应用多媒体组合教学 提高教学质量

应用“多媒体组合教学设计原理”，改进“化学教学论”课程教学，从理论与实践结合上使教学内容、课程结构、教学方法和教学评价等实行同步改革，使教学过程中的教师、学科内容、媒体、学生等要素的组合和联系方式产生重大改变。     

商陆浆果黄酮类化合物和多糖的提取与测定研究

以商陆浆果为原料,用石油醚去脂-乙醇抽提-甲醇溶解的方法提取了商陆浆果中黄酮类化合物．采用石油醚去脂-热水提取-三氯乙酸法除蛋白-活性炭脱色-乙醇醇析提取、沉淀的提取工艺从商陆浆果中提取多糖,并用苯酚-硫酸比色测定法对其进行定量测定．结果表明：商陆浆果中粗多糖含量为3．77％．用HCl-镁粉、HCl-锌粉热水浴、2％AlCl3-乙醇、1％NaOH溶液和1％FeCl3-乙醇与95％乙醇提取物进行显色反应,呈现黄酮类化合物性质特征颜色;利用紫外分光光度法测定甲醇提取物的紫外吸收光谱,观察到黄酮类化合物的特征吸收峰带,表明样品提取物中含有黄酮类化合物．     

基于教育社会分层视角的农村学生社会流动 与教育选择

随着教育的发展和社会分化的加剧,教育的社会分层功能与农村学生的社会流动日益受到人们关注.当前,农村学生在社会流动与社会地位获得的过程中,由于受到以城市教育为中心的发展战略及重点学校制度的行政约束、高等教育分层和高等教育大众化对文凭筛选功能的影响、高考和就业场域潜规则的干预和农村社会文化价值体系的制约,教育所铺架的向上流动的阶梯出现了明显的短路,农村学生不管在高等教育数量获得还是高等教育质量获得方面,均处于相对劣势;农村大学生也没有从根本上改变自己的命运,他们的阶层地位仍然较低,非正规就业居多、职业地位低、收入低、被限制在边缘行业,结果导致部分农村学生弃考和辍学.     

实证分析二氧化碳排放量主要影响因素

能源是经济与社会发展的基本动力, 但能源燃烧排放二氧化碳所引起的地球温暖化和气候变化更危及人类的生存。采用1990—2009年煤炭、石油、天然气消费结构数据, 电力生产系数, 道琼斯工业指数, 城镇人口数, 国内生产总值及二氧化碳排放量等统计数据进行灰色关联分析和协整检验;反映二氧化碳排放量与城镇人口数, 国内生产总值, 煤炭、石油、天然气消费结构, 电力生产系数之间的规律性变化, 从而突出协整检验的优越性。结果表明:城镇人口数, 国内生产总值, 煤炭、天然气消费结构, 电力生产系数是二氧化碳排放量的主要影响因素;道琼斯工业指数、石油消费结构是次要影响因素。     

Modelling udder infection data using copula models for quadruples

We study copula models for correlated infection times in the four udder quarters of dairy cows. Both a semi-parametric and a nonparametric approach are considered to estimate the marginal survival functions, taking into account the effect of a binary udder quarter level covariate. We use a two-stage estimation approach and we briefly discuss the asymptotic behaviour of the estimators obtained in the first and the second stage of the estimation. A pseudo-likelihood ratio test is used to select an appropriate copula from the power variance copula family that describes the association between the outcomes in a cluster. We propose a new bootstrap algorithm to obtain the p-value for this test. This bootstrap algorithm also provides estimates for the standard errors of the estimated parameters in the copula. The proposed methods are applied to the udder infection data. A small simulation study for a setting similar to the setting of the udder infection data gives evidence that the proposed method provides a valid approach to select an appropriate copula within the power variance copula family.     

Use of log−log survival function in modeling time-covariate interactions in Cox regression

Plotting of log−log survival functions against time for different categories or combinations of categories of covariates is perhaps the easiest and most commonly used graphical tool for checking proportional hazards (PH) assumption. One problem in the utilization of the technique is that the covariates need to be categorical or made categorical through appropriate grouping of the continuous covariates. Subjectivity in the decision making on the basis of eye-judgment of the plots and frequent inconclusiveness arising in situations where the number of categories and/or covariates gets larger are among other limitations of this technique. This paper proposes a non-graphical (numerical) test of the PH assumption that makes use of log−log survival function. The test enables checking proportionality for categorical as well as continuous covariates and overcomes the other limitations of the graphical method. Observed power and size of the test are compared to some other tests of its kind through simulation experiments. Simulations demonstrate that the proposed test is more powerful than some of the most sensitive tests in the literature in a wide range of survival situations. An example of the test is given using the widely used gastric cancer data.     

Robust tests for the common principal components model

When dealing with several populations, the common principal components (CPC) model assumes equal principal axes but different variances along them. In this paper, a robust log-likelihood ratio statistic allowing to test the null hypothesis of a CPC model versus no restrictions on the scatter matrices is introduced. The proposal plugs into the classical log-likelihood ratio statistic robust scatter estimators. Using the same idea, a robust log-likelihood ratio and a robust Wald-type statistic for testing proportionality against a CPC model are considered. Their asymptotic distributions under the null hypothesis and their partial influence functions are derived. A small simulation study allows to compare the behavior of the classical and robust tests, under normal and contaminated data.     

A permutation approach for testing heterogeneity in two-sample categorical variables

In many sciences researchers often meet the problem of establishing if the distribution of a categorical variable is more concentrated, or less heterogeneous, in population P ₁ than in population P ₂. An approximate nonparametric solution to this problem is discussed within the permutation context. Such a solution has similarities to that of testing for stochastic dominance, that is, of testing under order restrictions, for ordered categorical variables. Main properties of given solution and a Monte Carlo simulation in order to evaluate its degree of approximation and its power behaviour are examined. Two application examples are also discussed.     

A new alternative to the standard F test for clustered data

The data collection process and the inherent population structure are the main causes for clustered data. The observations in a given cluster are correlated, and the magnitude of such correlation is often measured by the intra-cluster correlation coefficient. The intra-cluster correlation can lead to an inflated size of the standard F test in a linear model. In this paper, we propose a solution to this problem. Unlike previous adjustments, our method does not require estimation of the intra-class correlation, which is problematic especially when the number of clusters is small. Our simulation results show that the new method outperforms the existing methods.     

Confidence interval estimation of the difference between paired AUCs based on combined biomarkers

In many diagnostic studies, multiple diagnostic tests are performed on each subject or multiple disease markers are available. Commonly, the information should be combined to improve the diagnostic accuracy. We consider the problem of comparing the discriminatory abilities between two groups of biomarkers. Specifically, this article focuses on confidence interval estimation of the difference between paired AUCs based on optimally combined markers under the assumption of multivariate normality. Simulation studies demonstrate that the proposed generalized variable approach provides confidence intervals with satisfying coverage probabilities at finite sample sizes. The proposed method can also easily provide P-values for hypothesis testing. Application to analysis of a subset of data from a study on coronary heart disease illustrates the utility of the method in practice.