Background: The characterization of three types of Marche (Italy) honeys (Acacia, Multifloral, Honeydew) was carried out on the basis of the their quality parameters (pH, sugar content, humidity) and mineral content (Na, K, Ca, Mg, Cu, Fe, and Mn). Pattern recognition methods such as principal components analysis (PCA) and linear discriminant analysis (LDA) were performed in order to classify honey samples whose botanical origins were different, and identify the most discriminant parameters. Lastly, using ANOVA and correlations for all parameters, significant differences between diverse types of honey were examined.
Purpose This study is intended to investigate the reliability of different incident duration prediction models for real time application with a view to contribute to the development of a decision aid tool within the incident management process context where rough incident duration estimates are currently provided by traffic operators or police on the basis of their skill and past experience. Methods Five predictive models, ranging from parametric models, to non-parametric and neural network models, have been considered and compared evaluating their capacity of predicting incident duration. The data set used in this study for developing and testing the prediction models includes 237 incident events and contains information about the incident characteristics, the personnel and equipment involved to clear the incident and the related response times, including the beginning and ending time of the incident. Results Testing results have demonstrated that the proposed models are able to achieve good performance in terms of prediction accuracy especially for incidents with duration less than 90 min. This finding is partly due to the fact that the dataset has a relatively small number of severe incidents. Furthermore a linear combination of predictions from models was applied with negligible gain in accuracy. Conclusions A deeper investigation is suggested for a future work to evaluate potential improvements from the application of other combination methods. Moreover each proposed model is able to reach best performance for incidents within a particular duration range. Thus a preliminary incident classification scheme could be more convenient in order to select the more appropriate prediction model.
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