The development of maize(Zea mays)kernels is a complex physiological process regulated by numerous genes in a spatially and temporally coordinated manner.However,many regulatory genes involved in this process remain u...The development of maize(Zea mays)kernels is a complex physiological process regulated by numerous genes in a spatially and temporally coordinated manner.However,many regulatory genes involved in this process remain unidentified.In this study,we identified ZmZFP2,a gene encoding a C4HC3-type RING zinc finger protein,which regulates kernel size and weight.This discovery was based on suppression subtractive hybridization from maize endosperm in our previous research.We further investigated the role of ZmZFP2 in regulating kernel development.The zmzfp2-ems mutant exhibited significantly reduced kernel size and weight,accompanied by fewer endosperm cells and altered starch and protein accumulation.CRISPR/Cas9-mediated knockouts and overexpression lines confirmed that ZmZFP2 positively regulates kernel size and weight,with overexpression leading to increased kernel size and weight.Transcriptome analysis revealed that ZmZFP2 regulates genes involved in zeatin biosynthesis,starch metabolism,and protein processing,further supporting its role in kernel development.Additionally,ZmZFP2 was shown to interact with the transcription factor ZmEREB98,implicating it in the gene regulatory network during grain filling.Together,these findings demonstrate that ZmZFP2 is a key regulator of maize kernel size and weight,functioning through its E3 ubiquitin ligase activity and interactions with various metabolic pathways.This study provides novel insights into the genetic regulation of kernel development and presents potential strategies for improving maize yield and quality.展开更多
Applying domain knowledge in fuzzy clustering algorithms continuously promotes the development of clustering technology.The combination of domain knowledge and fuzzy clustering algorithms has some problems,such as ini...Applying domain knowledge in fuzzy clustering algorithms continuously promotes the development of clustering technology.The combination of domain knowledge and fuzzy clustering algorithms has some problems,such as initialization sensitivity and information granule weight optimization.Therefore,we propose a weighted kernel fuzzy clustering algorithm based on a relative density view(RDVWKFC).Compared with the traditional density-based methods,RDVWKFC can capture the intrinsic structure of the data more accurately,thus improving the initial quality of the clustering.By introducing a Relative Density based Knowledge Extraction Method(RDKM)and adaptive weight optimization mechanism,we effectively solve the limitations of view initialization and information granule weight optimization.RDKM can accurately identify high-density regions and optimize the initialization process.The adaptive weight mechanism can reduce noise and outliers’interference in the initial cluster centre selection by dynamically allocating weights.Experimental results on 14 benchmark datasets show that the proposed algorithm is superior to the existing algorithms in terms of clustering accuracy,stability,and convergence speed.It shows adaptability and robustness,especially when dealing with different data distributions and noise interference.Moreover,RDVWKFC can also show significant advantages when dealing with data with complex structures and high-dimensional features.These advancements provide versatile tools for real-world applications such as bioinformatics,image segmentation,and anomaly detection.展开更多
In this work,we proposed a strategy for the hydrolysis of native corn starch after the treatment of corn starch in an ionic liquid aqueous solution,and it is an awfully“green”and simple means to obtain starch with l...In this work,we proposed a strategy for the hydrolysis of native corn starch after the treatment of corn starch in an ionic liquid aqueous solution,and it is an awfully“green”and simple means to obtain starch with low molecular weight and amorphous state.X-ray diffraction results revealed that the natural starch crystalline region was largely disrupted by ionic liquid owing to the broken intermolecular and intramolecular hydrogen bonds.After hydrolysis,the morphology of starch changed from particles of native corn starch into little pieces,and their molecular weight could be effectively regulated during the hydrolysis process,and also the hydrolyzed starch samples exhibited decreased thermal stability with the extension of hydrolysis time.This work would counsel as a powerful tool for the development of native starch in realistic applications.展开更多
In this paper,we present a necessary and sufficient condition for hyponormal block Toeplitz operators T on the vector-valued weighted Bergman space with symbolsΦ(z)=G^(*)(z)+F(z),where F(z)=∑^(N)_(i)=1 A_(i)z^(i)and...In this paper,we present a necessary and sufficient condition for hyponormal block Toeplitz operators T on the vector-valued weighted Bergman space with symbolsΦ(z)=G^(*)(z)+F(z),where F(z)=∑^(N)_(i)=1 A_(i)z^(i)and G(z)=∑^(N)_(i)=1 A_(−i)z^(i),A_(i)ae culants.展开更多
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
With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy...With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments.展开更多
