Later prehistoric vegetation dynamics and Bronze Age agriculture at Hobbister, Orkney, Scotland

The Bronze Age in Britain was a time of major social and cultural changes, reflected in the division of the landscape into field systems and the establishment of new belief systems and ritual practices. Several hypotheses have been advanced to explain these changes, and assessment of many of them is dependent on the availability of detailed palaeoenvironmental data from the sites concerned. This paper explores the development of a later prehistoric landscape in Orkney, where a Bronze Age field system and an apparently ritually-deposited late Bronze Age axe head are located in an area of deep blanket peat from which high-resolution palaeoenvironmental sequences have been recovered. There is no indication that the field system was constructed to facilitate agricultural intensification, and it more likely reflects a cultural response to social fragmentation associated with a more dispersed settlement pattern. There is evidence for wetter conditions during the later Bronze Age, and the apparent votive deposit may reflect the efforts of the local population to maintain community integrity during a time of perceptible environmental change leading to loss of farmland. The study emphasises the advantages of close integration of palaeoenvironmental and archaeological data for interpretation of prehistoric human activity. The palaeoenvironmental data also provide further evidence for the complexity of prehistoric woodland communities in Orkney, hinting at greater diversity than is often assumed. Additionally, differing dates for woodland decline in the two sequences highlight the dangers of over-extrapolation from trends observed in a single pollen profile, even at a very local scale.

SORBS1 gene, a new candidate for diabetic nephropathy: results from a multi-stage genome-wide association study in patients with type 1 diabetes

Aims/hypothesis

Prediction of surface roughness during hard turning of AISI 4340 steel (69 HRC)

In this study, 39 sets of hard turning (HT) experimental trials were performed on a Mori-Seiki SL-25Y (4-axis) computer numerical controlled (CNC) lathe to study the effect of cutting parameters in influencing the machined surface roughness. In all the trials, AISI 4340 steel workpiece (hardened up to 69 HRC) was machined with a commercially available CBN insert (Warren Tooling Limited, UK) under dry conditions. The surface topography of the machined samples was examined by using a white light interferometer and a reconfirmation of measurement was done using a Form Talysurf. The machining outcome was used as an input to develop various regression models to predict the average machined surface roughness on this material. Three regression models - Multiple regression, Random Forest, and Quantile regression were applied to the experimental outcomes. To the best of the authors’ knowledge, this paper is the first to apply Random Forest or Quantile regression techniques to the machining domain. The performance of these models was compared to each other to ascertain how feed, depth of cut, and spindle speed affect surface roughness and finally to obtain a mathematical equation correlating these variables. It was concluded that the random forest regression model is a superior choice over multiple regression models for prediction of surface roughness during machining of AISI 4340 steel (69 HRC).