浅谈高等学校的学风建设

文章首先论述了高校优良学风的内涵和加强学风建设的重要意义，接着分析了当前高校存在学风不正的现象和主客观原因，并提出了切实加强高校学风建设的措施。     

浅谈影响高等数学学习质量的因素

从学生、老师、社会、学校等方面对影响学习质量的因素进行分析并给出初步解决措施.     

浅谈知识经济条件下的财务管理

阐述了知识经济条件下的财务管理的目标、内容、新观念及新方式。     

再析知识经济时代图书馆建设问题

从知识管理、数字化图书馆建设、人才建设等方面阐明了图书馆应从实际出发。抓住机遇，充分发挥图书馆的职能，更好地为社会主义经济建设服务。     

岗位知识含量指标体系的构建及实证

从工作分析角度入手,利用以PAQ为基础改造的《企业员工生产率调查问卷》,对不同类型的单位及工作岗位上进行知识含量调查,从中归纳出衡量一般岗位知识含量的指标体系,并将岗位按知识含量高低进行分级,初步验证了知识含量不同的岗位之间不同指标维度上的区别,从而为深入研究知识工作生产率问题奠定一个基础.     

因素空间理论与知识表示的数学框架——概念的反馈外延与因素的重合性

本文是文献（１，２）的继续，该文及后续的论文的将系统地研究因素空间理论及其在知识表示中的应用，首先引入反馈外延这一表示概念的新工具，它为表达概念提供了直接的操作方法，然后设计了反馈外延的包络，它是对概念外延的一种逼近，最后讨论了关于概念的重合性重合性测度的公理化定义。     

从私人知识到公共知识的建构

科学知识社会学的出现彻底击碎了"知识(含科学知识)是真理"的传统观念,完成了知识从"发现"到"建构"的变革,但在知识建构过程解释上仍然是混乱的不能系统回答的黑箱,主要是因为没能处理好知识建构的"科学化"与"社会化"两个环节的关系问题,两者混谈导致理论混乱,原因在"科学知识"被等同于"公共知识",根源于"私人知识"与"公共知识"及其互动关系理解上的误区.     

一些分圆域的极大实子域的幂元整基

讨论了分圆域Q(ζn)的极大实子域Q(ζn ζn-1)的幂元整基,其中n∈{5,7,8,9,12,16,20,24),ζn是n次本原单位根．     

Rough sets bankruptcy prediction models versus auditor signalling rates

Both international and US auditing standards require auditors to evaluate the risk of bankruptcy when planning an audit and to modify their audit report if the bankruptcy risk remains high at the conclusion of the audit. Bankruptcy prediction is a problematic issue for auditors as the development of a cause–effect relationship between attributes that may cause or be related to bankruptcy and the actual occurrence of bankruptcy is difficult. Recent research indicates that auditors only signal bankruptcy in about 50% of the cases where companies subsequently declare bankruptcy. Rough sets theory is a new approach for dealing with the problem of apparent indiscernibility between objects in a set that has had a reported bankruptcy prediction accuracy ranging from 76% to 88% in two recent studies. These accuracy levels appear to be superior to auditor signalling rates, however, the two prior rough sets studies made no direct comparisons to auditor signalling rates and either employed small sample sizes or non‐current data. This study advances research in this area by comparing rough set prediction capability with actual auditor signalling rates for a large sample of United States companies from the 1991 to 1997 time period. Prior bankruptcy prediction research was carefully reviewed to identify 11 possible predictive factors which had both significant theoretical support and were present in multiple studies. These factors were expressed as variables and data for 11 variables was then obtained for 146 bankrupt United States public companies during the years 1991–1997. This sample was then matched in terms of size and industry to 145 non‐bankrupt companies from the same time period. The overall sample of 291 companies was divided into development and validation subsamples. Rough sets theory was then used to develop two different bankruptcy prediction models, each containing four variables from the 11 possible predictive variables. The rough sets theory based models achieved 61% and 68% classification accuracy on the validation sample using a progressive classification procedure involving three classification strategies. By comparison, auditors directly signalled going concern problems via opinion modifications for only 54% of the bankrupt companies. However, the auditor signalling rate for bankrupt companies increased to 66% when other opinion modifications related to going concern issues were included. In contrast with prior rough sets theory research which suggested that rough sets theory offered significant bankruptcy predictive improvements for auditors, the rough sets models developed in this research did not provide any significant comparative advantage with regard to prediction accuracy over the actual auditors' methodologies. The current research results should be fairly robust since this rough sets theory based research employed (1) a comparison of the rough sets model results to actual auditor decisions for the same companies, (2) recent data, (3) a relatively large sample size, (4) real world bankruptcy/non‐bankruptcy frequencies to develop the variable classifications, and (5) a wide range of industries and company sizes. Copyright © 2003 John Wiley & Sons, Ltd.