Ultra-high molecular weight polyethylene(UHMWPE)is a key material for marine applications owing to its outstanding self-lubrication and corrosion resistance.However,its long-term performance is compromised by plastic ...Ultra-high molecular weight polyethylene(UHMWPE)is a key material for marine applications owing to its outstanding self-lubrication and corrosion resistance.However,its long-term performance is compromised by plastic deformation in seawater.In this study,we performed a comparative analysis of the UHMWPE dynamics under seawater and water conditions to investigate the plastic deformation of UHMWPE induced by seawater.The results show that the plastic deformation of UHMWPE is amplified in seawater relative to the water conditions.Under thin fluid conditions,frictional interfaces exhibit a higher interfacial friction force and interaction energy in seawater than in water.Compared to freely diffused water molecules,hydrated ions occupy larger interchain spaces within polyethylene.Furthermore,the diffusion of hydrated ions weakens the interchain interactions,promoting more severe polyethylene chain rearrangement and accelerating seawater-induced plastic deformation in UHMWPE during friction.Furthermore,the diffused seawater accelerated the disentangling of the polyethylene chains and enhanced the orderly orientation distribution of polyethylene.Compared to free water molecules,the water molecules of hydrated ions exhibit enhanced attraction to free-flowing water molecules,thereby accelerating seawater flow across submerged UHMWPE surfaces.This flow enhancement promotes surface polyethylene chain mobility in seawater.展开更多
In image analysis,high-precision semantic segmentation predominantly relies on supervised learning.Despite significant advancements driven by deep learning techniques,challenges such as class imbalance and dynamic per...In image analysis,high-precision semantic segmentation predominantly relies on supervised learning.Despite significant advancements driven by deep learning techniques,challenges such as class imbalance and dynamic performance evaluation persist.Traditional weighting methods,often based on pre-statistical class counting,tend to overemphasize certain classes while neglecting others,particularly rare sample categories.Approaches like focal loss and other rare-sample segmentation techniques introduce multiple hyperparameters that require manual tuning,leading to increased experimental costs due to their instability.This paper proposes a novel CAWASeg framework to address these limitations.Our approach leverages Grad-CAM technology to generate class activation maps,identifying key feature regions that the model focuses on during decision-making.We introduce a Comprehensive Segmentation Performance Score(CSPS)to dynamically evaluate model performance by converting these activation maps into pseudo mask and comparing them with Ground Truth.Additionally,we design two adaptive weights for each class:a Basic Weight(BW)and a Ratio Weight(RW),which the model adjusts during training based on real-time feedback.Extensive experiments on the COCO-Stuff,CityScapes,and ADE20k datasets demonstrate that our CAWASeg framework significantly improves segmentation performance for rare sample categories while enhancing overall segmentation accuracy.The proposed method offers a robust and efficient solution for addressing class imbalance in semantic segmentation tasks.展开更多
The accessibility of urban public transit directly influences residents’quality of life,travel behavior,and social equity.Its correlation with housing prices has garnered significant attention across disciplines such...The accessibility of urban public transit directly influences residents’quality of life,travel behavior,and social equity.Its correlation with housing prices has garnered significant attention across disciplines such as geography,economics,and urban planning.Although much existing research focuses on the impact of individual transportation facilities on housing prices,there is a notable gap in comprehensive analyses that assess the influence of overall urban transit accessibility on housing market dynamics.This study selected the main urban area of Hefei,China,as a case to investigate the spatial distribution of housing prices and evaluate public transit accessibility in 2022.Employing techniques such as the optimized parameter geographical detector and local spatial regression models,the study aimed to elucidate the effects and underlying mechanisms of urban transit accessibility on housing prices.The findings revealed that:1)housing prices in Hefei exhibited a clustered spatial pattern,with high prices concentrated in the city center and lower prices in peripheral areas,forming three distinct high-price hotspots with a‘belt-like’distribution;2)public transit accessibility showed a‘coreperiphery’structure,with accessibility declining in a‘circumferential’pattern around the city center.Based on the‘housing price-accessibility’dimension,four categories were identified:high price-high accessibility(37.25%),high price-low accessibility(19.07%),low price-high accessibility(21.95%),and low price-low accessibility(21.73%);3)the impact of transit accessibility on housing prices was spatially heterogeneous,with bus travel showing the strongest explanatory power(0.692),followed by automobile,subway,and bicycle travel.The interaction of these transportation modes generated a synergistic effect on housing price differentiation,with most influencing factors contributing more than 25%.These findings offer valuable insights for optimizing the spatial distribution of public transit infrastructure and improving both urban housing quality and residents’living standards.展开更多
