RESEARCH UP-DATES

Human Resource Impacts on Food Store Selection and Shopping Loyalty, by Harry F. Krueckberg; The Use of the Multi-Dimensional Database Spreadsheet (VP Planner) to Analyze Supermarket Revenue Data, by Angelo E. DiAntonio, Ulrich C. Toensmeyer; Qualitative Choice Models for Determining Factors Influencing Consumer's Preferences For Package Sizes of Selected Produce Items, by Jean Domanico, Conrado Gempesaw, Richard Bacon, U. Toensmeyer; Consumer Reaction Toward Promotional Tools Used to Induce Soft Drink Purchases in a Supermarket, by James J. Corbett, Wayne Texeira; Demand of Finfish and Shellfish Products Using Scanner Data, by Dr. Oral Capps Jr., Daniel Moen; Assessing Value-Added Agricultural Industries, by Ralph D. Christy, Roger A. Hinson; Size, Profitablility and Growth of Wholesale Food Firms, by Walter B. EppsResearch and Development/Tech Change/Emerging Technologies,

Non-dimensional Star-Identification

This study introduces a new "Non-Dimensional" star identification algorithm to reliably identify the stars observed by a wide field-of-view star tracker when the focal length and optical axis offset values are known with poor accuracy. This algorithm is particularly suited to complement nominal lost-in-space algorithms, which may identify stars incorrectly when the focal length and/or optical axis offset deviate from their nominal operational ranges. These deviations may be caused, for example, by launch vibrations or thermal variations in orbit. The algorithm performance is compared in terms of accuracy, speed, and robustness to the Pyramid algorithm. These comparisons highlight the clear advantages that a combined approach of these methodologies provides.Comment: 17 pages, 10 figures, 4 table

Recueil, traçabilité et restitution des données territoriales du programme ESPON

International audienceThe ESPON M4D Multi-Dimensional Database Design and Development Project entails integrating, verifying and presenting the territorial data produced by ESPON Applied Research Projects. The main challenges that the project faced were managing, standardising and coordinating a wealth of diverse data, ensuring data quality, traceability and creating display tools for territorial data. Implementing specialised methods and tools has provided solutions and opportunities for further analysis. It is clear that centralising the data management and processing helps a wider audience to access this data.Le projet ESPON M4D Multi Dimensional Database Design and Development consiste à intégrer, vérifier et restituer les données territoriales produites par les projets de recherche appliquée du programme européen ESPON. Les principaux enjeux de ce projet consistent à gérer la profusion de données hétérogènes, les normaliser et les harmoniser, évaluer leur qualité, assurer leur traçabilité, créer des outils de suivi et de restitution de ces données. La mise en place de tels méthodes et outils adaptés apportent quelques solutions et pistes de réflexion. Force est de constater que la centralisation de la gestion et du traitement de ces données aide à leur restitution à un très large public

Recueil, traçabilité et restitution des données territoriales du programme ESPON

International audienceThe ESPON M4D Multi-Dimensional Database Design and Development Project entails integrating, verifying and presenting the territorial data produced by ESPON Applied Research Projects. The main challenges that the project faced were managing, standardising and coordinating a wealth of diverse data, ensuring data quality, traceability and creating display tools for territorial data. Implementing specialised methods and tools has provided solutions and opportunities for further analysis. It is clear that centralising the data management and processing helps a wider audience to access this data.Le projet ESPON M4D Multi Dimensional Database Design and Development consiste à intégrer, vérifier et restituer les données territoriales produites par les projets de recherche appliquée du programme européen ESPON. Les principaux enjeux de ce projet consistent à gérer la profusion de données hétérogènes, les normaliser et les harmoniser, évaluer leur qualité, assurer leur traçabilité, créer des outils de suivi et de restitution de ces données. La mise en place de tels méthodes et outils adaptés apportent quelques solutions et pistes de réflexion. Force est de constater que la centralisation de la gestion et du traitement de ces données aide à leur restitution à un très large public