We present and explore a new shock-capturing particle hydrodynamics approach.Our starting point is a commonly used discretization of smoothed particle hydrodynamics.We enhance this discretization with Roe’s approx-im...We present and explore a new shock-capturing particle hydrodynamics approach.Our starting point is a commonly used discretization of smoothed particle hydrodynamics.We enhance this discretization with Roe’s approx-imate Riemann solver,we identify its dissipative terms,and in these terms,we use slope-limited linear reconstruction.All gradients needed for our method are calculated with linearly reproducing kernels that are constructed to enforce the two lowest-order consistency relations.We scrutinize our reproducing kernel implementation carefully on a“glass-like”particle distribution,and we find that constant and linear functions are recovered to machine precision.We probe our method in a series of challenging 3D benchmark problems ranging from shocks over instabilities to Schulz-Rinne-type vorticity-creating shocks.All of our simulations show excellent agreement with analytic/reference solutions.展开更多
This study evaluated corn kernel drying performance and quality changes using hot air drying(HAD)and infrared drying(ID)across temperatures ranging from 55℃ to 80℃.Optimal drying parameters were determined by using ...This study evaluated corn kernel drying performance and quality changes using hot air drying(HAD)and infrared drying(ID)across temperatures ranging from 55℃ to 80℃.Optimal drying parameters were determined by using the entropy weight method,with drying time,specific energy consumption,damage rate,fatty acids,starch,polyphenols,and flavonoids as indicators.Results demonstrated that ID significantly outperformed HAD,achieving drying times up to 20%shorter and reducing specific energy consumption and kernel damage by up to 79.3%and 66.7%,respectively,while also better preserving quality attributes.Both methods exhibited drying profiles characterized by acceleration,constant,and falling rate periods,although the constant rate phase was distinctly observable only at lower temperatures.The effective moisture diffusivity under ID was consistently higher than that under HAD,with a maximum increase of 20.4%.The optimal drying conditions were HAD at 65℃ and ID at 80℃.A BP model was also developed and it showed better predictive performance and adaptability than classical mathematical models.展开更多
This paper considers the following Marcinkiewicz type integrals■which can be regarded as an extension of the classical Marcinkiewicz integral po introduced by Stein in[Trans Amer Math Soc,88(1958):159-172],where Ω i...This paper considers the following Marcinkiewicz type integrals■which can be regarded as an extension of the classical Marcinkiewicz integral po introduced by Stein in[Trans Amer Math Soc,88(1958):159-172],where Ω is a homogeneous function of degree zero on R^(n)with mean value zero in the unit sphere S^(n-1),Under the assumption that Ω∈L^(∞)(S^(n-1)),the authors establish the L^(q)-estimate and weak(1,1)type estimate as well as the corresponding weighted estimates for po.s with 1<q<∞ and 0<β(q-1)n/q.Moreover,the bounds do not depend on β and the strong(q,q)type and weak(1,1)type estimates for the classical Marcinkiewicz integral po can be recovered from the above estimates of μΩ,β whenβ→0.展开更多
Under natural pollination （NP）, early-fertilized ovaries at the base of ear promote kernel abortion of late-fertilized ovades from the tip in maize （Zea mays L.）. Synchronous pollination （SP） improves maize kern...Under natural pollination （NP）, early-fertilized ovaries at the base of ear promote kernel abortion of late-fertilized ovades from the tip in maize （Zea mays L.）. Synchronous pollination （SP） improves maize kernel set, but the physiological masons behind this response are yet unclear. We e^amined maize kernel growth at the tip of ear subjected to NP or SP with different ~.ant density of 6 plants/rr~ or 9 plants/n~. Synchronous pollination of ears baged before silking was obtained by hand pollination on 3 days after silking （DAS） and samples were taken from natural and hand-pollinated ears on 8, 13, 18, and 23 DAS. At each sampling date, kemel fresh weight, volume and dry weight at the tip of ear were all higher for maize grown under SP than NP, and the contents of soluble sugar, sucrose, starch, nitro- gen and the ratios of soluble sugar to nitrogen （C/N） in kernel at the tip of ear were all higher too for maize grown under SP than NP.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
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
