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Incorporating kernelized multi-omics data improves the accuracy of genomic prediction 认领 引用 被引量：6
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作者 Mang Liang Bingxing An +10 位作者 Tianpeng Chang Tianyu Deng Lili Du Keanning Li Sheng Cao Yueying Du Lingyang Xu Lupei Zhang Xue Gao Junya Li Huijiang Gao 《Journal of Animal Science and Biotechnology》 SCIE CAS CSCD 2023年第1期88-97,共10页
Background:Genomic selection(GS)has revolutionized animal and plant breeding after the first implementation via early selection before measuring phenotypes.Besides genome,transcriptome and metabolome information are i... Background:Genomic selection(GS)has revolutionized animal and plant breeding after the first implementation via early selection before measuring phenotypes.Besides genome,transcriptome and metabolome information are increasingly considered new sources for GS.Difficulties in building the model with multi-omics data for GS and the limit of specimen availability have both delayed the progress of investigating multi-omics.Results:We utilized the Cosine kernel to map genomic and transcriptomic data as n×n symmetric matrix(G matrix and T matrix),combined with the best linear unbiased prediction(BLUP)for GS.Here,we defined five kernel-based prediction models:genomic BLUP(GBLUP),transcriptome-BLUP(TBLUP),multi-omics BLUP(MBLUP,M=ratio×G+(1-ratio)×T),multi-omics single-step BLUP(mss BLUP),and weighted multi-omics single-step BLUP(wmss BLUP)to integrate transcribed individuals and genotyped resource population.The predictive accuracy evaluations in four traits of the Chinese Simmental beef cattle population showed that(1)MBLUP was far preferred to GBLUP(ratio=1.0),(2)the prediction accuracy of wmss BLUP and mss BLUP had 4.18%and 3.37%average improvement over GBLUP,(3)We also found the accuracy of wmss BLUP increased with the growing proportion of transcribed cattle in the whole resource population.Conclusions:We concluded that the inclusion of transcriptome data in GS had the potential to improve accuracy.Moreover,wmss BLUP is accepted to be a promising alternative for the present situation in which plenty of individuals are genotyped when fewer are transcribed. 展开更多
关键词 BLUP Cosine kernel Genomic prediction Transcriptome
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Network Traffic Prediction Using Radial Kernelized-Tversky Indexes-Based Multilayer Classifier 认领 引用
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作者 M.Govindarajan V.Chandrasekaran S.Anitha 《Computer Systems Science & Engineering》 SCIE EI 2022年第3期851-863,共13页
Accurate cellular network traffic prediction is a crucial task to access Internet services for various devices at any time.With the use of mobile devices,communication services generate numerous data for every moment.... Accurate cellular network traffic prediction is a crucial task to access Internet services for various devices at any time.With the use of mobile devices,communication services generate numerous data for every moment.Given the increasing dense population of data,traffic learning and prediction are the main components to substantially enhance the effectiveness of demand-aware resource allocation.A novel deep learning technique called radial kernelized LSTM-based connectionist Tversky multilayer deep structure learning(RKLSTM-CTMDSL)model is introduced for traffic prediction with superior accuracy and minimal time consumption.The RKLSTM-CTMDSL model performs attribute selection and classification processes for cellular traffic prediction.In this model,the connectionist Tversky multilayer deep structure learning includes multiple layers for traffic prediction.A large volume of spatial-temporal data are considered as an input-to-input layer.Thereafter,input data are transmitted to hidden layer 1,where a radial kernelized long short-term memory architecture is designed for the relevant attribute selection using activation function results.After obtaining the relevant attributes,the selected attributes are given to the next layer.Tversky index function is used in this layer to compute similarities among the training and testing traffic patterns.Tversky similarity index outcomes are given to the output layer.Similarity value is used as basis to classify data as heavy network or normal traffic.Thus,cellular network traffic prediction is presented with minimal error rate using the RKLSTM-CTMDSL model.Comparative evaluation proved that the RKLSTM-CTMDSL model outperforms conventional methods. 展开更多
关键词 Cellular network traffic prediction connectionist Tversky multilayer deep structure learning attribute selection classification radial kernelized long short-term memory
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Kernelized fourth quantification theory for mineral target prediction 认领 引用
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作者 CHEN Yongliang LI Xuebin LIN Nan 《Global Geology》 2011年第4期265-278,共14页
This paper presents a nonlinear multidimensional scaling model, called kernelized fourth quantifica- tion theory, which is an integration of kernel techniques and the fourth quantification theory. The model can deal w... This paper presents a nonlinear multidimensional scaling model, called kernelized fourth quantifica- tion theory, which is an integration of kernel techniques and the fourth quantification theory. The model can deal with the problem of mineral prediction without defining a training area. In mineral target prediction, the pre-defined statistical cells, such as grid cells, can be implicitly transformed using kernel techniques from input space to a high-dimensional feature space, where the nonlinearly separable clusters in the input space are ex- pected to be linearly separable. Then, the transformed cells in the feature space are mapped by the fourth quan- tifieation theory onto a low-dimensional scaling space, where the sealed cells can be visually clustered according to their spatial locations. At the same time, those cells, which are far away from the cluster center of the majority of the sealed cells, are recognized as anomaly cells. Finally, whether the anomaly cells can serve as mineral potential target cells can be tested by spatially superimposing the known mineral occurrences onto the anomaly ceils. A case study shows that nearly all the known mineral occurrences spatially coincide with the anomaly cells with nearly the smallest scaled coordinates in one-dimensional sealing space. In the case study, the mineral target cells delineated by the new model are similar to those predicted by the well-known WofE model. 展开更多
