基于DEA超效率模型的中国985工程大学效率评价——整合中国大学评价和网大大学排行榜的改进模型

选取34所985一期工程高校为决策单位（DMU）,整合中国大学评价和网大大学排行榜构建指标体系,运用EMS软件进行超效率数据包络分析（SE-DEA）,得出34所高校的超效率得分并进行效率排序,进一步展开松弛变量分析（包括投入冗余指标和产出不足指标分析）,从而对大学绩效的改进和教育资源的优化配置提出政策建议,并对中国高等教育的现有形势和未来发展做总结和展望。     

中国股指期货推出对A股波动影响分析

基于发展较成熟的恒生指数日收益率波动情况,引入股指期货推出前后的虚拟变量,建立ARCH和GARCH模型,检验股指期货推出对股指波动的影响,有利于揭示股指期货的波动性规律,降低市场系统性风险。实证研究结果表明,GARCH（2,1）模型拟合效果最佳;股指期货的推出,不但没有增加股指的波动性,反而对其波动性有所降低,但这种影响从数值来看非常小。     

企业进入、产业波动与就业增长——对我国高技术产业的实证分析

考虑可能影响地区就业增长的微观和宏观因素,利用我国2000—2009年31个省市区高技术产业的数据,通过面板PDLS模型和分位数回归模型实证分析企业进入和产业波动对就业增长的影响,研究结果表明:企业进入对就业增长的影响呈现出波浪形的变化趋势,从短期和长期来看,企业进入对就业增长有正向的促进作用,但从中期来看则表现为负向影响;产业波动对就业增长的影响呈现出先正后负的变化趋势,在短期内产业波动可以促进就业增长,长期内则会抑制就业增长。政府应降低行政性准入壁垒对企业进入的消极影响,营造公平的市场进入环境和完善的产业发展环境。     

基于利用率的检索结果过滤

由于检索结果过于庞大、无效信息太多,用户难以判断信息质量、发现所需信息。为此引鉴信息过滤的全新理念和按点击率对网络资源进行排序的方法,提出对检索结果按用户使用率进行后续处理的新思路。分析实现的可行性并提供了有关计算公式,对存在的问题也提出相应解决方案。     

2010年世界男子锦标赛前16名球队名次排名的回归分析

本文对2010年世界男子篮球锦标赛前16名球队的11项技战术指标进行统计,运用逐步回归分析法和秩相关分析法对所得数据进行分析和检验.结果表明:运用逐步回归分析法对2010年世界男子篮球锦标赛前16名球队的名次排名情况分析具有较高可靠性,能够客观反映出2010年世界男子篮球锦标赛前16名球队的名次排名情况;影响2010年世界男子篮球锦标赛前16名球队名次排名情况的最主要因素是得分和防守篮板两项技术指标;2010年世界男子篮球锦标赛前16名球队可以根据数据分析进行有针对性的技术训练,从而提高球队的名次.     

Effective ranking and search techniques for Web resources considering semantic relationships

On the Semantic Web, the types of resources and the semantic relationships between resources are defined in an ontology. By using that information, the accuracy of information retrieval can be improved.     

非常规油气开发在美国的兴起对全球油气市场的影响

<正>只要人类继续繁衍生息,又需要通过能源来发展经济,那么针对能源的动态博弈就会持续下去。目前,美国经济的首要问题之一仍是如何保障充足的能源需求。2008年,美国关注的能源问题是全球的液化天然气市场,美国被视为几个液化天然气项目的主要目标市场。然而到2014年,非常规油气在美国的能源新格局中占据了重要的位置。如果不出意外,预计到2020年,美国会成为     

Comparing rankings of search results on the Web

The Web has become an information source for professional data gathering. Because of the vast amounts of information on almost all topics, one cannot systematically go over the whole set of results, and therefore must rely on the ordering of the results by the search engine. It is well known that search engines on the Web have low overlap in terms of coverage. In this study we measure how similar are the rankings of search engines on the overlapping results.We compare rankings of results for identical queries retrieved from several search engines. The method is based only on the set of URLs that appear in the answer sets of the engines being compared. For comparing the similarity of rankings of two search engines, the Spearman correlation coefficient is computed. When comparing more than two sets Kendall’s W is used. These are well-known measures and the statistical significance of the results can be computed. The methods are demonstrated on a set of 15 queries that were submitted to four large Web search engines. The findings indicate that the large public search engines on the Web employ considerably different ranking algorithms.     

Measuring scientific contributions with modified fractional counting

We develop and propose a new counting method at the aggregate level for contributions to scientific publications called modified fractional counting (MFC). We show that, compared to traditional complete-normalized fractional counting, it eliminates the extreme differences in contributions over time that otherwise occur between scientists that mainly publish alone or in small groups and those that publish with large groups of co-authors. As an extra benefit we find that scientists in different areas of research turn out to have comparable average contributions to scientific articles. We test the method on scientists at Norway’s largest universities and find that, at an aggregate level, it indeed supports comparability across different co-authorship practices as well as between areas of research. MFC is thereby useful whenever the research output from institutions with different research profiles are compared, as e.g., in the Leiden Ranking. Finally, as MFC is actually a family of indicators, depending on a sensitivity parameter, it can be adapted to the circumstances.     

Ranking scientific articles based on bibliometric networks with a weighting scheme

As the volume of scientific articles has grown rapidly over the last decades, evaluating their impact becomes critical for tracing valuable and significant research output. Many studies have proposed various ranking methods to estimate the prestige of academic papers using bibliometric methods. However, the weight of the links in bibliometric networks has been rarely considered for article ranking in existing literature. Such incomplete investigation in bibliometric methods could lead to biased ranking results. Therefore, a novel scientific article ranking algorithm, W-Rank, is introduced in this study proposing a weighting scheme. The scheme assigns weight to the links of citation network and authorship network by measuring citation relevance and author contribution. Combining the weighted bibliometric networks and a propagation algorithm, W-Rank is able to obtain article ranking results that are more reasonable than existing PageRank-based methods. Experiments are conducted on both arXiv hep-th and Microsoft Academic Graph datasets to verify the W-Rank and compare it with three renowned article ranking algorithms. Experimental results prove that the proposed weighting scheme assists the W-Rank in obtaining ranking results of higher accuracy and, in certain perspectives, outperforming the other algorithms.