偏正态单向分类随机效应模型下暴露水平的Bootstrap推断

为评估工作环境中的暴露水平,基于偏正态单向分类随机效应模型,研究暴露水平的区间估计和假设检验问题。首先,利用EM算法给出未知参数的极大似然估计。进而,基于Bootstrap方法,构造个体平均暴露水平的三种Bootstrap置信区间。Monte Carlo模拟结果表明,修正的Bootstrap百分位置信区间在覆盖概率意义下优于其他两种Bootstrap置信区间,Bootstrap标准置信区间在置信上限意义下优于其他两种Bootstrap置信区间。最后,将上述方法应用于苯乙烯暴露数据的案例分析,以验证所提出方法的有效性和合理性。

The improved artificial bee colony algorithm for mixed additive and multiplicative random error model and the bootstrap method for its precision estimation

To solve the complex weight matrix derivative problem when using the weighted least squares method to estimate the parameters of the mixed additive and multiplicative random error model(MAM error model),we use an improved artificial bee colony algorithm without derivative and the bootstrap method to estimate the parameters and evaluate the accuracy of MAM error model.The improved artificial bee colony algorithm can update individuals in multiple dimensions and improve the cooperation ability between individuals by constructing a new search equation based on the idea of quasi-affine transformation.The experimental results show that based on the weighted least squares criterion,the algorithm can get the results consistent with the weighted least squares method without multiple formula derivation.The parameter estimation and accuracy evaluation method based on the bootstrap method can get better parameter estimation and more reasonable accuracy information than existing methods,which provides a new idea for the theory of parameter estimation and accuracy evaluation of the MAM error model.

A Bootstrapping-based Method to Automatically Identify Data-usage Statements in Publications

Purpose： Our study proposes a bootstrapping-based method to automatically extract data- usage statements from academic texts. Design/methodology/approach： The method for data-usage statements extraction starts with seed entities and iteratively learns patterns and data-usage statements from unlabeled text. In each iteration, new patterns are constructed and added to the pattern list based on their calculated score. Three seed-selection strategies are also proposed in this paper. Findings： The performance of the method is verified by means of experiments on real data collected from computer science journals. The results show that the method can achieve satisfactory performance regarding precision of extraction and extensibility of obtained patterns. Research limitations： While the triple representation of sentences is effective and efficient for extracting data-usage statements, it is unable to handle complex sentences. Additional features that can address complex sentences should thus be explored in the future. Practical implications： Data-usage statements extraction is beneficial for data-repository construction and facilitates research on data-usage tracking, dataset-based scholar search, and dataset evaluation. Originality/value： To the best of our knowledge, this paper is among the first to address the important task of automatically extracting data-usage statements from real data.

基于Bootstrap-C_(pu)的机床进给机构装配过程能力评估方法

机床进给机构装配过程是其装配质量形成的过程,由于受5M1E因素影响,进给机构装配过程能力可能不足,会导致进给机构装配质量出现较大波动,甚至出现不合格产品。为此,基于C_(pu)和Bootstrap理论,构建了基于Bootstrap-C_(pu)的进给机构装配过程能力评估方法,并进行了实际应用。结果表明:在95%置信度条件下,该批机床进给机构装配过程能力是足够的,其装配质量波动较小。

基于支持向量回归的Bootstrap数据扩充方法及其在小子样可靠性评估中的应用

针对航空发动机小子样特性下的可靠性评估问题,提出一种基于支持向量回归(Support Vector Regression, SVR)的Bootstrap数据扩充方法:建立并训练SVR模型,采用邻域抽样法构造输入集,输入训练好的模型中获得扩充样本。仿真结果表明,采用该方法获得的扩充样本较传统Bootstrap方法更接近真实分布,且有效拓展了样本取值区间。以某航空发动机涡轮盘小子样疲劳寿命试验数据为例:(1)非参数方法,采用两种方法获得的平均疲劳寿命十分接近,但新方法获得的置信区间更大,这与样本取值区间的拓展相关;(2)参数方法,对新方法扩充的样本进行参数估计得到的结果与参考值更为接近,最大相对偏差为-1.290 2%,而传统方法的最大相对偏差达到了29.477 6%,两种方法下计算得到的平均疲劳寿命与参考值均较接近,但新方法得到的置信区间与参考区间更为接近。综合来看,所提出的方法能够有效实现样本扩充,具有一定的应用价值。