关键词 kernel function feature space fourth quantification theory nonlinear transformation mineral target prediction
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Multi-View Dynamic Kernelized Evidential Clustering 认领 引用
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作者 Jinyi Xu Zuowei Zhang +2 位作者 Ze Lin Yixiang Chen Weiping Ding 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第12期2435-2450,共16页
It is challenging to cluster multi-view data in which the clusters have overlapping areas.Existing multi-view clustering methods often misclassify the indistinguishable objects in overlapping areas by forcing them int... It is challenging to cluster multi-view data in which the clusters have overlapping areas.Existing multi-view clustering methods often misclassify the indistinguishable objects in overlapping areas by forcing them into single clusters,increasing clustering errors.Our solution,the multi-view dynamic kernelized evidential clustering method(MvDKE),addresses this by assigning these objects to meta-clusters,a union of several related singleton clusters,effectively capturing the local imprecision in overlapping areas.MvDKE offers two main advantages:firstly,it significantly reduces computational complexity through a dynamic framework for evidential clustering,and secondly,it adeptly handles non-spherical data using kernel techniques within its objective function.Experiments on various datasets confirm MvDKE's superior ability to accurately characterize the local imprecision in multi-view non-spherical data,achieving better efficiency and outperforming existing methods in overall performance. 展开更多
关键词 Evidential clustering imprecision characterizing kernel technique multi-view clustering
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Kernelized Correlation Filter Target Tracking Algorithm Based on Saliency Feature Selection 认领 引用
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作者 Minghua Liu Zhikao Ren +1 位作者 Chuansheng Wang Xianlun Wang 《国际计算机前沿大会会议论文集》 EI 2019年第2期176-178,共3页
To address the problem of using fixed feature and single apparent model which is difficult to adapt to the complex scenarios, a Kernelized correlation filter target tracking algorithm based on online saliency feature ... To address the problem of using fixed feature and single apparent model which is difficult to adapt to the complex scenarios, a Kernelized correlation filter target tracking algorithm based on online saliency feature selection and fusion is proposed. It combined the correlation filter tracking framework and the salient feature model of the target. In the tracking process, the maximum Kernel correlation filter response values of different feature models were calculated respectively, and the response weights were dynamically set according to the saliency of different features. According to the filter response value, the final target position was obtained, which improves the target positioning accuracy. The target model was dynamically updated in an online manner based on the feature saliency measurement results. The experimental results show that the proposed method can effectively utilize the distinctive feature fusion to improve the tracking effect in complex environments. 展开更多
关键词 Kernel correlation filter Feature selection Patch-based target tracking Saliency detection
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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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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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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：1
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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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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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Production of Activated Biochar from Palm Kernel Shell for Methylene Blue Removal 认领 引用
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作者 Sarina Sulaiman Muhammad Faris 《Journal of Renewable Materials》 EI CAS 2026年第1期92-104,共13页
In this study,Palm kernel shell(PKS)is utilized as a raw material to produce activated biochar as adsorbent for dye removal from wastewater,specifically methylene blue(MB)dye,by utilizing a simplified and costeffectiv... In this study,Palm kernel shell(PKS)is utilized as a raw material to produce activated biochar as adsorbent for dye removal from wastewater,specifically methylene blue(MB)dye,by utilizing a simplified and costeffective approach.Production of activated biocharwas carried out using both a furnace and a domesticmicrowave oven without an inert atmosphere.Three samples of palm kernel shell(PKS)based activated biochar labeled as samples A,B and C were carbonized inside the furnace at 800℃ for 1 h and then activated using the microwave-heating technique with varying heating times(0,5,10,and 15 min).The heating was conducted in the absence of an inert gas.Fourier Transform Infrared Spectroscopy(FTIR)highlighted a significant Si-O stretching vibration between 1040.5 to 692.7 cm−1,indicating the presence of key components(Silica and Alumina)in all PKS-based activated biochar samples.For wastewater treatment,activated biochar samples were tested against a 20 mg/LMethylene Blue(MB)solution,and the MB percentage removal was calculated for each run using a standard curve.Central Composite Design(CCD)experiments were conducted for optimization,with activated biochar Sample C exhibiting the highest adsorption capacity at 88.14%MB removal under specific conditions.ANOVA analysis confirmed the significance of the quadratic model,with a p-value of 0.0222 and R2=0.9438.In conclusion,the results demonstrated the efficiency of PKS-based activated biochar as an adsorbent for MB removal in comparison to other commercial adsorbents. 展开更多
关键词 Palm kernel shell biochar methylene blue dye microwave heating adsorption
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