基于GPGPU-sim的多kernel场景下GPGPU性能优化实验方法

该文介绍了基于GPGPU-sim的多kernel环境下GPGPU性能优化实验方法,旨在为初学者开展多kernenl场景下GPGPU性能优化研究提供实验方法参考,也能为计算机系统结构教学提供案例。文中重点分析讨论了基于GPGPU-sim模拟器、多kernel场景下的一种自适应线程块调度方法的改进思想、实验方法及过程,还对GPGPU的微系统结构、GPGPU-sim模拟器及源代码结构进行了介绍。实验结果表明,该文阐述的实验方法可行,相对于基准方法,该文提出的改进策略可以提升多kernel场景下GPGPU的执行效率。

Incorporating kernelized multi-omics data improves the accuracy of genomic prediction

Background:Genomic selection(GS)has revolutionized animal and plant breeding after the first implementation via early selection before measuring phenotypes.Besides genome,transcriptome and metabolome information are increasingly considered new sources for GS.Difficulties in building the model with multi-omics data for GS and the limit of specimen availability have both delayed the progress of investigating multi-omics.Results:We utilized the Cosine kernel to map genomic and transcriptomic data as n×n symmetric matrix(G matrix and T matrix),combined with the best linear unbiased prediction(BLUP)for GS.Here,we defined five kernel-based prediction models:genomic BLUP(GBLUP),transcriptome-BLUP(TBLUP),multi-omics BLUP(MBLUP,M=ratio×G+(1-ratio)×T),multi-omics single-step BLUP(mss BLUP),and weighted multi-omics single-step BLUP(wmss BLUP)to integrate transcribed individuals and genotyped resource population.The predictive accuracy evaluations in four traits of the Chinese Simmental beef cattle population showed that(1)MBLUP was far preferred to GBLUP(ratio=1.0),(2)the prediction accuracy of wmss BLUP and mss BLUP had 4.18%and 3.37%average improvement over GBLUP,(3)We also found the accuracy of wmss BLUP increased with the growing proportion of transcribed cattle in the whole resource population.Conclusions:We concluded that the inclusion of transcriptome data in GS had the potential to improve accuracy.Moreover,wmss BLUP is accepted to be a promising alternative for the present situation in which plenty of individuals are genotyped when fewer are transcribed.

Adaptive multi-step piecewise interpolation reproducing kernel method for solving the nonlinear time-fractional partial differential equation arising from financial economics

This paper is aimed at solving the nonlinear time-fractional partial differential equation with two small parameters arising from option pricing model in financial economics.The traditional reproducing kernel(RK)method which deals with this problem is very troublesome.This paper proposes a new method by adaptive multi-step piecewise interpolation reproducing kernel(AMPIRK)method for the first time.This method has three obvious advantages which are as follows.Firstly,the piecewise number is reduced.Secondly,the calculation accuracy is improved.Finally,the waste time caused by too many fragments is avoided.Then four numerical examples show that this new method has a higher precision and it is a more timesaving numerical method than the others.The research in this paper provides a powerful mathematical tool for solving time-fractional option pricing model which will play an important role in financial economics.

Multi-output Gaussian Process Regression Model with Combined Kernel Function for Polyester Esterification Processes

In polyester fiber industrial processes,the prediction of key performance indicators is vital for product quality.The esterification process is an indispensable step in the polyester polymerization process.It has the characteristics of strong coupling,nonlinearity and complex mechanism.To solve these problems,we put forward a multi-output Gaussian process regression(MGPR)model based on the combined kernel function for the polyester esterification process.Since the seasonal and trend decomposition using loess(STL)can extract the periodic and trend characteristics of time series,a combined kernel function based on the STL and the kernel function analysis is constructed for the MGPR.The effectiveness of the proposed model is verified by the actual polyester esterification process data collected from fiber production.

