国产羊毛毛丛直径形态特征的估计

分析国产羊毛毛丛纤维直径形态特征(FDP),采用对国产羊毛毛丛进行简化测量的方法研究毛丛FDP形态特征.使用三次样条插值数学方法预测不同简化数量级的纤维直径,估计全毛丛的FDP形态特征参数.结果表明,利用全毛丛16.2%~52.7%的测试结果可以对整个毛丛的纤维直径形态的所有特征参数进行比较准确的预测和估计,其纤维直径插值样条曲线的形态与全毛丛纤维直径实际形态曲线有较好的拟合效果(R2=0.91~0.98),所估计的FDP特征参数和全毛丛实际FDP特征参数具有很高的相关性(R2=0.96~1.00).     

基于ARIMA模型的短时交通流量预测算法研究

针对造成基于线性最小方差预报原理的Astrom算法在多步预测过程中误差逐步增大的原因,通过增加误差动态修正因子,提出一种改进的短时交通流量预测算法.该算法基于ARIMA模型结构的时间序列分析方法,采用矩估计法进行参数初估计,用最小二乘法进行参数精估计,用BIC准则为模型定阶.对大量实测数据进行仿真实验,对多个统计量进行误差分析.结果表明,改进算法在应用于时变性强的短时交通流量预测时,相对于Astrom算法具有更好的预测性能.     

食源性致病菌污染估计中删失数据分析的研究进展

食源性致病菌污染水平的确定是开展微生物定量风险评估的重要前提,而删失数据的存在易造成对食品中致病菌整体污染水平的估计产生偏差。对检测过程中出现的删失数据进行分析研究已逐渐成为食源性致病菌定量建模工作的重要内容之一。本文对国内外相关研究进行综述,介绍了食源性致病菌污染检测中删失数据的分类,比较了替代法、参数估计法、非参数估计法和多重填补法这4 类常用分析方法,简述了不同特征的致病菌污染检测数据集及相关统计学方法在食源性致病菌污染水平估计中的应用。最后,基于目前食源性致病菌污染水平估计中存在的问题进行探讨,指出降低估计结果不确定性的同时不可忽视检测数据的变异性,并对未来的风险监测、风险评估及风险交流相关研究作出展望。     

基于扰动模型的非线性ODE局部影响分析

通过引入B样条基函数,给出非线性常微分方程中未知参数的两步估计法.基于扰动模型以及广义影响函数的概念,进行局部影响分析问题的讨论,给出基于扰动模型的广义Cook距离的计算公式.结果显示该算法可降低计算量,提高计算效率.     

食品冻结过程温度场及冻结时间的数值模拟与实验研究

计算流体力学(CFD)在食品传热传质及流体流动相关领域内广泛应用,并取得了很好的效果.本文以冰箱冷冻室内的土豆为研究对象,建立三维非稳态数值计算模型,选用冻结模式,并引入食品热物性多项式对其进行传热分析与计算.实验验证表明模型与实际吻合较好.模拟研究揭示了食品在冰箱中冻结各阶段的温度分布状态、冻结状况等,并较准确地预测了食品的冻结时间.     

关于多元无界连续函数逼近的渐近估计

讨论了多元无界连续函数逼近的渐近估计.利用扩展乘数法讨论了多元线性正算子改造为逼近多元无界连续函数的渐近估计,给出了具有一般性的较为实用的渐近公式.作为实例,研究了多元非乘积型的Landau多项式算子逼近多元无界连续函数的渐近估计式,推广了前人的若干结论.     

缺失响应数据下二阶段估计的渐近性质

在缺失响应数据下考虑半参数回归模型Y=X'β+g(T)+ε,建立该模型参数β,g(t)和σ2的二阶段估计(β)n,(g)n(t)和(σ)2,并通过对每个缺失响应数据Yi进行插值,得到了响应数据的均值.研究表明,这些参数的估计具有渐近正态性,并且(g)n(t)具有较好的收敛速度.     

误差为AANA序列的部分线性模型的弱相合性

研究误差为渐近几乎负相依序列的部分线性模型的性质.利用最小二乘法和非参数加权的估计方法,得到参数、非参数和误差方差的估计,并在合适的条件下得到这些估计量的弱相合性,推广了误差为负相依序列的部分线性模型的相关结论.     

多项式环上分次模的重数计算

给出了计算多项式环K[x0 ,x1,… ,xn]上的分次模M =K[x0 ,x1,… ,xn]/I的重数计算的简单估计式 .该式不需要求出I的投射Hilbert多项式 ,而由I的Groebner基直接得出分次模的重数估计式 .     

