The magnetic exchange interactions in chromium chalcogenide spinels

The chromium chalcogenide spinels, MCr2X4 (M = Zn, Cd, Hg; X = O, S, Se), have been the subject of considerable interest in recent years. In each case the crystal structure is that of a normal spinel with the chromium ions exclusively occupying the octahedral (B) sites, so that when diamagnetic ions are located at the tetrahedral (A) sites the only magnetic interactions present are those between B-site ions. Despite such apparently simple circumstances a rich variety of magnetic behaviour is exhibited. For the oxides the ground state spin configurations are antiferromagnetic whilst for the selenides ferromagnetic interactions dominate and several authors have drawn attention to the fact that the nature of the dominant interaction is a function of the nearest neighbour chromium - chromium separation. However, at least two of the compounds exhibit spiral structures and it has been proved difficult to account for the various spin configurations within a unified theory of the magnetic interactions involved. More recently, the possibility of formulating a simplified interpretation of the magnetic interactions has been provided by the discovery that the crystal struture of spinels does not always conform to the centrosymmetrical symmetry Fd3m that has been conventionally assumed. The deviation from this symmetry is associated with small < 111> displacements of the octahedrally coordinated metal ions and the structures so obtained are more correctly referred to the non-centrosymmetrical space group F43m. In the present study, therefore, extensive X-ray diffraction data have been collected from four chromium chalcogenide specimens and used to refine the corresponding structural parameters assuming F43m symmetry and also with conventional symmetry. The diffracted intensities from three of the compounds concerned cannot be satisfactorily accounted for on the basis of conventional symmetry and new locations have been found for the chromium ions in these cases. It is shown, however, that these displacements in chromium positions only partially resolve the difficulties in interpreting the magnetic behaviour. A re-examination of the magnetic data from different authors indicates much greater uncertainty in their measurements than they had claimed. By taking this into consideration it is shown that a unified theory of magnetic behaviour for the chromium chalcogenide spinels is a real possibility.

A preliminary study into emergent behaviours in a lattice of interacting nonlinear resonators and oscillators

Future sensor arrays will be composed of interacting nonlinear components with complex behaviours with no known analytic solutions. This paper provides a preliminary insight into the expected behaviour through numerical and analytical analysis. Specically, the complex behaviour of a periodically driven nonlinear Duffing resonator coupled elastically to a van der Pol oscillator is investigated as a building block in a 2D lattice of such units with local connectivity. An analytic treatment of the 2-device unit is provided through a two-time-scales approach and the stability of the complex dynamic motion is analysed. The pattern formation characteristics of a 2D lattice composed of these units coupled together through nearest neighbour interactions is analysed numerically for parameters appropriate to a physical realisation through MEMS devices. The emergent patterns of global and cluster synchronisation are investigated with respect to system parameters and lattice size.

The influence of non-metallic inclusions upon the properties of linepipe steels

The principal aim of this work was to determine the role of non-metallic inclusions in the process of hydrogen stepwise cracking (SWC). Additionally, the influence of inclusions upon the notch ductility of hydrogen charged (HC) and uncharged (UN) tensile specimens was examined. To obtain a basis for experiment a series of low carbon-manganese steels were prepared by induction melting. In order to produce variations in the composition, morphology, volume fraction, size and distribution of the inclusions the steel chemistry was adjusted prior to casting by additions of deoxidiser and Ca-Si injection. Sections of each ingot were hot rolled. Metallography, image analysis, mechanical tests and hydrogen SWC tests were then carried out. The volume fraction, morphology, and shape of inclusions influenced the tensile ductility of the steels. Marked anisotropy was found in the steels containing type II MnS inclusions at all rolling temperatures, whereas the fully Ca treated steel was isotropic. It was found that several inclusion parameters (projected length PL, mean free distance MFD, nearest-neighbour distance NND) correlated with fracture strain. An increase in inclusion volume fraction and/or the dimension of inclusions on a plane parallel to the plane of fracture led to a decrease in fracture strain. The inclusion parameters did not correlate with the fracture strains for the HC tensile specimens. However, large or clusters of inclusions acted as the principal sites for crack initiation. `Fisheyes' or areas of `flat' fracture were often found on these fracture surfaces. The criteria for SWC initiation was found to be either large inclusions or clusters of inclusions. As the PL of inclusions increased the probability of large SWCs occurring increased. SWC initiation at inclusions was believed to occur at a critical concentration of hydrogen. Factors which assisted the concentration of hydrogen at inclusions were discussed. None of the proposed mechanisms of hydrogen embrittlement could be identified as the single cause of SWC.