Variation in weather conditions during grain filling has substantial effects on maize kernel weight(KW). The objective of this work was to characterize variation in KW with sowing date-associated weather conditions an...Variation in weather conditions during grain filling has substantial effects on maize kernel weight(KW). The objective of this work was to characterize variation in KW with sowing date-associated weather conditions and examine the relationship between KW, grain filling parameters, and weather factors. Maize was sown on eight sowing dates(SD) at 15–20-day intervals from mid-March to mid-July during 2012 and 2013 in the North China Plain. With sowing date delay, KW increased initially and later declined, and the greatest KW was obtained at SD6 in both years. The increased KW at SD6 was attributed mainly to kernel growth rate(Gmean), and effective grain-filling period(P). Variations in temperature and radiation were the primary factors that influenced KW and grain-filling parameters. When the effective cumulative temperature(AT) and radiation(Ra)during grain filling were 950 °C and 1005.4 MJ m-2, respectively, P and KW were greatest. High temperatures(daily maximum temperature [Tmax] > 30.2 °C) during grain filling under early sowing conditions, or low temperatures(daily minimum temperature [Tmin] < 20.7 °C) under late sowing conditions combined with high diurnal temperature range(Tmax-min> 7.1 °C) decreased kernel growth rate and ultimately final KW. When sowing was performed from May 25 through June 27, higher KW and yield of maize were obtained. We conclude that variations in environmental conditions(temperature and radiation) during grain filling markedly affect growth rate and duration of grain filling and eventually affect kernel weight and yield of maize.展开更多
Multi-model approach can significantly improve the prediction performance of soft sensors in the process with multiple operational conditions.However,traditional clustering algorithms may result in overlapping phenome...Multi-model approach can significantly improve the prediction performance of soft sensors in the process with multiple operational conditions.However,traditional clustering algorithms may result in overlapping phenomenon in subclasses,so that edge classes and outliers cannot be effectively dealt with and the modeling result is not satisfactory.In order to solve these problems,a new feature extraction method based on weighted kernel Fisher criterion is presented to improve the clustering accuracy,in which feature mapping is adopted to bring the edge classes and outliers closer to other normal subclasses.Furthermore,the classified data are used to develop a multiple model based on support vector machine.The proposed method is applied to a bisphenol A production process for prediction of the quality index.The simulation results demonstrate its ability in improving the data classification and the prediction performance of the soft sensor.展开更多
In this paper, we shall deal with the boundedness of the Littlewood-Paley operators with rough kernel. We prove the boundedness of the Lusin-area integral μΩs and Littlewood-Paley functions μΩ and μλ^＊ on the w...In this paper, we shall deal with the boundedness of the Littlewood-Paley operators with rough kernel. We prove the boundedness of the Lusin-area integral μΩs and Littlewood-Paley functions μΩ and μλ^＊ on the weighted amalgam spaces （Lω^q,L^p）^α（R^n）as 1〈q≤α〈p≤∞.展开更多
This paper is devoted to studying the commutators of the multilinear singular integral operators with the non-smooth kernels and the weighted Lipschitz functions. Some mapping properties for two types of commutators o...This paper is devoted to studying the commutators of the multilinear singular integral operators with the non-smooth kernels and the weighted Lipschitz functions. Some mapping properties for two types of commutators on the weighted Lebesgue spaces, which extend and generalize some previous results, are obtained.展开更多
Kernel weight(KW), together with kernel number per unit area, determines yield of cereal crops. Here,two barley recombinant inbred lines(RILs) populations with a shared parent were used to identify loci controlling KW...Kernel weight(KW), together with kernel number per unit area, determines yield of cereal crops. Here,two barley recombinant inbred lines(RILs) populations with a shared parent were used to identify loci controlling KW. One is Baudin/AWCS276(BA) for which a linkage map was available. Several largeeffect QTL controlling KW were detected in this population. Another is Morex/AWCS276(MA). A linkage map with 5273 makers formed 1454 clusters, was constructed by the genotyping by sequence(GBS) data of 201 RILs from this population. A single marker was selected to represent each of the clusters and the linkage map constructed with these markers covers a total length of 1022.4 c M with an average interval of approximately 0.7 cM between loci. Three of the large-effect loci controlling KW(located on 2 HL, 6 HL,and 7 HL, respectively) identified from the BA population were also detected in the MA