A generative design system based on evolutionary and mathematical functions

Previous work by Professor John Frazer on Evolutionary Architecture provides a basis for the development of a system evolving architectural envelopes in a generic and abstract manner. Recent research by the authors has focused on the implementation of a virtual environment for the automatic generation and exploration of complex forms and architectural envelopes based on solid modelling techniques and the integration of evolutionary algorithms, enhanced computational and mathematical models. Abstract data types are introduced for genotypes in a genetic algorithm order to develop complex models using generative and evolutionary computing techniques. Multi-objective optimisation techniques are employed for defining the fitness function in the evaluation process.

An examination of the utility of the AUDIT in people with schizophrenia

Objective: To examine the reliability and validity of the Alcohol Use Disorders Identification Test (AUDIT) compared to a structured diagnostic interview, the Composite international Diagnostic Interview (CIDI; 12-month version) in psychiatric patients with a diagnosis of schizophrenia. Method: Patients (N = 71, 53 men) were interviewed using the CIDI (Alcohol Misuse Section; 12-month version) and then completed the AUDIT. Results: The CIDI identified 32.4% of the sample as having an alcohol use disorder. Of these, 5 (7.0%) met diagnostic criteria for harmful use of alcohol, 1 (1.4%) met diagnostic criteria for alcohol abuse and 17 (23.9%) met diagnostic criteria for alcohol dependence. The AUDIT was found to have good internal reliability (coefficient = 0.85). An AUDIT cutoff of greater than or equal to 8 had a sensitivity of 87% and specificity of 90% in detecting CIDI-diagnosed alcohol disorders. All items except Item 9 contributed significantly to discriminant validity. Conclusions: The findings replicate and extend previous findings of high rates of alcohol use disorders in people with severe mental illness. The AUDIT was found to be reliable and valid in this sample and can be used with confidence as a screening instrument for alcohol use disorders in people with schizophrenia.

The utility and challenges of using ICD codes in child maltreatment research : a review of existing literature

Objective: The objectives of this article are to explore the extent to which the International Statistical Classification of Diseases and Related Health Problems (ICD) has been used in child abuse research, to describe how the ICD system has been applied and to assess factors affecting the reliability of ICD coded data in child abuse research.----- Methods: PubMed, CINAHL, PsychInfo and Google Scholar were searched for peer reviewed articles written since 1989 that used ICD as the classification system to identify cases and research child abuse using health databases. Snowballing strategies were also employed by searching the bibliographies of retrieved references to identify relevant associated articles. The papers identified through the search were independently screened by two authors for inclusion, resulting in 47 studies selected for the review. Due to heterogeneity of studies metaanalysis was not performed.----- Results: This paper highlights both utility and limitations of ICD coded data. ICD codes have been widely used to conduct research into child maltreatment in health data systems. The codes appear to be used primarily to determine child maltreatment patterns within identified diagnoses or to identify child maltreatment cases for research.----- Conclusions: A significant impediment to the use of ICD codes in child maltreatment research is the under-ascertainment of child maltreatment by using coded data alone. This is most clearly identified and, to some degree, quantified, in research where data linkage is used. Practice Implications: The importance of improved child maltreatment identification will assist in identifying risk factors and creating programs that can prevent and treat child maltreatment and assist in meeting reporting obligations under the CRC.

The Autistic Behavioural Indicators Instrument (ABII) : development and instrument utility in discriminating autistic disorder from speech and language impairment and typical development

The Autistic Behavioural Indicators Instrument (ABII) is an 18-item instrument developed to identify children with Autistic Disorder (AD) based on the presence of unique autistic behavioural indicators. The ABII was administered to 20 children with AD, 20 children with speech and language impairment (SLI) and 20 typically developing (TD) children aged 2-6 years. Results indicated that the ABII discriminated children diagnosed with AD from those diagnosed with SLI and those who were TD, based on the presence of specific social attention, sensory, and behavioural symptoms. A combination of symptomology across these domains correctly classified 100% of children with and without AD. The paper concludes that the ABII shows considerable promise as an instrument for the early identification of AD.

Krylov subspace methods for approximating functions of symmetric positive definite matrices with applications to applied statistics and anomalous diffusion

Matrix function approximation is a current focus of worldwide interest and finds application in a variety of areas of applied mathematics and statistics. In this thesis we focus on the approximation of A^(-α/2)b, where A ∈ ℝ^(n×n) is a large, sparse symmetric positive definite matrix and b ∈ ℝ^n is a vector. In particular, we will focus on matrix function techniques for sampling from Gaussian Markov random fields in applied statistics and the solution of fractional-in-space partial differential equations. Gaussian Markov random fields (GMRFs) are multivariate normal random variables characterised by a sparse precision (inverse covariance) matrix. GMRFs are popular models in computational spatial statistics as the sparse structure can be exploited, typically through the use of the sparse Cholesky decomposition, to construct fast sampling methods. It is well known, however, that for sufficiently large problems, iterative methods for solving linear systems outperform direct methods. Fractional-in-space partial differential equations arise in models of processes undergoing anomalous diffusion. Unfortunately, as the fractional Laplacian is a non-local operator, numerical methods based on the direct discretisation of these equations typically requires the solution of dense linear systems, which is impractical for fine discretisations. In this thesis, novel applications of Krylov subspace approximations to matrix functions for both of these problems are investigated. Matrix functions arise when sampling from a GMRF by noting that the Cholesky decomposition A = LL^T is, essentially, a `square root' of the precision matrix A. Therefore, we can replace the usual sampling method, which forms x = L^(-T)z, with x = A^(-1/2)z, where z is a vector of independent and identically distributed standard normal random variables. Similarly, the matrix transfer technique can be used to build solutions to the fractional Poisson equation of the form ϕn = A^(-α/2)b, where A is the finite difference approximation to the Laplacian. Hence both applications require the approximation of f(A)b, where f(t) = t^(-α/2) and A is sparse. In this thesis we will compare the Lanczos approximation, the shift-and-invert Lanczos approximation, the extended Krylov subspace method, rational approximations and the restarted Lanczos approximation for approximating matrix functions of this form. A number of new and novel results are presented in this thesis. Firstly, we prove the convergence of the matrix transfer technique for the solution of the fractional Poisson equation and we give conditions by which the finite difference discretisation can be replaced by other methods for discretising the Laplacian. We then investigate a number of methods for approximating matrix functions of the form A^(-α/2)b and investigate stopping criteria for these methods. In particular, we derive a new method for restarting the Lanczos approximation to f(A)b. We then apply these techniques to the problem of sampling from a GMRF and construct a full suite of methods for sampling conditioned on linear constraints and approximating the likelihood. Finally, we consider the problem of sampling from a generalised Matern random field, which combines our techniques for solving fractional-in-space partial differential equations with our method for sampling from GMRFs.