe the dynamics of neurodegeneration,incorporating key biological factors such as functioning and infected neurons,extracellular alpha-synuclein,microglia,and T-cells.A fundamental assumption of the model is that neuronal deterioration is influenced by memory effects,where past states impact current disease progression,making fractional-order calculus more suitable than traditional integer-order models.The model accounts for the secretion and clearance of alpha-synuclein,the activation of immune responses,and the role of microglia in mitigating or exacerbating neuronal damage.Sensitivity analysis emphasizes the crucial role of factors like neuronal cells production IIN,infection prevalenceγ,and stimulation of microglial cellsΘ.Numerical simulations support the long-run neuroinflammatory feedback mechanism,revealing that smaller values of fractional orderη<1reduce disease progression.This is based on the premise that increased memory(ηvalues less than one)leads to slower transmission of pathological protein aggregation.The study demonstrates that building a surrogate machine learning model of the NARX-BRBNN type,calibrated using numerical solver output,not only decreases computing complexity but also accurately replicates the dynamics of the fractional equation.This comparison underscores the necessity of employing fractional-order numerical schemes for accurately modeling complex neurobiological systems.The study proposes focused treatment approaches and provides insightful information on the course of neurodegenerative diseases. 展开更多
关键词 Neurodegenerative disorder modeling Mittag-Leffler kernel sensitivity analysis ANN
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Dynamic parameters prediction of the spatial deployable mechanism:a hybrid approach combining physics-based and data-driven models 认领 引用
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作者 Yanhe Tao Qintao Guo +4 位作者 Jin Zhou Cheng Yi You Zhang Xiaofei Liu Ruiqi Chen 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第5期535-549,共15页
Accurate prediction of the spatial mechanism's dynamic parameters in microgravity deployment simulations is crucial for identifying potential faults and ensuring precise gravitational compensation.Traditional engi... Accurate prediction of the spatial mechanism's dynamic parameters in microgravity deployment simulations is crucial for identifying potential faults and ensuring precise gravitational compensation.Traditional engineering models are often inaccurate,primarily because of insufficient experimental data and incomplete understanding of physical phenomena,which impedes model bias reduction in information-poor scenarios.We present a novel hybrid approach aimed at improving the predictive accuracy of the dynamic behavior of spatial deployable mechanisms.The graph convolutional network-temporal convolutional network(GCN-TCN)model,a type of deep learning architecture,is utilized for its expertise in forecasting spatio-temporal data through multi-step predictions.Next,the adaptive bandwidth kernel density estimation technique is applied to estimate the probability density function of residuals from the testing set of the GCN-TCN,quantifying predictive uncertainty.The predictive information is further refined using Bayesian inference,integrating a priori knowledge from physics-based models with data from data-driven models to yield robust posterior predictions.The proposed methodology is validated and shown to be robust through rigorous numerical simulations and experimental validation,demonstrating its ability to provide accurate and reliable predictions for the deployment of spatial mechanisms. 展开更多
关键词 Deep learning Kernel density estimation Data fusion Spatial deployable mechanisms Uncertainty quantification Hybrid approach Bayesian
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Sustainable waste-to-value approach:Walnut kernel waste-derived hard carbon with high-rate and cycle life for sodium-ion batteries 认领 引用
