An empirical analysis of the factors and measures of Enterprise Architecture Management success

Enterprise Architecture Management (EAM) is discussed in academia and industry as a vehicle to guide IT implementations, alignment, compliance assessment, or technology management. Still, a lack of knowledge prevails about how EAM can be successfully used, and how positive impact can be realized from EAM. To determine these factors, we identify EAM success factors and measures through literature reviews and exploratory interviews and propose a theoretical model that explains key factors and measures of EAM success. We test our model with data collected from a cross-sectional survey of 133 EAM practitioners. The results confirm the existence of an impact of four distinct EAM success factors, ‘EAM product quality’, ‘EAM infrastructure quality’, ‘EAM service delivery quality’, and ‘EAM organizational anchoring’, and two important EAM success measures, ‘intentions to use EAM’ and ‘Organizational and Project Benefits’ in a confirmatory analysis of the model. We found the construct ‘EAM organizational anchoring’ to be a core focal concept that mediated the effect of success factors such as ‘EAM infrastructure quality’ and ‘EAM service quality’ on the success measures. We also found that ‘EAM satisfaction’ was irrelevant to determining or measuring success. We discuss implications for theory and EAM practice.

Building a better world: Can architecture shape behaviour?

In 1966, a British planner called Maurice Broady came up with a new term for the architectural lexicon: architectural determinism. This was to describe the practice of groundlessly asserting that design solutions would change behaviour in a predictable and positive way. It was a new phrase but the belief system behind it – that buildings shape behaviour – had allowed the heroes of architecture to make all kinds of outlandish claims.

What can we do to improve the coordination of care for cancer patients?

The delivery of cancer services has become increasingly complex as we move to more personalized diagnostic and treatment approaches and cancer is recognized as a chronic disease. Cancer patients today typically come into contact with multiple health services over long periods. Healthcare reforms in many countries also mean that the various services involved in cancer care are funded by a confusing mix of personal, private insurer, and government sources. It is not surprising then that many patients today tell us their cancer experience is disjointed. Importantly, this lack of coordination of cancer care results in poor outcomes that can range from minor inconvenience at best to potentially life-threatening safety incidents at worst.

A LIN inspired optical bus for signal isolation in multilevel or modular power electronic converters

Proposed in this paper is a low-cost, half-duplex optical communication bus for control signal isolation in modular or multilevel power electronic converters. The concept is inspired by the Local Interconnect Network (LIN) serial network protocol as used in the automotive industry. The proposed communications bus utilises readily available optical transceivers and is suitable for use with low-cost microcontrollers for distributed control of multilevel converters. As a signal isolation concept, the proposed optical bus enables very high cell count modular multilevel cascaded converters (MMCCs) for high-bandwidth, high-voltage and high-power applications. Prototype hardware is developed and the optical bus concept is validated experimentally in a 33-level MMCC converter operating at 120 Vrms and 60 Hz.

Design of a lightweight, modular robotic vehicle for the sustainable intensification of broadacre agriculture

This thesis presents the design process and the prototyping of a lightweight, modular robotic vehicle for the sustainable intensification of broadacre agriculture. Achieved by the joint operation of multiple autonomous vehicles to improve energy consumption, reduce labour, and increase efficiency in the application of inputs for the management of crops. The Small Robotic Farm Vehicle (SRFV) is a lightweight and energy efficient robotic vehicle with a configurable, modular design. It is capable of undertaking a range of agricultural tasks, including fertilising and weed management through mechanical intervention and precision spraying, whilst being more than an order of magnitude lower in weight than existing broadacre agricultural equipment.

Crystal palaces: Copyright law and public architecture

This paper investigates copyright law and public architecture in the context of cultural institutions of Australia. Part 1 examines the case of the Sydney Opera House to illustrate the past position of architects in respect of copyright law. It goes onto consider the framework laid down by the Copyright Amendment (Moral Rights) Act 2000 (Cth) to resolve copyright disputes over moral rights and architecture. Part 2 considers the argument over the proposed renovations to the National Gallery of Australia between Dr Brian Kennedy and the original architect Colin Madigan. Part 3 finally deals with the allegations that Ashton Raggatt McDougall, the architects of the National Museum of Australia, plagiarised the designs of Daniel Libeskind for the Jewish Berlin Museum.

