Finding vikings in the Danelaw

Historical, artefactual and place-name evidence indicates that Scandinavian migrants moved to eastern England in the ninth century AD, settling in the Danelaw. However, only a handful of characteristically Scandinavian burials have been found in the region. One, widely held, explanation is that most of these Scandinavian settlers quickly adopted local Christian burial customs, thus leaving Scandinavians indistinguishable from the Anglo-Saxon population. We undertook osteological and isotopic analysis to investigate the presence of first-generation Scandinavian migrants. Burials from Masham were typical of the later Anglo-Saxon period and included men, women and children. The location and positioning of the four adult burials from Coppergate, however, are unusual for Anglo-Scandinavian York. None of the skeletons revealed interpersonal violence. Isotopic evidence did not suggest a marine component in the diet of either group, but revealed migration on a regional, and possibly an international, scale. Combined strontium and oxygen isotope analysis should be used to investigate further both regional and Scandinavian migration in the later Anglo-Saxon period.

Efficiency of evolutionary algorithms in water network pipe sizing

The pipe sizing of water networks via evolutionary algorithms is of great interest because it allows the selection of alternative economical solutions that meet a set of design requirements. However, available evolutionary methods are numerous, and methodologies to compare the performance of these methods beyond obtaining a minimal solution for a given problem are currently lacking. A methodology to compare algorithms based on an efficiency rate (E) is presented here and applied to the pipe-sizing problem of four medium-sized benchmark networks (Hanoi, New York Tunnel, GoYang and R-9 Joao Pessoa). E numerically determines the performance of a given algorithm while also considering the quality of the obtained solution and the required computational effort. From the wide range of available evolutionary algorithms, four algorithms were selected to implement the methodology: a PseudoGenetic Algorithm (PGA), Particle Swarm Optimization (PSO), a Harmony Search and a modified Shuffled Frog Leaping Algorithm (SFLA). After more than 500,000 simulations, a statistical analysis was performed based on the specific parameters each algorithm requires to operate, and finally, E was analyzed for each network and algorithm. The efficiency measure indicated that PGA is the most efficient algorithm for problems of greater complexity and that HS is the most efficient algorithm for less complex problems. However, the main contribution of this work is that the proposed efficiency ratio provides a neutral strategy to compare optimization algorithms and may be useful in the future to select the most appropriate algorithm for different types of optimization problems.