傈僳族新创文字研究综述

新傈僳文自1957年创制试行,已历经半个世纪。有关傈僳文的研究积累了翔实的调查资料,并在此基础上取得了可观的研究成果,但尚存在一些不足之处:第一,视野狭窄,方法单一。当前的研究应从多学科的角度,运用多种、多重研究方法,力求在研究中有所创新和突破。第二,描述性研究较多,解释性研究较少。今后的新傈僳文研究应多关注傈僳族文化传承与发展、语言文字多样性保护、文化价值及现实意义等问题。第三,回顾性研究多,前瞻性研究少。学界应对新傈僳文的使用现状进行深层次的前瞻性研究,提出适合现代社会发展规律的、具有应用意义和可操作性的应对策略。     

“江郎才尽”新解

“颠覆阅读”的“难以为继”,在文学史上的印证就是“江郎才尽”。对“江郎才尽”,文学史上有多种解释,此处认为,江淹是以“颠覆阅读”的手段来使自己的诗歌达到一种独特性,故意用一种怪诞或隐晦的文风,用一种奇哨怪仄的意境或修辞手段,来实现自己诗歌的新鲜感甚或冲击力,以震撼人们视觉、听觉,但这种文风难以为继,文学史告诉我们,“颠覆阅读”也是需要创新的。     

广西西江流域工业化发展分析

目前广西西江流域工业化发展处于工业化中前期阶段。西江流域工业化发展有其优势和劣势。充分发挥政府的作用、加快基础设施建设、加快工业园区建设和促进民营工业的发展,是推进西江流域工业化快速发展的战略路径。     

罗马帝国与海盗

为当前打击海盗提供借鉴和参考,就罗马帝国时期海盗问题进行研究。分析认为,海盗的存在有其历史原因和现实原因;当时海盗已影响到罗马帝国的社会稳定和政治安全;罗马帝国采取组建帝国舰队、特设专职官吏等措施打击海盗;蛮族海盗的入侵加速了晚期罗马帝国的灭亡。     

“岂不怀匡济,天运当何如”──论天然禅师的安攘匡济情怀与通脱的应世方式

清初岭南佛门领袖天然禅师,年轻时抱有强烈的报国安民的情怀。虽然在明亡之前即已剃度,但安攘济世之心并未泯灭。甲申、乙酉之后,身为僧人的他,并未真正弃绝世事。他的一些行为仍与变乱的时局密切相关。其后,他在诗歌中不断地抒写着故国丘墟之感。其沧海桑田之作俨然前明遗民。尽管如此,他并没有拒绝与新朝官员的交接。甚至其诗歌中还有一些肯定新朝官员的作品。可以看出天然禅师在应世方式上是相当通脱的。     

孙悟空的名号与《西游记》的主旨

《西游记》中,孙悟空的名号有一个美猴王-孙悟空-弼马温-齐天大圣-孙行者-斗战胜佛的变化,亦可称之为心与魔之间相交的变化。二者相交反映着孙悟空的心路历程,即求心-束心-放心-收心-正心的过程,而这也正是小说的主旨。且心魔相交之说在《西游记》主旨探求过程中始终是一条重要支脉而延绵不断,这与晚明大行其道的心学思潮有着紧密关联。     

荒诞派戏剧的深层特质探析

荒诞派戏剧是后现代主义文学的重要流派,后现代主义文学是一种无视任何既定规范的极度自由的“破坏性”文学,是某种意义上的“反文学”。     

Cox regression analysis in presence of collinearity: an application to assessment of health risks associated with occupational radiation exposure

This paper considers the analysis of time to event data in the presence of collinearity between covariates. In linear and logistic regression models, the ridge regression estimator has been applied as an alternative to the maximum likelihood estimator in the presence of collinearity. The advantage of the ridge regression estimator over the usual maximum likelihood estimator is that the former often has a smaller total mean square error and is thus more precise. In this paper, we generalized this approach for addressing collinearity to the Cox proportional hazards model. Simulation studies were conducted to evaluate the performance of the ridge regression estimator. Our approach was motivated by an occupational radiation study conducted at Oak Ridge National Laboratory to evaluate health risks associated with occupational radiation exposure in which the exposure tends to be correlated with possible confounders such as years of exposure and attained age. We applied the proposed methods to this study to evaluate the association of radiation exposure with all-cause mortality.     

ELICITING A DIRECTED ACYCLIC GRAPH FOR A MULTIVARIATE TIME SERIES OF VEHICLE COUNTS IN A TRAFFIC NETWORK

The problem of modelling multivariate time series of vehicle counts in traffic networks is considered. It is proposed to use a model called the linear multiregression dynamic model (LMDM). The LMDM is a multivariate Bayesian dynamic model which uses any conditional independence and causal structure across the time series to break down the complex multivariate model into simpler univariate dynamic linear models. The conditional independence and causal structure in the time series can be represented by a directed acyclic graph (DAG). The DAG not only gives a useful pictorial representation of the multivariate structure, but it is also used to build the LMDM. Therefore, eliciting a DAG which gives a realistic representation of the series is a crucial part of the modelling process. A DAG is elicited for the multivariate time series of hourly vehicle counts at the junction of three major roads in the UK. A flow diagram is introduced to give a pictorial representation of the possible vehicle routes through the network. It is shown how this flow diagram, together with a map of the network, can suggest a DAG for the time series suitable for use with an LMDM.     

Using a mixture model for multiple imputation in the presence of outliers: the 'Healthy for life' project

Summary.  We consider the problem of obtaining population-based inference in the presence of missing data and outliers in the context of estimating the prevalence of obesity and body mass index measures from the 'Healthy for life' study. Identifying multiple outliers in a multivariate setting is problematic because of problems such as masking, in which groups of outliers inflate the covariance matrix in a fashion that prevents their identification when included, and swamping, in which outliers skew covariances in a fashion that makes non-outlying observations appear to be outliers. We develop a latent class model that assumes that each observation belongs to one of K unobserved latent classes, with each latent class having a distinct covariance matrix. We consider the latent class covariance matrix with the largest determinant to form an 'outlier class'. By separating the covariance matrix for the outliers from the covariance matrices for the remainder of the data, we avoid the problems of masking and swamping. As did Ghosh-Dastidar and Schafer, we use a multiple-imputation approach, which allows us simultaneously to conduct inference after removing cases that appear to be outliers and to promulgate uncertainty in the outlier status through the model inference. We extend the work of Ghosh-Dastidar and Schafer by embedding the outlier class in a larger mixture model, consider penalized likelihood and posterior predictive distributions to assess model choice and model fit, and develop the model in a fashion to account for the complex sample design. We also consider the repeated sampling properties of the multiple imputation removal of outliers.