Extending genetic linkage analysis to diffusion tensor images to map single gene effects on brain fiber architecture

We extended genetic linkage analysis - an analysis widely used in quantitative genetics - to 3D images to analyze single gene effects on brain fiber architecture. We collected 4 Tesla diffusion tensor images (DTI) and genotype data from 258 healthy adult twins and their non-twin siblings. After high-dimensional fluid registration, at each voxel we estimated the genetic linkage between the single nucleotide polymorphism (SNP), Val66Met (dbSNP number rs6265), of the BDNF gene (brain-derived neurotrophic factor) with fractional anisotropy (FA) derived from each subject's DTI scan, by fitting structural equation models (SEM) from quantitative genetics. We also examined how image filtering affects the effect sizes for genetic linkage by examining how the overall significance of voxelwise effects varied with respect to full width at half maximum (FWHM) of the Gaussian smoothing applied to the FA images. Raw FA maps with no smoothing yielded the greatest sensitivity to detect gene effects, when corrected for multiple comparisons using the false discovery rate (FDR) procedure. The BDNF polymorphism significantly contributed to the variation in FA in the posterior cingulate gyrus, where it accounted for around 90-95% of the total variance in FA. Our study generated the first maps to visualize the effect of the BDNF gene on brain fiber integrity, suggesting that common genetic variants may strongly determine white matter integrity.

Brain fiber architecture, genetics, and intelligence: a high angular resolution diffusion imaging (HARDI) study

We developed an analysis pipeline enabling population studies of HARDI data, and applied it to map genetic influences on fiber architecture in 90 twin subjects. We applied tensor-driven 3D fluid registration to HARDI, resampling the spherical fiber orientation distribution functions (ODFs) in appropriate Riemannian manifolds, after ODF regularization and sharpening. Fitting structural equation models (SEM) from quantitative genetics, we evaluated genetic influences on the Jensen-Shannon divergence (JSD), a novel measure of fiber spatial coherence, and on the generalized fiber anisotropy (GFA) a measure of fiber integrity. With random-effects regression, we mapped regions where diffusion profiles were highly correlated with subjects' intelligence quotient (IQ). Fiber complexity was predominantly under genetic control, and higher in more highly anisotropic regions; the proportion of genetic versus environmental control varied spatially. Our methods show promise for discovering genes affecting fiber connectivity in the brain.

Mapping genetic influences on brain fiber architecture with high angular resolution diffusion imaging (HARDI)

We report the first 3D maps of genetic effects on brain fiber complexity. We analyzed HARDI brain imaging data from 90 young adult twins using an information-theoretic measure, the Jensen-Shannon divergence (JSD), to gauge the regional complexity of the white matter fiber orientation distribution functions (ODF). HARDI data were fluidly registered using Karcher means and ODF square-roots for interpol ation; each subject's JSD map was computed from the spatial coherence of the ODFs in each voxel's neighborhood. We evaluated the genetic influences on generalized fiber anisotropy (GFA) and complexity (JSD) using structural equation models (SEM). At each voxel, genetic and environmental components of data variation were estimated, and their goodness of fit tested by permutation. Color-coded maps revealed that the optimal models varied for different brain regions. Fiber complexity was predominantly under genetic control, and was higher in more highly anisotropic regions. These methods show promise for discovering factors affecting fiber connectivity in the brain.

Genetics of brain fiber architecture and intellectual performance

The study is the first to analyze genetic and environmental factors that affect brain fiber architecture and its genetic linkage with cognitive function. We assessed white matter integrity voxelwise using diffusion tensor imaging at high magnetic field (4 Tesla), in 92 identical and fraternal twins. White matter integrity, quantified using fractional anisotropy (FA), was used to fit structural equation models (SEM) at each point in the brain, generating three-dimensional maps of heritability. We visualized the anatomical profile of correlations between white matter integrity and full-scale, verbal, and performance intelligence quotients (FIQ, VIQ, and PIQ). White matter integrity (FA) was under strong genetic control and was highly heritable in bilateral frontal (a 2 = 0.55, p = 0.04, left; a 2 = 0.74, p = 0.006, right), bilateral parietal (a 2 = 0.85, p < 0.001, left; a 2 = 0.84, p < 0.001, right), and left occipital (a 2 = 0.76, p = 0.003) lobes, and was correlated with FIQ and PIQ in the cingulum, optic radiations, superior fronto- occipital fasciculus, internal capsule, callosal isthmus, and the corona radiata (p = 0.04 for FIQ and p = 0.01 for PIQ, corrected for multiple comparisons). In a cross-trait mapping approach, common genetic factors mediated the correlation between IQ and white matter integrity, suggesting a common physiological mechanism for both, and common genetic determination. These genetic brain maps reveal heritable aspects of white matter integrity and should expedite the discovery of single-nucleotide polymorphisms affecting fiber connectivity and cognition.

