基于语料库的“はずだ”语义用法分析

日语众多表示推论的助动词中,"はずだ"使用频率高、表现形式多,往往被翻译为"应该",但其在不同语境下具有多种语义用法,利用语料库对"はずだ"的对译情况、用法分布、接续谓语、助词搭配等进行考察,从推论的前提与结论入手,梳理"推测""反事实""确认""想起"四种主要用法的特征与内在联系,为使用、理解该助动词提供参考。各用法在命题的真实性与形式上有所不同,但在逻辑关系层面又相互关联,表示预定与确认的用法不属于特殊用法,均可用推论的逻辑加以解释,前接谓语的时态性在不同用法中的体现有所偏差。     

Performance analysis and prediction in triathlon

Performance in triathlon is dependent upon factors that include somatotype, physiological capacity, technical proficiency and race strategy. Given the multidisciplinary nature of triathlon and the interaction between each of the three race components, the identification of target split times that can be used to inform the design of training plans and race pacing strategies is a complex task. The present study uses machine learning techniques to analyse a large database of performances in Olympic distance triathlons (2008–2012). The analysis reveals patterns of performance in five components of triathlon (three race “legs” and two transitions) and the complex relationships between performance in each component and overall performance in a race. The results provide three perspectives on the relationship between performance in each component of triathlon and the final placing in a race. These perspectives allow the identification of target split times that are required to achieve a certain final place in a race and the opportunity to make evidence-based decisions about race tactics in order to optimise performance.     

右中立过程的支撑

针对非参数贝叶斯中的右中立过程先验的支撑问题展开了讨论,给出了右中立过程在两种特殊情形下的支撑:当它对应的Levy表示中没有非随机部分时,它以概率1离散;当它对应的Levy测度的支撑为(0,∞)×[0,1]时,支撑是(0,∞)上所有分布函数构成的集合.     

用于炉热状态控制的模糊专家系统设计

正确判断高炉炉况及其变化趋势从而优化操作,是高炉生产的关键技术。文章在科学分析和预测影响炉热状态的诸因素的基础上,根据专家经验,运用模糊推理的原理和方法,重点分析了炉热状态知识信息的获取与表示方法,给出了知识库的建立及模糊推理规则,并研究设计了用于炉热状态控制的模糊专家系统。     

Meta‐Analytic Methodology and Inferences About the Efficacy of Formative Assessment

In a recent article published in EM:IP, Kingston and Nash report on the results of a meta‐analysis on the efficacy of formative assessment. They conclude that the average effect of formative assessment on student achievement is about .20 SD units. This would seem to dispel the myth that effects between .40 and .70 can be attributed to formative assessment. They also find that there is considerable variability in effect sizes across studies, and that only the content area in which the treatment is situated explains a significant proportion of study variability. However, there are issues in the meta‐analytic methodology employed by the authors that make their findings somewhat equivocal. This commentary focuses on four methodological concerns about the Kingston and Nash meta‐analysis: (1) the approach taken to select studies for inclusion, (2) the application of study inclusion criteria, (3) the extent to which the effect sizes being combined are biased, and (4) the relationship between effect size magnitude and characteristics of outcome measures. After examining these issues in the context of the Kingston and Nash review, it appears that considerable uncertainty remains about the effect that formative assessment practices have on student achievement.     

试析关联理论在大学英语教学中的运用

英语教学的目的不只是教给学生基本的语言知识，而应是使他们获得交际能力。关联理论能够指导教师在教学中帮助学生在交际中寻找关联，通过推理，认知交际意图，用英语成功进行交际。     

大、中学生叙事语篇阅读中主题推理能力与协调性整合

研究目的是对比大学生与初中生主题推理能力和协调性整合能力,以及确定协调性整合对主题推理能力是否存在影响。结果表明,大学高主题推理能力组出现了协调性整合,而低主题推理能力组没有出现,这说明协调性整合的确会对主题推理能力产生影响;初中被试无论主题推理能力如何,都没有出现协调性整合。     

Emphasis on emotions in student learning: Analyzing relationships between overexcitabilities and the learning approach using Bayesian MIMIC modeling

The aim of this study is to investigate interrelationships between overexcitability and learning patterns from the perspective of personality development according to Dabrowski’s theory of positive disintegration. To this end, Bayesian structural equation modeling (BSEM) is applied which allows for the simultaneous inclusion in the measurement model of all, approximate zero cross-loadings and residual covariances based on zero-mean, small-variance priors, and represents substantive theory better. Our BSEM analysis with a sample of 516 students in higher education yields positive results regarding the validity of the model, in contrast to a frequentist approach to validation, and reveals that overexcitability – the degree and nature of which is characteristic of the potential for advanced personality development, according to Dabrowski’s theory – is substantially related to the way in which information is processed, as well as to the regulation strategies that are used for this purpose and to study motivation. Overexcitability is able to explain variations in learning patterns to varying degrees, ranging from weakly (3.3% for reproduction-directed learning for the female group) to rather strongly (46.1% for meaning-directed learning for males), with intellectual overexcitability representing the strongest indicator of deep learning. This study further argues for the relevance of including emotion dynamics – taking into account their multilevelness – in the study of the learning process.     

A Systematic Evaluation and Comparison Between Exploratory Structural Equation Modeling and Bayesian Structural Equation Modeling

In this study, we contrast two competing approaches, not previously compared, that balance the rigor of CFA/SEM with the flexibility to fit realistically complex data. Exploratory SEM (ESEM) is claimed to provide an optimal compromise between EFA and CFA/SEM. Alternatively, a family of three Bayesian SEMs (BSEMs) replace fixed-zero estimates with informative, small-variance priors for different subsets of parameters: cross-loadings (CL), residual covariances (RC), or CLs and RCs (CLRC). In Study 1, using three simulation studies, results showed that (1) BSEM-CL performed more closely to ESEM; (2) BSEM-CLRC did not provide more accurate model estimation compared with BSEM-CL; (3) BSEM-RC provided unstable estimation; and (4) different specifications of targeted values in ESEM and informative priors in BSEM have significant impacts on model estimation. The real data analysis (Study 2) showed that the differences in estimation between different models were largely consistent with those in Study1 but somewhat smaller.     

基于Bayesian网络的大型油船货油泵透平驱动装置故障诊断

为提高大型油船货油泵透平驱动装置的安全性,保证船舶的正常航行,通过对大型油船货油泵透平驱动装置结构、工况、故障模式的分析研究,建立故障树模型,并通过Bayesian网络对模型进行分析。建立故障诊断系统,并通过案例对模型进行验证。结果表明,将Bayesian网络应用于货油泵透平驱动装置的故障诊断是合理的,模型是可靠的。