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作者 Muhammad Ishaq Maher Jabeen +4 位作者 Yana Li Yixing Shen Shuzhi Zhao Xiang Zhang Zifeng Ma 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2026年第4期115-125,共11页
The pursuit of environmentally benign and cost-effective hard carbon(HC)anode materials has been expedited by the growing demand for sustainable sodium energy storage solutions.Herein,a waste-tovalue method is pioneer... The pursuit of environmentally benign and cost-effective hard carbon(HC)anode materials has been expedited by the growing demand for sustainable sodium energy storage solutions.Herein,a waste-tovalue method is pioneered to produce HC from walnut kernel(WK)biowaste from agro-industries,via pre-hydrothermal carbonization in a KOH/water solvent system,followed by post-high temperature treatment at 1200℃(H-WKHC-KW-12).The influence of synthesis parameters on the structural characteristics and interfacial sodium storage behavior of H-WKHC-KW-12 was systematically investigated.As an anode material for sodium-ion batteries(SIBs),the optimized H-WKHC-KW-12 electrode exhibits impressive electrochemical properties including a high reversible capacity of 311.95 m A·h·g-1 at 0.1C,excellent rate performance with 247.7 m A·h·g-1 retained at 10 C,and robust long-term cycling stability,retaining 98.87%of its capacity at 0.1C after 100 cycles and 92.36%at 1C after 1350 cycles.Furthermore,the material delivers a favorable initial Coulombic efficiency(ICE)of 81%,demonstrating its viability for practical sodium storage applications.The study demonstrates the feasibility of converting WK processing waste from agro-industries into high-performance HC anode materials,supporting circular economy principles and furthering the creation of affordable,environmentally friendly SIBs technology. 展开更多
关键词 Hard carbon Walnut kernel biowaste Sodium-ion batteries
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Optimizing maize yield and kernel quality via leguminous green manure intercropping with deficit irrigation in arid agroecosystem 认领 引用
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作者 Diaoliang Zhang Yunyou Nan +4 位作者 Zhilong Fan Qiang Chai Gary Y.Gan Wen Yin Falong Hu 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2026年第7期3017-3030,共14页
Intercropping with leguminous green manure represents a sustainable approach to enhance agroecosystem resilience through improved soil fertility and resource-use efficiency.However,the synergistic mechanisms between l... Intercropping with leguminous green manure represents a sustainable approach to enhance agroecosystem resilience through improved soil fertility and resource-use efficiency.However,the synergistic mechanisms between leguminous green manure intercropping and regulated deficit irrigation in maintaining maize yield stability and enhancing kernel profiles under arid conditions remain inadequately understood.A three-year(2021–2023)split-plot field experiment incorporated main plots consisting of three green manure incorporation practices:full green manure incorporation(M||V-P),green manure stubble retention(M||V-R),and maize without green manure(maize sole cropping,SM);while split plots comprised three irrigation regimes:conventional(I3;400 mm),15%deficit(I2;340 mm),and 30%deficit(I1;280 mm).The study examined maize grain yield,kernel quality(protein,fat,starch,and essential amino acid content),net photosynthetic rate(Pn)of maize,and soil nitrate-ammonium nitrogen content.M||V-P and M||V-R increased maize grain yield compared to SM,with M||V-P producing 5.7%higher yields than M||V-R.Notably,M||V-PI2 achieved comparable yield to M||V-PI3 while reducing irrigation by 15%,demonstrating an 18.3%yield increase over SMI3.M||V-P and M||V-R enhanced kernel quality compared to SM,exhibiting higher protein,fat,starch,and