晚清数学的发展

晚清数学的发展主要是由技术进步的派生需求引导的,数学发展的目标与中体西用的要求之间存在冲突,这种矛盾最终导致中国数学的全盘西化。西学东渐改变了中算知识的结构却未能改变知识价值的结构,数学会通的效率受到考核技术的制约,考核技术的水平受到中体西用的制约。数学概念的进化因而受到阻碍,会通工作陷入效益递减的境地,最终导致中算被完全取代。     

Timing the Use of Breast Cancer Risk Information in Biopsy Decision‐Making

Available clinical evidence is inconclusive on whether radiologists should use the patient risk profile information when interpreting mammograms. On the one hand, risk profile information is informative and can improve radiologists’ performance, but on the other hand, it may impair their judgment by introducing biases in mammography interpretation. Therefore, it is important to assess whether and when profile information use translates into improved outcomes. We model the use of profile information in mammography, using a decision theoretic approach and explore the value of profile information using three process design choices: mammography only, unbiased, and biased reading. We estimate the parameters of our model using clinical data and find that using profile information along with the mammography information can achieve a better performance than not using the profile information. However, the better performance is contingent on the weight assigned to the profile information as well as the extent of bias due to profile information. Translating our findings into clinical practice would require properly designed experiments aiming to quantify the effect of the timing and the use of profile information on performance while accounting for radiologist and patient characteristics. When conducting an experiment is not feasible, a uniform operational sequence for interpreting mammograms and related guidelines may be a useful starting point to improve the quality of mammography operations.     

Planning Implications of the Information Systems Strategic Grid: An Empirical Investigation

In recent years, information systems (IS) planning has come to represent a key IS management tool to practitioners and researchers. Concurrently, there has been increasing recognition by organizations that their IS can be viewed as a strategic resource. This research is an attempt to explore the relationship between these two aspects of IS. It analyzes, in an empirical setting, the contingent nature of some IS planning-related variables in the context of the strategic relevance of an organization's IS. The basis for the empirical test is the IS strategic grid framework developed by Cash, McFarlan, McKenney, and Vitale [6]. A questionnaire survey method is employed to elicit information on the strategic orientation of IS and IS planning issues within respondent organizations. The difference in emphasis on various planning aspects in organizations with different IS environments is analyzed. Results of the analysis indicate that there is empirical support for the strategic grid framework and that there are differences in planning aspects among organizations, depending on their location in the grid. The implications of these findings for IS and IS planning are then discussed.     

On the Performance of Sequential Regression Multiple Imputation Methods with Non Normal Error Distributions

Sequential regression multiple imputation has emerged as a popular approach for handling incomplete data with complex features. In this approach, imputations for each missing variable are produced based on a regression model using other variables as predictors in a cyclic manner. Normality assumption is frequently imposed for the error distributions in the conditional regression models for continuous variables, despite that it rarely holds in real scenarios. We use a simulation study to investigate the performance of several sequential regression imputation methods when the error distribution is flat or heavy tailed. The methods evaluated include the sequential normal imputation and its several extensions which adjust for non normal error terms. The results show that all methods perform well for estimating the marginal mean and proportion, as well as the regression coefficient when the error distribution is flat or moderately heavy tailed. When the error distribution is strongly heavy tailed, all methods retain their good performances for the mean and the adjusted methods have robust performances for the proportion; but all methods can have poor performances for the regression coefficient because they cannot accommodate the extreme values well. We caution against the mechanical use of sequential regression imputation without model checking and diagnostics.     

Telecommunication steering committees and their implications

Rapid advances in telecommunications technology (TT) and its usage have elevated it to a strategic role. This strategic thrust of TT has obvious implications for TT management. This paper reports on a study conducted to identify and analyze the impact of using a distinct steering committee to manage telecommunication technology. Based on statistical analysis of the responses to a survey instrument it is found that having a separate steering committee to manage telecommunication technology has a positive impact on various factors affecting telecommunications usage and management. Implications of our findings for successful TT management are discussed.     

Interorganizational Collaborative Forecasting and Replenishment Systems and Supply Chain Implications

Collaborative Forecasting and Replenishment (CFAR) is a new interorganizational system that enables retailers and manufacturers to forecast demand and schedule production jointly. The capabilities of CFAR exceed those of the traditional EDI because CFAR allows exchange of complex decision support models and manufacturer/retailer strategies. The proponents of CFAR claim that the fastest way for retailers and manufacturers to slash inventory costs and product shortages is by coordinating their decisions. The impact of CFAR on the supply chain using a model consisting of a manufacturer selling its product through two independent identical retailers is analyzed. The model assumes that the manufacturer doesn't face capacity constraints, the demand is stationary, and retailer prices are constant over time; the shortages are backordered at the manufacturer and retailers to isolate the effects of information sharing in CFAR. The most significant findings relate to the increase in the cost incurred by the nonparticipant in CFAR, the increase in the manufacturer's cost of serving the nonparticipant if the participant can obtain delivery guarantees in return for demand information sharing, and the incentives of the players to move towards universal retailer participation in CFAR. The findings are explained using the risk structure faced by the players in the supply chain. The managerial implications of the impact of CFAR on the supply chain structure are also discussed.     

The Use of Stochastic Simulation in Knowledge-Based Systems

Knowledge-based systems support the decision-making process with the help of domain specific knowledge bases. The knowledge bases almost always have uncertainty associated with them. A variety of approaches have been proposed in the artificial intelligence (AI) literature for the construction of and reasoning with uncertain knowledge bases. Building on this stream of research, we focus on how stochastic simulation can be used to construct and reason with knowledge bases that have uncertainties. An advantage of the simulation methodology is that it may not have to make many of the assumptions made by other approaches. It also allows the designer of the knowledge-based system to control the methodology based on accuracy and time requirements. The simulation approach to knowledge base construction is a modified version of the concept induction procedure used in AI. However, it incorporates, as does simulation modeling, statistical tests to identify the best rule that describes the relationship among the variables. We show that when simulation is used to reason with uncertain knowledge bases, under certain conditions, the number of simulation trials needed to achieve a given level of accuracy is independent of the characteristics, such as the size, of the knowledge base. Empirical results obtained from an experiment confirm our theoretical results and provide evidence that simulation methodology is practical for real life knowledge-based systems.