Applications of coupled LC-GC: A review

Although coupled liquid chromatographygas chromatography (LC-GC) was first demonstrated ten years ago, only in the last few years has there been a sudden surge of interest in the technique. Approximately 70% of the total number of LC-GC applications have been published in the last two years (1987–88) alone. This review categorizes LC-GC publications into four main application areas: fossil fuels, foods, environmental samples, biologiical/pharmaceutical samples, and miscellaneous samples. Multidimensional separations carried out using other coupled-column chromatographic techniques (such as supercritical fluid chromatography (SFC) with GC, and on-line trace enrichment-GC) have also been included in this review.     

Fluorescence spectra of different configurations of the diphenyl molecule in solid paraffin solutions

Translated from Zhurnal Prikladnoi Spektroskopii, Vol. 51, No. 2, pp. 212–217, August, 1989.     

A transfer map for singular fibrations

Using a new group of singular chains on polyhedra, a transfer map is defined on the chain level for fibrations with singularities. These include usual transfer on homology as well as Oliver transfer and other cases.     

Positive definite functions on the Heisenberg group

Work supported in part by the NSF     

Error analysis: a tool for selecting travel demand models

In developing travel demand models it is generally assumed that the base-year data used in developing the parameters, as well as the forecasted data to be used as independent variables for the design year, are of acceptable quality. The purpose of this paper is to present the application of error propagation theory in assesing the predictive quality of one type of travel demand forecasting model (multinomial logit models) and to demonstrate how error considerations can be used as a tool for identifying the optimal model. The general conclusions of this study are that: (1) it is indeed possible to quantify errors in dependent variables in logit models as a consequence of errors in independent variables; and (2) error consideration can be used as a tool for identifying the optimal model from a set of candidate models. Further research is recommended to develop better insights into the phenomenon of error propagation so that the consideration of errors can be a factor in decisions on model selection.