A data mining driven crash risk profiling method for road asset management

This thesis takes a new data mining approach for analyzing road/crash data by developing models for the whole road network and generating a crash risk profile. Roads with an elevated crash risk due to road surface friction deficit are identified. The regression tree model, predicting road segment crash rate, is applied in a novel deployment coined regression tree extrapolation that produces a skid resistance/crash rate curve. Using extrapolation allows the method to be applied across the network and cope with the high proportion of missing road surface friction values. This risk profiling method can be applied in other domains.

Customer equity management : the impact of strategy drivers and customer data management on customer equity outcomes

This research contributes to the field of customer equity by examining how important the strategy drivers of consumption and customer data management are in contributing to the value of the customer asset. A mixed methods approach focused on one sector: the Australian accommodation hotels. From this research, a deeper understanding of how to theorise, conceptualise and practice customer equity management has been achieved.

A data mining driven risk profiling method for road asset management

Road surface skid resistance has been shown to have a strong relationship to road crash risk, however, applying the current method of using investigatory levels to identify crash prone roads is problematic as they may fail in identifying risky roads outside of the norm. The proposed method analyses a complex and formerly impenetrable volume of data from roads and crashes using data mining. This method rapidly identifies roads with elevated crash-rate, potentially due to skid resistance deficit, for investigation. A hypothetical skid resistance/crash risk curve is developed for each road segment, driven by the model deployed in a novel regression tree extrapolation method. The method potentially solves the problem of missing skid resistance values which occurs during network-wide crash analysis, and allows risk assessment of the major proportion of roads without skid resistance values.

How much is that data in the window? Advertising your research data with QUT Library

Queensland University of Technology (QUT) Library offers a range of resources and services to researchers as part of their research support portfolio. This poster will present key features of two of the data management services offered by research support staff at QUT Library. The first service is QUT Research Data Finder (RDF), a product of the Australian National Data Service (ANDS) funded Metadata Stores project. RDF is a data registry (metadata repository) that aims to publicise datasets that are research outputs arising from completed QUT research projects. The second is a software and code registry, which is currently under development with the sole purpose of improving discovery of source code and software as QUT research outputs. RESEARCH DATA FINDER As an integrated metadata repository, Research Data Finder aligns with institutional sources of truth, such as QUT’s research administration system, ResearchMaster, as well as QUT’s Academic Profiles system to provide high quality data descriptions that increase awareness of, and access to, shareable research data. The repository and its workflows are designed to foster better data management practices, enhance opportunities for collaboration and research, promote cross-disciplinary research and maximise the impact of existing research data sets. SOFTWARE AND CODE REGISTRY The QUT Library software and code registry project stems from concerns amongst researchers with regards to development activities, storage, accessibility, discoverability and impact, sharing, copyright and IP ownership of software and code. As a result, the Library is developing a registry for code and software research outputs, which will use existing Research Data Finder architecture. The underpinning software for both registries is VIVO, open source software developed by Cornell University. The registry will use the Research Data Finder service instance of VIVO and will include a searchable interface, links to code/software locations and metadata feeds to Research Data Australia. Key benefits of the project include:improving the discoverability and reuse of QUT researchers’ code and software amongst QUT and the QUT research community; increasing the profile of QUT research outputs on a national level by providing a metadata feed to Research Data Australia, and; improving the metrics for access and reuse of code and software in the repository.

Children's understanding of graphic representations of quantitative data

Children are encountering more and more graphic representations of data in their learning and everyday life. Much of this data occurs in quantitative forms as different forms of measurement are incorporated into the graphics during their construction. In their formal education, children are required to learn to use a range of these quantitative representations in subjects across the school curriculum. Previous research that focuses on the use of information processing and traditional approaches to cognitive psychology concludes that the development of an understanding of such representations of data is a complex process. An alternative approach is to investigate the experiences of children as they interact with graphic representations of quantitative data in their own life-worlds. This paper demonstrates how a phenomenographic approach may be used to reveal the qualitatively different ways in which children in Australian primary and secondary education understand the phenomenon of graphic representations of quantitative data. Seven variations of the children’s understanding were revealed. These have been described interpretively in the article and confirmed through the words of the children. A detailed outcome space demonstrates how these seven variations are structurally related.

