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
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
The authors consider the issue of hypothesis testing in varying-coefficient regression models with high-dimensional data.Utilizing kernel smoothing techniques,the authors propose a locally concerned U-statistic method...The authors consider the issue of hypothesis testing in varying-coefficient regression models with high-dimensional data.Utilizing kernel smoothing techniques,the authors propose a locally concerned U-statistic method to assess the overall significance of the coefficients.The authors establish that the proposed test is asymptotically normal under both the null hypothesis and local alternatives.Based on the locally concerned U-statistic,the authors further develop a globally concerned U-statistic to test whether the coefficient function is zero.A stochastic perturbation method is employed to approximate the distribution of the globally concerned test statistic.Monte Carlo simulations demonstrate the validity of the proposed test in finite samples.展开更多
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 R^(2)=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
Accurate prediction of remaining useful life serves as a reliable basis for maintenance strategies,effectively reducing both the frequency of failures and associated costs.As a core component of PHM,RUL prediction pla...Accurate prediction of remaining useful life serves as a reliable basis for maintenance strategies,effectively reducing both the frequency of failures and associated costs.As a core component of PHM,RUL prediction plays a crucial role in preventing equipment failures and optimizing maintenance decision-making.However,deep learning models often falter when processing raw,noisy temporal signals,fail to quantify prediction uncertainty,and face challenges in effectively capturing the nonlinear dynamics of equipment degradation.To address these issues,this study proposes a novel deep learning framework.First,a newbidirectional long short-termmemory network integrated with an attention mechanism is designed to enhance temporal feature extraction with improved noise robustness.Second,a probabilistic prediction framework based on kernel density estimation is constructed,incorporating residual connections and stochastic regularization to achieve precise RUL estimation.Finally,extensive experiments on the C-MAPSS dataset demonstrate that our method achieves competitive performance in terms of RMSE and Score metrics compared to state-of-the-artmodels.More importantly,the probabilistic output provides a quantifiablemeasure of prediction confidence,which is crucial for risk-informed maintenance planning,enabling managers to optimize maintenance strategies based on a quantifiable understanding of failure risk.展开更多
This paper introduces a novel fractional-order model based on the Caputo-Fabrizio(CF)derivative for analyzing computer virus propagation in networked environments.The model partitions the computer population into four...This paper introduces a novel fractional-order model based on the Caputo-Fabrizio(CF)derivative for analyzing computer virus propagation in networked environments.The model partitions the computer population into four compartments:susceptible,latently infected,breaking-out,and antivirus-capable systems.By employing the CF derivative—which uses a nonsingular exponential kernel—the framework effectively captures memory-dependent and nonlocal characteristics intrinsic to cyber systems,aspects inadequately represented by traditional integer-order models.Under Lipschitz continuity and boundedness assumptions,the existence and uniqueness of solutions are rigorously established via fixed-point theory.We develop a tailored two-step Adams-Bashforth numerical scheme for the CF framework and prove its second-order accuracy.Extensive numerical simulations across various fractional orders reveal that memory effects significantly influence virus transmission and control dynamics;smaller fractional orders produce more pronounced memory effects,delaying both infection spread and antivirus activation.Further theoretical analysis,including Hyers-Ulam stability and sensitivity assessments,reinforces the model’s robustness and identifies key parameters governing virus dynamics.The study also extends the framework to incorporate stochastic effects through a stochastic CF formulation.These results underscore fractional-order modeling as a powerful analytical tool for developing robust and effective cybersecurity strategies.展开更多
Kernelization algorithms for graph modification problems are important ingredients in parameterized computation theory. In this paper, we survey the kernelization algorithms for four types of graph modification proble...Kernelization algorithms for graph modification problems are important ingredients in parameterized computation theory. In this paper, we survey the kernelization algorithms for four types of graph modification problems, which include vertex deletion problems, edge editing problems, edge deletion problems, and edge completion problems. For each type of problem, we outline typical examples together with recent results, analyze the main techniques, and provide some suggestions for future research in this field.展开更多
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
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.