population under different environments. The locus on 6 HL was detected in each of the experiments conducted for both populations thus was selected for developing near isogenic lines(NILs). Apart from KW, the two isolines for each pair of the putative NILs obtained showed no significant difference for any of the morphological characteristics assessed. The average difference in KW between the isolines for all the NILs obtained was about 15% based on assessments under both glasshouse and field conditions. Taken advantage that high quality genome assemblies for both Morex and AWCS276 are available, we identified candidate genes underlying two of the three loci based on an orthologous analysis. The NILs developed and the candidate genes identified in this study should facilitate the cloning and functional analysis of genes regulating KW in barley.展开更多
Suppose T^k,l and T^k,2 are singular integrals with variable kernels and mixed homogeneity or ±I （the identity operator）. Denote the Toeplitz type operator by T^b=k=1∑^QT^k,1M^bT^k,2 where M^bf= bf. In this pa...Suppose T^k,l and T^k,2 are singular integrals with variable kernels and mixed homogeneity or ±I （the identity operator）. Denote the Toeplitz type operator by T^b=k=1∑^QT^k,1M^bT^k,2 where M^bf= bf. In this paper, the boundedness of Tb on weighted Morrey space are obtained when b belongs to the weighted Lipschitz function space and weighted BMO function space, respectively.展开更多
Let T be the singular integral operator with variable kernel, T＊ be the adjoint of T and T# be the pseudo-adjoint of T. Let TIT2 be the product of T1 and T2, T1 o T2 be the pseudo product of T1 and T2. In this paper,...Let T be the singular integral operator with variable kernel, T＊ be the adjoint of T and T# be the pseudo-adjoint of T. Let TIT2 be the product of T1 and T2, T1 o T2 be the pseudo product of T1 and T2. In this paper, we establish the boundedness for commutators of these operators and the fractional differentiation operator D^γ on the weighted Morrey spaces.展开更多
In this paper, we will study the boundedness of the singular integral operator with variable Calder′on-Zygmund kernel on the weighted Morrey spaces Lp,κ(ω) for q′≤ p < ∞and 0 < κ < 1. Furthermore, the ...In this paper, we will study the boundedness of the singular integral operator with variable Calder′on-Zygmund kernel on the weighted Morrey spaces Lp,κ(ω) for q′≤ p < ∞and 0 < κ < 1. Furthermore, the boundedness for the commutator with BMO functions is also obtained.展开更多
In this paper,a new full-Newton step primal-dual interior-point algorithm for solving the special weighted linear complementarity problem is designed and analyzed.The algorithm employs a kernel function with a linear ...In this paper,a new full-Newton step primal-dual interior-point algorithm for solving the special weighted linear complementarity problem is designed and analyzed.The algorithm employs a kernel function with a linear growth term to derive the search direction,and by introducing new technical results and selecting suitable parameters,we prove that the iteration bound of the algorithm is as good as best-known polynomial complexity of interior-point methods.Furthermore,numerical results illustrate the efficiency of the proposed method.展开更多
In order to clarify the impact posed by wheat powdery mildew （Blumeria graminis f. sp. tritici） on the yield and yield components in different epidemic seasons, field trials were conducted in three growing seasons, ...In order to clarify the impact posed by wheat powdery mildew （Blumeria graminis f. sp. tritici） on the yield and yield components in different epidemic seasons, field trials were conducted in three growing seasons, 2009-2010, 2010-2011 and 2011-2012, in Langfang City, Hebei Province, China. The relationships between 1000-kernel weight, crude protein content of grain and yield and disease index （DI）, as well as area under disease progress curve （AUDPC） were studied. The models of the percentage of loss of 1000-kernel weight, crude protein content and yield were constructed using DI at critical point （CP） of growth stages （GS） and AUDPC in the three growing seasons, respectively. The CPs for estimating 1 000-kernel weight, crude protein content of grain and yield of wheat caused by powdery mildew were GS 11.1, GS 10.5.3 and GS l 0.5.3, respectively. Models based on DI at CP to estimate the percentage of loss of 1000-kernel weight, crude protein content of grain and yield were better than models based on AUDPC. And models of the percentage of loss of 1000-kernel weight, crude protein content and yield for 2011-2012 season were significant different from these for 2009-2010 and 2010-2011 seasons. These results indicated that besides powdery mildew, weather conditions also had influence on 1 000-kernel weight, crude protein content of grain and yield loss of wheat when powdery mildew occurred.展开更多
基金supported by the National Natural Science Foundation of China(31971962,31771812,and 32272129 to Yuling Li)Zhongyuan Scholars in Henan Province(22400510003 to Yuling Li)+3 种基金the Major Public Welfare Projects of Henan Province(201300111100 to Yuling Li)Tackle Program of Agricultural Seed in Henan Province(2022010201 to Yuling Li)Technical System of Maize Industry in Henan Province(HARS62922-02-S to Yuling Li)Key Scientific Research Projects for Higher Education of Henan Province(19zx001 to Yuling Li).