我国外汇储备变化走势的非参数统计分析

采用1993~2009年的数据,对我国外汇储备总量占国内GDP总量比重的变化走势进行了统计研究。利用线性模型和非参数回归模型,对我国外汇储备总量占国内GDP总量比重的变化走势进行了拟合分析。结果表明,我国外汇储备总量占国内GDP总量比重的变化走势大致呈线性增长。另外,从拟合效果来说,相对于线性回归模型而言,非参数回归模型更能有效地拟合该比重的变化走势,最后给出了建议。     

Estimation of ingestion time for suspected food in food-poisoning outbreaks

We previously constructed a database of 341 food-poisoning cases involving various causative agents. Using this database, we developed a new method for estimating the ingestion time of a food suspected of causing a food-poisoning outbreak. We confirmed that the method is superior to conventional methods. In this report, estimation equations specific for seven separate causative agents were obtained using the original database. We analyzed 301 cases in the database and 14 cases not included in the database using the seven estimation equations, the estimation equation in our previous report and the conventional methods. The values estimated with the improved method showed higher correspondence and accuracy than those estimated by using the conventional methods or the method in our previous report. These results confirmed that the improved method is superior to the conventional methods and our previous method.     

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.     

Stress and the single cell: intrapopulation diversity is a mechanism to ensure survival upon exposure to stress

The application of Artificial Neural Networks (ANN) in predictive microbiology is presented in this paper. This technique was used to build up a predictive model of the joint effect of NaCl concentration, pH level and storage temperature on kinetic parameters of the growth curve of Lactobacillus plantarum using ANN and Response Surface Model (RSM). Sigmoid functions were fitted to the data and kinetic parameters were estimated and used to build the models in which the independent variables were the factors mentioned above (NaCl, pH, temperature), and in some models, the values of the optical densities (OD) vs. time of the growth curve were also included in order to improve the error of estimation. The determination of the proper size of an ANN was the first step of the estimation. This study shows the usefulness of an ANN pruning methodology. The pruning of the network is a process consisting of removing unnecessary parameters (weights) and nodes during the training process of the network without losing its generalization capacity. The best architecture has been sought using genetic algorithms (GA) in conjunction with pruning algorithms and regularization methods in which the initial distribution of the parameters (weights) of the network is not uniform. The ANN model has been compared with the response surface model by means of the Standard Error of Prediction (SEP). The best values obtained were 14.04% of SEP for the growth rate and 14.84% for the lag estimation by the best ANN model, which were much better than those obtained by the RSM, 35.63% and 39.30%, respectively. These were very promising results that, in our opinion, open up an extremely important field of research.     

Parametric correlation functions to model the structure of permanent environmental (co)variances in milk yield random regression models

The objective of the present study was to estimate milk yield genetic parameters applying random regression models and parametric correlation functions combined with a variance function to model animal permanent environmental effects. A total of 152,145 test-day milk yields from 7,317 first lactations of Holstein cows belonging to herds located in the southeastern region of Brazil were analyzed. Test-day milk yields were divided into 44 weekly classes of days in milk. Contemporary groups were defined by herd-test-day comprising a total of 2,539 classes. The model included direct additive genetic, permanent environmental, and residual random effects. The following fixed effects were considered: contemporary group, age of cow at calving (linear and quadratic regressions), and the population average lactation curve modeled by fourth-order orthogonal Legendre polynomial. Additive genetic effects were modeled by random regression on 1/6/2010orthogonal Legendre polynomials of days in milk, whereas permanent environmental effects were estimated using a stationary or nonstationary parametric correlation function combined with a variance function of different orders. The structure of residual variances was modeled using a step function containing 6 variance classes. The genetic parameter estimates obtained with the model using a stationary correlation function associated with a variance function to model permanent environmental effects were similar to those obtained with models employing orthogonal Legendre polynomials for the same effect. A model using a sixth-order polynomial for additive effects and a stationary parametric correlation function associated with a seventh-order variance function to model permanent environmental effects would be sufficient for data fitting.     

基于纤维长度分布的浮游纤维变速仿真

采用非参数核密度估计的方法获得纤维长度分布的密度函数,随机生成牵伸区纤维分布样本。利用多项式回归拟合出JF型双短胶圈环锭细纱牵伸装置的纵向摩擦力界分布函数,结合纤维变速的数学模型,采用计算机方法模拟出JF型牵伸装置在纤维等长均匀分布和核密度估计分布下的变速点分布形态的直方图。结果表明：等长分布纤维的变速点高度集中于前钳口;核密度估计分布纤维变速点分度离散,近似于正态分布,表明纤维尾端离开后钳口即会有变速可能,可见核密度估计分布下纤维变速点分布的模拟结果更接近于真实情况。此方法可用于对实际牵伸工艺和牵伸机构的优化和预测。     

