螺旋弹簧快速疲劳试验加载优化方法

新设计以及投产加工制造的高应力圆柱螺旋弹簧都需进行快速疲劳试验,试验时间短。要求圆柱螺旋弹簧加载频率高,使用寿命长,实际疲劳断裂主要为正断型疲劳断裂模式。金属材料扭转疲劳断裂机制图诠释了材料的疲劳断裂寿命、屈服强度、外施交变切应力振幅和疲劳断裂模式之间的关系。利用原理图表述不同试验条件下引发疲劳断裂模式发生的替代变化。缩短快速试验时间的主要方法:提高加载应力水平;最大正应力不变条件下,降低应力比值;对圆柱螺旋弹簧进行优化的二次喷丸处理等。给出快速疲劳试验的一般试验程序。     

基于逐步Ⅱ型删失恒定应力部分加速寿命试验下Burr Ⅲ分布的统计分析

在可靠性工程试验中,恒定应力部分加速寿命试验不仅能提高效率节省成本,且不需要试验设备的加速方程,这种寿命试验被广泛应用。基于逐步Ⅱ型删失数据,在恒定应力部分加速寿命试验下,对加速因子和Burr Ⅲ分布中的形状参数进行估计。运用极大似然法和贝叶斯方法得到了Burr Ⅲ分布形状参数和加速因子的估计式。此外,利用Monte-Carlo模拟,对上述2种估计方法的效果进行了对比分析。模拟结果表明,在均方误差（MSE）准则下,贝叶斯估计与极大似然估计近似。     

逐步增加Ⅱ型截尾试验下广义指数分布的参数估计

基于逐步增加II型截尾试验,在单参数情况下,利用极大似然估计方法给出了广义指数分布形状参数和可靠度函数的极大似然估计.同时,在逐步增加II型截尾试验下,证明了极大似然估计的相合性和渐近正态性.     

步态时间序列的神经网络模拟和混沌检测

本文应用研究小样本时间序列的神经网络方法估计一类混沌时间序列,步态时间序列的李亚普诺夫指数,相关维数和相关熵。给出了步态时间序列复杂性的判据。     

猪鞋面革应力松弛时间谱的研究(Ⅱ)——二阶近似法研究猪鞋面革应力松弛谱的形态

研究了利用Maxwell模型和Schwarzl二阶近似法求得的猪鞋面革应力松弛时间谱图的形态,并利用试验数据检验了其应力松弛时间谱的精确程度。通过检验发现:得到的应力松弛谱有较高的精确性,证明此松弛时间谱能够较准确地描绘猪鞋面革的应力松弛行为。     

有限压缩拉格朗日描述本构模型

对铅的有限变形压缩试验进行了分析,给出了基于拉格朗日描述的有限压缩本构模型,对设计侧向约束的复杂应力状态进行了理论探讨.无量纲压力的理论模拟值同试验结果一致,表明基于拉格朗日描述的本构模型可应用于复杂应力状态情形.     

一类异步群集系统的稳定性分析

在考虑异步假设下一类一维离散时间群集系统稳定性的基础上,讨论了群集运动的数学模型及此模型在异步假设条件下的稳定性,给出了在部分异步条件下关于模型收敛时间的一个上界估计,并给出了群集运动的数值模拟结果.     

马铃薯渣应力松弛模型的建立及参数分析

为改善马铃薯渣机械处理方式及效果,在不同喂入量下进行了薯渣应力松弛试验.获取了应力松弛曲线并分析了弹性模量随时间变化的规律,建立了3种广义Maxwell模型用于描述薯渣应力松弛行为,得到了应力松弛时间、平衡弹性模量、衰变弹性模量、松弛时间比率等特性参数;同时分析了3种喂入量对薯渣应力松弛特性参数的影响.结果表明:可用五...     

塑料接线盒的型腔残余应力正交优化

由于塑料接线盒结构比较复杂,面上和侧边有多个小孔,对于注塑成型时熔体流动的阻碍改向较多,易产生较大的型腔内残余应力,影响塑件强度。笔者设计了正交试验方案列表,并对每个方案组合进行了Moldflow分析,研究了模具温度、注射时间、保压时间、冷却时间、熔体温度和保压压力6个参数对型腔残余应力的影响,找到了不同因素的影响力大小,合理进行了工艺参数组合的优化,找到了残余应力最小时的最佳工艺参数组合。试验结果表明:正交试验可以针对试验指标结果进行某一注塑参数的精准优化,避免大量无序摸索,节省了注塑企业的时间和材料成本。     

