中小企业CRM系统的OLAP设计与实现

根据中小企业CRM发展需求,构建了OLAP分析模型,为企业提供决策支持.     

构建以人为本的英语教学模式

传统的英语教育模式忽视了学生的个性意识、个性特点和个性需要,违背了人本主义的教育教学理念。如何提高教学效率一直是外语教师主要探讨的问题。文章运用人本主义的教学理念,结合实际探讨了如何构建以人为本的新型教学模式以改进英语教学,提高教学效率。以人为本的新型教学模式包括:创立民主愉悦的课堂教学环境、构建个性化的学习空间、设计师生共同探索的教学活动、建立协作性的教学模式。     

大学计算机专业“基础原理”课的教学探讨

本文针对现代大学生学习计算机专业“基础原理”课程存在的种种客观问题,通过分析这些问题找出其中原因,并介绍对这些问题所采取的解决方案。教学实践表明,这些方案对学生学习“基础原理课”起到了较好的效果,提高了这类课程的教学质量。     

翻译理论新标准与语言学理论

语言学的发展一直在不断地影响着翻译理论的发展,只不过有时这种影响很间接,尼达的译学思想在中国翻译界一向占据着重要的地位,但这位大师最近宣布其观点已经发生根本变化,尼达学术观点的变化无疑会给目前译学建设带来一定的负面影响。本文着重从翻译理论的作用、翻译标准及语言学与翻译的关系三个角度对尼达翻译思想的转变加以分析;重新阐述理论在译学框架中的地位及作用,理论和实践的关系及翻译理论的意义。     

Generalized additive models for location, scale and shape

Summary.  A general class of statistical models for a univariate response variable is presented which we call the generalized additive model for location, scale and shape (GAMLSS). The model assumes independent observations of the response variable y given the parameters, the explanatory variables and the values of the random effects. The distribution for the response variable in the GAMLSS can be selected from a very general family of distributions including highly skew or kurtotic continuous and discrete distributions. The systematic part of the model is expanded to allow modelling not only of the mean (or location) but also of the other parameters of the distribution of y , as parametric and/or additive nonparametric (smooth) functions of explanatory variables and/or random-effects terms. Maximum (penalized) likelihood estimation is used to fit the (non)parametric models. A Newton–Raphson or Fisher scoring algorithm is used to maximize the (penalized) likelihood. The additive terms in the model are fitted by using a backfitting algorithm. Censored data are easily incorporated into the framework. Five data sets from different fields of application are analysed to emphasize the generality of the GAMLSS class of models.     

造粒塔内液滴传热数学模型的建立

通过在竖直向上流动的空气中逆向运动的颗粒的速率方程．单颗粒与流动气流间的Ranz和Marshall传热关联式．以及能量守恒方程和传热速率方程建立简化的液滴粒径与塔高间的定量关系．从而为研究喷雾造粒工艺提供参考．     

Bayesian analysis of a marked point process: Application in seismic hazard assessment

Point processes are the stochastic models most suitable for describing physical phenomena that appear at irregularly spaced times, such as the earthquakes. These processes are uniquely characterized by their conditional intensity, that is, by the probability that an event will occur in the infinitesimal interval (t, t+Δt), given the history of the process up tot. The seismic phenomenon displays different behaviours on different time and size scales; in particular, the occurrence of destructive shocks over some centuries in a seismogenic region may be explained by the elastic rebound theory. This theory has inspired the so-called stress release models: their conditional intensity translates the idea that an earthquake produces a sudden decrease in the amount of strain accumulated gradually over time along a fault, and the subsequent event occurs when the stress exceeds the strength of the medium. This study has a double objective: the formulation of these models in the Bayesian framework, and the assignment to each event of a mark, that is its magnitude, modelled through a distribution that depends at timet on the stress level accumulated up to that instant. The resulting parameter space is constrained and dependent on the data, complicating Bayesian computation and analysis. We have resorted to Monte Carlo methods to solve these problems.     

信度函数在政府形象评价模型中的应用

在分析政府形象效能评价系统和信息置信度的基础上,建立了基于信度函数的政府形象评价模型。运用该函数建模分析了政府形象评估中的理念识别系统、行为识别系统、视觉识别系统、环境识别系统以及个人识别系统等五大不确定性评价问题。经过理论分析,该函数对政府形象的评估有较大的参考价值。此外,该函数也可以用于对其他类似复杂性系统的效能评估,具有普遍适用性。     

HEAVY-TAILED-DISTRIBUTED THRESHOLD STOCHASTIC VOLATILITY MODELS IN FINANCIAL TIME SERIES

To capture mean and variance asymmetries and time‐varying volatility in financial time series, we generalize the threshold stochastic volatility (THSV) model and incorporate a heavy‐tailed error distribution. Unlike existing stochastic volatility models, this model simultaneously accounts for uncertainty in the unobserved threshold value and in the time‐delay parameter. Self‐exciting and exogenous threshold variables are considered to investigate the impact of a number of market news variables on volatility changes. Adopting a Bayesian approach, we use Markov chain Monte Carlo methods to estimate all unknown parameters and latent variables. A simulation experiment demonstrates good estimation performance for reasonable sample sizes. In a study of two international financial market indices, we consider two variants of the generalized THSV model, with US market news as the threshold variable. Finally, we compare models using Bayesian forecasting in a value‐at‐risk (VaR) study. The results show that our proposed model can generate more accurate VaR forecasts than can standard models.     

Bayesian updating of atmospheric dispersion after a nuclear accident

Summary.  We consider a Bayesian forecasting system to predict the dispersal of contamination on a large scale grid in the event of an accidental release of radioactivity. The statistical model is built on a physical model for atmospheric dispersion and transport called MATCH. Our spatiotemporal model is a dynamic linear model where the state parameters are the (essentially, deterministic) predictions of MATCH; the distributions of these are updated sequentially in the light of monitoring data. One of the distinguishing features of the model is that the number of these parameters is very large (typically several hundreds of thousands) and we discuss practical issues arising in its implementation as a realtime model. Our procedures have been checked against a variational approach which is used widely in the atmospheric sciences. The results of the model are applied to test data from a tracer experiment.