A statistical strategy to select optimal structural health monitoring data in operational modal analysis

Operational modal analysis (OMA) is prevalent in modal identifi cation of civil structures. It asks for response measurements of the underlying structure under ambient loads. A valid OMA method requires the excitation be white noise in time and space. Although there are numerous applications of OMA in the literature, few have investigated the statistical distribution of a measurement and the infl uence of such randomness to modal identifi cation. This research has attempted modifi ed kurtosis to evaluate the statistical distribution of raw measurement data. In addition, a windowing strategy employing this index has been proposed to select quality datasets. In order to demonstrate how the data selection strategy works, the ambient vibration measurements of a laboratory bridge model and a real cable-stayed bridge have been respectively considered. The analysis incorporated with frequency domain decomposition (FDD) as the target OMA approach for modal identifi cation. The modal identifi cation results using the data segments with different randomness have been compared. The discrepancy in FDD spectra of the results indicates that, in order to fulfi l the assumption of an OMA method, special care shall be taken in processing a long vibration measurement data. The proposed data selection strategy is easy-to-apply and verifi ed effective in modal analysis.

Utility of COI, CAD and morphological data for resolving relationships within the genus Sarcophaga (sensu lato) (Diptera: Sarcophagidae) : a preliminary study

Currently there are ~3000 known species of Sarcophagidae (Diptera), which are classified into 173 genera in three subfamilies. Almost 25% of sarcophagids belong to the genus Sarcophaga (sensu lato) however little is known about the validity of, and relationships between the ~150 (or more) subgenera of Sarcophaga s.l. In this preliminary study, we evaluated the usefulness of three sources of data for resolving relationships between 35 species from 14 Sarcophaga s.l. subgenera: the mitochondrial COI barcode region, ~800. bp of the nuclear gene CAD, and 110 morphological characters. Bayesian, maximum likelihood (ML) and maximum parsimony (MP) analyses were performed on the combined dataset. Much of the tree was only supported by the Bayesian and ML analyses, with the MP tree poorly resolved. The genus Sarcophaga s.l. was resolved as monophyletic in both the Bayesian and ML analyses and strong support was obtained at the species-level. Notably, the only subgenus consistently resolved as monophyletic was Liopygia. The monophyly of and relationships between the remaining Sarcophaga s.l. subgenera sampled remain questionable. We suggest that future phylogenetic studies on the genus Sarcophaga s.l. use combined datasets for analyses. We also advocate the use of additional data and a range of inference strategies to assist with resolving relationships within Sarcophaga s.l.

Secured e-health data retrieval in DaaS and Big Data

Big Data is a rising IT trend similar to cloud computing, social networking or ubiquitous computing. Big Data can offer beneficial scenarios in the e-health arena. However, one of the scenarios can be that Big Data needs to be kept secured for a long period of time in order to gain its benefits such as finding cures for infectious diseases and protecting patient privacy. From this connection, it is beneficial to analyse Big Data to make meaningful information while the data is stored securely. Therefore, the analysis of various database encryption techniques is essential. In this study, we simulated 3 types of technical environments, namely, Plain-text, Microsoft Built-in Encryption, and custom Advanced Encryption Standard, using Bucket Index in Data-as-a-Service. The results showed that custom AES-DaaS has a faster range query response time than MS built-in encryption. Furthermore, while carrying out the scalability test, we acknowledged that there are performance thresholds depending on physical IT resources. Therefore, for the purpose of efficient Big Data management in eHealth it is noteworthy to examine their scalability limits as well even if it is under a cloud computing environment. In addition, when designing an e-health database, both patient privacy and system performance needs to be dealt as top priorities.

Improving level crossing safety through enhanced data recording and reporting : The CRC for Rail Innovation’s Baseline Rail Level Crossing Video project

This paper describes the work being conducted in the baseline rail level crossing project, supported by the Australian rail industry and the Cooperative Research Centre for Rail Innovation. The paper discusses the limitations of near-miss data for analysis obtained using current level crossing occurrence reporting practices. The project is addressing these limitations through the development of a data collection and analysis system with an underlying level crossing accident causation model. An overview of the methodology and improved data recording process are described. The paper concludes with a brief discussion of benefits this project is expected to provide the Australian rail industry.