Persistence in a random bond ising model of socio-econo dynamics

We study the persistence phenomenon in a socio-econo dynamics model using computer simulations at a nite temperature on hypercubic lattices in dimensions up to ve. The model includes a \social" local eld which contains the magnetization at time t. The nearest neighbour quenched interactions are drawn from a binary distribution which is a function of the bond concentration, p. The decay of the persistence probability in the model depends on both the spatial dimension and p. We nd no evidence of \blocking" in this model. We also discuss the implications of our results for possible applications in the social and economic elds. It is suggested that the absence, or otherwise, of blocking could be used as a criterion to decide on the validity of a given model in dierent scenarios.

A molecular dynamics model of the atomic structure of dysprosium alumino-phosphate glass

Molecular dynamics (MD) has been used to identify the relative distribution of dysprosium in the phosphate glass DyAl0.30P3.05O9.62. The MD model has been compared directly with experimental data obtained from neutron diffraction to enable a detailed comparison beyond the total structure factor level. The MD simulation gives Dy ... Dy correlations at 3.80(5) and 6.40(5) angstrom with relative coordination numbers of 0.8(1) and 7.3(5), thus providing evidence of minority rare-earth clustering within these glasses. The nearest neighbour Dy-O peak occurs at 2.30 angstrom with each Dy atom having on average 5.8 nearest neighbour oxygen atoms. The MD simulation is consistent with the phosphate network model based on interlinked PO4 tetrahedra where the addition of network modifiers Dy3+ depolymerizes the phosphate network through the breakage of P-(O)-P bonds whilst leaving the tetrahedral units intact. The role of aluminium within the network has been taken into explicit account, and A1 is found to be predominantly (78 tetrahedrally coordinated. In fact all four A1 bonds are found to be to P (via an oxygen atom) with negligible amounts of Al-O-Dy bonds present. This provides an important insight into the role of Al additives in improving the mechanical properties of these glasses.

A structural investigation of the alkali metal site distribution within bioactive glass using neutron diffraction and multinuclear solid state NMR

The atomic-scale structure of Bioglass and the effect of substituting lithium for sodium within these glasses have been investigated using neutron diffraction and solid state magic angle spinning (MAS) NMR. Applying an effective isomorphic substitution difference function to the neutron diffraction data has enabled the Na-O and Li-O nearest-neighbour correlations to be isolated from the overlapping Ca-O, O-(P)-O and O-(Si)-O correlations. These results reveal that Na and Li behave in a similar manner within the glassy matrix and do not disrupt the short range order of the network former. Residual differences are attributed solely to the variation in ionic radius between the two species. Successful simplification of the 2 nearest neighbour correlations to be deconvolved. The diffraction data provides the first direct experimental evidence of split Na-O nearest-neighbour correlations in these melt quench bioactive glasses, and an analogous splitting of the Li-O correlations. The observed correlations are attributed to the metal ions bonded either to bridging or to non-bridging oxygen atoms. 23Na triple quantum MAS (3QMAS) NMR data corroborates the split Na-O correlations. The structural sites present will be intimately related to the release properties of the glass system in physiological fluids such as plasma and saliva, and hence to the bioactivity of the material. Detailed structural knowledge is therefore a prerequisite for optimizing material design.

An examination of the calcium and strontium site distribution in bioactive glasses through isomorphic neutron diffraction, X-ray diffraction, EXAFS and multinuclear solid state NMR.

