论诗歌翻译中译者再创造的局限性

本文对诗歌翻译中译者再创造的局限性进行了论证与分析。     

明清海禁政策与海上商业力量的消长——以王直、林凤、张元隆海商集团为对象的个案分析

明清时期,国际海上商业环境的变化和国内海洋经济的勃兴促进了海上商业力量的发展和兴盛。武装海商集团的出现则是这种张力的独特表现形式。但同时,明清朝廷对海疆安全愈加关注,并出台了一系列限民防外的海禁政策。16世纪以来,以王直、林凤、郑芝龙为代表的海上商业势力相继叱咤于东南沿海兴盛一时,但由于缺乏适宜生存的社会土壤和发展空间,终至走向败亡。以张元隆为代表的清代海商是和平发展海上贸易的典型,却仍不能见容于清王朝。张氏欲依靠自己的船队走向世界以与西方海上势力争锋,这种锐意宏图最后只能在中国海上商业力量发展史上再增添一幕时代的悲剧。     

Facing Time Limits

Abstract

With reauthorization of the 1996 Personal Responsibility and Work Opportunity Reconciliation Act (PRWORA) on the horizon, policy makers need to have a sound understanding of the act's consequences for children and families. In contrast to earlier studies, the present study quantifies the impact of a 24-month state imposed welfare time limit on foster care placements. It also examines the impact of a “hardship” provision that temporarily excuses select families from meeting a two-year time limit on foster care placements in Nevada. In order to determine how these factors, in conjunction with other relevant welfare experiences and personal factors, affect parent-child separation, a multivariate logit model was tested. Key findings reveal that families who have more months counted toward their time limits also are more likely to have at least one child removed and placed in foster care. On the other hand, hardship families are not more likely to have their children placed in foster care than non-hardship families. The findings lend support to the notion of providing family-centered casework services to those identified to be at risk of approaching their welfare time limits. The findings also lend support to inter-agency collaboration between TANF and child welfare systems.     

Prediction of order statistics and record values from two independent sequences

In this paper, we discuss the problem of predicting future order statistics based on observed record values and similarly, the prediction of future records based on observed order statistics. The coverage probabilities of these intervals are exact and are free of the parent distribution F. Finally, two data sets are used to illustrate the proposed procedures.     

The lindstrom-madden method for serties systems with repeated components

The Lindstrom-Madden method of approximating lower confidence limits for series systems with unlike components is extended to series systems with repeated components utilizing the methods of Buehler, Sudakov and of Harris and Sons. An exact solution is given for no failures and key test results, together with an approximation for the general case. Numerical examples are also provided.     

On the construction of bayesian prediction limits for the weibull distribution

This paper is concerned with a BAYESian construction of the prediction limits for the Weibull distribution as an example of extreme value distributions. Thus, considering Weibull and Uniform distributions for the parameters, the predictive functions, which may lead to approximative evaluation of the prediction limits, is determined by using simulation methods     

国际航空侵权行为法律制度的新发展——以《非法干扰公约》为视角

人类社会步入新世纪以来,面对日益猖獗的恐怖主义局面,国际民航组织启动了航空器对地（水）面第三人损害赔偿制度的现代化（罗马公约现代化）进程,并初步形成了两个赔偿公约草案。与此同时,欧美的航空法学界对此给予了前所未有的重视,可谓百家争鸣。文章全面展示了罗马公约现代化的全景图．点评欧美各家之观点,最后指出,与华沙公约的成功现代化相比,罗马公约现代化要完成理论到实践的转变必定道路崎岖、命运多舛,需要各国政治家们在利益博弈的基础上,充分发挥灵活性找到各方都能接受的方案。     

Bayesian and frequentist prediction limits for the Poisson distribution

Prediction limits for Poisson distribution are useful in real life when predicting the occurrences of some phenomena, for example, the number of infections from a disease per year among school children, or the number of hospitalizations per year among patients with cardiovascular disease. In order to allocate the right resources and to estimate the associated cost, one would want to know the worst (i.e., an upper limit) and the best (i.e., the lower limit) scenarios. Under the Poisson distribution, we construct the optimal frequentist and Bayesian prediction limits, and assess frequentist properties of the Bayesian prediction limits. We show that Bayesian upper prediction limit derived from uniform prior distribution and Bayesian lower prediction limit derived from modified Jeffreys non informative prior coincide with their respective frequentist limits. This is not the case for the Bayesian lower prediction limit derived from a uniform prior and the Bayesian upper prediction limit derived from a modified Jeffreys prior distribution. Furthermore, it is shown that not all Bayesian prediction limits derived from a proper prior can be interpreted in a frequentist context. Using a counterexample, we state a sufficient condition and show that Bayesian prediction limits derived from proper priors satisfying our condition cannot be interpreted in a frequentist context. Analysis of simulated data and data on Atlantic tropical storm occurrences are presented.