Building and testing age models for radiocarbon dates in Lateglacial and Early Holocene sediments

The growing importance of understanding past abrupt climate variability at a regional and global scale has led to the realisation that independent chronologies of past environmental change need to be compared between various archives. This has in turn led to attempts at significant improvements in the required precision at which records can be dated. Radiocarbon dating is still the most prominent method for dating organic material from terrestrial and marine archives, and as such many of the recent developments in improving precision have been aimed at this technique. These include: (1) selection of the most suitable datable fractions within a record, (2) the development of better calibration curves, and (3) more precise age modelling techniques. While much attention has been focussed oil the first two items, testing the possibilities of the relatively new age modelling approaches has not received much attention. Here, we test the potential for methods designed to significantly improve precision in radiocarbon-based age models, wiggle match dating and various forms of Bayesian analyses. We demonstrate that while all of the methods can perform very well, in some scenarios, caution must be taken when applying them. It appears that an integrated approach is required in real life dating situations where more than one model is applied, with strict error calculation, and with the integration of radiocarbon data with sedimentological analyses of site formation processes. (C) 2007 Elsevier Ltd. All rights reserved.

Viscoelastic Material Models of Polypropylene for Thermoforming Applications

Polypropylene (PP), a semi-crystalline material, is typically solid phase thermoformed at temperatures associated with crystalline melting, generally in the 150° to 160°Celsius range. In this very narrow thermoforming window the mechanical properties of the material rapidly decline with increasing temperature and these large changes in properties make Polypropylene one of the more difficult materials to process by thermoforming. Measurement of the deformation behaviour of a material under processing conditions is particularly important for accurate numerical modelling of thermoforming processes. This paper presents the findings of a study into the physical behaviour of industrial thermoforming grades of Polypropylene. Practical tests were performed using custom built materials testing machines and thermoforming equipment at Queen′s University Belfast. Numerical simulations of these processes were constructed to replicate thermoforming conditions using industry standard Finite Element Analysis software, namely ABAQUS and custom built user material model subroutines. Several variant constitutive models were used to represent the behaviour of the Polypropylene materials during processing. This included a range of phenomenological, rheological and blended constitutive models. The paper discusses approaches to modelling industrial plug-assisted thermoforming operations using Finite Element Analysis techniques and the range of material models constructed and investigated. It directly compares practical results to numerical predictions. The paper culminates discussing the learning points from using Finite Element Methods to simulate the plug-assisted thermoforming of Polypropylene, which presents complex contact, thermal, friction and material modelling challenges. The paper makes recommendations as to the relative importance of these inputs in general terms with regard to correlating to experimentally gathered data. The paper also presents recommendations as to the approaches to be taken to secure simulation predictions of improved accuracy.

A New Gradient Descent Approach for Local Learning of Fuzzy Neural Models

The majority of reported learning methods for Takagi-Sugeno-Kang fuzzy neural models to date mainly focus on the improvement of their accuracy. However, one of the key design requirements in building an interpretable fuzzy model is that each obtained rule consequent must match well with the system local behaviour when all the rules are aggregated to produce the overall system output. This is one of the distinctive characteristics from black-box models such as neural networks. Therefore, how to find a desirable set of fuzzy partitions and, hence, to identify the corresponding consequent models which can be directly explained in terms of system behaviour presents a critical step in fuzzy neural modelling. In this paper, a new learning approach considering both nonlinear parameters in the rule premises and linear parameters in the rule consequents is proposed. Unlike the conventional two-stage optimization procedure widely practised in the field where the two sets of parameters are optimized separately, the consequent parameters are transformed into a dependent set on the premise parameters, thereby enabling the introduction of a new integrated gradient descent learning approach. A new Jacobian matrix is thus proposed and efficiently computed to achieve a more accurate approximation of the cost function by using the second-order Levenberg-Marquardt optimization method. Several other interpretability issues about the fuzzy neural model are also discussed and integrated into this new learning approach. Numerical examples are presented to illustrate the resultant structure of the fuzzy neural models and the effectiveness of the proposed new algorithm, and compared with the results from some well-known methods.

