浅谈化学实验教学的改革与创新

化学课程相对于其他的课程而言,实验部分占据了重要的比例。而实验教学的灵活性,给化学课程的教学又提了新的要求。在化学实验中,针对目前存在的一些问题,应该采取相应的措施进行解决,才能真正实现实验教学的创新与改革发展,给化学实验的发展带去新的局面。     

以就业为导向的计算机教学改革

当前计算机专业学生就业形势严峻,出现了需求与供给不对称。为了改变这一现状,在教学中应以市场为导向,加强校企合作。高校要在教学实际中结合自身优势和办学条件,及时调整人才培养方案,建立与信息产业发展相适应的教学内容和课程体系,打造富有特色的计算机专业教育,培养适应市场需求的计算机专业大学生。     

会计电算化研究趋势刍议

会计电算化已广泛应用 ,但是仍存在许多问题 ,将来应在以下几个方面重点研究 ,即会计软件可靠性模型 ,会计软件开发工具 ,利于二次开发的专用软件包的开发 ,Web软件的开发 ,智能化 (分析型 )会计软件的研制 ,软件适应性研究等方面。     

关于高师计算机基础课程教学改革的思考

文章分析了当前高等师范院校计算机基础课程教学的现状，说明高等师范院校计算机基础课程的教学改革势在必行，并提出了改革的思路和措施。     

强化电工学课程教学改革培养学生综合能力

以提高学生的综合能力为目的,根据电工学课程的教学现状,提出了一些对电工学教学改革的看法及改革的具体实施方法.     

《企业管理》课程教学方法改革初探——计算机模拟企业竞争

企业竞争模拟是国际上通行的培养管理人才的最佳方法 ,它是利用计算机进行辅助教学 ,它把参加学习人员进行分组 ,让其分别代表不同企业扮演决策者的角色 ,如生产经理、营销经理、财务经理、人事经理、总经理等让其既有分工 ,又有合作 ,利用所学的管理知识 ,对企业的管理进行运作 ,逐期做出决策 ,锻炼学员的应变能力 ,为今后对企业的管理打下良好的基础。     

Buying Insurance for Disaster-Type Risks: Experimental Evidence

This paper presents a series of experiments that confront subjects with low probability, high loss situations. A rich parameter set is examined and we find subjects respond to low probability, high loss risks in predictable ways. As loss events become more likely, or loss amounts get larger, or the cost of insurance falls, subjects are more likely to buy indemnifying insurance, even for the class of low probability risks that usually presents problems for standard expected utility theory. A novel application of Cameron's method to estimate willingness to pay from dichotomous choice responses allows us to estimate willingness to pay for insurance. We do not observe the bimodal distribution of bids found in other studies of similar risk situations.     

清除计算机病毒对.EXE文件的入侵

分析了计算机中文件型病毒对可执行文件的攻击方式 ,给出了清除方法 ,详细说明了清除步骤     

On approximations to the inverse student’s-t distribution function

The inverse of the Student's t-distribution is often needed in computer simulation and applied statistics, e.g, in generating random variates from t-distributions and in computing tables needed for statistical procedures which do not assume known variances. The t-distribution algorithm of Dudewicz and Dalal (1972) can be used to approximate the inverset t distribution function. The author notes an algorithm for evaluation of this inverse d.f. which can be implemented in a fast, accurate and short computer program. The error analysis is also reported. An application is considered for the problem of testing the hypothesis that a sequence of random variates follows Student's-t distribution.     

Sampling methods and sensitivity analysis for large parameter sets

Models with large parameter (i.e., hundreds or thousands of parameters) often behave as if they depend upon only a few parameters, with the rest having comparatively little influence. One challenge of sensitivity analysis with such models is screening parameters to identify the influential ones, and then characterizing their influences.

Large models often require significant computing resources to evaluate their output, and so a good screening mechanism should be efficient: it should minimize the number of times a model must be exercised. This paper describes an efficient procedure to perform sensitivity analysis on deterministic models with specified ranges or probability distributions for each parameter.

It is based on repeated exercising of the model, which can be treated as a black box. Statistical checks can ensure that the screening identified parameters that account for the bulk of the model variation. Subsequent sensitivity analysis can use the screening information to reduce the investment required to characterize the influence of influential and other parameters.

The procedure exploits simplifications in the dependence of a model output on model inputs. It works best where a small number of parameters are much more influential than all the rest. The method is much more sensitive to the number of influential parameters than to the total number of parameters. It is most effective when linear or quadratic effects dominate higher order effects and complex interactions.

The paper presents a set of M athematica functions that can be used to create a variety of types of experimental designs useful for sensitivity analysis, including simple random, latin hypercube and fractional factorial sampling. Each sampling method can use discretization, folding, grouping and replication to create composite designs. These techniques have beencombined in a composite approach called Iterated Fractional Factorial Design (IFFD).

The procedure is applied to model of nuclear fuel waste disposal, and to simplified example models to demonstrate the concepts involved.