essential amino acid content.Decreased irrigation led to increased kernel protein content but reduced fat and starch contents.The kernel protein content under M||V-PI2 showed no significant difference from M||V-PI1,while maintaining fat,starch,and essential amino acid content similar to M||V-PI3.M||V-PI2 improved all kernel quality parameters relative to SMI3.These enhancements primarily resulted from maize intercropped with leguminous green manure in combination with 15%deficit irrigation,which increased maize Pn by 14.3%,and elevated soil nitrate-ammonium nitrogen by 12.5 and 5.2%,respectively.These findings demonstrate a scalable approach for sustainable maize production though the integration of leguminous green manure intercropping in water-limited regions. 展开更多
关键词 intercropping with leguminous green manure yield—water tradeoff kernel quality maize
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Spatial-temporal correlation between surface distress and internal damage in pavement structure 认领 引用
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作者 Yuhui Zhang Peiguo Yuan +5 位作者 Zhongping Wang Zepeng Fan Haotian Lyu Fujiao Tang Binglei Xie Dawei Wang 《Journal of Road Engineering》 EI CAS 2026年第2期232-244,共13页
A recurrent phenomenon is the reappearance of distress conditions on the same road section,both before and after maintenance interventions.The maintenance work essentially addresses the superficial symptoms rather tha... A recurrent phenomenon is the reappearance of distress conditions on the same road section,both before and after maintenance interventions.The maintenance work essentially addresses the superficial symptoms rather than the root causes,since the internal relationships between various forms of distress remain unclear.This study quantitatively evaluates the correlation between surface distress and internal defects based on field detection data and statistical methods,effectively complementing existing qualitative analytical method.Approximately 200 defect locations data were collected from the RIOHTrack full-scale ring road,and targeted evaluation metrics reflecting pavement performance were proposed.Then,the Ripley's K-function was employed to analyze the spatial aggregation of surface and internal cracks,and to further verify their macroscopic correlation during the spatio-temporal evolution process.Next,kernel density estimation and relative risk assessment were used to investigate the relationships between the surface distress and internal defects.Experimental results reveal that the loading position significantly affects surface distress,but exhibits no obvious correlation with hidden damage,and there is also no spatial aggregation phenomenon between them.However,for semi-rigid base asphalt pavement,internal cracks and surface cracks show a strong correlation,while demonstrating only a weak association with loading position.Finally,a sensitivity analysis was performed based on the results obtained at different distance thresholds,and r=0.5 m was designated as the optimal spatial correlation distance threshold.This threshold was then used to determine the pavement structure offering the best crack resistance performance,providing a key reference for the design and maintenance of heavy-duty highway pavements.This study provides a reference for road active maintenance and supports the transformation of maintenance strategies from passive response to active intervention. 展开更多
关键词 Pavement structure RIOHTrack Ripley's K-function Kernel density estimation Surface distress Internal defects
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全要素视域下中国城市碳汇效率的时空格局及趋势预测 认领 引用
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作者 陈明华 李亚婷 +1 位作者 耿树伟 谢琳霄 《中国土地科学》 CSSCI CSCD 北大核心 2026年第3期79-90,共12页