文摘The development of maize(Zea mays)kernels is a complex physiological process regulated by numerous genes in a spatially and temporally coordinated manner.However,many regulatory genes involved in this process remain unidentified.In this study,we identified ZmZFP2,a gene encoding a C4HC3-type RING zinc finger protein,which regulates kernel size and weight.This discovery was based on suppression subtractive hybridization from maize endosperm in our previous research.We further investigated the role of ZmZFP2 in regulating kernel development.The zmzfp2-ems mutant exhibited significantly reduced kernel size and weight,accompanied by fewer endosperm cells and altered starch and protein accumulation.CRISPR/Cas9-mediated knockouts and overexpression lines confirmed that ZmZFP2 positively regulates kernel size and weight,with overexpression leading to increased kernel size and weight.Transcriptome analysis revealed that ZmZFP2 regulates genes involved in zeatin biosynthesis,starch metabolism,and protein processing,further supporting its role in kernel development.Additionally,ZmZFP2 was shown to interact with the transcription factor ZmEREB98,implicating it in the gene regulatory network during grain filling.Together,these findings demonstrate that ZmZFP2 is a key regulator of maize kernel size and weight,functioning through its E3 ubiquitin ligase activity and interactions with various metabolic pathways.This study provides novel insights into the genetic regulation of kernel development and presents potential strategies for improving maize yield and quality.
文摘Applying domain knowledge in fuzzy clustering algorithms continuously promotes the development of clustering technology.The combination of domain knowledge and fuzzy clustering algorithms has some problems,such as initialization sensitivity and information granule weight optimization.Therefore,we propose a weighted kernel fuzzy clustering algorithm based on a relative density view(RDVWKFC).Compared with the traditional density-based methods,RDVWKFC can capture the intrinsic structure of the data more accurately,thus improving the initial quality of the clustering.By introducing a Relative Density based Knowledge Extraction Method(RDKM)and adaptive weight optimization mechanism,we effectively solve the limitations of view initialization and information granule weight optimization.RDKM can accurately identify high-density regions and optimize the initialization process.The adaptive weight mechanism can reduce noise and outliers’interference in the initial cluster centre selection by dynamically allocating weights.Experimental results on 14 benchmark datasets show that the proposed algorithm is superior to the existing algorithms in terms of clustering accuracy,stability,and convergence speed.It shows adaptability and robustness,especially when dealing with different data distributions and noise interference.Moreover,RDVWKFC can also show significant advantages when dealing with data with complex structures and high-dimensional features.These advancements provide versatile tools for real-world applications such as bioinformatics,image segmentation,and anomaly detection.
文摘In this work,we proposed a strategy for the hydrolysis of native corn starch after the treatment of corn starch in an ionic liquid aqueous solution,and it is an awfully“green”and simple means to obtain starch with low molecular weight and amorphous state.X-ray diffraction results revealed that the natural starch crystalline region was largely disrupted by ionic liquid owing to the broken intermolecular and intramolecular hydrogen bonds.After hydrolysis,the morphology of starch changed from particles of native corn starch into little pieces,and their molecular weight could be effectively regulated during the hydrolysis process,and also the hydrolyzed starch samples exhibited decreased thermal stability with the extension of hydrolysis time.This work would counsel as a powerful tool for the development of native starch in realistic applications.
文摘In this paper,we present a necessary and sufficient condition for hyponormal block Toeplitz operators T on the vector-valued weighted Bergman space with symbolsΦ(z)=G^(*)(z)+F(z),where F(z)=∑^(N)_(i)=1 A_(i)z^(i)and G(z)=∑^(N)_(i)=1 A_(−i)z^(i),A_(i)ae culants.
基金supported by the China Agriculture Research System of MOF and MARAthe National Natural Science Foundation of China (31872337 and 31501919)the Agricultural Science and Technology Innovation Project,China (ASTIP-IAS02)。
文摘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.
文摘With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments.