我国护理人力资源区域差异的演变特征——基于Dagum基尼系数分解和Kernel核密度估计的实证研究

目的分析我国护理人力资源的区域差异及分布动态演进,为我国护理人力资源的合理配置和规划提供参考。方法基于2011-2022年省级护理人力资源面板数据,通过测算Kernel密度和Dagum基尼系数对我国护理人力资源的区域差异及分布动态演进进行分析评价。结果2011-2022年,在空间分布上,全国及各地区护理人力资源总量呈增加趋势,各区域差异逐步降低,且两极化特征明显;在区域差异上,我国护理人力资源总体差异均值为0.1149;区域内呈东部>西部>中部>东北区域的梯度逐步递增趋势;区域间差异占总体差异的40.61%。结论全国护理人力资源总体差异处于相对合理状态,区域间差异是主要来源,均等化水平逐步提升;政府应针对各区域精准施策,进一步稳定护理人力资源队伍,完善护理人力资源结构以促进护理人力资源的优质均衡发展。

The cytosolic isoform of triosephosphate isomerase,ZmTPI4,is required for kernel development and starch synthesis in maize(Zea mays L.)

Triosephosphate isomerase(TPI)is an enzyme that functions in plant energy production,accumulation,and conversion.To understand its function in maize,we characterized a maize TPI mutant,zmtpi4.In comparison to the wild type,zmtpi4 mutants showed altered ear development,reduced kernel weight and starch content,modified starch granule morphology,and altered amylose and amylopectin content.Protein,ATP,and pyruvate contents were reduced,indicating ZmTPI4 was involved in glycolysis.Although subcellular localization confirmed ZmTPI4 as a cytosolic rather than a plastid isoform of TPI,the zmtpi4 mutant showed reduced leaf size and chlorophyll content.Overexpression of ZmTPI4 in Arabidopsis led to enlarged leaves and increased seed weight,suggesting a positive regulatory role of ZmTPI4 in kernel weight and starch content.We conclude that ZmTPI4 functions in maize kernel development,starch synthesis,glycolysis,and photosynthesis.

HEAT KERNEL ON RICCI SHRINKERS(II)

This paper is the sequel to our study of heat kernel on Ricci shrinkers[29].In this paper,we improve many estimates in[29]and extend the recent progress of Bamler[2].In particular,we drop the compactness and curvature boundedness assumptions and show that the theory of F-convergence holds naturally on any Ricci flows induced by Ricci shrinkers.

Multi-omics analysis reveals the pivotal role of phytohormone homeostasis in regulating maize grain water content

Grain water content(GWC)is a key determinant for mechanical harvesting of maize(Zea mays).In our previous research,we identified a quantitative trait locus,qGWC1,associated with GWC in maize.Here,we examined near-isogenic lines(NILs)NILL and NILH that differed at the qGWC1 locus.Lower GWC in NILL was primarily attributed to reduced grain water weight(GWW)and smaller fresh grain size,rather than the accumulation of dry matter.The difference in GWC between the NILs became more pronounced approximately 35 d after pollination(DAP),arising from a faster dehydration rate in NILL.Through an integrated analysis of the transcriptome,proteome,and metabolome,coupled with an examination of hormones and their derivatives,we detected a marked decrease in JA,along with an increase in cytokinin,storage forms of IAA(IAA-Glu,IAA-ASP),and IAA precursor IPA in immature NILL kernels.During kernel development,genes associated with sucrose synthases,starch biosynthesis,and zein production in NILL,exhibited an initial up-regulation followed by a gradual down-regulation,compared to those in NILH.This discovery highlights the crucial role of phytohormone homeostasis and genes related to kernel development in balancing GWC and dry matter accumulation in maize kernels.

Quantification of the adulteration concentration of palm kernel oil in virgin coconut oil using near-infrared hyperspectral imaging

The adulteration concentration of palm kernel oil(PKO)in virgin coconut oil(VCO)was quantified using near-infrared(NIR)hyperspectral imaging.Nowadays,some VCO is adulterated with lower-priced PKO to reduce production costs,which diminishes the quality of the VCO.This study used NIR hyperspectral imaging in the wavelength region 900-1,650 nm to create a quantitative model for the detection of PKO contaminants(0-100%)in VCO and to develop predictive mapping.The prediction equation for the adulteration of VCO with PKO was constructed using the partial least squares regression method.The best predictive model was pre-processed using the standard normal variate method,and the coefficient of determination of prediction was 0.991,the root mean square error of prediction was 2.93%,and the residual prediction deviation was 10.37.The results showed that this model could be applied for quantifying the adulteration concentration of PKO in VCO.The prediction adulteration concentration mapping of VCO with PKO was created from a calibration model that showed the color level according to the adulteration concentration in the range of 0-100%.NIR hyperspectral imaging could be clearly used to quantify the adulteration of VCO with a color level map that provides a quick,accurate,and non-destructive detection method.