单向复合材料微观应变场的h-p型有限元模拟

单向复合材料的纤维和机体是两种典型的异质材料,两者在界面处及各自内部应力/应变场的分布较复杂。目前,很多学者对多尺度模拟方法的研究都是建立在对微观应力/应变场进行模拟的基础上进行的。通常,p型有限元在异质材料细节应力/应变场的分析方面有其优越性。从这个角度出发,采用一定h细化的p型有限元即h-p型有限元模型,分别在多种力载荷和热载荷作用下,利用代表体元(RVE)技术,给出了单向复合材料纤维和机体界面及其各自内部的Mises应变和J1的分布;同时,为了表明计算结果的精度,同时给出了模型的能范误差估计。这种微观力学模拟方法对复合材料结构的多尺度模拟有较重要参考价值。     

Economic burden from health losses due to foodborne illness in the United States

The Centers for Disease Control and Prevention (CDC) recently revised their estimates for the annual number of foodborne illnesses; 48 million Americans suffer from domestically acquired foodborne illness associated with 31 identified pathogens and a broad category of unspecified agents. Consequently, economic studies based on the previous estimates are now obsolete. This study was conducted to provide improved and updated estimates of the cost of foodborne illness by adding a replication of the 2011 CDC model to existing cost-of-illness models. The basic cost-of-illness model includes economic estimates for medical costs, productivity losses, and illness-related mortality (based on hedonic value-of-statistical-life studies). The enhanced cost-of-illness model replaces the productivity loss estimates with a more inclusive pain, suffering, and functional disability measure based on monetized quality-adjusted life year estimates. Costs are estimated for each pathogen and a broader class of unknown pathogens. The addition of updated cost data and improvements to methodology enhanced the performance of each existing economic model. Uncertainty in these models was characterized using Monte Carlo simulations in @Risk version 5.5. With this model, the average cost per case of foodborne illness was $1,626 (90% credible interval [CI], $607 to $3,073) for the enhanced cost-of-illness model and $1,068 (90% CI, $683 to $1,646) for the basic model. The resulting aggregated annual cost of illness was $77.7 billion (90% CI, $28.6 to $144.6 billion) and $51.0 billion (90% CI, $31.2 to $76.1 billion) for the enhanced and basic models, respectively.     

Simulation study on covariance component estimation for two binary traits in an underlying continuous scale

The usefulness of the variance and covariance component estimation methods based on a threshold model was studied in a multiple-trait situation with two binary traits. Estimation equations that yield marginal maximum likelihood estimates of variance components on the underlying continuous variable scale and point estimates of location parameters with empirical Bayesian properties are described. Methods were tested on simulated data sets that were generated to exhibit three different incidences, 25, 15, and 5%. Results were compared with analyses of the same data sets with a REML method based on normal distribution and a linear model. Heritabilities and residual correlations calculated from discrete observations were transformed to underlying parameters. In estimation of heritabilities, all methods performed equally well at all incidence levels and with no detectable bias. As suggested by threshold theory, the genetic correlation was accurately estimated directly from the observations without any need of correction for incidence. Marginal maximum likelihood estimates of genetic correlations were similar to linear model estimates; discrepancies from the true parameters were consistent with both methods. In estimation of residual correlations, the method with the linear model approach yielded satisfactory estimates only at the highest incidence level, 25%. For 5% incidence, the uncorrected estimate of residual correlation was 50% less than the true value, and after correction for incidence, the parameter was overestimated by 90%. The estimates of residual correlation from the threshold model were regarded fair, except at the lowest level of incidence, where the estimate was 27% higher than the true value. Results indicated that when an accurate estimate of residual correlation is needed, the marginal maximum likelihood estimates are superior to the estimates calculated with the linear model. Using correction for the incidence level for residual correlation did not work well except at the highest incidence level.     

Short communication: Heritability of susceptibility to infection by Mycobacterium avium ssp. paratuberculosis in Holstein cattle

