翻译中心智距离存在的客观性

翻译难是一个众所周知的事实。难的原因有多种,涉及许多内在和外在诸因素,其中有一个不容忽视的成因是译者与原作者之间客观地存在着“心智距离”,即译者在语际传译过程中,受自身诸种内在因素的制约,不可能真正地深入到原作者的内心中去,与原作者同思维、共呼吸,与原作者的意旨彻底复合。当代西方流行的接受美学理论为这一现象作了很好的佐证。     

测试实数编码遗传算法的困难度

分析了遗传算法困难度测试在遗传算法研究中的重要意义. 鉴于当前这方面的研究集 中于二进制编码遗传算法,对实数编码遗传算法的困难度测试进行了分析. 探讨了实数编码遗 传算法困难度分析的适应值与距离相关系数测试法与基于随机游走模型的相关函数测试法, 提出了最佳一阶函数逼近测试法,做了大量实验,并根据实证分析结果对三种方法进行了比较 与评价.     

高校远程教学模式探析

信息技术的迅速发展,促进现代远程教育的教学手段和教学模式不断改革.由于我国许多地区的学校互联网接入条件比较差,造成使用费用较高,从而影响了互联网信息资源的有效传播,以低成本运行网络为前提讨论远程教学模式.     

基于VARK学习风格的远程社会情感学习课程研究

基于VARK学习风格模型,以对应分析方法对IY、第二步、CKCC和积极行动四门远程社会情感学习课程的研究发现：课程学习风格偏好具有差异,其教学活动体现出不同的风格搭配且混合式学习是实现不同学习风格的主要方式。其中,IY偏好视觉和动觉的学习风格,教学活动主要包括观看视频,然后根据指示进行有道具的情境模拟活动,主要以视频课作为远程教学方式;第二步偏好读写的学习风格,教学活动主要包括阅读图片文字材料,然后完成书写类的作业,主要以数字课程作为实现方式;CKCC同样偏好读写的学习风格,教学活动主要包括教材阅读,同时伴随讨论和讲述,主要以直播课为远程教学方式;积极行动偏好听觉的学习风格,更加注重同伴间的讨论和互动,同样以在线直播课程为远程教学方式。这四门课程为其它远程社会情感学习课程开发提供了借鉴,包括课程特色和教学目标与学习风格需匹配,以及混合式学习促进不同学习风格活动效果。     

资源环境约束下中国TFP增长的空间差异和影响因素

运用DDF模型和ML生产率指数,对中国分省资源环境约束下的区域全要素生产率进行测算,采用Theil指数测算资源环境约束下中国全要素生产率增长的地区差异并按照多种空间尺度进行区域分解;构建空间面板数据模型,采用广义空间面板自回归最小二乘法对资源环境约束下全要素生产率增长的影响因素进行实证研究。研究结果表明,在2010年之前,资源环境约束下中国全要素生产率增长的空间差异总体上呈下降态势,地区内而非地区间差距是造成总体空间差异的主要来源。空间计量模型回归估计结果表明,资源环境约束下全要素生产率增长存在显著的正向空间溢出效应,经济发展水平、贸易开放和科技创新水平对资源环境约束下全要素生产率增长存在显著的促进作用,产业结构、能源结构和要素禀赋结构等结构因素对全要素生产率增长存在显著的负向影响,外商直接投资和环境规制水平对全要素生产率增长的影响在统计上并不显著。     

模糊环境下考虑缺货和延期支付的Stackelberg均衡策略

在具有价格弹性需求的两层供应链系统中,考虑到价格弹性指数的模糊性和供应链延期支付策略,建立了含缺货的制造商-零售商协调模型.运用符号距离反模糊化方法将其转化为确定模型.基于遗传算法,设计了其求解方法,获得了制造商最优信用期和零售商最优零售价.数值结果表明:模糊环境下的延期支付策略降低了产品的市场零售价,同时增加了供应链中各成员的利润,因此实现了供应链协调.进一步分析显示当模糊价格弹性指数的上(下)界变化反映出的不确定性减弱时,制造商提供更长的信用期,且制造商和零售商的利润均增加(减少).     

真我型领导与员工创造力:中介性调节机制

有关领导与创造力关系的研究更多关注领导效能的作用,很少探讨真我型领导通过何种机制对创造力产生影响.从自我决定理论视角构建真我型领导影响员工创造力的内在作用机制模型,并以两家民营企业集团中的532名员工及其89名主管为对象,借助中介性调节作用模型及相关技术,验证工作激情和权力距离在真我型领导与员工创造力关系中的中介和调节作用.研究结果表明:1)真我型领导对工作激情和员工创造力均有显著的正向影响;2)工作激情能完全中介真我型领导和员工创造力间关系;3)员工的权力距离感越高,真我型领导对工作激情的正向作用越强,对员工创造力的促进作用也更显著.     

Feature selection based on distance correlation: a filter algorithm

Feature selection (FS) is one of the most powerful techniques to cope with the curse of dimensionality. In the study, a new filter approach to feature selection based on distance correlation is presented (DCFS, for short), which keeps the model-free advantage without any pre-specified parameters. Our method consists of two steps: hard step (forward selection) and soft step (backward selection). In the hard step, two types of associations, between univariate feature and the classes and between group feature and the classes, are involved to pick out the most relevant features with respect to the target classes. Due to the strict screening condition in the first step, some of the useful features are likely removed. Therefore, in the soft step, a feature-relationship gain (like feature score) based on the distance correlation is introduced, which is concerned with five kinds of associations. We sort the feature gain values and implement the backward selection procedure until the errors stop declining. The simulation results show that our method becomes more competitive on several datasets compared with some of the representative feature selection methods based on several classification models.     

Computation of an efficient and robust estimator in a semiparametric mixture model

In this article, we propose an efficient and robust estimation for the semiparametric mixture model that is a mixture of unknown location-shifted symmetric distributions. Our estimation is derived by minimizing the profile Hellinger distance (MPHD) between the model and a nonparametric density estimate. We propose a simple and efficient algorithm to find the proposed MPHD estimation. Monte Carlo simulation study is conducted to examine the finite sample performance of the proposed procedure and to compare it with other existing methods. Based on our empirical studies, the newly proposed procedure works very competitively compared to the existing methods for normal component cases and much better for non-normal component cases. More importantly, the proposed procedure is robust when the data are contaminated with outlying observations. A real data application is also provided to illustrate the proposed estimation procedure.     

A Hellinger distance approach to MCMC diagnostics

Bayesian analysis often requires the researcher to employ Markov Chain Monte Carlo (MCMC) techniques to draw samples from a posterior distribution which in turn is used to make inferences. Currently, several approaches to determine convergence of the chain as well as sensitivities of the resulting inferences have been developed. This work develops a Hellinger distance approach to MCMC diagnostics. An approximation to the Hellinger distance between two distributions f and g based on sampling is introduced. This approximation is studied via simulation to determine the accuracy. A criterion for using this Hellinger distance for determining chain convergence is proposed as well as a criterion for sensitivity studies. These criteria are illustrated using a dataset concerning the Anguilla australis, an eel native to New Zealand.