A wealth distribution model with a non-Maxwellian collision kernel

A non-Maxwellian collision kernel is employed to study the evolution of wealth distribution in a multi-agent society.The collision kernel divides agents into two different groups under certain conditions. Applying the kinetic theory of rarefied gases, we construct a two-group kinetic model for the evolution of wealth distribution. Under the continuous trading limit, the Fokker–Planck equation is derived and its steady-state solution is obtained. For the non-Maxwellian collision kernel, we find a suitable redistribution operator to match the taxation. Our results illustrate that taxation and redistribution have the property to change the Pareto index.

Nuclear charge radius predictions by kernel ridge regression with odd-even effects

The extended kernel ridge regression(EKRR)method with odd-even effects was adopted to improve the description of the nuclear charge radius using five commonly used nuclear models.These are:(i)the isospin-dependent A^(1∕3) formula,(ii)relativistic continuum Hartree-Bogoliubov(RCHB)theory,(iii)Hartree-Fock-Bogoliubov(HFB)model HFB25,(iv)the Weizsacker-Skyrme(WS)model WS*,and(v)HFB25*model.In the last two models,the charge radii were calculated using a five-parameter formula with the nuclear shell corrections and deformations obtained from the WS and HFB25 models,respectively.For each model,the resultant root-mean-square deviation for the 1014 nuclei with proton number Z≥8 can be significantly reduced to 0.009-0.013 fm after considering the modification with the EKRR method.The best among them was the RCHB model,with a root-mean-square deviation of 0.0092 fm.The extrapolation abilities of the KRR and EKRR methods for the neutron-rich region were examined,and it was found that after considering the odd-even effects,the extrapolation power was improved compared with that of the original KRR method.The strong odd-even staggering of nuclear charge radii of Ca and Cu isotopes and the abrupt kinks across the neutron N=126 and 82 shell closures were also calculated and could be reproduced quite well by calculations using the EKRR method.

Combined application of organic fertilizer and chemical fertilizer alleviates the kernel position effect in summer maize by promoting post-silking nitrogen uptake and dry matter accumulation

Adjusting agronomic measures to alleviate the kernel position effect in maize is important for ensuring high yields.In order to clarify whether the combined application of organic fertilizer and chemical fertilizer(CAOFCF)can alleviate the kernel position effect of summer maize,field experiments were conducted during the 2019 and 2020 growing seasons,and five treatments were assessed:CF,100%chemical fertilizer;OFCF1,15%organic fertilizer+85%chemical fertilizer;OFCF2,30%organic fertilizer+70%chemical fertilizer;OFCF3,45%organic fertilizer+55%chemical fertilizer;and OFCF4,60%organic fertilizer+40%chemical fertilizer.Compared with the CF treatment,the OFCF1 and OFCF2 treatments significantly alleviated the kernel position effect by increasing the weight ratio of inferior kernels to superior kernels and reducing the weight gap between the superior and inferior kernels.These effects were largely due to the improved filling and starch accumulation of inferior kernels.However,there were no obvious differences in the kernel position effect among plants treated with CF,OFCF3,or OFCF4 in most cases.Leaf area indexes,post-silking photosynthetic rates,and net assimilation rates were higher in plants treated with OFCF1 or OFCF2 than in those treated with CF,reflecting an enhanced photosynthetic capacity and improved postsilking dry matter accumulation(DMA)in the plants treated with OFCF1 or OFCF2.Compared with the CF treatment,the OFCF1 and OFCF2 treatments increased post-silking N uptake by 66.3 and 75.5%,respectively,which was the major factor driving post-silking photosynthetic capacity and DMA.Moreover,the increases in root DMA and zeatin riboside content observed following the OFCF1 and OFCF2 treatments resulted in reduced root senescence,which is associated with an increased post-silking N uptake.Analyses showed that post-silking N uptake,DMA,and grain yield in summer maize were negatively correlated with the kernel position effect.In conclusion,the combined application of 15-30%organic fertilizer and 70-85%chemical fertilizer alleviated the kernel position effect in summer maize by improving post-silking N uptake and DMA.These results provide new insights into how CAOFCF can be used to improve maize productivity.

