Exploring a Favorable Tradeoff for Finding Every Efficient Path in Large-Scale Networks.

Multiobjective shortest path problem (MSPP) is one of the most critical issues in network optimization, aimed at identifying all efficient paths across conflicting objectives. Nowadays, existing methods face substantial bottlenecks in addressing the diverse preferences of decision makers and high spatiotemporal overhead caused by the calculation process, particularly in cases with large-scale networks. To overcome these obstacles, a generalized MSPP in large-scale networks is investigated with the aim of solving it with diverse preferences of decision makers satisfied and low spatiotemporal overhead.

Assessing the surveillance use of 2018 EFP/AAP classification of periodontitis: A validation study and clustering analysis.

Background: The performance of the 2018 European Federation of Periodontology/American Academy of Periodontology (EFP/AAP) classification of periodontitis for epidemiology surveillance purposes remains to be investigated. This study assessed the surveillance use of the 2018 EFP/AAP classification and its agreement with the unsupervised clustering method compared with the 2012 Centers for Disease Control and Prevention(CDC)/AAP case definition.

Methods: Participants (n = 9424) in the National Health and Nutrition Examination Survey (NHANES) were staged by the 2018 EFP/AAP classification and classified into subgroups via k-medoids clustering.