Johne's disease in cattle is the result of infection of the small intestine by Mycobacterium avium ssp. paratuberculosis (MAP), leading to an incurable inflammatory bowel disease (Johne's disease or paratuberculosis). The disease is a concern both for its direct cost to dairy producers and for its zoonotic potential. The objective of this study was to estimate the heritability for susceptibility to infection of cattle by MAP using Johne's testing records (ELISA test for presence of antibodies to MAP in milk or blood) from US Holstein cattle from 2009 to 2016. Data sets were edited to include records from herds with 100 or more total records and sires with 50 or more daughters. Data sets were further edited to include (1) only herds with at least 1 positive test, (2) herds with at least 2.5% positive test results, and (3) herds with at least 5% positive test results to examine the effect of data from herds with higher proportions of positive tests, and presumably higher pathogen exposure, on heritability estimates. Two models were used in this study, a linear sire model and a binary threshold-probit sire model. Both were mixed models considering fixed effects of herd and age at test, the latter as a covariate accounting for linear and quadratic effects; random effects included sire and residual. Analyses were conducted using a restricted maximum likelihood method. Heritability estimates (±standard error) from the linear model were 0.041 ± 0.004, 0.050 ± 0.004, and 0.062 ± 0.007 for data from herds with at least 1 positive test, ≥2.5% positive tests, and ≥5% positive tests, respectively. Heritability estimates from the threshold model were 0.157 ± 0.014, 0.174 ± 0.016, and 0.186 ± 0.021 for data from herds with at least 1 positive test, ≥2.5% positive tests, and ≥5% positive tests, respectively. Heritability estimates from the linear model were affected by population incidence for positive tests, in contrast to estimates from the threshold model, likely accounting for the difference in magnitude of heritability estimates between models and suggesting that the threshold model analysis is the better choice. Heritability estimates increased as data were restricted to herds with presumed higher MAP exposure for both linear model and threshold model analyses. These estimates are similar to previous estimates in other dairy cattle populations and suggest the potential for selection to lessen susceptibility to MAP infection.     

Underestimation of uncertainty in statistical regression of environmental models: influence of model structure uncertainty

As the quality of sensors increases, systematic discrepancies between measurements and model outputs become more apparent Applying regression type analysis in these cases leads to autocorrelated residuals, biased parameter estimates, and underestimation of uncertainty. This paper examines how parameter estimates are affected by model structure uncertainty for an application from wastewater treatment. A Monod model is fitted to synthetic data generated by a reference system exhibiting predefined Tessier kinetics and a known error process. A range of methods are suggested to test if the resulting residuals fulfill the IID (independent and identically distributed)-requirement visual examination of time series, autocorrelation, and partial autocorrelation functions, the Jarque-Bera normality test, the Runs test for independence, and the BDS test for IID. The tests are shown to perform well at low measurement noise but not at higher levels of noise where transferring the parameter estimates gained from a batch system leads to erroneous estimation of steady state concentrations in a completely stirred tank reactor. Additional diagnostics are suggested which include second order autocorrelation functions of the residuals in the case of a single experiment and examination of moving averages of residuals in the case of multiple experiments.     

Comparison of two models for the estimation of usual intake addressing zero consumption and non-normality

Various models exist for estimating the usual intake distribution from dietary intake data. In this paper, we compare two of these models, the Iowa State University Foods (ISUF) model and the betabinomial-normal (BBN) model and apply them to three different datasets. Intake data are obtained by aggregating over multiple food products and are often non-normal. The ISUF and BBN model both address non-normality. While the two models have similar structures, they show some differences. The ISUF model includes an additional spline transformation for improving the normality of the intake amount distribution, while the BBN model includes the possibility of addressing covariates, such as age or sex. Our analyses showed that for two of the example datasets both models produced similar estimates of the higher percentiles of the usual intake distribution. However, for the third dataset, where the intake amount distribution appear to be multimodal, both models produced different percentile estimates.     

Genetic parameters for reproductive traits of Holstein cattle in California and Minnesota

Genetic parameters for five reproductive traits were estimated using data from 51,528 Holstein cows that were inseminated from April to September 1998 in 1717 herds in California and Minnesota. Nonreturn rate and veterinary-confirmed pregnancy rate at 60 and 90 d after insemination were evaluated using linear and threshold models, including an additive genetic effect for the cow being inseminated and a random environmental effect for the service bull. Interval from calving to first insemination was evaluated using a linear model, including an additive genetic effect for the cow being inseminated. Linear model heritability estimates for 60-d (90-d) nonreturn rate were 1.4% (1.5%) in California and 4.1% (2.7%) in Minnesota. Corresponding estimates for 60-d (90-d) confirmed pregnancy rate were 1.4% (2.3%) in California and 1.0% (2.0%) in Minnesota; the proportion of cows with veterinary data available 60 d after breeding was 86% in California and 55% in Minnesota. Threshold model heritability estimates were slightly higher than linear model estimates in California but were lower in Minnesota, presumably because 25% of the herd-season classes in Minnesota contained either all successes or all failures. Linear model repeatability estimates for the service bull effect on 60-d (90-d) nonreturn rate were 0.5% (0.4%) in California and 0.3% (0.3%) in Minnesota. Corresponding estimates for 60-d (90-d) confirmed pregnancy rates were 0.6% (0.2%) in California and 0.1% (0.4%) in Minnesota. Threshold model estimates were slightly higher than linear model estimates in both states. Heritability estimates for the interval from calving to first insemination were 5.8% in California and 6.1% in Minnesota. Despite the low parameter estimates, variation was present among animals, and it should be possible to identify sires that possess superior or inferior reproductive characteristics.     