Association of a GRIA3 gene polymorphism with migraine in an Australian case-control cohort

BACKGROUND: The excitatory neurotransmitter glutamate has been implicated in both the hyperexcitability required for cortical spreading depression as well as activation of the trigeminovascular system required for the allodynia associated with migraine. Polymorphisms in the glutamate receptor ionotropic amino-3-hydroxy-5-methyl-4-isoxazole-propionin acid 1 (GRIA1) and GRIA3 genes that code for 2 of 4 subunits of the glutamate receptor have been previously associated with migraine in an Italian population. In addition, the GRIA3 gene is coded within a previously identified migraine susceptibility locus at Xq24. This study investigated the previously associated polymorphisms in both genes in an Australian case-control population. METHODS: Variants in GRIA1 and GRIA3 were genotyped in 472 unrelated migraine cases and matched controls, and data were analyzed for association. RESULTS: Analysis showed no association between migraine and the GRIA1 gene. However, association was observed with the GRIA3 single nucleotide polymorphism (SNP) rs3761555 (P = .008). CONCLUSION: The results of this study confirmed the previous report of association at the rs3761555 SNP within the migraine with aura subgroup of migraineurs. However, the study identified association with the inverse allele suggesting that rs3761555 may not be the causative SNP but is more likely in linkage disequilibrium with another causal variant in both populations. This study supports the plethora of evidence suggesting that glutamate dysfunction may contribute to migraine susceptibility, warranting further investigation of the glutamatergic system and particularly of the GRIA3 gene.

An X chromosome association scan of the Norfolk Island genetic isolate provides evidence for a novel migraine susceptibility locus at Xq12

Migraine is a common and debilitating neurovascular disorder with a complex envirogenomic aetiology. Numerous studies have demonstrated a preponderance of women affected with migraine and previous pedigree linkage studies in our laboratory have identified susceptibility loci on chromosome Xq24-Xq28. In this study we have used the genetic isolate of Norfolk Island to further analyse the X chromosome for migraine susceptibility loci. An association approach was employed to analyse 14,124 SNPs spanning the entire X chromosome. Genotype data from 288 individuals comprising a large core-pedigree, of which 76 were affected with migraine, were analysed. Although no SNP reached chromosome-wide significance (empirical α = 1×10−5) ranking by P-value revealed two primary clusters of SNPs in the top 25. A 10 SNP cluster represents a novel migraine susceptibility locus at Xq12 whilst a 11 SNP cluster represents a previously identified migraine susceptibility locus at Xq27. The strongest association at Xq12 was seen for rs599958 (OR = 1.75, P = 8.92×10−4), whilst at Xq27 the strongest association was for rs6525667 (OR = 1.53, P = 1.65×10−4). Further analysis of SNPs at these loci was performed in 5,122 migraineurs from the Women’s Genome Health Study and provided additional evidence for association at the novel Xq12 locus (P<0.05). Overall, this study provides evidence for a novel migraine susceptibility locus on Xq12. The strongest effect SNP (rs102834, joint P = 1.63×10−5) is located within the 5′UTR of the HEPH gene, which is involved in iron homeostasis in the brain and may represent a novel pathway for involvement in migraine pathogenesis.

Traffic density estimation of signalised arterials with stop line detector and probe data

This research aims to develop a reliable density estimation method for signalised arterials based on cumulative counts from upstream and downstream detectors. In order to overcome counting errors associated with urban arterials with mid-link sinks and sources, CUmulative plots and Probe Integration for Travel timE estimation (CUPRITE) is employed for density estimation. The method, by utilizing probe vehicles’ samples, reduces or cancels the counting inconsistencies when vehicles’ conservation is not satisfied within a section. The method is tested in a controlled environment, and the authors demonstrate the effectiveness of CUPRITE for density estimation in a signalised section, and discuss issues associated with the method.

