Development of an advanced artificial intelligent reliability analysis tool to enhance ship operations and maintenance activities

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Modelling the prevalence of wildlife diseases using simulated diagnostic test data

Bayesian Latent Class Models (BLCMs) are algorithms that are used to infer disease prevalence when true disease statuses and gold-standard diagnostic tests are not available. However, limited attention has been given to the specification and validation of BLCMs, which are necessary if credible estimates of diagnostic test performance and disease prevalence are to result. Across six technical chapters, this thesis investigates the fundamental principles of specification and validation via a series of experiments that apply BLCMs to ante-mortem diagnostic test data. To achieve this, simulated arrays of diagnostic test data are generated to reflect the reality of the imperfect trapping and testing efforts that take place in nature. Moreover, the classic Hui-Walter algorithm is generalised within a Bayesian framework to unlock the capability of BLCMs to handle both varying prior information and varying hypotheses simultaneously. Methods to validate BLCMs are developed and then scaled up across a wide range of possible diagnostic testing scenarios via the creation of procedures to explore high-dimensional parameter spaces. For the first time, it is demonstrated that the credibility of BLCM inferences is in fact predictable. Among the key findings discovered are dependence structures that are critical to the identifiability of BLCMs; these structures are uncovered at the limits of parameter spaces, and between the means and variances of the inferred statistics. Accordingly, methods are explored to mitigate for these structures as a further prerequisite to obtaining credible estimates. Attention then turns to testing the core assumptions used to specify the generalised Hui-Walter algorithm. The assumptions about where the true values of diagnostic test performance and disease prevalence exist are removed, and the resulting sensitivity analyses provide confirmation that the findings reported throughout the thesis are indeed generalisable, even to unusual testing scenarios. With a rigorous validation protocol in place, a novel class of time-dependent BLCMs is specified, and then provided with data from one of the world’s longest running wildlife studies. New and rigorously validated inferences of disease prevalence are revealed, and anecdotal trends are corroborated, highlighting the real-world applications of this thesis

An overview of statistical decomposition techniques applied to complex systems

The current state of the art in applied decomposition techniques is summarized within a comparative uniform framework. These techniques are classified by the parametric or information theoretic approaches they adopt. An underlying structural model common to all parametric approaches is outlined. The nature and premises of a typical information theoretic approach are stressed. Some possible application patterns for an information theoretic approach are illustrated. Composition is distinguished from decomposition by pointing out that the former is not a simple reversal of the latter. From the standpoint of application to complex systems, a general evaluation is provided

Understanding and measuring the complex relationship between natural disasters and violence against children

Background:Violence against children is thought to increase after natural disasters, but evidence is limited. Methodological questions of how to measure possible associations are similarly unanswered. This thesis addresses these gaps by analyzing the relationship between natural disasters and violence against children, with emphasis on the 2010 Haitian earthquake,and by advancing design-based approaches for inference. Methods:The thesis is comprised of four related studies: (i) a systematic review and meta-analysis of the association between natural disasters and violence against children; (ii) a systematic review of pathways to violence; (iii) a matched-pairs analysis of violence against girls and boys after internal displacement from the 2010 Haitian earthquake; and (iv) a simulation comparing bias reduction properties and accuracy of matching designs,with sexual violence against girls displaced to a camp as the motivating example. The first two components synthesize background literature, the third component is empirical, and the fourth is methodological. Results: Themeta-analysis found no clear association or directional effect, albeit with a limited number of studies that exhibited methodological weaknesses. Further systematic review identified five pathways to violence. In delving into one aspect of exposure, internal displacement from the 2010 Haitian earthquake was not associated with long-term violence. Sensitivity analysis, however, indicated that sexual violence against girls and physical violence perpetrated by authority figures against boys were sensitive to Unobserved covariates. Full matching incorporating an instrumental variable can mitigate measured and unmeasured biases to increase the accuracy of inference. Conclusion:This thesis begins to elucidate and quantify the relationship between natural disasters and violence against children. The findings identify gaps in knowledge and pathways to violence for future study. Additional high-quality research is needed to unpack the complex relationship. The methods piloted in this thesis present promising tools, particularly after rapid-onset natural disasters and in resource scarce settings