基金financially supported by the National Natural Science Foundation of China(Nos.51909023 and 51775077)the Natural Science Foundation of Liaoning Province(No.2021-MS-140)the Fundamental Research Funds for the Central Universities(No.3132025114)。
文摘Ultra-high molecular weight polyethylene(UHMWPE)is a key material for marine applications owing to its outstanding self-lubrication and corrosion resistance.However,its long-term performance is compromised by plastic deformation in seawater.In this study,we performed a comparative analysis of the UHMWPE dynamics under seawater and water conditions to investigate the plastic deformation of UHMWPE induced by seawater.The results show that the plastic deformation of UHMWPE is amplified in seawater relative to the water conditions.Under thin fluid conditions,frictional interfaces exhibit a higher interfacial friction force and interaction energy in seawater than in water.Compared to freely diffused water molecules,hydrated ions occupy larger interchain spaces within polyethylene.Furthermore,the diffusion of hydrated ions weakens the interchain interactions,promoting more severe polyethylene chain rearrangement and accelerating seawater-induced plastic deformation in UHMWPE during friction.Furthermore,the diffused seawater accelerated the disentangling of the polyethylene chains and enhanced the orderly orientation distribution of polyethylene.Compared to free water molecules,the water molecules of hydrated ions exhibit enhanced attraction to free-flowing water molecules,thereby accelerating seawater flow across submerged UHMWPE surfaces.This flow enhancement promotes surface polyethylene chain mobility in seawater.
基金supported by the Funds for Central-Guided Local Science and Technology Development(Grant No.202407AC110005)Key Technologies for the Construction of a Whole-Process Intelligent Service System for Neuroendocrine Neoplasm.Supported by 2023 Opening Research Fund of Yunnan Key Laboratory of Digital Communications(YNJTKFB-20230686,YNKLDC-KFKT-202304).
文摘In image analysis,high-precision semantic segmentation predominantly relies on supervised learning.Despite significant advancements driven by deep learning techniques,challenges such as class imbalance and dynamic performance evaluation persist.Traditional weighting methods,often based on pre-statistical class counting,tend to overemphasize certain classes while neglecting others,particularly rare sample categories.Approaches like focal loss and other rare-sample segmentation techniques introduce multiple hyperparameters that require manual tuning,leading to increased experimental costs due to their instability.This paper proposes a novel CAWASeg framework to address these limitations.Our approach leverages Grad-CAM technology to generate class activation maps,identifying key feature regions that the model focuses on during decision-making.We introduce a Comprehensive Segmentation Performance Score(CSPS)to dynamically evaluate model performance by converting these activation maps into pseudo mask and comparing them with Ground Truth.Additionally,we design two adaptive weights for each class:a Basic Weight(BW)and a Ratio Weight(RW),which the model adjusts during training based on real-time feedback.Extensive experiments on the COCO-Stuff,CityScapes,and ADE20k datasets demonstrate that our CAWASeg framework significantly improves segmentation performance for rare sample categories while enhancing overall segmentation accuracy.The proposed method offers a robust and efficient solution for addressing class imbalance in semantic segmentation tasks.
基金Under the auspices of the National Natural Science Foundation of China(No.42271224,41901193)Ministry of Edu cation Humanities and Social Sciences Research Planning Fund Project of China(No.24YJAZH190)+1 种基金Anhui Province Excellent Youth Research Project in Universities(No.2022AH030019)Anhui Social Sciences Innovation Development Research Project(No.2024CXQ503)。
文摘The accessibility of urban public transit directly influences residents’quality of life,travel behavior,and social equity.Its correlation with housing prices has garnered significant attention across disciplines such as geography,economics,and urban planning.Although much existing research focuses on the impact of individual transportation facilities on housing prices,there is a notable gap in comprehensive analyses that assess the influence of overall urban transit accessibility on housing market dynamics.This study selected the main urban area of Hefei,China,as a case to investigate the spatial distribution of housing prices and evaluate public transit accessibility in 2022.Employing techniques such as the optimized parameter geographical detector and local spatial regression models,the study aimed to elucidate the effects and underlying mechanisms of urban transit accessibility on housing prices.The findings revealed that:1)housing prices in Hefei exhibited a clustered spatial pattern,with high prices concentrated in the city center and lower prices in peripheral areas,forming three distinct high-price hotspots with a‘belt-like’distribution;2)public transit accessibility showed a‘coreperiphery’structure,with accessibility declining in a‘circumferential’pattern around the city center.Based on the‘housing price-accessibility’dimension,four categories were identified:high price-high accessibility(37.25%),high price-low accessibility(19.07%),low price-high accessibility(21.95%),and low price-low accessibility(21.73%);3)the impact of transit accessibility on housing prices was spatially heterogeneous,with bus travel showing the strongest explanatory power(0.692),followed by automobile,subway,and bicycle travel.The interaction of these transportation modes generated a synergistic effect on housing price differentiation,with most influencing factors contributing more than 25%.These findings offer valuable insights for optimizing the spatial distribution of public transit infrastructure and improving both urban housing quality and residents’living standards.
