Weak acid extractable metals in Bramble Bay, Queensland, Australia : temporal behaviour, enrichment and source apportionment

Sediment samples were taken from six sampling sites in Bramble Bay, Queensland, Australia between February and November in 2012. They were analysed for a range of heavy metals including Al, Fe, Mn, Ti, Ce, Th, U, V, Cr, Co, Ni, Cu, Zn, As, Cd, Sb, Te, Hg, Tl and Pb. Fraction analysis, enrichment factors and Principal Component Analysis –Absolute Principal Component Scores (PCA-APCS) were carried out in order to assess metal pollution, potential bioavailability and source apportionment. Cr and Ni exceeded the Australian Interim Sediment Quality Guidelines at some sampling sites, while Hg was found to be the most enriched metal. Fraction analysis identified increased weak acid soluble Hg and Cd during the sampling period. Source apportionment via PCA-APCS found four sources of metals pollution, namely, marine sediments, shipping, antifouling coatings and a mixed source. These sources need to be considered in any metal pollution control measure within Bramble Bay.

Assessment and application of clustering techniques to atmospheric particle number size distribution for the purpose of source apportionment

Long-term measurements of particle number size distribution (PNSD) produce a very large number of observations and their analysis requires an efficient approach in order to produce results in the least possible time and with maximum accuracy. Clustering techniques are a family of sophisticated methods which have been recently employed to analyse PNSD data, however, very little information is available comparing the performance of different clustering techniques on PNSD data. This study aims to apply several clustering techniques (i.e. K-means, PAM, CLARA and SOM) to PNSD data, in order to identify and apply the optimum technique to PNSD data measured at 25 sites across Brisbane, Australia. A new method, based on the Generalised Additive Model (GAM) with a basis of penalised B-splines, was proposed to parameterise the PNSD data and the temporal weight of each cluster was also estimated using the GAM. In addition, each cluster was associated with its possible source based on the results of this parameterisation, together with the characteristics of each cluster. The performances of four clustering techniques were compared using the Dunn index and Silhouette width validation values and the K-means technique was found to have the highest performance, with five clusters being the optimum. Therefore, five clusters were found within the data using the K-means technique. The diurnal occurrence of each cluster was used together with other air quality parameters, temporal trends and the physical properties of each cluster, in order to attribute each cluster to its source and origin. The five clusters were attributed to three major sources and origins, including regional background particles, photochemically induced nucleated particles and vehicle generated particles. Overall, clustering was found to be an effective technique for attributing each particle size spectra to its source and the GAM was suitable to parameterise the PNSD data. These two techniques can help researchers immensely in analysing PNSD data for characterisation and source apportionment purposes.

Source characterisation of urban road surface pollutants for enhanced water quality predictions

This study developed a comprehensive research methodology for identification and quantification of sources responsible for pollutant build-up and wash-off from urban road surfaces. The study identified soil and asphalt wear, and non-combusted diesel fuel as the most influential sources for metal and hydrocarbon pollution respectively. The study also developed mathematical models to relate contributions from identified sources to underlying site specific factors such as land use and traffic. Developed mathematical model will play a key role in urban planning practices, enabling the implementation of effective water pollution control strategies.

Fine-grained plant classification using convolutional neural networks for feature extraction

We present an overview of the QUT plant classification system submitted to LifeCLEF 2014. This system uses generic features extracted from a convolutional neural network previously used to perform general object classification. We examine the effectiveness of these features to perform plant classification when used in combination with an extremely randomised forest. Using this system, with minimal tuning, we obtained relatively good results with a score of 0:249 on the test set of LifeCLEF 2014.

The combination of plant-expressed cellobiohydrolase and low dosages of cellulases for the hydrolysis of sugar cane bagasse

Background The expression of biomass-degrading enzymes (such as cellobiohydrolases) in transgenic plants has the potential to reduce the costs of biomass saccharification by providing a source of enzymes to supplement commercial cellulase mixtures. Cellobiohydrolases are the main enzymes in commercial cellulase mixtures. In the present study, a cellobiohydrolase was expressed in transgenic corn stover leaf and assessed as an additive for two commercial cellulase mixtures for the saccharification of pretreated sugar cane bagasse obtained by different processes. Results Recombinant cellobiohydrolase in the senescent leaves of transgenic corn was extracted using a simple buffer with no concentration step. The extract significantly enhanced the performance of Celluclast 1.5 L (a commercial cellulase mixture) by up to fourfold on sugar cane bagasse pretreated at the pilot scale using a dilute sulfuric acid steam explosion process compared to the commercial cellulase mixture on its own. Also, the extracts were able to enhance the performance of Cellic CTec2 (a commercial cellulase mixture) up to fourfold on a range of residues from sugar cane bagasse pretreated at the laboratory (using acidified ethylene carbonate/ethylene glycol, 1-butyl-3-methylimidazolium chloride, and ball-milling) and pilot (dilute sodium hydroxide and glycerol/hydrochloric acid steam explosion) scales. We have demonstrated using tap water as a solvent (under conditions that mimic an industrial process) extraction of about 90% recombinant cellobiohydrolase from senescent, transgenic corn stover leaf that had minimal tissue disruption. Conclusions The accumulation of recombinant cellobiohydrolase in senescent, transgenic corn stover leaf is a viable strategy to reduce the saccharification cost associated with the production of fermentable sugars from pretreated biomass. We envisage an industrial-scale process in which transgenic plants provide both fibre and biomass-degrading enzymes for pretreatment and enzymatic hydrolysis, respectively.

