做好文稿的退修工作

为了确保科技信息准确，快速，广泛地传播，编辑在做文稿退修时须把握好以下几点：1）注意对文稿内容，结构及统计数据的审查与质疑。2）注意对文稿标准化的审查与修改，使题名，摘要，关键词，名词术语，计量单位等符合科技期刊的标准化规范化要求。3）尊重作者劳动，退修意见力求准确，全面，具体，要求明确，确保一次退修成功。     

Using Context Clue in Reading

The context of a word consists of the other words surrounding it and the situation in which it is used.When we come across a new word in our reading,instead of looking it up in a dictionary for its definition,we can guess its meaning from its context.     

春秋战国时期军队的跑跳训练

在冷兵器时代，军队行军强调速度对作战胜负具有极为重要作用。因此，我国古代兵家十分重视加强对军队的体能训练。跑跳项目就是当时训练提高体能的重要手段，在军事理论和军事体育实践中积累了宝贵经验。春秋战国时期军队的跑跳训练便是冷兵器时代军队体能训练的典型。     

对于建设长江三角洲体育圈的战略构想

从苏、浙、沪地理位置和经济、社会、体育的实际情况和发展趋势出发,对于建设长江三角洲体育圈的指导思想、基本框架等方面,提出了一些设想和建议。     

演讲要讲究"三美"

出色的演讲首先形象要美，这样才会给观众留下美好的第一印象．为演讲成功打下良好的基础。演讲本身就是语言的艺术，语言的优劣直接关系到演讲的成败．所以演讲者的语言一定要美，并且演讲者口齿要清楚．控制好发音．使之优美动听．铿锵有力。演讲不但要传播思想，而且还要给人一种美的享受，这就要求演讲者使用准确、协调、优美、灵活自如的态势语言．做到态势美。     

大学生篮球运动员自我暗示语体系的研究

运用走访法、问卷法、实验法和心理测试等方法,初步构建了大学生篮球运动员积极自我暗示语体系,即赛前、赛中和赛后积极自我暗示语共计25条,并对其使用效果进行评价,从整体来讲,使用积极自我暗示语对改善大学生篮球运动员的比赛情景的认知水平是有效的,并且效果有性别差异,有角色重要性差异。     

论叠词和联绵词与陶渊明审美之联系

陶渊明的诗文中运用了大量的叠词和联绵词 ,这不是偶然的现象 ,而是这两类词的召唤特性和陶渊明的审美观的宿命的遇合 ,其结果自然形成了这两类词在陶诗文中的独特表现力     

现实的话语权:一种贯通的精神——从苏轼和赵树理想到的

中国知识分子有一个倔强的性格,那就是始终要拥有话语权,这种性格显现的方式历来不同,但对于现实的热情与关注却是几千年来不变的核心,始终不绝如缕、极其顽强。苏轼与赵树理就是不同时期杰出的代表。然而近些年,这种精神却有所萎缩。     

Unique unto itself: The records of the Federal Bureau of Investigation 1908 to 1945

This paper addresses several misunderstandings about the nature of the FBI's Central Records System. Although depicted in popular culture and much of FBI historiography as a tool for the Cold War suppression of American radicals, the FBI's records system grew out of Department of Justice systems already in place before J. Edgar Hoover became Director in 1924. The system grew in size and complexity according to the demands placed on it by the changing priorities of the FBI's criminal and intelligence missions, guided by a “progressive” drive for efficiency through centralization.     

Automated Classification to Improve the Efficiency of Weeding Library Collections

Previous studies have shown that weeding a library collection benefits patrons and increases circulation rates. However, the time required to review the collection and make weeding decisions presents a formidable obstacle. This study empirically evaluated methods for automatically classifying weeding candidates. A data set containing 80,346 items from a large-scale weeding project running from 2011 to 2014 at Wesleyan University was used to train six machine learning classifiers to predict a weeding decision of either ‘Keep’ or ‘Weed’ for each candidate. The study found statistically significant agreement (p?=?0.001) between classifier predictions and librarian judgments for all classifier types. The naive Bayes and linear support vector machine classifiers had the highest recall (fraction of items weeded by librarians that were identified by the algorithm), while the k-nearest-neighbor classifier had the highest precision (fraction of recommended candidates that librarians had chosen to weed). The variables found to be most relevant were: librarian and faculty votes for retention, item age, and the presence of copies in other libraries.