基金supported by the Special Fund for Agro-scientific Research in the Public Interest(No.201203096)the National Key Technology R&D Program of China(Nos.2013BAD07B00 and 2013BAD08B00)the China Agriculture Research System(No.CARS-02)
文摘Variation in weather conditions during grain filling has substantial effects on maize kernel weight(KW). The objective of this work was to characterize variation in KW with sowing date-associated weather conditions and examine the relationship between KW, grain filling parameters, and weather factors. Maize was sown on eight sowing dates(SD) at 15–20-day intervals from mid-March to mid-July during 2012 and 2013 in the North China Plain. With sowing date delay, KW increased initially and later declined, and the greatest KW was obtained at SD6 in both years. The increased KW at SD6 was attributed mainly to kernel growth rate(Gmean), and effective grain-filling period(P). Variations in temperature and radiation were the primary factors that influenced KW and grain-filling parameters. When the effective cumulative temperature(AT) and radiation(Ra)during grain filling were 950 °C and 1005.4 MJ m-2, respectively, P and KW were greatest. High temperatures(daily maximum temperature [Tmax] > 30.2 °C) during grain filling under early sowing conditions, or low temperatures(daily minimum temperature [Tmin] < 20.7 °C) under late sowing conditions combined with high diurnal temperature range(Tmax-min> 7.1 °C) decreased kernel growth rate and ultimately final KW. When sowing was performed from May 25 through June 27, higher KW and yield of maize were obtained. We conclude that variations in environmental conditions(temperature and radiation) during grain filling markedly affect growth rate and duration of grain filling and eventually affect kernel weight and yield of maize.
基金Supported by the National Natural Science Foundation of China(61273070)the Foundation of Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘Multi-model approach can significantly improve the prediction performance of soft sensors in the process with multiple operational conditions.However,traditional clustering algorithms may result in overlapping phenomenon in subclasses,so that edge classes and outliers cannot be effectively dealt with and the modeling result is not satisfactory.In order to solve these problems,a new feature extraction method based on weighted kernel Fisher criterion is presented to improve the clustering accuracy,in which feature mapping is adopted to bring the edge classes and outliers closer to other normal subclasses.Furthermore,the classified data are used to develop a multiple model based on support vector machine.The proposed method is applied to a bisphenol A production process for prediction of the quality index.The simulation results demonstrate its ability in improving the data classification and the prediction performance of the soft sensor.
基金supported in part by National Natural Foundation of China (Grant No. 11161042 and No. 11071250)
文摘In this paper, we shall deal with the boundedness of the Littlewood-Paley operators with rough kernel. We prove the boundedness of the Lusin-area integral μΩs and Littlewood-Paley functions μΩ and μλ^＊ on the weighted amalgam spaces （Lω^q,L^p）^α（R^n）as 1〈q≤α〈p≤∞.
基金Supported by the National Natural Science Foundation of China (10771054,11071200)the NFS of Fujian Province of China (No. 2010J01013)
文摘This paper is devoted to studying the commutators of the multilinear singular integral operators with the non-smooth kernels and the weighted Lipschitz functions. Some mapping properties for two types of commutators on the weighted Lebesgue spaces, which extend and generalize some previous results, are obtained.