研究目的:测度并探索城市碳汇效率的时空分异及长期转移趋势,以期为提升区域生态—经济系统韧性提供重要参考。研究方法:基于DEA-EBM模型对2010—2021年中国城市碳汇效率进行测算,并采用Dagum基尼系数、空间Kernel密度估计与地理探测器... 研究目的:测度并探索城市碳汇效率的时空分异及长期转移趋势,以期为提升区域生态—经济系统韧性提供重要参考。研究方法:基于DEA-EBM模型对2010—2021年中国城市碳汇效率进行测算,并采用Dagum基尼系数、空间Kernel密度估计与地理探测器等方法分析其空间异质性、长期转移趋势及驱动机制。研究结果:(1)全国及四大地区的城市碳汇效率显著提升,呈现“西高东低”的分布格局。(2)中国城市碳汇效率的空间异质性较为明显,区域间空间差异是主要来源;除中部外,其他地区的城市碳汇效率差异均呈缩小趋势。(3)全国整体及中、西、东北三大地区均面临“低效跃迁”与“高效退化”,而东部地区则相反。考虑空间因素时,整体城市碳汇效率正向溢出效应明显,但东部地区则存在“以邻为壑”的负向效应。(4)科技创新是影响全国整体及东、西部地区城市碳汇效率时空演变的主导因素,而与西部或东北部地区相关联的区域间城市碳汇效率时空演进则主要受人口密度和禀赋结构驱动。研究结论:全要素视角下中国城市碳汇效率存在区域发展异质性,需进一步构建差异化治理体系并创新区域协同发展路径,深化治理合作与空间溢出效应引导,系统构建优势互补的协同新机制。 展开更多
关键词 城市碳汇效率 碳中和 Dagum基尼系数 空间Kernel密度 长期转移趋势
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YOLO-SDD:An Improved YOLOv5 for Storm Drain Detection in Street-Level View 认领 引用
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作者 WANG Jing FANG Zhiqiang +4 位作者 LI Qianqian TANG Zhiwei HUANG Zhangyang HONG Zhonghua HE Haiyang 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第2期359-374,共16页
Urban drainage pipe system is an important part of city management.Automated detection of the status of storm drain in street-level images through current technologies in computer vision and AI is an important aspect ... Urban drainage pipe system is an important part of city management.Automated detection of the status of storm drain in street-level images through current technologies in computer vision and AI is an important aspect of smart city construction.In this paper,a framework based on YOLOv5s for storm drain detection(YOLOSDD)in street view is proposed.By analyzing the characteristics of small-scale targets,YOLO-SDD focuses on optimizing the Backbone network and its loss function.Series of experiments demonstrated that in the task of detecting different states of storm drain under various environmental conditions,the mean average precision(mAP@.5)of the YOLO-SDD can reach 89.6%,increasing by 2%compared with the baseline model YOLOv5s.In the presence and absence of occlusion,the average precision of storm drain detection increased by 0.9%and 3.1%,respectively.In addition,the effectiveness and generalization ability of YOLO-SDD were further validated using the storm drain dataset of Urbana-Champaign(SDUC)from Illinois,USA,and the dataset for object detection in aerial images(DOTA).Finally,this work has deployed the YOLO-SDD on the Android system,which verifies its ability of real-time detecting storm drain in different states in street scenes. 展开更多
关键词 storm drain detection YOLOv5s selective kernel attention spatial pyramid pooling cross stage partial connection SCYLLA-IoU
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新医改以来我国基层医疗服务效率的区域差异及动态演进分析 认领 引用
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作者 李丽清 邝骁睿 +1 位作者 万里晗 陈振生 《中国卫生统计》 CSCD 北大核心 2026年第1期105-110,共6页
目的探究新医改以来我国基层医疗服务效率的区域差异及动态演进特征,旨在为提升基层医疗服务效率和推动基层医疗卫生事业高质量发展提供科学的决策依据和参考。方法利用超效率slacks-based measure(SBM)模型测算2010—2021年我国31个省... 目的探究新医改以来我国基层医疗服务效率的区域差异及动态演进特征,旨在为提升基层医疗服务效率和推动基层医疗卫生事业高质量发展提供科学的决策依据和参考。方法利用超效率slacks-based measure(SBM)模型测算2010—2021年我国31个省份基层医疗服务效率,并采用Dagum基尼系数对基层医疗服务效率的区域差异进行分析,通过Kernel核密度估计法进一步探讨其动态演进特征和极化程度。结果新医改以来我国基层医疗服务效率均值为0.9245,距离前沿面的差距较小,但区域差异性较为显著,超变密度是总体差异的主要来源;基层医疗服务效率分布呈明显的两级分化现象,高效率地区和低效率地区间的差距不断扩大。结论为推动基层医疗服务提质增效,可从持续深化医改并加强监管、合理优化区域医疗资源配置、推进医联体网格化布局等方面入手,提升基层医疗服务效率并缩小区域差异。 展开更多
关键词 基层医疗服务效率 超效率slacks-baseda measure模型 Dagum基尼系数 Kernel核密度估计法
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中国影子银行发展的时空演化与分布动态 认领 引用
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作者 张兴旺 南欣 《科技和产业》 2026年第10期80-88,共9页
以2008—2020年中国31个省份(因数据缺失,未包含港澳台地区)为样本,采用Dagum基尼系数分解方法与Kernel函数,研究中国影子银行发展的区域差异及分布动态演进。结果表明:影子银行整体发展水平逐年稳步上升;影子银行发展存在区域不平衡现... 以2008—2020年中国31个省份(因数据缺失,未包含港澳台地区)为样本,采用Dagum基尼系数分解方法与Kernel函数,研究中国影子银行发展的区域差异及分布动态演进。结果表明:影子银行整体发展水平逐年稳步上升;影子银行发展存在区域不平衡现象,呈现“东部-中部-西部”阶梯型分布;影子银行区域发展不均衡程度未明显改善,极化现象整体有所好转。研究结论对于优化我国金融结构,改善金融生态,提高金融资源配置效率具有一定的政策启示。 展开更多
关键词 影子银行 时空演化特征 基尼系数 Kernel核密度 分布动态
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中国地方政府债务风险的区域差异以及分布动态——基于Dagum基尼系数和Kernel密度估计法 认领 引用