Identification of reservoir types in deep carbonates based on mixedkernel machine learning using geophysical logging data

Identification of reservoir types in deep carbonates has always been a great challenge due to complex logging responses caused by the heterogeneous scale and distribution of storage spaces.Traditional cross-plot analysis and empirical formula methods for identifying reservoir types using geophysical logging data have high uncertainty and low efficiency,which cannot accurately reflect the nonlinear relationship between reservoir types and logging data.Recently,the kernel Fisher discriminant analysis(KFD),a kernel-based machine learning technique,attracts attention in many fields because of its strong nonlinear processing ability.However,the overall performance of KFD model may be limited as a single kernel function cannot simultaneously extrapolate and interpolate well,especially for highly complex data cases.To address this issue,in this study,a mixed kernel Fisher discriminant analysis(MKFD)model was established and applied to identify reservoir types of the deep Sinian carbonates in central Sichuan Basin,China.The MKFD model was trained and tested with 453 datasets from 7 coring wells,utilizing GR,CAL,DEN,AC,CNL and RT logs as input variables.The particle swarm optimization(PSO)was adopted for hyper-parameter optimization of MKFD model.To evaluate the model performance,prediction results of MKFD were compared with those of basic-kernel based KFD,RF and SVM models.Subsequently,the built MKFD model was applied in a blind well test,and a variable importance analysis was conducted.The comparison and blind test results demonstrated that MKFD outperformed traditional KFD,RF and SVM in the identification of reservoir types,which provided higher accuracy and stronger generalization.The MKFD can therefore be a reliable method for identifying reservoir types of deep carbonates.

Influence of broken kernels content on soybean quality during storage

Although it is recognized that the post-harvest system is most responsible for the loss of soybean quality,the real impact of this loss is still unknown.Brazilian regulation allows 15%and 30%of broken soybean for group I and group II(quality groups),respectively.However,the industry is not informed about the loss in the quality parameters of soybeans and its impacts during long-term storage.Therefore,the objective was to evaluate the effect of the breakage kernel percentage of soybean stored for 12 months.Content of 15% of breakage kernels did not affect soybean quality.However,content of 30% of breakage kernels affected significantly soybean quality,which was evidenced by the increase of up to 75% in moldy soybeans,72% in acidity,50% in leached solids,27% in electrical conductivity,and the decrease of up to 12% in soluble protein,6.4% in germination and 3.5% in thousand kernel weight after 8 months of storage.Although the legislation establishes a percentage limit,it is recommended to store soybeans with up to 15% breakage kernels.On the contrary,values higher than that can cause a significant reduction in soybean quality,resulting in commercial losses.

Convergence analysis for complementary-label learning with kernel ridge regression

Complementary-label learning(CLL)aims at finding a classifier via samples with complementary labels.Such data is considered to contain less information than ordinary-label samples.The transition matrix between the true label and the complementary label,and some loss functions have been developed to handle this problem.In this paper,we show that CLL can be transformed into ordinary classification under some mild conditions,which indicates that the complementary labels can supply enough information in most cases.As an example,an extensive misclassification error analysis was performed for the Kernel Ridge Regression(KRR)method applied to multiple complementary-label learning(MCLL),which demonstrates its superior performance compared to existing approaches.

Deep neural network based on multi-level wavelet and attention for structured illumination microscopy

Structured illumination microscopy(SIM)is a popular and powerful super-resolution(SR)technique in biomedical research.However,the conventional reconstruction algorithm for SIM heavily relies on the accurate prior knowledge of illumination patterns and signal-to-noise ratio(SNR)of raw images.To obtain high-quality SR images,several raw images need to be captured under high fluorescence level,which further restricts SIM’s temporal resolution and its applications.Deep learning(DL)is a data-driven technology that has been used to expand the limits of optical microscopy.In this study,we propose a deep neural network based on multi-level wavelet and attention mechanism(MWAM)for SIM.Our results show that the MWAM network can extract high-frequency information contained in SIM raw images and accurately integrate it into the output image,resulting in superior SR images compared to those generated using wide-field images as input data.We also demonstrate that the number of SIM raw images can be reduced to three,with one image in each illumination orientation,to achieve the optimal tradeoff between temporal and spatial resolution.Furthermore,our MWAM network exhibits superior reconstruction ability on low-SNR images compared to conventional SIM algorithms.We have also analyzed the adaptability of this network on other biological samples and successfully applied the pretrained model to other SIM systems.

