Dating the emergence of dairying by the first farmers of Central Europe using 14C analysis of fatty acids preserved in pottery vessels

Direct, accurate, and precise dating of archaeological pottery vessels is now achievable using a recently developed approach based on the radiocarbon dating of purified molecular components of food residues preserved in the walls of pottery vessels. The method targets fatty acids from animal fat residues, making it uniquely suited for directly dating the inception of new food commodities in prehistoric populations. Here, we report a large-scale application of the method by directly dating the introduction of dairying into Central Europe by the Linearbandkeramik (LBK) cultural group based on dairy fat residues. The radiocarbon dates (n=27) from the 54th century BC from the western and eastern expansion of the LBK suggest dairy exploitation arrived with the first settlers in the respective regions and were not gradually adopted later. This is particularly significant, as contemporaneous LBK sites showed an uneven distribution of dairy exploitation. Significantly, our findings demonstrate the power of directly dating the introduction of new food commodities, hence removing taphonomic uncertainties when assessing this indirectly based on associated cultural materials or other remainsPeer reviewe

Économies et société des populations rubanées de la vallée de l’Aisne

Le projet collectif de recherche, initié en 2007, vise à proposer un premier niveau de synthèse et de modélisation du fonctionnement socio-économique des populations du Néolithique ancien de la vallée de l’Aisne. Ce projet triennal se fonde sur les données accumulées depuis 30 ans sur une quinzaine de sites rubanés fouillés dans le cadre d’un programme pluri-institutionnel sur près de 80 km le long de l’Aisne. Il inclut ainsi le matériel lithique, céramique et faunique des 15 sites rubanés ay..

Predicting energy usage of high dynamic range video on mobile devices

High-end mobile devices now support displaying video in High Dynamic Range (HDR), delivering a significantly enhanced viewing experience over Standard Dynamic Range (SDR). However, more energy may be required to play HDR, impacting device battery life and reducing overall quality of experience. We present a new methodology for predicting the real-time energy usage of a mobile device playing video content. Thirty-seven video clips were encoded into 12 combinations of different resolution, frame-rate, bit-rate, and dynamic range. An external power monitor was used to measure the voltage and current drawn by the device while playing the content. These measurements were used to train a neural network to predict the energy requirements of playing any clip. We show that our model can predict the energy usage of videos with RMS error of 4.88%, achieving a substantial improvement over existing methods that use linear regression, symbolic regression, or trust-region optimisation