Effects of ethylene-octene copolymer (POE) on the brittle to ductile transition of high-density polyethylene/POE blends

Three kinds of ethylene-octene copolymers (POE) were melt-blended with high-density polyethylene (PE-HD) in different proportions. Detailed characterizations were conducted to analyze their structural differences of POE and its effects in toughening PE-HD. The higher molecular weight POE can improve the toughness of PE-HD. 60:40 PE-HD/POE is elongated to break up to 700% while impact strength is 84.7 kJ/m² at −30°C, which is 21-fold of PE-HD. In the brittle to ductile transition (BDT) during impact, the fracture mechanism changes from the crazing mode to the shear yield-plastic deformation mode. The BDT temperature decreases as the POE molecular weight and its content increase. The interface strength in tension is estimated to access their effects. The Boltzmann-type models were successfully extended to describe the typical S-shaped curves in BDT of notched impact strength vs POE content or temperature. The supplementary decay model is suggested for the attenuation in toughening. Transition map in impact is proposed to select the use range of composition (c ) and temperature (T ) for high toughness. The curves are converted into 3D graph of T -c -impact strength for illustrating their coupling-separate effects, and further into the contour map of impact strength in T -c space for finding their partial equivalence.     

Convergence analysis of the alternating RGLS algorithm for the identification of the reduced complexity Volterra model

In this paper we provide a convergence analysis of the alternating RGLS (Recursive Generalized Least Square) algorithm used for the identification of the reduced complexity Volterra model describing stochastic non-linear systems. The reduced Volterra model used is the 3rd order SVD-PARAFC-Volterra model provided using the Singular Value Decomposition (SVD) and the Parallel Factor (PARAFAC) tensor decomposition of the quadratic and the cubic kernels respectively of the classical Volterra model. The Alternating RGLS (ARGLS) algorithm consists on the execution of the classical RGLS algorithm in alternating way. The ARGLS convergence was proved using the Ordinary Differential Equation (ODE) method. It is noted that the algorithm convergence canno׳t be ensured when the disturbance acting on the system to be identified has specific features. The ARGLS algorithm is tested in simulations on a numerical example by satisfying the determined convergence conditions. To raise the elegies of the proposed algorithm, we proceed to its comparison with the classical Alternating Recursive Least Squares (ARLS) presented in the literature. The comparison has been built on a non-linear satellite channel and a benchmark system CSTR (Continuous Stirred Tank Reactor). Moreover the efficiency of the proposed identification approach is proved on an experimental Communicating Two Tank system (CTTS).     

金属配合物与核酸作用机理的理论计算

通过计算机模拟金属配合物与核酸作用机理,对其理论模型能量计算和结构优化。在不同的化学环境条件下得到不同的实验模拟结果,对真实的化学反应过程有重要的指导意义和参考意义。通过Gaussian03,Gauss View等软件的结合应用,分别应用于优化计算和理论建模,熟悉了对化学软件操作应用。     

Kinetic analysis of magnesium aluminate spinel formation: Effect of MgO:Al2O3 ratio and titania dopant

The mechanistic pathway of MgO-Al₂O₃ reaction in solid state to form MgAl₂O₄ spinel was investigated to correlate the kinetic parameters with ratio of reactants (MgO:Al₂O₃) and with the presence of a doping agent, TiO₂. The time-temperature-expansion data of oxide compacts was analyzed using several model free analyses and model based (linear and non-linear) kinetic algorithms. These indicated that spinel formation process can be best described by single step with n-dimensional Avrami equation for every MgO:Al₂O₃ ratio, irrespective of titania dopant. The activation energy (E_a) of the process was proportional to % spinel formed in each system and validated with quantitative XRD analysis. The higher value of Avrami coefficient (n) in 90 wt% Al₂O₃ compositions has been explained with geometric considerations of powder packing. Incorporations of 1% TiO₂ in the MgO: Al₂O₃ oxide compact did not markedly affect the reaction model, frequency factor and Activation energy.     