The filling rate and the starch accumulation in developing maize kernel were analyzed. The changes of enzyme activities associated with sucrose metabolism and starch biosynthesis were investigated. The purpose is to d...The filling rate and the starch accumulation in developing maize kernel were analyzed. The changes of enzyme activities associated with sucrose metabolism and starch biosynthesis were investigated. The purpose is to discuss the enzymatic mechanisms responsible for starch synthesis. Two types of maize cultivars （Zea mays）, high starch maize （Feiyu 3） and normal maize （Yuyu 22）, were grown in a corn field. The factors involved in starch synthesis were performed during the growth period. The kernel filling rate, the sucrose content, the starch accumulating rates and the activities of SS （sucrose synthase）, GBSS （granule-bound starch synthase）, SBE （starch branching enzyme） of Feiyu 3, which has high starch content, were significantly higher than those of Yuyu 22, which has low starch content, after 10 DAP （days after pollination）. Correlation analysis indicated that ADPGPPase （ADP-glucose pyrophosphorylase） and DBE （starch debranching enzyme） were not correlated with the starch accumulating rates and the kernel filling rate, but the SS activity at the middle and late period were highly significantly correlated with the starch accumulating rates and the kernel filling rate. The GBSS activity was highly significantly correlated with the amylose accumulating rate, but not correlated with the kernel filling rate. The SBE activity was highly significantly correlated with the amylopectin accumulating rate and the kernel filling rate. It was not ADPGPPase and DBE, but SS was the rate-limiting factor of starch biosynthesis in developing maize kernels. GBSS had an important effect on amylose accumulation, and SBE had a significant effect on amylopectin accumulation.展开更多
Let T be a singular integral operator bounded on Lp(Rn) for some p, 1 < p < ∞. The authors give a sufficient condition on the kernel of T so that when b ∈BMO, the commutator [b,T](f) = T(bf) - bT(f) is bounded...Let T be a singular integral operator bounded on Lp(Rn) for some p, 1 < p < ∞. The authors give a sufficient condition on the kernel of T so that when b ∈BMO, the commutator [b,T](f) = T(bf) - bT(f) is bounded on the space Lp for all p, 1 < p < ∞. The condition of this paper is weaker than the usual pointwise Hormander condition.展开更多
This paper is concerned with certain multilinear commutators of BMO functions and multilinear singular integral operators with non-smooth kernels. By the sharp maximal functions estimates, the weighted norm inequaliti...This paper is concerned with certain multilinear commutators of BMO functions and multilinear singular integral operators with non-smooth kernels. By the sharp maximal functions estimates, the weighted norm inequalities for this kind of commutators are established.展开更多
Cloud radiative kernels were built by BCC_RAD(Beijing Climate Center radiative transfer model)radiative transfer code.Then,short-term cloud feedback and its mechanisms in East Asia(0.5°S−60.5°N,69.5°−15...Cloud radiative kernels were built by BCC_RAD(Beijing Climate Center radiative transfer model)radiative transfer code.Then,short-term cloud feedback and its mechanisms in East Asia(0.5°S−60.5°N,69.5°−150.5°E)were analyzed quantitatively using the kernels combined with MODIS satellite data from July 2002 to June 2018.According to the surface and monsoon types,four subregions in East Asia-the Tibetan Plateau,northwest,temperate monsoon(TM),and subtropical monsoon(SM)—were selected.The average longwave,shortwave,and net cloud feedbacks in East Asia are−0.68±1.20,1.34±1.08,and 0.66±0.40 W m^−2 K^−1(±2σ),respectively,among which the net feedback is dominated by the positive shortwave feedback.Positive feedback in SM is the strongest of all subregions,mainly due to the contributions of nimbostratus and stratus.In East Asia,short-term feedback in spring is primarily caused by marine stratus in SM,in summer is primarily driven by deep convective cloud in TM,in autumn is mainly caused by land nimbostratus in SM,and in winter is mainly driven by land stratus in SM.Cloud feedback in East Asia is chiefly driven by decreases in mid-level and low cloud fraction owing to the changes in relative humidity,and a decrease in low cloud optical thickness due to the changes in cloud water content.展开更多
基金supported by the Swedish Research Council(VR)under grant number 2020-05044by the research environment grant"Gravitational Radiation and Electromagnetic Astrophysical Transients"(GREAT)funded by the Swedish Research Council(VR)under Dnr 2016-06012+2 种基金by the Knut and Alice Wallenberg Foundation under grant Dnr.KAW 2019.0112by the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation)under Germany's Excellence Strategy-EXC 2121"Quantum Universe"-390833306by the European Research Council(ERC)Advanced Grant INSPIRATION under the European Union's Horizon 2020 Research and Innovation Programme(Grant agreement No.101053985).