Modeling the cumulative genetic risk for multiple sclerosis from genome-wide association data

Background: Multiple sclerosis (MS) is the most common cause of chronic neurologic disability beginning in early to middle adult life. Results from recent genome-wide association studies (GWAS) have substantially lengthened the list of disease loci and provide convincing evidence supporting a multifactorial and polygenic model of inheritance. Nevertheless, the knowledge of MS genetics remains incomplete, with many risk alleles still to be revealed. Methods: We used a discovery GWAS dataset (8,844 samples, 2,124 cases and 6,720 controls) and a multi-step logistic regression protocol to identify novel genetic associations. The emerging genetic profile included 350 independent markers and was used to calculate and estimate the cumulative genetic risk in an independent validation dataset (3,606 samples). Analysis of covariance (ANCOVA) was implemented to compare clinical characteristics of individuals with various degrees of genetic risk. Gene ontology and pathway enrichment analysis was done using the DAVID functional annotation tool, the GO Tree Machine, and the Pathway-Express profiling tool. Results: In the discovery dataset, the median cumulative genetic risk (P-Hat) was 0.903 and 0.007 in the case and control groups, respectively, together with 79.9% classification sensitivity and 95.8% specificity. The identified profile shows a significant enrichment of genes involved in the immune response, cell adhesion, cell communication/ signaling, nervous system development, and neuronal signaling, including ionotropic glutamate receptors, which have been implicated in the pathological mechanism driving neurodegeneration. In the validation dataset, the median cumulative genetic risk was 0.59 and 0.32 in the case and control groups, respectively, with classification sensitivity 62.3% and specificity 75.9%. No differences in disease progression or T2-lesion volumes were observed among four levels of predicted genetic risk groups (high, medium, low, misclassified). On the other hand, a significant difference (F = 2.75, P = 0.04) was detected for age of disease onset between the affected misclassified as controls (mean = 36 years) and the other three groups (high, 33.5 years; medium, 33.4 years; low, 33.1 years). Conclusions: The results are consistent with the polygenic model of inheritance. The cumulative genetic risk established using currently available genome-wide association data provides important insights into disease heterogeneity and completeness of current knowledge in MS genetics.

Optical data encryption using time-dependent dynamics of refractive index changes in LiNbO3

We present a method for optical encryption of information, based on the time-dependent dynamics of writing and erasure of refractive index changes in a bulk lithium niobate medium. Information is written into the photorefractive crystal with a spatially amplitude modulated laser beam which when overexposed significantly degrades the stored data making it unrecognizable. We show that the degradation can be reversed and that a one-to-one relationship exists between the degradation and recovery rates. It is shown that this simple relationship can be used to determine the erasure time required for decrypting the scrambled index patterns. In addition, this method could be used as a straightforward general technique for determining characteristic writing and erasure rates in photorefractive media.

Principal component and linkage analysis of cardiovascular risk traits in the Norfolk isolate

OBJECTIVE(S): An individual's risk of developing cardiovascular disease (CVD) is influenced by genetic factors. This study focussed on mapping genetic loci for CVD-risk traits in a unique population isolate derived from Norfolk Island. METHODS: This investigation focussed on 377 individuals descended from the population founders. Principal component analysis was used to extract orthogonal components from 11 cardiovascular risk traits. Multipoint variance component methods were used to assess genome-wide linkage using SOLAR to the derived factors. A total of 285 of the 377 related individuals were informative for linkage analysis. RESULTS: A total of 4 principal components accounting for 83% of the total variance were derived. Principal component 1 was loaded with body size indicators; principal component 2 with body size, cholesterol and triglyceride levels; principal component 3 with the blood pressures; and principal component 4 with LDL-cholesterol and total cholesterol levels. Suggestive evidence of linkage for principal component 2 (h(2) = 0.35) was observed on chromosome 5q35 (LOD = 1.85; p = 0.0008). While peak regions on chromosome 10p11.2 (LOD = 1.27; p = 0.005) and 12q13 (LOD = 1.63; p = 0.003) were observed to segregate with principal components 1 (h(2) = 0.33) and 4 (h(2) = 0.42), respectively. CONCLUSION(S): This study investigated a number of CVD risk traits in a unique isolated population. Findings support the clustering of CVD risk traits and provide interesting evidence of a region on chromosome 5q35 segregating with weight, waist circumference, HDL-c and total triglyceride levels.