时间转换对中国学生语篇阅读理解影响的研究

采用探测词再认和照应句阅读时间法的研究范式,运用移动窗口技术,以英语叙事语篇为实验材料,以中国英语学习者提取先前信息的准确度和速度为指标,考察叙事语篇时间转换对第二语言学习者阅读理解加工过程的影响。结果表明,叙事语篇时间转换导致第二语言学习者提取先前信息准确度降低、速度减慢。研究结论进一步验证了强印象假设和事件标记模型理论的普遍性。     

科学的看待高校思想政治教育进网络工作

网络给传统的思想政治教育工作带来冲击,我们应以辩证的眼光,实事求是地看待它,既要反对无用论,又要反对机械论和万能论;在用网络为思想政治教育服务上,坚持网络素养与网络思想政治教育的可持续发展;更为重要的是思想政治教育进网络工作必须确立教育者先行教育理念。     

借鉴先秦重民思想 始终代表人民利益

“重民思想”是先秦时期中国传统政治领导思想的核心,“始终代表人民利益”的思想是世纪之交江泽民总书记创造性地提出的具有时代新蕴的“三个代表”思想之一。二者在出发点、理论根据以及最终目的等方面具有本质的区别,但二者也有相似之处;在任何时候都不能忽视人民的利益,决不能无视民众的愿望和思想。现在重温先秦“重民思想”,可以给我们三点启示。     

邓小平对毛泽东公平效率观的新拓展

公平效率观是社会主义建设理论的重要部分。在新的历史条件下 ,邓小平对毛泽东的公平效率观作出新的拓展 :其一 ,提出“效率优先 ,兼顾公平”的原则 ,找到了实现公平的物质依托。其二 ,主张以效率促公平 ,发现了达到“效率优先 ,兼顾公平”的根本途径。其三 ,倡导“科学技术是第一生产力” ,确定了“效率优先 ,兼顾公平”的实现方式     

从美的产生看事物的使用价值先于其审美价值

生活中,人与人的关系是互助合作关系,在劳动中人类是作为自由创造的主体而存在的。首先人类用自己的劳动创造了事物的实用价值,然后创造了美。因而在美的产生过程中,事物的使用价值先于其审美价值。     

域名抢注法律问题研究

域名抢注引起的域名争议有增无减。在我国有人认为应用商标法和反不正当竞争法来规范域名抢注行为 ,但无法解决由此带来的矛盾 ,近年来我国颁布的相关法规仍不能全面有效地解决域名抢注问题 ,笔者认为应建立解决域名争议的独立法律体系 ,将相关法律法规统一于有权威性的法律之中 ,并吸收“WIPO新规则”中的政策 ,进一步完善相关法律     

From statistical power to statistical assurance: It's time for a paradigm change in clinical trial design

A well-designed clinical trial requires an appropriate sample size with adequate statistical power to address trial objectives. The statistical power is traditionally defined as the probability of rejecting the null hypothesis with a pre-specified true clinical treatment effect. This power is a conditional probability conditioned on the true but actually unknown effect. In practice, however, this true effect is never a fixed value. Thus, we discuss a newly proposed alternative to this conventional statistical power: statistical assurance, defined as the unconditional probability of rejecting the null hypothesis. This kind of assurance can then be obtained as an expected power where the expectation is based on the prior probability distribution of the unknown treatment effect, which leads to the Bayesian paradigm. In this article, we outline the transition from conventional statistical power to the newly developed assurance and discuss the computations of assurance using Monte Carlo simulation-based approach.     

Bayesian change-point problem using Bayes factor with hierarchical prior distribution

We consider the hierarchical Bayesian models of change-point problem in a sequence of random variables having either normal population or skew-normal population. Further, we consider the problem of detecting an influential point concerning change point using Bayes factors. Our proposed models are illustrated with the real data example, the annual flow volume data of Nile River at Aswan from 1871 to 1970. The result using our proposed models indicated the largest influential observation in the year 1888 among outliers. We have shown that it is useful to measure the influence of observations on Bayes factors. Here, we consider omitting single observation as well.     

Planning a Bayesian early-phase phase I/II study for human vaccines in HER2 carcinomas

Recent innovative statistical approaches for phase I/II clinical trials allow one to jointly model the toxicity and efficacy of a new treatment, taking into account the information gathered during the trial. Prior probabilities are then updated with interim data and thus predictive probabilities become more accurate as the trial progresses. In this study, prior distribution elicited from a physician's opinion on the available dose levels planned for a vaccination dose-finding trial, with human DNA in patients with HER2-positive tumours in terms of toxicity and therapeutic response is presented and discussed. A simulation study was conducted in order to quantify the impact of the choice of prior on study results, i.e. the recommended dose level at the end of the trial.     

On the estimation problem of periodic autoregressive time series: symmetric and asymmetric innovations

Periodic autoregressive (PAR) models with symmetric innovations are widely used on time series analysis, whereas its asymmetric counterpart inference remains a challenge, because of a number of problems related to the existing computational methods. In this paper, we use an interesting relationship between periodic autoregressive and vector autoregressive (VAR) models to study maximum likelihood and Bayesian approaches to the inference of a PAR model with normal and skew-normal innovations, where different kinds of estimation methods for the unknown parameters are examined. Several technical difficulties which are usually complicated to handle are reported. Results are compared with the existing classical solutions and the practical implementations of the proposed algorithms are illustrated via comprehensive simulation studies. The methods developed in the study are applied and illustrate a real-time series. The Bayes factor is also used to compare the multivariate normal model versus the multivariate skew-normal model.