基于Bootstrap框架的响应式网页设计与实现

目的:研究基于Bootstrap框架的响应式网页设计及实现方式,使网页能够适应不同尺寸、型号、品牌的终端,并表现出良好的呈现效果,以期能促进网页设计的发展。方法:深入了解Bootstrap框架的相关内容,并明确响应式网页设计的技术要求,为后续实现方式提供具体的依据,在此基础上探讨基于Bootstrap框架响应式网页的具体设计与实现。结果:结合具体案例对基于Bootstrap框架响应式网页设计与实现进行了详细阐述,明确了响应式网页设计时需要达成的内容及一般方法。结论:Bootstrap框架能够为响应式网页设计及实现提供完备且强大的功能支持,为响应式网页设计及技术应用与实现创造了更多的可行性及拓展性,使网页能够在不同终端上均呈现出理想表达效果。

基于Bootstrap方法的多组配对数据风险差的一致性检验

在Dallal模型下构建3个统计量,研究小样本多组配对数据风险差的一致性检验问题.通过Monte-Carlo数值模拟,比较小样本下渐近方法和Bootstrap方法在检验过程中的性能.研究发现,Bootstrap方法在第一类错误率和功效上表现更优;特别地,基于Score检验的Bootstrap方法的第一类错误率较稳健,且有较高的功效,检验性能最优.

Truncated Geometric Bootstrap Method for Time Series Stationary Process

This paper introduced a bootstrap method called truncated geometric bootstrap method for time series stationary process. We estimate the parameters of a geometric distribution which has been truncated as a probability model for the bootstrap algorithm. This probability model was used in resampling blocks of random length, where the length of each blocks has a truncated geometric distribution. The method was able to determine the block sizes b and probability p attached to its random selections. The mean and variance were estimated for the truncated geometric distribution and the bootstrap algorithm developed based on the proposed probability model.

Automatic determination method of optimal threshold based on the bootstrapping technology

In order to predict the extreme load of the mechanical components during the entire life,an automatic method based on the bootstrapping technology(BT)is proposed to determine the most suitable threshold.Based on all the turning points of the load history and a series of thresholds estimated in advance,the generalized Pareto distribution is established to fit the exceedances.The corresponding distribution parameters are estimated with the maximum likelihood method.Then,BT is employed to calculate the mean squared error(MSE)of each estimated threshold based on the exceedances and the specific distribution parameters.Finally,the threshold with the smallest MSE will be the optimal one.Compared to the kurtosis method and the mean excess function method,the average deviation of the probability density function of exceedances determined by BT reduces by 38.52%and 29.25%,respectively.Moreover,the quantile-quantile plot of the exceedances determined by BT is closer to a straight line.The results suggest the improvement of the modeling flexibility and the determined threshold precision.If the exceedances are insufficient,BT will enlarge their amount by resampling to solve the instability problem of the original distribution parameters.

Simulation Evaluation Study of System Reliability Based on Improved BayesBootstrap Method

For the disadvantages of analyzing system reliability using common Bayes method,an evaluation method based on improved Bayes-Bootstrap method is presented.Firstly,the data obliteration problem existing in traditional Bayes method is analyzed,then an improved sampling arithmetic is proposed on the foundation of an experience function.Finally,effectiveness of this method is proved by an example.The simulating result shows that this method is more accurate than common Bayes method.

On the Application of Bootstrap Method to Stationary Time Series Process

This article introduces a resampling procedure called the truncated geometric bootstrap method for stationary time series process. This procedure is based on resampling blocks of random length, where the length of each blocks has a truncated geometric distribution and capable of determining the probability p and number of block b. Special attention is given to problems with dependent data, and application with real data was carried out. Autoregressive model was fitted and the choice of order determined by Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The normality test was carried out on the residual variance of the fitted model using Jargue-Bera statistics, and the best model was determined based on root mean square error of the forecasting values. The bootstrap method gives a better and a reliable model for predictive purposes. All the models for the different block sizes are good. They preserve and maintain stationary data structure of the process and are reliable for predictive purposes, confirming the efficiency of the proposed method.