Strontium has been substituted for calcium in the glass series (SiO2)49.46(Na2O)26.38(P2O5)1.07(CaO)23.08x(SrO)x (where x = 0, 11.54, 23.08) to elucidate their underlying atomic-scale structural characteristics as a basis for understanding features related to the bioactivity. These bioactive glasses have been investigated using isomorphic neutron and X-ray diffraction, Sr K-edge EXAFS and solid state 17O, 23Na, 29Si, 31P and 43Ca magic-angle-spinning (MAS) NMR. An effective isomorphic substitution first-order difference function has been applied to the neutron diffraction data, confirming that Ca and Sr behave in a similar manner within the glass network, with residual differences attributed to solely the variation in ionic radius between the two species. The diffraction data provides the first direct experimental evidence of split Ca–O nearest-neighbour correlations in these melt quench bioactive glasses, together with an analogous splitting of the Sr–O correlations; the correlations are attributed to the metal ions correlated either to bridging or to non-bridging oxygen atoms. Triple quantum (3Q) 43Ca MAS NMR corroborates the split Ca–O correlations. Successful simplification of the 2 < r (A) < 3 region via the difference method has also revealed two distinct Na environments. These environments are attributed to sodium correlated either to bridging or to nonbridging oxygen atoms. Complementary multinuclear MAS NMR, Sr K-edge EXAFS and X-ray diffraction data supports the structural model presented. The structural sites present will be intimately related to their release properties in physiological fluids such as plasma and saliva, and hence the bioactivity of the material. Detailed structural knowledge is therefore a prerequisite for optimising material design.

Structure of glassy GeO2

The full set of partial structure factors for glassy germania, or GeO2, were accurately measured by using the method of isotopic substitution in neutron diffraction in order to elucidate the nature of the pair correlations for this archetypal strong glass former. The results show that the basic tetrahedral Ge(O-1/2)(4) building blocks share corners with a mean inter-tetrahedral Ge-O-Ge bond angle of 132(2)degrees. The topological and chemical ordering in the resultant network displays two characteristic length scales at distances greater than the nearest neighbour. One of these describes the intermediate range order, and manifests itself by the appearance of a first sharp diffraction peak in the measured diffraction patterns at a scattering vector k(FSDP) approximate to 1.53 angstrom(-1), while the other describes so-called extended range order, and is associated with the principal peak at k(PP) = 2.66( 1) angstrom(-1). We find that there is an interplay between the relative importance of the ordering on these length scales for tetrahedral network forming glasses that is dominated by the extended range ordering with increasing glass fragility. The measured partial structure factors for glassy GeO2 are used to reproduce the total structure factor measured by using high energy x-ray diffraction and the experimental results are also compared to those obtained by using classical and first principles molecular dynamics simulations.

Topological versus chemical ordering in network glasses at intermediate and extended length scales

Atomic ordering in network glasses on length scales longer than nearest-neighbour length scales has long been a source of controversy(1-6). Detailed experimental information is therefore necessary to understand both the network properties and the fundamentals of glass formation. Here we address the problem by investigating topological and chemical ordering in structurally disordered AX2 systems by applying the method of isotopic substitution in neutron diffraction to glassy ZnCl2. This system may be regarded as a prototypical ionic network forming glass, provided that ion polarization effects are taken into account(7), and has thus been the focus of much attention(8-14). By experiment, we show that both the topological and chemical ordering are described by two length scales at distances greater than nearest-neighbour length scales. One of these is associated with the intermediate range, as manifested by the appearance in the measured diffraction patterns of a first sharp diffraction peak at 1.09( 3) angstrom(-1); the other is associated with an extended range, which shows ordering in the glass out to 62( 4) angstrom. We also find that these general features are characteristic of glassy GeSe2, a prototypical covalently bonded network material(15,16). The results therefore offer structural insight into those length scales that determine many important aspects of supercooled liquid and glass phenomenology(11).

