DEVELOPMENT OF THE WAVE CONTOURING RAFT.

The wave contouring raft system is the outcome of ideas initiated and developed by Sir Christopher Cockerell from 1972 onwards. His objective was to develop a wave energy device which is within the bounds of current technology. It should consist of simple, relatively small units, amenable to quantity production, which would enable a power generating system to be built up and commissioned in stages according to needs and production capability. This thinking led to the investigation of chains of pontoons, hinged together so that the passage of a wave down the chain causes the pontoons to oscillate relative to one another. Energy is extracted from the sea by applying a torque about the hinges to damp the motion. The work has involved extensive model testing in wave tanks and the building and testing of a 3-unit 1/10 scale power generating installation in the Solent, as well as design studies for a full size installation for Atlantic conditions.

A novel method of extraction of blend component structure from SANS measurements of homopolymer bimodal blends

A new method is presented for the extraction of single-chain form factors and interchain interference functions from a range of small-angle neutron scattering (SANS) experiments on bimodal homopolymer blends. The method requires a minimum of three blends, made up of hydrogenated and deuterated components with matched degree of polymerization at two different chain lengths, but with carefully varying deuteration levels. The method is validated through an experimental study on polystyrene homopolymer bimodal blends with M A≈1/2MB. By fitting Debye functions to the structure factors, it is shown that there is good agreement between the molar mass of the components obtained from SANS and from chromatography. The extraction method also enables, for the first time, interchain scattering functions to be produced for scattering between chains of different lengths. © 2014 The Authors. Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A metal-polymer composite with unusual properties

Electrically conductive composites that contain conductive filler dispersed in an insulating polymer matrix are usually prepared by the vigorous mixing of the components. This affects the structure of the filler particles and thereby the properties of the composite. It is shown that by careful mixing nano-scale features on the surface of the filler particles can be retained. The fillers used possess sharp surface protrusions similar to the tips used in scanning tunnelling microscopy. The electric field strength at these tips is very large and results in field assisted (Fowler-Nordheim) tunnelling. In addition the polymer matrix intimately coats the filler particles and the particles do not come into direct physical contact. This prevents the formation of chains of filler particles in close contact as the filler content increases. In consequence the composite has an extremely high resistance even at filler loadings above the expected percolation threshold. The retention of filler particle morphology and the presence of an insulating polymer layer between them endow the composite with a number of unusual properties. These are presented here together with appropriate physical models. © 2005 IOP Publishing Ltd.

Metal-polymer composite sensors for volatile organic compounds: Part 1. Flow-through chemi-resistors

A new type of chemi-resistor based on a novel metal-polymer composite is described. The composite contains nickel particles with sharp nano-scale surface features, which are intimately coated by the polymer matrix so that they do not come into direct physical contact. No conductive chains of filler particles are formed even at loadings above the percolation threshold and the composite is intrinsically insulating. However, when subjected to compression the composite becomes conductive, with sample resistance falling from ≥ 1012 Ω to < 0.01 Ω. The composite can be formed into insulating granules, which display similar properties to the bulk form. A bed of granules compressed between permeable frits provides a porous structure with a start resistance set by the degree of compression while the granules are free to swell when exposed to volatile organic compounds (VOCs). The granular bed presents a large surface area for the adsorption of VOCs from the gas stream flowing through it. The response of this system to a variety of vapours has been studied for two different sizes of the granular bed and for different matrix polymers. Large responses, ΔR/R0 ≥ 10^7, are observed when saturated vapours are passed through the chemi-resistor. Rapid response allows real time sensing of VOCs and the initial state is recovered in a few seconds by purging with an inert gas stream. The variation in response as a function of VOC concentration is determined.

Syllable language models for Mandarin speech recognition: exploiting character language models.

Mandarin Chinese is based on characters which are syllabic in nature and morphological in meaning. All spoken languages have syllabiotactic rules which govern the construction of syllables and their allowed sequences. These constraints are not as restrictive as those learned from word sequences, but they can provide additional useful linguistic information. Hence, it is possible to improve speech recognition performance by appropriately combining these two types of constraints. For the Chinese language considered in this paper, character level language models (LMs) can be used as a first level approximation to allowed syllable sequences. To test this idea, word and character level n-gram LMs were trained on 2.8 billion words (equivalent to 4.3 billion characters) of texts from a wide collection of text sources. Both hypothesis and model based combination techniques were investigated to combine word and character level LMs. Significant character error rate reductions up to 7.3% relative were obtained on a state-of-the-art Mandarin Chinese broadcast audio recognition task using an adapted history dependent multi-level LM that performs a log-linearly combination of character and word level LMs. This supports the hypothesis that character or syllable sequence models are useful for improving Mandarin speech recognition performance.

Temporal difference models describe higher-order learning in humans.

The ability to use environmental stimuli to predict impending harm is critical for survival. Such predictions should be available as early as they are reliable. In pavlovian conditioning, chains of successively earlier predictors are studied in terms of higher-order relationships, and have inspired computational theories such as temporal difference learning. However, there is at present no adequate neurobiological account of how this learning occurs. Here, in a functional magnetic resonance imaging (fMRI) study of higher-order aversive conditioning, we describe a key computational strategy that humans use to learn predictions about pain. We show that neural activity in the ventral striatum and the anterior insula displays a marked correspondence to the signals for sequential learning predicted by temporal difference models. This result reveals a flexible aversive learning process ideally suited to the changing and uncertain nature of real-world environments. Taken with existing data on reward learning, our results suggest a critical role for the ventral striatum in integrating complex appetitive and aversive predictions to coordinate behaviour.