Exposure of the Irish population to PBDEs in food: consideration of parameter uncertainty and variability for risk assessment

Polybrominated diphenyl ethers (PBDEs) are brominated flame retardants used to retard the ignition and/or spread of fire. PBDEs are used in various consumer products, such as textiles, mattresses and TV screens. This study presents a chemical risk assessment for the Irish population based on exposure to PBDEs from food. Special regard is given to the influence of parameter uncertainty and variability on the margins of safety. To quantitatively model uncertainty and variability in concentration data and variability in consumer behavior, a hierarchical probabilistic model was constructed. This model was evaluated using a two-dimensional Monte Carlo simulation (2D-MCS) approach. By considering uncertainty and variability in concentration data, margins of safety (MOS) were derived that are lower by a factor of ～2 compared to MOS based on dose estimates that only consider variability. The lowest MOS is 7.5?×?10⁴ for BDE-99, with impaired spermatogenesis as toxic endpoint. Assuming an MOS of 10⁴ as acceptable, we conclude that there is no significant risk for human health through intake of contaminated food. To investigate whether additional measurements could improve the quality of dose estimates, the statistic “uncertainty-to-variability (UVR)” was developed. By applying the UVR to our dose estimates, we show that, in our case, the datasets contain little uncertainty and additional measurements would not significantly improve the quality of dose estimates.     

Choice models with mixtures: An application to a cocktail experiment

Data from choice experiments are analyzed using choice models. Proper modeling allows a good understanding of the consumer preferences, the identification of consumer segments and the optimization of products, and, eventually, offers opportunities for competitive advantage. However, in the analysis of choice data, products that are mixtures of ingredients have been largely overlooked. In this paper, we combine traditional choice models and traditional mixture models. We apply the resulting mixture choice models to data from a real-life experiment in which consumers made pair-wise comparisons involving seven cocktails. For the choice models, we first assume consumer homogeneity. Next, we allow for heterogeneity among individuals. Therefore, we discuss the multinomial logit model, the mixed logit model and the latent class model. For identifying segments, besides the latent class model, we explore various two-stage segmentation approaches in which forces, hierarchical Bayes estimates and Firth individual-level estimates are used as input for a cluster analysis. The results show that the mixed logit model and the latent class model describe the data better than the multinomial logit model. The optimal cocktails for the segments we identified using the various methods are not identical, but show many similarities.     

Modified Jack-knife estimation of parameter uncertainty in bilinear modelling by partial least squares regression (PLSR)

A method for assessing the uncertainty of the individual bilinear model parameters from two-block regression modelling by multivariate partial least squares regression (PLSR) is presented. The method is based on the so-called “Jack-knife” resampling, comparing the perturbed model parameter estimates from cross-validation with the estimates from the full model. The conventional jack-knifing from ordinary least squares regression is modified in order to compensate for rotational ambiguities of bilinear modelling. The method is intended to make “do-it-yourself” multivariate data-analysis by non-statisticians more safe, in particular in cases with many collinear and noisy regressor and -regressand variables (which is very common in practice). Its use is illustrated by a real example, where the chemical and physical properties of different cocoa drinks are predicted from sensory analysis.     

Bayesian inference for categorical traits with an application to variance component estimation

We implemented statistical models of Bayesian inference that included direct and maternal genetic effects for genetic parameter estimation of categorical traits by Gibbs sampling. The estimation errors and variances of estimates of animal versus sire and maternal grandsire models, of linear versus threshold models, of single-trait versus multiple-trait models, and of treating herd-year-season as fixed versus random effects in the model were compared. The results indicated that linear models yielded biased estimates of genetic parameters for categorical traits. The animal model was improper for analysis of categorical traits using a threshold model and the Gibbs sampler. Moreover, linear versus threshold models and animal versus sire-maternal grandsire models resulted in larger Monte Carlo errors and increased auto-correlations among posterior samples. Treating herd-year-seasons as random effects in the threshold models decreased the Monte Carlo error, auto-correlations, and the variances of estimates. Efficiency of the single-trait threshold sire model, as measured by the variance of the estimates, was lower than for a multiple-trait model that included a correlated continuous trait, but both estimates were unbiased. Therefore, the threshold single-trait sire and maternal grandsire model is a feasible alternative to the multiple-trait model for analysis of variance components of categorical traits affected by direct and maternal genetic factors.