文摘We present and explore a new shock-capturing particle hydrodynamics approach.Our starting point is a commonly used discretization of smoothed particle hydrodynamics.We enhance this discretization with Roe’s approx-imate Riemann solver,we identify its dissipative terms,and in these terms,we use slope-limited linear reconstruction.All gradients needed for our method are calculated with linearly reproducing kernels that are constructed to enforce the two lowest-order consistency relations.We scrutinize our reproducing kernel implementation carefully on a“glass-like”particle distribution,and we find that constant and linear functions are recovered to machine precision.We probe our method in a series of challenging 3D benchmark problems ranging from shocks over instabilities to Schulz-Rinne-type vorticity-creating shocks.All of our simulations show excellent agreement with analytic/reference solutions.
基金financially supported by the Natural Science Foundation of Hunan Province(project No.2024JJ8037:https://kjt.hunan.gov.cn/kjt/xxgk/tzgg/tzgg_1/202403/t20240311_33144606.html,accessed on 27 October 2025)。
文摘This study evaluated corn kernel drying performance and quality changes using hot air drying(HAD)and infrared drying(ID)across temperatures ranging from 55℃ to 80℃.Optimal drying parameters were determined by using the entropy weight method,with drying time,specific energy consumption,damage rate,fatty acids,starch,polyphenols,and flavonoids as indicators.Results demonstrated that ID significantly outperformed HAD,achieving drying times up to 20%shorter and reducing specific energy consumption and kernel damage by up to 79.3%and 66.7%,respectively,while also better preserving quality attributes.Both methods exhibited drying profiles characterized by acceleration,constant,and falling rate periods,although the constant rate phase was distinctly observable only at lower temperatures.The effective moisture diffusivity under ID was consistently higher than that under HAD,with a maximum increase of 20.4%.The optimal drying conditions were HAD at 65℃ and ID at 80℃.A BP model was also developed and it showed better predictive performance and adaptability than classical mathematical models.
文摘This paper considers the following Marcinkiewicz type integrals■which can be regarded as an extension of the classical Marcinkiewicz integral po introduced by Stein in[Trans Amer Math Soc,88(1958):159-172],where Ω is a homogeneous function of degree zero on R^(n)with mean value zero in the unit sphere S^(n-1),Under the assumption that Ω∈L^(∞)(S^(n-1)),the authors establish the L^(q)-estimate and weak(1,1)type estimate as well as the corresponding weighted estimates for po.s with 1<q<∞ and 0<β(q-1)n/q.Moreover,the bounds do not depend on β and the strong(q,q)type and weak(1,1)type estimates for the classical Marcinkiewicz integral po can be recovered from the above estimates of μΩ,β whenβ→0.
基金Supported by National Natural Science Foundation of China(31271645)Agricultural Science and Technology Project of Shanxi Province(20140311007-4)~~
文摘Under natural pollination （NP）, early-fertilized ovaries at the base of ear promote kernel abortion of late-fertilized ovades from the tip in maize （Zea mays L.）. Synchronous pollination （SP） improves maize kernel set, but the physiological masons behind this response are yet unclear. We e^amined maize kernel growth at the tip of ear subjected to NP or SP with different ~.ant density of 6 plants/rr~ or 9 plants/n~. Synchronous pollination of ears baged before silking was obtained by hand pollination on 3 days after silking （DAS） and samples were taken from natural and hand-pollinated ears on 8, 13, 18, and 23 DAS. At each sampling date, kemel fresh weight, volume and dry weight at the tip of ear were all higher for maize grown under SP than NP, and the contents of soluble sugar, sucrose, starch, nitro- gen and the ratios of soluble sugar to nitrogen （C/N） in kernel at the tip of ear were all higher too for maize grown under SP than NP.