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作者 李林汉 刘丽 关雪飞 《金融理论探索》 2026年第2期64-80,共17页
研究地方政府债务风险及其区域差异,对防范系统性金融风险、维护财政可持续性具有重要现实意义,可为差异化制定债务管控政策、优化区域资源配置与推动经济高质量发展提供决策参考。本文构建了包含债务举借、使用、偿还三个维度的省域地... 研究地方政府债务风险及其区域差异,对防范系统性金融风险、维护财政可持续性具有重要现实意义,可为差异化制定债务管控政策、优化区域资源配置与推动经济高质量发展提供决策参考。本文构建了包含债务举借、使用、偿还三个维度的省域地方政府债务风险评价体系,运用熵值-TOPSIS法、Dagum基尼系数法、Kernel密度估计法等,分析了2016—2024年我国31个省份(不包含港澳台地区)地方政府债务风险的区域差异与动态演进。结果显示:我国地方政府债务风险总体可控,呈小幅下降的周期波动;空间上呈现内陆和沿边地区高、沿海地区低的梯度分布。其中,债务使用环节对地方政府债务风险影响最大。总体区域差距呈“升—降—升”波动,在区域内差异上沿海最大,而区域间差异是地方政府债务风险总体不平衡的主因。地方政府债务风险水平总体下降,但极化问题显著,沿海地区风险集中度提高,内陆内部差异扩大,沿边风险则逐步降低且分散。据此提出强化债务资金使用监督、实施差异化防控策略、构建跨区域的债务风险协同管理机制和深化财税体制改革等建议。 展开更多
关键词 地方政府债务风险 区域差异 分布动态 Dagum基尼系数 Kernel密度估计
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多任务场景下kernel特征感知的动态SM划分 认领 引用
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作者 张军 邓尚豪 刘立森 《计算机工程与设计》 北大核心 2026年第5期1242-1250,共9页
针对GPGPU多任务并行处理场景中如何高效划分流式多处理器(streaming multiprocessor,SM)的问题,提出了一种kernel特征感知的动态SM划分方法。kernel的计算操作占比和单位周期全局内存访问次数这两个特征往往存在较大差异,这种特征的差... 针对GPGPU多任务并行处理场景中如何高效划分流式多处理器(streaming multiprocessor,SM)的问题,提出了一种kernel特征感知的动态SM划分方法。kernel的计算操作占比和单位周期全局内存访问次数这两个特征往往存在较大差异,这种特征的差异导致了kernel对SM的需求也呈现出差异性。该方法根据并行kernel的不同组合类型,结合kernel的计算操作占比和全局内存访问次数两个特征进行SM动态划分,使SM划分更加合理,并有效提升SM资源的利用率。实验结果表明,相对于Even划分策略和Dynamic Optimizations策略,平均系统吞吐量分别获得了10.5%和4.37%的提升;平均周转时间分别降低了9.33%和4.05%。 展开更多
关键词 GPGPU 空间多任务 SM划分 kernel特征感知 多任务场景 并行计算 系统吞吐量
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新质生产力行业创新生态系统韧性测度 认领 引用
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作者 朱俏俏 苗永震 《资源开发与市场》 CAS 2026年第2期161-172,共12页
构建新质生产力行业创新生态系统韧性评价指标体系,运用熵权TOPSIS法、聚类分析、系统耦合模型、Kernel密度、障碍因子模型、预警模型对2008—2022年新质生产力行业创新生态系统韧性进行统计测度与动态预警研究。结果显示:新质生产力行... 构建新质生产力行业创新生态系统韧性评价指标体系,运用熵权TOPSIS法、聚类分析、系统耦合模型、Kernel密度、障碍因子模型、预警模型对2008—2022年新质生产力行业创新生态系统韧性进行统计测度与动态预警研究。结果显示:新质生产力行业创新生态系统韧性整体水平不断提高,但行业间发展差距逐渐变大,“极化”现象愈发明显;新质生产力行业创新生态系统韧性多样性、进化性、流动性、缓冲性四大子系统间联系紧密,协同发展能力强,呈现出“高耦合度、高协调度”特征;四大子系统中流动性维度、缓冲性维度障碍度较高,是制约行业创新生态系统韧性发展的关键因素;新质生产力行业创新生态系统韧性警戒度逐渐下降,警情由“危机”转向“安全”。 展开更多
关键词 新质生产力行业 创新生态系统韧性 Kernel密度链 障碍因子模型 预警模型
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中国省域能源韧性水平测度、区域差异及动态演进特征 认领 引用
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作者 张倩倩 张俊玮 《统计与决策》 CSSCI 北大核心 2026年第8期52-57,共6页
文章基于2010—2023年中国30个省份的面板数据,测度中国能源韧性水平,并采用Dagum基尼系数及其分解法、Kernel密度估计及空间马尔可夫链探究其区域差异、动态演进特征及空间转移规律。研究发现:中国能源韧性水平呈上升趋势,其中,抵御力... 文章基于2010—2023年中国30个省份的面板数据,测度中国能源韧性水平,并采用Dagum基尼系数及其分解法、Kernel密度估计及空间马尔可夫链探究其区域差异、动态演进特征及空间转移规律。研究发现:中国能源韧性水平呈上升趋势,其中,抵御力和适应力较强,恢复力较弱;区域能源韧性水平呈现东部>西部>中部>东北的特点,全国能源韧性水平的区域差异逐渐缩小,区域间差异是造成整体差异的主要原因;中部地区能源韧性水平的区域内差异逐步缩小,而东部、西部和东北地区呈扩大趋势;各省份维持原能源韧性等级的概率较高,跨等级转移的概率较小;能源韧性存在空间溢出效应,较高水平省份会带动周边省份能源韧性水平提升。 展开更多
关键词 能源韧性 区域差异 动态演进 Dagum基尼系数及其分解法 Kernel密度估计
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Kernelization in Parameterized Computation: A Survey 认领 引用
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作者 Qilong Feng Qian Zhou +1 位作者 Wenjun Li Jianxin Wang 《Tsinghua Science and Technology》 SCIE EI CAS 2014年第4期338-345,共8页
Parameterized computation is a new method dealing with NP-hard problems, which has attracted a lot of attentions in theoretical computer science. As a practical preprocessing method for NP-hard problems, kernelizaiton... Parameterized computation is a new method dealing with NP-hard problems, which has attracted a lot of attentions in theoretical computer science. As a practical preprocessing method for NP-hard problems, kernelizaiton in parameterized computation has recently become an active research area. In this paper, we discuss several kernelizaiton techniques, such as crown decomposition, planar graph vertex partition, randomized methods, and kernel lower bounds, which have been used widely in the kernelization of many hard problems. 展开更多
关键词 parameterized computation kernelization parameterized algorithm NP-hard
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