制导炸弹半实物仿真系统误差对仿真结果的影响

在制导炸弹半实物仿真试验中经常发现,同一发制导炸弹多次仿真的俯仰角差别较大,同一批次制导炸弹投弹试验的遥测俯仰角与仿真俯仰角差别也较大,但是俯仰角的收敛趋势均一致,实测脱靶量与仿真脱靶量都合格,且具有一致性。为了分析出现上述现象的原因,基于协方差分析描述函数法(CADFT),建立了制导炸弹半实物仿真系统的误差传播理论,计算了仿真姿态角和姿态角速度的误差。分析了仿真姿态角和姿态角速度的误差对仿真结果的影响。结果表明,在仿真系统各种误差中,气动力和气动力矩误差是主要误差,是引起仿真俯仰角差别的重要原因; 姿态角的动态验模方法不适用于制导炸弹仿真模型,而脱靶量的静态验模方法适用于制导炸弹仿真模型。     

Group sequential crossover trial designs with strong control of the familywise error rate

Crossover designs are an extremely useful tool to investigators, and group sequential methods have proven highly proficient at improving the efficiency of parallel group trials. Yet, group sequential methods and crossover designs have rarely been paired together. One possible explanation for this could be the absence of a formal proof of how to strongly control the familywise error rate in the case when multiple comparisons will be made. Here, we provide this proof, valid for any number of initial experimental treatments and any number of stages, when results are analyzed using a linear mixed model. We then establish formulae for the expected sample size and expected number of observations of such a trial, given any choice of stopping boundaries. Finally, utilizing the four-treatment, four-period TOMADO trial as an example, we demonstrate that group sequential methods in this setting could have reduced the trials expected number of observations under the global null hypothesis by over 33%.     

水文序列相依变异识别的RIC定阶准则——以自回归模型为例

水文过程相依性是水文变异的主要表现形式之一,应用自回归模型对其进行拟合时合理确定模型阶数是一个难点问题。本文在分析AIC和BIC准则的基础上,提出了一种以原序列与其相依成分的相关系数作为拟合度指标,同时借用信息熵形式的函数式,作为模型不确定性度量指标的自回归模型定阶准则(简称RIC准则)。以AR(1)、AR(2)、AR(3)和AR(4)模型为例进行统计试验,将不同序列长度下该准则的定阶准确率与其他定阶准则进行比较,试验结果表明,RIC准则对于上述模型均具有较好的适应性,且定阶准确率远高于AIC准则,其中对于前三阶模型RIC准则优于BIC准则,但四阶模型略低于BIC准则。RIC准则的优势是可以同时满足模型定阶、相依程度分级与模型检验的需求,将其应用于实测水文序列分析,结果显示,该准则能较准确地识别自回归模型的阶数,且符合提出的"相依有变异而残差无变异的最小阶数"的检验标准。     

Modelling consumer choice through the random regret minimization model: An application in the food domain

In line with findings on post-purchase food-choice regret, one can expect that pre-purchase anticipated regret with respect to forgone (non-chosen) alternatives has an impact on consumer food choices, especially when the choice is considered to be important. The traditional Random Utility Maximization (RUM) models for discrete choices may not fully capture this impact. This study investigates the usefulness and potential in the food domain of a discrete choice model that follows the regret minimization principle, the Random Regret Minimization (RRM) model, as an alternative and complement to existing RUM models. The two models are applied to consumer stated choices of cheese in a choice experiment. The study also investigates whether and to what extent a number of personality traits determine whether particular consumers rather choose according to utility-maximization, or regret-minimization principles. Results show that at the aggregate level the two models have a similar goodness of fit to the data and prediction ability. Still, each of them shows better fit for particular subgroups of consumers, based on personality traits. Hence, the present study reveals a potential for the RRM model applications in the food domain, and adds to the empirical literature supporting previous findings on the RRM model found in other contexts. Further research is needed to explore in which situations and for which consumer segments the RRM model is the most useful model.