Bootstrap样本大数据模型和分布式集成学习方法

传统Bootstrap抽样和Bagging集成学习通常以串行方式实现,计算效率低,且存在样本不可重用、扩展性差等问题,不适合高效的大规模Bagging集成学习。从大数据分布式计算的思维入手,提出新的Bootstrap样本划分(BSP)大数据模型和分布式集成学习方法。BSP数据模型通过分布式生成算法将训练数据表达成分布式Bootstrap样本集的集合,存储成HDFS分布式数据文件,为后续的分布式集成学习提供数据支持。分布式集成学习方法从BSP数据模型中随机选取多个BSP数据块,读入集群各个节点的虚拟机,用串行算法对选取的数据块并行计算统计量或训练建模,再将所有的计算子结果回传至主节点中,生成最终的集成学习结果,此过程中可加入对子结果的质量选择以进一步提高预测效果。BSP数据模型的生成和分布式集成学习采用非Map-Reduce计算范式进行,每个数据块的计算独立完成,减少了计算节点间的数据通信开销。提出的算法在Spark开源系统中以新的算子方式实现,供Spark应用程序调用。实验表明,新方法可以高效地生成训练数据的BSP数据模型,提高数据样本的可重用性,在基于有监督机器学习算法构建的大规模Bagging集成学习实验中,计算效率能提高50%以上,同时预测精度进一步提高约2%。

广西紧密型县域医共体试点地区乡镇卫生院运行效率研究——基于Bootstrap-DEA和Tobit模型

目的:分析近年来广西紧密型县域医共体试点地区乡镇卫生院的运行效率及其影响因素,提出对策建议。方法:运用Bootstrap-DEA模型测算2015—2020年试点地区乡镇卫生院静态、动态运行效率,借助Tobit模型检验各因素对效率的影响。结果:2015—2020年试点地区乡镇卫生院综合效率、纯技术效率、规模效率均小于0.9,未达到有效状态;6年间全要素生产率小于1,处于退步状态;医护比、全科医生数占比、医师日均担负诊疗人次、人均GDP、重点人群数量对综合效率有正向影响,出院者平均住院日、门急诊病人人均费用对综合效率有负向影响。结论:紧密型医共体试点地区乡镇卫生院运行效率退步,原因主要在于技术进步指数下降,建议完善乡镇卫生院管理制度,提升管理与医疗技术水平,强化医共体信息平台建设,提升乡镇卫生院运行效率。

A Review on Sources,Extractions and Analysis Methods of a Sustainable Biomaterial:Tannins

Condensed and hydrolysable tannins are non-toxic natural polyphenols that are a commercial commodity industrialized for tanning hides to obtain leather and for a growing number of other industrial applications mainly to substitute petroleum-based products.They are a definite class of sustainable materials of the forestry industry.They have been in operation for hundreds of years to manufacture leather and now for a growing number of applications in a variety of other industries,such as wood adhesives,metal coating,pharmaceutical/medical applications and several others.This review presents the main sources,either already or potentially commercial of this forestry by-materials,their industrial and laboratory extraction systems,their systems of analysis with their advantages and drawbacks,be these methods so simple to even appear primitive but nonetheless of proven effectiveness,or very modern and instrumental.It constitutes a basic but essential summary of what is necessary to know of these sustainable materials.In doing so,the review highlights some of the main challenges that remain to be addressed to deliver the quality and economics of tannin supply necessary to fulfill the industrial production requirements for some materials-based uses.

The Performance of Double Bootstrap Method for Large Sampling Sequence

Studies on the iteration procedure in double bootstrap method have given a great impact on confidence interval performance. However, the procedure was claimed to be complicated and demand intensive computer processor. Considering this problem, an alternative procedure was proposed in this research. Despite of using small sampling sequence, this research was aimed to increase the accuracy estimation using a second replication number which resulted in a large sampling sequence of double bootstrap. In this paper, the alternative double bootstrap method was hybrid onto an example model and its performance was based on Studentised interval. The performance was examined in simulation study and real sample data of sukuk Ijarah. The result showed that hybrid double bootstrap model gave more accurate estimation in terms of its shorter length when dealing with various parameter values and has shown to improve the single bootstrap estimation.

Drilling-based measuring method for the c-φ parameter of rock and its field application

The technology of drilling tests makes it possible to obtain the strength parameter of rock accurately in situ. In this paper, a new rock cutting analysis model that considers the influence of the rock crushing zone(RCZ) is built. The formula for an ultimate cutting force is established based on the limit equilibrium principle. The relationship between digital drilling parameters(DDP) and the c-φ parameter(DDP-cφ formula, where c refers to the cohesion and φ refers to the internal friction angle) is derived, and the response of drilling parameters and cutting ratio to the strength parameters is analyzed. The drillingbased measuring method for the c-φ parameter of rock is constructed. The laboratory verification test is then completed, and the difference in results between the drilling test and the compression test is less than 6%. On this basis, in-situ rock drilling tests in a traffic tunnel and a coal mine roadway are carried out, and the strength parameters of the surrounding rock are effectively tested. The average difference ratio of the results is less than 11%, which verifies the effectiveness of the proposed method for obtaining the strength parameters based on digital drilling. This study provides methodological support for field testing of rock strength parameters.