Hierarchical Bayesian models in ecology: Reconstructing species interaction networks from non-homogeneous species abundance data

The relationships among organisms and their surroundings can be of immense complexity. To describe and understand an ecosystem as a tangled bank, multiple ways of interaction and their effects have to be considered, such as predation, competition, mutualism and facilitation. Understanding the resulting interaction networks is a challenge in changing environments, e.g. to predict knock-on effects of invasive species and to understand how climate change impacts biodiversity. The elucidation of complex ecological systems with their interactions will benefit enormously from the development of new machine learning tools that aim to infer the structure of interaction networks from field data. In the present study, we propose a novel Bayesian regression and multiple changepoint model (BRAM) for reconstructing species interaction networks from observed species distributions. The model has been devised to allow robust inference in the presence of spatial autocorrelation and distributional heterogeneity. We have evaluated the model on simulated data that combines a trophic niche model with a stochastic population model on a 2-dimensional lattice, and we have compared the performance of our model with L1-penalized sparse regression (LASSO) and non-linear Bayesian networks with the BDe scoring scheme. In addition, we have applied our method to plant ground coverage data from the western shore of the Outer Hebrides with the objective to infer the ecological interactions. (C) 2012 Elsevier B.V. All rights reserved.

Ages of 24 widespread tephras erupted since 30,000 years ago in New Zealand, with re-evaluation of the timing and palaeoclimatic implications of the Lateglacial cool episode recorded at Kaipo bog

Tephras are important for the NZ-INTIMATE project because they link all three records comprising the composite inter-regional stratotype developed for the New Zealand climate event stratigraphy (NZ-CES). Here we firstly report new calendar ages for 24 widespread marker tephras erupted since 30,000 calendar (cal.) years ago in New Zealand to help facilitate their use as chronostratigraphic dating tools for the NZ-CES and for other palaeoenvironmental and geological applications. The selected tephras comprise 12 rhyolitic tephras from Taupo, nine rhyolitic tephras from Okataina, one peralkaline rhyolitic tephra from Tuhua, and one andesitic tephra each from Tongariro and Egmont/Taranaki volcanic centres. Age models for the tephras were obtained using three methods: (i) C-based wiggle-match dating of wood from trees killed by volcanic eruptions (these dates published previously); (ii) flexible depositional modelling of a high-resolution C-dated age-depth sequence at Kaipo bog using two Bayesian-based modelling programs, Bacon and OxCal's P_Sequence function, and the IntCal09 data set (with SH offset correction-44±17yr); and (iii) calibration of C ages using OxCal's Tau_Boundary function and the SHCal04 and IntCal09 data sets. Our preferred dates or calibrated ages for the 24 tephras are as follows (youngest to oldest, all mid-point or mean ages of 95% probability ranges): Kaharoa AD 1314±12; Taupo (Unit Y) AD 232±10; Mapara (Unit X) 2059±118cal.yrBP; Whakaipo (Unit V) 2800±60cal.yrBP; Waimihia (Unit S) 3401±108cal.yrBP; Stent (Unit Q) 4322±112cal.yrBP; Unit K 5111±210cal.yrBP; Whakatane 5526±145cal.yrBP; Tuhua 6577±547cal.yrBP; Mamaku 7940±257cal.yrBP; Rotoma 9423±120cal.yrBP; Opepe (Unit E) 9991±160cal.yrBP; Poronui (Unit C) 11,170±115cal.yrBP; Karapiti (Unit B) 11,460±172cal.yrBP; Okupata 11,767±192cal.yrBP; Konini (bed b) 11,880±183cal.yrBP; Waiohau 14,009±155cal.yrBP; Rotorua 15,635±412cal.yrBP; Rerewhakaaitu 17,496±462cal.yrBP; Okareka 21,858±290cal.yrBP; Te Rere 25,171±964cal.yrBP; Kawakawa/Oruanui 25,358±162cal.yrBP; Poihipi 28,446±670cal.yrBP; and Okaia 28,621±1428cal.yrBP.Secondly, we have re-dated the start and end of the Lateglacial cool episode (climate event NZce-3 in theNZ-CES), previously referred to as the Lateglacial climate reversal, as defined at Kaipo bog in eastern North Island, New Zealand, using both Bacon and OxCal P_Sequence modelling with the IntCal09 data set. The ca1200-yr-long cool episode, indicated by a lithostratigraphic change in the Kaipo peat sequence to grey mudwith lowered carbon content, and a high-resolution pollen-derived cooling signal, began 13,739±125cal.yrBP and ended 12,550±140cal.yrBP (mid-point ages of the 95% highest posterior density regions, Bacon modelling). The OxCal modelling, generating almost identical ages, confirmed these ages. The Lateglacial cool episode (ca 13.8-12.6cal.kaBP) thus overlaps a large part of the entire Antarctic Cold Reversal chronozone (ca 14.1-12.4cal.kaBP or ca 14.6-12.8cal.kaBP), and an early part of the Greenland Stadial-1 (Younger Dryas) chronozone (ca 12.9-11.7cal.kaBP). The timing of the Lateglacial cool episode at Kaipo is broadly consistent with the latitudinal patterns in the Antarctic Cold Reversal signal suggested for the New Zealand archipelago from marine and terrestrial records, and with records from southern South America. © 2012 Elsevier Ltd.