Visualisation of bioinformatics datasets

Analysing the molecular polymorphism and interactions of DNA, RNA and proteins is of fundamental importance in biology. Predicting functions of polymorphic molecules is important in order to design more effective medicines. Analysing major histocompatibility complex (MHC) polymorphism is important for mate choice, epitope-based vaccine design and transplantation rejection etc. Most of the existing exploratory approaches cannot analyse these datasets because of the large number of molecules with a high number of descriptors per molecule. This thesis develops novel methods for data projection in order to explore high dimensional biological dataset by visualising them in a low-dimensional space. With increasing dimensionality, some existing data visualisation methods such as generative topographic mapping (GTM) become computationally intractable. We propose variants of these methods, where we use log-transformations at certain steps of expectation maximisation (EM) based parameter learning process, to make them tractable for high-dimensional datasets. We demonstrate these proposed variants both for synthetic and electrostatic potential dataset of MHC class-I. We also propose to extend a latent trait model (LTM), suitable for visualising high dimensional discrete data, to simultaneously estimate feature saliency as an integrated part of the parameter learning process of a visualisation model. This LTM variant not only gives better visualisation by modifying the project map based on feature relevance, but also helps users to assess the significance of each feature. Another problem which is not addressed much in the literature is the visualisation of mixed-type data. We propose to combine GTM and LTM in a principled way where appropriate noise models are used for each type of data in order to visualise mixed-type data in a single plot. We call this model a generalised GTM (GGTM). We also propose to extend GGTM model to estimate feature saliencies while training a visualisation model and this is called GGTM with feature saliency (GGTM-FS). We demonstrate effectiveness of these proposed models both for synthetic and real datasets. We evaluate visualisation quality using quality metrics such as distance distortion measure and rank based measures: trustworthiness, continuity, mean relative rank errors with respect to data space and latent space. In cases where the labels are known we also use quality metrics of KL divergence and nearest neighbour classifications error in order to determine the separation between classes. We demonstrate the efficacy of these proposed models both for synthetic and real biological datasets with a main focus on the MHC class-I dataset.

Implicit Feature Selection with the Value Difference Metric

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A tight-binding analysis of the electronic properties of III-nitride semiconductors

This thesis divides into two distinct parts, both of which are underpinned by the tight-binding model. The first part covers our implementation of the tight-binding model in conjunction with the Berry phase theory of electronic polarisation to probe the atomistic origins of spontaneous polarisation and piezoelectricity as well as attempting to accurately calculate the values and coefficients associated with these phenomena. We first develop an analytic model for the polarisation of a one-dimensional linear chain of atoms. We compare the zincblende and ideal wurtzite structures in terms of effective charges, spontaneous polarisation and piezoelectric coefficients, within a first nearest neighbour tight-binding model. We further compare these to real wurtzite structures and conclude that accurate quantitative results are beyond the scope of this model but qualitative trends can still be described. The second part of this thesis deals with implementing the tight-binding model to investigate the effect of local alloy fluctuations in bulk AlGaN alloys and InGaN quantum wells. We calculate the band gap evolution of Al1_xGaxN across the full composition range and compare it to experiment as well as fitting bowing parameters to the band gap as well as to the conduction band and valence band edges. We also investigate the wavefunction character of the valence band edge to determine the composition at which the optical polarisation switches in Al1_xGaxN alloys. Finally, we examine electron and hole localisation in InGaN quantum wells. We show how the built-in field localises the carriers along the c-axis and how local alloy fluctuations strongly localise the highest hole states in the c-plane, while the electrons remain delocalised in the c-plane. We show how this localisation affects the charge density overlap and also investigate the effect of well width fluctuations on the localisation of the electrons.

Performance Evaluation of a Statistical and a Neural Network Model for Nonrigid Shape-Based Registration

Shape-based registration methods frequently encounters in the domains of computer vision, image processing and medical imaging. The registration problem is to find an optimal transformation/mapping between sets of rigid or nonrigid objects and to automatically solve for correspondences. In this paper we present a comparison of two different probabilistic methods, the entropy and the growing neural gas network (GNG), as general feature-based registration algorithms. Using entropy shape modelling is performed by connecting the point sets with the highest probability of curvature information, while with GNG the points sets are connected using nearest-neighbour relationships derived from competitive hebbian learning. In order to compare performances we use different levels of shape deformation starting with a simple shape 2D MRI brain ventricles and moving to more complicated shapes like hands. Results both quantitatively and qualitatively are given for both sets.

