Raw X-ray fluorescence (XRF) scannings and radiocarbon age of sediment cores GeoB8331-4, and GeoB8322-2

Variations in the sediment input to the Namaqualand mudbelt during the Holocene are assessed using an integrative terrestrial to marine, source to sink approach. Geochemical and Sr and Nd isotopic signatures are used to distinguish fluvial sediment source areas. Relative to the sediments of the Olifants River, craton outcrops in the northern Orange River catchment have a more radiogenic Sr and a more unradiogenic Nd isotopic signature. Furthermore, upper Orange River sediments are rich in heavier elements such as Ti and Fe derived from the chemical weathering of Drakensberg flood basalt. Suspension load signatures change along the Orange River's westward transit as northern catchments contribute physical weathering products from the Fish and Molopo River catchment area. Marine cores offshore of the Olifants (GeoB8323-2) and Orange (GeoB8331-4) River mouths show pulses of increased contribution of Olifants River and upper Orange River input, respectively. These pulses coincide with intervals of increased terrestrial organic matter flux and increased paleo-production at the respective core sites. We attribute this to an increase in fluvial activity and vegetation cover in the adjacent catchments during more humid climate conditions. The contrast in the timing of these wet phases in the catchment areas reflects the bipolar behavior of the South African summer and winter rainfall zones. While rainfall in the Orange River catchment is related to southward shifts in the ICTZ, rainfall in the Olifants catchment is linked to northward shifts in Southern Hemisphere Westerly storm tracks. The later may also have increased southern Benguela upwelling in the past by reducing the shedding of Agulhas eddies into the Atlantic. The high-resolution records of latitudinal shifts in these atmospheric circulation systems correspond to late Holocene centennial-millennial scale climate variability evident in Antarctic ice core records. The mudbelt cores indicate that phases of high summer rainfall zone and low winter rainfall zone humidity (at ca. 2.8 and 1 ka BP) may be synchronous with Antarctic warming events. On the other hand, dry conditions in the summer rainfall zone along with wet conditions in the winter rainfall zone (at ca 3.3, 2 and 0.5 ka BP) may be associated with Antarctic cooling events.

Experimental evaluation of the response of micro-channel plate detector to ions with 10s of MeV energies

The absolute calibration of a microchannel plate (MCP) assembly using a Thomson spectrometer for laser-driven ion beams is described. In order to obtain the response of the whole detection system to the particles’ impact, a slotted solid state nuclear track detector (CR-39) was installed in front of the MCP to record the ions simultaneously on both detectors. The response of the MCP (counts/particles) was measured for 5–58 MeV carbon ions and for protons in the energy range2–17.3 MeV. The response of the MCP detector is non-trivial when the stopping range of particles becomes larger than the thickness of the detector. Protons with energiesE>~ 10 MeV are energetic enough that they can pass through the MCP detector. Quantitative analysis of the pits formed in CR-39 and the signal generated in the MCP allowed to determine the MCP response to particles in this energy range. Moreover, a theoretical model allows to predict the response of MCP at even higher proton energies. This suggests that in this regime the MCP response is a slowly decreasing function of energy, consistently with the decrease of the deposited energy. These calibration data will enable particle spectra to be obtained in absolute terms over a broad energy range.

Concerning the house museum and the picturesque landscape in the work of Sir John Soane : with raw hints for post-heritage museum space

The picturesque aesthetic in the work of Sir John Soane, architect and collector, resonates in the major work of his very personal practice – the development of his house museum, now the Soane Museum in Lincoln’s Inn Fields in London. Soane was actively involved with the debates, practices and proponents of picturesque and classical practices in architecture and landscape and his lectures reveal these influences in the making of The Soane, which was built to contain and present diverse collections of classical and contemporary art and architecture alongside scavenged curiosities. The Soane Museum has been described as a picturesque landscape, where a pictorial style, together with a carefully defined itinerary, has resulted in the ‘apotheosis of the Picturesque interior’. Soane also experimented with making mock ruinscapes within gardens, which led him to construct faux architectures alluding to archaeological practices based upon the ruin and the fragment. These ideas framed the making of interior landscapes expressed through spatial juxtapositions of room and corridor furnished with the collected object that characterise The Soane Museum. This paper is a personal journey through the Museum which describes and then reviews aspects of Soane’s work in the context of contemporary theories on ‘new’ museology. It describes the underpinning picturesque practices that Soane employed to exceed the boundaries between interior and exterior landscapes and the collection. It then applies particular picturesque principles drawn from visiting The Soane to a speculative project for a house/landscape museum for the Oratunga historic property in outback South Australia, where the often, normalising effects of conservation practices are reviewed using minimal architectural intervention through a celebration of ruinous states.

Urban network travel time estimation from stop-line loop detector data and signal controller data

This paper presents a model to estimate travel time using cumulative plots. Three different cases considered are i) case-Det, for only detector data; ii) case-DetSig, for detector data and signal controller data and iii) case-DetSigSFR: for detector data, signal controller data and saturation flow rate. The performance of the model for different detection intervals is evaluated. It is observed that detection interval is not critical if signal timings are available. Comparable accuracy can be obtained from larger detection interval with signal timings or from shorter detection interval without signal timings. The performance for case-DetSig and for case-DetSigSFR is consistent with accuracy generally more than 95% whereas, case-Det is highly sensitive to the signal phases in the detection interval and its performance is uncertain if detection interval is integral multiple of signal cycles.