基金supported by the Natural Science Foundation of Henan Province(242300421028)the National Natural Science Foundation of China(32372091)+3 种基金the Science and Technology Innovation Fund of Henan Agricultural University(202023CXZX002)to ZY.F.the National Key Research and Development Program of China(2021YFF1000304)to Q-W.S.the Natural Science Foundation Youth Fund project of Henan Province(232300421261)to Q-Q.Z.the China Postdoctoral Science Foundation(2024M750812),and Henan Postdoctoral Foundation.
文摘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.
基金supported by the National Social Science Foundation of China under Grant No.23&ZD126National Science Foundation of China under Grant No.12471256+1 种基金Natural Science Foundation of Shanxi Province under Grant No.202203021221219Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi under Grant No.2023L164。
文摘The authors consider the issue of hypothesis testing in varying-coefficient regression models with high-dimensional data.Utilizing kernel smoothing techniques,the authors propose a locally concerned U-statistic method to assess the overall significance of the coefficients.The authors establish that the proposed test is asymptotically normal under both the null hypothesis and local alternatives.Based on the locally concerned U-statistic,the authors further develop a globally concerned U-statistic to test whether the coefficient function is zero.A stochastic perturbation method is employed to approximate the distribution of the globally concerned test statistic.Monte Carlo simulations demonstrate the validity of the proposed test in finite samples.
文摘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 R^(2)=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.
基金funded by scientific research projects under Grant JY2024B011.
文摘Accurate prediction of remaining useful life serves as a reliable basis for maintenance strategies,effectively reducing both the frequency of failures and associated costs.As a core component of PHM,RUL prediction plays a crucial role in preventing equipment failures and optimizing maintenance decision-making.However,deep learning models often falter when processing raw,noisy temporal signals,fail to quantify prediction uncertainty,and face challenges in effectively capturing the nonlinear dynamics of equipment degradation.To address these issues,this study proposes a novel deep learning framework.First,a newbidirectional long short-termmemory network integrated with an attention mechanism is designed to enhance temporal feature extraction with improved noise robustness.Second,a probabilistic prediction framework based on kernel density estimation is constructed,incorporating residual connections and stochastic regularization to achieve precise RUL estimation.Finally,extensive experiments on the C-MAPSS dataset demonstrate that our method achieves competitive performance in terms of RMSE and Score metrics compared to state-of-the-artmodels.More importantly,the probabilistic output provides a quantifiablemeasure of prediction confidence,which is crucial for risk-informed maintenance planning,enabling managers to optimize maintenance strategies based on a quantifiable understanding of failure risk.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2601).
文摘This paper introduces a novel fractional-order model based on the Caputo-Fabrizio(CF)derivative for analyzing computer virus propagation in networked environments.The model partitions the computer population into four compartments:susceptible,latently infected,breaking-out,and antivirus-capable systems.By employing the CF derivative—which uses a nonsingular exponential kernel—the framework effectively captures memory-dependent and nonlocal characteristics intrinsic to cyber systems,aspects inadequately represented by traditional integer-order models.Under Lipschitz continuity and boundedness assumptions,the existence and uniqueness of solutions are rigorously established via fixed-point theory.We develop a tailored two-step Adams-Bashforth numerical scheme for the CF framework and prove its second-order accuracy.Extensive numerical simulations across various fractional orders reveal that memory effects significantly influence virus transmission and control dynamics;smaller fractional orders produce more pronounced memory effects,delaying both infection spread and antivirus activation.Further theoretical analysis,including Hyers-Ulam stability and sensitivity assessments,reinforces the model’s robustness and identifies key parameters governing virus dynamics.The study also extends the framework to incorporate stochastic effects through a stochastic CF formulation.These results underscore fractional-order modeling as a powerful analytical tool for developing robust and effective cybersecurity strategies.
基金supported by the National Natural Science Foundation of China (Nos. 61070224, 61232001, and 61173051)the China Postdoctoral Science Foundation (No. 2012M521551)
文摘Kernelization algorithms for graph modification problems are important ingredients in parameterized computation theory. In this paper, we survey the kernelization algorithms for four types of graph modification problems, which include vertex deletion problems, edge editing problems, edge deletion problems, and edge completion problems. For each type of problem, we outline typical examples together with recent results, analyze the main techniques, and provide some suggestions for future research in this field.
基金supported by the National Natural Science Foundation of China (Nos. 61173051, 61103033, and 61232001)
文摘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.
