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。     

电力市场理论的若干基本概念辨析

本文通过对电力市场、电价与电力交易模式等电力经济基础概念的分析,指出了现有概念体系的不足,并提出了作者的认识,尤其是对电力市场一般与市场特殊作出详尽分析,力图为构建竞争性的电力市场经济理论奠定微观概念的基础。     

基于FCM的动态结合全局图像阈值分割

全局阈值分割对于小目标物效果不理想,动态阈值容易产生阴影等干扰,但综合考虑全局阈值和动态阈值可以达到比较理想的结果。模糊C均值算法用于灰度图像分割是一种非监督模糊聚类后再标定的过程,该文在不明显增加运算量的前提下,利用模糊C均值自动聚类的功能分别得到全局阈值和动态阈值,完成对阈值矩阵的构造和图像的分割。     

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.     

Detection and correction of artificial shifts in climate series

Summary.  Many long instrumental climate records are available and might provide useful information in climate research. These series are usually affected by artificial shifts, due to changes in the conditions of measurement and various kinds of spurious data. A comparison with surrounding weather-stations by means of a suitable two-factor model allows us to check the reliability of the series. An adapted penalized log-likelihood procedure is used to detect an unknown number of breaks and outliers. An example concerning temperature series from France confirms that a systematic comparison of the series together is valuable and allows us to correct the data even when no reliable series can be taken as a reference.     

优化专业结构,提高毕业生就业竞争力

随着社会以及高等教育改革的不断深化,高校毕业生就业形势日益严峻,根据用人单位对大学毕业生综合素质的要求和高校毕业学生对学校教学的意见反馈以及就业工作的现状,高等学校应优化专业结构,调整教学计划和课程设置体系,改革人才培养模式,努力提高毕业生就业竞争力。