印度表演艺术与敦煌变文讲唱

印度表演艺术与敦煌变文讲唱之间存在很深的渊源关系。二者在授受流变过程中,体现出异质文化融会时的过滤机制。佛教入华,亦带来了印度历史悠久的讲唱艺术,促成中土释门梵呗、转读、唱导、俗讲的发展繁荣。另一方面,梵剧途经西域传入中国,在当地文化影响下一路蜕变,最终在敦煌完全汉化,和变文讲唱融为一体。     

A new heteroscedastic regression to analyze mass loss of wood in civil construction in Brazil

A heteroscedastic regression based on the odd log-logistic Marshall–Olkin normal (OLLMON) distribution is defined by extending previous models. Some structural properties of this distribution are presented. The estimation of the parameters is addressed by maximum likelihood. For different parameter settings, sample sizes and some scenarios, various simulations investigate the performance of the heteroscedastic OLLMON regression. We use residual analysis to detect influential observations and to check the model assumptions. The new regression explains the mass loss of different wood species in civil construction in Brazil.     

PROGRAMMED DECISION STRUCTURES - FUNCTIONAL APPROACH TO QUANTITATIVE UNDERGRADUATE CORE

Administrators and professionals must make social decisions within the environment of the computer revolution. This means that precise skills of logical problem solving and quantitative methods may assume an increasingly prominent place in college preparation for business and social sciences. At the same time, colleges are under pressure not to “select out” persons who are disadvantaged in quantitative areas. With a widening “quantitative gap,” major surgery of existing math and stat courses is unlikely to meet the challenge of this new social environment. Programmed Decisions Structures (PDS) is experimental coursework in a quantitative undergraduate core for business and social sciences. Particular features of PDS were developed to emphasize (1) motivation of the student, (2) credible simulation of realistic business and social environment, (3) awareness of logically developed structures, and (4) principles of applied scientific method. Constructed around the above four points, it is hoped that PDS will close the “quantitative gap” more effectively than the 20 quarter hours of conventional math, stat and computer courses which it replaces.     

AN INDEX FOR THE MEASUREMENT OF CONSUMER LOYALTY*

The concept of consumer “loyalty” (i.e., preference for a particular store or brand) is intriguing to marketing scholars and practitioners. Research in this area would be enhanced by the acceptance of an operational measure of consumer loyalty. Loyalty appears to be a tri-dimensional concept; per cent of budget, allocated to the store or brand, amount of switching, and number of alternatives explored have been used to measure loyalty. Consequently, the authors propose a “loyalty index” which combines these three measures. An empirical test of the proposed index showed that the index did discriminate among degrees of consumer loyalty, and was related to consumer psychological characteristics in a manner similar to that of the generally used single measures of consumer loyalty.     

APPLICATIONS OF SPECTRAL ANALYSIS

The basic concepts and application of spectral analysis are explained. Stationary time series and autocorrelation are first defined. Autocorrelation is related to the familiar concepts of variance and covariance. The use of autocorrelation analysis is explained in estimating the interdependent relationship of a time series over discrete time lags. In order to measure the behavior of the time series using autocorrelation, it would be necessary to examine a very large number of autocorrelation lags. Alternatively, the technique of Fourier analysis can be used to transform the autocorrelation function of the time series into a continuous function, termed a spectrum. The spectrum has a one to one correspondence to the autocorrelation for the time series and has the advantage of representing all possible autocorrelations over the discrete time lags. The spectrum can then be examined as a measure of the behavior of the time series. Spectral analysis indicates the reliability of the analysis of autocorrelated variables when familiar statistical techniques such as sample means and variances are used. The application of spectral analysis to management science problems in three general areas is illustrated: (1) inventory demand, (2) transportation simulation, and (3) stock market price behavior. Spectral analysis was used to detect cycles and trends in the data. Analyses were focused on the spectrum which provides a measure of the relative contribution of cycles in a band of frequencies to the total variance of the data.     

USING DUMMY VARIABLES TO CHECK FOR ROUNDING ERROR IN COMPUTERIZED REGRESSION PROGRAMS*

After a brief review of the role of dummy variables in regression analysis and the current state-of-the art in rounding/truncation error detection in computerized least squares programs, this paper presents a theorem that can be used to detect this type of error whenever an analyst is running a regression program that has one (or more) dummy variables as independent variables.     

EXPLORING MARKET PRICING STRATEGIES VIA DYNAMIC PROGRAMMING

Price determinants as well as strategies can be studies by use of simulation, particularly if cost and price relationships can be related to market activity [1] [9] [11]. But, through the use of dynamic programming, given the market conditions, one can extend the analysis to include an optimal strategy. This paper describes a dynamic programming approach to studying price strategy. A model is developed to show that in a market characterized by cost/volume and price/volume relationships, profitability can be extended beyond that resulting from a dominant market strategy to an optimal maximizing strategy. Extension of the model is suggested for studying (a) sensitivity of a strategy (solution) to price level and cost changes, (b) optimal timing of withdrawal, and (c) present value analysis.     

DETERMINING OPTIMAL SHIFT SCHEDULES FOR TELEPHONE TRAFFIC EXCHANGE OPERATORS

A branch and bound algorithm is presented for determining the minimum number of telephone operators, and their shift schedules, required to meet demand that varies over a 24 hour operating period. An integer linear programming formulation is used, and the algorithm is described in terms of its separation, relaxation, fathoming, and branching procedures. Computational results are provided, using actual operating data. The results indicate that practical sized problems can be solved by the algorithm, involving as many as 100 different shift type variables and demand profiles typical of those encountered in many telephone traffic exchanges.