CL2ES-KDBC:A Novel Covariance Embedded Selection Based on Kernel Distributed Bayes Classifier for Detection of Cyber-Attacks in IoT Systems

The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks.In this framework,a Covariance Linear Learning Embedding Selection(CL2ES)methodology is used at first to extract the features highly associated with the IoT intrusions.Then,the Kernel Distributed Bayes Classifier(KDBC)is created to forecast attacks based on the probability distribution value precisely.In addition,a unique Mongolian Gazellas Optimization(MGO)algorithm is used to optimize the weight value for the learning of the classifier.The effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyber-attack datasets,The obtained results are then compared with current classification methods regarding accuracy(97%),precision(96.5%),and other factors.Computational analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed,which provides valuable insight into its performance,efficiency,and suitability for securing IoT networks.

Hexavalent Chromium Cr (VI) Removal from Water by Mango Kernel Powder

Metal trace elements (MTE) are among the most harmful micropollutants of natural waters. Eliminating them helps improve the quality and safety of drinking water and protect human health. In this work, we used mango kernel powder (MKP) as bioadsorbent material for removal of Cr (VI) from water. Uv-visible spectroscopy was used to monitor and quantify Cr (VI) during processing using the Beer-Lambert formula. Some parameters such as pH, mango powder, mass and contact time were optimized to determine adsorption capacity and chromium removal rate. Adsorption kinetics, equilibrium, isotherms and thermodynamic parameters such as ΔG˚, ΔH˚, and ΔS˚, as well as FTIR were studied to better understand the Cr (VI) removal process by MKP. The adsorption capacity reached 94.87 mg/g, for an optimal contact time of 30 min at 298 K. The obtained results are in accordance with a pseudo-second order Freundlich adsorption isotherm model. Finally FTIR was used to monitor the evolution of absorption bands, while Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Spectroscopy (EDS) were used to evaluate surface properties and morphology of the adsorbent.

AHermitian C^(2) Differential Reproducing Kernel Interpolation Meshless Method for the 3D Microstructure-Dependent Static Flexural Analysis of Simply Supported and Functionally Graded Microplates

This work develops a Hermitian C^(2) differential reproducing kernel interpolation meshless(DRKIM)method within the consistent couple stress theory(CCST)framework to study the three-dimensional(3D)microstructuredependent static flexural behavior of a functionally graded(FG)microplate subjected to mechanical loads and placed under full simple supports.In the formulation,we select the transverse stress and displacement components and their first-and second-order derivatives as primary variables.Then,we set up the differential reproducing conditions(DRCs)to obtain the shape functions of the Hermitian C^(2) differential reproducing kernel(DRK)interpolant’s derivatives without using direct differentiation.The interpolant’s shape function is combined with a primitive function that possesses Kronecker delta properties and an enrichment function that constituents DRCs.As a result,the primary variables and their first-and second-order derivatives satisfy the nodal interpolation properties.Subsequently,incorporating ourHermitianC^(2)DRKinterpolant intothe strong formof the3DCCST,we develop a DRKIM method to analyze the FG microplate’s 3D microstructure-dependent static flexural behavior.The Hermitian C^(2) DRKIM method is confirmed to be accurate and fast in its convergence rate by comparing the solutions it produces with the relevant 3D solutions available in the literature.Finally,the impact of essential factors on the transverse stresses,in-plane stresses,displacements,and couple stresses that are induced in the loaded microplate is examined.These factors include the length-to-thickness ratio,the material length-scale parameter,and the inhomogeneity index,which appear to be significant.

Heat Kernel Estimates on Simple Random Walks and On-Diagonal Upper Bounds

We primarily provide several estimates for the heat kernel defined on the 2-dimensional simple random walk. Additionally, we offer an estimate for the heat kernel on high-dimensional random walks, demonstrating that the heat kernel in higher dimensions converges rapidly. We also compute the constants involved in the estimate for the 1-dimensional heat kernel. Furthermore, we discuss the general case of on-diagonal estimates for the heat kernel.