The impact of climate change on the treatability of dissolved organic matter (DOM) in upland water supplies: A UK perspective

Climate change in the UK is expected to cause increases in temperatures, altered precipitation patterns and more frequent and extreme weather events. In this review we discuss climate effects on dissolved organic matter (DOM), how altered DOM and water physico-chemical properties will affect treatment processes and assess the utility of techniques used to remove DOM and monitor water quality. A critical analysis of the literature has been undertaken with a focus on catchment drivers of DOM character, removal of DOM via coagulation and the formation of disinfectant by-products (DBPs). We suggest that: (1) upland catchments recovering from acidification will continue to produce more DOM with a greater hydrophobic fraction as solubility controls decrease; (2) greater seasonality in DOM export is likely in future due to altered precipitation patterns; (3) changes in species diversity and water properties could encourage algal blooms; and (4) that land management and vegetative changes may have significant effects on DOM export and treatability but require further research. Increases in DBPs may occur where catchments have high influence from peatlands or where algal blooms become an issue. To increase resilience to variable DOM quantity and character we suggest that one or more of the following steps are undertaken at the treatment works: a) ‘enhanced coagulation’ optimised for DOM removal; b) switching from aluminium to ferric coagulants and/or incorporating coagulant aids; c) use of magnetic ion-exchange (MIEX) pre-coagulation; and d) activated carbon filtration post-coagulation. Fluorescence and UV absorbance techniques are highlighted as potential methods for low-cost, rapid on-line process optimisation to improve DOM removal and minimise DBPs.

Neurocognitive control in dance perception and performance

Dance is a rich source of material for researchers interested in the integration of movement and cognition. The multiple aspects of embodied cognition involved in performing and perceiving dance have inspired scientists to use dance as a means for studying motor control, expertise, and action-perception links. The aim of this review is to present basic research on cognitive and neural processes implicated in the execution, expression, and observation of dance, and to bring into relief contemporary issues and open research questions. The review addresses six topics: 1) dancers’ exemplary motor control, in terms of postural control, equilibrium maintenance, and stabilization; 2) how dancers’ timing and on-line synchronization are influenced by attention demands and motor experience; 3) the critical roles played by sequence learning and memory; 4) how dancers make strategic use of visual and motor imagery; 5) the insights into the neural coupling between action and perception yielded through exploration of the brain architecture mediating dance observation; and 6) a neuroaesthetics perspective that sheds new light on the way audiences perceive and evaluate dance expression. Current and emerging issues are presented regarding future directions that will facilitate the ongoing dialogue between science and dance.

Individual differences in early language learning: a study of English learners of French

The present longitudinal study examines the interaction of learner variables (gender, motivation, self-efficacy and first language literacy) and their influence on second language learning outcomes. The study follows English learners of French from Year 5 in primary school (aged 9-10) to the first year in secondary school (Year 7 aged 11-12). Language outcomes were measured by two oral production tasks; a sentence repetition task and a photo description task both of which were administered at three time points. Longitudinal data on learner attitudes and motivation were collected via questionnaires. Teacher assessment data for general first language literacy attainment were also provided. The results show a great deal of variation in learner attitudes and outcomes and that there is a complex relationship between first language literacy, self-efficacy, gender and attainment. For example, in general, girls held more positive attitudes to boys and were more successful. However, the inclusion of first language ability, which explained 30-40% of variation, shows that gender differences in attitudes and outcomes are likely mediated by first language literacy and prior learning experience.

English history teachers’ views on what substantive content young people should be taught

Public and policy discourse about the content of history curricula is frequently contested, but the voice of history teachers is often absent from such debate. Drawing on a large scale on-line survey of history teachers in England, this paper explores their responses to major curriculum reforms proposed by the Coalition government in February 2013. In particular it examines teachers' responses to government plans to prescribe a list of topics, events and individuals to be taught chronologically that all students would be expected to study. Nearly 550 teachers responded to the survey, and more than two-thirds of them provided additional written comments on the curriculum proposals. This paper examines these comments, with reference to a range of curriculum models. The study reveals a deep antagonism towards the proposals for various reasons, including concerns about the extent and nature of the substantive content proposed and the way in which it should be sequenced. Analysis of these reactions provides an illuminating insight into history teachers’ perspectives. While the rationales that underpin their thinking seem to have connections to a variety of different theoretical models, the analysis suggests that more attention could usefully be devoted to the idea of developing frameworks of reference.

A fast adaptive tunable RBF network for nonstationary systems

This paper describes a novel on-line learning approach for radial basis function (RBF) neural network. Based on an RBF network with individually tunable nodes and a fixed small model size, the weight vector is adjusted using the multi-innovation recursive least square algorithm on-line. When the residual error of the RBF network becomes large despite of the weight adaptation, an insignificant node with little contribution to the overall system is replaced by a new node. Structural parameters of the new node are optimized by proposed fast algorithms in order to significantly improve the modeling performance. The proposed scheme describes a novel, flexible, and fast way for on-line system identification problems. Simulation results show that the proposed approach can significantly outperform existing ones for nonstationary systems in particular.