A comparison study on structure-function relationship of polysaccharides obtained from sea buckthorn berries using different methods:antioxidant and bile acid-binding capacity

In this study,the structural characters,antioxidant activities and bile acid-binding ability of sea buckthorn polysaccharides(HRPs)obtained by the commonly used hot water(HRP-W),pressurized hot water(HRP-H),ultrasonic(HRP-U),acid(HRP-C)and alkali(HRP-A)assisted extraction methods were investigated.The results demonstrated that extraction methods had significant effects on extraction yield,monosaccharide composition,molecular weight,particle size,triple-helical structure,and surface morphology of HRPs except for the major linkage bands.Thermogravimetric analysis showed that HRP-U with filamentous reticular microstructure exhibited better thermal stability.The HRP-A with the lowest molecular weight and highest arabinose content possessed the best antioxidant activities.Moreover,the rheological analysis indicated that HRPs with higher galacturonic acid content and molecular weight showed higher viscosity and stronger crosslinking network(HRP-C,HRP-W and HRP-U),which exhibited stronger bile acid binding capacity.The present findings provide scientific evidence in the preparation technology of sea buckthorn polysaccharides with good antioxidant and bile acid binding capacity which are related to the structure affected by the extraction methods.

Material point method simulation of hydro-mechanical behaviour in twophase porous geomaterials: A state-of-the-art review

The material point method(MPM)has been gaining increasing popularity as an appropriate approach to the solution of coupled hydro-mechanical problems involving large deformation.In this paper,we survey the current state-of-the-art in the MPM simulation of hydro-mechanical behaviour in two-phase porous geomaterials.The review covers the recent advances and developments in the MPM and their extensions to capture the coupled hydro-mechanical problems involving large deformations.The focus of this review is aiming at providing a clear picture of what has or has not been developed or implemented for simulating two-phase coupled large deformation problems,which will provide some direct reference for both practitioners and researchers.

Deciphering gastric inflammation-induced tumorigenesis through multi-omics data and AI methods

Gastric cancer(GC), the fifth most common cancer globally, remains the leading cause of cancer deaths worldwide. Inflammation-induced tumorigenesis is the predominant process in GC development;therefore, systematic research in this area should improve understanding of the biological mechanisms that initiate GC development and promote cancer hallmarks. Here, we summarize biological knowledge regarding gastric inflammation-induced tumorigenesis, and characterize the multi-omics data and systems biology methods for investigating GC development. Of note, we highlight pioneering studies in multi-omics data and state-of-the-art network-based algorithms used for dissecting the features of gastric inflammation-induced tumorigenesis, and we propose translational applications in early GC warning biomarkers and precise treatment strategies. This review offers integrative insights for GC research, with the goal of paving the way to novel paradigms for GC precision oncology and prevention.

Volumetric lattice Boltzmann method for pore-scale mass diffusionadvection process in geopolymer porous structures

Porous materials present significant advantages for absorbing radioactive isotopes in nuclear waste streams.To improve absorption efficiency in nuclear waste treatment,a thorough understanding of the diffusion-advection process within porous structures is essential for material design.In this study,we present advancements in the volumetric lattice Boltzmann method(VLBM)for modeling and simulating pore-scale diffusion-advection of radioactive isotopes within geopolymer porous structures.These structures are created using the phase field method(PFM)to precisely control pore architectures.In our VLBM approach,we introduce a concentration field of an isotope seamlessly coupled with the velocity field and solve it by the time evolution of its particle population function.To address the computational intensity inherent in the coupled lattice Boltzmann equations for velocity and concentration fields,we implement graphics processing unit(GPU)parallelization.Validation of the developed model involves examining the flow and diffusion fields in porous structures.Remarkably,good agreement is observed for both the velocity field from VLBM and multiphysics object-oriented simulation environment(MOOSE),and the concentration field from VLBM and the finite difference method(FDM).Furthermore,we investigate the effects of background flow,species diffusivity,and porosity on the diffusion-advection behavior by varying the background flow velocity,diffusion coefficient,and pore volume fraction,respectively.Notably,all three parameters exert an influence on the diffusion-advection process.Increased background flow and diffusivity markedly accelerate the process due to increased advection intensity and enhanced diffusion capability,respectively.Conversely,increasing the porosity has a less significant effect,causing a slight slowdown of the diffusion-advection process due to the expanded pore volume.This comprehensive parametric study provides valuable insights into the kinetics of isotope uptake in porous structures,facilitating the development of porous materials for nuclear waste treatment applications.