La Sardegna, unica zona di approvvigionamento in ossidiana per la Corsica durante il Neolitico ? Il caso del sito neolitico medio-finale di A Fuata (NW della Corsica)

International audienc

La Sardegna, unica zona di approvvigionamento in ossidiana per la Corsica durante il Neolitico ? Il caso del sito neolitico medio-finale di A Fuata (NW della Corsica)

International audienc

Western Mediterranean obsidians characterization by SEM-EDS and the Neolithic site of A Fuata (Corsica)

International audienceDuring the Neolithic, obsidians of the Monte Arci (Sardinia) volcanic complex were by far more used in the northern Tyrrhenian area than those of the three other source-islands (Lipari, Palmarola, Pantelleria) in the western Mediterranean. It is shown that merely determinations of content for six major elements with a scanning electron microscope by energy dispersion spectrometry (SEM-EDS) are sufficient to distinguish the four types of Monte Arci obsidians. Because of the compositional similarities between these obsidian types, a multivariate analysis is recommended in provenance studies. Although SEM-EDS, electron microprobe-wavelength dispersion spectrometry (EMP-WDS) and particle induced X-ray emission (PIXE) give essentially concordant results in the determination of these six element contents, subtle technique-related biases prevent the combination of SEM-EDS, EMP-WDS and PIXE data on source samples for provenance purposes. An SEM-EDS test-study reveals the first occurrence of obsidians of Lipari for the A Fuata Middle to Late Neolithic site of NW Corsica (north of Sardinia), in addition to the usual Monte Arci obsidians. Similar to EMP-WDS, the SEM-EDS technique requires only millimeter-sized fragments

Natural and anthropogenic dynamics of the coastal environment in northwestern Corsica (western Mediterranean) over the past six millennia

The present paper provides new insights into the climatic and anthropic factors that influenced a 6000-year coastal evolution in northwestern Corsica, the third largest island of the western Mediterranean. Pollen, microcharcoal, sedimentary and geochemical analyses were carried out on a core drilled in the Crovani coastal wetland to reconstruct the regional drivers of landscape change. We show that anthropogenic and climate-induced fires favoured the development of Mediterranean maquis, dominated by Erica and Quercus ilex, from ca. 6000 to 3350 cal. BP. A change in arboreal vegetation triggered a short but intense sediment input in the Crovani pond between ca. 3350 and 3200 cal. BP. This is consistent with a coeval process of runoff recorded in several coastal sites of western Corsica and related to an arid climate change occurred in many sites of the western Mediterranean around 3200 years ago. We provide evidence of agriculture during the Late Neolithic from ca. 3900 BC, which is much earlier than any archaeological evidence previously available in this area of Corsica, followed by a progressive decline of arable farming practices. Human impact has been responsible for a degradation of the maquis only from approximately 3000 cal. BP, and it intensified in Roman times, when the area experienced the first phase of galena exploitation from the Argentella mines. Over the last 500 years, the present work evidences a major development of Castanea related to cultivation during the Genoese administration of Corsica. Our findings suggest that solar activity and the North Atlantic Oscillation had an influence on centennial-scale forest cover variations during the last 6000 years

An operational bidding framework for aggregated electric vehicles on the electricity spot market

Fluctuating electricity prices offer potential economic savings for the consumption of electricity by flexible assets such as Electric Vehicles (EVs). This study proposes an operational bidding framework that minimizes the charging costs of an EV fleet by submitting an optimized bid to the day-ahead electricity market. The framework consists of a bidding module that determines the most cost-effective bid by considering an electricity price and an EV charging demand forecast module. In this study we develop and evaluate several regression and machine learning models that forecast the electricity price and EV charging demand. Furthermore, we examine the composition of a most optimal operational bidding framework by comparing the outcome of the bidding module when fed with each of the forecast models. This is determined by considering the day-ahead electricity price and imbalance costs due to forecast errors. The study demonstrates that the best performing self-contained forecast models with the objective of electricity price and EV charging demand forecasting, do not deliver the best overall results when included in the bidding framework. Additionally, the results show that the best performing framework obtains a 26% cost savings compared to a reference case where EVs are charged inflexibly. This corresponds to an achieved savings potential of 92%. Consequently, along with the developed bidding framework, these results provide a fundamental basis for effective electricity trading on the day-ahead market.Electrical Engineering, Mathematics and Computer ScienceNumerical Analysi