CTCombine : rotating and combining CT data with an accurate detector model, to simulate radiotherapy portal imaging at non-zero beam angles

Established Monte Carlo user codes BEAMnrc and DOSXYZnrc permit the accurate and straightforward simulation of radiotherapy experiments and treatments delivered from multiple beam angles. However, when an electronic portal imaging detector (EPID) is included in these simulations, treatment delivery from non-zero beam angles becomes problematic. This study introduces CTCombine, a purpose-built code for rotating selected CT data volumes, converting CT numbers to mass densities, combining the results with model EPIDs and writing output in a form which can easily be read and used by the dose calculation code DOSXYZnrc. The geometric and dosimetric accuracy of CTCombine’s output has been assessed by simulating simple and complex treatments applied to a rotated planar phantom and a rotated humanoid phantom and comparing the resulting virtual EPID images with the images acquired using experimental measurements and independent simulations of equivalent phantoms. It is expected that CTCombine will be useful for Monte Carlo studies of EPID dosimetry as well as other EPID imaging applications.

What counts as evidence and equity?

Editorial introduction to Vol. 34 of Review of Research in Education (American Educational Research Association/Sage).

Addressing issues in sparseness, ecological bias and formulation of the adjacency matrix in Bayesian spatio-temporal analysis of disease counts

The main objective of this PhD was to further develop Bayesian spatio-temporal models (specifically the Conditional Autoregressive (CAR) class of models), for the analysis of sparse disease outcomes such as birth defects. The motivation for the thesis arose from problems encountered when analyzing a large birth defect registry in New South Wales. The specific components and related research objectives of the thesis were developed from gaps in the literature on current formulations of the CAR model, and health service planning requirements. Data from a large probabilistically-linked database from 1990 to 2004, consisting of fields from two separate registries: the Birth Defect Registry (BDR) and Midwives Data Collection (MDC) were used in the analyses in this thesis. The main objective was split into smaller goals. The first goal was to determine how the specification of the neighbourhood weight matrix will affect the smoothing properties of the CAR model, and this is the focus of chapter 6. Secondly, I hoped to evaluate the usefulness of incorporating a zero-inflated Poisson (ZIP) component as well as a shared-component model in terms of modeling a sparse outcome, and this is carried out in chapter 7. The third goal was to identify optimal sampling and sample size schemes designed to select individual level data for a hybrid ecological spatial model, and this is done in chapter 8. Finally, I wanted to put together the earlier improvements to the CAR model, and along with demographic projections, provide forecasts for birth defects at the SLA level. Chapter 9 describes how this is done. For the first objective, I examined a series of neighbourhood weight matrices, and showed how smoothing the relative risk estimates according to similarity by an important covariate (i.e. maternal age) helped improve the model’s ability to recover the underlying risk, as compared to the traditional adjacency (specifically the Queen) method of applying weights. Next, to address the sparseness and excess zeros commonly encountered in the analysis of rare outcomes such as birth defects, I compared a few models, including an extension of the usual Poisson model to encompass excess zeros in the data. This was achieved via a mixture model, which also encompassed the shared component model to improve on the estimation of sparse counts through borrowing strength across a shared component (e.g. latent risk factor/s) with the referent outcome (caesarean section was used in this example). Using the Deviance Information Criteria (DIC), I showed how the proposed model performed better than the usual models, but only when both outcomes shared a strong spatial correlation. The next objective involved identifying the optimal sampling and sample size strategy for incorporating individual-level data with areal covariates in a hybrid study design. I performed extensive simulation studies, evaluating thirteen different sampling schemes along with variations in sample size. This was done in the context of an ecological regression model that incorporated spatial correlation in the outcomes, as well as accommodating both individual and areal measures of covariates. Using the Average Mean Squared Error (AMSE), I showed how a simple random sample of 20% of the SLAs, followed by selecting all cases in the SLAs chosen, along with an equal number of controls, provided the lowest AMSE. The final objective involved combining the improved spatio-temporal CAR model with population (i.e. women) forecasts, to provide 30-year annual estimates of birth defects at the Statistical Local Area (SLA) level in New South Wales, Australia. The projections were illustrated using sixteen different SLAs, representing the various areal measures of socio-economic status and remoteness. A sensitivity analysis of the assumptions used in the projection was also undertaken. By the end of the thesis, I will show how challenges in the spatial analysis of rare diseases such as birth defects can be addressed, by specifically formulating the neighbourhood weight matrix to smooth according to a key covariate (i.e. maternal age), incorporating a ZIP component to model excess zeros in outcomes and borrowing strength from a referent outcome (i.e. caesarean counts). An efficient strategy to sample individual-level data and sample size considerations for rare disease will also be presented. Finally, projections in birth defect categories at the SLA level will be made.

Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.

Identifying large truck hot spots using crash counts and PDOEs

Large trucks are involved in a disproportionately small fraction of the total crashes but a disproportionately large fraction of fatal crashes. Large truck crashes often result in significant congestion due to their large physical dimensions and from difficulties in clearing crash scenes. Consequently, preventing large truck crashes is critical to improving highway safety and operations. This study identifies high risk sites (hot spots) for large truck crashes in Arizona and examines potential risk factors related to the design and operation of the high risk sites. High risk sites were identified using both state of the practice methods (accident reduction potential using negative binomial regression with long crash histories) and a newly proposed method using Property Damage Only Equivalents (PDOE). The hot spots identified via the count model generally exhibited low fatalities and major injuries but large minor injuries and PDOs, while the opposite trend was observed using the PDOE methodology. The hot spots based on the count model exhibited large AADTs, whereas those based on the PDOE showed relatively small AADTs but large fractions of trucks and high posted speed limits. Documented site investigations of hot spots revealed numerous potential risk factors, including weaving activities near freeway junctions and ramps, absence of acceleration lanes near on-ramps, small shoulders to accommodate large trucks, narrow lane widths, inadequate signage, and poor lighting conditions within a tunnel.