Strong inference in functional neuroimaging

A recurring question for cognitive science is whether functional neuroimaging data can provide evidence for or against psychological theories. As posed, the question reflects an adherence to a popular scientific method known as 'strong inference'. The method entails constructing multiple hypotheses (Hs) and designing experiments so that alternative possible outcomes will refute at least one (i.e., 'falsify' it). In this article, after first delineating some well-documented limitations of strong inference, I provide examples of functional neuroimaging data being used to test Hs from rival modular information-processing models of spoken word production. 'Strong inference' for neuroimaging involves first establishing a systematic mapping of 'processes to processors' for a common modular architecture. Alternate Hs are then constructed from psychological theories that attribute the outcome of manipulating an experimental factor to two or more distinct processing stages within this architecture. Hs are then refutable by a finding of activity differentiated spatially and chronometrically by experimental condition. When employed in this manner, the data offered by functional neuroimaging may be more useful for adjudicating between accounts of processing loci than behavioural measures.

The multivariate A/C/E model and the genetics of fiber architecture

We present a new algorithm to compute the voxel-wise genetic contribution to brain fiber microstructure using diffusion tensor imaging (DTI) in a dataset of 25 monozygotic (MZ) twins and 25 dizygotic (DZ) twin pairs (100 subjects total). First, the structural and DT scans were linearly co-registered. Structural MR scans were nonlinearly mapped via a 3D fluid transformation to a geometrically centered mean template, and the deformation fields were applied to the DTI volumes. After tensor re-orientation to realign them to the anatomy, we computed several scalar and multivariate DT-derived measures including the geodesic anisotropy (GA), the tensor eigenvalues and the full diffusion tensors. A covariance-weighted distance was measured between twins in the Log-Euclidean framework [2], and used as input to a maximum-likelihood based algorithm to compute the contributions from genetics (A), common environmental factors (C) and unique environmental ones (E) to fiber architecture. Quanititative genetic studies can take advantage of the full information in the diffusion tensor, using covariance weighted distances and statistics on the tensor manifold.

Scalar connectivity measures from fast-marching tractography reveal heritability of white matter architecture

Recent advances in diffusion-weighted MRI (DWI) have enabled studies of complex white matter tissue architecture in vivo. To date, the underlying influence of genetic and environmental factors in determining central nervous system connectivity has not been widely studied. In this work, we introduce new scalar connectivity measures based on a computationally-efficient fast-marching algorithm for quantitative tractography. We then calculate connectivity maps for a DTI dataset from 92 healthy adult twins and decompose the genetic and environmental contributions to the variance in these metrics using structural equation models. By combining these techniques, we generate the first maps to directly examine genetic and environmental contributions to brain connectivity in humans. Our approach is capable of extracting statistically significant measures of genetic and environmental contributions to neural connectivity.

Genetic architecture of subcortical brain regions: Common and region-specific genetic contributions

Understanding the aetiology of patterns of variation within and covariation across brain regions is key to advancing our understanding of the functional, anatomical and developmental networks of the brain. Here we applied multivariate twin modelling and principal component analysis (PCA) to investigate the genetic architecture of the size of seven subcortical regions (caudate nucleus, thalamus, putamen, pallidum, hippocampus, amygdala and nucleus accumbens) in a genetically informative sample of adolescents and young adults (N=1038; mean age=21.6±3.2years; including 148 monozygotic and 202 dizygotic twin pairs) from the Queensland Twin IMaging (QTIM) study. Our multivariate twin modelling identified a common genetic factor that accounts for all the heritability of intracranial volume (0.88) and a substantial proportion of the heritability of all subcortical structures, particularly those of the thalamus (0.71 out of 0.88), pallidum (0.52 out of 0.75) and putamen (0.43 out of 0.89). In addition, we also found substantial region-specific genetic contributions to the heritability of the hippocampus (0.39 out of 0.79), caudate nucleus (0.46 out of 0.78), amygdala (0.25 out of 0.45) and nucleus accumbens (0.28 out of 0.52). This provides further insight into the extent and organization of subcortical genetic architecture, which includes developmental and general growth pathways, as well as the functional specialization and maturation trajectories that influence each subcortical region. This multivariate twin study identifies a common genetic factor that accounts for all the heritability of intracranial volume (0.88) and a substantial proportion of the heritability of all subcortical structures, particularly those of the thalamus (0.71 out of 0.88), pallidum (0.52 out of 0.75) and putamen (0.43 out of 0.89). In parallel, it also describes substantial region-specific genetic contributions to the heritability of the hippocampus (0.39 out of 0.79), caudate nucleus (0.46 out of 0.78), amygdala (0.25 out of 0.45) and nucleus accumbens (0.28 out of 0.52).