Validity of the geriatric depression scale and the collateral source version of the geriatric depression scale in nursing homes

Background Depression is a common psychiatric disorder in older people. The study aimed to examine the screening accuracy of the Geriatric Depression Scale (GDS) and the Collateral Source version of the Geriatric Depression Scale (CS-GDS) in the nursing home setting. Methods Eighty-eight residents from 14 nursing homes were assessed for depression using the GDS and the CS-GDS, and validated against clinician diagnosed depression using the Semi-structured Clinical Diagnostic Interview for DSM-IV-TR Axis I Disorders (SCID) for residents without dementia and the Provisional Diagnostic Criteria for Depression in Alzheimer Disease (PDCdAD) for those with dementia. The screening performances of five versions of the GDS (30-, 15-, 10-, 8-, and 4-item) and two versions of the CS-GDS (30- and 15-item) were analyzed using receiver operating characteristic (ROC) curves. Results Among residents without dementia, both the self-rated (AUC = 0.75–0.79) and proxy-rated (AUC = 0.67) GDS variations performed significantly better than chance in screening for depression. However, neither instrument adequately identified depression among residents with dementia (AUC between 0.57 and 0.70). Among the GDS variations, the 4- and 8-item scales had the highest AUC and the optimal cut-offs were >0 and >3, respectively. Conclusions The validity of the GDS in detecting depression requires a certain level of cognitive functioning. While the CS-GDS is designed to remedy this issue by using an informant, it did not have adequate validity in detecting depression among residents with dementia. Further research is needed on informant selection and other factors that can potentially influence the validity of proxy-based measures in the nursing home setting.

Positioning as a source of competitive advantage: Benchmarking Rotorua’s position as a domestic short break holiday destination

Travellers are spoilt by holiday choice, and yet will usually only seriously consider a few destinations during the decision process. With thousands of destination marketing organisations (DMOs) competing for attention, places are becoming increasingly substitutable. The study of destination competitiveness is an emerging field, and thesis contributes to an enhanced understanding by addressing three topics that have received relatively little attention in the tourism literature: destination positioning, the context of short break holidays, and domestic travel in New Zealand. A descriptive model of positioning as a source of competitive advantage is developed, and tested through 12 propositions. The destination of interest is Rotorua, which was arguably New Zealand’s first tourist destination. The market of interest is Auckland, which is Rotorua’s largest visitor market. Rotorua’s history is explored to identify factors that may have contributed to the destination’s current image in the Auckland market. A mix of qualitative and quantitative procedures is then utilised to determine Rotorua’s position, relative to a competing set of destinations. Based on an applied research problem, the thesis attempts to bridge the gap between academia and industry by providing useable results and benchmarks for five regional tourism organisations (RTOs). It is proposed that, in New Zealand, the domestic short break market represents a valuable opportunity not explicitly targeted by the competitive set of destinations. Conceptually, the thesis demonstrates the importance of analysing a destination’s competitive position, from the demand perspective, in a travel context; and then the value of comparing this ‘ideal’ position with that projected by the RTO. The thesis concludes Rotorua’s market position in the Auckland short break segment represents a source of comparative advantage, but is not congruent with the current promotional theme, which is being used in all markets. The findings also have implications for destinations beyond the context of the thesis. In particular, a new definition for ‘destination attractiveness’ is proposed, which warrants consideration in the design of future destination positioning analyses.

A Bayesian model of source monitoring

The Source Monitoring Framework is a promising model of constructive memory, yet fails because it is connectionist and does not allow content tagging. The Dual-Process Signal Detection Model is an improvement because it reduces mnemic qualia to a single memory signal (or degree of belief), but still commits itself to non-discrete representation. By supposing that ‘tagging’ means the assignment of propositional attitudes to aggregates of anemic characteristics informed inductively, then a discrete model becomes plausible. A Bayesian model of source monitoring accounts for the continuous variation of inputs and assignment of prior probabilities to memory content. A modified version of the High-Threshold Dual-Process model is recommended to further source monitoring research.