基金supported by the National Natural Science Foundation of China(31771794)the National Key Research and Development Program of China(2017YFD0100900 and 2016YFD0101004)+1 种基金the Outstanding Youth Foundation of the Department of Science and Technology of Sichuan Province(2016JQ0040)the International Science&Technology Cooperation Program of the Bureau of Science and Technology of Chengdu China(2015DFA306002015-GH03-00008-HZ)。
文摘Kernel weight(KW), together with kernel number per unit area, determines yield of cereal crops. Here,two barley recombinant inbred lines(RILs) populations with a shared parent were used to identify loci controlling KW. One is Baudin/AWCS276(BA) for which a linkage map was available. Several largeeffect QTL controlling KW were detected in this population. Another is Morex/AWCS276(MA). A linkage map with 5273 makers formed 1454 clusters, was constructed by the genotyping by sequence(GBS) data of 201 RILs from this population. A single marker was selected to represent each of the clusters and the linkage map constructed with these markers covers a total length of 1022.4 c M with an average interval of approximately 0.7 cM between loci. Three of the large-effect loci controlling KW(located on 2 HL, 6 HL,and 7 HL, respectively) identified from the BA population were also detected in the MA population under different environments. The locus on 6 HL was detected in each of the experiments conducted for both populations thus was selected for developing near isogenic lines(NILs). Apart from KW, the two isolines for each pair of the putative NILs obtained showed no significant difference for any of the morphological characteristics assessed. The average difference in KW between the isolines for all the NILs obtained was about 15% based on assessments under both glasshouse and field conditions. Taken advantage that high quality genome assemblies for both Morex and AWCS276 are available, we identified candidate genes underlying two of the three loci based on an orthologous analysis. The NILs developed and the candidate genes identified in this study should facilitate the cloning and functional analysis of genes regulating KW in barley.
文摘Suppose T^k,l and T^k,2 are singular integrals with variable kernels and mixed homogeneity or ±I （the identity operator）. Denote the Toeplitz type operator by T^b=k=1∑^QT^k,1M^bT^k,2 where M^bf= bf. In this paper, the boundedness of Tb on weighted Morrey space are obtained when b belongs to the weighted Lipschitz function space and weighted BMO function space, respectively.
基金supported by NSF of China (Grant No. 11471033)NCET of China (Grant No. NCET-11-0574)the Fundamental Research Funds for the Central Universities (FRF-TP-12-006B)
文摘Let T be the singular integral operator with variable kernel, T＊ be the adjoint of T and T# be the pseudo-adjoint of T. Let TIT2 be the product of T1 and T2, T1 o T2 be the pseudo product of T1 and T2. In this paper, we establish the boundedness for commutators of these operators and the fractional differentiation operator D^γ on the weighted Morrey spaces.
基金Supported by the NSFC(11001001)Supported by the Natural Science Foundation from the Education Department of Anhui Province(KJ2013A235,KJ2013Z279)
文摘In this paper, we will study the boundedness of the singular integral operator with variable Calder′on-Zygmund kernel on the weighted Morrey spaces Lp,κ(ω) for q′≤ p < ∞and 0 < κ < 1. Furthermore, the boundedness for the commutator with BMO functions is also obtained.
基金Supported by University Science Research Project of Anhui Province(2023AH052921)Outstanding Youth Talent Project of Anhui Province(gxyq2021254)。
文摘In this paper,a new full-Newton step primal-dual interior-point algorithm for solving the special weighted linear complementarity problem is designed and analyzed.The algorithm employs a kernel function with a linear growth term to derive the search direction,and by introducing new technical results and selecting suitable parameters,we prove that the iteration bound of the algorithm is as good as best-known polynomial complexity of interior-point methods.Furthermore,numerical results illustrate the efficiency of the proposed method.
基金financially supported by the National Basic Research Program of China(2010CB951503)the Special Fund for Agro-Scientific Research in the Public Interest,China(201303016)the National Key Technologies R&D Program of China(2012BAD19B04)
文摘In order to clarify the impact posed by wheat powdery mildew （Blumeria graminis f. sp. tritici） on the yield and yield components in different epidemic seasons, field trials were conducted in three growing seasons, 2009-2010, 2010-2011 and 2011-2012, in Langfang City, Hebei Province, China. The relationships between 1000-kernel weight, crude protein content of grain and yield and disease index （DI）, as well as area under disease progress curve （AUDPC） were studied. The models of the percentage of loss of 1000-kernel weight, crude protein content and yield were constructed using DI at critical point （CP） of growth stages （GS） and AUDPC in the three growing seasons, respectively. The CPs for estimating 1 000-kernel weight, crude protein content of grain and yield of wheat caused by powdery mildew were GS 11.1, GS 10.5.3 and GS l 0.5.3, respectively. Models based on DI at CP to estimate the percentage of loss of 1000-kernel weight, crude protein content of grain and yield were better than models based on AUDPC. And models of the percentage of loss of 1000-kernel weight, crude protein content and yield for 2011-2012 season were significant different from these for 2009-2010 and 2010-2011 seasons. These results indicated that besides powdery mildew, weather conditions also had influence on 1 000-kernel weight, crude protein content of grain and yield loss of wheat when powdery mildew occurred.
