The Holocene vegetation cover of Britain and Ireland: overcoming problems of scale and discerning patterns of openness

The vegetation of Europe has undergone substantial changes during the course of the Holocene epoch, resulting from range expansion of plants following climate amelioration, competition between taxa and disturbance through anthropogenic activities. Much of the detail of this pattern is understood from
decades of pollen analytical work across Europe, and this understanding has been used to address questions relating to vegetation-climate feedback, biogeography and human impact. Recent advances in modelling the relationship between pollen and vegetation now make it possible to transform pollen
proportions into estimates of vegetation cover at both regional and local spatial scales, using the Landscape Reconstruction Algorithm (LRA), i.e. the REVEALS (Regional Estimates of VEgetation Abundance from Large Sites) and the LOVE (LOcal VEgetation) models. This paper presents the compilation and analysis of 73 pollen stratigraphies from the British Isles, to assess the application of the LRA and describe the pattern of landscape/woodland openness (i.e. the cover of low herb and bushy vegetation) through the Holocene. The results show that multiple small sites can be used as an effective replacement for a single large site for the reconstruction of regional vegetation cover. The REVEALS vegetation estimates imply that the British Isles had a greater degree of landscape/woodland openness at the regional scale than areas on the European mainland. There is considerable spatial bias in the British Isles dataset towards wetland areas and uplands, which may explain higher estimates of landscape openness compared with Europe. Where multiple estimates of regional vegetation are available from within the same region inter-regional differences are greater than intra-regional differences, supporting the use of the REVEALS model to the estimation of regional vegetation from pollen data.

Dry computational approaches for wet medical problems

This is a report on the 4th international conference in 'Quantitative Biology and Bioinformatics in Modern Medicine' held in Belfast (UK), 19-20 September 2013. The aim of the conference was to bring together leading experts from a variety of different areas that are key for Systems Medicine to exchange novel findings and promote interdisciplinary ideas and collaborations.

Distal volcanic ash layers in the Lateglacial Interstadial (GI-1): problems of stratigraphic discrimination

A new Icelandic ash layer has been detected in mid-Interstadial sediments in a number of Scottish Lateglacial sequences and has been named the Penifiler Tephra. It is rhyolitic in composition and possesses a chemistry, which is similar to the Borrobol Tephra of early Lateglacial Interstadial age, which also occurs in a number of these same sequences. Where the Borrobol Tephra has been identified in these sequences it consistently exhibits a diffuse distribution accompanied in some cases by stratigraphic bimodality. A number of sedimentological and taphonomic factors are considered in order to account for this distribution. One possibility is that these distributions are produced by taphonomic factors. Another possibility is that the Borrobol Tephra may not be the product of a single Icelandic eruption, but of two events closely spaced in time. In at least two of the sequences investigated in this study, basaltic shards were found in association with the Penifiler and Borrobol tephras, suggesting either a basaltic phase associated with these eruptions, or coincident eruptions from a separate basaltic volcanic centre. The discovery of the new Penifiler Tephra makes a contribution to the regional tephrostratigraphic framework, and provides an additional isochron for assessing the synchroneity of palaeoenvironmental changes during the Interstadial. The true stratigraphic nature and age of the Borrobol Tephra, however, remains unresolved and, therefore, its use as an isochron is more problematic. The possible occurrence of basaltic populations may strengthen correlations with basaltic tephras recently detected in the NGRIP ice-core.

Pitfalls in Protection: How Theory Can Enrich Our Understanding of Regulatory Compliance Problems in Planning Practice

This paper responds to demands for greater academic investigation into environmental protection, specifically the practical and structural problems which underpin regulatory compliance in the planning system. It critiques traditional theories of regulation and answers calls for the development of a thematic lens to facilitate the scrutiny of not only operational practice, but also the broader institutional regime. An empirical investigation builds upon the construct of really responsive regulation to study planning control and it becomes apparent that not only are there significant procedural planning difficulties facing regulatory compliance, but also that a much wider raft of issues must be considered if the complex equation is to be solved. The findings demonstrate how theory can be applied to enrich our rudimentary understanding of deep-seated problems and foster insights into areas of structural importance which are relevant to both planning and the wider regulatory arena.

Solving Decision Problems with Limited Information

We present a new algorithm for exactly solving decision-making problems represented as an influence diagram. We do not require the usual assumptions of no forgetting and regularity, which allows us to solve problems with limited information. The algorithm, which implements a sophisticated variable elimination procedure, is empirically shown to outperform a state-of-the-art algorithm in randomly generated problems of up to 150 variables and 10^64 strategies.

Semi-Qualitative Probabilistic Networks in Computer Vision Problems

This paper explores the application of semi-qualitative probabilistic networks (SQPNs) that combine numeric and qualitative information to computer vision problems. Our version of SQPN allows qualitative influences and imprecise probability measures using intervals. We describe an Imprecise Dirichlet model for parameter learning and an iterative algorithm for evaluating posterior probabilities, maximum a posteriori and most probable explanations. Experiments on facial expression recognition and image segmentation problems are performed using real data.

Some current problems in perovskite nano-ferroelectrics and multiferroics: kinetically-limited systems of finite lateral size

We describe some unsolved problems of current interest; these involve quantum critical points in
ferroelectrics and problems which are not amenable to the usual density functional theory, nor to
classical Landau free energy approaches (they are kinetically limited), nor even to the Landau–
Kittel relationship for domain size (they do not satisfy the assumption of infinite lateral diameter)
because they are dominated by finite aperiodic boundary conditions.

An integrated approach for real-time model-based state-of-charge estimation of lithium-ion batteries

Lithium-ion batteries have been widely adopted in electric vehicles (EVs), and accurate state of charge (SOC) estimation is of paramount importance for the EV battery management system. Though a number of methods have been proposed, the SOC estimation for Lithium-ion batteries, such as LiFePo4 battery, however, faces two key challenges: the flat open circuit voltage (OCV) vs SOC relationship for some SOC ranges and the hysteresis effect. To address these problems, an integrated approach for real-time model-based SOC estimation of Lithium-ion batteries is proposed in this paper. Firstly, an auto-regression model is adopted to reproduce the battery terminal behaviour, combined with a non-linear complementary model to capture the hysteresis effect. The model parameters, including linear parameters and non-linear parameters, are optimized off-line using a hybrid optimization method that combines a meta-heuristic method (i.e., the teaching learning based optimization method) and the least square method. Secondly, using the trained model, two real-time model-based SOC estimation methods are presented, one based on the real-time battery OCV regression model achieved through weighted recursive least square method, and the other based on the state estimation using the extended Kalman filter method (EKF). To tackle the problem caused by the flat OCV-vs-SOC segments when the OCV-based SOC estimation method is adopted, a method combining the coulombic counting and the OCV-based method is proposed. Finally, modelling results and SOC estimation results are presented and analysed using the data collected from LiFePo4 battery cell. The results confirmed the effectiveness of the proposed approach, in particular the joint-EKF method.