Water vapor adsorption isotherms of agar-based nanocomposite films

Adsorption isotherms of agar and agar/clay nanocomposite films prepared with different types of nanoclays, that is, a natural montmorillonite (Cloisite Na(+) ) and 2 organically modified montmorillonites (Cloisite 30B and Cloisite 20A), were determined at 3 different temperatures (10, 25, and 40 °C). The water vapor adsorption behavior of the nanocomposite films was found to be greatly influenced with the type of clay. The Guggenheim-Anderson-de Boer (GAB) isotherm model parameters were estimated by using both polynomial regression and nonlinear regression methods and it was found that the GAB model fitted adequately for describing experimental adsorption isotherm data for the film samples. The monolayer moisture content (m(o) ) of the film samples was also greatly affected by the type of nanoclay used, that is, m(o) of nanocomposite films was significantly lower than that of the neat agar film. Nanocomposite films prepared with hydrophobic nanoclays (Cloisite 30B and Cloisite 20A) exhibited lower m(o) values than those prepared with hydrophilic nanoclay (Cloisite Na(+) ).     

Development of a new estimation method of ingestion time to suspected food in food-poisoning outbreaks

In food-poisoning outbreaks, the ingestion time of suspect foods has been estimated using the Sartwell method (Hirayama method), the graphical method and maximum likelihood estimation. These methods are based upon the assumption that the incubation period is log-normally distributed. However, these methods may not always be valid because the bias and variability of the estimates are large. We constructed a database from 341 food-poisoning cases in the literature. Using this database, we developed a method to estimate the actual ingestion time based on the period from the ingestion time to the first outbreak, using a multiple linear regression equation. Since only 9.09% of cases showed log-normal distribution, the above three methods would not be valid in most cases. The new method was applicable in all cases. In addition, the values estimated by the new method showed higher correspondence and accuracy than the values estimated by the other methods. Therefore, we consider that the new method is superior to those methods.     

PLSR modelling of quality changes of lager and malt beer during storage

The aim of this research was to create mathematical models for describing the changes in beer properties by using two chemometric methods applied on the experimental data. The models are intended to be useful and trustworthy for calculating four beer properties based on three easily measured ones. For that purpose, lager and malt beer were packaged in glass bottles, while lager beer was also packaged in polyethylene terephthalate (PET). Samples were placed at room temperature in the dark for 6 months. Fifteen physical and chemical properties of the beer were measured before bottling, immediately after bottling and once per month for the next 6 months. Standard MEBAK and Analytica‐EBC methods of analysis were applied. During the 6 month period, seven properties changed >1%. Two partial least squares regression methods [polynomial regression, partial least squares regression with polynomial regression (PLSR‐PR) and response surface method (PLSR‐RSM)] were used for modelling the relationships amongst multivariate measurements. Models with high statistical significance were determined and two PLSR methods were compared. Both chemometric methods were found to be suitable for modelling physical and chemical changes in the beers during their commercial shelf‐life. The PLS‐RSM method was found to be the more precise and confident method in describing property changes for lager and malt beer in glass bottles, while the polynomial regression model was found to be better for the lager beer packaged in PET. The R² values determined for polynomial regression model were up to 0.939, while for the random surface method model the values were up to 1.000. Copyright © 2016 The Institute of Brewing & Distilling     

Analysis of water intake and dry matter intake using different lactation curve models

The objective was to evaluate 6 different lactation curve models for daily water and dry matter intake. Data originated from the Futterkamp dairy research farm of the Chamber of Agriculture of Schleswig-Holstein in Germany. A data set of about 23,000 observations from 193 Holstein cows was used. Average daily water and dry matter intake were 82.3 and 19.8 kg, respectively. The basic linear mixed model included the fixed effects of parity and test-day within feeding group. Additionally, 6 different functions were tested for the fixed effect of lactation curve and the individual (random) effect of cow-lactation curve. Furthermore, the autocorrelation between repeated measures was modeled with the spatial (power) covariance structure. Model fit was evaluated by the likelihood ratio test, Akaike's and Bayesian information criteria, and the analysis of mean residual at different days in milk. The Ali and Schaeffer function was best suited for modeling the fixed lactation curve for both traits. A Legendre polynomial of order 4 delivered the best model fit for the random effect of cow-lactation. Applying the error covariance structure led to a significantly better model fit and indicated that repeated measures were autocorrelated. Generally, the best information criteria values were yielded by the most complex model using the Ali and Schaeffer function and Legendre polynomial of order 4 to model the average lactation and cow-specific lactation curves, respectively, with inclusion of the spatial (power) error covariance structure. This model is recommended for the analysis of water and dry matter intake including missing observations to obtain estimation of correct statistical inference and valid variance components.