Rotina computacional de obtenção, tratamento e visualização de dados oceanográficos

Atualmente, sensores remotos e computadores de alto desempenho estão sendo utilizados como instrumentos principais na coleta e produção de dados oceanográficos. De posse destes dados, é possível realizar estudos que permitem simular e prever o comportamento do oceano por meio de modelos numéricos regionais. Dentre os fatores importantes no estudo da oceanografia, podem ser destacados àqueles referentes aos impactos ambientais, de contaminação antrópica, utilização de energias renováveis, operações portuárias e etc. Contudo, devido ao grande volume de dados gerados por instituições ambientais, na forma de resultados de modelos globais como o HYCOM (Hybrid Coordinate Ocean Model) e dos programas de Reanalysis da NOAA (National Oceanic and Atmospheric Administration), torna-se necessária a criação de rotinas computacionais para realizar o tratamento de condições iniciais e de contorno, de modo que possam ser aplicadas a modelos regionais como o TELEMAC3D (www.opentelemac.org). Problemas relacionados a baixa resolução, ausência de dados e a necessidade de interpolação para diferentes malhas ou sistemas de coordenadas verticais, tornam necessária a criação de um mecanismo computacional que realize este tratamento adequadamente. Com isto, foram desenvolvidas rotinas na linguagem de programação Python, empregando interpoladores de vizinho mais próximo, de modo que, a partir de dados brutos dos modelos HYCOM e do programa de Reanalysis da NOAA, foram preparadas condições iniciais e de contorno para a realização de uma simulação numérica teste. Estes resultados foram confrontados com outro resultado numérico onde, as condições foram construídas a partir de um método de interpolação mais sofisticado, escrita em outra linguagem, e que já vem sendo utilizada no laboratório. A análise dos resultados permitiu concluir que, a rotina desenvolvida no âmbito deste trabalho, funciona adequadamente para a geração de condições iniciais e de contorno do modelo TELEMAC3D. Entretanto, um interpolador mais sofisticado deve ser desenvolvido de forma a aumentar a qualidade nas interpolações, otimizar o custo computacional, e produzir condições que sejam mais realísticas para a utilização do modelo TELEMAC3D.

Statistical forecasting of the Australian macadamia crop

Three types of forecasts of the total Australian production of macadamia nuts (t nut-in-shell) have been produced early each year since 2001. The first is a long-term forecast, based on the expected production from the tree census data held by the Australian Macadamia Society, suitably scaled up for missing data and assumed new plantings each year. These long-term forecasts range out to 10 years in the future, and form a basis for industry and market planning. Secondly, a statistical adjustment (termed the climate-adjusted forecast) is made annually for the coming crop. As the name suggests, climatic influences are the dominant factors in this adjustment process, however, other terms such as bienniality of bearing, prices and orchard aging are also incorporated. Thirdly, industry personnel are surveyed early each year, with their estimates integrated into a growers and pest-scouts forecast. Initially conducted on a 'whole-country' basis, these models are now constructed separately for the six main production regions of Australia, with these being combined for national totals. Ensembles or suites of step-forward regression models using biologically-relevant variables have been the major statistical method adopted, however, developing methodologies such as nearest-neighbour techniques, general additive models and random forests are continually being evaluated in parallel. The overall error rates average 14% for the climate forecasts, and 12% for the growers' forecasts. These compare with 7.8% for USDA almond forecasts (based on extensive early-crop sampling) and 6.8% for coconut forecasts in Sri Lanka. However, our somewhatdisappointing results were mainly due to a series of poor crops attributed to human reasons, which have now been factored into the models. Notably, the 2012 and 2013 forecasts averaged 7.8 and 4.9% errors, respectively. Future models should also show continuing improvement, as more data-years become available.