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grants from the National Natural Science Foundation of China （30471025） Agricultural Science and Technology Span Project （2003, 19） the High Level Person Start-Up Fund of Qingdao Agricultural University, China （630629）.
文摘The filling rate and the starch accumulation in developing maize kernel were analyzed. The changes of enzyme activities associated with sucrose metabolism and starch biosynthesis were investigated. The purpose is to discuss the enzymatic mechanisms responsible for starch synthesis. Two types of maize cultivars （Zea mays）, high starch maize （Feiyu 3） and normal maize （Yuyu 22）, were grown in a corn field. The factors involved in starch synthesis were performed during the growth period. The kernel filling rate, the sucrose content, the starch accumulating rates and the activities of SS （sucrose synthase）, GBSS （granule-bound starch synthase）, SBE （starch branching enzyme） of Feiyu 3, which has high starch content, were significantly higher than those of Yuyu 22, which has low starch content, after 10 DAP （days after pollination）. Correlation analysis indicated that ADPGPPase （ADP-glucose pyrophosphorylase） and DBE （starch debranching enzyme） were not correlated with the starch accumulating rates and the kernel filling rate, but the SS activity at the middle and late period were highly significantly correlated with the starch accumulating rates and the kernel filling rate. The GBSS activity was highly significantly correlated with the amylose accumulating rate, but not correlated with the kernel filling rate. The SBE activity was highly significantly correlated with the amylopectin accumulating rate and the kernel filling rate. It was not ADPGPPase and DBE, but SS was the rate-limiting factor of starch biosynthesis in developing maize kernels. GBSS had an important effect on amylose accumulation, and SBE had a significant effect on amylopectin accumulation.
文摘Let T be a singular integral operator bounded on Lp(Rn) for some p, 1 < p < ∞. The authors give a sufficient condition on the kernel of T so that when b ∈BMO, the commutator [b,T](f) = T(bf) - bT(f) is bounded on the space Lp for all p, 1 < p < ∞. The condition of this paper is weaker than the usual pointwise Hormander condition.
基金Supported by the National Natural Science Foundation of China (10771054, 10771221, 11071200)the Youth Foundation of Wuyi University (No. xq0930)
文摘This paper is concerned with certain multilinear commutators of BMO functions and multilinear singular integral operators with non-smooth kernels. By the sharp maximal functions estimates, the weighted norm inequalities for this kind of commutators are established.
基金supported by the National Key R&D Program of China(Grant No.2017YFA0603502)the National Natural Science Foundation of China(Grant Nos.91644211 and 41575002).
文摘Cloud radiative kernels were built by BCC_RAD(Beijing Climate Center radiative transfer model)radiative transfer code.Then,short-term cloud feedback and its mechanisms in East Asia(0.5°S−60.5°N,69.5°−150.5°E)were analyzed quantitatively using the kernels combined with MODIS satellite data from July 2002 to June 2018.According to the surface and monsoon types,four subregions in East Asia-the Tibetan Plateau,northwest,temperate monsoon(TM),and subtropical monsoon(SM)—were selected.The average longwave,shortwave,and net cloud feedbacks in East Asia are−0.68±1.20,1.34±1.08,and 0.66±0.40 W m^−2 K^−1(±2σ),respectively,among which the net feedback is dominated by the positive shortwave feedback.Positive feedback in SM is the strongest of all subregions,mainly due to the contributions of nimbostratus and stratus.In East Asia,short-term feedback in spring is primarily caused by marine stratus in SM,in summer is primarily driven by deep convective cloud in TM,in autumn is mainly caused by land nimbostratus in SM,and in winter is mainly driven by land stratus in SM.Cloud feedback in East Asia is chiefly driven by decreases in mid-level and low cloud fraction owing to the changes in relative humidity,and a decrease in low cloud optical thickness due to the changes in cloud water content.
