New age estimates and climatostratigraphic correlations for the Borrobol and Penifiler Tephras: evidence from Abernethy Forest, Scotland

The emerging tephrostratigraphy of NW Europe spanning the last termination (ca. 15–9 ka) provides the potential for synchronizing marine, ice-core and terrestrial records, but is currently compromised by stratigraphic complications, geochemical ambiguity and imprecise age estimates for some layers. Here we present new tephrostratigraphic, radiocarbon and chironomid-based
palaeotemperature data from Abernethy Forest, Scotland, that refine the ages and stratigraphic positions of the Borrobol and Penifiler tephras. The Borrobol Tephra (14.14–13.95 cal ka BP) was deposited in a relatively warm period equated with Greenland Interstadial sub-stage GI-1e. The younger Penifiler Tephra (14.09–13.65 cal ka BP) is closely associated with a cold oscillation equated with GI-
1d. We also present evidence for a previously undescribed tephra layer that has a major-element chemical signature identical to the Vedde Ash. It is associated with the warming trend at the end of the Younger Dryas, and dates between 11.79 and 11.20 cal ka BP.

Rough Waters, Dark Shadows and Cross-Cutting Winds:the impact of European Union enlargement on Turkey’s EU accession negotiations

For a multiplicity of socio-economic, geo-political, strategic and identity-based reasons, Turkey’s progress towards EU membership is often treated as a sui generis case. Yet although Turkey’s accession negotiations with the European Union (EU) are essentially a bilateral – and often stormy – affair, they take place within a wider and dynamic process of enlargement in which not only can the gloomy – sometimes dark – shadows of past and prospective enlargements be clearly detected, but so too can the often chill winds from ongoing, parallel negotiations with other candidates. How the EU negotiates accession and what it expects from candidates has continued to evolve since the EU began drawing up its framework for negotiations with Turkey ten years ago. This paper charts this evolution by first identifying changes in the light of Croatia’s negotiating experience, the ‘lessons learnt’ by the EU in meeting the challenges of Bulgarian and Romanian accession, the EU’s handling of Iceland’s membership bid and accession negotiations, and the revised approach to negotiating accession evident in the more recent frameworks for accession negotiations with Montenegro and Serbia. The paper then explores the extent to which these changes have impacted on the approach the EU has adopted in framing and progressing accession negotiations with Turkey. In doing so, it questions both the consistency with which the EU’s negotiates accession and the extent to which Turkey’s progress towards EU membership is conditioned by the broader dynamics of EU enlargement as opposed to simply the dynamics within EU-Turkey relations and domestic Turkish reform efforts.

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).

Air pollutant contamination and acidification of surface waters in the North York Moors, UK: Multi-proxy evidence from the sediments of a moorland pool

Despite the extensive geographical range of palaeolimnological studies designed to assess the extent of surface water acidification in the United Kingdom during the 1980s, little attention was paid to the status of surface waters in the North York Moors (NYM). In this paper, we present sediment core data from a moorland pool in the NYM that provide a record of air pollution contamination and surface water acidification. The 41-cm-long core was divided into three lithostratigraphic units. The lower two comprise peaty soils and peats, respectively, that date to between approximately 8080 and 6740 cal. BP. The uppermost unit comprises peaty lake muds dating from between approximately ad 1790 and the present day (ad 2006). The lower two units contain pollen dominated by forest taxa, whereas the uppermost unit contains pollen indicative of open landscape conditions similar to those of the present. Heavy metal, spheroidal carbonaceous particle, mineral magnetics and stable isotope analysis of the upper sediments show clear evidence of contamination by air pollutants derived from fossil-fuel combustion over the last c. 150years, and diatom analysis indicates that the naturally acidic pool became more acidic during the 20th century. We conclude that the exceptionally acidic surface waters of the pool at present (pH=c. 4.1) are the result of a long history of air pollution and not because of naturally acidic local conditions. We argue that the highly acidic surface waters elsewhere in the NYM are similarly acidified and that the lack of evidence of significant recovery from acidification, despite major reductions in the emissions of acidic gases that have taken place over the last c. 30years, indicates the continuing influence of pollutant sulphur stored in catchment peats, a legacy of over 150years of acid deposition.

Predicting forest attributes In southeast Alaska using artificial neural networks

Artificial neural network (ANN) methods are used to predict forest characteristics. The data source is the Southeast Alaska (SEAK) Grid Inventory, a ground survey compiled by the USDA Forest Service at several thousand sites. The main objective of this article is to predict characteristics at unsurveyed locations between grid sites. A secondary objective is to evaluate the relative performance of different ANNs. Data from the grid sites are used to train six ANNs: multilayer perceptron, fuzzy ARTMAP, probabilistic, generalized regression, radial basis function, and learning vector quantization. A classification and regression tree method is used for comparison. Topographic variables are used to construct models: latitude and longitude coordinates, elevation, slope, and aspect. The models classify three forest characteristics: crown closure, species land cover, and tree size/structure. Models are constructed using n-fold cross-validation. Predictive accuracy is calculated using a method that accounts for the influence of misclassification as well as measuring correct classifications. The probabilistic and generalized regression networks are found to be the most accurate. The predictions of the ANN models are compared with a classification of the Tongass national forest in southeast Alaska based on the interpretation of satellite imagery and are found to be of similar accuracy.