预设推理与语篇情景组合模式

预设推理是一种以读者经验争认知结构为基础的认知活动.对语篇语义的理解,既需要读者解读语言因素又需要读者的非语言因素的参与,即与语篇相关的预设推理.语篇情景组合就是作者利用先前存入的知识以及认知能力并与文本中的符号产生联系,以互明的认知情境为栽体,在话语之间建构最佳关联,以取得最大语境效果,创设情景的心理认知过程.文章拟从读者在解读语篇时所采用的预设推理方法以及作者在建构语篇时所采用的语篇情景组合模式两方面来探讨含义推导的心理认知过程.     

试从关联理论角度看语用翻译

文章根据关联理论的明示-推理交际模式,提出了语用翻译实际上是双明示-推理的交际过程,是涉及到原文作者、译者、译文读者三个交际主体的动态的三元翻译,并探讨了认知语用学的关联理论对语用翻译的启发和指导意义,进而尝试将关联理论的研究成果应用于解决翻译实践中的文化、语用类问题。     

战略管理视角下的供应链绩效评价研究

传统的供应链绩效评价指标体系一般着重评价整个供应链效率或者评价节点企业而忽视了各个节点企业之间的联系、影响和制约。本文从建立学习型供应链企业战略的视角出发,构建以客户绩效、社会绩效、财务绩效和学习绩效为一级评价指标的供应链绩效评价指标体系。在评价方法上采用了层次分析法和灰色关联分析相结合的模型,对四个模拟供应链节点企业的实际数据进行分析,证实该指标评价体系和数学方法在实际操作中是可行的,比较准确地反映了供应链节点企业运营的实际效果。     

多焊接电源控制技术

现代的膜式水冷壁制造工艺要求多达２０台焊接电源同时工作,当外网原因使车间供电电压波动的时候,焊接电源群同时进行调整。群焊接电源的冲击性载荷对电网进行反激,导致电网的更大波动。文章引入优先级的概念,将自适应神经模糊推理系统(ANFIS)应用到焊接电源群的优先级控制上,设计了自适应神经模糊推理网络,并利用MATLAB平台进行整个焊接系统的建模和仿真。结果表明应用该优先级群控策略,各焊机调整能力加强,调整时间缩短,动态性能得到改善。     

基于FAHP的农村超市服务质量评价模型研究

随着供需条件的变化和市场竞争的日益加剧,服务质量已经成为农村超市生存和发展的关键因素。在构建农村超市服务指标体系的基础上,运用模糊层次分析法（FAHP）构建了农村超市服务质量综合评价模型,并通过实际算例对模型的应用和适应性进行了验证。结果表明该模型所得结论具有较高的可信度,能够对农村超市服务质量进行全面评价,为农村超市服务质量管理明确重点和方向。     

Averaging Lorenz curves

A large number of functional forms has been suggested in the literature for estimating Lorenz curves that describe the relationship between income and population shares. The traditional way of overcoming functional-form uncertainty when estimating a Lorenz curve is to choose the function that best fits the data in some sense. In this paper we describe an alternative approach for accommodating functional-form uncertainty, namely, how to use Bayesian model averaging to average the alternative functional forms. In this averaging process, the different Lorenz curves are weighted by their posterior probabilities of being correct. Unlike a strategy of picking the best-fitting function, Bayesian model averaging gives posterior standard deviations that reflect the functional-form uncertainty. Building on our earlier work (Chotikapanich and Griffiths, 2002), we construct likelihood functions using the Dirichlet distribution and estimate a number of Lorenz functions for Australian income units. Prior information is formulated in terms of the Gini coefficient and the income shares of the poorest 10% and poorest 90% of the population. Posterior density functions for these quantities are derived for each Lorenz function and are averaged over all the Lorenz functions.     

毛泽东诗词的历史观解读

毛泽东的历史观不仅以理论形态见诸于他的理论著作,同时也蕴含于他的大量诗词作品中。毛泽东诗词中的历史观是其唯物史观的诗化反映,它表现为革命史观、发展史观和人民史观的高度融合。     

Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations

Summary.  Structured additive regression models are perhaps the most commonly used class of models in statistical applications. It includes, among others, (generalized) linear models, (generalized) additive models, smoothing spline models, state space models, semiparametric regression, spatial and spatiotemporal models, log-Gaussian Cox processes and geostatistical and geoadditive models. We consider approximate Bayesian inference in a popular subset of structured additive regression models, latent Gaussian models , where the latent field is Gaussian, controlled by a few hyperparameters and with non-Gaussian response variables. The posterior marginals are not available in closed form owing to the non-Gaussian response variables. For such models, Markov chain Monte Carlo methods can be implemented, but they are not without problems, in terms of both convergence and computational time. In some practical applications, the extent of these problems is such that Markov chain Monte Carlo sampling is simply not an appropriate tool for routine analysis. We show that, by using an integrated nested Laplace approximation and its simplified version, we can directly compute very accurate approximations to the posterior marginals. The main benefit of these approximations is computational: where Markov chain Monte Carlo algorithms need hours or days to run, our approximations provide more precise estimates in seconds or minutes. Another advantage with our approach is its generality, which makes it possible to perform Bayesian analysis in an automatic, streamlined way, and to compute model comparison criteria and various predictive measures so that models can be compared and the model under study can be challenged.     

SEMIPARAMETRIC REGRESSION AND GRAPHICAL MODELS

Semiparametric regression models that use spline basis functions with penalization have graphical model representations. This link is more powerful than previously established mixed model representations of semiparametric regression, as a larger class of models can be accommodated. Complications such as missingness and measurement error are more naturally handled within the graphical model architecture. Directed acyclic graphs, also known as Bayesian networks, play a prominent role. Graphical model-based Bayesian 'inference engines', such as bugs and vibes , facilitate fitting and inference. Underlying these are Markov chain Monte Carlo schemes and recent developments in variational approximation theory and methodology.     

On reliability estimation based on fuzzy lifetime data

Real lifetime data are never precise numbers but more or less non-precise, also called fuzzy. This kind of imprecision is connected with all measurement results of continuous variables, therefore also with time observations. Imprecision is different from errors and variability. Therefore estimation methods for reliability characteristics have to be adapted to the situation of fuzzy lifetimes in order to obtain realistic results.