二维Frenkel激子有效质量与温度的关系

本文计及晶格的原子结构讨论了二维Frenkel激子的性质。用二次量子化方法处理晶格振动及其与激子的相互作用,导出了二维Frenkel激子的自陷能以及它的有效质量与温度的关系。     

信息化与西部农业和农村经济发展

西部农业要参与世界市场大循环就必须改变落后面貌。而信息化就是农业和农村经济跨跃式发展的有效途径。信息化可以使农业和农村经济实现可持续发展。农业信息技术的应用,全面改造和提升了西部传统农业。促进西部农业由传统农业向现代农业的现代化转变。信息化使西部农村的信息流、物流、知识流协调发展。促进西部农业和农村经济实现跨跃发展。利用信息平台对农民实施全面的农业现代科技知识培训伞面提升西北农民素质,完成西北农业向农业现代化的转变。     

What is(n’t) a friend? Dimensions of the friendship concept among adolescents

Much research in network analysis of adolescent friendships assumes that friendships represent liking and social interaction, friendships are directed, and friendships are equivalent to one another. This study investigates the meaning of friendship for eight diverse cohorts of sixth graders. Analysis of focus group and survey data suggests that these adolescents construe friendship as a multidimensional role relation composed primarily of relational norms, expectations for mutual behavior. Their friendship definitions may also include mutual liking and interaction, and other structural expectations such as reciprocity, homophily, and transitivity. Lastly, boys and girls weight these dimensions differently in defining friendship.     

教研交互策略在药学类学生科研素养建设中的实践与启示——以福建某医科大学药学类专业教学为例

“培养一流创新人才，产出一流科研成果”和提高“教学科研和社会服务的水平”是创建“双一流”高等院校的重要宗旨。文章从提高本科生科研素养角度，阐述教研交互策略在药学类学生科研素养建设中的实践与启示。该“教研交互”教学法是通过基于与分析化学课程群教学融合，增加科研实践平台和启蒙、长周期、延续性的科研教学内涵，机会，可有效提高学生科研素养水平。强化了学生在教学过程的延续获益，有效提高学生科研素养水平，同时促进学科改革，教学相长。     

弗兰德斯互动分析系统在幼儿园绘本教学中的应用

弗兰德斯互动分析系统被用来记录和分析教学中师生言语互动的过程以及产生的影响，并通过量化的数据分析判断课堂教学的结构、教师教学倾向和教学风格等。但是，在幼儿园绘本教学案例研究中发现，由于弗兰德斯分析系统本身所具有较强的结构性和量化的特点，在一定程度上会影响研究结论的客观性和真实性。为此，我们根据绘本教学的特性提出分工协作、“质量结合”、采集录像、细化编码等优化改进策略。     

教师特征对托班师幼互动质量的影响

构建“数量”和“质量”双重保障的托育服务体系是解决“托育难”“幼无所托”等民生难题以及实现人口发展战略重要配套措施。师幼互动是托育服务质量的核心指标,影响婴幼儿身心发展的多个方面。托班师幼互动质量水平不高,尤其不能提供适宜的学习支持,而教师学历是影响师幼互动质量的重要因素。因此,需要提高托班教师“学历”门槛,建立职前职后“师幼互动”一体化培训体系,重视师幼互动质量的评估,切实提高教师师幼互动能力。     

Stepwise Regression in Mixed Quantitative Linear Models with Autocorrelated Errors

ABSTRACT

In the stepwise procedure of selection of a fixed or a random explanatory variable in a mixed quantitative linear model with errors following a Gaussian stationary autocorrelated process, we have studied the efficiency of five estimators relative to Generalized Least Squares (GLS): Ordinary Least Squares (OLS), Maximum Likelihood (ML), Restricted Maximum Likelihood (REML), First Differences (FD), and First-Difference Ratios (FDR). We have also studied the validity and power of seven derived testing procedures, to assess the significance of the slope of the candidate explanatory variable x ₂ to enter the model in which there is already one regressor x ₁. In addition to five testing procedures of the literature, we considered the FDR t-test with n ? 3 df and the modified t-test with n? ? 3 df for partial correlations, where n? is Dutilleul's effective sample size. Efficiency, validity, and power were analyzed by Monte Carlo simulations, as functions of the nature, fixed vs. random (purely random or autocorrelated), of x ₁ and x ₂, the sample size and the autocorrelation of random terms in the regression model. We report extensive results for the autocorrelation structure of first-order autoregressive [AR(1)] type, and discuss results we obtained for other autocorrelation structures, such as spherical semivariogram, first-order moving average [MA(1)] and ARMA(1,1), but we could not present because of space constraints. Overall, we found that:

1. the efficiency of slope estimators and the validity of testing procedures depend primarily on the nature of x ₂, but not on that of x ₁;

2. FDR is the most inefficient slope estimator, regardless of the nature of x ₁ and x ₂;

3. REML is the most efficient of the slope estimators compared relative to GLS, provided the specified autocorrelation structure is correct and the sample size is large enough to ensure the convergence of its optimization algorithm;

4. the FDR t-test, the modified t-test and the REML t-test are the most valid of the testing procedures compared, despite the inefficiency of the FDR and OLS slope estimators for the former two;

5. the FDR t-test, however, suffers from a lack of power that varies with the nature of x ₁ and x ₂; and

6. the modified t-test for partial correlations, which does not require the specification of an autocorrelation structure, can be recommended when x ₁ is fixed or random and x ₂ is random, whether purely random or autocorrelated. Our results are illustrated by the environmental data that motivated our work.

     

Model-Robust Design of Conjoint Choice Experiments

Within the context of choice experimental designs, most authors have proposed designs for the multinomial logit model under the assumption that only the main effects matter. Very little attention has been paid to designs for attribute interaction models. In this article, three types of Bayesian D-optimal designs for the multinomial logit model are studied: main-effects designs, interaction-effects designs, and composite designs. Simulation studies are used to show that in situations where a researcher is not sure whether or not attribute interaction effects are present, it is best to take into account interactions in the design stage. In particular, it is shown that a composite design constructed by including an interaction-effects model and a main-effects model in the design criterion is most robust against misspecification of the underlying model when it comes to making precise predictions.     

Score tests in the two - way layout of counts

For the balanced two-way layout of a count response variable Y classified by fixed or random factors A and B, we address the problems of (i) testing for individual and interactive effects on Y of two fixed factors, and (ii) testing for the effect of a fixed factor in the presence of a random factor and conversely. In case (i), we assume independent Poisson responses with µ_ij= E(Y| A=i,B=j) = α_iβ_jγ_ij corresponding respectively to the multiplicative

interactive and non-interactive cases. For case (ii) with factor A random, we derive a multivariate gamma-Poisson model by mixing on the random variable associated with each level of A. In each case Neyman C(α) score tests are derived. We present simulation results,and apply the interaction test to a data set, to evaluate and compare the size and power of the score test for interaction between two fixed factors, the competing Poisson-based likelihood ratio test, and the F-tests based on the assumptions that √Y+1 or log(Y+1) are approximately normal. Our results provide strong evidence that the normal-theory based F-tests typically are very far from nominal size, and that the likelihood ratio test is somewhat more liberal than the score test.